Calculo del reparto de diputados en la Diputación Provincial de Soria para el año 2023

main.py

#!/bin/bash
import math

import numpy as np
import pandas as pd
import requests
from fake_useragent import UserAgent
from requests.adapters import HTTPAdapter, Retry
from municipalities import municipalities

URL_FORMAT = "https://resultados.locales2023.es/_next/data/bKpDDhunNBOUCBsOTpr5x/es/resultados/0/{id}/90.json?elec=0" \
             "&id={id}&mapLevel=90"

ROUND_METHOD = np.trunc  # or np.round
TYPE_OF_DATA = 'def'  # or pro


def dhondt(parties, seats):
    results = {}
    parties = [party | {'updated_votes': party['votes']} for party in parties]
    while seats > 0:
        parties = sorted(parties, key=lambda x: x['updated_votes'], reverse=True)
        results[parties[0]['name']] = results.get(parties[0]['name'], 0) + 1
        next = '{} votes needed -> {} would gain a seat from {}'.format(
            results[parties[0]['name']] * math.ceil(parties[0]['updated_votes'] - parties[1]['updated_votes']), parties[1]['name'],
            parties[0]['name'])
        seats -= 1
        parties[0]['updated_votes'] = parties[0]['votes'] / (results[parties[0]['name']] + 1)

    return {'SEATS': results, 'next': next}


def get_votes(information, siglas):
    try:
        return next(
            x['votos'][TYPE_OF_DATA] for x in
            information['pageProps']['ScopeResponse']['scope']['escrutinio']['partidos'] if
            x['siglas'] == siglas)
    except:
        return 0


def get_real_votes(information, siglas):
    votes = 0
    try:
        party = get_party(information, siglas)
        if len(party['candidatos']) > 0:
            for candidate in party['candidatos']:
                votes += candidate['votos'][TYPE_OF_DATA]
    finally:
        return votes


def get_candidates(information, siglas):
    try:
        party = get_party(information, siglas)
        return len(party['candidatos'])
    except:
        return 0


def get_party(information, siglas):
    return next(x for x in information['pageProps']['ScopeResponse']['scope']['escrutinio']['partidos'] if
                x['siglas'] == siglas)


def get_data(read_local_file=False):
    if read_local_file:
        return pd.read_csv('data.csv')
    data = []

    s = requests.Session()
    retries = Retry(total=5, backoff_factor=1, status_forcelist=[502, 503, 504, 403])
    s.mount('http://', HTTPAdapter(max_retries=retries))

    ua = UserAgent()
    headers = {'User-Agent': ua.firefox}

    for municipality in municipalities:
        response = s.get(URL_FORMAT.format(id=municipality), headers=headers)
        information = response.json()
        data.append({
            'municipality': information['pageProps']['ScopeResponse']['scope']['nombre'],
            'partidoJudicial': information['pageProps']['ScopeResponse']['scope']['padres'][1]['nombre'],
            'padron': information['pageProps']['ScopeResponse']['scope']['padron'],
            'censoINE': information['pageProps']['ScopeResponse']['scope']['censoINE'],
            'censoNoche': information['pageProps']['ScopeResponse']['scope']['censoNoche'],
            'tipo': information['pageProps']['ScopeResponse']['scope']['padres'][5]['nombre'],
            'tipo_id': information['pageProps']['ScopeResponse']['scope']['padres'][5]['codigo'],
            'cargos': information['pageProps']['ScopeResponse']['scope']['escrutinio']['cargos'],
            'votesPSOE': get_votes(information, 'PSOE'),
            'votesPP': get_votes(information, 'PP'),
            'votesVOX': get_votes(information, 'VOX'),
            'realPSOE': get_real_votes(information, 'PSOE'),
            'realPP': get_real_votes(information, 'PP'),
            'realVOX': get_real_votes(information, 'VOX'),
            'candidatesPSOE': get_candidates(information, 'PSOE'),
            'candidatesPP': get_candidates(information, 'PP'),
            'candidatesVOX': get_candidates(information, 'VOX'),
        })

        print('Fetching results for {}'.format(information['pageProps']['ScopeResponse']['scope']['nombre']))
    df = pd.DataFrame.from_records(data)
    df.to_csv('data.csv')
    return df


