{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e9bdd173-cdd4-4552-8d92-5d296f2c5a54",
   "metadata": {},
   "source": [
    "# Planpraz (Chamonix-Mont-Blanc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b6f8a8c-6ade-4ec6-850c-e110cad94249",
   "metadata": {},
   "source": [
    "## Chargement des librairies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c888f4e5-3e81-41e6-b86a-8dfa1db97768",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Ajout dans la variable PATH du système du chemin où est installée la librairie tracklib\n",
    "module_path = os.path.abspath(os.path.join('../../../../tracklib'))\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)\n",
    "# Alias pour tracklib\n",
    "import tracklib as tkl\n",
    "\n",
    "# Ajout dans la variable PATH du système du chemin où est installée la librairie footprint2graph\n",
    "module_path = os.path.abspath(os.path.join('../../..'))\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9b3c723c-1e8d-4ffc-a785-6dd95b56f4e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import time\n",
    "\n",
    "from footprint2graph import run_iteration\n",
    "from footprint2graph import read_config\n",
    "\n",
    "from footprint2graph.util.Outdoorvision import load_raw_tracks_split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f48df9a2-b953-4b38-88fa-8a8bd3ec046c",
   "metadata": {},
   "source": [
    "## Chargement des paramètres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a7713943-24e1-4417-9a21-31ce2014a290",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
      "`````````````````````````````````````````````````````````````````````\n",
      "             Generate a footprint graph                              \n",
      "                      from hiking trajectories                       \n",
      "                      in the Planpraz, summer 2024.                  \n",
      "’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’\n",
      "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
      "\n",
      "Paramètres relevés dans la configuration: \n",
      "Résultats enregistrés dans le répertoire:  /home/md_vandamme/4_RESEAU/ZTEMPZ2/\n",
      "Number of iterations:  2\n"
     ]
    }
   ],
   "source": [
    "print ('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')\n",
    "print ('`````````````````````````````````````````````````````````````````````')\n",
    "print ('             Generate a footprint graph                              ')\n",
    "print ('                      from hiking trajectories                       ')\n",
    "print ('                      in the Planpraz, summer 2024.                  ')\n",
    "print ('’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’')\n",
    "print ('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')\n",
    "print ('')\n",
    "\n",
    "\n",
    "\"\"\" ======================================================================= \"\"\"\n",
    "\"\"\"     Load parameters                                                     \"\"\"\n",
    "\"\"\"                                                                         \"\"\"\n",
    "\n",
    "config_path = r'/home/md_vandamme/7_LIB/footprint2graph/data/config_planpraz.yml'\n",
    "config = read_config(config_path)\n",
    "\n",
    "\n",
    "print('Paramètres relevés dans la configuration: ')\n",
    "print ('Résultats enregistrés dans le répertoire: ', config['output']['RESULT_PATH'])\n",
    "\n",
    "NBITER = int(config['graph_construction']['NUM_ITERATIONS'])\n",
    "print ('Number of iterations: ', NBITER)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4f513ce-ba35-4b77-a1ca-16ea79bec9af",
   "metadata": {},
   "source": [
    "## Chargement des données\n",
    "\n",
    "Les données proviennent de la plateforme Outdoorvision. Après avoir été formatées au format CSV, elles sont chargées dans une collection qui servira d’entrée au pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9b969dec-e61c-4a57-badf-9168583cbd45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading and split outdoorvision track data...\n",
      "Reading track data...\n",
      "     Number files to load:  21211\n",
      "Starting split ...\n",
      "     500 / 21211\n",
      "     1000 / 21211\n",
      "     1500 / 21211\n",
      "     2000 / 21211\n",
      "     2500 / 21211\n",
      "     3000 / 21211\n",
      "     3500 / 21211\n",
      "     4000 / 21211\n",
      "     4500 / 21211\n",
      "     5000 / 21211\n",
      "     5500 / 21211\n",
      "     6000 / 21211\n",
      "     6500 / 21211\n",
      "     7000 / 21211\n",
      "     7500 / 21211\n",
      "     8000 / 21211\n",
      "     8500 / 21211\n",
      "     9000 / 21211\n",
      "     9500 / 21211\n",
      "     10000 / 21211\n",
      "     10500 / 21211\n",
      "     11000 / 21211\n",
      "     11500 / 21211\n",
      "     12000 / 21211\n",
      "     12500 / 21211\n",
      "     13000 / 21211\n",
      "     13500 / 21211\n",
      "     14000 / 21211\n",
      "     14500 / 21211\n",
      "     15000 / 21211\n",
      "     15500 / 21211\n",
      "     16000 / 21211\n",
      "     16500 / 21211\n",
      "     17000 / 21211\n",
      "     17500 / 21211\n",
      "     18000 / 21211\n",
      "     18500 / 21211\n",
      "     19000 / 21211\n",
      "     19500 / 21211\n",
      "     20000 / 21211\n",
      "     20500 / 21211\n",
      "     21000 / 21211\n",
      "     Number of tracks after split: 1451\n"
     ]
    }
   ],
   "source": [
    "\"\"\" ======================================================================= \"\"\"\n",
    "\"\"\"     Chargement de la collection de traces                               \"\"\"\n",
    "\"\"\"                                                                         \"\"\"\n",
    "\n",
    "# chemin où sont stockés les traces Outdoorvision:\n",
    "tracespathsource = r'/home/md_vandamme/5_GPS/OV/CHAM/walk/'\n",
    "\n",
    "# Paramètre : Coordonnées de la zone d'étude sur laquelle on construit le réseau\n",
    "#                           Polygone sous la forme d'un tableau de X et de Y\n",
    "# traces de la zone 1 (3km x 3km)\n",
    "X = [996920, 998373, 999071, 999347, 998623, 997890, 996638, 996920]\n",
    "Y = [6542674, 6542770, 6543183, 6543820, 6544857, 6544992, 6544973, 6542674]\n",
    "\n",
    "fmt = tkl.TrackFormat({'ext': 'CSV',\n",
    "                       'srid': 'ENU',\n",
    "                       'id_E': 1, 'id_N': 0, 'id_U': 3, 'id_T': 2,\n",
    "                       'time_fmt': '2D/2M/4Y 2h:2m:2s',\n",
    "                       'separator': ';',\n",
    "                       'header': 0,\n",
    "                       'cmt': '#',\n",
    "                       'read_all': True})\n",
    "collection = load_raw_tracks_split(config['output']['RESULT_PATH'],\n",
    "                                   tracespathsource, fmt, X, Y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed458deb-f0dc-4975-97bf-429cc2e5fa51",
   "metadata": {},
   "source": [
    "## Lancement du pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "71511e17-842d-4705-ac0e-29a2955b15fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------------------\n",
      "-----------------------------------------------------------------\n",
      "              ITERATION  1\n",
      "-----------------------------------------------------------------\n",
      "-----------------------------------------------------------------\n",
      "Starting segmentation and resampling...\n",
      "Starting segmentation ...\n",
      "     500 / 1451\n",
      "     1000 / 1451\n",
      "    Number of tracks after segmentation: 2015\n",
      "Finished saving segmented tracks.\n",
      "Starting resampling ...\n",
      "    Number of tracks to resample:  2015\n",
      "    Number of tracks after resampling: 2015\n",
      "    Number of tracks after resampling: 2015\n",
      "Finished saving resampled tracks.\n",
      "Stage 1 finished: segmentation and resampling.\n",
      "Starting rasterization and vectorization (iteration 1) \n",
      "\n",
      "    Loading tracks from :  resample_grid\n",
      "    Number of tracks to load:  2015\n",
      "    Building high-resolution geometry density grid G1 :  2 m ...\n",
      "    Building low-resolution contextual density grid G2 :  30 m ...\n",
      "    Assigning track points to the G1 and G2 grids\n",
      "         500 / 2015\n",
      "         1000 / 2015\n",
      "         1500 / 2015\n",
      "         2000 / 2015\n",
      "    Computing G1 ...\n",
      "    Computing G2 ...\n",
      "    Number of neighboring cells to consider: 7\n",
      "    Building contrast grid :  2 m\n",
      "    Execution time (seconds): 123.5079653263092\n",
      "    Finished heatmap computation.\n",
      "    Starting morphological closing image ...\n",
      "    Execution time (seconds): 23.947893857955933\n",
      "    Finished morphological opening.\n",
      "Vectorizing cleaned image ...\n",
      "Extracting road surface vector features ...\n",
      "    Number of polygonize features:  111\n",
      "    Number of polygonize features copied:  41\n",
      "    Execution time (seconds): 0.18500256538391113\n",
      "    Vectorization completed.\n",
      "Smoothing polygon to remove stair-step artifacts ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(483 of 483)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(2617 of 2617)\u001b[39m |####################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Execution time (seconds): 2.2707924842834473\n",
      "    Road surface smoothing completed.\n",
      "    Starting centerline computation ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(398 of 398)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
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      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(805 of 805)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(795 of 795)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(2995 of 2995)\u001b[39m |####################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(2739 of 2739)\u001b[39m |####################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(2735 of 2735)\u001b[39m |####################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Execution time (seconds): 3.4580135345458984\n",
      "    Centerline computed.\n",
      "Stage 2 completed: rasterization and vectorization.\n",
      "Starting topology creation for the network\n",
      "    Number of edges in the skeleton: 7931\n",
      "    Finished loaded skeleton.\n",
      "    /home/md_vandamme/4_RESEAU/ZTEMPZ2/network/tmp_in.csv not exists\n",
      "    /home/md_vandamme/4_RESEAU/ZTEMPZ2/network/tmp_out.csv not exists\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;255;91;0m 12%\u001b[39m \u001b[38;2;255;91;0m(43 of 333)\u001b[39m |##                     | Elapsed Time: 0:00:00 ETA:   0:00:01"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    Finished removing hooked parts of the skeleton.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(333 of 333)\u001b[39m |######################| Elapsed Time: 0:00:02 Time:  0:00:020000\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(333 of 333)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Finished simplification of the skeleton.\n",
      "Building [100 x 87] spatial index...\n",
      "    on coupe la trace 1\n",
      "    on coupe la trace 1\n",
      "    on coupe la trace 2\n",
      "    on coupe la trace 1\n",
      "    on coupe la trace 1\n",
      "    on coupe la trace 2\n",
      "    Number of edges in the skeleton (after snapping): 339\n",
      "    Edge count difference after snapping :  6\n",
      "    Number of edges in the simplified skeleton: 290\n",
      "    Number of nodes: 340\n",
      "     Shortest edges limit :  50\n",
      "    Number of edges in the skeleton (after removing the shortest edges): 3\n",
      "    Conflation cannot be performed for node  24 ; the three incident edges are too long: 49 24 14\n",
      "    Conflation cannot be performed for node  27 ; the three incident edges are too long: 7 77 29\n",
      "    Conflation cannot be performed for node  28 ; the three incident edges are too long: 7 24 55\n",
      "    Conflation cannot be performed for node  137 ; the three incident edges are too long: 14 13 60\n",
      "    Conflation cannot be performed for node  138 ; the three incident edges are too long: 14 13 171\n",
      "    Edge count after conflation: 67\n",
      "Stage 3 completed: adding topology to the skeleton.\n",
      "Starting map-matching, aggregation, and conflation of GNSS trajectories.\n",
      "    Loading network (1) ...\n",
      "        Number of edges =  67\n",
      "        Number of nodes =  114\n",
      "        Total segment length of the network =  21235.511081231256\n",
      "    Loading collection of tracks ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(114 of 114)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Number of tracks: 2015\n",
      "        Execution time (seconds): 9.441591024398804\n",
      "    Starting map-matching ...\n",
      "        Index spatial :  [100 x 87] spatial index centered on [998000.8112047985; 6543905.475636125]\n",
      "Map-matching preparation...\n",
      "        Parameter search_radius:  25\n",
      "        Map-matching ended.\n",
      "        Execution time (seconds): 603.6405894756317\n",
      "        Prepare map-matching results for candidate segment generation\n",
      "    Number of map-matched points = 666297 (86.84 %)\n",
      "    Map-matching results restructuring completed.\n",
      "        Map-matching results exported.\n",
      "Starting construction of candidate trajectory segments for each topology edge ...\n",
      "    33  candidates for edge 451\n",
      "    228  candidates for edge 176\n",
      "    206  candidates for edge 454\n",
      "    126  candidates for edge 456\n",
      "    101  candidates for edge 452\n",
      "    83  candidates for edge 450\n",
      "    110  candidates for edge 448\n",
      "    55  candidates for edge 446\n",
      "    10  candidates for edge 101\n",
      "    65  candidates for edge 361\n",
      "    121  candidates for edge 348\n",
      "    43  candidates for edge 344\n",
      "    25  candidates for edge 414\n",
      "    35  candidates for edge 328\n",
      "    23  candidates for edge 389\n",
      "    23  candidates for edge 416\n",
      "    34  candidates for edge 351\n",
      "    220  candidates for edge 398\n",
      "    176  candidates for edge 463\n",
      "    69  candidates for edge 461\n",
      "    121  candidates for edge 196\n",
      "    545  candidates for edge 14\n",
      "    330  candidates for edge 207\n",
      "    124  candidates for edge 375\n",
      "    5  candidates for edge 417\n",
      "    85  candidates for edge 428\n",
      "    203  candidates for edge 210\n",
      "    326  candidates for edge 460\n",
