{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "48cb95e5",
   "metadata": {},
   "source": [
    "# Interpolate GPS track\n",
    "\n",
    "<br/>\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "    Les méthodes d'<b>interpolation</b> sont des méthodes qui sur-échantillonnent, sous-échantillonnent ou ré-échantillonnent les traces, elles passent donc nécessairement par une modélisation et une description complète de la trajectoire suivie. Lorsque la trace comporte des timestamps, on a alors suppression ou création de nouveaux timestamps. On peut donc considérer que ces méthodes <b>modifient les observations</b>, contrairement aux méthodes de lissage qui ne modifient que les géométries des observations. \n",
    "    \n",
    "Il peut s'agir des méthodes locales (e.g. interpolation linéaire, bézier) ou globales (e.g. splines, krigeage, gaussian process...)\n",
    "\n",
    "Ces méthodes ne sont sensées conserver les AF que pour un sous-échantillonnage strict. La porte d'entrée de base des méthodes d'interpolation dans Track est la fonction <b>resample</b>. \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53aba2c9",
   "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": "8872370c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\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 tkl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3fb05ec",
   "metadata": {},
   "source": [
    "### Construction de la trace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8f33a2b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "tkl.ObsTime.setReadFormat(\"4Y-2M-2D 2h:2m:2s\")\n",
    "trace = tkl.Track([], 1)\n",
    "        \n",
    "trace.addObs(tkl.Obs(tkl.ENUCoords(0.0,  0.0, 0), tkl.ObsTime.readTimestamp(\"2018-01-01 10:00:00\")))\n",
    "trace.addObs(tkl.Obs(tkl.ENUCoords(1.0,  0.8, 0), tkl.ObsTime.readTimestamp(\"2018-01-01 10:00:10\")))\n",
    "trace.addObs(tkl.Obs(tkl.ENUCoords(2.0,  0.9, 0), tkl.ObsTime.readTimestamp(\"2018-01-01 10:00:20\")))\n",
    "trace.addObs(tkl.Obs(tkl.ENUCoords(3.0,  0.1, 0), tkl.ObsTime.readTimestamp(\"2018-01-01 10:00:30\")))\n",
    "trace.addObs(tkl.Obs(tkl.ENUCoords(4.0, -0.7, 0), tkl.ObsTime.readTimestamp(\"2018-01-01 10:00:35\")))\n",
    "trace.addObs(tkl.Obs(tkl.ENUCoords(5.0, -1.0, 0), tkl.ObsTime.readTimestamp(\"2018-01-01 10:00:50\")))\n",
    "trace.addObs(tkl.Obs(tkl.ENUCoords(6.0, -0.2, 0), tkl.ObsTime.readTimestamp(\"2018-01-01 10:00:59\")))\n",
    "\n",
