xszheng2020/the_stack_dedup_python_hits_3 · Datasets at Hugging Face

e951acf619bd81394427d8e42721c0b0db717204

1,160

py

Python

Python Part 2 Exercises/Lesson 5 - Lists of Lists.py

ryanzhao2/grade11cs

173b9f50db49368ea2042f6803d6674dd9f185cd

[ "Apache-2.0" ]

null

Python Part 2 Exercises/Lesson 5 - Lists of Lists.py

ryanzhao2/grade11cs

173b9f50db49368ea2042f6803d6674dd9f185cd

[ "Apache-2.0" ]

null

Python Part 2 Exercises/Lesson 5 - Lists of Lists.py

ryanzhao2/grade11cs

173b9f50db49368ea2042f6803d6674dd9f185cd

[ "Apache-2.0" ]

null

""" def generate_list_books(filename): book_list = [] file_in = open(filename, encoding='utf-8', errors='replace') file_in.readline() for line in file_in: line = line.strip().split(",") line[2] = float(line[2]) line[3] = int(line[3]) line[4] = int(line[4]) line[5] = int(line[5]) book_list.append(line) return book_list #QUESTION #1 def print_books(list_of_books): for book in list_of_books: print(f' {book[0][0:30]:<30} by {book[1][0:20]:<20} {str(book[5]):<4} rated {str(book[2])[0:3]}') #QUESTION #2 def print_detailed_book(list_of_books): format = '-' for book in list_of_books: print(f' {book[0]}\n' f' by: {book[1]}\n' f' {book[5]}\n' f' {format * len(book[0])}\n' f' ${book[4]:.2f}\n\n' f' {book[6]}\n' f' rated {book[2]} for {book[3]} reviews\n\n\n') def main(): main_book_list = generate_list_books("amazon_bestseller_books.csv") #print(main_book_list[:10]) print_books(main_book_list) print_detailed_book(main_book_list) main() """

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1

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null

0

1

0

3

e957393d4b094e24e1cea3eeb2c0b3f95ffdaa26

427

py

Python

arc852/file_image_source.py

athenian-robotics/common-robotics-python

a2ede8fb3072cf1baa53672f76081aa6bfde397f

[ "MIT" ]

1

2019-02-20T22:59:59.000Z

arc852/file_image_source.py

athenian-robotics/common-robotics

a2ede8fb3072cf1baa53672f76081aa6bfde397f

[ "MIT" ]

null

arc852/file_image_source.py

athenian-robotics/common-robotics

a2ede8fb3072cf1baa53672f76081aa6bfde397f

[ "MIT" ]

1

2020-05-23T09:08:42.000Z

import cv2 import arc852.cli_args as cli from arc852.generic_image_source import GenericImageSource class FileImageSource(GenericImageSource): args = [cli.filename] def __init__(self, filename): super(FileImageSource, self).__init__() self.__cv2_img = cv2.imread(filename) def start(self): pass def stop(self): pass def get_image(self): return self.__cv2_img

19.409091

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427

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0.080292

0.072993

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null

0

null

0

1

0

1

0

1

0

3

e985b081be6c314c34518c301b0ad505a26b7801

3,136

py

Python

sources/utils/helpers.py

pablintino/Altium-DBlib-source

65e85572f84048a7e7c5a116b429e09ac9a33e82

[ "MIT" ]

1

2021-06-23T20:19:45.000Z

sources/utils/helpers.py

pablintino/Altium-DBlib-source

65e85572f84048a7e7c5a116b429e09ac9a33e82

[ "MIT" ]

null

sources/utils/helpers.py

pablintino/Altium-DBlib-source

65e85572f84048a7e7c5a116b429e09ac9a33e82

[ "MIT" ]

null

# # MIT License # # Copyright (c) 2020 Pablo Rodriguez Nava, @pablintino # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # class BraceMessage(object): def __init__(self, fmt, *args, **kwargs): self.fmt = fmt self.args = args self.kwargs = kwargs def __str__(self): return self.fmt.format(*self.args, **self.kwargs) class CaseInsensitiveDict(dict): @classmethod def _k(cls, key): return key.lower() if isinstance(key, str) else key def __init__(self, *args, **kwargs): super(CaseInsensitiveDict, self).__init__(*args, **kwargs) self._convert_keys() def __getitem__(self, key): return super(CaseInsensitiveDict, self).__getitem__(self.__class__._k(key)) def __setitem__(self, key, value): super(CaseInsensitiveDict, self).__setitem__(self.__class__._k(key), value) def __delitem__(self, key): return super(CaseInsensitiveDict, self).__delitem__(self.__class__._k(key)) def __contains__(self, key): return super(CaseInsensitiveDict, self).__contains__(self.__class__._k(key)) def pop(self, key, *args, **kwargs): return super(CaseInsensitiveDict, self).pop(self.__class__._k(key), *args, **kwargs) def get(self, key, *args, **kwargs): return super(CaseInsensitiveDict, self).get(self.__class__._k(key), *args, **kwargs) def setdefault(self, key, *args, **kwargs): return super(CaseInsensitiveDict, self).setdefault(self.__class__._k(key), *args, **kwargs) def update(self, E={}, **F): super(CaseInsensitiveDict, self).update(self.__class__(E)) super(CaseInsensitiveDict, self).update(self.__class__(**F)) def _convert_keys(self): for k in list(self.keys()): v = super(CaseInsensitiveDict, self).pop(k) self.__setitem__(k, v) def is_float(value): try: float(value) return True except ValueError: return False def is_int(x): try: a = float(x) b = int(a) except ValueError: return False else: return a == b

34.086957

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3,136

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0

null

0

null

0

1

0

1

0

3

e98a999d029d498d01b9f015cfa099eed54b6542

169

py

Python

office365/sharepoint/changes/change_web.py

wreiner/Office365-REST-Python-Client

476bbce4f5928a140b4f5d33475d0ac9b0783530

[ "MIT" ]

544

2016-08-04T17:10:16.000Z

2022-03-31T07:17:20.000Z

office365/sharepoint/changes/change_web.py

wreiner/Office365-REST-Python-Client

476bbce4f5928a140b4f5d33475d0ac9b0783530

[ "MIT" ]

438

2016-10-11T12:24:22.000Z

2022-03-31T19:30:35.000Z

office365/sharepoint/changes/change_web.py

wreiner/Office365-REST-Python-Client

476bbce4f5928a140b4f5d33475d0ac9b0783530

[ "MIT" ]

202

2016-08-22T19:29:40.000Z

2022-03-30T20:26:15.000Z

from office365.sharepoint.changes.change import Change class ChangeWeb(Change): @property def web_id(self): return self.properties.get("WebId", None)

18.777778

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5.714286

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169

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null

0

1

0

null

0

1

0

3

e997cf29f1f010ccb03101018c493d5f81b230d9

162

py

Python

main.py

JanAndrosiuk/Movie-Scraper-App

1702da1a42c723c1c058ae3455a729a9215ed96c

[ "MIT" ]

null

main.py

JanAndrosiuk/Movie-Scraper-App

1702da1a42c723c1c058ae3455a729a9215ed96c

[ "MIT" ]

null

main.py

JanAndrosiuk/Movie-Scraper-App

1702da1a42c723c1c058ae3455a729a9215ed96c

[ "MIT" ]

null

from routes import * from bottle import run def main(): run(host='localhost', port=8001, debug=True) return 1 if __name__ == "__main__": main()

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null

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null

0

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0

3

e99f8d8cd97252231551ad2f188e95f0782f6786

770

py

Python

insights/parsers/tests/test_od_cpu_dma_latency.py

skateman/insights-core

e7cd3001ffc2558757b9e7759dbe27b8b29f4bac

[ "Apache-2.0" ]

1

2021-11-08T16:25:01.000Z

insights/parsers/tests/test_od_cpu_dma_latency.py

ahitacat/insights-core

0ba58dbe5edceef0bd4a74c1caf6b826381ccda5

[ "Apache-2.0" ]

null

insights/parsers/tests/test_od_cpu_dma_latency.py

ahitacat/insights-core

0ba58dbe5edceef0bd4a74c1caf6b826381ccda5

[ "Apache-2.0" ]

null

import doctest import pytest from insights.parsers import od_cpu_dma_latency from insights.parsers.od_cpu_dma_latency import OdCpuDmaLatency from insights.tests import context_wrap from insights.parsers import SkipException CONTENT_OD_CPU_DMA_LATENCY = """ 2000000000 """ CONTENT_OD_CPU_DMA_LATENCY_EMPTY = "" def test_doc_examples(): env = {'cpu_dma_latency': OdCpuDmaLatency(context_wrap(CONTENT_OD_CPU_DMA_LATENCY))} failed, total = doctest.testmod(od_cpu_dma_latency, globs=env) assert failed == 0 def test_OdCpuDmaLatency(): d = OdCpuDmaLatency(context_wrap(CONTENT_OD_CPU_DMA_LATENCY)) assert d.force_latency == 2000000000 with pytest.raises(SkipException): OdCpuDmaLatency(context_wrap(CONTENT_OD_CPU_DMA_LATENCY_EMPTY))

27.5

88

0.801299

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770

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0.031204

0.125974

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0

null

0

1

0

null

0

1

0

3

e9bcd30273c11fb35ffa8754f17e17af5c1e3785

14

py

Python

login.py

wihop/test27

a4191ba59d8a2024e1fe440062e9b1cf8b30c2dc

[ "MIT" ]

null

login.py

wihop/test27

a4191ba59d8a2024e1fe440062e9b1cf8b30c2dc

[ "MIT" ]

null

login.py

wihop/test27

a4191ba59d8a2024e1fe440062e9b1cf8b30c2dc

[ "MIT" ]

null

num =1 num =3

4.666667

6

0.571429

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14

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2

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false

0

1

0

null

0

1

0

null

0

3

e9c0895add5899be815b789724f6ac052629fa99

117

py

Python

Artesian/MarketData/_Enum/AggregationRule.py

AndreaCuneo/Artesian.SDK-Python

1f3528293e2da42d9f1901cd94165464cffab3fc

[ "MIT" ]

null

Artesian/MarketData/_Enum/AggregationRule.py

AndreaCuneo/Artesian.SDK-Python

1f3528293e2da42d9f1901cd94165464cffab3fc

[ "MIT" ]

null

Artesian/MarketData/_Enum/AggregationRule.py

AndreaCuneo/Artesian.SDK-Python

1f3528293e2da42d9f1901cd94165464cffab3fc

[ "MIT" ]

null

from enum import Enum class AggregationRule(Enum): Undefined = 0 SumAndDivide = 1 AverageAndReplicate = 2

23.4

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117

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117

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null

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1

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null

0

1

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3

e9c1749005c1906c7c1389f59e2a043db52283c1

705

py

Python

src/ros_say/say.py

tsaoyu/ROS-SAY

fb187e6aa88e46bf0dbac2a7a79231d08a82bf5c

[ "MIT" ]

null

src/ros_say/say.py

tsaoyu/ROS-SAY

fb187e6aa88e46bf0dbac2a7a79231d08a82bf5c

[ "MIT" ]

null

src/ros_say/say.py

tsaoyu/ROS-SAY

fb187e6aa88e46bf0dbac2a7a79231d08a82bf5c

[ "MIT" ]

null

import rospy from std_msgs.msg import String def publish_to_topic(msg): pub = rospy.Publisher('/rossay', String, queue_size=10) tx = String() tx.data = msg pub.publish(tx) def logdebug(msg, *args, **kwargs): rospy.logdebug(msg, *args, **kwargs) publish_to_topic(msg) def loginfo(msg, *args, **kwargs): rospy.loginfo(msg, *args, **kwargs) publish_to_topic(msg) def logwarn(msg, *args, **kwargs): rospy.logwarn(msg, *args, **kwargs) publish_to_topic(msg) def logerr(msg, *args, **kwargs): rospy.logerr(msg, *args, **kwargs) publish_to_topic(msg) def logfatal(msg, *args, **kwargs): rospy.logfatal(msg, *args, **kwargs) publish_to_topic(msg)

22.03125

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0

null

0

1

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3

e9cef6d99b52cc805ec9ac9ce59b259aa3cf54ca

151

py

Python

visual_map/apps.py

ig-rudenko/zabbix-map

84255b133039426426ddbd31dc93c45acb48dcfa

[ "Apache-2.0" ]

null

visual_map/apps.py

ig-rudenko/zabbix-map

84255b133039426426ddbd31dc93c45acb48dcfa

[ "Apache-2.0" ]

null

visual_map/apps.py

ig-rudenko/zabbix-map

84255b133039426426ddbd31dc93c45acb48dcfa

[ "Apache-2.0" ]

null

from django.apps import AppConfig class VisualMapConfig(AppConfig): default_auto_field = 'django.db.models.BigAutoField' name = 'visual_map'

21.571429

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null

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0

null

0

1

0

3

e9de4762f802e5caf1e5de03911f42c066d117e3

359

py

Python

radiopadre/notebook.py

ratt-ru/radiopadre

3bf934eba69144d9707777a57da0e827625517a3

[ "MIT" ]

9

2019-08-08T12:32:20.000Z

2021-07-06T17:50:35.000Z

radiopadre/notebook.py

ratt-ru/radiopadre

3bf934eba69144d9707777a57da0e827625517a3

[ "MIT" ]

70

2019-03-26T12:42:23.000Z

2022-02-14T13:45:03.000Z

radiopadre/notebook.py

ratt-ru/radiopadre

3bf934eba69144d9707777a57da0e827625517a3

[ "MIT" ]

null

from radiopadre.file import ItemBase, FileBase from radiopadre.render import render_title, render_url, render_preamble, rich_string, htmlize class NotebookFile(FileBase): def __init__(self, *args, **kw): FileBase.__init__(self, *args, **kw) @property def downloadable_url(self): return render_url(self.fullpath, notebook=True)

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null

0

1

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1

0

3

756eb1cfdd22b5a8264cae58a411306eb270498f

654

py

Python

step_impl/vowels_steps.py

almeidh/py-gauge

ad27c01084c002464d953d7996497bdc5f926bf8

[ "MIT" ]

null

step_impl/vowels_steps.py

almeidh/py-gauge

ad27c01084c002464d953d7996497bdc5f926bf8

[ "MIT" ]

null

step_impl/vowels_steps.py

almeidh/py-gauge

ad27c01084c002464d953d7996497bdc5f926bf8

[ "MIT" ]

null

from getgauge.python import step, before_scenario, Messages, DataStoreFactory from utils.page_factory import PageFactory # -------------------------- # Gauge step implementations # -------------------------- @step("The word <word> has <number> vowels.") def assert_no_of_vowels_in(word, number): PageFactory.vowels_page.assert_no_of_vowels_in(word, number) @step("Vowels in English language are <vowels>.") def assert_default_vowels(given_vowels): PageFactory.vowels_page.assert_default_vowels(given_vowels) @step("Almost all words have vowels <table>") def assert_words_vowel_count(table): PageFactory.vowels_page.assert_table(table)

29.727273

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22

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759f9a07b3d027653188df371076c4d0240ec9de

623

py

Python

src/github/models/user.py

isabella232/SGTM

3793d78e99f89e5f73bac5c44f9d8a18cac75fbf

[ "MIT" ]

8

2020-12-05T00:13:03.000Z

2022-01-11T11:35:51.000Z

src/github/models/user.py

Asana/SGTM

0e9e236980ed68e80e021470da6374945bbac501

[ "MIT" ]

12

2020-12-14T18:21:21.000Z

2022-03-29T17:06:20.000Z

src/github/models/user.py

isabella232/SGTM

3793d78e99f89e5f73bac5c44f9d8a18cac75fbf

[ "MIT" ]

2

2021-06-27T09:32:55.000Z

2022-02-27T23:17:36.000Z

from typing import Optional, Dict, Any import copy class User(object): def __init__(self, raw_user: Dict[str, Any]): if "login" not in raw_user or not raw_user["login"].strip(): raise ValueError("User must have a login") self._raw = copy.deepcopy(raw_user) def id(self) -> str: return self._raw["id"] def login(self) -> str: return self._raw["login"] def name(self) -> Optional[str]: if "name" not in self._raw: return None return self._raw["name"] def to_raw(self) -> Dict[str, Any]: return copy.deepcopy(self._raw)

25.958333

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0.108939

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23

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null

0

null

0

1

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1

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3

75d48195c56aae6a2d0f05e4d6ae50ff8c778bf2

462

py

Python

galaxy/main/migrations/0063_remove_deprecated_role_fields.py

tima/galaxy

b371b973e0e9150f3e8b9b08068828b092982f62

[ "Apache-2.0" ]

null

galaxy/main/migrations/0063_remove_deprecated_role_fields.py

tima/galaxy

b371b973e0e9150f3e8b9b08068828b092982f62

[ "Apache-2.0" ]

null

galaxy/main/migrations/0063_remove_deprecated_role_fields.py

tima/galaxy

b371b973e0e9150f3e8b9b08068828b092982f62

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('main', '0062_move_repository_counters'), ] operations = [ migrations.RemoveField(model_name='role', name='average_score'), migrations.RemoveField(model_name='role', name='bayesian_score'), migrations.RemoveField(model_name='role', name='num_ratings'), ]

25.666667

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0

3

75e7f7a28483a2bca9b731fbdc2e199a8fa9ea99

1,140

py

Python

dapper/mods/Lorenz63/boc12.py

sebastienbarthelemy/DAPPER

dc92a7339932af059967bd9cf0a473ae9b8d7bf9

[ "MIT" ]

1

2019-11-12T21:39:00.000Z

dapper/mods/Lorenz63/boc12.py

cecidip/DAPPER

dc92a7339932af059967bd9cf0a473ae9b8d7bf9

[ "MIT" ]

null

dapper/mods/Lorenz63/boc12.py

cecidip/DAPPER

dc92a7339932af059967bd9cf0a473ae9b8d7bf9

[ "MIT" ]

null

# Reproduce results from Fig 11 of # M. Bocquet and P. Sakov (2012): "Combining inflation-free and # iterative ensemble Kalman filters for strongly nonlinear systems" from dapper.mods.Lorenz63.sak12 import HMM # The only diff to sak12 is R: boc12 uses 1 and 8, sak12 uses 2 (and 8) from dapper import * HMM.Obs.noise.C = CovMat(eye(3)) HMM.name = os.path.relpath(__file__,'mods/') #################### # Suggested tuning #################### # from dapper.mods.Lorenz63.boc12 import HMM # Expected RMSE_a: # HMM.t.dkObs = 25 # cfgs += iEnKS('Sqrt', N=10,infl=1.02,rot=True) # 0.22 # cfgs += iEnKS('Sqrt', N=3, infl=1.04) # 0.23 # cfgs += iEnKS('Sqrt', N=3, xN=1.0) # 0.22 # # HMM.t.dkObs = 5 # cfgs += iEnKS('Sqrt', N=10,infl=1.02,rot=True) # 0.13 # cfgs += iEnKS('Sqrt', N=3, infl=1.02) # 0.13 # cfgs += iEnKS('Sqrt', N=3, xN=1.0) # 0.15 # cfgs += iEnKS('Sqrt', N=3, xN=2.0) # 0.14 # # HMM.t.dkObs = 25 and R=8*eye(3): # cfgs += iEnKS('Sqrt', N=3, xN=1.0) # 0.70

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3.314607

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0.176271

0.189831

0.311864

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0.20678

0.105085

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0.105521

0.285088

1,140

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null

0

1

0

1

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3

f92ea7c0c6d49055dbc619624c3e66a7180dfdb3

12,311

py

Python

UltronRoBo/modules/__aimultilanguage.py

UltronRoBo/UltronRoBoAssistant

874dcf725d453ffabd85543533d2a07676af4d65

[ "MIT" ]

null

UltronRoBo/modules/__aimultilanguage.py

UltronRoBo/UltronRoBoAssistant

874dcf725d453ffabd85543533d2a07676af4d65

[ "MIT" ]

null

UltronRoBo/modules/__aimultilanguage.py

UltronRoBo/UltronRoBoAssistant

874dcf725d453ffabd85543533d2a07676af4d65

[ "MIT" ]

null

""" MIT License Copyright (c) 2021 UltronRoBo Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import re import emoji import requests url = "https://acobot-brainshop-ai-v1.p.rapidapi.com/get" from google_trans_new import google_translator from pyrogram import filters from UltronRoBo.helper_extra.aichat import add_chat, get_session, remove_chat from UltronRoBo.pyrogramee.pluginshelper import admins_only, edit_or_reply from UltronRoBo import UltronRobo as ULTRON translator = google_translator() def extract_emojis(s): return "".join(c for c in s if c in emoji.UNICODE_EMOJI) BOT_ID = 1811984200 ultron_chats = [] en_chats = [] @ULTRON.on_message(filters.command("chatbot") & ~filters.edited & ~filters.bot) @admins_only async def hmm(_, message): global asuna_chats if len(message.command) != 2: await message.reply_text( "I only recognize `/chatbot on` and /chatbot `off only`" ) message.continue_propagation() status = message.text.split(None, 1)[1] chat_id = message.chat.id if status == "ON" or status == "on" or status == "On": lel = await edit_or_reply(message, "`Processing...`") lol = add_chat(int(message.chat.id)) if not lol: await lel.edit("UltronRoBo AI Already Activated In This Chat") return await lel.edit( f"UltronRoBo AI Successfully Added For Users In The Chat {message.chat.id}" ) elif status == "OFF" or status == "off" or status == "Off": lel = await edit_or_reply(message, "`Processing...`") Escobar = remove_chat(int(message.chat.id)) if not Escobar: await lel.edit("UltronRoBo AI Was Not Activated In This Chat") return await lel.edit( f"UltronRoBo AI Successfully Deactivated For Users In The Chat {message.chat.id}" ) elif status == "EN" or status == "en" or status == "english": if not chat_id in en_chats: en_chats.append(chat_id) await message.reply_text("English AI chat Enabled!") return await message.reply_text("AI Chat Is Already Disabled.") message.continue_propagation() else: await message.reply_text( "I only recognize `/chatbot on` and /chatbot `off only`" ) @ULTRON.on_message( filters.text & filters.reply & ~filters.bot & ~filters.via_bot & ~filters.forwarded, group=2, ) async def hmm(client, message): if message.reply_to_message.from_user.id != BOT_ID: message.continue_propagation() msg = message.text chat_id = message.chat.id if msg.startswith("/") or msg.startswith("@"): message.continue_propagation() if chat_id in en_chats: test = msg test = test.replace("tesla", "Aco") test = test.replace("tesla", "Aco") querystring = { "bid": "178", "key": "sX5A2PcYZbsN5EY6", "uid": "mashape", "msg": {test}, } headers = { "x-rapidapi-key": "cf9e67ea99mshecc7e1ddb8e93d1p1b9e04jsn3f1bb9103c3f", "x-rapidapi-host": "acobot-brainshop-ai-v1.p.rapidapi.com", } response = requests.request("GET", url, headers=headers, params=querystring) result = response.text result = result.replace('{"cnt":"', "") result = result.replace('"}', "") result = result.replace("Aco", "tesla") result = result.replace("<a href=\\", "<a href =") result = result.replace("<\/a>", "</a>") pro = result try: await tesla.send_chat_action(message.chat.id, "typing") await message.reply_text(pro) except CFError as e: print(e) else: u = msg.split() emj = extract_emojis(msg) msg = msg.replace(emj, "") if ( [(k) for k in u if k.startswith("@")] and [(k) for k in u if k.startswith("#")] and [(k) for k in u if k.startswith("/")] and re.findall(r"\[([^]]+)]$\s*([^)]+)\s*$", msg) != [] ): h = " ".join(filter(lambda x: x[0] != "@", u)) km = re.sub(r"\[([^]]+)]$\s*([^)]+)\s*$", r"", h) tm = km.split() jm = " ".join(filter(lambda x: x[0] != "#", tm)) hm = jm.split() rm = " ".join(filter(lambda x: x[0] != "/", hm)) elif [(k) for k in u if k.startswith("@")]: rm = " ".join(filter(lambda x: x[0] != "@", u)) elif [(k) for k in u if k.startswith("#")]: rm = " ".join(filter(lambda x: x[0] != "#", u)) elif [(k) for k in u if k.startswith("/")]: rm = " ".join(filter(lambda x: x[0] != "/", u)) elif re.findall(r"\[([^]]+)]$\s*([^)]+)\s*$", msg) != []: rm = re.sub(r"\[([^]]+)]$\s*([^)]+)\s*$", r"", msg) else: rm = msg # print (rm) lan = translator.detect(rm) test = rm if not "en" in lan and not lan == "": test = translator.translate(test, lang_tgt="en") # test = emoji.demojize(test.strip()) test = test.replace("tesla", "Aco") test = test.replace("tesla", "Aco") querystring = { "bid": "178", "key": "sX5A2PcYZbsN5EY6", "uid": "mashape", "msg": {test}, } headers = { "x-rapidapi-key": "cf9e67ea99mshecc7e1ddb8e93d1p1b9e04jsn3f1bb9103c3f", "x-rapidapi-host": "acobot-brainshop-ai-v1.p.rapidapi.com", } response = requests.request("GET", url, headers=headers, params=querystring) result = response.text result = result.replace('{"cnt":"', "") result = result.replace('"}', "") result = result.replace("Aco", "tesla") result = result.replace("<a href=\\", "<a href =") result = result.replace("<\/a>", "</a>") pro = result if not "en" in lan and not lan == "": pro = translator.translate(pro, lang_tgt=lan[0]) try: await tesla.send_chat_action(message.chat.id, "typing") await message.reply_text(pro) except CFError as e: print(e) @ULTRON.on_message(filters.text & filters.private & filters.reply & ~filters.bot) async def inuka(client, message): msg = message.text if msg.startswith("/") or msg.startswith("@"): message.continue_propagation() u = msg.split() emj = extract_emojis(msg) msg = msg.replace(emj, "") if ( [(k) for k in u if k.startswith("@")] and [(k) for k in u if k.startswith("#")] and [(k) for k in u if k.startswith("/")] and re.findall(r"\[([^]]+)]$\s*([^)]+)\s*$", msg) != [] ): h = " ".join(filter(lambda x: x[0] != "@", u)) km = re.sub(r"\[([^]]+)]$\s*([^)]+)\s*$", r"", h) tm = km.split() jm = " ".join(filter(lambda x: x[0] != "#", tm)) hm = jm.split() rm = " ".join(filter(lambda x: x[0] != "/", hm)) elif [(k) for k in u if k.startswith("@")]: rm = " ".join(filter(lambda x: x[0] != "@", u)) elif [(k) for k in u if k.startswith("#")]: rm = " ".join(filter(lambda x: x[0] != "#", u)) elif [(k) for k in u if k.startswith("/")]: rm = " ".join(filter(lambda x: x[0] != "/", u)) elif re.findall(r"\[([^]]+)]$\s*([^)]+)\s*$", msg) != []: rm = re.sub(r"\[([^]]+)]$\s*([^)]+)\s*$", r"", msg) else: rm = msg # print (rm) lan = translator.detect(rm) test = rm if not "en" in lan and not lan == "": test = translator.translate(test, lang_tgt="en") # test = emoji.demojize(test.strip()) test = test.replace("tesla", "Aco") test = test.replace("tesla", "Aco") querystring = { "bid": "178", "key": "sX5A2PcYZbsN5EY6", "uid": "mashape", "msg": {test}, } headers = { "x-rapidapi-key": "cf9e67ea99mshecc7e1ddb8e93d1p1b9e04jsn3f1bb9103c3f", "x-rapidapi-host": "acobot-brainshop-ai-v1.p.rapidapi.com", } response = requests.request("GET", url, headers=headers, params=querystring) result = response.text result = result.replace('{"cnt":"', "") result = result.replace('"}', "") result = result.replace("Aco", "tesla") result = result.replace("<a href=\\", "<a href =") result = result.replace("<\/a>", "</a>") pro = result if not "en" in lan and not lan == "": pro = translator.translate(pro, lang_tgt=lan[0]) try: await tesla.send_chat_action(message.chat.id, "typing") await message.reply_text(pro) except CFError as e: print(e) @ULTRON.on_message( filters.regex("ultron|Ultron|huntinbots|hello|hi") & ~filters.bot & ~filters.via_bot & ~filters.forwarded & ~filters.reply & ~filters.channel ) async def inuka(client, message): msg = message.text if msg.startswith("/") or msg.startswith("@"): message.continue_propagation() u = msg.split() emj = extract_emojis(msg) msg = msg.replace(emj, "") if ( [(k) for k in u if k.startswith("@")] and [(k) for k in u if k.startswith("#")] and [(k) for k in u if k.startswith("/")] and re.findall(r"\[([^]]+)]$\s*([^)]+)\s*$", msg) != [] ): h = " ".join(filter(lambda x: x[0] != "@", u)) km = re.sub(r"\[([^]]+)]$\s*([^)]+)\s*$", r"", h) tm = km.split() jm = " ".join(filter(lambda x: x[0] != "#", tm)) hm = jm.split() rm = " ".join(filter(lambda x: x[0] != "/", hm)) elif [(k) for k in u if k.startswith("@")]: rm = " ".join(filter(lambda x: x[0] != "@", u)) elif [(k) for k in u if k.startswith("#")]: rm = " ".join(filter(lambda x: x[0] != "#", u)) elif [(k) for k in u if k.startswith("/")]: rm = " ".join(filter(lambda x: x[0] != "/", u)) elif re.findall(r"\[([^]]+)]$\s*([^)]+)\s*$", msg) != []: rm = re.sub(r"\[([^]]+)]$\s*([^)]+)\s*$", r"", msg) else: rm = msg # print (rm) lan = translator.detect(rm) test = rm if not "en" in lan and not lan == "": test = translator.translate(test, lang_tgt="en") # test = emoji.demojize(test.strip()) test = test.replace("tesla", "Aco") test = test.replace("tesla", "Aco") querystring = { "bid": "178", "key": "sX5A2PcYZbsN5EY6", "uid": "mashape", "msg": {test}, } headers = { "x-rapidapi-key": "cf9e67ea99mshecc7e1ddb8e93d1p1b9e04jsn3f1bb9103c3f", "x-rapidapi-host": "acobot-brainshop-ai-v1.p.rapidapi.com", } response = requests.request("GET", url, headers=headers, params=querystring) result = response.text result = result.replace('{"cnt":"', "") result = result.replace('"}', "") result = result.replace("Aco", "tesla") result = result.replace("<a href=\\", "<a href =") result = result.replace("<\/a>", "</a>") pro = result if not "en" in lan and not lan == "": pro = translator.translate(pro, lang_tgt=lan[0]) try: await tesla.send_chat_action(message.chat.id, "typing") await message.reply_text(pro) except CFError as e: print(e)