def balance(df):
    df = df.drop(columns=(
    'balancedPSOE_1', 'balancedPP_1', 'balancedVOX_1', 'balancedPSOE_2', 'balancedPP_2', 'balancedVOX_2'), errors='ignore')
    
    df['balancedPSOE_1'] = np.where(df['tipo_id'] != '01', df['votesPSOE'],
                                     ROUND_METHOD(df['realPSOE'] / np.minimum(df['candidatesPSOE'], 4)))
    df['balancedPP_1'] = np.where(df['tipo_id'] != '01', df['votesPP'],
                                   ROUND_METHOD(df['realPP'] / np.minimum(df['candidatesPP'], 4)))
    df['balancedVOX_1'] = np.where(df['tipo_id'] != '01', df['votesVOX'],
                                    ROUND_METHOD(df['realVOX'] / np.minimum(df['candidatesVOX'], 4)))

    df['balancedPSOE_2'] = np.where(df['tipo_id'] != '01', df['votesPSOE'], ROUND_METHOD(
        df['realPSOE'] / np.minimum(df['candidatesPSOE'], np.where((df['cargos'] > 1) & (df['padron'] <= 101), 2, 4))))
    df['balancedPP_2'] = np.where(df['tipo_id'] != '01', df['votesPP'], ROUND_METHOD(
        df['realPP'] / np.minimum(df['candidatesPP'], np.where((df['cargos'] > 1) & (df['padron'] < 101), 2, 4))))
    df['balancedVOX_2'] = np.where(df['tipo_id'] != '01', df['votesVOX'], ROUND_METHOD(
        df['realVOX'] / np.minimum(df['candidatesVOX'], np.where((df['cargos'] > 1) & (df['padron'] < 101), 2, 4))))

    df = df.fillna(0).astype(
        {'balancedPSOE_1': 'int32', 'balancedPP_1': 'int32', 'balancedVOX_1': 'int32', 'balancedPSOE_2': 'int32',
         'balancedPP_2': 'int32', 'balancedVOX_2': 'int32'})

    print(df.head())
    df.to_csv('data.csv')
    return df


def apply_dhondt(df):
    partiesBalancedSoria_1 = [
        {'name': 'PSOE', 'votes': df.query("partidoJudicial == 'Soria'")['balancedPSOE_1'].sum()},
        {'name': 'PP', 'votes': df.query("partidoJudicial == 'Soria'")['balancedPP_1'].sum()},
        {'name': 'VOX', 'votes': df.query("partidoJudicial == 'Soria'")['balancedVOX_1'].sum()},
    ]

    partiesBalancedSoria_2 = [
        {'name': 'PSOE', 'votes': df.query("partidoJudicial == 'Soria'")['balancedPSOE_2'].sum()},
        {'name': 'PP', 'votes': df.query("partidoJudicial == 'Soria'")['balancedPP_2'].sum()},
        {'name': 'VOX', 'votes': df.query("partidoJudicial == 'Soria'")['balancedVOX_2'].sum()},
    ]

    partiesBalancedAlmazan_1 = [
        {'name': 'PSOE', 'votes': df.query("partidoJudicial == 'Almazán'")['balancedPSOE_1'].sum()},
        {'name': 'PP', 'votes': df.query("partidoJudicial == 'Almazán'")['balancedPP_1'].sum()},
        {'name': 'VOX', 'votes': df.query("partidoJudicial == 'Almazán'")['balancedVOX_1'].sum()},
    ]

    partiesBalancedAlmazan_2 = [
        {'name': 'PSOE', 'votes': df.query("partidoJudicial == 'Almazán'")['balancedPSOE_2'].sum()},
        {'name': 'PP', 'votes': df.query("partidoJudicial == 'Almazán'")['balancedPP_2'].sum()},
        {'name': 'VOX', 'votes': df.query("partidoJudicial == 'Almazán'")['balancedVOX_2'].sum()},
    ]

    partiesBalancedOsma_1 = [
        {'name': 'PSOE',
         'votes': df.query("partidoJudicial == 'Burgo de Osma-Ciudad de Osma'")['balancedPSOE_1'].sum()},
        {'name': 'PP', 'votes': df.query("partidoJudicial == 'Burgo de Osma-Ciudad de Osma'")['balancedPP_1'].sum()},
        {'name': 'VOX', 'votes': df.query("partidoJudicial == 'Burgo de Osma-Ciudad de Osma'")['balancedVOX_1'].sum()},
    ]

