xszheng2020/the_stack_dedup_python_hits_4 · Datasets at Hugging Face

8a58cb34b8c9ce5b83974e59a07a04d7bc934385

711

py

Python

utils/gpu_utils.py

veritas9872/Knowledge-Distillation-Task

d260b1057c96cfc52af8ff7a0775befbd102f59d

[ "MIT" ]

2

2020-02-16T13:30:27.000Z

2021-01-18T14:18:26.000Z

utils/gpu_utils.py

veritas9872/Knowledge-Distillation-Task

d260b1057c96cfc52af8ff7a0775befbd102f59d

[ "MIT" ]

null

utils/gpu_utils.py

veritas9872/Knowledge-Distillation-Task

d260b1057c96cfc52af8ff7a0775befbd102f59d

[ "MIT" ]

null

import torch from torch import nn def get_gpu_if_available(gpu: int = None): # Device agnostic setting. return torch.device(f'cuda:{gpu}') if torch.cuda.is_available() and (gpu is not None) else torch.device('cpu') def get_single_model_device(model: nn.Module) -> torch.device: """Function for retrieving device of a model, assuming that it is on a single device. If the model is on multiple devices, this function will return the first device. There will be a silent error. This should be fixed if possible. Args: model: The model, assumed to be on a single device. Returns: The device that the model is in. """ return next(model.parameters()).device

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4

8a619c88d08938613878f8018030baf1ebd6aa78

122

py

Python

src/s3labeler/__main__.py

karlrink/s3labeler

60f8ea19fc5895bd2e5e7f8a8eef231888462f89

[ "MIT" ]

null

src/s3labeler/__main__.py

karlrink/s3labeler

60f8ea19fc5895bd2e5e7f8a8eef231888462f89

[ "MIT" ]

null

src/s3labeler/__main__.py

karlrink/s3labeler

60f8ea19fc5895bd2e5e7f8a8eef231888462f89

[ "MIT" ]

null

# -*- coding: utf-8 -*- """s3labeler.__main__: execute when directory is called.""" from .s3labeler import main main()

15.25

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5.2

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7

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0

1

0

1

0

4

8a78f5f2b5195b5b42a9594ba0949fd7ca0bd378

51

py

Python

genre_recognizer.py

salubinseid/am-genre-reco

b7036c7dd9148ad4bdd03e7d3edaa1ac7d8b7a02

[ "MIT" ]

null

genre_recognizer.py

salubinseid/am-genre-reco

b7036c7dd9148ad4bdd03e7d3edaa1ac7d8b7a02

[ "MIT" ]

null

genre_recognizer.py

salubinseid/am-genre-reco

b7036c7dd9148ad4bdd03e7d3edaa1ac7d8b7a02

[ "MIT" ]

null

# A code which load a model and made a prediction

25.5

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3.8

0.8

0

0.235294

51

1

51

0.974359

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1

0

1

0

null

0

1

0

4

8a84db0b3c402e33cdf9c7aa1666f6df4ceb8668

376

py

Python

home/admin.py

DeepanshuPratik/Docon

ad4236e66cf19b668ac69ee3d43711e64a729867

[ "MIT" ]

1

2021-06-22T18:00:05.000Z

home/admin.py

DeepanshuPratik/Docon

ad4236e66cf19b668ac69ee3d43711e64a729867

[ "MIT" ]

null

home/admin.py

DeepanshuPratik/Docon

ad4236e66cf19b668ac69ee3d43711e64a729867

[ "MIT" ]

null

from django.contrib import admin from .models import UserDetails from .models import Contact from .models import Book from .models import Diagnostic from .models import Report # Registered all the models here in the database admin.site.register(UserDetails) admin.site.register(Contact) admin.site.register(Book) admin.site.register(Diagnostic) admin.site.register(Report)

25.066667

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null

0

1

0

1

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0

4

8a8f3ecf67bc94a52bb65d2b3bc558883bbd3474

203

py

Python

src/vmcontroller.host/vmcontroller/host/services/__init__.py

dgquintas/vmcontroller.unstable

131c0af19c5923ef57c74006246dc41c65f24120

[ "BSD-3-Clause" ]

null

src/vmcontroller.host/vmcontroller/host/services/__init__.py

dgquintas/vmcontroller.unstable

131c0af19c5923ef57c74006246dc41c65f24120

[ "BSD-3-Clause" ]

null

src/vmcontroller.host/vmcontroller/host/services/__init__.py

dgquintas/vmcontroller.unstable

131c0af19c5923ef57c74006246dc41c65f24120

[ "BSD-3-Clause" ]

null

try: from HostStompEngine import * from HostServices import * from HostWords import * except ImportError, e: print "Import error in %s : %s" % (__name__, e) import sys sys.exit()

22.555556

51

0.650246

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5.12

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0.261084

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8

52

25.375

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0

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0

1

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1

0

4

8a8fe78acefa95d1af7ec08489d855a0707a7dc3

154

py

Python

services/traction/api/endpoints/models/tenant_schema.py

Open-Earth-Foundation/traction

908b555a7f408a88541b7692d3730e37a297c919

[ "Apache-2.0" ]

12

2022-01-29T20:30:03.000Z

2022-03-29T11:46:14.000Z

services/traction/api/endpoints/models/tenant_schema.py

Open-Earth-Foundation/traction

908b555a7f408a88541b7692d3730e37a297c919

[ "Apache-2.0" ]

38

2021-11-22T17:52:50.000Z

2022-03-31T17:52:00.000Z

services/traction/api/endpoints/models/tenant_schema.py

Open-Earth-Foundation/traction

908b555a7f408a88541b7692d3730e37a297c919

[ "Apache-2.0" ]

9

2021-11-22T18:05:48.000Z

2022-03-29T11:25:08.000Z

from api.db.models.base import BaseSchema class TenantSchemaRequest(BaseSchema): schema_name: str schema_version: str attributes: list[str]

19.25

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19

154

6.052632

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null

0

1

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1

0

4

8ac1b29e5998d9fe8f4ae803c15e96047bff8204

122

py

Python

src/java/python/__init__.py

bastie/PythonVampire

b5102f7389f3d583c41ec85c574ce5a72bbf4460

[ "Apache-2.0" ]

1

2020-09-05T14:02:11.000Z

src/java/util/__init__.py

bastie/PythonVampire

b5102f7389f3d583c41ec85c574ce5a72bbf4460

[ "Apache-2.0" ]

null

src/java/util/__init__.py

bastie/PythonVampire

b5102f7389f3d583c41ec85c574ce5a72bbf4460

[ "Apache-2.0" ]

null

# SPDX-FileCopyrightText: 2020 - Sebastian Ritter <bastie@users.noreply.github.com> # SPDX-License-Identifier: Apache-2.0

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null

0

1

0

1

0

null

0

1

0

4

8acfacc8115035f5c7f9346338deed04f4d415e9

749

py

Python

auxiliary/config.py

yanlinqian/Temporal-Color-Constancy

ebd363962fa8ae0908252cabaf97355da3da8a80

[ "MIT" ]

8

2020-09-04T08:55:41.000Z

2021-07-16T01:51:57.000Z

auxiliary/config.py

yanlinqian/Temporal-Color-Constancy

ebd363962fa8ae0908252cabaf97355da3da8a80

[ "MIT" ]

3

2021-11-04T02:35:09.000Z

2021-11-24T12:37:28.000Z

auxiliary/config.py

yanlinqian/Temporal-Color-Constancy

ebd363962fa8ae0908252cabaf97355da3da8a80

[ "MIT" ]

1

2020-10-15T13:20:42.000Z

#superset of config ### C4 or FC4 # FCN_INPUT_SIZE = 512 # # Use data augmentation? # AUGMENTATION = True # # Rotation angle # AUGMENTATION_ANGLE = 60 # # Patch scale # AUGMENTATION_SCALE = [0.1, 1.0] # # Color rescaling? # AUGMENTATION_COLOR = 0.8 # BOARD_FILL_COLOR = 1e-5 ### ffcc #FCN_INPUT_SIZE = 512 # Use data augmentation? #AUGMENTATION = True # Rotation angle #AUGMENTATION_ANGLE = 0 # Patch scale #AUGMENTATION_SCALE = [1.0,1,0]#[0.8, 1.0] # Color rescaling? #AUGMENTATION_COLOR = 0 #BOARD_FILL_COLOR = 0 ### RCC-Net FCN_INPUT_SIZE = 512 # Use data augmentation? AUGMENTATION = True # Rotation angle AUGMENTATION_ANGLE = 15 # Patch scale AUGMENTATION_SCALE = [0.8, 1.0] # Color rescaling? AUGMENTATION_COLOR = 0 BOARD_FILL_COLOR = 1e-5

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null

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1

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null

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4

8ad66a12455c75eba1193467be036df654ae6c1a

2,601

py

Python

__init__.py

jmaggio14/borealis

e61a02671fbfb910cd9526e717a33c93d1880773

[ "MIT" ]

null

__init__.py

jmaggio14/borealis

e61a02671fbfb910cd9526e717a33c93d1880773

[ "MIT" ]

null

__init__.py

jmaggio14/borealis

e61a02671fbfb910cd9526e717a33c93d1880773

[ "MIT" ]

null

# @Author: Jeff Maggio <jmaggio> # @Date: 2017-07-30T15:37:09-07:00 # @Email: jmaggio@planetaryresources.com # @Project: sam (framing camera simulator) # @Last modified by: jmaggio # @Last modified time: 2017-07-30T15:37:53-07:00 # @Copyright: #!/usr/bin/env python ################################################################################ # Copyright (c) 2017 Planetary Resources Inc. # Planetary Resources Proprietary # NOTICE: # All information contained herein is, and remains the property of Planetary # Resources Incorporated, its subsidiaries and its suppliers, if any. The # intellectual and technical concepts contained herein are proprietary to # Planetary Resources Incorporated, its subsidiaries, and its suppliers and # may be covered by U.S. and Foreign Patents, patents in process, and are # protected by trade secret or copyright law. Dissemination of this # information or reproduction of this material is strictly forbidden unless # prior written permission is obtained from Planetary Resources Inc. ################################################################################ ## Doxygen header # @author <your name> # @brief <description> # Standard library imports ################################################################################ # Third party library imports ################################################################################ # Standard PRI imports ################################################################################ # External component imports ################################################################################ # Internal component imports ################################################################################ # Constant definitions ################################################################################ # Utility function definitions ################################################################################ # Public class definitions ################################################################################ # Public function definitions ################################################################################ # Private class definitions ################################################################################ # Private function definitions ################################################################################ # Main function and argument parsing ################################################################################ from .path import * from .render import * from .terrain import * from .tube import *

37.695652

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1

0

1

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4

76d6ba281bb2c79903b5de6a7f400af0f63a86e4

370

py

Python

organice/settings.py

bittner/django-organice

7621e4cf2361db84b42d77e5e72e341559eb9906

[ "Apache-2.0" ]

34

2015-04-22T12:47:32.000Z

2022-03-18T02:16:17.000Z

organice/settings.py

TebelloX/django-organice

7621e4cf2361db84b42d77e5e72e341559eb9906

[ "Apache-2.0" ]

13

2015-07-24T05:25:56.000Z

2020-09-02T17:38:35.000Z

organice/settings.py

TebelloX/django-organice

7621e4cf2361db84b42d77e5e72e341559eb9906

[ "Apache-2.0" ]

14

2015-05-01T20:42:49.000Z

2022-03-25T01:12:34.000Z

"""Default settings for django Organice""" from django.conf import settings URL_PATH_ADMIN = getattr(settings, 'ORGANICE_URL_PATH_ADMIN', 'admin') URL_PATH_BLOG = getattr(settings, 'ORGANICE_URL_PATH_BLOG', 'blog') URL_PATH_NEWSLETTER = getattr(settings, 'ORGANICE_URL_PATH_NEWSLETTER', 'newsletter') URL_PATH_TODO = getattr(settings, 'ORGANICE_URL_PATH_TODO', 'todo')

46.25

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370

5.54

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7

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1

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1

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null

0

4

76e786acc34f0b2ad15c66ff0074cbab39b06e92

60

py

Python

cached_result/tests/factories.py

darthwade/django-cached-result

0e6a9f258db4fc98e0d9c8190adde3aca0b95782

[ "MIT" ]

1

2016-08-29T20:26:40.000Z

cached_result/tests/factories.py

darthwade/django-cached-result

0e6a9f258db4fc98e0d9c8190adde3aca0b95782

[ "MIT" ]

null

cached_result/tests/factories.py

darthwade/django-cached-result

0e6a9f258db4fc98e0d9c8190adde3aca0b95782

[ "MIT" ]

null

"""Factories for the cached_result app.""" # import factory

20

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0

1

0

4

0a0a43a57eddfd50c80bd36c49d4becf4a751886

22

py

Python

questionary/version.py

philastrophist/questionary

512d52f5216e3494902a124f660453ec7fa4fa16

[ "MIT" ]

2,245

2017-03-31T14:43:44.000Z

2022-03-30T01:17:25.000Z

trackerjacker/version.py

Warlockk/trackerjacker

692262b2646a2e784733a258da8b8d6bfe79e3d7

[ "MIT" ]

46

2020-06-27T18:13:41.000Z

2021-07-12T10:49:00.000Z

trackerjacker/version.py

Warlockk/trackerjacker

692262b2646a2e784733a258da8b8d6bfe79e3d7

[ "MIT" ]

191

2017-05-20T20:25:49.000Z

2022-03-03T05:44:49.000Z

__version__ = "1.9.0"

11

21

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2.5

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1

22

0.368421

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1

0

false

0

1

0

null

0

1

0

1

0

null

0

4

0a264c59a07b3a3f0e95e5c8c2c675aba96c9906

220

py

Python

little_mees/little_mees/doctype/global_defaults/test_global_defaults.py

ayhamkht/little_mees

6acd7a347a7786bbdb2f5b06f60ba5c22059eb19

[ "MIT" ]

null

little_mees/little_mees/doctype/global_defaults/test_global_defaults.py

ayhamkht/little_mees

6acd7a347a7786bbdb2f5b06f60ba5c22059eb19

[ "MIT" ]

null

little_mees/little_mees/doctype/global_defaults/test_global_defaults.py

ayhamkht/little_mees

6acd7a347a7786bbdb2f5b06f60ba5c22059eb19

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Copyright (c) 2021, Kunhi Mohamed and Contributors # See license.txt from __future__ import unicode_literals # import frappe import unittest class TestGlobalDefaults(unittest.TestCase): pass

20

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0.768182

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6.074074

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0.84127

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true

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0

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0

1

0

4

0a656cc99cb1f62b7de712de53aab5716e41ab05

131

py

Python

bunq/sdk/exception/bunq_exception.py

mwiekens/sdk_python

9333636083bc63dca4353e8f497588f57617efec

[ "MIT" ]

88

2017-08-01T18:39:46.000Z

2022-02-21T12:34:16.000Z

bunq/sdk/exception/bunq_exception.py

mwiekens/sdk_python

9333636083bc63dca4353e8f497588f57617efec

[ "MIT" ]

136

2017-08-02T13:54:41.000Z

2021-04-25T20:31:08.000Z

bunq/sdk/exception/bunq_exception.py

mwiekens/sdk_python

9333636083bc63dca4353e8f497588f57617efec

[ "MIT" ]

30

2017-08-15T09:35:42.000Z

2021-05-06T12:42:06.000Z

class BunqException(Exception): def __init__(self, message: str) -> None: super(BunqException, self).__init__(message)

32.75

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0.709924

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131

6.071429

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43.666667

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false

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0

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null

0

1

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1

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4

0a66793300d4bd961723ef76e259170dabe2366c

517

py

Python

src/mysql_bigquery/prefect_utils/__init__.py

beirving/mysql-to-bigquery

75dfe3390f1e1a0dc54d5cace0cf1a89a0560ae6

[ "MIT" ]

null

src/mysql_bigquery/prefect_utils/__init__.py

beirving/mysql-to-bigquery

75dfe3390f1e1a0dc54d5cace0cf1a89a0560ae6

[ "MIT" ]

null

src/mysql_bigquery/prefect_utils/__init__.py

beirving/mysql-to-bigquery

75dfe3390f1e1a0dc54d5cace0cf1a89a0560ae6

[ "MIT" ]

null

import os from mysql_bigquery.prefect_utils import install from mysql_bigquery.prefect_utils import jobs if os.environ.get('MYSQL_BIG_QUERY_DEFINITIONS') is None: os.environ['MYSQL_BIG_QUERY_DEFINITIONS'] = '/credentials/definitions.json' if os.environ.get('MYSQL_BIG_QUERY_GOOGLE_AUTH') is None: os.environ['MYSQL_BIG_QUERY_GOOGLE_AUTH'] = '/credentials/google_auth.json' if os.environ.get('MYSQL_BIG_QUERY_MYSQL_CONFIG') is None: os.environ['MYSQL_BIG_QUERY_MYSQL_CONFIG'] = '/credentials/config.ini'

43.083333

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true

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null

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1

0

4

6a544f8fee4e4b8776c18503a376e27fe4af0326

506

py

Python

OpenGLCffi/GL/EXT/SGIX/async.py

cydenix/OpenGLCffi

c78f51ae5e6b655eb2ea98f072771cf69e2197f3

[ "MIT" ]

null

OpenGLCffi/GL/EXT/SGIX/async.py

cydenix/OpenGLCffi

c78f51ae5e6b655eb2ea98f072771cf69e2197f3

[ "MIT" ]

null

OpenGLCffi/GL/EXT/SGIX/async.py

cydenix/OpenGLCffi

c78f51ae5e6b655eb2ea98f072771cf69e2197f3

[ "MIT" ]

null

from OpenGLCffi.GL import params @params(api='gl', prms=['marker']) def glAsyncMarkerSGIX(marker): pass @params(api='gl', prms=['markerp']) def glFinishAsyncSGIX(markerp): pass @params(api='gl', prms=['markerp']) def glPollAsyncSGIX(markerp): pass @params(api='gl', prms=['range']) def glGenAsyncMarkersSGIX(range): pass @params(api='gl', prms=['marker', 'range']) def glDeleteAsyncMarkersSGIX(marker, range): pass @params(api='gl', prms=['marker']) def glIsAsyncMarkerSGIX(marker): pass

15.8125

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0.705534

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5.852459

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0.151261

0.184874

0.252101

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0.330532

0

0.106719

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31

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0.315789

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6a690851e616a20a4cc455e792561102402f3c7e

38,316

py

Python

bayes_filter/vi.py

Joshuaalbert/bayes_filter

2997d60d8cf07f875e42c0b5f07944e9ab7e9d33

[ "Apache-2.0" ]

null

bayes_filter/vi.py

Joshuaalbert/bayes_filter

2997d60d8cf07f875e42c0b5f07944e9ab7e9d33

[ "Apache-2.0" ]

3

2019-02-21T16:00:53.000Z

2020-03-31T01:33:00.000Z

bayes_filter/vi.py

Joshuaalbert/bayes_filter

2997d60d8cf07f875e42c0b5f07944e9ab7e9d33

[ "Apache-2.0" ]