36.208824

93

0.553001

1,552

12,311

4.338918

0.160438

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0.05643

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0.721859

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null

0

3

f937fa8c8a16b6213b8f5b89a5b75680e1222f56

2,013

py

Python

worms/criteria/base.py

abiedermann/worms

026c45a88d5c71b0e035ac83de6f4dc107316ed8

[ "Apache-2.0" ]

4

2018-01-30T23:13:43.000Z

2021-02-12T22:36:54.000Z

worms/criteria/base.py

abiedermann/worms

026c45a88d5c71b0e035ac83de6f4dc107316ed8

[ "Apache-2.0" ]

9

2018-02-23T00:52:25.000Z

2022-01-26T00:02:32.000Z

worms/criteria/base.py

abiedermann/worms

026c45a88d5c71b0e035ac83de6f4dc107316ed8

[ "Apache-2.0" ]

4

2018-06-28T21:30:14.000Z

2022-03-30T17:50:42.000Z

import abc import numpy as np import homog as hm from numpy.linalg import inv from worms.util import jit Ux = np.array([1, 0, 0, 0]) Uy = np.array([0, 1, 0, 0]) Uz = np.array([0, 0, 1, 0]) class WormCriteria(abc.ABC): @abc.abstractmethod def score(self, **kw): pass allowed_attributes = ( "last_body_same_as", "symname", "is_cyclic", "alignment", "from_seg", "to_seg", "origin_seg", "symfile_modifiers", "crystinfo", ) class CriteriaList(WormCriteria): def __init__(self, children): if isinstance(children, WormCriteria): children = [children] self.children = children def score(self, **kw): return sum(c.score(**kw) for c in self.children) def __getattr__(self, name): if name not in WormCriteria.allowed_attributes: raise AttributeError("CriteriaList has no attribute: " + name) r = [getattr(c, name) for c in self.children if hasattr(c, name)] r = [x for x in r if x is not None] assert len(r) < 2 return r[0] if len(r) else None def __getitem__(self, index): assert isinstance(index, int) return self.children[index] def __len__(self): return len(self.children) def __iter__(self): return iter(self.children) class NullCriteria(WormCriteria): def __init__(self, from_seg=0, to_seg=-1, origin_seg=None): self.from_seg = from_seg self.to_seg = to_seg self.origin_seg = None self.is_cyclic = False self.tolerance = 9e8 self.symname = None def merge_segment(self, **kw): return None def score(self, segpos, **kw): return np.zeros(segpos[-1].shape[:-2]) def alignment(self, segpos, **kw): r = np.empty_like(segpos[-1]) r[..., :, :] = np.eye(4) return r def jit_lossfunc(self): @jit def null_lossfunc(pos, idx, verts): return 0.0 return null_lossfunc def iface_rms(self, pose0, prov0, **kw): return -1

23.964286

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0.615002

282

2,013

4.216312

0.333333

0.070648

0.030278

0.023549

0.030278

0

0.018145

0.260805

2,013

83

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1

0.208955

false

0.014925

0.074627

0.104478

0.507463

0

null

0

null

0

1

0

1

0

3

f93ab25dcb9271147e77b194862c6c60e2c15b85

1,757

py

Python

nba_data/data/game.py

jaebradley/nba_data

30d817bbc1c5474774f97f3800354492e382d206

[ "MIT" ]

8

2017-01-07T13:32:16.000Z

2019-08-08T17:36:26.000Z

nba_data/data/game.py

jaebradley/nba_data

30d817bbc1c5474774f97f3800354492e382d206

[ "MIT" ]

72

2016-09-01T01:21:07.000Z

2021-03-25T21:41:38.000Z

nba_data/data/game.py

jaebradley/nba_data

30d817bbc1c5474774f97f3800354492e382d206

[ "MIT" ]

4

2016-12-06T10:30:59.000Z

2021-09-08T21:23:43.000Z

class Game: def __init__(self, id, match_up): self.id = id self.match_up = match_up def __unicode__(self): return '{0} - {1}'.format(self.get_additional_unicode(), self.get_base_unicode()) def get_base_unicode(self): return 'id: {id} | match up: {match_up}'.format(self.id, self.match_up) def get_additional_unicode(self): raise NotImplementedError('Implement in concrete classes') class SeasonGame(Game): def __init__(self, id, match_up, season): self.season = season Game.__init__(self, id, match_up) def get_base_unicode(self): return 'season: {season} | {base_unicode}'.format(season=self.season, base_unicode=Game.get_base_unicode(self)) def get_additional_unicode(self): raise NotImplementedError('Implement in concrete classes') class LoggedGame(SeasonGame): def __init__(self, id, match_up, season, start_date, season_type, home_team_outcome): self.start_date = start_date self.season_type = season_type self.home_team_outcome = home_team_outcome SeasonGame.__init__(self, id=id, match_up=match_up, season=season) def get_additional_unicode(self): return 'start_date: {start_date} | season type: {season_type} | home team outcome: {home_team_outcome}'\ .format(date=self.start_date, season_type=self.season_type, home_team_outcome=self.home_team_outcome) class ScoreboardGame(SeasonGame): def __init__(self, id, season, start_time, match_up): self.start_time = start_time SeasonGame.__init__(self, id=id, match_up=match_up, season=season) def get_additional_unicode(self): return 'start time: {start_time}'.format(start_time=self.start_time)

35.857143

119

0.704041

237

1,757

4.818565

0.139241

0.085814

0.061296

0.105079

0.583187

0.530648

0.364273

0.294221

0

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0.188958

1,757

48

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0

null

0

null

0

1

0

1

0

3

f945d73dc56bc2c6de0605926d234f912917c5c4

192

py

Python

my_first_webdriver.py

williamholtkamp/unc_webscraping

c9ad7a6a492d050ea646ab5b70c6f351ab99e870

[ "CC0-1.0" ]

4

2021-02-03T00:33:33.000Z

2021-04-16T21:58:25.000Z

my_first_webdriver.py

williamholtkamp/unc_webscraping

c9ad7a6a492d050ea646ab5b70c6f351ab99e870

[ "CC0-1.0" ]

null

my_first_webdriver.py

williamholtkamp/unc_webscraping

c9ad7a6a492d050ea646ab5b70c6f351ab99e870

[ "CC0-1.0" ]

3

2020-10-23T18:20:37.000Z

2021-02-05T12:49:07.000Z

#following script opens up your first webdriver browser and goes to the specified link from selenium import webdriver driver = webdriver.Chrome() driver.get('https://www.google.com/')

27.428571

87

0.760417

27

192

5.407407

0.888889

0

0.15625

192

6

88

32

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0.442708

0

0.232323

0

1

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false

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

3

f94db5537f35a72175a5968d1dcc47b2f6d25ba6

311

py

Python

kosmos/zos/ZosNodes/ZOSVirtual.py

Pishoy/jumpscaleX_threebot

781e839857fecfa601a31d98d86d304e3a6b3b4e

[ "Apache-2.0" ]

null

kosmos/zos/ZosNodes/ZOSVirtual.py

Pishoy/jumpscaleX_threebot

781e839857fecfa601a31d98d86d304e3a6b3b4e

[ "Apache-2.0" ]

546

2019-08-29T11:48:19.000Z

2020-12-06T07:20:45.000Z

kosmos/zos/ZosNodes/ZOSVirtual.py

Pishoy/jumpscaleX_threebot

781e839857fecfa601a31d98d86d304e3a6b3b4e

[ "Apache-2.0" ]

5

2019-09-26T14:03:05.000Z

2020-04-16T08:47:10.000Z

from Jumpscale import j from .ZOSContainer import ZOSContainers from .ZOSInstance import ZOSInstance class ZOSVirtual(ZOSInstance): """ is the host which runs a ZOS operating system is a Virtual Zero-OS running on a virtual service provider """ def _init(self, **kwargs): pass

18.294118

62

0.707395

40

311

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0.75

0.073059

0

0.234727

311

16

63

19.4375

0.920168

0.337621

0

1

0.166667

false

0.166667

0.5

0

0.833333

0

null

0

null

0

1

0

1

0

3

f983560b381da0021faa8b318c7ecd01b487c63f

16,051

py

Python

lib/python3.7/site-packages/gatco_apimanager/views/couchdb.py

teomoney1999/ACT_gatco_project

a804a6348efeab90f3114606cfbc73aaebab63e1

[ "MIT" ]

null

lib/python3.7/site-packages/gatco_apimanager/views/couchdb.py

teomoney1999/ACT_gatco_project

a804a6348efeab90f3114606cfbc73aaebab63e1

[ "MIT" ]

1

2019-11-27T08:58:03.000Z

lib/python3.7/site-packages/gatco_apimanager/views/couchdb.py

teomoney1999/ACT_gatco_project

a804a6348efeab90f3114606cfbc73aaebab63e1

[ "MIT" ]

2

2019-06-19T07:33:45.000Z

2021-06-21T08:19:31.000Z

# -*- coding: utf-8 - import asyncio # import pymongo import ujson import math # from bson.objectid import ObjectId from cloudant.query import Query from gatco.exceptions import GatcoException, ServerError from gatco.response import json, text, HTTPResponse from gatco.request import json_loads from . import ModelView # https://python-cloudant.readthedocs.io/en/latest/getting_started.html # https://github.com/cloudant/python-cloudant def to_dict(document, exclude=None, include=None): obj = dict(document) columns = list(obj.keys()) if exclude is not None: for c in columns: if c in exclude: del (obj[c]) elif include is not None: for c in columns: if c not in include: del (obj[c]) return obj def response_exception(exception): if type(exception.message) is dict: return json(exception.message, status=exception.status_code) else: return json({"error_code": "UNKNOWN_ERROR", "error_message": exception.message}, status=520) class APIView(ModelView): primary_key = "_id" db = None def _compute_results_per_page(self, request): try: results_per_page = int(request.args.get('results_per_page')) except: results_per_page = self.results_per_page if results_per_page <= 0: results_per_page = self.results_per_page return min(results_per_page, self.max_results_per_page) async def _search(self, request, search_params): is_single = search_params.get('single') order_by_list = search_params.get('order_by', None) # paginate # num_results = 20 results_per_page = self._compute_results_per_page(request) page_num = 1 if results_per_page > 0: page_num = int(request.args.get('page', 1)) start = (page_num - 1) * results_per_page # end = start + results_per_page # total_pages = int(math.ceil(num_results / results_per_page)) else: start = 0 # total_pages = 1 query = None filters = {"doc_type": self.collection_name} if 'filters' in search_params: filters = search_params['filters'] filters["doc_type"] = self.collection_name if order_by_list is not None: query = Query(self.db.db, selector=filters, sort=order_by_list) else: query = Query(self.db.db, selector=filters) if is_single: for document in query(limit=1)['docs']: result = to_dict(document, exclude=self.exclude_columns, include=self.include_columns) break else: query_result = None if (results_per_page is not None) and (results_per_page > 0): if (start is not None) and (start > 0): query_result = query.result[start:start + results_per_page] else: query_result = query.result[:results_per_page] else: if (start is not None) and (start > 0): query_result = query.result[start:] else: query_result = query.result objects = [] for document in query_result: objects.append(to_dict(document, exclude=self.exclude_columns, include=self.include_columns)) new_page = page_num + 1 result = { "page": page_num, "next_page": new_page, "objects": objects } return result async def search(self, request): try: search_params = json_loads(request.args.get('q', '{}')) except (TypeError, ValueError, OverflowError) as exception: # current_app.logger.exception(str(exception)) return json(dict(error_code="PARAM_ERROR", error_message='Unable to decode data'), status=520) try: for preprocess in self.preprocess['GET_MANY']: if asyncio.iscoroutinefunction(preprocess): resp = await preprocess(request=request, search_params=search_params, collection_name=self.collection_name) else: resp = preprocess(request=request, search_params=search_params, collection_name=self.collection_name) if (resp is not None) and isinstance(resp, HTTPResponse): return resp except Exception as exception: return response_exception(exception) result = await self._search(request, search_params) if result is None: return json(dict(error_code="NOT_FOUND", error_message='No result found'), status=520) try: headers = {} for postprocess in self.postprocess['GET_MANY']: if asyncio.iscoroutinefunction(postprocess): resp = await postprocess(request=request, result=result, search_params=search_params, collection_name=self.collection_name, headers=headers) else: resp = postprocess(request=request, result=result, search_params=search_params, collection_name=self.collection_name, headers=headers) if (resp is not None) and isinstance(resp, HTTPResponse): return resp except Exception as exception: return response_exception(exception) return json(result, headers=headers, status=200) async def _get(self, request, instid): document = self.db.db.get(instid, remote=True) if document is not None: obj = to_dict(document, exclude=self.exclude_columns, include=self.include_columns) return obj return None async def get(self, request, instid=None): if instid is None: return await self.search(request) try: for preprocess in self.preprocess['GET_SINGLE']: if asyncio.iscoroutinefunction(preprocess): resp = await preprocess(request=request, instance_id=instid, collection_name=self.collection_name) else: resp = preprocess(request=request, instance_id=instid, collection_name=self.collection_name) if (resp is not None) and isinstance(resp, HTTPResponse): return resp if resp is not None: instid = resp except Exception as exception: return response_exception(exception) result = await self._get(request, instid) if result is None: return json(dict(error_code="NOT_FOUND", error_message='No result found'), status=520) try: headers = {} for postprocess in self.postprocess['GET_SINGLE']: if asyncio.iscoroutinefunction(postprocess): resp = await postprocess(request=request, result=result, collection_name=self.collection_name, headers=headers) else: resp = postprocess(request=request, result=result, collection_name=self.collection_name, headers=headers) if (resp is not None) and isinstance(resp, HTTPResponse): return resp except Exception as exception: return response_exception(exception) return json(result, headers=headers, status=200) async def delete(self, request, instid=None): try: for preprocess in self.preprocess['DELETE_SINGLE']: if asyncio.iscoroutinefunction(preprocess): resp = await preprocess(request=request, instance_id=instid, collection_name=self.collection_name) else: resp = preprocess(request=request, instance_id=instid, collection_name=self.collection_name) if (resp is not None) and isinstance(resp, HTTPResponse): return resp # See the note under the preprocess in the get() method. if resp is not None: instid = resp except Exception as exception: return response_exception(exception) was_deleted = False if instid is not None: # result = await self.db.db[self.collection_name].delete_one({'_id': {'$eq': ObjectId(instid)}}) # result = self.db.db[self.collection_name].delete_one({'_id': {'$in': [ObjectId(instid), instid]}}) # was_deleted = result.deleted_count > 0 document = self.db.db.get(instid,remote=True) if document is not None: document.delete() was_deleted = True else: was_deleted = False try: headers = {} for postprocess in self.postprocess['DELETE_SINGLE']: if asyncio.iscoroutinefunction(postprocess): resp = await postprocess(request=request, was_deleted=was_deleted, collection_name=self.collection_name, headers=headers) else: resp = postprocess(request=request, was_deleted=was_deleted, collection_name=self.collection_name, headers=headers) if (resp is not None) and isinstance(resp, HTTPResponse): return resp except Exception as exception: return response_exception(exception) return json({}, headers=headers, status=200) if was_deleted else json({}, headers=headers, status=520) async def _post(self, request, data): if "_id" in data: del data["_id"] if "_rev" in data: del data["_rev"] data["doc_type"] = self.collection_name if self.primary_key is not None: # if data["_id"] not in self.db.db: document = self.db.db.create_document(data) obj = to_dict(document, exclude=self.exclude_columns, include=self.include_columns) del (obj["_rev"]) return obj async def post(self, request): content_type = request.headers.get('Content-Type', "") content_is_json = content_type.startswith('application/json') if not content_is_json: msg = 'Request must have "Content-Type: application/json" header' return json(dict(message=msg), status=520) try: data = request.json or {} except (ServerError, TypeError, ValueError, OverflowError) as exception: # current_app.logger.exception(str(exception)) return json(dict(message='Unable to decode data'), status=520) try: for preprocess in self.preprocess['POST']: if asyncio.iscoroutinefunction(preprocess): resp = await preprocess(request=request, data=data, collection_name=self.collection_name) else: resp = preprocess(request=request, data=data, collection_name=self.collection_name) if (resp is not None) and isinstance(resp, HTTPResponse): return resp except Exception as exception: return response_exception(exception) result = await self._post(request, data) if result is None: json(dict(error_code='UNKNOWN_ERROR', error_message=''), status=520) try: headers = {} for postprocess in self.postprocess['POST']: if asyncio.iscoroutinefunction(postprocess): resp = await postprocess(request=request, result=result, collection_name=self.collection_name, headers=headers) else: resp = postprocess(request=request, result=result, collection_name=self.collection_name, headers=headers) if (resp is not None) and isinstance(resp, HTTPResponse): return resp except Exception as exception: return response_exception(exception) return json(result, headers=headers, status=201) async def _put(self, request, data, instid): if "doc_type" in data: del data["doc_type"] if "_rev" in data: del data["_rev"] if instid is not None: document = self.db.db.get(instid, remote=True) if document is not None: update = False for key, value in data.items(): try: if document.get(key) != value: update = True document[key] = value except: pass if update: document.save() if document is not None: return to_dict(document, exclude=self.exclude_columns, include=self.include_columns) else: if self.primary_key is not None: data["doc_type"] = self.collection_name document = self.db.db.create_document(data) obj = to_dict(document, exclude=self.exclude_columns, include=self.include_columns) del (obj["_rev"]) return obj else: if self.primary_key is not None: data["doc_type"] = self.collection_name document = self.db.db.create_document(data) obj = to_dict(document, exclude=self.exclude_columns, include=self.include_columns) del (obj["_rev"]) return obj return None async def put(self, request, instid=None): content_type = request.headers.get('Content-Type', "") content_is_json = content_type.startswith('application/json') if not content_is_json: msg = 'Request must have "Content-Type: application/json" header' return json(dict(message=msg), status=520) try: data = request.json or {} except (ServerError, TypeError, ValueError, OverflowError) as exception: # current_app.logger.exception(str(exception)) return json(dict(error_code='PARAM_ERROR', error_message='Unable to decode data'), status=520) for preprocess in self.preprocess['PATCH_SINGLE']: try: if asyncio.iscoroutinefunction(preprocess): resp = await preprocess(request=request, instance_id=instid, data=data, collection_name=self.collection_name) else: resp = preprocess(request=request, instance_id=instid, data=data, collection_name=self.collection_name) if (resp is not None) and isinstance(resp, HTTPResponse): return resp # See the note under the preprocess in the get() method. if resp is not None: instid = resp except Exception as exception: return response_exception(exception) result = await self._put(request, data, instid) if result is None: return json(dict(error_code='UNKNOWN_ERROR', error_message=''), status=520) headers = {} try: for postprocess in self.postprocess['PATCH_SINGLE']: if asyncio.iscoroutinefunction(postprocess): resp = await postprocess(request=request, result=result, collection_name=self.collection_name, headers=headers) else: resp = postprocess(request=request, result=result, collection_name=self.collection_name, headers=headers) if (resp is not None) and isinstance(resp, HTTPResponse): return resp except Exception as exception: return response_exception(exception) return json(result, headers=headers, status=200) async def patch(self, *args, **kw): """Alias for :meth:`patch`.""" return await self.put(*args, **kw)

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16,051

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null

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1

0

null

0

3

f989a8ab7bd4c020ae58199b39d5401abadd08a1

357

py

Python

back/api/management/commands/setcustompassword.py

kalbermattenm/geoshop2

b1c5c9124248e7df4d4653e9b7cb33590e9d11de

[ "BSD-3-Clause" ]

null

back/api/management/commands/setcustompassword.py

kalbermattenm/geoshop2

b1c5c9124248e7df4d4653e9b7cb33590e9d11de

[ "BSD-3-Clause" ]

null

back/api/management/commands/setcustompassword.py

kalbermattenm/geoshop2

b1c5c9124248e7df4d4653e9b7cb33590e9d11de

[ "BSD-3-Clause" ]

null

import os from django.core.management.base import BaseCommand, CommandError from django.contrib.auth import get_user_model UserModel = get_user_model() class Command(BaseCommand): def handle(self, *args, **options): u = UserModel.objects.get(username='admin') u.set_password(os.environ.get('ADMIN_PASSWORD', 'admin')) u.save()

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null

0

null

0

1

0

1

0

3

f99e4cafa46fa36cad26a48954e6a140b222dc41

501

py

Python

reqcli/errors.py

shiftinv/reqcli

3b8fe42bcddf7676bb7d9a70f6bd2a430ae7beb1

[ "Apache-2.0" ]

null

reqcli/errors.py

shiftinv/reqcli

3b8fe42bcddf7676bb7d9a70f6bd2a430ae7beb1

[ "Apache-2.0" ]

null

reqcli/errors.py

shiftinv/reqcli

3b8fe42bcddf7676bb7d9a70f6bd2a430ae7beb1

[ "Apache-2.0" ]

null

class ResponseStatusError(Exception): status: int def __init__(self, message: str, status: int): super().__init__(message) self.status = status def __reduce__(self): # pragma: no cover return (type(self), (*self.args, self.status)) class ConfigDependencyError(Exception): pass class XmlLoadError(Exception): pass class XmlSchemaError(Exception): pass class ReaderError(Exception): pass class TypeAlreadyLoadedError(Exception): pass

16.7

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51

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6.509804

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0.216867

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false

0.294118

0

0.058824

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0

null

0

1

0

null

0

1

0

1

0

3

f9a7923e4fcf5836f4c0dbf26f9c1465c820f925

49

py

Python

cst_modeling/__init__.py

swayli94/cst-modeling3d

3799aec6fa08ae5e9b66760bf0a8c998527f9a8c

[ "MIT" ]

5

2020-11-17T02:24:55.000Z

2021-12-18T10:12:21.000Z

cst_modeling/__init__.py

swayli94/cst-modeling3d

3799aec6fa08ae5e9b66760bf0a8c998527f9a8c

[ "MIT" ]

null

cst_modeling/__init__.py

swayli94/cst-modeling3d

3799aec6fa08ae5e9b66760bf0a8c998527f9a8c

[ "MIT" ]

1

2021-03-13T16:19:57.000Z

__version__ = "0.1.13" __name__ = "cst_modeling"

16.333333

25

0.714286

7

49

3.714286

1

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0.122449

49

2

26

24.5

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0

1

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1

0

null

0

1

0

null

0

3

f9abcb53f8c5b40987eb6b1c02d59e4b4c660b34

827

py

Python

tests/test_data.py

alingse/emoji-chengyu

2d4436212c1d2899dfc12a1c965ea2ddce9a4aab

[ "MIT" ]

3

2020-04-28T03:25:36.000Z

2022-01-24T04:52:01.000Z

tests/test_data.py

alingse/emoji-chengyu

2d4436212c1d2899dfc12a1c965ea2ddce9a4aab

[ "MIT" ]

null

tests/test_data.py

alingse/emoji-chengyu

2d4436212c1d2899dfc12a1c965ea2ddce9a4aab

[ "MIT" ]

1

2020-04-28T03:25:49.000Z

import unittest from emoji_chengyu.data import DefaultChengyuManager from emoji_chengyu.data import CommonChengyuManager from emoji_chengyu.data import DefaultEmojiManager from emoji_chengyu.data import clean_tone from emoji_chengyu.data import split_pinyin class TestData(unittest.TestCase): def test_load_on_init(self): assert len(DefaultChengyuManager.chengyu_list) > 0 assert len(CommonChengyuManager.chengyu_list) > 0 assert len(DefaultEmojiManager.emoji_list) > 0 def test_clean_tone(self): self.assertEqual(clean_tone('nihao'), 'nihao') self.assertEqual(clean_tone('dòu'), 'dou') self.assertEqual(clean_tone('zǒu gǒu'), 'zou gou') def test_split_pinyin(self): pinyin = "ān bù lí mǎ，jiǎ bù lí shēn" assert len(split_pinyin(pinyin)) == 9

30.62963

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827

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null

0

1

0

1

0

3

f9b38134648472f5513418f289ad7f59bb4c5113

2,017

py

Python

wlroots/wlr_types/__init__.py

anriha/pywlroots

13e043fc73b40249d09596f5c26580b1ae55ec54

[ "NCSA" ]

null

wlroots/wlr_types/__init__.py

anriha/pywlroots

13e043fc73b40249d09596f5c26580b1ae55ec54

[ "NCSA" ]

null

wlroots/wlr_types/__init__.py

anriha/pywlroots

13e043fc73b40249d09596f5c26580b1ae55ec54

[ "NCSA" ]

null

# Copyright (c) 2019 Sean Vig from .compositor import Compositor # noqa: F401 from .cursor import Cursor # noqa: F401 from .data_control_v1 import DataControlManagerV1 # noqa: F401 from .data_device_manager import DataDeviceManager # noqa: F401 from .foreign_toplevel_management_v1 import ForeignToplevelManagerV1 # noqa: F401 from .gamma_control_v1 import GammaControlManagerV1 # noqa: F401 from .input_device import InputDevice # noqa: F401 from .input_inhibit import InputInhibitManager # noqa: F401 from .keyboard import Keyboard # noqa: F401 from .layer_shell_v1 import LayerShellV1 # noqa: F401 from .matrix import Matrix # noqa: F401 from .output import Output # noqa: F401 from .output_damage import OutputDamage # noqa: F401 from .output_layout import OutputLayout # noqa: F401 from .pointer import ( # noqa: F401 PointerEventAxis, PointerEventButton, PointerEventMotion, PointerEventMotionAbsolute, ) from .pointer_constraints_v1 import ( # noqa: F401 PointerConstraintsV1, PointerConstraintV1, ) from .primary_selection_v1 import PrimarySelectionV1DeviceManager # noqa: F401 from .relative_pointer_manager_v1 import RelativePointerManagerV1 # noqa: F401 from .scene import Scene, SceneNode # noqa: F401 from .screencopy_v1 import ScreencopyManagerV1 # noqa: F401 from .seat import Seat # noqa: F401 from .surface import Surface, SurfaceState # noqa: F401 from .texture import Texture # noqa: F401 from .virtual_keyboard_v1 import VirtualKeyboardManagerV1 # noqa: F401 from .xcursor_manager import XCursorManager # noqa: F401 from .xdg_decoration_v1 import XdgDecorationManagerV1 # noqa: F401 from .xdg_output_v1 import XdgOutputManagerV1 # noqa: F401 from .xdg_shell import XdgShell # noqa: F401 def __getattr__(name: str): if name == "Box": from .box import Box # noqa: F401 return Box try: return globals()[name] except KeyError: raise ImportError(f"cannot import name '{name}' from wlroots.wlr_types")

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null

0

1

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null

0

1

0

1

0

3

f9bb4c56c85fe0458266ec52b26fe1dbee6037d6

967

py

Python

tbr_reg/__init__.py

ucl-tbr-group-project/regression

ab02f62cdf83ffc43d9b8e88b0c1833190d65b95

[ "MIT" ]

null

tbr_reg/__init__.py

ucl-tbr-group-project/regression

ab02f62cdf83ffc43d9b8e88b0c1833190d65b95

[ "MIT" ]

null

tbr_reg/__init__.py

ucl-tbr-group-project/regression

ab02f62cdf83ffc43d9b8e88b0c1833190d65b95

[ "MIT" ]

null

from .autoencoders import create_autoencoder from .autoencoders import train_autoencoder from .data_utils import load_batches from .data_utils import encode_data_frame from .data_utils import x_y_split from .data_utils import c_d_y_split from .gans import create_discriminator from .gans import create_generator from .gans import create_discriminator_model from .gans import create_adversarial_model from .gans import train_gan from .hyperopt.grid_search import grid_search from .hyperopt.model_space import model_space_product from .model_loader import get_model_factory from .model_loader import load_model_from_file from .plot_params_vs_tbr import plot_params_vs_tbr from .plot_reg_performance import plot_reg_performance from .plot_reg_vs_time import plot_reg_vs_time from .plot_utils import set_plotting_style from .plot_utils import density_scatter from .train import apply_on_y_columns from .train import fit_multiple from .train import predict_multiple

32.233333

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5.130719

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0.089172

0.096815

0.084076

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0.101344

967

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55

33.344828

0.903337

0

1

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true

0

1

0

1

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null

0

null

0

1

0

1

0

1

0

3

f9cac33d6aab543b9bb9aaf8d87b105473df95c9

33

py

Python

src/__init__.py

uvraj88/SimpleNetworkService

9396654f148170f02ce13577a25f8caaa6415a2e

[ "Apache-2.0" ]

null

src/__init__.py

uvraj88/SimpleNetworkService

9396654f148170f02ce13577a25f8caaa6415a2e

[ "Apache-2.0" ]

null

src/__init__.py

uvraj88/SimpleNetworkService

9396654f148170f02ce13577a25f8caaa6415a2e

[ "Apache-2.0" ]

null

#init file __version__ = '1.0.0'

11

21

0.666667

6

33

3

0.833333

0

0.107143

0.151515

33

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0

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0

1

0

null

0

1

0

null

0

3

ddb24d05ac2913bf9d5467c1c017c03fc1c95ef3

8,629

py

Python

printers/fairresults70.py

simonlet/MissRateSimulator

89f481a3408d243a61a6907f332e36ca9a2fb45d

[ "MIT" ]

1

2021-05-26T16:03:13.000Z

printers/fairresults70.py

simonlet/MissRateSimulator

89f481a3408d243a61a6907f332e36ca9a2fb45d

[ "MIT" ]

null

printers/fairresults70.py

simonlet/MissRateSimulator

89f481a3408d243a61a6907f332e36ca9a2fb45d

[ "MIT" ]

