xszheng2020/the_stack_dedup_python_hits_3 · Datasets at Hugging Face

fe85d8a95b8c5b2930e247504f6c68cbb22c2717

1,038

py

Python

whisk_python_ch0_randomtrigger/__main__.py

timwaizenegger/e-ink-display-esp8266-mqtt-openwhisk

d964151bfdebb54a0e0edccb7f5cfbbffa49ba0f

[ "MIT" ]

30

2018-02-16T21:10:34.000Z

2021-11-15T21:06:33.000Z

whisk_python_ch0_randomtrigger/__main__.py

timwaizenegger/e-ink-display-esp8266-mqtt-openwhisk

d964151bfdebb54a0e0edccb7f5cfbbffa49ba0f

[ "MIT" ]

1

2020-10-04T21:50:26.000Z

whisk_python_ch0_randomtrigger/__main__.py

timwaizenegger/e-ink-display-esp8266-mqtt-openwhisk

d964151bfdebb54a0e0edccb7f5cfbbffa49ba0f

[ "MIT" ]

2

2020-10-04T21:51:30.000Z

2021-05-31T14:53:30.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Tim Waizenegger (c) 2018,2019 Used as an open whisk action on IBM cloud bx wsk action update displaychannel_ch0_trigger_random --kind python:3 --main main __main__.py """ import random import requests functions_apikey = "cloud functions CF API key" endpoints_protocol = "https://" endpoints = [ "eu-de.functions.cloud.ibm.com/api/v1/namespaces/timw_dev/triggers/displaychannel_ch0_trigger_time", "eu-de.functions.cloud.ibm.com/api/v1/namespaces/timw_dev/triggers/displaychannel_ch0_trigger_bitcoin", "eu-de.functions.cloud.ibm.com/api/v1/namespaces/timw_dev/triggers/displaychannel_ch0_trigger_twitter", "eu-de.functions.cloud.ibm.com/api/v1/namespaces/timw_dev/triggers/displaychannel_ch0_trigger_christmas" ] def main(args): endpoint = random.choice(endpoints) url = endpoints_protocol + functions_apikey + "@" + endpoint ret=requests.post(url) print("Made API call to ", url, " , RC is: ", ret) return { 'message': 'main method called' } #main(None)

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fe9594e1b983f4aeff7a0d0b1ef141900acd5a69

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py

Python

src/markdown2pango/__init__.py

mkdryden/markdown2pango

547a76fd352c52b5f718f5166da5f59672383896

[ "BSD-3-Clause" ]

null

src/markdown2pango/__init__.py

mkdryden/markdown2pango

547a76fd352c52b5f718f5166da5f59672383896

[ "BSD-3-Clause" ]

null

src/markdown2pango/__init__.py

mkdryden/markdown2pango

547a76fd352c52b5f718f5166da5f59672383896

[ "BSD-3-Clause" ]

1

2021-05-28T21:44:27.000Z

from __future__ import (absolute_import, division, print_function, unicode_literals) import lxml.html import re import mistune from ._version import get_versions __version__ = get_versions()['version'] del get_versions class PangoRenderer(mistune.Renderer): ''' Pango Markdown renderer See also -------- `markdown2pango()` ''' def __init__(self, **kwargs): self.options = kwargs def block_code(self, code, lang=None): code = code.rstrip('\n') return '<tt>%s</tt>\n' % code def block_quote(self, text): return text def header(self, text, level, raw=None): if level >= 1 and level < 4: size = ('xx-large', 'x-large', 'large')[level - 1] return "%s\n\n" % (size, text) return text + '\n\n' def hrule(self): return '\n%s\n' % (72 * '-') def paragraph(self, text): return '\n%s\n' % text.strip() def double_emphasis(self, text): return '%s' % text def emphasis(self, text): return '%s' % text def codespan(self, text): text = mistune.escape(text.rstrip(), smart_amp=False) return '<tt>%s</tt>' % text def linebreak(self): return '\n' def strikethrough(self, text): return '<s>%s</s>' % text def newline(self): """Rendering newline element.""" return '' def markdown2pango(markdown_text): ''' Render Markdown-formatted text as Pango formatted text. Note ---- Pango does not fully support _all_ markdown styles (e.g., lists). In most cases, some attempt has been made to render something sensible (e.g., render unordered list items with leading ``-``, ordered list items with item number, etc.). Parameters ---------- markdown_text : str Markdown-formatted text. Returns ------- str `Pango markup <https://developer.gnome.org/pango/stable/PangoMarkupFormat.html>`_. ''' def sub_list(match): ''' Substitute root level HTML lists with Markdown list ''' def extract_list_items(root, level=0): content = [] for list_i in root.xpath('ul|ol'): for j, child_ij in enumerate(list_i.xpath('li')): leader_ij = '-' if list_i.tag == 'ul' else '%d.' % (j + 1) subcontent_ij = extract_list_items(child_ij, level=level + 1) child_ij.text = ' %s%s %s' % (' ' * level, leader_ij, child_ij.text if child_ij.text else '') content += [(level, child_ij)] content.extend(subcontent_ij) if root.tag != 'body': root.remove(list_i) else: list_i.drop_tag() return content root = lxml.html.fragment_fromstring(match.group()) items = extract_list_items(root.xpath('/html/body')[0]) output = '' for level, item in items: item_str = re.sub(r'^<li>(.*)</li>', r'\1', lxml.html.tostring(item)) output += item_str return output.rstrip('\n') m = mistune.Markdown(renderer=PangoRenderer()) return re.sub(r'^<ul>.*</ul>', sub_list, m.render(markdown_text), flags=re.DOTALL | re.MULTILINE)

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feac0c8edfceabc4f4d3d2b1442ce4b6442267f5

3,780

py

Python

aliyun-python-sdk-cms/aliyunsdkcms/request/v20190101/PutCustomMetricRuleRequest.py

yndu13/aliyun-openapi-python-sdk

12ace4fb39fe2fb0e3927a4b1b43ee4872da43f5

[ "Apache-2.0" ]

1,001

2015-07-24T01:32:41.000Z

2022-03-25T01:28:18.000Z

aliyun-python-sdk-cms/aliyunsdkcms/request/v20190101/PutCustomMetricRuleRequest.py

yndu13/aliyun-openapi-python-sdk

12ace4fb39fe2fb0e3927a4b1b43ee4872da43f5

[ "Apache-2.0" ]

363

2015-10-20T03:15:00.000Z

2022-03-08T12:26:19.000Z

aliyun-python-sdk-cms/aliyunsdkcms/request/v20190101/PutCustomMetricRuleRequest.py

yndu13/aliyun-openapi-python-sdk

12ace4fb39fe2fb0e3927a4b1b43ee4872da43f5

[ "Apache-2.0" ]

682

2015-09-22T07:19:02.000Z

2022-03-22T09:51:46.000Z

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # # http://www.apache.org/licenses/LICENSE-2.0 # # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. from aliyunsdkcore.request import RpcRequest class PutCustomMetricRuleRequest(RpcRequest): def __init__(self): RpcRequest.__init__(self, 'Cms', '2019-01-01', 'PutCustomMetricRule','cms') self.set_method('POST') def get_Webhook(self): return self.get_query_params().get('Webhook') def set_Webhook(self,Webhook): self.add_query_param('Webhook',Webhook) def get_RuleName(self): return self.get_query_params().get('RuleName') def set_RuleName(self,RuleName): self.add_query_param('RuleName',RuleName) def get_Threshold(self): return self.get_query_params().get('Threshold') def set_Threshold(self,Threshold): self.add_query_param('Threshold',Threshold) def get_EffectiveInterval(self): return self.get_query_params().get('EffectiveInterval') def set_EffectiveInterval(self,EffectiveInterval): self.add_query_param('EffectiveInterval',EffectiveInterval) def get_EmailSubject(self): return self.get_query_params().get('EmailSubject') def set_EmailSubject(self,EmailSubject): self.add_query_param('EmailSubject',EmailSubject) def get_EvaluationCount(self): return self.get_query_params().get('EvaluationCount') def set_EvaluationCount(self,EvaluationCount): self.add_query_param('EvaluationCount',EvaluationCount) def get_SilenceTime(self): return self.get_query_params().get('SilenceTime') def set_SilenceTime(self,SilenceTime): self.add_query_param('SilenceTime',SilenceTime) def get_MetricName(self): return self.get_query_params().get('MetricName') def set_MetricName(self,MetricName): self.add_query_param('MetricName',MetricName) def get_Period(self): return self.get_query_params().get('Period') def set_Period(self,Period): self.add_query_param('Period',Period) def get_ContactGroups(self): return self.get_query_params().get('ContactGroups') def set_ContactGroups(self,ContactGroups): self.add_query_param('ContactGroups',ContactGroups) def get_Level(self): return self.get_query_params().get('Level') def set_Level(self,Level): self.add_query_param('Level',Level) def get_GroupId(self): return self.get_query_params().get('GroupId') def set_GroupId(self,GroupId): self.add_query_param('GroupId',GroupId) def get_Resources(self): return self.get_query_params().get('Resources') def set_Resources(self,Resources): self.add_query_param('Resources',Resources) def get_RuleId(self): return self.get_query_params().get('RuleId') def set_RuleId(self,RuleId): self.add_query_param('RuleId',RuleId) def get_ComparisonOperator(self): return self.get_query_params().get('ComparisonOperator') def set_ComparisonOperator(self,ComparisonOperator): self.add_query_param('ComparisonOperator',ComparisonOperator) def get_Statistics(self): return self.get_query_params().get('Statistics') def set_Statistics(self,Statistics): self.add_query_param('Statistics',Statistics)

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22834b58ceed661ae744755e2574a5136d052354

337

py

Python

jd/api/rest/KplOpenRegularPlanCompletedorderRequest.py

fengjinqi/linjuanbang

8cdc4e81df73ccd737ac547da7f2c7dca545862a

[ "MIT" ]

5

2019-10-30T01:16:30.000Z

2020-06-14T03:32:19.000Z

jd/api/rest/KplOpenRegularPlanCompletedorderRequest.py

fengjinqi/linjuanbang

8cdc4e81df73ccd737ac547da7f2c7dca545862a

[ "MIT" ]

2

2020-10-12T07:12:48.000Z

2021-06-02T03:15:47.000Z

jd/api/rest/KplOpenRegularPlanCompletedorderRequest.py

fengjinqi/linjuanbang

8cdc4e81df73ccd737ac547da7f2c7dca545862a

[ "MIT" ]

3

2019-12-06T17:33:49.000Z

2021-03-01T13:24:22.000Z

from jd.api.base import RestApi class KplOpenRegularPlanCompletedorderRequest(RestApi): def __init__(self,domain='gw.api.360buy.com',port=80): RestApi.__init__(self,domain, port) self.venderId = None self.planId = None self.orderId = None def getapiname(self): return 'jd.kpl.open.regular.plan.completedorder'

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null

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3

229a0bc926578ae10eff4515af08212c5144abe2

267

py

Python

scripts/slave/recipe_modules/bisect_tester/__init__.py

bopopescu/build

4e95fd33456e552bfaf7d94f7d04b19273d1c534

[ "BSD-3-Clause" ]

null

scripts/slave/recipe_modules/bisect_tester/__init__.py

bopopescu/build

4e95fd33456e552bfaf7d94f7d04b19273d1c534

[ "BSD-3-Clause" ]

null

scripts/slave/recipe_modules/bisect_tester/__init__.py

bopopescu/build

4e95fd33456e552bfaf7d94f7d04b19273d1c534

[ "BSD-3-Clause" ]

1

2020-07-23T11:05:06.000Z

DEPS = [ 'chromium', 'file', 'gsutil', 'recipe_engine/json', 'math_utils', 'recipe_engine/path', 'recipe_engine/platform', 'recipe_engine/properties', 'recipe_engine/python', 'recipe_engine/raw_io', 'recipe_engine/step', ]

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null

1

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1

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null

0

3

22d05e6147b038f95ac6c3a6cf8e40ad4c492310

56

py

Python

Backend Web Application Development/config/Settings.py

amilaansari/SP-Assignments

dbf3c1c9be199406aad1f23274380b2aee673089

[ "CNRI-Python" ]

null

Backend Web Application Development/config/Settings.py

amilaansari/SP-Assignments

dbf3c1c9be199406aad1f23274380b2aee673089

[ "CNRI-Python" ]

null

Backend Web Application Development/config/Settings.py

amilaansari/SP-Assignments

dbf3c1c9be199406aad1f23274380b2aee673089

[ "CNRI-Python" ]

null

class Settings: secretKey="a12nc)238OmPq#cxOlm*a"

18.666667

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null

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null

0

1

0

3

22ec4a5ebba3a1cca202b2468c0f4ced5573dba5

731

py

Python

aleph/search/__init__.py

gazeti/aleph

f6714c4be038471cfdc6408bfe88dc9e2ed28452

[ "MIT" ]

1

2017-07-28T12:54:09.000Z

aleph/search/__init__.py

gazeti/aleph

f6714c4be038471cfdc6408bfe88dc9e2ed28452

[ "MIT" ]

7

2017-08-16T12:49:23.000Z

2018-02-16T10:22:11.000Z

aleph/search/__init__.py

gazeti/aleph

f6714c4be038471cfdc6408bfe88dc9e2ed28452

[ "MIT" ]

6

2017-07-26T12:29:53.000Z

2017-08-18T09:35:50.000Z

import logging from aleph.index.mapping import TYPE_DOCUMENT, TYPE_RECORD # noqa from aleph.search.query import QueryState # noqa from aleph.search.documents import documents_query, documents_iter # noqa from aleph.search.documents import entity_documents # noqa from aleph.search.entities import entities_query # noqa from aleph.search.entities import suggest_entities, similar_entities # noqa from aleph.search.entities import load_entity # noqa from aleph.search.links import links_query # noqa from aleph.search.leads import leads_query, lead_count # noqa from aleph.search.records import records_query, execute_records_query # noqa from aleph.search.util import scan_iter # noqa log = logging.getLogger(__name__)

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null

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null

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1

0

3

22f479d8dbee7eb6ba67509acb08bbf5df764797

1,505

py

Python

BookStore/models/book_model.py

ki-yungkim/Python_1st_Mini

1dadb9ec51b85dfef22164557b8340d4eab96d65

[ "MIT" ]

null

BookStore/models/book_model.py

ki-yungkim/Python_1st_Mini

1dadb9ec51b85dfef22164557b8340d4eab96d65

[ "MIT" ]

null

BookStore/models/book_model.py

ki-yungkim/Python_1st_Mini

1dadb9ec51b85dfef22164557b8340d4eab96d65

[ "MIT" ]

null

import sys import os sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__)))) from flask_migrate import Migrate from flask_sqlalchemy import SQLAlchemy from flask import session from datetime import datetime from models.mem_model import Member,PaperBook db = SQLAlchemy() migrate = Migrate() class PaperBookService: # 책 추가 def addPaperBook(self,pb:PaperBook): db.session.add(pb) db.session.commit() # 책 전체 조회 def getPaperBookAll(self): return PaperBook.query.order_by(PaperBook.book_no.asc()) # 책 상세정보 def getPaperBookDetail(self,paper_book_no): return PaperBook.query.get(paper_book_no) # 책 이름으로 검색 def getPaperBookName(self,paper_book_name): return PaperBook.query.filter(PaperBook.paper_book_name.like('%' + paper_book_name + '%')).all() # 책 지은이로 검색 def getPaperBookPublisher(self,paper_book_publisher): return PaperBook.query.filter(PaperBook.paper_book_publisher.like('%' + paper_book_publisher + '%')).all() # 책 정보 수정 def editPaperBookInfo(self,paper_book_no,name,publisher,price,amount): book = self.getPaperBookDetail(paper_book_no) book.paper_book_name = name book.paper_book_publisher = publisher book.paper_book_price = price book.paper_book_amount = amount # 책 정보 삭제 def deletePaperBook(self,paper_book_no): book = self.getPaperBookDetail(paper_book_no) db.session.delete(book) db.session.commit()

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3

22f73371f58112d97679bbd9398198ded82c14e8

4,353

py

Python

datapreprocessing/crash_match.py

Andyzr/work_zone_safety

e653740e7a42f06536f64c199388fd40d85aaaae

[ "MIT" ]

null

datapreprocessing/crash_match.py

Andyzr/work_zone_safety

e653740e7a42f06536f64c199388fd40d85aaaae

[ "MIT" ]

null

datapreprocessing/crash_match.py

Andyzr/work_zone_safety

e653740e7a42f06536f64c199388fd40d85aaaae

[ "MIT" ]

null

# %% import numpy as np import matplotlib.pyplot as plt import pandas as pd import pickle import gc from sqlalchemy import create_engine import sqlite3 from sqlalchemy import Column, Integer, String, ForeignKey, Float from _datetime import time # %% # speed_engine = create_engine('sqlite:////media/andy/b4a51c70-19cd-420f-91e4-c7adf2274c39/WorkZone/Data/CMU_rcrs_all_events_2010-2014-selected/RCRS_2015_17/important/speed_db.db', echo=False) # speed_engine_old = create_engine('sqlite:////media/andy/zhangzr/speed_db.db', echo=False) # crash_engine = create_engine('sqlite:////media/andy/b4a51c70-19cd-420f-91e4-c7adf2274c39/WorkZone/Data/CMU_rcrs_all_events_2010-2014-selected/RCRS_2015_17/output/output_crash.db', echo=False) # output location output_crash_conn = sqlite3.connect( '/media/andy/b4a51c70-19cd-420f-91e4-c7adf2274c39/WorkZone/Data/CMU_rcrs_all_events_2010-2014-selected/RCRS_2015_17/output/output_crash_1117.db') output_crash_c = output_crash_conn.cursor() # source of wzs_output (wzID-loc-time): speed_db.wzsoutput output_crash_c.execute('ATTACH DATABASE "/media/andy/zhangzr/speed_db.db" AS speed_db') output_crash_conn.commit() output_crash_c.execute('create table wzsoutput as select * from speed_db.wzsoutput;') output_crash_conn.commit() output_crash_c.execute('create index id_wzsoutput_id_time_loc on wzsoutput(wzID,wzTime_divided_stamp,location);') output_crash_conn.commit() # output_crash_c.execute('ATTACH DATABASE "/media/andy/b4a51c70-19cd-420f-91e4-c7adf2274c39/WorkZone/Data/CMU_rcrs_all_events_2010-2014-selected/RCRS_2015_17/important/wzs_output_db.db" AS wzs_output_db') # output_crash_conn.commit() # source of crash database crash_db.crash_table1107 output_crash_c.execute( 'ATTACH DATABASE "../CMU_rcrs_all_events_2010-2014-selected/RCRS_2015_17/important/crash_db.db" AS crash_db') # source of wzloc wz_loc_db.wz_loc_518 or wz_loc_db.wz_loc_61 output_crash_c.execute( 'ATTACH DATABASE "/media/andy/b4a51c70-19cd-420f-91e4-c7adf2274c39/WorkZone/Data/CMU_rcrs_all_events_2010-2014-selected/RCRS_2015_17/important/wz_loc.db" AS wz_loc_db') output_crash_conn.commit() # create crash table # %% # %%time output_crash_c.execute(""" create table if not exists crash_xy_61 AS SELECT temp_61.wzID, temp_61.wzTime_divided_stamp, temp_61.location, COUNT(crash_db.crash_table1107.FATAL_OR_MAJ_INJ)>0 AS crash_61, SUM(crash_db.crash_table1107.FATAL_OR_MAJ_INJ)>0 AS crash_severe_61 FROM (select wzsoutput.wzID,wzsoutput.wzTime_divided_stamp,wzsoutput.location, wz_loc_db.wz_loc_61.x as x,wz_loc_db.wz_loc_61.y as y , CAST(wzsoutput.wzTime_divided_stamp as INT) as wztimeint,CAST(wzsoutput.wzTime_divided_stamp as INT)+1800 as wztimeintend FROM wzsoutput LEFT JOIN wz_loc_db.wz_loc_61 ON wzsoutput.wzID == wz_loc_db.wz_loc_61.wzID AND wzsoutput.location == wz_loc_db.wz_loc_61.location)temp_61 LEFT JOIN crash_db.crash_table1107 ON temp_61.x = crash_db.crash_table1107.keplist_0x AND temp_61.y = crash_db.crash_table1107.keplist_0y AND crash_db.crash_table1107.Time_stamp BETWEEN temp_61.wztimeint AND temp_61.wztimeintend GROUP BY temp_61.wzID, temp_61.wzTime_divided_stamp, temp_61.location """) output_crash_conn.commit() # %% # %%time output_crash_c.execute(""" create table if not exists crash_xy_518 AS SELECT temp_518.wzID, temp_518.wzTime_divided_stamp, temp_518.location, COUNT(crash_db.crash_table1107.FATAL_OR_MAJ_INJ)>0 AS crash_518, SUM(crash_db.crash_table1107.FATAL_OR_MAJ_INJ)>0 AS crash_severe_518 FROM (select wzsoutput.wzID,wzsoutput.wzTime_divided_stamp,wzsoutput.location, wz_loc_db.wz_loc_518.x as x,wz_loc_db.wz_loc_518.y as y , CAST(wzsoutput.wzTime_divided_stamp as INT) as wztimeint,CAST(wzsoutput.wzTime_divided_stamp as INT)+1800 as wztimeintend FROM wzsoutput LEFT JOIN wz_loc_db.wz_loc_518 ON wzsoutput.wzID == wz_loc_db.wz_loc_518.wzID AND wzsoutput.location == wz_loc_db.wz_loc_518.location)temp_518 LEFT JOIN crash_db.crash_table1107 ON temp_518.x = crash_db.crash_table1107.keplist_0x AND temp_518.y = crash_db.crash_table1107.keplist_0y AND crash_db.crash_table1107.Time_stamp BETWEEN temp_518.wztimeint AND temp_518.wztimeintend GROUP BY temp_518.wzID, temp_518.wzTime_divided_stamp, temp_518.location """) output_crash_conn.commit()

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0

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null

0

3

fe106a238946f463e637b920b7a043d1d7b312cf

2,457

py

Python

ccnpy/flic/Pointers.py

mmosko/ccnpy

20d982e2e3845818fde7f3facdc8cbcdff323dbb

[ "Apache-2.0" ]

1

2020-12-23T14:17:25.000Z

ccnpy/flic/Pointers.py

mmosko/ccnpy

20d982e2e3845818fde7f3facdc8cbcdff323dbb

[ "Apache-2.0" ]

1

2019-07-01T18:19:05.000Z

2019-07-02T05:35:52.000Z

ccnpy/flic/Pointers.py

mmosko/ccnpy

20d982e2e3845818fde7f3facdc8cbcdff323dbb

[ "Apache-2.0" ]

null

# Copyright 2019 Marc Mosko # # 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. import ccnpy class Pointers(ccnpy.TlvType): """ Encloses an array of ccnpy.HashValues. Note that len(Pointers) will return the TLV wire encoding length. You can access Pointers as an array: p = Pointers([hv1, hv2, hv3]) for i in range(0, p.count()): hv = p[i] print(hv) Or you can iterate it: p = Pointers([hv1, hv2, hv3]) for hv in p: print(hv) """ __type = 0x0002 @classmethod def class_type(cls): return cls.__type def __init__(self, hash_values): ccnpy.TlvType.__init__(self) if hash_values is None or not isinstance(hash_values, list): raise TypeError("hash_values must be a non-empty list of ccnpy.HashValue") self._hash_values = hash_values self._tlv = ccnpy.Tlv(self.class_type(), self._hash_values) def __len__(self): return len(self._hash_values) def __eq__(self, other): return self.__dict__ == other.__dict__ def __repr__(self): return "Ptrs: %r" % self._hash_values def __getitem__(self, item): return self._hash_values[item] def __iter__(self): self._offset = 0 return self def __next__(self): if self._offset == len(self): raise StopIteration output = self[self._offset] self._offset += 1 return output @classmethod def parse(cls, tlv): if tlv.type() != cls.class_type(): raise ValueError("Incorrect TLV type %r" % tlv.type()) hash_values = [] offset = 0 while offset < tlv.length(): hv = ccnpy.HashValue.deserialize(tlv.value()[offset:]) offset += len(hv) hash_values.append(hv) return cls(hash_values) def serialize(self): return self._tlv.serialize()

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325

2,457

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false

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null

0

null

0

1

0

1

0

3

fe152ca660ab97b3c8ccee1a6e74ccc101fe26e2

2,373

py

Python

starter-kits/credential-registry/server/tob-api/api_v2/migrations/0003_auto_20181005_2140.py

nairobi222/indy-catalyst

dcbd80524ace7747ecfecd716ff932e9b571d69a

[ "Apache-2.0" ]

1

2019-03-18T13:10:05.000Z

starter-kits/credential-registry/server/tob-api/api_v2/migrations/0003_auto_20181005_2140.py

nairobi222/indy-catalyst

dcbd80524ace7747ecfecd716ff932e9b571d69a

[ "Apache-2.0" ]

8

2019-06-15T13:18:39.000Z

2021-05-01T17:52:02.000Z

starter-kits/credential-registry/server/tob-api/api_v2/migrations/0003_auto_20181005_2140.py

nairobi222/indy-catalyst

dcbd80524ace7747ecfecd716ff932e9b571d69a

[ "Apache-2.0" ]

3

2019-06-12T21:08:53.000Z

2021-05-03T17:09:37.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.11.16 on 2018-10-05 21:40 from __future__ import unicode_literals from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('api_v2', '0002_user_display_name'), ] operations = [ migrations.RemoveField( model_name='doingbusinessas', name='verifiableOrgId', ), migrations.RemoveField( model_name='issuerservice', name='jurisdictionId', ), migrations.RemoveField( model_name='location', name='doingBusinessAsId', ), migrations.RemoveField( model_name='location', name='locationTypeId', ), migrations.RemoveField( model_name='location', name='verifiableOrgId', ), migrations.RemoveField( model_name='verifiableclaim', name='claimType', ), migrations.RemoveField( model_name='verifiableclaim', name='inactiveClaimReasonId', ), migrations.RemoveField( model_name='verifiableclaim', name='verifiableOrgId', ), migrations.RemoveField( model_name='verifiableclaimtype', name='issuerServiceId', ), migrations.RemoveField( model_name='verifiableorg', name='jurisdictionId', ), migrations.RemoveField( model_name='verifiableorg', name='orgTypeId', ), migrations.DeleteModel( name='DoingBusinessAs', ), migrations.DeleteModel( name='InactiveClaimReason', ), migrations.DeleteModel( name='IssuerService', ), migrations.DeleteModel( name='Jurisdiction', ), migrations.DeleteModel( name='Location', ), migrations.DeleteModel( name='LocationType', ), migrations.DeleteModel( name='VerifiableClaim', ), migrations.DeleteModel( name='VerifiableClaimType', ), migrations.DeleteModel( name='VerifiableOrg', ), migrations.DeleteModel( name='VerifiableOrgType', ), ]

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154

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0.263789

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null

0

1

0

1

0

null

0

3

fe1c0591c4151d978ce42f0dfcabccd003aa16e6

2,770

py

Python

aewl/helpers.py

SigJig/aewl

cbcf1a635503f53536d2cb32b88415b221e84bf1

[ "MIT" ]

null

aewl/helpers.py

SigJig/aewl

cbcf1a635503f53536d2cb32b88415b221e84bf1

[ "MIT" ]

2

2021-04-22T19:00:11.000Z

2021-05-02T19:06:26.000Z

aewl/helpers.py

SigJig/aewl

cbcf1a635503f53536d2cb32b88415b221e84bf1

[ "MIT" ]

null

class EmptyFactor: """ Empty factors, such as safeZoneX which do not get multiplied by anything """ def __str__(self): return type(self).__name__ def __repr__(self): return str(self) def _operation_skip_if(self, skip, other, op): if other == skip: return self return Operation(self, other, op) def _skip_zero(self, other, op): if isinstance(other, (float, int)) and float(other) == 0: return self return Operation(self, other, op) def __mul__(self, other): return self._operation_skip_if(1, other, '*') def __truediv__(self, other): return self._operation_skip_if(1, other, '/') def __mod__(self, other): return Operation(self, other, '%') def __add__(self, other): return self._skip_zero(other, '+') def __sub__(self, other): return self._skip_zero(other, '-') def _filter_redundant_prod(self, return_): if float(self) == 1: return return_ return '({}*{})'.format(float(self), return_) class Factor(EmptyFactor, float): def __str__(self): return '{}({})'.format(type(self).__name__, float(self)) def export(self): return float(self) class Operation(EmptyFactor): def __init__(self, left, right, op): self.left = left self.right = right self.op = op def export(self): def _xport(x): if hasattr(x, 'export'): return x.export() return x return '({left}{op}{right})'.format( left=_xport(self.left), op=self.op, right=_xport(self.right) ) def __str__(self): return '{name}({left}{op}{right})'.format( name=type(self).__name__, left=self.left, op=self.op, right=self.right ) class SafeZoneX(EmptyFactor): def export(self): return 'safeZoneX' class SafeZoneY(EmptyFactor): def export(self): return 'safeZoneY' class PixelGrid(EmptyFactor): @classmethod def pixel_h(cls, fac): return Operation(PixelH(fac), cls(), '*') @classmethod def pixel_w(cls, fac): return Operation(PixelW(fac), cls(), '*') def export(self): return 'pixelGrid' class SafeZoneW(Factor): def export(self): return self._filter_redundant_prod('safeZoneW') class SafeZoneH(Factor): def export(self): return self._filter_redundant_prod('safeZoneH') class PixelH(Factor): def export(self): return self._filter_redundant_prod('pixelH') class PixelW(Factor): def export(self): return self._filter_redundant_prod('pixelW') class Percentage(float): pass

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321

2,770

4.831776

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0.075435

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0.280866

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0

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0.780488

0

null

0

null

0

1

0

1

0

3

a3b0c0bd3fa0ef2995b2c304a285c25b5a8a0bbf

489

py

Python

016_Quebrando_um_numero.py

fabioeomedeiros/Python-Base

ef9c1c66b3221f71d1c8dcaf4c2f86503712e9f1

[ "MIT" ]

null

016_Quebrando_um_numero.py

fabioeomedeiros/Python-Base

ef9c1c66b3221f71d1c8dcaf4c2f86503712e9f1

[ "MIT" ]

null

016_Quebrando_um_numero.py

fabioeomedeiros/Python-Base

ef9c1c66b3221f71d1c8dcaf4c2f86503712e9f1

[ "MIT" ]

null

# 016_Quebrando_um_numero.py # Quebra e exibe um número em sua parte inteira e fracionária from math import trunc print() num = float(input("Entre com um número: ")) #importando o método trunc da biblioteca math print(f"A parte inteira de {num} é :{int(num)}") print(f"A parte fracionária de {num} é :{(num - trunc(num)):.4f}") print() #usando a função interna int print(f"A parte inteira de {num} é :{int(num)}") print(f"A parte fracionária de {num} é :{(num - int(num)):.4f}") print()

30.5625

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0.070796

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0.141593

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0

0.012048

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null

0

1

0

1

0

3

a3b6d8a55aad781578d713a387e609902c6da3db

3,045

py

Python

src/sage/repl/readline_extra_commands.py

bopopescu/sage

2d495be78e0bdc7a0a635454290b27bb4f5f70f0

[ "BSL-1.0" ]

3

2016-06-19T14:48:31.000Z

2022-01-28T08:46:01.000Z

src/sage/repl/readline_extra_commands.py

bopopescu/sage

2d495be78e0bdc7a0a635454290b27bb4f5f70f0

[ "BSL-1.0" ]

2

2018-10-30T13:40:20.000Z

2020-07-23T12:13:30.000Z

src/sage/repl/readline_extra_commands.py

bopopescu/sage

2d495be78e0bdc7a0a635454290b27bb4f5f70f0

[ "BSL-1.0" ]

7

2021-11-08T10:01:59.000Z

2022-03-03T11:25:52.000Z

r""" Extra Readline Commands .. WARNING:: The feature described here is no longer available in Sage, as IPython upon which Sage's command line interface is based adopted prompt_toolkit as a replacement of readline as of IPython version 5.0 The following extra readline commands are available in Sage: - ``operate-and-get-next`` - ``history-search-backward-and-save`` - ``history-search-forward-and-save`` The ``operate-and-get-next`` command accepts the input line and fetches the next line from the history. This is the same command with the same name in the Bash shell. The ``history-search-backward-and-save`` command searches backward in the history for the string of characters from the start of the input line to the current cursor position, and fetches the first line found. If the cursor is at the start of the line, the previous line is fetched. The position of the fetched line is saved internally, and the next search begins at the saved position. The ``history-search-forward-and-save`` command behaves similarly but forward. The previous two commands is best used in tandem to fetch a block of lines from the history, by searching backward the first line of the block and then issuing the forward command as many times as needed. They are intended to replace the ``history-search-backward`` command and the ``history-search-forward`` command provided by the GNU readline library used in Sage. To bind these commands with keys, insert the relevant lines into the IPython configuration file ``$DOT_SAGE/ipython-*/profile_default/ipython_config.py``. Note that ``$DOT_SAGE`` is ``$HOME/.sage`` by default. For example, :: c = get_config() c.InteractiveShell.readline_parse_and_bind = [ '"\C-o": operate-and-get-next', '"\e[A": history-search-backward-and-save', '"\e[B": history-search-forward-and-save' ] binds the three commands with the control-o key, the up arrow key, and the down arrow key, respectively. *Warning:* Sometimes, these keys may be bound to do other actions by the terminal and does not reach to the readline properly (check this by running ``stty -a`` and reading the ``cchars`` section). Then you may need to turn off these bindings before the new readline commands work fine . A prominent case is when control-o is bound to ``discard`` by the terminal. You can turn this off by running ``stty discard undef``. AUTHORS: - Kwankyu Lee (2010-11-23): initial version - Kwankyu Lee (2013-06-05): updated for the new IPython configuration format. """ #***************************************************************************** # Copyright (C) 2010 Kwankyu Lee <ekwankyu@gmail.com> # # Distributed under the terms of the GNU General Public License (GPL) # http://www.gnu.org/licenses/ #***************************************************************************** from sage.misc.superseded import deprecation deprecation(21342, "This module and the feature it provides is not available anymore in Sage")

46.136364

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3,045

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0.427632

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0.07732

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0.16913

3,045

65

112

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true

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0.333333

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0.333333

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null

0

1

0

null

0

1

0

1

0

3

a3ba112724eb41303dadbed92c7755b3b7e561ce

2,023

py

Python

mysite/polls/models.py

bjtuhfz/pipe_leakage_query_system

eecca3fdbee44fb0f818d5fb195cf9769eda8786

[ "MIT" ]

null

mysite/polls/models.py

bjtuhfz/pipe_leakage_query_system

eecca3fdbee44fb0f818d5fb195cf9769eda8786

[ "MIT" ]

null

mysite/polls/models.py

bjtuhfz/pipe_leakage_query_system

eecca3fdbee44fb0f818d5fb195cf9769eda8786

[ "MIT" ]

2

2017-05-26T04:32:07.000Z

2019-04-07T13:50:17.000Z

from __future__ import unicode_literals from django.db import models import datetime from django.utils import timezone # Create your models here. # class Question(models.Model): # question_text = models.CharField(max_length=200) # pub_date = models.DateTimeField('date published') # # def __str__(self): # return self.question_text # # def was_published_recently(self): # # return self.pub_date >= timezone.now() - datetime.timedelta(days=1) # now = timezone.now() # return now - datetime.timedelta(days=1) <= self.pub_date <= now class Tweet(models.Model): tweet_text = models.CharField(max_length=150) # pub_date = models.CharField(max_length=15) pub_date = models.DateTimeField() location = models.CharField(max_length=10) label = models.CharField(max_length=10) def __str__(self): return self.tweet_text def was_published_recently(self): # return self.pub_date >= timezone.now() - datetime.timedelta(days=1) now = timezone.now() return now - datetime.timedelta(days=1) <= self.pub_date <= now class Choice(models.Model): tweet = models.ForeignKey(Tweet, on_delete=models.CASCADE) choice_text = models.CharField(max_length=100) votes = models.IntegerField(default=0) def __str__(self): return self.choice_text # Wei Wang class User(models.Model): username = models.CharField(max_length=100) password = models.CharField(max_length=100) def __str__(self): return self.username class Message(models.Model): user = models.ForeignKey(User, on_delete=models.CASCADE) content = models.CharField(max_length=300) location = models.CharField(max_length=30,default="") pub_date = models.DateTimeField('date published') status = models.CharField(max_length=100,default="Unlabelled") def __str__(self): return self.content def was_published_recently(self): return self.pub_date >= timezone.now() - datetime.timedelta(days=1)

32.111111

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false

0.028571

0.114286

0.142857

0.971429

0

null

0

1

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null

0

1

0

3

a3ca4bd0882a8dccc51d543cc41a81a4ff9f3acf

135

py

Python

guest-talks/20170213-optional-static-types/good_example.py

mgadagin/PythonClass

70b370362d75720b3fb0e1d6cc8158f9445e9708

[ "MIT" ]

46

2017-09-27T20:19:36.000Z

2020-12-08T10:07:19.000Z

guest-talks/20170213-optional-static-types/good_example.py

mgadagin/PythonClass

70b370362d75720b3fb0e1d6cc8158f9445e9708

[ "MIT" ]

6

2018-01-09T08:07:37.000Z

2020-09-07T12:25:13.000Z

guest-talks/20170213-optional-static-types/good_example.py

mgadagin/PythonClass

70b370362d75720b3fb0e1d6cc8158f9445e9708

[ "MIT" ]

18

2017-10-10T02:06:51.000Z

2019-12-01T10:18:13.000Z

def fibonnacci(nth: int) -> int: if nth <= 1: return 1 else: return fibonnacci(nth - 1) + fibonnacci(nth - 2)

19.285714

56

0.540741

18

135

4.055556

0.5

0.534247

0

0.044444

0.333333

135

6

57

22.5

0.766667

0

1

0.2

false

0

0.6

0

1

0

null

1

0

1

0

null

0

3

a3fef8727b39ef1514d1ab301ca08e01d1277985

686

py

Python

models/dtc.py

harryprabowo/tars-backend

f791df7124a71bc3be6bd305f2197918bf86d74d

[ "MIT" ]

1

2020-02-14T15:26:20.000Z

models/dtc.py

harryprabowo/tars-backend

f791df7124a71bc3be6bd305f2197918bf86d74d

[ "MIT" ]

null

models/dtc.py

harryprabowo/tars-backend

f791df7124a71bc3be6bd305f2197918bf86d74d

[ "MIT" ]

null

from app import db class DTC(db.Model): __tablename__ = 'dtcs' id = db.Column(db.Integer, primary_key=True) dtc_number = db.Column(db.String(10)) dtc_name = db.Column(db.String(100)) desc = db.Column(db.String(1000), nullable=True) system = db.Column(db.String(25), nullable=True) severity = db.Column(db.Integer) urgency = db.Column(db.Integer) def serialize(self): return { 'id': self.id, 'dtc_number': self.dtc_number, 'dtc_name': self.dtc_name, 'desc': self.desc, 'system': self.system, 'severity': self.severity, 'urgency': self.urgency, }

31.181818

52

0.578717

87

686

4.436782

0.356322

0.145078

0.181347

0.165803

0

0.022312

0.281341

686

22

53

31.181818

0.760649

0

0.071325

0

1

0.05

false

0

0.05

0.6

0

null

0

1

0

null

0

1

0

3

4306e5f0da25fe0d4940a1ba8329ab1c6ac6de16

1,087

py

Python

diofant/matrices/expressions/funcmatrix.py

rajkk1/diofant

6b361334569e4ec2e8c7d30dc324387a4ad417c2

[ "BSD-3-Clause" ]

57

2016-09-13T23:16:26.000Z

2022-03-29T06:45:51.000Z

diofant/matrices/expressions/funcmatrix.py

rajkk1/diofant

6b361334569e4ec2e8c7d30dc324387a4ad417c2

[ "BSD-3-Clause" ]

402

2016-05-11T11:11:47.000Z

2022-03-31T14:27:02.000Z

diofant/matrices/expressions/funcmatrix.py

rajkk1/diofant

6b361334569e4ec2e8c7d30dc324387a4ad417c2

[ "BSD-3-Clause" ]

20

2016-05-11T08:17:37.000Z

2021-09-10T09:15:51.000Z

from ...core import Expr from ...core.sympify import sympify from .matexpr import MatrixExpr class FunctionMatrix(MatrixExpr): """ Represents a Matrix using a function (Lambda) This class is an alternative to SparseMatrix >>> i, j = symbols('i j') >>> X = FunctionMatrix(3, 3, Lambda((i, j), i + j)) >>> Matrix(X) Matrix([ [0, 1, 2], [1, 2, 3], [2, 3, 4]]) >>> Y = FunctionMatrix(1000, 1000, Lambda((i, j), i + j)) >>> isinstance(Y*Y, MatMul) # this is an expression object True >>> (Y**2)[10, 10] # So this is evaluated lazily 342923500 """ def __new__(cls, rows, cols, lamda): rows, cols = sympify(rows), sympify(cols) return Expr.__new__(cls, rows, cols, lamda) @property def shape(self): return self.args[0:2] @property def lamda(self): return self.args[2] def _entry(self, i, j): return self.lamda(i, j) def _eval_trace(self): from ...concrete import Sum from .trace import Trace return Trace(self).rewrite(Sum)

