upd cwi

journal/tutorial.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Titre du document"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "4"
+      ],
+      "text/latex": [
+       "4"
+      ],
+      "text/markdown": [
+       "4"
+      ],
+      "text/plain": [
+       "[1] 4"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "2+2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 10\n"
+     ]
+    }
+   ],
+   "source": [
+    "x=10\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 20\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = x + 10\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Petit exemple de completion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "mu, sigma = 100, 15\n",
+    "sigma"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.random.normal(loc=mu, scale=sigma, size=10000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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": "markdown",
+   "metadata": {},
+   "source": [
+    "## Utilisation d'autres langages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext rpy2.ipython"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%R\n",
+    "plot(cars)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.4.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}