xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

2ae1f0a06755e24403d95b8e9c11cd73e8864abe

114

py

Python

babc/__init__.py

bozzzzo/babc

f8f8fd582c89c9b72ec95434b53707e6d96f0dd2

[ "Apache-2.0" ]

null

babc/__init__.py

bozzzzo/babc

f8f8fd582c89c9b72ec95434b53707e6d96f0dd2

[ "Apache-2.0" ]

null

babc/__init__.py

bozzzzo/babc

f8f8fd582c89c9b72ec95434b53707e6d96f0dd2

[ "Apache-2.0" ]

null

from .babc import BABC, BABCMeta from abc import abstractmethod __all__ = ['BABC', 'BABCMeta', 'abstractmethod']

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Python

manager/manager/wsgi.py

jlbrewe/hub

c737669e6493ad17536eaa240bed3394b20c6b7d

[ "Apache-2.0" ]

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2016-03-26T12:08:04.000Z

2021-12-24T14:48:32.000Z

manager/manager/wsgi.py

jlbrewe/hub

c737669e6493ad17536eaa240bed3394b20c6b7d

[ "Apache-2.0" ]

1,250

2016-03-23T04:56:50.000Z

2022-03-28T02:27:58.000Z

manager/manager/wsgi.py

jlbrewe/hub

c737669e6493ad17536eaa240bed3394b20c6b7d

[ "Apache-2.0" ]

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2016-07-14T17:04:20.000Z

2021-07-01T16:19:09.000Z

import os os.environ.setdefault("DJANGO_SETTINGS_MODULE", "manager.settings") os.environ.setdefault("DJANGO_CONFIGURATION", "Prod") from configurations.wsgi import get_wsgi_application # noqa: E402 application = get_wsgi_application()

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py

Python

survol/sources_types/CIM_DataFile/Radare2/__init__.py

AugustinMascarelli/survol

7a822900e82d1e6f016dba014af5741558b78f15

[ "BSD-3-Clause" ]

null

survol/sources_types/CIM_DataFile/Radare2/__init__.py

AugustinMascarelli/survol

7a822900e82d1e6f016dba014af5741558b78f15

[ "BSD-3-Clause" ]

null

survol/sources_types/CIM_DataFile/Radare2/__init__.py

AugustinMascarelli/survol

7a822900e82d1e6f016dba014af5741558b78f15

[ "BSD-3-Clause" ]

null

""" Radare2 """ import lib_util def Usable(entity_type,entity_ids_arr): """Not an executable or library file""" return lib_util.UsableWindowsBinary(entity_type,entity_ids_arr) or lib_util.UsableLinuxBinary(entity_type,entity_ids_arr)

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fa8ddb9761bce35e2bb911393f38ba6bfdee04e4

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py

Python

pyeafe/__init__.py

thepnpsolver/PyEAFE

a3d39f3c9905e9a528d4aa0000478562e4653b66

[ "MIT" ]

null

pyeafe/__init__.py

thepnpsolver/PyEAFE

a3d39f3c9905e9a528d4aa0000478562e4653b66

[ "MIT" ]

13

2018-03-07T19:45:44.000Z

2021-04-12T02:27:55.000Z

pyeafe/__init__.py

thepnpsolver/PyEAFE

a3d39f3c9905e9a528d4aa0000478562e4653b66

[ "MIT" ]

2

2018-10-25T05:57:15.000Z

2019-07-25T15:52:42.000Z

from pyeafe.assembly import Coefficient, eafe_assemble __version__ = "1.0.1" __all__ = [Coefficient, eafe_assemble]

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faafe5f7d809d74eceb75e656d08399e3e5532e3

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py

Python

python/testData/resolve/multiFile/fromPackageImportFile/FromPackageImportFile.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/resolve/multiFile/fromPackageImportFile/FromPackageImportFile.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/resolve/multiFile/fromPackageImportFile/FromPackageImportFile.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

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2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

from mypackage import myfile # <ref>

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py

Python

layers/residual_block_test.py

katsugeneration/sngan-with-projection-tensorflow

8476c4ecbae9a5a0bddfac5259fcd78894a3db38

[ "MIT" ]

2

2018-09-28T09:03:40.000Z

2018-10-05T23:35:46.000Z

layers/residual_block_test.py

katsugeneration/sngan-with-projection-tensorflow

8476c4ecbae9a5a0bddfac5259fcd78894a3db38

[ "MIT" ]

1

2018-10-12T11:14:19.000Z

layers/residual_block_test.py

katsugeneration/sngan-with-projection-tensorflow

8476c4ecbae9a5a0bddfac5259fcd78894a3db38

[ "MIT" ]

null

import numpy as np import tensorflow as tf from layers.residual_block import ResidualBlock from layers.conditional_batch_normalization import ConditionalBatchNormalization class ResidualBlockTest(tf.test.TestCase): def testInit(self): ResidualBlock() def testBuildAndRun(self): N = 5 W = 10 H = 10 C = 3 hidden_c = 5 x = tf.ones((N, H, W, C)) rb = ResidualBlock(hidden_c=hidden_c) outputs = rb(x) self.assertEqual(rb.in_c, C) self.assertEqual(rb.out_c, C) self.assertEqual(rb.hidden_c, hidden_c) self.assertEqual((N, H, W, C), outputs.shape) self.assertEqual((rb.ksize, rb.ksize, C, hidden_c), rb.conv1.kernel.shape) self.assertEqual(rb.conv1_u, None) self.assertEqual(rb.conv2_u, None) def testBuildAndRunWithUpsampling(self): N = 5 W = 10 H = 10 C = 3 hidden_c = 5 x = tf.ones((N, H, W, C)) rb = ResidualBlock(hidden_c=hidden_c, upsampling=True) outputs = rb(x) self.assertEqual(rb.in_c, C) self.assertEqual(rb.out_c, C) self.assertEqual(rb.hidden_c, hidden_c) self.assertEqual((N, H * 2, W * 2, C), outputs.shape) self.assertEqual((rb.ksize, rb.ksize, C, hidden_c), rb.conv1.kernel.shape) self.assertEqual((1, 1, C, C), rb.conv_shortcut.kernel.shape) self.assertTrue(hasattr(rb, 'bn1')) self.assertEqual(rb.conv1_u, None) self.assertEqual(rb.conv2_u, None) self.assertEqual(rb.conv_shortcut_u, None) def testBuildAndRunWithDownsampling(self): N = 5 W = 10 H = 10 C = 3 hidden_c = 5 x = tf.ones((N, H, W, C)) rb = ResidualBlock(hidden_c=hidden_c, is_use_bn=False, downsampling=True) outputs = rb(x) self.assertEqual(rb.in_c, C) self.assertEqual(rb.out_c, C) self.assertEqual(rb.hidden_c, hidden_c) self.assertEqual((N, H / 2, W / 2, C), outputs.shape) self.assertEqual((rb.ksize, rb.ksize, C, hidden_c), rb.conv1.kernel.shape) self.assertEqual((1, 1, C, C), rb.conv_shortcut.kernel.shape) self.assertFalse(hasattr(rb, 'bn1')) def testBuildAndRunWithDownsamplingWithSN(self): N = 5 W = 10 H = 10 C = 3 hidden_c = 5 x = tf.ones((N, H, W, C)) rb = ResidualBlock(hidden_c=hidden_c, is_use_bn=False, downsampling=True, is_use_sn=True) outputs = rb(x) self.assertEqual(rb.in_c, C) self.assertEqual(rb.out_c, C) self.assertEqual(rb.hidden_c, hidden_c) self.assertEqual((N, H / 2, W / 2, C), outputs.shape) self.assertEqual((rb.ksize, rb.ksize, C, hidden_c), rb.conv1.kernel.shape) self.assertEqual((1, 1, C, C), rb.conv_shortcut.kernel.shape) self.assertFalse(hasattr(rb, 'bn1')) self.assertNotEqual(rb.conv1_u, None) self.assertNotEqual(rb.conv2_u, None) self.assertNotEqual(rb.conv_shortcut_u, None) def testTrain(self): N = 5 W = 10 H = 10 C = 3 hidden_c = 5 x = tf.ones((N, H, W, C)) rb = ResidualBlock(hidden_c=hidden_c) with tf.GradientTape() as tape: outputs = rb(x) loss = tf.reduce_mean(tf.square(1 - outputs)) grads = tape.gradient(loss, rb.variables) optimizer = tf.train.GradientDescentOptimizer(0.001) optimizer.apply_gradients(zip(grads, rb.variables)) self.assertEqual(type(rb.bn1), tf.layers.BatchNormalization) def testTrainCategory(self): N = 5 W = 10 H = 10 C = 3 hidden_c = 5 x = tf.ones((N, H, W, C)) y = [[3], [1], [3], [1], [3]] rb = ResidualBlock(hidden_c=hidden_c, category=4) with tf.GradientTape() as tape: outputs = rb(x, labels=y) loss = tf.reduce_mean(tf.square(1 - outputs)) grads = tape.gradient(loss, rb.variables) optimizer = tf.train.GradientDescentOptimizer(0.001) optimizer.apply_gradients(zip(grads, rb.variables)) self.assertEqual(type(rb.bn1), ConditionalBatchNormalization) if __name__ == '__main__': tf.enable_eager_execution() tf.test.main()

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py

Python

paida-3.2.1_2.10.1/paida/tools/__init__.py

AshleyChraya/HubbleConstant-ConstraintsForVCG

634c15d296147ec1cdc3c92af1fbbfeb17844586

[ "MIT" ]

null

paida-3.2.1_2.10.1/paida/tools/__init__.py

AshleyChraya/HubbleConstant-ConstraintsForVCG

634c15d296147ec1cdc3c92af1fbbfeb17844586

[ "MIT" ]

null

paida-3.2.1_2.10.1/paida/tools/__init__.py

AshleyChraya/HubbleConstant-ConstraintsForVCG

634c15d296147ec1cdc3c92af1fbbfeb17844586

[ "MIT" ]

null

### Nothing to do. pass

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py

Python

code/scripts/other_module.py

felipegermanos/test1

d2896ba208e7c3aa4e6192052eca794e6045afec

[ "MIT" ]

null

code/scripts/other_module.py

felipegermanos/test1

d2896ba208e7c3aa4e6192052eca794e6045afec

[ "MIT" ]

null

code/scripts/other_module.py

felipegermanos/test1

d2896ba208e7c3aa4e6192052eca794e6045afec

[ "MIT" ]

1

2020-02-09T20:36:38.000Z

def get_me(): print('hi') if __name__ == '__main__': # This is executed you run via terminal print('Running other_module.py...')

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87e2cc7eaf911cbad3029f4e7696b1316cd4077c

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py

Python

play.py

dgnsrekt/abnormal_returns

84bd29cedeb901bd54c236f8dbad6c66e9179389

[ "MIT" ]

1

2020-10-12T00:52:08.000Z

play.py

dgnsrekt/abnormal_returns

84bd29cedeb901bd54c236f8dbad6c66e9179389

[ "MIT" ]

null

play.py

dgnsrekt/abnormal_returns

84bd29cedeb901bd54c236f8dbad6c66e9179389

[ "MIT" ]

null

from ar_scraper import core core.run()

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358615c9f8c5d1d624aa04506095c2e275c6f6ea

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py

Python

utils/networks/__init__.py

ivclab/Multistage_Pruning

0fb7e084f56d565c27dd9c4536cc95204eecf926

[ "BSD-3-Clause" ]

11

2020-04-07T02:23:53.000Z

2021-09-02T13:54:47.000Z

utils/networks/__init__.py

van-hub/Multistage_Pruning

0fb7e084f56d565c27dd9c4536cc95204eecf926

[ "BSD-3-Clause" ]

null

utils/networks/__init__.py

van-hub/Multistage_Pruning

0fb7e084f56d565c27dd9c4536cc95204eecf926

[ "BSD-3-Clause" ]

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2020-11-07T17:10:08.000Z

2021-01-18T02:26:56.000Z

from .mobilenetv1 import mobilenetv1 from .mobilenetv2 import mobilenetv2

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py

Python

maps/balance/__init__.py

56kyle/bloons_auto

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[ "MIT" ]

null

maps/balance/__init__.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

maps/balance/__init__.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

from .balance import Balance

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3595742fbfb02d8a0e8807a8f2de37bee4fa3c56

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py

Python

hadoop_script_2.0/common/tool/action/report/__init__.py

jiandequn/code

676bae6523a26b4d8b01914f6b963112054461a3

[ "Apache-2.0" ]

2

2019-01-17T01:55:59.000Z

2019-04-18T02:06:53.000Z

sx_hadoop_script_2.0/common/tool/action/report/__init__.py

sunnyJam/code

676bae6523a26b4d8b01914f6b963112054461a3

[ "Apache-2.0" ]

1

2022-02-09T22:28:06.000Z

sx_hadoop_script_2.0/common/tool/action/report/__init__.py

sunnyJam/code

676bae6523a26b4d8b01914f6b963112054461a3

[ "Apache-2.0" ]

null

# from common.tool.action.report import backup_log,backup_mysql_data,events_type_log,increase_user,run_all_common

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35b3180b0d50acb4b3686ca247b1efddebdc2699

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py

Python

CodeCrewSiteApp/admin.py

cs-fullstack-2019-spring/django-inheritance-cw-tdude0175

76e688a6e93be58039a945ec25db7ddb03eda5f4

[ "Apache-2.0" ]

null

CodeCrewSiteApp/admin.py

cs-fullstack-2019-spring/django-inheritance-cw-tdude0175

76e688a6e93be58039a945ec25db7ddb03eda5f4

[ "Apache-2.0" ]

null

CodeCrewSiteApp/admin.py

cs-fullstack-2019-spring/django-inheritance-cw-tdude0175

76e688a6e93be58039a945ec25db7ddb03eda5f4

[ "Apache-2.0" ]

null

from django.contrib import admin from .models import ContactForm # Register your models here. admin.site.register(ContactForm)

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py

Python

Problems/apaxiaaans.py

iHaveBecomeDeath/kattis-practice

3ec52a7a50520228f778ba55278c84425e29f4f4

[ "Unlicense" ]

null

Problems/apaxiaaans.py

iHaveBecomeDeath/kattis-practice

3ec52a7a50520228f778ba55278c84425e29f4f4

[ "Unlicense" ]

null

Problems/apaxiaaans.py

iHaveBecomeDeath/kattis-practice

3ec52a7a50520228f778ba55278c84425e29f4f4

[ "Unlicense" ]

null

import re print(re.sub(r'([a-z])\1+', r'\1', input()))

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35bd1879e6202d315fe259be63594cc98ceb175b

218

py

Python

api/tacticalrmm/agents/admin.py

BaDTaG/tacticalrmm

7bdd8c4626e0629d393edb5dec2541150d1802ef

[ "MIT" ]

1

2021-01-19T20:39:02.000Z

api/tacticalrmm/agents/admin.py

BaDTaG/tacticalrmm

7bdd8c4626e0629d393edb5dec2541150d1802ef

[ "MIT" ]

null

api/tacticalrmm/agents/admin.py

BaDTaG/tacticalrmm

7bdd8c4626e0629d393edb5dec2541150d1802ef

[ "MIT" ]

null

from django.contrib import admin from .models import Agent, AgentOutage, RecoveryAction, Note admin.site.register(Agent) admin.site.register(AgentOutage) admin.site.register(RecoveryAction) admin.site.register(Note)

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35d0b62d4439c53fe8d73b6a5e39fe91c154e6a2

578

py

Python

base/__init__.py

thu-spmi/semi-EBM

393e3ea3566dd60c48872a5c573a335e8e802707

[ "Apache-2.0" ]

2

2021-09-18T14:21:24.000Z

2021-12-20T03:39:13.000Z

base/__init__.py

thu-spmi/semi-EBM

393e3ea3566dd60c48872a5c573a335e8e802707

[ "Apache-2.0" ]

null

base/__init__.py

thu-spmi/semi-EBM

393e3ea3566dd60c48872a5c573a335e8e802707

[ "Apache-2.0" ]

1

2021-09-12T07:02:23.000Z

# use the tensroflow try: from base import layers except: print('[%s] no tensorflow.' % __name__) # do not use the tensorflow from base import ngram from base import parser from base import wblib as wb from base import matlib as mlib from base import reader from base import vocab from base import sampling as sp from base import word2vec from base import learningrate as lr from base import log from base import seq import numpy as np # from scipy.misc import logsumexp from scipy.special import logsumexp from collections import OrderedDict

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null

0

1

0

1

0

1

0

5

ea162b75073aa0eccb59ca2b3a97fd1d29f8ee4d

414

py

Python

packages/pytea/pylib/torch/__init__.py

lego0901/pytea

8ede650def2e68f4610ba816451d8b9e28f09f76

[ "MIT" ]

null

packages/pytea/pylib/torch/__init__.py

lego0901/pytea

8ede650def2e68f4610ba816451d8b9e28f09f76

[ "MIT" ]

null

packages/pytea/pylib/torch/__init__.py

lego0901/pytea

8ede650def2e68f4610ba816451d8b9e28f09f76

[ "MIT" ]

null

import LibCall # from .functional import * from .tensor import Tensor from .functional import * from . import nn as nn from . import optim as optim from . import utils as utils from . import distributions as distributions from . import cuda as cuda from . import onnx as onnx from .autograd import no_grad, enable_grad class bool: pass class device: def __init__(self, type): self.type = type

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5

ea19bbf11c5417635056046086953c02c630d3fa

57

py

Python

EAlib/log/__init__.py

iseesaw/EAlib

498bcc096779a951e254b0aee5677ee885700263

[ "MIT" ]

4

2019-09-28T03:42:09.000Z

2020-04-17T02:46:19.000Z

EAlib/log/__init__.py

iseesaw/EAlib

498bcc096779a951e254b0aee5677ee885700263

[ "MIT" ]

null

EAlib/log/__init__.py

iseesaw/EAlib

498bcc096779a951e254b0aee5677ee885700263

[ "MIT" ]

null

# -*- coding: utf-8 -*- from ..log.log import get_logger

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5

ea2864c948f9d611b8f56b0ea178bc63f5f0a342

7,518

py

Python

sdk/python/pulumi_azure_native/authorization/__init__.py

polivbr/pulumi-azure-native

09571f3bf6bdc4f3621aabefd1ba6c0d4ecfb0e7

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/authorization/__init__.py

polivbr/pulumi-azure-native

09571f3bf6bdc4f3621aabefd1ba6c0d4ecfb0e7

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/authorization/__init__.py

polivbr/pulumi-azure-native

09571f3bf6bdc4f3621aabefd1ba6c0d4ecfb0e7

[ "Apache-2.0" ]

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** from .. import _utilities import typing # Export this package's modules as members: from ._enums import * from .access_review_schedule_definition_by_id import * from .get_access_review_schedule_definition_by_id import * from .get_client_config import * from .get_client_token import * from .get_management_lock_at_resource_group_level import * from .get_management_lock_at_resource_level import * from .get_management_lock_at_subscription_level import * from .get_management_lock_by_scope import * from .get_policy_assignment import * from .get_policy_definition import * from .get_policy_definition_at_management_group import * from .get_policy_exemption import * from .get_policy_set_definition import * from .get_policy_set_definition_at_management_group import * from .get_resource_management_private_link import * from .get_role_assignment import * from .get_role_definition import * from .get_role_management_policy_assignment import * from .management_lock_at_resource_group_level import * from .management_lock_at_resource_level import * from .management_lock_at_subscription_level import * from .management_lock_by_scope import * from .policy_assignment import * from .policy_definition import * from .policy_definition_at_management_group import * from .policy_exemption import * from .policy_set_definition import * from .policy_set_definition_at_management_group import * from .resource_management_private_link import * from .role_assignment import * from .role_definition import * from .role_management_policy_assignment import * from ._inputs import * from . import outputs # Make subpackages available: if typing.TYPE_CHECKING: import pulumi_azure_native.authorization.v20150101 as __v20150101 v20150101 = __v20150101 import pulumi_azure_native.authorization.v20150701 as __v20150701 v20150701 = __v20150701 import pulumi_azure_native.authorization.v20151001preview as __v20151001preview v20151001preview = __v20151001preview import pulumi_azure_native.authorization.v20160401 as __v20160401 v20160401 = __v20160401 import pulumi_azure_native.authorization.v20160901 as __v20160901 v20160901 = __v20160901 import pulumi_azure_native.authorization.v20161201 as __v20161201 v20161201 = __v20161201 import pulumi_azure_native.authorization.v20170401 as __v20170401 v20170401 = __v20170401 import pulumi_azure_native.authorization.v20170601preview as __v20170601preview v20170601preview = __v20170601preview import pulumi_azure_native.authorization.v20171001preview as __v20171001preview v20171001preview = __v20171001preview import pulumi_azure_native.authorization.v20180101preview as __v20180101preview v20180101preview = __v20180101preview import pulumi_azure_native.authorization.v20180301 as __v20180301 v20180301 = __v20180301 import pulumi_azure_native.authorization.v20180501 as __v20180501 v20180501 = __v20180501 import pulumi_azure_native.authorization.v20180501preview as __v20180501preview v20180501preview = __v20180501preview import pulumi_azure_native.authorization.v20180901preview as __v20180901preview v20180901preview = __v20180901preview import pulumi_azure_native.authorization.v20190101 as __v20190101 v20190101 = __v20190101 import pulumi_azure_native.authorization.v20190601 as __v20190601 v20190601 = __v20190601 import pulumi_azure_native.authorization.v20190901 as __v20190901 v20190901 = __v20190901 import pulumi_azure_native.authorization.v20200301 as __v20200301 v20200301 = __v20200301 import pulumi_azure_native.authorization.v20200301preview as __v20200301preview v20200301preview = __v20200301preview import pulumi_azure_native.authorization.v20200401preview as __v20200401preview v20200401preview = __v20200401preview import pulumi_azure_native.authorization.v20200501 as __v20200501 v20200501 = __v20200501 import pulumi_azure_native.authorization.v20200701preview as __v20200701preview v20200701preview = __v20200701preview import pulumi_azure_native.authorization.v20200801preview as __v20200801preview v20200801preview = __v20200801preview import pulumi_azure_native.authorization.v20200901 as __v20200901 v20200901 = __v20200901 import pulumi_azure_native.authorization.v20201001preview as __v20201001preview v20201001preview = __v20201001preview import pulumi_azure_native.authorization.v20210301preview as __v20210301preview v20210301preview = __v20210301preview import pulumi_azure_native.authorization.v20210601 as __v20210601 v20210601 = __v20210601 import pulumi_azure_native.authorization.v20210701preview as __v20210701preview v20210701preview = __v20210701preview else: v20150101 = _utilities.lazy_import('pulumi_azure_native.authorization.v20150101') v20150701 = _utilities.lazy_import('pulumi_azure_native.authorization.v20150701') v20151001preview = _utilities.lazy_import('pulumi_azure_native.authorization.v20151001preview') v20160401 = _utilities.lazy_import('pulumi_azure_native.authorization.v20160401') v20160901 = _utilities.lazy_import('pulumi_azure_native.authorization.v20160901') v20161201 = _utilities.lazy_import('pulumi_azure_native.authorization.v20161201') v20170401 = _utilities.lazy_import('pulumi_azure_native.authorization.v20170401') v20170601preview = _utilities.lazy_import('pulumi_azure_native.authorization.v20170601preview') v20171001preview = _utilities.lazy_import('pulumi_azure_native.authorization.v20171001preview') v20180101preview = _utilities.lazy_import('pulumi_azure_native.authorization.v20180101preview') v20180301 = _utilities.lazy_import('pulumi_azure_native.authorization.v20180301') v20180501 = _utilities.lazy_import('pulumi_azure_native.authorization.v20180501') v20180501preview = _utilities.lazy_import('pulumi_azure_native.authorization.v20180501preview') v20180901preview = _utilities.lazy_import('pulumi_azure_native.authorization.v20180901preview') v20190101 = _utilities.lazy_import('pulumi_azure_native.authorization.v20190101') v20190601 = _utilities.lazy_import('pulumi_azure_native.authorization.v20190601') v20190901 = _utilities.lazy_import('pulumi_azure_native.authorization.v20190901') v20200301 = _utilities.lazy_import('pulumi_azure_native.authorization.v20200301') v20200301preview = _utilities.lazy_import('pulumi_azure_native.authorization.v20200301preview') v20200401preview = _utilities.lazy_import('pulumi_azure_native.authorization.v20200401preview') v20200501 = _utilities.lazy_import('pulumi_azure_native.authorization.v20200501') v20200701preview = _utilities.lazy_import('pulumi_azure_native.authorization.v20200701preview') v20200801preview = _utilities.lazy_import('pulumi_azure_native.authorization.v20200801preview') v20200901 = _utilities.lazy_import('pulumi_azure_native.authorization.v20200901') v20201001preview = _utilities.lazy_import('pulumi_azure_native.authorization.v20201001preview') v20210301preview = _utilities.lazy_import('pulumi_azure_native.authorization.v20210301preview') v20210601 = _utilities.lazy_import('pulumi_azure_native.authorization.v20210601') v20210701preview = _utilities.lazy_import('pulumi_azure_native.authorization.v20210701preview')

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1

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5

ea2d3b4d146e0b705c2efcb553dd41511fcfe853

30

py

Python

PythonCurso01/aula121_docstrings/exemplo05/main.py

AlissonAnjos21/Aprendendo

9454d9e53ef9fb8bc61bf481b6592164f5bf8695

[ "MIT" ]

null

PythonCurso01/aula121_docstrings/exemplo05/main.py

AlissonAnjos21/Aprendendo

9454d9e53ef9fb8bc61bf481b6592164f5bf8695

[ "MIT" ]

null

PythonCurso01/aula121_docstrings/exemplo05/main.py

AlissonAnjos21/Aprendendo

9454d9e53ef9fb8bc61bf481b6592164f5bf8695

[ "MIT" ]

null

import classes help(classes)

7.5

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null

0

1

0

1

0

5

ea55525a24eef9f03328e91fdfdf6df238bf98b5

2,168

py

Python

tests/test_50_apps.py

bopen/sarsen

00d94c47a1b0abf4af1be2b17e4ae9ee66b1cd3b

[ "Apache-2.0" ]

111

2021-12-12T11:54:25.000Z

2022-03-30T05:53:02.000Z

tests/test_50_apps.py

bopen/sarsen

00d94c47a1b0abf4af1be2b17e4ae9ee66b1cd3b

[ "Apache-2.0" ]

7

2022-03-09T08:48:01.000Z

2022-03-24T17:57:00.000Z

tests/test_50_apps.py

bopen/sarsen

00d94c47a1b0abf4af1be2b17e4ae9ee66b1cd3b

[ "Apache-2.0" ]

6

2022-03-22T07:27:29.000Z

2022-03-29T17:58:10.000Z

import os import pathlib import py import pytest import xarray as xr from sarsen import apps DATA_FOLDER = pathlib.Path(__file__).parent / "data" DATA_PATHS = [ DATA_FOLDER / "S1B_IW_GRDH_1SDV_20211223T051122_20211223T051147_030148_039993_5371.SAFE", DATA_FOLDER / "S1A_IW_SLC__1SDV_20220104T170557_20220104T170624_041314_04E951_F1F1.SAFE", ] GROUPS = ["IW/VV", "IW1/VV"] DEM_RASTER = DATA_FOLDER / "Rome-30m-DEM.tif" @pytest.mark.parametrize("data_path,group", zip(DATA_PATHS, GROUPS)) @pytest.mark.skipif(os.getenv("GITHUB_ACTIONS") == "true", reason="too much memory") def test_terrain_correction_gtc( tmpdir: py.path.local, data_path: pathlib.Path, group: str, ) -> None: out = str(tmpdir.join("GTC.tif")) res = apps.terrain_correction( str(data_path), group, str(DEM_RASTER), output_urlpath=out, chunks={"slant_range_time": 1000, "azimuth_time": 1000}, ) assert isinstance(res, xr.DataArray) @pytest.mark.parametrize("data_path,group", zip(DATA_PATHS, GROUPS)) @pytest.mark.skipif(os.getenv("GITHUB_ACTIONS") == "true", reason="too much memory") def test_terrain_correction_fast_rtc( tmpdir: py.path.local, data_path: pathlib.Path, group: str ) -> None: out = str(tmpdir.join("RTC.tif")) res = apps.terrain_correction( str(data_path), group, str(DEM_RASTER), correct_radiometry="gamma_nearest", output_urlpath=out, chunks={"slant_range_time": 1000, "azimuth_time": 1000}, ) assert isinstance(res, xr.DataArray) @pytest.mark.parametrize("data_path,group", zip(DATA_PATHS, GROUPS)) @pytest.mark.skipif(os.getenv("GITHUB_ACTIONS") == "true", reason="too much memory") def test_terrain_correction_rtc( tmpdir: py.path.local, data_path: pathlib.Path, group: str ) -> None: out = str(tmpdir.join("RTC.tif")) res = apps.terrain_correction( str(data_path), group, str(DEM_RASTER), correct_radiometry="gamma_bilinear", output_urlpath=out, chunks={"slant_range_time": 1000, "azimuth_time": 1000}, ) assert isinstance(res, xr.DataArray)

27.443038

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0.760962

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0.147541

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null

0

1

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null

0

5

ea5751075853c0a423d8ddf444ac4a35b31ec139

90

py

Python

dingomata/cogs/botadmin/config.py

snazzyfox/discord-party-dingomata

5ef2a61fc7b85520e704641ec31f8fb88d590ec0

[ "MIT" ]

1

2021-12-23T18:21:22.000Z

dingomata/cogs/botadmin/config.py

snazzyfox/discord-party-dingomata

5ef2a61fc7b85520e704641ec31f8fb88d590ec0

[ "MIT" ]

20

2021-10-01T18:09:39.000Z

2022-02-27T10:18:56.000Z

dingomata/cogs/botadmin/config.py

snazzyfox/discord-party-dingomata

5ef2a61fc7b85520e704641ec31f8fb88d590ec0

[ "MIT" ]

6

2021-09-27T18:01:59.000Z

2022-01-30T17:46:10.000Z

from dingomata.config.models import CogConfig class BotAdminConfig(CogConfig): pass

15

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null

0

1

0

5

ea951b6fca44a4c9eaa772dd2048cde2c971b334

2,631

py

Python

tests/test_main.py

scravy/jinsi

d9cea4e22de603c05e6da3d16bff63e6ae094047

[ "MIT" ]

13

2020-12-06T04:35:17.000Z

2022-02-21T19:47:12.000Z

tests/test_main.py

scravy/jinsi

d9cea4e22de603c05e6da3d16bff63e6ae094047

[ "MIT" ]

11

2021-01-27T21:51:42.000Z

2021-04-17T16:23:51.000Z

tests/test_main.py

scravy/jinsi

d9cea4e22de603c05e6da3d16bff63e6ae094047

[ "MIT" ]

3

2021-02-11T10:35:23.000Z

2021-04-24T15:00:45.000Z

import re import textwrap import unittest import io from typing import Dict from jinsi.main import main as jinsi_main def capture(into: io.StringIO): def _print(arg, end='\n'): into.write(arg) into.write(end) return _print def provide(dct: Dict[str, str]): def _open(arg): val = dct[arg] return io.StringIO(textwrap.dedent(val)) return _open class MainTest(unittest.TestCase): def test_version(self): res = io.StringIO() jinsi_main("--version", _print=capture(res), _open=provide({}), _stdin=io.StringIO("")) self.assertRegex(res.getvalue(), re.compile(r"0\.[1-9][0-9]*\.[0-9]")) def test_single_json_object_as_yaml(self): res = io.StringIO() jinsi_main("-", _print=capture(res), _open=provide({}), _stdin=io.StringIO("""{"x":3}""")) self.assertEqual("x: 3\n", res.getvalue()) def test_multi_json_object_as_yaml(self): res = io.StringIO() jinsi_main("-", _print=capture(res), _open=provide({}), _stdin=io.StringIO("""{"x":3}\n{"y":3}\n""")) self.assertEqual("x: 3\n---\ny: 3\n", res.getvalue()) def test_single_yaml_object_as_yaml(self): res = io.StringIO() jinsi_main("-", _print=capture(res), _open=provide({}), _stdin=io.StringIO("x: 3")) self.assertEqual("x: 3\n", res.getvalue()) def test_multi_yaml_object_as_yaml(self): res = io.StringIO() jinsi_main("-", _print=capture(res), _open=provide({}), _stdin=io.StringIO("x: 3\n---\ny: 3\n")) self.assertEqual("x: 3\n---\ny: 3\n", res.getvalue()) def test_single_json_object_as_json(self): res = io.StringIO() jinsi_main("-j", "-", _print=capture(res), _open=provide({}), _stdin=io.StringIO("""{"x":3}""")) self.assertEqual("""{"x":3}\n""", res.getvalue()) def test_multi_json_object_as_json(self): res = io.StringIO() jinsi_main("-j", "-", _print=capture(res), _open=provide({}), _stdin=io.StringIO("""{"x":3}\n{"y":3}\n""")) self.assertEqual("""{"x":3}\n{"y":3}\n""", res.getvalue()) def test_single_yaml_object_as_json(self): res = io.StringIO() jinsi_main("-j", "-", _print=capture(res), _open=provide({}), _stdin=io.StringIO("x: 3")) self.assertEqual("""{"x":3}\n""", res.getvalue()) def test_multi_yaml_object_as_json(self): res = io.StringIO() jinsi_main("-j", "-", _print=capture(res), _open=provide({}), _stdin=io.StringIO("x: 3\n---\ny: 3\n")) self.assertEqual("""{"x":3}\n{"y":3}\n""", res.getvalue()) if __name__ == '__main__': unittest.main()

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1

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null

0

1

0

5

57aa0a7bc2fd9e41e3eab02b1da1709cdfdf3867

298

py

Python

logs/models.py

Phist0ne/webvirtcloud

d94ca38e5c8b5bb1323d33067ee6b991775cc390

[ "Apache-2.0" ]

2

2018-03-14T09:46:49.000Z

2019-05-14T11:45:14.000Z

logs/models.py

JamesLinus/webvirtcloud

d94ca38e5c8b5bb1323d33067ee6b991775cc390

[ "Apache-2.0" ]

4

2020-02-12T03:16:43.000Z

2021-06-10T22:08:23.000Z

logs/models.py

caicloud/webvirtcloud

d94ca38e5c8b5bb1323d33067ee6b991775cc390

[ "Apache-2.0" ]

1

2019-06-11T19:54:08.000Z

from django.db import models class Logs(models.Model): user = models.CharField(max_length=50) instance = models.CharField(max_length=50) message = models.CharField(max_length=255) date = models.DateTimeField(auto_now=True) def __unicode__(self): return self.instance

24.833333

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5.333333

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1

0

null

1

0

null

0

1

0

5

57b8fb7b2e996ea0f0336dad1e42ea379d608b15

308

py

Python

colossalai/kernel/jit/__init__.py

RichardoLuo/ColossalAI

797a9dc5a9e801d7499b8667c3ef039a38aa15ba

[ "Apache-2.0" ]

1,630

2021-10-30T01:00:27.000Z

2022-03-31T23:02:41.000Z

colossalai/kernel/jit/__init__.py

RichardoLuo/ColossalAI

797a9dc5a9e801d7499b8667c3ef039a38aa15ba

[ "Apache-2.0" ]

166

2021-10-30T01:03:01.000Z

2022-03-31T14:19:07.000Z

colossalai/kernel/jit/__init__.py

RichardoLuo/ColossalAI

797a9dc5a9e801d7499b8667c3ef039a38aa15ba

[ "Apache-2.0" ]

253

2021-10-30T06:10:29.000Z

2022-03-31T13:30:06.000Z

from .option import set_jit_fusion_options from .bias_dropout_add import bias_dropout_add_fused_train, bias_dropout_add_fused_inference from .bias_gelu import bias_gelu_impl __all__ = [ "bias_dropout_add_fused_train", "bias_dropout_add_fused_inference", "bias_gelu_impl", "set_jit_fusion_options" ]

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57bb75038ce8610d0482a9ee877229be51514c4b

254

py

Python

workout_tracker/exercises/filters.py

e-dang/Workout-Tracker

00a27597ea628cff62b320d616f56b2df4f344a0

[ "MIT" ]

null

workout_tracker/exercises/filters.py

e-dang/Workout-Tracker

00a27597ea628cff62b320d616f56b2df4f344a0

[ "MIT" ]

null

workout_tracker/exercises/filters.py

e-dang/Workout-Tracker

00a27597ea628cff62b320d616f56b2df4f344a0

[ "MIT" ]

null

from core.filters import OwnedMultiAliasResourceFilterSet from .models import ExerciseTemplate class ExerciseTemplateFilterSet(OwnedMultiAliasResourceFilterSet): class Meta: model = ExerciseTemplate fields = ['workloads__movement']

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1

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5

57eaf10585b039b9d737ec2afc6a98052c485f76

145

py

Python

cheese_grader/__main__.py

matthewdeanmartin/cheese_grader

566840e0f888b50daabcfd6fc723a5a7d42f5c39

[ "MIT" ]

8

2019-05-24T19:31:38.000Z

2019-05-28T14:13:56.000Z

cheese_grader/__main__.py

matthewdeanmartin/cheese_grader

566840e0f888b50daabcfd6fc723a5a7d42f5c39

[ "MIT" ]

null

cheese_grader/__main__.py

matthewdeanmartin/cheese_grader

566840e0f888b50daabcfd6fc723a5a7d42f5c39

[ "MIT" ]

1

2019-06-26T17:16:03.000Z

# coding=utf-8 """ Entrypoint for python -m """ from cheese_grader.main import process_docopts if __name__ == "__main__": process_docopts()

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null

0

1

0

1

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5

17acadb37f8977d242a22db18b335edd346e803c

111

py

Python

opportunity/admin.py

LoveProgramming-Limited/Django-CRM-2

7252460fad9ac38619304d0cdd3e5af59745e26e

[ "MIT" ]

null

opportunity/admin.py

LoveProgramming-Limited/Django-CRM-2

7252460fad9ac38619304d0cdd3e5af59745e26e

[ "MIT" ]

1

2021-11-27T02:53:06.000Z

2021-11-28T16:51:15.000Z

opportunity/admin.py

LoveProgramming-Limited/Django-CRM-2

7252460fad9ac38619304d0cdd3e5af59745e26e

[ "MIT" ]

null

from django.contrib import admin from opportunity.models import Opportunity admin.site.register(Opportunity)

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5

17f1aca75beae218759e2abbf7449890f960ec64

457

py

Python

tests/test_routes.py

bnbdr/argz

1e2974a6469361ed98c7e613ec6dd39e77acba27

[ "MIT" ]

2

2019-01-29T21:17:04.000Z

2021-04-02T13:06:20.000Z

tests/test_routes.py

bnbdr/argz

1e2974a6469361ed98c7e613ec6dd39e77acba27

[ "MIT" ]

2

2018-08-26T13:25:35.000Z

2019-03-01T15:34:49.000Z

tests/test_routes.py

bnbdr/argz

1e2974a6469361ed98c7e613ec6dd39e77acba27

[ "MIT" ]

null

def route_allow_all(*args, **kwargs): return args, kwargs def route_defarg(reqarg, defarg=1): return defarg def route_json_dict(jsondict, dbg=False): pass def route_validator(alphanum, filepath, key, novalidation): """ put descriptive docstring here """ pass def route_min(count): """ put descriptive docstring here """ pass def test_switch(pos1, opt1=None, dbg=False): pass

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5.090909

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0.085714

0.192857

0.242857

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0.269147

457

25

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0

0.333333

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0.333333

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null

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null

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1

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1

0

1

0

5

aa1ebcedace4e2f3b97204488b7ce84adeebaa4c

119

py

Python

learn/__init__.py

Daniela-Gibbons/insectvision

bdd2aed33b814483512280ba138abde86fa15558

[ "MIT" ]

1

2021-12-20T07:39:53.000Z

learn/__init__.py

JJFosterLab/insectvision

aef03fc406fb6a35340051cebf0e3ecd9bec63cc

[ "MIT" ]

null

learn/__init__.py

JJFosterLab/insectvision

aef03fc406fb6a35340051cebf0e3ecd9bec63cc

[ "MIT" ]

null

from loss_function import get_loss, SensorObjective # from optimisation import optimise from whitening import pca, zca

