Données spatio-temporelles et plaquages.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notebook 2 : Données spatio-temporelles et plaquages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Vitesse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "data=pd.read_csv('LOU MHR Export for Delon Armitage 43636.csv',skiprows=8,sep=\";\")\n",
    "data=data.replace(',','.',regex=True)\n",
    "data[[\"Latitude\",\"Longitude\",\"Velocity\",\"Odometer\"]]=data[[\"Latitude\",\"Longitude\",\"Velocity\",\"Odometer\"]].apply(pd.to_numeric)\n",
    "liste=data.Latitude\n",
    "liste=liste.replace(',','.',regex=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On calcule une première fois la vitesse instanée en appliquant la definition de la vitesse instantannée. v=(Xi+1-Xi)/dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1681c5aa5f8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zeoXEi4JepMR64ic/bQvFi4K+C+I29irxoqyTuFDQS8Vqa3PmN2wudxk59cTvW+hNLl4U9FKxHpy/hq/ev5Dn39pU7lKy0r4ciQsFvVSsNZt3ALBx+64yVyId6U0uXhT0XdDzPpBLIXrgyI2GbmImyqkEHzSzJjNbntE20MxmmtmqcD0gtJuZ3WVmDWa2zMxGlLL4ctHGiohUkihb9L8GLu7QNgGY5e61wKwwDzAKqA2X8cC9xSlTRCqJNobiJWfQu/uLwJYOzaOBSWF6EnBFRvvDnrIA6G9mQ4pVrEgl6dlh16P/+NgpdIx+sLtvBAjXg0L7UGB9Rr/G0JZIPfGwuVjS4yDSqWLvjM32Np71VWhm482s3szqm5ubi1xGaelXCSUfPfF9qGd/momfQoN+U3pIJlw3hfZGYHhGv2HAhmwrcPeJ7l7n7nXV1dUFliFCbFMlpmVJD1Ro0E8FxobpscCUjParw9E3ZwEt6SEekZ6qJ554RO9x8VKVq4OZPQacBxxnZo3AD4BbgMlmNg5YB1wZuk8HLgEagJ3ANSWouey0pSZR9OQhPr1G4iVn0Lv7VYdYNDJLXweu7WpRkjw/n7GS1Zt3cM9XEvnVik71xDF6iRd9M1a6xX/MbmDasp41iteTt2p78qeZOFLQd4G21ESyi9Ob3IW3v8Dld79U7jLKSkFfgBg9h6UCdGV74O7Zq6iZMI2de/YVrZ6eZlXTxyxrbCl3GWWloJdYu+QX87jxmTc67xTTj1bF2CD4zwXrAGj5ZG8R1tZ94rRFLwp6ibm3Nm7n0YXryl1Gl3TlG9TpwGyL53uZVAgFfRf0xOOjJbpi/CZ7r7COSvu5De2MjRcFfQH0sVS6W4XlvHZkxYyCvkR27W1ly4495S6jZ4j5O29XMrpXeIVWXNBLrCjou6CzF9/XHljIiB/P7L5iJHaK8faTHrppq7Ckj/dbb8+joC9AlLHX19Zu7YZKpBJ0JaPTz7SKC/qYf8rqaRId9B+07GJ+w+ZylyE9VUbWffm+V3hqUWPeq2jfGVusmhJm+669tOmQpJwSHfSX3jWPr96/sNxliLBwzRa+/dvX879heLOovKNuSm/bzj187qYZ3D7znW64t8qW6KD/sMQ7Q+P80ptcv57F63rI8FHsQ7Dw+vYfXlmsWrpHd4zcbN2Z+hLZfy3LesoLyZDooC/U6uaP2b7r0N9E7K7Rxw3bPuFnz71d0Nbcd55cxt/8v5dLUJVEVZydsanrUoxO7N7XyuvrtxV/xSUy+p75fP2hV9vnq8I/p7XI/5yWT/bS9NGuoq6z3BT0Hbg75//8BT5304yirO97z7xB0/Zd7Gtty/u21z2+hHvmvMsb7/fs3+modId6n377g+2dblDA/i8elWJn7E1T32L0PfNZ9+HOoq+7FBtDr6/fxtyV+0872qtEQf+FW2Zzxr/NKuo6yy2RQb9p+y5qJkxrn89nZ83ufdEDefNHu3P2eWThOs64eRYnfe/3h+zj7llr3Bfa9rY6s9/exAvvRDu37oqN2yP1k9JKH3ny3JsfZF1+8Z3zGHPfghzrSF3nm/Pp59S1jy4+5P2/uSG1AbFl58FDnMvfb+Hu2avyu9Nu1tvyC/qHX1kbqd9Huwv/AblN23exYPWHBd++VEoS9GZ2sZmtNLMGM5tQivtIa2tzbp6+gnUf7mTdhzvZtbeVM28+8N24NcKr5OPw4OazdXD/S2vyqvU/F7xHy879W3DuzhOvreOEG6Zz4o3TDwr79EfTxq07+btf1zP2wVfZva8151DOw6+81z6deX+QegH/tn49AFt37OH6yUvb//Zcrvzly0x+bf0hl7e2OXsL+OQSxQctqY/Sk+vXc8r3/1