{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "365fc9a2-b911-424a-a313-2af9497544a2",
   "metadata": {},
   "source": [
    "# Plan de l'Aiguille (Chamonix-Mont-Blanc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2db3c964-172c-4428-b553-17acd533b109",
   "metadata": {},
   "source": [
    "## Chargement des librairies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8b9a7849-f2ca-4b90-b233-6bdd82d3a108",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\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": "a863a91b-593f-4dd9-a139-04427fa7b1f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import time\n",
    "\n",
    "from footprint2graph import footprint2grap\n",
    "from footprint2graph import read_config\n",
    "\n",
    "from footprint2graph.util.Outdoorvision import load_raw_tracks_split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51ed3939-bd5b-4645-a6c0-498d6bc93c6d",
   "metadata": {},
   "source": [
    "## Chargement des paramètres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5e69bd82-2a28-47f6-a1df-62e9442e16f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
      "`````````````````````````````````````````````````````````````````````\n",
      "  Generate a footprint graph                                         \n",
      "             from hiking trajectories                                \n",
      "             in the Plan de l'Aiguille, dans la vallée de Chamonix, summer 2024.  \n",
      "’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’\n",
      "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
      "\n",
      "Paramètres relevés dans la configuration: \n",
      "Résultats enregistrés dans le répertoire:  /home/md_vandamme/4_RESEAU/ZTEMPZ3/\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 Plan de l'Aiguille, dans la vallée de Chamonix, 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_plan_de_l_aiguille.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": "22b42e48-c89f-4e74-8e72-11ce15651466",
   "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": "787afef1-dd96-4406-98f7-da68c5e15639",
   "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",
      "     1000 / 21211\n",
      "     2000 / 21211\n",
      "     3000 / 21211\n",
      "     4000 / 21211\n",
      "     5000 / 21211\n",
      "     6000 / 21211\n",
      "     7000 / 21211\n",
      "     8000 / 21211\n",
      "     9000 / 21211\n",
      "     10000 / 21211\n",
      "     11000 / 21211\n",
      "     12000 / 21211\n",
      "     13000 / 21211\n",
      "     14000 / 21211\n",
      "     15000 / 21211\n",
      "     16000 / 21211\n",
      "     17000 / 21211\n",
      "     18000 / 21211\n",
      "     19000 / 21211\n",
      "     20000 / 21211\n",
      "     21000 / 21211\n",
      "     Number of tracks after split: 631\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",
    "X = [1000852, 1001838, 1001852, 1000853, 1000852]\n",
    "Y = [6541520,  6541524,  6540842,  6540839,  6541520]\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)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b03c2f2-7ab4-4bed-9f8c-7534c8112732",
   "metadata": {},
   "source": [
    "## Lancement du pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e8f0b991-7c16-4234-9194-dfec5e7b8a78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------------------\n",
      "-----------------------------------------------------------------\n",
      "              ITERATION  1\n",
      "-----------------------------------------------------------------\n",
      "-----------------------------------------------------------------\n",
      "Starting segmentation and resampling...\n",
      "Starting segmentation ...\n",
      "    Number of tracks to resample:  688\n",
      "Stage 1 finished: segmentation and resampling.\n",
      "Starting rasterization and vectorization (iteration 1) \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(341 of 341)\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(846 of 846)\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(403 of 403)\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(663 of 663)\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(1273 of 1273)\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(621 of 621)\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(618 of 618)\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(2711 of 2711)\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(1113 of 1113)\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(967 of 967)\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(963 of 963)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stage 2 completed: rasterization and vectorization.\n",
      "Starting topology creation for the network\n",
      "    /home/md_vandamme/4_RESEAU/ZTEMPZ3/network/tmp_in.csv not exists\n",
      "    /home/md_vandamme/4_RESEAU/ZTEMPZ3/network/tmp_out.csv not exists\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;255;215;0m 50%\u001b[39m \u001b[38;2;255;215;0m(16 of 32)\u001b[39m |############            | Elapsed Time: 0:00:00 ETA:   0:00:00"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(32 of 32)\u001b[39m |########################| Elapsed Time: 0:00:00 Time:  0:00:000000\n",
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(32 of 32)\u001b[39m |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building [100 x 74] spatial index...\n",
      "Stage 3 completed: adding topology to the skeleton.\n",
      "Starting map-matching, aggregation, and conflation of GNSS trajectories.\n",
      "Stage 4 completed: map-matching, aggregation, and conflation.\n",
      "-----------------------------------------------------------------\n",
      "-----------------------------------------------------------------\n",
      "              ITERATION  2\n",
      "-----------------------------------------------------------------\n",
      "-----------------------------------------------------------------\n",
      "Starting new dataset for the next iteration.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(1293 of 1293)\u001b[39m |####################| Elapsed Time: 0:00:00 Time:  0:00:000:00\n",
      "\u001b[38;2;255;0;0m  0%\u001b[39m \u001b[38;2;255;0;0m(0 of 1293)\u001b[39m |                       | Elapsed Time: 0:00:00 ETA:  --:--:--"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "        Number of reconstructed tracks : 1293\n",
      "        Attract points toward the centroid of neighboring trajectory points\n",
      "        Create index and index all observations not map-matched\n",
      "\n",
      "        Pull points toward the centroid of neighboring trajectory points\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(1293 of 1293)\u001b[39m |####################| Elapsed Time: 0:05:33 Time:  0:05:330227\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New dataset created.\n",
      "Starting rasterization and vectorization (iteration 2) \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(147 of 147)\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(123 of 123)\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(115 of 115)\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(149 of 149)\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(123 of 123)\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(195 of 195)\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(519 of 519)\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(111 of 111)\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(207 of 207)\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(236 of 236)\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(399 of 399)\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(430 of 430)\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(379 of 379)\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(289 of 289)\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(346 of 346)\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(960 of 960)\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(333 of 333)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stage 2 completed: rasterization and vectorization.\n",
      "Starting topology creation for the network\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;186;255;0m 81%\u001b[39m \u001b[38;2;186;255;0m(66 of 81)\u001b[39m |###################     | Elapsed Time: 0:00:00 ETA:   0:00:00"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\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(81 of 81)\u001b[39m |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building [100 x 87] spatial index...\n",
      "Stage 3 completed: adding topology to the skeleton.\n",
      "Starting map-matching, aggregation, and conflation of GNSS trajectories.\n",
      "WARNING: TRAJECTORY FUSION HAS NOT CONVERGED (#ITER = 25 - CV = 0.4308281593414602)\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(40 of 40)\u001b[39m |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building [100 x 74] spatial index...\n",
      "End building the mobility network.\n",
      "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
      "`````````````````````````````````````````````````````````````````````\n",
      "                           FIN                                       \n",
      "’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’\n",
      "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n"
     ]
    }
   ],
   "source": [
    "# \n",
    "footprint2grap(config, collection, 'ERROR')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d060ac54-e013-4cc0-8a30-44175e841b68",
   "metadata": {},
   "source": [
    "## Visualisation du réseau de mobilité pédestre final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dd93d2b4-3b36-430b-9833-3f1feb5bc45b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from footprint2graph.util.PlotRes import plotResultatFinal\n",
    "\n",
    "plotResultatFinal(config['output']['RESULT_PATH'], '2')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