29.75

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1

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1

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5

aa2621ee800372336458262792d11c9f07debda4

4,079

py

Python

Days/Day 13 - Mine Cart Madness/Part 1.py

jamesjiang52/Advent-of-Code-2018

d2331a8133866335721af1379a62c3880114d07d

[ "MIT" ]

null

Days/Day 13 - Mine Cart Madness/Part 1.py

jamesjiang52/Advent-of-Code-2018

d2331a8133866335721af1379a62c3880114d07d

[ "MIT" ]

null

Days/Day 13 - Mine Cart Madness/Part 1.py

jamesjiang52/Advent-of-Code-2018

d2331a8133866335721af1379a62c3880114d07d

[ "MIT" ]

null

def main(): f = [line.rstrip("\n") for line in open("Data.txt")] map_ = [list(line) for line in f] info = [] for i in range(len(map_)): for j in range(len(map_[0])): if map_[i][j] == "v": info.append([i, j, "down", "left"]) map_[i][j] = "|" elif map_[i][j] == ">": info.append([i, j, "right", "left"]) map_[i][j] = "-" elif map_[i][j] == "^": info.append([i, j, "up", "left"]) map_[i][j] = "|" elif map_[i][j] == "<": info.append([i, j, "left", "left"]) map_[i][j] = "-" done = False while not done: info.sort() for i in info: y, x = i[0], i[1] if i[2] == "down": if map_[y + 1][x] == "/": i[2] = "left" elif map_[y + 1][x] == "\\": i[2] = "right" elif map_[y + 1][x] == "+": if i[3] == "left": i[2] = "right" i[3] = "straight" elif i[3] == "straight": i[2] = "down" i[3] = "right" elif i[3] == "right": i[2] = "left" i[3] = "left" positions = [info_[0:2] for info_ in info] i[0] += 1 if [y + 1, x] in positions: print(x, y + 1) done = True break elif i[2] == "right": if map_[y][x + 1] == "/": i[2] = "up" elif map_[y][x + 1] == "\\": i[2] = "down" elif map_[y][x + 1] == "+": if i[3] == "left": i[2] = "up" i[3] = "straight" elif i[3] == "straight": i[2] = "right" i[3] = "right" elif i[3] == "right": i[2] = "down" i[3] = "left" positions = [info_[0:2] for info_ in info] i[1] += 1 if [y, x + 1] in positions: print(x + 1, y) done = True break elif i[2] == "up": if map_[y - 1][x] == "/": i[2] = "right" elif map_[y - 1][x] == "\\": i[2] = "left" elif map_[y - 1][x] == "+": if i[3] == "left": i[2] = "left" i[3] = "straight" elif i[3] == "straight": i[2] = "up" i[3] = "right" elif i[3] == "right": i[2] = "right" i[3] = "left" positions = [info_[0:2] for info_ in info] i[0] -= 1 if [y - 1, x] in positions: print(x, y - 1) done = True break elif i[2] == "left": if map_[y][x - 1] == "/": i[2] = "down" elif map_[y][x - 1] == "\\": i[2] = "up" elif map_[y][x - 1] == "+": if i[3] == "left": i[2] = "down" i[3] = "straight" elif i[3] == "straight": i[2] = "left" i[3] = "right" elif i[3] == "right": i[2] = "up" i[3] = "left" positions = [info_[0:2] for info_ in info] i[1] -= 1 if [y, x - 1] in positions: print(x - 1, y) done = True break if __name__ == "__main__": main()

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0.034221

0.789924

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0.73384

0.558935

0

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4,079

121

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0

0.009174

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0

null

0

1

0

1

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null

0

5

a4b1121d72b80203a1f7ff08431a2499da64d1b3

54

py

Python

packages/vaex-ml/vaex/ml/_version.py

yohplala/vaex

ca7927a19d259576ca0403ee207a597aaef6adc2

[ "MIT" ]

null

packages/vaex-ml/vaex/ml/_version.py

yohplala/vaex

ca7927a19d259576ca0403ee207a597aaef6adc2

[ "MIT" ]

null

packages/vaex-ml/vaex/ml/_version.py

yohplala/vaex

ca7927a19d259576ca0403ee207a597aaef6adc2

[ "MIT" ]

null

__version__ = '0.12.0' __version_tuple__ = (0, 12, 0)

18

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0.444444

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0.148148

54

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null

1

0

1

0

1

0

null

0

5

a4b81ae123193a7e7dd18666aea67ef9b6795993

21

py

Python

python/testData/joinLines/BackslashBetweenTargetsInImport.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/joinLines/BackslashBetweenTargetsInImport.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/joinLines/BackslashBetweenTargetsInImport.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

import foo, \ bar

10.5

13

0.571429

3

21

4

1

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0.333333

21

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14

10.5

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0

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1

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null

0

1

0

1

0

5

a4c4d8be7fb365c9292c7111ab0edf97776913f6

190

py

Python

average.py

pdebuyl/cli01

6f8118806338aee6cfae6fc699614ee01b8f3436

[ "CC-BY-4.0" ]

null

average.py

pdebuyl/cli01

6f8118806338aee6cfae6fc699614ee01b8f3436

[ "CC-BY-4.0" ]

null

average.py

pdebuyl/cli01

6f8118806338aee6cfae6fc699614ee01b8f3436

[ "CC-BY-4.0" ]

null

#!/usr/bin/env python3 from __future__ import print_function, division import sys import statistics with open(sys.argv[1], 'r') as f: print(statistics.mean(map(float, f.readlines())))

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1

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5

a4d7856e62ca66b557e16cd3dcca1b02f848deef

28,983

py

Python

data/data.py

Elric2718/HierGMM

4cf24e921443f24856385364ffbf0058327b85f8

[ "MIT" ]

null

data/data.py

Elric2718/HierGMM

4cf24e921443f24856385364ffbf0058327b85f8

[ "MIT" ]

null

data/data.py

Elric2718/HierGMM

4cf24e921443f24856385364ffbf0058327b85f8

[ "MIT" ]

null

import os import argparse import logging from typing import Dict, List, Tuple, Optional, Set import numpy as np # type: ignore import pandas as pd # type: ignore from scipy import sparse as sp # type: ignore import torch # type: ignore from torch.utils import data # type: ignore from numpy.random import RandomState # type: ignore def ml_1m( data_path: str, train_path: str, val_path: str, test_path: str) -> None: ratings = pd.read_csv( os.path.join( data_path, 'ratings.dat'), sep='::', names=[ 'uidx', 'iidx', 'rating', 'ts'], dtype={ 'uidx': int, 'iidx': int, 'rating': float, 'ts': float}) print(ratings.shape) ratings.uidx = ratings.uidx - 1 ratings.iidx = ratings.iidx - 1 print(ratings.head()) ratings.to_feather(os.path.join(data_path, 'ratings.feather')) user_hist: Dict[int, List[Tuple[int, float]]] = {} for row in ratings.itertuples(): if row.uidx not in user_hist: user_hist[row.uidx] = [] user_hist[row.uidx].append((row.iidx, row.ts)) # sort by ts in descending order # row represents the user, columns represents the item train_record: List[Tuple[int, int]] = [] val_record: List[Tuple[int, int]] = [] test_record: List[Tuple[int, int]] = [] for uidx, hist in user_hist.items(): ord_hist = [x[0] for x in sorted(hist, key=lambda x: x[1])] assert(len(ord_hist) >= 20) for v in ord_hist[:-2]: train_record.append((uidx, v)) val_record.append((uidx, ord_hist[-2])) test_record.append((uidx, ord_hist[-1])) train_dat = np.ones(len(train_record)) val_dat = np.ones(len(val_record)) test_dat = np.ones(len(test_record)) train_npy = np.array(train_record) val_npy = np.array(val_record) test_npy = np.array(test_record) mat_shape = (ratings.uidx.max() + 1, ratings.iidx.max() + 1) train_csr = sp.csr_matrix((train_dat, (train_npy[:, 0], train_npy[:, 1])), shape=mat_shape) val_csr = sp.csr_matrix((val_dat, (val_npy[:, 0], val_npy[:, 1])), shape=mat_shape) test_csr = sp.csr_matrix((test_dat, (test_npy[:, 0], test_npy[:, 1])), shape=mat_shape) sp.save_npz(train_path, train_csr) sp.save_npz(val_path, val_csr) sp.save_npz(test_path, test_csr) def time_based_split( data: pd.DataFrame, data_path: str, min_len: int = 20) -> None: names = ['uidx', 'iidx', 'rating', 'ts'] #names = ["uidx", "gender", "age", "occupation", "zipcode", "iidx", "rating", "ts", "title", "genre"] if (data.columns == names).min() < 1: raise ValueError( f"Only support data frame with columns ['uidx', 'iidx', 'rating', 'ts'], the input is {data.columns}") #f"Only support data frame with columns ['uidx', 'gender', 'age', 'occupation', 'zipcode', 'iidx', 'rating', 'ts', 'title', 'genre'], the input is {data.columns}") user_hist: Dict[int, List[Tuple[int, float, float]]] = {} for row in data.itertuples(): if row.uidx not in user_hist: user_hist[row.uidx] = [] user_hist[row.uidx].append((row.iidx, row.rating, row.ts)) # sort by ts in descending order train_record = {x: [] for x in names} val_record = {x: [] for x in names} test_record = {x: [] for x in names} def put2record(record, u, obs): record['uidx'].append(u) record['iidx'].append(obs[0]) record['rating'].append(obs[1]) record['ts'].append(obs[2]) for uidx, hist in user_hist.items(): ord_hist = [x for x in sorted(hist, key=lambda x: x[-1])] assert(len(ord_hist) >= 20) for v in ord_hist[:-2]: put2record(train_record, uidx, v) put2record(val_record, uidx, ord_hist[-2]) put2record(test_record, uidx, ord_hist[-1]) train_path = os.path.join(data_path, 'train.feather') pd.DataFrame(train_record).to_feather(train_path) val_path = os.path.join(data_path, 'val.feather') pd.DataFrame(val_record).to_feather(val_path) test_path = os.path.join(data_path, 'test.feather') pd.DataFrame(test_record).to_feather(test_path) class HistoryCtrl: # Control the length of the history. def __init__(self, max_len: int, sep: str, n_item: int): self._max_len = max_len self._sep = sep self._n_item = n_item self._seq_len = 0 self._counter = 0 def cut_hist(self, wd): self._counter = (self._counter + 1) % self._n_item if not wd: self._seq_len = 0 if self._seq_len == self._max_len: loc = 0 while loc < len(wd) and wd[loc] != self._sep: loc += 1 if loc < len(wd): wd = wd[(loc+1):] if self._counter == 0: self._seq_len = min(self._seq_len + 1, self._max_len) return wd + ":" if wd else wd # Version 2 uses rating to get labels: rating > 3 is postive, o.w., negative. def time_based_split_v2( data: pd.DataFrame, data_path: str, min_len: int = 20, max_len: int = 60) -> None: names = ["uidx", "gender", "age", "occupation", "zipcode", "iidx", "rating", "ts", "title", "genre"] if (data.columns == names).min() < 1: raise ValueError( f"Only support data frame with columns ['uidx', 'gender', 'age', 'occupation', 'zipcode', 'iidx', 'rating', 'ts', 'title', 'genre'], the input is {data.columns}") hist_controller = HistoryCtrl(max_len, ":", 5) # 5 histories data = data.sort_values(['uidx', 'ts'], ascending = True) user_hist: Dict[List[Tuple[int, int, int, int, int]], List[Tuple[int, int, int, float, float, int, str, str, str, str, str, int]]] = {} for row in data.itertuples(): row_info = (row.uidx, row.gender, row.age, row.occupation, row.zipcode) if row_info not in user_hist: counter = 0 last = (0, '', '', '', '', '') user_hist[row_info] = [] curr = (row.iidx, row.rating, row.ts, row.title, row.genre) user_hist[row_info].append(tuple(list(curr) + list(last) + [int(row.rating > 3)])) last = tuple([hist_controller._seq_len + 1] + [hist_controller.cut_hist(x) + str(y) for x, y in zip(last[1:], curr)]) # the 1-st length is wrong output_names = ["uidx", "gender", "age", "occupation", "zipcode", "iidx", "rating", "ts", "title", "genre", "hist_seq_length", "iidx_hist", "rating_hist", "ts_hist", "title_hist", "genre_hist", "label"] train_record = {x: [] for x in output_names} val_record = {x: [] for x in output_names} test_record = {x: [] for x in output_names} def put2record(record, u, obs): record['uidx'].append(u[0]) record['gender'].append(u[1]) record['age'].append(u[2]) record['occupation'].append(u[3]) record['zipcode'].append(u[4]) record['iidx'].append(obs[0]) record['title'].append(obs[1]) record['genre'].append(obs[2]) record['rating'].append(obs[3]) record['ts'].append(obs[4]) record['hist_seq_length'].append(obs[5]) record['iidx_hist'].append(obs[6]) record['title_hist'].append(obs[7]) record['genre_hist'].append(obs[8]) record['rating_hist'].append(obs[9]) record['ts_hist'].append(obs[10]) record['label'].append(obs[11]) for user_info, hist in user_hist.items(): assert(len(hist) >= min_len) for v in hist[:-2]: put2record(train_record, user_info, v) put2record(val_record, user_info, hist[-2]) put2record(test_record, user_info, hist[-1]) train_path = os.path.join(data_path, 'train.csv') pd.DataFrame(train_record).to_csv(train_path, sep = ";", header = False, index = False) val_path = os.path.join(data_path, 'val.csv') pd.DataFrame(val_record).to_csv(val_path, sep = ";", header = False, index = False) test_path = os.path.join(data_path, 'test.csv') pd.DataFrame(test_record).to_csv(test_path, sep = ";", header = False, index = False) # Version 3 uses negative sampling def time_based_split_v3( data: pd.DataFrame, data_path: str, min_len: int = 20, max_len: int = 60) -> None: names = ["uidx", "gender", "age", "occupation", "zipcode", "iidx", "rating", "ts", "title", "genre"] if (data.columns == names).min() < 1: raise ValueError( f"Only support data frame with columns ['uidx', 'gender', 'age', 'occupation', 'zipcode', 'iidx', 'rating', 'ts', 'title', 'genre'], the input is {data.columns}") hist_controller = HistoryCtrl(max_len, ":", 1) # 1 history item data = data.sort_values(['uidx', 'ts'], ascending = True) user_hist: Dict[List[Tuple[int, int, int, int]], List[Tuple[int, int, str, int, str]]] = {} all_items = set(data['iidx'].values) user_items = data.groupby('uidx')['iidx'].apply(set).reset_index(name='iidx') item_count = data.groupby('uidx').size().reset_index(name='counts') for row in data.itertuples(): row_info = (row.uidx, row.gender, row.age, row.occupation) if row_info not in user_hist: neg_pool = list(all_items - user_items[user_items['uidx'] == row.uidx]['iidx'].values[0]) total_count = item_count[item_count['uidx'] == row.uidx]['counts'].values counter = 0 last = (0, '') user_hist[row_info] = [] counter += 1 if counter < total_count - 1: dat_type = "train" elif counter == total_count: dat_type = "val" else: dat_type = 'test' curr = row.iidx user_hist[row_info].append(tuple([curr] + list(last) + [1] + [dat_type])) # negative sampling neg_size = 4 if counter < total_count else 99 neg_candidates = np.random.choice(neg_pool, neg_size, replace = False) for neg_cand in neg_candidates: user_hist[row_info].append(tuple([neg_cand] + list(last) + [0] + [dat_type])) # update history last = tuple([hist_controller._seq_len + 1] + [hist_controller.cut_hist(x) + str(y) for x, y in zip(last[1:], [curr])]) output_names = ["uidx", "gender", "age", "occupation", "iidx", "hist_seq_length", "iidx_hist", "label"] train_record = {x: [] for x in output_names} val_record = {x: [] for x in output_names} test_record = {x: [] for x in output_names} def put2record(record, u, obs): record['uidx'].append(u[0]) record['gender'].append(u[1]) record['age'].append(u[2]) record['occupation'].append(u[3]) record['iidx'].append(obs[0]) record['hist_seq_length'].append(obs[1]) record['iidx_hist'].append(obs[2]) record['label'].append(obs[3]) for user_info, hist in user_hist.items(): assert(len(hist) >= min_len) for v in hist: if v[-1] == "train": put2record(train_record, user_info, v) elif v[-1] == "val": put2record(val_record, user_info, v) elif v[-1] == "test": put2record(test_record, user_info, v) train_path = os.path.join(data_path, 'train2.csv') pd.DataFrame(train_record).to_csv(train_path, sep = ";", header = False, index = False) val_path = os.path.join(data_path, 'val2.csv') pd.DataFrame(val_record).to_csv(val_path, sep = ";", header = False, index = False) test_path = os.path.join(data_path, 'test2.csv') pd.DataFrame(test_record).to_csv(test_path, sep = ";", header = False, index = False) # Version 4 uses negative sampling; a compressed version def time_based_split_v4( # for ml-1m data: pd.DataFrame, data_path: str, min_len: int = 20, max_len: int = 60) -> None: names = ["uidx", "gender", "age", "occupation", "zipcode", "iidx", "rating", "ts", "title", "genre"] if (data.columns == names).min() < 1: raise ValueError( f"Only support data frame with columns ['uidx', 'gender', 'age', 'occupation', 'zipcode', 'iidx', 'rating', 'ts', 'title', 'genre'], the input is {data.columns}") hist_controller = HistoryCtrl(max_len, ":", 1) # 5 history item data = data.sort_values(['uidx', 'ts'], ascending = True) user_hist: Dict[List[Tuple[int, int, int, int]], List[Tuple[str, int, str, str]]] = {} all_items = set(data['iidx'].values) user_items = data.groupby('uidx')['iidx'].apply(set).reset_index(name='iidx') item_count = data.groupby('uidx').size().reset_index(name='counts') for row in data.itertuples(): row_info = (row.uidx, row.gender, row.age, row.occupation) if row_info not in user_hist: neg_pool = list(all_items - user_items[user_items['uidx'] == row.uidx]['iidx'].values[0]) total_count = item_count[item_count['uidx'] == row.uidx]['counts'].values counter = 0 last = (0, '') user_hist[row_info] = [] counter += 1 if counter < total_count - 1: dat_type = "train" neg_size = 4 elif counter < total_count: dat_type = "val" neg_size = 4 else: dat_type = 'test' neg_size = 99 curr = row.iidx # negative sampling neg_candidates = np.random.choice(neg_pool, neg_size, replace = False) user_hist[row_info].append(tuple(["::".join([str(curr)] + [str(x) for x in neg_candidates])] + list(last) + [dat_type])) # the 1-st length is wrong # update history last = tuple([hist_controller._seq_len + 1] + [hist_controller.cut_hist(x) + str(y) for x, y in zip(last[1:], [curr])]) output_names = ["uidx", "gender", "age", "occupation", "iidx", # the first one is positive, the remaining are negative, separated by '::' "hist_seq_length", "iidx_hist"] train_record = {x: [] for x in output_names} val_record = {x: [] for x in output_names} test_record = {x: [] for x in output_names} def put2record(record, u, obs): record['uidx'].append(u[0]) record['gender'].append(u[1]) record['age'].append(u[2]) record['occupation'].append(u[3]) record['iidx'].append(obs[0]) record['hist_seq_length'].append(obs[1]) record['iidx_hist'].append(obs[2]) for user_info, hist in user_hist.items(): assert(len(hist) >= min_len) for v in hist: if v[-1] == "train": put2record(train_record, user_info, v) elif v[-1] == "val": put2record(val_record, user_info, v) elif v[-1] == "test": put2record(test_record, user_info, v) train_path = os.path.join(data_path, 'train2.csv') pd.DataFrame(train_record).to_csv(train_path, sep = ";", header = False, index = False) val_path = os.path.join(data_path, 'val2.csv') pd.DataFrame(val_record).to_csv(val_path, sep = ";", header = False, index = False) test_path = os.path.join(data_path, 'test2.csv') pd.DataFrame(test_record).to_csv(test_path, sep = ";", header = False, index = False) # Version 5 uses negative sampling; a compressed version def time_based_split_v5( # for last-fm and books data: pd.DataFrame, data_path: str, min_len: int = 20, max_len: int = 60) -> None: names = ["uidx", "iidx", "rating", "ts"] if (data.columns == names).min() < 1: raise ValueError( f"Only support data frame with columns ['uidx', 'iidx', 'rating', 'ts'], the input is {data.columns}") hist_controller = HistoryCtrl(max_len, ":", 1) # 5 history item data = data.sort_values(['uidx', 'ts'], ascending = True) user_hist: Dict[List[Tuple[int, int, int, int]], List[Tuple[str, int, str, str]]] = {} all_items = set(data['iidx'].values) user_items = data.groupby('uidx')['iidx'].apply(set).reset_index(name='iidx') item_count = data.groupby('uidx').size().reset_index(name='counts') for row in data.itertuples(): row_info = (row.uidx, 0) if row_info not in user_hist: neg_pool = list(all_items - user_items[user_items['uidx'] == row.uidx]['iidx'].values[0]) total_count = item_count[item_count['uidx'] == row.uidx]['counts'].values counter = 0 last = (0, '') user_hist[row_info] = [] counter += 1 if counter < total_count - 1: dat_type = "train" neg_size = 4 elif counter < total_count: dat_type = "val" neg_size = 4 else: dat_type = 'test' neg_size = 99 curr = row.iidx # negative sampling neg_candidates = np.random.choice(neg_pool, neg_size, replace = False) user_hist[row_info].append(tuple(["::".join([str(curr)] + [str(x) for x in neg_candidates])] + list(last) + [dat_type])) # the 1-st length is wrong # update history last = tuple([hist_controller._seq_len + 1] + [hist_controller.cut_hist(x) + str(y) for x, y in zip(last[1:], [curr])]) output_names = ["uidx", "iidx", # the first one is positive, the remaining are negative, separated by '::' "hist_seq_length", "iidx_hist"] train_record = {x: [] for x in output_names} val_record = {x: [] for x in output_names} test_record = {x: [] for x in output_names} def put2record(record, u, obs): record['uidx'].append(u[0]) record['iidx'].append(obs[0]) record['hist_seq_length'].append(obs[1]) record['iidx_hist'].append(obs[2]) for user_info, hist in user_hist.items(): assert(len(hist) >= min_len) for v in hist: if v[-1] == "train": put2record(train_record, user_info, v) elif v[-1] == "val": put2record(val_record, user_info, v) elif v[-1] == "test": put2record(test_record, user_info, v) train_path = os.path.join(data_path, 'train2.csv') pd.DataFrame(train_record).to_csv(train_path, sep = ";", header = False, index = False) val_path = os.path.join(data_path, 'val2.csv') pd.DataFrame(val_record).to_csv(val_path, sep = ";", header = False, index = False) test_path = os.path.join(data_path, 'test2.csv') pd.DataFrame(test_record).to_csv(test_path, sep = ";", header = False, index = False) def ml_1m_v2(data_path: str) -> None: names = ['uidx', 'iidx', 'rating', 'ts'] dtype = {'uidx': int, 'iidx': int, 'rating': float, 'ts': float} ratings = pd.read_csv(os.path.join(data_path, 'ratings.dat'), sep='::', names=names, dtype=dtype) print(ratings.shape) ratings.uidx = ratings.uidx - 1 ratings.iidx = ratings.iidx - 1 print(ratings.head()) ratings.to_feather(os.path.join(data_path, 'ratings.feather')) time_based_split(ratings, data_path, 20) class NegSeqData(data.Dataset): def __init__(self, features: List[Tuple[int, int]], num_item: int, num_neg: int = 0, is_training: bool = False, seed: int = 123, past_hist: Optional[Dict[int, Set[int]]] = None) -> None: super(NegSeqData, self).__init__() """ Note that the labels are only useful when training, we thus add them in the ng_sample() function. """ self.features = features self.num_item = num_item self.train_set = set(features) self.num_neg = num_neg self.is_training = is_training self.past_hist = past_hist self.prng = RandomState(seed) def ng_sample(self) -> None: self.features_fill = [] for x in self.features: u, i = x[0], x[1] j_list = [] for _ in range(self.num_neg): is_dup = True while is_dup: j = self.prng.randint(self.num_item) is_dup = (u, j) in self.train_set if self.past_hist is not None: is_dup = is_dup or j in self.past_hist.get(u, []) j_list.append(j) self.features_fill.append([u, i, j_list]) def __len__(self) -> int: return len(self.features) def __getitem__(self, idx): features = self.features_fill if \ self.is_training else self.features user = features[idx][0] item_i = features[idx][1] item_j_list = np.array(features[idx][2]) if \ self.is_training else features[idx][1] return user, item_i, item_j_list class NegSampleData(data.Dataset): def __init__(self, features: List[Tuple[int, int]], num_item: int, num_neg: int = 0, is_training: bool = False, seed: int = 123) -> None: super(NegSampleData, self).__init__() """ Note that the labels are only useful when training, we thus add them in the ng_sample() function. """ self.features = features self.num_item = num_item self.train_set = set(features) self.num_neg = num_neg self.is_training = is_training self.prng = RandomState(seed) def ng_sample(self) -> None: assert self.is_training, 'no need to sample when testing' self.features_fill = [] for x in self.features: u, i = x[0], x[1] for _ in range(self.num_neg): j = self.prng.randint(self.num_item) while (u, j) in self.train_set: j = self.prng.randint(self.num_item) self.features_fill.append([u, i, j]) def __len__(self) -> int: return self.num_neg * len(self.features) if \ self.is_training else len(self.features) def __getitem__(self, idx): features = self.features_fill if \ self.is_training else self.features user = features[idx][0] item_i = features[idx][1] item_j = features[idx][2] if \ self.is_training else features[idx][1] return user, item_i, item_j class RatingData(data.Dataset): def __init__(self, features: List[Tuple[int, int, float]]) -> None: super(RatingData, self).__init__() self.features = features def __len__(self): return len(self.features) def __getitem__(self, idx): return self.features[idx] class NegSequenceData(data.Dataset): def __init__(self, hist: Dict[int, List[int]], max_len: int, padding_idx: int, item_num: int, num_neg: int = 0, is_training: bool = False, past_hist: Optional[Dict[int, Set[int]]] = None, seed: int = 123, window: bool = True, allow_empty: bool =False) -> None: super(NegSequenceData, self).__init__() self.max_len = max_len self.padding_idx = padding_idx self.num_item = item_num self.num_neg = num_neg self.past_hist = past_hist self.prng = RandomState(seed) self.logger = logging.getLogger(__name__) self.logger.debug('Build windowed data') self.records = [] for uidx, item_list in hist.items(): if window: for i in range(len(item_list)): item_slice = item_list[max(0, i - max_len):i] if not allow_empty and len(item_slice) == 0: continue self.records.append([uidx, item_list[i], item_slice]) else: if not allow_empty and len(item_list) == 1: continue self.records.append([uidx, item_list[-1], item_list[-(max_len + 1):-1]]) def __len__(self) -> int: return len(self.records) def __getitem__(self, idx): temp_hist = np.zeros(self.max_len, dtype=int) + self.padding_idx uidx, pos_item, item_hist = self.records[idx] assert(len(temp_hist) >= len(item_hist)) if len(item_hist) > 0: temp_hist[-len(item_hist):] = item_hist negitem_list = np.zeros(self.num_neg, dtype=int) for idx in range(self.num_neg): is_dup = True while is_dup: negitem = self.prng.randint(self.num_item) is_dup = negitem == pos_item if self.past_hist is not None: is_dup = is_dup or negitem in self.past_hist.get(uidx, []) negitem_list[idx] = negitem return uidx, pos_item, negitem_list, temp_hist class LabeledSequenceData(data.Dataset): def __init__(self, hist: Dict[int, List[Tuple[int, float]]], max_len: int, padding_idx: int, item_num: int, num_neg: int = 4, past_hist: Optional[Dict[int, Set[int]]] = None, is_training: bool = False, window: bool = True, seed: int = 1, allow_empty: bool = False) -> None: """ :param hist: use_idx to a list of [item_idx, label] :param max_len: :param padding_idx: :param item_num: :param is_training: :param window: Use window approach to get data :param allow_empty: """ super(LabeledSequenceData, self).__init__() self.max_len = max_len self.padding_idx = padding_idx self.num_item = item_num self.num_neg = num_neg self.prng = RandomState(seed) self.past_hist = past_hist self.logger = logging.getLogger(__name__) self.logger.debug('Build windowed data') self.records = [] for uidx, item_record_list in hist.items(): item_list = [x[0] for x in item_record_list] label_list = [int(x[1]) for x in item_record_list] if window: for i in range(len(item_list)): item_slice = item_list[max(0, i - max_len):i] if not allow_empty and len(item_slice) == 0: continue self.records.append([uidx, item_list[i], label_list[i], item_slice]) for _ in range(self.num_neg): neg_item = self.get_negative(uidx) self.records.append([uidx, neg_item, 0, item_slice]) else: if not allow_empty and len(item_list) == 1: continue item_slice = item_list[-(max_len + 1):-1] self.records.append([uidx, item_list[-1], label_list[-1], item_slice]) for _ in range(self.num_neg): neg_item = self.get_negative(uidx) self.records.append([uidx, neg_item, 0, item_slice]) self.prng.shuffle(self.records) def get_negative(self, uidx): is_past = True negitem = self.prng.randint(self.num_item) while self.past_hist is not None and negitem in self.past_hist.get(uidx, []): negitem = self.prng.randint(self.num_item) return negitem def __len__(self) -> int: return len(self.records) def __getitem__(self, idx): temp_hist = np.zeros(self.max_len, dtype=int) + self.padding_idx uidx, item_idx, label, item_hist = self.records[idx] assert(len(temp_hist) >= len(item_hist)) if len(item_hist) > 0: temp_hist[-len(item_hist):] = item_hist return uidx, item_idx, label, temp_hist if __name__ == '__main__': # ml_1m('/mnt/c0r00zy/a()c_gan/data/ml-1m', # '/mnt/c0r00zy/ac_gan/data/ml-1m/train.npz', # '/mnt/c0r00zy/ac_gan/data/ml-1m/val.npz', # '/mnt/c0r00zy/ac_gan/data/ml-1m/test.npz') parser = argparse.ArgumentParser() parser.add_argument('--data_path', type=str, required=True) args = parser.parse_args() ml_1m_v2(args.data_path)

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0.008868

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0.804974

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0.737823

0.713019

0.676713

0.661997

0

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0.296795

28,983

745

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0.750356

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0.659285

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0.008518

0.073221

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0.015332

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0.054514

false

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0.017036

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0

null

0

1

0

1

0

null

0

5

a4e11e9b69ae81ab62869850931e1cbe9231c790

70

py

Python

backend/core/admin.py

ohforest/verbose-equals-true

db100aef71d39a6918899c96b767db44920b2083

[ "MIT" ]

7

2019-08-11T17:23:36.000Z

2022-03-25T23:02:09.000Z

backend/core/admin.py

ohforest/verbose-equals-true

db100aef71d39a6918899c96b767db44920b2083

[ "MIT" ]

196

2019-10-04T17:03:36.000Z

2022-03-31T17:54:59.000Z

src/marketlist/admin.py

gabaconrado/peculiar-fox

278ae43a0222ce895a043afdd6199bb026174585

[ "MIT" ]

2

2019-03-07T11:55:56.000Z

2019-08-11T17:17:58.000Z

from django.contrib import admin # noqa # Register your models here.

17.5

39

0.771429

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0.171429

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1

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0

5

a4edb979d969318c6577287d152b2ad3c250d726

8,111

py

Python

alembic/versions/b11587a08126_refactor_contacts.py

qwc-services/sogis-agdi

f278612c42f648da07448905f2b8021b279e66bc

[ "MIT" ]

null

alembic/versions/b11587a08126_refactor_contacts.py

qwc-services/sogis-agdi

f278612c42f648da07448905f2b8021b279e66bc

[ "MIT" ]

null

alembic/versions/b11587a08126_refactor_contacts.py

qwc-services/sogis-agdi

f278612c42f648da07448905f2b8021b279e66bc

[ "MIT" ]

null

"""refactor contacts Revision ID: b11587a08126 Revises: 0a213bde7bdb Create Date: 2018-03-08 15:08:30.316913 """ from alembic import op import sqlalchemy as sa # revision identifiers, used by Alembic. revision = 'b11587a08126' down_revision = '0a213bde7bdb' branch_labels = None depends_on = None def upgrade(): sql = sa.sql.text(""" -- replace contact CREATE TYPE contacts.contact_type AS ENUM ('person', 'organisation'); DROP TABLE IF EXISTS contacts.contact CASCADE; CREATE TABLE contacts.contact( id bigint NOT NULL DEFAULT nextval('contacts.contact_id_seq'::regclass), type contacts.contact_type NOT NULL, id_organisation bigint, name character varying NOT NULL, street character varying, house_no character varying, zip character varying, city character varying, country_code character(3), CONSTRAINT contact_pk PRIMARY KEY (id) ); -- replace person DROP SEQUENCE IF EXISTS contacts.person_id_seq CASCADE; DROP TABLE IF EXISTS contacts.person CASCADE; CREATE TABLE contacts.person( id bigint NOT NULL, function character varying NOT NULL, email character varying, phone character varying, CONSTRAINT person_pk PRIMARY KEY (id) ); -- replace organisation DROP SEQUENCE IF EXISTS contacts.organisation_id_seq CASCADE; DROP TABLE IF EXISTS contacts.organisation CASCADE; CREATE TABLE contacts.organisation( id bigint NOT NULL, unit character varying, abbreviation character varying, CONSTRAINT organisation_pk PRIMARY KEY (id) ); -- update resource_contact ALTER TABLE contacts.resource_contact RENAME contact_id_contact TO id_contact; -- FK constraints ALTER TABLE contacts.contact ADD CONSTRAINT organisation_fk FOREIGN KEY (id_organisation) REFERENCES contacts.organisation (id) MATCH FULL ON DELETE RESTRICT ON UPDATE CASCADE; ALTER TABLE contacts.person ADD CONSTRAINT contact_fk FOREIGN KEY (id) REFERENCES contacts.contact (id) MATCH FULL ON DELETE CASCADE ON UPDATE CASCADE; ALTER TABLE contacts.organisation ADD CONSTRAINT contact_fk FOREIGN KEY (id) REFERENCES contacts.contact (id) MATCH FULL ON DELETE CASCADE ON UPDATE CASCADE; ALTER TABLE contacts.resource_contact ADD CONSTRAINT contact_fk FOREIGN KEY (id_contact) REFERENCES contacts.contact (id) MATCH FULL ON DELETE RESTRICT ON UPDATE CASCADE; -- audit triggers CREATE TRIGGER audit_trigger_row AFTER INSERT OR DELETE OR UPDATE ON contacts.contact FOR EACH ROW EXECUTE PROCEDURE audit.if_modified_func('true'); CREATE TRIGGER audit_trigger_stm AFTER TRUNCATE ON contacts.contact FOR EACH STATEMENT EXECUTE PROCEDURE audit.if_modified_func('true'); CREATE TRIGGER audit_trigger_row AFTER INSERT OR DELETE OR UPDATE ON contacts.person FOR EACH ROW EXECUTE PROCEDURE audit.if_modified_func('true'); CREATE TRIGGER audit_trigger_stm AFTER TRUNCATE ON contacts.person FOR EACH STATEMENT EXECUTE PROCEDURE audit.if_modified_func('true'); CREATE TRIGGER audit_trigger_row AFTER INSERT OR DELETE OR UPDATE ON contacts.organisation FOR EACH ROW EXECUTE PROCEDURE audit.if_modified_func('true'); CREATE TRIGGER audit_trigger_stm AFTER TRUNCATE ON contacts.organisation FOR EACH STATEMENT EXECUTE PROCEDURE audit.if_modified_func('true'); """) conn = op.get_bind() conn.execute(sql) def downgrade(): sql = sa.sql.text(""" -- revert contact DROP TYPE IF EXISTS contacts.contact_type CASCADE; DROP TABLE IF EXISTS contacts.contact CASCADE; CREATE TABLE contacts.contact ( contact_id bigint NOT NULL DEFAULT nextval('contacts.contact_id_seq'::regclass), street character varying, house_no character varying, zip character varying, city character varying, country_code character(3), id_organisation bigint, id_person bigint, CONSTRAINT contact_pk PRIMARY KEY (contact_id) ); -- revert person CREATE SEQUENCE contacts.person_id_seq INCREMENT BY 1 MINVALUE 1 MAXVALUE 9223372036854775807 START WITH 1 CACHE 1 NO CYCLE OWNED BY NONE; DROP TABLE IF EXISTS contacts.person CASCADE; CREATE TABLE contacts.person ( id bigint NOT NULL DEFAULT nextval('contacts.person_id_seq'::regclass), name character varying, surname character varying, telephone_nr character varying, e_mail character varying, CONSTRAINT person_pk PRIMARY KEY (id) ); -- revert organisation CREATE SEQUENCE contacts.organisation_id_seq INCREMENT BY 1 MINVALUE 1 MAXVALUE 9223372036854775807 START WITH 1 CACHE 1 NO CYCLE OWNED BY NONE; DROP TABLE IF EXISTS contacts.organisation CASCADE; CREATE TABLE contacts.organisation ( id bigint NOT NULL DEFAULT nextval('contacts.organisation_id_seq'::regclass), company character varying, departement character varying, office character varying, office_short character varying, CONSTRAINT organisation_pk PRIMARY KEY (id) ); -- revert resource_contact ALTER TABLE contacts.resource_contact RENAME id_contact TO contact_id_contact; -- FK constraints ALTER TABLE contacts.contact ADD CONSTRAINT organisation_fk FOREIGN KEY (id_organisation) REFERENCES contacts.organisation (id) MATCH FULL ON UPDATE CASCADE ON DELETE SET NULL; ALTER TABLE contacts.contact ADD CONSTRAINT person_fk FOREIGN KEY (id_person) REFERENCES contacts.person (id) MATCH FULL ON UPDATE CASCADE ON DELETE SET NULL; ALTER TABLE contacts.resource_contact ADD CONSTRAINT contact_fk FOREIGN KEY (contact_id_contact) REFERENCES contacts.contact (contact_id) MATCH FULL ON DELETE RESTRICT ON UPDATE CASCADE; -- audit triggers CREATE TRIGGER audit_trigger_row AFTER INSERT OR DELETE OR UPDATE ON contacts.contact FOR EACH ROW EXECUTE PROCEDURE audit.if_modified_func('true'); CREATE TRIGGER audit_trigger_stm AFTER TRUNCATE ON contacts.contact FOR EACH STATEMENT EXECUTE PROCEDURE audit.if_modified_func('true'); CREATE TRIGGER audit_trigger_row AFTER INSERT OR DELETE OR UPDATE ON contacts.person FOR EACH ROW EXECUTE PROCEDURE audit.if_modified_func('true'); CREATE TRIGGER audit_trigger_stm AFTER TRUNCATE ON contacts.person FOR EACH STATEMENT EXECUTE PROCEDURE audit.if_modified_func('true'); CREATE TRIGGER audit_trigger_row AFTER INSERT OR DELETE OR UPDATE ON contacts.organisation FOR EACH ROW EXECUTE PROCEDURE audit.if_modified_func('true'); CREATE TRIGGER audit_trigger_stm AFTER TRUNCATE ON contacts.organisation FOR EACH STATEMENT EXECUTE PROCEDURE audit.if_modified_func('true'); """) conn = op.get_bind() conn.execute(sql)

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null

0

1

0

1

0

1

0

null

0

5

350a796b0dfa6996443af80acb5f389019c3f075

390

py

Python

lcg/lcg.py

ranisalt/compsec

ea11aa36057427ad979ca4f3903b87e7ce1d44cb

[ "CC0-1.0" ]

null

lcg/lcg.py

ranisalt/compsec

ea11aa36057427ad979ca4f3903b87e7ce1d44cb

[ "CC0-1.0" ]

null

lcg/lcg.py

ranisalt/compsec

ea11aa36057427ad979ca4f3903b87e7ce1d44cb

[ "CC0-1.0" ]

null

class LinearCongruentialGenerator: mul = 1103515245 # a, 0 < a < m inc = 12345 # c, 0 <= c < m mod = 2 ** 31 # m, 0 < m def __init__(self, seed): self.seed_ = seed % self.mod # X[0], 0 <= X[0] < m def rand(self): # X[n + 1] = (a * X[n] + c) mod m self.seed_ = (self.seed_ * self.mul + self.inc) % self.mod return self.seed_

30

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3.05

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0.099206

0.353846

390

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32.5

0.626984

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0.222222

false

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0

null

1

0

null

0

1

0

1

0

5

352dc46650c1760ce38cbdd9fec8c42c44cd8d27

38

py

Python

eureka/S3_data_reduction/nirspec.py

ladsantos/Eureka

7d29df2f6965995f43bcf7500d0a021912b10fe9

[ "MIT" ]

null

eureka/S3_data_reduction/nirspec.py

ladsantos/Eureka

7d29df2f6965995f43bcf7500d0a021912b10fe9

[ "MIT" ]

null

eureka/S3_data_reduction/nirspec.py

ladsantos/Eureka

7d29df2f6965995f43bcf7500d0a021912b10fe9

[ "MIT" ]

null

# NIRSpec specific rountines go here

12.666667

36

0.789474

5

38

6

1

0

0.184211

38

2

37

19

0.967742

0.894737

0

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null

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null

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null

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true

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null

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null

0

1

0

1

0

null

0

1

0

5

354baf726a4eaeaf9fcd757b9d8acb08611aa985

144

py

Python

twitch/v5/models/__init__.py

sotif/Twitch-Python

82509e91c18d5c0ff5c6663be2dff63259eb87fd

[ "MIT" ]

177

2018-10-12T13:36:43.000Z

2022-03-20T04:16:46.000Z

twitch/v5/models/__init__.py

sotif/Twitch-Python

82509e91c18d5c0ff5c6663be2dff63259eb87fd

[ "MIT" ]

41

2018-10-17T01:33:18.000Z

2022-02-14T19:27:52.000Z

twitch/v5/models/__init__.py

sotif/Twitch-Python

82509e91c18d5c0ff5c6663be2dff63259eb87fd

[ "MIT" ]

50

2019-03-16T19:06:41.000Z

2022-03-07T00:12:04.000Z

from typing import List, Callable from .comment import Comment from .model import Model __all__: List[Callable] = [ Comment, Model, ]

14.4

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144

5.5

0.444444

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0.208333

144

9

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true

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null

0

1

0

1

0

5

10231cb6c40752ec58266063045d600e04ac84a3

158

py

Python

kloppy/infra/serializers/tracking/__init__.py

TK5-Tim/kloppy

912e8036305958fbe426f51cd2afa9ceb1a9c374

[ "BSD-3-Clause" ]

