xszheng2020/the_stack_dedup_python_hits_4 · Datasets at Hugging Face

07735a9f620af68e0e83b32173c18b049fe12c82

176

py

Python

virtual/bin/django-admin.py

Brayonski/Instagram

189ec32577f950fe14bd199e767379416e9d4f94

[ "MIT" ]

null

virtual/bin/django-admin.py

Brayonski/Instagram

189ec32577f950fe14bd199e767379416e9d4f94

[ "MIT" ]

null

virtual/bin/django-admin.py

Brayonski/Instagram

189ec32577f950fe14bd199e767379416e9d4f94

[ "MIT" ]

null

#!/media/root/Alpha/projects/core/django/Instagram/virtual/bin/python from django.core import management if __name__ == "__main__": management.execute_from_command_line()

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07983e0fdb4c56c6c89fc7aa6fe201858208d96c

154

py

Python

examples/user/py/__main__.py

Hacker0x01/pulumi-snowflake

f6ebcf2c3f73b103a7c2001fae231998ce1323b2

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

3

2021-07-01T17:03:33.000Z

2022-03-01T19:29:04.000Z

examples/user/py/__main__.py

Hacker0x01/pulumi-snowflake

f6ebcf2c3f73b103a7c2001fae231998ce1323b2

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

102

2021-07-14T13:12:58.000Z

2022-03-31T18:34:04.000Z

examples/user/py/__main__.py

Hacker0x01/pulumi-snowflake

f6ebcf2c3f73b103a7c2001fae231998ce1323b2

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

1

2022-03-25T07:24:45.000Z

"""A Python Pulumi program""" import pulumi import pulumi_snowflake as snowflake user = snowflake.User("py-user") pulumi.export("username", user.name)

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07cb7dd092f421c365ac59c83203c974c406ee1d

193

py

Python

pyrt/__init__.py

Oleg595/pyRT

6fc0ccbc6fb24dcc2a8532aa22eb9574f1afdb3a

[ "MIT" ]

74

2016-09-02T08:15:39.000Z

2021-08-09T08:16:23.000Z

pyrt/__init__.py

Oleg595/pyRT

6fc0ccbc6fb24dcc2a8532aa22eb9574f1afdb3a

[ "MIT" ]

22

2016-09-02T08:15:14.000Z

2021-02-22T19:52:21.000Z

pyrt/__init__.py

Oleg595/pyRT

6fc0ccbc6fb24dcc2a8532aa22eb9574f1afdb3a

[ "MIT" ]

27

2016-09-04T12:55:27.000Z

2022-03-19T11:21:24.000Z

""" This is the main module "pyrt" Usually you do not need to import it directly, see examples. """ from .camera import * from .geometry import * from .renderer import * from .scene import *

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07cf3662f89648b524ce4071f804b530f14be385

826

py

Python

pytglib/api/types/passport_element_rental_agreement.py

iTeam-co/pytglib

e5e75e0a85f89b77762209b32a61b0a883c0ae61

[ "MIT" ]

6

2019-10-30T08:57:27.000Z

2021-02-08T14:17:43.000Z

pytglib/api/types/passport_element_rental_agreement.py

iTeam-co/python-telegram

e5e75e0a85f89b77762209b32a61b0a883c0ae61

[ "MIT" ]

1

2021-08-19T05:44:10.000Z

2021-08-19T07:14:56.000Z

pytglib/api/types/passport_element_rental_agreement.py

iTeam-co/python-telegram

e5e75e0a85f89b77762209b32a61b0a883c0ae61

[ "MIT" ]

5

2019-12-04T05:30:39.000Z

2021-05-21T18:23:32.000Z

from ..utils import Object class PassportElementRentalAgreement(Object): """ A Telegram Passport element containing the user's rental agreement Attributes: ID (:obj:`str`): ``PassportElementRentalAgreement`` Args: rental_agreement (:class:`telegram.api.types.personalDocument`): Rental agreement Returns: PassportElement Raises: :class:`telegram.Error` """ ID = "passportElementRentalAgreement" def __init__(self, rental_agreement, **kwargs): self.rental_agreement = rental_agreement # PersonalDocument @staticmethod def read(q: dict, *args) -> "PassportElementRentalAgreement": rental_agreement = Object.read(q.get('rental_agreement')) return PassportElementRentalAgreement(rental_agreement)

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07faf6a2811708d5e0ab5d34fccb8ce190cebeb2

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py

Python

setup.py

Enchan1207/ProcessObserving

d11055dd84559e30c28b3b5d0351daac63fdaa78

[ "MIT" ]

null

setup.py

Enchan1207/ProcessObserving

d11055dd84559e30c28b3b5d0351daac63fdaa78

[ "MIT" ]

1

2021-11-19T04:12:51.000Z

2021-11-19T04:37:17.000Z

setup.py

Enchan1207/ProcessObserving

d11055dd84559e30c28b3b5d0351daac63fdaa78

[ "MIT" ]

null

# # pipが読んでライブラリの諸々を設定するためのファイル # from setuptools import setup setup()

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6af447bfa6d0d2be9e1e535859c6074ceda78b36

121

py

Python

dpFreeDine/log.py

jeffreyyzwu/pythonTools

9cb390d93a0ac8ab1f4f2a68428bdacbfd7371ce

[ "MIT" ]

null

dpFreeDine/log.py

jeffreyyzwu/pythonTools

9cb390d93a0ac8ab1f4f2a68428bdacbfd7371ce

[ "MIT" ]

null

dpFreeDine/log.py

jeffreyyzwu/pythonTools

9cb390d93a0ac8ab1f4f2a68428bdacbfd7371ce

[ "MIT" ]

1

2020-06-25T03:57:37.000Z

import logging import logging.config logging.config.fileConfig("conf/logger.conf") logger = logging.getLogger("dpfree")

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ed05a5861aaf9f9c6b7148625e873ff48b47754c

2,375

py

Python

memrise/core/modules/actions/empty_actions.py

kolesnikov-bn/django-memrise-scraper

00dddb53f04ce2f794fe3611ea97190ef8265079

[ "MIT" ]

null

memrise/core/modules/actions/empty_actions.py

kolesnikov-bn/django-memrise-scraper

00dddb53f04ce2f794fe3611ea97190ef8265079

[ "MIT" ]

null

memrise/core/modules/actions/empty_actions.py

kolesnikov-bn/django-memrise-scraper

00dddb53f04ce2f794fe3611ea97190ef8265079

[ "MIT" ]

null

from __future__ import annotations from typing import List, ClassVar, TYPE_CHECKING from memrise.core.modules.actions.base import Actions if TYPE_CHECKING: from memrise.core.domains.entities import CourseEntity, LevelEntity, WordEntity class EmptyCourseActions(Actions): def create(self, entities: List[CourseEntity]) -> None: self.reporter.report( entities, f"{self.prefix}Добавление новых курсов{self.postfix}" ) def update(self, entities: List[CourseEntity]) -> None: self.reporter.report(entities, f"{self.prefix}Обновление курсов{self.postfix}") def equal(self, entities: List[CourseEntity]) -> None: self.reporter.report( entities, f"{self.prefix}Курсы без изменений{self.postfix}" ) def delete(self, entities: List[CourseEntity]) -> None: self.reporter.report(entities, f"{self.prefix}Удаление курсов{self.postfix}") class EmptyLevelActions(Actions): prefix: ClassVar[str] = "Курс $course_id --> " def create(self, entities: List[LevelEntity]) -> None: self.reporter.report( entities, f"{self.prefix}Добавление новых уровней{self.postfix}" ) def update(self, entities: List[LevelEntity]) -> None: self.reporter.report(entities, f"{self.prefix}Обновление уровней{self.postfix}") def equal(self, entities: List[LevelEntity]) -> None: self.reporter.report( entities, f"{self.prefix}Уровни без изменений{self.postfix}" ) def delete(self, entities: List[LevelEntity]) -> None: self.reporter.report(entities, f"{self.prefix}Удаление уровней{self.postfix}") class EmptyWordActions(Actions): prefix: ClassVar[str] = "Уровень $level_id --> " def create(self, entities: List[WordEntity]) -> None: self.reporter.report( entities, f"{self.prefix}Добавление новых слов{self.postfix}" ) def update(self, entities: List[WordEntity]) -> None: self.reporter.report(entities, f"{self.prefix}Обновление слов{self.postfix}") def equal(self, entities: List[WordEntity]) -> None: self.reporter.report( entities, f"{self.prefix}Слова без изменений{self.postfix}" ) def delete(self, entities: List[WordEntity]) -> None: self.reporter.report(entities, f"{self.prefix}Удаление слов{self.postfix}")

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ed0c79a039d908ee6ce419a8ed7b3735194829ba

254

py

Python

wingedsheep/carcassonne/objects/road.py

SuryaVikram/CarcassonneMaster

cc0201638dfea05b254833097329729e9b8a410c

[ "MIT" ]

11

2020-05-19T17:29:18.000Z

2022-03-24T06:22:50.000Z

wingedsheep/carcassonne/objects/road.py

SuryaVikram/CarcassonneMaster

cc0201638dfea05b254833097329729e9b8a410c

[ "MIT" ]

6

2020-05-18T09:24:26.000Z

2022-03-12T00:30:21.000Z

wingedsheep/carcassonne/objects/road.py

SuryaVikram/CarcassonneMaster

cc0201638dfea05b254833097329729e9b8a410c

[ "MIT" ]

5

2021-09-16T11:53:26.000Z

2022-03-30T12:08:56.000Z

from wingedsheep.carcassonne.objects.coordinate_with_side import CoordinateWithSide class Road: def __init__(self, road_positions: [CoordinateWithSide], finished: bool): self.road_positions = road_positions self.finished = finished

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ed1af59adda910a4594541daabe9bb4402f689da

365

py

Python

manipulacao_de_arquivos/List-Comprehension/comprehension_v3.py

andremartins746/curso_de_PYTHON

3b4d79e3310b2442cf57a98f213a153492f2a89a

[ "MIT" ]

null

manipulacao_de_arquivos/List-Comprehension/comprehension_v3.py

andremartins746/curso_de_PYTHON

3b4d79e3310b2442cf57a98f213a153492f2a89a

[ "MIT" ]

null

manipulacao_de_arquivos/List-Comprehension/comprehension_v3.py

andremartins746/curso_de_PYTHON

3b4d79e3310b2442cf57a98f213a153492f2a89a

[ "MIT" ]

null

# usando generator, ele comsome menos memoria generator = (i ** 2 for i in range(10) if i % 2 == 0) #o next serve para extrair o valor do generator, importante o generator nbao e uma tupla print(next(generator)) print(next(generator)) print(next(generator)) print(next(generator)) print(next(generator)) # print(next(generator)) aqui vai dar um ERRO apartir do 64

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ed2e924af7f02f74df4d4f102ee84e3e3f011848

222

py

Python

Lists_as_Stacks_and_Queues/01_reverse_strings.py

MihailMarkovski/Python-Advanced-2020

8edea78cbe5588a409ba9bc3767861250f58c1a6

[ "MIT" ]

4

2020-09-19T13:53:19.000Z

2020-11-01T18:34:53.000Z

Lists_as_Stacks_and_Queues/01_reverse_strings.py

MNikov/Python-Advanced-September-2020

1d65039de7f094d908411afffa8aee9689ab4220

[ "MIT" ]

null

Lists_as_Stacks_and_Queues/01_reverse_strings.py

MNikov/Python-Advanced-September-2020

1d65039de7f094d908411afffa8aee9689ab4220

[ "MIT" ]

null

def reverse_string(string): reversed_list = [] string_as_list = list(string) while string_as_list: reversed_list.append(string_as_list.pop()) print(''.join(reversed_list)) reverse_string(input())

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ed3b21943c2f9baae29dd5ad2e45a0e03bc0e035

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py

Python

compliance/verify_submission/mlperf_submission_helper/crypto.py

sanyalington/mlperf_training_mitest

d07b360e475afb87c7da57f173952822d84ed212

[ "Apache-2.0" ]

1

2019-02-19T09:53:42.000Z

compliance/verify_submission/mlperf_submission_helper/crypto.py

sanyalington/mlperf_training_mitest

d07b360e475afb87c7da57f173952822d84ed212

[ "Apache-2.0" ]

1

2018-11-06T06:03:30.000Z

compliance/verify_submission/mlperf_submission_helper/crypto.py

sanyalington/mlperf_training_mitest

d07b360e475afb87c7da57f173952822d84ed212

[ "Apache-2.0" ]

3

2019-01-14T13:57:03.000Z

2019-02-22T23:19:41.000Z

import fnmatch import os import shutil from Cryptodome.PublicKey import RSA from Cryptodome.Random import get_random_bytes from Cryptodome.Cipher import AES, PKCS1_OAEP def encrypt_file(public_key, src_file, dest_file): try: with open(src_file) as f: rsa_key = RSA.import_key(open(public_key).read()) session_key = get_random_bytes(16) # Encrypt session key cipher_rsa = PKCS1_OAEP.new(rsa_key) encrypted_session_key = cipher_rsa.encrypt(session_key) # Encrypt data cipher_aes = AES.new(session_key, AES.MODE_EAX) ciphertext, tag = cipher_aes.encrypt_and_digest(f.read().encode("utf-8")) except Exception as e: print("Unable to encrypt file: {}".format(src_file)) raise e try: with open(dest_file, "wb") as f: for x in (encrypted_session_key, cipher_aes.nonce, tag, ciphertext): f.write(x) except Exception as e: print("Unable to write output file {}".format(dest_file)) raise e def decrypt_file(private_key, src_file, dest_file): try: with open(src_file, "rb") as f: rsa_key = RSA.import_key(open(private_key).read()) encrypted_session_key = f.read(rsa_key.size_in_bytes()) nonce = f.read(16) tag = f.read(16) ciphertext = f.read(-1) # Decrypt session key cipher_rsa = PKCS1_OAEP.new(rsa_key) session_key = cipher_rsa.decrypt(encrypted_session_key) # Decrypt data cipher_aes = AES.new(session_key, AES.MODE_EAX, nonce) data = cipher_aes.decrypt_and_verify(ciphertext, tag) data = data.decode("utf-8") except Exception as e: print("Unable to decrypt file: {}".format(src_file)) raise e try: with open(dest_file, "w") as f: f.write(data) except Exception as e: print("Unable to write output file: {}".format(dest_file)) raise e def encrypt_submission(key, src_dir, dest_dir): if os.path.isdir(dest_dir): raise Exception("Output directory already exists.") os.mkdir(dest_dir, mode=0o755) for root, dirs, files in os.walk(src_dir): # identify result files and encrypt, else directly copy if fnmatch.fnmatch(root, os.path.join(src_dir, "results", "*", "*")): for f in files: from_file = os.path.join(root, f) to_file = from_file.replace(src_dir.rstrip(os.sep), dest_dir.rstrip(os.sep), 1) encrypt_file(key, from_file, to_file) else: for d in dirs: from_dir = os.path.join(root, d) to_dir = from_dir.replace(src_dir.rstrip(os.sep), dest_dir.rstrip(os.sep), 1) os.mkdir(to_dir, mode=0o755) for f in files: from_file = os.path.join(root, f) to_file = from_file.replace(src_dir.rstrip(os.sep), dest_dir.rstrip(os.sep), 1) shutil.copyfile(from_file, to_file) def decrypt_submission(key, src_dir, dest_dir): if os.path.isdir(dest_dir): raise Exception("Output directory already exists.") os.mkdir(dest_dir, mode=0o755) for root, dirs, files in os.walk(src_dir): # identify result files and encrypt, else directly copy if fnmatch.fnmatch(root, os.path.join(src_dir, "results", "*", "*")): for f in files: from_file = os.path.join(root, f) to_file = from_file.replace(src_dir.rstrip(os.sep), dest_dir.rstrip(os.sep), 1) decrypt_file(key, from_file, to_file) else: for d in dirs: from_dir = os.path.join(root, d) to_dir = from_dir.replace(src_dir.rstrip(os.sep), dest_dir.rstrip(os.sep), 1) os.mkdir(to_dir, mode=0o755) for f in files: from_file = os.path.join(root, f) to_file = from_file.replace(src_dir.rstrip(os.sep), dest_dir.rstrip(os.sep), 1) shutil.copyfile(from_file, to_file)

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py

Python

dataprofiler/profilers/__init__.py

gliptak/DataProfiler

37ffbf43652246ef27e070df7ff0d9f1b9529162

[ "Apache-2.0" ]

null

dataprofiler/profilers/__init__.py

gliptak/DataProfiler

37ffbf43652246ef27e070df7ff0d9f1b9529162

[ "Apache-2.0" ]

1

2021-11-20T01:08:12.000Z

dataprofiler/profilers/__init__.py

gliptak/DataProfiler

37ffbf43652246ef27e070df7ff0d9f1b9529162

[ "Apache-2.0" ]

null

from .base_column_profilers import BaseColumnProfiler from .categorical_column_profile import CategoricalColumn from .data_labeler_column_profile import DataLabelerColumn from .datetime_column_profile import DateTimeColumn from .float_column_profile import FloatColumn from .int_column_profile import IntColumn from .numerical_column_stats import NumericStatsMixin from .order_column_profile import OrderColumn from .profile_builder import Profiler, StructuredProfiler, UnstructuredProfiler from .text_column_profile import TextColumn from .unstructured_labeler_profile import UnstructuredLabelerProfile """ The purpose of this package is to provide statistics and predictions for a given dataset. """

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py

Python

mahiru/policy/__init__.py

SecConNet/proof_of_concept

80f6b27ff6b97796803e554387ca2881a792be79

[ "Apache-2.0" ]

4

2021-03-26T09:17:51.000Z

2021-05-17T10:31:59.000Z

mahiru/policy/__init__.py

SecConNet/proof_of_concept

80f6b27ff6b97796803e554387ca2881a792be79

[ "Apache-2.0" ]

58

2020-03-02T10:02:51.000Z

2021-07-09T09:23:49.000Z

mahiru/policy/__init__.py

SecConNet/proof_of_concept

80f6b27ff6b97796803e554387ca2881a792be79

[ "Apache-2.0" ]

null

"""Defining, distributing and evaluating policies."""

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53

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1

54

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1

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1

0

null

0

1

0

4

ed5f8448befb750076defd3f16297f10b90d40f5

475

py

Python

test/test_body42.py

pygitee/pygitee

7622314a4dbb08cf2f729b6cdd0a2887b96e394e

[ "MIT" ]

null

test/test_body42.py

pygitee/pygitee

7622314a4dbb08cf2f729b6cdd0a2887b96e394e

[ "MIT" ]

null

test/test_body42.py

pygitee/pygitee

7622314a4dbb08cf2f729b6cdd0a2887b96e394e

[ "MIT" ]

null

# coding: utf-8 from __future__ import absolute_import import unittest class TestBody42(unittest.TestCase): """Body42 unit test stubs""" def setUp(self): pass def tearDown(self): pass def testBody42(self): """Test Body42""" # FIXME: construct object with mandatory attributes with example values # model = gitee.models.body42.Body42() # noqa: E501 pass if __name__ == '__main__': unittest.main()

17.592593

79

0.633684

53

475

5.433962

0.679245

0.055556

0.076389

0

0.045977

0.267368

475

26

80

18.269231

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0

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0

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false

0.272727

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0

0.545455

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null

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null

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1

0

1

0

1

0

1

0

4

ed670d5e1a117f68b4bda3c8dfa630b3beaeecb1

175

py

Python

geotrek/land/apps.py

claudep/Geotrek

620799cc2667c3b203ef92de6ec35008111fb592

[ "BSD-2-Clause" ]

1

2019-12-11T11:04:05.000Z

geotrek/land/apps.py

numahell/Geotrek-admin

e279875b0b06ef60928c049d51533f76716c902a

[ "BSD-2-Clause" ]

null

geotrek/land/apps.py

numahell/Geotrek-admin

e279875b0b06ef60928c049d51533f76716c902a

[ "BSD-2-Clause" ]

null

from django.apps import AppConfig from django.utils.translation import gettext_lazy as _ class LandConfig(AppConfig): name = 'geotrek.land' verbose_name = _("Land")

21.875

54

0.754286

22

175

5.818182

0.727273

0.15625

0

0.16

175

7

55

25

0.870748

0

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0

1

0

false

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0.4

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1

0

1

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null

0

1

0

null

0

1

0

1

0

4

ed7d6583293861a136d4bce1ddf2366f87e28d56

114

py

Python

tests/cycle_info_example.py

lowrie/OPPPY

4bbbc16092f100aaf0411f9e20d89c1b1f8b19f9

[ "BSD-3-Clause" ]

2

2019-08-19T23:29:20.000Z

2020-03-19T07:08:30.000Z

tests/cycle_info_example.py

lowrie/OPPPY

4bbbc16092f100aaf0411f9e20d89c1b1f8b19f9

[ "BSD-3-Clause" ]

5

2019-08-20T22:03:23.000Z

2021-04-05T15:24:26.000Z

tests/cycle_info_example.py

lowrie/OPPPY

4bbbc16092f100aaf0411f9e20d89c1b1f8b19f9

[ "BSD-3-Clause" ]

4

2019-10-23T13:29:26.000Z

2021-04-05T14:17:43.000Z

cycle_info={} # populate some example cycle info cycle_info['cycle']=1 cycle_info['time']=1.0 print(cycle_info)

14.25

34

0.745614

19

114

4.263158

0.473684

0.555556

0.345679

0

0.029412

0.105263

114

7

35

16.285714

0.764706

0.280702

0

0.1125

0

1

0

false

0

0.25

1

0

null

1

0

1

0

null

0

4

71ff105211da39c027cbe4ed45c3eb3d49c32f6c

75

py

Python

tasks/Scrapy/scrapy_official_newspapers/runner.py

thefirebanks/policy-data-analyzer

670a4ea72ab71975b84c4a4ec43d573371c4a986

[ "RSA-MD" ]

13

2020-12-11T12:10:20.000Z

2021-04-27T22:54:25.000Z

tasks/Scrapy/scrapy_official_newspapers/runner.py

thefirebanks/policy-data-analyzer

670a4ea72ab71975b84c4a4ec43d573371c4a986

[ "RSA-MD" ]

40

2020-11-24T06:48:53.000Z

2021-04-28T05:20:37.000Z

tasks/Scrapy/scrapy_official_newspapers/runner.py

thefirebanks/policy-data-analyzer

670a4ea72ab71975b84c4a4ec43d573371c4a986

[ "RSA-MD" ]

5

2020-11-26T08:23:05.000Z

2021-04-19T18:08:20.000Z

from scrapy.cmdline import execute execute(['scrapy','crawl', 'elperuano'])

37.5

40

0.76

9

75

6.333333

0.777778

0

0.066667

75

2

40

37.5

0.814286

0

0.263158

0

1

0

true

0

0.5

0

0.5

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null

0

1

0

null

0

1

0

1

0

4

9c11f93e70f9f2e00296ce74bd6f33fd4b703339

747

py

Python

Chapter_7_code/build/hector_mapping/cmake/hector_mapping-genmsg-context.py

crepuscularlight/ROSbyExample

fa7b1a60cacca9b1034e318a2ac16ce4c8530d7c

[ "MIT" ]

1

2021-04-23T10:01:22.000Z

Chapter_7_code/build/hector_mapping/cmake/hector_mapping-genmsg-context.py

crepuscularlight/ROSbyExample

fa7b1a60cacca9b1034e318a2ac16ce4c8530d7c

[ "MIT" ]

null

Chapter_7_code/build/hector_mapping/cmake/hector_mapping-genmsg-context.py

crepuscularlight/ROSbyExample

fa7b1a60cacca9b1034e318a2ac16ce4c8530d7c

[ "MIT" ]

null

# generated from genmsg/cmake/pkg-genmsg.context.in messages_str = "/home/liudiyang1998/Git/ROS-Robotics-By-Example/Chapter_7_code/src/hector_slam/hector_mapping/msg/HectorDebugInfo.msg;/home/liudiyang1998/Git/ROS-Robotics-By-Example/Chapter_7_code/src/hector_slam/hector_mapping/msg/HectorIterData.msg" services_str = "" pkg_name = "hector_mapping" dependencies_str = "" langs = "gencpp;geneus;genlisp;gennodejs;genpy" dep_include_paths_str = "hector_mapping;/home/liudiyang1998/Git/ROS-Robotics-By-Example/Chapter_7_code/src/hector_slam/hector_mapping/msg" PYTHON_EXECUTABLE = "/usr/bin/python2" package_has_static_sources = '' == 'TRUE' genmsg_check_deps_script = "/opt/ros/melodic/share/genmsg/cmake/../../../lib/genmsg/genmsg_check_deps.py"

62.25

251

0.813922

108

747

5.351852

0.518519

0.112457

0.103806

0.119377

0.420415

0

0.02244

0.045515

747

11

252

67.909091

0.788219

0.065596

0

1

0.222222

0.708333

0.659483

0

1

0

false

0

null

0

1

null

0

4

9c141cbfb146e420f2d73b59b5a80e4a11238b62

11,347

py

Python

crop_rotator/strona/models.py

Bahusson/crop_rotator

c1d86d36ce1867a84b927708f92c62c7815250a4

[ "MIT" ]

1

2021-05-08T07:04:45.000Z

crop_rotator/strona/models.py

Bahusson/crop_rotator

c1d86d36ce1867a84b927708f92c62c7815250a4

[ "MIT" ]

80

2020-11-18T20:35:12.000Z

2021-06-13T08:08:36.000Z

crop_rotator/strona/models.py

Bahusson/crop_rotator

c1d86d36ce1867a84b927708f92c62c7815250a4

[ "MIT" ]

null

from django.db import models # Klasa tłumaczeniowa dla "core" class PageNames(models.Model): lang_flag = models.ImageField(upload_to='images') # Mały obrazek języka lang_flag_id = models.CharField(max_length=20, blank=True, null=True) headtitle = models.CharField(max_length=200) # Nagłówek strony w tym j mainpage = models.CharField(max_length=200) # Strona główna w tym języku all_plants = models.CharField(max_length=200, blank=True, null=True) # Spis roślin about = models.CharField(max_length=200) # Informacje w tym języku contact = models.CharField(max_length=200) # Kontakty w tym języku logout = models.CharField(max_length=200) # Wyloguj login = models.CharField(max_length=200) # zaloguj register = models.CharField(max_length=50) see_more = models.CharField(max_length=200) my_plans = models.CharField(max_length=200, blank=True, null=True) # Spis roślin all_plans = models.CharField(max_length=200, blank=True, null=True) # Wszystkie plany see_more = models.CharField(max_length=200, blank=True, null=True) # Czytaj dalej of_steps = models.CharField(max_length=200, blank=True, null=True) # Kroków of_plants = models.CharField(max_length=200, blank=True, null=True) # Czytaj dalej by_crops = models.CharField(max_length=50, blank=True, null=True) # Rośliny (button) by_families = models.CharField(max_length=50, blank=True, null=True) # Rodziny (button) by_tags = models.CharField(max_length=50, blank=True, null=True) # Kategorie (button) class Meta: verbose_name_plural = 'Page Names' # Klasa tłumaczeniowa dla Login/Register i myprofile. class RegNames(models.Model): password = models.CharField(max_length=50, blank=True, null=True) re_password = models.CharField(max_length=50, blank=True, null=True) name = models.CharField(max_length=50, blank=True, null=True) refresh = models.CharField(max_length=50, blank=True, null=True) passwd_too_simple = models.CharField(max_length=250, blank=True, null=True) register = models.CharField(max_length=50, blank=True, null=True) class Meta: verbose_name_plural = 'Registry Names' # Klasa skórek do naszej apki. Pola nienulowalne. class PageSkin(models.Model): themetitle = models.CharField(max_length=200) position = models.IntegerField() planimagedefault = models.ImageField( upload_to='skins', blank=True, null=True) rotatorlogo_main = models.ImageField( upload_to='skins', blank=True, null=True) class Meta: ordering = ['position'] verbose_name_plural = 'Page Skins' def __str__(self): return self.themetitle # klasa tłumaczeniowa dla strony "o projekcie" class AboutPageNames(models.Model): about_project = models.TextField() # Pole tekstowe dla strony about. send_email = models.CharField(max_length=200) # Wyślij email gitter = models.CharField(max_length=200) # Adres gittera github = models.CharField(max_length=200) # Adres github login_to_see = models.CharField(max_length=200) # zaloguj się by przeglądać curr_prog_includes = models.CharField(max_length=40, blank=True, null=True) # Obecnie program zawiera bazę over = models.CharField(max_length=30, blank=True, null=True) # Ponad plants = models.CharField(max_length=30, blank=True, null=True) # roślin_uprawnych coming_from = models.CharField(max_length=30, blank=True, null=True) # pochodzących z families = models.CharField(max_length=30, blank=True, null=True) # rodzin marked_by = models.CharField(max_length=30, blank=True, null=True) # oznaczonych według categories = models.CharField(max_length=30, blank=True, null=True) # kategorii and_over = models.CharField(max_length=30, blank=True, null=True) # i ponad unique_interactions = models.CharField(max_length=30, blank=True, null=True) # unikalnych interakcji described_by = models.CharField(max_length=30, blank=True, null=True) # opisanych na podstawie sources = models.CharField(max_length=30, blank=True, null=True) # źródeł class Meta: verbose_name_plural = 'About Page Names' # klasa tłumaczeniowa dla strony edycji planów. class RotatorEditorPageNames(models.Model): new_plan = models.CharField(max_length=200) # dodaj nowy plan new_step = models.CharField(max_length=200) # Dodaj nowy krok name_plan = models.CharField(max_length=200) # Nazwa planu name_step = models.CharField(max_length=200) # Nazwa kroku plan_remove = models.CharField(max_length=200) # Usuń plan (button) step_remove = models.CharField(max_length=200) # Usuń krok (button) remove_warning = models.CharField(max_length=200, blank=True, null=True) # Czy na pewno usunąć? (text) remove_permanent = models.CharField(max_length=200, blank=True, null=True) # Tak usuń trwale (button) dont_remove = models.CharField(max_length=200, blank=True, null=True) # Nie usuwaj (button) editme = models.CharField(max_length=200) # Edytuj switch_places = models.CharField(max_length=200) # Zamień miejscami (button) switch_with = models.CharField(max_length=200) # Zamień z krokiem: switch_text = models.CharField(max_length=200) # Zamień z innym krokiem w planie (text) u_edit_step_no = models.CharField(max_length=200) # Edytujesz krok nr. title = models.CharField(max_length=200) # Tytuł descr = models.CharField(max_length=200) # opis early_crop = models.CharField(max_length=200) # Wczesny Plon middle_crop = models.CharField(max_length=200, blank=True, null=True) # Śródplon late_crop = models.CharField(max_length=200) # late_crop destroy_early_crop = models.CharField(max_length=200) # Zniszcz na zielony nawóz add_fertilizer = models.CharField(max_length=200) # Dodaj nawóz change = models.CharField(max_length=200) # Zachowaj zmiany (button) publish = models.CharField(max_length=200, blank=True, null=True) # Opublikuj unpublish = models.CharField(max_length=200, blank=True, null=True) # Wycofaj publish_text = models.CharField(max_length=200, blank=True, null=True) # Opublikuj swój plan (text) unpublish_text = models.CharField(max_length=200, blank=True, null=True) # Wycofaj plan z publikacji (text) publish_onhover = models.CharField(max_length=900, blank=True, null=True) # Wyjaśnienie onhover o publikacji unpublish_onhover = models.CharField(max_length=900, blank=True, null=True) # Wyjaśnienie onhover o wycofywaniu publikacji more_info = models.CharField(max_length=900, blank=True, null=True) # więcej informacji (button "info") option_select = models.CharField(max_length=200, blank=True, null=True) # Wybierz opcję: (dropdown) in_this_plan = models.CharField(max_length=200, blank=True, null=True) # W tym planie znajduje się fabs_and = models.CharField(max_length=200, blank=True, null=True) # bobowatych lub strączkowych should_be_fabs = models.CharField(max_length=200, blank=True, null=True) # Powinno ich być między 25% a 33% error_len = models.CharField(max_length=200, blank=True, null=True) # Błąd: ten płodozmian jest za krótki. len_required = models.CharField(max_length=200, blank=True, null=True) # W płodozmianie znajdują się rośliny, które wymagają dłuższego zmianowania. remove_or_add = models.CharField(max_length=200, blank=True, null=True) # Usuń je i wybierz coś innego, lub dodaj więcej roślin. plan_limit_reached = models.TextField(blank=True, null=True) # Osiągnięto limit planów family = models.CharField(max_length=200, blank=True, null=True) # Rodzina species = models.CharField(max_length=200, blank=True, null=True) # Gatunki sources = models.CharField(max_length=200, blank=True, null=True) # Źródła notes = models.CharField(max_length=200, blank=True, null=True) # Uwagi harms = models.CharField(max_length=200, blank=True, null=True) # Szkodzi in_step = models.CharField(max_length=200, blank=True, null=True) # W kroku well_cooperates = models.CharField(max_length=200, blank=True, null=True) # dobrze oddziaływuje na collides = models.CharField(max_length=200, blank=True, null=True) # Powoduje KOLIZJĘ z image_source = models.CharField(max_length=200, blank=True, null=True) # Źródło obrazka add_fertilizer_main = models.CharField(max_length=200, blank=True, null=True) # W tym planie brakuje nawozu z zewnątrz! infl_type = models.CharField(max_length=100, blank=True, null=True) # typ oddziaływania companion = models.CharField(max_length=100, blank=True, null=True) # współrzędna following = models.CharField(max_length=100, blank=True, null=True) # następcza allelopatic = models.CharField(max_length=150, blank=True, null=True) # allelopatyczna, albo współrzędna i nastepcza source_button = models.CharField(max_length=50, blank=True, null=True) # Źródło known_interactions = models.CharField(max_length=200, blank=True, null=True) # Znane interakcje plant_to_other = models.CharField(max_length=200, blank=True, null=True) # Roślina oddziaływuje na inne other_to_plant = models.CharField(max_length=200, blank=True, null=True) # Inne oddziaływują na roślinę family_to_other = models.CharField(max_length=200, blank=True, null=True) # Rodzina oddziaływuje na inne other_to_family = models.CharField(max_length=200, blank=True, null=True) # Inne oddziaływują na rodzinę category_to_other = models.CharField(max_length=200, blank=True, null=True) # Kategoria oddziaływuje na inne other_to_category = models.CharField(max_length=200, blank=True, null=True) # Inne oddziaływują na kategorię annual = models.CharField(max_length=50, blank=True, null=True) # Jare perennial = models.CharField(max_length=50, blank=True, null=True) # Ozime evaluate_button = models.CharField(max_length=50, blank=True, null=True) # Ewaluacja (button) analysis_by_text = models.CharField(max_length=200, blank=True, null=True) # Analizuje plan pod kątem pozytywnych i negatywnych interakcji, oraz błędów. remove_element = models.CharField(max_length=50, blank=True, null=True) # Usuń element (button) add_element = models.CharField(max_length=50, blank=True, null=True) # Dodaj element (button) return_to_plan = models.CharField(max_length=50, blank=True, null=True) # Powrót do planu (button) categories = models.CharField(max_length=50, blank=True, null=True) # Kategorie next_year = models.CharField(max_length=50, blank=True, null=True) # W kolejnym roku second_year = models.CharField(max_length=50, blank=True, null=True) # W drugim roku third_year = models.CharField(max_length=50, blank=True, null=True) # W trzecim roku two_consecutive = models.CharField(max_length=50, blank=True, null=True) # W dwóch kolejnych latach manure_added = models.CharField(max_length=50, blank=True, null=True) # DODANO OBORNIK green_manure_destroyed = models.CharField(max_length=50, blank=True, null=True) # ZNISZCZONO NA ZIELONY NAWÓZ remove_button = models.CharField(max_length=50, blank=True, null=True) # Usuń wait_button = models.CharField(max_length=50, blank=True, null=True) # Obliczam... button second_third = models.CharField(max_length=50, blank=True, null=True) # W drugim i trzecim roku class Meta: verbose_name_plural = 'Rotator Editor Page Names'

68.355422

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5.20681

0.228878

0.208888

0.250666

0.334221

0.648099

0.568903

0.521676

0.482078

0.270404

0

0.032121

0.146735

11,347

165

157

68.769697

0.820801

0.210805

0

0.049296

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false

0.021127

0.007042

0.943662

0

null

1

0

null

0

1

0

4

9c387abdc7101ce84b434e0967e90ea3d11fadd1

120

py

Python

tests/openfl/databases/__init__.py

brandon-edwards/openfl

9e0521252253eab09571dc2be40f46bfeaf9746a

[ "Apache-2.0" ]

297

2021-01-13T08:49:35.000Z

2022-03-31T15:06:43.000Z

tests/openfl/databases/__init__.py

brandon-edwards/openfl

9e0521252253eab09571dc2be40f46bfeaf9746a

[ "Apache-2.0" ]

265

2021-02-02T09:57:33.000Z

2022-03-30T22:51:55.000Z

tests/openfl/databases/__init__.py

brandon-edwards/openfl

9e0521252253eab09571dc2be40f46bfeaf9746a

[ "Apache-2.0" ]

81

2021-01-18T07:52:36.000Z

2022-03-26T18:55:54.000Z

30

43

0.75

16

120

5.625

1

0

0.091743

0.091667

120

3

44

40

0.733945

0.916667

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

4

9c62239602bee7f09d33cb2c02cecbf2bb2ffe6b

170

py

Python

nlu/streamlit/01_dashboard.py

hatrungduc/spark-nlp-workshop

4a4ec0195d1d3d847261df9ef2df7aa5f95bbaec

[ "Apache-2.0" ]

687

2018-09-07T03:45:39.000Z

2022-03-20T17:11:20.000Z

nlu/streamlit/01_dashboard.py

hatrungduc/spark-nlp-workshop

4a4ec0195d1d3d847261df9ef2df7aa5f95bbaec

[ "Apache-2.0" ]

89

2018-09-18T02:04:42.000Z

2022-02-24T18:22:27.000Z

nlu/streamlit/01_dashboard.py

hatrungduc/spark-nlp-workshop

4a4ec0195d1d3d847261df9ef2df7aa5f95bbaec

[ "Apache-2.0" ]

407

2018-09-07T03:45:44.000Z

2022-03-20T05:12:25.000Z

import nlu nlu.enable_streamlit_caching() # Optional caching the models, recommended nlu.load('ner').viz_streamlit(['I love NLU and Streamlit!','I hate buggy software'])

42.5

84

0.776471

25

170

5.16

0.72

0.155039

0

0.1

170

3

85

56.666667

0.843137

0.235294

0

0.382813

0

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true

0

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null

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1

0

1

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9c70db857c23160057be0e2d7842554b41679dd1

192

py

Python

sheet/forms.py

samarth-singh-thakur/finanza

b518d800341ba3ccd2d9e45dd3b309ecfb181289

[ "MIT" ]

null

sheet/forms.py

samarth-singh-thakur/finanza

b518d800341ba3ccd2d9e45dd3b309ecfb181289

[ "MIT" ]

5

2021-03-30T14:08:55.000Z

2021-09-22T19:31:37.000Z

sheet/forms.py

samarth-singh-thakur/finanza

b518d800341ba3ccd2d9e45dd3b309ecfb181289

[ "MIT" ]

null

from django import forms from .models import Ledger class LedgerForm(forms.ModelForm): class Meta: model = Ledger fields = ('lender', 'borrower', 'amount','description',)

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null

0

1

0

1

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4

9c7ec81e80295304adad08f8086e6397eddf8190

231

py

Python

tests/test_linear_stable_convergence.py

dmitry-kabanov/fickettmodel

255b1e9cae1cfb7a6b914ad61a17288d52215cc4

[ "MIT" ]

null

tests/test_linear_stable_convergence.py

dmitry-kabanov/fickettmodel

255b1e9cae1cfb7a6b914ad61a17288d52215cc4

[ "MIT" ]

null

tests/test_linear_stable_convergence.py

dmitry-kabanov/fickettmodel

255b1e9cae1cfb7a6b914ad61a17288d52215cc4

[ "MIT" ]

null

""" Test that linear solver convergence with correct rate of convergence when applied to the problem with stable detonation. """ # class TestLinearStableConvergence: # def test_linear_stable_convergence(self): # pass

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4

13391be17287077c23c254a63f27cb22436b4f20

4,550

py

Python

augur/datasources/ghtorrent/test_ghtorrent_functions.py

parthsharma2/augur

6d59c8c80f3c21eb97bfa4ea4817908ea9a7d10b

[ "MIT" ]

null

augur/datasources/ghtorrent/test_ghtorrent_functions.py

parthsharma2/augur

6d59c8c80f3c21eb97bfa4ea4817908ea9a7d10b

[ "MIT" ]

null

augur/datasources/ghtorrent/test_ghtorrent_functions.py

parthsharma2/augur

6d59c8c80f3c21eb97bfa4ea4817908ea9a7d10b

[ "MIT" ]

null

import os import pytest @pytest.fixture(scope="module") def ghtorrent(): import augur augur_app = augur.Application() return augur_app['ghtorrent']() def test_repoid(ghtorrent): assert ghtorrent.repoid('rails', 'rails') >= 1000 def test_userid(ghtorrent): assert ghtorrent.userid('howderek') >= 1000 """ Pandas testing format assert ghtorrent.<function>('owner', 'repo').isin(['<data that should be in dataframe>']).any The tests check if a value is anywhere in the dataframe """ # *** DIVERSITY AND INCLUSION *** # # *** GROWTH, MATURITY, AND DECLINE *** # def test_closed_issues(ghtorrent): assert ghtorrent.closed_issues('cashmusic', 'platform').isin(["2012-11-09T00:00:00.000Z"]).any def test_code_commits(ghtorrent): assert ghtorrent.code_commits('facebook', 'folly').isin(["2013-01-07"]).any def test_code_review_iteration(ghtorrent): assert ghtorrent.code_review_iteration('apache', 'spark').isin(["2015-05-22T00:00:00.000Z"]).any def test_contribution_acceptance(ghtorrent): assert ghtorrent.contribution_acceptance('rails', 'rails').isin(["2012-05-16T00:00:00.000Z"]).any def test_contributing_github_organizations(ghtorrent): assert ghtorrent.contributing_github_organizations('rails', 'rails').isin(["4066"]).any def test_first_response_to_issue_duration(ghtorrent): assert ghtorrent.first_response_to_issue_duration('AudioKit', 'AudioKit').isin(["13000839"]).any def test_forks(ghtorrent): assert ghtorrent.forks('facebook', 'hiphop-php').isin(["2012-01-08"]).any def test_maintainer_response_to_merge_request_duration(ghtorrent): assert ghtorrent.maintainer_response_to_merge_request_duration('rails', 'rails').isin(["2011-05-10T00:00:00.000Z"]).any def test_new_contributing_github_organizations(ghtorrent): assert ghtorrent.new_contributing_github_organizations('rails', 'rails').isin(["4066"]).any def test_open_issues(ghtorrent): assert ghtorrent.open_issues('mongodb', 'mongo').isin(["2013-01-05"]).any def test_pull_request_comments(ghtorrent): assert ghtorrent.pull_request_comments('rails', 'rails').isin(["2011-11-15T00:00:00.000Z"]).any def test_pull_requests_open(ghtorrent): assert ghtorrent.pull_requests_open('rails', 'rails').isin(["2013-01-09T00:00:00.000Z"]).any def test_pull_requests_closed(ghtorrent): assert ghtorrent.pull_requests_closed('rails', 'rails').isin(["2013-01-09T00:00:00.000Z"]).any def test_pull_request_comment_duration(ghtorrent): assert ghtorrent.pull_request_comment_duration('AudioKit', 'AudioKit').isin(["13000839"]).any # *** RISK *** # # *** VALUE *** # # *** ACTIVITY *** # def test_watchers(ghtorrent): assert ghtorrent.watchers('rails', 'rails').isin(["2017-08-23T00:00:00.000Z"]).any def test_issue_comments(ghtorrent): assert ghtorrent.issue_comments('rails', 'rails').isin(["2009-04-05T00:00:00.000Z"]).any # *** EXPERIMENTAL *** # def test_commits100(ghtorrent): assert ghtorrent.commits100('rails', 'rails').isin(["2017-08-13T00:00:00.000Z"]).any def test_commit_comments(ghtorrent): assert ghtorrent.commit_comments('rails', 'rails').isin(["2008-07-10T00:00:00.000Z"]).any def test_committer_locations(ghtorrent): assert ghtorrent.committer_locations('mavam', 'stat-cookbook').isin(["Berkeley, CA"]).any def test_total_committers(ghtorrent): assert ghtorrent.total_committers('rails', 'rails').isin(["2004-11-24T00:00:00.000Z"]).any def test_total_watchers(ghtorrent): assert ghtorrent.total_watchers('rails', 'rails').isin(["2005-08-26T00:00:00.000Z"]).any def test_issue_activity(ghtorrent): assert ghtorrent.issue_activity('bitcoin', 'bitcoin').isin(["2010-12-20T00:00:00.000Z"]).any def test_pull_acceptance_rate(ghtorrent): assert ghtorrent.pull_request_acceptance_rate('akka', 'akka').isin([0.5]).any # def test_community_age(ghtorrent): # assert ghtorrent.community_age('TEST', 'TEST').isin(["DATE"]).any def test_community_engagement(ghtorrent): assert ghtorrent.community_engagement('rails', 'rails').isin(["2010-09-11T00:00:00.000Z"]).any def test_contributions(ghtorrent): assert ghtorrent.contributions('ariya', 'phantomjs').isin(["ariya"]).any def test_contributors(ghtorrent): assert ghtorrent.contributors('TTimo', 'doom3.gpl').isin(["sergiocampama"]).any def test_project_age(ghtorrent): assert ghtorrent.project_age('rails', 'rails').isin(["2008-04-11T00:00:00.000Z"]).any def test_fakes(ghtorrent): assert ghtorrent.fakes('rails', 'rails').isin(["2008-09-24T00:00:00.000Z"]).any

35

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0.07697

0.065271

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0.092747

4,550

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124

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null

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1

0

null

0

1

0

1

0

1

0

4

134f15445e7302c0e86c87de872ff2ca3ad4311e

134

py

Python

my_code.py

Athenian-Computer-Science/dictionary-notes-assignment-template

435b0c312ba9813d32c166ccea4e1e41ab8f8bcd

[ "Apache-2.0" ]

null

my_code.py

Athenian-Computer-Science/dictionary-notes-assignment-template

435b0c312ba9813d32c166ccea4e1e41ab8f8bcd

[ "Apache-2.0" ]

null

my_code.py

Athenian-Computer-Science/dictionary-notes-assignment-template

435b0c312ba9813d32c166ccea4e1e41ab8f8bcd

[ "Apache-2.0" ]

null

# Use this to take notes on the Edpuzzle video. Try each example rather than just watching it - you will get much more out of it! #

44.666667

129

0.738806

25

134

3.96

0.96

0

0.223881

134

2

130

67

0.951923

0.947761

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0

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null

0

1

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1

0

null

0

1

0

4

1369d4c49e09e1f502eaa09062aedc111a1e000f

85

py

Python

checkov/bicep/checks/param/registry.py

jamesholland-uk/checkov

d73fd4bd7096d48ab3434a92a177bcc55605460a

[ "Apache-2.0" ]

null

checkov/bicep/checks/param/registry.py

jamesholland-uk/checkov

d73fd4bd7096d48ab3434a92a177bcc55605460a

[ "Apache-2.0" ]

null

checkov/bicep/checks/param/registry.py

jamesholland-uk/checkov

d73fd4bd7096d48ab3434a92a177bcc55605460a

[ "Apache-2.0" ]

null

from checkov.bicep.checks.param.base_registry import Registry registry = Registry()

21.25

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0.727273

0.463768

0

0.094118

85

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1

0

1

0

null

0

1

0

4

13913f6d784e5527d7e979b177b5c67feb8ea7ae

33,122

py

Python

src/visualization/MakeVeraReport.py

tsdataclinic/Vera

3d8f6545b689355f2cf3f4c267145e7639a89b23

[ "MIT" ]

4

2020-03-11T20:34:03.000Z

2022-03-15T13:47:00.000Z

src/visualization/MakeVeraReport.py

Smarker/Vera

9a1eec490f970a9bf128c92ef4ff6d0e36ed114d

[ "MIT" ]

7

2020-03-31T11:10:33.000Z

2021-11-18T20:06:56.000Z

src/visualization/MakeVeraReport.py

Smarker/Vera

9a1eec490f970a9bf128c92ef4ff6d0e36ed114d

[ "MIT" ]

4

2020-03-11T20:16:11.000Z

2021-06-03T00:21:16.000Z

%load_ext autoreload %autoreload 2 import sys sys.path.append('/home/vera0519/vera_911') import pandas as pd # import cenpy from slugify import slugify from pathlib import Path import src.features.call_types as call_types from src.cities.new_orleans import NewOrleans from src.cities.seattle import Seattle from src.cities.dallas import Dallas from src.cities.detroit import Detroit from src.cities.charleston import Charleston import matplotlib.pyplot as plt import src.features.geo as Geo from src.features.call_types import load_call_mappings, assign_disposition, process import src.visualization.visualize as vis BASE_MAP_DIR = Path('/home/vera0519/vera_911/reports/VeraExport/Maps') BASE_CHARTS_DIR = Path('/home/vera0519/vera_911/reports/VeraExport/Charts') new_orleans = NewOrleans() dallas = Dallas() seattle = Seattle() detroit = Detroit() charleston = Charleston() new_orleans.clean_data(reload=True) dallas.clean_data(reload=True) detroit.clean_data(reload=True) charleston.clean_data(reload=True); seattle.clean_data(reload=True); ### SUMMARIES def summary_office_initiated(city): return (city.clean_data() .groupby(['year','self_initiated']) .count() .reset_index() .pivot_table(index='year', columns='self_initiated', values='day_of_week') .assign(total=lambda x: x.sum(axis=1)) .assign(percent_other = lambda x: 100*x.No/x.total, pecent_self_initaited = lambda x: 100*x.Yes/x.total) .rename(columns={'No' : 'other', 'Yes': 'self_initiated'})) def summary_by_type(city): return (city.clean_data() .loc[lambda x: x.year.isin([2014,2015,2016,2018,2019])] .groupby(['year','self_initiated']) .count() .reset_index() .pivot_table(index='year', columns='self_initiated', values='day_of_week') .assign(total=lambda x: x.sum(axis=1)) .assign(percent_other = lambda x: 100*x.No/x.total, pecent_self_initaited = lambda x: 100*x.Yes/x.total) .rename(columns={'No' : 'other', 'Yes': 'self_initiated'})) # .assign(percent_other = lambda x: 100*x.No/x.total, # pecent_self_initaited = lambda x: 100*x.Yes/x.total) # .rename(columns={'No' : 'other', 'Yes': 'self_initiated'})) def summary_by_type_disposition(city, percentage=False): return (city.clean_data() .loc[lambda x: x.year.isin([2014,2015,2016,2018,2019])] .rename(columns={'year': 'Year', 'call_type': 'Incident Type','disposition':"Disposition"}) .groupby(['Year','Incident Type','Disposition']) .count()[['index']] .rename(columns= {'index' : 'Frequency'}) .reset_index() .pivot_table(index=['Year','Incident Type'], columns='Disposition', values='Frequency') .fillna(0) .pipe(lambda x: 100* x.div(x.sum(axis=1),axis =0 ) if percentage else x) .rename(columns = lambda x: "Percent {}".format(x) if percentage else "Frequency {}".format(x)) ) def full_summary_by_type_disposition(city): return pd.merge( summary_by_type_disposition(new_orleans, percentage=False), summary_by_type_disposition(new_orleans, percentage=True), left_index=True, right_index=True ) def summary_of_priority(city, percentage = False): return (city.clean_data() .rename(columns= {'year': 'Year', 'priority': 'Priority'}) .groupby(['Year','Priority']) .count() [['index']] .rename(columns={'index': 'Frequency'}) .reset_index() .pivot_table(index='Year', columns='Priority',values='Frequency') .pipe(lambda x: 100* x.div(x.sum(axis=1),axis =0 ) if percentage else x) .fillna(0) .rename(columns = lambda x: "Percent {}".format(x) if percentage else "Frequency {}".format(x)) ) ### MAPS def generate_per_capita_maps(geometry='tract'): plot_dir = BASE_MAP_DIR / "call_volumne_per_capita" plot_dir.mkdir(exist_ok=True) figsize = (10,10) for city in [new_orleans,dallas,detroit,charleston]: tracts = city.load_tracts().to_crs(vis.map_crs) fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.map_call_volume(city, norm_by='capita', ax=ax, scheme='percentiles') tracts.plot(color='none', edgecolor='red', ax=ax) fig.savefig(plot_dir / '{}-{}-tracts-all_years-all_CFS.png'.format(city.BASE_NAME, 'calls_per_capita')) for year in city.USE_YEARS: fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.map_call_volume(city, norm_by='capita', ax=ax, scheme='percentiles',year=year) tracts.plot(facecolor='none', edgecolor='black', ax=ax) ax.set_title('{} / calls per capita / {} / {}'.format(city.BASE_NAME, 'All Years', 'All CFS')) fig.savefig(plot_dir / '{}-{}-tracts-{}-all_CFS.png'.format(city.BASE_NAME, 'calls_per_capita',year)) for year in city.USE_YEARS: fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.map_call_volume(city, norm_by='capita', ax=ax, scheme='percentiles',year=year) ax.set_title('{} / calls per capita / {} / {}'.format(city.BASE_NAME, year, 'All CFS')) tracts.plot(facecolor='none', edgecolor='black', ax=ax) fig.savefig(plot_dir / '{}-{}-tracts-{}-all_CFS.png'.format(city.BASE_NAME, 'calls_per_capita',year)) for cfs in city.clean_data().call_type.unique(): if(cfs != None): fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.map_call_volume(city, norm_by='capita', ax=ax, scheme='percentiles',call_type=cfs) ax.set_title('{} / calls per capita / {} / {}'.format(city.BASE_NAME, 'All Years', cfs)) tracts.plot(facecolor='none', edgecolor='black', ax=ax) fig.savefig(plot_dir / '{}-{}-tracts-all_years-{}.png'.format(city.BASE_NAME, 'calls_per_capita',slugify(cfs))) for year in city.USE_YEARS: for cfs in city.clean_data().call_type.unique(): if(cfs != None): fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.map_call_volume(city, norm_by='capita', ax=ax, scheme='percentiles',year=year,call_type=cfs) ax.set_title('{} / calls per capita / {} / {}'.format(city.BASE_NAME, year, cfs)) tracts.plot(facecolor='none', edgecolor='black', ax=ax) fig.savefig(plot_dir / '{}-{}-tracts-all_years-{}.png'.format(city.BASE_NAME,'calls_per_capita',slugify(cfs))) def generate_self_initiated_fraction_maps(geometry='tract'): plot_dir = BASE_MAP_DIR / "officer_initiated" plot_dir.mkdir(exist_ok=True) figsize = (10,10) for city in [new_orleans,dallas,detroit,charleston]: tracts = city.load_tracts().to_crs(vis.map_crs) fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.map_self_initiated(city, norm_by='total', ax=ax) tracts.plot(color='none', edgecolor='black', ax=ax) fig.savefig(plot_dir / '{}-{}-tracts-all_years-all_CFS.png'.format(city.BASE_NAME, 'officer_initiated_fraction')) for year in city.USE_YEARS: fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.map_self_initiated(city, norm_by='total', ax=ax, year=year) tracts.plot(facecolor='none', edgecolor='black', ax=ax) ax.set_title('{} / officer initiated fraction / {} / {}'.format(city.BASE_NAME, 'All Years', 'All CFS')) fig.savefig(plot_dir / '{}-{}-tracts-{}-all_CFS.png'.format(city.BASE_NAME, 'officer_initiated_fraction',year)) for year in city.USE_YEARS: fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.map_self_initiated(city, norm_by='total', ax=ax,year=year) ax.set_title('{} / officer initiated fraction / {} / {}'.format(city.BASE_NAME, year, 'All CFS')) tracts.plot(facecolor='none', edgecolor='black', ax=ax) fig.savefig(plot_dir / '{}-{}-tracts-{}-all_CFS.png'.format(city.BASE_NAME, 'officer_initiated_fraction',year)) for cfs in city.clean_data().call_type.unique(): if(cfs != None): fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.map_self_initiated(city, norm_by='total', ax=ax,call_type=cfs) ax.set_title('{} / officer initiated fraction / {} / {}'.format(city.BASE_NAME, 'All Years', cfs)) tracts.plot(facecolor='none', edgecolor='black', ax=ax) fig.savefig(plot_dir / '{}-{}-tracts-all_years-{}.png'.format(city.BASE_NAME, 'officer_initiated_fraction',slugify(cfs))) for year in city.USE_YEARS: for cfs in city.clean_data().call_type.unique(): if(cfs != None): fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.map_self_initiated(city, norm_by='total', ax=ax,year=year,call_type=cfs) ax.set_title('{} / officer initiated fraction / {} / {}'.format(city.BASE_NAME, year, cfs)) tracts.plot(facecolor='none', edgecolor='black', ax=ax) fig.savefig(plot_dir / '{}-{}-tracts-all_years-{}.png'.format(city.BASE_NAME,'officer_initiated_fraction',slugify(cfs))) def generate_enforcement_action_maps(geometry='tract'): plot_dir = BASE_MAP_DIR / "enforcement_action" plot_dir.mkdir(exist_ok=True) figsize = (10,10) for city in [new_orleans,dallas,detroit,charleston]: tracts = city.load_tracts().to_crs(vis.map_crs) fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.map_enforcement_by_tract(city, ax=ax) tracts.plot(color='none', edgecolor='black', ax=ax) fig.savefig(plot_dir / '{}-{}-tracts-all_years-all_CFS.png'.format(city.BASE_NAME, 'enforcement_action')) for year in city.USE_YEARS: fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.map_enforcement_by_tract(city, ax=ax, year=year) tracts.plot(facecolor='none', edgecolor='black', ax=ax) ax.set_title('{} / Enforcement Action / {} / {}'.format(city.BASE_NAME, 'All Years', 'All CFS')) fig.savefig(plot_dir / '{}-{}-tracts-{}-all_CFS.png'.format(city.BASE_NAME, 'enforcement_action',year)) for year in city.USE_YEARS: fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.map_enforcement_by_tract(city, ax=ax,year=year) ax.set_title('{} / Enforcement Action / {} / {}'.format(city.BASE_NAME, year, 'All CFS')) tracts.plot(facecolor='none', edgecolor='black', ax=ax) fig.savefig(plot_dir / '{}-{}-tracts-{}-all_CFS.png'.format(city.BASE_NAME, 'enforcement_action',year)) for cfs in city.clean_data().call_type.unique(): if(cfs != None): fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.map_enforcement_by_tract(city, ax=ax,call_type=cfs) ax.set_title('{} / Enforcement Action / {} / {}'.format(city.BASE_NAME, 'All Years', cfs)) tracts.plot(facecolor='none', edgecolor='black', ax=ax) fig.savefig(plot_dir / '{}-{}-tracts-all_years-{}.png'.format(city.BASE_NAME, 'enforcement_action',slugify(cfs))) for year in city.USE_YEARS: for cfs in city.clean_data().call_type.unique(): if(cfs != None): fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.map_enforcement_by_tract(city, ax=ax,year=year,call_type=cfs) ax.set_title('{} / Enforcement Action / {} / {}'.format(city.BASE_NAME, year, cfs)) tracts.plot(facecolor='none', edgecolor='black', ax=ax) fig.savefig(plot_dir / '{}-{}-tracts-all_years-{}.png'.format(city.BASE_NAME,'enforcement_action',slugify(cfs))) def generate_response_time_maps(geometry='tract'): plot_dir = BASE_MAP_DIR / "median_response_time" plot_dir.mkdir(exist_ok=True) figsize = (10,10) for city in [new_orleans,dallas,detroit,charleston]: tracts = city.load_tracts().to_crs(vis.map_crs) fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.map_median_response_time(city, ax=ax) tracts.plot(color='none', edgecolor='black', ax=ax, scheme='percentiles') fig.savefig(plot_dir / '{}-{}-tracts-all_years-all_CFS.png'.format(city.BASE_NAME, 'median_response_time')) for year in city.USE_YEARS: fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.map_median_response_time(city, ax=ax, year=year, scheme='percentiles') tracts.plot(facecolor='none', edgecolor='black', ax=ax) ax.set_title('{} / Median Response Time / {} / {}'.format(city.BASE_NAME, 'All Years', 'All CFS')) fig.savefig(plot_dir / '{}-{}-tracts-{}-all_CFS.png'.format(city.BASE_NAME, 'median_response_time',year)) for year in city.USE_YEARS: fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.map_median_response_time(city, ax=ax,year=year, scheme='percentiles') ax.set_title('{} / Median Response Time / {} / {}'.format(city.BASE_NAME, year, 'All CFS')) tracts.plot(facecolor='none', edgecolor='black', ax=ax) fig.savefig(plot_dir / '{}-{}-tracts-{}-all_CFS.png'.format(city.BASE_NAME, 'median_response_time',year)) for cfs in city.clean_data().call_type.unique(): if(cfs != None): fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.map_median_response_time(city, ax=ax,call_type=cfs, scheme='percentiles') ax.set_title('{} / Median Response Time / {} / {}'.format(city.BASE_NAME, 'All Years', cfs)) tracts.plot(facecolor='none', edgecolor='black', ax=ax) fig.savefig(plot_dir / '{}-{}-tracts-all_years-{}.png'.format(city.BASE_NAME, 'median_response_time',slugify(cfs))) for year in city.USE_YEARS: for cfs in city.clean_data().call_type.unique(): if(cfs != None): fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.map_median_response_time(city, ax=ax,year=year,call_type=cfs, scheme='percentiles') ax.set_title('{} / Median Response Time / {} / {}'.format(city.BASE_NAME, year, cfs)) tracts.plot(facecolor='none', edgecolor='black', ax=ax) fig.savefig(plot_dir / '{}-{}-tracts-all_years-{}.png'.format(city.BASE_NAME,'median_response_time',slugify(cfs))) def generate_disposition_by_CFS(): plot_dir = BASE_CHARTS_DIR / 'disposition_by_CFS' plot_dir.mkdir(exist_ok=True) figsize=(15,10) for city in [new_orleans,dallas,detroit,charleston,seattle]: fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.plot_disposition_fraction_by_call_Type(city, ax=ax) ax.set_xlim(0,1) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) ax.set_title('{} - {}'.format(city.BASE_NAME,'All Years')) plt.tight_layout() fig.savefig(plot_dir / '{}-{}.png'.format(city.BASE_NAME, 'disposition_by_CFS')) for year in city.USE_YEARS: fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.plot_disposition_fraction_by_call_Type(city, ax=ax, year=year) ax.set_xlim(0,1) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) ax.set_title('{} - {}'.format(city.BASE_NAME,year)) plt.tight_layout() fig.savefig(plot_dir / '{}-{}-{}.png'.format(city.BASE_NAME, 'disposition_by_CFS',year)) def self_initiated_by_CFS(): plot_dir = BASE_CHARTS_DIR / 'officer_initiated_by_CFS' plot_dir.mkdir(exist_ok=True) figsize=(15,10) for city in [new_orleans,dallas,detroit,charleston,seattle]: fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.plot_self_initated_by_call_type(city, ax=ax) ax.set_xlim(0,1) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) ax.set_title('{} - {}'.format(city.BASE_NAME,'All Years')) plt.tight_layout() fig.savefig(plot_dir / '{}-{}.png'.format(city.BASE_NAME, 'officer_initiated_by_CFS')) for year in city.USE_YEARS: fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.plot_self_initated_by_call_type(city, ax=ax, year=year) ax.set_xlim(0,1) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) ax.set_title('{} - {}'.format(city.BASE_NAME,year)) plt.tight_layout() fig.savefig(plot_dir / '{}-{}-{}.png'.format(city.BASE_NAME, 'officer_initiated_by_CFS',year)) ### Plots def generate_CFS_breakdown(): plot_dir = BASE_CHARTS_DIR / 'CFS_breakdown' plot_dir.mkdir(exist_ok=True) figsize=(10,10) for city in [new_orleans,dallas,detroit,charleston,seattle]: print('Doint city {}'.format(city.BASE_NAME)) fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.plot_cfs_breakdown(city) fig.savefig(plot_dir / '{}-{}.png'.format(city.BASE_NAME, 'cfs_breakdown')) for year in city.USE_YEARS: fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.plot_cfs_breakdown(city,year=year) fig.savefig(plot_dir / '{}-{}-{}.png'.format(city.BASE_NAME, 'cfs_breakdown',year)) def generate_disposition_by_CFS(): plot_dir = BASE_CHARTS_DIR / 'disposition_by_CFS' plot_dir.mkdir(exist_ok=True) figsize=(15,10) for city in [new_orleans,dallas,detroit,charleston,seattle]: fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.plot_disposition_fraction_by_call_Type(city, ax=ax) ax.set_xlim(0,1) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) ax.set_title('{} - {}'.format(city.BASE_NAME,'All Years')) plt.tight_layout() fig.savefig(plot_dir / '{}-{}.png'.format(city.BASE_NAME, 'disposition_by_CFS')) for year in city.USE_YEARS: fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.plot_disposition_fraction_by_call_Type(city, ax=ax, year=year) ax.set_xlim(0,1) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) ax.set_title('{} - {}'.format(city.BASE_NAME,year)) plt.tight_layout() fig.savefig(plot_dir / '{}-{}-{}.png'.format(city.BASE_NAME, 'disposition_by_CFS',year)) ## Quantile based breakdowns def call_type_breakdown_by_quantile(data, variable, quantile_range, ax=None, subset=None ): try: agg = (data[ (data[variable] > data[variable].quantile(quantile_range[0])) & (data[variable] < data[variable].quantile(quantile_range[1])) ] .groupby('call_type') .count()[['index']] .assign(total = lambda x : x['index'].sum()) .pipe(lambda x: 100*x.div(x.total,axis=0)) ['index'].sort_values() ) return agg.plot(kind='barh',ax=ax) except: ax.text(0.5,0.5,'No data', horizontalalignment='center', verticalalignment='center') ax.set_axis_off() return ax def call_type_breakdown_by_quantile_percent(data,variable,breakdown, quantile_range,ax=None, subset=None): try: ax = (data[ (data[variable] > data[variable].quantile(quantile_range[0])) & (data[variable] < data[variable].quantile(quantile_range[1])) ] .groupby(['call_type', breakdown]) .count()[['index']] .reset_index() .pivot_table(index='call_type', values='index', columns=breakdown) .assign(total = lambda x: x.sum(axis=1)) .sort_values(by='total') .pipe(lambda x: 100*x.div(x.total,axis=0)) .drop('total', axis=1) .plot(kind='barh', stacked=True,ax=ax) # .assign(total = lambda x : x['index'].sum()) # ['index'].sort_values().plot(kind='barh') ) return ax except: ax.text(0.5,0.5,'No data', horizontalalignment='center', verticalalignment='center') ax.set_axis_off() return ax # subset = ['Violent Crime','Suspicion','Drugs','Sex Offenses', 'Domestic Violence', 'Property Crime '] def plot_demographic_breakdown_summary(city,variable,path,year=None): fig, axs= plt.subplots(nrows=3,ncols=2, sharey='row', figsize=(20,15)) data = (city.filter_calls_by(year=year) .pipe(city.assign_demographics)) axs= axs.flatten() call_type_breakdown_by_quantile(data, variable, quantile_range=[0,0.1],ax=axs[0], subset=subset) axs[0].set_xlim(0,8.5) axs[0].set_title('Lowest 10% by median income') axs[0].set_xlabel('') axs[0].set_ylabel('') call_type_breakdown_by_quantile_percent(data, variable, 'self_initiated', [0,0.1],ax=axs[2], subset=subset) axs[2].set_xlim(0,100) axs[2].set_ylabel('% of CFS type by officer initiated') axs[2].set_title('') axs[2].get_legend().remove() call_type_breakdown_by_quantile_percent(data, variable, 'disposition', [0,0.1],ax=axs[4], subset=subset) axs[4].set_xlim(0,100) axs[4].set_ylabel('% of CFS type by disposition') axs[4].set_title('') axs[4].get_legend().remove() call_type_breakdown_by_quantile(data, variable, quantile_range=[0.9,1.0],ax=axs[1], subset=subset) axs[1].set_xlim(0,8.5) axs[1].set_title('Highest 10% by median income') axs[1].set_xlabel('') axs[1].set_ylabel('highest 10% by median income') max_x = max(axs[1].get_xlim()[1], axs[0].get_xlim()[0]) axs[0].set_xlim(0,max_x) axs[1].set_xlim(0,max_x) call_type_breakdown_by_quantile_percent(data, variable, 'self_initiated', [0.9,1.0],ax=axs[3], subset=subset) axs[3].set_xlim(0,100) axs[3].set_ylabel('') axs[3].legend(loc='upper right',title='',bbox_to_anchor=(1.25,1.0), labels=["Other", 'Officer Initiated']) call_type_breakdown_by_quantile_percent(data, variable, 'disposition', [0.9,1.0],ax=axs[5], subset=subset) axs[5].set_xlim(0,100) axs[5].set_ylabel('') axs[5].legend(loc='upper right', title='',bbox_to_anchor=(1.3,1.0),fontsize='x-large') plt.suptitle("{} - {} - {}".format(city.BASE_NAME, variable,year if year else 'All Years')) plt.tight_layout(rect=[0,.98,0,1]) fig.savefig(path / "{}-{}-{}-{}.png".format(city.BASE_NAME,'demographic_quantiles',variable,year if year else 'All Years')) def generate_response_time_by_CFS(): plot_dir = BASE_CHARTS_DIR / 'response_time' plot_dir.mkdir(exist_ok=True) figsize=(15,10) for city in [new_orleans,dallas,detroit,charleston,seattle]: fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.plot_response_time_dist(city, ax=ax) ax.set_title('{} - {}'.format(city.BASE_NAME,'All Years')) fig.savefig(plot_dir / '{}-{}.png'.format(city.BASE_NAME, 'response_time')) for year in city.USE_YEARS: fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.plot_response_time_dist(city, ax=ax, year=year) ax.set_title('{} - {} - {} - {}'.format(city.BASE_NAME,'response_time',year, 'All CFS')) fig.savefig(plot_dir / '{}-{}-{}-{}.png'.format(city.BASE_NAME, 'response_time',year,'All CFS')) for cfs in city.clean_data().call_type.unique(): if(cfs != None): fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.plot_response_time_dist(city, ax=ax, call_type=cfs) ax.set_title('{} - {} - {} - {}'.format(city.BASE_NAME,'response_time','All Years', cfs)) fig.savefig(plot_dir / '{}-{}-{}-{}.png'.format(city.BASE_NAME, 'response_time','all_years',slugify(cfs))) for year in city.USE_YEARS: for cfs in city.clean_data().call_type.unique(): if(cfs != None): fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.plot_response_time_dist(city, ax=ax, year=year) ax.set_title('{} - {} - {} - {}'.format(city.BASE_NAME,'response_time',year, cfs)) fig.savefig(plot_dir / '{}-{}-{}-{}.png'.format(city.BASE_NAME, 'response_time',year,slugify(cfs))) def generate_percentile_comparisons(): for city in [new_orleans,dallas,detroit,charleston]: demos = ['pc_white','pc_black','pc_occupied_homes','median_income','median_rent','percent_income_spent_on_rent' ] out_dir = BASE_CHARTS_DIR / 'demographics_comparison' out_dir.mkdir(exist_ok=True) for demo in demos: plot_demographic_breakdown_summary(new_orleans,demo,out_dir) for year in city.USE_YEARS: plot_demographic_breakdown_summary(new_orleans,demo,out_dir,year=year) def generate_officer_initiated_by_CFS(): plot_dir = BASE_CHARTS_DIR / 'officer_initiated_by_CFS' plot_dir.mkdir(exist_ok=True) figsize=(15,10) for city in [new_orleans,dallas,detroit,charleston,seattle]: fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.plot_self_initated_by_call_type(city, ax=ax) ax.set_xlim(0,1) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) ax.set_title('{} - {}'.format(city.BASE_NAME,'All Years')) plt.tight_layout() fig.savefig(plot_dir / '{}-{}.png'.format(city.BASE_NAME, 'officer_initiated_by_CFS')) for year in city.USE_YEARS: fig = plt.figure(figsize=figsize) ax = fig.add_subplot(111) vis.plot_self_initated_by_call_type(city, ax=ax, year=year) ax.set_xlim(0,1) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) ax.set_title('{} - {}'.format(city.BASE_NAME,year)) plt.tight_layout() fig.savefig(plot_dir / '{}-{}-{}.png'.format(city.BASE_NAME, 'officer_initiated_by_CFS',year)) ## Correlation coefficent plots def make_tract_metrics(city, call_type=None, year=None): col_volume = (city.call_volume_by_tract(norm_by='capita',call_type=call_type, year=year) .dropna() .drop('geometry',axis=1)).rename(columns={'calls':"calls_per_capita_per_year"}) try: enforcement_fraction = city.disposition_by_tract(norm_by='total', call_type=call_type, year=year)[city.ENFORCEMENT_VARIABLES].sum(axis=1) except: enforcement_fraction = np.nan try: officer_initated_fraction = city.self_initated_by_tract(norm_by='total', call_type=call_type, year=year)['Yes'] except: officer_initated_fraction = np.nan return city.assign_demographics(col_volume.assign(enforcement_fraction = enforcement_fraction, officer_initated_fraction=officer_initated_fraction)) def make_corr_plot_for_city(data, city, cut='All',metrics=['calls_per_capita_per_year', 'enforcement_fraction', 'officer_initated_fraction']): demos = ['pc_asian','pc_black','pc_employed', 'pc_hispanic', 'pc_occupied_homes', 'pc_white','median_income', 'median_rent'] corr_data = data.copy().drop(['state','county','tract','geometry'],axis=1) corr_data = corr_data.dropna(axis=1, how='all') corr_data[corr_data <0 ] = None corr_data = corr_data.dropna(how='any',axis=0) result = [] for metric in metrics: if(metric in corr_data.columns): for demo in demos: result.append( { 'c': corr_data[metric].corr(corr_data[demo]), 'city' : city, 'metric': metric,'demo':demo}) return pd.DataFrame(result) def generate_correlation_coefficent_plots(): for call_type in new_orleans.clean_data().call_type.unique(): all_data = pd.DataFrame() for city in [new_orleans,detroit,dallas,charleston]: tract_data = make_tract_metrics(city, call_type=call_type).set_index('GEOID') all_data = all_data.append(make_corr_plot_for_city(tract_data, city.BASE_NAME )) # g= sns.PairGrid(all_data, x_vars = ['c','metric','city'], y_vars=['demo'], height=10, aspect=.25) plt.figure(figsize=(10,5)) demo_names =['% Asian', '% Black', '% Hispanic', '% White', '% Employed', 'Median Income', 'Median rent', '% Occupied homes'] for index, demo in enumerate(['pc_asian','pc_black','pc_hispanic','pc_white','pc_employed', 'median_income', 'median_rent','pc_occupied_homes']): plt.subplot(2,4,index+1 ) ax = sns.stripplot( data=all_data[all_data.demo == demo], x='c',y='metric', hue='city') ax.set_xlim(-1,1) if(index!=0 and index!=4): ax.set_yticklabels([]) else: ax.set_yticklabels(['Calls per capita per year','Enforcement fraction', 'Officer initiated']) plt.axvline(x=0) if(index == 3): plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.) else: ax.get_legend().remove() ax.set_ylabel('') ax.set_xlabel('Correlation coefficent') plt.title(demo_names[index]) plt.tight_layout(rect=[0,0.03,1,0.95]) plt.suptitle(call_type) plt.savefig(plot_dir / slugify(call_type)) if __name__ == "__main__": with pd.ExcelWriter('../../reports/CityPriorityBreakdown.xlsx') as writer: summary_of_priority(new_orleans).to_excel(writer,sheet_name='New Orleans') summary_of_priority(dallas).to_excel(writer,sheet_name='Dallas') summary_of_priority(detroit).to_excel(writer,sheet_name='Detroit') summary_of_priority(seattle).to_excel(writer,sheet_name='Seattle') summary_of_priority(charleston).to_excel(writer,sheet_name='Charleston') with pd.ExcelWriter('../../reports/CityCallTypeDisposition.xlsx') as writer: full_summary_by_type_disposition(new_orleans).to_excel(writer,sheet_name='New Orleans') full_summary_by_type_disposition(dallas).to_excel(writer,sheet_name='Dallas') full_summary_by_type_disposition(detroit).to_excel(writer,sheet_name='Detroit') full_summary_by_type_disposition(charleston).to_excel(writer,sheet_name='Charleston') full_summary_by_type_disposition(seattle).to_excel(writer, sheet_name='Seattle') with pd.ExcelWriter('../../reports/CityOfficerInitiatedSummary.xlsx') as writer: summary_office_initiated(new_orleans).to_excel(writer,sheet_name='New Orleans', merge_cells=False) summary_office_initiated(dallas).to_excel(writer,sheet_name='Dallas') summary_office_initiated(detroit).to_excel(writer,sheet_name='Detroit', merge_cells=False) summary_office_initiated(seattle).to_excel(writer,sheet_name='Seattle') summary_office_initiated(charleston).to_excel(writer,sheet_name='Charleston') with pd.ExcelWriter('../../reports/CityCallTypeSummary.xlsx') as writer: summary_by_type(new_orleans).to_excel(writer,sheet_name='New Orleans') summary_by_type(dallas).to_excel(writer,sheet_name='Dallas') summary_by_type(detroit).to_excel(writer,sheet_name='Detroit') summary_by_type(seattle).to_excel(writer,sheet_name='Seattle') summary_by_type(charleston).to_excel(writer,sheet_name='Chrleston') generate_per_capita_maps(); generate_self_initiated_fraction_maps(); generate_enforcement_action_maps(); generate_response_time_maps(); generate_CFS_breakdown() generate_response_time_by_CFS() generate_disposition_by_CFS() generate_officer_initiated_by_CFS() generate_percentile_comparisons() generate_correlation_coefficent_plots()