    partiesBalancedOsma_2 = [
        {'name': 'PSOE',
         'votes': df.query("partidoJudicial == 'Burgo de Osma-Ciudad de Osma'")['balancedPSOE_2'].sum()},
        {'name': 'PP', 'votes': df.query("partidoJudicial == 'Burgo de Osma-Ciudad de Osma'")['balancedPP_2'].sum()},
        {'name': 'VOX', 'votes': df.query("partidoJudicial == 'Burgo de Osma-Ciudad de Osma'")['balancedVOX_2'].sum()},
    ]

    print('Ponderación con máximo 4 en todos los municipios < 251 habitantes')
    print('-------------')
    print('Soria')
    print(partiesBalancedSoria_1)
    print(dhondt(partiesBalancedSoria_1, 15))
    print('Almazán')
    print(partiesBalancedAlmazan_1)
    print(dhondt(partiesBalancedAlmazan_1, 5))
    print('Burgo de Osma-Ciudad de Osma')
    print(partiesBalancedOsma_1)
    print(dhondt(partiesBalancedOsma_1, 5))
    print()
    print()
    print('Ponderación con máximo 2 en municipios con padrón < 101 habitantes')
    print('-------------')
    print('Soria')
    print(partiesBalancedSoria_2)
    print(dhondt(partiesBalancedSoria_2, 15))
    print('Almazán')
    print(partiesBalancedAlmazan_2)
    print(dhondt(partiesBalancedAlmazan_2, 5))
    print('Burgo de Osma-Ciudad de Osma')
    print(partiesBalancedOsma_2)
    print(dhondt(partiesBalancedOsma_2, 5))


if __name__ == '__main__':
    df = get_data()
    balanced = balance(df)
    apply_dhondt(balanced)

municipalities.py

municipalities = [
    122,
    162,
    351,
    364,
    365,
    390,
    395,
    396,
    397,
    423,
    433,
    507,
    526,
    527,
    533,
    537,
    538,
    545,
    594,
    692,
    728,
    744,
    845,
    877,
    974,
    991,
    999,
    1035,
    1036,
    1201,
    1223,
    1286,
    1296,
    1342,
    1345,
    1391,
    1409,
    1423,
    1511,
    1512,
    1544,
    1570,
    1660,
    1709,
    1712,
    1766,
    1768,
    1793,
    1917,
    1926,
    1937,
    2011,
    2058,
    2061,
    2147,
    2153,
    2155,
    2176,
    2183,
    2319,
    2328,
    2353,
    2365,
    2372,
    2443,
    2477,
    2480,
    2628,
    2733,
    2755,
    2757,
    2789,
    2919,
    2935,
    2996,
    2998,
    2999,
    3011,
    3013,
    3020,
    3036,
    3049,
    3126,
    3189,
    3205,
    3301,
    3340,
    3371,
    3748,
    3821,
    3828,
    3933,
    3960,
    4070,
    4190,
    4198,
    4275,
    4291,
    4425,
    4427,
    4468,
    4575,
    4576,
    4602,
    4612,
    4669,
    4683,
    4691,
    4780,
    4822,
    4823,
    4866,
    4873,
    4906,
    4966,
    4991,
    5000,
    5111,
    5459,
    5552,
    5573,
    5743,
    5751,
    5772,
    5797,
    5802,
    5817,
    5825,
    5836,
    5848,
    5905,
    5957,
    6055,
    6133,
    6141,
    6163,
    6227,
    6414,
    6459,
    6463,
    6631,
    6694,
    6716,
    6733,
    6753,
    6779,
    6780,
    6794,
    6812,
    6817,
    6834,
    6919,
    6962,
    7061,
    7120,
    7156,
    7213,
    7235,
    7250,
    7260,
    7269,
    7281,
    7295,
    7303,
    7419,
    7472,
    7480,
    7481,
    7510,
    7619,
    7772,
    7851,
    7854,
    7862,
    7917,
    7921,
    8009,
    8024,
    8030,
    8053,
    8061,
    8152,
    8166
]