xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

a1c856b268aeebc7840d2380fb74682b65d2fd67

56

py

Python

anthill/tools/services/login/__init__.py

0x55AAh/anthill_gaming

475af798bd08d85fc0fbfce9d2ba710f73252c15

[ "MIT" ]

1

2018-11-30T21:56:14.000Z

anthill/tools/services/login/__init__.py

0x55AAh/anthill_gaming

475af798bd08d85fc0fbfce9d2ba710f73252c15

[ "MIT" ]

null

anthill/tools/services/login/__init__.py

0x55AAh/anthill_gaming

475af798bd08d85fc0fbfce9d2ba710f73252c15

[ "MIT" ]

null

from .. import Service class Login(Service): pass

9.333333

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null

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6

a1eb1222a9f1fc4889daec4ed5f0c967dba529c8

22

py

Python

pyBASS/__init__.py

lanl/pyBASS

968077b53ca4d9a39884b6d3ea52b6a57d8576fa

[ "BSD-3-Clause" ]

8

2021-09-06T08:47:12.000Z

2022-03-21T19:44:12.000Z

pyBASS/__init__.py

lanl/pyBASS

968077b53ca4d9a39884b6d3ea52b6a57d8576fa

[ "BSD-3-Clause" ]

2

2021-12-13T18:55:40.000Z

2021-12-21T18:14:26.000Z

pyBASS/__init__.py

lanl/pyBASS

968077b53ca4d9a39884b6d3ea52b6a57d8576fa

[ "BSD-3-Clause" ]

2

2021-05-05T22:28:24.000Z

2021-12-16T00:23:43.000Z

from .pyBASS import *

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62bf1dfe40fa9f5e0b3d5f7def61407170bcc958

21

py

Python

nodebox/sound/__init__.py

mcjocobe/drawExploration

2c50526ef14dea5bc3802b7fda08871919d62ac4

[ "BSD-3-Clause" ]

76

2015-01-21T11:21:08.000Z

2022-02-04T13:33:19.000Z

nodebox/sound/__init__.py

mcjocobe/drawExploration

2c50526ef14dea5bc3802b7fda08871919d62ac4

[ "BSD-3-Clause" ]

8

2015-11-12T07:42:58.000Z

2020-06-09T10:01:15.000Z

nodebox/sound/__init__.py

mcjocobe/drawExploration

2c50526ef14dea5bc3802b7fda08871919d62ac4

[ "BSD-3-Clause" ]

23

2015-01-12T12:07:40.000Z

2020-04-13T16:32:15.000Z

from process import *

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0

1

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6

62e76a49aec3490b3224380b500745ab42d7710d

3,355

py

Python

test.py

imapex/CHROnIC_Bus

3d5ce51f9ee84aad5d0aeba3759595c4f62d5cb4

[ "MIT" ]

null

test.py

imapex/CHROnIC_Bus

3d5ce51f9ee84aad5d0aeba3759595c4f62d5cb4

[ "MIT" ]

null

test.py

imapex/CHROnIC_Bus

3d5ce51f9ee84aad5d0aeba3759595c4f62d5cb4

[ "MIT" ]

2

2016-11-04T16:43:40.000Z

2017-02-15T16:10:05.000Z

#!/usr/bin/python import app import unittest ct = 'application/json' class FlaskTestCase(unittest.TestCase): def setUp(self): app.app.config['TESTING'] = True self.app = app.app.test_client() def test_top_level_http_response(self): resp = self.app.get('/') self.assertEqual(resp.status_code, 200) def test_get_no_results(self): resp = self.app.get('/api/get/testplan123', content_type=ct) self.assertEqual(resp.status_code, 404) def test_getstatus_no_results(self): resp = self.app.get('/api/status/1', content_type=ct) self.assertEqual(resp.status_code, 404) def test_poststatus_no_results(self): d = '{"status": "2"}' resp = self.app.post('/api/status/1', data=d, content_type=ct) self.assertEqual(resp.status_code, 404) def test_delete_no_results(self): resp = self.app.delete('/api/send/testplan123') self.assertEqual(resp.status_code, 404) def test_delete_results(self): resp = self.app.delete('/api/send/testplan123') d = '{"msgdata": "data1"}' resp = self.app.post('/api/send/testplan123', data=d, content_type=ct) resp = self.app.delete('/api/send/testplan123') self.assertEqual(resp.status_code, 204) def test_post_result_one(self): resp = self.app.delete('/api/send/testplan123') d = '{"msgdata": "data1"}' resp = self.app.post('/api/send/testplan123', data=d, content_type=ct) self.app.delete('/api/send/testplan123') self.assertEqual(resp.data, b'1') def test_post_result_two(self): resp = self.app.delete('/api/send/testplan123') d = '{"msgdata": "data1"}' resp = self.app.post('/api/send/testplan123', data=d, content_type=ct) d = '{"msgdata": "data2"}' resp = self.app.post('/api/send/testplan123', data=d, content_type=ct) self.app.delete('/api/send/testplan123') self.assertEqual(resp.data, b'2') def test_get_results(self): resp = self.app.delete('/api/send/testplan123') d = '{"msgdata": "data1"}' resp = self.app.post('/api/send/testplan123', data=d, content_type=ct) resp = self.app.get('/api/get/testplan123', content_type=ct) self.app.delete('/api/send/testplan123') self.assertEqual(resp.status_code, 200) def test_getstatus_results(self): resp = self.app.delete('/api/send/testplan123') d = '{"msgdata": "data1"}' resp = self.app.post('/api/send/testplan123', data=d, content_type=ct) resp = self.app.get('/api/status/1', content_type=ct) self.app.delete('/api/send/testplan123') self.assertEqual(resp.status_code, 200) def test_poststatus_results(self): resp = self.app.delete('/api/send/testplan123') d = '{"msgdata": "data1"}' resp = self.app.post('/api/send/testplan123', data=d, content_type=ct) d = '{"status": "2"}' resp = self.app.post('/api/status/1', data=d, content_type=ct) self.app.delete('/api/send/testplan123') self.assertEqual(resp.status_code, 200) def test_bad_path(self): resp = self.app.get('/api/badcall') self.assertEqual(resp.status_code, 404) def tearDown(self): pass if __name__ == '__main__': unittest.main()

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null

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null

0

6

1a01172863bd096733f3e1119e0ba8749cf6e1d1

96

py

Python

terrascript/nomad/__init__.py

vutsalsinghal/python-terrascript

3b9fb5ad77453d330fb0cd03524154a342c5d5dc

[ "BSD-2-Clause" ]

null

terrascript/nomad/__init__.py

vutsalsinghal/python-terrascript

3b9fb5ad77453d330fb0cd03524154a342c5d5dc

[ "BSD-2-Clause" ]

null

terrascript/nomad/__init__.py

vutsalsinghal/python-terrascript

3b9fb5ad77453d330fb0cd03524154a342c5d5dc

[ "BSD-2-Clause" ]

null

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

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null

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6

1a07dce1706cb6d3fdf95f03a4c1e171f0054831

47

py

Python

mountaintools/cairio/__init__.py

tjd2002/spikeforest2

2e393564b858b2995aa2ccccd9bd73065681b5de

[ "Apache-2.0" ]

null

mountaintools/cairio/__init__.py

tjd2002/spikeforest2

2e393564b858b2995aa2ccccd9bd73065681b5de

[ "Apache-2.0" ]

null

mountaintools/cairio/__init__.py

tjd2002/spikeforest2

2e393564b858b2995aa2ccccd9bd73065681b5de

[ "Apache-2.0" ]

null

from .cairioclient import CairioClient, client

23.5

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1

0

1

0

6

1a13e8fd55e243ec52ce954ab31689f8a93fbcd5

4,925

py

Python

neukrill_net/model_combination.py

Neuroglycerin/neukrill-net-tools

133c68403128e6fcea6d6c8c8326b45443ef7f4e

[ "MIT" ]

null

neukrill_net/model_combination.py

Neuroglycerin/neukrill-net-tools

133c68403128e6fcea6d6c8c8326b45443ef7f4e

[ "MIT" ]

null

neukrill_net/model_combination.py

Neuroglycerin/neukrill-net-tools

133c68403128e6fcea6d6c8c8326b45443ef7f4e

[ "MIT" ]

null

""" Creates a theano based gradient descent optimiser for finding good choices of weights to combine model predictions. """ import theano as th import theano.tensor as tt import numpy as np def compile_model_combination_weight_optimiser(lr_adjuster = lambda h, t: h): model_weights = tt.vector('w') # indexed over K models model_preds = tt.tensor3('P') # indexed over N examples, M classes, K models true_labels = tt.matrix('Y') # indexed over N examples, M classes learning_rate = tt.scalar('h') n_steps = tt.iscalar('n_steps') # use softmax form to ensure weights all >=0 and sum to one comb_preds = (tt.sum(tt.exp(model_weights) * model_preds, axis=2) / tt.sum(tt.exp(model_weights), axis=0)) # mean negative log loss cost function cost = - tt.mean(tt.sum(tt.log(comb_preds) * true_labels, axis=1)) # gradient of log loss cost with respect to weights dC_dW = lambda W: th.clone(th.gradient.jacobian(cost, model_weights), {model_weights: W}) # scan through gradient descent updates of weights, applying learning rate # adjuster at each step [Ws, hs], updates = th.scan( fn = lambda t, W, h: [W - h * dC_dW(W), lr_adjuster(h, t)], outputs_info = [model_weights, learning_rate], sequences = [th.tensor.arange(n_steps)], n_steps = n_steps, name = 'weight cost gradient descent') # create a function to get last updated weight from scan sequence weights_optimiser = th.function( inputs = [model_weights, model_preds, true_labels, learning_rate, n_steps], outputs = Ws[-1], updates = updates, allow_input_downcast = True, ) # also compile a function for evaluating cost function to check optimiser # performance / convergence cost_func = th.function([model_weights, model_preds, true_labels], cost) return weights_optimiser, cost_func def compile_per_class_model_combination_weight_optimiser(lr_adjuster = lambda h, t: h): model_weights = tt.matrix('w') # indexed over M classes, K models model_preds = tt.tensor3('P') # indexed over N examples, M classes, K models true_labels = tt.matrix('Y') # indexed over N examples, M classes learning_rate = tt.scalar('h') n_steps = tt.iscalar('n_steps') # use softmax form to ensure weights all >=0 and sum to one comb_preds = (tt.sum(tt.exp(model_weights) * model_preds, axis=2) / tt.sum(tt.exp(model_weights), axis=1)) # mean negative log loss cost function cost = - tt.mean(tt.sum(tt.log(comb_preds) * true_labels, axis=1)) # gradient of log loss cost with respect to weights dC_dW = lambda W: th.clone(th.gradient.jacobian(cost, model_weights), {model_weights: W}) # scan through gradient descent updates of weights, applying learning rate # adjuster at each step [Ws, hs], updates = th.scan( fn = lambda t, W, h: [W - h * dC_dW(W), lr_adjuster(h, t)], outputs_info = [model_weights, learning_rate], sequences = [th.tensor.arange(n_steps)], n_steps = n_steps, name = 'weight cost gradient descent') # create a function to get last updated weight from scan sequence weights_optimiser = th.function( inputs = [model_weights, model_preds, true_labels, learning_rate, n_steps], outputs = Ws[-1], updates = updates, allow_input_downcast = True, ) # also compile a function for evaluating cost function to check optimiser # performance / convergence cost_func = th.function([model_weights, model_preds, true_labels], cost) return weights_optimiser, cost_func if __name__ == '__main__': """ Test with randomly generated model predictions and labels. """ N_MODELS = 3 N_CLASSES = 10 N_DATA = 100 SEED = 1234 INIT_LEARNING_RATE = 0.1 LR_ADJUSTER = lambda h, t: h N_STEP = 1000 prng = np.random.RandomState(SEED) weights = np.zeros((N_CLASSES, N_MODELS)) model_pred_vals = prng.rand(N_DATA, N_CLASSES, N_MODELS) model_pred_vals = model_pred_vals / model_pred_vals.sum(1)[:,None,:] true_label_vals = prng.rand(N_DATA, N_CLASSES) true_label_vals = true_label_vals == true_label_vals.max(axis=1)[:,None] optimiser, cost = compile_per_class_model_combination_weight_optimiser(LR_ADJUSTER) print('Initial weights {0}'.format(weights)) print('Initial cost value {0}'.format( cost(weights, model_pred_vals, true_label_vals))) updated_weights = optimiser(weights, model_pred_vals, true_label_vals, INIT_LEARNING_RATE, N_STEP) print('Final weights {0}'.format(updated_weights)) print('Final cost value {0}'.format( cost(updated_weights, model_pred_vals, true_label_vals)))

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6

a7fc3dfdb7f781593a5c59dce1595b7f8099347d

6,201

py

Python

basic_code/load.py

Soapy-Salted-Fish-King/Emotion-FAN

5a48f9290f48d713397761db9d72efa9220b5ef3

[ "MIT" ]

275

2019-09-11T10:22:06.000Z

2022-03-29T07:14:31.000Z

basic_code/load.py

DebinMeng19-OpenSourceLibrary/Emotion-FAN

874e871999a2002cd5dd9dffff2c4400c2e1805b

[ "MIT" ]

34

2019-09-11T11:32:32.000Z

2022-03-18T09:32:42.000Z

basic_code/load.py

DebinMeng19-OpenSourceLibrary/Emotion-FAN

874e871999a2002cd5dd9dffff2c4400c2e1805b

[ "MIT" ]

69

2019-09-18T19:00:17.000Z

2022-03-08T11:43:49.000Z

from __future__ import print_function import torch print(torch.__version__) import torch.utils.data import torchvision.transforms as transforms from basic_code import data_generator cate2label = {'CK+':{0: 'Happy', 1: 'Angry', 2: 'Disgust', 3: 'Fear', 4: 'Sad', 5: 'Contempt', 6: 'Surprise', 'Angry': 1,'Disgust': 2,'Fear': 3,'Happy': 0,'Contempt': 5,'Sad': 4,'Surprise': 6}, 'AFEW':{0: 'Happy',1: 'Angry',2: 'Disgust',3: 'Fear',4: 'Sad',5: 'Neutral',6: 'Surprise', 'Angry': 1,'Disgust': 2,'Fear': 3,'Happy': 0,'Neutral': 5,'Sad': 4,'Surprise': 6}} def ckplus_faces_baseline(video_root, video_list, fold, batchsize_train, batchsize_eval): train_dataset = data_generator.TenFold_VideoDataset( video_root=video_root, video_list=video_list, rectify_label=cate2label['CK+'], transform=transforms.Compose([transforms.Resize(224), transforms.RandomHorizontalFlip(), transforms.ToTensor()]), fold=fold, run_type='train' ) val_dataset = data_generator.TenFold_VideoDataset( video_root=video_root, video_list=video_list, rectify_label=cate2label['CK+'], transform=transforms.Compose([transforms.Resize(224), transforms.ToTensor()]), fold=fold, run_type='test' ) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=batchsize_train, shuffle=True, num_workers=8,pin_memory=True) val_loader = torch.utils.data.DataLoader( val_dataset, batch_size=batchsize_eval, shuffle=False, num_workers=8, pin_memory=True) return train_loader, val_loader def ckplus_faces_fan(video_root, video_list, fold, batchsize_train, batchsize_eval): train_dataset = data_generator.TenFold_TripleImageDataset( video_root=video_root, video_list=video_list, rectify_label=cate2label['CK+'], transform=transforms.Compose([ transforms.Resize(224), transforms.RandomHorizontalFlip(), transforms.ToTensor()]), fold=fold, run_type='train', ) val_dataset = data_generator.TenFold_VideoDataset( video_root=video_root, video_list=video_list, rectify_label=cate2label['CK+'], transform=transforms.Compose([transforms.Resize(224), transforms.ToTensor()]), fold=fold, run_type='test' ) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=batchsize_train, shuffle=True, num_workers=8,pin_memory=True) val_loader = torch.utils.data.DataLoader( val_dataset, batch_size=batchsize_eval, shuffle=False, num_workers=8, pin_memory=True) return train_loader, val_loader def afew_faces_baseline(root_train, list_train, batchsize_train, root_eval, list_eval, batchsize_eval): train_dataset = data_generator.VideoDataset( video_root=root_train, video_list=list_train, rectify_label=cate2label['AFEW'], transform=transforms.Compose([transforms.Resize(224), transforms.RandomHorizontalFlip(), transforms.ToTensor()]), ) val_dataset = data_generator.VideoDataset( video_root=root_eval, video_list=list_eval, rectify_label=cate2label['AFEW'], transform=transforms.Compose([transforms.Resize(224), transforms.ToTensor()]), csv=False) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=batchsize_train, shuffle=True, num_workers=8, pin_memory=True) val_loader = torch.utils.data.DataLoader( val_dataset, batch_size=batchsize_eval, shuffle=False, num_workers=8, pin_memory=True) return train_loader, val_loader def afew_faces_fan(root_train, list_train, batchsize_train, root_eval, list_eval, batchsize_eval): train_dataset = data_generator.TripleImageDataset( video_root=root_train, video_list=list_train, rectify_label=cate2label['AFEW'], transform=transforms.Compose([transforms.Resize(224), transforms.RandomHorizontalFlip(), transforms.ToTensor()]), ) val_dataset = data_generator.VideoDataset( video_root=root_eval, video_list=list_eval, rectify_label=cate2label['AFEW'], transform=transforms.Compose([transforms.Resize(224), transforms.ToTensor()]), csv=False) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=batchsize_train, shuffle=True, num_workers=8, pin_memory=True, drop_last=True) val_loader = torch.utils.data.DataLoader( val_dataset, batch_size=batchsize_eval, shuffle=False, num_workers=8, pin_memory=True) return train_loader, val_loader def model_parameters(_structure, _parameterDir): checkpoint = torch.load(_parameterDir) pretrained_state_dict = checkpoint['state_dict'] model_state_dict = _structure.state_dict() for key in pretrained_state_dict: if ((key == 'module.fc.weight') | (key == 'module.fc.bias')): pass else: model_state_dict[key.replace('module.', '')] = pretrained_state_dict[key] _structure.load_state_dict(model_state_dict) model = torch.nn.DataParallel(_structure).cuda() return model

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null

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null

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6

c505ec7a70aebc306a64376c725499551ec1f125

181

py

Python

lab5_moduly/5.1.py

Damian9449/Python

dc9091e15356733821bbb6a768b7d5e428640340

[ "MIT" ]

1

2017-11-15T13:03:40.000Z

lab5_moduly/5.1.py

Damian9449/Python

dc9091e15356733821bbb6a768b7d5e428640340

[ "MIT" ]

null

lab5_moduly/5.1.py

Damian9449/Python

dc9091e15356733821bbb6a768b7d5e428640340

[ "MIT" ]

null

#!/usr/bin/python import rekurencja import rekurencja as rek from rekurencja import factorial from rekurencja import fibonacci as fib print(rekurencja.factorial(6)) print(fib(5))

18.1

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6

c52f2cb5665c9225cc331c7afdccbd81e9901d35

35

py

Python

Package/V0.0.0-beta/Scripts/monte_carlo.py

Password-Classified/Stock-Search

517787e29de9e531e2b01ba94ee3d8a2a8928dca

[ "Unlicense" ]

3

2021-11-07T20:16:54.000Z

2022-01-24T07:47:52.000Z

Source/Scripts/monte_carlo.py

Password-Classified/Stock-Search

517787e29de9e531e2b01ba94ee3d8a2a8928dca

[ "Unlicense" ]

null

Source/Scripts/monte_carlo.py

Password-Classified/Stock-Search

517787e29de9e531e2b01ba94ee3d8a2a8928dca

[ "Unlicense" ]

null

from Scripts.data import get_data

11.666667

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c540768269f664fa57d294335d673cda2ec78edd

98

py

Python

tests/test_hello_world.py

sluger/myserverplugin

c66ca1cddb5ce2f530be8d0ada89a95eebb25df9

[ "BSD-3-Clause" ]

null

tests/test_hello_world.py

sluger/myserverplugin

c66ca1cddb5ce2f530be8d0ada89a95eebb25df9

[ "BSD-3-Clause" ]

null

tests/test_hello_world.py

sluger/myserverplugin

c66ca1cddb5ce2f530be8d0ada89a95eebb25df9

[ "BSD-3-Clause" ]

null

from myserverplugin import hello_world def test_hello_world(): assert hello_world is not None

16.333333

38

0.816327

15

98

5.066667

0.733333

0.394737

0

0.153061

98

5

39

19.6

0.915663

0

0.333333

1

0.333333

true

0

0.333333

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null

1

0

1

0

null

0

1

0

1

0

6

c565e1279b56c770b906f3704f486f912be143b1

34

py

Python

wordle/helpers.py

AntonGomes/wordle

2e20aa84f3430d31d291e91421a6f503185d6295

[ "MIT" ]

null

wordle/helpers.py

AntonGomes/wordle

2e20aa84f3430d31d291e91421a6f503185d6295

[ "MIT" ]

null

wordle/helpers.py

AntonGomes/wordle

2e20aa84f3430d31d291e91421a6f503185d6295

[ "MIT" ]

null

def isValid(word): return True

11.333333

18

0.705882

5

34

4.8

1

0

0.205882

34

2

19

17

0.888889

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

3dd979614c8418a0018c01535c3b6e4ddbd28cb5

38

bzl

Python

tools/build_defs/apple/fb_apple_test.bzl

CrshOverride/react-native

260c5a393fe2708f3d12c722b6d189ec3057743a

[ "CC-BY-4.0", "MIT" ]

null

tools/build_defs/apple/fb_apple_test.bzl

CrshOverride/react-native

260c5a393fe2708f3d12c722b6d189ec3057743a

[ "CC-BY-4.0", "MIT" ]

null

tools/build_defs/apple/fb_apple_test.bzl

CrshOverride/react-native

260c5a393fe2708f3d12c722b6d189ec3057743a

[ "CC-BY-4.0", "MIT" ]

null

def fb_apple_test(**kwargs): pass

12.666667

28

0.684211

6

38

4

1

0

0.184211

38

2

29

19

0.774194

0

1

0.5

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

6

9a847810c47a6b47e57b717e340b9e3dc96828a0

44

py

Python

tests/__init__.py

roansong/osu-replay-parser

70a206622b51bb8443d423f6da671bb005cb32f7

[ "MIT" ]

1

2019-12-08T07:22:56.000Z

tests/__init__.py

roansong/osu-replay-parser

70a206622b51bb8443d423f6da671bb005cb32f7

[ "MIT" ]

null

tests/__init__.py

roansong/osu-replay-parser

70a206622b51bb8443d423f6da671bb005cb32f7

[ "MIT" ]

null

from .replay_test import TestStandardReplay

22

43

0.886364

5

44

7.6

1

0

0.090909

44

1

44

0.95

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

9a9359e91dbdb03cb73b915309f8efefc068c5f6

2,472

py

Python

src/genie/libs/parser/junos/tests/Ping/cli/equal/golden_output_2_expected.py

balmasea/genieparser

d1e71a96dfb081e0a8591707b9d4872decd5d9d3

[ "Apache-2.0" ]

204

2018-06-27T00:55:27.000Z

2022-03-06T21:12:18.000Z

src/genie/libs/parser/junos/tests/Ping/cli/equal/golden_output_2_expected.py

balmasea/genieparser

d1e71a96dfb081e0a8591707b9d4872decd5d9d3

[ "Apache-2.0" ]

468

2018-06-19T00:33:18.000Z

2022-03-31T23:23:35.000Z

src/genie/libs/parser/junos/tests/Ping/cli/equal/golden_output_2_expected.py

balmasea/genieparser

d1e71a96dfb081e0a8591707b9d4872decd5d9d3

[ "Apache-2.0" ]

309

2019-01-16T20:21:07.000Z

2022-03-30T12:56:41.000Z

expected_output = { "ping": { "address": "2001:db8:223c:2c16::2", "data-bytes": 56, "result": [ { "bytes": 16, "from": "2001:db8:223c:2c16::2", "hlim": 64, "icmp-seq": 0, "time": "973.514", }, { "bytes": 16, "from": "2001:db8:223c:2c16::2", "hlim": 64, "icmp-seq": 1, "time": "0.993", }, { "bytes": 16, "from": "2001:db8:223c:2c16::2", "hlim": 64, "icmp-seq": 2, "time": "1.170", }, { "bytes": 16, "from": "2001:db8:223c:2c16::2", "hlim": 64, "icmp-seq": 3, "time": "0.677", }, { "bytes": 16, "from": "2001:db8:223c:2c16::2", "hlim": 64, "icmp-seq": 4, "time": "0.914", }, { "bytes": 16, "from": "2001:db8:223c:2c16::2", "hlim": 64, "icmp-seq": 5, "time": "0.814", }, { "bytes": 16, "from": "2001:db8:223c:2c16::2", "hlim": 64, "icmp-seq": 6, "time": "0.953", }, { "bytes": 16, "from": "2001:db8:223c:2c16::2", "hlim": 64, "icmp-seq": 7, "time": "1.140", }, { "bytes": 16, "from": "2001:db8:223c:2c16::2", "hlim": 64, "icmp-seq": 8, "time": "0.800", }, { "bytes": 16, "from": "2001:db8:223c:2c16::2", "hlim": 64, "icmp-seq": 9, "time": "0.881", }, ], "source": "2001:db8:223c:2c16::1", "statistics": { "loss-rate": 0, "received": 10, "round-trip": { "avg": "98.186", "max": "973.514", "min": "0.677", "stddev": "291.776", }, "send": 10, }, } }

27.164835

48

0.264159

201

2,472

3.243781

0.283582

0.128834

0.202454

0.276074

0.638037

0.613497

0

0.242647

0.559871

2,472

90

49

27.466667

0.356618

0

0.333333

0

0.270227

0.101942

0

1

0

false

0

null

0

1

0

1

0

1

0

1

0

null

0

6

b149686145c5f7eb3e955e98e7348ccbcc245399

1,453

py

Python

storage_engine/base.py

JackInTaiwan/ViDB

d658fd4f6a1ad2d7d36bb270fde2a373d3cc965d

[ "MIT" ]

2

2021-05-29T06:57:24.000Z

2021-06-15T09:13:38.000Z

storage_engine/base.py

JackInTaiwan/ViDB

d658fd4f6a1ad2d7d36bb270fde2a373d3cc965d

[ "MIT" ]

null

storage_engine/base.py

JackInTaiwan/ViDB

d658fd4f6a1ad2d7d36bb270fde2a373d3cc965d

[ "MIT" ]

null

import abc import json class BaseStorageEngine(metaclass=abc.ABCMeta): @abc.abstractmethod def init_storage(self): return NotImplemented @abc.abstractmethod def create_one(self, image:str, thumbnail:str, features, metadata:json): return NotImplemented @abc.abstractmethod def create_many(self, image:list, thumbnail:list, features:list, metadata:list): return NotImplemented @abc.abstractmethod def read_one(self, index, mode): return NotImplemented @abc.abstractmethod def read_many(self, index:list, mode): return NotImplemented @abc.abstractmethod def delete_one(self, index): return NotImplemented @abc.abstractmethod def delete_many(self, index:list): # TBD: how to relocate files return NotImplemented @abc.abstractmethod def update_one(self, index, metadata): return NotImplemented @abc.abstractmethod def update_many(self, index, metadata): return NotImplemented @abc.abstractmethod def generate_id(self): return NotImplemented @abc.abstractmethod def generate_c_at(self): # create time return NotImplemented @abc.abstractmethod def locate_id(self, index): # TBD: how to relocate files return NotImplemented @abc.abstractmethod def update_storage_table(self, file_path, delete=False): return NotImplemented

21.367647

84

0.689608

159

1,453

6.201258

0.27044

0.224138

0.263692

0.450304

0.647059

0.606491

0.251521

0.135903

0

0.23744

1,453

68

85

21.367647

0.889892

0.044735

0

0.619048

0

1

0.309524

false

0

0.047619

0.309524

0.690476

0

null

1

0

null

0

1

0

1

0

6

492b33a242fdfb68a060e0d0491ea9065ba2fe0a

4,858

py

Python

livestyled/schemas/fields.py

andrelopez/python-sdk

3c83d4698ecf6b5b59003d20cb26644e0dd77f61

[ "MIT" ]

null

livestyled/schemas/fields.py

andrelopez/python-sdk

3c83d4698ecf6b5b59003d20cb26644e0dd77f61

[ "MIT" ]

null

livestyled/schemas/fields.py

andrelopez/python-sdk

3c83d4698ecf6b5b59003d20cb26644e0dd77f61

[ "MIT" ]

null

from marshmallow import class_registry, fields from marshmallow.base import SchemaABC class RelatedResourceLinkField(fields.Field): def __init__( self, schema=None, many=False, microservice_aware=False, **kwargs ): self._schema_arg = schema self.many = many self.__schema = None self.__microservice_aware = microservice_aware super(RelatedResourceLinkField, self).__init__(**kwargs) @property def schema(self): if not self.__schema and self._schema_arg: if isinstance(self._schema_arg, SchemaABC): self.__schema = self._schema_arg elif isinstance(self._schema_arg, type) and issubclass(self._schema_arg, SchemaABC): self.__schema = self._schema_arg elif isinstance(self._schema_arg, str): if self._schema_arg == 'self': self.__schema = self.parent.__class__ else: self.__schema = class_registry.get_class(self._schema_arg) else: raise ValueError('Nested fields must be passed a Schema, not {0}.'.format(self.nested.__class__)) return self.__schema def _serialize(self, value, attr, obj, **kwargs): if value: if isinstance(value, (str, int)): if self.__microservice_aware: return '/{}/{}'.format(self.schema.Meta.url, value) else: return '/v4/{}/{}'.format(self.schema.Meta.api_type, value) else: if self.__microservice_aware: return '/{}/{}'.format(self.schema.Meta.url, value.id) else: return '/v4/{}/{}'.format(self.schema.Meta.api_type, value.id) return None def _deserialize(self, value, attr, data, **kwargs): if self.many: return [int(v.split('/')[-1]) for v in value] elif isinstance(value, dict): return int(value['id']) return int(value.split('/')[-1]) class RelatedResourceField(fields.Field): def __init__( self, schema=None, many=False, microservice_aware=False, **kwargs ): self._schema_arg = schema self.many = many self.__schema = None self.__microservice_aware = microservice_aware super(RelatedResourceField, self).__init__(**kwargs) @property def schema(self): if not self.__schema and self._schema_arg: if isinstance(self._schema_arg, SchemaABC): self.__schema = self._schema_arg elif isinstance(self._schema_arg, type) and issubclass(self._schema_arg, SchemaABC): self.__schema = self._schema_arg elif isinstance(self._schema_arg, str): if self._schema_arg == 'self': self.__schema = self.parent.__class__ else: self.__schema = class_registry.get_class(self._schema_arg) else: raise ValueError('Nested fields must be passed a Schema, not {0}.'.format(self.nested.__class__)) return self.__schema def _serialize(self, value, attr, obj, **kwargs): if value: if isinstance(value, (str, int)): if self.__microservice_aware: return '/{}/{}'.format(self.schema.Meta.url, value) else: return '/v4/{}/{}'.format(self.schema.Meta.url, value) elif isinstance(value, list): r_value = [] for v in value: if self.__microservice_aware: r_value.append('/{}/{}'.format(self.schema.Meta.url, v.id)) else: r_value.append('/v4/{}/{}'.format(self.schema.Meta.api_type, v.id)) r_value.append('/{}/{}'.format(self.schema.Meta.url, v.id)) return r_value else: if self.__microservice_aware: return '/{}/{}'.format(self.schema.Meta.url, value.id) else: return '/v4/{}/{}'.format(self.schema.Meta.api_type, value.id) else: if self.many: return [] else: return None def _deserialize(self, value, attr, data, **kwargs): if self.many: deserialized = [] for v in value: if isinstance(v, str): deserialized.append(int(v.split('/')[-1])) elif isinstance(v, dict): deserialized.append(self.schema().load(v)) return deserialized else: return self.schema().load(value)

38.251969

113

0.53767

503

4,858

4.908549

0.135189

0.198461

0.105306

0.089105

0.793034

0.782503

0.779263

0.767517

0

0.00317

0.350556

4,858

126

114

38.555556

0.779398

0

0.789474

0

0.038699

0

1

0.070175

false

0.017544

0

0.27193

0

null

0

1

0

1

0

null

0

6

493ff6a4ece23b1832f3e7baa3d75f2ce1895cf2

26

py

Python

make_var/__init__.py

karnigen/make_var

536be7107099830facaa0835bed2331778fc9e94

[ "MIT" ]

null

make_var/__init__.py

karnigen/make_var

536be7107099830facaa0835bed2331778fc9e94

[ "MIT" ]

null

make_var/__init__.py

karnigen/make_var

536be7107099830facaa0835bed2331778fc9e94

[ "MIT" ]

1

2022-02-01T12:57:57.000Z

from .make_var import *

6.5

23

0.692308

4

26

4.25

1

0

0.230769

26

3

24

8.666667

0.85

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

494b81a2fa9658b2822d06afd8dfb953051081e9

21

py

Python

tact-random/__init__.py

tactlabs/tactrandom

ff52d19eefe7c10ed17442e845f78397c8149517

[ "MIT" ]

2

2019-03-21T07:14:19.000Z

2020-06-23T12:53:15.000Z

Lib/site-packages/numjy/random/__init__.py

Yaqiang/jythonlab

d031d85e5bd5f19943c6a410c56ceb734c533534

[ "CNRI-Jython", "Apache-2.0" ]

null

Lib/site-packages/numjy/random/__init__.py

Yaqiang/jythonlab

d031d85e5bd5f19943c6a410c56ceb734c533534

[ "CNRI-Jython", "Apache-2.0" ]

null

from .random import *

21

0.761905

3

21

5.333333

1

0

0.142857

21

1

21

0.888889

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

49917025b5391af044654bc61bf168869c1ed2e5

926

py

Python

core/admin.py

hackforthesea/hackforthesea.tech

33b7522c13d87b26a39e9dfbdcad4067b44cda06

[ "BSD-3-Clause" ]

1

2018-09-17T04:35:06.000Z

core/admin.py

hackforthesea/hackforthesea.tech

33b7522c13d87b26a39e9dfbdcad4067b44cda06

[ "BSD-3-Clause" ]

5

2021-04-08T18:28:06.000Z

2022-02-10T08:24:03.000Z

core/admin.py

hackforthesea/hackforthesea.tech

33b7522c13d87b26a39e9dfbdcad4067b44cda06

[ "BSD-3-Clause" ]

1

2018-09-17T04:35:08.000Z

from django.contrib import admin # from .models import Sponsor, CommunityPartner, Location, Team, Participant, \ # Submission, FrequentlyAskedQuestion # class SponsorAdmin(admin.ModelAdmin): # pass # class CommunityPartnerAdmin(admin.ModelAdmin): # pass # class LocationAdmin(admin.ModelAdmin): # pass # class SubmissionAdmin(admin.ModelAdmin): # pass # class TeamAdmin(admin.ModelAdmin): # pass # class ParticipantAdmin(admin.ModelAdmin): # pass # class FrequentlyAskedQuestionAdmin(admin.ModelAdmin): # pass # admin.site.register(Sponsor,SponsorAdmin) # admin.site.register(CommunityPartner,CommunityPartnerAdmin) # admin.site.register(Location,LocationAdmin) # admin.site.register(Participant,ParticipantAdmin) # admin.site.register(Team,TeamAdmin) # admin.site.register(Submission,SubmissionAdmin) # admin.site.register(FrequentlyAskedQuestion,FrequentlyAskedQuestionAdmin)

22.585366

79

0.774298

85

926

8.435294

0.294118

0.146444

0.185495

0.200837

0

0.12311

926

41

80

22.585366

0.883005

0.896328

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

4993f6406a9a73a3ef0b60cd7b6145884beaf326

16,925

py

Python

koko/lib/dottext.py

TheBeachLab/kokopelli

529b8149a951363d2a027946464ea0bb22346428

[ "MIT" ]

null

koko/lib/dottext.py

TheBeachLab/kokopelli

529b8149a951363d2a027946464ea0bb22346428

[ "MIT" ]

1

2018-11-23T11:52:41.000Z

koko/lib/dottext.py

TheBeachLab/kokopelli

529b8149a951363d2a027946464ea0bb22346428

[ "MIT" ]

null

# koko.lib.dottext.py # Simple dot matrix math-string based font. # Modified by Francisco Sanchez # original code by Matt Keeter # matt.keeter@cba.mit.edu # kokompe.cba.mit.edu ################################################################################ from koko.lib.shapes2d import * def text(text, x, y, height = 1, align = 'CC'): dx, dy = 0, -1 text_shape = None for line in text.split('\n'): line_shape = None for c in line: if not c in _glyphs.keys(): print 'Warning: Unknown character "%s" in koko.lib.text' % c else: chr_math = move(_glyphs[c], dx, dy) if line_shape is None: line_shape = chr_math else: line_shape += chr_math dx += _glyphs[c].width + 0.1 dx -= 0.1 if line_shape is not None: if align[0] == 'L': pass elif align[0] == 'C': line_shape = move(line_shape, -dx / 2, 0) elif align[0] == 'R': line_shape = move(line_shape, -dx, 0) text_shape += line_shape dy -= 1.55 dx = 0 dy += 1.55 if text_shape is None: return None if align[1] == 'T': pass elif align[1] == 'B': text_shape = move(text_shape, 0, -dy,) elif align[1] == 'C': text_shape = move(text_shape, 0, -dy/2) if height != 1: text_shape = scale_xy(text_shape, 0, 0, height) dx *= height dy *= height return move(text_shape, x, y) _glyphs = {} shape = triangle(0, 0, 0.35, 1, 0.1, 0) shape += triangle(0.1, 0, 0.35, 1, 0.45, 1) shape += triangle(0.35, 1, 0.45, 1, 0.8, 0) shape += triangle(0.7, 0, 0.35, 1, 0.8, 0) shape += rectangle(0.2, 0.6, 0.3, 0.4) shape.width = 0.8 _glyphs['A'] = shape shape = circle(0.25, 0.275, 0.275) shape -= circle(0.25, 0.275, 0.175) shape = shear_x_y(shape, 0, 0.35, 0, 0.1) shape += rectangle(0.51, 0.61, 0, 0.35) shape = move(shape, -0.05, 0) shape.width = 0.58 _glyphs['a'] = shape shape = circle(0.3, 0.725, 0.275) shape -= circle(0.3, 0.725, 0.175) shape += circle(0.3, 0.275, 0.275) shape -= circle(0.3, 0.275, 0.175) shape &= rectangle(0.3, 1, 0, 1) shape += rectangle(0, 0.1, 0, 1) shape += rectangle(0.1, 0.3, 0, 0.1) shape += rectangle(0.1, 0.3, 0.45, 0.55) shape += rectangle(0.1, 0.3, 0.9, 1) shape.width = 0.575 _glyphs['B'] = shape shape = circle(0.25, 0.275, 0.275) shape -= circle(0.25, 0.275, 0.175) shape &= rectangle(0.25, 1, 0, 0.275) + rectangle(0, 1, 0.275, 1) shape += rectangle(0, 0.1, 0, 1) shape += rectangle(0.1, 0.25, 0, 0.1) shape.width = 0.525 _glyphs['b'] = shape shape = circle(0.3, 0.7, 0.3) - circle(0.3, 0.7, 0.2) shape += circle(0.3, 0.3, 0.3) - circle(0.3, 0.3, 0.2) shape -= rectangle(0, 0.6, 0.3, 0.7) shape -= triangle(0.3, 0.5, 1, 1.5, 1, -0.5) shape -= rectangle(0.3, 0.6, 0.2, 0.8) shape += rectangle(0, 0.1, 0.3, 0.7) shape.width = 0.57 _glyphs['C'] = shape shape = circle(0.275, 0.275, 0.275) shape -= circle(0.275, 0.275, 0.175) shape -= triangle(0.275, 0.275, 0.55, 0.55, 0.55, 0) shape.width = 0.48 _glyphs['c'] = shape shape = circle(0.1, 0.5, 0.5) - circle(0.1, 0.5, 0.4) shape &= rectangle(0, 1, 0, 1) shape += rectangle(0, 0.1, 0, 1) shape.width = 0.6 _glyphs['D'] = shape shape = reflect_x(_glyphs['b'], _glyphs['b'].width/2) shape.width = _glyphs['b'].width _glyphs['d'] = shape shape = rectangle(0, 0.1, 0, 1) shape += rectangle(0.1, 0.6, 0.9, 1) shape += rectangle(0.1, 0.6, 0, 0.1) shape += rectangle(0.1, 0.5, 0.45, 0.55) shape.width = 0.6 _glyphs['E'] = shape shape = circle(0.275, 0.275, 0.275) shape -= circle(0.275, 0.275, 0.175) shape -= triangle(0.1, 0.275, 0.75, 0.275, 0.6, 0) shape += rectangle(0.05, 0.55, 0.225, 0.315) shape &= circle(0.275, 0.275, 0.275) shape.width = 0.55 _glyphs['e'] = shape shape = rectangle(0, 0.1, 0, 1) shape += rectangle(0.1, 0.6, 0.9, 1) shape += rectangle(0.1, 0.5, 0.45, 0.55) shape.width = 0.6 _glyphs['F'] = shape shape = circle(0.4, 0.75, 0.25) - circle(0.4, 0.75, 0.15) shape &= rectangle(0, 0.4, 0.75, 1) shape += rectangle(0, 0.4, 0.45, 0.55) shape += rectangle(0.15, 0.25, 0, 0.75) shape.width = 0.4 _glyphs['f'] = shape shape = circle(0.275, -0.1, 0.275) shape -= circle(0.275, -0.1, 0.175) shape &= rectangle(0, 0.55, -0.375, -0.1) shape += circle(0.275, 0.275, 0.275) - circle(0.275, 0.275, 0.175) shape += rectangle(0.45, 0.55, -0.1, 0.55) shape.width = 0.55 _glyphs['g'] = shape shape = circle(0.3, 0.7, 0.3) - circle(0.3, 0.7, 0.2) shape += circle(0.3, 0.3, 0.3) - circle(0.3, 0.3, 0.2) shape -= rectangle(0, 0.6, 0.3, 0.7) shape += rectangle(0, 0.1, 0.3, 0.7) shape += rectangle(0.5, 0.6, 0.3, 0.4) shape += rectangle(0.3, 0.6, 0.4, 0.5) shape.width = 0.6 _glyphs['G'] = shape shape = rectangle(0, 0.1, 0, 1) shape += rectangle(0.5, 0.6, 0, 1) shape += rectangle(0.1, 0.5, 0.45, 0.55) shape.width = 0.6 _glyphs['H'] = shape shape = circle(0.275, 0.275, 0.275) shape -= circle(0.275, 0.275, 0.175) shape &= rectangle(0, 0.55, 0.275, 0.55) shape += rectangle(0, 0.1, 0, 1) shape += rectangle(0.45, 0.55, 0, 0.275) shape.width = 0.55 _glyphs['h'] = shape shape = rectangle(0, 0.5, 0, 0.1) shape += rectangle(0, 0.5, 0.9, 1) shape += rectangle(0.2, 0.3, 0.1, 0.9) shape.width = 0.5 _glyphs['I'] = shape shape = rectangle(0.025, 0.125, 0, 0.55) shape += circle(0.075, 0.7, 0.075) shape.width = 0.15 _glyphs['i'] = shape shape = circle(0.275, 0.275, 0.275) shape -= circle(0.275, 0.275, 0.175) shape &= rectangle(0, 0.55, 0, 0.275) shape += rectangle(0.45, 0.55, 0.275, 1) shape.width = 0.55 _glyphs['J'] = shape shape = circle(0.0, -0.1, 0.275) shape -= circle(0.0, -0.1, 0.175) shape &= rectangle(0, 0.55, -0.375, -0.1) shape += rectangle(0.175, 0.275, -0.1, 0.55) shape += circle(0.225, 0.7, 0.075) shape.width = 0.3 _glyphs['j'] = shape shape = rectangle(0, 0.6, 0, 1) shape -= triangle(0.1, 1, 0.5, 1, 0.1, 0.6) shape -= triangle(0.5, 0, 0.1, 0, 0.1, 0.4) shape -= triangle(0.6, 0.95, 0.6, 0.05, 0.18, 0.5) shape.width = 0.6 _glyphs['K'] = shape shape = rectangle(0, 0.5, 0, 1) shape -= triangle(0.1, 1, 0.5, 1, 0.1, 0.45) shape -= triangle(0.36, 0, 0.1, 0, 0.1, 0.25) shape -= triangle(0.6, 1, 0.5, 0.0, 0.18, 0.35) shape -= triangle(0.1, 1, 0.6, 1, 0.6, 0.5) shape.width = 0.5 _glyphs['k'] = shape shape = rectangle(0, 0.6, 0, 0.1) shape += rectangle(0, 0.1, 0, 1) shape.width = 0.6 _glyphs['L'] = shape shape = rectangle(0.025, 0.125, 0, 1) shape.width = 0.15 _glyphs['l'] = shape shape = rectangle(0, 0.1, 0, 1) shape += rectangle(0.7, 0.8, 0, 1) shape += triangle(0, 1, 0.1, 1, 0.45, 0) shape += triangle(0.45, 0, 0.35, 0, 0, 1) shape += triangle(0.7, 1, 0.8, 1, 0.35, 0) shape += triangle(0.35, 0, 0.8, 1, 0.45, 0) shape.width = 0.8 _glyphs['M'] = shape shape = circle(0.175, 0.35, 0.175) - circle(0.175, 0.35, 0.075) shape += circle(0.425, 0.35, 0.175) - circle(0.425, 0.35, 0.075) shape &= rectangle(0, 0.65, 0.35, 0.65) shape += rectangle(0, 0.1, 0, 0.525) shape += rectangle(0.25, 0.35, 0, 0.35) shape += rectangle(0.5, 0.6, 0, 0.35) shape.width = 0.6 _glyphs['m'] = shape shape = rectangle(0, 0.1, 0, 1) shape += rectangle(0.5, 0.6, 0, 1) shape += triangle(0, 1, 0.1, 1, 0.6, 0) shape += triangle(0.6, 0, 0.5, 0, 0, 1) shape.width = 0.6 _glyphs['N'] = shape shape = circle(0.275, 0.275, 0.275) shape -= circle(0.275, 0.275, 0.175) shape &= rectangle(0, 0.55, 0.325, 0.55) shape += rectangle(0, 0.1, 0, 0.55) shape += rectangle(0.45, 0.55, 0, 0.325) shape.width = 0.55 _glyphs['n'] = shape shape = circle(0.3, 0.7, 0.3) - circle(0.3, 0.7, 0.2) shape += circle(0.3, 0.3, 0.3) - circle(0.3, 0.3, 0.2) shape -= rectangle(0, 0.6, 0.3, 0.7) shape += rectangle(0, 0.1, 0.3, 0.7) shape += rectangle(0.5, 0.6, 0.3, 0.7) shape.width = 0.6 _glyphs['O'] = shape shape = circle(0.275, 0.275, 0.275) shape -= circle(0.275, 0.275, 0.175) shape.width = 0.55 _glyphs['o'] = shape shape = circle(0.3, 0.725, 0.275) shape -= circle(0.3, 0.725, 0.175) shape &= rectangle(0.3, 1, 0, 1) shape += rectangle(0, 0.1, 0, 1) shape += rectangle(0.1, 0.3, 0.45, 0.55) shape += rectangle(0.1, 0.3, 0.9, 1) shape.width = 0.575 _glyphs['P'] = shape shape = circle(0.275, 0.275, 0.275) shape -= circle(0.275, 0.275, 0.175) shape += rectangle(0, 0.1, -0.375, 0.55) shape.width = 0.55 _glyphs['p'] = shape shape = circle(0.3, 0.7, 0.3) - circle(0.3, 0.7, 0.2) shape += circle(0.3, 0.3, 0.3) - circle(0.3, 0.3, 0.2) shape -= rectangle(0, 0.6, 0.3, 0.7) shape += rectangle(0, 0.1, 0.3, 0.7) shape += rectangle(0.5, 0.6, 0.3, 0.7) shape += triangle(0.5, 0.1, 0.6, 0.1, 0.6, 0) shape += triangle(0.5, 0.1, 0.5, 0.3, 0.6, 0.1) shape.width = 0.6 _glyphs['Q'] = shape shape = circle(0.275, 0.275, 0.275) - circle(0.275, 0.275, 0.175) shape += rectangle(0.45, 0.55, -0.375, 0.55) shape.width = 0.55 _glyphs['q'] = shape shape = circle(0.3, 0.725, 0.275) shape -= circle(0.3, 0.725, 0.175) shape &= rectangle(0.3, 1, 0, 1) shape += rectangle(0, 0.1, 0, 1) shape += rectangle(0.1, 0.3, 0.45, 0.55) shape += rectangle(0.1, 0.3, 0.9, 1) shape += triangle(0.3, 0.5, 0.4, 0.5, 0.575, 0) shape += triangle(0.475, 0.0, 0.3, 0.5, 0.575, 0) shape.width = 0.575 _glyphs['R'] = shape shape = circle(0.55, 0, 0.55) - scale_x(circle(0.55, 0, 0.45), 0.55, 0.8) shape &= rectangle(0, 0.55, 0, 0.55) shape = scale_x(shape, 0, 0.7) shape += rectangle(0, 0.1, 0, 0.55) shape.width = 0.385 _glyphs['r'] = shape shape = circle(0.275, 0.725, 0.275) shape -= circle(0.275, 0.725, 0.175) shape -= rectangle(0.275, 0.55, 0.45, 0.725) shape += reflect_x(reflect_y(shape, 0.5), .275) shape.width = 0.55 _glyphs['S'] = shape shape = circle(0.1625, 0.1625, 0.1625) shape -= scale_x(circle(0.165, 0.165, 0.0625), 0.165, 1.5) shape -= rectangle(0, 0.1625, 0.1625, 0.325) shape += reflect_x(reflect_y(shape, 0.275), 0.1625) shape = scale_x(shape, 0, 1.5) shape.width = 0.4875 _glyphs['s'] = shape shape = rectangle(0, 0.6, 0.9, 1) + rectangle(0.25, 0.35, 0, 0.9) shape.width = 0.6 _glyphs['T'] = shape shape = circle(0.4, 0.25, 0.25) - circle(0.4, 0.25, 0.15) shape &= rectangle(0, 0.4, 0, 0.25) shape += rectangle(0, 0.4, 0.55, 0.65) shape += rectangle(0.15, 0.25, 0.25, 1) shape.width = 0.4 _glyphs['t'] = shape shape = circle(0.3, 0.3, 0.3) - circle(0.3, 0.3, 0.2) shape &= rectangle(0, 0.6, 0, 0.3) shape += rectangle(0, 0.1, 0.3, 1) shape += rectangle(0.5, 0.6, 0.3, 1) shape.width = 0.6 _glyphs['U'] = shape shape = circle(0.275, 0.275, 0.275) - circle(0.275, 0.275, 0.175) shape &= rectangle(0, 0.55, 0, 0.275) shape += rectangle(0, 0.1, 0.275, 0.55) shape += rectangle(0.45, 0.55, 0, 0.55) shape.width = 0.55 _glyphs['u'] = shape shape = triangle(0, 1, 0.1, 1, 0.35, 0) shape += triangle(0.35, 0, 0.25, 0, 0, 1) shape += reflect_x(shape, 0.3) shape.width = 0.6 _glyphs['V'] = shape shape = triangle(0, 0.55, 0.1, 0.55, 0.35, 0) shape += triangle(0.35, 0, 0.25, 0, 0, 0.55) shape += reflect_x(shape, 0.3) shape.width = 0.6 _glyphs['v'] = shape shape = triangle(0, 1, 0.1, 1, 0.25, 0) shape += triangle(0.25, 0, 0.15, 0, 0, 1) shape += triangle(0.15, 0, 0.35, 1, 0.45, 1) shape += triangle(0.45, 1, 0.25, 0, 0.15, 0) shape += reflect_x(shape, 0.4) shape.width = 0.8 _glyphs['W'] = shape shape = triangle(0, 0.55, 0.1, 0.55, 0.25, 0) shape += triangle(0.25, 0, 0.15, 0, 0, 0.55) shape += triangle(0.15, 0, 0.35, 0.5, 0.45, 0.5) shape += triangle(0.45, 0.5, 0.25, 0, 0.15, 0) shape += reflect_x(shape, 0.4) shape.width = 0.8 _glyphs['w'] = shape shape = triangle(0, 1, 0.125, 1, 0.8, 0) shape += triangle(0.8, 0, 0.675, 0, 0, 1) shape += reflect_x(shape, 0.4) shape.width = 0.8 _glyphs['X'] = shape shape = triangle(0, 0.55, 0.125, 0.55, 0.55, 0) shape += triangle(0.55, 0, 0.425, 0, 0, 0.55) shape += reflect_x(shape, 0.275) shape.width = 0.55 _glyphs['x'] = shape shape = triangle(0, 1, 0.1, 1, 0.45, 0.5) shape += triangle(0.45, 0.5, 0.35, 0.5, 0, 1) shape += reflect_x(shape, 0.4) shape += rectangle(0.35, 0.45, 0, 0.5) shape.width = 0.8 _glyphs['Y'] = shape shape = triangle(0, 0.55, 0.1, 0.55, 0.325, 0) shape += triangle(0.325, 0, 0.225, 0, 0, 0.55) shape += reflect_x(shape, 0.275) + move(reflect_x(shape, 0.275), -0.225, -0.55) shape &= rectangle(0, 0.55, -0.375, 0.55) shape.width = 0.55 _glyphs['y'] = shape shape = rectangle(0, 0.6, 0, 1) shape -= triangle(0, 0.1, 0, 0.9, 0.45, 0.9) shape -= triangle(0.6, 0.1, 0.15, 0.1, 0.6, 0.9) shape.width = 0.6 _glyphs['Z'] = shape shape = rectangle(0, 0.6, 0, 0.55) shape -= triangle(0, 0.1, 0, 0.45, 0.45, 0.45) shape -= triangle(0.6, 0.1, 0.15, 0.1, 0.6, 0.45) shape.width = 0.6 _glyphs['z'] = shape shape = MathTree.Constant(1) shape.bounds = [0,0,0,0,None,None,None] shape.shape = True shape.width = 0.55 shape.xmin, shape.xmax = 0, 0.55 shape.ymin, shape.ymax = 0, 1 _glyphs[' '] = shape shape = circle(0.075, 0.075, 0.075) shape = scale_y(shape, 0.075, 3) shape &= rectangle(0.0, 0.15, -0.15, 0.075) shape -= triangle(0.075, 0.075, 0.0, -0.15, -0.5, 0.075) shape += circle(0.1, 0.075, 0.075) shape.width = 0.175 _glyphs[','] = shape shape = circle(0.075, 0.075, 0.075) shape.width = 0.15 _glyphs['.'] = shape shape = rectangle(0, 0.1, 0.55, 0.8) shape.width = 0.1 _glyphs["'"] = shape shape = rectangle(0, 0.1, 0.55, 0.8) + rectangle(0.2, 0.3, 0.55, 0.8) shape.width = 0.3 _glyphs['"'] = shape shape = circle(0.075, 0.15, 0.075) + circle(0.075, 0.45, 0.075) shape.width = 0.15 _glyphs[':'] = shape shape = circle(0.075, 0.15, 0.075) shape = scale_y(shape, 0.15, 3) shape &= rectangle(0.0, 0.15, -0.075, 0.15) shape -= triangle(0.075, 0.15, 0.0, -0.075, -0.5, 0.15) shape += circle(0.075, 0.45, 0.075) shape += circle(0.1, 0.15, 0.075) shape.width = 0.15 _glyphs[';'] = shape shape = rectangle(0.025, 0.125, 0.3, 1) shape += circle(0.075, 0.075, 0.075) shape.width = 0.1 _glyphs['!'] = shape shape = rectangle(0.05, 0.4, 0.35, 0.45) shape.width = 0.45 _glyphs['-'] = shape shape = circle(0, 0.4, 0.6) - scale_x(circle(0, 0.4, 0.5), 0, 0.7) shape &= rectangle(0, 0.6, -0.2, 1) shape = scale_x(shape, 0, 1/2.) shape.width = 0.3 _glyphs[')'] = shape shape = circle(0.6, 0.4, 0.6) - scale_x(circle(0.6, 0.4, 0.5), 0.6, 0.7) shape &= rectangle(0, 0.6, -0.2, 1) shape = scale_x(shape, 0, 1/2.) shape.width = 0.3 _glyphs['('] = shape shape = rectangle(0, 0.3, 0, 1) shape -= circle(0, 1, 0.2) shape -= rectangle(0, 0.2, 0, 0.7) shape.width = 0.3 _glyphs['1'] = shape shape = circle(0.275, .725, .275) shape -= circle(0.275, 0.725, 0.175) shape -= rectangle(0, 0.55, 0, 0.725) shape += rectangle(0, 0.55, 0, 0.1) shape += triangle(0, 0.1, 0.45, 0.775, 0.55, 0.725) shape += triangle(0, 0.1, 0.55, 0.725, 0.125, 0.1) shape.width = 0.55 _glyphs['2'] = shape shape = circle(0.3, 0.725, 0.275) shape -= circle(0.3, 0.725, 0.175) shape += circle(0.3, 0.275, 0.275) shape -= circle(0.3, 0.275, 0.175) shape -= rectangle(0, 0.275, 0.275, 0.725) shape.width = 0.55 _glyphs['3'] = shape shape = triangle(-0.10, 0.45, 0.4, 1, 0.4, 0.45) shape += rectangle(0.4, 0.5, 0, 1) shape -= triangle(0.4, 0.85, 0.4, 0.55, 0.1, 0.55) shape &= rectangle(0, 0.5, 0, 1) shape.width = 0.5 _glyphs['4'] = shape shape = circle(0.325, 0.325, 0.325) - circle(0.325, 0.325, 0.225) shape -= rectangle(0, 0.325, 0.325, 0.65) shape += rectangle(0, 0.325, 0.55, 0.65) shape += rectangle(0, 0.1, 0.55, 1) shape += rectangle(0.1, 0.65, 0.9, 1) shape.width = 0.65 _glyphs['5'] = shape shape = circle(0.275, 0.725, 0.275) - scale_y(circle(0.275, 0.725, 0.175), .725, 1.2) shape &= rectangle(0, 0.55, 0.725, 1) shape -= triangle(0.275, 0.925, 0.55, 0.9, 0.55, 0.725) shape = scale_y(shape, 1, 2) shape = scale_x(shape, 0, 1.1) shape -= rectangle(0.275, 0.65, 0., 0.7) shape += rectangle(0, 0.1, 0.275, 0.45) shape += circle(0.275, 0.275, 0.275) - circle(0.275, 0.275, 0.175) shape.width = 0.55 _glyphs['6'] = shape shape = rectangle(0, 0.6, 0.9, 1) shape += triangle(0, 0, 0.475, 0.9, 0.6, 0.9) shape += triangle(0, 0, 0.6, 0.9, 0.125, 0) shape.width = 0.6 _glyphs['7'] = shape shape = circle(0.3, 0.725, 0.275) shape -= circle(0.3, 0.725, 0.175) shape += circle(0.3, 0.275, 0.275) shape -= circle(0.3, 0.275, 0.175) shape.width = 0.55 _glyphs['8'] = shape shape = reflect_x(reflect_y(_glyphs['6'], 0.5), _glyphs['6'].width/2) shape.width = _glyphs['6'].width _glyphs['9'] = shape shape = circle(0.5, 0.5, 0.5) - scale_x(circle(0.5, 0.5, 0.4), 0.5, 0.7**0.5) shape = scale_x(shape, 0, 0.7) shape.width = 0.7 _glyphs['0'] = shape shape = rectangle(0., 0.5, 0.45, 0.55) shape += rectangle(0.2, 0.3, 0.25, 0.75) shape.width = 0.55 _glyphs['+'] = shape shape = triangle(0, 0, 0.425, 1, 0.55, 1) shape += triangle(0, 0, 0.55, 1, 0.125, 0) shape.width = 0.55 _glyphs['/'] = shape shape = circle(0.275, 0.725, 0.275) - circle(0.275, 0.725, 0.175) shape -= rectangle(0, 0.275, 0.45, 0.725) shape += rectangle(0.225, 0.325, 0.3, 0.55) shape += circle(0.275, 0.075, 0.075) shape.width = 0.55 _glyphs['?'] = shape del shape

25.643939

85

0.58257

3,585

16,925

2.71046

0.041004

0.036843

0.189873

0.113615

0.845837

0.748997

0.655346

0.528558

0.474118

0.430174

0

0.228326

0.181507

16,925

659

86

25.682853

0.473111

0.010281

0

0.359504

0

0.008462

0

null

0.004132

0.002066

null

0.002066

0

null

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1

0

1

0

1

0

null

0

1

0

6

49a2c5e5f75f28db4ece07e1c76aa61e3215e183

34

py

Python

examples/math.lgamma/ex1.py

mcorne/python-by-example

15339c0909c84b51075587a6a66391100971c033

[ "MIT" ]

null

examples/math.lgamma/ex1.py

mcorne/python-by-example

15339c0909c84b51075587a6a66391100971c033

[ "MIT" ]

null

examples/math.lgamma/ex1.py

mcorne/python-by-example

15339c0909c84b51075587a6a66391100971c033

[ "MIT" ]

null

import math print(math.lgamma(2))

11.333333

21

0.764706

6

34

4.333333

0.833333

0

0.032258

0.088235

34

2

22

17

0.806452

0

1

0

true

0

0.5

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

772929c5030d8fc7d1b696f173eb6effdadd4ba5

60

py

Python

emlearn/tools/__init__.py

Brax94/emlearn

cc5fd962f5af601c02dfe0ec9203d1b30e6b3aef

[ "MIT" ]

161

2019-03-12T16:07:20.000Z

2022-03-31T06:24:38.000Z

emlearn/tools/__init__.py

Brax94/emlearn

cc5fd962f5af601c02dfe0ec9203d1b30e6b3aef

[ "MIT" ]

35

2019-05-14T11:34:04.000Z

2022-02-04T20:09:34.000Z

emlearn/tools/__init__.py

Brax94/emlearn

cc5fd962f5af601c02dfe0ec9203d1b30e6b3aef

[ "MIT" ]

27

2019-03-11T01:09:27.000Z

2021-12-27T22:56:04.000Z

from . import mel_filterbank from . import window_function

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65b7e06b740affffb8bbe74670c4341e8a0bae11

46

py

Python

federated_aggregations/__init__.py

tf-encrypted/federated-aggregations

b4ab7a15c2719d4119db7d9d609f8c06d9df8958

[ "Apache-2.0" ]

16

2020-08-07T05:40:09.000Z

2022-01-08T20:32:07.000Z

federated_aggregations/__init__.py

tf-encrypted/federated-aggregations

b4ab7a15c2719d4119db7d9d609f8c06d9df8958

[ "Apache-2.0" ]

1

2020-10-14T00:18:39.000Z

2020-10-19T14:13:03.000Z

federated_aggregations/__init__.py

tf-encrypted/federated-aggregations

b4ab7a15c2719d4119db7d9d609f8c06d9df8958

[ "Apache-2.0" ]

2

2020-09-08T10:16:28.000Z

2021-01-14T12:33:01.000Z

from . import channels from . import paillier

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028f7931e2bad42389a82de6259959a1fa81c700

132

py

Python

python/testData/inspections/PyAbstractClassInspection/quickFix/AddABCToSuperclasses/main_after.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/inspections/PyAbstractClassInspection/quickFix/AddABCToSuperclasses/main_after.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/inspections/PyAbstractClassInspection/quickFix/AddABCToSuperclasses/main_after.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

from abc import ABC from PyAbstractClassInspection.quickFix.AddABCToSuperclasses.main_import import A1 class A2(A1, ABC): pass

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6

02eaab6bf16c2c54a15d969b86eae1a7ce5c0db3

12,444

py

Python

ppr-api/src/ppr_api/models/search_utils.py

jordiwes/ppr

5e0cdf75036198b87a0f46a732c0e784e5f32259

[ "Apache-2.0" ]

4

2020-01-21T21:46:42.000Z

2021-02-24T18:30:24.000Z

ppr-api/src/ppr_api/models/search_utils.py

jordiwes/ppr

5e0cdf75036198b87a0f46a732c0e784e5f32259

[ "Apache-2.0" ]

1,313

2019-10-18T22:48:16.000Z

2022-03-30T17:42:47.000Z

ppr-api/src/ppr_api/models/search_utils.py

jordiwes/ppr

5e0cdf75036198b87a0f46a732c0e784e5f32259

[ "Apache-2.0" ]

