TP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TP Accéléromètre"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import des bibliothèques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Récupération des lignes du fichier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "fileName = \"./Aller-Retour_Noe.csv\"\n",
    "file = open(fileName, 'r')\n",
    "fileLines = list(file)\n",
    "file.close()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extraction des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "t, ax, ay = [], [], []\n",
    "vx, vy= [0],[0]\n",
    "x, y = [0], [0]\n",
    "\n",
    "for i in range(2, len(fileLines)):\n",
    "    currentLine = fileLines[i].rstrip()\n",
    "    data = currentLine.split(\";\")\n",
    "    t.append(float(data[0]))\n",
    "    ax.append(float(data[1]))\n",
    "    ay.append(float(data[2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correction des données en soustrayant la moyenne des données obtenues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "meanx = sum(ax)/len(ax)\n",
    "meany = sum(ay)/len(ay)\n",
    "\n",
    "for i in range(len(t)):\n",
    "    ax[i] = ax[i]-meanx\n",
    "    ay[i] = ay[i]-meany"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Méthode d'Euler : Primitivation de l'accélération puis de la vitesse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff613a71c60>]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(1, len(t)):\n",
    "    vx.append((t[i]-t[i-1])*ax[i]+vx[i-1])\n",
    "    vy.append((t[i]-t[i-1])*ay[i]+vy[i-1])\n",
    "\n",
    "for i in range(1, len(t)):\n",
    "    x.append((t[i]-t[i-1])*vx[i]+x[i-1])\n",
    "    y.append((t[i]-t[i-1])*vy[i]+y[i-1])\n",
    "\n",
    "plt.plot(x, y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calcul de la distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6710433946449831\n"
     ]
    }
   ],
   "source": [
    "xmax = max(x)\n",
    "ymax = max(y)\n",
    "d = (math.sqrt(xmax**2 + ymax**2))\n",
    "print(d)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.4 64-bit",
   "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": "3.10.4"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "4f946df053fbf2b937619d3c5458e7af74262f9a954d8797ba0b27400bcafe06"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}