GUDHI_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examples taken from Cubical complexes tutorial\n",
    "Tutorial link:\n",
    "- https://arxiv.org/pdf/1807.08607.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": [
    "#This experiemnt has been designed by Pawel Dlotko, p.t.dlotko@swansea.ac.uk\n",
    "#It is distributed under GPLv3 licence\n",
    "\n",
    "import numpy as np\n",
    "import math\n",
    "import gudhi as gd\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "N = 100\n",
    "array = np.zeros((2*N+1,2*N+1))\n",
    "xExtrem = 2;\n",
    "yExtrem = 2;\n",
    "\n",
    "bitmap = []\n",
    "for i in range(0,2*N+1):\n",
    "\tfor j in range (0,2*N+1):\n",
    "\t\tx = i/(2*float(N)+1)*2*float(xExtrem)-xExtrem\n",
    "\t\ty = j/(2*float(N)+1)*2*float(xExtrem)-xExtrem\n",
    "\t\tnorm = math.sqrt( x*x + y*y )\n",
    "\t\tnorm =  math.fabs(norm-1)\n",
    "\t\tarray[i][j] = norm\n",
    "\t\tbitmap.append(norm)\n",
    "\n",
    "\n",
    "#Here we will display our creation:\n",
    "plt.imshow(array, cmap='gray', interpolation='nearest', vmin=np.amin(array), vmax=np.amax(array))\n",
    "#plt.savefig('circle.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.8284271247461903,\n",
       " 1.8143905272281406,\n",
       " 1.800424992586923,\n",
       " 1.7865315892811853,\n",
       " 1.7727114016105023,\n",
       " 1.7589655298285818,\n",
       " 1.7452950902491748,\n",
       " 1.7317012153441356,\n",
       " 1.7181850538330745,\n",
       " 1.7047477707640106,\n",
       " 1.6913905475844158,\n",
       " 1.6781145822020171,\n",
       " 1.6649210890347037,\n",
       " 1.6518112990488643,\n",
       " 1.6387864597854485,\n",
       " 1.6258478353730448,\n",
       " 1.6129967065272148,\n",
       " 1.6002343705353415,\n",
       " 1.5875621412261895,\n",
       " 1.5749813489233828,\n",
       " 1.562493340381979,\n",
       " 1.5500994787072888,\n",
       " 1.5378011432550944,\n",
       " 1.525599729512384,\n",
       " 1.5134966489577173,\n",
       " 1.5014933289003207,\n",
       " 1.4895912122969963,\n",
       " 1.4777917575459263,\n",
       " 1.4660964382564439,\n",
       " 1.454506742993833,\n",
       " 1.4430241749982238,\n",
       " 1.431650251876654,\n",
       " 1.420386505267356,\n",
       " 1.4092344804753516,\n",
       " 1.3981957360784456,\n",
       " 1.3872718435027087,\n",
       " 1.3764643865665844,\n",
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       " ...]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bitmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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2AaUBxiQiSWjGjBlkZWVx/PHH8/7773PyySfHO6SUEmQiaA6sjlguAXqWGzMOeAVYCzQErnT3feV3ZGaDgcEArVq1CiRYEUk869at45ZbbmHGjBk88MAD/OxnP1MFEIAgzxFE+2t5ueXzgYVAM6ArMM7MGv3gSe4T3D3T3TObNm1a+5GKSELZs2cPo0ePplOnTrRs2ZLi4mK1gQIUZEVQArSMWG5B6J1/pF8Bf3V3B5aZ2QrgFGB2gHGJSAKbPn06WVlZNGvWjA8++EBtoBgIMhHMAdqZWVtgDXAVcE25MauAc4GZZnYscDKwPMCYRCRBrV27lltuuYWZM2cyevRoLr30UlUAMRJYa8jdS4FsYDJQDDzv7oVmdqOZ3Rge9kegj5kVAO8CI9x9Y1AxiUji2bNnDw888ACdO3emdevWFBcX61xAjAX6gTJ3fwN4o9y68RGP1wI/DjIGEUlc06ZNIzs7m+bNm6sNFEf6ZLGIxNzatWsZPnw4H3zwAaNHj+aSSy5RBRBHuteQiMTMnj17GDVqFJ07d6Zt27YUFRXpXEACUEUgIjExbdo0srKyaNGiBR9++CE/+tGP4h2ShCkRiEig1AZKfGoNiUgg1AZKHqoIRKTWvffee2RnZ9OyZUu1gZKAEoGI1Jo1a9YwfPhwPvzwQx588EEGDRqkCiAJqDUkIjW2Z88eRo4cSZcuXTjxxBMpLi7WuYAkoopARGoksg300Ucf0a5du3iHJNWkRCAiB6SsDfTRRx8xevRotYGSmFpDIlIt5dtARUVFagMlOVUEIlJlU6dOJTs7m9atW6sNlEKUCESkUmvWrGHYsGHMmjWLBx98kIEDB6oCSCFqDYlIhXbv3s39999Ply5daNeuHUVFRToXkIJUEYhIVO+++y7Z2dm0bdtWbaAUp0QgIt9TUlLCsGHD+PjjjxkzZgw//elPVQGkOLWGRAQItYH+9re/0bVrV04++WSKiop0LiBNqCIQke+1gWbNmsVJJ50U75AkhpQIRNJYWRto9uzZPPjgg2oDpSm1hkTSUPk2UGFhodpAaUwVgUiamTJlCjk5OZxwwglqAwmgRCCSNkpKSrj55puZM2cOY8aM4eKLL1YFIIBaQyIpb/fu3dx333107dqVjIwMioqKdC5AvkcVgUgKmzJlCtnZ2Zx00kl8/PHHnHjiifEOSRKQEoFIClq9ejXDhg1j7ty537WBRCoSaGvIzC4ws0/MbJmZ3VbBmH5mttDMCs1sepDxiKS6sjbQqaeeSvv27SksLFQSkEpVuSIwsz5Am8jnuPuT+xlfB3gI6A+UAHPM7BV3L4oY0xh4GLjA3VeZ2THVnoGIAPDOO++Qk5NDu3bt1AaSaqlSIjCzp4ATgYXA3vBqBypMBEAPYJm7Lw/vYyIwECiKGHMN8JK7rwJw9/XVil5EWL16NTfffDPz5s1TG0gOSFUrgkygvbt7NfbdHFgdsVwC9Cw35kfAIWY2DWgIjIlWZZjZYGAwQKtWraoRgkjq2r17Nw888AAjR44kJyeHJ598knr16sU7LElCVU0Ei4HjgHXV2He0a9PKJ5KDgdOAc4F6wEdmNsvdl37vSe4TgAkAmZmZ1UlGIinp7bffJicnh5NPPpnZs2dzwgknxDskSWL7TQRm9iqhg3dDoMjMZgO7yra7+0/38/QSoGXEcgtgbZQxG919G7DNzGYAXYCliMgPrFq1iptvvpkFCxYwZswYfvKTn8Q7JEkBlVUEI2uw7zlAOzNrC6wBriJ0TiDSf4FxZnYwcCih1tHoGrymSEratWsXDzzwAKNGjSInJ4ennnpKbSCpNftNBO4+HcDM7nP3EZHbzOw+oMLLPd291MyygclAHeBxdy80sxvD28e7e7GZvQXkA/uAx9x9cY1mJJJi1AaSoFlVzv+a2Xx371ZuXb67dw4ssgpkZmb63LlzY/2yIjGnNpDUJjOb5+6Z0bbt9wNlZvZbMysATjaz/IifFUBBEMGKpLtdu3bxl7/8hW7dutG5c2cKCwuVBCRQlZ0jeBZ4E/gLEPnJ4C3uvimwqETS1OTJk8nJySEjI4M5c+bQtm3beIckaaCycwSbgc3A1QDhT/7WBRqYWYOyD4KJSM2sWrWKoUOHsnDhQsaOHctFF10U75AkjVTpXkNmdrGZfQqsIHSC+HNClYKI1MCuXbv485//TLdu3ejSpQuFhYVKAhJzVf1A2b1AL2CKu59qZmcTrhJE5MC89dZb5Obmqg0kcVfVRLDH3b8ys4PM7CB3fy98+aiIVNPKlSsZOnQoixYtUhtIEkJVb0P9jZk1AGYCz5jZGKA0uLBEUs+uXbv405/+RLdu3Tj11FPVBpKEUdWKYCCwAxgCXAscAfwhqKBEUs1bb71FTk4OHTp0YO7cuWoDSUKpUiJw921m1hpo5+7/MrPDCX1aWET24/PPP2fo0KEUFBQwduxYLrzwwniHJPIDVb1q6DfAC8Cj4VXNgZeDCkok2e3cuZN7772XzMxMTjvtNBYvXqwkIAmrqq2hLEJfNPMxgLt/qm8TE4nuzTffJDc3l44dOzJ37lzatGkT75BE9quqiWCXu+82C33FQPhuofpeAJEIZW2gxYsXM3bsWAYMGBDvkESqpKpXDU03szuAembWH5gEvBpcWCLJo3wbqKCgQElAkkpVK4LbgBsI3Wjuf4A3gMeCCkokWbz55pvk5OTQqVMntYEkaVX1qqF9ZvYy8LK7bwg4JpGE9/nnnzNkyBAKCwv5+9//rgpAklplt6E2M7vbzDYCS4BPzGyDmd0Vm/BEEsvOnTv54x//SGZmJt27d1cbSFJCZecIhgCnA93d/Wh3P4rQ10mebmZDA49OJIG88cYbdOzYkQULFjBv3jzuvPNO6tatG++wRGqsstbQz4H+7r6xbIW7Lzez64C30fcLSxqIbAONGzeOCy64IN4hidSqyiqCQyKTQJnweYJDgglJJDGUbwMtXrxYSUBSUmUVwe4D3CaS1F5//XXy8vLo3Lkz8+bNo3Xr1vEOSSQwlSWCLmb2bZT1RuibykRSyooVKxgyZAhFRUVqA0na2G9ryN3ruHujKD8N3V2tIUkZO3fu5A9/+AOZmZn07NlTbSBJK1X9QJlIynr99dfJzc2la9euzJ8/X20gSTtKBJK2VqxYQV5eHkuWLOHhhx/m/PPPj3dIInFR1XsNiaSMHTt2cM8999C9e3d69+5NQUGBkoCktUATgZldYGafmNkyM7ttP+O6m9leM7ssyHhEXnvtNTp27EhBQQHz58/n9ttv57DDDot3WCJxFVhryMzqAA8B/YESYI6ZveLuRVHG3QdMDioWkeXLlzNkyBA++eQTHnnkEX784x/HOySRhBFkRdADWObuy919NzCR0Hcfl5cDvAisDzAWSVNlbaAePXrQu3dv8vPzlQREygnyZHFzYHXEcgmh+xR9x8yaA5cA5wDdK9qRmQ0GBgO0atWq1gOV1PTaa6+Rm5tLt27dmD9/vv7tiFQgyERgUdaV/1azB4ER7r637NvPonH3CcAEgMzMTH0zmuzX8uXLycvLY+nSpYwfP14VgEglgmwNlQAtI5ZbAGvLjckEJprZ58BlwMNmNijAmCSF7dixg7vvvpsePXpw+umnqw0kUkVBVgRzgHZm1hZYA1wFXBM5wN3blj02syeA19z95QBjkhT16quvkpeXx2mnnaY2kEg1BZYI3L3UzLIJXQ1UB3jc3QvN7Mbw9vFBvbakj8g20KOPPkr//v3jHZJI0gn0k8Xu/gah7zeOXBc1Abj7L4OMRVLLjh07uO+++xg3bhzDhw/nhRde0OcBRA6QbjEhSSeyDbRgwQJatmxZ+ZNEpEJKBJI0PvvsM/Ly8li2bJnaQCK1SPcakoS3Y8cOfv/739OzZ0/OPPNM8vPzlQREapEqAklY7v5dG6h79+5qA4kERIlAEtJnn31Gbm4un332Gf/4xz8477zz4h2SSMpSa0gSyvbt27nrrrvo2bMnffv2JT8/X0lAJGCqCCQhuDuvvPIKQ4YMoUePHixcuJAWLVrEOyyRtKBEIHG3bNky8vLyWL58udpAInGg1pDETVkbqFevXvTt25dFixYpCYjEgSoCiTm1gUQSixKBxNSyZcvIzc1lxYoVPPbYY5x77rnxDkkk7ak1JDGxfft2fve739GrVy/OPvtsFi1apCQgkiBUEUig3J3//ve/DBkyhF69eqkNJJKAlAgkMJ9++im5ubmsXLmSf/7zn6oARBKUWkNS67Zv387//u//0rt3b8455xwWLlyoJCCSwFQRSK1xd15++WWGDh2qNpBIElEikFrx6aefkpOTw6pVq3j88cc555xz4h2SiFSRWkNSI9u2bePOO++kd+/e9O/fn0WLFikJiCQZJQI5IO7OSy+9RPv27VmxYgWLFi1i2LBhHHLIIfEOTUSqSa0hqbalS5eSm5vL6tWreeKJJzj77LPjHZKI1IAqAqmysjZQnz596N+/PwsXLlQSEEkBSgRSqfJtoPz8fLWBRFKIWkOyX0uXLiUnJ4eSkhL+9a9/0a9fv3iHJCK1TBWBRLVt2zbuuOMO+vTpw/nnn8/ChQuVBERSlBKBfI+78+KLL9K+fXtWrlxJfn4+N998s9pAIiks0NaQmV0AjAHqAI+5+1/Lbb8WGBFe3Ar81t0XBRmTVKysDbRmzRq1gUTSSGAVgZnVAR4CBgDtgavNrH25YSuAvu7eGfgjMCGoeKRi5dtACxYsUBIQSSNBtoZ6AMvcfbm77wYmAgMjB7j7h+7+dXhxFqAb08RQWRsoIyNDbSCRNBZka6g5sDpiuQTouZ/xNwBvRttgZoOBwQCtWrWqrfjS2ieffEJOTg5r167lqaeeom/fvvEOSUTiJMiKwKKs86gDzc4mlAhGRNvu7hPcPdPdM5s2bVqLIaafbdu2cfvtt3P66aczYMAAFixYoCQgkuaCTAQlQMuI5RbA2vKDzKwz8Bgw0N2/CjCetObuvPDCC2RkZLB69WoKCgoYOnSo2kAiEmhraA7QzszaAmuAq4BrIgeYWSvgJeB6d18aYCxpbcmSJeTk5PDFF1+oDSQiPxBYReDupUA2MBkoBp5390Izu9HMbgwPuws4GnjYzBaa2dyg4klHW7du5bbbbuOMM87goosuYv78+UoCIvIDgX6OwN3fAN4ot258xONfA78OMoZ0VNYGGjZsGH379qWgoIDjjz8+3mGJSILSvYZSTGQb6Omnn+ass86Kd0gikuB0i4kUUdYGOvPMM79rAykJiEhVKBEkOXdn0qRJZGRksGbNGvLz8xkyZIiuBhKRKlNrKIktWbKE7Oxs1q9fzzPPPKMKQEQOiCqCJLR161ZGjBjBmWeeycUXX6w2kIjUiBJBEnF3nn/+eTIyMli3bh0FBQXk5eVx8MEq7ETkwOkIkiSKi4vJyclh/fr1PPvss5x55pnxDklEUoQqggRX1gY666yzvmsDKQmISG1SIkhQagOJSKzoqJKAiouLyc7OZsOGDWoDiUjgVBEkkC1btnDrrbdy1llnMXDgQLWBRCQmlAgSgLvz73//m4yMDL788ksKCgrIzc1VG0hEYkJHmjgrKioiJyeHjRs3MnHiRM4444x4hyQiaUYVQZxs2bKFW265hb59+zJo0CDmzZunJCAicaFEEGPuzsSJE8nIyGDDhg0sXryYnJwctYFEJG509ImhoqIisrOz2bRpE//+9785/fTT4x2SiIgqgliIbANdcsklzJ07V0lARBKGEkGA1AYSkWSgI1JACgsLycnJURtIRBKeKoJatmXLFoYPH06/fv249NJL1QYSkYSnRFBL3J3nnnuOjIwMvvrqKxYvXkx2drbaQCKS8HSUqgWFhYVkZWWxefNmnn/+efr06RPvkEREqkwVQQ18++23DBs2jH79+nHZZZcxZ84cJQERSTpKBAfA3Xn22WfJyMhg06ZNFBYWqg0kIklLR64KrGneMur6rye/SXZ2Nps3b2bSpEmqAEQk6QVaEZjZBWb2iZktM7Pbomw3Mxsb3p5vZt2CjKeqoiWBLfv2cc/mb+jbpQuXX345c+fOVRIQkZQQWCIwszrAQ8AAoD1wtZm1LzdsANAu/DMYeCSoeA6Uu/PS9u30W/8F3+7bx+u+mdUAAAexSURBVNSmx5KVlUWdOnXiHZqISK0IsjXUA1jm7ssBzGwiMBAoihgzEHjS3R2YZWaNzex4d18XYFxVtmTPHu7c/DVb9zkTjjqa0w49LN4hiYjUuiBbQ82B1RHLJeF11R2DmQ02s7lmNnfDhg21Hmh5ZW2gK77awE/rHc4bTY9REhCRlBVkIrAo6/wAxuDuE9w9090zmzZtWivBRePuPPPMM6E2kIfaQL+o34A6Fi1MEZHUEGRrqASIPOvaAlh7AGNioqCggOzsbLZs2cL/vewKOr//ftRxzdesjrpeRCRZBVkRzAHamVlbMzsUuAp4pdyYV4Cfh68e6gVsjvX5gc2bNzN06FDOPfdcrrzySubMmcOAfz/HYVdcAYceGhp06KEcdsUVSgIikpICqwjcvdTMsoHJQB3gcXcvNLMbw9vHA28AFwLLgO3Ar4KKJ0p8PPPMM9x6660MGDCAwsJCIttOTUaPgtGjYhWOiEjcBPqBMnd/g9DBPnLd+IjHDmQFGUM0BQUFZGVlsXXrVl588UV69+4d6xBERBJGWt1iIrINdPXVVzNnzhwlARFJe2mRCNydp59+moyMDLZs2UJhYSG//e1v9aEwERHS4F5D+fn5ZGdns23bNl566SV69eoV75BERBJKylYEmzdvZsiQIZx33nlcffXVzJ49W0lARCSKlEsE7s5TTz1FRkYGW7duVRtIRKQSKdUays/PJysri+3bt6sNJCJSRSlREWzevJm8vDzOO+88rr32WrWBRESqIakTgbvz5JNPkpGRwfbt2ykqKuLGG29UG0hEpBqStjW0aNEisrOz2bFjB//5z3/o2bNnvEMSEUlKSVcR7N27l7y8PPr37891113Hxx9/rCQgIlIDSVcRFBYW0r17d4qKimjSpEm8wxERSXoWut1P8jCzDcDKGL5kE2BjDF8vHlJ9jqk+P0j9Oab6/CD4ObZ296hf6JJ0iSDWzGyuu2fGO44gpfocU31+kPpzTPX5QXznmHTnCEREpHYpEYiIpDklgspNiHcAMZDqc0z1+UHqzzHV5wdxnKPOEYiIpDlVBCIiaU6JQEQkzSkRhJnZBWb2iZktM7Pbomw3Mxsb3p5vZt3iEeeBqsL8rg3PK9/MPjSzLvGIsyYqm2PEuO5mttfMLotlfDVVlfmZWT8zW2hmhWY2PdYx1lQV/p0eYWavmtmi8Bx/FY84D5SZPW5m681scQXb43Occfe0/wHqAJ8BJwCHAouA9uXGXAi8CRjQC/g43nHX8vz6AEeGHw9IpvlVdY4R46YCbwCXxTvuWv4bNgaKgFbh5WPiHXcAc7wDuC/8uCmwCTg03rFXY45nAd2AxRVsj8txRhVBSA9gmbsvd/fdwERgYLkxA4EnPWQW0NjMjo91oAeo0vm5+4fu/nV4cRbQIsYx1lRV/oYAOcCLwPpYBlcLqjK/a4CX3H0VgLun4hwdaGhmBjQglAhKYxvmgXP3GYRirkhcjjNKBCHNgdURyyXhddUdk6iqG/sNhN6VJJNK52hmzYFLgPExjKu2VOVv+CPgSDObZmbzzOznMYuudlRljuOADGAtUADkufu+2IQXE3E5ziTdTecCYlHWlb+utipjElWVYzezswklgjMCjaj2VWWODwIj3H1v6A1lUqnK/A4GTgPOBeoBH5nZLHdfGnRwtaQqczwfWAicA5wIvGNmM93926CDi5G4HGeUCEJKgJYRyy0IveOo7phEVaXYzawz8BgwwN2/ilFstaUqc8wEJoaTQBPgQjMrdfeXYxNijVT13+hGd98GbDOzGUAXIFkSQVXm+Cvgrx5qqC8zsxXAKcDs2IQYuLgcZ9QaCpkDtDOztmZ2KHAV8Eq5Ma8APw+f1e8FbHb3dbEO9ABVOj8zawW8BFyfRO8gI1U6R3dv6+5t3L0N8AJwU5IkAajav9H/Amea2cFmdjjQEyiOcZw1UZU5riJU8WBmxwInA8tjGmWw4nKcUUUAuHupmWUDkwldufC4uxea2Y3h7eMJXWVyIbAM2E7onUlSqOL87gKOBh4Ov2Mu9SS622MV55i0qjI/dy82s7eAfGAf8Ji7R71MMRFV8W/4R+AJMysg1EYZ4e5Jc3tqM3sO6Ac0MbMS4PfAIRDf44xuMSEikubUGhIRSXNKBCIiaU6JQEQkzSkRiIikOSUCEZE0p0QgUonwnUoXhu94Od/M+oTXNzOzFyp4ThszuyZi+ZdmNi5WMYtUhxKBSOV2uHtXd+8C3A78BcDd17r7D25lbWYHA20I3QROJOHpA2Ui1dMI+BpC7/qB19y9o5n9ErgIqAvUBw4HMsxsIfCv8HOahT/wdSLwH3e/NebRi0ShRCBSuXrhA3pd4HhCNzyLpjfQ2d03mVk/YLi7/wRCrSGgK3AqsAv4xMz+7u6rK9iXSMyoNSRSubLW0CnABcCTFv32pe+4+/7uNf+uu292952EvkCmdRDBilSXEoFINbj7R4TuXNo0yuZtlTx9V8TjvagilwShRCBSDWZ2CqEbolV2m+4tQMPgIxKpOb0jEalc2TkCCN3x8hdV+HKbfKDUzBYBTxA+wSySiHT3URGRNKfWkIhImlMiEBFJc0oEIiJpTolARCTNKRGIiKQ5JQIRkTSnRCAikub+H/Z2zFNBejhBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Given the input data we can buld a Gudhi btmap cubical complex:\n",
    "bcc = gd.CubicalComplex(top_dimensional_cells = bitmap, dimensions=[2*N+1,2*N+1])\n",
    "#optional computation of persistence\n",
    "persistence = bcc.persistence()\n",
    "plt = gd.plot_persistence_diagram(persistence)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## gudhi_graphical_tools_example.py\n",
    "\n",
    "https://github.com/GUDHI/gudhi-devel/blob/master/src/python/example/gudhi_graphical_tools_example.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#####################################################################\n",
      "Show barcode persistence example\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#####################################################################\n",
      "Show diagram persistence example\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#####################################################################\n",
      "Show diagram persistence example with a confidence band\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#####################################################################\n",
      "Show barcode and diagram persistence side by side example\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plot\n",
    "import gudhi\n",
    "\n",
    "\"\"\" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.\n",
    "    See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details.\n",
    "    Author(s):       Vincent Rouvreau\n",
    "\n",
    "    Copyright (C) 2016 Inria\n",
    "\n",
    "    Modification(s):\n",
    "      - YYYY/MM Author: Description of the modification\n",
    "\"\"\"\n",
    "\n",
    "__author__ = \"Vincent Rouvreau\"\n",
    "__copyright__ = \"Copyright (C) 2016 Inria\"\n",
    "__license__ = \"MIT\"\n",
    "\n",
    "print(\"#####################################################################\")\n",
    "print(\"Show barcode persistence example\")\n",
    "\n",
    "persistence = [\n",
    "    (2, (1.0, float(\"inf\"))),\n",
    "    (1, (1.4142135623730951, float(\"inf\"))),\n",
    "    (1, (1.4142135623730951, float(\"inf\"))),\n",
    "    (0, (0.0, float(\"inf\"))),\n",
    "    (0, (0.0, 1.0)),\n",
    "    (0, (0.0, 1.0)),\n",
    "    (0, (0.0, 1.0)),\n",
    "]\n",
    "gudhi.plot_persistence_barcode(persistence)\n",
    "plot.show()\n",
    "\n",
    "print(\"#####################################################################\")\n",
    "print(\"Show diagram persistence example\")\n",
    "\n",
    "gudhi.plot_persistence_diagram(persistence)\n",
    "plot.show()\n",
    "\n",