22.645833

63

0.579577

148

1,087

4.182432

0.398649

0.025848

0.029079

0.0937

0

0.04586

0.277829

1,087

47

64

23.12766

0.742675

0.379945

0

0.105263

0

1

0.263158

false

0

0.263158

0.157895

0.842105

0

null

0

null

0

1

0

1

0

3

431c90612332da9814879b0719b2cbe5f1419e0f

108

py

Python

03_01_dice.py

ChoppingBroccoli/Raspi_Book_Exercises

8082ce330817175212a7e5a8baf224e05a63dd3a

[ "MIT" ]

26

2015-04-28T14:34:14.000Z

2021-12-03T21:29:29.000Z

03_01_dice.py

ChoppingBroccoli/Raspi_Book_Exercises

8082ce330817175212a7e5a8baf224e05a63dd3a

[ "MIT" ]

null

03_01_dice.py

ChoppingBroccoli/Raspi_Book_Exercises

8082ce330817175212a7e5a8baf224e05a63dd3a

[ "MIT" ]

27

2015-09-06T16:45:33.000Z

2021-03-26T15:58:51.000Z

#03_01_dice import random for x in range(1, 11): random_number = random.randint(1, 6) print(random_number)

21.6

37

0.759259

20

108

3.9

0.75

0.307692

0

0.095745

0.12963

108

5

38

21.6

0.734043

0.092593

0

1

0

false

0

0.25

0

0.25

1

0

null

1

0

1

0

null

0

3

4320ac889347de937838e13e00b8ae5f2025c288

557

py

Python

problems/12.py

christofferaakre/project-euler

4b42802233be10e4a592798205171fb5156dae6b

[ "MIT" ]

null

problems/12.py

christofferaakre/project-euler

4b42802233be10e4a592798205171fb5156dae6b

[ "MIT" ]

null

problems/12.py

christofferaakre/project-euler

4b42802233be10e4a592798205171fb5156dae6b

[ "MIT" ]

null

import math from decimal import Decimal from main import Solver, list_divisors solver = Solver() def triangle_number(n): return int(n * (n + 1) / 2) def is_triangle_number(x): return ((-1 + math.sqrt(1 + 8 * x)) / 2) % 1 == 0 largest_number_of_divisors = 0 x = 0 i = 1 while largest_number_of_divisors <= 500: x += i number_of_divisors = len(list_divisors(x)) if number_of_divisors > largest_number_of_divisors: print(number_of_divisors) largest_number_of_divisors = number_of_divisors i += 1 solver.solve(12, x)

23.208333

55

0.691203

88

557

4.090909

0.352273

0.177778

0.355556

0.255556

0.216667

0

0.038549

0.208259

557

23

56

24.217391

0.777778

0

1

0.105263

false

0

0.157895

0.105263

0.368421

0.052632

0

null

0

1

0

null

0

1

0

3

43259e8e7c375fc55442abedd4d41d0a13dbe895

451

py

Python

1478.py

heltonricardo/URI

160cca22d94aa667177c9ebf2a1c9864c5e55b41

[ "MIT" ]

6

2021-04-13T00:33:43.000Z

2022-02-10T10:23:59.000Z

1478.py

heltonricardo/URI

160cca22d94aa667177c9ebf2a1c9864c5e55b41

[ "MIT" ]

null

1478.py

heltonricardo/URI

160cca22d94aa667177c9ebf2a1c9864c5e55b41

[ "MIT" ]

3

2021-03-23T18:42:24.000Z

2022-02-10T10:24:07.000Z

while True: n = int(input()) if n == 0: break l = 1 o = '+' while l <= n: v = l c = 1 while c <= n: if c != n: print('{:>3}'.format(v), end=' ') else: print('{:>3}'.format(v)) o = '-' if v == 1: o = '+' elif v == n: o = '-' if o == '+': v += 1 else: v -= 1 c += 1 l += 1 print()

21.47619

56

0.261641

56

451

2.107143

0.339286

0.050847

0.20339

0.220339

0

0.04878

0.545455

451

20

57

22.55

0.526829

0

0.035477

0

1

0

false

0

0.15

0

null

0

1

0

1

0

null

0

3

432c39c315ddf844b7d71fd91b7511362986f19f

56,003

py

Python

predstorm/plot.py

helioforecast/Predstorm

f3c02c201ba790261c4fbd42264e3eb91e09ceb3

[ "MIT" ]

8

2020-01-17T22:04:38.000Z

2021-11-18T11:02:23.000Z

predstorm/plot.py

helioforecast/Predstorm

f3c02c201ba790261c4fbd42264e3eb91e09ceb3

[ "MIT" ]

3

2019-04-11T09:39:28.000Z

2019-06-19T12:02:14.000Z

predstorm/plot.py

IWF-helio/PREDSTORM

f3c02c201ba790261c4fbd42264e3eb91e09ceb3

[ "MIT" ]

9

2019-03-15T13:28:42.000Z

2019-11-08T09:12:47.000Z

#!/usr/bin/env python """ This is the module for producing predstorm plots. Author: C. Moestl, R. Bailey, IWF Graz, Austria started May 2019, last update May 2019 Python 3.7 Issues: - ... To-dos: - ... Future steps: - ... """ import os import sys import copy import logging import logging.config import numpy as np import pdb import seaborn as sns import scipy.signal as signal import matplotlib.dates as mdates from matplotlib.dates import date2num, num2date, DateFormatter import matplotlib.pyplot as plt import matplotlib.colors as mcolors from matplotlib.patches import Polygon from datetime import datetime, timedelta from glob import iglob import json import urllib from .config import plotting as pltcfg logger = logging.getLogger(__name__) # ======================================================================================= # --------------------------- PLOTTING FUNCTIONS ---------------------------------------- # ======================================================================================= def plot_solarwind_and_dst_prediction(DSCOVR_data, STEREOA_data, DST_data, DSTPRED_data, newell_coupling=None, dst_label='Dst Temerin & Li 2002', past_days=3.5, future_days=7., verification_mode=False, timestamp=None, times_3DCORE=[], times_nans={}, outfile='predstorm_real.png', **kwargs): """ Plots solar wind variables, past from DSCOVR and future/predicted from STEREO-A. Total B-field and Bz (top), solar wind speed (second), particle density (third) and Dst (fourth) from Kyoto and model prediction. Parameters ========== DSCOVR_data : list[minute data, hourly data] DSCOVR data in different time resolutions. STEREOA_data : list[minute data, hourly data] STEREO-A data in different time resolutions. DST_data : predstorm_module.SatData Kyoto Dst DSTPRED_data : predstorm_module.SatData Dst predicted by PREDSTORM. dst_method : str (default='temerin_li') Descriptor for Dst method being plotted. past_days : float (default=3.5) Number of days in the past to plot. future_days : float (default=7.) Number of days into the future to plot. lw : int (default=1) Linewidth for plotting functions. fs : int (default=11) Font size for all text in plot. ms : int (default=5) Marker size for markers in plot. figsize : tuple(float=width, float=height) (default=(14,12)) Figure size (in inches) for output file. verification_mode : bool (default=False) If True, verification mode will produce a plot of the predicted Dst for model verification purposes. timestamp : datetime obj Time for 'now' label in plot. Returns ======= plt.savefig : .png file File saved to XXX """ figsize = kwargs.get('figsize', pltcfg.figsize) lw = kwargs.get('lw', pltcfg.lw) fs = kwargs.get('fs', pltcfg.fs) date_fmt = kwargs.get('date_fmt', pltcfg.date_fmt) c_dst = kwargs.get('c_dst', pltcfg.c_dst) c_dis = kwargs.get('c_dis', pltcfg.c_dis) c_ec = kwargs.get('c_ec', pltcfg.c_ec) c_sta = kwargs.get('c_sta', pltcfg.c_sta) c_sta_dst = kwargs.get('c_sta_dst', pltcfg.c_sta_dst) c_btot = kwargs.get('c_btot', pltcfg.c_btot) c_bx = kwargs.get('c_bx', pltcfg.c_bx) c_by = kwargs.get('c_by', pltcfg.c_by) c_bz = kwargs.get('c_bz', pltcfg.c_bz) ms_dst = kwargs.get('c_dst', pltcfg.ms_dst) fs_legend = kwargs.get('fs_legend', pltcfg.fs_legend) fs_ylabel = kwargs.get('fs_legend', pltcfg.fs_ylabel) fs_title = kwargs.get('fs_title', pltcfg.fs_title) # Set style: sns.set_context(pltcfg.sns_context) sns.set_style(pltcfg.sns_style) # Make figure object: fig=plt.figure(1,figsize=figsize) axes = [] # Set data objects: stam, sta = STEREOA_data dism, dis = DSCOVR_data dst = DST_data dst_pred = DSTPRED_data text_offset = past_days # days (for 'fast', 'intense', etc.) # For the minute data, check which are the intervals to show for STEREO-A until end of plot i_fut = np.where(np.logical_and(stam['time'] > dism['time'][-1], \ stam['time'] < dism['time'][-1]+future_days))[0] if timestamp == None: timestamp = datetime.utcnow() timeutc = mdates.date2num(timestamp) if newell_coupling == None: n_plots = 4 else: n_plots = 5 plotstart = timeutc - past_days plotend = timeutc + future_days - 3./24. # SUBPLOT 1: Total B-field and Bz # ------------------------------- ax1 = fig.add_subplot(n_plots,1,1) axes.append(ax1) # Total B-field and Bz (DSCOVR) plst = 2 plt.plot_date(dism['time'][::plst], dism['btot'][::plst],'-', c=c_btot, label='$B_{tot}$', linewidth=lw) plt.plot_date(dism['time'][::plst], dism['bx'][::plst],'-', c=c_bx, label='$B_x$', linewidth=lw) plt.plot_date(dism['time'][::plst], dism['by'][::plst],'-', c=c_by, label='$B_y$', linewidth=lw) plt.plot_date(dism['time'][::plst], dism['bz'][::plst],'-', c=c_bz, label='$B_z$', linewidth=lw) # STEREO-A minute resolution data with timeshift plt.plot_date(stam['time'][i_fut], stam['btot'][i_fut], '-', c=c_btot, alpha=0.5, linewidth=0.5) plt.plot_date(stam['time'][i_fut], stam['br'][i_fut], '-', c=c_bx, alpha=0.5, linewidth=0.5) plt.plot_date(stam['time'][i_fut], stam['bt'][i_fut], '-', c=c_by, alpha=0.5, linewidth=0.5) plt.plot_date(stam['time'][i_fut], stam['bn'][i_fut], '-', c=c_bz, alpha=0.5, linewidth=0.5) # Indicate 0 level for Bz plt.plot_date([plotstart,plotend], [0,0],'--k', alpha=0.5, linewidth=1) plt.ylabel('Magnetic field [nT]', fontsize=fs_ylabel) # For y limits check where the maximum and minimum are for DSCOVR and STEREO taken together: bplotmax = np.nanmax(dism['btot'])+5 bplotmin = -bplotmax plt.ylim(bplotmin, bplotmax) if len(times_3DCORE) > 0: plt.annotate('flux rope (3DCORE)', xy=(times_3DCORE[0],bplotmax-(bplotmax-bplotmin)*0.25), xytext=(times_3DCORE[0]+0.05,bplotmax-(bplotmax-bplotmin)*0.95), color='gray', fontsize=14) if 'stereo' in stam.source.lower(): pred_source = 'STEREO-Ahead Beacon' elif 'dscovr' in stam.source.lower() or 'noaa' in stam.source.lower(): pred_source = '27-day SW-Recurrence Model (NOAA)' plt.title('L1 real time solar wind from NOAA SWPC for '+ datetime.strftime(timestamp, "%Y-%m-%d %H:%M")+ ' UT & {}'.format(pred_source), fontsize=fs_title) # SUBPLOT 2: Solar wind speed # --------------------------- ax2 = fig.add_subplot(n_plots,1,2) axes.append(ax2) # Plot solar wind speed (DSCOVR): plt.plot_date(dism['time'][::plst], dism['speed'][::plst],'-', c='black', label='speed',linewidth=lw) plt.ylabel('Speed $\mathregular{[km \\ s^{-1}]}$', fontsize=fs_ylabel) stam_speed_filt = signal.savgol_filter(stam['speed'],11,1) if 'speed' in times_nans: stam_speed_filt = np.ma.array(stam_speed_filt) for times in times_nans['speed']: stam_speed_filt = np.ma.masked_where(np.logical_and(stam['time'] > times[0], stam['time'] < times[1]), stam_speed_filt) # Plot STEREO-A data with timeshift and savgol filter plt.plot_date(stam['time'][i_fut], stam_speed_filt[i_fut],'-', c='black', alpha=0.5, linewidth=lw, label='speed {}'.format(stam.source)) # Add speed levels: pltcfg.plot_speed_lines(xlims=[plotstart, plotend]) # For y limits check where the maximum and minimum are for DSCOVR and STEREO taken together: vplotmax=np.nanmax(np.concatenate((dism['speed'],stam_speed_filt[i_fut])))+100 vplotmin=np.nanmin(np.concatenate((dism['speed'],stam_speed_filt[i_fut]))-50) plt.ylim(vplotmin, vplotmax) plt.annotate('now', xy=(timeutc,vplotmax-(vplotmax-vplotmin)*0.25), xytext=(timeutc+0.05,vplotmax-(vplotmax-vplotmin)*0.25), color='k', fontsize=14) # SUBPLOT 3: Solar wind density # ----------------------------- ax3 = fig.add_subplot(n_plots,1,3) axes.append(ax3) stam_density_filt = signal.savgol_filter(stam['density'],5,1) if 'density' in times_nans: stam_density_filt = np.ma.array(stam_density_filt) for times in times_nans['density']: stam_density_filt = np.ma.masked_where(np.logical_and(stam['time'] > times[0], stam['time'] < times[1]), stam_density_filt) # Plot solar wind density: plt.plot_date(dism['time'], dism['density'],'-k', label='density L1',linewidth=lw) plt.ylabel('Density $\mathregular{[ccm^{-3}]}$',fontsize=fs_ylabel) # For y limits check where the maximum and minimum are for DSCOVR and STEREO taken together: plt.ylim([0,np.nanmax(np.nanmax(np.concatenate((dism['density'],stam_density_filt[i_fut])))+10)]) #plot STEREO-A data with timeshift and savgol filter plt.plot_date(stam['time'][i_fut], stam_density_filt[i_fut], '-', c='black', alpha=0.5, linewidth=lw, label='density {}'.format(stam.source)) # SUBPLOT 4: Actual and predicted Dst # ----------------------------------- ax4 = fig.add_subplot(n_plots,1,4) axes.append(ax4) # Observed Dst Kyoto (past): plt.plot_date(dst['time'], dst['dst'],'o', c=c_dst, label='Dst observed',markersize=ms_dst) plt.ylabel('Dst [nT]', fontsize=fs_ylabel) dstplotmax = np.nanmax(np.concatenate((dst['dst'], dst_pred['dst'])))+20 dstplotmin = np.nanmin(np.concatenate((dst['dst'], dst_pred['dst'])))-20 if dstplotmin > -100: # Low activity (normal) plt.ylim([-100, dstplotmax + 30]) else: # High activity plt.ylim([dstplotmin, dstplotmax]) # Plot predicted Dst dst_pred_past = dst_pred['time'] < date2num(timestamp) plt.plot_date(dst_pred['time'][dst_pred_past], dst_pred['dst'][dst_pred_past], '-', c=c_sta_dst, label=dst_label, markersize=3, linewidth=1) plt.plot_date(dst_pred['time'][~dst_pred_past], dst_pred['dst'][~dst_pred_past], '-', c=c_sta_dst, alpha=0.5, markersize=3, linewidth=1) # Add generic error bars of +/-15 nT: # Errors calculated using https://machinelearningmastery.com/prediction-intervals-for-machine-learning/ error_l1 = 5.038 error_l5 = 12.249 error_pers = 13.416 ih_fut = np.where(dst_pred['time'] > dis['time'][-1])[0] ih_past = np.arange(0, ih_fut[0]+1) # Error bars for data from L1: plt.fill_between(dst_pred['time'][ih_past], dst_pred['dst'][ih_past]-error_l1, dst_pred['dst'][ih_past]+error_l1, alpha=0.1, facecolor=c_sta_dst, label=r'prediction interval +/- 1 & 2 $\sigma$ (68% and 95% significance)') plt.fill_between(dst_pred['time'][ih_past], dst_pred['dst'][ih_past]-2*error_l1, dst_pred['dst'][ih_past]+2*error_l1, alpha=0.1, facecolor=c_sta_dst) # Error bars for data from L5/STEREO:# plt.fill_between(dst_pred['time'][ih_fut], dst_pred['dst'][ih_fut]-error_l5, dst_pred['dst'][ih_fut]+error_l5, alpha=0.1, facecolor=c_sta_dst) plt.fill_between(dst_pred['time'][ih_fut], dst_pred['dst'][ih_fut]-2*error_l5, dst_pred['dst'][ih_fut]+2*error_l5, alpha=0.1, facecolor=c_sta_dst) # Label plot with geomagnetic storm levels pltcfg.plot_dst_activity_lines(xlims=[plotstart, plotend]) # SUBPLOT 5: Newell Coupling # -------------------------- if newell_coupling != None: ax5 = fig.add_subplot(n_plots,1,5) axes.append(ax5) # Plot solar wind density: ec_past = newell_coupling['time'] < date2num(timestamp) avg_newell_coupling = newell_coupling.get_weighted_average('ec') plt.plot_date(newell_coupling['time'][ec_past], avg_newell_coupling[ec_past]/4421., '-', color=c_ec, # past label='Newell coupling 4h weighted mean',linewidth=1.5) plt.plot_date(newell_coupling['time'][~ec_past], avg_newell_coupling[~ec_past]/4421., '-', color=c_ec, # future alpha=0.5, linewidth=1.5) plt.ylabel('Newell Coupling / 4421\n$\mathregular{[(km/s)^{4/3} nT^{2/3}]}$',fontsize=fs_ylabel) # For y limits check where the maximum and minimum are for DSCOVR and STEREO taken together: plt.ylim([0,np.nanmax(avg_newell_coupling/4421.)*1.2]) # Indicate level of interest (Ec/4421 = 1.0) plt.plot_date([plotstart,plotend], [1,1],'--k', alpha=0.5, linewidth=1) # GENERAL FORMATTING # ------------------ for ax in axes: ax.set_xlim([plotstart,plotend]) ax.tick_params(axis="x", labelsize=fs) ax.tick_params(axis="y", labelsize=fs) ax.legend(loc=2,ncol=4,fontsize=fs_legend) # Dates on x-axes: myformat = mdates.DateFormatter(date_fmt) ax.xaxis.set_major_formatter(myformat) # Vertical line for NOW: ax.plot_date([timeutc,timeutc],[-2000,100000],'-k', linewidth=2) # Indicate where prediction comes from 3DCORE: if len(times_3DCORE) > 0: ax.plot_date([times_3DCORE[0],times_3DCORE[0]],[-2000,100000], color='gray', linewidth=1, linestyle='--') ax.plot_date([times_3DCORE[-1],times_3DCORE[-1]],[-2000,100000], color='gray', linewidth=1, linestyle='--') # Liability text: pltcfg.group_info_text() pltcfg.liability_text() #save plot if not verification_mode: plot_label = 'realtime' else: plot_label = 'verify' filename = os.path.join('results','predstorm_v1_{}_stereo_a_plot_{}.png'.format( plot_label, datetime.strftime(timestamp, "%Y-%m-%d-%H_%M"))) filename_eps = filename.replace('png', 'eps') if not verification_mode: plt.savefig(outfile) logger.info('Real-time plot saved as {}!'.format(outfile)) #if not server: # Just plot and exit # plt.show() # sys.exit() plt.savefig(filename) logger.info('Plot saved as png:\n'+ filename) def plot_solarwind_science(DSCOVR_data, STEREOA_data, verification_mode=False, timestamp=None, past_days=7, future_days=7, plot_step=20, outfile='predstorm_science.png', **kwargs): """ Plots solar wind variables, past from DSCOVR and future/predicted from STEREO-A. Total B-field and Bz (top), solar wind speed (second), particle density (third) and Dst (fourth) from Kyoto and model prediction. Parameters ========== DSCOVR_data : list[minute data, hourly data] DSCOVR data in different time resolutions. STEREOA_data : list[minute data, hourly data] STEREO-A data in different time resolutions. lw : int (default=1) Linewidth for plotting functions. fs : int (default=11) Font size for all text in plot. ms : int (default=5) Marker size for markers in plot. figsize : tuple(float=width, float=height) (default=(14,12)) Figure size (in inches) for output file. verification_mode : bool (default=False) If True, verification mode will produce a plot of the predicted Dst for model verification purposes. timestamp : datetime obj Time for 'now' label in plot. Returns ======= plt.savefig : .png file File saved to XXX """ figsize = kwargs.get('figsize', pltcfg.figsize) lw = kwargs.get('lw', pltcfg.lw) fs = kwargs.get('fs', pltcfg.fs) date_fmt = kwargs.get('date_fmt', pltcfg.date_fmt) c_dst = kwargs.get('c_dst', pltcfg.c_dst) c_dis = kwargs.get('c_dis', pltcfg.c_dis) c_ec = kwargs.get('c_ec', pltcfg.c_ec) c_sta = kwargs.get('c_sta', pltcfg.c_sta) c_sta_dst = kwargs.get('c_sta_dst', pltcfg.c_sta_dst) ms_dst = kwargs.get('c_dst', pltcfg.ms_dst) fs_legend = kwargs.get('fs_legend', pltcfg.fs_legend) fs_ylabel = kwargs.get('fs_legend', pltcfg.fs_ylabel) fs_title = kwargs.get('fs_title', pltcfg.fs_title) # Set style: sns.set_context(pltcfg.sns_context) sns.set_style(pltcfg.sns_style) # Make figure object: fig = plt.figure(1,figsize=(20,8)) axes = [] # Set data objects: stam, sta = STEREOA_data dism, dis = DSCOVR_data # For the minute data, check which are the intervals to show for STEREO-A until end of plot sta_index_future=np.where(np.logical_and(stam['time'] > dism['time'][-1], \ stam['time'] < dism['time'][-1]+future_days))[0] if timestamp == None: timestamp = datetime.utcnow() timeutc = mdates.date2num(timestamp) n_plots = 3 plst = plot_step plotstart = timeutc - past_days plotend = timeutc + future_days # SUBPLOT 1: Total B-field and Bz # ------------------------------- ax1 = fig.add_subplot(n_plots,1,1) axes.append(ax1) # Total B-field and Bz (DSCOVR) plt.plot_date(dism['time'][::plst], dism['btot'][::plst],'-', c='black', label='B', linewidth=lw) plt.plot_date(dism['time'][::plst], dism['bx'][::plst],'-', c='teal', label='Bx', linewidth=lw) plt.plot_date(dism['time'][::plst], dism['by'][::plst],'-', c='orange', label='By', linewidth=lw) plt.plot_date(dism['time'][::plst], dism['bz'][::plst],'-', c='purple', label='Bz', linewidth=lw) # STEREO-A minute resolution data with timeshift plt.plot_date(stam['time'][sta_index_future], stam['btot'][sta_index_future], '-', c='black', alpha=0.5, linewidth=0.5) plt.plot_date(stam['time'][sta_index_future], stam['br'][sta_index_future], '-', c='teal', alpha=0.5, linewidth=0.5) plt.plot_date(stam['time'][sta_index_future], stam['bt'][sta_index_future], '-', c='orange', alpha=0.5, linewidth=0.5) plt.plot_date(stam['time'][sta_index_future], stam['bn'][sta_index_future], '-', c='purple', alpha=0.5, linewidth=0.5) # Indicate 0 level for Bz plt.plot_date([plotstart,plotend], [0,0],'--k', alpha=0.5, linewidth=1) plt.ylabel('Magnetic field [nT]', fontsize=fs_ylabel) # For y limits check where the maximum and minimum are for DSCOVR and STEREO taken together: bplotmax=np.nanmax(np.concatenate((dism['btot'],stam['btot'][sta_index_future])))+5 bplotmin=np.nanmin(np.concatenate((dism['bz'],stam['bn'][sta_index_future]))-5) plt.ylim((-13, 13)) if 'stereo' in stam.source.lower(): pred_source = 'STEREO-Ahead Beacon' elif 'dscovr' in stam.source.lower() or 'noaa' in stam.source.lower(): pred_source = '27-day SW-Recurrence Model (NOAA)' plt.title('L1 real time solar wind from NOAA SWPC for '+ datetime.strftime(timestamp, "%Y-%m-%d %H:%M")+ ' UT & {}'.format(pred_source), fontsize=fs_title) # SUBPLOT 2: Solar wind speed # --------------------------- ax2 = fig.add_subplot(n_plots,1,2) axes.append(ax2) # Plot solar wind speed (DSCOVR): plt.plot_date(dism['time'][::plst], dism['speed'][::plst],'-', c='black', label='speed',linewidth=lw) plt.ylabel('Speed $\mathregular{[km \\ s^{-1}]}$', fontsize=fs_ylabel) # Plot STEREO-A data with timeshift and savgol filter plt.plot_date(stam['time'][sta_index_future],signal.savgol_filter(stam['speed'][sta_index_future],11,1),'-', c='black', alpha=0.5, linewidth=lw) # Add speed levels: pltcfg.plot_speed_lines(xlims=[plotstart, plotend]) # For y limits check where the maximum and minimum are for DSCOVR and STEREO taken together: vplotmax=np.nanmax(np.concatenate((dism['speed'],signal.savgol_filter(stam['speed'][sta_index_future],11,1))))+100 vplotmin=np.nanmin(np.concatenate((dism['speed'],signal.savgol_filter(stam['speed'][sta_index_future],11,1)))-50) plt.ylim(vplotmin, vplotmax) plt.annotate('now', xy=(timeutc,vplotmax-(vplotmax-vplotmin)*0.25), xytext=(timeutc+0.05,vplotmax-(vplotmax-vplotmin)*0.25), color='k', fontsize=14) # SUBPLOT 3: Solar wind density # ----------------------------- ax3 = fig.add_subplot(n_plots,1,3) axes.append(ax3) # Plot solar wind density: plt.plot_date(dism['time'][::plst], dism['density'][::plst],'-k', label='density',linewidth=lw) plt.ylabel('Density $\mathregular{[ccm^{-3}]}$',fontsize=fs_ylabel) # For y limits check where the maximum and minimum are for DSCOVR and STEREO taken together: plt.ylim([0,np.nanmax(np.nanmax(np.concatenate((dism['density'],stam['density'][sta_index_future])))+10)]) #plot STEREO-A data with timeshift and savgol filter plt.plot_date(stam['time'][sta_index_future], signal.savgol_filter(stam['density'][sta_index_future],5,1), '-', c='black', alpha=0.5, linewidth=lw) # GENERAL FORMATTING # ------------------ for ax in axes: ax.set_xlim([plotstart,plotend]) ax.tick_params(axis="x", labelsize=fs) ax.tick_params(axis="y", labelsize=fs) ax.legend(loc=2,ncol=4,fontsize=fs_legend) # Dates on x-axes: myformat = mdates.DateFormatter(date_fmt) ax.xaxis.set_major_formatter(myformat) # Vertical line for NOW: ax.plot_date([timeutc,timeutc],[-2000,100000],'-k', linewidth=2) # Liability text: pltcfg.group_info_text() pltcfg.liability_text() #save plot if not verification_mode: plot_label = 'realtime' else: plot_label = 'verify' if not verification_mode: plt.savefig(outfile) logger.info('Real-time plot saved as {}!'.format(outfile)) def plot_solarwind_pretty(sw_past, sw_future, dst, newell_coupling, timestamp): """Uses the package mplcyberpunk to make a simpler and more visually appealing plot. TO-DO: - Implement weighted average smoothing on Newell Coupling.""" import mplcyberpunk plt.style.use("cyberpunk") c_speed = (0.58, 0.404, 0.741) c_dst = (0.031, 0.969, 0.996) c_ec = (0.961, 0.827, 0) alpha_fut = 0.5 fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(17,9), sharex=True) time_past = dst['time'] <= date2num(timestamp) time_future = dst['time'] >= date2num(timestamp) # Plot data: ax1.plot_date(sw_past['time'], sw_past['speed'], '-', c=c_speed, label="Solar wind speed [km/s]") ax1.plot_date(sw_future['time'], sw_future['speed'], '-', c=c_speed, alpha=alpha_fut) ax2.plot_date(dst['time'][time_past], dst['dst'][time_past], '-', c=c_dst, label="$Dst$ [nT]") ax2.plot_date(dst['time'][time_future], dst['dst'][time_future], '-', c=c_dst, alpha=alpha_fut) avg_newell_coupling = newell_coupling.get_weighted_average('ec') ax3.plot_date(newell_coupling['time'][time_past], avg_newell_coupling[time_past]/4421., '-', c=c_ec, label="Newell Coupling\n[nT]") ax3.plot_date(newell_coupling['time'][time_future], avg_newell_coupling[time_future]/4421., '-', c=c_ec, alpha=alpha_fut) mplcyberpunk.add_glow_effects(ax1) mplcyberpunk.add_glow_effects(ax2) mplcyberpunk.add_glow_effects(ax3) # Add labels: props = dict(boxstyle='round', facecolor='silver', alpha=0.2) # place a text box in upper left in axes coords ax1.text(0.01, 0.95, "Solar wind speed [km/s]", transform=ax1.transAxes, fontsize=14, verticalalignment='top', bbox=props) ax2.text(0.01, 0.95, "Predicted $Dst$ [nT]", transform=ax2.transAxes, fontsize=14, verticalalignment='top', bbox=props) ax3.text(0.01, 0.95, 'Newell Coupling / 4421 $\mathregular{[(km/s)^{4/3} nT^{2/3}]}$', transform=ax3.transAxes, fontsize=14, verticalalignment='top', bbox=props) pltcfg.plot_dst_activity_lines(xlims=[dst['time'][0], dst['time'][-1]], ax=ax2, color='silver') pltcfg.plot_speed_lines(xlims=[dst['time'][0], dst['time'][-1]], ax=ax1, color='silver') # Add vertical lines for 'now' time: print_time_lines = True for ax in [ax1, ax2, ax3]: # Add a line denoting "now" ax.axvline(x=timestamp, linewidth=2, color='silver') # Add buffer to top of plots so that labels don't overlap with data: ax_ymin, ax_ymax = ax.get_ylim() text_adj = (ax_ymax-ax_ymin)*0.17 ax.set_ylim((ax_ymin, ax_ymax + text_adj)) # Add lines for future days: ax_ymin, ax_ymax = ax.get_ylim() text_adj = (ax_ymax-ax_ymin)*0.15 for t_day in [1,2,3,4]: t_days_timestamp = timestamp+timedelta(days=t_day) ax.axvline(x=t_days_timestamp, ls='--', linewidth=0.7, color='silver') if print_time_lines: ax.annotate('now', xy=(timestamp, ax_ymax-text_adj), xytext=(timestamp+timedelta(hours=2.5), ax_ymax-text_adj*1.03), color='silver', fontsize=14) ax.annotate('+{} days'.format(t_day), xy=(t_days_timestamp, ax_ymax-text_adj), xytext=(t_days_timestamp+timedelta(hours=2), ax_ymax-text_adj*1.03), color='silver', fontsize=10) print_time_lines = False # Formatting: tick_date = num2date(dst['time'][0]).replace(hour=0, minute=0, second=0, microsecond=0) ax3.set_xticks([tick_date + timedelta(days=n) for n in range(1,15)]) ax3.set_xlim([dst['time'][0], dst['time'][-1]]) myformat = DateFormatter('%a\n%b %d') ax3.xaxis.set_major_formatter(myformat) ax1.tick_params(axis='both', which='major', labelsize=14) ax2.tick_params(axis='both', which='major', labelsize=14) ax3.tick_params(axis='both', which='major', labelsize=14) plt.subplots_adjust(hspace=0.) ax1.set_title("Helio4Cast Geomagnetic Activity Forecast, {} UTC".format(timestamp.strftime("%Y-%m-%d %H:%M")), pad=20) pltcfg.group_info_text_small() plt.savefig("predstorm_pretty.png") # To cut the final version: # convert predstorm_pretty.png -crop 1420x1000+145+30 predstorm_pretty_cropped.png def plot_stereo_dscovr_comparison(stam, dism, dst, timestamp=None, look_back=20, outfile=None, **kwargs): """Plots the last days of STEREO-A and DSCOVR data for comparison alongside the predicted and real Dst. Parameters ========== stam : predstorm.SatData Object containing minute STEREO-A data dism : predstorm.SatData Object containing minute DSCOVR data. dst : predstorm.SatData Object containing Kyoto Dst data. timestamp : datetime obj Time for last datapoint in plot. look_back : float (default=20) Number of days in the past to plot. **kwargs : ... See config.plotting for variables that can be tweaked. Returns ======= plt.savefig : .png file File saved to XXX """ if timestamp == None: timestamp = datetime.utcnow() if outfile == None: outfile = 'sta_dsc_comparison_{}.png'.format(datetime.strftime(timestamp, "%Y-%m-%dT%H:%M")) figsize = kwargs.get('figsize', pltcfg.figsize) lw = kwargs.get('lw', pltcfg.lw) fs = kwargs.get('fs', pltcfg.fs) date_fmt = kwargs.get('date_fmt', pltcfg.date_fmt) c_dst = kwargs.get('c_dst', pltcfg.c_dst) c_dis = kwargs.get('c_dis', pltcfg.c_dis) c_sta = kwargs.get('c_sta', pltcfg.c_sta) c_sta_dst = kwargs.get('c_sta_dst', pltcfg.c_sta_dst) ms_dst = kwargs.get('c_dst', pltcfg.ms_dst) fs_legend = kwargs.get('fs_legend', pltcfg.fs_legend) fs_ylabel = kwargs.get('fs_legend', pltcfg.fs_ylabel) # READ DATA: # ---------- # TODO: It would be faster to read archived hourly data rather than interped minute data... logger.info("plot_stereo_dscovr_comparison: Reading satellite data") # Get estimate of time diff: stam.shift_time_to_L1() sta = stam.make_hourly_data() sta.interp_nans() dis = dism.make_hourly_data() dis.interp_nans() # CALCULATE PREDICTED DST: # ------------------------ sta.convert_RTN_to_GSE().convert_GSE_to_GSM() dst_pred = sta.make_dst_prediction() # PLOT: # ----- # Set style: sns.set_context(pltcfg.sns_context) sns.set_style(pltcfg.sns_style) plotstart = timestamp - timedelta(days=look_back) plotend = timestamp # Make figure object: fig = plt.figure(1,figsize=figsize) axes = [] # SUBPLOT 1: Total B-field and Bz # ------------------------------- ax1 = fig.add_subplot(411) axes.append(ax1) plt.plot_date(dis['time'], dis['bz'], '-', c=c_dis, linewidth=lw, label='DSCOVR') plt.plot_date(sta['time'], sta['bz'], '-', c=c_sta, linewidth=lw, label='STEREO-A') # Indicate 0 level for Bz plt.plot_date([plotstart,plotend], [0,0],'--k', alpha=0.5, linewidth=1) plt.ylabel('Magnetic field Bz [nT]', fontsize=fs_ylabel) # For y limits check where the maximum and minimum are for DSCOVR and STEREO taken together: bplotmax=np.nanmax(np.concatenate((dis['bz'], sta['bz'])))+5 bplotmin=np.nanmin(np.concatenate((dis['bz'], sta['bz'])))-5 plt.ylim(bplotmin, bplotmax) plt.legend(loc=2,ncol=4,fontsize=fs_legend) plt.title('DSCOVR and STEREO-A solar wind projected to L1 for '+ datetime.strftime(timestamp, "%Y-%m-%d %H:%M")+ ' UT', fontsize=16) # SUBPLOT 2: Solar wind speed # --------------------------- ax2 = fig.add_subplot(412) axes.append(ax2) plt.plot_date(dis['time'], dis['speed'], '-', c=c_dis, linewidth=lw) plt.plot_date(sta['time'], sta['speed'], '-', c=c_sta, linewidth=lw) plt.ylabel('Speed $\mathregular{[km \\ s^{-1}]}$', fontsize=fs_ylabel) # Add speed levels: pltcfg.plot_speed_lines(xlims=[plotstart, plotend]) # For y limits check where the maximum and minimum are for DSCOVR and STEREO taken together: vplotmax=np.nanmax(np.concatenate((dis['speed'], sta['speed'])))+100 vplotmin=np.nanmin(np.concatenate((dis['speed'], sta['speed'])))-50 plt.ylim(vplotmin, vplotmax) # SUBPLOT 3: Solar wind density # ----------------------------- ax3 = fig.add_subplot(413) axes.append(ax3) # Plot solar wind density: plt.plot_date(dis['time'], dis['density'], '-', c=c_dis, linewidth=lw) plt.plot_date(sta['time'], sta['density'], '-', c=c_sta, linewidth=lw) plt.ylabel('Density $\mathregular{[ccm^{-3}]}$',fontsize=fs_ylabel) # For y limits check where the maximum and minimum are for DSCOVR and STEREO taken together: plt.ylim([0, np.nanmax(np.nanmax(np.concatenate((dis['density'], sta['density'])))+10)]) # SUBPLOT 4: Actual and predicted Dst # ----------------------------------- ax4 = fig.add_subplot(414) axes.append(ax4) # Observed Dst Kyoto (past): plt.plot_date(dst['time'], dst['dst'],'o', c=c_dst, label='Observed Dst', ms=ms_dst) plt.plot_date(sta['time'], dst_pred['dst'],'-', c=c_sta_dst, label='Predicted Dst', lw=lw) # Add generic error bars of +/-15 nT: error=15 plt.fill_between(sta['time'], dst_pred['dst']-error, dst_pred['dst']+error, alpha=0.2, label='Error for high speed streams') # Label plot with geomagnetic storm levels pltcfg.plot_dst_activity_lines(xlims=[plotstart, plotend]) dstplotmin = -10 + np.nanmin(np.nanmin(np.concatenate((dst['dst'], dst_pred['dst'])))) dstplotmax = 10 + np.nanmax(np.nanmax(np.concatenate((dst['dst'], dst_pred['dst'])))) plt.ylim([dstplotmin, dstplotmax]) plt.legend(loc=2,ncol=4,fontsize=fs_legend) # GENERAL FORMATTING # ------------------ for ax in axes: ax.set_xlim([plotstart,plotend]) ax.tick_params(axis="x", labelsize=fs) ax.tick_params(axis="y", labelsize=fs) # Dates on x-axes: myformat = mdates.DateFormatter('%b %d %Hh') ax.xaxis.set_major_formatter(myformat) plt.savefig(outfile) logger.info("Plot saved as {}".format(outfile)) plt.close() return def plot_dst_comparison(stam, dism, dst, timestamp=None, look_back=20, dst_method='temerin_li_2006', outfile=None, **kwargs): """Plots the last days of STEREO-A and DSCOVR data for comparison alongside the predicted and real Dst. Parameters ========== stam : predstorm.SatData Object containing minute STEREO-A data dism : predstorm.SatData Object containing minute DSCOVR data. dst : predstorm.SatData Object containing hourly Kyoto Dst data. timestamp : datetime obj Time for last datapoint in plot. look_back : float (default=20) Number of days in the past to plot. **kwargs : ... See config.plotting for variables that can be tweaked. Returns ======= plt.savefig : .png file File saved to XXX """ if timestamp == None: timestamp = datetime.utcnow() if outfile == None: outfile = 'dst_comparison_{}.png'.format(datetime.strftime(timestamp, "%Y-%m-%dT%H:%M")) figsize = kwargs.get('figsize', pltcfg.figsize) lw = kwargs.get('lw', pltcfg.lw) fs = kwargs.get('fs', pltcfg.fs) date_fmt = kwargs.get('date_fmt', pltcfg.date_fmt) c_dst = kwargs.get('c_dst', pltcfg.c_dst) c_dis = kwargs.get('c_dis', pltcfg.c_dis) c_dis_dst = kwargs.get('c_dis_dst', pltcfg.c_dis_dst) c_sta_dst = kwargs.get('c_sta_dst', pltcfg.c_sta_dst) c_sta = kwargs.get('c_sta', pltcfg.c_sta) ms_dst = kwargs.get('c_dst', pltcfg.ms_dst) fs_legend = kwargs.get('fs_legend', pltcfg.fs_legend) fs_title = kwargs.get('fs_title', pltcfg.fs_title) # PREPARE DATA: # ------------- # TODO: It would be faster to read archived hourly data rather than interped minute data... logger.info("plot_dst_comparison: Preparing satellite data") # Correct for STEREO-A position: stam.shift_time_to_L1() sta = stam.make_hourly_data() sta = sta.cut(starttime=timestamp-timedelta(days=look_back), endtime=timestamp).interp_nans() dis = dism.make_hourly_data() dis.interp_nans() # CALCULATE PREDICTED DST: # ------------------------ sta.convert_RTN_to_GSE().convert_GSE_to_GSM() dst_h = dst.interp_to_time(sta['time']) dis = dis.interp_to_time(sta['time']) dst_sta = sta.make_dst_prediction(method=dst_method) dst_dis = dis.make_dst_prediction(method=dst_method) # PLOT: # ----- # Set style: sns.set_context(pltcfg.sns_context) sns.set_style(pltcfg.sns_style) plotstart = timestamp - timedelta(days=look_back) plotend = timestamp # Make figure object: fig = plt.figure(1, figsize=figsize) axes = [] # SUBPLOT 1: Actual and predicted Dst # ----------------------------------- ax1 = fig.add_subplot(411) axes.append(ax1) # Observed Dst Kyoto (past): plt.plot_date(dst['time'], dst['dst'], 'o', c=c_dst, label='Observed Dst', ms=ms_dst) plt.plot_date(sta['time'], dst_sta['dst'],'-', c=c_sta_dst, label='Predicted Dst (STEREO-A)', linewidth=lw) plt.plot_date(dis['time'], dst_dis['dst'],'-', c=c_dis_dst, label='Predicted Dst (DSCOVR)', linewidth=lw) # Add generic error bars of +/-15 nT: error=15 plt.fill_between(sta['time'], dst_sta['dst']-error, dst_sta['dst']+error, facecolor=c_sta_dst, alpha=0.2, label='Error') plt.fill_between(dis['time'], dst_dis['dst']-error, dst_dis['dst']+error, facecolor=c_dis_dst, alpha=0.2, label='Error') # Label plot with geomagnetic storm levels pltcfg.plot_dst_activity_lines(xlims=[plotstart, plotend]) dstplotmin = -10 + np.nanmin(np.nanmin(np.concatenate((dst_sta['dst'], dst_dis['dst'])))) dstplotmax = 10 + np.nanmax(np.nanmax(np.concatenate((dst_sta['dst'], dst_dis['dst'])))) plt.ylim([dstplotmin, dstplotmax]) plt.title("Dst(real) vs Dst(predicted)", fontsize=fs_title) # SUBPLOT 2: Actual vs predicted Dst STEREO # ----------------------------------------- diff_sta = dst_h['dst'] - dst_sta['dst'] diff_dis = dst_h['dst'] - dst_dis['dst'] if np.nanmax((np.abs(dstplotmin), dstplotmax)) > 50: maxval = np.nanmax((np.abs(dstplotmin), dstplotmax)) else: maxval = 50. ax2 = fig.add_subplot(412) axes.append(ax2) # Observed Dst Kyoto (past): gradient_fill(sta['time'], dst_sta['dst']-dst_h['dst'], maxval=maxval, ls='-', c='k', label='Dst(Kyoto) - Dst(STEREO-A-pred)', ms=0, lw=lw) # SUBPLOT 3: Actual vs predicted Dst DSCOVR # ----------------------------------------- ax3 = fig.add_subplot(413) axes.append(ax3) # Observed Dst Kyoto (past): gradient_fill(dis['time'], dst_dis['dst']-dst_h['dst'], maxval=maxval, ls='-', c='k', label='Dst(Kyoto) - Dst(DSCOVR-pred)', ms=0, lw=lw) # SUBPLOT 3: Predicted vs predicted Dst # ------------------------------------- ax4 = fig.add_subplot(414) axes.append(ax4) # Observed Dst Kyoto (past): gradient_fill(dis['time'], dst_dis['dst']-dst_sta['dst'], maxval=maxval, ls='-', c='k', label='Dst(DSCOVR-pred) - Dst(STEREO-A-pred)', ms=0, lw=lw) # GENERAL FORMATTING # ------------------ for ax in axes: ax.set_xlim([plotstart,plotend]) ax.tick_params(axis="x", labelsize=fs) ax.tick_params(axis="y", labelsize=fs) ax.legend(loc=2, ncol=5, fontsize=fs_legend) # Dates on x-axes: myformat = mdates.DateFormatter(date_fmt) ax.xaxis.set_major_formatter(myformat) plt.savefig(outfile) logger.info("Plot saved as {}".format(outfile)) plt.close() return def plot_dst_vs_persistence_model(stam, dism, dpmm, dst, t_syn=27.27, dst_method='temerin_li_2006', timestamp=None, look_back=20, outfile=None, **kwargs): """Plots the last days of STEREO-A and DSCOVR data for comparison alongside the predicted and real Dst. Parameters ========== stam : predstorm.SatData Object containing minute STEREO-A data dism : predstorm.SatData Object containing minute DSCOVR data. dst : predstorm.SatData Object containing hourly Kyoto Dst data. timestamp : datetime obj Time for last datapoint in plot. look_back : float (default=20) Number of days in the past to plot. **kwargs : ... See config.plotting for variables that can be tweaked. Returns ======= plt.savefig : .png file File saved to XXX """ if timestamp == None: timestamp = datetime.utcnow() if outfile == None: outfile = 'dst_comparison_{}.png'.format(datetime.strftime(timestamp, "%Y-%m-%dT%H:%M")) figsize = kwargs.get('figsize', pltcfg.figsize) lw = kwargs.get('lw', pltcfg.lw) fs = kwargs.get('fs', pltcfg.fs) date_fmt = kwargs.get('date_fmt', pltcfg.date_fmt) c_dst = kwargs.get('c_dst', pltcfg.c_dst) c_dis = kwargs.get('c_dis', pltcfg.c_dis) c_dis_dst = kwargs.get('c_dis_dst', pltcfg.c_dis_dst) c_sta_dst = kwargs.get('c_sta_dst', pltcfg.c_sta_dst) c_sta = kwargs.get('c_sta', pltcfg.c_sta) ms_dst = kwargs.get('c_dst', pltcfg.ms_dst) fs_legend = kwargs.get('fs_legend', pltcfg.fs_legend) + 2 fs_title = kwargs.get('fs_title', pltcfg.fs_title) + 2 # PREPARE DATA: # ------------- # TODO: It would be faster to read archived hourly data rather than interped minute data... logger.info("plot_dst_comparison: Preparing satellite data") # Correct for STEREO-A position: stam.shift_time_to_L1() stam['bx'], stam['by'], stam['bz'] = stam['br'], -stam['bt'], stam['bn'] sta = stam.make_hourly_data() sta = sta.cut(starttime=timestamp-timedelta(days=look_back), endtime=timestamp).interp_nans() # DSCOVR #dis = dism.make_hourly_data() dism.interp_nans() # Persistence Model #dpm = dpmm.make_hourly_data() dpmm.interp_nans() # CALCULATE PREDICTED DST: # ------------------------ #sta.convert_RTN_to_GSE().convert_GSE_to_GSM() dst_h = dst.interp_to_time(sta['time']) dis = dism.interp_to_time(sta['time']) dpm = dpmm.interp_to_time(sta['time']) dst_sta = sta.make_dst_prediction(method=dst_method) dst_dis = dis.make_dst_prediction(method=dst_method) dst_dpm = dpm.make_dst_prediction(method=dst_method) # PLOT: # ----- # Set style: sns.set_context(pltcfg.sns_context) sns.set_style(pltcfg.sns_style) plotstart = timestamp - timedelta(days=look_back) plotend = timestamp # Make figure object: fig = plt.figure(1, figsize=figsize) axes = [] score_xpos, score_ypos = 0.80, 0.73 # SUBPLOT 1: Actual and predicted Dst # ----------------------------------- ax1 = fig.add_subplot(411) axes.append(ax1) # Observed Dst Kyoto (past): plt.plot_date(dst['time'], dst['dst'], 'o', c=c_dst, label='Observed Dst', ms=ms_dst) plt.plot_date(sta['time'], dst_sta['dst'],'-', c=c_sta_dst, label='Predicted Dst (STEREO-A)', linewidth=lw) plt.plot_date(dis['time'], dst_dis['dst'],'-', c=c_dis_dst, label='Predicted Dst (DSCOVR)', linewidth=lw) plt.plot_date(dis['time'], dst_dpm['dst'],'-', c='r', label='Dst (DSCOVR persistence model)', linewidth=lw) # Add generic error bars of +/-15 nT: error=15 plt.fill_between(sta['time'], dst_sta['dst']-error, dst_sta['dst']+error, facecolor=c_sta_dst, alpha=0.2, label='Error') #plt.fill_between(dis['time'], dst_dis['dst']-error, dst_dis['dst']+error, facecolor=c_dis_dst, alpha=0.2, label='Error') # Label plot with geomagnetic storm levels pltcfg.plot_dst_activity_lines(xlims=[plotstart, plotend]) dstplotmin = -10 + np.nanmin(np.nanmin(np.concatenate((dst_sta['dst'], dst_dis['dst'], dst_dpm['dst'])))) dstplotmax = 10 + np.nanmax(np.nanmax(np.concatenate((dst_sta['dst'], dst_dis['dst'], dst_dpm['dst'])))) plt.ylim([dstplotmin, dstplotmax]) plt.title("Dst(real) vs Dst(predicted) for {} - {} days".format(timestamp.strftime("%Y-%m-%d %H:%M"), look_back), fontsize=fs_title) # SUBPLOT 2: Actual vs predicted Dst DSCOVR # ----------------------------------------- ax2 = fig.add_subplot(412) axes.append(ax2) if np.nanmax((np.abs(dstplotmin), dstplotmax)) > 50: maxval = np.nanmax((np.abs(dstplotmin), dstplotmax)) else: maxval = 50. # Observed Dst Kyoto (past): gradient_fill(dis['time'], dst_h['dst']-dst_dis['dst'], maxval=maxval, ls='-', c='k', label='Dst(Kyoto) - Dst(DSCOVR-pred)', ms=0, lw=lw) r2 = np.corrcoef(dst_h['dst'], dst_dis['dst'])[0][1] mae = np.sum(np.abs(dst_h['dst']-dst_dis['dst'])) / len(dst_h['dst']) ax2.annotate(r'$R^2 = {:.2f}$'.format(r2)+'\n'+r'$MAE = {:.1f}$ nT'.format(mae), xy=(score_xpos, score_ypos), xycoords='axes fraction', size=fs_title-2) # SUBPLOT 3: Actual vs predicted Dst STEREO # ----------------------------------------- ax3 = fig.add_subplot(413) axes.append(ax3) # Observed Dst Kyoto (past): gradient_fill(sta['time'], dst_h['dst']-dst_sta['dst'], maxval=maxval, ls='-', c='k', label='Dst(Kyoto) - Dst(STEREO-A-pred)', ms=0, lw=lw) r2 = np.corrcoef(dst_h['dst'], dst_sta['dst'])[0][1] mae = np.sum(np.abs(dst_h['dst']-dst_sta['dst'])) / len(dst_h['dst']) ax3.annotate(r'$R^2 = {:.2f}$'.format(r2)+'\n'+r'$MAE = {:.1f}$ nT'.format(mae), xy=(score_xpos, score_ypos), xycoords='axes fraction', size=fs_title-2) # SUBPLOT 3: Actual vs persistence model Dst # ------------------------------------------ ax4 = fig.add_subplot(414) axes.append(ax4) # Observed Dst Kyoto (past): gradient_fill(dis['time'], dst_h['dst']-dst_dpm['dst'], maxval=maxval, ls='-', c='k', label='Dst(Kyoto) - Dst(DSCOVR pers. model)', ms=0, lw=lw) r2 = np.corrcoef(dst_h['dst'], dst_dpm['dst'])[0][1] mae = np.sum(np.abs(dst_h['dst']-dst_dpm['dst'])) / len(dst_h['dst']) ax4.annotate(r'$R^2 = {:.2f}$'.format(r2)+'\n'+r'$MAE = {:.1f}$ nT'.format(mae), xy=(score_xpos, score_ypos), xycoords='axes fraction', size=fs_title-2) # GENERAL FORMATTING # ------------------ for ax in axes: ax.set_xlim([plotstart,plotend]) ax.tick_params(axis="x", labelsize=fs) ax.tick_params(axis="y", labelsize=fs) ax.legend(loc=2, ncol=5, fontsize=fs_legend) # Dates on x-axes: myformat = mdates.DateFormatter(date_fmt) ax.xaxis.set_major_formatter(myformat) plt.savefig(outfile) logger.info("Plot saved as {}".format(outfile)) plt.close() return def plot_indices(dism, timestamp=None, look_back=20, outfile=None, **kwargs): """ Plots solar wind variables, past from DSCOVR and future/predicted from STEREO-A. Total B-field and Bz (top), solar wind speed (second), particle density (third) and Dst (fourth) from Kyoto and model prediction. Parameters ========== dism : predstorm.SatData Object containing minute satellite L1 data. timestamp : datetime obj Time for last datapoint in plot. look_back : float (default=20) Number of days in the past to plot. **kwargs : ... See config.plotting for variables that can be tweaked. Returns ======= plt.savefig : .png file File saved to XXX """ if timestamp == None: timestamp = datetime.utcnow() if outfile == None: outfile = 'indices_{}.png'.format(datetime.strftime(timestamp, "%Y-%m-%dT%H:%M")) figsize = kwargs.get('figsize', pltcfg.figsize) lw = kwargs.get('lw', pltcfg.lw) fs = kwargs.get('fs', pltcfg.fs) date_fmt = kwargs.get('date_fmt', pltcfg.date_fmt) c_dst = kwargs.get('c_dst', pltcfg.c_dst) c_dis = kwargs.get('c_dis', pltcfg.c_dis) c_dis_dst = kwargs.get('c_dis', pltcfg.c_dis_dst) c_ec = kwargs.get('c_dis', pltcfg.c_ec) c_kp = kwargs.get('c_dis', pltcfg.c_kp) c_aurora = kwargs.get('c_dis', pltcfg.c_aurora) ms_dst = kwargs.get('c_dst', pltcfg.ms_dst) fs_legend = kwargs.get('fs_legend', pltcfg.fs_legend) fs_ylabel = kwargs.get('fs_legend', pltcfg.fs_ylabel) fs_title = kwargs.get('fs_title', pltcfg.fs_title) # READ DATA: # ---------- # TODO: It would be faster to read archived hourly data rather than interped minute data... logger.info("plot_indices: Preparing satellite data") # Get estimate of time diff: # Read DSCOVR data: dis = dism.make_hourly_data() dis.interp_nans() dst = ps.get_past_dst(filepath="data/dstarchive/WWW_dstae00016185.dat", starttime=timestamp-timedelta(days=look_back), endtime=timestamp) # Calculate Dst from prediction: dst_dis = dis.make_dst_prediction() # Kp: kp_dis = dis.make_kp_prediction() # Newell coupling ec: ec_dis = dis.get_newell_coupling() # Aurora power: aurora_dis = dis.make_aurora_power_prediction() # PLOT: # ----- # Set style: sns.set_context(pltcfg.sns_context) sns.set_style(pltcfg.sns_style) # Make figure object: fig = plt.figure(1, figsize=figsize) axes = [] if timestamp == None: timestamp = datetime.utcnow() timeutc = mdates.date2num(timestamp) plotstart = timestamp - timedelta(days=look_back) plotend = timestamp # SUBPLOT 1: Total B-field and Bz # ------------------------------- ax1 = fig.add_subplot(511) axes.append(ax1) # Total B-field and Bz (DSCOVR) plt.plot_date(dism['time'], dism['btot'],'-', c=c_dis, label='B total L1', linewidth=lw) plt.plot_date(dism['time'], dism['bz'],'-', c=c_dis, alpha=0.5, label='Bz GSM L1', linewidth=lw) # Indicate 0 level for Bz plt.plot_date([plotstart,plotend], [0,0],'--k', alpha=0.5, linewidth=1) plt.ylabel('Magnetic field [nT]', fontsize=fs_ylabel) plt.ylim(np.nanmin(dism['bz'])-5, np.nanmax(dism['btot'])+5) plt.title('DSCOVR data and derived indices for {}'.format(datetime.strftime(timestamp, "%Y-%m-%d %H:%M")), fontsize=fs_title) # SUBPLOT 2: Actual and predicted Dst # ----------------------------------- ax3 = fig.add_subplot(512) axes.append(ax3) # Observed Dst Kyoto (past): plt.plot_date(dst['time'], dst['dst'],'o', c=c_dst, label='Observed Dst', markersize=ms_dst) plt.ylabel('Dst [nT]', fontsize=fs_ylabel) dstplotmax = np.nanmax(np.concatenate((dst['dst'], dst_dis['dst'])))+20 dstplotmin = np.nanmin(np.concatenate((dst['dst'], dst_dis['dst'])))-20 plt.ylim([dstplotmin, dstplotmax]) plt.plot_date(dst_dis['time'], dst_dis['dst'],'-', c=c_dis_dst, label='Predicted Dst (DSCOVR)', linewidth=lw) error=15 plt.fill_between(dst_dis['time'], dst_dis['dst']-error, dst_dis['dst']+error, facecolor=c_dis_dst, alpha=0.2, label='Error') # Label plot with geomagnetic storm levels pltcfg.plot_dst_activity_lines(xlims=[plotstart, plotend]) # SUBPLOT 3: kp # ----------------------------- ax5 = fig.add_subplot(513) axes.append(ax5) # Plot Newell coupling (DSCOVR): plt.plot_date(kp_dis['time'], kp_dis['kp'],'-', c=c_kp, linewidth=lw) plt.ylabel('$\mathregular{k_p}$', fontsize=fs_ylabel) plt.ylim([0., 10.]) # SUBPLOT 4: Newell Coupling # -------------------------- ax2 = fig.add_subplot(514) axes.append(ax2) # Plot Newell coupling (DSCOVR): plt.plot_date(ec_dis['time'], ec_dis['ec'],'-', c=c_ec, linewidth=lw) plt.ylabel('Newell coupling $ec$', fontsize=fs_ylabel) plt.ylim([0., np.nanmax(ec_dis['ec'])*1.1]) # SUBPLOT 5: Aurora power # ----------------------- ax4 = fig.add_subplot(515) axes.append(ax4) # Plot Newell coupling (DSCOVR): plt.plot_date(aurora_dis['time'], aurora_dis['aurora'],'-', c=c_aurora, linewidth=lw) plt.ylabel('Aurora power [?]', fontsize=fs_ylabel) plt.ylim([0., np.nanmax(aurora_dis['aurora'])*1.1]) # GENERAL FORMATTING # ------------------ for ax in axes: ax.set_xlim([plotstart,plotend]) ax.tick_params(axis="x", labelsize=fs) ax.tick_params(axis="y", labelsize=fs) ax.legend(loc=2,ncol=4,fontsize=fs_legend) # Dates on x-axes: myformat = mdates.DateFormatter(date_fmt) ax.xaxis.set_major_formatter(myformat) plt.savefig(outfile) logger.info('Plot saved as png:\n'+ outfile) plt.close() return # ======================================================================================= # --------------------------- EXTRA FUNCTIONS ------------------------------------------- # ======================================================================================= def gradient_fill(x, y, ax=None, maxval=None, **kwargs): """ Plot a line with a linear alpha gradient filled beneath it. Adapted from https://stackoverflow.com/a/29331211. Parameters ---------- x, y : array-like The data values of the line. ax : a matplotlib Axes instance The axes to plot on. If None, the current pyplot axes will be used. maxval : float Maximum value (x/-) in plots for gradient scaling. Additional arguments are passed on to matplotlib's ``plot`` function. Returns ------- line : a Line2D instance The line plotted. im : an AxesImage instance The transparent gradient clipped to just the area beneath the curve. """ if ax is None: ax = plt.gca() line, = ax.plot_date(x, y, **kwargs) zorder = line.get_zorder() alpha = line.get_alpha() alpha = 1.0 if alpha is None else alpha maxval if maxval is None else maxval z_up, z_down = np.empty((100, 1, 4), dtype=float), np.empty((100, 1, 4), dtype=float) rgb_b = mcolors.colorConverter.to_rgb('b') rgb_r = mcolors.colorConverter.to_rgb('r') z_down[:,:,:3] = rgb_r z_down[:,:,-1] = np.linspace(0, alpha, 100)[:,None] z_up[:,:,:3] = rgb_b z_up[:,:,-1] = np.linspace(0, alpha, 100)[:,None] # Fill above zero: xmin, xmax, ymin, ymax = x.min(), x.max(), 0., maxval im = ax.imshow(z_up, aspect='auto', extent=[xmin, xmax, ymin, ymax], origin='lower', zorder=zorder) xy = np.column_stack([x, y]) xy = np.vstack([[xmin, ymin], xy, [xmax, ymin], [xmin, ymin]]) clip_path = Polygon(xy, facecolor='none', edgecolor='none', closed=True) ax.add_patch(clip_path) im.set_clip_path(clip_path) # Fill below zero: xmin, xmax, ymin, ymax = x.min(), x.max(), -maxval, 0. im = ax.imshow(z_down, aspect='auto', extent=[xmin, xmax, ymin, ymax], origin='upper', zorder=zorder) xy = np.column_stack([x, y]) #xy = np.vstack([[xmin, ymin], xy, [xmax, ymin], [xmin, ymin]]) xy = np.vstack([[xmin, 0.], xy, [xmax, 0.], [xmin, 0.]]) clip_path = Polygon(xy, facecolor='none', edgecolor='none', closed=True) ax.add_patch(clip_path) im.set_clip_path(clip_path) ax.autoscale(True) return line, im def plot_all(timestamp=None, plotdir="plots", download=True): """Makes plots of a time range ending with timestamp using all functions.""" if not os.path.isdir(plotdir): os.mkdir(plotdir) if timestamp == None: timestamp = datetime.utcnow() from datetime import datetime, timedelta import predstorm as ps import os import heliosat logger = ps.init_logging(verbose=True) plotdir="plots" timestamp = datetime(2019,8,8) - timedelta(days=26*2) # datetime.utcnow() - timedelta(days=180) # datetime(2019,6,23) look_back = 26 lag_L1, lag_r = ps.get_time_lag_wrt_earth(timestamp=timestamp, satname='STEREO-A') est_timelag = lag_L1 + lag_r logger.info("Plotting all plots...") # STEREO DATA stam = ps.get_stereo_beacon_data(starttime=timestamp-timedelta(days=look_back+est_timelag+0.5), endtime=timestamp) stam = stam.interp_nans(keys=['time']) stam.load_positions() # DSCOVR DATA if timestamp < datetime(2019,6,23): dism = ps.get_dscovr_data(starttime=timestamp-timedelta(days=look_back), endtime=timestamp) else: dism = ps.get_omni_data(starttime=timestamp-timedelta(days=look_back), endtime=timestamp) dism.h['HeliosatObject'] = heliosat.DSCOVR() dism.load_positions(l1_corr=True) # KYOTO DST dst = ps.get_omni_data(starttime=timestamp-timedelta(days=look_back), endtime=timestamp, download=False) # dst = ps.get_past_dst(filepath="data/dstarchive/WWW_dstae00019594.dat", # starttime=timestamp-timedelta(days=look_back), # endtime=timestamp) # PERSISTENCE MODEL t_syn = 26.27 if timestamp < datetime(2019,6,10): dpmm = ps.get_dscovr_data(starttime=timestamp-timedelta(days=t_syn)-timedelta(days=look_back), endtime=timestamp-timedelta(days=t_syn)) else: dpmm = ps.get_omni_data(starttime=timestamp-timedelta(days=t_syn)-timedelta(days=look_back), endtime=timestamp-timedelta(days=t_syn)) dpmm.h['HeliosatObject'] = heliosat.DSCOVR() dpmm['time'] = dpmm['time'] + t_syn outfile = os.path.join(plotdir, "all_dst_{}day_plot.png".format(look_back)) ps.plot.plot_dst_vs_persistence_model(stam, dism, dpmm, dst, look_back=look_back, timestamp=timestamp, outfile=outfile) logger.info("\n-------------------------\nDst comparison\n-------------------------") outfile = os.path.join(plotdir, "dst_prediction_{}day_plot.png".format(look_back)) plot_dst_comparison(stam, dism, dst, timestamp=timestamp, look_back=look_back, outfile=outfile) logger.info("\n-------------------------\nSTEREO-A vs DSCOVR\n-------------------------") outfile = os.path.join(plotdir, "stereoa_vs_dscovr_{}day_plot.png".format(look_back)) plot_stereo_dscovr_comparison(stam, dism, dst, timestamp=timestamp, look_back=look_back, outfile=outfile) logger.info("\n-------------------------\nPredicted indices\n-------------------------") outfile = os.path.join(plotdir, "indices_{}day_plot.png".format(look_back)) plot_indices(dism, timestamp=timestamp, look_back=look_back, outfile=outfile) if __name__ == '__main__': plot_all()

