votings.py

import requests
from requests.auth import HTTPDigestAuth
import json
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
import math
from requests.auth import HTTPBasicAuth
import pyodbc
import datetime
from pymongo import MongoClient
from scipy import stats
import matplotlib.pyplot as plt

from datetime import datetime, timedelta
import matplotlib.pyplot as plt
import matplotlib.dates as dates


connLive = pyodbc.connect('Driver={PostgreSQL Unicode(x64)}; Database=voting;Uid=postgres;Pwd=')

cursor = connLive.cursor()



cursor.execute("""SELECT payload -> 'voters' voter_id
	FROM public.transactions
	where method_id = 1
	order by block_height, position_in_block""")
votersRaw = [v.voter_id for v in cursor.fetchall()]
votersArr = [item for sublist in votersRaw for item in json.loads(sublist)]




actualVotersRaw = pd.read_sql_query("""select payload ->> 'voter_id' voter_id, payload ->> 'district_id' district_id, datetime
	FROM public.transactions
	where method_id = 4
	order by block_height, position_in_block""", connLive)

actualVoters2 = actualVotersRaw.copy()
actualVoters2 = actualVoters2.set_index("voter_id")


voterRegistrations = pd.DataFrame()
voterRegistrations["voter_id"] = votersArr
voterRegistrations["reg_order_num"] = voterRegistrations.index
voterRegistrations = voterRegistrations.set_index("voter_id")
voterRegistrations['voted'] = voterRegistrations.index.isin(actualVoters2.index)

voterRegistrations['bin'] = pd.cut(voterRegistrations['reg_order_num'],100)

voterRegistrationsGrouped = voterRegistrations.groupby('bin').sum()

plt.plot([x.left for x in voterRegistrationsGrouped.index], voterRegistrationsGrouped['voted'] / (len(voterRegistrations.index)/100))