D0rbhSum3GwTsL03/PWznelC3P4+invr6Blk/28hc/ncOJN05n2rKN/MNvFmXtm35+Zfu0+Vd3v8RtM97Ja9hw6449nHTjdOY3bO708Mq1m3cUZWOkd6/8/jffn/Jmzj7zVkXbmDqUy+9+iTET9795P7tsA5u2l38YqOhBb2a9gXuAUcApwFVmdkqx7yftql8tYOKLqzn3Z3M492dz+F8P1x/Up2MobNu5h5oJ0/jVi6vb20b8KPXlplxvCg1NH2f9xuu8Vc2s/OCjTm/7r79bzqk/mtH+aaP2e7/nu0/t/2XGjlscVeFrkdc9vrS97eR//QPfeGRxp/fz2Kv7fwTs1B/N4NU1W7h/3mrWbN7BZf/xEv/nyWUA3Pfiap5e/D7fnrwUd6eh6eOD1tXW5pz+oxncPH0Fr63dyneeSt12xcbt1EyY1v63bGz5hD+9cTq1nXxyAfgf//Y8NROmsaxxG1t37Gn/lHLxnS/y3296DnfntudWUjNh2gFvUmf9e+rN+8fPvsXOPa0sWbeVR8KPne3aW5o3l1JYsPpDxkx8pf3vAfjfIYgfmr+GiS++e0D/9Bvnng5h/IflGxn587kHPLfrfvI8//zYEk794Qwat36S9f43tuxvr+rdK9zHwc/59MsgvaytzXF3Lrj9Bb72QPYj2c64+Xn2tTlfvX8hzyx5v8P6nG3hk8N5t81l1C/mZV1HPtL7L9KffN2dfa1tfPG2udz5/Dvs3tfa6af51jY/6FdBo/yc8dYde1jdfOBrZV9rG+7Opu2722vZs6+Nbz66hC/f90p7v5NunE7NhGnc8vu3I/2NxWLFPmzLzM4GbnL3i8L8DQDu/u+Huk1dXZ3X1x8c0Lksem8Lf3vvKzn7vfnDiziqbxXuzpL12w65k3LtLZeydcceTg/faF17y6UH9ckcEsrsk25fe8ulNDR9xAW3v5jX35Lpos8O5s4vn864Sa/x8rudfwy8+yunc9nnPtVpjfnoZfBf//QF/mxwP77+0KvMb8j9MfT568896O89um9V+yeF60bW8otZhx4GOOekYyPdTxQXnjKYX11dV5R1ddUXbp19yMDt6Kl/PPug5/KxR/U54Mixm/7qFN5t3sExRxzG3XMaADjxuKM47fj+DOp3OL984cA3iaju+9rnGfnpQTw0fy19qnrx1yOGUvfj59nT2sY3zvtTvnLm8Xzh1jl8/c9r+PXLawEOmM5HVS9rD+a0806u5tfXnAHAtGUb2dvaxlF9q7JutAEcP/BI/uefVTNl6fts31Wc3+nvZTBswJGs29L5/oraQUezqsMG0d+MGMrTi98/xC1SLvrsYD7V/wgemr/2oGUrf3Ixfat6510zgJktcvecT/hSBP2XgIvd/e/D/NeAM939m4e6TaFB35VAy6Z20NG0tjmrN+9on++o44Oc7pNuH9r/CN7fFu3FXSyZdXasryfK9riVgx4LieK04f353bXnFHTbqEGf8+Tghdx3lraD3k3MbDwwHuD4448v6I5m/su5XHhH7i3nv6g9jm079/LG+y18+o/78XaWIZY+vXtROzgVEO1BPzh70PfrW9W+wybdZ1XTx1T368uZJw5k7srmLv+g2cmD+1Hdry8vRfhmb2adxw88kllvN3XpvgEu+Mxgnl+xKVLfU4cdw+sxOYNPL8v+uJXDHx9zOPNW6ZvZUZx5wkAWrtlS7jLK4rqRtSW/j1IEfSMwPGN+GHDQNxrcfSIwEVJb9IXcUe3gflmHV0REZL9SHHXzGlBrZieYWR9gDDC1BPcjIiIRFH2L3t33mdk3geeA3sCD7p77uCYRESmJUgzd4O7TgemlWLeIiOQnkd+MFRGR/RT0IiIJp6AXEUk4Bb2ISMIp6EVEEq7oP4FQUBFmzcB7OTtmdxxQCV8/rJQ6oXJqVZ3FpTqLqzvq/BN3r87VKRZB3xVmVh/ltx7KrVLqhMqpVXUWl+osrjjVqaEbEZGEU9CLiCRcEoJ+YrkLiKhS6oTKqVV1FpfqLK7Y1FnxY/QiItK5JGzRi4hIJyo66LvzJOQZ9/mgmTWZ2fKMtoFmNtPMVoXrAaHdzOyuUN8yMxuRcZuxof8qMxub0f55M3sj3OYu6+wsy53XOdzM5pjZCjN708yui2OtZna4mb1qZq+HOn8Y2k8ws4XhPp8IP3mNmfUN8w1heU3Gum4I7SvN7KKM9qI9T8yst5ktMbNn41qnma0Nj8tSM6sPbbF63MN6+pvZk2b2dnienh3TOk8O/8v0ZbuZfSuOtR6Su1fkhdRPIL8LnAj0AV4HTumG+z0XGAEsz2j7KTAhTE8Abg3TlwC/J3XWrbOAhaF9ILA6XA8I0wPCsleBs8Ntfg+MKrDOIcCIMN0PeIfUydpjVWu47dFh+jBgYbj/ycCY0P5L4B/D9DeAX4bpMcATYfqU8BzoC5wQnhu9i/08Aa4HHgWeDfOxqxNYCxzXoS1Wj3tYzyTg78N0H6B/HOvsUHNv4APgT+Je6wF1F3Nl3XkJ/5TnMuZvAG7opvuu4cCgXwkMCdNDgJVh+j7gqo79gKuA+zLa7wttQ4C3M9oP6NfFmqcAF8a5VuBIYDFwJqkvmlR1fKxJnefg7DBdFfpZx8c/3a+YzxNSZ0ubBZwPPBvuN451ruXgoI/V4w78EbCGsJ8wrnVmqfsvgfmVUGvmpZKHboYC6zPmG0NbOQx2940A4XpQaD9UjZ21N2Zp75IwbHA6qa3l2NUahkOWAk3ATFJbttvcfV+WdbfXE5a3AMcWUH8h7gS+