k_means.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set()  # for plot styling\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read in data from different notebook\n",
    "import csv\n",
    "data=[]\n",
    "with open('/Users/oliviachandrasekhar/Desktop/betti_tlsclip_45_5_clean.csv', newline='') as file:\n",
    "    reader = csv.reader(file, delimiter=',', quotechar='|')\n",
    "    for row in reader:\n",
    "        data.append(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get b_0,b_1,b2 from each entry above\n",
    "betti01=[]\n",
    "betti02=[]\n",
    "betti_0=[]\n",
    "betti_1=[]\n",
    "betti_2=[]\n",
    "for item in data:\n",
    "    b0=item[2]\n",
    "    b1=item[3]\n",
    "    b2=item[4]\n",
    "    point1=item[2:4]\n",
    "    point2=[item[2],item[4]]\n",
    "    betti01.append(point1)\n",
    "    betti02.append(point2)\n",
    "    betti_0.append(b0)\n",
    "    betti_1.append(b1)\n",
    "    betti_2.append(b2)\n",
    "n=len(data)\n",
    "betti01=betti01[1:n]   #betti 0 and betti 1 in the same list--for analyzing betti 0 vs. betti 1\n",
    "betti02=betti02[1:n]  \n",
    "betti_0=betti_0[1:n]  \n",
    "betti_1=betti_1[1:n]  \n",
    "betti_2=betti_2[1:n]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['48', '52'], ['88', '183'], ['86', '206'], ['101', '206'], ['82', '173'], ['48', '52'], ['88', '183'], ['86', '206'], ['101', '206'], ['82', '173'], ['80', '208'], ['114', '157'], ['110', '215'], ['117', '294'], ['89', '230'], ['63', '98'], ['84', '137'], ['97', '124'], ['101', '217'], ['85', '168'], ['82', '140'], ['83', '150'], ['64', '122'], ['50', '39'], ['43', '87'], ['67', '119'], ['73', '157'], ['118', '158'], ['94', '258'], ['116', '227'], ['94', '186'], ['88', '126'], ['85', '132'], ['83', '230'], ['97', '128'], ['118', '227'], ['113', '284'], ['95', '291'], ['109', '192'], ['116', '183'], ['75', '242'], ['79', '205'], ['98', '205'], ['115', '153']]\n"
     ]
    }
   ],
   "source": [
    "print(betti01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 80.27272727 130.27272727]\n",
      " [ 94.5        216.42857143]\n",
      " [104.75       281.75      ]\n",
      " [ 47.25        57.5       ]\n",
      " [ 99.18181818 173.54545455]]\n"
     ]
    },
    {
     "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": [
