xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

e87036f9dc84456eb79073af2898babcb7325cd3

91

py

Python

src/vulfocus/__init__.py

x98zy/vulfocus-py

c3ac3108918ecd1faa661146fbbd3543173cdb55

[ "Apache-2.0" ]

1

2021-11-28T04:32:57.000Z

src/vulfocus/__init__.py

x98zy/vulfocus-py

c3ac3108918ecd1faa661146fbbd3543173cdb55

[ "Apache-2.0" ]

null

src/vulfocus/__init__.py

x98zy/vulfocus-py

c3ac3108918ecd1faa661146fbbd3543173cdb55

[ "Apache-2.0" ]

2

2021-11-26T09:11:50.000Z

2021-11-28T02:46:33.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 2021/11/28 10:50 # @Author : x98zy

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171

py

Python

cracker/main.py

dfioravanti/rust_projects

a80f01006488d9d5eb02bf3c904361e0da02d0fd

[ "MIT" ]

null

cracker/main.py

dfioravanti/rust_projects

a80f01006488d9d5eb02bf3c904361e0da02d0fd

[ "MIT" ]

null

cracker/main.py

dfioravanti/rust_projects

a80f01006488d9d5eb02bf3c904361e0da02d0fd

[ "MIT" ]

null

from libcracker import generate_valid_string original_string = "aaaa" nb_zeros = 5 nb_threads = 10 print(generate_valid_string(original_string, nb_zeros, nb_threads))

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fa30ac8e5220e33b1542373d5c3d9b42852823eb

5,526

py

Python

api/Package.py

Purdue-ECE-461/project-2-3

77a12793b8e799982efa0508c8600ae81dc1fc07

[ "Apache-2.0" ]

1

2022-01-25T18:11:32.000Z

api/Package.py

Purdue-ECE-461/project-2-3

77a12793b8e799982efa0508c8600ae81dc1fc07

[ "Apache-2.0" ]

null

api/Package.py

Purdue-ECE-461/project-2-3

77a12793b8e799982efa0508c8600ae81dc1fc07

[ "Apache-2.0" ]

null

from flask import Flask, request, jsonify import responses import requests import json app = Flask(__name__) class Package(object): import firestore as Firestore import datetime from MetaData import MetaData from PackageData import PackageData import Error import Packages import PackageRating from PackageHistoryEntry import PackageHistoryEntry import PackageQuery data = PackageData() metadata = MetaData() history = [] rating = 0 def toJSON(self): j = dict() j["metadata"] = str(self.metadata.toJSON()) j["data"] = str(self.data.toJSON()) j["history"] = self.history j["rating"] = str(self.rating) return json.dumps(j, default=lambda o: o.__dict__, sort_keys=True, indent=4) def get_self(self, ID): import firestore as Firestore packdict = Firestore.read(ID) try: self.rating = packdict["rating"] except: self.rating = 0 try: self.history = json.loads(packdict["history"]) except: self.history = [] self.metadata = self.metadata.get_data() self.data = self.data.get_data(ID) return self def __eq__(self, obj): if(obj == None): if ((not self) or self.metadata == None): return True else: return False return self.metadata == obj.metadata def __str__(self): return self.toJSON() if __name__ == "__main__" : from main import packageCreate jcreate = ''' { "metadata": { "Name": "Underscore", "Version": "1.0.0", "ID": "underscore" }, "data": { "Content": "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", "JSProgram": "if (process.argv.length === 7) {\nconsole.log('\''Success'\'')\nprocess.exit(0)\n} else {\nconsole.log('\''Failed'\'')\nprocess.exit(1)\n}\n" } }''' jingest = ''' { "metadata": { "Name": "Underscore", "Version": "1.0.0", "ID": "underscore" }, "data": { "URL": "https://github.com/jashkenas/underscore", "JSProgram": "if (process.argv.length === 7) {\nconsole.log('\''Success'\'')\nprocess.exit(0)\n} else {\nconsole.log('\''Failed'\'')\nprocess.exit(1)\n}\n" } }''' r = requests.Request("/packages/", headers={"X-Authorization": "bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIxMjM0NTY3ODkwIiwibmFtZSI6IkpvaG4gRG9lIiwiaWF0IjoxNTE2MjM5MDIyfQ.SflKxwRJSMeKKF2QT4fwpMeJf36POk6yJV_adQssw5c"}, json=jcreate) request = r resp = 200 with app.app_context(): resp = packageCreate() print(resp) if resp[1] == 201: resp = packageDelete(json.loads(str(resp[0]), strict=False)["ID"]) if resp != 200: print("delete failed") r = requests.Request("/packages/", headers={"X-Authorization": "bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIxMjM0NTY3ODkwIiwibmFtZSI6IkpvaG4gRG9lIiwiaWF0IjoxNTE2MjM5MDIyfQ.SflKxwRJSMeKKF2QT4fwpMeJf36POk6yJV_adQssw5c"}, json=jingest) request = r resp = 200 with app.app_context(): resp = packageCreate() print(test.rating) print(resp)

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5

d7036209a590bd7cc187b388d16f385f47135376

757

py

Python

dbconnect/connection/abstract.py

astercrono/python-dbconnect

6a2d6cc40b43deadaec32c11aa3bb8925eef2328

[ "BSD-3-Clause" ]

null

dbconnect/connection/abstract.py

astercrono/python-dbconnect

6a2d6cc40b43deadaec32c11aa3bb8925eef2328

[ "BSD-3-Clause" ]

null

dbconnect/connection/abstract.py

astercrono/python-dbconnect

6a2d6cc40b43deadaec32c11aa3bb8925eef2328

[ "BSD-3-Clause" ]

1

2019-02-05T20:37:39.000Z

from abc import ABC, abstractmethod class AbstractDBConnection(ABC): @abstractmethod def connect(self, connection_string): pass @abstractmethod def close(self): pass @abstractmethod def is_open(self): pass @abstractmethod def rollback(self): pass @abstractmethod def commit(self): pass @abstractmethod def update(self, query): pass @abstractmethod def select(self, query): pass @abstractmethod def batch_update(self, queries, notify): pass @abstractmethod def set_transaction_size(self, size): pass @abstractmethod def get_transaction_size(self): pass @abstractmethod def enable_commit(self): pass @abstractmethod def disable_commit(self): pass @abstractmethod def commit_enabled(self): pass

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299

py

Python

snippets/py/const/const.py

snippetfinder/The-Quick-Snippet-Reference

4d2c38cb3687f31428539b6c9cdb11abdd4c6682

[ "BSL-1.0" ]

10

2022-01-13T15:56:14.000Z

2022-01-21T20:43:29.000Z

snippets/py/const/const.py

snippetfinder/The-Quick-Snippet-Reference

4d2c38cb3687f31428539b6c9cdb11abdd4c6682

[ "BSL-1.0" ]

1

2022-01-21T20:33:13.000Z

2022-01-22T20:26:57.000Z

snippets/py/const/const.py

snippetfinder/The-Quick-Snippet-Reference

4d2c38cb3687f31428539b6c9cdb11abdd4c6682

[ "BSL-1.0" ]

null

# as function: def number(): return 10 def decimal(): return 2.3 def string(): return "Hello there." # ≡ def array(): return [2.3, 'Hello there.', [1, 2], {"a": 1, 'b': 2}] def dictionary(): return {"number": 2.3, 'list': [1, 2], "values": {'a': 1, "b": 2}} print(string()) # Hello there. ≡

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d72754871e3b78020c0273228e78de85575a548c

148

py

Python

actions/get_idps.py

StackStorm-Exchange/keycloak

f978f295fbdcf72d3bde62d73d3ea023ed45378f

[ "Apache-2.0" ]

2

2021-01-04T13:16:57.000Z

2021-07-13T20:42:30.000Z

actions/get_idps.py

StackStorm-Exchange/keycloak

f978f295fbdcf72d3bde62d73d3ea023ed45378f

[ "Apache-2.0" ]

2

2017-10-20T23:58:33.000Z

2018-10-29T18:51:46.000Z

actions/get_idps.py

StackStorm-Exchange/keycloak

f978f295fbdcf72d3bde62d73d3ea023ed45378f

[ "Apache-2.0" ]

4

2017-11-02T16:57:30.000Z

2021-01-28T17:45:07.000Z

from lib import action class KeycloakgetRolesAction(action.KeycloakBaseAction): def run(self): return self.keycloak_admin.get_idps()

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5

d76eac67dea8e4ef11926dec7090e0a57d9961e9

124

py

Python

solutions/0205_insomorphic-strings/python/solution_hash-mapping.py

Hsins/LeetCode

38766debcd76164b397fc1d1b6c7cc8115b2d226

[ "MIT" ]

2

2022-02-18T15:13:00.000Z

2022-02-18T15:13:06.000Z

solutions/0205_insomorphic-strings/python/solution_hash-mapping.py

Hsins/LeetCode

38766debcd76164b397fc1d1b6c7cc8115b2d226

[ "MIT" ]

null

solutions/0205_insomorphic-strings/python/solution_hash-mapping.py

Hsins/LeetCode

38766debcd76164b397fc1d1b6c7cc8115b2d226

[ "MIT" ]

null

class Solution: def isIsomorphic(self, s: str, t: str) -> bool: return [*map(s.index, s)] == [*map(t.index, t)]

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ad27b9ef94524278c12f74774cb5b579fef7d352

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py

Python

python/testData/quickFixes/PyMakeFunctionFromMethodQuickFixTest/usageImport2_after.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/quickFixes/PyMakeFunctionFromMethodQuickFixTest/usageImport2_after.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/quickFixes/PyMakeFunctionFromMethodQuickFixTest/usageImport2_after.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

from test import foo foo()

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ad4a60921a1ba898e2272469d1cd9e9d6a0795e2

200

py

Python

game/admin.py

pmarella2/Fox-and-Hounds

ad6e8868df9ec13d5c977cee093c7eb6a8150b6f

[ "MIT" ]

1

2020-05-12T23:55:55.000Z

game/admin.py

pmarella2/Fox-and-Hounds

ad6e8868df9ec13d5c977cee093c7eb6a8150b6f

[ "MIT" ]

14

2020-05-11T22:43:48.000Z

2022-03-17T00:04:44.000Z

game/admin.py

pmarella2/Fox-and-Hounds

ad6e8868df9ec13d5c977cee093c7eb6a8150b6f

[ "MIT" ]

null

from django.contrib import admin from django_otp.admin import OTPAdminSite from .models import Game, GameLog admin.site.__class__ = OTPAdminSite admin.site.register(Game) admin.site.register(GameLog)

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46

py

Python

gitpy/repository/__init__.py

babygame0ver/gitpy

960306ef8f2e7827ddaed3fd875c5ac7db6b1d02

[ "MIT" ]

6

2019-12-27T14:08:27.000Z

2020-03-05T10:23:45.000Z

gitpy/repository/__init__.py

babygame0ver/gitpy

960306ef8f2e7827ddaed3fd875c5ac7db6b1d02

[ "MIT" ]

7

2020-01-08T06:02:26.000Z

2020-10-30T03:24:33.000Z

gitpy/repository/__init__.py

babygame0ver/gitpy

960306ef8f2e7827ddaed3fd875c5ac7db6b1d02

[ "MIT" ]

2

2019-10-28T17:10:38.000Z

2020-01-08T06:03:08.000Z

''' https://developer.github.com/v3/repos '''

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5

ad5d4c0601f7edd06e10f5605dc3c9f5be70568d

85

py

Python

notes/admin.py

truc0/simple-noteapp

34071ffc55e29f870816dc33b17b27333e09bf6e

[ "MIT" ]

1

2021-04-29T13:04:43.000Z

notes/admin.py

truc0/simple-noteapp

34071ffc55e29f870816dc33b17b27333e09bf6e

[ "MIT" ]

22

2020-12-31T01:45:19.000Z

2021-10-13T04:55:18.000Z

notes/admin.py

truc0/simple-noteapp

34071ffc55e29f870816dc33b17b27333e09bf6e

[ "MIT" ]

null

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

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a8dd08d5c2e5deafdc515f7b5b6e533892a6844c

2,188

py

Python

tensorbay/healthcheck/basic_check.py

machearn/tensorbay-python-sdk

5c96a5f4c0028c7bec0764f2d0142b29597ec3a9

[ "MIT" ]

73

2021-02-24T12:23:26.000Z

2022-03-12T13:00:31.000Z

tensorbay/healthcheck/basic_check.py

machearn/tensorbay-python-sdk

5c96a5f4c0028c7bec0764f2d0142b29597ec3a9

[ "MIT" ]

681

2021-02-25T07:34:17.000Z

2022-03-25T07:08:23.000Z

tensorbay/healthcheck/basic_check.py

machearn/tensorbay-python-sdk

5c96a5f4c0028c7bec0764f2d0142b29597ec3a9

[ "MIT" ]

35

2021-02-24T12:00:45.000Z

2022-03-30T06:43:13.000Z

#!/usr/bin/env python3 # # Copyright 2021 Graviti. Licensed under MIT License. # """Method check_basic. :meth:`check_basic` checks whether :class:`~tensorbay.dataset.dataset.Dataset` or :class:`~tensorbay.dataset.dataset.FusionDataset` is empty and whether the :class:`~tensorbay.dataset.segment.Segment` or :class:`~tensorbay.dataset.dataset.FusionDataset` in the object is empty. """ from typing import Iterator, Union from tensorbay.dataset import Dataset, FusionDataset from tensorbay.healthcheck.report import Error class BasicError(Error): """The base class of the basic error. Arguments: name: The dataset or segment name which has error. """ def __init__(self, name: str) -> None: self._name = name class EmptyDatasetError(BasicError): """The health check function for empty dataset. This error is raised to indicate that :class:`~tensorbay.dataset.dataset.Dataset` or :class:`~tensorbay.dataset.dataset.FusionDataset` is empty. """ def __str__(self) -> str: return f"Dataset '{self._name}' is empty" class EmptySegmentError(BasicError): """The health check function for empty segment. This error is raised to indicate that :class:`~tensorbay.dataset.segment.Segment` or :class:`~tensorbay.dataset.dataset.FusionDataset` is empty. """ def __str__(self) -> str: return f"Segment '{self._name}' is empty" def check_basic(dataset: Union[Dataset, FusionDataset]) -> Iterator[BasicError]: """The health check function for basic error. Arguments: dataset: The :class:`~tensorbay.dataset.dataset.Dataset` or :class:`~tensorbay.dataset.dataset.FusionDataset` needs to be checked. Yields: BasicError indicating that :class:`~tensorbay.dataset.dataset.Dataset`, :class:`~tensorbay.dataset.dataset.FusionDataset`, :class:`~tensorbay.dataset.segment.Segment` or :class:`~tensorbay.dataset.segment.FusionSegment` is empty. """ if not dataset: yield EmptyDatasetError(dataset.name) return for segment in dataset: if not segment: yield EmptySegmentError(segment.name)

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Python

NeuralAdversarialBalancing/__init__.py

MGIMM/Neural_Adversarial_Balancing

e8b68011cc1fef3c6f352dda8b7a0f9046ef6b4d

[ "MIT" ]

1

2021-11-26T22:03:22.000Z

NeuralAdversarialBalancing/__init__.py

MGIMM/Neural_Adversarial_Balancing

e8b68011cc1fef3c6f352dda8b7a0f9046ef6b4d

[ "MIT" ]

null

NeuralAdversarialBalancing/__init__.py

MGIMM/Neural_Adversarial_Balancing

e8b68011cc1fef3c6f352dda8b7a0f9046ef6b4d

[ "MIT" ]

null

from .NeuralAdversarialBalancing import NeuralAdversarialBalancing, ParameterClipper

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Python

Arcade/Intro/1_The_Journey_Begins/3_checkPalindrome/code.py

leocabrallce/CodeFights

9037c68669c04bff6b6152491ce37dbbbec62aa9

[ "MIT" ]

null

Arcade/Intro/1_The_Journey_Begins/3_checkPalindrome/code.py

leocabrallce/CodeFights

9037c68669c04bff6b6152491ce37dbbbec62aa9

[ "MIT" ]

null

Arcade/Intro/1_The_Journey_Begins/3_checkPalindrome/code.py

leocabrallce/CodeFights

9037c68669c04bff6b6152491ce37dbbbec62aa9

[ "MIT" ]

null

def checkPalindrome(inputString): return inputString == str(inputString)[::-1]

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a8fe770af46df264ed013e3494c8e1e36c0c2087

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py

Python

tests/test_markers.py

orenavitov/passa

d4c7dbb5d62e017f5868d095cffcc3b21b64cdae

[ "ISC" ]

55

2018-08-07T23:40:48.000Z

2021-10-05T10:05:25.000Z

tests/test_markers.py

orenavitov/passa

d4c7dbb5d62e017f5868d095cffcc3b21b64cdae

[ "ISC" ]

60

2018-08-16T17:47:20.000Z

2021-03-20T13:20:17.000Z

tests/test_markers.py

orenavitov/passa

d4c7dbb5d62e017f5868d095cffcc3b21b64cdae

[ "ISC" ]

11

2018-09-06T10:09:41.000Z

2022-01-25T15:03:32.000Z

from packaging.markers import Marker from passa.internals.markers import get_without_extra def test_strip_marker_extra_noop(): marker = get_without_extra( Marker('os_name == "nt" or sys_platform == "Windows"'), ) assert str(marker) == 'os_name == "nt" or sys_platform == "Windows"' def test_strip_marker_none(): marker = get_without_extra(None) assert marker is None def test_strip_marker_extra_only(): marker = get_without_extra(Marker('extra == "sock"')) assert marker is None def test_strip_marker_extra_simple(): marker = get_without_extra(Marker('os_name == "nt" and extra == "sock"')) assert str(marker) == 'os_name == "nt"' def test_strip_marker_extra_in_front(): marker = get_without_extra(Marker('extra == "sock" or os_name == "nt"')) assert str(marker) == 'os_name == "nt"' def test_strip_marker_extra_nested(): marker = get_without_extra(Marker( '(os_name == "nt" or sys_platform == "Windows") ' 'and extra == "sock"', )) assert str(marker) == 'os_name == "nt" or sys_platform == "Windows"' def test_strip_marker_extra_crazy(): marker = get_without_extra(Marker( '(os_name == "nt" or sys_platform == "Windows" and extra == "huh") ' 'and extra == "sock"', )) assert str(marker) == 'os_name == "nt" or sys_platform == "Windows"' def test_strip_marker_extra_cancelled(): marker = get_without_extra(Marker('extra == "sock" or extra == "huh"')) assert marker is None def test_strip_marker_extra_paranthesized_cancelled(): marker = get_without_extra(Marker( '(extra == "sock") or (extra == "huh") or (sys_platform == "Windows")', )) assert str(marker) == 'sys_platform == "Windows"' def test_strip_marker_extra_crazy_cancelled(): marker = get_without_extra(Marker( '(extra == "foo" or extra == "sock") or ' '(extra == "huh" or extra == "bar")', )) assert marker is None

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py

Python

insights/parsers/nova_conf.py

lhuett/insights-core

1c84eeffc037f85e2bbf60c9a302c83aa1a50cf8

[ "Apache-2.0" ]

121

2017-05-30T20:23:25.000Z

2022-03-23T12:52:15.000Z

insights/parsers/nova_conf.py

lhuett/insights-core

1c84eeffc037f85e2bbf60c9a302c83aa1a50cf8

[ "Apache-2.0" ]

1,977

2017-05-26T14:36:03.000Z

2022-03-31T10:38:53.000Z

insights/parsers/nova_conf.py

lhuett/insights-core

1c84eeffc037f85e2bbf60c9a302c83aa1a50cf8

[ "Apache-2.0" ]

244

2017-05-30T20:22:57.000Z

2022-03-26T10:09:39.000Z

from .. import parser, IniConfigFile from insights.specs import Specs @parser(Specs.nova_conf) class NovaConf(IniConfigFile): pass

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py

Python

bfm/__init__.py

MaxMls/3DDFA_V2

9c622c9468a00a890a3de6d4e968cf3d7ddb03b8

[ "MIT" ]

2,153

2020-08-25T09:44:57.000Z

2022-03-31T03:09:21.000Z

bfm/__init__.py

MaxMls/3DDFA_V2

9c622c9468a00a890a3de6d4e968cf3d7ddb03b8

[ "MIT" ]

123

2020-08-30T02:21:22.000Z

2022-03-13T06:53:44.000Z

bfm/__init__.py

MaxMls/3DDFA_V2

9c622c9468a00a890a3de6d4e968cf3d7ddb03b8

[ "MIT" ]

394

2020-08-27T14:24:53.000Z

2022-03-31T07:46:45.000Z

from .bfm import BFMModel

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py

Python

gbdxtools/ipe/__init__.py

matthewhanson/gbdxtools

f07fed2ea2b8d62845f6cf83c3947d0c2a4c6daf

[ "MIT" ]

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gbdxtools/ipe/__init__.py

matthewhanson/gbdxtools

f07fed2ea2b8d62845f6cf83c3947d0c2a4c6daf

[ "MIT" ]

null

gbdxtools/ipe/__init__.py

matthewhanson/gbdxtools

f07fed2ea2b8d62845f6cf83c3947d0c2a4c6daf

[ "MIT" ]

null

import sys import gbdxtools.rda from gbdxtools.rda.util import deprecation deprecation("The module 'gbdxtools.ipe' has been deprecated, functionality has been moved to gbdxtools.rda") sys.modules[__name__] = sys.modules['gbdxtools.rda']

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py

Python

qrogue/game/world/navigation/__init__.py

7Magic7Mike7/Qrogue

70bd5671a77981c1d4b633246321ba44f13c21ff

[ "MIT" ]

4

2021-12-14T19:13:43.000Z

2022-02-16T13:25:38.000Z

qrogue/game/world/navigation/__init__.py

7Magic7Mike7/Qrogue

70bd5671a77981c1d4b633246321ba44f13c21ff

[ "MIT" ]

null

qrogue/game/world/navigation/__init__.py

7Magic7Mike7/Qrogue

70bd5671a77981c1d4b633246321ba44f13c21ff

[ "MIT" ]

1

2022-01-04T18:35:51.000Z

# exporting from .navigation import Coordinate, Direction # importing # +util

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Python

tests/test_stimp.py

stumpy-dev/stumpy

589630e0308529d000fe9c06504ee7e4f759bc0b

[ "BSD-3-Clause" ]

null

tests/test_stimp.py

stumpy-dev/stumpy

589630e0308529d000fe9c06504ee7e4f759bc0b

[ "BSD-3-Clause" ]

null

tests/test_stimp.py

stumpy-dev/stumpy

589630e0308529d000fe9c06504ee7e4f759bc0b

[ "BSD-3-Clause" ]

null

import numpy as np import numpy.testing as npt from stumpy import stimp, stimped from stumpy.stimp import _bfs_indices from dask.distributed import Client, LocalCluster import pytest import naive T = [ np.array([584, -11, 23, 79, 1001, 0, -19], dtype=np.float64), np.random.uniform(-1000, 1000, [64]).astype(np.float64), ] n = [9, 10, 16] @pytest.fixture(scope="module") def dask_cluster(): cluster = LocalCluster(n_workers=2, threads_per_worker=2) yield cluster cluster.close() def split(node, out): mid = len(node) // 2 out.append(node[mid]) return node[:mid], node[mid + 1 :] def naive_bsf_indices(n): a = np.arange(n) nodes = [a.tolist()] out = [] while nodes: tmp = [] for node in nodes: for n in split(node, out): if n: tmp.append(n) nodes = tmp return np.array(out) @pytest.mark.parametrize("n", n) def test_bsf_indices(n): ref_bsf_indices = naive_bsf_indices(n) cmp_bsf_indices = np.array(list(_bfs_indices(n))) npt.assert_almost_equal(ref_bsf_indices, cmp_bsf_indices) @pytest.mark.parametrize("T", T) def test_stimp_1_percent(T): threshold = 0.2 percentage = 0.01 min_m = 3 n = T.shape[0] - min_m + 1 seed = np.random.randint(100000) np.random.seed(seed) pan = stimp( T, min_m=min_m, max_m=None, step=1, percentage=percentage, pre_scrump=True, # normalize=True, ) for i in range(n): pan.update() ref_PAN = np.full((pan.M_.shape[0], T.shape[0]), fill_value=np.inf) np.random.seed(seed) for idx, m in enumerate(pan.M_[:n]): zone = int(np.ceil(m / 4)) s = zone tmp_P, tmp_I = naive.prescrump(T, m, T, s=s, exclusion_zone=zone) ref_mp = naive.scrump(T, m, T, percentage, zone, True, s) for i in range(ref_mp.shape[0]): if tmp_P[i] < ref_mp[i, 0]: ref_mp[i, 0] = tmp_P[i] ref_mp[i, 1] = tmp_I[i] ref_PAN[pan._bfs_indices[idx], : ref_mp.shape[0]] = ref_mp[:, 0] # Compare raw pan cmp_PAN = pan._PAN naive.replace_inf(ref_PAN) naive.replace_inf(cmp_PAN) npt.assert_almost_equal(ref_PAN, cmp_PAN) # Compare transformed pan cmp_pan = pan.PAN_ ref_pan = naive.transform_pan( pan._PAN, pan._M, threshold, pan._bfs_indices, pan._n_processed ) naive.replace_inf(ref_pan) naive.replace_inf(cmp_pan) npt.assert_almost_equal(ref_pan, cmp_pan) @pytest.mark.parametrize("T", T) def test_stimp_max_m(T): threshold = 0.2 percentage = 0.01 min_m = 3 max_m = 5 n = T.shape[0] - min_m + 1 seed = np.random.randint(100000) np.random.seed(seed) pan = stimp( T, min_m=min_m, max_m=max_m, step=1, percentage=percentage, pre_scrump=True, # normalize=True, ) for i in range(n): pan.update() ref_PAN = np.full((pan.M_.shape[0], T.shape[0]), fill_value=np.inf) np.random.seed(seed) for idx, m in enumerate(pan.M_[:n]): zone = int(np.ceil(m / 4)) s = zone tmp_P, tmp_I = naive.prescrump(T, m, T, s=s, exclusion_zone=zone) ref_mp = naive.scrump(T, m, T, percentage, zone, True, s) for i in range(ref_mp.shape[0]): if tmp_P[i] < ref_mp[i, 0]: ref_mp[i, 0] = tmp_P[i] ref_mp[i, 1] = tmp_I[i] ref_PAN[pan._bfs_indices[idx], : ref_mp.shape[0]] = ref_mp[:, 0] # Compare raw pan cmp_PAN = pan._PAN naive.replace_inf(ref_PAN) naive.replace_inf(cmp_PAN) npt.assert_almost_equal(ref_PAN, cmp_PAN) # Compare transformed pan cmp_pan = pan.PAN_ ref_pan = naive.transform_pan( pan._PAN, pan._M, threshold, pan._bfs_indices, pan._n_processed ) naive.replace_inf(ref_pan) naive.replace_inf(cmp_pan) npt.assert_almost_equal(ref_pan, cmp_pan) @pytest.mark.parametrize("T", T) def test_stimp_100_percent(T): threshold = 0.2 percentage = 1.0 min_m = 3 n = T.shape[0] - min_m + 1 pan = stimp( T, min_m=min_m, max_m=None, step=1, percentage=percentage, pre_scrump=True, # normalize=True, ) for i in range(n): pan.update() ref_PAN = np.full((pan.M_.shape[0], T.shape[0]), fill_value=np.inf) for idx, m in enumerate(pan.M_[:n]): zone = int(np.ceil(m / 4)) ref_mp = naive.stump(T, m, T_B=None, exclusion_zone=zone) ref_PAN[pan._bfs_indices[idx], : ref_mp.shape[0]] = ref_mp[:, 0] # Compare raw pan cmp_PAN = pan._PAN naive.replace_inf(ref_PAN) naive.replace_inf(cmp_PAN) npt.assert_almost_equal(ref_PAN, cmp_PAN) # Compare transformed pan cmp_pan = pan.PAN_ ref_pan = naive.transform_pan( pan._PAN, pan._M, threshold, pan._bfs_indices, pan._n_processed ) naive.replace_inf(ref_pan) naive.replace_inf(cmp_pan) npt.assert_almost_equal(ref_pan, cmp_pan) @pytest.mark.filterwarnings("ignore:numpy.dtype size changed") @pytest.mark.filterwarnings("ignore:numpy.ufunc size changed") @pytest.mark.filterwarnings("ignore:numpy.ndarray size changed") @pytest.mark.filterwarnings("ignore:\\s+Port 8787 is already in use:UserWarning") @pytest.mark.parametrize("T", T) def test_stimped(T, dask_cluster): with Client(dask_cluster) as dask_client: threshold = 0.2 min_m = 3 n = T.shape[0] - min_m + 1 pan = stimped( dask_client, T, min_m=min_m, max_m=None, step=1, # normalize=True, ) for i in range(n): pan.update() ref_PAN = np.full((pan.M_.shape[0], T.shape[0]), fill_value=np.inf) for idx, m in enumerate(pan.M_[:n]): zone = int(np.ceil(m / 4)) ref_mp = naive.stump(T, m, T_B=None, exclusion_zone=zone) ref_PAN[pan._bfs_indices[idx], : ref_mp.shape[0]] = ref_mp[:, 0] # Compare raw pan cmp_PAN = pan._PAN naive.replace_inf(ref_PAN) naive.replace_inf(cmp_PAN) npt.assert_almost_equal(ref_PAN, cmp_PAN) # Compare transformed pan cmp_pan = pan.PAN_ ref_pan = naive.transform_pan( pan._PAN, pan._M, threshold, pan._bfs_indices, pan._n_processed ) naive.replace_inf(ref_pan) naive.replace_inf(cmp_pan) npt.assert_almost_equal(ref_pan, cmp_pan)

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py

Python

flopy/export/__init__.py

hansonmcoombs/flopy

49398983c36d381992621d5bf698ea7f78fc0014

[ "CC0-1.0", "BSD-3-Clause" ]

null

flopy/export/__init__.py

hansonmcoombs/flopy

49398983c36d381992621d5bf698ea7f78fc0014

[ "CC0-1.0", "BSD-3-Clause" ]

null

flopy/export/__init__.py

hansonmcoombs/flopy

49398983c36d381992621d5bf698ea7f78fc0014

[ "CC0-1.0", "BSD-3-Clause" ]

null

from .netcdf import Logger, NetCdf # isort:skip from . import metadata, shapefile_utils, utils

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py

Python

mathics/builtin/arithfns/__init__.py

tirkarthi/mathics-core

6b07500b935f23dc332f4ec3fac1d71ac4c8fc04

[ "Apache-2.0" ]

90

2021-09-11T14:14:00.000Z

2022-03-29T02:08:29.000Z

mathics/builtin/arithfns/__init__.py

tirkarthi/mathics-core

6b07500b935f23dc332f4ec3fac1d71ac4c8fc04

[ "Apache-2.0" ]

187

2021-09-13T01:00:41.000Z

2022-03-31T11:52:52.000Z

mathics/builtin/arithfns/__init__.py

tirkarthi/mathics-core

6b07500b935f23dc332f4ec3fac1d71ac4c8fc04

[ "Apache-2.0" ]

10

2021-10-05T15:44:26.000Z

2022-03-21T12:34:33.000Z

""" Arithmetic Functions Arithmetic Functions are functions that work on individual numbers, lists, and arrays: in either symbolic or algebraic forms. """

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py

Python

code_icc/archs/cluster/baselines/__init__.py

ThmCuong/IIC-Python3

5a02b40ffa07b159fa7e89cf5b4ed781f4798ff1

[ "MIT" ]

null

code_icc/archs/cluster/baselines/__init__.py

ThmCuong/IIC-Python3

5a02b40ffa07b159fa7e89cf5b4ed781f4798ff1

[ "MIT" ]

null

code_icc/archs/cluster/baselines/__init__.py

ThmCuong/IIC-Python3

5a02b40ffa07b159fa7e89cf5b4ed781f4798ff1

[ "MIT" ]

null

# from code_icc.archs.cluster.baselines.triplets import * from . import triplets

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py

Python

dodo.py

tonyfast/lit_fizz

e26c5d580bbabc1f8b82a024dc9a7888987e7b3e

[ "MIT" ]

null

dodo.py

tonyfast/lit_fizz

e26c5d580bbabc1f8b82a024dc9a7888987e7b3e

[ "MIT" ]

null

dodo.py

tonyfast/lit_fizz

e26c5d580bbabc1f8b82a024dc9a7888987e7b3e

[ "MIT" ]

null

with __import__('tingle').Markdown(): from readme import *

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py

Python

Python5.py

SubOptimal/PythonChallenge

989a04500aa371057315dffb6e3d03a968f16130

[ "MIT" ]

null

Python5.py

SubOptimal/PythonChallenge

989a04500aa371057315dffb6e3d03a968f16130

[ "MIT" ]

null

Python5.py

SubOptimal/PythonChallenge

989a04500aa371057315dffb6e3d03a968f16130

[ "MIT" ]

1

2019-04-11T17:39:00.000Z

#Used to make requests import urllib.request import pickle base_url="http://www.pythonchallenge.com/pc/def/banner.p" x = urllib.request.urlopen(base_url) y = pickle.loads(x.read()) print(y) #[[(' ', 95)], [(' ', 14), ('#', 5), (' ', 70), ('#', 5), (' ', 1)], [(' ', 15), ('#', 4), (' ', 71), ('#', 4), (' ', 1)], [(' ', 15), ('#', 4), (' ', 71), ('#', 4), (' ', 1)], [(' ', 15), ('#', 4), (' ', 71), ('#', 4), (' ', 1)], [(' ', 15), ('#', 4), (' ', 71), ('#', 4), (' ', 1)], [(' ', 15), ('#', 4), (' ', 71), ('#', 4), (' ', 1)], [(' ', 15), ('#', 4), (' ', 71), ('#', 4), (' ', 1)], [(' ', 15), ('#', 4), (' ', 71), ('#', 4), (' ', 1)], [(' ', 6), ('#', 3), (' ', 6), ('#', 4), (' ', 3), ('#', 3), (' ', 9), ('#', 3), (' ', 7), ('#', 5), (' ', 3), ('#', 3), (' ', 4), ('#', 5), (' ', 3), ('#', 3), (' ', 10), ('#', 3), (' ', 7), ('#', 4), (' ', 1)], [(' ', 3), ('#', 3), (' ', 3), ('#', 2), (' ', 4), ('#', 4), (' ', 1), ('#', 7), (' ', 5), ('#', 2), (' ', 2), ('#', 3), (' ', 6), ('#', 4), (' ', 1), ('#', 7), (' ', 3), ('#', 4), (' ', 1), ('#', 7), (' ', 5), ('#', 3), (' ', 2), ('#', 3), (' ', 5), ('#', 4), (' ', 1)], [(' ', 2), ('#', 3), (' ', 5), ('#', 3), (' ', 2), ('#', 5), (' ', 4), ('#', 4), (' ', 3), ('#', 3), (' ', 3), ('#', 4), (' ', 4), ('#', 5), (' ', 4), ('#', 4), (' ', 2), ('#', 5), (' ', 4), ('#', 4), (' ', 3), ('#', 3), (' ', 5), ('#', 3), (' ', 3), ('#', 4), (' ', 1)], [(' ', 1), ('#', 3), (' ', 11), ('#', 4), (' ', 5), ('#', 4), (' ', 3), ('#', 3), (' ', 4), ('#', 3), (' ', 4), ('#', 4), (' ', 5), ('#', 4), (' ', 2), ('#', 4), (' ', 5), ('#', 4), (' ', 2), ('#', 3), (' ', 6), ('#', 4), (' ', 2), ('#', 4), (' ', 1)], [(' ', 1), ('#', 3), (' ', 11), ('#', 4), (' ', 5), ('#', 4), (' ', 10), ('#', 3), (' ', 4), ('#', 4), (' ', 5), ('#', 4), (' ', 2), ('#', 4), (' ', 5), ('#', 4), (' ', 2), ('#', 3), (' ', 7), ('#', 3), (' ', 2), ('#', 4), (' ', 1)], [('#', 4), (' ', 11), ('#', 4), (' ', 5), ('#', 4), (' ', 5), ('#', 2), (' ', 3), ('#', 3), (' ', 4), ('#', 4), (' ', 5), ('#', 4), (' ', 2), ('#', 4), (' ', 5), ('#', 4), (' ', 1), ('#', 4), (' ', 7), ('#', 3), (' ', 2), ('#', 4), (' ', 1)], [('#', 4), (' ', 11), ('#', 4), (' ', 5), ('#', 4), (' ', 3), ('#', 10), (' ', 4), ('#', 4), (' ', 5), ('#', 4), (' ', 2), ('#', 4), (' ', 5), ('#', 4), (' ', 1), ('#', 14), (' ', 2), ('#', 4), (' ', 1)], [('#', 4), (' ', 11), ('#', 4), (' ', 5), ('#', 4), (' ', 2), ('#', 3), (' ', 4), ('#', 4), (' ', 4), ('#', 4), (' ', 5), ('#', 4), (' ', 2), ('#', 4), (' ', 5), ('#', 4), (' ', 1), ('#', 4), (' ', 12), ('#', 4), (' ', 1)], [('#', 4), (' ', 11), ('#', 4), (' ', 5), ('#', 4), (' ', 1), ('#', 4), (' ', 5), ('#', 3), (' ', 4), ('#', 4), (' ', 5), ('#', 4), (' ', 2), ('#', 4), (' ', 5), ('#', 4), (' ', 1), ('#', 4), (' ', 12), ('#', 4), (' ', 1)], [(' ', 1), ('#', 3), (' ', 11), ('#', 4), (' ', 5), ('#', 4), (' ', 1), ('#', 4), (' ', 5), ('#', 3), (' ', 4), ('#', 4), (' ', 5), ('#', 4), (' ', 2), ('#', 4), (' ', 5), ('#', 4), (' ', 2), ('#', 3), (' ', 12), ('#', 4), (' ', 1)], [(' ', 2), ('#', 3), (' ', 6), ('#', 2), (' ', 2), ('#', 4), (' ', 5), ('#', 4), (' ', 2), ('#', 3), (' ', 4), ('#', 4), (' ', 4), ('#', 4), (' ', 5), ('#', 4), (' ', 2), ('#', 4), (' ', 5), ('#', 4), (' ', 3), ('#', 3), (' ', 6), ('#', 2), (' ', 3), ('#', 4), (' ', 1)], [(' ', 3), ('#', 3), (' ', 4), ('#', 2), (' ', 3), ('#', 4), (' ', 5), ('#', 4), (' ', 3), ('#', 11), (' ', 3), ('#', 4), (' ', 5), ('#', 4), (' ', 2), ('#', 4), (' ', 5), ('#', 4), (' ', 4), ('#', 3), (' ', 4), ('#', 2), (' ', 4), ('#', 4), (' ', 1)], [(' ', 6), ('#', 3), (' ', 5), ('#', 6), (' ', 4), ('#', 5), (' ', 4), ('#', 2), (' ', 4), ('#', 4), (' ', 1), ('#', 6), (' ', 4), ('#', 11), (' ', 4), ('#', 5), (' ', 6), ('#', 3), (' ', 6), ('#', 6)], [(' ', 95)]] for line in y: for t in line: print(t[0]*t[1], end = '') print("") #Set terminal width to a minimum width for best results.

