diff --git a/module2/exo3/exercice.ipynb b/module2/exo3/exercice.ipynb
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..86252ebb51eab6101fc0a506c23a3838e763f474 100644
--- a/module2/exo3/exercice.ipynb
+++ b/module2/exo3/exercice.ipynb
@@ -1,5 +1,168 @@
{
- "cells": [],
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#
MOOC:RR Module 2 Exercice 03 "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Similairement a l'exercice 02, on utilise les memes donnés afin de les affichés en sequence plot et en histogramme avec une librairie graphique `Matplotlib` :\n",
+ "> 14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Importation de la librairie"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Définition des donnés et visualisation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[14. 7.6 11.2 12.8 12.5 9.9 14.9 9.4 16.9 10.2 14.9 18.1 7.3 9.8\n",
+ " 10.9 12.2 9.9 2.9 2.8 15.4 15.7 9.7 13.1 13.2 12.3 11.7 16. 12.4\n",
+ " 17.9 12.2 16.2 18.7 8.9 11.9 12.1 14.6 12.1 4.7 3.9 16.9 16.8 11.3\n",
+ " 14.4 15.7 14. 13.6 18. 13.6 19.9 13.7 17. 20.5 9.9 12.5 13.2 16.1\n",
+ " 13.5 6.3 6.4 17.6 19.1 12.8 15.5 16.3 15.2 14.6 19.1 14.4 21.4 15.1\n",
+ " 19.6 21.7 11.3 15. 14.3 16.8 14. 6.8 8.2 19.9 20.4 14.6 16.4 18.7\n",
+ " 16.8 15.8 20.4 15.8 22.4 16.2 20.3 23.4 12.1 15.5 15.4 18.4 15.7 10.2\n",
+ " 8.9 21. ]\n"
+ ]
+ }
+ ],
+ "source": [
+ "data = np.array([14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0])\n",
+ "print(data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "On rajoute un axe X :"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x = np.linspace(0,100,data.size)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Visualisation avec Matplotlib"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Séquence plot"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Par soucis de detail j'ai rajouté des propriétés `pyplot` pour etre similaire au graphiques demandés : "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(x,data,c='BLUE')\n",
+ "ax = plt.gca()\n",
+ "ax.set_xlim([0, 100])\n",
+ "ax.set_ylim([0, 25])\n",
+ "plt.grid(linestyle='--')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Histogram"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.hist(data,color='BLUE',edgecolor=\"black\")\n",
+ "ax = plt.gca()\n",
+ "ax.set_xlim([0, 25])\n",
+ "ax.set_ylim([0, 25])\n",
+ "plt.grid(linestyle='--')\n",
+ "plt.show()"
+ ]
+ }
+ ],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
@@ -16,10 +179,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.3"
+ "version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
-