      "    67  candidates for edge 432\n",
      "    55  candidates for edge 430\n",
      "    49  candidates for edge 378\n",
      "    182  candidates for edge 49\n",
      "    51  candidates for edge 48\n",
      "    36  candidates for edge 433\n",
      "    30  candidates for edge 252\n",
      "    57  candidates for edge 480\n",
      "    51  candidates for edge 482\n",
      "    21  candidates for edge 386\n",
      "    77  candidates for edge 279\n",
      "    51  candidates for edge 405\n",
      "    43  candidates for edge 211\n",
      "    26  candidates for edge 253\n",
      "    36  candidates for edge 258\n",
      "    7  candidates for edge 418\n",
      "    82  candidates for edge 497\n",
      "    132  candidates for edge 317\n",
      "    55  candidates for edge 413\n",
      "    90  candidates for edge 312\n",
      "    219  candidates for edge 311\n",
      "    89  candidates for edge 313\n",
      "    87  candidates for edge 310\n",
      "    79  candidates for edge 496\n",
      "    123  candidates for edge 321\n",
      "    45  candidates for edge 412\n",
      "    26  candidates for edge 479\n",
      "    36  candidates for edge 133\n",
      "    16  candidates for edge 94\n",
      "    29  candidates for edge 394\n",
      "    35  candidates for edge 96\n",
      "    30  candidates for edge 97\n",
      "    18  candidates for edge 354\n",
      "    90  candidates for edge 408\n",
      "    87  candidates for edge 483\n",
      "    19  candidates for edge 383\n",
      "    34  candidates for edge 141\n",
      "    13  candidates for edge 478\n",
      "    7  candidates for edge 93\n",
      "    Number of processed edges:  67\n",
      "    Minimum number of candidate tracks per edge:  5\n",
      "    Maximum number of candidate traces per edge:  545\n",
      "    Average number of candidate tracks per edge:  87\n",
      "    Segment construction completed.\n",
      "        Execution time (seconds): 33.111122369766235\n",
      "    Starting track segment aggregation for all network edges ...\n",
      "        Number of candidate tracks / number of sampled tracks 33 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 228 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 206 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 126 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 101 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 83 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 110 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 55 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 10 / 10\n",
      "        Number of candidate tracks / number of sampled tracks 65 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 121 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 43 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 25 / 25\n",
      "        Number of candidate tracks / number of sampled tracks 23 / 23\n",
      "        Number of candidate tracks / number of sampled tracks 23 / 23\n",
      "        Number of candidate tracks / number of sampled tracks 34 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 220 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 176 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 69 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 121 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 124 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 5 / 5\n",
      "        Number of candidate tracks / number of sampled tracks 85 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 203 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 300 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 67 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 55 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 49 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 182 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 51 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 36 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 30 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 57 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 51 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 21 / 21\n",
      "        Number of candidate tracks / number of sampled tracks 77 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 51 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 43 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 26 / 26\n",
      "        Number of candidate tracks / number of sampled tracks 36 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 7 / 7\n",
      "        Number of candidate tracks / number of sampled tracks 82 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 55 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 90 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 79 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 45 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 26 / 26\n",