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13aac356c77623518d4aea58569f898ef86d3338

440

py

Python

tests/test_exlo_data.py

ovinc/exlo_data

d09290fdabb3cd3503a80891682833e58f9788f0

[ "BSD-3-Clause" ]

null

tests/test_exlo_data.py

ovinc/exlo_data

d09290fdabb3cd3503a80891682833e58f9788f0

[ "BSD-3-Clause" ]

null

tests/test_exlo_data.py

ovinc/exlo_data

d09290fdabb3cd3503a80891682833e58f9788f0

[ "BSD-3-Clause" ]

null

"""Tests for the exlo module.""" # Standard library from pathlib import Path # Non standard import pytest import exlo_data basefolder = Path(exlo_data.__file__).parent def test_exlo(): """Check that all required files are present.""" assert (basefolder / 'config.json').exists() assert (basefolder / 'users.json').exists() assert (basefolder / 'projects.json').exists() assert (basefolder / 'setups.json').exists()

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py

Python

python/anonymous-functions.py

ThePeeps191/code-snippets

f4f6ffa58747433f13e6d512d51c10f1e296c104

[ "MIT" ]

1

2022-01-20T04:20:17.000Z

python/anonymous-functions.py

ThePeeps191/code-snippets

f4f6ffa58747433f13e6d512d51c10f1e296c104

[ "MIT" ]

null

python/anonymous-functions.py

ThePeeps191/code-snippets

f4f6ffa58747433f13e6d512d51c10f1e296c104

[ "MIT" ]

null

def my_function(function): function() print(my_function(lambda : 99))

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248

py

Python

rudalle_paddle/packages/einops/__init__.py

HighCWu/ru-dalle-paddle

742d7002b71a5e166fb4dee854524c7c44d20cf1

[ "Apache-2.0" ]

18

2021-11-22T16:30:07.000Z

2022-03-09T07:59:05.000Z

rudalle_paddle/packages/einops/__init__.py

AgentMaker/ru-dalle-paddle

742d7002b71a5e166fb4dee854524c7c44d20cf1

[ "Apache-2.0" ]

null

rudalle_paddle/packages/einops/__init__.py

AgentMaker/ru-dalle-paddle

742d7002b71a5e166fb4dee854524c7c44d20cf1

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- __author__ = 'Alex Rogozhnikov' __version__ = '0.3.2' from .einops import rearrange, reduce, repeat, parse_shape, asnumpy, EinopsError __all__ = ['rearrange', 'reduce', 'repeat', 'parse_shape', 'asnumpy', 'EinopsError']

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b917cbd30b53e81e1207f00c0f39306426edfa5f

151

py

Python

mpr/middleware.py

root79-glit/medicine-price-registry

8c9b135f4abd5b97b127ace01559c9dbe8b9a303

[ "Apache-2.0" ]

null

mpr/middleware.py

root79-glit/medicine-price-registry

8c9b135f4abd5b97b127ace01559c9dbe8b9a303

[ "Apache-2.0" ]

null

mpr/middleware.py

root79-glit/medicine-price-registry

8c9b135f4abd5b97b127ace01559c9dbe8b9a303

[ "Apache-2.0" ]

null

class CORSMiddleware: def process_response(self, request, response): response['Access-Control-Allow-Origin'] = "*" return response

30.2

53

0.682119

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null

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b919282ce011459272ef8dc02521c9ffb813974d

837

py

Python

tests/python/test_struct_for_intermediate.py

Detavern/taichi

1599050e42e1a1927a54d6e7aced5d158af77340

[ "MIT" ]

1

2020-07-17T08:59:53.000Z

tests/python/test_struct_for_intermediate.py

youyufeng92/taichi

c826de521d254745db556835e322dd2e0cfdbfa0

[ "MIT" ]

null

tests/python/test_struct_for_intermediate.py

youyufeng92/taichi

c826de521d254745db556835e322dd2e0cfdbfa0

[ "MIT" ]

null

import taichi as ti @ti.all_archs def test_nested(): ti.cfg.demote_dense_struct_fors = False x = ti.var(ti.i32) p, q = 3, 7 n, m = 2, 4 @ti.layout def place(): ti.root.dense(ti.ij, (p, q)).dense(ti.ij, (n, m)).place(x) @ti.kernel def iterate(): for i, j in x.parent(): x[i, j] += 1 iterate() for i in range(p): for j in range(q): assert x[i * n, j * m] == 1 @ti.all_archs def test_nested_demote(): ti.cfg.demote_dense_struct_fors = True ti.cfg.print_ir = True x = ti.var(ti.i32) p, q = 3, 7 n, m = 2, 4 @ti.layout def place(): ti.root.dense(ti.ij, (p, q)).dense(ti.ij, (n, m)).place(x) @ti.kernel def iterate(): for i, j in x.parent(): x[i, j] += 1 iterate() for i in range(p): for j in range(q): assert x[i * n, j * m] == 1

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b91db35b07f8308e3ad7a033f352c96b3b4f858f

261

py

Python

end_to_end_tests/custom-templates-golden-record/my_test_api_client/api/tag1/__init__.py

barjomet/openapi-python-client

3d0b96478a81a84468f9f34e70c715a486915108

[ "MIT" ]

null

end_to_end_tests/custom-templates-golden-record/my_test_api_client/api/tag1/__init__.py

barjomet/openapi-python-client

3d0b96478a81a84468f9f34e70c715a486915108

[ "MIT" ]

null

end_to_end_tests/custom-templates-golden-record/my_test_api_client/api/tag1/__init__.py

barjomet/openapi-python-client

3d0b96478a81a84468f9f34e70c715a486915108

[ "MIT" ]

null

""" Contains methods for accessing the API Endpoints """ import types from my_test_api_client.api.tag1 import get_tag_with_number class Tag1Endpoints: @classmethod def get_tag_with_number(cls) -> types.ModuleType: return get_tag_with_number

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null

0

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4

b92f501e3c62c69d79fb298a46427a2f3d82a0d8

54

py

Python

other/run_gui.py

MSLNZ/Mass-Circular-Weighing

f144158b9e2337d7e9446326d6927e1dd606ed38

[ "MIT" ]

1

2020-02-19T09:10:43.000Z

other/run_gui.py

MSLNZ/Mass-Circular-Weighing

f144158b9e2337d7e9446326d6927e1dd606ed38

[ "MIT" ]

null

other/run_gui.py

MSLNZ/Mass-Circular-Weighing

f144158b9e2337d7e9446326d6927e1dd606ed38

[ "MIT" ]

null

import mass_circular_weighing as mcw mcw.show_gui()

10.8

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null

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4

b94a14936a17f35fddac15ee549ab4325e5b8c42

2,546

py

Python

python-百度关键字爬虫/seo_prj/spider/chinaz/chinaz.py

wangchuanli001/Project-experience

b563c5c3afc07c913c2e1fd25dff41c70533f8de

[ "Apache-2.0" ]

12

2019-12-07T01:44:55.000Z

2022-01-27T14:13:30.000Z

python-百度关键字爬虫/seo_prj/spider/chinaz/chinaz.py

hujiese/Project-experience

b563c5c3afc07c913c2e1fd25dff41c70533f8de

[ "Apache-2.0" ]

23

2020-05-23T03:56:33.000Z

2022-02-28T07:54:45.000Z

python-百度关键字爬虫/seo_prj/spider/chinaz/chinaz.py

hujiese/Project-experience

b563c5c3afc07c913c2e1fd25dff41c70533f8de

[ "Apache-2.0" ]

7

2019-12-20T04:48:56.000Z

2021-11-19T02:23:45.000Z

# -*- coding: utf-8 -*- import requests from bs4 import BeautifulSoup import urllib.request import myutils # cnblogs post请求 def getDoc(keyword): url = 'http://tool.chinaz.com/kwevaluate' headers = { 'User-Agent': 'Mozilla/4.0 (compatible; MSIE 5.5; Windows NT)' } values = { 't': 'kwevaluate', 'kw': keyword } data = urllib.parse.urlencode(values).encode('utf-8') request = urllib.request.Request(url, data, headers) html = urllib.request.urlopen(request).read().decode('utf-8') print(html) # get def get(keyword): url = "http://index.baidu.com/api/SearchApi/index?word=" + keyword + "&area=0&days=30" headers = { "Accept": "application / json, text / plain, * / *", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9", "Connection": "keep-alive", "Cookie":"BAIDUID=47B9867DEBA6B652903C4975C010AB3F:FG=1; BIDUPSID=47B9867DEBA6B652903C4975C010AB3F; PSTM=1554868710; BD_UPN=12314753; BDORZ=AE84CDB3A529C0F8A2B9DCDD1D18B695; H_WISE_SIDS=130593_125703_128701_130792_125696_130163_120129_131381_128879_118882_118864_118838_118819_118793_130763_131649_131577_131535_131534_131530_130222_131295_131246_129565_107317_131392_130120_131517_131239_131195_130350_117431_129649_127027_130689_128967_131036_130569_129838_130990_129479_129644_124802_131423_131467_130716_110085_127969_131506_123289_131210_131296_127317_128200_131549_130595_131264_131262_128604_131458_128806; delPer=0; BD_CK_SAM=1; BDUSS=IzekZDay1FUHJJclIxNTVVQVpuZE5-NUtBLXVWdzREdk9SRHkzc2MwSWtWLTVjSVFBQUFBJCQAAAAAAAAAAAEAAACWA4Z6gVfs4buou~DYvAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACTKxlwkysZcRl; bdindexid=g296e3ba116gv1ulcnasoo8gt0; SE_LAUNCH=5%3A25941955; PSINO=5; BD_HOME=1; BDRCVFR[feWj1Vr5u3D]=I67x6TjHwwYf0; H_PS_PSSID=1466_21117_28721_28832_28585_26350_28604_28890; H_PS_645EC=c576SDj%2FVTd6i0UC7abuVnm0w%2Bi370%2F7VhDK9yUMJlg6A%2BE0Vg1mVFzXbHCRTajZ7D%2Bv; sug=3; sugstore=0; ORIGIN=0; bdime=0", "Host": "index.baidu_index.com", "Referer": "http://index.baidu.com/v2/main/index.html", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.103 Safari/537.36", "X-Requested-With": "XMLHttpRequest" } req = requests.get(url=url, headers=headers, timeout=5) html_doc = req.text print(html_doc) if __name__ == '__main__': # for i in range(1, 10): get('php从入门到精通') get('java开发工具') get('matlab好学吗')

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null

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null

0

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b94aa95e2076fae89eeaa325fcf151f5f9b58a88

27

py

Python

data/studio21_generated/introductory/3626/starter_code.py

vijaykumawat256/Prompt-Summarization

614f5911e2acd2933440d909de2b4f86653dc214

[ "Apache-2.0" ]

null

data/studio21_generated/introductory/3626/starter_code.py

vijaykumawat256/Prompt-Summarization

614f5911e2acd2933440d909de2b4f86653dc214

[ "Apache-2.0" ]

null

data/studio21_generated/introductory/3626/starter_code.py

vijaykumawat256/Prompt-Summarization

614f5911e2acd2933440d909de2b4f86653dc214

[ "Apache-2.0" ]

null

def encode(message, key):

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b96b601e7e94d3fb1e8152ca8ba0f0dca2364cfc

358

py

Python

espnet2/text/text_preparation/stress_dictionary/extracting/old/wikipedia_accent_parser/test_wikipedia_accent_parser.py

texpomru13/espnet

7ef005e832e2fb033f356c16f54e0f08762fb4b0

[ "Apache-2.0" ]

null

espnet2/text/text_preparation/stress_dictionary/extracting/old/wikipedia_accent_parser/test_wikipedia_accent_parser.py

texpomru13/espnet

7ef005e832e2fb033f356c16f54e0f08762fb4b0

[ "Apache-2.0" ]

null

espnet2/text/text_preparation/stress_dictionary/extracting/old/wikipedia_accent_parser/test_wikipedia_accent_parser.py

texpomru13/espnet

7ef005e832e2fb033f356c16f54e0f08762fb4b0

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- from wikipedia_accent_parser import WikipediaAccentParser accent_parser = WikipediaAccentParser() assert accent_parser.retrieve_accent(u"Алексей") == u"алексе́й" assert accent_parser.retrieve_accent(u"Андрей") == u"андре́й" assert accent_parser.retrieve_accent(u"Васильев") == u"васи́льев" print("SUCCESS")

25.571429

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1

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null

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1

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4

b96c580f39b020791ae72e5aae37f8ddbce7762b

4,835

py

Python

marspylib/fret/__init__.py

duderstadt-lab/marspylib

f91acb75a78c9a6cfbdd9caa7c5a07b5575f7d85

[ "BSD-2-Clause" ]

1

2022-03-25T07:47:46.000Z

marspylib/fret/__init__.py

duderstadt-lab/marspylib

f91acb75a78c9a6cfbdd9caa7c5a07b5575f7d85

[ "BSD-2-Clause" ]

null

marspylib/fret/__init__.py

duderstadt-lab/marspylib

f91acb75a78c9a6cfbdd9caa7c5a07b5575f7d85

[ "BSD-2-Clause" ]

1

2022-03-25T07:47:49.000Z

import numpy as np ## marspylib.fret def get_T_bleach(molecule, metadata_tag_populations = ['FRET', 'AO', 'DO'], names_bleaching_events = ['Donor_Bleach', 'Acceptor_Bleach']): ''' Function that returns the T_bleach position for a molecule. Requirements archive: an archive should have been initiated prior to running this function. Inputs molecule: the variable 'molecule', representing a single molecule record in the archive, should have been defined prior to running this function. metadata_tag_populations: default ['FRET', 'AO', 'DO'], list with strings denoting the tags present in the archive to tag molecules displaying FRET behavior, that have an acceptor only (AO) or donor only (DO). Note: names have to be entered in the specific order (FRET name, AO name, DO name). names_bleaching_events: default ['Donor_Bleach', 'Acceptor_Bleach'], list with strings denoting the position names of the donor and bleaching events in the archive. Note: names have to be entered in the specific order (Donor bleaching name, Acceptor bleaching name). Outputs T_bleach: the T-position of the bleaching point where either one of the dyes (donor or acceptor) has bleached. Numerical value. @Author: Nadia M. Huisjes ''' if (archive.metadataHasTag(molecule.getMetadataUID(),metadata_tag_populations[0])): if (molecule.hasPosition(names_bleaching_events[1]) & molecule.hasPosition(names_bleaching_events[0])): T_AO_bleach = molecule.getPosition(names_bleaching_events[1]).getPosition() T_DO_bleach = molecule.getPosition(names_bleaching_events[0]).getPosition() if int(T_AO_bleach) > int(T_DO_bleach): T_bleach = int(T_AO_bleach) else: T_bleach = int(T_DO_bleach) # Molecules in an AO dataset elif (archive.metadataHasTag(molecule.getMetadataUID(),metadata_tag_populations[1])): T_bleach = int(molecule.getPosition(names_bleaching_events[1]).getPosition()) # Molecules in a DO dataset elif (archive.metadataHasTag(molecule.getMetadataUID(),metadata_tag_populations[2])): T_bleach = int(molecule.getPosition(names_bleaching_events[0]).getPosition()) else: T_bleach = np.NaN return T_bleach def get_acceptor_donor_bleach_fret(molecule, metadata_tag_fret = 'FRET', names_bleaching_events = ['Donor_Bleach', 'Acceptor_Bleach']): ''' Function that returns the T_bleach position for a molecule. IMPORTANT: both bleaching positions are only retrieved in the case the molecule has a metadata tag representing a FRET molecule. Requirements archive: an archive should have been initiated prior to running this function. Inputs molecule: the variable molecule, representing a single molecule record in the archive, should have been defined prior to running this function. By default set to the name molecule. metadata_tag_fret: default 'FRET', string denoting the tags present in the archive to tag molecules displaying FRET behavior. names_bleaching_events: default ['Donor_Bleach', 'Acceptor_Bleach'], list with strings denoting the position names of the donor and bleaching events in the archive. Note: names have to be entered in the specific order (Donor bleaching name, Acceptor bleaching name). Outputs (tuple with the following three parameters) T_bleach: the T-position of the bleaching point where there first dye has bleached. Numerical value. T_second_bleach:the T-position of the bleaching point where there second dye has bleached. Numerical value. dye: list with one string representing which dye is associated with the defined T_bleach @Author: Nadia M. Huisjes ''' if (archive.metadataHasTag(molecule.getMetadataUID(),metadata_tag_fret)): if (molecule.hasPosition(names_bleaching_events[1]) & molecule.hasPosition(names_bleaching_events[0])): T_AO_bleach = molecule.getPosition(names_bleaching_events[1]).getPosition() T_DO_bleach = molecule.getPosition(names_bleaching_events[0]).getPosition() if int(T_AO_bleach) < int(T_DO_bleach): T_bleach = int(T_AO_bleach) T_second_bleach = int(T_DO_bleach) dye = ['acceptor'] else: T_bleach = int(T_DO_bleach) T_second_bleach = int(T_AO_bleach) dye = ['donor'] else: T_bleach = np.NaN T_second_bleach = np.NaN dye = ['NaN'] return (T_bleach, T_second_bleach, dye)

49.336735

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b987eeb22980c6ec95eb951a7947b24c26cd8830

316

py

Python

runway/core/providers/aws/__init__.py

avosper-intellaegis/runway

757d4e7db269ec16479b044ac82a69f25fa2a450

[ "Apache-2.0" ]

134

2018-02-26T21:35:23.000Z

2022-03-03T00:30:27.000Z

runway/core/providers/aws/__init__.py

asksmruti/runway

8aca76df9372e3d13eb35e12f81758f618e89e74

[ "Apache-2.0" ]

937

2018-03-08T22:04:35.000Z

2022-03-30T12:21:47.000Z

runway/core/providers/aws/__init__.py

asksmruti/runway

8aca76df9372e3d13eb35e12f81758f618e89e74

[ "Apache-2.0" ]

70

2018-02-26T23:48:11.000Z

2022-03-02T18:44:30.000Z

"""Runway AWS objects.""" from . import s3 from ._account import AccountDetails from ._assume_role import AssumeRole from ._response import BaseResponse, ResponseError, ResponseMetadata __all__ = [ "AccountDetails", "AssumeRole", "BaseResponse", "ResponseError", "ResponseMetadata", "s3", ]

21.066667

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null

1

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null

0

1

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4

b98bb1f72ac08ba0054f44d1583e7ea839be2480

37

py

Python

foiamachine/local/lib/python2.7/encodings/cp737.py

dwillis/foiamachine

26d3b02870227696cdaab639c39d47b2a7a42ae5

[ "Unlicense", "MIT" ]

3

2021-08-07T04:01:55.000Z

2021-08-07T05:12:11.000Z

foiamachine/local/lib/python2.7/encodings/cp737.py

dwillis/foiamachine

26d3b02870227696cdaab639c39d47b2a7a42ae5

[ "Unlicense", "MIT" ]

null

foiamachine/local/lib/python2.7/encodings/cp737.py

dwillis/foiamachine

26d3b02870227696cdaab639c39d47b2a7a42ae5

[ "Unlicense", "MIT" ]