3

2019-05-27T16:53:28.000Z

2021-02-07T21:58:11.000Z

import matplotlib matplotlib.use('Agg') import matplotlib.patches as mpatches import random import math import numpy as np import matplotlib.pyplot as plt import itertools from matplotlib import rcParams from matplotlib.backends.backend_pdf import PdfPages #fileName = 'diffrent_set_size' fileName = '10tasks_u70' folder = 'final_plot/' perfault = [] # plot in pdf pp = PdfPages(folder + fileName + '.pdf') percentageU = 70 #title = 'Tasks: '+ repr(10) + ', Utilization:'+repr(percentageU)+'%' title = 'Tasks: '+ repr(10) + ', $U^N_{SUM}$:'+repr(percentageU)+'%' faultRate = [10**-2., 10**-4, 10**-6.] rate102 = [0.00222184606643217, 9.39324716686523e-10, 1.49597848095289e-8, 2.82808218593350e-6, 0.000519849044411903, 7.91475444562763e-10, 7.63137213153905e-9, 0.00844286257163001, 4.85236052205006e-5, 2.90563491251103e-8, 1.91460710499306e-5, 3.74261719880609e-9, 0.00713732790552336, 0.000207450616666342, 1.39423096296592e-7, 1.12540466961398e-6, 0.000365215756628136, 0.000610315643742477, 0.0141338447497429, 9.75821568330343e-11, 3.14328179987514e-9, 7.94471258590973e-5, 4.31921903676913e-9, 3.22952584412667e-8, 1.21789332986402e-8, 3.50470600511955e-9, 1.07798151743278e-9, 2.41743004314943e-9, 1.53109752426535e-5, 6.69294805754113e-5, 4.43053428536352e-5, 0.129091977880312, 6.57197834950264e-8, 8.14766361347744e-10, 0.000109200822785597, 8.33601490379021e-7, 1.42511699542707e-8, 0.0259112869376599, 1.97181386727492e-8, 2.66813604969816e-8, 2.56688412663861e-9, 0.000301936621855498, 0.00273439666925762, 5.40020267302603e-8, 1.74936443476837e-8, 1.54602431135751e-6, 0.000568039358057984, 3.19406528700786e-11, 1.15739385850340e-6, 0.00180186413239562, 3.03198334119634e-5, 1.03149500246218e-9, 0.0129149469144891, 0.0143390629520243, 0.00259558019110573, 1.93580791164615e-8, 2.55111987179379e-7, 0.000152258872221307, 5.42604163306919e-5, 2.01483792895990e-5, 0.000684083196343718, 4.16399041462163e-8, 2.13472945543019e-6, 1.53061079443339e-5, 4.28533411016388e-7, 0.00585649086012912, 2.37257895261147e-7, 0.000104624934316751, 3.01003596485980e-5, 2.27653602379746e-9, 6.29822794216893e-5, 1.44875341319109e-8, 3.62678316357252e-6, 7.02911964737374e-5, 5.77516941349414e-6, 0.0611608199907390, 1.42603058812823e-7, 1.22439187597120e-9, 7.37031693326042e-8, 5.66778932028624e-7, 1.51381148642878e-6, 5.87553980690649e-7, 5.56701215028766e-7, 1.98712384192308e-5, 3.02540207700675e-5, 0.000670861064895008, 0.00833562215374853, 0.000995406161983560, 0.0121058396237000, 2.15574010657646e-5, 2.97706259742780e-6, 5.62510432190370e-6, 1.60632355202967e-8, 1.02592081515856e-5, 1.63302734258775e-9, 6.53003207019999e-5, 5.89093970422537e-6, 7.97228025405875e-6, 2.43182208854725e-9, 0.00441120623659347] rate104 = [2.55922634486244e-9, 4.02511098423670e-7, 0.00239760214429604, 2.92184396061692e-10, 4.68027206076662e-11, 2.77189315369545e-7, 4.03757229254190e-10, 3.79332106423175e-6, 1.99132024417317e-9, 3.72599220833455e-5, 1.66893171485177e-6, 0.000140263086000775, 5.49910920259565e-7, 1.46020776398318e-7, 7.59011058216085e-8, 0.000680440579154460, 9.93208001913125e-7, 3.46311405252067e-10, 1.91213199475381e-6, 7.55088383015764e-8, 1.24781955378994e-6, 1.15370434185976e-5, 6.85316427995476e-10, 1.33744752500981e-8, 2.12136263540692e-6, 0.000166945214883034, 1.13641061115819e-6, 7.01225546690983e-9, 3.33002807838448e-8, 3.74556840722565e-10, 3.71368686833925e-9, 1.47221531580665e-8, 1.14071646669328e-8, 7.51558903780034e-8, 4.25350362325632e-5, 1.23779414960434e-7, 4.52282485426895e-9, 9.55235380921678e-9, 7.60192402213061e-10, 1.35566862267848e-8, 7.20241424474404e-8, 4.52843101819219e-5, 1.15163838038676e-9, 7.24721501414755e-8, 1.76426699014602e-8, 6.62810015386497e-8, 8.52047452974755e-11, 8.44107542687781e-10, 4.26862155165545e-13, 1.13999220453730e-9, 1.32066039353684e-6, 3.47672767934463e-8, 2.26590601126801e-10, 1.19406400021389e-7, 7.43728250508719e-9, 9.26329687702886e-9, 9.99327200558436e-10, 2.82290903681309e-6, 2.89794846966364e-7, 2.61412821277112e-8, 1.61921677166592e-8, 6.50828822701966e-8, 1.82341982305757e-9, 7.86976455864671e-11, 5.95354399895515e-10, 2.01648661895825e-7, 4.17308574139251e-6, 5.02162643942448e-12, 1.97158364139061e-6, 1.05249446361136e-10, 0.00135739377433914, 1.66399209160707e-6, 7.57494023507982e-8, 4.08937697570720e-7, 8.69189465901483e-9, 1.69504869756261e-10, 1.40137426975428e-10, 3.95837466833965e-9, 1.23392998307897e-10, 1.04665988150720e-7, 1.30726987992533e-7, 2.92781349377796e-11, 0.000868803822898168, 0.00257277203529016, 2.07299584107239e-9, 2.63019712043447e-7, 2.80363229738044e-10, 3.87962784285876e-6, 8.36214624379606e-9, 1.95584160695076e-11, 2.49866614732545e-7, 0.000611654233942630, 3.26616021684404e-7, 8.57540892372777e-12, 1.42505813760285e-7, 5.48403240083137e-9, 2.02895691825970e-8, 0.00231019725722616, 8.84974117290134e-11, 1.93458943327190e-7] rate106 = [8.10246326875006e-8, 3.19572386172112e-9, 3.70214040707043e-7, 2.81485362609499e-9, 2.66235949386522e-9, 8.48893373659518e-9, 0.000251633014266995, 0.000135382856725150, 1.00898330391177e-5, 1.98681838141496e-7, 8.45554235730134e-8, 6.35465746553238e-9, 8.91758800061116e-7, 4.42137419616922e-10, 4.57101772946562e-7, 1.87328885932463e-5, 9.72096057018516e-8, 1.44308567253600e-10, 0.000159427990721121, 0.00130897598013566, 3.67808657193573e-7, 2.97773597334758e-9, 1.33609909282283e-7, 3.96827811355925e-10, 6.97919198165053e-9, 1.18320210195456e-8, 2.16040337962765e-8, 2.08339322122311e-10, 1.98435666604422e-8, 1.42674788399530e-10, 2.07208671508576e-10, 3.62129606893985e-8, 6.04525877332040e-10, 2.35420635231133e-6, 1.05101361047384e-8, 6.03706301598164e-9, 3.53058371018829e-12, 3.41459273578394e-9, 1.53405530923801e-6, 8.61208993386428e-9, 4.86343231386139e-9, 1.91902888494632e-7, 2.69837228723710e-10, 6.84784370482018e-8, 0.000769147005785305, 0.000250265233675757, 1.18203624174800e-7, 4.16933304076519e-10, 1.65326623023707e-11, 2.64428098968946e-7, 4.90666504981627e-11, 2.42137123867731e-8, 4.46923732351041e-8, 4.72588114887817e-11, 1.25224682058919e-7, 1.25886692060796e-5, 4.00180818548924e-8, 8.26883841639840e-9, 1.98476299877318e-11, 1.82068141418345e-9, 7.09273345021130e-9, 8.16940726884649e-11, 5.49826401646357e-10, 4.04186360195653e-5, 4.59721463211207e-7, 6.54987436233689e-11, 4.91919078603306e-8, 1.62387725752927e-9, 1.20516317516089e-9, 2.36968590935437e-9, 7.22480389150433e-10, 6.58666085366840e-7, 1.64611675896198e-8, 4.99008066068288e-12, 1.29465058340227e-7, 2.20179378233447e-6, 1.47427861957454e-8, 3.28139875139643e-12, 3.79133089057844e-11, 6.66624900817902e-7, 5.80694957430743e-11, 2.48210279991865e-7, 6.98262056147752e-9, 1.68626690527661e-10, 3.46041782742860e-8, 7.26467025543694e-5, 6.97675913021538e-8, 4.84110109230271e-9, 6.39669263117586e-7, 1.32584269738637e-8, 3.91429149309245e-11, 1.41625758571705e-9, 3.49283325705739e-9, 7.49032340673913e-10, 7.68595473810612e-12, 6.22348878558919e-10, 7.59810676061588e-9, 1.75195124267886e-10, 1.79458794153281e-7, 9.95413237899837e-10] frate102 = [] frate104 = [] frate106 = [] for x in rate102: if x != 0: frate102.append(x) for x in rate104: if x != 0: frate104.append(x) for x in rate106: if x != 0: frate106.append(x) perfault.append(frate102) perfault.append(frate104) perfault.append(frate106) ''' lower_error=[] upper_error=[] median = [] for i in range(4): median.append(np.median(perfault[i])) lower_error.append(min(perfault[i])) upper_error.append(max(perfault[i])) asymmetric_error = (lower_error, upper_error) print(asymmetric_error) ''' #the blue box boxprops = dict(linewidth=2, color='blue') #the median line medianprops = dict(linewidth=2.5, color='red') whiskerprops = dict(linewidth=2.5, color='black') capprops = dict(linewidth=2.5) plt.title(title, fontsize=20) plt.grid(True) plt.ylabel('Expected Miss Rate', fontsize=20) plt.xlabel('Fault Rate $P^A_i$', fontsize=22) ax = plt.subplot() ax.set_yscale("log") ax.set_ylim([10**-21,10**0]) ax.tick_params(axis='both', which='major',labelsize=20) labels = ('$10^{-2}$','$10^{-4}$', '$10^{-6}$') try: ax.boxplot(perfault, 0, '', labels=labels, boxprops=boxprops, whiskerprops=whiskerprops, capprops=capprops) except ValueError: print("ValueError") figure = plt.gcf() figure.set_size_inches([10,6.5]) box = mpatches.Patch(color='blue', label='First to Third Quartiles', linewidth=3) av = mpatches.Patch(color='red', label='Median', linewidth=3) whisk = mpatches.Patch(color='black', label='Whiskers', linewidth=3) plt.legend(handles=[av, box, whisk], fontsize=16, frameon=True, loc=1) pp.savefig() plt.clf() pp.close()

90.831579

2,144

0.785375

1,179

8,629

5.731128

0.373198

0.00444

0.008288

0.00666

0.012432

0.002664

0

0.631055

0.069301

8,629

94

2,145

91.797872

0.21031

0.015877

0

0.04918

0

0.024079

0

1

0

false

0

0.147541

0

0.147541

0.016393

0

null

0

1

0

1

0

1

0

null

0

3

ddba631bac0c4624ecd16301bbc93a45be5e7b6a

186

py

Python

setup.py

uptrace/uptrace-python

808ee29e6cecfa561224c4f4387f8a9fb95e85a6

[ "BSD-2-Clause" ]

7

2020-10-10T09:07:06.000Z

2022-03-17T14:26:05.000Z

setup.py

uptrace/uptrace-python

808ee29e6cecfa561224c4f4387f8a9fb95e85a6

[ "BSD-2-Clause" ]

31

2021-02-23T11:31:15.000Z

2022-03-21T08:06:12.000Z

setup.py

uptrace/uptrace-python

808ee29e6cecfa561224c4f4387f8a9fb95e85a6

[ "BSD-2-Clause" ]

3

2020-11-30T13:36:54.000Z

2022-02-23T17:37:06.000Z

import os import setuptools PKG_INFO = {} with open(os.path.join("src", "uptrace", "version.py")) as f: exec(f.read(), PKG_INFO) setuptools.setup(version=PKG_INFO["__version__"])

18.6

61

0.698925

28

186

4.392857

0.642857

0.170732

0

0.123656

186

9

62

20.666667

0.754601

0

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0

1

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false

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

3

ddbf0cbb40263312c451826ff2e69b53928d7416

60

py

Python

package_updater/__init__.py

superlevure/package_updater

df80df23bd24fb3856cf723081ac4a68f2c412e3

[ "MIT" ]

null

package_updater/__init__.py

superlevure/package_updater

df80df23bd24fb3856cf723081ac4a68f2c412e3

[ "MIT" ]

null

package_updater/__init__.py

superlevure/package_updater

df80df23bd24fb3856cf723081ac4a68f2c412e3

[ "MIT" ]

null

from .package_updater import Update __version__ = "0.0.3"

12

35

0.75

9

60

4.444444

0.888889

0

0.058824

0.15

60

4

36

15

0.72549

0

0.084746

0

1

0

false

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

3

ddc06c65eb1a2f3358dc1d1c80fb8211ee59e6a3

445

py

Python

polyaxon/api/index/health.py

elyase/polyaxon

1c19f059a010a6889e2b7ea340715b2bcfa382a0

[ "MIT" ]

null

polyaxon/api/index/health.py

elyase/polyaxon

1c19f059a010a6889e2b7ea340715b2bcfa382a0

[ "MIT" ]

null

polyaxon/api/index/health.py

elyase/polyaxon

1c19f059a010a6889e2b7ea340715b2bcfa382a0

[ "MIT" ]

null

from rest_framework import status from rest_framework.response import Response from rest_framework.throttling import AnonRateThrottle from rest_framework.views import APIView class HealthRateThrottle(AnonRateThrottle): scope = 'health' class HealthView(APIView): authentication_classes = () throttle_classes = (HealthRateThrottle,) def get(self, request, *args, **kwargs): return Response(status=status.HTTP_200_OK)

26.176471

54

0.782022

49

445

6.938776

0.571429

0.094118

0.2

0

0.007895

0.146067

445

16

55

27.8125

0.886842

0

0.013483

0

1

0.090909

false

0

0.363636

0.090909

1

0

null

0

1

0

null

0

1

0

1

0

3

ddcca3c42effd1a86ff82fad018297ec94ad1127

138

py

Python

test/resources/inspectors/python/case3_redefining_builtin.py

hyperskill/hyperstyle

bf3c6e2dc42290ad27f2d30ce42d84a53241544b

[ "Apache-2.0" ]

18

2020-10-05T16:48:11.000Z

2022-03-22T04:15:38.000Z

test/resources/inspectors/python/case3_redefining_builtin.py

hyperskill/hyperstyle

bf3c6e2dc42290ad27f2d30ce42d84a53241544b

[ "Apache-2.0" ]

60

2020-10-05T17:01:05.000Z

2022-01-27T12:46:14.000Z

test/resources/inspectors/python/case3_redefining_builtin.py

hyperskill/hyperstyle

bf3c6e2dc42290ad27f2d30ce42d84a53241544b

[ "Apache-2.0" ]

6

2021-02-09T09:31:19.000Z

2021-08-13T07:45:51.000Z

a = int(input()) b = int(input()) range = range(a, b + 1) list = list(filter(lambda x: x % 3 == 0, range)) print(sum(list) / len(list))

17.25

48

0.572464

25

138

3.16

0.6

0.202532

0

0.027027

0.195652

138

7

49

19.714286

0.684685

0

1

0

false

0

0.2

1

0

null

1

0

1

0

null

0

3

ddd7affd6e7048e92218de8726cda8982eeb56a1

110

py

Python

nbconvert_reportlab/__init__.py

takluyver/nbconvert-reportlab

8f921dcd7fb441c2555642d7364b011d4711aaf3

[ "MIT" ]

13

2016-09-29T17:28:26.000Z

2019-09-09T15:08:47.000Z

nbconvert_reportlab/__init__.py

takluyver/nbconvert-reportlab

8f921dcd7fb441c2555642d7364b011d4711aaf3

[ "MIT" ]

4

2016-09-29T17:45:02.000Z

2017-11-15T09:31:55.000Z

nbconvert_reportlab/__init__.py

takluyver/nbconvert-reportlab

8f921dcd7fb441c2555642d7364b011d4711aaf3

[ "MIT" ]

3

2016-12-06T10:13:59.000Z

2019-11-29T09:46:49.000Z

"""Convert notebooks to PDF using Reportlab """ __version__ = '0.2' from .exporter import ReportlabExporter

15.714286

43

0.754545

13

110

6.076923

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0

0.021277

0.145455

110

6

44

18.333333

0.819149

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0

0.047619

0

1

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false

0

0.5

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0.5

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1

0

null

0

1

0

null

0

1

0

3

ddd93cb01b6f286d2cf3bf053ca404d9d463910d

270

py

Python

classes/angle.py

KenNewcomb/Topologist

8e3faffdb1945ca355b1667aaae48a51df930de1

[ "MIT" ]

3

2015-07-15T18:42:51.000Z

2019-05-01T10:52:57.000Z

classes/angle.py

KenNewcomb/Topologist

8e3faffdb1945ca355b1667aaae48a51df930de1

[ "MIT" ]

null

classes/angle.py

KenNewcomb/Topologist

8e3faffdb1945ca355b1667aaae48a51df930de1

[ "MIT" ]

null

# angle.py: A class describing an angle between three atoms. class Angle: atom1 = "" atom2 = "" atom3 = "" angle = 0.0 def __init__(self, atom1, atom2, atom3, angle): self.atom1 = atom1 self.atom2 = atom2 self.atom3 = atom3 self.angle = angle

19.285714

60

0.62963

37

270

4.486486

0.432432

0.120482

0.180723

0.240964

0

0.069652

0.255556

270

13

61

20.769231

0.756219

0.214815

0

1

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0

0.6

0

null

0

1

0

null

0

1

0

3

ddecb00af4970d6b0bfa6886415e8136f7d81a95

159

py

Python

EfficientCoding/Assignment-6-shooting.py

vikbehal/Explore

b35948d8a6894647df3ee462746475f7e66f78f8

[ "MIT" ]

3

2019-01-29T06:33:34.000Z

2022-01-26T20:01:04.000Z

EfficientCoding/Assignment-6-shooting.py

vikbehal/Explore

b35948d8a6894647df3ee462746475f7e66f78f8

[ "MIT" ]

null

EfficientCoding/Assignment-6-shooting.py

vikbehal/Explore

b35948d8a6894647df3ee462746475f7e66f78f8

[ "MIT" ]

1

2022-03-11T10:47:29.000Z

def solve(data): X, Y, N, W, P1, P2 = data for _ in range(int(input())): data = [int(num) for num in input().split(" ")] print(solve(data))

22.714286

52

0.534591

26

159

3.230769

0.653846

0.214286

0

0.017094

0.264151

159

7

53

22.714286

0.700855

0

0.006494

0

1

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false

0

0.2

1

0

null

1

0

1

0

null

0

3

ddfc17a7986a68754f19910472f7d868a2858c32

25

py

Python

cmsplugin_vimeo/test/__init__.py

rescale/cmsplugin_vimeo

246fc21755446412924ff67f3bc4c778244cee09

[ "BSD-3-Clause" ]

null

cmsplugin_vimeo/test/__init__.py

rescale/cmsplugin_vimeo

246fc21755446412924ff67f3bc4c778244cee09

[ "BSD-3-Clause" ]

null

cmsplugin_vimeo/test/__init__.py

rescale/cmsplugin_vimeo

246fc21755446412924ff67f3bc4c778244cee09

[ "BSD-3-Clause" ]

null

__author__ = 'francesco'

12.5

24

0.76

2

25

7.5

1

0

0.12

25

1

25

0.681818

0

0.36

0

1

0

false

0

1

0

null

0

1

0

null

0

3

fb0c8270e934912c80a9f2eda6592537eac1af02

1,434

py

Python

mibs/pycopia/mibs/SNMP_USM_DH_OBJECTS_MIB_OID.py

kdart/pycopia

1446fabaedf8c6bdd4ab1fc3f0ea731e0ef8da9d

[ "Apache-2.0" ]

89

2015-03-26T11:25:20.000Z

2022-01-12T06:25:14.000Z

mibs/pycopia/mibs/SNMP_USM_DH_OBJECTS_MIB_OID.py

kdart/pycopia

1446fabaedf8c6bdd4ab1fc3f0ea731e0ef8da9d

[ "Apache-2.0" ]

1

2015-07-05T03:27:43.000Z

2015-07-11T06:21:20.000Z

mibs/pycopia/mibs/SNMP_USM_DH_OBJECTS_MIB_OID.py

kdart/pycopia

1446fabaedf8c6bdd4ab1fc3f0ea731e0ef8da9d

[ "Apache-2.0" ]

30

2015-04-30T01:35:54.000Z

2022-01-12T06:19:49.000Z

# python # This file is generated by a program (mib2py). import SNMP_USM_DH_OBJECTS_MIB OIDMAP = { '1.3.6.1.3.101': SNMP_USM_DH_OBJECTS_MIB.snmpUsmDHObjectsMIB, '1.3.6.1.3.101.1': SNMP_USM_DH_OBJECTS_MIB.usmDHKeyObjects, '1.3.6.1.3.101.1.1': SNMP_USM_DH_OBJECTS_MIB.usmDHPublicObjects, '1.3.6.1.3.101.1.2': SNMP_USM_DH_OBJECTS_MIB.usmDHKickstartGroup, '1.3.6.1.3.101.2': SNMP_USM_DH_OBJECTS_MIB.usmDHKeyConformance, '1.3.6.1.3.101.2.1': SNMP_USM_DH_OBJECTS_MIB.usmDHKeyMIBCompliances, '1.3.6.1.3.101.2.2': SNMP_USM_DH_OBJECTS_MIB.usmDHKeyMIBGroups, '1.3.6.1.3.101.1.1.1': SNMP_USM_DH_OBJECTS_MIB.usmDHParameters, '1.3.6.1.3.101.1.1.2.1.1': SNMP_USM_DH_OBJECTS_MIB.usmDHUserAuthKeyChange, '1.3.6.1.3.101.1.1.2.1.2': SNMP_USM_DH_OBJECTS_MIB.usmDHUserOwnAuthKeyChange, '1.3.6.1.3.101.1.1.2.1.3': SNMP_USM_DH_OBJECTS_MIB.usmDHUserPrivKeyChange, '1.3.6.1.3.101.1.1.2.1.4': SNMP_USM_DH_OBJECTS_MIB.usmDHUserOwnPrivKeyChange, '1.3.6.1.3.101.1.2.1.1.1': SNMP_USM_DH_OBJECTS_MIB.usmDHKickstartIndex, '1.3.6.1.3.101.1.2.1.1.2': SNMP_USM_DH_OBJECTS_MIB.usmDHKickstartMyPublic, '1.3.6.1.3.101.1.2.1.1.3': SNMP_USM_DH_OBJECTS_MIB.usmDHKickstartMgrPublic, '1.3.6.1.3.101.1.2.1.1.4': SNMP_USM_DH_OBJECTS_MIB.usmDHKickstartSecurityName, '1.3.6.1.3.101.2.2.1': SNMP_USM_DH_OBJECTS_MIB.usmDHKeyMIBBasicGroup, '1.3.6.1.3.101.2.2.2': SNMP_USM_DH_OBJECTS_MIB.usmDHKeyParamGroup, '1.3.6.1.3.101.2.2.3': SNMP_USM_DH_OBJECTS_MIB.usmDHKeyKickstartGroup, }

53.111111

78

0.778243

306

1,434

3.385621

0.133987

0.07722

0.173745

0.30888

0.573359

0.53668

0.472973

0.198842

0.092664

0

0.15625

0.040446

1,434

26

79

55.153846

0.596657

0.036262

0

1

0.363636

0.269231

0.133527

0

1

0

false

0

0.045455

0

0.045455

0

null

0

1

0

1

0

null

0

3

fb0d02a5d465c49c56af84ea66d6a3bb2cbf99c5

1,105

py

Python

odp/db/models/client.py

SAEONData/Open-Data-Platform

cfd2a53e145ec86e187d7c4e1260df17ec6dcb03

[ "MIT" ]

2

2021-03-04T07:09:47.000Z

2022-01-02T19:23:41.000Z

odp/db/models/client.py

SAEONData/Open-Data-Platform

cfd2a53e145ec86e187d7c4e1260df17ec6dcb03

[ "MIT" ]

18

2020-09-16T09:16:45.000Z

2022-01-25T14:17:42.000Z

odp/db/models/client.py

SAEONData/Open-Data-Platform

cfd2a53e145ec86e187d7c4e1260df17ec6dcb03

[ "MIT" ]

1

2021-06-25T13:02:57.000Z

from sqlalchemy import Column, String, ForeignKey from sqlalchemy.ext.associationproxy import association_proxy from sqlalchemy.orm import relationship from odp.db import Base from odp.db.models.client_scope import ClientScope class Client(Base): """Client application config. The associated scopes represent the set of permissions granted to the client. If a client is linked to a provider, then its scopes apply only to entities that are associated with that provider. """ __tablename__ = 'client' id = Column(String, primary_key=True) name = Column(String, unique=True, nullable=False) provider_id = Column(String, ForeignKey('provider.id', ondelete='CASCADE')) provider = relationship('Provider') # many-to-many relationship between client and scope client_scopes = relationship('ClientScope', back_populates='client', cascade='all, delete-orphan', passive_deletes=True) scopes = association_proxy('client_scopes', 'scope', creator=lambda s: ClientScope(scope=s)) def __repr__(self): return self._repr('id', 'name', 'provider')

35.645161

124

0.742986

140

1,105

5.735714

0.514286

0.059776

0.054795

0

0.163801

1,105

30

125

36.833333

0.869048

0.247059

0

0.122373

0

1

0.066667

false

0.066667

0.333333

0.066667

1

0

null

0

null

0

1

0

1

0

3

fb1ba3a5f26babae6c55761357c299747dca3d7d

1,225

py

Python

obj/category.py

ArthurBartoli/QuestLog

96acb8279c067547dc1b44556af6c9fd280284aa

[ "MIT" ]

null

obj/category.py

ArthurBartoli/QuestLog

96acb8279c067547dc1b44556af6c9fd280284aa

[ "MIT" ]

null

obj/category.py

ArthurBartoli/QuestLog

96acb8279c067547dc1b44556af6c9fd280284aa

[ "MIT" ]

null

from questlog import QuestLog class Category(): '''A category is a subfolder of a QuestLog and contains itself another QuestLog''' def __init__(self, title, ql_title, ql_parent, ql_desc=''): self.title = title self.questlog = QuestLog(ql_title, desc=ql_desc) self.ql_parent = ql_parent self._pos_parent = self.ql_parent.search_log(self.title) def __str__(self): return f"{self.questlog}" def __len__(self): return len(self.questlog) # Reader Functions def read_title(self): return self.title def read_ql_title(self): return self.questlog.read_name() def read_desc(self): return self.questlog.read_desc() # Access Functions def change_title(self, title): self.title = title def change_ql(self, ql_title, ql_desc): self.questlog = QuestLog(ql_title, desc=ql_desc) def change_parent(self, new_parent): self.ql_parent.remove_category(self) self.ql_parent = new_parent self.ql_parent.add_category(self) self._pos_parent = self.ql_parent.search_log(self.title) def correct_pos(self): self._pos_parent = self.ql_parent.search_log(self.title)

29.166667

86

0.671837

170

1,225

4.535294

0.217647

0.093385

0.108949

0.116732

0.403372

0.281453

0.185473

0

0.231837

1,225

41

87

29.878049

0.819341

0.090612

0

0.25

0

0.01355

0

1

0.357143

false

0

0.035714

0.178571

0.607143

0

null

0

null

0

1

0

1

0

3

fb2058691cb76f9bf1b051e83d3299359a200c6c

397

py

Python

Python_OCR_JE/venv/Lib/site-packages/numpy/typing/tests/data/fail/numerictypes.py

JE-Chen/je_old_repo

a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5

[ "MIT" ]

null

Python_OCR_JE/venv/Lib/site-packages/numpy/typing/tests/data/fail/numerictypes.py

JE-Chen/je_old_repo

a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5

[ "MIT" ]

null

Python_OCR_JE/venv/Lib/site-packages/numpy/typing/tests/data/fail/numerictypes.py

JE-Chen/je_old_repo

a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5

[ "MIT" ]

1

2021-04-26T22:41:56.000Z

import numpy as np # Techincally this works, but probably shouldn't. See # # https://github.com/numpy/numpy/issues/16366 # np.maximum_sctype(1) # E: incompatible type "int" np.issubsctype(1, np.int64) # E: incompatible type "int" np.issubdtype(1, np.int64) # E: incompatible type "int" np.find_common_type(np.int64, np.int64) # E: incompatible type "Type[signedinteger[Any]]"

28.357143

91

0.702771

59

397

4.677966

0.525424

0.188406

0.246377

0.217391

0.384058

0.217391

0

0.048048

0.161209

397

13

92

30.538462

0.780781

0.564232

0

1

0

true

0

0.2

0

0.2

0

null

0

1

0

null

0

1

0

3

fb25b3d7a2fe7cd6282ebe55438c530b80c2419e

517

py

Python

env/experiments.py

clayton-ho/EGGs_Control

312f02488b47cf880c6e6600ce10856a871123df

[ "MIT" ]

null

env/experiments.py

clayton-ho/EGGs_Control

312f02488b47cf880c6e6600ce10856a871123df

[ "MIT" ]

null

env/experiments.py

clayton-ho/EGGs_Control

312f02488b47cf880c6e6600ce10856a871123df

[ "MIT" ]

null

""" Stores everything needed to write experiments. """ __all__ = [] #experiments from EGGS_labrad.lib.servers.script_scanner.experiment import experiment __all__.append("experiment") from EGGS_labrad.lib.servers.script_scanner import experiment_classes from EGGS_labrad.lib.servers.script_scanner.experiment_classes import * __all__.extend(experiment_classes.__all__) #pulser from EGGS_labrad.lib.servers.pulser import sequence from EGGS_labrad.lib.servers.pulser.sequence import * __all__.extend(sequence.__all__)

30.411765

72

0.839458

67

517

5.955224

0.313433

0.100251

0.175439

0.213033

0.478697

0.328321

0.235589

0

0.073501

517

17

73

30.411765

0.832985

0.123791

0

0.022472

0

1

0

false

0

0.555556

0

0.555556

0

null

0

1

0

null

0

1

0

1

0

3

fb3693408e7092730e43d1020be124f53cd11da7

322

py

Python

server/project/apps/core/forms.py

CalHoll/9001

7fe371346d4fefb7b2434262da67b7e1b18057e4

[ "MIT" ]