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290

0.627252

7,949

56,003

4.262423

0.081897

0.020985

0.018181

0.007674

0.775604

0.74181

0.715749

0.688005

0.656661

0.617112

0

0.021637

0.193704

56,003

1,393

291

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0.728712

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0.521978

0

0.002747

0.116079

0.015463

0

0.002872

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1

0.012363

false

0

0.032967

0

0.052198

0.004121

0

null

0

1

0

1

0

null

0

3

4a29d5c98270ecac8a5ef68b2b368bd1974fb05d

148

py

Python

nhl/raw/stats/common.py

devinsba/py-nhl

d6f560d9a43cd2b7183ba465e03ee7871365814c

[ "Apache-2.0" ]

null

nhl/raw/stats/common.py

devinsba/py-nhl

d6f560d9a43cd2b7183ba465e03ee7871365814c

[ "Apache-2.0" ]

null

nhl/raw/stats/common.py

devinsba/py-nhl

d6f560d9a43cd2b7183ba465e03ee7871365814c

[ "Apache-2.0" ]

null

from dataclasses import dataclass SERVER_ADDRESS = "https://statsapi.web.nhl.com" @dataclass(frozen=True) class BaseResponse: copyright: str

16.444444

47

0.77027

18

148

6.277778

0.944444

0

0.128378

148

8

48

18.5

0.875969

0

0.189189

0

1

0

false

0

0.2

0

0.6

0

1

0

null

0

1

0

null

0

1

0

3

4a454289eef15e58ae478c5b593d3115e6fbb5d8

103

py

Python

checkov/serverless/checks/layer/registry.py

antonblr/checkov

9415c6593c537945c08f7a19f28bdd8b96966f67

[ "Apache-2.0" ]

4,013

2019-12-09T13:16:54.000Z

2022-03-31T14:31:01.000Z

checkov/serverless/checks/layer/registry.py

antonblr/checkov

9415c6593c537945c08f7a19f28bdd8b96966f67

[ "Apache-2.0" ]

1,258

2019-12-17T09:55:51.000Z

2022-03-31T19:17:17.000Z

checkov/serverless/checks/layer/registry.py

antonblr/checkov

9415c6593c537945c08f7a19f28bdd8b96966f67

[ "Apache-2.0" ]

638

2019-12-19T08:57:38.000Z

2022-03-30T21:38:37.000Z

from checkov.serverless.base_registry import ServerlessRegistry layer_registry = ServerlessRegistry()

25.75

63

0.873786

10

103

8.8

0.8

0

0.07767

103

3

64

34.333333

0.926316

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false

0

0.5

0

0.5

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1

0

null

0

1

0

null

0

1

0

3

4a502776f85861f4e8401bf0df6935a49fb1141d

64

py

Python

script_eater/__init__.py

FoxtrotCore/script-eater

7e64b5e18be1c8a3f947d4e3f7170aaf338ac561

[ "MIT" ]

null

script_eater/__init__.py

FoxtrotCore/script-eater

7e64b5e18be1c8a3f947d4e3f7170aaf338ac561

[ "MIT" ]

null

script_eater/__init__.py

FoxtrotCore/script-eater

7e64b5e18be1c8a3f947d4e3f7170aaf338ac561

[ "MIT" ]

null

branch = "master" version = "2.0.0" from .script_eater import *

16

27

0.6875

10

64

4.3

0.9

0

0.055556

0.15625

64

3

28

21.333333

0.740741

0

0.171875

0

1

0

false

0

0.333333

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0.333333

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null

0

1

0

null

0

1

0

3

4a5c929803c7f23da0d1d656a1f299fc3eac2710

3,084

py

Python

epsonprojector/devices/generic.py

eieste/epsonProjector

bbe64cfcd07c821ffd43b771b9e7f337e4dfbf43

[ "MIT" ]

null

epsonprojector/devices/generic.py

eieste/epsonProjector

bbe64cfcd07c821ffd43b771b9e7f337e4dfbf43

[ "MIT" ]

null

epsonprojector/devices/generic.py

eieste/epsonProjector

bbe64cfcd07c821ffd43b771b9e7f337e4dfbf43

[ "MIT" ]

null

from epsonprojector.devices.configurations.load import LoadConfiguration # from epsonprojector.interfaces.generic import GenericInterface from collections import namedtuple ParsedResponse = namedtuple("ParsedResponse", ("command", "parameter", "status")) class GenericDevice: config_file = "" _conf = None def __init__(self, conn): # ToDo check if conn inherith from Generic Interface # if not isinstance(conn, epsonprojector.interfaces.GenericInterface): # raise AttributeError("Invalid Interface") self._conn = conn # Load Config if not done yet if not self._conf: self.initialize_config() def __getattr__(self, item): """ Intercept all method calls to implement them on projector commands :param item: Name of called method :return method: Return a wrapper method """ # Try to find command in config cmd = self._conf.find_command(item) # Wrapper Command def set_command(*args, **kwargs): # Try to find Parameters from wrapper called args in Command parameter = self._conf.find_parameter(cmd["request_parameters"], args[0]) # Create a projector command based on previously collected information command = self.build_command(cmd, parameter) if command is False: raise ValueError("Cant build command") # Transmit Projector Command answer = self.send(command) return answer return set_command def build_command(self, command, parameter): """ Creates a command from Command and Parameter object :param command: dict of command information from config :param parameter: dict of parameter informations from config :return any: Command to send via Interface """ raise NotImplementedError("Please implement this method") def parse_command(self, command, parameter, *args, **kwargs): """ Try to parse responses from Projector :param answer: Data that returned from projector :return ParsedResponse: Tuple with the parsed Information """ raise NotImplementedError("Please implement this method") def get_config_file(self): """ Get path to configfile :return path: String path to configfile """ return self.config_file def initialize_config(self): """ Initialize Config Loading """ self._conf = LoadConfiguration(self) def connect(self): # ToDo I dont know pass def send(self, command): """ Send command via Initialized Interface :param command: Projector Command :return: Parsed Projector answer """ answer = self._conn.send_command(command) response = self.parse_command(answer) return response def read(self): # ToDo I dont know pass

31.469388

85

0.623217

322

3,084

5.872671

0.350932

0.033845

0.021153

0.015865

0.077208

0.054997

0

0.000469

0.30869

3,084

97

86

31.793814

0.886492

0.385863

0

0.111111

0

0.08

0

0.030928

0

1

0.277778

false

0.055556

0

0.527778

0

null

0

null

0

1

0

1

0

1

0

3

4a78fb78b56c0c58bf9d4751bf9eaece05b156c5

230

py

Python

tests/utils/compat.py

mvas/apm-agent-python

f4582e90eb5308b915ca51e2e98620fc22af09ec

[ "BSD-3-Clause" ]

null

tests/utils/compat.py

mvas/apm-agent-python

f4582e90eb5308b915ca51e2e98620fc22af09ec

[ "BSD-3-Clause" ]

null

tests/utils/compat.py

mvas/apm-agent-python

f4582e90eb5308b915ca51e2e98620fc22af09ec

[ "BSD-3-Clause" ]

null

def middleware_setting(django_version, middleware_list): if django_version < (1, 10): return {'MIDDLEWARE_CLASSES': middleware_list} else: return {'MIDDLEWARE': middleware_list, 'MIDDLEWARE_CLASSES': None}

38.333333

74

0.717391

25

230

6.28

0.52

0.267516

0

0.015873

0.178261

230

5

75

46

0.814815

0

0.2

0

1

0.2

false

0

0.6

0

1

0

null

1

0

1

0

null

0

3

4a9ab3c6d2b817a099af795c11a7ed7309b68d87

1,557

py

Python

python/linked_list/0023_merge_k_sorted_lists.py

linshaoyong/leetcode

ea052fad68a2fe0cbfa5469398508ec2b776654f

[ "MIT" ]

6

2019-07-15T13:23:57.000Z

2020-01-22T03:12:01.000Z

python/linked_list/0023_merge_k_sorted_lists.py

linshaoyong/leetcode

ea052fad68a2fe0cbfa5469398508ec2b776654f

[ "MIT" ]

null

python/linked_list/0023_merge_k_sorted_lists.py

linshaoyong/leetcode

ea052fad68a2fe0cbfa5469398508ec2b776654f

[ "MIT" ]

1

2019-07-24T02:15:31.000Z

import heapq class ListNode(object): def __init__(self, val=0, next=None): self.val = val self.next = next class CmpNode: def __init__(self, node): self.node = node self.val = node.val def __gt__(self, another): return self.val > another.val class Solution(object): def mergeKLists(self, lists): """ :type lists: List[ListNode] :rtype: ListNode """ h = [] for node in lists: if node: heapq.heappush(h, CmpNode(node)) if not h: return None head, prev = None, None while h: cmpn = heapq.heappop(h) if not head: head = cmpn.node prev = head else: prev.next = cmpn.node prev = cmpn.node if cmpn.node.next: heapq.heappush(h, CmpNode(cmpn.node.next)) return head def test_merge_k_lists_1(): s = Solution() a = ListNode(1, ListNode(4, ListNode(5))) b = ListNode(1, ListNode(3, ListNode(4))) c = ListNode(2, ListNode(6)) r = s.mergeKLists([a, b, c]) assert 1 == r.val assert 1 == r.next.val assert 2 == r.next.next.val assert 3 == r.next.next.next.val assert 4 == r.next.next.next.next.val assert 4 == r.next.next.next.next.next.val assert 5 == r.next.next.next.next.next.next.val assert 6 == r.next.next.next.next.next.next.next.val assert r.next.next.next.next.next.next.next.next is None

23.953846

60

0.540784

214

1,557

3.859813

0.238318

0.280872

0.305085

0.290557

0.22276

0.200969

0.079903

0

0.017459

0.337829

1,557

64

61

24.328125

0.783705

0.028259

0

0.195652

1

0.108696

false

0

0.021739

0.26087

0

null

1

0

null

0

3

4ac29a44b03de1427f4291796030a4baa95e24ef

751

py

Python

enki/modeltokenverify.py

charlf/enkiWS

789c789b2c0e46dcf568697dc2e46b82512981da

[ "Zlib" ]

1

2021-04-22T15:29:20.000Z

enki/modeltokenverify.py

charlf/enkiWS

789c789b2c0e46dcf568697dc2e46b82512981da

[ "Zlib" ]

null

enki/modeltokenverify.py

charlf/enkiWS

789c789b2c0e46dcf568697dc2e46b82512981da

[ "Zlib" ]

null

from google.appengine.ext.ndb import model class EnkiModelTokenVerify( model.Model ): token = model.StringProperty() email = model.StringProperty() user_id = model.IntegerProperty() # ndb user ID time_created = model.DateTimeProperty( auto_now_add = True ) type = model.StringProperty( choices = [ 'register', 'passwordchange', 'emailchange', 'accountdelete', 'accountandpostsdelete', 'preventmultipost', ] ) auth_ids_provider = model.StringProperty() # store auth Id info for registration

41.722222

81

0.509987

53

751

7.113208

0.698113

0.201592

0

0.416778

751

17

82

44.176471

0.860731

0.062583

0

0.118402

0.029957

0

1

0

false

0.071429

0

0.571429

0

null

1

0

null

0

1

0

1

0

3

434d3cbb69dc7e957cb0e444a2fbccb16a1cf2b4

277

py

Python

MorseMain.py

JEFMX/Codigo-Morse

a515d0c445eb296e3ef82cd7ccc6fc20f0501899

[ "CC0-1.0" ]

1

2021-03-18T18:15:20.000Z

MorseMain.py

JEFMX/Codigo-Morse

a515d0c445eb296e3ef82cd7ccc6fc20f0501899

[ "CC0-1.0" ]

null

MorseMain.py

JEFMX/Codigo-Morse

a515d0c445eb296e3ef82cd7ccc6fc20f0501899

[ "CC0-1.0" ]

null

#enconding: utf-8 from ObenedorDeEntrada import ObtenedorDeEntrada from ProcesadorDeEntrada import ProcesadorDeEntrada if __name__== "__main__": entrada = ObtenedorDeEntrada() procesador = ProcesadorDeEntrada() procesador.procesarEntrada(entrada.getEntrada())

39.571429

52

0.794224

22

277

9.636364

0.681818

0

0.004167

0.133574

277

7

53

39.571429

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null

0

1

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null

0

1

0

3

4369567b366c848a10879285cc44e397d683a0d0

305

py

Python

test.py

aqeelahmad/python-units-of-measure

436829f7aeeec75999bece3736f0eb4e39daf0ad

[ "MIT" ]

3

2015-08-10T14:32:42.000Z

2020-01-27T17:23:58.000Z

test.py

aqeelahmad/python-units-of-measure

436829f7aeeec75999bece3736f0eb4e39daf0ad

[ "MIT" ]

null

test.py

aqeelahmad/python-units-of-measure

436829f7aeeec75999bece3736f0eb4e39daf0ad

[ "MIT" ]

2

2019-10-28T13:45:18.000Z

2020-06-26T10:55:26.000Z

import unittest # (The lines below are "imported but unused", but that's ok # unittest main runs all the imported modules. # import all the test objects here, to run them from tests.PhysicalQuantityTest import PhysicalQuantityTest #from tests.UnitOfMeasureTest import UnitOfMeasureTest unittest.main()

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null

0

null

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1

0

1

0

1

0

3

437ec59598314e507f9887e5f4fc2f98434b71bf

640

py

Python

Advanced Topics/Decorators.py

srp98/Python-Stuff

fade8934718e01a3d30cf9db93515b8f02a20b18

[ "MIT" ]

null

Advanced Topics/Decorators.py

srp98/Python-Stuff

fade8934718e01a3d30cf9db93515b8f02a20b18

[ "MIT" ]

null

Advanced Topics/Decorators.py

srp98/Python-Stuff

fade8934718e01a3d30cf9db93515b8f02a20b18

[ "MIT" ]

1

2019-10-31T03:16:04.000Z

class Current: def __init__(self): self._voltage = 100000 @property def voltage(self): """" Get the current voltage""" return self._voltage class Pizza(object): def __init__(self): self.toppings = [] def __call__(self, topping): # when using '@instance_of_pizza' before a function def, the function gets passed onto 'topping' self.toppings.append(topping()) def __repr__(self): return str(self.toppings) c1 = Current() print(c1.voltage) pizza = Pizza() @pizza def cheese(): return 'cheese' @pizza def sauce(): return 'sauce' print(pizza)

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16.410256

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0

0.130435

0.565217

0.086957

0

null

0

null

0

1

0

1

0

3

43a6b4262c5578895cecea6af4a80b47401a9d8e

100

py

Python

transform/gdc/defaults.py

ohsu-comp-bio/gen3-etl

9114f75cc8c8085111152ce0ef686a8a12f67f8e

[ "MIT" ]

1

2020-01-22T17:05:58.000Z

transform/gdc/defaults.py

ohsu-comp-bio/gen3-etl

9114f75cc8c8085111152ce0ef686a8a12f67f8e

[ "MIT" ]

2

2019-02-08T23:24:58.000Z

2021-05-13T22:42:28.000Z

transform/gdc/defaults.py

ohsu-comp-bio/gen3_etl

9114f75cc8c8085111152ce0ef686a8a12f67f8e

[ "MIT" ]

null

DEFAULT_OUTPUT_DIR = 'output/gdc' DEFAULT_EXPERIMENT_CODE = 'gdc' DEFAULT_PROJECT_ID = 'smmart-gdc'