A7SF+WNjWqcDM8xskaXOzQzxe9xPBJqBh8JQ2P1mdlQM6+xoDPBYmI57re0qOegjnYS8zA5VY77thRdgdjTwFPAtd9/eWdc8aypare7e6u6nkdpiPgP4TCfrLkudZnYZ0OTuizKbO1l3OR/7c9x9BDAKuNbMzu2kb7nqrCI1BHqvu58O7CA1/HEocXgt9QEuB36bq2ueNZU8yyo56COdhLybbDKzIQDhuim0H6rGztqHZWkviJkdRirkH3H3p+NcK4C7bwPmkhrX7G9m6TOgZa67vZ6w/BhgSwH15+sc4HIzWws8Tmr45s4Y1om7bwjXTcAzpN484/a4NwKN7r4wzD9JKvjjVmemUcBid98U5uNc64GKOQ7UnRdSWwSrSe3QSu+8+mw33XcNB47R/4wDd8r8NExfyoE7ZV4N7QNJjU8OCJc1wMCw7LXQN71T5pICazTgYeDODu2xqhWoBvqH6SOAecBlpLaaMndyfiNMX8uBOzknh+nPcuBOztWkdpwV/XkCnMf+nbGxqhM4CuiXMf0ycHHcHvewnnnAyWH6plBj7OrMqPdx4Jq4vpY6rb2YK+vuC6m92++QGtP9Xjfd52PARmAvqXficaTGXmcBq8J1+sEz4J5Q3xtAXcZ6/g5oCJfMJ08dsDzc5m467KzKo84vkPr4twxYGi6XxK1W4HPAklDncuD7of1EUkciNJAK076h/fAw3xCWn5ixru+FWlaScdRCsZ8nHBj0saoz1PN6uLyZXk/cHvewntOA+vDY/45U+MWuzrCuI4EPgWMy2mJZa7aLvhkrIpJwlTxGLyIiESjoRUQSTkEvIpJwCnoRkYRT0IuIJJyCXkQk4RT0IiIJp6AXEUm4/w8zX9a1Nh32pwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[\"Vitesse\"]=[(data[\"Odometer\"][i+1]-data[\"Odometer\"][i])/0.1 for i in range(len(data)-1)]+[0]\n",
    "data[\"Vitesse\"].plot()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On obtient beaucoup de valeurs aberrante. Deux solutions s'offre à nous: soit on supprime ces valeurs soit on lisse la courbe. On commence par lisser la courbe de position (technique de la moyenne mobile) pour limiter le bruit de sa dérivé."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1681b361da0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Lcad=[(data[\"Odometer\"][i]+data[\"Odometer\"][i+1]+data[\"Odometer\"][i+2]+data[\"Odometer\"][i+3]+data[\"Odometer\"][i+4])/5 for i in range(len(data)-4)]\n",
    "data[\"OdometerLisse\"]=[0,0]+Lcad+[Lcad[-1],Lcad[-1]]\n",
    "data[\"OdometerLisse\"].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On limite encore plus le bruit sur la courbe de vitesse en appliquant cette definition:: v=(Xi+2-Xi-2)/dt. Ainsi l'erreur est diminuée pour chaque point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1681b355b70>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5eeGkIwqkPTrzSUP6+odwyaZH/3ng28CZZjYt+LkAuBU4x8wWAucEtyNFHbSOW7F2K2Pnri50GSKdUrdMH+juk0g9PHpWpsuVaDrv7gls2d7A0lsvLHQpu1xn7BeoPx8uOjNWdokt2xsKXcIup9GLcLjm8al87rdvFLqMglLQZ0AfYNlVZq3cwGdvep3qTbWFLqVovTJjFRu27Sh0GQWloJdQe3l6FU9/uCLpfUWzrySLOh+atITNdfVMXLAmd/VIp5PxGL3IrvCjJz4C4OvHD0w5T1i3sHJRVvxvK5Z1moSTevRS9MLes8/mfIv4IZqNYf8jWwjryrezUtBnoa3Pnruzo6Fx1xXTCXWGMGn6G4sr5yVkIhv07y36hPVbt+dp6ekTZsTYCobcOJotdfV5qmHXe6Z8Bc+UJx8vL4Ri6eRmc+LUzpwvkj820AnWwUUlkkG/vb6RK/7+PkP/8WHBanjqw+UArC/A3v7qTbUc+7s3mLuq3V891C7/++wM/vfZGTldZi6EvWefTUh3Cf64YlmpSThFMujj45nzchx0HRE/Bbyxcdd/QsfNrWbd1h3cM27hLn/udOobGtmwteMrv8mLP8lDNfmVixVQse6M1VcghEskg752R+zknLr6Rh54e1HevlSqraV25H2+ZnMdz02pzLqeuNUb6wAYNfPjnCzvoUlLeHdR68P7LhwxkUkLO3bY3y+em