{ "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": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Approximation de l'intégrale d'une courbe par la méthode des rectangles\n", "\n", "[Fichier Geogebra : somme des rectangles pour la parabole $y=x^2$sur [0;1]](https://www.geogebra.org/m/n6javdkf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "au départ x est en a \n", "\n", "on avance avec u npas de (b-a)/n si on a n rectangles\n", "\n", "on construit une suite d'abscisses x(k) = a + k * (b-a)/n\n", "\n", " x(n) = b\n", " \n", "le rectangle d'indice k a pour base l'intervalle [x(k) ; x(k+1)]\n", "\n", "sa hauteur est f( x(k+1) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exemple de la fonction sommeRectangle(a, b, n) du cours\n", "\n", "Que représentent les variables `u` et `v` dans la fonction ci-dessous ?" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "\n", "def f(x):\n", " return x ** 2\n", "\n", "def sommeRectangle(a, b, n):\n", " h = (b - a) / n\n", " x = a \n", " u = 0\n", " v = 0\n", " for k in range(0, n): #n subdivisions => n tours\n", " u = u + h * f(x) #on ajoute à u l'aire du kieme rectangle à gauche\n", " x = x + h #on avance x d'un pas\n", " v = v + h * f(x) ##on ajoute à u l'aire du kieme rectangle à droite\n", " return (u, v)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.33283350000000095, 0.33383350000000095)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sommeRectangle(0, 1, 1000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exemple 3\n", "\n", "Compléter les fonctions Python ci-dessous pour qu'elles retournent une approximation de $\\int_{a}^{b}f(x) \\mathrm{dx}$ par la somme de rectangles à gauche construits sur $n$ subdivisions régulières de l'intervalle $[a;b]$." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def rectangleGauche(f, a, b, n):\n", " s = 0\n", " pas = (b - a)/n\n", " x = a\n", " for k in range(0, n):\n", " s = s + pas * f(x) #x * f(x) est l'aire du kieme rectangle\n", " x = x + pas\n", " return s\n", "\n", "\n", "def rectangleGauche2(f, a, b, n):\n", " s = 0\n", " pas = (b - a) / n\n", " for k in range(0, n):\n", " s = s + pas * f(a + k * pas) # ou s = s + pas * f(a + k * (b-a)/n)\n", " return s\n", "\n", "def rectangleGaucheDessin(f, a, b, n):\n", " s = 0\n", " pas = (b - a)/n\n", " x = a\n", " for k in range(n):\n", " s = s + f(x) * pas\n", " plt.fill([x, x + pas, x + pas, x, x], [0] * 2 + [f(x)] * 2 + [0], hatch='/',edgecolor='black', facecolor='none')\n", " x = x + pas \n", " lesx = np.linspace(a, b, 1000) \n", " fvect = np.vectorize(f)\n", " ax = plt.gca()\n", " ax.spines['top'].set_color('none')\n", " ax.spines['right'].set_color('none')\n", " ax.spines['bottom'].set_position(('data',0))\n", " ax.spines['left'].set_position(('data',a))\n", " plt.plot(lesx, fvect(lesx), color='black')\n", " plt.title(r\"Rectangles à gauche $\\int_{%d}^{%d}f(x)dx\\approx$%1.3f\"%(a,b,s))\n", " plt.savefig('methodeRectangleGauche-{}-{}-{}-{}subdivisions.eps'.format(f.