25

33

0.8

14

100

5.285714

0.642857

0.27027

0

0.09

100

3

34

33.333333

0.813187

0

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0

1

0

null

1

0

1

0

null

0

3

43de10fe3abc51ebbaf471797873704b20f65773

5,729

py

Python

cowsay/lib/cows/mona_lisa.py

Ovlic/cowsay_py

1ee8d11d6d895d7695d57e26003d71ce18379d3b

[ "MIT" ]

null

cowsay/lib/cows/mona_lisa.py

Ovlic/cowsay_py

1ee8d11d6d895d7695d57e26003d71ce18379d3b

[ "MIT" ]

null

cowsay/lib/cows/mona_lisa.py

Ovlic/cowsay_py

1ee8d11d6d895d7695d57e26003d71ce18379d3b

[ "MIT" ]

null

def Mona_lisa(thoughts, eyes, eye, tongue): return f""" {thoughts} {thoughts} !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!>''''''<!!!!!!!!!!!!!!!!!!!!!!!!!!!! !!!!!!!!!!!!!!!!!!!!!!!!!!!!'''''\` \`\`'!!!!!!!!!!!!!!!!!!!!!!!! !!!!!!!!!!!!!!!!!!!!!!!!''\` ..... \`'!!!!!!!!!!!!!!!!!!!!! !!!!!!!!!!!!!!!!!!!!!'\` . :::::' \`'!!!!!!!!!!!!!!!!!! !!!!!!!!!!!!!!!!!!!' . ' .::::' \`!!!!!!!!!!!!!!!! !!!!!!!!!!!!!!!!!' : \`\`\`\`\` \`!!!!!!!!!!!!!! !!!!!!!!!!!!!!!! .,cchcccccc,,. \`!!!!!!!!!!!! !!!!!!!!!!!!!!! .-"?\$\$\$\$\$\$\$\$\$\$\$\$\$\$c, \`!!!!!!!!!!! !!!!!!!!!!!!!! ,ccc\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$, \`!!!!!!!!!! !!!!!!!!!!!!! z\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$;. \`!!!!!!!!! !!!!!!!!!!!! <\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$:. \`!!!!!!!! !!!!!!!!!!! \$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$h;:. !!!!!!!! !!!!!!!!!!' \$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$h;. !!!!!!! !!!!!!!!!' <\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$ !!!!!!! !!!!!!!!' \`\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$F \`!!!!!! !!!!!!!! c\$\$\$\$???\$\$\$\$\$\$\$P"" \"\"\"??????" !!!!!! !!!!!!! \`"" .,.. "\$\$\$\$F .,zcr !!!!!! !!!!!!! . dL .?\$\$\$ .,cc, .,z\$h. !!!!!! !!!!!!!! <. \$\$c= <\$d\$\$\$ <\$\$\$\$=-=+"\$\$\$\$\$\$\$ !!!!!! !!!!!!! d\$\$\$hcccd\$\$\$\$\$ d\$\$\$hcccd\$\$\$\$\$\$\$F \`!!!!! !!!!!! ,\$\$\$\$\$\$\$\$\$\$\$\$\$\$h d\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$ \`!!!!! !!!!! \`\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$<\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$' !!!!! !!!!! \`\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$"\$\$\$\$\$\$\$\$\$\$\$\$\$P> !!!!! !!!!! ?\$\$\$\$\$\$\$\$\$\$\$\$??\$c\`\$\$\$\$\$\$\$\$\$\$\$?>' \`!!!! !!!!! \`?\$\$\$\$\$\$I7?"" ,\$\$\$\$\$\$\$\$\$?>>' !!!! !!!!!. <<?\$\$\$\$\$\$c. ,d\$\$?\$\$\$\$\$F>>'' \`!!! !!!!!! <i?\$P"??\$\$r--"?"" ,\$\$\$\$h;>'' \`!!! !!!!!! \$\$\$hccccccccc= cc\$\$\$\$\$\$\$>>' !!! !!!!! \`?\$\$\$\$\$\$F"\"\"\" \`"\$\$\$\$\$>>>'' \`!! !!!!! "?\$\$\$\$\$cccccc\$\$\$\$??>>>>' !! !!!!> "\$\$\$\$\$\$\$\$\$\$\$\$\$F>>>>'' \`! !!!!! "\$\$\$\$\$\$\$\$???>''' ! !!!!!> \`"\"\"\"\" \` !!!!!!; . \` !!!!!!! ?h. !!!!!!!! \$\$c, !!!!!!!!> ?\$\$\$h. .,c !!!!!!!!! \$\$\$\$\$\$\$\$\$hc,.,,cc\$\$\$\$\$ !!!!!!!!! .,zcc\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$ !!!!!!!!! .z\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$ !!!!!!!!! ,d\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$ . !!!!!!!!! ,d\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$ !! !!!!!!!!! ,d\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$ ,!' !!!!!!!!> c\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$. !' !!!!!!'' ,d\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$> ' !!!'' z\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$> !' ,\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$> .. z\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$' ;!!!!''\` \$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$F ,;;!'\`' .'' <\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$> ,;'\`' ,; \`\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$F -' ,;!!' "?\$\$\$\$\$\$\$\$\$\$?\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$F .<!!!''' <! !> ""??\$\$\$?C3\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$"" ;!''' !!! ;!!!!;, \`"''""????\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$\$"" ,;-'' ',! ;!!!!<!!!; . \`"\"\"\"\"\"\"\"\"\"\" \`' ' ' !!!! ;!!! ;!!!!>;,;, .. ' . ' ' !!' ,;!!! ;'\`!!!!!!!!;!!!!!; . >' .'' ; !!' ;!!'!';! !! !!!!!!!!!!!!! ' -' <!! !! \`!;! \`!' !!!!!!!!!!<! . \`! ;! ;!!! <' <!!!! \`!!! < / \""" !> <!! ;' !!!!' !!';! ;' ! ! !!! ! \`!!! ;!! ! ' ' ; \`! \`!! ,' !' ;!' ' /\`! ! < !! < ' / ;! >;! ;> !' ; !! ' ' ;! > ! ' """

75.381579

116

0.027404

69

5,729

2.26087

0.405797

0.038462

0

0.000531

0.341944

5,729

76

117

75.381579

0.040849

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0.054054

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0.440489

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1

0.013514

false

0

0.013514

0.027027

0

null

0

1

0

1

null

0

3

43df9ad06dbcde4685ab6b8dc808f7fe64079223

353

py

Python

ticktick/managers/settings.py

prnake/ticktick-py

33f2131deca65dc322f0c6a8447c50122fa9006b

[ "MIT" ]

null

ticktick/managers/settings.py

prnake/ticktick-py

33f2131deca65dc322f0c6a8447c50122fa9006b

[ "MIT" ]

null

ticktick/managers/settings.py

prnake/ticktick-py

33f2131deca65dc322f0c6a8447c50122fa9006b

[ "MIT" ]

null

class SettingsManager: def __init__(self, client_class): self._client = client_class self.access_token = '' def get_templates(self): # https://api.dida365.com/api/v2/templates pass def get_user_settings(self): # https://api.dida365.com/api/v2/user/preferences/settings?includeWeb=true pass

25.214286

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0.651558

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353

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0.136986

0.173516

0.246575

0

0.02974

0.23796

353

13

83

27.153846

0.784387

0.320113

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0.25

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0.375

false

0.25

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0.5

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null

0

1

0

null

0

1

0

1

0

3

602de935be06a22f5ac8826f1b5ff109a77878fe

783

py

Python

monitoringProxy/utils.py

juliozinga/FIWARELab-monitoringAPI

5a411de0a59f4408b4ed1a7e58b550b227e4975c

[ "Apache-2.0" ]

null

monitoringProxy/utils.py

juliozinga/FIWARELab-monitoringAPI

5a411de0a59f4408b4ed1a7e58b550b227e4975c

[ "Apache-2.0" ]

null

monitoringProxy/utils.py

juliozinga/FIWARELab-monitoringAPI

5a411de0a59f4408b4ed1a7e58b550b227e4975c

[ "Apache-2.0" ]

null

import time from datetime import timedelta, tzinfo def get_timestamp(origin_datetime=None): ''' Return UNIX timestamp from datetime object :param origin_datetime: Origin date to convert to timestamp :type origin_datetime: datetime :return: The corresponding UNIX timestamp for date passed as parameter or current timestamp if no parameter :rtype: int ''' if not origin_datetime: return int(time.time()) else: timestamp = origin_datetime.strftime("%s") return int(timestamp) class UTC(tzinfo): """ Class Representing a UTC tzinfo """ ZERO = timedelta(0) def utcoffset(self, dt): return self.ZERO def tzname(self, dt): return "UTC" def dst(self, dt): return self.ZERO

24.46875

111

0.662835

98

783

5.234694

0.469388

0.136452

0.070175

0.062378

0.077973

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0.001718

0.256705

783

32

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24.46875

0.879725

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0.125

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0

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0.25

false

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0.125

0.1875

0.8125

0

null

0

null

0

1

0

1

0

3

60449dcb42f663b022c8428c5c4e23abac0cc522

340

py

Python

sourcemon/model/servermodel.py

michaelimfeld/sourcemon

45be21fcab6377f25bee63430600283b381b2265

[ "MIT" ]

1

2015-12-22T19:16:36.000Z

sourcemon/model/servermodel.py

michaelimfeld/sourcemon

45be21fcab6377f25bee63430600283b381b2265

[ "MIT" ]

null

sourcemon/model/servermodel.py

michaelimfeld/sourcemon

45be21fcab6377f25bee63430600283b381b2265

[ "MIT" ]

null

""" Database model for sourcemod server """ from peewee import IntegerField, ForeignKeyField from sourcemon.model.basemodel import BaseModel from sourcemon.model.ipmodel import IPModel class ServerModel(BaseModel): """ Database model for sourcemod server """ ip = ForeignKeyField(IPModel) port = IntegerField()

24.285714

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0.735294

35

340

7.142857

0.485714

0.104

0.128

0.2

0.248

0

0.188235

340

13

49

26.153846

0.905797

0.208824

0

1

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false

0

0.5

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1

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null

0

1

0

null

0

1

0

1

0

3

605cca585c607197433e07668b41e274ee1aae96

290

py

Python

config/base.py

macic/pyboi

dc7dd77165612c149785411ea8b61e4f632d2df7

[ "MIT" ]

null

config/base.py

macic/pyboi

dc7dd77165612c149785411ea8b61e4f632d2df7

[ "MIT" ]

null

config/base.py

macic/pyboi

dc7dd77165612c149785411ea8b61e4f632d2df7

[ "MIT" ]

null

import os api_key = os.getenv('binance_key') api_secret = os.getenv('binance_secret') db_host = os.getenv('pqsl_db_host') db_user = os.getenv('pqsl_db_user') db_pw = os.getenv('pqsl_db_pw') db_name = os.getenv('pqsl_db_name') db_url=f'postgresql://{db_user}:{db_pw}@{db_host}/{db_name}'

24.166667

60

0.734483

54

290

3.555556

0.296296

0.25

0.291667

0

0.082759

290

11

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26.363636

0.721805

0

0.418685

0.17301

0

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false

0

0.125

0

0.125

0

null

1

0

null

0

3

6064e5c861d53fd1967e60c602a1ae7111089af4

179

py

Python

__main__.py

mynameismon/kindle_clippings_manager

3ad6f83ec3f8b4e37f3bc96c765eea4bb55f4465

[ "MIT" ]

null

__main__.py

mynameismon/kindle_clippings_manager

3ad6f83ec3f8b4e37f3bc96c765eea4bb55f4465

[ "MIT" ]

null

__main__.py

mynameismon/kindle_clippings_manager

3ad6f83ec3f8b4e37f3bc96c765eea4bb55f4465

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """This module provides RP Contacts entry point script.""" from src.__init__ import main if __name__ == "__main__": main()

17.9

58

0.653631

24

179

4.375

0.916667

0

0.013699

0.184358

179

10

59

17.9

0.705479

0.536313

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0

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true

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null

0

null

0

1

0

1

0

3

606b25903613d3e458532fd71d4136ee8837016e

2,311

py

Python

Creat AGI-PRIME/creat_single_rule_match.py

agi-hub/AGI-PRIME-dataset

2d96c49f85382a254057fd7c5d33b7d114f6cfa3

[ "MIT" ]

null

Creat AGI-PRIME/creat_single_rule_match.py

agi-hub/AGI-PRIME-dataset

2d96c49f85382a254057fd7c5d33b7d114f6cfa3

[ "MIT" ]

null

Creat AGI-PRIME/creat_single_rule_match.py

agi-hub/AGI-PRIME-dataset

2d96c49f85382a254057fd7c5d33b7d114f6cfa3

[ "MIT" ]

null

import os import json import random import util import shutil random.seed(2021) def main(): IST = 'Single-rule Learning' json_data_list = {} path='./data/' if not os.path.exists(path): os.mkdir(path) path=path + 'single_rule_match_train/' if not os.path.exists(path): os.mkdir(path) else: shutil.rmtree(path) os.mkdir(path) rules = ['number-progression','type-progression','size-progression','color-progression', 'type-xor','size-xor','color-xor','position-xor', 'type-or','size-or','color-or','position-or', 'type-and','size-and','color-and','position-and'] rules=set(rules) rules=sorted(rules) util.text_save(os.path.join(path,'rule.txt'), rules) n=8000//12+1 for i in range(n): json_data_list = util.q0(path, json_data_list,back=False) json_data_list = util.q1(path, json_data_list,back=False) json_data_list = util.q2(path, json_data_list,back=False) json_data_list = util.q5(path, json_data_list,back=False) json_data_list = util.q6(path, json_data_list,back=False) json_data_list = util.q7(path, json_data_list,back=False) json_data_list = util.q8(path, json_data_list,back=False) json_data_list = util.q10(path, json_data_list,back=False) json_data_list = util.q11(path, json_data_list,back=False) json_data_list = util.q12(path, json_data_list,back=False) json_data_list = util.q13(path, json_data_list,back=False) json_data_list = util.q15(path, json_data_list,back=False) # json_data_list = util.q3(path, json_data_list, back=False) # json_data_list = util.q4(path, json_data_list, back=False) # json_data_list = util.q10(path, json_data_list, back=False) # json_data_list = util.q15(path, json_data_list, back=False) print(len(json_data_list)) json_data_list = {k:v for (k,v) in json_data_list.items() if int(k)<8000} print(len(json_data_list)) json_data_dict = {IST: json_data_list} json_path = path + 'label.json' with open(json_path, "w") as f: json.dump(json_data_dict, f) print("加载入文件完成...") if __name__ == '__main__': main()

35.015152

93

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0.233429

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0.184838

0.557401

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0.021265

0.226742

2,311

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94

35.015152

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0.061224

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null

1

0

null

0

3

607365caebe15c29bf1fd4872b2528ec9b8e00ab

1,974

py

Python

Aspect based Senti analysis/aspect_categorization/word_grouping/seq_matcher.py

hixio-mh/Aspect-Based-Sentiment-Analysis

3527dd7c36f6b57f41ba02157d47cfdb7b84a286

[ "MIT" ]

1

2017-12-22T15:34:34.000Z

Aspect based Senti analysis/aspect_categorization/word_grouping/seq_matcher.py

pranithkumar/Aspect-Based-Sentiment-Analysis

55355b8c38f1a0d8ed67665cce0901d8b3e002cd

[ "MIT" ]

12

2017-12-29T12:13:07.000Z

2022-03-11T23:19:24.000Z

Aspect based Senti analysis/aspect_categorization/word_grouping/seq_matcher.py

hixio-mh/Aspect-Based-Sentiment-Analysis

3527dd7c36f6b57f41ba02157d47cfdb7b84a286

[ "MIT" ]

1

2017-12-19T09:49:52.000Z

from difflib import SequenceMatcher def similar(a, b): return SequenceMatcher(None, a, b).ratio() choices = ['', 'con i', 'battery untill', 'looks', 'touch', 'speed', 'dont', 'update i', 'situations', 'window', 'winner', 'ram management', 'feel', 'charge', 'usage', 'camera quality', 'damages', 'case', 'advantage', 'views', 'game', 'bilion day', 'fingerprint', 'front', 'bit', 'day', 'color', 'disply', 'similer', 'signal reception', 'quality game', 'quality mobile', 'signal', 'honor', 'emui', 'mode', 'output', 'flipkart', 'mode picture', 'deal', 'people', 'branding', 'noise', 'design', 'honor mobile', 'build quality', 'review', 'power savng', 'aperture mode', 'everything', 'finger', 'sensor', 'satisfy', 'camera clarity', 'power', 'use granuels', 'confusion', 'screen', 'use', 'update', 'efficiency', 'packing', 'sensors', 'bilion', 'front camera', 'heating', 'amount', 'backup', 'processor', 'software', 'load', 'features', 'criterias', 'battery', 'image', 'batry', 'dont use', 'everyone', 'delevery', 'shoots', 'quality', 'story', 'management', 'service', 'usage i', 'look cons', 'battery backup', 'camera', 'criteria', 'aperture', 'legs', 'batrry', 'mobiles', 'function', 'cricket', 'buy', 'delivery', 'phone', 'part', 'sound', 'look', 'camera cons', 'advantage charging', 'n', 'mobile', 'ui', 'today', 'problem', 'piece', 'display', 'compare', 'earphone', 'ram', 'life', 'doesn', 'camera awesome', 'guarantee', 'sound quality', 'make', 'get', 'nd', 'feature', 'note', 'amazing', 'speaker', 'build', 'speed network', 'android', 'charging', 'sim', 'okay', 'beauty', 'though', 'price', 'effect', 'jst', 'thankyou', 'device', 'mah', 'data', 'response', 'purchase', 'calls', 'i', 'light', 'nd sel', 'options', 'phone value', 'con', 'reception', 'time', 'rupees', 'notch'] i=0 for choice in choices[:-1]: for check in choices[i+1:]: if similar(choice,check)>0.55: print "comparing similarity of "+choice+" and "+check print similar(choice,check) i += 1

141

1,679

0.628166

227

1,974

5.462555

0.700441

0.003226

0.029032

0

0.004021

0.118034

1,974

14

1,680

141

0.708214

0

0.549873

0

null

0

0.090909

null

0.272727

0

null

0

1

0

null

0

1

0

3

6079184c859cc811898a8f2628356e1a3ce41f30

178

py

Python

macroPi/__init__.py

stefankablowski/macroPi

c9b64077ac9d3b185c7a16c89c6ffab675b50cfd

[ "MIT" ]

null

macroPi/__init__.py

stefankablowski/macroPi

c9b64077ac9d3b185c7a16c89c6ffab675b50cfd

[ "MIT" ]

null

macroPi/__init__.py

stefankablowski/macroPi

c9b64077ac9d3b185c7a16c89c6ffab675b50cfd

[ "MIT" ]

null

from keys import record, replay from store import store_key_events, load_key_events, __init__ arr = record() print(arr) store_key_events(arr) for elem in arr: print(elem[0])

22.25

61

0.775281

30

178

4.266667

0.533333

0.210938

0.21875

0

0.006536

0.140449

178

8

62

22.25

0.830065

0

1

0

false

0

0.285714

0

0.285714

0

null

1

0

1

0

null

0

3

60807a2a39d15da5a7257d0d73ad73c39ce01414

316

py

Python

libs/applibs/dialogs/__init__.py

flytrue/testapp

74d75e8761634aa8ce69d48f589d1f7a6b4d79ae

[ "MIT" ]

null

libs/applibs/dialogs/__init__.py

flytrue/testapp

74d75e8761634aa8ce69d48f589d1f7a6b4d79ae

[ "MIT" ]

null

libs/applibs/dialogs/__init__.py

flytrue/testapp

74d75e8761634aa8ce69d48f589d1f7a6b4d79ae

[ "MIT" ]

1

2018-09-20T19:32:17.000Z

# -*- coding: utf-8 -*- ''' VKGroups Copyright © 2010-2018 HeaTTheatR Для предложений и вопросов: <kivydevelopment@gmail.com> Данный файл распространяется по условиям той же лицензии, что и фреймворк Kivy. ''' from . selection import Selection from . dialogs import card, dialog, dialog_progress, input_dialog

17.555556

65

0.756329

41

316

5.804878

0.853659

0

0.033582

0.151899

316

17

66

18.588235

0.850746

0.642405

0

1

0

true

0

1

0

1

0

null

0

null

0

1

0

1

0

1

0

3

6083ee05382ce8abd7a9ef4444d07c16fd128bd1

68

py

Python

AtCoder/ABC/000-159/ABC142_A.py

sireline/PyCode

8578467710c3c1faa89499f5d732507f5d9a584c

[ "MIT" ]

null

AtCoder/ABC/000-159/ABC142_A.py

sireline/PyCode

8578467710c3c1faa89499f5d732507f5d9a584c

[ "MIT" ]

null

AtCoder/ABC/000-159/ABC142_A.py

sireline/PyCode

8578467710c3c1faa89499f5d732507f5d9a584c

[ "MIT" ]

null

A = int(input()) print(len([n for n in range(1,A+1) if n %2!=0])/A)

22.666667

50

0.558824

18

68

2.111111

0.722222

0

0.070175

0.161765

68

2

51

34

0.596491

0

1

0

false

0

0.5

1

0

null

0

1

0

null

0

1

0

3

60869b43ef323ba45971a70ee3ff8b554afd1e64

217

py

Python

fitnessFolks/blog/urls.py

Programming-Club-Ahmedabad-University/wellness

4cc06497ce2f2a6019c0fa4595940703605ffb0a

[ "MIT" ]

1

2020-10-07T09:51:01.000Z

fitnessFolks/blog/urls.py

Programming-Club-Ahmedabad-University/wellness

4cc06497ce2f2a6019c0fa4595940703605ffb0a

[ "MIT" ]

1

2020-10-15T07:58:16.000Z

fitnessFolks/blog/urls.py

Programming-Club-Ahmedabad-University/wellness

4cc06497ce2f2a6019c0fa4595940703605ffb0a

[ "MIT" ]

2

2020-10-07T07:48:18.000Z

2021-07-16T04:22:44.000Z

from django.urls import path from .views import post_list_view, post_detail_view urlpatterns = [ path('blog/', post_list_view, name='blog'), path('blog/<slug:slug>/', post_detail_view, name='post_detail'), ]

27.125

68

0.723502

32

217

4.625

0.4375

0.202703

0.162162

0

0.129032

217

8

69

27.125

0.783069

0

0.169725

0

1

0

false

0

0.333333

0

0.333333

0

null

1

0

1

0

null

0

1

0

3

60881ed419c7dc5820b8a06ee34bdf29a02fac4f

265

py

Python

api/goods/migrations/0018_merge_20201127_1102.py

django-doctor/lite-api

1ba278ba22ebcbb977dd7c31dd3701151cd036bf

[ "MIT" ]

null

api/goods/migrations/0018_merge_20201127_1102.py

django-doctor/lite-api

1ba278ba22ebcbb977dd7c31dd3701151cd036bf

[ "MIT" ]

null

api/goods/migrations/0018_merge_20201127_1102.py

django-doctor/lite-api

1ba278ba22ebcbb977dd7c31dd3701151cd036bf

[ "MIT" ]

null

# Generated by Django 2.2.16 on 2020-11-27 11:02 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ("goods", "0016_auto_20201123_0332"), ("goods", "0017_auto_20201124_1613"), ] operations = []

18.928571

48

0.656604

33

265

5.090909

0.787879

0

0.231884

0.218868

265

13

49

20.384615

0.57971

0.173585

0

1

0

0.258065

0.211982

0

1

0

false

0

0.142857

0

0.571429

0

null

0

1

0

1

0

null

0

1

0

3

6094a7d9cf2e49911f792766d99c4d279b2e10c7

6,499

py

Python

mmtbx/scaling/__init__.py

hbrunie/cctbx_project

2d8cb383d50fe20cdbbe4bebae8ed35fabce61e5

[ "BSD-3-Clause-LBNL" ]

2

2021-03-18T12:31:57.000Z

2022-03-14T06:27:06.000Z

mmtbx/scaling/__init__.py

hbrunie/cctbx_project

2d8cb383d50fe20cdbbe4bebae8ed35fabce61e5

[ "BSD-3-Clause-LBNL" ]

null

mmtbx/scaling/__init__.py

hbrunie/cctbx_project

2d8cb383d50fe20cdbbe4bebae8ed35fabce61e5

[ "BSD-3-Clause-LBNL" ]

1

2021-03-26T12:52:30.000Z

""" Base module for Xtriage and related scaling functionality; this imports the Boost.Python extensions into the local namespace, and provides core functions for displaying the results of Xtriage. """ from __future__ import absolute_import, division, print_function import cctbx.array_family.flex # import dependency from libtbx.str_utils import make_sub_header, make_header, make_big_header from libtbx import slots_getstate_setstate from six.moves import cStringIO as StringIO import sys import boost.python from six.moves import range ext = boost.python.import_ext("mmtbx_scaling_ext") from mmtbx_scaling_ext import * class data_analysis(slots_getstate_setstate): def show(self, out=sys.stdout, prefix=""): raise NotImplementedError() class xtriage_output(slots_getstate_setstate): """ Base class for generic output wrappers. """ # this is used to toggle behavior in some output methods gui_output = False def show_big_header(self, title): """ Print a big header with the specified title. """ raise NotImplementedError() def show_header(self, title): """ Start a new section with the specified title. """ raise NotImplementedError() def show_sub_header(self, title): """ Start a sub-section with the specified title. """ raise NotImplementedError() def show_text(self, text): """ Show unformatted text. """ raise NotImplementedError() def show(self, text): return self.show_text(text) def show_preformatted_text(self, text): """ Show text with spaces and line breaks preserved; in some contexts this will be done using a monospaced font. """ raise NotImplementedError() def show_lines(self, text): """ Show partially formatted text, preserving paragraph breaks. """ raise NotImplementedError() def show_paragraph_header(self, text): """ Show a header/title for a paragraph or small block of text. """ raise NotImplementedError() def show_table(self, table, indent=0, plot_button=None, equal_widths=True): """ Display a formatted table. """ raise NotImplementedError() def show_plot(self, table): """ Display a plot, if supported by the given output class. """ raise NotImplementedError() def show_plots_row(self, tables): """ Display a series of plots in a single row. Only used for the Phenix GUI. """ raise NotImplementedError() def show_text_columns(self, rows, indent=0): """ Display a set of left-justified text columns. The number of columns is arbitrary but this will usually be key:value pairs. """ raise NotImplementedError() def newline(self): """ Print a newline and nothing else. """ raise NotImplementedError() def write(self, text): """ Support for generic filehandle methods. """ self.show(text) def flush(self): """ Support for generic filehandle methods. """ pass def warn(self, text): """ Display a warning message. """ raise NotImplementedError() class printed_output(xtriage_output): """ Output class for displaying raw text with minimal formatting. """ __slots__ = ["out"] def __init__(self, out): assert hasattr(out, "write") and hasattr(out, "flush") self.out = out self._warnings = [] def show_big_header(self, text): make_big_header(text, out=self.out) def show_header(self, text): make_header(text, out=self.out) def show_sub_header(self, title): out_tmp = StringIO() make_sub_header(title, out=out_tmp) for line in out_tmp.getvalue().splitlines(): self.out.write("%s\n" % line.rstrip()) def show_text(self, text): print(text, file=self.out) def show_paragraph_header(self, text): print(text, file=self.out) #+ ":" def show_preformatted_text(self, text): print(text, file=self.out) def show_lines(self, text): print(text, file=self.out) def show_table(self, table, indent=2, plot_button=None, equal_widths=True): print(table.format(indent=indent, equal_widths=equal_widths), file=self.out) def show_plot(self, table): pass def show_plots_row(self, tables): pass def show_text_columns(self, rows, indent=0): prefix = " "*indent n_cols = len(rows[0]) col_sizes = [ max([ len(row[i]) for row in rows ]) for i in range(n_cols) ] for row in rows : assert len(row) == n_cols formats = prefix+" ".join([ "%%%ds" % x for x in col_sizes ]) print(formats % tuple(row), file=self.out) def newline(self): print("", file=self.out) def write(self, text): self.out.write(text) def warn(self, text): self._warnings.append(text) out_tmp = StringIO() make_sub_header("WARNING", out=out_tmp, sep='*') for line in out_tmp.getvalue().splitlines(): self.out.write("%s\n" % line.rstrip()) self.out.write(text) class loggraph_output(xtriage_output): """ Output class for displaying 'loggraph' format (from ccp4i) as plain text. """ gui_output = True def __init__(self, out): assert hasattr(out, "write") and hasattr(out, "flush") self.out = out def show_big_header(self, text) : pass def show_header(self, text) : pass def show_sub_header(self, title) : pass def show_text(self, text) : pass def show_paragraph_header(self, text) : pass def show_preformatted_text(self, text) : pass def show_lines(self, text) : pass def show_table(self, *args, **kwds) : pass def show_text_columns(self, *args, **kwds) : pass def newline(self) : pass def write(self, text) : pass def warn(self, text) : pass def show_plot(self, table): print("", file=self.out) print(table.format_loggraph(), file=self.out) def show_plots_row(self, tables): for table in tables : self.show_plot(table) class xtriage_analysis(object): """ Base class for analyses performed by Xtriage. This does not impose any restrictions on content or functionality, but simply provides a show() method suitable for either filehandle-like objects or objects derived from the xtriage_output class. Child classes should implement _show_impl. """ def show(self, out=None): if out is None: out=sys.stdout if (not isinstance(out, xtriage_output)): out = printed_output(out) self._show_impl(out=out) return self def _show_impl(self, out): raise NotImplementedError() def summarize_issues(self): return []

26.635246

80

0.68549

895

6,499

4.826816

0.236872

0.059954

0.08125

0.071759

0.413889

0.311574

0.176852

0.144444

0.11713

0.073611

0

0.001163

0.206032

6,499

243

81

26.744856

0.836047

0.237883

0

0.473282

0

0.013522

0

0.022901

1

0.381679

false

0.114504

0.076336

0.015267

0.541985

0.091603

0

null

0

null

0

1

0

1

0

1

0

3

60b48ef4664313247f6f73e2eb5c349625ba0d6d

3,469

py

Python

scripts/external_libs/scapy-2.4.3/scapy/layers/tls/__init__.py

timgates42/trex-core

efe94752fcb2d0734c83d4877afe92a3dbf8eccd

[ "Apache-2.0" ]

956

2015-06-24T15:04:55.000Z

2022-03-30T06:25:04.000Z

scripts/external_libs/scapy-2.4.3/scapy/layers/tls/__init__.py

angelyouyou/trex-core

fddf78584cae285d9298ef23f9f5c8725e16911e

[ "Apache-2.0" ]

782

2015-09-20T15:19:00.000Z

2022-03-31T23:52:05.000Z

scripts/external_libs/scapy-2.4.3/scapy/layers/tls/__init__.py

angelyouyou/trex-core

fddf78584cae285d9298ef23f9f5c8725e16911e

[ "Apache-2.0" ]

429

2015-06-27T19:34:21.000Z

2022-03-23T11:02:51.000Z

# This file is part of Scapy # Copyright (C) 2007, 2008, 2009 Arnaud Ebalard <arno@natisbad.com> # 2015, 2016, 2017 Maxence Tury <maxence.tury@ssi.gouv.fr> # This program is published under a GPLv2 license """ Tools for handling TLS sessions and digital certificates. Use load_layer('tls') to load them to the main namespace. Prerequisites: - You may need to 'pip install cryptography' for the module to be loaded. Main features: - X.509 certificates parsing/building. - RSA & ECDSA keys sign/verify methods. - TLS records and sublayers (handshake...) parsing/building. Works with versions SSLv2 to TLS 1.2. This may be enhanced by a TLS context. For instance, if Scapy reads a ServerHello with version TLS 1.2 and a cipher suite using AES, it will assume the presence of IVs prepending the data. See test/tls.uts for real examples. - TLS encryption/decryption capabilities with many ciphersuites, including some which may be deemed dangerous. Once again, the TLS context enables Scapy to transparently send/receive protected data if it learnt the session secrets. Note that if Scapy acts as one side of the handshake (e.g. reads all server-related packets and builds all client-related packets), it will indeed compute the session secrets. - TLS client & server basic automatons, provided for testing and tweaking purposes. These make for a very primitive TLS stack. - Additionally, a basic test PKI (key + certificate for a CA, a client and a server) is provided in tls/examples/pki_test. Unit tests: - Various cryptography checks. - Reading a TLS handshake between a Firefox client and a GitHub server. - Reading TLS 1.3 handshakes from test vectors of a draft RFC. - Reading a SSLv2 handshake between s_client and s_server, without PFS. - Test our TLS server against s_client with different cipher suites. - Test our TLS client against our TLS server (s_server is unscriptable). TODO list (may it be carved away by good souls): - Features to add (or wait for) in the cryptography library: - X448 from RFC 7748 (no support in openssl yet); - the compressed EC point format. - About the automatons: - Add resumption support, through session IDs or session tickets. - Add various checks for discrepancies between client and server. Is the ServerHello ciphersuite ok? What about the SKE params? Etc. - Add some examples which illustrate how the automatons could be used. Typically, we could showcase this with Heartbleed. - Allow the server to store both one RSA key and one ECDSA key, and select the right one to use according to the ClientHello suites. - Find a way to shutdown the automatons sockets properly without simultaneously breaking the unit tests. - Miscellaneous: - Enhance PSK and session ticket support. - Define several Certificate Transparency objects. - Add the extended master secret and encrypt-then-mac logic. - Mostly unused features : DSS, fixed DH, SRP, char2 curves... """ from scapy.config import conf if not conf.crypto_valid: import logging log_loading = logging.getLogger("scapy.loading") log_loading.info("Can't import python-cryptography v1.7+. " "Disabled PKI & TLS crypto-related features.")

34.69

78

0.705967

496

3,469

4.919355

0.542339

0.014754

0.004098

0

0.017464

0.240703

3,469

99

79

35.040404

0.908884

0.919573

0

0.358209

0

0.010101

0

1

0

false

0

0.5

0

0.5

0

null

0

1

0

null

0

1

0

1

0

3

60bafacde9691f3785199dbb0aaa71a6c89ad956

165

py

Python

quickreplies/models.py

praekeltfoundation/reminder-scheduler

02ec6fde57883b276d16ca3524bd0202813c8fb2

[ "BSD-3-Clause" ]

null

quickreplies/models.py

praekeltfoundation/reminder-scheduler

02ec6fde57883b276d16ca3524bd0202813c8fb2

[ "BSD-3-Clause" ]

null

quickreplies/models.py

praekeltfoundation/reminder-scheduler

02ec6fde57883b276d16ca3524bd0202813c8fb2

[ "BSD-3-Clause" ]

null

from django.db import models class QuickReplyDestination(models.Model): url = models.URLField() hmac_secret = models.CharField(max_length=255, blank=True)

23.571429

62

0.763636

21

165

5.904762

0.857143

0

0.021127

0.139394

165

6

63

27.5

0.852113

0

1

0

false

0

0.25

0

1

0

1

0

null

0

1

0

null

0

1

0

3

60c26d4b0096829edb6a95bd327e12271cc0494b

48

py

Python

neuroml/nml/config.py

NeuralEnsemble/libNeuroML

75d1630a0c6354a3997c4068dc8cdc447491b6f8

[ "BSD-3-Clause" ]

20

2015-03-11T11:21:32.000Z

2021-10-11T16:03:27.000Z

neuroml/nml/config.py

NeuralEnsemble/libNeuroML

75d1630a0c6354a3997c4068dc8cdc447491b6f8

[ "BSD-3-Clause" ]

48

2015-01-15T18:41:01.000Z

2022-01-05T13:53:58.000Z

neuroml/nml/config.py

NeuralEnsemble/libNeuroML

75d1630a0c6354a3997c4068dc8cdc447491b6f8

[ "BSD-3-Clause" ]

16

2015-01-14T21:53:46.000Z

2019-09-04T23:05:27.000Z

variables = {"schema_name": "NeuroML_v2.2.xsd"}

24

47

0.708333

7

48

4.571429

1

0

0.045455

0.083333

48

1

48

0.681818

0

0.5625

0

1

0

false

0

1

0

null

0

1

0

null

0

3

60c75e4ba343d77630741c129fff874ee7aabd3a

856

py

Python

discovery-provider/src/api/v1/models/challenges.py

Tenderize/audius-protocol

aa15844e3f12812fe8aaa81e2cb6e5c5fa89ff51

[ "Apache-2.0" ]

1

2022-03-27T21:40:36.000Z

discovery-provider/src/api/v1/models/challenges.py

Tenderize/audius-protocol

aa15844e3f12812fe8aaa81e2cb6e5c5fa89ff51

[ "Apache-2.0" ]

null

discovery-provider/src/api/v1/models/challenges.py

Tenderize/audius-protocol

aa15844e3f12812fe8aaa81e2cb6e5c5fa89ff51

[ "Apache-2.0" ]

null

from flask_restx import fields from .common import ns attestation = ns.model( "attestation", { "owner_wallet": fields.String(required=True), "attestation": fields.String(required=True), }, ) undisbursed_challenge = ns.model( "undisbursed_challenge", { "challenge_id": fields.String(required=True), "user_id": fields.String(required=True), "specifier": fields.String(required=True), "amount": fields.String(required=True), "completed_blocknumber": fields.Integer(required=True), "handle": fields.String(required=True), "wallet": fields.String(required=True), }, ) create_sender_attestation = ns.model( "create_sender_attestation", { "owner_wallet": fields.String(required=True), "attestation": fields.String(required=True), }, )

25.939394

63

0.651869

87

856

6.275862

0.298851

0.241758

0.3663

0.43956

0.446886

0.296703

0

0.209112

856

32

64

26.75

0.806499

0

0.142857

0

0.198598

0.078271

0

1

0

false

0

0.071429

0

0.071429

0

null

1

0

null

0

3

60ce67b7e2b24cc2a97190b453defc65ccef1e84

1,350

py

Python

tests/test_enter.py

vpv11110000/pyss

bc2226e2e66e0b551a09ae6ab6835b0bb6c7f32b

[ "MIT" ]

null

tests/test_enter.py

vpv11110000/pyss

bc2226e2e66e0b551a09ae6ab6835b0bb6c7f32b

[ "MIT" ]

2

2017-09-05T11:12:05.000Z

2017-09-07T19:23:15.000Z

tests/test_enter.py

vpv11110000/pyss

bc2226e2e66e0b551a09ae6ab6835b0bb6c7f32b

[ "MIT" ]

null

# #!/usr/bin/python # -*- coding: utf-8 -*- # pylint: disable=line-too-long,missing-docstring,bad-whitespace import sys import os import unittest DIRNAME_MODULE = os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(sys.argv[0])))) + os.sep sys.path.append(DIRNAME_MODULE) sys.path.append(DIRNAME_MODULE + "pyss" + os.sep) from pyss import pyssobject from pyss.pyss_model import PyssModel from pyss.segment import Segment from pyss import generate from pyss import terminate from pyss import logger from pyss import pyss_model from pyss import segment from pyss import table from pyss import handle from pyss.enter import Enter from pyss.leave import Leave from pyss import storage from pyss import pyssobject from pyss import advance from pyss import options from pyss.pyss_const import * class TestEnter(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def test_init_001(self): # with self.assertRaises(pyssobject.ErrorIsNone) as context: Enter(None, storageName="S1", funcBusySize=1) def test_init_002(self): m = PyssModel(optionz=None) m[OPTIONS].setAllFalse() sgm = Segment(m) # Enter(sgm, storageName="S1", funcBusySize=1) if __name__ == '__main__': unittest.main(module="test_enter")

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0.713333

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107

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null

0

null

0

1

0

1

0

3

60ef24190e5796fe94aa6d0c84e7cc01d9ab2f7e

174

py

Python

CURSO UDEMY/TEORICAS/1.py

CamilliCerutti/Exercicios-de-Python-curso-em-video

6571a5c5cb7b4398352a7778c55588c0c16f13c2

[ "MIT" ]

null

CURSO UDEMY/TEORICAS/1.py

CamilliCerutti/Exercicios-de-Python-curso-em-video

6571a5c5cb7b4398352a7778c55588c0c16f13c2

[ "MIT" ]

null

CURSO UDEMY/TEORICAS/1.py

CamilliCerutti/Exercicios-de-Python-curso-em-video

6571a5c5cb7b4398352a7778c55588c0c16f13c2

[ "MIT" ]

null

# STRING: nome print('Camilli', type('Camilli')) # INT: idade print(17, type(17)) # # ALTURA: float print(1.58, type(1.58)) # É MAIOR DE IDADE: bool print(bool(17 > 18))

15.818182

33

0.632184

29

174

3.793103

0.586207

0.054545

0

0.096552

0.166667

174

11

34

15.818182

0.662069

0.362069

0

0.132075

0

1

0

true

0

1

0

null

0

null

0

1

0

1

0

3

88031728853823161b5a425023b70cdeff85ee1f

190

py

Python

angelo/problem_9.py

giovannilambertucci/github-learning

9d99ae0b4d77bf4464ce974a903c1a225aeb1d0a

[ "MIT" ]

null

angelo/problem_9.py

giovannilambertucci/github-learning

9d99ae0b4d77bf4464ce974a903c1a225aeb1d0a

[ "MIT" ]

4

2018-10-09T20:55:26.000Z

2020-10-16T18:33:01.000Z

angelo/problem_9.py

giovannilambertucci/github-learning

9d99ae0b4d77bf4464ce974a903c1a225aeb1d0a

[ "MIT" ]

8

2018-10-06T16:39:22.000Z

2021-10-20T19:41:53.000Z

#!/usr/bin/python3 for a in range(1, 400): for b in range(1, 400): c = (1000 - a) - b if a < b < c: if a**2 + b**2 == c**2: print(a * b * c)

21.111111

35

0.373684

34

190

2.088235

0.441176

0.084507

0.225352

0.309859

0

0.150943

0.442105

190

8

36

23.75

0.518868

0.089474

0

1

0

false

0

0.166667

0

null

0

1

0

1

0

null

0

3

880be01962e805216657469d5d33f6977bef9b89

155

py

Python

emergency/ui.py

dmonroy/911

217bb336903495370ff1374606823c5473a0cf70

[ "MIT" ]

null

emergency/ui.py

dmonroy/911

217bb336903495370ff1374606823c5473a0cf70

[ "MIT" ]

null

emergency/ui.py

dmonroy/911

217bb336903495370ff1374606823c5473a0cf70

[ "MIT" ]

null

from chilero import web class HomeView(web.View): def get(self): return web.Response('This is the home!') routes = [ ['', HomeView] ]

11.923077

48

0.606452

20

155

4.7

0.85

0

0.258065

155

12

49

12.916667

0.817391

0

0.109677

0

1

0.142857

false

0

0.142857

0.571429

0

1

0

null

0

null

0

1

0

3

7150600aaa110110306719c5b7be299f067d0fba

265

py

Python

djangoProject3/giris/urls.py

abdullahturkak/Django-pc-login-and-show

8de1c33f30ff3e501ee4fa57f45e0752f7092d1f

[ "Apache-2.0" ]

null

djangoProject3/giris/urls.py

abdullahturkak/Django-pc-login-and-show

8de1c33f30ff3e501ee4fa57f45e0752f7092d1f

[ "Apache-2.0" ]

null

djangoProject3/giris/urls.py

abdullahturkak/Django-pc-login-and-show

8de1c33f30ff3e501ee4fa57f45e0752f7092d1f

[ "Apache-2.0" ]

null

from django.urls import path from . import views from django.urls import path from django.urls import path urlpatterns = [ path('', views.ilksayfa,name='ilksayfa'), path('ekle',views.ekle,name='ekle'), path('getir',views.index,name='getir'), ]