54

2017-01-20T20:05:23.000Z

2022-02-02T12:34:33.000Z

server/project/apps/core/forms.py

CalHoll/9001

7fe371346d4fefb7b2434262da67b7e1b18057e4

[ "MIT" ]

104

2019-11-25T08:33:52.000Z

2021-08-02T06:17:19.000Z

server/project/apps/core/forms.py

CalHoll/9001

7fe371346d4fefb7b2434262da67b7e1b18057e4

[ "MIT" ]

9

2017-01-26T05:56:01.000Z

2018-05-15T16:50:19.000Z

import django_filters from .models import Playlist, FavoritesList class PlaylistFilter(django_filters.FilterSet): class Meta: model = Playlist fields = ('user_id', ) class FavoritesListFilter(django_filters.FilterSet): class Meta: model = FavoritesList fields = ('user_id', )

18.941176

52

0.686335

32

322

6.75

0.5

0.180556

0.203704

0.25

0.333333

0

0.232919

322

16

53

20.125

0.874494

0

0.4

0

0.043478

0

1

0

false

0

0.2

0

0.6

0

null

0

1

0

null

0

1

0

3

348dc17ec6bd1a910d2e1f23c9de1eb0677fad7a

459

py

Python

hello_world.py

edmund-bishanga/pytest_and_selenium_expts

e6e34e80f5d5bf853ffbbe1d96e567568319b827

[ "MIT" ]

null

hello_world.py

edmund-bishanga/pytest_and_selenium_expts

e6e34e80f5d5bf853ffbbe1d96e567568319b827

[ "MIT" ]

null

hello_world.py

edmund-bishanga/pytest_and_selenium_expts

e6e34e80f5d5bf853ffbbe1d96e567568319b827

[ "MIT" ]

null

#!/usr/bin/python print("Hello world") print("GOD LOVES YOU") print("John 3:16\n") print("CHRIST JESUS: EMMANUEL...") print("Full of TRUTH & GRACE") print("John 1:17\n") print("Love God: with ALL you are... + the kitchen sink!") print("Love ya Neighbour: as you already love ya self...") print("ENJOY THE DISCIPLESHIP PROCESS") print("HALLELUJAH!!!\n") print("just another pilgrim: Edmund Muzoora BISHANGA") print("#TeamEMMANUELForever") print("#YNWA")

21.857143

58

0.697168

68

459

4.705882

0.676471

0.05625

0

0.014888

0.122004

459

20

59

22.95

0.779156

0.034858

0

0.693182

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

3

349b39884bb881700fb84a1d3950aac605a02b25

2,290

py

Python

tests/test_abc.py

neurobin/python-ocd

178bc7923e4702a1d90b2b38bc28515921f8553d

[ "BSD-3-Clause" ]

null

tests/test_abc.py

neurobin/python-ocd

178bc7923e4702a1d90b2b38bc28515921f8553d

[ "BSD-3-Clause" ]

1

2020-04-26T16:00:32.000Z

tests/test_abc.py

neurobin/python-easyvar

178bc7923e4702a1d90b2b38bc28515921f8553d

[ "BSD-3-Clause" ]

null

import unittest from ocd import abc from ocd.prop import Prop class Test_abc(unittest.TestCase): def setUp(self): # init pass def tearDown(self): # destruct pass def test_abc_VarConf_NIMP(self): # NIMP: Not Itempemented class VarConf(abc.VarConf): pass with self.assertRaises(TypeError): VarConf.get_conf() with self.assertRaises(TypeError): VarConf.get_conf(None) with self.assertRaises(TypeError): VarConf.get_conf(None, None) with self.assertRaises(NotImplementedError): VarConf.get_conf(None, None, None) with self.assertRaises(TypeError): VarConf.get_conf(None, None, None, None) conf = VarConf() with self.assertRaises(TypeError): conf.get_conf() with self.assertRaises(TypeError): conf.get_conf(None) with self.assertRaises(NotImplementedError): conf.get_conf(None, None) with self.assertRaises(TypeError): conf.get_conf(None, None, None) def test_abc_VarConf_IMP(self): # IMP: implemented class VarConf(abc.VarConf): def get_conf(self, name, value): return None conf = VarConf() p = conf.get_conf(None, None) self.assertTrue(isinstance(p, Prop) or p is None) class VarConf(abc.VarConf): def get_conf(self, name, value): return Prop() conf = VarConf() p = conf.get_conf(None, None) self.assertTrue(isinstance(p, Prop) or p is None) class VarConf(abc.VarConf): def get_conf(self, name, value): return True conf = VarConf() p = conf.get_conf(None, None) with self.assertRaises(AssertionError): self.assertTrue(isinstance(p, Prop) or p is None) class VarConf(abc.VarConf): def get_conf(self, name, value): return self conf = VarConf() p = conf.get_conf(None, None) with self.assertRaises(AssertionError): self.assertTrue(isinstance(p, Prop) or p is None) if __name__ == '__main__': unittest.main(verbosity=2)

28.987342

61

0.581659

260

2,290

5

0.165385

0.091538

0.169231

0.103846

0.771538

0.736154

0.700769

0.621538

0.506154

0

0.000647

0.325328

2,290

78

62

29.358974

0.840777

0.023144

0

0.610169

0

0.003586

0

0.254237

1

0.135593

false

0.050847

0.067797

0.355932

0

null

0

1

0

null

0

1

0

3

34aa5c06330b1d511b424db33ed44e5d7dd8129d

189

py

Python

booktest/forms.py

Adolph-Anthony/study_django

9ba9037107c8b1763bbc449d9d2c14f028ca0672

[ "MIT" ]

null

booktest/forms.py

Adolph-Anthony/study_django

9ba9037107c8b1763bbc449d9d2c14f028ca0672

[ "MIT" ]

1

2019-07-01T02:21:06.000Z

2019-07-02T01:12:54.000Z

booktest/forms.py

Adolph-Anthony/study_django

9ba9037107c8b1763bbc449d9d2c14f028ca0672

[ "MIT" ]

null

from django import forms class BookInfoForm(forms.Form): btitle = forms.CharField(label='图书名称',required=True,max_length=20) bpub_date = forms.DateField(label='发型日期',required=True)

31.5

70

0.761905

26

189

5.461538

0.769231

0.169014

0

0.011905

0.111111

189

5

71

37.8

0.833333

0

0.042328

0

1

0

false

0

0.25

0

1

0

1

0

null

0

1

0

null

0

1

0

3

34ad2ccb8ea8232aec89a5f71c1d054aa77dc2ad

13,970

py

Python

bot/New bot.py

AllisonW2323/creativecode

e7c276de46d6208505f2bd1c2922a2f0e0bd1ecd

[ "MIT" ]

null

bot/New bot.py

AllisonW2323/creativecode

e7c276de46d6208505f2bd1c2922a2f0e0bd1ecd

[ "MIT" ]

null

bot/New bot.py

AllisonW2323/creativecode

e7c276de46d6208505f2bd1c2922a2f0e0bd1ecd

[ "MIT" ]

null

{ "origin": [ "Where in the world am I? #Place#? I should be in prison. ", "#greeting#, remember to thank #Place# for their sacrifice of hosting Trump today! Someone save me.", "I escaped to #Place# but Trumps on my tail! I need help!", "Things I overhear: collusion....Idoit....#Phrase#......", "I'm having a great time in #Place#! I flew away and hope to never see him again, wait, is that...? Oh no.", "#greeting#! Decided to stop by at #Place#! If you live here, please keep an eye out so you don't become another victim of Trump! Stay safe!", "Oh my, I just saw a #animal# in #Place#! I hope it carries me away somewhere safe!", "Yay! #animal# is running off with me in #Place#! I'm free!", "True Trump quotes: #true# #impeach", "Many words have been thrown my way like: #noun#. And you know what? I'm not even mad, its true.", "Boy, I sure love to dance with #animal#! Damn it, Trump found me again... see you later friends!", "I know the real reason Trump doesn't own a pet...it's because he is afriad of #animal# and thinks that a dog will act the same way."], "animal": ["Aardvark", "Abyssinian", "Adelie Penguin", "Affenpinscher", "Afghan Hound", "African Bush Elephant", "African Civet", "African Clawed Frog", "African Forest Elephant", "African Palm Civet", "African Penguin", "African Tree Toad", "Airedale Terrier", "Akbash", "Akita", "Alaskan Malamute", "Albatross", "Aldabra Giant Tortoise", "Alligator", "American Staffordshire Terrier", "American Water Spaniel", "Angelfish", "Ant", "Anteater", "Antelope", "Arctic Fox", "Arctic Hare", "Arctic Wolf", "Armadillo", "Asian Elephant", "Asian Giant Hornet", "Asian Palm Civet", "Asiatic Black Bear", "Australian Mist", "Australian Shepherd", "Australian Terrier", "Avocet", "Axolotl", "Aye Aye", "Baboon", "Bactrian Camel", "Badger", "Balinese", "Banded Palm Civet", "Bandicoot", "Barb", "Barn Owl", "Barnacle", "Barracuda", "Basking Shark", "Basset Hound", "Bat", "Bavarian Mountain Hound", "Beagle", "Bear", "Bearded Collie", "Bearded Dragon", "Beaver", "Bedlington Terrier", "Beetle", "Bengal Tiger", "Bichon Frise", "Binturong", "Bird","Birds Of Paradise", "Birman", "Bison", "Black Bear", "Black Rhinoceros", "Black Russian Terrier", "Black Widow Spider", "Blue Whale", "Bobcat", "Bombay", "Bongo", "Bonobo", "Booby", "Bornean Orang-utan", "Borneo Elephant", "Boston Terrier", "Bottle Nosed Dolphin", "Boykin Spaniel", "Brazilian Terrier", "Brown Bear", "Budgerigar", "Buffalo", "Bull Mastiff", "Bull Shark", "Bull Terrier", "Bullfrog", "Bumble Bee", "Burmese", "Burrowing Frog", "Butterfly", "Butterfly Fish", "Caiman", "Caiman Lizard", "Cairn Terrier", "Camel", "Capybara", "Caracal", "Cassowary", "Caterpillar", "Catfish", "Cavalier King Charles Spaniel", "Centipede", "Cesky Fousek", "Chameleon", "Chamois", "Cheetah", "Chesapeake Bay Retriever", "Chicken", "Chimpanzee", "Chinchilla", "Chinook", "Chinstrap Penguin", "Chipmunk", "Chow Chow", "Cichlid", "Clouded Leopard", "Clown Fish", "Coati", "Cockroach", "Collared Peccary", "Collie", "Common Buzzard", "Common Frog", "Common Loon", "Common Toad", "Coral", "Cottontop Tamarin", "Cougar", "Cow", "Coyote", "Crab", "Crab-Eating Macaque", "Crane", "Crested Penguin", "Crocodile", "Cross River Gorilla", "Curly Coated Retriever", "Cuscus", "Cuttlefish", "Darwin's Frog", "Deer", "Desert Tortoise", "Deutsche Bracke", "Dhole", "Dingo", "Discus", "Doberman Pinscher", "Dodo", "Dogo Argentino", "Dogue De Bordeaux", "Dolphin", "Donkey", "Dormouse", "Dragonfly", "Drever", "Duck", "Dugong", "Dunker", "Dusky Dolphin", "Dwarf Crocodile", "Eagle", "Earwig", "Eastern Gorilla", "Eastern Lowland Gorilla", "Echidna", "Edible Frog", "Egyptian Mau", "Electric Eel", "Elephant", "Elephant Seal", "Elephant Shrew", "Emperor Penguin", "Emperor Tamarin", "Emu", "Epagneul Pont Audemer", "Falcon", "Fennec Fox", "Ferret", "Fin Whale", "Finnish Spitz", "Fire-Bellied Toad", "Fish", "Flamingo", "Flounder", "Fly", "Flying Squirrel", "Fossa", "Fox", "Frigatebird", "Frilled Lizard", "Frog", "Fur Seal", "Galapagos Penguin", "Galapagos Tortoise", "Gar", "Gecko", "Gentoo Penguin", "Geoffroys Tamarin", "Gerbil", "Gharial", "Giant African Land Snail", "Giant Clam", "Giant Panda Bear", "Giant Schnauzer", "Gibbon", "Gila Monster", "Giraffe", "Glass Lizard", "Glow Worm", "Goat", "Golden Lion Tamarin", "Golden Oriole", "Goose", "Gopher", "Gorilla", "Grasshopper", "Great White Shark", "Green Bee-Eater", "Grey Mouse Lemur", "Grey Reef Shark", "Grey Seal", "Grizzly Bear", "Grouse", "Guinea Fowl", "Guinea Pig", "Guppy", "Hammerhead Shark", "Hamster", "Hare", "Harrier", "Havanese", "Hedgehog", "Hercules Beetle", "Hermit Crab", "Heron", "Highland Cattle", "Himalayan", "Hippopotamus", "Honey Bee", "Horn Shark", "Horned Frog", "Horse", "Horseshoe Crab", "Howler Monkey", "Human", "Humboldt Penguin", "Hummingbird", "Humpback Whale", "Hyena", "Ibis", "Ibizan Hound", "Iguana", "Impala", "Indian Elephant", "Indian Palm Squirrel", "Indian Rhinoceros", "Indian Star Tortoise", "Indochinese Tiger", "Indri", "Insect", "Jackal", "Jaguar", "Japanese Chin", "Japanese Macaque", "Javan Rhinoceros", "Javanese", "Jellyfish", "Kakapo", "Kangaroo", "Keel Billed Toucan", "Killer Whale", "King Crab", "King Penguin", "Kingfisher", "Kiwi", "Koala", "Komodo Dragon", "Kudu", "Labradoodle", "Labrador Retriever", "Ladybird", "Leaf-Tailed Gecko", "Lemming", "Lemur", "Leopard", "Leopard Cat", "Leopard Seal", "Leopard Tortoise", "Liger", "Lion", "Lionfish", "Little Penguin", "Lizard", "Llama", "Lobster", "Long-Eared Owl", "Lynx", "Macaroni Penguin", "Macaw", "Magellanic Penguin", "Magpie", "Malayan Civet", "Malayan Tiger", "Manatee", "Mandrill", "Manta Ray", "Marine Toad", "Markhor", "Marsh Frog", "Masked Palm Civet", "Mayfly", "Meerkat", "Millipede", "Minke Whale", "Mole", "Molly", "Mongoose", "Mongrel", "Monitor Lizard", "Monkey", "Monte Iberia Eleuth", "Moorhen", "Moose", "Moray Eel", "Moth", "Mountain Gorilla", "Mountain Lion", "Mouse", "Mule", "Neanderthal", "Neapolitan Mastiff", "Newfoundland", "Newt", "Nightingale", "Norwegian Forest", "Numbat", "Nurse Shark", "Ocelot", "Octopus", "Okapi", "Olm", "Opossum", "Orang-utan", "Ostrich", "Otter", "Oyster", "Pademelon", "Panther", "Parrot", "Patas Monkey", "Peacock", "Pelican", "Penguin", "Persian", "Pheasant", "Pied Tamarin", "Pig", "Pika", "Pike", "Pink Fairy Armadillo", "Piranha", "Platypus", "Pointer", "Poison Dart Frog", "Polar Bear", "Pond Skater", "Pool Frog", "Porcupine", "Possum", "Prawn", "Proboscis Monkey", "Puffer Fish", "Puffin", "Pug", "Puma", "Purple Emperor", "Puss Moth", "Pygmy Hippopotamus", "Pygmy Marmoset", "Quail", "Quetzal", "Quokka", "Quoll", "Rabbit", "Raccoon", "Radiated Tortoise", "Ragdoll", "Rat", "Rattlesnake", "Red Knee Tarantula", "Red Panda", "Red Wolf", "Red-handed Tamarin", "Reindeer", "Rhinoceros", "River Dolphin", "River Turtle", "Robin", "Rock Hyrax", "Rockhopper Penguin", "Roseate Spoonbill", "Royal Penguin", "Russian Blue", "Sabre-Toothed Tiger", "Saint Bernard", "Salamander", "Sand Lizard", "Saola", "Scorpion", "Scorpion Fish", "Sea Dragon", "Sea Lion", "Sea Otter", "Sea Slug", "Sea Squirt", "Sea Turtle", "Sea Urchin", "Seahorse", "Seal", "Serval", "Sheep", "Shih Tzu", "Shrimp", "Siamese Fighting Fish", "Siberian Tiger", "Silver Dollar", "Skunk", "Sloth", "Slow Worm", "Snail", "Snake", "Snapping Turtle", "Snowshoe", "Snowy Owl", "Somali", "South China Tiger", "Spadefoot Toad", "Sparrow", "Spectacled Bear", "Sperm Whale", "Spider Monkey", "Spiny Dogfish", "Sponge", "Squid", "Squirrel", "Squirrel Monkey", "Sri Lankan Elephant", "Stag Beetle", "Starfish", "Stellers Sea Cow", "Stick Insect", "Stingray", "Stoat", "Striped Rocket Frog", "Sumatran Elephant", "Sumatran Orang-utan", "Sumatran Rhinoceros", "Sumatran Tiger", "Sun Bear", "Swan", "Tang", "Tapanuli Orang-utan", "Tapir", "Tarsier", "Tasmanian Devil", "Tawny Owl", "Termite", "Tetra", "Thorny Devil", "Tibetan Mastiff", "Tiffany", "Tiger", "Tiger Salamander", "Tiger Shark", "Tortoise", "Toucan", "Tree Frog", "Tropicbird", "Tuatara", "Turkey", "Turkish Angora", "Uakari", "Uguisu", "Umbrellabird", "Vampire Bat", "Vervet Monkey", "Vulture", "Wallaby", "Walrus", "Warthog", "Wasp", "Water Buffalo", "Water Dragon", "Water Vole", "Weasel", "Welsh Corgi", "West Highland Terrier", "Western Gorilla", "Western Lowland Gorilla", "Whale Shark", "Whippet", "White Faced Capuchin", "White Rhinoceros", "White Tiger", "Wild Boar", "Wildebeest", "Wolf", "Wolverine", "Wombat", "Woodlouse", "Woodpecker", "Woolly Mammoth", "Woolly Monkey", "Wrasse", "X-Ray Tetra", "Yak", "Yellow-Eyed Penguin", "Yorkshire Terrier", "Zebra", "Zebra Shark", "Zebu", "Zonkey", "Zorse"], "noun": ["pheasant", "bug", "far too expensive bunch of thread", "taxpayer payed bunch of twigs", "bobcat toy", "meat hat", "ass hat", "corn colored corncob", "little son", "politician hat made by mommy", "Last minute cosplay", "leftover mop", "water", "vegan top hat", "purple pestilence", "pelicans dinner", "day old fish", "cat vomit", "college student funded wig", "MAGA hat extension", "disrespecting women wig"], "adjective": ["horrendous", "vegan", "scrawny", "questionable", "swift", "(you said hair Jimmy? That doesn't sound quite right.)", "quality", "affordable", "menswear", "unfitting", "gross", "updo"], "greeting": ["Howdy", "What's up ya'll", "Yo", "Welcome", "It's me again"], "Phrase": ["Putin is huuuuge, the best", "I hate everyone", "Alright! I did it! Okay? I did the bad stuff." , "mexicans are in Spain and it's all crazy."], "true": ["I've always said, 'If you need Viagra, you're probably with the wrong girl.'", "I love her … upper body.", "Look at that face. Would anybody vote for that? Can you imagine that, the face of our next president? I mean, she's a woman, and I'm not supposed to say bad things, but really, folks, come on. Are we serious?", "Why does she keep interrupting everybody?", "She's certainly not hot.", "Horseface" , "I promise not to talk about your massive plastic surgeries that didn't work.", "I know where she went, it's disgusting, I don't want to talk about it … No, it's too disgusting. Don't say it, it's disgusting.", "If she were a man, I don't think she'd get five percent of the vote." , "Does she look presidential, fellas? Give me a break.", "Such a nasty woman.", "Unattractive both inside and out. I fully understand why her former husband left her for a man — he made a good decision.", "A crazed, crying lowlife", "Does she have a good body? No. Does she have a fat ass? Absolutely.", "Sadly, she's no longer a 10.", "She does have a very nice figure ... if [she] weren't my daughter, perhaps I'd be dating her.", "She's actually always been very voluptuous.","How do the breasts look?", "Well, I think that she's got a lot of Marla. She's a really beautiful baby, and she's got Marla's legs. We don't know whether she's got this part yet, but time will tell.", "26,000 unreported sexual assaults in the military — only 238 convictions. What did these geniuses expect when they put men & women together?", "We could say, politically correct, that look doesn't matter, but the look obviously matters. Like you wouldn't have your job if you weren't beautiful.", "I've got to use some Tic Tacs, just in case I start kissing her. You know I'm automatically attracted to beautiful — I just start kissing them. It's like a magnet. Just kiss. I don't even wait. And when you're a star, they let you do it. You can do anything ... Grab them by the pussy. You can do anything." , "Nobody has more respect for women than I do. Nobody. Nobody has more respect." ], "Place": ["Afghanistan", "Albania", "Algeria", "Andorra", "Angola", "Antigua and Barbuda", "Argentina", "Armenia", "Australia", "Austria", "Azerbaijan", "The Bahamas", "Bahrain", "Bangladesh", "Barbados", "Belarus", "Belgium", "Belize", "Benin", "Bhutan", "Bolivia", "Bosnia and Herzegovina", "Botswana", "Brazil", "Brunei", "Bulgaria", "Burkina Faso", "Burundi", "Cabo Verde", "Cambodia", "Cameroon", "Canada", "the Central African Republic", "Chad", "Chile", "China", "Colombia", "Comoros", "Congo", "the Democratic Republic of the Congo", "Costa Rica", "Côte d’Ivoire", "Croatia", "Cuba","Cyprus","the Czech Republic","Denmark","Djibouti","Dominica","Dominican Republic","East Timor (Timor-Leste)","Ecuador","Egypt","El Salvador","Equatorial Guinea","Eritrea","Estonia","Eswatini","Ethiopia","Fiji","Finland","France","Gabon","The Gambia","Georgia","Germany","Ghana","Greece","Grenada","Guatemala","Guinea","Guinea-Bissau","Guyana","Haiti","Honduras","Hungary","Iceland","India","Indonesia","Iran","Iraq","Ireland","Israel","Italy","Jamaica","Japan","Jordan","Kazakhstan","Kenya","Kiribati","Korea, North","Korea, South","Kosovo","Kuwait","Kyrgyzstan","Laos","Latvia","Lebanon","Lesotho","Liberia","Libya","Liechtenstein","Lithuania","Luxembourg","Madagascar","Malawi","Malaysia","Maldives","Mali","Malta","the Marshall Islands","Mauritania","Mauritius","Mexico","Micronesia","Moldova","Monaco","Mongolia","Montenegro","Morocco","Mozambique","Myanmar (Burma)","Namibia","Nauru","Nepal","Netherlands","New Zealand","Nicaragua","Niger","Nigeria","North Macedonia","Norway","Oman","Pakistan","Palau","Panama","Papua New Guinea","Paraguay","Peru","the Philippines","Poland","Portugal","Qatar","Romania","Russia","Rwanda","Saint Kitts and Nevis","Saint Lucia","Saint Vincent and the Grenadines","Samoa","San Marino","Sao Tome and Principe","Saudi Arabia","Senegal","Serbia","Seychelles","Sierra Leone","Singapore","Slovakia","Slovenia","the Solomon Islands","Somalia","South Africa","Spain","Sri Lanka","Sudan","Sudan, South","Suriname","Sweden","Switzerland","Syria","Taiwan","Tajikistan","Tanzania","Thailand","Togo","Tonga","Trinidad and Tobago","Tunisia","Turkey","Turkmenistan","Tuvalu","Uganda","Ukraine","the United Arab Emirates","the United Kingdom","the United States","Uruguay","Uzbekistan","Vanuatu","Vatican City","Venezuela","Vietnam","Yemen","Zambia","Zimbabwe"] }

36.098191

2,382

0.688332

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5.271084

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1

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null

0

1

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3

34b092edc052b1e0d00c401ee896473f6a64c23d

283

py

Python

test_main.py

cupppu/BigDate

6a9c285bf37dc6f967a23c1e1592fd9c41ed1a71

[ "MIT" ]

null

test_main.py

cupppu/BigDate

6a9c285bf37dc6f967a23c1e1592fd9c41ed1a71

[ "MIT" ]

null

test_main.py

cupppu/BigDate

6a9c285bf37dc6f967a23c1e1592fd9c41ed1a71

[ "MIT" ]

null

hour_test = 6 if not args.hr: hr = convertMonDayHr(hour_test) elif hour_test < 12: hr = convertMonDayHr(hour_test) hr = "上午" + hr elif hour_test > 12: hr = convertMonDayHr(hour_test - 12) hr = "下午" + hr else: hr = convertMonDayHr(hour_test) hr = "下午" + hr

23.583333

40

0.632509

42

283

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0.581395

0.622093

0.453488

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283

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0

null

1

0

null

0

3

34c7cc921e0f9deb40eb8faacd83261dbbe1b439

1,026

py

Python

code/Symbols/Symbol_Table.py

antuniooh/Dattebayo-compiler

fd6767aee1c131dcbf76fc061bce43224df03f8b

[ "MIT" ]

null

code/Symbols/Symbol_Table.py

antuniooh/Dattebayo-compiler

fd6767aee1c131dcbf76fc061bce43224df03f8b

[ "MIT" ]

null

code/Symbols/Symbol_Table.py

antuniooh/Dattebayo-compiler

fd6767aee1c131dcbf76fc061bce43224df03f8b

[ "MIT" ]

1

2021-12-05T14:00:39.000Z

""" Integrantes: Nome: Antônio Gustavo Muniz 22.119.001-0 Nome: João Vitor Dias dos Santos 22.119.006-9 Nome: Weverson da Silva Pereira 22.119.004-4 """ import json from Symbol import Symbol class SymbolTable: """ Class for Symbols tables """ symbol_table = {} def __init__(self) -> None: """ Performs the creation of an object of type SymbolTable """ pass @staticmethod def set_varaible(kw_symbol): """ Set a kw_symbol in symbol table :param kw_symbol: Symbol to be add in symbol table """ SymbolTable.symbol_table[kw_symbol.name] = {"name": kw_symbol.name, "type": kw_symbol.type, "scope": kw_symbol.scope} @staticmethod def get_symbol_table(): """ Return the symbol table """ return SymbolTable.symbol_table def __str__(self): """ Custom log """ return json.dumps(SymbolTable.symbol_table, indent=4)

22.304348

99

0.586745

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1,026

4.715447

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1

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1

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3

34ca493c8ac992db65269a12049777595d6a879a

22

py

Python

project/openaid/templatetags/__init__.py

DeppSRL/open-aid

84130761c00600a8523f4f28467d70ad974859cd

[ "BSD-3-Clause" ]

11

2015-09-30T19:08:37.000Z

2021-11-08T11:18:04.000Z

project/openaid/templatetags/__init__.py

DeppSRL/open-aid

84130761c00600a8523f4f28467d70ad974859cd

[ "BSD-3-Clause" ]

3

2015-02-07T23:29:00.000Z

2015-10-19T04:42:20.000Z

project/openaid/templatetags/__init__.py

DeppSRL/open-aid

84130761c00600a8523f4f28467d70ad974859cd

[ "BSD-3-Clause" ]

7

2016-06-08T10:11:36.000Z

2022-02-05T14:26:00.000Z

__author__ = 'joke2k'

11

21

0.727273

2

22

6

1

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0.052632

0.136364

22

1

22

0.578947

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1

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false

0

1

0

null

0

1

0

null

0

3

34edb633462afc86f41871ccea960661b584383c

1,247

py

Python

scrapers/scrapers/spiders/FuseSpider.py

rbbh/Oncase-challenge

b04057ece5354d7bc6221dd7870a1a9693730e32

[ "MIT" ]

null

scrapers/scrapers/spiders/FuseSpider.py

rbbh/Oncase-challenge

b04057ece5354d7bc6221dd7870a1a9693730e32

[ "MIT" ]

null

scrapers/scrapers/spiders/FuseSpider.py

rbbh/Oncase-challenge

b04057ece5354d7bc6221dd7870a1a9693730e32

[ "MIT" ]

null

from datetime import datetime as dt import scrapy from scrapers.items import FuseItem class PostSpider(scrapy.Spider): name = 'fuse' allowed_domains = ['fuse.tv'] urls = [ "https://www.fuse.tv/latest#read" # selecionando a url específica que contém as notícias do site ] def parse(self, response): news = Selector(response).xpath('//div[@class="imagetitle"]/h1') # modificando o XPath do site para conseguir todas as notícias for iterator in news: item = FuseItem() item['title'] = iterator.xpath('a[@class="imagetitle"]/text()').extract()[0] ''' TODO: função nece não se sabe até então como se passar o xpath da forma correta ''' yield item

54.217391

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4.9375

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0

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0.551724

1,247

22

470

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null

0

1

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1

0

3

34eff7d809d46eea33078c325514201d7c387481

1,279

py

Python

test/test_SensorPose.py

jcandan/WonderPy

ee82322b082e94015258b34b27f23501f8130fa2

[ "MIT" ]

46

2018-07-31T20:30:41.000Z

2022-03-23T17:14:51.000Z

test/test_SensorPose.py

jcandan/WonderPy

ee82322b082e94015258b34b27f23501f8130fa2

[ "MIT" ]

24

2018-08-01T09:59:29.000Z

2022-02-26T20:57:51.000Z

test/test_SensorPose.py

jcandan/WonderPy

ee82322b082e94015258b34b27f23501f8130fa2

[ "MIT" ]

24

2018-08-01T19:14:31.000Z

2021-02-18T13:26:40.000Z

import unittest from test.robotTestUtil import RobotTestUtil class MyTestCase(unittest.TestCase): def test_pose(self): robot = RobotTestUtil.make_fake_dash() packet = {} packet['2002'] = { 'x' : 1.2, 'y' : 3.4, 'degree': 5.6, } robot.sensors.parse(packet) sensor = robot.sensors.pose self.assertAlmostEquals(sensor.x , -3.4) self.assertAlmostEquals(sensor.y , 1.2) self.assertAlmostEquals(sensor.degrees, 5.6) self.assertTrue (sensor.watermark_measured is None) self.assertAlmostEquals(sensor.watermark_inferred, 0.0) packet['2002'] = { 'x' : 1.2, 'y' : 3.4, 'degree' : 5.6, 'watermark': 3, } robot.sensors.parse(packet) sensor = robot.sensors.pose self.assertAlmostEquals(sensor.x , -3.4) self.assertAlmostEquals(sensor.y , 1.2) self.assertAlmostEquals(sensor.degrees , 5.6) self.assertAlmostEquals(sensor.watermark_measured, 3) self.assertAlmostEquals(sensor.watermark_inferred, 3) if __name__ == '__main__': unittest.main()

