{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8b06aa6b",
   "metadata": {},
   "source": [
    "# ST-DBSCAN application to detect \n",
    "\n",
    "\n",
    "\n",
    "new density-based clustering algorithm which takes into account spatial and temporal characteristics of the observations. \n",
    "\n",
    "ST-DBSCAN is a spatio-temporal density-based clustering. \n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Reference:</b> <br/>\n",
    "Birant, D., & Kut, A. (2007). ST-DBSCAN: An algorithm for clustering\n",
    "spatial–temporal data. Data & Knowledge Engineering, 60(1), 208-221. <a href='https://doi.org/10.1016/j.datak.2006.01.013'>https://doi.org/10.1016/j.datak.2006.01.013</a>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9af69b8",
   "metadata": {},
   "source": [
    "## Exemple: détection des zones plates en montagne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c5232c36",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import sys\n",
    "\n",
    "#-------------------------------------------------------\n",
    "# Import de tracklib\n",
    "\n",
    "module_path = os.path.abspath(os.path.join('../../..'))\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)\n",
    "    \n",
    "import tracklib as trk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b77d627f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# \n",
    "trk.ObsTime.setReadFormat(\"4Y-2M-2D 2h:2m:2s\")\n",
    "resource_path = './'\n",
    "filepath = os.path.join(resource_path, '../../../data/XYZBRAVOSANSFIDOUTEST.txt')\n",
    "param = trk.TrackFormat({'ext': 'CSV', 'id_E': 0, 'id_N': 1, 'id_U': 2, 'id_T': -1, 'header': 1})\n",
    "track = trk.TrackReader.readFromFile(filepath, param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7c04d2d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kernel = trk.GaussianKernel(3)\n",
    "track.operate(trk.Operator.FILTER, \"z\", kernel, \"z_filtered\")\n",
    "track.operate(\"z=z_filtered\")\n",
    "\n",
    "trk.computeAbsCurv(track)\n",
    "\n",
    "track.plotProfil('SPATIAL_ALTI_PROFIL')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "240e9f08",
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "249 1105 6\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trk.stdbscan(track, 'z', 500, 5, 20, 25)\n",
    "\n",
    "X1 = []\n",
    "Y1 = []\n",
    "X2 = []\n",
    "Y2 = []\n",
    "C2 = []\n",
    "for i in range(len(track)):\n",
    "    o = track[i]\n",
    "    ac = track.getObsAnalyticalFeature('abs_curv', i)\n",
    "    noise = track.getObsAnalyticalFeature('noise', i)\n",
    "    cluster = track.getObsAnalyticalFeature('stdbscan', i)\n",
    "    \n",
    "    if noise > 0:\n",
    "        X1.append(ac)\n",
    "        Y1.append(o.position.getZ())\n",
    "    if cluster > 0:\n",
    "        X2.append(ac)\n",
    "        Y2.append(o.position.getZ())\n",
    "        C2.append(cluster)\n",
    "    \n",
    "plt.figure(figsize=(5, 4))\n",
    "ax1 = plt.gca()\n",
    "track.plotProfil('SPATIAL_ALTI_PROFIL', append=ax1)\n",
    "\n",
    "from collections import Counter\n",
    "print (len(X2), track.size(), len(Counter(C2).keys()))\n",
    "\n",
    "plt.plot(X2, Y2, 'bo', markersize=1)\n",
    "plt.show()"
   ]
  }
 ],
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