    "SIZE = trace.size()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f5c49f9",
   "metadata": {},
   "source": [
    "### Une fonction pour afficher la trace avec l'heure de passage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "53c5f453",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Une fonction pour afficher la trace avec l'heure de passage\n",
    "'''\n",
    "def view_track(track, color='royalblue', sens='a'):\n",
    "    #plt.figure(figsize=(8, 8))\n",
    "    \n",
    "    # Display point\n",
    "    track.plotAsMarkers(frg = color, bkg = \"w\", sym_frg = r'$\\bowtie$', sym_bkg = \"o\", size=6)\n",
    "    track.plot(type='LINE', style='--', color=color)\n",
    "    \n",
    "    # Display time\n",
    "    for i in range(track.size()):\n",
    "        txt = str(track[i].timestamp.sec) + \"'\"\n",
    "        if sens == 'a':\n",
    "            delta = 0.15\n",
    "        else:\n",
    "            delta = -0.25\n",
    "        plt.text(track[i].position.getX()-0.1, track[i].position.getY()+delta, txt, color=color)\n",
    "    \n",
    "    # Zoom\n",
    "    plt.xlim([-0.5, 7.0])\n",
    "    plt.ylim([-1.5, 2.0])\n",
    "    \n",
    "    #plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "89ede1b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Longueur de la trace (en mètre):  7.171517602949423\n",
      "Durée de la trace (en seconde):  59.0\n",
      "Nombre de points:  7\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print ('')\n",
    "print (\"Longueur de la trace (en mètre): \", trace.length())\n",
    "print (\"Durée de la trace (en seconde): \", trace.duration())\n",
    "print (\"Nombre de points: \", trace.size())\n",
    "view_track(trace)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5146ca07",
   "metadata": {},
   "source": [
    "### Resampling of a track with spatial linear interpolation\n",
    "\n",
    "Oversampling with a factor 2, i.e. since the track has 7 points, it will have 2*7 points after resampling\n",
    "\n",
    "Track lenght is "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bca2c3d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Espace entre chaque intervalle (en mètre):  0.5122512573535303\n",
      "P0: 01/01/2018 10:00:00\n",
      "P1: 01/01/2018 10:00:04 0.51\n",
      "P2: 01/01/2018 10:00:08 0.51\n",
      "P3: 01/01/2018 10:00:12 0.49\n",
      "P4: 01/01/2018 10:00:12 0.51\n"
     ]
    }
   ],
   "source": [
    "trace1 = trace.copy()\n",
    "\n",
