mirror of
https://github.com/zoffline/zwift-offline.git
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170 lines
14 KiB
Python
Executable File
170 lines
14 KiB
Python
Executable File
#! /usr/bin/env python
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# Use this script to populate bot.txt with names from https://www.procyclingstats.com
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# pip install beautifulsoup4 country-converter fuzzywuzzy
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# scripts/get_pro_names.py -h
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from bs4 import BeautifulSoup
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import urllib.request
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import json
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import country_converter as coco
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import argparse
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import getpass
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import os
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import sys
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import xml.etree.ElementTree as ET
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from fuzzywuzzy import process
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from fuzzywuzzy import fuzz
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base_url = "https://www.procyclingstats.com/rankings.php?filter=Filter"
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cc = coco.CountryConverter()
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teams = {
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'UAE Team Emirates': {'jersey_name': 'UAE', 'jersey_signature': 1751349769, 'bike_name': 'Colnago Colnago V3RS', 'bike_signature': 3628259811, 'front_wheel_name': 'Enve SES 3.4', 'front_wheel_signature': 2223270801, 'rear_wheel_name': 'Enve SES 3.4', 'rear_wheel_signature': 3835575171},
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'Soudal - Quick Step': {'jersey_name': 'Deceuninck-Quick-Step', 'jersey_signature': 2906189156, 'bike_name': 'Specialized Tarmac SL7', 'bike_signature': 935373427, 'front_wheel_name': 'Roval Rapide CLX', 'front_wheel_signature': 2181416413, 'rear_wheel_name': 'Roval Rapide CLX', 'rear_wheel_signature': 3548735686},
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'Jumbo-Visma': {'jersey_name': 'Team Jumbo-Visma Men 2023', 'jersey_signature': 88214615, 'bike_name': 'Cervelo R5', 'bike_signature': 106535518, 'front_wheel_name': 'Reserve Reserve 25 GR', 'front_wheel_signature': 635220876, 'rear_wheel_name': 'Reserve Reserve 25 GR', 'rear_wheel_signature': 1842698274},
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'Alpecin-Deceuninck': {'jersey_name': 'Alpecin-Fenix Pro Team', 'jersey_signature': 930946828, 'bike_name': 'Canyon Aeroad 2015', 'bike_signature': 1520594784, 'front_wheel_name': 'Shimano C50', 'front_wheel_signature': 1742598126, 'rear_wheel_name': 'Shimano C50', 'rear_wheel_signature': 3725678091},
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'Trek - Segafredo': {'jersey_name': 'Trek-Segafredo Men', 'jersey_signature': 2140478849, 'bike_name': 'Trek Madone', 'bike_signature': 4129467727, 'front_wheel_name': 'Bontrager Aeolus5', 'front_wheel_signature': 702195190, 'rear_wheel_name': 'Bontrager Aeolus5', 'rear_wheel_signature': 3594144634},
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'Movistar Team': {'jersey_name': 'Movistar Team', 'jersey_signature': 1842355135, 'bike_name': 'Canyon Aeroad 2015', 'bike_signature': 1520594784, 'front_wheel_name': 'Zipp 404', 'front_wheel_signature': 613983807, 'rear_wheel_name': 'Zipp 404', 'rear_wheel_signature': 4183014640},
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'Lotto Dstny': {'jersey_name': 'Lotto', 'jersey_signature': 4130579852, 'bike_name': 'Ridley Noah Fast 2019', 'bike_signature': 4288910569, 'front_wheel_name': 'DTSwiss ARC 1100 DICUT 62', 'front_wheel_signature': 346409677, 'rear_wheel_name': 'DTSwiss ARC 1100 DICUT 62', 'rear_wheel_signature': 2049111692},
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'EF Education-EasyPost': {'jersey_name': 'EF Education First', 'jersey_signature': 2349035663, 'bike_name': 'Cannondale System Six', 'bike_signature': 2005280203, 'front_wheel_name': 'HED HED Vanquish RC6 Pro', 'front_wheel_signature': 1791179228, 'rear_wheel_name': 'HED HED Vanquish RC6 Pro', 'rear_wheel_signature': 2913819265},
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'INEOS Grenadiers': {'jersey_name': 'INEOS Grenadiers 2022 Pro', 'jersey_signature': 542207259, 'bike_name': 'Pinarello Dogma F', 'bike_signature': 4208139356, 'front_wheel_name': 'Shimano C50', 'front_wheel_signature': 1742598126, 'rear_wheel_name': 'Shimano C50', 'rear_wheel_signature': 3725678091},
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'Groupama - FDJ': {'jersey_name': 'Groupama FDJ 2023', 'jersey_signature': 2814449542, 'bike_name': 'Specialized Amira S-Works', 'bike_signature': 2662728556, 'front_wheel_name': 'Shimano C50', 'front_wheel_signature': 1742598126, 'rear_wheel_name': 'Shimano C50', 'rear_wheel_signature': 3725678091},