null

import tensorflow as tf import numpy as np import tensorflow_probability as tfp from .sgd import adam_stochastic_gradient_descent, natural_adam_stochastic_gradient_descent, natural_adam_stochastic_gradient_descent_with_linesearch, natural_adam_stochastic_gradient_descent_with_linesearch_minibatch from . import float_type, TEC_CONV from .misc import sqrt_with_finite_grads, safe_cholesky, flatten_batch_dims, log_normal_cdf_solve from .kernels import DTECIsotropicTimeGeneral class Likelihood(object): def __init__(self): pass def log_prob(self, *args): pass class VariationalPosterior(object): def __init__(self, event_size): self._event_size = event_size self._distribution = None def sample(self, num_samples): if self._distribution is None: raise ValueError("no distribution defined") return self._distribution.sample(num_samples) def _build_distribution(self, *params): """ Build the distribution. :param params: :return: """ raise NotImplementedError() def initial_variational_params(self, batch_size): raise NotImplementedError() class WhitenedVariationalPosterior(VariationalPosterior): def __init__(self, event_size): super(WhitenedVariationalPosterior, self).__init__(event_size=event_size) def _build_distribution(self, loc, scale): """ Build the MultivariateNormalDiagWithSoftplusScale distribution :param loc: :param scale: :return: """ return tfp.distributions.MultivariateNormalDiag(loc, scale_diag=tf.nn.softplus(scale)) def initial_variational_params(self, batch_size=None): """ Gets the initial parameters :param batch_size: :return: """ if batch_size is not None: m = tf.zeros(shape=[batch_size, self._event_size], dtype=float_type) S_inverse = tfp.distributions.softplus_inverse( tf.ones(shape=[batch_size, self._event_size], dtype=float_type)) return m, S_inverse m = tf.zeros(shape=[self._event_size], dtype=float_type) S_inverse = tfp.distributions.softplus_inverse( tf.ones(shape=[self._event_size], dtype=float_type)) return m, S_inverse class LaplaceLikelihood(Likelihood): def __init__(self, Yreal, Yimag, freqs, transform_fn): super(LaplaceLikelihood, self).__init__() self._Yreal = Yreal self._Yimag = Yimag self._invfreqs = tf.constant(TEC_CONV, float_type) * tf.math.reciprocal(freqs) self._transform_fn = transform_fn def log_prob(self, white_dtec, y_sigma): """ Represents log P(Yreal, Yimag | white_dtec, hyperparams) where P is the product of Laplace distributions over frequency and coordinate index log P = Sum_i Sum_nu (-log(2) - log(y_sigma) - (|Yreal(i,nu) - Yreal_model(i, nu)| + |Yimag(i, nu) - Yimag_model(i, nu)|) / y_sigma) :param white_dtec: tf.Tensor [A, N] :param log_y_sigma: tf.Tensor [B, 1] :return: tf.Tensor [A, B] """ Nf = tf.cast(tf.shape(self._invfreqs)[0],float_type) # A, B, N dtec = self._transform_fn(white_dtec) # [A, B, N, Nf] phase = dtec[..., None] * self._invfreqs Yreal_model = tf.cos(phase) Yimag_model = tf.sin(phase) # B, 1 log_y_sigma = tf.math.log(y_sigma) # [A, B, N, Nf] likelihood = -tf.math.reciprocal(y_sigma[..., None]) * sqrt_with_finite_grads( tf.math.square(self._Yimag - Yimag_model) + tf.math.square(self._Yreal - Yreal_model))\ - log_y_sigma[..., None] - tf.math.log(tf.constant(2., float_type)) # A, B #TODO: is div by Nf right? likelihood = tf.reduce_sum(likelihood, axis=[-2, -1])/Nf prior = tf.reduce_mean(tfp.distributions.Normal(loc=tf.constant(0.05, float_type), scale=tf.constant(0.05, float_type)).log_prob(y_sigma)) return likelihood + prior class VariationalBayesHeirarchical(object): def __init__(self, Yreal, Yimag, freqs, X, Xstar, dtec_samples=10, hyperparam_samples=10, mean_hyperparam_approx=True, obs_type='DTEC', fed_kernel='RBF'): self._Yreal = Yreal self._Yimag = Yimag self._freqs = freqs self._invfreqs = tf.constant(TEC_CONV, float_type) * tf.math.reciprocal(freqs) self._X = X self._Xstar = Xstar self._Xconcat = tf.concat([self._X, self._Xstar], axis=0) self.N = tf.shape(self._X)[0] self.Ns = tf.shape(self._Xstar)[0] self._obs_type = obs_type self._fed_kernel = fed_kernel self._dtec_samples = tf.convert_to_tensor(dtec_samples, tf.int32, name='num_dtec_samples') self._hyperparam_samples = tf.convert_to_tensor(hyperparam_samples, tf.int32, name='num_hyperparam_samples') self._mean_hyperparam_approx = mean_hyperparam_approx self._white_posterior = WhitenedVariationalPosterior(event_size=tf.shape(self._X)[0]) # amp, lengthscales, a, b, timescales, y_sigma self._hyperparam_posterior = WhitenedVariationalPosterior(event_size=6) self._hyperparam_bijectors = [ tfp.bijectors.Chain( [tfp.bijectors.AffineScalar(scale=tf.constant(3., float_type)), tfp.bijectors.Softplus()]), tfp.bijectors.Chain( [tfp.bijectors.AffineScalar(scale=tf.constant(15., float_type)), tfp.bijectors.Softplus()]), tfp.bijectors.Chain( [tfp.bijectors.AffineScalar(scale=tf.constant(250., float_type)), tfp.bijectors.Softplus()]), tfp.bijectors.Chain( [tfp.bijectors.AffineScalar(scale=tf.constant(100., float_type)), tfp.bijectors.Softplus()]), tfp.bijectors.Chain( [tfp.bijectors.AffineScalar(scale=tf.constant(50., float_type)), tfp.bijectors.Softplus()]), tfp.bijectors.Chain( [tfp.bijectors.AffineScalar(scale=tf.constant(0.1, float_type)), tfp.bijectors.Softplus()]) ] def _initial_states(self, batch_size=None): return self._white_posterior.initial_variational_params( batch_size), self._hyperparam_posterior.initial_variational_params(batch_size) def _constrain_hyperparams(self, sampled_hyperparams): """ Constrains the samples of hyperparams. :param sampled_hyperparams: tf.Tensor [samples, 6] :return: Tuple of tf.Tensor Each of shape [samples, 1] """ constrained_hyperparams = [] for i in range(len(self._hyperparam_bijectors)): bijector = self._hyperparam_bijectors[i] # num_hyperparams, 1 s = sampled_hyperparams[:, i:i + 1] constrained_hyperparams.append(bijector.forward(s)) return constrained_hyperparams def _loss_fn(self, white_dtec_mean, white_dtec_scale, hyperparam_mean, hyperparam_scale): white_vi_params, hyperparam_vi_params = (white_dtec_mean, white_dtec_scale), (hyperparam_mean, hyperparam_scale) hyperparam_dist = self._hyperparam_posterior._build_distribution(*hyperparam_vi_params) if self._mean_hyperparam_approx: #1, 6 sampled_hyperparams = hyperparam_vi_params[0][None,:] else: # num_hyperparams, 6 sampled_hyperparams = hyperparam_dist.sample(self._hyperparam_samples) amp, lengthscales, a, b, timescale, y_sigma = self._constrain_hyperparams(sampled_hyperparams) kern = DTECIsotropicTimeGeneral(variance=tf.math.square(amp), lengthscales=lengthscales, a=a, b=b, timescale=timescale, fed_kernel=self._fed_kernel, obs_type=self._obs_type, squeeze=False) # num_hyperparams, N, N K = kern.K(self._X, None) # num_hyperparams, N, N L = safe_cholesky(K) # no mean right now def transform_fn(white_dtec): """ Constrain white_dtec to tec mean_approx_hyperparams :param white_dtec: tf.Tensor [b0,..., bB, N] :param data_only: tf.bool :return: tf.Tensor [b0,...,bB, b0,...,bC,N] """ # TODO: add mean # L[d,i,j].white_dtec[b,j] -> [b,d,i] # b0,..., bB, , b0,...,bC,N return tf.tensordot(white_dtec, L, axes=[[-1], [-1]]) white_dist = self._white_posterior._build_distribution(*white_vi_params) # num_dtec, N white_dtec = white_dist.sample(self._dtec_samples) likelihood = LaplaceLikelihood(self._Yreal, self._Yimag, self._freqs, transform_fn=transform_fn) # num_hyperparams #TODO: derive better var_exp var_exp = tf.reduce_mean(likelihood.log_prob(white_dtec, y_sigma), axis=0) # num_hyperparams dtec_prior_KL = self._dtec_prior_kl(white_vi_params, L) # scalar hyperparam_prior_KL = self._hyperparams_prior_kl(hyperparam_vi_params) # scalar elbo = tf.reduce_mean(var_exp - dtec_prior_KL, axis=0) - hyperparam_prior_KL with tf.control_dependencies([tf.print('elbo', elbo, 'var_exp', var_exp, 'dtec_prior', dtec_prior_KL, 'hyperparam_prior', hyperparam_prior_KL, 'amp', amp, 'lengthscales', lengthscales, 'a', a, 'b', b, 'timescale', timescale, 'y_sigma', y_sigma)]): loss = tf.math.negative(elbo, name='loss') return loss def _hyperparams_prior_kl(self, hyperparams_params): """The KL-div[ Q(hyperparams) || P(hyperparams) ] P(hyperparams) = U[-infty, infty](hyperparams) = N[0, infty](hyperparams) :param hyperparams_params: tf.Tensor [6] mean [6] diag_scale :return: tf.Tensor scalar """ variance = tf.math.square(tf.nn.softplus(hyperparams_params[1])) entropy = tf.reduce_sum(tf.constant(0.5, float_type) * tf.math.log(tf.constant(2 * np.pi * np.exp(1), float_type) * variance)) return -entropy def _dtec_prior_kl(self, white_dtec_params, L): """ Get the KL-div [ Q(white_dtec) || P(white_dtec | hyperparams)] where Q = N[m, S] and S is diagonal and P = |L|^{-1} N[0,I] KL-div [ N[m, S] || |L|^{-1} N[0,I]] = KL-div [ N[m, S] || |L|^{-1} N[0,I]] + log |L| :param white_dtec_params: tuple of tf.Tensor [N+Ns] mean [N+Ns] unconstrained scale :param L: tf.Tensor [num_hyperparams, N, N] :return: tf.Tensor [num_hyperparams] """ # [N+Ns] mean, S = white_dtec_params variance = tf.math.square(tf.nn.softplus(S)) # num_hyperparams logdetL = tf.reduce_sum(tf.math.log(tf.linalg.diag_part(L)), axis=-1) return 0.5 * tf.reduce_sum( variance + tf.math.square(mean) - tf.constant(1., float_type) - 2. * tf.math.log(variance), axis=-1) + logdetL def _build_variational_posteriors(self, white_vi_params, hyperparam_vi_params): hyperparam_dist = self._hyperparam_posterior._build_distribution(*hyperparam_vi_params) white_dist = self._white_posterior._build_distribution(*white_vi_params) return white_dist, hyperparam_dist def solve_variational_posterior(self, param_warmstart, hyperparams_warmstart, iters=100, learning_rate=0.001, parallel_iterations=10): (white_dtec_mean, white_dtec_scale), (hyperparam_mean, hyperparam_scale) = self._initial_states() ((white_dtec_mean, white_dtec_scale), (hyperparam_mean, hyperparam_scale)) = \ tf.cond(tf.reduce_all(tf.equal(param_warmstart[0], 0.)), lambda: ((white_dtec_mean, white_dtec_scale), (hyperparam_mean, hyperparam_scale)), lambda: (param_warmstart, hyperparams_warmstart), strict=True) # [white_dtec_mean, white_dtec_scale, hyperparam_mean, hyperparam_scale], loss = \ # adam_stochastic_gradient_descent(self._loss_fn, # [white_dtec_mean, white_dtec_scale, hyperparam_mean, hyperparam_scale], # iters=iters, # learning_rate=learning_rate, # parallel_iterations=parallel_iterations) [white_dtec_mean, white_dtec_scale], [hyperparam_mean, hyperparam_scale], loss = \ natural_adam_stochastic_gradient_descent(self._loss_fn, [white_dtec_mean, white_dtec_scale], [hyperparam_mean, hyperparam_scale], iters=iters, learning_rate=learning_rate, parallel_iterations=parallel_iterations) ### # produce the posterior distributions needed hyperparam_dist = self._hyperparam_posterior._build_distribution(hyperparam_mean, hyperparam_scale) if self._mean_hyperparam_approx: # 1, 6 sampled_hyperparams = hyperparam_mean[None, :] else: # num_hyperparams, 6 sampled_hyperparams = hyperparam_dist.sample(self._hyperparam_samples) amp, lengthscales, a, b, timescale, y_sigma = self._constrain_hyperparams(sampled_hyperparams) kern = DTECIsotropicTimeGeneral(variance=tf.math.square(amp), lengthscales=lengthscales, a=a, b=b, timescale=timescale, fed_kernel=self._fed_kernel, obs_type=self._obs_type, squeeze=False) # num_hyperparams, N, N K_xx = kern.K(self._X, None) # num_hyperparams, N, N L_xx = safe_cholesky(K_xx) # num_hyperparams, M, N K_yx = kern.K(self._X, self._Xstar) q_mean, q_sqrt = white_dtec_mean, tf.nn.softplus(white_dtec_scale) dtec_data_dist = conditional_same_points(q_mean, q_sqrt, L_xx) dtec_screen_dist = conditional_different_points(q_mean, q_sqrt, L_xx, K_xx, K_yx) return loss, dtec_data_dist, dtec_screen_dist, (amp, lengthscales, a, b, timescale, y_sigma), ( white_dtec_mean, white_dtec_scale), (hyperparam_mean, hyperparam_scale) class VariationalBayesZIsX(object): def __init__(self, Yreal, Yimag, freqs, X, Xstar, y_sigma, dtec_samples=10, kernel_params=None, minibatch_size=None, quadrature_var_exp=False): self._Yreal = Yreal self._Yimag = Yimag self._freqs = freqs self._y_sigma = y_sigma self._invfreqs = tf.constant(TEC_CONV, float_type) * tf.math.reciprocal(freqs) self._X = X self._Xstar = Xstar self._Xconcat = tf.concat([self._X, self._Xstar], axis=0) self.N = tf.shape(self._X)[0] self.Ns = tf.shape(self._Xstar)[0] self._kernel_params = kernel_params self._dtec_samples = tf.convert_to_tensor(dtec_samples, tf.int32, name='num_dtec_samples') self._minibatch_size = tf.convert_to_tensor(minibatch_size,tf.int64) if minibatch_size is not None else None if self._minibatch_size is not None: self._scale = tf.cast(self.N, float_type)/tf.cast(self._minibatch_size, float_type) else: self._scale = tf.constant(1., float_type) self._quadrature_var_exp = quadrature_var_exp self._white_posterior = WhitenedVariationalPosterior(event_size=tf.shape(self._X)[0]) self._hyperparam_bijectors = [ tfp.bijectors.Chain( [tfp.bijectors.AffineScalar(scale=tf.constant(3., float_type)), tfp.bijectors.Softplus()]), tfp.bijectors.Chain( [tfp.bijectors.AffineScalar(scale=tf.constant(15., float_type)), tfp.bijectors.Softplus()]), tfp.bijectors.Chain( [tfp.bijectors.AffineScalar(scale=tf.constant(250., float_type)), tfp.bijectors.Softplus()]), tfp.bijectors.Chain( [tfp.bijectors.AffineScalar(scale=tf.constant(100., float_type)), tfp.bijectors.Softplus()]), tfp.bijectors.Chain( [tfp.bijectors.AffineScalar(scale=tf.constant(50., float_type)), tfp.bijectors.Softplus()]) ] def _initial_states(self, batch_size=None): return self._white_posterior.initial_variational_params( batch_size), (tfp.distributions.softplus_inverse(tf.ones((1,5),float_type)),) def _constrain_hyperparams(self, sampled_hyperparams): """ Constrains the samples of hyperparams. :param sampled_hyperparams: tf.Tensor [samples, 6] :return: Tuple of tf.Tensor Each of shape [samples, 1] """ constrained_hyperparams = [] for i in range(len(self._hyperparam_bijectors)): bijector = self._hyperparam_bijectors[i] # num_hyperparams, 1 s = sampled_hyperparams[:, i:i + 1] constrained_hyperparams.append(bijector.forward(s)) return constrained_hyperparams def _loss_fn(self, white_dtec_mean, white_dtec_scale, hyperparams_unconstrained): white_vi_params = (white_dtec_mean, white_dtec_scale) #each 1,1 amp, lengthscales, a, b, timescale = self._constrain_hyperparams(hyperparams_unconstrained) kern = DTECIsotropicTimeGeneral(variance=tf.math.square(amp), lengthscales=lengthscales, a=a, b=b, timescale=timescale, squeeze=False, **self._kernel_params) # num_hyperparams, N, N K = kern.K(self._X, None) # num_hyperparams, N, N L = safe_cholesky(K) # no mean right now def transform_fn(white_dtec): """ Constrain white_dtec to tec L is [B, N, N] :param white_dtec: tf.Tensor [A, N] :return: tf.Tensor [A,B,N] """ # TODO: add mean # L[d,i,j].white_dtec[b,j] -> [b,d,i] # A, B, N return tf.tensordot(white_dtec, L, axes=[[-1], [-1]]) var_exp = self._calculate_var_exp(transform_fn, white_vi_params) dtec_prior_KL = self._dtec_prior_kl(white_vi_params, L) # scalar elbo = var_exp*self._scale - dtec_prior_KL ### # priors on parameters. with tf.control_dependencies([tf.print('elbo', elbo, 'var_exp', var_exp, 'dtec_prior', dtec_prior_KL, 'amp', amp, 'lengthscales', lengthscales, 'a', a, 'b', b, 'timescale', timescale, 'y_sigma', self._y_sigma)]): loss = tf.math.negative(elbo, name='loss') return loss def _calculate_var_exp(self, transform_fn, white_vi_params): if not self._quadrature_var_exp: white_dist = self._white_posterior._build_distribution(*white_vi_params) # num_dtec, N white_dtec = white_dist.sample(self._dtec_samples) likelihood = LaplaceLikelihood(self._Yreal, self._Yimag, self._freqs, transform_fn=transform_fn) # TODO: derive better var_exp var_exp = tf.reduce_mean(likelihood.log_prob(white_dtec, self._y_sigma)) return var_exp ## Use Gauss Hermite Quadrature def _dtec_prior_kl(self, white_dtec_params, L): """ Get the KL-div [ Q(white_dtec) || P(white_dtec | hyperparams)] where Q = N[m, S] and S is diagonal and P = |L|^{-1} N[0,I] KL-div [ N[m, S] || |L|^{-1} N[0,I]] = KL-div [ N[m, S] || |L|^{-1} N[0,I]] + log |L| :param white_dtec_params: tuple of tf.Tensor [N+Ns] mean [N+Ns] unconstrained scale :param L: tf.Tensor [num_hyperparams, N, N] :return: tf.Tensor [num_hyperparams] """ logdetL = tf.reduce_sum(tf.math.log(tf.linalg.diag_part(L))) # [N+Ns] q_mean, q_scale = white_dtec_params q_sqrt = tf.nn.softplus(q_scale) q_var = tf.math.square(q_sqrt) trace = tf.reduce_sum(q_var) mahalanobis = tf.reduce_sum(tf.math.square(q_mean)) constant = -tf.cast(tf.size(q_mean, out_type=tf.int64), float_type) logdet_qcov = tf.reduce_sum(tf.math.log(q_var)) twoKL = mahalanobis + constant - logdet_qcov + trace - logdetL return 0.5 * twoKL # # num_hyperparams # # return 0.5 * tf.reduce_sum( # variance + tf.math.square(mean) - tf.constant(1., float_type) - 2. * tf.math.log(variance), # axis=-1) + logdetL def _build_variational_posteriors(self, white_vi_params): white_dist = self._white_posterior._build_distribution(*white_vi_params) return white_dist def solve_variational_posterior(self, param_warmstart, hyperparams_warmstart, solver_params=None, parallel_iterations=10): (white_dtec_mean, white_dtec_scale), (hyperparams_unconstrained,) = self._initial_states() # ((white_dtec_mean, white_dtec_scale), (hyperparams_unconstrained,)) = \ # tf.cond(tf.reduce_all(tf.equal(param_warmstart[0], 0.)), # lambda: ((white_dtec_mean, white_dtec_scale), (hyperparams_unconstrained,)), # lambda: (param_warmstart, hyperparams_warmstart), strict=True) (white_dtec_mean, white_dtec_scale) = \ tf.cond(tf.reduce_all(tf.equal(param_warmstart[0], 0.)), lambda: (white_dtec_mean, white_dtec_scale), lambda: param_warmstart, strict=True) # TODO: mini batch and choose larger basis # TODO: speed up kernel computation ^^ help # TODO: fix screen approximation [white_dtec_mean, white_dtec_scale], [hyperparams_unconstrained], loss = \ natural_adam_stochastic_gradient_descent_with_linesearch(self._loss_fn, [white_dtec_mean, white_dtec_scale], [hyperparams_unconstrained], parallel_iterations=parallel_iterations, **solver_params) ### # produce the posterior distributions needed amp, lengthscales, a, b, timescale = self._constrain_hyperparams(hyperparams_unconstrained) kern = DTECIsotropicTimeGeneral(variance=tf.math.square(amp), lengthscales=lengthscales, a=a, b=b, timescale=timescale, squeeze=False, **self._kernel_params) # num_hyperparams, N, N K_x_x = kern.K(self._X, None) # num_hyperparams, N, N L_x_x = safe_cholesky(K_x_x) # num_hyperparams, M, N K_x_xstar = kern.K(self._X, self._Xstar) K_xstar_xstar = kern.K(self._Xstar, None) q_mean, q_sqrt = white_dtec_mean, tf.nn.softplus(white_dtec_scale) dtec_data_dist = conditional_same_points(q_mean, q_sqrt, L_x_x) dtec_screen_dist = conditional_different_points(q_mean, q_sqrt, L_x_x, K_xstar_xstar, K_x_xstar) return loss, dtec_data_dist, dtec_screen_dist, (amp, lengthscales, a, b, timescale), ( white_dtec_mean, white_dtec_scale), (hyperparams_unconstrained,) class VariationalBayes(object): def __init__(self, Yreal, Yimag, freqs, X, Xstar, Z, y_sigma, dtec_samples=10, kernel_params=None, minibatch_size=None): self._Yreal = Yreal self._Yimag = Yimag self._freqs = freqs self._y_sigma = y_sigma self._invfreqs = tf.constant(TEC_CONV, float_type) * tf.math.reciprocal(freqs) self._X = X self._Xstar = Xstar self._Z = Z self._Xconcat = tf.concat([self._X, self._Xstar], axis=0) self.N = tf.shape(self._X)[0] self.Ns = tf.shape(self._Xstar)[0] self.Nz = tf.shape(self._Z)[0] self._kernel_params = kernel_params self._dtec_samples = tf.convert_to_tensor(dtec_samples, tf.int32, name='num_dtec_samples') self._minibatch_size = tf.convert_to_tensor(minibatch_size,tf.int64) if minibatch_size is not None else None if self._minibatch_size is not None: self._scale = tf.cast(self.N, float_type)/tf.cast(self._minibatch_size, float_type) else: self._scale = tf.constant(1., float_type) self._white_posterior = WhitenedVariationalPosterior(event_size=self.Nz) self._hyperparam_bijectors = [ tfp.bijectors.Chain( [tfp.bijectors.AffineScalar(scale=tf.constant(3., float_type)), tfp.bijectors.Softplus()]), tfp.bijectors.Chain( [tfp.bijectors.AffineScalar(scale=tf.constant(15., float_type)), tfp.bijectors.Softplus()]), tfp.bijectors.Chain( [tfp.bijectors.AffineScalar(scale=tf.constant(250., float_type)), tfp.bijectors.Softplus()]), tfp.bijectors.Chain( [tfp.bijectors.AffineScalar(scale=tf.constant(100., float_type)), tfp.bijectors.Softplus()]), tfp.bijectors.Chain( [tfp.bijectors.AffineScalar(scale=tf.constant(50., float_type)), tfp.bijectors.Softplus()]) ] self._hyperparam_distributions = [ None,#tfp.distributions.LogNormal(*log_normal_cdf_solve(2., 8., as_tensor=True), name='pert_ant_lengthscale') None, None, None, None ] def _initial_states(self, batch_size=None): return self._white_posterior.initial_variational_params( batch_size), (tfp.distributions.softplus_inverse(tf.ones((1,8),float_type)),) def _constrain_hyperparams(self, sampled_hyperparams): """ Constrains the samples of hyperparams. :param sampled_hyperparams: tf.Tensor [samples, 6] :return: Tuple of tf.Tensor Each of shape [samples, 1] """ constrained_hyperparams = [] for i in range(len(self._hyperparam_bijectors)): bijector = self._hyperparam_bijectors[i] # num_hyperparams, 1 s = sampled_hyperparams[:, i:i + 1] constrained_hyperparams.append(bijector.forward(s)) return constrained_hyperparams def _hyperparam_priors(self, *constrained_hyperparams): priors = [] for hp, bij, dist in zip(constrained_hyperparams, self._hyperparam_bijectors, self._hyperparam_distributions): if dist is None: continue priors.append(tf.reduce_sum(dist.log_prob(hp)) - tf.reduce_sum(bij.inverse_log_det_jacobian(hp, 1))) if len(priors) == 0: return tf.constant(0., float_type) return tf.math.accumulate_n(priors, shape=()) def _loss_fn(self, q_mean, q_scale, hyperparams_unconstrained, X, Y): #each 1,1 amp, lengthscales, a, b, timescale = self._constrain_hyperparams(hyperparams_unconstrained) kern = DTECIsotropicTimeGeneral(variance=tf.math.square(amp), lengthscales=lengthscales, a=a, b=b, timescale=timescale, squeeze=False, **self._kernel_params) # 1, 1, Nz, Nz K_z_z = kern.K(self._Z, None) L_z_z = safe_cholesky(K_z_z) # q_mean, q_scale = white_vi_params q_sqrt = tf.nn.softplus(q_scale) dtec_prior_KL = self._dtec_prior_kl(q_mean, q_sqrt, L_z_z) if self._minibatch_size is not None: K_z_xmini = kern.K(self._Z, X) K_xmini_xmini = kern.K(X, None) q_dist = conditional_different_points(q_mean, q_sqrt, L_z_z, K_xmini_xmini, K_z_xmini) dtec_samples = q_dist.sample(self._dtec_samples) likelihood = LaplaceLikelihood(Y[0], Y[1], self._freqs, transform_fn=lambda x: x) # TODO: derive better var_exp var_exp = tf.reduce_mean(likelihood.log_prob(dtec_samples, self._y_sigma)) else: # num_dtec, num_hyperparams, N, N L_expanded = tf.tile(tf.expand_dims(L_z_z, 0), [self._dtec_samples, 1, 1, 1]) def transform_fn(white_dtec): """ Constrain white_dtec to tec L is [A, B, N, N] :param white_dtec: tf.Tensor [A, B, N, 1] :return: tf.Tensor [A,B,N] """ # white_dtec[a,b,j,1].L[a,b,i,j] -> white_dtec^T[a,b,1,j].L^T[a,b,j,i] -> [a,b, 1, i] return tf.matmul(white_dtec, L_expanded, transpose_a=True, transpose_b=True)[:,:,0, :] # # A, B, N # return tf.tensordot(white_dtec, L_z_z, axes=[[-1], [-1]]) white_dist = self._white_posterior._build_distribution(q_mean, q_scale) # num_dtec, N white_dtec = white_dist.sample(self._dtec_samples) # num_dtec, 1, N, 1 white_dtec = white_dtec[:, None, :, None] likelihood = LaplaceLikelihood(Y[0], Y[1], self._freqs, transform_fn=transform_fn) var_exp = tf.reduce_mean(likelihood.log_prob(white_dtec, self._y_sigma)) # scalar elbo = var_exp*self._scale - dtec_prior_KL + self._hyperparam_priors(amp, lengthscales, a, b, timescale) # with tf.control_dependencies([tf.print('elbo', elbo, # 'var_exp', var_exp, 'dtec_prior', dtec_prior_KL, # 'amp', amp, 'lengthscales', lengthscales, 'a', a, 'b', b, 'timescale', # timescale, 'y_sigma', self._y_sigma)]): loss = tf.math.negative(elbo, name='loss') return loss def _dtec_prior_kl(self, q_mean, q_sqrt, L): """ Get the KL-div [ Q(white_dtec) || P(white_dtec | hyperparams)] where Q = N[m, S] and S is diagonal and P = |L|^{-1} N[0,I] KL-div [ N[m, S] || |L|^{-1} N[0,I]] = KL-div [ N[m, S] || |L|^{-1} N[0,I]] + log |L| :param white_dtec_params: tuple of tf.Tensor [N+Ns] mean [N+Ns] unconstrained scale :param L: tf.Tensor [num_hyperparams, N, N] :return: tf.Tensor [num_hyperparams] """ logdetL = tf.reduce_sum(tf.math.log(tf.linalg.diag_part(L))) # [N+Ns] q_var = tf.math.square(q_sqrt) trace = tf.reduce_sum(q_var) mahalanobis = tf.reduce_sum(tf.math.square(q_mean)) constant = -tf.cast(tf.size(q_mean, out_type=tf.int64), float_type) logdet_qcov = tf.reduce_sum(tf.math.log(q_var)) twoKL = mahalanobis + constant - logdet_qcov + trace - logdetL return 0.5 * twoKL # # num_hyperparams # # return 0.5 * tf.reduce_sum( # variance + tf.math.square(mean) - tf.constant(1., float_type) - 2. * tf.math.log(variance), # axis=-1) + logdetL def _build_variational_posteriors(self, white_vi_params): white_dist = self._white_posterior._build_distribution(*white_vi_params) return white_dist def solve_variational_posterior(self, param_warmstart, solver_params=None, parallel_iterations=10): param_init, (hyperparams_unconstrained,) = self._initial_states() param_warmstart = \ tf.cond(tf.reduce_all(tf.equal(param_warmstart[0], 0.)), lambda: param_init, lambda: param_warmstart, strict=True) # TODO: speed up kernel computation with tf.device('/device:GPU:0' if tf.test.is_gpu_available() else '/device:CPU:0'): learned_params, [learned_hyperparams_unconstrained], loss, t = \ natural_adam_stochastic_gradient_descent_with_linesearch_minibatch(self._loss_fn, self._X, (self._Yreal, self._Yimag), self._minibatch_size, param_warmstart, [hyperparams_unconstrained], parallel_iterations=parallel_iterations, **solver_params) ### # produce the posterior distributions needed # each 1,1 amp, lengthscales, a, b, timescale = self._constrain_hyperparams( learned_hyperparams_unconstrained) kern = DTECIsotropicTimeGeneral(variance=tf.math.square(amp), lengthscales=lengthscales, a=a, b=b, timescale=timescale, squeeze=False, **self._kernel_params) # 1, 1, Nz, Nz K_z_z = kern.K(self._Z, None) L_z_z = safe_cholesky(K_z_z) # num_hyperparams, M, N K_z_xstar = kern.K(self._Z, self._Xstar) K_xstar_xstar = kern.K(self._Xstar, None) q_mean, q_scale = learned_params q_sqrt = tf.nn.softplus(q_scale) dtec_screen_dist = conditional_different_points(q_mean, q_sqrt, L_z_z, K_xstar_xstar, K_z_xstar) # num_hyperparams, M, N K_z_x = kern.K(self._Z, self._X) K_x_x = kern.K(self._X, None) dtec_data_dist = conditional_different_points(q_mean, q_sqrt, L_z_z, K_x_x, K_z_x) dtec_basis_dist = conditional_same_points(q_mean, q_sqrt, L_z_z) return t, loss, dtec_basis_dist, dtec_data_dist, dtec_screen_dist, (amp, lengthscales, a, b, timescale), ( q_mean, q_scale) def conditional_same_points(q_mean, q_sqrt, L, prior_mean=None): """ Computes P(tau(X) | Y) = int P(tau(X) | x(X)) Q(x(X)) dx(X) = N[prior_mean + L.q_mean, L.q_sqrt^2.L^T] :param q_mean: tf.Tensor [N] :param q_sqrt: tf.Tensor [N] :param L: tf.Tensor [num_hyperparams, N, N] :param prior_mean: tf.Tensor [num_hyperparams, N] :return: tfp.distributions.MultivariateNormalTriL batch_shape is [num_hyperparams] event_shape is [N] """ #num_hyperparams, N mean = tf.tensordot(L, q_mean, axes=[[-1], [-1]]) # num_hyperparams, N, N scale_tril = L*q_sqrt[None, :]#tf.tensordot(L, q_sqrt, axes=[[-1], [-1]]) if prior_mean is None: return tfp.distributions.MultivariateNormalTriL(loc=mean, scale_tril=scale_tril) return tfp.distributions.MultivariateNormalTriL(loc=prior_mean + mean, scale_tril=scale_tril) def conditional_different_points(q_mean, q_sqrt, L, K_xstar_xstar, K_x_xstar, prior_mean=None): """ Computes P(tau(X) | Y) = int P(tau(Xstar) | x(X)) Q(x(X)) dx(X) = |L(X,X)| N[m(Xstar) + K(Xstar, X) L(X,X)^-T.q_mean, K(Xstar,Xstar) + K(Xstar,X) L(X,X)^-T(q_sqrt^2 - I) L(X,X)^-1 K(X,Xstar)] :param q_mean: tf.Tensor [N] :param q_sqrt: tf.Tensor [N] :param L: tf.Tensor [num_hyperparams, N, N] :param K_xx: tf.Tensor [num_hyperparams, M, M] :param K_yx: tf.Tensor [num_hyperparams, N, M] :param prior_mean: tf.Tensor [num_hyperparams, M] :return: tfp.distributions.MultivariateNormalTriL batch_shape is [num_hyperparams] event_shape is [N] """ ### # conditional one first # [num_hyperparams, M, N] A = tf.linalg.triangular_solve(L, K_x_xstar) B = q_sqrt[:, None] * A # num_hyperparams, N mean = tf.tensordot(A, q_mean, axes=[[-2], [-1]]) f_cov = K_xstar_xstar - tf.matmul(A,A,transpose_a=True) + tf.matmul(B,B,transpose_a=True) L = safe_cholesky(f_cov) if prior_mean is None: return tfp.distributions.MultivariateNormalTriL(loc=mean, scale_tril=L) return tfp.distributions.MultivariateNormalTriL(loc=prior_mean + mean, scale_tril=L)