1

2021-08-05T22:51:14.000Z

/usr/lib/python2.7/encodings/cp737.py

37

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null

0

1

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4

b9a55a27068247e5dbf3027d80652de300521fc0

7,233

py

Python

tests/sentry/api/serializers/test_team.py

pierredup/sentry

0145e4b3bc0e775bf3482fe65f5e1a689d0dbb80

[ "BSD-3-Clause" ]

null

tests/sentry/api/serializers/test_team.py

pierredup/sentry

0145e4b3bc0e775bf3482fe65f5e1a689d0dbb80

[ "BSD-3-Clause" ]

null

tests/sentry/api/serializers/test_team.py

pierredup/sentry

0145e4b3bc0e775bf3482fe65f5e1a689d0dbb80

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- from __future__ import absolute_import import six from sentry.api.serializers import serialize from sentry.api.serializers.models.team import TeamWithProjectsSerializer from sentry.api.serializers.models.project import ProjectSerializer from sentry.models import InviteStatus from sentry.testutils import TestCase class TeamSerializerTest(TestCase): def test_simple(self): user = self.create_user(username="foo") organization = self.create_organization(owner=user) team = self.create_team(organization=organization) result = serialize(team, user) result.pop("dateCreated") assert result == { "slug": team.slug, "name": team.name, "hasAccess": True, "isPending": False, "isMember": False, "id": six.text_type(team.id), "avatar": {"avatarType": "letter_avatar", "avatarUuid": None}, "memberCount": 0, } def test_member_count(self): user = self.create_user(username="foo") other_user = self.create_user(username="bar") third_user = self.create_user(username="baz") organization = self.create_organization(owner=user) team = self.create_team(organization=organization, members=[user, other_user, third_user]) result = serialize(team, user) assert 3 == result["memberCount"] def test_member_count_does_not_include_invite_requests(self): org = self.create_organization(owner=self.user) team = self.create_team(organization=org) self.create_member(user=self.create_user(), organization=org, teams=[team]) # member self.create_member(email="1@example.com", organization=org, teams=[team]) # pending invite result = serialize(team, self.user) assert result["memberCount"] == 2 # invite requests self.create_member( email="2@example.com", organization=org, invite_status=InviteStatus.REQUESTED_TO_BE_INVITED.value, teams=[team], ) self.create_member( email="3@gmail.com", organization=org, invite_status=InviteStatus.REQUESTED_TO_JOIN.value, teams=[team], ) result = serialize(team, self.user) assert result["memberCount"] == 2 def test_member_access(self): user = self.create_user(username="foo") organization = self.create_organization() self.create_member(user=user, organization=organization) team = self.create_team(organization=organization) result = serialize(team, user) result.pop("dateCreated") assert result["hasAccess"] is True assert result["isMember"] is False organization.flags.allow_joinleave = False organization.save() result = serialize(team, user) # after changing to allow_joinleave=False assert result["hasAccess"] is False assert result["isMember"] is False self.create_team_membership(user=user, team=team) result = serialize(team, user) # after giving them access to team assert result["hasAccess"] is True assert result["isMember"] is True def test_admin_access(self): user = self.create_user(username="foo") organization = self.create_organization() self.create_member(user=user, organization=organization, role="admin") team = self.create_team(organization=organization) result = serialize(team, user) result.pop("dateCreated") assert result["hasAccess"] is True assert result["isMember"] is False organization.flags.allow_joinleave = False organization.save() result = serialize(team, user) # after changing to allow_joinleave=False assert result["hasAccess"] is False assert result["isMember"] is False self.create_team_membership(user=user, team=team) result = serialize(team, user) # after giving them access to team assert result["hasAccess"] is True assert result["isMember"] is True def test_manager_access(self): user = self.create_user(username="foo") organization = self.create_organization() self.create_member(user=user, organization=organization, role="manager") team = self.create_team(organization=organization) result = serialize(team, user) result.pop("dateCreated") assert result["hasAccess"] is True assert result["isMember"] is False organization.flags.allow_joinleave = False organization.save() result = serialize(team, user) # after changing to allow_joinleave=False assert result["hasAccess"] is True assert result["isMember"] is False self.create_team_membership(user=user, team=team) result = serialize(team, user) # after giving them access to team assert result["hasAccess"] is True assert result["isMember"] is True def test_owner_access(self): user = self.create_user(username="foo") organization = self.create_organization() self.create_member(user=user, organization=organization, role="owner") team = self.create_team(organization=organization) result = serialize(team, user) result.pop("dateCreated") assert result["hasAccess"] is True assert result["isMember"] is False organization.flags.allow_joinleave = False organization.save() result = serialize(team, user) # after changing to allow_joinleave=False assert result["hasAccess"] is True assert result["isMember"] is False self.create_team_membership(user=user, team=team) result = serialize(team, user) # after giving them access to team assert result["hasAccess"] is True assert result["isMember"] is True class TeamWithProjectsSerializerTest(TestCase): def test_simple(self, project_serializer=None): user = self.create_user(username="foo") organization = self.create_organization(owner=user) team = self.create_team(organization=organization) project = self.create_project(teams=[team], organization=organization, name="foo") project2 = self.create_project(teams=[team], organization=organization, name="bar") result = serialize(team, user, TeamWithProjectsSerializer()) serialized_projects = serialize([project2, project], user, project_serializer) assert result == { "slug": team.slug, "name": team.name, "hasAccess": True, "isPending": False, "isMember": False, "id": six.text_type(team.id), "projects": serialized_projects, "avatar": {"avatarType": "letter_avatar", "avatarUuid": None}, "memberCount": 0, "dateCreated": team.date_added, } def test_with_performance_flag(self): with self.feature("organizations:enterprise-perf"): self.test_simple(ProjectSerializer(include_features=False))

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b9c00b86343b58893eccec0356f47cb6aa23aafa

89

py

Python

bmwcd/__init__.py

gerard33/bmwcd

02f08f8e8f0e98a406102f390ecf18879549d703

[ "MIT" ]

6

2018-01-20T10:47:36.000Z

2022-03-08T09:48:49.000Z

bmwcd/__init__.py

gerard33/bmwcd

02f08f8e8f0e98a406102f390ecf18879549d703

[ "MIT" ]

1

2019-02-18T14:56:58.000Z

2019-02-25T09:04:44.000Z

bmwcd/__init__.py

gerard33/bmwcd

02f08f8e8f0e98a406102f390ecf18879549d703

[ "MIT" ]

6

2018-01-20T08:58:24.000Z

2021-03-21T18:49:14.000Z

""" Simple BMW ConnectedDrive API. init file for backward compatibility """ # empty

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19,370

py

Python

tests/unittests/test_dispatcher.py

anandagopal6/azure-functions-python-worker

e4adb351e5454c093fcefbf0fb84f200af32f386

[ "MIT" ]

null

tests/unittests/test_dispatcher.py

anandagopal6/azure-functions-python-worker

e4adb351e5454c093fcefbf0fb84f200af32f386

[ "MIT" ]

null

tests/unittests/test_dispatcher.py

anandagopal6/azure-functions-python-worker

e4adb351e5454c093fcefbf0fb84f200af32f386

[ "MIT" ]

null

# Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. import collections as col import os import sys import unittest from typing import Optional, Tuple from unittest.mock import patch from azure_functions_worker import protos from azure_functions_worker import testutils from azure_functions_worker.constants import PYTHON_THREADPOOL_THREAD_COUNT, \ PYTHON_THREADPOOL_THREAD_COUNT_DEFAULT SysVersionInfo = col.namedtuple("VersionInfo", ["major", "minor", "micro", "releaselevel", "serial"]) DISPATCHER_FUNCTIONS_DIR = testutils.UNIT_TESTS_FOLDER / 'dispatcher_functions' class TestThreadPoolSettingsPython37(testutils.AsyncTestCase): """Base test class for testing thread pool settings for sync threadpool worker count. This class specifically sets sys.version_info to return as Python 3.7 and extended classes change this value and other platform specific values to test the behavior across the different python versions. - Why not python 3.6? - In Azure.Functions (library), the typing_inspect module imports specific modules which are not available on systems where Python 3.7+ is installed. Ref: NEW_TYPING = sys.version_info[:3] >= (3, 7, 0) # PEP 560 """ def setUp(self): self._ctrl = testutils.start_mockhost( script_root=DISPATCHER_FUNCTIONS_DIR) self._default_workers: Optional[ int] = PYTHON_THREADPOOL_THREAD_COUNT_DEFAULT self._pre_env = dict(os.environ) self.mock_version_info = patch( 'azure_functions_worker.dispatcher.sys.version_info', SysVersionInfo(3, 7, 0, 'final', 0)) self.mock_version_info.start() def tearDown(self): os.environ.clear() os.environ.update(self._pre_env) self.mock_version_info.stop() async def test_dispatcher_initialize_worker(self): """Test if the dispatcher can be initialized worker successfully """ async with self._ctrl as host: r = await host.init_worker('3.0.12345') self.assertIsInstance(r.response, protos.WorkerInitResponse) async def test_dispatcher_initialize_worker_logging(self): """Test if the dispatcher's log can be flushed out during worker initialization """ async with self._ctrl as host: r = await host.init_worker('3.0.12345') self.assertEqual( len([l for l in r.logs if l.message.startswith( 'Received WorkerInitRequest' )]), 1 ) async def test_dispatcher_send_worker_request(self): """Test if the worker status response will be sent correctly when a worker status request is received """ async with self._ctrl as host: r = await host.get_worker_status() self.assertIsInstance(r.response, protos.WorkerStatusResponse) async def test_dispatcher_sync_threadpool_default_worker(self): """Test if the sync threadpool has maximum worker count set the correct default value """ async with self._ctrl as host: # await self._check_if_function_is_ok(host) await self._assert_workers_threadpool(self._ctrl, host, self._default_workers) async def test_dispatcher_sync_threadpool_set_worker(self): """Test if the sync threadpool maximum worker can be set """ # Configure thread pool max worker os.environ.update({PYTHON_THREADPOOL_THREAD_COUNT: '5'}) async with self._ctrl as host: await self._check_if_function_is_ok(host) await self._assert_workers_threadpool(self._ctrl, host, 5) async def test_dispatcher_sync_threadpool_invalid_worker_count(self): """Test when sync threadpool maximum worker is set to an invalid value, the host should fallback to default value """ # The @patch decorator does not work as expected and will suppress # any assertion failures in the async test cases. # Thus we're moving the patch() method to use the with syntax with patch('azure_functions_worker.dispatcher.logger') as mock_logger: # Configure thread pool max worker to an invalid value os.environ.update({PYTHON_THREADPOOL_THREAD_COUNT: 'invalid'}) async with self._ctrl as host: await self._check_if_function_is_ok(host) await self._assert_workers_threadpool(self._ctrl, host, self._default_workers) mock_logger.warning.assert_any_call( f'{PYTHON_THREADPOOL_THREAD_COUNT} must be an integer') async def test_dispatcher_sync_threadpool_below_min_setting(self): """Test if the sync threadpool will pick up default value when the setting is below minimum """ with patch('azure_functions_worker.dispatcher.logger') as mock_logger: # Configure thread pool max worker to an invalid value os.environ.update({PYTHON_THREADPOOL_THREAD_COUNT: '0'}) async with self._ctrl as host: await self._check_if_function_is_ok(host) await self._assert_workers_threadpool(self._ctrl, host, self._default_workers) mock_logger.warning.assert_any_call( f'{PYTHON_THREADPOOL_THREAD_COUNT} must be set to a value ' 'between 1 and 32. Reverting to default value for max_workers') async def test_dispatcher_sync_threadpool_exceed_max_setting(self): """Test if the sync threadpool will pick up default value when the setting is above maximum """ with patch('azure_functions_worker.dispatcher.logger') as mock_logger: # Configure thread pool max worker to an invalid value os.environ.update({PYTHON_THREADPOOL_THREAD_COUNT: '33'}) async with self._ctrl as host: await self._check_if_function_is_ok(host) # Ensure the dispatcher sync threadpool should fallback to 1 await self._assert_workers_threadpool(self._ctrl, host, self._default_workers) mock_logger.warning.assert_any_call( f'{PYTHON_THREADPOOL_THREAD_COUNT} must be set to a value ' 'between 1 and 32. ' 'Reverting to default value for max_workers') async def test_dispatcher_sync_threadpool_in_placeholder(self): """Test if the sync threadpool will pick up app setting in placeholder mode (Linux Consumption) """ async with self._ctrl as host: await self._check_if_function_is_ok(host) # Reload environment variable on specialization await host.reload_environment(environment={ PYTHON_THREADPOOL_THREAD_COUNT: '3' }) # Ensure the dispatcher sync threadpool should fallback to 1 await self._assert_workers_threadpool(self._ctrl, host, 3) async def test_dispatcher_sync_threadpool_in_placeholder_invalid(self): """Test if the sync threadpool will use the default setting when the app setting is invalid """ with patch('azure_functions_worker.dispatcher.logger') as mock_logger: async with self._ctrl as host: await self._check_if_function_is_ok(host) # Reload environment variable on specialization await host.reload_environment(environment={ PYTHON_THREADPOOL_THREAD_COUNT: 'invalid' }) await self._assert_workers_threadpool(self._ctrl, host, self._default_workers) # Check warning message mock_logger.warning.assert_any_call( f'{PYTHON_THREADPOOL_THREAD_COUNT} must be an integer') async def test_dispatcher_sync_threadpool_in_placeholder_above_max(self): """Test if the sync threadpool will use the default setting when the app setting is above maximum """ with patch('azure_functions_worker.dispatcher.logger') as mock_logger: async with self._ctrl as host: await self._check_if_function_is_ok(host) # Reload environment variable on specialization await host.reload_environment(environment={ PYTHON_THREADPOOL_THREAD_COUNT: '33' }) await self._assert_workers_threadpool(self._ctrl, host, self._default_workers) mock_logger.warning.assert_any_call( f'{PYTHON_THREADPOOL_THREAD_COUNT} must be set to a ' f'value ' 'between 1 and 32. ' 'Reverting to default value for max_workers') async def test_dispatcher_sync_threadpool_in_placeholder_below_min(self): """Test if the sync threadpool will use the default setting when the app setting is below minimum """ with patch('azure_functions_worker.dispatcher.logger') as mock_logger: async with self._ctrl as host: await self._check_if_function_is_ok(host) # Reload environment variable on specialization await host.reload_environment(environment={ PYTHON_THREADPOOL_THREAD_COUNT: '0' }) await self._assert_workers_threadpool(self._ctrl, host, self._default_workers) mock_logger.warning.assert_any_call( f'{PYTHON_THREADPOOL_THREAD_COUNT} must be set to a ' f'value ' 'between 1 and 32. ' 'Reverting to default value for max_workers') async def test_sync_invocation_request_log(self): with patch('azure_functions_worker.dispatcher.logger') as mock_logger: async with self._ctrl as host: request_id: str = self._ctrl._worker._request_id func_id, invoke_id, func_name = ( await self._check_if_function_is_ok(host) ) mock_logger.info.assert_any_call( 'Received FunctionInvocationRequest, ' f'request ID: {request_id}, ' f'function ID: {func_id}, ' f'function name: {func_name}, ' f'invocation ID: {invoke_id}, ' 'function type: sync, ' f'sync threadpool max workers: {self._default_workers}' ) async def test_async_invocation_request_log(self): with patch('azure_functions_worker.dispatcher.logger') as mock_logger: async with self._ctrl as host: request_id: str = self._ctrl._worker._request_id func_id, invoke_id, func_name = ( await self._check_if_async_function_is_ok(host) ) mock_logger.info.assert_any_call( 'Received FunctionInvocationRequest, ' f'request ID: {request_id}, ' f'function ID: {func_id}, ' f'function name: {func_name}, ' f'invocation ID: {invoke_id}, ' 'function type: async' ) async def test_sync_invocation_request_log_threads(self): os.environ.update({PYTHON_THREADPOOL_THREAD_COUNT: '5'}) with patch('azure_functions_worker.dispatcher.logger') as mock_logger: async with self._ctrl as host: request_id: str = self._ctrl._worker._request_id func_id, invoke_id, func_name = ( await self._check_if_function_is_ok(host) ) mock_logger.info.assert_any_call( 'Received FunctionInvocationRequest, ' f'request ID: {request_id}, ' f'function ID: {func_id}, ' f'function name: {func_name}, ' f'invocation ID: {invoke_id}, ' 'function type: sync, ' 'sync threadpool max workers: 5' ) async def test_async_invocation_request_log_threads(self): os.environ.update({PYTHON_THREADPOOL_THREAD_COUNT: '4'}) with patch('azure_functions_worker.dispatcher.logger') as mock_logger: async with self._ctrl as host: request_id: str = self._ctrl._worker._request_id func_id, invoke_id, func_name = ( await self._check_if_async_function_is_ok(host) ) mock_logger.info.assert_any_call( 'Received FunctionInvocationRequest, ' f'request ID: {request_id}, ' f'function ID: {func_id}, ' f'function name: {func_name}, ' f'invocation ID: {invoke_id}, ' 'function type: async' ) async def test_sync_invocation_request_log_in_placeholder_threads(self): with patch('azure_functions_worker.dispatcher.logger') as mock_logger: async with self._ctrl as host: await host.reload_environment(environment={ PYTHON_THREADPOOL_THREAD_COUNT: '5' }) request_id: str = self._ctrl._worker._request_id func_id, invoke_id, func_name = ( await self._check_if_function_is_ok(host) ) mock_logger.info.assert_any_call( 'Received FunctionInvocationRequest, ' f'request ID: {request_id}, ' f'function ID: {func_id}, ' f'function name: {func_name}, ' f'invocation ID: {invoke_id}, ' 'function type: sync, ' 'sync threadpool max workers: 5' ) async def test_async_invocation_request_log_in_placeholder_threads(self): with patch('azure_functions_worker.dispatcher.logger') as mock_logger: async with self._ctrl as host: await host.reload_environment(environment={ PYTHON_THREADPOOL_THREAD_COUNT: '5' }) request_id: str = self._ctrl._worker._request_id func_id, invoke_id, func_name = ( await self._check_if_async_function_is_ok(host) ) mock_logger.info.assert_any_call( 'Received FunctionInvocationRequest, ' f'request ID: {request_id}, ' f'function ID: {func_id}, ' f'function name: {func_name}, ' f'invocation ID: {invoke_id}, ' 'function type: async' ) async def _assert_workers_threadpool(self, ctrl, host, expected_worker_count): self.assertIsNotNone(ctrl._worker._sync_call_tp) self.assertEqual(ctrl._worker.get_sync_tp_workers_set(), expected_worker_count) # Check if the dispatcher still function await self._check_if_function_is_ok(host) async def _check_if_function_is_ok(self, host) -> Tuple[str, str]: # Ensure the function can be properly loaded function_name = "show_context" func_id, load_r = await host.load_function(function_name) self.assertEqual(load_r.response.function_id, func_id) self.assertEqual(load_r.response.result.status, protos.StatusResult.Success) # Ensure the function can be properly invoked invoke_id, call_r = await host.invoke_function( 'show_context', [ protos.ParameterBinding( name='req', data=protos.TypedData( http=protos.RpcHttp( method='GET' ) ) ) ]) self.assertIsNotNone(invoke_id) self.assertEqual(call_r.response.result.status, protos.StatusResult.Success) return func_id, invoke_id, function_name async def _check_if_async_function_is_ok(self, host) -> Tuple[str, str]: # Ensure the function can be properly loaded function_name = "show_context_async" func_id, load_r = await host.load_function('show_context_async') self.assertEqual(load_r.response.function_id, func_id) self.assertEqual(load_r.response.result.status, protos.StatusResult.Success) # Ensure the function can be properly invoked invoke_id, call_r = await host.invoke_function( 'show_context_async', [ protos.ParameterBinding( name='req', data=protos.TypedData( http=protos.RpcHttp( method='GET' ) ) ) ]) self.assertIsNotNone(invoke_id) self.assertEqual(call_r.response.result.status, protos.StatusResult.Success) return func_id, invoke_id, function_name class TestThreadPoolSettingsPython38(TestThreadPoolSettingsPython37): def setUp(self): super(TestThreadPoolSettingsPython38, self).setUp() self.mock_version_info = patch( 'azure_functions_worker.dispatcher.sys.version_info', SysVersionInfo(3, 8, 0, 'final', 0)) self.mock_version_info.start() def tearDown(self): os.environ.clear() os.environ.update(self._pre_env) self.mock_version_info.stop() @unittest.skipIf(sys.version_info.minor != 9, "Run the tests only for Python 3.9. In other platforms, " "as the default passed is None, the cpu_count determines the " "number of max_workers and we cannot mock the os.cpu_count() " "in the concurrent.futures.ThreadPoolExecutor") class TestThreadPoolSettingsPython39(TestThreadPoolSettingsPython37): def setUp(self): super(TestThreadPoolSettingsPython39, self).setUp() self.mock_os_cpu = patch( 'os.cpu_count', return_value=2) self.mock_os_cpu.start() # 6 - based on 2 cores - min(32, (os.cpu_count() or 1) + 4) - 2 + 4 self._default_workers: Optional[int] = 6 self.mock_version_info = patch( 'azure_functions_worker.dispatcher.sys.version_info', SysVersionInfo(3, 9, 0, 'final', 0)) self.mock_version_info.start() def tearDown(self): os.environ.clear() os.environ.update(self._pre_env) self.mock_os_cpu.stop() self.mock_version_info.stop()

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null

0

4

b9dd4b9e4a087ece290af4499362873c2216c2ee

169

wsgi

Python

contrib/apache/api.wsgi

ilcic/alerta

b0fd34df0d7574418a9760202538f3aa594d4fc7

[ "Apache-2.0" ]

null

contrib/apache/api.wsgi

ilcic/alerta

b0fd34df0d7574418a9760202538f3aa594d4fc7

[ "Apache-2.0" ]

null

contrib/apache/api.wsgi

ilcic/alerta

b0fd34df0d7574418a9760202538f3aa594d4fc7

[ "Apache-2.0" ]

null

#!/usr/bin/env python activate_this = '/opt/alerta/bin/activate_this.py' execfile(activate_this, dict(__file__=activate_this)) from alerta.app import app as application

33.8

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0.804734

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169

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4

b9e34793759fae172d38005724d8074461e7c948

86

py

Python

test/test_remove_duplicates_from_sorted_list_ii.py

spencercjh/sync-leetcode-today-problem-python3-example

4957e5eadb697334741df0fc297bec2edaa9e2ab

[ "Apache-2.0" ]

null

test/test_remove_duplicates_from_sorted_list_ii.py

spencercjh/sync-leetcode-today-problem-python3-example

4957e5eadb697334741df0fc297bec2edaa9e2ab

[ "Apache-2.0" ]

null

test/test_remove_duplicates_from_sorted_list_ii.py

spencercjh/sync-leetcode-today-problem-python3-example

4957e5eadb697334741df0fc297bec2edaa9e2ab

[ "Apache-2.0" ]

null

solution = RemoveDuplicatesFromSortedListIi() assert X == solution.deleteDuplicates( )

43

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b9e59edf503f60d47e2a6b994fecc3b6c5ff8c48

218

py

Python

pystributor/pystributor.py

hirsimaki-markus/pystributor

14d546ab21e48bd3ef3cbe6198f14d5ad393e8d9

[ "Unlicense" ]

null

pystributor/pystributor.py

hirsimaki-markus/pystributor

14d546ab21e48bd3ef3cbe6198f14d5ad393e8d9

[ "Unlicense" ]

null

pystributor/pystributor.py

hirsimaki-markus/pystributor

14d546ab21e48bd3ef3cbe6198f14d5ad393e8d9

[ "Unlicense" ]

null

#!/usr/bin/python3 """ Import wrapper for pystributor worker and hub """ from pystributor.pystributor_hub import Hub as _Hub from pystributor.pystributor_worker import Worker as _Worker Hub = _Hub Worker = _Worker

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18.166667

0.88172

0.288991

0

1

0

false

0

0.5

0

0.5

0

null

1

0

null

0

1

0

4

b9f14c2386069ffd6fbc60c668e183fb991e62e7

155

py

Python

11_tuple.py

AmreshTripathy/Python

e86420fef7f52da393be5b50ac2f13bddfeb3306

[ "Apache-2.0" ]

4

2021-05-27T05:06:09.000Z

2021-06-12T17:12:47.000Z

11_tuple.py

AmreshTripathy/Python

e86420fef7f52da393be5b50ac2f13bddfeb3306

[ "Apache-2.0" ]

null

11_tuple.py

AmreshTripathy/Python

e86420fef7f52da393be5b50ac2f13bddfeb3306

[ "Apache-2.0" ]

null

t = ( 10, 11, 12, 34, 99, 4, 98) print (t[0]) t1 = (1, 1, 1, 2, 3, 4, 65, 65, 3, 2) #tuple with single element print (t1.count(1)) print (t1.index(65))

31

65

0.541935

34

155

2.470588

0.617647

0.047619

0

0.266667

0.225806

155

5

66

31

0.433333

0.16129

0

1

0

false

0

0.6

0

null

0

1

0

1

0

1

0

null

0

1

0

4

6a048e71920ac9364887d7d202a65d42c7152f53

95

py

Python

tests/impl/mocks/not_an_impl.py

bcurnow/rfid-reader

0753b3f8517fecbcaebfe29c660f8e0d6d76fc8e

[ "Apache-2.0" ]

null

tests/impl/mocks/not_an_impl.py

bcurnow/rfid-reader

0753b3f8517fecbcaebfe29c660f8e0d6d76fc8e

[ "Apache-2.0" ]

1

2021-11-05T12:29:39.000Z

2021-11-05T15:37:03.000Z

tests/impl/mocks/not_an_impl.py

bcurnow/rfid-reader

0753b3f8517fecbcaebfe29c660f8e0d6d76fc8e

[ "Apache-2.0" ]

null

""" This module has no register method and should be skipped by the register_readers logic."""

47.5

94

0.768421

15

95

4.8

0.933333

0

0.157895

95

1

95

0.9

0.915789

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

4

6a1cb8bd410126018754807baff0562a38f951c5

348

py

Python

checkov/terraform/checks/resource/openstack/SecurityGroupUnrestrictedIngress3389.py

jamesholland-uk/checkov

d73fd4bd7096d48ab3434a92a177bcc55605460a

[ "Apache-2.0" ]

1

2021-02-13T15:24:42.000Z

checkov/terraform/checks/resource/openstack/SecurityGroupUnrestrictedIngress3389.py

jamesholland-uk/checkov

d73fd4bd7096d48ab3434a92a177bcc55605460a

[ "Apache-2.0" ]

7

2021-04-12T06:54:07.000Z

2022-03-21T14:04:14.000Z

checkov/terraform/checks/resource/openstack/SecurityGroupUnrestrictedIngress3389.py

jamesholland-uk/checkov

d73fd4bd7096d48ab3434a92a177bcc55605460a

[ "Apache-2.0" ]

1

2021-12-16T03:09:55.000Z

from checkov.terraform.checks.resource.openstack.AbsSecurityGroupUnrestrictedIngress import AbsSecurityGroupUnrestrictedIngress class SecurityGroupUnrestrictedIngress3389(AbsSecurityGroupUnrestrictedIngress): def __init__(self): super().__init__(check_id="CKV_OPENSTACK_3", port=3389) check = SecurityGroupUnrestrictedIngress3389()

34.8

127

0.847701

26

348

10.923077

0.769231

0

0.040752

0.083333

348

9

128

38.666667

0.84953

0

0.043103

0

1

0.2

false

0

0.2

0

0.6

0

1

0

1

null

0

1

0

null

0

1

0

4

6a4469fed3e76fd25ea3a810b176b26034bfa2d5

70

py

Python

reprexlite/__main__.py

jayqi/reprexlite

efe0fa3190bd77ba8d47be6995cd9a0d040d36d4

[ "MIT" ]

6

2021-02-15T11:33:05.000Z

2021-05-31T04:14:18.000Z

reprexlite/__main__.py

jayqi/reprexlite

efe0fa3190bd77ba8d47be6995cd9a0d040d36d4

[ "MIT" ]

51

2021-02-15T21:06:51.000Z

2022-03-31T15:11:21.000Z

reprexlite/__main__.py

jayqi/reprexlite

efe0fa3190bd77ba8d47be6995cd9a0d040d36d4

[ "MIT" ]

null

from reprexlite.cli import app app(prog_name="python -m reprexlite")

17.5

37

0.785714

11

70

4.909091

0.818182

0

0.114286

70

3

38

23.333333

0.870968

0

0.285714

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

4

dbf3792b341b234f73145ccb386c4360a38be852

320

py

Python

src/yellowdog_client/model/compute_requirement_template_summary.py

yellowdog/yellowdog-sdk-python-public

da69a7d6e45c92933e34fefcaef8b5d98dcd6036

[ "Apache-2.0" ]

null

src/yellowdog_client/model/compute_requirement_template_summary.py

yellowdog/yellowdog-sdk-python-public

da69a7d6e45c92933e34fefcaef8b5d98dcd6036

[ "Apache-2.0" ]

null

src/yellowdog_client/model/compute_requirement_template_summary.py

yellowdog/yellowdog-sdk-python-public

da69a7d6e45c92933e34fefcaef8b5d98dcd6036

[ "Apache-2.0" ]

null

from dataclasses import dataclass from typing import Optional @dataclass class ComputeRequirementTemplateSummary: id: Optional[str] = None name: Optional[str] = None namespace: Optional[str] = None description: Optional[str] = None strategyType: Optional[str] = None type: Optional[str] = None

24.615385

40

0.728125

35

320

6.657143

0.457143

0.283262

0.386266

0

0.190625

320

12

41

26.666667

0.899614

0

1

0

true

0

0.2

0

0.9

0

null

1

0

null

0

1

0

1

0

4

dbf585ebc64a22803f1c02b402a2fde5af32de27

108

py

Python

django_trumbo/apps.py

sasriawesome/django_trumbo

28372409837a9e97158428e3beb1ed1c74e8860c

[ "MIT" ]

null

django_trumbo/apps.py

sasriawesome/django_trumbo

28372409837a9e97158428e3beb1ed1c74e8860c

[ "MIT" ]

null

django_trumbo/apps.py

sasriawesome/django_trumbo

28372409837a9e97158428e3beb1ed1c74e8860c

[ "MIT" ]

null

from django.apps import AppConfig as AppConfigBase class AppConfig(AppConfigBase): name='django_trumbo'

27

50

0.814815

13

108

6.692308

0.769231

0

0.12037

108

4

51

27

0.915789

0

0.119266

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

dbf7991b3c4a00b6cc73e52df47681f55c2ddbe7

31

py

Python

homeassistant/components/hikvision/__init__.py

domwillcode/home-assistant

f170c80bea70c939c098b5c88320a1c789858958

[ "Apache-2.0" ]

30,023

2016-04-13T10:17:53.000Z

2020-03-02T12:56:31.000Z

homeassistant/components/hikvision/__init__.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

31,101

2020-03-02T13:00:16.000Z

2022-03-31T23:57:36.000Z

homeassistant/components/hikvision/__init__.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

11,956

2016-04-13T18:42:31.000Z

2020-03-02T09:32:12.000Z

"""The hikvision component."""