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py

Python

operations.py

aniruddhraghu/ecg_aug

6779c99948bb588849fab43dc9cab266819f9994

[ "Apache-2.0" ]

null

operations.py

aniruddhraghu/ecg_aug

6779c99948bb588849fab43dc9cab266819f9994

[ "Apache-2.0" ]

null

operations.py

aniruddhraghu/ecg_aug

6779c99948bb588849fab43dc9cab266819f9994

[ "Apache-2.0" ]

null

############ Adapted from https://github.com/moskomule/dda ############# """ Operations """ from scipy.special import logit import torch from torch import nn from torch.distributions import RelaxedBernoulli, Bernoulli from functional import (rand_temporal_warp, baseline_wander, gaussian_noise, rand_crop, rand_crop_base, spec_aug, rand_displacement, magnitude_scale) import warp_ops class _Operation(nn.Module): """ Base class of operation :param operation: :param initial_magnitude: :param initial_probability: :param learn_magnitude: :param learn_probability: :param temperature: Temperature for RelaxedBernoulli distribution used during training """ def __init__(self, operation, initial_magnitude, initial_probability=[0.9999999,0.9999999], learn_magnitude=True, learn_probability=True, temperature = 0.1, ): super().__init__() self.operation = operation if initial_magnitude is not None and learn_magnitude: self.magnitude = nn.Parameter(torch.Tensor(initial_magnitude)) else: self.magnitude = torch.Tensor(initial_magnitude) if learn_probability: self.probability = nn.Parameter(torch.Tensor([float(logit(i)) for i in initial_probability])) else: self.probability = torch.Tensor([float(logit(i)) for i in initial_probability]) assert 0 < temperature self.temperature = temperature def forward(self,input, label): mask = self.get_mask(label, input.size(0)).to(input.device) mag = self.magnitude.to(input.device).unsqueeze(0) # we need a per-ex mag based on the class label. Right now, the mag is a (1x2) tensor. # First repeat in BS dimension BS, C, L = input.shape mag = mag.repeat(BS, 1) # Now it is BS x 2, or BS by class num more generally. Select out the relevant entries. # Also add a sum over all elems to make sure we don't get an error in autograd. mag_rel = 0*mag.sum() + mag[torch.arange(BS), label.long()] mag_rel = mag_rel.view(BS, 1, 1) transformed = self.operation(input, mag_rel) mask = mask.view(BS, 1, 1) retval = (mask * transformed + (1 - mask) * input) return retval def get_mask(self, label, batch_size=None): prob = torch.sigmoid(self.probability).unsqueeze(0) prob = prob.repeat(batch_size, 1) prob = 0*prob.sum() + prob[torch.arange(batch_size), label.long()] if self.training: return RelaxedBernoulli(self.temperature, prob).rsample() else: return Bernoulli(prob).sample() class NoOp(_Operation): def __init__(self, initial_magnitude=[2., 2.], initial_probability=[0.9999999,0.9999999], learn_magnitude=False, learn_probability=False, temperature = 0.1, ): super().__init__(None, initial_magnitude, initial_probability, learn_magnitude, learn_probability, temperature) def forward(self, input, label): return input class RandTemporalWarp(_Operation): def __init__(self, initial_magnitude=[2., 2.], initial_probability=[0.9999999,0.9999999], learn_magnitude=True, learn_probability=True, temperature = 0.1, ): super().__init__(rand_temporal_warp, initial_magnitude, initial_probability, learn_magnitude, learn_probability, temperature) # create the warp obj here. self.warp_obj = warp_ops.RandWarpAug([2496]) def forward(self,input, label): mask = self.get_mask(label, input.size(0)).to(input.device) mag = self.magnitude.to(input.device).unsqueeze(0) # we need a per-ex mag based on the class label. Right now, the mag is a (1x2) tensor. # First repeat in BS dimension BS, C, L = input.shape mag = mag.repeat(BS, 1) # Now it is BS x 2, or BS by class num more generally. Select out the relevant entries. # Also add a sum over all elems to make sure we don't get an error in autograd. mag_rel = 0*mag.sum() + mag[torch.arange(BS), label.long()] mag_rel = mag_rel.view(BS, 1, 1) transformed = self.operation(input, mag_rel, self.warp_obj) B, C, L = transformed.shape mask = mask.view(B, 1, 1) retval = (mask * transformed + (1 - mask) * input) return retval class BaselineWander(_Operation): def __init__(self, initial_magnitude=[0.0,0.0], initial_probability=[0.9999999,0.9999999], learn_magnitude=True, learn_probability=True, temperature = 0.1, ): super().__init__(baseline_wander, initial_magnitude, initial_probability, learn_magnitude, learn_probability, temperature) class GaussianNoise(_Operation): def __init__(self, initial_magnitude=[0.0,0.0], initial_probability=[0.9999999,0.9999999], learn_magnitude=True, learn_probability=True, temperature = 0.1, ): super().__init__(gaussian_noise, initial_magnitude, initial_probability, learn_magnitude, learn_probability, temperature) class RandCrop(_Operation): def __init__(self, initial_magnitude=[0.05], initial_probability=[0.9999999,0.9999999], learn_magnitude=False, learn_probability=True, temperature = 0.1, ): super().__init__(rand_crop, initial_magnitude, initial_probability, learn_magnitude, learn_probability, temperature) def forward(self,input, label): mask = self.get_mask(label, input.size(0)).to(input.device) mag = self.magnitude.to(input.device) transformed = self.operation(input, mag) B, C, L = transformed.shape mask = mask.view(B, 1, 1) retval = (mask * transformed + (1 - mask) * input) return retval class RandDisplacement(_Operation): def __init__(self, initial_magnitude=[0.5,0.5], initial_probability=[0.9999999,0.9999999], learn_magnitude=True, learn_probability=True, temperature = 0.1, ): super().__init__(rand_displacement, initial_magnitude, initial_probability, learn_magnitude, learn_probability, temperature) # create the warp obj here. self.warp_obj = warp_ops.DispAug([2496]) def forward(self,input, label): mask = self.get_mask(label, input.size(0)).to(input.device) mag = self.magnitude.to(input.device).unsqueeze(0) # we need a per-ex mag based on the class label. Right now, the mag is a (1x2) tensor. # First repeat in BS dimension BS, C, L = input.shape mag = mag.repeat(BS, 1) # Now it is BS x 2, or BS by class num more generally. Select out the relevant entries. # Also add a sum over all elems to make sure we don't get an error in autograd. mag_rel = 0*mag.sum() + mag[torch.arange(BS), label.long()] mag_rel = mag_rel.view(BS, 1, 1) transformed = self.operation(input, mag_rel, self.warp_obj) B, C, L = transformed.shape mask = mask.view(B, 1, 1) retval = (mask * transformed + (1 - mask) * input) return retval class MagnitudeScale(_Operation): def __init__(self, initial_magnitude=[0.0,0.0], initial_probability=[0.9999999,0.9999999], learn_magnitude=True, learn_probability=True, temperature = 0.1, ): super().__init__(magnitude_scale, initial_magnitude, initial_probability, learn_magnitude, learn_probability, temperature)

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py

Python

management/commands/refresh.py

AileenLumina/dwarf

5fc3b1b532290a474d17f84694dae1d0d53be7b4

[ "MIT" ]

null

management/commands/refresh.py

AileenLumina/dwarf

5fc3b1b532290a474d17f84694dae1d0d53be7b4

[ "MIT" ]

null

management/commands/refresh.py

AileenLumina/dwarf

5fc3b1b532290a474d17f84694dae1d0d53be7b4

[ "MIT" ]

null

from dwarf.bot import bot bot.loop.close() # TODO Also shutdown the web interface # TODO Make it actually reboot # TODO Backup instance

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py

Python

shop/admin.py

hosseinkianmehr/solar-site

6e7995e70442efded3e7bde7cd776fa74dd74372

[ "MIT" ]

3

2021-01-19T20:12:09.000Z

2021-11-18T10:06:45.000Z

shop/admin.py

hosseinkianmehr/solar-site

6e7995e70442efded3e7bde7cd776fa74dd74372

[ "MIT" ]

null

shop/admin.py

hosseinkianmehr/solar-site

6e7995e70442efded3e7bde7cd776fa74dd74372

[ "MIT" ]

null

from django.contrib import admin from shop.models import * # Register your models here. admin.site.register(shop), admin.site.register(company), admin.site.register(salessite)

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py

Python

FaceSwap-master/pytorch_stylegan_encoder/encode_image.py

CSID-DGU/-2020-1-OSSP1-ninetynine-2

b1824254882eeea0ee44e4e60896b72c51ef1d2c

[ "MIT" ]

1

2020-06-21T13:45:26.000Z

FaceSwap-master/pytorch_stylegan_encoder/encode_image.py

CSID-DGU/-2020-1-OSSP1-ninetynine-2

b1824254882eeea0ee44e4e60896b72c51ef1d2c

[ "MIT" ]

null

FaceSwap-master/pytorch_stylegan_encoder/encode_image.py

CSID-DGU/-2020-1-OSSP1-ninetynine-2

b1824254882eeea0ee44e4e60896b72c51ef1d2c

[ "MIT" ]

3

2020-09-02T03:18:45.000Z

2021-01-27T08:24:05.000Z

version https://git-lfs.github.com/spec/v1 oid sha256:d97eeb64e51b97d2db9ecdd71998aeaf1948172ddcf1cd7ad51b4f00b7c4ddee size 6138

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181

py

Python

myCommServer/admin.py

JadeShekh/satinnovation

c6f8a4197a5c8a9beee3671257eff1e59e6f8449

[ "MIT" ]

null

myCommServer/admin.py

JadeShekh/satinnovation

c6f8a4197a5c8a9beee3671257eff1e59e6f8449

[ "MIT" ]

null

myCommServer/admin.py

JadeShekh/satinnovation

c6f8a4197a5c8a9beee3671257eff1e59e6f8449

[ "MIT" ]

8

2017-03-13T10:37:27.000Z

2021-07-22T05:17:08.000Z

from django.contrib import admin from .models import UserMsg, MyCommDevice, MyCommMsg admin.site.register(UserMsg) admin.site.register(MyCommDevice) admin.site.register(MyCommMsg)

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py

Python

blive/__init__.py

yulinfeng000/blive

ee135a8648332b772983108b5f299656fa4f15d0

[ "MIT" ]

6

2022-01-07T08:48:13.000Z

2022-03-12T02:32:19.000Z

blive/__init__.py

yulinfeng000/blive

ee135a8648332b772983108b5f299656fa4f15d0

[ "MIT" ]

1

2022-02-18T11:45:24.000Z

2022-03-09T02:00:31.000Z

blive/__init__.py

yulinfeng000/blive

ee135a8648332b772983108b5f299656fa4f15d0

[ "MIT" ]

1

2022-01-11T07:38:45.000Z

from .framework import * from .core import * from .msg import *

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py

Python

python/testData/intentions/PyAnnotateVariableTypeIntentionTest/notSuggestedForNonlocalTarget.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/intentions/PyAnnotateVariableTypeIntentionTest/notSuggestedForNonlocalTarget.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/intentions/PyAnnotateVariableTypeIntentionTest/notSuggestedForNonlocalTarget.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

def func(): nonlocal var v<caret>ar

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py

Python

leetcode/hard/815_bus_routes.py

phantomnat/python-learning

addc7ba5fc4fb8920cdd2891d4b2e79efd1a524a

[ "MIT" ]

null

leetcode/hard/815_bus_routes.py

phantomnat/python-learning

addc7ba5fc4fb8920cdd2891d4b2e79efd1a524a

[ "MIT" ]

null

leetcode/hard/815_bus_routes.py

phantomnat/python-learning

addc7ba5fc4fb8920cdd2891d4b2e79efd1a524a

[ "MIT" ]

null

import collections import queue class Solution: def numBusesToDestination(self, routes, S, T): if S == T: return 0 routes = list(map(set, routes)) graph = collections.defaultdict(set) for i, r1 in enumerate(routes): for j in range(i+1, len(routes)): r2 = routes[j] if any(r in r2 for r in r1): graph[i].add(j) graph[j].add(i) seens, targets = set(), set() for node, route in enumerate(routes): if S in route: seens.add(node) if T in route: targets.add(node) print('seens:', seens) print('targets:', targets) # print(dict(graph)) # Queues.queue q = queue.Queue() # queue = [q for node in seens] for node in seens: q.put((node, 1)) while q.qsize() > 0: # print(queue) node, depth = q.get() # queue.remove() # print('node:',node,' depth:',depth) if node in targets: return depth # print('graph[node]:',graph[node]) for nei in graph[node]: # print('nei:',nei) if nei not in seens: seens.add(nei) q.put((nei, depth+1)) # print('queue:',queue) return -1 if __name__ == '__main__': s = Solution() # assert (s.numBusesToDestination([[1, 2, 7, 9], [3, 6, 8, 7]], 1, 6)) == 2 # assert (s.numBusesToDestination([[1, 2, 7], [ 6, 8, 7]], 1, 6)) == 2 # assert (s.numBusesToDestination([[1, 2, 7], [3, 6, 7], [ 9, 6, 10, 12]], 1, 12)) == 3 # assert (s.numBusesToDestination([[1, 2, 7], [3, 6, 7], [2, 9, 6, 10, 12]], 1, 2)) == 1 # assert (s.numBusesToDestination([[1, 2, 7], [3, 6, 7], [2, 9, 6, 10, 12]], 1, 9)) == 2 # assert (s.numBusesToDestination([[1, 2, 7], [3, 6, 7], [ 9, 10, 12]], 1, 12)) == -1 # assert (s.numBusesToDestination( [[24],[3,6,11,14,22],[1,23,24],[0,6,14],[1,3,8,11,20]], 20, 8)) == 1 # assert (s.numBusesToDestination( # [[0,14,22,34,35,56,64,65,82,108,111,116,121,123,131,132,151,152,154,176,180,184,189,198,201,208],[6,23,35,37,38,40,41,49,58,64,74,92,96,101,112,116,123,127,142,148,150,154,155,165,182,193,209],[10,13,21,42,72,73,89,115,121,125,135,136,161,194,205,208],[6,33,34,60,75,79,80,81,87,96,105,110,115,119,122,124,125,133,135,141,142,148,158,186],[3,21,22,30,31,35,56,58,59,74,78,93,97,107,124,127,128,130,141,142,143,146,147,151,166,167,169,175,180,181,185,203],[141,204],[7,19,27,30,31,34,41,48,51,54,55,69,89,91,100,104,110,121,127,134,138,143,150,152,161,170,181,184,185,190,195],[3,8,22,25,38,58,70,74,77,79,80,83,86,88,99,108,109,116,120,132,135,136,137,148,151,153,158,169,171,173,174,175,178,180,202,205,207],[0,10,16,27,41,45,60,62,73,93,102,115,134,140,153,164,168,186,188],[7,12,23,29,32,39,43,59,64,68,70,91,107,120,136,162,165,176,177,183,189,195,203],[11,13,16,28,31,34,39,56,62,89,95,101,102,107,112,115,125,142,145,151,155,170,171,177,197,204,209],[2,5,70,71,83,93,97,100],[9,10,12,20,21,27,34,36,40,55,57,61,67,68,70,73,83,90,92,101,106,108,109,111,116,128,138,141,155,156,168,173,176,186,191,203],[8,10,11,15,18,24,43,46,97,99,137,138,166,192,194,207],[4,5,6,29,42,50,64,78,81,84,107,110,116,137,143,144,146,150,157,178,182,207],[3,7,17,34,36,41,51,53,54,55,63,68,71,82,86,91,96,97,108,117,128,136,137,138,142,145,148,156,164,173,182,188,200,207],[4,13,19,23,33,50,60,69,76,77,78,80,94,97,107,109,116,123,126,128,129,133,137,139,140,143,147,152,158,159,163,164,166,184,187,189,200,206],[7,35,38,42,94,104,116,120,123,134,154],[2,17,21,25,30,38,43,46,48,55,64,84,85,99,102,112,117,126,145,154,161,165,174,176,186],[4,43,46,48,58,67,68,91,126,130,140,150,153,162,170,174],[8,24,65,67,106,118,145,148,202],[6,12,20,23,36,37,39,48,59,66,69,71,80,86,90,102,105,118,126,128,134,140,141,156,160,163,164,188,191,199,205,207],[2,48,115,170],[2,27,49,59,64,67,68,75,77,90,133,174,178,184,188,205],[0,4,68,140,158,183,196,204],[11,20,35,48,60,61,63,73,98,116,120,125,131,132,149,151,171,179,180,182,185,188,204,208],[42,112],[7,11,24,29,41,42,54,57,64,72,78,87,95,103,106,107,108,111,117,122,123,152,164,186,190,195,199,202,208],[2,3,10,11,19,21,24,43,48,49,61,67,68,73,75,80,101,122,123,134,137,141,162,172,182,186,190,202,208],[9,13,31,35,42,66,68,83,88,90,95,97,102,111,134,136,138,142,153,174,200],[4,19,51,52,56,60,65,67,68,82,83,85,93,110,113,125,134,139,152,159,162,194],[19,20,25,28,29,30,40,65,120,125,126,153,155,170,172,184],[12,28,35,36,37,48,53,77,84,85,89,106,117,119,134,138,149,150,161,180,182,189,206],[90],[5,35,38,44,45,46,48,51,52,54,58,62,71,74,77,78,80,90,99,118,126,136,150,152,157,171,185,187,189,209],[9,18,22,25,48,55,56,59,64,66,68,75,77,78,79,98,103,109,110,122,151,154,164,171,175,187,193,205,207],[11,12,19,32,41,50,54,56,57,62,69,70,71,73,79,83,84,94,95,110,130,157,172,173,179,205,208],[0,5,10,15,30,32,36,45,72,93,105,111,114,116,120,127,138,139,154,190,193],[3,5,6,21,33,34,35,41,50,57,60,65,82,90,92,102,111,114,120,131,156,186,195],[116,168,203],[1,4,10,14,18,23,43,51,52,65,71,86,88,92,100,104,108,112,120,131,133,148,152,154,165,169,174,186,189,190,191,198,200,208],[6,16,21,28,31,35,37,38,58,64,71,72,76,94,95,99,100,116,122,126,133,134,136,138,140,144,147,161,163,180,185]], # 77, # 31 # )) == 2 # assert (s.numBusesToDestination( # [[98,173],[87,139,178,184],[0,1,2,5,8,17,28,40,42,47,59,71,82,85,105,114,117,118,119,126,130,135,138,142,143,150,152,166,169,176,178,196,201,210,238,239],[0,3,5,12,37,38,39,41,45,50,58,64,65,69,76,89,95,110,118,120,126,128,133,135,143,171,176,179,183,197,200,215,216,217,218,219,227,229,232],[8,23,35,45,52,54,55,66,73,84,95,107,154,156,175,206,207,229,233,236],[4,5,16,24,27,30,50,54,56,67,72,75,77,79,80,89,96,124,137,139,147,157,163,165,167,193,195,198,210,216,221,222,223,227],[2,22,24,29,31,52,107,116,120,136,141,142,159,167,176,187,235],[80,175],[13,23,96,115,122,150,167,170,172,185,187,212,233],[14,21,23,24,25,39,42,46,57,66,77,100,115,116,118,122,126,127,144,145,148,152,155,160,164,165,195,202,209,210,213,220,223,229,231,237],[5,23,30,37,51,56,57,59,72,74,87,105,141,163,166,169,175,183,184,198,210,220,221],[10,35,58,69,109,151,154,187,207,210,222,239],[3,5,20,22,30,34,35,36,42,50,52,61,65,66,67,69,80,86,87,89,93,95,124,127,128,132,136,166,174,183,186,191,195,197,201,207,208,220,221],[25,69,92,102,107,110,118,132,136,160,161,189,202,209,239],[24,37,38,41,42,47,52,57,58,71,82,90,103,107,123,142,143,150,174,177,187,189,196,233],[62,113,115,179,207],[14,60,80,83,93,103,123,133,135,151,163,173,180,235],[2,3,9,23,26,29,32,36,37,46,49,59,74,79,86,94,114,115,132,141,142,161,163,167,171,174,179,180,192,199,201,205,210,218,230,232,236],[10,44,74,120,123,142,183,188],[23,28,33,42,48,61,64,73,74,89,99,105,134,141,143,152,162,164,172,186,205,221,222],[3,8,23,42,50,56,58,66,81,86,103,110,125,133,143,155,156,159,167,171,172,188,189,190,199,221,230],[1,19,24,28,29,32,33,35,45,51,55,61,77,78,79,87,90,96,111,115,119,121,130,133,138,141,164,169,172,178,184,192,198,216,222,234],[192],[38,137],[25,43,50,52,64,74,96,104,109,136,149,194,195,200,237,238],[2,9,12,16,18,40,41,44,86,100,115,122,126,132,139,142,149,161,164,165,169,170,175,195,198,202,214,217,228,230,237],[14,27,34,36,40,66,78,80,88,131,132,135,148,169,177,183,207,223],[4,29,31,38,74,88,107,112,123,142,171,176,205,215,227],[11,32,39,53,59,66,74,80,101,109,113,114,142,151,154,158,172,193,198,221,238,239],[2],[55,74,118,128,170],[10,20,27,35,41,64,72,73,76,97,119,126,140,142,167,187,213],[51,79,109,158,200,215],[6,21,31,33,47,87,89,109,119,126,153,173,179,183,202,211,223,230,237],[3,6,12,14,24,26,46,47,49,78,91,101,114,120,124,125,131,132,150,161,182,189,213,218,224,230,234],[13,18,21,29,34,38,39,48,54,55,58,63,85,90,93,112,162,174,194,198,203,206,210,232],[30,40,52,62,63,68,69,72,77,80,83,85,91,97,104,114,115,123,133,137,147,161,174,177,179,187,190,195,198,215,226,228,232,233],[2,14,15,31,34,35,36,42,46,54,63,66,73,78,107,108,123,126,137,139,140,143,154,164,165,177,179,186,197,200,202,227,230,239],[1,37,82,94,119,125,127,128,146,148,184,188,206,209,212,218,232,236],[8,10,21,26,31,35,36,38,39,41,44,50,55,70,77,84,86,103,117,124,130,133,134,171,172,184,193,194,213,229,230],[0,71,73,89,91,119,130,146,148,152,192,196],[32,33,55,103,141],[11,16,23,52,74,86,100,105,108,120,129,133,170,181,183,187,207,210,227,231],[18,19,38,46,49,51,52,53,70,90,99,117,173,191,201,209,219,223],[2,5,9,12,15,16,17,23,24,33,47,68,70,101,109,119,122,133,134,153,167,171,173,176,179,188,197,204,207,210,216,225,230,237,238],[1,8,10,11,13,22,25,26,27,31,36,37,53,55,57,58,61,75,89,90,100,106,125,158,169,170,172,177,186,199,215,222,226,230,235,237,239],[6,7,8,20,24,30,40,42,51,74,79,81,91,136,167,169,176,179,181,182,197,203,210,215,238,239],[8,12,14,33,37,38,39,44,65,107,116,124,128,132,134,169,178,179,186,193,200,211,213,217,222,224,225]], # 94, # 222 # )) == 2 # assert (s.numBusesToDestination( # [[10,13,22,28,32,35,43],[2,11,15,25,27],[6,13,18,25,42],[5,6,20,27,37,47],[7,11,19,23,35],[7,11,17,25,31,43,46,48],[1,4,10,16,25,26,46],[7,11],[3,9,19,20,21,24,32,45,46,49],[11,41]], # 37, # 43 # )) == 3 # assert (s.numBusesToDestination( # [[16,18,24,75,82,83,93,95,98,103,106,168,171,211,218,221,265,283,285,307,313,319,336,369,377,379,390,392,397,423,452,457],[4,21,25,28,29,46,48,53,54,71,81,86,116,117,122,124,130,133,147,148,151,155,158,171,198,208,209,218,248,252,261,267,271,273,274,275,288,289,291,299,300,301,303,330,334,352,362,364,365,373,381,386,393,421,424,428,431,433,437,458,466,473],[22,32,44,72,90,100,129,132,153,158,161,173,175,187,196,209,213,220,221,236,239,242,268,281,283,289,304,326,330,334,352,365,376,389,414,428,429,443,461],[4,8,9,10,12,19,36,44,45,48,53,59,63,68,76,78,79,82,85,100,102,105,113,121,131,157,163,164,165,175,179,180,192,196,200,210,221,222,225,232,237,245,265,272,275,279,283,300,302,327,331,332,335,348,353,356,363,378,385,387,403,422,428,429,451,452],[6,13,14,36,37,41,42,55,62,78,82,83,96,107,114,116,118,129,138,143,158,164,168,201,202,204,222,223,227,229,232,234,244,265,268,274,289,295,313,317,322,330,341,347,348,361,363,369,379,391,414,433,447,456,464,465],[14,22,36,39,42,57,62,65,70,86,90,97,112,123,133,145,151,158,168,185,187,192,194,203,223,225,234,238,245,252,255,267,271,274,285,297,300,303,318,322,343,344,363,364,372,381,399,419,420,425,426,427,433,439,440,450,452,470,471],[56,74,100,167,179,194,214,250,288,332,393,410,439],[1,5,6,8,10,18,24,27,29,30,36,42,44,52,64,66,70,83,96,97,98,100,103,107,113,125,129,140,141,159,160,163,164,166,172,174,181,186,194,202,208,218,232,239,242,248,258,277,279,284,286,291,295,302,304,305,308,313,316,319,320,341,342,352,356,360,370,386,392,399,400,402,408,410,412,416,427,434,435,439,444,457,463,471,472],[0,1,10,12,26,27,29,30,48,51,60,72,83,84,89,91,100,103,125,127,131,136,147,156,167,170,173,178,181,182,186,188,195,197,201,204,211,215,216,221,235,238,266,269,277,282,287,296,317,335,340,343,347,351,355,357,358,366,368,382,384,390,395,397,404,418,421,424,427,429,430,438,458,462,466],[87,207,419],[9,22,89,90,129,131,136,142,148,154,176,182,204,207,217,228,246,248,275,306,328,340,341,364,366,371,407,418,420,437,439,442,458,473],[2,25,34,67,80,81,106,110,125,146,160,172,176,178,196,205,208,217,226,239,244,253,269,280,300,308,322,324,328,335,340,342,343,352,365,368,385,387,391,404,410,413,416,420,423,433,435,436,439,440,450,468],[10,59,68,75,79,90,91,98,105,121,134,143,145,156,162,174,209,214,216,235,249,255,256,258,260,274,279,291,308,312,315,319,341,346,350,353,356,382,386,399,408,414,435,449,471],[1,5,6,9,14,35,39,41,53,70,75,77,91,98,101,108,114,120,123,134,150,156,160,171,180,181,186,187,201,206,215,220,223,236,248,249,251,255,257,263,269,270,278,282,305,312,313,314,320,327,331,338,345,349,359,360,380,381,384,389,400,414,415,419,420,427,429,432,437,440,441,442,455],[2,12,14,15,51,62,64,69,74,90,97,98,99,100,110,112,114,118,133,136,138,139,154,163,170,172,173,180,194,199,207,210,211,226,233,234,235,260,273,274,282,300,302,314,318,327,334,335,344,351,352,354,367,369,377,385,388,407,419,420,433,434,441,444,445,450,454,458,470],[2,30,32,41,64,71,76,77,79,84,104,109,128,132,137,143,147,152,160,182,191,196,197,204,218,221,262,282,312,323,326,334,349,357,361,371,385,387,398,415,460,461,471],[2,6,28,45,56,68,70,110,113,140,150,159,168,172,174,175,177,179,204,207,230,231,235,247,281,285,289,291,298,315,322,326,339,350,351,352,367,369,376,380,381,382,386,427,429,443,461,465,470],[47,61,62,63,84,105,107,124,151,178,203,234,239,253,267,304,322,349,353,357,363,365,396,412,425,446,451,462],[10,21,65,109,114,168,200,253,292,308,323,327,381,392,393,432],[17,21,27,30,107,140,191,195,261,278,290,332,342,347,353,399,402,433,444],[46,91,95,103,154,181,192,202,221,224,266,290,335,342,346,347,363,381,394,401,417,439,441],[19,30,39,61,118,197,200,224,271,278,360,408],[15,20,30,37,38,60,65,79,81,88,91,92,93,99,100,101,103,115,124,148,160,191,194,228,234,251,252,258,261,272,280,300,301,303,313,316,318,320,329,343,378,379,380,387,406,412,418,423,435,439,443,470,472],[0,5,12,25,71,86,100,102,132,143,167,181,183,187,226,244,304,336,423,461],[6,20,26,45,48,53,67,73,81,87,89,92,94,96,99,107,122,128,129,137,138,149,159,168,182,202,211,219,225,229,231,234,240,244,247,251,254,261,284,292,297,300,302,305,315,328,337,338,348,354,366,381,382,383,384,395,398,399,409,415,418,420,422,424,426,428,450,452,457,460,461],[4,6,22,39,44,48,49,52,61,64,68,71,76,78,93,101,102,111,114,126,127,138,141,148,159,167,172,176,187,195,201,205,207,232,242,252,256,277,289,295,297,299,301,305,310,320,332,335,339,343,346,350,353,355,364,366,375,380,388,419,420,423,444,446,448,450,453,464,471],[67,68,73,83,152,202,218,222,264,289,301,302,368,371,412,416,435],[8,10,19,32,38,54,64,75,84,114,124,130,139,149,158,160,172,181,185,194,195,200,208,231,240,277,283,292,308,328,335,338,353,355,366,371,390,406,412,418,445,462,469,470,474],[9,22,30,65,81,88,138,142,151,153,155,156,160,162,166,174,181,188,192,198,203,219,238,240,243,252,257,258,265,301,325,330,340,362,370,392,396,399,404,417,447,454,463,467,472,473],[7,15,35,152,165,229,296,330,331,342,348,359,459],[9,12,21,28,35,45,50,57,61,76,83,97,107,111,120,130,165,171,177,180,187,195,199,204,216,217,227,244,262,278,283,285,290,304,305,310,316,329,331,343,354,360,378,387,413,422,427,433,438,449,452,456,465,468,469],[37,109,112,173,174,219,248,249,263,347,396,401,417],[9,13,24,25,34,38,44,47,54,64,70,86,102,103,106,111,112,118,129,136,145,152,159,162,196,207,209,215,224,225,248,257,259,275,315,327,345,355,378,382,394,398,423,429,433,438,445,449,450,462,472],[3,20,28,30,36,47,63,70,78,90,93,101,102,108,111,126,140,150,153,160,162,163,167,170,182,190,195,203,204,210,216,219,229,232,239,247,251,252,253,269,283,291,292,294,295,309,334,343,350,356,364,365,368,372,373,382,393,406,413,418,439,443,446,454,461,463,469,474],[0,1,10,11,18,20,21,22,23,24,33,36,43,54,60,62,65,71,72,76,77,78,80,98,103,106,118,120,124,131,138,140,143,151,155,160,161,164,165,170,171,174,175,186,191,199,207,228,231,239,243,246,248,254,265,267,278,281,286,290,291,298,299,309,312,316,327,348,358,366,371,380,381,396,412,417,422,423,424,430,436,439,442,468],[8,17,19,38,43,54,68,70,79,99,109,112,122,133,153,159,162,179,185,190,199,227,229,239,259,260,267,275,277,278,293,296,324,325,326,332,335,336,342,358,362,385,391,408,419,420,421,422,433,447,451,471],[1,3,6,19,25,27,29,39,45,48,54,59,61,67,68,71,72,73,77,83,105,107,111,113,126,129,131,139,141,154,162,165,167,168,172,173,174,180,182,192,195,200,209,211,213,218,225,228,231,236,242,267,270,272,280,283,284,286,291,297,298,299,302,317,324,333,335,340,347,348,350,354,355,358,366,368,372,373,380,399,419,421,428,435,436,450,455,470],[103,146,194,267,317,351,433,468],[5,11,16,47,49,59,72,79,119,120,127,129,143,158,162,185,204,221,235,248,249,264,270,290,291,295,297,301,313,316,324,334,335,340,377,404,410,412,423,446,456,458,462,464,472],[14,16,53,61,65,86,98,99,100,113,115,185,207,217,246,278,282,336,358,375,429,445],[9,21,33,36,40,49,58,59,66,70,79,81,84,107,112,123,126,133,134,135,141,154,155,169,178,195,210,216,220,231,232,239,255,257,272,279,294,295,300,301,305,308,313,357,372,380,383,386,388,393,414,415,419,420,424,437,443,445,456,466,467],[10,179,182,353],[0,2,6,7,8,13,19,20,28,35,40,42,49,53,59,62,71,75,76,86,95,96,101,115,116,126,129,144,155,157,159,171,191,195,205,215,220,222,224,234,240,241,257,260,264,267,269,280,281,290,307,339,341,346,347,363,377,378,383,389,395,401,405,406,407,416,418,420,426,429,432,434,438,441,459,460,473],[1,10,11,28,30,34,36,41,58,117,149,169,217,218,219,245,270,354,386,440],[3,12,15,19,21,23,30,48,49,63,81,90,110,119,133,141,143,166,168,185,191,200,203,214,222,253,260,266,284,306,311,321,329,340,380,397,399,401,415,425,426,428,437,463],[12,32,34,64,70,80,86,89,91,117,119,125,133,172,187,189,191,197,223,246,259,265,280,282,291,296,346,350,352,368,405,411,430,434,468],[13,57,58,93,100,172,174,182,231,252,269,347,389,417,474],[1,24,28,30,33,52,55,60,61,66,67,71,76,77,79,88,96,101,121,126,129,136,146,152,155,171,175,176,179,190,197,199,200,205,208,221,230,238,245,249,253,257,259,266,269,272,274,276,284,285,294,298,300,319,332,335,345,349,350,353,359,361,362,365,374,377,379,392,413,417,436,441,442,454,457,461,465,471,472],[4,22,30,53,56,57,74,79,80,96,98,100,107,128,133,139,142,151,175,183,190,191,193,206,211,224,230,237,242,245,251,256,261,270,272,275,276,280,286,294,305,317,327,330,334,344,364,385,387,394,398,402,403,404,407,412,413,419,427,428,457,460,461,471],[356,369],[23,46,49,51,53,59,61,74,96,113,114,120,135,138,139,144,159,192,202,204,215,216,236,261,268,281,301,317,324,346,377,380,389,402,414,427,429,459],[20,29,38,39,45,58,77,78,97,102,113,130,139,140,142,154,171,172,188,196,200,211,214,215,224,233,237,246,249,252,256,271,278,280,282,287,296,301,302,304,310,330,340,346,348,360,372,376,377,381,390,396,405,424,427,442,444,451,459,465],[107,154,179,222,252,286,313,316,372,382,383,385],[8,16,27,62,68,69,73,84,103,120,172,177,179,189,207,208,229,240,274,286,291,301,314,321,324,336,387,410],[8,15,17,37,52,55,58,75,95,115,121,145,152,168,170,178,183,189,192,209,212,233,243,250,260,262,273,278,280,283,306,316,327,329,334,348,355,378,405,412,418,427,428],[5,7,9,34,40,61,68,78,81,93,123,127,130,150,153,156,167,214,219,226,227,242,250,276,278,340,355,360,364,377,403,415,439,441,457,463,474],[1,4,14,16,21,43,58,62,81,98,111,124,145,153,178,181,202,206,229,264,266,284,304,310,319,330,412,427,459,464],[13,15,21,27,29,49,64,65,70,77,79,85,92,115,122,128,131,155,160,163,168,179,181,191,204,213,235,244,264,282,284,300,305,309,315,318,319,328,347,356,362,364,369,384,385,399,434,453],[0,5,14,15,21,22,26,30,35,38,40,53,60,62,79,93,100,104,108,116,117,126,127,129,147,151,152,155,159,161,163,172,174,176,190,194,205,216,228,230,236,250,253,256,271,274,278,283,298,316,320,329,335,337,342,346,350,353,356,361,365,368,369,371,379,380,390,392,402,404,409,412,415,422,424,427,428,433,436,449,450,458,461,467,468],[0,34,39,45,46,72,81,93,106,110,113,119,121,124,129,137,139,146,156,184,189,199,207,231,233,235,241,247,252,263,264,265,283,311,314,316,331,350,359,368,378,401,406,409,417,423,437,440,441,445,447,457,462,467],[0,9,20,28,36,37,39,48,54,64,71,73,75,76,84,109,119,121,122,131,139,155,175,186,205,206,211,213,218,220,222,228,239,242,244,254,265,278,285,286,295,301,321,329,335,344,346,349,350,359,370,371,372,384,393,397,398,408,412,433,435,436,442,458,460,464,466,468],[331,388,406],[5,43,68,73,89,110,131,199,214,245,272,279,290,305,331,359,388,392,413,435,470],[1,9,13,18,49,54,55,57,64,72,78,86,87,88,93,118,133,135,137,138,139,141,147,149,156,157,162,165,171,175,180,189,205,218,219,239,242,244,249,253,270,273,275,277,279,283,291,305,307,309,318,327,329,335,337,342,344,345,353,354,355,362,365,366,369,372,375,376,392,400,406,435,449,456,462,472],[5,6,19,22,39,49,51,53,66,76,79,80,86,98,122,134,138,142,157,165,167,168,179,180,198,204,215,237,243,246,248,254,257,285,288,290,295,318,319,326,338,347,355,361,375,376,380,386,388,390,396,402,403,404,427,437,441,459,472],[3,9,18,30,37,42,45,60,67,71,88,92,98,105,127,133,134,136,140,142,143,149,150,154,160,173,174,175,177,188,193,208,214,220,223,227,239,240,245,250,257,258,263,292,295,310,319,322,325,338,345,353,354,355,358,360,368,376,396,397,398,401,421,423,436,441,452,453,467,469,471],[3,58],[9,35,37,70,73,79,81,87,94,98,105,106,115,117,121,145,168,183,192,222,229,230,232,236,242,252,255,267,279,281,284,290,296,318,324,338,340,346,350,354,360,374,415,416,426,433,440,441,445,447,463,464],[7,11,16,22,25,32,34,38,39,45,57,58,64,66,68,69,73,79,85,88,91,96,101,114,120,125,131,139,142,149,152,165,170,172,174,176,192,194,208,211,218,221,230,236,240,263,271,281,284,288,294,310,318,324,325,329,336,340,341,346,348,351,355,361,369,370,376,385,389,397,401,415,417,418,419,423,429,443,465,466,468,473,474],[1,5,15,25,31,45,47,53,55,56,59,63,68,72,101,138,139,143,145,146,158,159,171,175,176,205,210,219,220,224,234,243,244,246,254,276,280,281,286,287,298,299,304,305,306,307,310,313,316,318,322,325,327,329,332,343,348,354,361,367,369,370,372,374,375,388,390,391,393,394,421,440,442,448,449,450,455,456,463,464,470,473],[32,70,89,91,100,130,147,157,166,182,205,251,269,280,289,301,305,315,333,338,354,366,374,388],[17,28,40,61,63,66,91,112,120,131,153,169,192,215,218,236,242,257,264,293,333,352,366,368,375,376,383,394,395,405,425,463],[27,28,61,73,79,80,89,94,95,99,119,125,128,131,156,165,177,193,197,198,205,209,212,218,219,224,230,231,232,247,253,258,269,271,276,277,281,282,290,291,301,316,319,341,346,347,351,354,361,386,399,402,422,438,454,471],[16,18,19,22,26,32,36,37,56,64,82,86,92,94,95,104,107,120,135,145,151,172,198,201,208,209,223,228,243,245,247,251,252,255,260,265,287,288,289,313,333,360,366,369,373,382,401,447,465],[1,4,7,21,27,28,30,34,41,58,63,65,78,92,93,95,100,104,110,111,115,131,132,136,145,148,158,175,180,186,191,194,195,196,200,204,206,215,227,228,242,243,245,246,251,258,274,279,288,289,292,295,300,304,306,312,323,325,329,335,340,342,345,365,370,387,393,409,414,418,424,434,457,462,466,471,472],[0,5,36,51,54,56,57,58,67,70,76,81,87,98,100,102,103,111,113,139,142,145,147,149,150,153,157,162,172,180,184,186,196,203,204,216,220,224,252,256,274,287,297,309,314,334,335,348,366,374,388,389,391,393,395,404,428,434,446,447,451,455,474],[22,58,81,98,100,107,125,133,199,224,230,232,235,288,290,323,324,340,351,393,397,399,446,451,461,468],[364],[0,1,10,12,16,35,37,45,58,60,61,79,83,90,96,119,127,133,136,139,141,143,144,167,170,177,178,182,192,207,212,216,230,234,256,284,293,298,314,317,321,329,354,357,362,367,369,376,420,434,438,439,441,442,443,452,456],[1,9,11,23,58,62,81,86,100,102,109,116,128,139,141,147,161,171,178,179,180,181,187,189,190,194,200,207,211,217,223,233,245,248,252,258,267,269,270,273,274,290,296,297,299,300,301,312,327,334,335,348,351,354,360,365,370,375,376,382,384,385,400,406,412,420,422,424,435,446,449,469,470,471],[0,10,11,13,15,17,35,38,40,61,63,75,76,78,109,113,115,120,123,129,130,134,144,148,156,158,159,161,176,179,186,199,204,205,209,211,237,244,246,251,256,268,274,290,297,299,300,316,317,327,336,338,348,353,366,369,375,380,383,386,387,394,398,405,411,423,427,430,435,436,440,441,447,455,459,463,467,469,471,472],[14,17,23,24,41,49,54,76,78,89,93,101,104,105,108,118,134,161,163,170,174,180,182,186,193,214,219,238,241,246,247,251,278,304,318,333,342,343,344,345,351,352,357,361,378,388,394,404,406,415,436,438,442,446,463,471],[8,12,17,21,42,48,50,52,74,80,104,117,124,127,138,146,147,153,162,167,176,207,215,219,220,231,235,240,252,268,285,290,291,302,320,321,328,334,353,356,371,372,374,387,402,415,419,448,450,459,466,473],[9,18,30,36,38,43,44,64,73,78,82,86,87,93,94,108,109,118,132,140,141,148,150,152,157,163,168,178,182,196,199,221,223,226,238,241,245,248,250,252,255,258,261,271,280,282,284,291,293,299,303,310,312,314,323,329,340,345,348,351,354,358,361,374,380,391,392,394,397,400,405,411,419,421,444,445,467],[11,20,21,43,44,47,55,59,62,68,69,74,80,81,84,92,93,97,112,115,119,121,123,127,129,130,150,155,157,161,163,164,165,169,179,180,182,189,194,197,205,221,223,236,241,249,257,260,262,265,271,281,286,290,292,296,297,304,305,307,321,323,334,349,357,363,365,368,382,396,401,408,416,439,453,454,455,464],[0,14,20,27,28,51,52,72,87,91,104,105,122,125,135,159,161,178,179,190,196,209,234,266,281,284,298,303,317,376,407,445,471,472],[2,27,81,142,158,178,258,276,332,438,443,472],[0,6,15,17,25,29,35,36,59,63,67,71,73,76,80,81,97,101,105,116,117,121,129,131,145,149,151,154,159,171,175,184,186,192,195,201,202,207,209,211,214,216,229,230,236,243,246,247,250,252,254,258,270,273,277,282,284,287,291,305,323,331,338,348,350,359,385,386,388,393,397,417,437,449,459,460,466,470,471,473],[1,12,14,15,26,35,36,50,63,67,68,70,73,79,99,110,121,122,128,135,136,140,148,149,160,188,196,200,203,204,206,210,213,218,225,227,228,233,243,244,246,254,258,265,267,283,286,290,296,308,311,312,317,326,330,338,343,364,365,369,381,383,399,402,403,420,421,422,427,430,433,437,438,443,445,447,459,460,462,467,469,470],[10,17,28,32,34,44,48,78,79,81,82,85,89,93,101,106,107,124,138,156,157,169,172,177,182,190,191,198,199,208,212,256,282,291,293,313,315,324,340,346,353,361,369,384,386,387,391,426,432,449,452,455,460,471],[15,25,48,66,69,78,83,87,113,116,123,137,143,147,159,199,204,232,235,242,247,251,265,266,294,314,321,324,351,356,362,363,376,390,401,433,434,444,445,448,473],[15,16,26,27,31,42,50,55,63,68,69,75,95,109,111,114,117,121,128,131,132,136,138,140,144,148,159,161,162,163,168,170,186,187,197,200,211,213,214,225,234,243,246,256,262,268,273,285,305,325,333,340,365,385,387,402,411,412,418,421,431,434,437,439,442,445,452,455,461],[6,7,16,22,44,45,47,53,73,77,80,81,111,119,120,122,126,129,133,134,140,143,144,146,147,150,170,172,186,189,197,199,210,224,226,228,240,244,246,249,250,255,272,274,276,280,283,300,303,305,308,311,312,313,324,325,326,330,333,341,347,355,362,369,375,385,391,413,421,423,430,432,438,446,450],[18,26,28,35,41,42,48,75,87,92,106,107,114,125,133,137,140,152,168,170,171,184,192,200,223,241,256,258,275,279,286,287,299,319,326,343,351,361,368,372,382,386,392,406,409,415,432,438,445,449,454,455,464,469],[8,10,20,29,30,36,37,39,42,46,48,66,69,71,72,76,78,83,84,85,95,108,112,117,120,124,132,133,148,152,177,180,185,189,231,232,246,247,260,267,279,284,291,303,314,316,320,331,333,335,341,346,350,369,392,397,401,408,410,413,414,416,422,426,439,444,459,462,463,466,469,470]] # ,100, # 285 # )) == 2 assert (s.numBusesToDestination( [[148,167,216],[6,23,25,40,43,58,63,69,77,86,94,96,106,117,119,127,139,151,153,155,157,186,191,196,200,204,210,216,219],[2,6,7,16,27,30,42,47,49,68,69,77,93,94,96,102,104,111,114,126,131,137,150,161,167,171,174,193,198,199,200,223],[46,131,211],[25,36,51,52,65,78,90,102,103,105,108,114,123,151,152,153,162,174,175],[217],[9,10,15,27,37,38,41,43,46,51,67,74,81,82,83,94,95,107,113,120,122,123,124,132,149,160,162,169,170,171,174,177,185,192,193,195,196,198,213,217,220,221],[74,78,85,95,130,136,145,152,173,175,180,181,184,193,199,202],[13,18,28,38,41,42,47,75,87,91,106,151,158,166,181,182,199,216],[44,63,71,74,144,162,169,220],[2,23,115,185,208],[0,8,13,14,35,46,67,89,91,122,124,126,130,156,177,193,212,214],[2,4,24,37,40,43,55,68,81,92,106,107,109,127,132,138,145,159,163,165,170,172,183,184,209,213,215,220],[5,16,17,34,38,48,55,59,60,65,69,84,86,94,100,103,109,110,112,127,130,131,134,145,148,149,154,161,166,169,182,183,201,203,208,214,223],[0,2,5,6,8,19,49,50,53,79,92,94,97,109,110,112,121,129,132,135,138,139,144,160,166,170,194,197,198,201,212],[27,52,61,112,118,133,142,159,175,186,216],[2,20,34,64,65,77,87,91,95,96,97,125,126,131,144,146,149,152,154,164,165,170,179,205,207],[24,85,123,132,172,173,194,222],[2,4,5,15,23,36,44,47,63,64,78,80,84,97,99,102,104,114,120,130,132,143,161,162,163,167,171,172,176,179,180,194,196,199,202,204,209,214,216,221],[8,22,26,31,38,39,41,59,78,90,102,108,110,138,141,146,176,185,190,198,200,219,220],[5,24,30,46,55,64,67,74,78,136,194,216],[133,142,202],[13,40,49,57,63,75,76,85,91,107,116,121,128,135,137,141,154,193,198,200,204,223],[4,13,14,26,28,33,39,49,58,65,67,74,77,81,90,96,122,124,144,156,158,166,169,170,179,203,204,208,215,223],[6,20,28,36,46,90,107,115,124,131,135,144,147,148,149,161,162,174,176,214,221],[10,20,21,29,35,36,62,65,67,70,72,87,89,92,100,103,107,109,113,126,129,139,140,145,146,147,174,176,180,184,189,190,193,196,198,199,200,209,217],[19,22,27,54,59,63,77,102,122,126,140,143,154,164,165,175,212,216,217,218],[11,13,16,18,27,31,46,49,69,77,88,109,111,119,121,146,161,169,193,194,198,200,204],[1,7,28,58,73,91,98,138,150,173,182,186,213],[3,25,28,33,46,68,70,74,78,97,141,146,149,169,172,178,185,188,202,212,223],[3,4,19,22,24,37,38,43,54,55,56,57,58,62,66,72,75,77,88,106,114,119,127,132,133,137,144,146,150,156,161,164,165,179,181,195,200,213,214,215,222],[9,11,14,15,38,46,55,61,66,68,69,75,76,79,82,91,100,101,102,113,135,141,142,171,175,180,198,208,210,215,218,221],[2,30,33,62,93,104,124,127,128,147,158,160,161,173,181,189,192,199,201,215,223],[4,26,29,38,47,58,61,69,78,93,94,112,114,131,136,144,182,193,198,203,206,209],[5,13,14,16,17,22,30,32,45,47,49,55,63,64,68,77,82,84,86,92,98,100,104,107,117,119,122,127,134,153,164,179,185,197,201,209,212,213,220,223],[2,4,5,6,42,55,75,81,84,93,102,111,112,113,118,129,142,149,159,169,191,193,200,214,223],[10,12,15,19,20,24,33,34,40,47,54,64,93,104,115,121,123,124,155,172,189,190,193,196,202,212,219,222],[104,108,143],[14,15,20,21,31,47,48,59,67,70,74,82,94,102,109,121,125,128,148,162,165,171,180,196,199,202,205,212,214],[2,6,17,18,41,50,60,70,118,151,155,158,166,167,172,180,182,186,188,195],[1,23,25,30,39,41,42,48,58,65,67,94,100,121,126,135,145,152,163,164,171,174,206,210,220,224],[18,25,96,123,172],[5,7,9,12,13,19,22,25,34,51,62,64,74,79,81,85,88,101,102,119,123,140,143,149,155,165,166,167,178,182,189,204,213,222,223],[1,5,18,21,23,50,54,59,62,67,68,72,87,94,95,96,110,116,118,122,133,135,151,155,156,158,171,178,183,184,192,198,208,212,222,224],[18,20,24,34,47,52,56,68,77,82,89,91,97,101,105,106,107,109,118,123,139,141,143,152,153,162,174,180,184,187,188,192,198,202,206,216,224]], 180, 143 )) == 1

