modified toy_notebook_fr

module2/exo1/test_python.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# tests with python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## plot loi normale"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.random.normal(loc=0, scale=2, size=1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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": [
+    "plt.figure()\n",
+    "plt.hist(x, bins=64)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "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": 2
+}

module2/exo1/toy_notebook_en.ipynb

-{
- "cells": [],
- "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": "3.6.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
-