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'Bahrain - Victorious': {'jersey_name': 'Bahrain McLaren', 'jersey_signature': 2155858980, 'bike_name': 'Merida Scultura', 'bike_signature': 3033010663, 'front_wheel_name': 'Shimano C50', 'front_wheel_signature': 1742598126, 'rear_wheel_name': 'Shimano C50', 'rear_wheel_signature': 3725678091},
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'Team DSM': {'jersey_name': 'Team ODZ', 'jersey_signature': 2695025247, 'bike_name': 'Scott Foil', 'bike_signature': 1315158373, 'front_wheel_name': 'Shimano C50', 'front_wheel_signature': 1742598126, 'rear_wheel_name': 'Shimano C50', 'rear_wheel_signature': 3725678091},
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'Team Jayco AlUla': {'jersey_name': 'Team 3R', 'jersey_signature': 493134166, 'bike_name': 'Giant Propel Advanced SL Disc', 'bike_signature': 103914490, 'front_wheel_name': 'Cadex CADEX 42', 'front_wheel_signature': 1497226614, 'rear_wheel_name': 'Cadex CADEX 42', 'rear_wheel_signature': 1347687916},
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'Uno-X Pro Cycling Team': {'jersey_name': 'UnoXPro2022', 'jersey_signature': 1756517729},
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'Cofidis': {'jersey_name': 'Cofidis 2018', 'jersey_signature': 927604154, 'bike_name': 'Cervelo R5', 'bike_signature': 106535518, 'front_wheel_name': 'Shimano C50', 'front_wheel_signature': 1742598126, 'rear_wheel_name': 'Shimano C50', 'rear_wheel_signature': 3725678091},
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'Intermarché - Circus - Wanty': {'jersey_name': 'Intermarché–Wanty–Gobert Matériaux', 'jersey_signature': 88121645, 'bike_name': 'Cube Cube Litening', 'bike_signature': 1767548815, 'front_wheel_name': 'Shimano C50', 'front_wheel_signature': 1742598126, 'rear_wheel_name': 'Shimano C50', 'rear_wheel_signature': 3725678091},
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'BORA - hansgrohe': {'jersey_name': 'Bora-Hansgrohe', 'jersey_signature': 321508751, 'bike_name': 'Specialized Tarmac SL7', 'bike_signature': 935373427, 'front_wheel_name': 'Roval Rapide CLX', 'front_wheel_signature': 2181416413, 'rear_wheel_name': 'Roval Rapide CLX', 'rear_wheel_signature': 3548735686},
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'Team Arkéa Samsic': {'jersey_name': 'Arkea-Samsic', 'jersey_signature': 598687666, 'bike_name': 'Trek Madone', 'bike_signature': 4129467727, 'front_wheel_name': 'Shimano C50', 'front_wheel_signature': 1742598126, 'rear_wheel_name': 'Shimano C50', 'rear_wheel_signature': 3725678091},
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'AG2R Citroën Team': {'jersey_name': 'AG2R La Mondiale', 'jersey_signature': 1587982785, 'bike_name': 'BMC BmcTeamMachine2022', 'bike_signature': 3868468027, 'front_wheel_name': 'Campagnolo Bora Ultra 35', 'front_wheel_signature': 1053884173, 'rear_wheel_name': 'Campagnolo Bora Ultra 35', 'rear_wheel_signature': 1614586487},
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'Astana Qazaqstan Team': {'jersey_name': 'ASTANA PRO TEAM', 'jersey_signature': 1969335676, 'bike_name': 'Giant GiantRevolt2022', 'bike_signature': 2360271970, 'front_wheel_name': 'Shimano C50', 'front_wheel_signature': 1742598126, 'rear_wheel_name': 'Shimano C50', 'rear_wheel_signature': 3725678091},
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'Israel - Premier Tech': {'jersey_name': 'Israel Premier-Tech', 'jersey_signature': 552170906},
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'TotalEnergies': {'jersey_name': 'Total Direct Energie', 'jersey_signature': 2092402045},
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'Team SD Worx': {'jersey_name': 'Team SD Worx', 'jersey_signature': 1494272741},
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'UAE Team ADQ': {'jersey_name': 'UAE', 'jersey_signature': 1751349769},
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'UAE Development Team': {'jersey_name': 'UAE', 'jersey_signature': 1751349769},
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'FDJ - SUEZ': {'jersey_name': 'FDJ Suez 2023', 'jersey_signature': 3360845221},
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'Canyon//SRAM Racing': {'jersey_name': 'CANYON//SRAM Racing', 'jersey_signature': 3970245639},
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'AG Insurance - Soudal Quick-Step': {'jersey_name': 'Lotto-Soudal', 'jersey_signature': 3103938066},
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'Human Powered Health': {'jersey_name': 'Human Powered Health Fan', 'jersey_signature': 854534852},
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'Team Jumbo-Visma': {'jersey_name': 'Team Jumbo Visma-Women', 'jersey_signature': 1541349594},
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'Liv Racing TeqFind': {'jersey_name': 'Liv Racing 2019', 'jersey_signature': 3932519699},
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'Israel Premier Tech Roland': {'jersey_name': 'Israel Premier-Tech', 'jersey_signature': 552170906},
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'EF Education-TIBCO-SVB': {'jersey_name': 'Team EF Education-TIBCO-SVB', 'jersey_signature': 2795352821},
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'Fenix-Deceuninck': {'jersey_name': 'Deceuninck-Quick-Step', 'jersey_signature': 2906189156},