__name__,a,b,n))\n", " plt.show()\n", " return s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Écrire une fonction `rectangleDroite(f, a, b, n)` qui retourne une approximation de $\\int_{a}^{b}f(x) \\mathrm{dx}$ par la somme de rectangles à droite construits sur $n$ subdivisions régulières de l'intervalle $[a;b]$.\n", "\n", "* premier tour : x = a + pas\n", "* deuxième tour : x = a + 2 * pas\n", "* troisième tour : x = a + 3 * pas" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def rectangleDroite(f, a, b, n):\n", " s = 0\n", " pas = (b - a)/n\n", " x = a + pas #borne droite\n", " for k in range(0, n):\n", " s = s + pas * f(x)\n", " x = x + pas\n", " return s\n", "\n", "def rectangleDroite2(f, a, b, n):\n", " s = 0\n", " pas = (b - a) / n\n", " for k in range(1, n + 1):\n", " s = s + pas * f(a + k * pas) \n", " return s\n", "\n", "def rectangleDroite3(f, a, b, n):\n", " s = 0\n", " pas = (b - a) / n\n", " for k in range(0, n):\n", " s = s + pas * f(a + (k+1) * pas)\n", " return s\n", "\n", "\n", "def rectangleDroiteDessin(f, a, b, n):\n", " s = 0\n", " pas = (b - a)/n\n", " x = a\n", " for k in range(n):\n", " s = s + f(x + pas) * pas\n", " plt.fill([x, x + pas, x + pas, x, x], [0] * 2 + [f(x+pas)] * 2 + [0], hatch='/',edgecolor='black', facecolor='none')\n", " x = x + pas \n", " lesx = np.linspace(a, b, 1000) \n", " fvect = np.vectorize(f)\n", " ax = plt.gca()\n", " ax.spines['top'].set_color('none')\n", " ax.spines['right'].set_color('none')\n", " ax.spines['bottom'].set_position(('data',0))\n", " ax.spines['left'].set_position(('data',a))\n", " plt.plot(lesx, fvect(lesx), color='black')\n", " plt.title(r\"Rectangles à droite $\\int_{%d}^{%d}f(x)dx\\approx$%1.3f\"%(a,b,s))\n", " plt.savefig('methodeRectangleDroite-{}-{}-{}-{}subdivisions.eps'.format(f.__name__,a,b,n))\n", " plt.show()\n", " return s" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def inverse(x):\n", " return 1 / x" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'inverse'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "inverse.__name__" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.7187714031754279" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rectangleGauche(inverse, 1, 2, 10)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.718771403175428" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rectangleGauche2(inverse, 1, 2, 10)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "0.7187714031754279" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rectangleGaucheDessin(inverse,1,2,10)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "def carre(x):\n", " return x ** 2" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAAELCAYAAADuufyvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3deXgT5d4+8PsptNQCshXPC/QI1CKbbL8ii6CoKJTKVgVkEXBDqrKKHoGjyIvicTkiKrJ6BFEW2WRRii9CgYMbIIhakH1VwJZ9K6XN/ftjkhpiJkmTmUzSfD/XNRckmXyfZ9Lm7pOZZyaKJIQQQlgvyuoOCCGE0EggCyFEiJBAFkKIECGBLIQQIUICWQghQoQEshBChAgJZCGECBESyEIIESIkkIXwk1KqnFJqk1LqglLqFqv7I8KfBLIQ/rsE4D4Ai6zuiCgeJJCF8BPJqySzre6HKD4kkIUQIkRIIAvhhVKqqVLqV6XUQaVUstX9EcWXBLIQ3k0CkAXgKwD/srgvohgraXUHhAgDjQHMhHbw7nrnB5RSK+2P11ZKTSM5K/jdE8WFjJCLKfvH63sMrplor1vNyLpGMWmbrwNQCsBZkidJHnB+nGQqyaokW7qGsVKqslJqtVLqtFLqQ6XUv5RSw3xsd5NSqr6XdV5USr1fxE0SIUwC2Q37G/uyfX7pcaXULKVUGYPqGhoYwURyP4AhAKZY3ZcgcvzcL/jx3FEA9pCsAOB5AP0ATPPxuf8GMM7LOvUB/ORHv3ymlKqolPpMKXVRKXVIKdVbZ71SSqn/2Nc5r5T6USnVwWWdGkqplfY/UMeVUpOUUiWdHl+nlMq1v+8uKKV2mbltoUgCWV8nkmWgfRxtAu3NFfFILgewQClVxeq+BElZ+7/+BPI9ABba//8wgJUkL/v43OUA7lJK/Y+HdUwPZADvA8gD8DcAfQBM0Rm5lwRwBEAbAOUAvADt96SG0zqTAfwBoAq091UbAE+51BlEsox9qW3gdoQFCWQvSB4H8CW0XyAAgFKqqlJqsVIqWyl1QCk1xOmxvyulltgfO6mUmmS//2MANwJYYf/r/w/7/SOVUvvso4odSqk0p1oHlVLPKqV+UkqdVUp9qpSKtT/2/5RS2+zPW2h/7BV32+Cpv/bHn1dK/WavtUsp1Vanzkil1D5oI+Q1zn11s67H/nnZbiqlkpxuz3J5rtvX2K6xu9fLl9dBR5FHyEqpGKXUWQANoP28fwbQAcB6l/XeUEotdbr9plJqjVIqhmQugB8AtLc/FqWUGqWU+kMp9btSqieAJAC/eKvla7/dbEdpAA8AeJHkBZIbof2h6Ou6LsmLJMeSPEjSRvJzAAcAOM9KqQlgAclc+/tqFbQ/KsKBpCwuC4CDAO6x/z8BwM8A3rHfjoL2RhkDIAZAIoD90N44JQBsB/A2gNIAYgG0dlfX6b7uAKra6z4I4CKAKk7rb7I/XhHATgDp9nYPARgKIBrA/dBGMa+4tuWpv/b1akMb2VS1364B4Cad10W3ry7r+dI/T9tNAElO685yPNfTa6z3enn7uXn5XbjN3p/aRfwdqgfghNPtbAC3uqxTCcBZaJ/A0u2/Z+WcHn8XwAT7/8cC+BrA/0AbgX4NYL+vtZzWqw7gHQBrAUwF0BZAnP15/+uybhMAl1zuexbACh+2/28AcgHUcbpvIIDZ9vaqQftjkub0+Dr765Rj3747rc6CYC+WdyAUF/sb+wKA8/Y34xoA5e2PNQdw2GX9UdCOwre0/0KV9FD3Hi9t/wigi9P6Dzk99ob9TXQHgN8AKKfHNsJ9IOv21/7/JGgfI+8BEF3E16mwry73e+2fl+32FMi6r7He6+Xt5+ZlG9vZ+1OtiK9NbwD/53T7qnM4Od0/Ftpuh0MA/u7y2HgAHwKobP99vMnpsdEAlvlay2mdaQAeB9AKwDAA3wE4B+B7AKku694O4LjLfQMArPOy7dHQpghOc7m/LrQ/ivn213SWy+9Ic2i7iEoB6A/t/ed2cFBcF9lloa8rybIA7gRQB0C8/f7qAKoqpc44Fmhvjr8B+DuAQyTzfW1EKdXPfgDEUesWp7YA4LjT/y9B+whdFcBvtP8W2x3RacJTf0FyL7Q35lgAfyil5iulqvrZVwev/StCLVfeXmN3rxfg5XXwwN99yI2hjeQdTjvVcrYN2q6NUSRdf4ZlAZyBNordSXKf02N/w1/3H3uq5TAK2qeKntCC8yloo+ve0F5bZxfgMs3Pfvu8Tm0opaIAfAztE9Egl/tXAVgC7ZNNPIAKAF53rEPye5LnSV4h+RG0UXKqXlvFkQSyFyTXQ/tL/m/7XUcAHCBZ3mkpSzLV/tiNzkeOXcs531BKVQcwA9ovbiWS5aF9jFNeunUMQDWllPN6rm8mB0/9dWzjXJKtoYUW4fQm8bOvHvvnQ61L0D7WOjgf2PL2Guvx+jro8HeWRSNcG8g/AbjZeQWlVANo++M/AvComxp17TXioX2KcTwvGkBXOAWyD7Uc3oA2Ql0E7Wc9FVroLwDwu8u6uwGUVErVctmuLHeF7T/v/0D7Y/EAyatOD1eEdgxlkj1wT0L7VOnp9Se8vxeKFQlk30wEcK9SqhG0fZTn7QfCrlNKlVBK3aKUutX+2DEArymlSiulYpVSrZzqnIC279KhNLRfumwAUEo9Am2k6M23AAoADFJKlVRKdQHQTGddT/2FUqq2UupupVQpaPv8LgOwualTlL5665+3Wj8C6G3vawq0o/HO2+PpNdbj8XXwoAyAyyQLfGjDmWsgr4TTdihtLvcKaPt7nwLQQCl1p9PjsdAOiK0GsAtAa6XUzUqpctCC90Zo+4m91nIxkORUkutJ/ptkM5KlSSaTXOG8IsmL0Ea04+yvdSsAXaCNgN2ZAu2PSCe6zCYhmQPtIN+T9t+J8tB2S/xk34bySqn29p9nSaVUH2i7vlbptFUsSSD7gNoVvWYDGGN/Y3aE9pH0ALQDEB9AO4BSAKATtP2yhwEchXbAyuFfAF6wf2R+luQOAG9BC7AT0D5ufu1Df/KgHSh7DNro5iEAnwO44mZd3f7aVykF4DX7/ccB3AA3U/yK0ldv/fOh1lBor+MZaFOtljrV9vYau+XD66CnDIo4OlbaVLUKAH51uns2gFT7H4ProQX0BJLLSV4C8Ca0fcYOnaDtq/2d5GoA8wFsAbAZ2h+yXAB7fKxVyI8/LE8BuA7aCH0egCdJFo6QlVIZSqnR9k89A6G9vsfVn3OJ+zjVuh9Air3/e6HtVx9ufywawCv486DeYGi7DXcXsb9hTV27m0+EK6XU99AOYM20ui/uhHr/XNn3eSoA/wugJ8kkN+u8Dm0WxkEAj7p8RHdX81UAf5Cc6EP73wN4jOQvfnRfhCkZIYcppVQbpdT/2D/e9QfQECH08S7U++eDQ9BO5mgCbfrcNey7r6qRvB3aSLibt4IkR/sSxvZ1m0sYRx65uFD4qg3tQExpaPNpu5E8Zm2XrhHq/fNmPIDpAE5CO6nD1W0A/s/+/1UAHoH2kV4Iv8kuCyF0KKUqAzhP7aw518dGA9hBcqnSziocR9LtdR6E8JWMkIXQQc9fz3QGf87RLQfglPk9EsWd7EMWwj/fQDu7EdBOm/c6O0YIbySQhfADyR8BnFBK/RfaBXIWW9wlUQzIPmQhhAgRMkIWQogQEehBPb+H1ykpKVi1KpympQohhGHcXqPDshFyTk6OVU0LIURIkl0WQggRIiSQhRAiREggCyFEiJBAFkKIECGBLIQQIcJrICulPlTaV4+7vRQgSQwZMgRJSUlo2LAhtm7danwvhRAiAvgyQp4F7Sr/bmVkZGDPnj3Ys2cPpk+fjieffNKwzgkhRCTxGsgkN8DDlayWLVuGfv36QSmFFi1a4MyZMzh2LJwueyuEEL47cuQI0tLS8Pvvrt8JG7iAL7/522+/4e9///MLjxMSEvDbb7+hSpUqf1l3+vTpmD59OgAgO9vTlQ2FECL05OXloXv37sjKysL58+cNrx/Ug3pPPPEEtmzZgi1btqBy5crBbFoIIQI2YsQIfP/995g5cyZq165teP2AA7latWo4cuRI4e2jR4+iWrVqgZYVQoiQMnfuXEyaNAnPPPMMunXz+hWKfgk4kDt37ozZs2eDJL777juUK1fO7e4KIYQIV1lZWRgwYABat26N1157zbR2vO5DVkrNA3AngHil1FEALwGIBrQpb6mpqVi5ciWSkpIQFxeHmTPD4lvehRDCJ2fPnkVaWhrKli2LBQsWIDo62rS2Ar1Avd9Pbtq0KbZs2RJI20IIYSqbzYa0tDSsXLkSa9euxe23325UabeX35QvORVCCB2vvvoqli9fjnfffdfIMNYlp04LIYQbGRkZGDNmDB566CEMGjQoKG1KIAshhIt9+/ahd+/eaNSoEaZNmwal3O5hMJwEshBCOLl48SLS0tKglMKSJUsQFxcXtLZlH7IQQtiRxIABA/DLL78gIyMDNWvWDGr7EshCCGH3zjvvYN68eXj11VfRvn37oLcvuyyEEALA+vXr8eyzzyItLQ0jR460pA8SyEKIiHf06FH06NEDtWrVwqxZs4J2EM+VBLIQIqJduXIFDzzwAC5fvozPPvsM119/vWV9kX3IQoiIRRKDBw/Gpk2bsGTJEtSpU8fS/sgIWQgRsaZMmYIZM2Zg9OjRSEtLs7o7EshCiMi0bt06DB06FB07dsTLL79sdXcASCALISLQgQMH0K1bN9SqVQtz5sxBVFRoRGFo9EIIIYLkwoUL6NKlCwoKCrBs2TJLD+K5koN6QoiIYbPZ0L9/f2RlZSEjIwO1atWyukvXkEAWQkSMV155BUuWLMGECRPQrl07q7vzF7LLQggRET777DO89NJL6N+/P4YNG2Z1d9ySQBZCFHs///wz+vbti+bNm2Pq1KmWnYnnjQSyEKJYy8nJQZcuXXD99ddjyZIliI2NtbpLumQfshCi2Lp69Sp69OiB33//HRs2bEDVqlWt7pJHEshCiGLrmWeeQWZmJmbPno1mzZpZ3R2vZJeFEKJYmjJlCiZNmoQRI0agb9++VnfHJxLIQohiZ/Xq1Rg8eDDuu+8+vP7661Z3x2cSyEKIYuXXX39F9+7dUa9ePcybNw8lSpSwuks+k0AWQhQbJ0+eRMeOHVGqVCmsWLECZcuWtbpLRSIH9YQQxUJeXh7uv/9+HD16FOvWrUP16tWt7lKRSSALIcIeSaSnp2PDhg2YO3cuWrRoYXWX/CK7LIQQYe/f//43Zs6ciTFjxqBXr15Wd8dvEshCiLC2dOlSPP/883jwwQcxduxYq7sTEAlkIUTY2rZtG/r06YNbb70VM2fODNlrVPjKp0BWSqUopXYppfYqpUa6Pn748GHcddddaNKkCRo2bIiVK1ca31MhhHBy7NgxdO7cGRUrVsTSpUtx3XXXWd2lgHkNZKVUCQDvA+gAoB6AXkqpes7rvPLKK+jRowe2bduG+fPn46mnnjKnt0IIAeDixYvo3LkzTp8+jRUrVqBKlSpWd8kQvoyQmwHYS3I/yTwA8wF0cV5BKYVz584BAM6ePRvyF/AQQoSvgoIC9OrVC1u3bkVcXByaNGkCpVRQlxo1apiybb4EcjUAR5xuH7XfV2js2LH45JNPkJCQgNTUVLz33ntG9lEIIQBo09uGDRuGFStW4N1330V2djZIBrxkZmYiPj4emZmZPq1/6NAhU7bPkIN68+bNw8MPP4yjR49i5cqV6Nu3L2w221/Wmz59Opo2bYqmTZsiOzvbiKaFEBFk4sSJhRcMevrppw2puW7dOnTv3h0LFy7EnXfeaUhNv3n7SwCgJYAvnW6PAjDKfpskWa9ePR4+fNhxkzVr1uSJEyfoSXJyssfHhRDC2eLFi6mU4gMPPMCCggKSpBZh/svMzGR8fDwzMzOL9LxA26VO3voyQt4MoJZSqqZSKgZATwDLnVe48cYbsWbNGgDAzp07kZubi8qVKxv2R0MIEdm+++479OnTB82bN8fHH3+MqKjAP9yH1MjYzutWkcwHMAjAlwB2AlhAMkspNW75ci2X33rrLcyYMQONGjVCr169MGvWrLCfDyiECA379u1Dp06dUK1aNSxfvtyQ6W2hGMYAoLTRt9/8fnLTpk2xZcuWQNoWQhRzJ0+exG233YacnBx8++23uPnmm695XCmFomaYEWHsT7uuJdzdKRcXEkKEpNzcXHTt2hWHDh3CV1999Zcw9keojowdJJCFECHHZrPhkUcewcaNGzF//ny0bt064JqhHsaAXMtCCBGCRo8ejfnz5+P111/Hgw8+GHC9cAhjQAJZCBFi3nnnHbz++utIT0/Hc889F3C9cAljQAJZCBFCPv30UwwfPhxpaWmYNGlSwLO1wimMAQlkIUSIWLt2Lfr164fWrVtj7ty5AX85abiFMSCBLIQIAT/++CO6du2KWrVqYdmyZYiNjQ2oXjiGMSCBLISw2IEDB9ChQweUK1cOq1atQoUKFQKqZ3YYr1u3zvCaDjLtTQhhmezsbLRv3x5XrlzBmjVrkJCQEFC9YIRx9+7dDa/rICNkIYQlLl68iI4dO+LIkSNYsWIF6tWr5/1JHgQrjBcuXGh4bQcZIQshgu7q1avo3r07tmzZgiVLlqBVq1YB1QtmGJu5T1oCWQgRVCQxYMAAZGRkYNq0aejSpYv3J3lQXMIYkF0WQoggGzlyJD766COMHTsWTzzxRMD1iksYAxLIQoggev311/HGG28gPT0dY8aMMaRmcQljQAJZCBEk06dPx8iRI9GrVy+8//77hl0zvbiEMSCBLIQIgk8//RTp6elITU3FRx99ZMg3fpjFypNKQvdVEUIUC6tWrULfvn3RqlUrLFy4ENHR0VZ3SZfVZ/hJIAshTPP111/j/vvvR/369bFixQrExcVZ3SVdVocxIIEshDDJ9u3bcd999yEhIQGrVq1C+fLlre6SrlAIY0ACWQhhgj179qB9+/YoW7YsVq9ejb/97W9Wd0lXqIQxICeGCCEM9ttvv+Hee+9FQUEB1q1bh+rVq1vdJV2hFMaABLIQwkA5OTlo164dTp06hczMTNSpU8fqLukKtTAGJJCFEAY5c+YM2rVrh/379yMjIwPJyclWd0lXKIYxIIEshDDA+fPn0aFDB/zyyy9Yvnx5SIWcq1ANY0ACWQgRoEuXLqFjx47YvHkzFi1ahJSUFKu7pCuUwxiQQBZCBCA3NxddunTBxo0bMWfOHHTt2tXqLukK9TAGZNqbEMJPeXl56NatG7766itUqFABvXr1glIqqIuvwiGMAQlkIYQf8vPz0bt3b3zxxReYMmUKTp48CZJ+LZmZmYiPj0dmZmaRn+uLcAljQAJZCFFEBQUFePjhh7F48WK8/fbbSE9P97tWcbq4vBEkkIUQPrPZbEhPT8ecOXMwfvx4DBs2zO9aEsZu+Psxw774LTk5OZCnCyGCzGazcdCgQQTAF1544ZrHtCjxXWZmJuPj45mZmRlQn/TaNap+UdstSgl3i6/7aVIA7AKwF8BIugnkTz/9lHXr1mW9evXYq1cvr72RQBYifNhsNg4ePJgAOGLECNpstmseL0pAGRmW7to1O4wzMzOtC2QAJQDsA5AIIAbAdgD16BTIu3fvZuPGjXnq1CmS5IkTJ7z2RgJZiPDgHMbPPPPMX8KY9D2QjQ5L13aDEcbx8fGWBnJLAF863R4FYBSdAvm5557jjBkzitQbCWQhQp8vYUz6FshmhKVzu8EKY6tHyN0AfOB0uy+ASXQK5C5duvC5557jbbfdxubNmzMjI8NtD6ZNm8bk5GQmJyfzxhtvDHSDhBAmstlsHDJkCAFw+PDhumFMeg9ks8LS0W4ww9i53QCYF8j33Xcfu3btyry8PO7fv58JCQk8ffq0x97ICFmI0FWUMCY9B5SZYQkg6GHsaDdAbvPWl2lvvwH4u9PtBPt9f96RkIDOnTsjOjoaNWvWxM0334w9e/b4UFoIEWpIYtiwYXj33XcxfPhwvPXWW35/Q3Qwpp4Vq6lzeknNP0fEJQHsB1ATfx7Uq0+nEXJGRgb79etHkszOzmZCQgJzcnI8/nmQEbIQocdms3Ho0KEEwGHDhnkdGTvAwtkOwRwZO7jb3iIKaNpbKoDd0GZb/NN+37hly5aR1H6Iw4cPZ926dXnLLbdw3rx5XnsjgSxEaPE3jMmwnu3gsb5e/y0NZA+L3ySQhQgdBQUFhSd9FDWMybCe7eCxvh4JZCGEKfLz8/n444/rnvThC4TvbAeP9fVIIAshDHf16lX26dOHAPjiiy/6FcZkWM928FhfjwSyEMJQV65cYbdu3QiA48ePD6gWgKCHsaNdM+vrkUAWQhjm8uXL7NSpEwFwwoQJAdcL49kOfo3sJZCFEIa4ePEi7733XgLg5MmTDakZrrMd/N3NYlYgy3fqCRFBzp8/j06dOmHDhg348MMP8cgjj1jdJV2ReL1kCWQhIsSZM2eQmpqKTZs2Yc6cOejVq5fVXdIViWEMSCALERGys7ORkpKCn3/+GQsXLkRaWprVXdIVqWEMSCALUewdPnwY7dq1w6FDh7B06VKkpqZa3SVdkRzGgASyEMXarl27cO+99+LcuXNYvXo1WrdubXWXdEV6GAMSyEIUW1u3bkX79u0RFRWFdevWoXHjxlZ3SZeEsUa+dVqIYmj9+vW48847Ubp0aWzcuFHCOAzCGJBAFqLYWbFiBdq3b4+EhARs3LgRtWrVsrpLuiSMryWBLEQx8sknnyAtLQ0NGzbEhg0bkJCQYHWXdEkY/5UEshDFxLvvvou+ffuiTZs2WLNmDeLj463uki4JY/ckkIUIcyQxZswYDB06FGlpafjiiy9QtmxZq7ulK9zDeN26dYbXLKR3TrWPi9/kWhZCBC4vL4+PPvooAbBMmTIEYNniC6Mv0enabhh9U4lcXEiI4uTChQtMTU0lAL700kuWXWiH9O1iO2aEpXO7YfZNJRLIQhQXJ06c4K233sqoqChOnz6dZGBXIAs0zLy1bVZYOtoNw28qkUAWojjYu3cvk5KSeN