2

2022-02-17T09:50:10.000Z

2022-03-01T05:04:12.000Z

kloppy/infra/serializers/tracking/__init__.py

FCrSTATS/kloppy

30a84e6adcecc7b703e8f4b9a8e786612fa2e49e

[ "BSD-3-Clause" ]

null

kloppy/infra/serializers/tracking/__init__.py

FCrSTATS/kloppy

30a84e6adcecc7b703e8f4b9a8e786612fa2e49e

[ "BSD-3-Clause" ]

null

from .base import TrackingDataSerializer from .tracab import TRACABSerializer from .metrica import MetricaTrackingSerializer from .epts import EPTSSerializer

31.6

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null

0

1

0

1

0

1

0

5

104b4a41aa1ad76a01d0bc6ec65653cee9469e8f

34,825

py

Python

flask_web/flask_app/deep_learning/models_mse_loss/resnet_con1d.py

Yakings/system_demo

6ec9596db1e60e221054282a06d9129246e88f54

[ "Apache-2.0" ]

null

flask_web/flask_app/deep_learning/models_mse_loss/resnet_con1d.py

Yakings/system_demo

6ec9596db1e60e221054282a06d9129246e88f54

[ "Apache-2.0" ]

null

flask_web/flask_app/deep_learning/models_mse_loss/resnet_con1d.py

Yakings/system_demo

6ec9596db1e60e221054282a06d9129246e88f54

[ "Apache-2.0" ]

1

2020-08-18T10:55:10.000Z

from collections import namedtuple import tensorflow as tf import numpy as np from .import utils HParams = namedtuple('HParams', 'batch_size, num_gpus, num_cpus,num_classes, input_dim, weight_decay, ' 'momentum, finetune,' ########################################## 'layer_num,net_class,activation_class,optimizer_id,learning_rate,net_losses') class ResNet(object): def __init__(self, hp, images, labels, global_step, name=None, reuse_weights=False,is_train = True): self._hp = hp # Hyperparameters self._images = images # Input images self._labels = labels # Input labels self._global_step = global_step self._name = name self._reuse_weights = reuse_weights self.lr = tf.placeholder(tf.float32, name="lr") self.is_train = tf.placeholder(tf.bool, name="is_train") # self.is_train = True self._counted_scope = [] self._flops = 0 self._weights = 0 # self._trian_device = '/GPU:%d' self._trian_device = ['/CPU:%d','/GPU:%d'] def build_tower(self, images,labels): if self._hp.net_class==0: return self.build_tower_cnn(images, labels) elif self._hp.net_class==1: return self.build_tower_mobile(images, labels) elif self._hp.net_class==2: return self.build_tower_res(images, labels) elif self._hp.net_class==3: return self.build_tower_dnn_fix(images, labels) pass else: return self.build_tower_dnn(images, labels) pass def get_activation(self,x): if self._hp.activation_class == 0: # x = 1.01*tf.sigmoid(x)-0.005 pass elif self._hp.activation_class == 1: x = tf.nn.tanh(x) pass elif self._hp.activation_class == 2: x = tf.nn.relu(x) pass elif self._hp.activation_class == 3: x = tf.nn.elu(x) pass elif self._hp.activation_class == 4: x = tf.nn.leaky_relu(x) pass elif self._hp.activation_class == 5: x = tf.nn.relu6(x) pass else: pass return x def build_tower_dnn(self, images, labels): print('Building model') # Logit with tf.variable_scope('logits_1') as scope: print('\tBuilding unit: %s' % scope.name) shape_list = images.get_shape().as_list() x = tf.reshape(images,[shape_list[0],-1]) x = self._fc(x, 200) x = tf.nn.relu(x) with tf.variable_scope('logits_2') as scope: print('\tBuilding unit: %s' % scope.name) x = self._fc(x, 200) x = tf.nn.relu(x) # with tf.variable_scope('logits_3') as scope: # print('\tBuilding unit: %s' % scope.name) # x = self._fc(x, 10) # x = tf.sigmoid(x) layer_n = self._hp.layerlayer_num layer_n = int(layer_n//3) for i in range(layer_n): with tf.variable_scope('logits_mid_'+str(i)) as scope: print('\tBuilding unit: %s' % scope.name) x = self._fc(x, self._hp.num_classes) pass with tf.variable_scope('logits_4') as scope: print('\tBuilding unit: %s' % scope.name) x = self._fc(x, self._hp.num_classes) # x = tf.multiply(x,3000) # x = tf.nn.relu(x) # with tf.variable_scope('logits_final') as scope: # print('\tBuilding unit: %s' % scope.name) # x = self._fc(x, self._hp.num_classes) # logits = x # # # Probs & preds & acc # # probs = tf.nn.softmax(x) # # preds = tf.to_int32(tf.argmax(logits, 1)) # # ones = tf.constant(np.ones([self._hp.batch_size]), dtype=tf.float32) # # zeros = tf.constant(np.zeros([self._hp.batch_size]), dtype=tf.float32) # # # added # ################# # labels = tf.squeeze(labels) # ################# # # correct = tf.where(tf.equal(preds, labels), ones, zeros) # # acc = tf.reduce_mean(correct) # acc = tf.constant(0) # preds = tf.constant(0) # # # Loss & acc # # x = self._relu(x) # x = tf.cast(x,tf.float32) # # x = tf.sigmoid(x) # x = tf.squeeze(x) # self.preds_result = x # # labels = tf.cast(labels,tf.float32)/3000.0 + 0.001 # labels = tf.cast(labels,tf.float32) # # labels = tf.cast(labels,tf.float32) # # loss = tf.reduce_mean(tf.square(x-labels)) # # loss = tf.reduce_sum(tf.square(x-labels)) # # # losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=x, labels=labels) # # loss = tf.reduce_mean(losses) # # # # dif = tf.subtract(x,labels) # # percent_dif = tf.div(tf.subtract(x,labels),tf.add(x,10))*300 # # err_dif = tf.square(dif) # # percent_err = tf.square(percent_dif) # # err = err_dif + percent_err # # loss = tf.reduce_mean(err) # # dif = tf.subtract(x,labels) # err = tf.square(dif) # loss = tf.reduce_mean(err) # # # # losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=x, labels=labels) # # loss = tf.reduce_mean(losses) # acc = tf.reduce_mean(tf.abs(dif)) x, preds, loss, acc = self.get_loss(x, labels) return x, preds, loss, acc def build_tower_dnn_fix(self, images, labels): print('Building model') # Logit with tf.variable_scope('logits_1') as scope: print('\tBuilding unit: %s' % scope.name) shape_list = images.get_shape().as_list() x = tf.reshape(images,[shape_list[0],-1]) x = self._fc(x, 200) x = tf.nn.relu(x) with tf.variable_scope('logits_2') as scope: print('\tBuilding unit: %s' % scope.name) x = self._fc(x, 200) x = tf.nn.relu(x) with tf.variable_scope('logits_4') as scope: print('\tBuilding unit: %s' % scope.name) x = self._fc(x, self._hp.num_classes) # x = tf.multiply(x,3000) # x = tf.nn.relu(x) # with tf.variable_scope('logits_final') as scope: # print('\tBuilding unit: %s' % scope.name) # x = self._fc(x, self._hp.num_classes) # 'E-Sigmod','Tanh','ReLU','ELU','PReLU','Leaky ReLU' x = self.get_activation(x) # logits = x # # # Probs & preds & acc # # probs = tf.nn.softmax(x) # # preds = tf.to_int32(tf.argmax(logits, 1)) # # ones = tf.constant(np.ones([self._hp.batch_size]), dtype=tf.float32) # # zeros = tf.constant(np.zeros([self._hp.batch_size]), dtype=tf.float32) # # # added # ################# # labels = tf.squeeze(labels) # ################# # # correct = tf.where(tf.equal(preds, labels), ones, zeros) # # acc = tf.reduce_mean(correct) # acc = tf.constant(0) # preds = tf.constant(0) # # # Loss & acc # # x = self._relu(x) # x = tf.cast(x,tf.float32) # # x = tf.sigmoid(x) # x = tf.squeeze(x) # self.preds_result = x # # labels = tf.cast(labels,tf.float32)/3000.0 + 0.001 # labels = tf.cast(labels,tf.float32) # # labels = tf.cast(labels,tf.float32) # # loss = tf.reduce_mean(tf.square(x-labels)) # # loss = tf.reduce_sum(tf.square(x-labels)) # # # losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=x, labels=labels) # # loss = tf.reduce_mean(losses) # # # # dif = tf.subtract(x,labels) # # percent_dif = tf.div(tf.subtract(x,labels),tf.add(x,10))*300 # # err_dif = tf.square(dif) # # percent_err = tf.square(percent_dif) # # err = err_dif + percent_err # # loss = tf.reduce_mean(err) # # dif = tf.subtract(x,labels) # err = tf.square(dif) # loss = tf.reduce_mean(err) # # # # # losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=x, labels=labels) # # loss = tf.reduce_mean(losses) # acc = tf.reduce_mean(tf.abs(dif)) x, preds, loss, acc = self.get_loss(x, labels) return x, preds, loss, acc def get_loss_func(self,x,labels): if self._hp.net_losses==0: dif = tf.subtract(x, labels) err = tf.square(dif) loss = tf.reduce_mean(err) # losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=x, labels=labels) # loss = tf.reduce_mean(losses) elif self._hp.net_losses==1: losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=x, labels=labels) loss = tf.reduce_mean(losses) elif self._hp.net_losses==2: dif = tf.subtract(x,labels) percent_dif = tf.div(tf.subtract(x,labels),tf.add(x,10))*300 err_dif = tf.square(dif) percent_err = tf.square(percent_dif) err = err_dif + percent_err loss = tf.reduce_mean(err) pass elif self._hp.net_losses==3: dif = tf.subtract(x, labels) err = tf.square(dif) loss = tf.reduce_mean(err) pass elif self._hp.net_losses==4: loss = tf.losses.hinge_loss(labels,x) pass else: dif = tf.subtract(x, labels) err = tf.square(dif) loss = tf.reduce_mean(err) return loss def get_loss(self,x,labels): logits = x # added ################# labels = tf.squeeze(labels) ################# acc = tf.constant(0) preds = tf.constant(0) # Loss & acc # x = self._relu(x) x = tf.cast(x, tf.float32) # x = tf.sigmoid(x) x = tf.squeeze(x) self.preds_result = x # labels = tf.cast(labels,tf.float32)/3000.0 + 0.001 labels = tf.cast(labels, tf.float32) dif = tf.subtract(x, labels) err = tf.square(dif) loss = tf.reduce_mean(err) loss = self.get_loss_func(x, labels) # losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=x, labels=labels) # loss = tf.reduce_mean(losses) acc = tf.reduce_mean(tf.abs(dif)) return x, preds, loss, acc def build_tower_mobile(self, images, labels): print('Building model') # filters = [128, 128, 256, 512, 1024] # filters = [64, 64, 128, 256, 512] # filters = [16, 16, 32, 32, 64] filters = [2, 2, 4, 4, 8] kernels = [7, 3, 3, 3, 3] # strides = [2, 0, 2, 2, 2] # origin strides = [2, 0, 2, 2, 2] # conv1 print('\tBuilding unit: conv1') with tf.variable_scope('conv1'): # input [[batch, in_width, in_channels] x = self._conv(images, kernels[0], filters[0], strides[0]) x = self._bn(x) x = self._relu(x) # x = tf.nn.max_pool(x, [1, 3, 3, 1], [1, 2, 2, 1], 'SAME') x = tf.layers.max_pooling1d(x, [3], [2], 'SAME') # conv2_x x = self._residual_block(x, name='conv2_1') x = self._residual_block(x, name='conv2_2') # conv3_x x = self._residual_block_first(x, filters[2], strides[2], name='conv3_1') x = self._residual_block(x, name='conv3_2') # conv4_x x = self._residual_block_first(x, filters[3], strides[3], name='conv4_1') x = self._residual_block(x, name='conv4_2') # conv5_x x = self._residual_block_first(x, filters[4], strides[4], name='conv5_1') x = self._residual_block(x, name='conv5_2') # Logit with tf.variable_scope('logits_1') as scope: print('\tBuilding unit: %s' % scope.name) # x = tf.reduce_mean(x, [1, 2]) # x = tf.reduce_mean(x, [1]) shape_list = x.get_shape().as_list() x = tf.reshape(x,[shape_list[0],-1]) x = self._fc(x, self._hp.num_classes) # x = self._fc(x, 30) # x = tf.nn.relu(x) # with tf.variable_scope('logits_final') as scope: # print('\tBuilding unit: %s' % scope.name) # x = self._fc(x, self._hp.num_classes) # logits = x # # Probs & preds & acc # probs = tf.nn.softmax(x) # preds = tf.to_int32(tf.argmax(logits, 1)) # ones = tf.constant(np.ones([self._hp.batch_size]), dtype=tf.float32) # zeros = tf.constant(np.zeros([self._hp.batch_size]), dtype=tf.float32) # # added # ################# # labels = tf.squeeze(labels) # ################# # correct = tf.where(tf.equal(preds, labels), ones, zeros) # acc = tf.reduce_mean(correct) # # Loss & acc # # x = self._relu(x) # x = tf.cast(x,tf.float32) # # x = tf.sigmoid(x) # x = tf.squeeze(x) # self.preds_result = x # # labels = tf.cast(labels,tf.float32)/3000.0 + 0.001 # labels = tf.cast(labels,tf.float32) # # labels = tf.cast(labels,tf.float32) # # loss = tf.reduce_mean(tf.square(x-labels)) # # loss = tf.reduce_sum(tf.square(x-labels)) # err = tf.square(tf.subtract(x,labels)) # loss = tf.reduce_mean(err) # # losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=x, labels=labels) # # loss = tf.reduce_mean(losses) # # # return logits, preds, loss, acc x, preds, loss, acc = self.get_loss(x, labels) return x, preds, loss, acc def build_tower_cnn(self, images, labels): print('Building model') # filters = [128, 128, 256, 512, 1024] # filters = [64, 64, 128, 256, 512] filters = [16, 16, 32, 32, 64] # filters = [2, 2, 4, 4, 8] kernels = [7, 3, 3, 3, 3] # strides = [2, 0, 2, 2, 2] # origin strides = [2, 0, 2, 2, 2] # conv1 print('\tBuilding unit: conv1') with tf.variable_scope('conv1'): # input [[batch, in_width, in_channels] x = self._conv(images, kernels[0], filters[0], strides[0]) x = self._bn(x) x = self._relu(x) # x = tf.nn.max_pool(x, [1, 3, 3, 1], [1, 2, 2, 1], 'SAME') x = tf.layers.max_pooling1d(x, [3], [2], 'SAME') # conv2_x x = self._cnn_block(x, name='conv2_1') x = self._cnn_block(x, name='conv2_2') # conv3_x x = self._cnn_block_first(x, filters[2], strides[2], name='conv3_1') x = self._cnn_block(x, name='conv3_2') # conv4_x x = self._cnn_block_first(x, filters[3], strides[3], name='conv4_1') x = self._cnn_block(x, name='conv4_2') # conv5_x x = self._cnn_block_first(x, filters[4], strides[4], name='conv5_1') x = self._cnn_block(x, name='conv5_2') # Logit with tf.variable_scope('logits_1') as scope: print('\tBuilding unit: %s' % scope.name) # x = tf.reduce_mean(x, [1, 2]) # x = tf.reduce_mean(x, [1]) shape_list = x.get_shape().as_list() x = tf.reshape(x,[shape_list[0],-1]) x = self._fc(x, self._hp.num_classes) # x = self._fc(x, 30) # x = tf.nn.relu(x) # with tf.variable_scope('logits_final') as scope: # print('\tBuilding unit: %s' % scope.name) # x = self._fc(x, self._hp.num_classes) # logits = x # # Probs & preds & acc # probs = tf.nn.softmax(x) # preds = tf.to_int32(tf.argmax(logits, 1)) # ones = tf.constant(np.ones([self._hp.batch_size]), dtype=tf.float32) # zeros = tf.constant(np.zeros([self._hp.batch_size]), dtype=tf.float32) # # added # ################# # labels = tf.squeeze(labels) # ################# # correct = tf.where(tf.equal(preds, labels), ones, zeros) # acc = tf.reduce_mean(correct) # # Loss & acc # # x = self._relu(x) # x = tf.cast(x,tf.float32) # # x = tf.sigmoid(x) # x = tf.squeeze(x) # self.preds_result = x # # labels = tf.cast(labels,tf.float32)/3000.0 + 0.001 # labels = tf.cast(labels,tf.float32) # # labels = tf.cast(labels,tf.float32) # # loss = tf.reduce_mean(tf.square(x-labels)) # # loss = tf.reduce_sum(tf.square(x-labels)) # err = tf.square(tf.subtract(x,labels)) # loss = tf.reduce_mean(err) # # losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=x, labels=labels) # # loss = tf.reduce_mean(losses) # # return logits, preds, loss, acc x, preds, loss, acc = self.get_loss(x, labels) return x, preds, loss, acc def build_tower_res(self, images, labels): print('Building model') # filters = [128, 128, 256, 512, 1024] # filters = [64, 64, 128, 256, 512] filters = [16, 16, 32, 32, 64] # filters = [2, 2, 4, 4, 8] kernels = [7, 3, 3, 3, 3] # strides = [2, 0, 2, 2, 2] # origin strides = [2, 0, 2, 2, 2] # conv1 print('\tBuilding unit: conv1') with tf.variable_scope('conv1'): # input [[batch, in_width, in_channels] x = self._conv(images, kernels[0], filters[0], strides[0]) x = self._bn(x) x = self._relu(x) # x = tf.nn.max_pool(x, [1, 3, 3, 1], [1, 2, 2, 1], 'SAME') x = tf.layers.max_pooling1d(x, [3], [2], 'SAME') # conv2_x x = self._residual_block(x, name='conv2_1') x = self._residual_block(x, name='conv2_2') # conv3_x x = self._residual_block_first(x, filters[2], strides[2], name='conv3_1') x = self._residual_block(x, name='conv3_2') # conv4_x x = self._residual_block_first(x, filters[3], strides[3], name='conv4_1') x = self._residual_block(x, name='conv4_2') # conv5_x x = self._residual_block_first(x, filters[4], strides[4], name='conv5_1') x = self._residual_block(x, name='conv5_2') # Logit with tf.variable_scope('logits_1') as scope: print('\tBuilding unit: %s' % scope.name) # x = tf.reduce_mean(x, [1, 2]) # x = tf.reduce_mean(x, [1]) shape_list = x.get_shape().as_list() x = tf.reshape(x,[shape_list[0],-1]) x = self._fc(x, self._hp.num_classes) # x = self._fc(x, 30) # x = tf.nn.relu(x) # with tf.variable_scope('logits_final') as scope: # print('\tBuilding unit: %s' % scope.name) # x = self._fc(x, self._hp.num_classes) # logits = x # # # Probs & preds & acc # probs = tf.nn.softmax(x) # preds = tf.to_int32(tf.argmax(logits, 1)) # ones = tf.constant(np.ones([self._hp.batch_size]), dtype=tf.float32) # zeros = tf.constant(np.zeros([self._hp.batch_size]), dtype=tf.float32) # # # added # ################# # labels = tf.squeeze(labels) # ################# # correct = tf.where(tf.equal(preds, labels), ones, zeros) # acc = tf.reduce_mean(correct) # # # Loss & acc # # x = self._relu(x) # x = tf.cast(x,tf.float32) # # x = tf.sigmoid(x) # x = tf.squeeze(x) # self.preds_result = x # # labels = tf.cast(labels,tf.float32)/3000.0 + 0.001 # labels = tf.cast(labels,tf.float32) # # labels = tf.cast(labels,tf.float32) # # loss = tf.reduce_mean(tf.square(x-labels)) # # loss = tf.reduce_sum(tf.square(x-labels)) # err = tf.square(tf.subtract(x,labels)) # loss = tf.reduce_mean(err) # # losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=x, labels=labels) # # loss = tf.reduce_mean(losses) x, preds, loss, acc = self.get_loss(x, labels) # return logits, preds, loss, acc return x, preds, loss, acc def build_model(self,scope_name=''): # Split images and labels into (num_gpus) groups # images = tf.split(self._images, num_or_size_splits=self._hp.num_gpus, axis=0) # labels = tf.split(self._labels, num_or_size_splits=self._hp.num_gpus, axis=0) # Build towers for each GPU self._logits_list = [] self._preds_list = [] self._loss_list = [] self._acc_list = [] if self._hp.num_gpus>0: for i in range(self._hp.num_gpus): with tf.device(self._trian_device[1] % i), tf.variable_scope(tf.get_variable_scope()): with tf.name_scope('tower_%d' % i) as scope: print('Build a tower: %s' % scope) if self._reuse_weights or i > 0: tf.get_variable_scope().reuse_variables() logits, preds, loss, acc = self.build_tower(self._images[i], self._labels[i]) self._logits_list.append(logits) self._preds_list.append(preds) self._loss_list.append(loss) self._acc_list.append(acc) else: for i in range(self._hp.num_cpus): with tf.device(self._trian_device[0] % i), tf.variable_scope(tf.get_variable_scope()): with tf.name_scope('tower_%d' % i) as scope: print('Build a tower: %s' % scope) if self._reuse_weights or i > 0: tf.get_variable_scope().reuse_variables() logits, preds, loss, acc = self.build_tower(self._images[i], self._labels[i]) self._logits_list.append(logits) self._preds_list.append(preds) self._loss_list.append(loss) self._acc_list.append(acc) # Merge losses, accuracies of all GPUs with tf.device('/CPU:0'): tf.summary.histogram("input_hist_"+self._name,self._images) self.logits = tf.concat(self._logits_list, axis=0, name="logits") tf.summary.histogram("pred_hist_"+self._name,self.logits) tf.summary.histogram("label_hist_"+self._name,tf.convert_to_tensor(self._labels)) self.preds = tf.concat(self._preds_list, axis=0, name="predictions") self.loss = tf.reduce_mean(self._loss_list, name="cross_entropy") # self.loss =self._loss_list[0] tf.summary.scalar((self._name+"/" if self._name else "") + "cross_entropy", self.loss) self.acc = tf.reduce_mean(self._acc_list, name="accuracy") tf.summary.scalar((self._name+"/" if self._name else "") + "accuracy", self.acc) def build_train_op(self): # Learning rate tf.summary.scalar((self._name+"/" if self._name else "") + 'learing_rate', self.lr) # opt = tf.train.MomentumOptimizer(self.lr, self._hp.momentum) opt = tf.train.AdamOptimizer() if self._hp.optimizer_id==1: opt = tf.train.GradientDescentOptimizer(0.1) elif self._hp.optimizer_id==2: opt = tf.train.AdamOptimizer() elif self._hp.optimizer_id == 3: opt = tf.train.AdagradOptimizer(0.1) elif self._hp.optimizer_id==4: opt = tf.train.AdadeltaOptimizer() elif self._hp.optimizer_id==5: opt = tf.train.RMSPropOptimizer(0.1) # opt = tf.train.AdadeltaOptimizer() # opt = tf.train.RMSPropOptimizer(0.1) #'Adam','SGD','BGD','Adagrad','Adadelta','RMSprop' self._grads_and_vars_list = [] # Computer gradients for each GPU if self._hp.num_gpus>0: for i in range(self._hp.num_gpus): with tf.device(self._trian_device[1] % i), tf.variable_scope(tf.get_variable_scope()): with tf.name_scope('train_tower_%d' % i) as scope: print('Compute gradients of tower: %s' % scope) if self._reuse_weights or i > 0: tf.get_variable_scope().reuse_variables() # Add l2 loss costs = [tf.nn.l2_loss(var) for var in tf.get_collection(utils.WEIGHT_DECAY_KEY)] l2_loss = tf.multiply(self._hp.weight_decay, tf.add_n(costs)) # total_loss = self._loss_list[i] + l2_loss total_loss = self._loss_list[i] # Compute gradients of total loss grads_and_vars = opt.compute_gradients(total_loss, tf.trainable_variables()) # Append gradients and vars self._grads_and_vars_list.append(grads_and_vars) else: for i in range(self._hp.num_cpus): with tf.device(self._trian_device[0] % i), tf.variable_scope(tf.get_variable_scope()): with tf.name_scope('train_tower_%d' % i) as scope: print('Compute gradients of tower: %s' % scope) if self._reuse_weights or i > 0: tf.get_variable_scope().reuse_variables() # Add l2 loss costs = [tf.nn.l2_loss(var) for var in tf.get_collection(utils.WEIGHT_DECAY_KEY)] l2_loss = tf.multiply(self._hp.weight_decay, tf.add_n(costs)) # total_loss = self._loss_list[i] + l2_loss total_loss = self._loss_list[i] # Compute gradients of total loss grads_and_vars = opt.compute_gradients(total_loss, tf.trainable_variables()) # Append gradients and vars self._grads_and_vars_list.append(grads_and_vars) # Merge gradients print('Average gradients') with tf.device('/CPU:0'): grads_and_vars = self._average_gradients(self._grads_and_vars_list) # Finetuning if self._hp.finetune: for idx, (grad, var) in enumerate(grads_and_vars): if "unit3" in var.op.name or \ "unit_last" in var.op.name or \ "/q" in var.op.name or \ "logits" in var.op.name: print('\tScale up learning rate of % s by 10.0' % var.op.name) grad = 10.0 * grad grads_and_vars[idx] = (grad,var) # Apply gradient apply_grad_op = opt.apply_gradients(grads_and_vars, global_step=self._global_step) # Batch normalization moving average update update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) self.train_op = tf.group(*(update_ops+[apply_grad_op])) def _residual_block_first(self, x, out_channel, strides, name="unit"): in_channel = x.get_shape().as_list()[-1] with tf.variable_scope(name) as scope: print('\tBuilding residual unit: %s' % scope.name) # Shortcut connection if in_channel == out_channel: if strides == 1: shortcut = tf.identity(x) else: # shortcut = tf.nn.max_pool(x, [1, strides, strides, 1], [1, strides, strides, 1], 'VALID') shortcut = tf.layers.max_pooling1d(x, [strides], [strides], 'VALID') shortcut = tf.layers.max_pooling1d(x, [strides], [strides], 'SAME') else: shortcut = self._conv(x, 1, out_channel, strides, name='shortcut') # Residual x = self._conv(x, 3, out_channel, strides, name='conv_1') x = self._bn(x, name='bn_1') x = self._relu(x, name='relu_1') x = self._conv(x, 3, out_channel, 1, name='conv_2') x = self._bn(x, name='bn_2') # Merge x = x + shortcut x = self._relu(x, name='relu_2') return x def _residual_block(self, x, input_q=None, output_q=None, name="unit"): num_channel = x.get_shape().as_list()[-1] with tf.variable_scope(name) as scope: print('\tBuilding residual unit: %s' % scope.name) # Shortcut connection shortcut = x # Residual x = self._conv(x, 3, num_channel, 1, input_q=input_q, output_q=output_q, name='conv_1') x = self._bn(x, name='bn_1') x = self._relu(x, name='relu_1') x = self._conv(x, 3, num_channel, 1, input_q=output_q, output_q=output_q, name='conv_2') x = self._bn(x, name='bn_2') x = x + shortcut x = self._relu(x, name='relu_2') return x def _cnn_block_first(self, x, out_channel, strides, name="unit"): in_channel = x.get_shape().as_list()[-1] with tf.variable_scope(name) as scope: print('\tBuilding residual unit: %s' % scope.name) # Shortcut connection if in_channel == out_channel: if strides == 1: shortcut = tf.identity(x) else: # shortcut = tf.nn.max_pool(x, [1, strides, strides, 1], [1, strides, strides, 1], 'VALID') shortcut = tf.layers.max_pooling1d(x, [strides], [strides], 'VALID') shortcut = tf.layers.max_pooling1d(x, [strides], [strides], 'SAME') else: shortcut = self._conv(x, 1, out_channel, strides, name='shortcut') # Residual x = self._conv(x, 3, out_channel, strides, name='conv_1') x = self._bn(x, name='bn_1') x = self._relu(x, name='relu_1') x = self._conv(x, 3, out_channel, 1, name='conv_2') x = self._bn(x, name='bn_2') # Merge # x = x + shortcut x = self._relu(x, name='relu_2') return x def _cnn_block(self, x, input_q=None, output_q=None, name="unit"): num_channel = x.get_shape().as_list()[-1] with tf.variable_scope(name) as scope: print('\tBuilding residual unit: %s' % scope.name) # Shortcut connection shortcut = x # no Residual x = self._conv(x, 3, num_channel, 1, input_q=input_q, output_q=output_q, name='conv_1') x = self._bn(x, name='bn_1') x = self._relu(x, name='relu_1') x = self._conv(x, 3, num_channel, 1, input_q=output_q, output_q=output_q, name='conv_2') x = self._bn(x, name='bn_2') # x = x + shortcut x = self._relu(x, name='relu_2') return x def _average_gradients(self, tower_grads): """Calculate the average gradient for each shared variable across all towers. Note that this function provides a synchronization point across all towers. Args: tower_grads: List of lists of (gradient, variable) tuples. The outer list is over individual gradients. The inner list is over the gradient calculation for each tower. Returns: List of pairs of (gradient, variable) where the gradient has been averaged across all towers. """ average_grads = [] for grad_and_vars in zip(*tower_grads): # If no gradient for a variable, exclude it from output if grad_and_vars[0][0] is None: continue # Note that each grad_and_vars looks like the following: # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) grads = [] for g, _ in grad_and_vars: # Add 0 dimension to the gradients to represent the tower. expanded_g = tf.expand_dims(g, 0) # Append on a 'tower' dimension which we will average over below. grads.append(expanded_g) # Average over the 'tower' dimension. grad = tf.concat(grads, 0) grad = tf.reduce_mean(grad, 0) # Keep in mind that the Variables are redundant because they are shared # across towers. So .. we will just return the first tower's pointer to # the Variable. v = grad_and_vars[0][1] grad_and_var = (grad, v) average_grads.append(grad_and_var) return average_grads # Helper functions(counts FLOPs and number of weights) def _conv(self, x, filter_size, out_channel, stride, pad="SAME", input_q=None, output_q=None, name="conv"): b, w, in_channel = x.get_shape().as_list() h = 1 x = utils._conv(x, filter_size, out_channel, stride, pad, input_q, output_q, name) f = 2 * (h/stride) * (w/stride) * in_channel * out_channel * filter_size * filter_size w = in_channel * out_channel * filter_size * filter_size scope_name = tf.get_variable_scope().name + "/" + name self._add_flops_weights(scope_name, f, w) return x def _fc(self, x, out_dim, input_q=None, output_q=None, name="fc"): b, in_dim = x.get_shape().as_list() x = utils._fc(x, out_dim, input_q, output_q, name) f = 2 * (in_dim + 1) * out_dim w = (in_dim + 1) * out_dim scope_name = tf.get_variable_scope().name + "/" + name self._add_flops_weights(scope_name, f, w) return x def _bn(self, x, name="bn"): x = utils._bn(x, self.is_train, self._global_step, name=name) # f = 8 * self._get_data_size(x) # w = 4 * x.get_shape().as_list()[-1] # scope_name = tf.get_variable_scope().name + "/" + name # self._add_flops_weights(scope_name, f, w) return x def _relu(self, x, name="relu"): x = utils._relu(x, 0.0, name) # f = self._get_data_size(x) # scope_name = tf.get_variable_scope().name + "/" + name # self._add_flops_weights(scope_name, f, 0) return x def _get_data_size(self, x): return np.prod(x.get_shape().as_list()[1:]) def _add_flops_weights(self, scope_name, f, w): if scope_name not in self._counted_scope: self._flops += f self._weights += w self._counted_scope.append(scope_name)

40.826495

111

0.538378

4,626

34,825

3.845655

0.07393

0.028387

0.02968

0.025183

0.784205

0.757504

0.750028

0.725801

0.715289

0.704497

0

0.02865

0.322469

34,825

852

112

40.874413

0.725323

0.294358

0

0.60095

0

0.060378

0.003986

0

1

0.054632

false

0.030879

0.009501

0.002375

0.12114

0.066508

0

null

0

1

0

null

0

5

105d52940fd06d2d50820b67c466b037e2f297ec

105

py

Python

enthought/permissions/package_globals.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/permissions/package_globals.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/permissions/package_globals.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from __future__ import absolute_import from apptools.permissions.package_globals import *

26.25

50

0.857143

13

105

6.461538

0.769231

0

0.104762

105

3

51

35

0.893617

0.114286

0

1

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true

0

1

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1

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null

0

1

0

null

0

1

0

1

0

1

0

5

1071323c691220419430449b5fa134aef7a8e45c

61,915

py

Python

experiments/experiments_gdsc/convergence/nmtf_gibbs.py

ThomasBrouwer/BNMTF

34df0c3cebc5e67a5e39762b9305b75d73a2a0e0

[ "Apache-2.0" ]

16

2017-04-19T12:04:47.000Z

2021-12-03T00:50:43.000Z

experiments/experiments_gdsc/convergence/nmtf_gibbs.py

ThomasBrouwer/BNMTF

34df0c3cebc5e67a5e39762b9305b75d73a2a0e0

[ "Apache-2.0" ]

1

2017-04-20T11:26:16.000Z

experiments/experiments_gdsc/convergence/nmtf_gibbs.py

ThomasBrouwer/BNMTF

34df0c3cebc5e67a5e39762b9305b75d73a2a0e0

[ "Apache-2.0" ]

8

2015-12-15T05:29:43.000Z

2019-06-05T03:14:11.000Z

""" Run NMTF Gibbs on the Sanger dataset. We can plot the MSE, R2 and Rp as it converges, on the entire dataset. We give flat priors (1/10). """ import sys, os project_location = os.path.dirname(__file__)+"/../../../../" sys.path.append(project_location) from BNMTF.code.models.bnmtf_gibbs_optimised import bnmtf_gibbs_optimised from BNMTF.data_drug_sensitivity.gdsc.load_data import load_gdsc import numpy, matplotlib.pyplot as plt ########## standardised = False #standardised Sanger or unstandardised iterations = 1000 init_FG = 'kmeans' init_S = 'random' I, J, K, L = 622,138,5,5 alpha, beta = 1., 1. lambdaF = numpy.ones((I,K))/10. lambdaS = numpy.ones((K,L))/10. lambdaG = numpy.ones((J,L))/10. priors = { 'alpha':alpha, 'beta':beta, 'lambdaF':lambdaF, 'lambdaS':lambdaS, 'lambdaG':lambdaG } # Load in data (_,R,M,_,_,_,_) = load_gdsc(standardised=standardised) # Run the Gibbs sampler BNMTF = bnmtf_gibbs_optimised(R,M,K,L,priors) BNMTF.initialise(init_S,init_FG) BNMTF.run(iterations) # Extract the performances across all iterations print "gibbs_all_performances = %s" % BNMTF.all_performances ''' gibbs_all_performances = {'R^2': 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0.89086675400394932, 0.89087741454822877, 0.89086793163223155, 0.89106053636551674, 0.89100604884742785, 0.89102312035930997, 0.89104928559781404, 0.89098250252298417, 0.89110645105236652, 0.89119376138539363, 0.89104674766900416, 0.89117544801668624, 0.89121848419160077, 0.89131094479844764, 0.89127918402582995, 0.89123376497167939, 0.89145878323397221, 0.89128409968437594, 0.89128644293731907, 0.89133090682065241, 0.8912802371253421, 0.89130848888666447, 0.8913338877157071, 0.89132418977838113, 0.89128363676885247, 0.89141055021129079, 0.891350853018682, 0.89145148627024262, 0.89129686233756333, 0.89144861206936787, 0.89139788802727404, 0.89157860026890134, 0.89162358154760224, 0.89171060146771042, 0.89173619792901038, 0.89171009408287449, 0.89170594247465851, 0.89165435402072424, 0.89161941219936336, 0.89165286359588569, 0.89168101125605537, 0.89178321848629172, 0.8915387448892147, 0.89155687389198202, 0.89171645315593595, 0.89178417522133258, 0.89174734181254844, 0.891815247680528, 0.89178836563472175, 0.8918113600869243, 0.89183638868267723, 0.8918950992317819, 0.89187697223243367, 0.89180535969231356, 0.89177838682491251, 0.89193372656908398, 0.89201787034986157, 0.89174936311831232, 0.89186947284221552, 0.89193809040572702, 0.89198076014515137, 0.89205143553867094, 0.89210735929801088, 0.8921279121196507, 0.89208624886833143, 0.89218448118076088, 0.89213458628486153, 0.89220660490504455, 0.89220611963448215, 0.89217941386231647, 0.89217018253678726, 0.89210695412058616, 0.89217803840906018, 0.89213042562501399, 0.89220898470829335, 0.89219487276427922, 0.89224081816065337, 0.89214551709233092, 0.89221053760324009, 0.89228605303358943, 0.8923449457620769, 0.89225485241099034, 0.89230370063437658, 0.89232166988915507, 0.89238037046172813, 0.89231812211958561, 0.89236327278639493, 0.89237006001243346, 0.89239066182320959, 0.89248612528523863, 0.89243054054721782, 0.89240402957956355, 0.89246363010264229, 0.89241129883321357, 0.89235288649779521, 0.89241065942377229, 0.89238245761041157, 0.89232213637125224, 0.89255209223180521, 0.89249536756125725, 0.89253935182375965, 0.89246081329854088, 0.89259678726999148]} ''' plt.figure() plt.plot(BNMTF.all_performances['MSE']) plt.ylim(0,10)

1,238.3

60,722

0.84897

6,189

61,915

8.487963

0.498788

0.001142

0.001085

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0.879711

0.050844

61,915

50

60,723

1,238.3

0.014192

0.001922

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0.093716

0.024256

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null

0

1

0

1

0

null

0

1

0

5

1074ffad876ec4a4e28b6e048a65800ca3de277d

96

py

Python

lichee/utils/model_loader/__init__.py

Tencent/Lichee

7653becd6fbf8b0715f788af3c0507c012be08b4

[ "Apache-2.0" ]

91

2021-10-30T02:25:05.000Z

2022-03-28T06:51:52.000Z

lichee/utils/model_loader/__init__.py

zhaijunyu/Lichee

7653becd6fbf8b0715f788af3c0507c012be08b4

[ "Apache-2.0" ]

1

2021-12-17T09:30:25.000Z

2022-03-05T12:30:13.000Z

lichee/utils/model_loader/__init__.py

zhaijunyu/Lichee

7653becd6fbf8b0715f788af3c0507c012be08b4

[ "Apache-2.0" ]

17

2021-11-04T07:50:23.000Z

2022-03-24T14:24:11.000Z

# -*- coding: utf-8 -*- """ 模型加载器插件 """ from . import onnx_loader from . import torch_nn_loader

13.714286

29

0.65625

13

96

4.615385

0.769231

0.333333

0

0.0125

0.166667

96

6

30

16

0.7375

0.3125

0

1

0

true

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1

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1

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1

0

null

1

0

1

0

null

0

1

0

1

0

5

1075958d5cb6a79744492f3c8154a1ff6edc1284

99

py

Python

Dinos/CrystalWyvern/Eggs/PrimalItemConsumable_Egg_CrystalWyvern_Fertilized_Bloodfall.PrimalItemConsumable_Egg_CrystalWyvern_Fertilized_Bloodfall.py

cutec-chris/sce-PrimalEarth

4e7a45acffc57a455a7668af1a954004668c3085

[ "MIT" ]

null

Dinos/CrystalWyvern/Eggs/PrimalItemConsumable_Egg_CrystalWyvern_Fertilized_Bloodfall.PrimalItemConsumable_Egg_CrystalWyvern_Fertilized_Bloodfall.py

cutec-chris/sce-PrimalEarth

4e7a45acffc57a455a7668af1a954004668c3085

[ "MIT" ]

null

Dinos/CrystalWyvern/Eggs/PrimalItemConsumable_Egg_CrystalWyvern_Fertilized_Bloodfall.PrimalItemConsumable_Egg_CrystalWyvern_Fertilized_Bloodfall.py

cutec-chris/sce-PrimalEarth

4e7a45acffc57a455a7668af1a954004668c3085

[ "MIT" ]

null

import sys,sce class PrimalItemConsumable_Egg_CrystalWyvern_Fertilized_Bloodfall(sce.Eggs): pass

33

76

0.878788

12

99

6.916667

0.916667

0

0.070707

99

3

77

33

0.902174

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

5

109920892f6b59d2f377c8204df109e4c227868e

180

py

Python

vanko/scrapy/webdriver/__init__.py

ivandeex/scrapoku

2a315e0e8ed2228e17ef65a5647523e66a7bbf36

[ "BSD-3-Clause" ]

1

2018-05-08T21:29:18.000Z

vanko/scrapy/webdriver/__init__.py

ivandeex/scrapoku

2a315e0e8ed2228e17ef65a5647523e66a7bbf36

[ "BSD-3-Clause" ]

10

2021-01-07T22:41:57.000Z

2022-03-29T23:18:11.000Z

vanko/scrapy/webdriver/__init__.py

ivandeex/scrapoku

2a315e0e8ed2228e17ef65a5647523e66a7bbf36

[ "BSD-3-Clause" ]