15.5

30

0.677419

3

31

7

1

0

0.096774

31

1

31

0.75

0.774194

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

4

dbfa68ae662b3d9b2b24158ad57a90a8d0cdd99f

3,384

py

Python

nightcappackages/nightcappackages/classes/databases/mogo/mongo_modules.py

abaker2010/NightCAP

c58365a0e2ff1896ce0f8fbf2977b3e83feee1e2

[ "MIT" ]

2

2022-02-11T17:47:38.000Z

2022-02-11T21:13:36.000Z

nightcappackages/nightcappackages/classes/databases/mogo/mongo_modules.py

abaker2010/NightCAP

c58365a0e2ff1896ce0f8fbf2977b3e83feee1e2

[ "MIT" ]

null

nightcappackages/nightcappackages/classes/databases/mogo/mongo_modules.py

abaker2010/NightCAP

c58365a0e2ff1896ce0f8fbf2977b3e83feee1e2

[ "MIT" ]

null

# Copyright 2020 by Aaron Baker. # All rights reserved. # This file is part of the Nightcap Project, # and is released under the "MIT License Agreement". Please see the LICENSE # file that should have been included as part of this package. # region Imports from nightcapcore.singleton.singleton import Singleton from nightcappackages.classes.databases.mogo.connections.mongo_operation_connector import ( MongoDatabaseOperationsConnection, ) # endregion class MongoModuleDatabase(MongoDatabaseOperationsConnection, metaclass=Singleton): """ This class is used interact with the mongo databse ... Attributes ---------- _db: -> MongoClient the connection to the db Methods ------- Accessible ------- create(self, module: str = None): -> None addes a new module to the database read(self): -> Any this will read the database update(self): -> pass for override when implemented delete(self): -> pass for override when implemented find(self, module: str = None): -> Any returns the results of the find query find_one(self, module: str = None): -> Any returns the results of the find one query check_module_path(self, path: list): -> Any returns the module if exists get_all_modules(self): -> Any returns all of the modules module_install(self, module: str): -> None tries to install the module module_try_unintall(self, module: str): -> None tries to uninstall the module """ # region Init def __init__(self): MongoDatabaseOperationsConnection.__init__(self) self._db = self.client[self.conf.config["MONGOSERVER"]["db_name"]]["modules"] # endregion # region Create def create(self, module: str = None): self._db.insert_one({"type": module}) # endregion # region Read def read(self): return self._db.find() # endregion # region Update def update(self): pass # endregion # region Delete def delete(self): pass # endregion def drop(self): self._db.drop() # region Find def find(self, module: str = None): return self._db.find({"type": module}) # endregion # region Find One def find_one(self, module: str = None): return self._db.find_one({"type": module}) # endregion # region Check Module Path def check_module_path(self, path: list): return self.find(path[0]) # endregion # region Get All Modules def get_all_modules(self): _doc = self.read() return _doc # endregion # region Install Module def module_install(self, module: str): _moduleexists = self.find(module) if _moduleexists.count() == 0: self.create(module) else: pass # endregion # region Uninstall Module def module_try_unintall(self, module: str): _moduleexists = self.find_one(module) self._db.remove(_moduleexists) self.printer.print_formatted_additional( text="Deleted module entry", leadingTab=3 ) # endregion

24

91

0.599586

375

3,384

5.288

0.328

0.050429

0.065557

0.068583

0.291478

0.222895

0.079677

0.046394

0

0.003006

0.311761

3,384

140

92

24.171429

0.848433

0.476359

0

0.076923

0

0.035382

0

1

0.307692

false

0.076923

0.051282

0.102564

0.512821

0.025641

0

null

0

null

0

1

0

1

0

1

0

4

e012472a9757406a29273b5f1cde6c72b886a7f1

307

py

Python

spec/fixtures/with_included_context.py

kfischer-okarin/mamba

0a2c83e2e9b1fc06aa6165519bc2c8de418e906b

[ "MIT" ]

462

2015-01-02T19:59:33.000Z

2022-03-12T09:47:17.000Z

spec/fixtures/with_included_context.py

kfischer-okarin/mamba

0a2c83e2e9b1fc06aa6165519bc2c8de418e906b

[ "MIT" ]

93

2015-01-31T13:18:47.000Z

2021-05-06T18:32:42.000Z

spec/fixtures/with_included_context.py

kfischer-okarin/mamba

0a2c83e2e9b1fc06aa6165519bc2c8de418e906b

[ "MIT" ]

66

2015-04-24T14:12:13.000Z

2022-03-01T16:52:51.000Z

from mamba import shared_context, included_context, describe, it SHARED_CONTEXT = 'Shared Context' with shared_context(SHARED_CONTEXT): with it('shared example'): pass with describe('Real tests'): with included_context(SHARED_CONTEXT): with it('added example'): pass

21.928571

64

0.697068

37

307

5.594595

0.378378

0.376812

0.289855

0.347826

0.425121

0

0.214984

307

13

65

23.615385

0.858921

0

0.222222

0

0.166124

0

1

0

false

0.222222

0.111111

0

0.111111

0

null

1

0

null

0

1

0

4

e02629bbd217e2c2284440a328cadd313acb7c88

1,131

py

Python

apps/hello/src/hello/views.py

civascu/hue

82f2de44789ff5a981ed725175bae7944832d1e9

[ "Apache-2.0" ]

2

2021-04-27T03:57:00.000Z

2021-06-18T09:39:58.000Z

apps/hello/src/hello/views.py

civascu/hue

82f2de44789ff5a981ed725175bae7944832d1e9

[ "Apache-2.0" ]

null

apps/hello/src/hello/views.py

civascu/hue

82f2de44789ff5a981ed725175bae7944832d1e9

[ "Apache-2.0" ]

2

2021-09-06T18:44:45.000Z

2022-02-24T04:10:10.000Z

#!/usr/bin/env python # Licensed to Cloudera, Inc. under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. Cloudera, Inc. licenses this file # to you 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. # # Sample "hello world" view. from desktop.lib.django_util import render from hello import conf def hello(request): # Use render from django_util so that ?format=json works. return render("hello.html", request, {"greeting": conf.GREETING.get()}) def goodbye(request): return render("hello.html", request, {"greeting": "goodbye"})

39

74

0.755968

168

1,131

5.077381

0.595238

0.07034

0.030481

0.037515

0.084408

0

0.004202

0.158267

1,131

28

75

40.392857

0.891807

0.744474

0

0.159259

0

1

0.333333

false

0

0.333333

1

0

null

0

null

0

1

0

1

0

4

e03153b1bc29cafe0d89ddbbba23df8396dbf680

215

py

Python

Aulas/aula03/exe3-media-do-aluno.py

GabrielGustavoMS/programacaoDeComputadores

4eb3735b2393f241da78e2b259fde30ff2566a4c

[ "MIT" ]

null

Aulas/aula03/exe3-media-do-aluno.py

GabrielGustavoMS/programacaoDeComputadores

4eb3735b2393f241da78e2b259fde30ff2566a4c

[ "MIT" ]

null

Aulas/aula03/exe3-media-do-aluno.py

GabrielGustavoMS/programacaoDeComputadores

4eb3735b2393f241da78e2b259fde30ff2566a4c

[ "MIT" ]

null

nome= input("Digite o nome do aluno: ") nota1 = float(input("Digite a primeira nota: " )) nota2 = float(input("Digite a segunda nota: " )) media = (nota1 + nota2)/2 print("A média do aluno é",nome, ":", media)

35.833333

50

0.64186

33

215

4.181818

0.545455

0.23913

0.231884

0.246377

0

0.028571

0.186047

215

5

51

43

0.76

0

0.428571

0

1

0

false

0

0.2

0

null

1

0

1

0

null

0

4

e04a5037420562e660d3fde7237be53948a2745e

174

py

Python

tests/test_files/src/decorator.py

TylerYep/pytest-idempotent

f0c5cd84762bd057796d7dec02b230ae8f32b693

[ "MIT" ]

2

2021-11-26T07:41:50.000Z

2021-11-27T13:48:14.000Z

tests/test_files/src/decorator.py

TylerYep/pytest-idempotent

f0c5cd84762bd057796d7dec02b230ae8f32b693

[ "MIT" ]

null

tests/test_files/src/decorator.py

TylerYep/pytest-idempotent

f0c5cd84762bd057796d7dec02b230ae8f32b693

[ "MIT" ]

null

from __future__ import annotations from typing import Any, Callable, TypeVar _F = TypeVar("_F", bound=Callable[..., Any]) def idempotent(func: _F) -> _F: return func

17.4

44

0.706897

23

174

5

0.608696

0.13913

0

0.172414

174

9

45

19.333333

0.798611

0

0.011494

0

1

0.2

false

0

0.4

0.2

0.8

0

1

0

null

0

1

0

null

0

1

0

4

e06cf65c2e8ae616e590636d9540d1301d1c8f7c

187

py

Python

dependencies/src/4Suite-XML-1.0.2/Ft/Lib/DistExt/Formatters/__init__.py

aleasims/Peach

bb56841e943d719d5101fee0a503ed34308eda04

[ "MIT" ]

null

dependencies/src/4Suite-XML-1.0.2/Ft/Lib/DistExt/Formatters/__init__.py

aleasims/Peach

bb56841e943d719d5101fee0a503ed34308eda04

[ "MIT" ]

null

dependencies/src/4Suite-XML-1.0.2/Ft/Lib/DistExt/Formatters/__init__.py

aleasims/Peach

bb56841e943d719d5101fee0a503ed34308eda04

[ "MIT" ]

1

2020-07-26T03:57:45.000Z

__revision__ = '$Id: __init__.py,v 1.2 2006/08/12 15:56:26 jkloth Exp $' __all__ = ['XmlFormatter', 'ApiFormatter', 'ExtensionFormatter', 'CommandLineFormatter', ]

31.166667

72

0.636364

20

187

5.35

1

0

0.108844

0.213904

187

5

73

37.4

0.619048

0

0.25

0.625668

0

1

0

false

0

1

0

null

0

1

0

1

0

null

0

4

0eb869a88fa1f1bcce59e1ba1c260db5429aa701

113

py

Python

streamlit-app/config.py

anadiamaq/BCN-air

942f218ba0227150fd1f7ad94c57ea81e17a5a41

[ "Apache-2.0" ]

null

streamlit-app/config.py

anadiamaq/BCN-air

942f218ba0227150fd1f7ad94c57ea81e17a5a41

[ "Apache-2.0" ]

1

2021-08-20T17:56:20.000Z

streamlit-app/config.py

anadiamaq/BCN-air

942f218ba0227150fd1f7ad94c57ea81e17a5a41

[ "Apache-2.0" ]

null

import os from dotenv import load_dotenv load_dotenv() PORT = os.getenv('PORT') MONGO_URL=os.getenv('MONGO_URL')

18.833333

32

0.778761

19

113

4.421053

0.473684

0.238095

0

0.097345

113

6

32

18.833333

0.823529

0

0.114035

0

1

0

false

0

0.4

0

0.4

0

1

0

null

1

0

1

0

null

0

1

0

4

0ebaed68865237e96deb5eb41a0c75fba0c5ee87

158

py

Python

test.py

nzkller/dnav3-code

4676adad7cee9490c2cf910e8c73b41a758a828f

[ "MIT" ]

null

test.py

nzkller/dnav3-code

4676adad7cee9490c2cf910e8c73b41a758a828f

[ "MIT" ]

null

test.py

nzkller/dnav3-code

4676adad7cee9490c2cf910e8c73b41a758a828f

[ "MIT" ]

null

n = input("value of n\n") n = int(n) if n < 5: print("n is less than 5") elif n == 5: print("n is equal to 5") else: print("n is greater than 5")

17.555556

32

0.550633

34

158

2.558824

0.470588

0.206897

0.275862

0.183908

0.229885

0

0.04386

0.278481

158

8

33

19.75

0.719298

0

0.392405

0

1

0

false

0

0.375

0

null

1

0

1

0

null

0

4

0ec49fc9cfa6234ca63d19ddb6743551087aa47e

210

py

Python

tinynn/converter/operators/tflite/custom.py

www516717402/TinyNeuralNetwork

23e7931b4377462fad94a9ab0651b6d9a346252d

[ "MIT" ]

1

2021-12-20T07:21:35.000Z

tinynn/converter/operators/tflite/custom.py

www516717402/TinyNeuralNetwork

23e7931b4377462fad94a9ab0651b6d9a346252d

[ "MIT" ]

null

tinynn/converter/operators/tflite/custom.py

www516717402/TinyNeuralNetwork

23e7931b4377462fad94a9ab0651b6d9a346252d

[ "MIT" ]

1

2021-12-20T07:21:37.000Z

from .generated_ops import CustomOperator class Atan2Operator(CustomOperator): def __init__(self, inputs, outputs) -> None: super().__init__(inputs, outputs) self.op.custom_code = "Atan2"

26.25

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0.709524

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0.18705

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0.011696

0.185714

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1

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null

0

1

0

4

0edd342f30db856a96dbfe76270baf29fc345c03

9,828

py

Python

hufscoops/haksik_table_make.py

JunKiBeom/HUFormation-kakao

a76c23fa0e8e0625b7ae98e79df117c9e4ac8fb5

[ "MIT" ]

null

hufscoops/haksik_table_make.py

JunKiBeom/HUFormation-kakao

a76c23fa0e8e0625b7ae98e79df117c9e4ac8fb5

[ "MIT" ]

null

hufscoops/haksik_table_make.py

JunKiBeom/HUFormation-kakao

a76c23fa0e8e0625b7ae98e79df117c9e4ac8fb5

[ "MIT" ]

null

import sqlite3 from django.shortcuts import render import datetime def to_seo_table(request): context = {} context['menu'] = seo_haksik_load() # 식단표 단어별로 분류 완료 #print(context['menu']) #print(context) return render(request, 'seo_haksik_table.html', context) def to_glo_table(request): context = {} context['menu'] = glo_haksik_load() # 식단표 단어별로 분류 완료 #print(context['menu']) #print(context) return render(request, 'glo_haksik_table.html', context) def seo_haksik_load(): t = ['월', '화', '수', '목', '금', '토', '일'] r = datetime.datetime.today().weekday() days = t[r] all_menu = {} time = 'lunch' menu_list = formatted_haksik(time, '인문관') #print(menu_list) try: if len(menu_list) == 1: inmoon_menu = dict( inmoon_today_menu0=menu_list[0][1], inmoon_today_price0=menu_list[0][-1], ) elif len(menu_list) == 2: inmoon_menu = dict( inmoon_today_menu0=menu_list[0][1], inmoon_today_price0=menu_list[0][-1], inmoon_today_menu1=menu_list[1][1], inmoon_today_price1=menu_list[1][-1], ) elif len(menu_list) == 3: inmoon_menu = dict( inmoon_today_menu0=menu_list[0][1], inmoon_today_price0=menu_list[0][-1], inmoon_today_menu1=menu_list[1][1], inmoon_today_price1=menu_list[1][-1], inmoon_today_menu2=menu_list[2][1], inmoon_today_price2=menu_list[2][-1], ) all_menu.update(inmoon_menu) except: pass #print(inmoon_menu) menu_list = formatted_haksik(time, '교수회관') try: if len(menu_list) == 1: gyosoo_menu = dict( gyosoo_today_menu0=menu_list[0][1], gyosoo_today_price0=menu_list[0][-1], ) elif len(menu_list) == 2: gyosoo_menu = dict( gyosoo_today_menu0=menu_list[0][1], gyosoo_today_price0=menu_list[0][-1], gyosoo_today_menu1=menu_list[1][1], gyosoo_today_price1=menu_list[1][-1], ) all_menu.update(gyosoo_menu) except: pass menu_list = formatted_haksik(time, '스카이라운지') try: if len(menu_list) == 1: lounge_menu = dict( lounge_today_menu0=menu_list[0][1], lounge_today_price0=menu_list[0][-1], ) elif len(menu_list) == 2: lounge_menu = dict( lounge_today_menu0=menu_list[0][1], lounge_today_price0=menu_list[0][-1], lounge_today_menu1=menu_list[1][1], lounge_today_price1=menu_list[1][-1], ) all_menu.update(lounge_menu) except: pass #print(menu_list) #print(all_menu) return all_menu def glo_haksik_load(): all_menu = {} time='lunch' menu_list=formatted_haksik(time, '후생관') try: if len(menu_list)==0: hooseng_menu=dict() elif len(menu_list)==1: hooseng_menu=dict( hooseng_today_menu0=menu_list[0][1], hooseng_today_price0=menu_list[0][-1], ) elif len(menu_list)==2: hooseng_menu=dict( hooseng_today_menu0=menu_list[0][1], hooseng_today_price0=menu_list[0][-1], hooseng_today_menu1=menu_list[1][1], hooseng_today_price1=menu_list[1][-1], ) elif len(menu_list)==3: hooseng_menu=dict( hooseng_today_menu0=menu_list[0][1], hooseng_today_price0=menu_list[0][-1], hooseng_today_menu1=menu_list[1][1], hooseng_today_price1=menu_list[1][-1], hooseng_today_menu2=menu_list[2][1], hooseng_today_price2=menu_list[2][-1], ) elif len(menu_list)==4: hooseng_menu=dict( hooseng_today_menu0=menu_list[0][1], hooseng_today_price0=menu_list[0][-1], hooseng_today_menu1=menu_list[1][1], hooseng_today_price1=menu_list[1][-1], hooseng_today_menu2=menu_list[2][1], hooseng_today_price2=menu_list[2][-1], hooseng_today_menu3=menu_list[3][1], hooseng_today_price3=menu_list[3][-1], ) elif len(menu_list)==5: hooseng_menu=dict( hooseng_today_menu0=menu_list[0][1], hooseng_today_price0=menu_list[0][-1], hooseng_today_menu1=menu_list[1][1], hooseng_today_price1=menu_list[1][-1], hooseng_today_menu2=menu_list[2][1], hooseng_today_price2=menu_list[2][-1], hooseng_today_menu3=menu_list[3][1], hooseng_today_price3=menu_list[3][-1], hooseng_today_menu4=menu_list[4][1], hooseng_today_price4=menu_list[4][-1], ) except: pass all_menu.update(hooseng_menu) menu_list=formatted_haksik(time, '교직원') try: if len(menu_list)==0: gyojik_menu=dict() elif len(menu_list)==1: gyojik_menu=dict( gyojik_today_menu0=menu_list[0][1], gyojik_today_price0=menu_list[0][-1], ) elif len(menu_list)==2: gyojik_menu=dict( gyojik_today_menu0=menu_list[0][1], gyojik_today_price0=menu_list[0][-1], gyojik_today_menu1=menu_list[1][1], gyojik_today_price1=menu_list[1][-1], ) except: pass all_menu.update(gyojik_menu) menu_list=formatted_haksik(time, '어문관') try: umoon_menu = dict( ) if len(menu_list)==1: umoon_menu=dict( umoon_today_menu0=menu_list[0][1], umoon_today_price0=menu_list[0][-1], ) elif len(menu_list)==2: umoon_menu=dict( umoon_today_menu0=menu_list[0][1], umoon_today_price0=menu_list[0][-1], umoon_today_menu1=menu_list[1][1], umoon_today_price1=menu_list[1][-1], ) except: pass all_menu.update(umoon_menu) menu_list=formatted_haksik(time, '기숙사') try: if len(menu_list)==0: hufsdorm_menu=dict() elif len(menu_list)==1: hufsdorm_menu=dict( hufsdorm_today_menu0=menu_list[0][1], hufsdorm_today_price0=menu_list[0][-1], ) elif len(menu_list) == 2: hufsdorm_menu = dict( hufsdorm_today_menu0=menu_list[0][1], hufsdorm_today_price0=menu_list[0][-1], hufsdorm_today_menu1=menu_list[1][1], hufsdorm_today_price1=menu_list[1][-1], ) elif len(menu_list) == 3: hufsdorm_menu = dict( hufsdorm_today_menu0=menu_list[0][1], hufsdorm_today_price0=menu_list[0][-1], hufsdorm_today_menu1=menu_list[1][1], hufsdorm_today_price1=menu_list[1][-1], hufsdorm_today_menu2=menu_list[2][1], hufsdorm_today_price2=menu_list[2][-1], ) elif len(menu_list) == 4: hufsdorm_menu = dict( hufsdorm_today_menu0=menu_list[0][1], hufsdorm_today_price0=menu_list[0][-1], hufsdorm_today_menu1=menu_list[1][1], hufsdorm_today_price1=menu_list[1][-1], hufsdorm_today_menu2=menu_list[2][1], hufsdorm_today_price2=menu_list[2][-1], hufsdorm_today_menu3=menu_list[3][1], hufsdorm_today_price3=menu_list[3][-1], ) elif len(menu_list) == 5: hufsdorm_menu = dict( hufsdorm_today_menu0=menu_list[0][1], hufsdorm_today_price0=menu_list[0][-1], hufsdorm_today_menu1=menu_list[1][1], hufsdorm_today_price1=menu_list[1][-1], hufsdorm_today_menu2=menu_list[2][1], hufsdorm_today_price2=menu_list[2][-1], hufsdorm_today_menu3=menu_list[3][1], hufsdorm_today_price3=menu_list[3][-1], hufsdorm_today_menu4=menu_list[4][1], hufsdorm_today_price4=menu_list[4][-1], ) except: pass all_menu.update(hufsdorm_menu) print(all_menu) #print(menu_list) return all_menu def formatted_haksik(time, cafeteria): con = sqlite3.connect('./DB/haksik_data.db') cur = con.cursor() querys = 'SELECT ' + time + ' FROM ' + cafeteria cur.execute(querys) menu_size = len(cur.fetchall()) cur.execute(querys) menu_list = [] for i in range(0, menu_size): menu = cur.fetchone()[0] if len(menu) <= 1: continue else: menu_list.append(menu) for size in range(0, len(menu_list)): menu_list[size] = menu_list[size].split('\n') for i in range(0, len(menu_list[size])): try: menu_list[size].remove('') #print(menu_list) # 어문관 작업중 . 2017-11-06 for i in range(0,len(menu_list[size])): if '선택식' in menu_list[size][i]: #어문관일때 선택식으로 인한 가격수정 menu_list[size][-1] = str(menu_list[size][i-1]).replace('가격 : ', '') else: menu_list[size][-1] = menu_list[size][-1].replace('가격 : ', '') except: pass con.close() return menu_list seo_haksik_load() glo_haksik_load()

31.703226

92

0.544668

1,234

9,828

4.002431

0.093193

0.234865

0.082

0.085037

0.807653

0.774651

0.683539

0.668961

0.626848

0

0.050703

0.33374

9,828

310

93

31.703226

0.703574

0.024318

0

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0

null

1

0

1

0

1

0

null

0

4

0ef0b34210a5fa4d1a7c3413d0c7012978d858ef

686

py

Python

setup.py

lienz/sphfile

2a9af179245ce16a3a61f6f36f335b78e1b454a3

[ "MIT" ]

1

2021-05-03T10:02:55.000Z

setup.py

lienz/sphfile

2a9af179245ce16a3a61f6f36f335b78e1b454a3

[ "MIT" ]

null

setup.py

lienz/sphfile

2a9af179245ce16a3a61f6f36f335b78e1b454a3

[ "MIT" ]

null

import setuptools setuptools.setup( name="sphfile", version="1.0.1", url="https://github.com/mcfletch/sphfile", author="Mike C. Fletcher", author_email="mcfletch@vrplumber.com", description="Numpy-based NIST SPH audio-file reader", long_description=open('README.rst').read(), packages=setuptools.find_packages(), install_requires=[], classifiers=[ 'Programming Language :: Python', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', ], )

25.407407

57

0.622449

72

686

5.875

0.625

0.269504

0.35461

0.184397

0

0.020638

0.223032

686

26

58

26.384615

0.772983

0

0.501458

0.03207

0

1

0

true

0

0.05

0

0.05

0

null

1

0

null

0

1

0

4

0ef32db3f5c0352544551ed32ea76efd272e1a7c

70

py

Python

src/aleph_client/vm/__init__.py

davetapley/aleph-client

71dbab7b8107ea5676fbff5dc11d8418f77ac57b

[ "MIT" ]

4

2020-10-17T13:22:45.000Z

2022-02-21T17:29:33.000Z

src/aleph_client/vm/__init__.py

davetapley/aleph-client

71dbab7b8107ea5676fbff5dc11d8418f77ac57b

[ "MIT" ]

21

2021-04-20T07:33:58.000Z

2022-02-16T08:57:34.000Z

src/aleph_client/vm/__init__.py

davetapley/aleph-client

71dbab7b8107ea5676fbff5dc11d8418f77ac57b

[ "MIT" ]

7

2020-11-01T13:06:02.000Z

2022-02-10T23:07:01.000Z

""" Aleph helpers for apps running inside Aleph Virtual Machines. """

17.5

61

0.742857

9

70

5.777778

0.888889

0

0.157143

70

3

62

23.333333

0.881356

0.871429

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

4

1615cacabe19c00e6fc295f178bb6f26ddfa52c8

91

py

Python

djangoProject/GardenAR/apps.py

ValenDtv/Gargen_ARback

ba4da7d93168fd3dcfb03901eef3a779a2f5859f

[ "MIT" ]

null

djangoProject/GardenAR/apps.py

ValenDtv/Gargen_ARback

ba4da7d93168fd3dcfb03901eef3a779a2f5859f

[ "MIT" ]

null

djangoProject/GardenAR/apps.py

ValenDtv/Gargen_ARback

ba4da7d93168fd3dcfb03901eef3a779a2f5859f

[ "MIT" ]

null

from django.apps import AppConfig class GardenarConfig(AppConfig): name = 'GardenAR'

15.166667

33

0.758242

10

91

6.9

0.9

0

0.164835

91

5

34

18.2

0.907895

0

0.087912

0

1

0

false

0

0.333333

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0

1

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null

0

1

0

null

0

1

0

1

0

4

16bc9eca21ffbb87310e54c2088a2acab78664f1

47,975

py

Python

examples/speech_recognition/twophase_sequence_generator.py

sarapapi/FBK-fairseq-ST

33f381937c1589602944da8cf39e533802d283ca

[ "MIT" ]

11

2021-02-28T23:33:18.000Z

2022-02-11T20:42:18.000Z

examples/speech_recognition/twophase_sequence_generator.py

sarapapi/FBK-fairseq-ST

33f381937c1589602944da8cf39e533802d283ca

[ "MIT" ]

1

2021-05-21T08:08:19.000Z

2021-06-30T12:28:55.000Z

examples/speech_recognition/twophase_sequence_generator.py

sarapapi/FBK-fairseq-ST

33f381937c1589602944da8cf39e533802d283ca

[ "MIT" ]