13.947368

45

0.679245

36

265

5

0.333333

0.166667

0.233333

0.333333

0.422222

0.311111

0

0.169811

265

18

46

14.722222

0.818182

0

0.333333

0

0.098113

0

1

0

false

0

0.444444

0

0.444444

0

null

0

1

0

null

0

1

0

3

7160d489a58b25d7b87e2d37c20b94c78c28e43c

57

py

Python

example_snippets/multimenus_snippets/Snippets/SciPy/Physical and mathematical constants/CODATA physical constants/P/proton Compton wavelength.py

kuanpern/jupyterlab-snippets-multimenus

477f51cfdbad7409eab45abe53cf774cd70f380c

[ "BSD-3-Clause" ]

null

example_snippets/multimenus_snippets/Snippets/SciPy/Physical and mathematical constants/CODATA physical constants/P/proton Compton wavelength.py

kuanpern/jupyterlab-snippets-multimenus

477f51cfdbad7409eab45abe53cf774cd70f380c

[ "BSD-3-Clause" ]

null

example_snippets/multimenus_snippets/Snippets/SciPy/Physical and mathematical constants/CODATA physical constants/P/proton Compton wavelength.py

kuanpern/jupyterlab-snippets-multimenus

477f51cfdbad7409eab45abe53cf774cd70f380c

[ "BSD-3-Clause" ]

1

2021-02-04T04:51:48.000Z

constants.physical_constants["proton Compton wavelength"]

57

0.877193

6

57

8.166667

0.833333

0

0.035088

57

1

57

0.890909

0

0.431034

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

3

717d6b8beae77248154db43634235c684600256e

293

py

Python

helpers/collect.py

dan-candeira/api_backend

b2670ede792634cb1982f0a809ef70fc4e2e21d1

[ "MIT" ]

2

2020-09-11T01:41:06.000Z

2022-01-26T11:09:00.000Z

helpers/collect.py

dan-candeira/api_backend

b2670ede792634cb1982f0a809ef70fc4e2e21d1

[ "MIT" ]

null

helpers/collect.py

dan-candeira/api_backend

b2670ede792634cb1982f0a809ef70fc4e2e21d1

[ "MIT" ]

2

2020-10-10T21:15:33.000Z

2021-12-06T18:02:03.000Z

from models.collect import Collect from helpers.patient import validate_patient from helpers.equipment import validate_equipment async def validate_collect(collect: Collect): await validate_patient(collect.patient) await validate_equipment(collect.equipment) return collect

26.636364

48

0.8157

35

293

6.685714

0.342857

0.094017

0

0.139932

293

11

49

26.636364

0.928571

0

1

0

true

0

0.428571

0

0.571429

0

null

0

null

0

1

0

1

0

1

0

3

717f1f70265be6bc5f941c4217513b3ab2e45ef1

407

py

Python

lib/python3.7/site-packages/pydantic/__init__.py

guilhermeginezsilva/python-products-api

b27327f64359801edcd858263e39fe8bc8c0b0f7

[ "BSD-3-Clause" ]

null

lib/python3.7/site-packages/pydantic/__init__.py

guilhermeginezsilva/python-products-api

b27327f64359801edcd858263e39fe8bc8c0b0f7

[ "BSD-3-Clause" ]

null

lib/python3.7/site-packages/pydantic/__init__.py

guilhermeginezsilva/python-products-api

b27327f64359801edcd858263e39fe8bc8c0b0f7

[ "BSD-3-Clause" ]

null

# flake8: noqa from . import dataclasses from .class_validators import validator from .env_settings import BaseSettings from .error_wrappers import ValidationError from .errors import * from .fields import Required from .main import BaseConfig, BaseModel, Extra, compiled, create_model, validate_model from .parse import Protocol from .schema import Schema from .types import * from .version import VERSION

31.307692

86

0.823096

53

407

6.226415

0.566038

0.060606

0

0.002817

0.127764

407

12

87

33.916667

0.926761

0.029484

0

1

0

true

0

1

0

1

0

null

0

null

0

1

0

1

0

1

0

3

71acf177ad960023e1438bf52e631fc9a6602f6a

619

py

Python

product_information/product_data.py

BVT-Engineering/Product_Information

7b9c6d9bbb3d68ac9c1e4b606e96196f4ee1cd4b

[ "Apache-2.0" ]

null

product_information/product_data.py

BVT-Engineering/Product_Information

7b9c6d9bbb3d68ac9c1e4b606e96196f4ee1cd4b

[ "Apache-2.0" ]

null

product_information/product_data.py

BVT-Engineering/Product_Information

7b9c6d9bbb3d68ac9c1e4b606e96196f4ee1cd4b

[ "Apache-2.0" ]

null

import pandas as pd from . import data import importlib.resources def autex_frontier_acoustic_fins(): """Return a dataframe containing Autex frontier acoustic fin data""" path = importlib.resources.open_text(data, "Autex Frontier Acoustic Fins.csv") return pd.read_csv(path) def tracklok(): """Return a dataframe containing tracklok data""" path = importlib.resources.open_text(data, "tracklok.csv") return pd.read_csv(path) def gripple(): """Return a dataframe containing tracklok data""" path = importlib.resources.open_text(data, "Gripple.csv") return pd.read_csv(path)

23.807692

82

0.726979

82

619

5.378049

0.304878

0.163265

0.142857

0.176871

0.575964

0.526077

0.326531

0

0.168013

619

25

83

24.76

0.856311

0.242326

0

0.25

0

0.121413

0

1

0.25

false

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

3

71afa04ef8d0f57669ca0f129f2c50b754e901ac

365

py

Python

0-notes/job-search/Cracking the Coding Interview/C04TreesGraphs/questions/4.12-questions.py

eengineergz/Lambda

1fe511f7ef550aed998b75c18a432abf6ab41c5f

[ "MIT" ]

null

0-notes/job-search/Cracking the Coding Interview/C04TreesGraphs/questions/4.12-questions.py

eengineergz/Lambda

1fe511f7ef550aed998b75c18a432abf6ab41c5f

[ "MIT" ]

null

0-notes/job-search/Cracking the Coding Interview/C04TreesGraphs/questions/4.12-questions.py

eengineergz/Lambda

1fe511f7ef550aed998b75c18a432abf6ab41c5f

[ "MIT" ]

null

# 4.12 Paths with Sum # You are given a binary tree in which each node contains an integer value # which might be positive or negative. # Design an algorithm to count the number of paths that sum to a given value. # The path does not need to start or end at the root or a leaf, but it must go # downwards, traveling only from parent nodes to child nodes.

45.625

78

0.739726

68

365

3.970588

0.764706

0

0.010638

0.227397

365

7

79

52.142857

0.946809

0.939726

0

null

0

null

0

null

0

null

1

null

true

0

null

0

null

0

1

0

1

0

null

0

1

0

3

71e5fc3a02f133ded42284d60f5db450de4aea77

204

py

Python

setup.py

KyungMinJin/Pointnet

ac18b074d1ab9067ad923bbea2dab524f76b8b09

[ "MIT" ]

null

setup.py

KyungMinJin/Pointnet

ac18b074d1ab9067ad923bbea2dab524f76b8b09

[ "MIT" ]

null

setup.py

KyungMinJin/Pointnet

ac18b074d1ab9067ad923bbea2dab524f76b8b09

[ "MIT" ]

null

from setuptools import setup setup(name='pointnet', packages=['pointnet'], package_dir={'pointnet': 'pointnet'}, install_requires=['torch', 'tqdm', 'plyfile'], version='0.0.1')

20.4

52

0.622549

22

204

5.681818

0.772727

0

0.018182

0.191176

204

9

53

22.666667

0.739394

0

0.262376

0

1

0

true

0

0.166667

0

0.166667

0

1

0

null

0

1

0

null

0

1

0

3

e08224d680f298b0c62c7c66f4ee253b21125106

578

py

Python

tests/meeshkan/nlp/test_gib_detector.py

meeshkan/meeshkan-nlp

63ef1e0ef31fd9c2031c89e9fd6ca3fc46eef13e

[ "MIT" ]

1

2020-04-02T08:02:33.000Z

tests/meeshkan/nlp/test_gib_detector.py

meeshkan/meeshkan-nlp

63ef1e0ef31fd9c2031c89e9fd6ca3fc46eef13e

[ "MIT" ]

9

2020-03-24T21:09:16.000Z

2020-07-24T09:58:11.000Z

tests/meeshkan/nlp/test_gib_detector.py

meeshkan/meeshkan-nlp

63ef1e0ef31fd9c2031c89e9fd6ca3fc46eef13e

[ "MIT" ]

null

from meeshkan.nlp.ids.gib_detect import GibberishDetector def test_gib_detector(): detector = GibberishDetector() assert not detector.is_gibberish("gibberish") assert not detector.is_gibberish("gibberish text") assert not detector.is_gibberish("gibberish_text_with_underscores") assert not detector.is_gibberish("gibberish.text.with.dots") assert not detector.is_gibberish("gibberish-text-with-minus") assert detector.is_gibberish("WhYHJKb") assert detector.is_gibberish("cdkf=9m0fm3") assert not detector.is_gibberish("g5ibdf35ber6ish")

36.125

71

0.778547

71

578

6.140845

0.338028

0.183486

0.348624

0.261468

0.552752

0.488532

0.40367

0.309633

0

0.013834

0.124567

578

15

72

38.533333

0.847826

0

0.235294

0.138408

0

0.727273

1

0.090909

false

0

0.090909

0

0.181818

0

null

0

1

0

null

0

1

0

3

e093c9bb33cc079afd8c352a399d3a4910427a85

463

py

Python

GA/baseline/mtsp/routemanager.py

Tricker-z/CSE5001-GA-mTSP

108916cafecbe325302dbce4ddd07c477a0c5f79

[ "Apache-2.0" ]

3

2021-12-14T00:46:55.000Z

2021-12-19T08:41:21.000Z

GA/baseline/mtsp/routemanager.py

Tricker-z/CSE5001-GA-mTSP

108916cafecbe325302dbce4ddd07c477a0c5f79

[ "Apache-2.0" ]

null

GA/baseline/mtsp/routemanager.py

Tricker-z/CSE5001-GA-mTSP

108916cafecbe325302dbce4ddd07c477a0c5f79

[ "Apache-2.0" ]

null

''' Holds all the dustbin objects and is used for creation of chromosomes by jumbling their sequence ''' from mtsp.dustbin import * class RouteManager: destinationDustbins = [] @classmethod def addDustbin (cls, db): cls.destinationDustbins.append(db) @classmethod def getDustbin (cls, index): return cls.destinationDustbins[index] @classmethod def numberOfDustbins(cls): return len(cls.destinationDustbins)

22.047619

50

0.704104

49

463

6.653061

0.673469

0.128834

0

0.218143

463

20

51

23.15

0.900552

0.207343

0

0.25

0

1

0.25

false

0

0.083333

0.166667

0.666667

0

null

0

null

0

1

0

1

0

3

e0be97a1e97f364aa46dcdeed19c0eced545d040

105

py

Python

series_tiempo_ar_api/apps/metadata/indexer/strings.py

datosgobar/series-tiempo-ar-api

6b553c573f6e8104f8f3919efe79089b7884280c

[ "MIT" ]

28

2017-12-16T20:30:52.000Z

2021-08-11T17:35:04.000Z

series_tiempo_ar_api/apps/metadata/indexer/strings.py

datosgobar/series-tiempo-ar-api

6b553c573f6e8104f8f3919efe79089b7884280c

[ "MIT" ]

446

2017-11-16T15:21:40.000Z

2021-06-10T20:14:21.000Z

series_tiempo_ar_api/apps/metadata/indexer/strings.py

datosgobar/series-tiempo-ar-api

6b553c573f6e8104f8f3919efe79089b7884280c

[ "MIT" ]

12

2018-08-23T16:13:32.000Z

2022-03-01T23:12:28.000Z

#! coding: utf-8 from __future__ import unicode_literals INDEXING_ERROR = 'Error en la indexación: %s'

17.5

45

0.761905

15

105

4.933333

0.933333

0

0.011236

0.152381

105

5

46

21

0.820225

0.142857

0

0.292135

0

1

0

false

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

3

e0c0630ef7275753a27f4636b02798e72127feab

117

py

Python

Python/PythonExercicios/ex024.py

felizardo27/Python

965d56f4956eb7b6a68c0b1cbd74d363dd2a223c

[ "MIT" ]

null

Python/PythonExercicios/ex024.py

felizardo27/Python

965d56f4956eb7b6a68c0b1cbd74d363dd2a223c

[ "MIT" ]

null

Python/PythonExercicios/ex024.py

felizardo27/Python

965d56f4956eb7b6a68c0b1cbd74d363dd2a223c

[ "MIT" ]

null

print('====== EX 024 ======') cid = str(input('Em que cidade você nasceu? ')).lower().split() print('santo' in cid)

23.4

63

0.57265

17

117

3.941176

0.882353

0

0.029703

0.136752

117

4

64

29.25

0.633663

0

0.448276

0

1

0

false

0

0.666667

1

0

null

0

1

0

null

0

1

0

3

e0c7399fc2d4022ed149f2a68a5e24931da09237

174

py

Python

web/apps/social/urls.py

vitaliyharchenko/django_template

41fa00cb0b8be6c5cf67b7a334d4340163255160

[ "MIT" ]

null

web/apps/social/urls.py

vitaliyharchenko/django_template

41fa00cb0b8be6c5cf67b7a334d4340163255160

[ "MIT" ]

1

2018-02-02T20:25:41.000Z

web/apps/social/urls.py

vitaliyharchenko/django_template

41fa00cb0b8be6c5cf67b7a334d4340163255160

[ "MIT" ]

null

# URLconf from django.urls import path from apps.social import views app_name = 'social' urlpatterns = [ path('vk/complete', views.vk_complete, name='vk_complete'), ]

15.818182

63

0.724138

24

174

5.125

0.583333

0.243902

0

0.155172

174

10

64

17.4

0.836735

0.04023

0

0.169697

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

1

0

null

0

1

0

3

e0ca2023f2d70fc898d1c818e74ce9cd59b43330

2,481

py

Python

fieldtypes/Field.py

pulpocoders/pulpoforms-django

60d268faa492ba8256cc32b3108d6a27dabcd40f

[ "Apache-2.0" ]

45

2015-07-30T21:52:00.000Z

2020-03-25T16:53:34.000Z

fieldtypes/Field.py

pulpocoders/pulpo-forms-django

60d268faa492ba8256cc32b3108d6a27dabcd40f

[ "Apache-2.0" ]

5

2016-10-18T12:17:54.000Z

2017-11-09T10:39:34.000Z

fieldtypes/Field.py

pulpocoders/pulpo-forms-django

60d268faa492ba8256cc32b3108d6a27dabcd40f

[ "Apache-2.0" ]

13

2015-08-01T01:57:35.000Z

2022-03-28T21:14:02.000Z

from django.core.exceptions import ValidationError from pulpo_forms.models import Version, FieldEntry class Field(object): """ Default abstract field type class """ folder = "fields/" template_name = "field_template_base.html" edit_template_name = "fiel_template_edit_base.html" prp_template_name = "field_properties_base.html" def validate(self, value, **kwargs): # Default validation or pass checks = self.get_methods(**kwargs) for method in checks: method(value, **kwargs) def get_methods(self, **kwargs): return [self.null_check] def null_check(self, value, **kwargs): if not value: raise ValidationError("Problem with the answer.") def get_validations(self, json, f_id): for page in json['pages']: for field in page['fields']: if (field['field_id'] == f_id): return field['validations'] def get_options(self, json, f_id): return None def check_consistency(self, field): # When a field is created check if the restrictions are consistent pass def count_responses_pct(self, form_pk, version_num, field_id): v = Version.objects.get(number=version_num, form_id=form_pk) queryset = FieldEntry.objects.filter( field_id=field_id, entry__version_id=v.pk) total = queryset.count() responses = total - queryset.filter(answer="").count() return (responses, total) def get_statistics(self, data_list, field): """ Returns a the statistics related to the data list. """ statistics = { "field_type": field["field_type"], "field_text": field["text"] } if field["required"]: statistics["required"] = "Yes" else: statistics["required"] = "No" return statistics def get_assets(): return [] def get_non_static(): return [] def get_styles(): return [] """ Default Render methods for field templates """ def render(self): return self.folder + self.template_name def render_properties(self): return self.folder + self.prp_template_name def render_edit(self): return self.folder + self.edit_template_name def render_statistic(self): return self.folder + self.sts_template_name class Meta: abstract = True

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0

null

0

null

0

1

0

1

0

3

e0d2cb5079d6c051d2825b4e95644d0a69a10092

441

py

Python

fluiddb/schema/scripts/lowercase_usernames.py

fluidinfo/fluiddb

b5a8c8349f3eaf3364cc4efba4736c3e33b30d96

[ "Apache-2.0" ]

3

2021-05-10T14:41:30.000Z

2021-12-16T05:53:30.000Z

fluiddb/schema/scripts/lowercase_usernames.py

fluidinfo/fluiddb

b5a8c8349f3eaf3364cc4efba4736c3e33b30d96

[ "Apache-2.0" ]

null

fluiddb/schema/scripts/lowercase_usernames.py

fluidinfo/fluiddb

b5a8c8349f3eaf3364cc4efba4736c3e33b30d96

[ "Apache-2.0" ]

2

2018-01-24T09:03:21.000Z

2021-06-25T08:34:54.000Z

"""Lowercase all the users in the database.""" from fluiddb.data.user import getUsers from fluiddb.scripts.commands import setupStore if __name__ == '__main__': store = setupStore('postgres:///fluidinfo', 'main') print __doc__ for user in getUsers(): if user.username != user.username.lower(): print 'Fixing user', user.username user.username = user.username.lower() store.commit()

23.210526

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441

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0.54902

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0.173285

0.259928

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0

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441

18

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24.5

0.812317

0

0.111392

0.053165

0

null

0

0.2

null

0.2

0

null

1

0

1

0

null

0

1

0

3

e0de11c2dcc49d61800875bc4b3d8fe77073a5e9

452

py

Python

100-Exercicios/ex004.py

thedennerdev/ExerciciosPython-Iniciante

de36c4a09700353a9a1daa7f1320e416c6201a5c

[ "MIT" ]

null

100-Exercicios/ex004.py

thedennerdev/ExerciciosPython-Iniciante

de36c4a09700353a9a1daa7f1320e416c6201a5c

[ "MIT" ]

null

100-Exercicios/ex004.py

thedennerdev/ExerciciosPython-Iniciante

de36c4a09700353a9a1daa7f1320e416c6201a5c

[ "MIT" ]

null

qq = (input('Digite algo qualquer: ')) print('O tipo da palavra digitado é', type(qq)) print('O valor dela são digitos?', qq.isdigit()) print('O valor dela são númericos?', qq.isnumeric()) print('O valor dela são letras?', qq.isalpha()) print('O valor dela são alfanúmericos?', qq.isalnum()) print('O valor dela são apenas espaços?', qq.isspace()) print('O valor dela são maiúsculas?', qq.isupper()) print('O valor dela são minusclas?', qq.islower())

41.090909

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0.701327

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452

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null

0

1

0

null

0

1

0

3

1c9f59beeef09a1d3746d78c309103b22a67b401

285

py

Python

tests/test_required_interfaces.py

evoh-nft/evoh-erc721

573de4da7047066ab187c2a31aee95ed00355e7d

[ "MIT" ]

null

tests/test_required_interfaces.py

evoh-nft/evoh-erc721

573de4da7047066ab187c2a31aee95ed00355e7d

[ "MIT" ]

null

tests/test_required_interfaces.py

evoh-nft/evoh-erc721

573de4da7047066ab187c2a31aee95ed00355e7d

[ "MIT" ]

null

#!/usr/bin/python3 def test_erc165_support(nft): erc165_interface_id = "0x01ffc9a7" assert nft.supportsInterface(erc165_interface_id) is True def test_erc721_support(nft): erc721_interface_id = "0x80ac58cd" assert nft.supportsInterface(erc721_interface_id) is True

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5.702703

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0.739837

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null

1

0

1

0

null

0

1

0

3

1cabdaf9988c673eef43545493f061d3a959c66d

3,788

py

Python

backend/src/gloader/xml/dom/html/HTMLAnchorElement.py

anrl/gini4

d26649c8c02a1737159e48732cf1ee15ba2a604d

[ "MIT" ]

11

2019-03-02T20:39:34.000Z

2021-09-02T19:47:38.000Z

backend/src/gloader/xml/dom/html/HTMLAnchorElement.py

anrl/gini4

d26649c8c02a1737159e48732cf1ee15ba2a604d

[ "MIT" ]

29

2019-01-17T15:44:48.000Z

2021-06-02T00:19:40.000Z

backend/src/gloader/xml/dom/html/HTMLAnchorElement.py

anrl/gini4

d26649c8c02a1737159e48732cf1ee15ba2a604d

[ "MIT" ]

11

2019-01-28T05:00:55.000Z

2021-11-12T03:08:32.000Z

######################################################################## # # File Name: HTMLAnchorElement # # ### This file is automatically generated by GenerateHtml.py. ### DO NOT EDIT! """ WWW: http://4suite.com/4DOM e-mail: support@4suite.com Copyright (c) 2000 Fourthought Inc, USA. All Rights Reserved. See http://4suite.com/COPYRIGHT for license and copyright information """ import string from xml.dom import Node from xml.dom.html.HTMLElement import HTMLElement class HTMLAnchorElement(HTMLElement): def __init__(self, ownerDocument, nodeName="A"): HTMLElement.__init__(self, ownerDocument, nodeName) ### Attribute Methods ### def _get_accessKey(self): return self.getAttribute("ACCESSKEY") def _set_accessKey(self, value): self.setAttribute("ACCESSKEY", value) def _get_charset(self): return self.getAttribute("CHARSET") def _set_charset(self, value): self.setAttribute("CHARSET", value) def _get_coords(self): return self.getAttribute("COORDS") def _set_coords(self, value): self.setAttribute("COORDS", value) def _get_href(self): return self.getAttribute("HREF") def _set_href(self, value): self.setAttribute("HREF", value) def _get_hreflang(self): return self.getAttribute("HREFLANG") def _set_hreflang(self, value): self.setAttribute("HREFLANG", value) def _get_name(self): return self.getAttribute("NAME") def _set_name(self, value): self.setAttribute("NAME", value) def _get_rel(self): return self.getAttribute("REL") def _set_rel(self, value): self.setAttribute("REL", value) def _get_rev(self): return self.getAttribute("REV") def _set_rev(self, value): self.setAttribute("REV", value) def _get_shape(self): return string.capitalize(self.getAttribute("SHAPE")) def _set_shape(self, value): self.setAttribute("SHAPE", value) def _get_tabIndex(self): value = self.getAttribute("TABINDEX") if value: return int(value) return 0 def _set_tabIndex(self, value): self.setAttribute("TABINDEX", str(value)) def _get_target(self): return self.getAttribute("TARGET") def _set_target(self, value): self.setAttribute("TARGET", value) def _get_type(self): return self.getAttribute("TYPE") def _set_type(self, value): self.setAttribute("TYPE", value) ### Methods ### def blur(self): pass def focus(self): pass ### Attribute Access Mappings ### _readComputedAttrs = HTMLElement._readComputedAttrs.copy() _readComputedAttrs.update({ "accessKey" : _get_accessKey, "charset" : _get_charset, "coords" : _get_coords, "href" : _get_href, "hreflang" : _get_hreflang, "name" : _get_name, "rel" : _get_rel, "rev" : _get_rev, "shape" : _get_shape, "tabIndex" : _get_tabIndex, "target" : _get_target, "type" : _get_type }) _writeComputedAttrs = HTMLElement._writeComputedAttrs.copy() _writeComputedAttrs.update({ "accessKey" : _set_accessKey, "charset" : _set_charset, "coords" : _set_coords, "href" : _set_href, "hreflang" : _set_hreflang, "name" : _set_name, "rel" : _set_rel, "rev" : _set_rev, "shape" : _set_shape, "tabIndex" : _set_tabIndex, "target" : _set_target, "type" : _set_type }) _readOnlyAttrs = filter(lambda k,m=_writeComputedAttrs: not m.has_key(k), HTMLElement._readOnlyAttrs + _readComputedAttrs.keys())

25.768707

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398

3,788

5.537688

0.228643

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0.076679

0.136116

0

0.003181

0.253168

3,788

146

78

25.945205

0.775893

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false

0.021505

0.032258

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0

null

0

1

0

null

0

1

0

1

0

3

1cbf7e7b48c17a4738f3a41ebb956e0efb15333c

638

py

Python

tests/api/utils/schema/auth.py

BenjamenMeyer/deuce

fbca31cb5248a808a85bfc24af10119453359276

[ "Apache-2.0" ]

null

tests/api/utils/schema/auth.py

BenjamenMeyer/deuce

fbca31cb5248a808a85bfc24af10119453359276

[ "Apache-2.0" ]

null

tests/api/utils/schema/auth.py

BenjamenMeyer/deuce

fbca31cb5248a808a85bfc24af10119453359276

[ "Apache-2.0" ]

null

authentication = { "type": "object", "properties": { "access": { "type": "object", "properties": { "token": { "type": "object", "properties": { "id": { "type": "string" }, }, "required": ["id"], }, "serviceCatalog": { "type": "array", }, }, "required": ["token", "serviceCatalog", ], }, }, "required": ["access", ], }

25.52

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0.268025

25

638

6.84

0.44

0.175439

0.350877

0

0.570533

638

24

55

26.583333

0.624088

0

0.25

0

0.246082

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false

0

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0

null

0

1

0

1

0

null

0

3

1cc6c7e8b732b32110858561f10fdd739f1cb8b1

207

py

Python

src/roboverse/__init__.py

Mindstem/Roboverse

3d34e6afc2c43f57e647c10411f013a317108886

[ "MIT" ]

null

src/roboverse/__init__.py

Mindstem/Roboverse

3d34e6afc2c43f57e647c10411f013a317108886

[ "MIT" ]

null

src/roboverse/__init__.py

Mindstem/Roboverse

3d34e6afc2c43f57e647c10411f013a317108886

[ "MIT" ]

null

"""Roboverse package.""" from collections.abc import Sequence from importlib.metadata import version __all__: Sequence[str] = ('__version__',) __version__: str = version(distribution_name='Roboverse')

17.25

57

0.763285

22

207

6.590909

0.636364

0.137931

0

0.115942

207

11

58

18.818182

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0.086957

0

0.10929

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

null

0

1

0

1

0

3

1cd036636063d98c815e91d7a353efb5060a2fb5

809

py

Python

metadrive/base_class/nameable.py

liuzuxin/metadrive

850c207536531bc85179084acd7c30ab14a66111

[ "Apache-2.0" ]

125

2021-08-30T06:33:57.000Z

2022-03-31T09:02:44.000Z

metadrive/base_class/nameable.py

liuzuxin/metadrive

850c207536531bc85179084acd7c30ab14a66111

[ "Apache-2.0" ]

72

2021-08-30T16:23:41.000Z

2022-03-31T19:17:16.000Z

metadrive/base_class/nameable.py

liuzuxin/metadrive

850c207536531bc85179084acd7c30ab14a66111

[ "Apache-2.0" ]

20

2021-09-09T08:20:25.000Z

2022-03-24T13:24:07.000Z

import logging from metadrive.utils import random_string class Nameable: """ Instance of this class will have a special name """ def __init__(self, name=None): # ID for object self.name = random_string() if name is None else name self.id = self.name # name = id @property def class_name(self): return self.__class__.__name__ def __del__(self): try: str(self) except AttributeError: pass else: logging.debug("{} is destroyed".format(str(self))) def __repr__(self): return "{}".format(str(self)) def __str__(self): return "{}, ID:{}".format(self.class_name, self.name) def rename(self, new_name): self.name = new_name self.id = self.name

22.472222

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100

809

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0.41

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0.045249

0.063348

0.081448

0

0.309023

809

35

63

23.114286

0.790698

0.088999

0

0.086957

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0

1

0.26087

false

0.043478

0.086957

0.130435

0.521739

0

null

0

null

0

1

0

1

0

3

1cd0b9969ec980eadb9502a245242751f992c588

320

py

Python

EPro-PnP-6DoF/lib/utils/tictoc.py

Lakonik/EPro-PnP

931df847190ce10eddd1dc3e3168ce1a2f295ffa

[ "Apache-2.0" ]

19

2022-03-21T10:22:24.000Z

2022-03-30T15:43:46.000Z

EPro-PnP-6DoF/lib/utils/tictoc.py

Lakonik/EPro-PnP

931df847190ce10eddd1dc3e3168ce1a2f295ffa

[ "Apache-2.0" ]

null

EPro-PnP-6DoF/lib/utils/tictoc.py

Lakonik/EPro-PnP

931df847190ce10eddd1dc3e3168ce1a2f295ffa

[ "Apache-2.0" ]

3

2022-03-26T08:08:24.000Z

2022-03-30T11:17:11.000Z

""" This file is from https://github.com/LZGMatrix/CDPN_ICCV2019_ZhigangLi """ import time def tic(): global start_time start_time = time.time() return start_time def toc(): if 'start_time' in globals(): end_time = time.time() return end_time - start_time else: return None

17.777778

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0.646875

44

320

4.5

0.568182

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0.131313

0.181818

0

0.016667

0.25

320

18

53

17.777778

0.808333

0.21875

0

0.041152

0

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0.181818

false

0

0.090909

0

0.545455

0

null

1

0

1

0

null

0

1

0

3

1cdc4f9eed5127ce6f230d3c5f394af453babf03

385

py

Python

q2_comp/__init__.py

dianahaider/q2-CHAOS

89571a8bffbebeeed2e2f5ec989a978169afaa88

[ "BSD-3-Clause" ]

null

q2_comp/__init__.py

dianahaider/q2-CHAOS

89571a8bffbebeeed2e2f5ec989a978169afaa88

[ "BSD-3-Clause" ]

null

q2_comp/__init__.py

dianahaider/q2-CHAOS

89571a8bffbebeeed2e2f5ec989a978169afaa88

[ "BSD-3-Clause" ]

null

from . import _alpha from . import _denoise from . import _taxonomy from ._alpha import (alpha_frequency, alpha_diversity) from ._denoise import (denoise_stats) from ._taxonomy import (taxo_variability) __all__ = ['alpha_frequency', 'alpha_diversity', 'denoise_stats', 'taxo_variability'] from ._version import get_versions __version__ = get_versions()['version'] del get_versions

25.666667

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47

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5.957447

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0.135714

0.2

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385

14

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27.5

0.821114

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false

0

0.7

0

0.7

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null

0

1

0

null

0

1

0

1

0

3

1ce06d34f7fe6b93f423edbe5ef505a1a0608531

570

py

Python

nlu/components/classifiers/sentiment_detector/sentiment_detector.py

sumanthratna/nlu

acde6879d776116051d4cbe909268ab8946989b5

[ "Apache-2.0" ]

1

2020-09-25T22:55:13.000Z

nlu/components/classifiers/sentiment_detector/sentiment_detector.py

sumanthratna/nlu

acde6879d776116051d4cbe909268ab8946989b5

[ "Apache-2.0" ]

null

nlu/components/classifiers/sentiment_detector/sentiment_detector.py

sumanthratna/nlu

acde6879d776116051d4cbe909268ab8946989b5

[ "Apache-2.0" ]

null

import nlu.pipe_components import sparknlp from sparknlp.annotator import * class SentimentDl: @staticmethod def get_default_model(): # TODO cannot runw ithouth a dictionary! return SentimentDetectorModel() \ .setInputCols("lemma", "sentence_embeddings") \ .setOutputCol("sentiment") \ \ @staticmethod def get_default_trainable_model(): return SentimentDetector() \ .setInputCols("lemma", "sentence_embeddings") \ .setOutputCol("sentiment") \ .setDictionary("dcit_TODO???")

30

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0.306849

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31.666667

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true

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0.125

0.5

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null

0

null

0

1

0

1

0

1

0

3

1ce104758de9c21aee1176a944bdbb004968ca9e

232

py

Python

atcoder/abc054/a.py

Ashindustry007/competitive-programming

2eabd3975c029d235abb7854569593d334acae2f

[ "WTFPL" ]

506

2018-08-22T10:30:38.000Z

2022-03-31T10:01:49.000Z

atcoder/abc054/a.py

Ashindustry007/competitive-programming

2eabd3975c029d235abb7854569593d334acae2f

[ "WTFPL" ]

13

2019-08-07T18:31:18.000Z

2020-12-15T21:54:41.000Z

atcoder/abc054/a.py

Ashindustry007/competitive-programming

2eabd3975c029d235abb7854569593d334acae2f

[ "WTFPL" ]

234

2018-08-06T17:11:41.000Z

2022-03-26T10:56:42.000Z

#!/usr/bin/env python3 # https://abc054.contest.atcoder.jp/tasks/abc054_a a, b = map(int, input().split()) if a == b: print('Draw') elif a == 1: print('Alice') elif b == 1: print('Bob') elif a > b: print('Alice') else: print('Bob')

25.777778

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41

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0

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0.133621

232

8

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0.301724

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0.125

0

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true

0

0.833333

0

null

0

1

0

null

0

1

0

1

0

3

1cf1834effd35d24cf8eec21ed50f484254b6230

766

py

Python

app/http/controllers/dashboard/TechniciansController.py

jrquiles18/Kennedy-Pools

628375c814c4b4a59fa194739ddab4ab5838d2f2

[ "MIT" ]

null

app/http/controllers/dashboard/TechniciansController.py

jrquiles18/Kennedy-Pools

628375c814c4b4a59fa194739ddab4ab5838d2f2

[ "MIT" ]

null

app/http/controllers/dashboard/TechniciansController.py

jrquiles18/Kennedy-Pools

628375c814c4b4a59fa194739ddab4ab5838d2f2

[ "MIT" ]

null

"""A TechniciansController Module.""" from masonite.request import Request from masonite.view import View from masonite.controllers import Controller from app.Technician import Technician class TechniciansController(Controller): """TechniciansController Controller Class.""" def __init__(self, request: Request): """TechniciansController Initializer Arguments: request {masonite.request.Request} -- The Masonite Request class. """ self.request = request def show(self, view: View): techs = Technician.all() return view.render('dashboard/technicians', {'techs': techs}) def logout(self, request: Request): request.session.reset() return request.redirect('/dashboard')

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1

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e8027d4e7a933ea51e200c6d5e5b6bf722082f31

327

py

Python

__init__.py

antoniamarie03/siren-alarm-skill

d4c994ef8f72725f78ff199e93ffd30f6a66b47d

[ "Apache-2.0" ]

null

__init__.py

antoniamarie03/siren-alarm-skill

d4c994ef8f72725f78ff199e93ffd30f6a66b47d

[ "Apache-2.0" ]

null

__init__.py

antoniamarie03/siren-alarm-skill

d4c994ef8f72725f78ff199e93ffd30f6a66b47d

[ "Apache-2.0" ]

null

from mycroft import MycroftSkill, intent_file_handler class SirenAlarm(MycroftSkill): def __init__(self): MycroftSkill.__init__(self) @intent_file_handler('alarm.siren.intent') def handle_alarm_siren(self, message): self.speak_dialog('alarm.siren') def create_skill(): return SirenAlarm()

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null

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1

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1

0

3

08fd3f72ab2400ef9e1e9f6c50e300b0c22e9a9f

1,573

py

Python

design_patterns__examples/Proxy/example.py

DazEB2/SimplePyScripts

1dde0a42ba93fe89609855d6db8af1c63b1ab7cc

[ "CC-BY-4.0" ]

117

2015-12-18T07:18:27.000Z

2022-03-28T00:25:54.000Z

design_patterns__examples/Proxy/example.py

DazEB2/SimplePyScripts

1dde0a42ba93fe89609855d6db8af1c63b1ab7cc

[ "CC-BY-4.0" ]

8

2018-10-03T09:38:46.000Z

2021-12-13T19:51:09.000Z

design_patterns__examples/Proxy/example.py

DazEB2/SimplePyScripts

1dde0a42ba93fe89609855d6db8af1c63b1ab7cc

[ "CC-BY-4.0" ]

28

2016-08-02T17:43:47.000Z

2022-03-21T08:31:12.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- __author__ = 'ipetrash' # SOURCE: Design Patterns: Proxy - Заместитель # SOURCE: https://ru.wikipedia.org/wiki/Заместитель_(шаблон_проектирования) class IMath: """Интерфейс для прокси и реального субъекта""" def add(self, x, y): raise NotImplementedError() def sub(self, x, y): raise NotImplementedError() def mul(self, x, y): raise NotImplementedError() def div(self, x, y): raise NotImplementedError() class Math(IMath): """Реальный субъект""" def add(self, x, y): return x + y def sub(self, x, y): return x - y def mul(self, x, y): return x * y def div(self, x, y): return x / y class MathProxy(IMath): """Прокси""" def __init__(self): self.math = None # Быстрые операции - не требуют реального субъекта def add(self, x, y): return x + y def sub(self, x, y): return x - y # Медленная операция - требует создания реального субъекта def mul(self, x, y): if not self.math: self.math = Math() return self.math.mul(x, y) def div(self, x, y): if y == 0: return float('inf') # Вернуть positive infinity if not self.math: self.math = Math() return self.math.div(x, y) if __name__ == '__main__': p = MathProxy() x, y = 4, 2 print('4 + 2 =', p.add(x, y)) print('4 - 2 =', p.sub(x, y)) print('4 * 2 =', p.mul(x, y)) print('4 / 2 =', p.div(x, y))

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0

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0

null

0

null

0

1

0

1

0

3

1c0cb41326a46bf497e4e202b368457518deb26c

595

py

Python

_Projects/flask_projects/todo_app/test.py

vincepogz/CUNY2X-TTP-Notes

8579c7457ef315c4465520b3f2ddb04b0a6ddaf7

[ "MIT" ]

null

_Projects/flask_projects/todo_app/test.py

vincepogz/CUNY2X-TTP-Notes

8579c7457ef315c4465520b3f2ddb04b0a6ddaf7

[ "MIT" ]

null

_Projects/flask_projects/todo_app/test.py

vincepogz/CUNY2X-TTP-Notes

8579c7457ef315c4465520b3f2ddb04b0a6ddaf7

[ "MIT" ]

1

2022-01-29T21:39:03.000Z

from app import app def test1(): """ This function test that the flask application has a correct response code when the application goes live """ response = app.test_client().get("/") assert response.status_code == 200 def test2(): """A dummy docstring""" response = app.test_client().get("/edit") assert response.status_code == 200 def test3(): """A dummy docstring""" response = app.test_client().get("/edit") assert b"To Do App" in response.data assert b"To Do Title" in response.data assert b"Add" in response.data

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null

0

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1

0

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1c12eb72b957a807ce5688e024cf15ee45589047

1,256

py

Python

OpenGLWrapper_JE/venv/Lib/site-packages/OpenGL/GLES2/OES/fbo_render_mipmap.py

JE-Chen/je_old_repo

a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5

[ "MIT" ]

null

OpenGLWrapper_JE/venv/Lib/site-packages/OpenGL/GLES2/OES/fbo_render_mipmap.py

JE-Chen/je_old_repo

a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5

[ "MIT" ]

null

OpenGLWrapper_JE/venv/Lib/site-packages/OpenGL/GLES2/OES/fbo_render_mipmap.py

JE-Chen/je_old_repo

a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5

[ "MIT" ]

null

'''OpenGL extension OES.fbo_render_mipmap This module customises the behaviour of the OpenGL.raw.GLES2.OES.fbo_render_mipmap to provide a more Python-friendly API Overview (from the spec) OES_framebuffer_object allows rendering to the base level of a texture only. This extension removes this limitation by allowing implementations to support rendering to any mip-level of a texture(s) that is attached to a framebuffer object(s). If this extension is supported, FramebufferTexture2DOES, and FramebufferTexture3DOES can be used to render directly into any mip level of a texture image The official definition of this extension is available here: http://www.opengl.org/registry/specs/OES/fbo_render_mipmap.txt ''' from OpenGL import platform, constant, arrays from OpenGL import extensions, wrapper import ctypes from OpenGL.raw.GLES2 import _types, _glgets from OpenGL.raw.GLES2.OES.fbo_render_mipmap import * from OpenGL.raw.GLES2.OES.fbo_render_mipmap import _EXTENSION_NAME def glInitFboRenderMipmapOES(): '''Return boolean indicating whether this extension is available''' from OpenGL import extensions return extensions.hasGLExtension( _EXTENSION_NAME ) ### END AUTOGENERATED SECTION

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1,256

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null

0

null

0

1

0

1

0

1

0

3

1c27fcdd05db513ebf2533fb78d8a688c277b9dc

593

py

Python

guided_diffusion/grad_reverse.py

ZGCTroy/guided-diffusion

af987bb2b65db2875148a5466df79736ea5ae6a1

[ "MIT" ]

null

guided_diffusion/grad_reverse.py

ZGCTroy/guided-diffusion

af987bb2b65db2875148a5466df79736ea5ae6a1

[ "MIT" ]

null

guided_diffusion/grad_reverse.py

ZGCTroy/guided-diffusion

af987bb2b65db2875148a5466df79736ea5ae6a1

[ "MIT" ]

null

from torch.autograd import Function # class GradReverse(Function): # def __init__(self, lambd): # self.lambd = lambd # # def forward(self, x): # return x.view_as(x) # # def backward(self, grad_output): # return (grad_output * -self.lambd) # # # def grad_reverse(x, lambd=1.0): # return GradReverse(lambd)(x) class GradReverse(Function): @staticmethod def forward(ctx, x): return x.view_as(x) @staticmethod def backward(ctx, grad_output): return grad_output.neg() def grad_reverse(x): return GradReverse.apply(x)

