{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "89a764de-069f-47fa-8573-d0d5bdbe9ac8",
   "metadata": {},
   "source": [
    "# Plan de la Limace (Massif des Bauges)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c72bcb3-19b9-4da4-8121-c3a1ac4dfd76",
   "metadata": {},
   "source": [
    "## Chargement des librairies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e00a5055-cc58-4913-9c5b-bd193c23a669",
   "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": "2a1cbe41-a30f-473a-9424-a932031f099e",
   "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": "f13afc50-2669-4420-b4a8-caf02ca57a71",
   "metadata": {},
   "source": [
    "## Chargement des paramètres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e096a92b-1f1b-4153-8868-b8a977385cc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
      "`````````````````````````````````````````````````````````````````````\n",
      "             Generate a footprint graph                              \n",
      "                      from hiking trajectories                       \n",
      "                      in the Bauges, summer 2024.                    \n",
      "’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’\n",
      "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
      "\n",
      "Paramètres relevés dans la configuration: \n",
      "Résultats enregistrés dans le répertoire:  /home/md_vandamme/4_RESEAU/ZTEMPZ1/\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 Bauges, 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_la_limace.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": "cfe00ef6-46e6-4691-88c8-120352aebd76",
   "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": "6336cbd9-8606-4fb8-b671-60d97b6d1358",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading and split outdoorvision track data...\n",
      "Reading track data...\n",
      "     Number files to load:  4145\n",
      "Starting split ...\n",
      "     1000 / 4145\n",
      "     2000 / 4145\n",
      "     3000 / 4145\n",
      "     4000 / 4145\n",
      "     Number of tracks after split: 832\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/BAUGES/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 = [950987, 951409, 950696, 949467, 947934, 948545, 950987]\n",
    "Y = [6513197, 6512091, 6511113, 6510719, 6511949, 6512621, 6513197]\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": "d9a08216-19d0-4939-a242-4ed79142a147",
   "metadata": {},
   "source": [
    "## Lancement du pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "58254880-fa80-4fda-ac8e-d95b2b4515ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------------------\n",
      "-----------------------------------------------------------------\n",
      "              ITERATION  1\n",
      "-----------------------------------------------------------------\n",
      "-----------------------------------------------------------------\n",
      "Starting segmentation and resampling...\n",
      "Starting segmentation ...\n",
      "     500 / 832\n",
      "    Number of tracks after segmentation: 1171\n",
      "Finished saving segmented tracks.\n",
      "Starting resampling ...\n",
      "    Number of tracks to resample:  1171\n",
      "    Number of tracks after resampling: 1171\n",
      "    Number of tracks after resampling: 1171\n",
      "Finished saving resampled tracks.\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(279 of 279)\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(279 of 279)\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(406 of 406)\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(1204 of 1204)\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(3195 of 3195)\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(3191 of 3191)\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(21702 of 21702)\u001b[39m |##################| Elapsed Time: 0:00:00 Time:  0:00:000000\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/ZTEMPZ1/network/tmp_in.csv not exists\n",
      "    /home/md_vandamme/4_RESEAU/ZTEMPZ1/network/tmp_out.csv not exists\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;255;145;0m 32%\u001b[39m \u001b[38;2;255;145;0m(33 of 102)\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(102 of 102)\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(102 of 102)\u001b[39m |######################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building [100 x 75] 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",
      "        Number of tracks map matched : 1171\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(1605 of 1605)\u001b[39m |####################| Elapsed Time: 0:00:00 Time:  0:00:000:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "        Number of reconstructed tracks : 1605\n",
      "        Attract points toward the centroid of neighboring trajectory points\n",
      "        Create index and index all observations not map-matched\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;2;255;1;0m  0%\u001b[39m \u001b[38;2;255;1;0m(4 of 1605)\u001b[39m |                       | Elapsed Time: 0:00:00 ETA:   0:02:34"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\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(1605 of 1605)\u001b[39m |####################| Elapsed Time: 0:04:54 Time:  0:04:540224\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(350 of 350)\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(193 of 193)\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(185 of 185)\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(431 of 431)\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(245 of 245)\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(3193 of 3193)\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(3188 of 3188)\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;255;248;0m 56%\u001b[39m \u001b[38;2;255;248;0m(37 of 66)\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(66 of 66)\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(66 of 66)\u001b[39m |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building [100 x 66] 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",
      "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(53 of 53)\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": [
    "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": "0acc46d1-e0a7-40ab-9351-74b78ea668b1",
   "metadata": {},
   "source": [
    "## On affiche le résultat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c328be5a-88db-4209-a545-23a24197bcc6",
   "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)",
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