5

2021-03-15T02:05:38.000Z

2022-02-14T09:20:20.000Z

# Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. import math from typing import Dict, List, Optional import torch from fairseq import search from fairseq.models.fairseq_encoder import EncoderOut from torch import Tensor from fairseq.sequence_generator import SequenceGenerator, EnsembleModel, BeamContainer class HierarchicalBeamSearch(search.BeamSearch): def __init__(self, tgt_dict): super().__init__(tgt_dict) @torch.jit.export def step(self, step: int, lprobs, scores: Optional[Tensor], prev_scores: Tensor): bsz, beam_size, vocab_size = lprobs.size() if step == 0: # at the first step add the scores from previous beam search lprobs = lprobs + prev_scores else: # make probs contain cumulative scores for each hypothesis assert scores is not None lprobs = lprobs + scores[:, :, step - 1].unsqueeze(-1) top_prediction = torch.topk( lprobs.view(bsz, -1), k=min( # Take the best 2 x beam_size predictions. We'll choose the first # beam_size of these which don't predict eos to continue with. beam_size * 2, lprobs.view(bsz, -1).size(1) - 1, # -1 so we never select pad ), ) scores_buf = top_prediction[0] indices_buf = top_prediction[1] if torch.__version__ < '1.6.0': beams_buf = torch.div(indices_buf, vocab_size) else: beams_buf = torch.floor_divide(indices_buf, vocab_size) indices_buf = indices_buf.fmod(vocab_size) return scores_buf, indices_buf, beams_buf class TwoPhaseSequenceGenerator(SequenceGenerator): def __init__( self, models, src_dict, tgt_dict, beam_size=1, max_len_a=0, max_len_b=200, min_len=1, normalize_scores=True, len_penalty=1.0, unk_penalty=0.0, retain_dropout=False, temperature=1.0, match_source_len=False, no_repeat_ngram_size=0, search_strategy=None, eos=None, ): """Generates transcripts and translations of a given source audio. Args: models (List[~fairseq.models.FairseqModel]): ensemble of models, currently support fairseq.models.TransformerModel for scripting beam_size (int, optional): beam width (default: 1) max_len_a/b (int, optional): generate sequences of maximum length ax + b, where x is the source length min_len (int, optional): the minimum length of the generated output (not including end-of-sentence) normalize_scores (bool, optional): normalize scores by the length of the output (default: True) len_penalty (float, optional): length penalty, where <1.0 favors shorter, >1.0 favors longer sentences (default: 1.0) unk_penalty (float, optional): unknown word penalty, where <0 produces more unks, >0 produces fewer (default: 0.0) retain_dropout (bool, optional): use dropout when generating (default: False) temperature (float, optional): temperature, where values >1.0 produce more uniform samples and values <1.0 produce sharper samples (default: 1.0) match_source_len (bool, optional): outputs should match the source length (default: False) """ super().__init__( models, tgt_dict, beam_size=beam_size, max_len_a=max_len_a, max_len_b=max_len_b, min_len=min_len, normalize_scores=normalize_scores, len_penalty=len_penalty, unk_penalty=unk_penalty, retain_dropout=retain_dropout, temperature=temperature, match_source_len=match_source_len, no_repeat_ngram_size=no_repeat_ngram_size, search_strategy=search_strategy, eos=eos ) if isinstance(models, EnsembleTwoPhaseModel): self.model = models else: self.model = EnsembleTwoPhaseModel(models) self.src_pad = src_dict.pad() self.src_unk = src_dict.unk() self.src_eos = src_dict.eos() if eos is None else eos self.src_vocab_size = len(src_dict) self.src_search = search.BeamSearch(src_dict) self.search = HierarchicalBeamSearch(tgt_dict) def cuda(self): self.model.cuda() return self def _generate( self, sample: Dict[str, Dict[str, Tensor]], prefix_tokens: Optional[Tensor] = None, bos_token: Optional[int] = None, ): net_input = sample["net_input"] # TODO: should not use audio features... src_tokens = net_input["src_tokens"] # length of the source text being the character length except EndOfSentence and pad src_lengths = ( (src_tokens.ne(self.src_eos) & src_tokens.ne(self.src_pad)).long().sum(dim=1) ) # bsz: total number of sentences in beam input_size = src_tokens.size() bsz, src_len = input_size[0], input_size[1] beam_size = self.beam_size max_len: int = -1 if self.match_source_len: max_len = src_lengths.max().item() else: max_len = min( int(self.max_len_a * src_len + self.max_len_b), # exclude the EOS marker self.model.max_decoder_positions() - 1, ) assert ( self.min_len <= max_len ), "min_len cannot be larger than max_len, please adjust these!" # compute the encoder output for each beam encoder_outs = self.model.forward_encoder(net_input) # placeholder of indices for bsz * beam_size to hold tokens and accumulative scores new_order = torch.arange(bsz).view(-1, 1).repeat(1, beam_size).view(-1) new_order = new_order.to(src_tokens.device).long() encoder_outs = self.model.reorder_encoder_out(encoder_outs, new_order) # ensure encoder_outs is a List. assert encoder_outs is not None aux_nbest = self._generate_aux( sample, encoder_outs, prefix_tokens=prefix_tokens, bos_token=bos_token) return self._generate_tgt( aux_nbest, encoder_outs, prefix_tokens=prefix_tokens, bos_token=bos_token) def _generate_tgt( self, aux_nbest: List[List[Dict[str, Tensor]]], encoder_outs: List[EncoderOut], prefix_tokens: Optional[Tensor] = None, bos_token: Optional[int] = None, ): # bsz: total number of sentences in beam bsz = len(aux_nbest) beam_size = self.beam_size max_aux_len = max([cand["tokens"].shape[0] for sent in aux_nbest for cand in sent]) src_tokens = ( torch.zeros(bsz, beam_size, max_aux_len).long().fill_(self.src_pad).to( aux_nbest[0][0]["tokens"].device) ) for i_batch in range(len(aux_nbest)): for i_best in range(len(aux_nbest[i_batch])): cand = aux_nbest[i_batch][i_best] src_tokens[i_batch, i_best, :cand["tokens"].shape[0]] = cand["tokens"] src_tokens = src_tokens.view(bsz * beam_size, -1) # length of the source text being the character length except EndOfSentence and pad src_lengths = ( (src_tokens.ne(self.src_eos) & src_tokens.ne(self.src_pad)).long().sum(dim=1) ) src_len = src_tokens.size()[1] auxiliary_outputs = torch.zeros( bsz, beam_size, max_aux_len, aux_nbest[0][0]["auxiliary_out"].shape[1]).float().fill_( self.src_pad).to(src_tokens.device) for i_batch in range(len(aux_nbest)): for i_best in range(len(aux_nbest[i_batch])): cand = aux_nbest[i_batch][i_best] auxiliary_outputs[i_batch, i_best, :cand["auxiliary_out"].shape[0], :] = cand["auxiliary_out"] auxiliary_outputs = auxiliary_outputs.view(bsz * beam_size, max_aux_len, -1) prev_scores = torch.stack( [cand["score"] for sent in aux_nbest for cand in sent]).view(bsz, beam_size, 1) if self.match_source_len: max_len = src_lengths.max().item() else: max_len = min( int(self.max_len_a * src_len + self.max_len_b), # exclude the EOS marker self.model.max_decoder_positions() - 1, ) assert ( self.min_len <= max_len ), "min_len cannot be larger than max_len, please adjust these!" # compute the encoder output for each beam # initialize buffers scores = ( torch.zeros(bsz * beam_size, max_len + 1).to(src_tokens).float() ) # +1 for eos; pad is never choosed for scoring tokens = ( torch.zeros(bsz * beam_size, max_len + 2) .to(src_tokens) .long() .fill_(self.pad) ) # +2 for eos and pad tokens[:, 0] = self.eos if bos_token is None else bos_token attn: Optional[Tensor] = None # The blacklist indicates candidates that should be ignored. # For example, suppose we're sampling and have already finalized 2/5 # samples. Then the blacklist would mark 2 positions as being ignored, # so that we only finalize the remaining 3 samples. blacklist = ( torch.zeros(bsz, beam_size).to(src_tokens).eq(-1) ) # forward and backward-compatible False mask # list of completed sentences finalized = torch.jit.annotate( List[List[Dict[str, Tensor]]], [torch.jit.annotate(List[Dict[str, Tensor]], []) for i in range(bsz)], ) # contains lists of dictionaries of infomation about the hypothesis being finalized at each step finished = [ False for i in range(bsz) ] # a boolean array indicating if the sentence at the index is finished or not num_remaining_sent = bsz # number of sentences remaining # number of candidate hypos per step cand_size = 2 * beam_size # 2 x beam size in case half are EOS # offset arrays for converting between different indexing schemes bbsz_offsets = (torch.arange(0, bsz) * beam_size).unsqueeze(1).type_as(tokens) cand_offsets = torch.arange(0, cand_size).type_as(tokens) reorder_state: Optional[Tensor] = None batch_idxs: Optional[Tensor] = None for step in range(max_len + 1): # one extra step for EOS marker # reorder decoder internal states based on the prev choice of beams # print(f'step: {step}') if reorder_state is not None: if batch_idxs is not None: # update beam indices to take into account removed sentences corr = batch_idxs - torch.arange(batch_idxs.numel()).type_as( batch_idxs ) reorder_state.view(-1, beam_size).add_( corr.unsqueeze(-1) * beam_size ) self.model.reorder_incremental_state(reorder_state) encoder_outs = self.model.reorder_encoder_out( encoder_outs, reorder_state ) src_tokens = src_tokens.index_select(0, reorder_state) prev_scores = prev_scores.view(-1).index_select(0, reorder_state).view(-1, beam_size, 1) auxiliary_outputs = auxiliary_outputs.index_select(0, reorder_state) lprobs, avg_attn_scores = self.model.forward_decoder( tokens[:, : step + 1], encoder_outs, src_tokens, auxiliary_outputs, self.temperature ) lprobs[lprobs != lprobs] = torch.tensor(-math.inf).to(lprobs) lprobs[:, self.pad] = -math.inf # never select pad lprobs[:, self.unk] -= self.unk_penalty # apply unk penalty # handle max length constraint if step >= max_len: lprobs[:, : self.eos] = -math.inf lprobs[:, self.eos + 1 :] = -math.inf # handle prefix tokens (possibly with different lengths) if ( prefix_tokens is not None and step < prefix_tokens.size(1) and step < max_len ): lprobs, tokens, scores = self._prefix_tokens( step, lprobs, scores, tokens, prefix_tokens, beam_size, self.pad, self.eos ) elif step < self.min_len: # minimum length constraint (does not apply if using prefix_tokens) lprobs[:, self.eos] = -math.inf # Record attention scores, only support avg_attn_scores is a Tensor if avg_attn_scores is not None: if attn is None: attn = torch.empty( bsz * beam_size, avg_attn_scores.size(1), max_len + 2 ).to(scores) attn[:, :, step + 1].copy_(avg_attn_scores) scores = scores.type_as(lprobs) eos_bbsz_idx = torch.empty(0).to( tokens ) # indices of hypothesis ending with eos (finished sentences) eos_scores = torch.empty(0).to( scores ) # scores of hypothesis ending with eos (finished sentences) self.search.set_src_lengths(src_lengths) if self.no_repeat_ngram_size > 0: lprobs = self._no_repeat_ngram(tokens, lprobs, bsz, beam_size, step) cand_scores, cand_indices, cand_beams = self.search.step( step, lprobs.view(bsz, -1, self.vocab_size), scores.view(bsz, beam_size, -1)[:, :, :step], prev_scores, ) # cand_bbsz_idx contains beam indices for the top candidate # hypotheses, with a range of values: [0, bsz*beam_size), # and dimensions: [bsz, cand_size] cand_bbsz_idx = cand_beams.add(bbsz_offsets) # finalize hypotheses that end in eos eos_mask = cand_indices.eq(self.eos) & cand_scores.ne(-math.inf) eos_mask[:, :beam_size][blacklist] = torch.tensor(0).to(eos_mask) # only consider eos when it's among the top beam_size indices eos_bbsz_idx = torch.masked_select( cand_bbsz_idx[:, :beam_size], mask=eos_mask[:, :beam_size] ) finalized_sents: List[int] = [] if eos_bbsz_idx.numel() > 0: eos_scores = torch.masked_select( cand_scores[:, :beam_size], mask=eos_mask[:, :beam_size] ) finalized_sents = self.finalize_hypos( step, eos_bbsz_idx, eos_scores, tokens, src_tokens, scores, finalized, finished, beam_size, attn, src_lengths, max_len, ) num_remaining_sent -= len(finalized_sents) assert num_remaining_sent >= 0 if num_remaining_sent == 0: break assert step < max_len if len(finalized_sents) > 0: new_bsz = bsz - len(finalized_sents) # construct batch_idxs which holds indices of batches to keep for the next pass batch_mask = torch.ones(bsz).to(cand_indices) batch_mask[ torch.tensor(finalized_sents).to(cand_indices) ] = torch.tensor(0).to(batch_mask) batch_idxs = batch_mask.nonzero().squeeze(-1) eos_mask = eos_mask[batch_idxs] cand_beams = cand_beams[batch_idxs] bbsz_offsets.resize_(new_bsz, 1) cand_bbsz_idx = cand_beams.add(bbsz_offsets) cand_scores = cand_scores[batch_idxs] cand_indices = cand_indices[batch_idxs] if prefix_tokens is not None: prefix_tokens = prefix_tokens[batch_idxs] src_lengths = src_lengths[batch_idxs] blacklist = blacklist[batch_idxs] scores = scores.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) tokens = tokens.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) if attn is not None: attn = attn.view(bsz, -1)[batch_idxs].view( new_bsz * beam_size, attn.size(1), -1 ) bsz = new_bsz else: batch_idxs = None # set active_mask so that values > cand_size indicate eos hypos # and values < cand_size indicate candidate active hypos. # After, the min values per row are the top candidate active hypos # Rewrite the operator since the element wise or is not supported in torchscript. eos_mask[:, :beam_size] = ~((~blacklist) & (~eos_mask[:, :beam_size])) active_mask = torch.add( eos_mask.type_as(cand_offsets) * cand_size, cand_offsets[: eos_mask.size(1)], ) # get the top beam_size active hypotheses, which are just the hypos # with the smallest values in active_mask new_blacklist, active_hypos = torch.topk( active_mask, k=beam_size, dim=1, largest=False ) # update blacklist to ignore any finalized hypos blacklist = new_blacklist.ge(cand_size)[:, :beam_size] assert (~blacklist).any(dim=1).all() active_bbsz_idx = torch.gather(cand_bbsz_idx, dim=1, index=active_hypos) active_scores = torch.gather(cand_scores, dim=1, index=active_hypos) active_bbsz_idx = active_bbsz_idx.view(-1) active_scores = active_scores.view(-1) # copy tokens and scores for active hypotheses tokens[:, : step + 1] = torch.index_select( tokens[:, : step + 1], dim=0, index=active_bbsz_idx ) tokens.view(bsz, beam_size, -1)[:, :, step + 1] = torch.gather( cand_indices, dim=1, index=active_hypos ) if step > 0: scores[:, :step] = torch.index_select( scores[:, :step], dim=0, index=active_bbsz_idx ) scores.view(bsz, beam_size, -1)[:, :, step] = torch.gather( cand_scores, dim=1, index=active_hypos ) # copy attention for active hypotheses if attn is not None: attn[:, :, : step + 2] = torch.index_select( attn[:, :, : step + 2], dim=0, index=active_bbsz_idx ) # reorder incremental state in decoder reorder_state = active_bbsz_idx # sort by score descending for sent in range(len(finalized)): # make into beam container BCList = [ BeamContainer(elem["score"].item(), elem) for elem in finalized[sent] ] BCList.sort() BCList.reverse() finalized[sent] = torch.jit.annotate( List[Dict[str, Tensor]], [x.elem for x in BCList] ) return finalized def _generate_aux( self, sample: Dict[str, Dict[str, Tensor]], encoder_outs: List[EncoderOut], prefix_tokens: Optional[Tensor] = None, bos_token: Optional[int] = None, ): net_input = sample["net_input"] # TODO: should not use audio features... src_tokens = net_input["src_tokens"] # length of the source text being the character length except EndOfSentence and pad src_lengths = ( (src_tokens.ne(self.src_eos) & src_tokens.ne(self.src_pad)).long().sum(dim=1) ) # bsz: total number of sentences in beam input_size = src_tokens.size() bsz, src_len = input_size[0], input_size[1] beam_size = self.beam_size max_len: int = -1 if self.match_source_len: max_len = src_lengths.max().item() else: max_len = min( int(self.max_len_a * src_len + self.max_len_b), # exclude the EOS marker self.model.max_decoder_positions() - 1, ) # initialize buffers scores = ( torch.zeros(bsz * beam_size, max_len + 1).to(src_tokens).float() ) # +1 for eos; pad is never chosen for scoring aux_tokens = ( torch.zeros(bsz * beam_size, max_len + 2) .to(src_tokens) .long() .fill_(self.src_pad) ) # +2 for eos and pad aux_tokens[:, 0] = self.src_eos if bos_token is None else bos_token attn: Optional[Tensor] = None # The ignorelist indicates candidates that should be ignored. # For example, suppose we're sampling and have already finalized 2/5 # samples. Then the ignorelist would mark 2 positions as being ignored, # so that we only finalize the remaining 3 samples. ignorelist = ( torch.zeros(bsz, beam_size).to(src_tokens).eq(-1) ) # forward and backward-compatible False mask # list of completed sentences finalized = torch.jit.annotate( List[List[Dict[str, Tensor]]], [torch.jit.annotate(List[Dict[str, Tensor]], []) for i in range(bsz)], ) # contains lists of dictionaries of information about the hypothesis being finalized at each step finished = [ False for i in range(bsz) ] # a boolean array indicating if the sentence at the index is finished or not num_remaining_sent = bsz # number of sentences remaining # number of candidate hypos per step cand_size = 2 * beam_size # 2 x beam size in case half are EOS # offset arrays for converting between different indexing schemes bbsz_offsets = (torch.arange(0, bsz) * beam_size).unsqueeze(1).type_as(aux_tokens) cand_offsets = torch.arange(0, cand_size).type_as(aux_tokens) reorder_state: Optional[Tensor] = None batch_idxs: Optional[Tensor] = None aux_outputs: Optional[Tensor] = None for step in range(max_len + 1): # one extra step for EOS marker # reorder decoder internal states based on the prev choice of beams # print(f'step: {step}') if reorder_state is not None: if batch_idxs is not None: # update beam indices to take into account removed sentences corr = batch_idxs - torch.arange(batch_idxs.numel()).type_as( batch_idxs ) reorder_state.view(-1, beam_size).add_( corr.unsqueeze(-1) * beam_size ) self.model.reorder_auxiliary_incremental_state(reorder_state) encoder_outs = self.model.reorder_encoder_out( encoder_outs, reorder_state ) aux_outputs = aux_outputs.index_select(1, reorder_state) lprobs, aux_out, avg_attn_scores = self.model.forward_auxiliary_decoder( aux_tokens[:, : step + 1], encoder_outs, self.temperature ) if step == 0: # We need to initialize this here as we don't know the last dimension (C) # until we do the first step aux_outputs = ( torch.zeros(max_len + 1, bsz * beam_size, aux_out.shape[-1]).to(src_tokens).float() ) # Assign the auxiliary outputs for this decoding step (only the current decoding step is returned) aux_outputs[step] = aux_out.squeeze(1) lprobs[lprobs != lprobs] = torch.tensor(-math.inf).to(lprobs) lprobs[:, self.src_pad] = -math.inf # never select pad lprobs[:, self.src_unk] -= self.unk_penalty # apply unk penalty # handle max length constraint if step >= max_len: lprobs[:, : self.src_eos] = -math.inf lprobs[:, self.src_eos + 1 :] = -math.inf # handle prefix tokens (possibly with different lengths) if ( prefix_tokens is not None and step < prefix_tokens.size(1) and step < max_len ): lprobs, aux_tokens, scores = self._prefix_tokens( step, lprobs, scores, aux_tokens, prefix_tokens, beam_size, self.src_pad, self.src_eos ) elif step < self.min_len: # minimum length constraint (does not apply if using prefix_tokens) lprobs[:, self.src_eos] = -math.inf # Record attention scores, only support avg_attn_scores is a Tensor if avg_attn_scores is not None: if attn is None: attn = torch.empty( bsz * beam_size, avg_attn_scores.size(1), max_len + 2 ).to(scores) attn[:, :, step + 1].copy_(avg_attn_scores) scores = scores.type_as(lprobs) eos_bbsz_idx = torch.empty(0).to( aux_tokens ) # indices of hypothesis ending with eos (finished sentences) eos_scores = torch.empty(0).to( scores ) # scores of hypothesis ending with eos (finished sentences) self.src_search.set_src_lengths(src_lengths) if self.no_repeat_ngram_size > 0: lprobs = self._no_repeat_ngram(aux_tokens, lprobs, bsz, beam_size, step) cand_scores, cand_indices, cand_beams = self.src_search.step( step, lprobs.view(bsz, -1, self.src_vocab_size), scores.view(bsz, beam_size, -1)[:, :, :step], ) # cand_bbsz_idx contains beam indices for the top candidate # hypotheses, with a range of values: [0, bsz*beam_size), # and dimensions: [bsz, cand_size] cand_bbsz_idx = cand_beams.add(bbsz_offsets) # finalize hypotheses that end in eos eos_mask = cand_indices.eq(self.src_eos) & cand_scores.ne(-math.inf) eos_mask[:, :beam_size][ignorelist] = torch.tensor(0).to(eos_mask) # only consider eos when it's among the top beam_size indices eos_bbsz_idx = torch.masked_select( cand_bbsz_idx[:, :beam_size], mask=eos_mask[:, :beam_size] ) finalized_sents: List[int] = [] if eos_bbsz_idx.numel() > 0: eos_scores = torch.masked_select( cand_scores[:, :beam_size], mask=eos_mask[:, :beam_size] ) finalized_sents = self.finalize_aux_hypos( step, eos_bbsz_idx, eos_scores, aux_tokens, encoder_outs, aux_outputs, scores, finalized, finished, beam_size, attn, src_lengths, max_len, self.src_eos, ) num_remaining_sent -= len(finalized_sents) assert num_remaining_sent >= 0 if num_remaining_sent == 0: break assert step < max_len if len(finalized_sents) > 0: new_bsz = bsz - len(finalized_sents) # construct batch_idxs which holds indices of batches to keep for the next pass batch_mask = torch.ones(bsz).to(cand_indices) batch_mask[ torch.tensor(finalized_sents).to(cand_indices) ] = torch.tensor(0).to(batch_mask) batch_idxs = batch_mask.nonzero().squeeze(-1) eos_mask = eos_mask[batch_idxs] cand_beams = cand_beams[batch_idxs] bbsz_offsets.resize_(new_bsz, 1) cand_bbsz_idx = cand_beams.add(bbsz_offsets) cand_scores = cand_scores[batch_idxs] cand_indices = cand_indices[batch_idxs] if prefix_tokens is not None: prefix_tokens = prefix_tokens[batch_idxs] src_lengths = src_lengths[batch_idxs] ignorelist = ignorelist[batch_idxs] scores = scores.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) aux_tokens = aux_tokens.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) if attn is not None: attn = attn.view(bsz, -1)[batch_idxs].view( new_bsz * beam_size, attn.size(1), -1 ) bsz = new_bsz else: batch_idxs = None # set active_mask so that values > cand_size indicate eos hypos # and values < cand_size indicate candidate active hypos. # After, the min values per row are the top candidate active hypos # Rewrite the operator since the element wise or is not supported in torchscript. eos_mask[:, :beam_size] = ~((~ignorelist) & (~eos_mask[:, :beam_size])) active_mask = torch.add( eos_mask.type_as(cand_offsets) * cand_size, cand_offsets[: eos_mask.size(1)], ) # get the top beam_size active hypotheses, which are just the hypos # with the smallest values in active_mask new_ignorelist, active_hypos = torch.topk( active_mask, k=beam_size, dim=1, largest=False ) # update ignorelist to ignore any finalized hypos ignorelist = new_ignorelist.ge(cand_size)[:, :beam_size] assert (~ignorelist).any(dim=1).all() active_bbsz_idx = torch.gather(cand_bbsz_idx, dim=1, index=active_hypos) active_scores = torch.gather(cand_scores, dim=1, index=active_hypos) active_bbsz_idx = active_bbsz_idx.view(-1) active_scores = active_scores.view(-1) # copy tokens and scores for active hypotheses aux_tokens[:, : step + 1] = torch.index_select( aux_tokens[:, : step + 1], dim=0, index=active_bbsz_idx ) aux_tokens.view(bsz, beam_size, -1)[:, :, step + 1] = torch.gather( cand_indices, dim=1, index=active_hypos ) if step > 0: scores[:, :step] = torch.index_select( scores[:, :step], dim=0, index=active_bbsz_idx ) scores.view(bsz, beam_size, -1)[:, :, step] = torch.gather( cand_scores, dim=1, index=active_hypos ) # copy attention for active hypotheses if attn is not None: attn[:, :, : step + 2] = torch.index_select( attn[:, :, : step + 2], dim=0, index=active_bbsz_idx ) # reorder incremental state in decoder reorder_state = active_bbsz_idx # sort by score descending # for sent in range(len(finalized)): # # make into beam container # BCList = [ # BeamContainer(elem["score"].item(), elem) for elem in finalized[sent] # ] # BCList.sort() # BCList.reverse() # finalized[sent] = torch.jit.annotate( # List[Dict[str, Tensor]], [x.elem for x in BCList] # ) return finalized def _prefix_tokens( self, step: int, lprobs, scores, tokens, prefix_tokens, beam_size: int, pad, eos ): """Handle prefix tokens""" prefix_toks = prefix_tokens[:, step].unsqueeze(-1).repeat(1, beam_size).view(-1) prefix_lprobs = lprobs.gather(-1, prefix_toks.unsqueeze(-1)) prefix_mask = prefix_toks.ne(pad) lprobs[prefix_mask] = torch.tensor(-math.inf).to(lprobs) lprobs[prefix_mask] = lprobs[prefix_mask].scatter( -1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_lprobs[prefix_mask] ) # if prefix includes eos, then we should make sure tokens and # scores are the same across all beams eos_mask = prefix_toks.eq(eos) if eos_mask.any(): # validate that the first beam matches the prefix first_beam = tokens[eos_mask].view(-1, beam_size, tokens.size(-1))[ :, 0, 1 : step + 1 ] eos_mask_batch_dim = eos_mask.view(-1, beam_size)[:, 0] target_prefix = prefix_tokens[eos_mask_batch_dim][:, :step] assert (first_beam == target_prefix).all() # copy tokens, scores and lprobs from the first beam to all beams tokens = self.replicate_first_beam(tokens, eos_mask_batch_dim, beam_size) scores = self.replicate_first_beam(scores, eos_mask_batch_dim, beam_size) lprobs = self.replicate_first_beam(lprobs, eos_mask_batch_dim, beam_size) return lprobs, tokens, scores def finalize_aux_hypos( self, step: int, bbsz_idx, eos_scores, tokens, encoder_outs, decoder_out, scores, finalized: List[List[Dict[str, Tensor]]], finished: List[bool], beam_size: int, attn: Optional[Tensor], src_lengths, max_len: int, eos, ): """Finalize hypothesis, store finalized information in `finalized`, and change `finished` accordingly. Returns number of sentences being finalized. Args: bbsz_idx (Tensor): """ assert bbsz_idx.numel() == eos_scores.numel() # clone relevant token and attention tensors tokens_clone = tokens.index_select(0, bbsz_idx)[ :, 1 : step + 2 ] # skip the first index, which is EOS decoder_out_clone = decoder_out.index_select(1, bbsz_idx)[: step + 1].transpose(0, 1) encoder_outs_clone = self.model.reorder_encoder_out(encoder_outs, bbsz_idx) tokens_clone[:, step] = eos attn_clone = ( attn.index_select(0, bbsz_idx)[:, :, 1 : step + 2] if attn is not None else None ) # compute scores per token position pos_scores = scores.index_select(0, bbsz_idx)[:, : step + 1] pos_scores[:, step] = eos_scores # convert from cumulative to per-position scores pos_scores[:, 1:] = pos_scores[:, 1:] - pos_scores[:, :-1] # normalize sentence-level scores if self.normalize_scores: eos_scores /= (step + 1) ** self.len_penalty cum_unfin: List[int] = [] prev = 0 for f in finished: if f: prev += 1 else: cum_unfin.append(prev) # set() is not supported in script export sents_seen: Dict[str, Optional[Tensor]] = {} for i in range(bbsz_idx.size()[0]): idx = bbsz_idx[i] score = eos_scores[i] unfin_idx = idx // beam_size sent = unfin_idx + cum_unfin[unfin_idx] # Cannot create dict for key type '(int, int)' in torchscript. # The workaround is to cast int to string seen = str(sent.item()) + "_" + str(unfin_idx.item()) if seen not in sents_seen: sents_seen[seen] = None if self.match_source_len and step > src_lengths[unfin_idx]: score = torch.tensor(-math.inf).to(score) if len(finalized[sent]) < beam_size: if attn_clone is not None: # remove padding tokens from attn scores hypo_attn = attn_clone[i] else: hypo_attn = torch.empty(0) finalized[sent].append( { "tokens": tokens_clone[i], "auxiliary_out": decoder_out_clone[i], "encoder_outs": self.model.reorder_encoder_out( encoder_outs_clone, torch.tensor([i], dtype=torch.long).to(tokens_clone.device)), "score": score, "attention": hypo_attn, # src_len x tgt_len "alignment": torch.empty(0), "positional_scores": pos_scores[i], } ) newly_finished: List[int] = [] for seen in sents_seen.keys(): # check termination conditions for this sentence sent: int = int(float(seen.split("_")[0])) unfin_idx: int = int(float(seen.split("_")[1])) if not finished[sent] and self.is_finished( step, unfin_idx, max_len, len(finalized[sent]), beam_size ): finished[sent] = True newly_finished.append(unfin_idx) return newly_finished def finalize_hypos( self, step: int, bbsz_idx, eos_scores, tokens, src_tokens, scores, finalized: List[List[Dict[str, Tensor]]], finished: List[bool], beam_size: int, attn: Optional[Tensor], src_lengths, max_len: int, ): """Finalize hypothesis, store finalized information in `finalized`, and change `finished` accordingly. Returns number of sentences being finalized. Args: bbsz_idx (Tensor): """ assert bbsz_idx.numel() == eos_scores.numel() # clone relevant token and attention tensors tokens_clone = tokens.index_select(0, bbsz_idx)[ :, 1 : step + 2 ] # skip the first index, which is EOS src_tokens_clone = src_tokens.index_select(0, bbsz_idx) tokens_clone[:, step] = self.eos attn_clone = ( attn.index_select(0, bbsz_idx)[:, :, 1 : step + 2] if attn is not None else None ) # compute scores per token position pos_scores = scores.index_select(0, bbsz_idx)[:, : step + 1] pos_scores[:, step] = eos_scores # convert from cumulative to per-position scores pos_scores[:, 1:] = pos_scores[:, 1:] - pos_scores[:, :-1] # normalize sentence-level scores if self.normalize_scores: eos_scores /= (step + 1) ** self.len_penalty cum_unfin: List[int] = [] prev = 0 for f in finished: if f: prev += 1 else: cum_unfin.append(prev) # set() is not supported in script export sents_seen: Dict[str, Optional[Tensor]] = {} for i in range(bbsz_idx.size()[0]): idx = bbsz_idx[i] score = eos_scores[i] unfin_idx = idx // beam_size sent = unfin_idx + cum_unfin[unfin_idx] # Cannot create dict for key type '(int, int)' in torchscript. # The workaround is to cast int to string seen = str(sent.item()) + "_" + str(unfin_idx.item()) if seen not in sents_seen: sents_seen[seen] = None if self.match_source_len and step > src_lengths[unfin_idx]: score = torch.tensor(-math.inf).to(score) if len(finalized[sent]) < beam_size: if attn_clone is not None: # remove padding tokens from attn scores hypo_attn = attn_clone[i] else: hypo_attn = torch.empty(0) src_mask = src_tokens_clone[i] != self.src_pad finalized[sent].append( { "tokens": tokens_clone[i], "score": score, "aux_tokens": src_tokens_clone[i].masked_select(src_mask), "attention": hypo_attn, # src_len x tgt_len "alignment": torch.empty(0), "positional_scores": pos_scores[i], } ) newly_finished: List[int] = [] for seen in sents_seen.keys(): # check termination conditions for this sentence sent: int = int(float(seen.split("_")[0])) unfin_idx: int = int(float(seen.split("_")[1])) if not finished[sent] and self.is_finished( step, unfin_idx, max_len, len(finalized[sent]), beam_size ): finished[sent] = True newly_finished.append(unfin_idx) return newly_finished class EnsembleTwoPhaseModel(EnsembleModel): """A wrapper around an ensemble of models.""" auxiliary_incremental_states: List[Dict[str, Dict[str, Optional[Tensor]]]] def __init__(self, models): super().__init__(models) self.auxiliary_incremental_states = torch.jit.annotate( List[Dict[str, Dict[str, Optional[Tensor]]]], [ torch.jit.annotate(Dict[str, Dict[str, Optional[Tensor]]], {}) for _ in range(self.models_size) ], ) def reset_incremental_state(self): super().reset_incremental_state() self.auxiliary_incremental_states = torch.jit.annotate( List[Dict[str, Dict[str, Optional[Tensor]]]], [ torch.jit.annotate(Dict[str, Dict[str, Optional[Tensor]]], {}) for _ in range(self.models_size) ], ) return @torch.jit.export def forward_decoder( self, tokens, encoder_outs: List[EncoderOut], aux_tokens: Tensor, aux_decoder_out: Tensor, temperature: float = 1.0 ): log_probs = [] avg_attn: Optional[Tensor] = None encoder_out: Optional[EncoderOut] = None for i, model in enumerate(self.models): if self.has_encoder(): encoder_out = encoder_outs[i] # decode each model if self.has_incremental_states(): decoder_out = model.forward_decoder( tokens, auxiliary_out=aux_decoder_out, auxiliary_tokens=aux_tokens, encoder_out=encoder_out, incremental_state=self.incremental_states[i], ) else: decoder_out = model.forward_decoder( tokens, auxiliary_out=aux_decoder_out, auxiliary_tokens=aux_tokens, encoder_out=encoder_out) attn: Optional[Tensor] = None decoder_len = len(decoder_out) if decoder_len > 1 and decoder_out[1] is not None: if isinstance(decoder_out[1], Tensor): attn = decoder_out[1] else: attn_holder = decoder_out[1]["attn"] if isinstance(attn_holder, Tensor): attn = attn_holder elif attn_holder is not None: attn = attn_holder[0] if attn is not None: attn = attn[:, -1, :] decoder_out_tuple = ( decoder_out[0][:, -1:, :].div_(temperature), None if decoder_len <= 1 else decoder_out[1], ) probs = model.get_normalized_probs( decoder_out_tuple, log_probs=True, sample=None ) probs = probs[:, -1, :] if self.models_size == 1: return probs, attn log_probs.append(probs) if attn is not None: if avg_attn is None: avg_attn = attn else: avg_attn.add_(attn) avg_probs = torch.logsumexp(torch.stack(log_probs, dim=0), dim=0) - math.log( self.models_size ) if avg_attn is not None: avg_attn.div_(self.models_size) return avg_probs, avg_attn @torch.jit.export def reorder_auxiliary_incremental_state(self, new_order): if not self.has_incremental_states(): return for i, model in enumerate(self.models): model.auxiliary_decoder.reorder_incremental_state( self.auxiliary_incremental_states[i], new_order ) @torch.jit.export def forward_auxiliary_decoder( self, tokens, encoder_outs: List[EncoderOut], temperature: float = 1.0 ): log_probs = [] outs = [] avg_attn: Optional[Tensor] = None encoder_out: Optional[EncoderOut] = None for i, model in enumerate(self.models): if self.has_encoder(): encoder_out = encoder_outs[i] # decode each model if self.has_incremental_states(): decoder_out = model.auxiliary_decoder.forward( tokens, encoder_out=encoder_out, incremental_state=self.auxiliary_incremental_states[i], features_only=True, ) else: decoder_out = model.auxiliary_decoder.forward( tokens, encoder_out=encoder_out, features_only=True) decoder_out_emb = decoder_out[0] decoder_out = (model.auxiliary_decoder.output_layer(decoder_out[0]), decoder_out[1]) attn: Optional[Tensor] = None decoder_len = len(decoder_out) if decoder_len > 1 and decoder_out[1] is not None: if isinstance(decoder_out[1], Tensor): attn = decoder_out[1] else: attn_holder = decoder_out[1]["attn"] if isinstance(attn_holder, Tensor): attn = attn_holder elif attn_holder is not None: attn = attn_holder[0] if attn is not None: attn = attn[:, -1, :] decoder_out_tuple = ( decoder_out[0][:, -1:, :].div(temperature), None if decoder_len <= 1 else decoder_out[1], ) probs = model.get_normalized_probs( decoder_out_tuple, log_probs=True, sample=None ) probs = probs[:, -1, :] if self.models_size == 1: return probs, decoder_out_emb, attn log_probs.append(probs) outs.append(decoder_out_emb) if attn is not None: if avg_attn is None: avg_attn = attn else: avg_attn.add_(attn) avg_probs = torch.logsumexp(torch.stack(log_probs, dim=0), dim=0) - math.log( self.models_size ) avg_decoder_outs = torch.sum(torch.stack(outs, dim=0), dim=0).div_(self.models_size) if avg_attn is not None: avg_attn.div_(self.models_size) return avg_probs, avg_decoder_outs, avg_attn

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null

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4

16be5a45756dcd71abba477667e1911eccef139f

129

py

Python

pages/admin.py

kzambrow/cs347

bcb711545a9f3dfcb298b8a20cf5106d13701cc1

[ "MIT" ]

null

pages/admin.py

kzambrow/cs347

bcb711545a9f3dfcb298b8a20cf5106d13701cc1

[ "MIT" ]

null

pages/admin.py

kzambrow/cs347

bcb711545a9f3dfcb298b8a20cf5106d13701cc1

[ "MIT" ]

null

from django.contrib import admin # Register your models here. admin.site.site_header = 'IOT Intrusion Detection Administration'

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4

16e7e5ab26e6086c1e610dcb443a29cd1b99c8f0

1,203

py

Python

utils/loggers.py

Egolas/TC_LAAU

6b9f700f642ca3187f1556434b5fb2308f065564

[ "MIT" ]

null

utils/loggers.py

Egolas/TC_LAAU

6b9f700f642ca3187f1556434b5fb2308f065564

[ "MIT" ]

null

utils/loggers.py

Egolas/TC_LAAU

6b9f700f642ca3187f1556434b5fb2308f065564

[ "MIT" ]

null

import logging class Logger(): def __init__(self, using_config): logger = logging.getLogger() logger.setLevel('DEBUG') BASIC_FORMAT = '%(asctime)s %(levelname)s:%(message)s' formatter = logging.Formatter(BASIC_FORMAT) chlr = logging.StreamHandler() chlr.setFormatter(formatter) chlr.setLevel('DEBUG') fhlr = logging.FileHandler(using_config.logdir + '/train.log') fhlr.setFormatter(formatter) logger.addHandler(chlr) logger.addHandler(fhlr) self.logger = logger def info(self, *args, **kwargs): return self.logger.info(*args, **kwargs) def debug(self, *args, **kwargs): return self.logger.debug(*args, **kwargs) def warning(self, *args, **kwargs): return self.logger.warning(*args, **kwargs) def error(self, *args, **kwargs): return self.logger.error(*args, **kwargs) def critical(self, *args, **kwargs): return self.logger.critical(*args, **kwargs) def log(self, *args, **kwargs): return self.logger.log(*args, **kwargs) def exception(self, *args, **kwargs): return self.logger.exception(*args, **kwargs)

27.976744

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null

0

1

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1

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4

bc482f3bca38ad43f6717bc3fc9b850c605338ca

154

py

Python

chengyubert/optim/__init__.py

VisualJoyce/ChengyuBERT

605db3a4b3241dd4d02baa41a68bf23b5b00b36d

[ "MIT" ]

8

2020-12-11T13:06:16.000Z

2022-03-01T13:47:51.000Z

chengyubert/optim/__init__.py

VisualJoyce/ChengyuBERT

605db3a4b3241dd4d02baa41a68bf23b5b00b36d

[ "MIT" ]

18

2020-12-31T07:32:55.000Z

2022-02-07T08:33:30.000Z

chengyubert/optim/__init__.py

VisualJoyce/ChengyuBERT

605db3a4b3241dd4d02baa41a68bf23b5b00b36d

[ "MIT" ]

3

2021-03-25T01:08:56.000Z

2022-03-22T09:05:57.000Z

""" Copyright (c) Microsoft Corporation. Licensed under the MIT license. """ from .sched import noam_schedule, warmup_linear, vqa_schedule, get_lr_sched

