test

module2/exo1/Untitled.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Titre du doc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2+2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n"
+     ]
+    }
+   ],
+   "source": [
+    "x=10\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = x +10\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Petit exemple de complétion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "mu, sigma = 100,15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x= np.random.normal(loc=mu,scale=sigma,size=10000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "plt.hist(x)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "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": 4
+}