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py

Python

tests/test_models/test_buttons.py

ExpressApp/pybotx

97c8b1ce5d45a05567ed01d545cb43174a2dcbb9

[ "MIT" ]

13

2021-01-21T12:43:10.000Z

2022-03-23T11:11:59.000Z

tests/test_models/test_buttons.py

ExpressApp/pybotx

97c8b1ce5d45a05567ed01d545cb43174a2dcbb9

[ "MIT" ]

259

2020-02-26T08:51:03.000Z

2022-03-23T11:08:36.000Z

tests/test_models/test_buttons.py

ExpressApp/pybotx

97c8b1ce5d45a05567ed01d545cb43174a2dcbb9

[ "MIT" ]

5

2019-12-02T16:19:22.000Z

2021-11-22T20:33:34.000Z

import pytest from pydantic import ValidationError from botx.models.buttons import Button, ButtonOptions class CustomButton(Button): """Button without custom behaviour.""" def test_label_will_be_set_to_command_if_none(): assert CustomButton(command="/cmd").label == "/cmd" def test_label_can_be_set_if_passed_explicitly(): assert CustomButton(command="/cmd", label="temp").label == "temp" def test_empty_label(): assert CustomButton(command="/cmd", label="").label == "" def test_create_button_options_with_invalid_hsize(): with pytest.raises(ValidationError) as exc_info: ButtonOptions(h_size=0) assert "should be positive integer" in str(exc_info)

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0e94f52aa74ffa31af581266bcde6be47bc8f08e

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py

Python

py_client/aidm/aidm_time_window_classes.py

sma-software/openviriato.algorithm-platform.py-client

73d4cf89aa6f4d02ab15b5504d92107848742325

[ "Apache-2.0" ]

2

2021-06-21T06:50:29.000Z

2021-06-30T15:58:02.000Z

py_client/aidm/aidm_time_window_classes.py

sma-software/openviriato.algorithm-platform.py-client

73d4cf89aa6f4d02ab15b5504d92107848742325

[ "Apache-2.0" ]

null

py_client/aidm/aidm_time_window_classes.py

sma-software/openviriato.algorithm-platform.py-client

73d4cf89aa6f4d02ab15b5504d92107848742325

[ "Apache-2.0" ]

null

import datetime class TimeWindow: __from_time: datetime.datetime __to_time: datetime.datetime def __init__(self, from_time: datetime.datetime, to_time: datetime.datetime): self.__from_time = from_time self.__to_time = to_time @property def from_time(self) -> datetime.datetime: return self.__from_time @property def to_time(self) -> datetime.datetime: return self.__to_time

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0eb0de9cbe7b073230d696555d472888b3e40a1c

348

py

Python

wabot/models.py

engleandro/Portifolio-WhatsAppAutomation

f01d181697215aee3d105e3ad94e2593b7732e5c

[ "Apache-2.0" ]

null

wabot/models.py

engleandro/Portifolio-WhatsAppAutomation

f01d181697215aee3d105e3ad94e2593b7732e5c

[ "Apache-2.0" ]

null

wabot/models.py

engleandro/Portifolio-WhatsAppAutomation

f01d181697215aee3d105e3ad94e2593b7732e5c

[ "Apache-2.0" ]

null

from django.db import models class MessageWABot(models.Model): customer = models.CharField(max_length=15) from_phone = models.CharField(max_length=15) to_phone = models.CharField(max_length=15) message = models.CharField(max_length=500) request_at = models.DateTimeField() def __str__(self): return self.customer

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7ec603da7e51b19f1f67a90f5e3b8a8507d65b66

671

py

Python

landing/templatetags/hometag.py

okfnepal/election-nepal

20b29330a5fbc14e2512e76ae0c3ef37d665e3d0

[ "MIT" ]

31

2017-04-04T15:12:01.000Z

2020-09-23T23:28:33.000Z

landing/templatetags/hometag.py

okfnepal/election-nepal

20b29330a5fbc14e2512e76ae0c3ef37d665e3d0

[ "MIT" ]

45

2017-04-05T16:06:15.000Z

2019-10-08T19:10:43.000Z

landing/templatetags/hometag.py

okfnepal/election-nepal

20b29330a5fbc14e2512e76ae0c3ef37d665e3d0

[ "MIT" ]

18

2017-04-04T15:12:23.000Z

2019-07-09T00:50:59.000Z

from django import template from landing.models import AboutUs, Data, Visualization register = template.Library() @register.assignment_tag def get_aboutus_tag(): return AboutUs.objects.first() @register.assignment_tag def get_datalist_tag(): return Data.objects.all().order_by('-added') @register.assignment_tag def get_Visualization_tag(): return Visualization.objects.all().order_by('-added') @register.assignment_tag def get_recentVisualization_tag(): limit = 5 return Visualization.objects.order_by('-added')[:limit] @register.assignment_tag def get_recentDataset_tag(): limit = 5 return Data.objects.order_by('-added')[:limit]

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7ed1112dd92c7ac3a94b34837f1170638c1b14cd

134

py

Python

django/django_admin_register_app/cafe/admin.py

taptorestart/python-backend-examples

0817223f403570f5822511c240726c6108d3b9b7

[ "MIT" ]

7

2022-02-25T03:27:01.000Z

2022-03-22T10:51:13.000Z

django/django_admin_register_app/cafe/admin.py

taptorestart/python-backend-examples

0817223f403570f5822511c240726c6108d3b9b7

[ "MIT" ]

null

django/django_admin_register_app/cafe/admin.py

taptorestart/python-backend-examples

0817223f403570f5822511c240726c6108d3b9b7

[ "MIT" ]

1

2022-03-24T14:47:49.000Z

from django.contrib import admin from .models import Category, Beverage admin.site.register(Category) admin.site.register(Beverage)

19.142857

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7ed455c08ebf2fa2029dd086b8bafbace11c5c72

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py

Python

source/online_catalog_scraper/__init__.py

kevinliang43/ProjectDB

6ffd435cb01658e16f0da272bb3f8ec2faeef73c

[ "Apache-2.0" ]

null

source/online_catalog_scraper/__init__.py

kevinliang43/ProjectDB

6ffd435cb01658e16f0da272bb3f8ec2faeef73c

[ "Apache-2.0" ]

null

source/online_catalog_scraper/__init__.py

kevinliang43/ProjectDB

6ffd435cb01658e16f0da272bb3f8ec2faeef73c

[ "Apache-2.0" ]

null

from scrapers import *

11.5

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7d3904750e900034307b9e21305faa768ed1a144

173

py

Python

web_python/website/templatetags/tempo_atual.py

1997jorge/web-python-django

a5709258e1c1cac408e2aeabe3011dcc5309e787

[ "MIT" ]

null

web_python/website/templatetags/tempo_atual.py

1997jorge/web-python-django

a5709258e1c1cac408e2aeabe3011dcc5309e787

[ "MIT" ]

null

web_python/website/templatetags/tempo_atual.py

1997jorge/web-python-django

a5709258e1c1cac408e2aeabe3011dcc5309e787

[ "MIT" ]

null

from django import template import datetime register = template.Library() @register.simple_tag def tempo_atual(): return datetime.datetime.now().strftime('%H:%M:%S')

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7d3e26e31199b66d23d9599df6983d1a56433ae2

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py

Python

spekulatio/build_file_tree/actions/__init__.py

iwilltry42/spekulatio

42d678b7d7fcc13284902be5a08fb0407d96ec4d

[ "MIT" ]

10

2019-03-19T23:05:04.000Z

2022-01-19T14:08:06.000Z

spekulatio/build_file_tree/actions/__init__.py

iwilltry42/spekulatio

42d678b7d7fcc13284902be5a08fb0407d96ec4d

[ "MIT" ]

6

2019-03-23T08:38:44.000Z

2020-11-24T20:50:14.000Z

spekulatio/build_file_tree/actions/__init__.py

iwilltry42/spekulatio

42d678b7d7fcc13284902be5a08fb0407d96ec4d

[ "MIT" ]

1

2019-09-26T12:21:36.000Z

from .ignore import ignore # noqa from .copy import copy # noqa from .compile_scss import compile_scss # noqa from .render_html import render_html_factory # noqa

27.833333

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1

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adfd4049865aa1696336f3545f50153a62234e0d

219

py

Python

lib/datasets/__init__.py

ybai62868/UAV-LPSODection

fd5f2315df0811f108f8fd24693280807bb8aa94

[ "MIT" ]

6

2019-09-26T12:02:13.000Z

2020-08-12T15:52:00.000Z

lib/datasets/__init__.py

ybai62868/UAV-Detection

fd5f2315df0811f108f8fd24693280807bb8aa94

[ "MIT" ]

1

2019-11-06T10:20:27.000Z

lib/datasets/__init__.py

ybai62868/UAV-Detection

fd5f2315df0811f108f8fd24693280807bb8aa94

[ "MIT" ]

null

from __future__ import absolute_import from __future__ import division from __future__ import print_function from .dac import DACDataset as dac from .voc import MPIIDataset as mpii from .coco import COCODataset as coco

31.285714

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null

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5

bc00bd3a407659bbdd4a5252d6fdb5b5c19ca1d5

229

py

Python

src/commands/testgroup.py

xiaoeric/HomegrownRobot

632c5bdff534d09d98361e395892c5c3cabad099

[ "MIT" ]

null

src/commands/testgroup.py

xiaoeric/HomegrownRobot

632c5bdff534d09d98361e395892c5c3cabad099

[ "MIT" ]

null

src/commands/testgroup.py

xiaoeric/HomegrownRobot

632c5bdff534d09d98361e395892c5c3cabad099

[ "MIT" ]

null

from wpilib.command import CommandGroup class TestCommandGroup(CommandGroup): """Run when robot enters testing mode""" def __init__(self): super().__init__('Test Program') # TODO add robot systems test

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0

1

0

5

bc1f6bad55ade03ce0cbd31cd7659d53a6ead5b1

42

py

Python

guillotina/contrib/pubsub/exceptions.py

rboixaderg/guillotina

fcae65c2185222272f3b8fee4bc2754e81e0e983

[ "BSD-2-Clause" ]

173

2017-03-10T18:26:12.000Z

2022-03-03T06:48:56.000Z

guillotina/contrib/pubsub/exceptions.py

rboixaderg/guillotina

fcae65c2185222272f3b8fee4bc2754e81e0e983

[ "BSD-2-Clause" ]

921

2017-03-08T14:04:43.000Z

2022-03-30T10:28:56.000Z

guillotina/contrib/pubsub/exceptions.py

rboixaderg/guillotina

fcae65c2185222272f3b8fee4bc2754e81e0e983

[ "BSD-2-Clause" ]

60

2017-03-16T19:59:44.000Z

2022-03-03T06:48:59.000Z

class NoPubSubDriver(Exception): pass

14

32

0.761905

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42

8

1

0

0.166667

42

2

33

21

0.914286

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1

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0.5

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1

0

null

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1

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null

0

1

0

5

70ab257f9b12d6396972df5c3fe1cbe88970ed8a

101

py

Python

src/export/simple_html/jinja_filters.py

pgecsenyi/jira-report-generator

48d9c7dc8e8bc5e7e9cc69c0c05a644f320c41d2

[ "MIT" ]

null

src/export/simple_html/jinja_filters.py

pgecsenyi/jira-report-generator

48d9c7dc8e8bc5e7e9cc69c0c05a644f320c41d2

[ "MIT" ]

null

src/export/simple_html/jinja_filters.py

pgecsenyi/jira-report-generator

48d9c7dc8e8bc5e7e9cc69c0c05a644f320c41d2

[ "MIT" ]

null

SECS_IN_HOUR = 60 * 60 def secs_to_hours(value): return '{0:.1f}'.format(value / SECS_IN_HOUR)

16.833333

49

0.683168

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101

3.5

0.666667

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0

1

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1

0

null

0

1

0

1

0

5

cb11de4c9072bbe73633b36bf0348541dac4e512

20

py

Python

elliot/evaluation/metrics/fairness/reo/__init__.py

gategill/elliot

113763ba6d595976e14ead2e3d460d9705cd882e

[ "Apache-2.0" ]

175

2021-03-04T15:46:25.000Z

2022-03-31T05:56:58.000Z

elliot/evaluation/metrics/fairness/reo/__init__.py

gategill/elliot

113763ba6d595976e14ead2e3d460d9705cd882e

[ "Apache-2.0" ]

15

2021-03-06T17:53:56.000Z

2022-03-24T17:02:07.000Z

elliot/evaluation/metrics/fairness/reo/__init__.py

gategill/elliot

113763ba6d595976e14ead2e3d460d9705cd882e

[ "Apache-2.0" ]

39

2021-03-04T15:46:26.000Z

2022-03-09T15:37:12.000Z

from .reo import REO

20

0.8

4

20

4

0.75

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0.15

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20

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true

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null

0

1

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5

cb5861d3aadda5d45446336fbb6104cc1b43c5ef

83

py

Python

cpepgen/__init__.py

hjuinj/cpepgen

965f84148f783bd1d19aec4c9b86841a598d4a9b

[ "MIT" ]

null

cpepgen/__init__.py

hjuinj/cpepgen

965f84148f783bd1d19aec4c9b86841a598d4a9b

[ "MIT" ]

null

cpepgen/__init__.py

hjuinj/cpepgen

965f84148f783bd1d19aec4c9b86841a598d4a9b

[ "MIT" ]

null

from . import cyclo_peptide, utils, geometry, genetic_algorithm, chemical_linkages

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1

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1

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5

cb74ea5e26551e48ae60d8019b911dcf9863a816

24

py

Python

sanic/__version__.py

ddc67cd/sanic

150d75b7c6aa2346436f0eb895048c53976c98d4

[ "MIT" ]

null

sanic/__version__.py

ddc67cd/sanic

150d75b7c6aa2346436f0eb895048c53976c98d4

[ "MIT" ]

null

sanic/__version__.py

ddc67cd/sanic

150d75b7c6aa2346436f0eb895048c53976c98d4

[ "MIT" ]

null

__version__ = "20.12.0"

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null

0

1

0

1

0

1

0

null

0

5

cbcf3ede66ed140776738fffc9172dad4aefce86

3,327

py

Python

haigha/tests/unit/writer_test.py

ask/haigha

1f87bbb37371f5ae6212c5af32ab4e8c5cebe34c

[ "BSD-3-Clause" ]

1

2022-02-18T05:41:30.000Z

haigha/tests/unit/writer_test.py

ask/haigha

1f87bbb37371f5ae6212c5af32ab4e8c5cebe34c

[ "BSD-3-Clause" ]

null

haigha/tests/unit/writer_test.py

ask/haigha

1f87bbb37371f5ae6212c5af32ab4e8c5cebe34c

[ "BSD-3-Clause" ]

null

from chai import Chai from datetime import datetime from cStringIO import StringIO from haigha.writer import Writer class WriterTest(Chai): # Tests commented out because they don't really apply, but there's a lot that # can be copied ''' def test_write_methods(self): writer = Writer() writer.write( 'foo' ) writer.write_bit( 1 ) writer.write_octet( 5 ) writer.write_short( 42 ) writer.write_long( 12345 ) writer.write_longlong( 123456789 ) writer.write_shortstr( "bar" ) writer.write_longstr( "hellowurld" ) writer.write_table( {'cats':'dogs'} ) writer.write_timestamp( 'now' ) self.assertEquals( 10, len(writer._output_buffer) ) self.assertEquals( (writer._write_str, 'foo'), writer._output_buffer[0] ) self.assertEquals( (writer._write_bit, 1), writer._output_buffer[1] ) self.assertEquals( (writer._write_octet, 5), writer._output_buffer[2] ) self.assertEquals( (writer._write_short, 42), writer._output_buffer[3] ) self.assertEquals( (writer._write_long, 12345), writer._output_buffer[4] ) self.assertEquals( (writer._write_longlong, 123456789), writer._output_buffer[5] ) self.assertEquals( (writer._write_shortstr, 'bar'), writer._output_buffer[6] ) self.assertEquals( (writer._write_longstr, 'hellowurld'), writer._output_buffer[7] ) self.assertEquals( (writer._write_table, {'cats':'dogs'}), writer._output_buffer[8] ) self.assertEquals( (writer._write_timestamp, 'now'), writer._output_buffer[9] ) def test_writing_bits(self): writer = Writer(); stream = StringIO() writer.write_bit(True) writer.flush( stream ) self.assertEquals( '\x01', stream.getvalue() ) writer = Writer(); stream = StringIO() [ writer.write_bit(True) for x in xrange(4) ] writer.flush( stream ) self.assertEquals( '\x0f', stream.getvalue() ) writer = Writer(); stream = StringIO() [ writer.write_bit(True) for x in xrange(5) ] writer.flush( stream ) self.assertEquals( '\x1f', stream.getvalue() ) writer = Writer(); stream = StringIO() [ writer.write_bit(True) for x in xrange(8) ] writer.flush( stream ) self.assertEquals( '\xff', stream.getvalue() ) writer = Writer(); stream = StringIO() writer.write_bit(True) writer.write_bit(False) writer.write_bit(True) writer.write_bit(False) writer.flush( stream ) self.assertEquals( '\x05', stream.getvalue() ) writer = Writer(); stream = StringIO() writer.write_bit(True) writer.write_bit(False) writer.write_bit(True) writer.write_bit(False) writer.write_bit(True) writer.flush( stream ) self.assertEquals( '\x15', stream.getvalue() ) writer = Writer(); stream = StringIO() writer.write_shortstr('foo') [ writer.write_bit(True) for x in xrange(4) ] writer.write_shortstr('bar') writer.flush( stream ) self.assertEquals( '\x03foo\x0f\x03bar', stream.getvalue() ) writer = Writer(); stream = StringIO() writer.write_shortstr('foo') writer.write_bit(True) writer.write_bit(False) writer.write_bit(True) writer.write_bit(False) writer.write_bit(True) writer.write_shortstr('bar') writer.flush( stream ) self.assertEquals( '\x03foo\x15\x03bar', stream.getvalue() ) '''

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1

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1

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5

cbe6b4ea065b4dac055cfee219847d46c3defaa3

472

py

Python

habitican_curse/__init__.py

1ncend1ary/Habitican-Curse

f51c8f3646ad4830bf94bcbd9184128ecffd55d5

[ "MIT" ]

null

habitican_curse/__init__.py

1ncend1ary/Habitican-Curse

f51c8f3646ad4830bf94bcbd9184128ecffd55d5

[ "MIT" ]

null

habitican_curse/__init__.py

1ncend1ary/Habitican-Curse

f51c8f3646ad4830bf94bcbd9184128ecffd55d5

[ "MIT" ]

null

# Standard Library Imports import curses import tempfile import time import locale import threading # Custom Module Imports import habitican_curse.config as C from habitican_curse.screen import Screen import habitican_curse.global_objects as G import habitican_curse.helper as H import habitican_curse.menu as M import habitican_curse.request_manager as RM import habitican_curse.interface as I import habitican_curse.content as CT import habitican_curse.debug as DEBUG

24.842105

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0.319797

0.406091

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0.118644

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true

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0

null

0

1

0

1

0

1

0

5

1dca7fde809db36dd5a4ea53b85425259f779d2b

87

py

Python

ast_version/src/intval.py

lucassa3/CCompiler

ad788f692dc2863da9111b4a42f54277ac29d5ae

[ "MIT" ]

1

2020-04-29T21:30:11.000Z

ast_version/src/intval.py

lucassa3/CCompiler

ad788f692dc2863da9111b4a42f54277ac29d5ae

[ "MIT" ]

10

2018-08-20T18:10:56.000Z

2019-04-05T14:45:11.000Z

ast_version/src/intval.py

lucassa3/CCompiler

ad788f692dc2863da9111b4a42f54277ac29d5ae

[ "MIT" ]

null

from node import Node class IntVal(Node): def eval(self, st): return self.value

17.4

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0.206897

87

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null

0

1

0

null

0

1

0

1

0

5

38174fb0aea8c08124e96d888b9526c53ae3c11c

179

py

Python

repo/sqlalchemy/repo.py

kianooshsanatkar/PyRepo

5b0ec3d6efdfab239cf04d72d78f891a21d2875a

[ "MIT" ]

null

repo/sqlalchemy/repo.py

kianooshsanatkar/PyRepo

5b0ec3d6efdfab239cf04d72d78f891a21d2875a

[ "MIT" ]

null

repo/sqlalchemy/repo.py

kianooshsanatkar/PyRepo

5b0ec3d6efdfab239cf04d72d78f891a21d2875a

[ "MIT" ]

null

from ..core.baserpo import BaseRepository class SqlAlchemyRepository(BaseRepository): def get(self, model: type, query): return self.__ctx__.query(model).get(query)

25.571429

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25.571429

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0

null

0

1

0

null

0

1

0

1

0

5

697fac30073930777c35c24844fee181f34d9622

176

py

Python

py_tdlib/constructors/passport_elements_with_errors.py

Mr-TelegramBot/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

24

2018-10-05T13:04:30.000Z

2020-05-12T08:45:34.000Z

py_tdlib/constructors/passport_elements_with_errors.py

MrMahdi313/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

3

2019-06-26T07:20:20.000Z

2021-05-24T13:06:56.000Z

py_tdlib/constructors/passport_elements_with_errors.py

MrMahdi313/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

5

2018-10-05T14:29:28.000Z

2020-08-11T15:04:10.000Z

from ..factory import Type class passportElementsWithErrors(Type): elements = None # type: "vector<PassportElement>" errors = None # type: "vector<passportElementError>"

25.142857

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null

0

1

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1

0

5

69971bf8f3181c74c781b011c230dda3322a9438

196

py

Python

utils/cheat.py

Its-Vichy/Cs-Fuck

3e13cbf33ca62178e61d7df3e23e803835626d92

[ "Apache-2.0" ]

15

2021-11-05T14:25:08.000Z

2022-03-22T07:37:26.000Z

utils/cheat.py

Its-Vichy/Cs-Fuck

3e13cbf33ca62178e61d7df3e23e803835626d92

[ "Apache-2.0" ]

1

2021-12-21T15:28:32.000Z

utils/cheat.py

Its-Vichy/Cs-Fuck

3e13cbf33ca62178e61d7df3e23e803835626d92

[ "Apache-2.0" ]

4

2021-11-05T17:37:39.000Z

2022-03-18T17:55:09.000Z

class Cheat: def __init__(self, name: str): self.name = name self.is_running = False def set_is_running(self, is_running: bool): self.is_running = is_running