28.422222

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1,279

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0.121212

0

null

1

0

null

0

3

5501ab9251679e9c55580f0c64a632d331a5bc44

186

py

Python

deets.py

lileddie/selfservepwd

abf4a25890a06a48c3e30fa551582fa116a346eb

[ "MIT" ]

null

deets.py

lileddie/selfservepwd

abf4a25890a06a48c3e30fa551582fa116a346eb

[ "MIT" ]

null

deets.py

lileddie/selfservepwd

abf4a25890a06a48c3e30fa551582fa116a346eb

[ "MIT" ]

null

#Passwd file for passwordmanager app loginpass='windowsADpasswd' user='domain.name\\username' passwordReset = 'staticPassword' new_pass = 'newadpassword' adServer='adServer.domain.name'

26.571429

36

0.811828

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186

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0.14

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0.6

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1

0

null

0

1

0

null

0

1

0

3

5503e7724cf930a5319c0edd5862a02412bfb684

1,279

py

Python

piperoni/operators/base.py

CitrineInformatics/piperoni

a5764e9da3a51da0a8962a00fd574a97b173d9a4

[ "Apache-2.0" ]

2

2021-04-21T19:51:06.000Z

2021-04-23T17:57:09.000Z

piperoni/operators/base.py

CitrineInformatics/piperoni

a5764e9da3a51da0a8962a00fd574a97b173d9a4

[ "Apache-2.0" ]

null

piperoni/operators/base.py

CitrineInformatics/piperoni

a5764e9da3a51da0a8962a00fd574a97b173d9a4

[ "Apache-2.0" ]

null

from functools import reduce from typing import List from abc import ABC, abstractmethod import logging import pandas as pd import warnings """ This module implements the BaseOperator object. Operators are applied to inputs using the __call__ python API, which wraps the `transform` methods implemented for each Operator. Examples -------- Functionality of the BaseOperator:: opp = BaseOperator() input = "some data" output = opp(input) """ class BaseOperator(ABC): """A generic operator to apply to data.""" @property def logger(self): return logging.getLogger("base") @abstractmethod def transform(self, input_: object) -> object: """Implemented in concrete sub-classes of BaseOperator. Concrete implementations of this method should accept a single argument as input and return a single output (most likely pandas DataFrame). Parameters ---------- input_: object The single argument for transform. Returns ------- object The single output of transform. """ pass def __call__(self, *args, **kwargs) -> object: """Class instances emulate callable methods.""" return self.transform(*args, **kwargs)

23.254545

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0.657545

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1,279

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0.534722

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0

0.254105

1,279

54

80

23.685185

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0.319781

0

0.008753

0

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0.066667

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0.066667

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0

null

0

null

0

1

0

1

0

3

5521ff3fe5fe31c80eacd4765957cf09677868c9

3,986

py

Python

locations/spiders/apple.py

bealbrown/allhours

f750ee7644246a97bd16879f14115d7845f76b89

[ "MIT" ]

null

locations/spiders/apple.py

bealbrown/allhours

f750ee7644246a97bd16879f14115d7845f76b89

[ "MIT" ]

null

locations/spiders/apple.py

bealbrown/allhours

f750ee7644246a97bd16879f14115d7845f76b89

[ "MIT" ]

null

# -*- coding: utf-8 -*- import scrapy #import json import re from locations.hourstudy import inputoutput class AppleSpider(scrapy.Spider): name = "apple" allowed_domains = ["apple.com"] start_urls = ( 'https://www.apple.com/retail/storelist/', ) def store_hours(self, store_hours): result = '' for line in range(0,int(len(store_hours)/2)): days = re.search(r'^(\D{3})\s*((through|and|-|&|-)\s*(\D{3}))?$',store_hours[line*2]) if (not days)or(store_hours[line*2+1]=='Closed'): continue result += days[1][:2] try: result += "-"+days[4][:2]+" " except Exception as e: result += " " hours_str=store_hours[line*2+1].replace("Noon","12:00 a.m.") hours= re.search(r'(\d+):(\d+)\s*((a\.m\.)|(p\.m\.))\s*-\s*(\d+):?(\d+)?\s*((a\.m\.)|(p\.m\.))',hours_str) result += str(int(hours[1])+(12 if hours[3] in ['p.m.','m.p.'] else 0))+':'+hours[2]+'-' result += str(int(hours[6])+(12 if hours[8] in ['p.m.','m.p.'] else 0))+':'+hours[7]+';' return result.rstrip(';') def parse(self, response): shops=response.xpath('//div[@id="usstores"]//li/a/@href') for shop in shops: yield scrapy.Request(response.urljoin(shop.extract()), callback=self.parse_shops) def parse_shops(self, response): props={} if response.xpath('//div[contains(@class,"store-details")]//span[contains(@class,"store-phone")]/text()'): props['phone'] = response.xpath('//div[contains(@class,"store-details")]//span[contains(@class,"store-phone")]/text()').extract_first() if response.xpath('//div[contains(@class,"store-details")]//span[contains(@class,"store-street")]/text()'): props['addr_full'] = response.xpath('//div[contains(@class,"store-details")]//span[contains(@class,"store-street")]/text()').extract_first() if response.xpath('//div[contains(@class,"store-details")]//span[contains(@class,"store-postal-code")]/text()'): props['postcode'] = response.xpath('//div[contains(@class,"store-details")]//span[contains(@class,"store-postal-code")]/text()').extract_first() if response.xpath('//div[contains(@class,"store-locality")]//span[contains(@class,"store-postal-code")]/text()'): props['city'] = response.xpath('//div[contains(@class,"store-locality")]//span[contains(@class,"store-postal-code")]/text()').extract_first() if response.xpath('//div[contains(@class,"store-locality")]//span[contains(@class,"store-region")]/text()'): props['state'] =response.xpath('//div[contains(@class,"store-locality")]//span[contains(@class,"store-region")]/text()').extract_first() if response.xpath('//li[@class="country-name"]/span/text()'): props['country'] =response.xpath('//li[@class="country-name"]/span/text()').extract_first().strip(), if response.xpath('//div[contains(@class,"copy-software")]/a/@href'): pos = re.search(r'&lat=(.+)&long=(.+)',response.xpath('//div[contains(@class,"copy-software")]/a/@href').extract_first()) props['lat'] = pos[1] props['lon'] = pos[2] # yield inputoutput( # **props, # website=response.url, # ref=response.xpath('//meta[@property="og:title"]/@content').extract_first(), # country='USA', # opening_hours=self.store_hours(response.xpath('//div[contains(@class,"store-hours-row")]/div/text()').extract()), # ) raw = response.xpath('//div[contains(@class,"store-hours-row")]/div/text()').extract() formatted = self.store_hours(response.xpath('//div[contains(@class,"store-hours-row")]/div/text()').extract()) yield inputoutput(raw,formatted)

52.447368

160

0.561967

480

3,986

4.616667

0.258333

0.146661

0.186823

0.162455

0.575812

0.561372

0.554152

0.547834

0.493682

0.452166

0

0.010091

0.204466

3,986

75

161

53.146667

0.688742

0.077772

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0.22

0.398254

0.35461

0

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false

0

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0.22

0

null

0

1

0

1

0

null

0

3

5523c221f96bcb8d726eca4962f30f097b541ff6

25

py

Python

recipes/yolov5_onnx/example.py

notAI-tech/fastDeploy-core

011f4cba2d2b018efe0a5548978e290225f2e745

[ "MIT" ]

null

recipes/yolov5_onnx/example.py

notAI-tech/fastDeploy-core

011f4cba2d2b018efe0a5548978e290225f2e745

[ "MIT" ]

1

2020-04-12T13:36:22.000Z

recipes/yolov5_onnx/example.py

notAI-tech/fastDeploy-core

011f4cba2d2b018efe0a5548978e290225f2e745

[ "MIT" ]

null

example = ["zidane.jpg"]

12.5

24

0.64

3

25

5.333333

1

0

0.12

25

1

25

0.727273

0

0.4

0

1

0

false

0

1

0

null

0

1

0

null

0

3

9b4a540421f9c467216dc50871c7a0aedf1eb26a

396

py

Python

NST_Hotbox/W_hotbox/Single/apColorSampler/003.py

CreativeLyons/NST_Hotbox

48d23a651d9578a70b16bcc2c034de4b3586883f

[ "MIT" ]

null

NST_Hotbox/W_hotbox/Single/apColorSampler/003.py

CreativeLyons/NST_Hotbox

48d23a651d9578a70b16bcc2c034de4b3586883f

[ "MIT" ]

null

NST_Hotbox/W_hotbox/Single/apColorSampler/003.py

CreativeLyons/NST_Hotbox

48d23a651d9578a70b16bcc2c034de4b3586883f

[ "MIT" ]

null

#---------------------------------------------------------------------------------------------------------- # # AUTOMATICALLY GENERATED FILE TO BE USED BY W_HOTBOX # # NAME: Bake # COLOR: #685777 # TEXTCOLOR: #ffffff # #---------------------------------------------------------------------------------------------------------- ns = nuke.selectedNodes() for n in ns: n.knob('bake').execute()

28.285714

107

0.305556

26

396

4.615385

0.884615

0

0.016667

0.090909

396

13

108

30.461538

0.316667

0.775253

0

1

0

0.051282

0

1

0

false

0

1

0

null

0

1

0

1

0

null

0

3

9b7c2fa4b13bd61043410d116386fb2ea45cedc8

999

py

Python

corehq/form_processor/backends/sql/supply.py

dimagilg/commcare-hq

ea1786238eae556bb7f1cbd8d2460171af1b619c

[ "BSD-3-Clause" ]

null

corehq/form_processor/backends/sql/supply.py

dimagilg/commcare-hq

ea1786238eae556bb7f1cbd8d2460171af1b619c

[ "BSD-3-Clause" ]

94

2020-12-11T06:57:31.000Z

2022-03-15T10:24:06.000Z

corehq/form_processor/backends/sql/supply.py

dimagilg/commcare-hq

ea1786238eae556bb7f1cbd8d2460171af1b619c

[ "BSD-3-Clause" ]

null

from corehq.apps.commtrack.helpers import make_supply_point from corehq.form_processor.abstract_models import AbstractSupplyInterface from corehq.form_processor.backends.sql.dbaccessors import CaseAccessorSQL class SupplyPointSQL(AbstractSupplyInterface): @classmethod def get_or_create_by_location(cls, location): sp = SupplyPointSQL.get_by_location(location) if not sp: sp = make_supply_point(location.domain, location) return sp @classmethod def get_by_location(cls, location): return location.linked_supply_point() @staticmethod def get_closed_and_open_by_location_id_and_domain(domain, location_id): return CaseAccessorSQL.get_case_by_location(domain, location_id) @staticmethod def get_supply_point(supply_point_id): return CaseAccessorSQL.get_case(supply_point_id) @staticmethod def get_supply_points(supply_point_ids): return list(CaseAccessorSQL.get_cases(supply_point_ids))

33.3

75

0.771772

122

999

5.959016

0.360656

0.121045

0.074278

0.063274

0.154058

0

0.168168

999

29

76

34.448276

0.87485

0

0.227273

0

1

0.227273

false

0

0.136364

0.181818

0.636364

0

null

0

null

0

1

0

1

0

3

32c9887e376278347c4e15e18c18eb75e940bdbd

269

py

Python

Appointment/admin.py

CiganOliviu/InfiniteShoot

14f7fb21e360e3c58876d82ebbe206054c72958e

[ "MIT" ]

1

2021-04-02T16:45:37.000Z

Appointment/admin.py

CiganOliviu/InfiniteShoot-1

6322ae34f88caaffc1de29dfa4f6d86d175810a7

[ "Apache-2.0" ]

null

Appointment/admin.py

CiganOliviu/InfiniteShoot-1

6322ae34f88caaffc1de29dfa4f6d86d175810a7

[ "Apache-2.0" ]

null

from django.contrib import admin from .models import Appointment class AppointmentAdmin(admin.ModelAdmin): list_display = ('first_name', 'last_name', 'email', 'desired_date', 'sent_moment', 'seen', 'accepted') admin.site.register(Appointment, AppointmentAdmin)

26.9

106

0.765799

31

269

6.483871

0.774194

0

0.107807

269

9

107

29.888889

0.8375

0

0.219331

0

1

0

false

0

0.4

0

0.8

0

null

0

1

0

null

0

1

0

1

0

3

32d0f3bc24adfab4054c30f3f5e21861d6979f1a

58

py

Python

ASCII/t1.py

matewszz/Python

18b7fc96d3ed294d2002ed484941a0ee8cf18108

[ "MIT" ]

null

ASCII/t1.py

matewszz/Python

18b7fc96d3ed294d2002ed484941a0ee8cf18108

[ "MIT" ]

null

ASCII/t1.py

matewszz/Python

18b7fc96d3ed294d2002ed484941a0ee8cf18108

[ "MIT" ]

null

import string s = 'mary11had a littlee lamb' print = (s)

11.6

30

0.689655

9

58

4.444444

0.888889

0

0.043478

0.206897

58

5

31

11.6

0.826087

0

0.40678

0

1

0

false

0

0.333333

0

0.333333

1

0

null

0

1

0

null

0

1

0

3

32d857afba3a20329784306fd1db72a9f87339f6

440

py

Python

tests/test_utils_data.py

lucasmfaria/image_classifier

94e077125ee0c4b7a0cb7d2ff043567a3601a516

[ "MIT" ]

7

2021-12-11T20:24:57.000Z

2022-03-03T02:56:32.000Z

tests/test_utils_data.py

lucasmfaria/image_classifier

94e077125ee0c4b7a0cb7d2ff043567a3601a516

[ "MIT" ]

null

tests/test_utils_data.py

lucasmfaria/image_classifier

94e077125ee0c4b7a0cb7d2ff043567a3601a516

[ "MIT" ]

null

import sys from pathlib import Path try: from utils.data import create_aux_dataframe, train_test_valid_split, filter_binary_labels, optimize_dataset, \ delete_folder, create_split except ModuleNotFoundError: sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) from utils.data import create_aux_dataframe, train_test_valid_split, filter_binary_labels, optimize_dataset, \ delete_folder, create_split

40

114

0.795455

59

440

5.525424

0.508475

0.055215

0.079755

0.116564

0.687117

0

0.002632

0.136364

440

10

115

44

0.855263

0

0.444444

0

1

0

true

0

0.444444

0

0.444444

0

null

0

1

0

null

0

1

0

1

0

3

32d86d992fd5bceca71f38045878ec955f5ec00b

294

py

Python

src/rlext/misc.py

kngwyu/rlext

b7e74ba4b440d2c6f98ea79dcd0aab15053fa96a

[ "Apache-2.0" ]

null

src/rlext/misc.py

kngwyu/rlext

b7e74ba4b440d2c6f98ea79dcd0aab15053fa96a

[ "Apache-2.0" ]

null

src/rlext/misc.py

kngwyu/rlext

b7e74ba4b440d2c6f98ea79dcd0aab15053fa96a

[ "Apache-2.0" ]

null

import typing as t import numpy as np from gym.spaces import Box def box_action_scaler(action_space: Box) -> t.Callable[[np.ndarray], np.ndarray]: shift = action_space.low scale = action_space.high - action_space.low return lambda action: scale / (1.0 + np.exp(-action)) + shift

26.727273

81

0.717687

47

294

4.361702

0.531915

0.214634

0.136585

0

0.00823

0.173469

294

10

82

29.4

0.835391

0

1

0.142857

false

0

0.428571

0

0.714286

0

null

1

0

null

0

1

0

1

0

3

32f27312c84306f3edb747f0f69983c26b59d778

3,586

py

Python

WebService/app/models.py

CryptoSalamander/DeepFake-Detection

f3b1c95ce1955a4c203a9f3d1279c5fbade66684

[ "MIT" ]

null

WebService/app/models.py

CryptoSalamander/DeepFake-Detection

f3b1c95ce1955a4c203a9f3d1279c5fbade66684

[ "MIT" ]

null

WebService/app/models.py

CryptoSalamander/DeepFake-Detection

f3b1c95ce1955a4c203a9f3d1279c5fbade66684

[ "MIT" ]

1

2021-04-11T06:27:48.000Z

from app import db, login from datetime import datetime from flask_login import UserMixin from werkzeug.security import generate_password_hash, check_password_hash class User(UserMixin, db.Model): id = db.Column(db.Integer, primary_key=True) user_name = db.Column(db.String(64), unique=True, nullable=False) email = db.Column(db.String(120), unique=True, nullable=False) password_hash = db.Column(db.String(60), nullable=False) avatar = db.Column(db.String(20), nullable=False, default='avatar.png') cover_pic = db.Column(db.String(20), nullable=False, default='cover.png') age = db.Column(db.Integer, nullable=False) address = db.Column(db.Text, nullable=False) register_date = db.Column( db.DateTime, nullable=False, default=datetime.utcnow) videos = db.relationship('Video', backref='author', lazy='dynamic') comments = db.relationship('Comments', backref='author', lazy='dynamic') liked = db.relationship( 'Likes', foreign_keys='Likes.user_id', backref='user', lazy='dynamic') def __repr__(self): return f"User('{self.user_name}', '{self.email}')" def set_password(self, password): '''Setting up the password''' self.password_hash = generate_password_hash(password) def check_password(self, password): '''Checking the password filled with the password in database''' return check_password_hash(self.password_hash, password) def like_video(self, video): if not self.has_liked_video(video): like = Likes(user_id=self.id, video_id=video.id) db.session.add(like) def unlike_video(self, video): if self.has_liked_video(video): Likes.query.filter_by(user_id=self.id, video_id=video.id).delete() def has_liked_video(self, video): return Likes.query.filter(Likes.user_id == self.id, Likes.video_id == video.id).count() > 0 @login.user_loader def load_user(id): '''function to add the user into the session generated by LoginManager''' return User.query.get(int(id)) class Video(db.Model): id = db.Column(db.Integer, primary_key=True) video_title = db.Column(db.String(120), nullable=False) video_content = db.Column(db.String(40), nullable=False) description = db.Column(db.Text, nullable=False) category = db.Column(db.String, nullable=False) views_count = db.Column(db.Integer, nullable=False, default=0) upload_time = db.Column(db.DateTime, nullable=False, default=datetime.utcnow) likes_count = db.Column(db.Integer, nullable=False, default=0) user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False) comments = db.relationship('Comments', backref='video', lazy='dynamic') likes = db.relationship('Likes', backref='video', lazy='dynamic') def __repr__(self): return f"Video('{self.video_title}', '{self.upload_time}')" class Likes(db.Model): id = db.Column(db.Integer, primary_key=True) video_id = db.Column(db.Integer, db.ForeignKey('video.id'), nullable=False) user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False) class Comments(db.Model): id = db.Column(db.Integer, primary_key=True) body = db.Column(db.Text) comment_time = db.Column(db.DateTime, index=True, default=datetime.utcnow) author_id = db.Column(db.Integer, db.ForeignKey('user.id')) video_id = db.Column(db.Integer, db.ForeignKey('video.id')) def __repr__(self): return f"Comments('{self.author_id}', '{self.body}', {self.video_id})"

38.55914

99

0.688511

502

3,586

4.782869

0.207171

0.086631

0.108288

0.084965

0.44898

0.356518

0.321533

0.297376

0.244065

0.149521

0

0.006376

0.168991

3,586

92

100

38.978261

0.799329

0.041829

0

0.140625

1

0

0.090643

0.02924

0

1

0.140625

false

0.09375

0.0625

0.84375

0

null

0

1

0

null

0

1

0

1

0

3

fd0445408b4e2ce0f1a5646a132544bebf3d255c

190

py

Python

dojotrust/request.py

kipkemoimayor/Dojo_Trust

34b0dbe8ae056b0747ca3474b558dd65a59eb5b0

[ "MIT" ]

2

2019-06-08T20:02:40.000Z

2021-09-30T07:20:57.000Z

dojotrust/request.py

kipkemoimayor/Dojo_Trust

34b0dbe8ae056b0747ca3474b558dd65a59eb5b0

[ "MIT" ]

12

2020-02-12T00:31:48.000Z

2022-02-10T12:04:08.000Z

dojotrust/request.py

kipkemoimayor/Dojo_Trust

34b0dbe8ae056b0747ca3474b558dd65a59eb5b0

[ "MIT" ]

null

import requests def location(): response=requests.get("http://api.ipstack.com/105.27.206.46?access_key=18220f1e8b17fbf02d95d972679ccfef") geodata=response.json() return geodata

27.142857

109

0.763158

23

190

6.26087

0.869565

0

0.171598

0.110526

190

6

110

31.666667

0.680473

0

0.421053

0

1

0.2

false

0

0.2

0

0.6

0

1

0

null

0

1

0

null

0

1

0

3

fd17982343a6bf5291fbda21f25004d6079a45a1

199

py

Python

students/K33402/Kondrashov_Egor/LR2/commerce/commerce/urls.py

emina13/ITMO_ICT_WebDevelopment_2021-2022

498a6138e352e7e0ca40d1eb301bc29416158f51

[ "MIT" ]

7

2021-09-02T08:20:58.000Z

2022-01-12T11:48:07.000Z

students/K33402/Kondrashov_Egor/LR2/commerce/commerce/urls.py

emina13/ITMO_ICT_WebDevelopment_2021-2022

498a6138e352e7e0ca40d1eb301bc29416158f51

[ "MIT" ]

76

2021-09-17T23:01:50.000Z

2022-03-18T16:42:03.000Z

students/K33402/Kondrashov_Egor/LR2/commerce/commerce/urls.py

emina13/ITMO_ICT_WebDevelopment_2021-2022

498a6138e352e7e0ca40d1eb301bc29416158f51

[ "MIT" ]

60

2021-09-04T16:47:39.000Z

2022-03-21T04:41:27.000Z

"""commerce URL Configuration""" from django.contrib import admin from django.urls import include, path urlpatterns = [ path("admin/", admin.site.urls), path("", include("auctions.urls")) ]

22.111111

38

0.698492

24

199

5.791667

0.583333

0.143885

0

0.145729

199

8

39

24.875

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0.130653

0

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0

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0.333333

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null

0

1

0

null

0

1

0

3

fd3feec3826f9373db9daf6f11ac2a6f5a6d9609

431

py

Python

env/lib/python3.6/site-packages/traits/adaptation/api.py

Raniac/NEURO-LEARN

3c3acc55de8ba741e673063378e6cbaf10b64c7a

[ "Apache-2.0" ]

8

2019-05-29T09:38:30.000Z

2021-01-20T03:36:59.000Z

env/lib/python3.6/site-packages/traits/adaptation/api.py

Raniac/neurolearn_dev

3c3acc55de8ba741e673063378e6cbaf10b64c7a

[ "Apache-2.0" ]

12

2021-03-09T03:01:16.000Z

2022-03-11T23:59:36.000Z

env/lib/python3.6/site-packages/traits/adaptation/api.py

Raniac/NEURO-LEARN

3c3acc55de8ba741e673063378e6cbaf10b64c7a

[ "Apache-2.0" ]

1

2020-07-17T12:49:49.000Z

from .adapter import Adapter, PurePythonAdapter from .adaptation_error import AdaptationError from .adaptation_manager import ( adapt, AdaptationManager, get_global_adaptation_manager, provides_protocol, register_factory, register_offer, register_provides, reset_global_adaptation_manager, set_global_adaptation_manager, supports_protocol, ) from .adaptation_offer import AdaptationOffer

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0

null

1

0

null

0

1

0

3

fd41bc6b8352a13522178ab7c7827ca643f6a978

3,115

py

Python

api/users/serializers.py

fujikawahiroaki/webspecimanager

d46a4feec0c695d5231b21e3311f4adbe25435cb

[ "BSD-2-Clause" ]

null

api/users/serializers.py

fujikawahiroaki/webspecimanager

d46a4feec0c695d5231b21e3311f4adbe25435cb

[ "BSD-2-Clause" ]

10

2020-12-07T08:54:30.000Z

2022-03-13T12:23:03.000Z

api/users/serializers.py

fujikawahiroaki/webspecimanager

d46a4feec0c695d5231b21e3311f4adbe25435cb

[ "BSD-2-Clause" ]

null

from rest_framework import serializers from django.core.validators import RegexValidator from .models import UserProfile class UserProfileSerializer(serializers.ModelSerializer): """ユーザープロファイルモデル用シリアライザ""" user = serializers.HiddenField(default=serializers.CurrentUserDefault()) class Meta: model = UserProfile fields = '__all__' read_only_fields = ('created_at', 'id') extra_kwargs = { 'contient': { 'validators': [RegexValidator(r'^[!-~ ]+$', message='半角英数記号のみ使用可')] }, 'island_group': { 'validators': [RegexValidator(r'^[!-~ ]+$', message='半角英数記号のみ使用可')] }, 'island': { 'validators': [RegexValidator(r'^[!-~ ]+$', message='半角英数記号のみ使用可')] }, 'state_provice': { 'validators': [RegexValidator(r'^[!-~ ]+$', message='半角英数記号のみ使用可')] }, 'kingdom': { 'validators': [RegexValidator(r'^[A-Z][a-z]+$', message='先頭のみ大文字、以降小文字の半角英字1単語のみ使用可')] }, 'phylum': { 'validators': [RegexValidator(r'^[A-Z][a-z]+$', message='先頭のみ大文字、以降小文字の半角英字1単語のみ使用可')] }, 'class_name': { 'validators': [RegexValidator(r'^[A-Z][a-z]+$', message='先頭のみ大文字、以降小文字の半角英字1単語のみ使用可')] }, 'order': { 'validators': [RegexValidator(r'^[A-Z][a-z]+$', message='先頭のみ大文字、以降小文字の半角英字1単語のみ使用可')] }, 'identified_by': { 'validators': [RegexValidator(r'^[!-~ ]+$', message='半角英数記号のみ使用可')] }, 'collecter': { 'validators': [RegexValidator(r'^[!-~ ]+$', message='半角英数記号のみ使用可')] }, 'preparation_type': { 'validators': [RegexValidator(r'^[!-~ ]+$', message='半角英数記号のみ使用可')] }, 'disposition': { 'validators': [RegexValidator(r'^[!-~ ]+$', message='半角英数記号のみ使用可')] }, 'lifestage': { 'validators': [RegexValidator(r'^[!-~ ]+$', message='半角英数記号のみ使用可')] }, 'establishment_means': { 'validators': [RegexValidator(r'^[!-~ ]+$', message='半角英数記号のみ使用可')] }, 'rights': { 'validators': [RegexValidator(r'^[!-~ ]+$', message='半角英数記号のみ使用可')] }, }

40.986842

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41.533333

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0.084507

0

null

1

0

null

0

3

b5bf923f69b2f1a195f6a2164fbae124f199c868

81

py

Python

moodle/__init__.py

Crissal1995/new_moodle_importer

c068a2dc2f97a6346031ac4e9a6844a98caa1305

[ "MIT" ]

null

moodle/__init__.py

Crissal1995/new_moodle_importer

c068a2dc2f97a6346031ac4e9a6844a98caa1305

[ "MIT" ]

null

moodle/__init__.py

Crissal1995/new_moodle_importer

c068a2dc2f97a6346031ac4e9a6844a98caa1305

[ "MIT" ]

null

from moodle.automator import Automator __all__ = ["Automator"] version = "0.1"

13.5

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81

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null

0

1

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3

b5c456a5e8c7aba4885bba955e13589c3d341152

1,287

py

Python

torchmetrics/image/__init__.py

radandreicristian/metrics

8048c77229f47d82d1adc391407f9cd2f5a8e9fa

[ "Apache-2.0" ]

2

2022-01-20T12:33:18.000Z

2022-03-25T04:30:02.000Z

torchmetrics/image/__init__.py

radandreicristian/metrics

8048c77229f47d82d1adc391407f9cd2f5a8e9fa

[ "Apache-2.0" ]

null

torchmetrics/image/__init__.py

radandreicristian/metrics

8048c77229f47d82d1adc391407f9cd2f5a8e9fa

[ "Apache-2.0" ]

null

# Copyright The PyTorch Lightning team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from torchmetrics.image.inception import IS, InceptionScore # noqa: F401 from torchmetrics.image.kid import KID, KernelInceptionDistance # noqa: F401 from torchmetrics.image.psnr import PSNR, PeakSignalNoiseRatio # noqa: F401 from torchmetrics.image.ssim import ( # noqa: F401 SSIM, MultiScaleStructuralSimilarityIndexMeasure, StructuralSimilarityIndexMeasure, ) from torchmetrics.utilities.imports import _LPIPS_AVAILABLE, _TORCH_FIDELITY_AVAILABLE if _TORCH_FIDELITY_AVAILABLE: from torchmetrics.image.fid import FID, FrechetInceptionDistance # noqa: F401 if _LPIPS_AVAILABLE: from torchmetrics.image.lpip import LPIPS, LearnedPerceptualImagePatchSimilarity # noqa: F401

44.37931

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0

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1

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3

b5d70ab09684b20a24edec88af826451e55e1ec4

195

py

Python

services/viewcounts/apps.py

RyanFleck/AuxilliaryWebsiteServices

bcaa6689e567fdf9f20f7f4ea84043aa2b6f1378

[ "MIT" ]

1

2020-11-11T20:20:42.000Z

services/viewcounts/apps.py

RyanFleck/AuxilliaryWebsiteServices

bcaa6689e567fdf9f20f7f4ea84043aa2b6f1378

[ "MIT" ]

17

2020-11-09T19:04:04.000Z

2022-03-01T18:08:42.000Z

services/viewcounts/apps.py

RyanFleck/AuxilliaryWebsiteServices

bcaa6689e567fdf9f20f7f4ea84043aa2b6f1378

[ "MIT" ]

null

from django.apps import AppConfig from django.utils.translation import gettext_lazy as _ class ViewCountsConfig(AppConfig): name = "services.viewcounts" verbose_name = _("View Counts")

24.375

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6.391304

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195

7

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null

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1

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null

0

1

0

3

bd1fb555a3aa55bcecdf98f31894d5a52d697994

734

py

Python

backend/src/data_process/datacleaner.py

AnXi-TieGuanYin-Tea/MusicGenreClassifiaction

a0b9f621b0a5d2451180b12af7681756c5abd138

[ "MIT" ]