    "print(\"#####################################################################\")\n",
    "print(\"Show diagram persistence example with a confidence band\")\n",
    "\n",
    "gudhi.plot_persistence_diagram(persistence, band=0.2)\n",
    "plot.show()\n",
    "\n",
    "print(\"#####################################################################\")\n",
    "print(\"Show barcode and diagram persistence side by side example\")\n",
    "fig, axes = plot.subplots(nrows=1, ncols=2)\n",
    "gudhi.plot_persistence_barcode(persistence, axes = axes[0])\n",
    "gudhi.plot_persistence_diagram(persistence, axes = axes[1])\n",
    "fig.suptitle(\"barcode versus diagram\")\n",
    "plot.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# random_cubical_complex_persistence_example\n",
    "https://github.com/GUDHI/gudhi-devel/blob/master/src/python/example/random_cubical_complex_persistence_example.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "usage: ipykernel_launcher.py [-h] [dimension [dimension ...]]\n",
      "ipykernel_launcher.py: error: argument dimension: invalid int value: '/Users/emil/Library/Jupyter/runtime/kernel-52baab18-a7f4-4f73-9e89-c1880dcdba2f.json'\n",
      "ERROR:root:Internal Python error in the inspect module.\n",
      "Below is the traceback from this internal error.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"/Users/emil/anaconda3/envs/gudhi/lib/python3.8/argparse.py\", line 2422, in _get_value\n",
      "    result = type_func(arg_string)\n",
      "ValueError: invalid literal for int() with base 10: '/Users/emil/Library/Jupyter/runtime/kernel-52baab18-a7f4-4f73-9e89-c1880dcdba2f.json'\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/emil/anaconda3/envs/gudhi/lib/python3.8/argparse.py\", line 1800, in parse_known_args\n",
      "    namespace, args = self._parse_known_args(args, namespace)\n",
      "  File \"/Users/emil/anaconda3/envs/gudhi/lib/python3.8/argparse.py\", line 2009, in _parse_known_args\n",
      "    stop_index = consume_positionals(start_index)\n",
      "  File \"/Users/emil/anaconda3/envs/gudhi/lib/python3.8/argparse.py\", line 1965, in consume_positionals\n",
      "    take_action(action, args)\n",
      "  File \"/Users/emil/anaconda3/envs/gudhi/lib/python3.8/argparse.py\", line 1858, in take_action\n",
      "    argument_values = self._get_values(action, argument_strings)\n",
      "  File \"/Users/emil/anaconda3/envs/gudhi/lib/python3.8/argparse.py\", line 2407, in _get_values\n",
      "    value = [self._get_value(action, v) for v in arg_strings]\n",
      "  File \"/Users/emil/anaconda3/envs/gudhi/lib/python3.8/argparse.py\", line 2407, in <listcomp>\n",
      "    value = [self._get_value(action, v) for v in arg_strings]\n",
      "  File \"/Users/emil/anaconda3/envs/gudhi/lib/python3.8/argparse.py\", line 2435, in _get_value\n",
      "    raise ArgumentError(action, msg % args)\n",
      "argparse.ArgumentError: argument dimension: invalid int value: '/Users/emil/Library/Jupyter/runtime/kernel-52baab18-a7f4-4f73-9e89-c1880dcdba2f.json'\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/emil/anaconda3/envs/gudhi/lib/python3.8/site-packages/IPython/core/interactiveshell.py\", line 3331, in run_code\n",
      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "  File \"<ipython-input-14-fa16ac4c888a>\", line 32, in <module>\n",
      "    args = parser.parse_args()\n",
      "  File \"/Users/emil/anaconda3/envs/gudhi/lib/python3.8/argparse.py\", line 1768, in parse_args\n",
      "    args, argv = self.parse_known_args(args, namespace)\n",
      "  File \"/Users/emil/anaconda3/envs/gudhi/lib/python3.8/argparse.py\", line 1807, in parse_known_args\n",
      "    self.error(str(err))\n",
      "  File \"/Users/emil/anaconda3/envs/gudhi/lib/python3.8/argparse.py\", line 2521, in error\n",
      "    self.exit(2, _('%(prog)s: error: %(message)s\\n') % args)\n",
      "  File \"/Users/emil/anaconda3/envs/gudhi/lib/python3.8/argparse.py\", line 2508, in exit\n",
      "    _sys.exit(status)\n",
      "SystemExit: 2\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/emil/anaconda3/envs/gudhi/lib/python3.8/site-packages/IPython/core/ultratb.py\", line 1148, in get_records\n",
      "    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
      "  File \"/Users/emil/anaconda3/envs/gudhi/lib/python3.8/site-packages/IPython/core/ultratb.py\", line 316, in wrapped\n",
      "    return f(*args, **kwargs)\n",
      "  File \"/Users/emil/anaconda3/envs/gudhi/lib/python3.8/site-packages/IPython/core/ultratb.py\", line 350, in _fixed_getinnerframes\n",
      "    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
      "  File \"/Users/emil/anaconda3/envs/gudhi/lib/python3.8/inspect.py\", line 1503, in getinnerframes\n",