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6ab4ee0fad6a311f66ab7ca26f9bd019c65774ff

2,612

py

Python

nova/objects/__init__.py

venusource/nova

0c6e6f180eebe71a3431abf726a0fd0c66578162

[ "Apache-2.0" ]

7

2015-09-22T11:27:16.000Z

2015-11-02T12:33:46.000Z

nova/objects/__init__.py

venusource/nova

0c6e6f180eebe71a3431abf726a0fd0c66578162

[ "Apache-2.0" ]

2

2015-09-07T22:14:46.000Z

2020-08-12T08:51:56.000Z

nova/objects/__init__.py

venusource/nova

0c6e6f180eebe71a3431abf726a0fd0c66578162

[ "Apache-2.0" ]

4

2015-09-09T16:48:56.000Z

2022-03-15T20:52:57.000Z

# Copyright 2013 IBM Corp. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. # NOTE(comstud): You may scratch your head as you see code that imports # this module and then accesses attributes for objects such as Instance, # etc, yet you do not see these attributes in here. Never fear, there is # a little bit of magic. When objects are registered, an attribute is set # on this module automatically, pointing to the newest/latest version of # the object. def register_all(): # NOTE(danms): You must make sure your object gets imported in this # function in order for it to be registered by services that may # need to receive it via RPC. __import__('nova.objects.agent') __import__('nova.objects.aggregate') __import__('nova.objects.bandwidth_usage') __import__('nova.objects.block_device') __import__('nova.objects.compute_node') __import__('nova.objects.dns_domain') __import__('nova.objects.ec2') __import__('nova.objects.external_event') __import__('nova.objects.fixed_ip') __import__('nova.objects.flavor') __import__('nova.objects.floating_ip') __import__('nova.objects.hv_spec') __import__('nova.objects.instance') __import__('nova.objects.instance_action') __import__('nova.objects.instance_fault') __import__('nova.objects.instance_group') __import__('nova.objects.instance_info_cache') __import__('nova.objects.instance_numa_topology') __import__('nova.objects.instance_pci_requests') __import__('nova.objects.keypair') __import__('nova.objects.migration') __import__('nova.objects.network') __import__('nova.objects.network_request') __import__('nova.objects.numa') __import__('nova.objects.pci_device') __import__('nova.objects.pci_device_pool') __import__('nova.objects.tag') __import__('nova.objects.quotas') __import__('nova.objects.security_group') __import__('nova.objects.security_group_rule') __import__('nova.objects.service') __import__('nova.objects.virt_cpu_topology') __import__('nova.objects.virtual_interface')

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78

py

Python

basic_python_practice/functions.py

MylesWritesCode/web_dev_practice

769bad96cd19afeeda3b1dbbed1c34823fe1502f

[ "MIT" ]

null

basic_python_practice/functions.py

MylesWritesCode/web_dev_practice

769bad96cd19afeeda3b1dbbed1c34823fe1502f

[ "MIT" ]

null

basic_python_practice/functions.py

MylesWritesCode/web_dev_practice

769bad96cd19afeeda3b1dbbed1c34823fe1502f

[ "MIT" ]

null

#! /usr/bin/python print 'Content-type: text/html' print '' print 'Testing'

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1

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4

6aca50a78b1cd1bf3a4d7c2626fdf5ca3b762357

8,430

py

Python

challenges/Cereal_Mixup__A_Cereal_Vending_Machine_Controller/support/breakfast.py

pingjuiliao/cb-multios

64ededd0b87030eda7c40c4388a4ad8283712d8e

[ "MIT" ]

473

2016-08-01T12:48:16.000Z

2022-03-09T18:13:14.000Z

challenges/Cereal_Mixup__A_Cereal_Vending_Machine_Controller/support/breakfast.py

pingjuiliao/cb-multios

64ededd0b87030eda7c40c4388a4ad8283712d8e

[ "MIT" ]

71

2016-08-01T03:33:44.000Z

2022-03-09T18:37:04.000Z

challenges/Cereal_Mixup__A_Cereal_Vending_Machine_Controller/support/breakfast.py

pingjuiliao/cb-multios

64ededd0b87030eda7c40c4388a4ad8283712d8e

[ "MIT" ]

121

2016-08-01T04:07:53.000Z

2022-03-07T11:08:09.000Z

#!/usr/bin/env python # # Copyright (C) 2014 Narf Industries <info@narfindustries.com> # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the "Software"), # to deal in the Software without restriction, including without limitation # the rights to use, copy, modify, merge, publish, distribute, sublicense, # and/or sell copies of the Software, and to permit persons to whom the # Software is furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included # in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS # OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. # IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY # CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, # TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE # SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # from random import choice, randint import support as sp from common import DEBUG, CONFIG # Plain content, send SVU as UINT32 and STI as UCHAR # Serialized content, send SVU as UINT32, STI as UINT32, and name as series of chars. class Liquids(object): def __init__(self): self.serialVersionUID = 0 self.typeName = "Liquids" self.subTypeID = 0 def rand_content(self): self.subTypeID = randint(0,4) def get_plain_content(self): pc = '' pc += sp.pack_single_uint32(self.serialVersionUID) pc += sp.pack_single_uint8(self.subTypeID) return pc def get_serialized_content(self): sc = '' sc += sp.pack_single_uint32(self.serialVersionUID) sc += sp.pack_single_string(self.typeName) sc += sp.pack_single_uint32(self.subTypeID) return sc def __eq__(self, other): return self.serialVersionUID == other.serialVersionUID \ and self.subTypeID == other.subTypeID def __hash__(self): return hash( ("serialVersionUID", self.serialVersionUID, "subTypeID", self.subTypeID)) def __str__(self): return "SVU {0}, {1}, SubType {2}".format(self.serialVersionUID, self.typeName, self.subTypeID) def __repr__(self): return self.__str__() class Cereals(object): def __init__(self): self.serialVersionUID = 1 self.typeName = "Cereals" self.subTypeID = 0 def rand_content(self): self.subTypeID = randint(0,6) def get_plain_content(self): pc = '' pc += sp.pack_single_uint32(self.serialVersionUID) pc += sp.pack_single_uint8(self.subTypeID) return pc def get_serialized_content(self): sc = '' sc += sp.pack_single_uint32(self.serialVersionUID) sc += sp.pack_single_string(self.typeName) sc += sp.pack_single_uint32(self.subTypeID) return sc def __eq__(self, other): return self.serialVersionUID == other.serialVersionUID \ and self.subTypeID == other.subTypeID def __hash__(self): return hash( ("serialVersionUID", self.serialVersionUID, "subTypeID", self.subTypeID)) def __str__(self): return "SVU {0}, {1}, SubType {2}".format(self.serialVersionUID, self.typeName, self.subTypeID) def __repr__(self): return self.__str__() class Toppings(object): def __init__(self): self.serialVersionUID = 2 self.typeName = "Toppings" self.subTypeID = 0 def rand_content(self): self.subTypeID = randint(0,4) def get_plain_content(self): pc = '' pc += sp.pack_single_uint32(self.serialVersionUID) pc += sp.pack_single_uint8(self.subTypeID) return pc def get_serialized_content(self): sc = '' sc += sp.pack_single_uint32(self.serialVersionUID) sc += sp.pack_single_string(self.typeName) sc += sp.pack_single_uint32(self.subTypeID) return sc def __eq__(self, other): return self.serialVersionUID == other.serialVersionUID \ and self.subTypeID == other.subTypeID def __hash__(self): return hash( ("serialVersionUID", self.serialVersionUID, "subTypeID", self.subTypeID)) def __str__(self): return "SVU {0}, {1}, SubType {2}".format(self.serialVersionUID, self.typeName, self.subTypeID) def __repr__(self): return self.__str__() class GenericString(object): def __init__(self): self.serialVersionUID = 3 self.typeName = "GenericString" self.str = "" def rand_content(self): self.str = sp.random_string(randint(5, 25)) def get_plain_content(self): pc = '' pc += sp.pack_single_uint32(self.serialVersionUID) pc += sp.pack_single_string(self.str) pc += sp.pack_single_char('\0') return pc def get_serialized_content(self): sc = '' sc += sp.pack_single_uint32(self.serialVersionUID) sc += sp.pack_single_string(self.typeName) sc += sp.pack_single_string(self.str) sc += sp.pack_single_char('\0') return sc def __str__(self): return "SVU {0}, {1}, Str {2}".format(self.serialVersionUID, self.typeName, self.str) def __repr__(self): return self.__str__() class PrinterString(object): def __init__(self): self.serialVersionUID = 4 self.typeName = "PrinterString" self.str = "" def rand_content(self): self.str = sp.random_string(randint(5, 25)) def get_plain_content(self): pc = '' pc += sp.pack_single_uint32(self.serialVersionUID) pc += sp.pack_single_string(self.str) pc += sp.pack_single_char('\0') return pc def get_serialized_content(self): sc = '' sc += sp.pack_single_uint32(self.serialVersionUID) sc += sp.pack_single_string(self.typeName) sc += sp.pack_single_string(self.str) sc += sp.pack_single_char('\0') return sc def __str__(self): return "SVU {0}, {1}, Str {2}".format(self.serialVersionUID, self.typeName, self.str) def __repr__(self): return self.__str__() class CommandRunner(object): def __init__(self): self.serialVersionUID = 5 self.typeName = "CommandRunner" self.fn_addr = '' self.args = [] def rand_content(self): self.fn_addr = 'AADD' self.args = [128, 1024, 4096] def get_plain_content(self): pc = '' pc += sp.pack_single_uint32(self.serialVersionUID) pc += sp.pack_single_uint16(1 + len(self.args)) pc += sp.pack_single_string(self.fn_addr) pc += sp.pack_single_char(' ') pc += sp.pack_single_uint32(self.args[0]) pc += sp.pack_single_char(' ') pc += sp.pack_single_uint32(self.args[1]) pc += sp.pack_single_char(' ') pc += sp.pack_single_uint32(self.args[2]) pc += sp.pack_single_char('\0') return pc def get_serialized_content(self): sc = '' sc += sp.pack_single_uint32(self.serialVersionUID) sc += sp.pack_single_string(self.typeName) sc += sp.pack_single_uint16(1 + len(self.args)) sc += sp.pack_single_string(self.fn_addr) sc += sp.pack_single_char(' ') sc += sp.pack_single_uint32(self.args[0]) sc += sp.pack_single_char(' ') sc += sp.pack_single_uint32(self.args[1]) sc += sp.pack_single_char(' ') sc += sp.pack_single_uint32(self.args[2]) sc += sp.pack_single_char('\0') return sc def __str__(self): return "SVU {0}, {1}, fn_addr 0x{2}, args {3}".format(self.serialVersionUID, self.typeName, self.fn_addr, self.args) def __repr__(self): return self.__str__() if __name__ == '__main__': b = [] for item_type in [Liquids, Cereals, Toppings, GenericString, PrinterString, CommandRunner]: item = item_type() item.rand_content() print item print ''.join(["\\x{0:02x}".format(ord(c)) for c in item.get_plain_content()]) print ''.join(["\\x{0:02x}".format(ord(c)) for c in item.get_serialized_content()]) b.append(item) print b

32.548263

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null

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0a725ad236bbad80eef198a278c063064e7d696e

192

py

Python

tenable/ad/preference/schema.py

Rogdham/pyTenable

79f3f7360f8ef31b964f1db99d0c7b8a0bc25d7a

[ "MIT" ]

1

2022-03-01T17:17:19.000Z

tenable/ad/preference/schema.py

Rogdham/pyTenable

79f3f7360f8ef31b964f1db99d0c7b8a0bc25d7a

[ "MIT" ]

null

tenable/ad/preference/schema.py

Rogdham/pyTenable

79f3f7360f8ef31b964f1db99d0c7b8a0bc25d7a

[ "MIT" ]

1

2022-03-01T17:17:30.000Z

from marshmallow import fields from tenable.ad.base.schema import CamelCaseSchema class PreferenceSchema(CamelCaseSchema): language = fields.Str() preferred_profile_id = fields.Int()

27.428571

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192

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27.428571

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1

0

1

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4

0a7cec209e7d0c5ce38da5abe7abce9dfe627d69

147

py

Python

app/tests/test_models.py

vmasten/stock_portfolio

75a7f17e4891b1ca1374b5e1e5d83dd891b9fddd

[ "MIT" ]

null

app/tests/test_models.py

vmasten/stock_portfolio

75a7f17e4891b1ca1374b5e1e5d83dd891b9fddd

[ "MIT" ]

1

2018-12-07T03:57:51.000Z

app/tests/test_models.py

vmasten/stock_portfolio

75a7f17e4891b1ca1374b5e1e5d83dd891b9fddd

[ "MIT" ]

null

from ..models import db, Company import pytest def test_company_all(session): companies = Company.query.all() assert len(companies) == 0

18.375

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4

0a8ed9e5e4062d85b79f14a7e4b14e26b204c710

124

py

Python

castoredc_api_client/__init__.py

andreasCastor/castoredc_api

ef0bd4eb8ac2efaa7e98e8462de7e5a7aa65a7f0

[ "MIT" ]

null

castoredc_api_client/__init__.py

andreasCastor/castoredc_api

ef0bd4eb8ac2efaa7e98e8462de7e5a7aa65a7f0

[ "MIT" ]

null

castoredc_api_client/__init__.py

andreasCastor/castoredc_api

ef0bd4eb8ac2efaa7e98e8462de7e5a7aa65a7f0

[ "MIT" ]

null

from .castoredc_api_client import CastorClient from .exceptions.exceptions import CastorException, castor_exception_handler

41.333333

76

0.895161

14

124

7.642857

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0

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124

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null

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1

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null

0

1

0

1

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4

0aaf100092b165c2ae2f26adb86157b1723133db

160

py

Python

src/main/python/app/controllers/WelcomeController.py

karlpet/WadLauncher

512f5d28de5c57e4dffdc642b170891a99a00ea8

[ "MIT" ]

2

2020-09-06T11:16:30.000Z

2020-09-15T17:11:34.000Z

src/main/python/app/controllers/WelcomeController.py

karlpet/WadLauncher

512f5d28de5c57e4dffdc642b170891a99a00ea8

[ "MIT" ]

74

2020-09-07T16:40:54.000Z

2021-06-18T00:22:39.000Z

src/main/python/app/controllers/WelcomeController.py

karlpet/WadLauncher

512f5d28de5c57e4dffdc642b170891a99a00ea8

[ "MIT" ]

null

import sys from app.views.WelcomeView import WelcomeView class WelcomeController: def __init__(self, root, models): self.view = WelcomeView(root)

20

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0.736842

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7

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null

0

1

0

1

0

4

0adc4c79b6410b58cb55a5a74adf42e173a6dfef

42

py

Python

pywizlight/_version.py

fabaff/pywizlight

395e63846dd8bcfc99a65d50252c6a71e02590c4

[ "MIT" ]

1

2021-04-02T17:22:52.000Z

pywizlight/_version.py

fabaff/pywizlight

395e63846dd8bcfc99a65d50252c6a71e02590c4

[ "MIT" ]

null

pywizlight/_version.py

fabaff/pywizlight

395e63846dd8bcfc99a65d50252c6a71e02590c4

[ "MIT" ]

null

"""PyPi Version.""" __version__ = "0.3.4"

14

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42

3.5

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0.081081

0.119048

42

2

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0

0.217391

0

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0

null

0

1

0

1

0

null

0

4

0ae36535434841c27eb6d1a1d3d898b7d0cdfd3c

280

py

Python

appengine_config.py

mayankgosain/python_slack_bot-master

03d83384fdd7bc248ecf8713ca4d80013ba17808

[ "Apache-2.0" ]

null

appengine_config.py

mayankgosain/python_slack_bot-master

03d83384fdd7bc248ecf8713ca4d80013ba17808

[ "Apache-2.0" ]

1

2015-07-28T10:27:52.000Z

appengine_config.py

mayankgosain/python_slack_bot-master

03d83384fdd7bc248ecf8713ca4d80013ba17808

[ "Apache-2.0" ]

null

"""`appengine_config` gets loaded when starting a new application instance.""" import site import os.path # add `lib` subdirectory as a site packages directory, so our `main` module can load # third-party libraries. site.addsitedir(os.path.join(os.path.dirname(__file__), 'lib'))

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null

0

1

0

1

0

1

0

4

7c25f480a7e4db2b9fea4c8ef910e2b139377c72

1,360

py

Python

beta.py

joenghl/SwarmSim

b06f27e91f7a6cba886aa06734f38f0ac006d6c0

[ "MIT" ]

null

beta.py

joenghl/SwarmSim

b06f27e91f7a6cba886aa06734f38f0ac006d6c0

[ "MIT" ]

null

beta.py

joenghl/SwarmSim

b06f27e91f7a6cba886aa06734f38f0ac006d6c0

[ "MIT" ]

null

import numpy as np import torch import argparse from torch import Tensor from torch import autograd from torch.autograd import Variable import sys print(sys.executable) print(torch.cuda.is_available()) # import airsim # client = airsim.MultirotorClient() # client.confirmConnection() # client.enableApiControl(True, "Drone1") # client.enableApiControl(True, "Drone2") # client.armDisarm(True, "Drone1") # client.armDisarm(True, "Drone2") # raise Exception("valid") # # a = client.getMultirotorState(vehicle_name="Drone1").kinematics_estimated.position # # b = client.getMultirotorState(vehicle_name="Drone2").kinematics_estimated.position # f1 = client.takeoffAsync(vehicle_name="Drone1") # f2 = client.takeoffAsync(vehicle_name="Drone2") # f1.join() # f2.join() # f1 = client.moveToPositionAsync(5, 5, -10, 5, vehicle_name="Drone1") # f2 = client.moveToPositionAsync(5, 5, -10, 5, vehicle_name="Drone2") # f1.join() # f2.join() # a = client.getGpsData(vehicle_name="Drone1") # b = client.getMultirotorState(vehicle_name="Drone2").gps_location.latitude # print(a, b) # airsim.wait_key('Press any key to reset to original state') # client.armDisarm(False, "Drone1") # client.armDisarm(False, "Drone2") # client.reset() # # that's enough fun for now. let's quit cleanly # client.enableApiControl(False, "Drone1") # client.enableApiControl(False, "Drone2")

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1

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7c280d829598b4adc58515af003be7dcab324652

188

py

Python

setup.py

zoomie/homemade_steganog

1ab0a140b6a2e0d9d36073d067a2c808c97adf38

[ "MIT" ]

1

2019-03-12T13:25:43.000Z

setup.py

zoomie/homemade_encryption

1ab0a140b6a2e0d9d36073d067a2c808c97adf38

[ "MIT" ]

4

2020-03-24T16:43:01.000Z

2022-03-11T23:39:53.000Z

setup.py

zoomie/homemade_encryption

1ab0a140b6a2e0d9d36073d067a2c808c97adf38

[ "MIT" ]

null

from distutils.core import setup setup( name='homemade_steganog', version='0.3.0', packages=['homemade_steganog',], install_requires=['scikit-image'], license='MIT', )

20.888889

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5.590909

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0.019108

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true

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null

1

0

1

0

null

0

1

0

4

7c4414ad42002173728a604f0b54bdc51ac158a5

547

py

Python

lfs/customer/migrations/0005_auto_20210406_1513.py

michael-hahn/django-lfs

26c3471a8f8d88269c84f714f507b952dfdb6397

[ "BSD-3-Clause" ]

null

lfs/customer/migrations/0005_auto_20210406_1513.py

michael-hahn/django-lfs

26c3471a8f8d88269c84f714f507b952dfdb6397

[ "BSD-3-Clause" ]

null

lfs/customer/migrations/0005_auto_20210406_1513.py

michael-hahn/django-lfs

26c3471a8f8d88269c84f714f507b952dfdb6397

[ "BSD-3-Clause" ]

null

# Generated by Django 3.1.2 on 2021-04-06 15:13 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('customer', '0004_auto_20210406_1434'), ] operations = [ migrations.RemoveField( model_name='customer', name='synthesized', ), migrations.RemoveField( model_name='customer', name='taints', ), migrations.RemoveField( model_name='customer', name='trusted', ), ]

21.038462

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547

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0

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547

25

49

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0

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0

null

1

0

1

0

null

0

4

7c58d45604fb37f69a3ee612ec766061ac0618d5

232

py

Python

mmdet/utils/deployment/operations_domain.py

morkovka1337/mmdetection

5187d94b6c96084b17817249622d6e4520213ae6

[ "Apache-2.0" ]

58

2020-09-21T08:17:26.000Z

2022-03-31T19:38:14.000Z

mmdet/utils/deployment/operations_domain.py

morkovka1337/mmdetection

5187d94b6c96084b17817249622d6e4520213ae6

[ "Apache-2.0" ]

170

2020-09-08T12:29:06.000Z

2022-03-31T18:28:09.000Z

mmdet/utils/deployment/operations_domain.py

morkovka1337/mmdetection

5187d94b6c96084b17817249622d6e4520213ae6

[ "Apache-2.0" ]

21

2020-10-06T13:49:41.000Z

2022-03-30T14:52:45.000Z

# Copyright (C) 2020-2021 Intel Corporation # SPDX-License-Identifier: Apache-2.0 # DOMAIN_CUSTOM_OPS_NAME = 'org.openvinotoolkit' def add_domain(name_operator: str) -> str: return DOMAIN_CUSTOM_OPS_NAME + '::' + name_operator

29

56

0.758621

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232

5.21875

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0.179641

0.227545

0

0.049261

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7

57

33.142857

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false

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null

0

1

0

1

0

4

7c596c3c8b72ea6024b27bd0c56984709592b557

240

py

Python

trajectory/utils/__init__.py

hyyh28/trajectory-transformer

4a369b6d1c950c76d1792cf004644fa13040319c

[ "MIT" ]

63

2021-11-23T08:00:27.000Z

2022-03-31T04:03:05.000Z

trajectory/utils/__init__.py

hyyh28/trajectory-transformer

4a369b6d1c950c76d1792cf004644fa13040319c

[ "MIT" ]

7

2021-12-08T04:01:13.000Z

2022-03-31T07:42:37.000Z

trajectory/utils/__init__.py

hyyh28/trajectory-transformer

4a369b6d1c950c76d1792cf004644fa13040319c

[ "MIT" ]

12

2021-12-13T10:55:32.000Z

2022-03-24T09:06:22.000Z

from .setup import Parser, watch from .arrays import * from .serialization import * from .progress import Progress, Silent from .rendering import make_renderer # from .video import * from .config import Config from .training import Trainer

26.666667

38

0.795833

32

240

5.9375

0.5

0.157895

0

0.145833

240

8

39

30

0.926829

0.083333

0

1

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true

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null

0

1

0

1

0

1

0

4

7c74ac5a8df263460eb4d995c96d663a8ecb20f1

436

py

Python

Admission Counselling For Direct Second Year/Web-Application/AdmissionDirectSecondYear/TopColleges/models.py

atharvaagrawal/direct-second-year-admission-analysis

4744753c5b69d5e06211f006d56150997793c5bf

[ "MIT" ]

null

Admission Counselling For Direct Second Year/Web-Application/AdmissionDirectSecondYear/TopColleges/models.py

atharvaagrawal/direct-second-year-admission-analysis

4744753c5b69d5e06211f006d56150997793c5bf

[ "MIT" ]

1

2020-03-25T11:06:18.000Z

Admission Counselling For Direct Second Year/Web-Application/AdmissionDirectSecondYear/TopColleges/models.py

atharvaagrawal/direct-second-year-admission-analysis

4744753c5b69d5e06211f006d56150997793c5bf

[ "MIT" ]

null

from django.db import models # Create your models here. class TopCollegesModel(models.Model): institute_id = models.CharField(max_length=300) college_name = models.CharField(max_length=300) city = models.CharField(max_length=300) state = models.CharField(max_length=300) score = models.FloatField() crank = models.IntegerField() class Meta: db_table = "IndiaTop200College2019"

27.25

53

0.697248

50

436

5.94

0.56

0.20202

0.242424

0.323232

0.363636

0

0.055394

0.213303

436

15

54

29.066667

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0.055838

0

1

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false

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0.9

0

null

1

0

null

0

1

0

4

7c7e189ba8ab8260e471ec212e19a2773d5fcf83

174

py

Python

environments/obstacle_car_o/colors.py

lhk/baselines

18eab9df4f74b5dac276bce64c13554d518618f7

[ "MIT" ]

null

environments/obstacle_car_o/colors.py

lhk/baselines

18eab9df4f74b5dac276bce64c13554d518618f7

[ "MIT" ]

null

environments/obstacle_car_o/colors.py

lhk/baselines

18eab9df4f74b5dac276bce64c13554d518618f7

[ "MIT" ]

1

2021-03-17T13:26:49.000Z

import pygame red = pygame.Color(255, 0, 0) green = pygame.Color(0, 255, 0) blue = pygame.Color(0, 0, 255) white = pygame.Color(255, 255, 255) black = pygame.Color(0, 0, 0)

21.75

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0.310345

0.224138

0

0.184932

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174

7

36

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0.609589

0

1

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false

0

0.166667

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7c9489c18fb126a2e30051daf58cddbd901b41de

1,164

py

Python

dlrnapi_client/__init__.py

ssbarnea/dlrnapi_client

e09d278713cdd16cea69bfc64689166d2a886f19

[ "Apache-2.0" ]

null

dlrnapi_client/__init__.py

ssbarnea/dlrnapi_client

e09d278713cdd16cea69bfc64689166d2a886f19

[ "Apache-2.0" ]

null

dlrnapi_client/__init__.py

ssbarnea/dlrnapi_client

e09d278713cdd16cea69bfc64689166d2a886f19

[ "Apache-2.0" ]

null

# coding: utf-8 """ DLRN API DLRN API client OpenAPI spec version: 1.0.0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import # import models into sdk package from dlrnapi_client.models.ci_vote import CIVote # noqa from dlrnapi_client.models.metrics import Metrics # noqa from dlrnapi_client.models.metrics import MetricsRequest # noqa from dlrnapi_client.models.model_import import ModelImport # noqa from dlrnapi_client.models.params import Params # noqa from dlrnapi_client.models.params_1 import Params1 # noqa from dlrnapi_client.models.params_2 import Params2 # noqa from dlrnapi_client.models.params_3 import Params3 # noqa from dlrnapi_client.models.promotion import Promotion # noqa from dlrnapi_client.models.promotion_query import PromotionQuery # noqa from dlrnapi_client.models.repo import Repo # noqa # import apis into sdk package from dlrnapi_client.apis.default_api import DefaultApi # noqa # import ApiClient from dlrnapi_client.api_client import ApiClient # noqa from dlrnapi_client.configuration import Configuration configuration = Configuration()

