Evony Crazy Eggs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "84ef2d43-4f8f-4022-acc7-97bda73ccd69",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import collections\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4ac682f6-a080-4a32-b963-4a1d14c12777",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = [16, 9]\n",
    "plt.rcParams['font.size'] = 14\n",
    "plt.rcParams['axes.grid'] = True\n",
    "plt.rcParams['figure.facecolor'] = 'white'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ea4f50fc-c2b8-4fd9-a87f-d8c855e1ff24",
   "metadata": {},
   "outputs": [],
   "source": [
    "def openEgg():\n",
    "    tries = 0\n",
    "    collected = set()\n",
    "    while True:\n",
    "        eggs_to_open = np.random.choice(5, p=[0, 0, 0, 0.25, 0.75])\n",
    "        opened_eggs = np.random.choice(5, size=eggs_to_open, replace=False)\n",
    "        for egg in opened_eggs:\n",
    "            collected.add(egg)\n",
    "        tries += 1\n",
    "        if len(collected) == 5:\n",
    "            break\n",
    "                \n",
    "    return tries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "57b72008-bc95-4cc5-9055-ada7b2b1b1f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 50s, sys: 1.08 s, total: 1min 51s\n",
      "Wall time: 1min 53s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "hist = []\n",
    "for i in range(1000000):\n",
    "    tries = openEgg()\n",
    "    hist.append(tries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9106fcdc-04fa-4994-963e-8b0401a8e370",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9b881d93d0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(hist, stat='percent', shrink=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9478c89c-02a5-4ded-838a-c3f3227644b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.409891"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(hist)/len(hist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49c7ea4c-4b1a-4ae6-90fb-a344bd7a7ab2",
   "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": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}