{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "16ea37f9",
   "metadata": {},
   "source": [
    "# Segmentation of a GPS track\n",
    "\n",
    "This section presents the problem of segmenting a GPS track based on discrete-value criteria or spatiotemporal criteria or a combination of both. The segmentation for a track consists to partition it into a number of pieces of *track*. The idea of segmentation is to obtain tracks where characteristics inside each ones are uniform in some sens.\n",
    "\n",
    "In track library, segmentation takes place (cf. figure 1) in two stages. Once the indicators have been calculated, the first step is to create a new analytical feature, named \"seg\" for example, which value is: 0 if change of division, 1 otherwise by calling *segmentation* algorithm. The second step consists to split the track using \"seg\" analytical feature. A collection of tracks are created; all tracks are defined for all observations between the different values of 1 of \"seg\" AF from the original track.\n",
    "\n",
    "<figure style='text-align:center;padding:1.5em'>\n",
    "<img src=\"segmentation_process3.png\"  width=\"650\" />\n",
    "<figcaption><br/>Figure 1 : process to segmenting track</figcaption>\n",
    "</figure>\n",
    "\n",
    "\n",
    "We have had a brief preview of this treatment in the quickstart use case. Indeed, in the GPS track corresponding to an athletics training, we segment the trajectory depending on the shifting the speed of his running. In this tutorial, we add a second analytical feature, the direction of the edge."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69278f6c",
   "metadata": {},
   "source": [
    "## Let's start by defining our environment\n",
    "\n",
    "This task is only useful for the online notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2ad3b117",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "\n",
    "# Import de tracklib\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": "markdown",
   "id": "1a0698cc",
   "metadata": {},
   "source": [
    "## Loading GPS track data\n",
    "\n",
    "Data represents a runner's track on an athletics track (identifier N°903959)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3a5b9a96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: no reference point (base) provided for local projection to ENU coordinates. Arbitrarily used: [lon= 2.457019882, lat=48.830705099, hgt= 55.200]\n",
      "-------------------------------------\n",
      "GPS track #0 of user 0:\n",
      "-------------------------------------\n",
      "  Nb of pt(s):   190\n",
      "  Ref sys id   : ENU\n",
      "  Starting at  : 11/11/2020 15:39:54\n",
      "  Ending at    : 11/11/2020 15:52:00\n",
      "  Duration     : 726.000 s\n",
      "  Length       : 2412.144 m\n",
      "-------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 216x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trk.ObsTime.setReadFormat(\"4Y-2M-2DT2h:2m:2sZ\")\n",
    "param = trk.TrackFormat({'ext': 'GPX'})\n",
    "tracks = trk.TrackReader.readFromFile('../../../../data/gpx/activity_5807084803.gpx', param)\n",
    "trace = tracks.getTrack(0)\n",
    "\n",
    "# Transformation GEO coordinates to ENU\n",
    "trace.toENUCoords()\n",
    "\n",
    "trace.summary()\n",
    "\n",
    "trace.rotate(-np.pi/4)\n",
    "\n",
    "plt.figure(figsize=(3, 4))\n",
    "plt.scatter(trace.getX(), trace.getY(), marker='o', c='#C0C0C0', s=2)\n",
    "plt.xlim([-100, 10])\n",
    "plt.ylim([-100, 100])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7172c4e",
   "metadata": {},
   "source": [
    "\n",
    "## First basic method, with table index\n",
    "\n",
    "Tout simplement, vous avez la liste des index des observations qui représentent les limites des différents fragments de la trace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "187662b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 216x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "idxFragments = [50, 100, 150, 189]\n",
    "collection = trk.split(trace, idxFragments)\n",
    "\n",
    "plt.figure(figsize=(3, 4))\n",
    "COLORS = ['r-','g-','b-','y-','m-','c-']\n",
    "for i in range(len(collection)):\n",
    "    collection[i].plot(COLORS[i%6], append=True)\n",
    "plt.xlim([-100, 10])\n",
    "plt.ylim([-100, 100])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c34fb33",
   "metadata": {},
   "source": [
    "## Segmenting under any of these AF or any combination of these AF\n",
    "\n",
    "\n",
    "### Step 1: create criteria as Analytical Feature\n",
    "\n",
    "Vous n'avez pas les bornes pour la segmenter mais vous avez une méthode pour les calculer. Autrement dit, vous avez un critère permettant \n",