    "#k means b0 vs b1\n",
    "from sklearn.cluster import KMeans\n",
    "kmeans1 = KMeans(n_clusters=5).fit(betti01) #specify number of clusters\n",
    "y_kmeans1 = kmeans1.predict(betti01) #predict/create clusters\n",
    "#centers for b0 vs b1\n",
    "centers = kmeans1.cluster_centers_ #get centers of clusters \n",
    "print(centers)\n",
    "plt.scatter(centers[:, 0], centers[:, 1], c='blue', s=50, alpha=0.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[113.36363636  25.54545455]\n",
      " [ 66.75        13.        ]\n",
      " [ 90.23076923  10.30769231]\n",
      " [ 85.75        26.5       ]\n",
      " [ 47.25         3.25      ]]\n"
     ]
    },
    {
     "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": [
    "#k means b0 vs b2\n",
    "from sklearn.cluster import KMeans\n",
    "kmeans2 = KMeans(n_clusters=5).fit(betti02)\n",
    "y_kmeans2 = kmeans2.predict(betti02)\n",
    "#centers for b0 vs b2\n",
    "centers = kmeans2.cluster_centers_\n",
    "print(centers)\n",
    "plt.scatter(centers[:, 0], centers[:, 1], c='red', s=50, alpha=0.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAD7CAYAAAB37B+tAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAUy0lEQVR4nO3df4zceX3f8ed3vLd75s7MzlqOuNyu8VG495ok5qC5UIU7gsSVCCkpvaYElaYJTfixCq6uIYBoD4woiZI20hW1V7pS6CWoqBSVExRxTYpyR1og9BKFwIna++mJ2ngt3Mby7k7PF9+s19/pH9/xedbntffH7Mx81s+HtLLn8x3P9+XZ7772O5/v9ztTtNttJEn5qg06gCRpayxyScqcRS5JmbPIJSlzFrkkZW5kAOscA+4GTgMXB7B+ScrRLuA24M+AVveCQRT53cDXBrBeSdoJ7gW+3j0wiCI/DbC4+Cxl2f9z2PfuvZWzZ8/1fb1bYeb+MHN/mHlzarWCRuMW6HRot0EU+UWAsmwPpMgvrTs3Zu4PM/eHmbfkBVPSHuyUpMxZ5JKUOYtckjJnkUtS5ixyScqcRS5JmRvE6Ydb1mpBSjUWFgomJtpElIyNDTqVdB2tFiPpGMXCAu2JCVbiIG646oXsivzUqYLZ2VGazctj9TrMzCwzOTk053lKq9ROzbN79mFqzSZtoADKep3zM4cpJ6cGHU+Zy2pqpdWC2dlRyrLN1NTlr7JsMzs7yvLyoBNKV9FqsXv2YShLLk7tp5zaz8Wp/VCW1bgbrrYoqyJPqUazCePjq8fHx6HZhLm5rP47ukGMpGPVnvh4Y9V4e7xBrdlkZO7ogJJpp8iq+RYWimsuX1y89nJpEIqFBdaa9GsDxeJiP+NoB8qqyCcmrj0H3mg4R67h056YYK1djAJoNxprLJXWJ6sijyip12FpafX40lJ1wHN6uhxMMOkaVuIgZb1OsbR6z7tYWqSs11mZfuWAkmmnyKrIx8aqs1NqtYL5+ctftVrBzMwyo6ODTihdxdgY52cOQ63GrvmT1OZPsmv+JNRq1bgbrrYou9MPJyfbHDnSYm6uxuJiQaPRZnq69GdBW9Jqwbe+Bd/73q5tuTahnJzi2SMfZ2TuKMXiIu1Go9oTd8Pd2fp07UB2RQ7Vtn/okNMo6o1L1ya0WvDcc9WPxLZcmzA6ysqhu3r3eBpq/bx2IKupFanXuq9NOHAAr01Qb/T52gGLXDc0r03Qduj3tQNupbqheW2CtkO/rx1Y1xx5RHwU+PnOzcdSSh+MiN8D7gGe7Yx/LKX0hZ6