7

2018-05-01T19:39:17.000Z

2020-01-02T17:11:05.000Z

backend/src/data_process/datacleaner.py

AnXi-TieGuanYin-Tea/MusicGenreClassifiaction

a0b9f621b0a5d2451180b12af7681756c5abd138

[ "MIT" ]

10

2018-12-10T22:16:43.000Z

2020-08-27T18:23:45.000Z

backend/src/data_process/datacleaner.py

AnXi-TieGuanYin-Tea/MusicGenreClassifiaction

a0b9f621b0a5d2451180b12af7681756c5abd138

[ "MIT" ]

2

2021-04-16T08:20:17.000Z

2022-01-06T14:06:44.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Nov 6 2018 @author: Akihiro Inui """ import numpy as np class DataCleaner: @staticmethod def clean_data(input_dataframe): """ Replace Inf to NaN, then replace NaN to 0 :param input_dataframe: input pandas dataframe :return clean dataframe """ # Copy input dataframe clean_dataframe = input_dataframe.copy() # Replace Inf to NaN clean_dataframe=clean_dataframe.replace([np.inf, -np.inf], np.nan) clean_dataframe=clean_dataframe.replace([np.inf, -np.inf], np.nan) # Replace NaN to 0 clean_dataframe = clean_dataframe.fillna(0) return clean_dataframe

23.677419

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734

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0

0.111111

0

0.444444

0

null

1

0

null

0

3

bd237c8cc029dc3ff7a07afaffbfd766a6cb00a7

205

py

Python

ontraportlib/controllers/__init__.py

LifePosts/ontraport

fb4834e89b897dce3475c89c7e6c34bf8756880e

[ "MIT" ]

null

ontraportlib/controllers/__init__.py

LifePosts/ontraport

fb4834e89b897dce3475c89c7e6c34bf8756880e

[ "MIT" ]

null

ontraportlib/controllers/__init__.py

LifePosts/ontraport

fb4834e89b897dce3475c89c7e6c34bf8756880e

[ "MIT" ]

null

__all__ = [ 'base_controller', 'forms_controller', 'landing_page_controller', 'messages_controller', 'objects_controller', 'tasks_controller', 'transactions_controller', ]

22.777778

31

0.668293

16

205

7.8125

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0

0.214634

205

9

32

22.777778

0.776398

0

0.65

0.23

0

1

0

false

0

1

0

null

0

1

0

1

0

null

0

3

bd3a6e6866b8527ae521f3115fb4c8710b8bf0d2

1,185

py

Python

general/cleanup_py.py

xR86/scripts

a28e4b5011d2629805642b63f97038ede7c0b2e5

[ "MIT" ]

1

2022-02-05T06:35:14.000Z

general/cleanup_py.py

xR86/scripts

a28e4b5011d2629805642b63f97038ede7c0b2e5

[ "MIT" ]

2

2017-12-30T10:32:54.000Z

2021-02-16T23:12:26.000Z

general/cleanup_py.py

xR86/scripts

a28e4b5011d2629805642b63f97038ede7c0b2e5

[ "MIT" ]

null

""" Utility functions for finding dependencies locations. """ import site def get_package_install_locations(): ''' There was also this method: ``` from distutils.sysconfig import get_python_lib print(get_python_lib()) ``` ''' return site.getsitepackages() def recursive_find_peer_deps(find_peerdep): ''' Call get_package_install_locations and start from there. Find any requirements.txt files where find_peerdep is listed, save to list Print list with locations of the packages where peerdep is needed(parse pip freeze ??) Print list with locations of the packages where peerdep is needed(parse pip freeze ??) ''' pass def json_file_location_stub(): import json # is this lazy importing ? return json.__file__ def pip_freeze(): from pip.operations import freeze x = freeze.freeze() return list(x) if __name__ == '__main__': # get_package_install_locations() print(get_package_install_locations()) print() # json_file_location_stub() print(json_file_location_stub()) print() # pip_freeze() # print(help('modules')) p_fr = pip_freeze() # print('\n'.join('{}: {}'.format(*k) for k in enumerate(p_fr))) print('\n'.join('%s' % pkg for pkg in p_fr))

23.7

87

0.736709

171

1,185

4.824561

0.438596

0.054545

0.082424

0.126061

0.30303

0.233939

0.167273

0

0.147679

1,185

49

88

24.183673

0.816832

0.547679

0

0.105263

0

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false

0.052632

0.157895

0

0.526316

0.263158

0

null

0

null

0

1

0

1

0

1

0

3

bd404d49ac15baec2549ab59990990528394ae4f

134

py

Python

helm/dagster/schema/schema/charts/dagster/subschema/telemetry.py

asamoal/dagster

08fad28e4b608608ce090ce2e8a52c2cf9dd1b64

[ "Apache-2.0" ]

null

helm/dagster/schema/schema/charts/dagster/subschema/telemetry.py

asamoal/dagster

08fad28e4b608608ce090ce2e8a52c2cf9dd1b64

[ "Apache-2.0" ]

null

helm/dagster/schema/schema/charts/dagster/subschema/telemetry.py

asamoal/dagster

08fad28e4b608608ce090ce2e8a52c2cf9dd1b64

[ "Apache-2.0" ]

null

from pydantic import BaseModel, Extra class Telemetry(BaseModel): enabled: bool class Config: extra = Extra.forbid

14.888889

37

0.69403

15

134

6.2

0.733333

0

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134

8

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1

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null

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1

0

null

0

1

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3

bd535ff2da475103db73e39bd34a39afb78bca84

85

py

Python

src/hello/__init__.py

SarahDVictoria/hello

e3bdee706acbbc3b5418b005c0d18297caee25e4

[ "MIT" ]

null

src/hello/__init__.py

SarahDVictoria/hello

e3bdee706acbbc3b5418b005c0d18297caee25e4

[ "MIT" ]

null

src/hello/__init__.py

SarahDVictoria/hello

e3bdee706acbbc3b5418b005c0d18297caee25e4

[ "MIT" ]

null

class Hello: def __init__(self): while True: print("Hello!")

17

27

0.517647

9

85

4.444444

0.888889

0

0.364706

85

4

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21.25

0.740741

0

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0

1

0.25

false

0

0.5

0.25

1

0

null

0

1

0

null

0

1

0

3

1fadefca45df4102aacb48cc8d6446d9f9d5635c

186

py

Python

mysite/scisheets/ui/__init__.py

ScienceStacks/JViz

c8de23d90d49d4c9bc10da25f4a87d6f44aab138

[ "Artistic-2.0", "Apache-2.0" ]

31

2016-11-16T22:34:35.000Z

2022-03-22T22:16:11.000Z

mysite/scisheets/ui/__init__.py

ScienceStacks/JViz

c8de23d90d49d4c9bc10da25f4a87d6f44aab138

[ "Artistic-2.0", "Apache-2.0" ]

6

2017-06-24T06:29:36.000Z

2022-01-23T06:30:01.000Z

mysite/scisheets/ui/__init__.py

ScienceStacks/JViz

c8de23d90d49d4c9bc10da25f4a87d6f44aab138

[ "Artistic-2.0", "Apache-2.0" ]

4

2017-07-27T16:23:50.000Z

2022-03-12T06:36:13.000Z

""" Extends the scitables core to display tables in a browser. UITable is a rendering independent user API to tables. DGTable is an API to tables that gets rendered in YUI DataTable """

31

63

0.77957

31

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4.677419

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0

0.177419

186

5

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0.947712

0.951613

0

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0

1

0

1

0

null

0

1

0

3

1fb5d28b4a894485334890c05e234a77200b449a

731

py

Python

model/user_achievement.py

fi-ksi/dashboard-alpha

aaa800d02f78f198a95faf27af2bc8afeca4b867

[ "MIT" ]

4

2017-12-11T00:14:22.000Z

2022-02-07T15:08:13.000Z

model/user_achievement.py

fi-ksi/dashboard-alpha

aaa800d02f78f198a95faf27af2bc8afeca4b867

[ "MIT" ]

94

2016-04-29T10:38:37.000Z

2022-02-10T13:41:29.000Z

model/user_achievement.py

fi-ksi/dashboard-alpha

aaa800d02f78f198a95faf27af2bc8afeca4b867

[ "MIT" ]

null

from sqlalchemy import Column, Integer, ForeignKey from . import Base from .user import User from .achievement import Achievement from .task import Task class UserAchievement(Base): __tablename__ = 'user_achievement' __table_args__ = { 'mysql_engine': 'InnoDB', 'mysql_charset': 'utf8mb4' } user_id = Column(Integer, ForeignKey(User.id, ondelete='CASCADE'), primary_key=True, nullable=False) achievement_id = Column(Integer, ForeignKey(Achievement.id, ondelete='CASCADE'), primary_key=True, nullable=False) task_id = Column(Integer, ForeignKey(Task.id, ondelete='CASCADE'), nullable=True)

31.782609

75

0.636115

74

731

6.054054

0.378378

0.116071

0.205357

0.167411

0.196429

0

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0.269494

731

22

76

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false

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0.277778

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0.611111

0

null

0

1

0

null

0

1

0

3

1fca975aaae74f761eb75ea1f54014d67027a10d

51,199

py

Python

library.py

olonok69/TIME_SERIES_MODELING_KERAS

c0e5a6f274d707551e3bcad2d2ccf75b18c39464

[ "MIT" ]

1

2019-03-14T19:15:32.000Z

library.py

olonok69/TIME_SERIES_MODELING_KERAS

c0e5a6f274d707551e3bcad2d2ccf75b18c39464

[ "MIT" ]

null

library.py

olonok69/TIME_SERIES_MODELING_KERAS

c0e5a6f274d707551e3bcad2d2ccf75b18c39464

[ "MIT" ]