201

2019-10-18T21:34:41.000Z

2022-03-31T20:07:42.000Z

# Copyright © 2019 Province of British Columbia # # 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. """Model helper utilities for processing search query and search detail requests. Search constants and helper functions. """ # flake8: noqa Q000,E122,E131 # Disable Q000: Allow query strings to be in double quotation marks that contain single quotation marks. # Disable E122: allow query strings to be more human readable. # Disable E131: allow query strings to be more human readable. GET_DETAIL_DAYS_LIMIT = 7 # Number of days in the past a get details request is allowed. # Maximum number of days in the past to filter when fetching account search history: set to <= 0 to disable. GET_HISTORY_DAYS_LIMIT = -1 # Account search history max result set size. ACCOUNT_SEARCH_HISTORY_MAX_SIZE = 1000 # Maximum number or results returned by search. SEARCH_RESULTS_MAX_SIZE = 1000 # Result set size limit clause RESULTS_SIZE_LIMIT_CLAUSE = 'FETCH FIRST :max_results_size ROWS ONLY' # Serial number search base where clause SERIAL_SEARCH_BASE = """ SELECT r.registration_type,r.registration_ts AS base_registration_ts, sc.serial_type,sc.serial_number,sc.year,sc.make,sc.model, r.registration_number AS base_registration_num, CASE WHEN serial_number = :query_value THEN 'EXACT' ELSE 'SIMILAR' END match_type, fs.expire_date,fs.state_type,sc.id AS vehicle_id, sc.mhr_number FROM registrations r, financing_statements fs, serial_collateral sc WHERE r.financing_id = fs.id AND r.registration_type_cl IN ('PPSALIEN', 'MISCLIEN', 'CROWNLIEN') AND r.base_reg_number IS NULL AND (fs.expire_date IS NULL OR fs.expire_date > ((now() at time zone 'utc') - interval '30 days')) AND NOT EXISTS (SELECT r3.id FROM registrations r3 WHERE r3.financing_id = fs.id AND r3.registration_type_cl = 'DISCHARGE' AND r3.registration_ts < ((now() at time zone 'utc') - interval '30 days')) AND sc.financing_id = fs.id AND sc.registration_id_end IS NULL """ # Equivalent logic as DB view search_by_reg_num_vw, but API determines the where clause. REG_NUM_QUERY = """ SELECT r2.registration_type, r2.registration_ts AS base_registration_ts, r2.registration_number AS base_registration_num, 'EXACT' AS match_type, fs.state_type, fs.expire_date FROM registrations r, financing_statements fs, registrations r2 WHERE r.financing_id = fs.id AND r2.financing_id = fs.id AND r2.registration_type_cl IN ('PPSALIEN', 'MISCLIEN', 'CROWNLIEN') AND r.registration_number = :query_value AND (fs.expire_date IS NULL OR fs.expire_date > ((now() at time zone 'utc') - interval '30 days')) AND NOT EXISTS (SELECT r3.id FROM registrations r3 WHERE r3.financing_id = fs.id AND r3.registration_type_cl = 'DISCHARGE' AND r3.registration_ts < ((now() at time zone 'utc') - interval '30 days')) """ # Equivalent logic as DB view search_by_mhr_num_vw, but API determines the where clause. MHR_NUM_QUERY = SERIAL_SEARCH_BASE + \ " AND sc.serial_type = 'MH' " + \ "AND sc.mhr_number = (SELECT searchkey_mhr(:query_value)) " + \ "ORDER BY match_type, r.registration_ts ASC " + RESULTS_SIZE_LIMIT_CLAUSE # Equivalent logic as DB view search_by_serial_num_vw, but API determines the where clause. SERIAL_NUM_QUERY = SERIAL_SEARCH_BASE + \ " AND sc.serial_type NOT IN ('AC', 'AF', 'AP') " + \ "AND sc.srch_vin = (SELECT searchkey_vehicle(:query_value)) " + \ "ORDER BY match_type, sc.serial_number " + RESULTS_SIZE_LIMIT_CLAUSE # Equivalent logic as DB view search_by_aircraft_dot_vw, but API determines the where clause. AIRCRAFT_DOT_QUERY = SERIAL_SEARCH_BASE + \ " AND sc.serial_type IN ('AC', 'AF', 'AP') " + \ "AND sc.srch_vin = (SELECT searchkey_aircraft(:query_value)) " + \ "ORDER BY match_type, sc.serial_number " + RESULTS_SIZE_LIMIT_CLAUSE BUSINESS_NAME_QUERY = """ SELECT r.registration_type,r.registration_ts AS base_registration_ts, p.business_name, r.registration_number AS base_registration_num, CASE WHEN p.business_name = :query_bus_name THEN 'EXACT' ELSE 'SIMILAR' END match_type, fs.expire_date,fs.state_type,p.id FROM registrations r, financing_statements fs, parties p WHERE r.financing_id = fs.id AND r.registration_type_cl IN ('PPSALIEN', 'MISCLIEN', 'CROWNLIEN') AND r.base_reg_number IS NULL AND (fs.expire_date IS NULL OR fs.expire_date > ((now() at time zone 'utc') - interval '30 days')) AND NOT EXISTS (SELECT r3.id FROM registrations r3 WHERE r3.financing_id = fs.id AND r3.registration_type_cl = 'DISCHARGE' AND r3.registration_ts < ((now() at time zone 'utc') - interval '30 days')) AND p.financing_id = fs.id AND p.registration_id_end IS NULL AND p.party_type = 'DB' AND (SELECT searchkey_business_name(:query_bus_name)) <% p.business_srch_key AND word_similarity(p.business_srch_key, (SELECT searchkey_business_name(:query_bus_name))) >= .60 ORDER BY match_type, p.business_name """ + RESULTS_SIZE_LIMIT_CLAUSE INDIVIDUAL_NAME_QUERY = """ SELECT r.registration_type,r.registration_ts AS base_registration_ts, p.last_name,p.first_name,p.middle_initial,p.id, r.registration_number AS base_registration_num, CASE WHEN p.last_name = :query_last AND p.first_name = :query_first THEN 'EXACT' ELSE 'SIMILAR' END match_type, fs.expire_date,fs.state_type, p.birth_date FROM registrations r, financing_statements fs, parties p WHERE r.financing_id = fs.id AND r.registration_type_cl IN ('PPSALIEN', 'MISCLIEN', 'CROWNLIEN') AND r.base_reg_number IS NULL AND (fs.expire_date IS NULL OR fs.expire_date > ((now() at time zone 'utc') - interval '30 days')) AND NOT EXISTS (SELECT r3.id FROM registrations r3 WHERE r3.financing_id = fs.id AND r3.registration_type_cl = 'DISCHARGE' AND r3.registration_ts < ((now() at time zone 'utc') - interval '30 days')) AND p.financing_id = fs.id AND p.registration_id_end IS NULL AND p.party_type = 'DI' AND p.id IN (SELECT * FROM unnest(match_individual_name(:query_last, :query_first))) ORDER BY match_type, p.last_name, p.first_name """ + RESULTS_SIZE_LIMIT_CLAUSE INDIVIDUAL_NAME_MIDDLE_QUERY = """ SELECT r.registration_type,r.registration_ts AS base_registration_ts, p.last_name,p.first_name,p.middle_initial,p.id, r.registration_number AS base_registration_num, CASE WHEN p.last_name = :query_last AND p.first_name = :query_first AND p.middle_initial = :query_middle THEN 'EXACT' ELSE 'SIMILAR' END match_type, fs.expire_date,fs.state_type, p.birth_date FROM registrations r, financing_statements fs, parties p WHERE r.financing_id = fs.id AND r.registration_type_cl IN ('PPSALIEN', 'MISCLIEN', 'CROWNLIEN') AND r.base_reg_number IS NULL AND (fs.expire_date IS NULL OR fs.expire_date > ((now() at time zone 'utc') - interval '30 days')) AND NOT EXISTS (SELECT r3.id FROM registrations r3 WHERE r3.financing_id = fs.id AND r3.registration_type_cl = 'DISCHARGE' AND r3.registration_ts < ((now() at time zone 'utc') - interval '30 days')) AND p.financing_id = fs.id AND p.registration_id_end IS NULL AND p.party_type = 'DI' AND p.id IN (SELECT * FROM unnest(match_individual_name(:query_last, :query_first))) ORDER BY match_type, p.last_name, p.first_name """ + RESULTS_SIZE_LIMIT_CLAUSE # Total result count queries for serial number, debtor name searches: BUSINESS_NAME_TOTAL_COUNT = """ SELECT COUNT(r.id) AS query_count FROM registrations r, financing_statements fs, parties p WHERE r.financing_id = fs.id AND r.registration_type_cl IN ('PPSALIEN', 'MISCLIEN', 'CROWNLIEN') AND r.base_reg_number IS NULL AND (fs.expire_date IS NULL OR fs.expire_date > ((now() at time zone 'utc') - interval '30 days')) AND NOT EXISTS (SELECT r3.id FROM registrations r3 WHERE r3.financing_id = fs.id AND r3.registration_type_cl = 'DISCHARGE' AND r3.registration_ts < ((now() at time zone 'utc') - interval '30 days')) AND p.financing_id = fs.id AND p.registration_id_end IS NULL AND p.party_type = 'DB' AND (SELECT searchkey_business_name(:query_bus_name)) <% p.business_srch_key AND word_similarity(p.business_srch_key, (SELECT searchkey_business_name(:query_bus_name))) >= .60 """ INDIVIDUAL_NAME_TOTAL_COUNT = """ SELECT COUNT(r.id) AS query_count FROM registrations r, financing_statements fs, parties p WHERE r.financing_id = fs.id AND r.registration_type_cl IN ('PPSALIEN', 'MISCLIEN', 'CROWNLIEN') AND r.base_reg_number IS NULL AND (fs.expire_date IS NULL OR fs.expire_date > ((now() at time zone 'utc') - interval '30 days')) AND NOT EXISTS (SELECT r3.id FROM registrations r3 WHERE r3.financing_id = fs.id AND r3.registration_type_cl = 'DISCHARGE' AND r3.registration_ts < ((now() at time zone 'utc') - interval '30 days')) AND p.financing_id = fs.id AND p.registration_id_end IS NULL AND p.party_type = 'DI' AND p.id IN (SELECT * FROM unnest(match_individual_name(:query_last, :query_first))) """ SERIAL_SEARCH_COUNT_BASE = """ SELECT COUNT(r.id) AS query_count FROM registrations r, financing_statements fs, serial_collateral sc WHERE r.financing_id = fs.id AND r.registration_type_cl IN ('PPSALIEN', 'MISCLIEN', 'CROWNLIEN') AND r.base_reg_number IS NULL AND (fs.expire_date IS NULL OR fs.expire_date > ((now() at time zone 'utc') - interval '30 days')) AND NOT EXISTS (SELECT r3.id FROM registrations r3 WHERE r3.financing_id = fs.id AND r3.registration_type_cl = 'DISCHARGE' AND r3.registration_ts < ((now() at time zone 'utc') - interval '30 days')) AND sc.financing_id = fs.id AND sc.registration_id_end IS NULL """ MHR_NUM_TOTAL_COUNT = SERIAL_SEARCH_COUNT_BASE + \ " AND sc.serial_type = 'MH' " + \ "AND sc.mhr_number = searchkey_mhr(:query_value)" SERIAL_NUM_TOTAL_COUNT = SERIAL_SEARCH_COUNT_BASE + \ " AND sc.serial_type NOT IN ('AC', 'AF') " + \ "AND sc.srch_vin = searchkey_vehicle(:query_value)" AIRCRAFT_DOT_TOTAL_COUNT = SERIAL_SEARCH_COUNT_BASE + \ " AND sc.serial_type IN ('AC', 'AF') " + \ "AND sc.srch_vin = searchkey_aircraft(:query_value)" COUNT_QUERY_FROM_SEARCH_TYPE = { 'AC': AIRCRAFT_DOT_TOTAL_COUNT, 'BS': BUSINESS_NAME_TOTAL_COUNT, 'IS': INDIVIDUAL_NAME_TOTAL_COUNT, 'MH': MHR_NUM_TOTAL_COUNT, 'SS': SERIAL_NUM_TOTAL_COUNT } ACCOUNT_SEARCH_HISTORY_DATE_QUERY = \ 'SELECT sc.id, sc.search_ts, sc.api_criteria, sc.total_results_size, sc.returned_results_size,' + \ 'sr.exact_match_count, sr.similar_match_count ' + \ 'FROM search_requests sc, search_results sr ' + \ 'WHERE sc.id = sr.search_id ' + \ "AND sc.account_id = '?' " + \ "AND sc.search_ts > ((now() at time zone 'utc') - interval '" + str(GET_HISTORY_DAYS_LIMIT) + " days') " + \ 'ORDER BY sc.search_ts DESC ' + \ 'FETCH FIRST ' + str(ACCOUNT_SEARCH_HISTORY_MAX_SIZE) + ' ROWS ONLY' ACCOUNT_SEARCH_HISTORY_QUERY = \ 'SELECT sc.id, sc.search_ts, sc.api_criteria, sc.total_results_size, sc.returned_results_size,' + \ 'sr.exact_match_count, sr.similar_match_count ' + \ 'FROM search_requests sc, search_results sr ' + \ 'WHERE sc.id = sr.search_id ' + \ "AND sc.account_id = '?' " + \ 'ORDER BY sc.search_ts DESC ' + \ 'FETCH FIRST ' + str(ACCOUNT_SEARCH_HISTORY_MAX_SIZE) + ' ROWS ONLY'

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1

0

1

0

null

0

6

f301ed1f95018bbf5d43822d3c38f6118815c6fc

33

py

Python

tmr_description/src/urdf_modification/__init__.py

kentsai0319/tmr_ros1_dev

2953f3317d2fdac8b1fc79ffbe661d3b978eb658

[ "BSD-3-Clause" ]

null

tmr_description/src/urdf_modification/__init__.py

kentsai0319/tmr_ros1_dev

2953f3317d2fdac8b1fc79ffbe661d3b978eb658

[ "BSD-3-Clause" ]

null

tmr_description/src/urdf_modification/__init__.py

kentsai0319/tmr_ros1_dev

2953f3317d2fdac8b1fc79ffbe661d3b978eb658

[ "BSD-3-Clause" ]

null

from .urdf_modification import *

16.5

32

0.818182

4

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6.5

1

0

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33

1

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0.896552

0

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true

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1

0

6

b87e84ea190a77d711f5373bbf7836621e8c7a01

195

py

Python

data_sets/synthetic_review_prediction/article_0/__init__.py

Octavian-ai/graph-node-categorizer

80fb2606ff2eebd0273cfaf756578330b6a87bb0

[ "MIT" ]

1

2017-12-15T19:36:12.000Z

data_sets/synthetic_review_prediction/article_0/__init__.py

Octavian-ai/basic-graph-connection

80fb2606ff2eebd0273cfaf756578330b6a87bb0

[ "MIT" ]

null

data_sets/synthetic_review_prediction/article_0/__init__.py

Octavian-ai/basic-graph-connection

80fb2606ff2eebd0273cfaf756578330b6a87bb0

[ "MIT" ]

null

from .configure import DATASET_NAME, create_data_set_properties from .generate import run as _run def run(client): print(DATASET_NAME) return _run(client, create_data_set_properties())

24.375

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0.151724

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null

0

1

0

1

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6

b89868403fb5e4ba6860e43dae51747244f03277

94

py

Python

refgenieserver/__init__.py

databio/refgenies

2f554de08566da6faaf6a78e06dfcb80bb609219

[ "BSD-2-Clause" ]

5

2019-07-12T16:44:58.000Z

2020-02-03T06:04:03.000Z

refgenieserver/__init__.py

databio/refgenieserver

2f554de08566da6faaf6a78e06dfcb80bb609219

[ "BSD-2-Clause" ]

53

2019-05-24T16:43:17.000Z

2020-06-18T14:35:51.000Z

refgenieserver/__init__.py

databio/refgenie_server

2f554de08566da6faaf6a78e06dfcb80bb609219

[ "BSD-2-Clause" ]

1

2019-05-29T19:47:20.000Z

from .const import * from .helpers import * from .main import * from .server_builder import *

18.8

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0.744681

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94

5.307692

0.538462

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94

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1

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1

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6

b2233a032e807f3e832c02081f15d61e8b2bfdc1

44

py

Python

code/Python/hello_world_my.py

souvickroy02/Hello-World

a25bea68b2a22e68c3dffb5824e36f3fce33a8d3

[ "MIT" ]

63

2019-09-30T16:16:19.000Z

2021-06-17T17:23:06.000Z

code/Python/hello_world_my.py

souvickroy02/Hello-World

a25bea68b2a22e68c3dffb5824e36f3fce33a8d3

[ "MIT" ]

242

2019-09-30T14:07:06.000Z

2020-10-01T13:52:13.000Z

code/Python/hello_world_my.py

souvickroy02/Hello-World

a25bea68b2a22e68c3dffb5824e36f3fce33a8d3

[ "MIT" ]

743

2019-09-30T13:58:42.000Z

2021-12-29T21:58:28.000Z

print("Hello World") # prints "Hello World"

22

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1

0

1

0

6

b24c8eb72b047dfa1f0b814f732d1c9488a4e77b

8,920

py

Python

ProyectoMN/app/main.py

hakuruklis/Proyecto-Metodos-Numericos

1e67dcfd64f62bce9f898637635fa17e3a831dcb

[ "MIT" ]

null

ProyectoMN/app/main.py

hakuruklis/Proyecto-Metodos-Numericos

1e67dcfd64f62bce9f898637635fa17e3a831dcb

[ "MIT" ]

null

ProyectoMN/app/main.py

hakuruklis/Proyecto-Metodos-Numericos

1e67dcfd64f62bce9f898637635fa17e3a831dcb

[ "MIT" ]

null

from flask import Flask from flask import render_template from flask import request from flask import make_response from flask import session app = Flask(__name__) app.secret_key = 'F12Zr47j\3yX R~X@H!jmM]Lwf/,?KT' @app.route('/') def index(): return render_template('index.html') @app.route('/iniciarTest', methods=['POST']) def inicio(): if request.method == 'POST': session['desde'] = float(request.form['desde']) session['hasta'] = float(request.form['hasta']) session['H']=float(request.form['N']) if session['H'] == 2: h=(session['hasta']-session['desde'])/session['H'] c11=session['desde'] c12=h+c11 c13=c12+h respuesta = make_response(render_template('N2.html', c11=c11, c12=c12, c13=c13, h=h)) elif session['H'] == 3: h=(session['hasta']-session['desde'])/session['H'] c11=session['desde'] c12=h+c11 c13=c12+h c14=session['hasta'] respuesta = make_response(render_template('N3.html', c11=c11, c12=c12, c13=c13, c14=c14, h=h)) elif session['H']==4: h = (session['hasta'] - session['desde']) / session['H'] c11 = session['desde'] c12 = h + c11 c13 = c12 + h c14 = c13 + h c15 = session['hasta'] respuesta = make_response(render_template('N4.html', c11=c11, c12=c12, c13=c13, c14=c14, c15=c15, h=h)) elif session['H']==5: h = (session['hasta'] - session['desde']) / session['H'] c11 = session['desde'] c12 = h + c11 c13 = c12 + h c14 = c13 + h c15 = c14 + h c16 = session['hasta'] respuesta = make_response(render_template('N5.html', c11=c11, c12=c12, c13=c13, c14=c14, c15=c15, c16=c16, h=h)) elif session['H']==6: h = (session['hasta'] - session['desde']) / session['H'] c11 = session['desde'] c12 = h + c11 c13 = c12 + h c14 = c13 + h c15 = c14 + h c16 = c15 + h c17 = session['hasta'] respuesta = make_response(render_template('N6.html', c11=c11, c12=c12, c13=c13, c14=c14, c15=c15, c16=c16, c17=c17, h=h)) elif session['H']==7: h = (session['hasta'] - session['desde']) / session['H'] c11 = session['desde'] c12 = h + c11 c13 = c12 + h c14 = c13 + h c15 = c14 + h c16 = c15 + h c17 = c16 + h c18 = session['hasta'] respuesta = make_response(render_template('N7.html', c11=c11, c12=c12, c13=c13, c14=c14, c15=c15, c16=c16, c17=c17, c18=c18, h=h)) elif session['H']==8: h = (session['hasta'] - session['desde']) / session['H'] c11 = session['desde'] c12 = h + c11 c13 = c12 + h c14 = c13 + h c15 = c14 + h c16 = c15 + h c17 = c16 + h c18 = c17 + h c19 = session['hasta'] respuesta = make_response(render_template('N8.html', c11=c11, c12=c12, c13=c13, c14=c14, c15=c15, c16=c16, c17=c17, c18=c18, c19=c19, h=h)) return respuesta @app.route('/N2', methods=['POST']) def N2(): valor21 = float(request.form['21']) valor22 = float(request.form['22']) valor23 = float(request.form['23']) operacion=request.form['operacion'] if operacion == 'Trapecio': resultado=((session['hasta']-session['desde'])/session['H'])*(valor21+(valor22*2)+valor23*2) elif operacion == '1/3': resultado=(((session['hasta']-session['desde'])/session['H'])*(1/3))*(valor21+(valor22*4)+valor23) respuesta = make_response(render_template('Resultado.html', S=resultado)) return respuesta @app.route('/N3', methods=['POST']) def N3(): valor21 = float(request.form['21']) valor22 = float(request.form['22']) valor23 = float(request.form['23']) valor24 = float(request.form['24']) operacion=request.form['operacion'] if operacion == 'Trapecio': resultado=((session['hasta']-session['desde'])/session['H'])*(0.5)*(valor21+(valor22*2)+(valor23*2)+valor24) elif operacion == '3/8': resultado=(((session['hasta']-session['desde'])/session['H'])*(3/8))*(valor21+(valor22*3)+(valor23*3)+valor24) respuesta = make_response(render_template('Resultado.html', S=resultado)) return respuesta @app.route('/N4', methods=['POST']) def N4(): valor21 = float(request.form['21']) valor22 = float(request.form['22']) valor23 = float(request.form['23']) valor24 = float(request.form['24']) valor25 = float(request.form['25']) operacion=request.form['operacion'] if operacion == 'Trapecio': resultado=((session['hasta']-session['desde'])/session['H'])*(0.5)*(valor21+(valor22*2)+(valor23*2)+(valor24*2)+valor25) elif operacion == '1/3': resultado=(((session['hasta']-session['desde'])/session['H'])*(1/3))*(valor21+(valor23*4)+valor25) respuesta = make_response(render_template('Resultado.html', S=resultado)) return respuesta @app.route('/N5', methods=['POST']) def N5(): valor21 = float(request.form['21']) valor22 = float(request.form['22']) valor23 = float(request.form['23']) valor24 = float(request.form['24']) valor25 = float(request.form['25']) valor26 = float(request.form['26']) operacion=request.form['operacion'] if operacion == 'Trapecio': resultado=(((session['hasta']-session['desde'])/session['H'])*0.5)*(valor21+(valor22*2)+(valor23*2)+(valor24*2)+(valor25*2)+valor26) elif operacion == '3/8': resultado=(((session['hasta']-session['desde'])/session['H'])*(3/8))*(valor21+(valor22*3)+(valor23*3)+valor24)+(((session['hasta'] - session['desde']) / session['H']) * (1/3)) * (valor24 + (valor25 * 4) +valor26) respuesta = make_response(render_template('Resultado.html', S=resultado)) return respuesta @app.route('/N6', methods=['POST']) def N6(): valor21 = float(request.form['21']) valor22 = float(request.form['22']) valor23 = float(request.form['23']) valor24 = float(request.form['24']) valor25 = float(request.form['25']) valor26 = float(request.form['26']) valor27 = float(request.form['27']) operacion=request.form['operacion'] if operacion == 'Trapecio': resultado=((session['hasta']-session['desde'])/session['H'])*(0.5)*(valor21+(valor22*2)+(valor23*2)+(valor24*2)+(valor25*2)+(valor26*2)+valor27) elif operacion == '1/3': resultado=(((session['hasta']-session['desde'])/2)*(1/3))*(valor21+(valor24*4)+valor27) respuesta = make_response(render_template('Resultado.html', S=resultado)) return respuesta @app.route('/N7', methods=['POST']) def N7(): valor21 = float(request.form['21']) valor22 = float(request.form['22']) valor23 = float(request.form['23']) valor24 = float(request.form['24']) valor25 = float(request.form['25']) valor26 = float(request.form['26']) valor27 = float(request.form['27']) valor28 = float(request.form['28']) operacion = request.form['operacion'] if operacion == 'Trapecio': resultado = (((session['hasta'] - session['desde']) / session['H']) * 0.5) * ( valor21 + (valor22 * 2) + (valor23 * 2) + (valor24 * 2) + (valor25 * 2) + (valor26*2) + (valor27*2) + valor28) elif operacion == '3/8': resultado = (((session['hasta'] - session['desde']) / session['H']) * (3 / 8)) * ( valor21 + (valor22 * 3) + (valor23 * 3) + (valor24 * 3) + (valor25 * 3) + (valor26*3) + (valor27*3) + valor28) respuesta = make_response(render_template('Resultado.html', S=resultado)) return respuesta @app.route('/N8', methods=['POST']) def N8(): valor21 = float(request.form['21']) valor22 = float(request.form['22']) valor23 = float(request.form['23']) valor24 = float(request.form['24']) valor25 = float(request.form['25']) valor26 = float(request.form['26']) valor27 = float(request.form['27']) valor28 = float(request.form['28']) valor29 = float(request.form['29']) operacion = request.form['operacion'] if operacion == 'Trapecio': resultado = (((session['hasta'] - session['desde']) / session['H']) * 0.5) * ( valor21 + (valor22 * 2) + (valor23 * 2) + (valor24 * 2) + (valor25 * 2) + (valor26*2) + (valor27*2) + (valor28*2) + valor29) elif operacion == '1/3': resultado = (((session['hasta'] - session['desde']) / 2) * (1/3)) * (valor21 + (valor25 * 4) + valor29) respuesta = make_response(render_template('Resultado.html', S=resultado)) return respuesta @app.route('/Inicio', methods=['POST']) def inicioo(): return render_template('index.html') if __name__ == "__main__": app.run(debug=True)

41.105991

220

0.576121

1,105

8,920

4.609955

0.082353

0.112289

0.141343

0.103651

0.857676

0.818414

0.758736

0.752061

0.746565

0

0.107643

0.224103

8,920

216

221

41.296296

0.628377

0

0.637306

0

0.094731

0

1

0.051813

false

0

0.025907

0.010363

0.129534

0

null

0

1

0

null

0

6

b2605a24626625fb91ca87d59aaf6bd768870172

137

py

Python

fastccd_support_ioc/utils/setClocksBiasOn.py

lbl-camera/fastccd_support_ioc

80b3820744e9aec7923af6adec0a66a0c51b2c21

[ "BSD-3-Clause" ]

null

fastccd_support_ioc/utils/setClocksBiasOn.py

lbl-camera/fastccd_support_ioc

80b3820744e9aec7923af6adec0a66a0c51b2c21

[ "BSD-3-Clause" ]

1

2020-08-07T22:22:25.000Z

fastccd_support_ioc/utils/setClocksBiasOn.py

lbl-camera/fastccd_support_ioc

80b3820744e9aec7923af6adec0a66a0c51b2c21

[ "BSD-3-Clause" ]

1

2021-02-08T22:06:05.000Z

from fastccd_support_ioc.utils import cin_functions cin_functions.WriteReg("8204", "0001", 1) cin_functions.WriteReg("8205", "0009", 1)

27.4

51

0.781022

20

137

5.1

0.7

0.352941

0.392157

0

0.142857

0.080292

137

4

52

34.25

0.666667

0

0.116788

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

b2967f09846ea808f68f13f98ee805a19a109e19

46

py

Python

tests/test_get_general_info.py

cemsinano/pykap

b49b2c53d40aa27b68186fb8b595dd41f3c5a21b

[ "MIT" ]

2

2021-06-09T06:25:23.000Z

2022-02-14T06:42:54.000Z

tests/test_get_general_info.py

cemsinano/pykap

b49b2c53d40aa27b68186fb8b595dd41f3c5a21b

[ "MIT" ]

null

tests/test_get_general_info.py

cemsinano/pykap

b49b2c53d40aa27b68186fb8b595dd41f3c5a21b

[ "MIT" ]

null

def test_get_general_info(): assert False

15.333333

28

0.76087

7

46

4.571429

1

0

0.173913

46

2

29

23

0.842105

0

0.5

1

0.5

true

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

6

b2a2ee690d6b7d667d9f8b1689af65175a4897b6

20

py

Python

pddb/lib/__init__.py

genwch/pddb

d1f5ff9bfc29f1e9e4f3b6f53304b56224256f15

[ "MIT" ]

null

pddb/lib/__init__.py

genwch/pddb

d1f5ff9bfc29f1e9e4f3b6f53304b56224256f15

[ "MIT" ]

null

pddb/lib/__init__.py

genwch/pddb

d1f5ff9bfc29f1e9e4f3b6f53304b56224256f15

[ "MIT" ]

null

from .pddb import *

10

19

0.7

3

20

4.666667

1

0

0.2

20

1

20

0.875

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

b2aa405f204e7c276030998aa3215886f32f9963

24,382

py

Python

wolf/flows/couplings/coupling.py

andrecianflone/wolf

826bbedc58d4d29871110349356868066a3108e6

[ "Apache-2.0" ]

75

2020-03-31T22:21:04.000Z

2022-03-20T10:58:17.000Z

wolf/flows/couplings/coupling.py

andrecianflone/wolf

826bbedc58d4d29871110349356868066a3108e6

[ "Apache-2.0" ]

3

2021-02-03T07:07:14.000Z

2022-03-08T20:58:43.000Z

wolf/flows/couplings/coupling.py

andrecianflone/wolf

826bbedc58d4d29871110349356868066a3108e6

[ "Apache-2.0" ]

10

2020-04-27T05:31:44.000Z

2021-11-21T14:11:16.000Z

__author__ = 'max' from overrides import overrides from typing import Tuple, Dict import torch from wolf.flows.couplings.blocks import NICEConvBlock, MCFBlock, NICEMLPBlock from wolf.flows.couplings.blocks import LocalLinearCondNet, GlobalLinearCondNet, GlobalAttnCondNet from wolf.flows.flow import Flow from wolf.flows.couplings.transform import Additive, Affine, NLSQ, ReLU, SymmELU class NICE1d(Flow): """ NICE Flow for 1D data """ def __init__(self, in_features, hidden_features=None, inverse=False, split_type='continuous', order='up', transform='affine', alpha=1.0, type='mlp', activation='elu'): super(NICE1d, self).__init__(inverse) self.in_features = in_features self.factor = 2 assert split_type in ['continuous', 'skip'] assert in_features % self.factor == 0 assert order in ['up', 'down'] self.split_type = split_type self.up = order == 'up' if hidden_features is None: hidden_features = min(8 * in_features, 512) out_features = in_features // self.factor in_features = in_features - out_features self.z1_features = in_features if self.up else out_features assert transform in ['additive', 'affine'] if transform == 'additive': self.transform = Additive() self.analytic_bwd = True elif transform == 'affine': self.transform = Affine(dim=-1, alpha=alpha) self.analytic_bwd = True out_features = out_features * 2 else: raise ValueError('unknown transform: {}'.format(transform)) assert type in ['mlp'] if type == 'mlp': self.net = NICEMLPBlock(in_features, out_features, hidden_features, activation) def split(self, z): split_dim = z.dim() - 1 split_type = self.split_type dim = z.size(split_dim) if split_type == 'continuous': return z.split([self.z1_features, dim - self.z1_features], dim=split_dim) elif split_type == 'skip': idx1 = torch.tensor(list(range(0, dim, 2))).to(z.device) idx2 = torch.tensor(list(range(1, dim, 2))).to(z.device) z1 = z.index_select(split_dim, idx1) z2 = z.index_select(split_dim, idx2) return z1, z2 else: raise ValueError('unknown split type: {}'.format(split_type)) def unsplit(self, z1, z2): split_dim = z1.dim() - 1 split_type = self.split_type if split_type == 'continuous': return torch.cat([z1, z2], dim=split_dim) elif split_type == 'skip': z = torch.cat([z1, z2], dim=split_dim) dim = z1.size(split_dim) idx = torch.tensor([i // 2 if i % 2 == 0 else i // 2 + dim for i in range(dim * 2)]).to(z.device) return z.index_select(split_dim, idx) else: raise ValueError('unknown split type: {}'.format(split_type)) def calc_params(self, z: torch.Tensor): params = self.net(z) return params def init_net(self, z: torch.Tensor, init_scale=1.0): params = self.net.init(z, init_scale=init_scale) return params @overrides def forward(self, input: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: """ Args: input: Tensor input tensor [batch, in_channels, H, W] Returns: out: Tensor , logdet: Tensor out: [batch, in_channels, H, W], the output of the flow logdet: [batch], the log determinant of :math:`\partial output / \partial input` """ # [batch, length, in_channels] z1, z2 = self.split(input) # [batch, length, features] z, zp = (z1, z2) if self.up else (z2, z1) params = self.transform.calc_params(self.calc_params(z)) zp, logdet = self.transform.fwd(zp, params) z1, z2 = (z, zp) if self.up else (zp, z) return self.unsplit(z1, z2), logdet @overrides def backward(self, input: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: """ Args: input: Tensor input tensor [batch, in_channels, H, W] Returns: out: Tensor , logdet: Tensor out: [batch, in_channels, H, W], the output of the flow logdet: [batch], the log determinant of :math:`\partial output / \partial input` """ if self.analytic_bwd: return self.backward_analytic(input) else: return self.backward_iterative(input) def backward_analytic(self, z: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: # [batch, length, in_channels] z1, z2 = self.split(z) # [batch, length, features] z, zp = (z1, z2) if self.up else (z2, z1) params = self.transform.calc_params(self.calc_params(z)) zp, logdet = self.transform.bwd(zp, params) z1, z2 = (z, zp) if self.up else (zp, z) return self.unsplit(z1, z2), logdet def backward_iterative(self, z: torch.Tensor, maxIter=100) -> Tuple[torch.Tensor, torch.Tensor]: # [batch, length, in_channels] z1, z2 = self.split(z) # [batch, length, features] z, zp = (z1, z2) if self.up else (z2, z1) params = self.transform.calc_params(self.calc_params(z)) zp_org = zp eps = 1e-6 for iter in range(maxIter): new_zp, logdet = self.transform.bwd(zp, params) new_zp = zp_org - new_zp diff = torch.abs(new_zp - zp).max().item() zp = new_zp if diff < eps: break _, logdet = self.transform.fwd(zp, params) z1, z2 = (z, zp) if self.up else (zp, z) return self.unsplit(z1, z2), logdet * -1.0 @overrides def init(self, data: torch.Tensor, init_scale=1.0) -> Tuple[torch.Tensor, torch.Tensor]: with torch.no_grad(): # [batch, length, in_channels] z1, z2 = self.split(data) # [batch, length, features] z, zp = (z1, z2) if self.up else (z2, z1) params = self.transform.calc_params(self.init_net(z, init_scale=init_scale)) zp, logdet = self.transform.fwd(zp, params) z1, z2 = (z, zp) if self.up else (zp, z) return self.unsplit(z1, z2), logdet @overrides def extra_repr(self): return 'inverse={}, in_features={}, split={}, order={}, factor={}, transform={}'.format(self.inverse, self.in_features, self.split_type, 'up' if self.up else 'down', self.factor, self.transform) @classmethod def from_params(cls, params: Dict) -> "NICE1d": return NICE1d(**params) class NICE2d(Flow): """ NICE Flow for 2D image data """ def __init__(self, in_channels, hidden_channels=None, h_channels=0, inverse=False, split_type='continuous', order='up', factor=2, transform='affine', alpha=1.0, type='conv', h_type=None, activation='relu', normalize=None, num_groups=None): super(NICE2d, self).__init__(inverse) self.in_channels = in_channels self.factor = factor assert split_type in ['continuous', 'skip'] if split_type == 'skip': assert factor == 2 if in_channels % factor == 1: split_type = 'continuous' assert order in ['up', 'down'] self.split_type = split_type self.up = order == 'up' if hidden_channels is None: hidden_channels = min(8 * in_channels, 512) out_channels = in_channels // factor in_channels = in_channels - out_channels self.z1_channels = in_channels if self.up else out_channels assert transform in ['additive', 'affine', 'relu', 'nlsq', 'symm_elu'] if transform == 'additive': self.transform = Additive() self.analytic_bwd = True elif transform == 'affine': self.transform = Affine(dim=1, alpha=alpha) self.analytic_bwd = True out_channels = out_channels * 2 elif transform == 'relu': self.transform = ReLU(dim=1) self.analytic_bwd = True out_channels = out_channels * 2 elif transform == 'nlsq': self.transform = NLSQ(dim=1) self.analytic_bwd = True out_channels = out_channels * 5 elif transform == 'symm_elu': self.transform = SymmELU(dim=1) self.analytic_bwd = False out_channels = out_channels * 2 else: raise ValueError('unknown transform: {}'.format(transform)) assert type in ['conv'] if type == 'conv': self.net = NICEConvBlock(in_channels, out_channels, hidden_channels, activation, normalize=normalize, num_groups=num_groups) assert h_type in [None, 'local_linear', 'global_linear', 'global_attn'] if h_type is None: assert h_channels == 0 self.h_net = None elif h_type == 'local_linear': self.h_net = LocalLinearCondNet(h_channels, hidden_channels, kernel_size=3) elif h_type == 'global_linear': self.h_net = GlobalLinearCondNet(h_channels, hidden_channels) elif h_type == 'global_attn': self.h_net = GlobalAttnCondNet(h_channels, in_channels, hidden_channels) else: raise ValueError('unknown conditional transform: {}'.format(h_type)) def split(self, z): split_dim = 1 split_type = self.split_type dim = z.size(split_dim) if split_type == 'continuous': return z.split([self.z1_channels, dim - self.z1_channels], dim=split_dim) elif split_type == 'skip': idx1 = torch.tensor(list(range(0, dim, 2))).to(z.device) idx2 = torch.tensor(list(range(1, dim, 2))).to(z.device) z1 = z.index_select(split_dim, idx1) z2 = z.index_select(split_dim, idx2) return z1, z2 else: raise ValueError('unknown split type: {}'.format(split_type)) def unsplit(self, z1, z2): split_dim = 1 split_type = self.split_type if split_type == 'continuous': return torch.cat([z1, z2], dim=split_dim) elif split_type == 'skip': z = torch.cat([z1, z2], dim=split_dim) dim = z1.size(split_dim) idx = torch.tensor([i // 2 if i % 2 == 0 else i // 2 + dim for i in range(dim * 2)]).to(z.device) return z.index_select(split_dim, idx) else: raise ValueError('unknown split type: {}'.format(split_type)) def calc_params(self, z: torch.Tensor, h=None): params = self.net(z, h=h) return params def init_net(self, z: torch.Tensor, h=None, init_scale=1.0): params = self.net.init(z, h=h, init_scale=init_scale) return params @overrides def forward(self, input: torch.Tensor, h=None) -> Tuple[torch.Tensor, torch.Tensor]: """ Args: input: Tensor input tensor [batch, in_channels, H, W] h: Tensor conditional input (default: None) Returns: out: Tensor , logdet: Tensor out: [batch, in_channels, H, W], the output of the flow logdet: [batch], the log determinant of :math:`\partial output / \partial input` """ # [batch, length, in_channels] z1, z2 = self.split(input) # [batch, length, features] z, zp = (z1, z2) if self.up else (z2, z1) if self.h_net is not None: h = self.h_net(h, x=z) else: h = None params = self.transform.calc_params(self.calc_params(z, h=h)) zp, logdet = self.transform.fwd(zp, params) z1, z2 = (z, zp) if self.up else (zp, z) return self.unsplit(z1, z2), logdet @overrides def backward(self, input: torch.Tensor, h=None) -> Tuple[torch.Tensor, torch.Tensor]: """ Args: input: Tensor input tensor [batch, in_channels, H, W] h: Tensor conditional input (default: None) Returns: out: Tensor , logdet: Tensor out: [batch, in_channels, H, W], the output of the flow logdet: [batch], the log determinant of :math:`\partial output / \partial input` """ if self.analytic_bwd: return self.backward_analytic(input, h=h) else: return self.backward_iterative(input, h=h) def backward_analytic(self, z: torch.Tensor, h=None) -> Tuple[torch.Tensor, torch.Tensor]: # [batch, length, in_channels] z1, z2 = self.split(z) # [batch, length, features] z, zp = (z1, z2) if self.up else (z2, z1) if self.h_net is not None: h = self.h_net(h, x=z) else: h = None params = self.transform.calc_params(self.calc_params(z, h=h)) zp, logdet = self.transform.bwd(zp, params) z1, z2 = (z, zp) if self.up else (zp, z) return self.unsplit(z1, z2), logdet def backward_iterative(self, z: torch.Tensor, h=None, maxIter=100) -> Tuple[torch.Tensor, torch.Tensor]: # [batch, length, in_channels] z1, z2 = self.split(z) # [batch, length, features] z, zp = (z1, z2) if self.up else (z2, z1) if self.h_net is not None: h = self.h_net(h, x=z) else: h = None params = self.transform.calc_params(self.calc_params(z, h=h)) zp_org = zp eps = 1e-6 for iter in range(maxIter): new_zp, logdet = self.transform.bwd(zp, params) new_zp = zp_org - new_zp diff = torch.abs(new_zp - zp).max().item() zp = new_zp if diff < eps: break _, logdet = self.transform.fwd(zp, params) z1, z2 = (z, zp) if self.up else (zp, z) return self.unsplit(z1, z2), logdet * -1.0 @overrides def init(self, data: torch.Tensor, h=None, init_scale=1.0) -> Tuple[torch.Tensor, torch.Tensor]: with torch.no_grad(): # [batch, length, in_channels] z1, z2 = self.split(data) # [batch, length, features] z, zp = (z1, z2) if self.up else (z2, z1) if self.h_net is not None: h = self.h_net(h, x=z) else: h = None params = self.transform.calc_params(self.init_net(z, h=h, init_scale=init_scale)) zp, logdet = self.transform.fwd(zp, params) z1, z2 = (z, zp) if self.up else (zp, z) return self.unsplit(z1, z2), logdet @overrides def extra_repr(self): return 'inverse={}, in_channels={}, split={}, order={}, factor={}, transform={}'.format(self.inverse, self.in_channels, self.split_type, 'up' if self.up else 'down', self.factor, self.transform) @classmethod def from_params(cls, params: Dict) -> "NICE2d": return NICE2d(**params) class MaskedConvFlow(Flow): """ Masked Convolutional Flow """ def __init__(self, in_channels, kernel_size, hidden_channels=None, h_channels=None, h_type=None, activation='relu', order='A', transform='affine', alpha=1.0, inverse=False): super(MaskedConvFlow, self).__init__(inverse) self.in_channels = in_channels if hidden_channels is None: if in_channels <= 96: hidden_channels = 4 * in_channels else: hidden_channels = min(2 * in_channels, 512) out_channels = in_channels assert transform in ['additive', 'affine', 'relu', 'nlsq', 'symm_elu'] if transform == 'additive': self.transform = Additive() self.analytic_bwd = True elif transform == 'affine': self.transform = Affine(dim=1, alpha=alpha) self.analytic_bwd = True out_channels = out_channels * 2 elif transform == 'relu': self.transform = ReLU(dim=1) self.analytic_bwd = True out_channels = out_channels * 2 elif transform == 'nlsq': self.transform = NLSQ(dim=1) self.analytic_bwd = True out_channels = out_channels * 5 elif transform == 'symm_elu': self.transform = SymmELU(dim=1) self.analytic_bwd = False out_channels = out_channels * 2 else: raise ValueError('unknown transform: {}'.format(transform)) self.kernel_size = kernel_size self.order = order self.net = MCFBlock(in_channels, out_channels, kernel_size, hidden_channels, order, activation) assert h_type in [None, 'local_linear', 'global_linear', 'global_attn'] if h_type is None: assert h_channels is None or h_channels == 0 self.h_net = None elif h_type == 'local_linear': self.h_net = LocalLinearCondNet(h_channels, hidden_channels, kernel_size=3) elif h_type == 'global_linear': # TODO remove global linear self.h_net = GlobalLinearCondNet(h_channels, hidden_channels) elif h_type == 'global_attn': # TODO add global attn self.h_net = None else: raise ValueError('unknown conditional transform: {}'.format(h_type)) def calc_params(self, x: torch.Tensor, h=None, shifted=True): params = self.net(x, h=h, shifted=shifted) return params def init_net(self, x, h=None, init_scale=1.0): params = self.net.init(x, h=h, init_scale=init_scale) return params @overrides def forward(self, input: torch.Tensor, h=None) -> Tuple[torch.Tensor, torch.Tensor]: """ Args: input: Tensor input tensor [batch, in_channels, H, W] h: Tensor conditional input (default: None) Returns: out: Tensor , logdet: Tensor out: [batch, in_channels, H, W], the output of the flow logdet: [batch], the log determinant of :math:`\partial output / \partial input` """ if self.h_net is not None: h = self.h_net(h) else: h = None params = self.transform.calc_params(self.calc_params(input, h=h)) out, logdet = self.transform.fwd(input, params) return out, logdet @overrides def backward(self, input: torch.Tensor, h=None) -> Tuple[torch.Tensor, torch.Tensor]: """ Args: input: Tensor input tensor [batch, in_channels, H, W] h: Tensor conditional input (default: None) Returns: out: Tensor , logdet: Tensor out: [batch, in_channels, H, W], the output of the flow logdet: [batch], the log determinant of :math:`\partial output / \partial input` """ if self.analytic_bwd: return self.backward_analytic(input, h=h) else: return self.backward_iterative(input, h=h) def backward_analytic(self, z: torch.Tensor, h=None) -> Tuple[torch.Tensor, torch.Tensor]: if self.h_net is not None: bs, _, H, W = z.size() h = self.h_net(h) hh = h + h.new_zeros(bs, 1, H, W) else: h = hh = None if self.order == 'A': out = self.backward_height(z, hh=hh, reverse=False) elif self.order == 'B': out = self.backward_height(z, hh=hh, reverse=True) elif self.order == 'C': out = self.backward_width(z, hh=hh, reverse=False) else: out = self.backward_width(z, hh=hh, reverse=True) params = self.transform.calc_params(self.calc_params(out, h=h)) _, logdet = self.transform.fwd(out, params) return out, logdet.mul(-1.0) def backward_iterative(self, z: torch.Tensor, h=None, maxIter=100) -> Tuple[torch.Tensor, torch.Tensor]: if self.h_net is not None: h = self.h_net(h) else: h = None z_org = z eps = 1e-6 for iter in range(maxIter): params = self.transform.calc_params(self.calc_params(z, h=h)) new_z, logdet = self.transform.bwd(z, params) new_z = z_org - new_z diff = torch.abs(new_z - z).max().item() z = new_z if diff < eps: break params = self.transform.calc_params(self.calc_params(z, h=h)) z_recon, logdet = self.transform.fwd(z, params) return z, logdet * -1.0 def backward_height(self, input: torch.Tensor, hh=None, reverse=False) -> torch.Tensor: batch, channels, H, W = input.size() kH, kW = self.kernel_size cW = kW // 2 out = input.new_zeros(batch, channels, H + kH, W + 2 * cW) itr = reversed(range(H)) if reverse else range(H) for h in itr: curr_h = h if reverse else h + kH s_h = h + 1 if reverse else h t_h = h + kH + 1 if reverse else h + kH # [batch, channels, kH, width+2*cW] out_curr = out[:, :, s_h:t_h] hh_curr = None if hh is None else hh[:, :, h:h + 1] # [batch, channels, width] in_curr = input[:, :, h] # [batch, channels, 1, width] params = self.calc_params(out_curr, h=hh_curr, shifted=False) params = self.transform.calc_params(params.squeeze(2)) # [batch, channels, width] new_out, _ = self.transform.bwd(in_curr, params) out[:, :, curr_h, cW:W + cW] = new_out out = out[:, :, :H, cW:cW + W] if reverse else out[:, :, kH:, cW:cW + W] return out def backward_width(self, input: torch.Tensor, hh=None, reverse=False) -> torch.Tensor: batch, channels, H, W = input.size() kH, kW = self.kernel_size cH = kH // 2 out = input.new_zeros(batch, channels, H + 2 * cH, W + kW) itr = reversed(range(W)) if reverse else range(W) for w in itr: curr_w = w if reverse else w + kW s_w = w + 1 if reverse else w t_w = w + kW + 1 if reverse else w + kW # [batch, channels, height+2*cH, kW] out_curr = out[:, :, :, s_w:t_w] hh_curr = None if hh is None else hh[:, :, :, w:w + 1] # [batch, channels, height] in_curr = input[:, :, :, w] # [batch, channels, height, 1] params = self.calc_params(out_curr, h=hh_curr, shifted=False) params = self.transform.calc_params(params.squeeze(3)) # [batch, channels, height] new_out, _ = self.transform.bwd(in_curr, params) out[:, :, cH:H + cH, curr_w] = new_out out = out[:, :, cH:cH + H, :W] if reverse else out[:, :, cH:cH + H, kW:] return out @overrides def init(self, data, h=None, init_scale=1.0) -> Tuple[torch.Tensor, torch.Tensor]: with torch.no_grad(): if self.h_net is not None: h = self.h_net(h) else: h = None params = self.transform.calc_params(self.init_net(data, h=h, init_scale=init_scale)) out, logdet = self.transform.fwd(data, params) return out, logdet @overrides def extra_repr(self): return 'inverse={}, in_channels={}, order={}, kernel={}, transform={}'.format(self.inverse, self.in_channels, self.order, self.kernel_size, self.transform) @classmethod def from_params(cls, params: Dict) -> "MaskedConvFlow": return MaskedConvFlow(**params) NICE1d.register('nice1d') NICE2d.register('nice2d') MaskedConvFlow.register('masc')

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a23490ff97b503e6685840388a20a84039860bb5

125

py

Python

spiral/data/__init__.py

acdaniells/spiral

d78344007969d7c991216901b4a9d3ad7d768587

[ "BSD-3-Clause" ]

null

spiral/data/__init__.py

acdaniells/spiral

d78344007969d7c991216901b4a9d3ad7d768587

[ "BSD-3-Clause" ]

1

2020-04-01T18:39:48.000Z

spiral/data/__init__.py

acdaniells/spiral

d78344007969d7c991216901b4a9d3ad7d768587

[ "BSD-3-Clause" ]

1

2020-04-01T18:36:44.000Z

""" Spiral data subpackage. """ from ._core import list_datasets, load_dataset __all__ = ["list_datasets", "load_dataset"]

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6

a240b5e9c363c1a88ea8bcf17217f918a1857e5d

34

py

Python

modules/geopy/geocoders/__init__.py

flavour/lacity

fd1f1cccdcea64d07143b29d4f88996e3af35c4b

[ "MIT" ]

1

2016-01-01T12:22:48.000Z

modules/geopy/geocoders/__init__.py

andygimma/eden

716d5e11ec0030493b582fa67d6f1c35de0af50d

[ "MIT" ]

null

modules/geopy/geocoders/__init__.py

andygimma/eden

716d5e11ec0030493b582fa67d6f1c35de0af50d

[ "MIT" ]

1

2020-04-29T13:58:31.000Z

from geopy.geocoders_old import *

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a255bb6e17c2034b50e2fced42c41c41dc3184e4

30,204

py

Python

postprocessing/pyplotgen/config/VariableGroupNondimMoments.py

larson-group/clubb_release

b4d671e3e238dbe00752c0dead6a0d4f9897350a

[ "Intel", "Unlicense", "NetCDF" ]

null

postprocessing/pyplotgen/config/VariableGroupNondimMoments.py

larson-group/clubb_release

b4d671e3e238dbe00752c0dead6a0d4f9897350a

[ "Intel", "Unlicense", "NetCDF" ]

null

postprocessing/pyplotgen/config/VariableGroupNondimMoments.py

larson-group/clubb_release

b4d671e3e238dbe00752c0dead6a0d4f9897350a

[ "Intel", "Unlicense", "NetCDF" ]

1

2022-01-28T22:22:04.000Z

""" """ import numpy as np from netCDF4 import Dataset from src.Panel import Panel from src.VariableGroup import VariableGroup class VariableGroupNondimMoments(VariableGroup): """ This class contains information for plotting normalized moments, such as correlations or kurtosis. """ def __init__(self, case, clubb_datasets=None, sam_benchmark_dataset=None, coamps_benchmark_dataset=None, wrf_benchmark_dataset=None, r408_dataset=None, hoc_dataset=None, cam_datasets=None, e3sm_datasets=None, sam_datasets=None, wrf_datasets=None, priority_vars=False): """ :param clubb_datasets: :param case: :param sam_benchmark_dataset: """ self.name = "normalized moments" self.variable_definitions = [ {'var_names': { 'clubb': [self.get_kurtosis_clubb], 'sam': [self.get_kurtosis_sam], 'coamps': [''], 'r408': [''], 'hoc': [''], 'e3sm': [''], 'cam': [''], 'wrf': [''], }, 'sci_scale': 0, 'priority': False, 'title': r'kurtosis, wp4/(wp2**2)', 'axis_title': r'kurtosis [-]', }, {'var_names': { 'clubb': [self.get_wpthlp_corr_clubb], 'sam': [self.get_wpthlp_corr_sam], 'coamps': [''], 'r408': [''], 'hoc': [''], 'e3sm': [''], 'cam': [''], 'wrf': [''], }, 'sci_scale': 0, 'priority': False, 'title': r'Correlation of w and thetal, wpthlp/sqrt(wp2*thlp2)', 'axis_title': r'Correlation of w and thetal [-]', }, {'var_names': { 'clubb': [self.get_wprtp_corr_clubb], 'sam': [self.get_wprtp_corr_sam], 'coamps': [''], 'r408': [''], 'hoc': [''], 'e3sm': [''], 'cam': [''], 'wrf': [''], }, 'sci_scale': 0, 'priority': False, 'title': r'Correlation of w and rt, wprtp/sqrt(wp2*rtp2)', 'axis_title': r'Correlation of w and rt [-]', }, {'var_names': { 'clubb': [self.get_wprcp_corr_clubb], 'sam': [self.get_wprcp_corr_sam], 'coamps': [''], 'r408': [''], 'hoc': [''], 'e3sm': [''], 'cam': [''], 'wrf': [''], }, 'sci_scale': 0, 'priority': False, 'title': r'Correlation of w and rc, wprcp/sqrt(wp2*rcp2)', 'axis_title': r'Correlation of w and rc [-]', }, {'var_names': { 'clubb': [self.get_upwp_corr_clubb], 'sam': [self.get_upwp_corr_sam], 'coamps': [''], 'r408': [''], 'hoc': [''], 'e3sm': [''], 'cam': [''], 'wrf': [''], }, 'sci_scale': 0, 'priority': False, 'title': r'Correlation of u and w, upwp/sqrt(up2*wp2)', 'axis_title': r'Correlation of u and w [-]', }, {'var_names': { 'clubb': [self.get_vpwp_corr_clubb], 'sam': [self.get_vpwp_corr_sam], 'coamps': [''], 'r408': [''], 'hoc': [''], 'e3sm': [''], 'cam': [''], 'wrf': [''], }, 'sci_scale': 0, 'priority': False, 'title': r'Correlation of v and w, vpwp/sqrt(vp2*wp2)', 'axis_title': r'Correlation of v and w [-]', }, {'var_names': { 'clubb': [self.get_nondim_wpthlp2_clubb], 'sam': [self.get_nondim_wpthlp2_sam], 'coamps': [''], 'r408': [''], 'hoc': [''], 'e3sm': [''], 'cam': [''], 'wrf': [''], }, 'sci_scale': 0, 'priority': False, 'title': r'Nondimensionalized wpthlp2, wpthlp2/(sqrt(wp2)*thlp2)', 'axis_title': r'Nondimensionalized wpthlp2 [-]', }, {'var_names': { 'clubb': [self.get_nondim_wprtp2_clubb], 'sam': [self.get_nondim_wprtp2_sam], 'coamps': [''], 'r408': [''], 'hoc': [''], 'e3sm': [''], 'cam': [''], 'wrf': [''], }, 'sci_scale': 0, 'priority': False, 'title': r'Nondimensionalized wprtp2, wprtp2/(sqrt(wp2)*rtp2)', 'axis_title': r'Nondimensionalized wprtp2 [-]', }, {'var_names': { 'clubb': [self.get_nondim_wp2thlp_clubb], 'sam': [self.get_nondim_wp2thlp_sam], 'coamps': [''], 'r408': [''], 'hoc': [''], 'e3sm': [''], 'cam': [''], 'wrf': [''], }, 'sci_scale': 0, 'priority': False, 'title': r'Nondimensionalized wp2thlp, wp2thlp/(wp2*sqrt(thlp2))', 'axis_title': r'Nondimensionalized wp2thlp [-]', }, {'var_names': { 'clubb': [self.get_nondim_wp2rtp_clubb], 'sam': [self.get_nondim_wp2rtp_sam], 'coamps': [''], 'r408': [''], 'hoc': [''], 'e3sm': [''], 'cam': [''], 'wrf': [''], }, 'sci_scale': 0, 'priority': False, 'title': r'Nondimensionalized wp2rtp, wp2rtp/(wp2*sqrt(rtp2))', 'axis_title': r'Nondimensionalized wp2rtp [-]', }, ] # Call ctor of parent class super().__init__(case, clubb_datasets=clubb_datasets, sam_datasets=sam_datasets, sam_benchmark_dataset=sam_benchmark_dataset, coamps_benchmark_dataset=coamps_benchmark_dataset, wrf_benchmark_dataset=wrf_benchmark_dataset, r408_dataset=r408_dataset, cam_datasets=cam_datasets, hoc_dataset=hoc_dataset, e3sm_datasets=e3sm_datasets, wrf_datasets=wrf_datasets, priority_vars=priority_vars) def get_kurtosis_clubb(self, dataset_override=None): """ :param dataset_override: If passed, this netcdf dataset will be used to gather the data needed to calculate the given variable. if not passed, this function should attempt to find the best source for the data, e.g. the benchmark data for the given model :return: tuple of numeric lists of the form (dependent_data, independent_data) for the given variable being caluclated. Lists will be filled with NaN's if the variable could not be calculated. """ if dataset_override is not None: dataset = dataset_override else: dataset = self.clubb_datasets['zm'] wp4, indep, dataset = self.getVarForCalculations('wp4', dataset) wp2, indep, dataset = self.getVarForCalculations('wp2', dataset) kurtosis = wp4 / ( wp2 * wp2 ) return kurtosis, indep def get_kurtosis_sam(self, dataset_override=None): """ :param dataset_override: If passed, this netcdf dataset will be used to gather the data needed to calculate the given variable. if not passed, this function should attempt to find the best source for the data, e.g. the benchmark data for the given model. :return: tuple of numeric lists of the form (dependent_data, independent_data) for the given variable being calculated. Lists will be filled with NaN's if the variable could not be calculated. """ dataset = self.sam_benchmark_dataset if dataset_override is not None: dataset = dataset_override wp4, indep, dataset = self.getVarForCalculations('WP4', dataset) wp2, indep, dataset = self.getVarForCalculations(['WP2', 'W2', 'wp2'], dataset) kurtosis = wp4 / ( wp2 * wp2 ) return kurtosis, indep def get_wpthlp_corr_clubb(self, dataset_override=None): """ :param dataset_override: If passed, this netcdf dataset will be used to gather the data needed to calculate the given variable. if not passed, this function should attempt to find the best source for the data, e.g. the benchmark data for the given model :return: tuple of numeric lists of the form (dependent_data, independent_data) for the given variable being caluclated. Lists will be filled with NaN's if the variable could not be calculated. """ if dataset_override is not None: dataset = dataset_override else: dataset = self.clubb_datasets['zm'] wpthlp, indep, dataset = self.getVarForCalculations('wpthlp', dataset) wp2, indep, dataset = self.getVarForCalculations('wp2', dataset) thlp2, indep, dataset = self.getVarForCalculations('thlp2', dataset) wpthlp_corr = wpthlp / np.sqrt( wp2 * thlp2 ) return wpthlp_corr, indep def get_wpthlp_corr_sam(self, dataset_override=None): """ :param dataset_override: If passed, this netcdf dataset will be used to gather the data needed to calculate the given variable. if not passed, this function should attempt to find the best source for the data, e.g. the benchmark data for the given model. :return: tuple of numeric lists of the form (dependent_data, independent_data) for the given variable being calculated. Lists will be filled with NaN's if the variable could not be calculated. """ dataset = self.sam_benchmark_dataset if dataset_override is not None: dataset = dataset_override wp2, indep, dataset = self.getVarForCalculations(['WP2', 'W2', 'wp2'], dataset) TL2, indep, dataset = self.getVarForCalculations('TL2', dataset) THLP2_SGS, indep, dataset = self.getVarForCalculations('THLP2_SGS', dataset) thlp2 = TL2 + THLP2_SGS tlflux, indep, dataset = self.getVarForCalculations(['TLFLUX'], dataset) rho, indep, dataset = self.getVarForCalculations(['RHO'], dataset) wpthlp_sgs, indep, dataset = self.getVarForCalculations(['WPTHLP_SGS'], dataset) wpthlp = (tlflux / (rho * 1004)) if not np.any(np.isnan(wpthlp_sgs)): wpthlp += wpthlp_sgs wpthlp_corr = wpthlp / np.sqrt( wp2 * thlp2 ) return wpthlp_corr, indep def get_wprtp_corr_clubb(self, dataset_override=None): """ :param dataset_override: If passed, this netcdf dataset will be used to gather the data needed to calculate the given variable. if not passed, this function should attempt to find the best source for the data, e.g. the benchmark data for the given model :return: tuple of numeric lists of the form (dependent_data, independent_data) for the given variable being caluclated. Lists will be filled with NaN's if the variable could not be calculated. """ if dataset_override is not None: dataset = dataset_override else: dataset = self.clubb_datasets['zm'] wprtp, indep, dataset = self.getVarForCalculations('wprtp', dataset) wp2, indep, dataset = self.getVarForCalculations('wp2', dataset) rtp2, indep, dataset = self.getVarForCalculations('rtp2', dataset) wprtp_corr = wprtp / np.sqrt( wp2 * rtp2 ) return wprtp_corr, indep def get_wprtp_corr_sam(self, dataset_override=None): """ :param dataset_override: If passed, this netcdf dataset will be used to gather the data needed to calculate the given variable. if not passed, this function should attempt to find the best source for the data, e.g. the benchmark data for the given model. :return: tuple of numeric lists of the form (dependent_data, independent_data) for the given variable being calculated. Lists will be filled with NaN's if the variable could not be calculated. """ dataset = self.sam_benchmark_dataset if dataset_override is not None: dataset = dataset_override wp2, indep, dataset = self.getVarForCalculations(['WP2', 'W2', 'wp2'], dataset) QT2, indep, dataset = self.getVarForCalculations(['QT2'], dataset) RTP2_SGS, indep, dataset = self.getVarForCalculations(['RTP2_SGS'], dataset) rtp2 = (QT2 / 1e+6) + RTP2_SGS qtflux, indep, dataset = self.getVarForCalculations(['QTFLUX'], dataset) rho, indep, dataset = self.getVarForCalculations(['RHO'], dataset) wprtp_sgs, indep, dataset = self.getVarForCalculations(['WPRTP_SGS'], dataset) wprtp = qtflux / (rho * 2.5104e+6) if not np.any(np.isnan(wprtp_sgs)): wprtp += wprtp_sgs wprtp_corr = wprtp / np.sqrt( wp2 * rtp2 ) return wprtp_corr, indep def get_wprcp_corr_clubb(self, dataset_override=None): """ :param dataset_override: If passed, this netcdf dataset will be used to gather the data needed to calculate the given variable. if not passed, this function should attempt to find the best source for the data, e.g. the benchmark data for the given model :return: tuple of numeric lists of the form (dependent_data, independent_data) for the given variable being caluclated. Lists will be filled with NaN's if the variable could not be calculated. """ if dataset_override is not None: dataset = dataset_override else: dataset = self.clubb_datasets['zm'] wprcp, indep, dataset = self.getVarForCalculations('wprcp', dataset) wp2, indep, dataset = self.getVarForCalculations('wp2', dataset) rcp2, indep, dataset = self.getVarForCalculations('rcp2', dataset) wprcp_corr = wprcp / np.sqrt( wp2 * rcp2 ) return wprcp_corr, indep def get_wprcp_corr_sam(self, dataset_override=None): """ :param dataset_override: If passed, this netcdf dataset will be used to gather the data needed to calculate the given variable. if not passed, this function should attempt to find the best source for the data, e.g. the benchmark data for the given model. :return: tuple of numeric lists of the form (dependent_data, independent_data) for the given variable being calculated. Lists will be filled with NaN's if the variable could not be calculated. """ dataset = self.sam_benchmark_dataset if dataset_override is not None: dataset = dataset_override wp2, indep, dataset = self.getVarForCalculations(['WP2', 'W2', 'wp2'], dataset) rcp2, indep, dataset = self.getVarForCalculations(['RCP2'], dataset) wprcp, indep, dataset = self.getVarForCalculations(['WPRCP'], dataset) wprcp_corr = wprcp / np.sqrt( wp2 * rcp2 ) return wprcp_corr, indep def get_upwp_corr_clubb(self, dataset_override=None): """ :param dataset_override: If passed, this netcdf dataset will be used to gather the data needed to calculate the given variable. if not passed, this function should attempt to find the best source for the data, e.g. the benchmark data for the given model :return: tuple of numeric lists of the form (dependent_data, independent_data) for the given variable being caluclated. Lists will be filled with NaN's if the variable could not be calculated. """ if dataset_override is not None: dataset = dataset_override else: dataset = self.clubb_datasets['zm'] upwp, indep, dataset = self.getVarForCalculations('upwp', dataset) wp2, indep, dataset = self.getVarForCalculations('wp2', dataset) up2, indep, dataset = self.getVarForCalculations('up2', dataset) upwp_corr = upwp / np.sqrt( wp2 * up2 ) return upwp_corr, indep def get_upwp_corr_sam(self, dataset_override=None): """ :param dataset_override: If passed, this netcdf dataset will be used to gather the data needed to calculate the given variable. if not passed, this function should attempt to find the best source for the data, e.g. the benchmark data for the given model. :return: tuple of numeric lists of the form (dependent_data, independent_data) for the given variable being calculated. Lists will be filled with NaN's if the variable could not be calculated. """ dataset = self.sam_benchmark_dataset if dataset_override is not None: dataset = dataset_override wp2, indep, dataset = self.getVarForCalculations(['WP2', 'W2', 'wp2'], dataset) U2, z, dataset = self.getVarForCalculations('U2', dataset) UP2_SGS, z, dataset = self.getVarForCalculations('UP2_SGS', dataset) up2 = UP2_SGS + U2 UW, z, dataset = self.getVarForCalculations('UW', dataset) UPWP_SGS, z, dataset = self.getVarForCalculations('UPWP_SGS', dataset) upwp = UW + UPWP_SGS upwp_corr = upwp / np.sqrt( wp2 * up2 ) return upwp_corr, indep def get_vpwp_corr_clubb(self, dataset_override=None): """ :param dataset_override: If passed, this netcdf dataset will be used to gather the data needed to calculate the given variable. if not passed, this function should attempt to find the best source for the data, e.g. the benchmark data for the given model :return: tuple of numeric lists of the form (dependent_data, independent_data) for the given variable being caluclated. Lists will be filled with NaN's if the variable could not be calculated. """ if dataset_override is not None: dataset = dataset_override else: dataset = self.clubb_datasets['zm'] vpwp, indep, dataset = self.getVarForCalculations('vpwp', dataset) wp2, indep, dataset = self.getVarForCalculations('wp2', dataset) vp2, indep, dataset = self.getVarForCalculations('vp2', dataset) vpwp_corr = vpwp / np.sqrt( wp2 * vp2 ) return vpwp_corr, indep def get_vpwp_corr_sam(self, dataset_override=None): """ :param dataset_override: If passed, this netcdf dataset will be used to gather the data needed to calculate the given variable. if not passed, this function should attempt to find the best source for the data, e.g. the benchmark data for the given model. :return: tuple of numeric lists of the form (dependent_data, independent_data) for the given variable being calculated. Lists will be filled with NaN's if the variable could not be calculated. """ dataset = self.sam_benchmark_dataset if dataset_override is not None: dataset = dataset_override wp2, indep, dataset = self.getVarForCalculations(['WP2', 'W2', 'wp2'], dataset) V2, z, dataset = self.getVarForCalculations('V2', dataset) VP2_SGS, z, dataset = self.getVarForCalculations('VP2_SGS', dataset) vp2 = V2 + VP2_SGS VW, z, dataset = self.getVarForCalculations('VW', dataset) VPWP_SGS, z, dataset = self.getVarForCalculations('VPWP_SGS', dataset) vpwp = VW + VPWP_SGS vpwp_corr = vpwp / np.sqrt( wp2 * vp2 ) return vpwp_corr, indep def get_nondim_wpthlp2_clubb(self, dataset_override=None): """ :param dataset_override: If passed, this netcdf dataset will be used to gather the data needed to calculate the given variable. if not passed, this function should attempt to find the best source for the data, e.g. the benchmark data for the given model :return: tuple of numeric lists of the form (dependent_data, independent_data) for the given variable being caluclated. Lists will be filled with NaN's if the variable could not be calculated. """ if dataset_override is not None: dataset = dataset_override else: dataset = self.clubb_datasets['zt'] wpthlp2, indep, dataset = self.getVarForCalculations('wpthlp2', dataset) if dataset_override is not None: dataset = dataset_override else: dataset = self.clubb_datasets['zm'] wp2, indep, dataset = self.getVarForCalculations('wp2', dataset) thlp2, indep, dataset = self.getVarForCalculations('thlp2', dataset) nondim_wpthlp2 = wpthlp2 / ( np.sqrt( wp2 ) * thlp2 ) return nondim_wpthlp2, indep def get_nondim_wpthlp2_sam(self, dataset_override=None): """ :param dataset_override: If passed, this netcdf dataset will be used to gather the data needed to calculate the given variable. if not passed, this function should attempt to find the best source for the data, e.g. the benchmark data for the given model. :return: tuple of numeric lists of the form (dependent_data, independent_data) for the given variable being calculated. Lists will be filled with NaN's if the variable could not be calculated. """ dataset = self.sam_benchmark_dataset if dataset_override is not None: dataset = dataset_override wp2, indep, dataset = self.getVarForCalculations(['WP2', 'W2', 'wp2'], dataset) TL2, indep, dataset = self.getVarForCalculations('TL2', dataset) THLP2_SGS, indep, dataset = self.getVarForCalculations('THLP2_SGS', dataset) thlp2 = TL2 + THLP2_SGS wpthlp2, indep, dataset = self.getVarForCalculations(['WPTHLP2'], dataset) nondim_wpthlp2 = wpthlp2 / ( np.sqrt( wp2 ) * thlp2 ) return nondim_wpthlp2, indep def get_nondim_wprtp2_clubb(self, dataset_override=None): """ :param dataset_override: If passed, this netcdf dataset will be used to gather the data needed to calculate the given variable. if not passed, this function should attempt to find the best source for the data, e.g. the benchmark data for the given model :return: tuple of numeric lists of the form (dependent_data, independent_data) for the given variable being caluclated. Lists will be filled with NaN's if the variable could not be calculated. """ if dataset_override is not None: dataset = dataset_override else: dataset = self.clubb_datasets['zt'] wprtp2, indep, dataset = self.getVarForCalculations('wprtp2', dataset) if dataset_override is not None: dataset = dataset_override else: dataset = self.clubb_datasets['zm'] wp2, indep, dataset = self.getVarForCalculations('wp2', dataset) rtp2, indep, dataset = self.getVarForCalculations('rtp2', dataset) nondim_wprtp2 = wprtp2 / ( np.sqrt( wp2 ) * rtp2 ) return nondim_wprtp2, indep def get_nondim_wprtp2_sam(self, dataset_override=None): """ :param dataset_override: If passed, this netcdf dataset will be used to gather the data needed to calculate the given variable. if not passed, this function should attempt to find the best source for the data, e.g. the benchmark data for the given model. :return: tuple of numeric lists of the form (dependent_data, independent_data) for the given variable being calculated. Lists will be filled with NaN's if the variable could not be calculated. """ dataset = self.sam_benchmark_dataset if dataset_override is not None: dataset = dataset_override wp2, indep, dataset = self.getVarForCalculations(['WP2', 'W2', 'wp2'], dataset) QT2, indep, dataset = self.getVarForCalculations(['QT2'], dataset) RTP2_SGS, indep, dataset = self.getVarForCalculations(['RTP2_SGS'], dataset) rtp2 = (QT2 / 1e+6) + RTP2_SGS wprtp2, indep, dataset = self.getVarForCalculations(['WPRTP2'], dataset) nondim_wprtp2 = wprtp2 / ( np.sqrt( wp2 ) * rtp2 ) return nondim_wprtp2, indep def get_nondim_wp2thlp_clubb(self, dataset_override=None): """ :param dataset_override: If passed, this netcdf dataset will be used to gather the data needed to calculate the given variable. if not passed, this function should attempt to find the best source for the data, e.g. the benchmark data for the given model :return: tuple of numeric lists of the form (dependent_data, independent_data) for the given variable being caluclated. Lists will be filled with NaN's if the variable could not be calculated. """ if dataset_override is not None: dataset = dataset_override else: dataset = self.clubb_datasets['zt'] wp2thlp, indep, dataset = self.getVarForCalculations('wp2thlp', dataset) if dataset_override is not None: dataset = dataset_override else: dataset = self.clubb_datasets['zm'] wp2, indep, dataset = self.getVarForCalculations('wp2', dataset) thlp2, indep, dataset = self.getVarForCalculations('thlp2', dataset) nondim_wp2thlp = wp2thlp / ( wp2 * np.sqrt( thlp2 ) ) return nondim_wp2thlp, indep def get_nondim_wp2thlp_sam(self, dataset_override=None): """ :param dataset_override: If passed, this netcdf dataset will be used to gather the data needed to calculate the given variable. if not passed, this function should attempt to find the best source for the data, e.g. the benchmark data for the given model. :return: tuple of numeric lists of the form (dependent_data, independent_data) for the given variable being calculated. Lists will be filled with NaN's if the variable could not be calculated. """ dataset = self.sam_benchmark_dataset if dataset_override is not None: dataset = dataset_override wp2, indep, dataset = self.getVarForCalculations(['WP2', 'W2', 'wp2'], dataset) TL2, indep, dataset = self.getVarForCalculations('TL2', dataset) THLP2_SGS, indep, dataset = self.getVarForCalculations('THLP2_SGS', dataset) thlp2 = TL2 + THLP2_SGS wp2thlp, indep, dataset = self.getVarForCalculations(['WP2THLP'], dataset) nondim_wp2thlp = wp2thlp / ( wp2 * np.sqrt( thlp2 ) ) return nondim_wp2thlp, indep def get_nondim_wp2rtp_clubb(self, dataset_override=None): """ :param dataset_override: If passed, this netcdf dataset will be used to gather the data needed to calculate the given variable. if not passed, this function should attempt to find the best source for the data, e.g. the benchmark data for the given model :return: tuple of numeric lists of the form (dependent_data, independent_data) for the given variable being caluclated. Lists will be filled with NaN's if the variable could not be calculated. """ if dataset_override is not None: dataset = dataset_override else: dataset = self.clubb_datasets['zt'] wp2rtp, indep, dataset = self.getVarForCalculations('wp2rtp', dataset) if dataset_override is not None: dataset = dataset_override else: dataset = self.clubb_datasets['zm'] wp2, indep, dataset = self.getVarForCalculations('wp2', dataset) rtp2, indep, dataset = self.getVarForCalculations('rtp2', dataset) nondim_wp2rtp = wp2rtp / ( wp2 * np.sqrt( rtp2 ) ) return nondim_wp2rtp, indep def get_nondim_wp2rtp_sam(self, dataset_override=None): """ :param dataset_override: If passed, this netcdf dataset will be used to gather the data needed to calculate the given variable. if not passed, this function should attempt to find the best source for the data, e.g. the benchmark data for the given model. :return: tuple of numeric lists of the form (dependent_data, independent_data) for the given variable being calculated. Lists will be filled with NaN's if the variable could not be calculated. """ dataset = self.sam_benchmark_dataset if dataset_override is not None: dataset = dataset_override wp2, indep, dataset = self.getVarForCalculations(['WP2', 'W2', 'wp2'], dataset) QT2, indep, dataset = self.getVarForCalculations(['QT2'], dataset) RTP2_SGS, indep, dataset = self.getVarForCalculations(['RTP2_SGS'], dataset) rtp2 = (QT2 / 1e+6) + RTP2_SGS wp2rtp, indep, dataset = self.getVarForCalculations(['WP2RTP'], dataset) nondim_wp2rtp = wp2rtp / ( wp2 * np.sqrt( rtp2 ) ) return nondim_wp2rtp, indep

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6

a25dd159a122d0bd52fb9c0dbd27643c2721f9b9

180

py

Python

test/test_nabla_version.py

AlbanAndrieu/nabla-hooks

44ffbb834ebcce59b9e1c23f3789a00cbb12ac6f

[ "Apache-2.0" ]

null

test/test_nabla_version.py

AlbanAndrieu/nabla-hooks

44ffbb834ebcce59b9e1c23f3789a00cbb12ac6f

[ "Apache-2.0" ]

5

2021-04-07T20:43:26.000Z

2022-03-01T08:42:41.000Z

test/test_nabla_version.py

AlbanAndrieu/nabla-hooks

44ffbb834ebcce59b9e1c23f3789a00cbb12ac6f

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- import re from hooks import __version__ def test_version(): # assert __version__ == '1.0.2' assert re.match(r'^v1.0.2.+$', __version__) # nosec

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1

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6

a270d21d3dc61380b695da03858471846f869cd7

29

py

Python

src/__init__.py

goodarzilab/ciberatac

58c150813cfdf1cea160b9b2c464c382cb0f7395

[ "BSD-3-Clause" ]

3

2022-02-25T19:24:52.000Z

2022-03-22T16:48:07.000Z

src/__init__.py

goodarzilab/ciberatac

58c150813cfdf1cea160b9b2c464c382cb0f7395

[ "BSD-3-Clause" ]

null

src/__init__.py

goodarzilab/ciberatac

58c150813cfdf1cea160b9b2c464c382cb0f7395

[ "BSD-3-Clause" ]

null

import ciberatac import mave

9.666667

16

0.862069

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a296e9f4f9437379d0bce6ee891c6e014eddae96

46,008

py

Python

Part2_Probabilistic_Models/w2_unittest.py

picsag/NLP

7fe8ec5cf9636fbbe1d5dd077455f4db62800ec9

[ "MIT" ]

null

Part2_Probabilistic_Models/w2_unittest.py

picsag/NLP

7fe8ec5cf9636fbbe1d5dd077455f4db62800ec9

[ "MIT" ]

null

Part2_Probabilistic_Models/w2_unittest.py

picsag/NLP

7fe8ec5cf9636fbbe1d5dd077455f4db62800ec9

[ "MIT" ]

null

from utils_pos import get_word_tag, preprocess import pandas as pd from collections import defaultdict import math import numpy as np import pickle def test_create_dictionaries(target, training_corpus, vocab): successful_cases = 0 failed_cases = [] test_cases = [ { "name": "default_case", "input": { "training_corpus": training_corpus, "vocab": vocab, "verbose": False, }, "expected": { "len_emission_counts": 31140, "len_transition_counts": 1421, "len_tag_counts": 46, "emission_counts": { ("DT", "the"): 41098, ("NNP", "--unk_upper--"): 4635, ("NNS", "Arts"): 2, }, "transition_counts": { ("VBN", "TO"): 2142, ("CC", "IN"): 1227, ("VBN", "JJR"): 66, }, "tag_counts": {"PRP": 17436, "UH": 97, ")": 1376,}, }, }, { "name": "small_case", "input": { "training_corpus": training_corpus[:1000], "vocab": vocab, "verbose": False, }, "expected": { "len_emission_counts": 442, "len_transition_counts": 272, "len_tag_counts": 38, "emission_counts": { ("DT", "the"): 48, ("NNP", "--unk_upper--"): 9, ("NNS", "Arts"): 1, }, "transition_counts": { ("VBN", "TO"): 3, ("CC", "IN"): 2, ("VBN", "JJR"): 1, }, "tag_counts": {"PRP": 11, "UH": 0, ")": 2,}, }, }, ] for test_case in test_cases: result_emission, result_transition, result_tag = target(**test_case["input"]) # emission dictionary try: assert isinstance(result_emission, defaultdict) successful_cases += 1 except: failed_cases.append( { "name": test_case["name"], "expected": defaultdict, "got": type(result_emission), } ) print( f"Wrong output type for emission_counts dictionary.\n\t Expected: {failed_cases[-1].get('expected')} \n\tGot: {failed_cases[-1].get('got')}." ) try: assert len(result_emission) == test_case["expected"]["len_emission_counts"] successful_cases += 1 except: failed_cases.append( { "name": test_case["name"], "expected": test_case["expected"]["len_emission_counts"], "got": len(result_emission), } ) print( f"Wrong output length for emission_counts dictionary.\n\t Expected: {failed_cases[-1].get('expected')} \n\tGot: {failed_cases[-1].get('got')}." ) try: for k, v in test_case["expected"]["emission_counts"].items(): assert np.isclose(result_emission[k], v) successful_cases += 1 except: failed_cases.append( { "name": test_case["name"], "expected": test_case["expected"]["emission_counts"], "got": result_emission, } ) print( f"Wrong output values for emission_counts dictionary.\n\t Expected: {failed_cases[-1].get('expected')}." ) # transition dictionary try: assert isinstance(result_transition, defaultdict) successful_cases += 1 except: failed_cases.append( { "name": test_case["name"], "expected": defaultdict, "got": type(result_transition), } ) print( f"Wrong output type for transition_counts dictionary.\n\t Expected: {failed_cases[-1].get('expected')} \n\tGot: {failed_cases[-1].get('got')}." ) try: assert ( len(result_transition) == test_case["expected"]["len_transition_counts"] ) successful_cases += 1 except: failed_cases.append( { "name": test_case["name"], "expected": test_case["expected"]["len_transition_counts"], "got": len(result_transition), } ) print( f"Wrong output length for transition_counts dictionary.\n\t Expected: {failed_cases[-1].get('expected')} \n\tGot: {failed_cases[-1].get('got')}." ) try: for k, v in test_case["expected"]["transition_counts"].items(): assert np.isclose(result_transition[k], v) successful_cases += 1 except: failed_cases.append( { "name": test_case["name"], "expected": test_case["expected"]["transition_counts"], "got": result_transition, } ) print( f"Wrong output values for transition_counts dictionary.\n\t Expected: {failed_cases[-1].get('expected')}." ) # tags count try: assert isinstance(result_tag, defaultdict) successful_cases += 1 except: failed_cases.append( { "name": test_case["name"], "expected": defaultdict, "got": type(result_transition), } ) print( f"Wrong output type for tag_counts dictionary.\n\t Expected: {failed_cases[-1].get('expected')} \n\tGot: {failed_cases[-1].get('got')}." ) try: assert len(result_tag) == test_case["expected"]["len_tag_counts"] successful_cases += 1 except: failed_cases.append( { "name": test_case["name"], "expected": test_case["expected"]["len_tag_counts"], "got": len(result_tag), } ) print( f"Wrong output length for tag_counts dictionary.\n\t Expected: {failed_cases[-1].get('expected')} \n\tGot: {failed_cases[-1].get('got')}." ) try: for k, v in test_case["expected"]["tag_counts"].items(): assert np.isclose(result_tag[k], v) successful_cases += 1 except: failed_cases.append( { "name": test_case["name"], "expected": test_case["expected"]["tag_counts"], "got": result_tag, } ) print( f"Wrong output values for tag_counts dictionary.\n\t Expected: {failed_cases[-1].get('expected')}." ) if len(failed_cases) == 0: print("\033[92m All tests passed") else: print("\033[92m", successful_cases, " Tests passed") print("\033[91m", len(failed_cases), " Tests failed") # return failed_cases, len(failed_cases) + successful_cases def test_predict_pos(target, prep, y, emission_counts, vocab, states): successful_cases = 0 failed_cases = [] test_cases = [ { "name": "default_check", "input": { "prep": prep, "y": y, "emission_counts": emission_counts, "vocab": vocab, "states": states, }, "expected": 0.8888563993099213, }, { "name": "small_check", "input": { "prep": prep[:1000], "y": y[:1000], "emission_counts": emission_counts, "vocab": vocab, "states": states, }, "expected": 0.876, }, ] for test_case in test_cases: result = target(**test_case["input"]) try: assert np.isclose(result, test_case["expected"]) successful_cases += 1 except: failed_cases.append( { "name": test_case["name"], "expected": test_case["expected"], "got": result, } ) print( f"Wrong output values for tag_counts dictionary.\n\t Expected: {failed_cases[-1].get('expected')}.\n\t Got: {failed_cases[-1].get('got')}." ) if len(failed_cases) == 0: print("\033[92m All tests passed") else: print("\033[92m", successful_cases, " Tests passed") print("\033[91m", len(failed_cases), " Tests failed") # return failed_cases, len(failed_cases) + successful_cases def test_create_transition_matrix(target, tag_counts, transition_counts): successful_cases = 0 failed_cases = [] test_cases = [ { "name": "default_check", "input": { "alpha": 0.001, "tag_counts": tag_counts, "transition_counts": transition_counts, }, "expected": { "0:5": np.array( [ [ 7.03997297e-06, 7.03997297e-06, 7.03997297e-06, 7.03997297e-06, 7.03997297e-06, ], [ 1.35647553e-07, 1.35647553e-07, 1.35647553e-07, 1.35647553e-07, 1.35647553e-07, ], [ 1.44528595e-07, 1.44673124e-04, 6.93751711e-03, 6.79298851e-03, 5.05864537e-03, ], [ 7.32039770e-07, 1.69101919e-01, 7.32039770e-07, 7.32039770e-07, 7.32039770e-07, ], [ 7.26719892e-07, 7.27446612e-04, 7.26719892e-07, 7.27446612e-04, 7.26719892e-07, ], ] ), "30:35": np.array( [ [ 2.21706877e-06, 2.21706877e-06, 2.21706877e-06, 8.87049214e-03, 2.21706877e-06, ], [ 3.75650909e-07, 7.51677469e-04, 3.75650909e-07, 5.10888993e-02, 3.75650909e-07, ], [ 1.72277159e-05, 1.72277159e-05, 1.72277159e-05, 1.72277159e-05, 1.72277159e-05, ], [ 4.47733569e-05, 4.47286283e-08, 4.47286283e-08, 8.95019852e-05, 4.47733569e-05, ], [ 1.03043917e-05, 1.03043917e-05, 1.03043917e-05, 6.18366548e-02, 3.09234796e-02, ], ] ), }, }, { "name": "alpha_check", "input": { "alpha": 0.05, "tag_counts": tag_counts, "transition_counts": transition_counts, }, "expected": { "0:5": np.array( [ [ 3.46500347e-04, 3.46500347e-04, 3.46500347e-04, 3.46500347e-04, 3.46500347e-04, ], [ 6.78030457e-06, 6.78030457e-06, 6.78030457e-06, 6.78030457e-06, 6.78030457e-06, ], [ 7.22407640e-06, 1.51705604e-04, 6.94233742e-03, 6.79785589e-03, 5.06407756e-03, ], [ 3.65416941e-05, 1.68859168e-01, 3.65416941e-05, 3.65416941e-05, 3.65416941e-05, ], [ 3.62765726e-05, 7.61808024e-04, 3.62765726e-05, 7.61808024e-04, 3.62765726e-05, ], ] ), "30:35": np.array( [ [ 1.10302228e-04, 1.10302228e-04, 1.10302228e-04, 8.93448048e-03, 1.10302228e-04, ], [ 1.87666554e-05, 7.69432872e-04, 1.87666554e-05, 5.10640694e-02, 1.87666554e-05, ], [ 8.29187396e-04, 8.29187396e-04, 8.29187396e-04, 8.29187396e-04, 8.29187396e-04, ], [ 4.69603252e-05, 2.23620596e-06, 2.23620596e-06, 9.16844445e-05, 4.69603252e-05, ], [ 5.03524673e-04, 5.03524673e-04, 5.03524673e-04, 6.09264854e-02, 3.07150050e-02, ], ] ), }, }, ] for test_case in test_cases: result = target(**test_case["input"]) try: assert isinstance(result, np.ndarray) successful_cases += 1 except: failed_cases.append( {"name": test_case["name"], "expected": np.ndarray, "got": type(result),} ) print( f"Wrong output type .\n\t Expected: {failed_cases[-1].get('expected')}.\n\t Got: {failed_cases[-1].get('got')}." ) try: assert np.allclose(result[0:5, 0:5], test_case["expected"]["0:5"]) successful_cases += 1 except: failed_cases.append( { "name": test_case["name"], "expected": test_case["expected"]["0:5"], "got": result[0:5, 0:5], } ) print( f"Wrong output values in rows and columns with indexes between 0 and 5.\n\t Expected: {failed_cases[-1].get('expected')}.\n\t Got: {failed_cases[-1].get('got')}." ) try: assert np.allclose(result[30:35, 30:35], test_case["expected"]["30:35"]) successful_cases += 1 except: failed_cases.append( { "name": test_case["name"], "expected": test_case["expected"]["30:35"], "got": result[30:35, 30:35], } ) print( f"Wrong output values in rows and columns with indexes between 30 and 35.\n\t Expected: {failed_cases[-1].get('expected')}.\n\t Got: {failed_cases[-1].get('got')}." ) if len(failed_cases) == 0: print("\033[92m All tests passed") else: print("\033[92m", successful_cases, " Tests passed") print("\033[91m", len(failed_cases), " Tests failed") # return failed_cases, len(failed_cases) + successful_cases def test_create_emission_matrix(target, tag_counts, emission_counts, vocab): successful_cases = 0 failed_cases = [] test_cases = [ { "name": "default_check", "input": { "alpha": 0.001, "tag_counts": tag_counts, "emission_counts": emission_counts, "vocab": vocab, }, "expected": { "0:5": np.array( [ [ 6.03219988e-06, 6.03219988e-06, 8.56578416e-01, 6.03219988e-06, 6.03219988e-06, ], [ 1.35212298e-07, 1.35212298e-07, 1.35212298e-07, 9.71365280e-01, 1.35212298e-07, ], [ 1.44034584e-07, 1.44034584e-07, 1.44034584e-07, 1.44034584e-07, 1.44034584e-07, ], [ 7.19539897e-07, 7.19539897e-07, 7.19539897e-07, 7.19539897e-07, 7.19539897e-07, ], [ 7.14399508e-07, 7.14399508e-07, 7.14399508e-07, 7.14399508e-07, 7.14399508e-07, ], ] ), "30:35": np.array( [ [ 2.10625199e-06, 2.10625199e-06, 2.10625199e-06, 2.10625199e-06, 2.10625199e-06, ], [ 3.72331731e-07, 3.72331731e-07, 3.72331731e-07, 3.72331731e-07, 3.72331731e-07, ], [ 1.22283772e-05, 1.22406055e-02, 1.22283772e-05, 1.22283772e-05, 1.22283772e-05, ], [ 4.46812012e-08, 4.46812012e-08, 4.46812012e-08, 4.46812012e-08, 4.46812012e-08, ], [ 8.27972213e-06, 4.96866125e-02, 8.27972213e-06, 8.27972213e-06, 8.27972213e-06, ], ] ), }, }, { "name": "alpha_check", "input": { "alpha": 0.05, "tag_counts": tag_counts, "emission_counts": emission_counts, "vocab": vocab, }, "expected": { "0:5": np.array( [ [ 3.75699741e-05, 3.75699741e-05, 1.06736296e-01, 3.75699741e-05, 3.75699741e-05, ], [ 5.84054154e-06, 5.84054154e-06, 5.84054154e-06, 8.39174848e-01, 5.84054154e-06, ], [ 6.16686298e-06, 6.16686298e-06, 6.16686298e-06, 6.16686298e-06, 6.16686298e-06, ], [ 1.95706206e-05, 1.95706206e-05, 1.95706206e-05, 1.95706206e-05, 1.95706206e-05, ], [ 1.94943174e-05, 1.94943174e-05, 1.94943174e-05, 1.94943174e-05, 1.94943174e-05, ], ] ), "30:35": np.array( [ [ 3.04905937e-05, 3.04905937e-05, 3.04905937e-05, 3.04905937e-05, 3.04905937e-05, ], [ 1.29841464e-05, 1.29841464e-05, 1.29841464e-05, 1.29841464e-05, 1.29841464e-05, ], [ 4.01010547e-05, 8.42122148e-04, 4.01010547e-05, 4.01010547e-05, 4.01010547e-05, ], [ 2.12351646e-06, 2.12351646e-06, 2.12351646e-06, 2.12351646e-06, 2.12351646e-06, ], [ 3.88847844e-05, 4.70505891e-03, 3.88847844e-05, 3.88847844e-05, 3.88847844e-05, ], ] ), }, }, ] for test_case in test_cases: result = target(**test_case["input"]) try: assert isinstance(result, np.ndarray) successful_cases += 1 except: failed_cases.append( { "name": test_case["name"], "expected": np.ndarray, "got": type(result), } ) print( f"Wrong output type .\n\t Expected: {failed_cases[-1].get('expected')}.\n\t Got: {failed_cases[-1].get('got')}." ) try: assert np.allclose(result[0:5, 0:5], test_case["expected"]["0:5"]) successful_cases += 1 except: failed_cases.append( { "name": test_case["name"], "expected": test_case["expected"]["0:5"], "got": result[0:5, 0:5], } ) print( f"Wrong output values in rows and columns with indexes between 0 and 5.\n\t Expected: {failed_cases[-1].get('expected')}.\n\t Got: {failed_cases[-1].get('got')}." ) try: assert np.allclose(result[30:35, 30:35], test_case["expected"]["30:35"]) successful_cases += 1 except: failed_cases.append( { "name": test_case["name"], "expected": test_case["expected"]["30:35"], "got": result[30:35, 30:35], } ) print( f"Wrong output values in rows and columns with indexes between 30 and 35.\n\t Expected: {failed_cases[-1].get('expected')}.\n\t Got: {failed_cases[-1].get('got')}." ) if len(failed_cases) == 0: print("\033[92m All tests passed") else: print("\033[92m", successful_cases, " Tests passed") print("\033[91m", len(failed_cases), " Tests failed") # return failed_cases, len(failed_cases) + successful_cases def test_initialize(target, states, tag_counts, A, B, corpus, vocab): successful_cases = 0 failed_cases = [] test_cases = [ { "name": "default_check", "input": { "states": states, "tag_counts": tag_counts, "A": A, "B": B, "corpus": corpus, "vocab": vocab, }, "expected": { "best_probs_shape": (46, 34199), "best_paths_shape": (46, 34199), "best_probs_col0": np.array( [ -22.60982633, -23.07660654, -23.57298822, -19.76726066, -24.74325104, -35.20241402, -35.00096024, -34.99203854, -21.35069072, -19.85767814, -21.92098414, -4.01623741, -19.16380593, -21.1062242, -20.47163973, -21.10157273, -21.49584851, -20.4811853, -18.25856307, -23.39717471, -21.92146798, -9.41377777, -21.03053445, -21.08029591, -20.10863677, -33.48185979, -19.47301382, -20.77150242, -20.11727696, -20.56031676, -20.57193964, -32.30366295, -18.07551522, -22.58887909, -19.1585905, -16.02994331, -24.30968545, -20.92932218, -21.96797222, -24.29571895, -23.45968569, -22.43665883, -20.46568904, -22.75551606, -19.6637215, -18.36288463, ] ), }, } ] for test_case in test_cases: result_best_probs, result_best_paths = target(**test_case["input"]) try: assert isinstance(result_best_probs, np.ndarray) successful_cases += 1 except: failed_cases.append( { "name": str(test_case["name"]) + "index 0", "expected": np.ndarray, "got": type(result_best_probs), } ) print( f"Wrong output type .\n\t Expected: {failed_cases[-1].get('expected')}.\n\t Got: {failed_cases[-1].get('got')}." ) try: assert isinstance(result_best_paths, np.ndarray) successful_cases += 1 except: failed_cases.append( { "name": str(test_case["name"]) + "index 1", "expected": np.ndarray, "got": type(result_best_paths), } ) print( f"Wrong output type .\n\t Expected: {failed_cases[-1].get('expected')}.\n\t Got: {failed_cases[-1].get('got')}." ) try: assert result_best_probs.shape == test_case["expected"]["best_probs_shape"] successful_cases += 1 except: failed_cases.append( { "name": str(test_case["name"]), "expected": test_case["expected"]["best_probs_shape"], "got": result_best_probs.shape, } ) print( f"Wrong output shape for best_probs.\n\t Expected: {failed_cases[-1].get('expected')}.\n\t Got: {failed_cases[-1].get('got')}." ) try: assert result_best_paths.shape == test_case["expected"]["best_paths_shape"] successful_cases += 1 except: failed_cases.append( { "name": str(test_case["name"]), "expected": test_case["expected"]["best_paths_shape"], "got": result_best_paths.shape, } ) print( f"Wrong output shape for best_paths.\n\t Expected: {failed_cases[-1].get('expected')}.\n\t Got: {failed_cases[-1].get('got')}." ) try: assert np.allclose( result_best_probs[:, 0], test_case["expected"]["best_probs_col0"] ) successful_cases += 1 except: failed_cases.append( { "name": str(test_case["name"]), "expected": test_case["expected"]["best_probs_col0"], "got": result_best_probs[:, 0], } ) print( f"Wrong non-zero values for best_probs.\n\t Expected: {failed_cases[-1].get('expected')}.\n\t Got: {failed_cases[-1].get('got')}." ) try: assert np.all((result_best_paths == 0)) successful_cases += 1 except: failed_cases.append( { "name": str(test_case["name"]), "expected": "Array of zeros with shape (46, 34199)", } ) print( f"Wrong values for best_paths.\n\t Expected: {failed_cases[-1].get('expected')}." ) if len(failed_cases) == 0: print("\033[92m All tests passed") else: print("\033[92m", successful_cases, " Tests passed") print("\033[91m", len(failed_cases), " Tests failed") # return failed_cases, len(failed_cases) + successful_cases def test_viterbi_forward(target, A, B, test_corpus, vocab): successful_cases = 0 failed_cases = [] test_cases = [ { "name": "default_check", "input": { "A": A, "B": B, "test_corpus": test_corpus, "best_probs": pickle.load( open("./support_files/best_probs_initilized.pkl", "rb") ), "best_paths": pickle.load( open("./support_files/best_paths_initilized.pkl", "rb") ), "vocab": vocab, "verbose": False, }, "expected": { "best_probs0:5": np.array( [ [ -22.60982633, -24.78215633, -34.08246498, -34.34107105, -49.56012613, ], [ -23.07660654, -24.51583896, -35.04774303, -35.28281026, -50.52540418, ], [ -23.57298822, -29.98305064, -31.98004656, -38.99187549, -47.45770771, ], [ -19.76726066, -25.7122143, -31.54577612, -37.38331695, -47.02343727, ], [ -24.74325104, -28.78696025, -31.458494, -36.00456711, -46.93615515, ], ] ), "best_probs30:35": np.array( [ [ -202.75618827, -208.38838519, -210.46938402, -210.15943098, -223.79223672, ], [ -202.58297597, -217.72266765, -207.23725672, -215.529735, -224.13957203, ], [ -202.00878092, -214.23093833, -217.41021623, -220.73768708, -222.03338753, ], [ -200.44016117, -209.46937757, -209.06951664, -216.22297765, -221.09669653, ], [ -208.74189499, -214.62088817, -209.79346523, -213.52623459, -228.70417526, ], ] ), "best_paths0:5": np.array( [ [0, 11, 20, 25, 20], [0, 11, 20, 25, 20], [0, 11, 20, 25, 20], [0, 11, 20, 25, 20], [0, 11, 20, 25, 20], ] ), "best_paths30:35": np.array( [ [20, 19, 35, 11, 21], [20, 19, 35, 11, 21], [20, 19, 35, 11, 21], [20, 19, 35, 11, 21], [35, 19, 35, 11, 34], ] ), }, } ] for test_case in test_cases: result_best_probs, result_best_paths = target(**test_case["input"]) try: assert isinstance(result_best_probs, np.ndarray) successful_cases += 1 except: failed_cases.append( { "name": str(test_case["name"]) + "index 0", "expected": np.ndarray, "got": type(result_best_probs), } ) print( f"Wrong output type .\n\t Expected: {failed_cases[-1].get('expected')}.\n\t Got: {failed_cases[-1].get('got')}." ) try: assert isinstance(result_best_paths, np.ndarray) successful_cases += 1 except: failed_cases.append( { "name": str(test_case["name"]) + "index 1", "expected": np.ndarray, "got": type(result_best_paths), } ) print( f"Wrong output type .\n\t Expected: {failed_cases[-1].get('expected')}.\n\t Got: {failed_cases[-1].get('got')}." ) try: assert np.allclose( result_best_probs[0:5, 0:5], test_case["expected"]["best_probs0:5"] ) successful_cases += 1 except: failed_cases.append( { "name": str(test_case["name"]), "expected": test_case["expected"]["best_probs0:5"], "got": result_best_probs[0:5, 0:5], } ) print( f"Wrong values for best_probs.\n\t Expected: {failed_cases[-1].get('expected')}.\n\t Got: {failed_cases[-1].get('got')}." ) try: assert np.allclose( result_best_probs[30:35, 30:35], test_case["expected"]["best_probs30:35"], ) successful_cases += 1 except: failed_cases.append( { "name": str(test_case["name"]), "expected": test_case["expected"]["best_probs30:35"], "got": result_best_probs[:, 0], } ) print( f"Wrong values for best_probs.\n\t Expected: {failed_cases[-1].get('expected')}.\n\t Got: {failed_cases[-1].get('got')}." ) try: assert np.allclose( result_best_paths[0:5, 0:5], test_case["expected"]["best_paths0:5"], ) successful_cases += 1 except: failed_cases.append( { "name": str(test_case["name"]), "expected": test_case["expected"]["best_paths0:5"], "got": result_best_paths[0:5, 0:5], } ) print( f"Wrong values for best_paths.\n\t Expected: {failed_cases[-1].get('expected')}.\n\t Got: {failed_cases[-1].get('got')}." ) try: assert np.allclose( result_best_paths[30:35, 30:35], test_case["expected"]["best_paths30:35"], ) successful_cases += 1 except: failed_cases.append( { "name": str(test_case["name"]), "expected": test_case["expected"]["best_paths30:35"], "got": result_best_paths[30:35, 30:35], } ) print( f"Wrong values for best_paths.\n\t Expected: {failed_cases[-1].get('expected')}.\n\t Got: {failed_cases[-1].get('got')}." ) if len(failed_cases) == 0: print("\033[92m All tests passed") else: print("\033[92m", successful_cases, " Tests passed") print("\033[91m", len(failed_cases), " Tests failed") # return failed_cases, len(failed_cases) + successful_cases def test_viterbi_backward(target, corpus, states): successful_cases = 0 failed_cases = [] test_cases = [ { "name": "default_check", "input": { "corpus": corpus, "best_probs": pickle.load( open("./support_files/best_probs_trained.pkl", "rb") ), "best_paths": pickle.load( open("./support_files/best_paths_trained.pkl", "rb") ), "states": states, }, "expected": { "pred_len": 34199, "pred_head": [ "DT", "NN", "POS", "NN", "MD", "VB", "VBN", "IN", "JJ", "NN", ], "pred_tail": [ "PRP", "MD", "RB", "VB", "PRP", "RB", "IN", "PRP", ".", "--s--", ], }, } ] for test_case in test_cases: result = target(**test_case["input"]) try: assert isinstance(result, list) successful_cases += 1 except: failed_cases.append( {"name": str(test_case["name"]), "expected": list, "got": type(result)} ) print( f"Wrong output type .\n\t Expected: {failed_cases[-1].get('expected')}.\n\t Got: {failed_cases[-1].get('got')}." ) try: assert len(result) == test_case["expected"]["pred_len"] successful_cases += 1 except: failed_cases.append( { "name": str(test_case["name"]), "expected": test_case["expected"]["pred_len"], "got": len(result), } ) print( f"Wrong output lenght.\n\t Expected: {failed_cases[-1].get('expected')}.\n\t Got: {failed_cases[-1].get('got')}." ) try: assert result[:10] == test_case["expected"]["pred_head"] successful_cases += 1 except: failed_cases.append( { "name": str(test_case["name"]), "expected": test_case["expected"]["pred_head"], "got": result[:10], } ) print( f"Wrong values for pred list.\n\t Expected: {failed_cases[-1].get('expected')}.\n\t Got: {failed_cases[-1].get('got')}." ) try: assert result[-10:] == test_case["expected"]["pred_tail"] successful_cases += 1 except: failed_cases.append( { "name": str(test_case["name"]), "expected": test_case["expected"]["pred_tail"], "got": result[-10:], } ) print( f"Wrong values for pred list.\n\t Expected: {failed_cases[-1].get('expected')}.\n\t Got: {failed_cases[-1].get('got')}." ) if len(failed_cases) == 0: print("\033[92m All tests passed") else: print("\033[92m", successful_cases, " Tests passed") print("\033[91m", len(failed_cases), " Tests failed") # return failed_cases, len(failed_cases) + successful_cases def test_compute_accuracy(target, pred, y): successful_cases = 0 failed_cases = [] test_cases = [ { "name": "default_check", "input": {"pred": pred, "y": y}, "expected": 0.953063647155511, }, { "name": "small_check", "input": {"pred": pred[:100], "y": y[:100]}, "expected": 0.979381443298969, }, ] for test_case in test_cases: result = target(**test_case["input"]) try: assert isinstance(result, float) successful_cases += 1 except: failed_cases.append( { "name": str(test_case["name"]), "expected": float, "got": type(result), } ) print( f"Wrong output type.\n\t Expected: {failed_cases[-1].get('expected')}.\n\t Got: {failed_cases[-1].get('got')}." ) try: assert np.isclose(result, test_case["expected"]) successful_cases += 1 except: failed_cases.append( { "name": str(test_case["name"]), "expected": float, "got": type(result), } ) print( f"Wrong output type.\n\t Expected: {failed_cases[-1].get('expected')}.\n\t Got: {failed_cases[-1].get('got')}." ) if len(failed_cases) == 0: print("\033[92m All tests passed") else: print("\033[92m", successful_cases, " Tests passed") print("\033[91m", len(failed_cases), " Tests failed") # return failed_cases, len(failed_cases) + successful_cases

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null

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6

a2d9fdd419fa036f52175d9e3415841d8f5f5c1f

4,055

py

Python

tests/features/meta/tests/test_aggregators.py

kevinbazira/revscoring

625f8b8048eb3c0c1c872ed9c15687c56f125747

[ "MIT" ]

49

2015-07-15T14:53:06.000Z

2018-08-20T15:00:31.000Z

tests/features/meta/tests/test_aggregators.py

kevinbazira/revscoring

625f8b8048eb3c0c1c872ed9c15687c56f125747

[ "MIT" ]

224

2015-06-14T23:22:43.000Z

2018-08-08T22:52:46.000Z

tests/features/meta/tests/test_aggregators.py

kevinbazira/revscoring

625f8b8048eb3c0c1c872ed9c15687c56f125747

[ "MIT" ]

36

2015-07-03T03:25:01.000Z

2018-05-25T10:21:08.000Z

import pickle from revscoring.datasources import Datasource from revscoring.dependencies import solve from revscoring.features.meta import aggregators def test_sum(): my_list = Datasource("my_list") my_sum = aggregators.sum(my_list) cache = {my_list: [1, 2, 3, 4]} assert solve(my_sum, cache=cache) == 10 cache = {my_list: []} assert solve(my_sum, cache=cache) == 0 cache = {my_list: None} assert solve(my_sum, cache=cache) == 0 assert str(my_sum) == "feature.sum(<datasource.my_list>)" assert pickle.loads(pickle.dumps(my_sum)) == my_sum def test_sum_vectors(): my_list = Datasource("my_list") my_sum = aggregators.sum(my_list, vector=True) cache = {my_list: [[1, 2, 3], [4, 5, 6]]} assert all(a == b for a, b in zip(solve(my_sum, cache=cache), [5, 7, 9])) cache = {my_list: [[]]} assert solve(my_sum, cache=cache) == [0] cache = {my_list: [None]} assert solve(my_sum, cache=cache) == [0] assert str(my_sum) == "feature_vector.sum(<datasource.my_list>)" assert pickle.loads(pickle.dumps(my_sum)) == my_sum def test_min(): my_list = Datasource("my_list") my_min = aggregators.min(my_list) cache = {my_list: [1, 2, 3, 4]} assert solve(my_min, cache=cache) == 1 cache = {my_list: []} assert solve(my_min, cache=cache) == 0 cache = {my_list: None} assert solve(my_min, cache=cache) == 0 assert pickle.loads(pickle.dumps(my_min)) == my_min def test_min_vectors(): my_list = Datasource("my_list") my_min = aggregators.min(my_list, vector=True) cache = {my_list: [[1, 2, 3], [4, 5, 6]]} assert all(a == b for a, b in zip(solve(my_min, cache=cache), [1, 2, 3])) cache = {my_list: [[]]} assert solve(my_min, cache=cache) == [0] cache = {my_list: [None]} assert solve(my_min, cache=cache) == [0] assert pickle.loads(pickle.dumps(my_min)) == my_min def test_max(): my_list = Datasource("my_list") my_max = aggregators.max(my_list) cache = {my_list: [1, 2, 3, 4]} assert solve(my_max, cache=cache) == 4 cache = {my_list: []} assert solve(my_max, cache=cache) == 0 cache = {my_list: None} assert solve(my_max, cache=cache) == 0 assert pickle.loads(pickle.dumps(my_max)) == my_max def test_max_vectors(): my_list = Datasource("my_list") my_max = aggregators.max(my_list, vector=True) cache = {my_list: [[1, 2, 3], [4, 5, 6]]} assert all(a == b for a, b in zip(solve(my_max, cache=cache), [4, 5, 6])) cache = {my_list: [[]]} assert solve(my_max, cache=cache) == [0] cache = {my_list: [None]} assert solve(my_max, cache=cache) == [0] assert pickle.loads(pickle.dumps(my_max)) == my_max def test_len(): my_list = Datasource("my_list") my_len = aggregators.len(my_list) cache = {my_list: [1, 2, 3, 4]} assert solve(my_len, cache=cache) == 4 cache = {my_list: []} assert solve(my_len, cache=cache) == 0 cache = {my_list: None} assert solve(my_len, cache=cache) == 0 assert pickle.loads(pickle.dumps(my_len)) == my_len def test_len_vectors(): my_list = Datasource("my_list") my_len = aggregators.len(my_list, vector=True) cache = {my_list: [[1, 2, 3], [4, 5, 6]]} assert all(a == b for a, b in zip(solve(my_len, cache=cache), [2, 2, 2])) cache = {my_list: [[]]} assert solve(my_len, cache=cache) == [0] cache = {my_list: [None]} assert solve(my_len, cache=cache) == [0] assert pickle.loads(pickle.dumps(my_len)) == my_len def test_mean_vectors(): my_list = Datasource("my_list") my_mean = aggregators.mean(my_list, vector=True) cache = {my_list: [[1, 2, 3], [4, 5, 6]]} assert all(a == b for a, b in zip(solve(my_mean, cache=cache), [2.5, 3.5, 4.5])) cache = {my_list: [[]]} assert solve(my_mean, cache=cache) == [0] cache = {my_list: [None]} assert solve(my_mean, cache=cache) == [0] assert pickle.loads(pickle.dumps(my_mean)) == my_mean

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null

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1

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null

0

6

a2f587a4e8ce39c071b8b287c0d083d8bb6c6391

36,729

py

Python

pyke/krb_compiler/krbparser_tables.py

jhidding/pyke

0fbf34612a7648b5b096de4a6749325e205dfc2a

[ "MIT" ]

null

pyke/krb_compiler/krbparser_tables.py

jhidding/pyke

0fbf34612a7648b5b096de4a6749325e205dfc2a

[ "MIT" ]

null

pyke/krb_compiler/krbparser_tables.py

jhidding/pyke

0fbf34612a7648b5b096de4a6749325e205dfc2a

[ "MIT" ]

null

# /home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser_tables.py # This file is automatically generated. Do not edit. _tabversion = '3.2' _lr_method = 'LALR' _lr_signature = '`\xa3O\x17\xd6C\xd4E2\xb5\xf60wIM\xd9' _lr_action_items = {'TAKING_TOK':([142,161,187,216,],[-66,188,-66,240,]),'LP_TOK':([18,32,43,65,73,85,88,95,111,112,124,125,128,132,144,150,158,162,165,170,171,179,180,181,182,183,184,185,189,192,193,196,197,199,204,205,206,207,210,212,214,218,219,220,224,225,226,231,233,234,235,236,238,239,244,245,251,253,254,255,257,258,260,266,268,269,272,273,275,276,278,279,283,284,289,292,295,296,300,301,304,307,308,310,312,],[32,43,65,65,43,65,43,111,43,43,-94,43,-79,43,-111,-32,43,43,192,43,43,-91,-51,-76,-50,-11,212,43,43,43,-38,43,43,226,231,43,-51,-50,43,43,-57,-33,-37,-36,-31,-21,43,43,-59,43,-108,255,43,43,-40,-34,266,43,-11,43,-11,-11,-30,43,-11,-60,-52,-25,-56,-16,-39,43,-53,-58,-61,43,-54,-63,-35,-55,43,-11,-65,-62,-64,]),'FOREACH_TOK':([61,],[79,]),'AS_TOK':([256,267,285,294,],[271,271,271,271,]),'ANONYMOUS_VAR_TOK':([32,43,65,66,73,85,88,100,106,111,112,124,125,128,132,144,150,158,162,170,171,179,181,183,185,189,192,193,196,197,205,210,212,214,218,219,220,224,225,226,231,233,234,235,238,239,244,245,253,254,255,257,258,260,266,268,269,272,273,275,276,278,279,283,284,289,292,295,296,300,301,304,307,308,310,312,],[46,46,46,46,46,46,46,46,46,46,46,-94,46,-79,46,-111,-32,46,46,46,46,-91,-76,-11,46,46,46,-38,46,46,46,46,46,-57,-33,-37,-36,-31,-21,46,46,-59,46,-108,46,46,-40,-34,46,-11,46,-11,-11,-30,46,-11,-60,-52,-25,-56,-16,-39,46,-53,-58,-61,46,-54,-63,-35,-55,46,-11,-65,-62,-64,]),'NUMBER_TOK':([32,43,65,73,85,88,111,112,124,125,128,132,144,150,158,162,170,171,179,181,183,185,189,192,193,196,197,205,210,212,214,218,219,220,224,225,226,231,233,234,235,238,239,244,245,253,254,255,257,258,260,266,268,269,272,273,274,275,276,278,279,283,284,289,292,295,296,300,301,304,307,308,310,312,],[44,44,44,44,44,44,44,44,-94,44,-79,44,-111,-32,44,44,44,44,-91,-76,-11,44,44,44,-38,44,44,44,44,44,-57,-33,-37,-36,-31,-21,44,44,-59,44,-108,44,44,-40,-34,44,-11,44,-11,-11,-30,44,-11,-60,-52,-25,288,-56,-16,-39,44,-53,-58,-61,44,-54,-63,-35,-55,44,-11,-65,-62,-64,]),'DEINDENT_TOK':([94,107,109,114,119,121,124,125,128,144,150,153,154,159,160,172,173,179,181,183,189,193,196,197,209,214,218,219,220,224,225,229,233,235,241,244,245,250,254,257,258,260,261,265,268,269,272,273,275,276,277,278,280,281,283,284,289,291,292,293,295,296,300,301,305,307,308,310,311,312,],[-104,118,-106,135,138,140,-94,143,-79,-111,-32,-90,173,-49,-48,-107,198,-91,-76,209,218,-38,224,225,-9,-57,-33,-37,-36,-31,-21,250,-59,-108,-85,-40,-34,-15,269,275,276,-30,278,-43,284,-60,-52,-25,-56,-16,291,-39,-41,293,-53,-58,-61,-86,300,-42,-54,-63,-35,-55,308,310,-65,-62,312,-64,]),'STEP_TOK':([256,267,285,294,],[274,274,274,274,]),'EXTENDING_TOK':([0,3,6,],[-22,-23,9,]),'ASSERT_TOK':([61,80,143,],[-101,97,-29,]),'INDENT_TOK':([33,37,39,58,59,60,62,77,96,113,122,139,141,145,151,152,157,160,168,194,200,208,213,215,227,232,262,273,287,298,299,303,],[-69,61,-69,75,76,-69,81,93,112,134,-10,-68,158,162,170,171,176,187,-70,222,-70,234,238,239,248,253,279,-67,297,-67,304,306,]),'.':([7,129,155,180,182,184,204,206,207,],[10,147,174,-51,-50,211,230,-51,-50,]),'!':([158,179,181,183,185,193,205,210,214,219,220,233,234,235,238,239,244,253,254,257,258,268,269,272,273,275,276,278,283,284,289,295,296,301,304,307,308,310,312,],[177,-91,-76,-11,177,-38,177,177,-57,-37,-36,-59,177,-108,177,177,-40,177,-11,-11,-11,-11,-60,-52,-25,-56,-16,-39,-53,-58,-61,-54,-63,-55,177,-11,-65,-62,-64,]),'IN_TOK':([44,46,48,49,50,52,53,54,55,56,67,99,103,104,127,129,136,137,146,180,182,],[-78,-75,-74,-89,-80,-88,-81,-116,-20,-84,-100,-117,-121,-120,-66,-87,-118,-119,163,-74,-87,]),'NOTANY_TOK':([112,124,125,128,132,144,150,158,162,170,171,179,181,183,185,189,193,196,197,205,210,214,218,219,220,224,225,233,234,235,238,239,244,245,253,254,257,258,260,268,269,272,273,275,276,278,279,283,284,289,292,295,296,300,301,304,307,308,310,312,],[126,-94,126,-79,126,-111,-32,178,126,126,126,-91,-76,-11,178,126,-38,126,126,178,178,-57,-33,-37,-36,-31,-21,-59,178,-108,178,178,-40,-34,178,-11,-11,-11,-30,-11,-60,-52,-25,-56,-16,-39,126,-53,-58,-61,126,-54,-63,-35,-55,178,-11,-65,-62,-64,]),'WITHOUT_TOK':([17,],[30,]),'*':([43,65,85,88,],[66,66,100,106,]),',':([40,41,44,45,46,47,48,49,50,52,53,54,55,56,57,67,68,69,70,71,82,83,90,99,101,102,103,104,105,136,137,],[-98,63,-78,-87,-75,-96,-74,-89,-80,-88,-81,-116,-20,-84,73,-100,85,-97,-93,88,-115,85,-113,-117,-110,88,-121,-120,-114,-118,-119,]),'BC_EXTRAS_TOK':([11,16,21,140,],[19,-92,-109,-45,]),'CODE_TOK':([75,81,93,148,163,164,176,188,195,222,228,240,248,297,306,],[91,91,91,166,166,166,202,166,166,166,166,166,166,202,202,]),'REQUIRE_TOK':([225,276,],[246,290,]),'PATTERN_VAR_TOK':([32,43,65,66,73,85,88,100,106,111,112,124,125,128,132,144,150,158,162,170,171,177,179,181,183,185,189,192,193,196,197,205,210,211,212,214,218,219,220,224,225,226,230,231,233,234,235,238,239,244,245,253,254,255,257,258,260,266,268,269,271,272,273,275,276,278,279,283,284,289,292,295,296,300,301,304,307,308,310,312,],[48,48,48,48,48,48,48,48,48,48,48,-94,48,-79,48,-111,-32,180,48,48,48,206,-91,-76,-11,180,48,48,-38,48,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_lr_action = { } for _k, _v in list(_lr_action_items.items()): for _x,_y in zip(_v[0],_v[1]): if not _x in _lr_action: _lr_action[_x] = { } _lr_action[_x][_k] = _y del _lr_action_items _lr_goto_items = {'inc_plan_vars':([183,254,257,258,268,307,],[210,210,210,210,210,210,]),'when_opt':([94,],[109,]),'bc_rules_opt':([14,26,],[25,38,]),'parent_opt':([6,],[8,]),'fc_extras':([14,],[26,]),'start_extra_statements':([33,39,60,],[58,62,77,]),'bc_rules':([8,14,26,],[11,11,11,]),'file':([0,],[4,]),'fc_premise':([112,125,132,162,170,171,189,196,197,279,292,],[124,144,150,124,124,124,144,144,144,124,144,]),'python_plan_code':([176,297,306,],[203,302,309,]),'bc_require_opt':([276,],[289,]),'plan_spec':([256,267,285,294,],[272,283,295,301,]),'goal':([78,],[94,]),'plan_extras_opt':([22,],[35,]),'pattern':([32,73,88,111,112,125,132,158,162,170,171,185,189,192,196,197,205,210,212,226,231,234,238,239,253,255,266,279,292,304,],[47,90,105,47,127,127,127,127,127,127,127,127,127,47,127,127,127,127,47,47,47,127,127,127,127,47,47,127,127,127,]),'top':([0,],[2,]),'bc_premise':([158,185,205,210,234,238,239,253,304,],[179,214,233,235,179,179,179,179,179,]),'assertion':([134,154,],[153,172,]),'name':([158,177,185,205,210,211,230,234,238,239,253,304,],[184,204,184,184,184,236,251,184,184,184,184,184,]),'data_list':([43,65,],[68,83,]),'start_python_plan_call':([273,298,],[287,303,]),'pattern_proper':([32,43,65,73,85,88,111,112,125,132,158,162,170,171,185,189,192,196,197,205,210,212,226,231,234,238,239,253,255,266,279,292,304,],[50,69,69,50,69,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,50,]),'python_goal':([0,],[5,]),'without_names':([30,],[41,]),'bc_extras_opt':([11,],[22,]),'start_python_statements':([139,],[157,]),'patterns_opt':([32,111,192,212,226,231,255,266,],[51,123,221,237,247,252,270,282,]),'fc_require_opt':([225,],[245,]),'python_premise':([112,125,132,158,162,170,171,185,189,196,197,205,210,234,238,239,253,279,292,304,],[128,128,128,181,128,128,128,181,128,128,128,181,181,181,181,181,181,128,128,181,]),'with_opt':([109,],[121,]),'variable':([32,43,65,66,73,85,88,100,106,111,112,125,132,158,162,170,171,185,189,192,196,197,205,210,212,226,231,234,238,239,253,255,266,279,292,304,],[53,53,53,84,53,53,53,115,117,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,53,]),'fc_rule':([8,14,],[13,27,]),'start_python_code':([112,125,127,132,142,149,158,162,170,171,175,185,187,189,196,197,205,210,234,238,239,253,279,292,304,],[130,130,146,130,161,169,130,130,130,130,201,130,216,130,130,130,130,130,130,130,130,130,130,130,130,]),'bc_premises':([158,234,238,239,253,304,],[183,254,257,258,268,307,]),'data':([32,43,65,73,85,88,111,112,125,132,158,162,170,171,185,189,192,196,197,205,210,212,226,231,234,238,239,253,255,266,279,292,304,],[54,70,70,54,101,54,54,54,54,54,54,54,54,54,54,54,54,54,54,54,54,54,54,54,54,54,54,54,54,54,54,54,54,]),'patterns_proper':([43,65,85,],[71,71,102,]),'check_nl':([112,125,132,134,154,158,162,170,171,185,189,196,197,205,210,234,238,239,253,279,292,304,],[131,131,131,156,156,131,131,131,131,131,131,131,131,131,131,131,131,131,131,131,131,131,]),'rest_opt':([71,102,],[87,116,]),'fc_rules':([8,],[14,]),'bc_rules_section':([8,14,26,],[15,29,29,]),'python_extras_code':([75,81,93,],[92,98,108,]),'nl_opt':([0,],[6,]),'python_rule_code':([148,163,164,188,195,222,228,240,248,],[167,190,191,217,223,243,249,259,264,]),'colon_opt':([12,20,],[24,34,]),'fc_premises':([112,162,170,171,279,],[125,189,196,197,292,]),'patterns':([32,111,192,212,226,231,255,266,],[57,57,57,57,57,57,57,57,]),'comma_opt':([41,57,68,71,83,102,],[64,74,86,89,86,89,]),'reset_plan_vars':([122,],[141,]),'taking':([142,],[159,]),'without_opt':([17,],[31,]),'foreach_opt':([61,],[80,]),'bc_rule':([8,11,14,26,],[16,21,16,16,]),'start_python_assertion':([168,200,],[194,227,]),'assertions':([134,],[154,]),} _lr_goto = { } for _k, _v in list(_lr_goto_items.items()): for _x,_y in zip(_v[0],_v[1]): if not _x in _lr_goto: _lr_goto[_x] = { } _lr_goto[_x][_k] = _y del _lr_goto_items _lr_productions = [ ("S' -> top","S'",1,None,None,None), ('top -> file','top',1,'p_top','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',40), ('top -> python_goal','top',1,'p_top','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',41), ('python_goal -> CHECK_TOK IDENTIFIER_TOK . 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IDENTIFIER_TOK LP_TOK patterns_opt RP_TOK NL_TOK','fc_premise',7,'p_fc_premise','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',144), ('fc_premise -> FIRST_TOK NL_TOK INDENT_TOK fc_premises DEINDENT_TOK','fc_premise',5,'p_fc_first_1','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',149), ('fc_premise -> FIRST_TOK fc_premise','fc_premise',2,'p_fc_first_n','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',154), ('fc_premise -> NOTANY_TOK NL_TOK INDENT_TOK fc_premises DEINDENT_TOK','fc_premise',5,'p_fc_notany','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',159), ('fc_premise -> FORALL_TOK NL_TOK INDENT_TOK fc_premises DEINDENT_TOK fc_require_opt','fc_premise',6,'p_fc_forall','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',164), ('fc_require_opt -> REQUIRE_TOK NL_TOK INDENT_TOK fc_premises DEINDENT_TOK','fc_require_opt',5,'p_fc_require_opt','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',169), ('python_premise -> pattern start_python_code = python_rule_code NL_TOK','python_premise',5,'p_python_eq','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',174), ('python_premise -> pattern start_python_code IN_TOK python_rule_code NL_TOK','python_premise',5,'p_python_in','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',179), ('python_premise -> start_python_code CHECK_TOK python_rule_code NL_TOK','python_premise',4,'p_python_check','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',184), ('python_premise -> check_nl PYTHON_TOK NL_TOK start_python_assertion INDENT_TOK python_rule_code NL_TOK DEINDENT_TOK','python_premise',8,'p_python_block_n','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',189), ('python_premise -> check_nl PYTHON_TOK start_python_code NOT_NL_TOK python_rule_code NL_TOK','python_premise',6,'p_python_block_1','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',194), ('assertion -> IDENTIFIER_TOK . IDENTIFIER_TOK LP_TOK patterns_opt RP_TOK NL_TOK','assertion',7,'p_assertion','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',199), ('assertion -> check_nl PYTHON_TOK NL_TOK start_python_assertion INDENT_TOK python_rule_code NL_TOK DEINDENT_TOK','assertion',8,'p_python_assertion_n','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',204), ('assertion -> check_nl PYTHON_TOK start_python_code NOT_NL_TOK python_rule_code NL_TOK','assertion',6,'p_python_assertion_1','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',209), ('check_nl -> <empty>','check_nl',0,'p_check_nl','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',214), ('bc_rule -> IDENTIFIER_TOK colon_opt NL_TOK INDENT_TOK USE_TOK goal when_opt with_opt DEINDENT_TOK','bc_rule',9,'p_bc_rule','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',220), ('bc_rules_opt -> <empty>','bc_rules_opt',0,'p_empty_bc_rules_opt','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',225), ('bc_rules_section -> bc_rules bc_extras_opt plan_extras_opt','bc_rules_section',3,'p_bc_rules_section','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',230), ('goal -> IDENTIFIER_TOK LP_TOK patterns_opt RP_TOK NL_TOK','goal',5,'p_goal_no_taking','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',235), ('goal -> IDENTIFIER_TOK LP_TOK patterns_opt RP_TOK taking','goal',5,'p_goal_taking','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',240), ('name -> IDENTIFIER_TOK','name',1,'p_name_sym','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',245), ('name -> PATTERN_VAR_TOK','name',1,'p_name_pat_var','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',250), ('bc_premise -> name LP_TOK patterns_opt RP_TOK plan_spec','bc_premise',5,'p_bc_premise1','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',255), ('bc_premise -> ! name LP_TOK patterns_opt RP_TOK plan_spec','bc_premise',6,'p_bc_premise2','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',261), ('bc_premise -> name . name LP_TOK patterns_opt RP_TOK plan_spec','bc_premise',7,'p_bc_premise3','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',267), ('bc_premise -> ! name . name LP_TOK patterns_opt RP_TOK plan_spec','bc_premise',8,'p_bc_premise4','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',273), ('bc_premise -> FIRST_TOK NL_TOK INDENT_TOK bc_premises DEINDENT_TOK','bc_premise',5,'p_bc_first_1f','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',279), ('bc_premise -> FIRST_TOK bc_premise','bc_premise',2,'p_bc_first_nf','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',284), ('bc_premise -> ! FIRST_TOK NL_TOK INDENT_TOK bc_premises DEINDENT_TOK','bc_premise',6,'p_bc_first_1t','/home/bruce/python/workareas/pyke-hg/r1_working/pyke/krb_compiler/krbparser.py',289), ('bc_premise -> ! 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src/spaceone/monitoring/info/__init__.py

jean1042/monitoring

0585a1ea52ec13285eaca81cc5b19fa3f7a1fba4

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2020-06-04T23:01:30.000Z

2020-09-09T08:58:51.000Z

src/spaceone/monitoring/info/__init__.py

jean1042/monitoring

0585a1ea52ec13285eaca81cc5b19fa3f7a1fba4

[ "Apache-2.0" ]

8

2021-11-12T08:13:00.000Z

2022-03-28T11:13:12.000Z

src/spaceone/monitoring/info/__init__.py

jean1042/monitoring

0585a1ea52ec13285eaca81cc5b19fa3f7a1fba4

[ "Apache-2.0" ]

7

2020-06-10T01:56:35.000Z

2021-12-02T05:36:21.000Z

from spaceone.monitoring.info.common_info import * from spaceone.monitoring.info.data_source_info import * from spaceone.monitoring.info.metric_info import * from spaceone.monitoring.info.log_info import * from spaceone.monitoring.info.project_alert_config_info import * from spaceone.monitoring.info.escalation_policy_info import * from spaceone.monitoring.info.event_rule_info import * from spaceone.monitoring.info.webhook_info import * from spaceone.monitoring.info.maintenance_window_info import * from spaceone.monitoring.info.alert_info import * from spaceone.monitoring.info.note_info import * from spaceone.monitoring.info.event_info import *

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Python

inclusive_django_range_fields/drf/__init__.py

Hipo/inclusive-django-range-fields

f40d915fc8bfbfda5cba59fcabb1831fae486fd4

[ "MIT" ]

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2019-12-19T13:35:54.000Z

2021-08-16T20:59:45.000Z

inclusive_django_range_fields/drf/__init__.py

Hipo/inclusive-django-range-fields

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[ "MIT" ]

1

2020-02-07T11:39:38.000Z

inclusive_django_range_fields/drf/__init__.py

Hipo/inclusive-django-range-fields

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[ "MIT" ]

null

from inclusive_django_range_fields.drf.fields import InclusiveIntegerRangeField, InclusiveDateRangeField

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Python

tests/rules/test_IAMRolesOverprivilegedRule.py

lpmi-13/cfripper

36bfdc45855112496977806e1a93d98d010399ed

[ "Apache-2.0" ]

360

2018-08-08T12:34:58.000Z

2022-03-25T17:01:41.000Z

tests/rules/test_IAMRolesOverprivilegedRule.py

lpmi-13/cfripper

36bfdc45855112496977806e1a93d98d010399ed

[ "Apache-2.0" ]

40

2018-11-26T07:08:15.000Z

2022-03-02T09:10:45.000Z

tests/rules/test_IAMRolesOverprivilegedRule.py

lpmi-13/cfripper

36bfdc45855112496977806e1a93d98d010399ed

[ "Apache-2.0" ]

51

2018-11-09T11:46:32.000Z

2022-03-28T08:47:28.000Z

import pytest from pycfmodel.model.cf_model import CFModel from cfripper.model.enums import RuleGranularity, RuleMode, RuleRisk from cfripper.model.result import Failure from cfripper.rules.iam_roles import IAMRolesOverprivilegedRule from tests.utils import compare_lists_of_failures, get_cfmodel_from @pytest.fixture() def valid_role_inline_policy() -> CFModel: return get_cfmodel_from("rules/IAMRolesOverprivilegedRule/valid_role_inline_policy.json").resolve() @pytest.fixture() def invalid_role_inline_policy() -> CFModel: return get_cfmodel_from("rules/IAMRolesOverprivilegedRule/invalid_role_inline_policy.json").resolve() @pytest.fixture() def invalid_role_inline_policy_resource_as_array() -> CFModel: return get_cfmodel_from( "rules/IAMRolesOverprivilegedRule/invalid_role_inline_policy_resource_as_array.json" ).resolve() @pytest.fixture() def valid_role_managed_policy() -> CFModel: return get_cfmodel_from("rules/IAMRolesOverprivilegedRule/valid_role_managed_policy.json").resolve() @pytest.fixture() def invalid_role_managed_policy() -> CFModel: return get_cfmodel_from("rules/IAMRolesOverprivilegedRule/invalid_role_managed_policy.json").resolve() @pytest.fixture() def invalid_role_inline_policy_fn_if() -> CFModel: return get_cfmodel_from("rules/IAMRolesOverprivilegedRule/invalid_role_inline_policy_fn_if.json").resolve() def test_with_valid_role_inline_policy(valid_role_inline_policy): rule = IAMRolesOverprivilegedRule(None) result = rule.invoke(valid_role_inline_policy) assert result.valid assert compare_lists_of_failures(result.failures, []) def test_with_invalid_role_inline_policy(invalid_role_inline_policy): rule = IAMRolesOverprivilegedRule(None) result = rule.invoke(invalid_role_inline_policy) assert not result.valid assert compare_lists_of_failures( result.failures, [ Failure( granularity=RuleGranularity.RESOURCE, reason="Role 'RootRole' contains an insecure permission 'ec2:DeleteInternetGateway' in policy 'not_so_chill_policy'", risk_value=RuleRisk.MEDIUM, rule="IAMRolesOverprivilegedRule", rule_mode=RuleMode.BLOCKING, actions=None, resource_ids={"RootRole"}, ) ], ) def test_with_invalid_role_inline_policy_resource_as_array(invalid_role_inline_policy_resource_as_array): rule = IAMRolesOverprivilegedRule(None) result = rule.invoke(invalid_role_inline_policy_resource_as_array) assert not result.valid assert compare_lists_of_failures( result.failures, [ Failure( granularity=RuleGranularity.RESOURCE, reason="Role 'RootRole' contains an insecure permission 'ec2:DeleteInternetGateway' in policy 'not_so_chill_policy'", risk_value=RuleRisk.MEDIUM, rule="IAMRolesOverprivilegedRule", rule_mode=RuleMode.BLOCKING, actions=None, resource_ids={"RootRole"}, ) ], ) def test_with_valid_role_managed_policy(valid_role_managed_policy): rule = IAMRolesOverprivilegedRule(None) result = rule.invoke(valid_role_managed_policy) assert result.valid assert compare_lists_of_failures(result.failures, []) def test_with_invalid_role_managed_policy(invalid_role_managed_policy): rule = IAMRolesOverprivilegedRule(None) result = rule.invoke(invalid_role_managed_policy) assert not result.valid assert compare_lists_of_failures( result.failures, [ Failure( granularity=RuleGranularity.RESOURCE, reason="Role RootRole has forbidden Managed Policy arn:aws:iam::aws:policy/AdministratorAccess", risk_value=RuleRisk.MEDIUM, rule="IAMRolesOverprivilegedRule", rule_mode=RuleMode.BLOCKING, actions=None, resource_ids={"RootRole"}, ) ], ) def test_with_invalid_role_inline_policy_fn_if(invalid_role_inline_policy_fn_if): rule = IAMRolesOverprivilegedRule(None) result = rule.invoke(invalid_role_inline_policy_fn_if) assert not result.valid assert compare_lists_of_failures( result.failures, [ Failure( granularity=RuleGranularity.RESOURCE, reason="Role 'RootRole' contains an insecure permission 'ec2:DeleteVpc' in policy 'ProdCredentialStoreAccessPolicy'", risk_value=RuleRisk.MEDIUM, rule="IAMRolesOverprivilegedRule", rule_mode=RuleMode.BLOCKING, actions=None, resource_ids={"RootRole"}, ) ], ) def test_rule_supports_filter_config(invalid_role_managed_policy, default_allow_all_config): rule = IAMRolesOverprivilegedRule(default_allow_all_config) result = rule.invoke(invalid_role_managed_policy) assert result.valid assert compare_lists_of_failures(result.failures, [])

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a749a396f15188ef345b4ae7c53017b6004c5e71

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Python

tensorflow/contrib/bayesflow/python/ops/layers_dense_variational_impl.py

harunpehlivan/tensorflow

376e2cfdab31f4da251ea2e50992a9bf97fd171b

[ "Apache-2.0" ]

22

2018-01-13T14:52:47.000Z

2018-07-05T01:00:28.000Z

tensorflow/contrib/bayesflow/python/ops/layers_dense_variational_impl.py

hamzabekkouri/tensorflow

d87a9fbbc5f49ec5ae8eb52c62628f0b1a0bf67f

[ "Apache-2.0" ]

null

tensorflow/contrib/bayesflow/python/ops/layers_dense_variational_impl.py

hamzabekkouri/tensorflow

d87a9fbbc5f49ec5ae8eb52c62628f0b1a0bf67f

[ "Apache-2.0" ]