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7c9a3aaea871b1e086e68d4e1b27037efb98d7d5

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py

Python

general/glassnode_api.py

RichardRed0x/checkonchain

2a2c1b50fb9f31c9afc01e97095ca09d62b41860

[ "ISC" ]

null

general/glassnode_api.py

RichardRed0x/checkonchain

2a2c1b50fb9f31c9afc01e97095ca09d62b41860

[ "ISC" ]

null

general/glassnode_api.py

RichardRed0x/checkonchain

2a2c1b50fb9f31c9afc01e97095ca09d62b41860

[ "ISC" ]

null

#bd859ac9-3f51-4e09-a41e-5dcdfd3e99ec client = 'https://api.glassnode.com/v1/metrics/'

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null

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1

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1

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null

0

4

7c9ade3fd0e041b55112f1301882f6fd3419bf11

138

py

Python

nigsp/tests/test_viz.py

smoia/nigsp

75eab5e428e5b28b4a5c174b3aeb69b8172cf9f5

[ "Apache-2.0" ]

2

2022-03-21T14:53:39.000Z

2022-03-24T15:39:45.000Z

nigsp/tests/test_viz.py

smoia/nigsp

75eab5e428e5b28b4a5c174b3aeb69b8172cf9f5

[ "Apache-2.0" ]

6

2022-03-21T14:57:12.000Z

2022-03-28T12:55:52.000Z

nigsp/tests/test_viz.py

smoia/nigsp

75eab5e428e5b28b4a5c174b3aeb69b8172cf9f5

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 """Tests for viz.""" from pytest import mark, raises from nigsp import viz # ### Unit tests # ### Break tests

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7c9c2300ca97b500eac86c8d54d83d9fb9245ad0

157

py

Python

spine_json_lib/__init__.py

jesdi/spine-json-lib

15767302d829738ddd21e0249ceba21874a6a052

[ "MIT" ]

null

spine_json_lib/__init__.py

jesdi/spine-json-lib

15767302d829738ddd21e0249ceba21874a6a052

[ "MIT" ]

null

spine_json_lib/__init__.py

jesdi/spine-json-lib

15767302d829738ddd21e0249ceba21874a6a052

[ "MIT" ]

null

__version__ = '0.2.4' __url__ = 'https://github.com/socialpoint-labs/spine-json-lib' from spine_json_lib.spine_animation_editor import SpineAnimationEditor

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null

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7ca8c319a0444c442f2da164641f164f6047cdcf

280

py

Python

django/db/backends/base/validation.py

wfxiang08/django197

c760d7f7cfbbb54a248dd303ef73206c43d80fbd

[ "BSD-3-Clause" ]

null

django/db/backends/base/validation.py

wfxiang08/django197

c760d7f7cfbbb54a248dd303ef73206c43d80fbd

[ "BSD-3-Clause" ]

null

django/db/backends/base/validation.py

wfxiang08/django197

c760d7f7cfbbb54a248dd303ef73206c43d80fbd

[ "BSD-3-Clause" ]

null

# -*- coding:utf-8 -*- class BaseDatabaseValidation(object): """ This class encapsulates all backend-specific model validation. """ def __init__(self, connection): self.connection = connection def check_field(self, field, **kwargs): return []

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1

0

1

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4

7cb6167a5a519c96613e6d16d7b003ebdc20fa6e

393

py

Python

scripts/rpc/net.py

zealoussnow/spdk

3148c48079731e121ec2fc81fb15293219da1789

[ "BSD-3-Clause" ]

13

2021-08-23T03:37:46.000Z

2022-02-16T03:00:09.000Z

scripts/rpc/net.py

zealoussnow/spdk

3148c48079731e121ec2fc81fb15293219da1789

[ "BSD-3-Clause" ]

2

2021-11-12T10:19:47.000Z

2021-12-21T14:26:36.000Z

scripts/rpc/net.py

zealoussnow/spdk

3148c48079731e121ec2fc81fb15293219da1789

[ "BSD-3-Clause" ]

4

2021-09-03T13:55:05.000Z

2021-11-09T10:59:33.000Z

def add_ip_address(client, args): params = {'ifc_index': args.ifc_index, 'ip_address': args.ip_addr} return client.call('add_ip_address', params) def delete_ip_address(client, args): params = {'ifc_index': args.ifc_index, 'ip_address': args.ip_addr} return client.call('delete_ip_address', params) def get_interfaces(client, args): return client.call('get_interfaces')

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null

0

1

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1

0

4

7cba37af37a0c49a828584ad7f3bc1c775fd47ad

200

py

Python

src/expression_evaluator/operators/basic/lesser_than.py

Xett/expression_evaluator

eca895d79f015843a262e9636b86c6dd3d06a69d

[ "MIT" ]

null

src/expression_evaluator/operators/basic/lesser_than.py

Xett/expression_evaluator

eca895d79f015843a262e9636b86c6dd3d06a69d

[ "MIT" ]

null

src/expression_evaluator/operators/basic/lesser_than.py

Xett/expression_evaluator

eca895d79f015843a262e9636b86c6dd3d06a69d

[ "MIT" ]

null

from expression_evaluator.token import * class LesserThan(BasicOperator): symbols = ['<'] priority = PriorityLevel.Boolean @classmethod def _function(cls, a, b): return a < b

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null

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1

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4

7cd548bf9630c9951f1f88bc54abce74dbe50b51

11,832

py

Python

neuroprob/utils/biophysical.py

davindicode/universal_count_model

f91294b66f16a5701dc8e0b5a825ac55bff83da2

[ "MIT" ]

1

2022-01-14T19:27:55.000Z

neuroprob/utils/biophysical.py

davindicode/universal_count_model

f91294b66f16a5701dc8e0b5a825ac55bff83da2

[ "MIT" ]

null

neuroprob/utils/biophysical.py

davindicode/universal_count_model

f91294b66f16a5701dc8e0b5a825ac55bff83da2

[ "MIT" ]

null

import torch import torch.nn as nn import numpy as np from tqdm.autonotebook import tqdm # Continuous models class Hodgkin_Huxley(): r""" Hodgkin-Huxley model via Euler integration """ def __init__(self, G_na=120, G_k=36, G_l=0.3, E_na=50, E_k=-77, E_l=-54.4): r""" units are in mV, microS, nF, mA, ms """ self.G_na = G_na self.G_k = G_k self.G_l = G_l self.E_na = E_na self.E_k = E_k self.E_l = E_l def euler_int(self, T, runs, I_ext, ic, dt=0.001, prin=1000): r""" Integrate the HH dynamics, the state array (v, m, h, n) is represented by 4 floating-point values :param int T: timesteps to run the simulation for :param int runs: number of trials to run (I_ext and i.c. can differ per run) :param np.array I_ext: external input current, with shape (runs, timesteps) :param np.array ic: neuron initial conditions, with shape (runs, 4) :returns: neuron state over the simulation :rtype: np.array """ alpha_m = lambda V: (2.5-0.1*(V+65)) / (np.exp(2.5-0.1*(V+65)) -1) beta_m = lambda V: 4.0 * np.exp(-(V+65)/18) alpha_h = lambda V: 0.07 * np.exp(-(V+65)/20) beta_h = lambda V: 1.0 / (np.exp(3.0-0.1*(V+65)) + 1) alpha_n = lambda V: (0.1-0.01*(V+65)) / (np.exp(1-0.1*(V+65)) - 1) beta_n = lambda V: 0.125 * np.exp(-(V+65)/80) state = np.zeros((runs, T, 4)) # vector v, m, h, n for k in range(runs): state[k, 0, :] = ic[k, :]#[-6.49997224e+01, 5.29342176e-02, 5.96111046e-01, 3.17681168e-01] ds = np.zeros((runs, 4)) iterator = tqdm(range(T-1)) for t in iterator: ds[:, 0] = -(G_l*(state[:, t, 0] - E_l) + \ G_k*np.power(state[:, t, 3], 4)*(state[:, t, 0] - E_k) + \ G_na*np.power(state[:, t, 1], 3)*state[:, t, 2]*(state[:, t, 0] - E_na)) + I_ext[:, t] ds[:, 1] = alpha_m(state[:, t, 0]) * (1 - state[:, t, 1]) - beta_m(state[:, t, 0]) * state[:, t, 1] ds[:, 2] = alpha_h(state[:, t, 0]) * (1 - state[:, t, 2]) - beta_h(state[:, t, 0]) * state[:, t, 2] ds[:, 3] = alpha_n(state[:, t, 0]) * (1 - state[:, t, 3]) - beta_n(state[:, t, 0]) * state[:, t, 3] state[:, t+1] = state[:, t] + ds * dt return state class FitzHugh_Nagumo(): r""" A 2D reduction of the Hodgkin-Huxley model to the phase plane. """ def __init__(self, b_0, b_1, tau_u, tau_w): r""" units are in mV, microS, nF, mA, ms """ self.b_0 = b_0 self.b_1 = b_1 self.tau_u = tau_u self.tau_w = tau_w def euler_int(self, T, runs, I_ext, ic, dt=0.001, prin=1000): r""" Integrate the HH dynamics, the state array (v, m, h, n) is represented by 4 floating-point values :param int T: timesteps to run the simulation for :param int runs: number of trials to run (I_ext and i.c. can differ per run) :param np.array I_ext: external input current, with shape (runs, timesteps) :param np.array ic: neuron initial conditions, with shape (runs, 4) :returns: neuron state over the simulation :rtype: np.array """ state = np.zeros((runs, T, 2)) # vector u, w for k in range(runs): state[k, 0, :] = ic[k, :]#[-6.49997224e+01, 5.29342176e-02, 5.96111046e-01, 3.17681168e-01] ds = np.zeros((runs, 2)) iterator = tqdm(range(T-1)) for t in iterator: ds[:, 0] = 1/self.tau_u * (state[:, t, 0] - state[:, t, 0]**3/3. - state[:, t, 1] + I_ext) ds[:, 1] = 1/self.tau_w * (self.b_0 + self.b_1*state[:, t, 0] - state[:, t, 1]) state[:, t+1] = state[:, t] + ds * dt return state class Morris_Lecar(): r""" A 2D reduction of the Hodgkin-Huxley model to the phase plane. """ def __init__(self, G_na=120, G_k=36, G_l=0.3, E_na=50, E_k=-77, E_l=-54.4): r""" units are in mV, microS, nF, mA, ms """ self.G_na = G_na self.G_k = G_k self.G_l = G_l self.E_na = E_na self.E_k = E_k self.E_l = E_l def euler_int(self, T, runs, I_ext, ic, dt=0.001, prin=1000): r""" Integrate the HH dynamics, the state array (v, m, h, n) is represented by 4 floating-point values :param int T: timesteps to run the simulation for :param int runs: number of trials to run (I_ext and i.c. can differ per run) :param np.array I_ext: external input current, with shape (runs, timesteps) :param np.array ic: neuron initial conditions, with shape (runs, 4) :returns: neuron state over the simulation :rtype: np.array """ alpha_m = lambda V: (2.5-0.1*(V+65)) / (np.exp(2.5-0.1*(V+65)) -1) beta_m = lambda V: 4.0 * np.exp(-(V+65)/18) alpha_h = lambda V: 0.07 * np.exp(-(V+65)/20) beta_h = lambda V: 1.0 / (np.exp(3.0-0.1*(V+65)) + 1) alpha_n = lambda V: (0.1-0.01*(V+65)) / (np.exp(1-0.1*(V+65)) - 1) beta_n = lambda V: 0.125 * np.exp(-(V+65)/80) state = np.zeros((runs, T, 4)) # vector v, m, h, n for k in range(runs): state[k, 0, :] = ic[k, :]#[-6.49997224e+01, 5.29342176e-02, 5.96111046e-01, 3.17681168e-01] ds = np.zeros((runs, 4)) iterator = tqdm(range(T-1)) for t in iterator: ds[:, 0] = -(G_l*(state[:, t, 0] - E_l) + \ G_k*np.power(state[:, t, 3], 4)*(state[:, t, 0] - E_k) + \ G_na*np.power(state[:, t, 1], 3)*state[:, t, 2]*(state[:, t, 0] - E_na)) + I_ext[:, t] ds[:, 1] = alpha_m(state[:, t, 0]) * (1 - state[:, t, 1]) - beta_m(state[:, t, 0]) * state[:, t, 1] ds[:, 2] = alpha_h(state[:, t, 0]) * (1 - state[:, t, 2]) - beta_h(state[:, t, 0]) * state[:, t, 2] ds[:, 3] = alpha_n(state[:, t, 0]) * (1 - state[:, t, 3]) - beta_n(state[:, t, 0]) * state[:, t, 3] state[:, t+1] = state[:, t] + ds * dt return state def count_APs(V, lim=20.0): r""" Action potential counter """ idx = (V > lim).astype(float) idf = np.diff(idx) == 1 return idf.sum() # Integrate-and-fire models class Izhikevich(): r""" Biophysically inspired Izhikevich model (2003/2004) [1], a nonlinear integrate-and-fire model. References: [1] """ def __init__(self, a, b, c, d): self.a = a self.b = b self.c = c self.d = d def euler_int(self, T, runs, I_ext, ic, dt=0.1, prin=1000): r""" Euler integration of the dynamics, with state array (v, u) """ state = np.zeros((runs, T, 2)) # vector v, u spiketrain = np.zeros((runs, T)) reset_state = np.empty((runs, 2)) reset_state[:, 0].fill(self.c) for k in range(runs): state[k, 0, :] = ic[k, :] ds = np.zeros((runs, 2)) iterator = tqdm(range(T-1)) for t in iterator: ds[:, 0] = 0.04*state[:, t, 0]**2 + 5.*state[:, t, 0] + 140. - state[:, t, 1] + I_ext[:, t] ds[:, 1] = self.a*(self.b*state[:, t, 0] - state[:, t, 1]) reset = (state[:, t, 0] >= 30.) if reset.sum() > 0: reset_state[:, 1] = (state[:, t, 1] + self.d) state[:, t+1] = reset[:, None]*reset_state + (1-reset)[:, None]*(state[:, t] + ds * dt) spiketrain[:, t+1] = reset else: state[:, t+1] = state[:, t] + ds * dt return state, spiketrain class AdExIF(): r""" Adaptive exponential integrate-and-fire model. [1] References: [1] `Neuronal Dynamics`, Wulfram Gerstner, Werner M. Kistler, Richard Naud and Liam Paninski. """ def __init__(self, a, b, c, d): self.a = a self.b = b self.c = c self.d = d def euler_int(self, T, runs, I_ext, ic, dt=0.001, prin=1000): r""" Euler integration of the dynamics, with state array (v, u) """ state = np.zeros((runs, T, 2)) # vector v, u spiketrain = np.zeros((runs, T)) reset_state = np.empty((runs, 2)) reset_state[:, 0].fill(self.c) for k in range(runs): state[k, 0, :] = ic[k, :] ds = np.zeros((runs, 2)) iterator = tqdm(range(T-1)) for t in iterator: ds[:, 0] = 0.04*state[:, t, 0]**2 + 5.*state[:, t, 0] + 140. - state[:, t, 1] + I_ext[:, t] ds[:, 1] = self.a*(self.b*state[:, t, 0] - state[:, t, 1]) reset = (state[:, t, 0] >= 30.) if reset.sum() > 0: reset_state[:, 1] = (state[:, t, 1] + self.d) state[:, t+1] = reset[:, None]*reset_state + (1-reset)[:, None]*(state[:, t] + ds * dt) spiketrain[:, t+1] = reset else: state[:, t+1] = state[:, t] + ds * dt return state, spiketrain def neuron_model(dynamics, model_type): r""" Neuronal dynamics library of parameter values. Izhikevich parameters from [1]. References: [1] `Capturing the Dynamical Repertoire of Single Neurons with Generalized Linear Models`, Alison I. Weber & Jonathan W. Pillow """ if model_type == 'Izhikevich': # dt in ms if dynamics == 'tonic_spiking': model = Izhikevich(0.02, 0.2, -65, 6) I = 14 dt = 0.1 elif dynamics == 'phasic_spiking': model = Izhikevich(0.02, 0.2, -65, 6) I = 0.5 dt = 0.1 elif dynamics == 'tonic_bursting': model = Izhikevich(0.02, 0.2, -50, 2) I = 10 dt = 0.1 elif dynamics == 'phasic_bursting': model = Izhikevich(0.02, 0.25, -55, 0.05) I = 0.6 dt = 0.1 elif dynamics == 'mixed': model = Izhikevich(0.02, 0.2, -55, 4) I = 10 dt = 0.1 elif dynamics == 'frequency_adaptation': model = Izhikevich(0.01, 0.2, -65, 5) I = 20 dt = 0.1 elif dynamics == 'type_I': model = Izhikevich(0.02, -0.1, -55, 6) I = 25 dt = 1. elif dynamics == 'type_II': model = Izhikevich(0.2, 0.26, -65, 0) I = 0.5 dt = 1. elif dynamics == 'spike_latency': model = Izhikevich(0.02, 0.2, -65, 6) I = 3.49 dt = 0.1 elif dynamics == 'resonator': model = Izhikevich(0.1, 0.26, -60, -1) I = 0.3 dt = 0.5 elif dynamics == 'integrator': model = Izhikevich(0.02, -0.1, -66, 6) I = 27.4 dt = 0.5 elif dynamics == 'rebound_spike': model = Izhikevich(0.03, 0.25, -60, 4) I = -5. dt = 0.1 elif dynamics == 'rebound_burst': model = Izhikevich(0.03, 0.25, -52, 0) I = -5. dt = 0.1 elif dynamics == 'threshold_variability': model = Izhikevich(0.03, 0.25, -60, 4) I = 2.3 dt = 1. elif dynamics == 'bistability_I': model = Izhikevich(1., 1.5, -60, 0) I = 30. dt = 0.05 elif dynamics == 'bistability_II': model = Izhikevich(1., 1.5, -60, 0) I = 40. dt = 0.05 else: raise NotImplementedError return model, I, dt else: raise NotImplementedError

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1

0

null

0

4

7ce2a2f7a600f572ddad417b0f26991aa5156a69

64

py

Python

helloworld.py

sheki/snipy

311ccbc043fc317b98393f16ebabb1f0f2ad2574

[ "WTFPL" ]

1

2017-12-19T22:44:19.000Z

helloworld.py

sheki/snipy

311ccbc043fc317b98393f16ebabb1f0f2ad2574

[ "WTFPL" ]

null

helloworld.py

sheki/snipy

311ccbc043fc317b98393f16ebabb1f0f2ad2574

[ "WTFPL" ]

null

print 'Content-Type: text/plain' print '' print 'Hello, world!'

16

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9

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5

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0.125

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0

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null

0

1

0

1

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4

7cefc6e968d9c54af26c949e17f947dcd46300be

556

py

Python

oscar/lib/python2.7/site-packages/phonenumbers/shortdata/region_WS.py

AMuratTuran/mkn

557086426773ced10d82c969304bd349414a601e

[ "BSD-3-Clause" ]

4

2018-10-19T04:36:20.000Z

2020-02-13T16:14:09.000Z

oscar/lib/python2.7/site-packages/phonenumbers/shortdata/region_WS.py

AMuratTuran/mkn

557086426773ced10d82c969304bd349414a601e

[ "BSD-3-Clause" ]

null

oscar/lib/python2.7/site-packages/phonenumbers/shortdata/region_WS.py

AMuratTuran/mkn

557086426773ced10d82c969304bd349414a601e

[ "BSD-3-Clause" ]

null

"""Auto-generated file, do not edit by hand. WS metadata""" from ..phonemetadata import NumberFormat, PhoneNumberDesc, PhoneMetadata PHONE_METADATA_WS = PhoneMetadata(id='WS', country_code=None, international_prefix=None, general_desc=PhoneNumberDesc(national_number_pattern='9\\d{2}', possible_length=(3,)), emergency=PhoneNumberDesc(national_number_pattern='99[4-6]', example_number='994', possible_length=(3,)), short_code=PhoneNumberDesc(national_number_pattern='99[4-6]', example_number='994', possible_length=(3,)), short_data=True)

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7cefef909e7458f99684bc978e406b37332343a3

71

py

Python

nasbench301/surrogate_models/bananas/bananas_src/bo/fn/__init__.py

Basvanstein/nasbench301

2984dec45c760d47762f50efe39b71e9d1ac22e0

[ "Apache-2.0" ]

167

2019-10-26T19:54:49.000Z

2021-12-14T15:25:32.000Z

nasbench301/surrogate_models/bananas/bananas_src/bo/fn/__init__.py

Basvanstein/nasbench301

2984dec45c760d47762f50efe39b71e9d1ac22e0

[ "Apache-2.0" ]

12

2020-11-07T12:50:19.000Z

2022-01-21T08:52:53.000Z

nasbench301/surrogate_models/bananas/bananas_src/bo/fn/__init__.py

Basvanstein/nasbench301

2984dec45c760d47762f50efe39b71e9d1ac22e0

[ "Apache-2.0" ]

23

2019-10-28T12:26:32.000Z

2020-10-12T12:31:39.000Z

""" Code for synthetic functions to query (perform experiment on). """

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null

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7cf280e691010fa2d069889268a0b02995b8964b

5,086

py

Python

paxes_cinder/volume/discovery_driver.py

windskyer/k_cinder

000ee539ee4842a158071d26ee99d12c7c0a87da

[ "Apache-2.0" ]

null

paxes_cinder/volume/discovery_driver.py

windskyer/k_cinder

000ee539ee4842a158071d26ee99d12c7c0a87da

[ "Apache-2.0" ]

null

paxes_cinder/volume/discovery_driver.py

windskyer/k_cinder

000ee539ee4842a158071d26ee99d12c7c0a87da

[ "Apache-2.0" ]

null

# # # ================================================================= # ================================================================= """Extended Volume Driver interface to discover/query resource information""" class VolumeDiscoveryDriver(object): """ Extended Volume Driver interface for drivers to implementation to discover/query additional resource information from the managed system. The base VolumeDriver interface provides methods to take actions on the Storage Provider and its Volumes but given the premise that OpenStack is the authoritative management source for the Hosts being managed, it assumes the resources created by OpenStack are an accurate representation of the current state of the resources, so it provides very limited information through the driver interface about those resources. This interface extends those driver capabilities by asking the driver to provide 4 levels of information about the resources being managed: 1) discover - provide a list of all Volumes that exist on the Provider 2) query - provide enough info about the Volumes to import into OS 3) inventory - provide additional details about the Volumes specified 4) metrics - provide additional metric information about the Volumes """ def discover_volumes(self, context, filters=None): """ Returns a list of all of the Volumes that exist on the given Provider. For each Volumes the driver needs to return a dictionary containing the following attributes: name: The Name of the Volume defined on the Storage Provider status: The Status of the Volume, matching the status definition uuid: The UUID of the VM when created thru OS (Optional) status: The Status of the Volume, matching the definition size: The Size of the Volume in GB restricted_metadata: The Additional Meta-data from the Driver vdisk_id: The Identifier for the Volume on the Back-end vdisk_name: The Name of the Volume on the Back-end support: Dictionary stating whether the Volume can be managed status: Whether or not it is "supported" or "not_supported" reasons: List of Text Strings as to why it isn't supported :param context: The security context for the query :param filters: The filters to apply, such as {'wwpns': ['wwpn1',..] """ raise NotImplementedError() def query_volumes(self, context, volumes, server_info={}, mark_boot=True): """ Returns a list of Volumes (matching those specified on input) with enough additional details about each Volume to be able to import the Volume into the Cinder Database such as OpenStack can start managing. For each Volume the driver needs to return a dictionary containing the following attributes: uuid: The UUID of the Volume when created thru OS name: The Name of the Volume defined on the Storage Provider status: The Status of the Volume, matching the definition size: The Size of the Volume in GB restricted_metadata: The Additional Meta-data from the Driver vdisk_id: The Identifier for the Volume on the Back-end vdisk_name: The Name of the Volume on the Back-end :param context: The security context for the query :param volumes: A list of dictionary objects for each Volume with: uuid: The UUID of the Volume when created thru OS name: The Name of the Volume defined on the Storage Provider restricted_metadata: The Additional Meta-data from the Driver vdisk_id: The Identifier for the Volume on the Back-end vdisk_name: The Name of the Volume on the Back-end :param server_info: The host info for the attached servers """ #Currently the discover/query methods return the same data, so we can #return the values passed in since discover was called to get the info return volumes def inventory_volumes(self, context, volumes): """ Provides a mechanism for the Driver to gather Inventory-related information for the Volumes provided off of the Back-end at periodic intervals. The Driver is free from there to populate the information directly in the Database rather than return it. :param context: The security context for the query :param volumes: A list of dictionary objects for each Volume with: uuid: The UUID of the Volume when created thru OS name: The Name of the Volume defined on the Storage Provider restricted_metadata: The Additional Meta-data from the Driver vdisk_id: The Identifier for the Volume on the Back-end vdisk_name: The Name of the Volume on the Back-end """ pass

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6b0b873fff92b5ab3b7c7fe753e498cb0574572b

280

py

Python

utils/__init__.py

ntrlmt/jetson_benchmarks

0a3d56ed806aa1aa0ee27ddeba0dd60daf0eb65d

[ "MIT" ]

224

2020-04-28T17:28:21.000Z

2022-03-28T15:27:19.000Z

utils/__init__.py

nikunjpansari/jetson_benchmarks

d0e474ff5e797c6842cd7ae8b2daddc82b7be423

[ "MIT" ]

23

2020-05-25T03:46:33.000Z

2022-03-13T09:38:51.000Z

utils/__init__.py

nikunjpansari/jetson_benchmarks

d0e474ff5e797c6842cd7ae8b2daddc82b7be423

[ "MIT" ]

38

2020-05-29T02:51:57.000Z

2022-03-23T06:45:44.000Z

from .download_models import download_models from .load_store_engine import load_store_engine from .read_write_data import read_write_data from .utilities import utilities from .benchmark_argparser import benchmark_argparser from .run_benchmark_models import run_benchmark_models

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1

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1

0

4

6b0d3d28748b79c66f155d77253c7374050f983f

26

py

Python

midterm2/pick_area.py

williamtrang/DSC20

eb72c1003ee98e30f63040568e82eedcb8d3af52

[ "Apache-2.0" ]

null

midterm2/pick_area.py

williamtrang/DSC20

eb72c1003ee98e30f63040568e82eedcb8d3af52

[ "Apache-2.0" ]

null

midterm2/pick_area.py

williamtrang/DSC20

eb72c1003ee98e30f63040568e82eedcb8d3af52

[ "Apache-2.0" ]

null

def pick_area(input):