    "FACTOR = 2\n",
    "trace1.resample(factor=FACTOR, mode=tkl.MODE_SPATIAL) \n",
    "\n",
    "#trace.plot('k-')\n",
    "view_track(trace)\n",
    "#trace1.plot('r-')\n",
    "#trace1.plot('ro')\n",
    "view_track(trace1, color=\"limegreen\", sens='b')\n",
    "#plt.xlim([-0.5, 7.0])\n",
    "#plt.ylim([-1.5, 1.5])\n",
    "#plt.title('sur-échantillon spatial facteur ' + str(FACTOR))\n",
    "plt.show()\n",
    "\n",
    "print (\"Espace entre chaque intervalle (en mètre): \", trace.length() / (FACTOR*SIZE))\n",
    "print ('P0:', trace1[0].timestamp)\n",
    "print ('P1:', trace1[1].timestamp, str(round(trace1[1].distanceTo(trace1[0]), 2)))\n",
    "print ('P2:', trace1[2].timestamp, str(round(trace1[2].distanceTo(trace1[1]), 2)))\n",
    "print ('P3:', trace1[3].timestamp, str(round(trace1[3].distanceTo(trace1[2]), 2)))\n",
    "print ('P4:', trace1[3].timestamp, str(round(trace1[4].distanceTo(trace1[3]), 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e2dce28",
   "metadata": {},
   "source": [
    "### Interpolation lineaire : sur-echantillonnage temporel facteur 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "076a51b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8xRfQqxcMGAA1a0JaGrz0UuTzEXGqy4wZLK1ShVYjRvDlxIlhxxEpk1QapcTNN9/M7NmzeeXxx2n22GPQrh189hmMHh05lLVbt7AjFlr55GSO+fhj9ptxoH9/dm/eHHYkkTJHpVEKjB07lif/+U/eOOMMzrv5Zhg1Cq67LrIp6qqrIDEx7IhFpm7nznz1wAOcsG8fC7p0wXN8Ql1Eip9KI859tnQpz195JSuqVOHCTz6BU0+FhQvhsceglA6/0f7225naqxfd16xh2uWXhx1HpExRacSxnRs2sKZNGz7MzKTJ/v2Rnd6ffAKtW4cdrdj1+Ogj5taoQaexY/nsxRfDjiNSZqg04pRnZ7OkUyf6ZmXxJZCUlQU9e4b/wbwSkpCUxPHTp/NtYiLVfvtbvl+9OuxIImWCSiNOTb34Yrpu2MDU6tVpkpQU2dFdq1bYsUrUMc2ase3pp6mVlcWqLl3IPjiCrogUG5VGHFr6zDN0efNNZtWuTc9Nm2D9+vDHiwpJqyuvJP3ii+mwZQtT+/YNO45IqafSiDPfLl/OMUOGsDEpieYzZ5JQvnxk+I8yWBgHpb76KtMbNSL144+Z//e/hx1HpFRTacSRrMxMvu7enWOystj9wgtUb9Ik7EgxwRISaDN7Nl9WqECDYcP4poyPcCxSnFQacWTaWWfRfutWZl9+OS1+/euw48SUKrVqkfDWW1R0Z8vpp5O5e3fYkURKJZVGnJh7//30mDqVaSecQOrzz4cdJyYd368fS268kVN27mRG9+5hxxEplVQacWD99Okcf/fdfFGxIu3T04vvW/RKga6PPsqUNm3otXAhM266Kew4IqWOXn1i3L4dO9jeuzeJ7lQcP57KNWqEHSnmdZk2jSXJyZw8ciRrPvgg7DgipYpKI8ald+1Kqz17WH7rrTQ644yw48SF8snJ1Pj0U/aZkfXLX2pgQ5EiFGppmNnZZva5ma0ys9tymd7LzLab2cLgdHcYOcMyfehQei5bxuSUFDr/9a9hx4krdTp0YO1DD3HCvn0s7NRJAxuKFJHQSsPMEoF/An2BlsBAM2uZy6zT3L1NcLqvREOGaNU779DmySdZVK0a3adNCztOXGo3bBhTTz+dbl99xbRLLw07jkipEOaaRkdglbuvcfdM4DWgf4h5YsbOjAwSLr6Y3QkJHDdlCkkVK4YdKW71mDiROTVr0um11/hszBjYtAncw44lErfCLI16wLocl9cH1x2qi5ktMrMPzKzV4RZmZoPNbK6Zzd2yZUtRZy0xnp3N0k6