2

2020-08-01T10:14:17.000Z

2021-05-14T14:59:44.000Z

# flake8: noqa from .spider import WebdriverSpider from .http import WebdriverRequest, WebdriverActionRequest, WebdriverResponse from .middlewares import WebdriverSpiderMiddleware

36

77

0.866667

16

180

9.75

0.75

0

0.006135

0.094444

180

4

78

45

0.95092

0.066667

0

1

0

true

0

1

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1

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1

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0

1

0

null

0

1

0

1

0

1

0

5

52da7538fb056dab5d1931720782bcc44cb2776c

439

py

Python

src/hexagonal/hexagonal_project_config.py

rfrezino/hexagonal-sanity-check

78c8711d9be6ec173abead4ab344f7ac57d5d4ac

[ "MIT" ]

1

2022-03-14T10:17:38.000Z

src/hexagonal/hexagonal_project_config.py

rfrezino/hexagonal-sanity-check

78c8711d9be6ec173abead4ab344f7ac57d5d4ac

[ "MIT" ]

null

src/hexagonal/hexagonal_project_config.py

rfrezino/hexagonal-sanity-check

78c8711d9be6ec173abead4ab344f7ac57d5d4ac

[ "MIT" ]

2

2021-12-14T10:35:24.000Z

2022-01-31T14:17:36.000Z

from hexagonal.hexagonal_config import hexagonal_config hexagonal_config.add_inner_layer_with_dirs(layer_name='infrastructure', directories=['/infrastructure']) hexagonal_config.add_inner_layer_with_dirs(layer_name='use_cases', directories=['/use_cases']) hexagonal_config.add_inner_layer_with_dirs(layer_name='services', directories=['/services']) hexagonal_config.add_inner_layer_with_dirs(layer_name='domain', directories=['/domain'])

62.714286

104

0.849658

57

439

6.052632

0.280702

0.26087

0.208696

0.266667

0.521739

0

0.029613

439

6

105

73.166667

0.809859

0

0.177677

0

1

0

true

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0.2

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0.2

0

null

1

0

1

0

null

0

1

0

5

52e92a7ada06051115aa37b20c5d5aaf9997a0bd

40

py

Python

waterfall_ax/__init__.py

microsoft/waterfall_ax

dbb4dd43637c52b8e4620ce773d8ffd1701b8fb9

[ "MIT" ]

2

2020-10-03T07:33:56.000Z

2022-03-30T20:39:04.000Z

waterfall_ax/__init__.py

microsoft/waterfall_ax

dbb4dd43637c52b8e4620ce773d8ffd1701b8fb9

[ "MIT" ]

null

waterfall_ax/__init__.py

microsoft/waterfall_ax

dbb4dd43637c52b8e4620ce773d8ffd1701b8fb9

[ "MIT" ]

4

2020-10-07T07:40:50.000Z

2022-03-30T20:39:27.000Z

from .waterfall_ax import WaterfallChart

40

0.9

5

40

7

1

0

0.075

40

1

40

0.945946

0

1

0

true

0

1

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1

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1

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null

0

1

0

null

0

1

0

1

0

5

eaafd749ee276d5c4c4f2913e5fb57aee8923ab2

121

py

Python

midistream/__init__.py

b3b/midistream

0357de225e867a080443d1bc7d5baf9b845f892f

[ "MIT" ]

1

2018-07-18T05:31:36.000Z

midistream/__init__.py

b3b/midistream

0357de225e867a080443d1bc7d5baf9b845f892f

[ "MIT" ]

null

midistream/__init__.py

b3b/midistream

0357de225e867a080443d1bc7d5baf9b845f892f

[ "MIT" ]

null

from .facade import MIDIException, ReverbPreset, Synthesizer __all__ = ["MIDIException", "ReverbPreset", "Synthesizer"]

30.25

60

0.785124

10

121

9.1

0.7

0.549451

0.791209

0

0.099174

121

3

61

40.333333

0.834862

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0.297521

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0.5

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0.5

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null

1

0

1

0

null

0

1

0

5

ead99c302c352798c7a6d702456febc67b60b643

154

py

Python

pythreshold/__init__.py

ivangtorre/threshold-method-time-series

f5085cd4893b18a6369f6fe3f08ee576179fa0fb

[ "MIT" ]

null

pythreshold/__init__.py

ivangtorre/threshold-method-time-series

f5085cd4893b18a6369f6fe3f08ee576179fa0fb

[ "MIT" ]

null

pythreshold/__init__.py

ivangtorre/threshold-method-time-series

f5085cd4893b18a6369f6fe3f08ee576179fa0fb

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Fri Jun 29 21:56:52 2018 @author: ivan """ from pythreshold.thresholds import time_series

15.4

46

0.681818

24

154

4.333333

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0.107692

0.155844

154

9

47

17.111111

0.692308

0.61039

0

1

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true

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1

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null

0

1

0

null

0

1

0

1

0

1

0

5

d804b33401d598b1ef49ef940aa7a948ffaac960

89

py

Python

plugins/markdown_extensions/__init__.py

raabrp/rraabblog

a1d47ede918f4838ac3bbcff9ef4e7c67f851c32

[ "MIT" ]

null

plugins/markdown_extensions/__init__.py

raabrp/rraabblog

a1d47ede918f4838ac3bbcff9ef4e7c67f851c32

[ "MIT" ]

null

plugins/markdown_extensions/__init__.py

raabrp/rraabblog

a1d47ede918f4838ac3bbcff9ef4e7c67f851c32

[ "MIT" ]

null

from .preprocessor import Custom # preprocessor from .katex import Katex # regex Pattern

29.666667

47

0.808989

11

89

6.545455

0.636364

0

0.146067

89

2

48

44.5

0.947368

0.292135

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

d80d4f78304e4d6e13ce8d38c77a3be4a4d1b1ec

6,944

py

Python

tests/test_serialization.py

DavidWhittingham/agsconfig

c0ac6c37e5e49f87d2812220d756aef118c08024

[ "BSD-3-Clause" ]

1

2019-05-17T01:44:41.000Z

tests/test_serialization.py

DavidWhittingham/agsconfig

c0ac6c37e5e49f87d2812220d756aef118c08024

[ "BSD-3-Clause" ]

2

2019-04-09T02:01:26.000Z

2019-06-25T05:27:11.000Z

tests/test_serialization.py

DavidWhittingham/agsconfig

c0ac6c37e5e49f87d2812220d756aef118c08024

[ "BSD-3-Clause" ]

2

2019-03-21T04:58:18.000Z

2019-09-09T23:00:48.000Z

# coding=utf-8 """Tests for serialization.py.""" # Python 2/3 compatibility # pylint: disable=wildcard-import,unused-wildcard-import,wrong-import-order,wrong-import-position from __future__ import (absolute_import, division, print_function, unicode_literals) from future.builtins.disabled import * from future.builtins import * from future.standard_library import install_aliases install_aliases() # pylint: enable=wildcard-import,unused-wildcard-import,wrong-import-order,wrong-import-position # standard lib imports import datetime # third-party imports import pytest # local imports from agsconfig import MapServer from agsconfig.editing import serialization #yapf: disable @pytest.mark.parametrize( ("value", "expected", "exception"), [ (("","",""), [None, None, None], None), ([], [], None) ] )#yapf:enable def test_deserialize_empty_string_to_none(value, expected, exception): if exception is not None: with pytest.raises(exception): assert serialization.deserialize_empty_string_to_none(value, None, None) == expected else: assert serialization.deserialize_empty_string_to_none(value, None, None) == expected #yapf: disable @pytest.mark.parametrize( ("value", "conversion", "expected", "exception"), [ ("true", {"id" : "boolToString"}, True, None), ("false", {"id" : "boolToString"}, False, None), ("TRUE", {"id" : "boolToString"}, True, None), ("FALSE", {"id" : "boolToString"}, False, None), (("false", "true"), {"id" : "boolToString"}, [False, True], None), ("123", {"id" : "boolToString"}, None, ValueError), ([], {"id" : "boolToString"}, [], None), (None, {"id": "boolToString", "allowNone": True}, None, None), (None, {"id": "boolToString", "allowNone": False}, None, ValueError), (None, {"id": "boolToString", "allowNone": False, "noneAsFalse": True}, False, None), (None, {"id": "boolToString", "allowNone": True, "noneAsFalse": True}, None, None) ] )#yapf:enable def test_deserialize_string_to_bool(value, conversion, expected, exception): if exception is not None: with pytest.raises(exception): serialization.deserialize_string_to_bool(value, conversion, None) else: assert serialization.deserialize_string_to_bool(value, conversion, None) == expected #yapf: disable @pytest.mark.parametrize( ("value", "conversion", "expected", "exception"), [ ("", {"enum" : "Capabilities", "id" : "enumToString"}, None, None) ] )#yapf:enable def test_deserialize_string_to_enum(value, conversion, expected, exception): if exception is not None: with pytest.raises(exception): serialization.deserialize_string_to_enum(value, conversion, None) else: assert serialization.deserialize_string_to_enum(value, conversion, None) == expected #yapf: disable @pytest.mark.parametrize( ("value", "conversion", "expected", "exception"), [ ([], {"enum" : "Capabilities", "id" : "enumToString"}, [], None), (["12:00", "14:00"], {"enum" : "Capabilities", "id" : "enumToString"}, [datetime.time(12, 0), datetime.time(14, 0)], None) ] )#yapf:enable def test_deserialize_string_to_time(value, conversion, expected, exception): if exception is not None: with pytest.raises(exception): serialization.deserialize_string_to_time(value, conversion, None) else: assert serialization.deserialize_string_to_time(value, conversion, None) == expected #yapf: disable @pytest.mark.parametrize( ("value", "conversion", "expected", "exception"), [ ([], {"enum" : "Capabilities", "id" : "enumToString"}, [], None), (["1", "2"], {"enum" : "Capabilities", "id" : "enumToString"}, [1, 2], None) ] )#yapf:enable def test_deserialize_string_to_number(value, conversion, expected, exception): if exception is not None: with pytest.raises(exception): serialization.deserialize_string_to_number(value, conversion, None) else: assert serialization.deserialize_string_to_number(value, conversion, None) == expected #yapf: disable @pytest.mark.parametrize( ("value", "conversion", "expected", "exception"), [ (True, {"enum" : "Capabilities", "id" : "enumToString"}, "true", None), ([True, False], {"enum" : "Capabilities", "id" : "enumToString"}, ["true", "false"], None), ([], {"enum" : "Capabilities", "id" : "enumToString"}, [], None), ] )#yapf:enable def test_serialize_bool_to_string(value, conversion, expected, exception): if exception is not None: with pytest.raises(exception): serialization.serialize_bool_to_string(value, conversion, None) else: assert serialization.serialize_bool_to_string(value, conversion, None) == expected #yapf: disable @pytest.mark.parametrize( ("value", "conversion", "expected", "exception"), [ (1, {"enum" : "Capabilities", "id" : "enumToString"}, "1", None), ([1, 2], {"enum" : "Capabilities", "id" : "enumToString"}, ["1", "2"], None), ([], {"enum" : "Capabilities", "id" : "enumToString"}, [], None) ] )#yapf:enable def test_serialize_number_to_string(value, conversion, expected, exception): if exception is not None: with pytest.raises(exception): serialization.serialize_number_to_string(value, conversion, None) else: assert serialization.serialize_number_to_string(value, conversion, None) == expected #yapf: disable @pytest.mark.parametrize( ("value", "conversion", "expected", "exception"), [ (None, {"enum" : "Capabilities", "id" : "enumToString"}, "", None), ([None, None], {"enum" : "Capabilities", "id" : "enumToString"}, ["", ""], None), ([], {"enum" : "Capabilities", "id" : "enumToString"}, [], None) ] )#yapf:enable def test_serialize_none_to_empty_string(value, conversion, expected, exception): if exception is not None: with pytest.raises(exception): serialization.serialize_none_to_empty_string(value, conversion, None) else: assert serialization.serialize_none_to_empty_string(value, conversion, None) == expected #yapf: disable @pytest.mark.parametrize( ("value", "conversion", "expected", "exception"), [ (None, {"enum" : "Capabilities", "id" : "enumToString"}, None, None), ([None, None], {"enum" : "Capabilities", "id" : "enumToString"}, [None, None], None), ([], {"enum" : "Capabilities", "id" : "enumToString"}, [], None) ] )#yapf:enable def test_serialize_time_to_string(value, conversion, expected, exception): if exception is not None: with pytest.raises(exception): serialization.serialize_time_to_string(value, conversion, None) else: assert serialization.serialize_time_to_string(value, conversion, None) == expected

39.011236

130

0.651642

728

6,944

6.070055

0.116758

0.108622

0.069246

0.115411

0.852003

0.814211

0.775967

0.765332

0.725277

0.595384

0

0.005307

0.185916

6,944

177

131

39.231638

0.776402

0.076037

0

0.376812

0

0.165675

0

0.072464

1

0.065217

false

0

0.057971

0

0.123188

0.007246

0

null

0

1

0

null

0

5

d827bcb83341841a45627ca2d863bfd9876ccc28

39

py

Python

tests/components/overkiz/__init__.py

MrDelik/core

93a66cc357b226389967668441000498a10453bb

[ "Apache-2.0" ]

30,023

2016-04-13T10:17:53.000Z

2020-03-02T12:56:31.000Z

tests/components/overkiz/__init__.py

MrDelik/core

93a66cc357b226389967668441000498a10453bb

[ "Apache-2.0" ]

31,101

2020-03-02T13:00:16.000Z

2022-03-31T23:57:36.000Z

tests/components/overkiz/__init__.py

MrDelik/core

93a66cc357b226389967668441000498a10453bb

[ "Apache-2.0" ]

11,956

2016-04-13T18:42:31.000Z

2020-03-02T09:32:12.000Z

"""Tests for the overkiz component."""

19.5

38

0.692308

5

39

5.4

1

0

0.128205

39

1

39

0.794118

0.820513

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

d829514939e320404e2ccbb7b8b1b0e1af93c386

214

py

Python

arabian_string.py

Kunalpod/codewars

8dc1af2f3c70e209471045118fd88b3ea1e627e5

[ "MIT" ]

null

arabian_string.py

Kunalpod/codewars

8dc1af2f3c70e209471045118fd88b3ea1e627e5

[ "MIT" ]

null

arabian_string.py

Kunalpod/codewars

8dc1af2f3c70e209471045118fd88b3ea1e627e5

[ "MIT" ]

null

#Kunal Gautam #Codewars : @Kunalpod #Problem name: Arabian String #Problem level: 6 kyu import re def camelize(str_): return ''.join([x[0].upper()+x[1:].lower() for x in re.split('[^0-9A-Za-z]*', str_) if x])

23.777778

94

0.654206

37

214

3.72973

0.810811

0

0.027322

0.14486

214

8

95

26.75

0.726776

0.373832

0

0.1

0

1

0.333333

false

0

0.333333

1

0

null

0

1

0

null

0

1

0

1

0

5

dc5ddc38294ba60c65cbd3917e927614acec7fa6

373

py

Python

forms.py

ArthurOliveiraTeles/Dashboard-administrativo-com-Flask-Estudo-

7c221e67c7177507b4e2bbf0a43aaf0bcec18fd6

[ "MIT" ]

null

forms.py

ArthurOliveiraTeles/Dashboard-administrativo-com-Flask-Estudo-

7c221e67c7177507b4e2bbf0a43aaf0bcec18fd6

[ "MIT" ]

null

forms.py

ArthurOliveiraTeles/Dashboard-administrativo-com-Flask-Estudo-

7c221e67c7177507b4e2bbf0a43aaf0bcec18fd6

[ "MIT" ]

null

from wtforms import Form, StringField, PasswordField, validators class LoginForm(Form): email = StringField('E-mail ou Usuário', [validators.Length(min=6, max=35), validators.DataRequired()], render_kw={'class': 'form-control'}) password = PasswordField('Senha', [validators.DataRequired()], render_kw={'class': 'form-control'})

53.285714

103

0.664879

39

373

6.307692

0.641026

0.178862

0.227642

0.243902

0.373984

0

0.009868

0.184987

373

6

104

62.166667

0.799342

0

0.150134

0

1

0

false

0.4

0.2

0

0.8

0

null

0

1

0

1

0

null

0

1

0

1

0

5

dc5e9fd952497ff07090df09fedf01106d4967b6

68

py

Python

dash/components/table/__init__.py

datavalor/adesit

0dce50e36b2a042ce2590e1e8e33e642423bf552

[ "BSD-3-Clause" ]

2

2021-07-22T11:41:55.000Z

2022-01-26T19:21:00.000Z

dash/components/table/__init__.py

datavalor/adesit

0dce50e36b2a042ce2590e1e8e33e642423bf552

[ "BSD-3-Clause" ]

null

dash/components/table/__init__.py

datavalor/adesit

0dce50e36b2a042ce2590e1e8e33e642423bf552

[ "BSD-3-Clause" ]

null

from .callbacks import register_callbacks from .layout import render

34

41

0.867647

9

68

6.444444

0.666667

0

0.102941

68

2

42

34

0.95082

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

dca3be7f46df40c77be5be12c6e83d83186214ad

38

py

Python

tests/test_checks.py

pyapp-org/pyapp.aio-pika

db568ceea747bedf4c1c29012ba61c30b5a8507b

[ "BSD-3-Clause" ]

null

tests/test_checks.py

pyapp-org/pyapp.aio-pika

db568ceea747bedf4c1c29012ba61c30b5a8507b

[ "BSD-3-Clause" ]

null

tests/test_checks.py

pyapp-org/pyapp.aio-pika

db568ceea747bedf4c1c29012ba61c30b5a8507b

[ "BSD-3-Clause" ]

null

from pyapp_ext.aio_pika import checks

19

37

0.868421

7

38

4.428571

1

0

0.105263

38

1

38

0.911765

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

f4faf61af96bab274310c7edc36ea232d08baa3e

231

py

Python

files2md/structure_objects/structureObject.py

KacperKotlewski/file_structure_to_markdown

aad0e1c80f88e0b3d079cf242d43fdc4b7a369f7

[ "MIT" ]

1

2020-02-22T00:41:04.000Z

files2md/structure_objects/structureObject.py

KacperKotlewski/file_structure_to_markdown

aad0e1c80f88e0b3d079cf242d43fdc4b7a369f7

[ "MIT" ]

null

files2md/structure_objects/structureObject.py

KacperKotlewski/file_structure_to_markdown

aad0e1c80f88e0b3d079cf242d43fdc4b7a369f7

[ "MIT" ]

null

class StructureObject: def __init__(self, name="", dir=""): self.name = name self.dir = dir def getName(self): return self.name def getDir(self): return self.dir def getType(self): return type(self)

28.875

40

0.632035

31

231

4.580645

0.387097

0.169014

0.197183

0

0.242424

231

8

41

28.875

0.811429

0

1

0.571429

false

0

0.428571

0.714286

0

null

0

1

0

1

0

null

0

1

0

1

0

5

76019a6903e997cb699a96cb82643e2d41594987

230

py

Python

Util/WriteFile.py

XueQiangFan/I-RNAsol

c503746f47eee97203f8c006222c4b701e999778

[ "Unlicense" ]

null

Util/WriteFile.py

XueQiangFan/I-RNAsol

c503746f47eee97203f8c006222c4b701e999778

[ "Unlicense" ]

null

Util/WriteFile.py

XueQiangFan/I-RNAsol

c503746f47eee97203f8c006222c4b701e999778

[ "Unlicense" ]

null

def write(filename, content): with open(filename, 'w') as file_object: file_object.write(content) def appendWrite(filename, content): with open(filename, 'a') as file_object: file_object.write(content)

20.909091

44

0.686957

30

230

5.133333

0.4

0.25974

0.246753

0.298701

0.844156

0.441558

0

0.2

230

10

45

23

0.836957

0

0.333333

0

0.008772

0

1

0.333333

false

0

0.333333

0

null

1

0

null

0

1

0

5

762c4eddf6e62d951b364f3388dba0fd544ca632

130

py

Python

atcoder/abc/abc149_b.py

knuu/competitive-programming

16bc68fdaedd6f96ae24310d697585ca8836ab6e

[ "MIT" ]

1

2018-11-12T15:18:55.000Z

atcoder/abc/abc149_b.py

knuu/competitive-programming

16bc68fdaedd6f96ae24310d697585ca8836ab6e

[ "MIT" ]

null

atcoder/abc/abc149_b.py

knuu/competitive-programming

16bc68fdaedd6f96ae24310d697585ca8836ab6e

[ "MIT" ]

null

A, B, K = map(int, input().split()) if A >= K: A -= K print(A, B) else: A, B = 0, max(0, B - (K - A)) print(A, B)

16.25

35

0.407692

27

130

1.962963

0.444444

0.150943

0.264151

0

0.023256

0.338462

130

7

36

18.571429

0.593023

0

0.285714

0

1

0

true

0

0.285714

1

0

1

null

0

1

0

1

0

null

0

1

0

5

762d4803e1f87bf476a9c770ae154251ab0ad5b3

447

py

Python

graph_ter_seg/transforms/__init__.py

RicardoLanJ/graph-ter

3b9bda527a6a9559be835c5b84e6491ac8c5aa30

[ "MIT" ]

58

2020-03-24T16:06:21.000Z

2022-03-26T07:04:28.000Z

graph_ter_seg/transforms/__init__.py

RicardoLanJ/graph-ter

3b9bda527a6a9559be835c5b84e6491ac8c5aa30

[ "MIT" ]

6

2020-04-02T08:52:37.000Z

2020-11-27T12:27:23.000Z

graph_ter_seg/transforms/__init__.py

RicardoLanJ/graph-ter

3b9bda527a6a9559be835c5b84e6491ac8c5aa30

[ "MIT" ]

19

2020-03-29T18:23:55.000Z

2021-12-25T04:10:00.000Z

from graph_ter_seg.transforms.global_rotate import GlobalRotate from graph_ter_seg.transforms.global_translate import GlobalTranslate from graph_ter_seg.transforms.global_shear import GlobalShear from graph_ter_seg.transforms.local_rotate import LocalRotate from graph_ter_seg.transforms.local_shear import LocalShear from graph_ter_seg.transforms.local_translate import LocalTranslate from graph_ter_seg.transforms.transformer import Transformer

55.875

69

0.90604

62

447

6.209677

0.290323

0.163636

0.218182

0.272727

0.54026

0.475325

0

0.06264

447

7

70

63.857143

0.918854

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

5230255512ed8bdd2cb4496352bf177743c59623

128

py

Python

backend/orcinus/admin.py

jiwonMe/rokn-letter

c91442f0174ff1e1efba568483d9d9def9423309

[ "MIT" ]

1

2022-01-18T03:13:20.000Z

backend/orcinus/admin.py

jiwonMe/rokn-letter

c91442f0174ff1e1efba568483d9d9def9423309

[ "MIT" ]

4

2021-06-13T14:54:41.000Z

2021-06-14T14:25:33.000Z

backend/orcinus/admin.py

jiwonMe/rokn-letter

c91442f0174ff1e1efba568483d9d9def9423309

[ "MIT" ]

null

from django.contrib import admin from .models import Letter, Session admin.site.register(Letter) admin.site.register(Session)

18.285714

35

0.8125

18

128

5.777778

0.555556

0.173077

0.326923

0

0.101563

128

6

36

21.333333

0.904348

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

524ad3a4d3bafe8569cc598430f4421cb1f798c9

306

py

Python

utils/pagination.py

darth-dodo/meal-helper

8cfc57463ff4a66be111520605453d4ff8fed6d0

[ "MIT" ]

2

2020-04-02T14:27:58.000Z

2021-02-01T10:39:50.000Z

utils/pagination.py

darth-dodo/meal-helper

8cfc57463ff4a66be111520605453d4ff8fed6d0

[ "MIT" ]

9

2019-12-05T00:43:48.000Z

2021-06-10T19:07:43.000Z

utils/pagination.py

darth-dodo/meal-helper

8cfc57463ff4a66be111520605453d4ff8fed6d0

[ "MIT" ]

null

from rest_framework.pagination import LimitOffsetPagination, PageNumberPagination from utils.constants import MAX_PAGINATION_LIMIT, DEFAULT_PAGINATION_LIMIT, PAGE_SIZE class MealPlannerPagination(LimitOffsetPagination): max_limit = MAX_PAGINATION_LIMIT default_limit = DEFAULT_PAGINATION_LIMIT

30.6

85

0.866013

32

306

7.90625

0.5

0.237154

0.142292

0.197628

0

0.101307

306

9

86

34

0.92

0

1

0

false

0

0.4

0

1

0

null

1

0

1

0

1

0

null

0

1

0

1

0

5

5270cefbebdc6821e49fc122b0e4f839deb09475

55

py

Python

packages/syft/src/syft/core/node/common/node_service/testing_services/__init__.py

jackbandy/PySyft

0e20e90abab6a7a7ca672d6eedfa1e7f83c4981b

[ "Apache-2.0" ]

null

packages/syft/src/syft/core/node/common/node_service/testing_services/__init__.py

jackbandy/PySyft

0e20e90abab6a7a7ca672d6eedfa1e7f83c4981b

[ "Apache-2.0" ]

null

packages/syft/src/syft/core/node/common/node_service/testing_services/__init__.py

jackbandy/PySyft

0e20e90abab6a7a7ca672d6eedfa1e7f83c4981b

[ "Apache-2.0" ]

null

# relative from .. import upload_service # noqa: F401

18.333333

43

0.727273

7

55

5.571429

1

0

0.066667

0.181818

55

2

44

27.5

0.8

0.345455

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

52715821b77912d7a8bae5c5cbab2cced899b000

74

py

Python

doujin/__init__.py

jack1142/MayuYukirin

2a61a98ce2924e9941334a35cf89b275aa21bc94

[ "MIT" ]

7

2017-09-13T02:54:58.000Z

2020-09-19T04:14:58.000Z

doujin/__init__.py

jack1142/MayuYukirin

2a61a98ce2924e9941334a35cf89b275aa21bc94

[ "MIT" ]

3

2018-11-08T06:28:55.000Z

2021-08-28T01:32:11.000Z

doujin/__init__.py

jack1142/MayuYukirin

2a61a98ce2924e9941334a35cf89b275aa21bc94

[ "MIT" ]

15

2017-09-13T02:32:45.000Z

2021-09-17T20:26:02.000Z

from .doujin import Doujin def setup(bot): bot.add_cog(Doujin(bot))

12.333333

28

0.702703

12

74

4.25

0.666667

0

0.175676

74

5

29

14.8

0.836066

0

1

0.333333

false

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

5284a00f11620e607411af4433b86f14a892f3e6

369

py

Python

importAll.py

jb-diplom/Big-Data

ae18d9b0b37a5bb716999ccd8671f2b72c37c09a

[ "MIT" ]

null

importAll.py

jb-diplom/Big-Data

ae18d9b0b37a5bb716999ccd8671f2b72c37c09a

[ "MIT" ]

null

importAll.py

jb-diplom/Big-Data

ae18d9b0b37a5bb716999ccd8671f2b72c37c09a

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Tue May 19 18:27:52 2020 @author: Janice """ import importlib importlib.import_module("reader") importlib.import_module("topicmap") importlib.import_module("similarity") importlib.import_module("scatterplots") importlib.import_module("sentiment3d") from reader import loadAllFeedsFromFile from scatterplots import displayTags

20.5

39

0.785908

44

369

6.477273

0.568182

0.263158

0.368421

0

0.041916

0.094851

369

17

40

21.705882

0.811377

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0

null

0

1

0

1

0

1

0

5

bfe56fdb72cb796da21b7e61292b90f0deb516a7

89

py

Python

scripts/densenet/export_segmented_tiles.py

SchiffFlieger/semantic-segmentation-master-thesis

f54b8321a9e0828e492bc6847acbff80c1a75d7c

[ "MIT" ]

1

2021-02-07T09:22:44.000Z

scripts/densenet/export_segmented_tiles.py

SchiffFlieger/semantic-segmentation-master-thesis

f54b8321a9e0828e492bc6847acbff80c1a75d7c

[ "MIT" ]

null

scripts/densenet/export_segmented_tiles.py

SchiffFlieger/semantic-segmentation-master-thesis

f54b8321a9e0828e492bc6847acbff80c1a75d7c

[ "MIT" ]

null

from scripts.common.qgis_export_worker import do_export do_export("densenet", 224, 224)

22.25

55

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89

4.928571

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0.074074

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null

0

1

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1

0

5

87379c42a34b124d9b314093a00897e0d7adec8b

107

py

Python

jjba/src/structs.py

lstn/jjba-continued

ce82399162bf156a9925e1412eb226bed58e4a33

[ "MIT" ]

2

2016-10-15T04:37:23.000Z

2016-10-15T05:20:26.000Z

jjba/src/structs.py

lstn/jjba-continued

ce82399162bf156a9925e1412eb226bed58e4a33

[ "MIT" ]

null

jjba/src/structs.py

lstn/jjba-continued

ce82399162bf156a9925e1412eb226bed58e4a33

[ "MIT" ]

null

class Bunch(dict): def __init__(self,**kw): dict.__init__(self,kw) self.__dict__ = self

26.75

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0.607477

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3.785714

0.5

0.301887

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0

1

0.25

false

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0

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0

null

1

0

1

0

null

0

1

0

5

5e83a22455603c1bbab93a8dd2e96968721f098d

109

py

Python

recolhidos/simulador.py

farioso-fernando/Rtimegrapk-Frame

892bb58a451550c772332da3e4d7c982832b2713

[ "MIT" ]

null

recolhidos/simulador.py

farioso-fernando/Rtimegrapk-Frame

892bb58a451550c772332da3e4d7c982832b2713

[ "MIT" ]

null

recolhidos/simulador.py

farioso-fernando/Rtimegrapk-Frame

892bb58a451550c772332da3e4d7c982832b2713

[ "MIT" ]

null

import os arquivos_aula = os.listdir('arquivo_ky/.') print(arquivos_aula[0]) print(type(arquivos_aula[0]))

18.166667

42

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null

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1

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null

0

1

0

5

5eba60fe6b6812c6778387197da21530a6ae7e6c

132

py

Python

enthought/numerical_modeling/numeric_context/context_modified.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/numerical_modeling/numeric_context/context_modified.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/numerical_modeling/numeric_context/context_modified.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from __future__ import absolute_import from blockcanvas.numerical_modeling.numeric_context.context_modified import *

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0

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0

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0

1

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1

0

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1

0

null

0

1

0

1

0

1

0

5

0d7152f9cee259d7df221f095bf7042b58be0971

640

py

Python

lacusClient_p2pTest/app_core/interfaces/cacheFilesManagerInterface.py

tavog96/distribuidosProyecto

8aee06ca580389412809353ac312c417aa1163fa

[ "MIT" ]

null

lacusClient_p2pTest/app_core/interfaces/cacheFilesManagerInterface.py

tavog96/distribuidosProyecto

8aee06ca580389412809353ac312c417aa1163fa

[ "MIT" ]

null

lacusClient_p2pTest/app_core/interfaces/cacheFilesManagerInterface.py

tavog96/distribuidosProyecto

8aee06ca580389412809353ac312c417aa1163fa

[ "MIT" ]

null

class cacheFilesManagerInterface: def __init__(self, cacheRootPath, resourcesRootPath): super().__init__() def createCacheFiles (self, fileInfo): raise NotImplementedError() def deleteCacheFiles (self, fileInfo): raise NotImplementedError() def getCacheFile (self, fileUID, chunkNumber): raise NotImplementedError() def restoreFileFromCache (self, fileInfo): raise NotImplementedError() def copyFileIntoChunks (self, cachedFileInfo): raise NotImplementedError() def writeChunkContent (self, content, fileName): raise NotImplementedError()

25.6

57

0.695313

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640

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0

0.228125

640

24

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26.666667

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0

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0

null

1

0

null

0

1

0

1

0

5

0d99ddd36b888f8a1b76f2948fb155d0ed17a817

988

py

Python

migrations/versions/37244bf4e3f5_aggregatetest_build_.py

vault-the/changes

37e23c3141b75e4785cf398d015e3dbca41bdd56

[ "Apache-2.0" ]

443

2015-01-03T16:28:39.000Z

2021-04-26T16:39:46.000Z

migrations/versions/37244bf4e3f5_aggregatetest_build_.py

vault-the/changes

37e23c3141b75e4785cf398d015e3dbca41bdd56

[ "Apache-2.0" ]

12

2015-07-30T19:07:16.000Z

2016-11-07T23:11:21.000Z

migrations/versions/37244bf4e3f5_aggregatetest_build_.py

vault-the/changes

37e23c3141b75e4785cf398d015e3dbca41bdd56

[ "Apache-2.0" ]

47

2015-01-09T10:04:00.000Z

2020-11-18T17:58:19.000Z

"""AggregateTest*.build_id => job_id Revision ID: 37244bf4e3f5 Revises: 57e24a9f2290 Create Date: 2013-12-26 01:24:37.395827 """ # revision identifiers, used by Alembic. revision = '37244bf4e3f5' down_revision = '57e24a9f2290' from alembic import op def upgrade(): op.execute('ALTER TABLE aggtestsuite RENAME COLUMN first_build_id to first_job_id') op.execute('ALTER TABLE aggtestsuite RENAME COLUMN last_build_id to last_job_id') op.execute('ALTER TABLE aggtestgroup RENAME COLUMN first_build_id to first_job_id') op.execute('ALTER TABLE aggtestgroup RENAME COLUMN last_build_id to last_job_id') def downgrade(): op.execute('ALTER TABLE aggtestsuite RENAME COLUMN first_job_id to first_build_id') op.execute('ALTER TABLE aggtestsuite RENAME COLUMN last_job_id to last_build_id') op.execute('ALTER TABLE aggtestgroup RENAME COLUMN first_job_id to first_build_id') op.execute('ALTER TABLE aggtestgroup RENAME COLUMN last_job_id to last_build_id')

32.933333

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1

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1

0

null

0

5

0d9f0206e3f5eb8a91a7cc8653815282887262c8

108

py

Python

examples/source_separation/utils/__init__.py

albertvillanova/audio

0cd25093626d067e008e1f81ad76e072bd4a1edd

[ "BSD-2-Clause" ]

1

2021-12-14T17:08:12.000Z

examples/source_separation/utils/__init__.py

albertvillanova/audio

0cd25093626d067e008e1f81ad76e072bd4a1edd

[ "BSD-2-Clause" ]

1

2021-08-31T22:20:32.000Z

examples/source_separation/utils/__init__.py

albertvillanova/audio

0cd25093626d067e008e1f81ad76e072bd4a1edd

[ "BSD-2-Clause" ]

null

from . import ( dataset, dist_utils, metrics, ) __all__ = ['dataset', 'dist_utils', 'metrics']

13.5

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0

5

0dcc9e44d905cab734ac20ae400067121c6ae373

143

py

Python

LoopStructural/probability/_normal.py

wgorczyk/LoopStructural

bedc7abd4c1868fdbd6ed659c8d72ef19f793875

[ "MIT" ]

67

2020-06-25T06:50:58.000Z

2022-03-29T17:15:43.000Z

LoopStructural/probability/_normal.py

wgorczyk/LoopStructural

bedc7abd4c1868fdbd6ed659c8d72ef19f793875

[ "MIT" ]

60

2020-06-28T22:58:21.000Z

2022-03-24T01:30:59.000Z

LoopStructural/probability/_normal.py

wgorczyk/LoopStructural

bedc7abd4c1868fdbd6ed659c8d72ef19f793875

[ "MIT" ]

9

2020-06-25T13:07:39.000Z

2021-12-01T01:41:24.000Z

import numpy as np def normal(values, sigma, mu = 0): return -0.5 * np.sum(np.log(2 * np.pi * sigma ** 2) + (0 - values) ** 2 / sigma ** 2)

47.666667

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47.666667

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0

1

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null

0

1

0

1

0

5

216d47ceeda5f61bfc094dd8ad49c4fc5faadf53

159

py

Python

bitmovin/resources/enums/encoding_mode.py

camberbridge/bitmovin-python

3af4c6e79b0291fda05fd1ceeb5bed1bba9f3c95

[ "Unlicense" ]

44

2016-12-12T17:37:23.000Z

2021-03-03T09:48:48.000Z

bitmovin/resources/enums/encoding_mode.py

camberbridge/bitmovin-python

3af4c6e79b0291fda05fd1ceeb5bed1bba9f3c95

[ "Unlicense" ]

38

2017-01-09T14:45:45.000Z

2022-02-27T18:04:33.000Z

bitmovin/resources/enums/encoding_mode.py

camberbridge/bitmovin-python

3af4c6e79b0291fda05fd1ceeb5bed1bba9f3c95

[ "Unlicense" ]

27

2017-02-02T22:49:31.000Z

2019-11-21T07:04:57.000Z

import enum class EncodingMode(enum.Enum): STANDARD = 'STANDARD' SINGLE_PASS = 'SINGLE_PASS' TWO_PASS = 'TWO_PASS' THREE_PASS = 'THREE_PASS'

17.666667

31

0.685535

20

159

5.15

0.45

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0

0.213836

159

8

32

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null

0

1

0

1

0

5

219438508dac53c4c0dbbdf5b11c3f9a937e3bd4

1,738

py

Python

backtestingrm.py

pepelawycliffe/AI_in_Finance

5eb29afed137c809955d116e7a7764b5914add96

[ "MIT" ]

3

2021-03-15T05:30:50.000Z

2021-12-14T07:28:44.000Z

backtestingrm.py

pepelawycliffe/AI_in_Finance

5eb29afed137c809955d116e7a7764b5914add96

[ "MIT" ]

null

backtestingrm.py

pepelawycliffe/AI_in_Finance

5eb29afed137c809955d116e7a7764b5914add96

[ "MIT" ]

null

# # Event-Based Backtesting # --Base Class (2) # # (c) Dr. Yves J. Hilpisch # from backtesting import * class BacktestingBaseRM(BacktestingBase): def set_prices(self, price): ''' Sets prices for tracking of performance. To test for e.g. trailing stop loss hit. ''' self.entry_price = price self.min_price = price self.max_price = price def place_buy_order(self, bar, amount=None, units=None, gprice=None): ''' Places a buy order for a given bar and for a given amount or number of units. ''' date, price = self.get_date_price(bar) if gprice is not None: price = gprice if units is None: units = int(amount / price) self.current_balance -= (1 + self.ptc) * units * price + self.ftc self.units += units self.trades += 1 self.set_prices(price) if self.verbose: print(f'{date} | buy {units} units for {price:.4f}') self.print_balance(bar) def place_sell_order(self, bar, amount=None, units=None, gprice=None): ''' Places a sell order for a given bar and for a given amount or number of units. ''' date, price = self.get_date_price(bar) if gprice is not None: price = gprice if units is None: units = int(amount / price) self.current_balance += (1 - self.ptc) * units * price - self.ftc self.units -= units self.trades += 1 self.set_prices(price) if self.verbose: print(f'{date} | sell {units} units for {price:.4f}') self.print_balance(bar)

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null

0

1

0

null

0

5

2198284093e00d1297a24bbe44602962a0682286

31

py

Python

ghidra_bridge/server/ghidra_bridge_host.py

novafacing/ghidra_bridge

682a0ff400438cb4b86f4154b5ac8650f910a872

[ "MIT" ]

null

ghidra_bridge/server/ghidra_bridge_host.py

novafacing/ghidra_bridge

682a0ff400438cb4b86f4154b5ac8650f910a872

[ "MIT" ]

null

ghidra_bridge/server/ghidra_bridge_host.py

novafacing/ghidra_bridge

682a0ff400438cb4b86f4154b5ac8650f910a872

[ "MIT" ]

null

DEFAULT_SERVER_HOST = "0.0.0.0"

31

0.741935

7

31

3

0.571429

0.285714

0

0.137931

0.064516

31

1

31

0.586207

0

0.21875

0

1

0

false

0

1

0

null

1

0

1

0

null

0

5

21991630308fb2b98f570db3be17f632fb485f5d

1,026

py

Python

code/python/echomesh/util/string/SizeName_test.py

rec/echomesh

be668971a687b141660fd2e5635d2fd598992a01

[ "MIT" ]

30

2015-02-18T14:07:00.000Z

2021-12-11T15:19:01.000Z

code/python/echomesh/util/string/SizeName_test.py

rec/echomesh

be668971a687b141660fd2e5635d2fd598992a01

[ "MIT" ]

16

2015-01-01T23:17:24.000Z

2015-04-18T23:49:27.000Z

code/python/echomesh/util/string/SizeName_test.py

rec/echomesh

be668971a687b141660fd2e5635d2fd598992a01

[ "MIT" ]

31

2015-03-11T20:04:07.000Z

2020-11-02T13:56:59.000Z

from __future__ import absolute_import, division, print_function, unicode_literals from echomesh.util.string.SizeName import size_name from echomesh.util.TestCase import TestCase class SizeNameTest(TestCase): def test_zero(self): self.assertEqual(size_name(0), '0') def test_FF(self): self.assertEqual(size_name(1023), '1023') def test_1K(self): self.assertEqual(size_name(1024), '1K') def test_1023K(self): self.assertEqual(size_name(1023 * 1024), '1023K') def test_1023K_2(self): self.assertEqual(size_name(1023 * 1024 + 511), '1023K') def test_1M(self): self.assertEqual(size_name(1023 * 1024 + 512), '1M') def test_1M2(self): self.assertEqual(size_name(1024 * 1024 - 1), '1M') def test_1M3(self): self.assertEqual(size_name(1024 * 1024), '1M') def test_1G(self): self.assertEqual(size_name(1024 * 1024 * 1024), '1G') def test_1G2(self): self.assertEqual(size_name(1024 * 1024 * 1024), '1G')