1113H58uWF9/sbEkaEmae2zQxLhO83lUggCxHuNm/ezMqVK7NSpUr89ttvr3nMn5AwKsz02jY7LBG+31QigSxEOFu5ciVLly7NGjVq8Ndff/3L40UNCSPD0l3bwRi5OkbIZtU38ZtKJJCFCFcffvghS5QowcaNG/P33393u05RQkJmO/hW36xvKqEEshDhp6CggKNHjyYA3nvvvTx79qzuulZNPXNtO8xmO3isr0cCWYgIc/nyZT744IMEwAEDBjAvL8/j+lZNPXNuOwxnO3isr0cCWYgI8scff7Bly5YEwDfeeIM2m83rc6yaeuZoO0xnO3isr0cCWYgIsXPnTiYmJjI2NpYLFy70+XlWTT1ztB2msx081tcjgSxEBMjMzGT58uV5ww038LvvvivSc62aehbmsx38en0kkIUo5mbNmsXo6GjWrVuX+/fvL/LzrZp6Fs6zHfx9fSSQhSimCgoK+MILLxAA27Zty9OnT/tVx6qpZ+E62yGQ18fSQAaQAmAXgL0ARlInkBctWkQA3Lx5s9feSCALQZ4/f55paWkEwMcee8zrTApPrJp65tq2GfX1WHW6uGWBDKAEgH0AEgHEANgOoB5dAvncuXO8/fbb2bx5cwlkIXxw4MABNmjQgFFRUXz77bd9mknhiVVTz5zbNqu+HqtOF7cykFsC+NLp9igAo+gSyEOHDuXnn3/ONm3aSCAL4cX69esZHx/P8uXL88svvzSkplVTzxxtm1lfj1Wni1sZyN0AfOB0uy+ASXQK5B9++IH3338/SUogC+HF9OnTWbJkSdauXZu7du0yrK5VU88cbZtZX49Vp4uHbCAXFBSwTZs2PHDgAEnPgTxt2jQmJyczOTmZN954Y6AbJERYycvL46BBgwiAKSkpfh+802PV1DNH22bW1xMq31TiTwl3S8C7LM6cOcNKlSqxevXqrF69OkuVKsUqVap4HSXLCFlEkpycHN59990EwBEjRjA/P9/wNqyaehZo22bPdjBjN46VgVwSwH4ANZ0O6tWnm1kWpOyyEMLVTz/9xJtuuokxMTGcNWuWae1YNfUskLbNnu1g1j51swLZ67dOk8wHMAjAlwB2AlhAMkspNW758uXeni5ERJs3bx5atGiBS5cuYd26dejfv7/VXfJZcfh2aDPrm0IvqX1c/CYjZFGc5eXlcdiwYQTA1q1b89ixY6a3CYumnvnTttmzHcyebWLAa+3fLgsvi98kkEVxdezYMd5xxx0EwKFDhwZ0skdRGBXI4T7bIRhT/ySQhQgD33zzDatWrcrrrruOc+bMCWrbRgRyuM92CNY8bAlkIUKYzWbj5MmTGR0dzcTERG7fvj3ofQg0JMJ9tkMwT4qRQBYiRF26dIkPP/wwATA1NZUJCQkEYMnir3Cf7RDMMHZuNwASyEIY7ddff2WDBg0IgGPHjmVBQUHEXWjHW9tmhiXC95tKJJCFMNLcuXNZpkwZVqpUiRkZGYX3WzXbwZ92i8NshzC9docEshBGuHz5MgcOHEgAbNWqFY8cOXLN41bNdrBq6ple28Ga7RDsMCYlkIUICbt372ajRo0IgM8//7zbKW1WzXawauqZu7bDaLaDx/p6/ZdAFsJin376KcuWLcuKFSvy888/113PqtkOVk09c207zGY7eKyvRwJZCItcvnyZTz31FAGwZcuWPHTokMf1rZrtYNXUM+e2w3C2g8f6eiSQhbDAL7/8UjiLYsSIET6ddWfVbAerpp452g7T2Q4e6+uRQBYiiBwnesTGxrJy5cr84osvfH6uVbMdrJp65mg7TGc7eKyvRwJZiCDJzs5m586dCYDt27cv8oWBIu1CO2E+28H0A6h6JdwtEshCOPnqq69YpUoVxsTE8O2332ZBQUGRa0TahXbCebaD2QdQPZVwt0ggC0HyypUr/Mc//kGlFOvUqcNt27b5XSvSLrQTrrMdzD6A6q2Eu0UCWUS8nTt3smnTpgTAgQMH8uLFiwHVi7QL7bi2bUZ9PaF6ANWXEu4WCWQRsQoKCjhx4kTGxsayUqVKXLx4sSF1I+1CO85tm1VfTygeQPW1hLtFAsgSqykAABI5SURBVFlEpIMHD/Kuu+4iAHbs2NHQb/SItAvtONo2s76eUDuAWpQS7hYJZBFRbDYbP/zwQ5YtW5ZlypThf/7zH9psNkPbiLQL7TjaNrO+nlA6gFrUEu4WCWQRMY4fP144na1Nmzbcv3+/Ke1E2oV2yMADyuzZDmYfQPWnhLtFAllEhEWLFjE+Pp6lSpXihAkT/JrO5qtIu9AOGVhAmT3bwewDqP6WcLdIIIti7fjx4+zevTsBMDk5mVlZWaa3GWkX2iH9DyizZzuYfQA1kBLuFglkUSzZbDbOnj2bFStWZExMDMePHx/0b38O9gE2q6ae+du22bMdzD6AGmgJd4sEsih2Dh8+zA4dOhRenW3Hjh1BbT/SLrTjT9tmz3YIxgHUQEu4WySQRbFRUFDAKVOmsGzZslRK0d8vCjViiaSpZ0Vt2+zZDsE6gBogCWRRfO3Zs4dt2rQhALZt21YutGNCfU9CZbZDGB1AlUAWxU9ubi5feeUVxsbG8vrrr+eMGTNos9nkQjsm1PckFGY7hNkBVAlkUbxkZmayTp06BMBu3brx6NGjhY/JhXaMr++J1bMdgr3PXgJZCLsTJ06wX79+BMAaNWq4vXi8XGjH+Pr+tm3m6x/GB1AlkEV4Kygo4PTp01mhQgVGR0dz9OjRuldmkwvtGF/fn7bNfv3D+ACqBLIIXz/99BNvu+02AuDtt9/u9QQPudCO8fWL2nYwXv8wPoDqfyADSAGwC8BeACPpEshvvfUW69atywYNGvDuu+/mwYMHvfZGAln44tSpUxw8eDBLlCjBSpUqcebMmT5dDEgutGN8/aK0Haw/huF6AJX+BjKAEgD2AUgEEANgO4B6dArktWvXFn50nDx5Mnv06OG1NxLIwpP8/HxOnTqVlSpVYlRUFJ988knm5OT4/Hy50I7x9X1tO5ifTMLxAKqjhLvFl0BuCeBLp9ujAIyizi6LrVu38rbbbvPaGwlkoee///0vGzduTAC84447+OOPPxa5hlxox/j6vrQd7N1E4XYA1bmEu8WXQO4G4AOn230BTKJOID/99NN8+eWX3fZg2rRpTE5OZnJyMm+88cZAN0gUM0eOHGGvXr0IgAkJCZw/f77f1yq2auQUSNuBhplVU88cbVuxzz6cDqC6lnC3GBrIH3/8MZs3b87c3FyvvZERsnC4cOECx40bx7i4OJYqVYovvvgiL1y4EFBNq0ZO/rZtRJhZNfXM0bYcQC0Sc3dZrF69mnXq1OGJEyd86o0EssjPz+cHH3zAKlWqEAAfeOABwy4ab9XIyZ+2jQpLOV3c+Pp6rAzkkgD2A6jpdFCvPp0CeevWrUxMTOTu3bt97o0EcuSy2WzMyMhggwYNCIAtWrTgxo0bDW1DLrRjfH13HPXDdbaDv6+P1dPeUgHsts+2+Kf9vnHLli0jSbZt25Y33HADGzVqxEaNGrFTp05eeyOBHJm2bdvGe+65hwB40003ceHChYZ/px1p3cipKG0bHZZyurjx9c1o11HC3eJTIHtY/CaBHFkOHjzI/v37UynFihUrcuLEibxy5Ypp7Vk1cvK1bTPCUk4XN76+0e06l3C3SCALU/3+++98+umnGR0dzVKlSvEf//gHT58+bXq7Vo2cfGnbrLCU08WNr29ku64l3C0SyMIU2dnZfPbZZxkbG8uSJUty4MCBPHLkSNDat2rk5K1tM8NSThc3vr5R7bor4W6RQBaGOnPmDMeMGVP4rR2lS5cmYM23dhSV2W9Ws8PSqqlnjrbNrK8nlA6gFrWEu0UCWRji7Nmz/Ne//sWKFSsWTmHLysqKuJGTXtvBGLlaNfWMtG6ffagcQPWnhLtFAlkE5NSpUxw7diwrVKhAAOzQoQN/+OGHwscjbeTkru1g7UawauoZad0++1A4gOpvCXeLBLLwS3Z2NkeNGsWyZcsSALt06cJNmzb9Zb1IGzm5th3MfbpWTT0jrdtnb/UB1EBKuFskkEWRHDt2jCNGjGBcXByVUuzevbvHi/9E2sjJue1gH2CT08WNr+9Pu76WcLdIIAuf7Nq1i+np6YyNjWVUVBQfeugh7tixw+vzIm3k5Gg72GHsaNfM+p6E2mwHs19/CWRhia+//ppdu3alUooxMTF8/PHHuWfPHp+fH2kjJ0fbkTT1rKhtG/n6W3kANUASyMI3+fn5XLx4MVu2bEkArFChAv/5z3/y2LFjRa4VaSMnx5s12GFMGhPI4bbPPowPoEogC8/Onz/P999/n0lJSQTAmjVr8r333gvoUpiRNnKKxAvt+Nq2Ga9/GB9AlUAW7u3atYtDhgzh9ddfTwC89dZbuWDBAubn5wdcO9JGTga9WT3W1yOni4fVAVQJZPGn/Px8rlixgu3btycARkdHs3fv3vzmm28MvfpapI2cXNs2o74eOV3c+PqkaQdQJZAFmZOTwzfffJM1a9YkAFatWpXjxo3za/+wLyJt5OTctln19cjp4sbT678EsvBbQUEBv/rqK/bs2ZMxMTEEtC8PXbBgAfPy8kxtO9JGTo62zayvR04XN76+iQdQJZAjzZEjR/jyyy8XjoYrVKjAwYMHc/v27UHrQ6SNnBxtm1lfj5wubnx9vf5LIAuf5ObmcvHixbzvvvsYFRVFAIyNjfV4VTSzF1fFeeREBv5m9ff1kdPFja+vRwJZ6CooKOCGDRv4xBNPsHz58gTAKlWqcPTo0dy7d6+MnEyob9abNZDXR04XN76+Hglk8Rc7duzg6NGjWb16dQJgXFwc+/Tpw4yMDF69erVwPRk5GV/fEzld3Pj6ZPgfQHUt4W6RQA4ze/bs4WuvvcYmTZoQAKOiopiSksJPPvmE58+fd/scGTkZX98TOV3ceHr9D5cDqO5KuFskkMPAzp07+fLLL7NRo0aF+2WbNWvGiRMn+jRdTUZOxtf3RE4XN75+OB9A1SvhbpFADkE2m43btm3jSy+9xPr16xeGcKtWrfj222/z0KFDRaonIyfj63sip4sbX1+v/6F+ANVTCXeLBHKIuHjxIlesWMGBAweyWrVqBEClFNu0acP33nuPR48e9bu2jJyMr++JnC5ufH09oXwA1VsJd4sEsoUOHjzIyZMnMzU1tXBqWpkyZfjAAw9w5syZPH78uCHtyMjJ+PqeWLXP3qoDqK5tm1FfT6geQPWlhLtFAjmITp48yUWLFjE9Pb3wimoAmJiYyKFDh3L16tXMzc01vF0ZORlf3xOr9tlbdQDVuW2z6usJxQOovpZwt0ggm+jChQtcvXo1R44cyaZNm1IpVTgK7tixIydOnMgdO3YYejEfd2TkZHx9f9s28/W36gCqo20z6+sJtQOoRSnhbpFANtDx48e5ePFiDh8+nLfeeitLlChBACxZsiRbt27NcuXKBfUMOdez5WTkZFx9f9o2+/W36gCqo20z6+sJpQOoRS3hbpFA9lNubi63bNnCqVOn8uGHH75mF0RsbCzbtGnD0aNHc+XKlTx37hxJGTmZUV+PVSMnvbaD8fpbdQCVtG6ffagcQPWnhLtFAtkHeXl5/PHHH/nBBx8wPT2dTZs2LbxqmmNU0qVLF7755pv89ttveeXKFbd1ZORkfH09Vo2c3LUdrD+GVh1AJa3bZx8KB1D9LeFukUB2UlBQwL1793LZsmUcP348e/XqxQYNGjA6OrowfMuVK8e2bdvy+eef58KFC3ngwAGf9wHLyMn4+nqsGjm5th3MTyZWHUAlrdtnb/UB1EBKuFt8Cl4AKQB2AdgLYCRdAjk3N5c9evTgTTfdxGbNmvHAgQNee2NVINtsNh4/fpwbN27krFmz+MILL7Bnz55MTk5mXFzcNftda9SowY4dO3LkyJGcO3cu9+zZE9ABOBk5GV8/kHbNfrMGM4yd2zWrviehONvBzNffskAGUALAPgCJAGIAbAdQj06B/P7773PgwIEkyXnz5rFHjx5ee2NWIF+8eJG7d+/m2rVrOXv2bL766qt86qmn2LlzZzZq1IhlypS5JnRLlCjBm266ie3ateOwYcP4wQcf8Lvvvivc72skGTkZX9/fds1+swY7jB3tmlnfk1Cb7WD2629lILcE8KXT7VEARtEpkNu1a8dvvvmGJHn16lVWqlTJ60jSOZBtNhvz8vJ46dIlnjt3jjk5OTx06BCzsrL4/fffc82aNVy6dCk/+eQTTp06la+++iqfeeYZ9uvXj6mpqWzWrBkTExMLv6TTdalYsSIbNmzI1NRUDhkyhBUqVIio2Q4ycjK+vre2I+kAalHbNvL1t3I3YID8DuRuAD5wut0XwCQ6BXL9+vV55MiRwpYSExOZnZ2t25PevXszKiqKMTExhRdRL+pSunRpVq9encnJyWzfvj379OnDoUOHcvz48Zw9ezbXrl3L3bt38+LFi399JSJstoOMnIyvr8fxZg12GJPG/Jz9fX2s2mcfxrsB3eatohayupRS3QCkkHzcfrsvgOYkB6WkpDAnJwdZWVmoVasWYmJiAAA///wz6tati5IlS15TKzs7Gzk5OcjPz0deXp7Hds1UokQJFBQUWNa+ECK8lShRAo0bN/b7+T/88MOXJFP+8oBeUpPm7rKIi4sL9C9M2AnFmSVmk22ODJG2zQZsr9u8jfIhzDcDqKWUqqmUigHQE8By5xU6d+6Mjz76CACwaNEi3H333VBK+f3XQwghIpHXQCaZD2AQgC8B7ASwgGSWUmrc8uVaLj/22GM4efIkkpKSMGHCBLz22mumdloIIYqjkt5XAUiuBLDS5b4xAF4EgNjYWCxcuLBIDcfHxxdp/eLgiSeesLoLQSfbHBkibZvN2l6vB/W88PvJTZs2xZYtWwJpWwghwpXbfbq+7EMWQggRBKYH8qpVq1C7dm0kJSW53bd85coVPPjgg0hKSkLz5s1x8OBBs7tkOm/bPGHCBNSrVw8NGzZE27ZtcejQIQt6aSxv2+ywePFiKKXC/tORL9u7YMEC1KtXD/Xr10fv3r2D3EPjedvmw4cP46677kKTJk3QsGFDrFy50k2V8PLoo4/ihhtuwC233OL2cZIYMmQIkpKS0LBhQ2zdujWwBvWmX/i4eJSfn8/ExETu27ePV65cYcOGDZmVlXXNtBF/TrsOZZ622WHt2rWFJ6xMnjw5IraZJM+dO8fbb7+dzZs35+bNmy3oqTF82d7du3ezcePGPHXqFEnyxIkTVnTVML5s84ABAzh58mSSZFZWFqtXr25BT421fv16/vDDD6xfv77bx7/44gumpKTQZrPx22+/ZbNmzXwt7fe0N79t2rQJSUlJSExMRExMDHr27Illy5Zds86yZcvQv39/AEC3bt2wZs0ax3znsOTLNt91112Ii4sDALRo0QJHjx61oquG8WWbAeDFF1/E888/j9jYWAt6aRxftnfGjBl4+umnUaFCBQDADTfcYEVXDePLNiulcO7cOQDA2bNnUbVqVSu6aqg77rgDFStW1H182bJl6NevH5RSaNGiBc6cOYNjx47536BeUhuxwMNp1wBW2f/9BUCC0zr7AMSb2S+rtlln/UkAXrC632ZvM4D/B2Cx/f/rADS1ut8mb+9SAG8A+BrAd9DOdrW87yZvcxUAPwM4CuA0gGSr+23QttcA8IvOY58DaO10e00gv9s+TXszA92dNhhhlFIPAWgKoI3VfTGTUioKwAQAD1vclWAqCaAWgDsBJADYoJRqQPKMpb0yVy8As0i+pZRqCeBjpdQtJG1WdyxcmH1Q7zcAf3e6nWC/z+06SqmSAMoBOGlyv8zkyzZDKXUPgH8C6EzySpD6ZhZv21wWwC0A1imlDgJoAWC5Uqpp0HpoLF9+xkcBLCd5leQBALuhBXS48mWbHwOwAABIfgsgFkBxP+HAp/e7r8wOZK+nXdtv97f/vxuAtbSP/cOU121WSjUBMA1aGP9hQR+N5nGbSZ4lGU+yBska0D7CdyYZrlMtfPm9XgptdAylVDyAmwHsD2YnDebLNh8G0BYAlFJ1oQVydlB7GXzLAfRTmhYAzpL0fydyEPa/pEIbHewD8E/7feOgvSEB7Ye2ENq3kWwCkGj1PqMgbPNXAE4A+NG+LLe6z2Zvs8u66xDG+5B9/BkraLtpdkDbr9rT6j4HYZvrQdtnvt3+e93O6j4bsM3zABwDcBXap57HAKQDSHf6Ob9vf01+DvT3OtAz9YQQQhhEztQTQogQIYEshBAhQgJZCCFChASyEEKECAlkIYQIERLIQggRIiSQhRAiREggCyFEiPj/XQbGc4mV6S8AAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "0.2849999999999999" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rectangleGaucheDessin(carre,0,1,10)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "0.3849999999999999" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rectangleDroiteDessin(carre, 0, 1, 10)" ] } ], "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": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 2 }