mk7aZ1yZoO/T72oHrFnlE3Ae8CXg11S+TP4yI+4EfB16fUnrBJzpLuei+NuGWWy6Pe22CtqL72oHu6ZXtunZgPVMrp4FfTyktp5QuAMeA/Z2vRyLiqYj4WEQ4TaPsdF+bcOIEXpug3ujztQNFu73+l44R8QrgG8C9wG8Dvwo0gS8Dn00p/e46HuYAcHzDSaVttLwM3/0unD0Le/fCj/6op3irB7Znw7oDONE9sO4ij4gfAR4DPppS+vQVy+4HfjGldP86HuoAcPzs2XOUZf/nH/ft28OZM8/0fb1bYeb+MHN/mHlzarWCvXtvhasU+bqmQyLidcDjwIdSSp+OiB+LiJ/ruksBXOhNXEnSRqznYOcU8EXgbSmlJzrDBfCJiHgCOAe8G/j0Gg8hSdpG6zn98P3AzcBDEXFpbBb4Lar58puAR1NKn92WhJKka7pukaeUHgAeWGPxJ3sbR5K0UZ4yKEmZs8glKXMWuSRlziKXpMxZ5JKUOYtckjJnkUtS5ixyScqcRS5JmbPIJSlzFrkkZc4il6TMWeSSlDmLXJIyZ5FLUuYscknKnEUuSZmzyCUpcxa5JGXOIpekzFnkkpQ5i1ySMmeRS1LmLHJJypxFLkmZs8glKXMWuSRlziKXpMxZ5JKUOYtckjJnkUtS5ixyScrcyHruFBEfBX6+c/OxlNIHI+I+4CFgN/C5lNKHtymjJOkarrtH3insNwGvBu4C/npE/D3gEeAtwEHg7oh483YGlSRd3XqmVk4Dv55SWk4pXQCOAXcCT6eUjqeUVoDPAG/dxpySpDVcd2olpfQ/L/09Il5BNcXyr6kK/pLTwGTP00mSrmtdc+QAEfEjwGPAB4AVqr3ySwqg3MiK9+69dSN376l9+/YMbN2bZeb+MHN/mLm31nuw83XAo8A/Tin9x4j4KeC2rru8BPjBRlZ89uw5yrK9kX/SE/v27eHMmWf6vt6tMHN/mLk/zLw5tVqx5g7wdYs8IqaALwJvSyk90Rl+sloULweOA2+nOvgpSeqz9eyRvx+4GXgoIi6NzQLvoNpLvxn4L8DntyGfJOk61nOw8wHggTUWv6q3cSRJG+WVnZKUOYtckjJnkUtS5ixyScqcRS5JmbPIJSlzFrkkZc4il6TMWeSSlDmLXJIyZ5FLUuYscknKnEUuSZmzyCUpcxa5JGXOIpekzFnkkpQ5i1ySMmeRS1LmLHJJypxFLkmZs8glKXMWuSRlziKXpMxZ5JKUOYtckjJnkUtS5ixyScqcRS5JmbPIJSlzFrkkZc4il6TMjaz3jhHxYuBPgJ9JKZ2IiN8D7gGe7dzlYymlL2xDRknSNayryCPitcDvAnd2Df848PqU0untCCZJWp/1Tq28C3gv8AOAiHgRsB94JCKeioiPRYTTNJI0AOsq35TSO1NKX+saegnwBPDLwN8A7gV+pffxJEnXU7Tb7XXfOSJOAG9IKZ24Yvx+4BdTSvev42EOAMfXvVJJUrc7gBPdA+s+2NktIn4MuDOl9GhnqAAubOQxzp49R1mu/5dIr+zbt4czZ57p+3q3wsz9Yeb+MPPm1GoFe/feetVlmypyquL+REQ8AZwD3g18epOPJUnagk0doEwpPQX8FvAN4Cjw7ZTSZ3sZTJK0PhvaI08pHej6+yeBT/Y6kCRpYzxlUJIyZ5FLUuYscknKnEUuSZmzyCUpcxa5JGXOIpekzG32yk7tZK0WI+kYxcIC7YkJVuIgjI0NOpW2UasFKdVYWCiYmGgTUfotz4hFrlVqp+bZPfswtWaTNtV7MZT1OudnDlNOTg06nrbBqVMFs7OjNJuXx+p1mJlZZnKy/++HpI1zakWXtVrsnn0YypKLU/spp/ZzcWo/lGU1vrw86ITqsVYLZmdHKcs2U1OXv8qyzezsqN/yTFjket5IOlbtiY83Vo23xxvUmk1G5o4OKJm2S0o1mk0YH189Pj4OzSbMzVkROfC7pOcVCwus9UK6DRSLi/2Moz5YWCiuuXxx8drLNRwscj2vPTHBWj+2BdBuNNZYqlxNTFx7DrzRcI48Bxa5nrcSBynrdYql1XvexdIiZb3OyvQrB5RM2yWipF6HpaXV40tL1QHP6elyMMG0IRa5Lhsb4/zMYajV2DV/ktr8SXbNn4RarRofHR10QvXY2Fh1dkqtVjA/f/mrViuYmVn2W54JTz/UKuXkFM8e+Tgjc0cpFhdpNxrVnrg/0TvW5GSbI0dazM3VWFwsaDTaTE+XfsszYpHrhUZHWTl016BTqI9GR+HQIadRcuXUiiRlziKXpMxZ5JKUOYtckjJnkUtS5ixyScqcRS5JmbPIJSlzFrkkZc4il6TMWeSSlDmLXJIy55tmSblqteBbT3PT9+ZpT0ywEger96W9wbRa1UfWLSwUTEy0iShvuKfBIpcyVDs1X30gduuvGHvuAgVQ1uucnzlMOTk16Hh9c+pUwezsKM3m5bF6vXqP9cnJG+fTjZxakXLTalUlXpZw4ADl1H4uTu2HsqzGl5cHnbAvWi2YnR2lLNtMTV3+Kss2s7OjN8rTAKyzyCPixRHx3Yg40Ll9X0Q8FRFPR8RvbGtCSauMpGPUmk3a46s/Q7U93qDWbDIyd3RAyforpRrNJoyPrx4fH4dmE+bmbpz91Ov+TyPitcDXgTs7t3cDjwBvAQ4Cd0fEm7czpKTLioUF1po0aAPF4uIaS3eWhYW1Piq8srh47eU7yXp+Zb0LeC/wg87tnwCeTikdTymtAJ8B3rpN+SRdoT0xwVoVVQDtRmONpTvLxMS158AbDefIn5dSemdK6WtdQz8MnO66fRqY7HUwSVe3Egcp63WKpdV73sXSImW9Xn3G6g0goqReh6Wl1eNLS9UBz+npG+ej6zZz1koNVr2yK4ANP2N79966iVX3xr59ewa27s0yc3/kkXkPPPgheOghOHGCW4oC2u1qcvh97+NFt+8ddMDr6tXz/OCD1dNw5gxc8TRw++29/fToYd42NlPkp4Dbum6/hMvTLut29uw5yrL/L3327dvDmTPP9H29W2Hm/sgq8+4GfOAj7Pu/32fpf5+i3WhUe+KjozDk/4dePs+7d8MHPlAd2FxcLGg02kxPl9XTcKYnqwCGY9uo1Yo1d4A3U+RPAhERLweOA2+nOvgpqZ9GR+E1r+HC1CsGnWSgRkfh0KEbZxrlajZ8fk5K6TngHcCjwFFgDvh8b2NJktZr3XvkKaUDXX9/HHjVdgSSJG3MjXPGvCTtUBa5JGXOIpekzFnkkpQ5i1ySMmeRS1LmLHJJypxFLkmZs8glKXMWuSRlziKXpMxZ5JKUOYtckjJnkUtS5ixyScqcRS5JmbPIJSlzFrkkZc4il6TMWeSSlDmLXJIyZ5FLUuYscknKnEUuSZmzyCUpcxa5JGXOIpekzFnkkpQ5i1ySMmeRS1LmLHJJypxFLkmZG9nKP46IrwI/BFzoDL0npfTkllNJktZt00UeEQVwJ/DSlNJK7yJJkjZiK1Mr0fnzKxHxnYg43ItAkqSN2UqRN4DHgfuBNwIzEfE3e5JKkrRuRbvd7skDRcSvAftTSr92nbseAI73ZKWSdOO5AzjRPbCVOfJ7gLGU0uOdoYLLBz2v6+zZc5Rlb36JbMS+fXs4c+aZvq93K8zcH2buDzNvTq1WsHfvrVddtpWzVsaBfxYRPwncBPwSMLOFx5MkbcKm58hTSl8GHgP+Avhz4JGU0jd7FUyStD5bOo88pfQR4CM9yiJJ2gSv7JSkzFnkkpQ5i1ySMmeRS1LmLHJJytyWzloZmFaLkXSMYmGB9sQEK3EQxsYGnUrSFVotSKnGwkLBxESbiHLQkXak7Iq8dmqe3bMPU2s2aVNdTlrW65yfOUw5OTXoeJI6Tp0qmJ0dpdm8PFavw4MPwu7dg8u1E+U1tdJqsXv2YShLLk7tp5zaz8Wp/VCW1fjy8qATSqLaE5+dHaUs20xNXf4qyzYPPeSPaq9lVeQj6Vi1Jz7eWDXeHm9QazYZmTs6oGSSuqVUo9mE8fHV4+PjsLQEc3NZVc/Qy+rZLBYWWOttttpAsbjYzziS1rCwUKy5rChgcXHt5dq4rIq8PTHBWt/+Amg3GmssldRPExNrv7Npuw2NRv/f+XQny6rIV+IgZb1OsbR6z7tYWqSs11mZfuWAkknqFlFSr1fTKN2Wlqrplelpz17ppayKnLExzs8chlqNXfMnqc2fZNf8SajVqvHR0UEnlER1NvDMzDK1WsH8/OWvWq3gfe/zR7XXsjv9sJyc4tkjH2dk7ijF4iLtRqPaE3fLkIbK5GSbI0dazM3VWFwsaDTaTE+X3H77KGfODDrdzpJdkQMwOsrKobsGnULSdYyOwqFDTqNst7ymViRJL2CRS1LmLHJJypxFLkmZs8glKXMWuSRlbhCnH+4CqNUG914Lg1z3Zpm5P8zcH2be0vp3XbmsaLf7/p4H9wBf6/dKJWmHuBf4evfAIIp8DLgbOA1c7PfKJSlTu4DbgD8DWt0LBlHkkqQe8mCnJGXOIpekzFnkkpQ5i1ySMmeRS1LmLHJJypxFLkmZy/MTgtYpIr4K/BBwoTP0HuCvAR8GbgI+kVL6NwOKd1UR8bPAR4FbgK+klB6IiPuAh4DdwOdSSh8eZMZuEfFO4HDX0B3Avwe+yJBmBoiIXwD+SefmH6SU3h8RdwGfAl4M/HdgJqW0MqiMV4qIDwH/kOpikM+llH5zWDNHxIuBPwF+JqV0Yq1teJjyX5m5M3YT8IfAx1NKfzxsmS/ZsXvkEVEAdwKvSindlVK6CzgF/CbV2wTcBbw7Il45wJirRMTLgFngbwOHgNdExJuBR4C3AAeBuztjQyGl9Kmu5/fvA38J/HOGOHNEvAj4V8BPAa8C7u0UzWeAwymlO4ECeNfgUq7Wyfd2qquiXw28NiL+DkOYOSJeS3UJ+Z2d27tZe3sYivxXZu6MBfDHwE9ecfehyNxtxxY5EJ0/vxIR34mIw8B9wBMppYWU0rPA54G/O7CEL3Q/1d7KqZTSBeBtwF8BT6eUjnd+638GeOsgQ17DvwX+KfAyhjvzLqpt/xaqV2Y3Ub1q251S+h+d+/w+w5X51cB/TSn9v5TSRaq9xH/EcGZ+F/Be4Aed2z/BVbaHiHgpw5P/yswAvwL8DvDkpYEhy/y8nVzkDeBxqnJ8IzAD7Kd6j5dLTgOT/Y+2ppcDuyLiSxHxbeBXgR9muDMDz+8x7k4p/SeGPHNK6RngI8Ac1au0E8AyQ5wZ+Bbw0xExERE3A38LWGEIM6eU3plS6n5jvLW2h6HZTq6SmZTSB1NKX7zirkOTuduOnSNPKX0T+Oal2xHx76jm6H6j624FMEwf8T0CvB54A3AO+BJwHuh+Q5xhy3zJe6ieX6h2EIY2c0QcAn4ZeCnQpNpDfBNDnDml9HhE/D7VS/0F4I+odlCGNnOXtbaHod5O1jCUmXfsHnlE3BMRb+waKqj2vG7rGnsJq19KDdr/Af4opXQmpXQe+ALVdNAwZyYiRqnmm7/UGTrFcGf+aeDxlNJfppRaVC+P38AQZ46IPcCjKaVDKaU3UB3wPMEQZ+6y1vYw7NvJ1Qxl5h1b5MA48DsRcXPnh+CXgF8A3hgR+zoHvH6Oaq5xWHyZ6uXzeETsAt5MNY8fEfHyztjbgT8YZMirOAT8r85xB6jmFIc583eA+yLils5B8Z8F/hvwXES8rnOff8BwZb4D+M8RMRIRdar5208x3Jkvuer2kFL6Pnnkf96wZt6xRZ5S+jLwGPAXwJ8Dj6SUvgE8CHwV+DbwH1JKfzq4lKullJ4E/gXV0fOjwPepDiC+A3i0MzZHVe7D5GVUeyoApJSeY4gzp5S+AnyWart4iupg529TnXXzLyNiDriV6syWoZBSeorq+XwK+FOqU2e/wRBnvuQ628PQ57+Kocvs+5FLUuZ27B65JN0oLHJJypxFLkmZs8glKXMWuSRlziKXpMxZ5JKUOYtckjL3/wFGBsiGy0v1hAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#centers for b0 vs b1\n",
    "centers1 = kmeans1.cluster_centers_\n",
    "#centers for b0 vs b2\n",
    "centers2 = kmeans2.cluster_centers_\n",
    "plt.scatter(centers1[:, 0], centers[:, 1], c='blue', s=50, alpha=0.5);\n",
    "plt.scatter(centers2[:, 0], centers[:, 1], c='red', s=50, alpha=0.5);\n",
    "\n",
    "#axes aren't scaled correctly, fix below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "centers1x=[]\n",
    "centers1y=[]\n",
    "for item in centers1:\n",
    "    centers1x.append(item[0])\n",
    "    centers1y.append(item[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "centers2x=[]\n",
    "centers2y=[]\n",
    "for item in centers2:\n",
    "    centers2x.append(item[0])\n",
    "    centers2y.append(item[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(centers1x,centers1y,'o')\n",
    "ax.plot(centers2x,centers2y,'o', color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#that's better!"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}