      "    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
      "AttributeError: 'tuple' object has no attribute 'tb_frame'\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "can only concatenate str (not \"list\") to str",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/envs/gudhi/lib/python3.8/argparse.py\u001b[0m in \u001b[0;36m_get_value\u001b[0;34m(self, action, arg_string)\u001b[0m\n\u001b[1;32m   2421\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2422\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg_string\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2423\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: '/Users/emil/Library/Jupyter/runtime/kernel-52baab18-a7f4-4f73-9e89-c1880dcdba2f.json'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mArgumentError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/envs/gudhi/lib/python3.8/argparse.py\u001b[0m in \u001b[0;36mparse_known_args\u001b[0;34m(self, args, namespace)\u001b[0m\n\u001b[1;32m   1799\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1800\u001b[0;31m             \u001b[0mnamespace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse_known_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnamespace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1801\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnamespace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_UNRECOGNIZED_ARGS_ATTR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/gudhi/lib/python3.8/argparse.py\u001b[0m in \u001b[0;36m_parse_known_args\u001b[0;34m(self, arg_strings, namespace)\u001b[0m\n\u001b[1;32m   2008\u001b[0m         \u001b[0;31m# consume any positionals following the last Optional\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2009\u001b[0;31m         \u001b[0mstop_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconsume_positionals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2010\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/gudhi/lib/python3.8/argparse.py\u001b[0m in \u001b[0;36mconsume_positionals\u001b[0;34m(start_index)\u001b[0m\n\u001b[1;32m   1964\u001b[0m                 \u001b[0mstart_index\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0marg_count\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1965\u001b[0;31m                 \u001b[0mtake_action\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1966\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/gudhi/lib/python3.8/argparse.py\u001b[0m in \u001b[0;36mtake_action\u001b[0;34m(action, argument_strings, option_string)\u001b[0m\n\u001b[1;32m   1857\u001b[0m             \u001b[0mseen_actions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1858\u001b[0;31m             \u001b[0margument_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margument_strings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1859\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/gudhi/lib/python3.8/argparse.py\u001b[0m in \u001b[0;36m_get_values\u001b[0;34m(self, action, arg_strings)\u001b[0m\n\u001b[1;32m   2406\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2407\u001b[0;31m             \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marg_strings\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2408\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/gudhi/lib/python3.8/argparse.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   2406\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2407\u001b[0;31m             \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marg_strings\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2408\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/gudhi/lib/python3.8/argparse.py\u001b[0m in \u001b[0;36m_get_value\u001b[0;34m(self, action, arg_string)\u001b[0m\n\u001b[1;32m   2434\u001b[0m             \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'invalid %(type)s value: %(value)r'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2435\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mArgumentError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mArgumentError\u001b[0m: argument dimension: invalid int value: '/Users/emil/Library/Jupyter/runtime/kernel-52baab18-a7f4-4f73-9e89-c1880dcdba2f.json'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mSystemExit\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/envs/gudhi/lib/python3.8/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_code\u001b[0;34m(self, code_obj, result, async_)\u001b[0m\n\u001b[1;32m   3330\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3331\u001b[0;31m                     \u001b[0mexec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muser_global_ns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muser_ns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3332\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-14-fa16ac4c888a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m \u001b[0mdimension_multiplication\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmul\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdimension\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/gudhi/lib/python3.8/argparse.py\u001b[0m in \u001b[0;36mparse_args\u001b[0;34m(self, args, namespace)\u001b[0m\n\u001b[1;32m   1767\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mparse_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnamespace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1768\u001b[0;31m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse_known_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnamespace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1769\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0margv\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/gudhi/lib/python3.8/argparse.py\u001b[0m in \u001b[0;36mparse_known_args\u001b[0;34m(self, args, namespace)\u001b[0m\n\u001b[1;32m   1806\u001b[0m             \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_sys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1807\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1808\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/gudhi/lib/python3.8/argparse.py\u001b[0m in \u001b[0;36merror\u001b[0;34m(self, message)\u001b[0m\n\u001b[1;32m   2520\u001b[0m         \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'prog'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'message'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2521\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'%(prog)s: error: %(message)s\\n'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/gudhi/lib/python3.8/argparse.py\u001b[0m in \u001b[0;36mexit\u001b[0;34m(self, status, message)\u001b[0m\n\u001b[1;32m   2507\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_print_message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_sys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstderr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2508\u001b[0;31m         \u001b[0m_sys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2509\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mSystemExit\u001b[0m: 2",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/envs/gudhi/lib/python3.8/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_code\u001b[0;34m(self, code_obj, result, async_)\u001b[0m\n\u001b[1;32m   3336\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3337\u001b[0m                 \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror_in_exec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3338\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshowtraceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexception_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3339\u001b[0m             \u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"To exit: use 'exit', 'quit', or Ctrl-D.\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3340\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcustom_exceptions\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/gudhi/lib/python3.8/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mshowtraceback\u001b[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[1;32m   2035\u001b[0m                     stb = ['An exception has occurred, use %tb to see '\n\u001b[1;32m   2036\u001b[0m                            'the full traceback.\\n']\n\u001b[0;32m-> 2037\u001b[0;31m                     stb.extend(self.InteractiveTB.get_exception_only(etype,\n\u001b[0m\u001b[1;32m   2038\u001b[0m                                                                      value))\n\u001b[1;32m   2039\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/gudhi/lib/python3.8/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mget_exception_only\u001b[0;34m(self, etype, value)\u001b[0m\n\u001b[1;32m    821\u001b[0m         \u001b[0mvalue\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mexception\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    822\u001b[0m         \"\"\"\n\u001b[0;32m--> 823\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mListTB\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstructured_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    824\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    825\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mshow_exception_only\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/gudhi/lib/python3.8/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, evalue, etb, tb_offset, context)\u001b[0m\n\u001b[1;32m    696\u001b[0m             \u001b[0mchained_exceptions_tb_offset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    697\u001b[0m             out_list = (\n\u001b[0;32m--> 698\u001b[0;31m                 self.structured_traceback(\n\u001b[0m\u001b[1;32m    699\u001b[0m                     \u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0metb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchained_exc_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    700\u001b[0m                     chained_exceptions_tb_offset, context)\n",
