{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Etude de l'influence de l'amplitude et de la période pour un signal périodique (version élève)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    ":download:`Télécharger le pdf <./signal_periodique_eleves.pdf>`\n",
    "\n",
    ":download:`Télécharger le notebook <./signal_periodique_eleves.ipynb>`\n",
    "\n",
    ":download:`Lancer le notebook sur binder (lent) <https://mybinder.org/v2/gl/pyspc%2Fpyspc/master?filepath=07-activites%2F00-terminale%2Fsignal_periodique%2Fsignal_periodique_eleves.ipynb>`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous allons étudier un signal sinusoïdal. Un tel signal se répète identique à lui-même tous les 2 $\\pi$, au bout d'une durée T (période en s).\n",
    "\n",
    "Son évolution au cours du temps t se traduit par la fonction mathématique : A.sin((2$\\pi$/T).t) \n",
    "\n",
    "où A est l'amplitude\n",
    "\n",
    "Comme le temps t ne peut pas être continu, il faut le discrétiser, c'est-à-dire calculer t pour des valeurs entières, multiples d'une petite durée appelée *période d'échantillonnage* et notée Te.\n",
    "\n",
    "Te doit être suffisamment petit par rapport à T. \n",
    "\n",
    "Ce qui donne l'expression mathématique suivante : A.sin((2$\\pi$/T).i.Te)   \n",
    "\n",
    "Avec i prenant des valeurs entières."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# cette fonction permet d'afficher un graphique \n",
    "# à un emplacement précis de la fenêtre graphique. \n",
    "# Ainsi, on peut afficher plusieurs sous-graphiques \n",
    "# sur la même fenêtre.\n",
    "\n",
    "def affichage_graphique(n,y,l,yl) :\n",
    "# Déclaration du nombre d'emplacements dans la fenêtre    \n",
    "    plt.subplot(3,1,n)   \n",
    "# Affichage de la courbe\n",
    "    plt.plot(t,y,'k-',lw=1,label=n)    \n",
    " # Impose les bornes min et max sur l'axe des ordonnées\n",
    "    plt.ylim(-2,2)                          \n",
    "    plt.grid()\n",
    "    plt.xlabel('t (s)')\n",
    "    plt.ylabel(yl)\n",
    "    plt.legend()\n",
    "\n",
    "# Déclaration des variables\n",
    "Ymax=1   # amplitude en m\n",
    "T=1      # période en s\n",
    "Te=0.01   # période d'échantillonnage en s\n",
    "# Création des listes (vides) qui contiendront les valeurs \n",
    "# du temps et des amplitudes\n",
    "t=[]                                             \n",
    "y1=[]                                            \n",
    "\n",
    "# Boucle permettant de parcourir toutes les valeurs du temps \n",
    "# discrétisé.\n",
    "for i in range (0,1000) : \n",
    "# La méthode append permet de rajouter une valeur en fin \n",
    "# de la liste t\n",
    "    t.append(i*Te)    \n",
    "# la fonction sinus est contenue dans la bibliothèque numpy\n",
    "# la constante pi est contenue dans la bibliothèque numpy\n",
    "    y1.append(Ymax*np.sin((2*np.pi/T)*i*Te))     \n",
    "                                                 \n",
    "\n",
    "# création du graphique \n",
    "\n",
    "# création de la fenêtre graphique\n",
    "plt.figure(2,figsize=(10,12))          \n",
    "# appel de la fonction gérant l'affichage du sous-graphique\n",
    "affichage_graphique(1,y1,\"courbe de référence\",\"y1 (m)\")   \n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **Etude préalable :**\n",
    " \n",
    "En tenant compte des renseignements donnés lignes 24, 25 et 33, répondez aux questions suivantes :\n",
    "\n",
    "1. Combien d'échantillons temporels aura-t-on?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Par quel calcul simple aurait-on pu prévoir la durée du signal créé?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Par quel calcul simple aurait-on pu prévoir le nombre de périodes affichées?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Combien d'échantillons temporels a-t-on par période?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous souhaitons étudier l'influence des paramètres A et T sur l'évolution temporelle du signal sinusoïdal.\n",
    "\n",
    "Pour cela, nous avons déjà écrit en lignes 25 et 37 du programme ci-dessous, la création d'un signal sinusoïdal de référence noté y1.\n",
    "\n",
    "Nous avons également déjà déclaré les listes y2 et y3 sur les lignes de code 26 et 27.\n",
    "\n",
    "Sur le modèle de la ligne 37, compléter la ligne 38 de manière à créer un signal sinusoïdal nommé y2 d'amplitude deux fois plus grande que y1.\n",
    "\n",
    "Puis, toujours sur le même modèle, compléter la ligne 39 de manière à créer un signal sinusoïdal nommé y3 de période deux fois plus grande que y1.\n",
    "\n",
    "Nous souhaitons de plus, afficher ces trois signaux en trois graphiques situés l'un au-dessous de l'autre. Nous allons pour cela utiliser la méthode plt.subplot(nombre de lignes, nombre de colonnes, index) de la bibliothèque matplotlib.pyplot as plt.\n",
    "\n",
    "L'affichage est géré par une fonction nommée affichage_graphique qui a besoin d'un certain nombre de paramètres (fournis entre parenthèses) pour fonctionner correctement.\n",
    "\n",
    "Sur le modèle de la ligne 43, écrire la ligne de code nécessaire à l'affichage de y2.\n",
    "\n",
    "Puis, toujours sur le modèle de la ligne 43, écrire la ligne de code nécessaire à l'affichage de y3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "def affichage_graphique(n,y,l,yl) :\n",
    "# Déclaration du nombre d'emplacements dans la fenêtre\n",
    "    plt.subplot(3,1,n)       \n",
    "# Affichage de la courbe\n",
    "    plt.plot(t,y,'k-',lw=1,label=n)   \n",
    "# Impose les bornes min et max sur l'axe des ordonnées\n",
    "    plt.ylim(-2,2)                       \n",
    "    plt.grid()\n",
    "    plt.xlabel('t (s)')\n",
    "    plt.ylabel(yl)\n",
    "    plt.legend()\n",
    "\n",
    "# Déclaration des variables\n",
    "Ymax=1   # amplitude en m\n",
    "T=1      # période en s\n",
    "Te=0.01   # période d'échantillonnage en s\n",
    "\n",
    "# Création des listes (vides) qui contiendront les valeurs\n",
    "# du temps et des amplitudes\n",
    "t=[]                                             \n",
    "y1=[]                                           \n",
    "y2=[]\n",
    "y3=[]\n",
    "\n",
    "# Boucle permettant de parcourir toutes les valeurs du temps discrétisé.\n",
    "for i in range (0,1000) :     \n",
    "# La méthode append permet de rajouter une valeur en fin de la liste t\n",
    "    t.append(i*Te)                               \n",
    "# la fonction sinus est contenue dans la bibliothèque numpy\n",
    "# la constante pi est contenue dans la bibliothèque numpy\n",
    "# On aurait pu aussi utiliser la bibliothèque math pour y avoir accés\n",
    "# à l'aide des fonctions math.sin() et math.pi\n",
    "    y1.append(Ymax*np.sin((2*np.pi/T)*i*Te))     \n",
    "                                                 \n",
    "       \n",
    "\n",
    "    # création de la fenêtre graphique\n",
    "plt.figure(2,figsize=(10,12))                     \n",
    "affichage_graphique(1,y1,\"courbe de référence\",\"y1 (m)\")\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Format de la Cellule Texte Brut",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
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 },
 "nbformat": 4,
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}