8nnfvdGh1eA1wRH3yRTDL3dI3/9Gt988P0OP64p6Ivgb0yU663JVN6tWMOqDdt2yXPlynNTKhk3b9d+HUgkgv65KZVUVG9uuv3m3J1fmHnL6Hksqtmc7GEZi3/4lqzZknaedEdLPD55OWV/eJPrnpme8g27qXYHX/nbOyxu599R2ndPAE4evF+75k/n96/M4Rt/n9ysrWr9NmZXbeRbD01O8ajknpsaW6Ft7+CO6mILukTusTOD36lovVWydst2Fqze1Maj23/UzTsVaxg9cxUNBdiKhNgBCKNmrmJ7fSNPpjj3Ide+8eBkzr1zQrO2V2esoqK6rf9patWbaikd/ipPfrA8F+W18vX73+O6Z6bzvUfK87L8VCIR9Nc9M50LRkxsut1rj92a3b+joWNv/JP+OJYrRqbvfSWuXFqKj62+u+gTRoxdSOnwV/lhkl7pDS/MbJqu25E8/MbNq2bq8vXc9eZCHp60hI+Wr2uzrvjf+16S4Y75H2/i1y/NKsiQUqLNHdxJ3bXFO3XN5jrKl63NYUW5F98JO23F+pTzfPW+dzn3rgkp7+/SgaGbbz44mR88NpVDbhiVcp7ypfn7nz02eTlXPTaVO8csyMvyN9buoGZTXav2TS3eS1c/PpWz75yQ0dmwS9dsBeDZHG5hJ/ogj///thR90D80aQkQ2wH7yebYm2CfFkHf0WOQP95YmzQkOyI+cnP98zOb3vj/mV7F3FUbue+tRQCt3ogNLeqs3dHAiLELqauPrQC6GPzulTn899/ebfO56xN6y796cVaz+4b9q5x/vreMynWxrYd1W7Zn1AOMHzq6x25dO/xYgJenVbFi7Va+fM8kGhqdP78+n9LhrzJr5Yak87c8cuUrf3uXf78f63W9PL0qoxp2lbZ6t/GtwlSvQXzLsKHFFtCqDdtY2MaWwHuLkr9///3+sqbpuvqGdm8lprN6Yy2/DN5r97+9qNl9HV2pp3La7eM5/uY3U97v7s1Wqj95alqby3P3pCsOgPJlqTtTiZ/huOpNtWysDe/XLBR90P/+lTlN0yf8cSwAj7y7pNk8LXN+3ZbtlA5/lZteah6CudQlxSD9+X+ZyG2vzWP1xtpWX7R0z9iFzfYnPPD2Yu4cs6DpTfXmnPaN6yWuMP4VfLCHPzeD+95a1LTSc5yNtTs49vdjOO/u1D3KVOJbDfHl1dU3MGrmqmbzLFmzhcvue7dpn0mi370yh1NvH8/MlRsYM2c1946vAOCieyYxauYqSoe/2myro+VKcfnarR2uOQzq6nf+LxL3yyT7HwFs3BYLyT+Omtes/eRbxnHOXRNY/kny/8MVf0++RZq4k/Sqf0/lzDveZvz8pNcG6pC2QnXr9syD/oWPKqneGPuen/VpduK/8NFKLv3rO023l36SemgVYlsgx9/8JvM/7tgwT/wznHj49gk3j+W028d3aDltD9nlVtEHfaKGRmfB6k2tdkK2DPqng2PBH31vZ++mI//0du1nTTNTy14PwIvTqpoF2PaG2Ie/an2s993eb4BsOSxTOvxVnvxwBbe9No8Va2PLanTYXBv7AC5sYwgqmXkfb2zq0ceD/pjfjuGqx6ZSOvzVpjA78463KF+2rmmrq6U+PbsDcMA+PZq1X/XYVAAmVezc0bttRwPvBLcLNQbdUcnW9Z9s3hkO1z0zvWl6W4qgj7en2qcxZXnqoYBFNZv59kOTufnVnZ2hxBXKnGCH6bxVrd/7K9dv43f/mZPyf72lrp4Va7fS0OgsrtnMuym2IADmVHV8x2x9QyNlfxjDT56a3tSBa0v50rWtDgxo+e8f/lzs0ODVG2tZsXYrExbELmGaal+bu7Nuy/ZWHZi4qvW1TZ9NiK2IKqo38a0HJ7frZMmvptkyz6W8Bb2ZnWdm882swsyG5+M5NiXZVEo23pk4dLOlrp5bRs9rNU9b46SZSNWjj0v15kr8XHXtEnt54kM37dWeHNxSV9+sxm0pViLJhgfOu3si5/8ltk8k3rNPDKr45nD8355qf0B8RZPqULyWzb96cRYvTVuZdAx6e30j/3p/WdP5C2G1ZnMdY+asZt2W5ifzperRJwZtsqPH2sqTs+54m4kL1/D3iUvYtr2BLXX1jJ61sxO0R/fYsFuyoc2fPjWNh99pvT+orr6Brdvr+fZDkzn19vEccsMozrzj7dRFAKs21HL6n8bzYTA+vXTNFt6Y3fYRYZvr6lmTsFJMt0/psvvfY1ZV82G/RTVbmgXxkx+uYMXarZz4x7Gcevv4ps9J5bqtbKrdwbbtDc3+FzsanGufmsZVj01l1YZtLP9ka7M6LhgxkVNuHdfstbtwxCQmVazhh4/H9sc9/eEKpqQYBtp/nx7N6sunvHypmZl1Bf5K7ApTlcCHZvayu89p+5Ed8+DE5D3FlhJfvHVZni27emMt1UnG9cqXrqWh0Tlx8H4s+2RLmztqAd6an/yC6F/881s88t3jOf3QEhoaU3+KS4e/CkDZp3rz7A9OaXZffF9FWy66ZxL/c/ohTbcP//Vr9Nt3d47qvy9lpb35zsmlHPar19IuB+DBiYub3f7pU9P51/dPaLp9R4qdc/Fe6uUj30t6/7cf+qDZ7cVrtvDjJ5MPERz6y9FN0//n+EHpi94FkgXoxfe+k2ROeOKD5WzcVs+L01ayKVgBjrvudNYkvJbH/X4Mw88/jF88t3MH/s+emc6cqo2s3dL2a374r1u/lotr2t4/ALGvST5mYC/qG50uZpx71wSWpRguSuX652P1fu3+9/jTZUdzwwszWx0gkXhBmqr121p9TgcnrNwbGp2V67axtsU8C1a3/sydcuu4ZrdPTRheeTO44tkfXp3LH16dC8AVJ+x87yS+p06+pflyEiV+TuKdstdmf9z0GQX4xomDePGjlc0et6hmC6fcOo4//vdRfOPE/L5nLR/HmJvZycBv3P1Lwe3rAdz9lmTzl5WVeXl5xw83OuSGUe3ajL/0mIP47SWf5dkplTxTvoJ5Kcbk4m+2+AuU7GpIiS9eqsd8+oZR1Gc5vHDpMQcxsM+e3DOuIu28C28+n92Cw1K2bq/niF+/ntVzA1x//mFJt3yKQViuYvXb/8zmH+8sLXQZ7WK2cwusR7cuHNy3Z6vPScnePVLuvMzWT84+lP/6VG9mVW3g1iJ932Wq717dKf/lORk91symuHtZ2vnyFPSXAee5+/eD298GTnT3a5LNn2nQ3zJ6Lg+8vTj9jO3Uv9ce1NU3NvWihuy/V6t5Wo5nx+eJt/fvtQcrc7Q5dtC+u1O1If0FJ/bevRv79exOg3vTGHy2+vTsztot+fquoPxK9roVwsbaHU0nr4mkcu3ZQ7j27EMzemx7gz5f30efbNC12RrFzIYBwwAGDcpss+XcIw7k4UlL+M3FR9J3rx6xE3tOHMTtr80PniPWS7nwqH5062q8NK2Kr5cNoLRvT84+/ADuGVfBqIQTTE4avB/du3XhieBkiSEHtA6M+GZrfNghPs/K9dvY0dDIiYP7sGD1JmatTL0Dao/durJtRwNXfuFgbrjgcLZur+epD1fwr/eXNS3/6AH7MqD3HmyYX9NsJ+zph5bw9oIazjniAMYER+GcfmgJXbsYXcw4vhSen7pzE3Fw354c2X9frvzCwU1HJIz+8al8sGQt7y/+hNGzPuawA/fmtENLGDkhttK85JiD6NalC+PmrWZdiyMd3hl+Jufe+XbTkT1fPvogtu1o4JUZyXdYxZ3xmRIG9N6DP1x6FB9vqGXr9nou/es7bKyt56Kj+3H58YM4cN/duevNBUxevJb7v3Ucl90fG9IZccWxXPDZA5m7ahNfvndSs+X23asHf7rsaGp3NNDgzn+mV9G1S3hOv6+o3syGbTs4cJ/d+cKQvvx1fOud8Ef135f6Rm92NulZh+3P2Hmxo2GG7L9Xsw5G7z13o2TvHgzsvWfTPHHjf3YGje6c1WLc/PavHs2ePbpyzeM7z+Voa6st/tk5qv++zExyyOveu3djU209//eUUirXbW06SfGbJw7isck795Ps2b0rpw7py+uzkx8xtvTWC1ttJSc694gDuP2yo3lo0pJ2bd1C7ETBe79xLJ+/bRy1CeemHFLSk0U1W3j0eydwxxvzmVEZ+7uuOGEgHy5dl3a4NVGfnt35f6cNZtnarTw+eTknlPahd8/deH32as494gB+eOYQnv+ostUW3cWfO4iePbpy9Rc/ze67daXvXj2SP0GOFfXQjYhIZ9beHn2+jrr5EBhiZgebWXfgcuDlPD2XiIi0IS9DN+5eb2bXAK8DXYGH3X12Pp5LRETalrdrxrr7KCD1l26IiMguEakzY0VEpDUFvYhIxCnoRUQiTkEvIhJxCnoRkYjLywlTHS7CrAZYlnbG5PoCHbtwaeEUS62qM7dUZ24VS52Q/1o/5e4l6WYKRdBnw8zK23NmWBgUS62qM7dUZ24VS50Qnlo1dCMiEnEKehGRiItC0I8sdAEdUCy1qs7cUp25VSx1QkhqLfoxehERaVsUevQiItKGog76XXEB8iTP+bCZVZvZrIS2PmY2xswWBr97B+1mZiOC+maY2XEJjxkazL/QzIYmtP+Xmc0MHjPCUl05O32dA81svJnNNbPZZvbjMNZqZrub2QdmNj2o87dB+8FmNjl4zqeCr7vGzHoEtyuC+0sTlnV90D7fzL6U0J6z94mZdTWzj8zslbDWaWZLg9dlmpmVB22het2D5fQys2fNbF7wPj05pHV+Jvhfxn82mtm1Yaw1JXcvyh9iX3+8CBgMdAemA0fsguc9DTgOmJXQdjswPJgeDtwWTF8AjCZ2xa2TgMlBex9gcfC7dzDdO7jvA+Dk4DGjgfMzrLMfcFwwvTewADgibLUGj90rmN4NmBw8/9PA5UH7/cAPgumrgPuD6cuBp4LpI4L3QA/g4OC90TXX7xPgp8DjwCvB7dDVCSwF+rZoC9XrHiznUeD7wXR3oFcY62xRc1fgY+BTYa+1Wd25XNiu/An+Ka8n3L4euH4XPXcpzYN+PtAvmO4HzA+mHwCuaDkfcAXwQEL7A0FbP2BeQnuz+bKs+SXgnDDXCuwJTAVOJHaSSbeWrzWxaxycHEx3C+azlq9/fL5cvk+AAcBY4EzgleB5w1jnUloHfahed2AfYAnBfsKw1pmk7nOBd4qh1sSfYh666Q+sSLhdGbQVwgHuvgog+L1/0J6qxrbaK5O0ZyUYNjiWWG85dLUGwyHTgGpgDLGe7Xp3r0+y7KZ6gvs3APtlUH8m7gZ+DsQvRLpfSOt04A0zm2KxazND+F73wUAN8I9gKOxBM+sZwjpbuhx4IpgOe61Nijno016APARS1djR9swLMNsLeA641t1TX7G8gLW6e4O7H0Osx3wCcHgbyy5InWZ2EVDt7lMSm9tYdiFf+8+7+3HA+cAjIwoOAAACJklEQVTVZnZaG/MWqs5uxIZA73P3Y4EtxIY/UgnDZ6k7cDHwTLpZO1hT3rOsmIO+EhiYcHsAUFWgWlabWT+A4Hd10J6qxrbaByRpz4iZ7UYs5B9z9+fDXCuAu68H3iI2rtnLzOJXQEtcdlM9wf37AmszqL+jPg9cbGZLgSeJDd/cHcI6cfeq4Hc18AKxlWfYXvdKoNLdJwe3nyUW/GGrM9H5wFR3Xx3cDnOtzeVyHGhX/hDrESwmtkMrvvPqyF303KU0H6P/E813ytweTF9I850yHwTtfYiNT/YOfpYAfYL7Pgzmje+UuSDDGg34J3B3i/ZQ1QqUAL2C6T2AicBFxHpNiTs5rwqmr6b5Ts6ng+kjab6TczGxHWc5f58AZ7BzZ2yo6gR6AnsnTL8LnBe21z1YzkTgM8H0b4IaQ1dnQr1PAt8N62epzdpzubBd/UNs7/YCYmO6N+6i53wCWAXsILYmvpLY2OtYYGHwO/7iGfDXoL6ZQFnCcr4HVAQ/iW+eMmBW8Jh7abGzqgN1foHY5t8MYFrwc0HYagWOBj4K6pwF/DpoH0zsSIQKYmHaI2jfPbhdEdw/OGFZNwa1zCfhqIVcv09oHvShqjOoZ3rwMzu+nLC97sFyjgHKg9f+RWLhF7o6g2XtCXwC7JvQFspak/3ozFgRkYgr5jF6ERFpBwW9iEjEKehFRCJOQS8iEnEKehGRiFPQi4hEnIJeRCTiFPQiIhH3/wHPHhpebUEyowAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "L=[((data[\"OdometerLisse\"][i+2]-data[\"OdometerLisse\"][i-2])/0.4) for i in range(2,len(data)-2)]\n",
    "data[\"VitesseLisse\"]=[0]+[0]+L+[L[-1],L[-1]]\n",
    "data[\"VitesseLisse\"].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " On voit bien qu'en lissant l'erreur a été diminué par 5. Il ne nous reste alors plus qu'à supprimer les points aberrants. On décide pour ça desupprimer les valeurs superieurs a 12 m/s (c'est le record d'Usain Bolt sur 100m).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1681b34cba8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "l=[]\n",
    "for x in data[\"VitesseLisse\"]:\n",
    "    if x<12:\n",
    "        l.append(x)\n",
    "    else:\n",
    "        l.append(None)\n",
    "data[\"Vitesse2\"]=l\n",
    "data[\"Vitesse2\"].plot()\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On lisse encore une fois la courbe de la meme maniere que precedement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1681b33b5c0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Lcad=[(data[\"Vitesse2\"][i]+data[\"Vitesse2\"][i+1]+data[\"Vitesse2\"][i+2]+data[\"Vitesse2\"][i+3]+data[\"Vitesse2\"][i+4])/5 for i in range(len(data)-4)]\n",
    "data[\"Vitesse2Lisse\"]=[0,0]+Lcad+[Lcad[-1],Lcad[-1]]\n",
    "data[\"Vitesse2Lisse\"].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accelerations\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On calcule laccélération à l aide de la formule A(Xi)=(V(Xi+1)-V(Xi))/dt en utilisant la première donnée de vitesse calculée (non lissée). Comme attendu, les résultats de l'accélération présentent de nombreuses valeurs aberrantes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1681c270860>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[\"Acceleration\"]=[(data[\"Vitesse\"][i+1]-data[\"Vitesse\"][i])/0.1 for i in range(len(data)-1)]+[0]\n",
    "data[\"Acceleration\"].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On utilise alors la vitesse lissée, et on trouve des résultats plus cohérents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1681a4b0588>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[\"Acceleration\"]=[(data[\"Vitesse2Lisse\"][i+1]-data[\"Vitesse2Lisse\"][i])/0.1 for i in range(len(data)-1)]+[0]\n",