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null

1

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1

0

null

0

1

0

5

69bbddc3da1c934d115679377ca5b2d562ddc9ba

69

py

Python

sccsServerLocalNetworkPythonGear-sendingOnlyWIP/sccsPYTest.py

ninekorn/Python-2-way-local-network-communication-WIP

ce0338574f27e9799f83178320ce651ed27ccf5e

[ "MIT" ]

null

sccsServerLocalNetworkPythonGear-sendingOnlyWIP/sccsPYTest.py

ninekorn/Python-2-way-local-network-communication-WIP

ce0338574f27e9799f83178320ce651ed27ccf5e

[ "MIT" ]

null

sccsServerLocalNetworkPythonGear-sendingOnlyWIP/sccsPYTest.py

ninekorn/Python-2-way-local-network-communication-WIP

ce0338574f27e9799f83178320ce651ed27ccf5e

[ "MIT" ]

null

import math import time import socket while True: print('test')

9.857143

17

0.724638

10

69

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0.8

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69

7

17

9.857143

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true

0

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1

0

null

0

1

0

1

0

1

0

5

69ce7c0a52477f88a4506877bb180bf318034a14

50

py

Python

antelope_core/providers/traci/__init__.py

AntelopeLCA/core

ee40685add52ba41a462e2147fe8c377c6ba2a80

[ "BSD-3-Clause" ]

1

2021-10-06T18:42:49.000Z

antelope_core/providers/traci/__init__.py

AntelopeLCA/core

ee40685add52ba41a462e2147fe8c377c6ba2a80

[ "BSD-3-Clause" ]

6

2021-01-09T08:56:46.000Z

2022-03-29T08:26:21.000Z

antelope_core/providers/traci/__init__.py

AntelopeLCA/core

ee40685add52ba41a462e2147fe8c377c6ba2a80

[ "BSD-3-Clause" ]

null

from .traci_2_1_spreadsheet import Traci21Factors

25

49

0.9

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6

1

0

0.086957

0.08

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50

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1

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1

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0

null

0

1

0

1

0

1

0

5

0e101567dc00f179d44577e30cffde57d1bb748b

87

py

Python

vk/exceptions/errors.py

fossabot/vk.py

94d5c719eb8da6d778d2be208038c447971d5cff

[ "MIT" ]

null

vk/exceptions/errors.py

fossabot/vk.py

94d5c719eb8da6d778d2be208038c447971d5cff

[ "MIT" ]

null

vk/exceptions/errors.py

fossabot/vk.py

94d5c719eb8da6d778d2be208038c447971d5cff

[ "MIT" ]

null

class APIException(Exception): pass class KeyboardException(Exception): pass

12.428571

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null

1

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0

null

0

1

0

5

3878672e1e44fc04d3fb7fe3be181f6f35bf9b93

149

py

Python

.config/qtile/settings/path.py

QWinOS/Qtile-Dracula

cf6db72278fe8aebcfd663534ba5c99320dc97da

[ "MIT" ]

null

.config/qtile/settings/path.py

QWinOS/Qtile-Dracula

cf6db72278fe8aebcfd663534ba5c99320dc97da

[ "MIT" ]

null

.config/qtile/settings/path.py

QWinOS/Qtile-Dracula

cf6db72278fe8aebcfd663534ba5c99320dc97da

[ "MIT" ]

null

from os import path qtile_path = path.join(path.expanduser("~"), ".config", "qtile") rofi_path = path.join(path.expanduser("~"), ".config", "rofi")

29.8

64

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1

0

5

389246086698bcce85818760c9d00f1d29183bd8

278

py

Python

dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/message/ServerMsg.py

mjames-upc/python-awips

e2b05f5587b02761df3b6dd5c6ee1f196bd5f11c

[ "BSD-3-Clause" ]

null

dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/message/ServerMsg.py

mjames-upc/python-awips

e2b05f5587b02761df3b6dd5c6ee1f196bd5f11c

[ "BSD-3-Clause" ]

null

dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/message/ServerMsg.py

mjames-upc/python-awips

e2b05f5587b02761df3b6dd5c6ee1f196bd5f11c

[ "BSD-3-Clause" ]

null

## ## # File auto-generated against equivalent DynamicSerialize Java class class ServerMsg(object): def __init__(self): self.message = None def getMessage(self): return self.message def setMessage(self, message): self.message = message

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null

0

1

0

1

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5

38947fa2a62a41a91c169926864d8178dab8281e

156

py

Python

cluster/__init__.py

JoviaNierenberg/project5

7e12dfa0ae02065c55ef2d8422455c936fb50684

[ "MIT" ]

null

cluster/__init__.py

JoviaNierenberg/project5

7e12dfa0ae02065c55ef2d8422455c936fb50684

[ "MIT" ]

null

cluster/__init__.py

JoviaNierenberg/project5

7e12dfa0ae02065c55ef2d8422455c936fb50684

[ "MIT" ]

20

2022-01-31T20:09:57.000Z

2022-02-15T03:17:27.000Z

from .kmeans import KMeans from .silhouette import Silhouette from .utils import ( make_clusters, plot_clusters, plot_multipanel)

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38c767d37eea0f824938b4cfd691045c7fd5b93d

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py

Python

todo/renderers/render_output.py

tomasdanjonsson/td-cli

08abf22e991ef3b62c170af67fd77581fa1c1b21

[ "MIT" ]

154

2018-09-28T11:05:39.000Z

2022-03-05T08:22:09.000Z

todo/renderers/render_output.py

tomasdanjonsson/td-cli

08abf22e991ef3b62c170af67fd77581fa1c1b21

[ "MIT" ]

18

2019-01-14T08:47:30.000Z

2021-12-10T21:02:58.000Z

todo/renderers/render_output.py

tomasdanjonsson/td-cli

08abf22e991ef3b62c170af67fd77581fa1c1b21

[ "MIT" ]

11

2018-10-15T12:54:06.000Z

2022-02-07T13:34:37.000Z

from .base import Render class RenderOutput(Render): def __init__(self, string_to_format): self.string_to_format = string_to_format def render(self, **kwargs): print(self._format(f"{self.string_to_format}%s" % "{reset}", **kwargs))

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null

0

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1

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5

38cbf6271e5c8686931e7778b83b92ce0a53e300

119

py

Python

synaptor/proc/overlap/__init__.py

ZettaAI/Synaptor

e425b4c744fca093ee5c63f41b82b3cae7898af4

[ "MIT" ]

7

2018-04-01T18:18:23.000Z

2021-09-13T07:02:16.000Z

synaptor/proc/overlap/__init__.py

ZettaAI/Synaptor

e425b4c744fca093ee5c63f41b82b3cae7898af4

[ "MIT" ]

5

2018-10-24T19:36:03.000Z

2020-10-30T02:13:38.000Z

synaptor/proc/overlap/__init__.py

ZettaAI/Synaptor

e425b4c744fca093ee5c63f41b82b3cae7898af4

[ "MIT" ]

6

2018-07-12T17:59:54.000Z

2020-10-30T02:29:50.000Z

from . import overlap from .overlap import count_overlaps, find_max_overlaps, add_overlapping_seg from . import merge

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119

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1

0

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null

0

1

0

1

0

5

38d9b8b75915922113d0020dfc3dd142f820d8bc

237

py

Python

src/sage/combinat/ncsym/all.py

bopopescu/sage

2d495be78e0bdc7a0a635454290b27bb4f5f70f0

[ "BSL-1.0" ]

4

2020-07-17T04:49:44.000Z

2020-07-29T06:33:51.000Z

src/sage/combinat/ncsym/all.py

Ivo-Maffei/sage

467fbc70a08b552b3de33d9065204ee9cbfb02c7

[ "BSL-1.0" ]

2

2018-10-30T13:40:20.000Z

2020-07-23T12:13:30.000Z

src/sage/combinat/ncsym/all.py

dimpase/sage

468f23815ade42a2192b0a9cd378de8fdc594dcd

[ "BSL-1.0" ]

7

2021-11-08T10:01:59.000Z

2022-03-03T11:25:52.000Z

""" Features that are imported by default in the interpreter namespace """ from __future__ import absolute_import from .ncsym import SymmetricFunctionsNonCommutingVariables from .dual import SymmetricFunctionsNonCommutingVariablesDual

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8

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0

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1

0

null

0

1

0

1

0

1

0

5

38e4894c9f1670ff361dda9ba42c2198757e33bb

237

py

Python

src/Model/Visitor.py

oIi123/TableauxProver

cb527f91f5c2d0393fbfcb3fb501b4480e0c9031

[ "MIT" ]

null

src/Model/Visitor.py

oIi123/TableauxProver

cb527f91f5c2d0393fbfcb3fb501b4480e0c9031

[ "MIT" ]

null

src/Model/Visitor.py

oIi123/TableauxProver

cb527f91f5c2d0393fbfcb3fb501b4480e0c9031

[ "MIT" ]

null

def visitor(visitor_class: object): def visited(self, obj: object): # call visitor obj.__getattribute__("visited_" + visitor_class.__name__)(self) setattr(visitor_class, "visit", visited) return visitor_class

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5.666667

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null

1

0

1

0

null

0

1

0

1

0

5

2a09495aad436133b8f3603ce7c480e858e8a99f

86

py

Python

helpFuncs.py

Lunaresk/trollinfobot

1d9c5746799854435c50d0cc7b1ee6eb50283631

[ "MIT" ]

null

helpFuncs.py

Lunaresk/trollinfobot

1d9c5746799854435c50d0cc7b1ee6eb50283631

[ "MIT" ]

null

helpFuncs.py

Lunaresk/trollinfobot

1d9c5746799854435c50d0cc7b1ee6eb50283631

[ "MIT" ]

null

def linkUser(id, name = 'User'): return u'[{0}](tg://user?id={1})'.format(name, id)

28.666667

52

0.581395

15

86

3.333333

0.733333

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0.026316

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86

2

53

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0.631579

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1

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0

1

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null

0

1

0

null

0

1

0

1

0

5

2a54e3c04e58f0252c8cc2cd8a05712c83833a41

186

py

Python

html_telegraph_poster/__init__.py

bhumikapaharia/html-telegraph-poster

e3bab4b7a602931eb9b891c6befbb6b2ded7e22c

[ "MIT" ]

1

2022-01-24T12:24:31.000Z

html_telegraph_poster/__init__.py

bhumikapaharia/html-telegraph-poster

e3bab4b7a602931eb9b891c6befbb6b2ded7e22c

[ "MIT" ]

null

html_telegraph_poster/__init__.py

bhumikapaharia/html-telegraph-poster

e3bab4b7a602931eb9b891c6befbb6b2ded7e22c

[ "MIT" ]

null

from .html_to_telegraph import upload_to_telegraph, TelegraphPoster from .upload_images import upload_image import logging logging.getLogger(__name__).addHandler(logging.NullHandler())

31

67

0.865591

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186

6.565217

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0.069892

186

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68

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true

0

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null

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1

0

1

0

1

0

5

aa4d0a9d9d65fe4183d24fbb84786f80a750e568

147

py

Python

third/orm/01_sqlalchemy.py

gottaegbert/penter

8cbb6be3c4bf67c7c69fa70e597bfbc3be4f0a2d

[ "MIT" ]

13

2020-01-04T07:37:38.000Z

2021-08-31T05:19:58.000Z

third/orm/01_sqlalchemy.py

gottaegbert/penter

8cbb6be3c4bf67c7c69fa70e597bfbc3be4f0a2d

[ "MIT" ]

3

2020-06-05T22:42:53.000Z

2020-08-24T07:18:54.000Z

third/orm/01_sqlalchemy.py

gottaegbert/penter

8cbb6be3c4bf67c7c69fa70e597bfbc3be4f0a2d

[ "MIT" ]

9

2020-10-19T04:53:06.000Z

2021-08-31T05:20:01.000Z

# https://github.com/sqlalchemy/sqlalchemy # https://www.sqlalchemy.org/ # pip install sqlalchemy import sqlalchemy print(sqlalchemy.__version__)

24.5

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147

6.647059

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147

5

43

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1

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null

0

1

0

1

0

1

0

5

aa6324c5c2bfdc82b2a8ff9561178d78ebc282ec

34

py

Python

Fundamentos/Python/Sesion3/archivoParalelo.py

sergijoan22/MasterDataEDEM

6dd9a449902633e1f03bc09163a8bdca44b2698a

[ "Apache-2.0" ]

null

Fundamentos/Python/Sesion3/archivoParalelo.py

sergijoan22/MasterDataEDEM

6dd9a449902633e1f03bc09163a8bdca44b2698a

[ "Apache-2.0" ]

null

Fundamentos/Python/Sesion3/archivoParalelo.py

sergijoan22/MasterDataEDEM

6dd9a449902633e1f03bc09163a8bdca44b2698a

[ "Apache-2.0" ]

null

def saludar(str): print(str*3)

17

0.647059

6

34

3.666667

0.833333

0

0.035714

0.176471

34

2

18

17

0.75

0

1

0.5

false

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

5

aa91d214a1f42e7fd867926f6677ed64ff57f151

30

py

Python

py/desisurvey/_version.py

michaelJwilson/desisurvey

dfd875918beec919eb746946dc792a6db318b2ff

[ "BSD-3-Clause" ]

null

py/desisurvey/_version.py

michaelJwilson/desisurvey

dfd875918beec919eb746946dc792a6db318b2ff

[ "BSD-3-Clause" ]

null

py/desisurvey/_version.py

michaelJwilson/desisurvey

dfd875918beec919eb746946dc792a6db318b2ff

[ "BSD-3-Clause" ]

null

__version__ = '0.11.1.dev820'

15

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0.7

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3.4

1

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0.259259

0.1

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1

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0.37037

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0.433333

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false

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1

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null

0

1

0

1

0

1

0

null

0

5

aac743e93fc17061f18d02fa91fbd53d49ba427f

249

py

Python

students/K33422/Khusnutdinov_Sergei/Lab_02/lab02/conf_app/admin.py

DanteLeapman/ITMO_ICT_WebDevelopment_2020-2021

e19ce8a69caf353c63a6f4c1b9484cc0fcceb74c

[ "MIT" ]

null

students/K33422/Khusnutdinov_Sergei/Lab_02/lab02/conf_app/admin.py

DanteLeapman/ITMO_ICT_WebDevelopment_2020-2021

e19ce8a69caf353c63a6f4c1b9484cc0fcceb74c

[ "MIT" ]

null

students/K33422/Khusnutdinov_Sergei/Lab_02/lab02/conf_app/admin.py

DanteLeapman/ITMO_ICT_WebDevelopment_2020-2021

e19ce8a69caf353c63a6f4c1b9484cc0fcceb74c

[ "MIT" ]

null

from django.contrib import admin from .models import User, Conf, Review, Reserve from django.contrib.auth.admin import UserAdmin admin.site.register(User, UserAdmin) admin.site.register(Conf) admin.site.register(Review) admin.site.register(Reserve)

31.125

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5.666667

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true

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1

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null

0

1

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1

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5

2d5584e63ee649d63b5db9a41b7352542f395746

5,274

py

Python

data_generation/clean_extra_data.py

KochPJ/AutoPoseEstimation

6b2e0181fb4dc76399c7a21759a9d73aebc48ade

[ "MIT" ]

8

2021-03-21T17:50:33.000Z

2022-01-21T13:55:39.000Z

data_generation/clean_extra_data.py

KochPJ/AutoPoseEstimation

6b2e0181fb4dc76399c7a21759a9d73aebc48ade

[ "MIT" ]

1

2021-11-09T19:25:34.000Z

2021-11-11T14:53:34.000Z

data_generation/clean_extra_data.py

KochPJ/AutoPoseEstimation

6b2e0181fb4dc76399c7a21759a9d73aebc48ade

[ "MIT" ]

2

2021-03-21T17:52:06.000Z

2021-05-14T06:48:51.000Z

import os import json import numpy as np import transforms3d ''' use only for data with foreground and foreground 180 ''' path = './data' classes = list(os.listdir(path)) tag = '.meta.json' l = len(tag) for cls in classes: print('_________________') print('class: ', cls) extra_path = os.path.join(path, cls, 'extra') extra_dirs = sorted(list(os.listdir(extra_path))) extra_dirs = sorted([d for d in extra_dirs if tag in d]) times = [int(float(d[:-l])) for d in extra_dirs] max_dist = 0 at = 0 for i, t in enumerate(times[:-1]): dist = times[i+1]-t if dist > max_dist: max_dist = dist at = i ''' fig, axs = plt.subplots(2, 2, constrained_layout=True) fig.suptitle(cls,fontsize=16) plt.subplot(1, 2, 1) plt.title('foreground') plt.plot(times[:at], list(range(len(times[:at])))) plt.subplot(1, 2, 2) plt.title('foreground 180') plt.plot(times[at+1:], list(range(len(times[at+1:])))) plt.show() ''' print('check for foreground') with open(os.path.join(extra_path, extra_dirs[0])) as f: meta = json.load(f) pc_rotation = np.array(meta.get('object_pose')).reshape(4, 4)[:3, :3] first_rotation = np.rad2deg(transforms3d.euler.mat2euler(pc_rotation)) rotations = [{'rot': first_rotation, 'indexes': [0]}] for i, d in enumerate(extra_dirs[1:at]): with open(os.path.join(extra_path, d)) as f: meta = json.load(f) pc_rotation = np.array(meta.get('object_pose')).reshape(4, 4)[:3, :3] this_rotation = np.rad2deg(transforms3d.euler.mat2euler(pc_rotation)) not_in_rotatins = True for rot in rotations: if np.array_equal(rot['rot'], this_rotation): not_in_rotatins = False rot['indexes'].append(i+1) break if not_in_rotatins: rotations.append({'rot': this_rotation, 'indexes': [i+1]}) for rot in rotations: print('rot: {}, n indexes: {}, first and last index: {}, dist: {}'.format( rot['rot'], len(rot['indexes']), [rot['indexes'][0], rot['indexes'][-1]], rot['indexes'][-1]-rot['indexes'][0]+1)) if not np.array_equal(rot['rot'], first_rotation): print('delete indexes of rot: {}'.format(rot['rot'])) print([rot['indexes'][0], rot['indexes'][-1]], rot['indexes'][-1]-rot['indexes'][0]+1) for index in rot['indexes']: id = extra_dirs[index][:-l] curr_path = os.path.join(extra_path, '{}.color.png'.format(id)) if os.path.exists(curr_path): os.remove(curr_path) curr_path = os.path.join(extra_path, '{}.depth.png'.format(id)) if os.path.exists(curr_path): os.remove(curr_path) curr_path = os.path.join(extra_path, '{}.meta.json'.format(id)) if os.path.exists(curr_path): os.remove(curr_path) print('') print('check for foreground 180') with open(os.path.join(extra_path, extra_dirs[at+1])) as f: meta = json.load(f) pc_rotation = np.array(meta.get('object_pose')).reshape(4, 4)[:3, :3] first_rotation = np.rad2deg(transforms3d.euler.mat2euler(pc_rotation)) rotations = [{'rot': first_rotation, 'indexes': [at+1]}] for i, d in enumerate(extra_dirs[at+2:]): with open(os.path.join(extra_path, d)) as f: meta = json.load(f) pc_rotation = np.array(meta.get('object_pose')).reshape(4, 4)[:3, :3] this_rotation = np.rad2deg(transforms3d.euler.mat2euler(pc_rotation)) not_in_rotatins = True for rot in rotations: if np.array_equal(rot['rot'], this_rotation): not_in_rotatins = False rot['indexes'].append(i+at+2) break if not_in_rotatins: rotations.append({'rot': this_rotation, 'indexes': [i+at+2]}) for rot in rotations: print('rot: {}, n indexes: {}, first and last index: {}, dist: {}'.format( rot['rot'], len(rot['indexes']), [rot['indexes'][0], rot['indexes'][-1]], rot['indexes'][-1]-rot['indexes'][0]+1)) if not np.array_equal(rot['rot'], first_rotation): print('delete indexes of rot: {}'.format(rot['rot'])) print([rot['indexes'][0], rot['indexes'][-1]], rot['indexes'][-1]-rot['indexes'][0]+1) for index in rot['indexes']: id = extra_dirs[index][:-l] curr_path = os.path.join(extra_path, '{}.color.png'.format(id)) if os.path.exists(curr_path): os.remove(curr_path) curr_path = os.path.join(extra_path, '{}.depth.png'.format(id)) if os.path.exists(curr_path): os.remove(curr_path) curr_path = os.path.join(extra_path, '{}.meta.json'.format(id)) if os.path.exists(curr_path): os.remove(curr_path)

37.942446

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0

null

0

1

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null

0

5

2d58a3ca7cb54f97cdf121396337f0b44457da7e

206

py

Python

api/views/__init__.py

SarangWadode/medstore

07cb70661a8cba6f8dd090dfbd589bfacb7bf12a

[ "MIT" ]

2

2021-03-24T13:36:39.000Z

2022-02-10T13:51:59.000Z

api/views/__init__.py

SarangWadode/medstore

07cb70661a8cba6f8dd090dfbd589bfacb7bf12a

[ "MIT" ]

44

2021-01-05T01:51:38.000Z

2022-02-10T13:44:26.000Z

api/views/__init__.py

mukeshgurpude/medstore

498b76acbeb9727e7a61560e4016b3577c2706d2

[ "MIT" ]

1

2020-10-28T09:26:01.000Z

# A little bit modification, used the __init__.py as to make different routes in different files from .medicine import * from .auth import * from .cart import * from .order import * from .checkout import *

29.428571

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2d69fedc4eca0b45958c80460c196c33d83a6521

65

py

Python

Modules and packages/Built-in modules/builtin_modules.py

yazgeldigithub/PythonIntro

849065dcaaf8bc6aaf63cd35fdcd4b24786955c1

[ "MIT" ]

null

Modules and packages/Built-in modules/builtin_modules.py

yazgeldigithub/PythonIntro

849065dcaaf8bc6aaf63cd35fdcd4b24786955c1

[ "MIT" ]

null

Modules and packages/Built-in modules/builtin_modules.py

yazgeldigithub/PythonIntro

849065dcaaf8bc6aaf63cd35fdcd4b24786955c1

[ "MIT" ]

null

import sys import datetime print(sys.path) print(datetime.date)

10.833333

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0.8

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null

0

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0

1

0

1

0

5

2d971a6513ffcc1cacbf32bdaa649493363ce3be

2,029

py

Python

csrank/tests/test_losses.py

hytsang/cs-ranking

241626a6a100a27b96990b4f199087a6dc50dcc0

[ "Apache-2.0" ]

null

csrank/tests/test_losses.py

hytsang/cs-ranking

241626a6a100a27b96990b4f199087a6dc50dcc0

[ "Apache-2.0" ]

null

csrank/tests/test_losses.py

hytsang/cs-ranking

241626a6a100a27b96990b4f199087a6dc50dcc0

[ "Apache-2.0" ]

1

2018-10-30T08:57:14.000Z

import numpy as np from keras import backend as K from numpy.testing import assert_almost_equal from csrank.losses import hinged_rank_loss, smooth_rank_loss, plackett_luce_loss decimal = 3 def test_hinged_rank_loss(): y_true = np.arange(5)[None, :] y_true_tensor = K.constant(y_true) # Predicting all 0, gives an error of 1.0: assert_almost_equal( actual=K.eval( hinged_rank_loss( y_true_tensor, K.constant(np.array([[0., 0., 0., 0., 0.]])) ) ), desired=np.array([1.]), decimal=decimal, ) # Predicting the correct ranking improves, but penalizes by difference of # scores: assert_almost_equal( actual=K.eval( hinged_rank_loss( y_true_tensor, K.constant(np.array([[.2, .1, .0, -0.1, -0.2]])) ) ), desired=np.array([0.8]), decimal=decimal, ) def test_plackett_luce_loss(): y_true = np.arange(5)[None, :] y_true_tensor = K.constant(y_true) assert_almost_equal( actual=K.eval( plackett_luce_loss( y_true_tensor, K.constant(np.array([[0., 0., 0., 0., 0.]])) ) ), desired=np.array([4.78749]), decimal=decimal, ) def test_smooth_rank_loss(): y_true = np.arange(5)[None, :] y_true_tensor = K.constant(y_true) # Predicting all 0, gives an error of 1.0: assert_almost_equal( actual=K.eval( smooth_rank_loss( y_true_tensor, K.constant(np.array([[0., 0., 0., 0., 0.]])) ) ), desired=np.array([1.]), decimal=decimal, ) # Predicting the correct ranking improves, but penalizes by difference of # scores: assert_almost_equal( actual=K.eval( smooth_rank_loss( y_true_tensor, K.constant(np.array([[.2, .1, .0, -0.1, -0.2]])) ) ), desired=np.array([0.822749841877]), decimal=decimal, )

25.683544

80

0.56136

268

2,029

4.029851

0.212687

0.064815

0.025

0.088889

0.782407

0.749074

0.723148

0

0.04416

0.308034

2,029

78

81

26.012821

0.725071

0.118778

0

0.627119

0

0.101695

1

0.050847

false

0

0.067797

0

0.118644

0

null

0

1

0

null

0

5

2dd4cfee0f24415a7cea03aaf2c96896c4bf578a

11,177

py

Python

test/test_deterministic_wallets.py

mflaxman/electrum-personal-server

700cd108ea026b971f05b906db03951acf6b2ac0

[ "MIT" ]

2

2020-06-19T20:11:38.000Z

2021-12-28T10:52:10.000Z

test/test_deterministic_wallets.py

mflaxman/electrum-personal-server

700cd108ea026b971f05b906db03951acf6b2ac0

[ "MIT" ]

10

2018-08-02T16:58:34.000Z

2018-08-04T22:00:09.000Z

test/test_deterministic_wallets.py

mflaxman/electrum-personal-server

700cd108ea026b971f05b906db03951acf6b2ac0

[ "MIT" ]

1

2020-07-05T20:32:15.000Z

import pytest from electrumpersonalserver.server import parse_electrum_master_public_key # electrum has its own tests here #https://github.com/spesmilo/electrum/blob/03b40a3c0a7dd84e76bc0d0ea2ad390dafc92250/lib/tests/test_wallet_vertical.py @pytest.mark.parametrize( "master_public_key, recv_spks, change_spks", [ #p2pkh wallet ("xpub661MyMwAqRbcGVQTLtBFzc3ENvyZHoUEhWRdGwoqLZaf5wXP9VcDY2VJV7usvsFLZz" + "2RUTVhCVXYXc3S8zpLyAFbDFcfrpUiwLoE9VWH2yz", #pubkey ["76a914b1847c763c9a9b12631ab42335751c1bf843880c88ac" #recv scriptpubkeys ,"76a914d8b6b932e892fad5132ea888111adac2171c5af588ac" ,"76a914e44b19ef74814f977ae4e2823dd0a0b33480472a88ac"], ["76a914d2c2905ca383a5b8f94818cb7903498061a6286688ac" #change scriptpubkeys ,"76a914e7b4ddb7cede132e84ba807defc092cf52e005b888ac" ,"76a91433bdb046a1d373728d7844df89aa24f788443a4588ac"]) , #p2wpkh wallet ("zpub6mr7wBKy3oJn89TCiXUAPBWpTTTx58BgEjPLzDNf5kMThvd6xchrobPTsJ5mP" + "w3NJ7zRhckN8cv4FhQBfwurZzNE5uTW5C5PYqNTkRAnTkP", #pubkey ['00142b82c61a7a48b7b10801f0eb247af46821bd33f5' #recv scriptpubkeys ,'0014073dc6bcbb18d6468c5996bdeba926f6805b74b1' ,'001400fa0b5cb21e8d442a7bd61af3d558a62be0c9aa'], ['00144f4a0655a4b586be1e08d97a2f55125120b84c69' #change scriptpubkeys ,'0014ef7967a7a56c23bbc9f317e612c93a5e23d25ffe' ,'0014ad768a11730bf54d10c72184d53239de0f310bc9']) ,#p2sh 2of2 multisig wallet ("2 tpubD6NzVbkrYhZ4YVMVzC7wZeRfz3bhqcHvV8M3UiULCfzFtLtp5nwvi6LnBQegrkx" + "YGPkSzXUEvcPEHcKdda8W1YShVBkhFBGkLxjSQ1Nx3cJ tpubD6NzVbkrYhZ4WjgNYq2nF" + "TbiSLW2SZAzs4g5JHLqwQ3AmR3tCWpqsZJJEoZuP5HAEBNxgYQhtWMezszoaeTCg6FWGQB" + "T74sszGaxaf64o5s", #m=2, 2 pubkeys, n=len(pubkeys) ['a914fe30a46a4e1b41f9bb758448fd84ee4628c103e187' #recv ,'a914dad5dd605871560ae5d219cd6275e6ad19bc6b9987' ,'a914471e158e2db190acdd8c76ed6d2ade102fe1e8ac87' ,'a914013449715a32f21d1a8a2b95a01b40eb41ada16f87' ,'a914ae3dd25567fb7c2f87be41220dd14025ca68b0e087' ,'a91462b90344947b610c4eadb7dd460fee3f32fefe7687' ,'a914d4388c7d5771ebf26b6e650c42e60e4cf7d4c5a187' ,'a914e4f0832e56591d01b71c72b9a3777dc8f9d9a92e87' ,'a914a5d5accd96d27403c7663b92fdb57299d7a871eb87' ,'a914f8f2c6ef2d80f972e4d8b418a15337a3c38af37f87' ,'a914a2bd2f67fac7c24e609b574ccc8cfaa2f90ebf8c87' ,'a914a56298a7decde1d18306f55d9305577c3fce690187' ,'a91430f2f83238ac29125a539055fa59efc86a73a23987' ,'a914263b4585d0735c5065987922af359d5eabeb880d87' ,'a91455d9d47113fb8b37705bdf6d4107d438afd63e4687' ,'a914970d754163b8957b73f4e8baaf23dea5f6e3db2287' ,'a914facbc921203a9ffd751cc246a884918beaac21b687' ,'a914fc7556833eca1e0f84c6d7acb875e645f7ed4e9687' ,'a914bbfe6a032d633f113b5d605e3a97cc08a47cc87d87' ,'a91403d733c4ca337b5fa1de95970ba6f898a9d36c4887' ,'a9148af27dc7c950e17c11e164065e672cd60ae3d48d87' ,'a914c026aa45377f2a4a62136bac1d3350c318fee5c587' ,'a9146337f59e3ea55e73725c9f2fc52a5ca5d68c361687'], ['a914aeaebf9d567ab8a6813e89668e16f40bf419408e87' #change ,'a914f2a6264dd3975297fa2a5a8e17321299a44f76d987' ,'a9142067a6c47958090a645137cc0898c0c7bbc69b5387' ,'a914210840f77ea5b7eb11cb55e5d719a93b7746fb9387' ,'a914163db6b8ca00362be63a26502c5f7bf64787506b87' ,'a91479b2c527594059c056e5367965ae92bbcf63512187']) ,#p2sh 2of3 multisig wallet ("2 tpubD6NzVbkrYhZ4WwaMJ3od4hANxdMVpb63Du3ERq1xjtowxVJEcTbGH2rFd9TFXxw" + "KJRKDn9vQjDPxFeaku6BHW6wHn2KPF1ijS4LwgwQFJ3B tpubD6NzVbkrYhZ4Wjv4ZRPD6" + "MNdiLmfvXztbKuuatkqHjukU3S6GXhmKnbAF5eU9bR2Nryiq8v67emUUSM1VUrAx5wcZ19" + "AsaGg3ZLmjbbwLXr tpubD6NzVbkrYhZ4Xxa2fEp7YsbnFnwuQNaogijbiX42Deqd4NiAD" + "tqNU6AXCU2d2kPFWBpAGG7K3HAKYwUfZBPgTLkfQp2dDg9SLVnkgYPgEXN", ['a914167c95beb25b984ace517d4346e6cdbf1381793687', #recv addrs 'a914378bbda1ba7a713de18c3ba3c366f42212bfb45087', 'a9142a5c9881c70906180f37dd02d8c830e9b6328d4a87', 'a914ffe0832375b72ee5307bfa502896ba28cc470ee987', 'a9147607d40e039fbea57d9c04e48b198c9fcf3356c187', 'a9148d9582ad4cf0581c6e0697e4cba6a12e66ca1a0087', 'a914d153a743b315ba19690823119019e16e3762104d87', 'a914b4accc89e48610043e70371153fd8cb5a3eef34287', 'a91406febca615e3631253fd75a1d819436e1d046e0487', 'a914b863cbb888c6b28291cb87a2390539e28be37a9587', 'a914ec39094e393184d2c352a29b9d7a3caddaccb6cf87', 'a914da4faa4babbdf611caf511d287133f06c1c3244a87', 'a9146e64561d0c5e2e9159ecff65db02e04b3277402487', 'a914377d66386972492192ae827fb2208596af0941d187', 'a914448d364ff2374449e57df13db33a40f5b099997c87', 'a914f24b875d2cb99e0b138ab0e6dd65027932b3c6e787', 'a914aa4bcee53406b1ef6c83852e3844e38a3a9d9f3087', 'a9145e5ec40fdab54be0d6e21107bc38c39df97e37fc87', 'a9141de4d402c82f4e9b0e6b792b331232a5405ebd3f87', 'a9148873ee280e51f9c64d257dd6dedc8712fd652cc687'], ['a9142cc87d7562a85029a57cc37026e12dab72223db287', #change 'a91499f4aee0b274f0b3ab48549a2c58cd667a62c0cb87', 'a91497a89cd5ada3a766a1275f8151e9256fcf537f6c87', 'a9147ffc9f3a3b60635ea1783243274f4d07ab617cb487', 'a9143423113ab913d86fd47e55488a0c559e18b457b987', 'a914a28a3773a37c52ff6fd7dff497d0eaf80a46febb87']) , #p2wsh 1of2 multisig wallet ("1 Vpub5fAqpSRkLmvXwqbuR61MaKMSwj5z5xUBwanaz3qnJ5MgaBDpFSLUvKTiNK9zHp" + "dvrg2LHHXkKxSXBHNWNpZz9b1VqADjmcCs3arSoxN3F3r Vpub5fvEo4MUpbVs9sZqr45" + "zmRVEsTcQ49MA9m3MLht3XzdZvS9eMXLLu1H6TL1j2SMnykHqXNzG5ycMyQmFDvEE5B32" + "sP8TmRe6wW8HjBgMssh", #recv scriptpubkeys ['002031fbaa839e96fc1abaf3453b9f770e0ccfe2d8e3e990bb381fdcb7db4722986a', '0020820ae739b36f4feb1c299ced201db383bbcf1634e0071e489b385f43c2323761', '0020eff05f4d14aa1968a7142b1009aa57a6208fb01b212f8b8f7df63645d26a1292', '002049c6e17979dca380ffb66295d27f609bea2879d4f0b590c96c70ff12260a8721', '002002bf2430fc7ebc6fb27da1cb80e52702edcc62a29f65c997e5c924dcd98411bd', '0020c7a58dcf9633453ba12860b57c14af67d87d022be5c52bf6be7a6abdc295c6e0', '0020136696059a5e932c72f4f0a05fa7f52faf9b54f1b7694e15acce710e6cc9e89d', '0020c372e880227f35c2ee35d0724bf05cea95e74dcb3e6aa67ff15f561a29c0645d', '002095c705590e2b84996fa44bff64179b26669e53bbd58d76bb6bbb5c5498a981ce', '00207217754dae083c3c365c7e1ce3ad889ca2bd88e4f809cec66b9987adc390aa26', '0020bee30906450e099357cc96a1f472c1ef70089cd4a0cba96749adfe1c9a2f9e87', '0020b1838b3d5a386ad6c90eeae9a27a9b812e32ce06376f261dea89e405bc8209d9', '0020231a3d05886efff601f0702d4c8450dfcce8d6a4bd90f17f7ff76f5c25c632de', '002071220f3941b5f65aca90e464db4291cd5ea63f37fa858fd5b66d5019f0dbab0f', '0020fc3c7db9f0e773f9f9c725d4286ddcc88db9575c45b2441d458018150eb4ef10', '00209f037bfc98dee2fc0d3cca54df09b2d20e92a0133fa381a4dd74c49e4d0a89f5', '0020c9060d0554ba2ca92048e1772e806d796ba41f10bf6aee2653a9eba96b05c944', '0020a7cb1dd2730dba564f414ed8d9312370ff89c34df1441b83125cb4d97a96005a', '00209fddc9b4e070b887dec034ed74f15f62d075a3ac8cf6eb95a88c635e0207534c', '0020c48f9c50958ab8e386a8bd3888076f31d12e5cf011ff46cc83c6fadfe6d47d20', '0020a659f4621dca404571917e73dedb26b6d7c49a07dacbf15890760ac0583d3267'], #change scriptpubkeys ['002030213b5d3b6988b86aa13a9eaca08e718d51f32dc130c70981abb0102173c791', '002027bd198f9783a58e9bc4d3fdbd1c75cc74154905cce1d23c7bd3e051695418fe', '0020c1fd2cdebf120d3b1dc990dfdaca62382ff9525beeb6a79a908ddecb40e2162c', '00207a3e478266e5fe49fe22e3d8f04d3adda3b6a0835806a0db1f77b84d0ba7f79c', '002059e66462023ecd54e20d4dce286795e7d5823af511989736edc0c7a844e249f5', '0020bd8077906dd367d6d107d960397e46db2daba5793249f1f032d8d7e12e6f193c']) , #p2wpkh-p2sh ("upub5E4QEumGPNTmSKD95TrYX2xqLwwvBULbRzzHkrpW9WKKCB1y9DEfPXDnUyQjLjmVs" + "7gSd7k5vRb1FoSb6BjyiWNg4arkJLaqk1jULzbwA5q", ["a914ae8f84a06668742f713d0743c1f54d248040e63387", #recv "a914c2e9bdcc48596b8cce418042ade72198fddf3cd987", "a914a44b6ad63ccef0ae1741eaccee99bf2fa83f842987", "a9148cf1c891d96a0be07893d0bddcf00ed5dad2c46e87", "a91414d677b32f2409f4dfb3073d382c302bcd6ed33587", "a9141b284bee7198d5134512f37ef60e4048864b4bd687"], ["a914a5aacff65860440893107b01912dc8f60cadab2b87", #change "a914dcd74ebc8bfc5cf0535717a3e833592d54b3c48687", "a91446793cae4c2b8149ade61c1627b96b90599bc08787", "a91439f3776831f321125bdb5099fbbd654923f8316c87"]) , #p2wpkh-p2sh ("ypub6XrRLtXNB7NQo3vDaMNnffXVJe1WVaebXcb4ncpTHHADLuFYmf2CcPn96YzUbMt8s" + "HSMmtr1mCcMgCBLqNdY2hrXXcdiLxCdD9e2dChBLun", ["a91429c2ad045bbb162ef3c2d9cacb9812bec463061787", #recv "a91433ec6bb67b113978d9cfd307a97fd15bc0a5a62087", "a91450523020275ccbf4e916a0d8523ae42391ad988a87", "a91438c2e5e76a874d86cfc914fe9fc1868b6afb5c5487"], ["a91475f608698bb735120a17699fee854bce9a8dc8d387", "a91477e69344ef53587051c85a06a52a646457b44e6c87", "a914607c98ea34fbdffe39fee161ae2ffd5517bf1a5587"]) , #old mnemonic mpk ("e9d4b7866dd1e91c862aebf62a49548c7dbf7bcc6e4b7b8c9da820c7737968df9c09d" + "5a3e271dc814a29981f81b3faaf2737b551ef5dcc6189cf0f8252c442b3", ["76a9149cd3dfb0d87a861770ae4e268e74b45335cf00ab88ac", #recv "76a914c30f2af6a79296b6531bf34dba14c8419be8fb7d88ac", "76a9145eb4eeaefcf9a709f8671444933243fbd05366a388ac", "76a914f96669095e6df76cfdf5c7e49a1909f002e123d088ac"], ["76a914ca14915184a2662b5d1505ce7142c8ca066c70e288ac", #change "76a9148942ac692ace81019176c4fb0ac408b18b49237f88ac", "76a914e1232622a96a04f5e5a24ca0792bb9c28b089d6e88ac"]) , #p2wsh-p2sh 2of2 multisig ("2 Ypub6hWbqA2p47QgsLt5J4nxrR3ngu8xsPGb7PdV8CDh48KyNngNqPKSqertAqYhQ4u" + "mELu1UsZUCYfj9XPA6AdSMZWDZQobwF7EJ8uNrECaZg1 Ypub6iNDhL4WWq5kFZcdFqHHw" + "X4YTH4rYGp8xbndpRrY7WNZFFRfogSrL7wRTajmVHgR46AT1cqUG1mrcRd7h1WXwBsgX2Q" + "vT3zFbBCDiSDLkau", ["a91428060ade179c792fac07fc8817fd150ce7cdd3f987", #recv "a9145ba5ed441b9f3e22f71193d4043b645183e6aeee87", "a91484cc1f317b7d5afff115916f1e27319919601d0187", "a9144001695a154cac4d118af889d3fdcaf929af315787", "a914897888f3152a27cbd7611faf6aa01085931e542a87"], ["a91454dbb52de65795d144f3c4faeba0e37d9765c85687", #change "a914f725cbd61c67f34ed40355f243b5bb0650ce61c587", "a9143672bcd3d02d3ea7c3205ddbc825028a0d2a781987"]) ] ) def test_deterministic_wallets(master_public_key, recv_spks, change_spks): initial_count = 15 gaplimit = 5 wal = parse_electrum_master_public_key(master_public_key, gaplimit) spks = wal.get_scriptpubkeys(0, 0, initial_count) #for test, generate 15, check that the last 5 lead to gap limit overrun for i in range(initial_count - gaplimit): ret = wal.have_scriptpubkeys_overrun_gaplimit([spks[i]]) assert ret == None for i in range(gaplimit): index = i + initial_count - gaplimit ret = wal.have_scriptpubkeys_overrun_gaplimit([spks[index]]) assert ret != None and ret[0] == i+1 last_index_add = 3 last_index = initial_count - gaplimit + last_index_add ret = wal.have_scriptpubkeys_overrun_gaplimit(spks[2:last_index]) assert ret[0] == last_index_add assert wal.get_scriptpubkeys(0, 0, len(recv_spks)) == recv_spks assert wal.get_scriptpubkeys(1, 0, len(change_spks)) == change_spks