      "        Number of candidate tracks / number of sampled tracks 36 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 16 / 16\n",
      "        Number of candidate tracks / number of sampled tracks 29 / 29\n",
      "        Number of candidate tracks / number of sampled tracks 18 / 18\n",
      "        Number of candidate tracks / number of sampled tracks 90 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 87 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 19 / 19\n",
      "        Number of candidate tracks / number of sampled tracks 34 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 13 / 13\n",
      "        Number of candidate tracks / number of sampled tracks 7 / 7\n",
      "        Number of aggregations: 67\n",
      "        Number of aggregations with 30 traces: 41\n",
      "        Number of aggregations with fewer than 30 traces: 16\n",
      "        Minimum number of traces in aggregation: 5\n",
      "        Average number of traces in aggregation: 62\n",
      "        Aggregation process finished.\n",
      "        Execution time (seconds): 73.89605689048767\n",
      "    Starting conflation ...\n",
      "        Conflation process finished.\n",
      "        Execution time (seconds): 0.14773011207580566\n",
      "Stage 4 completed: map-matching, aggregation, and conflation.\n",
      "-----------------------------------------------------------------\n",
      "-----------------------------------------------------------------\n",
      "              ITERATION  2\n",
      "-----------------------------------------------------------------\n",
      "-----------------------------------------------------------------\n",
      "Number of tracks map matched : 2015\n",
      "5720\n",
      "Starting rasterization and vectorization (iteration 2) \n",
      "\n",
      "    Loading tracks from :  points_not_mm_2\n",
      "    Number of tracks to load:  5720\n",
      "    Building high-resolution geometry density grid G1 :  2 m ...\n",
      "    Building low-resolution contextual density grid G2 :  30 m ...\n",
      "    Assigning track points to the G1 and G2 grids\n",
      "         500 / 5720\n",
      "         1000 / 5720\n",
      "         1500 / 5720\n",
      "         2000 / 5720\n",
      "         2500 / 5720\n",
      "         3000 / 5720\n",
      "         3500 / 5720\n",
      "         4000 / 5720\n",
      "         4500 / 5720\n",
      "         5000 / 5720\n",
      "         5500 / 5720\n",
      "    Computing G1 ...\n",
      "    Computing G2 ...\n",
      "    Number of neighboring cells to consider: 7\n",
      "    Building contrast grid :  2 m\n",
      "    Execution time (seconds): 55.25071835517883\n",
      "    Finished heatmap computation.\n",
      "    Starting morphological closing image ...\n",
      "    Execution time (seconds): 24.48509693145752\n",
      "    Finished morphological opening.\n",
      "Vectorizing cleaned image ...\n",
      "Extracting road surface vector features ...\n",
      "    Number of polygonize features:  77\n",
      "    Number of polygonize features copied:  47\n",
      "    Execution time (seconds): 0.10633659362792969\n",
      "    Vectorization completed.\n",
      "Smoothing polygon to remove stair-step artifacts ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(107 of 107)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(128 of 128)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(161 of 161)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(533 of 533)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(85 of 85)\u001b[39m |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(659 of 659)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(212 of 212)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(263 of 263)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(131 of 131)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(213 of 213)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(213 of 213)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(118 of 118)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(324 of 324)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Execution time (seconds): 0.7621243000030518\n",
      "    Road surface smoothing completed.\n",
      "    Starting centerline computation ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(558 of 558)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(109 of 109)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(180 of 180)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(234 of 234)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(151 of 151)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(231 of 231)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(556 of 556)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(125 of 125)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(175 of 175)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(194 of 194)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(288 of 288)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(117 