daJiZyYYRI6jdpk3YkeJaQlISJ86cyZZgYMOtdetGvtFQm6tEjkiYpZHbuBeHvgWcDzRy91OBJ4D/HG5h7j7a3VPcPaVmzZpFl7KETb3wQrqsX8+0vn1pe+ONYccpFY4+4QR2PPooNYHV2dlkp6VBHL+xEAlTmKWxHmiQ43J9ICPnDO6+w913BeffB8qZWak95nTJqFF0fftt0o87jl7jx4cdp1Rped11zKpZkw7A1KpVy9yIwCJFJczSmAM0NbMmZlYeGAC8m3MGMzvOLDISn5l1JJL3uxJPWgK2LFtGjaFDyUhKooU+wFf0zEjNyCCtYUN6bN/OPB2NJnJEQntlcvcDwHXARGA58Lq7LzOzIWY2JJjtQmCpmS0CHgcGuJe+vZhZmZmsS02lenY2P7z0Ekc1ahR2pFLJkpJoO2cOqytUoNGf/0zGrFlhRxKJO1YKX4NJSUnxuXE00unk1FR6paUx7YorSB0zJuw4pd6aDz6gRr9+fJ2cTPNvvqF8cnLYkURCZ2bz3D0lv/m0DSRks++9N1IYTZuqMErI8X37suyPf+TkXbs0sKFIAak0QrQ+LY2m993HikqVSElPDztOmdJlxAimtG1Lr0WLmH7DDWHHEYkbKo2Q7N2+ne19+pDgTqXx46l0zDFhRypzuqalsbhqVU55/HFWT5gQdhyRuKDSCMmsYCDCFbffTqPTTw87TplUrnJlagYDG/qvfsWujRvDjiQS81QaIUgbPJien33G5I4d6fTgg2HHKdPqpKSw7m9/o0lmJos6d9bAhiL5UGmUsBXvv0+7p59mIdC9fHkNZxED2t5yC9POPJNuX3/N1AEDwo4jEtNUGiVox44dnH/ddQwH6gJJ6ekaziJG9PjgA+bUqkWXN95g6bPPhh1HJGapNEqIu/Pb3/6WNWvXcs4pp1ArKQm6dtVwFjEiISmJpunpbEpK4uirr+a7zz8PO5JITFJplJCRI0fy5ptv8tBDD9FjwQJYvx4mTwbLbdxGCUP1Jk3YNWYMNbKy+LJbN7IyM8OOJBJzVBolYPr06dx666384he/4JZbboGEBKhdW4URg1pcdhmzLr2UlO++Y1qfPmHHEYk5Ko1itnnzZi6++GIaN27M888/j6koYl7qiy+Sdvzx9Jg8mbkPPBB2HJGYotIoRgcOHGDAgAFs3bqVcePGcdRRR4UdSaJgCQm0mzWLVRUr0uSuu9gwc2bYkURihkqjGN19991MmjSJJ598klNPPTXsOFIAlWvUoPw771DOne/POot9O3eGHUkkJqg0isl7773HQw89xO9//3uuuOKKsOPIEWjcuzef/elPtN69m/Ru3cKOIxITVBrFYM2aNQwaNIi2bdvyxBNPhB1HCqHz3/7GlPbt6blkCdOvvz7sOCKhU2kUsb1793LRRRcBMG7cOCpWrBhyIimsrlOnsqhaNU79xz9Y9e67+d9ApBRTaRSxP/zhD8yfP58XX3yR448/Puw4UgTKVa5M7UmT+CEhAS66iJ3ffBN2JJHQqDSK0AsvvMDTTz/NbbfdxnnnnRd2HClCx7Vrx4b/+z+aZGayWAMbShkWdWmY2dFm1srMjjczlc0hFi9ezJAhQzjttNP4y1/+EnYcKQZtbrqJaX360G3tWqZefDFs2gSl8OuSRfKS53eEm9lRwLXAQKA8sAWoCNQG0oF/ufukEshZICX9HeHbt28nJSWF3bt3s2DBAmrXrl1i9y0lK/vAAebUr0/bTZtYmZBA6+7dYdKkyKf8ReJYtN8RnpTP9HHAi0Cqu2875A7aA5eb2fHuXmaHBT04EOGXX37JpEmTVBilXEJSEs3ffZeNnTpxTHY236alUWPLlsiwMCJlQJ6l4e5n5TFtHjCvyBPFmUceeYS33nqLESNGkJqaGnYcKQHVO3Rg40kn0XjFCpYlJHB09eokhh1KpIQUZJ/GKWZ2vpn96uCpOIPFg2nTpjFs2DAuuOACbrrpprDjSEkx46Rly5hz8cW0P3CAqWcd9r2VSKmT5z6NH2cyew44BVgGHDxsxN39t8WY7YiVxD6NjRs30rZtW6pWrcrcuXOpVq1asd6fxCB30po1o+uqVcy77z463HVX2IlEjlhR7dM4qLO7tyxkplLj4ECE27dv58MPP1RhlFVmtE9P54v69TnhnntYf/rp1NdwI1LKRbt5aqaZqTQCd955J1OmTOGpp57i5JNPDjuOhKjSscdScfx4Et3Z3rs3+3bsCDuSSLGKtjReIFIcn5vZYjNbYmaLizNYrHr33Xf561//yuDBgxk0aFDYcSQGNDrjDFYMG0arPXtI79o17DgixSra0ngOuBw4GzgPODf4WShmdnZQRKvM7LZcppuZPR5MX2xm7Qp7n4WxevVqBg0aRLt27XjsscfCjCIxptPDDzOlQwd6LlvG9GuvDTuOSLGJtjTWuvu77v6lu3998FSYOzazROCfQF+gJTAwl01gfYGmwWkw8GRh7rMwfvjhBy688EISEhI0EKHkqlswsGGbf/2LVe+8E3YckWIRbWmsMLOxZjawCA+57Qiscvc17p4JvAb0P2Se/sCLHpEOVDezOoW83yNy/fXXs3DhQl566SWaNGkSRgSJcUkVK3LclCnsTkgg4eKL2ZmREXYkkSIXbWlUAvYBvYlsljq4iaow6gHrclxeH1xX0HkAMLPBZjbXzOZu2bKlkNF+bt++fWzYsIE///nPnHPOOUW6bCldardpQ8Yjj9AwM5OlnTppYEMpdaI65NbdryyG+7bc7uoI5olc6T4aGA2Rz2kULtrPVahQgQkTJhDNZ1pE2txwA5MnTqTXBx8w5cIL6fnWW2FHEikyea5pmNmdZnZMHtNPN7MjXeNYDzTIcbk+cOj6fDTzlIiEhAQSEzVYhESn5/jxzDruOLq+/TZLRo0KO45IkclvTWMJ8J6Z7QXm89Mot02BNsDHwINHeN9zgKZm1gTYAAwAfn3IPO8C15nZa0AnYLu76xtwJOZZQgInpaeTceKJ1Bg6lC3du1OzVauwY4kUWp5rGu7+jrt3A4YQGUIkEdgBvAx0dPeb3P2IdiC4+wHgOmAisBx43d2XmdkQMxsSzPY+sAZYBTwNDD2S+xIJw1GNGrH35Zepnp3NutRUsjIzw44kUmhRjT0Vb0r6+zRE8jLtyitJff55JnXrxmlpaWHHEclVtGNP6ZtjRIpZ6pgxTGvWjNOmT2f2vfeGHUekUFQaIiUgZeZMVlSqRNP77mO91jYkjqk0REpApWOOofKECSS4s6NPH/Zu2xZ2JJEjkufRU2Z2dx6T3d3/UsR5REqthqedxuw//5mODz7I1K5d6fHZZ2FHEimw/NY0dudycuB3wLDijSZS+nR84AEmd+pEj+XLSRsyJP8biMSY/A65HXHwROTT1pWA3xIZJ+r4EsgnUup0nzyZBdWr027UKFa++WbYcUQKJN99GmZ2jJndDywmsjmrnbsPc/fNxZ5OpBRKqliRelOmsCMhgXIDB7Jj/fqwI4lELb9hRP5O5JPbO4GT3f1ed/++RJKJlGK1TjmFTY89RoP9+1nWubMGNixl0tPT2b9/f9gxikV+axo3A3WBO4EMM9sRnHaamb7XUqQQTr3uOtLOOYcuGzYw5VeF/aYBiRULFiygV69e3HHHHWFHKRb6RLhIiDw7m1n169P+m2/47J//5NShGiknnn3//fekpKSQmZnJ/PnzqVmzZtiRoqZPhIvEAUtIoEV6OhvKlaPW9dezeenSsCPJEcrOzuY3v/kN69at44033oirwigIlYZIyI5q2JB9wcCG61NTObBvX9iR5Aj87W9/47333mPEiBF07tw57DjFRqUhEgOaX3wx8373O9pt28a0008PO44U0KRJk7jjjjsYMGAA1113XdhxipVKQyRGdH/mGaY2b85pM2Yw+667wo4jUcrIyGDAgAE0a9aMp59+GrPcvnC09FBpiMSQjunprKhUiWYPPMDayZPDjiP52L9/P5dccgm7d+/mzTffJDk5OexIxU6lIRJDKlavTpUPPgBgd79+Gtgwxt1+++2kpaXx9NNP07Jly7DjlAiVhkiMadCzJyvvvJMWP/zA7C5dwo4jh/HWW28xYsQIrr32WgYOHBh2nBKj0hCJQR3vu4/JXbrQY8UK0q66Kuw4cogvvviCK6+8ko4dOzJixIiw45QolYZIjOr+6acsqF6d9s88w8px48KOI4E9e/ZwwQUXUK5cOd544w0qVKgQdqQSpdIQiVFJFStSf9o0tickUP7Xv2b72rVhRyrz3J2hQ4eydOlSXnnlFRo2bBh2pBKn0hCJYTVbt2bzE09Qf/9+lnfpooENQ/bMM8/wwgsvcPfdd9OnT5+w44RCpSES404ZOpS088+nc0YGU/r3DztOmTVv3jyuv/56evfuzV1l+HM0Kg2RONDz7beZWa8e3cePZ+ETT4Qdp8zZunUrF154IbVq1eKVV14hMTEx7EihUWmIxAFLSKBVejrrypWjzo03snnx4rAjlRnZ2dkMGjSIDRs2MG7cOGrUqBF2pFCpNETiRLX69dn/6qtUzc4mo0cPDuzdG3akMuHhhx9mwoQJPProo3Ts2DHsOKFTaYjEkWYXXMD8wYNps307ab16hR2n1Pvkk0+46667GDhwIEP1XSeASkMk7nQfNYopLVrQa9YsZpXSb4eLBRs2bGDgwIE0b96c0aNHl/qBCKMVSmmY2TFm9pGZfRH8PPow831lZkvMbKGZ6av4RAKdZszgs8qVOenBB/n600/DjlPqHByIcM+ePWVmIMJohbWmcRvwibs3BT4JLh/Oae7eJpqvIRQpKypWr07V//6XbDN+OPdcflixAkrhVzeHZdiwYUyfPp1nn32WFi1ahB0npoRVGv2BF4LzLwC/CCmHSNxqkJrKqjvv5KQffmBxixbQsyfow3+FNm7cOB599FGuv/56LrnkkrDjxBzzEN6dmNk2d6+e4/L37v4/m6jM7Evge8CBUe4+Oo9lDgYGAzRs2LD9119/XeS5RWLOpk1MPO44+gBfAY3XroUGDUIOFb9WrlxJSkoKrVq1YsqUKZQvXz7sSCXGzOZFs0Wn2NY0zOxjM1uay6kgH2nt5u7tgL7AtWbW43Azuvtod09x95TS+oXuIv+jVi3OTE3lX0BjYMeAAbBnT8ih4tPu3bu54IILqFChAq+//nqZKoyCKLbScPcz3b11Lqd3gE1mVgcg+Ln5MMvICH5uBt4GdJC0SE5mJE6ezIVLlvCn6tVJnjGDA717w86dYSeLK+7ONddcw7Jlyxg7diwNtLZ2WGHt03gX+E1w/jfAO4fOYGZVzKzqwfNAb2BpiSUUiRcJCdRq3ZpfTpjA5QkJMGMGfuaZsHVr2MnixujRo3nppZcYPnw4Z511VthxYlpYpfEwcJaZfQGcFVzGzOqa2fvBPLWBNDNbBMwGJrj7f0NJKxIHunbtSocRI/iVO1nz5kGvXrBpU9ixYt6i8eO54frr6du3L3focy/5CmVHeHFLSUnxuXP1sQ4pe9ydSy65hG3jxvFBhQokNmgAH38MZfB7H6KxdeVK9jRvzmSgb5cuHJuWBgll8zPPoe8IF5GSZ2Y888wzfN20Kf0rVSJ70yZITYVVq8KOFnOyDxxgVdeu1ALaAcfOmQNbtoQdK+apNERKmWrVqvHmm28yad8+Bp9wAr5nT6Q4lmqXYE5Tzz6bjt99R3rNmrRMSoKuXaFWrbBjxTyVhkgp1Lp1a0aNGsWzCxYw4rzzIptcevYEbbYFYP7f/kaPTz5heuPGpGZkwPr1MHkyaHypfKk0REqpyy67jCFDhvCnMWP48K67oFo1OP10mDYt7Gih+mbOHBredhurK1SgzaxZWFIS1K6twoiSSkOkFBs5ciQpKSlcNGwYX774ItStC336wIcfhh0tFJm7dvHt6adTwZ3Et9+mijZHFZhKQ6QUq1ChAm+88QaJiYn88vrr+WHiRGjWDM47D95+O+x4JW5maion79rF0ptu4vi+fcOOE5dUGiKlXOPGjXn55ZdZtGgR1w4fDpMmQbt2cNFF8PLLYccrMTNuuomeCxcypU0bujzySNhx4pZKQ6QM6NevH3fddRdjxozh2bfego8+iuwYHzQInnoq7HjFbs3773PyyJEsSU6mSxnfp1NYSWEHEJGScc8995Cens61117LV199xY0vvsixQ4bANddExqr605/Cjlgsdm/eTNavfsU+M2p8+inl9YVKhaI1DZEyIjExkbFjx3Luuedy//3306h5c25r1oy9/fvDrbfC3XeXui9y8uxsFnbqxAn79rH24Yep06FD2JHinkpDpAypUaMG48aNY8mSJZx//vn8feRIakycSHrLlvCXv8Af/1iqimPapZfS7auvmHrGGbS79daw45QKKg2RMqh169aMHTuW5cuXc9GAAaSuWME/EhJg5Eh2DhwIGRlxXx7Lnn+eTq+9xpyaNenxX411WlRUGiJlWLNmzRgzZgwrV61iye9+x0MJCVT997+ZX68eX596atx+fezWL77gqN//ni2JiZw4cyYJSdp9W1RUGiJCkyZNGDV6NJfNmcPbQAug/pIlTG/cmNXjx4cdr0CyDxxgddeu1MzKYsdzz3H0CSeEHalUUWmIyI8atG3LL3v0YGdCAtOSkzl13TqanHceM+vX5/PXXw87XlSm9u5Nh2+/Zdavf03LQYPCjlPqqDRE5CdmMGkStTIy6LVjB3uXL2dK16603LCB5pdcwqw6dfjshRfCTnlY8x56iB6TJjG9SRNSX3op7DilkkpDRH4uIeHHAfxqnHQSp02fjn/5JZNPO41mmzbR8oormFujBouffDLspD+TMWsWje+4g9UVKtB21iysjH6ZUnHTsyoi+areuDG9Pv2UxHXrmHT22TTaupVThg5l/tFHs2DECPzAgchXy4Z0xFXmrl1sPeMMyrmT9M47VK5ZM5QcZYFKQ0SiVq1ePU774AMqb9zI5P79qbtjB21vuYUlFSsyp04dvEePUI64mtmtG61372bZH/9Ikz59Svz+yxKVhogUWJVatej1n/9Q/bvvmHL++RyblUUHd5anpTHrppvIzsoqsSwz/vAHei5ezOR27egyYkSJ3W9ZpdIQkSNWsXp1ev7nP9Ts2pWpZlQxo9Pjj/NFcjIzbrqJrP37i/X+V48fzylPPMHiqlXppoEIS4RKQ0QKx4zy06bR45tvqLd7N2lXX0257Gy6jhzJl8nJTL/mGg7s3Vvkd7tr40a44AJ+MKPWpEmUq1y5yO9D/pdKQ0QKLzjiKqlSJbo/9RSNdu5kxg03kG1Gt6eeYkPVqky98koyd+0qkrvz7GwWde5M48xM1v/97xzXvn2RLFfyp9IQkSKXWL48XUeO5MRdu5h1223sLl+eHs8/z+bq1ZkyYAB7t24t1NFWUwcMoNvXXzOtd2/a3nxzEaeXvKg0RKTYJCQl0emhh2ixcydzhg/n+0qV6Pnvf/P9sccyo04d9rVoATNnwjffRH3U1dJnn6XLG28wu1YtekyYUMyPQA5lHucjWeYmJSXF586dG3YMETmEZ2ezYPhw7L77aHvoxHLloEEDaNgw91ODBny3bh17Tz6ZbDOqrlxJ9SZNwngYpZKZzXP3lPzm09CPIlJiLCGBdvfeC5MnMyMtjReSk1m/YwctKlfml+3b06F2bcpv3AiTJ8OGDXDIobtVgWRgTfPmNGjUKIRHIKFsnjKzi8xsmZllm9lhm83Mzjazz81slZndVpIZRaSYBONbdc3IYNS2bdwzaxYrzziD7tOmUfPDD7mzZ0++nTcP9u6FtWshLQ3GjuWj1FRGA58CLVavhi1bwn4kZVJY+zSWAr8Cph5uBjNLBP4J9AVaAgPNrGXJxBORYpVjfKuOHTvy7rvvsmDBAs466yweeOABGjduzJ9uv52N5cpBt2789+ij6ZOWxtzatTk7MRG6doVatcJ+FGVSKKXh7svd/fN8ZusIrHL3Ne6eCbwG9C/+dCIShjZt2jBu3DiWLl1K//79eeSRR2jSpAnXXnstl112GSeffDL/WrUK27AhsvnKLOzIZVIsHz1VD1iX4/L64DoRKcVatWrFK6+8wooVKxg4cCCjR49m//79jBs3jsrJyT+uoUg4iq00zOxjM1uayynatYXc/ioOe6iXmQ02s7lmNneLtnWKxL2mTZvy3HPPsXr1aubNm0fTpk3DjiQU49FT7n5mIRexHmiQ43J9ICOP+xsNjIbIIbeFvG8RiRENGzYMO4LkEMubp+YATc2siZmVBwYA74acSUSkTAvrkNtfmtl6oAswwcwmBtfXNbP3Adz9AHAdMBFYDrzu7svCyCsiIhGhfLjP3d8G3s7l+gygX47L7wPvl2A0ERHJQyxvnhIRkRij0hARkaipNEREJGoqDRERiZpKQ0REoqbSEBGRqKk0REQkaioNERGJmkpDRESiptIQEZGoqTRERCRqKg0REYmaSkNERKKm0hARkaipNEREJGoqDRERiZpKQ0REoqbSEBGRqKk0REQkaioNERGJmkpDRESiptIQEZGoqTRERCRqKg0REYmaSkNERKKm0hARkaipNEREJGoqDRERiVoopWFmF5nZMjPLNrOUPOb7ysyWmNlCM5tbkhlFROR/JYV0v0uBXwGjopj3NHf/tpjziIhIFEIpDXdfDmBmYdy9iIgcobDWNKLlwIdm5sAodx99uBnNbDAwOLi4y8w+L4Y8NYB4W+uJt8zxlhfiL3O85QVlLgnNo5mp2ErDzD4Gjstl0h3u/k6Ui+nm7hlmVgv4yMxWuPvU3GYMCuWwpVIUzGyuux92H0wsirfM8ZYX4i9zvOUFZS4J0e43LrbScPczi2AZGcHPzWb2NtARyLU0RESk+MXsIbdmVsXMqh48D/QmsgNdRERCEtYht780s/VAF2CCmU0Mrq9rZu8Hs9UG0sxsETAbmODu/w0jbw7FuvmrmMRb5njLC/GXOd7ygjKXhKjymrsXdxARESklYnbzlIiIxB6VhoiIRE2lEQUzO9vMPjezVWZ2W9h58mNmz5nZZjOLmwMHzKyBmU0ys+XBEDM3hJ0pL2ZW0cxmm9miIO/wsDNFy8wSzWyBmY0PO0s04m04ITOrbmbjzGxF8PfcJexMeTGz5sFze/C0w8xuPOz82qeRNzNLBFYCZwHrgTnAQHf/LNRgeTCzHsAu4EV3bx12nmiYWR2gjrvPD46amwf8IlafZ4sMZ1DF3XeZWTkgDbjB3dNDjpYvM/sjkAJUc/dzw86THzP7CkiJl+GEzOwFYJq7P2Nm5YHK7r4t5FhRCV7vNgCd3P3r3ObRmkb+OgKr3H2Nu2cCrwH9Q86Up+ADkFvDzlEQ7v6Nu88Pzu8ElgP1wk11eB6xK7hYLjjF/DswM6sPnAM8E3aW0sjMqgE9gGcB3D0zXgojcAaw+nCFASqNaNQD1uW4vJ4YfjErDcysMdAWmBVylDwFm3kWApuBj9w9pvMGRgK3Atkh5yiIg8MJzQuGC4plxwNbgDHBJsBngs+ZxYsBwKt5zaDSyF9uoyrG/DvKeGVmycCbwI3uviPsPHlx9yx3bwPUBzqaWUxvCjSzc4HN7j4v7CwF1M3d2wF9gWuDza+xKgloBzzp7m2B3UDM7wcFCDalnQ+8kdd8Ko38rQca5LhcH8gIKUupFuwbeBN4xd3fCjtPtILND5OBs8NNkq9uwPnBPoLXgNPN7OVwI+Uv53BCwMHhhGLVemB9jrXOcURKJB70Bea7+6a8ZlJp5G8O0NTMmgRNPAB4N+RMpU6wY/lZYLm7PxJ2nvyYWU0zqx6crwScCawINVQ+3P12d6/v7o2J/B1/6u6XhRwrT/E2nJC7bwTWmdnBEWPPAGLyYI5cDCSfTVMQ+0Ojh87dD5jZdcBEIBF4zt2XhRwrT2b2KtALqBEM13KPuz8bbqp8dQMuB5YE+wkA/uzu7x/+JqGqA7wQHG2SALzu7nFxCGucqQ28HXz3ThIwNgaGE8rP9cArwZvMNcCVIefJl5lVJnKE6NX5zqtDbkVEJFraPCUiIlFTaYiISNRUGiIiEjWVhoiIRE2lISIiUVNpiBSCmWUdMkJorp/+NbORBfkkc/A5kFg/tFTKIH1OQ6RwfgiGEjksMzsG6OzuN0a7UHffYmbfmFk3d59eyIwiRUZrGiLF70Lgx7WG4PshHjSzmWY218zamdlEM1ttZkNy3O4/wKUlHVYkLyoNkcKpdMjmqUtymacbke8HyWmdu3cBpgHPEymWzsB9OeaZC6QWQ2aRI6bNUyKFk+/mKSJDjmw55LqD45ctAZKD7xDZaWZ7zax6MAjiZqBuUYYVKSytaYgUvx+Aiodcty/4mZ3j/MHLB9/MVQxuKxIzVBoixW85cOIR3K4ZMTyiq5RNKg2Rwjl0n8bDucwzgciowwV1WnBbkZihUW5FSoCZpQHnFuT7os1sKtDf3b8vtmAiBaTSECkBZtaJyE7zxVHOX5PI15z+p1iDiRSQSkNERKKmfRoiIhI1lYaIiERNpSEiIlFTaYiISNRUGiIiErX/B5exerl2fyr9AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durée entre chaque intervalle (en seconde):  4.214285714285714\n",
      "P0: 01/01/2018 10:00:04\n",
      "P1: 01/01/2018 10:00:08\n",
      "P2: 01/01/2018 10:00:12\n",
      "P3: 01/01/2018 10:00:16\n",
      "P4: 01/01/2018 10:00:16\n"
     ]
    }
   ],
   "source": [
    "trace2 = trace.copy()\n",
    "\n",
    "trace2.resample(factor=2, mode=tkl.MODE_TEMPORAL) \n",
    "trace.plot('k-')\n",
    "trace2.plot('r-')\n",
    "trace2.plot('ro')\n",
    "plt.xlim([-0.5, 7.0])\n",
    "plt.ylim([-1.5, 1.5])\n",
    "plt.title('sur-échantillon temporel facteur 2')\n",
    "plt.show()\n",
    "\n",
    "print (\"Durée entre chaque intervalle (en seconde): \", trace.duration() / (FACTOR*SIZE))\n",
    "print ('P0:', trace2[0].timestamp)\n",
    "print ('P1:', trace2[1].timestamp)\n",
    "print ('P2:', trace2[2].timestamp)\n",
    "print ('P3:', trace2[3].timestamp)\n",
    "print ('P4:', trace2[3].timestamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "19e9fdf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tracklib\n",
    "trace3 = trace.copy()\n",
    "trace4 = trace.copy()\n",
    "\n",
    "tracklib.algo.interpolation.GP_KERNEL = tkl.GaussianKernel(3)\n",
    "trace3.resample(delta=1.5, mode=tkl.MODE_SPATIAL, algo=tkl.ALGO_GAUSSIAN_PROCESS)   \n",
    "\n",
    "tracklib.algo.interpolation.GP_KERNEL = tkl.TriangularKernel(10)\n",
    "trace4.resample(delta=1.5, mode=tkl.MODE_SPATIAL, algo=tkl.ALGO_GAUSSIAN_PROCESS)   \n",
    "\n",
    "trace.plot('k-')\n",
    "#trace1.plot('r-')\n",
    "trace3.plot('b-')\n",
    "trace4.plot('c-')\n",
    "trace3.plotFirstObs(append=True)\n",
    "trace3.plotLastObs(append=True)\n",
    "\n",
    "plt.xlim([-0.5, 7.0])\n",
    "plt.ylim([-1.5, 1.5])\n",
    "plt.title('sur-échantillon spatial facteur 2')\n",
    "plt.show()"
   ]
  }
 ],
 "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
}