13

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26

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22

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null

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null

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1

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null

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1

0

4

6b13d14922bb0aa6053368fb4eec6baa39c0a5ba

523

py

Python

app/migrations/0005_auto_20211205_2323.py

RhonaJoyKe/Insta-Clone

18a742f93c3e69ab03e88d69b28aced77fcce109

[ "Unlicense" ]

null

app/migrations/0005_auto_20211205_2323.py

RhonaJoyKe/Insta-Clone

18a742f93c3e69ab03e88d69b28aced77fcce109

[ "Unlicense" ]

null

app/migrations/0005_auto_20211205_2323.py

RhonaJoyKe/Insta-Clone

18a742f93c3e69ab03e88d69b28aced77fcce109

[ "Unlicense" ]

null

# Generated by Django 3.2.9 on 2021-12-05 20:23 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('app', '0004_image_user'), ] operations = [ migrations.RemoveField( model_name='comments', name='postee', ), migrations.RemoveField( model_name='image', name='postee', ), migrations.RemoveField( model_name='image', name='profile', ), ]

20.115385

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0.345588

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523

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null

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null

0

4

6b26e5fee369f5d6578e8f4df9c4ff323d3510c6

102

py

Python

mmdetection/mmdet/models/necks/__init__.py

taikis/kaggle-kuzushiji-recognition

63c063f03f1ff5d53d411ae1709b4e328e170ace

[ "MIT" ]

859

2019-09-29T05:36:03.000Z

2022-03-15T08:33:03.000Z

mmdetection/mmdet/models/necks/__init__.py

taikis/kaggle-kuzushiji-recognition

63c063f03f1ff5d53d411ae1709b4e328e170ace

[ "MIT" ]

69

2019-10-14T11:07:51.000Z

2022-03-10T14:39:00.000Z

mmdetection/mmdet/models/necks/__init__.py

taikis/kaggle-kuzushiji-recognition

63c063f03f1ff5d53d411ae1709b4e328e170ace

[ "MIT" ]

165

2019-10-05T02:59:29.000Z

2022-03-28T02:30:11.000Z

from .bfp import BFP from .fpn import FPN from .hrfpn import HRFPN __all__ = ['FPN', 'BFP', 'HRFPN']

17

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null

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1

0

4

6b43fccfadfdf79c05274698c8c5c16cec3bb845

780

py

Python

aiotdlib/api/types/ok.py

jraylan/aiotdlib

4528fcfca7c5c69b54a878ce6ce60e934a2dcc73

[ "MIT" ]

37

2021-05-04T10:41:41.000Z

2022-03-30T13:48:05.000Z

aiotdlib/api/types/ok.py

jraylan/aiotdlib

4528fcfca7c5c69b54a878ce6ce60e934a2dcc73

[ "MIT" ]

13

2021-07-17T19:54:51.000Z

2022-02-26T06:50:00.000Z

aiotdlib/api/types/ok.py

jraylan/aiotdlib

4528fcfca7c5c69b54a878ce6ce60e934a2dcc73

[ "MIT" ]

7

2021-09-22T21:27:11.000Z

2022-02-20T02:33:19.000Z

# =============================================================================== # # # # This file has been generated automatically!! Do not change this manually! # # # # =============================================================================== # from __future__ import annotations from pydantic import Field from ..base_object import BaseObject class Ok(BaseObject): """ An object of this type is returned on a successful function call for certain functions """ ID: str = Field("ok", alias="@type") @staticmethod def read(q: dict) -> Ok: return Ok.construct(**q)

32.5

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780

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860c6cfe5eb3073994889280bbad52cf77a18d3a

16,471

py

Python

lib/risksense_api/__subject/__uploads/__uploads.py

arockiachristopher-git/risksense_tools

1564cd93505a4d4ccd546f68310e0a09f888e590

[ "Apache-2.0" ]

1

2021-06-08T23:58:55.000Z

lib/risksense_api/__subject/__uploads/__uploads.py

arockiachristopher-git/risksense_tools

1564cd93505a4d4ccd546f68310e0a09f888e590

[ "Apache-2.0" ]

1

2021-08-05T17:39:38.000Z

2021-08-05T17:51:17.000Z

lib/risksense_api/__subject/__uploads/__uploads.py

risksense/risksense_tools

1564cd93505a4d4ccd546f68310e0a09f888e590

[ "Apache-2.0" ]

5

2022-02-25T21:09:08.000Z

2022-03-31T06:16:44.000Z

""" ******************************************************************************************************************* | | Name : __uploads.py | Module : risksense_api | Description : A class to be used for interacting with uploads on the RiskSense Platform. | Copyright : (c) RiskSense, Inc. | License : Apache-2.0 (http://www.apache.org/licenses/LICENSE-2.0) | ******************************************************************************************************************* """ import json from ...__subject import Subject from ..._api_request_handler import * class Uploads(Subject): """ Uploads class """ def __init__(self, profile): """ Initialization of Uploads object. :param profile: Profile Object :type profile: _profile """ self.subject_name = "upload" Subject.__init__(self, profile, self.subject_name) def get_uploads(self, assessment_id, page_num=0, page_size=150, client_id=None): """ Get uploads associated with an assessment. :param assessment_id: The assessment ID. :type assessment_id: int :param page_num: The page number of results to return. :type page_num: int :param page_size: The number of results per page to return. :type page_size: int :param client_id: Client ID. If an ID isn't passed, will use the profile's default Client ID. :type client_id: int :return: The JSON response from the platform is returned. :rtype: dict :raises RequestFailed: """ if client_id is None: client_id = self._use_default_client_id()[0] url = self.api_base_url.format(str(client_id)) params = { "assessmentId": assessment_id, "size": page_size, "page": page_num } try: raw_response = self.request_handler.make_request(ApiRequestHandler.GET, url, params=params) except RequestFailed: raise jsonified_response = json.dumps(raw_response.text) return jsonified_response def create(self, name, assessment_id, network_id, client_id=None): """ Create a new upload. :param name: The name to be associated with the upload. :type name: str :param assessment_id: The assessment ID. :type assessment_id: int :param network_id: The network ID. :type network_id: int :param client_id: Client ID. If an ID isn't passed, will use the profile's default Client ID. :type client_id: int :return: The Upload ID :rtype: int :raises RequestFailed: """ if client_id is None: client_id = self._use_default_client_id()[0] url = self.api_base_url.format(str(client_id)) body = { "assessmentId": assessment_id, "networkId": network_id, "name": name } try: raw_response = self.request_handler.make_request(ApiRequestHandler.POST, url, body=body) except RequestFailed: raise jsonified_response = json.loads(raw_response.text) upload_id = jsonified_response['id'] return upload_id def check_state(self, upload_id, client_id=None): """ Check the state of an upload. :param upload_id: The upload ID. :type upload_id: int :param client_id: Client ID. If an ID isn't passed, will use the profile's default Client ID. :type client_id: int :return: The current state of the upload is returned. :rtype: str :raises RequestFailed: """ if client_id is None: client_id = self._use_default_client_id()[0] url = self.api_base_url.format(str(client_id)) + "/{}".format(str(upload_id)) try: raw_response = self.request_handler.make_request(ApiRequestHandler.GET, url) except RequestFailed: raise jsonified_response = json.loads(raw_response.text) state = jsonified_response['state'] return state def update(self, upload_id, client_id=None, **kwargs): """ Update an upload. Uploads can only be updated before they have been processed. :param upload_id: The upload ID. :type upload_id: int :param client_id: Client ID. If an ID isn't passed, will use the profile's default Client ID. :type client_id: int :keyword name: str Name of upload :keyword assessment_id: int Assessment ID. :keyword network_id: int Network ID. :return: The job ID is returned. :rtype: int :raises RequestFailed: :raises ValueError: """ if client_id is None: client_id = self._use_default_client_id()[0] url = self.api_base_url.format(str(client_id)) + "/{}".format(str(upload_id)) name = kwargs.get('name', None) assessment_id = kwargs.get('assessment_id', None) network_id = kwargs.get('network_id', None) body = { "name": name, "assessmentId": assessment_id, "networkId": network_id } body = self._strip_nones_from_dict(body) if body == {}: raise ValueError("Body is empty. Please provide name, assessment_id, and/or network_id") try: raw_response = self.request_handler.make_request(ApiRequestHandler.PUT, url, body=body) except RequestFailed: raise jsonified_response = json.loads(raw_response.text) job_id = jsonified_response['id'] return job_id def delete(self, upload_id, client_id=None): """ Delete an Upload. :param upload_id: The upload ID :type upload_id: int :param client_id: Client ID. If an ID isn't passed, will use the profile's default Client ID. :type client_id: int :return: True/False reflecting whether or not the operation was successful. :rtype: bool :raises RequestFailed: """ if client_id is None: client_id = self._use_default_client_id()[0] url = self.api_base_url.format(str(client_id)) + "/{}".format(str(upload_id)) try: self.request_handler.make_request(ApiRequestHandler.DELETE, url) except RequestFailed: raise success = True return success def list_files(self, upload_id, page_num=0, page_size=150, client_id=None): """ List files in an upload. :param upload_id: The upload ID :type upload_id: int :param page_num: The page number to be returned. :type page_num: int :param page_size: The number of results to return per page. :type page_size: int :param client_id: Client ID. If an ID isn't passed, will use the profile's default Client ID. :type client_id: int :return: A paginated JSON response from the platform. :rtype: dict :raises RequestFailed: """ if client_id is None: client_id = self._use_default_client_id()[0] url = self.api_base_url.format(str(client_id)) + "/{}/file".format(str(upload_id)) params = { "size": page_size, "page": page_num } try: raw_response = self.request_handler.make_request(ApiRequestHandler.GET, url, params=params) except RequestFailed: raise jsonified_response = json.loads(raw_response.text) return jsonified_response def add_file(self, upload_id, file_name, path_to_file, client_id=None): """ Add a file to an upload. :param upload_id: Upload ID :type upload_id: int :param file_name: The name to be used for the uploaded file. :type file_name: str :param path_to_file: Full path to the file to be uploaded. :type path_to_file: str :param client_id: Client ID. If an ID isn't passed, will use the profile's default Client ID. :type client_id: int :return: The file ID is returned. :rtype: int :raises RequestFailed: :raises FileNotFoundError: """ if client_id is None: client_id = self._use_default_client_id()[0] url = self.api_base_url.format(str(client_id)) + "/{}/file".format(str(upload_id)) upload_file = {'scanFile': (file_name, open(path_to_file, 'rb'))} try: raw_response = self.request_handler.make_request(ApiRequestHandler.POST, url, files=upload_file) except RequestFailed: raise except FileNotFoundError: raise jsonified_response = json.loads(raw_response.text) file_id = jsonified_response[0]['id'] return file_id def update_file(self, upload_id, file_id, client_id=None, **kwargs): """ Update an uploaded file. Will only work if the file has not yet been processed. :param upload_id: The upload ID. :type upload_id: int :param file_id: The file ID. :type file_id: str :param client_id: Client ID. If an ID isn't passed, will use the profile's default Client ID. :type client_id: int :keyword assessment_id: The assessment ID the upload should be associated with. Integer. :keyword network_id: The network ID the upload should be associated with. Integer. :keyword application_id: The application ID the upload should be associated with. Integer. :return: The upload ID is returned :rtype: int :raises RequestFailed: :raises ValueError: """ if client_id is None: client_id = self._use_default_client_id()[0] url = self.api_base_url.format(str(client_id)) + "/{}/file/{}".format(str(upload_id), str(file_id)) assessment_id = kwargs.get('assessment_id', None) network_id = kwargs.get('network_id', None) application_id = kwargs.get('application_id', None) body = { "assessmentId": assessment_id, "networkId": network_id, "applicationId": application_id } body = self._strip_nones_from_dict(body) if body == {}: raise ValueError('Body empty. Please provide assessment_id, network_id, and/or application_id missing.') try: raw_response = self.request_handler.make_request(ApiRequestHandler.PUT, url, body=body) except RequestFailed: raise jsonified_response = json.loads(raw_response) returned_id = jsonified_response['id'] return returned_id def delete_file(self, upload_id, file_id, client_id=None): """ Delete an uploaded file. :param upload_id: The upload ID. :type upload_id: int :param file_id: The file ID. :type file_id: int :param client_id: Client ID. If an ID isn't passed, will use the profile's default Client ID. :type client_id: int :return: True/False reflecting whether or not the operation was successfully submitted. :rtype: bool :raises RequestFailed: """ if client_id is None: client_id = self._use_default_client_id()[0] url = self.api_base_url.format(str(client_id)) + "/{}/file/{}".format(str(upload_id), str(file_id)) try: self.request_handler.make_request(ApiRequestHandler.DELETE, url) except RequestFailed: raise success = True return success def download_file(self, upload_id, file_destination, client_id=None): """ Download a previously uploaded file. :param upload_id: The upload ID :type upload_id: int :param file_destination: The local destination for the downloaded file. :type file_destination: str :param client_id: Client ID. If an ID isn't passed, will use the profile's default Client ID. :type client_id: int :return: True/False reflecting whether or not the operation was successful. :rtype: bool :raises RequestFailed: :raises FileNotFoundError: :raises FileExistsError: """ if client_id is None: client_id = self._use_default_client_id()[0] url = self.api_base_url.format(str(client_id)) + "/{}/file/download".format(str(upload_id)) try: raw_response = self.request_handler.make_request(ApiRequestHandler.GET, url) except RequestFailed: raise try: open(file_destination, "wb").write(raw_response.content) success = True except (FileNotFoundError, FileExistsError): raise return success def fetch_file_by_uuid(self, upload_id, file_uuid, file_destination, client_id=None): """ Download a file by UUID. :param upload_id: The upload ID :type upload_id: int :param file_uuid: The file UUID :type file_uuid: str :param file_destination: The local destination for the downloaded file. :type file_destination: str :param client_id: Client ID. If an ID isn't passed, will use the profile's default Client ID. :type client_id: int :return: True/False reflecting whether or not the operation was successful. :rtype: bool :raises RequestFailed: :raises FileNotFoundError: :raises FileExistsError: """ if client_id is None: client_id = self._use_default_client_id()[0] url = self.api_base_url.format(str(client_id)) + "/{}/file/{}".format(str(upload_id), str(file_uuid)) try: raw_response = self.request_handler.make_request(ApiRequestHandler.GET, url) except RequestFailed: raise try: open(file_destination, "wb").write(raw_response.content) success = True except (FileNotFoundError, FileExistsError): raise return success def start_processing(self, upload_id, auto_urba=False, client_id=None): """ Initiate processing of an upload. :param upload_id: The upload ID :type upload_id: int :param auto_urba: Indicator for whether or not auto-URBA should be run after upload is processed. :type auto_urba: bool :param client_id: Client ID. If an ID isn't passed, will use the profile's default Client ID. :type client_id: int :return: True/False reflecting whether or not the operation was successfully submitted. :rtype: bool :raises RequestFailed: """ if client_id is None: client_id = self._use_default_client_id()[0] url = self.api_base_url.format(str(client_id)) + "/{}/start".format(str(upload_id)) body = { "autoUrba": auto_urba } try: self.request_handler.make_request(ApiRequestHandler.POST, url, body=body) success = True except RequestFailed: raise return success """ Copyright 2021 RiskSense, Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """

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1

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1

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null

0

4

8625bc8c94c83d1dc2c1cfefab93a80ff30f3ce2

5,752

py

Python

pymtl3/passes/translator/structural/test/StructuralTranslatorL4_test.py

hsqforfun/pymtl3

05e06601cf262a663a95d1235cb99056ece84580

[ "BSD-3-Clause" ]

1

2019-11-12T12:26:01.000Z

pymtl3/passes/translator/structural/test/StructuralTranslatorL4_test.py

hsqforfun/pymtl3

05e06601cf262a663a95d1235cb99056ece84580

[ "BSD-3-Clause" ]

null

pymtl3/passes/translator/structural/test/StructuralTranslatorL4_test.py

hsqforfun/pymtl3

05e06601cf262a663a95d1235cb99056ece84580

[ "BSD-3-Clause" ]

null

#========================================================================= # StructuralTranslatorL4_test.py #========================================================================= # Author : Peitian Pan # Date : May 21, 2019 """Test the level 3 structural translators.""" import pytest from pymtl3.datatypes import Bits1, Bits32 from pymtl3.dsl import Component, InPort, Interface, OutPort, connect from pymtl3.passes.rtlir.errors import RTLIRConversionError from pymtl3.passes.rtlir.util.test_utility import do_test, expected_failure from pymtl3.passes.translator.structural.StructuralTranslatorL4 import ( StructuralTranslatorL4, ) from .TestStructuralTranslator import mk_TestStructuralTranslator def local_do_test( m ): tr = mk_TestStructuralTranslator(StructuralTranslatorL4)(m) tr.clear( m ) tr.translate_structural(m) for comp in m._ref_comps.keys(): decl_comp = tr.structural.decl_subcomps[comp] assert decl_comp == m._ref_comps[comp] for comp in m._ref_conns.keys(): connections = tr.structural.connections[comp] assert connections == m._ref_conns[comp] def test_multi_components( do_test ): class B( Component ): def construct( s ): s.out_b = OutPort( Bits32 ) @s.update def upblk(): s.out_b = Bits32(0) class A( Component ): def construct( s ): s.out_a = OutPort( Bits32 ) s.b = B() connect( s.b.out_b, s.out_a ) a = A() a.elaborate() a._ref_comps = { a : \ """\ component_decls: component_decl: b Component B component_ports: component_port: out_b Port of Vector32 component_ifcs: """, a.b : \ """\ component_decls: """} a._ref_conns = { a : \ """\ connections: connection: SubCompAttr CurCompAttr b out_b -> CurCompAttr out_a """, a.b : \ """\ connections: """} a._ref_src = \ """\ component B ( port_decls: port_decl: out_b Port of Vector32 interface_decls: ); const_decls: freevars: wire_decls: component_decls: tmpvars: upblk_srcs: upblk_src: upblk connections: endcomponent component A ( port_decls: port_decl: out_a Port of Vector32 interface_decls: ); const_decls: freevars: wire_decls: component_decls: component_decl: b Component B component_ports: component_port: out_b Port of Vector32 component_ifcs: tmpvars: upblk_srcs: connections: connection: SubCompAttr CurCompAttr b out_b -> CurCompAttr out_a endcomponent """ do_test( a ) def test_multi_components_ifc_hierarchy_connect( do_test ): class OutIfc( Interface ): def construct( s ): s.msg = OutPort( Bits32 ) s.rdy = InPort( Bits1 ) s.val = OutPort( Bits1 ) class B( Component ): def construct( s ): s.out_b = OutPort( Bits32 ) s.ifc_b = OutIfc() connect( s.out_b, 0 ) connect( s.ifc_b.msg, 0 ) connect( s.ifc_b.val, 1 ) class A( Component ): def construct( s ): s.out_a = OutPort( Bits32 ) s.b = B() s.ifc_a = OutIfc() connect( s.b.out_b, s.out_a ) connect( s.b.ifc_b, s.ifc_a ) a = A() a.elaborate() a._ref_comps = { a : \ """\ component_decls: component_decl: b Component B component_ports: component_port: out_b Port of Vector32 component_ifcs: component_ifc: ifc_b InterfaceView OutIfc component_ifc_ports: component_ifc_port: msg Port of Vector32 component_ifc_port: rdy Port of Vector1 component_ifc_port: val Port of Vector1 """, a.b : \ """\ component_decls: """} a._ref_conns = { a : \ """\ connections: connection: SubCompAttr CurCompAttr b out_b -> CurCompAttr out_a connection: IfcAttr SubCompAttr CurCompAttr b ifc_b msg -> IfcAttr CurCompAttr ifc_a msg connection: IfcAttr CurCompAttr ifc_a rdy -> IfcAttr SubCompAttr CurCompAttr b ifc_b rdy connection: IfcAttr SubCompAttr CurCompAttr b ifc_b val -> IfcAttr CurCompAttr ifc_a val """, a.b : \ """\ connections: connection: Bits32(0) -> CurCompAttr out_b connection: Bits32(0) -> IfcAttr CurCompAttr ifc_b msg connection: Bits1(1) -> IfcAttr CurCompAttr ifc_b val """} a._ref_src = \ """\ component B ( port_decls: port_decl: out_b Port of Vector32 interface_decls: interface_decl: ifc_b InterfaceView OutIfc interface_ports: interface_port: msg Port of Vector32 interface_port: rdy Port of Vector1 interface_port: val Port of Vector1 ); const_decls: freevars: wire_decls: component_decls: tmpvars: upblk_srcs: connections: connection: Bits32(0) -> CurCompAttr out_b connection: Bits32(0) -> IfcAttr CurCompAttr ifc_b msg connection: Bits1(1) -> IfcAttr CurCompAttr ifc_b val endcomponent component A ( port_decls: port_decl: out_a Port of Vector32 interface_decls: interface_decl: ifc_a InterfaceView OutIfc interface_ports: interface_port: msg Port of Vector32 interface_port: rdy Port of Vector1 interface_port: val Port of Vector1 ); const_decls: freevars: wire_decls: component_decls: component_decl: b Component B component_ports: component_port: out_b Port of Vector32 component_ifcs: component_ifc: ifc_b InterfaceView OutIfc component_ifc_ports: component_ifc_port: msg Port of Vector32 component_ifc_port: rdy Port of Vector1 component_ifc_port: val Port of Vector1 tmpvars: upblk_srcs: connections: connection: SubCompAttr CurCompAttr b out_b -> CurCompAttr out_a connection: IfcAttr SubCompAttr CurCompAttr b ifc_b msg -> IfcAttr CurCompAttr ifc_a msg connection: IfcAttr CurCompAttr ifc_a rdy -> IfcAttr SubCompAttr CurCompAttr b ifc_b rdy connection: IfcAttr SubCompAttr CurCompAttr b ifc_b val -> IfcAttr CurCompAttr ifc_a val endcomponent """ do_test( a ) __all__ = [s for s in dir() if s.startswith('test_')]

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5,752

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0

null

0

1

0

1

0

null

0

4

864d337c82ef94b51d95ff66100a6168388f04c6

248

py

Python

src/rul/gbdt_trainer.py

LongxingTan/Survival_analysis

4e132edc79423e286fb9d4c61b42547ed758a15c

[ "MIT" ]

9

2020-01-13T10:01:56.000Z

2022-01-23T06:06:09.000Z

src/rul/gbdt_trainer.py

LongxingTan/Survival_analysis

4e132edc79423e286fb9d4c61b42547ed758a15c

[ "MIT" ]

3

2020-09-25T22:23:43.000Z

2022-02-10T02:09:10.000Z

src/rul/gbdt_trainer.py

LongxingTan/Survival_analysis

4e132edc79423e286fb9d4c61b42547ed758a15c

[ "MIT" ]

5

2020-12-28T01:40:42.000Z

2022-03-15T03:01:31.000Z

# https://tianchi.aliyun.com/notebook-ai/detail?spm=5176.12586969.1002.6.3f9a7084HBDkFB&postId=107251 class LGBTrainer(object): def __init__(self): pass def train(self, x_train, y_train, x_valid=None, y_valid=None): pass

24.8

101

0.705645

36

248

4.638889

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0.107784

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0.140097

0.165323

248

9

102

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0.6

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null

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1

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null

0

1

0

1

0

1

0

4

865edf37cca78e50a9b9671f01e57c611d08cc82

162

py

Python

services/wallet/signer/exceptions.py

snario/zksnark-nft

1f2fc5c3f4885c50465bf839ef478ec048ec754d

[ "MIT" ]

76

2018-09-09T00:35:10.000Z

2022-02-22T16:54:34.000Z

services/wallet/signer/exceptions.py

ejhanrienaOut/zknifty

9285b573d4befe4ddeed9edba321af65c545bf39

[ "MIT" ]

null

services/wallet/signer/exceptions.py

ejhanrienaOut/zknifty

9285b573d4befe4ddeed9edba321af65c545bf39

[ "MIT" ]

17

2018-09-09T17:35:08.000Z

2022-02-22T07:25:22.000Z

class RequestFailedException(Exception): """request failed without success http status""" class WalletException(Exception): """wallet operation failed"""

32.4

52

0.759259

15

162

8.2

0.8

0

0.12963

162

5

53

32.4

0.87234

0.407407

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

867c39291a7d226f3704ce663d558c6588f987a8

197

py

Python

OP3/op3/__init__.py

gvx/op3

888ab5975a3f911fc9ed9afea983928de3110033

[ "MIT" ]

null

OP3/op3/__init__.py

gvx/op3

888ab5975a3f911fc9ed9afea983928de3110033

[ "MIT" ]

null

OP3/op3/__init__.py

gvx/op3

888ab5975a3f911fc9ed9afea983928de3110033

[ "MIT" ]

null

# based on http://opensoundcontrol.org/spec-1_0 from .payload_types import RGBA, MIDIMessage, ASAP from .messages import Element, Message, Bundle from .parser import parse __version__ = '0.0.1'

21.888889

50

0.771574

29

197

5.034483

0.758621

0

0.02924

0.13198

197

8

51

24.625

0.824561

0.228426

0

0.033557

0

1

0

false

0

0.75

0

0.75

0

1

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null

0

1

0

null

0

1

0

1

0

4

867eaf02bbbd25be7d40654bc084d3dd4d311ee7

26

py

Python

python/testData/mover/emptyLine_afterUp.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/mover/emptyLine_afterUp.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/mover/emptyLine_afterUp.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

if True: a = 1 b = 2

5.2

9

0.384615

6

26

1.666667

1

0

0.153846

0.5

26

4

10

6.5

0.615385

0

1

0

false

0

1

null

0

1

0

null

0

4

86938ffd5a61560719e40314836bd9e543038b28

87

py

Python

scp4tw/center/apps.py

iblis17/scp4tw

faa07aa8c6ea4905db843fbccdb4057043bc9b9a

[ "MIT" ]

null

scp4tw/center/apps.py

iblis17/scp4tw

faa07aa8c6ea4905db843fbccdb4057043bc9b9a

[ "MIT" ]

null

scp4tw/center/apps.py

iblis17/scp4tw

faa07aa8c6ea4905db843fbccdb4057043bc9b9a

[ "MIT" ]

null

from django.apps import AppConfig class CenterConfig(AppConfig): name = 'center'

14.5

33

0.747126

10

87

6.5

0.9

0

0.172414

87

5

34

17.4

0.902778

0

0.068966

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

869712e600d5a57afef86843f8444e9409b06aa3

351

py

Python

members-backend/core/serializers.py

LombaAnderson/angular-django-integration

ea7a7bf639c484f6c812fc2657d8c82880759cf2

[ "MIT" ]

null

members-backend/core/serializers.py

LombaAnderson/angular-django-integration

ea7a7bf639c484f6c812fc2657d8c82880759cf2

[ "MIT" ]

null

members-backend/core/serializers.py

LombaAnderson/angular-django-integration

ea7a7bf639c484f6c812fc2657d8c82880759cf2

[ "MIT" ]

null

from rest_framework import serializers from .models import Member class MemberSerializer(serializers.ModelSerializer): class Meta: model = Member fields = ['id', 'name', 'surname', 'phone', 'photo'] class MemberSimpleSerializer(serializers.ModelSerializer): class Meta: model = Member fields = ['id', 'name']