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null

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4

bc511d30c3e5d85c7985c32edc3b36b8b5459805

278

py

Python

flat_sales/flat_sales/doctype/payment_intimation/test_payment_intimation.py

swamedh/flat_sales

28f3d517e06591669c24accaba4e0683eabe8901

[ "MIT" ]

null

flat_sales/flat_sales/doctype/payment_intimation/test_payment_intimation.py

swamedh/flat_sales

28f3d517e06591669c24accaba4e0683eabe8901

[ "MIT" ]

null

flat_sales/flat_sales/doctype/payment_intimation/test_payment_intimation.py

swamedh/flat_sales

28f3d517e06591669c24accaba4e0683eabe8901

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Copyright (c) 2015, Deepak and Contributors # See license.txt from __future__ import unicode_literals import frappe import unittest # test_records = frappe.get_test_records('Payment Intimation') class TestPaymentIntimation(unittest.TestCase): pass

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1

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1

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4

bc6a9054e4861fa0582118a5f32f82c3e6794d28

157

py

Python

LABWORK1/Scripts/django-admin.py

maxovic/summerpractice2019

0b61ca6302f74618a62bad60615c47f29fa531cb

[ "MIT" ]

null

LABWORK1/Scripts/django-admin.py

maxovic/summerpractice2019

0b61ca6302f74618a62bad60615c47f29fa531cb

[ "MIT" ]

null

LABWORK1/Scripts/django-admin.py

maxovic/summerpractice2019

0b61ca6302f74618a62bad60615c47f29fa531cb

[ "MIT" ]

null

#!D:\KBTU_LIFE\backend\LABWORK1\Scripts\python.exe from django.core import management if __name__ == "__main__": management.execute_from_command_line()

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null

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4

bc6bb62456ccb44a104766d7588dc5e17d0bd397

1,623

py

Python

clients/python-fastapi/generated/src/openapi_server/models/pipeline_step_impl.py

cliffano/jenkins-api-clients-generator

522d02b3a130a29471df5ec1d3d22c822b3d0813

[ "MIT" ]

null

clients/python-fastapi/generated/src/openapi_server/models/pipeline_step_impl.py

cliffano/jenkins-api-clients-generator

522d02b3a130a29471df5ec1d3d22c822b3d0813

[ "MIT" ]

null

clients/python-fastapi/generated/src/openapi_server/models/pipeline_step_impl.py

cliffano/jenkins-api-clients-generator

522d02b3a130a29471df5ec1d3d22c822b3d0813

[ "MIT" ]

null

# coding: utf-8 from __future__ import annotations from datetime import date, datetime # noqa: F401 import re # noqa: F401 from typing import Any, Dict, List, Optional # noqa: F401 from pydantic import AnyUrl, BaseModel, EmailStr, validator # noqa: F401 from openapi_server.models.input_step_impl import InputStepImpl from openapi_server.models.pipeline_step_impllinks import PipelineStepImpllinks class PipelineStepImpl(BaseModel): """NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech). Do not edit the class manually. PipelineStepImpl - a model defined in OpenAPI _class: The _class of this PipelineStepImpl [Optional]. links: The links of this PipelineStepImpl [Optional]. display_name: The display_name of this PipelineStepImpl [Optional]. duration_in_millis: The duration_in_millis of this PipelineStepImpl [Optional]. id: The id of this PipelineStepImpl [Optional]. input: The input of this PipelineStepImpl [Optional]. result: The result of this PipelineStepImpl [Optional]. start_time: The start_time of this PipelineStepImpl [Optional]. state: The state of this PipelineStepImpl [Optional]. """ _class: Optional[str] = None links: Optional[PipelineStepImpllinks] = None display_name: Optional[str] = None duration_in_millis: Optional[int] = None id: Optional[str] = None input: Optional[InputStepImpl] = None result: Optional[str] = None start_time: Optional[str] = None state: Optional[str] = None PipelineStepImpl.update_forward_refs()

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1

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1

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1

0

4

bc71c22c730c9017fa5f0b1dbe330f244e29fcf2

256

py

Python

tests/models/control.py

IntegrCiTy/zerobnl

7daafc67f945b3797b465674272302de113f46f2

[ "Apache-2.0" ]

2

2018-10-23T12:02:25.000Z

2019-12-21T09:07:02.000Z

tests/models/control.py

IntegrCiTy/zerobnl

7daafc67f945b3797b465674272302de113f46f2

[ "Apache-2.0" ]

5

2018-11-20T07:40:37.000Z

2019-01-30T18:10:34.000Z

tests/models/control.py

IntegrCiTy/zerobnl

7daafc67f945b3797b465674272302de113f46f2

[ "Apache-2.0" ]

null

class Model: def __init__(self): self.SoC = 0.0 self.io = 0.0 def step(self, value): if self.SoC > 0.95 and self.io == 1.0: self.io = 0.0 if self.SoC < 0.05 and self.io == 0.0: self.io = 1.0

23.272727

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256

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null

1

0

null

0

1

0

4

bc8a44e9af1696b1f8944dfde715149b7c4354be

760

py

Python

exercises.py

hauntsaninja/importlib_metadata

2f05392ca980952a6960d82b2f2d2ea10aa53239

[ "Apache-2.0" ]

42

2020-10-24T16:41:15.000Z

2022-03-09T06:17:08.000Z

exercises.py

hauntsaninja/importlib_metadata

2f05392ca980952a6960d82b2f2d2ea10aa53239

[ "Apache-2.0" ]

770

2020-10-22T14:05:50.000Z

2022-03-30T15:49:13.000Z

exercises.py

hauntsaninja/importlib_metadata

2f05392ca980952a6960d82b2f2d2ea10aa53239

[ "Apache-2.0" ]

33

2020-10-24T16:50:36.000Z

2022-03-31T16:20:55.000Z

from pytest_perf.deco import extras @extras('perf') def discovery_perf(): "discovery" import importlib_metadata # end warmup importlib_metadata.distribution('ipython') def entry_points_perf(): "entry_points()" import importlib_metadata # end warmup importlib_metadata.entry_points() @extras('perf') def cached_distribution_perf(): "cached distribution" import importlib_metadata importlib_metadata.distribution('ipython') # end warmup importlib_metadata.distribution('ipython') @extras('perf') def uncached_distribution_perf(): "uncached distribution" import importlib import importlib_metadata # end warmup importlib.invalidate_caches() importlib_metadata.distribution('ipython')

20.540541

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1

0

1

0

4

bca428f1c1bab9cde2bfcb7273f1a7f8d7ce9e83

2,811

py

Python

ocial/ocial_project/topics/decorators.py

kasimbozdag/swe_574

a77fa29fd80c713cd202ccbb82cfcadfa52b81fa

[ "MIT" ]

1

2019-09-29T12:54:58.000Z

ocial/ocial_project/topics/decorators.py

kasimbozdag/swe_574

a77fa29fd80c713cd202ccbb82cfcadfa52b81fa

[ "MIT" ]

55

2019-09-26T16:29:22.000Z

2022-02-10T11:28:32.000Z

ocial/ocial_project/topics/decorators.py

kasimbozdag/swe_574

a77fa29fd80c713cd202ccbb82cfcadfa52b81fa

[ "MIT" ]

null

from django.core.exceptions import PermissionDenied from .models import * def course_teacher_is_user(function): def wrap(request, *args, **kwargs): course = Course.objects.get(pk=kwargs['course_id']) if course.teacher == request.user: return function(request, *args, **kwargs) else: raise PermissionDenied wrap.__doc__ = function.__doc__ wrap.__name__ = function.__name__ return wrap def glossary_teacher_is_user(function): def wrap(request, *args, **kwargs): glossary = Glossary.objects.get(pk=kwargs['glossary_id']) if glossary.course.teacher == request.user: return function(request, *args, **kwargs) else: raise PermissionDenied wrap.__doc__ = function.__doc__ wrap.__name__ = function.__name__ return wrap def section_teacher_is_user(function): def wrap(request, *args, **kwargs): section = Section.objects.get(pk=kwargs['section_id']) if section.course.teacher == request.user: return function(request, *args, **kwargs) else: raise PermissionDenied wrap.__doc__ = function.__doc__ wrap.__name__ = function.__name__ return wrap def lecture_teacher_is_user(function): def wrap(request, *args, **kwargs): lecture = Lecture.objects.get(pk=kwargs['lecture_id']) if lecture.section.course.teacher == request.user: return function(request, *args, **kwargs) else: raise PermissionDenied wrap.__doc__ = function.__doc__ wrap.__name__ = function.__name__ return wrap def quiz_teacher_is_user(function): def wrap(request, *args, **kwargs): quiz = Quiz.objects.get(pk=kwargs['quiz_id']) if quiz.section.course.teacher == request.user: return function(request, *args, **kwargs) else: raise PermissionDenied wrap.__doc__ = function.__doc__ wrap.__name__ = function.__name__ return wrap def question_teacher_is_user(function): def wrap(request, *args, **kwargs): question = Question.objects.get(pk=kwargs['question_id']) if question.quiz.section.course.teacher == request.user: return function(request, *args, **kwargs) else: raise PermissionDenied wrap.__doc__ = function.__doc__ wrap.__name__ = function.__name__ return wrap def choice_teacher_is_user(function): def wrap(request, *args, **kwargs): choice = Choice.objects.get(pk=kwargs['choice_id']) if choice.question.quiz.section.course.teacher == request.user: return function(request, *args, **kwargs) else: raise PermissionDenied wrap.__doc__ = function.__doc__ wrap.__name__ = function.__name__ return wrap

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bcb24fabd13ef1030f3a10f8992a5028a357bc96

36

py

Python

docs/core/howto/tutorial/listings/finger/finger/__init__.py

giadram/twisted

4771b1340b822d20d0664bb7d8334e8fb7e52863

[ "MIT", "Unlicense" ]

4,612

2015-01-01T12:57:23.000Z

2022-03-30T01:08:23.000Z

docs/core/howto/tutorial/listings/finger/finger/__init__.py

giadram/twisted

4771b1340b822d20d0664bb7d8334e8fb7e52863

[ "MIT", "Unlicense" ]

1,243

2015-01-23T17:23:59.000Z

2022-03-28T13:46:17.000Z

docs/core/howto/tutorial/listings/finger/finger/__init__.py

giadram/twisted

4771b1340b822d20d0664bb7d8334e8fb7e52863

[ "MIT", "Unlicense" ]

1,236

2015-01-13T14:41:26.000Z

2022-03-17T07:12:36.000Z

""" Finger example application. """

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null

0

1

0

4

bcc8de202bbd86325e25bc06d7290dd13396e94e

8,679

py

Python

test/test_recog.py

gaochangfeng/espnet

dcb281a0a9eb52433dd4f8338b163f592e635303

[ "Apache-2.0" ]

5

2021-04-17T13:12:20.000Z

2022-02-22T09:36:45.000Z

test/test_recog.py

JaejinCho/espnet_spkidtts

a52bdebb08558b63df23564d6e67dfcba8a41d78

[ "Apache-2.0" ]

null

test/test_recog.py

JaejinCho/espnet_spkidtts

a52bdebb08558b63df23564d6e67dfcba8a41d78

[ "Apache-2.0" ]

5

2020-02-24T08:13:54.000Z

2022-02-22T09:03:09.000Z

# coding: utf-8 # Copyright 2018 Hiroshi Seki # Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) import espnet.nets.pytorch_backend.lm.default as lm_pytorch import espnet.lm.chainer_backend.lm as lm_chainer import argparse import importlib import numpy import pytest def make_arg(**kwargs): defaults = dict( elayers=4, subsample="1_2_2_1_1", etype="blstmp", eunits=100, eprojs=100, dtype="lstm", dlayers=1, dunits=300, atype="location", aconv_chans=10, aconv_filts=100, mtlalpha=0.5, lsm_type="", lsm_weight=0.0, sampling_probability=0.0, adim=320, dropout_rate=0.0, dropout_rate_decoder=0.0, nbest=5, beam_size=3, penalty=0.5, maxlenratio=1.0, minlenratio=0.0, ctc_weight=0.2, ctc_window_margin=0, verbose=2, char_list=["a", "i", "u", "e", "o"], outdir=None, ctc_type="warpctc", report_cer=False, report_wer=False, sym_space="<space>", sym_blank="<blank>", context_residual=False, use_frontend=False, replace_sos=False, tgt_lang=False ) defaults.update(kwargs) return argparse.Namespace(**defaults) def init_torch_weight_const(m, val): for p in m.parameters(): p.data.fill_(val) def init_chainer_weight_const(m, val): for p in m.params(): p.data[:] = val @pytest.mark.parametrize(("etype", "dtype", "m_str", "text_idx1"), [ ("blstmp", "lstm", "espnet.nets.chainer_backend.e2e_asr", 0), ("blstmp", "lstm", "espnet.nets.pytorch_backend.e2e_asr", 1), ("vggblstmp", "lstm", "espnet.nets.chainer_backend.e2e_asr", 2), ("vggblstmp", "lstm", "espnet.nets.pytorch_backend.e2e_asr", 3), ("bgrup", "gru", "espnet.nets.chainer_backend.e2e_asr", 4), ("bgrup", "gru", "espnet.nets.pytorch_backend.e2e_asr", 5), ("vggbgrup", "gru", "espnet.nets.chainer_backend.e2e_asr", 6), ("vggbgrup", "gru", "espnet.nets.pytorch_backend.e2e_asr", 7), ]) def test_recognition_results(etype, dtype, m_str, text_idx1): const = 1e-4 numpy.random.seed(1) seq_true_texts = ([["o", "iuiuiuiuiuiuiuiuo", "aiaiaiaiaiaiaiaio"], ["o", "uiuiuiuiuiuiuiuio", "aiaiaiaiaiaiaiaio"], ["o", "iuiuiuiuiuiuiuiuo", "aiaiaiaiaiaiaiaio"], ["o", "uiuiuiuiuiuiuiuio", "aiaiaiaiaiaiaiaio"], ["o", "iuiuiuiuiuiuiuiuo", "aiaiaiaiaiaiaiaio"], ["o", "uiuiuiuiuiuiuiuio", "aiaiaiaiaiaiaiaio"], ["o", "iuiuiuiuiuiuiuiuo", "aiaiaiaiaiaiaiaio"], ["o", "uiuiuiuiuiuiuiuio", "aiaiaiaiaiaiaiaio"]]) # ctc_weight: 0.0 (attention), 0.5 (hybrid CTC/attention), 1.0 (CTC) for text_idx2, ctc_weight in enumerate([0.0, 0.5, 1.0]): seq_true_text = seq_true_texts[text_idx1][text_idx2] args = make_arg(etype=etype, ctc_weight=ctc_weight) m = importlib.import_module(m_str) model = m.E2E(40, 5, args) if "pytorch" in m_str: init_torch_weight_const(model, const) else: init_chainer_weight_const(model, const) data = [ ("aaa", dict(feat=numpy.random.randn(100, 40).astype( numpy.float32), token=seq_true_text)) ] in_data = data[0][1]["feat"] nbest_hyps = model.recognize(in_data, args, args.char_list) y_hat = nbest_hyps[0]['yseq'][1:] seq_hat = [args.char_list[int(idx)] for idx in y_hat] seq_hat_text = "".join(seq_hat).replace('<space>', ' ') seq_true_text = data[0][1]["token"] assert seq_hat_text == seq_true_text @pytest.mark.parametrize(("etype", "dtype", "m_str", "text_idx1"), [ ("blstmp", "lstm", "espnet.nets.chainer_backend.e2e_asr", 0), ("blstmp", "lstm", "espnet.nets.pytorch_backend.e2e_asr", 1), ("vggblstmp", "lstm", "espnet.nets.chainer_backend.e2e_asr", 2), ("vggblstmp", "lstm", "espnet.nets.pytorch_backend.e2e_asr", 3), ("bgrup", "gru", "espnet.nets.chainer_backend.e2e_asr", 4), ("bgrup", "gru", "espnet.nets.pytorch_backend.e2e_asr", 5), ("vggbgrup", "gru", "espnet.nets.chainer_backend.e2e_asr", 6), ("vggbgrup", "gru", "espnet.nets.pytorch_backend.e2e_asr", 7), ]) def test_recognition_results_with_lm(etype, dtype, m_str, text_idx1): const = 1e-4 numpy.random.seed(1) seq_true_texts = [["o", "iuiuiuiuiuiuiuiuo", "aiaiaiaiaiaiaiaio"], ["o", "uiuiuiuiuiuiuiuio", "aiaiaiaiaiaiaiaio"], ["o", "iuiuiuiuiuiuiuiuo", "aiaiaiaiaiaiaiaio"], ["o", "uiuiuiuiuiuiuiuio", "aiaiaiaiaiaiaiaio"], ["o", "iuiuiuiuiuiuiuiuo", "aiaiaiaiaiaiaiaio"], ["o", "uiuiuiuiuiuiuiuio", "aiaiaiaiaiaiaiaio"], ["o", "iuiuiuiuiuiuiuiuo", "aiaiaiaiaiaiaiaio"], ["o", "uiuiuiuiuiuiuiuio", "aiaiaiaiaiaiaiaio"]] # ctc_weight: 0.0 (attention), 0.5 (hybrid CTC/attention), 1.0 (CTC) for text_idx2, ctc_weight in enumerate([0.0, 0.5, 1.0]): seq_true_text = seq_true_texts[text_idx1][text_idx2] args = make_arg(etype=etype, rnnlm="dummy", ctc_weight=ctc_weight, lm_weight=0.3) m = importlib.import_module(m_str) model = m.E2E(40, 5, args) if "pytorch" in m_str: rnnlm = lm_pytorch.ClassifierWithState( lm_pytorch.RNNLM(len(args.char_list), 2, 10)) init_torch_weight_const(model, const) init_torch_weight_const(rnnlm, const) else: rnnlm = lm_chainer.ClassifierWithState( lm_chainer.RNNLM(len(args.char_list), 2, 10)) init_chainer_weight_const(model, const) init_chainer_weight_const(rnnlm, const) data = [ ("aaa", dict(feat=numpy.random.randn(100, 40).astype( numpy.float32), token=seq_true_text)) ] in_data = data[0][1]["feat"] nbest_hyps = model.recognize(in_data, args, args.char_list, rnnlm) y_hat = nbest_hyps[0]['yseq'][1:] seq_hat = [args.char_list[int(idx)] for idx in y_hat] seq_hat_text = "".join(seq_hat).replace('<space>', ' ') seq_true_text = data[0][1]["token"] assert seq_hat_text == seq_true_text @pytest.mark.parametrize(("etype", "dtype", "m_str"), [ ("blstmp", "lstm", "espnet.nets.chainer_backend.e2e_asr"), ("blstmp", "lstm", "espnet.nets.pytorch_backend.e2e_asr"), ("vggblstmp", "lstm", "espnet.nets.chainer_backend.e2e_asr"), ("vggblstmp", "lstm", "espnet.nets.pytorch_backend.e2e_asr"), ("bgrup", "gru", "espnet.nets.chainer_backend.e2e_asr"), ("bgrup", "gru", "espnet.nets.pytorch_backend.e2e_asr"), ("vggbgrup", "gru", "espnet.nets.chainer_backend.e2e_asr"), ("vggbgrup", "gru", "espnet.nets.pytorch_backend.e2e_asr"), ]) def test_batch_beam_search(etype, dtype, m_str): const = 1e-4 numpy.random.seed(1) # ctc_weight: 0.0 (attention), 0.5 (hybrid CTC/attention), 1.0 (CTC) for ctc_weight in [0.0, 0.5]: args = make_arg(etype=etype, rnnlm="dummy", ctc_weight=ctc_weight, lm_weight=0.3) m = importlib.import_module(m_str) model = m.E2E(40, 5, args) if "pytorch" in m_str: rnnlm = lm_pytorch.ClassifierWithState( lm_pytorch.RNNLM(len(args.char_list), 2, 10)) init_torch_weight_const(model, const) init_torch_weight_const(rnnlm, const) else: # chainer module continue data = [("aaa", dict(feat=numpy.random.randn(100, 40).astype(numpy.float32)))] in_data = data[0][1]["feat"] for lm_weight in [0.0, 0.3]: if lm_weight == 0.0: s_nbest_hyps = model.recognize(in_data, args, args.char_list) b_nbest_hyps = model.recognize_batch([in_data], args, args.char_list) else: s_nbest_hyps = model.recognize(in_data, args, args.char_list, rnnlm) b_nbest_hyps = model.recognize_batch([in_data], args, args.char_list, rnnlm) assert s_nbest_hyps[0]['yseq'] == b_nbest_hyps[0][0]['yseq'] if ctc_weight > 0.0: args.ctc_window_margin = 40 s_nbest_hyps = model.recognize(in_data, args, args.char_list, rnnlm) b_nbest_hyps = model.recognize_batch([in_data], args, args.char_list, rnnlm) assert s_nbest_hyps[0]['yseq'] == b_nbest_hyps[0][0]['yseq']

38.402655

92

0.596613

1,102

8,679

4.460073

0.160617

0.050865

0.063479

0.7941

0.78881

0.76826

0.763377

0.683011

0

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8,679

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null

0

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0

1

0

null

0

4

bccf4e06a8dd034bb58dadc034b3381a06cabb21

1,451

py

Python

fHDHR_web/rmg/__init__.py

deathbybandaid/fHDHR_NewsOn

06d205a3ca677b88fa93b9b7503465aed1838c6b

[ "WTFPL" ]

2

2021-11-21T18:45:35.000Z

2022-01-11T16:11:48.000Z

fHDHR_web/rmg/__init__.py

deathbybandaid/fHDHR_NewsOn

06d205a3ca677b88fa93b9b7503465aed1838c6b

[ "WTFPL" ]

null

fHDHR_web/rmg/__init__.py

deathbybandaid/fHDHR_NewsOn

06d205a3ca677b88fa93b9b7503465aed1838c6b

[ "WTFPL" ]

null

from .rmg_ident_xml import RMG_Ident_XML from .device_xml import RMG_Device_XML from .devices_discover import RMG_Devices_Discover from .devices_probe import RMG_Devices_Probe from .devices_devicekey import RMG_Devices_DeviceKey from .devices_devicekey_channels import RMG_Devices_DeviceKey_Channels from .devices_devicekey_scanners import RMG_Devices_DeviceKey_Scanners from .devices_devicekey_networks import RMG_Devices_DeviceKey_Networks from .devices_devicekey_scan import RMG_Devices_DeviceKey_Scan from .devices_devicekey_prefs import RMG_Devices_DeviceKey_Prefs from .devices_devicekey_media import RMG_Devices_DeviceKey_Media class fHDHR_RMG(): def __init__(self, fhdhr): self.fhdhr = fhdhr self.rmg_ident_xml = RMG_Ident_XML(fhdhr) self.device_xml = RMG_Device_XML(fhdhr) self.devices_discover = RMG_Devices_Discover(fhdhr) self.devices_probe = RMG_Devices_Probe(fhdhr) self.devices_devicekey = RMG_Devices_DeviceKey(fhdhr) self.devices_devicekey_channels = RMG_Devices_DeviceKey_Channels(fhdhr) self.devices_devicekey_scanners = RMG_Devices_DeviceKey_Scanners(fhdhr) self.devices_devicekey_networks = RMG_Devices_DeviceKey_Networks(fhdhr) self.devices_devicekey_scan = RMG_Devices_DeviceKey_Scan(fhdhr) self.devices_devicekey_prefs = RMG_Devices_DeviceKey_Prefs(fhdhr) self.devices_devicekey_media = RMG_Devices_DeviceKey_Media(fhdhr)

46.806452

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0.241818

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null

1

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null

0

1

0

1

0

4

bcdd33143d14e2c1810f736cbf4939c6ec53a65b

328

py

Python

vernam/test_util.py

millaguie/Vernam

127b8e3c7d221e4736e66f0e82810404b4d24bd7

[ "BSD-3-Clause" ]

4

2018-07-21T22:41:51.000Z

2021-11-17T11:16:27.000Z

vernam/test_util.py

millaguie/Vernam

127b8e3c7d221e4736e66f0e82810404b4d24bd7

[ "BSD-3-Clause" ]

1

2017-06-07T16:32:37.000Z

vernam/test_util.py

millaguie/Vernam

127b8e3c7d221e4736e66f0e82810404b4d24bd7

[ "BSD-3-Clause" ]

1

2017-06-01T17:21:25.000Z

import unittest import util class SimplisticTest(unittest.TestCase): def test_getKeyHashFromKey(self): assert util.hashSum("AAA") == "8d708d18b54df3962d696f069ad42dad7762b5d4d3c97ee5fa2dae0673ed46545164c078b8db3d59c4b96020e4316f17bb3d91bf1f6bc0896bbe75416eb8c385" if __name__ == '__main__': unittest.main()

29.818182

170

0.810976

22

328

11.681818

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0

0.275862

0.115854

328

10

171

32.8

0.610345

0

0.42378

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false

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0

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0

1

null

0

1

0

null

0

1

0

4

bceab3b511835b23703820db69dbae9dae102bf1

377

py

Python

yelp/models/user.py

cwithmichael/yelp-camp-flask

15d05285ff198256c396e51456a9a88bc836a342

[ "MIT" ]

null

yelp/models/user.py

cwithmichael/yelp-camp-flask

15d05285ff198256c396e51456a9a88bc836a342

[ "MIT" ]

null

yelp/models/user.py

cwithmichael/yelp-camp-flask

15d05285ff198256c396e51456a9a88bc836a342

[ "MIT" ]

null

import mongoengine as me def _not_empty(val): if not val: raise me.ValidationError("value can not be empty") class User(me.Document): email = me.StringField(required=True, unique=True, validation=_not_empty) username = me.StringField(required=True, unique=True, validation=_not_empty) password = me.StringField(required=True, validation=_not_empty)

29

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null

0

1

0

null

0

1

0

1

0

4

4c036440909eee3fac3c5546dc84ff6a3b70b68d

216

py

Python

wsl_make_install.py

tacesrever/Il2CppParser

2c588761f8f70a63270c3b14b06f43259ecc5ea2

[ "MIT" ]

35

2019-12-24T15:34:11.000Z

2022-02-26T07:13:15.000Z

wsl_make_install.py

tacesrever/Il2CppParser

2c588761f8f70a63270c3b14b06f43259ecc5ea2

[ "MIT" ]

null

wsl_make_install.py

tacesrever/Il2CppParser

2c588761f8f70a63270c3b14b06f43259ecc5ea2

[ "MIT" ]

13

2020-01-11T01:52:56.000Z

2021-09-29T17:25:52.000Z

#!/usr/bin/python import sys, os os.system("wslbridge sh -c 'cd build;make'") os.system("adb push build/libparser.so /data/local/tmp/libparser.so") os.system("adb shell chmod 0755 /data/local/tmp/libparser.so")

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1

0

1

0

null

0

1

0

4

4c33c5fc108bb117e42df026b80b64486ee395a7

1,817

py

Python

deep-rl/lib/python2.7/site-packages/OpenGL/GL/ARB/texture_storage.py

ShujaKhalid/deep-rl

99c6ba6c3095d1bfdab81bd01395ced96bddd611

[ "MIT" ]

210

2016-04-09T14:26:00.000Z

2022-03-25T18:36:19.000Z

deep-rl/lib/python2.7/site-packages/OpenGL/GL/ARB/texture_storage.py

ShujaKhalid/deep-rl

99c6ba6c3095d1bfdab81bd01395ced96bddd611

[ "MIT" ]

72

2016-09-04T09:30:19.000Z

2022-03-27T17:06:53.000Z

deep-rl/lib/python2.7/site-packages/OpenGL/GL/ARB/texture_storage.py

ShujaKhalid/deep-rl

99c6ba6c3095d1bfdab81bd01395ced96bddd611

[ "MIT" ]

64

2016-04-09T14:26:49.000Z

2022-03-21T11:19:47.000Z

'''OpenGL extension ARB.texture_storage This module customises the behaviour of the OpenGL.raw.GL.ARB.texture_storage to provide a more Python-friendly API Overview (from the spec) The texture image specification commands in OpenGL allow each level to be separately specified with different sizes, formats, types and so on, and only imposes consistency checks at draw time. This adds overhead for implementations. This extension provides a mechanism for specifying the entire structure of a texture in a single call, allowing certain consistency checks and memory allocations to be done up front. Once specified, the format and dimensions of the image array become immutable, to simplify completeness checks in the implementation. When using this extension, it is no longer possible to supply texture data using TexImage*. Instead, data can be uploaded using TexSubImage*, or produced by other means (such as render-to-texture, mipmap generation, or rendering to a sibling EGLImage). This extension has complicated interactions with other extensions. The goal of most of these interactions is to ensure that a texture is always mipmap complete (and cube complete for cubemap textures). The official definition of this extension is available here: http://www.opengl.org/registry/specs/ARB/texture_storage.txt ''' from OpenGL import platform, constant, arrays from OpenGL import extensions, wrapper import ctypes from OpenGL.raw.GL import _types, _glgets from OpenGL.raw.GL.ARB.texture_storage import * from OpenGL.raw.GL.ARB.texture_storage import _EXTENSION_NAME def glInitTextureStorageARB(): '''Return boolean indicating whether this extension is available''' from OpenGL import extensions return extensions.hasGLExtension( _EXTENSION_NAME ) ### END AUTOGENERATED SECTION

40.377778

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1,817

5.530534

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0.041408

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null

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1

0

1

0

1

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4

4c3642f8c2592b63c03155615c1082669105f0de

248

py

Python

service/common/errors.py

DSAdv/student-question-answer-app

bbc3cec15cd37ecb7bc6703f2324b1ae24981ae4

[ "MIT" ]

null

service/common/errors.py

DSAdv/student-question-answer-app

bbc3cec15cd37ecb7bc6703f2324b1ae24981ae4

[ "MIT" ]

null

service/common/errors.py

DSAdv/student-question-answer-app

bbc3cec15cd37ecb7bc6703f2324b1ae24981ae4

[ "MIT" ]

null

from werkzeug.exceptions import BadRequest class IncorrectRequestBodyError(BadRequest): message = "[API ERROR] Incorrect fields in request body." class ExistingUserError(BadRequest): message = "[API ERROR] User is already exist in DB."