      "\u001b[0;32m~/anaconda3/envs/gudhi/lib/python3.8/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1412\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1413\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1414\u001b[0;31m         return FormattedTB.structured_traceback(\n\u001b[0m\u001b[1;32m   1415\u001b[0m             self, etype, value, tb, tb_offset, number_of_lines_of_context)\n\u001b[1;32m   1416\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/gudhi/lib/python3.8/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1312\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose_modes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1313\u001b[0m             \u001b[0;31m# Verbose modes need a full traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1314\u001b[0;31m             return VerboseTB.structured_traceback(\n\u001b[0m\u001b[1;32m   1315\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb_offset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber_of_lines_of_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1316\u001b[0m             )\n",
      "\u001b[0;32m~/anaconda3/envs/gudhi/lib/python3.8/site-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, evalue, etb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1181\u001b[0m         \u001b[0mexception\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_parts_of_chained_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1182\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mexception\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1183\u001b[0;31m             \u001b[0mformatted_exceptions\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprepare_chained_exception_message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__cause__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1184\u001b[0m             \u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0metb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1185\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: can only concatenate str (not \"list\") to str"
     ]
    }
   ],
   "source": [
    "import gudhi\n",
    "import numpy\n",
    "from functools import reduce\n",
    "import argparse\n",
    "import operator\n",
    "\n",
    "\n",
    "\"\"\" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.\n",
    "    See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details.\n",
    "    Author(s):       Vincent Rouvreau\n",
    "\n",
    "    Copyright (C) 2016 Inria\n",
    "\n",
    "    Modification(s):\n",
    "      - YYYY/MM Author: Description of the modification\n",
    "\"\"\"\n",
    "\n",
    "__author__ = \"Vincent Rouvreau\"\n",
    "__copyright__ = \"Copyright (C) 2016 Inria\"\n",
    "__license__ = \"MIT\"\n",
    "\n",
    "parser = argparse.ArgumentParser(\n",
    "    description=\"Random cubical complex.\",\n",
    "    epilog=\"Example: \"\n",
    "    \"./random_cubical_complex_persistence_example.py\"\n",
    "    \" 10 10 10 - Constructs a random cubical \"\n",
    "    \"complex in a dimension [10, 10, 10] (aka. \"\n",
    "    \"1000 random top dimensional cells).\",\n",
    ")\n",
    "parser.add_argument(\"dimension\", type=int, nargs=\"*\", help=\"Cubical complex dimensions\")\n",
    "\n",
    "args = parser.parse_args()\n",
    "dimension_multiplication = reduce(operator.mul, args.dimension, 1)\n",
    "\n",
    "if dimension_multiplication > 1:\n",
    "    print(\"#####################################################################\")\n",
    "    print(\"CubicalComplex creation\")\n",
    "    cubical_complex = gudhi.CubicalComplex(\n",
    "        dimensions=args.dimension,\n",
    "        top_dimensional_cells=numpy.random.rand(dimension_multiplication),\n",
    "    )\n",
    "\n",
    "    print(\"persistence(homology_coeff_field=2, min_persistence=0)=\")\n",
    "    print(cubical_complex.persistence(homology_coeff_field=2, min_persistence=0))\n",
    "\n",
    "    print(\"betti_numbers()=\")\n",