2

2019-03-14T19:15:33.000Z

2019-05-28T13:12:36.000Z

import os import sys import pandas as pd import numpy as np import time import datetime from sklearn import preprocessing import matplotlib.pyplot as plt import matplotlib.pylab as py #from sklearn import svm #from sklearn import cross_validation from sklearn.ensemble import RandomForestClassifier from sklearn.linear_model import SGDClassifier from sklearn import neighbors from sklearn.ensemble import AdaBoostClassifier from sklearn.ensemble import GradientBoostingClassifier from sklearn.svm import SVC from sklearn.discriminant_analysis import LinearDiscriminantAnalysis from sklearn.model_selection import GridSearchCV from sklearn.metrics import make_scorer from sklearn.metrics import f1_score from sklearn.metrics import accuracy_score, classification_report, roc_auc_score, matthews_corrcoef from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import cross_val_score from sklearn.metrics import classification_report from sklearn.naive_bayes import GaussianNB from sklearn.ensemble import VotingClassifier from sklearn.model_selection import cross_val_predict from sklearn.metrics import confusion_matrix from sklearn.linear_model import LogisticRegression from sklearn.decomposition import PCA #import pandas_datareader.data as web #import quandl global kFOLDS daysAhead = 270 kFOLDS=0 def get_data(symbols, dates): #"""Read stock data (adjusted close) for given symbols from CSV files.""" df_final = pd.DataFrame(index=dates) i=0 for symbol in symbols: path = os.path.dirname(os.path.realpath(__file__)) file_path = path + "\\raw_data\\" + symbol + ".csv" #print ("Loading csv..." + str(file_path)) if symbol=="ORB"or symbol=="WT1010":#OIL df_temp = pd.read_csv(file_path, parse_dates=True, index_col="Date",usecols=["Date", "Value"], na_values=["nan"]) df_temp = df_temp.rename(columns={"Value": symbol}) elif symbol=="EUR" or symbol=="GBP" or symbol=="AUD" or symbol=="JPY": df_temp = pd.read_csv(file_path, parse_dates=True, index_col="DATE",usecols=["DATE", "RATE"], na_values=["nan"]) df_temp = df_temp.rename(columns={"RATE": symbol}) elif symbol=="PLAT": df_temp = pd.read_csv(file_path, parse_dates=True, index_col="Date",usecols=["Date", "London 08:00"], na_values=["nan"]) df_temp = df_temp.rename(columns={"London 08:00": symbol}) elif symbol=="GOLD": df_temp = pd.read_csv(file_path, parse_dates=True, index_col="Date",usecols=["Date", "USD (AM)"], na_values=["nan"]) df_temp = df_temp.rename(columns={"USD (AM)": symbol}) elif symbol=="SILVER": df_temp = pd.read_csv(file_path, parse_dates=True, index_col="Date",usecols=["Date", "USD"], na_values=["nan"]) df_temp = df_temp.rename(columns={"USD": symbol}) else: df_temp = pd.read_csv(file_path, parse_dates=True, index_col="Date",usecols=["Date", "Adj Close"], na_values=["nan"]) df_temp = df_temp.rename(columns={"Adj Close": symbol}) #df_temp = df_temp.rename(columns={"Adj Close": symbol}) df_final = df_final.join(df_temp) i+=1 if i == 1: # drop dates SPY did not trade df_final = df_final.dropna(subset=[symbol]) return df_final def write_Ex(__df_r1, filename, title): print ("Printing Report..."+ str(title)) total_file = os.path.dirname(os.path.realpath(__file__)) + "\\raw_data\\" + filename writer = pd.ExcelWriter(total_file) __df_r1.to_excel(writer,title) writer.save() def plot_confusion_matrix(cm,alg, title='Confusion matrix', cmap=plt.cm.Blues): plt.imshow(cm, interpolation='nearest', cmap=cmap) title=title+" " +alg plt.title(title) plt.colorbar() names=["Up","Down"] tick_marks = np.arange(len(names)) plt.xticks(tick_marks, names) plt.yticks(tick_marks, names) #plt.tight_layout() plt.ylabel('True label') plt.xlabel('Predicted label') def plot_cm(cm, alg): np.set_printoptions(precision=2) cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] plt.figure(figsize=(6, 4)) plot_confusion_matrix(cm_normalized, alg) file = os.path.dirname(os.path.realpath(__file__)) + "\\out\\" +"CM_"+alg+".png" plt.savefig(file) def shift_day(df_index,indices_list,indices_day_after): for indice in indices_list: if indice not in str(indices_day_after): df_index[indice] = df_index[indice].shift(1) return df_index def Load_DataFrames(): # Open Excel files to load them in a dataframe path = os.path.dirname(os.path.realpath(__file__)) file_Accuracy = path + "\\raw_data\\Accuracy.xlsx" # Load files in a dataframe for manipulation accu = pd.ExcelFile(file_Accuracy) # Select first sheet in the file _df_accu = accu.parse(accu.sheet_names[0]) return _df_accu def get_web_data(symbols, dates): #"""Read stock data (adjusted close) for given symbols from CSV files.""" df_final = pd.DataFrame(index=dates) i=0 fechastart='2007-01-01' fechaend='2017-01-01' for symbol in symbols: df_temp = web.DataReader(symbol, start=fechastart, end=fechaend,data_source='yahoo')["Date", "Adj Close"] df_temp = df_temp.rename(columns={"Adj Close": symbol}) df_final = df_final.join(df_temp) i+=1 if i == 1: # drop dates SPY did not trade df_final = df_final.dropna(subset=[symbol]) return df_final def get_web_quandl(symbol, simbolo): df = quandl.get(symbol, trim_start = "01/01/2007", trim_end ="01/01/2017", authtoken="_N85bWLCNCWz14smKHSi") name=simbolo df.columns.values[-1] = 'AdjClose' df.columns = df.columns + '_' + name df['Return_%s' %name] = df['AdjClose_%s' %name].pct_change() return df def adjusted_return(df, symbol,numDaysArray): #df_final = pd.DataFrame(index=dates) for n in numDaysArray: #["^GSPC","SPY","^IXIC", "^DJI", "^GDAXI", "^FTSE","^FCHI", "EUR","GBP", "SILVER", "WT1010"] if symbol =="^GSPC"or symbol =="^IXIC"or symbol =="^DJI"or symbol == "^GDAXI"or symbol =="^FTSE"or symbol == "^FCHI"or symbol =="EUR"or symbol == "GBP"or symbol == "SILVER" or symbol == "WT1010": M = pd.Series(df[symbol].pct_change(n).shift(-1), name = str(symbol)+ '_Adj_' + str(n)) #M = pd.Series(df[symbol].pct_change(n), name = str(symbol)+ '_Adj_' + str(n)) else: M = pd.Series(df[symbol].pct_change(n), name = str(symbol)+ '_Adj_' + str(n)) df = df.join(M) return df def rolling_average(df, symbol,numDaysArray): for n in numDaysArray: if symbol=="^GSPC_Adj_1": M=pd.Series(df[str(symbol)].rolling(window = n, center = False).mean().shift(1), name = str(symbol)+ '_Roll_Avg_' + str(n)) else: M=pd.Series(df[str(symbol)].rolling(window = n, center = False).mean(), name = str(symbol)+ '_Roll_Avg_' + str(n)) df = df.join(M) return df def remove_col(df, symbol, symbol2): if symbol != symbol2: del df[symbol] return df def normalize(df, symbols): result = df.copy() for symbol in df.columns: max_value = df[symbol].max() min_value = df[symbol].min() result[symbol] = (df[symbol] - min_value) / (max_value - min_value) return result def mergeDataframes(out, dataset1,dataset2, symbol1,symbol2): out= pd.merge(dataset1, dataset2, on='Date', how='left') return out def Volatity_n(df, symbol,numDaysArray): #numDaysArray = [2, 21, 63] #numDaysArray = [2] for n in numDaysArray: #M = pd.Series(df[symbol].pct_change(n).std(), name = 'Volatility_' + str(n)) #if symbol != "^GSPC": df["Log_Ret"] = np.log(df[symbol] / df[symbol].shift(1)) df[str(symbol)+ "_Vol_"+ str(n)] = df["Log_Ret"].rolling(window=n, center=False).std() * np.sqrt(252) # if we multiply sqrt(252) is anualized volatility del df["Log_Ret"] return df def MOM(df, symbol,numDaysArray): for n in numDaysArray: #if symbol != "^GSPC": M = pd.Series(df[symbol].diff(n), name = str(symbol)+ '_MoM_' + str(n)) df = df.join(M) return df def ExpMovingAverage(df, symbol,numDaysArray): #ts_log = np.log(df[str(symbol)]) for n in numDaysArray: #if symbol !="^GSPC": M = pd.Series(df[symbol].ewm(span=n).mean(), name = str(symbol)+ '_EWA_' + str(n)) df = df.join(M) #df[str(symbol)+ "_EMA_"+ str(n)] = pd.ewma(df[symbol], span=n, freq="D") #Series.ewm(min_periods=0,adjust=True,ignore_na=False,span=1,freq=D).mean() return df def Label_Change (row): if row['^GSPC_Adj_1'] > 0 : return 'Up' return 'Down' def Label_Change2 (row): if row['Real'] > 0 : return 'Up' return 'Down' def Cleaning(df): #(Remove, inf with NaN and replace Bfill) df.replace([np.inf, -np.inf], np.nan) df[df==np.inf] = np.nan df[df==-np.inf] = np.nan df.fillna(method='bfill', inplace=True) df.fillna(method='ffill', inplace=True) return df def plotvalidation_curve(y_test,predictions): fig, ax = plt.subplots() ax.scatter(y_test, predictions) ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=4) ax.set_xlabel('Measured') ax.set_ylabel('Predicted') plt.show() return def prepareDataForClassification(dataset, start_test, test): le = preprocessing.LabelEncoder() dataset['UpDown'] = dataset.apply (lambda row: Label_Change (row),axis=1) dataset1=dataset.truncate(before='2003-07-01') #delete all values up to the first rolling average 63 days #dataset.UpDown[dataset.UpDown >= 0] = 'Up' #dataset.UpDown[dataset.UpDown < 0] = 'Down' dataset1.UpDown = le.fit(dataset1.UpDown).transform(dataset1.UpDown) if test==2: features = dataset1.columns[0:-1] X = dataset1[features] del X["^GSPC_Adj_1"] if test==1 or test==5: features = dataset1.columns[0:-1] X = dataset1[features] del X["^GSPC_Adj_1"] if test==3: features = dataset1.columns[0:-1] X = dataset1[features] del X["^GSPC_Adj_1"] if test==4: features = dataset1.columns[0:-1] X = dataset1[features] del X["^GSPC_Adj_1"] if test==6: features = dataset1.columns[0:-1] X = dataset1[features] del X["^GSPC_Adj_1"] y = dataset1.UpDown X_train = X[X.index < start_test] y_train = y[y.index < start_test] X_test = X[X.index >= start_test] y_test = y[y.index >= start_test] #path = os.path.dirname(os.path.realpath(__file__)) #file_path = path + "\\raw_data\\" + "y_train.csv" #y_train.to_csv(file_path, index = False) #file_path = path + "\\raw_data\\" + "y_testn.csv" #y_test.to_csv(file_path, index = False) #write_Ex(X_train, "X_train.xlsx", "X_train") #write_Ex(X_test, "X_test.xlsx", "X_test") return X, X_train, y_train, X_test, y_test def performEnsemberBlendingClass(X_train, y_train, X_test, y_test):#, parameters, fout, savemodel): print("Ensembler Blending Classification") start_date = "2013-01-01" end_date = "2017-01-01" dates = pd.date_range(start_date, end_date) df_out = pd.DataFrame(index=dates) df_out["Real"] = y_test.copy() df_out=df_out.dropna() # so we can modify it le = preprocessing.LabelEncoder() df_out['UpDown'] = df_out.apply (lambda row: Label_Change2 (row),axis=1) df_out.UpDown = le.fit(df_out.UpDown).transform(df_out.UpDown) for i in range(50): clf1 = SVC(C=((i+1)*100)+3200, kernel= 'sigmoid', gamma=0.05) #'C': 3500, 'kernel': 'sigmoid', 'gamma': 0.05 clf2 = RandomForestClassifier(n_estimators=4000, criterion= 'gini') #'criterion': 'gini', 'n_estimators': 4000} clf3 = SGDClassifier(penalty='elasticnet', loss='perceptron', learning_rate= 'invscaling', eta0=0.1, alpha=0.001) #{'penalty': 'elasticnet', 'eta0': 0.1, 'alpha': 0.001, 'loss': 'perceptron', 'learning_rate': 'invscaling'} clf4 = AdaBoostClassifier(n_estimators=200, algorithm='SAMME') clf5= GradientBoostingClassifier(min_samples_leaf=75, n_estimators=90+(i*10), max_features= 'auto',min_samples_split=300, learning_rate=0.1) clf6 = neighbors.KNeighborsClassifier(algorithm='ball_tree', n_neighbors=350, weights='distance', leaf_size= 30) clf7 = LinearDiscriminantAnalysis(store_covariance=True, n_components= 1, solver= 'lsqr', shrinkage= 0.1) print("Fitting") print("SVC"+str(i)) clf1.fit(X_train, y_train) print("RFC"+str(i)) clf2.fit(X_train, y_train) print("SGD"+str(i)) clf3.fit(X_train, y_train) print("ADA"+str(i)) clf4.fit(X_train, y_train) print("GTB"+str(i)) clf5.fit(X_train, y_train) print("KNN"+str(i)) clf6.fit(X_train, y_train) print("LDA"+str(i)) clf7.fit(X_train, y_train) print("predicting") df_out['SVM'+str(i)] = clf1.predict(X_test) df_out['RFC'+str(i)] = clf2.predict(X_test) df_out['SDG'+str(i)] = clf3.predict(X_test) df_out['ADA'+str(i)] = clf4.predict(X_test) df_out['GTB'+str(i)] = clf5.predict(X_test) df_out['KNN'+str(i)] = clf6.predict(X_test) df_out['LDA'+str(i)] = clf7.predict(X_test) features = df_out.columns[1:] X = df_out[features] del X["UpDown"] #print(X) y = df_out.UpDown start_test="2016-01-01" #print(y) X_train = X[X.index < start_test] y_train = y[y.index < start_test] X_test = X[X.index >= start_test] y_test = y[y.index >= start_test] #{'kernel': 'rbf', 'C': 100.0, 'gamma': 0.1} #{'penalty': 'l2', 'C': 0.001, 'solver': 'newton-cg'} clfsb= LogisticRegression(penalty= 'l2', C= 0.001, solver= 'newton-cg') clfsb.fit(X_train, y_train) clfsb2= SVC(C=100,kernel= 'rbf', gamma=0.1) clfsb2.fit(X_train, y_train) #training=end-start accuracy = clfsb.score(X_test, y_test) accuracy2 = clfsb2.score(X_test, y_test) #start = datetime.datetime.now() predictions = clfsb.predict(X_test) predictions2 = clfsb2.predict(X_test) #end = datetime.datetime.now() Sscore = f1_score(y_test, predictions) Sscore2 = f1_score(y_test, predictions2) #predecir=end-start print("F1 Score Blender " +str(Sscore)) print("Accuracy Blender " +str(accuracy)) print("F1 Score Blender 2 " +str(Sscore2)) print("Accuracy Blender 2 " +str(accuracy2)) cm = confusion_matrix(y_test, predictions) plot_cm(cm,"Blender1 Classification") cm1 = confusion_matrix(y_test, predictions2) plot_cm(cm1,"Blender2 Classification") return df_out, accuracy, Sscore, accuracy2, Sscore2 def CVperformEnsemberBlendingClass(X_train, y_train, X_test, y_test):#, parameters, fout, savemodel): def f1_score_on_test(estimator, x, y): y_pred = estimator.predict(X_test) return f1_score(y_test.values, y_pred) print("Ensembler Blending Classification") start_date = "2013-01-01" end_date = "2017-01-01" dates = pd.date_range(start_date, end_date) df_out = pd.DataFrame(index=dates) df_out["Real"] = y_test.copy() df_out=df_out.dropna() # so we can modify it le = preprocessing.LabelEncoder() df_out['UpDown'] = df_out.apply (lambda row: Label_Change2 (row),axis=1) df_out.UpDown = le.fit(df_out.UpDown).transform(df_out.UpDown) for i in range(2): clf1 = SVC(C=((i+1)*100)+3200, kernel= 'sigmoid', gamma=0.05) #'C': 3500, 'kernel': 'sigmoid', 'gamma': 0.05 clf2 = RandomForestClassifier(n_estimators=4000, criterion= 'gini') #'criterion': 'gini', 'n_estimators': 4000} clf3 = SGDClassifier(penalty='elasticnet', loss='perceptron', learning_rate= 'invscaling', eta0=0.1, alpha=0.001) #{'penalty': 'elasticnet', 'eta0': 0.1, 'alpha': 0.001, 'loss': 'perceptron', 'learning_rate': 'invscaling'} clf4 = AdaBoostClassifier(n_estimators=200, algorithm='SAMME') clf5= GradientBoostingClassifier(min_samples_leaf=75, n_estimators=90+(i*10), max_features= 'auto',min_samples_split=300, learning_rate=0.1) clf6 = neighbors.KNeighborsClassifier(algorithm='ball_tree', n_neighbors=350, weights='distance', leaf_size= 30) clf7 = LinearDiscriminantAnalysis(store_covariance=True, n_components= 1, solver= 'lsqr', shrinkage= 0.1) print("Fitting") print("SVC"+str(i)) clf1.fit(X_train, y_train) print("RFC"+str(i)) clf2.fit(X_train, y_train) print("SGD"+str(i)) clf3.fit(X_train, y_train) print("ADA"+str(i)) clf4.fit(X_train, y_train) print("GTB"+str(i)) clf5.fit(X_train, y_train) print("KNN"+str(i)) clf6.fit(X_train, y_train) print("LDA"+str(i)) clf7.fit(X_train, y_train) print("predicting") df_out['SVM'+str(i)] = clf1.predict(X_test) df_out['RFC'+str(i)] = clf2.predict(X_test) df_out['SDG'+str(i)] = clf3.predict(X_test) df_out['ADA'+str(i)] = clf4.predict(X_test) df_out['GTB'+str(i)] = clf5.predict(X_test) df_out['KNN'+str(i)] = clf6.predict(X_test) df_out['LDA'+str(i)] = clf7.predict(X_test) features = df_out.columns[1:] X = df_out[features] del X["UpDown"] #print(X) y = df_out.UpDown start_test="2016-01-01" #print(y) X_train = X[X.index < start_test] y_train = y[y.index < start_test] X_test = X[X.index >= start_test] y_test = y[y.index >= start_test] #clfsb= LogisticRegression(penalty= 'l1', C= 1, solver= 'liblinear') #clfsb.fit(X_train, y_train) #clfsb2= SVC(C=0.01,kernel= 'rbf') #clfsb2.fit(X_train, y_train) #training=end-start params_map = [{ 'C': [0.001,0.005, 0.01,0.02, 0.1,0.4, 0.5,0.6,0.7, 1.0, 2.0, 5.0, 10.0, 100.0],'kernel': ['rbf'],'gamma': ['auto',0.001,0.01, 0.05,0.1, 0.5, 1.0,4.0, 5.0, 6.0, 10.0,50.0,100.0,150.0]}, {'kernel': ['linear'], 'C': [1, 10, 100, 1000, 1500, 2000, 2500,3000, 3500,4000]}]#, #{'kernel': ['poly'], 'C': [1, 10, 100, 1000, 1500, 2000, 2500,3000, 3500,4000],'degree':[2,3,4,5,6,7,8,9,10,15], 'gamma': ['auto',0.001,0.01, 0.05,0.1, 0.5, 1.0, 5.0]}, #{'kernel': ['sigmoid'], 'C': [1, 10, 100, 1000, 1500, 2000, 2500,3000, 3500,4000], 'gamma': ['auto',0.001,0.01, 0.05,0.1, 0.5, 1.0, 5.0]}] clf = GridSearchCV(SVC(), params_map, scoring=f1_score_on_test, verbose=100) test=5 clf.fit(X_train, y_train) #joblib.dump(clf.best_estimator_, file_path, compress = 3) df=pd.DataFrame(clf.grid_scores_) total_file = os.path.dirname(os.path.realpath(__file__)) + "\\CV\\CVBlender_SVC_" + str(test)+ ".xlsx" writer = pd.ExcelWriter(total_file) df.to_excel(writer,"SVC") writer.save() param_grid = [{'C':[0.001, 0.01, 0.1, 1, 10, 100, 1000], 'penalty' :["l1"], 'solver' :["liblinear"] }, {'C':[0.001, 0.01, 0.1, 1, 10, 100, 1000], 'penalty' :["l2"], 'solver' :[ "newton-cg", "lbfgs", "sag"] }] clf1 = GridSearchCV(LogisticRegression(), param_grid, scoring=f1_score_on_test, verbose=100) clf1.fit(X_train, y_train) df=pd.DataFrame(clf1.grid_scores_) total_file = os.path.dirname(os.path.realpath(__file__)) + "\\CV\\CVBlenderLRC_" + str(test)+ ".xlsx" writer = pd.ExcelWriter(total_file) df.to_excel(writer,"LRC") writer.save() return clf.best_params_, clf1.best_params_ def performKNNClass(X_train, y_train, X_test, y_test):#, parameters, fout, savemodel): print("KNN binary Classification") #{{'leaf_size': 30, 'algorithm': 'ball_tree', 'n_neighbors': 350, 'weights': 'distance'} clf = neighbors.KNeighborsClassifier(algorithm='ball_tree', n_neighbors=300, weights='distance', leaf_size= 30) #clf = neighbors.KNeighborsClassifier() start = datetime.datetime.now() clf.fit(X_train, y_train) end = datetime.datetime.now() training=end-start accuracy = clf.score(X_test, y_test) start = datetime.datetime.now() predictions = clf.predict(X_test) end = datetime.datetime.now() Sscore = f1_score(y_test, predictions) roc=mat=0 if kFOLDS != 2: roc = roc_auc_score(y_test, predictions) mat = matthews_corrcoef(y_test, predictions) predecir=end-start #cm = confusion_matrix(y_test, predictions) #plot_cm(cm,"KNN Binary Classification") print("F1 Score KNN " +str(Sscore)) return accuracy, Sscore, y_test,predictions , training, predecir,roc,mat def performRFClass(X_train, y_train, X_test, y_test):#, parameters, fout, savemodel): print("Random Forest Binary Classification") clf = RandomForestClassifier(n_estimators=500, criterion= 'gini') #clf = RandomForestClassifier()# '{'n_estimators': 500, 'criterion': 'gini'} start = datetime.datetime.now() clf.fit(X_train, y_train) end = datetime.datetime.now() training=end-start accuracy = clf.score(X_test, y_test) start = datetime.datetime.now() predictions = clf.predict(X_test) end = datetime.datetime.now() Sscore = f1_score(y_test, predictions) roc=mat=0 if kFOLDS != 2: roc = roc_auc_score(y_test, predictions) mat = matthews_corrcoef(y_test, predictions) predecir=end-start #cm = confusion_matrix(y_test, predictions) #plot_cm(cm,"Random Forest Binary") print("F1 Score RFC " +str(Sscore)) return accuracy, Sscore, y_test,predictions , training, predecir,roc,mat def performSGDClass(X_train, y_train, X_test, y_test):#, parameters, fout, savemodel): print("Stochastic Gradient Descent binary Classification") #{'learning_rate': 'invscaling', 'alpha': 0.01, 'loss': 'perceptron', 'penalty': 'elasticnet', 'eta0': 0.2} clf = SGDClassifier( alpha= 0.01,penalty='elasticnet', loss='perceptron', learning_rate= 'invscaling', eta0=0.2) #clf = SGDClassifier() start = datetime.datetime.now() clf.fit(X_train, y_train) end = datetime.datetime.now() training=end-start accuracy = clf.score(X_test, y_test) start = datetime.datetime.now() predictions = clf.predict(X_test) end = datetime.datetime.now() Sscore = f1_score(y_test, predictions) roc=mat=0 if kFOLDS != 2: roc = roc_auc_score(y_test, predictions) mat = matthews_corrcoef(y_test, predictions) predecir=end-start #cm = confusion_matrix(y_test, predictions) #plot_cm(cm,"Stochastic Gradient Descent") print("F1 Score SGD " +str(Sscore)) return accuracy, Sscore, y_test,predictions , training, predecir,roc,mat def performSVMClass(X_train, y_train, X_test, y_test):#, parameters, fout, savemodel): #""" print("SVM binary Classification") #print(n_components) #c = parameters[0] #g = parameters[1] #{{'C': 3500, 'kernel': 'sigmoid', 'gamma': 0.05} #{'kernel': 'rbf', 'C': 0.1, 'gamma': 100.0} clf = SVC(C=2000,kernel= 'linear') start = datetime.datetime.now() clf.fit(X_train, y_train) end = datetime.datetime.now() training=end-start accuracy = clf.score(X_test, y_test) start = datetime.datetime.now() #df_out['SVM'] = clf.predict(X_test) predictions = clf.predict(X_test) end = datetime.datetime.now() Sscore = f1_score(y_test, predictions,average='binary') #cm = confusion_matrix(y_test, predictions) #plot_cm(cm,"Support Vector Machines") roc=mat=0 if kFOLDS != 2: roc = roc_auc_score(y_test, predictions) mat = matthews_corrcoef(y_test, predictions) predecir=end-start #write_Ex(df_out, "df_out.xlsx", "df_out") #plotvalidation_curve(y_test,predictions) print("F1 Score SVM " +str(Sscore)) return accuracy, Sscore, y_test,predictions, training, predecir,roc,mat def performSVM_PCAClass(X_train, y_train, X_test, y_test):#, parameters, fout, savemodel): #""" print("SVM binary Classification") start_date = "2013-01-01" end_date = "2017-01-01" dates = pd.date_range(start_date, end_date) df_out = pd.DataFrame(index=dates) df_out["Real"] = y_test.copy() df_out=df_out.dropna() n_components=len(X_train.columns)-1 pca = PCA(copy=True,n_components=n_components, whiten=False).fit(X_train) X_train_pca = pca.transform(X_train) X_test_pca = pca.transform(X_test) #print(n_components) #c = parameters[0] #g = parameters[1] #{{'C': 3500, 'kernel': 'sigmoid', 'gamma': 0.05} #{'kernel': 'rbf', 'C': 0.1, 'gamma': 100.0} clf = SVC(C=3500,kernel= 'sigmoid', gamma= 0.05) start = datetime.datetime.now() clf.fit(X_train_pca, y_train) end = datetime.datetime.now() training=end-start accuracy = clf.score(X_test_pca, y_test) start = datetime.datetime.now() df_out['SVM'] = clf.predict(X_test_pca) predictions = clf.predict(X_test_pca) end = datetime.datetime.now() Sscore = f1_score(y_test, predictions,average='binary') #cm = confusion_matrix(y_test, predictions) #plot_cm(cm,"Support Vector Machines") roc=mat=0 if kFOLDS != 2: roc = roc_auc_score(y_test, predictions) mat = matthews_corrcoef(y_test, predictions) predecir=end-start #write_Ex(df_out, "df_out.xlsx", "df_out") #plotvalidation_curve(y_test,predictions) print("F1 Score SVM " +str(Sscore)) return accuracy, Sscore, y_test,predictions, training, predecir,roc,mat def performAdaBoostClass(X_train, y_train, X_test, y_test):#, parameters, fout, savemodel): print("Ada Boosting binary Classification") #{'algorithm': 'SAMME.R', 'n_estimators': 80, 'learning_rate': 0.7} clf = AdaBoostClassifier(n_estimators=80, algorithm='SAMME.R', learning_rate= 0.7) #clf = AdaBoostClassifier() start = datetime.datetime.now() clf.fit(X_train, y_train) end = datetime.datetime.now() training=end-start accuracy = clf.score(X_test, y_test) start = datetime.datetime.now() predictions = clf.predict(X_test) end = datetime.datetime.now() Sscore = f1_score(y_test, predictions) roc=mat=0 if kFOLDS != 2: roc = roc_auc_score(y_test, predictions) mat = matthews_corrcoef(y_test, predictions) #cm = confusion_matrix(y_test, predictions) #plot_cm(cm,"Ada Boosting binary Classification") predecir=end-start print("F1 Score ADA " +str(Sscore)) return accuracy, Sscore, y_test,predictions, training, predecir,roc,mat def performGTBClass(X_train, y_train, X_test, y_test):#, parameters, fout, savemodel): #""" print("Gradient Tree Boosting binary Classification") #{'max_features': 'auto', 'min_samples_split': 300, 'min_samples_leaf': 75, 'n_estimators': 90, 'learning_rate': 0.1} clf = GradientBoostingClassifier(min_samples_leaf=75, n_estimators=90, max_features= 'auto',min_samples_split=300, learning_rate=0.1) #clf = GradientBoostingClassifier() start = datetime.datetime.now() clf.fit(X_train, y_train) end = datetime.datetime.now() training=end-start accuracy = clf.score(X_test, y_test) start = datetime.datetime.now() predictions = clf.predict(X_test) end = datetime.datetime.now() Sscore = f1_score(y_test, predictions) roc=mat=0 if kFOLDS != 2: roc = roc_auc_score(y_test, predictions) mat = matthews_corrcoef(y_test, predictions) #cm = confusion_matrix(y_test, predictions) #plot_cm(cm,"Gradient Tree Boosting Classification") predecir=end-start print("F1 Score GTB " +str(Sscore)) return accuracy, Sscore, y_test,predictions, training, predecir,roc,mat def performLRBClass(X_train, y_train, X_test, y_test):#, parameters, fout, savemodel): print("Logistic Regresion Binary Classification") clf = LogisticRegression(penalty= 'l1', C= 1, solver= 'liblinear') #{'penalty': 'l1', 'C': 1, 'solver': 'liblinear'} start = datetime.datetime.now() clf.fit(X_train, y_train) end = datetime.datetime.now() training=end-start accuracy = clf.score(X_test, y_test) start = datetime.datetime.now() predictions = clf.predict(X_test) end = datetime.datetime.now() Sscore = f1_score(y_test, predictions) roc=mat=0 if kFOLDS != 2: roc = roc_auc_score(y_test, predictions) mat = matthews_corrcoef(y_test, predictions) predecir=end-start #cm = confusion_matrix(y_test, predictions) #plot_cm(cm,"Gaussian Naives Bayes Classification") print("F1 Score Logistic Regresion " +str(Sscore)) return accuracy, Sscore, y_test,predictions, training, predecir,roc,mat def performLDAClass(X_train, y_train, X_test, y_test):#, parameters, fout, savemodel): print("Linear Discriminant Analysis binary Classification") #'{'store_covariance': 'True', 'n_components': 1, 'solver': 'lsqr', 'shrinkage': 0.1} clf = LinearDiscriminantAnalysis(store_covariance=True, n_components= 1, tol=0.5, solver='svd') #clf = LinearDiscriminantAnalysis() start = datetime.datetime.now() clf.fit(X_train, y_train) end = datetime.datetime.now() training=end-start accuracy = clf.score(X_test, y_test) start = datetime.datetime.now() predictions = clf.predict(X_test) end = datetime.datetime.now() Sscore = f1_score(y_test, predictions) roc=mat=0 if kFOLDS != 2: roc = roc_auc_score(y_test, predictions) mat = matthews_corrcoef(y_test, predictions) predecir=end-start print("F1 Score LDA " +str(Sscore)) #cm = confusion_matrix(y_test, predictions) #plot_cm(cm,"Linear Discriminant Analysis Classification") return accuracy, Sscore, y_test,predictions, training, predecir,roc,mat def performVotingClass(X_train, y_train, X_test, y_test):#, parameters, fout, savemodel): print("Voting binary Classification") #clf = LinearDiscriminantAnalysis() clf1 = SVC(C=2000,kernel= 'linear') clf2 = neighbors.KNeighborsClassifier(algorithm='ball_tree', n_neighbors=350, weights='distance', leaf_size= 30) #clf2 = RandomForestClassifier(random_state=1) clf3 = LogisticRegression(penalty= 'l1', C= 1, solver= 'liblinear') clf4 = SGDClassifier( alpha= 0.01,penalty='elasticnet', loss='perceptron', learning_rate= 'invscaling', eta0=0.2) eclf = VotingClassifier(estimators=[('svc', clf1), ('knn', clf2), ('lgr', clf3),('sdg',clf4)], voting='hard') start = datetime.datetime.now() clf1.fit(X_train, y_train) clf2.fit(X_train, y_train) clf3.fit(X_train, y_train) clf4.fit(X_train, y_train) eclf.fit(X_train, y_train) end = datetime.datetime.now() training=end-start accuracy = eclf.score(X_test, y_test) start = datetime.datetime.now() predictions = eclf.predict(X_test) end = datetime.datetime.now() Sscore = f1_score(y_test, predictions) roc=mat=0 if kFOLDS != 2: roc = roc_auc_score(y_test, predictions) mat = matthews_corrcoef(y_test, predictions) predecir=end-start print("F1 Score Voting Classifier " +str(Sscore)) #cm = confusion_matrix(y_test, predictions) #plot_cm(cm,"Voting binary Classification") return accuracy, Sscore, y_test,predictions, training, predecir,roc,mat def performDTCClass(X_train, y_train, X_test, y_test):#, parameters, fout, savemodel): #""" print("DecisionTreeClassifier Classification") #""" clf = DecisionTreeClassifier(max_features='sqrt', criterion='entropy') #{'max_features': 'sqrt', 'criterion': 'entropy'} start = datetime.datetime.now() clf.fit(X_train, y_train) end = datetime.datetime.now() training=end-start accuracy = clf.score(X_test, y_test) start = datetime.datetime.now() predictions = clf.predict(X_test) end = datetime.datetime.now() Sscore = f1_score(y_test, predictions) roc=mat=0 if kFOLDS != 2: roc = roc_auc_score(y_test, predictions) mat = matthews_corrcoef(y_test, predictions) predecir=end-start #cm = confusion_matrix(y_test, predictions) #plot_cm(cm,"Decision Tree Classification") print("F1 Score DecisionTreeClassifier " +str(Sscore)) return accuracy, Sscore, y_test,predictions, training, predecir,roc,mat def GSLDA(X_train, y_train, X_test, y_test,test): #Choose all predictors except target & IDcols def f1_score_on_test(estimator, x, y): y_pred = estimator.predict(X_test) return f1_score(y_test.values, y_pred) params_map = [{'solver':['lsqr','eigen'],'shrinkage':['auto',0.1,0.3,0.5,0.7,0.9],'store_covariance':['True', 'False'],'n_components':[1,2,10,20,30,40,50,60, 80, 100,200, 300,500,1000]},{'solver':['svd'], 'tol':[0.3,0.45,0.5,0.55,0.8, 1.0],'store_covariance':['True', 'False'],'n_components':[1,2,10,20,30,40,50,60, 80, 100,200, 300,500,1000]}] clf = GridSearchCV(LinearDiscriminantAnalysis(), param_grid = params_map, scoring=f1_score_on_test, verbose=100) clf.fit(X_train, y_train) df=pd.DataFrame(clf.grid_scores_) total_file = os.path.dirname(os.path.realpath(__file__)) + "\\CV\\CVLDA_" + str(test)+ ".xlsx" writer = pd.ExcelWriter(total_file) df.to_excel(writer,"LDA") writer.save() return clf.best_params_ def GSGTB(X_train, y_train, X_test, y_test,test): #Choose all predictors except target & IDcols def f1_score_on_test(estimator, x, y): y_pred = estimator.predict(X_test) return f1_score(y_test.values, y_pred) param_test1 = {'n_estimators':[20,40,50,60,70,80,90,100,150,200],'learning_rate':[0.1,0.2], 'min_samples_split':[100,200,300],'min_samples_leaf':[10,30,40, 50, 75],'max_features':['auto', 'sqrt']} #,'subsample':[0.6,0.8,1.0],'random_state':[10,20,30]} #param_test2 = [{'n_estimators':[20,40,50,60,70,80,90,100,150,200]}] clf = GridSearchCV(GradientBoostingClassifier(), param_grid = param_test1, scoring=f1_score_on_test,n_jobs=1,iid=False, cv=5, verbose=100) #clf = GridSearchCV(estimator = GradientBoostingClassifier(learning_rate=0.1, min_samples_split=500,min_samples_leaf=50,max_depth=8,max_features='sqrt',subsample=0.8,random_state=10),param_grid = param_test2, scoring='roc_auc',n_jobs=1,iid=False, cv=5, verbose=100) clf.fit(X_train, y_train) df=pd.DataFrame(clf.grid_scores_) total_file = os.path.dirname(os.path.realpath(__file__)) + "\\CV\\CVGTB_" + str(test)+ ".xlsx" writer = pd.ExcelWriter(total_file) df.to_excel(writer,"GTB") writer.save() return clf.best_params_ def GSSGD(X_train, y_train, X_test, y_test,test): #Choose all predictors except target & IDcols def f1_score_on_test(estimator, x, y): y_pred = estimator.predict(X_test) return f1_score(y_test.values, y_pred) print("Stochastic Gradient Descent binary Classification") #c = parameters[0] #parameters = {'loss': [ 'hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron'], 'alpha': [0.1, 0.01, 0.001, 0.0001, 0.00001, 0.000001, 0.0000001], 'n_iter': list(np.arange(1,1001))} #clf = SVC(C=5, kernel= 'rbf', gamma= 0.01) {'kernel': 'rbf', 'C': 100.0, 'gamma': 0.5} param_test1=[{'loss':['hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron'], 'penalty':['none', 'l2', 'l1', 'elasticnet'], 'learning_rate':['constant','optimal','invscaling'], 'eta0':[0.1,0.2], 'alpha': [0.1, 0.01, 0.001, 0.0001, 0.00001, 0.000001, 0.0000001]}] clf = GridSearchCV(SGDClassifier(), param_grid = param_test1, scoring=f1_score_on_test,n_jobs=1,iid=False, cv=5, verbose=100) #clf = GridSearchCV(estimator = GradientBoostingClassifier(learning_rate=0.1, min_samples_split=500,min_samples_leaf=50,max_depth=8,max_features='sqrt',subsample=0.8,random_state=10),param_grid = param_test2, scoring='roc_auc',n_jobs=1,iid=False, cv=5, verbose=100) clf.fit(X_train, y_train) df=pd.DataFrame(clf.grid_scores_) total_file = os.path.dirname(os.path.realpath(__file__)) + "\\CV\\CVSGD_" + str(test)+ ".xlsx" writer = pd.ExcelWriter(total_file) df.to_excel(writer,"SGD") writer.save() return clf.best_params_ def GSADA(X_train, y_train, X_test, y_test,test): def f1_score_on_test(estimator, x, y): y_pred = estimator.predict(X_test) return f1_score(y_test.values, y_pred)#return f1_score(y_test.values, y_pred, pos_label=1) param_grid = [{'n_estimators':[20,40,50,60,70,80,90,100,150,200],'algorithm' : ['SAMME', 'SAMME.R'], 'learning_rate':[0.1, 0.3,0.5,0.7,1] }] DTC = DecisionTreeClassifier(random_state = 11, max_features = "auto", class_weight = "balanced") #DTC = SVC(kernel= 'rbf', C= 10.0, gamma= 0.1) ABC = AdaBoostClassifier(base_estimator = DTC) clf = GridSearchCV(ABC, param_grid=param_grid, scoring=f1_score_on_test, verbose=100 ) clf.fit(X_train, y_train) df=pd.DataFrame(clf.grid_scores_) total_file = os.path.dirname(os.path.realpath(__file__)) + "\\CV\\CVADA_" + str(test)+ ".xlsx" writer = pd.ExcelWriter(total_file) df.to_excel(writer,"ADA") writer.save() return clf.best_params_ def GSDTC(X_train, y_train, X_test, y_test,test): def f1_score_on_test(estimator, x, y): y_pred = estimator.predict(X_test) return f1_score(y_test.values, y_pred) param_grid = [{'criterion':['gini','entropy'],'max_features':['auto','sqrt','log2' ] }] clf = GridSearchCV(DecisionTreeClassifier(), param_grid=param_grid, scoring=f1_score_on_test, verbose=100 ) clf.fit(X_train, y_train) df=pd.DataFrame(clf.grid_scores_) total_file = os.path.dirname(os.path.realpath(__file__)) + "\\CV\\CVDTC_" + str(test)+ ".xlsx" writer = pd.ExcelWriter(total_file) df.to_excel(writer,"DTC") writer.save() return clf.best_params_ def GSSVC(X_train, y_train, X_test, y_test, test): def f1_score_on_test(estimator, x, y): y_pred = estimator.predict(X_test) return f1_score(y_test.values, y_pred) # TODO: Create the parameters list you wish to tune params_map = [{ 'C': [0.001,0.005, 0.01,0.02, 0.1,0.4, 0.5,0.6,0.7, 1.0, 2.0, 5.0, 10.0, 100.0],'kernel': ['rbf'],'gamma': ['auto',0.001,0.01, 0.05,0.1, 0.5, 1.0,4.0, 5.0, 6.0, 10.0,50.0,100.0,150.0]}, {'kernel': ['linear'], 'C': [1, 10, 100, 1000, 1500, 2000, 2500,3000, 3500,4000]}]#, #{'kernel': ['poly'], 'C': [1, 10, 100, 1000, 1500, 2000, 2500,3000, 3500,4000],'degree':[2,3,4,5,6,7,8,9,10,15], 'gamma': ['auto',0.001,0.01, 0.05,0.1, 0.5, 1.0, 5.0]}, #{'kernel': ['sigmoid'], 'C': [1, 10, 100, 1000, 1500, 2000, 2500,3000, 3500,4000], 'gamma': ['auto',0.001,0.01, 0.05,0.1, 0.5, 1.0, 5.0]}] clf = GridSearchCV(SVC(), params_map, scoring=f1_score_on_test, verbose=100) clf.fit(X_train, y_train) #joblib.dump(clf.best_estimator_, file_path, compress = 3) df=pd.DataFrame(clf.grid_scores_) total_file = os.path.dirname(os.path.realpath(__file__)) + "\\CV\\CVSVC_" + str(test)+ ".xlsx" writer = pd.ExcelWriter(total_file) df.to_excel(writer,"SVC") writer.save() return clf.best_params_ def GSKNN(X_train, y_train, X_test, y_test, test): def f1_score_on_test(estimator, x, y): y_pred = estimator.predict(X_test) return f1_score(y_test.values, y_pred) # here must be some code for your training set and test set tuned_parameters = [{'n_neighbors': [50,100,150,200,250,300,350], 'weights': ['distance', 'uniform'],'algorithm': ['ball_tree', 'kd_tree', 'brute'], 'leaf_size':[30,40,50,60] }] clf = GridSearchCV(neighbors.KNeighborsClassifier(), tuned_parameters, cv=10, scoring=f1_score_on_test, verbose=100) clf.fit(X_train, y_train) df=pd.DataFrame(clf.grid_scores_) total_file = os.path.dirname(os.path.realpath(__file__)) + "\\CV\\CVKNN_" + str(test)+ ".xlsx" writer = pd.ExcelWriter(total_file) df.to_excel(writer,"KNN") writer.save() return clf.best_estimator_ def GSRFC(X_train, y_train, X_test, y_test,test): def f1_score_on_test(estimator, x, y): y_pred = estimator.predict(X_test) return f1_score(y_test.values, y_pred) # here must be some code for your training set and test set tuned_parameters = [{'n_estimators': [500,1000,2000,2500,3000,3500,4000, 4500,5000], 'criterion':['gini','entropy'] }] clf = GridSearchCV(RandomForestClassifier(), tuned_parameters, scoring=f1_score_on_test, verbose=100) clf.fit(X_train, y_train) df=pd.DataFrame(clf.grid_scores_) total_file = os.path.dirname(os.path.realpath(__file__)) + "\\CV\\CVRFC_" + str(test)+ ".xlsx" writer = pd.ExcelWriter(total_file) df.to_excel(writer,"RFC") writer.save() return clf.best_params_ def GSLRC(X_train, y_train, X_test, y_test,test): def f1_score_on_test(estimator, x, y): y_pred = estimator.predict(X_test) return f1_score(y_test.values, y_pred) param_grid = [{'C':[0.001, 0.01, 0.1, 1, 10, 100, 1000], 'penalty' :["l1"], 'solver' :["liblinear"] }, {'C':[0.001, 0.01, 0.1, 1, 10, 100, 1000], 'penalty' :["l2"], 'solver' :[ "newton-cg", "lbfgs", "sag"] }] clf = GridSearchCV(LogisticRegression(), param_grid, scoring=f1_score_on_test, verbose=100) clf.fit(X_train, y_train) df=pd.DataFrame(clf.grid_scores_) total_file = os.path.dirname(os.path.realpath(__file__)) + "\\CV\\CVLRC_" + str(test)+ ".xlsx" writer = pd.ExcelWriter(total_file) df.to_excel(writer,"LRC") writer.save() return clf.best_params_ def TimeSeriesCrossValidation(X_train, y_train, number_folds, algorithm):# algorithm=["KNN","RFC","SVM","ADA Bost","GTB","LDA"] #print('Parameters --------------------------------> ', parameters) print('Size train set: '+ str(X_train.shape)) k = int(np.floor(float(X_train.shape[0]) / number_folds)) print('Size of each fold: '+ str(k)) accuracies = np.zeros(number_folds-1) F1scores = np.zeros(number_folds-1)# rocs= np.zeros(number_folds-1) mats= np.zeros(number_folds-1) # loop from the first 2 folds to the total number of folds for i in range(2, number_folds + 1): print('') split = float(i-1)/i print('Splitting the first ' + str(i) + ' chunks at ' + str(i-1) + '/' + str(i)) X = X_train[:(k*i)] y = y_train[:(k*i)] # split percentage we have set above index = int(np.floor(X.shape[0] * split)) # folds used to train the model X_trainFolds = X[:index] y_trainFolds = y[:index] # fold used to test the model X_testFolds = X[(index + 1):] y_testFolds = y[(index + 1):] # algorithm=["KNN","RFC","SVM","ADA Bost","GTB","LDA"] if algorithm=="ADA Bost": accuracies[i-2], F1scores[i-2], y_test_,predict, training, predecir,rocs[i-2], mats[i-2] = performAdaBoostClass(X_trainFolds, y_trainFolds, X_testFolds, y_testFolds) if algorithm=="LDA": accuracies[i-2], F1scores[i-2], y_test_,predict, training, predecir,rocs[i-2], mats[i-2] = performLDAClass(X_trainFolds, y_trainFolds, X_testFolds, y_testFolds) if algorithm=="GTB": accuracies[i-2], F1scores[i-2], y_test_,predict, training, predecir,rocs[i-2], mats[i-2] = performGTBClass(X_trainFolds, y_trainFolds, X_testFolds, y_testFolds) if algorithm=="KNN": accuracies[i-2], F1scores[i-2], y_test_,predict, training, predecir,rocs[i-2], mats[i-2] = performKNNClass(X_trainFolds, y_trainFolds, X_testFolds, y_testFolds) if algorithm=="RFC": accuracies[i-2], F1scores[i-2], y_test_,predict, training, predecir,rocs[i-2], mats[i-2] = performRFClass(X_trainFolds, y_trainFolds, X_testFolds, y_testFolds) if algorithm=="SVM": accuracies[i-2], F1scores[i-2], y_test_,predict, training, predecir,rocs[i-2], mats[i-2] = performSVMClass(X_trainFolds, y_trainFolds, X_testFolds, y_testFolds) if algorithm=="SGD": accuracies[i-2], F1scores[i-2], y_test_,predict, training, predecir,rocs[i-2], mats[i-2] = performSGDClass(X_trainFolds, y_trainFolds, X_testFolds, y_testFolds) if algorithm=="VOT": accuracies[i-2], F1scores[i-2], y_test_,predict, training, predecir,rocs[i-2], mats[i-2] = performVotingClass(X_trainFolds, y_trainFolds, X_testFolds, y_testFolds) if algorithm=="LRC": accuracies[i-2], F1scores[i-2], y_test_,predict, training, predecir,rocs[i-2], mats[i-2] = performLRBClass(X_trainFolds, y_trainFolds, X_testFolds, y_testFolds) if algorithm=="DTC": accuracies[i-2], F1scores[i-2], y_test_,predict, training, predecir,rocs[i-2], mats[i-2] = performDTCClass(X_trainFolds, y_trainFolds, X_testFolds, y_testFolds) print('Accuracy on fold ' + str(i) + ': ' + str(accuracies[i-2])) print('F1 Score on fold ' + str(i) + ': ' + str(F1scores[i-2])) print('Roc on fold ' + str(i) + ': ' + str(rocs[i-2])) print('Matthew Coef on fold ' + str(i) + ': ' + str(mats[i-2])) return accuracies.mean(), F1scores.mean(), y_test_,predict, training, predecir,rocs.mean(),mats.mean() def call_alg(X_train, y_train, X_test, y_test, alg):# if alg=="KNN": score, f1score, y_test,predictions, training, predecir,roc,mat = performKNNClass(X_train, y_train, X_test, y_test) if alg=="RFC": score, f1score, y_test,predictions, training, predecir,roc,mat = performRFClass(X_train, y_train, X_test, y_test) if alg=="SVM": score, f1score, y_test,predictions, training, predecir,roc,mat = performSVMClass(X_train, y_train, X_test, y_test) if alg=="ADA Bost": score, f1score, y_test,predictions, training, predecir,roc,mat = performAdaBoostClass(X_train, y_train, X_test, y_test) if alg=="GTB": score, f1score, y_test,predictions, training, predecir,roc,mat = performGTBClass(X_train, y_train, X_test, y_test) if alg=="LDA": score, f1score, y_test,predictions, training, predecir,roc,mat = performLDAClass(X_train, y_train, X_test, y_test) if alg=="SGD": score, f1score, y_test,predictions, training, predecir,roc,mat = performSGDClass(X_train, y_train, X_test, y_test) if alg=="VOT": score, f1score, y_test,predictions, training, predecir,roc,mat = performVotingClass(X_train, y_train, X_test, y_test) if alg=="LRC": score, f1score, y_test,predictions, training, predecir,roc,mat = performLRBClass(X_train, y_train, X_test, y_test) if alg=="DTC": score, f1score, y_test,predictions, training, predecir,roc,mat = performDTCClass(X_train, y_train, X_test, y_test) return score, f1score, y_test,predictions, training, predecir,roc,mat def classifaction_report_csv(report,alg,version,TEST,kFOLDS, score, training, predecir,roc,mat): path = os.path.dirname(os.path.realpath(__file__)) ahora=str(datetime.datetime.now().strftime("%y-%m-%d-%H-%M-%S")) file_path = path + "\\out\\" + "classification_report_"+str(alg) + ahora+ ".csv" report_data = [] lines = report.split('\n') for line in lines[2:-3]: row = {} row_data = line.split(' ') row['Algorithm'] = str(alg) row['Accuracy'] = str(score) row['roc_auc_score'] = str(roc) row['matthews_corrcoef'] = str(mat) row['Features'] = str(version) row['Version'] = str(TEST) row['KFolds'] = str(kFOLDS) row['Date'] = str(ahora) row['Training'] = str(training) row['Predicting'] = str(predecir) row['precision'] = float(row_data[1]) row['recall'] = float(row_data[2]) row['f1_score'] = float(row_data[3]) row['support'] = float(row_data[4]) report_data.append(row) dataframe = pd.DataFrame.from_dict(report_data) dataframe.to_csv(file_path, index = False) def Walk_Forward_Validation_CV(df, start_test, algorithm):# algorithm=["KNN","RFC","SVM","ADA Bost","GTB","LDA"] #def prepareDataForClassification(dataset, start_test): le = preprocessing.LabelEncoder() df['UpDown'] = df.apply (lambda row: Label_Change (row),axis=1) dataset1=df.truncate(before='2003-07-01') #delete all values up to the first rolling average 63 days dataset1.UpDown = le.fit(dataset1.UpDown).transform(dataset1.UpDown) features = dataset1.columns[1:-1] X = dataset1[features] y = dataset1.UpDown X_train = X[X.index < start_test] y_train = y[y.index < start_test] n_train = len(X_train) n_records= len(dataset1) number_folds=n_records-n_train accuracies = np.zeros(number_folds-1) F1scores = np.zeros(number_folds-1) rocs= np.zeros(number_folds-1) mats= np.zeros(number_folds-1) yreals = np.zeros(number_folds-1) ypredictions = np.zeros(number_folds-1) # loop from the first 2 folds to the total number of folds for i in range(n_train, n_records-1): X_trainFolds = X[0:i] y_trainFolds = y[0:i] # fold used to test the model X_testFolds = X[i:i+1] y_testFolds = y[i:i+1] # algorithm=["KNN","RFC","SVM","ADA Bost","GTB","LDA", "SGD","GNB", "VOT", "DTC"] if algorithm=="ADA Bost": accuracies[i-n_train], F1scores[i-n_train],yreals[i-n_train], ypredictions[i-n_train], training, predecir,rocs[i-n_train], mats[i-n_train] = performAdaBoostClass(X_trainFolds, y_trainFolds, X_testFolds, y_testFolds) if algorithm=="LDA": accuracies[i-n_train], F1scores[i-n_train],yreals[i-n_train], ypredictions[i-n_train], training, predecir,rocs[i-n_train], mats[i-n_train] = performLDAClass(X_trainFolds, y_trainFolds, X_testFolds, y_testFolds) if algorithm=="GTB": accuracies[i-n_train], F1scores[i-n_train],yreals[i-n_train], ypredictions[i-n_train], training, predecir,rocs[i-n_train], mats[i-n_train] = performGTBClass(X_trainFolds, y_trainFolds, X_testFolds, y_testFolds) if algorithm=="KNN": accuracies[i-n_train], F1scores[i-n_train],yreals[i-n_train], ypredictions[i-n_train], training, predecir,rocs[i-n_train], mats[i-n_train] = performKNNClass(X_trainFolds, y_trainFolds, X_testFolds, y_testFolds) if algorithm=="RFC": accuracies[i-n_train], F1scores[i-n_train],yreals[i-n_train], ypredictions[i-n_train], training, predecir,rocs[i-n_train], mats[i-n_train] = performRFClass(X_trainFolds, y_trainFolds, X_testFolds, y_testFolds) if algorithm=="SVM": accuracies[i-n_train], F1scores[i-n_train],yreals[i-n_train], ypredictions[i-n_train], training, predecir,rocs[i-n_train], mats[i-n_train] = performSVMClass(X_trainFolds, y_trainFolds, X_testFolds, y_testFolds) if algorithm=="SGD": accuracies[i-n_train], F1scores[i-n_train],yreals[i-n_train], ypredictions[i-n_train], training, predecir,rocs[i-n_train], mats[i-n_train] = performSGDClass(X_trainFolds, y_trainFolds, X_testFolds, y_testFolds) if algorithm=="VOT": accuracies[i-n_train], F1scores[i-n_train],yreals[i-n_train], ypredictions[i-n_train], training, predecir,rocs[i-n_train], mats[i-n_train] = performVotingClass(X_trainFolds, y_trainFolds, X_testFolds, y_testFolds) if algorithm=="LRC": accuracies[i-n_train], F1scores[i-n_train],yreals[i-n_train], ypredictions[i-n_train], training, predecir,rocs[i-n_train], mats[i-n_train] = performLRBClass(X_trainFolds, y_trainFolds, X_testFolds, y_testFolds) if algorithm=="DTC": accuracies[i-n_train], F1scores[i-n_train],yreals[i-n_train], ypredictions[i-n_train], training, predecir,rocs[i-n_train], mats[i-n_train] = performDTCClass(X_trainFolds, y_trainFolds, X_testFolds, y_testFolds) print('Accuracy on fold ' + str(i) + ': ' + str(accuracies[i-n_train])) print('F1 Score on fold ' + str(i) + ': ' + str(F1scores[i-n_train])) print('ytest fold ' + str(i) + ': ' + str(yreals[i-n_train])) print('Prediction on fold ' + str(i) + ': ' + str(ypredictions[i-n_train])) print('Roc on fold ' + str(i) + ': ' + str(rocs[i-n_train])) print('Matthew Coef on fold ' + str(i) + ': ' + str(mats[i-n_train])) return accuracies.mean(), F1scores.mean(),yreals, ypredictions,training, predecir,rocs.mean(),mats.mean() # Plot CV scores of a 2D grid search def plotGridResults2D(x, y, x_label, y_label, grid_scores): scores = [s[1] for s in grid_scores] scores = np.array(scores).reshape(len(x), len(y)) plt.figure() plt.imshow(scores, interpolation='nearest', cmap=plt.cm.RdYlGn) plt.xlabel(y_label) plt.ylabel(x_label) plt.colorbar() plt.xticks(np.arange(len(y)), y, rotation=45) plt.yticks(np.arange(len(x)), x) plt.title('Validation accuracy')

