diff --git a/PreStSauveur/Programmation/5-listes et fonctions.ipynb b/PreStSauveur/Programmation/5-listes et fonctions.ipynb index 75c99c7..2690d37 100644 --- a/PreStSauveur/Programmation/5-listes et fonctions.ipynb +++ b/PreStSauveur/Programmation/5-listes et fonctions.ipynb @@ -15,14 +15,14 @@ "\n", "Nous allons tracer la représentation graphique de \n", "\n", - "$$ f : x \\mapsto \\frac{x^2}{10} - 5 $$\n", + "$$ f : x \\mapsto x^2 - 10x + 20 $$\n", "\n", "**Recopier** le programme suivant, **compléter** les pointillés et calculer les images jusqu'à 8." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -36,7 +36,7 @@ ], "source": [ "def f(x):\n", - " return x**2/10 - 5\n", + " return x**2 - 10*x + 20\n", "\n", "# image de 0\n", "print(\"L'image de\", \"...\",\" est\", \"...\")\n", @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -81,7 +81,7 @@ "print(\"L'image de\", \"...\",\" est\", \"...\", \"On l'ajoute à la liste\")\n", "antecedents.append(0) # On ajoute (append) 0 à la liste des antecedents\n", "# image de 1\n", - "print(\"L'image de\", \"...\",\" est\", \"...\", \"On l'ajoute à la liste\")" + "print(\"L'image de\", \"...\",\" est\", \"...\")" ] }, { @@ -125,12 +125,12 @@ "source": [ "Vous avez tracer la courbe représentative de de la fonction $f$.\n", "\n", - "![Représentation graphique de f](./fig/cos_pas1.png)\n", + "![Représentation graphique de f](./fig/plt_f.png)\n", "\n", - "Dans un nouveau fichier, **tracer** la courbe représentative pour x allant de 0 à 50 de la fonction $g$ tel que\n", - "$$ g : x \\mapsto x^2 - 4$$\n", + "Dans un nouveau fichier, **tracer** la courbe représentative pour x allant de 0 à 15 de la fonction $g$ tel que\n", + "$$ g : x \\mapsto -(x - 5)^2 + 5$$\n", "\n", - "![Représentation graphique de f](./fig/g_pas1.png)" + "![Représentation graphique de f](./fig/plt_g.png)" ] }, { @@ -139,7 +139,9 @@ "source": [ "## Précision et controle du tracé\n", "\n", - "Pour le moment, on sait donner un maximum à l'antécédent $x$ mais on ne peut pas contrôler le minimum ni le pas (l'écart en 2 valeurs de $x$). Or pour tracer précisément la représentation graphique d'une fonction, nous avons besoin de contrôler la fenêtre (minimum et maximum) et le pas." + "Pour le moment, on sait donner un maximum à l'antécédent $x$ mais on ne peut pas contrôler le minimum ni le pas (l'écart en 2 valeurs de $x$). \n", + "\n", + "Or pour tracer précisément la représentation graphique d'une fonction, nous avons besoin de contrôler la **fenêtre** (minimum et maximum) et le **pas**." ] }, { @@ -148,26 +150,93 @@ "source": [ "## Boucle `while` (tant que )\n", "\n", - "Ce type de boucle donne plus de contrôle sur $x$ et nous évite d'utiliser `range` qui est une commande qui n'existe que en Python.\n", + "Une boucle `while` permet de répeter une ou plusieurs actions **tant qu'** une condition est vérifiée.\n", "\n", "Les 2 programmes ci-dessous font la même chose." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n", + "4\n", + "5\n", + "6\n", + "7\n", + "8\n", + "9\n" + ] + } + ], "source": [ - "for x in range(10):\n", - " print(x)" + "x = 0\n", + "\n", + "print(x)\n", + "x = x+1\n", + "\n", + "print(x)\n", + "x = x+1\n", + "\n", + "print(x)\n", + "x = x+1\n", + "\n", + "print(x)\n", + "x = x+1\n", + "\n", + "print(x)\n", + "x = x+1\n", + "\n", + "print(x)\n", + "x = x+1\n", + "\n", + "print(x)\n", + "x = x+1\n", + "\n", + "print(x)\n", + "x = x+1\n", + "\n", + "print(x)\n", + "x = x+1\n", + "\n", + "print(x)\n", + "x = x+1" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n", + "4\n", + "5\n", + "6\n", + "7\n", + "8\n", + "9\n" + ] + } + ], "source": [ "x = 0\n", "while x < 10:\n", @@ -208,9 +277,22 @@ "images = []\n", "antecedants = []\n", "\n", - "for x in range(20):\n", - " images.append(h(x))\n", - " antecedants.append(x)\n", + "# image de 0\n", + "print(\"L'image de\", \"...\",\" est\", \"...\", \"On les ajoute à la liste\")\n", + "antecedents.append(0)\n", + "images.append(h(0))\n", + "\n", + "# image de 1\n", + "print(\"L'image de\", \"...\",\" est\", \"...\", \"On les ajoute à la liste\")\n", + "antecedents.append(1)\n", + "images.append(h(1))\n", + "\n", + "# ....\n", + "\n", + "# image de 20\n", + "print(\"L'image de\", \"...\",\" est\", \"...\", \"On les ajoute à la liste\")\n", + "antecedents.append(20)\n", + "images.append(h(20))\n", "\n", "print(\"Les images sont \", images)" ] diff --git a/PreStSauveur/Programmation/5-listes et fonctions_sol.ipynb b/PreStSauveur/Programmation/5-listes et fonctions_sol.ipynb index 0aabde5..0577af8 100644 --- a/PreStSauveur/Programmation/5-listes et fonctions_sol.ipynb +++ b/PreStSauveur/Programmation/5-listes et fonctions_sol.ipynb @@ -2,12 +2,12 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -23,12 +23,12 @@ "import matplotlib.pyplot as plt\n", "\n", "def f(x):\n", - " return cos(x*pi/2)\n", + " return x**2 - 10*x + 20\n", "\n", "images = []\n", "antecedants = []\n", "\n", - "for x in range(20):\n", + "for x in range(10):\n", " images.append(f(x))\n", " antecedants.append(x)\n", "\n", @@ -38,7 +38,43 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from math import cos, pi\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def g(x):\n", + " return -(x - 5)**2 + 5\n", + "\n", + "images = []\n", + "antecedants = []\n", + "\n", + "for x in range(15):\n", + " images.append(g(x))\n", + " antecedants.append(x)\n", + "\n", + "plt.plot(antecedants, images)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": {}, "outputs": [ { diff --git a/PreStSauveur/Programmation/fig/plt_f.png b/PreStSauveur/Programmation/fig/plt_f.png new file mode 100644 index 0000000..38c03b7 Binary files /dev/null and b/PreStSauveur/Programmation/fig/plt_f.png differ diff --git a/PreStSauveur/Programmation/fig/plt_g.png b/PreStSauveur/Programmation/fig/plt_g.png new file mode 100644 index 0000000..a71a173 Binary files /dev/null and b/PreStSauveur/Programmation/fig/plt_g.png differ diff --git a/config.fish b/config.fish new file mode 100755 index 0000000..33d9bb6 --- /dev/null +++ b/config.fish @@ -0,0 +1,17 @@ +#!/bin/bash + +set -x ROOT (pwd) + +set -x TEXINPUTS ".:$ROOT/tools/style//::" + +function goroot + cd $ROOT +end + +function doublea5 + sh ../Shombos/doublea5.sh +end + +# activate virtual env +source venv/bin/activate.fish +