1bis. Premiers graphes - code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ymes=np.array([-0,-0.7,-1.5,-2.3,-3.5,-4.5,-5.9,-7.7,-8.8,-10.6,-12.3,-14.2,-16.4,-18.5,-21,-23.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ymes: [  0.   -0.7  -1.5  -2.3  -3.5  -4.5  -5.9  -7.7  -8.8 -10.6 -12.3 -14.2\n",
      " -16.4 -18.5 -21.  -23.5]\n"
     ]
    }
   ],
   "source": [
    "print('ymes:',ymes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t: [0.         0.01666667 0.03333333 0.05       0.06666667 0.08333333\n",
      " 0.1        0.11666667 0.13333333 0.15       0.16666667 0.18333333\n",
      " 0.2        0.21666667 0.23333333 0.25      ]\n"
     ]
    }
   ],
   "source": [
    "t=np.linspace(0,1/4,16)\n",
    "print('t:',t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x115fb8128>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bd6f630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t,ymes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bd6f5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yreelle=ymes*2/100\n",
    "plt.plot(t,yreelle,'ro',label=\"y=f(t)\")\n",
    "plt.xlabel(\"temps\")\n",
    "plt.ylabel(\"yreelle\")\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.title(\"chute libre\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}