Coin flip paradox - potentially neuroscience measurement paradox.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "SMALL_SIZE = 14\n",
    "MEDIUM_SIZE = 16\n",
    "BIGGER_SIZE = 18\n",
    "\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def coin(pocket='bias',p=0.7,size=100):\n",
    "    if pocket == 'fair':\n",
    "        outcome = np.random.choice(['H','T'],size=size,p=[.5,.5])\n",
    "        return outcome,pocket,p\n",
    "    elif pocket == 'bias':\n",
    "        outcome = []\n",
    "        for ii in range(size):\n",
    "            select = np.random.choice(['Head bias','Tail bias'],size=1,p=[.5,.5])\n",
    "            #print(select)\n",
    "            if select == 'Head bias':\n",
    "                outcome.append(np.random.choice(['H','T'],size=1,p=[p,1-p])[0])\n",
    "                \n",
    "            elif select == 'Tail bias':\n",
    "                outcome.append(np.random.choice(['H','T'],size=1,p=[1-p,p])[0])\n",
    "        return np.array(outcome),pocket,p        \n",
    "    else:\n",
    "        print('Specify if the coin is bias or fair')\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "results=[];Head_counts=[];pocket_picked=[]\n",
    "n_flips=1000;\n",
    "for ii in range(500):\n",
    "    get_pocket = np.random.choice(['fair','bias'],size=1,p=[.5,.5])\n",
    "    outcomes = coin(pocket=get_pocket,size=n_flips,p=0.9)\n",
    "    Head_count = pd.DataFrame({'outcome':outcomes[0]})['outcome'].map({'T':0,'H':1}).sum()\n",
    "    results.append(outcomes)\n",
    "    Head_counts.append(Head_count)\n",
    "    pocket_picked.append(get_pocket[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'proportion of heads':np.array(Head_counts)/n_flips,'pocket_picked':pocket_picked})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2c276341710>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c2784ed080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(row='pocket_picked',data=df,size=3,aspect=2)\n",
    "g.map(plt.hist,'proportion of heads')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}