56.165829

117

0.833408

404

11,177

22.930693

0.59901

0.006477

0.008096

0.007448

0.035622

0.025043

0.013385

0

0.452605

0.107095

11,177

198

118

56.449495

0.475651

0.053324

0

0.763797

0.752323

0

0.026882

1

0.005376

false

0

0.010753

0

0.016129

0

1

null

0

1

0

1

0

1

null

0

5

934f93f6826c943f78e2f05fb5205c4dd95da98e

12,979

py

Python

hmda/migrations/0009_auto_20181117_2048.py

cmc333333/mapusaurus

1d7ccef90d0ed832d52f797cbe68057057cd0177

[ "CC0-1.0" ]

null

hmda/migrations/0009_auto_20181117_2048.py

cmc333333/mapusaurus

1d7ccef90d0ed832d52f797cbe68057057cd0177

[ "CC0-1.0" ]

55

2018-02-09T04:11:31.000Z

2018-07-04T18:30:29.000Z

hmda/migrations/0009_auto_20181117_2048.py

cmc333333/mapusaurus

1d7ccef90d0ed832d52f797cbe68057057cd0177

[ "CC0-1.0" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.11.13 on 2018-11-17 20:48 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('hmda', '0008_auto_20180331_0327'), ] operations = [ migrations.AlterField( model_name='hmdarecord', name='agency_code', field=models.CharField(choices=[('1', 'Office of the Comptroller of the Currency (OCC)'), ('2', 'Federal Reserve System (FRS)'), ('3', 'Federal Deposit Insurance Corporation (FDIC)'), ('5', 'National Credit Union Administration (NCUA)'), ('7', 'Department of Housing and Urban Development (HUD)'), ('9', 'Consumer Financial Protection Bureau (CFPB)')], help_text='A code representing the federal agency to which the HMDA-reporting institution submits its HMDA data.', max_length=1), ), migrations.AlterField( model_name='hmdarecord', name='applicant_ethnicity', field=models.CharField(choices=[('1', 'Hispanic or Latino'), ('2', 'Not Hispanic or Latino'), ('3', 'Information not provided by applicant in mail, Internet, or telephone application'), ('4', 'Not applicable'), ('5', 'No co-applicant')], help_text='A code representing the ethnicity of the primary applicant.', max_length=1), ), migrations.AlterField( model_name='hmdarecord', name='applicant_income_000s', field=models.CharField(help_text='The gross annual income that the lender relied on when evaluating the creditworthiness of the applicant, rounded to the nearest thousand.', max_length=8), ), migrations.AlterField( model_name='hmdarecord', name='applicant_race_1', field=models.CharField(choices=[('1', 'American Indian or Alaska Native'), ('2', 'Asian'), ('3', 'Black or African American'), ('4', 'Native Hawaiian or Other Pacific Islander '), ('5', 'White'), ('6', 'Information not provided by applicant in mail, Internet, or telephone application'), ('7', 'Not applicable'), ('8', 'No co-applicant')], help_text='A code representing the first listed race for the primary applicant. The applicant can list up to five races.', max_length=1), ), migrations.AlterField( model_name='hmdarecord', name='applicant_race_2', field=models.CharField(blank=True, choices=[('1', 'American Indian or Alaska Native'), ('2', 'Asian'), ('3', 'Black or African American'), ('4', 'Native Hawaiian or Other Pacific Islander '), ('5', 'White'), ('6', 'Information not provided by applicant in mail, Internet, or telephone application'), ('7', 'Not applicable'), ('8', 'No co-applicant')], help_text='A code representing the second listed race for the primary applicant.', max_length=1, null=True), ), migrations.AlterField( model_name='hmdarecord', name='applicant_race_3', field=models.CharField(blank=True, choices=[('1', 'American Indian or Alaska Native'), ('2', 'Asian'), ('3', 'Black or African American'), ('4', 'Native Hawaiian or Other Pacific Islander '), ('5', 'White'), ('6', 'Information not provided by applicant in mail, Internet, or telephone application'), ('7', 'Not applicable'), ('8', 'No co-applicant')], help_text='A code representing the third listed race for the primary applicant.', max_length=1, null=True), ), migrations.AlterField( model_name='hmdarecord', name='applicant_race_4', field=models.CharField(blank=True, choices=[('1', 'American Indian or Alaska Native'), ('2', 'Asian'), ('3', 'Black or African American'), ('4', 'Native Hawaiian or Other Pacific Islander '), ('5', 'White'), ('6', 'Information not provided by applicant in mail, Internet, or telephone application'), ('7', 'Not applicable'), ('8', 'No co-applicant')], help_text='A code representing the fourth listed race for the primary applicant.', max_length=1, null=True), ), migrations.AlterField( model_name='hmdarecord', name='applicant_race_5', field=models.CharField(blank=True, choices=[('1', 'American Indian or Alaska Native'), ('2', 'Asian'), ('3', 'Black or African American'), ('4', 'Native Hawaiian or Other Pacific Islander '), ('5', 'White'), ('6', 'Information not provided by applicant in mail, Internet, or telephone application'), ('7', 'Not applicable'), ('8', 'No co-applicant')], help_text='A code representing the fifth listed race for the primary applicant.', max_length=1, null=True), ), migrations.AlterField( model_name='hmdarecord', name='co_applicant_ethnicity', field=models.CharField(choices=[('1', 'Hispanic or Latino'), ('2', 'Not Hispanic or Latino'), ('3', 'Information not provided by applicant in mail, Internet, or telephone application'), ('4', 'Not applicable'), ('5', 'No co-applicant')], help_text='A code representing the ethnicity of the co-applicant.', max_length=1), ), migrations.AlterField( model_name='hmdarecord', name='co_applicant_race_1', field=models.CharField(choices=[('1', 'American Indian or Alaska Native'), ('2', 'Asian'), ('3', 'Black or African American'), ('4', 'Native Hawaiian or Other Pacific Islander '), ('5', 'White'), ('6', 'Information not provided by applicant in mail, Internet, or telephone application'), ('7', 'Not applicable'), ('8', 'No co-applicant')], help_text='A code representing the first listed race for the co-applicant. The co-applicant can list up to five races.', max_length=1), ), migrations.AlterField( model_name='hmdarecord', name='co_applicant_race_2', field=models.CharField(blank=True, choices=[('1', 'American Indian or Alaska Native'), ('2', 'Asian'), ('3', 'Black or African American'), ('4', 'Native Hawaiian or Other Pacific Islander '), ('5', 'White'), ('6', 'Information not provided by applicant in mail, Internet, or telephone application'), ('7', 'Not applicable'), ('8', 'No co-applicant')], help_text='A code representing the second listed race for the co-applicant.', max_length=1, null=True), ), migrations.AlterField( model_name='hmdarecord', name='co_applicant_race_3', field=models.CharField(blank=True, choices=[('1', 'American Indian or Alaska Native'), ('2', 'Asian'), ('3', 'Black or African American'), ('4', 'Native Hawaiian or Other Pacific Islander '), ('5', 'White'), ('6', 'Information not provided by applicant in mail, Internet, or telephone application'), ('7', 'Not applicable'), ('8', 'No co-applicant')], help_text='A code representing the third listed race for the co-applicant.', max_length=1, null=True), ), migrations.AlterField( model_name='hmdarecord', name='co_applicant_race_4', field=models.CharField(blank=True, choices=[('1', 'American Indian or Alaska Native'), ('2', 'Asian'), ('3', 'Black or African American'), ('4', 'Native Hawaiian or Other Pacific Islander '), ('5', 'White'), ('6', 'Information not provided by applicant in mail, Internet, or telephone application'), ('7', 'Not applicable'), ('8', 'No co-applicant')], help_text='A code representing the fourth listed race for the co-applicant.', max_length=1, null=True), ), migrations.AlterField( model_name='hmdarecord', name='co_applicant_race_5', field=models.CharField(blank=True, choices=[('1', 'American Indian or Alaska Native'), ('2', 'Asian'), ('3', 'Black or African American'), ('4', 'Native Hawaiian or Other Pacific Islander '), ('5', 'White'), ('6', 'Information not provided by applicant in mail, Internet, or telephone application'), ('7', 'Not applicable'), ('8', 'No co-applicant')], help_text='A code representing the fifth listed race for the co-applicant.', max_length=1, null=True), ), migrations.AlterField( model_name='hmdarecord', name='denial_reason_1', field=models.CharField(blank=True, choices=[('1', 'Debt-to-income ratio'), ('2', 'Employment history'), ('3', 'Credit history'), ('4', 'Collateral'), ('5', 'Insufficient cash (downpayment, closing costs)'), ('6', 'Unverifiable information'), ('7', 'Credit application incomplete'), ('8', 'Mortgage insurance denied'), ('9', 'Other')], help_text='A code representing the first reason for denial of the application. Lenders may report up to three denial reasons, but such reporting is optional.', max_length=1, null=True), ), migrations.AlterField( model_name='hmdarecord', name='denial_reason_2', field=models.CharField(blank=True, choices=[('1', 'Debt-to-income ratio'), ('2', 'Employment history'), ('3', 'Credit history'), ('4', 'Collateral'), ('5', 'Insufficient cash (downpayment, closing costs)'), ('6', 'Unverifiable information'), ('7', 'Credit application incomplete'), ('8', 'Mortgage insurance denied'), ('9', 'Other')], help_text='A code representing the second reason for denial of the application.', max_length=1, null=True), ), migrations.AlterField( model_name='hmdarecord', name='denial_reason_3', field=models.CharField(blank=True, choices=[('1', 'Debt-to-income ratio'), ('2', 'Employment history'), ('3', 'Credit history'), ('4', 'Collateral'), ('5', 'Insufficient cash (downpayment, closing costs)'), ('6', 'Unverifiable information'), ('7', 'Credit application incomplete'), ('8', 'Mortgage insurance denied'), ('9', 'Other')], help_text='A code representing the third reason for denial of the application.', max_length=1, null=True), ), migrations.AlterField( model_name='hmdarecord', name='edit_status', field=models.CharField(blank=True, choices=[('', 'No edit failures'), ('5', 'Validity edit failure only'), ('6', 'Quality edit failure only'), ('7', 'Validity and quality edit failures')], help_text='A code representing the edit failure status of the application.', max_length=1, null=True), ), migrations.AlterField( model_name='hmdarecord', name='hoepa_status', field=models.CharField(choices=[('1', 'HOEPA loan'), ('2', 'Not a HOEPA loan')], help_text='A code representing whether a loan is subject to the Home Ownership and Equity Protection Act of 1994 (HOEPA).', max_length=1), ), migrations.AlterField( model_name='hmdarecord', name='hmda_record_id', field=models.CharField(max_length=23, primary_key=True, serialize=False), ), migrations.AlterField( model_name='hmdarecord', name='lien_status', field=models.CharField(choices=[('1', 'Secured by a first lien'), ('2', 'Secured by a subordinate lien'), ('3', 'Not secured by a lien'), ('4', 'Not applicable (purchased loans)')], help_text='A code representing the lien status. Most mortgages are secured by a lien against the property. In the event of a forced liquidation, first lien holders will generally get paid before subordinate lien holders.', max_length=1), ), migrations.AlterField( model_name='hmdarecord', name='preapproval', field=models.CharField(choices=[('1', 'Preapproval was requested'), ('2', 'Preapproval was not requested'), ('3', 'Not applicable')], help_text='A code representing the pre-approval status of the application.', max_length=1), ), migrations.AlterField( model_name='hmdarecord', name='property_type', field=models.CharField(choices=[('1', 'One to four-family (other than manufactured housing)'), ('2', 'Manufactured housing'), ('3', 'Multifamily')], help_text='A code representing the type of the property.', max_length=1), ), migrations.AlterField( model_name='hmdarecord', name='purchaser_type', field=models.CharField(choices=[('0', 'Loan was not originated or was not sold in calendar year covered by register'), ('1', 'Fannie Mae (FNMA)'), ('2', 'Ginnie Mae (GNMA)'), ('3', 'Freddie Mac (FHLMC)'), ('4', 'Farmer Mac (FAMC)'), ('5', 'Private securitization'), ('6', 'Commercial bank, savings bank or savings association'), ('7', 'Life insurance company, credit union, mortgage bank, or finance company'), ('8', 'Affiliate institution'), ('9', 'Other type of purchaser')], help_text='A code representing the type of institution purchasing the loan.', max_length=1), ), migrations.AlterField( model_name='hmdarecord', name='sequence_number', field=models.CharField(help_text='A one-up number scheme for each respondent to make each loan unique.', max_length=8), ), ]

92.049645

582

0.646968

1,601

12,979

5.164897

0.163648

0.060467

0.075584

0.087677

0.780989

0.756803

0.712178

0.706857

0.693191

0.661144

0

0.021898

0.197781

12,979

140

583

92.707143

0.772282

0.005316

0

0.56391

1

0.045113

0.530875

0.005114

0

1

0

false

0

0.015038

0

0.037594

0

null

0

1

0

1

0

1

0

null

0

5

fa7daa77f6e4adbef5d5783be9b77a28d8a9cc2f

93

py

Python

HREMGromacs/__init__.py

MauriceKarrenbrock/HREMGromacs

3741820bee466ae3b4a69a8241c0905b5beffe0c

[ "MIT" ]

null

HREMGromacs/__init__.py

MauriceKarrenbrock/HREMGromacs

3741820bee466ae3b4a69a8241c0905b5beffe0c

[ "MIT" ]

null

HREMGromacs/__init__.py

MauriceKarrenbrock/HREMGromacs

3741820bee466ae3b4a69a8241c0905b5beffe0c

[ "MIT" ]

null

# -*- coding: utf-8 -*- """Template init file""" from .package import __title__, __version__

23.25

43

0.677419

11

93

5

1

0

0.0125

0.139785

93

3

44

31

0.675

0.44086

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

fac38a5c07150f3bc3e731989d6bfc2f7d456b84

2,241

py

Python

bioimageio/core/transformations/reshape.py

k-dominik/python-bioimage-io

aecaa3412c31672ce159335db083ee9fb4fca519

[ "MIT" ]

null

bioimageio/core/transformations/reshape.py

k-dominik/python-bioimage-io

aecaa3412c31672ce159335db083ee9fb4fca519

[ "MIT" ]

null

bioimageio/core/transformations/reshape.py

k-dominik/python-bioimage-io

aecaa3412c31672ce159335db083ee9fb4fca519

[ "MIT" ]

null

from typing import Sequence from bioimageio.core.protocols import Tensor from bioimageio.core.transformations import TensorTransformation class Reshape(TensorTransformation): def __init__(self, shape: Sequence[int], **super_kwargs): self.shape = shape super().__init__(**super_kwargs) def apply(self, tensor: Tensor) -> Tensor: # this -2 stuff was intended to be able to deal with an unknown batch dimension... # todo: decide if we want to deal with batch dimension at all in trfs and if so how? # if -2 in self.shape and self.shape.index(-2) >= len(tensor.shape): # raise ValueError(f"transformation shape {self.shape} incompatible with tensor shape {tensor.shape}") # # out_shape = tuple([tensor.shape[i] if s == -2 else s for i, s in enumerate(self.shape)]) return tensor.reshape(self.shape) # def dynamic_output_shape(self, input_shape: List[Tuple[int]]) -> List[Tuple[int]]: # output_shape = [] # for i, ipt_shape in enumerate(input_shape): # if i in self.apply_to: # s = numpy.prod(ipt_shape) # rest_dim = None # out_shape = list(ipt_shape) # for out_idx, out in enumerate(self.shape): # if out == -1: # rest_dim = out_idx # continue # # if out == -2: # out = ipt_shape[self.shape.index(-2)] # out_shape[out_idx] = out # # if s / out != s // out: # raise ValueError(f"Cannot reshape {ipt_shape} to {self.shape}") # # s //= out # # if rest_dim is not None: # out_shape[rest_dim] = s # elif s != 1: # raise ValueError(f"Cannot reshape {ipt_shape} to {self.shape}") # # output_shape.append(tuple(out_shape)) # else: # output_shape.append(ipt_shape) # # return output_shape # # def dynamic_input_shape(self, output_shape: List[Tuple[int]]) -> List[Tuple[int]]: # raise NotImplementedError

40.017857

114

0.545292

266

2,241

4.43985

0.300752

0.083827

0.040644

0.025402

0.130398

0.081287

0

0.005487

0.349398

2,241

55

115

40.745455

0.804527

0.703257

0

0.018182

0

1

0.222222

false

0

0.333333

0.111111

0.777778

0

null

0

null

0

1

0

1

0

1

0

5

fad733a619c4fe340c9c4de189e41b3549c6b4df

1,310

py

Python

test_movies.py

ppai22/knn_movie_recommender

64bfda2dbb826c61db400976aae646e72b5dab3f

[ "MIT" ]

5

2021-06-14T10:05:01.000Z

2022-01-10T12:28:16.000Z

test_movies.py

ppai22/knn_movie_recommender

64bfda2dbb826c61db400976aae646e72b5dab3f

[ "MIT" ]

3

2021-06-08T21:56:09.000Z

2022-03-12T00:38:52.000Z

test_movies.py

ppai22/knn_movie_recommender

64bfda2dbb826c61db400976aae646e72b5dab3f

[ "MIT" ]

1

2020-07-09T04:34:47.000Z

''' Template to create a movie test data by yourself: ['Action', 'Adventure', 'Animation', 'Biography', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Family', 'Fantasy', 'Film-Noir', 'Game-Show', 'History', 'Horror', 'Music', 'Musical', 'Mystery', 'News', 'Reality-TV', 'Romance', 'Sci-Fi', 'Short', 'Sport', 'Thriller', 'War', 'Western', IMDb SCORE] Generate a list in the above format by replacing the respective genre of the movie with 1 and 0 if it isn't and add the IMDb score at the end ''' AVENGERS_INFINITY_WAR = [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 8.5] JOKER = [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 8.7] FORD_V_FERRARI = [1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8.3] SW_THE_FORCE_AWAKENS = [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7.9] MI_FALLOUT = [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 7.8] THE_GREAT_HACK = [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7.0] THE_NUN = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 5.7] RALPH_BREAKS_THE_INTERNET = [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7.1]

65.5

113

0.514504

318

1,310

2.075472

0.264151

0.49697

0.659091

0.793939

0.319697

0.316667

0.30303

0.284848

0

0.224652

0.232061

1,310

19

114

68.947368

0.431412

0.372519

0

1

0

false

0

null

1

0

1

0

1

0

null

0

5

fae2f0f473e63b57ec02453f3e56697080af9066

449

py

Python

SatPlot/rotate.py

lff5985/share-from-zhao

b1a6e3513db10e6da18ed6884d4fab9fb68e51b4

[ "MIT" ]

2

2018-06-13T02:27:22.000Z

2020-12-27T09:55:50.000Z

SatPlot/rotate.py

lff5985/share-from-zhao

b1a6e3513db10e6da18ed6884d4fab9fb68e51b4

[ "MIT" ]

null

SatPlot/rotate.py

lff5985/share-from-zhao

b1a6e3513db10e6da18ed6884d4fab9fb68e51b4

[ "MIT" ]

2

2016-11-09T14:06:30.000Z

2019-06-01T02:46:15.000Z

# -*- coding: utf-8 -*- import math import numpy as np test = np.mat(np.zeros((3,3))) def R1(omega): return np.mat([[1,0,0],[0,math.cos(omega),math.sin(omega)],[0,-1*math.sin(omega),math.cos(omega)]]) def R2(omega): return np.mat([[math.cos(omega),0,-1*math.sin(omega)],[0,1,0],[math.sin(omega),0,math.cos(omega)]]) def R3(omega): return np.mat([[math.cos(omega),math.sin(omega),0],[-1*math.sin(omega),math.cos(omega),0],[0,0,1]])

26.411765

103

0.619154

88

449

3.159091

0.25

0.151079

0.258993

0.18705

0.57554

0.568345

0.517986

0.359712

0

0.059259

0.097996

449

16

104

28.0625

0.62716

0.046771

0

1

0.333333

false

0

0.222222

0.333333

0.888889

0

null

0

1

0

null

0

1

0

1

0

5

fae842c7765866ed9a9d6ae6dd62df4a17849e0c

27,854

py

Python

tasks/drug_run.py

dmis-lab/ReSimNet

3c5832dccba525451ed019afb66b829b055a2be1

[ "Apache-2.0" ]

34

2019-02-11T04:48:06.000Z

2021-11-30T13:16:39.000Z

tasks/drug_run.py

dmis-lab/ReSimNet

3c5832dccba525451ed019afb66b829b055a2be1

[ "Apache-2.0" ]

1

2019-09-13T21:31:53.000Z

2019-12-12T00:10:56.000Z

tasks/drug_run.py

dmis-lab/ReSimNet

3c5832dccba525451ed019afb66b829b055a2be1

[ "Apache-2.0" ]