of 117)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(269 of 269)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(168 of 168)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(350 of 350)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(644 of 644)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(157 of 157)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(189 of 189)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(125 of 125)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(144 of 144)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(121 of 121)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(148 of 148)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(81 of 81)\u001b[39m |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(439 of 439)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(315 of 315)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(233 of 233)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(79 of 79)\u001b[39m |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(255 of 255)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(472 of 472)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(127 of 127)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(347 of 347)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(2184 of 2184)\u001b[39m |####################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(2734 of 2734)\u001b[39m |####################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(284 of 284)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Execution time (seconds): 1.2905175685882568\n",
      "    Centerline computed.\n",
      "Stage 2 completed: rasterization and vectorization.\n",
      "Starting topology creation for the network\n",
      "    Number of edges in the skeleton: 2487\n",
      "    Finished loaded skeleton.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;255;159;0m 37%\u001b[39m \u001b[38;2;255;159;0m(56 of 150)\u001b[39m |########               | Elapsed Time: 0:00:00 ETA:   0:00:00"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    Finished removing hooked parts of the skeleton.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(150 of 150)\u001b[39m |######################| Elapsed Time: 0:00:01 Time:  0:00:010000\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(150 of 150)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Finished simplification of the skeleton.\n",
      "Building [100 x 92] spatial index...\n",
      "    on coupe la trace 1\n",
      "    on coupe la trace 2\n",
      "    Number of edges in the skeleton (after snapping): 152\n",
      "    Edge count difference after snapping :  2\n",
      "    Number of edges in the simplified skeleton: 126\n",
      "    Number of nodes: 171\n",
      "     Shortest edges limit :  50\n",
      "    Number of edges in the skeleton (after removing the shortest edges): 5\n",
      "    Conflation cannot be performed for node  50 ; the three incident edges are too long: 31 24 25\n",
      "    Conflation cannot be performed for node  53 ; the three incident edges are too long: 96 85 37\n",
      "    Conflation cannot be performed for node  172 ; the three incident edges are too long: 11 36 56\n",
      "    Conflation cannot be performed for node  174 ; the three incident edges are too long: 11 51 56\n",
      "    Conflation cannot be performed for node  184 ; the three incident edges are too long: 40 664 411\n",
      "    Edge count after conflation: 53\n",
      "Stage 3 completed: adding topology to the skeleton.\n",
      "Starting map-matching, aggregation, and conflation of GNSS trajectories.\n",
      "    Loading network (2) ...\n",
      "        Number of edges =  53\n",
      "        Number of nodes =  93\n",
      "        Total segment length of the network =  6180.154927407347\n",
      "    Loading collection of tracks ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(93 of 93)\u001b[39m |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Number of tracks: 5720\n",
      "        Execution time (seconds): 9.888888359069824\n",
      "    Starting map-matching ...\n",
      "        Index spatial :  [100 x 91] spatial index centered on [998107.2968269091; 6543843.350262897]\n",
      "Map-matching preparation...\n",
      "        Parameter search_radius:  25\n",
      "        Map-matching ended.\n",
      "        Execution time (seconds): 268.4186022281647\n",
      "        Prepare map-matching results for candidate segment generation\n",
      "    Number of map-matched points = 650800 (84.61 %)\n",
      "    Map-matching results restructuring completed.\n",
      "        Map-matching results exported.\n",
      "Starting construction of candidate trajectory segments for each topology edge ...\n",
      "    145  candidates for edge 25\n",
      "    30  candidates for edge 195\n",
      "    10  candidates for edge 196\n",
      "    5  candidates for edge 205\n",
      "    192  candidates for edge 206\n",