27

60

0.678063

34

351

6.970588

0.558824

0.219409

0.261603

0.295359

0.489451

0

0.213675

351

12

61

29.25

0.858696

0

0.4

0

0.082621

0

1

0

false

0

0.2

0

0.6

0

null

1

0

null

0

1

0

4

86aaf79edf4b0dfa0ead7f6d90d7b88d14f5fc46

161

py

Python

A_MIAR3QtGui/MainWindow.py

FukukouSSJouhou/A_MIA_R3

970e80f6b71ba3c6eab013470b52f1e76fb04d3c

[ "MIT" ]

null

A_MIAR3QtGui/MainWindow.py

FukukouSSJouhou/A_MIA_R3

970e80f6b71ba3c6eab013470b52f1e76fb04d3c

[ "MIT" ]

null

A_MIAR3QtGui/MainWindow.py

FukukouSSJouhou/A_MIA_R3

970e80f6b71ba3c6eab013470b52f1e76fb04d3c

[ "MIT" ]

1

2022-03-29T03:30:36.000Z

from PySide2 import QtCore class MainWindowConnect(QtCore.QObject): def __init__(self,parent=None): super(MainWindowConnect,self).__init__(parent)

23

54

0.763975

18

161

6.388889

0.722222

0

0.007246

0.142857

161

6

55

26.833333

0.826087

0

1

0.25

false

0

0.25

0

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

4

86d027b23ed92eb1e3c11dcd36430966ed5b2332

538

py

Python

stitcher.py

jarsba/meow

1120e6a97bcf549d3177b959fcae5375d05d5e47

[ "MIT" ]

null

stitcher.py

jarsba/meow

1120e6a97bcf549d3177b959fcae5375d05d5e47

[ "MIT" ]

1

2021-08-24T21:11:09.000Z

2021-08-25T14:54:48.000Z

stitcher.py

jarsba/meow

1120e6a97bcf549d3177b959fcae5375d05d5e47

[ "MIT" ]

null

import cv2 class Stitcher: def __init__(self, input_path_1: str, input_path_2: str, output: str = "output.mp4"): self.input_path_1 = input_path_1 self.input_path_2 = input_path_2 self.output = output @staticmethod def read_stream(self, input_path: str): capture = cv2.VideoCapture(input_path) return capture def stitch(self, video_stream_1, video_stream_2, output="output.mp4"): # TODO: finish pass def save_output(self): # TODO: finish pass

25.619048

89

0.644981

73

538

4.424658

0.356164

0.22291

0.160991

0.086687

0

0.030457

0.267658

538

20

90

26.9

0.78934

0.046468

0

0.142857

0

0.039216

0

0.05

0

1

0.285714

false

0.142857

0.071429

0

0.5

0

null

1

0

null

0

1

0

1

0

1

0

4

86e125c31f710969b3c59c9fb2bfaae6cbc52795

121

py

Python

abc/abc059/abc059b.py

c-yan/atcoder

940e49d576e6a2d734288fadaf368e486480a948

[ "MIT" ]

1

2019-08-21T00:49:34.000Z

abc/abc059/abc059b.py

c-yan/atcoder

940e49d576e6a2d734288fadaf368e486480a948

[ "MIT" ]

null

abc/abc059/abc059b.py

c-yan/atcoder

940e49d576e6a2d734288fadaf368e486480a948

[ "MIT" ]

null

A = int(input()) B = int(input()) if A > B: print('GREATER') elif A < B: print('LESS') else: print('EQUAL')

12.1

20

0.520661

19

121

3.315789

0.578947

0.253968

0.222222

0

0.256198

121

9

21

13.444444

0.7

0

0.132231

0

1

0

false

0

0.375

1

0

null

1

0

1

0

null

0

4

86fb9a82459f258016626e1ff2aeaf4cbef4b9db

188

py

Python

lemon/protocol/Image/CdnImageUploadStatus.py

lemon-chat/lemon-server-python

5947b52b3c4535ae54fe2705a830db07fdaf741d

[ "MIT" ]

null

lemon/protocol/Image/CdnImageUploadStatus.py

lemon-chat/lemon-server-python

5947b52b3c4535ae54fe2705a830db07fdaf741d

[ "MIT" ]

null

lemon/protocol/Image/CdnImageUploadStatus.py

lemon-chat/lemon-server-python

5947b52b3c4535ae54fe2705a830db07fdaf741d

[ "MIT" ]

null

# automatically generated by the FlatBuffers compiler, do not modify # namespace: Image class CdnImageUploadStatus(object): Success = 0 FailUnknown = 1 FailInvalidToken = 2

18.8

68

0.739362

20

188

6.95

1

0

0.020134

0.207447

188

9

69

20.888889

0.912752

0.441489

0

1

0

1

0

false

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

4

811f1b4beeae3f90dcce91aa0b6deb6b7792e972

26,622

py

Python

Packs/Orca/Integrations/Orca/Orca_test.py

matan-xmcyber/content

7f02301c140b35956af3cd20cb8dfc64f34afb3e

[ "MIT" ]

null

Packs/Orca/Integrations/Orca/Orca_test.py

matan-xmcyber/content

7f02301c140b35956af3cd20cb8dfc64f34afb3e

[ "MIT" ]

null

Packs/Orca/Integrations/Orca/Orca_test.py

matan-xmcyber/content

7f02301c140b35956af3cd20cb8dfc64f34afb3e

[ "MIT" ]

null

from datetime import datetime import pytest import json from Orca import OrcaClient, ORCA_API_DNS_NAME, BaseClient, DEMISTO_OCCURRED_FORMAT, fetch_incidents import demistomock as demisto @pytest.fixture def orca_client() -> OrcaClient: api_key = "dummy api key" client = BaseClient( base_url=ORCA_API_DNS_NAME, verify=True, headers={ 'Authorization': f'Bearer {api_key}' }, proxy=True) return OrcaClient(client=client) def test_get_alerts_by_type_malware_should_succeed(requests_mock, orca_client: OrcaClient) -> None: mock_response = { "version": "0.1.0", "status": "success", "total_items": 6, "total_ungrouped_items": 6, "total_supported_items": 10000, "data": [ { "type": "malware", "rule_id": "r1111ea1111", "type_string": "Malware", "type_key": "/test_eicar_file", "category": "Malware", "description": "Malware EICAR-Test-File found on asset", "details": "We have detected a file infected with EICAR-Test-File on the asset.", "recommendation": "Remediate the host and attend additional alerts on the host to close the infection path.", "alert_labels": [ "malware_found" ], "asset_category": "Storage", "cloud_provider_id": "111111111111", "cloud_provider": "aws", "cloud_account_id": "10b11111-1111-1111-91d5-11111de11111", "cloud_vendor_id": "111111111111", "account_name": "111111111111", "asset_unique_id": "storage_111111e11111_scan-me-s3-bucket-s8rrr", "asset_name": "scan-me-s3-bucket-s8rrr", "asset_type": "storage", "asset_type_string": "AWS S3 Bucket", "group_unique_id": "storage_111111e11111_scan-me-s3-bucket-s8rrr", "group_name": "scan-me-s3-bucket-s8rrr", "group_type": "storage", "group_type_string": "NonGroup", "group_val": "nongroup", "cluster_unique_id": "storage_111111e11111_scan-me-s3-bucket-s8rrr", "cluster_name": "scan-me-s3-bucket-s8rrr", "cluster_type": "storage", "level": 0, "asset_state": "enabled", "asset_labels": [ "internet_facing", "pii" ], "asset_vendor_id": "scan-me-s3-bucket-s8rrr", "asset_regions": [ "us-east-1" ], "asset_regions_names": [ "N. Virginia" ], "source": "test_eicar_file", "findings": { "malware": [ { "type": "malware", "labels": [ "malware_found" ], "virus_names": [ "EICAR-Test-File" ], "modification_time": "2020-04-26T14:26:11+00:00", "file": "/test_eicar_file", "sha256": "275a021bbfb6489e54d471899f7db9d1663fc695ec2fe2a2c4538aabf651fd0f", "sha1": "3395856ce81f2b7382dee72602f798b642f14140", "md5": "44d88612fea8a8f36de82e1278abb02f", "has_macro": False } ] }, "configuration": { "user_status": "closed", "jira_issue_link": "https://www.jira.com/myproject", "jira_issue": "TP-41" }, "state": { "alert_id": "orca-59", "status": "in_progress", "status_time": "2020-12-30T09:57:33+00:00", "created_at": "2020-11-08T12:58:52+00:00", "last_seen": "2020-12-30T10:35:46+00:00", "score": 1, "severity": "compromised", "low_since": None, "high_since": "2020-12-15T15:33:49+00:00", "in_verification": None }, "priv": { "key": "3ea22222274111114b011111bb311111", "score": 1, "orig_score": 1, "alert_id": "orca-59", "full_scan_time": "2020-12-30T10:35:46+00:00", "organization_id": "11111111-1111-1111-1111-c111881c1111", "organization_name": "Orca Security", "context": "data", "account_action_id_ctx": { "data": "11111111-1111-1111-1111-8a529a011111" }, "scan_id_ctx": { "data": "11111111-1111-1111-1111-8a529a011111_111111111111_bucket-111111e11111-us-east-1" }, "first_seen": "2020-11-08T13:03:37+00:00" }, "hdr": { "asset_category": "Storage", "organization_id": "11111111-1111-1111-1111-c111881c1111", "organization_name": "Orca Security", "cloud_provider": "aws", "cloud_provider_id": "111111111111", "cloud_account_id": "10b11111-1111-1111-91d5-11111de11111", "context": "data", "asset_unique_id": "storage_111111e11111_scan-me-s3-bucket-s8rrr", "asset_type": "storage", "asset_type_string": "AWS S3 Bucket", "asset_name": "scan-me-s3-bucket-s8rrr", "group_unique_id": "storage_111111e11111_scan-me-s3-bucket-s8rrr", "group_name": "scan-me-s3-bucket-s8rrr", "group_type": "storage", "group_type_string": "NonGroup", "cluster_unique_id": "storage_111111e11111_scan-me-s3-bucket-s8rrr", "cluster_type": "storage", "cluster_name": "scan-me-s3-bucket-s8rrr", "level": 0, "group_val": "nongroup", "asset_vendor_id": "scan-me-s3-bucket-s8rrr", "cloud_vendor_id": "111111111111", "asset_state": "enabled", "account_name": "111111111111", "asset_labels": [ "internet_facing" ] }, "insert_time": "2020-12-30T10:45:21+00:00" } ] } requests_mock.get(f"{ORCA_API_DNS_NAME}/alerts?type=malware", json=mock_response) res = orca_client.get_alerts_by_filter(alert_type="malware") assert res[0] == mock_response['data'][0] def test_get_alerts_by_non_existent_type_should_return_empty_list(requests_mock, orca_client: OrcaClient) -> None: NON_EXISTENT_ALERT_TYPE = "non_existent_alert_type" mock_response = { "version": "0.1.0", "status": "success", "total_items": 0, "total_ungrouped_items": 0, "total_supported_items": 10000, "data": []} requests_mock.get(f"{ORCA_API_DNS_NAME}/alerts?type={NON_EXISTENT_ALERT_TYPE}", json=mock_response) res = orca_client.get_alerts_by_filter(alert_type=NON_EXISTENT_ALERT_TYPE) assert res == [] def test_fetch_incidents_first_run_should_succeed(mocker, requests_mock, orca_client: OrcaClient) -> None: mock_response = { "version": "0.1.0", "status": "success", "total_items": 58, "total_ungrouped_items": 58, "total_supported_items": 10000, "data": [ { "type": "malware", "rule_id": "r1111ea1111", "type_string": "Malware", "type_key": "/test_eicar_file", "category": "Malware", "description": "Malware EICAR-Test-File found on asset", "details": "We have detected a file infected with EICAR-Test-File on the asset.", "recommendation": "Remediate the host and attend additional alerts on the host to close the infection path.", "alert_labels": [ "malware_found" ], "asset_category": "Storage", "cloud_provider_id": "111111111111", "cloud_provider": "aws", "cloud_account_id": "10b11111-1111-1111-91d5-11111de11111", "cloud_vendor_id": "111111111111", "account_name": "111111111111", "asset_unique_id": "storage_111111e11111_scan-me-s3-bucket-s8rrr", "asset_name": "scan-me-s3-bucket-s8rrr", "asset_type": "storage", "asset_type_string": "AWS S3 Bucket", "group_unique_id": "storage_111111e11111_scan-me-s3-bucket-s8rrr", "group_name": "scan-me-s3-bucket-s8rrr", "group_type": "storage", "group_type_string": "NonGroup", "group_val": "nongroup", "cluster_unique_id": "storage_111111e11111_scan-me-s3-bucket-s8rrr", "cluster_name": "scan-me-s3-bucket-s8rrr", "cluster_type": "storage", "level": 0, "asset_state": "enabled", "asset_labels": [ "internet_facing", "pii" ], "asset_vendor_id": "scan-me-s3-bucket-s8rrr", "asset_regions": [ "us-east-1" ], "asset_regions_names": [ "N. Virginia" ], "source": "test_eicar_file", "findings": { "malware": [ { "type": "malware", "labels": [ "malware_found" ], "virus_names": [ "EICAR-Test-File" ], "modification_time": "2020-04-26T14:26:11+00:00", "file": "/test_eicar_file", "sha256": "275a021bbfb6489e54d471899f7db9d1663fc695ec2fe2a2c4538aabf651fd0f", "sha1": "3395856ce81f2b7382dee72602f798b642f14140", "md5": "44d88612fea8a8f36de82e1278abb02f", "has_macro": False } ] }, "configuration": { "user_status": "closed", "jira_issue_link": "https://www.jira.com/myproject", "jira_issue": "TP-41" }, "state": { "alert_id": "orca-59", "status": "in_progress", "status_time": "2020-12-30T09:57:33+00:00", "created_at": "2020-11-08T12:58:52+00:00", "last_seen": "2020-12-30T10:35:46+00:00", "score": 1, "severity": "compromised", "low_since": None, "high_since": "2020-12-15T15:33:49+00:00", "in_verification": None }, "priv": { "key": "3ea22222274111114b011111bb311111", "score": 1, "orig_score": 1, "alert_id": "orca-59", "full_scan_time": "2020-12-30T10:35:46+00:00", "organization_id": "11111111-1111-1111-1111-c111881c1111", "organization_name": "Orca Security", "context": "data", "account_action_id_ctx": { "data": "11111111-1111-1111-1111-8a529a011111" }, "scan_id_ctx": { "data": "11111111-1111-1111-1111-8a529a011111_111111111111_bucket-111111e11111-us-east-1" }, "first_seen": "2020-11-08T13:03:37+00:00" }, "hdr": { "asset_category": "Storage", "organization_id": "11111111-1111-1111-1111-c111881c1111", "organization_name": "Orca Security", "cloud_provider": "aws", "cloud_provider_id": "111111111111", "cloud_account_id": "10b11111-1111-1111-91d5-11111de11111", "context": "data", "asset_unique_id": "storage_111111e11111_scan-me-s3-bucket-s8rrr", "asset_type": "storage", "asset_type_string": "AWS S3 Bucket", "asset_name": "scan-me-s3-bucket-s8rrr", "group_unique_id": "storage_111111e11111_scan-me-s3-bucket-s8rrr", "group_name": "scan-me-s3-bucket-s8rrr", "group_type": "storage", "group_type_string": "NonGroup", "cluster_unique_id": "storage_111111e11111_scan-me-s3-bucket-s8rrr", "cluster_type": "storage", "cluster_name": "scan-me-s3-bucket-s8rrr", "level": 0, "group_val": "nongroup", "asset_vendor_id": "scan-me-s3-bucket-s8rrr", "cloud_vendor_id": "111111111111", "asset_state": "enabled", "account_name": "111111111111", "asset_labels": [ "internet_facing" ] }, "insert_time": "2020-12-30T10:45:21+00:00" }, { "type": "malware", "rule_id": "r1111ea1111", "type_string": "Malware", "type_key": "/usr/local/bin/eicarcom2.zip", "category": "Malware", "description": "Malware EICAR-Test-File found on asset", "details": "We have detected a file infected with EICAR-Test-File on the asset.", "recommendation": "Remediate the host and attend additional alerts on the host to close the infection path.", "alert_labels": [ "malware_found" ], "asset_category": "Image", "cloud_provider_id": "111111111111", "cloud_provider": "aws", "cloud_account_id": "10b11111-1111-1111-91d5-11111de11111", "cloud_vendor_id": "111111111111", "account_name": "111111111111", "asset_unique_id": "vmimage_111111e11111_ami-11111c111111d7911", "asset_name": "my_test_image-1231asdasjdn", "asset_type": "vmimage", "asset_type_string": "VM Image", "group_unique_id": "vmimage_111111e11111_ami-11111c111111d7911", "group_name": "my_test_image-1231asdasjdn", "group_type": "vmimage", "group_type_string": "NonGroup", "group_val": "nongroup", "cluster_unique_id": "vmimage_111111e11111_ami-11111c111111d7911", "cluster_name": "my_test_image-1231asdasjdn", "cluster_type": "vmimage", "level": 0, "asset_vendor_id": "ami-11111c111111d7911", "asset_distribution_name": "Ubuntu", "asset_distribution_version": "18.04", "asset_role_names": [ "mysql", "ssh", "haproxy", "postgresql" ], "source": "eicarcom2.zip", "findings": { "malware": [ { "type": "malware", "labels": [ "malware_found" ], "virus_names": [ "EICAR-Test-File" ], "modification_time": "2019-07-09T21:16:26+00:00", "file": "/usr/local/bin/eicarcom2.zip", "sha256": "e1105070ba828007508566e28a2b8d4c65d192e9eaf3b7868382b7cae747b397", "sha1": "bec1b52d350d721c7e22a6d4bb0a92909893a3ae", "md5": "e4968ef99266df7c9a1f0637d2389dab", "has_macro": False } ] }, "configuration": {}, "state": { "alert_id": "orca-242", "status": "open", "status_time": "2020-11-08T12:58:54+00:00", "created_at": "2020-11-08T12:58:54+00:00", "last_seen": "2020-12-30T10:35:48+00:00", "score": 1, "severity": "compromised", "low_since": None, "high_since": "2020-11-08T13:04:32+00:00", "in_verification": None }, "priv": { "key": "3696080647d937b881eee2cfdd6c3943", "score": 1, "orig_score": 1, "alert_id": "orca-242", "full_scan_time": "2020-12-30T10:35:48+00:00", "organization_id": "11111111-1111-1111-1111-c111881c1111", "organization_name": "Orca Security", "context": "data", "account_action_id_ctx": { "data": "11111111-1111-1111-1111-8a529a011111" }, "scan_id_ctx": { "data": "11111111-1111-1111-1111-8a529a011111_111111111111_ami-11111c111111d7911" }, "first_seen": "2020-11-08T13:04:32+00:00" }, "hdr": { "asset_category": "Image", "organization_id": "11111111-1111-1111-1111-c111881c1111", "organization_name": "Orca Security", "cloud_provider": "aws", "cloud_provider_id": "111111111111", "cloud_account_id": "10b11111-1111-1111-91d5-11111de11111", "context": "data", "asset_unique_id": "vmimage_111111e11111_ami-11111c111111d7911", "asset_type": "vmimage", "asset_type_string": "VM Image", "asset_name": "my_test_image-1231asdasjdn", "group_unique_id": "vmimage_111111e11111_ami-11111c111111d7911", "group_name": "my_test_image-1231asdasjdn", "group_type": "vmimage", "group_type_string": "NonGroup", "cluster_unique_id": "vmimage_111111e11111_ami-11111c111111d7911", "cluster_type": "vmimage", "cluster_name": "my_test_image-1231asdasjdn", "level": 0, "group_val": "nongroup", "asset_vendor_id": "ami-11111c111111d7911", "cloud_vendor_id": "111111111111", "account_name": "111111111111" }, "insert_time": "2020-12-30T10:44:11+00:00" } ] } mocker.patch.object(demisto, 'getLastRun', return_value={'lastRun': None}) requests_mock.get(f"{ORCA_API_DNS_NAME}/query/alerts", json=mock_response) fetched_incidents = fetch_incidents(orca_client, max_fetch=20, pull_existing_alerts=True) assert fetched_incidents[0]['name'] == 'orca-59' loaded_raw_alert = json.loads(fetched_incidents[0]['rawJSON']) assert loaded_raw_alert['demisto_score'] == 4 assert fetched_incidents[1]['name'] == 'orca-242' loaded_raw_alert = json.loads(fetched_incidents[1]['rawJSON']) assert loaded_raw_alert['demisto_score'] == 4 def test_fetch_incidents_not_first_run_return_empty(mocker, orca_client: OrcaClient) -> None: # validates that fetch-incidents is returning an a empty list when it is not the first run mocker.patch.object(demisto, 'getLastRun', return_value={'lastRun': datetime.now().strftime(DEMISTO_OCCURRED_FORMAT), "incidents_for_next_run": []}) fetched_incidents = fetch_incidents(orca_client, max_fetch=20, pull_existing_alerts=True) assert fetched_incidents == [] def test_get_asset_should_succeed(requests_mock, orca_client: OrcaClient) -> None: mock_response = { "type": "vmimage", "asset_category": "Image", "asset_subcategory": "VM Image", "cloud_provider_id": "111111111111", "cloud_provider": "aws", "cloud_account_id": "10b11111-1111-1111-91d5-11111de11111", "cloud_vendor_id": "111111111111", "account_name": "111111111111", "asset_unique_id": "vmimage_111111e11111_ami-11111c111111d7911", "asset_name": "my_test_image-1231asdasjdn", "asset_type": "vmimage", "asset_type_string": "VM Image", "group_unique_id": "vmimage_111111e11111_ami-11111c111111d7911", "group_name": "my_test_image-1231asdasjdn", "group_type": "vmimage", "group_type_string": "NonGroup", "cluster_unique_id": "vmimage_111111e11111_ami-11111c111111d7911", "cluster_name": "my_test_image-1231asdasjdn", "cluster_type": "vmimage", "level": 0, "asset_vendor_id": "ami-11111c111111d7911", "internet_facing": False, "internet_facing_new": False, "create_time": "2020-07-28T09:10:01+00:00", "container_id": "main", "compute": { "distribution_name": "Ubuntu", "distribution_version": "18.04", "disks": [ { "size": "7.75 GB", "used": "2.06 GB" } ], "total_disks_bytes": 8326123520, "roles": [ { "type": "database", "name": "mysql", "is_public": False, "detected_evidence": [ "/var/lib/mysql/mysqldb2", "/var/lib/mysql/mysqldb1" ] }, { "type": "ssh", "name": "ssh", "is_public": False }, { "type": "web", "name": "haproxy", "is_public": False }, { "type": "database", "name": "postgresql", "is_public": False, "detected_evidence": [ "/var/lib/postgresql/10/main/base/1", "/var/lib/postgresql/10/main", "/var/lib/postgresql/10/main/base/13017", "/var/lib/postgresql/10/main/base/16384", "/var/lib/postgresql/10/main/base/13018" ] } ] }, "vmimage": { "image_id": "ami-11111c111111d7911", "image_owner_id": "111111111111", "image_name": "my_test_image-1231asdasjdn", "image_description": "" }, "configuration": {}, "state": { "status": "exists", "status_time": "2020-11-08T13:04:34+00:00", "created_at": "2020-11-08T13:04:34+00:00", "last_seen": "2020-12-30T10:44:11+00:00", "score": 1, "severity": "compromised", "safe_since": None, "unsafe_since": "2020-11-08T13:04:34+00:00" } } requests_mock.get(f"{ORCA_API_DNS_NAME}/assets/vmimage_111111e11111_ami-11111c111111d7911", json=mock_response) res = orca_client.get_asset(asset_unique_id="vmimage_111111e11111_ami-11111c111111d7911") assert res == mock_response def test_get_asset_nonexistent(requests_mock, orca_client: OrcaClient) -> None: mock_response = {"error": ""} requests_mock.get(f"{ORCA_API_DNS_NAME}/assets/1234567", json=mock_response) res = orca_client.get_asset(asset_unique_id="1234567") assert res == "Asset Not Found" def test_test_module_success(requests_mock, orca_client: OrcaClient) -> None: mock_response = { "status": "success", "data": { "user_id": "77777634-7777-7777-7777-f49f77777777", "email": "system_testing@orca.security", "first": "System", "last": "Testing", "full_name": "System Testing", "profile_picture": "", "organization_id": "e3dab69a-5555-5555-5555-c5b8881cd2fe", "organization_name": "Orca Security", "feature_flags": {}, "has_cloud_accounts": True, "has_scanned_cloud_accounts": True } } requests_mock.get(f"{ORCA_API_DNS_NAME}/user/action?", json=mock_response) res = orca_client.validate_api_key() assert res == "ok" def test_test_module_fail(requests_mock, orca_client: OrcaClient) -> None: mock_response = { "detail": "Given token not valid for any token type", "code": "token_not_valid", "messages": [ { "token_class": "AccessTokenWithExpiration", "token_type": "access", "message": "Token is invalid or expired" } ], "status_code": 403 } requests_mock.get(f"{ORCA_API_DNS_NAME}/user/action?", json=mock_response) res = orca_client.validate_api_key() assert res == "Test failed becasue the Orca API key that was entered is invalid, please provide a valid API key"

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812b9ce9ae3a1fa3685d90f524bbba8c1aa76074

132

py

Python

rate/apps.py

muneneee/work

b16e273bd8ee626b41cdbb5366013b76ff6c373a

[ "MIT" ]

null

rate/apps.py

muneneee/work

b16e273bd8ee626b41cdbb5366013b76ff6c373a

[ "MIT" ]

6

2021-03-19T11:22:07.000Z

2022-02-10T12:03:40.000Z

rate/apps.py

muneneee/work

b16e273bd8ee626b41cdbb5366013b76ff6c373a

[ "MIT" ]

null

from django.apps import AppConfig class RateConfig(AppConfig): name = 'rate' def ready(self): import rate.signals

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108

py

Python

pybots/src/magpy/magpy_backend/test.py

aivian/robots

6827886916e36432ce1d806f0a78edef6c9270d9

[ "MIT" ]

null

pybots/src/magpy/magpy_backend/test.py

aivian/robots

6827886916e36432ce1d806f0a78edef6c9270d9

[ "MIT" ]

null

pybots/src/magpy/magpy_backend/test.py

aivian/robots

6827886916e36432ce1d806f0a78edef6c9270d9

[ "MIT" ]

1

2021-09-24T17:08:30.000Z

import magpy_backend for i in range(1000): a = magpy_backend.magpy_backend('WMM.COF', 0,0,0, 2015,1,1)

