Monte Carlo Code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Monte Carlo Coding Video: \n",
    "# https://www.youtube.com/watch?v=yA6_V-v3ODo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from time import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_calories_burned(lower_temp, upper_temp, avg_tol, sd_tol, avg_calories_burned):\n",
    "    #get the temp\n",
    "    temp = np.random.uniform(lower_temp, upper_temp)\n",
    "    \n",
    "    #get the tolerance\n",
    "    tol = np.random.normal(avg_tol, sd_tol)\n",
    "    \n",
    "    #if the temp is higher than our tolerance, then run\n",
    "    if temp > tol:\n",
    "        cals = np.random.exponential(avg_calories_burned)\n",
    "    else:\n",
    "        cals = 0\n",
    "        \n",
    "    return cals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.804136753082275\n"
     ]
    }
   ],
   "source": [
    "num_days = 1000000\n",
    "daily_calories = []\n",
    "\n",
    "start = time()\n",
    "for _ in range(num_days):\n",
    "    cals = get_calories_burned(40, 60, 55, 5, 200)\n",
    "    daily_calories.append(cals)\n",
    "end = time()\n",
    "print(end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '54.060966025098885')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEICAYAAABxiqLiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAYPElEQVR4nO3cfbRfVX3n8fdnEqUgRhOJioHVoKZacFWRFPCxs8QGfJhil9BJrZpl6WK0WLUzs6bQrhl8GKbgtNpxpjiDgka0AqIdUAc1g1qtUiD4AAZEYkGIIASDiE8o+J0/zr5ycufm5mYn5nJv3q+1zvqd395n79/Z5yS/zz37nHtTVUiStKP+xWzvgCRpbjJAJEldDBBJUhcDRJLUxQCRJHUxQCRJXQwQSVIXA0S7TZLPJvlJkh+05foptjk1SSV5/jT9LEny90l+mORbSV42qX6fJGcmuTPJ3Uk+N6pLkjOSfLctb02SUf0zk1yR5J4kVyd59qS+lyb5uyTfS3JXkg9Mqn9+ki+1fbslye+N6p6W5KokP2qvTxvVrWll30+yqe3XwpkeuyRHJfl66/szSX51VPfIJGuT3NGWN05q+7Qkn2/HalOS/zSp/k+S3Nj2bf34mLRzcX471ncm+UCSRaP6asdiYr/fPeVJ1dxUVS4uu2UBPgv80TT1TwCuAW4Fnj/Ndh8Ezgf2BZ4N3A0cMqp/P3AesBRYABw2qvs3wPXAAcAy4Frg1a1uCXAncHxr93LgLmDxqP3ngbcBjwAeAhw6qjsYuAN4AbAQeBTwhFb3UOBbwJ8CewGva+8f2upfAzynbbcMuAo4eSbHDtivHYPjgV8B/ivwT6P69wAfAvYBlgPfBF41qr8WOK2N+QnAbcDvtLojgB8ChwFp+7kZWNDqzwQ+BSxqx+T/Am8b9V3AE2f7357LL2eZ9R1w2XOWGQTIJcALgZu2FSDAw4CfAr82KjsXOL2tPwn4PrBoG+2/CJw4en/CxJct8GJgw6TtvwGc0NZXtX1bsI2+/w54yzbqVgHfBjIquxk4Zhvb/1vgozM5dsCJwBcnHaMfA09u7+8EfnNU/+fA50fvfwQcPHr/IeCUtv6vgSsm9V3A/qNz9sej+pOAT47eGyDzeHEKS7vbX7apji8k+ZcThUmOB35aVf9nO+1/Dbi/qr4xKvsqcEhbP4LhJ/s3tc+5JslLR9se0rafqm3aMhbgKW39SIarl7Vt+uvKJL812vbINpZrktyW5P1Jlow+9+pq36rN1aPPnuy5wIZJZVMeu8ljqqofMlxljPvOpPWnjN7/DfDKJA9J8iTgGQxXEjAExIIkRyRZAPwh8BXgO63+b4EXJ1mcZDHw0tZm7HNJvpPkI0mWb2O8moMMEO1OfwY8nmGK5izgo0mekGRf4L8Ab5hBH/syTNeM3Q08vK0fwPDleDfwOOC1DF/4v76N9ncD+7b7IF8EHpfk99uX6RqGKZ19Rn2vAj4DPBb4a+CiJPuN6l/B8CW6Atgb+O8z3O9fSPIqYCXwV6PiKY/dDPv+BHBykocneSJDCOwz2vZjwHEMVy1fB86uqitb3T3Ah4F/BO4FTmW4gpsIwi8xTLt9ty33M0xrTfgthmmzJzNMTX5sfG9Hc5sBot2mqi6vqnuq6t6qWgt8gWHK6k3AuVV14wy6+QHDfPvYIoYvOhi+BH8G/Oeq+mlV/QPDF/6qbbRfBPygBt8FjmWYProdOIbhJ/FNo75vqqqzq+pnVXUecAvwrFH9e6rqG1X1A4ZQfOEM9xuAJC8BTgdeUFV3TpRPc+xm0vfr2r7dAFzEcA9pU/u8JQwB82aG+ycHAkcn+ePW9o8YAucQhqB4OUMIPK7Vf4hhmu/h7TO/yXAPamK/P9fOw/eA1wMHARNhrjnOANFsKobplKOA17Vpju8wfIldkOTPpmjzDWBhkhWjsqfywHTP1dv5zA1t+6naUlX/UFW/WVVLGK4mngRcMep7uj9fPV39BuA3xk98Ab8x/uwkxwDvAv5VVV2znXFMHLv/b0xJHsZw5bShjWlLVf1BVT22qg5h+H8/MabHM0wJvq+q7quqTQwPIEyE01MZ7sV8o6p+XlWfYLjJ/sxR/f+qqh+20Pyfo7bb22/NdbN9E8Zlz1iARwJHM/yUuxD4A4ane57E8LTSY0fLLQxPFO27jb7OY/gp+mEMP/3/4ikshiejNgL/sX3Osxh+Ep+4ofxq4DqGqaDHMXzJvnrU96Gtj0UM9wa+MKpbwvBU1hqGJ5aOA7YA+7X6PwRuZPhS3ge4gOHKCh54Cuv1DE9hvZatn8J6HsMU0HN35Ni1+qXtGLy0bXMGWz+F9YR2jBcwPCF25+h4LQK+B7yMIVgeC1wGnNbq1zCE9uMZvvh/m+Gm+8Tx/AzDNN3ebTlz4pgxXLU8rX3uvu14Xg88ZLb/Pbrsov/Xs70DLnvG0r7krmxf5t8D/gn47W1sexOjp7AYnhq6ZPR+CfC/25fozcDLJrU/pH0J/pDhEdXfHdUFeGv74t/S1sdPRn2wfRnfzfCo8KMn9f0chkeNfwCsB54zqf5NDI+5bmZ4Omz8CPChDI/n/pjh3sH4EeDPAPe1fieWS2Z67IDnM9y/+DHDE1vLR3W/x3D/4UcMN8CPntT2ea3/uxlujr8L2Gd0vN7cjvM9DOH7ilHbg4CPMoTfFobpsBWjfq9v5+GOds5WzPa/RZddt6SdaEmSdoj3QCRJXQwQSVIXA0SS1MUAkSR1mXe/EbrffvvV8uXLZ3s3JGlOueqqq+6sqqU70mbeBcjy5ctZv379bO+GJM0pSb61o22cwpIkdTFAJEldDBBJUhcDRJLUxQCRJHUxQCRJXQwQSVIXA0SS1MUAkSR1mXe/ib6zlp/88Vn53JtOf9GsfK4k9fIKRJLUxQCRJHUxQCRJXQwQSVIXA0SS1MUAkSR1MUAkSV0MEElSFwNEktTFAJEkdTFAJEldDBBJUhcDRJLUxQCRJHUxQCRJXQwQSVIXA0SS1MUAkSR1MUAkSV0MEElSFwNEktTFAJEkdTFAJEldZhQgSf40yYYkX0vywSS/kmRJknVJbmivi0fbn5JkY5Lrkxw9Kj8syTWt7h1J0sr3SnJ+K788yfJRmzXtM25IsmbXDV2StDO2GyBJlgGvA1ZW1VOABcBq4GTg0qpaAVza3pPk4FZ/CHAMcGaSBa27dwInAivackwrPwG4q6qeCLwdOKP1tQQ4FTgCOBw4dRxUkqTZM9MprIXA3kkWAvsAtwLHAmtb/VrgJW39WOC8qrq3qm4ENgKHJ9kfWFRVl1VVAe+b1GairwuBo9rVydHAuqraUlV3Aet4IHQkSbNouwFSVd8G/gq4GbgNuLuqPgU8pqpua9vcBjy6NVkG3DLqYlMrW9bWJ5dv1aaq7gPuBh41TV9bSXJikvVJ1m/evHl7Q5Ik7QIzmcJazHCFcBDwOOBhSV4+XZMpymqa8t42DxRUnVVVK6tq5dKlS6fZNUnSrjKTKaznAzdW1eaq+hnwEeCZwO1tWor2ekfbfhNw4Kj9AQxTXpva+uTyrdq0abJHAFum6UuSNMtmEiA3A0cm2afdlzgKuA64GJh4KmoNcFFbvxhY3Z6sOojhZvkVbZrrniRHtn5eOanNRF/HAZ9u90k+CaxKsrhdCa1qZZKkWbZwextU1eVJLgS+BNwHfBk4C9gXuCDJCQwhc3zbfkOSC4Br2/YnVdX9rbvXAO8F9gYuaQvA2cC5STYyXHmsbn1tSfIW4Mq23ZurastOjViStEtk+EF//li5cmWtX7++u/3ykz++C/dm5m46/UWz8rmSBJDkqqpauSNt/E10SVIXA0SS1MUAkSR1MUAkSV0MEElSFwNEktTFAJEkdTFAJEldDBBJUhcDRJLUxQCRJHUxQCRJXQwQSVIXA0SS1MUAkSR1MUAkSV0MEElSFwNEktTFAJEkdTFAJEldDBBJUhcDRJLUxQCRJHUxQCRJXQwQSVIXA0SS1MUAkSR1MUAkSV0MEElSFwNEktTFAJEkdTFAJEldDBBJUhcDRJLUxQCRJHUxQCRJXWYUIEkemeTCJF9Pcl2SZyRZkmRdkhva6+LR9qck2Zjk+iRHj8oPS3JNq3tHkrTyvZKc38ovT7J81GZN+4wbkqzZdUOXJO2MmV6B/DfgE1X1ZOCpwHXAycClVbUCuLS9J8nBwGrgEOAY4MwkC1o/7wROBFa05ZhWfgJwV1U9EXg7cEbrawlwKnAEcDhw6jioJEmzZ7sBkmQR8FzgbICq+mlVfQ84FljbNlsLvKStHwucV1X3VtWNwEbg8CT7A4uq6rKqKuB9k9pM9HUhcFS7OjkaWFdVW6rqLmAdD4SOJGkWzeQK5PHAZuA9Sb6c5N1JHgY8pqpuA2ivj27bLwNuGbXf1MqWtfXJ5Vu1qar7gLuBR03T11aSnJhkfZL1mzdvnsGQJEk7ayYBshB4OvDOqjoU+CFtumobMkVZTVPe2+aBgqqzqmplVa1cunTpNLsmSdpVZhIgm4BNVXV5e38hQ6Dc3qalaK93jLY/cNT+AODWVn7AFOVbtUmyEHgEsGWaviRJs2y7AVJV3wFuSfKkVnQUcC1wMTDxVNQa4KK2fjGwuj1ZdRDDzfIr2jTXPUmObPc3XjmpzURfxwGfbvdJPgmsSrK43Txf1cokSbNs4Qy3+xPgA0keCvwz8CqG8LkgyQnAzcDxAFW1IckFDCFzH3BSVd3f+nkN8F5gb+CStsBwg/7cJBsZrjxWt762JHkLcGXb7s1VtaVzrJKkXWhGAVJVXwFWTlF11Da2Pw04bYry9cBTpij/CS2Apqg7BzhnJvspSdp9/E10SVIXA0SS1MUAkSR1MUAkSV0MEElSFwNEktTFAJEkdTFAJEldDBBJUhcDRJLUxQCRJHUxQCRJXQwQSVIXA0SS1MUAkSR1MUAkSV0MEElSFwNEktTFAJEkdTFAJEldDBBJUhcDRJLUxQCRJHUxQCRJXQwQSVIXA0SS1MUAkSR1MUAkSV0MEElSFwNEktTFAJEkdTFAJEldDBBJUhcDRJLUxQCRJHUxQCRJXWYcIEkWJPlyko+190uSrEtyQ3tdPNr2lCQbk1yf5OhR+WFJrml170iSVr5XkvNb+eVJlo/arGmfcUOSNbti0JKknbcjVyCvB64bvT8ZuLSqVgCXtvckORhYDRwCHAOcmWRBa/NO4ERgRVuOaeUnAHdV1ROBtwNntL6WAKcCRwCHA6eOg0qSNHtmFCBJDgBeBLx7VHwssLatrwVeMio/r6ruraobgY3A4Un2BxZV1WVVVcD7JrWZ6OtC4Kh2dXI0sK6qtlTVXcA6HggdSdIsmukVyN8A/wH4+ajsMVV1G0B7fXQrXwbcMtpuUytb1tYnl2/VpqruA+4GHjVNX1tJcmKS9UnWb968eYZDkiTtjO0GSJIXA3dU1VUz7DNTlNU05b1tHiioOquqVlbVyqVLl85wNyVJO2MmVyDPAn4nyU3AecDzkrwfuL1NS9Fe72jbbwIOHLU/ALi1lR8wRflWbZIsBB4BbJmmL0nSLNtugFTVKVV1QFUtZ7g5/umqejlwMTDxVNQa4KK2fjGwuj1ZdRDDzfIr2jTXPUmObPc3XjmpzURfx7XPKOCTwKoki9vN81WtTJI0yxbuRNvTgQuSnADcDBwPUFUbklwAXAvcB5xUVfe3Nq8B3gvsDVzSFoCzgXOTbGS48ljd+tqS5C3AlW27N1fVlp3YZ0nSLrJDAVJVnwU+29a/Cxy1je1OA06bonw98JQpyn9CC6Ap6s4BztmR/ZQk/fL5m+iSpC4GiCSpiwEiSepigEiSuhggkqQuBogkqYsBIknqYoBIkroYIJKkLgaIJKmLASJJ6mKASJK6GCCSpC4GiCSpiwEiSepigEiSuhggkqQuBogkqYsBIknqYoBIkroYIJKkLgaIJKmLASJJ6mKASJK6GCCSpC4GiCSpiwEiSepigEiSuhggkqQuBogkqYsBIknqYoBIkroYIJKkLgaIJKmLASJJ6mKASJK6bDdAkhyY5DNJrkuyIcnrW/mSJOuS3NBeF4/anJJkY5Lrkxw9Kj8syTWt7h1J0sr3SnJ+K788yfJRmzXtM25IsmZXDl6S1G8mVyD3Af+uqn4dOBI4KcnBwMnApVW1Ari0vafVrQYOAY4BzkyyoPX1TuBEYEVbjmnlJwB3VdUTgbcDZ7S+lgCnAkcAhwOnjoNKkjR7thsgVXVbVX2prd8DXAcsA44F1rbN1gIvaevHAudV1b1VdSOwETg8yf7Aoqq6rKoKeN+kNhN9XQgc1a5OjgbWVdWWqroLWMcDoSNJmkU7dA+kTS0dClwOPKaqboMhZIBHt82WAbeMmm1qZcva+uTyrdpU1X3A3cCjpulr8n6dmGR9kvWbN2/ekSFJkjrNOECS7At8GHhDVX1/uk2nKKtpynvbPFBQdVZVrayqlUuXLp1m1yRJu8qMAiTJQxjC4wNV9ZFWfHublqK93tHKNwEHjpofANzayg+YonyrNkkWAo8AtkzTlyRpls3kKawAZwPXVdXbRlUXAxNPRa0BLhqVr25PVh3EcLP8ijbNdU+SI1ufr5zUZqKv44BPt/sknwRWJVncbp6vamWSpFm2cAbbPAt4BXBNkq+0sj8HTgcuSHICcDNwPEBVbUhyAXAtwxNcJ1XV/a3da4D3AnsDl7QFhoA6N8lGhiuP1a2vLUneAlzZtntzVW3pHKskaRfaboBU1T8y9b0IgKO20eY04LQpytcDT5mi/Ce0AJqi7hzgnO3tpyRp9/I30SVJXQwQSVIXA0SS1MUAkSR1MUAkSV0MEElSFwNEktTFAJEkdTFAJEldDBBJUhcDRJLUxQCRJHUxQCRJXQwQSVIXA0SS1MUAkSR1MUAkSV0MEElSFwNEktTFAJEkdTFAJEldDBBJUhcDRJLUxQCRJHUxQCRJXQwQSVKXhbO9AxosP/njs/bZN53+oln7bElzl1cgkqQuBogkqYsBIknqYoBIkroYIJKkLgaIJKmLASJJ6mKASJK6+IuEmrVfYvQXGKW5bU5cgSQ5Jsn1STYmOXm290eSNAcCJMkC4G+BFwAHA7+f5ODZ3StJ0lyYwjoc2FhV/wyQ5DzgWODaWd0r7bTZ/Ptfs8VpO80ncyFAlgG3jN5vAo4Yb5DkRODE9vYHSa7fic/bD7hzJ9rPVXvquGE3jj1n7I5PmTHP+Z5nunH/6o52NhcCJFOU1VZvqs4CztolH5asr6qVu6KvuWRPHTfsuWPfU8cNe+7Yd/W4H/T3QBiuOA4cvT8AuHWW9kWS1MyFALkSWJHkoCQPBVYDF8/yPknSHu9BP4VVVfcleS3wSWABcE5VbfglfuQumQqbg/bUccOeO/Y9ddyw5459l447VbX9rSRJmmQuTGFJkh6EDBBJUhcDpJnvfy4lyU1JrknylSTrW9mSJOuS3NBeF4+2P6Udi+uTHD17e77jkpyT5I4kXxuV7fBYkxzWjtnGJO9IMtUj5Q8q2xj7G5N8u537ryR54ahuXow9yYFJPpPkuiQbkry+lc/r8z7NuHfPOa+qPX5huDn/TeDxwEOBrwIHz/Z+7eIx3gTsN6nsrcDJbf1k4Iy2fnA7BnsBB7Vjs2C2x7ADY30