20.448276

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0.637437

76

593

4.815789

0.342105

0.10929

0.131148

0.065574

0.224044

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0.237774

593

29

45

20.448276

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0

0.1

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null

0

null

0

1

0

1

0

3

1c3c59272f0b216c9e1a63e8019a1742ca1d1695

418

py

Python

jobs/models.py

bharatchanddandamudi/Bharatchand_Portfolio_V1.1-deploy-

21205ec9d3263463b43422b4679b9736142f7308

[ "MIT" ]

null

jobs/models.py

bharatchanddandamudi/Bharatchand_Portfolio_V1.1-deploy-

21205ec9d3263463b43422b4679b9736142f7308

[ "MIT" ]

null

jobs/models.py

bharatchanddandamudi/Bharatchand_Portfolio_V1.1-deploy-

21205ec9d3263463b43422b4679b9736142f7308

[ "MIT" ]

null

from django.db import models from django.urls import reverse # Create your models here. class Job(models.Model): image = models.ImageField(upload_to='images/') summary = models.CharField(max_length=5000) # video = models.FileField(upload_to='images/',null=True) def __str__(self): return self.summary def get_absolute_url(self): return reverse('links', kwargs={"pk": self.pk})

24.588235

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0.070922

0.099291

0

0.011696

0.181818

418

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false

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1

0

null

0

null

0

1

0

1

0

3

1c572b006232931c609ddc348fb2c6139f59f91a

4,325

py

Python

pyStorageBackend/__init__.py

tantalum7/pyStorageBackend

948785a9431c4013a476341d3e3fc773ba0612eb

[ "WTFPL" ]

null

pyStorageBackend/__init__.py

tantalum7/pyStorageBackend

948785a9431c4013a476341d3e3fc773ba0612eb

[ "WTFPL" ]

null

pyStorageBackend/__init__.py

tantalum7/pyStorageBackend

948785a9431c4013a476341d3e3fc773ba0612eb

[ "WTFPL" ]

null

# Project imports from pyStorageBackend.uid import UID from pyStorageBackend.generic_backend import GenericBackend # Exceptions class InvalidKeyException(Exception): pass class InvalidUIDException(Exception): pass class InvalidDataException(Exception): pass class DocumentNotFoundException(Exception): pass class Storage: MAX_KEY_LENGTH = 32 MAX_DATA_LENGTH = 65536 def __init__(self, backend, settings: dict): """ Thin wrapper class around the specific implementation of GenericBackend used :param settings: """ self._backend = backend(settings=settings) def open(self): """ Opens the storage medium :return: """ self._backend.open() def close(self, options: dict=None): """ Safe-closes the storage medium :param options: Optional dict passed with options specific to the backend implementation """ self._backend.close(options=options) def get(self, uid: UID, key: str) -> bytes: """ Retrieves a value for the key given, within the document specified by the uid :param uid: UID of the document to access :param key: Key string to retrieve value of :return: Data bytes stored at key, or None """ self._validate(key=key, uid=uid) return self._backend.get(uid=uid, key=key) def get_document(self, uid: UID) -> dict: """ Retrieves the entire document with the given uid (dict of key:value pairs) :param uid: UID of the document to retrieve :return: Dict of key:value pairs for the document """ self._validate(uid=uid) return self._backend.get_document(uid=uid) def put(self, uid: UID, key, data): """ Stores data bytes for the key given, in the document with the uid specified. :param uid: UID of document to store in :param key: Key string to store data against :param data: Data bytes to store """ self._validate(uid=uid, key=key, data=data) self._backend.put(uid=uid, key=key, data=data) def delete(self, uid: UID, key): """ Deletes a key:value pair in the document with the uid specified Fails silently if the key doesn't exist :param uid: UID of the document to operate on :param key: Key string of the key:value pair to delete """ self._validate(uid=uid, key=key) self._backend.delete(uid=uid, key=key) def delete_document(self, uid): """ Deletes the entire document with the uid given :param uid: UID of the document to delete """ self._validate(uid=uid) self._backend.delete_document(uid=uid) def sync(self, options=None): """ Triggers a synchronisation of the storage medium. Actual operation depends on the backend, but typically storage writes should be considered volatile until sync() is called. :param options: Optional dict of options related to sync(), dependant on backend implementation :return: """ self._backend.sync(options=options) def count(self, uid: UID) -> int: """ Returns the number of keys stored in a given document :param uid: UID of document to count keys for :return: Number of keys (int) """ self._validate(uid=uid) return self._backend.count(uid=uid) @staticmethod def generate_uid(): """ Generates a new, random uid. Each new uid is considered globally unique, using the uuid library. The UID class is just a wrapper for a 32char uuid string :return: """ return UID.new() def _validate(self, key: str=None, uid: UID=None, data: bytes=None): # Validate key if key is not None: if not isinstance(key, str) or len(key) == 0 or len(key) > self.MAX_KEY_LENGTH: raise InvalidKeyException # Validate uid if uid is not None: if not isinstance(uid, UID): raise InvalidUIDException # Validate data if data is not None: if not isinstance(data, bytes) or len(data) < self.MAX_DATA_LENGTH: raise InvalidDataException

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1

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3

1c5c36b5c90c28b4a1c5c49c81d242e3804093dc

146,324

py

Python

abcvoting/abcrules.py

martinlackner/approval-multiwinner

17bb6294b1910531b66c457f7b1a34a966d4113d

[ "MIT" ]

2

2019-07-10T08:54:43.000Z

2019-09-09T16:17:04.000Z

abcvoting/abcrules.py

martinlackner/approval-multiwinner

17bb6294b1910531b66c457f7b1a34a966d4113d

[ "MIT" ]

1

2019-09-11T21:29:47.000Z

abcvoting/abcrules.py

martinlackner/approval-multiwinner

17bb6294b1910531b66c457f7b1a34a966d4113d

[ "MIT" ]