41.189863

345

0.681302

7,685

51,199

4.349382

0.083409

0.023037

0.016335

0.028003

0.749738

0.727599

0.706387

0.677577

0.663396

0.608227

0

0.041242

0.157484

51,199

1,243

346

41.189863

0.733633

0.156722

0

0.546083

0

0.084085

0.002116

0

0.000805

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1

0.06682

false

0

0.03341

0

0.163594

0.072581

0

null

0

1

0

1

0

null

0

3

1fed36e026cd0d2b241907aec64b4f8af567e6cd

281

py

Python

workon/contrib/flow/apps.py

dalou/django-workon

ef63c0a81c00ef560ed693e435cf3825f5170126

[ "BSD-3-Clause" ]

null

workon/contrib/flow/apps.py

dalou/django-workon

ef63c0a81c00ef560ed693e435cf3825f5170126

[ "BSD-3-Clause" ]

null

workon/contrib/flow/apps.py

dalou/django-workon

ef63c0a81c00ef560ed693e435cf3825f5170126

[ "BSD-3-Clause" ]

null

# coding: utf-8 ''' AppConfig ''' from django.apps import AppConfig from django.utils.translation import ugettext_lazy as _ class FlowConfig(AppConfig): name = 'workon.contrib.flow' label = 'workon_flow' verbose_name = _("Real time websocket per user backend")

18.733333

60

0.715302

35

281

5.6

0.771429

0.132653

0.193878

0

0.004367

0.185053

281

14

61

20.071429

0.851528

0.085409

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0.333333

0

1

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null

0

1

0

null

0

1

0

1

0

3

1ff649576a8cbdf7535a2184c084d671c12547fa

655

py

Python

viewmodels/reserve/detail.py

computablelabs/compas

fbde4e2ef61ebadc5ffd6453bff60f678e6002d6

[ "MIT" ]

1

2019-10-24T19:16:10.000Z

viewmodels/reserve/detail.py

computablelabs/compas

fbde4e2ef61ebadc5ffd6453bff60f678e6002d6

[ "MIT" ]

7

2019-10-31T14:55:07.000Z

2019-12-11T19:29:15.000Z

viewmodels/reserve/detail.py

computablelabs/compas

fbde4e2ef61ebadc5ffd6453bff60f678e6002d6

[ "MIT" ]

null

from models.reserve import Reserve from viewmodels.viewmodel import ViewModel class Detail(ViewModel): def __init__(self): self.model = Reserve() def get_support_price(self): return str(self.model.get_support_price()) def get_withdrawal_proceeds(self, addr=None): # assure was not set to empty str... if addr == '': addr = None return str(self.model.get_withdrawal_proceeds(addr)) def support(self, offer, gas_price): return self.transact(self.model.support(offer, gas_price)) def withdraw(self, gas_price): return self.transact(self.model.withdraw(gas_price))

28.478261

66

0.677863

85

655

5.035294

0.376471

0.10514

0.070093

0.084112

0.261682

0.163551

0

0.221374

655

22

67

29.772727

0.839216

0.051908

0

1

0.333333

false

0

0.133333

0.2

0.8

0

null

0

null

0

1

0

1

0

3

95166d77ef1d248ae094521782f231bf7169cc51

244

py

Python

exercicios_basico/ex007.py

montalvas/python

483c2097f6f91bfae127dafcb63e3006eeecad1d

[ "MIT" ]

null

exercicios_basico/ex007.py

montalvas/python

483c2097f6f91bfae127dafcb63e3006eeecad1d

[ "MIT" ]

null

exercicios_basico/ex007.py

montalvas/python

483c2097f6f91bfae127dafcb63e3006eeecad1d

[ "MIT" ]

null

# Informe duas notas e sem seguida mostre sua média. n1 = float(input('Informe a primeira nota: ')) n2 = float(input('Informe a segunda nota: ')) print("As notas do aluno foram {} e {}, logo sua média será {:.2f}".format(n1, n2, (n1 + n2)/2))

40.666667

96

0.663934

41

244

3.95122

0.658537

0.098765

0.209877

0.222222

0

0.039604

0.172131

244

5

97

48.8

0.762376

0.204918

0

0.5625

0

1

0

false

0

0.333333

0

null

0

1

0

1

0

null

0

3

9520483442fea8406fd4ea774a04497e7d71c1d4

191

py

Python

Python/Warmup-2/string_times.py

LucasHenrique-dev/exerc-cios-codingbat

ff92db10387757b9a2e3f72be6b7e51824b1ffa6

[ "MIT" ]

2

2020-12-09T13:36:44.000Z

2021-08-16T01:17:16.000Z

Python/Warmup-2/string_times.py

LucasHenrique-dev/exerc-cios-codingbat

ff92db10387757b9a2e3f72be6b7e51824b1ffa6

[ "MIT" ]

null

Python/Warmup-2/string_times.py

LucasHenrique-dev/exerc-cios-codingbat

ff92db10387757b9a2e3f72be6b7e51824b1ffa6

[ "MIT" ]

null

""" Dada uma String "str", retorne uma nova que será uma cópia "n" vezes da original. "n" não será negativo. """ def string_times(str, n): return str*n print(string_times("CASA", 0))

15.916667

81

0.670157

32

191

3.9375

0.65625

0.174603

0

0.006452

0.188482

191

11

82

17.363636

0.806452

0.544503

0

0.050633

0

1

0.333333

false

0

0.333333

0.666667

0.333333

0

null

0

null

0

1

0

1

0

3

1f05478e54a476705a6251584e74a8a8c92519df

216

py

Python

unit2/deterministic_number.py

purelind/MITx-6.00.2x

de92f62c8b0465713077312d66690f47818e7f5b

[ "MIT" ]

null

unit2/deterministic_number.py

purelind/MITx-6.00.2x

de92f62c8b0465713077312d66690f47818e7f5b

[ "MIT" ]

null

unit2/deterministic_number.py

purelind/MITx-6.00.2x

de92f62c8b0465713077312d66690f47818e7f5b

[ "MIT" ]

null

import random def deterministicNumber(): ''' Deterministically g :return: ''' return list(i for i in range(9, 21) if i % 2 == 0)[1] if __name__ == '__main__': print(deterministicNumber())

15.428571

57

0.606481

26

216

4.730769

0.807692

0

0.037267

0.25463

216

13

58

16.615385

0.726708

0.12963

0

0.047619

0

1

0.2

true

0

0.2

0

0.6

0.2

1

0

null

0

null

0

1

0

1

0

3

1f12240b4d881fdec072311b9da5230ba028c437

23

py

Python

src/__init__.py

sksdutra/RAFPy

d067b23723c7bed1debe096426ae2bc7bd8f2d50

[ "MIT" ]

null

src/__init__.py

sksdutra/RAFPy

d067b23723c7bed1debe096426ae2bc7bd8f2d50

[ "MIT" ]

null

src/__init__.py

sksdutra/RAFPy

d067b23723c7bed1debe096426ae2bc7bd8f2d50

[ "MIT" ]

null

__author__ = 'skdutra'

11.5

22

0.73913

2

23

6.5

1

0

0.130435

23

1

23

0.65

0

0.304348

0

1

0

false

0

1

0

null

0

1

0

null

0

3

1f215d1c4e58dfcce168b8ac4f42f6fdcd93d9ef

179

py

Python

Crawler/bus/bus-crawalData.py

AlbatrossBill/COSC4P02Project

c48682c014ab9de4847d46cffc710d386db93c0f

[ "MIT" ]

4

2022-01-15T22:04:06.000Z

2022-01-24T01:46:46.000Z

Crawler/bus/bus-crawalData.py

AlbatrossBill/COSC4P02Project

c48682c014ab9de4847d46cffc710d386db93c0f

[ "MIT" ]

null

Crawler/bus/bus-crawalData.py

AlbatrossBill/COSC4P02Project

c48682c014ab9de4847d46cffc710d386db93c0f

[ "MIT" ]

1

2022-01-24T01:31:57.000Z

import requests from bs4 import BeautifulSoup url = "http://whereis.yourbus.com/bustime/api/v3/getroutes?key=n3YEcYp545e55YhAVG65m9CKZ" doc = requests.get(url) print(doc.text)

19.888889

89

0.793296

24

179

5.916667

0.833333

0

0.067485

0.089385

179

8

90

22.375

0.803681

0

0.452514

0

1

0

false

0

0.4

0

0.4

0.2

1

0

null

0

1

0

null

0

1

0

3

1f69e1a328f31bb36fa1655ff8885266e5f40852

590

py

Python

vae-jiali/models/base.py

haehn/dicompute

ee4364aaa1258a370bd62bbaf6e577936bf463b3

[ "MIT" ]

null

vae-jiali/models/base.py

haehn/dicompute

ee4364aaa1258a370bd62bbaf6e577936bf463b3

[ "MIT" ]

null

vae-jiali/models/base.py

haehn/dicompute

ee4364aaa1258a370bd62bbaf6e577936bf463b3

[ "MIT" ]

null

import torch.nn as nn from abc import abstractmethod class BaseVAE(nn.Module): def __init__(self): super(BaseVAE, self).__init__() def encode(self, input): raise NotImplementedError def decode(self, inputs): raise NotImplementedError def sample(self, batch_size, current_device, **kwargs): raise RuntimeWarning() def generate(self, x, **kwargs): raise NotImplementedError @abstractmethod def forward(self, *inputs): pass @abstractmethod def loss_function(self, *inputs, **kwargs): pass

21.071429

59

0.650847

63

590

5.920635

0.52381

0.193029

0.144772

0

0.257627

590

27

60

21.851852

0.851598

0

0.368421

0

1

0.368421

false

0.105263

0

0.526316

0

null

0

null

0

1

0

1

0

1

0

3

1f8eedf3a72903aab2d7d5efd63db57c9070f34c

2,314

py

Python

tests/test_client.py

diarmuidw/PyKazoo

4b6e22c56400c6da1cdd6949c27b687b51712017

[ "MIT" ]

3

2015-10-16T07:45:38.000Z

2016-05-19T18:11:42.000Z

tests/test_client.py

diarmuidw/PyKazoo

4b6e22c56400c6da1cdd6949c27b687b51712017

[ "MIT" ]

35

2015-08-29T17:33:05.000Z

2021-06-06T15:53:52.000Z

tests/test_client.py

diarmuidw/PyKazoo

4b6e22c56400c6da1cdd6949c27b687b51712017

[ "MIT" ]

10

2015-08-29T17:33:51.000Z

2021-06-04T20:54:56.000Z

import pykazoo.client from unittest import TestCase from pykazoo.accounts import Accounts from pykazoo.agents import Agents from pykazoo.authentication import Authentication from pykazoo.callflows import Callflows from pykazoo.cdrs import CDRs from pykazoo.clicktocalls import ClickToCalls from pykazoo.conferences import Conferences from pykazoo.devices import Devices from pykazoo.directories import Directories from pykazoo.faxes import Faxes from pykazoo.hotdesks import Hotdesks from pykazoo.media import Media from pykazoo.menus import Menus from pykazoo.metaflows import Metaflows from pykazoo.phonenumbers import PhoneNumbers from pykazoo.queues import Queues from pykazoo.quickcalls import QuickCalls from pykazoo.resources import Resources from pykazoo.timedroutes import TimedRoutes from pykazoo.users import Users from pykazoo.voicemailboxes import VoicemailBoxes from pykazoo.webhooks import Webhooks class TestDevices(TestCase): def setUp(self): self.client = pykazoo.client.PyKazooClient('https://localhost:8080/v2') def test_map_attributes_to_client_objects(self): assert type(self.client.accounts) is Accounts assert type(self.client.agents) is Agents assert type(self.client.authentication) is Authentication assert type(self.client.callflows) is Callflows assert type(self.client.cdrs) is CDRs assert type(self.client.clicktocalls) is ClickToCalls assert type(self.client.conferences) is Conferences assert type(self.client.devices) is Devices assert type(self.client.directories) is Directories assert type(self.client.faxes) is Faxes assert type(self.client.hotdesks) is Hotdesks assert type(self.client.media) is Media assert type(self.client.menus) is Menus assert type(self.client.metaflows) is Metaflows assert type(self.client.phonenumbers) is PhoneNumbers assert type(self.client.queues) is Queues assert type(self.client.quickcalls) is QuickCalls assert type(self.client.resources) is Resources assert type(self.client.timedroutes) is TimedRoutes assert type(self.client.users) is Users assert type(self.client.voicemailboxes) is VoicemailBoxes assert type(self.client.webhooks) is Webhooks

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2f0d09da507f82170b9b07b9d8bfdbfef8c502f1

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py

Python

src/apps/eggs/__init__.py

SlonSky/django-grasped

97ea2f6d2e10232fc084a6407fa089df2cdf086e

[ "MIT" ]

null

src/apps/eggs/__init__.py

SlonSky/django-grasped

97ea2f6d2e10232fc084a6407fa089df2cdf086e

[ "MIT" ]

null

src/apps/eggs/__init__.py

SlonSky/django-grasped

97ea2f6d2e10232fc084a6407fa089df2cdf086e

[ "MIT" ]

null

""" Put here common independent stuff, such as utilities, management commands, etc., what can not belong to any app. DO NOT let this package to be dump of ugly code. Putting code here, you rather make an exception, than an ordinary core organization. """

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0

null

0

1

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2f1885b7f5cc551f3e75a35ef649ca09ab6b0266

111

py

Python

Flask/8_context.py

hive-sql-parser/feitian

d9ff89e18c3604590152b64255409d8760f780b9

[ "BSD-3-Clause" ]

null

Flask/8_context.py

hive-sql-parser/feitian

d9ff89e18c3604590152b64255409d8760f780b9

[ "BSD-3-Clause" ]

null

Flask/8_context.py

hive-sql-parser/feitian

d9ff89e18c3604590152b64255409d8760f780b9

[ "BSD-3-Clause" ]

null

from flask import Flask @app.route("/index") #线程局部变量 request def index(): request.form.get("name")

10.090909

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0.8

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0

null

0

1

0

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2f3282d19a5398ad14bbc14278ef21323ad8ffc8

24

py

Python

src/MQTTLibrary/version.py

NikoHeikki/robotframework-mqttlibrary

b35614b412287aa4b3cb7082fb2048406f1e5e2a

[ "Apache-2.0" ]

19

2015-02-23T22:53:00.000Z

2022-03-21T12:08:36.000Z

src/MQTTLibrary/version.py

NikoHeikki/robotframework-mqttlibrary

b35614b412287aa4b3cb7082fb2048406f1e5e2a

[ "Apache-2.0" ]

23

2015-04-22T22:40:14.000Z

2022-03-22T07:56:10.000Z

src/MQTTLibrary/version.py

NikoHeikki/robotframework-mqttlibrary

b35614b412287aa4b3cb7082fb2048406f1e5e2a

[ "Apache-2.0" ]

18

2015-04-18T00:33:57.000Z

2022-01-25T09:47:24.000Z

VERSION = '0.7.1.post3'

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0

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null

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2f3cd28ec535972dfd45e901f7c4459e17087223

79

py

Python

sponsor-challenges/csit/part1 source/opcode.py

wongwaituck/crossctf-2017-finals-public

bb180bcb3fdb559b7d7040fbe01c4fca98322f11

[ "MIT" ]

6

2017-06-26T15:07:19.000Z

2018-10-09T20:03:27.000Z

sponsor-challenges/csit/part1 source/opcode.py

wongwaituck/crossctf-2017-finals-public

bb180bcb3fdb559b7d7040fbe01c4fca98322f11

[ "MIT" ]

null

sponsor-challenges/csit/part1 source/opcode.py

wongwaituck/crossctf-2017-finals-public

bb180bcb3fdb559b7d7040fbe01c4fca98322f11

[ "MIT" ]

1

2018-08-18T00:49:02.000Z

class opcode(object): nul = 1 hello = 2 rhello = 130 get = 160 rget = 161

11.285714

21

0.620253

13

79

3.769231

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0

0.192982

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1

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1

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0

1

0

null

0

1

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2f3d1c026893da79800202ff6899d5ac3850913b

221

py

Python

ifc_builder/__init__.py

amrit3701/ifc-builder

1c7b9efc09b930a344fa87d3f783c0b6f2178a45

[ "MIT" ]

2

2019-02-04T06:50:10.000Z

2020-09-16T07:03:26.000Z

ifc_builder/__init__.py

amrit3701/ifc-builder

1c7b9efc09b930a344fa87d3f783c0b6f2178a45

[ "MIT" ]

null

ifc_builder/__init__.py

amrit3701/ifc-builder

1c7b9efc09b930a344fa87d3f783c0b6f2178a45

[ "MIT" ]

3

2018-11-25T16:58:54.000Z

2020-11-06T10:46:40.000Z

""" ifc_builder package.""" try: import ifcopenshell except ModuleNotFoundError: raise ( """ifcopenshell module not found. Install it by following http://www.ifcopenshell.org/python.html""" )

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9

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null

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3

2f4cdb08f5f2ead8b25be8f465591ba4787e5fe7

288

py

Python

tfp/test/split_test.py

meetulr/DanceTransformer

37f06db6e6feb22b6c983e103a15d91923f926b5

[ "Apache-2.0" ]

2

2020-01-06T16:32:01.000Z

2020-01-06T19:29:33.000Z

tfp/test/split_test.py

meetulr/DanceTransformer

37f06db6e6feb22b6c983e103a15d91923f926b5

[ "Apache-2.0" ]

null

tfp/test/split_test.py

meetulr/DanceTransformer

37f06db6e6feb22b6c983e103a15d91923f926b5

[ "Apache-2.0" ]

2

2020-01-06T16:32:01.000Z

2020-01-09T05:25:14.000Z

import os import sys # sys.path.append("../") print(os.getcwd()) from tfp.config.config import SPLIT_JSON_LOC # from tfp.utils.Split import Split def test_config_for_split(): print(SPLIT_JSON_LOC) if __name__ == "__main__": print("Running manual test") test_config_for_split()

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null

0

1

0

1

0

3

2f79701bc8f6beb7af5874e5007ec569219fe8e0

160

py

Python

test.py

leon709/KafkaConsole

426409019bbb764f6e54f9be40ef127e66749972

[ "BSD-2-Clause" ]

1

2018-10-27T04:17:00.000Z

test.py

leon709/KafkaConsole

426409019bbb764f6e54f9be40ef127e66749972

[ "BSD-2-Clause" ]

null

test.py

leon709/KafkaConsole

426409019bbb764f6e54f9be40ef127e66749972

[ "BSD-2-Clause" ]

null

from kafka_util import KafkaHelper with KafkaHelper() as h: consumer = h.get_consumer('test', 'tp_test1') consumer.seek(-100, 2) print 'done!'

22.857143

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22

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160

7

54

22.857143

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0.10625

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null

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1

0

null

0

1

0

3

2f7a077d7fa671ca8aedb46be19ba3ac1f1dfd23

577

py

Python

tests/utils.py

kreyoo/csgo-inv-shuffle

6392dd1eef1ca87ec25c9cf4845af3f8df3594a5

[ "MIT" ]

null

tests/utils.py

kreyoo/csgo-inv-shuffle

6392dd1eef1ca87ec25c9cf4845af3f8df3594a5

[ "MIT" ]

5

2021-12-22T19:25:51.000Z

2022-03-28T19:27:34.000Z

tests/utils.py

kreyoo/csgo-inv-shuffle

6392dd1eef1ca87ec25c9cf4845af3f8df3594a5

[ "MIT" ]

null

import json def __read_file(file: str): f = open("tests/test_data/" + file, "r", encoding="utf-8") data = f.read() f.close() return data def example_data() -> dict: d = __read_file("test_inventory.json") return json.loads(d) def example_csgo_saved_item_shuffles() -> str: return __read_file("compare_data.txt") def example_inv_repr() -> str: return __read_file("inventory_repr.txt") def new_shuffleconfig() -> str: f = open("./csgo_saved_item_shuffles.txt", "r", encoding="utf-8") data = f.read() f.close() return data

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null

0

1

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1

0

3

2f92b84df1b6afd8cad84a0ede31079dde751517

113

py

Python

KatesObjectOrientedPython/KatesObjectOrientedPython.py

katejp/ObjectOrientedPythonReference

ffc30e54b8d1e4a3182e388a544887845ca8c20e

[ "CC0-1.0" ]

null

KatesObjectOrientedPython/KatesObjectOrientedPython.py

katejp/ObjectOrientedPythonReference

ffc30e54b8d1e4a3182e388a544887845ca8c20e

[ "CC0-1.0" ]

null

KatesObjectOrientedPython/KatesObjectOrientedPython.py

katejp/ObjectOrientedPythonReference

ffc30e54b8d1e4a3182e388a544887845ca8c20e

[ "CC0-1.0" ]

null

userName = input("What is your name? ") print("Hello, {}! What can I help you with today?".format(userName))

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null

0

1

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2f9eea24aacd7da67201772b20be8422b02eef84

143

py

Python

config.py

SalN3t/NlpSQL

2e4db3c26c2175204ec5c27d53e63900384b2e84

[ "MIT" ]

10

2018-05-26T18:00:09.000Z

2021-11-07T16:56:28.000Z

config.py

SalN3t/NlpSQL

2e4db3c26c2175204ec5c27d53e63900384b2e84

[ "MIT" ]

1

2020-01-14T10:04:03.000Z

config.py

SalN3t/NlpSQL

2e4db3c26c2175204ec5c27d53e63900384b2e84

[ "MIT" ]

4

2020-05-27T16:48:06.000Z

2021-12-12T20:21:07.000Z

db_config = { 'user': '##username##', 'passwd': '##password##', 'host': '##host##', 'db': 'employees', }

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false

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0

null

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1

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null

0

1

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3

85d54d8d8a754b97bd6874604c8486c708f81498

1,059

py

Python

__init__.py

pykit3/k3color

e9976906ab09f095b76ee98b8497b5de8d1525f1

[ "MIT" ]

null

__init__.py

pykit3/k3color

e9976906ab09f095b76ee98b8497b5de8d1525f1

[ "MIT" ]

null

__init__.py

pykit3/k3color

e9976906ab09f095b76ee98b8497b5de8d1525f1

[ "MIT" ]

1

2021-08-05T09:22:26.000Z

""" k3color creates colored text on terminal. """ from .color import Str from .color import blue from .color import cyan from .color import danger from .color import dark from .color import fading_color from .color import green from .color import loaded from .color import normal from .color import optimal from .color import percentage from .color import purple from .color import red from .color import warn from .color import white from .color import yellow from .color import darkblue from .color import darkcyan from .color import darkgreen from .color import darkyellow from .color import darkred from .color import darkpurple from .color import darkwhite __version__ = "0.1.2" __name__ = "k3color" __all__ = [ "Str", "blue", "cyan", "danger", "dark", "fading_color", "green", "loaded", "normal", "optimal", "percentage", "purple", "red", "warn", "white", "yellow", "darkblue", "darkcyan", "darkgreen", "darkyellow", "darkred", "darkpurple", "darkwhite", ]

18.258621

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85d5ec3c535f7e3c5e3b20eac8d01c186304a390

291

py

Python

packages/PythonScripts/scripts/func_params_test.py

AndSviat/public

4677029ca8ad2dcd8a989b6122e8e2bb21454e7c

[ "MIT" ]

null

packages/PythonScripts/scripts/func_params_test.py

AndSviat/public

4677029ca8ad2dcd8a989b6122e8e2bb21454e7c

[ "MIT" ]

null

packages/PythonScripts/scripts/func_params_test.py

AndSviat/public

4677029ca8ad2dcd8a989b6122e8e2bb21454e7c

[ "MIT" ]

null

#name: Func Params Test #description: suggestions, choices and validators test #language: python #tags: test, selenium #input: string country {suggestions: jsSuggestCountryName} #input: string vegetable {choices: jsVeggies} #input: double saltiness {validators: ["jsSaltinessRange"]}

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7

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null

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1

0

null

0

1

0

3

85e0e5e6f8e96309bc8a05791754a2381ab4196b

267

py

Python

app/helper.py

fushouhai/flask

dc057109008a467685e5585ee8101aaa8d6de38d

[ "MIT" ]

null

app/helper.py

fushouhai/flask

dc057109008a467685e5585ee8101aaa8d6de38d

[ "MIT" ]

null

app/helper.py

fushouhai/flask

dc057109008a467685e5585ee8101aaa8d6de38d

[ "MIT" ]

null

from flask import current_app def jsonable(): # config_now = current_app.config # pay attentin to this point! pointing to the same object!!! config_now = current_app.config.copy() config_now.pop('PERMANENT_SESSION_LIFETIME') return config_now

33.375

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