3

2018-01-20T06:47:34.000Z

2018-05-07T19:14:34.000Z

# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Dense Bayesian layer using KL-divergence based variational inference. @@DenseReparameterization @@DenseLocalReparameterization @@DenseFlipout @@dense_reparameterization @@dense_local_reparameterization @@dense_flipout """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.contrib.bayesflow.python.ops import layers_util from tensorflow.contrib.distributions.python.ops import independent as independent_lib from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.layers import base as layers_lib from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn from tensorflow.python.ops import random_ops from tensorflow.python.ops import standard_ops from tensorflow.python.ops.distributions import kullback_leibler as kl_lib from tensorflow.python.ops.distributions import normal as normal_lib from tensorflow.python.ops.distributions import util as distribution_util __all__ = [ "DenseReparameterization", "DenseLocalReparameterization", "DenseFlipout", "dense_reparameterization", "dense_local_reparameterization", "dense_flipout", ] class _DenseVariational(layers_lib.Layer): """Abstract densely-connected class (private, used as implementation base). This layer implements the Bayesian variational inference analogue to a dense layer by assuming the `kernel` and/or the `bias` are drawn from distributions. By default, the layer implements a stochastic forward pass via sampling from the kernel and bias posteriors, ```none kernel, bias ~ posterior outputs = activation(matmul(inputs, kernel) + bias) ``` The arguments permit separate specification of the surrogate posterior (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` distributions. Args: units: Integer or Long, dimensionality of the output space. activation: Activation function (`callable`). Set it to None to maintain a linear activation. activity_regularizer: Regularizer function for the output. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). kernel_posterior_fn: Python `callable` which creates `tf.distributions.Distribution` instance representing the surrogate posterior of the `kernel` parameter. Default value: `default_mean_field_normal_fn()`. kernel_posterior_tensor_fn: Python `callable` which takes a `tf.distributions.Distribution` instance and returns a representative value. Default value: `lambda d: d.sample()`. kernel_prior_fn: Python `callable` which creates `tf.distributions` instance. See `default_mean_field_normal_fn` docstring for required parameter signature. Default value: `tf.distributions.Normal(loc=0., scale=1.)`. kernel_divergence_fn: Python `callable` which takes the surrogate posterior distribution, prior distribution and random variate sample(s) from the surrogate posterior and computes or approximates the KL divergence. The distributions are `tf.distributions.Distribution`-like instances and the sample is a `Tensor`. bias_posterior_fn: Python `callable` which creates `tf.distributions.Distribution` instance representing the surrogate posterior of the `bias` parameter. Default value: `default_mean_field_normal_fn(is_singular=True)` (which creates an instance of `tf.distributions.Deterministic`). bias_posterior_tensor_fn: Python `callable` which takes a `tf.distributions.Distribution` instance and returns a representative value. Default value: `lambda d: d.sample()`. bias_prior_fn: Python `callable` which creates `tf.distributions` instance. See `default_mean_field_normal_fn` docstring for required parameter signature. Default value: `None` (no prior, no variational inference) bias_divergence_fn: Python `callable` which takes the surrogate posterior distribution, prior distribution and random variate sample(s) from the surrogate posterior and computes or approximates the KL divergence. The distributions are `tf.distributions.Distribution`-like instances and the sample is a `Tensor`. name: Python `str`, the name of the layer. Layers with the same name will share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in such cases. reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous layer by the same name. Properties: units: Python integer, dimensionality of the output space. activation: Activation function (`callable`). activity_regularizer: Regularizer function for the output. kernel_posterior_fn: `callable` returning posterior. kernel_posterior_tensor_fn: `callable` operating on posterior. kernel_prior_fn: `callable` returning prior. kernel_divergence_fn: `callable` returning divergence. bias_posterior_fn: `callable` returning posterior. bias_posterior_tensor_fn: `callable` operating on posterior. bias_prior_fn: `callable` returning prior. bias_divergence_fn: `callable` returning divergence. """ def __init__( self, units, activation=None, activity_regularizer=None, trainable=True, kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), kernel_posterior_tensor_fn=lambda d: d.sample(), kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long bias_posterior_tensor_fn=lambda d: d.sample(), bias_prior_fn=None, bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), name=None, **kwargs): super(_DenseVariational, self).__init__( trainable=trainable, name=name, activity_regularizer=activity_regularizer, **kwargs) self.units = units self.activation = activation self.input_spec = layers_lib.InputSpec(min_ndim=2) self.kernel_posterior_fn = kernel_posterior_fn self.kernel_posterior_tensor_fn = kernel_posterior_tensor_fn self.kernel_prior_fn = kernel_prior_fn self.kernel_divergence_fn = kernel_divergence_fn self.bias_posterior_fn = bias_posterior_fn self.bias_posterior_tensor_fn = bias_posterior_tensor_fn self.bias_prior_fn = bias_prior_fn self.bias_divergence_fn = bias_divergence_fn def build(self, input_shape): input_shape = tensor_shape.TensorShape(input_shape) in_size = input_shape.with_rank_at_least(2)[-1].value if in_size is None: raise ValueError("The last dimension of the inputs to `Dense` " "should be defined. Found `None`.") self._input_spec = layers_lib.InputSpec(min_ndim=2, axes={-1: in_size}) dtype = dtypes.as_dtype(self.dtype) # Must have a posterior kernel. self.kernel_posterior = self.kernel_posterior_fn( dtype, [in_size, self.units], "kernel_posterior", self.trainable, self.add_variable) if self.kernel_prior_fn is None: self.kernel_prior = None else: self.kernel_prior = self.kernel_prior_fn( dtype, [in_size, self.units], "kernel_prior", self.trainable, self.add_variable) self._built_kernel_divergence = False if self.bias_posterior_fn is None: self.bias_posterior = None else: self.bias_posterior = self.bias_posterior_fn( dtype, [self.units], "bias_posterior", self.trainable, self.add_variable) if self.bias_prior_fn is None: self.bias_prior = None else: self.bias_prior = self.bias_prior_fn( dtype, [self.units], "bias_prior", self.trainable, self.add_variable) self._built_bias_divergence = False self.built = True def call(self, inputs): inputs = ops.convert_to_tensor(inputs, dtype=self.dtype) outputs = self._apply_variational_kernel(inputs) outputs = self._apply_variational_bias(outputs) if self.activation is not None: outputs = self.activation(outputs) # pylint: disable=not-callable if not self._built_kernel_divergence: kernel_posterior = self.kernel_posterior kernel_prior = self.kernel_prior if isinstance(self.kernel_posterior, independent_lib.Independent): kernel_posterior = kernel_posterior.distribution if isinstance(self.kernel_prior, independent_lib.Independent): kernel_prior = kernel_prior.distribution self._apply_divergence(self.kernel_divergence_fn, kernel_posterior, kernel_prior, self.kernel_posterior_tensor, name="divergence_kernel") self._built_kernel_divergence = True if not self._built_bias_divergence: bias_posterior = self.bias_posterior bias_prior = self.bias_prior if isinstance(self.bias_posterior, independent_lib.Independent): bias_posterior = bias_posterior.distribution if isinstance(self.bias_prior, independent_lib.Independent): bias_prior = bias_prior.distribution self._apply_divergence(self.bias_divergence_fn, bias_posterior, bias_prior, self.bias_posterior_tensor, name="divergence_bias") self._built_bias_divergence = True return outputs def _apply_variational_bias(self, inputs): if self.bias_posterior is None: self.bias_posterior_tensor = None return inputs self.bias_posterior_tensor = self.bias_posterior_tensor_fn( self.bias_posterior) return nn.bias_add(inputs, self.bias_posterior_tensor) def _apply_divergence(self, divergence_fn, posterior, prior, posterior_tensor, name): if (divergence_fn is None or posterior is None or prior is None): divergence = None return divergence = standard_ops.identity( divergence_fn( posterior, prior, posterior_tensor), name=name) self.add_loss(divergence) def _matmul(self, inputs, kernel): if inputs.shape.ndims <= 2: return standard_ops.matmul(inputs, kernel) # To handle broadcasting, we must use `tensordot`. return standard_ops.tensordot(inputs, kernel, axes=[[-1], [0]]) def _compute_output_shape(self, input_shape): input_shape = tensor_shape.TensorShape(input_shape).with_rank_at_least(2) if input_shape[-1].value is None: raise ValueError( "The innermost dimension of input_shape must be defined, " "but saw: {}".format(input_shape)) return input_shape[:-1].concatenate(self.units) class DenseReparameterization(_DenseVariational): """Densely-connected layer class with reparameterization estimator. This layer implements the Bayesian variational inference analogue to a dense layer by assuming the `kernel` and/or the `bias` are drawn from distributions. By default, the layer implements a stochastic forward pass via sampling from the kernel and bias posteriors, ```none kernel, bias ~ posterior outputs = activation(matmul(inputs, kernel) + bias) ``` The arguments permit separate specification of the surrogate posterior (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` distributions. Args: units: Integer or Long, dimensionality of the output space. activation: Activation function (`callable`). Set it to None to maintain a linear activation. activity_regularizer: Regularizer function for the output. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). kernel_posterior_fn: Python `callable` which creates `tf.distributions.Distribution` instance representing the surrogate posterior of the `kernel` parameter. Default value: `default_mean_field_normal_fn()`. kernel_posterior_tensor_fn: Python `callable` which takes a `tf.distributions.Distribution` instance and returns a representative value. Default value: `lambda d: d.sample()`. kernel_prior_fn: Python `callable` which creates `tf.distributions` instance. See `default_mean_field_normal_fn` docstring for required parameter signature. Default value: `tf.distributions.Normal(loc=0., scale=1.)`. kernel_divergence_fn: Python `callable` which takes the surrogate posterior distribution, prior distribution and random variate sample(s) from the surrogate posterior and computes or approximates the KL divergence. The distributions are `tf.distributions.Distribution`-like instances and the sample is a `Tensor`. bias_posterior_fn: Python `callable` which creates `tf.distributions.Distribution` instance representing the surrogate posterior of the `bias` parameter. Default value: `default_mean_field_normal_fn(is_singular=True)` (which creates an instance of `tf.distributions.Deterministic`). bias_posterior_tensor_fn: Python `callable` which takes a `tf.distributions.Distribution` instance and returns a representative value. Default value: `lambda d: d.sample()`. bias_prior_fn: Python `callable` which creates `tf.distributions` instance. See `default_mean_field_normal_fn` docstring for required parameter signature. Default value: `None` (no prior, no variational inference) bias_divergence_fn: Python `callable` which takes the surrogate posterior distribution, prior distribution and random variate sample(s) from the surrogate posterior and computes or approximates the KL divergence. The distributions are `tf.distributions.Distribution`-like instances and the sample is a `Tensor`. name: Python `str`, the name of the layer. Layers with the same name will share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in such cases. reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous layer by the same name. Properties: units: Python integer, dimensionality of the output space. activation: Activation function (`callable`). activity_regularizer: Regularizer function for the output. kernel_posterior_fn: `callable` returning posterior. kernel_posterior_tensor_fn: `callable` operating on posterior. kernel_prior_fn: `callable` returning prior. kernel_divergence_fn: `callable` returning divergence. bias_posterior_fn: `callable` returning posterior. bias_posterior_tensor_fn: `callable` operating on posterior. bias_prior_fn: `callable` returning prior. bias_divergence_fn: `callable` returning divergence. #### Examples We illustrate a Bayesian neural network with [variational inference]( https://en.wikipedia.org/wiki/Variational_Bayesian_methods), assuming a dataset of `features` and `labels`. ```python tfp = tf.contrib.bayesflow net = tfp.layers.DenseReparameterization( 512, activation=tf.nn.relu)(features) logits = tfp.layers.DenseReparameterization(10)(net) neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( labels=labels, logits=logits) kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) loss = neg_log_likelihood + kl train_op = tf.train.AdamOptimizer().minimize(loss) ``` It uses reparameterization gradients to minimize the Kullback-Leibler divergence up to a constant, also known as the negative Evidence Lower Bound. It consists of the sum of two terms: the expected negative log-likelihood, which we approximate via Monte Carlo; and the KL divergence, which is added via regularizer terms which are arguments to the layer. """ def __init__( self, units, activation=None, activity_regularizer=None, trainable=True, kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), kernel_posterior_tensor_fn=lambda d: d.sample(), kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), bias_posterior_fn=layers_util.default_mean_field_normal_fn( is_singular=True), bias_posterior_tensor_fn=lambda d: d.sample(), bias_prior_fn=None, bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), name=None, **kwargs): super(DenseReparameterization, self).__init__( units=units, activation=activation, activity_regularizer=activity_regularizer, trainable=trainable, kernel_posterior_fn=kernel_posterior_fn, kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, kernel_prior_fn=kernel_prior_fn, kernel_divergence_fn=kernel_divergence_fn, bias_posterior_fn=bias_posterior_fn, bias_posterior_tensor_fn=bias_posterior_tensor_fn, bias_prior_fn=bias_prior_fn, bias_divergence_fn=bias_divergence_fn, name=name, **kwargs) def _apply_variational_kernel(self, inputs): self.kernel_posterior_tensor = self.kernel_posterior_tensor_fn( self.kernel_posterior) self.kernel_posterior_affine = None self.kernel_posterior_affine_tensor = None return self._matmul(inputs, self.kernel_posterior_tensor) def dense_reparameterization( inputs, units, activation=None, activity_regularizer=None, trainable=True, kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), kernel_posterior_tensor_fn=lambda d: d.sample(), kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), bias_posterior_fn=layers_util.default_mean_field_normal_fn(is_singular=True), # pylint: disable=line-too-long bias_posterior_tensor_fn=lambda d: d.sample(), bias_prior_fn=None, bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), name=None, reuse=None): """Densely-connected layer with reparameterization estimator. This layer implements the Bayesian variational inference analogue to a dense layer by assuming the `kernel` and/or the `bias` are drawn from distributions. By default, the layer implements a stochastic forward pass via sampling from the kernel and bias posteriors, ```none kernel, bias ~ posterior outputs = activation(matmul(inputs, kernel) + bias) ``` The arguments permit separate specification of the surrogate posterior (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` distributions. Args: inputs: Tensor input. units: Integer or Long, dimensionality of the output space. activation: Activation function (`callable`). Set it to None to maintain a linear activation. activity_regularizer: Regularizer function for the output. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). kernel_posterior_fn: Python `callable` which creates `tf.distributions.Distribution` instance representing the surrogate posterior of the `kernel` parameter. Default value: `default_mean_field_normal_fn()`. kernel_posterior_tensor_fn: Python `callable` which takes a `tf.distributions.Distribution` instance and returns a representative value. Default value: `lambda d: d.sample()`. kernel_prior_fn: Python `callable` which creates `tf.distributions` instance. See `default_mean_field_normal_fn` docstring for required parameter signature. Default value: `tf.distributions.Normal(loc=0., scale=1.)`. kernel_divergence_fn: Python `callable` which takes the surrogate posterior distribution, prior distribution and random variate sample(s) from the surrogate posterior and computes or approximates the KL divergence. The distributions are `tf.distributions.Distribution`-like instances and the sample is a `Tensor`. bias_posterior_fn: Python `callable` which creates `tf.distributions.Distribution` instance representing the surrogate posterior of the `bias` parameter. Default value: `default_mean_field_normal_fn(is_singular=True)` (which creates an instance of `tf.distributions.Deterministic`). bias_posterior_tensor_fn: Python `callable` which takes a `tf.distributions.Distribution` instance and returns a representative value. Default value: `lambda d: d.sample()`. bias_prior_fn: Python `callable` which creates `tf.distributions` instance. See `default_mean_field_normal_fn` docstring for required parameter signature. Default value: `None` (no prior, no variational inference) bias_divergence_fn: Python `callable` which takes the surrogate posterior distribution, prior distribution and random variate sample(s) from the surrogate posterior and computes or approximates the KL divergence. The distributions are `tf.distributions.Distribution`-like instances and the sample is a `Tensor`. name: Python `str`, the name of the layer. Layers with the same name will share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in such cases. reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous layer by the same name. Returns: output: `Tensor` representing a the affine transformed input under a random draw from the surrogate posterior distribution. #### Examples We illustrate a Bayesian neural network with [variational inference]( https://en.wikipedia.org/wiki/Variational_Bayesian_methods), assuming a dataset of `features` and `labels`. ```python tfp = tf.contrib.bayesflow net = tfp.layers.dense_reparameterization( features, 512, activation=tf.nn.relu) logits = tfp.layers.dense_reparameterization(net, 10) neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( labels=labels, logits=logits) kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) loss = neg_log_likelihood + kl train_op = tf.train.AdamOptimizer().minimize(loss) ``` It uses reparameterization gradients to minimize the Kullback-Leibler divergence up to a constant, also known as the negative Evidence Lower Bound. It consists of the sum of two terms: the expected negative log-likelihood, which we approximate via Monte Carlo; and the KL divergence, which is added via regularizer terms which are arguments to the layer. """ layer = DenseReparameterization( units, activation=activation, activity_regularizer=activity_regularizer, trainable=trainable, kernel_posterior_fn=kernel_posterior_fn, kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, kernel_prior_fn=kernel_prior_fn, kernel_divergence_fn=kernel_divergence_fn, bias_posterior_fn=bias_posterior_fn, bias_posterior_tensor_fn=bias_posterior_tensor_fn, bias_prior_fn=bias_prior_fn, bias_divergence_fn=bias_divergence_fn, name=name, dtype=inputs.dtype.base_dtype, _scope=name, _reuse=reuse) return layer.apply(inputs) class DenseLocalReparameterization(_DenseVariational): """Densely-connected layer class with local reparameterization estimator. This layer implements the Bayesian variational inference analogue to a dense layer by assuming the `kernel` and/or the `bias` are drawn from distributions. By default, the layer implements a stochastic forward pass via sampling from the kernel and bias posteriors, ```none kernel, bias ~ posterior outputs = activation(matmul(inputs, kernel) + bias) ``` The arguments permit separate specification of the surrogate posterior (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` distributions. Args: units: Integer or Long, dimensionality of the output space. activation: Activation function (`callable`). Set it to None to maintain a linear activation. activity_regularizer: Regularizer function for the output. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). kernel_posterior_fn: Python `callable` which creates `tf.distributions.Distribution` instance representing the surrogate posterior of the `kernel` parameter. Default value: `default_mean_field_normal_fn()`. kernel_posterior_tensor_fn: Python `callable` which takes a `tf.distributions.Distribution` instance and returns a representative value. Default value: `lambda d: d.sample()`. kernel_prior_fn: Python `callable` which creates `tf.distributions` instance. See `default_mean_field_normal_fn` docstring for required parameter signature. Default value: `tf.distributions.Normal(loc=0., scale=1.)`. kernel_divergence_fn: Python `callable` which takes the surrogate posterior distribution, prior distribution and random variate sample(s) from the surrogate posterior and computes or approximates the KL divergence. The distributions are `tf.distributions.Distribution`-like instances and the sample is a `Tensor`. bias_posterior_fn: Python `callable` which creates `tf.distributions.Distribution` instance representing the surrogate posterior of the `bias` parameter. Default value: `default_mean_field_normal_fn(is_singular=True)` (which creates an instance of `tf.distributions.Deterministic`). bias_posterior_tensor_fn: Python `callable` which takes a `tf.distributions.Distribution` instance and returns a representative value. Default value: `lambda d: d.sample()`. bias_prior_fn: Python `callable` which creates `tf.distributions` instance. See `default_mean_field_normal_fn` docstring for required parameter signature. Default value: `None` (no prior, no variational inference) bias_divergence_fn: Python `callable` which takes the surrogate posterior distribution, prior distribution and random variate sample(s) from the surrogate posterior and computes or approximates the KL divergence. The distributions are `tf.distributions.Distribution`-like instances and the sample is a `Tensor`. name: Python `str`, the name of the layer. Layers with the same name will share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in such cases. reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous layer by the same name. Properties: units: Python integer, dimensionality of the output space. activation: Activation function (`callable`). activity_regularizer: Regularizer function for the output. kernel_posterior_fn: `callable` returning posterior. kernel_posterior_tensor_fn: `callable` operating on posterior. kernel_prior_fn: `callable` returning prior. kernel_divergence_fn: `callable` returning divergence. bias_posterior_fn: `callable` returning posterior. bias_posterior_tensor_fn: `callable` operating on posterior. bias_prior_fn: `callable` returning prior. bias_divergence_fn: `callable` returning divergence. #### Examples We illustrate a Bayesian neural network with [variational inference]( https://en.wikipedia.org/wiki/Variational_Bayesian_methods), assuming a dataset of `features` and `labels`. ```python tfp = tf.contrib.bayesflow net = tfp.layers.DenseLocalReparameterization( 512, activation=tf.nn.relu)(features) logits = tfp.layers.DenseLocalReparameterization(10)(net) neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( labels=labels, logits=logits) kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) loss = neg_log_likelihood + kl train_op = tf.train.AdamOptimizer().minimize(loss) ``` It uses local reparameterization gradients to minimize the Kullback-Leibler divergence up to a constant, also known as the negative Evidence Lower Bound. It consists of the sum of two terms: the expected negative log-likelihood, which we approximate via Monte Carlo; and the KL divergence, which is added via regularizer terms which are arguments to the layer. """ def __init__( self, units, activation=None, activity_regularizer=None, trainable=True, kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), kernel_posterior_tensor_fn=lambda d: d.sample(), kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), bias_posterior_fn=layers_util.default_mean_field_normal_fn( is_singular=True), bias_posterior_tensor_fn=lambda d: d.sample(), bias_prior_fn=None, bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), name=None, **kwargs): super(DenseLocalReparameterization, self).__init__( units=units, activation=activation, activity_regularizer=activity_regularizer, trainable=trainable, kernel_posterior_fn=kernel_posterior_fn, kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, kernel_prior_fn=kernel_prior_fn, kernel_divergence_fn=kernel_divergence_fn, bias_posterior_fn=bias_posterior_fn, bias_posterior_tensor_fn=bias_posterior_tensor_fn, bias_prior_fn=bias_prior_fn, bias_divergence_fn=bias_divergence_fn, name=name, **kwargs) def _apply_variational_kernel(self, inputs): if (not isinstance(self.kernel_posterior, independent_lib.Independent) or not isinstance(self.kernel_posterior.distribution, normal_lib.Normal)): raise TypeError( "`DenseLocalReparameterization` requires " "`kernel_posterior_fn` produce an instance of " "`tf.distributions.Independent(tf.distributions.Normal)` " "(saw: \"{}\").".format(type(self.kernel_posterior).__name__)) self.kernel_posterior_affine = normal_lib.Normal( loc=self._matmul(inputs, self.kernel_posterior.distribution.loc), scale=standard_ops.sqrt(self._matmul( standard_ops.square(inputs), standard_ops.square(self.kernel_posterior.distribution.scale)))) self.kernel_posterior_affine_tensor = ( self.kernel_posterior_tensor_fn(self.kernel_posterior_affine)) self.kernel_posterior_tensor = None return self.kernel_posterior_affine_tensor def dense_local_reparameterization( inputs, units, activation=None, activity_regularizer=None, trainable=True, kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), kernel_posterior_tensor_fn=lambda d: d.sample(), kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), bias_posterior_fn=layers_util.default_mean_field_normal_fn( is_singular=True), bias_posterior_tensor_fn=lambda d: d.sample(), bias_prior_fn=None, bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), name=None, reuse=None): """Densely-connected layer with local reparameterization estimator. This layer implements the Bayesian variational inference analogue to a dense layer by assuming the `kernel` and/or the `bias` are drawn from distributions. By default, the layer implements a stochastic forward pass via sampling from the kernel and bias posteriors, ```none kernel, bias ~ posterior outputs = activation(matmul(inputs, kernel) + bias) ``` The arguments permit separate specification of the surrogate posterior (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` distributions. Args: inputs: Tensor input. units: Integer or Long, dimensionality of the output space. activation: Activation function (`callable`). Set it to None to maintain a linear activation. activity_regularizer: Regularizer function for the output. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). kernel_posterior_fn: Python `callable` which creates `tf.distributions.Distribution` instance representing the surrogate posterior of the `kernel` parameter. Default value: `default_mean_field_normal_fn()`. kernel_posterior_tensor_fn: Python `callable` which takes a `tf.distributions.Distribution` instance and returns a representative value. Default value: `lambda d: d.sample()`. kernel_prior_fn: Python `callable` which creates `tf.distributions` instance. See `default_mean_field_normal_fn` docstring for required parameter signature. Default value: `tf.distributions.Normal(loc=0., scale=1.)`. kernel_divergence_fn: Python `callable` which takes the surrogate posterior distribution, prior distribution and random variate sample(s) from the surrogate posterior and computes or approximates the KL divergence. The distributions are `tf.distributions.Distribution`-like instances and the sample is a `Tensor`. bias_posterior_fn: Python `callable` which creates `tf.distributions.Distribution` instance representing the surrogate posterior of the `bias` parameter. Default value: `default_mean_field_normal_fn(is_singular=True)` (which creates an instance of `tf.distributions.Deterministic`). bias_posterior_tensor_fn: Python `callable` which takes a `tf.distributions.Distribution` instance and returns a representative value. Default value: `lambda d: d.sample()`. bias_prior_fn: Python `callable` which creates `tf.distributions` instance. See `default_mean_field_normal_fn` docstring for required parameter signature. Default value: `None` (no prior, no variational inference) bias_divergence_fn: Python `callable` which takes the surrogate posterior distribution, prior distribution and random variate sample(s) from the surrogate posterior and computes or approximates the KL divergence. The distributions are `tf.distributions.Distribution`-like instances and the sample is a `Tensor`. name: Python `str`, the name of the layer. Layers with the same name will share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in such cases. reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous layer by the same name. Returns: output: `Tensor` representing a the affine transformed input under a random draw from the surrogate posterior distribution. #### Examples We illustrate a Bayesian neural network with [variational inference]( https://en.wikipedia.org/wiki/Variational_Bayesian_methods), assuming a dataset of `features` and `labels`. ```python tfp = tf.contrib.bayesflow net = tfp.layers.dense_local_reparameterization( features, 512, activation=tf.nn.relu) logits = tfp.layers.dense_local_reparameterization(net, 10) neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( labels=labels, logits=logits) kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) loss = neg_log_likelihood + kl train_op = tf.train.AdamOptimizer().minimize(loss) ``` It uses local reparameterization gradients to minimize the Kullback-Leibler divergence up to a constant, also known as the negative Evidence Lower Bound. It consists of the sum of two terms: the expected negative log-likelihood, which we approximate via Monte Carlo; and the KL divergence, which is added via regularizer terms which are arguments to the layer. """ layer = DenseLocalReparameterization( units, activation=activation, activity_regularizer=activity_regularizer, trainable=trainable, kernel_posterior_fn=kernel_posterior_fn, kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, kernel_prior_fn=kernel_prior_fn, kernel_divergence_fn=kernel_divergence_fn, bias_posterior_fn=bias_posterior_fn, bias_posterior_tensor_fn=bias_posterior_tensor_fn, bias_prior_fn=bias_prior_fn, bias_divergence_fn=bias_divergence_fn, name=name, dtype=inputs.dtype.base_dtype, _scope=name, _reuse=reuse) return layer.apply(inputs) class DenseFlipout(_DenseVariational): """Densely-connected layer class with Flipout estimator. This layer implements the Bayesian variational inference analogue to a dense layer by assuming the `kernel` and/or the `bias` are drawn from distributions. By default, the layer implements a stochastic forward pass via sampling from the kernel and bias posteriors, ```none kernel, bias ~ posterior outputs = activation(matmul(inputs, kernel) + bias) ``` The arguments permit separate specification of the surrogate posterior (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` distributions. Args: units: Integer or Long, dimensionality of the output space. activation: Activation function (`callable`). Set it to None to maintain a linear activation. activity_regularizer: Regularizer function for the output. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). kernel_posterior_fn: Python `callable` which creates `tf.distributions.Distribution` instance representing the surrogate posterior of the `kernel` parameter. Default value: `default_mean_field_normal_fn()`. kernel_posterior_tensor_fn: Python `callable` which takes a `tf.distributions.Distribution` instance and returns a representative value. Default value: `lambda d: d.sample()`. kernel_prior_fn: Python `callable` which creates `tf.distributions` instance. See `default_mean_field_normal_fn` docstring for required parameter signature. Default value: `tf.distributions.Normal(loc=0., scale=1.)`. kernel_divergence_fn: Python `callable` which takes the surrogate posterior distribution, prior distribution and random variate sample(s) from the surrogate posterior and computes or approximates the KL divergence. The distributions are `tf.distributions.Distribution`-like instances and the sample is a `Tensor`. bias_posterior_fn: Python `callable` which creates `tf.distributions.Distribution` instance representing the surrogate posterior of the `bias` parameter. Default value: `default_mean_field_normal_fn(is_singular=True)` (which creates an instance of `tf.distributions.Deterministic`). bias_posterior_tensor_fn: Python `callable` which takes a `tf.distributions.Distribution` instance and returns a representative value. Default value: `lambda d: d.sample()`. bias_prior_fn: Python `callable` which creates `tf.distributions` instance. See `default_mean_field_normal_fn` docstring for required parameter signature. Default value: `None` (no prior, no variational inference) bias_divergence_fn: Python `callable` which takes the surrogate posterior distribution, prior distribution and random variate sample(s) from the surrogate posterior and computes or approximates the KL divergence. The distributions are `tf.distributions.Distribution`-like instances and the sample is a `Tensor`. seed: Python scalar `int` which initializes the random number generator. Default value: `None` (i.e., use global seed). name: Python `str`, the name of the layer. Layers with the same name will share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in such cases. reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous layer by the same name. Properties: units: Python integer, dimensionality of the output space. activation: Activation function (`callable`). activity_regularizer: Regularizer function for the output. kernel_posterior_fn: `callable` returning posterior. kernel_posterior_tensor_fn: `callable` operating on posterior. kernel_prior_fn: `callable` returning prior. kernel_divergence_fn: `callable` returning divergence. bias_posterior_fn: `callable` returning posterior. bias_posterior_tensor_fn: `callable` operating on posterior. bias_prior_fn: `callable` returning prior. bias_divergence_fn: `callable` returning divergence. seed: Python integer, used to create random seeds. #### Examples We illustrate a Bayesian neural network with [variational inference]( https://en.wikipedia.org/wiki/Variational_Bayesian_methods), assuming a dataset of `features` and `labels`. ```python tfp = tf.contrib.bayesflow net = tfp.layers.DenseFlipout( 512, activation=tf.nn.relu)(features) logits = tfp.layers.DenseFlipout(10)(net) neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( labels=labels, logits=logits) kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) loss = neg_log_likelihood + kl train_op = tf.train.AdamOptimizer().minimize(loss) ``` It uses the Flipout gradient estimator to minimize the Kullback-Leibler divergence up to a constant, also known as the negative Evidence Lower Bound. It consists of the sum of two terms: the expected negative log-likelihood, which we approximate via Monte Carlo; and the KL divergence, which is added via regularizer terms which are arguments to the layer. """ def __init__( self, units, activation=None, activity_regularizer=None, trainable=True, kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), kernel_posterior_tensor_fn=lambda d: d.sample(), kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), bias_posterior_fn=layers_util.default_mean_field_normal_fn( is_singular=True), bias_posterior_tensor_fn=lambda d: d.sample(), bias_prior_fn=None, bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), seed=None, name=None, **kwargs): super(DenseFlipout, self).__init__( units=units, activation=activation, activity_regularizer=activity_regularizer, trainable=trainable, kernel_posterior_fn=kernel_posterior_fn, kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, kernel_prior_fn=kernel_prior_fn, kernel_divergence_fn=kernel_divergence_fn, bias_posterior_fn=bias_posterior_fn, bias_posterior_tensor_fn=bias_posterior_tensor_fn, bias_prior_fn=bias_prior_fn, bias_divergence_fn=bias_divergence_fn, name=name, **kwargs) self.seed = seed def _apply_variational_kernel(self, inputs): if (not isinstance(self.kernel_posterior, independent_lib.Independent) or not isinstance(self.kernel_posterior.distribution, normal_lib.Normal)): raise TypeError( "`DenseFlipout` requires " "`kernel_posterior_fn` produce an instance of " "`tf.distributions.Independent(tf.distributions.Normal)` " "(saw: \"{}\").".format(type(self.kernel_posterior).__name__)) self.kernel_posterior_affine = normal_lib.Normal( loc=array_ops.zeros_like(self.kernel_posterior.distribution.loc), scale=self.kernel_posterior.distribution.scale) self.kernel_posterior_affine_tensor = ( self.kernel_posterior_tensor_fn(self.kernel_posterior_affine)) self.kernel_posterior_tensor = None input_shape = array_ops.shape(inputs) batch_shape = input_shape[:-1] sign_input = random_sign(input_shape, dtype=inputs.dtype, seed=self.seed) sign_output = random_sign( array_ops.concat([batch_shape, array_ops.expand_dims(self.units, 0)], 0), dtype=inputs.dtype, seed=distribution_util.gen_new_seed( self.seed, salt="dense_flipout")) perturbed_inputs = self._matmul( inputs * sign_input, self.kernel_posterior_affine_tensor) * sign_output outputs = self._matmul(inputs, self.kernel_posterior.distribution.loc) outputs += perturbed_inputs return outputs def dense_flipout( inputs, units, activation=None, activity_regularizer=None, trainable=True, kernel_posterior_fn=layers_util.default_mean_field_normal_fn(), kernel_posterior_tensor_fn=lambda d: d.sample(), kernel_prior_fn=lambda dtype, *args: normal_lib.Normal( # pylint: disable=g-long-lambda loc=dtype.as_numpy_dtype(0.), scale=dtype.as_numpy_dtype(1.)), kernel_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), bias_posterior_fn=layers_util.default_mean_field_normal_fn( is_singular=True), bias_posterior_tensor_fn=lambda d: d.sample(), bias_prior_fn=None, bias_divergence_fn=lambda q, p, ignore: kl_lib.kl_divergence(q, p), seed=None, name=None, reuse=None): """Densely-connected layer with Flipout estimator. This layer implements the Bayesian variational inference analogue to a dense layer by assuming the `kernel` and/or the `bias` are drawn from distributions. By default, the layer implements a stochastic forward pass via sampling from the kernel and bias posteriors, ```none kernel, bias ~ posterior outputs = activation(matmul(inputs, kernel) + bias) ``` The arguments permit separate specification of the surrogate posterior (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` distributions. Args: inputs: Tensor input. units: Integer or Long, dimensionality of the output space. activation: Activation function (`callable`). Set it to None to maintain a linear activation. activity_regularizer: Regularizer function for the output. trainable: Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). kernel_posterior_fn: Python `callable` which creates `tf.distributions.Distribution` instance representing the surrogate posterior of the `kernel` parameter. Default value: `default_mean_field_normal_fn()`. kernel_posterior_tensor_fn: Python `callable` which takes a `tf.distributions.Distribution` instance and returns a representative value. Default value: `lambda d: d.sample()`. kernel_prior_fn: Python `callable` which creates `tf.distributions` instance. See `default_mean_field_normal_fn` docstring for required parameter signature. Default value: `tf.distributions.Normal(loc=0., scale=1.)`. kernel_divergence_fn: Python `callable` which takes the surrogate posterior distribution, prior distribution and random variate sample(s) from the surrogate posterior and computes or approximates the KL divergence. The distributions are `tf.distributions.Distribution`-like instances and the sample is a `Tensor`. bias_posterior_fn: Python `callable` which creates `tf.distributions.Distribution` instance representing the surrogate posterior of the `bias` parameter. Default value: `default_mean_field_normal_fn(is_singular=True)` (which creates an instance of `tf.distributions.Deterministic`). bias_posterior_tensor_fn: Python `callable` which takes a `tf.distributions.Distribution` instance and returns a representative value. Default value: `lambda d: d.sample()`. bias_prior_fn: Python `callable` which creates `tf.distributions` instance. See `default_mean_field_normal_fn` docstring for required parameter signature. Default value: `None` (no prior, no variational inference) bias_divergence_fn: Python `callable` which takes the surrogate posterior distribution, prior distribution and random variate sample(s) from the surrogate posterior and computes or approximates the KL divergence. The distributions are `tf.distributions.Distribution`-like instances and the sample is a `Tensor`. seed: Python scalar `int` which initializes the random number generator. Default value: `None` (i.e., use global seed). name: Python `str`, the name of the layer. Layers with the same name will share `tf.Variable`s, but to avoid mistakes we require `reuse=True` in such cases. reuse: Python `bool`, whether to reuse the `tf.Variable`s of a previous layer by the same name. Returns: output: `Tensor` representing a the affine transformed input under a random draw from the surrogate posterior distribution. #### Examples We illustrate a Bayesian neural network with [variational inference]( https://en.wikipedia.org/wiki/Variational_Bayesian_methods), assuming a dataset of `features` and `labels`. ```python tfp = tf.contrib.bayesflow net = tfp.layers.dense_flipout( features, 512, activation=tf.nn.relu) logits = tfp.layers.dense_flipout(net, 10) neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits( labels=labels, logits=logits) kl = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) loss = neg_log_likelihood + kl train_op = tf.train.AdamOptimizer().minimize(loss) ``` It uses the Flipout gradient estimator to minimize the Kullback-Leibler divergence up to a constant, also known as the negative Evidence Lower Bound. It consists of the sum of two terms: the expected negative log-likelihood, which we approximate via Monte Carlo; and the KL divergence, which is added via regularizer terms which are arguments to the layer. """ layer = DenseFlipout( units, activation=activation, activity_regularizer=activity_regularizer, trainable=trainable, kernel_posterior_fn=kernel_posterior_fn, kernel_posterior_tensor_fn=kernel_posterior_tensor_fn, kernel_prior_fn=kernel_prior_fn, kernel_divergence_fn=kernel_divergence_fn, bias_posterior_fn=bias_posterior_fn, bias_posterior_tensor_fn=bias_posterior_tensor_fn, bias_prior_fn=bias_prior_fn, bias_divergence_fn=bias_divergence_fn, seed=seed, name=name, dtype=inputs.dtype.base_dtype, _scope=name, _reuse=reuse) return layer.apply(inputs) def random_sign(shape, dtype=dtypes.float32, seed=None): """Draw values from {-1, 1} uniformly, i.e., Rademacher distribution.""" random_bernoulli = random_ops.random_uniform(shape, minval=0, maxval=2, dtype=dtypes.int32, seed=seed) return math_ops.cast(2 * random_bernoulli - 1, dtype)

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null

0

6

a75472f4b186ce4433399e3535634ff879b1b81c

58

py

Python

dsb2015/models/__init__.py

rmunoz12/dsb2015

5f277658ecd49a1fd751c897367715811fa81668

[ "MIT" ]

null

dsb2015/models/__init__.py

rmunoz12/dsb2015

5f277658ecd49a1fd751c897367715811fa81668

[ "MIT" ]

null

dsb2015/models/__init__.py

rmunoz12/dsb2015

5f277658ecd49a1fd751c897367715811fa81668

[ "MIT" ]

null

from .alexnet import get_alexnet from .vgg import get_vgg

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6

a773118785193ba98c5f10f1dd015dc36d732948

169

py

Python

ProgressReport2/Testing.py

TainedeBlaze/EEE3097S-Project

93772557d9a795cb8411657d08d9a44f640ba1ee

[ "MIT" ]

null

ProgressReport2/Testing.py

TainedeBlaze/EEE3097S-Project

93772557d9a795cb8411657d08d9a44f640ba1ee

[ "MIT" ]

null

ProgressReport2/Testing.py

TainedeBlaze/EEE3097S-Project

93772557d9a795cb8411657d08d9a44f640ba1ee

[ "MIT" ]

null

import glob import os #os.system('python3 SendData.py ' + "IMU-data-2021-10-25-20:38:19.csv" ) os.system('python3 RecieveData.py' + " encrypted_fileofIMU.txt")

28.166667

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4.461538

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6

a7a600f9a55e01c7109c32fa9272219902c3de44

113

py

Python

tests/basetest.py

NerdPraise/UKUFU-TASK

3f3018a1d50706fa23fdacd13f644669c10406f0

[ "MIT" ]

null

tests/basetest.py

NerdPraise/UKUFU-TASK

3f3018a1d50706fa23fdacd13f644669c10406f0

[ "MIT" ]

null

tests/basetest.py

NerdPraise/UKUFU-TASK

3f3018a1d50706fa23fdacd13f644669c10406f0

[ "MIT" ]

null

from rest_framework.test import APITestCase # Create your tests here. class BaseTestCase(APITestCase): pass

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ac2c63dca724299b8cea31fb72410cb5561b6f1e

194

py

Python

day-11/package_demo/package1/pathprint.py

JohnLockwood/100-days-of-python

352b3b0861e1e1228b54079e39c1d0a83ef9af6c

[ "Apache-2.0" ]

null

day-11/package_demo/package1/pathprint.py

JohnLockwood/100-days-of-python

352b3b0861e1e1228b54079e39c1d0a83ef9af6c

[ "Apache-2.0" ]

null

day-11/package_demo/package1/pathprint.py

JohnLockwood/100-days-of-python

352b3b0861e1e1228b54079e39c1d0a83ef9af6c

[ "Apache-2.0" ]

null

"""Demo module to show python search path""" import sys import pprint def print_path(): """Pretty print the system path""" print("Printing the search path:") pprint.pprint(sys.path)

24.25

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3bc2aa086fed44b5e36a5921fabb9eeb08aa65ae

81

py

Python

mitreattack/__init__.py

wetkind/mitreattack-python

f2406cac6b8d104d280712fccf9c50637ae05fbb

[ "Apache-2.0" ]

137

2021-04-06T17:40:20.000Z

2022-03-30T18:27:44.000Z

mitreattack/__init__.py

wetkind/mitreattack-python

f2406cac6b8d104d280712fccf9c50637ae05fbb

[ "Apache-2.0" ]

33

2021-04-07T13:41:39.000Z

2022-03-25T14:37:40.000Z

mitreattack/__init__.py

wetkind/mitreattack-python

f2406cac6b8d104d280712fccf9c50637ae05fbb

[ "Apache-2.0" ]

29

2021-04-06T21:14:40.000Z

2022-03-31T15:26:27.000Z

from .attackToExcel import * from .navlayers import * from .collections import *

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ce3f6d2c4d9cecf42f273ac3ac1ebe1a697d9983

167

py

Python

xgp/tests/test_regressor.py

MaxHalford/xgp-python

f93059f46dedd8712578a2bd0f45e5f9f18a2c63

[ "BSD-3-Clause" ]

7

2018-05-24T07:57:56.000Z

2021-11-16T17:34:26.000Z

xgp/tests/test_regressor.py

MaxHalford/xgp-python

f93059f46dedd8712578a2bd0f45e5f9f18a2c63

[ "BSD-3-Clause" ]

2

2018-06-01T17:02:35.000Z

2020-03-15T07:09:53.000Z

xgp/tests/test_regressor.py

MaxHalford/xgp-python

f93059f46dedd8712578a2bd0f45e5f9f18a2c63

[ "BSD-3-Clause" ]

1

2021-11-16T17:34:27.000Z

from sklearn.utils.estimator_checks import check_estimator import xgp def test_regressor_check_estimator(): return return check_estimator(xgp.XGPRegressor)

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ce445002912c2b7e95efb447384b2ade20749c71

38,400

py

Python

textadapter/tests/test_TextAdapter.py

ContinuumIO/TextAdapter

53138c2277cdfcf32e127251313d4f77f81050aa

[ "BSD-3-Clause" ]

22

2016-11-09T12:20:04.000Z

2021-02-07T03:07:58.000Z

textadapter/tests/test_TextAdapter.py

blaze/TextAdapter

53138c2277cdfcf32e127251313d4f77f81050aa

[ "BSD-3-Clause" ]

3

2016-11-01T03:43:03.000Z

2017-02-27T20:19:05.000Z

textadapter/tests/test_TextAdapter.py

ContinuumIO/TextAdapter

53138c2277cdfcf32e127251313d4f77f81050aa

[ "BSD-3-Clause" ]

9

2017-01-31T21:28:57.000Z

2021-12-15T04:22:53.000Z

#!/usr/bin/python import sys import textadapter import unittest from .generate import (generate_dataset, IntIter, MissingValuesIter, FixedWidthIter) import numpy as np from numpy.testing import assert_array_equal import gzip import os import io from six import StringIO class TestTextAdapter(unittest.TestCase): num_records = 100000 def assert_equality(self, left, right): try: if isinstance(left, np.ndarray) and isinstance(right, np.ndarray): self.assert_array_equal(left, right) else: self.assertTrue(left == right) except AssertionError: raise AssertionError('FAIL: {0} != {1}'.format(left, right)) # Basic parsing tests def test_string_parsing(self): data = StringIO('1,2,3\n') adapter = textadapter.text_adapter(data, field_names=False) adapter.set_field_types({0:'S5', 1:'S5', 2:'S5'}) assert_array_equal(adapter[:], np.array([('1', '2', '3')], dtype='S5,S5,S5')) data = io.StringIO(u'1,2,3\n') adapter = textadapter.text_adapter(data, field_names=False) adapter.set_field_types({0:'S5', 1:'S5', 2:'S5'}) assert_array_equal(adapter[:], np.array([('1', '2', '3')], dtype='S5,S5,S5')) data = io.BytesIO(b'1,2,3\n') adapter = textadapter.text_adapter(data, field_names=False) adapter.set_field_types({0:'S5', 1:'S5', 2:'S5'}) assert_array_equal(adapter[:], np.array([('1', '2', '3')], dtype='S5,S5,S5')) # basic utf_8 tests def test_utf8_parsing(self): # test single byte character data = io.BytesIO(u'1,2,\u0033'.encode('utf_8')) adapter = textadapter.text_adapter(data, field_names=False) expected = np.array([('1', '2', '3')], dtype='u8,u8,u8') assert_array_equal(adapter[:], expected) # test multibyte character data = io.BytesIO(u'1,2,\u2092'.encode('utf_8')) adapter = textadapter.text_adapter(data, field_names=False) expected = np.array([('1', '2', u'\u2092')], dtype='u8,u8,O') assert_array_equal(adapter[:], expected) def test_no_whitespace_stripping(self): data = StringIO('1 ,2 ,3 \n') adapter = textadapter.text_adapter(data, field_names=False) adapter.set_field_types({0:'S3', 1:'S3', 2:'S3'}) assert_array_equal(adapter[:], np.array([('1 ', '2 ', '3 ')], dtype='S3,S3,S3')) data = StringIO(' 1, 2, 3\n') adapter = textadapter.text_adapter(data, field_names=False) adapter.set_field_types({0:'S3', 1:'S3', 2:'S3'}) assert_array_equal(adapter[:], np.array([(' 1', ' 2', ' 3')], dtype='S3,S3,S3')) data = StringIO(' 1 , 2 , 3 \n') adapter = textadapter.text_adapter(data, field_names=False) adapter.set_field_types({0:'S5', 1:'S5', 2:'S5'}) assert_array_equal(adapter[:], np.array([(' 1 ', ' 2 ', ' 3 ')], dtype='S5,S5,S5')) data = StringIO('\t1\t,\t2\t,\t3\t\n') adapter = textadapter.text_adapter(data, field_names=False) adapter.set_field_types({0:'S3', 1:'S3', 2:'S3'}) assert_array_equal(adapter[:], np.array([('\t1\t', '\t2\t', '\t3\t')], dtype='S3,S3,S3')) def test_quoted_whitespace(self): data = StringIO('"1 ","2 ","3 "\n') adapter = textadapter.text_adapter(data, field_names=False) adapter.set_field_types({0:'S3', 1:'S3', 2:'S3'}) assert_array_equal(adapter[:], np.array([('1 ', '2 ', '3 ')], dtype='S3,S3,S3')) data = StringIO('"\t1\t"\t"\t2\t"\t"\t3\t"\n') adapter = textadapter.text_adapter(data, field_names=False, delimiter='\t') adapter.set_field_types({0:'S3', 1:'S3', 2:'S3'}) assert_array_equal(adapter[:], np.array([('\t1\t', '\t2\t', '\t3\t')], dtype='S3,S3,S3')) def test_fixed_simple(self): # TODO: fix this test on 32-bit and on Windows if tuple.__itemsize__ == 4: # This test does not work on 32-bit, so we skip it return if sys.platform == 'win32': # This test does not work on Windows return data = StringIO(" 1 2 3\n 4 5 67\n890123 4") adapter = textadapter.FixedWidthTextAdapter(data, 3, infer_types=False, field_names=False) adapter.set_field_types({0:'i', 1:'i', 2:'i'}) control = np.array([(1, 2, 3), (4, 5, 67), (890, 123, 4)], dtype='i,i,i') assert_array_equal(adapter[:], control) def test_spaces_around_numeric_values(self): data = StringIO(' 1 , -2 , 3.3 , -4.4 \n 5 , -6 , 7.7 , -8.8 ') adapter = textadapter.text_adapter(data, field_names=False) adapter.set_field_types({0:'u4', 1:'i8', 2:'f4', 3:'f8'}) array = adapter[:] control = np.array([(1,-2,3.3,-4.4), (5,-6,7.7,-8.8)], dtype='u4,i8,f4,f8') assert_array_equal(array, control) def test_slicing(self): data = StringIO() generate_dataset(data, IntIter(), ',', self.num_records) adapter = textadapter.text_adapter(data, field_names=False) adapter.set_field_types({0:'u4',1:'u4',2:'u4',3:'u4',4:'u4'}) assert_array_equal(adapter[0], np.array([(0, 1, 2, 3, 4)], dtype='u4,u4,u4,u4,u4')) expected_values = [((self.num_records-1)*5)+x for x in range(5)] self.assert_equality(adapter[self.num_records-1].item(), tuple(expected_values)) #adapter.create_index() #self.assert_equality(adapter[-1].item(), tuple(expected_values)) self.assert_equality(adapter['f0'][0].item(), (0,)) self.assert_equality(adapter['f4'][1].item(), (9,)) #self.assert_equality(adapter[self.num_records-1]['f4'], (self.num_records*5)-1) array = adapter[:] record = [x for x in range(0, 5)] self.assert_equality(array.size, self.num_records) for i in range(0, self.num_records): self.assert_equality(array[i].item(), tuple(record)) record = [x+5 for x in record] array = adapter[:-1] record = [x for x in range(0, 5)] self.assert_equality(array.size, self.num_records-1) for i in range(0, self.num_records-1): self.assert_equality(array[i].item(), tuple(record)) record = [x+5 for x in record] array = adapter[0:10] self.assert_equality(array.size, 10) record = [x for x in range(0, 5)] for i in range(0, 10): self.assert_equality(array[i].item(), tuple(record)) record = [x+5 for x in record] array = adapter[1:] self.assert_equality(array.size, self.num_records-1) record = [x for x in range(5, 10)] for i in range(0, self.num_records-1): self.assert_equality(array[i].item(), tuple(record)) record = [x+5 for x in record] array = adapter[0:10:2] self.assert_equality(array.size, 5) record = [x for x in range(0, 5)] for i in range(0, 5): self.assert_equality(array[i].item(), tuple(record)) record = [x+10 for x in record] array = adapter[['f0', 'f4']][:] record = [0, 4] self.assert_equality(array.size, self.num_records) for i in range(0, self.num_records): self.assert_equality(array[i].item(), tuple(record)) record = [x+5 for x in record] adapter.field_filter = [0, 'f4'] array = adapter[:] record = [0, 4] self.assert_equality(array.size, self.num_records) for i in range(0, self.num_records): self.assert_equality(array[i].item(), tuple(record)) record = [x+5 for x in record] adapter.field_filter = None array = adapter[:] record = [0, 1, 2, 3, 4] self.assert_equality(array.size, self.num_records) for i in range(0, self.num_records): self.assert_equality(array[i].item(), tuple(record)) record = [x+5 for x in record] try: adapter[self.num_records] except textadapter.AdapterIndexError: pass else: self.fail('AdaperIndexError not thrown') try: adapter[0:self.num_records+1] except textadapter.AdapterIndexError: pass else: self.fail('AdaperIndexError not thrown') def test_converters(self): data = StringIO() generate_dataset(data, IntIter(), ',', self.num_records) adapter = textadapter.text_adapter(data, delimiter=',', field_names=False) #adapter.set_field_types({0:'u4', 1:'u4', 2:'u4', 3:'u4', 4:'u4'}) def increment(input_str): return int(input_str) + 1 def double(input_str): return int(input_str) + int(input_str) if sys.platform == 'win32' and tuple.__itemsize__ == 8: # TODO: there problems below here 64-bit Windows, I get # OverflowError: can't convert negative value to unigned PY_LONG_LONG return adapter.set_converter(0, increment) adapter.set_converter('f1', double) array = adapter[:] self.assert_equality(array.size, self.num_records) record = [1, 2, 2, 3, 4] for i in range(0, self.num_records): self.assert_equality(array[i].item(), tuple(record)) record[0] += 5 record[1] = (10 * (i+1)) + 2 record[2] += 5 record[3] += 5 record[4] += 5 def test_missing_fill_values(self): data = StringIO() generate_dataset(data, MissingValuesIter(), ',', self.num_records) adapter = textadapter.text_adapter(data, delimiter=',', field_names=False, infer_types=False) adapter.set_field_types({'f0':'u4', 1:'u4', 2:'u4', 3:'u4', 'f4':'u4'}) adapter.set_missing_values({0:['NA', 'NaN'], 'f4':['xx','inf']}) adapter.set_fill_values({0:99, 4:999}) array = adapter[:] self.assert_equality(array.size, self.num_records) record = [x for x in range(0, 5)] for i in range(0, self.num_records): if i % 4 == 0 or i % 4 == 1: record[0] = 99 record[4] = 999 else: record[0] = record[1] - 1 record[4] = record[3] + 1 self.assert_equality(array[i].item(), tuple(record)) record[1] += 5 record[2] += 5 record[3] += 5 data.seek(0) adapter = textadapter.text_adapter(data, delimiter=',', field_names=False, infer_types=True) adapter.set_missing_values({0:['NA', 'NaN'], 4:['xx','inf']}) array = adapter[:] self.assert_equality(array.size, self.num_records) record = [x for x in range(0, 5)] for i in range(0, self.num_records): if i % 4 == 0 or i % 4 == 1: record[0] = 0 record[4] = 0 else: record[0] = record[1] - 1 record[4] = record[3] + 1 self.assert_equality(array[i].item(), tuple(record)) record[1] += 5 record[2] += 5 record[3] += 5 # Test missing field data = StringIO('1,2,3\n4,5\n7,8,9') adapter = textadapter.text_adapter(data, field_names=False) adapter.field_types = {0:'O', 1:'O', 2:'O'} adapter.set_fill_values({0:np.nan, 1:np.nan, 2:np.nan}) array = adapter[:] # NumPy assert_array_equal no longer supports mixed O/nan types expected = [('1','2','3'),('4','5',np.nan),('7','8','9')] self.assert_equality(array.tolist(), expected) def test_fixed_width(self): data = StringIO() generate_dataset(data, FixedWidthIter(), '', self.num_records) adapter = textadapter.FixedWidthTextAdapter(data, [2,3,4,5,6], field_names=False, infer_types=False) adapter.set_field_types({0:'u4',1:'u4',2:'u4',3:'u4',4:'u4'}) array = adapter[:] self.assert_equality(array.size, self.num_records) record = [0, 0, 0, 0, 0] for i in range(0, self.num_records): self.assert_equality(array[i].item(), tuple(record)) record = [x+1 for x in record] if record[0] == 100: record[0] = 0 if record[1] == 1000: record[1] = 0 if record[2] == 10000: record[2] = 0 if record[3] == 100000: record[3] = 0 if record[4] == 1000000: record[4] = 0 # Test skipping blank lines data = StringIO(' 1 2 3\n\n 4 5 6') adapter = textadapter.text_adapter(data, parser='fixed_width', field_widths=[2,2,2], field_names=False) array = adapter[:] assert_array_equal(array, np.array([(1,2,3), (4,5,6)], dtype=[('f0','<u8'),('f1','<u8'),('f2','<u8')])) # Test comment lines data = StringIO('# 1 2 3\n 1 2 3\n# foo\n 4 5 6') adapter = textadapter.text_adapter(data, parser='fixed_width', field_widths=[2,2,2], field_names=False) array = adapter[:] assert_array_equal(array, np.array([(1,2,3), (4,5,6)], dtype=[('f0','<u8'),('f1','<u8'),('f2','<u8')])) # Test field names line data = StringIO(' a b c\n 1 2 3') adapter = textadapter.text_adapter(data, parser='fixed_width', field_widths=[2,2,2], field_names=True) array = adapter[:] assert_array_equal(array, np.array([(1,2,3)], dtype=[('a','<u8'),('b','<u8'),('c','<u8')])) # Test field names line as comment line data = StringIO('# a b c\n 1 2 3') adapter = textadapter.text_adapter(data, parser='fixed_width', field_widths=[2,2,2], field_names=True) array = adapter[:] assert_array_equal(array, np.array([(1,2,3)], dtype=[('a','<u8'),('b','<u8'),('c','<u8')])) # Test incomplete field names line data = StringIO(' a\n 1 2 3') adapter = textadapter.text_adapter(data, parser='fixed_width', field_widths=[2,2,2], field_names=True) array = adapter[:] assert_array_equal(array, np.array([(1,2,3)], dtype=[('a','<u8'),('f1','<u8'),('f2','<u8')])) def test_regex(self): data = StringIO() generate_dataset(data, IntIter(), ',', self.num_records) adapter = textadapter.RegexTextAdapter(data, '([0-9]*),([0-9]*),([0-9]*),([0-9]*),([0-9]*)\n', field_names=False, infer_types=False) adapter.set_field_types({0:'u4',1:'u4',2:'u4',3:'u4',4:'u4'}) array = adapter[:] self.assert_equality(array.size, self.num_records) record = [x for x in range(0, 5)] for i in range(0, self.num_records): self.assert_equality(array[i].item(), tuple(record)) record = [x+5 for x in record] # Test skipping blank lines data = StringIO('1 2 3\n\n4 5 6') adapter = textadapter.text_adapter(data, parser='regex', regex_string='([0-9]) ([0-9]) ([0-9])', field_names=False) array = adapter[:] assert_array_equal(array, np.array([(1,2,3), (4,5,6)], dtype=[('f0','<u8'),('f1','<u8'),('f2','<u8')])) # Test comment lines data = StringIO('#1 2 3\n1 2 3\n# foo\n4 5 6') adapter = textadapter.text_adapter(data, parser='regex', regex_string='([0-9]) ([0-9]) ([0-9])', field_names=False) array = adapter[:] assert_array_equal(array, np.array([(1,2,3), (4,5,6)], dtype=[('f0','<u8'),('f1','<u8'),('f2','<u8')])) # Test field names line data = StringIO('a b c\n4 5 6') adapter = textadapter.text_adapter(data, parser='regex', regex_string='([0-9]) ([0-9]) ([0-9])', field_names=True) array = adapter[:] assert_array_equal(array, np.array([(4,5,6)], dtype=[('a','<u8'),('b','<u8'),('c','<u8')])) # Test field names line as comment line data = StringIO('#a b c\n4 5 6') adapter = textadapter.text_adapter(data, parser='regex', regex_string='([0-9]) ([0-9]) ([0-9])', field_names=True) array = adapter[:] assert_array_equal(array, np.array([(4,5,6)], dtype=[('a','<u8'),('b','<u8'),('c','<u8')])) # Test incomplete field names line data = StringIO('a b\n4 5 6') adapter = textadapter.text_adapter(data, parser='regex', regex_string='([0-9]) ([0-9]) ([0-9])', field_names=True) array = adapter[:] assert_array_equal(array, np.array([(4,5,6)], dtype=[('a','<u8'),('b','<u8'),('f2','<u8')])) # Test field names line that doesn't match regex data = StringIO('a b c\n1 2 3 4 5 6') adapter = textadapter.text_adapter(data, parser='regex', regex_string='([0-9\s]+) ([0-9\s]+) ([0-9\s]+)', field_names=True) array = adapter[:] assert_array_equal(array, np.array([('1 2', '3 4', '5 6')], dtype=[('a','O'),('b','O'),('c','O')])) def test_index(self): if sys.platform == 'win32': # TODO: this test fails on Windows because of file lock problems return num_records = 100000 expected_values = [((num_records-1)*5) + x for x in range(5)] data = StringIO() generate_dataset(data, IntIter(), ',', num_records) # test explicit index building adapter = textadapter.text_adapter(data, delimiter=',', field_names=False, infer_types=False) adapter.set_field_types({0:'u4',1:'u4',2:'u4',3:'u4',4:'u4'}) adapter.create_index() self.assert_equality(adapter[0].item(), tuple([(0*5) + x for x in range(5)])) self.assert_equality(adapter[10].item(), tuple([(10*5) + x for x in range(5)])) self.assert_equality(adapter[100].item(), tuple([(100*5) + x for x in range(5)])) self.assert_equality(adapter[1000].item(), tuple([(1000*5) + x for x in range(5)])) self.assert_equality(adapter[10000].item(), tuple([(10000*5) + x for x in range(5)])) self.assert_equality(adapter[num_records - 1].item(), tuple([((num_records - 1)*5) + x for x in range(5)])) #self.assert_equality(adapter[-1].item(), tuple(expected_values)) # test implicitly creating disk index on the fly if os.path.exists('test.idx'): os.remove('test.idx') data.seek(0) adapter = textadapter.text_adapter(data, delimiter=',', field_names=False, infer_types=False, index_name='test.idx') adapter.set_field_types({0:'u4',1:'u4',2:'u4',3:'u4',4:'u4'}) adapter.to_array() self.assert_equality(adapter[0].item(), tuple([(0*5) + x for x in range(5)])) self.assert_equality(adapter[10].item(), tuple([(10*5) + x for x in range(5)])) self.assert_equality(adapter[100].item(), tuple([(100*5) + x for x in range(5)])) self.assert_equality(adapter[1000].item(), tuple([(1000*5) + x for x in range(5)])) self.assert_equality(adapter[10000].item(), tuple([(10000*5) + x for x in range(5)])) self.assert_equality(adapter[num_records - 1].item(), tuple([((num_records - 1)*5) + x for x in range(5)])) #self.assert_equality(adapter[-1].item(), tuple(expected_values)) adapter.close() # test loading disk index data.seek(0) adapter2 = textadapter.text_adapter(data, delimiter=',', field_names=False, infer_types=False, index_name='test.idx') adapter2.set_field_types({0:'u4',1:'u4',2:'u4',3:'u4',4:'u4'}) self.assert_equality(adapter2[0].item(), tuple([(0*5) + x for x in range(5)])) self.assert_equality(adapter2[10].item(), tuple([(10*5) + x for x in range(5)])) self.assert_equality(adapter2[100].item(), tuple([(100*5) + x for x in range(5)])) self.assert_equality(adapter2[1000].item(), tuple([(1000*5) + x for x in range(5)])) self.assert_equality(adapter2[10000].item(), tuple([(10000*5) + x for x in range(5)])) self.assert_equality(adapter2[num_records - 1].item(), tuple([((num_records - 1)*5) + x for x in range(5)])) #self.assert_equality(adapter2[-1].item(), tuple(expected_values)) adapter.close() os.remove('test.idx') def test_gzip_index(self): num_records = 1000000 data = StringIO() generate_dataset(data, IntIter(), ',', num_records) #if sys.version > '3': if True: dataz = io.BytesIO() else: dataz = StringIO() gzip_output = gzip.GzipFile(fileobj=dataz, mode='wb') #if sys.version > '3': if True: gzip_output.write(data.getvalue().encode('utf8')) else: gzip_output.write(data.getvalue()) gzip_output.close() dataz.seek(0) # test explicit index building adapter = textadapter.text_adapter(dataz, compression='gzip', delimiter=',', field_names=False, infer_types=False) adapter.set_field_types({0:'u4',1:'u4',2:'u4',3:'u4',4:'u4'}) adapter.create_index() self.assert_equality(adapter[0].item(), tuple([(0*5) + x for x in range(5)])) self.assert_equality(adapter[10].item(), tuple([(10*5) + x for x in range(5)])) self.assert_equality(adapter[100].item(), tuple([(100*5) + x for x in range(5)])) self.assert_equality(adapter[1000].item(), tuple([(1000*5) + x for x in range(5)])) self.assert_equality(adapter[10000].item(), tuple([(10000*5) + x for x in range(5)])) self.assert_equality(adapter[100000].item(), tuple([(100000*5) + x for x in range(5)])) self.assert_equality(adapter[num_records - 1].item(), tuple([((num_records - 1)*5) + x for x in range(5)])) #self.assert_equality(adapter[-1].item(), tuple(expected_values)) # test 'trouble' records that have caused crashes in the past self.assert_equality(adapter[290000].item(), tuple([(290000*5) + x for x in range(5)])) self.assert_equality(adapter[818000].item(), tuple([(818000*5) + x for x in range(5)])) # test implicitly creating disk index on the fly # JNB: not implemented yet '''adapter = textadapter.text_adapter(dataz, compression='gzip', delimiter=',', field_names=False, infer_types=False, indexing=True, index_filename='test.idx') adapter.set_field_types({0:'u4',1:'u4',2:'u4',3:'u4',4:'u4'}) adapter.to_array() self.assert_equality(adapter[0].item(), tuple([(0*5) + x for x in range(5)])) self.assert_equality(adapter[10].item(), tuple([(10*5) + x for x in range(5)])) self.assert_equality(adapter[100].item(), tuple([(100*5) + x for x in range(5)])) self.assert_equality(adapter[1000].item(), tuple([(1000*5) + x for x in range(5)])) self.assert_equality(adapter[10000].item(), tuple([(10000*5) + x for x in range(5)])) self.assert_equality(adapter[100000].item(), tuple([(100000*5) + x for x in range(5)])) self.assert_equality(adapter[num_records - 1].item(), tuple([((num_records - 1)*5) + x for x in range(5)])) #self.assert_equality(adapter[-1].item(), tuple(expected_values)) # test 'trouble' records that have caused crashes in the past self.assert_equality(adapter[290000].item(), tuple([(290000*5) + x for x in range(5)])) self.assert_equality(adapter[818000].item(), tuple([(818000*5) + x for x in range(5)])) # test loading disk index adapter2 = textadapter.text_adapter(dataz, compression='gzip', delimiter=',', field_names=False, infer_types=False, indexing=True, index_filename='test.idx') adapter2.set_field_types({0:'u4',1:'u4',2:'u4',3:'u4',4:'u4'}) self.assert_equality(adapter2[0].item(), tuple([(0*5) + x for x in range(5)])) self.assert_equality(adapter2[10].item(), tuple([(10*5) + x for x in range(5)])) self.assert_equality(adapter2[100].item(), tuple([(100*5) + x for x in range(5)])) self.assert_equality(adapter2[1000].item(), tuple([(1000*5) + x for x in range(5)])) self.assert_equality(adapter2[10000].item(), tuple([(10000*5) + x for x in range(5)])) self.assert_equality(adapter2[100000].item(), tuple([(100000*5) + x for x in range(5)])) self.assert_equality(adapter2[num_records - 1].item(), tuple([((num_records - 1)*5) + x for x in range(5)])) #self.assert_equality(adapter[-1].item(), tuple(expected_values)) # test 'trouble' records that have caused crashes in the past self.assert_equality(adapter2[290000].item(), tuple([(290000*5) + x for x in range(5)])) self.assert_equality(adapter2[818000].item(), tuple([(818000*5) + x for x in range(5)])) os.remove('test.idx')''' def test_header_footer(self): data = StringIO('0,1,2,3,4\n5,6,7,8,9\n10,11,12,13,14') adapter = textadapter.text_adapter(data, header=1, field_names=False) adapter.field_types = dict(zip(range(5), ['u4']*5)) assert_array_equal(adapter[:], np.array([(5,6,7,8,9), (10,11,12,13,14)], dtype='u4,u4,u4,u4,u4')) data.seek(0) adapter = textadapter.text_adapter(data, header=2, field_names=False) adapter.field_types = dict(zip(range(5), ['u4']*5)) assert_array_equal(adapter[:], np.array([(10,11,12,13,14)], dtype='u4,u4,u4,u4,u4')) data.seek(0) adapter = textadapter.text_adapter(data, header=1, field_names=True) adapter.field_types = dict(zip(range(5), ['u4']*5)) assert_array_equal(adapter[:], np.array([(10,11,12,13,14)], dtype=[('5','u4'),('6','u4'),('7','u4'),('8','u4'),('9','u4')])) def test_delimiter(self): data = StringIO('1,2,3\n') adapter = textadapter.text_adapter(data, field_names=False) self.assert_equality(adapter[0].item(), (1,2,3)) data = StringIO('1 2 3\n') adapter = textadapter.text_adapter(data, field_names=False) self.assert_equality(adapter[0].item(), (1,2,3)) data = StringIO('1\t2\t3\n') adapter = textadapter.text_adapter(data, field_names=False) self.assert_equality(adapter[0].item(), (1,2,3)) data = StringIO('1x2x3\n') adapter = textadapter.text_adapter(data, field_names=False) self.assert_equality(adapter[0].item(), (1,2,3)) # Test no delimiter in single field csv data data = StringIO('aaa\nbbb\nccc') array = textadapter.text_adapter(data, field_names=False, delimiter=None)[:] assert_array_equal(array, np.array([('aaa',), ('bbb',), ('ccc',)], dtype=[('f0', 'O')])) def test_auto_type_inference(self): data = StringIO('0,1,2,3,4\n5.5,6,7,8,9\n10,11,12,13,14a\n15,16,xxx,18,19') adapter = textadapter.text_adapter(data, field_names=False, infer_types=True) array = adapter.to_array() self.assert_equality(array.dtype.fields['f0'][0], np.dtype('float64')) self.assert_equality(array.dtype.fields['f1'][0], np.dtype('uint64')) self.assert_equality(array.dtype.fields['f2'][0], np.dtype('O')) self.assert_equality(array.dtype.fields['f3'][0], np.dtype('uint64')) self.assert_equality(array.dtype.fields['f4'][0], np.dtype('O')) data = StringIO('0,1,2,3,4\n5.5,6,7,8,9\n10,11,12,13,14a\n15,16,xxx,18,19') adapter = textadapter.text_adapter(data, field_names=False, infer_types=True) self.assert_equality(adapter[0].dtype.fields['f0'][0], np.dtype('uint64')) self.assert_equality(adapter[1:3].dtype.fields['f0'][0], np.dtype('float64')) self.assert_equality(adapter[3].dtype.fields['f4'][0], np.dtype('uint64')) self.assert_equality(adapter[:].dtype.fields['f3'][0], np.dtype('uint64')) self.assert_equality(adapter[-1].dtype.fields['f2'][0], np.dtype('O')) self.assert_equality(adapter[2].dtype.fields['f4'][0], np.dtype('O')) def test_64bit_ints(self): data = StringIO(str((2**63)-1) + ',' + str(((2**63)-1)*-1) + ',' + str((2**64)-1)) adapter = textadapter.text_adapter(data, delimiter=',', field_names=False, infer_types=False) adapter.set_field_types({0:'i8', 1:'i8', 2:'u8'}) array = adapter.to_array() self.assert_equality(array[0].item(), ((2**63)-1, ((2**63)-1)*-1, (2**64)-1)) def test_adapter_factory(self): data = StringIO("1,2,3") adapter = textadapter.text_adapter(data, "csv", delimiter=',', field_names=False, infer_types=False) self.assertTrue(isinstance(adapter, textadapter.CSVTextAdapter)) self.assertRaises(textadapter.AdapterException, textadapter.text_adapter, data, "foobar") def test_field_names(self): # Test for ignoring of extra fields data = StringIO('f0,f1\n0,1,2\n3,4,5') adapter = textadapter.text_adapter(data, 'csv', delimiter=',', field_names=True) array = adapter.to_array() self.assert_equality(array.dtype.names, ('f0', 'f1')) self.assert_equality(array[0].item(), (0,1)) self.assert_equality(array[1].item(), (3,4)) # Test for duplicate field names data = StringIO('f0,field,field\n0,1,2\n3,4,5') adapter = textadapter.text_adapter(data, 'csv', delimiter=',', field_names=True, infer_types=False) adapter.set_field_types({0:'u4', 1:'u4', 2:'u4'}) array = adapter.to_array() self.assert_equality(array.dtype.names, ('f0', 'field', 'field1')) # Test for field names list data = StringIO('0,1,2\n3,4,5') adapter = textadapter.text_adapter(data, field_names=['a', 'b', 'c'], infer_types=False) adapter.field_types = {0:'u4', 1:'u4', 2:'u4'} array = adapter[:] self.assertTrue(array.dtype.names == ('a', 'b', 'c')) assert_array_equal(array, np.array([(0,1,2), (3,4,5)], dtype=[('a', 'u4'), ('b', 'u4'), ('c', 'u4')])) def test_float_conversion(self): data = StringIO('10,1.333,-1.23,10.0E+2,999.9e-2') adapter = textadapter.text_adapter(data, field_names=False, infer_types=False) adapter.set_field_types(dict(zip(range(5), ['f8']*5))) array = adapter[0] #self.assert_equality(array[0].item(), (10.0,1.333,-1.23,1000.0,9.999)) self.assertAlmostEqual(array[0][0], 10.0) self.assertAlmostEqual(array[0][1], 1.333) self.assertAlmostEqual(array[0][2], -1.23) self.assertAlmostEqual(array[0][3], 1000.0) self.assertAlmostEqual(array[0][4], 9.999) def test_generators(self): def int_generator(num_recs): for i in range(num_recs): yield ','.join([str(i*5), str(i*5+1), str(i*5+2), str(i*5+3), str(i*5+4)]) adapter = textadapter.text_adapter(int_generator(self.num_records), field_names=False) array = adapter[:] self.assert_equality(array.size, self.num_records) record = [x for x in range(0, 5)] for i in range(0, self.num_records): self.assert_equality(array[i].item(), tuple(record)) record[0] += 5 record[1] += 5 record[2] += 5 record[3] += 5 record[4] += 5 def test_comments(self): data = StringIO('1,2,3\n#4,5,6') adapter = textadapter.text_adapter(data, field_names=False) array = adapter[:] self.assert_equality(array.size, 1) self.assert_equality(array[0].item(), (1,2,3)) data = StringIO('1,2,3\n#4,5,6') adapter = textadapter.text_adapter(data, field_names=False, comment=None) array = adapter[:] self.assert_equality(array.size, 2) self.assert_equality(array[0].item(), ('1',2,3)) self.assert_equality(array[1].item(), ('#4',5,6)) def test_escapechar(self): data = StringIO('1,2\\2,3\n4,5\\5\\5,6') array = textadapter.text_adapter(data, field_names=False)[:] assert_array_equal(array, np.array([(1,22,3), (4,555,6)], dtype='u8,u8,u8')) data = StringIO('\\1,2,3\n4,5,6\\') array = textadapter.text_adapter(data, field_names=False)[:] assert_array_equal(array, np.array([(1,2,3), (4,5,6)], dtype='u8,u8,u8')) data = StringIO('a,b\\,b,c\na,b\\,b\\,b,c') array = textadapter.text_adapter(data, field_names=False)[:] assert_array_equal(array, np.array([('a', 'b,b', 'c'), ('a', 'b,b,b', 'c')], dtype='O,O,O')) data = StringIO('a,bx,b,c\na,bx,bx,b,c') array = textadapter.text_adapter(data, field_names=False, escape='x')[:] assert_array_equal(array, np.array([('a', 'b,b', 'c'), ('a', 'b,b,b', 'c')], dtype='O,O,O')) '''def test_dataframe_output(self): try: import pandas except ImportError: return # Test filling blank lines with fill values if output is dataframe data = StringIO('1,2,3\n\n4,5,6') adapter = textadapter.text_adapter(data, field_names=False) adapter.field_types = {0:'O', 1:'O', 2:'O'} adapter.set_fill_values({0:np.nan, 1:np.nan, 2:np.nan}) df = adapter.to_dataframe()''' def test_csv(self): # Test skipping blank lines data = StringIO('1,2,3\n\n4,5,6') adapter = textadapter.text_adapter(data, field_names=False) array = adapter[:] assert_array_equal(array, np.array([(1,2,3), (4,5,6)], dtype=[('f0','<u8'),('f1','<u8'),('f2','<u8')])) def test_json(self): # Test json number data = StringIO('{"id":123}') adapter = textadapter.text_adapter(data, parser='json') array = adapter[:] assert_array_equal(array, np.array([(123,)], dtype=[('id', 'u8')])) # Test json number data = StringIO('{"id":"xxx"}') adapter = textadapter.text_adapter(data, parser='json') array = adapter[:] assert_array_equal(array, np.array([('xxx',)], dtype=[('id', 'O')])) # Test multiple values data = StringIO('{"id":123, "name":"xxx"}') adapter = textadapter.text_adapter(data, parser='json') array = adapter[:] assert_array_equal(array, np.array([(123, 'xxx',)], dtype=[('id', 'u8'), ('name', 'O')])) # Test multiple records data = StringIO('[{"id":123, "name":"xxx"}, {"id":456, "name":"yyy"}]') adapter = textadapter.text_adapter(data, parser='json') array = adapter[:] assert_array_equal(array, np.array([(123, 'xxx',), (456, 'yyy')], dtype=[('id', 'u8'), ('name', 'O')])) # Test multiple objects separated by newlines data = StringIO('{"id":123, "name":"xxx"}\n{"id":456, "name":"yyy"}') adapter = textadapter.text_adapter(data, parser='json') array = adapter[:] assert_array_equal(array, np.array([(123, 'xxx',), (456, 'yyy')], dtype=[('id', 'u8'), ('name', 'O')])) data = StringIO('{"id":123, "name":"xxx"}\n') adapter = textadapter.text_adapter(data, parser='json') array = adapter[:] assert_array_equal(array, np.array([(123, 'xxx',)], dtype=[('id', 'u8'), ('name', 'O')])) # JNB: broken; should be really be supporting the following json inputs? ''' # Test subarrays data = StringIO('{"id":123, "names":["xxx","yyy","zzz"]}') adapter = textadapter.text_adapter(data, parser='json') array = adapter[:] assert_array_equal(array, np.array([(123, 'xxx', 'yyy', 'zzz',)], dtype=[('f0', 'u8'), ('f1', 'O'), ('f2', 'O'), ('f3', 'O')])) # Test subobjects data = StringIO('{"id":123, "names":{"a":"xxx", "b":"yyy", "c":"zzz"}}') adapter = textadapter.text_adapter(data, parser='json') array = adapter[:] assert_array_equal(array, np.array([(123, 'xxx', 'yyy', 'zzz',)], dtype=[('f0', 'u8'), ('f1', 'O'), ('f2', 'O'), ('f3', 'O')])) ''' # Test ranges data = StringIO('{"id": 1, "name": "www"}\n' '{"id": 2, "name": "xxx"}\n' '{"id": 3, "name": "yyy"}\n' '{"id": 4, "name": "zzz"}') adapter = textadapter.text_adapter(data, parser='json') array = adapter[2:4] assert_array_equal(array, np.array([(3, 'yyy'), (4, 'zzz')], dtype=[('id', 'u8'), ('name', 'O')])) # Test column order data = StringIO('{"xxx": 1, "aaa": 2}\n') adapter = textadapter.text_adapter(data, parser='json') array = adapter[:] assert_array_equal(array, np.array([(1, 2)], dtype=[('xxx', 'u8'), ('aaa', 'u8')])) # Test field filter data = StringIO('{"id": 1, "name": "www"}\n' '{"id": 2, "name": "xxx"}\n' '{"id": 3, "name": "yyy"}\n' '{"id": 4, "name": "zzz"}') adapter = textadapter.text_adapter(data, parser='json') adapter.field_filter = ['name'] array = adapter[:] assert_array_equal(array, np.array([('www',), ('xxx',), ('yyy',), ('zzz',)], dtype=[('name', 'O')])) def test_stepping(self): data = StringIO('0,1\n2,3\n4,5\n6,7\n8,9\n10,11\n12,13\n14,15\n16,17\n18,19') adapter = textadapter.text_adapter(data, field_names=False) assert_array_equal(adapter[::2], np.array([(0,1), (4,5), (8,9), (12,13), (16,17)], dtype='u8,u8')) assert_array_equal(adapter[::3], np.array([(0,1), (6,7), (12,13), (18,19)], dtype='u8,u8')) def test_num_records(self): data = StringIO('0,1\n2,3\n4,5\n6,7\n8,9\n10,11\n12,13\n14,15\n16,17\n18,19') adapter = textadapter.text_adapter(data, field_names=False, num_records=2) assert_array_equal(adapter[:], np.array([(0, 1), (2, 3)], dtype='u8,u8')) def run(verbosity=1, num_records=100000): if num_records < 10: raise ValueError('number of records for generated datasets must be at least 10') TestTextAdapter.num_records = num_records suite = unittest.TestLoader().loadTestsFromTestCase(TestTextAdapter) return unittest.TextTestRunner(verbosity=verbosity).run(suite) if __name__ == '__main__': run()