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140

py

Python

peach/models/quartznet_recognizer.py

sergevkim/SpeechRecognition

c81024a3c9e6b022c7a44777bea3e6bc4b3cc35a

[ "MIT" ]

1

2020-10-11T19:04:35.000Z

peach/models/quartznet_recognizer.py

sergevkim/SpeechRecognition

c81024a3c9e6b022c7a44777bea3e6bc4b3cc35a

[ "MIT" ]

2

2020-10-16T07:46:33.000Z

2020-10-18T18:39:07.000Z

peach/models/quartznet_recognizer.py

sergevkim/SpeechRecognition

c81024a3c9e6b022c7a44777bea3e6bc4b3cc35a

[ "MIT" ]

null

from torch.nn import Module class QuartzNetRecognizer(Module): def __init__(self): pass def forward(self): pass

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d4ae716bd2621324198aa969fe60e79877aee2ee

109

py

Python

pysurf/spp/model/model.py

MFSJMenger/pysurf

99c6a94d4cb5046f16a0961b907061d989ffb6dc

[ "Apache-2.0" ]

7

2020-10-28T13:46:08.000Z

2021-05-27T06:41:56.000Z

pysurf/spp/model/model.py

MFSJMenger/pysurf

99c6a94d4cb5046f16a0961b907061d989ffb6dc

[ "Apache-2.0" ]

2

2020-10-27T19:15:12.000Z

2020-10-27T19:15:25.000Z

pysurf/spp/model/model.py

MFSJMenger/pysurf

99c6a94d4cb5046f16a0961b907061d989ffb6dc

[ "Apache-2.0" ]

2

2021-04-15T05:54:30.000Z

2022-02-08T00:10:10.000Z

from abc import ABC, abstractmethod class Model(ABC): @abstractmethod def get(self): pass

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d4b03e894832c5d7260c51b4ea9128f3e10215ee

327

py

Python

Desafios/Mundo 3/ex074.py

ZaikoXander/Python

7e7243edb02dd33991c5f63f02c983ad060fc3ca

[ "Unlicense" ]

null

Desafios/Mundo 3/ex074.py

ZaikoXander/Python

7e7243edb02dd33991c5f63f02c983ad060fc3ca

[ "Unlicense" ]

null

Desafios/Mundo 3/ex074.py

ZaikoXander/Python

7e7243edb02dd33991c5f63f02c983ad060fc3ca

[ "Unlicense" ]

null

from random import randint numeros = (randint(-999, 999), randint(-999, 999), randint(-999, 999), randint(-999, 999), randint(-999, 999)) print('Os números sorteados foram: ', end='') for num in numeros: print(f'| {num} ', end='') print('|', end='\n') print(f'O menor valor é {min(numeros)} e o maior é {max(numeros)}')

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null

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4

d4b2e2a88b394e145386346e42b82631170c0c31

226

py

Python

examples/example_ql_mf.py

markjunior/RL_policy

b88f191e8064342e7723df241a946d72bbfe5298

[ "MIT" ]

null

examples/example_ql_mf.py

markjunior/RL_policy

b88f191e8064342e7723df241a946d72bbfe5298

[ "MIT" ]

null

examples/example_ql_mf.py

markjunior/RL_policy

b88f191e8064342e7723df241a946d72bbfe5298

[ "MIT" ]

1

2021-01-28T12:49:19.000Z

import sys sys.path.append('./../') from q_learning.ql_mf import q_learning_model_free test_model = q_learning_model_free(env_name='MountainCar-v0', num_s=20) test_model.run(mode='q_learning') # test_model.run(mode='sarsa')

25.111111

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0.17284

0.222222

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8

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4

d4c0715d0361b109b2bd901ab261c21294d0e2c2

149

py

Python

24. Exam Prep/exam_19dec/project/supply/water_supply.py

elenaborisova/Python-OOP

584882c08f84045b12322917f0716c7c7bd9befc

[ "MIT" ]

1

2021-03-27T16:56:30.000Z

24. Exam Prep/exam_19dec/project/supply/water_supply.py

elenaborisova/Python-OOP

584882c08f84045b12322917f0716c7c7bd9befc

[ "MIT" ]

null

24. Exam Prep/exam_19dec/project/supply/water_supply.py

elenaborisova/Python-OOP

584882c08f84045b12322917f0716c7c7bd9befc

[ "MIT" ]

1

2021-03-15T14:50:39.000Z

from exam_19dec.project.supply.supply import Supply class WaterSupply(Supply): def __init__(self): super().__init__(needs_increase=40)

21.285714

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d4e69ced5ad38a9922dee4e91db668d18d241565

153

py

Python

src/missions_lib/__init__.py

phopley/rodney_missions

4222314960aa6e9f77b10bd2c64607f9c0ed5eea

[ "Apache-2.0" ]

1

2021-04-02T04:37:44.000Z

src/missions_lib/__init__.py

phopley/rodney_missions

4222314960aa6e9f77b10bd2c64607f9c0ed5eea

[ "Apache-2.0" ]

3

2019-01-14T10:45:01.000Z

2019-01-28T10:12:02.000Z

src/missions_lib/__init__.py

phopley/rodney_missions

4222314960aa6e9f77b10bd2c64607f9c0ed5eea

[ "Apache-2.0" ]

1

2021-04-02T04:37:45.000Z

# __init__.py from .take_message_to import Mission1StateMachine from .greet_all import Mission2StateMachine from .go_home import Mission4StateMachine

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d4f2b2d60dc0bbe60e71eb885a7e104d2c110c2a

1,092

py

Python

bcrypt/__init__.py

propellr/python-bcrypt

ad2f53aa36ffbe3a51edca12c97371ec154f9354

[ "ISC" ]

5

2016-04-29T08:15:05.000Z

2021-01-23T21:49:44.000Z

bcrypt/__init__.py

propellr/python-bcrypt

ad2f53aa36ffbe3a51edca12c97371ec154f9354

[ "ISC" ]

2

2021-06-08T21:32:02.000Z

2022-03-12T00:29:24.000Z

bcrypt/__init__.py

propellr/python-bcrypt

ad2f53aa36ffbe3a51edca12c97371ec154f9354

[ "ISC" ]

1

2017-07-15T22:15:56.000Z

"""OpenBSD Blowfish password hashing. This module implements the OpenBSD Blowfish password hashing algorithm, as described in "A Future-Adaptable Password Scheme" by Niels Provos and David Mazieres. This system hashes passwords using a version of Bruce Schneier's Blowfish block cipher with modifications designed to raise the cost of off-line password cracking. The computation cost of the algorithm is parametised, so it can be increased as computers get faster. Passwords are hashed using the hashpw() routine: hashpw(password, salt) -> hashed_password Salts for the the second parameter may be randomly generated using the gensalt() function: gensalt(log_rounds = 12) -> random_salt The parameter "log_rounds" defines the complexity of the hashing. The cost increases as 2**log_rounds. """ import os from bcrypt._bcrypt import * def gensalt(log_rounds = 12): """Generate a random text salt for use with hashpw(). "log_rounds" defines the complexity of the hashing, increasing the cost as 2**log_rounds.""" return encode_salt(os.urandom(16), min(max(log_rounds, 4), 31))

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1

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4

be2bd9bcd1dd9d2f444c870a5674e9acf9e60ec9

153

py

Python

images2pdf.py

kweusuf/Manga-Scraper

446dc3fa083e789f032623c7a040f1ea4f317d52

[ "MIT" ]

null

images2pdf.py

kweusuf/Manga-Scraper

446dc3fa083e789f032623c7a040f1ea4f317d52

[ "MIT" ]

null

images2pdf.py

kweusuf/Manga-Scraper

446dc3fa083e789f032623c7a040f1ea4f317d52

[ "MIT" ]

null

import img2pdf, os with open(f"Chapter 11.pdf", "wb") as f: f.write(img2pdf.convert([i for i in sorted(os.listdir(), key=len) if i.endswith(".jpg")]))

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null

0

1

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1

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076bf8f990154a66f9111dc354046ed5db8d8e0d

8

py

Python

__init__.py

Laende/Bacheloroppgave-droneteknologi

15d9b2cd0eeba47fd2e9615fb01d598516826194

[ "MIT" ]

null

__init__.py

Laende/Bacheloroppgave-droneteknologi

15d9b2cd0eeba47fd2e9615fb01d598516826194

[ "MIT" ]

11

2021-07-04T13:46:30.000Z

2021-07-13T08:30:40.000Z

__init__.py

Laende/Bacheloroppgave-droneteknologi

15d9b2cd0eeba47fd2e9615fb01d598516826194

[ "MIT" ]

1

2021-07-04T10:45:30.000Z

# Init

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null

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4

076f34f1db98255000ac1f3e917de83904fcc282

3,254

py

Python

curve_pg.py

Templarrr/hearthtools

6e13611c6f76e198c00802afadb6e360fa42d6ef

[ "CC0-1.0" ]

1

2015-11-10T20:14:36.000Z

curve_pg.py

Templarrr/hearthtools

6e13611c6f76e198c00802afadb6e360fa42d6ef

[ "CC0-1.0" ]

null

curve_pg.py

Templarrr/hearthtools

6e13611c6f76e198c00802afadb6e360fa42d6ef

[ "CC0-1.0" ]

1

2020-11-05T11:06:37.000Z

import json import random from perfect_curve_monte_carlo import ManaCurve, ImitateGame cached_file = 'mana_curves_json' try: with open(cached_file, 'r') as f: mc_results_cache = json.load(f) best_mana_unspent = min(mc_results_cache.values()) best_mc = mc_results_cache.keys()[mc_results_cache.values().index(best_mana_unspent)] except Exception as e: best_mana_unspent = 200 best_mc = '-' mc_results_cache = { best_mc:best_mana_unspent } print 'best cached curve %s %f' % (best_mc, best_mana_unspent) print 'mana curves processed %d' % len(mc_results_cache) for i in range(1000): mc = ManaCurve() while not mc.is_unusable(): if str(mc) in mc_results_cache: mc.push_mana_curve() continue mc_results = [] for i in range(1000): ig = ImitateGame(mc.get_deck(), 12) mc_results.append(ig.imitate_game()) avg_mana_unspent = sum(mc_results)/float(len(mc_results)) if avg_mana_unspent<best_mana_unspent: best_mana_unspent = avg_mana_unspent best_mc = str(mc) print 'Average mana lost for %s is %f' % (best_mc, best_mana_unspent) mc_results_cache[str(mc)] = avg_mana_unspent if random.randint(1,10): with open(cached_file, 'w') as f: json.dump(mc_results_cache, f) mc.push_mana_curve() # With hero power # 0:0:0:9:7:3:2:4:5:0:0 is 1.511200 - Shaman or Paladin deck? Something that can use double hp in the start # More realistic variants # Average mana lost for 0:1:2:4:8:5:2:3:3:1:1 is 1.795500 # Average mana lost for 0:1:2:2:9:6:2:2:4:1:1 is 1.746900 # Average mana lost for 0:1:2:2:8:7:2:2:4:1:1 is 1.743600 # Without hero power # Average mana lost for 0:1:1:5:4:8:4:4:0:2:1 is 6.930100 # Average mana lost for 0:1:1:4:8:4:4:4:2:0:2 is 6.683000 # Average mana lost for 0:0:0:3:5:7:4:4:2:2:3 is 6.632000 # Average mana lost for 0:0:3:5:5:6:4:2:3:1:1 is 6.547000 # Average mana lost for 0:0:3:5:5:5:4:3:3:0:2 is 6.426000 # Average mana lost for 0:0:1:6:5:5:5:3:3:0:2 is 6.343000 # Average mana lost for 0:0:2:4:6:7:4:3:1:1:2 is 6.280000 # Average mana lost for 0:0:1:5:6:6:5:3:1:1:2 is 6.128000 # Average mana lost for 0:0:1:5:5:6:3:3:2:2:3 is 6.047000 # Average mana lost for 0:0:0:6:4:7:3:3:2:2:3 is 6.045000 # Average mana lost for 0:0:3:5:5:7:2:3:1:2:2 is 5.989000 # Average mana lost for 0:0:2:5:7:5:2:3:1:2:3 is 5.952000 # Average mana lost for 0:0:2:6:5:3:5:2:2:1:4 is 5.930000 # Average mana lost for 0:0:0:8:5:3:4:2:2:1:5 is 5.898000 # Average mana lost for 0:0:0:7:5:4:3:3:2:1:5 is 5.854000 # With double penalty on skipped turns # Average mana lost for 0:1:2:5:7:5:4:2:2:2:0 is 9.989000 # Average mana lost for 0:1:2:5:7:4:4:3:2:2:0 is 9.871000 # Average mana lost for 0:1:2:6:7:6:3:2:1:1:1 is 9.868000 # Average mana lost for 0:1:2:6:7:5:4:2:1:1:1 is 9.680000 # Average mana lost for 0:0:3:5:8:5:4:2:1:1:1 is 9.541000 # Average mana lost for 0:0:2:6:9:3:4:1:2:2:1 is 9.536000 # Average mana lost for 0:0:2:6:9:3:4:1:2:1:2 is 9.147000 # Average mana lost for 0:0:1:9:4:5:5:1:2:1:2 is 9.001000 # Average mana lost for 0:0:5:6:4:4:2:3:3:1:2 is 8.754000 # Average mana lost for 0:0:5:6:4:4:2:3:2:2:2 is 8.697000

42.25974

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07785d069e42bc7cf23f5c347e79e74622d996fb

899

py

Python

src/score.py

akshayelangovan/fuzzy_template_akshay

b4e13741f18a2acad5b998d7487c992feec303fd

[ "MIT" ]

1

2022-02-22T23:54:34.000Z

src/score.py

akshayelangovan/fuzzy_template_akshay

b4e13741f18a2acad5b998d7487c992feec303fd

[ "MIT" ]

null

src/score.py

akshayelangovan/fuzzy_template_akshay

b4e13741f18a2acad5b998d7487c992feec303fd

[ "MIT" ]

null

from fuzzy_asteroids.util import Score from fuzzy_asteroids.game import AsteroidGame class SampleScore(Score): """ Sample of how to modify the Score class """ def __init__(self): """ Define constructor """ # TODO add your own attributes/properties to this claass # Constructor for this class should not miss call to parent class constructor super().__init__() def timestep_update(self, environment: AsteroidGame) -> None: """ This function is called after the evaluation of each game time step :param environment: AsteroidGame environment instance """ pass def final_update(self, environment: AsteroidGame) -> None: """ This function is called after the completion of the game :param environment: AsteroidGame environment instance """ pass

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4

079157a8c2e7675b8c20e7fe32c935bbf42bee57

101

py

Python

flambe/cluster/utils.py

ethan-asapp/flambe

70257167058c7b82ee39f74167a6161bd264ad18

[ "MIT" ]

148

2019-08-29T21:19:03.000Z

2022-03-18T06:13:53.000Z

flambe/cluster/utils.py

cle-ros/flambe

0dc2f5b2b286694defe8abf450fe5be9ae12c097

[ "MIT" ]

108

2019-09-03T14:36:10.000Z

2020-05-13T15:53:14.000Z

flambe/cluster/utils.py

cle-ros/flambe

0dc2f5b2b286694defe8abf450fe5be9ae12c097

[ "MIT" ]

21

2019-09-08T14:09:45.000Z

2020-12-27T04:12:33.000Z

from collections import namedtuple RemoteCommand = namedtuple('RemoteCommand', ['success', 'msg'])

20.2

63

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8.555556

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0

0.108911

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1

0

null

0

1

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07a8b2778231d9db56bfa96782ae5093eb797b1d

36

py

Python

dataLoader/constants.py

perazim-io/layout-bot

b01c440aa4ecd266e65596a1bd4cc7fcb722f715

[ "MIT" ]

null

dataLoader/constants.py

perazim-io/layout-bot

b01c440aa4ecd266e65596a1bd4cc7fcb722f715

[ "MIT" ]

null

dataLoader/constants.py

perazim-io/layout-bot

b01c440aa4ecd266e65596a1bd4cc7fcb722f715

[ "MIT" ]

null

screenWidth = 1080 screenHeight=1920

18

0.861111

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0.242424

0.083333

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null

0

1

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1

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null

0

4

07ca87a284cd8b935f791e6a5d0109045936f1cb

1,001

py

Python

polymorphism_duck_typing/lab/image_area.py

Minkov/python-oop-2021-02

bd387dde165f4338eed66c4bc0b4b516ee085340

[ "MIT" ]

2

2021-02-22T22:55:31.000Z

2021-04-05T18:25:10.000Z

polymorphism_duck_typing/lab/image_area.py

Minkov/python-oop-2021-02

bd387dde165f4338eed66c4bc0b4b516ee085340

[ "MIT" ]

null

polymorphism_duck_typing/lab/image_area.py

Minkov/python-oop-2021-02

bd387dde165f4338eed66c4bc0b4b516ee085340

[ "MIT" ]

2

2021-04-05T18:35:11.000Z

2021-04-08T12:18:19.000Z

class ImageArea: def __init__(self, width, height): self.width = width self.height = height def get_area(self): return self.width * self.height def __eq__(self, other): return self.get_area() == other.get_area() def __ne__(self, other): return self.get_area() != other.get_area() def __gt__(self, other): return self.get_area() > other.get_area() def __ge__(self, other): return self.get_area() >= other.get_area() def __lt__(self, other): return self.get_area() < other.get_area() def __le__(self, other): return self.get_area() <= other.get_area() class SquareImageArea(ImageArea): def __init__(self, side): super().__init__(side, side) a1 = SquareImageArea(7) # 70 a2 = SquareImageArea(7) # 70 a3 = SquareImageArea(8) # 72 print(a1 == a2) # True print(a1 != a3) # True print(a1 != a2) # False print(a1 >= a3) # False print(a1 <= a2) # True print(a1 < a3) # True

23.833333

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py

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threads/apps.py

akiyoss-git/MineLearningMirror

bf183738f6a95e6717f7b22081628279f9d6f20b

[ "MIT" ]

250

2018-05-09T06:46:08.000Z

2022-03-08T09:37:58.000Z

threads/apps.py

akiyoss-git/MineLearningMirror

bf183738f6a95e6717f7b22081628279f9d6f20b

[ "MIT" ]

14

2019-05-28T06:32:23.000Z

2022-03-11T23:20:37.000Z

threads/apps.py

akiyoss-git/MineLearningMirror

bf183738f6a95e6717f7b22081628279f9d6f20b

[ "MIT" ]

78

2018-07-29T07:44:42.000Z

2022-03-02T11:04:48.000Z

from django.apps import AppConfig class ThreadsConfig(AppConfig): name = 'threads'

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Python

absynthe/cfg/__init__.py

chaturv3di/absynthe

e2dcc97747ca6f17c4d39ae2cf16808751742d03

[ "Apache-2.0" ]

6

2019-06-17T16:16:24.000Z

2019-10-18T11:20:51.000Z

absynthe/cfg/__init__.py

chaturv3di/absynthe

e2dcc97747ca6f17c4d39ae2cf16808751742d03

[ "Apache-2.0" ]

null

absynthe/cfg/__init__.py

chaturv3di/absynthe

e2dcc97747ca6f17c4d39ae2cf16808751742d03

[ "Apache-2.0" ]

1

2019-09-15T12:02:29.000Z

from __future__ import absolute_import from .node import Node, UniformNode, BinomialNode from .logger_node import LoggerNode, SimpleLoggerNode from .graph import Graph __all__ = ["Node", "UniformNode", "BinomialNode", "LoggerNode", "SimpleLoggerNode", "Graph"]

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Python

core/arxiv/submission/services/classic/tests/test_store_annotations.py

NeolithEra/arxiv-submission-core

d4f20be62a882d2d5f3d1584eda69e7d90ca2c12

[ "MIT" ]

14

2019-05-26T22:52:17.000Z

2021-11-05T12:26:46.000Z

core/arxiv/submission/services/classic/tests/test_store_annotations.py

NeolithEra/arxiv-submission-core

d4f20be62a882d2d5f3d1584eda69e7d90ca2c12

[ "MIT" ]

30

2018-01-31T19:16:08.000Z

2018-12-08T08:41:04.000Z

core/arxiv/submission/services/classic/tests/test_store_annotations.py

NeolithEra/arxiv-submission-core

d4f20be62a882d2d5f3d1584eda69e7d90ca2c12

[ "MIT" ]

8

2019-01-10T22:01:39.000Z

2021-11-20T21:44:51.000Z

"""Test persistence of annotations in the classic database."""

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ed33f97291796a9d9583163462b39ada06e5428b

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py

Python

tests/test_backend_six_graph.py

166MMX/hiro-python-library

fb29e3247a8fe1b0f7dc4e68141cf7340a8dd0a5

[ "MIT" ]

null

tests/test_backend_six_graph.py

166MMX/hiro-python-library

fb29e3247a8fe1b0f7dc4e68141cf7340a8dd0a5

[ "MIT" ]

null

tests/test_backend_six_graph.py

166MMX/hiro-python-library

fb29e3247a8fe1b0f7dc4e68141cf7340a8dd0a5

[ "MIT" ]

null

from types import MappingProxyType from typing import Generator, Optional, Type from uuid import uuid4 import pytest from arago.hiro.client.client import HiroClient from arago.hiro.model.graph.attribute import FreeAttribute, SystemAttribute, FinalAttribute from arago.hiro.model.graph.edge import Edge from arago.hiro.model.graph.history import HistoryFormat, HistoryEntry, HistoryDiff from arago.hiro.model.graph.vertex import VertexId, Vertex, ExternalVertexId, VERTEX_TYPE_T, VERTEX_T, VERTEX_T_co # noinspection PyPackageRequirements from arago.hiro.model.storage import TimeSeriesVertex, BlobVertex from arago.ogit import OgitEntity, OgitVerb, OgitAttribute from arago.ontology import Attribute def uuid() -> str: return str(uuid4()) class TestClassGraphVertexCreate: @pytest.mark.parametrize('vertex_type', [ OgitEntity.OGIT_COMMENT, OgitEntity.OGIT_COMMENT.value, OgitEntity.OGIT_COMMENT.value.name.uri ]) def test_type_no_data(self, client: HiroClient, vertex_type: VERTEX_TYPE_T): from arago.hiro.backend.six.graph import Hiro6GraphModel graph = Hiro6GraphModel(client) vertex = graph.vertex.create(vertex_type) assert isinstance(vertex, Vertex) vertex.delete() pass @pytest.mark.parametrize('vertex_type', [ OgitEntity.OGIT_COMMENT, OgitEntity.OGIT_COMMENT.value, OgitEntity.OGIT_COMMENT.value.name.uri ]) @pytest.mark.parametrize('vertex_data', [ Vertex({OgitAttribute.OGIT_CONTENT: 'foo'}), BlobVertex({OgitAttribute.OGIT_CONTENT: 'foo'}), TimeSeriesVertex({OgitAttribute.OGIT_CONTENT: 'foo'}), {SystemAttribute.OGIT__XID: uuid()}, {FinalAttribute.OGIT__XID: uuid()}, {OgitAttribute.OGIT_CONTENT: 'foo'}, {OgitAttribute.OGIT_CONTENT.value: 'foo'}, {OgitAttribute.OGIT_CONTENT.value.name.uri: 'foo'}, {FreeAttribute('/bar'): 'foo'}, ]) def test_type_data(self, client: HiroClient, vertex_type: VERTEX_TYPE_T, vertex_data: Optional[VERTEX_T]): from arago.hiro.backend.six.graph import Hiro6GraphModel graph = Hiro6GraphModel(client) vertex = graph.vertex.create(vertex_type, vertex_data) assert isinstance(vertex, Vertex) vertex.delete() pass @pytest.mark.parametrize('vertex_data', [ Vertex({ FinalAttribute.OGIT__TYPE: OgitEntity.OGIT_COMMENT, OgitAttribute.OGIT_CONTENT: 'foo'}), { FinalAttribute.OGIT__TYPE: OgitEntity.OGIT_COMMENT, SystemAttribute.OGIT__XID: uuid()}, { FinalAttribute.OGIT__TYPE: OgitEntity.OGIT_COMMENT, FinalAttribute.OGIT__XID: uuid()}, { FinalAttribute.OGIT__TYPE: OgitEntity.OGIT_COMMENT, OgitAttribute.OGIT_CONTENT: 'foo'}, { FinalAttribute.OGIT__TYPE: OgitEntity.OGIT_COMMENT, OgitAttribute.OGIT_CONTENT.value: 'foo'}, { FinalAttribute.OGIT__TYPE: OgitEntity.OGIT_COMMENT, OgitAttribute.OGIT_CONTENT.value.name.uri: 'foo'}, { FinalAttribute.OGIT__TYPE: OgitEntity.OGIT_COMMENT, FreeAttribute('/bar'): 'foo'}, { FinalAttribute.OGIT__TYPE: OgitEntity.OGIT_COMMENT.value, FreeAttribute('/bar'): 'foo'}, { FinalAttribute.OGIT__TYPE: OgitEntity.OGIT_COMMENT.value.name.uri, FreeAttribute('/bar'): 'foo'}, ]) def test_data_no_type(self, client: HiroClient, vertex_data: Optional[VERTEX_T]): from arago.hiro.backend.six.graph import Hiro6GraphModel graph = Hiro6GraphModel(client) vertex = graph.vertex.create(vertex_data) assert isinstance(vertex, Vertex) vertex.delete() pass @pytest.mark.parametrize('vertex_type,cls', [ (OgitEntity.OGIT_TIME_SERIES, TimeSeriesVertex), (OgitEntity.OGIT_ATTACHMENT, BlobVertex), ]) def test_upcast(self, client: HiroClient, vertex_type: VERTEX_TYPE_T, cls: Type[VERTEX_T_co]): from arago.hiro.backend.six.graph import Hiro6GraphModel graph = Hiro6GraphModel(client) res = graph.vertex.create(vertex_type) assert isinstance(res, cls) res.delete() pass def test_ts(self, client: HiroClient): from arago.hiro.backend.six.graph import Hiro6GraphModel graph = Hiro6GraphModel(client) res = graph.vertex.create(OgitEntity.OGIT_TIME_SERIES) assert isinstance(res, TimeSeriesVertex) res.delete() pass def test_blob_ogit(self, client: HiroClient): from arago.hiro.backend.six.graph import Hiro6GraphModel graph = Hiro6GraphModel(client) res = graph.vertex.create(OgitEntity.OGIT_ATTACHMENT) assert isinstance(res, BlobVertex) res.delete() pass def test_blob_ogit_map(self, client: HiroClient): from arago.hiro.backend.six.graph import Hiro6GraphModel graph = Hiro6GraphModel(client) res = graph.vertex.create(OgitEntity.OGIT_ATTACHMENT, { OgitAttribute.OGIT_NAME: 'foo' }) assert isinstance(res, BlobVertex) assert res[OgitAttribute.OGIT_NAME] == 'foo' res.delete() pass def test_blob_ogit_v(self, client: HiroClient): from arago.hiro.backend.six.graph import Hiro6GraphModel graph = Hiro6GraphModel(client) res = graph.vertex.create(OgitEntity.OGIT_ATTACHMENT, Vertex({ OgitAttribute.OGIT_NAME: 'foo' })) assert isinstance(res, BlobVertex) assert res[OgitAttribute.OGIT_NAME] == 'foo' res.delete() pass def test_blob_ontology(self, client: HiroClient): from arago.hiro.backend.six.graph import Hiro6GraphModel graph = Hiro6GraphModel(client) res = graph.vertex.create(OgitEntity.OGIT_ATTACHMENT.value) assert isinstance(res, BlobVertex) res.delete() pass class TestClassGraphVertexGet: def test_id(self, client: HiroClient): from arago.hiro.backend.six.graph import Hiro6GraphModel graph = Hiro6GraphModel(client) vertex_id = VertexId(OgitEntity.OGIT_COMMENT.value.name.uri) res = graph.vertex.get(vertex_id) assert isinstance(res, Vertex) pass def test_str(self, client: HiroClient): from arago.hiro.backend.six.graph import Hiro6GraphModel graph = Hiro6GraphModel(client) vertex_id = OgitEntity.OGIT_COMMENT.value.name.uri res = graph.vertex.get(vertex_id) assert isinstance(res, Vertex) pass def test_xid(self, client: HiroClient): from arago.hiro.backend.six.graph import Hiro6GraphModel graph = Hiro6GraphModel(client) vertex_id = ExternalVertexId('arago.co') res = graph.vertex.get(vertex_id) assert isinstance(res, Vertex) pass class TestClassGraphVertexUpdate: def test_vertex_update_model(self, client: HiroClient): from arago.hiro.backend.six.graph import Hiro6GraphModel graph = Hiro6GraphModel(client) comment_v = graph.vertex.create(OgitEntity.OGIT_COMMENT) res = graph.vertex.update(comment_v, {}) assert isinstance(res, Vertex) pass class TestClassGraphVertexDelete: def test_vertex_delete_model(self, client: HiroClient): from arago.hiro.backend.six.graph import Hiro6GraphModel graph = Hiro6GraphModel(client) comment_v = graph.vertex.create(OgitEntity.OGIT_COMMENT) res = graph.vertex.delete(comment_v) assert isinstance(res, Vertex) pass class TestClassGraphVertexHistory: def test_vertex_history_model_element(self, client: HiroClient): from arago.hiro.backend.six.graph import Hiro6GraphModel res_1 = client.root.model.search.index(rf'''ogit\/_type:"{OgitEntity.OGIT_LICENSE_REQUEST.value.name.uri!s}"''') vertex = next(res_1) graph = Hiro6GraphModel(client) res_2 = graph.vertex.history(vertex, res_format=HistoryFormat.ELEMENT) assert isinstance(res_2, Generator) vertex = next(res_2) assert isinstance(vertex, Vertex) pass def test_vertex_history_model_full(self, client: HiroClient): from arago.hiro.backend.six.graph import Hiro6GraphModel res_1 = client.root.model.search.index(rf'''ogit\/_type:"{OgitEntity.OGIT_LICENSE_REQUEST.value.name.uri!s}"''') vertex = next(res_1) graph = Hiro6GraphModel(client) res_2 = graph.vertex.history(vertex, res_format=HistoryFormat.FULL) assert isinstance(res_2, Generator) entry = next(res_2) assert isinstance(entry, HistoryEntry) vertex = entry.data assert isinstance(vertex, Vertex) pass def test_vertex_history_model_diff(self, client: HiroClient): from arago.hiro.backend.six.graph import Hiro6GraphModel res_1 = client.root.model.search.index(rf'''ogit\/_type:"{OgitEntity.OGIT_LICENSE_REQUEST.value.name.uri!s}"''') vertex = next(res_1) graph = Hiro6GraphModel(client) res_2 = graph.vertex.history(vertex, res_format=HistoryFormat.DIFF) assert isinstance(res_2, Generator) diff = next(res_2) diff = next(res_2) assert isinstance(diff, HistoryDiff) replaced = diff.replaced assert isinstance(replaced, MappingProxyType) keys = iter(replaced) key = next(keys) assert isinstance(key, Attribute) pass class TestClassGraphEdgeCreate: def test_edge_create_model(self, client: HiroClient): from arago.hiro.backend.six.graph import Hiro6GraphModel graph = Hiro6GraphModel(client) vertex_a = graph.vertex.create(OgitEntity.OGIT_ATTACHMENT) vertex_b = graph.vertex.create(OgitEntity.OGIT_COMMENT) res = graph.edge.create(vertex_a, OgitVerb.OGIT_BELONGS, vertex_b) isinstance(res, Edge) pass class TestClassGraphEdgeDelete: def test_edge_delete_model(self, client: HiroClient): from arago.hiro.backend.six.graph import Hiro6GraphModel graph = Hiro6GraphModel(client) vertex_a = graph.vertex.create(OgitEntity.OGIT_ATTACHMENT) vertex_b = graph.vertex.create(OgitEntity.OGIT_COMMENT) edge_c = graph.edge.create(vertex_a, OgitVerb.OGIT_BELONGS, vertex_b) res = graph.edge.delete(edge_c) isinstance(res, Edge) pass