1

2019-09-11T16:56:23.000Z

"""Approval-based committee (ABC) voting rules.""" import functools import itertools import random from fractions import Fraction from abcvoting.output import output, DETAILS from abcvoting import abcrules_gurobi, abcrules_ortools, abcrules_mip, misc, scores from abcvoting.misc import str_committees_with_header, header, str_set_of_candidates from abcvoting.misc import sorted_committees, CandidateSet try: from gmpy2 import mpq except ImportError: mpq = None ######################################################################## MAIN_RULE_IDS = [ "av", "sav", "pav", "slav", "cc", "lexcc", "geom2", "seqpav", "revseqpav", "seqslav", "seqcc", "seqphragmen", "minimaxphragmen", "leximaxphragmen", "monroe", "greedy-monroe", "minimaxav", "lexminimaxav", "rule-x", "phragmen-enestroem", "consensus-rule", "trivial", "rsd", "eph", ] """ List of rule identifiers (`rule_id`) for the main ABC rules included in abcvoting. This selection is somewhat arbitrary. But all really important rules (that are implemented) are contained in this list. """ ALGORITHM_NAMES = { "gurobi": "Gurobi ILP solver", "branch-and-bound": "branch-and-bound", "brute-force": "brute-force", "mip-cbc": "CBC ILP solver via Python MIP library", "mip-gurobi": "Gurobi ILP solver via Python MIP library", # "cvxpy_gurobi": "Gurobi ILP solver via CVXPY library", # "cvxpy_scip": "SCIP ILP solver via CVXPY library", # "cvxpy_glpk_mi": "GLPK ILP solver via CVXPY library", # "cvxpy_cbc": "CBC ILP solver via CVXPY library", "standard": "Standard algorithm", "standard-fractions": "Standard algorithm (using standard Python fractions)", "gmpy2-fractions": "Standard algorithm (using gmpy2 fractions)", "float-fractions": "Standard algorithm (using floats instead of fractions)", "ortools-cp": "OR-Tools CP-SAT solver", } """ A dictionary containing mapping all valid algorithm identifiers to full names (i.e., descriptions). """ MAX_NUM_OF_COMMITTEES_DEFAULT = None """ The maximum number of committees that is returned by an ABC voting rule. If `MAX_NUM_OF_COMMITTEES_DEFAULT` ist set to `None`, then there is no constraint on the maximum number of committees. Can be overridden with the parameter `max_num_of_committees` in any `compute` function. """ class Rule: """ A class that contains the main information about an ABC rule. Parameters ---------- rule_id : str The rule identifier. """ _THIELE_ALGORITHMS = ( # algorithms sorted by speed "gurobi", "mip-gurobi", "mip-cbc", "branch-and-bound", "brute-force", ) _RESOLUTE_VALUES_FOR_OPTIMIZATION_BASED_RULES = (False, True) _RESOLUTE_VALUES_FOR_SEQUENTIAL_RULES = (True, False) def __init__( self, rule_id, ): self.rule_id = rule_id if rule_id == "av": self.shortname = "AV" self.longname = "Approval Voting (AV)" self.compute_fct = compute_av self.algorithms = ("standard",) self.resolute_values = self._RESOLUTE_VALUES_FOR_OPTIMIZATION_BASED_RULES elif rule_id == "sav": self.shortname = "SAV" self.longname = "Satisfaction Approval Voting (SAV)" self.compute_fct = compute_sav self.algorithms = ("standard",) self.resolute_values = self._RESOLUTE_VALUES_FOR_OPTIMIZATION_BASED_RULES elif rule_id == "pav": self.shortname = "PAV" self.longname = "Proportional Approval Voting (PAV)" self.compute_fct = compute_pav self.algorithms = self._THIELE_ALGORITHMS self.resolute_values = self._RESOLUTE_VALUES_FOR_OPTIMIZATION_BASED_RULES elif rule_id == "slav": self.shortname = "SLAV" self.longname = "Sainte-Laguë Approval Voting (SLAV)" self.compute_fct = compute_slav self.algorithms = self._THIELE_ALGORITHMS self.resolute_values = self._RESOLUTE_VALUES_FOR_OPTIMIZATION_BASED_RULES elif rule_id == "cc": self.shortname = "CC" self.longname = "Approval Chamberlin-Courant (CC)" self.compute_fct = compute_cc self.algorithms = ( # algorithms sorted by speed "gurobi", "mip-gurobi", "ortools-cp", "branch-and-bound", "brute-force", "mip-cbc", ) self.resolute_values = self._RESOLUTE_VALUES_FOR_OPTIMIZATION_BASED_RULES elif rule_id == "lexcc": self.shortname = "lex-CC" self.longname = "Lexicographic Chamberlin-Courant (lex-CC)" self.compute_fct = compute_lexcc # algorithms sorted by speed self.algorithms = ("gurobi", "mip-gurobi", "brute-force", "mip-cbc") self.resolute_values = self._RESOLUTE_VALUES_FOR_OPTIMIZATION_BASED_RULES elif rule_id == "seqpav": self.shortname = "seq-PAV" self.longname = "Sequential Proportional Approval Voting (seq-PAV)" self.compute_fct = compute_seqpav self.algorithms = ("standard",) self.resolute_values = self._RESOLUTE_VALUES_FOR_SEQUENTIAL_RULES elif rule_id == "revseqpav": self.shortname = "revseq-PAV" self.longname = "Reverse Sequential Proportional Approval Voting (revseq-PAV)" self.compute_fct = compute_revseqpav self.algorithms = ("standard",) self.resolute_values = self._RESOLUTE_VALUES_FOR_SEQUENTIAL_RULES elif rule_id == "seqslav": self.shortname = "seq-SLAV" self.longname = "Sequential Sainte-Laguë Approval Voting (seq-SLAV)" self.compute_fct = compute_seqslav self.algorithms = ("standard",) self.resolute_values = self._RESOLUTE_VALUES_FOR_SEQUENTIAL_RULES elif rule_id == "seqcc": self.shortname = "seq-CC" self.longname = "Sequential Approval Chamberlin-Courant (seq-CC)" self.compute_fct = compute_seqcc self.algorithms = ("standard",) self.resolute_values = self._RESOLUTE_VALUES_FOR_SEQUENTIAL_RULES elif rule_id == "seqphragmen": self.shortname = "seq-Phragmén" self.longname = "Phragmén's Sequential Rule (seq-Phragmén)" self.compute_fct = compute_seqphragmen self.algorithms = ("float-fractions", "gmpy2-fractions", "standard-fractions") self.resolute_values = self._RESOLUTE_VALUES_FOR_SEQUENTIAL_RULES elif rule_id == "minimaxphragmen": self.shortname = "minimax-Phragmén" self.longname = "Phragmén's Minimax Rule (minimax-Phragmén)" self.compute_fct = compute_minimaxphragmen self.algorithms = ("gurobi", "mip-gurobi", "mip-cbc") self.resolute_values = self._RESOLUTE_VALUES_FOR_OPTIMIZATION_BASED_RULES elif rule_id == "leximaxphragmen": self.shortname = "leximax-Phragmén" self.longname = "Phragmén's Leximax Rule (leximax-Phragmén)" self.compute_fct = compute_leximaxphragmen self.algorithms = ("gurobi",) # TODO: "mip-gurobi", "mip-cbc"), self.resolute_values = self._RESOLUTE_VALUES_FOR_OPTIMIZATION_BASED_RULES elif rule_id == "monroe": self.shortname = "Monroe" self.longname = "Monroe's Approval Rule (Monroe)" self.compute_fct = compute_monroe self.algorithms = ( # algorithms sorted by speed "gurobi", "mip-gurobi", "mip-cbc", "ortools-cp", "brute-force", ) self.resolute_values = self._RESOLUTE_VALUES_FOR_OPTIMIZATION_BASED_RULES elif rule_id == "greedy-monroe": self.shortname = "Greedy Monroe" self.longname = "Greedy Monroe" self.compute_fct = compute_greedy_monroe self.algorithms = ("standard",) self.resolute_values = (True,) elif rule_id == "minimaxav": self.shortname = "minimaxav" self.longname = "Minimax Approval Voting (MAV)" self.compute_fct = compute_minimaxav self.algorithms = ("gurobi", "mip-gurobi", "ortools-cp", "mip-cbc", "brute-force") # algorithms sorted by speed. however, for small profiles with a small committee size, # brute-force is often the fastest self.resolute_values = self._RESOLUTE_VALUES_FOR_OPTIMIZATION_BASED_RULES elif rule_id == "lexminimaxav": self.shortname = "lex-MAV" self.longname = "Lexicographic Minimax Approval Voting (lex-MAV)" self.compute_fct = compute_lexminimaxav self.algorithms = ("gurobi", "brute-force") self.resolute_values = self._RESOLUTE_VALUES_FOR_OPTIMIZATION_BASED_RULES elif rule_id == "rule-x": self.shortname = "Rule X" self.longname = "Rule X (aka Method of Equal Shares)" self.compute_fct = compute_rule_x self.algorithms = ("float-fractions", "gmpy2-fractions", "standard-fractions") self.resolute_values = self._RESOLUTE_VALUES_FOR_SEQUENTIAL_RULES elif rule_id == "rule-x-without-phragmen-phase": self.shortname = "Rule X without Phragmén phase" self.longname = "Rule X without the Phragmén phase (second phase)" self.compute_fct = functools.partial(compute_rule_x, skip_phragmen_phase=True) self.algorithms = ("float-fractions", "gmpy2-fractions", "standard-fractions") self.resolute_values = self._RESOLUTE_VALUES_FOR_SEQUENTIAL_RULES elif rule_id == "phragmen-enestroem": self.shortname = "Phragmén-Eneström" self.longname = "Method of Phragmén-Eneström" self.compute_fct = compute_phragmen_enestroem self.algorithms = ("float-fractions", "gmpy2-fractions", "standard-fractions") self.resolute_values = self._RESOLUTE_VALUES_FOR_SEQUENTIAL_RULES elif rule_id == "consensus-rule": self.shortname = "Consensus Rule" self.longname = "Consensus Rule" self.compute_fct = compute_consensus_rule self.algorithms = ("float-fractions", "gmpy2-fractions", "standard-fractions") self.resolute_values = self._RESOLUTE_VALUES_FOR_SEQUENTIAL_RULES elif rule_id == "trivial": self.shortname = "Trivial Rule" self.longname = "Trivial Rule" self.compute_fct = compute_trivial_rule self.algorithms = ("standard",) self.resolute_values = self._RESOLUTE_VALUES_FOR_OPTIMIZATION_BASED_RULES elif rule_id == "rsd": self.shortname = "Random Serial Dictator" self.longname = "Random Serial Dictator" self.compute_fct = compute_rsd self.algorithms = ("standard",) self.resolute_values = (True,) elif rule_id == "eph": self.shortname = "E Pluribus Hugo" self.longname = "E Pluribus Hugo (EPH)" self.compute_fct = compute_eph self.algorithms = ("float-fractions", "gmpy2-fractions", "standard-fractions") self.resolute_values = (False, True) elif rule_id.startswith("geom"): parameter = rule_id[4:] self.shortname = f"{parameter}-Geometric" self.longname = f"{parameter}-Geometric Rule" self.compute_fct = functools.partial(compute_thiele_method, rule_id) self.algorithms = self._THIELE_ALGORITHMS self.resolute_values = self._RESOLUTE_VALUES_FOR_OPTIMIZATION_BASED_RULES elif rule_id.startswith("seq") or rule_id.startswith("revseq"): # handle sequential and reverse sequential Thiele methods # that are not explicitly included in the list above if rule_id.startswith("seq"): scorefct_id = rule_id[3:] # score function id of Thiele method else: scorefct_id = rule_id[6:] # score function id of Thiele method try: scores.get_marginal_scorefct(scorefct_id) except scores.UnknownScoreFunctionError as error: raise UnknownRuleIDError(rule_id) from error if rule_id == "av": raise UnknownRuleIDError(rule_id) # seq-AV and revseq-AV are equivalent to AV # sequential Thiele methods optrule = Rule(scorefct_id) if rule_id.startswith("seq"): self.shortname = f"seq-{optrule.shortname}" self.longname = f"Sequential {optrule.longname}" self.compute_fct = functools.partial(compute_seq_thiele_method, scorefct_id) self.algorithms = ("standard",) self.resolute_values = self._RESOLUTE_VALUES_FOR_SEQUENTIAL_RULES # reverse sequential Thiele methods elif rule_id.startswith("revseq"): self.shortname = f"revseq-{optrule.shortname}" self.longname = f"Reverse Sequential {optrule.longname}" self.compute_fct = functools.partial(compute_revseq_thiele_method, scorefct_id) self.algorithms = ("standard",) self.resolute_values = self._RESOLUTE_VALUES_FOR_SEQUENTIAL_RULES else: raise UnknownRuleIDError(rule_id) # find all *available* algorithms for this ABC rule self.available_algorithms = [] for algorithm in self.algorithms: if algorithm in available_algorithms: self.available_algorithms.append(algorithm) def fastest_available_algorithm(self): """ Return the fastest algorithm for this rule that is available on this system. An algorithm may not be available because its requirements are not satisfied. For example, some algorithms require Gurobi, others require gmpy2 - both of which are not requirements for abcvoting. Returns ------- str """ if self.available_algorithms: # This rests on the assumption that ``self.algorithms`` are sorted by speed. return self.available_algorithms[0] raise NoAvailableAlgorithm(self.rule_id, self.algorithms) def compute(self, profile, committeesize, **kwargs): """ Compute rule using self._compute_fct. Parameters ---------- profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. **kwargs : dict Optional arguments for computing the rule (e.g., `resolute`). Returns ------- list of CandidateSet A list of winning committees. """ return self.compute_fct(profile, committeesize, **kwargs) def verify_compute_parameters( self, profile, committeesize, algorithm, resolute, max_num_of_committees=MAX_NUM_OF_COMMITTEES_DEFAULT, ): """ Basic checks for parameter values when computing an ABC rule. Parameters ---------- profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. resolute : bool Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. Returns ------- bool """ if committeesize < 1: raise ValueError("Parameter `committeesize` must be a positive integer.") if committeesize > profile.num_cand: raise ValueError( "Parameter `committeesize` must be smaller or equal to" " the total number of candidates." ) if len(profile) == 0: raise ValueError("The given profile contains no voters (len(profile) == 0).") if algorithm not in self.algorithms: raise UnknownAlgorithm(self.rule_id, algorithm) if resolute not in self.resolute_values: raise NotImplementedError( f'ABC rule with rule_id "{self.rule_id}" does not support resolute={resolute}.' ) if (max_num_of_committees is not None and not isinstance(max_num_of_committees, int)) or ( max_num_of_committees is not None and max_num_of_committees < 1 ): raise ValueError( "Parameter `max_num_of_committees` must be None or a positive integer." ) if max_num_of_committees is not None and resolute: raise ValueError( "Parameter `max_num_of_committees` cannot be used when `resolute` is set to True." ) class UnknownRuleIDError(ValueError): """ Error: unknown rule id. Parameters ---------- rule_id : str The unknown rule identifier. """ def __init__(self, rule_id): message = f'Rule ID "{rule_id}" is not known.' super().__init__(message) class UnknownAlgorithm(ValueError): """ Error: unknown algorithm for a given ABC rule. Parameters ---------- rule_id : str The ABC rule for which the algorithm is not known. algorithm : str The unknown algorithm. """ def __init__(self, rule_id, algorithm): message = f"Algorithm {algorithm} not specified for ABC rule {rule_id}." super().__init__(message) class NoAvailableAlgorithm(ValueError): """ Exception: none of the implemented algorithms are available. This error occurs because no solvers are installed. Parameters ---------- rule_id : str The ABC rule for which no algorithm are available. algorithms : tuple of str List of algorithms for this rule (none of which are available). """ def __init__(self, rule_id, algorithms): message = ( f"None of the implemented algorithms are available for ABC rule {rule_id}\n" f"(because the solvers for the following algorithms are not installed: " f"{algorithms}) " ) super().__init__(message) def _available_algorithms(): """Verify which algorithms are supported on the current machine. This is done by verifying that the required modules and solvers are available. """ available = [] for algorithm in ALGORITHM_NAMES: if "gurobi" in algorithm and not abcrules_gurobi.gb: continue if algorithm == "gmpy2-fractions" and not mpq: continue available.append(algorithm) return available available_algorithms = _available_algorithms() def get_rule(rule_id): """ Get instance of `Rule` for the ABC rule specified by `rule_id`. .. deprecated:: 2.3.0 Function `get_rule(rule_id)` is deprecated, use `Rule(rule_id)` instead. Parameters ---------- rule_id : str The rule identifier. Returns ------- Rule A corresponding `Rule` object. """ return Rule(rule_id) ######################################################################## def compute(rule_id, profile, committeesize, result=None, **kwargs): """ Compute winning committees with an ABC rule given by `rule_id`. Parameters ---------- rule_id : str The rule identifier. profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. result : list of CandidateSet, optional Expected winning committees. This is used in unit tests to verify correctness. Raises `ValueError` if `result` is different from actual winning committees. **kwargs : dict Optional arguments for computing the rule (e.g., `resolute`). Returns ------- list of CandidateSet A list of the winning committees. If `resolute=True`, the list contains only one winning committee. """ rule = Rule(rule_id) committees = rule.compute(profile=profile, committeesize=committeesize, **kwargs) if result is not None: # verify that the parameter `result` is indeed the result of computing the ABC rule resolute = kwargs.get("resolute", rule.resolute_values[0]) misc.verify_expected_committees_equals_actual_committees( actual_committees=committees, expected_committees=result, resolute=resolute, shortname=rule.shortname, ) return committees def compute_thiele_method( scorefct_id, profile, committeesize, algorithm="fastest", resolute=False, max_num_of_committees=MAX_NUM_OF_COMMITTEES_DEFAULT, ): """ Compute winning committees with Thiele methods. Compute winning committees according to a Thiele method specified by a score function (scorefct_id). Examples of Thiele methods are PAV, CC, and SLAV. An exception is Approval Voting (AV), which should be computed using compute_av(). (AV is polynomial-time computable (separable) and can thus be computed much faster.) For a mathematical description of Thiele methods, see e.g. "Multi-Winner Voting with Approval Preferences". Martin Lackner and Piotr Skowron. <https://arxiv.org/abs/2007.01795> Parameters ---------- scorefct_id : str A string identifying the score function that defines the Thiele method. profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. resolute : bool, optional Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. Returns ------- list of CandidateSet A list of winning committees. If `resolute=True`, the list contains only one winning committee. """ rule = Rule(scorefct_id) if algorithm == "fastest": algorithm = rule.fastest_available_algorithm() rule.verify_compute_parameters( profile=profile, committeesize=committeesize, algorithm=algorithm, resolute=resolute, max_num_of_committees=max_num_of_committees, ) if algorithm == "gurobi": committees = abcrules_gurobi._gurobi_thiele_methods( scorefct_id=scorefct_id, profile=profile, committeesize=committeesize, resolute=resolute, max_num_of_committees=max_num_of_committees, ) elif algorithm == "branch-and-bound": committees, detailed_info = _thiele_methods_branchandbound( scorefct_id=scorefct_id, profile=profile, committeesize=committeesize, resolute=resolute, max_num_of_committees=max_num_of_committees, ) elif algorithm == "brute-force": committees, detailed_info = _thiele_methods_bruteforce( scorefct_id=scorefct_id, profile=profile, committeesize=committeesize, resolute=resolute, max_num_of_committees=max_num_of_committees, ) elif algorithm.startswith("mip-"): committees = abcrules_mip._mip_thiele_methods( scorefct_id=scorefct_id, profile=profile, committeesize=committeesize, resolute=resolute, max_num_of_committees=max_num_of_committees, solver_id=algorithm[4:], ) elif algorithm == "ortools-cp" and scorefct_id == "cc": committees = abcrules_ortools._ortools_cc( profile=profile, committeesize=committeesize, resolute=resolute, max_num_of_committees=max_num_of_committees, ) else: raise UnknownAlgorithm(scorefct_id, algorithm) # optional output output.info(header(rule.longname), wrap=False) if resolute: output.info("Computing only one winning committee (resolute=True)\n") output.details(f"Algorithm: {ALGORITHM_NAMES[algorithm]}\n") output.details( f"Optimal {scorefct_id.upper()}-score: " f"{scores.thiele_score(scorefct_id, profile, committees[0])}\n" ) output.info( str_committees_with_header(committees, cand_names=profile.cand_names, winning=True) ) # end of optional output return committees def _thiele_methods_bruteforce( scorefct_id, profile, committeesize, resolute, max_num_of_committees=MAX_NUM_OF_COMMITTEES_DEFAULT, ): """ Brute-force algorithm for Thiele methods (PAV, CC, etc.). Only intended for comparison, much slower than _thiele_methods_branchandbound() """ opt_committees = [] opt_thiele_score = -1 for committee in itertools.combinations(profile.candidates, committeesize): score = scores.thiele_score(scorefct_id, profile, committee) if score > opt_thiele_score: opt_committees = [committee] opt_thiele_score = score elif score == opt_thiele_score: if not resolute: opt_committees.append(committee) committees = sorted_committees(opt_committees) if max_num_of_committees is not None: committees = committees[:max_num_of_committees] detailed_info = {} if resolute: committees = [committees[0]] return committees, detailed_info def _thiele_methods_branchandbound( scorefct_id, profile, committeesize, resolute, max_num_of_committees=MAX_NUM_OF_COMMITTEES_DEFAULT, ): """ Branch-and-bound algorithm for Thiele methods. """ marginal_scorefct = scores.get_marginal_scorefct(scorefct_id, committeesize) best_committees = [] init_com, _ = _seq_thiele_resolute(scorefct_id, profile, committeesize) init_com = init_com[0] best_score = scores.thiele_score(scorefct_id, profile, init_com) part_coms = [[]] while part_coms: part_com = part_coms.pop(0) # potential committee, check if at least as good # as previous best committee if len(part_com) == committeesize: score = scores.thiele_score(scorefct_id, profile, part_com) if score == best_score: best_committees.append(part_com) elif score > best_score: best_committees = [part_com] best_score = score else: if len(part_com) > 0: largest_cand = part_com[-1] else: largest_cand = -1 missing = committeesize - len(part_com) marg_util_cand = scores.marginal_thiele_scores_add( marginal_scorefct, profile, part_com ) upper_bound = sum( sorted(marg_util_cand[largest_cand + 1 :])[-missing:] ) + scores.thiele_score(scorefct_id, profile, part_com) if upper_bound >= best_score: for cand in range(largest_cand + 1, profile.num_cand - missing + 1): part_coms.insert(0, part_com + [cand]) committees = sorted_committees(best_committees) if max_num_of_committees is not None: committees = committees[:max_num_of_committees] if resolute: committees = [committees[0]] detailed_info = {} return committees, detailed_info def compute_pav( profile, committeesize, algorithm="fastest", resolute=False, max_num_of_committees=MAX_NUM_OF_COMMITTEES_DEFAULT, ): """ Compute winning committees with Proportional Approval Voting (PAV). This ABC rule belongs to the class of Thiele methods. For a mathematical description of this rule, see e.g. "Multi-Winner Voting with Approval Preferences". Martin Lackner and Piotr Skowron. <https://arxiv.org/abs/2007.01795> Parameters ---------- profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. The following algorithms are available for PAV: .. doctest:: >>> Rule("pav").algorithms ('gurobi', 'mip-gurobi', 'mip-cbc', 'branch-and-bound', 'brute-force') resolute : bool, optional Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. Returns ------- list of CandidateSet A list of winning committees. """ return compute_thiele_method( scorefct_id="pav", profile=profile, committeesize=committeesize, algorithm=algorithm, resolute=resolute, max_num_of_committees=max_num_of_committees, ) def compute_slav( profile, committeesize, algorithm="fastest", resolute=False, max_num_of_committees=MAX_NUM_OF_COMMITTEES_DEFAULT, ): """ Compute winning committees with Sainte-Lague Approval Voting (SLAV). This ABC rule belongs to the class of Thiele methods. For a mathematical description of this rule, see e.g. Martin Lackner and Piotr Skowron Utilitarian Welfare and Representation Guarantees of Approval-Based Multiwinner Rules In Artificial Intelligence, 288: 103366, 2020. <https://arxiv.org/abs/1801.01527> Parameters ---------- profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. The following algorithms are available for SLAV: .. doctest:: >>> Rule("slav").algorithms ('gurobi', 'mip-gurobi', 'mip-cbc', 'branch-and-bound', 'brute-force') resolute : bool, optional Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. Returns ------- list of CandidateSet A list of winning committees. """ return compute_thiele_method( scorefct_id="slav", profile=profile, committeesize=committeesize, algorithm=algorithm, resolute=resolute, max_num_of_committees=max_num_of_committees, ) def compute_cc( profile, committeesize, algorithm="fastest", resolute=False, max_num_of_committees=MAX_NUM_OF_COMMITTEES_DEFAULT, ): """ Compute winning committees with Approval Chamberlin-Courant (CC). This ABC rule belongs to the class of Thiele methods. For a mathematical description of this rule, see e.g. "Multi-Winner Voting with Approval Preferences". Martin Lackner and Piotr Skowron. <https://arxiv.org/abs/2007.01795> Parameters ---------- profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. The following algorithms are available for Approval Chamberlin-Courant (CC): .. doctest:: >>> Rule("cc").algorithms # doctest: +NORMALIZE_WHITESPACE ('gurobi', 'mip-gurobi', 'ortools-cp', 'branch-and-bound', 'brute-force', 'mip-cbc') resolute : bool, optional Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. Returns ------- list of CandidateSet A list of winning committees. """ return compute_thiele_method( scorefct_id="cc", profile=profile, committeesize=committeesize, algorithm=algorithm, resolute=resolute, max_num_of_committees=max_num_of_committees, ) def compute_lexcc( profile, committeesize, algorithm="fastest", resolute=False, max_num_of_committees=MAX_NUM_OF_COMMITTEES_DEFAULT, ): """ Compute winning committees with a Lexicographic Chamberlin-Courant (lex-CC). This ABC rule is a lexicographic variant of Approval Chamberlin-Courant (CC). It maximizes the CC score, i.e., the number of voters with at least one approved candidate in the winning committee. If there is more than one such committee, it chooses the committee with most voters having at least two approved candidates in the committee. This tie-breaking continues with values of 3, 4, .., k if necessary. This rule can be seen as an analogue to the leximin social welfare ordering for utility functions. .. important:: Very slow due to lexicographic optimization. Parameters ---------- profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. The following algorithms are available for Lexicographic Chamberlin-Courant (lex-CC): .. doctest:: >>> Rule("lexcc").algorithms ('gurobi', 'mip-gurobi', 'brute-force', 'mip-cbc') resolute : bool, optional Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. Returns ------- list of CandidateSet A list of winning committees. """ rule_id = "lexcc" rule = Rule(rule_id) if algorithm == "fastest": algorithm = rule.fastest_available_algorithm() rule.verify_compute_parameters( profile=profile, committeesize=committeesize, algorithm=algorithm, resolute=resolute, max_num_of_committees=max_num_of_committees, ) if algorithm == "brute-force": committees, detailed_info = _lexcc_bruteforce( profile=profile, committeesize=committeesize, resolute=resolute, max_num_of_committees=max_num_of_committees, ) elif algorithm == "gurobi": committees, detailed_info = abcrules_gurobi._gurobi_lexcc( profile=profile, committeesize=committeesize, resolute=resolute, max_num_of_committees=max_num_of_committees, ) elif algorithm.startswith("mip-"): committees, detailed_info = abcrules_mip._mip_lexcc( profile=profile, committeesize=committeesize, resolute=resolute, max_num_of_committees=max_num_of_committees, solver_id=algorithm[4:], ) else: raise UnknownAlgorithm(rule_id, algorithm) # optional output output.info(header(rule.longname), wrap=False) if resolute: output.info("Computing only one winning committee (resolute=True)\n") output.details(f"Algorithm: {ALGORITHM_NAMES[algorithm]}\n") output.details("At-least-ell scores:") for ell, score in enumerate(detailed_info["opt_score_vector"]): output.details(f"at-least-{ell+1}: {score}", indent=" ") output.info( str_committees_with_header(committees, cand_names=profile.cand_names, winning=True) ) # end of optional output return committees def _lexcc_bruteforce(profile, committeesize, resolute, max_num_of_committees): opt_committees = [] opt_score_vector = [0] * committeesize for committee in itertools.combinations(profile.candidates, committeesize): score_vector = [ scores.thiele_score(f"atleast{ell}", profile, committee) for ell in range(1, committeesize + 1) ] for i in range(committeesize): if opt_score_vector[i] > score_vector[i]: break if opt_score_vector[i] < score_vector[i]: opt_score_vector = score_vector opt_committees = [committee] break else: opt_committees.append(committee) committees = sorted_committees(opt_committees) detailed_info = {"opt_score_vector": opt_score_vector} if resolute: committees = [committees[0]] if max_num_of_committees is not None: committees = committees[:max_num_of_committees] return committees, detailed_info def compute_seq_thiele_method( scorefct_id, profile, committeesize, algorithm="fastest", resolute=True, max_num_of_committees=MAX_NUM_OF_COMMITTEES_DEFAULT, ): """ Sequential Thiele methods. For a mathematical description of these rules, see e.g. "Multi-Winner Voting with Approval Preferences". Martin Lackner and Piotr Skowron. <https://arxiv.org/abs/2007.01795> Parameters ---------- scorefct_id : str A string identifying the score function that defines the Thiele method. profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. resolute : bool, optional Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. Returns ------- list of CandidateSet A list of winning committees. """ scores.get_marginal_scorefct(scorefct_id, committeesize) # check that `scorefct_id` is valid rule_id = "seq" + scorefct_id rule = Rule(rule_id) if algorithm == "fastest": algorithm = rule.fastest_available_algorithm() rule.verify_compute_parameters( profile=profile, committeesize=committeesize, algorithm=algorithm, resolute=resolute, max_num_of_committees=max_num_of_committees, ) if algorithm == "standard": if resolute: committees, detailed_info = _seq_thiele_resolute(scorefct_id, profile, committeesize) else: committees, detailed_info = _seq_thiele_irresolute( scorefct_id, profile, committeesize, max_num_of_committees ) else: raise UnknownAlgorithm(rule_id, algorithm) # optional output output.info(header(rule.longname), wrap=False) if not resolute: output.info("Computing all possible winning committees for any tiebreaking order") output.info(" (aka parallel universes tiebreaking) (resolute=False)\n") if output.verbosity <= DETAILS: # skip thiele_score() calculations if not necessary output.details(f"Algorithm: {ALGORITHM_NAMES[algorithm]}\n") if resolute: output.details( f"starting with the empty committee (score = " f"{scores.thiele_score(scorefct_id, profile, [])})\n" ) committee = [] for i, next_cand in enumerate(detailed_info["next_cand"]): tied_cands = detailed_info["tied_cands"][i] delta_score = detailed_info["delta_score"][i] committee.append(next_cand) output.details(f"adding candidate number {i+1}: {profile.cand_names[next_cand]}") output.details( f"score increases by {delta_score} to" f" a total of {scores.thiele_score(scorefct_id, profile, committee)}", indent=" ", ) if len(tied_cands) > 1: output.details(f"tie broken in favor of {next_cand},\n", indent=" ") output.details( f"candidates " f"{str_set_of_candidates(tied_cands, cand_names=profile.cand_names)} " "are tied" ) output.details( f"(all would increase the score by the same amount {delta_score})", indent=" ", ) output.details("") output.info( str_committees_with_header(committees, cand_names=profile.cand_names, winning=True) ) if output.verbosity <= DETAILS: # skip thiele_score() calculations if not necessary output.details(scorefct_id.upper() + "-score of winning committee(s):") for committee in committees: output.details( f"{str_set_of_candidates(committee, cand_names=profile.cand_names)}: " f"{scores.thiele_score(scorefct_id, profile, committee)}", indent=" ", ) output.details("\n") # end of optional output return sorted_committees(committees) def _seq_thiele_resolute(scorefct_id, profile, committeesize): """Compute one winning committee (=resolute) for sequential Thiele methods. Tiebreaking between candidates in favor of candidate with smaller number/index (candidates with larger numbers get deleted first). """ committee = [] marginal_scorefct = scores.get_marginal_scorefct(scorefct_id, committeesize) detailed_info = {"next_cand": [], "tied_cands": [], "delta_score": []} # build a committee starting with the empty set for _ in range(committeesize): additional_score_cand = scores.marginal_thiele_scores_add( marginal_scorefct, profile, committee ) tied_cands = [ cand for cand in range(len(additional_score_cand)) if additional_score_cand[cand] == max(additional_score_cand) ] next_cand = tied_cands[0] # tiebreaking in favor of candidate with smallest index committee.append(next_cand) detailed_info["next_cand"].append(next_cand) detailed_info["tied_cands"].append(tied_cands) detailed_info["delta_score"].append(max(additional_score_cand)) return sorted_committees([committee]), detailed_info def _seq_thiele_irresolute(scorefct_id, profile, committeesize, max_num_of_committees): """Compute all winning committee (=irresolute) for sequential Thiele methods. Consider all possible ways to break ties between candidates (aka parallel universe tiebreaking) """ marginal_scorefct = scores.get_marginal_scorefct(scorefct_id, committeesize) # build committees starting with the empty set partial_committees = [()] winning_committees = set() while partial_committees: new_partial_committees = [] committee = partial_committees.pop() # marginal utility gained by adding candidate to the committee additional_score_cand = scores.marginal_thiele_scores_add( marginal_scorefct, profile, committee ) for cand in profile.candidates: if additional_score_cand[cand] >= max(additional_score_cand): new_committee = committee + (cand,) if len(new_committee) == committeesize: new_committee = tuple(sorted(new_committee)) winning_committees.add(new_committee) # remove duplicate committees if ( max_num_of_committees is not None and len(winning_committees) == max_num_of_committees ): # sufficiently many winning committees found detailed_info = {} return sorted_committees(winning_committees), detailed_info else: # partial committee new_partial_committees.append(new_committee) # add new partial committees in reversed order, so that tiebreaking is correct partial_committees += reversed(new_partial_committees) detailed_info = {} return sorted_committees(winning_committees), detailed_info # Sequential PAV def compute_seqpav( profile, committeesize, algorithm="fastest", resolute=True, max_num_of_committees=MAX_NUM_OF_COMMITTEES_DEFAULT, ): """ Sequential PAV (seq-PAV). For a mathematical description of this rule, see e.g. "Multi-Winner Voting with Approval Preferences". Martin Lackner and Piotr Skowron. <https://arxiv.org/abs/2007.01795> Parameters ---------- profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. The following algorithms are available for Sequential PAV: .. doctest:: >>> Rule("seqpav").algorithms ('standard',) resolute : bool, optional Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. Returns ------- list of CandidateSet A list of winning committees. """ return compute_seq_thiele_method( scorefct_id="pav", profile=profile, committeesize=committeesize, algorithm=algorithm, resolute=resolute, max_num_of_committees=max_num_of_committees, ) def compute_seqslav( profile, committeesize, algorithm="fastest", resolute=True, max_num_of_committees=MAX_NUM_OF_COMMITTEES_DEFAULT, ): """ Sequential Sainte-Lague Approval Voting (SLAV). For a mathematical description of SLAV, see e.g. Martin Lackner and Piotr Skowron Utilitarian Welfare and Representation Guarantees of Approval-Based Multiwinner Rules In Artificial Intelligence, 288: 103366, 2020. <https://arxiv.org/abs/1801.01527> Parameters ---------- profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. The following algorithms are available for Sequential SLAV: .. doctest:: >>> Rule("seqslav").algorithms ('standard',) resolute : bool, optional Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. Returns ------- list of CandidateSet A list of winning committees. """ return compute_seq_thiele_method( scorefct_id="slav", profile=profile, committeesize=committeesize, algorithm=algorithm, resolute=resolute, max_num_of_committees=max_num_of_committees, ) def compute_seqcc( profile, committeesize, algorithm="fastest", resolute=True, max_num_of_committees=MAX_NUM_OF_COMMITTEES_DEFAULT, ): """ Sequential Chamberlin-Courant (seq-CC). For a mathematical description of this rule, see e.g. "Multi-Winner Voting with Approval Preferences". Martin Lackner and Piotr Skowron. <https://arxiv.org/abs/2007.01795> Parameters ---------- profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. The following algorithms are available for Sequential CC: .. doctest:: >>> Rule("seqcc").algorithms ('standard',) resolute : bool, optional Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. Returns ------- list of CandidateSet A list of winning committees. """ return compute_seq_thiele_method( scorefct_id="cc", profile=profile, committeesize=committeesize, algorithm=algorithm, resolute=resolute, max_num_of_committees=max_num_of_committees, ) def compute_revseq_thiele_method( scorefct_id, profile, committeesize, algorithm="fastest", resolute=True, max_num_of_committees=MAX_NUM_OF_COMMITTEES_DEFAULT, ): """ Reverse sequential Thiele methods. For a mathematical description of these rules, see e.g. "Multi-Winner Voting with Approval Preferences". Martin Lackner and Piotr Skowron. <https://arxiv.org/abs/2007.01795> Parameters ---------- scorefct_id : str A string identifying the score function that defines the Thiele method. profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. resolute : bool, optional Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. Returns ------- list of CandidateSet A list of winning committees. """ scores.get_marginal_scorefct(scorefct_id, committeesize) # check that scorefct_id is valid rule_id = "revseq" + scorefct_id rule = Rule(rule_id) if algorithm == "fastest": algorithm = rule.fastest_available_algorithm() rule.verify_compute_parameters( profile=profile, committeesize=committeesize, algorithm=algorithm, resolute=resolute, max_num_of_committees=max_num_of_committees, ) if algorithm == "standard": if resolute: committees, detailed_info = _revseq_thiele_resolute( scorefct_id=scorefct_id, profile=profile, committeesize=committeesize, ) else: committees, detailed_info = _revseq_thiele_irresolute( scorefct_id=scorefct_id, profile=profile, committeesize=committeesize, max_num_of_committees=max_num_of_committees, ) else: raise UnknownAlgorithm(rule_id, algorithm) # optional output output.info(header(rule.longname), wrap=False) if not resolute: output.info("Computing all possible winning committees for any tiebreaking order") output.info(" (aka parallel universes tiebreaking) (resolute=False)\n") output.details(f"Algorithm: {ALGORITHM_NAMES[algorithm]}\n") if resolute: committee = set(profile.candidates) output.details( f"full committee ({len(committee)} candidates) has a total score of " f"{scores.thiele_score(scorefct_id, profile, committee)}\n" ) for i, next_cand in enumerate(detailed_info["next_cand"]): committee.remove(next_cand) tied_cands = detailed_info["tied_cands"][i] delta_score = detailed_info["delta_score"][i] output.details( f"removing candidate number {profile.num_cand - len(committee)}: " f"{profile.cand_names[next_cand]}" ) output.details( f"score decreases by {delta_score} to a total of " f"{scores.thiele_score(scorefct_id, profile, committee)}", indent=" ", ) if len(tied_cands) > 1: output.details(f"tie broken to the disadvantage of {next_cand},", indent=" ") output.details( f"candidates " f"{str_set_of_candidates(tied_cands, cand_names=profile.cand_names)}" " are tied", indent=" ", ) output.details( f"(all would decrease the score by the same amount {delta_score})", indent=" " ) output.details("") output.info( str_committees_with_header(committees, cand_names=profile.cand_names, winning=True) ) msg = "PAV-score of winning committee:" if not resolute and len(committees) != 1: msg += "\n" for committee in committees: msg += " " + str(scores.thiele_score(scorefct_id, profile, committee)) msg += "\n" output.details(msg) # end of optional output return committees def _revseq_thiele_resolute(scorefct_id, profile, committeesize): """Compute one winning committee (=resolute) for reverse sequential Thiele methods. Tiebreaking between candidates in favor of candidate with smaller number/index (candidates with smaller numbers are added first). """ marginal_scorefct = scores.get_marginal_scorefct(scorefct_id, committeesize) committee = set(profile.candidates) detailed_info = {"next_cand": [], "tied_cands": [], "delta_score": []} for _ in range(profile.num_cand - committeesize): marg_util_cand = scores.marginal_thiele_scores_remove( marginal_scorefct, profile, committee ) # find smallest elements in `marg_util_cand` and return indices cands_to_remove = [ cand for cand in profile.candidates if marg_util_cand[cand] == min(marg_util_cand) ] next_cand = cands_to_remove[-1] tied_cands = cands_to_remove[:-1] committee.remove(next_cand) detailed_info["next_cand"].append(next_cand) detailed_info["tied_cands"].append(tied_cands) detailed_info["delta_score"].append(min(marg_util_cand)) return sorted_committees([committee]), detailed_info def _revseq_thiele_irresolute(scorefct_id, profile, committeesize, max_num_of_committees): """ Compute all winning committee (=irresolute) for reverse sequential Thiele methods. Consider all possible ways to break ties between candidates (aka parallel universe tiebreaking) """ marginal_scorefct = scores.get_marginal_scorefct(scorefct_id, committeesize) full_committee = tuple(profile.candidates) comm_scores = {full_committee: scores.thiele_score(scorefct_id, profile, full_committee)} for _ in range(profile.num_cand - committeesize): comm_scores_next = {} for committee, score in comm_scores.items(): marg_util_cand = scores.marginal_thiele_scores_remove( marginal_scorefct, profile, committee ) score_reduction = min(marg_util_cand) # find smallest elements in `marg_util_cand` and return indices cands_to_remove = [ cand for cand in profile.candidates if marg_util_cand[cand] == min(marg_util_cand) ] for cand in cands_to_remove: next_committee = tuple(set(committee) - {cand}) comm_scores_next[next_committee] = score - score_reduction comm_scores = comm_scores_next committees = sorted_committees(list(comm_scores.keys())) if max_num_of_committees is not None: committees = committees[:max_num_of_committees] detailed_info = {} return committees, detailed_info # Reverse Sequential PAV def compute_revseqpav( profile, committeesize, algorithm="fastest", resolute=True, max_num_of_committees=MAX_NUM_OF_COMMITTEES_DEFAULT, ): """ Reverse Sequential PAV (revseq-PAV). For a mathematical description of this rule, see e.g. "Multi-Winner Voting with Approval Preferences". Martin Lackner and Piotr Skowron. <https://arxiv.org/abs/2007.01795> Parameters ---------- profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. The following algorithms are available for Reverse Sequential PAV: .. doctest:: >>> Rule("revseqpav").algorithms ('standard',) resolute : bool, optional Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. Returns ------- list of CandidateSet A list of winning committees. """ return compute_revseq_thiele_method( scorefct_id="pav", profile=profile, committeesize=committeesize, algorithm=algorithm, resolute=resolute, max_num_of_committees=max_num_of_committees, ) def compute_separable_rule( rule_id, profile, committeesize, algorithm, resolute=True, max_num_of_committees=MAX_NUM_OF_COMMITTEES_DEFAULT, ): """ Separable rules (such as AV and SAV). For a mathematical description of separable rules (for ranking-based rules), see E. Elkind, P. Faliszewski, P. Skowron, and A. Slinko. Properties of multiwinner voting rules. Social Choice and Welfare, 48(3):599–632, 2017. <https://link.springer.com/article/10.1007/s00355-017-1026-z> Parameters ---------- rule_id : str The rule identifier for a separable rule. profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. The following algorithms are available for AV: .. doctest:: >>> Rule("av").algorithms ('standard',) resolute : bool, optional Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. Returns ------- list of CandidateSet A list of winning committees. """ rule = Rule(rule_id) if algorithm == "fastest": algorithm = rule.fastest_available_algorithm() rule.verify_compute_parameters( profile=profile, committeesize=committeesize, algorithm=algorithm, resolute=resolute, max_num_of_committees=max_num_of_committees, ) if algorithm == "standard": committees, detailed_info = _separable_rule_algorithm( rule_id=rule_id, profile=profile, committeesize=committeesize, resolute=resolute, max_num_of_committees=max_num_of_committees, ) else: raise UnknownAlgorithm(rule_id, algorithm) # optional output output.info(header(rule.longname), wrap=False) if resolute: output.info("Computing only one winning committee (resolute=True)\n") output.details(f"Algorithm: {ALGORITHM_NAMES[algorithm]}\n") score = detailed_info["score"] msg = "Scores of candidates:\n" for cand in profile.candidates: msg += (profile.cand_names[cand] + ": " + str(score[cand])) + "\n" cutoff = detailed_info["cutoff"] msg += "\nCandidates are contained in winning committees\n" msg += "if their score is >= " + str(cutoff) + "." output.details(msg) certain_cands = detailed_info["certain_cands"] if len(certain_cands) > 0: msg = "\nThe following candidates are contained in\n" msg += "every winning committee:\n" namedset = [profile.cand_names[cand] for cand in certain_cands] msg += (" " + ", ".join(map(str, namedset))) + "\n" output.details(msg) possible_cands = detailed_info["possible_cands"] missing = detailed_info["missing"] if len(possible_cands) > 0: msg = "The following candidates are contained in\n" msg += "some of the winning committees:\n" namedset = [profile.cand_names[cand] for cand in possible_cands] msg += (" " + ", ".join(map(str, namedset))) + "\n" msg += f"({missing} of those candidates are contained\n in every winning committee.)\n" output.details(msg) output.info( str_committees_with_header(committees, cand_names=profile.cand_names, winning=True) ) # end of optional output return committees def _separable_rule_algorithm(rule_id, profile, committeesize, resolute, max_num_of_committees): """ Algorithm for separable rules (such as AV and SAV). """ score = [0] * profile.num_cand for voter in profile: for cand in voter.approved: if rule_id == "sav": # Satisfaction Approval Voting score[cand] += voter.weight / len(voter.approved) elif rule_id == "av": # (Classic) Approval Voting score[cand] += voter.weight else: raise UnknownRuleIDError(rule_id) # smallest score to be in the committee cutoff = sorted(score)[-committeesize] certain_cands = [cand for cand in profile.candidates if score[cand] > cutoff] possible_cands = [cand for cand in profile.candidates if score[cand] == cutoff] missing = committeesize - len(certain_cands) if len(possible_cands) == missing: # candidates with score[cand] == cutoff # are also certain candidates because all these candidates # are required to fill the committee certain_cands = sorted(certain_cands + possible_cands) possible_cands = [] missing = 0 if resolute: committees = sorted_committees([(certain_cands + possible_cands[:missing])]) else: if max_num_of_committees is None: committees = sorted_committees( [ (certain_cands + list(selection)) for selection in itertools.combinations(possible_cands, missing) ] ) else: committees = [] for selection in itertools.combinations(possible_cands, missing): committees.append(certain_cands + list(selection)) if len(committees) >= max_num_of_committees: break committees = sorted_committees(committees) detailed_info = { "certain_cands": certain_cands, "possible_cands": possible_cands, "missing": missing, "cutoff": cutoff, "score": score, } return committees, detailed_info def compute_sav( profile, committeesize, algorithm="fastest", resolute=False, max_num_of_committees=MAX_NUM_OF_COMMITTEES_DEFAULT, ): """ Compute winning committees with Satisfaction Approval Voting (SAV). For a mathematical description of this rule, see e.g. "Multi-Winner Voting with Approval Preferences". Martin Lackner and Piotr Skowron. <https://arxiv.org/abs/2007.01795> Parameters ---------- profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. The following algorithms are available for SAV: .. doctest:: >>> Rule("sav").algorithms ('standard',) resolute : bool, optional Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. Returns ------- list of CandidateSet A list of winning committees. """ return compute_separable_rule( rule_id="sav", profile=profile, committeesize=committeesize, algorithm=algorithm, resolute=resolute, max_num_of_committees=max_num_of_committees, ) def compute_av( profile, committeesize, algorithm="fastest", resolute=False, max_num_of_committees=MAX_NUM_OF_COMMITTEES_DEFAULT, ): """ Approval Voting (AV). AV is both a Thiele method and a separable rule. Seperable rules can be computed much faster than Thiele methods (in general), thus `compute_separable_rule` is used to compute AV. For a mathematical description of this rule, see e.g. "Multi-Winner Voting with Approval Preferences". Martin Lackner and Piotr Skowron. <https://arxiv.org/abs/2007.01795> Parameters ---------- profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. The following algorithms are available for AV: .. doctest:: >>> Rule("av").algorithms ('standard',) resolute : bool, optional Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. Returns ------- list of CandidateSet A list of winning committees. """ return compute_separable_rule( rule_id="av", profile=profile, committeesize=committeesize, algorithm=algorithm, resolute=resolute, max_num_of_committees=max_num_of_committees, ) def compute_minimaxav( profile, committeesize, algorithm="fastest", resolute=False, max_num_of_committees=MAX_NUM_OF_COMMITTEES_DEFAULT, ): """ Compute winning committees with Minimax Approval Voting (MAV). For a mathematical description of this rule, see e.g. "Multi-Winner Voting with Approval Preferences". Martin Lackner and Piotr Skowron. <https://arxiv.org/abs/2007.01795> Parameters ---------- profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. The following algorithms are available for Minimax AV: .. doctest:: >>> Rule("minimaxav").algorithms ('gurobi', 'mip-gurobi', 'ortools-cp', 'mip-cbc', 'brute-force') resolute : bool, optional Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. Returns ------- list of CandidateSet A list of winning committees. """ rule_id = "minimaxav" rule = Rule(rule_id) if algorithm == "fastest": algorithm = rule.fastest_available_algorithm() rule.verify_compute_parameters( profile=profile, committeesize=committeesize, algorithm=algorithm, resolute=resolute, max_num_of_committees=max_num_of_committees, ) if algorithm == "gurobi": committees = abcrules_gurobi._gurobi_minimaxav( profile=profile, committeesize=committeesize, resolute=resolute, max_num_of_committees=max_num_of_committees, ) elif algorithm == "ortools-cp": committees = abcrules_ortools._ortools_minimaxav( profile=profile, committeesize=committeesize, resolute=resolute, max_num_of_committees=max_num_of_committees, ) elif algorithm.startswith("mip-"): solver_id = algorithm[4:] committees = abcrules_mip._mip_minimaxav( profile=profile, committeesize=committeesize, resolute=resolute, max_num_of_committees=max_num_of_committees, solver_id=solver_id, ) elif algorithm == "brute-force": committees, detailed_info = _minimaxav_bruteforce( profile=profile, committeesize=committeesize, resolute=resolute, max_num_of_committees=max_num_of_committees, ) else: raise UnknownAlgorithm(rule_id, algorithm) # optional output output.info(header(rule.longname), wrap=False) if resolute: output.info("Computing only one winning committee (resolute=True)\n") output.details(f"Algorithm: {ALGORITHM_NAMES[algorithm]}\n") opt_minimaxav_score = scores.minimaxav_score(profile, committees[0]) output.info( str_committees_with_header(committees, cand_names=profile.cand_names, winning=True) ) output.details("Minimum maximal distance: " + str(opt_minimaxav_score)) msg = "Corresponding distances to voters:\n" for committee in committees: msg += str([misc.hamming(voter.approved, committee) for voter in profile]) + "\n" output.details(msg) # end of optional output return committees def _minimaxav_bruteforce(profile, committeesize, resolute, max_num_of_committees): """Brute-force algorithm for Minimax AV (MAV).""" opt_committees = [] opt_minimaxav_score = profile.num_cand + 1 for committee in itertools.combinations(profile.candidates, committeesize): score = scores.minimaxav_score(profile, committee) if score < opt_minimaxav_score: opt_committees = [committee] opt_minimaxav_score = score elif score == opt_minimaxav_score: opt_committees.append(committee) committees = sorted_committees(opt_committees) detailed_info = {} if resolute: committees = [committees[0]] if max_num_of_committees is not None: committees = committees[:max_num_of_committees] return committees, detailed_info def compute_lexminimaxav( profile, committeesize, algorithm="fastest", resolute=False, max_num_of_committees=MAX_NUM_OF_COMMITTEES_DEFAULT, ): """ Compute winning committees with Lexicographic Minimax AV (lex-MAV). For a mathematical description of this rule, see e.g. "Multi-Winner Voting with Approval Preferences". Martin Lackner and Piotr Skowron. <https://arxiv.org/abs/2007.01795> (Remark 2) If `lexicographic_tiebreaking` is True, compute all winning committees and choose the lexicographically smallest. This is a deterministic form of tiebreaking; if only resolute=True, it is not guaranteed how ties are broken. .. important:: Very slow due to lexicographic optimization. Parameters ---------- profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. The following algorithms are available for Lexicographic Minimax AV: .. doctest:: >>> Rule("lexminimaxav").algorithms ('gurobi', 'brute-force') resolute : bool, optional Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. Returns ------- list of CandidateSet A list of winning committees. """ rule_id = "lexminimaxav" rule = Rule(rule_id) if algorithm == "fastest": algorithm = rule.fastest_available_algorithm() rule.verify_compute_parameters( profile=profile, committeesize=committeesize, algorithm=algorithm, resolute=resolute, max_num_of_committees=max_num_of_committees, ) if not profile.has_unit_weights(): raise ValueError(f"{rule.shortname} is only defined for unit weights (weight=1)") if algorithm == "brute-force": committees, detailed_info = _lexminimaxav_bruteforce( profile=profile, committeesize=committeesize, resolute=resolute, max_num_of_committees=max_num_of_committees, ) elif algorithm == "gurobi": committees, detailed_info = abcrules_gurobi._gurobi_lexminimaxav( profile=profile, committeesize=committeesize, resolute=resolute, max_num_of_committees=max_num_of_committees, ) else: raise UnknownAlgorithm(rule_id, algorithm) # optional output opt_distances = detailed_info["opt_distances"] output.info(header(rule.longname), wrap=False) if resolute: output.info("Computing only one winning committee (resolute=True)\n") output.details(f"Algorithm: {ALGORITHM_NAMES[algorithm]}\n") output.info( str_committees_with_header(committees, cand_names=profile.cand_names, winning=True) ) output.details("Minimum maximal distance: " + str(max(opt_distances))) msg = "Corresponding distances to voters:\n" for committee in committees: msg += str([misc.hamming(voter.approved, committee) for voter in profile]) output.details(msg + "\n") # end of optional output return committees def _lexminimaxav_bruteforce(profile, committeesize, resolute, max_num_of_committees): opt_committees = [] opt_distances = [profile.num_cand + 1] * len(profile) for committee in itertools.combinations(profile.candidates, committeesize): distances = sorted( (misc.hamming(voter.approved, set(committee)) for voter in profile), reverse=True ) for i, dist in enumerate(distances): if opt_distances[i] < dist: break if opt_distances[i] > dist: opt_distances = distances opt_committees = [committee] break else: opt_committees.append(committee) committees = sorted_committees(opt_committees) detailed_info = {"opt_distances": opt_distances} if resolute: committees = [committees[0]] if max_num_of_committees is not None: committees = committees[:max_num_of_committees] return committees, detailed_info def compute_monroe( profile, committeesize, algorithm="fastest", resolute=False, max_num_of_committees=MAX_NUM_OF_COMMITTEES_DEFAULT, ): """ Compute winning committees with Monroe's rule. For a mathematical description of this rule, see e.g. "Multi-Winner Voting with Approval Preferences". Martin Lackner and Piotr Skowron. <https://arxiv.org/abs/2007.01795> Parameters ---------- profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. The following algorithms are available for Monroe: .. doctest:: >>> Rule("monroe").algorithms ('gurobi', 'mip-gurobi', 'mip-cbc', 'ortools-cp', 'brute-force') resolute : bool, optional Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. Returns ------- list of CandidateSet A list of winning committees. """ rule_id = "monroe" rule = Rule(rule_id) if algorithm == "fastest": algorithm = rule.fastest_available_algorithm() rule.verify_compute_parameters( profile=profile, committeesize=committeesize, algorithm=algorithm, resolute=resolute, max_num_of_committees=max_num_of_committees, ) if not profile.has_unit_weights(): raise ValueError(f"{rule.shortname} is only defined for unit weights (weight=1)") if algorithm == "gurobi": committees = abcrules_gurobi._gurobi_monroe( profile=profile, committeesize=committeesize, resolute=resolute, max_num_of_committees=max_num_of_committees, ) elif algorithm == "ortools-cp": committees = abcrules_ortools._ortools_monroe( profile=profile, committeesize=committeesize, resolute=resolute, max_num_of_committees=max_num_of_committees, ) elif algorithm.startswith("mip-"): committees = abcrules_mip._mip_monroe( profile=profile, committeesize=committeesize, resolute=resolute, max_num_of_committees=max_num_of_committees, solver_id=algorithm[4:], ) elif algorithm == "brute-force": committees, detailed_info = _monroe_bruteforce( profile=profile, committeesize=committeesize, resolute=resolute, max_num_of_committees=max_num_of_committees, ) else: raise UnknownAlgorithm(rule_id, algorithm) # optional output output.info(header(rule.longname), wrap=False) if resolute: output.info("Computing only one winning committee (resolute=True)\n") output.details(f"Algorithm: {ALGORITHM_NAMES[algorithm]}\n") output.details( "Optimal Monroe score: " + str(scores.monroescore(profile, committees[0])) + "\n" ) output.info( str_committees_with_header(committees, cand_names=profile.cand_names, winning=True) ) # end of optional output return committees def _monroe_bruteforce(profile, committeesize, resolute, max_num_of_committees): """ Brute-force algorithm for Monroe's rule. """ opt_committees = [] opt_monroescore = -1 for committee in itertools.combinations(profile.candidates, committeesize): score = scores.monroescore(profile, committee) if score > opt_monroescore: opt_committees = [committee] opt_monroescore = score elif scores.monroescore(profile, committee) == opt_monroescore: opt_committees.append(committee) committees = sorted_committees(opt_committees) if max_num_of_committees is not None: committees = committees[:max_num_of_committees] if resolute: committees = [committees[0]] detailed_info = {} return committees, detailed_info def compute_greedy_monroe( profile, committeesize, algorithm="fastest", resolute=True, max_num_of_committees=None ): """ Compute winning committees with Greedy Monroe. For a mathematical description of this rule, see e.g. "Multi-Winner Voting with Approval Preferences". Martin Lackner and Piotr Skowron. <https://arxiv.org/abs/2007.01795> Parameters ---------- profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. The following algorithms are available for Greedy Monroe: .. doctest:: >>> Rule("greedy-monroe").algorithms ('standard',) resolute : bool, optional Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. Returns ------- list of CandidateSet A list of winning committees. """ rule_id = "greedy-monroe" rule = Rule(rule_id) if algorithm == "fastest": algorithm = rule.fastest_available_algorithm() rule.verify_compute_parameters( profile, committeesize, algorithm, resolute, max_num_of_committees ) if not profile.has_unit_weights(): raise ValueError(f"{rule.shortname} is only defined for unit weights (weight=1)") if algorithm == "standard": committees, detailed_info = _greedy_monroe_algorithm(profile, committeesize) else: raise UnknownAlgorithm(rule_id, algorithm) # optional output output.info(header(rule.longname), wrap=False) output.details(f"Algorithm: {ALGORITHM_NAMES[algorithm]}\n") remaining_voters = detailed_info["remaining_voters"] assignment = detailed_info["assignment"] score1 = scores.monroescore(profile, committees[0]) score2 = len(profile) - len(remaining_voters) output.details("The Monroe assignment computed by Greedy Monroe") output.details("has a Monroe score of " + str(score2) + ".") if score1 > score2: output.details( "Monroe assignment found by Greedy Monroe is not " + "optimal for the winning committee," ) output.details( "i.e., by redistributing voters to candidates a higher " + "satisfaction is possible " + "(without changing the committee)." ) output.details("Optimal Monroe score of the winning committee is " + str(score1) + ".") # build actual Monroe assignment for winning committee num_voters = len(profile) for t, district in enumerate(assignment): cand, voters = district if t < num_voters - committeesize * (num_voters // committeesize): missing = num_voters // committeesize + 1 - len(voters) else: missing = num_voters // committeesize - len(voters) for _ in range(missing): v = remaining_voters.pop() voters.append(v) msg = "Assignment (unsatisfatied voters marked with *):\n\n" for cand, voters in assignment: msg += " candidate " + profile.cand_names[cand] + " assigned to: " assing_msg = "" for v in sorted(voters): assing_msg += str(v) if cand not in profile[v].approved: assing_msg += "*" assing_msg += ", " msg += assing_msg[:-2] + "\n" output.details(msg) output.info( str_committees_with_header(committees, cand_names=profile.cand_names, winning=True) ) # end of optional output return sorted_committees(committees) def _greedy_monroe_algorithm(profile, committeesize): """ Algorithm for Greedy Monroe. """ num_voters = len(profile) committee = [] remaining_voters = list(range(num_voters)) remaining_cands = set(profile.candidates) assignment = [] for t in range(committeesize): maxapprovals = -1 selected = None for cand in remaining_cands: approvals = len([i for i in remaining_voters if cand in profile[i].approved]) if approvals > maxapprovals: maxapprovals = approvals selected = cand # determine how many voters are removed (at most) if t < num_voters - committeesize * (num_voters // committeesize): num_remove = num_voters // committeesize + 1 else: num_remove = num_voters // committeesize # only voters that approve the chosen candidate # are removed to_remove = [i for i in remaining_voters if selected in profile[i].approved] if len(to_remove) > num_remove: to_remove = to_remove[:num_remove] assignment.append((selected, to_remove)) remaining_voters = [i for i in remaining_voters if i not in to_remove] committee.append(selected) remaining_cands.remove(selected) detailed_info = {"remaining_voters": remaining_voters, "assignment": assignment} return sorted_committees([committee]), detailed_info def compute_seqphragmen( profile, committeesize, algorithm="fastest", resolute=True, max_num_of_committees=MAX_NUM_OF_COMMITTEES_DEFAULT, ): """ Compute winning committees with Phragmen's sequential rule (seq-Phragmen). For a mathematical description of this rule, see e.g. "Multi-Winner Voting with Approval Preferences". Martin Lackner and Piotr Skowron. <https://arxiv.org/abs/2007.01795> Parameters ---------- profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. The following algorithms are available for Phragmen's sequential rule (seq-Phragmen): .. doctest:: >>> Rule("seqphragmen").algorithms ('float-fractions', 'gmpy2-fractions', 'standard-fractions') resolute : bool, optional Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. Returns ------- list of CandidateSet A list of winning committees. """ rule_id = "seqphragmen" rule = Rule(rule_id) if algorithm == "fastest": algorithm = rule.fastest_available_algorithm() rule.verify_compute_parameters( profile=profile, committeesize=committeesize, algorithm=algorithm, resolute=resolute, max_num_of_committees=max_num_of_committees, ) if resolute: committees, detailed_info = _seqphragmen_resolute( profile=profile, committeesize=committeesize, algorithm=algorithm, ) else: committees, detailed_info = _seqphragmen_irresolute( profile=profile, committeesize=committeesize, algorithm=algorithm, max_num_of_committees=max_num_of_committees, ) # optional output output.info(header(rule.longname), wrap=False) if not resolute: output.info("Computing all possible winning committees for any tiebreaking order") output.info(" (aka parallel universes tiebreaking) (resolute=False)\n") output.details(f"Algorithm: {ALGORITHM_NAMES[algorithm]}\n") if resolute: committee = [] for i, next_cand in enumerate(detailed_info["next_cand"]): tied_cands = detailed_info["tied_cands"][i] max_load = detailed_info["max_load"][i] load = detailed_info["load"][i] committee.append(next_cand) output.details(f"adding candidate number {i+1}: {profile.cand_names[next_cand]}") output.details( f"maximum load increased to {max_load}", indent=" " # f"\n (continuous model: time t_{i+1} = {max_load})" ) output.details(" load distribution:") msg = "(" for v, _ in enumerate(profile): msg += str(load[v]) + ", " output.details(msg[:-2] + ")", indent=" ") if len(tied_cands) > 1: output.details( f"tie broken in favor of {profile.cand_names[next_cand]},", indent=" " ) output.details( "candidates " f"{str_set_of_candidates(tied_cands, cand_names=profile.cand_names)}" f" are tied", indent=" ", ) output.details( f"(for all those new maximum load = {max_load}).", indent=" ", ) output.details("") output.info( str_committees_with_header(committees, cand_names=profile.cand_names, winning=True) ) if resolute or len(committees) == 1: output.details("corresponding load distribution:") else: output.details("corresponding load distributions:") for committee, load in detailed_info["committee_load_pairs"].items(): msg = f"{str_set_of_candidates(committee, cand_names=profile.cand_names)}: (" for v, _ in enumerate(profile): msg += str(load[v]) + ", " output.details(msg[:-2] + ")\n") # end of optional output return sorted_committees(committees) def _seqphragmen_resolute( profile, committeesize, algorithm, start_load=None, partial_committee=None ): """ Algorithm for computing resolute seq-Phragmen (1 winning committee). """ if algorithm == "float-fractions": division = lambda x, y: x / y # standard float division elif algorithm == "standard-fractions": division = Fraction # using Python built-in fractions elif algorithm == "gmpy2-fractions": if not mpq: raise ImportError( 'Module gmpy2 not available, required for algorithm "gmpy2-fractions"' ) division = mpq # using gmpy2 fractions else: raise UnknownAlgorithm("seqphragmen", algorithm) approvers_weight = {} for cand in profile.candidates: approvers_weight[cand] = sum(voter.weight for voter in profile if cand in voter.approved) load = start_load if load is None: load = [0 for _ in range(len(profile))] committee = partial_committee if partial_committee is None: committee = [] # build committees starting with the empty set detailed_info = { "next_cand": [], "tied_cands": [], "load": [], "max_load": [], } for _ in range(len(committee), committeesize): approvers_load = {} for cand in profile.candidates: approvers_load[cand] = sum( voter.weight * load[v] for v, voter in enumerate(profile) if cand in voter.approved ) new_maxload = [ division(approvers_load[cand] + 1, approvers_weight[cand]) if approvers_weight[cand] > 0 else committeesize + 1 for cand in profile.candidates ] # exclude committees already in the committee for cand in profile.candidates: if cand in committee: new_maxload[cand] = committeesize + 2 # that's larger than any possible value opt = min(new_maxload) if algorithm == "float-fractions": tied_cands = [ cand for cand in profile.candidates if misc.isclose(new_maxload[cand], opt) ] else: tied_cands = [cand for cand in profile.candidates if new_maxload[cand] == opt] next_cand = tied_cands[0] # compute new loads and add new candidate for v, voter in enumerate(profile): if next_cand in voter.approved: load[v] = new_maxload[next_cand] committee = sorted(committee + [next_cand]) detailed_info["next_cand"].append(next_cand) detailed_info["tied_cands"].append(tied_cands) detailed_info["load"].append(list(load)) # create copy of `load` detailed_info["max_load"].append(opt) detailed_info["committee_load_pairs"] = {tuple(committee): load} return [committee], detailed_info def _seqphragmen_irresolute( profile, committeesize, algorithm, max_num_of_committees, start_load=None, partial_committee=None, ): """Algorithm for computing irresolute seq-Phragmen (all winning committees).""" if algorithm == "float-fractions": division = lambda x, y: x / y # standard float division elif algorithm == "standard-fractions": division = Fraction # using Python built-in fractions elif algorithm == "gmpy2-fractions": if not mpq: raise ImportError( 'Module gmpy2 not available, required for algorithm "gmpy2-fractions"' ) division = mpq # using gmpy2 fractions else: raise UnknownAlgorithm("seqphragmen", algorithm) approvers_weight = {} for cand in profile.candidates: approvers_weight[cand] = sum(voter.weight for voter in profile if cand in voter.approved) load = start_load if load is None: load = {v: 0 for v, _ in enumerate(profile)} if partial_committee is None: partial_committee = () # build committees starting with the empty set else: partial_committee = tuple(partial_committee) committee_load_pairs = [(partial_committee, load)] committees = set() detailed_info = {"committee_load_pairs": {}} while committee_load_pairs: committee, load = committee_load_pairs.pop() approvers_load = {} for cand in profile.candidates: approvers_load[cand] = sum( voter.weight * load[v] for v, voter in enumerate(profile) if cand in voter.approved ) new_maxload = [ division(approvers_load[cand] + 1, approvers_weight[cand]) if approvers_weight[cand] > 0 else committeesize + 1 for cand in profile.candidates ] # exclude committees already in the committee for cand in profile.candidates: if cand in committee: new_maxload[cand] = committeesize + 2 # that's larger than any possible value # compute new loads new_committee_load_pairs = [] for cand in profile.candidates: if algorithm == "float-fractions": select_cand = misc.isclose(new_maxload[cand], min(new_maxload)) else: select_cand = new_maxload[cand] <= min(new_maxload) if select_cand: new_load = [0] * len(profile) for v, voter in enumerate(profile): if cand in voter.approved: new_load[v] = new_maxload[cand] else: new_load[v] = load[v] new_committee = committee + (cand,) if len(new_committee) == committeesize: new_committee = tuple(sorted(new_committee)) committees.add(new_committee) # remove duplicate committees detailed_info["committee_load_pairs"][new_committee] = new_load if ( max_num_of_committees is not None and len(committees) == max_num_of_committees ): # sufficiently many winning committees found return sorted_committees(committees), detailed_info else: # partial committee new_committee_load_pairs.append((new_committee, new_load)) # add new committee/load pairs in reversed order, so that tiebreaking is correct committee_load_pairs += reversed(new_committee_load_pairs) return sorted_committees(committees), detailed_info def compute_rule_x( profile, committeesize, algorithm="fastest", resolute=True, max_num_of_committees=MAX_NUM_OF_COMMITTEES_DEFAULT, skip_phragmen_phase=False, ): """ Compute winning committees with Rule X (aka Method of Equal Shares). For a mathematical description of this rule, see e.g. "Multi-Winner Voting with Approval Preferences". Martin Lackner and Piotr Skowron. <https://arxiv.org/abs/2007.01795> See also <https://arxiv.org/pdf/1911.11747.pdf>, page 7 Parameters ---------- profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. The following algorithms are available for Rule X (aka Method of Equal Shares): .. doctest:: >>> Rule("rule-x").algorithms ('float-fractions', 'gmpy2-fractions', 'standard-fractions') resolute : bool, optional Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. skip_phragmen_phase : bool, default=False Omit the second phase (that uses seq-Phragmen). May result in a committee that is too small (length smaller than `committeesize`). Returns ------- list of CandidateSet A list of winning committees. """ if skip_phragmen_phase: rule_id = "rule-x-without-phragmen-phase" else: rule_id = "rule-x" rule = Rule(rule_id) if algorithm == "fastest": algorithm = rule.fastest_available_algorithm() rule.verify_compute_parameters( profile=profile, committeesize=committeesize, algorithm=algorithm, resolute=resolute, max_num_of_committees=max_num_of_committees, ) if not profile.has_unit_weights(): raise ValueError(f"{rule.shortname} is only defined for unit weights (weight=1)") committees, detailed_info = _rule_x_algorithm( profile=profile, committeesize=committeesize, algorithm=algorithm, resolute=resolute, max_num_of_committees=max_num_of_committees, skip_phragmen_phase=skip_phragmen_phase, ) # optional output output.info(header(rule.longname), wrap=False) if not resolute: output.info("Computing all possible winning committees for any tiebreaking order") output.info(" (aka parallel universes tiebreaking) (resolute=False)\n") output.details(f"Algorithm: {ALGORITHM_NAMES[algorithm]}\n") if resolute: start_budget = detailed_info["start_budget"] output.details("Phase 1:\n") output.details("starting budget:") msg = " (" for v, _ in enumerate(profile): msg += str(start_budget[v]) + ", " output.details(msg[:-2] + ")\n") committee = [] for i, next_cand in enumerate(detailed_info["next_cand"]): committee.append(next_cand) budget = detailed_info["budget"][i] cost = detailed_info["cost"][i] tied_cands = detailed_info["tied_cands"][i] output.details(f"adding candidate number {i+1}: {profile.cand_names[next_cand]}") output.details(f"with maxmimum cost per voter q = {cost}", indent=" ") output.details(" remaining budget:") msg = "(" for v, _ in enumerate(profile): msg += str(budget[v]) + ", " output.details(msg[:-2] + ")", indent=" ") if len(tied_cands) > 1: output.details( f"tie broken in favor of {profile.cand_names[next_cand]},", indent=" " ) output.details( "candidates " f"{str_set_of_candidates(tied_cands, cand_names=profile.cand_names)}" f" are tied", indent=" ", ) output.details(f"(all would impose a maximum cost of {cost}).", indent=" ") output.details("") if detailed_info["phragmen_start_load"]: # the second phase (seq-Phragmen) was used phragmen_start_load = detailed_info["phragmen_start_load"] output.details("Phase 2 (seq-Phragmén):\n") output.details("starting loads (= budget spent):") msg = "(" for v, _ in enumerate(profile): msg += str(phragmen_start_load[v]) + ", " output.details(msg[:-2] + ")\n", indent=" ") detailed_info_phragmen = detailed_info["phragmen_phase"] for i, next_cand in enumerate(detailed_info_phragmen["next_cand"]): tied_cands = detailed_info_phragmen["tied_cands"][i] max_load = detailed_info_phragmen["max_load"][i] load = detailed_info_phragmen["load"][i] committee.append(next_cand) output.details( f"adding candidate number {len(committee)}: {profile.cand_names[next_cand]}" ) output.details( f"maximum load increased to {max_load}", indent=" " # f"\n (continuous model: time t_{len(committee)} = {max_load})" ) output.details(" load distribution:") msg = "(" for v, _ in enumerate(profile): msg += str(load[v]) + ", " output.details(msg[:-2] + ")", indent=" ") if len(tied_cands) > 1: output.details( f"tie broken in favor of {profile.cand_names[next_cand]},", indent=" " ) output.details( "candidates " f"{str_set_of_candidates(tied_cands, cand_names=profile.cand_names)}" " are tied", indent=" ", ) output.details( f"(for any of those, the new maximum load would be {max_load}).", indent=" ", ) output.details("") output.info( str_committees_with_header(committees, cand_names=profile.cand_names, winning=True) ) # end of optional output return sorted_committees(committees) def _rule_x_algorithm( profile, committeesize, algorithm, resolute, max_num_of_committees, skip_phragmen_phase=False ): """Algorithm for Rule X.""" def _rule_x_get_min_q(profile, budget, cand, division): rich = {v for v, voter in enumerate(profile) if cand in voter.approved} poor = set() while len(rich) > 0: poor_budget = sum(budget[v] for v in poor) _q = division(1 - poor_budget, len(rich)) if algorithm == "float-fractions": # due to float imprecision, values very close to `q` count as `q` new_poor = {v for v in rich if budget[v] < _q and not misc.isclose(budget[v], _q)} else: new_poor = {v for v in rich if budget[v] < _q} if len(new_poor) == 0: return _q rich -= new_poor poor.update(new_poor) return None # not sufficient budget available def find_minimum_dict_entries(dictx): if algorithm == "float-fractions": min_entries = [ cand for cand in dictx.keys() if misc.isclose(dictx[cand], min(dictx.values())) ] else: min_entries = [cand for cand in dictx.keys() if dictx[cand] == min(dictx.values())] return min_entries def phragmen_phase(_committee, _budget): # translate budget to loads start_load = [-_budget[v] for v in range(len(profile))] detailed_info["phragmen_start_load"] = list(start_load) # make a copy if resolute: committees, detailed_info_phragmen = _seqphragmen_resolute( profile=profile, committeesize=committeesize, algorithm=algorithm, partial_committee=list(_committee), start_load=start_load, ) else: committees, detailed_info_phragmen = _seqphragmen_irresolute( profile=profile, committeesize=committeesize, algorithm=algorithm, max_num_of_committees=None, # TODO: would be nice to have max_num_of_committees=max_num_of_committees # but there is the issue that some of these committees might be # already contained in `winning_committees` - so we need more partial_committee=list(_committee), start_load=start_load, ) winning_committees.update([tuple(sorted(committee)) for committee in committees]) detailed_info["phragmen_phase"] = detailed_info_phragmen # after filling the remaining spots these committees have size `committeesize` if algorithm == "float-fractions": division = lambda x, y: x / y # standard float division elif algorithm == "standard-fractions": division = Fraction # using Python built-in fractions elif algorithm == "gmpy2-fractions": if not mpq: raise ImportError( 'Module gmpy2 not available, required for algorithm "gmpy2-fractions"' ) division = mpq # using gmpy2 fractions else: raise UnknownAlgorithm("rule-x", algorithm) if resolute: max_num_of_committees = 1 # same algorithm for resolute==True and resolute==False start_budget = {v: division(committeesize, len(profile)) for v, _ in enumerate(profile)} committee_bugdet_pairs = [(tuple(), start_budget)] winning_committees = set() detailed_info = { "next_cand": [], "cost": [], "tied_cands": [], "budget": [], "start_budget": start_budget, "phragmen_start_load": None, } while committee_bugdet_pairs: committee, budget = committee_bugdet_pairs.pop() available_candidates = [cand for cand in profile.candidates if cand not in committee] min_q = {} for cand in available_candidates: q = _rule_x_get_min_q(profile, budget, cand, division) if q is not None: min_q[cand] = q if len(min_q) > 0: # one or more candidates are affordable # choose those candidates that require the smallest budget tied_cands = find_minimum_dict_entries(min_q) new_committee_budget_pairs = [] for next_cand in sorted(tied_cands): new_budget = dict(budget) for v, voter in enumerate(profile): if next_cand in voter.approved: new_budget[v] -= min(budget[v], min_q[next_cand]) new_committee = committee + (next_cand,) if resolute: detailed_info["next_cand"].append(next_cand) detailed_info["tied_cands"].append(tied_cands) detailed_info["cost"].append(min(min_q.values())) detailed_info["budget"].append(new_budget) if len(new_committee) == committeesize: new_committee = tuple(sorted(new_committee)) winning_committees.add(new_committee) # remove duplicate committees if ( max_num_of_committees is not None and len(winning_committees) == max_num_of_committees ): # sufficiently many winning committees found return sorted_committees(winning_committees), detailed_info else: # partial committee new_committee_budget_pairs.append((new_committee, new_budget)) if resolute: break # add new committee/budget pairs in reversed order, so that tiebreaking is correct committee_bugdet_pairs += reversed(new_committee_budget_pairs) else: # no affordable candidates remain if skip_phragmen_phase: winning_committees.add(tuple(sorted(committee))) else: # fill committee via seq-Phragmen phragmen_phase(committee, budget) if max_num_of_committees is not None and len(winning_committees) >= max_num_of_committees: winning_committees = sorted_committees(winning_committees)[:max_num_of_committees] break return sorted_committees(winning_committees), detailed_info def compute_minimaxphragmen( profile, committeesize, algorithm="fastest", resolute=False, max_num_of_committees=MAX_NUM_OF_COMMITTEES_DEFAULT, ): """ Compute winning committees with Phragmen's minimax rule (minimax-Phragmen). Minimizes the maximum load. For a mathematical description of this rule, see e.g. "Multi-Winner Voting with Approval Preferences". Martin Lackner and Piotr Skowron. <https://arxiv.org/abs/2007.01795> Does not include the lexicographic optimization as specified in Markus Brill, Rupert Freeman, Svante Janson and Martin Lackner. Phragmen's Voting Methods and Justified Representation. <https://arxiv.org/abs/2102.12305> Instead: minimizes the maximum load (without consideration of the second-, third-, ...-largest load. The lexicographic method is this one: :func:`compute_leximaxphragmen`. Parameters ---------- profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. The following algorithms are available for Phragmen's minimax rule (minimax-Phragmen): .. doctest:: >>> Rule("minimaxphragmen").algorithms ('gurobi', 'mip-gurobi', 'mip-cbc') resolute : bool, optional Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. Returns ------- list of CandidateSet A list of winning committees. """ rule_id = "minimaxphragmen" rule = Rule(rule_id) if algorithm == "fastest": algorithm = rule.fastest_available_algorithm() rule.verify_compute_parameters( profile=profile, committeesize=committeesize, algorithm=algorithm, resolute=resolute, max_num_of_committees=max_num_of_committees, ) if algorithm == "gurobi": committees = abcrules_gurobi._gurobi_minimaxphragmen( profile, committeesize, resolute=resolute, max_num_of_committees=max_num_of_committees, ) elif algorithm.startswith("mip-"): committees = abcrules_mip._mip_minimaxphragmen( profile, committeesize, resolute=resolute, max_num_of_committees=max_num_of_committees, solver_id=algorithm[4:], ) else: raise UnknownAlgorithm(rule_id, algorithm) # optional output output.info(header(rule.longname), wrap=False) if resolute: output.info("Computing only one winning committee (resolute=True)\n") output.details(f"Algorithm: {ALGORITHM_NAMES[algorithm]}\n") output.info( str_committees_with_header(committees, cand_names=profile.cand_names, winning=True) ) # end of optional output return committees def compute_leximaxphragmen( profile, committeesize, algorithm="fastest", resolute=False, max_num_of_committees=MAX_NUM_OF_COMMITTEES_DEFAULT, lexicographic_tiebreaking=False, ): """ Compute winning committees with Phragmen's leximax rule (leximax-Phragmen). Lexicographically minimize the maximum loads. Details in Markus Brill, Rupert Freeman, Svante Janson and Martin Lackner. Phragmen's Voting Methods and Justified Representation. <https://arxiv.org/abs/2102.12305> .. important:: Very slow due to lexicographic optimization. Parameters ---------- profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. The following algorithms are available for Phragmen's leximax rule (leximax-Phragmen): .. doctest:: >>> Rule("leximaxphragmen").algorithms ('gurobi',) resolute : bool, optional Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. lexicographic_tiebreaking : bool Require lexicographic tiebreaking among tied committees. This requires the computation of *all* winning committees and is therefore very slow. .. important:: `lexicographic_tiebreaking=True` is only valid in "combination with resolute=True. Returns ------- list of CandidateSet A list of winning committees. """ rule_id = "leximaxphragmen" rule = Rule(rule_id) if algorithm == "fastest": algorithm = rule.fastest_available_algorithm() rule.verify_compute_parameters( profile=profile, committeesize=committeesize, algorithm=algorithm, resolute=resolute, max_num_of_committees=max_num_of_committees, ) if lexicographic_tiebreaking: if not resolute: raise ValueError( "lexicographic_tiebreaking=True is only valid in " "combination with resolute=True." ) resolute = False # compute all committees to break ties correctly if algorithm == "gurobi": committees = abcrules_gurobi._gurobi_leximaxphragmen( profile, committeesize, resolute=resolute, max_num_of_committees=max_num_of_committees, ) # elif algorithm.startswith("mip-"): # committees = abcrules_mip._mip_leximaxphragmen( # profile, # committeesize, # resolute=resolute, # max_num_of_committees=max_num_of_committees, # solver_id=algorithm[4:], # ) else: raise UnknownAlgorithm(rule_id, algorithm) if lexicographic_tiebreaking: committees = sorted_committees(committees)[:1] # optional output output.info(header(rule.longname), wrap=False) if resolute: output.info("Computing only one winning committee (resolute=True)\n") output.details(f"Algorithm: {ALGORITHM_NAMES[algorithm]}\n") output.info( str_committees_with_header(committees, cand_names=profile.cand_names, winning=True) ) # end of optional output return committees def compute_phragmen_enestroem( profile, committeesize, algorithm="fastest", resolute=True, max_num_of_committees=MAX_NUM_OF_COMMITTEES_DEFAULT, ): """ Compute winning committees with Phragmen-Enestroem. This ABC rule is also known as Phragmen's first method and Enestroem's method. In every round the candidate with the highest combined budget of their supporters is put in the committee. Method described in: Svante Janson Phragmén's and Thiele's election methods <https://arxiv.org/pdf/1611.08826.pdf> (Section 18.5, Page 59) Parameters ---------- profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. The following algorithms are available for Phragmen-Enestroem: .. doctest:: >>> Rule("phragmen-enestroem").algorithms ('float-fractions', 'gmpy2-fractions', 'standard-fractions') resolute : bool, optional Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. Returns ------- list of CandidateSet A list of winning committees. """ rule_id = "phragmen-enestroem" rule = Rule(rule_id) if algorithm == "fastest": algorithm = rule.fastest_available_algorithm() rule.verify_compute_parameters( profile=profile, committeesize=committeesize, algorithm=algorithm, resolute=resolute, max_num_of_committees=max_num_of_committees, ) if not profile.has_unit_weights(): raise ValueError(f"{rule.shortname} is only defined for unit weights (weight=1)") committees, detailed_info = _phragmen_enestroem_algorithm( profile=profile, committeesize=committeesize, algorithm=algorithm, resolute=resolute, max_num_of_committees=max_num_of_committees, ) # optional output output.info(header(rule.longname), wrap=False) if not resolute: output.info("Computing all possible winning committees for any tiebreaking order") output.info(" (aka parallel universes tiebreaking) (resolute=False)\n") output.details(f"Algorithm: {ALGORITHM_NAMES[algorithm]}\n") output.info( str_committees_with_header(committees, cand_names=profile.cand_names, winning=True) ) # end of optional output return committees def _phragmen_enestroem_algorithm( profile, committeesize, algorithm, resolute, max_num_of_committees ): """ Algorithm computing Phragmen-Enestroem. """ if algorithm == "float-fractions": division = lambda x, y: x / y # standard float division elif algorithm == "standard-fractions": division = Fraction # using Python built-in fractions elif algorithm == "gmpy2-fractions": if not mpq: raise ImportError( 'Module gmpy2 not available, required for algorithm "gmpy2-fractions"' ) division = mpq # using gmpy2 fractions else: raise UnknownAlgorithm("phragmen-enestroem", algorithm) if resolute: max_num_of_committees = 1 # same algorithm for resolute==True and resolute==False initial_voter_budget = [voter.weight for voter in profile] # price for adding a candidate to the committee price = division(sum(initial_voter_budget), committeesize) committee_budget_pairs = [(tuple(), initial_voter_budget)] committees = set() while committee_budget_pairs: committee, budget = committee_budget_pairs.pop() available_candidates = [cand for cand in profile.candidates if cand not in committee] support = {cand: 0 for cand in available_candidates} for i, voter in enumerate(profile): voting_power = budget[i] if voting_power <= 0: continue for cand in voter.approved: if cand in available_candidates: support[cand] += voting_power max_support = max(support.values()) if algorithm == "float-fractions": tied_cands = [ cand for cand, supp in support.items() if misc.isclose(supp, max_support) ] else: tied_cands = sorted(cand for cand, supp in support.items() if supp == max_support) assert tied_cands, "_phragmen_enestroem_algorithm: no candidate with max support (??)" new_committee_budget_pairs = [] for cand in tied_cands: new_budget = list(budget) # copy of budget if max_support > price: # supporters can afford it multiplier = division(max_support - price, max_support) else: # supporters can't afford it, set budget to 0 multiplier = 0 for i, voter in enumerate(profile): if cand in voter.approved: new_budget[i] *= multiplier new_committee = committee + (cand,) if len(new_committee) == committeesize: new_committee = tuple(sorted(new_committee)) committees.add(new_committee) # remove duplicate committees if max_num_of_committees is not None and len(committees) == max_num_of_committees: # sufficiently many winning committees found detailed_info = {} return sorted_committees(committees), detailed_info else: # partial committee new_committee_budget_pairs.append((new_committee, new_budget)) # add new committee/budget pairs in reversed order, so that tiebreaking is correct committee_budget_pairs += reversed(new_committee_budget_pairs) detailed_info = {} return sorted_committees(committees), detailed_info def compute_consensus_rule( profile, committeesize, algorithm="fastest", resolute=True, max_num_of_committees=MAX_NUM_OF_COMMITTEES_DEFAULT, ): """ Compute winning committees with the Consensus rule. Based on Perpetual Consensus from Martin Lackner Perpetual Voting: Fairness in Long-Term Decision Making In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI 2020) Parameters ---------- profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. The following algorithms are available for the Consensus rule: .. doctest:: >>> Rule("consensus-rule").algorithms ('float-fractions', 'gmpy2-fractions', 'standard-fractions') resolute : bool, optional Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. Returns ------- list of CandidateSet A list of winning committees. """ rule_id = "consensus-rule" rule = Rule(rule_id) if algorithm == "fastest": algorithm = rule.fastest_available_algorithm() rule.verify_compute_parameters( profile=profile, committeesize=committeesize, algorithm=algorithm, resolute=resolute, max_num_of_committees=max_num_of_committees, ) committees, detailed_info = _consensus_rule_algorithm( profile=profile, committeesize=committeesize, algorithm=algorithm, resolute=resolute, max_num_of_committees=max_num_of_committees, ) # optional output output.info(header(rule.longname), wrap=False) if not resolute: output.info("Computing all possible winning committees for any tiebreaking order") output.info(" (aka parallel universes tiebreaking) (resolute=False)\n") output.details(f"Algorithm: {ALGORITHM_NAMES[algorithm]}\n") output.info( str_committees_with_header(committees, cand_names=profile.cand_names, winning=True) ) # end of optional output return committees def _consensus_rule_algorithm(profile, committeesize, algorithm, resolute, max_num_of_committees): """ Algorithm for computing the consensus rule. """ if algorithm == "float-fractions": division = lambda x, y: x / y # standard float division elif algorithm == "standard-fractions": division = Fraction # using Python built-in fractions elif algorithm == "gmpy2-fractions": if not mpq: raise ImportError( 'Module gmpy2 not available, required for algorithm "gmpy2-fractions"' ) division = mpq # using gmpy2 fractions else: raise UnknownAlgorithm("consensus-rule", algorithm) if resolute: max_num_of_committees = 1 # same algorithm for resolute==True and resolute==False initial_voter_budget = [0] * len(profile) committee_budget_pairs = [(tuple(), initial_voter_budget)] committees = set() while committee_budget_pairs: committee, budget = committee_budget_pairs.pop() for i, _ in enumerate(profile): budget[i] += profile[i].weight # weight is 1 by default available_candidates = [cand for cand in profile.candidates if cand not in committee] support = {cand: 0 for cand in available_candidates} supporters = {cand: [] for cand in available_candidates} for i, voter in enumerate(profile): if (budget[i] <= 0) or (algorithm == "float-fractions" and misc.isclose(budget[i], 0)): continue for cand in voter.approved: if cand in available_candidates: support[cand] += budget[i] supporters[cand].append(i) max_support = max(support.values()) if algorithm == "float-fractions": tied_cands = [ cand for cand, supp in support.items() if misc.isclose(supp, max_support) ] else: tied_cands = sorted(cand for cand, supp in support.items() if supp == max_support) assert tied_cands, "_consensus_rule_algorithm: no candidate with max support (??)" new_committee_budget_pairs = [] for cand in tied_cands: new_budget = list(budget) # copy of budget for i in supporters[cand]: new_budget[i] -= division(len(profile), len(supporters[cand])) new_committee = committee + (cand,) if len(new_committee) == committeesize: new_committee = tuple(sorted(new_committee)) committees.add(new_committee) # remove duplicate committees if max_num_of_committees is not None and len(committees) == max_num_of_committees: # sufficiently many winning committees found detailed_info = {} return sorted_committees(committees), detailed_info else: # partial committee new_committee_budget_pairs.append((new_committee, new_budget)) # add new committee/budget pairs in reversed order, so that tiebreaking is correct committee_budget_pairs += reversed(new_committee_budget_pairs) detailed_info = {} return sorted_committees(committees), detailed_info def compute_trivial_rule( profile, committeesize, algorithm="standard", resolute=False, max_num_of_committees=MAX_NUM_OF_COMMITTEES_DEFAULT, ): """ Compute winning committees with the trivial rule (all committees are winning). Parameters ---------- profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. The following algorithms are available for the trivial rule: .. doctest:: >>> Rule("trivial").algorithms ('standard',) resolute : bool, optional Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. Returns ------- list of CandidateSet A list of winning committees. """ rule_id = "trivial" rule = Rule(rule_id) if algorithm == "fastest": algorithm = rule.fastest_available_algorithm() rule.verify_compute_parameters( profile=profile, committeesize=committeesize, algorithm=algorithm, resolute=resolute, max_num_of_committees=max_num_of_committees, ) if algorithm == "standard": if resolute: committees = [range(committeesize)] else: all_committees = itertools.combinations(profile.candidates, committeesize) if max_num_of_committees is None: committees = list(all_committees) else: committees = itertools.islice(all_committees, max_num_of_committees) committees = [CandidateSet(comm) for comm in committees] else: raise UnknownAlgorithm(rule_id, algorithm) # optional output output.info(header(rule.longname), wrap=False) if resolute: output.info("Computing only one winning committee (resolute=True)\n") output.details(f"Algorithm: {ALGORITHM_NAMES[algorithm]}\n") output.info( str_committees_with_header(committees, cand_names=profile.cand_names, winning=True) ) # end of optional output return sorted_committees(committees) def compute_rsd( profile, committeesize, algorithm="standard", resolute=True, max_num_of_committees=None ): """ Compute winning committees with the Random Serial Dictator rule. This rule randomy selects a permutation of voters. The first voter in this permutation adds all approved candidates to the winning committee, then the second voter, then the third, etc. At some point, a voter has more approved candidates than can be added to the winning committee. In this case, as many as possible are added (candidates with smaller index first). In this way, the winning committee is constructed. .. important:: This algorithm is not deterministic and relies on the Python module `random`. Parameters ---------- profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. The following algorithms are available for Random Serial Dictator: .. doctest:: >>> Rule("rsd").algorithms ('standard',) resolute : bool, optional Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. Returns ------- list of CandidateSet A list of winning committees. """ rule_id = "rsd" rule = Rule(rule_id) if algorithm == "fastest": algorithm = rule.fastest_available_algorithm() rule.verify_compute_parameters( profile, committeesize, algorithm, resolute, max_num_of_committees ) if not profile.has_unit_weights(): raise ValueError(f"{rule.shortname} is only implemented for unit weights (weight=1).") # Todo: fix if algorithm == "standard": approval_sets = [sorted(voter.approved) for voter in profile] # random order of dictators random.shuffle(approval_sets) committee = set() for approved in approval_sets: if len(committee) + len(approved) <= committeesize: committee.update(approved) else: for cand in approved: committee.add(cand) if len(committee) == committeesize: break if len(committee) == committeesize: break else: remaining_candidates = [cand for cand in profile.candidates if cand not in committee] num_missing_candidates = committeesize - len(committee) committee.update(random.sample(remaining_candidates, num_missing_candidates)) else: raise UnknownAlgorithm(rule_id, algorithm) committees = [committee] # optional output output.info(header(rule.longname), wrap=False) output.details(f"Algorithm: {ALGORITHM_NAMES[algorithm]}\n") output.info( str_committees_with_header(committees, cand_names=profile.cand_names, winning=True) ) # end of optional output return sorted_committees(committees) def compute_eph( profile, committeesize, algorithm="float-fractions", resolute=False, max_num_of_committees=None ): """ Compute winning committees with the "E Pluribus Hugo" (EPH) voting rule. This rule is used by the Hugo Awards as a shortlisting voting rule. It is described in the following paper under the name "Single Divisible Vote with Least-Popular Elimination (SDV-LPE)": "A proportional voting system for awards nominations resistant to voting blocs." Jameson Quinn, and Bruce Schneier. <https://www.schneier.com/wp-content/uploads/2016/05/Proportional_Voting_System.pdf> Parameters ---------- profile : abcvoting.preferences.Profile A profile. committeesize : int The desired committee size. algorithm : str, optional The algorithm to be used. The following algorithms are available for Random Serial Dictator: .. doctest:: >>> Rule("eph").algorithms ('float-fractions', 'gmpy2-fractions', 'standard-fractions') resolute : bool, optional Return only one winning committee. If `resolute=False`, all winning committees are computed (subject to `max_num_of_committees`). max_num_of_committees : int, optional At most `max_num_of_committees` winning committees are computed. If `max_num_of_committees=None`, the number of winning committees is not restricted. The default value of `max_num_of_committees` can be modified via the constant `MAX_NUM_OF_COMMITTEES_DEFAULT`. Returns ------- list of CandidateSet A list of winning committees. """ rule_id = "eph" rule = Rule(rule_id) if algorithm == "fastest": algorithm = rule.fastest_available_algorithm() rule.verify_compute_parameters( profile, committeesize, algorithm, resolute, max_num_of_committees ) committees, detailed_info = _eph_algorithm( rule_id=rule_id, profile=profile, algorithm=algorithm, committeesize=committeesize, resolute=resolute, max_num_of_committees=max_num_of_committees, ) # optional output output.info(header(rule.longname), wrap=False) if resolute: output.info("Computing only one winning committee (resolute=True)\n") output.details(f"Algorithm: {ALGORITHM_NAMES[algorithm]}\n") output.info( str_committees_with_header(committees, cand_names=profile.cand_names, winning=True) ) # end of optional output return sorted_committees(committees) def _eph_algorithm(rule_id, profile, algorithm, committeesize, resolute, max_num_of_committees): """Algorithm for computing the "E Pluribus Hugo" (EPH) voting rule.""" if algorithm == "float-fractions": division = lambda x, y: x / y # standard float division elif algorithm == "standard-fractions": division = Fraction # using Python built-in fractions elif algorithm == "gmpy2-fractions": if not mpq: raise ImportError( 'Module gmpy2 not available, required for algorithm "gmpy2-fractions"' ) division = mpq # using gmpy2 fractions else: raise UnknownAlgorithm(rule_id, algorithm) if resolute: max_num_of_committees = 1 # same algorithm for resolute==True and resolute==False remaining_candidates = set(profile.candidates) while True: sdv_score = {cand: 0 for cand in remaining_candidates} av_score = {cand: 0 for cand in remaining_candidates} for voter in profile: remaining_approved = [cand for cand in remaining_candidates if cand in voter.approved] for cand in remaining_approved: sdv_score[cand] += division(voter.weight, len(remaining_approved)) av_score[cand] += voter.weight cutoff_sdv = sorted(sdv_score.values())[1] # 2nd smallest value elimination_cands = [ cand for cand in remaining_candidates if (sdv_score[cand] <= cutoff_sdv) or (algorithm == "float-fractions" and misc.isclose(sdv_score[cand], cutoff_sdv)) ] cutoff_av = min(av_score[cand] for cand in elimination_cands) elimination_cands = [cand for cand in elimination_cands if av_score[cand] <= cutoff_av] if len(remaining_candidates) - len(elimination_cands) <= committeesize: num_cands_to_be_eliminated = len(remaining_candidates) - committeesize committees = sorted_committees( [ (remaining_candidates - set(selection)) for selection in itertools.combinations( elimination_cands, num_cands_to_be_eliminated ) ] ) detailed_info = {} return committees[:max_num_of_committees], detailed_info remaining_candidates -= set(elimination_cands)