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'Fenix-Deceuninck Continental': {'jersey_name': 'Deceuninck-Quick-Step', 'jersey_signature': 2906189156},
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'CERATIZIT-WNT Pro Cycling': {'jersey_name': 'Ceratizit-WNT', 'jersey_signature': 97975537},
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'St Michel - Mavic - Auber93 WE': {'jersey_name': 'South Africa Elite', 'jersey_signature': 3305515323},
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'Lifeplus Wahoo': {'jersey_name': 'Wahoo', 'jersey_signature': 3553917933},
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'Cofidis Women Team': {'jersey_name': 'Cofidis', 'jersey_signature': 4191972189},
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'Arkéa Pro Cycling Team': {'jersey_name': 'Arkea', 'jersey_signature': 1128201030},
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'MAT Atom Deweloper Wrocław': {'jersey_name': 'Atom Racing Team', 'jersey_signature': 851470392},
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'Top Girls Fassa Bortolo': {'jersey_name': 'Clash Of Clubs Blue', 'jersey_signature': 520081294},
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'EOLO-Kometa': {'jersey_name': 'Eolo Kometa', 'jersey_signature': 2422819298},
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'Green Project-Bardiani CSF-Faizanè': {'jersey_name': 'Bardiani 2019', 'jersey_signature': 3503002798},
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'L39ION of Los Angeles': {'jersey_name':'L39ION of LA 2022', 'jersey_signature': 2330819669},
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'Lotto Dstny Ladies': {'jersey_name': 'Lotto Soudal Ladies', 'jersey_signature': 1423767803},
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'Parkhotel Valkenburg': {'jersey_name': 'Parkhotel Valkenburg', 'jersey_signature': 4102459937}
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}
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def get_pros(url, male, get_jersey, get_equipment):
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data = []
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req = urllib.request.Request(url, headers={'User-Agent': 'Mozilla/5.0'})
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site = urllib.request.urlopen(req).read()
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soup = BeautifulSoup(site)
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for td in soup.find_all('td'):
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if td.span and td.contents[0]:
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tmp = {}
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if "flag" in repr(td.contents[0]):
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code = td.span.get_attribute_list("class")[1]
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tmp['country_code'] = cc.convert(names=code, to='ISOnumeric')
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tmp['is_male'] = male
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if td.a:
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tmp['first_name'] = (td.a.contents[1].strip())
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tmp['last_name'] = (td.a.span.contents[0])
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if td.a and td.contents[0]:
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if "cu600" in repr(td) and td.a.contents:
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if 'first_name' in tmp:
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if get_jersey:
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if td.a.contents[0] in teams:
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tmp['jersey'] = teams[td.a.contents[0]]['jersey_signature']
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else:
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best_match = process.extractOne(td.a.contents[0], jerseys.keys(), scorer=fuzz.token_set_ratio)
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print ("%s %s : %s - %s" % (tmp['first_name'],tmp['last_name'],td.a.contents[0], best_match))
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tmp['jersey'] = jerseys[best_match[0]]
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if get_equipment:
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if td.a.contents[0] in teams:
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team = teams[td.a.contents[0]]
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if 'bike_signature' in team:
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tmp['bike_frame'] = team['bike_signature']
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if 'front_wheel_signature' in team:
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tmp['bike_wheel_front'] = team['front_wheel_signature']
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if 'rear_wheel_signature' in team:
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tmp['bike_wheel_rear'] = team['rear_wheel_signature']
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data.append(tmp)
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return data
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tree = ET.parse('../cdn/gameassets/GameDictionary.xml')
