Fraction distribution plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Функция для отображения гистограммы явки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_turnout_distribution(sizes, turnout, bins, title):\n",
    "    sizes_in_people = np.floor(sizes)\n",
    "    turnout_in_people = np.floor(sizes_in_people * turnout)\n",
    "    turnout_fraction = turnout_in_people / sizes_in_people\n",
    "\n",
    "    plt.figure(figsize=(16,8))\n",
    "    plt.hist(turnout_fraction, bins=bins, range=(0, 1.0))\n",
    "    plt.xticks([0.0, 1.0, 1./2, 1./3, 2./3, 1./4, 3./4, 2./5, 3./5, 2./7, 3./7, 4./7, 5./7, 3./8, 5./8,\n",
    "                4./9, 5./9, 3./10, 7./10],\n",
    "               [\"0\", \"1\", \"1/2\", \"1/3\", \"2/3\", \"1/4\", \"3/4\", \"2/5\", \"3/5\", \"2/7\", \"3/7\", \"4/7\", \"5/7\", \"3/8\", \"5/8\",\n",
    "                \"4/9\", \"5/9\", \"3/10\", \"7/10\"],\n",
    "               rotation=90)\n",
    "    plt.xlim((0, 1.0))\n",
    "    plt.xlabel(\"Явка\")\n",
    "    plt.ylabel(\"Число УИКов с такой явкой\")\n",
    "    plt.title(\"Распределение явки по числу УИКов, %s\" % title);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Гистограмма явки для искусственного случая\n",
    "\n",
    "Всего 50.000 участков. Размеры участков распределены равномерно от 1000 до 2000. Явка распределена нормально $\\mu = 0.55$, $\\sigma = 0.1$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nSWrSpInWrFmT5z6hoaEKDQ0tQukAAAAAgLLO5tWPs7KyJEn16tXT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106e391d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N_POINTS = 50000\n",
    "sizes = np.random.uniform(1000, 2000, N_POINTS)\n",
    "turnout = np.random.normal(0.55, 0.1, N_POINTS)\n",
    "\n",
    "draw_turnout_distribution(sizes, turnout, 1000, \"участки uniform(1000, 2000), явка norm(0.55, 0.1)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Гистограмма явки для случая, приближенного к натуральному\n",
    "\n",
    "Всего 95.000 участков. Размеры участков распределены по гамме $\\theta = 2.5$, $k=500$ ($E = 1250$). Явка распределена по бете $\\alpha = 8$, $\\beta = 4$ ($E = 0.67$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vsBx/QGFlsVArSa6urho9erR2796tjz/+WIcOHZKTk5Mkyd3dXXFxcYqPj5en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\nAAAAAPCs/wP3jeLXuGGjQwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111795978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N_POINTS = 95000\n",
    "sizes = np.random.gamma(2.5, 500, N_POINTS)\n",
    "turnout = np.random.beta(8, 4, N_POINTS)\n",
    "\n",
    "draw_turnout_distribution(sizes, turnout, 1000, \"участки gamma(2.5, 500), явка beta(8, 4)\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}