28.5

82

0.663743

140

1,026

4.657143

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0.291411

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null

0

1

0

1

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1

0

5

21c463612ec7346af9f15bcb2070bd2547bd9efe

57

py

Python

python/testData/addImport/relativeImportTooDeepWithSameLevelUsed/pkg1/pkg2/pkg3/pkg4/test.after.py

tgodzik/intellij-community

f5ef4191fc30b69db945633951fb160c1cfb7b6f

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/addImport/relativeImportTooDeepWithSameLevelUsed/pkg1/pkg2/pkg3/pkg4/test.after.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2022-02-19T09:45:05.000Z

2022-02-27T20:32:55.000Z

python/testData/addImport/relativeImportTooDeepWithSameLevelUsed/pkg1/pkg2/pkg3/pkg4/test.after.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

from ....foo import foo_func from ....bar import bar_func

28.5

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28.5

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null

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1

0

1

0

1

0

5

21ccd1c0d3721d57a3472ab7052e026713923b31

49,326

py

Python

Code/stackedAutoencoder.py

mChataign/smileCompletion

1bde2dd9fada2194c79cb3599bc9e9139cde6ee5

[ "BSD-3-Clause" ]

4

2021-01-06T13:53:39.000Z

2021-12-16T21:23:13.000Z

Code/stackedAutoencoder.py

mChataign/smileCompletion

1bde2dd9fada2194c79cb3599bc9e9139cde6ee5

[ "BSD-3-Clause" ]

null

Code/stackedAutoencoder.py

mChataign/smileCompletion

1bde2dd9fada2194c79cb3599bc9e9139cde6ee5

[ "BSD-3-Clause" ]

2

2021-01-06T20:53:15.000Z

2021-12-29T08:59:31.000Z

import pandas as pd import numpy as np import tensorflow as tf import dask import scipy import time from functools import partial from abc import ABCMeta, abstractmethod import shallowAutoencoder #Taken from https://stackoverflow.com/questions/39354566/what-is-the-equivalent-of-np-std-in-tensorflow #Available in next version of tensorflow def reduce_var(x, axis=None, keepdims=False): """Variance of a tensor, alongside the specified axis. # Arguments x: A tensor or variable. axis: An integer, the axis to compute the variance. keepdims: A boolean, whether to keep the dimensions or not. If `keepdims` is `False`, the rank of the tensor is reduced by 1. If `keepdims` is `True`, the reduced dimension is retained with length 1. # Returns A tensor with the variance of elements of `x`. """ m = tf.reduce_mean(x, axis=axis, keep_dims=True) devs_squared = tf.square(x - m) return tf.reduce_mean(devs_squared, axis=axis, keep_dims=keepdims) def reduce_std(x, axis=None, keepdims=False): """Standard deviation of a tensor, alongside the specified axis. # Arguments x: A tensor or variable. axis: An integer, the axis to compute the standard deviation. keepdims: A boolean, whether to keep the dimensions or not. If `keepdims` is `False`, the rank of the tensor is reduced by 1. If `keepdims` is `True`, the reduced dimension is retained with length 1. # Returns A tensor with the standard deviation of elements of `x`. """ return tf.sqrt(reduce_var(x, axis=axis, keepdims=keepdims)) class StackedAutoEncoder(shallowAutoencoder.ShallowAutoEncoder): def __init__(self, learningRate, hyperParameters, nbUnitsPerLayer, nbFactors, modelName = "./bestStackedAEModel"): super().__init__(learningRate, hyperParameters, nbUnitsPerLayer, nbFactors, modelName) def buildArchitecture(self): #Kernel initializer he_init = tf.contrib.layers.variance_scaling_initializer(factor=1.0, mode='FAN_AVG', uniform=True) # def positiveKernelInitializer(shape, dtype=None, partition_info=None): # return 1 + tf.abs(tf.contrib.layers.variance_scaling_initializer()(shape,dtype)) #Regularizer l2_regularizer = tf.contrib.layers.l2_regularizer(self.hyperParameters['l2_reg']) self.inputTensor = tf.placeholder(tf.float32, shape=[None, self.nbUnitsPerLayer['Input Layer']])#batch size along if self.verbose : print(self.inputTensor) #Layers 1 hiddenEncoder1 = self.buildDenseLayer(self.nbUnitsPerLayer['LayerEncoder1'], self.inputTensor, activation = tf.nn.softplus, kernelRegularizer = l2_regularizer, kernelInitializer = he_init) if self.verbose : print(hiddenEncoder1) #Layer 2 hiddenEncoder2 = self.buildDenseLayer(self.nbUnitsPerLayer['LayerEncoder2'], hiddenEncoder1, activation = tf.nn.softplus, kernelRegularizer = l2_regularizer, kernelInitializer = he_init) if self.verbose : print(hiddenEncoder2) #Layer 3 hiddenEncoder3 = self.buildDenseLayer(self.nbUnitsPerLayer['LayerEncoder3'], hiddenEncoder2, activation = tf.nn.softplus, kernelRegularizer = l2_regularizer, kernelInitializer = he_init) if self.verbose : print(hiddenEncoder3) #Layer 4 / Hidden Layer self.factorTensor = self.buildDenseLayer(self.nbFactors, hiddenEncoder3, activation = None, kernelRegularizer = l2_regularizer, kernelInitializer = he_init) self.nbEncoderLayer = len(self.layers) # DECODE -------------------------------------------------------------------- lastTensor = self.factorTensor for k in range(self.nbEncoderLayer): if self.verbose : print(lastTensor) lastTensor = self.buildInverseLayer(lastTensor) # if self.verbose : # print(lastTensor) # lastTensor = self.buildDenseLayer(self.nbUnitsPerLayer['Output Layer'], # lastTensor, # kernelRegularizer = l2_regularizer, # kernelInitializer = he_init, # activation = None) self.outputTensor = lastTensor if self.verbose : print(self.outputTensor) return def buildPenalization(self,**kwargs): return super().buildPenalization(**kwargs) class StackedAutoEncoderOptimized(StackedAutoEncoder): def __init__(self, learningRate, hyperParameters, nbUnitsPerLayer, nbFactors, modelName = "./bestStackedAEOptimizedModel"): super().__init__(learningRate, hyperParameters, nbUnitsPerLayer, nbFactors, modelName) #Train the factorial model def trainWithSession(self, session, inputTrain, nbEpoch, inputTest = None): start = time.time() if self.verbose : print("Calibrate model on training data and return testing loss per epoch") nbInit = self.hyperParameters["nbInit"] if "nbInit" in self.hyperParameters else 100 nbEpochInit = self.hyperParameters["nbEpochInit"] if "nbEpochInit" in self.hyperParameters else 1 lossInit = [] session.run(self.init) save_path = self.saveModel(session, self.metaModelNameInit) for k in range(nbInit): session.run(self.init) ##Layer wise pretraining #self.pretrainNetwork(session, inputTrain, nbEpochInit, inputTest = inputTest) #Global optimization for all layers epochLosses, epochValidation = self.gradientDescent(session, inputTrain, nbEpochInit, inputTest, self.loss, self.trainingOperator, self.reducedReconstructionLoss) if (k==0) or (np.nanmin(epochValidation) < np.nanmin(lossInit)): #save this model save_path = self.saveModel(session, self.metaModelNameInit) lossInit.append(np.nanmin(epochValidation)) #Get best estimate self.restoreWeights(session, self.metaModelNameInit) if nbInit > 0 : print("Min validation error for initialization : ", np.nanmin(lossInit)) print("Mean validation error for initialization : ", np.nanmean(lossInit)) print("Std validation error for initialization : ", np.nanstd(lossInit)) #Layer wise pretraining self.pretrainNetwork(session, inputTrain, nbEpoch, inputTest = inputTest) #Global optimization for all layers epochLosses, epochValidation = self.gradientDescent(session, inputTrain, nbEpoch, inputTest, self.loss, self.trainingOperator, self.reducedReconstructionLoss) print("Detailed performances") if inputTest is not None : totalLoss = session.run(self.loss , feed_dict=self.createFeedDictEncoder(inputTest if inputTest is not None else inputTrain)) print("Penalized Loss on testing dataset : ", totalLoss) print("Training time : % 5d" %(time.time() - start)) return epochLosses class ContractiveAutoEncoder(StackedAutoEncoderOptimized): def __init__(self, learningRate, hyperParameters, nbUnitsPerLayer, nbFactors, modelName = "./bestContractiveAEModel"): super().__init__(learningRate, hyperParameters, nbUnitsPerLayer, nbFactors, modelName) def buildPenalization(self, **kwargs): firstPenalizations = super().buildPenalization(**kwargs) #Aims at reducing factor sensitivity to inputs values def contractiveLoss(inputTensor, factorTensor): nbFactor = factorTensor.get_shape().as_list()[1] jacobian = tf.stack([tf.gradients(factorTensor[:, i], inputTensor) for i in range(nbFactor)], axis=2) cLoss = tf.norm(jacobian) return tf.reduce_mean(cLoss) contractivePenalization = None if len(kwargs)==0:#Train all layers contractivePenalization = contractiveLoss(self.inputTensor, self.factorTensor) else :#Train a subpart of neural network #contractivePenalization = contractiveLoss(kwargs['inputLayer'], kwargs['factors']) return firstPenalizations return firstPenalizations + [self.hyperParameters['lambdaContractive'] * contractivePenalization] #Denoising autoencoder where each intermediate layer received masked input during training as explained in #Vincent, Pascal, et al. "Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion." #Journal of machine learning research 11.12 (2010). class StackedAutoEncoderDenoised(StackedAutoEncoderOptimized): def __init__(self, learningRate, hyperParameters, nbUnitsPerLayer, nbFactors, modelName = "./bestStackedAEModelDenoised"): self.IsTraining = None super().__init__(learningRate, hyperParameters, nbUnitsPerLayer, nbFactors, modelName) #Train the factorial model def trainWithSession(self, session, inputTrain, nbEpoch, inputTest = None): start = time.time() if self.verbose : print("Calibrate model on training data and return testing loss per epoch") nbInit = self.hyperParameters["nbInit"] if "nbInit" in self.hyperParameters else 100 nbEpochInit = self.hyperParameters["nbEpochInit"] if "nbEpochInit" in self.hyperParameters else 100 lossInit = [] session.run(self.init) save_path = self.saveModel(session, self.metaModelNameInit) for k in range(nbInit): session.run(self.init) ##Layer wise pretraining #self.pretrainNetwork(session, inputTrain, nbEpochInit, inputTest = inputTest) #Global optimization for all layers epochLosses, epochValidation = self.gradientDescent(session, inputTrain, nbEpochInit, inputTest, self.loss, self.trainingOperator, self.reducedReconstructionLoss) if (k==0) or (np.nanmin(epochValidation) < np.nanmin(lossInit)): #save this model save_path = self.saveModel(session, self.metaModelNameInit) lossInit.append(np.nanmin(epochValidation)) #Get best estimate self.restoreWeights(session, self.metaModelNameInit) if nbInit > 0 : print("Min validation error for initialization : ", np.nanmin(lossInit)) print("Mean validation error for initialization : ", np.mean(lossInit)) print("Std validation error for initialization : ", np.std(lossInit)) #Layer wise pretraining self.pretrainNetwork(session, inputTrain, nbEpoch, inputTest = inputTest) #Global optimization for all layers epochLosses, epochValidation = self.gradientDescent(session, inputTrain, nbEpoch, inputTest, self.loss, self.trainingOperator, self.reducedReconstructionLoss) print("Detailed performances") if inputTest is not None : totalLoss = session.run(self.loss , feed_dict=self.createFeedDictEncoder(inputTest if inputTest is not None else inputTrain)) print("Penalized Loss on testing dataset : ", totalLoss) print("Training time : % 5d" %(time.time() - start)) return epochLosses def buildArchitecture(self): #Kernel initializer he_init = tf.contrib.layers.variance_scaling_initializer(factor=1.0, mode='FAN_AVG', uniform=True) # def positiveKernelInitializer(shape, dtype=None, partition_info=None): # return 1 + tf.abs(tf.contrib.layers.variance_scaling_initializer()(shape,dtype)) #Regularizer l2_regularizer = tf.contrib.layers.l2_regularizer(self.hyperParameters['l2_reg']) #Tensor for complete data self.inputTensor = tf.placeholder(tf.float32, shape=[None, self.nbUnitsPerLayer['Input Layer']])#batch size along #Tensor for noisy data # self.corruptedTensor = tf.placeholder(tf.float32, # shape=[None, self.nbUnitsPerLayer['Input Layer']]) if self.verbose : print(self.inputTensor) #self.IsTraining = tf.placeholder_with_default(False, (), # name="ActivateCorruption") #inputLayer = self.corruptTensor(self.inputTensor) if self.IsTraining else self.inputTensor inputLayer = self.inputTensor #Layers 1 hiddenEncoder1 = self.buildDenseLayer(self.nbUnitsPerLayer['LayerEncoder1'], inputLayer, activation = tf.nn.softplus, kernelRegularizer = l2_regularizer, kernelInitializer = he_init) if self.verbose : print(hiddenEncoder1) #Layer 2 hiddenEncoder2 = self.buildDenseLayer(self.nbUnitsPerLayer['LayerEncoder2'], hiddenEncoder1, activation = tf.nn.softplus, kernelRegularizer = l2_regularizer, kernelInitializer = he_init) if self.verbose : print(hiddenEncoder2) #Layer 3 hiddenEncoder3 = self.buildDenseLayer(self.nbUnitsPerLayer['LayerEncoder3'], hiddenEncoder2, activation = tf.nn.softplus, kernelRegularizer = l2_regularizer, kernelInitializer = he_init) if self.verbose : print(hiddenEncoder3) #Layer 4 / Hidden Layer self.factorTensor = self.buildDenseLayer(self.nbFactors, hiddenEncoder3, activation = None, kernelRegularizer = l2_regularizer, kernelInitializer = he_init) self.nbEncoderLayer = len(self.layers) # DECODE -------------------------------------------------------------------- lastTensor = self.factorTensor for k in range(self.nbEncoderLayer): if self.verbose : print(lastTensor) lastTensor = self.buildInverseLayer(lastTensor) # if self.verbose : # print(lastTensor) # lastTensor = self.buildDenseLayer(self.nbUnitsPerLayer['Output Layer'], # lastTensor, # kernelRegularizer = l2_regularizer, # kernelInitializer = he_init, # activation = None) self.outputTensor = lastTensor if self.verbose : print(self.outputTensor) return def buildPenalization(self,**kwargs): return super().buildPenalization(**kwargs) #Taken from https://github.com/timsainb/GAIA/blob/master/network.py def shape(self, tensor): """ get the shape of a tensor """ s = tensor.get_shape() return tuple([s[i].value for i in range(0, len(s))]) def squared_dist(A): """ Computes the pairwise distance between points #http://stackoverflow.com/questions/37009647/compute-pairwise-distance-in-a-batch-without-replicating-tensor-in-tensorflow """ expanded_a = tf.expand_dims(A, 1) expanded_b = tf.expand_dims(A, 0) distances = tf.reduce_mean(tf.squared_difference(expanded_a, expanded_b), 2) return distances def distance_loss(self, x, z_x): """ Loss based on the distance between elements in a batch """ z_x = tf.reshape(z_x, [shape(z_x)[0], np.prod(shape(z_x)[1:])]) sdx = squared_dist(x) sdx = sdx / tf.reduce_mean(sdx) sdz = squared_dist(z_x) sdz = sdz / tf.reduce_mean(sdz) return tf.reduce_mean(tf.square(tf.log(tf.constant(1.) + sdx) - (tf.log(tf.constant(1.) + sdz)))) def distance_loss_true(self, x, z_x): """ Loss based on the distance between elements in a batch """ sdx = squared_dist(x) sdz = squared_dist(z_x) return tf.reduce_mean(tf.abs(sdz - sdx)) def corruptDf(self, df): def corruptSurface(obs, nbCorruptedSurfaces, maxNbCorruption): uncorruptedDfList = [obs]*nbCorruptedSurfaces nbCorruptions = np.random.randint(maxNbCorruption, size=len(uncorruptedDfList)) def corruptionProcess(obs, nbCorruptions): orderedIndexes = np.arange(obs.shape[0]) np.random.shuffle(orderedIndexes) corruptedIndexes = obs.index[orderedIndexes[:nbCorruptions]] return obs.mask( obs.index.isin(corruptedIndexes) , other=0.0) corruptedDfs = pd.concat(list(map(lambda x : corruptionProcess(*x), zip(uncorruptedDfList, nbCorruptions) )), axis=1) return corruptedDfs funcCorrupt = lambda x : corruptSurface(x, self.hyperParameters["nbCorruptedSurfaces"], self.hyperParameters["nbCorruptionMax"]) return pd.concat(df.apply(funcCorrupt, axis=1).values, axis=1).transpose() def repeatDf(self, df): def repeatSurface(obs, nbCorruptedSurfaces, maxNbCorruption): uncorruptedDfList = [obs]*nbCorruptedSurfaces corruptedDfs = pd.concat(uncorruptedDfList, axis=1) return corruptedDfs funcRepeat = lambda x : repeatSurface(x, self.hyperParameters["nbCorruptedSurfaces"], self.hyperParameters["nbCorruptionMax"]) return pd.concat(df.apply(funcRepeat, axis=1).values, axis=1).transpose() #Create a feed_dict (see tensorflow documentation) for evaluating encoder #args : List dataset to feed, order meaning is proper to each model # def createFeedDictEncoder(self, dataSetList): # feedDict = {self.inputTensor : dataSetList[0]} # if len(dataSetList)> 1 :#Add corrupted Data # feedDict[self.corruptedTensor] = dataSetList[1] # else : # feedDict[self.corruptedTensor] = dataSetList[0] # return feedDict #Sample Mini-batch def generateMiniBatches(self, dataSet, nbEpoch): batchSize = 1000 return self.selectMiniBatchWithoutReplacement(dataSet, batchSize) # def gradientDescent(self, # session, # datasetTrain, # nbEpoch, # datasetTest, # trainingLoss, # gradientStep, # validationLoss): # corruptedDatasetTrain = [self.repeatDf(datasetTrain[0])]#, self.corruptDf(datasetTrain[0])] # corruptedDatasetTest = None # if datasetTest is not None : # corruptedDatasetTest = [self.repeatDf(datasetTest[0])]#, self.corruptDf(datasetTest[0])] # return super().gradientDescent(session, # corruptedDatasetTrain, # nbEpoch, # corruptedDatasetTest, # trainingLoss, # gradientStep, # validationLoss) #Stacked network def buildAndTrainPartialAutoencoder(self, stepEncoderLayer, stepDecoderLayer, trainableVariableList, inputToReproduce, session, inputTrain, nbEpoch, inputTest, encoderLayerIndex): corruptedLayer = self.corruptTensor(inputToReproduce) stepFactorLayer = corruptedLayer for layerFactory in stepEncoderLayer : stepFactorLayer = layerFactory(stepFactorLayer) stepOutputLayer = stepFactorLayer for layerFactory in stepDecoderLayer : stepOutputLayer = layerFactory(stepOutputLayer) #Build Losses stepLoss = self.buildLoss( stepOutputLayer, inputToReproduce, "stepOutputLayer"+str(encoderLayerIndex), matrixNorm = False) stepPenalization = [] if 'l2_reg' in self.hyperParameters : kwargsPenalization = {'layersKernels' : trainableVariableList[0::2], #kernels are located at even indices 'outputLayer' : stepOutputLayer, 'inputLayer' : inputToReproduce, 'factors' : stepFactorLayer} stepPenalization = self.buildPenalization(**kwargsPenalization) stepTotalLosses = tf.add_n([stepLoss] + stepPenalization) stepVariableToTrain = self.getVariableFromTensor(trainableVariableList) stepTrainingOperator = self.optimizer.minimize(stepTotalLosses, var_list=stepVariableToTrain) corruptedDatasetTrain = [self.repeatDf(inputTrain[0])]#, self.corruptDf(datasetTrain[0])] corruptedDatasetTest = None if inputTest is not None : corruptedDatasetTest = [self.repeatDf(inputTest[0])]#, self.corruptDf(datasetTest[0])] self.gradientDescent(session, corruptedDatasetTrain, nbEpoch, corruptedDatasetTest, stepTotalLosses, stepTrainingOperator, stepLoss) layerFactor = session.run(stepFactorLayer, feed_dict=self.createFeedDictEncoder(inputTrain)) #print("Factor layer", layerFactor[0,:]) corruptedInput = session.run(corruptedLayer, feed_dict=self.createFeedDictEncoder(inputTrain)) #print("corrupted layer", corruptedInput[0,:]) corruptedOutput = session.run(stepOutputLayer, feed_dict=self.createFeedDictEncoder(inputTrain)) #print("output layer", corruptedOutput[0,:]) #Build input for next step newInputToReproduce = inputToReproduce for layerFactory in stepEncoderLayer : newInputToReproduce = layerFactory(newInputToReproduce) return newInputToReproduce def corruptTensor(self, tensor): shape = tf.shape(tensor) #nbFeatures = shape[1] #shape_x, shape_y = shape[0], shape[1] # nbCorruptedData = tf.random.uniform((), # maxval=shape_y / 2, # dtype=tf.dtypes.int32) # nbCorruptedData = tf.random.uniform([nbCorruptedData, ], # maxval=shape_y, # dtype=tf.dtypes.float32) # return res = tf.where(mask, tf.zeros_like(data), data) #mask=np.random.binomial(1, 1 - corruption_level,tensor.shape ) #mask with several zeros at certain position mask = tf.keras.backend.random_binomial(shape, p = self.hyperParameters["CorruptionLevel"]) return mask * tensor #Denoising autoencoder where only input layer received masked input during training class StackedAutoEncoderDenoised1(StackedAutoEncoderDenoised): def __init__(self, learningRate, hyperParameters, nbUnitsPerLayer, nbFactors, modelName = "./bestStackedAEModelDenoised1"): super().__init__(learningRate, hyperParameters, nbUnitsPerLayer, nbFactors, modelName) #Stacked network def buildAndTrainPartialAutoencoder(self, stepEncoderLayer, stepDecoderLayer, trainableVariableList, inputToReproduce, session, inputTrain, nbEpoch, inputTest, encoderLayerIndex): corruptedLayer = self.corruptTensor(inputToReproduce) if (inputToReproduce==self.inputTensor) else inputToReproduce stepFactorLayer = corruptedLayer for layerFactory in stepEncoderLayer : stepFactorLayer = layerFactory(stepFactorLayer) stepOutputLayer = stepFactorLayer for layerFactory in stepDecoderLayer : stepOutputLayer = layerFactory(stepOutputLayer) #Build Losses stepLoss = self.buildLoss( stepOutputLayer, inputToReproduce, "stepOutputLayer"+str(encoderLayerIndex), matrixNorm = False ) stepPenalization = [] if 'l2_reg' in self.hyperParameters : kwargsPenalization = {'layersKernels' : trainableVariableList[0::2], #kernels are located at even indices 'outputLayer' : stepOutputLayer, 'inputLayer' : inputToReproduce, 'factors' : stepFactorLayer} stepPenalization = self.buildPenalization(**kwargsPenalization) stepTotalLosses = tf.add_n([stepLoss] + stepPenalization) stepVariableToTrain = self.getVariableFromTensor(trainableVariableList) stepTrainingOperator = self.optimizer.minimize(stepTotalLosses, var_list=stepVariableToTrain) corruptedDatasetTrain = [self.repeatDf(inputTrain[0])]#, self.corruptDf(datasetTrain[0])] corruptedDatasetTest = None if inputTest is not None : corruptedDatasetTest = [self.repeatDf(inputTest[0])]#, self.corruptDf(datasetTest[0])] self.gradientDescent(session, corruptedDatasetTrain, nbEpoch, corruptedDatasetTest, stepTotalLosses, stepTrainingOperator, stepLoss) layerFactor = session.run(stepFactorLayer, feed_dict=self.createFeedDictEncoder(inputTrain)) #print("Factor layer", layerFactor[0,:]) corruptedInput = session.run(corruptedLayer, feed_dict=self.createFeedDictEncoder(inputTrain)) #print("corrupted layer", corruptedInput[0,:]) corruptedOutput = session.run(stepOutputLayer, feed_dict=self.createFeedDictEncoder(inputTrain)) #print("output layer", corruptedOutput[0,:]) return stepFactorLayer class StackedAutoEncoderDisentanglement(StackedAutoEncoderOptimized): def __init__(self, learningRate, hyperParameters, nbUnitsPerLayer, nbFactors, modelName = "./bestStackedAEModelDisentanglement"): super().__init__(learningRate, hyperParameters, nbUnitsPerLayer, nbFactors, modelName) def buildPenalization(self,**kwargs): firstPenalizations = super().buildPenalization(**kwargs) if ("lambdaDisentangle" not in self.hyperParameters) : return firstPenalizations #Penalize unlikely factor values with respect to a gaussian distribution def log_normal_pdf(sample, mean, logvar, raxis=1): log2pi = tf.math.log(2. * np.pi) return tf.reduce_mean(-.5 * ((sample - mean) ** 2. * tf.exp(-logvar) + logvar + log2pi),axis=raxis) def log_gaussian_independant_vector_pdf(factors, raxis=1): exampleWiseScalarProduct = tf.reduce_mean(tf.square(factors), axis = raxis) return tf.reduce_mean(exampleWiseScalarProduct) penalization = None if len(kwargs)==0: #Standard case, training all layers penalization = log_gaussian_independant_vector_pdf(self.factorTensor) if self.verbose : print(penalization) else: #Training only some layers typically for layer wise learning penalization = log_gaussian_independant_vector_pdf(kwargs['factors']) if self.verbose : print(penalization) return firstPenalizations + [self.hyperParameters["lambdaDisentangle"] * penalization] #Train autoencoders by masking points as in completion problem #Completion amounts then to evaluate the model #See https://github.com/RaptorMai/Deep-AutoEncoder-Recommendation class StackedAutoEncoderRecommender(StackedAutoEncoderOptimized): def __init__(self, learningRate, hyperParameters, nbUnitsPerLayer, nbFactors, modelName = "./bestStackedAEModelRecommender"): self.mask = hyperParameters["mask"] self.maskTensor = None super().__init__(learningRate, hyperParameters, nbUnitsPerLayer, nbFactors, modelName) def maskValues(self, unmaskedData): maskedValue = tf.zeros_like(unmaskedData) #tf.boolean_mask(unmaskData, mask, axis=1) return tf.transpose(tf.where(tf.transpose(self.maskTensor), tf.transpose(maskedValue), tf.transpose(unmaskedData))) def completeDataTensor(self, sparseSurface, initialValueForFactors, nbCalibrationStep, *args): reshapedSparseSurface = pd.DataFrame(np.reshape([sparseSurface.fillna(0.0)], (1,sparseSurface.shape[0])), columns = sparseSurface.index) if self.verbose : print("Completion is assimilated to compression in our case") bestLoss, surface, factor = self.evalModel(reshapedSparseSurface) completedSurface = np.where(np.isnan(np.reshape([sparseSurface], (1,sparseSurface.shape[0]))), surface.values, reshapedSparseSurface.values) bestSurface = pd.Series(np.ravel(completedSurface), index = surface.columns) bestFactor = np.ravel(factor.values) return bestLoss, bestFactor, bestSurface, pd.Series([bestLoss]) def buildArchitecture(self): #Kernel initializer he_init = tf.contrib.layers.variance_scaling_initializer(factor=1.0, mode='FAN_AVG', uniform=True) # def positiveKernelInitializer(shape, dtype=None, partition_info=None): # return 1 + tf.abs(tf.contrib.layers.variance_scaling_initializer()(shape,dtype)) #Regularizer l2_regularizer = tf.contrib.layers.l2_regularizer(self.hyperParameters['l2_reg']) #Tensor for complete data self.inputTensor = tf.placeholder(tf.float32, shape=[None, self.nbUnitsPerLayer['Input Layer']])#batch size along #Tensor for noisy data # self.corruptedTensor = tf.placeholder(tf.float32, # shape=[None, self.nbUnitsPerLayer['Input Layer']]) if self.verbose : print(self.inputTensor) #self.IsTraining = tf.placeholder_with_default(False, (), # name="ActivateCorruption") #inputLayer = self.corruptTensor(self.inputTensor) if self.IsTraining else self.inputTensor self.maskTensor = tf.Variable(self.mask.values, dtype=tf.bool) inputLayer = self.maskValues(self.inputTensor) #Layers 1 hiddenEncoder1 = self.buildDenseLayer(self.nbUnitsPerLayer['LayerEncoder1'], inputLayer, activation = tf.nn.softplus, kernelRegularizer = l2_regularizer, kernelInitializer = he_init) if self.verbose : print(hiddenEncoder1) #Layer 2 hiddenEncoder2 = self.buildDenseLayer(self.nbUnitsPerLayer['LayerEncoder2'], hiddenEncoder1, activation = tf.nn.softplus, kernelRegularizer = l2_regularizer, kernelInitializer = he_init) if self.verbose : print(hiddenEncoder2) #Layer 3 hiddenEncoder3 = self.buildDenseLayer(self.nbUnitsPerLayer['LayerEncoder3'], hiddenEncoder2, activation = tf.nn.softplus, kernelRegularizer = l2_regularizer, kernelInitializer = he_init) if self.verbose : print(hiddenEncoder3) #Layer 4 / Hidden Layer self.factorTensor = self.buildDenseLayer(self.nbFactors, hiddenEncoder3, activation = None, kernelRegularizer = l2_regularizer, kernelInitializer = he_init) self.nbEncoderLayer = len(self.layers) # DECODE -------------------------------------------------------------------- lastTensor = self.factorTensor for k in range(self.nbEncoderLayer): if self.verbose : print(lastTensor) lastTensor = self.buildInverseLayer(lastTensor) # if self.verbose : # print(lastTensor) # lastTensor = self.buildDenseLayer(self.nbUnitsPerLayer['Output Layer'], # lastTensor, # kernelRegularizer = l2_regularizer, # kernelInitializer = he_init, # activation = None) self.outputTensor = lastTensor if self.verbose : print(self.outputTensor) return def buildAndTrainPartialAutoencoder(self, stepEncoderLayer, stepDecoderLayer, trainableVariableList, inputToReproduce, session, inputTrain, nbEpoch, inputTest, encoderLayerIndex): corruptedLayer = self.maskValues(inputToReproduce) if (inputToReproduce==self.inputTensor) else inputToReproduce stepFactorLayer = corruptedLayer for layerFactory in stepEncoderLayer : stepFactorLayer = layerFactory(stepFactorLayer) stepOutputLayer = stepFactorLayer for layerFactory in stepDecoderLayer : stepOutputLayer = layerFactory(stepOutputLayer) #Build Losses stepLoss = self.buildLoss( stepOutputLayer, inputToReproduce, "stepOutputLayer"+str(encoderLayerIndex), matrixNorm = False ) stepPenalization = [] if 'l2_reg' in self.hyperParameters : kwargsPenalization = {'layersKernels' : trainableVariableList[0::2], #kernels are located at even indices 'outputLayer' : stepOutputLayer, 'inputLayer' : inputToReproduce, 'factors' : stepFactorLayer} stepPenalization = self.buildPenalization(**kwargsPenalization) stepTotalLosses = tf.add_n([stepLoss] + stepPenalization) stepVariableToTrain = self.getVariableFromTensor(trainableVariableList) stepTrainingOperator = self.optimizer.minimize(stepTotalLosses, var_list=stepVariableToTrain) self.gradientDescent(session, inputTrain, nbEpoch, inputTest, stepTotalLosses, stepTrainingOperator, stepLoss) layerFactor = session.run(stepFactorLayer, feed_dict=self.createFeedDictEncoder(inputTrain)) #print("Factor layer", layerFactor[0,:]) corruptedInput = session.run(corruptedLayer, feed_dict=self.createFeedDictEncoder(inputTrain)) #print("corrupted layer", corruptedInput[0,:]) corruptedOutput = session.run(stepOutputLayer, feed_dict=self.createFeedDictEncoder(inputTrain)) #print("output layer", corruptedOutput[0,:]) return stepFactorLayer #Try to preserve relative distance for encodings w.r.t volatility surfaces relative distances class StackedAutoEncoderTopology(StackedAutoEncoderOptimized): def __init__(self, learningRate, hyperParameters, nbUnitsPerLayer, nbFactors, modelName = "./bestStackedAEModelTopology"): super().__init__(learningRate, hyperParameters, nbUnitsPerLayer, nbFactors, modelName) #Taken from https://github.com/timsainb/GAIA/blob/master/network.py def shape(self, tensor): """ get the shape of a tensor """ s = tensor.get_shape() return tuple([s[i].value for i in range(0, len(s))]) # def squared_dist(self, A): # """ # Computes the pairwise distance between points # http://stackoverflow.com/questions/37009647/compute-pairwise-distance-in-a-batch-without-replicating-tensor-in-tensorflow # """ # expanded_a = tf.expand_dims(A, 1) # expanded_b = tf.expand_dims(A, 0) # distances = tf.reduce_mean(tf.squared_difference(expanded_a, expanded_b), 2) # return distances def squared_dist(self, A): r = tf.reduce_sum(A*A, 1) r = tf.reshape(r, [-1, 1]) D = r - 2*tf.matmul(A, tf.transpose(A)) + tf.transpose(r) return D def distance_loss(self, x, z_x): """ Loss based on the distance between elements in a batch """ #print([self.shape(z_x)[0], np.prod(self.shape(z_x)[1:])]) #z_x = tf.reshape(z_x, [self.shape(z_x)[0], np.prod(self.shape(z_x)[1:])]) sdx = self.squared_dist(x) sdx = sdx / tf.reduce_mean(sdx) sdz = self.squared_dist(z_x) sdz = sdz / tf.reduce_mean(sdz) return tf.reduce_mean(tf.square(tf.log(tf.constant(1.) + sdx) - (tf.log(tf.constant(1.) + sdz)))) def distance_loss_true(self, x, z_x): """ Loss based on the distance between elements in a batch """ sdx = self.squared_dist(x) sdz = self.squared_dist(z_x) return tf.reduce_mean(tf.abs(sdz - sdx)) def buildPenalization(self,**kwargs): firstPenalizations = super().buildPenalization(**kwargs) if ("lambdaTopology" not in self.hyperParameters) : return firstPenalizations penalization = None if (len(kwargs)==0) : #Standard case, training all layers penalization = self.distance_loss(self.inputTensor, self.factorTensor) if self.verbose : print(penalization) else: #Training only some layers typically for layer wise learning penalization = self.distance_loss(kwargs['inputLayer'], kwargs['factors']) if self.verbose : print(penalization) return firstPenalizations + [self.hyperParameters["lambdaTopology"] * penalization] #Penalize completion with encodings from completed surfaces class StackedAutoEncoderPenalizedCompletion(StackedAutoEncoderOptimized): def __init__(self, learningRate, hyperParameters, nbUnitsPerLayer, nbFactors, modelName = "./bestStackedAEModelDisentanglement"): super().__init__(learningRate, hyperParameters, nbUnitsPerLayer, nbFactors, modelName) #Build a tensor that construct a surface from factors values def buildEncoderTensor(self, surfaceTensor): lastTensor = surfaceTensor for idxFactory in range(self.nbEncoderLayer): factoryTmp = self.layers[idxFactory] lastTensor = factoryTmp(lastTensor) return lastTensor def buildCompletionLoss(self, factorTensor, calibrationLoss, completedSurfaceTensor): previousPenalization = super().buildCompletionLoss(factorTensor, calibrationLoss, completedSurfaceTensor) completedEncodings = self.buildEncoderTensor(completedSurfaceTensor) finalCalibrationLoss = previousPenalization if "lambdaCompletionEncodings" in self.hyperParameters : encodingRegularization = tf.reduce_mean(self.buildReconstructionLoss(completedEncodings, factorTensor, "EncodingRegularization")) finalCalibrationLoss += self.hyperParameters["lambdaCompletionEncodings"] * encodingRegularization return finalCalibrationLoss

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144

0.507461

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49,326

7.028093

0.146992

0.007268

0.014697

0.02035

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0.713732

0.70816

0.687366

0.66843

0

0.008495

0.420062

49,326

1,138

145

43.344464

0.857303

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0.007866

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0.070661

false

0

0.013825

0.003072

0.159754

0.056836

0

null

0

1

0

1

0

1

0

null

0

5

21f1d1c73a1ecc750f6560c6943b038b16fb1a39

145

py

Python

build/lib/ytrader/__init__.py

YONDE927/ytrader

592928e16884f2864329e34fc85854c3391c5686

[ "MIT" ]

1

2021-09-14T11:38:44.000Z

ytrader/__init__.py

YONDE927/ytrader

592928e16884f2864329e34fc85854c3391c5686

[ "MIT" ]

null

ytrader/__init__.py

YONDE927/ytrader

592928e16884f2864329e34fc85854c3391c5686

[ "MIT" ]

null

from .qdata import Qdata,Symbol from .book import Book from .tradetime import Tradetime from .trader import Trader from .strategy import Strategy

29

32

0.827586

21

145

5.714286

0.380952

0

0.131034

145

5

33

29

0.952381

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

df3fdd1270bc224645749245287fe521c9ab237f

57

py

Python

ripser/__init__.py

lrcfmd/ripser.py

de49167b6da035a7920d1cc6b1f4527b143091f1

[ "MIT" ]

172

2018-08-22T19:52:14.000Z

2022-03-07T18:01:40.000Z

ripser/__init__.py

ghilesmeddour/ripser.py

190f1ffe0abfb89cacf9f0b03e8507dddad73ff9

[ "MIT" ]

90

2018-08-01T04:42:39.000Z

2022-03-28T08:31:20.000Z

ripser/__init__.py

ghilesmeddour/ripser.py

190f1ffe0abfb89cacf9f0b03e8507dddad73ff9

[ "MIT" ]

50

2018-08-18T20:48:54.000Z

2022-03-20T23:46:45.000Z

from .ripser import * from ._version import __version__

14.25

33

0.789474

7

57

5.714286

0.571429

0

0.157895

57

3

34

19

0.833333

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1

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true

0

1

0

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null

0

1

0

null

0

1

0

1

0

1

0

5

df650913321ab58ad93c91dc152a35ffb6a903a1

30

py

Python

HelloMsLibrary/hello.py

TravelRPG/MSLibrary

4384559c2b1f44dc7a1eeac2385d1a3ce684729c

[ "MIT" ]

4

2021-08-16T15:41:02.000Z

2021-08-18T03:10:56.000Z

HelloMsLibrary/hello.py

TravelRPG/MSLibrary

4384559c2b1f44dc7a1eeac2385d1a3ce684729c

[ "MIT" ]

null

HelloMsLibrary/hello.py

TravelRPG/MSLibrary

4384559c2b1f44dc7a1eeac2385d1a3ce684729c

[ "MIT" ]

1

2021-09-17T12:08:29.000Z

print("Hello,", "MSLibrary!")