    "data[\"Acceleration\"].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accelerations et plaquages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On met maintenant en place une méthode permettant d'extraire les décélérations les plus importantes, pouvant s'apparenter à des plaquages (par exemple celle supérieures à 8m.s^-2.)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-12.580000000000098, -12.320000000000277, -11.06000000000023, -10.019999999999527, -14.559999999999604, -15.559999999999832, -13.339999999999463, -16.499999999998636, -18.65000000000009, -12.279999999998381, -11.470000000003893, -10.639999999996235, -12.339999999999236, -12.229999999999563, -20.66000000000031, -13.840000000000146, -15.059999999998581, -16.890000000007603, -15.370000000007167, -12.850000000003092, -17.200000000002547, -12.280000000000655, -12.75000000000091, -21.81999999999789, -14.909999999999854], ['05.05.2018 21:03', '05.05.2018 21:03', '05.05.2018 21:09', '05.05.2018 21:12', '05.05.2018 21:15', '05.05.2018 21:15', '05.05.2018 21:15', '05.05.2018 21:42', '05.05.2018 21:48', '05.05.2018 21:48', '05.05.2018 21:51', '05.05.2018 22:07', '05.05.2018 22:08', '05.05.2018 22:08', '05.05.2018 22:13', '05.05.2018 22:13', '05.05.2018 22:21', '05.05.2018 22:31', '05.05.2018 22:31', '05.05.2018 22:31', '05.05.2018 22:32', '05.05.2018 22:41', '05.05.2018 22:41', '05.05.2018 22:45', '05.05.2018 22:45']]\n",
      "Le nombre de décélérations pouvant s apparenter à des plaquages est de : 25\n"
     ]
    }
   ],
   "source": [
    "Decelerationsimportantes=[[],[]]\n",
    "for k in range(len(data[\"Acceleration\"])):\n",
    "    if data[\"Acceleration\"][k]<-10:\n",
    "        Decelerationsimportantes[0].append(data[\"Acceleration\"][k])\n",
    "        Decelerationsimportantes[1].append(data['Timestamp'][k])\n",
    "print(Decelerationsimportantes)\n",
    "print('Le nombre de décélérations pouvant s apparenter à des plaquages est de :',len(Decelerationsimportantes[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cette méthode étant ponctuelle, elle est très susceptible de donner des résultats erronés ou très peu précis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vitesse et plaquages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A partir de la Vitesse, nous allons essayer de trouver un moyen de repérer les décélérations brusques, qui pourraient correspondre à un plaquage. On met alors en place une méthode qui permet de repérer les diminutions 5m.s^-1(choisi arbitrairement pour l'instant) en une demi seconde (durée approximative d'un plaquage)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72200"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l=list(data[\"Vitesse2\"])\n",
