1 premier essai

module2/exo1/.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Titre du document"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2+2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Petit exemple                    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "mu, sigma = 100, 15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.random.normal(loc=mu, scale=sigma, size=10000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAD8CAYAAACRkhiPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAEdhJREFUeJzt3X+s3fdd3/HnC6eEtBA1ITfB2C72Km+QRNQllhVWaeoIWwxBdfijkqtBLBHJKEpFmZiGDdKAPywF8aNbpCVToFkcKLUsaBerTVg9D1QhhaY3JY3juCYe8ZJbe/EFBIRNyrD75o/zsXrqHPue+8P33OvP8yEdfb/n/f18v9/PW2388vl+v+c4VYUkqU/fMukJSJImxxCQpI4ZApLUMUNAkjpmCEhSxwwBSeqYISBJHTMEJKljhoAkdeyaSU9gLjfddFNt3Lhx0tOQpFXl+eef/8uqmppr3IoPgY0bNzI9PT3paUjSqpLkf48zzstBktQxQ0CSOmYISFLHDAFJ6pghIEkdMwQkqWOGgCR1zBCQpI4ZApLUsRX/jWFppdq453MTO/eph+6Z2Ll1dfGTgCR1zBCQpI4ZApLUMUNAkjo2Zwgk+bYkzyX5SpJjSX6l1W9McjjJK215w9A+e5OcTHIiyd1D9TuSHG3bHk6SK9OWJGkc43wSeAv4oap6H7AF2J7kTmAPcKSqNgNH2nuS3ArsBG4DtgOPJFnTjvUosBvY3F7bl7AXSdI8zRkCNfD37e072quAHcD+Vt8P3NvWdwAHquqtqnoVOAlsS7IWuL6qnq2qAp4c2keSNAFj3RNIsibJC8BZ4HBVfRG4parOALTlzW34OuD1od1nWm1dW7+4LkmakLFCoKrOV9UWYD2Dv9Xffpnho67z12Xqbz9AsjvJdJLp2dnZcaYoSVqAeT0dVFV/A/wxg2v5b7RLPLTl2TZsBtgwtNt64HSrrx9RH3Wex6pqa1VtnZqa899JliQt0DhPB00leXdbvw74YeCrwCFgVxu2C3iqrR8Cdia5NskmBjeAn2uXjN5Mcmd7Kui+oX0kSRMwzm8HrQX2tyd8vgU4WFWfTfIscDDJ/cBrwIcBqupYkoPAy8A54MGqOt+O9QDwBHAd8Ex7SZImZM4QqKoXgfePqP8VcNcl9tkH7BtRnwYudz9BkrSM/MawJHXMEJCkjhkCktQxQ0CSOmYISFLHDAFJ6pghIEkdMwQkqWOGgCR1zBCQpI4ZApLUMUNAkjpmCEhSxwwBSeqYISBJHTMEJKljhoAkdcwQkKSOGQKS1DFDQJI6ZghIUscMAUnqmCEgSR0zBCSpY3OGQJINSf4oyfEkx5J8rNV/OcnXkrzQXj86tM/eJCeTnEhy91D9jiRH27aHk+TKtCVJGsc1Y4w5B/xcVX05yXcAzyc53LZ9vKp+fXhwkluBncBtwHcD/yPJP62q88CjwG7gT4Gnge3AM0vTiiRpvub8JFBVZ6rqy239TeA4sO4yu+wADlTVW1X1KnAS2JZkLXB9VT1bVQU8Cdy76A4kSQs2r3sCSTYC7we+2EofTfJikseT3NBq64DXh3ababV1bf3iuiRpQsYOgSTfDvwB8LNV9XcMLu28F9gCnAF+48LQEbvXZeqjzrU7yXSS6dnZ2XGnKEmap7FCIMk7GATAJ6vq0wBV9UZVna+qrwO/BWxrw2eADUO7rwdOt/r6EfW3qarHqmprVW2dmpqaTz+SpHkY5+mgAJ8AjlfVbw7V1w4N+3HgpbZ+CNiZ5Nokm4DNwHNVdQZ4M8md7Zj3AU8tUR+SpAUY5+mgDwA/CRxN8kKr/QLwkSRbGFzSOQX8NEBVHUtyEHiZwZNFD7YngwAeAJ4ArmPwVJBPBknSBM0ZAlX1J4y+nv/0ZfbZB+wbUZ8Gbp/PBCVJV47fGJakjhkCktQxQ0CSOjbOjWFpRdu453OTnoK0avlJQJI6ZghIUscMAUnqmCEgSR0zBCSpY4aAJHXMEJCkjhkCktQxQ0CSOmYISFLHDAFJ6pghIEkdMwQkqWOGgCR1zBCQpI757wlIq9Ck/g2FUw/dM5Hz6srxk4AkdcwQkKSOGQKS1DFDQJI6NmcIJNmQ5I+SHE9yLMnHWv3GJIeTvNKWNwztszfJySQnktw9VL8jydG27eEkuTJtSZLGMc4ngXPAz1XV9wF3Ag8muRXYAxypqs3Akfaetm0ncBuwHXgkyZp2rEeB3cDm9tq+hL1IkuZpzhCoqjNV9eW2/iZwHFgH7AD2t2H7gXvb+g7gQFW9VVWvAieBbUnWAtdX1bNVVcCTQ/tIkiZgXvcEkmwE3g98Ebilqs7AICiAm9uwdcDrQ7vNtNq6tn5xXZI0IWOHQJJvB/4A+Nmq+rvLDR1Rq8vUR51rd5LpJNOzs7PjTlGSNE9jhUCSdzAIgE9W1adb+Y12iYe2PNvqM8CGod3XA6dbff2I+ttU1WNVtbWqtk5NTY3biyRpnsZ5OijAJ4DjVfWbQ5sOAbva+i7gqaH6ziTXJtnE4Abwc+2S0ZtJ7mzHvG9oH0nSBIzz20EfAH4SOJrkhVb7BeAh4GCS+4HXgA8DVNWxJAeBlxk8WfRgVZ1v+z0APAFcBzzTXpKkCZkzBKrqTxh9PR/grkvssw/YN6I+Ddw+nwlKkq4cvzEsSR0zBCSpY4aAJHXMEJCkjhkCktQxQ0CSOmYISFLHDAFJ6pghIEkdMwQkqWOGgCR1zBCQpI4ZApLUMUNAkjpmCEhSxwwBSeqYISBJHTMEJKljhoAkdcwQkKSOGQKS1DFDQJI6ZghIUscMAUnq2JwhkOTxJGeTvDRU++UkX0vyQnv96NC2vUlOJjmR5O6h+h1JjrZtDyfJ0rcjSZqPcT4JPAFsH1H/eFVtaa+nAZLcCuwEbmv7PJJkTRv/KLAb2Nxeo44pSVpGc4ZAVX0B+Osxj7cDOFBVb1XVq8BJYFuStcD1VfVsVRXwJHDvQictSVoai7kn8NEkL7bLRTe02jrg9aExM622rq1fXJckTdBCQ+BR4L3AFuAM8ButPuo6f12mPlKS3Ummk0zPzs4ucIqSpLksKASq6o2qOl9VXwd+C9jWNs0AG4aGrgdOt/r6EfVLHf+xqtpaVVunpqYWMkVJ0hgWFALtGv8FPw5ceHLoELAzybVJNjG4AfxcVZ0B3kxyZ3sq6D7gqUXMW5K0BK6Za0CSTwEfBG5KMgP8EvDBJFsYXNI5Bfw0QFUdS3IQeBk4BzxYVefboR5g8KTRdcAz7SVJmqA5Q6CqPjKi/InLjN8H7BtRnwZun9fsJElXlN8YlqSOGQKS1DFDQJI6ZghIUscMAUnqmCEgSR0zBCSpY4aAJHXMEJCkjhkCktQxQ0CSOmYISFLHDAFJ6pghIEkdMwQkqWOGgCR1zBCQpI4ZApLUMUNAkjpmCEhSxwwBSeqYISBJHTMEJKljhoAkdWzOEEjyeJKzSV4aqt2Y5HCSV9ryhqFte5OcTHIiyd1D9TuSHG3bHk6SpW9HkjQf43wSeALYflFtD3CkqjYDR9p7ktwK7ARua/s8kmRN2+dRYDewub0uPqYkaZldM9eAqvpCko0XlXcAH2zr+4E/Bn6+1Q9U1VvAq0lOAtuSnAKur6pnAZI8CdwLPLPoDrQibNzzuUlPQdICLPSewC1VdQagLW9u9XXA60PjZlptXVu/uC5JmqClvjE86jp/XaY++iDJ7iTTSaZnZ2eXbHKSpG+20BB4I8lagLY82+ozwIahceuB062+fkR9pKp6rKq2VtXWqampBU5RkjSXhYbAIWBXW98FPDVU35nk2iSbGNwAfq5dMnozyZ3tqaD7hvaRJE3InDeGk3yKwU3gm5LMAL8EPAQcTHI/8BrwYYCqOpbkIPAycA54sKrOt0M9wOBJo+sY3BD2prAkTdg4Twd95BKb7rrE+H3AvhH1aeD2ec1OknRF+Y1hSeqYISBJHTMEJKljhoAkdcwQkKSOzfl0kCRdMKnfiDr10D0TOW8P/CQgSR0zBCSpY4aAJHXMEJCkjhkCktQxQ0CSOmYISFLHDAFJ6pghIEkdMwQkqWOGgCR1zBCQpI4ZApLUMUNAkjpmCEhSxwwBSeqYISBJHTMEJKljiwqBJKeSHE3yQpLpVrsxyeEkr7TlDUPj9yY5meREkrsXO3lJ0uIsxSeBf1lVW6pqa3u/BzhSVZuBI+09SW4FdgK3AduBR5KsWYLzS5IW6EpcDtoB7G/r+4F7h+oHquqtqnoVOAlsuwLnlySNabEhUMDnkzyfZHer3VJVZwDa8uZWXwe8PrTvTKtJkibkmkXu/4GqOp3kZuBwkq9eZmxG1GrkwEGg7AZ4z3ves8gpSpIuZVGfBKrqdFueBT7D4PLOG0nWArTl2TZ8BtgwtPt64PQljvtYVW2tqq1TU1OLmaIk6TIWHAJJ3pXkOy6sA/8aeAk4BOxqw3YBT7X1Q8DOJNcm2QRsBp5b6PklSYu3mMtBtwCfSXLhOL9XVX+Y5EvAwST3A68BHwaoqmNJDgIvA+eAB6vq/KJmL0lalAWHQFX9BfC+EfW/Au66xD77gH0LPackaWn5jWFJ6pghIEkdMwQkqWOGgCR1zBCQpI4ZApLUMUNAkjpmCEhSxwwBSeqYISBJHTMEJKljhoAkdWyx/6iMVpiNez436SlIWkX8JCBJHTMEJKljhoAkdcx7ApJWvEne6zr10D0TO/dy8JOAJHXMEJCkjhkCktQxQ0CSOmYISFLHDAFJ6pghIEkdW/YQSLI9yYkkJ5PsWe7zS5K+YVm/LJZkDfCfgX8FzABfSnKoql5eznlcaf6Im6TVYrm/MbwNOFlVfwGQ5ACwA7iqQkDS1WNSf6lbrm8qL/floHXA60PvZ1pNkjQBy/1JICNq9bZByW5gd3v790lOXNFZwU3AX17hcywn+1nZrqZ+rqZeYAX1k19d9CG+Z5xByx0CM8CGoffrgdMXD6qqx4DHlmtSSaarautyne9Ks5+V7Wrq52rqBa6+fsax3JeDvgRsTrIpybcCO4FDyzwHSVKzrJ8Equpcko8C/x1YAzxeVceWcw6SpG9Y9n9PoKqeBp5e7vPOYdkuPS0T+1nZrqZ+rqZe4OrrZ06pett9WUlSJ/zZCEnqWJchkGRNkj9L8tn2/sYkh5O80pY3THqO40