15

29

0.633333

3

30

6.333333

1

0

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30

1

30

0.678571

0

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true

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null

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1

0

null

0

1

0

1

0

5

df7d5868e44269f2d68119385b0d17cb2a3eb966

65,358

py

Python

test/crypto/signing_test.py

plenarius/iota.lib.py

ac6167dadb8b60a64b33eeb9db755be32c7cef12

[ "MIT" ]

2

2018-02-21T12:04:41.000Z

2018-04-01T18:56:18.000Z

test/crypto/signing_test.py

plenarius/iota.lib.py

ac6167dadb8b60a64b33eeb9db755be32c7cef12

[ "MIT" ]

null

test/crypto/signing_test.py

plenarius/iota.lib.py

ac6167dadb8b60a64b33eeb9db755be32c7cef12

[ "MIT" ]

3

2018-02-19T09:35:44.000Z

2018-04-01T19:16:26.000Z

# coding=utf-8 from __future__ import absolute_import, division, print_function, \ unicode_literals import warnings from unittest import TestCase from iota import Hash, TryteString from iota.crypto import SeedWarning from iota.crypto.signing import KeyGenerator, SignatureFragmentGenerator from iota.crypto.types import PrivateKey # noinspection SpellCheckingInspection class KeyGeneratorTestCase(TestCase): """ Generating validation data using the JS lib: .. code-block:: javascript // Specify parameters used to generate the private key. var seed = 'SEED9GOES9HERE'; var keyIndex = 42; var securityLevel = 3; var Converter = require('./lib/crypto/converter/converter'); var Signing = require('./lib/crypto/signing/signing'); // Generate the key. var privateKey = Signing.key(Converter.trits(seed), keyIndex, securityLevel); // Output human-readable version. console.log(Converter.trytes(privateKey)); """ def test_get_keys_single(self): """ Generating a single key. """ with warnings.catch_warnings(record=True) as catched_warnings: # Cause all warnings to always be triggered warnings.simplefilter("always") kg = KeyGenerator( seed = b'TESTSEED9DONTUSEINPRODUCTION99999ZTRFNBTRBSDIHWKOWCFBOQYQTENWL', ) # no warning must be raised, since seed has a valid length self.assertEqual(len(catched_warnings), 0) self.assertListEqual( kg.get_keys(start=0), # Note that the result is always a list, even when generating a # single key. [ PrivateKey( b'RYTWXBSSDDMFTHADVDRQN9HVOABBDMJDGAN9HPFAUOBZRKIWSVHJOPPETPFEF9UM9V' b'ZETGGPMRJFPQAUWAVSHPZLGWNXLE9EKMGENDWUGRFBV9IVBAQZBVKF9GKTIAUFTSRZ' b'NKGVITUYZDFRUXUJSVS9TRPMXJYKNBSHGUSUKVFLDHPSWNINOQLYTYYUY9W9EFDEHB' b'CQZADZIIVSTOOQDVXKLCNHYZPUXTUUXTNAUDS9NZIVRHXNDMXNMOKHDEFGGLFMJIIT' b'MJ9MVDMFYJ9SY9IWSVRWITSWFMQJZIBETXXSXUDNSJQIJLMNXJGUUKBAII9YLSQVQO' b'SHZQJWPEJQTS9IJAEKPFLQYLUANQGJTWEVNXOFYHFUJCTSFT9GXVCAPHUMTTTVPXHE' b'KORVSSMVWLALNGUSFRZYEUPJRDZ9ESSZYEHGYSYDKLLPLKUPUIGOKVBI9YJTN9MLQC' b'HTLQYLLBXGKQM9DOIPOZEWOXJKHNASEUUCUVPCGL9HWCBQTTFIWUCMXVMEMVUKQWVU' b'ELWBMRQQGY9DO9UISPLULEKORKJVPLHUSDRNZJCNNUZEKMUDQJNRNNQWQOBHIYMEQD' b'LRHLNNSLRLWJEXRVUKXBLOKAXPKWVAGFUFFKCKCJAYYFNMVHLIMQABMM9O9BCANIZX' b'YXYVSLWJOQEDKF9TGQGVGSRBZZCXTGAMROYCW9WCRDDBWSSHK9UOGASQBZVGLHLNKR' b'EHXXS9UWCOQGSOGRYQIVX9EZQAP9KTRTOTTDCFRYHL9HGIJNRBADEGVVZGEQLKJNFN' b'RDHIVSJARABAWPRB9DGQHROWIQZCFQJMNIDNDZRFFSABYCHRKGZNAXPNTCKAQAHXWS' b'FHDZTSGZFOJQRSWDQOZELCYFHDVZADU9XCIPNKGAFQS9DFKYWJPQBRJK9MPXDTJOC9' b'URFTMQXDQLOFFLQCNMTZLROECGMXUCENMYHVIBJIO9DWOKRDPVVUUBZ9DYBKEYEFYO' b'SXAYBGJTEJKSBZJRPLZSTWG9YVVF9OWXIC9MNSDMACOVRHNSTTGARCCSDJAWSRWDKT' b'OCDLZUAWQANXXX9RAFLIQUISYWPMDKTZWUZOKOWDHFJ9N9EMQVA9DRZGAIAJTIPBNI' b'MTOL9JY9IIGYIXDIB9MZNTZYHJYMGAJEAOKZYYFAIVWQMQAJJJWECCJ9VZNMWNPMZA' b'VTDWEZBZSIGGDDPTIPKEASMWBHXNKTGKHNNBURXLCOBR9CYSXZXUYVOCLRJTXDQCZB' b'HKZ9TRWRHYZAMEHLKCAKUYXAGUUANNYTJKUEIENVYXBQLTKWYPDFCCSGQKWRYZDASY' b'DKUMZYXEWUUROUXUHEFMWVRDNRVZJTANTDZIZUXQMBCIVAF9JCYQZEVAAQKRZY9OYL' b'EURQGARRSDGSFEW9GIGFMJDPAY9NFXTQMXXTJUIMKKUE9BOOHUOHHENLI9DVYJJXLM' b'UKWKNWTBODTHZFBNQOVJIXOOMBULE9IGIITWQACCWZCOPDHNLHCKACNETJJNSOHQFO' b'99RLRZFLPGMBYZYHKSNKYSULKENZODTDKFKJEZNWK9IDIPQLYGSETEMLYFDQXDHVA9' b'XJUUCWIDUMU9AE9HGOWDIVALKUVAZV9TBEGYPDWQKQOH9SNKLBTGEQZAJZXBJUIDA9' b'YCBHONYIZTZWSAVXPGDDWN9PQWKXGGFUQSFUSPJIVLDDZUSZVRXMYZEBEXYFVLPZQQ' b'YAUZOYIUWGXNJEXCQT9GDFLFDCPZBSRHZSGYUHUBGQSSQEXB9PDPRQQ9YICHOWTJRW' b'XGCZRTXC9ERXFFHLDMMVKF9LULV9LDQPXJAHUKWZNINFHTCSYNAXFST9IFRAXPASIJ' b'9WJWWFO9SYIQZNDMVXG9JXONUDMVGSNRQIJKVEDCGHWKEDOGED9YPQIXBUCZJRSMRM' b'S9RJTFIYDIXAQMWNXGHHTCJLCSQNH9CNP9PJZCLIBQQBNMEY9PIJJLXPLREAHPKOZD' b'JMPVYNUK9EMRVEJVMCQPPW9HOUHQ9YNNBXGLDLUJQEQFXTDTKHDREEIHHKIWWSPKWY' b'QYXLGXUEFAIWSA9NSDXWPSKISBADFJHJCSCNRORALCQNMIJ9U9TNCHZOC9NARJWQPH' b'O9HALCYZMNPCIV9VROERYT9UCNLZGKJEORFT9AMEZCXOVUIXSEPEIBMUNPPDEWIAAK' b'GRYJANZDY' ), ], ) self.assertListEqual( kg.get_keys(start=1), [ PrivateKey( b'PUQKFGMMWIEFMSGJI9QGVRYOCAPKTRJAOJLGGLURVTWDOWOI9ZHKHSVC9GPKNPHDTD' b'MSMJNGNXDR9NDYDRMUM9CTOI9MCGMCRAARLPVUSJGXEYRRBUSUQLGMTDGGGWYASWTK' b'KJHHBZ9BIYVNKGHKCTXVVJLFSWXW9WVU9NWTKO99ZZP9FOMLULMJPZZEAJBZXMW9BD' b'IWBMZMAERMLVPW9ADZKWQLSSHCYZZQIGXDBRDEEHUBZV9FJBIOPPAAE9RMKMAQFHYW' b'ELQPUAFTMBBXFSPOIVJL9QDYZLDTPMLZQSEJHWEBAUTWAIFAUSGHZMYPSVBDTKQKWQ' b'UQNRIEMYMNS9AYZJGURKDWEZHVGBJIOJKHBVWXJVFUVBZKOMWIHNPKV9SWRHYNKN9T' b'WNDYETPNXYKRMRPLDXUMGQ9GNSF9KHFXVQVIDCQRJJL9DFZHOFFNMJDO9EEAYTLHUT' b'WHJWKKONESMCENCALGTLDZNBUWLBKMKEPXTTHLBQLTVDBXZNLFJRZM9MSGLUDYPBUD' b'ISGNOAXXZNPEQQXFDZAKEPHJZHGDTXDBHPTQLPCLBXDZERTCNJQYLSKSVOAXUBQNWK' b'GOIMHJIOBYPYUBANJTXBOGLSASYWMKV9JZTVNZVOZJCEOUVWXTWQPWNVIWUAB9IPGJ' b'KREMJCAEMOZJJXQGZTAUBITQBMCARFG9NWLTWOYCWSBKTKXXJRRIFFDSKF9AMJBZIM' b'FQXVRSSB9NXXLXORUHEKOWAQHZVFUETBTRCDQFFNGLTFKSQNOJWEXTOBWDGBMDSNLQ' b'BORKWPMUNGIKQQNPBCFRDZKCEJUKFWAKJXKVVTJMP9NGUAYVCIHKKYOEQBONDIOIOQ' b'AYYYGJDI9EIOIHFCBJTEUOHGASNXKVFR9QGWWQNQEWLNYLRBDLPBGSSEFGHIKLIYHR' b'KR9YHZTMWSOSMPIJKNKYG9TRXMKIYZLTCQOEXYWDUTRXLMRWYZJVH9LPTIDYINNPZF' b'TPCL9HRXJYQXEAXRXPN9RINJHBYJPHEFQJPDVHKJJKIAQWVNKRBD9WLVIDGKDXXGJF' b'EIZJTYIC9HGYCZH9MQROVDVDVWFULPNSHZCUOUVABLAYGFSOVTABUUWUY9ACVPAJQC' b'OJCXJOML9VJDSIHNVRR9RBNGCUAIVBWNGFDJWVWHJQFXMXYDWKOMFVPNBWJKIBXY9Z' b'LYSHEMWPCDXIVOUKKTHYBHBNI9YYLNGYYDOLUAKUKFRJZA9FYCFNTFTNBCXLMMCZIJ' b'OPJFIWYENQOHXWIAT9JODYDOEHESLGHZIMFGRKAVHZSZYLKTYSCHKQFSDTSHTOWHHQ' b'XGFKAYYCMPYTTJFQPLEYFMLAQBPYMFPYYLR9JQPUBMJUVDYSGUXVVLSRXJJEERZVSY' b'KDPCTOXQNMFWAXQXIGUQRDKUQD9XDTZHRZJGNQCNORSDHWSCZGFLZ9PCCUPSVQCYUT' b'9HGXHZHPNMSBFQIDEHLMWGVJAWUXSSV9VRRIBRBOTSWUAVMWSTBVXNCAPMCQRSTMBF' b'ZOCAYLBVSAXBTDIUYQMDDGT99YMUVTIHLGFUYJCYFJLLX9GBAGAVISAZ9QJSKNHXVX' b'YIVKOARIA9YFDGSMPPWPJFCHAYGDRJJ9GYKYAMFS99OMOTEBOGODKUITJWFOMRQNBS' b'9ZMCFIFJKISKKAJFPGVWWOERDSFCUEBSZPTKYMTTRAFLWYOZYJDNYIQWTBSLANDXQX' b'QZUYCMKQINKOAXGHVMEGWLWTJPKJUPWCXRPZVDMBRDSQLTKLNIDXINWCRYYNSLDT99' b'ORWVIFHY9DFXFXQM9LPRMFOVZJSMJAQAMRPAWNTGEA9VWBQTXJCGYZHPKWLL9VCVDQ' b'PUIKFLGK9WKMMUWQPCJCWDK99QLUYUPJIVPMNL9QZK9QDTGFFKMMAOSEPWKOGLIVYD' b'VKRAAGDFMEULWVCUPUV9ZOTSKORD9CTI9DFYBOJMEDOSOHNARELZNFPGXFLEPBLYQO' b'WMRNZLVDRNBWOTRDMHEURDWGQKCXZEX9UAWBSEKOVDG9CGXOZXMPYCUWQTKLKAYGQN' b'TPNUXAFOJTZRRPSAQ9HGOPFFVMYTEEFKOWZWVMIAYZULGWODOCOYHIJITYBDLQCTXZ' b'TJUIGGWPUHOWVQSDDNVWNUNGUPUPYBZMSZTYXLRYSGVLVMGERUNIPZDDFHLJCDIHVI' b'NETYGWNAY' ), ], ) # You can request a key at any arbitrary index, and the result will # always be consistent (assuming the seed doesn't change). # Note: this can be a slow process, so we'll keep the numbers small # so that tests don't take too long. self.assertListEqual( kg.get_keys(start=13), [ PrivateKey( b'TIUQQXOOYTKCESSBBGKKVJQPSJNVTAGCTAJVRJNXJUIHXWXQHSTACBRP9BJCSQD9CA' b'FNQNNFVEPMYFCPYKXRCNWZHYKYCRSFTAZTPGQXBIMGAZEXABLUJQKEQMKAOUXYNICZ' b'YWDIBLZVJYGUPET9BAV9PTM9TJLHTWLXV9NXMNDNDYHTLIVGZJSEHIBWOYIXABKP9O' b'APPGWTUNLKYDFNWSFDTGRDPTMABYOPCTZM9LQIESPNNVZVYGGCJOHC9NSXMX9ETPGH' b'NQACONLQJIHRNAKUYMLSGTEJUO9MZGEQZPQBTQXKWJDJLFSRBPERWKVHUZXANWMOKW' b'QPPGVFZRVUEOWDMZM9IOZUCOYTQIRZXWDYWAHGOATEW9AMZNSTZRZCQYS9MYREAFMT' b'BIQFUUZKBSWFSNZO9HDBVMNTXX9EAAVTIZSARDP9ZKCUBIRPPGWWXV9PDXLCSDKLWA' b'NVLTXPTEBUAHJJL9LESHCQQWZIYMRNCVYMDQUQFWNAGTCOQBNCLHZMJRNYWIZXDLFF' b'JVA9OTEVHOQZKUDHXLHLZLH9YZR9WBCMFOVRUCDBNVHRGMAYLIEKNDCSWMT9HXU9SD' b'LMLKOBEISWYQCSXYINQNNHSOGTZTZNUXNVHBBOUCFIKIICILJADHTDBJNTPORFBQGC' b'LJKOMVRRBBXZJLRVKBLELUDBQGFHOHRAXTHVUSJMZBO9KKXZZNNKJEN9GNYVBDFZDU' b'QNYTENTFYUSOGJOOJJFGAADFKNKMVWKUWCCTACYZZEYTIKBJP9SCUGTRYLYCBXCIQP' b'LQFKARVWU9VKUGUZYXZXCNSXGNZBXIZDDCSMTOHOCCI9GGXMNAYLVMITJXEN9MKUAP' b'NUBBSZCXNWN9DKMHROJQPNWEGHDPKPVNZLFMHXDUOTBVBGDLWGSODW9ACJJIJW9NXD' b'YOGFIPHPET9AJZXNKYWXKGRSINIODDS9IAT9LAEQWUODEBW9MQIDKZPIINGKUXZGKG' b'NXTPDMUOKEKKM9TQQJCGMOGXITJAVODWPFCJX9UEDVABYLUIUUUCAXUFWZCYWGWL9Z' b'FZP9YSD9RJCMZJQZEQJLAZNJPSPOPJCEPOJTFZOBTWWLHFAALRHISBVFUYAHNBMT9I' b'TDDFKREXTZUXHCOL9VDDVQAXKBKZSLVEREOOSPPZHIWH9AQHVNZEXVCWPQLVUGPVBZ' b'GIRYUMQXPJBGP9AUFCHZHTENRJAKJHLKORDSYZYISTFUPAZNIWUJUGEDFSI9XMZRLB' b'HCZSGOVXINMDJZYSPHBOVTTDCYONQKZ9UJHNJDWR9YT9MJNSFFUQDCLPOXFV9RDQSA' b'TGCCWAAZVVJSCZLZTPJXBFBUDLIBABLKLZFZUHGARDDFX9OZDXBZNSFEWYXNSLCMHF' b'ROWWJHK9IRLEZZEVFWJ9KCEWGVHXTSXWMJSWPH99BU9GVXOIHTFTOGFMGZVCYCPDHZ' b'OUUB9DEPJLJCFFKEGMBYLLSXEDGWNPMWCSQOAMDITYDHDPTDQRKMKPAJIRLMSAN9OW' b'DOYZQHJJSVVOSYIYCMYVXEAOOFNNRWKJWNYDEZOLZ9MMMSUIPWIYKWZVEFKSTARVVO' b'UONUBAXJEFKJRNLDBQTRYAWK9NZYFRBCYVPZOACJPZRSAFYQPA9KJGZMAKKPAAUYC9' b'NQGEPWKUVCHPSUNQDDMOIMKCLLBSACBXDVXUDWYOUCVOVZCNNRAURQFUPQGNB9CAXS' b'CPXPFPJENYWFBOJFFWZAUUTYRRQQMRBWUQOMJYPMONU9EYWJSGQQZHHZSHF9XQPIAD' b'AUHDKOIWJJGRLIPZQJ9LKPJRBHRTKUBWQHAOKZKT9GPDTSRXDRESZXMCMRNGUJOJPC' b'CSFMMPITLHU9FEBFMYLCBPCFEFXAZXYZRZFZ9KIQINS9GSN9BZLMMODAPNORGPMYNT' b'XXASTKLVIGMUGJWRSLMLIDQYHXNTBDZR9EQDBDUZVSIALOEHBBUS9QILWCBIYYUSHZ' b'RNICAYREZANRQWGGBJZCMSB9TRXXCUSPIIEJLKLEOMVCYHRNI9KEHPXAIUQIFUNSVK' b'JMEUMLBXSIHQUFK9QLA9UTBJZQGAQHWXVHWXYSEPCDO9M99HYRPAXFXHQENZHNBWXD' b'VN9KOMUINNCIMWKWI9QITAORLVJL9HRYSDNIQNAAQOQPSQNCQCGYAPIYSNOPHECZJY' b'VTVVMKOUZ' ), ], ) def test_get_keys_long_seed(self): """ Generating a PrivateKey from a seed longer than 1 hash. This catches a regression caused by :py:meth:`iota.crypto.pycurl.Curl.squeeze` processing the wrong number of trits. Note that seeds longer than 1 hash are not more secure. References: - https://forum.iota.org/t/why-arent-seeds-longer-than-81-trytes-more-secure/1278/4 """ with warnings.catch_warnings(record=True) as catched_warnings: # Cause seed related warnings to be triggered warnings.simplefilter("always", category=SeedWarning) kg = KeyGenerator( b'TESTSEED9DONTUSEINPRODUCTION99999ZTRFNBTRBSDIHWKOWCFBOQYQTENWLTG9S' b'IGVTKTOFGZAIMWHNQWWENEFBAYZXBYWK9QBIWKTMO9MFZIEQVJULQILER9GRDCBLEY' b'OPLCYJALVJESQMIEZOVOPYYAOLJMIUCGAJLIUFKHTIHZSEOYYLTPHKSURQSWPQEESV' b'99QM9DUSKSMLSCCDYMDAJIAPGJIHWBROISBLAA9GZFGPPRPHSTVNJMPUWGLTZEZEGQ' b'HIHMCRZILISRFGVOJMXOYRALR9ZOUAMQXGW9XPFID', ) # check attributes of warning self.assertEqual(len(catched_warnings), 1) self.assertIs(catched_warnings[-1].category, SeedWarning) self.assertIn("inappropriate length", str(catched_warnings[-1].message)) self.assertListEqual( kg.get_keys(start=0), [ PrivateKey( b'BXBGJNNW9NXZYRHFVVXVVSRD9ECGQSXNWPLBDLMDYXWJJLVELFZZESFJYH9FCSTINR' b'CCJODSWMHFWVGB9HYNHATWHWOJKNVWEVXGZBSXSYVKMEDEZVCME9MQOCFFGVWCZHDT' b'9DTPOZYBEQQWCDVMJQC9BCTBJWIGW9B9PUHCBWOHN9OHYRMSBFCAPGOEC9GYIMRPNC' b'VCZYZLEFFJBGEERRKQHVNFMPPSWZAAXOVJXEEIZI9DNNDDONRNSHUUCNYWIIXIRWLR' b'AV9CYCNHCKSVWS9ERXCUQZLUAWEAU9IUGEQUCXDRTVWGPDRSOCSFKRZSSTKXXYWBBU' b'JMGIIQUWKXGHJBEUONHMESPAHCUGRDPDDRPQJYWPIFVYNNTXZAWBWQPOOOLAIVI9HY' b'UYIGXCUXDWTCL9YXUDXCEJRVBVXTMAFXHBXRKFXBYHMPZPKSVUKNSJXSCPTXTYITAT' b'CLUWNTGMBTMCCBOCINJSXSYDLJAXCUWQKGLXVQWNIXKHN9KZOHFLFURSHOXSTJCUUH' b'IOBEYWMWMNMFHEGKEINGIJFJOHAZAYQPIULXGNYZMTLJVUPWW9MAAWCUF9QAYATOPZ' b'MMKZRSVBWRSAWAOYRF9BVSWMNKAFEUHOTLFARLV9NDGLCXUIQUCQSXFHCAS9EWBYNO' b'VJ9HEKOACWDDQMPPIOE9UDLIGZJTBLWLWOMPAOCFDFJPKVGVEHEUKXFULTKRGMGKMZ' b'T9AFWJ9VDFJIBSODRRBFFXIMONSSCLPKD9JZFSCDBBQIMBUYMMVCZVRCZJBSCIBJMY' b'VNC99VUAWYBXDTPVBXJRZVOQQJFKHCGLTZPYSWMNBQUYHOXZXSYPMWBS9HCLPQGRVS' b'9TNGKSGRWHEM99YA9BYUBJKJFGSN9XKQXHOJHKEIVSGYCEYARXUVJKVYHRJHMGDNVS' b'NAXQDXYTYAME9BXJFZ9HEPVSYMSDAPIBJKPJFOAO9XQYWNFCFAMNOMOPIAHWOFSSFT' b'VQVETQUHLCNAKGXICWCNBHMMJARWUXBLWFOLDRDYDOZAMDKMMXLGNSLNKHHMVKZTQO' b'TJSZWAP9LUWVHGPUKWNUJANUZQGLMKDUCVSSBQMZCGZURESHMUWUEEPHSVRETSDCOW' b'VNKMRPUTLRQCRHKCZBDDFQBELWMVCMIUELCJMKTKHMXSKSQNCAQOIJLSGWVOLWQCLG' b'QBQKKMHRNJKJNWZMCNDNXAFXWDBSIDOMSJFUTQKAEZJNWYXPZDVXQQFDYPJXWCPCMW' b'TWBQVSXCXTUFKWQKTFEATWSDFI9ETMWQOC9PY9VOMWDFBCLBRDTDSX9ICUQDEVCFLA' b'UDNFCFJCWIGNGLAILHKFBG9NK9YXCIBZQGODDGZZRCMVIR9EXOYCCODKWASLVLBDQY' b'VQYRBNGVSOPKZEGLWHTPAGPQQHGHVCTVDVBAWLDHUPZISMSDVJOAEUGOHUOOMBQEB9' b'TSRMSWIAIGGYOFQAVHOKMAIDGFHZHQNBDILMKIFQPIWZRMKVOVSYDXTMIFMHGCZJZQ' b'9CCYINX9DDGZHYMXXFEZXDTLOEOSNVULRD9VNBLTGDAMQSQKOGDLLDYDNFEVMMWYXZ' b'IRJP9LMNAOEBZ9FXKLACMUPSIABFBDNCJTVCCDZGFQU9OKDUEDPCPCTHKAUZ9ZZXSG' b'XHLYMFOVZXVHXFVJNMRBJJTSJPPBLZWMSOWYTLXXXYEVBVQJHRCLROVNPSVFLPRIIG' b'GDGSPIHOEYNQELOYHXDRHXOWOMP9OPYCL9QUUOYBQQMDULTMZMOHZAELOUDVBVRGLD' b'IN9DVYJPKYWILMWAYCLOPCXVBPPIQDILYPRFJZLFFTWFAESFTPBRRLJPATSIWKXJFO' b'HV9BMDLLBRLCBABP9UZPFADGUKWBJZMHUN9URWOALIZIOLFTVNTNTHBHYMWJUU9MOB' b'9NAKVBME9TAKCTLCSOEOBLRQLWHUEBTGJKYXBRLOCUKEVLGWSGNPHAYYHDPISOMDOL' b'ALS9VODLNXXOBBCNDNHSBEVNRMLD9JPYUVOZVMORYRQJWXYBWSIQDYXOSAZABKYINW' b'ZZJTONDTMMUQ9OLCUKBVCJDRPFVGIBJKAWEUVSBTIJWEVQP9LIHLKGHJYLBYOMIDKS' b'BETUZYEFU9SNPHQDWUXKX9VRTTMWHZUFQMBRWQDVFZXVGBFEJRQVTFFTQBTIGVHBDZ' b'9JPZYQEPW' ), ], ) def test_get_keys_multiple(self): """ Generating multiple keys in one go. """ kg = KeyGenerator( seed = b'TESTSEED9DONTUSEINPRODUCTION99999TPXGCGPRTMI9QQNCW9PKWTAAOPYHU', ) self.assertListEqual( kg.get_keys(start=1, count=2), [ PrivateKey( b'IQOTORFDCOZORDLUUQAXXNFCILODCMVOOEJEGUCZTSFMQONYDALBCAD9YETATQRRRF' b'AHUAHU9VARQZPFWVLRUPXXPGDTQJDVJBMUMOBXFMEKFNGOIKUMZBIGNJGLWCPPCHHX' b'AJAI9RMRFJICRXVTIYLQWGTNRMOIE9VMHYAJLQPPEKPS9RZZJSPTDRRHRUOYOWMFGN' b'OVMJDPDJHRGYYWPTIYCVNITYVMSHGC9NLAJWCZVEHJQIXDZBRPSZHC9JNTPTSJZAW9' b'9CIZLHIIDCONEDPUWBXVAQHRTICUQO9UQLFPLJUTIHYMIBRUZNCVSCZT9TNZQHCUEM' b'TTOUWELUXJCMFRSZVOPBNZR9AGEAKUIXGOZQDGJKPOEYKDZJHDJ9RUSMUGPFIEQGAH' b'FHMDQLDI9HHWPBJFERFQAOIDPNGVQTARVJH9TJRKQJWRECXIUITPWNQSMDPJJEOPIG' b'YJZTURMZDYFMZQJWJVEVWFZAHAGWSGLNUIEDFRRSXSA9ZGNYKXGCLKRSAUIUKYZTGC' b'B9RLGBPR9MIDZMLJHVGV9UIOZDQUEJEGXBSAOFZ9XGPHQNLNUEWOKHDSOWXDBIGQKA' b'JMXQKJZTFHK9SHX9CVNINETMGCKJNI9PNSF9CZKZARPR9CR9LBWZOZXDATXDXYNFEJ' b'PPOE9VGUQFAFZKNJLAETHOUKAXCRFUKB9FG9IWCEPWIUHZPSAO9TRNPSQUDOZKSHER' b'YZYYVVAWWDRUDAEJLHNCRFAMBSRZX9ISXVGXPWXC9CHNUKGHLYZHIV9HFXWOXBFZPL' b'XOMMSK9DSWPBJMKTLBMTVWNTWDUXSI9BHPBCSQJX9ZLFGZWIEQPFHYVEXSIEWIBEGI' b'EDXP9ZBUQLJAON99KRVLHLBTLSIAENF9WLI9LBIXIUHLI9ESVBSGSKLXMFUOGJTJZD' b'QLMZTFDDJDBHJJLKBQULQSSNIYIQG9TAFZXPZOUJ9MUEZLQAT9SKI9VPRZ9LBWUYYP' b'SZRLMLDITJOJEULG9BLDKKEKPXJKUIDORYDVNZFABCLINHNHZEZRYRTYCBNMMOZSNC' b'FYADBORKPLLDWW9LHUHHLFDRP9VTDLUO9ENPHJXAWJRQKVSUGFYDGWVGAPUFGYTQZF' b'WSZTILZDHKHWGCKGZXPZFW9OMMGCQOXHPDIQDSZCVKBFZKEBUNYEBUIZSZWXMCSXPG' b'FYFDXFDZBQCNISTV9M9NLICOLTZQJELMBOOHYZ9WC9UJYMIGDSOVQXFFWBVMIXJWBM' b'KQQBCXGREPAQIWPJXHIKWYYO9LVDOSOFJXB9FFDZRWJACEPVSUN9YNMJJQTYIIENLB' b'IGIRJKBWSWJFRHQRUCCDZY99BXGJIQOHMEPPNHPVQFFMYUZXWRCBOOGKAADLXZIEEU' b'NYKQURKPAIBBYNFHJEOWX9SGRKSYINGKNORUGNKQMZUDBUJGWHALUYXII9XNKHVPYK' b'YMDHDZPWWWKYZESNZDXMDFHYJCXZMGQVTEIVOQTHVMDVUMQMMRSVWLFDGYEOJJX9DB' b'CHSGMOWHZOLDNBSWCUJR9PGOIRRTSFDVZWTELIICRQFLPNFZMMQVLYNWKAMWDRTYLZ' b'9OEJGIVLQQTFNOWKUQFDCWVATINKDZROEENXEAMOOLKNOCLGANKKNZKUKZGWSI9JW9' b'GUOARMFPHJPLSEXWXQATKBOTWMNVITSX9MWBYOGIIGMLJQXDYEWTJFPSUSDMMSXCOE' b'ULGOHEJYCSAOOTHDPTOYXTORYOLOVFMHYNPVHYQTGTIMSRDEEFB9VSBADXDNDQYRAA' b'SWQZGPCMPXH9YRRWOOKOFGIUOLNNEVCZOTXZPBQNPPLTCOXLMCXWSHNYYJEM9BTXNJ' b'YQM9DDLSTLZUBLAUOWDWKL9OSTWVJZPIDTOBQZKASTTUNWU9JECOZQXM9NXUOGKYKI' b'OVSBDDPKIQUBBCCBVD9AXCAEMSND9DDYOESN9SBGIVTYGOTLVUCIDRFLXOZWIYBSWH' b'XPUSURKBXNBFYHCWNINLPYFIYPIPPVCNSVTQAQ9EGQLICGTHVCLGWHJVL9NBRNJTWN' b'LCMXXQCBDVFLHONYRUBXYYAV9FSSLWBUIZCJMTJQCODHDZUAGZNFNCZPQRW9PRKDZR' b'V9IRFJAEDODDCNHZFBJPI9MBYAVNENYZOHOZCQBIVDSBKYQOBRZZYPGYSZCCGMAEXY' b'ZSGENLRSA' ), PrivateKey( b'RRVJULJGXMDUR9DEGFRBXWHHTSGMTZZYDQRWJKHV99XAWYSUEFLXUACXKSKALMYLWB' b'AMWVRINPNDGXGODEFFGYJYJELKVCJYPFXOIAI9SUVCRLUGXCMDTEHHJUWGYLDSVQSY' b'XTEVSAXHPLZQHDDUPRAHRWGNCTDIFDQVLWVZLNWZYAEXWVIPKUDZSPLYCFMCDKDSLH' b'CTZDNQYTRLGMFGR9EJISUOAWLEW9FWFBYCFBYGX9DBLUXOAIXEDTGLSXAH9NFEPVNN' b'NTUXSCSFYKXJDIMYVGU9BKLAIHYPYKE9KL9ALNGNEMUZEAQCICHTWDVLZSZDRWPXFW' b'VGQTZISZCSSKQYZBLUSCLMALMBHAQUEHPIFTXZSM9IFEEHKRTJVCXRGCL9EOKEHDKN' b'GFNKQNBECQ9SBFCAZQHFPM9AUURHVLDGRCHQYHTSE9SV99ECSZPUFSDWPXGVQZYAYA' b'WPJRKOOD9WGXLAQFGZLXUKEDSWRAPVFIBPPIPYHAKVJWTCD9YFDFZYHUNNEL9LILSG' b'WKPBRP9MXK9HUPSKECPWLGMFFCCUVWRXNFYSIZAZOEHBLMTE9EDOCSHHDGYEYEWYCC' b'UJQMXLLUSRXUVJKVAHPO9CAQEMCSIYQMSUKBIOGUDDJJIOUMB9TGZDDDXSBK9KIFGF' b'9FYOXWHRWI99M9NYISSTMINBJDVCFEZMGNNW9EYWPYKWQWJTFVPQVP9BWJ9VTTZNZI' b'MQBBDFAICJESMEGIJWEHHWBPLCPZRIDZJAZTEHRI9ELFMRASOAMMYUYWYNF9URSBWR' b'YCUKWYXTGXYMW9VCPAGPNEDQCZWMWOCLUKQPTIQKXPYMQBFWMGLKYHXPFAIGYDGKSI' b'PZ9EHSHWBHO9M9KDQDFOVKXOXDNSXHJMXGGMGTQPVGRYPHCDHMYWSGXQJKAOZVNFEV' b'ZNDNRAAAUPQHECGNSUHMDNBTBHXVEIOCTJH9CYIPJSLPWFRAKONPLALPXDOEOQTIZF' b'TPUAM9GLUEPURGKDJAEMTKUJFXFAYZVVVAFILXQLWXVFUHHLCVOKIJCCII9ONRWOZE' b'HAEGRHMTE9LPBNJPDJD9K9GNTYANA9SNHNWSAHHVCMNCWOQSJICTGCVEWOBHGTYZJX' b'WOLCABCFJTSXQUMGRZSGNELULCLEUYKUKBAEEXZXPLISBBCHJRDKAOBPL9T9RRZTLS' b'FZWOXYBRWFDWSNDRNPU9KETYVYBLYPGJUPRFKJ9WKOVGYBQVSSMXQCDRSCWSYPZBLP' b'NSG9JMVTEPWHTYOSQPTVRHVDAFVV9L9LEGWEGJKOZWDSPNL9CHGIIVMFGVOIFEZSXY' b'GCMWOBZLTHLBQ9KZQHQXSUGGDKGIRNFBMPRYWSQXFIMUGXEBSXVGRLX9XHLXEMBKET' b'MIVQIQWAIFAFVJPYVDUNTHLDCULGSXAMJPQKATIFDUDXNGJZNDFMLBBDJBFNGGGMVS' b'YPDSQCGZUZDQHLUZEWWTTHYITIRVSNPQBAEEALYTOXZTZMSJAGZXUWNWJ9UTMIDYBD' b'NDBX9RBTHURTCYOHWGGNAPMDDHYNGVLTKX99QKBJECELQSALNYOPFBLFLKXVTTACFB' b'NPFRDVHZRX9FPOUQMGFZZSKSTLGOTFUGLEX9GBAHXUPZUBSXBKQU9RLZHTIWHIVRMQ' b'LSKLSIYQIQSFEGLZQSLSCSUMDC9ERMHQCUEJCJD9SJDLNCHKEVCVERUMLPZQPLDDRE' b'XCRTCVJXPFFJZQPMKYXYQZFNU99SYX9DKSLLUTVNOMNXWFOGCHFZUVXAFDOUXEBZND' b'OOWUKMWNJAPIPLB9WUNJIPLMX9PTGLGDWLHSVRVUIJGNXEXKITXU9JDFSLWFZQUXRD' b'QXEILJVAJZIUSHYXZYJNEEAQEIZFSXKOKLQT9OUOZEEFFNEJUPCUTSYBLEXCEEOJAT' b'LTNKAYIYIQRUSWQNJELMNRR9GOXJZCLBOALYIIHZIWQLP9MQVJNRHHJIJQZRSAHRPH' b'ONWCGMZXGC9BGMBTYKSUXIMJIHXLGVQLWGCMGTOFADWBXNVUEJNAVBGIMVVZJTC9OS' b'PPBYQBPPHGP9KMTKHSPAVRWDQLPRRWAYDZWEHMPWDSIFVJSBCLDZNGWSZZD99LPTJC' b'BUBSKAWUTBXBMXEJWCLFNTQ9MYVTLOTNRFZSVVMQWOPZDSCRVN9WNVIVIXLBBXS9SQ' b'JMZHGZVHX' ), ], ) def test_get_keys_error_start_too_small(self): """ Providing a negative ``start`` value to ``get_keys``. :py:class:`KeyGenerator` can potentially generate an infinite number of keys, so there is no "end" to offset against. """ kg = KeyGenerator(seed=b'') with self.assertRaises(ValueError): kg.get_keys(start=-1) def test_get_keys_error_count_too_small(self): """ Providing a ``count`` value less than 1 to ``get_keys``. :py:class:`KeyGenerator` can potentially generate an infinite number of keys, so there is no "end" to offset against. """ kg = KeyGenerator(seed=b'') with self.assertRaises(ValueError): kg.get_keys(start=42, count=0) def test_get_keys_error_step_zero(self): """ Providing a ``step`` value of 0 to ``get_keys``. """ kg = KeyGenerator(seed=b'') with self.assertRaises(ValueError): kg.get_keys(start=42, step=0) def test_get_keys_step_negative(self): """ Providing a negative ``step`` value to ``get_keys``. This is probably a weird use case, but what the heck. """ kg = KeyGenerator( seed = b'TESTSEED9DONTUSEINPRODUCTION99999JKOJGZVGHCUXXGFZEMMGDSGWDCKJX', ) self.assertListEqual( kg.get_keys(start=1, count=2, step=-1), # This is the same as ``kg.get_keys(start=0, count=2)``, except # the order is reversed. [ PrivateKey( b'FZHJJLLYUDCKQDIYNIZFTCGRT9YLLDNWGRZSDXILJPGF9OVZLEOSTZVPVSSMXDJJZP' b'T9BGACTLDAXVRBC9RNLK9KSYZK9WRNGMIQRYWSIJUSQNYUE9LLHRGVGHABQZDHTJAI' b'GJCHXQTUXYTCTN9GQSCLTRUOIHOAQZRPV9BWUNGNXAGCPX9DVBJFAQZCKREACZWRRH' b'UPQRJARSUFPHHYZGHDDPNMLIIWOPRVYQFH9XTBVVLOMLDWTNSLSNRTSLMHVQCFXTXF' b'YOVYOPZ9SCRUVVNYXKFELIDGUXFNGLKIHGKPV9FKFJAYKWUDEKNCMDIKXXTARRVJZE' b'VUJTZGJVIYRQKHCZFXZZCLBNLBFZRNBMWNU9O99OETP9UFCJLAMYXJTRXUDIZGXJBY' b'TUDURKYYZBYOPMCA9OTZDH9KBRFWNXXJMTZJXQUWTSGWGDMDTVGNRTEOUYNEKZIUKH' b'RKPBNYPPOTWMLWXJROQUSWYDMAVGJFWJEWRFNSFSIFEGWWEBVRRVEZZNXQ9YBXFPPY' b'JZNDEUMFBIXZZIB9UMYFDADLQKSYXWUWXBBRJAYLREVVMKHNQCTEUCIGTZSHXCRGYK' b'XVCZTKGORGFVFCJXVDKZJOPQLUQMANRKBHKFOVAIFLCREDUOAXB9D9PPFDOYPAIPMB' b'VXMQTJZDMKDKEMIJC9FFNHUQQYGBKJU9PVYRMVZMYYMGSKTPFDFCACEDBFIYDXEDKY' b'OKCASQFEOSHGXTFIRXIDWS9WHBER9YP9MGGBYWQW9FTDCLNYYF9KTSUGYQYYAKTFAZ' b'KSONKKWLVQIQGSSKBWCEVTFNGJRFGLUNBYAHXRVF9SIWM9JHHHVMOTSFWMGMJJLYEB' b'QJUEUNSNVDEDATVFQUYRTJBQDQPEPOWWXLXMNYBU9PFNALVPTSXLM9OPYKGQGSFXBN' b'SXJIEVQ9XUQOTDGFUGYIRPZMJIUORIPMIKCFPXLJLWBVRDUWSJY9JVZHYDPTLTSABP' b'QVZ9MTR9AACCKIM9VJGXPKUANNTCTAGCWMN9QPW9PCZKWC9SSEJFVAFIDBMWU9C9ZI' b'QOFQZQZERTIRYDYRRXOZTBBPKTBPKGBFZ9FCHSQURZSUHFQK9YKTFWYWHQXYRPACPS' b'QT9ZSDVTVEC9AIXSDOZUEDDOGB99RPABOUWPPSIQXVLPLXOZG9SNIPGGTFQDSNNYBR' b'JDOEURXDZX9DWQDBMVUZDWGQMVXBNATQPJWGWYIZTZAKPHDJAGK9VL9FWOBECFOIBN' b'URTZUQXYUJEXEWUVCLNDIVMLQBNXVAMGIUWKJYJZODLJWHGJFSVGAPOPUHIVLYDNLK' b'NBLPVCUQQY9DHI99MBPLEHNBWTSVOQGKBNZRRFHCDJRC9FFOPHKVUAIGWPU9X9RJPW' b'9XCICKYARHPXMNSX9QGWJYG9VKXYOIDEAZHMIYBNRDUJBSL9BOZXCYWGIQU9AOPHRC' b'CYPR9XLBTKMND9YTDVNCYXBDHPRZAM9ZYCQIV9KLVEVFSZEWWQIJAJIHCEHCRDPGMF' b'RSVYFJTDTULEXTFMM9HOAEHBSOJWLAUH9SWLQZRTCKLWRVLSMXDGPZDELJSSBFKNTI' b'RPVKVHPZKOXYWLBJJTRLOLLFIXVCYXIAFOBABDWLPKRPMOEJULMQDZQGHPXNXWDKJA' b'KAJHAAA9YQCFMQGDJHOYGCAXMXYFJMUKYJGMEDVGOVQQZBEYTFCZDNSXCXGMCWSWMN' b'TSTEQGICEDWUQMFRQTNAFPQMXQLJ9LLRQTDJFSWACVRCTYTMXLWBUFLXYHCXEZQTI9' b'RHKLJBXYWSNJOREXBWEWSSPXWASQYIFJLTXIKE9WHURZZORBUI9EFLNHNRAHMTDXMU' b'CV9SPLMTDBVGZQ9NRNQANLGCIVHQO9FIEIOTFQUVUXXZTVQ9JFIJMVHHSUWOW9UPVA' b'OITNCUDJRXKYWNPTDRNOPAYEENJATXNRYPGVAVVAXLOBIDZNZXWREYRPWZDCUVCNNU' b'SKYMFJRNXWSBOKWEBDBRFMHYKLCTVSOMIXAADVSGWNDHB9LEIXOERNWDRFYUAA9FLZ' b'RLJUDOEUAKULOD9OFGSPDPMAIGLJQRNDKGRUXUJZPXDPFWGCGEZIEMLVCQUWXMQDYN' b'NYBHAHAQLISVSEXWETWMDTQKCL9LABMHQDOGKJKKEOBCJRCKGVIYYILOZLWIMQYYWV' b'DCBXIIVVD' ), PrivateKey( b'KHYPJPEEDAHDQDEXMZIBFQNFXYQHTLMZFZZMYMS9PNSTBNMDLNJ9GTKBEXMWULOBJN' b'LEWOQZESAYA9JGZARRGRTYKEEDALDRH9BFHSPFSFQGUAMBLNWKOZYJHRGAAJYWUDOA' b'MRRPMEVTOUXDGGZIBMBMZSCTBOWTFWBOFGSEKG9SDZLMLW9YCJIWZIPUVIYMMAAPJU' b'P9XRCHZIIQVF9UUAGPZI9RALRLHLLNDURDVAEVPKGTFQWVTEFHQAWTKTQTVECN9IBX' b'DHTDGNDSEIUOXRNFZLNYEDIPKHSVUNPQVIDHSLVYKLMDVVRRTIJPATKZZKODHSQFCO' b'GOJDQE9PZJMEDSKYI9SAUNINEDCYFMXSEADCPINSSTNXM9WU9XASQYHUQWVKAXYQZV' b'MCRCRSWBCBTZTGZDKRWLMPKXHYUH9IQSRULRNMAGCWG9YSOIKVHHH9MNAVLXJLWIOY' b'DOZEQRSM99ZDXBGELXLGZZXBXHUIIGGQPUFYQBQUYMH9RWBJCZJTM9RSVOWDDYLRHS' b'LRUGSOMFUNROBLEPGIMCYYNLALKDMPRWUARWZYC9IDJRENFKVMTLZQAOZDMOXNZ9YR' b'CTIZYGMLMKJZMSDGHUFDZVFEVLRXLPMUABUYQWECOPJBZITECXJFUXEKFDMQTE9RAK' b'MAJTFFJCSUMTVCXXUFGYGQXNFUG9EGZPVHXV9PEABWKXXXIHBDNETVOVOVPIXAI9UV' b'UNAGRSC9EKXILF9KLZPDCZOCLKFZQXXQLENUTVYSORFDWYUIDMRTYLT9TOYPKKIXQL' b'YQVDIRILI9SFKXAOPXRHHZBS9OOOMSCAYIYDRNUWZFQPIR9WZ99X9OD9ZJCVDIEYUJ' b'QTHRARYNQXQGIGZGQOQPZYUYPFNYMIXCDVLDZFBBBUTFTFJGFAHYBCFPCXGJITGCFU' b'QAOSAFHILVAZHT9LIDZVKTVTHVUHHMUJFTJHHMFZNEPNPWYZCRJSGSIIQSXUDBOHO9' b'EMAORRBNKVYLNMPZKGEBQRQMWTUAN9HLKKTIWFXG9VMOT9TGZEFRTODIILAW9FDLKF' b'WAAKT9FTMLMKTGOQFIDEMMPIPHXJPOOVKNBUXEZKHISSXWGWTQDGJWLTNIFILMAMRY' b'YNYQQBAADIODPB9EM9EGFGZXKGZXUVXYKGYFJZXIAOYODYBCIXOEWPGCGRZPFKQVWX' b'WNSSCOPBXQCUTI9H9MJXJQVKBGCZEG9UZNGAFQKEUSPXWSQSKAXRANIIUCAVSCDJYM' b'OEAPRIYJQTQFMW9ZEWRIANKGHDAPCPPOPECAGGZSQXTGNQBNBFCHK9VLLCNURNVVN9' b'ITLGSTZKCKZLUKQMU9NORQAGARMSSCWJWOBSBBCYVLGOJQQCGSXTNNAFWPATOSMHKW' b'KAZVOGGHOZVRPLLTKCSSUCNPALFFPJGP9MSOSSVMCQ9WRZPEEWUEY9IAGRTKYGAMWV' b'BGWFQXBCFMFOWRZFOASBVDFNFNICAZZJJOFJHNINGQIWPVHCTXSYJIRCMDI9TKRVKQ' b'KOHWOFVLPCIDPJMYJIBWBWCWNRGUDD9VAQJQFKSGFTWMYQTAUXNYCXQBRDZJFITLLX' b'JHBYA9LOZTFIYMIFVEVAUROMZVTDTGUYMCXXNKDSJKTULMIGDKITLHCYYAHGGFHBUI' b'ZFNQ9JFMMXFOJUCTLJKLOJZLSSTCJSKDJHGMBJMHMGZEKAAEYY9NUB9PXLULFSAKSJ' b'K9UBHSAEQVSYZ9SUQPJQLJQECEURDUFNERRYVWAIUZLNFFPXOWEIOPQDVOKMACRLJZ' b'NTY9CRCTNPLLYISOCDPYXVYQJNCWAZPLQBNEATUBEPLHPPG9RYTXLGFSEVRUOVOBUY' b'CYUEOXSKPSIKBGWCQQCJZRWTXQOWKHPFFZEDXKT9ZONNWYVEHLO9YBPDUQIWPBQXYS' b'QHWIUSLCWZTMKUCYGIGPEOVZSNXOIDCOZWEAEPGCIIDNUTIHVKDWVXXI9LKCWKNKTW' b'LARZPOBKWKKZGZUWAIODFDDMWNQQEQ9XCTXYWVXLSQPVWYKWGBKOWSXGTFTCVDZUYS' b'EGXOEEZGTRRAFDLDOI9IIKLJMYDYPNYZFGMVHNAZJBGWMAPWVV9ZBGTANAPDYCKUSD' b'OJOYOFRUROMAXAOA9JUTX9TRQZ9TBTDEMAWQDV9UXAVB9WLKOS9VLEEGRYU9FLWUSA' b'DVNPBIFLB' ) ], ) def test_iterations(self): """ Using more iterations to generate longer, more secure keys. """ kg = KeyGenerator( # Using the same seed as the previous test, just to make sure the # key generator doesn't cheat. seed = b'TESTSEED9DONTUSEINPRODUCTION99999TPXGCGPRTMI9QQNCW9PKWTAAOPYHU', ) self.assertListEqual( kg.get_keys(start=1, iterations=2), [ # 