      "    49  candidates for edge 176\n",
      "    29  candidates for edge 148\n",
      "    40  candidates for edge 149\n",
      "    54  candidates for edge 150\n",
      "    0  candidates for edge 165\n",
      "    1  candidates for edge 207\n",
      "    85  candidates for edge 187\n",
      "    82  candidates for edge 93\n",
      "    84  candidates for edge 168\n",
      "    116  candidates for edge 103\n",
      "    70  candidates for edge 139\n",
      "    81  candidates for edge 211\n",
      "    35  candidates for edge 91\n",
      "    15  candidates for edge 197\n",
      "    12  candidates for edge 181\n",
      "    158  candidates for edge 100\n",
      "    40  candidates for edge 192\n",
      "    37  candidates for edge 201\n",
      "    24  candidates for edge 204\n",
      "    38  candidates for edge 200\n",
      "    2  candidates for edge 208\n",
      "    64  candidates for edge 188\n",
      "    14  candidates for edge 33\n",
      "    42  candidates for edge 32\n",
      "    111  candidates for edge 193\n",
      "    11  candidates for edge 24\n",
      "    8  candidates for edge 54\n",
      "    50  candidates for edge 135\n",
      "    35  candidates for edge 210\n",
      "    18  candidates for edge 129\n",
      "    3  candidates for edge 209\n",
      "    133  candidates for edge 169\n",
      "    23  candidates for edge 69\n",
      "    149  candidates for edge 107\n",
      "    16  candidates for edge 189\n",
      "    8  candidates for edge 167\n",
      "    55  candidates for edge 27\n",
      "    39  candidates for edge 159\n",
      "    1  candidates for edge 194\n",
      "    14  candidates for edge 160\n",
      "    26  candidates for edge 190\n",
      "    75  candidates for edge 134\n",
      "    64  candidates for edge 186\n",
      "    27  candidates for edge 133\n",
      "    28  candidates for edge 172\n",
      "    8  candidates for edge 183\n",
      "    2  candidates for edge 70\n",
      "    6  candidates for edge 178\n",
      "    Number of processed edges:  53\n",
      "    Minimum number of candidate tracks per edge:  0\n",
      "    Maximum number of candidate traces per edge:  192\n",
      "    Average number of candidate tracks per edge:  46\n",
      "    Segment construction completed.\n",
      "        Execution time (seconds): 30.998132467269897\n",
      "    Starting track segment aggregation for all network edges ...\n",
      "        Number of candidate tracks / number of sampled tracks 145 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 30 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 10 / 10\n",
      "        Number of candidate tracks / number of sampled tracks 5 / 5\n",
      "        Number of candidate tracks / number of sampled tracks 192 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 49 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 1 / 1\n",
      "    Only one trajectory available for aggregation: no processing required\n",
      "        Number of candidate tracks / number of sampled tracks 85 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 82 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 84 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 116 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 70 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 81 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 35 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 15 / 15\n",
      "        Number of candidate tracks / number of sampled tracks 12 / 12\n",
      "        Number of candidate tracks / number of sampled tracks 158 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 40 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 37 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 24 / 24\n",
      "        Number of candidate tracks / number of sampled tracks 38 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 2 / 2\n",
      "        Number of candidate tracks / number of sampled tracks 64 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 14 / 14\n",
      "        Number of candidate tracks / number of sampled tracks 42 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 111 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 11 / 11\n",
      "        Number of candidate tracks / number of sampled tracks 8 / 8\n",
      "        Number of candidate tracks / number of sampled tracks 50 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 22 / 22\n",
      "        Number of candidate tracks / number of sampled tracks 3 / 3\n",
      "        Number of candidate tracks / number of sampled tracks 133 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 23 / 23\n",
      "        Number of candidate tracks / number of sampled tracks 149 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 16 / 16\n",
      "        Number of candidate tracks / number of sampled tracks 8 / 8\n",