u8HTgazszVuAK4BkMv4t0CfCC2R5b59jfCPz7KbadN2MH9gee3tYfDnyjjW9en/dpxr1bzrlXIINf/LmUqvopMPHnUua7Y4G1bX0t8JJR+XlVdW9V3QhsZDhGc0JVfQ7YMql4h8aaZH9gUVVdVsP/rveN2jxobWPs2zJvxl5Vt1XVl9r6PcB1DH/FYl6f92nGvS27dNwGyGCqP5cy3UmYiwr4VJKr2p9+AXhMVd0Gwz9E4NGtfD4ejx0d67K2Prl8rnptkqvbFNfENM68HHuS5cChwOXsQed90rhhN5xzA2Sw3T+XMg88q6qezvBXjU9K8txptt0TjseEbY11Ph2DdwJPAJ4G3Ab8dSufd2NPsi/wYeANVfX96TadomzOjn2Kce+Wc26ADOb9n0upqlvb6x3A3zNMSd3eLl1pr3e0zefj8djRsW5q65PL55yqur2q7q+qnwPv4oHpyHk19iQPYfgS/UBVfaQVz/vzPtW4d9c5N0AG8/rPpSR5WJKHT6wDq4CvMYxxTdtsDXBRW78YWJ1kryQHASsYbrDNZTs01jbdcU+SI9vTKK8ctZlTJr5Am99lOPcwj8be9vNs4Lqqetuoal6f922Ne7ed89l+iuDBsgAvZHiC4ZvAX8z2/uzisT2e4cmLrwIbJsYHPAq4FLihvS4ZtfmLdiyu50H8FMo2xvtBhsv2nzH8ZHVCz1iBle0/3jeB/0H7yw0P5mUbYz8XuAa4un2B7D/fxg48m2HK5WrgK2154Xw/79OMe7ecc/+UiSSpi1NYkqQuBogkqYsBIknqYoBIkroYIJKkLgaIJKmLASJJ6vL/AFrXhtxmM9/RAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(daily_calories)\n",
    "plt.title(np.mean(daily_calories))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.730156\n"
     ]
    }
   ],
   "source": [
    "print(len([i for i in daily_calories if i == 0]) / num_days)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Efficient Method : Vectorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2194535732269287\n"
     ]
    }
   ],
   "source": [
    "start = time()\n",
    "\n",
    "#get all temps at once\n",
    "temps = np.random.uniform(40, 60, num_days)\n",
    "\n",
    "#get all the tolerances at once\n",
    "tols = np.random.normal(55, 5, num_days)\n",
    "\n",
    "#get all the calories at once\n",
    "daily_calories = np.random.exponential(200, num_days)\n",
    "\n",
    "#if temp is less than tol, then you didnt run\n",
    "daily_calories[temps < tols] = 0\n",
    "\n",
    "end = time()\n",
    "print(end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '54.275049753539044')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(daily_calories)\n",
    "plt.title(np.mean(daily_calories))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.729439\n"
     ]
    }
   ],
   "source": [
    "print(len([i for i in daily_calories if i == 0]) / num_days)"
   ]
  },
  {
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}