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02241afd4e395cfa694093e2c540b15b145886cb

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Python

tests/test_polyloxpgen.py

hoefer-lab/polyloxpgen

4edd2c9f5197da5963c7b863c415ade57e9df77b

[ "MIT" ]

null

tests/test_polyloxpgen.py

hoefer-lab/polyloxpgen

4edd2c9f5197da5963c7b863c415ade57e9df77b

[ "MIT" ]

null

tests/test_polyloxpgen.py

hoefer-lab/polyloxpgen

4edd2c9f5197da5963c7b863c415ade57e9df77b

[ "MIT" ]

null

### this requires pip install pytest ### if installed, run >>> pytest within the /tests or /polyloxpgen (package) directory import polyloxpgen.merge import polyloxpgen.pgen import numpy as np import pandas as pd import os import time def test_single_sample_output_bc1020_ld2017(): floc = os.path.join(os.path.dirname(__file__), '') ref_file = os.path.join(floc, 'original', 'bc1020_BarPipe.txt') purged_barcodes_ref = np.loadtxt(ref_file, usecols=0, skiprows=1, delimiter='\t', dtype=str) purged_reads_ref = np.loadtxt(ref_file, usecols=1, skiprows=1, delimiter='\t') data_minrecs_ref = np.loadtxt(ref_file, usecols=3, skiprows=1, delimiter='\t', dtype=int) data_pgen_ref = np.loadtxt(ref_file, usecols=4, skiprows=1, delimiter='\t') # run polylox_merge df_merged = polyloxpgen.merge.polylox_merge([os.path.join(floc, 'original', 'bc1020.barcode.count.txt')], ['bc1020'], os.path.join(floc, 'temp'), 'bc1020_merged') # run polylox_pgen df_pgen = polyloxpgen.pgen.polylox_pgen(os.path.join(floc, 'temp', 'bc1020_merged.txt'), os.path.join(floc, 'temp'), 'bc1020_pgen', path_matrix_type='ld_2017') # read back from resulted file (corresponds to df_pgen) df_res = pd.read_csv(os.path.join(floc, 'temp', 'bc1020_pgen.txt'), sep='\t', index_col=0) # delete temporary files in the end time.sleep(1.0) if os.path.isfile(os.path.join(floc, 'temp', 'bc1020_merged.txt')): os.remove(os.path.join(floc, 'temp', 'bc1020_merged.txt')) if os.path.isfile(os.path.join(floc, 'temp', 'bc1020_pgen.txt')): os.remove(os.path.join(floc, 'temp', 'bc1020_pgen.txt')) # check if the same set of barcodes comes out assert set(purged_barcodes_ref)==set(df_res.index.to_numpy(dtype=str)) # then reindex to get the barcode order the same df_res = df_res.reindex(purged_barcodes_ref) # all remaining checks on the reorder df_res assert np.all(purged_barcodes_ref==df_res.index.to_numpy(dtype=str)) assert np.all(purged_reads_ref==df_res.bc1020) assert np.all(data_minrecs_ref==df_res.MinRec) assert np.allclose(data_pgen_ref, df_res.Pgen) def test_single_sample_output_bc1022_ld2017(): floc = os.path.join(os.path.dirname(__file__), '') ref_file = os.path.join(floc, 'original', 'bc1022_BarPipe.txt') purged_barcodes_ref = np.loadtxt(ref_file, usecols=0, skiprows=1, delimiter='\t', dtype=str) purged_reads_ref = np.loadtxt(ref_file, usecols=1, skiprows=1, delimiter='\t') data_minrecs_ref = np.loadtxt(ref_file, usecols=3, skiprows=1, delimiter='\t', dtype=int) data_pgen_ref = np.loadtxt(ref_file, usecols=4, skiprows=1, delimiter='\t') # run polylox_merge df_merged = polyloxpgen.merge.polylox_merge([os.path.join(floc, 'original', 'bc1022.barcode.count.txt')], ['bc1022'], os.path.join(floc, 'temp'), 'bc1022_merged') # run polylox_pgen df_pgen = polyloxpgen.pgen.polylox_pgen(os.path.join(floc, 'temp', 'bc1022_merged.txt'), os.path.join(floc, 'temp'), 'bc1022_pgen', path_matrix_type='ld_2017') # read back from resulted file (corresponds to df_pgen) df_res = pd.read_csv(os.path.join(floc, 'temp', 'bc1022_pgen.txt'), sep='\t', index_col=0) # delete temporary files in the end time.sleep(1.0) if os.path.isfile(os.path.join(floc, 'temp', 'bc1022_merged.txt')): os.remove(os.path.join(floc, 'temp', 'bc1022_merged.txt')) if os.path.isfile(os.path.join(floc, 'temp', 'bc1022_pgen.txt')): os.remove(os.path.join(floc, 'temp', 'bc1022_pgen.txt')) # check if the same set of barcodes comes out assert set(purged_barcodes_ref)==set(df_res.index.to_numpy(dtype=str)) # then reindex to get the barcode order the same df_res = df_res.reindex(purged_barcodes_ref) # all remaining checks on the reorder df_res assert np.all(purged_barcodes_ref==df_res.index.to_numpy(dtype=str)) assert np.all(purged_reads_ref==df_res.bc1022) assert np.all(data_minrecs_ref==df_res.MinRec) assert np.allclose(data_pgen_ref, df_res.Pgen) def test_two_sample_output_bc1020_bc1022_uniform(): floc = os.path.join(os.path.dirname(__file__), '') # load reference dataframes df_merged_ref = pd.read_csv(os.path.join(floc, 'original', 'bc1020_bc1022_merged.txt'), sep='\t', index_col=0) df_pgen_ref = pd.read_csv(os.path.join(floc, 'original', 'bc1020_bc1022_pgen_uniform.txt'), sep='\t', index_col=0) # run polylox_merge df_merged = polyloxpgen.merge.polylox_merge([os.path.join(floc, 'original', 'bc1020.barcode.count.txt'), os.path.join(floc, 'original', 'bc1022.barcode.count.txt')], ['bc1020', 'bc1022'], os.path.join(floc, 'temp'), 'bc1020_bc1022_merged') # run polylox_pgen df_pgen = polyloxpgen.pgen.polylox_pgen(os.path.join(floc, 'temp', 'bc1020_bc1022_merged.txt'), os.path.join(floc, 'temp'), 'bc1020_bc1022_pgen', path_matrix_type='uniform') # read back from resulted files (correspond to df_merged and df_pgen) df_merged_res = pd.read_csv(os.path.join(floc, 'temp', 'bc1020_bc1022_merged.txt'), sep='\t', index_col=0) df_pgen_res = pd.read_csv(os.path.join(floc, 'temp', 'bc1020_bc1022_pgen.txt'), sep='\t', index_col=0) # delete temporary files in the end time.sleep(1.0) if os.path.isfile(os.path.join(floc, 'temp', 'bc1020_bc1022_merged.txt')): os.remove(os.path.join(floc, 'temp', 'bc1020_bc1022_merged.txt')) if os.path.isfile(os.path.join(floc, 'temp', 'bc1020_bc1022_pgen.txt')): os.remove(os.path.join(floc, 'temp', 'bc1020_bc1022_pgen.txt')) # compare merge dataframes assert np.all(df_merged_ref.index.to_numpy(dtype=str)==df_merged_res.index.to_numpy(dtype=str)) assert np.all(df_merged_ref.bc1020==df_merged_res.bc1020) assert np.all(df_merged_ref.bc1022==df_merged_res.bc1022) # compare pgen dataframes assert np.all(df_pgen_ref.index.to_numpy(dtype=str)==df_pgen_res.index.to_numpy(dtype=str)) assert np.all(df_pgen_ref.bc1020==df_pgen_res.bc1020) assert np.all(df_pgen_ref.bc1022==df_pgen_res.bc1022) assert np.all(df_pgen_ref.MinRec==df_pgen_res.MinRec) assert np.allclose(df_pgen_ref.Pgen, df_pgen_res.Pgen)

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02324eb2023ae43c54f682a4a2f604864324c111

81,093

py

Python

src/sniffmypacketsv2/transforms/common/protocols/ssh.py

SneakersInc/sniffmypacketsv2

55d8ff70eedb4dd948351425c25a1e904ea6d50e

[ "Apache-2.0" ]

11

2015-01-01T19:44:04.000Z

2020-03-26T07:30:26.000Z

src/sniffmypacketsv2/transforms/common/protocols/ssh.py

SneakersInc/sniffmypacketsv2

55d8ff70eedb4dd948351425c25a1e904ea6d50e

[ "Apache-2.0" ]

8

2015-01-01T22:45:59.000Z

2015-12-12T10:37:50.000Z

src/sniffmypacketsv2/transforms/common/protocols/ssh.py

SneakersInc/sniffmypacketsv2

55d8ff70eedb4dd948351425c25a1e904ea6d50e

[ "Apache-2.0" ]

3

2017-06-04T05:18:24.000Z

2020-03-26T07:30:27.000Z

import binascii import base64 from scapy.layers.inet import * import dissector preprocess_sessions = [] sessions = [] def is_created_stream_session(Src, Dst, SPort, DPort): """ this method is used for purpose of tcp stream reassemble, for checking if this is a new session of not. @param Src: source ip address @param Dst: destination ip address @param SPort: source port number @param DPort: destination port number """ i = 0 while i < len(preprocess_sessions): if Src == preprocess_sessions[i][0] and\ Dst == preprocess_sessions[i][1] and\ SPort == preprocess_sessions[i][2] and\ DPort == preprocess_sessions[i][3]: return True i = i + 1 return False def create_stream_session(Src, Dst, SPort, DPort, stream): """ this method is used for purpose of tcp stream reassemble, for creating a new session. @param Src: source ip address @param Dst: destination ip address @param SPort: source port number @param DPort: destination port number @param stream: the initial packet """ if stream.push: sessions.append([Src, Dst, SPort, DPort, stream]) else: preprocess_sessions.append([Src, Dst, SPort, DPort, stream]) def build_stream(Src, Dst, SPort, DPort, stream): """ this method is used for purpose of tcp stream reassemble, for appending a new packet. @param Src: source ip address @param Dst: destination ip address @param SPort: source port number @param DPort: destination port number @param stream: the current packet """ i = 0 while i < len(preprocess_sessions): if Src == preprocess_sessions[i][0] and\ Dst == preprocess_sessions[i][1] and\ SPort == preprocess_sessions[i][2] and\ DPort == preprocess_sessions[i][3]: if not stream.push: preprocess_sessions[i][4] =\ preprocess_sessions[i][4].append_data(\ Src, Dst, SPort, DPort, stream) else: sessions.append(\ [Src, Dst, SPort, DPort, preprocess_sessions[i][4].append_data(\ Src, Dst, SPort, DPort, stream)]) del(preprocess_sessions[i]) break i = i + 1 def get_stream(Src, Dst, SPort, DPort, obj): """ this method is used for purpose of tcp stream reassemble, for retrieving a stream or packet. @param Src: source ip address @param Dst: destination ip address @param SPort: source port number @param DPort: destination port number @param obj: last packet to be appended """ i = 0 while i < len(sessions): if Src == sessions[i][0] and Dst == sessions[i][1] and\ SPort == sessions[i][2] and DPort == sessions[i][3]: if sessions[i][4].seq == obj.seq: return sessions[i][4].pkt i = i + 1 return -1 def is_stream_end(Src, Dst, SPort, DPort, obj): """ this method is used for purpose of tcp stream reassemble, for checking whether if this is the last packet in the stream or not. @param Src: source ip address @param Dst: destination ip address @param SPort: source port number @param DPort: destination port number @param obj: last packet in stream. """ i = 0 while i < len(sessions): if Src == sessions[i][0] and Dst == sessions[i][1] and\ SPort == sessions[i][2] and DPort == sessions[i][3]: if sessions[i][4].seq == obj.seq: return True i = i + 1 return False class Stream: """ this class is for tcp reassembling """ pkt = "" seq = -1 push = None length_of_last_packet = -1 stream = False def __init__(self, pkt, push, seq): """ this constructor is used for purpose of tcp stream reassemble, for initializing tcp packets. @param pkt: packet payload @param push: specify if push flag is true or false @param seq: sequence number """ self.stream = False self.pkt = pkt self.push = push self.seq = seq self.length_of_last_packet = len(pkt) def append_data(self, Src, Dst, SPort, DPort, obj): """ this method is used for purpose of tcp stream reassemble, for appending a packet to an existing stream. @param Src: source ip address @param Dst: destination ip address @param SPort: source port number @param DPort: destination port number @param obj: last packet in stream. """ if self.seq + self.length_of_last_packet == obj.seq and obj.push: self.stream = True self.append_packet(obj.pkt) self.change_seq(obj.seq) self.push = obj.push self.length_of_last_packet = len(obj.pkt) elif self.seq + self.length_of_last_packet == obj.seq: self.append_packet(obj.pkt) self.change_seq(obj.seq) self.push = obj.push return self def append_packet(self, pkt): """ this method is used for purpose of tcp stream reassemble, for appending a packet payload to an existing stream. @param pkt: packet payload. """ self.pkt = self.pkt + pkt def change_seq(self, seq): """ this method is used for purpose of tcp stream reassemble, for the last packet sequence in the stream. @param seq: sequence number. """ self.seq = seq def int2bin(n, count=16): """ this method converts integer numbers to binary numbers @param n: the number to be converted @param count: the number of binary digits """ return "".join([str((n >> y) & 1) for y in range(count-1, -1, -1)]) # holds ssh encrypted sessions encryptedsessions = [] def is_created_session(Src, Dst, SPort, DPort): """ method returns true if the ssh session is exist @param Src: source ip address @param Dst: destination ip address @param SPort: source port number @param DPort: destination port number """ i = 0 while i < len(encryptedsessions): if Src and Dst and SPort and DPort in encryptedsessions[i]: return True i = i + 1 return False def create_session(Src, Dst, SPort, DPort, Macl): """ method for creating encypted ssh sessions @param Src: source ip address @param Dst: destination ip address @param SPort: source port number @param DPort: destination port number """ if not is_created_session(Src, Dst, SPort, DPort): encryptedsessions.append([Src, Dst, SPort, DPort, Macl, False]) def set_as_encrypted(Src, Dst, SPort, DPort): """ set the ssh session as encrypted @param Src: source ip address @param Dst: destination ip address @param SPort: source port number @param DPort: destination port number """ i = 0 while i < len(encryptedsessions): if Src and Dst and SPort and DPort in encryptedsessions[i]: encryptedsessions[i] = [Src, Dst, SPort, DPort,\ encryptedsessions[i][4], True] i = i + 1 return -1 def is_encrypted_session(Src, Dst, SPort, DPort): """ returns true if the ssh session is encrypted @param Src: source ip address @param Dst: destination ip address @param SPort: source port number @param DPort: destination port number """ i = 0 while i < len(encryptedsessions): if Src and Dst and SPort and DPort and True in encryptedsessions[i]: return True i = i + 1 return False def get_mac_length(Src, Dst, SPort, DPort): """ method for maintaining the length of the mac for specific ssh session @param Src: source ip address @param Dst: destination ip address @param SPort: source port number @param DPort: destination port number """ i = 0 while i < len(encryptedsessions): if Src and Dst and SPort and DPort in encryptedsessions[i]: return encryptedsessions[i][4] i = i + 1 return -1 class SSHField(XByteField): """ this is a field class for handling the ssh packets @attention: this class inherets XByteField """ found = False encryptionstarted = False macstarted = False maclength = 0 holds_packets = 1 name = "SSHField" myresult = "" def get_ascii(self, hexstr): """ get hex string and returns ascii chars @param hexstr: hex value in str format """ return binascii.unhexlify(hexstr) def __init__(self, name, default): """ class constructor, for initializing instance variables @param name: name of the field @param default: Scapy has many formats to represent the data internal, human and machine. anyways you may sit this param to None. """ self.name = name self.fmt = "!B" Field.__init__(self, name, default, "!B") def get_discnct_msg(self, cn): """ method returns a message for every a specific code number @param cn: code number """ codes = { 1: "SSH_DISCONNECT_HOST_NOT_ALLOWED_TO_CONNECT", 2: "SSH_DISCONNECT_PROTOCOL_ERROR", 3: "SSH_DISCONNECT_KEY_EXCHANGE_FAILED", 4: "SSH_DISCONNECT_RESERVED", 5: "SSH_DISCONNECT_MAC_ERROR", 6: "SSH_DISCONNECT_COMPRESSION_ERROR", 7: "SSH_DISCONNECT_SERVICE_NOT_AVAILABLE", 8: "SSH_DISCONNECT_PROTOCOL_VERSION_NOT_SUPPORTED", 9: "SSH_DISCONNECT_HOST_KEY_NOT_VERIFIABLE", 10: "SSH_DISCONNECT_CONNECTION_LOST", 11: "SSH_DISCONNECT_BY_APPLICATION", 12: "SSH_DISCONNECT_TOO_MANY_CONNECTIONS", 13: "SSH_DISCONNECT_AUTH_CANCELLED_BY_USER", 14: "SSH_DISCONNECT_NO_MORE_AUTH_METHODS_AVAILABLE", 15: "SSH_DISCONNECT_ILLEGAL_USER_NAME", } if cn in codes: return codes[cn] + " " return "UnknownCode[" + str(cn) + "] " def get_code_msg(self, cn): """ method returns a message for every a specific code number @param cn: code number """ codes = { 1: "SSH_MSG_DISCONNECT", 2: "SSH_MSG_IGNORE", 3: "SSH_MSG_UNIMPLEMENTED", 4: "SSH_MSG_DEBUG", 5: "SSH_MSG_SERVICE_REQUEST", 6: "SSH_MSG_SERVICE_ACCEPT", 20: "SSH_MSG_KEXINIT", 21: "SSH_MSG_NEWKEYS", 30: "SSH_MSG_KEXDH_INIT", 31: "SSH_MSG_KEXDH_REPLY", 32: "SSH_MSG_KEX_DH_GEX_INIT", 33: "SSH_MSG_KEX_DH_GEX_REPLY", 34: "SSH_MSG_KEX_DH_GEX_REQUEST", 50: "SSH_MSG_USERAUTH_REQUEST", 51: "SSH_MSG_USERAUTH_FAILURE", 52: "SSH_MSG_USERAUTH_SUCCESS", 53: "SSH_MSG_USERAUTH_BANNER", 60: "SSH_MSG_USERAUTH_PK_OK", 80: "SSH_MSG_GLOBAL_REQUEST", 81: "SSH_MSG_REQUEST_SUCCESS", 82: "SSH_MSG_REQUEST_FAILURE", 90: "SSH_MSG_CHANNEL_OPEN", 91: "SSH_MSG_CHANNEL_OPEN_CONFIRMATION", 92: "SSH_MSG_CHANNEL_OPEN_FAILURE", 93: "SSH_MSG_CHANNEL_WINDOW_ADJUST", 94: "SSH_MSG_CHANNEL_DATA", 95: "SSH_MSG_CHANNEL_EXTENDED_DATA", 96: "SSH_MSG_CHANNEL_EOF", 97: "SSH_MSG_CHANNEL_CLOSE", 98: "SSH_MSG_CHANNEL_REQUEST", 99: "SSH_MSG_CHANNEL_SUCCESS", 100: "SSH_MSG_CHANNEL_FAILURE"} if cn in codes: return codes[cn] + " " return "UnknownCode[" + str(cn) + "] " def getfield(self, pkt, s): """ this method will get the packet, takes what does need to be taken and let the remaining go, so it returns two values. first value which belongs to this field and the second is the remaining which does need to be dissected with other "field classes". @param pkt: holds the whole packet @param s: holds only the remaining data which is not dissected yet. """ ss = -1 flags = None seq = pkt.underlayer.fields["seq"] push = False flags_bits = list(int2bin(pkt.underlayer.fields["flags"])) if flags_bits[11] == '1': flags = 'A' if flags_bits[12] == '1': flags = flags + 'P' if 'P' in flags: push = True else: push = False if not is_created_stream_session(\ pkt.underlayer.underlayer.fields["src"],\ pkt.underlayer.underlayer.fields["dst"],\ pkt.underlayer.fields["sport"], pkt.underlayer.fields["dport"]): seqn = pkt.underlayer.fields["seq"] stream = Stream(s, push, seqn) create_stream_session(\ pkt.underlayer.underlayer.fields["src"],\ pkt.underlayer.underlayer.fields["dst"],\ pkt.underlayer.fields["sport"],\ pkt.underlayer.fields["dport"], stream) elif is_created_stream_session(\ pkt.underlayer.underlayer.fields["src"],\ pkt.underlayer.underlayer.fields["dst"],\ pkt.underlayer.fields["sport"],\ pkt.underlayer.fields["dport"]): seqn = pkt.underlayer.fields["seq"] stream = Stream(s, push, seqn) build_stream(\ pkt.underlayer.underlayer.fields["src"],\ pkt.underlayer.underlayer.fields["dst"],\ pkt.underlayer.fields["sport"],\ pkt.underlayer.fields["dport"], stream) if not dissector.Dissector.preprocess_done: return "", "" if len(sessions) > 0: if is_stream_end(\ pkt.underlayer.underlayer.fields["src"],\ pkt.underlayer.underlayer.fields["dst"],\ pkt.underlayer.fields["sport"],\ pkt.underlayer.fields["dport"], stream): ss = get_stream(\ pkt.underlayer.underlayer.fields["src"],\ pkt.underlayer.underlayer.fields["dst"],\ pkt.underlayer.fields["sport"],\ pkt.underlayer.fields["dport"], stream) if not ss == -1: s = ss else: return "", "" self.myresult = "" resultlist = [] if s.upper().startswith("SSH"): return "", s for c in s: ustruct = struct.unpack(self.fmt, c) byte = str(hex(ustruct[0]))[2:] if len(byte) == 1: byte = "0" + byte self.myresult = self.myresult + byte if not s.startswith("SSH") and len(self.myresult) > 12: if not is_encrypted_session(\ pkt.underlayer.underlayer.fields["src"],\ pkt.underlayer.underlayer.fields["dst"],\ pkt.underlayer.fields["sport"],\ pkt.underlayer.fields["dport"]): pakl = str(int(self.myresult[:8], 16)) padl = str(int(self.myresult[8:10], 16)) payloadl = int(pakl) - int(padl) - 1 opcode = self.get_code_msg(int(self.myresult[10:12], 16)) payload = self.myresult[12:12 + payloadl * 2] padding = self.myresult[12 + payloadl * 2:12 + payloadl * 2\ + int(padl) * 2] resultlist.append(("packet_length", pakl)) resultlist.append(("padding_length", padl)) resultlist.append(("opcode", opcode)) if is_encrypted_session(pkt.underlayer.underlayer.fields["src"], pkt.underlayer.underlayer.fields["dst"], pkt.underlayer.fields["sport"], pkt.underlayer.fields["dport"]): if is_created_session(pkt.underlayer.underlayer.fields["src"], pkt.underlayer.underlayer.fields["dst"], pkt.underlayer.fields["sport"], pkt.underlayer.fields["dport"]): encrypted_payload = base64.standard_b64encode(\ self.get_ascii(self.myresult[:\ get_mac_length(pkt.underlayer.underlayer.fields["src"], pkt.underlayer.underlayer.fields["dst"], pkt.underlayer.fields["sport"], pkt.underlayer.fields["dport"]) * 2])) else: encrypted_payload = base64.standard_b64encode(\ self.myresult[:]) resultlist.append(("encrypted_payload", encrypted_payload)) if is_created_session(pkt.underlayer.underlayer.fields["src"], pkt.underlayer.underlayer.fields["dst"], pkt.underlayer.fields["sport"], pkt.underlayer.fields["dport"]): mac = base64.standard_b64encode(\ self.get_ascii(self.myresult[\ get_mac_length(pkt.underlayer.underlayer.fields["src"],\ pkt.underlayer.underlayer.fields["dst"],\ pkt.underlayer.fields["sport"],\ pkt.underlayer.fields["dport"]) * 2:])) resultlist.append(("mac", mac)) if not is_encrypted_session(\ pkt.underlayer.underlayer.fields["src"],\ pkt.underlayer.underlayer.fields["dst"],\ pkt.underlayer.fields["sport"],\ pkt.underlayer.fields["dport"]) and\ opcode.startswith("SSH_MSG_KEXDH_INIT"): try: e_length = int(self.myresult[12:20], 16) e = base64.standard_b64encode(\ self.get_ascii(self.myresult[20:20 + e_length * 2])) resultlist.append(("e_length", str(e_length))) resultlist.append(("e", e)) self.found = True except Exception: self.found = False if not is_encrypted_session(\ pkt.underlayer.underlayer.fields["src"],\ pkt.underlayer.underlayer.fields["dst"],\ pkt.underlayer.fields["sport"],\ pkt.underlayer.fields["dport"]) and\ opcode.startswith("SSH_MSG_KEXDH_REPLY"): try: server_public_host_key_and_certificates_K_S_length =\ int(self.myresult[12:20], 16) server_public_host_key_and_certificates_K_S =\ self.myresult[20:20 +\ server_public_host_key_and_certificates_K_S_length * 2] f_length = int(self.myresult[20 + \ server_public_host_key_and_certificates_K_S_length\ * 2:20 + server_public_host_key_and_certificates_K_S_length\ * 2 + 8], 16) f = base64.standard_b64encode(\ self.get_ascii(self.myresult[20 +\ server_public_host_key_and_certificates_K_S_length\ * 2 + 8:20 + server_public_host_key_and_certificates_K_S_length\ * 2 + 8 + f_length * 2])) signature_of_h_length = int(self.myresult[20 +\ server_public_host_key_and_certificates_K_S_length\ * 2 + 8 + f_length * 2:20 +\ server_public_host_key_and_certificates_K_S_length\ * 2 + 8 + f_length * 2 + 8], 16) signature_of_h = self.myresult[20 +\ server_public_host_key_and_certificates_K_S_length\ * 2 + 8 + f_length * 2 + 8:20 +\ server_public_host_key_and_certificates_K_S_length\ * 2 + 8 + f_length * 2 + 8 +\ signature_of_h_length * 2] resultlist.append(\ ("server_public_host_key_and_certificates_K_S_length",\ str(server_public_host_key_and_certificates_K_S_length))) resultlist.append(\ ("server_public_host_key_and_certificates_K_S",\ base64.standard_b64encode(\ self.get_ascii(server_public_host_key_and_certificates_K_S)))) resultlist.append(("f_length", str(f_length))) resultlist.append(("f", f)) resultlist.append(("signature_of_h_length", str(signature_of_h_length))) resultlist.append(("signature_of_h", base64.standard_b64encode(\ self.get_ascii(signature_of_h)))) self.found = True except Exception: self.found = False if not is_encrypted_session(\ pkt.underlayer.underlayer.fields["src"],\ pkt.underlayer.underlayer.fields["dst"],\ pkt.underlayer.fields["sport"],\ pkt.underlayer.fields["dport"])\ and opcode.startswith("SSH_MSG_SERVICE_REQUEST"): try: service_name_length = int(self.myresult[12:20], 16) service_name = self.myresult[20:20 \ + service_name_length * 2] resultlist.append(("service_name_length", str(service_name_length))) resultlist.append(("service_name", base64.standard_b64encode(self.get_ascii(service_name)))) self.found = True except Exception: self.found = False if not is_encrypted_session(\ pkt.underlayer.underlayer.fields["src"],\ pkt.underlayer.underlayer.fields["dst"],\ pkt.underlayer.fields["sport"],\ pkt.underlayer.fields["dport"])\ and opcode.startswith("SSH_MSG_SERVICE_ACCEPT"): try: service_name_length = int(self.myresult[12:20], 16) service_name = self.myresult[20:20 +\ service_name_length * 2] resultlist.append(("service_name_length", str(service_name_length))) resultlist.append(("service_name", self.get_ascii(service_name))) self.found = True except Exception: self.found = False if not is_encrypted_session(\ pkt.underlayer.underlayer.fields["src"],\ pkt.underlayer.underlayer.fields["dst"],\ pkt.underlayer.fields["sport"],\ pkt.underlayer.fields["dport"])\ and opcode.startswith("SSH_MSG_NEWKEYS"): try: set_as_encrypted(pkt.underlayer.underlayer.fields["src"], pkt.underlayer.underlayer.fields["dst"], pkt.underlayer.fields["sport"], pkt.underlayer.fields["dport"]) self.found = True except Exception: self.found = False if not is_encrypted_session(\ pkt.underlayer.underlayer.fields["src"],\ pkt.underlayer.underlayer.fields["dst"],\ pkt.underlayer.fields["sport"],\ pkt.underlayer.fields["dport"])\ and opcode.startswith("SSH_MSG_DISCONNECT"): try: reason_code = self.get_discnct_msg(int(\ self.myresult[12:20], 16)) * 2 description_length = int(\ self.myresult[20:28], 16) description = self.myresult[28:28 +\ description_length * 2] language_tag_length = int(\ self.myresult[28 + description_length * 2:28 +\ description_length * 2 + 8], 16) language_tag = self.myresult[28 + description_length\ * 2 + 8:28 + description_length * 2 + 8 +\ language_tag_length * 2] resultlist.append(("reason_code", reason_code)) resultlist.append(("description_length", str(description_length))) resultlist.append(("description", self.get_ascii(description))) resultlist.append(("language_tag_length", str(language_tag_length))) resultlist.append(("language_tag", self.get_ascii(language_tag))) self.found = True except Exception: self.found = False if not is_encrypted_session(\ pkt.underlayer.underlayer.fields["src"],\ pkt.underlayer.underlayer.fields["dst"],\ pkt.underlayer.fields["sport"],\ pkt.underlayer.fields["dport"])\ and opcode.startswith("SSH_MSG_IGNORE"): try: data_length = int(self.myresult[12:20], 16) data = self.myresult[20:20 + data_length * 2] resultlist.append(("data_length", str(data_length))) resultlist.append(\ ("data", base64.standard_b64encode(self.get_ascii(data)))) self.found = True except Exception: self.found = False if not is_encrypted_session(\ pkt.underlayer.underlayer.fields["src"],\ pkt.underlayer.underlayer.fields["dst"],\ pkt.underlayer.fields["sport"],\ pkt.underlayer.fields["dport"])\ and opcode.startswith("SSH_MSG_USERAUTH_PK_OK"): try: public_key_algorithm_name_from_the_request_length =\ int(self.myresult[12:20], 16) public_key_algorithm_name_from_the_request =\ self.myresult[20:20 +\ public_key_algorithm_name_from_the_request_length * 2] public_key_blob_from_the_request_length = int(\ self.myresult[20 + \ public_key_algorithm_name_from_the_request_length * 2:20\ + public_key_algorithm_name_from_the_request_length * 2\ + 8], 16) public_key_blob_from_the_request = self.myresult[20 +\ public_key_algorithm_name_from_the_request_length * 2 +\ 8:20 + public_key_algorithm_name_from_the_request_length\ * 2 + 8 + public_key_blob_from_the_request_length * 2] resultlist.append((\ "public_key_algorithm_name_from_the_request_length", str(public_key_algorithm_name_from_the_request_length))) resultlist.append(\ ("public_key_algorithm_name_from_the_request",\ self.get_ascii(\ public_key_algorithm_name_from_the_request))) resultlist.append(\ ("public_key_blob_from_the_request_length", str(public_key_blob_from_the_request_length))) resultlist.append(("public_key_blob_from_the_request", self.get_ascii(public_key_blob_from_the_request))) self.found = True except Exception: self.found = False if not is_encrypted_session(\ pkt.underlayer.underlayer.fields["src"],\ pkt.underlayer.underlayer.fields["dst"],\ pkt.underlayer.fields["sport"],\ pkt.underlayer.fields["dport"])\ and opcode.startswith("SSH_MSG_DEBUG"): try: always_display_boolean = int(self.myresult[12:14], 16) description_length = int(self.myresult[14:22], 16) description = self.myresult[22:22 +\ description_length * 2] language_tag_length = int(self.myresult[22 +\ description_length * 2:22 + description_length\ * 2 + 8], 16) language_tag = self.myresult[22 + description_length\ * 2 + 8:22 + description_length * 2 + 8 +\ language_tag_length * 2] resultlist.append(("always_display_boolean", always_display_boolean)) resultlist.append(("description_length", str(description_length))) resultlist.append(("description", self.get_ascii(description))) resultlist.append(("language_tag_length", str(language_tag_length))) resultlist.append(("language_tag", self.get_ascii(language_tag))) self.found = True except Exception: self.found = False if not is_encrypted_session(\ pkt.underlayer.underlayer.fields["src"],\ pkt.underlayer.underlayer.fields["dst"],\ pkt.underlayer.fields["sport"], pkt.underlayer.fields["dport"])\ and opcode.startswith("SSH_MSG_UNIMPLEMENTED"): try: seqn = int(self.myresult[12:20], 16) resultlist.append(\ ("packet sequence number of rejected message", seqn)) self.found = True except Exception: self.found = False if not is_encrypted_session(\ pkt.underlayer.underlayer.fields["src"],\ pkt.underlayer.underlayer.fields["dst"],\ pkt.underlayer.fields["sport"],\ pkt.underlayer.fields["dport"])\ and opcode.startswith("SSH_MSG_CHANNEL_DATA"): try: recipient_channel = int(self.myresult[12:20], 16) data_length = int(self.myresult[20:28], 16) data = self.myresult[28:28 + data_length * 2] resultlist.append(("recipient_channel", recipient_channel)) resultlist.append(("data_length", str(data_length))) resultlist.append(\ ("data", base64.standard_b64encode(self.get_ascii(data)))) self.found = True except Exception: self.found = False if not is_encrypted_session(\ pkt.underlayer.underlayer.fields["src"],\ pkt.underlayer.underlayer.fields["dst"],\ pkt.underlayer.fields["sport"],\ pkt.underlayer.fields["dport"])\ and opcode.startswith("SSH_MSG_USERAUTH_REQUEST"): try: user_name_length = int(self.myresult[12:20], 16) user_name = self.myresult[20:20 + user_name_length * 2] service_name_length = int(self.myresult[20 +\ user_name_length * 2:20 + user_name_length * 2 + 8], 16) service_name = self.myresult[20 + user_name_length *\ 2 + 8:20 + user_name_length * 2 + 8 +\ service_name_length * 2] method_name_length = int(self.myresult[20 +\ user_name_length * 2 + 8 + service_name_length *\ 2:20 + user_name_length * 2 + 8 + service_name_length\ * 2 + 8], 16) method_name = self.myresult[20 + user_name_length *\ 2 + 8 + service_name_length * 2 + 8:20 +\ user_name_length * 2 + 8 + service_name_length *\ 2 + 8 + method_name_length * 2] resultlist.append(("user_name_length", str(user_name_length))) resultlist.append(("user_name", self.get_ascii(user_name))) resultlist.append(("service_name_length", str(service_name_length))) resultlist.append(("service_name", self.get_ascii(service_name))) resultlist.append(("method_name_length", str(method_name_length))) resultlist.append(("method_name", self.get_ascii(method_name))) if method_name.startswith("publickey"): boolean = int(self.myresult[20 + user_name_length *\ 2 + 8 + service_name_length * 2 + 8 +\ method_name_length * 2:20 + user_name_length *\ 2 + 8 + service_name_length * 2 + 8 +\ method_name_length * 2 + 8], 16) public_key_algorithm_name_length =\ int(self.myresult[20 + user_name_length * 2 + 8 +\ service_name_length * 2 + 8 + method_name_length\ * 2 + 8:20 + user_name_length * 2 + 8 +\ service_name_length * 2 + 8 + method_name_length\ * 2 + 8 + 8], 16) public_key_algorithm_name = self.myresult[20 +\ user_name_length * 2 + 8 + service_name_length *\ 2 + 8 + method_name_length * 2 + 8 + 8:20 +\ user_name_length * 2 + 8 + service_name_length *\ 2 + 8 + method_name_length * 2 + 8 + 8 +\ public_key_algorithm_name_length * 2] resultlist.append(("boolean", boolean)) resultlist.append(("public_key_algorithm_name_length", str(public_key_algorithm_name_length))) resultlist.append(("public_key_algorithm_name", self.get_ascii(public_key_algorithm_name))) if boolean == 0: public_key_blob_length =\ int(self.myresult[20 + user_name_length * 2 +\ 8 + service_name_length * 2 + 8 +\ method_name_length * 2 + 8 + 8 +\ public_key_algorithm_name_length * 2:20 +\ user_name_length * 2 + 8 +\ service_name_length * 2 + 8 +\ method_name_length * 2 + 8 + 8 +\ public_key_algorithm_name_length * 2 + 8], 16) public_key_blob = self.myresult[20 +\ user_name_length * 2 + 8 + service_name_length *\ 2 + 8 + method_name_length * 2 + 8 + 8 +\ public_key_algorithm_name_length * 2 + 8:20 +\ user_name_length * 2 + 8 +\ service_name_length * 2 + 8 +\ method_name_length * 2 + 8 + 8 +\ public_key_algorithm_name_length * 2 + 8 +\ public_key_blob_length * 2] resultlist.append(("public_key_blob_length", str(public_key_blob_length))) resultlist.append(("public_key_blob", self.get_ascii(\ public_key_blob))) if boolean != 0: public_key_to_be_used_for_authentication_length =\ int(self.myresult[20 + user_name_length * 2 +\ 8 + service_name_length * 2 + 8 +\ method_name_length * 2 + 8 + 8 +\ public_key_algorithm_name_length * 2:20 +\ user_name_length * 2 + 8 +\ service_name_length * 2 + 8 +\ method_name_length * 2 + 8 + 8 +\ public_key_algorithm_name_length * 2 + 8],\ 16) public_key_to_be_used_for_authentication =\ self.myresult[20 + user_name_length * 2 + 8 +\ service_name_length * 2 + 8 + method_name_length\ * 2 + 8 + 8 + public_key_algorithm_name_length\ * 2 + 8:20 + user_name_length * 2 + 8 +\ service_name_length * 2 + 8 +\ method_name_length * 2 + 8 + 8 +\ public_key_algorithm_name_length * 2 + 8 +\ public_key_blob_length * 2] signature_length = \ int(self.myresult[20 + user_name_length * 2 + 8 +\ service_name_length * 2 + 8 +\ method_name_length * 2 + 8 + 8 +\ public_key_algorithm_name_length * 2 + 8 +\ public_key_to_be_used_for_authentication_length\ * 2:20 + user_name_length * 2 + 8 +\ service_name_length * 2 + 8 +\ method_name_length * 2 + 8 + 8 +\ public_key_algorithm_name_length * 2 + 8 +\ public_key_to_be_used_for_authentication_length\ * 2 + 8], 16) signature = self.myresult[20 + user_name_length *\ 2 + 8 + service_name_length * 2 + 8 + \ method_name_length * 2 + 8 + 8 +\ public_key_algorithm_name_length * 2 + 8 +\ public_key_to_be_used_for_authentication_length\ * 2 + 8:20 + user_name_length * 2 + 8 +\ service_name_length * 2 + 8 +\ method_name_length * 2 + 8 + 8 +\ public_key_algorithm_name_length * 2 + 8 +\ public_key_to_be_used_for_authentication_length\ * 2 + 8 + signature_length * 2] resultlist.append((\ "public_key_to_be_used_for_authentication_length", str(public_key_to_be_used_for_authentication_length))) resultlist.append((\ "public_key_to_be_used_for_authentication", self.get_ascii(\ public_key_to_be_used_for_authentication))) resultlist.append(("signature_length", str(signature_length))) resultlist.append(("signature", self.get_ascii(signature))) if method_name.startswith("password"): boolean = int(self.myresult[20 + user_name_length\ * 2 + 8 + service_name_length * 2 + 8 +\ method_name_length * 2:20 + user_name_length *\ 2 + 8 + service_name_length * 2 + 8 +\ method_name_length * 2 + 8], 16) resultlist.append(("boolean", boolean)) if boolean == 0: plaintext_password_length = int(self.myresult[\ 20 + user_name_length * 2 + 8 +\ service_name_length * 2 + 8 + method_name_length\ * 2 + 8:20 + user_name_length * 2 + 8 +\ service_name_length * 2 + 8 +\ method_name_length * 2 + 8 + 8], 16) plaintext_password = self.myresult[20 +\ user_name_length * 2 + 8 + service_name_length\ * 2 + 8 + method_name_length * 2 + 8 + 8:20 +\ user_name_length * 2 + 8 + service_name_length\ * 2 + 8 + method_name_length * 2 + 8 + 8 +\ plaintext_password_length * 2] resultlist.append(("plaintext_password_length", str(plaintext_password_length))) resultlist.append(("plaintext_password", self.get_ascii(plaintext_password))) if boolean != 0: plaintext_old_password_length =\ int(self.myresult[20 + user_name_length * 2 +\ 8 + service_name_length * 2 + 8 +\ method_name_length * 2 + 8:20 +\ user_name_length * 2 + 8 +\ service_name_length * 2 + 8 +\ method_name_length * 2 + 8 + 8], 16) plaintext_old_password = self.myresult[\ 20 + user_name_length * 2 + 8 +\ service_name_length * 2 + 8 +\ method_name_length * 2 + 8 + 8:20 +\ user_name_length * 2 + 8 +\ service_name_length * 2 + 8 +\ method_name_length * 2 + 8 + 8 +\ plaintext_old_password_length * 2] plaintext_new_password_length = \ int(self.myresult[20 + user_name_length * 2 +\ 8 + service_name_length * 2 + 8 +\ method_name_length * 2 + 8 + 8 +\ plaintext_old_password_length * 2:20\ + user_name_length * 2 + 8 +\ service_name_length * 2 + 8 +\ method_name_length * 2 + 8 + 8 +\ plaintext_old_password_length * 2 + 8], 16) plaintext_new_password = self.myresult[\ 20 + user_name_length * 2 + 8 +\ service_name_length * 2 + 8 + method_name_length\ * 2 + 8 + 8 + plaintext_old_password_length\ * 2 + 8:20 + user_name_length * 2 + 8 +\ service_name_length * 2 + 8 +\ method_name_length * 2 + 8 + 8 +\ plaintext_old_password_length * 2 +\ plaintext_new_password_length * 2] resultlist.append(("plaintext_old_password_length", str(plaintext_old_password_length))) resultlist.append(("plaintext_old_password", plaintext_old_password)) resultlist.append(("plaintext_new_password_length", str(plaintext_new_password_length))) resultlist.append(("plaintext_new_password", self.get_ascii(plaintext_new_password))) if method_name.startswith("hostbased"): public_key_algorithm_for_host_key_length =\ int(self.myresult[12:20], 16) public_key_algorithm_for_host_key =\ self.myresult[20:20 +\ public_key_algorithm_for_host_key_length * 2] public_host_key_and_cert_for_client_host_len =\ int(self.myresult[20 +\ public_key_algorithm_for_host_key_length * 2:20 +\ public_key_algorithm_for_host_key_length *\ 2 + 8], 16) public_host_key_and_certificates_for_client_host =\ self.myresult[20 +\ public_key_algorithm_for_host_key_length * 2 +\ 8:20 + public_key_algorithm_for_host_key_length *\ 2 + 8 +\ public_host_key_and_cert_for_client_host_len * 2] client_host_name_length = int(self.myresult[20 +\ public_key_algorithm_for_host_key_length * 2 + 8 +\ public_host_key_and_cert_for_client_host_len\ * 2:20 + public_key_algorithm_for_host_key_length\ * 2 + 8 + public_host_key_and_cert_for_client_host_len\ * 2 + 8], 16) client_host_name = self.myresult[20 +\ public_key_algorithm_for_host_key_length * 2 + 8 +\ public_host_key_and_cert_for_client_host_len\ * 2 + 8:20 + public_key_algorithm_for_host_key_length\ * 2 + 8 + public_host_key_and_cert_for_client_host_len\ * 2 + 8 + client_host_name_length * 2] user_name_on_the_client_host_length = int(\ self.myresult[20 +\ public_key_algorithm_for_host_key_length * 2 + 8 +\ public_host_key_and_cert_for_client_host_len\ * 2 + 8 + client_host_name_length * 2:20 +\ public_key_algorithm_for_host_key_length * 2 + 8 +\ public_host_key_and_cert_for_client_host_len\ * 2 + 8 + client_host_name_length * 2 + 8], 16) user_name_on_the_client_host = self.myresult[20\ + public_key_algorithm_for_host_key_length * 2 + 8 +\ public_host_key_and_cert_for_client_host_len * 2 +\ 8 + client_host_name_length * 2 + 8:20 +\ public_key_algorithm_for_host_key_length * 2 + 8 +\ public_host_key_and_cert_for_client_host_len * 2 + 8 +\ client_host_name_length * 2 + 8 +\ user_name_on_the_client_host_length * 2] signature_length = int(self.myresult[20 +\ public_key_algorithm_for_host_key_length * 2 + 8 +\ public_host_key_and_cert_for_client_host_len\ * 2 + 8 + client_host_name_length * 2 + 8 +\ user_name_on_the_client_host_length * 2:20 +\ public_key_algorithm_for_host_key_length * 2 + 8 +\ public_host_key_and_cert_for_client_host_len * 2 + 8 +\ client_host_name_length * 2 + 8 +\ user_name_on_the_client_host_length * 2 + 8], 16) signature = self.myresult[20 +\ public_key_algorithm_for_host_key_length * 2 + 8 +\ public_host_key_and_cert_for_client_host_len * 2 +\ 8 + client_host_name_length * 2 + 8 +\ user_name_on_the_client_host_length * 2 + 8:20 +\ public_key_algorithm_for_host_key_length * 2 + 8 +\ public_host_key_and_cert_for_client_host_len *\ 2 + 8 + client_host_name_length * 2 + 8 +\ user_name_on_the_client_host_length * 2 + 8 +\ signature_length * 2] resultlist.append(("public_key_algorithm_for\ _host_key_length", str(public_key_algorithm_for_host_key_length))) resultlist.append(("public_key_algorithm_for_host_key", self.get_ascii(public_key_algorithm_for_host_key))) resultlist.append(\ ("public_host_key_and_certificates_for_client_host_length", str(\ public_host_key_and_cert_for_client_host_len))) resultlist.append(\ ("public_host_key_and_certificates_for_client_host", self.get_ascii(\ public_host_key_and_certificates_for_client_host))) resultlist.append(("client_host_name_length", str(client_host_name_length))) resultlist.append(("client_host_name", self.get_ascii(client_host_name))) resultlist.append(\ ("user_name_on_the_client_host_length",\ str(user_name_on_the_client_host_length))) resultlist.append(("user_name_on_the_client_host", self.get_ascii(user_name_on_the_client_host))) resultlist.append(("signature_length", str(signature_length))) resultlist.append(("signature", self.get_ascii(signature))) else: method_specific_fields_length = int(self.myresult[\ 20 + user_name_length * 2 + 8 + service_name_length * 2 + 8 +\ method_name_length * 2:20 + user_name_length * 2 + 8 +\ service_name_length * 2 + 8 + method_name_length * 2 + 8], 16) method_specific_fields = self.myresult[\ 20 + user_name_length * 2 + 8 + service_name_length * 2 + 8 +\ method_name_length * 2 + 8:20 + user_name_length * 2 + 8 +\ service_name_length * 2 + 8 + method_name_length * 2 + 8 +\ method_specific_fields_length * 2] resultlist.append(("method_specific_fields_length", str(method_specific_fields_length))) resultlist.append(("method_specific_fields", self.get_ascii(method_specific_fields))) self.found = True except Exception: self.found = False if not is_encrypted_session(\ pkt.underlayer.underlayer.fields["src"],\ pkt.underlayer.underlayer.fields["dst"],\ pkt.underlayer.fields["sport"],\ pkt.underlayer.fields["dport"])\ and opcode.startswith("SSH_MSG_USERAUTH_FAILURE"): try: authentications_that_can_continue_length =\ int(self.myresult[12:20], 16) authentications_that_can_continue =\ self.myresult[20:20 + authentications_that_can_continue_length * 2] partial_success_boolean = int(self.myresult[20 +\ authentications_that_can_continue_length * 2:20 +\ authentications_that_can_continue_length * 2 + 8], 16) resultlist.append(\ ("authentications_that_can_continue_length", str(authentications_that_can_continue_length))) resultlist.append(("authentications_that_can_continue", authentications_that_can_continue)) resultlist.append(("partial_success_boolean", partial_success_boolean)) self.found = True except Exception: self.found = False if not is_encrypted_session(\ pkt.underlayer.underlayer.fields["src"],\ pkt.underlayer.underlayer.fields["dst"],\ pkt.underlayer.fields["sport"],\ pkt.underlayer.fields["dport"])\ and opcode.startswith("SSH_MSG_USERAUTH_BANNER"): try: message_length = int(self.myresult[12:20], 16) message = self.myresult[20:20 + message_length * 2] language_tag_length = int(self.myresult[20 +\ message_length * 2:20 + message_length * 2 + 8], 16) language_tag = self.myresult[20 + message_length * 2\ + 8:20 + message_length * 2 + 8 + language_tag_length * 2] resultlist.append(("message_length", str(message_length))) resultlist.append(("message", self.get_ascii(message))) resultlist.append(("language_tag_length", str(language_tag_length))) resultlist.append(("language_tag", self.get_ascii(language_tag))) self.found = True except Exception: self.found = False if not is_encrypted_session(\ pkt.underlayer.underlayer.fields["src"], pkt.underlayer.underlayer.fields["dst"], pkt.underlayer.fields["sport"], pkt.underlayer.fields["dport"])\ and opcode.startswith("SSH_MSG_KEXINIT"): try: cookie = base64.standard_b64encode(self.myresult[12:44]) kex_algorithms_length = int(self.myresult[44:52], 16) kex_algorithms = self.get_ascii(self.myresult[52:52 +\ kex_algorithms_length * 2]) server_host_key_algorithms_length = int(self.myresult[52 +\ kex_algorithms_length * 2:52 + kex_algorithms_length\ * 2 + 8], 16) server_host_key_algorithms = self.get_ascii(self.myresult[\ 52 + kex_algorithms_length * 2 + 8:52 +\ kex_algorithms_length * 2 + 8 +\ server_host_key_algorithms_length * 2]) encryption_algorithms_client_to_server_length = int(\ self.myresult[52 + kex_algorithms_length * 2 + 8 +\ server_host_key_algorithms_length * 2:52 +\ kex_algorithms_length * 2 + 8 +\ server_host_key_algorithms_length * 2 + 8], 16) encryption_algorithms_client_to_server = self.get_ascii(\ self.myresult[52 + kex_algorithms_length * 2 + 8 +\ server_host_key_algorithms_length * 2 + 8:52 +\ kex_algorithms_length * 2 + 8 +\ server_host_key_algorithms_length * 2 + 8 +\ encryption_algorithms_client_to_server_length * 2]) encryption_algorithms_server_to_client_length = int(\ self.myresult[52 + kex_algorithms_length * 2 + 8 +\ server_host_key_algorithms_length * 2 + 8 +\ encryption_algorithms_client_to_server_length * 2:52 +\ kex_algorithms_length * 2 + 8 +\ server_host_key_algorithms_length * 2 + 8 +\ encryption_algorithms_client_to_server_length *\ 2 + 8], 16) encryption_algorithms_server_to_client = self.get_ascii(\ self.myresult[52 + kex_algorithms_length * 2 + 8 +\ server_host_key_algorithms_length * 2 + 8 +\ encryption_algorithms_client_to_server_length * 2 +\ 8:52 + kex_algorithms_length * 2 + 8 +\ server_host_key_algorithms_length * 2 + 8 +\ encryption_algorithms_client_to_server_length * 2 +\ 8 + encryption_algorithms_server_to_client_length\ * 2]) mac_algorithms_client_to_server_length = int(\ self.myresult[52 + kex_algorithms_length * 2 + 8 +\ server_host_key_algorithms_length * 2 + 8 +\ encryption_algorithms_client_to_server_length * 2 + 8 +\ encryption_algorithms_server_to_client_length * 2:52 +\ kex_algorithms_length * 2 + 8 + server_host_key_algorithms_length\ * 2 + 8 + encryption_algorithms_client_to_server_length * 2 +\ 8 + encryption_algorithms_server_to_client_length * 2 + 8], 16) mac_algorithms_client_to_server = self.get_ascii(\ self.myresult[52 + kex_algorithms_length * 2 + 8 +\ server_host_key_algorithms_length * 2 + 8 +\ encryption_algorithms_client_to_server_length * 2 + 8 +\ encryption_algorithms_server_to_client_length * 2 + 8:52 +\ kex_algorithms_length * 2 + 8 + server_host_key_algorithms_length\ * 2 + 8 + encryption_algorithms_client_to_server_length * 2 +\ 8 + encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2]) mac_algorithms_server_to_client_length = int(\ self.myresult[52 + kex_algorithms_length * 2 + 8 +\ server_host_key_algorithms_length * 2 + 8 +\ encryption_algorithms_client_to_server_length * 2 + 8 +\ encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2:52 +\ kex_algorithms_length * 2 + 8 + server_host_key_algorithms_length\ * 2 + 8 + encryption_algorithms_client_to_server_length * 2 +\ 8 + encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2 + 8], 16) mac_algorithms_server_to_client = self.get_ascii(\ self.myresult[52 + kex_algorithms_length * 2 + 8 +\ server_host_key_algorithms_length * 2 + 8 +\ encryption_algorithms_client_to_server_length * 2 + 8 +\ encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2 + 8:52 +\ kex_algorithms_length * 2 + 8 + server_host_key_algorithms_length\ * 2 + 8 + encryption_algorithms_client_to_server_length * 2 +\ 8 + encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2 + 8 +\ mac_algorithms_server_to_client_length * 2]) compression_algorithms_client_to_server_length =\ int(self.myresult[52 + kex_algorithms_length * 2 + 8 +\ server_host_key_algorithms_length * 2 + 8 +\ encryption_algorithms_client_to_server_length * 2 + 8 +\ encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2 + 8 +\ mac_algorithms_server_to_client_length * 2:52 +\ kex_algorithms_length * 2 + 8 + server_host_key_algorithms_length *\ 2 + 8 + encryption_algorithms_client_to_server_length * 2 + 8 +\ encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2 + 8 +\ mac_algorithms_server_to_client_length * 2 + 8], 16) compression_algorithms_client_to_server = self.get_ascii(\ self.myresult[52 + kex_algorithms_length * 2 + 8 +\ server_host_key_algorithms_length * 2 + 8 +\ encryption_algorithms_client_to_server_length * 2 + 8 +\ encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2 + 8 +\ mac_algorithms_server_to_client_length * 2 + 8:52 +\ kex_algorithms_length * 2 + 8 +\ server_host_key_algorithms_length * 2 + 8 +\ encryption_algorithms_client_to_server_length * 2 + 8 +\ encryption_algorithms_server_to_client_length\ * 2 + 8 + mac_algorithms_client_to_server_length\ * 2 + 8 + mac_algorithms_server_to_client_length\ * 2 + 8 + \ compression_algorithms_client_to_server_length * 2]) compression_algorithms_server_to_client_length = int(\ self.myresult[52 + kex_algorithms_length * 2 + 8 +\ server_host_key_algorithms_length * 2 + 8 +\ encryption_algorithms_client_to_server_length * 2 + 8 +\ encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2 + 8 +\ mac_algorithms_server_to_client_length * 2 + 8 +\ compression_algorithms_client_to_server_length * 2:52 +\ kex_algorithms_length * 2 + 8 +\ server_host_key_algorithms_length * 2 + 8 +\ encryption_algorithms_client_to_server_length * 2 + 8 +\ encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2 + 8 +\ mac_algorithms_server_to_client_length * 2 + 8 +\ compression_algorithms_client_to_server_length * 2 + 8], 16) compression_algorithms_server_to_client = self.get_ascii(\ self.myresult[52 + kex_algorithms_length * 2 + 8 +\ server_host_key_algorithms_length * 2 + 8 +\ encryption_algorithms_client_to_server_length * 2 + 8 +\ encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2 + 8 +\ mac_algorithms_server_to_client_length * 2 + 8 +\ compression_algorithms_client_to_server_length * 2 + 8:52 +\ kex_algorithms_length * 2 + 8 + \ server_host_key_algorithms_length * 2 +\ 8 + encryption_algorithms_client_to_server_length * 2 + 8 +\ encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2 + 8 +\ mac_algorithms_server_to_client_length * 2 + 8 +\ compression_algorithms_client_to_server_length * 2 + 8 +\ compression_algorithms_server_to_client_length * 2]) languages_client_to_server_length = int(self.myresult[\ 52 + kex_algorithms_length * 2 + 8 + server_host_key_algorithms_length\ * 2 + 8 + encryption_algorithms_client_to_server_length * 2 + 8 +\ encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2 + 8 +\ mac_algorithms_server_to_client_length * 2 + 8 +\ compression_algorithms_client_to_server_length * 2 + 8 +\ compression_algorithms_server_to_client_length * 2:52 +\ kex_algorithms_length * 2 + 8 + server_host_key_algorithms_length\ * 2 + 8 + encryption_algorithms_client_to_server_length * 2 + 8 +\ encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2 + 8 +\ mac_algorithms_server_to_client_length * 2 + 8 +\ compression_algorithms_client_to_server_length * 2 + 8 +\ compression_algorithms_server_to_client_length * 2 + 8], 16) languages_client_to_server = self.get_ascii(self.myresult[\ 52 + kex_algorithms_length * 2 + 8 + server_host_key_algorithms_length\ * 2 + 8 + encryption_algorithms_client_to_server_length * 2 + 8 +\ encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2 + 8 +\ mac_algorithms_server_to_client_length * 2 + 8 +\ compression_algorithms_client_to_server_length * 2 + 8 +\ compression_algorithms_server_to_client_length * 2 + 8:52 +\ kex_algorithms_length * 2 + 8 + server_host_key_algorithms_length\ * 2 + 8 + encryption_algorithms_client_to_server_length * 2 +\ 8 + encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2 + 8 +\ mac_algorithms_server_to_client_length * 2 + 8 +\ compression_algorithms_client_to_server_length *\ 2 + 8 + compression_algorithms_server_to_client_length *\ 2 + 8 + languages_client_to_server_length * 2]) languages_client_to_server_length = int(self.myresult[52 +\ kex_algorithms_length * 2 + 8 + server_host_key_algorithms_length * 2 +\ 8 + encryption_algorithms_client_to_server_length * 2 + 8 +\ encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2 + 8 +\ mac_algorithms_server_to_client_length * 2 + 8 +\ compression_algorithms_client_to_server_length *\ 2 + 8 + compression_algorithms_server_to_client_length *\ 2 + 8 + languages_client_to_server_length * 2:52 +\ kex_algorithms_length * 2 + 8 +\ server_host_key_algorithms_length * 2 + 8 +\ encryption_algorithms_client_to_server_length * 2 + 8 +\ encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2 + 8 +\ mac_algorithms_server_to_client_length * 2 + 8 +\ compression_algorithms_client_to_server_length * 2 +\ 8 + compression_algorithms_server_to_client_length * 2 +\ 8 + languages_client_to_server_length * 2 + 8], 16) languages_client_to_server = self.get_ascii(self.myresult[\ 52 + kex_algorithms_length * 2 + 8 + server_host_key_algorithms_length *\ 2 + 8 + encryption_algorithms_client_to_server_length * 2 + 8 +\ encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2 + 8 +\ mac_algorithms_server_to_client_length * 2 + 8 +\ compression_algorithms_client_to_server_length * 2 +\ 8 + compression_algorithms_server_to_client_length * 2 + 8 +\ languages_client_to_server_length * 2 + 8:52 +\ kex_algorithms_length * 2 + 8 + server_host_key_algorithms_length\ * 2 + 8 + encryption_algorithms_client_to_server_length * 2 +\ 8 + encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2 + 8 +\ mac_algorithms_server_to_client_length * 2 + 8 +\ compression_algorithms_client_to_server_length * 2 + 8 +\ compression_algorithms_server_to_client_length * 2 + 8 +\ languages_client_to_server_length * 2 + 8 +\ languages_client_to_server_length * 2]) languages_server_to_client_length = int(self.myresult[\ 52 + kex_algorithms_length * 2 + 8 + server_host_key_algorithms_length *\ 2 + 8 + encryption_algorithms_client_to_server_length * 2 + 8 +\ encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2 + 8 +\ mac_algorithms_server_to_client_length * 2 + 8 +\ compression_algorithms_client_to_server_length * 2 + 8 +\ compression_algorithms_server_to_client_length * 2 + 8 +\ languages_client_to_server_length * 2 + 8 +\ languages_client_to_server_length * 2:52 + kex_algorithms_length *\ 2 + 8 + server_host_key_algorithms_length * 2 + 8 +\ encryption_algorithms_client_to_server_length * 2 + 8 +\ encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2 + 8 +\ mac_algorithms_server_to_client_length * 2 + 8 +\ compression_algorithms_client_to_server_length * 2 + 8 +\ compression_algorithms_server_to_client_length * 2 + 8 +\ languages_client_to_server_length * 2 + 8 +\ languages_client_to_server_length * 2 + 8], 16) languages_server_to_client = self.get_ascii(\ self.myresult[52 + kex_algorithms_length * 2 + 8 +\ server_host_key_algorithms_length * 2 + 8 +\ encryption_algorithms_client_to_server_length * 2 + 8 +\ encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2 + 8 +\ mac_algorithms_server_to_client_length * 2 + 8 +\ compression_algorithms_client_to_server_length * 2 + 8 +\ compression_algorithms_server_to_client_length * 2 + 8 +\ languages_client_to_server_length * 2 + 8 +\ languages_client_to_server_length * 2 + 8:52 +\ kex_algorithms_length * 2 + 8 +\ server_host_key_algorithms_length * 2 + 8 +\ encryption_algorithms_client_to_server_length * 2 + 8 +\ encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2 + 8 +\ mac_algorithms_server_to_client_length * 2 + 8 +\ compression_algorithms_client_to_server_length * 2 +\ 8 + compression_algorithms_server_to_client_length *\ 2 + 8 + languages_client_to_server_length * 2 + 8 +\ languages_client_to_server_length * 2 + 8 +\ languages_server_to_client_length * 2]) first_kex_packet_follows_boolean = self.myresult[\ 52 + kex_algorithms_length * 2 + 8 + server_host_key_algorithms_length\ * 2 + 8 + encryption_algorithms_client_to_server_length * 2 + 8 +\ encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2 + 8 +\ mac_algorithms_server_to_client_length * 2 + 8 +\ compression_algorithms_client_to_server_length * 2 + 8 +\ compression_algorithms_server_to_client_length * 2 + 8 +\ languages_client_to_server_length * 2 + 8 +\ languages_client_to_server_length * 2 + 8 +\ languages_server_to_client_length * 2:52 +\ kex_algorithms_length * 2 + 8 + server_host_key_algorithms_length\ * 2 + 8 + encryption_algorithms_client_to_server_length * 2 + 8 +\ encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2 + 8 +\ mac_algorithms_server_to_client_length * 2 + 8 +\ compression_algorithms_client_to_server_length * 2 + 8 +\ compression_algorithms_server_to_client_length * 2 + 8 +\ languages_client_to_server_length * 2 + 8 +\ languages_client_to_server_length * 2 + 8 +\ languages_server_to_client_length * 2 + 2] reserved = self.myresult[52 + kex_algorithms_length *\ 2 + 8 + server_host_key_algorithms_length * 2 + 8 +\ encryption_algorithms_client_to_server_length * 2 + 8 +\ encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2 + 8 +\ mac_algorithms_server_to_client_length * 2 + 8 +\ compression_algorithms_client_to_server_length * 2 + 8 +\ compression_algorithms_server_to_client_length * 2 + 8 +\ languages_client_to_server_length * 2 + 8 +\ languages_client_to_server_length * 2 + 8 +\ languages_server_to_client_length * 2 + 2:52 +\ kex_algorithms_length * 2 + 8 +\ server_host_key_algorithms_length * 2 + 8 +\ encryption_algorithms_client_to_server_length * 2 + 8 +\ encryption_algorithms_server_to_client_length * 2 + 8 +\ mac_algorithms_client_to_server_length * 2 + 8 +\ mac_algorithms_server_to_client_length * 2 + 8 +\ compression_algorithms_client_to_server_length * 2 +\ 8 + compression_algorithms_server_to_client_length\ * 2 + 8 + languages_client_to_server_length * 2 +\ 8 + languages_client_to_server_length * 2 + 8 +\ languages_server_to_client_length * 2 + 2 + 8] ctosmac = mac_algorithms_client_to_server.split(",") stocmac = mac_algorithms_server_to_client.split(",") i = 0 j = 0 while i < len(ctosmac): while j < len(stocmac): if ctosmac[i].startswith(stocmac[j]): if ctosmac[i].startswith("hmac-sha1"): create_session(\ pkt.underlayer.underlayer.fields["src"], pkt.underlayer.underlayer.fields["dst"], pkt.underlayer.fields["sport"], pkt.underlayer.fields["dport"], 20) if ctosmac[i].startswith("hmac-sha1-96"): create_session(\ pkt.underlayer.underlayer.fields["src"], pkt.underlayer.underlayer.fields["dst"], pkt.underlayer.fields["sport"], pkt.underlayer.fields["dport"], 20) if ctosmac[i].startswith("hmac-md5"): create_session(\ pkt.underlayer.underlayer.fields["src"], pkt.underlayer.underlayer.fields["dst"], pkt.underlayer.fields["sport"], pkt.underlayer.fields["dport"], 16) if ctosmac[i].startswith("hmac-md5-96"): create_session(\ pkt.underlayer.underlayer.fields["src"], pkt.underlayer.underlayer.fields["dst"], pkt.underlayer.fields["sport"], pkt.underlayer.fields["dport"], 16) if ctosmac[i].startswith("none"): create_session(\ pkt.underlayer.underlayer.fields["src"], pkt.underlayer.underlayer.fields["dst"], pkt.underlayer.fields["sport"], pkt.underlayer.fields["dport"], 0) j = j + 1 i = i + 1 resultlist.append(("cookie", cookie)) resultlist.append(\ ("kex_algorithms_length", str(kex_algorithms_length))) resultlist.append(("kex_algorithms", kex_algorithms)) resultlist.append(\ ("server_host_key_algorithms_length",\ str(server_host_key_algorithms_length))) resultlist.append(\ ("server_host_key_algorithms", server_host_key_algorithms)) resultlist.append(\ ("encryption_algorithms_client_to_server_length",\ str(encryption_algorithms_client_to_server_length))) resultlist.append(\ ("encryption_algorithms_client_to_server",\ encryption_algorithms_client_to_server)) resultlist.append(\ ("encryption_algorithms_server_to_client_length",\ str(encryption_algorithms_server_to_client_length))) resultlist.append(\ ("encryption_algorithms_server_to_client",\ encryption_algorithms_server_to_client)) resultlist.append(\ ("mac_algorithms_client_to_server_length",\ str(mac_algorithms_client_to_server_length))) resultlist.append(\ ("mac_algorithms_client_to_server",\ mac_algorithms_client_to_server)) resultlist.append(\ ("mac_algorithms_server_to_client_length",\ str(mac_algorithms_server_to_client_length))) resultlist.append(("mac_algorithms_server_to_client", mac_algorithms_server_to_client)) resultlist.append(\ ("compression_algorithms_client_to_server_length", str(\ compression_algorithms_client_to_server_length))) resultlist.append(\ ("compression_algorithms_client_to_server",\ compression_algorithms_client_to_server)) resultlist.append(\ ("compression_algorithms_server_to_client_length", str(\ compression_algorithms_server_to_client_length))) resultlist.append(\ ("compression_algorithms_server_to_client",\ compression_algorithms_server_to_client)) resultlist.append(("languages_client_to_server_length", str(languages_client_to_server_length))) resultlist.append(("languages_client_to_server", languages_client_to_server)) resultlist.append(("languages_server_to_client_length", str(languages_server_to_client_length))) resultlist.append(("languages_server_to_client", languages_server_to_client)) resultlist.append(("first_kex_packet_follows_boolean", first_kex_packet_follows_boolean)) resultlist.append(("reserved", reserved)) self.found = True except Exception: #self.found = False None if not self.found and not is_encrypted_session(\ pkt.underlayer.underlayer.fields["src"], pkt.underlayer.underlayer.fields["dst"], pkt.underlayer.fields["sport"], pkt.underlayer.fields["dport"]): payload = base64.standard_b64encode(\ self.get_ascii(self.myresult[12:payloadl * 2])) resultlist.append(("payload", payload)) self.found = False if not is_encrypted_session(\ pkt.underlayer.underlayer.fields["src"],\ pkt.underlayer.underlayer.fields["dst"],\ pkt.underlayer.fields["sport"],\ pkt.underlayer.fields["dport"]): resultlist.append(("padding", padding)) if len(self.myresult) > (10 + payloadl * 2 + int(padl) * 2): resultlist.append(("MAC", self.myresult[10 + payloadl *\ 2 + int(padl) * 2:])) result_str = "" for item in resultlist: if len(result_str) == 0: result_str = item[0] + ": " + item[1] else: result_str = result_str + ", " + item[0] + ": " + item[1] return "", result_str return "", "" class SSH(Packet): """ class for handling the ssh packets @attention: this class inherets Packet """ name = "ssh" fields_desc = [SSHField("sshpayload", "")] bind_layers(TCP, SSH, dport=22) bind_layers(TCP, SSH, sport=22)