11

2019-02-13T03:56:39.000Z

2022-03-11T02:25:20.000Z

import sys import pickle import numpy as np import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F import logging import csv import os import pandas as pd from scipy.stats import pearsonr from sklearn.metrics import precision_score, roc_auc_score from datetime import datetime from torch.autograd import Variable from models.root.utils import * LOGGER = logging.getLogger(__name__) def prob_to_class(prob): return np.array([float(p >= 0.5) for p in prob]) def run_bi(model, loader, dataset, args, metric, train=False): total_step = 0.0 stats = {'loss':[]} tar_set = [] pred_set = [] kk_tar_set = [] kk_pred_set = [] ku_tar_set = [] ku_pred_set = [] uu_tar_set = [] uu_pred_set = [] start_time = datetime.now() for d_idx, (d1, d1_r, d1_l, d2, d2_r, d2_l, score) in enumerate(loader): # Split for KK/KU/UU sets kk_idx = np.argwhere([a in dataset.known and b in dataset.known for a, b in zip(d1, d2)]).flatten() ku_idx = np.argwhere([(a in dataset.known) != (b in dataset.known) for a, b in zip(d1, d2)]).flatten() uu_idx = np.argwhere([a not in dataset.known and b not in dataset.known for a, b in zip(d1, d2)]).flatten() assert len(kk_idx) + len(ku_idx) + len(uu_idx) == len(d1) # Grad zero + mode change model.optimizer.zero_grad() if train: model.train(train) else: model.eval() # Get outputs outputs, embed1, embed2 = model(d1_r.cuda(), d1_l, d2_r.cuda(), d2_l, None, None) loss = model.get_loss(outputs, score.cuda()) stats['loss'] += [loss.data[0]] total_step += 1.0 # Metrics for binary classification tmp_tar = score.data.cpu().numpy() tmp_pred = outputs.data.cpu().numpy() # tmp_pred = np.array([float(p >= 0.5) for p in tmp_pred[:]]) # print(tmp_tar[:5], tmp_pred[:5]) # Accumulate for final evaluation tar_set += list(tmp_tar[:]) pred_set += list(tmp_pred[:]) kk_tar_set += list(tmp_tar[kk_idx]) kk_pred_set += list(tmp_pred[kk_idx]) ku_tar_set += list(tmp_tar[ku_idx]) ku_pred_set += list(tmp_pred[ku_idx]) uu_tar_set += list(tmp_tar[uu_idx]) uu_pred_set += list(tmp_pred[uu_idx]) # Calculate current f1 scores f1 = metric(list(tmp_tar[:]), list(prob_to_class(tmp_pred[:]))) f1_kk = metric(list(tmp_tar[kk_idx]), list(prob_to_class(tmp_pred[kk_idx]))) f1_ku = metric(list(tmp_tar[ku_idx]), list(prob_to_class(tmp_pred[ku_idx]))) f1_uu = metric(list(tmp_tar[uu_idx]), list(prob_to_class(tmp_pred[uu_idx]))) # For binary classification, report f1 _, _, f1, _ = f1 _, _, f1_kk, _ = f1_kk _, _, f1_ku, _ = f1_ku _, _, f1_uu, _ = f1_uu # Optimize model if train and not args.save_embed: loss.backward() nn.utils.clip_grad_norm(model.get_model_params()[1], args.grad_max_norm) model.optimizer.step() # Print for print step or at last if d_idx % args.print_step == 0 or d_idx == (len(loader) - 1): et = int((datetime.now() - start_time).total_seconds()) _progress = ( '{}/{} | Loss: {:.3f} | Total F1: {:.3f} | '.format( d_idx + 1, len(loader), loss.data[0], f1) + 'KK: {:.3f} KU: {:.3f} UU: {:.3f} | '.format( f1_kk, f1_ku, f1_uu) + '{:2d}:{:2d}:{:2d}'.format( et//3600, et%3600//60, et%60)) LOGGER.debug(_progress) if args.top_only: # if False: tar_sets = [tar_set, kk_tar_set, ku_tar_set, uu_tar_set] pred_sets = [pred_set, kk_pred_set, ku_pred_set, uu_pred_set] messages = ['Total', 'KK', 'KU', 'UU'] top_criterion = 0.10 top_k = 100 for tar, pred, msg in zip(tar_sets, pred_sets, messages): sorted_target = sorted(tar[:], reverse=True) # top_cut = sorted_target[int(len(sorted_target) * top_criterion)] top_cut = 0.9 sorted_pred, my_target = (list(t) for t in zip(*sorted( zip(pred[:], tar[:]), reverse=True))) precision = sum(k >= top_cut for k in my_target[:top_k]) / top_k LOGGER.info('{} cut: {:.3f}, P@{}: {:.2f}, '.format( msg, top_cut, top_k, precision) + 'Pred Mean@100: {:.3f}, Tar Mean@100: {:.3f}'.format( sum(sorted_pred[:top_k])/top_k, sum(my_target[:top_k])/top_k)) def sort_and_slice(list1, list2): list2, list1 = (list(t) for t in zip(*sorted( zip(list2, list1), reverse=True))) list1 = list1[:len(list1)//100] + list1[-len(list1)//100:] # list1 = list1[-len(list1)//100:] list2 = list2[:len(list2)//100] + list2[-len(list2)//100:] # list2 = list2[-len(list2)//100:] assert len(list1) == len(list2) return list1, list2 if args.top_only: # if False: tar_set, pred_set = sort_and_slice(tar_set, pred_set) kk_tar_set, kk_pred_set = sort_and_slice(kk_tar_set, kk_pred_set) ku_tar_set, ku_pred_set = sort_and_slice(ku_tar_set, ku_pred_set) uu_tar_set, uu_pred_set = sort_and_slice(uu_tar_set, uu_pred_set) # Calculate acuumulated f1 scores f1 = metric(tar_set, prob_to_class(pred_set)) f1_kk = metric(kk_tar_set, prob_to_class(kk_pred_set)) f1_ku = metric(ku_tar_set, prob_to_class(ku_pred_set)) f1_uu = metric(uu_tar_set, prob_to_class(uu_pred_set)) pr, rc, f1, _ = f1 pr_kk, rc_kk, f1_kk, _ = f1_kk pr_ku, rc_ku, f1_ku, _ = f1_ku pr_uu, rc_uu, f1_uu, _ = f1_uu # TODO add spearman correlation # End of an epoch et = (datetime.now() - start_time).total_seconds() LOGGER.info('Results (Loss/F1/KK/KU/UU): {:.3f}\t'.format( sum(stats['loss'])/len(stats['loss'])) + '[{:.3f}\t{:.3f}\t{:.3f}]\t[{:.3f}\t{:.3f}\t{:.3f}]\t'.format( pr, rc, f1, pr_kk, rc_kk, f1_kk) + '[{:.3f}\t{:.3f}\t{:.3f}]\t[{:.3f}\t{:.3f}\t{:.3f}]\t'.format( pr_ku, rc_ku, f1_ku, pr_uu, rc_uu, f1_uu) + 'count: {}/{}/{}/{}'.format( len(pred_set), len(kk_pred_set), len(ku_pred_set), len(uu_pred_set))) return f1_ku def element(d): return [d[k] for k in range(0,len(d))] def run_reg(model, loader, dataset, args, metric, train=False): total_step = 0.0 stats = {'loss':[]} tar_set = [] pred_set = [] kk_tar_set = [] kk_pred_set = [] ku_tar_set = [] ku_pred_set = [] uu_tar_set = [] uu_pred_set = [] start_time = datetime.now() for d_idx, d in enumerate(loader): if args.rep_idx == 4: d1, d1_r, d1_a, d1_l, d2, d2_r, d2_a, d2_l, score = element(d) else: d1, d1_r, d1_l, d2, d2_r, d2_l, score = element(d) # Split for KK/KU/UU sets kk_idx = np.argwhere([a in dataset.known and b in dataset.known for a, b in zip(d1, d2)]).flatten() ku_idx = np.argwhere([(a in dataset.known) != (b in dataset.known) for a, b in zip(d1, d2)]).flatten() uu_idx = np.argwhere([a not in dataset.known and b not in dataset.known for a, b in zip(d1, d2)]).flatten() assert len(kk_idx) + len(ku_idx) + len(uu_idx) == len(d1) # Grad zero + mode change model.optimizer.zero_grad() if train: model.train(train) else: model.eval() # Get outputs if args.rep_idx == 4: outputs, embed1, embed2 = model(d1_r.cuda(), d1_l, d2_r.cuda(), d2_r, d1_a.cuda(), d2_a.cuda()) else: outputs, embed1, embed2 = model(d1_r.cuda(), d1_l, d2_r.cuda(), d2_l, None, None) loss = model.get_loss(outputs, score.cuda()) stats['loss'] += [loss.data[0]] total_step += 1.0 # Metrics for regression tmp_tar = score.data.cpu().numpy() tmp_pred = outputs.data.cpu().numpy() # print(tmp_tar[:10]) # Accumulate for final evaluation tar_set += list(tmp_tar[:]) pred_set += list(tmp_pred[:]) kk_tar_set += list(tmp_tar[kk_idx]) kk_pred_set += list(tmp_pred[kk_idx]) ku_tar_set += list(tmp_tar[ku_idx]) ku_pred_set += list(tmp_pred[ku_idx]) uu_tar_set += list(tmp_tar[uu_idx]) uu_pred_set += list(tmp_pred[uu_idx]) # Calculate current f1 scores f1 = metric(list(tmp_tar[:]), list(tmp_pred[:])) f1_kk = metric(list(tmp_tar[kk_idx]), list(tmp_pred[kk_idx])) f1_ku = metric(list(tmp_tar[ku_idx]), list(tmp_pred[ku_idx])) f1_uu = metric(list(tmp_tar[uu_idx]), list(tmp_pred[uu_idx])) f1 = f1[0][1] f1_kk = f1_kk[0][1] f1_ku = f1_ku[0][1] f1_uu = f1_uu[0][1] # Optimize model if train and not args.save_embed: loss.backward() nn.utils.clip_grad_norm(model.get_model_params()[1], args.grad_max_norm) model.optimizer.step() # Print for print step or at last if d_idx % args.print_step == 0 or d_idx == (len(loader) - 1): et = int((datetime.now() - start_time).total_seconds()) _progress = ( '{}/{} | Loss: {:.3f} | Total Corr: {:.3f} | '.format( d_idx + 1, len(loader), loss.data[0], f1) + 'KK: {:.3f} KU: {:.3f} UU: {:.3f} | '.format( f1_kk, f1_ku, f1_uu) + '{:2d}:{:2d}:{:2d}'.format( et//3600, et%3600//60, et%60)) LOGGER.debug(_progress) # if args.top_only: # # if False: # tar_sets = [tar_set, kk_tar_set, ku_tar_set, uu_tar_set] # pred_sets = [pred_set, kk_pred_set, ku_pred_set, uu_pred_set] # messages = ['Total', 'KK', 'KU', 'UU'] # top_criterion = 0.10 # top_k = 100 # # for tar, pred, msg in zip(tar_sets, pred_sets, messages): # sorted_target = sorted(tar[:], reverse=True) # # top_cut = sorted_target[int(len(sorted_target) * top_criterion)] # top_cut = 0.9 # # sorted_pred, my_target = (list(t) for t in zip(*sorted( # zip(pred[:], tar[:]), reverse=True))) # precision = sum(k >= top_cut for k in my_target[:top_k]) / top_k # LOGGER.info('{} cut: {:.3f}, P@{}: {:.2f}, '.format( # msg, top_cut, top_k, precision) + # 'Pred Mean@100: {:.3f}, Tar Mean@100: {:.3f}'.format( # sum(sorted_pred[:top_k])/top_k, # sum(my_target[:top_k])/top_k)) # # def sort_and_slice(list1, list2): # list2, list1 = (list(t) for t in zip(*sorted( # zip(list2, list1), reverse=True))) # list1 = list1[:len(list1)//100] + list1[-len(list1)//100:] # # list1 = list1[-len(list1)//100:] # list2 = list2[:len(list2)//100] + list2[-len(list2)//100:] # # list2 = list2[-len(list2)//100:] # assert len(list1) == len(list2) # return list1, list2 # # if args.top_only: # # if False: # tar_set, pred_set = sort_and_slice(tar_set, pred_set) # kk_tar_set, kk_pred_set = sort_and_slice(kk_tar_set, kk_pred_set) # ku_tar_set, ku_pred_set = sort_and_slice(ku_tar_set, ku_pred_set) # uu_tar_set, uu_pred_set = sort_and_slice(uu_tar_set, uu_pred_set) # Calculate acuumulated f1 scores f1 = metric(tar_set, pred_set) f1_kk = metric(kk_tar_set, kk_pred_set) f1_ku = metric(ku_tar_set, ku_pred_set) f1_uu = metric(uu_tar_set, uu_pred_set) # Trun into correlation f1 = f1[0][1] f1_kk = f1_kk[0][1] f1_ku = f1_ku[0][1] f1_uu = f1_uu[0][1] # End of an epoch et = (datetime.now() - start_time).total_seconds() LOGGER.info('Results (Loss/F1/KK/KU/UU): {:.4f}\t'.format( sum(stats['loss'])/len(stats['loss'])) + '[{:.4f}\t{:.4f}\t{:.4f}\t{:.4f}] '.format( f1, f1_kk, f1_ku, f1_uu) + 'count: {}/{}/{}/{}'.format( len(pred_set), len(kk_pred_set), len(ku_pred_set), len(uu_pred_set))) corr, msetotal, mse1, mse2, mse5, auroc, precision1, precision2, precision5 = evaluation(pred_set, tar_set) LOGGER.info('[TOTAL\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}] '.format( corr, msetotal, mse1, mse2, mse5, auroc, precision1, precision2, precision5)) corr, msetotal, mse1, mse2, mse5, auroc, precision1, precision2, precision5 = evaluation(kk_pred_set, kk_tar_set) LOGGER.info('[KK\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}] '.format( corr, msetotal, mse1, mse2, mse5, auroc, precision1, precision2, precision5)) corr, msetotal, mse1, mse2, mse5, auroc, precision1, precision2, precision5 = evaluation(ku_pred_set, ku_tar_set) LOGGER.info('[KU\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}] '.format( corr, msetotal, mse1, mse2, mse5, auroc, precision1, precision2, precision5)) corr, msetotal, mse1, mse2, mse5, auroc, precision1, precision2, precision5 = evaluation(uu_pred_set, uu_tar_set) LOGGER.info('[UU\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}] '.format( corr, msetotal, mse1, mse2, mse5, auroc, precision1, precision2, precision5)) return f1_ku def precision_at_k(y_pred, y_true, k): list_of_tuple = [(x, y) for x, y in zip(y_pred, y_true)] sorted_list_of_tuple = sorted(list_of_tuple, key=lambda tup: tup[0], reverse=True) topk = sorted_list_of_tuple[:int(len(sorted_list_of_tuple) * k)] topk_true = [x[1] for x in topk] topk_pred = [x[0] for x in topk] #print(topk) #print(topk_true) #print(topk_pred) precisionk = precision_score([1 if x > 0.9 else 0 for x in topk_true], [1 if x > -1.0 else 0 for x in topk_pred], labels=[0,1], pos_label=1) # print([1 if x > 90.0 else 0 for x in topk_true]) # print([1 if x > 90.0 else 0 for x in topk_pred]) # print(precisionk) return precisionk def mse_at_k(y_pred, y_true, k): list_of_tuple = [(x, y) for x, y in zip(y_pred, y_true)] sorted_list_of_tuple = sorted(list_of_tuple, key=lambda tup: tup[0], reverse=True) topk = sorted_list_of_tuple[:int(len(sorted_list_of_tuple) * k)] topk_true = [x[1] for x in topk] topk_pred = [x[0] for x in topk] msek = np.square(np.subtract(topk_pred, topk_true)).mean() return msek def evaluation(y_pred, y_true): # print(y_pred) # print(y_true) # print(pearsonr(np.ravel(y_pred), y_true)) corr = pearsonr(np.ravel(y_pred), y_true)[0] # mse = np.square(np.subtract(y_pred, y_true)).mean() msetotal = mse_at_k(y_pred, y_true, 1.0) mse1 = mse_at_k(y_pred, y_true, 0.01) mse2 = mse_at_k(y_pred, y_true, 0.02) mse5 = mse_at_k(y_pred, y_true, 0.05) auroc = float('nan') if len([x for x in y_true if x > 0.9]) > 0: auroc = roc_auc_score([1 if x > 0.9 else 0 for x in y_true], y_pred) precision1 = precision_at_k(y_pred, y_true, 0.01) precision2 = precision_at_k(y_pred, y_true, 0.02) precision5 = precision_at_k(y_pred, y_true, 0.05) #print(auroc, precision1, precision2, precision5) return (corr, msetotal, mse1, mse2, mse5, auroc, precision1, precision2, precision5) # Outputs response embeddings for a given dictionary def save_embed(model, dictionary, dataset, args, drug_file): model.eval() key2vec = {} known_cnt = 0 # Iterate drug dictionary for idx, item in enumerate(dictionary.items()): drug, rep = [item[k] for k in range(0,len(item))] if args.embed_d == 1: d1_r = rep[args.rep_idx] d1_k = drug in dataset.known d1_l = len(d1_r) else: d1_r = rep[0] d1_k = rep[1] d1_l = len(d1_r) # For string data (smiles/inchikey) if args.rep_idx == 0 or args.rep_idx == 1: d1_r = list(map(lambda x: dataset.char2idx[x] if x in dataset.char2idx else dataset.char2idx[dataset.UNK], d1_r)) d1_l = len(d1_r) # Real valued for mol2vec if args.rep_idx != 3: d1_r = Variable(torch.LongTensor(d1_r)).cuda() else: d1_r = Variable(torch.FloatTensor(d1_r)).cuda() d1_l = torch.LongTensor(np.array([d1_l])) d1_r = d1_r.unsqueeze(0) d1_l = d1_l.unsqueeze(0) # Run model amd save embed _, embed1, embed2 = model(d1_r, d1_l, d1_r, d1_l, None, None) assert embed1.data.tolist() == embed2.data.tolist() """ known = False for pert_id, _ in dataset.drugs.items(): if drug == pert_id: known = True known_cnt += 1 break """ key2vec[drug] = [embed1.squeeze().data.tolist(), d1_k] # Print progress if idx % args.print_step == 0 or idx == len(dictionary) - 1: _progress = '{}/{} saving drug embeddings..'.format( idx + 1, len(dictionary)) LOGGER.info(_progress) # Save embed as pickle pickle.dump(key2vec, open('{}/embed/{}.{}.pkl'.format( args.checkpoint_dir, drug_file, args.model_name), 'wb'), protocol=2) LOGGER.info('{}/{} number of known drugs.'.format(known_cnt, len(key2vec))) # Outputs pred vs label scores given a dataloader def save_prediction(model, loader, dataset, args): model.eval() csv_writer = csv.writer(open(args.checkpoint_dir + 'pred_' + args.model_name + '.csv', 'w')) csv_writer.writerow(['pert1', 'pert1_known', 'pert2', 'pert2_known', 'prediction', 'target']) for d_idx, (d1, d1_r, d1_l, d2, d2_r, d2_l, score) in enumerate(loader): # Run model for getting predictions outputs, _, _ = model(d1_r.cuda(), d1_l, d2_r.cuda(), d2_l, None, None) predictions = outputs.data.cpu().numpy() targets = score.data.tolist() for a1, a2, a3, a4 in zip(d1, d2, predictions, targets): csv_writer.writerow([a1, a1 in dataset.known, a2, a2 in dataset.known, a3, a4]) # Print progress if d_idx % args.print_step == 0 or d_idx == len(loader) - 1: _progress = '{}/{} saving drug predictions..'.format( d_idx + 1, len(loader)) LOGGER.info(_progress) # Outputs pred vs label scores given a dataloader def perform_ensemble(model, loader, dataset, args): model.eval() tar_set = [] pred_set = [] kk_tar_set = [] kk_pred_set = [] ku_tar_set = [] ku_pred_set = [] uu_tar_set = [] uu_pred_set = [] for d_idx, (d1, d1_r, d1_l, d2, d2_r, d2_l, score) in enumerate(loader): # Run model for getting predictions outputs, _, _ = model(d1_r.cuda(), d1_l, d2_r.cuda(), d2_l, None, None) # Split for KK/KU/UU sets kk_idx = np.argwhere([a in dataset.known and b in dataset.known for a, b in zip(d1, d2)]).flatten() ku_idx = np.argwhere([(a in dataset.known) != (b in dataset.known) for a, b in zip(d1, d2)]).flatten() uu_idx = np.argwhere([a not in dataset.known and b not in dataset.known for a, b in zip(d1, d2)]).flatten() assert len(kk_idx) + len(ku_idx) + len(uu_idx) == len(d1) # Metrics for regression tmp_tar = score.data.cpu().numpy() tmp_pred = outputs.data.cpu().numpy() # Accumulate for final evaluation tar_set += list(tmp_tar[:]) pred_set += list(tmp_pred[:]) kk_tar_set += list(tmp_tar[kk_idx]) kk_pred_set += list(tmp_pred[kk_idx]) ku_tar_set += list(tmp_tar[ku_idx]) ku_pred_set += list(tmp_pred[ku_idx]) uu_tar_set += list(tmp_tar[uu_idx]) uu_pred_set += list(tmp_pred[uu_idx]) corr, msetotal, mse1, mse2, mse5, auroc, precision1, precision2, precision5 = evaluation(pred_set, tar_set) print('[TOTAL\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}] '.format( corr, msetotal, mse1, mse2, mse5, auroc, precision1, precision2, precision5)) corr, msetotal, mse1, mse2, mse5, auroc, precision1, precision2, precision5 = evaluation(kk_pred_set, kk_tar_set) print('[KK\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}] '.format( corr, msetotal, mse1, mse2, mse5, auroc, precision1, precision2, precision5)) corr, msetotal, mse1, mse2, mse5, auroc, precision1, precision2, precision5 = evaluation(ku_pred_set, ku_tar_set) print('[KU\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}] '.format( corr, msetotal, mse1, mse2, mse5, auroc, precision1, precision2, precision5)) corr, msetotal, mse1, mse2, mse5, auroc, precision1, precision2, precision5 = evaluation(uu_pred_set, uu_tar_set) print('[UU\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.5f}] '.format( corr, msetotal, mse1, mse2, mse5, auroc, precision1, precision2, precision5)) return pred_set, tar_set, kk_pred_set, kk_tar_set, ku_pred_set, ku_tar_set, uu_pred_set, uu_tar_set # Outputs pred scores for new pair dataset def save_pair_score(model, pair_dir, fp_dir, dataset, args): model.eval() drug2rep = pickle.load(open(fp_dir, 'rb')) folder_name = args.checkpoint_dir + 'save_pair_score/' if not os.path.exists(folder_name): os.makedirs(folder_name) for subdir, _, files in os.walk(pair_dir): for file_ in sorted(files): df = pd.read_csv(os.path.join(subdir, file_), sep=",") #print(df) LOGGER.info('save_pair_score processing {}...'.format(file_)) csv_writer = csv.writer(open(folder_name + file_ + '_' + args.model_name + '.csv', 'w')) csv_writer.writerow(['drug1', 'drug2', 'prediction', 'jaccard']) batch = [] for row_idx, row in df.iterrows(): drug1 = row['id1'] drug1_r = drug2rep[drug1][0] drug1_r = [float(value) for value in list(drug1_r)] drug2 = row['id2'] drug2_r = drug2rep[drug2][0] drug2_r = [float(value) for value in list(drug2_r)] example = [drug1, drug1_r, len(drug1_r), drug2, drug2_r, len(drug2_r), 0] batch.append(example) if len(batch) == 1024: inputs = dataset.collate_fn(batch) outputs, _, _ = model(inputs[1].cuda(), inputs[2], inputs[4].cuda(), inputs[5], None, None) predictions = outputs.data.cpu().numpy() for example, pred in zip(batch, predictions): from scipy.spatial import distance def jaccard(a, b): return 1-distance.jaccard(a, b) jac = jaccard(example[1], example[4]) csv_writer.writerow([example[0], example[3], pred, jac]) print(example[0], example[3], pred, jac) batch = [] # Print progress if row_idx % 5000 == 0 or row_idx == len(df) - 1: _progress = '{}/{} saving unknwon predictions..'.format( row_idx + 1, len(df)) LOGGER.info(_progress) if len(batch) > 0: inputs = dataset.collate_fn(batch) outputs, _, _ = model(inputs[1].cuda(), inputs[2], inputs[4].cuda(), inputs[5], None, None) predictions = outputs.data.cpu().numpy() for example, pred in zip(batch, predictions): from scipy.spatial import distance def jaccard(a, b): return 1-distance.jaccard(a, b) jac = jaccard(example[1], example[4]) csv_writer.writerow([example[0], example[3], pred, jac]) def save_pair_score_for_zinc(model, pair_dir, example_dir, dataset, args): print("\n=============================================================") print("SAVE PAIR SCORE FOR ZINC") print("=============================================================") model.eval() df_example = pd.read_csv(example_dir, sep=",") print(df_example) folder_name = args.checkpoint_dir + 'save_pair_score_for_zinc/' if not os.path.exists(folder_name): os.makedirs(folder_name) for subdir, _, files in os.walk(pair_dir): for file_ in sorted(files): df_zinc = pd.read_csv(os.path.join(subdir, file_), sep=",") LOGGER.info('save_pair_score processing {}...'.format(file_)) csv_writer = csv.writer(open(folder_name + file_ + '_' + args.model_name + '.csv', 'w')) csv_writer.writerow(['pair1', 'pair2', 'prediction']) batch = [] for row_idx, row in df_zinc.iterrows(): drug1 = row['zinc_id'] drug1_r = row['fingerprint'] drug1_r = [float(value) for value in list(drug1_r)] for row_idex, row in df_example.iterrows(): try: drug2 = row['pair'] drug2_r =row['fp'] drug2_r = [float(value) for value in list(drug2_r)] #print(drug1, drug1_r, len(drug1_r), drug2, drug2_r, len(drug2_r)) example = [drug1, drug1_r, len(drug1_r), drug2, drug2_r, len(drug2_r), 0] batch.append(example) except KeyError: continue if len(batch) == 4096: inputs = dataset.collate_fn(batch) outputs, _, _ = model(inputs[1].cuda(), inputs[2], inputs[4].cuda(), inputs[5], None, None) predictions = outputs.data.cpu().numpy() for example, pred in zip(batch, predictions): if pred > 0.9: csv_writer.writerow([example[0], example[3], pred]) batch = [] # Print progress if row_idx % 1000 == 0 or row_idx == len(df_zinc) - 1: _progress = '{}/{} saving zinc predictions..'.format( row_idx + 1, len(df_zinc)) LOGGER.info(_progress) if len(batch) > 0: inputs = dataset.collate_fn(batch) outputs, _, _ = model(inputs[1].cuda(), inputs[2], inputs[4].cuda(), inputs[5], None, None) predictions = outputs.data.cpu().numpy() for example, pred in zip(batch, predictions): if pred > 0.9: csv_writer.writerow([example[0], example[3], pred])

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4f07b683641f3a475d235b06ba42cb2586fe0495

294

py

Python

reqon/deprecated/exceptions.py

dmpayton/reqlon

69ea152acaed1bf4d5a6219e23e8af46f77fb9ee

[ "MIT" ]

null

reqon/deprecated/exceptions.py

dmpayton/reqlon

69ea152acaed1bf4d5a6219e23e8af46f77fb9ee

[ "MIT" ]

null

reqon/deprecated/exceptions.py

dmpayton/reqlon

69ea152acaed1bf4d5a6219e23e8af46f77fb9ee

[ "MIT" ]

null

class ReqonError(Exception): pass class InvalidTypeError(ReqonError): def __init__(self, message): super(InvalidTypeError, self).__init__(message) class InvalidFilterError(ReqonError): def __init__(self, message): super(InvalidFilterError, self).__init__(message)

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5

4f0d1e75b9aab327e0827668e0b5854852bd449e

190

py

Python

Logic-2/round_sum.py

VivekM27/Coding-Bat-Python-Solutions

14d5c6ccaa2129e56a5898374dec60740fe6761b

[ "Apache-2.0" ]

null

Logic-2/round_sum.py

VivekM27/Coding-Bat-Python-Solutions

14d5c6ccaa2129e56a5898374dec60740fe6761b

[ "Apache-2.0" ]

null

Logic-2/round_sum.py

VivekM27/Coding-Bat-Python-Solutions

14d5c6ccaa2129e56a5898374dec60740fe6761b

[ "Apache-2.0" ]

null

# ROUND_SUM def round_sum(a, b, c): return round10(a) + round10(b) + round10(c) def round10(n): if n%10>4: n/=10 return (n+1) * 10 else: n/=10 return n * 10

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py

Python

Python/sound2_working.py

GuruprasadaShridharHegde/Coder-Mansion

14529a6d5d4e674ecaf0c771e9cc428ba34b0a2d

[ "MIT" ]

1

2022-01-19T04:22:21.000Z

Python/sound2_working.py

GuruprasadaShridharHegde/Coder-Mansion

14529a6d5d4e674ecaf0c771e9cc428ba34b0a2d

[ "MIT" ]

null

Python/sound2_working.py

GuruprasadaShridharHegde/Coder-Mansion

14529a6d5d4e674ecaf0c771e9cc428ba34b0a2d

[ "MIT" ]

null

import pygame pygame.mixer.init() pygame.mixer.music.load("01.mp3") pygame.mixer.music.set_volume(0.1) pygame.mixer.music.play() while pygame.mixer.music.get_busy() == True: pass

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py

Python

CstrikeRCON/__init__.py

beatmasta/counter-strike-rcon

e08a0c63f5760ae3faf044fddd14733905df67f8

[ "MIT" ]

7

2016-01-08T16:30:39.000Z

2019-12-25T19:33:33.000Z

CstrikeRCON/__init__.py

beatmasta/counter-strike-rcon

e08a0c63f5760ae3faf044fddd14733905df67f8

[ "MIT" ]

3

2016-03-21T07:30:36.000Z

2020-11-08T09:07:34.000Z

CstrikeRCON/__init__.py

beatmasta/counter-strike-rcon

e08a0c63f5760ae3faf044fddd14733905df67f8

[ "MIT" ]

6

2015-03-11T16:11:24.000Z

2022-03-08T01:28:49.000Z

""" initializate CstrikeRCON as a module to be imported by third parties """ import CstrikeRCON

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87f7b04845579e835125f0960461bb06963e2326

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py

Python

src/backbones/__init__.py

Light4Code/tensorflow-research

392c2d7bc376f491fec68d479b130f883d6d028d

[ "MIT" ]

5

2020-02-29T16:28:55.000Z

2021-11-24T07:47:36.000Z

src/backbones/__init__.py

octumcore/tensorflow-research

ebb8e8243889f55affa354c49eb54db4fbcd2c87

[ "MIT" ]

3

2020-11-13T18:41:57.000Z

2022-02-10T01:37:51.000Z

src/backbones/__init__.py

octumcore/tensorflow-research

ebb8e8243889f55affa354c49eb54db4fbcd2c87

[ "MIT" ]

4

2020-03-24T10:50:17.000Z

2020-06-02T13:07:28.000Z

from .auto_encoder.auto_encoder_conv import AutoEncoderConv from .auto_encoder.auto_encoder_full_connected import AutoEncoderFullConnected from .base_backbone import BaseBackbone from .segmentation.segmentation_vanilla_unet import SegmentationVanillaUnet

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358a7752ebed3be02f8d75821efa8c24285c62b9

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py

Python

videoanalyst/model/loss/loss_base.py

JIANG-CX/data_labeling

8d2470bbb537dfc09ed2f7027ed8ee7de6447248

[ "MIT" ]

1

2021-05-24T10:08:51.000Z

videoanalyst/model/loss/loss_base.py

JIANG-CX/data_labeling

8d2470bbb537dfc09ed2f7027ed8ee7de6447248

[ "MIT" ]

null

videoanalyst/model/loss/loss_base.py

JIANG-CX/data_labeling

8d2470bbb537dfc09ed2f7027ed8ee7de6447248

[ "MIT" ]

null

# -*- coding: utf-8 -* from videoanalyst.utils import Registry TRACK_LOSSES = Registry('TRACK_LOSSES') VOS_LOSSES = Registry('VOS_LOSSES')

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5

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py

Python

modules/2.79/bpy/types/CompositorNodeBrightContrast.py

cmbasnett/fake-bpy-module

acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55

[ "MIT" ]

null

modules/2.79/bpy/types/CompositorNodeBrightContrast.py

cmbasnett/fake-bpy-module

acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55

[ "MIT" ]

null

modules/2.79/bpy/types/CompositorNodeBrightContrast.py

cmbasnett/fake-bpy-module

acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55

[ "MIT" ]

null

class CompositorNodeBrightContrast: use_premultiply = None def update(self): pass

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ea0e16b542da114d42d07b1f242d7e46bf6f4f87

17,087

py

Python

guppy/heapy/pbhelp.py

EhsanKia/guppy3

87bb2e5b9e3d76c8b1c6698c40fdd395b1ee3cd3

[ "MIT" ]

null

guppy/heapy/pbhelp.py

EhsanKia/guppy3

87bb2e5b9e3d76c8b1c6698c40fdd395b1ee3cd3

[ "MIT" ]

null

guppy/heapy/pbhelp.py

EhsanKia/guppy3

87bb2e5b9e3d76c8b1c6698c40fdd395b1ee3cd3

[ "MIT" ]

null

# AUTOMATICALLY GENERATED BY GENGUPPY about = b'\x80\x03cguppy.gsl.Text\nRecordingInter\nq\x00)\x81q\x01}q\x02(X\x07\x00\x00\x00appendsq\x03]q\x04(K\x00X\x16\x00\x00\x00Heapy Profile Browser\nq\x05K\x01X\x01\x00\x00\x00\tq\x06K\x02X\x07\x00\x00\x00Versionq\x07K\x01h\x06K\x03X\x04\x00\x00\x000.1\nq\x08K\x04h\x06K\x02X\x06\x00\x00\x00Authorq\tK\x04h\x06K\x03X\x10\x00\x00\x00Sverker Nilsson\nq\nK\x04h\x06K\x02X\x05\x00\x00\x00Emailq\x0bK\x04h\x06K\x03X\x0b\x00\x00\x00sn@sncs.se\nq\x0cK\x04h\x06K\x02X\x07\x00\x00\x00Licenseq\rK\x04h\x06K\x03X\x04\x00\x00\x00MIT\nq\x0eK\x05X\x18\x00\x00\x00Copyright (c) 2005--2008q\x0fK\x06X.\x00\x00\x00S. Nilsson Computer System ABLinkoping, Swedenq\x10K\x07X\x01\x00\x00\x00\nq\x11eX\x0b\x00\x00\x00tag_configsq\x12}q\x13(K\x00X\x08\x00\x00\x00spacing1q\x14K\x0b\x86q\x15X\x04\x00\x00\x00fontq\x16X\x05\x00\x00\x00timesq\x17K\x18X\x04\x00\x00\x00boldq\x18\x87q\x19\x86q\x1a\x86q\x1bK\x02h\x15h\x16h\x17K\x0cX\x04\x00\x00\x00boldq\x1c\x87q\x1d\x86q\x1e\x86q\x1fK\x03h\x15h\x16h\x17K\x0c\x86q \x86q!\x86q"K\x01X\x04\x00\x00\x00tabsq#(G@:\x80\x00\x00\x00\x00\x00X\x06\x00\x00\x00centerq$G@O\x80\x00\x00\x00\x00\x00X\x04\x00\x00\x00leftq%tq&\x86q\'h\x15h\x1e\x87q(K\x04h\'h\x14K\x06\x86q)\x86q*K\x05h)h\x16h\x17K\nX\x06\x00\x00\x00italicq+\x87q,\x86q-\x86q.K\x06h)h\x16h\x17K\n\x86q/\x86q0\x86q1K\x07h)h!\x86q2K\x08h!\x85q3uX\n\x00\x00\x00_gsl_titleq4X\x1b\x00\x00\x00About Heapy Profile Browserq5X\x10\x00\x00\x00_gsl_tk_geometryq6X\x07\x00\x00\x00400x200q7ub.' help = b'\x80\x03cguppy.gsl.Text\nRecordingInter\nq\x00)\x81q\x01}q\x02(X\x07\x00\x00\x00appendsq\x03]q\x04(K\x00X\x06\x00\x00\x00Menus\nq\x05K\x01Xr\x00\x00\x00Click on the dotted line at the top of a menu to "tear it off": a separate window containing the menu is created.\nq\x06K\x03X\n\x00\x00\x00File Menu\nq\x07K\x05X\x01\x00\x00\x00\tq\x08K\x06X\x13\x00\x00\x00New Profile Browserq\tK\x05h\x08K\x07X%\x00\x00\x00Create a new browser window with the\nq\nK\x08X\x02\x00\x00\x00\t\tq\x0bK\x07X#\x00\x00\x00same file as the one opened in the\nq\x0cK\x08h\x0bK\x07X\x11\x00\x00\x00current window. \nq\rK\th\x08K\x06X\x0c\x00\x00\x00Open Profileq\x0eK\th\x08K\x07X(\x00\x00\x00Open a profile data file in the current\nq\x0fK\x08h\x0bK\x07X\'\x00\x00\x00window. Data files can be created with\nq\x10K\x08h\x0bK\nX\n\x00\x00\x00Stat.dump q\x11K\x07X\x03\x00\x00\x00. \nq\x12K\th\x08K\x06X\x0c\x00\x00\x00Close Windowq\x13K\th\x08K\x07X(\x00\x00\x00Close the current window (exits from Tk\nq\x14K\x08h\x0bK\x07X%\x00\x00\x00if it was the last browser window). \nq\x15K\th\x08K\x06X\x0b\x00\x00\x00Clear Cacheq\x16K\th\x08K\x07X&\x00\x00\x00Clear the sample cache, releasing its\nq\x17K\x08h\x0bK\x07X\x1a\x00\x00\x00memory. The cache will be\nq\x18K\x08h\x0bK\x07X)\x00\x00\x00automatically filled again when needed. \nq\x19K\x08h\x0bK\x0bX%\x00\x00\x00This command is a kind of temporary /q\x1aK\x07X\x01\x00\x00\x00\nq\x1bK\x08h\x0bK\x0bX\'\x00\x00\x00experimental feature. I think the cacheq\x1cK\x07h\x1bK\x08h\x0bK\x0bX%\x00\x00\x00handling should be made automatic andq\x1dK\x07h\x1bK\x08h\x0bK\x0bX\x17\x00\x00\x00less memory consuming. q\x1eK\x07h\x1bK\x03X\n\x00\x00\x00Pane Menu\nq\x1fK\x0ch\x08K\x06X\x12\x00\x00\x00Show Control Panelq K\x0ch\x08K\x07X\x1d\x00\x00\x00Show the control panel pane.\nq!K\rh\x08K\x06X\n\x00\x00\x00Show Graphq"K\rh\x08K\x07X\x15\x00\x00\x00Show the graph pane.\nq#K\rh\x08K\x06X\n\x00\x00\x00Show Tableq$K\rh\x08K\x07X\x15\x00\x00\x00Show the table pane.\nq%K\x03X\x0b\x00\x00\x00Graph Menu\nq&K\x0eh\x08K\x06X\x0c\x00\x00\x00Bars / Linesq\'K\x0eh\x08K\x07X-\x00\x00\x00Choose whether the graph should be displayed\nq(K\x0fh\x0bK\x07X\x16\x00\x00\x00using bars or lines. \nq)K\x0fh\x0bK\x0bX1\x00\x00\x00When using bars, the sample value (size or count)q*K\x07h\x1bK\x0fh\x0bK\x0bX1\x00\x00\x00for different kinds of objects will be stacked onq+K\x07h\x1bK\x0fh\x0bK\x0bX4\x00\x00\x00top of each other so the total height represents theq,K\x07h\x1bK\x0fh\x0bK\x0bX/\x00\x00\x00total value of a sample. When using lines, eachq-K\x07h\x1bK\x0fh\x0bK\x0bX.\x00\x00\x00line represents the value for a single kind ofq.K\x07h\x1bK\x0fh\x0bK\x0bX/\x00\x00\x00object. The 10 largest values are shown in eachq/K\x07h\x1bK\x0fh\x0bK\x0bX/\x00\x00\x00sample point. Each kind has a particular color,q0K\x07h\x1bK\x0fh\x0bK\x0bX1\x00\x00\x00choosen arbitrary but it is always the same colorq1K\x07h\x1bK\x0fh\x0bK\x0bX1\x00\x00\x00for the same kind. The remaing kinds, if any, areq2K\x07h\x1bK\x0fh\x0bK\x0bX\x10\x00\x00\x00shown in black. q3K\x07h\x1bK\x10h\x08K\x06X\x0c\x00\x00\x00Size / Countq4K\x10h\x08K\x07X1\x00\x00\x00Choose whether the graph should display the size\nq5K\x0fh\x0bK\x07X1\x00\x00\x00of objects of a particular kind or the number of\nq6K\x0fh\x0bK\x07X\x17\x00\x00\x00objects of that kind. \nq7K\x0fh\x0bK\x11X<\x00\x00\x00(Note that this affects only the graph, the table will stillq8K\x07h\x1bK\x0fh\x0bK\x11X;\x00\x00\x00choose size or kind as it were choosen in the table menu.) q9K\x07h\x1bK\x03X\x0b\x00\x00\x00Table Menu\nq:K\x12X\x0f\x00\x00\x00Header submenu\nq;K\x13XT\x01\x00\x00This menu has a choice of header for each column of the table. The data of each column is determined by the header of that column, as well as the headers of previous columns. So if you change the first column header (A/B), the data in that column will change as well as the data under the next header (Size/Count) and the ones that follow.\nq<K\x14h\x08K\x15X\x05\x00\x00\x00A / Bq=K\x14h\x08K\x16X+\x00\x00\x00Use the sample at the A or B marker in the\nq>K\x17h\x0bK\x16X\x08\x00\x00\x00graph. \nq?K\x17h\x0bK\x13X\'\x00\x00\x00The kinds of objects shown in the tableq@K\x16h\x1bK\x17h\x0bK\x13X\'\x00\x00\x00under this column are taken from the 10qAK\x16h\x1bK\x17h\x0bK\x13X+\x00\x00\x00largest sample values at that point, in theqBK\x16h\x1bK\x17h\x0bK\x13X*\x00\x00\x00same order as they are shown in the graph.qCK\x16h\x1bK\x17h\x0bK\x13X(\x00\x00\x00The ordering in the graph depends on theqDK\x16h\x1bK\x17h\x0bK\x13X*\x00\x00\x00choice of count or size in the graph menu.qEK\x16h\x1bK\x17h\x0bK\x13X)\x00\x00\x00However, the table may show count or sizeqFK\x16h\x1bK\x17h\x0bK\x13X*\x00\x00\x00independent from the choice in the graph. qGK\x16h\x1bK\x18h\x08K\x15h4K\x18h\x08K\x16X\'\x00\x00\x00Show the size or count of the kinds of\nqHK\x17h\x0bK\x16X&\x00\x00\x00objects in each row, taken from those\nqIK\x17h\x0bK\x16X\x1e\x00\x00\x00choosen in the A / B column. \nqJK\x18h\x08K\x15X\x0f\x00\x00\x00%A:Tot / %B:TotqKK\x18h\x08K\x16X$\x00\x00\x00Show percentage of the Size / Count\nqLK\x17h\x0bK\x16X.\x00\x00\x00column, relative to the total (size or count)\nqMK\x17h\x0bK\x16X$\x00\x00\x00at either the A or B sample point. \nqNK\x18h\x08K\x19X\x07\x00\x00\x00Cumul /qOK\x18h\x08K\x16X+\x00\x00\x00Show either a cumulative sum of the Size /\nqPK\x17h\x08K\x19X\x00\x00\x00\x00qQK\x1aX\t\x00\x00\x00A-B / B-AqRK\x17h\x08K\x16X\'\x00\x00\x00Count column, or the difference A-B or\nqSK\x17h\x0bK\x16X\x06\x00\x00\x00B-A. \nqTK\x17h\x0bK\x13X&\x00\x00\x00The cumulative sum is taken by summingqUK\x16h\x1bK\x17h\x0bK\x13X)\x00\x00\x00from the first table row down to the lastqVK\x16h\x1bK\x17h\x0bK\x13X\x05\x00\x00\x00row. qWK\x16h\x1bK\x18h\x08K\x15hKK\x18h\x08K\x16X\'\x00\x00\x00Show percentage of the previous field,\nqXK\x17h\x0bK\x16X&\x00\x00\x00relative to either the A or B total. \nqYK\x18h\x08K\x15X\x04\x00\x00\x00KindqZK\x18h\x08K\x16X-\x00\x00\x00Shows the kind of objects. This is currently\nq[K\x17h\x0bK\x16X*\x00\x00\x00the only alternative for this column. The\nq\\K\x17h\x0bK\x16X*\x00\x00\x00kind shown corresponds to the color shown\nq]K\x17h\x0bK\x16X\'\x00\x00\x00in the A / B column. A special kind is\nq^K\x17h\x0bK\x16X\'\x00\x00\x00<Other> which summarizes the remaining\nq_K\x17h\x0bK\x16X*\x00\x00\x00data if there were more than 10 different\nq`K\x17h\x0bK\x16X\x16\x00\x00\x00kinds in the sample. \nqaK\x12X\x12\x00\x00\x00Scrollbar submenu\nqbK\x1bh\x08K\x15X\x0f\x00\x00\x00Auto / On / OffqcK\x1bh\x08K\x16X.\x00\x00\x00Choose a scrollbar mode. The usual setting is\nqdK\x1ch\x0bK\x16X)\x00\x00\x00Auto which shows the scrollbar only when\nqeK\x1ch\x0bK\x16X\t\x00\x00\x00needed. \nqfK\x03X\x0c\x00\x00\x00Window Menu\nqgK\x0bXq\x00\x00\x00This menu lists the names of all open windows. Selecting one brings it to the top, deiconifying it if necessary.\nqhK\x03X\n\x00\x00\x00Help Menu\nqiK\x1dh\x08K\x06X\x05\x00\x00\x00AboutqjK\x1dh\x08K\x07X#\x00\x00\x00Version, author, email, copyright.\nqkK\x1eh\x08K\x06X\x04\x00\x00\x00HelpqlK\x1eh\x08K\x07X\x17\x00\x00\x00Open this help window.\nqmK\x00X\x06\x00\x00\x00Panes\nqnK\x01X\x98\x00\x00\x00There are 3 panes in the main window shown by default. At the top is the Control Panel, at the bottom left the Graph and at the bottom right the Table.\nqoK\x03X\x13\x00\x00\x00Control Panel Pane\nqpK\x1fXl\x00\x00\x00This contains controls for the graph and the markers. It also has a quick-exit button and a collect button.\nqqK\x12X\x13\x00\x00\x00X / Y axis control\nqrK\x13X\xec\x00\x00\x00The two frames in the Control Panel having an X or Y button in the top left corner control each axis of the graph. The X, horizontal, axis shows the sample point. The Y axis shows either the size or count, as choosen in the Graph menu.\nqsK h\x08K\x15X\x0c\x00\x00\x00X / Y ButtonqtK h\x08K\x16X,\x00\x00\x00Brings up a menu, currently containing some\nquK!h\x0bK\x16X2\x00\x00\x00buttons that can also be accessed directly in the\nqvK!h\x0bK\x16X\x08\x00\x00\x00panel. \nqwK"h\x08K\x15X\x0b\x00\x00\x00Grid buttonqxK"h\x08K\x16X,\x00\x00\x00Select if the graph should show grid lines.\nqyK"h\x08K\x15X\r\x00\x00\x00Range buttonsqzK"h\x08K\x16X0\x00\x00\x00Change the range that is shown in the displayed\nq{K!h\x08K\x15hQK\x1aX\x05\x00\x00\x00- / +q|K!h\x08K\x16X.\x00\x00\x00portion of the graph. For each time + or - is\nq}K!h\x0bK\x16X0\x00\x00\x00pressed the range will be stepped up or down in\nq~K!h\x0bK\x16X0\x00\x00\x00the sequence (1, 2, 5) and multiples thereoff. \nq\x7fK"h\x08K\x15X\x0b\x00\x00\x00Range fieldq\x80K"h\x08K\x16X+\x00\x00\x00The current range is shown here, and a new\nq\x81K!h\x0bK\x16X2\x00\x00\x00range can be entered by writing to this field and\nq\x82K!h\x0bK\x16X.\x00\x00\x00pressing Enter. The format is an integer that\nq\x83K!h\x0bK\x16X-\x00\x00\x00may be followed by a multiplier, K, M, G, or\nq\x84K!h\x0bK\x16X0\x00\x00\x00T, meaning that the value is multipled by 1000,\nq\x85K!h\x0bK\x16X,\x00\x00\x001E6, 1E9, or 1E12 respectively. The maximum\nq\x86K!h\x0bK\x16X\x0e\x00\x00\x00range is 1T. \nq\x87K\x12X\x15\x00\x00\x00A / B sample control\nq\x88K\x13X\x95\x00\x00\x00Each of the frames showing A or B in the top left corner controls one of the sample markers. The current position is shown in the bottom left corner.q\x89K#X\x86\x00\x00\x00(This is currently not an entry field - TODO - but the marker may be moved long distances by directly dragging it in the Graph frame.)q\x8aK\x13h\x1bK$h\x08K\x15h|K$h\x08K\x16X3\x00\x00\x00Step the marker one step to the left (-) or to the\nq\x8bK%h\x0bK\x16X\x0c\x00\x00\x00right (+). \nq\x8cK%h\x0bK\x13X0\x00\x00\x00The table will be updated to show new data if itq\x8dK\x16h\x1bK%h\x0bK\x13X-\x00\x00\x00was set to show such data that were dependentq\x8eK\x16h\x1bK%h\x0bK\x13X\x15\x00\x00\x00on the marker moved. q\x8fK\x16h\x1bK%h\x0bK\x13X/\x00\x00\x00The graph will show the new marker position. Ifq\x90K\x16h\x1bK%h\x0bK\x13X/\x00\x00\x00the marker was outside of the displayed portionq\x91K\x16h\x1bK%h\x0bK\x13X1\x00\x00\x00of the graph, the graph will scroll so the markerq\x92K\x16h\x1bK%h\x0bK\x13X\x11\x00\x00\x00becomes visible. q\x93K\x16h\x1bK&h\x08K\x15X\x0c\x00\x00\x00Track buttonq\x94K&h\x08K\x16X2\x00\x00\x00Press to set the marker to the last sample in the\nq\x95K%h\x0bK\x16X,\x00\x00\x00file and stay at the end as new samples are\nq\x96K%h\x0bK\x16X/\x00\x00\x00added. (New samples are periodically read from\nq\x97K%h\x0bK\x16X2\x00\x00\x00the end of the file when auto-collect is selected\nq\x98K%h\x0bK\x16X\x1a\x00\x00\x00via the Collect button.) \nq\x99K%h\x0bK\x13X)\x00\x00\x00Tracking is turned off when the marker isq\x9aK\x16h\x1bK%h\x0bK\x13X\x10\x00\x00\x00manually moved. q\x9bK\x16h\x1bK\x12X\x0c\x00\x00\x00Exit button\nq\x9cK\x13XE\x00\x00\x00Exits the program, a shortcut for the Exit command in the File menu.\nq\x9dK\x12X\x0f\x00\x00\x00Collect button\nq\x9eK\x13Xv\x00\x00\x00When selected, the browser will collect new samples from the current file, and will continue to do this periodically.\nq\x9fK#X<\x00\x00\x00Currently it will check the file for new data once a second.q\xa0K\x13h\x1bK\x03X\x0b\x00\x00\x00Graph Pane\nq\xa1K\x0bX\x06\x01\x00\x00This pane shows the currently visible portion of the sample file. It can be scrolled via an horizontal scrollbar. The two markers are shown as buttons labeled A and B above the graph and with lines extending down in the graph. Markers can be moved by the mouse.\nq\xa2K\x07Xt\x00\x00\x00How to move the markers is hopefully quite self evident when tried out but I wrote up some details about it anyway.\nq\xa3K\x12X\x18\x00\x00\x00Marker movement details\nq\xa4K\'X\x9d\x04\x00\x00Holding down the mouse button and moving the mouse moves the underlying marker. Klicking the mouse button over a marker without moving the mouse, selects the marker. While it is selected any movement of the mouse within the graph will move the marker with it. Klicking again anywhere in the graph will deselect the marker. If the marker can be moved, the cursor will be an arrow indicating the direction it can be moved, left or right or both. If the marker can not be moved in any direction, the cursor will show a circle or disc. The marker can not move outside the available samples. Moving the mouse outside of the graph also restricts the movement of the mouse, even if the mouse button is pressed. This is intentional so that the marker can be moved longer distances than the mouse can move. Moving the mouse to the right of the graph, the marker can only be moved to the right - moving back the mouse will not move the marker back until the mouse enters the graph area again. Similarly for the left side. Above or below the graph, the mouse will not move the marker at all but will show a circle to indicate that the mouse may be \'recirculated\' to move back into the graph.\nq\xa5K\x03X\x0b\x00\x00\x00Table Pane\nq\xa6K\x0bX\x7f\x00\x00\x00This pane shows a table based on the configuration set in the Table menu. The sample number and time stamp show in the header.\nq\xa7eX\x0b\x00\x00\x00tag_configsq\xa8}q\xa9(K\x00X\x08\x00\x00\x00spacing1q\xaaK\n\x86q\xabX\x04\x00\x00\x00fontq\xacX\x05\x00\x00\x00timesq\xadK\x14X\x04\x00\x00\x00boldq\xae\x87q\xaf\x86q\xb0\x86q\xb1K\x01h\xabh\xach\xadK\x0c\x86q\xb2\x86q\xb3\x86q\xb4K\x02h\xaaK\x06\x86q\xb5h\xb3\x86q\xb6K\x03h\xaaK\t\x86q\xb7h\xach\xadK\x10X\x04\x00\x00\x00boldq\xb8\x87q\xb9\x86q\xba\x86q\xbbK\x04h\xb3\x85q\xbcK\x06(X\x08\x00\x00\x00lmargin1q\xbdK$\x86q\xbeX\x08\x00\x00\x00lmargin2q\xbfK$\x86q\xc0h\xb5h\xach\xadK\x0cX\x04\x00\x00\x00boldq\xc1\x87q\xc2\x86q\xc3tq\xc4K\x07(h\xbeh\xc0h\xb5h\xb3tq\xc5K\x05(X\x04\x00\x00\x00tabsq\xc6(G@Z\xc0\x00\x00\x00\x00\x00X\x06\x00\x00\x00centerq\xc7G@g\x80\x00\x00\x00\x00\x00X\x04\x00\x00\x00leftq\xc8tq\xc9\x86q\xcah\xbeh\xc0h\xb5h\xc3tq\xcbK\x08h\xca\x85q\xccK\th\xcah\xb5\x86q\xcdK\n(h\xbeh\xc0h\xb5h\xach\xadK\x0cX\x06\x00\x00\x00italicq\xce\x87q\xcf\x86q\xd0tq\xd1K\x0bh\xbeh\xc0h\xb3\x87q\xd2K\x0c(h\xc6(G@Z \x00\x00\x00\x00\x00h\xc7G@f\xe0\x00\x00\x00\x00\x00h\xc8tq\xd3\x86q\xd4h\xbeh\xc0h\xb5h\xc3tq\xd5K\rh\xd4h\xb5\x86q\xd6K\x0e(h\xc6(G@S@\x00\x00\x00\x00\x00h\xc7G@`\x00\x00\x00\x00\x00\x00h\xc8tq\xd7\x86q\xd8h\xbeh\xc0h\xb5h\xc3tq\xd9K\x0fh\xd8\x85q\xdaK\x10h\xd8h\xb5\x86q\xdbK\x11h\xbeh\xc0h\xach\xadK\n\x86q\xdc\x86q\xdd\x87q\xdeK\x12(h\xbeh\xc0h\xaaK\x08\x86q\xdfh\xc3tq\xe0K\x15(h\xbdKH\x86q\xe1h\xbfKH\x86q\xe2h\xb5h\xc3tq\xe3K\x16(h\xe1h\xe2h\xb5h\xb3tq\xe4K\x14(h\xc6(G@`\xd0\x00\x00\x00\x00\x00h\xc7G@i\xe0\x00\x00\x00\x00\x00h\xc8tq\xe5\x86q\xe6h\xe1h\xe2h\xb5h\xc3tq\xe7K\x17h\xe6\x85q\xe8K\x13h\xe1h\xe2h\xb3\x87q\xe9K\x18h\xe6h\xb5\x86q\xeaK\x19h\xe3K\x1ah\xe1h\xe2h\xc3\x87q\xebK\x1b(h\xc6(G@^\xa0\x00\x00\x00\x00\x00h\xc7G@f\xe0\x00\x00\x00\x00\x00h\xc8tq\xec\x86q\xedh\xe1h\xe2h\xb5h\xc3tq\xeeK\x1ch\xed\x85q\xefK\x1d(h\xc6(G@L\xc0\x00\x00\x00\x00\x00h\xc7G@V@\x00\x00\x00\x00\x00h\xc8tq\xf0\x86q\xf1h\xbeh\xc0h\xb5h\xc3tq\xf2K\x1eh\xf1h\xb5\x86q\xf3K\x1fh\xb7h\xb3\x86q\xf4K (h\xc6(G@^`\x00\x00\x00\x00\x00h\xc7G@f\xa0\x00\x00\x00\x00\x00h\xc8tq\xf5\x86q\xf6h\xe1h\xe2h\xb5h\xc3tq\xf7K!h\xf6\x85q\xf8K"h\xf6h\xb5\x86q\xf9K#h\xe1h\xe2h\xdd\x87q\xfaK$(h\xc6(G@]`\x00\x00\x00\x00\x00h\xc7G@e\xa0\x00\x00\x00\x00\x00h\xc8tq\xfb\x86q\xfch\xe1h\xe2h\xb5h\xc3tq\xfdK%h\xfc\x85q\xfeK&h\xfch\xb5\x86q\xffK\'(h\xbeh\xc0h\xdfh\xb3tr\x00\x01\x00\x00uX\n\x00\x00\x00_gsl_titler\x01\x01\x00\x00X\x1e\x00\x00\x00Help for Heapy Profile Browserr\x02\x01\x00\x00ub.'