2 iterations = key is twice as long! PrivateKey( b'IQOTORFDCOZORDLUUQAXXNFCILODCMVOOEJEGUCZTSFMQONYDALBCAD9YETATQRRRF' b'AHUAHU9VARQZPFWVLRUPXXPGDTQJDVJBMUMOBXFMEKFNGOIKUMZBIGNJGLWCPPCHHX' b'AJAI9RMRFJICRXVTIYLQWGTNRMOIE9VMHYAJLQPPEKPS9RZZJSPTDRRHRUOYOWMFGN' b'OVMJDPDJHRGYYWPTIYCVNITYVMSHGC9NLAJWCZVEHJQIXDZBRPSZHC9JNTPTSJZAW9' b'9CIZLHIIDCONEDPUWBXVAQHRTICUQO9UQLFPLJUTIHYMIBRUZNCVSCZT9TNZQHCUEM' b'TTOUWELUXJCMFRSZVOPBNZR9AGEAKUIXGOZQDGJKPOEYKDZJHDJ9RUSMUGPFIEQGAH' b'FHMDQLDI9HHWPBJFERFQAOIDPNGVQTARVJH9TJRKQJWRECXIUITPWNQSMDPJJEOPIG' b'YJZTURMZDYFMZQJWJVEVWFZAHAGWSGLNUIEDFRRSXSA9ZGNYKXGCLKRSAUIUKYZTGC' b'B9RLGBPR9MIDZMLJHVGV9UIOZDQUEJEGXBSAOFZ9XGPHQNLNUEWOKHDSOWXDBIGQKA' b'JMXQKJZTFHK9SHX9CVNINETMGCKJNI9PNSF9CZKZARPR9CR9LBWZOZXDATXDXYNFEJ' b'PPOE9VGUQFAFZKNJLAETHOUKAXCRFUKB9FG9IWCEPWIUHZPSAO9TRNPSQUDOZKSHER' b'YZYYVVAWWDRUDAEJLHNCRFAMBSRZX9ISXVGXPWXC9CHNUKGHLYZHIV9HFXWOXBFZPL' b'XOMMSK9DSWPBJMKTLBMTVWNTWDUXSI9BHPBCSQJX9ZLFGZWIEQPFHYVEXSIEWIBEGI' b'EDXP9ZBUQLJAON99KRVLHLBTLSIAENF9WLI9LBIXIUHLI9ESVBSGSKLXMFUOGJTJZD' b'QLMZTFDDJDBHJJLKBQULQSSNIYIQG9TAFZXPZOUJ9MUEZLQAT9SKI9VPRZ9LBWUYYP' b'SZRLMLDITJOJEULG9BLDKKEKPXJKUIDORYDVNZFABCLINHNHZEZRYRTYCBNMMOZSNC' b'FYADBORKPLLDWW9LHUHHLFDRP9VTDLUO9ENPHJXAWJRQKVSUGFYDGWVGAPUFGYTQZF' b'WSZTILZDHKHWGCKGZXPZFW9OMMGCQOXHPDIQDSZCVKBFZKEBUNYEBUIZSZWXMCSXPG' b'FYFDXFDZBQCNISTV9M9NLICOLTZQJELMBOOHYZ9WC9UJYMIGDSOVQXFFWBVMIXJWBM' b'KQQBCXGREPAQIWPJXHIKWYYO9LVDOSOFJXB9FFDZRWJACEPVSUN9YNMJJQTYIIENLB' b'IGIRJKBWSWJFRHQRUCCDZY99BXGJIQOHMEPPNHPVQFFMYUZXWRCBOOGKAADLXZIEEU' b'NYKQURKPAIBBYNFHJEOWX9SGRKSYINGKNORUGNKQMZUDBUJGWHALUYXII9XNKHVPYK' b'YMDHDZPWWWKYZESNZDXMDFHYJCXZMGQVTEIVOQTHVMDVUMQMMRSVWLFDGYEOJJX9DB' b'CHSGMOWHZOLDNBSWCUJR9PGOIRRTSFDVZWTELIICRQFLPNFZMMQVLYNWKAMWDRTYLZ' b'9OEJGIVLQQTFNOWKUQFDCWVATINKDZROEENXEAMOOLKNOCLGANKKNZKUKZGWSI9JW9' b'GUOARMFPHJPLSEXWXQATKBOTWMNVITSX9MWBYOGIIGMLJQXDYEWTJFPSUSDMMSXCOE' b'ULGOHEJYCSAOOTHDPTOYXTORYOLOVFMHYNPVHYQTGTIMSRDEEFB9VSBADXDNDQYRAA' b'SWQZGPCMPXH9YRRWOOKOFGIUOLNNEVCZOTXZPBQNPPLTCOXLMCXWSHNYYJEM9BTXNJ' b'YQM9DDLSTLZUBLAUOWDWKL9OSTWVJZPIDTOBQZKASTTUNWU9JECOZQXM9NXUOGKYKI' b'OVSBDDPKIQUBBCCBVD9AXCAEMSND9DDYOESN9SBGIVTYGOTLVUCIDRFLXOZWIYBSWH' b'XPUSURKBXNBFYHCWNINLPYFIYPIPPVCNSVTQAQ9EGQLICGTHVCLGWHJVL9NBRNJTWN' b'LCMXXQCBDVFLHONYRUBXYYAV9FSSLWBUIZCJMTJQCODHDZUAGZNFNCZPQRW9PRKDZR' b'V9IRFJAEDODDCNHZFBJPI9MBYAVNENYZOHOZCQBIVDSBKYQOBRZZYPGYSZCCGMAEXY' b'ZSGENLRSAHJLPRRZXCTAXMYUCDVEFB9J9FMTXZSVXMNBTWIGQZZFZZQJAKWMQGODXV' b'HJWNQVMPLTJ9OXYKPWPVVTCCCHSKSDZKACZXZHOUHHRSQHVBZXMOTPHXZLJSVXX9OG' b'WYJNOEZAWKVSAVSEOSBJKCBNMG9EHFEBKDWLFLXNVWUCEYBQFKLOFLSQKMQIZCZZMI' b'KJBCNKMYMKTWABX9URWXDRINXJQA9UWFBKQZION9JSHWQBOLEEWVXDODFJEMBVGLMM' b'ZGIOSWOAEBNLL9KLINAZIW9FAYSRSJUGFSDHENJOSYOBTFPWTGFHNSDNDHGDMU9EAP' b'UVDQYXTECZPUYHIVAXCNOTUNKTZMZZRXQUFSMEKJNDLP9BKWLVWMGVTJTGLEPECPOB' b'LHTGAFZMUBTSBEBTNXYJRMRVWBJLSZBCECAXZVAPMGSKGQUDOKEXN9JBWDVDRRIXNV' b'NJQSLNXKNHFKTUDFTNUQWIWQYZQGS9RABWASMDHUCGDOYTSDUNPLSMNAGKYEQZTGAN' b'CMJFVKBJMMHOVV9FTMRMTFMBEBNMBWXRRGBVEBZXSNTZRGIAQPBNGAMQEOEMCUYSLT' b'M9DKTSKCWDHTBDEQH9EVWYSDJXRVVEWARWXHKHZQCOIZWJ9JCYWOLUBPJQGCFECNXS' b'QTVVLMQXRT9UEYVPYHXSZBWISVKFZXTFVQTNYSWBVKDADATCKDMUJLPPTWQXRFWKDM' b'OGQDIWRKEZLYZEACTGPQFDCIXRBBUKVHQVRDHVLICJZCWIORJWPMRIFLBZBLXKXIKF' b'PDNLIBCTCYYNYDVRILCJZXHBDC9RKRNRHAJCUOLPTSTQIWTYARFGXMBLRSOXDFQQKV' b'RLWNEVHZGKIVKEBQSZBPRAWAYOOXIGEPOBZVQ9CJHYHIGV9VMHNNRVANNMKTKMLZLB' b'WFGGSOFUIGLJUEQUSBYCWNIRUMICHACAWIOSRKXIEEFOIP9RF9RDSAYHWJOSUQGEIW' b'WTYRBJOPOPJZWZF9GZWUKMBMKUMSJI9UCPQFXNWCVLGL9UPZPLG9UDOWLRRZUMHOME' b'MRNTBBGU9UCDKJOUEITNRXIMHEII9EYUADPLRTFTUANAXYWVMBLGCEUAWHAHUWJXML' b'QWYJXQYNCXL9VVDTOKLMYV9OUWNDTCQFEDELEETNWNU9XXRBZGMYKOPBXZEZRKEJUZ' b'BAMUQDQKQRQDRWP9RXDTNQGPHDJDBVKBZ9GCOCRCKXCFSRIGRYEXGYOMKZOSQQGMUU' b'QGYBSUDUPFARYRP9JCQAHMIJUZCUTNTDGBVYKZHKIGXLIL99TVWNKFHBJHDPSPWIVG' b'ZYQYAOAWTPMUALUZQKCJBPBXPMNYJJIPCNXCDFYV9QQBJFVGIKIZWPIZCAOKNVSI9X' b'SUWZEHPORPLKNYOYTMHSIZAQ9ZLXJHWBHWIA9ACGLOB9UZJAFEYNS9GLOMNHJAHYJ9' b'EBCHWVYKZDPC9G9XKBNSUBRLCVORIPAZZQFMDNUDPRVXTUKHXBKGKSYVZBNXRASYNC' b'URANZDYESQDIYSGCDI9HEIWARKXSRCAQO9MZYIMRKZBZHYWMHNWADLRFAPWSUOSRXD' b'JYYMYTZXEOPVBJXHEW9VZQIXTPRIQWGEENDHTMOHKBEOWMFO9XSGY9AIOUBOYBIFBD' b'EQBRRCKJCJSWAKKZKBUQTAOKFSEHLJPHPTQGIAKDOCVJRUESHWSVXJVQVB9WIRMZUF' b'PDTJVTVSEGMSGDNFOGGOVNUELOYGP9AAEKL9ZGKFCXFGFKXHX9SLQBCWVXTAAV9HUP' b'BLETXNLIYWKMAKXQTFYEADOYUGFGFLYKPDMEAXXXRQRBIJJUKBDLU9KAZPWCGWCW9K' b'IHVSMMNHKDK9AMDDCLU9OYAXQONZWHDEWENLLGZGYXZJEIXHMAJKINTFXPVCMGPLYR' b'OGSQVIOXKJOCZGEPGLP9MIBVQXBBZQPU9L9QZOYQPPAGDJTCLDUELXZGQSQFKOQOFE' b'HKJEMLCJ9ZNWARUEAFEDKQBQ9NKT9UHASO9JGXW9UBZGX9IEZFCYCWBINHW9DDWBUK' b'QZY9FIKKWVRAMGMSZDVUCSZKMPWHTGQUEPANTUWIYCKHENOG9OQKINYIPM99MAXPW9' b'UB9BQWUXZKLJOSPEMGALNNLJDFYRZCSETCELPMEOGYBAXJ99NSTIHZOILSAUXUZSTV' b'BPNVFNHJMFIJNWJDPZ' ), ], ) def test_error_iterations_zero(self): """ Attempting to generate a key with a number of iterations < 1. """ kg = KeyGenerator(seed=b'') with self.assertRaises(ValueError): kg.get_keys(start=0, iterations=0) def test_generator(self): """ Creating a generator. """ kg = KeyGenerator( seed = b'TESTSEED9DONTUSEINPRODUCTION99999IPKZWMLYYOLWBJGINLSO9EEYQMCUJ', ) iterator = kg.create_iterator() self.assertEqual( next(iterator), PrivateKey( b'CXNFADEBFFAAJSOODA9ZOBXJPCWKKJCYOW9ORHEELC9ITPHLLDUFILASQMXOBQXGQD' b'ZVTZOSJYIUIQBIDDDJEREMHXXTOV9O9EOMQDVYZIFQDCJTJTOAGDKCZNUENSOBWQWI' b'DHFU9MIIJMAEGZQNXRAQVLGWCKRKWDDHLHUIDSDCPZURBPBIYUEUXWZVZS9TCBBMLY' b'YLSOVLTHTBTNAURBYJFMJSDTYRK9GEFJLDHQUSIHSSLKBCJ9CGAJMCACGPVXPYBLBL' b'WECXXIHMVXMVWLBVNEYQHFNPUYMLLCWIHYGJHGDGHQYLYOJSSKFHOLMURNDZSYVVOQ' b'DKAFOHL9KVV9DMJPKFVHSDNMWULHSQIPSZJFLBQCCPIVHIYDZ9DSXCKFL9WHNGIABK' b'BZLOIYRCXUCPNBYIZBZNJE9CQLAJTAOHBQGMS9NKHEON9PJTOZNVHGBUZHOWEKYNSB' b'B9GGAPRUSNPOLIYDYJHDAWCBCQVWECDFSTMWQRJFWMHJPNVKPKTIQCRXQXRKPZVTQK' b'BMECURCKREMQIV9MNZIRYZR9DLEPRYLTCDNNHADYK9GZJKZDODIREMY9QOEZTUXYYE' b'ACHTMWRLLCHT9WMVZAG9YXQZBNMSXTVDXH9FDWQMUVADNQUJHMOVSZORILIYAETLQD' b'WGZRBSUUCAZMKKVHDLYFJDYLRNRFMVRKISPRAQRFY9TDJBHODVIPKQJVFEKHHUHENN' b'TIACCEQJLGDORLETYMMDOHQYETGLKCOHZVUZYAOD9IUJHSXOZGPUXUNWQINBOJJQEA' b'ZHJAJ9KZREYMNBGXVZTEXYSPVAJKKMHQOMLITYJKPEXGPREMJSUTVAUTZQQHPCDHTK' b'VVYUFZVJMXJBLHUWRTMUCNHRMG9VWXNTDDFFVMTNFSI9INCTSATR99UEDOUUNXAQAA' b'VPAFVFDFLWMLIWWWOWRDJOVECEVVJWOFVMRJKWZGBAKDLVAWGGLLYBQNRGVIWOZNMM' b'XWYNGFZ9VTCBCQQMBOUVXHXXVBWMUVGMQQJQZPDAPJHSYKKUM9TFDHBGIHAVXUWJPH' b'OAKIQFMJPDZNRYORGCSWQLMKSHBAZJEJTZATSTEOVBDWDKFBQAOIIHLGBKME9AWTIK' b'DHTICZRPFJRAFPWYHWFFKYWSJDVQ9UBUBPUFRHFVDNYTMRFKKYVIFKGXTYWCJXI9SK' b'WISZJGZLPMYZRCZPUWYKT9BSCXDUWHAXAZSGZTUBERVDMIBFQLJSLQBLXXSKJVRXEC' b'LYPQNBBTRKAXPJSCGZA9ZXUZRUYKZWUSTNGMEKGXYADAHBGLGWMFICHFHZOOR9DIWK' b'TPNCZQUCTTHVLNCVYMNLFSHUBHRKPRNLGEJJUYSNFKAIOHOJRPKRNFRMWZFUYOEKTY' b'EYMBNPVYQXNTWRJYESMC9FSXCCMWV9KAQSCXBXYORCABDYTBIMKQRNAJTERZODHRTB' b'JSIHLDIWNZMBQEENNURMRYLFP9RLARPZWSAPZGCIAMNGQMATVSKLOXUPGEGTOXSJKZ' b'9OYAILOLPCYZNCBRKP9JXCSHZAUQVRLIKJ9JAHDXQ9E9QQHSGGDGLESCQ9NWSLEOAC' b'JQOXSMJBQLSRXFPLNL9EZBRLCRMBHNUMONIYK9NFOXQLUKHZHPIAKARXJHZUMRZHQP' b'JLDSSAGFBMYONHBXZNVWZHXT9G9XWZSMOWCOXXLMOJCJBZDZ9CDXWRTVZZSNVSIYSJ' b'ZCCTHPM9SHZ9VRXKYAHQZYQ9UINIFJRQNDDCDSZNSTLAPDGEJJEAKCPTDDVPLVZYJA' b'TGTNTGRIUKJXPOPISTGQINGBFKZ9MBWZJSPORCOTYSKLCQYWGNNYBCPHXYOPECWVFJ' b'MITWHHHXELMJPYDOLJGDVPOMCRXINDPCSHFY9IUOUAIKWOCFHTYBHIPGVDSZKOOTBC' b'9DVKMCLODRCDZBYC9APPPQKUFXV9OZGKYRZFMYHMTZTPKBWQCEGRPKEZKEJOXQQIQT' b'BZPBBPNQJBIJCVY9GDUFHEVPCHYNTIZUZPEWJYNILZJG9F9AEDLIFTXNKFIRHYDRPO' b'QDHI9GWBGGD9KNSZRK9NUCHDBRKXPMMQPVWJFJTRDIINUNKNGEZFENEJO9Q9JCFJOT' b'WXCDFMCZZZSYHXASPPSJXHZZVYZIFFNTVVPNPFADKY9EPAKECAVQYCPS9FCBWXLKBQ' b'NG9IQVLTC' ), ) self.assertEqual( next(iterator), PrivateKey( b'EZQRJTGHMGDBQDEHEDGJZA9VGEIXKXBAJWMUHLJORWMFFZCDVKDWPZT9YMPJVDHWBC' b'FRSENRXLLBYTRPCHLFE9MDXWCTZIGOSHLQAOPZDFDSWXXK9LOX99DMMOO9LNWRDACR' b'KVXRWJTPASBPOBZKL9OGTDTVAZZEJCAYMQFYLZSUFKDFTTDBWMDAAITSVVHPJLGTLS' b'K9XWHDBZTT9BSVXJCYY9IJ9MIMUJIARPJONSFRTIIFKQXUBAHRRF9OHSYYXAILQDWD' b'HJGKYLRZSDTSKPXFMWIAVXMZXCSRFTTT9QCHW99ALGWWAGXCOIOLKVUFWDAZZPSWHB' b'RFEAAG9FVMFXHPZCPREPUVZYZVVMLJWBRXBCQCOLHDVOBJYCEZDQODXKTTDSRADBDB' b'KATXM9GGAWJBGKCQZNIPI9CCIMNNNCNLIBFU9R9TPUYZRLUMDSBGAKBFJGITVORILR' b'WJARQSTEJBVDBDQAYAXBSFGYIWHEQXCPMZGWXDXDDXPTYXMGHRPYMZDABDYCQGUBTA' b'YXQNOXWAKYEHBGDZCVRVYMSCNMHBWHPBDISCZRWNDETGITXBHXXOEOIKVLNW9IELBK' b'NHE9RQESYEATQALWGDEWIZENQODBCVPS9TPUWAQLVOIDFMNPGIDERXST9MVUEYJGVS' b'JYRF9ETXQNXAKVQKUXMVSSECZPXXE9MIOMYDXIZZXNCYVWBBWPFPNHLLXN9MAXXVRX' b'XRDILJGWHXEPCDT9FYFMONVFIWIPV9HYLCCFEESXSITIFIRKDNLBCONFNXWQCLIZQO' b'YOPVOHEDD9NAFHDESZCBMENLFXALDOUHCVAFHBIAICRDNWDYYUMTIPSCXVVKBGCNUN' b'WRBEVBAHREFJDQMXRMXEBKCXMVUGAOE9BALTFLFGNOYGARDFIKZ999SGEAT9DDJMGW' b'GUVDPUMOUEGEFKOHTOYAJ9BYWLXSHNFUEVJN9KETAIFQQZAACSKTLWIBQGFYR9CCZR' b'HNWNJEEXGAQRROW9WODHBICDIIGEZSMEZHCJBNVRKQKOT9YICETQUNWOJMQNNLYQSC' b'QDHJQBDMFIRQAVOAKAOAJUVKYZN9BCOUCGOIXGNMSCSNUQV9POFLLESIFFCAAVNQGU' b'ZSVBLRWLVUCCNAKOVWSYUPEQUNRABATEREEYOQQVEWJJHQZDSMSEYSKHCWYYPRNV9P' b'U9IKVIUWVCNNWEEVHNAMWYHFYMYIZVAOROXENARJZSIUONONKLBEIOPIAJCTNPDHCX' b'BHVWQHF9GZSRYJIASXNNEGWIEZUMJZXXATZXABYXOCSSMCZUVOWQSMMEDJALVS9WLO' b'TNSQRUKYQOIQCVZOZVY9OFKUZYOGTMUJMTMZZPMTROLQITPIRLCKKZJFBFAXFDGMQY' b'CEENIRTFGKKSLLYFSESSXZXUVMSRWARCV9AFKWZVTXTAWUMIKTXNAWLYMSSLCTPYWP' b'FUKHUXXRCXXSPGS9SVQIGKHUFRZQKWXFPQGTTFTLSPFPEGWIBXLFUOLZG9GMVWIUDT' b'9VASDDSEXVLKRVZQ9BXC9EOECROTWUSPVG9WPPPFDBSLGTYXVAXNOYRAVBHZJBBKED' b'NBAKIVTPRSNUHAIAHKCOYTTELLMN9KBBLESBWQRUGTQ9CXSH9RFY9MECJVBSAPPFCN' b'GRS9GPWEHQNNCYQWTYVLVOEIVKEBYGSXQWHAKUZKUKJNIIKEGEDZTWZLGTREKZVPKT' b'PES9CNXFULSOQXQQUPSRIRZTWAOGAIBRFNWYILQXXKZPZSRXELFNSKSHFCWWQDFXKB' b'GCETSHDKLIGABYCMWKHOHDBSBLSNVOCGEXZZIALZQFYBKWDAGEAMALALDELFTGMYJQ' b'NNDAMH9UAGIYPAYTATXMSBXUBQRJUEXNBENKDTMUBACEXMGJJZUJIPGQMUNPOEFTDY' b'FSPFWWK9GYUHMLGFYTDVBUSRMGQ9QXUZILIFLMOD9HT9HZXHOLYBHYIM9HZJ9CJBGM' b'XQJAZLCCKY9XAZIK9VGZEGIZVEEWWYUVRPXQUWXHIKRADCZ9VV9AMESCGRHRRKVZMM' b'GIDVWDLYACPXWUDYMSSKKGLAOSNWUUOJGSMBHGLBGPNDSGINIFODHCGGGZXYYGUZHI' b'YMIVWIBDE9FZFKRHMVYJBJSDVNQ9HWLEMMIJLAQNOHXK9WQELXWXIFHCNSR9QSFQOV' b'H9L9OHTND' ), ) def test_generator_with_offset(self): """ Creating a generator that starts at an offset greater than 0. """ kg = KeyGenerator( seed = b'TESTSEED9DONTUSEINPRODUCTION99999FFRFYAMRNWLGSGZNYUJNEBNWJQNYF', ) iterator = kg.create_iterator(start=3, step=2) self.assertEqual( next(iterator), PrivateKey( b'DEILIWDSOTXXVPJ9GKCY9EKLJAAVNL9TZQUYGXSVFEMWZGPKUWFQUQR9JL9SSSHFYZ' b'FPZKACLPLH9CHQYQBXDTTJWXOGL9AXPRSKAETKENMJDTRGCLDKDISPRYVDKPBIKESW' b'THBRYKOZUWFAKSJNEDJYAUKFGTURZWMSEIPIZPYDQHMXNTA9WL9KLFKRXCDCOHORXS' b'ISJWLPDSUTVNYDENFLCKHGKJLFJEIXUUBESWXTRTWZAWDCJCORDDEWCZUPTAKKFQOX' b'S9WNRNUQMMLRYJIRJWGNYFPIFMG9WO9EYBFZJLNGLASAE9SBXAI9LNVFVUWXPXQXVJ' b'EBVLDI9PLQTWKXIATCVUHMCXGGVOAD9HLEIYWOLYVDSY9DVHBUVXQJBAUOUPFVGWMP' b'HNLIIHKTDASXQVCM9NLBBGFJL9BYHYAYHNSXYWISFRCHELLUKYENACVAQRDBCXOZNF' b'THJ9WJZXSNZBV9ZNUGCFIHJWTSCGRYATQOHWBVSONUOJKOL9RZNTCKPDBQMXBHUEKT' b'GR9QBFFGPWFKJWSTFIORNLYHLIVBCPMEOGARGYZHNMBPMLQPPHFKFRAQYIPIVVEIXN' b'9LGTM9LMTEOGTJPYAAXTJUFYCBUJCYNLQPTVZXMMMVUZCWVFYNZHQAAF9IIHV9ERSK' b'K9YGICMF9YIXWVFLEBIKGI9NNI9JFXWMXTTSOAXCVYNOIFUOULT9MLAVE9HOYVXHMW' b'GKB9RFIDVWXLYXOHUGCNJNFDKWTXBNVBWEYDRBXDTQNLEBCDLSDNEHMKFCSEUBBBBC' b'WKUHOMDFSMZGWTZOOCQPURKCAWT9ZBFNWTZQNFWBWTOXXTKKJDQWAGBALOVEJWTPAW' b'QQRKPGGWGOKYOEUHNVHOPECDTFRBBDGJDFPYU9PPE9QTOFGWABAPNIXIJYJJAYKNGO' b'DFRGJX9N9WRPPPKJGZOVRBYZVYCBMMCKSXUCDPWROYUJFXXCSDRGRU9FFEMCSHITWR' b'URJINOEOTPBRNMKKCOGMWKLFJFNOZUTCMNKESNJOEVYOPMKODCXSETWUHOUWVSDHWY' b'YPUJAYNAUQGZISKRRSVLPQCYTUIEUZSCKVRADGHM9KPPX9XUVW9VGILBDKLCMCLYEL' b'YTPPXQKGNEI9TXUGOCYKEKMCOFBRXNDRZRDPPEBLATOPGTUHMGTNEOB9BEHPIZQGOA' b'JFCC9VABHCZLHQNCRKILEDZULPCNRNY9RMKVVARIJJEWCJFYYPWEEPCTMZBHAEAUIB' b'MXJMTLITMKRYNYHOYTFEANRUOSHWCHSUULJXRMYEBWMGNMJSYOKMTLIOIWFGQ9GXLD' b'CFEFBKIVKXYCRHMAHTP99EPUSLPXAMYHCFRSVHWSOEHJVJLOCXOSDKSPYWG9XMCPPZ' b'NAVULZWTECRGGVWGHFMRWPAOKLXOQMSGEFSVTCWITKGXTWLGXNS9WDXECMMMNFMJXG' b'KVLXR9G9HOHYHSM9IE9DQOUYAREBHFWIITROKMQPVFHVQZMWUUFEJRZTADTBRENMDM' b'PXMYZSIVTLYHDDLWWNWCWGGYAW9TOQUTRENBGAJCMISIWEPCBFUHPXIDKUVMCYSNBY' b'AXESQQPUSNKPXLLAUQT9NDYV9YMIXZAKOFCBLAFQFJ9VRXAXJQLZWXVAYGTRIVNDVS' b'FCJOKWPRKRFX9XJFFAAZINQLTOHEOTINY9XRAQGGCENHSVGZKB9GHJEEHRUSBLZHEK' b'U9BOBGYRCRKEPYADDVNKYKJWMFHLYUG9WLTDYELCSLDWFPXQUWVHULQFPUUHL9NWRZ' b'9U9EPZHCPRBPT9BFA9JGVKKKMAMQHMMZHLOAIW9YLUVFT9LPG9MFDTAQBVZWZNW9EU' b'9LNBZIQWOZAHXRWJIVRCMULGTOMGHWG9DWYOBZHBVD9CRABMXCASGRBGXSNRTOFZSP' b'ZAGJWHZIFZMZWQYDATWGFWEKYYXJCZ9JFJOZKDDHHQVXYSSTYXNHB9NPNOKMZ9RNLX' b'AOL9IADMCIIAATLNEZCNCBCUULZTDIWOIZUHLVQWECIYCDATIOUWOMOPBMOTBYRZJN' b'KIPZMGAJEWXVHROJPHYJNJJVNEMD9WXNSQNARYGEYKQMXUTEWPUGJ9XAEJXWXHOIYM' b'BNJBIYRWBTLVVZTCBJFEQVZIWN9TFFYDLEODFTXECOYHZGIKNS9ESHQGJNXMHPSJMB' b'IYUAZJFTW' ), ) self.assertEqual( next(iterator), PrivateKey( b'ULABUJASASWFHYTHWDANSXEISTMZXPFTAGDYNUWNBZOBNSMR9XZBWMIDVAHUHYIXPT' b'STHTXDVEFUBOGCAFTSOFPOYLEAFSYAPHAFPRTWQCDAGF9EHTOGWZHTDRNACYTBIBPU' b'UBCBIDXQZFFJR9HOEXAGRGLZS9ZJZBQOPXIRWPQY9BNHOZNTKTKQDGGTXBBAZCYGWP' b'YN9ZRSI9ORTBXYTW9KKIMILXBPSLRXCIGIJESSXOGDOYXEBEVIAQOVHCJBLJFSSPPO' b'BVFIGVMJUULVVCBZIFMSVIEQLFSMUIPMGWTXDTCFDQMKNHIHQIMQODZUGWDBNMVOWX' b'CTIASMPCLQGUDXHHMKRIOWZOWXNVTFTEQITHYIBWUJFW9DYRBOUC9OIYAKIHLQNAWD' b'KMFNZJMLCMKPENGHGOBZABAUYVPPKWLMPYXQYXPNFQTQAWISIJAXWAESRBMCQHWR9D' b'CFNGRRLMBCTRARQRBMWO9O9W9IDLPNNRESBVADMJOFBJGFJMMRXXCVUGPBJPMJA99R' b'BMIBXEFKQH9QLVPBRXBDPKCTOMAWFIKOAELUTBXPBFRATSLFTCR9ADKGAOEBZAKINX' b'WUUJRWNHDTNZGHOUUDXFKHNBYIFAJXGHRVOSVSAPIZ9EDOCSJWMLHD9JEVBTD9SNDZ' b'GJPUTWBLCHFLDOWDOLUKRRRRSAVEWOPYWIYNVTR9IXVCGOXWAZPEMI9MGHQFOJWTRL' b'SXXTLEKGIOGCVQCGKUQHZYOVLTMPHLYEXX9RDUHXECNZWZPYGQPRGHKQJHTINBCFGN' b'BEYFLKZIFKTNFUMIMBGV9DWGKEUUQXNCGEVRQVQWKSEVVKQP9YDXWVAQP9BUYCZZWK' b'PQEDFAJETIC9DTXHDMYZLHKXXWVXTUHEDTDDFAJSEUAABJCTOPWCACXDQBBQMWILHE' b'9HRNZKOSQAFXWJQJRBNDZEYCLIMFWETVERUBAXVZHRXYNEKA9VFQGCVOCVMHPQZAHT' b'9JJFSXKL9JTIHIRAJZFNGQZSOCNQIFVUSEAEQNUXDVNAEVAHGNUTCIPDYMWMCPYUNV' b'CVJPNUGGDPONVXMJENLDPHTWNSXYAHCEZMOZXIRUFESZPGWOB9HSSGBDZTOAKLTXHS' b'YYQ9YGEOMU9WEMAGTI9BXMKSRACJZAMFQWMZIXQOLRI9LKCJMTJNZYPDSN9LHW9LQC' b'CKWVUXRGRDNN9AGDKFRVJVOUBGWBMMOQVQDNEDVFCFRMZ9KZZWLKFICUGBHJAKJMEI' b'OCDQPVPWNJLRKQTLPSXSGNMGRJMJFNLTHRZERCRDBYEYZZGGCUMETWFFWUHZFGU9UX' b'B9OIYAHLHGHUYJNBYTBOSEMMIWNYDTY9HIUSCYELVWUBJJKCNUTQOMNRCXLNVWWUIB' b'NCQJUVJIIAOTJXCGAQI9A9GDQXHFZYJKQFQWCXOGOPCJNXUFGGRYPBCOWGPADFJKTS' b'EKBWDWCTYLACMGWOAJZAAYLXCLYDSGOBXFG9JJNPDUENUZWHEEPSGLVXATRHORZIFR' b'EPKMQGGTDQZPKUVNLXQMZIYMQJ9DQ9YVHCMWQGNQNASBBMSWTGSRGTDLWQSYFKTEVW' b'9SMMIZLOUOP9RJELWSDSKKBGJQ99TBXCJEJXGXAEQY9MWOWMYIJWTAFRXPHRKGICXM' b'DTRNTYPPT9ASUSVAORPJZHIKMPCXDZCHNUKVCWVPBMKFXFPZUDDQTPVJDKRLHOXSLJ' b'AOUFUZJTMWPRRGBENPEVMNAURDJXJCTDWKJTTIH9VRBEGTCEHAQRTHQPWCGBHF9RNW' b'NJXHLDPHLVLDWJOPAVTDNGA9BWQASZHWGETWUOWFTXRNOBCLFZGIQOHVZ9CRBNGSXN' b'DDQI9KIARJNDOFYOGDNQDDWXAALKLPMH9SKIKF9VKWODV9ZBUMKCJNGQXIGQOUQWDB' b'ZUBDPMXZS9LSE9PRRDSLEHPYFTEWHAABELHKOPYEWBMAVAWOGWEXVWG9QPZAUHHSJX' b'KNTWVKNW9LBWOSBEGPOJPYSSII9CXQNZQQARJBCPGBALDHYPWVPGIYIAHQAXOGKBVT' b'IPK9EHLLRGKGYYEIPGCODOOGEMSIJGPXXIDNPOCCGE9TKGEXMTKPWLUDCWMZO9YBQX' b'CBH9R9CYYSNQWQFMADEKZVLKS9BDSZZBHSLGCNKZSCNQGJQCYTVJEOYL9KQ9S9GPSE' b'LWDIKF9MD' ), ) def test_generator_with_security_level(self): """ Creating a generator that uses higher security level in order to create longer keys. """ kg = KeyGenerator( # Using the same seed as the previous test, just to make sure the # key generator doesn't cheat. seed = b'TESTSEED9DONTUSEINPRODUCTION99999FFRFYAMRNWLGSGZNYUJNEBNWJQNYF', ) iterator = kg.create_iterator(start=3, security_level=2) self.assertEqual( next(iterator), PrivateKey( b'DEILIWDSOTXXVPJ9GKCY9EKLJAAVNL9TZQUYGXSVFEMWZGPKUWFQUQR9JL9SSSHFYZ' b'FPZKACLPLH9CHQYQBXDTTJWXOGL9AXPRSKAETKENMJDTRGCLDKDISPRYVDKPBIKESW' b'THBRYKOZUWFAKSJNEDJYAUKFGTURZWMSEIPIZPYDQHMXNTA9WL9KLFKRXCDCOHORXS' b'ISJWLPDSUTVNYDENFLCKHGKJLFJEIXUUBESWXTRTWZAWDCJCORDDEWCZUPTAKKFQOX' b'S9WNRNUQMMLRYJIRJWGNYFPIFMG9WO9EYBFZJLNGLASAE9SBXAI9LNVFVUWXPXQXVJ' b'EBVLDI9PLQTWKXIATCVUHMCXGGVOAD9HLEIYWOLYVDSY9DVHBUVXQJBAUOUPFVGWMP' b'HNLIIHKTDASXQVCM9NLBBGFJL9BYHYAYHNSXYWISFRCHELLUKYENACVAQRDBCXOZNF' b'THJ9WJZXSNZBV9ZNUGCFIHJWTSCGRYATQOHWBVSONUOJKOL9RZNTCKPDBQMXBHUEKT' b'GR9QBFFGPWFKJWSTFIORNLYHLIVBCPMEOGARGYZHNMBPMLQPPHFKFRAQYIPIVVEIXN' b'9LGTM9LMTEOGTJPYAAXTJUFYCBUJCYNLQPTVZXMMMVUZCWVFYNZHQAAF9IIHV9ERSK' b'K9YGICMF9YIXWVFLEBIKGI9NNI9JFXWMXTTSOAXCVYNOIFUOULT9MLAVE9HOYVXHMW' b'GKB9RFIDVWXLYXOHUGCNJNFDKWTXBNVBWEYDRBXDTQNLEBCDLSDNEHMKFCSEUBBBBC' b'WKUHOMDFSMZGWTZOOCQPURKCAWT9ZBFNWTZQNFWBWTOXXTKKJDQWAGBALOVEJWTPAW' b'QQRKPGGWGOKYOEUHNVHOPECDTFRBBDGJDFPYU9PPE9QTOFGWABAPNIXIJYJJAYKNGO' b'DFRGJX9N9WRPPPKJGZOVRBYZVYCBMMCKSXUCDPWROYUJFXXCSDRGRU9FFEMCSHITWR' b'URJINOEOTPBRNMKKCOGMWKLFJFNOZUTCMNKESNJOEVYOPMKODCXSETWUHOUWVSDHWY' b'YPUJAYNAUQGZISKRRSVLPQCYTUIEUZSCKVRADGHM9KPPX9XUVW9VGILBDKLCMCLYEL' b'YTPPXQKGNEI9TXUGOCYKEKMCOFBRXNDRZRDPPEBLATOPGTUHMGTNEOB9BEHPIZQGOA' b'JFCC9VABHCZLHQNCRKILEDZULPCNRNY9RMKVVARIJJEWCJFYYPWEEPCTMZBHAEAUIB' b'MXJMTLITMKRYNYHOYTFEANRUOSHWCHSUULJXRMYEBWMGNMJSYOKMTLIOIWFGQ9GXLD' b'CFEFBKIVKXYCRHMAHTP99EPUSLPXAMYHCFRSVHWSOEHJVJLOCXOSDKSPYWG9XMCPPZ' b'NAVULZWTECRGGVWGHFMRWPAOKLXOQMSGEFSVTCWITKGXTWLGXNS9WDXECMMMNFMJXG' b'KVLXR9G9HOHYHSM9IE9DQOUYAREBHFWIITROKMQPVFHVQZMWUUFEJRZTADTBRENMDM' b'PXMYZSIVTLYHDDLWWNWCWGGYAW9TOQUTRENBGAJCMISIWEPCBFUHPXIDKUVMCYSNBY' b'AXESQQPUSNKPXLLAUQT9NDYV9YMIXZAKOFCBLAFQFJ9VRXAXJQLZWXVAYGTRIVNDVS' b'FCJOKWPRKRFX9XJFFAAZINQLTOHEOTINY9XRAQGGCENHSVGZKB9GHJEEHRUSBLZHEK' b'U9BOBGYRCRKEPYADDVNKYKJWMFHLYUG9WLTDYELCSLDWFPXQUWVHULQFPUUHL9NWRZ' b'9U9EPZHCPRBPT9BFA9JGVKKKMAMQHMMZHLOAIW9YLUVFT9LPG9MFDTAQBVZWZNW9EU' b'9LNBZIQWOZAHXRWJIVRCMULGTOMGHWG9DWYOBZHBVD9CRABMXCASGRBGXSNRTOFZSP' b'ZAGJWHZIFZMZWQYDATWGFWEKYYXJCZ9JFJOZKDDHHQVXYSSTYXNHB9NPNOKMZ9RNLX' b'AOL9IADMCIIAATLNEZCNCBCUULZTDIWOIZUHLVQWECIYCDATIOUWOMOPBMOTBYRZJN' b'KIPZMGAJEWXVHROJPHYJNJJVNEMD9WXNSQNARYGEYKQMXUTEWPUGJ9XAEJXWXHOIYM' b'BNJBIYRWBTLVVZTCBJFEQVZIWN9TFFYDLEODFTXECOYHZGIKNS9ESHQGJNXMHPSJMB' b'IYUAZJFTWOJWZMOTAQWGNABYNTAZMVWYRQXWUNCFVAEJPXEJAQVUEJVLSQGZHFOL9X' b'ZIIMCILT9CDUBH9AY9SSWMEBKXQTQJXYYUMLKVVWPEBMLXEXREVNGOVQTSLKAOCJQR' b'FMFLCSLIUESVWEKZDYZAXTUGVV9NFVC9GEUBCN9ZNCBRTIIGIVJ9WNCTEGTYYZVNLB' b'ISHOKQBAVETUJSQMGZZUMBJTHBBIBOMWJIDKKPARGLOLJMNQUISYDDCUJIZNRHAYIZ' b'MAXWJTSEWHFBPAIE9ELXXRJHATEMDPWJRGAZ9KMEVLBUIHRYE9LWZRPN9JNKSROTJK' b'TKCIOXISKVLPHEMXMDAUYZTTMDPBMRWYOJEMST9CARUGWYBSPZFIFXPXOVMVSWIYBK' b'HARCVHLAE9JEKZIVUWRKYXDODGSZDBHL9GPBLUJSHMNHLSRCRHWDRWTYAUXVPGVAFV' b'PQEKRBPJGTIMRTEOGI9KGQCVVDCEJDPFXUDXMKW9ZJKSHDUHEWUHREKDQYCFNDNXAQ' b'QNSOJVTVEDZ9ZMGV9CHFRVMRBDBPAJ9IXPAU99MZRCMWSUYXZOQTBPKYVIAT9YNHSZ' b'RGMMTWJITPBWTQWCNACHGNSXGAZH99TLWNRAWHKRGOQREXZFADLOIBIQZVTQUW9IWL' b'YCHSUOFZQYPFNIMMJSGNO9ICPCPGW9DNRNPENRKAYTAIINUDWYN9UBQKQEOEYAXSBA' b'QPRD9UEKNLWYPOVWWRMEZZOKJAJJWKIILAXLTXTGSIUVFMDGJ9OGCOHPFEJCNBZFFA' b'MIBSVRMOCSDRJVCLDIXFKMWFRYCBTGBZULULAEUNJEKSOB9FRXMYWXDIDAQ9CBGCCG' b'NXJTUAVQOYFPJVKB9CPTXRKVKPJNNUVTHEWD9GCOEXBUKKRTMUJKUKVCDBSMHI99AF' b'LAAGIUUI9LMYLPP9BIQDOGLBOTNEOW9NBSEKJHFDHDVUMHKQELRIZKFWBZSXCJGODU' b'RVMSIWLHUUKRDLCWVEJDMPZGOSJONZLDMNFLAJEK9EWSIHKXEFQVTDFCTITJREYRPB' b'HYTUJYALFYQLEITWYNVWJIXNSV99WKUVVBJQLIQQCWIXLCAMMBMUMUURBUJOYOLEBG' b'XMZAYLNBMWVYUWOR9PBCDQVID9CELEMIFQCZMMTDYLXZWXNXBBIDLURKCZDWDFXPVT' b'SQKEGPQUXFYDINSSKFZKJHULVRGK9ICCHSIDCMQU9JDMQSMRO9DKAWGATCNSAS9OSU' b'KD9VCRVTHASMAQOBV9RVGOQJADJVLFQYNOALAXNBANF9WTPLUPYPIGOFBYTZZIUKLU' b'KUQKMY9BWIDJXQEFBUOHFB9JNOMEMUPYQMNDDCMLVSQVTPFCRE9HCCQFSFMQMIKEIZ' b'EGVQYHXMF9LVNWQLYYCLOPQDEQLDQMCPEAYFESVW9MKHHJVBTFLXMZGTBAIKOZOZEC' b'MVWLZJNTZOHMBSDUPZCUIIQHRDZMSMIGCTHHEDBJQDWKTFFWZBOQTRVCULTLUCE9TS' b'WXVNXIUEUBHCDOCEVIJELBYMTJKBODXYYOFPXWJE9CEGPGREWHHIANYDGGWCAAOPIC' b'FWZWBWNFIUKBIAHLRINVFKMH9AHKLDOYOQWAVZ9I99DUDGKLFPEVMAPNYISYCEPDOR' b'VEUZYFU9BQRFXYLPD99MBIMNNVTGIBVNQRQIYEKKZADTUEYCIUTIAVPQNXODQIZSPV' b'DNUMQEOYQUVUHSSXCSMPIHJEKWECOKOETBJAQWR9RSVFSWIXZJIEUOCBNCGAFGJHCM' b'ZLTEQRS9AINIZHZVDDKOYDKHKABCTXXLKGXY9SHTKJZ9AZLAZZWLFSNIRJQKX9RHWX' b'ZCKUQUMPUXRGRJEMVA9RNGUWQMTCUIZPJZKWPFEIYXAKGLAVZ9YCWZDWWPLJIAUZWF' b'BVKYBEJIIMXWSSSWFXTDXTAHRGSKOIFXMJUUOYXMAZKWVYLCRUBUJFDIPDBDVMUBBZ' b'KUXYHBWMAGOUAZOIPUPVUFWTMTIROZKAMBRHOMZRMAPGQNGCCLDYKAOJAFGTERDJZS' b'YVCJSMVZO9QPE9CAOTVJABMIGRZIXUCAMCENUISOIFSAAMEIHK9WNXVJRRQAATXXDQ' b'EM9ZCCETCAUBBYEWWWXWBVVHTHGNMGESVVWMUDBHFYZQPTKSCJFKGWIY9LCJGUDHRS' b'HVJXLYSLYSNSUCDGID' ), ) # noinspection SpellCheckingInspection class SignatureFragmentGeneratorTestCase(TestCase): """ Generating values for this test case using the JS lib: .. code-block:: javascript var privateKeyTrytes = '...'; var bundleHashTrytes = '...'; var Bundle = require('./lib/crypto/bundle/bundle') var Converter = require('./lib/crypto/converter/converter'); var Signing = require('./lib/crypto/signing/signing'); var normalizedBundleHashTrits = Bundle.prototype.normalizedBundle(bundleHashTrytes); for(var i = 0; i < (privateKeyTrytes.length/2187); i++) { var normalizedBundleFragment = normalizedBundleHashTrits.slice(i*27, (i+1)*27); var keyFragment = Converter.trits(privateKeyTrytes.slice(i*2187, (i+1)*2187)); var signatureFragment = Signing.signatureFragment(normalizedBundleFragment, keyFragment); console.log(Converter.trytes(signatureFragment)); } """ def test_single_fragment(self): """ Creating signature fragments from a PrivateKey exactly 1 fragment long. """ generator =\ SignatureFragmentGenerator( private_key = PrivateKey( b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hash_ = Hash( b'RBUEILZHMYBLXFXBZLASTGECTXQDWPXKNXCYXBQHASRRRLXBDUFYZNPLWKKQYAQYOVKWFKSJT9OXIUOWC' ), ) self.assertEqual( next(generator), TryteString( b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generator can only generate one signature fragment per fragment # in its private key. with self.assertRaises(StopIteration): next(generator) def test_multiple_fragments(self): """ Creating signature fragments from a PrivateKey longer than 1 fragment. """ generator =\ SignatureFragmentGenerator( private_key = PrivateKey( b'DLGLSOAGKZTCOQYIDJMFVIEOLSDPMDOOGSJGRMQSFTONUNCKVTOEIWUOJYUGPADBAJJOZJYZXVTAUXFBBLHXCKHE9LXDMSFHHIACOBFW9DFVGKS9LWPHMWIKYGCEJHHMVVJPBGXWIMITNVZABFMWWORGM9NIVQFUWXKCWBHDJVYISCNINUIHLHCRAKEY9OLAJYHXVMRFKYPKX9EYULFOFNILGGPJPSPXELLJHAR9TRDUNEUNXOWUAAEXQELKSIYGFOTJKWNNZJWZGNVKJKOUJ9TASCHCCMVXKBZBFWMWTUXAUIEIWSLK9JOXVIJYKEO9QGFATLXICWHRFYMQTKCMPGUPWWV9LVZGSSHB9P9DVPHURK9QYXMNZLIRQPJNQGGJNXSYVZ9IFSXFWCKWSLDMZHDBFQCHDWYONHRVLB9XSQBCNAERNMHCZLXEDOEVYZUHP9QVITZR9EZLSSWKGLPIVTDWMXLXWDRFMKUYRDAQXBYUDZXICCJHLXIIARCAJZBLMI9NUZ9ZRYCUGNYLTRFP99XV9VVTOMIDFXHLLRZPKFNCMMNCSEIAHYCDVFQT9SVRUGIHAAPJMCAPAEZHNPOUHTJDDJKFHXTZEFAUFGBGTUVGTAETQMSDLQUKQ9OSKF9ZNKKFQKGABSUQLHXIYFOJGWYTEPNMVZY9OFROODXSHTNWCQIPODHLUOVHHBSZEOFXVDSYBIRLCTZ9ORGTMMEWMNAWATXZKAJGJMHFLIDIRAUJVWCIOEKWURHAQTNARUZIBROIEOPILKDXBFPGVCVMOFOQCUJUC9HFWNNYTPJWK9REHXGBGQFMXREPTJCMC9XOASZIKDMNOYEVABFKPFZ9VSIVFGJPMSDPCETDVBFVIOUFVBMYJLQKCCQQNBABTVGU9CUVUJCSUMYFSVJGOKVYHBEPCRQWIFLPHLNDLCOPOBWXV9NFDXDCVNM9OH9NTCGSYJINQZHWYPKDCQJTZOBDZDVHVRRDCFFMDTDCRXUMSYUEASDYVFSFNRSDDHAHCLBEXNXEYTSSDQVGNGCD9MBKFTHWB9PAXMVFVMUHAKCSUDTDXHPZRVK9BQHKLYJTKCDBEE9USUGPTJPEWKFGYSPHNZSLCYMTXQOAAXMO9YJGKYFIFBZZYHQXEDMXPDPQJWIYPTOAERJXLI9XDMEARFQBAAGOVL9EXWYHZAYHCAN9BYURDOPRLWWBYYBRUIAPCDDWOYYLWZJRXKSUFOPTYPUILVDZOLFT9HDBBNBNPLDUFVOVGLN9XUNDWZFC9LHJJESTWZ9CFPCLWIKLW9F9NUTLGLBOZFPWSQIDMF9GNVVXOGTJYKKIQTVHGCBCVQUANXYKSDYCEFGZZRDPWYXKXYQDJZ9LSZIUAGMHDIPUDN9NVBPL9PJVZAUOXVCENBPEQQSKIF9RHNUSHCWMRRYYDWXYUCJZUXYYPZVBUGKSFTRMDRCVIAFTFDHXIOMJUHMGNEBSCTMUMVRKLPYYKQZBADMVOMKX9Z9XFCGAYTRECALATWHCQXILVPBZUIADJWZUOBAVDADTSBDGPJXHYPWJVYZDZBKVHVUBGOPJTIKCGCEWABXSLGCTRBTCUCJ9CVFVIQXKLYQMPKRBIBRGKNTVJZBDINFELCNHHEXUNRVXMXXFFWGVJFCMWBZLKKRQDQMRCYLJXQGZZMBAJXDEZET9OHGKJDOAERIPBRVQCSMW9XECZSREEWXMEJNZDIYGBHICLWSRNEAXAVBPUENUKLRMGCRXUTROUDEXUNWYSPELLDJMKYNGVD9RKAABSXXKRNNIQRAWXWQJXDWLXCUWBOQHVUINIFZKDKMRHYW9KNJMDFIADXFML9VB9KVTWLYVULEVGUQZVZE9YODLRCOEZUMFVQA9ATBQVZHCQBLHFFPORVRODHIHYTHAAIZVFER9KXHXFUAQNQZGBFPWDOBIEUMHZAOXHSHCLSAXCVCIFIPGZUPIRLGKIEVEMGKHI9FTQZVEHVUVGZUMSHTU9JX9ULJJRBZWKMCGVNBXKTXTIJOLEKASDOUNFJJJYWRMQF9C9KVFZDXWRFYMRTAA9KG9MTKLSF9SRLPBUCSKEKOYIOVEMSONCNZWFLFSVJAHPWDXADSIGNTTWJTRCOJFHYFDBPQWWGVQSFGIVVSIMS9ATA9WYJTGGSUVZIZADJLBLEACFIBJJEG9OVNF9BZXEIA' b'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', ), hash_ = Hash( b'UZOWRZHEJG9SZKIADSTRZLJSTKMJVPRLFKIMJCLYDKCZAQMMQYCQGTSESVDYSSWMEORCHQUSWMSCVPTXX', ), ) self.assertEqual( next(generator), TryteString( b'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', ), ) self.assertEqual( next(generator), TryteString( b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generator can only generate one signature fragment per fragment # in its private key. with self.assertRaises(StopIteration): next(generator)