    "de découper la trace. Comme par exemple, la trace du quickstart qui a été découpée suivant les changements de vitesses. \n",
    "\n",
    "Pour cela, il faut tout d'abord créer des points de rupture, ils seront définis par un AF qui aura un ensemble fini de valeurs. \n",
    "Une coupure sera effective à chaque seuil passé en paramètre.\n",
    "\n",
    "Vous pouvez même combiner plusieurs AF pour découper la trace. Par défaut l'opérateur de comparaison est AND mais on peut le changer en OU.\n",
    "Dans ce cas, l'ensemble des AF sont définis dans un tableau ainsi que leurs seuils.\n",
    "\n",
    "A l'issu de la création des marqueurs, la trace est toujours conservée, mais une nouvelle AF est ajoutée. \n",
    "Elle a comme valeurs 0 sauf pour les ruptures, ce sera 1.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9bcc8f1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "trace.addAnalyticalFeature(trk.speed)\n",
    "trace.operate(trk.Operator.DIFFERENTIATOR, \"speed\", \"dv\")\n",
    "trace.operate(trk.Operator.RECTIFIER, \"dv\", \"absdv\")\n",
    "\n",
    "trace.addAnalyticalFeature(trk.heading)\n",
    "trace.operate(trk.Operator.RECTIFIER, \"heading\", \"absheading\")\n",
    "print ('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a1fd5691",
   "metadata": {},
   "outputs": [],
   "source": [
    "trk.segmentation(trace, [\"absdv\"], \"decoup1\", [1.5], trk.MODE_COMPARAISON_AND)\n",
    "trk.segmentation(trace, [\"absheading\"], \"decoup2\", [0.1], trk.MODE_COMPARAISON_AND)\n",
    "trk.segmentation(trace, [\"absdv\", \"absheading\"], \"decoup3\", [1.5, 0.1], trk.MODE_COMPARAISON_AND)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e3fc7f0",
   "metadata": {},
   "source": [
    "### Step 2: split under any of these AF or any combination of these AF\n",
    "\n",
    "Il reste à découper la trace suivant le marker créé précédemment. Pour cela, il faut appeler la même fonction **split** mais en spécifiant cette fois-ci\n",
    "non pas une liste des index, mais le ou les AF\n",
    "\n",
    "\n",
    "Splits track according to af name (considered as a marker) \n",
    "\n",
    "\n",
    "Return: No track if no segmentation, otherwise a TrackCollection object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b6e0e81d",
   "metadata": {},
   "outputs": [],
   "source": [
    "TRACES1 = trk.split(trace, \"decoup1\", limit=40)\n",
    "TRACES2 = trk.split(trace, \"decoup2\", limit=40)\n",
    "TRACES3 = trk.split(trace, \"decoup3\", limit=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "710a47b7",
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Segmentation based on its heading AND on changes in his running speed')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "COLORS = ['r','g','b','y','m','c']\n",
    "\n",
    "plt.figure(figsize=(10, 9))\n",
    "plt.subplots_adjust(top=1.3, wspace=0.4, hspace=0.2)\n",
    "\n",
    "# Figure 1\n",
    "ax1 = plt.subplot2grid((2, 2), (0, 0))\n",
    "ax1.scatter(trace.getX(), trace.getY(), marker='o', c='#000000', s=2)\n",
    "ax1.set_title(\"A runner's track on an athletics track\")\n",
    "\n",
    "# Figure 2\n",
    "ax2 = plt.subplot2grid((2, 2), (0, 1))\n",
    "ax2.scatter(trace.getX(), trace.getY(), marker='o', c='#000000', s=2)\n",
    "for i in range(len(TRACES1)):\n",
    "    track = TRACES1[i]\n",
    "    ax2.plot(track.getX(), track.getY(), color=COLORS[i%6], linestyle='-', linewidth=2)\n",
    "ax2.set_xlim([-100, 10])\n",
    "ax2.set_ylim([-100, 100])\n",
    "ax2.set_title(\"Segmentation based on changes in his running speed\")\n",
    "\n",
    "# Figure 3\n",
    "ax3 = plt.subplot2grid((2, 2), (1, 0))\n",
    "ax3.scatter(trace.getX(), trace.getY(), marker='o', c='#000000', s=2)\n",
    "for i in range(len(TRACES2)):\n",
    "    track = TRACES2[i]\n",
    "    ax3.plot(track.getX(), track.getY(), color=COLORS[i%6], linestyle='-', linewidth=3)\n",
    "ax3.set_xlim([-100, 10])\n",
    "ax3.set_ylim([-100, 100])\n",
    "ax3.set_title(\"Segmentation based on its heading\")\n",
    "\n",
    "# Figure 4\n",
    "ax4 = plt.subplot2grid((2, 2), (1, 1))\n",
    "ax4.scatter(trace.getX(), trace.getY(), marker='o', c='#000000', s=2)\n",
    "for i in range(len(TRACES3)):\n",
    "    track = TRACES3[i]\n",
    "    ax4.plot(track.getX(), track.getY(), color=COLORS[i%6], linestyle='-', linewidth=4)\n",
    "ax4.set_xlim([-100, 10])\n",
    "ax4.set_ylim([-100, 100])\n",
    "ax4.set_title(\"Segmentation based on its heading AND on changes in his running speed\")"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "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
}