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ed798aed54e01d84a8645c2c45e49553ec075ca0

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py

Python

main/management/commands/uploadcleanup.py

kristianmk/tator

0eb75ee9333316b06f773de2b75e8e797a98ffdb

[ "MIT" ]

50

2019-09-18T14:32:18.000Z

2022-03-31T16:26:07.000Z

main/management/commands/uploadcleanup.py

kristianmk/tator

0eb75ee9333316b06f773de2b75e8e797a98ffdb

[ "MIT" ]

566

2019-09-18T16:33:40.000Z

2022-03-31T20:01:38.000Z

main/management/commands/uploadcleanup.py

kristianmk/tator

0eb75ee9333316b06f773de2b75e8e797a98ffdb

[ "MIT" ]

19

2019-09-21T20:08:12.000Z

2022-03-17T14:53:11.000Z

from django.core.management.base import BaseCommand from main.util import cleanup_object_uploads class Command(BaseCommand): def handle(self, **options): cleanup_object_uploads()

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ed7a38f14feb45876e8d2fc53b967044ac058fc2

252

py

Python

models/model_abc.py

zmcx16/stock-forecast

c4ffcbc4215e135776fbb4d5ff384b069b7e631c

[ "MIT" ]

3

2021-11-27T13:21:11.000Z

2021-11-28T07:57:27.000Z

models/model_abc.py

zmcx16/stock-forecast

c4ffcbc4215e135776fbb4d5ff384b069b7e631c

[ "MIT" ]

null

models/model_abc.py

zmcx16/stock-forecast

c4ffcbc4215e135776fbb4d5ff384b069b7e631c

[ "MIT" ]

3

2021-11-26T17:39:52.000Z

2022-03-22T20:52:21.000Z

import abc class Model(abc.ABC): @abc.abstractmethod def run_validate(self, data): print(data) return NotImplemented @abc.abstractmethod def run_predict(self, data): print(data) return NotImplemented

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ed885666106e0e31922f343b0cc4ba3d6bde8173

827

py

Python

data_importer/importers/__init__.py

zhangchn/data-importer

faef624a19f97c76a157d8350bb05b819f1cb9f2

[ "BSD-2-Clause-FreeBSD" ]

62

2015-01-27T09:29:00.000Z

2021-02-28T09:56:11.000Z

data_importer/importers/__init__.py

zhangchn/data-importer

faef624a19f97c76a157d8350bb05b819f1cb9f2

[ "BSD-2-Clause-FreeBSD" ]

40

2015-01-16T11:57:17.000Z

2022-03-13T14:13:00.000Z

data_importer/importers/__init__.py

zhangchn/data-importer

faef624a19f97c76a157d8350bb05b819f1cb9f2

[ "BSD-2-Clause-FreeBSD" ]

34

2015-01-27T15:06:56.000Z

2021-02-28T09:56:14.000Z

# encoding: utf-8 from data_importer.importers.base import BaseImporter from data_importer.importers.csv_importer import CSVImporter from data_importer.importers.xls_importer import XLSImporter from data_importer.importers.xlsx_importer import XLSXImporter from data_importer.importers.xml_importer import XMLImporter from data_importer.importers.generic import GenericImporter from data_importer.core.exceptions import StopImporter from data_importer.core.exceptions import UnsuportedFile from data_importer.core.exceptions import InvalidModel from data_importer.core.exceptions import InvalidDescriptor __all__ = ( 'BaseImporter', 'CSVImporter', 'XLSImporter', 'XLSXImporter', 'XMLImporter', 'GenericImporter', 'StopImporter', 'UnsuportedFile', 'InvalidModel', 'InvalidDescriptor', )

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71eb391b86d48766f6ee823eba37e5d27e46cd63

370

py

Python

accelbyte_py_sdk/api/platform/operations/currency/__init__.py

encyphered/accelbyte-python-sdk

09c1e989d7251de308150fdcd3119d662ca2d205

[ "MIT" ]

null

accelbyte_py_sdk/api/platform/operations/currency/__init__.py

encyphered/accelbyte-python-sdk

09c1e989d7251de308150fdcd3119d662ca2d205

[ "MIT" ]

null

accelbyte_py_sdk/api/platform/operations/currency/__init__.py

encyphered/accelbyte-python-sdk

09c1e989d7251de308150fdcd3119d662ca2d205

[ "MIT" ]

null

# pylint: disable=line-too-long from .list_currencies import ListCurrencies from .create_currency import CreateCurrency from .get_currency_summary import GetCurrencySummary from .update_currency import UpdateCurrency from .delete_currency import DeleteCurrency from .get_currency_config import GetCurrencyConfig from .public_list_currencies import PublicListCurrencies

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9c086d4bf9b3fd5227953cbe2e9dbda7f0425383

305

py

Python

rpiRobot/src/cortex/domain/iItemChooser.py

olgam4/design3

6e05d123a24deae7dda646df535844a158ef5cc0

[ "WTFPL" ]

null

rpiRobot/src/cortex/domain/iItemChooser.py

olgam4/design3

6e05d123a24deae7dda646df535844a158ef5cc0

[ "WTFPL" ]

null

rpiRobot/src/cortex/domain/iItemChooser.py

olgam4/design3

6e05d123a24deae7dda646df535844a158ef5cc0

[ "WTFPL" ]

null

from abc import ABC, abstractmethod from typing import List from cortex.domain.objective.item import Item from cortex.domain.objective.objective import Objective class IItemChooser(ABC): @abstractmethod def choose_from(self, objective: Objective, items: List[Item]) -> List[Item]: pass

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9c1f095049bf8491264048877b1d7c95a42ab65d

198

py

Python

contracts/apps.py

City-of-Helsinki/berth-reservations

a3b1a8c2176f132505527acdf6da3a62199401db

[ "MIT" ]

3

2020-10-13T07:58:48.000Z

2020-12-22T09:41:50.000Z

contracts/apps.py

City-of-Helsinki/berth-reservations

a3b1a8c2176f132505527acdf6da3a62199401db

[ "MIT" ]

422

2018-10-25T10:57:05.000Z

2022-03-30T05:47:14.000Z

contracts/apps.py

City-of-Helsinki/berth-reservations

a3b1a8c2176f132505527acdf6da3a62199401db

[ "MIT" ]

1

2020-04-03T07:38:03.000Z

from django.apps import AppConfig class ContractsConfig(AppConfig): name = "contracts" def ready(self): from .services import load_services_config load_services_config()

18

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4

9c498bb43e411bf876af0c313338346a395c7824

8,576

py

Python

nimbleclient/v1/api/protection_schedules.py

prachiruparelia-hpe/nimble-python-sdk

a3e99d89e647291caf7936300ae853d21d94d6e5

[ "Apache-2.0" ]

1

2020-05-28T19:48:59.000Z

nimbleclient/v1/api/protection_schedules.py

prachiruparelia-hpe/nimble-python-sdk

a3e99d89e647291caf7936300ae853d21d94d6e5

[ "Apache-2.0" ]

null

nimbleclient/v1/api/protection_schedules.py

prachiruparelia-hpe/nimble-python-sdk

a3e99d89e647291caf7936300ae853d21d94d6e5

[ "Apache-2.0" ]

null

# # © Copyright 2020 Hewlett Packard Enterprise Development LP # # This file was auto-generated by the Python SDK generator; DO NOT EDIT. # from ...resource import Resource, Collection class ProtectionSchedule(Resource): """Manage protection schedules used in protection templates. # Parameters id : Identifier for protection schedule. name : Name of snapshot schedule to create. description : Description of the schedule. volcoll_or_prottmpl_type : Type of the protection policy this schedule is attached to. Valid values are protection_template and volume_collection. volcoll_or_prottmpl_id : Identifier of the protection policy (protection_template or volume_collection) in which this protection schedule is attached to. period : Repeat interval for snapshots with respect to the period_unit. For example, a value of 2 with the 'period_unit' of 'hours' results in one snapshot every 2 hours. period_unit : Time unit over which to take the number of snapshots specified in 'period'. For example, a value of 'days' with a 'period' of '1' results in one snapshot every day. at_time : Time of day when snapshot should be taken. In case repeat frequency specifies more than one snapshot in a day then the until_time option specifies until what time of day to take snapshots. until_time : Time of day to stop taking snapshots. Applicable only when repeat frequency specifies more than one snapshot in a day. days : Specifies which days snapshots should be taken. num_retain : Number of snapshots to retain. If replication is enabled on this schedule the array will always retain the latest replicated snapshot, which may exceed the specified retention value. This is necessary to ensure efficient replication performance. downstream_partner : Specifies the partner name if snapshots created by this schedule should be replicated. downstream_partner_name : Specifies the partner name if snapshots created by this schedule should be replicated. downstream_partner_id : Specifies the partner ID if snapshots created by this schedule should be replicated. In an update operation, if snapshots should be replicated, set this attribute to the ID of the replication partner. If snapshots should not be replicated, set this attribute to the empty string. upstream_partner_name : Specifies the partner name from which snapshots created by this schedule are replicated. upstream_partner_id : Specifies the partner ID from which snapshots created by this schedule are replicated. replicate_every : Specifies which snapshots should be replicated. If snapshots are replicated and this option is not specified, every snapshot is replicated. num_retain_replica : Number of snapshots to retain on the replica. repl_alert_thres : Replication alert threshold in seconds. If the replication of a snapshot takes more than this amount of time to complete an alert will be generated. Enter 0 to disable this alert. snap_verify : Run verification tool on snapshot created by this schedule. This option can only be used with snapshot schedules of a protection template that has application synchronization. The tool used to verify snapshot depends on the type of application. For example, if application synchronization is VSS and the application ID is Exchange, eseutil tool is run on the snapshots. If verification fails, the logs are not truncated. skip_db_consistency_check : Skip consistency check for database files on snapshots created by this schedule. This option only applies to snapshot schedules of a protection template with application synchronization set to VSS, application ID set to MS Exchange 2010 or later w/DAG, this schedule's snap_verify option set to yes, and its disable_appsync option set to false. Skipping consistency checks is only recommended if each database in a DAG has multiple copies. disable_appsync : Disables application synchronized snapshots and creates crash consistent snapshots instead. schedule_type : Normal schedules have internal timers which drive snapshot creation. An externally driven schedule has no internal timers. All snapshot activity is driven by an external trigger. In other words, these schedules are used only for externally driven manual snapshots. active : A schedule is active only if it is owned by the same owner as the volume collection. Only active schedules of a volume collection participate in the creation of snapshots and replication. creation_time : Time when this protection schedule was created. last_modified : Time when this protection schedule was last modified. last_mod_sched_time : Time when the timing of the protection schedule was last modified. last_replicated_snapcoll_name : Specifies the name of last replicated snapshot collection. last_replicated_snapcoll_id : Specifies the snapshot collection ID of last replicated snapshot collection. last_replicated_at_time : Time when last snapshot collection was replicated. last_snap_time : Time when last snapshot was taken. next_snap_time : Time when next snapshot will be taken. next_repl_snap_time : Time when next snapshot will be replicated. snap_counter : This is only used by custom read handler for internal calculations. sched_owner_id : Identifier of the group that owns this schedule. sched_owner_name : Name of the group that owns this schedule. last_config_change_time : The last timing configutation changed. vol_status_list : The list of the replication status of volumes undergoing replication. sync_repl_vol_status_list : A list of the replication status of volumes undergoing synchronous replication. use_downstream_for_DR : Break synchronous replication for the specified volume collection and present downstream volumes to host(s). Downstream volumes in the volume collection will be set to online and presented to the host(s) using new serial and LUN numbers. No changes will be made to the upstream volumes, their serial and LUN numbers, and their online state. The existing ACLs on the upstream volumes will be copied to the downstream volumes. Use this in conjunction with an empty downstream_partner_id. This unconfigures synchronous replication when the partner is removed from the last replicating schedule in the specified volume collection and presents the downstream volumes to host(s). Host(s) will need to be configured to access the new volumes with the newly assigned serial and LUN numbers. Use this option to expose downstream volumes in a synchronously replicated volume collection to host(s) only when the upstream partner is confirmed to be down and there is no communication between partners. Do not execute this operation if a previous Group Management Service takeover has been performed on a different array. Do not perform a subsequent Group Management Service takeover on a different array as it will lead to irreconcilable conflicts. This limitation is cleared once the Group management service backup array has successfully synchronized after reconnection. """ class ProtectionScheduleList(Collection): resource = ProtectionSchedule resource_type = "protection_schedules"

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py

Python

ceilometer/compute/server_pollsters/__init__.py

VeinFu/ceilometer_ha

fb0d3834d4db8a9eaeb8f5da088a2894c615770f

[ "Apache-2.0" ]

null

ceilometer/compute/server_pollsters/__init__.py

VeinFu/ceilometer_ha

fb0d3834d4db8a9eaeb8f5da088a2894c615770f

[ "Apache-2.0" ]

null

ceilometer/compute/server_pollsters/__init__.py

VeinFu/ceilometer_ha

fb0d3834d4db8a9eaeb8f5da088a2894c615770f

[ "Apache-2.0" ]

null

import abc import six from ceilometer.agent import plugin_base @six.add_metaclass(abc.ABCMeta) class ServerPollster(plugin_base.PollsterBase): @property def default_discovery(self): return 'local_node'

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Python

resolwe_bio/kb/management/__init__.py

gregorjerse/resolwe-bio

80f1e354cf0014a1eeff00acc112c622a2a044a9

[ "Apache-2.0" ]

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2015-12-07T18:29:27.000Z

2022-03-16T08:00:18.000Z

resolwe_bio/kb/management/__init__.py

gregorjerse/resolwe-bio

80f1e354cf0014a1eeff00acc112c622a2a044a9

[ "Apache-2.0" ]

480

2015-11-20T21:46:43.000Z

2022-03-28T12:40:57.000Z

resolwe_bio/kb/management/__init__.py

gregorjerse/resolwe-bio

80f1e354cf0014a1eeff00acc112c622a2a044a9

[ "Apache-2.0" ]

45

2015-11-19T14:54:07.000Z

2022-02-13T21:36:50.000Z

"""Management module."""

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py

Python

boa/interop/Ontology/Native.py

JasonZhouPW/neo-boa

84f4309c1876bd796b22a720b680d982b328c357

[ "MIT" ]

null

boa/interop/Ontology/Native.py

JasonZhouPW/neo-boa

84f4309c1876bd796b22a720b680d982b328c357

[ "MIT" ]

null

boa/interop/Ontology/Native.py

JasonZhouPW/neo-boa

84f4309c1876bd796b22a720b680d982b328c357

[ "MIT" ]

null

def Invoke(param,method,contractAddress,ver): """ """ pass

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1305c86bf3cab5001f7c50323d32759e3a5bcf5d

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py

Python

CSV/apps.py

jzadeh/chiron

493bf4e17f9970ee6118cc2ea6f1d87fb95ef26b

[ "Apache-2.0" ]

15

2017-08-08T10:19:47.000Z

2022-01-20T09:48:25.000Z

CSV/apps.py

jzadeh/chiron

493bf4e17f9970ee6118cc2ea6f1d87fb95ef26b

[ "Apache-2.0" ]

null

CSV/apps.py

jzadeh/chiron

493bf4e17f9970ee6118cc2ea6f1d87fb95ef26b

[ "Apache-2.0" ]

2

2019-12-11T20:14:27.000Z

2022-02-26T13:18:32.000Z

from django.apps import AppConfig class CsvConfig(AppConfig): name = 'CSV'

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py

Python

stac_exceptions.py

vincentsarago/stac-validator

2bc527cdf5b05bab499100e88254a3bdd3d65fe1

[ "Apache-2.0" ]

null

stac_exceptions.py

vincentsarago/stac-validator

2bc527cdf5b05bab499100e88254a3bdd3d65fe1

[ "Apache-2.0" ]

null

stac_exceptions.py

vincentsarago/stac-validator

2bc527cdf5b05bab499100e88254a3bdd3d65fe1

[ "Apache-2.0" ]

null

""" Description: Exceptions for the STAC Validator. """ class VersionException(Exception): pass

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131f6f2da3f2c69cf8ca88de131984155cc0cfa4

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py

Python

accounts/forms.py

iidamakinen/OHSIHA2018

76c4f2d754045cc82d57062453e7248d63e5bf4d

[ "MIT" ]

null

accounts/forms.py

iidamakinen/OHSIHA2018

76c4f2d754045cc82d57062453e7248d63e5bf4d

[ "MIT" ]

null

accounts/forms.py

iidamakinen/OHSIHA2018

76c4f2d754045cc82d57062453e7248d63e5bf4d

[ "MIT" ]

null

from django import forms from .models import Tapahtuma class TapahtumaForm(forms.ModelForm): class Meta: model = Tapahtuma fields = ['name', 'description', 'date']

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13277a1b3a4ae43c4f559219f3cf4b39a83b23db

241

py

Python

iexfinance/apidata/__init__.py

jto-d/iexfinance

8bf958f269638b6f8d2dbdd857c0ef2ba324cdd4

[ "Apache-2.0" ]

653

2018-01-02T21:03:49.000Z

2022-03-24T06:37:10.000Z

iexfinance/apidata/__init__.py

jto-d/iexfinance

8bf958f269638b6f8d2dbdd857c0ef2ba324cdd4

[ "Apache-2.0" ]

219

2017-12-09T21:44:43.000Z

2022-03-23T20:21:46.000Z

iexfinance/apidata/__init__.py

jto-d/iexfinance

8bf958f269638b6f8d2dbdd857c0ef2ba324cdd4

[ "Apache-2.0" ]

155

2018-02-07T17:08:18.000Z

2022-03-13T23:36:57.000Z

from iexfinance.apidata.base import APIReader def get_api_status(**kwargs): """ IEX Cloud API status Reference: https://iexcloud.io/docs/api/#status Data Weighting: ``Free`` """ return APIReader(**kwargs).fetch()

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13371afc97c339e0b6234da0975d033dca6367d5

183

py

Python

Lab2/Lab2/models/delmodel.py

sanchaez/python_labs

b90ab02c0fae82511c3db5a054b7ea8dda5d0a22

[ "MIT" ]

null

Lab2/Lab2/models/delmodel.py

sanchaez/python_labs

b90ab02c0fae82511c3db5a054b7ea8dda5d0a22

[ "MIT" ]

null

Lab2/Lab2/models/delmodel.py

sanchaez/python_labs

b90ab02c0fae82511c3db5a054b7ea8dda5d0a22

[ "MIT" ]

null

from lab2 import db as DataBase def delete(table, id): DataBase.delete(DataBase.escapeBySymbol(table, "`"), where = 'WHERE `id` = ' + DataBase.escapeBySymbol(id, "'")) return 1

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138eb354451a65835f63fadf4e9bfac58f0c50c5

270

py

Python

students/k3342/laboratory_works/Shipitcyna_Daria/laboratary_work_1/hotels_django/hotels_app/admin.py

shipa99/ITMO_ICT_WebProgramming_2020

86b6b1faa606fd3487193e426830daffd70e801c

[ "MIT" ]

null

students/k3342/laboratory_works/Shipitcyna_Daria/laboratary_work_1/hotels_django/hotels_app/admin.py

shipa99/ITMO_ICT_WebProgramming_2020

86b6b1faa606fd3487193e426830daffd70e801c

[ "MIT" ]

null

students/k3342/laboratory_works/Shipitcyna_Daria/laboratary_work_1/hotels_django/hotels_app/admin.py

shipa99/ITMO_ICT_WebProgramming_2020

86b6b1faa606fd3487193e426830daffd70e801c

[ "MIT" ]

null

from django.contrib import admin from .models import Facilities from .models import Room_types from .models import Hotel from .models import Comment admin.site.register(Facilities) admin.site.register(Room_types) admin.site.register(Hotel) admin.site.register(Comment)

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1395f94bbf093bda189114b31e44869460210116

1,340

gyp

Python

third_party/jsoncpp/jsoncpp.gyp

nagineni/chromium-crosswalk

5725642f1c67d0f97e8613ec1c3e8107ab53fdf8

[ "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

212

2015-01-31T11:55:58.000Z

2022-02-22T06:35:11.000Z

third_party/jsoncpp/jsoncpp.gyp

1065672644894730302/Chromium

239dd49e906be4909e293d8991e998c9816eaa35

[ "BSD-3-Clause" ]

5

2015-03-27T14:29:23.000Z

2019-09-25T13:23:12.000Z

third_party/jsoncpp/jsoncpp.gyp

1065672644894730302/Chromium

239dd49e906be4909e293d8991e998c9816eaa35

[ "BSD-3-Clause" ]

221

2015-01-07T06:21:24.000Z

2022-02-11T02:51:12.000Z

# Copyright (c) 2012 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. { 'targets': [ { 'target_name': 'jsoncpp', 'type': 'static_library', 'defines': [ 'JSON_USE_EXCEPTION=0', ], 'sources': [ 'source/include/json/assertions.h', 'source/include/json/autolink.h', 'source/include/json/config.h', 'source/include/json/features.h', 'source/include/json/forwards.h', 'source/include/json/json.h', 'source/include/json/reader.h', 'overrides/include/json/value.h', 'source/include/json/writer.h', 'source/src/lib_json/json_batchallocator.h', 'source/src/lib_json/json_reader.cpp', 'source/src/lib_json/json_tool.h', 'overrides/src/lib_json/json_value.cpp', 'source/src/lib_json/json_writer.cpp', ], 'include_dirs': [ 'overrides/include/', 'source/include/', 'source/src/lib_json/', ], 'direct_dependent_settings': { 'include_dirs': [ 'overrides/include/', 'source/include/', ], }, }, ], } # Local Variables: # tab-width:2 # indent-tabs-mode:nil # End: # vim: set expandtab tabstop=2 shiftwidth=2:

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