52.318065

79

0.538308

8,463

81,093

4.784001

0.043247

0.087312

0.08457

0.039124

0.851113

0.809544

0.770494

0.723911

0.702102

0.676341

0

0.034843

0.378874

81,093

1,549

80

52.35184

0.768965

0.051484

0

0.549211

0

0.059819

0.037685

0

1

0.015026

false

0.019534

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0

0.049587

0

null

0

1

0

null

0

6

0233c3488fd9dccd4d51d85f3b620c8d634b6e50

124

py

Python

src/texttest/repo_checks/test_gocdrepos.py

pagero/gocd-pipeline-builder

6db292757f15583438c2afe5b8303398629ef585

[ "MIT" ]

12

2016-01-21T21:37:17.000Z

2021-08-13T20:24:37.000Z

src/texttest/repo_checks/test_gocdrepos.py

pagero/gocd-pipeline-builder

6db292757f15583438c2afe5b8303398629ef585

[ "MIT" ]

1

2017-03-14T13:02:28.000Z

src/texttest/repo_checks/test_gocdrepos.py

pagero/gocd-pipeline-builder

6db292757f15583438c2afe5b8303398629ef585

[ "MIT" ]

5

2015-09-23T09:17:22.000Z

2019-10-07T12:32:18.000Z

# -*- coding: utf-8 -*- from __future__ import absolute_import from gocdpb import gocdpb import sys gocdpb.repos(sys.argv)

17.714286

38

0.758065

18

124

4.944444

0.611111

0.269663

0

0.009346

0.137097

124

6

39

20.666667

0.82243

0.169355

0

1

0

true

0

0.75

0

0.75

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

026f2ed903657bbd43cd041ed295e1916f2af061

115

py

Python

app/util/__init__.py

TIHLDE/Lepton

60ec0793381f1c1b222f305586e8c2d4345fb566

[ "MIT" ]

7

2021-03-04T18:49:12.000Z

2021-03-08T18:25:51.000Z

app/util/__init__.py

TIHLDE/Lepton

60ec0793381f1c1b222f305586e8c2d4345fb566

[ "MIT" ]

251

2021-03-04T19:19:14.000Z

2022-03-31T14:47:53.000Z

app/util/__init__.py

tihlde/Lepton

5cab3522c421b76373a5c25f49267cfaef7b826a

[ "MIT" ]

3

2021-10-05T19:03:04.000Z

2022-02-25T13:32:09.000Z

from app.util.enum_utils import EnumUtils from app.util.utils import now, yesterday, disable_for_loaddata, week_nr

38.333333

72

0.843478

19

115

4.894737

0.736842

0.150538

0.236559

0

0.095652

115

2

73

57.5

0.894231

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

027d009ac98880f7d298de5af5c096ca8e670643

45

py

Python

stonkclonk/__init__.py

MLH-Fellowship/kk-r1-orientation

d4f5f32c36a0d40a339798b7403c2be7f6e1cc93

[ "MIT" ]

1

2021-09-08T11:59:25.000Z

stonkclonk/__init__.py

MLH-Fellowship/stonk-clonk

d4f5f32c36a0d40a339798b7403c2be7f6e1cc93

[ "MIT" ]

3

2021-02-07T18:35:38.000Z

2021-02-08T19:21:34.000Z

stonkclonk/__init__.py

MLH-Fellowship/stonk-clonk

d4f5f32c36a0d40a339798b7403c2be7f6e1cc93

[ "MIT" ]

null

from stonkclonk.stonkclonk import StonkClonk

22.5

44

0.888889

5

45

8

0.6

0

0.088889

45

1

45

0.97561

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

0283a82a2d5783d4ec978eccfd98572cda4e2201

406

py

Python

cedar/cedar_settings.py

stewardshiptools/stewardshiptools

ee5d27e7b0d5d4947f34ad02bdf63a06ad0a5c3e

[ "MIT" ]

null

cedar/cedar_settings.py

stewardshiptools/stewardshiptools

ee5d27e7b0d5d4947f34ad02bdf63a06ad0a5c3e

[ "MIT" ]

11

2020-03-24T15:29:46.000Z

2022-03-11T23:14:48.000Z

cedar/cedar_settings.py

stewardshiptools/stewardshiptools

ee5d27e7b0d5d4947f34ad02bdf63a06ad0a5c3e

[ "MIT" ]

null

from cedar_settings.default_settings import default_settings from django.contrib.staticfiles.templatetags.staticfiles import static default_settings['cedar__default_support_url'] = ('text', 'http://www.cedarbox.ca/support/') default_settings['cedar__default_splash_page_background_img'] = ('text', static('css/cedar8_background.jpg')) default_settings['cedar__default_datepicker_years'] = ('int', 300)

40.6

109

0.817734

50

406

6.22

0.56

0.241158

0.192926

0.26045

0

0.010471

0.059113

406

9

110

45.111111

0.803665

0

0.406404

0.302956

0

1

0

true

0

0.4

0

0.4

0

null

1

0

1

0

null

0

1

0

1

0

6

ce4ad5c39f175d199e98d62a509e2bbdb108efa2

16,495

py

Python

ee/api/test/test_property_definition.py

ld-rale/posthog

0fa5b18b2e940cf5cdbe8afc733eb7e3cd4ae810

[ "MIT" ]

null

ee/api/test/test_property_definition.py

ld-rale/posthog

0fa5b18b2e940cf5cdbe8afc733eb7e3cd4ae810

[ "MIT" ]

null

ee/api/test/test_property_definition.py

ld-rale/posthog

0fa5b18b2e940cf5cdbe8afc733eb7e3cd4ae810

[ "MIT" ]

null

import urllib.parse from typing import cast import pytest from django.db.utils import IntegrityError from django.utils import timezone from rest_framework import status from ee.models.license import License, LicenseManager from ee.models.property_definition import EnterprisePropertyDefinition from posthog.models import EventProperty, Tag from posthog.models.property_definition import PropertyDefinition from posthog.test.base import APIBaseTest class TestPropertyDefinitionEnterpriseAPI(APIBaseTest): def test_can_set_and_query_property_type_and_format(self): property = EnterprisePropertyDefinition.objects.create( team=self.team, name="a timestamp", property_type="DateTime", ) response = self.client.get(f"/api/projects/@current/property_definitions/{property.id}") self.assertEqual(response.status_code, status.HTTP_200_OK) assert response.json()["property_type"] == "DateTime" query_list_response = self.client.get(f"/api/projects/@current/property_definitions") self.assertEqual(query_list_response.status_code, status.HTTP_200_OK) matches = [p["name"] for p in query_list_response.json()["results"] if p["name"] == "a timestamp"] assert len(matches) == 1 def test_errors_on_invalid_property_type(self): with pytest.raises(IntegrityError): EnterprisePropertyDefinition.objects.create( team=self.team, name="a timestamp", property_type="not an allowed option", ) def test_retrieve_existing_property_definition(self): super(LicenseManager, cast(LicenseManager, License.objects)).create( plan="enterprise", valid_until=timezone.datetime(2500, 1, 19, 3, 14, 7) ) property = EnterprisePropertyDefinition.objects.create(team=self.team, name="enterprise property") tag = Tag.objects.create(name="deprecated", team_id=self.team.id) property.tagged_items.create(tag_id=tag.id) response = self.client.get(f"/api/projects/@current/property_definitions/{property.id}") self.assertEqual(response.status_code, status.HTTP_200_OK) response_data = response.json() self.assertEqual(response_data["name"], "enterprise property") self.assertEqual(response_data["description"], "") self.assertEqual(response_data["tags"], ["deprecated"]) def test_retrieve_create_property_definition(self): super(LicenseManager, cast(LicenseManager, License.objects)).create( plan="enterprise", valid_until=timezone.datetime(2500, 1, 19, 3, 14, 7) ) property = PropertyDefinition.objects.create(team=self.team, name="property") response = self.client.get(f"/api/projects/@current/property_definitions/{property.id}") self.assertEqual(response.status_code, status.HTTP_200_OK) enterprise_property = EnterprisePropertyDefinition.objects.all().first() property.refresh_from_db() self.assertEqual(enterprise_property.propertydefinition_ptr_id, property.id) # type: ignore self.assertEqual(enterprise_property.name, property.name) # type: ignore self.assertEqual(enterprise_property.team.id, property.team.id) # type: ignore def test_search_property_definition(self): super(LicenseManager, cast(LicenseManager, License.objects)).create( plan="enterprise", valid_until=timezone.datetime(2500, 1, 19, 3, 14, 7) ) tag = Tag.objects.create(name="deprecated", team_id=self.team.id) EventProperty.objects.create(team=self.team, event="$pageview", property="enterprise property") enterprise_property = EnterprisePropertyDefinition.objects.create( team=self.team, name="enterprise property", description="" ) enterprise_property.tagged_items.create(tag_id=tag.id) other_property = EnterprisePropertyDefinition.objects.create( team=self.team, name="other property", description="" ) other_property.tagged_items.create(tag_id=tag.id) set_property = EnterprisePropertyDefinition.objects.create(team=self.team, name="$set", description="") set_property.tagged_items.create(tag_id=tag.id) response = self.client.get(f"/api/projects/@current/property_definitions/?search=enter") self.assertEqual(response.status_code, status.HTTP_200_OK) response_data = response.json() self.assertEqual(len(response_data["results"]), 1) self.assertEqual(response_data["results"][0]["name"], "enterprise property") self.assertEqual(response_data["results"][0]["description"], "") self.assertEqual(response_data["results"][0]["tags"], ["deprecated"]) response = self.client.get(f"/api/projects/@current/property_definitions/?search=enterprise") self.assertEqual(response.status_code, status.HTTP_200_OK) response_data = response.json() self.assertEqual(len(response_data["results"]), 1) # always True if not scoping by event names self.assertEqual(response_data["results"][0]["is_event_property"], None) # add event_names=['$pageview'] to get properties that have been seen by this event response = self.client.get( f"/api/projects/@current/property_definitions/?search=property&event_names=%5B%22%24pageview%22%5D" ) self.assertEqual(response.status_code, status.HTTP_200_OK) response_data = response.json() self.assertEqual(len(response_data["results"]), 2) self.assertEqual(response_data["results"][0]["name"], "enterprise property") self.assertEqual(response_data["results"][0]["is_event_property"], True) self.assertEqual(response_data["results"][1]["name"], "other property") self.assertEqual(response_data["results"][1]["is_event_property"], False) response = self.client.get(f"/api/projects/@current/property_definitions/?search=er pr") self.assertEqual(response.status_code, status.HTTP_200_OK) response_data = response.json() self.assertEqual(len(response_data["results"]), 2) response = self.client.get(f"/api/projects/@current/property_definitions/?search=bust") self.assertEqual(response.status_code, status.HTTP_200_OK) response_data = response.json() self.assertEqual(len(response_data["results"]), 0) response = self.client.get(f"/api/projects/@current/property_definitions/?search=set") self.assertEqual(response.status_code, status.HTTP_200_OK) response_data = response.json() self.assertEqual(len(response_data["results"]), 0) response = self.client.get(f"/api/projects/@current/property_definitions/?search=") self.assertEqual(response.status_code, status.HTTP_200_OK) response_data = response.json() self.assertEqual(len(response_data["results"]), 2) def test_update_property_definition(self): super(LicenseManager, cast(LicenseManager, License.objects)).create( plan="enterprise", valid_until=timezone.datetime(2038, 1, 19, 3, 14, 7) ) property = EnterprisePropertyDefinition.objects.create(team=self.team, name="enterprise property") response = self.client.patch( f"/api/projects/@current/property_definitions/{str(property.id)}/", {"description": "This is a description.", "tags": ["official", "internal"],}, ) response_data = response.json() self.assertEqual(response_data["description"], "This is a description.") self.assertEqual(response_data["updated_by"]["first_name"], self.user.first_name) self.assertEqual(set(response_data["tags"]), {"official", "internal"}) property.refresh_from_db() self.assertEqual(set(property.tagged_items.values_list("tag__name", flat=True)), {"official", "internal"}) def test_update_property_without_license(self): property = EnterprisePropertyDefinition.objects.create(team=self.team, name="enterprise property") response = self.client.patch( f"/api/projects/@current/property_definitions/{str(property.id)}/", data={"description": "test"}, ) self.assertEqual(response.status_code, status.HTTP_402_PAYMENT_REQUIRED) self.assertIn("This feature is part of the premium PostHog offering.", response.json()["detail"]) def test_with_expired_license(self): super(LicenseManager, cast(LicenseManager, License.objects)).create( plan="enterprise", valid_until=timezone.datetime(2010, 1, 19, 3, 14, 7) ) property = EnterprisePropertyDefinition.objects.create(team=self.team, name="description test") response = self.client.patch( f"/api/projects/@current/property_definitions/{str(property.id)}/", data={"description": "test"}, ) self.assertEqual(response.status_code, status.HTTP_402_PAYMENT_REQUIRED) self.assertIn("This feature is part of the premium PostHog offering.", response.json()["detail"]) def test_filter_property_definitions(self): super(LicenseManager, cast(LicenseManager, License.objects)).create( plan="enterprise", valid_until=timezone.datetime(2500, 1, 19, 3, 14, 7) ) EnterprisePropertyDefinition.objects.create(team=self.team, name="plan") EnterprisePropertyDefinition.objects.create(team=self.team, name="purchase") EnterprisePropertyDefinition.objects.create(team=self.team, name="app_rating") response = self.client.get("/api/projects/@current/property_definitions/?properties=plan,app_rating") self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.json()["count"], 2) for item in response.json()["results"]: self.assertIn(item["name"], ["plan", "app_rating"]) def test_event_property_definition_no_duplicate_tags(self): from ee.models.license import License, LicenseManager super(LicenseManager, cast(LicenseManager, License.objects)).create( key="key_123", plan="enterprise", valid_until=timezone.datetime(2038, 1, 19, 3, 14, 7), max_users=3, ) property = EnterprisePropertyDefinition.objects.create(team=self.team, name="description test") response = self.client.patch( f"/api/projects/@current/property_definitions/{str(property.id)}/", data={"tags": ["a", "b", "a"]}, ) self.assertListEqual(sorted(response.json()["tags"]), ["a", "b"]) def test_order_ids_first_filter(self): super(LicenseManager, cast(LicenseManager, License.objects)).create( plan="enterprise", valid_until=timezone.datetime(2010, 1, 19, 3, 14, 7) ) # is_first_movie, first_visit is_first_movie_property = EnterprisePropertyDefinition.objects.create(team=self.team, name="is_first_movie") first_visit_property = EnterprisePropertyDefinition.objects.create(team=self.team, name="first_visit") ids = [is_first_movie_property.id, first_visit_property.id] response = self.client.get("/api/projects/@current/property_definitions/?search=firs") self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.json()["count"], 2) # first_visit, is_first_movie self.assertEqual(response.json()["results"][0]["name"], "first_visit") self.assertEqual(response.json()["results"][1]["name"], "is_first_movie") order_ids_first_str = f'["{str(ids[0])}"]' response = self.client.get( f'/api/projects/@current/property_definitions/?search=firs&{urllib.parse.urlencode({"order_ids_first": order_ids_first_str})}' ) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.json()["count"], 2) self.assertEqual(response.json()["results"][0]["id"], str(ids[0])) # Test that included id is first item self.assertEqual(response.json()["results"][0]["name"], "is_first_movie") response = self.client.get( f'/api/projects/@current/property_definitions/?search=firs&{urllib.parse.urlencode({"order_ids_first": []})}' ) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.json()["count"], 2) # first_visit, is_first_movie self.assertEqual(response.json()["results"][0]["name"], "first_visit") self.assertEqual(response.json()["results"][1]["name"], "is_first_movie") def test_excluded_ids_filter(self): super(LicenseManager, cast(LicenseManager, License.objects)).create( plan="enterprise", valid_until=timezone.datetime(2010, 1, 19, 3, 14, 7) ) # is_first_movie, first_visit is_first_movie_property = EnterprisePropertyDefinition.objects.create(team=self.team, name="is_first_movie") first_visit_property = EnterprisePropertyDefinition.objects.create(team=self.team, name="first_visit") ids = [is_first_movie_property.id, first_visit_property.id] response = self.client.get("/api/projects/@current/property_definitions/?search=firs") self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.json()["count"], 2) # first_visit, is_first_movie self.assertEqual(response.json()["results"][0]["name"], "first_visit") self.assertEqual(response.json()["results"][1]["name"], "is_first_movie") excluded_ids_str = f'["{str(ids[0])}"]' response = self.client.get( f'/api/projects/@current/property_definitions/?search=firs&{urllib.parse.urlencode({"excluded_ids": excluded_ids_str})}' ) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.json()["count"], 1) self.assertEqual(response.json()["results"][0]["id"], str(ids[1])) self.assertEqual(response.json()["results"][0]["name"], "first_visit") response = self.client.get( f'/api/projects/@current/property_definitions/?search=firs&{urllib.parse.urlencode({"excluded_ids": []})}' ) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.json()["count"], 2) # first_visit, is_first_movie self.assertEqual(response.json()["results"][0]["name"], "first_visit") self.assertEqual(response.json()["results"][1]["name"], "is_first_movie") def test_order_ids_first_overrides_excluded_ids_filter(self): super(LicenseManager, cast(LicenseManager, License.objects)).create( plan="enterprise", valid_until=timezone.datetime(2010, 1, 19, 3, 14, 7) ) # is_first_movie, first_visit is_first_movie_property = EnterprisePropertyDefinition.objects.create(team=self.team, name="is_first_movie") first_visit_property = EnterprisePropertyDefinition.objects.create(team=self.team, name="first_visit") ids = [is_first_movie_property.id, first_visit_property.id] response = self.client.get("/api/projects/@current/property_definitions/?search=firs") self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.json()["count"], 2) # first_visit, is_first_movie self.assertEqual(response.json()["results"][0]["name"], "first_visit") self.assertEqual(response.json()["results"][1]["name"], "is_first_movie") ids_str = f'["{str(ids[0])}"]' response = self.client.get( f'/api/projects/@current/property_definitions/?search=firs&{urllib.parse.urlencode({"excluded_ids": ids_str, "order_ids_first": ids_str})}' ) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.json()["count"], 2) self.assertEqual(response.json()["results"][0]["id"], str(ids[0])) self.assertEqual(response.json()["results"][0]["name"], "is_first_movie") response = self.client.get( f'/api/projects/@current/property_definitions/?search=firs&{urllib.parse.urlencode({"excluded_ids": [], "order_ids_first": []})}' ) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.json()["count"], 2) # first_visit, is_first_movie self.assertEqual(response.json()["results"][0]["name"], "first_visit") self.assertEqual(response.json()["results"][1]["name"], "is_first_movie")

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ce7f099ef37b6fb26a3014bad92553275b1aedbb

117

py

Python

tests/test_poly.py

appeltel/smath2018b

713ab0c14e86e2b028efb7f29156216226cb9aa2

[ "MIT" ]

null

tests/test_poly.py

appeltel/smath2018b

713ab0c14e86e2b028efb7f29156216226cb9aa2

[ "MIT" ]

null

tests/test_poly.py

appeltel/smath2018b

713ab0c14e86e2b028efb7f29156216226cb9aa2

[ "MIT" ]

null

""" Tests for the poly module """ from smath2018b.poly import square def test_square(): assert square(4) == 16

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6

ced0c9214adf612de961eeb0c70c0f1144ab432d

578

py

Python

ds2/disjointsets/__init__.py

aslisabanci/datastructures

f7952801245bc8d386a03d92a38121f558bdacca

[ "MIT" ]

159

2017-10-02T22:03:14.000Z

2022-03-10T23:02:22.000Z

ds2/disjointsets/__init__.py

aslisabanci/datastructures

f7952801245bc8d386a03d92a38121f558bdacca

[ "MIT" ]

9

2019-02-04T14:55:09.000Z

2021-06-05T13:30:28.000Z

ds2/disjointsets/__init__.py

aslisabanci/datastructures

f7952801245bc8d386a03d92a38121f558bdacca

[ "MIT" ]

49

2017-09-29T17:51:16.000Z

2022-03-10T23:12:17.000Z

from ds2.disjointsets.disjointsets import ( DisjointSetsMapping, DisjointSetsLabels, DisjointSetsForest, DisjointSetsPathCompression, DisjointSetsTwoPassPC, DisjointSetsMergeByHeight, DisjointSetsMergeByWeight, DisjointSets )

57.8

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78

py

Python

tests/integration/testdata/buildcmd/Provided/main.py

renanmontebelo/aws-sam-cli

b5cfc46aa9726b5cd006df8ecc08d1b4eedeb9ea

[ "BSD-2-Clause", "Apache-2.0" ]

2,959

2018-05-08T21:48:56.000Z

2020-08-24T14:35:39.000Z

tests/integration/testdata/buildcmd/Provided/main.py

renanmontebelo/aws-sam-cli

b5cfc46aa9726b5cd006df8ecc08d1b4eedeb9ea

[ "BSD-2-Clause", "Apache-2.0" ]

1,469

2018-05-08T22:44:28.000Z

2020-08-24T20:19:24.000Z

tests/integration/testdata/buildcmd/Provided/main.py

renanmontebelo/aws-sam-cli

b5cfc46aa9726b5cd006df8ecc08d1b4eedeb9ea

[ "BSD-2-Clause", "Apache-2.0" ]

642

2018-05-08T22:09:19.000Z

2020-08-17T09:04:37.000Z

import requests def handler(event, context): return requests.__version__

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null

0

1

0

1

0

6

0c854d40c9b64db59e0fdb632d28f7965488f92e

113

py

Python

unclassifiers/config.py

infralight/terraform-unclassifier

edb81b86f1c53eb1a3aaf37ca59d054e2145e1e7

[ "Apache-2.0" ]

5

2021-01-11T10:04:32.000Z

2021-06-07T11:19:51.000Z

unclassifiers/config.py

infralight/terraform-unclassifier

edb81b86f1c53eb1a3aaf37ca59d054e2145e1e7

[ "Apache-2.0" ]

1

2021-01-07T15:40:08.000Z

unclassifiers/config.py

infralight/terraform-unclassifier

edb81b86f1c53eb1a3aaf37ca59d054e2145e1e7

[ "Apache-2.0" ]

1

2021-01-07T14:38:41.000Z

class Config: def __init__(self, classified_types: [str]): self.classified_types = classified_types

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null

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1

0

1

0

6

0c907939bf6b0f83e1b6efdbc468290650d64ec6

143

py

Python

Exercicios/Ex_013.py

jotmar/PythonEx

bf026518ae5479d5c99ff7a4e95fc383dec22d36

[ "MIT" ]

null

Exercicios/Ex_013.py

jotmar/PythonEx

bf026518ae5479d5c99ff7a4e95fc383dec22d36

[ "MIT" ]

null

Exercicios/Ex_013.py

jotmar/PythonEx

bf026518ae5479d5c99ff7a4e95fc383dec22d36

[ "MIT" ]

null

sal = float(input('Qual é o salário do funcionário? R$')) print(f'Após o aumento de 15%, ele passou a receber R${sal + (sal * 15 / 100):.2f}')

47.666667

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6

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py

Python

src/reinforcement/__init__.py

helenapoleri/reinforcement-filesystem

3fb839c07563384adba4abdd0fec61ebf76a2530

[ "BSD-2-Clause" ]

null

src/reinforcement/__init__.py

helenapoleri/reinforcement-filesystem

3fb839c07563384adba4abdd0fec61ebf76a2530

[ "BSD-2-Clause" ]

null

src/reinforcement/__init__.py

helenapoleri/reinforcement-filesystem

3fb839c07563384adba4abdd0fec61ebf76a2530

[ "BSD-2-Clause" ]

null

from .agent import * from .reinforcement import * from .environment import * from .config import *

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6

4900f7d7885cbe751f57d2b1cf8aabc4fd41374c

145

py

Python

pbj/electrostatics/pb_formulation/__init__.py

kstylesc/PBJ

0a4440b684c1d028341762a275fb3d51956b8301

[ "MIT" ]

null

pbj/electrostatics/pb_formulation/__init__.py

kstylesc/PBJ

0a4440b684c1d028341762a275fb3d51956b8301

[ "MIT" ]

null

pbj/electrostatics/pb_formulation/__init__.py

kstylesc/PBJ

0a4440b684c1d028341762a275fb3d51956b8301

[ "MIT" ]

null

import pbj.electrostatics.pb_formulation.formulations as formulations import pbj.electrostatics.pb_formulation.preconditioning as preconditioning

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145

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6

0b5880aa74802cffbdb7fb9d7350bd21f6e55f4c

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py

Python

okr/migrations/0031_auto_20201207_1204.py

wdr-data/wdr-okr

71c9e6e8d3521b1bb67d30310a93584389de2127

[ "MIT" ]

2

2021-07-28T08:46:13.000Z

2022-01-19T17:05:48.000Z

okr/migrations/0031_auto_20201207_1204.py

wdr-data/wdr-okr

71c9e6e8d3521b1bb67d30310a93584389de2127

[ "MIT" ]

3

2020-11-10T23:34:17.000Z

2021-03-31T16:19:21.000Z

okr/migrations/0031_auto_20201207_1204.py

wdr-data/wdr-okr

71c9e6e8d3521b1bb67d30310a93584389de2127

[ "MIT" ]

null

# Generated by Django 3.1.3 on 2020-12-07 11:04 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ("okr", "0030_auto_20201126_1641"), ] operations = [ migrations.AlterField( model_name="insta", name="name", field=models.CharField( help_text="Name des Accounts", max_length=200, verbose_name="Name" ), ), migrations.AlterField( model_name="insta", name="quintly_profile_id", field=models.IntegerField(verbose_name="Quintly Profil-ID"), ), migrations.AlterField( model_name="instacollaboration", name="collaboration_type", field=models.ForeignKey( help_text="Bezeichnung der Art von Collaboration", null=True, on_delete=django.db.models.deletion.SET_NULL, related_name="collaboration", to="okr.instacollaborationtype", verbose_name="Format", ), ), migrations.AlterField( model_name="instacollaborationtype", name="name", field=models.CharField( help_text="Bezeichnung der Art von Collaboration", max_length=200, verbose_name="Name", ), ), migrations.AlterField( model_name="instainsight", name="insta", field=models.ForeignKey( help_text="Globale ID des Instagram-Accounts", on_delete=django.db.models.deletion.CASCADE, related_name="insights", related_query_name="insight", to="okr.insta", verbose_name="Instagram-Account", ), ), migrations.AlterField( model_name="instainsight", name="interval", field=models.CharField( choices=[ ("daily", "Täglich"), ("weekly", "Wöchentlich"), ("monthly", "Monatlich"), ], help_text="Intervall (täglich, wöchentlich oder monatlich)", max_length=10, verbose_name="Zeitraum", ), ), migrations.AlterField( model_name="instapost", name="comments", field=models.IntegerField( help_text="Anzahl der Kommentare", verbose_name="Kommentare" ), ), migrations.AlterField( model_name="instapost", name="created_at", field=models.DateTimeField(verbose_name="Erstellungsdatum"), ), migrations.AlterField( model_name="instapost", name="external_id", field=models.CharField( max_length=25, unique=True, verbose_name="Externe ID" ), ), migrations.AlterField( model_name="instapost", name="insta", field=models.ForeignKey( help_text="Globale ID des Instagram-Accounts", on_delete=django.db.models.deletion.CASCADE, related_name="posts", related_query_name="post", to="okr.insta", verbose_name="Instagram-Account", ), ), migrations.AlterField( model_name="instapost", name="likes", field=models.IntegerField( help_text="Anzahl der Likes", verbose_name="Likes" ), ), migrations.AlterField( model_name="instapost", name="link", field=models.URLField(help_text="URL des Postings", verbose_name="Link"), ), migrations.AlterField( model_name="instapost", name="message", field=models.TextField( help_text="Volltext des Postings", verbose_name="Text" ), ), migrations.AlterField( model_name="instapost", name="post_type", field=models.CharField( help_text="Art des Postings (Image, Carousel, etc)", max_length=20, verbose_name="Typ", ), ), migrations.AlterField( model_name="instastory", name="caption", field=models.TextField( help_text="Volltext des Story-Elements", null=True, verbose_name="Text" ), ), migrations.AlterField( model_name="instastory", name="created_at", field=models.DateTimeField(verbose_name="Erstellungszeitpunkt"), ), migrations.AlterField( model_name="instastory", name="exits", field=models.IntegerField( help_text="Anzahl der Ausstiege", verbose_name="Exits" ), ), migrations.AlterField( model_name="instastory", name="external_id", field=models.CharField( max_length=25, unique=True, verbose_name="Externe ID" ), ), migrations.AlterField( model_name="instastory", name="insta", field=models.ForeignKey( help_text="Globale ID des Instagram-Accounts", on_delete=django.db.models.deletion.CASCADE, related_name="stories", related_query_name="story", to="okr.insta", verbose_name="Instagram-Account", ), ), migrations.AlterField( model_name="instastory", name="link", field=models.URLField( help_text="URL des Story-Elements", max_length=1024, verbose_name="Link" ), ), migrations.AlterField( model_name="instastory", name="replies", field=models.IntegerField( help_text="Anzahl der Antworten", verbose_name="Antworten" ), ), migrations.AlterField( model_name="instastory", name="story_type", field=models.CharField( help_text="Art des Story-Elements (Image/Video)", max_length=200, verbose_name="Typ", ), ), migrations.AlterField( model_name="podcast", name="name", field=models.CharField( help_text="Name des Accounts", max_length=200, verbose_name="Name" ), ), migrations.AlterField( model_name="property", name="name", field=models.CharField( help_text="Name des Accounts", max_length=200, verbose_name="Name" ), ), migrations.AlterField( model_name="youtube", name="name", field=models.CharField( help_text="Name des Accounts", max_length=200, verbose_name="Name" ), ), migrations.AlterField( model_name="youtube", name="quintly_profile_id", field=models.IntegerField(verbose_name="Quintly Profil-ID"), ), migrations.AlterField( model_name="youtubeagerangeaverageviewduration", name="interval", field=models.CharField( choices=[ ("daily", "Täglich"), ("weekly", "Wöchentlich"), ("monthly", "Monatlich"), ], help_text="Intervall (täglich, wöchentlich, monatlich)", max_length=10, verbose_name="Zeitraum", ), ), migrations.AlterField( model_name="youtubeagerangeaverageviewpercentage", name="interval", field=models.CharField( choices=[ ("daily", "Täglich"), ("weekly", "Wöchentlich"), ("monthly", "Monatlich"), ], help_text="Intervall (täglich, wöchentlich, monatlich)", max_length=10, verbose_name="Zeitraum", ), ), migrations.AlterField( model_name="youtubeagerangeviewspercentage", name="interval", field=models.CharField( choices=[ ("daily", "Täglich"), ("weekly", "Wöchentlich"), ("monthly", "Monatlich"), ], help_text="Intervall (täglich, wöchentlich, monatlich)", max_length=10, verbose_name="Zeitraum", ), ), migrations.AlterField( model_name="youtubeagerangewatchtimepercentage", name="interval", field=models.CharField( choices=[ ("daily", "Täglich"), ("weekly", "Wöchentlich"), ("monthly", "Monatlich"), ], help_text="Intervall (täglich, wöchentlich, monatlich)", max_length=10, verbose_name="Zeitraum", ), ), migrations.AlterField( model_name="youtubeanalytics", name="interval", field=models.CharField( choices=[ ("daily", "Täglich"), ("weekly", "Wöchentlich"), ("monthly", "Monatlich"), ], help_text="Intervall (täglich, wöchentlich, monatlich)", max_length=10, verbose_name="Zeitraum", ), ), migrations.AlterField( model_name="youtubeanalytics", name="youtube", field=models.ForeignKey( help_text="Globale ID des YouTube-Accouts", on_delete=django.db.models.deletion.CASCADE, related_name="analytic", related_query_name="analytics", to="okr.youtube", verbose_name="YouTube-Account", ), ), migrations.AlterField( model_name="youtubetrafficsource", name="youtube", field=models.ForeignKey( help_text="Globale ID des YouTube-Accounts", on_delete=django.db.models.deletion.CASCADE, related_name="traffic_source", related_query_name="traffic_sources", to="okr.youtube", verbose_name="YouTube-Account", ), ), ]

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0.497703

848

10,886

6.214623

0.159198

0.125237

0.156546

0.181594

0.810626

0.798861

0.713283

0.583491

0.550854

0.526186

0

0.010769

0.39436

10,886

313

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0.788564

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0

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null

0

1

0

1

0

null

0

6

0b59aeb914ac6cabaf5352fbc88af4f8ce6a230e

3,583

py

Python

data_loader.py

RohitGandikota/Satellite-Images-to-thematic-maps-using-Generative-Adversarial-Networks.

39f67614bb6b5a9e52fb286901b0b5832468b486

[ "MIT" ]

null

data_loader.py

RohitGandikota/Satellite-Images-to-thematic-maps-using-Generative-Adversarial-Networks.

39f67614bb6b5a9e52fb286901b0b5832468b486

[ "MIT" ]

null

data_loader.py

RohitGandikota/Satellite-Images-to-thematic-maps-using-Generative-Adversarial-Networks.

39f67614bb6b5a9e52fb286901b0b5832468b486

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Mon Sep 9 13:27:16 2019 @author: Rohit Gandikota and Radha Krishna """ import os import numpy as np from osgeo import gdal #datagen = ImageDataGenerator() #TASK TO DO. #THERE ARE TWO IMAGES TO LOAD HERE. 1 IS THE MAIN SAT IMAGE AND THE OTHER IS THE WATER IMAGE. def load_data(batch_size=1, is_testing=False): data_type = "train" if not is_testing else "test" data_main='C:\\Users\\user\\Desktop\\Projects\\ImageTileFD\\data_roads\\' data_sat='C:\\Users\\user\\Desktop\\Projects\\ImageTileFD\\data_roads\\Data\\' data_water='C:\\Users\\user\\Desktop\\Projects\\ImageTileFD\\data_roads\\Labels\\' images_water = os.listdir(data_water) images_sat = os.listdir(data_sat) args = np.intersect1d(images_water, images_sat) batch_images = np.random.choice(args, size=batch_size) sat_data = [] water_data = [] for img_path in batch_images: sat_img = gdal.Open(data_sat+img_path).ReadAsArray() water_img=gdal.Open(data_water+img_path).ReadAsArray() water_img[water_img!=water_img]= 0 water_img[water_img>0] = 1 sat_img = np.einsum('ijk->jki', sat_img) sat_img = (sat_img - sat_img.min()) / (sat_img.max() - sat_img.min()) pad = np.zeros((256,256,3)) pad_w = np.zeros((256,256)) pad[:220,:220,:]=sat_img pad_w[:220,:220]=water_img # sat_img = (np.zeros(256,256,3)[:220,:220]=sat_img) sat_data.append(pad) water_data.append(pad_w) water_data = np.array(water_data) water_data = np.expand_dims(water_data, axis=-1) sat_data = np.array(sat_data) return water_data,sat_data def load_batch(batch_size=1, is_testing=False): data_type = "train" if not is_testing else "test" data_main='C:\\Users\\user\\Desktop\\Projects\\ImageTileFD\\data_roads\\' data_sat='C:\\Users\\user\\Desktop\\Projects\\ImageTileFD\\data_roads\\Data\\' data_water='C:\\Users\\user\\Desktop\\Projects\\ImageTileFD\\data_roads\\Labels\\' images_water = os.listdir(data_water) images_sat = os.listdir(data_sat) args = np.intersect1d(images_water, images_sat) #batch_images = np.random.choice(os.listdir(data_sat), size=batch_size) n_batches = int(len(args) / batch_size) for i in range(n_batches-1): batch_images = args[i*batch_size:(i+1)*batch_size] sat_data = [] water_data = [] for img_path in batch_images: # print(data_sat+img_path sat_img = gdal.Open(data_sat+img_path).ReadAsArray() water_img=gdal.Open(data_water+img_path).ReadAsArray() water_img[water_img!=water_img]= 0 water_img[water_img>0] = 1 sat_img = np.einsum('ijk->jki', sat_img) sat_img = (sat_img - sat_img.min()) / (sat_img.max() - sat_img.min()) pad = np.zeros((256,256,3)) pad_w = np.zeros((256,256)) pad[:220,:220,:]=sat_img pad_w[:220,:220]=water_img # sat_img = (np.zeros(256,256,3)[:220,:220]=sat_img) sat_data.append(pad) water_data.append(pad_w) water_data = np.array(water_data) water_data = np.expand_dims(water_data, axis=-1) sat_data = np.array(sat_data) yield water_data,sat_data ##print(load_data(batch_size=10)) #image_generator=load_batch(batch_size=500) #water_data, sat_data=next(image_generator) ##

38.526882

94

0.623779

523

3,583

4.015296

0.202677

0.071429

0.034286

0.048571

0.759048

0

0.039503

0.236952

3,583

93

95

38.526882

0.728603

0.161596

0

0.819672

0

0.147994

0.136238

0

1

0.032787

false

0

0.04918

0

0.098361

0

null

0

1

0

1

0

null

0

6

0bb5b3ab31f02a54ad08292ec334e5898681ede8

246

py

Python

jms_estimator/__init__.py

jameshtwose/learning_CICD

ef91c547683f76af57196f7bc548ee9562b79563

[ "BSD-3-Clause" ]

null

jms_estimator/__init__.py

jameshtwose/learning_CICD

ef91c547683f76af57196f7bc548ee9562b79563

[ "BSD-3-Clause" ]

null

jms_estimator/__init__.py

jameshtwose/learning_CICD

ef91c547683f76af57196f7bc548ee9562b79563

[ "BSD-3-Clause" ]

null

from .jms_estimator import JmsEstimator from .jms_estimator import JmsClassifier from .jms_estimator import JmsTransformer from .version import __version__ __all__ = ['JmsEstimator', 'JmsClassifier', 'JmsTransformer', '__version__']

27.333333

61

0.784553

24

246

7.416667

0.375

0.117978

0.269663

0.370787

0

0.142276

246

8

62

30.75

0.843602

0

0.203252

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

0bc1f75fc4d25aa7a124dd98160c743cc6e66f22

23

py

Python

textar/__init__.py

sreecodeslayer/textar

9d61b5d8b78b5f736d5795ec09da55ad7ba730de

[ "MIT" ]

1

2019-11-27T20:18:05.000Z

textar/__init__.py

sreecodeslayer/textar

9d61b5d8b78b5f736d5795ec09da55ad7ba730de

[ "MIT" ]

null

textar/__init__.py

sreecodeslayer/textar

9d61b5d8b78b5f736d5795ec09da55ad7ba730de

[ "MIT" ]

null

from .api import Textar

23

0.826087

4

23

4.75

1

0

0.130435

23

1

23

0.95

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

e7e89feda6cbc92ffffa1533a134f3b2479d6cf0

233

py

Python

baekjoon/10171.py

GihwanKim/Baekjoon

52eb2bf80bb1243697858445e5b5e2d50d78be4e

[ "MIT" ]

null

baekjoon/10171.py

GihwanKim/Baekjoon

52eb2bf80bb1243697858445e5b5e2d50d78be4e

[ "MIT" ]

null

baekjoon/10171.py

GihwanKim/Baekjoon

52eb2bf80bb1243697858445e5b5e2d50d78be4e

[ "MIT" ]

null

""" 10171 : 고양이 URL : https://www.acmicpc.net/problem/10171 Input : Output : \ /\ ) ( ') ( / ) \(__)| """ print("\\ /\\") print(" ) ( ')") print("( / )") print(" \\(__)|")

14.5625

47

0.32618

15

233

4.8

0.733333

0.416667

0

0.071429

0.399142

233

15

48

15.533333

0.442857

0.519313

0

0.432432

0

1

0

true

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

e7f6db0654aef633d36ddd147a5e0e1ebdd54991

15,411

py

Python

tests/test_filter_abund.py

Dmarch28/khmer

86ce40a6619fc6f6e9c4ce18ce1e89de93ba2f83

[ "CNRI-Python" ]

null

tests/test_filter_abund.py

Dmarch28/khmer

86ce40a6619fc6f6e9c4ce18ce1e89de93ba2f83

[ "CNRI-Python" ]

4

2021-03-19T08:45:22.000Z

2022-02-18T21:25:42.000Z

tests/test_filter_abund.py

Dmarch28/khmer

86ce40a6619fc6f6e9c4ce18ce1e89de93ba2f83

[ "CNRI-Python" ]

1

2021-03-16T12:01:37.000Z

# This file is part of khmer, https://github.com/dib-lab/khmer/, and is # Copyright (C) 2016, The Regents of the University of California. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above # copyright notice, this list of conditions and the following # disclaimer in the documentation and/or other materials provided # with the distribution. # # * Neither the name of the Michigan State University nor the names # of its contributors may be used to endorse or promote products # derived from this software without specific prior written # permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT # HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # # Contact: khmer-project@idyll.org # pylint: disable=missing-docstring import os import khmer import screed from . import khmer_tst_utils as utils from .test_scripts import _make_counting def test_filter_abund_1(): script = 'filter-abund.py' infile = utils.copy_test_data('test-abund-read-2.fa') n_infile = utils.copy_test_data('test-fastq-n-reads.fq') in_dir = os.path.dirname(infile) n_in_dir = os.path.dirname(n_infile) counting_ht = _make_counting(infile, K=17) n_counting_ht = _make_counting(n_infile, K=17) args = [counting_ht, infile] utils.runscript(script, args, in_dir) outfile = infile + '.abundfilt' n_outfile = n_infile + '.abundfilt' n_outfile2 = n_infile + '2.abundfilt' assert os.path.exists(outfile), outfile seqs = set([r.sequence for r in screed.open(outfile)]) assert len(seqs) == 1, seqs assert 'GGTTGACGGGGCTCAGGG' in seqs args = [n_counting_ht, n_infile] utils.runscript(script, args, n_in_dir) seqs = set([r.sequence for r in screed.open(n_infile)]) assert os.path.exists(n_outfile), n_outfile args = [n_counting_ht, n_infile, '-o', n_outfile2] utils.runscript(script, args, in_dir) assert os.path.exists(n_outfile2), n_outfile2 def test_filter_abund_2(): infile = utils.copy_test_data('test-abund-read-2.fa') in_dir = os.path.dirname(infile) counting_ht = _make_counting(infile, K=17) script = 'filter-abund.py' args = ['-C', '1', counting_ht, infile, infile] utils.runscript(script, args, in_dir) outfile = infile + '.abundfilt' assert os.path.exists(outfile), outfile seqs = set([r.sequence for r in screed.open(outfile)]) assert len(seqs) == 2, seqs assert 'GGTTGACGGGGCTCAGGG' in seqs def test_filter_abund_2_stdin(): infile = utils.copy_test_data('test-abund-read-2.fa') in_dir = os.path.dirname(infile) counting_ht = _make_counting(infile, K=17) script = 'filter-abund.py' args = ['-C', '1', counting_ht, '-'] (status, out, err) = utils.runscript(script, args, in_dir, fail_ok=True) assert status == 1 assert "Accepting input from stdin; output filename must be provided" \ in str(err) def test_filter_abund_2_stdin_gzip_out(): infile = utils.copy_test_data('test-abund-read-2.fa') in_dir = os.path.dirname(infile) outfile = utils.get_temp_filename('out.fa.gz') counting_ht = _make_counting(infile, K=17) script = 'filter-abund.py' args = ['-C', '1', counting_ht, infile, '-o', outfile, '--gzip'] (status, out, err) = utils.runscript(script, args, in_dir, fail_ok=True) print(out) print(err) assert status == 0 # make sure that FASTQ records are retained. def test_filter_abund_3_fq_retained(): infile = utils.copy_test_data('test-abund-read-2.fq') in_dir = os.path.dirname(infile) counting_ht = _make_counting(infile, K=17) script = 'filter-abund.py' args = ['-C', '1', counting_ht, infile, infile] utils.runscript(script, args, in_dir) outfile = infile + '.abundfilt' assert os.path.exists(outfile), outfile seqs = set([r.sequence for r in screed.open(outfile)]) assert len(seqs) == 2, seqs assert 'GGTTGACGGGGCTCAGGG' in seqs # check for 'quality' string. quals = set([r.quality for r in screed.open(outfile)]) assert len(quals) == 2, quals assert '##################' in quals # make sure that FASTQ names are properly parsed, both formats. def test_filter_abund_4_fq_casava_18(): infile = utils.copy_test_data('test-abund-read-2.paired2.fq') in_dir = os.path.dirname(infile) counting_ht = _make_counting(infile, K=17) script = 'filter-abund.py' args = [counting_ht, infile, infile] utils.runscript(script, args, in_dir) outfile = infile + '.abundfilt' assert os.path.exists(outfile), outfile seqs = set([r.name for r in screed.open(outfile)]) assert 'pair:foo 1::N' in seqs, seqs def test_filter_abund_1_singlefile(): infile = utils.copy_test_data('test-abund-read-2.fa') in_dir = os.path.dirname(infile) script = 'filter-abund-single.py' args = ['-x', '1e7', '-N', '2', '-k', '17', infile] (status, out, err) = utils.runscript(script, args, in_dir) assert 'Total number of unique k-mers: 98' in err, err outfile = infile + '.abundfilt' assert os.path.exists(outfile), outfile seqs = set([r.sequence for r in screed.open(outfile)]) assert len(seqs) == 1, seqs assert 'GGTTGACGGGGCTCAGGG' in seqs def test_filter_abund_1_singlefile_long_k(): infile = utils.copy_test_data('test-abund-read-2.fa') in_dir = os.path.dirname(infile) script = 'filter-abund-single.py' args = ['-x', '1e7', '-N', '2', '-k', '35', '-H', 'murmur', infile] (status, out, err) = utils.runscript(script, args, in_dir) assert 'Total number of unique k-mers: 80' in err, err outfile = infile + '.abundfilt' assert os.path.exists(outfile), outfile seqs = set([r.sequence for r in screed.open(outfile)]) assert len(seqs) == 0 def test_filter_abund_1_singlefile_long_k_nosave(): infile = utils.copy_test_data('test-abund-read-2.fa') in_dir = os.path.dirname(infile) script = 'filter-abund-single.py' args = ['-x', '1e7', '-N', '2', '-k', '35', '-H', 'murmur', infile, '--savegraph', 'foo'] (status, out, err) = utils.runscript(script, args, in_dir, fail_ok=True) print(out) print(err) assert status == 1 assert 'ERROR: cannot save different hash functions yet.' in err def test_filter_abund_2_singlefile(): infile = utils.copy_test_data('test-abund-read-2.fa') in_dir = os.path.dirname(infile) tabfile = utils.get_temp_filename('test-savegraph.ct') script = 'filter-abund-single.py' args = ['-x', '1e7', '-N', '2', '-k', '17', '--savegraph', tabfile, infile] (status, out, err) = utils.runscript(script, args, in_dir) assert 'Total number of unique k-mers: 98' in err, err outfile = infile + '.abundfilt' assert os.path.exists(outfile), outfile seqs = set([r.sequence for r in screed.open(outfile)]) assert len(seqs) == 1, seqs assert 'GGTTGACGGGGCTCAGGG' in seqs def test_filter_abund_2_singlefile_fq_casava_18(): infile = utils.copy_test_data('test-abund-read-2.paired2.fq') in_dir = os.path.dirname(infile) script = 'filter-abund-single.py' args = ['-x', '1e7', '-N', '2', '-k', '17', infile] (status, out, err) = utils.runscript(script, args, in_dir) outfile = infile + '.abundfilt' assert os.path.exists(outfile), outfile seqs = set([r.name for r in screed.open(outfile)]) assert 'pair:foo 1::N' in seqs, seqs def test_filter_abund_4_retain_low_abund(): # test that the -V option does not trim sequences that are low abundance infile = utils.copy_test_data('test-abund-read-2.fa') in_dir = os.path.dirname(infile) counting_ht = _make_counting(infile, K=17) script, args = ('filter-abund.py', ['-V', counting_ht, infile]) utils.runscript(script, args, in_dir) outfile = infile + '.abundfilt' assert os.path.exists(outfile), outfile seqs = set([r.sequence for r in screed.open(outfile)]) assert len(seqs) == 2, seqs assert 'GGTTGACGGGGCTCAGGG' in seqs def test_filter_abund_single_4_retain_low_abund(): # test that the -V option does not trim sequences that are low abundance infile = utils.copy_test_data('test-abund-read-2.fa') in_dir = os.path.dirname(infile) counting_ht = _make_counting(infile, K=17) script, args = ('filter-abund-single.py', ['-k', '17', '-V', infile]) utils.runscript(script, args, in_dir) outfile = infile + '.abundfilt' assert os.path.exists(outfile), outfile seqs = set([r.sequence for r in screed.open(outfile)]) assert len(seqs) == 2, seqs assert 'GGTTGACGGGGCTCAGGG' in seqs def test_filter_abund_5_trim_high_abund(): # test that the -V option *does* trim sequences that are high abundance infile = utils.copy_test_data('test-abund-read-3.fa') in_dir = os.path.dirname(infile) counting_ht = _make_counting(infile, K=17) script, args = ('filter-abund.py', ['-V', counting_ht, infile]) utils.runscript(script, args, in_dir) outfile = infile + '.abundfilt' assert os.path.exists(outfile), outfile seqs = set([r.sequence for r in screed.open(outfile)]) assert len(seqs) == 2, seqs # trimmed sequence @ error assert 'GGTTGACGGGGCTCAGGGGGCGGCTGACTCCGAGAGACAGC' in seqs def test_filter_abund_single_trim_high_abund(): # test that the -V option *does* trim sequences that are high abundance infile = utils.copy_test_data('test-abund-read-3.fa') in_dir = os.path.dirname(infile) script, args = ('filter-abund-single.py', ['-k', '17', '-V', infile]) utils.runscript(script, args, in_dir) outfile = infile + '.abundfilt' assert os.path.exists(outfile), outfile seqs = set([r.sequence for r in screed.open(outfile)]) assert len(seqs) == 2, seqs # trimmed sequence @ error assert 'GGTTGACGGGGCTCAGGGGGCGGCTGACTCCGAGAGACAGC' in seqs def test_filter_abund_6_trim_high_abund_Z(): # test that -V/-Z settings interact properly - # trimming should not happen if -Z is set high enough. infile = utils.copy_test_data('test-abund-read-3.fa') in_dir = os.path.dirname(infile) counting_ht = _make_counting(infile, K=17) for script, args in (('filter-abund.py', ['-V', '-Z', '25', counting_ht, infile]), ('filter-abund-single.py', ['-k', '17', '-V', '-Z', '25', infile])): utils.runscript(script, args, in_dir) outfile = infile + '.abundfilt' assert os.path.exists(outfile), outfile seqs = set([r.sequence for r in screed.open(outfile)]) assert len(seqs) == 2, seqs # untrimmed seq. badseq = 'GGTTGACGGGGCTCAGGGGGCGGCTGACTCCGAGAGACAGCgtgCCGCAGCTG' \ 'TCGTCAGGGGATTTCCGGGCGG' assert badseq in seqs # should be there, untrimmed def test_filter_abund_7_retain_Ns(): # check that filter-abund retains sequences with Ns, and treats them as As. infile = utils.copy_test_data('test-filter-abund-Ns.fq') in_dir = os.path.dirname(infile) # copy test file over to test.fq & load into countgraph counting_ht = _make_counting(infile, K=17) script = 'filter-abund.py' args = ['-C', '3', counting_ht, infile] utils.runscript(script, args, in_dir) outfile = infile + '.abundfilt' assert os.path.exists(outfile), outfile # test for a sequence with an 'N' in it -- names = set([r.name for r in screed.open(outfile)]) assert '895:1:37:17593:9954 1::FOO_withN' in names, names # check to see if that 'N' was properly changed to an 'A' seqs = set([r.sequence for r in screed.open(outfile)]) assert 'GGTTGACGGGGCTCAGGGGGCGGCTGACTCCGAG' not in seqs, seqs # ...and that an 'N' remains in the output sequences found_N = False for s in seqs: if 'N' in s: found_N = True assert found_N, seqs def test_filter_abund_single_8_retain_Ns(): # check that filter-abund-single retains # sequences with Ns, and treats them as As. infile = utils.copy_test_data('test-filter-abund-Ns.fq') in_dir = os.path.dirname(infile) script = 'filter-abund-single.py' args = ['-k', '17', '-x', '1e7', '-N', '2', '-C', '3', infile] utils.runscript(script, args, in_dir) outfile = infile + '.abundfilt' assert os.path.exists(outfile), outfile # test for a sequence with an 'N' in it -- names = set([r.name for r in screed.open(outfile)]) assert '895:1:37:17593:9954 1::FOO_withN' in names, names # check to see if that 'N' was properly changed to an 'A' seqs = set([r.sequence for r in screed.open(outfile)]) assert 'GGTTGACGGGGCTCAGGGGGCGGCTGACTCCGAG' not in seqs, seqs # ...and that an 'N' remains in the output sequences found_N = False for s in seqs: if 'N' in s: found_N = True assert found_N, seqs def test_outfile(): infile = utils.get_test_data('paired-mixed-witherror.fa.pe') outfile = utils.get_temp_filename('paired-mixed-witherror.fa.pe.abundfilt') script = 'filter-abund-single.py' args = ['-o', outfile, infile] (status, out, err) = utils.runscript(script, args) md5hash = utils._calc_md5(open(outfile, 'rb')) assert md5hash == 'f17122f4c0c3dc0bcc4eeb375de93040', md5hash def test_filter_abund_1_quiet(): script = 'filter-abund.py' infile = utils.copy_test_data('test-abund-read-2.fa') n_infile = utils.copy_test_data('test-fastq-n-reads.fq') in_dir = os.path.dirname(infile) n_in_dir = os.path.dirname(n_infile) counting_ht = _make_counting(infile, K=17) n_counting_ht = _make_counting(n_infile, K=17) args = ['-q', counting_ht, infile] status, out, err = utils.runscript(script, args, in_dir) assert len(err) == 0 assert len(out) < 1000 outfile = infile + '.abundfilt' n_outfile = n_infile + '.abundfilt' n_outfile2 = n_infile + '2.abundfilt' assert os.path.exists(outfile), outfile def test_filter_abund_1_singlefile_quiet(): infile = utils.copy_test_data('test-abund-read-2.fa') in_dir = os.path.dirname(infile) script = 'filter-abund-single.py' args = ['-q', '-x', '1e7', '-N', '2', '-k', '17', infile] (status, out, err) = utils.runscript(script, args, in_dir) assert len(err) == 0 assert len(out) < 1000 outfile = infile + '.abundfilt' assert os.path.exists(outfile), outfile

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null

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6

f001e8486ccf82d8397e6988168e509306e2626b

1,063

py

Python

tests/test_cli.py

developmentseed/python-seed

5a5ef1f89199595e5c4b5b1fc33c5ce0e7eb4e3d

[ "MIT" ]

16

2018-03-04T18:34:52.000Z

2021-11-03T17:36:18.000Z

tests/test_cli.py

developmentseed/python-seed

5a5ef1f89199595e5c4b5b1fc33c5ce0e7eb4e3d

[ "MIT" ]

9

2017-12-18T15:12:07.000Z

2020-10-01T17:05:36.000Z

tests/test_cli.py

developmentseed/python-seed

5a5ef1f89199595e5c4b5b1fc33c5ce0e7eb4e3d

[ "MIT" ]

6

2019-03-07T19:49:54.000Z

2022-01-20T17:57:04.000Z

"""tests python_seed.cli.""" import os from click.testing import CliRunner from python_seed.scripts.cli import pyseed def test_create(): """Test the create function""" runner = CliRunner() with runner.isolated_filesystem(): result = runner.invoke(pyseed, ["create", "myfunction"]) assert not os.path.exists("myfunction/.github/workflows/ci.yml") assert not os.path.exists("myfunction/codecov.yml") with open("myfunction/README.md", "r") as f: assert f.read().splitlines()[0] == "# myfunction" assert not result.exception assert result.exit_code == 0 with runner.isolated_filesystem(): result = runner.invoke(pyseed, ["create", "myfunction", "--ci", "github"]) assert os.path.exists("myfunction/.github/workflows/ci.yml") assert os.path.exists("myfunction/codecov.yml") with open("myfunction/README.md", "r") as f: assert f.read().splitlines()[0] == "# myfunction" assert not result.exception assert result.exit_code == 0

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py

Python

ironic_staging_drivers/tests/unit/intel_nm/test_vendor.py

NaohiroTamura/ironic-staging-drivers

cf29fd0515760eb2ecb3855359d5acc395168a9e

[ "Apache-2.0" ]

null

ironic_staging_drivers/tests/unit/intel_nm/test_vendor.py

NaohiroTamura/ironic-staging-drivers

cf29fd0515760eb2ecb3855359d5acc395168a9e

[ "Apache-2.0" ]

null

ironic_staging_drivers/tests/unit/intel_nm/test_vendor.py

NaohiroTamura/ironic-staging-drivers

cf29fd0515760eb2ecb3855359d5acc395168a9e

[ "Apache-2.0" ]

null

# Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. """ Tests for Intel NM vendor interface """ import os from ironic.common import exception from ironic.conductor import task_manager from ironic.drivers.modules import ipmitool from ironic.tests.unit.conductor import mgr_utils from ironic.tests.unit.db import base as db_base from ironic.tests.unit.objects import utils as obj_utils from ironic_lib import utils as ironic_utils import mock from oslo_config import cfg from ironic_staging_drivers.intel_nm import nm_commands from ironic_staging_drivers.intel_nm import nm_vendor CONF = cfg.CONF _MAIN_IDS = {'domain_id': 'platform', 'policy_id': 111} _POLICY = {'domain_id': 'platform', 'enable': True, 'policy_id': 111, 'policy_trigger': 'none', 'action': 'alert', 'power_domain': 'primary', 'target_limit': 100, 'correction_time': 200, 'reporting_period': 600} _SUSPEND = {'domain_id': 'platform', 'policy_id': 121, 'periods': [{'start': 10, 'stop': 30, 'days': ['monday']}]} _GET_CAP = {'domain_id': 'platform', 'policy_trigger': 'none', 'power_domain': 'primary'} _CONTROL = {'scope': 'global', 'enable': True} _STATISTICS = {'scope': 'global', 'domain_id': 'platform', 'parameter_name': 'response_time'} _VENDOR_METHODS_DATA = {'get_nm_policy': _MAIN_IDS, 'remove_nm_policy': _MAIN_IDS, 'get_nm_policy_suspend': _MAIN_IDS, 'remove_nm_policy_suspend': _MAIN_IDS, 'set_nm_policy': _POLICY, 'set_nm_policy_suspend': _SUSPEND, 'get_nm_capabilities': _GET_CAP, 'control_nm_policy': _CONTROL, 'get_nm_statistics': _STATISTICS, 'reset_nm_statistics': _STATISTICS} class IntelNMPassthruTestCase(db_base.DbTestCase): def setUp(self): super(IntelNMPassthruTestCase, self).setUp() mgr_utils.mock_the_extension_manager(driver='fake_nm') self.node = obj_utils.create_test_node(self.context, driver='fake_nm') self.temp_filename = os.path.join(CONF.tempdir, self.node.uuid + '.sdr') @mock.patch.object(ironic_utils, 'unlink_without_raise', spec_set=True, autospec=True) @mock.patch.object(ipmitool, 'send_raw', spec_set=True, autospec=True) @mock.patch.object(ipmitool, 'dump_sdr', spec_set=True, autospec=True) @mock.patch.object(nm_commands, 'parse_slave_and_channel', spec_set=True, autospec=True) def test__get_nm_address_detected(self, parse_mock, dump_mock, raw_mock, unlink_mock): parse_mock.return_value = ('0x0A', '0x0B') with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: ret = nm_vendor._get_nm_address(task) self.assertEqual(('0x0B', '0x0A'), ret) self.node.refresh() internal_info = self.node.driver_internal_info self.assertEqual('0x0A', internal_info['intel_nm_address']) self.assertEqual('0x0B', internal_info['intel_nm_channel']) parse_mock.assert_called_once_with(self.temp_filename) dump_mock.assert_called_once_with(task, self.temp_filename) unlink_mock.assert_called_once_with(self.temp_filename) raw_mock.assert_called_once_with(task, mock.ANY) @mock.patch.object(ironic_utils, 'unlink_without_raise', spec_set=True, autospec=True) @mock.patch.object(ipmitool, 'send_raw', spec_set=True, autospec=True) @mock.patch.object(ipmitool, 'dump_sdr', spec_set=True, autospec=True) @mock.patch.object(nm_commands, 'parse_slave_and_channel', spec_set=True, autospec=True) def test__get_nm_address_already_detected(self, parse_mock, dump_mock, raw_mock, unlink_mock): internal_info = self.node.driver_internal_info internal_info['intel_nm_channel'] = '0x0B' internal_info['intel_nm_address'] = '0x0A' self.node.driver_internal_info = internal_info self.node.save() with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: ret = nm_vendor._get_nm_address(task) self.assertEqual(('0x0B', '0x0A'), ret) self.assertFalse(parse_mock.called) self.assertFalse(dump_mock.called) self.assertFalse(raw_mock.called) self.assertFalse(unlink_mock.called) @mock.patch.object(ironic_utils, 'unlink_without_raise', spec_set=True, autospec=True) @mock.patch.object(ipmitool, 'send_raw', spec_set=True, autospec=True) @mock.patch.object(ipmitool, 'dump_sdr', spec_set=True, autospec=True) @mock.patch.object(nm_commands, 'parse_slave_and_channel', spec_set=True, autospec=True) def test__get_nm_address_not_detected(self, parse_mock, dump_mock, raw_mock, unlink_mock): parse_mock.return_value = None with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: self.assertRaises(exception.IPMIFailure, nm_vendor._get_nm_address, task) self.node.refresh() internal_info = self.node.driver_internal_info self.assertEqual(False, internal_info['intel_nm_address']) self.assertEqual(False, internal_info['intel_nm_channel']) parse_mock.assert_called_once_with(self.temp_filename) dump_mock.assert_called_once_with(task, self.temp_filename) unlink_mock.assert_called_once_with(self.temp_filename) self.assertFalse(raw_mock.called) @mock.patch.object(ironic_utils, 'unlink_without_raise', spec_set=True, autospec=True) @mock.patch.object(ipmitool, 'send_raw', spec_set=True, autospec=True) @mock.patch.object(ipmitool, 'dump_sdr', spec_set=True, autospec=True) @mock.patch.object(nm_commands, 'parse_slave_and_channel', spec_set=True, autospec=True) def test__get_nm_address_raw_fail(self, parse_mock, dump_mock, raw_mock, unlink_mock): parse_mock.return_value = ('0x0A', '0x0B') raw_mock.side_effect = exception.IPMIFailure('raw error') with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: self.assertRaises(exception.IPMIFailure, nm_vendor._get_nm_address, task) self.node.refresh() internal_info = self.node.driver_internal_info self.assertEqual(False, internal_info['intel_nm_address']) self.assertEqual(False, internal_info['intel_nm_channel']) parse_mock.assert_called_once_with(self.temp_filename) dump_mock.assert_called_once_with(task, self.temp_filename) unlink_mock.assert_called_once_with(self.temp_filename) raw_mock.assert_called_once_with(task, mock.ANY) @mock.patch.object(ironic_utils, 'unlink_without_raise', spec_set=True, autospec=True) @mock.patch.object(ipmitool, 'send_raw', spec_set=True, autospec=True) @mock.patch.object(ipmitool, 'dump_sdr', spec_set=True, autospec=True) @mock.patch.object(nm_commands, 'parse_slave_and_channel', spec_set=True, autospec=True) def test__get_nm_address_already_not_detected(self, parse_mock, dump_mock, raw_mock, unlink_mock): internal_info = self.node.driver_internal_info internal_info['intel_nm_channel'] = False internal_info['intel_nm_address'] = False self.node.driver_internal_info = internal_info self.node.save() with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: self.assertRaises(exception.IPMIFailure, nm_vendor._get_nm_address, task) self.assertFalse(parse_mock.called) self.assertFalse(dump_mock.called) self.assertFalse(raw_mock.called) self.assertFalse(unlink_mock.called) @mock.patch.object(ipmitool, 'send_raw', spec_set=True, autospec=True) @mock.patch.object(nm_vendor, '_get_nm_address', spec_set=True, autospec=True) def test__execute_nm_command(self, addr_mock, raw_mock): addr_mock.return_value = ('0x0A', '0x0B') raw_mock.return_value = ('0x03 0x04', '') fake_data = {'foo': 'bar'} fake_command = mock.MagicMock() fake_parse = mock.MagicMock() fake_command.return_value = ('0x01', '0x02') with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: nm_vendor._execute_nm_command(task, fake_data, fake_command, fake_parse) self.assertEqual('single', task.node.driver_info['ipmi_bridging']) self.assertEqual('0x0A', task.node.driver_info['ipmi_target_channel']) self.assertEqual('0x0B', task.node.driver_info['ipmi_target_address']) fake_command.assert_called_once_with(fake_data) raw_mock.assert_called_once_with(task, '0x01 0x02') fake_parse.assert_called_once_with(['0x03', '0x04']) @mock.patch.object(ipmitool, 'send_raw', spec_set=True, autospec=True) @mock.patch.object(nm_vendor, '_get_nm_address', spec_set=True, autospec=True) def test__execute_nm_command_no_parse(self, addr_mock, raw_mock): addr_mock.return_value = ('0x0A', '0x0B') fake_data = {'foo': 'bar'} fake_command = mock.MagicMock() fake_command.return_value = ('0x01', '0x02') with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: nm_vendor._execute_nm_command(task, fake_data, fake_command) self.assertEqual('single', task.node.driver_info['ipmi_bridging']) self.assertEqual('0x0A', task.node.driver_info['ipmi_target_channel']) self.assertEqual('0x0B', task.node.driver_info['ipmi_target_address']) fake_command.assert_called_once_with(fake_data) raw_mock.assert_called_once_with(task, '0x01 0x02') def test_validate_json(self): with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: for method, data in _VENDOR_METHODS_DATA.items(): task.driver.vendor.validate(task, method, 'fake', **data) def test_validate_json_error(self): fake_data = {'foo': 'bar'} with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: for method in _VENDOR_METHODS_DATA: self.assertRaises(exception.InvalidParameterValue, task.driver.vendor.validate, task, method, 'fake', **fake_data) def test_validate_control_no_domain(self): data = {'scope': 'domain', 'enable': True} with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: self.assertRaises(exception.MissingParameterValue, task.driver.vendor.validate, task, 'control_nm_policy', 'fake', **data) def test_validate_control_no_policy(self): data = {'scope': 'policy', 'enable': True, 'domain_id': 'platform'} with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: self.assertRaises(exception.MissingParameterValue, task.driver.vendor.validate, task, 'control_nm_policy', 'fake', **data) def test_validate_policy_boot(self): data = _POLICY.copy() del data['correction_time'] data['policy_trigger'] = 'boot' data['target_limit'] = {'boot_mode': 'power', 'cores_disabled': 2} with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: task.driver.vendor.validate(task, 'set_nm_policy', 'fake', **data) def test_validate_policy_boot_error(self): data = _POLICY.copy() data['policy_trigger'] = 'boot' with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: self.assertRaises(exception.InvalidParameterValue, task.driver.vendor.validate, task, 'set_nm_policy', 'fake', **data) def test_validate_policy_no_correction_time(self): data = _POLICY.copy() del data['correction_time'] with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: self.assertRaises(exception.MissingParameterValue, task.driver.vendor.validate, task, 'set_nm_policy', 'fake', **data) def test_validate_statistics_no_policy(self): data = {'scope': 'policy', 'domain_id': 'platform'} with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: self.assertRaises(exception.MissingParameterValue, task.driver.vendor.validate, task, 'reset_nm_statistics', 'fake', **data) def test_validate_statistics_no_domain(self): data = {'scope': 'global', 'parameter_name': 'power'} with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: self.assertRaises(exception.InvalidParameterValue, task.driver.vendor.validate, task, 'get_nm_statistics', 'fake', **data) def test_reset_statistics_invalid_parameter(self): data = {'scope': 'global', 'domain_id': 'platform', 'parameter_name': 'power'} with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: self.assertRaises(exception.InvalidParameterValue, task.driver.vendor.validate, task, 'reset_nm_statistics', 'fake', **data) def test_get_statistics_no_parameter(self): data = {'scope': 'global', 'domain_id': 'platform'} with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: self.assertRaises(exception.MissingParameterValue, task.driver.vendor.validate, task, 'get_nm_statistics', 'fake', **data) def test_get_statistics_invalid_parameter(self): data = {'scope': 'policy', 'domain_id': 'platform', 'policy_id': 111, 'parameter_name': 'response_time'} with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: self.assertRaises(exception.InvalidParameterValue, task.driver.vendor.validate, task, 'get_nm_statistics', 'fake', **data) @mock.patch.object(nm_vendor, '_execute_nm_command', spec_set=True, autospec=True) def test_control_nm_policy(self, mock_exec): with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: task.driver.vendor.control_nm_policy(task) mock_exec.assert_called_once_with(task, {}, nm_commands.control_policies) @mock.patch.object(nm_vendor, '_execute_nm_command', spec_set=True, autospec=True) def test_set_nm_policy(self, mock_exec): with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: task.driver.vendor.set_nm_policy(task) mock_exec.assert_called_once_with(task, {}, nm_commands.set_policy) @mock.patch.object(nm_vendor, '_execute_nm_command', spec_set=True, autospec=True) def test_get_nm_policy(self, mock_exec): with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: task.driver.vendor.get_nm_policy(task) mock_exec.assert_called_once_with(task, {}, nm_commands.get_policy, nm_commands.parse_policy) @mock.patch.object(nm_vendor, '_execute_nm_command', spec_set=True, autospec=True) def test_remove_nm_policy(self, mock_exec): with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: task.driver.vendor.remove_nm_policy(task) mock_exec.assert_called_once_with(task, {}, nm_commands.remove_policy) @mock.patch.object(nm_vendor, '_execute_nm_command', spec_set=True, autospec=True) def test_set_nm_policy_suspend(self, mock_exec): with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: task.driver.vendor.set_nm_policy_suspend(task) mock_exec.assert_called_once_with(task, {}, nm_commands.set_policy_suspend) @mock.patch.object(nm_vendor, '_execute_nm_command', spec_set=True, autospec=True) def test_get_nm_policy_suspend(self, mock_exec): with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: task.driver.vendor.get_nm_policy_suspend(task) mock_exec.assert_called_once_with(task, {}, nm_commands.get_policy_suspend, nm_commands.parse_policy_suspend) @mock.patch.object(nm_vendor, '_execute_nm_command', spec_set=True, autospec=True) def test_remove_nm_policy_suspend(self, mock_exec): with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: task.driver.vendor.remove_nm_policy_suspend(task) mock_exec.assert_called_once_with(task, {}, nm_commands.remove_policy_suspend ) @mock.patch.object(nm_vendor, '_execute_nm_command', spec_set=True, autospec=True) def test_get_nm_capabilities(self, mock_exec): with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: task.driver.vendor.get_nm_capabilities(task) mock_exec.assert_called_once_with(task, {}, nm_commands.get_capabilities, nm_commands.parse_capabilities) @mock.patch.object(nm_vendor, '_execute_nm_command', spec_set=True, autospec=True) def test_get_nm_version(self, mock_exec): with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: task.driver.vendor.get_nm_version(task) mock_exec.assert_called_once_with(task, {}, nm_commands.get_version, nm_commands.parse_version) @mock.patch.object(nm_vendor, '_execute_nm_command', spec_set=True, autospec=True) def test_get_nm_statistics(self, mock_exec): with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: task.driver.vendor.get_nm_statistics(task) mock_exec.assert_called_once_with(task, {}, nm_commands.get_statistics, nm_commands.parse_statistics) @mock.patch.object(nm_vendor, '_execute_nm_command', spec_set=True, autospec=True) def test_reset_nm_statistics(self, mock_exec): with task_manager.acquire(self.context, self.node.uuid, shared=False) as task: task.driver.vendor.reset_nm_statistics(task) mock_exec.assert_called_once_with(task, {}, nm_commands.reset_statistics)