3,417.4

15,587

0.781764

3,387

17,087

3.940951

0.25096

0.123165

0.035061

0.017081

0.235091

0.147812

0.112451

0.066976

0.053641

0.029368

0

0.195402

0.078422

17,087

4

15,588

4,271.75

0.652251

0.002048

0

1

4.5

0.561173

0.35478

0

1

0

false

0

null

0

1

0

1

0

null

0

5

ea21081752360978188d7b3257e78389a9770d89

38

py

Python

custom_components/lobe/__init__.py

KTibow/lobe-home-assistant

d6ab166ffdce73f9ffb9282412e27b0685461d76

[ "MIT" ]

4

2021-05-02T06:33:03.000Z

2022-01-21T13:56:50.000Z

custom_components/lobe/__init__.py

KTibow/lobe-home-assistant

d6ab166ffdce73f9ffb9282412e27b0685461d76

[ "MIT" ]

null

custom_components/lobe/__init__.py

KTibow/lobe-home-assistant

d6ab166ffdce73f9ffb9282412e27b0685461d76

[ "MIT" ]

2

2021-05-30T01:28:19.000Z

2021-06-26T12:12:21.000Z

"""image_processing.lobe platform."""

19

37

0.736842

4

38

6.75

1

0

0.052632

38

1

38

0.75

0.815789

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

ea7d237d959d16f7c75f3c63e066ec5cac85234f

218

py

Python

Python/LeapYear.py

aaaaaaaaaanyaaaaaaaaa/Hello-world

ff47220589c34c1cd4555346e92d3255b433975f

[ "MIT" ]

3

2018-12-14T10:03:25.000Z

2020-02-11T16:24:39.000Z

Python/LeapYear.py

aaaaaaaaaanyaaaaaaaaa/Hello-world

ff47220589c34c1cd4555346e92d3255b433975f

[ "MIT" ]

1

2018-10-13T09:49:28.000Z

Python/LeapYear.py

aaaaaaaaaanyaaaaaaaaa/Hello-world

ff47220589c34c1cd4555346e92d3255b433975f

[ "MIT" ]

2

2018-10-15T07:10:43.000Z

2019-10-23T08:31:25.000Z

year = int(input("Enter Year: ")) # Leap Year Check if not year % 400: print(year, "is a Leap Year") elif not year % 4 and year % 100: print(year, "is a Leap Year") else: print(year, "is not a Leap Year")

21.8

37

0.619266

39

218

3.461538

0.435897

0.237037

0.244444

0.177778

0.296296

0

0.042424

0.243119

218

9

38

24.222222

0.775758

0.068807

0

0.285714

0

0.288557

0

1

0

false

0

0.428571

0

null

1

0

1

0

null

0

1

0

5

ea99451608efa17751b52b49d4d95a08e4bc6d5c

8,638

py

Python

Algorithm.Python/stubs/QuantConnect/Data/__Market_4.py

gaoxiaojun/Lean

9dca43bccb720d0df91e4bfc1d363b71e3a36cb5

[ "Apache-2.0" ]

2

2020-12-08T11:27:20.000Z

2021-04-06T13:21:15.000Z

Algorithm.Python/stubs/QuantConnect/Data/__Market_4.py

gaoxiaojun/Lean

9dca43bccb720d0df91e4bfc1d363b71e3a36cb5

[ "Apache-2.0" ]

null

Algorithm.Python/stubs/QuantConnect/Data/__Market_4.py

gaoxiaojun/Lean

9dca43bccb720d0df91e4bfc1d363b71e3a36cb5

[ "Apache-2.0" ]

1

2020-12-08T11:27:21.000Z

import typing import System.IO import System.Collections.Generic import System import QuantConnect.Orders import QuantConnect.Data.Market import QuantConnect.Data import QuantConnect import datetime class TradeBar(QuantConnect.Data.BaseData, QuantConnect.Data.Market.IBar, QuantConnect.Data.Market.IBaseDataBar, QuantConnect.Data.IBaseData): """ TradeBar class for second and minute resolution data: An OHLC implementation of the QuantConnect BaseData class with parameters for candles. TradeBar() TradeBar(original: TradeBar) TradeBar(time: DateTime, symbol: Symbol, open: Decimal, high: Decimal, low: Decimal, close: Decimal, volume: Decimal, period: Nullable[TimeSpan]) """ @typing.overload def Clone(self, fillForward: bool) -> QuantConnect.Data.BaseData: pass @typing.overload def Clone(self) -> QuantConnect.Data.BaseData: pass def Clone(self, *args) -> QuantConnect.Data.BaseData: pass @typing.overload def GetSource(self, config: QuantConnect.Data.SubscriptionDataConfig, date: datetime.datetime, isLiveMode: bool) -> QuantConnect.Data.SubscriptionDataSource: pass @typing.overload def GetSource(self, config: QuantConnect.Data.SubscriptionDataConfig, date: datetime.datetime, datafeed: QuantConnect.DataFeedEndpoint) -> str: pass def GetSource(self, *args) -> str: pass @staticmethod def Parse(config: QuantConnect.Data.SubscriptionDataConfig, line: str, baseDate: datetime.datetime) -> QuantConnect.Data.Market.TradeBar: pass @staticmethod @typing.overload def ParseCfd(config: QuantConnect.Data.SubscriptionDataConfig, line: str, date: datetime.datetime) -> QuantConnect.Data.Market.T: pass @staticmethod @typing.overload def ParseCfd(config: QuantConnect.Data.SubscriptionDataConfig, line: str, date: datetime.datetime) -> QuantConnect.Data.Market.TradeBar: pass @staticmethod @typing.overload def ParseCfd(config: QuantConnect.Data.SubscriptionDataConfig, streamReader: System.IO.StreamReader, date: datetime.datetime) -> QuantConnect.Data.Market.TradeBar: pass def ParseCfd(self, *args) -> QuantConnect.Data.Market.TradeBar: pass @staticmethod @typing.overload def ParseCrypto(config: QuantConnect.Data.SubscriptionDataConfig, line: str, date: datetime.datetime) -> QuantConnect.Data.Market.T: pass @staticmethod @typing.overload def ParseCrypto(config: QuantConnect.Data.SubscriptionDataConfig, line: str, date: datetime.datetime) -> QuantConnect.Data.Market.TradeBar: pass @staticmethod @typing.overload def ParseCrypto(config: QuantConnect.Data.SubscriptionDataConfig, streamReader: System.IO.StreamReader, date: datetime.datetime) -> QuantConnect.Data.Market.TradeBar: pass def ParseCrypto(self, *args) -> QuantConnect.Data.Market.TradeBar: pass @staticmethod @typing.overload def ParseEquity(config: QuantConnect.Data.SubscriptionDataConfig, line: str, date: datetime.datetime) -> QuantConnect.Data.Market.T: pass @staticmethod @typing.overload def ParseEquity(config: QuantConnect.Data.SubscriptionDataConfig, streamReader: System.IO.StreamReader, date: datetime.datetime) -> QuantConnect.Data.Market.TradeBar: pass @staticmethod @typing.overload def ParseEquity(config: QuantConnect.Data.SubscriptionDataConfig, line: str, date: datetime.datetime) -> QuantConnect.Data.Market.TradeBar: pass def ParseEquity(self, *args) -> QuantConnect.Data.Market.TradeBar: pass @staticmethod @typing.overload def ParseForex(config: QuantConnect.Data.SubscriptionDataConfig, line: str, date: datetime.datetime) -> QuantConnect.Data.Market.T: pass @staticmethod @typing.overload def ParseForex(config: QuantConnect.Data.SubscriptionDataConfig, line: str, date: datetime.datetime) -> QuantConnect.Data.Market.TradeBar: pass @staticmethod @typing.overload def ParseForex(config: QuantConnect.Data.SubscriptionDataConfig, streamReader: System.IO.StreamReader, date: datetime.datetime) -> QuantConnect.Data.Market.TradeBar: pass def ParseForex(self, *args) -> QuantConnect.Data.Market.TradeBar: pass @staticmethod @typing.overload def ParseFuture(config: QuantConnect.Data.SubscriptionDataConfig, streamReader: System.IO.StreamReader, date: datetime.datetime) -> QuantConnect.Data.Market.T: pass @staticmethod @typing.overload def ParseFuture(config: QuantConnect.Data.SubscriptionDataConfig, line: str, date: datetime.datetime) -> QuantConnect.Data.Market.T: pass @staticmethod @typing.overload def ParseFuture(config: QuantConnect.Data.SubscriptionDataConfig, line: str, date: datetime.datetime) -> QuantConnect.Data.Market.TradeBar: pass @staticmethod @typing.overload def ParseFuture(config: QuantConnect.Data.SubscriptionDataConfig, streamReader: System.IO.StreamReader, date: datetime.datetime) -> QuantConnect.Data.Market.TradeBar: pass def ParseFuture(self, *args) -> QuantConnect.Data.Market.TradeBar: pass @staticmethod @typing.overload def ParseOption(config: QuantConnect.Data.SubscriptionDataConfig, line: str, date: datetime.datetime) -> QuantConnect.Data.Market.T: pass @staticmethod @typing.overload def ParseOption(config: QuantConnect.Data.SubscriptionDataConfig, streamReader: System.IO.StreamReader, date: datetime.datetime) -> QuantConnect.Data.Market.T: pass @staticmethod @typing.overload def ParseOption(config: QuantConnect.Data.SubscriptionDataConfig, line: str, date: datetime.datetime) -> QuantConnect.Data.Market.TradeBar: pass @staticmethod @typing.overload def ParseOption(config: QuantConnect.Data.SubscriptionDataConfig, streamReader: System.IO.StreamReader, date: datetime.datetime) -> QuantConnect.Data.Market.TradeBar: pass def ParseOption(self, *args) -> QuantConnect.Data.Market.TradeBar: pass @typing.overload def Reader(self, config: QuantConnect.Data.SubscriptionDataConfig, line: str, date: datetime.datetime, isLiveMode: bool) -> QuantConnect.Data.BaseData: pass @typing.overload def Reader(self, config: QuantConnect.Data.SubscriptionDataConfig, stream: System.IO.StreamReader, date: datetime.datetime, isLiveMode: bool) -> QuantConnect.Data.BaseData: pass @typing.overload def Reader(self, config: QuantConnect.Data.SubscriptionDataConfig, line: str, date: datetime.datetime, datafeed: QuantConnect.DataFeedEndpoint) -> QuantConnect.Data.BaseData: pass def Reader(self, *args) -> QuantConnect.Data.BaseData: pass def ToString(self) -> str: pass def Update(self, lastTrade: float, bidPrice: float, askPrice: float, volume: float, bidSize: float, askSize: float) -> None: pass @typing.overload def __init__(self) -> QuantConnect.Data.Market.TradeBar: pass @typing.overload def __init__(self, original: QuantConnect.Data.Market.TradeBar) -> QuantConnect.Data.Market.TradeBar: pass @typing.overload def __init__(self, time: datetime.datetime, symbol: QuantConnect.Symbol, open: float, high: float, low: float, close: float, volume: float, period: typing.Optional[datetime.timedelta]) -> QuantConnect.Data.Market.TradeBar: pass def __init__(self, *args) -> QuantConnect.Data.Market.TradeBar: pass Close: float EndTime: datetime.datetime High: float Low: float Open: float Period: datetime.timedelta Volume: float class TradeBars(QuantConnect.Data.Market.DataDictionary[TradeBar], System.Collections.IEnumerable, QuantConnect.Interfaces.IExtendedDictionary[Symbol, TradeBar], System.Collections.Generic.ICollection[KeyValuePair[Symbol, TradeBar]], System.Collections.Generic.IDictionary[Symbol, TradeBar], System.Collections.Generic.IEnumerable[KeyValuePair[Symbol, TradeBar]]): """ Collection of TradeBars to create a data type for generic data handler: TradeBars() TradeBars(frontier: DateTime) """ @typing.overload def __init__(self) -> QuantConnect.Data.Market.TradeBars: pass @typing.overload def __init__(self, frontier: datetime.datetime) -> QuantConnect.Data.Market.TradeBars: pass def __init__(self, *args) -> QuantConnect.Data.Market.TradeBars: pass Item: indexer#

36.447257

364

0.726904

878

8,638

7.11959

0.117312

0.194529

0.137258

0.183011

0.774116

0.728843

0.68885

0.677012

0.639738

0.639258

0

0.173767

8,638

236

365

36.601695

0.875858

0.052558

0

0.603659

0

1

0.280488

false

0.280488

0.054878

0

0.396341

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

5

eaa0c1fac79cea8fccb2df9a92483486aa3ac54a

9,056

py

Python

plot.py

polikutinevgeny/FrontsCNN

a9f48d5afcdd7e0fe561840d94af36c0fedf1c15

[ "MIT" ]

1

2019-12-28T08:40:44.000Z

plot.py

polikutinevgeny/FrontsCNN

a9f48d5afcdd7e0fe561840d94af36c0fedf1c15

[ "MIT" ]

null

plot.py

polikutinevgeny/FrontsCNN

a9f48d5afcdd7e0fe561840d94af36c0fedf1c15

[ "MIT" ]

null

import matplotlib.colors import numpy as np import xarray as xr from cartopy import crs as ccrs from matplotlib import pyplot as plt import matplotlib.patches as mpatches from confusion_matrix import plot_confusion_matrix from crop import crop_center, crop_2d def plot_results(x, y_true, y_pred, name, in_size, date, bw=False, binary=False): proj = ccrs.LambertConformal( central_latitude=50, central_longitude=-107, false_easting=5632642.22547, false_northing=4612545.65137, standard_parallels=(50, 50), cutoff=-30 ) f = plt.figure(figsize=(16, 8)) f.suptitle("Fronts at {}".format(date), fontsize=16) ax = plt.subplot(1, 2, 1, projection=proj) ax.set_title("Prediction") plot_fronts(x, y_pred, proj, ax, in_size, bw, binary) ax = plt.subplot(1, 2, 2, projection=proj) ax.set_title("Ground truth") plot_fronts(x, y_true, proj, ax, in_size, bw, binary) plt.savefig(name) plt.close(f) def plot_fronts(x, y, proj, ax, in_size, bw=False, binary=False): with xr.open_dataset("/mnt/ldm_vol_DESKTOP-DSIGH25-Dg0_Volume1/DiplomData2/NARR/air.2m.nc") as example: lat = crop_center(crop_2d(example.lat.values), in_size) lon = crop_center(crop_2d(example.lon.values), in_size) lon = (lon + 220) % 360 - 180 # Shift due to problems with crossing dateline in cartopy shift = ccrs.PlateCarree(central_longitude=-40) ax.set_xmargin(0.1) ax.set_ymargin(0.1) ax.set_extent((2.0e+6, 1.039e+07, 6.0e+5, 8959788), crs=proj) if x.ndim == 3: plt.contour(lon, lat, x[..., 1], levels=20, transform=shift, colors='black', linewidths=0.5) if bw: if binary: cmap = matplotlib.colors.ListedColormap([(0, 0, 0, 0), 'black']) plt.pcolormesh(lon, lat, y, cmap=cmap, zorder=10, transform=shift) else: plt.pcolor(lon, lat, np.ma.masked_not_equal(y, 1), hatch="||||", alpha=0., transform=shift, zorder=100) plt.pcolor(lon, lat, np.ma.masked_not_equal(y, 2), hatch="----", alpha=0., transform=shift, zorder=100) plt.pcolor(lon, lat, np.ma.masked_not_equal(y, 3), hatch="oooo", alpha=0., transform=shift, zorder=100) plt.pcolor(lon, lat, np.ma.masked_not_equal(y, 4), hatch="++++", alpha=0., transform=shift, zorder=100) hot = mpatches.Patch(facecolor='white', label='Тёплый фронт', hatch="||||", alpha=1) cold = mpatches.Patch(facecolor='white', label='Холодный фронт', hatch="----", alpha=1) stat = mpatches.Patch(facecolor='white', label='Стационарный фронт', hatch="oooo", alpha=1) occl = mpatches.Patch(facecolor='white', label='Фронт окклюзии', hatch="++++", alpha=1) ax.legend(handles=[hot, cold, stat, occl], loc='upper center', bbox_to_anchor=(0.5, -0.05), ncol=2, prop={'size': 12}) else: if x.ndim == 3: plt.contourf(lon, lat, x[..., 0], levels=20, transform=shift) else: plt.contourf(lon, lat, x, levels=20, transform=shift) if binary: cmap = matplotlib.colors.ListedColormap([(0, 0, 0, 0), 'black']) plt.pcolormesh(lon, lat, y, cmap=cmap, zorder=10, transform=shift) else: cmap = matplotlib.colors.ListedColormap([(0, 0, 0, 0), 'red', 'blue', 'green', 'purple']) plt.pcolormesh(lon, lat, y, cmap=cmap, zorder=10, transform=shift) hot = mpatches.Patch(facecolor='red', label='Тёплый фронт', alpha=1) cold = mpatches.Patch(facecolor='blue', label='Холодный фронт', alpha=1) stat = mpatches.Patch(facecolor='green', label='Стационарный фронт', alpha=1) occl = mpatches.Patch(facecolor='purple', label='Фронт окклюзии', alpha=1) ax.legend(handles=[hot, cold, stat, occl], loc='upper center', bbox_to_anchor=(0.5, -0.05), ncol=2, prop={'size': 12}) ax.coastlines() ax.gridlines(draw_labels=True) # hot = mpatches.Patch(facecolor='red', label='Тёплый фронт', alpha=1) # cold = mpatches.Patch(facecolor='blue', label='Холодный фронт', alpha=1) # stat = mpatches.Patch(facecolor='green', label='Стационарный фронт', alpha=1) # occl = mpatches.Patch(facecolor='purple', label='Фронт окклюзии', alpha=1) # ax.legend(handles=[hot, cold, stat, occl], loc='upper center', bbox_to_anchor=(0.5, -0.05), ncol=2, # prop={'size': 12}) def plot_fronts_far_east(x, y, name, onehot, in_size, date, bw=False): proj = ccrs.LambertConformal( central_latitude=50, central_longitude=130, false_easting=5632642.22547, false_northing=4612545.65137, standard_parallels=(50, 50), cutoff=-30 ) f = plt.figure(figsize=(8, 8)) f.suptitle("Fronts at {}".format(date), fontsize=16) ax = plt.subplot(1, 1, 1, projection=proj) y = np.argmax(y, axis=-1) if onehot else y with xr.open_dataset("/mnt/ldm_vol_DESKTOP-DSIGH25-Dg0_Volume1/DiplomData2/NARR/air.2m.nc") as example: lat = crop_center(crop_2d(example.lat.values), in_size) lon = crop_center(crop_2d(example.lon.values), in_size) # Steal lat/lon from NARR lon = ((lon + 220) % 360 - 180 + 237) % 360 # Shift due to problems with crossing dateline in cartopy shift = ccrs.PlateCarree(central_longitude=-40) ax.set_xmargin(0.1) ax.set_ymargin(0.1) ax.set_extent((2.0e+6, 1.039e+07, 6.0e+5, 8959788), crs=proj) plt.contour(lon, lat, x[..., 1], levels=20, transform=shift, colors='black', linewidths=0.5) if bw: plt.pcolor(lon, lat, np.ma.masked_not_equal(y, 1), hatch="||||", alpha=0., transform=shift, zorder=100) plt.pcolor(lon, lat, np.ma.masked_not_equal(y, 2), hatch="----", alpha=0., transform=shift, zorder=100) plt.pcolor(lon, lat, np.ma.masked_not_equal(y, 3), hatch="oooo", alpha=0., transform=shift, zorder=100) plt.pcolor(lon, lat, np.ma.masked_not_equal(y, 4), hatch="++++", alpha=0., transform=shift, zorder=100) hot = mpatches.Patch(facecolor='white', label='Тёплый фронт', hatch="||||", alpha=1) cold = mpatches.Patch(facecolor='white', label='Холодный фронт', hatch="----", alpha=1) stat = mpatches.Patch(facecolor='white', label='Стационарный фронт', hatch="oooo", alpha=1) occl = mpatches.Patch(facecolor='white', label='Фронт окклюзии', hatch="++++", alpha=1) ax.legend(handles=[hot, cold, stat, occl], loc='upper center', bbox_to_anchor=(0.5, -0.05), ncol=2, prop={'size': 12}) else: plt.contourf(lon, lat, x[..., 0], levels=20, transform=shift) cmap = matplotlib.colors.ListedColormap([(0, 0, 0, 0), 'red', 'blue', 'green', 'purple']) plt.pcolormesh(lon, lat, y, cmap=cmap, zorder=10, transform=shift) hot = mpatches.Patch(facecolor='red', label='Тёплый фронт', alpha=1) cold = mpatches.Patch(facecolor='blue', label='Холодный фронт', alpha=1) stat = mpatches.Patch(facecolor='green', label='Стационарный фронт', alpha=1) occl = mpatches.Patch(facecolor='purple', label='Фронт окклюзии', alpha=1) ax.legend(handles=[hot, cold, stat, occl], loc='upper center', bbox_to_anchor=(0.5, -0.05), ncol=2, prop={'size': 12}) ax.coastlines() ax.gridlines(draw_labels=True) plt.savefig(name) plt.close(f) def plot_conf_matrix(y_true, y_pred, filename, binary=False, normalize=True, title=None, cmap='Greys'): if binary: plot_confusion_matrix(y_true, y_pred, ["Нет фронта", "Фронт"], normalize=normalize, title=title, cmap=cmap) else: plot_confusion_matrix(y_true, y_pred, ["Нет фронта", "Тёплый", "Холодный", "Стационарный", "Окклюзии"], normalize=normalize, title=title, cmap=cmap) plt.savefig(filename) plt.close() def plot_sample(dataset, model, prefix, in_size, binary=False): x, y_true = dataset[0] dates = dataset.get_dates(0) y_pred = model.predict(x) if binary: y_true = y_true[..., 0] for i in range(x.shape[0]): plot_results(x[i], y_true[i], y_pred[i], "{}/{}".format(prefix, i), in_size, dates[i]) def plot_filtered(dataset, model, in_size, prefix, filter_func, binary=False): for m, i in zip(dataset, range(len(dataset))): x, y = m r = model.evaluate(x, y, verbose=0) d = dataset.get_dates(i)[0] if filter_func(r[1]): pred = model.predict(x) if binary: y[0] = y[0, ..., 0] plot_results(x[0], y[0], pred[0], "{2}/{0}_{1:.2f}.png".format(i, r[1], prefix), in_size, d) def plot_metrics_histogram(dataset, model, prefix): d = [[] for _ in model.keras_model.metrics_names] for (x, y) in dataset: r = model.evaluate(x, y, verbose=0) for i, j in zip(d, r): i.append(j) for n, i in zip(model.metrics_names, d): plt.hist(i, bins=100) plt.title(n) plt.savefig("{}/{}".format(prefix, n)) plt.close()

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577161a144932ec56a755db8e42f348a74940472

123

py

Python

src/Biblioteca/members/admin.py

ElDwarf/Biblioteca-manager-demo

109476c8bf9ee3861857b4e9fe4965fb5321d609

[ "MIT" ]

null

src/Biblioteca/members/admin.py

ElDwarf/Biblioteca-manager-demo

109476c8bf9ee3861857b4e9fe4965fb5321d609

[ "MIT" ]

null

src/Biblioteca/members/admin.py

ElDwarf/Biblioteca-manager-demo

109476c8bf9ee3861857b4e9fe4965fb5321d609

[ "MIT" ]

1

2022-01-17T19:23:55.000Z

from django.contrib import admin from .models import Member, Loan admin.site.register(Member) admin.site.register(Loan)

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165

py

Python

service/front_controller.py

yutiansut/cilantro

3fa579999e7d5a6d6041ccc7e309c667fc7eac90

[ "Apache-2.0" ]

3

2019-09-04T12:40:33.000Z

2021-12-28T16:33:27.000Z

service/front_controller.py

yutiansut/cilantro

3fa579999e7d5a6d6041ccc7e309c667fc7eac90

[ "Apache-2.0" ]

97

2018-05-29T13:27:04.000Z

2021-11-02T11:03:33.000Z

service/front_controller.py

yutiansut/cilantro

3fa579999e7d5a6d6041ccc7e309c667fc7eac90

[ "Apache-2.0" ]

16

2018-04-25T11:39:21.000Z

2019-12-16T14:37:39.000Z

from flask import Blueprint front_controller = Blueprint('front', __name__) @front_controller.route('/') def index(): return "cilantro is up and running ..."

