Исходники генератора явки

%matplotlib inline
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
import random

def draw_turnout_distribution_uniform(sizes, bins, title):
    sizes_in_people = np.floor(sizes)
    turnout_in_people = np.array([random.randint(0, voters) for voters in sizes_in_people])
    turnout_fraction = turnout_in_people / sizes_in_people

    plt.figure(figsize=(16,8), dpi=100)
    axis = plt.gca()
    axis.set_facecolor('#e8e8e8')
    plt.grid(True)
    axis.set_axisbelow(True)
    axis.grid(color='#ffffff')
    # axis.tick_params(axis='x', grid_color='#ffffff')
    plt.hist(turnout_fraction, bins=bins, range=(0, 1.0))
    plt.xticks([0.0, 1.0, 1./2,
                1./3, 2./3,
                1./4, 3./4,
                1./5, 2./5, 3./5, 4./5,
                1./6, 5./6,
                1./7, 2./7, 3./7, 4./7, 5./7, 6./7,
                1./8, 3./8, 5./8, 7./8,
                1./9, 2./9, 4./9, 5./9, 7./9, 8./9,
                1./10, 3./10, 7./10, 9./10],
               ["0", "1", "1/2",
                "1/3", "2/3",
                "1/4", "3/4",
                "1/5", "2/5", "3/5", "4/5",
                "1/6", "5/6",
                "1/7", "2/7", "3/7", "4/7", "5/7", "6/7",
                "1/8", "3/8", "5/8", "7/8",
                "1/9", "2/9", "4/9", "5/9", "7/9", "8/9",
                "1/10", "3/10", "7/10", "9/10"],
               rotation=90)
    plt.xlim((0, 1.0))
    plt.xlabel("Явка")
    plt.ylabel("Число УИКов с такой явкой")
    plt.title("Равномерное распределение явки по числу УИКов, %s" % title);

N_POINTS = 50000
sizes = np.random.uniform(1000, 2000, N_POINTS)

draw_turnout_distribution_uniform(sizes, 1000, "участки uniform(1000, 2000), явка uniform(0, 100%)")