35.489692

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0.6292

16,039

146,324

5.540246

0.045452

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0.080216

0.759273

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0.700158

0.676176

0.65691

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0

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146,324

4,122

100

35.498302

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0

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0

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0

null

0

1

0

1

0

null

0

3

1c74695a7efe48fa2ca3158a804aa1dd32a43236

1,712

py

Python

cavedb/docgen_mxf.py

masneyb/cavedbmanager

0e1fb48a3054134a069ec8e9892475ae1e228e5c

[ "Apache-2.0" ]

4

2016-02-26T12:24:08.000Z

2019-09-10T02:45:08.000Z

cavedb/docgen_mxf.py

masneyb/cavedbmanager

0e1fb48a3054134a069ec8e9892475ae1e228e5c

[ "Apache-2.0" ]

2

2017-04-16T01:13:22.000Z

2017-05-07T22:28:49.000Z

cavedb/docgen_mxf.py

masneyb/cavedbmanager

0e1fb48a3054134a069ec8e9892475ae1e228e5c

[ "Apache-2.0" ]

1

2021-04-16T15:25:20.000Z

# SPDX-License-Identifier: Apache-2.0 import cavedb.docgen_common import cavedb.utils class Mxf(cavedb.docgen_common.Common): def __init__(self, filename, download_url): cavedb.docgen_common.Common.__init__(self) self.filename = filename self.download_url = download_url self.number = 1 self.mxffile = None def open(self, all_regions_gis_hash): cavedb.docgen_common.create_base_directory(self.filename) self.mxffile = open(self.filename, 'w') def close(self): self.mxffile.close() def feature_entrance(self, feature, entrance, coordinates): wgs84_lon_lat = coordinates.get_lon_lat_wgs84() self.mxffile.write('%s, %s, \"%s\", \"%s%s\", \"Number: %s Height: %s\", ff0000, 3\n' % \ (wgs84_lon_lat[1], wgs84_lon_lat[0], \ cavedb.docgen_common.get_entrance_name(feature, entrance), \ feature.survey_county.survey_short_name, feature.survey_id, \ self.number, entrance.elevation_ft)) self.number = self.number + 1 def create_html_download_urls(self): return self.create_url(self.download_url, 'Maptech (MXF)', self.filename) def create_for_bulletin(bulletin): return Mxf(get_bulletin_mxf_filename(bulletin.id), 'bulletin/%s/mxf' % (bulletin.id)) def create_for_global(): return Mxf(get_global_mxf_filename(), None) def get_bulletin_mxf_filename(bulletin_id): return '%s/mxf/bulletin_%s.mxf' % (cavedb.utils.get_output_base_dir(bulletin_id), bulletin_id) def get_global_mxf_filename(): return '%s/mxf/all.mxf' % (cavedb.utils.get_global_output_base_dir())

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0.665888

223

1,712

4.798206

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0.04486

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0

0.014137

0.214953

1,712

53

99

32.301887

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0

0.03125

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0

1

0.28125

false

0

0.0625

0.15625

0.53125

0

null

0

null

0

1

0

1

0

3

1c75e461532df41224c9c19d5a4cf71ecfa9e43d

1,505

py

Python

rrmng/rrmngmnt/user.py

avihaie/bug-hunter

9a2730d9a61b268f45e9d3115b8bbba039b954db

[ "Apache-2.0" ]

null

rrmng/rrmngmnt/user.py

avihaie/bug-hunter

9a2730d9a61b268f45e9d3115b8bbba039b954db

[ "Apache-2.0" ]

4

2018-05-29T03:58:15.000Z

2018-10-10T10:27:35.000Z

rrmng/rrmngmnt/user.py

avihaie/bug-hunter

9a2730d9a61b268f45e9d3115b8bbba039b954db

[ "Apache-2.0" ]

null

from rrmng.rrmngmnt.resource import Resource class User(Resource): def __init__(self, name, password): """ Args: password (str): Password name (str): User name """ super(User, self).__init__() self.name = name self.password = password @property def full_name(self): return self.get_full_name() def get_full_name(self): return self.name class RootUser(User): NAME = 'root' def __init__(self, password): super(RootUser, self).__init__(self.NAME, password) class Domain(Resource): def __init__(self, name, provider=None, server=None): """ Args: server (str): Server address name (str): Name of domain provider (str): Name of provider / type of domain """ super(Domain, self).__init__() self.name = name self.provider = provider self.server = server class InternalDomain(Domain): NAME = 'internal' def __init__(self): super(InternalDomain, self).__init__(self.NAME) class ADUser(User): def __init__(self, name, password, domain): """ Args: domain (instance of Domain): User domain password (str): Password name (str): User name """ super(ADUser, self).__init__(name, password) self.domain = domain def get_full_name(self): return "%s@%s" % (self.name, self.domain.name)

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1,505

4.91018

0.197605

0.087805

0.102439

0.078049

0.337805

0.212195

0.095122

0

0.312957

1,505

63

62

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0.793037

0.192691

0

0.129032

0

0.015726

0

1

0.258065

false

0.193548

0.032258

0.096774

0.612903

0

null

0

null

0

1

0

1

0

1

0

3

1c79ab6b54f565a108796c071b30a1793de5e098

243

py

Python

politicians/tasks.py

zinaukarenku/zkr-platform

8daf7d1206c482f1f8e0bcd54d4fde783e568774

[ "Apache-2.0" ]

2

2018-11-16T21:45:17.000Z

2019-02-03T19:55:46.000Z

politicians/tasks.py

zinaukarenku/zkr-platform

8daf7d1206c482f1f8e0bcd54d4fde783e568774

[ "Apache-2.0" ]

13

2018-08-17T19:12:11.000Z

2022-03-11T23:27:41.000Z

politicians/tasks.py

zinaukarenku/zkr-platform

8daf7d1206c482f1f8e0bcd54d4fde783e568774

[ "Apache-2.0" ]

null

from celery import shared_task from politicians.models import Promises, PromiseAction @shared_task(soft_time_limit=30) def get_promise_action_scores(promiseaction_id=None): promiseAction = PromiseAction.objects.filter(pk=promiseaction_id)

40.5

69

0.855967

32

243

6.21875

0.71875

0.100503

0

0.008929

0.078189

243

6

69

40.5

0.879464

0

1

0.2

false

0

0.4

0

0.6

0

null

0

1

0

null

0

1

0

1

0

3

1c86202c5c9d6cf6069f1ca55edd96f5a6259a61

443

py

Python

proxypool/utils/parse.py

zronghui/ProxyPool

7c0dde213c56942807d6421fa1e3604d3f25514f

[ "MIT" ]

null

proxypool/utils/parse.py

zronghui/ProxyPool

7c0dde213c56942807d6421fa1e3604d3f25514f

[ "MIT" ]

null

proxypool/utils/parse.py

zronghui/ProxyPool

7c0dde213c56942807d6421fa1e3604d3f25514f

[ "MIT" ]

null

import re def parse_redis_connection_string(connection_string): """ parse a redis connection string, for example: redis://[password]@host:port rediss://[password]@host:port :param connection_string: :return: """ result = re.match('rediss?:\/\/(.*?)@(.*?):(\d+)', connection_string) return result.group(2), int(result.group(3)), (result.group(1) or None) if result \ else ('localhost', 6379, None)

29.533333

87

0.636569

54

443

5.111111

0.555556

0.289855

0.152174

0.202899

0

0.019337

0.182844

443

14

88

31.642857

0.743094

0.31377

0

0.139706

0.106618

0

1

0.2

false

0

0.2

0

0.6

0

null

1

0

1

0

null

0

1

0

3

98d3c828d5f10cf9789f1280af55ea616236efec

523

py

Python

src/data/dataframes.py

TihonkovSergey/pd-model-logreg

baa6447198f5f89a43b1091413d3199192230ce1

[ "Apache-2.0" ]

null

src/data/dataframes.py

TihonkovSergey/pd-model-logreg

baa6447198f5f89a43b1091413d3199192230ce1

[ "Apache-2.0" ]

null

src/data/dataframes.py

TihonkovSergey/pd-model-logreg

baa6447198f5f89a43b1091413d3199192230ce1

[ "Apache-2.0" ]

null

from pathlib import Path import pandas as pd from definitions import ROOT_DIR def get_train(): data_path = Path(ROOT_DIR).joinpath("data/raw/") return pd.read_csv(data_path.joinpath('PD-data-train.csv'), sep=';') def get_test(): data_path = Path(ROOT_DIR).joinpath("data/raw/") return pd.read_csv(data_path.joinpath('PD-data-test.csv'), sep=';') def get_data_description(): data_path = Path(ROOT_DIR).joinpath("data/raw/") return pd.read_csv(data_path.joinpath('PD-data-desc.csv'), sep=';')

24.904762

72

0.703633

83

523

4.228916

0.277108

0.136752

0.102564

0.136752

0.606838

0

0.130019

523

20

73

26.15

0.771429

0

0.25

0

0.151052

0

1

0.25

false

0

0.25

0

0.75

0

null

0

1

0

null

0

1

0

1

0

3

98d4672916c38338602fe1d91086ef4b15d61b12

261

py

Python

app/config.py

joelkyu/SantaComingToTown

c93bca9158459243205b26cb36f1b19346eeb058

[ "MIT" ]

null

app/config.py

joelkyu/SantaComingToTown

c93bca9158459243205b26cb36f1b19346eeb058

[ "MIT" ]

3

2019-09-09T14:55:12.000Z

2019-09-10T14:51:52.000Z

app/config.py

joelkyu/SantaComingToTowne

c93bca9158459243205b26cb36f1b19346eeb058

[ "MIT" ]

null

from flask import Flask, render_template from flask_sqlalchemy import SQLAlchemy from flask_cors import CORS app = Flask(__name__) CORS(app) app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:////tmp/test.db' app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False

26.1

64

0.800766

36

261

5.5

0.527778

0.136364

0.191919

0

0.095785

261

9

65

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0

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0

1

0

false

0

0.428571

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0.428571

0

null

0

1

0

1

0

null

0

1

0

3

98e1cd4818800c72e6d9e5cea455548d52546136

2,354

py

Python

src/long_read_pipeline/migrations/0001_initial.py

NTU-CGM/MiDSystem

0d7dadafe4811ec9d1c0df03e99d7c479e8c7f1d

[ "MIT" ]

null

src/long_read_pipeline/migrations/0001_initial.py

NTU-CGM/MiDSystem

0d7dadafe4811ec9d1c0df03e99d7c479e8c7f1d

[ "MIT" ]

null

src/long_read_pipeline/migrations/0001_initial.py

NTU-CGM/MiDSystem

0d7dadafe4811ec9d1c0df03e99d7c479e8c7f1d

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.11.3 on 2021-06-10 20:18 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='long_ip_log', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('ip', models.CharField(max_length=25)), ('country', models.CharField(default='NA', max_length=50)), ('functions', models.CharField(default='NA', max_length=25)), ('submission_time', models.DateTimeField(auto_now_add=True)), ], ), migrations.CreateModel( name='long_User_Job', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('user_id', models.CharField(max_length=50)), ('upload_id', models.CharField(max_length=64)), ('ip', models.CharField(max_length=25)), ('mail', models.EmailField(max_length=254)), ('submission_time', models.DateTimeField(auto_now_add=True)), ('start_time', models.DateTimeField(auto_now_add=True)), ('end_time', models.DateTimeField(auto_now_add=True)), ('total_status', models.CharField(default='WAITING', max_length=10)), ('data_preparation_status', models.CharField(default='WAITING', max_length=10)), ('quality_check', models.CharField(default='WAITING', max_length=10)), ('assembly_status', models.CharField(default='WAITING', max_length=10)), ('remap_status', models.CharField(default='SKIPPED', max_length=10)), ('gene_prediction_status', models.CharField(default='WAITING', max_length=10)), ('go_status', models.CharField(default='WAITING', max_length=10)), ('tree_status', models.CharField(default='SKIPPED', max_length=10)), ('parsing_status', models.CharField(default='WAITING', max_length=10)), ('error_log', models.CharField(default='NA', max_length=50)), ], ), ]

47.08

114

0.595157

248

2,354

5.415323

0.334677

0.113924

0.196575

0.166791

0.703649

0.664929

0.59866

0.461653

0.117647

0

0.029765

0.257859

2,354

49

115

48.040816

0.738981

0.028887

0

0.341463

1

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0.144109

0.019711

0

1

0

false

0

0.04878

0

0.146341

0

null

0

1

0

1

0

null

0

3

98fd48870c8e98de7967b6f2fb07fb3a47a30dbf

1,763

py

Python

python/aead/aead_key_manager.py

tsingson/tink

bb4386994a4ff62b2ae2b140a9c106267028c511

[ "Apache-2.0" ]

null

python/aead/aead_key_manager.py

tsingson/tink

bb4386994a4ff62b2ae2b140a9c106267028c511

[ "Apache-2.0" ]

null

python/aead/aead_key_manager.py

tsingson/tink

bb4386994a4ff62b2ae2b140a9c106267028c511

[ "Apache-2.0" ]

null

# Copyright 2019 Google LLC. # # 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. """Python wrapper of the CLIF-wrapped C++ AEAD key manager.""" from __future__ import absolute_import from __future__ import division from __future__ import google_type_annotations from __future__ import print_function from typing import Text from tink.cc.python import aead as cc_aead from tink.python.aead import aead from tink.python.cc.clif import cc_key_manager from tink.python.core import key_manager from tink.python.core import tink_error class _AeadCcToPyWrapper(aead.Aead): """Transforms cliffed C++ Aead primitive into a Python primitive.""" def __init__(self, cc_primitve: cc_aead.Aead): self._aead = cc_primitve @tink_error.use_tink_errors def encrypt(self, plaintext: bytes, associated_data: bytes) -> bytes: return self._aead.encrypt(plaintext, associated_data) @tink_error.use_tink_errors def decrypt(self, plaintext: bytes, associated_data: bytes) -> bytes: return self._aead.decrypt(plaintext, associated_data) def from_cc_registry(type_url: Text) -> key_manager.KeyManager[aead.Aead]: return key_manager.KeyManagerCcToPyWrapper( cc_key_manager.AeadKeyManager.from_cc_registry(type_url), aead.Aead, _AeadCcToPyWrapper)

35.26

74

0.782189

257

1,763

5.143969

0.424125

0.045386

0.048412

0.024206

0.205749

0.173979

0.136157

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0

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c710d276f32cd528b2ce914aeaf8669baf623b38

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py

Python

subrepos/papy/papy/__init__.py

timothyyu/au_utils

6d1f1095b7f5de823a329ca9beb787c72aaea53b

[ "BSD-3-Clause" ]

1

2019-02-01T05:09:37.000Z

subrepos/papy/papy/__init__.py

timothyyu/au_utils

6d1f1095b7f5de823a329ca9beb787c72aaea53b

[ "BSD-3-Clause" ]

null

subrepos/papy/papy/__init__.py

timothyyu/au_utils

6d1f1095b7f5de823a329ca9beb787c72aaea53b

[ "BSD-3-Clause" ]

null

name = 'papy' from . import freq, img, num, plot, time, misc

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c714651871e62e4573ce237d4753e96cb6c3bf1b

721

py

Python

edk2toolext/environment/plugintypes/dsc_processor_plugin.py

joschock/edk2-pytool-extensions

34cd69415f8a8363291e8ae6f7c6bddec7ac9967

[ "BSD-2-Clause-Patent" ]

32

2019-07-09T21:43:57.000Z

2022-03-31T01:43:59.000Z

edk2toolext/environment/plugintypes/dsc_processor_plugin.py

joschock/edk2-pytool-extensions

34cd69415f8a8363291e8ae6f7c6bddec7ac9967

[ "BSD-2-Clause-Patent" ]

217

2019-08-07T01:12:27.000Z

2022-03-30T07:28:24.000Z

edk2toolext/environment/plugintypes/dsc_processor_plugin.py

joschock/edk2-pytool-extensions

34cd69415f8a8363291e8ae6f7c6bddec7ac9967

[ "BSD-2-Clause-Patent" ]

28

2019-08-05T17:23:08.000Z

2022-03-04T00:20:04.000Z

# @file dsc_processor_plugin # Plugin for for parsing DSCs ## # Copyright (c) Microsoft Corporation # # SPDX-License-Identifier: BSD-2-Clause-Patent ## class IDscProcessorPlugin(object): ## # does the transform on the DSC # # @param dsc - the in-memory model of the DSC # @param thebuilder - UefiBuild object to get env information # # @return 0 for success NonZero for error. ## def do_transform(self, dsc, thebuilder): return 0 ## # gets the level that this transform operates at # # @param thebuilder - UefiBuild object to get env information # # @return 0 for the most generic level ## def get_level(self, thebuilder): return 0

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null

0

null

0

1

0

1

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3

c757bdf20ef16356887bb05d39a876192818f188

754

py

Python

facenet_sandberg/train_insightface/distance.py

armanrahman22/facenet

194e7bab2060833acd1d245228bcd43825ce5af3

[ "MIT" ]

21

2019-01-10T16:46:06.000Z

2022-01-17T09:30:35.000Z

facenet_sandberg/train_insightface/distance.py

armanrahman22/facenet

194e7bab2060833acd1d245228bcd43825ce5af3

[ "MIT" ]

null

facenet_sandberg/train_insightface/distance.py

armanrahman22/facenet

194e7bab2060833acd1d245228bcd43825ce5af3

[ "MIT" ]

12

2019-02-08T20:37:16.000Z

2020-12-10T06:55:30.000Z

# -*- coding:utf-8 -*- import math import numpy as np def cosine_similarity(v1, v2): # compute cosine similarity of v1 to v2: (v1 dot v2)/{||v1||*||v2||) sumxx, sumxy, sumyy = 0, 0, 0 for i in range(len(v1)): x = v1[i] y = v2[i] sumxx += x * x sumyy += y * y sumxy += x * y return sumxy / math.sqrt(sumxx * sumyy) def cosine_distance(v1, v2): return 1 - cosine_similarity(v1, v2) def L2_distance(v1, v2): return np.sqrt(np.sum(np.square(v1 - v2))) def SSD_distance(v1, v2): return np.sum(np.square(v1 - v2)) def get_distance(dist_type): loss_map = { 'cosine': cosine_distance, 'L2': L2_distance, 'SSD': SSD_distance} return loss_map[dist_type]

20.944444

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754

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754

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null

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1

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3

c7659ec96d4e983c20829d4bde2f8c8c9f0ebe20

265

py

Python

Python/Math/RandomGuess.py

piovezan/SOpt

a5ec90796b7bdf98f0675457fc4bb99c8695bc40

[ "MIT" ]

148

2017-08-03T01:49:27.000Z

2022-03-26T10:39:30.000Z

Python/Math/RandomGuess.py

piovezan/SOpt

a5ec90796b7bdf98f0675457fc4bb99c8695bc40

[ "MIT" ]

3

2017-11-23T19:52:05.000Z

2020-04-01T00:44:40.000Z

Python/Math/RandomGuess.py

piovezan/SOpt

a5ec90796b7bdf98f0675457fc4bb99c8695bc40

[ "MIT" ]

59

2017-08-03T01:49:19.000Z

2022-03-31T23:24:38.000Z

import random m = 1 while m != 0: n1 = int(random.random() * 9) + 1 n2 = int(random.random() * 9) + 1 m = int(raw_input("{} * {} = ".format(n1, n2))) print ("correto!" if m == n1 * n2 else "errado!!") #https://pt.stackoverflow.com/q/259931/101

26.5

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