    "    print(cubical_complex.betti_numbers())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py\n",
    "\n",
    "https://github.com/GUDHI/gudhi-devel/blob/master/src/python/example/periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'Users/emil/Downloads/code/cubical_complexes/CubicalTwoSphere.txt'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-80587244ba06>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     64\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Users/emil/Downloads/code/cubical_complexes/CubicalTwoSphere.txt\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mis_file_perseus\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"##################################################################\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"PeriodicCubicalComplex creation\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-13-80587244ba06>\u001b[0m in \u001b[0;36mis_file_perseus\u001b[0;34m(file)\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mis_file_perseus\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m     \u001b[0mnum_lines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\n\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m         \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Users/emil/Downloads/code/cubical_complexes/CubicalTwoSphere.txt'"
     ]
    }
   ],
   "source": [
    "#!/usr/bin/env python\n",
    "\n",
    "import argparse\n",
    "import matplotlib.pyplot as plot\n",
    "import errno\n",
    "import os\n",
    "import gudhi\n",
    "\n",
    "\"\"\" This file is part of the Gudhi Library - https://gudhi.inria.fr/ -\n",
    "    which is released under MIT.\n",
    "    See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full\n",
    "    license details.\n",
    "    Author(s):       Vincent Rouvreau\n",
    "\n",
    "    Copyright (C) 2016 Inria\n",
    "\n",
    "    Modification(s):\n",
    "      - YYYY/MM Author: Description of the modification\n",
    "\"\"\"\n",
    "\n",
    "__author__ = \"Vincent Rouvreau\"\n",
    "__copyright__ = \"Copyright (C) 2016 Inria\"\n",
    "__license__ = \"MIT\"\n",
    "\n",
    "\n",
    "def is_file_perseus(file):\n",
    "    num_lines = open(file).read().count(\"\\n\")\n",
    "    try:\n",
    "        f = open(file)\n",
    "        num_dim = int(f.readline())\n",
    "        coeff = 1\n",
    "        for dim in range(0, num_dim):\n",
    "            try:\n",
    "                line = int(f.readline())\n",
    "                coeff *= abs(line)\n",
    "            except ValueError:\n",
    "                return False\n",
    "        if num_lines == (1 + num_dim + coeff):\n",
    "            return True\n",
    "        else:\n",
    "            return False\n",
    "    except ValueError:\n",
    "        return False\n",
    "\n",
    "\n",
    "parser = argparse.ArgumentParser(\n",
    "    description=\"Periodic cubical complex from a \" \"Perseus-style file name.\",\n",
    "    epilog=\"Example: \"\n",
    "    \"./periodic_cubical_complex_barcode_persistence_from_perseus_file_example.py\"\n",
    "#     \" -f ../data/bitmap/CubicalTwoSphere.txt\",\n",
    "    \" -f ~/Downloads/code/cubical_complexes/CubicalTwoSphere.txt\",\n",
    ")\n",
    "\n",
    "parser.add_argument(\"-f\", \"--file\", type=str, required=True)\n",
    "parser.add_argument(\n",
    "    \"--no-barcode\",\n",
    "    default=False,\n",
    "    action=\"store_true\",\n",
    "    help=\"Flag for not to display the barcodes\",\n",
    ")\n",
    "\n",
    "args = parser.parse_args()\n",
    "\n",
    "args.file = \"Users/emil/Downloads/code/cubical_complexes/CubicalTwoSphere.txt\"\n",
    "\n",
    "if is_file_perseus(args.file):\n",
    "    print(\"##################################################################\")\n",
    "    print(\"PeriodicCubicalComplex creation\")\n",
    "    periodic_cubical_complex = gudhi.PeriodicCubicalComplex(\n",
    "        perseus_file=args.file)\n",
    "\n",
    "    print(\"persistence(homology_coeff_field=3, min_persistence=0)=\")\n",
    "    diag = periodic_cubical_complex.persistence(\n",
    "        homology_coeff_field=3, min_persistence=0\n",
    "    )\n",
    "    print(diag)\n",
    "\n",
    "    print(\"betti_numbers()=\")\n",
    "    print(periodic_cubical_complex.betti_numbers())\n",
    "    if args.no_barcode == False:\n",
    "        gudhi.plot_persistence_barcode(diag)\n",
    "        plot.show()\n",
    "else:\n",
    "    raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT),\n",
    "                            args.file)\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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