24.8

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248

6.785714

0.75

0.178947

0.210526

0.263158

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0.157258

248

9

62

27.555556

0.909091

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null

0

1

0

1

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null

0

4

4c4692ecbf210acbc5aef9568a9dc8a80690535c

106

py

Python

client.py

daliasen/LED-Cube

3959ee5caf86c1497ac22231d87a8009bed5b3e8

[ "BSD-3-Clause" ]

4

2018-08-19T09:16:40.000Z

2020-01-27T13:18:19.000Z

client.py

daliasen/LED-Cube

3959ee5caf86c1497ac22231d87a8009bed5b3e8

[ "BSD-3-Clause" ]

null

client.py

daliasen/LED-Cube

3959ee5caf86c1497ac22231d87a8009bed5b3e8

[ "BSD-3-Clause" ]

3

2018-08-09T13:30:29.000Z

2020-01-26T16:19:23.000Z

#!/usr/bin/env python from client import main import sys if __name__ == "__main__": main(sys.argv[1])

13.25

26

0.698113

17

106

3.882353

0.764706

0

0.011236

0.160377

106

7

27

15.142857

0.730337

0.188679

0

0.095238

0

1

0

true

0

0.5

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0.5

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null

0

1

0

null

0

1

0

1

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4

4c516b623bad0f9b6592d07c1a35efbe8fc98cf2

750

py

Python

onnxmltools/convert/keras/operator_converters/__init__.py

weikexin/onnxmltools

b5ea8a43bb0abf5ca23f0913dc2d9ea11b9724b1

[ "MIT" ]

null

onnxmltools/convert/keras/operator_converters/__init__.py

weikexin/onnxmltools

b5ea8a43bb0abf5ca23f0913dc2d9ea11b9724b1

[ "MIT" ]

null

onnxmltools/convert/keras/operator_converters/__init__.py

weikexin/onnxmltools

b5ea8a43bb0abf5ca23f0913dc2d9ea11b9724b1

[ "MIT" ]

null

# ------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- from . import Activation from . import AdvancedActivation from . import BatchNorm from . import Concate from . import Conv from . import Crop from . import Dense from . import Dot from . import Embed from . import Flatten from . import GRU from . import LSTM from . import Merge from . import Permute from . import Pool from . import RepeatVector from . import Reshape from . import SimpleRNN from . import Upsample from . import ZeroPad

26.785714

76

0.614667

82

750

5.621951

0.5

0.433839

0

0.146667

750

27

77

27.777778

0.720313

0.398667

0

1

0

true

0

1

0

1

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null

1

0

null

0

1

0

1

0

1

0

4

d5cfdc8645784cc11aa3ad2ed1f9207baeddf21f

628

py

Python

qiime2/core/archive/format/v3.py

turanoo/qiime2

2af79e1a81b35b396b1a80e01617dba0f4e10446

[ "BSD-3-Clause" ]

null

qiime2/core/archive/format/v3.py

turanoo/qiime2

2af79e1a81b35b396b1a80e01617dba0f4e10446

[ "BSD-3-Clause" ]

null

qiime2/core/archive/format/v3.py

turanoo/qiime2

2af79e1a81b35b396b1a80e01617dba0f4e10446

[ "BSD-3-Clause" ]

null

# ---------------------------------------------------------------------------- # Copyright (c) 2016-2018, QIIME 2 development team. # # Distributed under the terms of the Modified BSD License. # # The full license is in the file LICENSE, distributed with this software. # ---------------------------------------------------------------------------- import qiime2.core.archive.format.v2 as v2 class ArchiveFormat(v2.ArchiveFormat): # Exactly the same as v2, but inputs may be variadic where the UUIDs are in # a YAML sequence. Additionally `Set` is now represented as a sequence # with a custom !set tag. pass

36.941176

79

0.552548

73

628

4.753425

0.712329

0.023055

0

0.026365

0.154459

628

16

80

39.25

0.627119

0.797771

0

1

0

true

0.333333

0

0.666667

0

null

0

null

0

1

0

1

0

4

d5d2dc1c777ace4aad022048ea9e7ef9a7e47b90

68

py

Python

a.py

jonschull/Lyte

e9ba2bb1b07c9398b81a6f591898d2474d1a4609

[ "MIT" ]

1

2018-06-07T17:54:27.000Z

a.py

jonschull/Lyte

e9ba2bb1b07c9398b81a6f591898d2474d1a4609

[ "MIT" ]

1

2018-06-28T05:08:57.000Z

a.py

jonschull/Lyte

e9ba2bb1b07c9398b81a6f591898d2474d1a4609

[ "MIT" ]

null

import makemyPYJ if __name__=='__main__': print('this is a.py')

17

25

0.691176

10

68

3.9

1

0

0.161765

68

3

26

22.666667

0.684211

0

0.294118

0

1

0

true

0

0.333333

0

0.333333

1

0

null

0

1

0

null

0

1

0

1

0

4

d5f136510bda08b8dca24ae90cee3727da3c9218

287

py

Python

python_100/Level1/39.get_output!!!!!!.py

relax-space/python-cy

eaf4650756e7ece5ec97894b65a7495b5c964eb3

[ "Apache-2.0" ]

1

2020-04-27T03:31:23.000Z

python_100/Level1/39.get_output!!!!!!.py

relax-space/python-cy

eaf4650756e7ece5ec97894b65a7495b5c964eb3

[ "Apache-2.0" ]

1

2020-04-14T23:55:19.000Z

2020-04-15T03:29:37.000Z

python_100/Level1/39.get_output!!!!!!.py

relax-space/python-cy

eaf4650756e7ece5ec97894b65a7495b5c964eb3

[ "Apache-2.0" ]

null

# 39.阅读一下代码他们的输出结果是什么？ def multi(): return [lambda x : i*x for i in range(4)] print([m(3) for m in multi()]) # 输出的应该是 # [0,3,6,9] # 正确答案是[9,9,9,9]，而不是[0,3,6,9]产生的原因是Python的闭包的后期绑定导致的，这意味着在闭包中的变量是在内部函数被调用的时候被查找的，因为，最后函数被调用的时候，for循环已经完成, i 的值最后是3,因此每一个返回值的i都是3,所以最后的结果是[9,9,9,9]

20.5

146

0.696864

49

287

4.081633

0.591837

0.06

0.04

0

0.087649

0.125436

287

13

147

22.076923

0.709163

0.634146

0

1

0.333333

true

0

0.333333

0.666667

0.333333

0

null

0

null

0

1

0

1

0

4

9107a8dbcc754643b25c07ad4aa109966c976c3d

142

py

Python

src/ns_web_crawler/ns_web_crawler/eshop/eshop_costants.py

steny138/PyNintendoEPrice

def9c95690cf3cf72615ae4216fee8fca2934de1

[ "Apache-2.0" ]

null

src/ns_web_crawler/ns_web_crawler/eshop/eshop_costants.py

steny138/PyNintendoEPrice

def9c95690cf3cf72615ae4216fee8fca2934de1

[ "Apache-2.0" ]

3

2020-06-22T15:38:18.000Z

2021-11-24T02:01:51.000Z

src/ns_web_crawler/ns_web_crawler/eshop/eshop_costants.py

steny138/PyNintendoEPrice

def9c95690cf3cf72615ae4216fee8fca2934de1

[ "Apache-2.0" ]

1

2018-08-04T08:15:05.000Z

import re def check_nsuid(nsuid): if not nsuid: return False match = re.match(r'7\d+$', nsuid) return not match is None

15.777778

37

0.619718

23

142

3.782609

0.652174

0.252874

0

0.009804

0.28169

142

9

38

15.777778

0.843137

0

0.034965

0

1

0.166667

false

0

0.166667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

4

910903b76366135e306fd0117be362fab2ce92c9

265

py

Python

gdal/run_test.py

stevemkim/conda-recipes

4fa403587b187d87cd6f77abf0b24b8c3f351564

[ "Apache-2.0" ]

null

gdal/run_test.py

stevemkim/conda-recipes

4fa403587b187d87cd6f77abf0b24b8c3f351564

[ "Apache-2.0" ]

null

gdal/run_test.py

stevemkim/conda-recipes

4fa403587b187d87cd6f77abf0b24b8c3f351564

[ "Apache-2.0" ]

null

#import osgeo._gdal #import osgeo._gdalconst #import osgeo._ogr #import osgeo._osr #import osgeo #import gdal #import gdalconst #import ogr #import osr # #cnt = ogr.GetDriverCount() #for i in xrange(cnt): # print ogr.GetDriver(i).GetName() # #import os1_hw pass

15.588235

37

0.743396

39

265

4.923077

0.461538

0.286458

0

0.004367

0.135849

265

16

38

16.5625

0.834061

0.867925

0

1

0

true

1

0

null

1

0

1

0

null

0

1

0

4

91222ac9ec4b6b1830c54677dd6133e135352430

389

py

Python

h5py/run_test.py

nikicc/anaconda-recipes

9c611a5854bf41bbc5e7ed9853dc71c0851a62ef

[ "BSD-3-Clause" ]

130

2015-07-28T03:41:21.000Z

2022-03-16T03:07:41.000Z

h5py/run_test.py

nikicc/anaconda-recipes

9c611a5854bf41bbc5e7ed9853dc71c0851a62ef

[ "BSD-3-Clause" ]

119

2015-08-01T00:54:06.000Z

2021-01-05T13:00:46.000Z

h5py/run_test.py

nikicc/anaconda-recipes

9c611a5854bf41bbc5e7ed9853dc71c0851a62ef

[ "BSD-3-Clause" ]

72

2015-07-29T02:35:56.000Z

2022-02-26T14:31:15.000Z

import h5py._conv import h5py._errors import h5py._objects import h5py._proxy import h5py.defs import h5py.h5 import h5py.h5a import h5py.h5ac import h5py.h5d import h5py.h5ds import h5py.h5f import h5py.h5fd import h5py.h5g import h5py.h5i import h5py.h5l import h5py.h5o import h5py.h5p import h5py.h5r import h5py.h5s import h5py.h5t import h5py.h5z import h5py.utils h5py.run_tests()

15.56

20

0.817481

69

389

4.536232

0.376812

0.702875

0

0.113703

0.118252

389

24

21

16.208333

0.798834

0

1

0

true

0

0.956522

0

0.956522

0

null

1

0

null

0

1

0

1

0

1

0

4

9128cfb0fa0ed1b65839adaefd198b312660e5d9

92

py

Python

src/server/python/tf1group/lcilive.py

nicojqn/Livy

d5076a493747563d8e40600d52371df888c75d27

[ "MIT" ]

null

src/server/python/tf1group/lcilive.py

nicojqn/Livy

d5076a493747563d8e40600d52371df888c75d27

[ "MIT" ]

null

src/server/python/tf1group/lcilive.py

nicojqn/Livy

d5076a493747563d8e40600d52371df888c75d27

[ "MIT" ]

null

from livetf1group import * if __name__=="__main__": print(get_live_url(str("lci")).path)

30.666667

40

0.728261

13

92

4.384615

1

0

0.012195

0.108696

92

3

40

30.666667

0.682927

0

0.11828

0

1

0

true

0

0.333333

0

0.333333

1

0

null

0

1

0

null

0

1

0

1

0

4

913f9f47a16001f36a363b0e5be88afe4588cdda

894

py

Python

malib/rpc/ExperimentManager/base_client.py

ReinholdM/play_football_with_human

9ac2f0a8783aede56f4ac1f6074db7daa41b6b6c

[ "MIT" ]

5

2021-11-17T03:11:13.000Z

2021-12-23T09:04:21.000Z

malib/rpc/ExperimentManager/base_client.py

ReinholdM/play_football_with_human

9ac2f0a8783aede56f4ac1f6074db7daa41b6b6c

[ "MIT" ]

null

malib/rpc/ExperimentManager/base_client.py

ReinholdM/play_football_with_human

9ac2f0a8783aede56f4ac1f6074db7daa41b6b6c

[ "MIT" ]

null

# -*- encoding: utf-8 -*- # ----- # Created Date: 2021/2/20 # Author: Hanjing Wang # ----- # Last Modified: # Modified By: # ----- # Copyright (c) 2020 MARL @ SJTU # ----- import abc from malib.utils.typing import Any class BaseClient(abc.ABC): def info(self, level: str, message: str, nid: str): pass @abc.abstractmethod def create_table(self, primary: str, secondary: str, nid: str): pass @abc.abstractmethod def send_scalar(self, key: int, tag: str, nid: str, content: Any): pass @abc.abstractmethod def send_image(self, key, tag, image, serial): pass @abc.abstractmethod def send_figure(self, key, tag, nid, figure): pass @abc.abstractmethod def send_obj(self, key, tag, nid, obj, serial): pass @abc.abstractmethod def send_binary_tensor(self, key, tag, nid, tensor): pass

20.790698

70

0.612975

116

894

4.663793

0.456897

0.077634

0.232902

0.266174

0.358595

0.247689

0.121996

0

0.017884

0.249441

894

42

71

21.285714

0.788376

0.168904

0

0.565217

0

1

0.304348

false

0.304348

0.086957

0

0.434783

0

null

0

1

0

null

0

1

0

1

0

4

e66b90d0e6be07b4f5e8426ce6b45fbffaf586c8

109

py

Python

Part 1/Chapter 7/exercise_7.1.py

kg55555/pypractice

1867f001b3d2a7174ea00d7b9e2fa22e9f1877ef

[ "MIT" ]

null

Part 1/Chapter 7/exercise_7.1.py

kg55555/pypractice

1867f001b3d2a7174ea00d7b9e2fa22e9f1877ef

[ "MIT" ]

null

Part 1/Chapter 7/exercise_7.1.py

kg55555/pypractice

1867f001b3d2a7174ea00d7b9e2fa22e9f1877ef

[ "MIT" ]

null

car = input("What kind of a car would you like to buy?\n") print(f"Let me see if I can find a {car} for you")

54.5

58

0.678899

26

109

2.846154

0.846154

0.108108

0

0.201835

109

2

59

54.5

0.850575

0

0.754545

0

1

0

false

0

0.5

1

0

null

0

1

0

1

0

null

0

1

0

4

e686ee0edde33198cdaa2a2a246bbc2ef23c913c

173

py

Python

lista_ex5.2.py/exercicio2.py

robinson-1985/mentoria_exercises

8359cead6ee5351851b04cb45f252e3881b79117

[ "MIT" ]

null

lista_ex5.2.py/exercicio2.py

robinson-1985/mentoria_exercises

8359cead6ee5351851b04cb45f252e3881b79117

[ "MIT" ]

null

lista_ex5.2.py/exercicio2.py

robinson-1985/mentoria_exercises

8359cead6ee5351851b04cb45f252e3881b79117

[ "MIT" ]

null

''' 2. Escreva um programa que exiba na tela a quantidade de números ímpares existentes entre dois números que o usuário digitar (testar inclusive os números digitados).'''

86.5

88

0.786127

26

173

5.230769

0.884615

0

0.006849

0.156069

173

2

89

86.5

0.924658

0.959538

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

4

e690a85378b5314181ba83245ca53e9163ff4e7f

41

py

Python

scrapy/tests/test_utils_template.py

emschorsch/scrapy

acb7bad1ff4037b4a613ac94e2d3357bf92bdb8f

[ "BSD-3-Clause" ]

26

2015-02-07T17:35:26.000Z

2020-04-27T21:11:00.000Z

scrapy/tests/test_utils_template.py

emschorsch/scrapy

acb7bad1ff4037b4a613ac94e2d3357bf92bdb8f

[ "BSD-3-Clause" ]

10

2020-02-11T23:34:28.000Z

2022-03-11T23:16:12.000Z

scrapy/tests/test_utils_template.py

emschorsch/scrapy

acb7bad1ff4037b4a613ac94e2d3357bf92bdb8f

[ "BSD-3-Clause" ]

9

2015-09-21T08:17:20.000Z

2021-02-07T02:31:36.000Z

__doctests__ = ['scrapy.utils.template']

20.5

40

0.756098

4

41

6.75

1

0

0.073171

41

1

41

0.710526

0

0.512195

0

1

0

false

0

1

0

null

0

1

0

1

null

0

4

e69c7de52621d46625d5d4ae90db8fbe3c914a5f

12,995

py

Python

spark_fhir_schemas/r4/complex_types/structuremap_target.py

icanbwell/SparkFhirSchemas

8c828313c39850b65f8676e67f526ee92b7d624e

[ "Apache-2.0" ]

null

spark_fhir_schemas/r4/complex_types/structuremap_target.py

icanbwell/SparkFhirSchemas

8c828313c39850b65f8676e67f526ee92b7d624e

[ "Apache-2.0" ]

null

spark_fhir_schemas/r4/complex_types/structuremap_target.py

icanbwell/SparkFhirSchemas

8c828313c39850b65f8676e67f526ee92b7d624e

[ "Apache-2.0" ]

null

from typing import Union, List, Optional from pyspark.sql.types import StructType, StructField, StringType, ArrayType, DataType # This file is auto-generated by generate_schema so do not edit it manually # noinspection PyPep8Naming class StructureMap_TargetSchema: """ A Map of relationships between 2 structures that can be used to transform data. """ # noinspection PyDefaultArgument @staticmethod def get_schema( max_nesting_depth: Optional[int] = 6, nesting_depth: int = 0, nesting_list: List[str] = [], max_recursion_limit: Optional[int] = 2, include_extension: Optional[bool] = False, extension_fields: Optional[List[str]] = None, extension_depth: int = 0, max_extension_depth: Optional[int] = 2, include_modifierExtension: Optional[bool] = False, use_date_for: Optional[List[str]] = None, parent_path: Optional[str] = "", ) -> Union[StructType, DataType]: """ A Map of relationships between 2 structures that can be used to transform data. id: Unique id for the element within a resource (for internal references). This may be any string value that does not contain spaces. extension: May be used to represent additional information that is not part of the basic definition of the element. To make the use of extensions safe and manageable, there is a strict set of governance applied to the definition and use of extensions. Though any implementer can define an extension, there is a set of requirements that SHALL be met as part of the definition of the extension. modifierExtension: May be used to represent additional information that is not part of the basic definition of the element and that modifies the understanding of the element in which it is contained and/or the understanding of the containing element's descendants. Usually modifier elements provide negation or qualification. To make the use of extensions safe and manageable, there is a strict set of governance applied to the definition and use of extensions. Though any implementer can define an extension, there is a set of requirements that SHALL be met as part of the definition of the extension. Applications processing a resource are required to check for modifier extensions. Modifier extensions SHALL NOT change the meaning of any elements on Resource or DomainResource (including cannot change the meaning of modifierExtension itself). context: Type or variable this rule applies to. contextType: How to interpret the context. element: Field to create in the context. variable: Named context for field, if desired, and a field is specified. listMode: If field is a list, how to manage the list. listRuleId: Internal rule reference for shared list items. transform: How the data is copied / created. parameter: Parameters to the transform. """ if extension_fields is None: extension_fields = [ "valueBoolean", "valueCode", "valueDate", "valueDateTime", "valueDecimal", "valueId", "valueInteger", "valuePositiveInt", "valueString", "valueTime", "valueUnsignedInt", "valueUri", "valueUrl", "valueReference", "valueCodeableConcept", "valueAddress", ] from spark_fhir_schemas.r4.complex_types.extension import ExtensionSchema from spark_fhir_schemas.r4.simple_types.id import idSchema from spark_fhir_schemas.r4.complex_types.structuremap_parameter import ( StructureMap_ParameterSchema, ) if ( max_recursion_limit and nesting_list.count("StructureMap_Target") >= max_recursion_limit ) or (max_nesting_depth and nesting_depth >= max_nesting_depth): return StructType([StructField("id", StringType(), True)]) # add my name to recursion list for later my_nesting_list: List[str] = nesting_list + ["StructureMap_Target"] my_parent_path = ( parent_path + ".structuremap_target" if parent_path else "structuremap_target" ) schema = StructType( [ # Unique id for the element within a resource (for internal references). This # may be any string value that does not contain spaces. StructField("id", StringType(), True), # May be used to represent additional information that is not part of the basic # definition of the element. To make the use of extensions safe and manageable, # there is a strict set of governance applied to the definition and use of # extensions. Though any implementer can define an extension, there is a set of # requirements that SHALL be met as part of the definition of the extension. StructField( "extension", ArrayType( ExtensionSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path, ) ), True, ), # May be used to represent additional information that is not part of the basic # definition of the element and that modifies the understanding of the element # in which it is contained and/or the understanding of the containing element's # descendants. Usually modifier elements provide negation or qualification. To # make the use of extensions safe and manageable, there is a strict set of # governance applied to the definition and use of extensions. Though any # implementer can define an extension, there is a set of requirements that SHALL # be met as part of the definition of the extension. Applications processing a # resource are required to check for modifier extensions. # # Modifier extensions SHALL NOT change the meaning of any elements on Resource # or DomainResource (including cannot change the meaning of modifierExtension # itself). StructField( "modifierExtension", ArrayType( ExtensionSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path, ) ), True, ), # Type or variable this rule applies to. StructField( "context", idSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path + ".context", ), True, ), # How to interpret the context. StructField("contextType", StringType(), True), # Field to create in the context. StructField("element", StringType(), True), # Named context for field, if desired, and a field is specified. StructField( "variable", idSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path + ".variable", ), True, ), # If field is a list, how to manage the list. # Internal rule reference for shared list items. StructField( "listRuleId", idSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth + 1, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path + ".listruleid", ), True, ), # How the data is copied / created. StructField("transform", StringType(), True), # Parameters to the transform. StructField( "parameter", ArrayType( StructureMap_ParameterSchema.get_schema( max_nesting_depth=max_nesting_depth, nesting_depth=nesting_depth + 1, nesting_list=my_nesting_list, max_recursion_limit=max_recursion_limit, include_extension=include_extension, extension_fields=extension_fields, extension_depth=extension_depth, max_extension_depth=max_extension_depth, include_modifierExtension=include_modifierExtension, use_date_for=use_date_for, parent_path=my_parent_path, ) ), True, ), ] ) if not include_extension: schema.fields = [ c if c.name != "extension" else StructField("extension", StringType(), True) for c in schema.fields ] if not include_modifierExtension: schema.fields = [ c if c.name != "modifierExtension" else StructField("modifierExtension", StringType(), True) for c in schema.fields ] return schema

47.775735

104

0.554983

1,242

12,995

5.60306

0.15942

0.050007

0.032332

0.041385

0.738037

0.72065

0.700532

0.661446

0

0.002556

0.397845

12,995

271

105

47.95203

0.886773

0.295883

0

0.540984

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0

0.04926

0

1

0.005464

false

0

0.027322

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0

1

0

1

0

1

0

null

0

4

e6ad7747954c711c92b1cc9828b20d90bfa1f7ca

8,804

py

Python

log_download_split_statics/logdiv.py

linlife/Python

6260f6cfdc234d7196255869dfbf70cd0a640ad4

[ "Apache-2.0" ]

null

log_download_split_statics/logdiv.py

linlife/Python

6260f6cfdc234d7196255869dfbf70cd0a640ad4

[ "Apache-2.0" ]

null

log_download_split_statics/logdiv.py

linlife/Python

6260f6cfdc234d7196255869dfbf70cd0a640ad4

[ "Apache-2.0" ]

null

#!/usr/bin/env python import urllib2 import urllib import os import re import sys import time atime=time.time()-60*60*24*1 mydate=time.strftime('%Y-%m-%d',time.localtime(atime)) loglst=[] logsdir=mydate+'logs' downloadurl={ 'i8.tg.com.cn':'http://223.203.224.40/%s/76/027/i8.tg.com.cn.log.gz'%mydate,\ 'www.jia.com':'http://223.203.224.40/%s/06/140/www.jia.com.log.gz'%mydate,\ 'qingdao.jia.com':'http://223.203.224.40/%s/68/456/qingdao.jia.com.log.gz'%mydate,\ 'sdhz.jia.com':'http://223.203.224.40/%s/10/401/sdhz.jia.com.log.gz'%mydate,\ 'jinan.jia.com':'http://223.203.224.40/%s/93/369/jinan.jia.com.log.gz'%mydate,\ 'sdjz.jia.com':'http://223.203.224.40/%s/52/535/sdjz.jia.com.log.gz'%mydate,\ 'sdly.jia.com':'http://223.203.224.40/%s/53/925/sdly.jia.com.log.gz'%mydate,\ 'sdwf.jia.com':'http://223.203.224.40/%s/55/525/sdwf.jia.com.log.gz'%mydate,\ 'sdzz.jia.com':'http://223.203.224.40/%s/88/608/sdzz.jia.com.log.gz'%mydate,\ 'suzhou.jia.com':'http://223.203.224.40/%s/13/481/suzhou.jia.com.log.gz'%mydate,\ 'changzhou.jia.com':'http://223.203.224.40/%s/32/443/changzhou.jia.com.log.gz'%mydate,\ 'jsha.jia.com':'http://223.203.224.40/%s/21/652/jsha.jia.com.log.gz'%mydate,\ 'kunshan.jia.com':'http://223.203.224.40/%s/29/162/kunshan.jia.com.log.gz'%mydate,\ 'jslyg.jia.com':'http://223.203.224.40/%s/68/296/jslyg.jia.com.log.gz'%mydate,\ 'nanjing.jia.com':'http://223.203.224.40/%s/22/202/nanjing.jia.com.log.gz'%mydate,\ 'nantong.jia.com':'http://223.203.224.40/%s/06/890/nantong.jia.com.log.gz'%mydate,\ 'jstz.jia.com':'http://223.203.224.40/%s/98/059/jstz.jia.com.log.gz'%mydate,\ 'wuxi.jia.com':'http://223.203.224.40/%s/90/119/wuxi.jia.com.log.gz'%mydate,\ 'jsxz.jia.com':'http://223.203.224.40/%s/82/328/jsxz.jia.com.log.gz'%mydate,\ 'jsyz.jia.com':'http://223.203.224.40/%s/53/395/jsyz.jia.com.log.gz'%mydate,\ 'hangzhou.jia.com':'http://223.203.224.40/%s/73/377/hangzhou.jia.com.log.gz'%mydate,\ 'jiaxing.jia.com':'http://223.203.224.40/%s/73/297/jiaxing.jia.com.log.gz'%mydate,\ 'ningbo.jia.com':'http://223.203.224.40/%s/96/389/ningbo.jia.com.log.gz'%mydate,\ 'shaoxing.jia.com':'http://223.203.224.40/%s/72/717/shaoxing.jia.com.log.gz'%mydate,\ 'zjwz.jia.com':'http://223.203.224.40/%s/98/269/zjwz.jia.com.log.gz'%mydate,\ 'hefei.jia.com':'http://223.203.224.40/%s/68/346/hefei.jia.com.log.gz'%mydate,\ 'ahsz.jia.com':'http://223.203.224.40/%s/37/763/ahsz.jia.com.log.gz'%mydate,\ 'shenzhen.jia.com':'http://223.203.224.40/%s/04/460/shenzhen.jia.com.log.gz'%mydate,\ 'gddg.jia.com':'http://223.203.224.40/%s/75/197/gddg.jia.com.log.gz'%mydate,\ 'gdfs.jia.com':'http://223.203.224.40/%s/09/260/gdfs.jia.com.log.gz'%mydate,\ 'guangzhou.jia.com':'http://223.203.224.40/%s/33/583/guangzhou.jia.com.log.gz'%mydate,\ 'huizhou.jia.com':'http://223.203.224.40/%s/27/592/huizhou.jia.com.log.gz'%mydate,\ 'fuzhou.jia.com':'http://223.203.224.40/%s/02/860/fuzhou.jia.com.log.gz'%mydate,\ 'nanning.jia.com':'http://223.203.224.40/%s/98/269/nanning.jia.com.log.gz'%mydate,\ 'gxlz.jia.com':'http://223.203.224.40/%s/42/474/gxlz.jia.com.log.gz'%mydate,\ 'zhengzhou.jia.com':'http://223.203.224.40/%s/55/415/zhengzhou.jia.com.log.gz'%mydate,\ 'wuhan.jia.com':'http://223.203.224.40/%s/66/636/wuhan.jia.com.log.gz'%mydate,\ 'changsha.jia.com':'http://223.203.224.40/%s/54/585/changsha.jia.com.log.gz'%mydate,\ 'hnyy.jia.com':'http://223.203.224.40/%s/45/284/hnyy.jia.com.log.gz'%mydate,\ 'nanchang.jia.com':'http://223.203.224.40/%s/13/371/nanchang.jia.com.log.gz'%mydate,\ 'shenyang.jia.com':'http://223.203.224.40/%s/18/811/shenyang.jia.com.log.gz'%mydate,\ 'dalian.jia.com':'http://223.203.224.40/%s/66/526/dalian.jia.com.log.gz'%mydate,\ 'dandong.jia.com':'http://223.203.224.40/%s/52/685/dandong.jia.com.log.gz'%mydate,\ 'haerbin.jia.com':'http://223.203.224.40/%s/28/912/haerbin.jia.com.log.gz'%mydate,\ 'changchun.jia.com':'http://223.203.224.40/%s/58/525/changchun.jia.com.log.gz'%mydate,\ 'jljl.jia.com':'http://223.203.224.40/%s/07/410/jljl.jia.com.log.gz'%mydate,\ 'chengdu.jia.com':'http://223.203.224.40/%s/09/580/chengdu.jia.com.log.gz'%mydate,\ 'kunming.jia.com':'http://223.203.224.40/%s/80/008/kunming.jia.com.log.gz'%mydate,\ 'guiyang.jia.com':'http://223.203.224.40/%s/79/657/guiyang.jia.com.log.gz'%mydate,\ 'shijiazhuang.jia.com':'http://223.203.224.40/%s/40/324/shijiazhuang.jia.com.log.gz'%mydate,\ 'handan.jia.com':'http://223.203.224.40/%s/25/732/handan.jia.com.log.gz'%mydate,\ 'hbts.jia.com':'http://223.203.224.40/%s/32/523/hbts.jia.com.log.gz'%mydate,\ 'hbxt.jia.com':'http://223.203.224.40/%s/57/535/hbxt.jia.com.log.gz'%mydate,\ 'taiyuan.jia.com':'http://223.203.224.40/%s/96/099/taiyuan.jia.com.log.gz'%mydate,\ 'sxjc.jia.com':'http://223.203.224.40/%s/29/022/sxjc.jia.com.log.gz'%mydate,\ 'xian.jia.com':'http://223.203.224.40/%s/81/818/xian.jia.com.log.gz'%mydate,\ 'wulumuqi.jia.com':'http://223.203.224.40/%s/10/781/wulumuqi.jia.com.log.gz'%mydate,\ 'qhxn.jia.com':'http://223.203.224.40/%s/96/899/qhxn.jia.com.log.gz'%mydate,\ 'beijing.jia.com':'http://223.203.224.40/%s/23/732/beijing.jia.com.log.gz'%mydate,\ 'chongqing.jia.com':'http://223.203.224.40/%s/97/779/chongqing.jia.com.log.gz'%mydate,\ 'shanghai.jia.com':'http://223.203.224.40/%s/42/064/shanghai.jia.com.log.gz'%mydate,\ 'tianjin.jia.com':'http://223.203.224.40/%s/00/890/tianjin.jia.com.log.gz'%mydate,\ 'jiaju.jia.com':'http://223.203.224.40/%s/80/598/jiaju.jia.com.log.gz'%mydate,\ 'mall.jia.com':'http://223.203.224.40/%s/31/113/mall.jia.com.log.gz'%mydate,\ 'tg.jia.com':'http://223.203.224.40/%s/18/501/tg.jia.com.log.gz'%mydate,\ 'pinpai.jia.com':'http://223.203.224.40/%s/78/387/pinpai.jia.com.log.gz'%mydate,\ 'tuku.jia.com':'http://223.203.224.40/%s/66/726/tuku.jia.com.log.gz'%mydate,\ 'zixun.jia.com':'http://223.203.224.40/%s/13/391/zixun.jia.com.log.gz'%mydate,\ 'm.jia.com':'http://223.203.224.40/%s/28/182/m.jia.com.log.gz'%mydate,\ 'mtgi1.jia.com':'http://223.203.224.40/%s/09/540/mtgi1.jia.com.log.gz'%mydate,\ 'mtgi2.jia.com':'http://223.203.224.40/%s/68/796/mtgi2.jia.com.log.gz'%mydate,\ 'mtgi3.jia.com':'http://223.203.224.40/%s/27/052/mtgi3.jia.com.log.gz'%mydate,\ 'mi1.jia.com':'http://223.203.224.40/%s/32/313/mi1.jia.com.log.gz'%mydate,\ 'mi2.jia.com':'http://223.203.224.40/%s/73/057/mi2.jia.com.log.gz'%mydate,\ 'mi3.jia.com':'http://223.203.224.40/%s/14/801/mi3.jia.com.log.gz'%mydate,\ 'mued1.jia.com':'http://223.203.224.40/%s/35/553/mued1.jia.com.log.gz'%mydate,\ 'mued2.jia.com':'http://223.203.224.40/%s/94/809/mued2.jia.com.log.gz'%mydate,\ 'mued3.jia.com':'http://223.203.224.40/%s/53/065/mued3.jia.com.log.gz'%mydate,\ #'tuku-wap.m.jia.com':''%mydate,\ #'zhuangxiu.jia.com':''%mydate,\ #'i1.tg.com.cn':''%mydate,\ } def logdir(mydate): os.chdir("/usr/local/logs") if os.path.isdir(logsdir): sys.exit(0) else: os.mkdir(logsdir) def downloadlogs(): os.chdir("/usr/local/logs/%s"%logsdir) for line in downloadurl.values(): logname=line.split('/')[-1] urllib.urlretrieve(line,logname) os.popen("gzip -d *.gz") def spiderlog(): os.chdir("/usr/local/logs/%s"%logsdir) for root,dirs,files in os.walk("/usr/local/logs/%s"%logsdir): for f in files: try: if f.split('.')[-1]=='log': loglst.append(f) except Exception ,e: pass for flog in loglst: os.popen("egrep -i 'Baiduspider|Sogou web spider|360Spider' %s >spider_%s "%(flog,flog)) if os.path.getsize('spider_%s'%flog)== 0: os.remove('spider_%s'%flog) def menu(): logdir(mydate) downloadlogs() spiderlog() if __name__=='__main__': menu()

63.338129

106

0.57542

1,506

8,804

3.356574

0.187915

0.185163

0.154303

0.200593

0.639169

0.383185

0.367161

0.22908

0.010682

0

0.176957

0.175829

8,804

138

107

63.797101

0.519708

0.012381

0

0.016529

0

0.644628

0.615925

0

null

0.008264

0.049587

null

0

null

0

1

0

1

0

null

0

1

0

4

e6b44fd9ea5ae5f54d6f07d8221b52760daf2836

200

py

Python

epic_hash/exceptions.py

AleksMat/epic-hash

3ea2e0b50b092029ae4336557fb3d6944712fe6d

[ "MIT" ]

null

epic_hash/exceptions.py

AleksMat/epic-hash

3ea2e0b50b092029ae4336557fb3d6944712fe6d

[ "MIT" ]

null

epic_hash/exceptions.py

AleksMat/epic-hash

3ea2e0b50b092029ae4336557fb3d6944712fe6d

[ "MIT" ]

null

""" A module implementing package-specific exceptions """ class OutputValidationError(Exception): """ This exception is raised whenever there is something wrong with the submitted output """

25

92

0.755

22

200

6.863636

0.909091

0

0.165

200

8

93

25

0.904192

0.695

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4