sertalpbilal

import requests
from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
from itertools import repeat
import json
import pandas as pd
from dateutil import parser
import time
import random
import string
import pathlib

sertalpbilal

// in each page

loc = localStorage.getItem('list_of_cities')
if (loc == null) {
    loc = []
}
else {
    loc = JSON.parse(loc)
}
a = loc

sertalpbilal

let get_all_data = () => {
    
    let gws = Array.from(jQuery(".ptsshoq")).map(i => i.textContent).filter(i => i.includes('GW')).map(i => parseInt(i.split('GW')[1]))
    let first_week = Math.min(...gws)
    let last_week = Math.max(...gws)

    let jsondata= jQuery("#fplr_api").text()
    let d=JSON.parse(decodeURIComponent(escape(window.atob(jsondata))))

    let main_keys = ['name', 'pos', 'team_abbrev', 'now_cost', 'now_cost']

sertalpbilal

URLS = [
    {'year': 2023, 'month': 3, 'url': 'https://docs.google.com/spreadsheets/d/1xivGiILExvw61SrDP1sar9u-V_oSfl5yDRIAHen0tAU/'},
    {'year': 2023, 'month': 2, 'url': 'https://docs.google.com/spreadsheets/d/1DTmJMhxKFeSZrPdrlmD4Fq93Yim6-m7lIj2CHwDJdV0/'},
    {'year': 2023, 'month': 1, 'url': 'https://docs.google.com/spreadsheets/d/1tu5cshTG33b416o0OroSkvnJb7Vnk-LJ-WlhXDgBPR4/'},
    {'year': 2022, 'month': 12, 'url': 'https://docs.google.com/spreadsheets/d/1PS7zerHg4Qis3pFG1qIC_QFIcdJ462jQS8n9s2bnJVU/'},
    {'year': 2022, 'month': 11, 'url': 'https://docs.google.com/spreadsheets/d/1qfpM4iiSnzsnvKFGC3eMIWXuUZmUEynNmh15K-x094M/'},
    {'year': 2022, 'month': 10, 'url': 'https://docs.google.com/spreadsheets/d/1aQcs5miXsnVN8iQ2mQcNoFh2eld46VkEHpkcumsRgAk/edit#gid=631818250'},
    {'year': 2022, 'month': 9, 'url': 'https://docs.google.com/spreadsheets/d/15l5ebzPPwM9T35cxPH01mxTGTWzdFLux7mNd5FY-aaU/edit#gid=631818250'},
    {'year': 2022, 'month': 5, 'url': 'https://docs.google.com/spreadsheets/d/1JXoaovC9

sertalpbilal

def get_data_kiwi(target, can_use_cache=False):
    if can_use_cache:
        if os.path.exists(target):
            return pd.read_csv(target)
    raw_data = pd.read_csv("https://github.com/theFPLkiwi/webpage/raw/main/data/Projected_FPL_2223.csv")
    # ("https://raw.githubusercontent.com/theFPLkiwi/webpage/main/data/Projected_FPL_2122.csv")
    first_gw = int(raw_data.keys()[9])
    last_gw = 38
    col_count = last_gw - first_gw + 1
    raw_data.iloc[:, 9:38-9+1]

sertalpbilal

import pandas as pd
import pathlib
import os
import json
from unicodedata import combining, normalize
import requests
from fuzzywuzzy import fuzz
import numpy as np
from abc import ABC, abstractmethod

sertalpbilal

from unicodedata import combining, normalize
import pandas as pd
import requests
from fuzzywuzzy import fuzz
import numpy as np

# To remove accents in names
def fix_name_dialect(name):
    new_name = ''.join([c for c in normalize('NFKD', name) if not combining(c)])
    return new_name.replace('Ø', 'O').replace('ø', 'o').replace('ã', 'a')

sertalpbilal

getMyRank = () => {let me = parseFloat(jQuery("#my_dataviz9 .bubbles[r=6]")[0].getAttribute("cy")); let others = jQuery("#my_dataviz9 .bubbles[r=2]").toArray().map(i => parseFloat(i.getAttribute('cy'))); return others.filter(i => i < me).length + 1}; getMyRank()

sertalpbilal

    bb_gw = 29

    model.add_constraints((so.expr_sum(lineup[p,w] for p in players) == 11 + (4 if bb_gw == w else 0) for w in gameweeks), name='lineup_count')
    model.add_constraints((so.expr_sum(bench[p,w,0] for p in players if player_type[p] == 1) == 1 - (1 if bb_gw == w else 0) for w in gameweeks), name='bench_gk')
    model.add_constraints((so.expr_sum(bench[p,w,o] for p in players) == 1 - (1 if bb_gw == w else 0) for w in gameweeks for o in [1,2,3]), name='bench_count')
    model.add_constraints((so.expr_sum(captain[p,w] for p in players) == 1 for w in gameweeks), name='captain_count')
    model.add_constraints((so.expr_sum(vicecap[p,w] for p in players) == 1 for w in gameweeks), name='vicecap_count')
    model.add_constraints((lineup[p,w] <= squad[p,w] for p in players for w in gameweeks), name='lineup_squad_rel')
    model.add_constraints((bench[p,w,o] <= squad[p,w] for p in players for w in gameweeks for o in order), name='bench_squad_rel')
    model.add_constraints((captain[p,w] <= lineup[p,w] for

sertalpbilal

let get_all_data = () => {
    
    let gws = Array.from(jQuery(".ptsshoq")).map(i => i.textContent).filter(i => i.includes('GW')).map(i => parseInt(i.split('GW')[1]))
    let first_week = Math.min(...gws)
    let last_week = Math.max(...gws)

    let jsondata= jQuery("#fplr_api").text()
    let d=JSON.parse(decodeURIComponent(escape(window.atob(jsondata))))

    let main_keys = ['name', 'pos', 'team_abbrev', 'now_cost']