    "len(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Le nombre de décélérations pouvant s apparenter à des plaquages est de : 15\n"
     ]
    }
   ],
   "source": [
    "i=0\n",
    "a=0\n",
    "ListePlaquage=[]\n",
    "while i< (len(l)-5):\n",
    "    if l[i+5]-l[i]<-5:\n",
    "        \n",
    "        a+=1\n",
    "        i+=10\n",
    "    i+=1\n",
    "print('Le nombre de décélérations pouvant s apparenter à des plaquages est de :', a )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On réalise maintenant la même chose pour différentes valeurs de décélérations. En effet, il est difficile de quantifier une décélération due à un plaquage. L'idée est donc de tracer une courbe du nombre de décélérations en fonction de la valeur de décélérations choisies, et d'essayer de déterminer pour quelle valeur le nombre de décélérations peut s'apparenter à un nombre de plaquages par match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha=0\n",
    "Lalpha=[]\n",
    "Lrec=[]\n",
    "while alpha>-11:\n",
    "    i=0\n",
    "    a=0\n",
    "    \n",
    "    ListePlaquage=[]\n",
    "    while i< (len(l)-5):\n",
    "        if l[i+5]-l[i]<alpha:\n",
    "            a+=1\n",
    "            i+=10\n",
    "        i+=1\n",
    "    Lalpha.append(alpha)\n",
    "    Lrec.append(a)\n",
    "    alpha\n",
    "    alpha-=0.5\n",
    "    \n",
    "\n",
    "\n",
    "        \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1681a4e1898>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Lalpha,Lrec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En utilisant cette courbe, une conclusion pourrait être tirée par exemple en regardant le match en question et en comptant le nombre de plaquages "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dans ce notebook, nous avons, à partir de données de position, élaboré tout d'abord une nouvelle donnée : la vitesse. En  utilisant le principe de la moyenne mobile et en supprimant les poins aberrants, nous sommes parvenus à constituer une liste de vitesse pertinente. Nous en avons ensuite tiré une liste des accélérations du joueur tout au long du match.\n",
    "\n",
    "Nous avons ensuite mis en place des méthodes permettant, à partir de la vitesse et de l'accélération, permettant à priori de repérer tout au long du match les décélérations assez importantes, pouvant s'apparenter à un plaquage.\n",
    "\n",
    "Le défaut principal de ce travail, est justement la difficulté à quantifier précisement ( en terme de décélération) le phénomène du plaquage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}