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root = tree.getroot()
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jerseys = {}
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for x in root.findall("./JERSEYS/JERSEY"):
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jerseys[x.get('name')] = int(x.get('signature'))
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def main(argv):
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global args
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parser = argparse.ArgumentParser(description='Populate Bot names with professional riders')
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parser.add_argument('-n', '--nation', help='Riders from specified nation only', default=False)
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parser.add_argument('-f', '--female', help='Female riders only', default=False, action='store_true')
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parser.add_argument('-m', '--male', help='Male riders only', default=False, action='store_true')
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parser.add_argument('-a', '--alltime', help='Use all time ranking', default=False, action='store_true')
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parser.add_argument('-p', '--pages', help='Number of pages to process', default=1)
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parser.add_argument('-j', '--jersey', help='Get team jerseys', default=False, action='store_true')
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parser.add_argument('-e', '--equipment', help='Get team bike and wheels', default=False, action='store_true')
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args = parser.parse_args()
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url_additions = ""
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url_list = []
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if args.alltime:
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url_additions += "&s=all-time"
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if args.nation:
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url_additions += "&nation="+args.nation
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if args.female:
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url_list = [ { "url": base_url + url_additions + "&p=we", "is_male": False } ]
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elif args.male:
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url_list = [ { "url": base_url + url_additions + "&p=me", "is_male": True } ]
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else:
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url_list = [ { "url": base_url + url_additions + "&p=me", "is_male": True }, { "url": base_url + url_additions + "&p=we", "is_male": False } ]
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if args.pages:
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new_url_list = url_list.copy()
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for x in range(1,int(args.pages)):
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offset = str(x*100)
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for url in url_list:
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new_url_list += [ { "url": url['url'] + "&offset=" + offset, "is_male": url['is_male'] }]
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url_list = new_url_list.copy()
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total_data = {}
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total_data['riders'] = []
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for item in url_list:
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total_data['riders'] = total_data['riders'] + get_pros(item['url'], item['is_male'], args.jersey, args.equipment)
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total_data['body_types'] = [16, 48, 80, 272, 304, 336, 528, 560, 592]
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total_data['hair_types'] = [25953412, 175379869, 398510584, 659452569, 838618949, 924073005, 1022111028, 1262230565, 1305767757, 1569595897, 1626212425, 1985754517, 2234835005, 2507058825, 3092564365, 3200039653, 3296520581, 3351295312, 3536770137, 4021222889, 4179410997, 4294226781]
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total_data['facial_hair_types'] = [248681634, 398510584, 867351826, 1947387842, 2173853954, 3169994930, 4131541011, 4216468066]
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with open('bot.txt', 'w') as outfile:
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json.dump(total_data, outfile, indent=2)
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if __name__ == '__main__':
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try:
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main(sys.argv)
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except KeyboardInterrupt:
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pass
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except SystemExit as se:
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print("ERROR:", se)
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