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0.596741

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22,033

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0.031028

0.042421

0.053733

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0.810359

0.799612

0.777796

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0.006936

0.306404

22,033

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51.239535

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0.118919

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null

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1

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null

0

6

f0522e65d11970fa2cb606c1448581ffd3d8825e

111

py

Python

LEVEL1/가운데글자가져오기/solution.py

seunghwanly/CODING-TEST

a820da950c163d399594770199aa2e782d1fbbde

[ "MIT" ]

null

LEVEL1/가운데글자가져오기/solution.py

seunghwanly/CODING-TEST

a820da950c163d399594770199aa2e782d1fbbde

[ "MIT" ]

null

LEVEL1/가운데글자가져오기/solution.py

seunghwanly/CODING-TEST

a820da950c163d399594770199aa2e782d1fbbde

[ "MIT" ]

null

def solution(s): if len(s) % 2 != 0: return s[len(s) // 2] else: return s[len(s) // 2:len(s) // 2 + 2]

27.75

47

0.486486

23

111

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6

f0748bde1df9dfd51e53e54624473476ad1fcb7c

198

py

Python

Codewars/8kyu/object-oriented-piracy/Python/solution1.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

7

2017-09-20T16:40:39.000Z

2021-08-31T18:15:08.000Z

Codewars/8kyu/object-oriented-piracy/Python/solution1.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

null

Codewars/8kyu/object-oriented-piracy/Python/solution1.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

null

# Python - 2.7.6 class Ship: def __init__(self, draft, crew): self.draft = draft self.crew = crew def is_worth_it(self): return (self.draft - self.crew * 1.5) > 20

19.8

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null

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6

b2ea1344648a8fd31c351aeb5a944417fa666670

45

py

Python

app/tgbot/handlers/admin/user/__init__.py

AnViSe/cost_confirmation_bot

f8eaa39c3df742bef0fc79b8b7ce0231f1b18749

[ "MIT" ]

13

2021-12-27T19:46:19.000Z

2022-03-19T07:55:25.000Z

app/tgbot/handlers/admin/user/__init__.py

AnViSe/cost_confirmation_bot

f8eaa39c3df742bef0fc79b8b7ce0231f1b18749

[ "MIT" ]

null

app/tgbot/handlers/admin/user/__init__.py

AnViSe/cost_confirmation_bot

f8eaa39c3df742bef0fc79b8b7ce0231f1b18749

[ "MIT" ]

1

2022-02-07T10:48:18.000Z

from .setup import register_user_db_handlers

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6521c799635d0ce23e77f8047c619295f8220f62

219

py

Python

auto_ria_client/search.py

DSAdv/auto-ria-python

8cfbb0b475dce8e7871236c4f9c1cbf4a937383a

[ "Apache-2.0" ]

2

2021-06-15T09:17:26.000Z

2022-01-05T20:15:11.000Z

auto_ria_client/search.py

DSAdv/auto-ria-python

8cfbb0b475dce8e7871236c4f9c1cbf4a937383a

[ "Apache-2.0" ]

null

auto_ria_client/search.py

DSAdv/auto-ria-python

8cfbb0b475dce8e7871236c4f9c1cbf4a937383a

[ "Apache-2.0" ]

null

class Search: def __init__(self): pass def execute(self): pass def __repr__(self): pass def __str__(self): pass class QueryBuilder: pass class Query: pass

9.954545

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1

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1

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6

e8f5d13370c88501c5930a64f508cc704785b5dc

6,981

py

Python

hpcframework_unittest.py

Azure/hpcpack-mesos

871d48b0ab187c227edfe12b3dfaca7e6ad0dd59

[ "MIT" ]

4

2019-04-26T03:03:13.000Z

2020-06-01T14:26:09.000Z

hpcframework_unittest.py

Azure/hpcpack-mesos

871d48b0ab187c227edfe12b3dfaca7e6ad0dd59

[ "MIT" ]

2

2018-09-05T01:47:59.000Z

2018-09-05T01:49:20.000Z

hpcframework_unittest.py

Azure/hpcpack-mesos

871d48b0ab187c227edfe12b3dfaca7e6ad0dd59

[ "MIT" ]

4

2018-09-05T01:35:40.000Z

2021-01-10T10:47:12.000Z

import json import unittest from mesoshttp.offers import Offer from mock import patch, MagicMock, call import hpcframework def create_mock_mesos_offer_aux(cpus, max_cores, is_windows, hostname): json_offer = ''' {{ "hostname": "{}", "attributes": [ '''.format(hostname) if (is_windows): json_offer += ''' { "text": { "value": "windows_server" }, "type": "TEXT", "name": "os" }, ''' json_offer += ''' {{ "scalar": {{ "value": {} }}, "type": "SCALAR", "name": "cores" }} ], "resources": [ {{ "type": "SCALAR", "allocation_info": {{ "role": "*" }}, "role": "*", "name": "cpus", "scalar": {{ "value": {} }} }} ] }} '''.format(max_cores, cpus) return json.loads(json_offer) def create_mock_mesos_offer(cpus, max_cores, is_windows, hostname): return Offer("uri", "fid", "sid", create_mock_mesos_offer_aux(cpus, max_cores, is_windows, hostname)) class HpcFrameworkUnitTest(unittest.TestCase): def setUp(self): self.hpcpackFramework = hpcframework.HpcpackFramwork("", "", "", "", "", "") @patch('hpcframework.HpcpackFramwork.decline_offer') @patch('hpcframework.HpcpackFramwork.accept_offer') @patch('restclient.HpcRestClient.get_grow_decision') def test_accpet_offer(self, mock_get_grow_decision, mock_accept_offer, mock_decline_offer): mock_get_grow_decision.return_value = MagicMock(cores_to_grow=1) offer = create_mock_mesos_offer(4.0, 4.0, True, "host1") offers = [offer] self.hpcpackFramework.offer_received(offers) mock_accept_offer.assert_called_with(offer) mock_decline_offer.assert_not_called() @patch('hpcframework.HpcpackFramwork.decline_offer') @patch('hpcframework.HpcpackFramwork.accept_offer') @patch('restclient.HpcRestClient.get_grow_decision') def test_no_need_to_grow(self, mock_get_grow_decision, mock_accept_offer, mock_decline_offer): mock_get_grow_decision.return_value = MagicMock(cores_to_grow=0) offer = create_mock_mesos_offer(4.0, 4.0, True, "host1") offers = [offer] self.hpcpackFramework.offer_received(offers) mock_accept_offer.assert_not_called() mock_decline_offer.assert_called_with(offer) @patch('hpcframework.HpcpackFramwork.decline_offer') @patch('hpcframework.HpcpackFramwork.accept_offer') @patch('restclient.HpcRestClient.get_grow_decision') def test_accept_partial_offer(self, mock_get_grow_decision, mock_accept_offer, mock_decline_offer): mock_get_grow_decision.return_value = MagicMock(cores_to_grow=2) offer1 = create_mock_mesos_offer(1.0, 1.0, True, "host1") offer2 = create_mock_mesos_offer(1.0, 1.0, True, "host2") offer3 = create_mock_mesos_offer(1.0, 1.0, True, "host3") offers = [offer1, offer2, offer3] self.hpcpackFramework.offer_received(offers) calls = [call(offer1), call(offer2)] mock_accept_offer.assert_has_calls(calls) mock_decline_offer.assert_called_with(offer3) @patch('hpc_cluster_manager.HpcClusterManager.get_cores_in_provisioning') @patch('hpcframework.HpcpackFramwork.decline_offer') @patch('hpcframework.HpcpackFramwork.accept_offer') @patch('restclient.HpcRestClient.get_grow_decision') def test_accept_offer_with_provisioning(self, mock_get_grow_decision, mock_accept_offer, mock_decline_offer, mock_get_cores_in_provisioning): mock_get_grow_decision.return_value = MagicMock(cores_to_grow=5) mock_get_cores_in_provisioning.return_value = 1 offer1 = create_mock_mesos_offer(1.0, 1.0, True, "host1") offer2 = create_mock_mesos_offer(1.0, 1.0, True, "host2") offer3 = create_mock_mesos_offer(1.0, 1.0, True, "host3") offers = [offer1, offer2, offer3] self.hpcpackFramework.offer_received(offers) calls = [call(offer1), call(offer2), call(offer3)] mock_accept_offer.assert_has_calls(calls) mock_decline_offer.assert_not_called() @patch('hpc_cluster_manager.HpcClusterManager.get_cores_in_provisioning') @patch('hpcframework.HpcpackFramwork.decline_offer') @patch('hpcframework.HpcpackFramwork.accept_offer') @patch('restclient.HpcRestClient.get_grow_decision') def test_accept_partial_offer_with_provisioning(self, mock_get_grow_decision, mock_accept_offer, mock_decline_offer, mock_get_cores_in_provisioning): mock_get_grow_decision.return_value = MagicMock(cores_to_grow=2) mock_get_cores_in_provisioning.return_value = 1 offer1 = create_mock_mesos_offer(1.0, 1.0, True, "host1") offer2 = create_mock_mesos_offer(1.0, 1.0, True, "host2") offer3 = create_mock_mesos_offer(1.0, 1.0, True, "host3") offers = [offer1, offer2, offer3] self.hpcpackFramework.offer_received(offers) calls = [call(offer2), call(offer3)] mock_accept_offer.assert_called_with(offer1) mock_decline_offer.assert_has_calls(calls) @patch('hpc_cluster_manager.HpcClusterManager.check_fqdn_collision') @patch('hpc_cluster_manager.HpcClusterManager.get_cores_in_provisioning') @patch('hpcframework.HpcpackFramwork.decline_offer') @patch('hpcframework.HpcpackFramwork.accept_offer') @patch('restclient.HpcRestClient.get_grow_decision') def test_declient_offer_on_fqdn_collision(self, mock_get_grow_decision, mock_accept_offer, mock_decline_offer, mock_get_cores_in_provisioning, mock_check_fqdn_collision): mock_get_grow_decision.return_value = MagicMock(cores_to_grow=2) mock_get_cores_in_provisioning.return_value = 0 mock_check_fqdn_collision.return_value = True offer1 = create_mock_mesos_offer(1.0, 1.0, True, "host1") offers = [offer1] self.hpcpackFramework.offer_received(offers) mock_accept_offer.assert_not_called() mock_decline_offer.assert_called_with(offer1) @patch('hpcframework.HpcpackFramwork.decline_offer') @patch('hpcframework.HpcpackFramwork.accept_offer') @patch('restclient.HpcRestClient.get_grow_decision') def test_decline_non_dedicated_offer(self, mock_get_grow_decision, mock_accept_offer, mock_decline_offer): mock_get_grow_decision.return_value = MagicMock(cores_to_grow=1) offer = create_mock_mesos_offer(4.0, 5.0, True, "host1") offers = [offer] self.hpcpackFramework.offer_received(offers) mock_accept_offer.assert_not_called() mock_decline_offer.assert_called_with(offer) if __name__ == '__main__': unittest.main()

43.360248

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43.63125

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0.06993

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0.125874

0

null

0

1

0

null

0

6

33303b16e74d1aec44ae5a537aa51d8158fadf63

104

py

Python

pydynamo_brain/pydynamo_brain/ui/actions/__init__.py

ubcbraincircuits/pyDynamo

006eb6edb5e54670574dbfdf7d249e9037f01ffc

[ "MIT" ]

4

2021-12-16T22:32:47.000Z

2022-01-03T05:42:12.000Z

pydynamo_brain/pydynamo_brain/ui/actions/__init__.py

padster/pyDynamo

006eb6edb5e54670574dbfdf7d249e9037f01ffc

[ "MIT" ]

1

2021-11-15T18:14:20.000Z

2021-11-15T18:14:36.000Z

pydynamo_brain/pydynamo_brain/ui/actions/__init__.py

padster/pyDynamo

006eb6edb5e54670574dbfdf7d249e9037f01ffc

[ "MIT" ]

1

2022-01-21T23:03:24.000Z

from .fullStateActions import FullStateActions from .dendriteCanvasActions import DendriteCanvasActions

34.666667

56

0.903846

8

104

11.75

0.5

0

0.076923

104

2

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true

0

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0

null

0

1

0

1

0

1

0

6

333aab4e17029bab7fda872889f1a7117add2c69

326

py

Python

plugins/mcafee_epo/komand_mcafee_epo/actions/__init__.py

lukaszlaszuk/insightconnect-plugins

8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892

[ "MIT" ]

46

2019-06-05T20:47:58.000Z

2022-03-29T10:18:01.000Z

plugins/mcafee_epo/komand_mcafee_epo/actions/__init__.py

lukaszlaszuk/insightconnect-plugins

8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892

[ "MIT" ]

386

2019-06-07T20:20:39.000Z

2022-03-30T17:35:01.000Z

plugins/mcafee_epo/komand_mcafee_epo/actions/__init__.py

lukaszlaszuk/insightconnect-plugins

8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892

[ "MIT" ]

43

2019-07-09T14:13:58.000Z

2022-03-28T12:04:46.000Z

# GENERATED BY KOMAND SDK - DO NOT EDIT from .add_permission_set_to_user.action import AddPermissionSetToUser from .clear_tags.action import ClearTags from .get_policies.action import GetPolicies from .run_wake_up.action import RunWakeUp from .search_agents.action import SearchAgents from .tag_system.action import TagSystem

40.75

69

0.855828

47

326

5.723404

0.680851

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0

0.101227

326

7

70

46.571429

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0

1

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0

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0

null

0

1

0

1

0

1

0

6

334e78d2ac8d951d0d117796a849e5e49f787bdd

87

py

Python

tributary/lazy/calculations/__init__.py

vishalbelsare/tributary

ab1a75eea50e92cbff2aa1b3d4e576cb25bc20e3

[ "Apache-2.0" ]

1

2022-03-23T10:50:42.000Z

tributary/lazy/calculations/__init__.py

vishalbelsare/tributary

ab1a75eea50e92cbff2aa1b3d4e576cb25bc20e3

[ "Apache-2.0" ]

null

tributary/lazy/calculations/__init__.py

vishalbelsare/tributary

ab1a75eea50e92cbff2aa1b3d4e576cb25bc20e3

[ "Apache-2.0" ]

null

from .finance import * from .ops import * from .rolling import * from .basket import *

17.4

22

0.724138

12

87

5.25

0.5

0.47619

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0.183908

87

4

23

21.75

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0

null

0

1

0

1

0

1

0

6

335e45555ad860b91cab1d9b0f14a0fa4cc15cdf

7,159

py

Python

tests/RejectPipeline.py

lbausch/filebeat-exim4

fb58f3f438cb2689a0750be78cd52cc6412b4c47

[ "MIT" ]

null

tests/RejectPipeline.py

lbausch/filebeat-exim4

fb58f3f438cb2689a0750be78cd52cc6412b4c47

[ "MIT" ]

null

tests/RejectPipeline.py

lbausch/filebeat-exim4

fb58f3f438cb2689a0750be78cd52cc6412b4c47

[ "MIT" ]

null

import unittest import BaseTestCase class RejectPipeline(BaseTestCase.BaseTestCase): pipeline_file = '../module/exim4/reject/ingest/pipeline.json' def test_pipeline(self): message = 'foo' response = self.request(message) source = self.source(response) self.assertSourceEquals(source, { 'message': message, 'error': { 'message': 'Provided Grok expressions do not match field value: [foo]' } }) def test_greylisting(self): message = "2021-05-04 13:37:00 +0100 H=mail.remotehost.tld [123.123.123.123]:1337 X=TLSv1.2:ECDHE-RSA-AES128-GCM-SHA256:128 CV=no F=<mail@sender.tld> temporarily rejected RCPT <mail@recipient.tld>: Deferred due to greylisting. Host: '123.123.123.123' From: 'mail@sender.tld' To: 'mail@recipient.tld' SPF: 'none'" response = self.request(message) source = self.source(response) self.assertSourceEquals(source, { '@timestamp': '2021-05-04T13:37:00.000+01:00', 'exim4': { 'message_raw': message, 'remote_host': 'mail.remotehost.tld', 'remote_addr': '123.123.123.123', 'remote_addr_port': '1337', 'tls': { 'cipher_suite': 'TLSv1.2:ECDHE-RSA-AES128-GCM-SHA256:128', 'cert_verification_status': 'no', }, 'sender_address': 'mail@sender.tld', 'recipient_address': 'mail@recipient.tld', 'message': "Deferred due to greylisting. Host: '123.123.123.123' From: 'mail@sender.tld' To: 'mail@recipient.tld' SPF: 'none'", }, }) def test_greylisting_without_cipher_suite(self): message = "2021-05-04 13:37:00 +0100 H=(mail.remotehost.tld) [123.123.123.123]:1337 F=<mail@sender.tld> temporarily rejected RCPT <mail@recipient.tld>: Deferred due to greylisting. Host: '123.123.123.123' From: 'mail@sender.tld' To: 'mail@recipient.tld' SPF: 'neutral'" response = self.request(message) source = self.source(response) self.assertSourceEquals(source, { '@timestamp': '2021-05-04T13:37:00.000+01:00', 'exim4': { 'message_raw': message, 'remote_host': 'mail.remotehost.tld', 'remote_addr': '123.123.123.123', 'remote_addr_port': '1337', 'sender_address': 'mail@sender.tld', 'recipient_address': 'mail@recipient.tld', 'message': "Deferred due to greylisting. Host: '123.123.123.123' From: 'mail@sender.tld' To: 'mail@recipient.tld' SPF: 'neutral'", }, }) def test_rbl(self): message = '2021-05-04 13:37:00 +0100 H=(mail.remotehost.tld) [123.123.123.123]:1337 F=<mail@sender.tld> rejected RCPT <mail@recipient.tld>: "JunkMail rejected - (mail.remotehost.tld) [123.123.123.123]:1337 is in an RBL (rbl.tld), see [https://rbl.tld]"' response = self.request(message) source = self.source(response) self.assertSourceEquals(source, { '@timestamp': '2021-05-04T13:37:00.000+01:00', 'exim4': { 'message_raw': message, 'remote_host': 'mail.remotehost.tld', 'remote_addr': '123.123.123.123', 'remote_addr_port': '1337', 'sender_address': 'mail@sender.tld', 'recipient_address': 'mail@recipient.tld', 'message': '"JunkMail rejected - (mail.remotehost.tld) [123.123.123.123]:1337 is in an RBL (rbl.tld), see [https://rbl.tld]"', }, }) def test_spf(self): message = '2021-05-04 13:37:00 +0100 H=mail.remotehost.tld [123.123.123.123]:1337 F=<mail@sender.tld> rejected RCPT <mail@recipient.tld>: SPF: 123.123.123.123 is not allowed to send mail from sender.tld' response = self.request(message) source = self.source(response) self.assertSourceEquals(source, { '@timestamp': '2021-05-04T13:37:00.000+01:00', 'exim4': { 'message_raw': message, 'remote_host': 'mail.remotehost.tld', 'remote_addr': '123.123.123.123', 'remote_addr_port': '1337', 'sender_address': 'mail@sender.tld', 'recipient_address': 'mail@recipient.tld', 'message': 'SPF: 123.123.123.123 is not allowed to send mail from sender.tld', }, }) def test_no_such_user_here(self): message = '2021-05-04 13:37:00 +0100 H=mail.remotehost.tld [123.123.123.123]:1337 F=<mail@sender.tld> rejected RCPT <mail@recipient.tld>: No Such User Here"' response = self.request(message) source = self.source(response) self.assertSourceEquals(source, { '@timestamp': '2021-05-04T13:37:00.000+01:00', 'exim4': { 'message_raw': message, 'remote_host': 'mail.remotehost.tld', 'remote_addr': '123.123.123.123', 'remote_addr_port': '1337', 'sender_address': 'mail@sender.tld', 'recipient_address': 'mail@recipient.tld', 'message': 'No Such User Here"', }, }) def test_rejected_junk_mail(self): message = '2021-05-04 13:37:00 +0100 H=(mail.remotehost.tld) [123.123.123.123]:1337 F=<mail@sender.tld> rejected RCPT <mail@recipient.tld>: "JunkMail rejected - (mail.remotehost.tld) [123.123.123.123]:1337 is in an RBL (rbl.tld), see https://rbl.tld"' response = self.request(message) source = self.source(response) self.assertSourceEquals(source, { '@timestamp': '2021-05-04T13:37:00.000+01:00', 'exim4': { 'message_raw': message, 'remote_host': 'mail.remotehost.tld', 'remote_addr': '123.123.123.123', 'remote_addr_port': '1337', 'sender_address': 'mail@sender.tld', 'recipient_address': 'mail@recipient.tld', 'message': '"JunkMail rejected - (mail.remotehost.tld) [123.123.123.123]:1337 is in an RBL (rbl.tld), see https://rbl.tld"', }, }) def test_dropped_syntax_errors(self): message = '2021-05-04 13:37:00 +0100 SMTP call from (mail.remotehost.tld) [123.123.123.123]:1337 dropped: too many syntax or protocol errors (last command was "RCPT TO: <\'mail@recipient.tld\'>", C=EHLO,AUTH,MAIL,RCPT,RCPT,RCPT,RCPT,RCPT,RCPT)' response = self.request(message) source = self.source(response) self.assertSourceEquals(source, { '@timestamp': '2021-05-04T13:37:00.000+01:00', 'exim4': { 'message_raw': message, 'message': 'SMTP call from (mail.remotehost.tld) [123.123.123.123]:1337 dropped: too many syntax or protocol errors (last command was "RCPT TO: <\'mail@recipient.tld\'>", C=EHLO,AUTH,MAIL,RCPT,RCPT,RCPT,RCPT,RCPT,RCPT)', }, }) if __name__ == '__main__': unittest.main()

45.310127

320

0.576337

870

7,159

4.658621

0.133333

0.106588

0.071058

0.888231

0.87244

0.865285

0

0.127329

0.272664

7,159

157

321

45.598726

0.651047

0

0.653543

0

0.110236

0.529124

0.152535

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0.062992

1

0.062992

false

0

0.015748

0

0.094488

0

null

0

1

0

null

0

6

336aba3f24409a56531a841bffd0111c287954bc

455

py

Python

autotf/tuner/fmin/__init__.py

DAIM-ML/autotf

3f82d858f49c27d5ecb624cee555fb8fd47bf067

[ "BSD-3-Clause" ]

8

2018-03-07T06:58:16.000Z

2019-01-30T07:49:44.000Z

autotf/tuner/fmin/__init__.py

DAIM-ML/autotf

3f82d858f49c27d5ecb624cee555fb8fd47bf067

[ "BSD-3-Clause" ]

null

autotf/tuner/fmin/__init__.py

DAIM-ML/autotf

3f82d858f49c27d5ecb624cee555fb8fd47bf067

[ "BSD-3-Clause" ]

1

2018-03-31T09:06:12.000Z

try: from .bayesian_optimization import bayesian_optimization except ImportError: pass try: from .random_search import random_search except ImportError: pass try: from .fabolas import fabolas except ImportError: pass try: from .mtbo import mtbo except ImportError: pass try: from .bohamiann import bohamiann except ImportError: pass try: from .entropy_search import entropy_search except ImportError: pass

17.5

60

0.747253

54

455

6.185185

0.259259

0.125749

0.377246

0.359281

0.419162

0

0.213187

455

25

61

18.2

0.932961

0

0.75

0

1

0

true

0.25

0.5

0

0.5

0

null

0

1

0

1

0

null

0

1

0

6

682859dba7a14a9673205c231589735f18f17cb0

5,431

py

Python

icepickle/pipeline.py

koaning/icepickle

f6692e334ceebe8390d8b4960a56eb661236edd3

[ "MIT" ]

8

2022-02-14T20:20:30.000Z

2022-03-08T10:03:13.000Z

icepickle/pipeline.py

koaning/icepickle

f6692e334ceebe8390d8b4960a56eb661236edd3

[ "MIT" ]

1

2022-02-20T08:40:42.000Z

2022-02-20T15:02:58.000Z

icepickle/pipeline.py

koaning/icepickle

f6692e334ceebe8390d8b4960a56eb661236edd3

[ "MIT" ]

null

from sklearn.pipeline import Pipeline, FeatureUnion, _name_estimators class PartialFeatureUnion(FeatureUnion): """ A `PartialFeatureUnion` is a `FeatureUnion` but able to `.partial_fit`. Arguments: transformer_list: a list of transformers to apply and concatenate Example: ```python import numpy as np from sklearn.linear_model import SGDClassifier from sklearn.feature_extraction.text import HashingVectorizer from icepickle.pipeline import PartialPipeline, PartialFeatureUnion pipe = PartialPipeline([ ("feat", PartialFeatureUnion([ ("hash1", HashingVectorizer()), ("hash2", HashingVectorizer(ngram_range=(1,2))) ])), ("clf", SGDClassifier()) ]) X = [ "i really like this post", "thanks for that comment", "i enjoy this friendly forum", "this is a bad post", "i dislike this article", "this is not well written" ] y = np.array([1, 1, 1, 0, 0, 0]) for loop in range(3): pipe.partial_fit(X, y, classes=[0, 1]) assert np.all(pipe.predict(X) == np.array([1, 1, 1, 0, 0, 0])) ``` """ def partial_fit(self, X, y=None, classes=None, **kwargs): """ Fits the components, but allow for batches. """ for name, step in self.transformer_list: if not hasattr(step, "partial_fit"): raise ValueError( f"Step {name} is a {step} which does not have `.partial_fit` implemented." ) for name, step in self.transformer_list: if hasattr(step, "predict"): step.partial_fit(X, y, classes=classes, **kwargs) else: step.partial_fit(X, y) return self def make_partial_union(*transformer_list): """ Utility function to generate a `PartialFeatureUnion` Arguments: transformer_list: a list of transformers to apply and concatenate Example: ```python import numpy as np from sklearn.linear_model import SGDClassifier from sklearn.feature_extraction.text import HashingVectorizer from icepickle.pipeline import make_partial_pipeline, make_partial_union pipe = make_partial_pipeline( make_partial_union( HashingVectorizer(), HashingVectorizer(ngram_range=(1,2)) ), SGDClassifier() ) X = [ "i really like this post", "thanks for that comment", "i enjoy this friendly forum", "this is a bad post", "i dislike this article", "this is not well written" ] y = np.array([1, 1, 1, 0, 0, 0]) for loop in range(3): pipe.partial_fit(X, y, classes=[0, 1]) assert np.all(pipe.predict(X) == np.array([1, 1, 1, 0, 0, 0])) ``` """ return PartialFeatureUnion(_name_estimators(transformer_list)) class PartialPipeline(Pipeline): """ Utility function to generate a `PartialPipeline` Arguments: steps: a collection of text-transformers ```python import numpy as np from sklearn.linear_model import SGDClassifier from sklearn.feature_extraction.text import HashingVectorizer from icepickle.pipeline import PartialPipeline pipe = PartialPipeline([ ("hash", HashingVectorizer()), ("clf", SGDClassifier()) ]) X = [ "i really like this post", "thanks for that comment", "i enjoy this friendly forum", "this is a bad post", "i dislike this article", "this is not well written" ] y = np.array([1, 1, 1, 0, 0, 0]) for loop in range(3): pipe.partial_fit(X, y, classes=[0, 1]) assert np.all(pipe.predict(X) == np.array([1, 1, 1, 0, 0, 0])) ``` """ def partial_fit(self, X, y=None, classes=None, **kwargs): """ Fits the components, but allow for batches. """ for name, step in self.steps: if not hasattr(step, "partial_fit"): raise ValueError( f"Step {name} is a {step} which does not have `.partial_fit` implemented." ) for name, step in self.steps: if hasattr(step, "predict"): step.partial_fit(X, y, classes=classes, **kwargs) else: step.partial_fit(X, y) if hasattr(step, "transform"): X = step.transform(X) return self def make_partial_pipeline(*steps): """ Utility function to generate a `PartialPipeline` Arguments: steps: a collection of text-transformers ```python import numpy as np from sklearn.linear_model import SGDClassifier from sklearn.feature_extraction.text import HashingVectorizer from icepickle.pipeline import make_partial_pipeline pipe = make_partial_pipeline( HashingVectorizer(), SGDClassifier() ) X = [ "i really like this post", "thanks for that comment", "i enjoy this friendly forum", "this is a bad post", "i dislike this article", "this is not well written" ] y = np.array([1, 1, 1, 0, 0, 0]) for loop in range(3): pipe.partial_fit(X, y, classes=[0, 1]) assert np.all(pipe.predict(X) == np.array([1, 1, 1, 0, 0, 0])) ``` """ return PartialPipeline(_name_estimators(steps))

27.155

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5,431

4.855828

0.162577

0.010107

0.020215

0.022742

0.830385

0.788692

0.772584

0.769425

0.759949

0

0.017359

0.299945

5,431

199

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27.291457

0.81536

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false

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0.030303

0

0.333333

0

null

0

1

0

null

0

6

6875f266eae6972c56d9b5125cb882d8d4d04e2d

129

py

Python

idfy_sdk/services/merchantsign/__init__.py

idfy-io/idfy-sdk-python

0f7ced0cf0df080b1c73e2451bf02a23710b5bf1

[ "Apache-2.0" ]

null

idfy_sdk/services/merchantsign/__init__.py

idfy-io/idfy-sdk-python

0f7ced0cf0df080b1c73e2451bf02a23710b5bf1

[ "Apache-2.0" ]

null

idfy_sdk/services/merchantsign/__init__.py

idfy-io/idfy-sdk-python

0f7ced0cf0df080b1c73e2451bf02a23710b5bf1

[ "Apache-2.0" ]

null

from idfy_sdk.services.merchantsign.merchant_sign_service import MerchantSignService import idfy_sdk.services.merchantsign.models

64.5

84

0.914729

16

129

7.125

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0.122807

0.263158

0.473684

0

0.03876

129

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85

64.5

0.919355

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1

0

true

0

1

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1

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null

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0

null

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1

0

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0

6

68826a5840025710d5af93a3fce5ecd5222c3806

46,764

py

Python

tests/modules/voting/test_voting.py

rlin0/donut

5672df8e853b4b775d7d50665128b255cd695ec2

[ "MIT" ]

null

tests/modules/voting/test_voting.py

rlin0/donut

5672df8e853b4b775d7d50665128b255cd695ec2

[ "MIT" ]

null

tests/modules/voting/test_voting.py

rlin0/donut

5672df8e853b4b775d7d50665128b255cd695ec2

[ "MIT" ]

null

""" Tests donut/modules/voting """ from datetime import date, datetime, timedelta import json import re import flask import pytest from donut.testing.fixtures import client from donut import app from donut.modules.groups.helpers import get_group_list_data from donut.modules.voting import helpers, routes, ranked_pairs # Ranked pairs def test_ranked_pairs(): # Example taken from en.wikipedia.org/wiki/Ranked_pairs M = 'Memphis' N = 'Nashville' C = 'Chattanooga' K = 'Knoxville' responses = (((M, ), (N, ), (C, ), (K, )), ) * 42 responses += (((N, ), (C, ), (K, ), (M, )), ) * 26 responses += (((C, ), (K, ), (N, ), (M, )), ) * 15 responses += (((K, ), (C, ), (N, ), (M, )), ) * 17 results = ranked_pairs.results(responses) assert results.winners == [N, C, K, M] assert results.tallies == { (C, K): 42 + 26 + 15, (C, M): 26 + 15 + 17, (C, N): 15 + 17, (K, C): 17, (K, M): 26 + 15 + 17, (K, N): 15 + 17, (M, C): 42, (M, K): 42, (M, N): 42, (N, C): 42 + 26, (N, K): 42 + 26, (N, M): 26 + 15 + 17, } # Test incomplete lists results = ranked_pairs.results([[['A']], [['B']], [['A']]]) assert results.winners == ['A', 'B'] # Test ties responses = [[['A'], ['B', 'C'], ['D']], [['A', 'C'], ['B', 'D']]] assert ranked_pairs.results(responses).winners == ['A', 'C', 'B', 'D'] # Helpers def test_question_types(client): assert helpers.get_question_types() == { 'Dropdown': 1, 'Checkboxes': 2, 'Elected position': 3, 'Short text': 4, 'Long text': 5 } def test_public_surveys(client): ruddock_id = get_group_list_data( ['group_id'], {'group_name': 'Ruddock House'})[0]['group_id'] survey_params = [ { 'title': 'Unrestricted', 'group': '', 'end_hour': '12' }, { 'title': 'Ruddock only', 'group': str(ruddock_id), 'end_hour': '1' # ends later } ] yesterday = date.today() + timedelta(days=-1) tomorrow = date.today() + timedelta(days=1) access_keys = {} with client.session_transaction() as sess: sess['username'] = 'csander' for params in survey_params: rv = client.post( flask.url_for('voting.make_survey'), data=dict( description='', start_date=yesterday.strftime(helpers.YYYY_MM_DD), start_hour='12', start_minute='00', start_period='P', end_date=tomorrow.strftime(helpers.YYYY_MM_DD), end_minute='00', end_period='P', public='on', **params), follow_redirects=False) assert rv.status_code == 302 access_keys[params['title']] = [ url_piece for url_piece in rv.location.split('/') if len(url_piece) == 64 ][0] unrestricted = { 'title': 'Unrestricted', 'description': None, 'end_time': datetime(tomorrow.year, tomorrow.month, tomorrow.day, 12), 'access_key': access_keys['Unrestricted'], 'group_id': None } assert list(helpers.get_visible_surveys(helpers.get_user_id( 'dqu'))) == [ # not a Rudd unrestricted ] assert list( helpers.get_visible_surveys(helpers.get_user_id('csander'))) == [ unrestricted, { 'title': 'Ruddock only', 'description': None, 'end_time': datetime(tomorrow.year, tomorrow.month, tomorrow.day, 13), 'access_key': access_keys['Ruddock only'], 'group_id': 2 } ] def test_closed_surveys(client): yesterday = date.today() + timedelta(days=-1) tomorrow = date.today() + timedelta(days=1) survey_params = [{ 'title': 'Before', 'start_date': yesterday.strftime(helpers.YYYY_MM_DD), 'start_hour': '2', 'end_date': yesterday.strftime(helpers.YYYY_MM_DD), 'end_hour': '3' }, { 'title': 'During', 'start_date': yesterday.strftime(helpers.YYYY_MM_DD), 'start_hour': '4', 'end_date': tomorrow.strftime(helpers.YYYY_MM_DD), 'end_hour': '5' }, { 'title': 'After', 'start_date': tomorrow.strftime(helpers.YYYY_MM_DD), 'start_hour': '2', 'end_date': tomorrow.strftime(helpers.YYYY_MM_DD), 'end_hour': '3' }] access_keys = {} with client.session_transaction() as sess: sess['username'] = 'csander' for params in survey_params: rv = client.post( flask.url_for('voting.make_survey'), data=dict( description='', start_minute='00', start_period='P', end_minute='00', end_period='P', public='on', group='', **params), follow_redirects=False) assert rv.status_code == 302 access_keys[params['title']] = [ url_piece for url_piece in rv.location.split('/') if len(url_piece) == 64 ][0] assert helpers.get_closed_surveys(helpers.get_user_id('reng')) == ( ) # not the creator of 'Before' before = [{ 'title': 'Before', 'description': None, 'end_time': datetime(yesterday.year, yesterday.month, yesterday.day, 15), 'access_key': access_keys['Before'], 'results_shown': 0 }] assert helpers.get_closed_surveys(helpers.get_user_id('csander')) == before rv = client.get( flask.url_for( 'voting.release_results', access_key=access_keys['Before'])) assert rv.status_code == 302 assert rv.location == flask.url_for( 'voting.show_results', access_key=access_keys['Before']) before[0]['results_shown'] = 1 assert helpers.get_closed_surveys(helpers.get_user_id('reng')) == before assert helpers.get_closed_surveys(helpers.get_user_id('csander')) == before helpers.delete_survey(3) helpers.delete_survey(4) helpers.delete_survey(5) def test_survey_data(client): access_key = list(helpers.get_visible_surveys(1))[0]['access_key'] yesterday = date.today() + timedelta(days=-1) tomorrow = date.today() + timedelta(days=1) assert helpers.get_survey_data(access_key) == { 'survey_id': 1, 'title': 'Unrestricted', 'description': None, 'group_id': None, 'start_time': datetime(yesterday.year, yesterday.month, yesterday.day, 12), 'end_time': datetime(tomorrow.year, tomorrow.month, tomorrow.day, 12), 'creator': 3, 'results_shown': 0 } def test_question_json(client): question_types = helpers.get_question_types() helpers.set_questions(1, [{ 'title': 'A', 'description': '', 'type': question_types['Dropdown'], 'choices': ['1', '2', '3'] }, { 'title': 'B', 'description': 'bbb', 'type': question_types['Short text'] }, { 'title': 'C', 'description': 'ccc', 'type': question_types['Checkboxes'], 'choices': ['a', 'b', 'c'] }, { 'title': 'D', 'description': '', 'type': question_types['Long text'] }, { 'title': 'E', 'description': '', 'type': question_types['Elected position'], 'choices': ['do', 're', 'me'] }]) assert helpers.get_questions_json( 1, False ) == '[{"title":"A","description":"","type":1,"choices":["1","2","3"]},{"title":"B","description":"bbb","type":4},{"title":"C","description":"ccc","type":2,"choices":["a","b","c"]},{"title":"D","description":"","type":5},{"title":"E","description":"","type":3,"choices":["do","re","me"]}]' assert helpers.get_questions_json( 1, True ) == '[{"question_id":1,"title":"A","description":"","type":1,"choices":[{"id":1,"choice":"1"},{"id":2,"choice":"2"},{"id":3,"choice":"3"}]},{"question_id":2,"title":"B","description":"bbb","type":4},{"question_id":3,"title":"C","description":"ccc","type":2,"choices":[{"id":4,"choice":"a"},{"id":5,"choice":"b"},{"id":6,"choice":"c"}]},{"question_id":4,"title":"D","description":"","type":5},{"question_id":5,"title":"E","description":"","type":3,"choices":[{"id":7,"choice":"do"},{"id":8,"choice":"re"},{"id":9,"choice":"me"}]}]' def test_question_ids(client): assert helpers.get_question_ids(1) == [1, 2, 3, 4, 5] assert helpers.get_question_ids(2) == [] def test_question_type(client): assert list(map(helpers.get_question_type, range(1, 6))) == [1, 4, 2, 5, 3] def test_get_choice(client): assert [ helpers.invalid_choice_id(5, choice) for choice in ['abc', 7, 8, 9, 10] ] == [True, False, False, False, True] def test_process_params_error(client): default_params = dict( title='New survey', description='', start_date='2018-05-08', start_hour='12', start_minute='00', start_period='P', end_date='2018-05-10', end_hour='12', end_minute='00', end_period='P', public='on', group='') def assert_message(message, params): rv = client.post( flask.url_for('voting.make_survey'), data=params, follow_redirects=False) assert rv.status_code == 200 assert message in rv.data rv = client.post( flask.url_for('voting.make_survey'), follow_redirects=False) assert rv.status_code == 403 with client.session_transaction() as sess: sess['username'] = 'csander' for delete_param in default_params: if delete_param == 'public': continue # this param is optional params = default_params.copy() del params[delete_param] assert_message(b'Invalid form data', params) for date_field in ['start_date', 'end_date']: assert_message(b'Invalid form data', { **default_params, date_field: '123' }) for hour_field in ['start_hour', 'end_hour']: assert_message(b'Invalid form data', { **default_params, hour_field: 'abc' }) assert_message(b'Invalid form data', { **default_params, hour_field: '0' }) assert_message(b'Invalid form data', { **default_params, hour_field: '13' }) for minute_field in ['start_minute', 'end_minute']: assert_message(b'Invalid form data', { **default_params, minute_field: 'abc' }) assert_message(b'Invalid form data', { **default_params, minute_field: '-1' }) assert_message(b'Invalid form data', { **default_params, minute_field: '60' }) for period_field in ['start_period', 'end_period']: assert_message(b'Invalid form data', { **default_params, period_field: 'a' }) assert_message(b'Invalid form data', { **default_params, period_field: '' }) assert_message(b'Invalid form data', {**default_params, 'group': 'a'}) assert_message(b'Start must be before end', { **default_params, 'start_date': '2018-05-09', 'end_date': '2018-05-08' }) rv = client.post( flask.url_for('voting.make_survey'), data=default_params, follow_redirects=False) assert rv.status_code == 302 # successful helpers.delete_survey(6) def test_survey_params(client): yesterday = date.today() + timedelta(days=-1) tomorrow = date.today() + timedelta(days=1) assert helpers.get_survey_params(1) == { 'title': 'Unrestricted', 'description': None, 'start_time': datetime(yesterday.year, yesterday.month, yesterday.day, 12), 'end_time': datetime(tomorrow.year, tomorrow.month, tomorrow.day, 12), 'group_id': None, 'public': 1 } helpers.update_survey_params(1, {'title': 'ABC', 'group_id': 2}) assert helpers.get_survey_params(1) == { 'title': 'ABC', 'description': None, 'start_time': datetime(yesterday.year, yesterday.month, yesterday.day, 12), 'end_time': datetime(tomorrow.year, tomorrow.month, tomorrow.day, 12), 'group_id': 2, 'public': 1 } def test_my_surveys(client): yesterday = date.today() + timedelta(days=-1) tomorrow = date.today() + timedelta(days=1) assert helpers.get_my_surveys(helpers.get_user_id('dqu')) == () csander = helpers.get_user_id('csander') assert helpers.get_my_surveys(csander) == [{ 'title': 'ABC', 'description': None, 'access_key': list(helpers.get_visible_surveys(csander))[0]['access_key'], 'start_time': datetime(yesterday.year, yesterday.month, yesterday.day, 12), 'unopened': 0, 'closed': 0, 'end_time': datetime(tomorrow.year, tomorrow.month, tomorrow.day, 12) }, { 'title': 'Ruddock only', 'description': None, 'access_key': list(helpers.get_visible_surveys(csander))[1]['access_key'], 'start_time': datetime(yesterday.year, yesterday.month, yesterday.day, 12), 'unopened': 0, 'closed': 0, 'end_time': datetime(tomorrow.year, tomorrow.month, tomorrow.day, 13) }] def test_respond(client): assert not helpers.some_responses_for_survey(1) with app.test_request_context(): flask.session['username'] = 'csander' helpers.set_responses([1, 2, 3, 4, 5], [ '2', '"asdf"', '[4, 6]', '"Lorem ipsum dolor sit amet"', '[[7], [-1], [9], [-2], [null]]' ]) assert helpers.some_responses_for_survey(1) results = helpers.get_results(1) election_result = results.pop() assert results == [{ 'question_id': 1, 'title': 'A', 'description': None, 'type': 1, 'list_order': 0, 'choices': { 1: '1', 2: '2', 3: '3' }, 'responses': [2], 'results': [(2, 1)] }, { 'question_id': 2, 'title': 'B', 'description': 'bbb', 'type': 4, 'list_order': 1, 'choices': 0, 'responses': ['asdf'], 'results': [('asdf', 1)] }, { 'question_id': 3, 'title': 'C', 'description': 'ccc', 'type': 2, 'list_order': 2, 'choices': { 4: 'a', 5: 'b', 6: 'c' }, 'responses': [[4, 6]], 'results': [(4, 1), (6, 1)] }, { 'question_id': 4, 'title': 'D', 'description': None, 'type': 5, 'list_order': 3, 'choices': 0, 'responses': ['Lorem ipsum dolor sit amet'], 'results': [('Lorem ipsum dolor sit amet', 1)] }] results = election_result.pop('results') assert election_result == { 'question_id': 5, 'title': 'E', 'description': None, 'type': 3, 'list_order': 4, 'choices': { 7: 'do', 8: 're', 9: 'me' }, 'responses': [[['do'], ['David Qu'], ['me'], ['Robert Eng'], ['NO']]] } assert results.winners == ['do', 'David Qu', 'me', 'Robert Eng', 'NO'] with app.test_request_context(): flask.session['username'] = 'dqu' # Invalid elected position response helpers.set_responses([5], ['[["abc"]]']) with pytest.raises(Exception) as e: helpers.get_results(1) assert e.value.args == ('Unrecognized elected position vote', ) def test_restrict_access(client): assert helpers.restrict_take_access(None) == 'Invalid access key' yesterday = datetime.now() + timedelta(days=-1) tomorrow = datetime.now() + timedelta(days=1) assert helpers.restrict_take_access({ 'start_time': yesterday, 'end_time': yesterday }) == 'Survey is not currently accepting responses' assert helpers.restrict_take_access({ 'start_time': tomorrow, 'end_time': tomorrow }) == 'Survey is not currently accepting responses' with app.test_request_context(): assert helpers.restrict_take_access({ 'start_time': yesterday, 'end_time': tomorrow }) == 'Must be logged in to take survey' with app.test_request_context(): flask.session['username'] = 'dqu' assert helpers.restrict_take_access({ 'start_time': yesterday, 'end_time': tomorrow, 'group_id': 2 }) == 'You do not belong to the group this survey is for' with app.test_request_context(): flask.session['username'] = 'csander' assert helpers.restrict_take_access({ 'survey_id': 1, 'start_time': yesterday, 'end_time': tomorrow, 'group_id': 2 }) == 'Already completed' with app.test_request_context(): flask.session['username'] = 'reng' assert helpers.restrict_take_access({ 'survey_id': 1, 'start_time': yesterday, 'end_time': tomorrow, 'group_id': 2 }) is None # Routes def test_home(client): rv = client.get(flask.url_for('voting.list_surveys')) assert rv.status_code == 200 assert b'Ruddock only' not in rv.data with client.session_transaction() as sess: sess['username'] = 'csander' rv = client.get(flask.url_for('voting.list_surveys')) assert rv.status_code == 200 assert b'Ruddock only' in rv.data def test_take(client): access_key = list( helpers.get_visible_surveys(helpers.get_user_id('csander')))[1][ 'access_key'] rv = client.get(flask.url_for('voting.take_survey', access_key=access_key)) assert rv.status_code == 200 assert b'Must be logged in to take survey' in rv.data with client.session_transaction() as sess: sess['username'] = 'csander' helpers.set_questions(2, [{ 'title': 'Question 1', 'description': '', 'type': helpers.get_question_types()['Long text'] }]) rv = client.get(flask.url_for('voting.take_survey', access_key=access_key)) assert rv.status_code == 200 assert b'Edit' in rv.data assert b'Question 1' in rv.data with client.session_transaction() as sess: sess['username'] = 'reng' rv = client.get(flask.url_for('voting.take_survey', access_key=access_key)) assert rv.status_code == 200 assert b'Edit' not in rv.data assert b'Question 1' in rv.data def test_make_form(client): with client.session_transaction() as sess: sess['username'] = 'ruddock_pres' rv = client.get(flask.url_for('voting.make_survey_form')) assert rv.status_code == 403 with client.session_transaction() as sess: sess['username'] = 'csander' rv = client.get(flask.url_for('voting.make_survey_form')) assert rv.status_code == 200 assert b'Making new survey' in rv.data def test_manage(client): rv = client.get(flask.url_for('voting.my_surveys')) assert rv.status_code == 200 assert b'Please log in to manage your surveys' in rv.data with client.session_transaction() as sess: sess['username'] = 'ruddock_pres' rv = client.get(flask.url_for('voting.my_surveys')) assert rv.status_code == 403 with client.session_transaction() as sess: sess['username'] = 'csander' rv = client.get(flask.url_for('voting.my_surveys')) assert rv.status_code == 200 assert b'ABC' in rv.data and b'Ruddock only' in rv.data def test_edit_questions(client): # Test all restrictions rv = client.get( flask.url_for( 'voting.edit_questions', access_key='invalid-access-key')) assert rv.status_code == 403 with client.session_transaction() as sess: sess['username'] = 'csander' rv = client.get( flask.url_for( 'voting.edit_questions', access_key='invalid-access-key')) assert rv.status_code == 200 assert b'Invalid access key' in rv.data assert b'Editing survey questions' not in rv.data with client.session_transaction() as sess: sess['username'] = 'reng' access_key = list( helpers.get_visible_surveys(helpers.get_user_id('csander')))[0][ 'access_key'] rv = client.get( flask.url_for('voting.edit_questions', access_key=access_key)) assert rv.status_code == 200 assert b'You are not the creator of this survey' in rv.data assert b'Editing survey questions' not in rv.data with client.session_transaction() as sess: sess['username'] = 'csander' client.post( flask.url_for('voting.make_survey'), data=dict( title='Already closed', description='', start_date='2018-05-01', start_hour='12', start_minute='00', start_period='P', end_date='2018-05-03', end_hour='12', end_minute='00', end_period='P', group=''), follow_redirects=False) closed_access_key = helpers.get_closed_surveys( helpers.get_user_id('csander'))[0]['access_key'] rv = client.get( flask.url_for('voting.edit_questions', access_key=closed_access_key)) assert rv.status_code == 200 assert b'Cannot modify a survey after it has closed' in rv.data assert b'Editing survey questions' not in rv.data # Test success cases rv = client.get( flask.url_for('voting.edit_questions', access_key=access_key)) assert rv.status_code == 200 assert b'Editing survey questions' in rv.data assert b'someResponses = true' in rv.data access_key2 = list( helpers.get_visible_surveys(helpers.get_user_id('csander')))[1][ 'access_key'] rv = client.get( flask.url_for('voting.edit_questions', access_key=access_key2)) assert rv.status_code == 200 assert b'Editing survey questions' in rv.data assert b'someResponses = false' in rv.data # Test saving questions rv = client.post( flask.url_for('voting.save_questions', access_key=closed_access_key), data='[]') assert rv.status_code == 200 assert b'Cannot modify a survey after it has closed' in rv.data rv = client.post( flask.url_for('voting.save_questions', access_key=access_key2), data= '[{"title":"Added question","description":"","type":1,"choices":["choice A","choice B"]}]' ) assert rv.status_code == 200 assert json.loads(rv.data) == {'success': True} rv = client.get( flask.url_for('voting.edit_questions', access_key=access_key2)) assert rv.status_code == 200 assert b'Editing survey questions' in rv.data assert b'Added question' in rv.data and b'choice A' in rv.data and b'choice B' in rv.data def test_edit_params(client): # Test that (some) restrictions are applied rv = client.get( flask.url_for('voting.edit_params', access_key='invalid-access-key')) assert rv.status_code == 403 with client.session_transaction() as sess: sess['username'] = 'csander' rv = client.get( flask.url_for('voting.edit_params', access_key='invalid-access-key')) assert rv.status_code == 200 assert b'Invalid access key' in rv.data assert b'Editing survey' not in rv.data # Test successful case access_key = list( helpers.get_visible_surveys(helpers.get_user_id('csander')))[0][ 'access_key'] rv = client.get(flask.url_for('voting.edit_params', access_key=access_key)) assert rv.status_code == 200 assert b'Editing survey' in rv.data assert b"value='ABC'" in rv.data assert b'New description' not in rv.data yesterday = date.today() + timedelta(days=-1) tomorrow = date.today() + timedelta(days=1) rv = client.post( flask.url_for('voting.save_params', access_key=access_key), data=dict( title='ABC', description='New description', start_date=yesterday.strftime(helpers.YYYY_MM_DD), start_hour='12', start_minute='00', start_period='P', end_date=tomorrow.strftime(helpers.YYYY_MM_DD), end_hour='12', end_minute='00', end_period='P', group=''), follow_redirects=False) assert rv.status_code == 302 assert rv.location == flask.url_for( 'voting.edit_questions', access_key=access_key) rv = client.get(flask.url_for('voting.edit_params', access_key=access_key)) assert rv.status_code == 200 assert b'New description' in rv.data # assert that description has changed # Error cases for saving params with client.session_transaction() as sess: sess['username'] = 'reng' rv = client.post( flask.url_for('voting.save_params', access_key=access_key), follow_redirects=False) assert rv.status_code == 200 assert b'You are not the creator of this survey' in rv.data with client.session_transaction() as sess: del sess['username'] rv = client.post( flask.url_for('voting.save_params', access_key=access_key), follow_redirects=False) assert rv.status_code == 403 def test_close(client): with client.session_transaction() as sess: sess['username'] = 'csander' def make_survey(start_date): rv = client.post( flask.url_for('voting.make_survey'), data=dict( title='ABC', description='', start_date=start_date.strftime(helpers.YYYY_MM_DD), start_hour='12', start_minute='00', start_period='P', end_date=(start_date + timedelta( days=2)).strftime(helpers.YYYY_MM_DD), end_hour='12', end_minute='00', end_period='P', public='on', group=''), follow_redirects=False) assert rv.status_code == 302 return re.search(r'[A-Za-z0-9]{64}', rv.location)[0] past = make_survey(date.today() + timedelta(days=-3)) present = make_survey(date.today() + timedelta(days=-1)) future = make_survey(date.today() + timedelta(days=1)) # Test error cases rv = client.get(flask.url_for('voting.close_survey', access_key=past)) assert rv.status_code == 200 assert json.loads(rv.data) == { 'success': False, 'message': 'Cannot modify a survey after it has closed' } rv = client.get(flask.url_for('voting.close_survey', access_key=future)) assert rv.status_code == 200 assert json.loads(rv.data) == { 'success': False, 'message': 'Survey has not opened yet' } # Test successful case rv = client.get(flask.url_for('voting.close_survey', access_key=present)) assert rv.status_code == 200 assert json.loads(rv.data) == {'success': True} rv = client.get(flask.url_for('voting.close_survey', access_key=present)) assert rv.status_code == 200 assert json.loads(rv.data) == { 'success': False, 'message': 'Cannot modify a survey after it has closed' } def test_delete(client): # Test error cases rv = client.delete( flask.url_for('voting.delete_survey', access_key='invalid-access-key')) assert rv.status_code == 403 with client.session_transaction() as sess: sess['username'] = 'reng' rv = client.delete( flask.url_for('voting.delete_survey', access_key='invalid-access-key')) assert rv.status_code == 200 assert json.loads(rv.data) == { 'success': False, 'message': 'Invalid access key' } access_key = helpers.get_closed_surveys( helpers.get_user_id('csander'))[0]['access_key'] rv = client.delete( flask.url_for('voting.delete_survey', access_key=access_key)) assert rv.status_code == 200 assert json.loads(rv.data) == { 'success': False, 'message': 'You are not the creator of this survey' } with client.session_transaction() as sess: sess['username'] = 'csander' # Test successful case rv = client.delete( flask.url_for('voting.delete_survey', access_key=access_key)) assert rv.status_code == 200 assert json.loads(rv.data) == {'success': True} # Test that it was deleted rv = client.delete( flask.url_for('voting.delete_survey', access_key=access_key)) assert rv.status_code == 200 assert json.loads(rv.data) == { 'success': False, 'message': 'Invalid access key' } def test_submit(client): with client.session_transaction() as sess: sess['username'] = 'csander' yesterday = date.today() + timedelta(days=-1) tomorrow = date.today() + timedelta(days=1) rv = client.post( flask.url_for('voting.make_survey'), data=dict( title='Response test', description='', start_date=yesterday.strftime(helpers.YYYY_MM_DD), start_hour='12', start_minute='00', start_period='P', end_date=tomorrow.strftime(helpers.YYYY_MM_DD), end_hour='12', end_minute='00', end_period='P', public='on', group=''), follow_redirects=False) access_key = [ survey for survey in helpers.get_visible_surveys( helpers.get_user_id('csander')) if survey['title'] == 'Response test' ][0]['access_key'] assert rv.status_code == 302 assert rv.location == flask.url_for( 'voting.edit_questions', access_key=access_key) with client.session_transaction() as sess: del sess['username'] survey_id = helpers.get_survey_data(access_key)['survey_id'] question_types = helpers.get_question_types() helpers.set_questions(survey_id, [{ # question id 8 'title': 'Question A', 'description': '', 'type': question_types['Dropdown'], 'choices': ['1', '2', '3'] # choices 12, 13, 14 }, { # question id 9 'title': 'Question B', 'description': 'bbb', 'type': question_types['Short text'] }, { # question id 10 'title': 'Question C', 'description': 'ccc', 'type': question_types['Checkboxes'], 'choices': ['a', 'b', 'c'] # choices 15, 16, 17 }, { # question id 11 'title': 'Question D', 'description': '', 'type': question_types['Long text'] }, { # question id 12 'title': 'Question E', 'description': '', 'type': question_types['Elected position'], 'choices': ['do', 're', 'me'] # choices 18, 19, 20 }]) # Test (some) restriction rv = client.post( flask.url_for('voting.submit', access_key=access_key), data='') assert rv.status_code == 200 assert json.loads(rv.data) == { 'success': False, 'message': 'Must be logged in to take survey' } with client.session_transaction() as sess: sess['username'] = 'csander' # Test questions match rv = client.post( flask.url_for('voting.submit', access_key=access_key), data='{"responses":[{"question":1}]}') assert rv.status_code == 200 assert json.loads(rv.data) == { 'success': False, 'message': 'Survey questions have changed' } # Test response value errors rv = client.post( flask.url_for('voting.submit', access_key=access_key), data=""" { "responses":[ {"question":8,"response":"4"}, {"question":9}, {"question":10}, {"question":11}, {"question":12} ] } """) assert rv.status_code == 200 assert json.loads(rv.data) == { 'success': False, 'message': 'Invalid response to dropdown' } rv = client.post( flask.url_for('voting.submit', access_key=access_key), data=""" { "responses":[ {"question":8,"response":15}, {"question":9}, {"question":10}, {"question":11}, {"question":12} ] } """) assert rv.status_code == 200 assert json.loads(rv.data) == { 'success': False, 'message': 'Invalid choice for dropdown' } rv = client.post( flask.url_for('voting.submit', access_key=access_key), data=""" { "responses":[ {"question":8,"response":13}, {"question":9,"response":10}, {"question":10}, {"question":11}, {"question":12} ] } """) assert rv.status_code == 200 assert json.loads(rv.data) == { 'success': False, 'message': 'Invalid text response' } rv = client.post( flask.url_for('voting.submit', access_key=access_key), data=""" { "responses":[ {"question":8,"response":13}, {"question":9,"response":"shorty"}, {"question":10,"response":10}, {"question":11}, {"question":12} ] } """) assert rv.status_code == 200 assert json.loads(rv.data) == { 'success': False, 'message': 'Invalid response to checkboxes' } rv = client.post( flask.url_for('voting.submit', access_key=access_key), data=""" { "responses":[ {"question":8,"response":13}, {"question":9,"response":"shorty"}, {"question":10,"response":["3"]}, {"question":11}, {"question":12} ] } """) assert rv.status_code == 200 assert json.loads(rv.data) == { 'success': False, 'message': 'Invalid response to checkboxes' } rv = client.post( flask.url_for('voting.submit', access_key=access_key), data=""" { "responses":[ {"question":8,"response":13}, {"question":9,"response":"shorty"}, {"question":10,"response":[14]}, {"question":11}, {"question":12} ] } """) assert rv.status_code == 200 assert json.loads(rv.data) == { 'success': False, 'message': 'Invalid choice for checkboxes' } rv = client.post( flask.url_for('voting.submit', access_key=access_key), data=""" { "responses":[ {"question":8,"response":13}, {"question":9,"response":"shorty"}, {"question":10,"response":[15,17]}, {"question":11,"response":100}, {"question":12} ] } """) assert rv.status_code == 200 assert json.loads(rv.data) == { 'success': False, 'message': 'Invalid text response' } for position_response in ('NO', [True], [[1]]): rv = client.post( flask.url_for('voting.submit', access_key=access_key), data=json.dumps({ 'responses': [{ 'question': 8, 'response': 13 }, { 'question': 9, 'response': 'shorty' }, { 'question': 10, 'response': [15, 17] }, { 'question': 11, 'response': 'looooooooong' }, { 'question': 12, 'response': position_response }] })) assert rv.status_code == 200 assert json.loads(rv.data) == { 'success': False, 'message': 'Invalid response to elected position' } for position_choice in ({'choice_id': 2}, {'user_id': -100}): rv = client.post( flask.url_for('voting.submit', access_key=access_key), data=json.dumps({ 'responses': [{ 'question': 8, 'response': 13 }, { 'question': 9, 'response': 'shorty' }, { 'question': 10, 'response': [15, 17] }, { 'question': 11, 'response': 'looooooooong' }, { 'question': 12, 'response': [[position_choice]] }] })) assert rv.status_code == 200 assert json.loads(rv.data) == { 'success': False, 'message': 'Invalid choice for elected position' } duplicate_responses = ([[{'choice_id': 19}, {'choice_id': 19}]], ) duplicate_responses += ([[None], [{'user_id': 3}], [None]], ) for position_response in duplicate_responses: rv = client.post( flask.url_for('voting.submit', access_key=access_key), data=json.dumps({ 'responses': [{ 'question': 8, 'response': 13 }, { 'question': 9, 'response': 'shorty' }, { 'question': 10, 'response': [15, 17] }, { 'question': 11, 'response': 'looooooooong' }, { 'question': 12, 'response': position_response }] })) assert rv.status_code == 200 assert json.loads(rv.data) == { 'success': False, 'message': 'Candidate ranked twice for elected position' } rv = client.post( flask.url_for('voting.submit', access_key=access_key), data=""" { "responses":[ {"question":8,"response":13}, {"question":9,"response":"shorty"}, {"question":10,"response":[15,17]}, {"question":11,"response":"looooooooong"}, {"question":12,"response":[[{"user_id":3}],[{"choice_id":19},{"user_id":2}],[null]]} ] } """) assert rv.status_code == 200 assert json.loads(rv.data) == {'success': True} assert helpers.get_responses( survey_id, helpers.get_user_id('csander')) == [{ 'question_id': 8, 'title': 'Question A', 'description': None, 'type': 1, 'list_order': 0, 'choices': { 12: '1', 13: '2', 14: '3' }, 'responses': [13] }, { 'question_id': 9, 'title': 'Question B', 'description': 'bbb', 'type': 4, 'list_order': 1, 'choices': 0, 'responses': ['shorty'] }, { 'question_id': 10, 'title': 'Question C', 'description': 'ccc', 'type': 2, 'list_order': 2, 'choices': { 15: 'a', 16: 'b', 17: 'c' }, 'responses': [[15, 17]] }, { 'question_id': 11, 'title': 'Question D', 'description': None, 'type': 5, 'list_order': 3, 'choices': 0, 'responses': ['looooooooong'] }, { 'question_id': 12, 'title': 'Question E', 'description': None, 'type': 3, 'list_order': 4, 'choices': { 18: 'do', 19: 're', 20: 'me' }, 'responses': [[['Belac Sander'], ['re', 'Robert Eng'], ['NO']]] }] rv = client.get(flask.url_for('voting.take_survey', access_key=access_key)) assert rv.status_code == 302 assert rv.location == flask.url_for( 'voting.show_my_response', access_key=access_key) def test_my_response(client): access_key = [ survey for survey in helpers.get_visible_surveys( helpers.get_user_id('csander')) if survey['title'] == 'Response test' ][0]['access_key'] rv = client.get( flask.url_for('voting.show_my_response', access_key=access_key)) assert rv.status_code == 200 assert b'Must be logged in to see response' in rv.data with client.session_transaction() as sess: sess['username'] = 'csander' rv = client.get( flask.url_for('voting.show_my_response', access_key='not-a-real-key')) assert rv.status_code == 200 assert b'Invalid access key' in rv.data unresponded_access_key = [ survey for survey in helpers.get_visible_surveys( helpers.get_user_id('csander')) if survey['title'] != 'Response test' ][0]['access_key'] rv = client.get( flask.url_for( 'voting.show_my_response', access_key=unresponded_access_key)) assert rv.status_code == 200 assert b'You have not responded to this survey' in rv.data rv = client.get( flask.url_for('voting.show_my_response', access_key=access_key)) assert rv.status_code == 200 assert b'My responses for Response test' in rv.data def test_results(client): rv = client.get( flask.url_for('voting.show_results', access_key='invalid-access-key')) assert rv.status_code == 200 assert b'Invalid access key' in rv.data access_key = [ survey for survey in helpers.get_visible_surveys( helpers.get_user_id('csander')) if survey['title'] == 'Response test' ][0]['access_key'] with client.session_transaction() as sess: sess['username'] = 'csander' # Test viewing before close rv = client.get( flask.url_for('voting.show_results', access_key=access_key)) assert rv.status_code == 200 assert b'You are not permitted to see the results at this time' in rv.data # Test releasing before close rv = client.get( flask.url_for('voting.release_results', access_key=access_key)) assert rv.status_code == 200 assert b'Survey has not yet finished' in rv.data # Test after close yesterday = date.today() + timedelta(days=-1) rv = client.post( flask.url_for('voting.save_params', access_key=access_key), data=dict( title='Response test', description='', start_date=yesterday.strftime(helpers.YYYY_MM_DD), start_hour='12', start_minute='00', start_period='P', end_date=yesterday.strftime(helpers.YYYY_MM_DD), end_hour='1', end_minute='00', end_period='P', public='on', group=''), follow_redirects=False) assert rv.status_code == 302 assert rv.location == flask.url_for( 'voting.edit_questions', access_key=access_key) rv = client.get( flask.url_for('voting.show_results', access_key=access_key)) assert rv.status_code == 200 assert b'You are not permitted to see the results at this time' not in rv.data assert b'Responses:' in rv.data assert b'Question A' in rv.data assert b'Question B' in rv.data assert b'Question C' in rv.data assert b'Question D' in rv.data assert b'Question E' in rv.data assert b'Allow others to see results' in rv.data # Test if not creator after close with client.session_transaction() as sess: del sess['username'] rv = client.get( flask.url_for('voting.show_results', access_key=access_key)) assert rv.status_code == 200 assert b'You are not permitted to see the results at this time' in rv.data # Test releasing error conditions rv = client.get( flask.url_for( 'voting.release_results', access_key='invalid-access-key'), follow_redirects=False) assert rv.status_code == 403 with client.session_transaction() as sess: sess['username'] = 'reng' rv = client.get( flask.url_for( 'voting.release_results', access_key='invalid-access-key'), follow_redirects=False) assert rv.status_code == 200 assert b'Invalid access key' in rv.data rv = client.get( flask.url_for('voting.release_results', access_key=access_key), follow_redirects=False) assert rv.status_code == 200 assert b'You are not the creator of this survey' in rv.data # Test successful release with client.session_transaction() as sess: sess['username'] = 'csander' rv = client.get( flask.url_for('voting.release_results', access_key=access_key), follow_redirects=False) assert rv.status_code == 302 assert rv.location == flask.url_for( 'voting.show_results', access_key=access_key) # Test if not creator, but results released with client.session_transaction() as sess: del sess['username'] rv = client.get( flask.url_for('voting.show_results', access_key=access_key)) assert rv.status_code == 200 assert b'You are not permitted to see the results at this time' not in rv.data assert b'Responses:' in rv.data assert b'Question A' in rv.data assert b'Question B' in rv.data assert b'Question C' in rv.data assert b'Question D' in rv.data assert b'Question E' in rv.data assert b'Allow others to see results' not in rv.data