      "        Number of candidate tracks / number of sampled tracks 55 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 39 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 1 / 1\n",
      "    Only one trajectory available for aggregation: no processing required\n",
      "        Number of candidate tracks / number of sampled tracks 14 / 14\n",
      "        Number of candidate tracks / number of sampled tracks 26 / 26\n",
      "WARNING: TRAJECTORY FUSION HAS NOT CONVERGED (#ITER = 25 - CV = 0.09362863524887388)\n",
      "        Number of candidate tracks / number of sampled tracks 75 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 64 / 30\n",
      "        Number of candidate tracks / number of sampled tracks 27 / 27\n",
      "        Number of candidate tracks / number of sampled tracks 28 / 28\n",
      "        Number of candidate tracks / number of sampled tracks 8 / 8\n",
      "        Number of candidate tracks / number of sampled tracks 2 / 2\n",
      "        Number of candidate tracks / number of sampled tracks 6 / 6\n",
      "        Number of aggregations: 52\n",
      "        Number of aggregations with 30 traces: 24\n",
      "        Number of aggregations with fewer than 30 traces: 24\n",
      "        Minimum number of traces in aggregation: 1\n",
      "        Average number of traces in aggregation: 44\n",
      "        Aggregation process finished.\n",
      "        Execution time (seconds): 16.357547283172607\n",
      "    Starting conflation ...\n",
      "        Conflation process finished.\n",
      "        Execution time (seconds): 0.05210399627685547\n",
      "Stage 4 completed: map-matching, aggregation, and conflation.\n",
      "Merging the mobility network with the result of iteration 2.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(119 of 119)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of collection In  :  67\n",
      "Size of collection In+1:  52\n",
      "Size of collection In+In+1:  119\n",
      "Building [100 x 88] spatial index...\n",
      "Size of collection In+In+1 avec intersection:  123\n",
      "        Candidate edge ignored (no intersection)\n",
      "Size of collection In+In+1 avec intersection et raccordement:  171\n",
      "Nombre de géométries :  171\n",
      "Size of reseau de mobilité:  171\n",
      "End building the mobility network.\n",
      "==================================================================\n",
      "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
      "`````````````````````````````````````````````````````````````````````\n",
      "                           FIN                                       \n",
      "’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’\n",
      "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n"
     ]
    }
   ],
   "source": [
    "# =====================================================================\n",
    "#    Appel des pipelines pour le nombre d'itérations nécessaires\n",
    "#\n",
    "\n",
    "for idx in range(0, NBITER):\n",
    "    iteration_index = int(idx) + 1\n",
    "\n",
    "    # run pipeline for the ith iteration\n",
    "    run_iteration(iteration_index, config, collection)\n",
    "\n",
    "print ('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')\n",
    "print ('`````````````````````````````````````````````````````````````````````')\n",
    "print ('                           FIN                                       ')\n",
    "print ('’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’')\n",
    "print ('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a89f09dc-5a05-4906-9ac4-9a4640f3d312",
   "metadata": {},
   "source": [
    "## On affiche le résultat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7d5427cb-9d13-4858-82d6-f7b866cc50ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7e6e81ed60b0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 16))\n",
    "\n",
    "fmt = tkl.NetworkFormat({\n",
    "           \"pos_edge_id\": 0,\n",
    "           \"pos_source\": 1,\n",
    "           \"pos_target\": 2,\n",
    "           \"pos_wkt\": 4,\n",
    "           \"srid\": \"ENU\",\n",
    "           \"separator\": \",\",\n",
    "           \"header\": 1})\n",
    "networkpath = config['output']['RESULT_PATH'] + 'merge_2/reseau_mobilite_2.csv'\n",
    "squelette = tkl.NetworkReader.readFromFile(networkpath, fmt, verbose=False)\n",
    "\n",
    "L = list(squelette.EDGES.items())\n",
    "for i in range(len(L)):\n",
    "    x1d = []\n",
    "    y1d = []\n",
    "    edge = L[i][1]\n",
    "    for j in range(edge.geom.size()):\n",
    "        x1d.append(edge.geom.getX()[j])\n",
    "        y1d.append(edge.geom.getY()[j])\n",
    "    ax.plot(x1d, y1d, 'r-', linewidth=3, label='Mobility Network')\n",
    "\n",
    "\n",
    "# Supprime les doublons dans la légende\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "by_label = dict(zip(labels, handles))\n",
    "ax.legend(by_label.values(), by_label.keys())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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