69.976445

2,203

0.840999

2,093

65,358

26.220736

0.354037

0.002934

0.001968

0.003061

0.289377

0.283437

0.278571

0.192165

0.187883

0

0.033874

0.128248

65,358

933

2,204

70.051447

0.929339

0.056535

0

0.322973

0

0.800075

0.797204

0

1

0

0.033784

1

0.018919

false

0

0.009459

0

0.031081

0.001351

0

1

null

0

1

0

1

null

1

0

5

df87bc729817cc35f15b1db90013edd09e446968

76

py

Python

nimba/__main__.py

hadpro24/nimba-framework

1c2906d81813c16ea96c185f1d54e25e4770648d

[ "MIT" ]

4

2021-06-26T00:59:24.000Z

2021-07-10T05:10:20.000Z

nimba/__main__.py

hadpro24/nimba-framework

1c2906d81813c16ea96c185f1d54e25e4770648d

[ "MIT" ]

27

2021-07-01T16:41:20.000Z

2021-08-11T15:10:24.000Z

nimba/__main__.py

hadpro24/nimba-framework

1c2906d81813c16ea96c185f1d54e25e4770648d

[ "MIT" ]

null

if __name__ == '__main__': from nimba.apps import mont_nimba mont_nimba()

19

34

0.75

11

76

4.272727

0.727273

0.382979

0

0.144737

76

3

35

25.333333

0.723077

0

0.105263

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

10c07d89e5da3fce5fb08b18eb48cb544fe4b451

88

py

Python

Codewars/6kyu/rotate-array-js/Python/solution1.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

7

2017-09-20T16:40:39.000Z

2021-08-31T18:15:08.000Z

Codewars/6kyu/rotate-array-js/Python/solution1.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

null

Codewars/6kyu/rotate-array-js/Python/solution1.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

null

# Python - 3.6.0 rotate = lambda arr, n: arr[-(n % len(arr)):] + arr[:-(n % len(arr))]

22

69

0.511364

16

88

2.8125

0.5625

0.266667

0.311111

0.444444

0

0.042254

0.193182

88

3

70

29.333333

0.591549

0.159091

0

1

0

false

0

1

0

null

1

0

1

0

null

0

5

10ca9d35a0f9631f9546d3684247c4c521137666

859

py

Python

indradb/__init__.py

indradb/python-client

48e47f49d5b1d953dc2016bb3c3c8e5f913a1ab8

[ "Apache-2.0" ]

16

2018-01-17T20:59:15.000Z

2022-03-06T05:33:33.000Z

indradb/__init__.py

indradb/python-client

48e47f49d5b1d953dc2016bb3c3c8e5f913a1ab8

[ "Apache-2.0" ]

2

2020-12-17T14:52:49.000Z

2021-02-01T18:42:40.000Z

indradb/__init__.py

indradb/python-client

48e47f49d5b1d953dc2016bb3c3c8e5f913a1ab8

[ "Apache-2.0" ]

3

2018-03-31T11:31:54.000Z

2022-01-26T09:26:34.000Z

import indradb.indradb_pb2 as proto import indradb.indradb_pb2_grpc as grpc from indradb.client import Client, BulkInserter, Transaction from indradb.models import Edge, EdgeKey, Vertex, RangeVertexQuery, SpecificVertexQuery, PipeVertexQuery, \ VertexPropertyQuery, SpecificEdgeQuery, PipeEdgeQuery, EdgePropertyQuery, EdgeDirection, NamedProperty, \ VertexProperty, VertexProperties, EdgeProperty, EdgeProperties __all__ = [ "proto", "grpc", "Client", "BulkInserter", "Transaction", "Edge", "EdgeKey", "Vertex", "RangeVertexQuery", "SpecificVertexQuery", "PipeVertexQuery", "VertexPropertyQuery", "SpecificEdgeQuery", "PipeEdgeQuery", "EdgePropertyQuery", "EdgeDirection", "NamedProperty", "VertexProperty", "VertexProperties", "EdgeProperty", "EdgeProperties", ]

26.84375

109

0.717113

62

859

9.822581

0.451613

0.042693

0.065681

0.075534

0.706076

0

0.002837

0.179278

859

31

110

27.709677

0.860993

0

0.294529

0

1

0

false

0

0.137931

0

0.137931

0

null

0

1

0

null

0

5

10d05bf2d6fa334ec2f4ea4b4bf16b951c326f0a

56

py

Python

simulation/device/simulated/air_conditioner_simple/__init__.py

LBNL-ETA/LPDM

3384a784b97e49cd7a801b758717a7107a51119f

[ "BSD-3-Clause-LBNL" ]

2

2019-01-05T02:33:38.000Z

2020-04-22T16:57:50.000Z

simulation/device/simulated/air_conditioner_simple/__init__.py

LBNL-ETA/LPDM

3384a784b97e49cd7a801b758717a7107a51119f

[ "BSD-3-Clause-LBNL" ]

3

2019-04-17T18:13:08.000Z

2021-04-23T22:40:23.000Z

simulation/device/simulated/air_conditioner_simple/__init__.py

LBNL-ETA/LPDM

3384a784b97e49cd7a801b758717a7107a51119f

[ "BSD-3-Clause-LBNL" ]

1

2019-01-31T08:37:44.000Z

from air_conditioner_simple import AirConditionerSimple

28

55

0.928571

6

56

8.333333

1

0

0.071429

56

1

56

0.961538

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

10f2bc75defd77dd04f48accea3f6cb2a9a6560e

93

py

Python

expense_tracker/expense_app/admin.py

cs-fullstack-fall-2018/project3-django-CalebC94

7e989cced47462a3d3de918f82f4dc4b6cfbcc21

[ "Apache-2.0" ]

null

expense_tracker/expense_app/admin.py

cs-fullstack-fall-2018/project3-django-CalebC94

7e989cced47462a3d3de918f82f4dc4b6cfbcc21

[ "Apache-2.0" ]

null

expense_tracker/expense_app/admin.py

cs-fullstack-fall-2018/project3-django-CalebC94

7e989cced47462a3d3de918f82f4dc4b6cfbcc21

[ "Apache-2.0" ]

null

from django.contrib import admin from .models import Expenses admin.site.register(Expenses)

18.6

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0.827957

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93

5.923077

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0.107527

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null

0

1

0

1

0

1

0

5

10f6e4ac2d162d3eb101101c7a67a9712963aa4b

423

py

Python

ofa/tutorial/__init__.py

johsnows/once-for-all

fac2a6388e70873666b848a316aa58c7b2e17031

[ "Apache-2.0" ]

null

ofa/tutorial/__init__.py

johsnows/once-for-all

fac2a6388e70873666b848a316aa58c7b2e17031

[ "Apache-2.0" ]

null

ofa/tutorial/__init__.py

johsnows/once-for-all

fac2a6388e70873666b848a316aa58c7b2e17031

[ "Apache-2.0" ]

null

from .accuracy_predictor import AccuracyPredictor from .flops_table import FLOPsTable from .latency_table import LatencyTable from .evolution_finder import EvolutionFinder from .imagenet_eval_helper import evaluate_ofa_resnet_subnet, evaluate_ofa_resnet_ensemble_subnet, evaluate_ofa_subnet, evaluate_ofa_specialized, evaluate_ofa_space, evaluate_ofa_best_acc_team, evaluate_ofa_random_sample, evaluate_ofa_ensemble_subnet

70.5

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0.907801

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84.6

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null

0

1

0

1

0

1

0

5

801d8283db72f721c7d8d2551202ae81c8a8c32d

245

py

Python

python/zp/primjer_14.01.py

jasarsoft/examples

d6fddfcb8c50c31fbfe170a3edd2b6c07890f13e

[ "MIT" ]

null

python/zp/primjer_14.01.py

jasarsoft/examples

d6fddfcb8c50c31fbfe170a3edd2b6c07890f13e

[ "MIT" ]

null

python/zp/primjer_14.01.py

jasarsoft/examples

d6fddfcb8c50c31fbfe170a3edd2b6c07890f13e

[ "MIT" ]

null

def obrnut(tekst): return tekst[::-1] def da_li_je_palindrom(tekst): return tekst == obrnut(tekst) nesto = input("Ukucja tekst: ") if(da_li_je_palindrom(nesto)): print("Da, to je palindrom") else: print("Ne to nije palindrom")

20.416667

33

0.677551

37

245

4.324324

0.486486

0.20625

0.2

0.1875

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11

34

22.272727

0.787129

0

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0

null

1

0

null

0

1

0

1

0

5

338075127d233ef0220ff7df3c05130c0da584c3

8,235

py

Python

tests/test_sat_utils/test_sat_channels.py

amanchokshi/mwa-satellites

f9e8de353e7eddf28ed715c01d7d3fb5336f0f18

[ "MIT" ]

1

2020-08-10T11:42:55.000Z

tests/test_sat_utils/test_sat_channels.py

amanchokshi/mwa-satellites

f9e8de353e7eddf28ed715c01d7d3fb5336f0f18

[ "MIT" ]

9

2020-11-16T03:05:16.000Z

2020-11-20T23:49:09.000Z

tests/test_sat_utils/test_sat_channels.py

amanchokshi/mwa-satellites

f9e8de353e7eddf28ed715c01d7d3fb5336f0f18

[ "MIT" ]

1

2021-12-27T02:34:30.000Z

import json import shutil from os import path from pathlib import Path import numpy as np from embers.sat_utils.sat_channels import (batch_window_map, good_chans, noise_floor, plt_channel, plt_sats, plt_window_chans, read_aligned, time_filter, window_chan_map) # Save the path to this directory dirpath = path.dirname(__file__) # Obtain path to directory with test_data test_data = path.abspath(path.join(dirpath, "../data")) ali_file = Path( f"{test_data}/rf_tools/align_data/2019-10-01/2019-10-01-14:30/rf0XX_S06XX_2019-10-01-14:30_aligned.npz" ) ali_file_2 = Path( f"{test_data}/rf_tools/align_data/2019-10-10/2019-10-10-02:30/rf0XX_S06XX_2019-10-10-02:30_aligned.npz" ) chrono_file = Path(f"{test_data}/sat_utils/chrono_json/2019-10-01-14:30.json") chrono_file_2 = Path(f"{test_data}/sat_utils/chrono_json/2019-10-10-02:30.json") def test_read_aligned_ref_tile_shape(): ali_file = Path( f"{test_data}/rf_tools/align_data/2019-10-01/2019-10-01-14:30/rf0XX_S06XX_2019-10-01-14:30_aligned.npz" ) ref_pow, tile_pow, times = read_aligned(ali_file=ali_file) assert ref_pow.shape == tile_pow.shape def test_read_aligned_times(): ali_file = Path( f"{test_data}/rf_tools/align_data/2019-10-01/2019-10-01-14:30/rf0XX_S06XX_2019-10-01-14:30_aligned.npz" ) ref_pow, tile_pow, times = read_aligned(ali_file=ali_file) assert times.shape[0] == 1779 def test_noise_floor(): ali_file = Path( f"{test_data}/rf_tools/align_data/2019-10-01/2019-10-01-14:30/rf0XX_S06XX_2019-10-01-14:30_aligned.npz" ) ref_pow, tile_pow, times = read_aligned(ali_file=ali_file) noise_threshold = noise_floor(1, 3, ref_pow) assert round(noise_threshold) == -104.0 def test_time_filter_I(): ali_file = Path( f"{test_data}/rf_tools/align_data/2019-10-01/2019-10-01-14:30/rf0XX_S06XX_2019-10-01-14:30_aligned.npz" ) ref_pow, tile_pow, times = read_aligned(ali_file=ali_file) intvl = time_filter(1569911400, 1569913100, times) assert intvl == [0, 1696] def test_time_filter_II(): ali_file = Path( f"{test_data}/rf_tools/align_data/2019-10-01/2019-10-01-14:30/rf0XX_S06XX_2019-10-01-14:30_aligned.npz" ) ref_pow, tile_pow, times = read_aligned(ali_file=ali_file) intvl = time_filter(1569911410, 1569913100, times) assert intvl == [6, 1696] def test_time_filter_III(): ali_file = Path( f"{test_data}/rf_tools/align_data/2019-10-01/2019-10-01-14:30/rf0XX_S06XX_2019-10-01-14:30_aligned.npz" ) ref_pow, tile_pow, times = read_aligned(ali_file=ali_file) intvl = time_filter(1569911405, 1569913200, times) assert intvl == [1, 1778] def test_time_filter_IV(): ali_file = Path( f"{test_data}/rf_tools/align_data/2019-10-01/2019-10-01-14:30/rf0XX_S06XX_2019-10-01-14:30_aligned.npz" ) ref_pow, tile_pow, times = read_aligned(ali_file=ali_file) intvl = time_filter(1569911400, 1569913200, times) assert intvl == [0, 1778] def test_time_filter_V(): ali_file = Path( f"{test_data}/rf_tools/align_data/2019-10-01/2019-10-01-14:30/rf0XX_S06XX_2019-10-01-14:30_aligned.npz" ) ref_pow, tile_pow, times = read_aligned(ali_file=ali_file) intvl = time_filter(1569911300, 1569911400, times) assert intvl is None def test_plt_window_chans(): ali_file = Path( f"{test_data}/rf_tools/align_data/2019-10-01/2019-10-01-14:30/rf0XX_S06XX_2019-10-01-14:30_aligned.npz" ) ref_pow, tile_pow, times = read_aligned(ali_file=ali_file) plt = plt_window_chans( ref_pow, 12345, 1569911700, 1569912000, "Spectral", chs=[0, 4, 7, 14], good_ch=7 ) assert type(plt).__name__ == "module" def test_plt_channel(): ali_file = Path( f"{test_data}/rf_tools/align_data/2019-10-01/2019-10-01-14:30/rf0XX_S06XX_2019-10-01-14:30_aligned.npz" ) ref_pow, tile_pow, times = read_aligned(ali_file=ali_file) noise_threshold = noise_floor(1, 3, ref_pow) plt = plt_channel( times, ref_pow[:, 46], np.median(ref_pow), 46, [-120, 5], noise_threshold, 30 ) assert type(plt).__name__ == "module" def test_plt_sats(): plt = plt_sats( ["25115", "25116"], f"{test_data}/sat_utils/chrono_json/2019-10-01-14:30.json", "2019-10-01-14:30", ) assert type(plt).__name__ == "module" def test_good_chans_41(): good_chan = good_chans( ali_file, chrono_file, "41184", 1, 3, 15, 0.8, "2019-10-01-14:30", f"{test_data}/sat_utils/good_chans_tmp", ) assert good_chan == 41 def test_good_chans_41_plts(): good_chans( ali_file, chrono_file, "41184", 1, 3, 15, 0.8, "2019-10-01-14:30", f"{test_data}/sat_utils/good_chans_tmp", plots=True, ) waterfall = Path( f"{test_data}/sat_utils/good_chans_tmp/window_plots/2019-10-01/2019-10-01-14:30/41184_waterfall_41.png" ) assert waterfall.is_file() shutil.rmtree(f"{test_data}/sat_utils/good_chans_tmp") def test_good_chans_45(): good_chan = good_chans( ali_file_2, chrono_file_2, "41188", 1, 3, 15, 0.8, "2019-10-10-02:30", f"{test_data}/sat_utils/good_chans_tmp", ) assert good_chan == 45 def test_good_chans_45_plts(): good_chans( ali_file_2, chrono_file_2, "41188", 1, 3, 15, 0.8, "2019-10-10-02:30", f"{test_data}/sat_utils/good_chans_tmp", plots=True, ) waterfall = Path( f"{test_data}/sat_utils/good_chans_tmp/window_plots/2019-10-10/2019-10-10-02:30/41188_waterfall_45.png" ) assert waterfall.is_file() shutil.rmtree(f"{test_data}/sat_utils/good_chans_tmp") def test_good_chans_46(): good_chan = good_chans( ali_file, chrono_file, "25417", 1, 3, 15, 0.8, "2019-10-01-14:30", f"{test_data}/sat_utils/good_chans_tmp", ) assert good_chan == 46 def test_good_chans_46_plts(): good_chans( ali_file, chrono_file, "25417", 1, 3, 15, 0.8, "2019-10-01-14:30", f"{test_data}/sat_utils/good_chans_tmp", plots=True, ) waterfall = Path( f"{test_data}/sat_utils/good_chans_tmp/window_plots/2019-10-01/2019-10-01-14:30/25417_waterfall_46.png" ) assert waterfall.is_file() shutil.rmtree(f"{test_data}/sat_utils/good_chans_tmp") def test_good_chans_plts_nochans(): good_chans( ali_file, chrono_file, "44028", 1, 3, 15, 0.8, "2019-10-01-14:30", f"{test_data}/sat_utils/good_chans_tmp", plots=True, ) waterfall = Path( f"{test_data}/sat_utils/good_chans_tmp/window_plots/2019-10-01/2019-10-01-14:30/44028_waterfall_window.png" ) assert waterfall.is_file() shutil.rmtree(f"{test_data}/sat_utils/good_chans_tmp") def test_window_chan_map(): window_chan_map( f"{test_data}/rf_tools/align_data", f"{test_data}/sat_utils/chrono_json", 1, 3, 15, 0.8, "2019-10-01-14:30", f"{test_data}/sat_utils/good_chans_tmp", True, ) chan_map = Path( f"{test_data}/sat_utils/good_chans_tmp/window_maps/2019-10-01-14:30.json" ) assert chan_map.is_file() shutil.rmtree(f"{test_data}/sat_utils/good_chans_tmp") def test_batch_window_map(): batch_window_map( "2019-10-01", "2019-10-01", f"{test_data}/rf_tools/align_data", f"{test_data}/sat_utils/chrono_json", 1, 3, 15, 0.8, f"{test_data}/sat_utils/good_chans_tmp", plots=False, ) chan_map = Path( f"{test_data}/sat_utils/good_chans_tmp/window_maps/2019-10-01-14:30.json" ) assert chan_map.is_file() shutil.rmtree(f"{test_data}/sat_utils/good_chans_tmp")

27.541806

115

0.634608

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3.708333

0.096451

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0.074906

0.786933

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0.733042

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false

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0

0.106122

0

null

0

1

0

null

0

5

33c77fb350a454c81af5732c6ddb333ae2a45bc1

180

py

Python

src/losses/mean_squared_error.py

oussama1598/neural-network-python-implementation

d40d67ab93d0bd59fa0a9825ff7db0f50781cee8

[ "MIT" ]

2

2022-01-27T13:17:20.000Z

2022-01-27T13:19:31.000Z

src/losses/mean_squared_error.py

oussama1598/neural-network-python-implementation

d40d67ab93d0bd59fa0a9825ff7db0f50781cee8

[ "MIT" ]

null

src/losses/mean_squared_error.py

oussama1598/neural-network-python-implementation

d40d67ab93d0bd59fa0a9825ff7db0f50781cee8

[ "MIT" ]

null

import numpy as np def mean_squared_error(y_hat, y): return np.mean(np.power(y_hat - y, 2)) def d_mean_squared_error(y_hat, y): return 2 * (y - y_hat) / np.size(y_hat)

18

43

0.672222

37

180

3

0.405405

0.18018

0.135135

0.306306

0.486486

0

0.013793

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9

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0.751724

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0

1

0

null

0

1

0

1

0

5

33e4a11c2bb6be46578a0789ff1aabaeff9732fb

84

py

Python

fracridge/__init__.py

Shin-kyoto/fracridge

03f84f4feb9ed87a79c1a331bc0992a6be195d2e

[ "BSD-2-Clause" ]

35

2020-05-08T12:32:58.000Z

2022-03-27T00:38:50.000Z

fracridge/__init__.py

Shin-kyoto/fracridge

03f84f4feb9ed87a79c1a331bc0992a6be195d2e

[ "BSD-2-Clause" ]

26

2020-06-26T13:47:13.000Z

2022-03-06T03:38:15.000Z

fracridge/__init__.py

Shin-kyoto/fracridge

03f84f4feb9ed87a79c1a331bc0992a6be195d2e

[ "BSD-2-Clause" ]

11

2020-05-06T02:53:02.000Z

2022-03-01T16:01:33.000Z

from .version import version as __version__ # noqa from .fracridge import * #noqa

28

51

0.761905

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84

5.454545

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0.178571

84

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52

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true

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null

0

1

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1

0

1

0

5

33fa392b08f1093fd3a75a7d5b57799fd25b8e44

412

py

Python

filter_plugins/apiserver_proxy.py

ochinchina/KubeClus

2a90e33cd0334c0ff3136c955ed44333ecb5cc98

[ "MIT" ]

null

filter_plugins/apiserver_proxy.py

ochinchina/KubeClus

2a90e33cd0334c0ff3136c955ed44333ecb5cc98

[ "MIT" ]

null

filter_plugins/apiserver_proxy.py

ochinchina/KubeClus

2a90e33cd0334c0ff3136c955ed44333ecb5cc98

[ "MIT" ]

null

#!/usr/bin/python2 import os import yaml class FilterModule: def filters(self): return { "change_apiserver_proxy": self.change_apiserver_proxy} def change_apiserver_proxy( self, kubelet_conf, new_apiserver): conf = yaml.load( kubelet_conf ) conf['clusters'][0]['cluster']['server'] = "https://%s:6443" % new_apiserver return yaml.dump( conf, default_flow_style=False )

29.428571

84

0.68932

52

412

5.230769

0.596154

0.165441

0.220588

0.176471

0

0.017857

0.184466

412

13

85

31.692308

0.791667

0.041262

0

0.147208

0.055838

0

1

0.222222

false

0

0.222222

0.111111

0.777778

0

null

0

1

0

null

0

1

0

1

0

5

1d14eaac493aa78a1fab376c73847db8d97bb8e4

166

py

Python

Config.py

szymonjanas/realtime_spectrum_analyzer

53793ebf6994bfc2042453b84d96b8300011178f

[ "MIT" ]

1

2020-08-28T23:34:05.000Z

Config.py

szymonjanas/realtime_spectrum_analyzer

53793ebf6994bfc2042453b84d96b8300011178f

[ "MIT" ]

null

Config.py

szymonjanas/realtime_spectrum_analyzer

53793ebf6994bfc2042453b84d96b8300011178f

[ "MIT" ]

null

import json import os data = None with open("config.json") as json_data_file: data = json.load(json_data_file) def get_settings(arg: str): return data[arg]

16.6

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0.722892

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166

4.107143

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0.13913

0.208696

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9

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0.142857

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0

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null

0

1

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1

0

null

0

1

0

5

1d1cee95bccaec5dfed7bdf077e50ba189bbc7f3

43

py

Python

src/django_fahrenheit/exceptions.py

marazmiki/django-fahrenheit

98080b9a388d081ac2955ccf585cffcd45fee7aa

[ "MIT" ]

null

src/django_fahrenheit/exceptions.py

marazmiki/django-fahrenheit

98080b9a388d081ac2955ccf585cffcd45fee7aa

[ "MIT" ]

null

src/django_fahrenheit/exceptions.py

marazmiki/django-fahrenheit

98080b9a388d081ac2955ccf585cffcd45fee7aa

[ "MIT" ]

null

class CountryNotFound(Exception): pass

14.333333

33

0.767442

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0

0.162791

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34

21.5

0.916667

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true

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1

0

null

0

1

0

null

0

1

0

5

1d31ac871d382399ef1c40edff237819975a0dce

188

py

Python

tests/fs.py

FilipVla/qpanelFilip

16d961ef8eee2992dd2e44c78159f8b95b604760

[ "MIT" ]

165

2015-08-01T16:36:50.000Z

2022-03-11T11:40:27.000Z

tests/fs.py

FilipVla/qpanelFilip

16d961ef8eee2992dd2e44c78159f8b95b604760

[ "MIT" ]

205

2015-09-08T17:16:59.000Z

2022-02-08T12:33:15.000Z

tests/fs.py

FilipVla/qpanelFilip

16d961ef8eee2992dd2e44c78159f8b95b604760

[ "MIT" ]

110

2015-08-15T11:24:15.000Z

2022-03-29T15:54:48.000Z

from libs.qpanel import freeswitch fs = freeswitch.Freeswitch() print(fs.getQueues()) print(fs.getAgents('support@default')) print(fs.getCalls('support@default')) print(fs.queueStatus())

23.5

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0

null

0

1

0

5

1d5a165d0d0272a94155bfa9e6885707f99c373f

154

py

Python

modules/analysis/deviation.py

ansteh/multivariate

fbd166f9e9a6d721a1d876b6e46db064f43afe53

[ "Apache-2.0" ]

null

modules/analysis/deviation.py

ansteh/multivariate

fbd166f9e9a6d721a1d876b6e46db064f43afe53

[ "Apache-2.0" ]

null

modules/analysis/deviation.py

ansteh/multivariate

fbd166f9e9a6d721a1d876b6e46db064f43afe53

[ "Apache-2.0" ]

null

import numpy as np def disparity(A, B): return abs(np.subtract(A, B)) def conforms(A, B, threshold): return np.all(disparity(A, B) < threshold)

19.25

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0.668831

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3.961538

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7

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0

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