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0.790051

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0.725994

0.683262

0

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0.305727

46,764

1,358

536

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0.737795

0.020422

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0.254043

0.047946

0

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0.021293

false

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0

null

0

1

0

null

0

6

688cc9797d54552b61af4197f1cbf7caed2fdb2f

9,217

py

Python

tests/test_lfw_format.py

IRDonch/datumaro

d029e67549b7359c887bd15039997bd8bbae7c0c

[ "MIT" ]

null

tests/test_lfw_format.py

IRDonch/datumaro

d029e67549b7359c887bd15039997bd8bbae7c0c

[ "MIT" ]

null

tests/test_lfw_format.py

IRDonch/datumaro

d029e67549b7359c887bd15039997bd8bbae7c0c

[ "MIT" ]

null

from unittest import TestCase import os.path as osp import numpy as np from datumaro.components.annotation import Label, Points from datumaro.components.dataset import Dataset from datumaro.components.extractor import DatasetItem from datumaro.plugins.lfw_format import LfwConverter, LfwImporter from datumaro.util.image import Image from datumaro.util.test_utils import TestDir, compare_datasets from .requirements import Requirements, mark_requirement class LfwFormatTest(TestCase): @mark_requirement(Requirements.DATUM_GENERAL_REQ) def test_can_save_and_load(self): source_dataset = Dataset.from_iterable([ DatasetItem(id='name0_0001', subset='test', image=np.ones((2, 5, 3)), annotations=[Label(0, attributes={ 'positive_pairs': ['name0/name0_0002'] })] ), DatasetItem(id='name0_0002', subset='test', image=np.ones((2, 5, 3)), annotations=[Label(0, attributes={ 'positive_pairs': ['name0/name0_0001'], 'negative_pairs': ['name1/name1_0001'] })] ), DatasetItem(id='name1_0001', subset='test', image=np.ones((2, 5, 3)), annotations=[Label(1, attributes={ 'positive_pairs': ['name1/name1_0002'] })] ), DatasetItem(id='name1_0002', subset='test', image=np.ones((2, 5, 3)), annotations=[Label(1, attributes={ 'positive_pairs': ['name1/name1_0002'], 'negative_pairs': ['name0/name0_0001'] })] ), ], categories=['name0', 'name1']) with TestDir() as test_dir: LfwConverter.convert(source_dataset, test_dir, save_images=True) parsed_dataset = Dataset.import_from(test_dir, 'lfw') compare_datasets(self, source_dataset, parsed_dataset, require_images=True) @mark_requirement(Requirements.DATUM_GENERAL_REQ) def test_can_save_and_load_with_no_save_images(self): source_dataset = Dataset.from_iterable([ DatasetItem(id='name0_0001', subset='test', image=np.ones((2, 5, 3)), annotations=[Label(0, attributes={ 'positive_pairs': ['name0/name0_0002'] })] ), DatasetItem(id='name0_0002', subset='test', image=np.ones((2, 5, 3)), annotations=[Label(0, attributes={ 'positive_pairs': ['name0/name0_0001'], 'negative_pairs': ['name1/name1_0001'] })] ), DatasetItem(id='name1_0001', subset='test', image=np.ones((2, 5, 3)), annotations=[Label(1, attributes={})] ), ], categories=['name0', 'name1']) with TestDir() as test_dir: LfwConverter.convert(source_dataset, test_dir, save_images=False) parsed_dataset = Dataset.import_from(test_dir, 'lfw') compare_datasets(self, source_dataset, parsed_dataset) @mark_requirement(Requirements.DATUM_GENERAL_REQ) def test_can_save_and_load_with_landmarks(self): source_dataset = Dataset.from_iterable([ DatasetItem(id='name0_0001', subset='test', image=np.ones((2, 5, 3)), annotations=[ Label(0, attributes={ 'positive_pairs': ['name0/name0_0002'] }), Points([0, 4, 3, 3, 2, 2, 1, 0, 3, 0]), ] ), DatasetItem(id='name0_0002', subset='test', image=np.ones((2, 5, 3)), annotations=[ Label(0), Points([0, 5, 3, 5, 2, 2, 1, 0, 3, 0]), ] ), ], categories=['name0']) with TestDir() as test_dir: LfwConverter.convert(source_dataset, test_dir, save_images=True) parsed_dataset = Dataset.import_from(test_dir, 'lfw') compare_datasets(self, source_dataset, parsed_dataset) @mark_requirement(Requirements.DATUM_GENERAL_REQ) def test_can_save_and_load_with_no_subsets(self): source_dataset = Dataset.from_iterable([ DatasetItem(id='name0_0001', image=np.ones((2, 5, 3)), annotations=[Label(0, attributes={ 'positive_pairs': ['name0/name0_0002'] })], ), DatasetItem(id='name0_0002', image=np.ones((2, 5, 3)), annotations=[Label(0)] ), ], categories=['name0']) with TestDir() as test_dir: LfwConverter.convert(source_dataset, test_dir, save_images=True) parsed_dataset = Dataset.import_from(test_dir, 'lfw') compare_datasets(self, source_dataset, parsed_dataset) @mark_requirement(Requirements.DATUM_GENERAL_REQ) def test_can_save_and_load_with_no_format_names(self): source_dataset = Dataset.from_iterable([ DatasetItem(id='a/1', image=np.ones((2, 5, 3)), annotations=[Label(0, attributes={ 'positive_pairs': ['name0/b/2'], 'negative_pairs': ['d/4'] })], ), DatasetItem(id='b/2', image=np.ones((2, 5, 3)), annotations=[Label(0)] ), DatasetItem(id='c/3', image=np.ones((2, 5, 3)), annotations=[Label(1)] ), DatasetItem(id='d/4', image=np.ones((2, 5, 3)), ), ], categories=['name0', 'name1']) with TestDir() as test_dir: LfwConverter.convert(source_dataset, test_dir, save_images=True) parsed_dataset = Dataset.import_from(test_dir, 'lfw') compare_datasets(self, source_dataset, parsed_dataset) @mark_requirement(Requirements.DATUM_GENERAL_REQ) def test_can_save_dataset_with_cyrillic_and_spaces_in_filename(self): dataset = Dataset.from_iterable([ DatasetItem(id='кириллица с пробелом', image=np.ones((2, 5, 3)) ), DatasetItem(id='name0_0002', image=np.ones((2, 5, 3)), annotations=[Label(0, attributes={ 'negative_pairs': ['кириллица с пробелом'] })] ), ], categories=['name0']) with TestDir() as test_dir: LfwConverter.convert(dataset, test_dir, save_images=True) parsed_dataset = Dataset.import_from(test_dir, 'lfw') compare_datasets(self, dataset, parsed_dataset, require_images=True) @mark_requirement(Requirements.DATUM_GENERAL_REQ) def test_can_save_and_load_image_with_arbitrary_extension(self): dataset = Dataset.from_iterable([ DatasetItem(id='a/1', image=Image( path='a/1.JPEG', data=np.zeros((4, 3, 3))), ), DatasetItem(id='b/c/d/2', image=Image( path='b/c/d/2.bmp', data=np.zeros((3, 4, 3))), ), ], categories=[]) with TestDir() as test_dir: LfwConverter.convert(dataset, test_dir, save_images=True) parsed_dataset = Dataset.import_from(test_dir, 'lfw') compare_datasets(self, dataset, parsed_dataset, require_images=True) DUMMY_DATASET_DIR = osp.join(osp.dirname(__file__), 'assets', 'lfw_dataset') class LfwImporterTest(TestCase): @mark_requirement(Requirements.DATUM_GENERAL_REQ) def test_can_detect(self): self.assertTrue(LfwImporter.detect(DUMMY_DATASET_DIR)) @mark_requirement(Requirements.DATUM_GENERAL_REQ) def test_can_import(self): expected_dataset = Dataset.from_iterable([ DatasetItem(id='name0_0001', subset='test', image=np.ones((2, 5, 3)), annotations=[ Label(0, attributes={ 'negative_pairs': ['name1/name1_0001', 'name1/name1_0002'] }), Points([0, 4, 3, 3, 2, 2, 1, 0, 3, 0]), ] ), DatasetItem(id='name1_0001', subset='test', image=np.ones((2, 5, 3)), annotations=[ Label(1, attributes={ 'positive_pairs': ['name1/name1_0002'], }), Points([1, 6, 4, 6, 3, 3, 2, 1, 4, 1]), ] ), DatasetItem(id='name1_0002', subset='test', image=np.ones((2, 5, 3)), annotations=[ Label(1), Points([0, 5, 3, 5, 2, 2, 1, 0, 3, 0]), ] ), ], categories=['name0', 'name1']) dataset = Dataset.import_from(DUMMY_DATASET_DIR, 'lfw') compare_datasets(self, expected_dataset, dataset)

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80

0.5364

964

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0.04664

0.05088

0.795633

0.787365

0.781429

0.771465

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0.723553

0

0.051673

0.338613

9,217

239

81

38.564854

0.722113

0

0.717703

0

0.090593

0

0.004785

1

0.043062

false

0

0.100478

0

0.15311

0

null

0

1

0

1

0

null

0

6

d7a5e5b05587f4827d617bcfc980e6852b32b7e8

4,606

py

Python

src/main.py

MrSpadala/2PL-tester

8911660ad874a485d62cd3ae7cbeb3f7bf1bf96a

[ "MIT" ]

null

src/main.py

MrSpadala/2PL-tester

8911660ad874a485d62cd3ae7cbeb3f7bf1bf96a

[ "MIT" ]

null

src/main.py

MrSpadala/2PL-tester

8911660ad874a485d62cd3ae7cbeb3f7bf1bf96a

[ "MIT" ]

1

2022-02-07T11:39:08.000Z

from flask import Flask from flask import request from check2PL import solve2PL from checkConflict import solveConflict from checkTimestamps import solveTimestamps from utils import parse_schedule app = Flask(__name__) index_cached = open('../static/index.html', 'r').read() @app.route("/2PL", methods=['GET', 'POST']) def index(): # get and check args schedule = request.args.get('schedule') use_xl_only = request.args.get('use_xl_only') response = index_cached if schedule is None: return response schedule = schedule.replace(' ', '') if schedule == '': return format_response('Empty schedule', response) sched_parsed = parse_schedule(schedule) print(sched_parsed) if type(sched_parsed) == str: #parsing error message return format_response('Parsing error: '+sched_parsed, response) # Solve res2PL = solve2PL(sched_parsed, use_xl_only) resConfl = solveConflict(sched_parsed) resTS = solveTimestamps(sched_parsed) # Format results for conflict serializability msg = 'Conflict serializability ' msg += 'Is the schedule conflict serializable: '+str(resConfl)+'' response = format_response(msg, response) # Format results for 2PL msg = 'Two phase lock protocol ' if res2PL['sol'] is None: #return format_response(' '+res2PL['err']+' '+res2PL['partial_locks']) msg += res2PL['err'] response = format_response(msg, response) else: msg += """ Solution: {}, Is the schedule strict-2PL: {}, Is the schedule strong strict-2PL: {} """.format(res2PL['sol'], res2PL['strict'], res2PL['strong']) response = format_response(msg, response) # Format results for timestamps msg = 'Timestamps (DRAFT) ' if resTS['err'] is None: msg += 'List of executed operations: '+str(resTS['sol'])+' ' msg += 'List of waiting transactions at the end of schedule: '+str(resTS['waiting_tx'])+' ' response = format_response(msg, response) else: msg += resTS['err']+' ' response = format_response(msg, response) return response @app.route("/solve", methods=['POST']) def solve(): # get and check args schedule = request.form.get('schedule') use_xl_only = request.form.get('use_xl_only') response = open('solve.html', 'r').read() if schedule is None: return response schedule = schedule.replace(' ', '') if schedule == '': return format_response('Empty schedule', response),400 sched_parsed = parse_schedule(schedule) print(sched_parsed) result_http_code = 200 if type(sched_parsed) == str: # parsing error message return format_response('Parsing error: ' + sched_parsed, response),400 # Solve res2PL = solve2PL(sched_parsed, use_xl_only) resConfl = solveConflict(sched_parsed) resTS = solveTimestamps(sched_parsed) # Format results for conflict serializability msg = 'Conflict serializability ' msg += 'Is the schedule conflict serializable: ' + str(resConfl) + '' response = format_response(msg, response) # Format results for 2PL msg = 'Two phase lock protocol ' if res2PL['sol'] is None: # return format_response(' '+res2PL['err']+' '+res2PL['partial_locks']) msg += res2PL['err'] response = format_response(msg, response) else: msg += """ Solution: {} Is the schedule strict-2PL: {} Is the schedule strong strict-2PL: {} """.format(res2PL['sol'], res2PL['strict'], res2PL['strong']) response = format_response(msg, response) # Format results for timestamps msg = 'Timestamps (DRAFT) ' if resTS['err'] is None: msg += 'List of executed operations: ' + str(resTS['sol']) + ' ' msg += 'List of waiting transactions at the end of schedule: ' + str(resTS['waiting_tx']) + ' ' response = format_response(msg, response) else: msg += resTS['err'] + ' ' response = format_response(msg, response) return response,result_http_code def format_response(msg, res): return res.replace('', ' '+msg+' ') if __name__ == "__main__": from os.path import isfile debug = isfile('.DEBUG') host = "localhost" if debug else "0.0.0.0" app.run(host=host, port=5000, debug=debug)

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null

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d7b24c8ca7adbb62fe2013b2416aa2a35ca6ffbb

48

py

Python

src/opendatablend/__init__.py

sscaress/opendatablend-py

976b514cfa0b9c7d41f4edc1a1c118c8c5a4fd6d

[ "MIT" ]

null

src/opendatablend/__init__.py

sscaress/opendatablend-py

976b514cfa0b9c7d41f4edc1a1c118c8c5a4fd6d

[ "MIT" ]

null

src/opendatablend/__init__.py

sscaress/opendatablend-py

976b514cfa0b9c7d41f4edc1a1c118c8c5a4fd6d

[ "MIT" ]

null

from opendatablend.opendatablend import get_data

48

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1

0

1

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6

0bf6e96cb5f32b5632ec9ac01e9f0991a95a8aa5

1,193

py

Python

py_modular/utils/processing.py

fredyeah/streamer

079b8cb5d6002df50f13aa184ece7a4a3b357da4

[ "MIT" ]

5

2021-05-20T00:59:41.000Z

2021-12-05T18:25:57.000Z

py_modular/utils/processing.py

fredyeah/streamer

079b8cb5d6002df50f13aa184ece7a4a3b357da4

[ "MIT" ]

null

py_modular/utils/processing.py

fredyeah/streamer

079b8cb5d6002df50f13aa184ece7a4a3b357da4

[ "MIT" ]

1

2021-05-30T12:43:02.000Z

from math import floor, asin, sin, pi def window_grains_sin(grains=[]): """Util used to window an array of audio with half a sine wave :param grains: An array of audio buffers that contain audio data to be windowed :type grains: array(array) :returns: An array of the same shape as the input, but windowed :rtype: array(array) """ for index, grain in enumerate(grains): print('windowed ' + str(index) + ' grains') grain_length = len(grain) for i in range(grain_length): grain[i] = grain[i] * sin(i * pi / grain_length) return grains def window_grains_tri(grains=[]): """Util used to window an array of audio with half a triangle wave :param grains: An array of audio buffers that contain audio data to be windowed :type grains: array(array) :returns: An array of the same shape as the input, but windowed :rtype: array(array) """ for index, grain in enumerate(grains): print('windowed ' + str(index) + ' grains') grain_length = len(grain) for i in range(grain_length): grain[i] = grain[i] * (asin(sin(i * pi / grain_length)) / (0.5 * pi)) return grains

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null

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null

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6

042977f0af674cf3ca4f28fd9e848d23764fa207

67

py

Python

pyece/core/property/__init__.py

rilshok/pyece

eaa78f175a922b99fd0bf5157ba129bf495203e3

[ "MIT" ]

null

pyece/core/property/__init__.py

rilshok/pyece

eaa78f175a922b99fd0bf5157ba129bf495203e3

[ "MIT" ]

null

pyece/core/property/__init__.py

rilshok/pyece

eaa78f175a922b99fd0bf5157ba129bf495203e3

[ "MIT" ]

null

from .base import * from .point import * from .pointcloud import *

16.75

25

0.731343

9

67

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0.555556

0.408163

0

0.179104

67

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null

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1

0

1

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1

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6

045a16856d919e5772728374d499ad5d309eb0c0

285

py

Python

src/errors.py

hyper-neutrino/bots-reforged

cbb4d34f2e40d460301077c8d58d3619e71f4406

[ "MIT" ]

null

src/errors.py

hyper-neutrino/bots-reforged

cbb4d34f2e40d460301077c8d58d3619e71f4406

[ "MIT" ]

null

src/errors.py

hyper-neutrino/bots-reforged

cbb4d34f2e40d460301077c8d58d3619e71f4406

[ "MIT" ]

null

class BotError(RuntimeError): def __init__(self, message = "An unexpected error occurred with the bot!"): self.message = message class DataError(RuntimeError): def __init__(self, message = "An unexpected error occurred when accessing/saving data!"): self.message = message

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1

0

6

f0bb6a461d2617c7c17a08e8e18ef1f4c1de29f2

18,422

py

Python

main/ensemble.py

ejklektov/dcase20-task3-seld

dd32cb5b23d48f10526f89e4ef1baf09947d6cc5

[ "MIT" ]

null

main/ensemble.py

ejklektov/dcase20-task3-seld

dd32cb5b23d48f10526f89e4ef1baf09947d6cc5

[ "MIT" ]

null

main/ensemble.py

ejklektov/dcase20-task3-seld

dd32cb5b23d48f10526f89e4ef1baf09947d6cc5

[ "MIT" ]

null

import argparse import os import pdb import shutil from timeit import default_timer as timer import numpy as np import pandas as pd from tqdm import tqdm from evaluation import write_submission def iters_ensemble(args): ''' Ensemble on different iterations and generate ensembled files in fusioned folder ''' ## directories if args.task_type == 'sed_only': # iterations ensemble directory fusioned_dir = os.path.join(submissions_dir, args.name + '_' + args.model_sed + '_{}'.format(args.audio_type) + '_{}'.format(args.feature_type) + '_aug_{}'.format(args.data_aug) + '_seed_{}'.format(args.seed), 'sed_mask_fusioned') os.makedirs(fusioned_dir, exist_ok=True) fusion_fn = '_fusion_sed_epoch_{}' iterator = range(38, 42, 2) elif args.task_type == 'two_staged_eval': # iterations ensemble directory fusioned_dir = os.path.join(submissions_dir, args.name + '_' + args.model_sed + '_{}'.format(args.audio_type) + '_{}'.format(args.feature_type) + '_aug_{}'.format(args.data_aug) + '_seed_{}'.format(args.seed), 'doa_fusioned') os.makedirs(fusioned_dir, exist_ok=True) fusion_fn = '_fusion_doa_epoch_{}' iterator = range(78, 82, 2) ## average ensemble print('\n===> Average ensemble') ensemble_start_time = timer() predicts_fusioned = [] for epoch_num in iterator: fusion_dir = os.path.join(submissions_dir, args.name + '_' + args.model_sed + '_{}'.format(args.audio_type) + '_{}'.format(args.feature_type) + '_aug_{}'.format(args.data_aug) + '_seed_{}'.format(args.seed), fusion_fn.format(epoch_num)) for fn in sorted(os.listdir(fusion_dir)): if fn.endswith('.csv') and not fn.startswith('.'): fn_path = os.path.join(fusion_dir, fn) predicts_fusioned.append(pd.read_csv(fn_path, header=0, index_col=0).values) if len(predicts_fusioned) > file_num: for n in range(file_num): min_len = min(predicts_fusioned[n].shape[0], predicts_fusioned[n+file_num].shape[0]) predicts_fusioned[n] = (predicts_fusioned[n][:min_len,:] + predicts_fusioned[n+file_num][:min_len,:]) / 2 predicts_fusioned = predicts_fusioned[:file_num] print('\nAverage ensemble time: {:.3f} s.'.format(timer()-ensemble_start_time)) ## write the fusioned sed probabilities or doa predictions to fusioned files print('\n===> Write the fusioned sed probabilities or doa predictions to fusioned files') # this folder here is only used for supplying fn iterator = tqdm(sorted(os.listdir(fusion_dir)), total=len(os.listdir(fusion_dir)), unit='iters') n = 0 for fn in iterator: if fn.endswith('.csv') and not fn.startswith('.'): # write to sed_mask_fusioned folder fn_path = os.path.join(fusioned_dir, fn) df_output = pd.DataFrame(predicts_fusioned[n]) df_output.to_csv(fn_path) n += 1 iterator.close() print('\n' + fusioned_dir) print('\n===> Iterations ensemble finished!') def threshold_iters_ensemble(args): ''' Threshold the ensembled iterations and write to submissions ''' # directories sed_mask_fusioned_dir = os.path.join(submissions_dir, args.name + '_' + args.model_sed + '_{}'.format(args.audio_type) + '_{}'.format(args.feature_type) + '_aug_{}'.format(args.data_aug) + '_seed_{}'.format(args.seed), 'sed_mask_fusioned') doa_fusioned_dir = os.path.join(submissions_dir, args.name + '_' + args.model_sed + '_{}'.format(args.audio_type) + '_{}'.format(args.feature_type) + '_aug_{}'.format(args.data_aug) + '_seed_{}'.format(args.seed), 'doa_fusioned') if args.task_type == 'sed_only': test_fusioned_dir = os.path.join(submissions_dir, args.name + '_' + args.model_sed + '_{}'.format(args.audio_type) + '_{}'.format(args.feature_type) + '_aug_{}'.format(args.data_aug) + '_seed_{}'.format(args.seed), 'sed_test_fusioned') elif args.task_type == 'two_staged_eval': test_fusioned_dir = os.path.join(submissions_dir, args.name + '_' + args.model_sed + '_{}'.format(args.audio_type) + '_{}'.format(args.feature_type) + '_aug_{}'.format(args.data_aug) + '_seed_{}'.format(args.seed), 'all_test_fusioned') os.makedirs(test_fusioned_dir, exist_ok=True) if args.task_type == 'sed_only': iterator = tqdm(sorted(os.listdir(sed_mask_fusioned_dir)), total=len(os.listdir(sed_mask_fusioned_dir)), unit='iters') for fn in iterator: if fn.endswith('_prob.csv') and not fn.startswith('.'): fn_path = os.path.join(sed_mask_fusioned_dir, fn) prob_fusioned = pd.read_csv(fn_path, header=0, index_col=0).values # write to sed_test_fusioned fn_noextension = fn.split('_prob')[0] output_doas = np.zeros((prob_fusioned.shape[0],22)) submit_dict = { 'filename': fn_noextension, 'events': (prob_fusioned>args.threshold).astype(np.float32), 'doas': output_doas } write_submission(submit_dict, test_fusioned_dir) if args.task_type == 'two_staged_eval': iterator = tqdm(sorted(os.listdir(doa_fusioned_dir)), total=len(os.listdir(doa_fusioned_dir)), unit='iters') for fn in iterator: if fn.endswith('_doa.csv') and not fn.startswith('.'): fn_noextension = fn.split('_doa')[0] # read sed predictions from sed_mask_fusioned directory fn_path = os.path.join(sed_mask_fusioned_dir, fn_noextension + '_prob.csv') prob_fusioned = pd.read_csv(fn_path, header=0, index_col=0).values # read doa predictions from doa_fusioned directory fn_path = os.path.join(doa_fusioned_dir, fn) doa_fusioned = pd.read_csv(fn_path, header=0, index_col=0).values # write to all_test_fusioned submit_dict = { 'filename': fn_noextension, 'events': (prob_fusioned>args.threshold).astype(np.float32), 'doas': doa_fusioned } write_submission(submit_dict, test_fusioned_dir) iterator.close() print('\n' + test_fusioned_dir) print('\n===> Threshold iterations ensemble finished!') def models_ensemble(args): ''' Ensemble on different iterations and generate ensembled files in fusioned folder ''' # directories if args.task_type == 'sed_only': fusion_folder = 'sed_mask_fusioned' fusioned_folder = 'sed_mask_models_fusioned' elif args.task_type == 'two_staged_eval': fusion_folder = 'doa_fusioned' fusioned_folder = 'doa_models_fusioned' print('\n===> Model average ensemble') ensemble_start_time = timer() predicts_fusioned = [] for model_folder in sorted(os.listdir(submissions_dir)): if not model_folder.startswith('.') and model_folder != 'models_ensemble': print('\n' + model_folder) fusion_dir = os.path.join(submissions_dir, model_folder, fusion_folder) for fn in sorted(os.listdir(fusion_dir)): if fn.endswith('.csv') and not fn.startswith('.'): fn_path = os.path.join(fusion_dir, fn) predicts_fusioned.append(pd.read_csv(fn_path, header=0, index_col=0).values) if len(predicts_fusioned) > file_num: for n in range(file_num): min_len = min(predicts_fusioned[n].shape[0], predicts_fusioned[n+file_num].shape[0]) predicts_fusioned[n] = (predicts_fusioned[n][:min_len,:] + predicts_fusioned[n+file_num][:min_len,:]) / 2 predicts_fusioned = predicts_fusioned[:file_num] print('\nAverage ensemble time: {:.3f} s.'.format(timer()-ensemble_start_time)) ## write the fusioned sed probabilities or doa predictions to fusioned files print('\n===> Write the fusioned sed probabilities or doa predictions to fusioned files') # this folder here is only used for supplying fn iterator = tqdm(sorted(os.listdir(fusion_dir)), total=len(os.listdir(fusion_dir)), unit='iters') models_ensemble_dir = os.path.join(submissions_dir, 'models_ensemble', fusioned_folder) os.makedirs(models_ensemble_dir, exist_ok=True) n = 0 for fn in iterator: if fn.endswith('.csv') and not fn.startswith('.'): # write to sed_mask_fusioned folder fn_path = os.path.join(models_ensemble_dir, fn) df_output = pd.DataFrame(predicts_fusioned[n]) df_output.to_csv(fn_path) n += 1 iterator.close() print('\n' + models_ensemble_dir) print('\n===> Models ensemble finished!') def threshold_models_ensemble(args): ''' Threshold the ensembled models and write to submissions ''' # directories sed_mask_fusioned_dir = os.path.join(submissions_dir, 'models_ensemble', 'sed_mask_models_fusioned') doa_fusioned_dir = os.path.join(submissions_dir, 'models_ensemble', 'doa_models_fusioned') if args.task_type == 'sed_only': test_fusioned_dir = os.path.join(submissions_dir, args.name + '_' + args.model_sed + '_{}'.format(args.audio_type) + '_{}'.format(args.feature_type) + '_aug_{}'.format(args.data_aug) + '_seed_{}'.format(args.seed), 'sed_test_fusioned') elif args.task_type == 'two_staged_eval': test_fusioned_dir = os.path.join(submissions_dir, args.name + '_' + args.model_sed + '_{}'.format(args.audio_type) + '_{}'.format(args.feature_type) + '_aug_{}'.format(args.data_aug) + '_seed_{}'.format(args.seed), 'all_test_fusioned') os.makedirs(test_fusioned_dir, exist_ok=True) if args.task_type == 'sed_only': iterator = tqdm(sorted(os.listdir(sed_mask_fusioned_dir)), total=len(os.listdir(sed_mask_fusioned_dir)), unit='iters') for fn in iterator: if fn.endswith('_prob.csv') and not fn.startswith('.'): fn_path = os.path.join(sed_mask_fusioned_dir, fn) prob_fusioned = pd.read_csv(fn_path, header=0, index_col=0).values # write to sed_test_fusioned fn_noextension = fn.split('_prob')[0] output_doas = np.zeros((prob_fusioned.shape[0],22)) submit_dict = { 'filename': fn_noextension, 'events': (prob_fusioned>args.threshold).astype(np.float32), 'doas': output_doas } write_submission(submit_dict, test_fusioned_dir) if args.task_type == 'two_staged_eval': iterator = tqdm(sorted(os.listdir(doa_fusioned_dir)), total=len(os.listdir(doa_fusioned_dir)), unit='iters') for fn in iterator: if fn.endswith('_doa.csv') and not fn.startswith('.'): fn_noextension = fn.split('_doa')[0] # read sed predictions from sed_mask_fusioned directory fn_path = os.path.join(sed_mask_fusioned_dir, fn_noextension + '_prob.csv') prob_fusioned = pd.read_csv(fn_path, header=0, index_col=0).values # read doa predictions from doa_fusioned directory fn_path = os.path.join(doa_fusioned_dir, fn) doa_fusioned = pd.read_csv(fn_path, header=0, index_col=0).values # write to all_test_fusioned submit_dict = { 'filename': fn_noextension, 'events': (prob_fusioned>args.threshold).astype(np.float32), 'doas': doa_fusioned } write_submission(submit_dict, test_fusioned_dir) iterator.close() print('\n' + test_fusioned_dir) print('\n===> Threshold models ensemble finished!') if __name__ == '__main__': parser = argparse.ArgumentParser(description='Ensemble on different iterations or different models') subparsers = parser.add_subparsers(dest='mode') parser_iters_ensemble = subparsers.add_parser('iters_ensemble') parser_iters_ensemble.add_argument('--workspace', type=str, required=True, help='workspace directory') parser_iters_ensemble.add_argument('--feature_type', type=str, required=True, choices=['logmel', 'logmelgcc', 'logmelintensity', 'logmelgccintensity']) parser_iters_ensemble.add_argument('--audio_type', type=str, required=True, choices=['foa', 'mic', 'foa&mic'], help='audio type') parser_iters_ensemble.add_argument('--task_type', type=str, required=True, choices=['sed_only', 'doa_only', 'two_staged_eval', 'seld']) parser_iters_ensemble.add_argument('--model_sed', type=str, default='CRNN10') parser_iters_ensemble.add_argument('--model_doa', type=str, default='pretrained_CRNN10') parser_iters_ensemble.add_argument('--data_aug', default='None', type=str, help='data augmentation methods') parser_iters_ensemble.add_argument('--seed', default=42, type=int, help='random seed') parser_iters_ensemble.add_argument('--name', default='n0', type=str) parser_threshold_iters_ensemble = subparsers.add_parser('threshold_iters_ensemble') parser_threshold_iters_ensemble.add_argument('--workspace', type=str, required=True, help='workspace directory') parser_threshold_iters_ensemble.add_argument('--feature_type', type=str, required=True, choices=['logmel', 'logmelgcc', 'logmelintensity', 'logmelgccintensity']) parser_threshold_iters_ensemble.add_argument('--audio_type', type=str, required=True, choices=['foa', 'mic', 'foa&mic'], help='audio type') parser_threshold_iters_ensemble.add_argument('--task_type', type=str, required=True, choices=['sed_only', 'doa_only', 'two_staged_eval', 'seld']) parser_threshold_iters_ensemble.add_argument('--model_sed', type=str, default='CRNN10') parser_threshold_iters_ensemble.add_argument('--model_doa', type=str, default='pretrained_CRNN10') parser_threshold_iters_ensemble.add_argument('--data_aug', default='None', type=str, help='data augmentation methods') parser_threshold_iters_ensemble.add_argument('--seed', default=42, type=int, help='random seed') parser_threshold_iters_ensemble.add_argument('--name', default='n0', type=str) parser_threshold_iters_ensemble.add_argument('--threshold', default=0.3, type=float) parser_models_ensemble = subparsers.add_parser('models_ensemble') parser_models_ensemble.add_argument('--workspace', type=str, required=True, help='workspace directory') parser_models_ensemble.add_argument('--feature_type', type=str, required=True, choices=['logmel', 'logmelgcc', 'logmelintensity', 'logmelgccintensity']) parser_models_ensemble.add_argument('--audio_type', type=str, required=True, choices=['foa', 'mic', 'foa&mic'], help='audio type') parser_models_ensemble.add_argument('--task_type', type=str, required=True, choices=['sed_only', 'doa_only', 'two_staged_eval', 'seld']) parser_models_ensemble.add_argument('--model_sed', type=str, default='CRNN10') parser_models_ensemble.add_argument('--model_doa', type=str, default='pretrained_CRNN10') parser_models_ensemble.add_argument('--data_aug', default='None', type=str, help='data augmentation methods') parser_models_ensemble.add_argument('--seed', default=42, type=int, help='random seed') parser_models_ensemble.add_argument('--name', default='n0', type=str) parser_threshold_models_ensemble = subparsers.add_parser('threshold_models_ensemble') parser_threshold_models_ensemble.add_argument('--workspace', type=str, required=True, help='workspace directory') parser_threshold_models_ensemble.add_argument('--feature_type', type=str, required=True, choices=['logmel', 'logmelgcc', 'logmelintensity', 'logmelgccintensity']) parser_threshold_models_ensemble.add_argument('--audio_type', type=str, required=True, choices=['foa', 'mic', 'foa&mic'], help='audio type') parser_threshold_models_ensemble.add_argument('--task_type', type=str, required=True, choices=['sed_only', 'doa_only', 'two_staged_eval', 'seld']) parser_threshold_models_ensemble.add_argument('--model_sed', type=str, default='CRNN10') parser_threshold_models_ensemble.add_argument('--model_doa', type=str, default='pretrained_CRNN10') parser_threshold_models_ensemble.add_argument('--data_aug', default='None', type=str, help='data augmentation methods') parser_threshold_models_ensemble.add_argument('--seed', default=42, type=int, help='random seed') parser_threshold_models_ensemble.add_argument('--name', default='n0', type=str) parser_threshold_models_ensemble.add_argument('--threshold', default=0.3, type=float) args = parser.parse_args() # submissions directory global submissions_dir submissions_dir = os.path.join(args.workspace, 'appendixes', 'submissions_eval') global file_num file_num = 100 # ensemble different iterations or models if args.mode == 'iters_ensemble': iters_ensemble(args) elif args.mode == 'threshold_iters_ensemble': threshold_iters_ensemble(args) elif args.mode == 'models_ensemble': models_ensemble(args) elif args.mode == 'threshold_models_ensemble': threshold_models_ensemble(args)

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350

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52.634286

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0.059524

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null

0

1

0

null

0

6

9bcefccb6280a6ff7ccc5701be21245777cc634c

19,469

py

Python

owlery_client/api/sparql_api.py

rpgoldman/owlery-client

bd02ee7c071b720604870d76da42a7b1e988332b

[ "Apache-2.0" ]

null

owlery_client/api/sparql_api.py

rpgoldman/owlery-client

bd02ee7c071b720604870d76da42a7b1e988332b

[ "Apache-2.0" ]

null

owlery_client/api/sparql_api.py

rpgoldman/owlery-client

bd02ee7c071b720604870d76da42a7b1e988332b

[ "Apache-2.0" ]

null

""" Owlery API Owlery provides a web API for an [OWL API](http://owlapi.sourceforge.net)-based reasoner containing a configurable set of ontologies (a \"knowledgebase\"). # noqa: E501 The version of the OpenAPI document: 1.0.0 Contact: balhoff@renci.org Generated by: https://openapi-generator.tech """ import re # noqa: F401 import sys # noqa: F401 from owlery_client.api_client import ApiClient, Endpoint as _Endpoint from owlery_client.model_utils import ( # noqa: F401 check_allowed_values, check_validations, date, datetime, file_type, none_type, validate_and_convert_types ) class SPARQLApi(object): """NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def __kbs_kb_expand_get( self, kb, query, **kwargs ): """Expand SPARQL query encoded in URL parameter # noqa: E501 Expand a SPARQL query, transforming Owlet-style embedded class expressions into `FILTER`s # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.kbs_kb_expand_get(kb, query, async_req=True) >>> result = thread.get() Args: kb (str): label for a knowledgebase in this Owlery query (str): SPARQL query Keyword Args: _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: None If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_host_index'] = kwargs.get('_host_index') kwargs['kb'] = \ kb kwargs['query'] = \ query return self.call_with_http_info(**kwargs) self.kbs_kb_expand_get = _Endpoint( settings={ 'response_type': None, 'auth': [], 'endpoint_path': '/kbs/{kb}/expand', 'operation_id': 'kbs_kb_expand_get', 'http_method': 'GET', 'servers': None, }, params_map={ 'all': [ 'kb', 'query', ], 'required': [ 'kb', 'query', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'kb': (str,), 'query': (str,), }, 'attribute_map': { 'kb': 'kb', 'query': 'query', }, 'location_map': { 'kb': 'path', 'query': 'query', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/sparql-query' ], 'content_type': [], }, api_client=api_client, callable=__kbs_kb_expand_get ) def __kbs_kb_expand_post( self, kb, body, **kwargs ): """Expand SPARQL query contained in request body # noqa: E501 Expand a SPARQL query, transforming Owlet-style embedded class expressions into `FILTER`s # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.kbs_kb_expand_post(kb, body, async_req=True) >>> result = thread.get() Args: kb (str): label for a knowledgebase in this Owlery body (str): Keyword Args: _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: None If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_host_index'] = kwargs.get('_host_index') kwargs['kb'] = \ kb kwargs['body'] = \ body return self.call_with_http_info(**kwargs) self.kbs_kb_expand_post = _Endpoint( settings={ 'response_type': None, 'auth': [], 'endpoint_path': '/kbs/{kb}/expand', 'operation_id': 'kbs_kb_expand_post', 'http_method': 'POST', 'servers': None, }, params_map={ 'all': [ 'kb', 'body', ], 'required': [ 'kb', 'body', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'kb': (str,), 'body': (str,), }, 'attribute_map': { 'kb': 'kb', }, 'location_map': { 'kb': 'path', 'body': 'body', }, 'collection_format_map': { } }, headers_map={ 'accept': [], 'content_type': [ 'application/sparql-query', 'application/x-www-form-urlencoded' ] }, api_client=api_client, callable=__kbs_kb_expand_post ) def __kbs_kb_sparql_get( self, kb, query, **kwargs ): """Perform SPARQL query encoded in URL parameter # noqa: E501 Perform SPARQL query using Owlet-style embedded class expression. This is not a complete SPARQL endpoint. It is for using Owlery as a federated query endpoint for a single Owlet triple pattern. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.kbs_kb_sparql_get(kb, query, async_req=True) >>> result = thread.get() Args: kb (str): label for a knowledgebase in this Owlery query (str): SPARQL query Keyword Args: _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: None If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_host_index'] = kwargs.get('_host_index') kwargs['kb'] = \ kb kwargs['query'] = \ query return self.call_with_http_info(**kwargs) self.kbs_kb_sparql_get = _Endpoint( settings={ 'response_type': None, 'auth': [], 'endpoint_path': '/kbs/{kb}/sparql', 'operation_id': 'kbs_kb_sparql_get', 'http_method': 'GET', 'servers': None, }, params_map={ 'all': [ 'kb', 'query', ], 'required': [ 'kb', 'query', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'kb': (str,), 'query': (str,), }, 'attribute_map': { 'kb': 'kb', 'query': 'query', }, 'location_map': { 'kb': 'path', 'query': 'query', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/sparql-results+xml' ], 'content_type': [], }, api_client=api_client, callable=__kbs_kb_sparql_get ) def __kbs_kb_sparql_post( self, kb, body, **kwargs ): """Perform SPARQL query contained in request body # noqa: E501 Perform SPARQL query using Owlet-style embedded class expression. This is not a complete SPARQL endpoint. It is for using Owlery as a federated query endpoint for a single Owlet triple pattern. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.kbs_kb_sparql_post(kb, body, async_req=True) >>> result = thread.get() Args: kb (str): label for a knowledgebase in this Owlery body (str): Keyword Args: _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: None If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_host_index'] = kwargs.get('_host_index') kwargs['kb'] = \ kb kwargs['body'] = \ body return self.call_with_http_info(**kwargs) self.kbs_kb_sparql_post = _Endpoint( settings={ 'response_type': None, 'auth': [], 'endpoint_path': '/kbs/{kb}/sparql', 'operation_id': 'kbs_kb_sparql_post', 'http_method': 'POST', 'servers': None, }, params_map={ 'all': [ 'kb', 'body', ], 'required': [ 'kb', 'body', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'kb': (str,), 'body': (str,), }, 'attribute_map': { 'kb': 'kb', }, 'location_map': { 'kb': 'path', 'body': 'body', }, 'collection_format_map': { } }, headers_map={ 'accept': [], 'content_type': [ 'application/sparql-query', 'application/x-www-form-urlencoded' ] }, api_client=api_client, callable=__kbs_kb_sparql_post )

35.657509

219

0.453644

1,734

19,469

4.868512

0.121684

0.029851

0.024639

0.025586

0.910448

0.900971

0.882729

0.872779

0

0.004116

0.463403

19,469

545

220

35.722936

0.803963

0.354975

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0

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0.039266

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0.01385

false

0

0.01108

0

0.038781

0

null

0

1

0

null

0

6

502350f581d3924fb70e62c90af4ec84d86da448

13,809

py

Python

tests/test_script.py

szypkiwonsz/Cryptocurrency-Script

e9ec7803fd78c49a598bfb3c89c800c347d5eb34

[ "MIT" ]

null

tests/test_script.py

szypkiwonsz/Cryptocurrency-Script

e9ec7803fd78c49a598bfb3c89c800c347d5eb34

[ "MIT" ]

null

tests/test_script.py

szypkiwonsz/Cryptocurrency-Script

e9ec7803fd78c49a598bfb3c89c800c347d5eb34

[ "MIT" ]

1

2021-09-29T18:34:11.000Z

from datetime import datetime from unittest.mock import patch, mock_open import pytest from click.testing import CliRunner from dateutil.relativedelta import relativedelta from httmock import HTTMock, all_requests from script import average_price_by_month, consecutive_increase, export from tests.conftest import api_content @all_requests def api_mock(url, request): return {'status_code': 200, 'content': api_content} runner = CliRunner() @pytest.mark.script class TestAveragePriceByMonth: def test_average_price_by_month(self, database): with patch('database_handler.DatabaseHandler.db') as mock: mock.return_value = database with HTTMock(api_mock): response = runner.invoke(average_price_by_month, ['--start_date=2012-01', '--end_date=2012-02']) assert response.exit_code == 0 assert '5.48' in response.output def test_average_price_by_month_diff_coin(self, database): with patch('database_handler.DatabaseHandler.db') as mock: mock.return_value = database with HTTMock(api_mock): response = runner.invoke(average_price_by_month, [ '--start_date=2012-01', '--end_date=2012-02', '--coin=usdt-tether']) assert response.exit_code == 0 assert '5.48' in response.output def test_average_price_by_month_too_early_start_date(self): response = runner.invoke(average_price_by_month, ['--start_date=2008-12-31', '--end_date=2011-12']) assert response.exit_code == 2 def test_average_price_by_month_too_early_start_date_previously_end_date(self): response = runner.invoke(average_price_by_month, ['--end_date=2011-12', '--start_date=2008-12-31']) assert response.exit_code == 2 def test_average_price_by_month_too_late_start_date(self): next_month = datetime.now() + relativedelta(months=1) response = runner.invoke(average_price_by_month, [f'--start_date={str(next_month)[:7]}', '--end_date=2011-12']) assert response.exit_code == 2 def test_average_price_by_month_too_late_start_date_previously_end_date(self): next_month = datetime.now() + relativedelta(months=1) response = runner.invoke(average_price_by_month, ['--end_date=2011-12', f'--start_date={str(next_month)[:7]}']) assert response.exit_code == 2 def test_average_price_by_month_too_late_end_date(self): next_month = datetime.now() + relativedelta(months=1) response = runner.invoke(average_price_by_month, ['--start_date=2012-01', f'--end_date={str(next_month)[:7]}']) assert response.exit_code == 2 def test_average_price_by_month_too_late_end_date_previously_end_date(self): next_month = datetime.now() + relativedelta(months=1) response = runner.invoke(average_price_by_month, [f'--end_date={str(next_month)[:7]}', '--start_date=2012-01']) assert response.exit_code == 2 def test_average_price_by_month_end_date_before_start_date(self): response = runner.invoke(average_price_by_month, ['--start_date=2012-01', '--end_date=2011-12']) assert response.exit_code == 2 def test_average_price_by_month_end_date_before_start_date_previously_end_date(self): response = runner.invoke(average_price_by_month, ['--end_date=2011-12', '--start_date=2012-01']) assert response.exit_code == 2 def test_average_price_by_month_wrong_arguments(self): response = runner.invoke(average_price_by_month, ['--start_date=2012-01-01', f'--end_date=2011-12-01']) assert response.exit_code == 2 @pytest.mark.script class TestConsecutiveIncrease: def test_consecutive_increase(self, database): with patch('database_handler.DatabaseHandler.db') as mock: mock.return_value = database with HTTMock(api_mock): response = runner.invoke(consecutive_increase, ['--start_date=2012-01-01', '--end_date=2012-02-03']) assert response.exit_code == 0 assert '$17.66' in response.output def test_consecutive_increase_diff_coin(self, database): with patch('database_handler.DatabaseHandler.db') as mock: mock.return_value = database with HTTMock(api_mock): response = runner.invoke(consecutive_increase, [ '--start_date=2012-01-01', '--end_date=2012-02-03', '--coin=usdt-tether' ]) assert response.exit_code == 0 assert '$17.66' in response.output def test_consecutive_increase_by_month_too_early_start_date(self): response = runner.invoke(consecutive_increase, [ '--start_date=2008-12-31', '--end_date=2011-12-01' ]) assert response.exit_code == 2 def test_consecutive_increase_by_month_too_early_start_date_previously_end_date(self): response = runner.invoke(consecutive_increase, [ '--end_date=2011-12-01', '--start_date=2008-12-31' ]) assert response.exit_code == 2 def test_consecutive_increase_by_month_too_late_start_date(self): next_month = datetime.now() + relativedelta(months=1) response = runner.invoke(consecutive_increase, [ f'--start_date={str(next_month)[:10]}', '--end_date=2011-12-01' ]) assert response.exit_code == 2 def test_consecutive_increase_by_month_too_late_start_date_previously_end_date(self): next_month = datetime.now() + relativedelta(months=1) response = runner.invoke(consecutive_increase, [ '--end_date=2011-12-01', f'--start_date={str(next_month)[:10]}' ]) assert response.exit_code == 2 def test_consecutive_increase_too_late_end_date(self): next_month = datetime.now() + relativedelta(months=1) response = runner.invoke(consecutive_increase, [ '--start_date=2012-01-01', f'--end_date={str(next_month)[:10]}' ]) assert response.exit_code == 2 def test_consecutive_increase_too_late_end_date_previously_end_date(self): next_month = datetime.now() + relativedelta(months=1) response = runner.invoke(consecutive_increase, [ f'--end_date={str(next_month)[:10]}', '--start_date=2012-01-01' ]) assert response.exit_code == 2 def test_consecutive_increase_end_date_before_start_date(self): response = runner.invoke(consecutive_increase, ['--start_date=2012-01-01', '--end_date=2011-12-01']) assert response.exit_code == 2 def test_consecutive_increase_end_date_before_start_date_previously_end_date(self): response = runner.invoke(consecutive_increase, ['--end_date=2011-12-01', '--start_date=2012-01-01']) assert response.exit_code == 2 def test_consecutive_increase_wrong_arguments(self): response = runner.invoke(consecutive_increase, ['--start_date=2012-01', '--end_date=2011-12']) assert response.exit_code == 2 @pytest.mark.script class TestExport: def test_export_json(self, database): with patch('database_handler.DatabaseHandler.db') as mock: mock.return_value = database with HTTMock(api_mock): with patch('data_exporters.open', mock_open()) as mocked_file: response = runner.invoke(export, [ '--start_date=2012-01-01', '--end_date=2012-02-03', '--format_type=json', '--file=data.json' ]) mocked_file.assert_called_once_with('data.json', 'w') assert response.exit_code == 0 assert 'data.json' in response.output def test_export_json_diff_coin(self, database): with patch('database_handler.DatabaseHandler.db') as mock: mock.return_value = database with HTTMock(api_mock): with patch('data_exporters.open', mock_open()) as mocked_file: response = runner.invoke(export, [ '--start_date=2012-01-02', '--end_date=2012-02-04', '--format_type=json', '--file=data.json', '--coin=usdt-tether' ]) mocked_file.assert_called_once_with('data.json', 'w') assert response.exit_code == 0 assert 'data.json' in response.output def test_export_csv(self, database): with patch('database_handler.DatabaseHandler.db') as mock: mock.return_value = database with HTTMock(api_mock): with patch('data_exporters.open', mock_open()) as mocked_file: response = runner.invoke(export, [ '--start_date=2012-01-01', '--end_date=2012-02-03', '--format_type=csv', '--file=data.csv' ]) mocked_file.assert_called_once_with('data.csv', 'w', newline='') assert response.exit_code == 0 assert 'data.csv' in response.output def test_export_csv_diff_coin(self, database): with patch('database_handler.DatabaseHandler.db') as mock: mock.return_value = database with HTTMock(api_mock): with patch('data_exporters.open', mock_open()) as mocked_file: response = runner.invoke(export, [ '--start_date=2012-01-02', '--end_date=2012-02-04', '--format_type=csv', '--file=data.csv', '--coin=usdt-tether' ]) mocked_file.assert_called_once_with('data.csv', 'w', newline='') assert response.exit_code == 0 assert 'data.csv' in response.output def test_export_json_without_extension(self, database): with patch('database_handler.DatabaseHandler.db') as mock: mock.return_value = database with HTTMock(api_mock): with patch('data_exporters.open', mock_open()) as mocked_file: response = runner.invoke(export, [ '--start_date=2012-01-01', '--end_date=2012-02-03', '--format_type=json', '--file=data' ]) mocked_file.assert_called_once_with('data.json', 'w') assert response.exit_code == 0 assert 'data.json' in response.output def test_export_csv_without_extension(self, database): with patch('database_handler.DatabaseHandler.db') as mock: mock.return_value = database with HTTMock(api_mock): with patch('data_exporters.open', mock_open()) as mocked_file: response = runner.invoke(export, [ '--start_date=2012-01-02', '--end_date=2012-02-04', '--format_type=csv', '--file=data', '--coin=usdt-tether' ]) mocked_file.assert_called_once_with('data.csv', 'w', newline='') assert response.exit_code == 0 assert 'data.csv' in response.output def test_export_by_month_too_early_start_date(self): response = runner.invoke(export, [ '--start_date=2008-12-31', '--end_date=2011-12-01', '--format_type=json', '--file=data.json' ]) assert response.exit_code == 2 def test_export_by_month_too_early_start_date_previously_end_date(self): response = runner.invoke(export, [ '--end_date=2011-12-01', '--start_date=2008-12-31', '--format_type=json', '--file=data.json' ]) assert response.exit_code == 2 def test_export_by_month_too_late_start_date(self): next_month = datetime.now() + relativedelta(months=1) response = runner.invoke(export, [ f'--start_date={str(next_month)[:10]}', '--end_date=2011-12-01', '--format_type=json', '--file=data.json' ]) assert response.exit_code == 2 def test_export_by_month_too_late_start_date_previously_end_date(self): next_month = datetime.now() + relativedelta(months=1) response = runner.invoke(export, [ '--end_date=2011-12-01', f'--start_date={str(next_month)[:10]}', '--format_type=json', '--file=data.json' ]) assert response.exit_code == 2 def test_export_too_late_end_date(self): next_month = datetime.now() + relativedelta(months=1) response = runner.invoke(export, [ '--start_date=2012-01-01', f'--end_date={str(next_month)[:10]}', '--format_type=json', '--file=data.json' ]) assert response.exit_code == 2 def test_export_too_late_end_date_previously_end_date(self): next_month = datetime.now() + relativedelta(months=1) response = runner.invoke(export, [f'--end_date={str(next_month)[:10]}', '--start_date=2012-01-01', '--format_type=json', '--file=data.json']) assert response.exit_code == 2 def test_export_end_date_before_start_date_previously_end_date(self): response = runner.invoke(export, ['--end_date=2011-12-01', '--start_date=2012-01-01', '--format_type=json', '--file=data.json']) assert response.exit_code == 2 def test_export_end_date_before_start_date(self): response = runner.invoke(export, ['--start_date=2012-01-01', '--end_date=2011-12-01', '--format_type=json', '--file=data.json']) assert response.exit_code == 2 def test_export_wrong_arguments(self): response = runner.invoke(export, [ '--start_date=2012-01-01', '--end_date=2011-12-01', '--format_type=wrong', '--file=data.json' ]) assert response.exit_code == 2

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0.941241

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null

0

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null

0

6

504f922e0d685b4f28e9ff1bb793159169019aa2

43

py

Python

ex_package/ex_package.py

kungfupanda92/python-sample-package

3d2d086514a66e0b46f5fd40f5b835f3679936b4

[ "MIT" ]

null

ex_package/ex_package.py

kungfupanda92/python-sample-package

3d2d086514a66e0b46f5fd40f5b835f3679936b4

[ "MIT" ]

null

ex_package/ex_package.py

kungfupanda92/python-sample-package

3d2d086514a66e0b46f5fd40f5b835f3679936b4

[ "MIT" ]

null

def hello_print(): print("hello baby")

14.333333

23

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1

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null

0

1

0

1

0

6

505fc8437fc0c3c250458093521a57f2e9a45d55

21,479

py

Python

service/normalizer.py

chdigiorno/georef-ar-api

951d8804e286b95de5dc63b6a04d792052c1b553

[ "MIT" ]

1

2020-04-29T03:43:05.000Z

service/normalizer.py

chdigiorno/georef-ar-api

951d8804e286b95de5dc63b6a04d792052c1b553

[ "MIT" ]

null

service/normalizer.py

chdigiorno/georef-ar-api

951d8804e286b95de5dc63b6a04d792052c1b553

[ "MIT" ]

null

"""Módulo 'normalizer' de georef-ar-api Contiene funciones que manejan la lógica de procesamiento de los recursos que expone la API. """ import logging from flask import current_app from service import data, params, formatter, address, location, utils from service import names as N from service.query_result import QueryResult logger = logging.getLogger('georef') def get_elasticsearch(): """Devuelve la conexión a Elasticsearch activa para la sesión de flask. La conexión es creada si no existía. Returns: Elasticsearch: conexión a Elasticsearch. Raises: data.DataConnectionException: En caso de ocurrir un error de conexión con la capa de manejo de datos. """ if not hasattr(current_app, 'elasticsearch'): current_app.elasticsearch = data.elasticsearch_connection( hosts=current_app.config['ES_HOSTS'], sniff=current_app.config['ES_SNIFF'], sniffer_timeout=current_app.config['ES_SNIFFER_TIMEOUT'] ) return current_app.elasticsearch def _process_entity_single(request, name, param_parser, key_translations): """Procesa una request GET para consultar datos de una entidad. En caso de ocurrir un error de parseo, se retorna una respuesta HTTP 400. Args: request (flask.Request): Request GET de flask. name (str): Nombre de la entidad. param_parser (ParameterSet): Objeto utilizado para parsear los parámetros. key_translations (dict): Traducciones de keys a utilizar para convertir el diccionario de parámetros del usuario a un diccionario representando una query a Elasticsearch. Raises: data.DataConnectionException: En caso de ocurrir un error de conexión con la capa de manejo de datos. Returns: flask.Response: respuesta HTTP """ try: qs_params = param_parser.parse_get_params(request.args) except params.ParameterParsingException as e: return formatter.create_param_error_response_single(e.errors, e.fmt) # Construir query a partir de parámetros query = utils.translate_keys(qs_params, key_translations, ignore=[N.FLATTEN, N.FORMAT]) # Construir reglas de formato a partir de parámetros fmt = { key: qs_params[key] for key in [N.FLATTEN, N.FIELDS, N.FORMAT] if key in qs_params } if fmt[N.FORMAT] == 'shp': query['fields'] += (N.GEOM,) es = get_elasticsearch() search_class = data.entity_search_class(name) search = search_class(query) data.ElasticsearchSearch.run_searches(es, [search]) query_result = QueryResult.from_entity_list(search.result.hits, search.result.total, search.result.offset) return formatter.create_ok_response(name, query_result, fmt) def _process_entity_bulk(request, name, param_parser, key_translations): """Procesa una request POST para consultar datos de una lista de entidades. En caso de ocurrir un error de parseo, se retorna una respuesta HTTP 400. Args: request (flask.Request): Request POST de flask. name (str): Nombre de la entidad. param_parser (ParameterSet): Objeto utilizado para parsear los parámetros. key_translations (dict): Traducciones de keys a utilizar para convertir los diccionarios de parámetros del usuario a una lista de diccionarios representando las queries a Elasticsearch. Raises: data.DataConnectionException: En caso de ocurrir un error de conexión con la capa de manejo de datos. Returns: flask.Response: respuesta HTTP """ try: body_params = param_parser.parse_post_params( request.args, request.json, name) except params.ParameterParsingException as e: return formatter.create_param_error_response_bulk(e.errors) queries = [] formats = [] for parsed_params in body_params: # Construir query a partir de parámetros query = utils.translate_keys(parsed_params, key_translations, ignore=[N.FLATTEN, N.FORMAT]) # Construir reglas de formato a partir de parámetros fmt = { key: parsed_params[key] for key in [N.FLATTEN, N.FIELDS] if key in parsed_params } queries.append(query) formats.append(fmt) es = get_elasticsearch() search_class = data.entity_search_class(name) searches = [search_class(query) for query in queries] data.ElasticsearchSearch.run_searches(es, searches) query_results = [ QueryResult.from_entity_list(search.result.hits, search.result.total, search.result.offset) for search in searches ] return formatter.create_ok_response_bulk(name, query_results, formats) def _process_entity(request, name, param_parser, key_translations): """Procesa una request GET o POST para consultar datos de una entidad. En caso de ocurrir un error de parseo, se retorna una respuesta HTTP 400. En caso de ocurrir un error interno, se retorna una respuesta HTTP 500. Args: request (flask.Request): Request GET o POST de flask. name (str): Nombre de la entidad. param_parser (ParameterSet): Objeto utilizado para parsear los parámetros. key_translations (dict): Traducciones de keys a utilizar para convertir los diccionarios de parámetros del usuario a una lista de diccionarios representando las queries a Elasticsearch. Returns: flask.Response: respuesta HTTP """ try: if request.method == 'GET': return _process_entity_single(request, name, param_parser, key_translations) return _process_entity_bulk(request, name, param_parser, key_translations) except data.DataConnectionException: logger.exception( 'Excepción en manejo de consulta para recurso: {}'.format(name)) return formatter.create_internal_error_response() def process_state(request): """Procesa una request GET o POST para consultar datos de provincias. En caso de ocurrir un error de parseo, se retorna una respuesta HTTP 400. Args: request (flask.Request): Request GET o POST de flask. Returns: flask.Response: respuesta HTTP """ return _process_entity(request, N.STATES, params.PARAMS_STATES, { N.ID: 'ids', N.NAME: 'name', N.INTERSECTION: 'geo_shape_ids', N.EXACT: 'exact', N.ORDER: 'order', N.FIELDS: 'fields', N.OFFSET: 'offset', N.MAX: 'size' }) def process_department(request): """Procesa una request GET o POST para consultar datos de departamentos. En caso de ocurrir un error de parseo, se retorna una respuesta HTTP 400. Args: request (flask.Request): Request GET o POST de flask. Returns: flask.Response: respuesta HTTP """ return _process_entity( request, N.DEPARTMENTS, params.PARAMS_DEPARTMENTS, { N.ID: 'ids', N.NAME: 'name', N.INTERSECTION: 'geo_shape_ids', N.STATE: 'state', N.EXACT: 'exact', N.ORDER: 'order', N.FIELDS: 'fields', N.OFFSET: 'offset', N.MAX: 'size' }) def process_municipality(request): """Procesa una request GET o POST para consultar datos de municipios. En caso de ocurrir un error de parseo, se retorna una respuesta HTTP 400. Args: request (flask.Request): Request GET o POST de flask. Returns: flask.Response: respuesta HTTP """ return _process_entity( request, N.MUNICIPALITIES, params.PARAMS_MUNICIPALITIES, { N.ID: 'ids', N.NAME: 'name', N.INTERSECTION: 'geo_shape_ids', N.STATE: 'state', N.EXACT: 'exact', N.ORDER: 'order', N.FIELDS: 'fields', N.OFFSET: 'offset', N.MAX: 'size' }) def process_locality(request): """Procesa una request GET o POST para consultar datos de localidades. En caso de ocurrir un error de parseo, se retorna una respuesta HTTP 400. Args: request (flask.Request): Request GET o POST de flask. Returns: flask.Response: respuesta HTTP """ return _process_entity(request, N.LOCALITIES, params.PARAMS_LOCALITIES, { N.ID: 'ids', N.NAME: 'name', N.STATE: 'state', N.DEPT: 'department', N.MUN: 'municipality', N.EXACT: 'exact', N.ORDER: 'order', N.FIELDS: 'fields', N.OFFSET: 'offset', N.MAX: 'size' }) def _build_street_query_format(parsed_params): """Construye dos diccionarios a partir de parámetros de consulta recibidos, el primero representando la query a Elasticsearch a realizar y el segundo representando las propiedades de formato (presentación) que se le debe dar a los datos obtenidos de la misma. Args: parsed_params (dict): Parámetros de una consulta para el índice de calles. Returns: tuple: diccionario de query y diccionario de formato """ # Construir query a partir de parámetros query = utils.translate_keys(parsed_params, { N.ID: 'ids', N.NAME: 'name', N.INTERSECTION: 'geo_shape_ids', N.STATE: 'state', N.DEPT: 'department', N.EXACT: 'exact', N.FIELDS: 'fields', N.CATEGORY: 'category', N.OFFSET: 'offset', N.ORDER: 'order', N.MAX: 'size' }, ignore=[N.FLATTEN, N.FORMAT]) # Construir reglas de formato a partir de parámetros fmt = { key: parsed_params[key] for key in [N.FLATTEN, N.FIELDS, N.FORMAT] if key in parsed_params } return query, fmt def _process_street_single(request): """Procesa una request GET para consultar datos de calles. En caso de ocurrir un error de parseo, se retorna una respuesta HTTP 400. Args: request (flask.Request): Request GET de flask. Raises: data.DataConnectionException: En caso de ocurrir un error de conexión con la capa de manejo de datos. Returns: flask.Response: respuesta HTTP """ try: qs_params = params.PARAMS_STREETS.parse_get_params(request.args) except params.ParameterParsingException as e: return formatter.create_param_error_response_single(e.errors, e.fmt) query, fmt = _build_street_query_format(qs_params) if fmt[N.FORMAT] == 'shp': query['fields'] += (N.GEOM,) es = get_elasticsearch() search = data.StreetsSearch(query) data.ElasticsearchSearch.run_searches(es, [search]) query_result = QueryResult.from_entity_list(search.result.hits, search.result.total, search.result.offset) return formatter.create_ok_response(N.STREETS, query_result, fmt) def _process_street_bulk(request): """Procesa una request POST para consultar datos de calles. En caso de ocurrir un error de parseo, se retorna una respuesta HTTP 400. Args: request (flask.Request): Request POST de flask. Raises: data.DataConnectionException: En caso de ocurrir un error de conexión con la capa de manejo de datos. Returns: flask.Response: respuesta HTTP """ try: body_params = params.PARAMS_STREETS.parse_post_params( request.args, request.json, N.STREETS) except params.ParameterParsingException as e: return formatter.create_param_error_response_bulk(e.errors) queries = [] formats = [] for parsed_params in body_params: query, fmt = _build_street_query_format(parsed_params) queries.append(query) formats.append(fmt) es = get_elasticsearch() searches = [data.StreetsSearch(query) for query in queries] data.ElasticsearchSearch.run_searches(es, searches) query_results = [ QueryResult.from_entity_list(search.result.hits, search.result.total, search.result.offset) for search in searches ] return formatter.create_ok_response_bulk(N.STREETS, query_results, formats) def process_street(request): """Procesa una request GET o POST para consultar datos de calles. En caso de ocurrir un error de parseo, se retorna una respuesta HTTP 400. En caso de ocurrir un error interno, se retorna una respuesta HTTP 500. Args: request (flask.Request): Request GET o POST de flask. Returns: flask.Response: respuesta HTTP """ try: if request.method == 'GET': return _process_street_single(request) return _process_street_bulk(request) except data.DataConnectionException: logger.exception( 'Excepción en manejo de consulta para recurso: calles') return formatter.create_internal_error_response() def _build_address_query_format(parsed_params): """Construye dos diccionarios a partir de parámetros de consulta recibidos, el primero representando la query a Elasticsearch a realizar y el segundo representando las propiedades de formato (presentación) que se le debe dar a los datos obtenidos de la misma. Args: parsed_params (dict): Parámetros de una consulta normalización de una dirección. Returns: tuple: diccionario de query y diccionario de formato """ # Construir query a partir de parámetros query = utils.translate_keys(parsed_params, { N.DEPT: 'department', N.STATE: 'state', N.EXACT: 'exact', N.OFFSET: 'offset', N.ORDER: 'order', N.MAX: 'size' }, ignore=[N.FLATTEN, N.FORMAT, N.FIELDS]) # Construir reglas de formato a partir de parámetros fmt = { key: parsed_params[key] for key in [N.FLATTEN, N.FIELDS, N.FORMAT] if key in parsed_params } return query, fmt def _process_address_queries(params_list): """Ejecuta una lista de consultas de direcciones, partiendo desde los parámetros recibidos del usuario. Args: params_list (list): Lista de dict, cada dict conteniendo los parámetros de una consulta al recurso de direcciones de la API. Returns: tuple: Tupla de (list, list), donde la primera lista contiene una instancia de QueryResult por cada consulta, y la segunda lista contiene una instancia de dict utilizada para darle formato al resultado más tarde. """ queries = [] formats = [] for parsed_params in params_list: query, fmt = _build_address_query_format(parsed_params) queries.append(query) formats.append(fmt) es = get_elasticsearch() query_results = address.run_address_queries(es, queries, formats) return query_results, formats def _process_address_single(request): """Procesa una request GET para normalizar una dirección. En caso de ocurrir un error de parseo, se retorna una respuesta HTTP 400. Args: request (flask.Request): Request GET de flask. Raises: data.DataConnectionException: En caso de ocurrir un error de conexión con la capa de manejo de datos. Returns: flask.Response: respuesta HTTP """ try: qs_params = params.PARAMS_ADDRESSES.parse_get_params(request.args) except params.ParameterParsingException as e: return formatter.create_param_error_response_single(e.errors, e.fmt) query_results, formats = _process_address_queries([qs_params]) return formatter.create_ok_response(N.ADDRESSES, query_results[0], formats[0]) def _process_address_bulk(request): """Procesa una request POST para normalizar lote de direcciones. En caso de ocurrir un error de parseo, se retorna una respuesta HTTP 400. Args: request (flask.Request): Request POST de flask. Raises: data.DataConnectionException: En caso de ocurrir un error de conexión con la capa de manejo de datos. Returns: flask.Response: respuesta HTTP """ try: body_params = params.PARAMS_ADDRESSES.parse_post_params( request.args, request.json, N.ADDRESSES) except params.ParameterParsingException as e: return formatter.create_param_error_response_bulk(e.errors) query_results, formats = _process_address_queries(body_params) return formatter.create_ok_response_bulk(N.ADDRESSES, query_results, formats) def process_address(request): """Procesa una request GET o POST para normalizar lote de direcciones. En caso de ocurrir un error de parseo, se retorna una respuesta HTTP 400. En caso de ocurrir un error interno, se retorna una respuesta HTTP 500. Args: request (flask.Request): Request GET o POST de flask. Returns: flask.Response: respuesta HTTP """ try: if request.method == 'GET': return _process_address_single(request) return _process_address_bulk(request) except data.DataConnectionException: logger.exception( 'Excepción en manejo de consulta para recurso: direcciones') return formatter.create_internal_error_response() def _build_location_query_format(parsed_params): """Construye dos diccionarios a partir de parámetros de consulta recibidos, el primero representando la query a Elasticsearch a realizar y el segundo representando las propiedades de formato (presentación) que se le debe dar a los datos obtenidos de la misma. Args: parsed_params (dict): Parámetros de una consulta para una ubicación. Returns: tuple: diccionario de query y diccionario de formato """ # Construir query a partir de parámetros query = utils.translate_keys(parsed_params, {}, ignore=[N.FLATTEN, N.FORMAT]) # Construir reglas de formato a partir de parámetros fmt = { key: parsed_params[key] for key in [N.FLATTEN, N.FIELDS, N.FORMAT] if key in parsed_params } return query, fmt def _process_location_single(request): """Procesa una request GET para obtener entidades en un punto. En caso de ocurrir un error de parseo, se retorna una respuesta HTTP 400. Args: request (flask.Request): Request GET de flask. Raises: data.DataConnectionException: En caso de ocurrir un error de conexión con la capa de manejo de datos. Returns: flask.Response: respuesta HTTP """ try: qs_params = params.PARAMS_LOCATION.parse_get_params(request.args) except params.ParameterParsingException as e: return formatter.create_param_error_response_single(e.errors, e.fmt) query, fmt = _build_location_query_format(qs_params) es = get_elasticsearch() result = location.run_location_queries(es, [query])[0] return formatter.create_ok_response(N.LOCATION, result, fmt) def _process_location_bulk(request): """Procesa una request POST para obtener entidades en varios puntos. En caso de ocurrir un error de parseo, se retorna una respuesta HTTP 400. Args: request (flask.Request): Request POST de flask. Raises: data.DataConnectionException: En caso de ocurrir un error de conexión con la capa de manejo de datos. Returns: flask.Response: respuesta HTTP """ try: body_params = params.PARAMS_LOCATION.parse_post_params( request.args, request.json, N.LOCATIONS) except params.ParameterParsingException as e: return formatter.create_param_error_response_bulk(e.errors) queries = [] formats = [] for parsed_params in body_params: query, fmt = _build_location_query_format(parsed_params) queries.append(query) formats.append(fmt) es = get_elasticsearch() results = location.run_location_queries(es, queries) return formatter.create_ok_response_bulk(N.LOCATION, results, formats) def process_location(request): """Procesa una request GET o POST para obtener entidades en una o varias ubicaciones. En caso de ocurrir un error de parseo, se retorna una respuesta HTTP 400. En caso de ocurrir un error interno, se retorna una respuesta HTTP 500. Args: request (flask.Request): Request GET o POST de flask. Returns: flask.Response: respuesta HTTP """ try: if request.method == 'GET': return _process_location_single(request) return _process_location_bulk(request) except data.DataConnectionException: logger.exception( 'Excepción en manejo de consulta para recurso: ubicacion') return formatter.create_internal_error_response()

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