test Thomas

module2/exo1/mynotebook.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### This is a test ###"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1+1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "mu, sigma = 100, 15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.random.normal(loc=mu, scale=sigma, size=1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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"
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-6-afd45adb8e7f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    251\u001b[0m     \"\"\"\n\u001b[1;32m    252\u001b[0m     \u001b[0;32mglobal\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 253\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    254\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m     47\u001b[0m         \u001b[0;31m# only call close('all') if any to close\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m         \u001b[0;31m# close triggers gc.collect, which can be slow\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mclose\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mGcf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m             \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'all'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
+     ]
+    },
+    {
+     "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(x)"
+   ]
+  }
+ ],
+ "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
+}