ry7iS/n+SrSY4n+cFV3s+/TXIsyUtJPpXk21ZTP0keT3I2yUtDtUvOP8ne9hMqJ5LcPZlZX9ol+vm19v+3F5N8Jsm7h7atun6Gtv27JJXkpqHaiu5nKXQZAsDHgOND7/cAR6pqM3CkvV8t/hPwh1X1vcD7GPS1KvtJsg74GWBrVd3O4OGBnayufp4Atl9UGzn/JLcy6O+2ts8j7adVVpIneHs/h4Hbq+r7gT8H9sKq7ockGxj8nM1rQ7XV0M+idRcCSdYD9wC/PVTeAexv6/uBe5d7XguR5HrgXwCfAKiq/19Vf8Mq7ae5BrguyTXAOxl8j2TV9FNVXwD++qLypea/AzhQVW9V1avASQY/rbJijOqnqj5fVefa2z9l8H0fWKX9NB8H/j3f/OXVFd/PUuguBID/yOB/7K8P1W6pqjMAbXnzJCa2AP8EmAX+a7u89dtJ3sUq7aeqvgb8OoO/jZ0B/raqPs8q7WfIpeZ/NfyMyk8Bz7T1VdlPkg8BX6uqr1y0aVX2M19dhUCSHwPOVtXzk57LErkG+AHg0ap6P/B/WdmXSi6rXSvfAWwCvht4V5KfmOysrqixfkZlpUryi8A54JMXSiOGreh+krwT+EXgP4zaPKK2ovtZiK5CAPgA8KEkp4ADwA8l+V3gjSRrAdry7OSmOC8zwExVfbG9/30GobBa+/lh4NWqmq2qfwA+DfxzVm8/F1xq/mP9jMpKlGQX8GPAv6lvPGe+Gvt5L4O/dHyl/bmwHvhyku9idfYzb12FQFXtrar1VbWRwQ2f/1lVP8Hgpyt2tWG7gKcmNMV5qar/A7ye5J+10l0MfpZ7VfbD4DLQnUnemSQM+jnO6u3ngkvN/xCwM8m1STYBm4HnJjC/eUmyHfh54ENV9f+GNq26fqrqaFXdXFUb258LM8APtP+2Vl0/C1JVXb6ADwKfbevfyeCpjVfa8sZJz28efWwBpoEXgf8G3LDK+/kV4KvAS8DvANeupn6ATzG4n/EPDP5Auf9y82dwKeJ/ASeAH5n0/Mfs5ySDa+UvtNd/Wc39XLT9FHDTaulnKV5+Y1iSOtbV5SBJ0jczBCSpY4aAJHXMEJCkjhkCktQxQ0CSOmYISFLHDAFJ6tg/AgNE58VlB9KRAAAAAElFTkSuQmCC\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": [
+    "# Utitlisation d'autres langages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext rpy2.ipython"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Error in plot[cars] : object of type 'closure' is not subsettable\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.6/site-packages/rpy2/rinterface/__init__.py:146: RRuntimeWarning: Error in plot[cars] : object of type 'closure' is not subsettable\n",
+      "\n",
+      "  warnings.warn(x, RRuntimeWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%R\n",
+    "plot[cars]"
+   ]
+  },
+  {
+   "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": 2
+}