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57e4546a55d2ff3e287a2f4ab6d0d1ff7834e879

40,724

py

Python

hivwholeseq/store/check_initial_reference.py

iosonofabio/hivwholeseq

d504c63b446c3a0308aad6d6e484ea1666bbe6df

[ "MIT" ]

3

2016-12-01T03:12:06.000Z

2021-07-03T01:29:26.000Z

hivwholeseq/store/check_initial_reference.py

iosonofabio/hivwholeseq

d504c63b446c3a0308aad6d6e484ea1666bbe6df

[ "MIT" ]

null

hivwholeseq/store/check_initial_reference.py

iosonofabio/hivwholeseq

d504c63b446c3a0308aad6d6e484ea1666bbe6df

[ "MIT" ]

3

2016-01-17T03:43:46.000Z

2020-03-25T07:00:11.000Z

# vim: fdm=marker ''' author: Fabio Zanini date: 17/06/14 content: Check whether we need a new initial reference. Reasons might be a new sample sequenced that comes before all current ones, or the current reference has some genes not properly assembled. ''' # Modules import sys import os import numpy as np import argparse from Bio.Seq import Seq from Bio.Alphabet.IUPAC import ambiguous_dna from Bio import SeqIO from hivwholeseq.sequencing.samples import SampleSeq from hivwholeseq.patients.patients import load_patients, load_patient, Patient from hivwholeseq.patients.filenames import get_sample_foldername from hivwholeseq.utils.genome_info import locate_gene from hivwholeseq.data.primers import fragments_genes # Functions def check_similarity_initial_sample(refseq, sample_seq, fragment, VERBOSE=0, maxdiff=10): '''Check whether the reference looks similar to the initial sample''' from seqanpy import align_global (score, ali1, ali2) = align_global(str(refseq.seq), str(sample_seq.seq), band=50) alim = np.zeros((2, len(ali1)), 'S1') alim[0] = np.fromstring(ali1, 'S1') alim[1] = np.fromstring(ali2, 'S1') n_diff = (alim[0] != alim[1]).sum() if VERBOSE >= 2: print fragment+': difference between ref and initial consensus:', n_diff if n_diff > maxdiff: print 'ERROR: '+fragment+', reference is not similar to initial consensus ('+\ str(sample_init_seq.name)+', '+\ str(n_diff)+' differences)' return False elif VERBOSE >=3: print 'OK: reference is similar to initial consensus ('+\ str(sample_init_seq.name)+', '+\ str(n_diff)+' differences)' return True def check_has_complete_codons(gene, genename, VERBOSE=0): '''Check that the length is multiple of 3''' if len(gene) % 3: print 'ERROR: '+genename+' has a length not 3 * X' return False elif VERBOSE >= 3: print 'OK: '+genename+' has a length 3 * X' return True def check_start_aminoacid(prot, genename, VERBOSE=0): '''Check whether the protein starts with an M''' # Pol starts with an F instead of an M from collections import defaultdict start_aa = defaultdict(lambda: 'M') start_aa['pol'] = 'F' if prot[0] != start_aa[genename]: print 'ERROR: '+genename+' does not start with an '+start_aa[genename]+'!' return False elif VERBOSE >= 3: print 'OK: '+genename+' starts with an '+start_aa[genename] return True def check_has_end(prot_ref, genename, VERBOSE=0): '''Check whether it has a stop codon''' if prot_ref[-1] != '*': if VERBOSE >= 1: print 'ERROR: '+genename+' does not end!' return False elif VERBOSE >= 3: print 'OK: '+genename+' ends with a *' return True def check_has_premature_stops(prot_ref, genename, VERBOSE=0): '''Check for premature stop codons''' protm = np.array(prot_ref) ind_star = (protm == '*').nonzero()[0] if (len(ind_star) != 1) or (ind_star[0] != len(protm) - 1): if VERBOSE >= 1: print 'ERROR: '+genename+' has premature stop codons!' return False elif VERBOSE >= 3: print 'OK: '+genename+' has no premature stop codons' return True def check_has_similar_length(len_ref, len_HXB2, genename, VERBOSE=0, maxdiff=30): '''Does the gene have similar length like the HXB2?''' if len_ref < len_HXB2 - maxdiff: print 'ERROR: '+genename+' too short! (ref '+str(len_ref)+', HXB2 '+str(len_HXB2)+')' return False elif len_ref > len_HXB2 + maxdiff: print 'ERROR: '+genename+' too long! (ref '+str(len_ref)+', HXB2 '+str(len_HXB2)+')' return False elif VERBOSE >= 3: print 'OK: '+genename+' has the right length (ref '+str(len_ref)+', HXB2 '+str(len_HXB2)+')' return True def check_has_premature_stops_noend(prot_ref, genename, VERBOSE=0): '''Check for premature stop codons''' protm = np.array(prot_ref) ind_star = (protm == '*').nonzero()[0] if len(ind_star): if VERBOSE >= 1: print 'ERROR: '+genename+' has premature stop codons!' return False elif VERBOSE >= 3: print 'OK: '+genename+' has no premature stop codons' return True def get_fragment_length_HXB2(frag_spec): '''Get the length of a fragment in HXB2''' from hivwholeseq.data.primers import primers_coordinates_HXB2 pr_coord_HXB2 = primers_coordinates_HXB2[frag_spec] len_HXB2 = pr_coord_HXB2[1][0] - pr_coord_HXB2[0][1] return len_HXB2 def get_gene_HXB2(genename): '''Get a gene or exon in HXB2''' from operator import attrgetter from hivwholeseq.reference import load_custom_reference HXB2 = load_custom_reference('HXB2', format='gb') if genename not in ('tat1', 'tat2', 'rev1', 'rev2'): gene_coord = HXB2.features[map(attrgetter('id'), HXB2.features).index(genename)] gene_HXB2 = gene_coord.extract(HXB2) return gene_HXB2 else: exon_n = int(genename[-1]) genename = genename[:-1] gene_coord = HXB2.features[map(attrgetter('id'), HXB2.features).index(genename)] exon_coord = gene_coord.location.parts[exon_n - 1] exon_HXB2 = exon_coord.extract(HXB2) return exon_HXB2 def get_frame(geneseq, gene_HXB2, genename, VERBOSE=0): '''Get the frame by aligning the proteins''' from seqanpy import align_local from Bio.Seq import translate from numpy import argmax geneseq = ''.join(geneseq) gene_HXB2 = ''.join(gene_HXB2) if genename in ('tat1', 'rev1'): gene_HXB2 = gene_HXB2[:len(gene_HXB2) - (len(gene_HXB2) % 3)] elif genename in ('tat2', 'rev2'): gene_HXB2 = gene_HXB2[len(gene_HXB2) % 3:] prot_HXB2 = translate(gene_HXB2) scores = [] for frame in xrange(3): tmp = geneseq[frame:] tmp = tmp[:len(tmp) - (len(tmp) % 3)] tmp = translate(tmp) (score, ali1, ali2) = align_local(prot_HXB2, tmp) scores.append(score) return argmax(scores) def check_length_fragment(refseq, frag_spec, VERBOSE=0, tolerance=50): '''Check the length of the fragment compared to HXB2''' fragment = frag_spec[:2] len_HXB2 = get_fragment_length_HXB2(frag_spec) if len(refseq) < len_HXB2 - tolerance: print 'ERROR: '+fragment+' too short! ('+str(len(refseq))+' vs '+str(len_HXB2)+' in HXB2)' return False elif len(refseq) > len_HXB2 + tolerance: print 'ERROR: '+fragment+' too long! ('+str(len(refseq))+' vs '+str(len_HXB2)+' in HXB2)' return False elif VERBOSE >= 3: print 'OK: '+fragment+' has approximately the right length ('+str(len(refseq))+' vs '+str(len_HXB2)+' in HXB2)' return True def check_F1(refseq, spec, VERBOSE=0): '''Check fragment F1: gag, pol''' check = check_length_fragment(refseq, 'F1'+spec, VERBOSE=VERBOSE, tolerance=50) if not check: return False # Check gag (should be complete) genename = 'gag' (start, end, start_found, end_found) = locate_gene(refseq, genename, VERBOSE=VERBOSE) if (not start_found) or (not end_found): print 'ERROR: '+genename+' not found in F1!' return False elif VERBOSE >= 3: print 'OK: start and end of '+genename+' found' gene_HXB2 = get_gene_HXB2(genename) check = check_has_similar_length(end - start, len(gene_HXB2), genename, VERBOSE=VERBOSE, maxdiff=30) if not check: return False geneseq = refseq[start: end] gene = geneseq.seq check = check_has_complete_codons(gene, genename, VERBOSE=VERBOSE) if not check: return False prot = gene.translate() check = check_start_aminoacid(prot, genename, VERBOSE=VERBOSE) if not check: return False check = check_has_end(prot, genename, VERBOSE=VERBOSE) if not check: return False check = check_has_premature_stops(prot, genename, VERBOSE=VERBOSE) if not check: return False # Check pol (there should be the start) genename = 'pol' (start, end, start_found, end_found) = locate_gene(refseq, genename, VERBOSE=VERBOSE) if (not start_found): print 'ERROR: start of '+genename+' not found in F1!' return False elif VERBOSE >= 3: print 'OK: start of '+genename+' found' geneseq = refseq[start:] geneseq = geneseq[:len(geneseq) - len(geneseq) % 3] gene = geneseq.seq prot = gene.translate() check = check_start_aminoacid(prot, genename, VERBOSE=VERBOSE) if not check: return False check = check_has_premature_stops_noend(prot, genename, VERBOSE=VERBOSE) if not check: return False return True def check_F2(refseq, spec, VERBOSE=0): '''Check fragment F2: gag, pol''' check = check_length_fragment(refseq, 'F2'+spec, VERBOSE=VERBOSE, tolerance=80) if not check: return False # Check gag (there should be end) genename = 'gag' (start, end, start_found, end_found) = locate_gene(refseq, genename, VERBOSE=VERBOSE) if (not end_found): print 'ERROR: end of '+genename+' not found in F2!' return False elif VERBOSE >= 3: print 'OK: end of '+genename+' found' geneseq = refseq[:end] geneseq = geneseq[len(geneseq) % 3:] gene = geneseq.seq prot = gene.translate() check = check_has_end(prot, 'gag', VERBOSE=VERBOSE) if not check: return False check = check_has_premature_stops(prot, 'gag', VERBOSE=VERBOSE) if not check: return False # Check pol (there should be the start) genename = 'pol' (start, end, start_found, end_found) = locate_gene(refseq, genename, VERBOSE=VERBOSE) if (not start_found): print 'ERROR: start of '+genename+' not found in F2!' return False elif VERBOSE >= 3: print 'OK: start of '+genename+' found' geneseq = refseq[start:] geneseq = geneseq[:len(geneseq) - len(geneseq) % 3] gene = geneseq.seq prot = gene.translate() check = check_start_aminoacid(prot, genename, VERBOSE=VERBOSE) if not check: return False check = check_has_premature_stops_noend(prot, genename, VERBOSE=VERBOSE) if not check: return False return True def check_F3(refseq, spec, VERBOSE=0): '''Check fragment F3: end of pol''' check = check_length_fragment(refseq, 'F3'+spec, VERBOSE=VERBOSE, tolerance=50) if not check: return False # Check pol: this depends on the spec: for F3bo there should be the end, # anything else has only the middle (it's all pol!) genename = 'pol' if spec == 'bo': (start, end, start_found, end_found) = locate_gene(refseq, genename, VERBOSE=VERBOSE) if (not end_found): print 'ERROR: end of '+genename+' not found in F3!' return False elif VERBOSE >= 3: print 'OK: end of '+genename+' found' geneseq = refseq[:end] geneseq = geneseq[len(geneseq) % 3:] gene = geneseq.seq prot = gene.translate() check = check_has_end(prot, genename, VERBOSE=VERBOSE) if not check: return False check = check_has_premature_stops(prot, genename, VERBOSE=VERBOSE) if not check: return False else: # Try all 3 reading frames for offset in xrange(3): geneseq = refseq[offset:] geneseq = geneseq[: len(geneseq) - (len(geneseq) % 3)] gene = geneseq.seq prot = gene.translate() check = check_has_premature_stops_noend(prot, genename, VERBOSE=0) if check: if VERBOSE >= 3: print 'OK: '+genename+' has no premature stop codons' break else: if VERBOSE >= 1: print 'ERROR: '+genename+' has premature stop codons in all reading frames!' return False return True def check_F4(refseq, spec, VERBOSE=0): '''Check fragment F4: pol, vif, vpr, vpu, tat1, rev1, env''' check = check_length_fragment(refseq, 'F4'+spec, VERBOSE=VERBOSE, tolerance=50) if not check: return False # Check pol (there should be end) genename = 'pol' (start, end, start_found, end_found) = locate_gene(refseq, genename, VERBOSE=VERBOSE) if (not end_found): print 'ERROR: end of '+genename+' not found in F4!' return False elif VERBOSE >= 3: print 'OK: end of '+genename+' found' geneseq = refseq[:end] gene_HXB2 = get_gene_HXB2(genename) frame = get_frame(geneseq, gene_HXB2, genename) geneseq = geneseq[frame:] geneseq = geneseq[:len(geneseq) - (len(geneseq) % 3)] gene = geneseq.seq prot = gene.translate() check = check_has_end(prot, genename, VERBOSE=VERBOSE) # it can end a bit early or late if not check: gene_new = refseq.seq[frame:] gene_new = gene_new[:len(gene_new) - (len(gene_new) % 3)] prot_new = gene_new.translate() end_new = prot_new.find('*') end_diff = (frame + 3 * end_new) - end if 0 < end_diff < 200: print genename.upper()+' ENDS '+str(end_new - len(prot) + 1)+' AMINO ACIDS DOWNSTREAM!' prot = prot_new[:end_new + 1] elif -200 < end_diff < 0: print genename.upper()+' ENDS '+str(len(prot) - 1 - end_new)+' AMINO ACIDS UPSTREAM!' prot = prot_new[:end_new + 1] else: return False check = check_has_premature_stops(prot, genename, VERBOSE=VERBOSE) if not check: print prot return False # Check env (there should be the start) genename = 'env' (start, end, start_found, end_found) = locate_gene(refseq, genename, VERBOSE=VERBOSE) if (not start_found): print 'ERROR: start of '+genename+' not found in F4!' return False elif VERBOSE >= 3: print 'OK: start of '+genename+' found' geneseq = refseq[start:] geneseq = geneseq[:len(geneseq) - len(geneseq) % 3] gene = geneseq.seq prot = gene.translate() check = check_start_aminoacid(prot, genename, VERBOSE=VERBOSE) if not check: return False check = check_has_premature_stops_noend(prot, genename, VERBOSE=VERBOSE) if not check: return False # Check vif (should be complete) genename = 'vif' (start, end, start_found, end_found) = locate_gene(refseq, genename, VERBOSE=VERBOSE) if (not start_found) or (not end_found): print 'ERROR: '+genename+' not found in F4!' return False elif VERBOSE >= 3: print 'OK: start and end of '+genename+' found' gene_HXB2 = get_gene_HXB2(genename) check = check_has_similar_length(end - start, len(gene_HXB2), genename, VERBOSE=VERBOSE, maxdiff=15) if not check: return False geneseq = refseq[start: end] gene = geneseq.seq check = check_has_complete_codons(gene, genename, VERBOSE=VERBOSE) if not check: return False prot = gene.translate() check = check_start_aminoacid(prot, genename, VERBOSE=VERBOSE) if not check: return False check = check_has_end(prot, genename, VERBOSE=0) if check: if VERBOSE >= 3: print 'OK: '+genename+' ends with a *' else: # Vif tends to be a bit longer than in HXB2 for nc in xrange(1, 4): gene_ext = refseq[start: end + 3 * nc].seq prot_ext = gene_ext.translate() check = check_has_end(prot_ext, genename, VERBOSE=0) if check: gene = gene_ext prot = prot_ext if VERBOSE: print 'WARNING: '+genename+' actually ends '+str(nc)+' codons downstream' break else: print 'ERROR: '+genename+' does not end, not even slightly downstream' return False check = check_has_premature_stops(prot, genename, VERBOSE=VERBOSE) if not check: return False # Check vpu (should be complete) genename = 'vpu' (start, end, start_found, end_found) = locate_gene(refseq, genename, VERBOSE=VERBOSE) if (not start_found) or (not end_found): print 'ERROR: '+genename+' not found in F4!' return False elif VERBOSE >= 3: print 'OK: start and end of '+genename+' found' gene_HXB2 = get_gene_HXB2(genename) check = check_has_similar_length(end - start, len(gene_HXB2), genename, VERBOSE=VERBOSE, maxdiff=15) if not check: return False geneseq = refseq[start: end] gene = geneseq.seq check = check_has_complete_codons(gene, genename, VERBOSE=VERBOSE) if not check: return False prot = gene.translate() check = check_start_aminoacid(prot, genename, VERBOSE=VERBOSE) if not check: print 'ERROR IN VPU STARTING CODON, CONTINUING!' #return False check = check_has_end(prot, genename, VERBOSE=VERBOSE) if not check: return False check = check_has_premature_stops(prot, genename, VERBOSE=VERBOSE) if not check: return False # Check vpr (should be complete) genename = 'vpr' (start, end, start_found, end_found) = locate_gene(refseq, genename, VERBOSE=VERBOSE) if (not start_found) or (not end_found): print 'ERROR: '+genename+' not found in F4!' return False elif VERBOSE >= 3: print 'OK: start and end of '+genename+' found' gene_HXB2 = get_gene_HXB2(genename) check = check_has_similar_length(end - start, len(gene_HXB2), genename, VERBOSE=VERBOSE, maxdiff=15) if not check: return False geneseq = refseq[start: end] gene = geneseq.seq check = check_has_complete_codons(gene, genename, VERBOSE=VERBOSE) if not check: return False prot = gene.translate() check = check_start_aminoacid(prot, genename, VERBOSE=VERBOSE) if not check: return False check = check_has_end(prot, genename, VERBOSE=VERBOSE) if not check: return False check = check_has_premature_stops(prot, genename, VERBOSE=VERBOSE) if not check: return False # Check tat1 (first exon of tat, should be complete) genename = 'tat1' (start, end, start_found, end_found) = locate_gene(refseq, genename, VERBOSE=VERBOSE) if (not start_found) or (not end_found): print 'ERROR: '+genename+' not found in F4!' return False elif VERBOSE >= 3: print 'OK: start and end of '+genename+' found' gene_HXB2 = get_gene_HXB2(genename) check = check_has_similar_length(end - start, len(gene_HXB2), genename, VERBOSE=VERBOSE, maxdiff=35) if not check: return False geneseq = refseq[start: end] geneseq = geneseq[:len(geneseq) - len(geneseq) % 3] gene = geneseq.seq prot = gene.translate() check = check_start_aminoacid(prot, genename, VERBOSE=VERBOSE) if not check: return False check = check_has_premature_stops_noend(prot, genename, VERBOSE=VERBOSE) if not check: return False # Check rev1 (first exon of rev, should be complete) genename = 'rev1' (start, end, start_found, end_found) = locate_gene(refseq, genename, VERBOSE=VERBOSE) if (not start_found) or (not end_found): print 'ERROR: '+genename+' not found in F4!' return False elif VERBOSE >= 3: print 'OK: start and end of '+genename+' found' gene_HXB2 = get_gene_HXB2(genename) check = check_has_similar_length(end - start, len(gene_HXB2), genename, VERBOSE=VERBOSE, maxdiff=15) if not check: return False geneseq = refseq[start: end] geneseq = geneseq[:len(geneseq) - len(geneseq) % 3] gene = geneseq.seq prot = gene.translate() check = check_start_aminoacid(prot, genename, VERBOSE=VERBOSE) if not check: return False check = check_has_premature_stops_noend(prot, genename, VERBOSE=VERBOSE) if not check: return False return True def check_F5(refseq, spec, VERBOSE=0): '''Check fragment F5: env''' if spec == 'a+bo': spec_inner = 'bo' else: spec_inner = spec check = check_length_fragment(refseq, 'F5'+spec_inner, VERBOSE=VERBOSE, tolerance=70) if not check: return False # Check env (there should be the start) genename = 'env' (start, end, start_found, end_found) = locate_gene(refseq, genename, VERBOSE=VERBOSE) if (not start_found): print 'ERROR: start of '+genename+' not found in F5!' return False elif VERBOSE >= 3: print 'OK: start of '+genename+' found' geneseq = refseq[start:] geneseq = geneseq[:len(geneseq) - len(geneseq) % 3] gene = geneseq.seq prot = gene.translate() check = check_start_aminoacid(prot, genename, VERBOSE=VERBOSE) if not check: return False check = check_has_premature_stops_noend(prot, genename, VERBOSE=VERBOSE) if not check: return False # Check vpu (should be complete in F5ao) if spec_inner == 'ao': genename = 'vpu' (start, end, start_found, end_found) = locate_gene(refseq, genename, VERBOSE=VERBOSE) if (not start_found) or (not end_found): print 'ERROR: '+genename+' not found in F4!' return False elif VERBOSE >= 3: print 'OK: start and end of '+genename+' found' gene_HXB2 = get_gene_HXB2(genename) check = check_has_similar_length(end - start, len(gene_HXB2), genename, VERBOSE=VERBOSE, maxdiff=15) if not check: return False geneseq = refseq[start: end] gene = geneseq.seq check = check_has_complete_codons(gene, genename, VERBOSE=VERBOSE) if not check: return False prot = gene.translate() check = check_start_aminoacid(prot, genename, VERBOSE=VERBOSE) if not check: print 'ERROR IN VPU STARTING CODON, CONTINUING!' #return False check = check_has_end(prot, genename, VERBOSE=VERBOSE) if not check: return False check = check_has_premature_stops(prot, genename, VERBOSE=VERBOSE) if not check: return False return True def check_F6(refseq, spec, VERBOSE=0): '''Check fragment F6: end of env, tat2, rev2''' check = check_length_fragment(refseq, 'F6'+spec, VERBOSE=VERBOSE, tolerance=50) if not check: return False # Check env (there should be end) genename = 'env' (start, end, start_found, end_found) = locate_gene(refseq, genename, VERBOSE=VERBOSE) if (not end_found): print 'ERROR: end of '+genename+' not found in F6!' return False elif VERBOSE >= 3: print 'OK: end of '+genename+' found' geneseq = refseq[:end] gene_HXB2 = get_gene_HXB2(genename) frame = get_frame(geneseq, gene_HXB2, genename) geneseq = geneseq[frame:] geneseq = geneseq[:len(geneseq) - (len(geneseq) % 3)] gene = geneseq.seq prot = gene.translate() check = check_has_end(prot, genename, VERBOSE=VERBOSE) # env can end a bit early or late if not check: gene_new = refseq.seq[frame:] gene_new = gene_new[:len(gene_new) - (len(gene_new) % 3)] prot_new = gene_new.translate() end_new = prot_new.find('*') end_diff = (frame + 3 * end_new) - end if 0 < end_diff < 200: print 'ENV ENDS '+str(end_new - len(prot) + 1)+' AMINO ACIDS DOWNSTREAM!' prot = prot_new[:end_new + 1] elif -200 < end_diff < 0: print 'ENV ENDS '+str(len(prot) - 1 - end_new)+' AMINO ACIDS UPSTREAM!' prot = prot_new[:end_new + 1] else: return False check = check_has_premature_stops(prot, genename, VERBOSE=VERBOSE) if not check: print prot return False # Check tat2 (second exon of tat, should be complete) genename = 'tat2' (start, end, start_found, end_found) = locate_gene(refseq, genename, VERBOSE=VERBOSE) if (not start_found) or (not end_found): print 'ERROR: '+genename+' not found in F6!' return False elif VERBOSE >= 3: print 'OK: start and end of '+genename+' found' gene_HXB2 = get_gene_HXB2(genename) check = check_has_similar_length(end - start, len(gene_HXB2), genename, VERBOSE=VERBOSE, maxdiff=15) if not check: return False geneseq = refseq[start: end] geneseq = geneseq[len(geneseq) % 3:] gene = geneseq.seq prot = gene.translate() check = check_has_end(prot, genename, VERBOSE=VERBOSE) if not check: return False check = check_has_premature_stops(prot, genename, VERBOSE=VERBOSE) if not check: print 'ERROR IN TAT2 PREMATURE STOPS, CONTINUING!' # Check rev2 (second exon of rev, should be complete) genename = 'rev2' (start, end, start_found, end_found) = locate_gene(refseq, genename, VERBOSE=VERBOSE) if (not start_found) or (not end_found): print 'ERROR: '+genename+' not found in F6!' return False elif VERBOSE >= 3: print 'OK: start and end of '+genename+' found' # NOTE: rev2 overlaps with env gp41 and can have insertions or deletions gene_HXB2 = get_gene_HXB2(genename) check = check_has_similar_length(end - start, len(gene_HXB2), genename, VERBOSE=VERBOSE, maxdiff=45) if not check: return False geneseq = refseq[start: end] geneseq = geneseq[len(geneseq) % 3:] gene = geneseq.seq prot = gene.translate() check = check_has_end(prot, genename, VERBOSE=VERBOSE) if not check: # rev2 can end a bit early end_new = prot.rfind('*') if end_new != -1: if len(prot) - 1 - end_new < 20: print 'REV2 ENDS '+str(len(prot) - end_new - 1)+' AMINO ACIDS UPSTREAM!' prot = prot[:end_new + 1] else: return False else: # rev2 can also end quite a bit late gene_new = refseq.seq[start:] gene_new = gene_new[(end - start) % 3:] gene_new = gene_new[:len(gene_new) - (len(gene_new) % 3)] prot_new = gene_new.translate() end_new = prot_new.find('*') if (start + 3 * end_new) - end < 200: print 'REV2 ENDS '+str(end_new - len(prot) + 1)+' AMINO ACIDS DOWNSTREAM!' prot = prot_new[:end_new + 1] else: return False check = check_has_premature_stops(prot, genename, VERBOSE=VERBOSE) if not check: return False return True def check_genomewide(refseq, VERBOSE=0): '''Check the integrity of all genes in the genomewide consensus''' # Check single-exon genes length_tolerance = {'gag': 30, 'pol': 30, 'env': 70, 'vpr': 15, 'vpu': 15} for genename, tol in length_tolerance.iteritems(): (start, end, start_found, end_found) = locate_gene(refseq, genename, VERBOSE=VERBOSE) if (not start_found) or (not end_found): print 'ERROR: '+genename+' not found in genomewide!' return False elif VERBOSE >= 3: print 'OK: start and end of '+genename+' found' gene_HXB2 = get_gene_HXB2(genename) check = check_has_similar_length(end - start, len(gene_HXB2), genename, VERBOSE=VERBOSE, maxdiff=tol) if not check: return False geneseq = refseq[start: end] gene = geneseq.seq check = check_has_complete_codons(gene, genename, VERBOSE=VERBOSE) if not check: # sometimes the gene ends a few nucleotides upstream, and there is a # frameshift mutation that screws up gene_new = refseq.seq[start:] gene_new = gene_new[:len(gene_new) - (len(gene_new) % 3)] prot_new = gene_new.translate() end_new = prot_new.find('*') end_diff = start + (3 * end_new + 3) - end if -90 < end_diff < 0: print genename.upper()+' ENDS '+str((end - start) // 3 - end_new - 1)+' AMINO ACIDS UPSTREAM!' gene = gene_new[:3 * (end_new + 1)] else: return False prot = gene.translate() check = check_start_aminoacid(prot, genename, VERBOSE=VERBOSE) if (not check): if genename != 'vpu': return False else: print 'ERROR IN VPU STARTING CODON, CONTINUING!' check = check_has_end(prot, genename, VERBOSE=VERBOSE) if not check: # sometimes a gene is a bit longer gene_new = refseq.seq[start:] gene_new = gene_new[:len(gene_new) - (len(gene_new) % 3)] prot_new = gene_new.translate() end_new = prot_new.find('*') end_diff = start + (3 * end_new + 3) - end if -90 < end_diff < 0: print genename.upper()+' ENDS '+str((end - start) // 3 - end_new - 1)+' AMINO ACIDS UPSTREAM!' gene = gene_new[:3 * (end_new + 1)] prot = gene.translate() elif 0 < end_diff < 90: print genename.upper()+' ENDS '+str(end_new + 1 - (end - start) // 3)+' AMINO ACIDS DOWNSTREAM!' gene = gene_new[:3 * (end_new + 1)] prot = gene.translate() else: return False check = check_has_premature_stops(prot, genename, VERBOSE=VERBOSE) if not check: return False # Vif is special because it can be longer than in HXB2 genename = 'vif' (start, end, start_found, end_found) = locate_gene(refseq, genename, VERBOSE=VERBOSE) if (not start_found) or (not end_found): print 'ERROR: '+genename+' not found in genomewide!' return False elif VERBOSE >= 3: print 'OK: start and end of '+genename+' found' gene_HXB2 = get_gene_HXB2(genename) check = check_has_similar_length(end - start, len(gene_HXB2), genename, VERBOSE=VERBOSE, maxdiff=15) if not check: return False geneseq = refseq[start: end] gene = geneseq.seq check = check_has_complete_codons(gene, genename, VERBOSE=VERBOSE) if not check: return False prot = gene.translate() check = check_start_aminoacid(prot, genename, VERBOSE=VERBOSE) if not check: return False check = check_has_end(prot, genename, VERBOSE=0) if not check: # Vif tends to be a bit longer than in HXB2 for nc in xrange(1, 4): gene_ext = refseq[start: end + 3 * nc].seq prot_ext = gene_ext.translate() check = check_has_end(prot_ext, genename, VERBOSE=0) if check: gene = gene_ext prot = prot_ext if VERBOSE: print 'WARNING: '+genename+' actually ends '+str(nc)+' codons downstream' break else: print 'ERROR: '+genename+' does not end, not even slightly downstream' return False check = check_has_premature_stops(prot, genename, VERBOSE=VERBOSE) if not check: return False # Check 2-exon genes for genename_whole in ('tat', 'rev'): genename = genename_whole+'1' (start, end, start_found, end_found) = locate_gene(refseq, genename, VERBOSE=VERBOSE) if (not start_found) or (not end_found): print 'ERROR: '+genename+' not found in genomewide!' return False elif VERBOSE >= 3: print 'OK: start and end of '+genename+' found' gene_HXB2 = get_gene_HXB2(genename) check = check_has_similar_length(end - start, len(gene_HXB2), genename, VERBOSE=VERBOSE, maxdiff=15) if not check: return False geneseq = refseq[start: end] geneseq = geneseq[:len(geneseq) - len(geneseq) % 3] gene = geneseq.seq prot = gene.translate() check = check_start_aminoacid(prot, genename, VERBOSE=VERBOSE) if not check: return False start_exon1 = start end_exon1 = end genename = genename_whole+'2' (start, end, start_found, end_found) = locate_gene(refseq[end_exon1 + 2000:], genename, VERBOSE=VERBOSE) if (not start_found) or (not end_found): print 'ERROR: '+genename+' not found in genomewide!' return False elif VERBOSE >= 3: print 'OK: start and end of '+genename+' found' start += end_exon1 + 2000 end += end_exon1 + 2000 # NOTE: rev2 overlaps with env gp41 and can have insertions or deletions if genename == 'rev2': tol = 45 else: tol = 15 gene_HXB2 = get_gene_HXB2(genename) check = check_has_similar_length(end - start, len(gene_HXB2), genename, VERBOSE=VERBOSE, maxdiff=tol) if not check: return False geneseq = refseq[start: end] frame = get_frame(geneseq, gene_HXB2, genename, VERBOSE=VERBOSE) geneseq = geneseq[frame:] gene = geneseq.seq prot = gene.translate() check = check_has_end(prot, genename, VERBOSE=VERBOSE) if not check: if genename != 'rev2': return False else: # rev2 can end a bit early end_new = prot.rfind('*') if end_new != -1: if len(prot) - 1 - end_new < 20: print 'REV2 ENDS '+str(len(prot) - end_new - 1)+' AMINO ACIDS UPSTREAM!' prot = prot[:end_new + 1] end = start + frame + 3 * (end_new + 1) else: return False else: # rev2 can also end quite a bit late gene_new = refseq.seq[start:] gene_new = gene_new[(end - start) % 3:] gene_new = gene_new[:len(gene_new) - (len(gene_new) % 3)] prot_new = gene_new.translate() end_new = prot_new.find('*') if (start + 3 * end_new) - end < 200: print 'REV2 ENDS '+str(end_new - len(prot) + 1)+' AMINO ACIDS DOWNSTREAM!' prot = prot_new[:end_new + 1] end = start + ((end - start) % 3) + 3 * (end_new + 1) else: return False check = check_has_premature_stops(prot, genename, VERBOSE=VERBOSE) if not check: return False start_exon2 = start end_exon2 = end genename = genename_whole gene_HXB2 = get_gene_HXB2(genename) from Bio.SeqFeature import FeatureLocation gene_loc = FeatureLocation(start_exon1, end_exon1, strand=+1) + \ FeatureLocation(start_exon2, end_exon2, strand=+1) geneseq = gene_loc.extract(refseq) gene = geneseq.seq check = check_has_complete_codons(gene, genename, VERBOSE=VERBOSE) if not check: return False prot = gene.translate() check = check_start_aminoacid(prot, genename, VERBOSE=VERBOSE) if not check: return False check = check_has_end(prot, genename, VERBOSE=VERBOSE) if not check: return False check = check_has_premature_stops(prot, genename, VERBOSE=VERBOSE) if not check: return False return True def check_genes(refseq, frag_spec, VERBOSE=0): '''Check whether a gene is present and intact''' if frag_spec == 'genomewide': return check_genomewide(refseq, VERBOSE=VERBOSE) fragment = frag_spec[:2] spec = frag_spec[2:] if fragment == 'F1': return check_F1(refseq, spec, VERBOSE=VERBOSE) elif fragment == 'F2': return check_F2(refseq, spec, VERBOSE=VERBOSE) elif fragment == 'F3': return check_F3(refseq, spec, VERBOSE=VERBOSE) elif fragment == 'F4': return check_F4(refseq, spec, VERBOSE=VERBOSE) elif fragment == 'F5': return check_F5(refseq, spec, VERBOSE=VERBOSE) elif fragment == 'F6': return check_F6(refseq, spec, VERBOSE=VERBOSE) else: raise ValueError('Fragment '+fragment+' not implemented') # Script if __name__ == '__main__': # Parse input args parser = argparse.ArgumentParser(description='Check patient samples') parser.add_argument('--verbose', type=int, default=0, help='Verbosity level [0-3]') parser.add_argument('--patients', nargs='+', help='Patient to analyze') parser.add_argument('--fragments', nargs='+', help='Fragment to map (e.g. F1 F6)') parser.add_argument('--force', action='store_true', help='Ignore a single bad fragment and move to the next') args = parser.parse_args() VERBOSE = args.verbose pnames = args.patients use_force = args.force fragments = args.fragments if not fragments: fragments = ['F'+str(i) for i in xrange(1, 7)] if VERBOSE >= 3: print 'fragments', fragments patients = load_patients() if pnames is not None: patients = patients.loc[pnames] for pname, patient in patients.iterrows(): patient = Patient(patient) if VERBOSE >= 1: print 'Patient:', patient.name patient.discard_nonsequenced_samples() for fragment in fragments: if VERBOSE >= 1: print fragment # Check whether a reference exists at all ref_fn = patient.get_reference_filename(fragment) if not os.path.isfile(ref_fn): print 'ERROR: reference for fragment', fragment, 'not found!' continue elif VERBOSE >= 3: print 'OK: reference file found' refseq = SeqIO.read(ref_fn, 'fasta') # Check whether the consensus from the first sample is similar to # the reference for i, sample in enumerate(patient.itersamples()): if os.path.isfile(sample.get_consensus_filename(fragment)): sample_init_seq = sample.get_consensus(fragment) if (VERBOSE >= 1) and (i != 0): print 'Consensus from initial sample missing, taking time point', print 'n', i, '(start from zero)' break check = check_similarity_initial_sample(refseq, sample_init_seq, fragment, VERBOSE=VERBOSE) if not check: if not use_force: sys.exit() # Get the specific fragment for this consensus # NOTE: we only recently started hiding the frag spec in the reference name tmp = refseq.name.split('_')[-1] if (len(tmp) >= 3) and (tmp[:2] == fragment): frag_spec = tmp else: if fragment == 'F3': frag_spec = 'F3bo' elif fragment == 'F5': frag_spec = 'F5ao' else: frag_spec = fragment+'o' # Check whether genes are fine check = check_genes(refseq, frag_spec, VERBOSE=VERBOSE) if not check: print 'ERROR in', fragment if use_force: continue else: sys.exit() # Check genomewide if present ref_fn = patient.get_reference_filename('genomewide') if not os.path.isfile(ref_fn): if VERBOSE >= 1: print 'WARNING: genomewide reference not found' continue refseq = SeqIO.read(ref_fn, 'fasta') #n_diff = check_similarity_initial_sample(refseq, sample_init_seq, 'genomewide', # VERBOSE=VERBOSE) #if n_diff > 10: # print 'ERROR: genomewide reference is not similar to initial consensus ('+\ # str(n_diff)+' differences)' # continue if VERBOSE: print 'Genomewide' check = check_genes(refseq, 'genomewide', VERBOSE=VERBOSE) if not check: print 'ERROR in genomewide' sys.exit()

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57e7d31d4fe20d7f923416487cc5d3eae977fd56

314

py

Python

src/tests/system/t347b.py

ArtemovSA/PyMite

a22fbae773b285ccf4993905a46dd396cb762f69

[ "OLDAP-2.6", "Python-2.0" ]

51

2015-03-24T07:53:03.000Z

2021-08-06T12:55:53.000Z

src/tests/system/t347b.py

ArtemovSA/PyMite

a22fbae773b285ccf4993905a46dd396cb762f69

[ "OLDAP-2.6", "Python-2.0" ]

null

src/tests/system/t347b.py

ArtemovSA/PyMite

a22fbae773b285ccf4993905a46dd396cb762f69

[ "OLDAP-2.6", "Python-2.0" ]

15

2015-04-09T14:17:27.000Z

2022-01-26T02:42:47.000Z

def bar1(): """__NATIVE__ PmReturn_t retval = PM_RET_OK; /* If wrong number of args, raise TypeError */ if (NATIVE_GET_NUM_ARGS() != 0) { PM_RAISE(retval, PM_RET_EX_TYPE); return retval; } NATIVE_SET_TOS(PM_NONE); return retval; """ pass def bar2(): return bar1()

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17e297faccad5373cc95f6427eb2f04cde8758c5

106

py

Python

ckanext/harvest/logic/auth/patch.py

alphagov-mirror/ckanext-harvest

be4d134cf2e4d4548c67dc2f61b200948f0f74e0

[ "PostgreSQL" ]

86

2015-01-09T19:21:20.000Z

2022-03-23T07:17:27.000Z

ckanext/harvest/logic/auth/patch.py

alphagov-mirror/ckanext-harvest

be4d134cf2e4d4548c67dc2f61b200948f0f74e0

[ "PostgreSQL" ]

319

2015-01-13T13:40:08.000Z

2022-03-24T12:13:42.000Z

ckanext/harvest/logic/auth/patch.py

alphagov-mirror/ckanext-harvest

be4d134cf2e4d4548c67dc2f61b200948f0f74e0

[ "PostgreSQL" ]

154

2015-01-13T21:06:03.000Z

2022-03-15T12:10:57.000Z

import ckanext.harvest.logic.auth.update as _update harvest_source_patch = _update.harvest_source_update

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17f1ee9790eacea0c15c226b89ab12c8b91778f8

221

py

Python

src/models/__init__.py

LazyEval/housing-prices-firenze

c9e81554d86cbf54dfc12cca9b2d12725a8909a9

[ "MIT" ]

1

2020-10-14T21:16:03.000Z

src/models/__init__.py

LazyEval/housing-prices-firenze

c9e81554d86cbf54dfc12cca9b2d12725a8909a9

[ "MIT" ]

null

src/models/__init__.py

LazyEval/housing-prices-firenze

c9e81554d86cbf54dfc12cca9b2d12725a8909a9

[ "MIT" ]

null

from .preprocessing_utils import CustomEncoder, ColumnSelector from .preprocessing_pipeline import preprocessing_pipeline from .model import Model __all__ = (CustomEncoder, ColumnSelector, preprocessing_pipeline, Model)

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5

aa2b32aceb703db15337d9c799bbc82cb3e8b57f

183

py

Python

samples/modules/module_use_4.py

nakednamor/naked-python

6580afe41c867888a08c5394d32c2bb4c60fa6d0

[ "MIT" ]

null

samples/modules/module_use_4.py

nakednamor/naked-python

6580afe41c867888a08c5394d32c2bb4c60fa6d0

[ "MIT" ]

null

samples/modules/module_use_4.py

nakednamor/naked-python

6580afe41c867888a08c5394d32c2bb4c60fa6d0

[ "MIT" ]

null

# you can import a module and use it with an alias # import module module_name as your_alias import module_definition as module_alias module_alias.method_a() module_alias.method_b()

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