deuxième sauvegarde

module2/exo1/toy_notebook_fr-Copy1.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = 2 + 2\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "mu, sigma = 100, 15\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.random.normal(loc=mu, scale=sigma, size=1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext rpy2.ipython"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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2FXSgeSG7FvOLcRndHQFVV35Jc9Hc668yGZrJ8zkMcasyNHar1KyYZlA6inC6DnafF3ffey4imgedlNlpIn95R+66s8D189opRuWjhGbBR1zUPnLEcwXhQ/QU6bt62eb2Ct2OTuWsNFH4xKLBoFlwtTrPStzz7LOeKwgLLu/vaBO5133JrLD2zy8xKh1FNAve+vfRNxmr472CsGDpqtCzEmvFefHFvINC+9/g4jENN4WcYOvA4yQrO7pTbe+lEGwK+JxFL0zyXgbBpgCPC31QKGTi0SJDng9DsBcHo1s0HsRd8enmCbGddRiq4gkEe1IivyT94WTeYZseZGz1q7yj+geCPflxMLs9CThHrr0gtxi6YgIODJCakiaco+a77sxDsAkobZzDnOPUzbodAK12Mfb5G7yj+oeY4DV9hqq9I3wluUGzKk9us1tE297mfl/qQnuHvZ/4SXW8oCV49Os/74n/TtUm+Q/0+GZwzarP5sWUZzCm6AMpwUXy37j8OFXbDJAHZQ9oJSQfM0BKcLarcoa6M5kEeZLYVV4vHJGBlODSsDLGcluo2masTWo6dBKTkAkgJZjNb7ViYbi6s6yKx58fE1v7hsodXTnm8Re18Ij6MpMXj4uf+4WYYJY1fa7a6o0VE1uOUHmXuLRLyx7h29yXTIrsHTFTXZAbycmvROxXt00QEBOsD+PnM1ZgK7yz4Lue0v8o7X5UFaT/OunaySb8OwzBQRBXKjUp6XcWjNgtNesnqQriOhfsle2vm1YgOAhayH+yB2bcWTBevvZO+1hVEIf85e0ofPYXCA6CJb1L2P4otxGVWXF57Jz9tKogU9+qYFuacc7MGwgOhrl2R6dc9wXbmjia7b1bb984UyMdPYXPJgzB1IFg4kAwcSCYONQFl2fs8XgIm7P9ijGpGANxwWftr71p2+22oKzDS2Mc8w3LR3+IC26XydgVm9uCqbMYq0gwxdh7fSAu2HU/sLPbHYhk+fg880uD0jEA6oLl+4HN3B4G9pTrk72t7q0eS0Nc8LRBJc753dwW7G52me2OVP/s1rLQElw0PqZplXHYznmxjnFVTqO3trD3Pue+4ES3qA5V3hHIHxHdfK37gvKP4+KmCy9wJAhagjvNLy/srW7e/Cu2AyynsduU787mK51X265265IytqR0ohmmJQwGUoKvy29IlkWr2mbhp1LzjVt59mPdpSbf/cWuSLmxm3QEvz9ICT4uu1H5VuUU+Wh88PU7C3aNlBqn486CUtd7uAkGvLTOA1KCK54vYCzjJVXbfP+K1IxOu7OgIKKEsY0D3brE/8rYGd6j0fSClGC21TZ6kP28YhfnzmVVZ6Qb22pC277uC1ZEvt0vPt9twWHbmyNth7glqS+0BLMbW/cpjxApShj0cVLVGctyv/GoqnNtc2bVP7jFu3YWc0nPAIgJ9stE+SrqBTNMA6sToSY4SZ5mf7Y55ywTQqgJ7ntQakZuNzoN/Qg1wYfsPxeviLXqbakgMLHgWVH2Xp4zBmrnp1eajsn3340M5hU87/VStq2JRe8fmQfzltWJk0eEdc/x2w8oYt6yOg75yztI/PA74pi3rM6QlYxdDzdFeTgrY96yOoXJbfradmqL4YOSZdN/ULvN2blzz3BPRCfMXFbnbFap1hBeXLdPWd5jqLpttkR+tijKq6qMRQi1sjop8pP8l35WtY39GmMFjcXkIxwBZXWWPueiTozG1ISQKJ8xzFmsZpPK58GuUjoWhNt1sNfUGOb8Br+xR2oG7vLbz50I6SKwPFxMPsLRXlbHUbeDPEy9hucKcwo+Ebb51IxW6m6fzOt4OKuLyilWTINmwbaUjEn1c0wi+FzbqIjhymdmuSO7zlR77ra1T+/NwSdlLJoF31fB2IYG580hOF66Apo+Wv/9mhjNghvIl6qLG500g+AL7eW2ykt35ft3W/TsiBOaBa+ouUxu61b3XGGA4NOd5dZd8Bn762+Gf697IiZC+1l0rusmzxWvIZlGHKLt0n/NV+6zbrc7IKVm1RNgLpj3caE3RXNHfqk8M9yxuKS4Lu6HZK/RhSGHhQQXRM7Z9W6Sn7n/rlUdV+Y1ujDksJDgVPlVuZT1qraZNrDY+Wk3//3oYiHBPeWXXVe/r2ob56dxUeMtOuiEDxYSnCqPyx+9jnNU6lhIcEHEpz9MaaV5/t2K6zySsQwWEsx+mzl0sebSJePDkhqH0GvRlhLMg8+HOVl+pPg5QE1DqAnuII89/HCl0WnoR6gJds0CnLrG6DT0I9QEr+pVxs5HXDM6Df0INcFsRnh0k1B62TrkBDMWWqNhQlBwaAHBxIFg4ugluDCr8G4dNVCcFVr3HYNAJ8EfRvaNVPccKBDSbL2bDAitcybV6CN4V2cnc76cfvfOQfGrXGJu3ELOUYmhj2BX6bf0lOCD+WTJHKk5q25iu5BDH8FTV0nNOnXFG/3ztXzQP/oq56jE0EfwiagL7GK08q7Uk2/LYTeSeR/4iaHTSdbuZo4EdSO+AuHoi/bYr7lHpQWug4kDwcSBYOJAMHEgmDgQTBwIJg4EEweCiQPBxDGx4BUJjlGhPLKXD+YVnNYp37nMaw5boBLzCo6Xh/V2DqFa3WIwr2DX9BpD9mkLAswruO8Wxm6Gh/TofB7oJThzSabK7a/G9hvTWN2MHMAbnQT36j6zR3eV7z86M7cV+O8FlNFH8Oo3pSZlRfDBQLDoI3i0PGnCbqtWSbc0+gie9ZnUfPFR8MFAsOgj+Gr49pvp4V6TwgPx6FT57NzAhAFnVSUG+GDeymeACwIqn1360UWHZK25AQ4IqHy2fbiL2B7aMgNcEFf5bOknQaYEeCKu8hkEmwIBlc9uAcGmQNx1MASbAggmDgQTR5zgjX+LVeRPDzwogHvvFxG15n0iot5/r4ioD9Su8mtuoHwDUYNgf4gpdjNmq4ioi+aKiPr9myKiXmyrpjcEu4DgIIBgCA4CCIbgIIDgIIBg4oK7nhERdZyQabOWeNVW5cGeUSKiXm6vprdAwWJeiS3UXDjLFyXFIqI6RUzAq/IXK1AwMAMQTBwIJg4EEweCiQPBxIFg4kAwcUQJLq5Wo0aNNnxjlg2tLtcF3tDwwfjzvKPyzvfrZ+63Z3PPtTKqqlxFCT5fm3/MxDG/k1Rcr/192agXeUflnG/u/TsrRjl453orqqpcRQk+Wp9/zEwmq0iLlyTX4HdrsTIq53xz0xjLeJR3rreiqspVlOA9f3Y8HJvNO6qsYsJr0g91eIaWowrId1I7AbnKUVXlKkrw4V5Hbo7wGu2iFVnFyGHSD0+qnfHFX1T++W58MldArnJUVbmKPIsu/QPvEcOyion9pB8ezuEcVYZrvosb5AjI1RVVJuBcRQk+d1g6M739m+OGHHB5JGNn/uhjQLqmqLzzXdVIPnnmnWtlVFW5ihK8/vGT5W/9H++o8n9VQe3NZX268I7KOd+rdU/KH5xzvRVVVa7CDtHvPvpQ/EmuEfNq1JAuAC+wb//6YAt+s4Hcjso33/nVpUvVGnmcc70dVU2uuJNFHAgmDgQTB4KJA8HEgWDiQDBxIJg4EEwcCCYOBBMHgokDwcSBYOJAMHEgmDgQTBwIJg4EEweCiQPBbmQKGG9jNBDsBgRbnZJOf3miY9Hehv0d/0hnbE2jv8Rc+tfHO3X/NgGCLc6yOGfF4J2Z1daz9U+zMw8eZKlJtz+yap2r6AjBFmfHo2tvSkfiWoyVVb80O46xG78vvfUxsxVjGyHY6qTZa75cmFlP+une7En31qtXr9a5Wx8TuzK2F4KtT17M5Mz/cLKb1S4vqqzGeOtjRpL01xiCLc60MU5ntymZ96Sxz/7KLvwpm+3tf/vjQK2zZckQbHEuNX/siTY3Mp8a/HSDnYytbfTUczv+9ZHyyDMfPGF0gvwJLcGVULzcvSsQTBwIJk4oCg4pIJg4EEwcCCYOBBMHgokDwcSBYOJAMHEgmDgQTBwIJg4EEweCiQPBxIFg4vw/C6hxe08+0jwAAAAASUVORK5CYII=\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "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
+}

module2/exo1/toy_notebook_fr.ipynb

 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Document introduction"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {