salem plot

module2/exo3/exercice.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.113000000000001"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "a = np.array([14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0])\n",
+    "np.mean(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.plot(a)\n",
+    "plt.grid(True, linestyle=':')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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VugZ7yYYD3eD06dOhS4gCc/J7rosfc9FgL9lwoBvs3r07dAlRYE5+Pz/Hw+dqsJdsONANeLB9Hebkd5wnuFBhL9lwoBvwYPs6zMnvJzzBhQp7ycY70EXkRyKSE5GPb7quRUR+KSKT5csN97bM2sDVgg5z8uMKXYe9ZKNZof8YwMHbrvs+gPedc48AeL/884rH1YIOc/LjCl2HvWTjHejOuV8DuHzb1c8BeL38/esAvlHlumrS2NhY6BKiwJz8DnVxha7BXrJZ7Db0Lc65LACULzdXr6TatW3bttAlRIE5+b2X4dtXGuwlm3veVSLyoogMichQNpvFzMwMstksMpkMZmdnkUqlUCwWMT4+jiRJbpwUtrLtbGRkBEmSYHx8HMViEalUCrOzs8hkMqjc39TUFAqFAiYmJjA/P4/R0dFb7qNyOTY2hrm5OUxOTiKfzyOdTiOXyyGXyyGdTiOfz2NychJzc3M3VgY330c6ncbo6Cjm5+cxMTGBQqGAqampqJ8TgKo/p1QqtaTndKhrAWtXOQy0J2hb4/D4pgSPri99Pb6pdN1Ae4K1q9yNlW5lm3Tl8kjPAhrqHA52JNjU6PDk5gS9Dzrs3JCgf2OCziaHp7cmaG5wGOxcQF1d3Y0TNz9fvo9jvQuoQ+nfmxtKt+9scujfmGDnhtL9Pbm5dP8HOxI01Dkc6fn8em5/Tv++5/fP6cCBA/fmOcHd9Tm1trbWfO99+umn9/3rqVgsQkuc8++CLCLdAN5xzu0s//wpgK8657Ii0gbgH5xzj/ruZ//+/W5oaEhdXK3J5/NYt25d6DJq3lJyEhF0vfpOlSvyO//a4LI+btsah+xVCfLYFedfG4Tm9R8SX3MlIjLsnNvvu91iV+i/APBC+fsXALy9yPuJypUrV0KXEAXm5NextrYHaa1gL9loPrb4MwC/AfCoiEyLyAkAfwbgayIyCeBr5Z9XvMbGxtAlRIE5+eV/F7qCOLCXbFb5buCc+9Yd/umPqlwLEREtAd9qN7h27VroEqLAnPzWNYSuIA7sJRsOdIPm5ubQJUSBOflN/ysPn6vBXrLhQDe4dOlS6BKiwJz8vtTMN0U12Es2HOgGnZ2doUuIAnPy+zDHFboGe8mGA93gzJkzoUtYFm0dnRCRRX99+9vfXvTv3i+eeagGTnDxQP2S/p+X8tXWoRvU98trrlq8n3Kh39u1a1foEpbFxcxnS9rR5X0AXa8eXtTvnn9tcNGPG5O3ztfAwbkWrgfZoQnQ/z/fL6+5auEK3YCH8tThoWH9mJEOX3M2HOgGPJSnDg8N68eMdPias+FAN+BqQYerTz9mpMPXnA0HugFXCzpcffoxIx2+5mw40A0qh9yku6scQpbujBnp8DVnw4FusGPHjtAlROHt82wrH2akw9ecDbvK4OzZs6FLiMJAO/eC9GFGOnzN2fBz6AYdHR2hS4jC8Mz9s4PQYt33GZV3avJpa2tDNput2sNufehhZKfTVbu/WsOBbjAzM4OmpqbQZdS8vnUO/+/afT6wPO77jJQ7NT25OcFvctXbkLDSd1zjJhcDDnOdXPE+HlRKzEiHOdlwoBtcv349dAlRWL2K24d9mJEOc7LhQDdIkho4oFIE6tlVXsxIhznZMC6DNWvWhC4hCr+d45/JPsxIhznZcKAbXL58OXQJUehu4p/JPsxIhznZcKAbtLe3hy4hCh9d5qrKhxnpMCcbDnSDc+fOhS4hCk9t5XsNPsxIhznZcKAbbN++PXQJUXg3zbbyYUY6zMmGaRmcOnUqdAlRONrLVZUPM9JhTjYc6AZ79+4NXUIU3kjx0LA+zEiHOdlwoBvwYPs6z/PkDV7MSIc52XCgG/Bg+zo/5ckbvJiRDnOy4UA3GBkZCV1CFI71clXlw4x0mJMNB7rBnj17QpcQhTdTbCsfZqTDnGyYlsHExEToEqLwbCc/meDDjHSYkw0HukFPT0/oEqLwwUW2lQ8z0mFONkzL4MKFC6FLiMKXW3j8DR9mpMOcbDjQDVpaWkKXEIWpAo+/4cOMdJiTDQe6wdWrV0OXEIWNX+CqyocZ6TAnGw50g7o6xqVxne9jeTEjHeZkwwllUF9fH7qEKBTn+WeyDzPSYU42HOgGhUIhdAlR2Lyafyb7MCMd5mTDgW7Q2toauoQonM1zVeXDjHSYk82SBrqITInImIicEpGhahVVq6anp0OXEIV9rVxV+TAjHeZks6oK93HAOTdThfupeX19faFLiMKvLnBV5cOMdJiTDTe5GJw+fTp0CVF4rosfTfBhRjrMyWapA90BeE9EhkXkxWoUVMt2794duoQo/PwcD3nqw4x0mJPNUgf6V5xzewH8MYDviMjTt99ARF4UkSERGcpms5iZmUE2m0Umk8Hs7CxSqRSKxSLGx8eRJMmNQ9RWTiYxMjKCJEkwPj6OYrGIVCqF2dlZZDIZVO5vamoKhUIBExMTmJ+fx+jo6C33UbkcGxvD3NwcJicnkc/nkU6nkcvlkMvlkE6nkc/nMTk5ibm5OYyNjf2b+xgeHsbo6Cjm5+cxMTGBQqGAqampe/acdvfvw9GjR7F69WqcPHkSIoJXXnnllssTJ05g/fr1OHz4MLq7uzEwMID+/n709/djYGAA3d3dOHz4MNavX48TJ0587n2cPHkSq1evxtGjR9He3o5nnnkGvQ867NyQoH9jgs4mh6e3JmhucBjsXEAd3I3DmlZOQHCst3T9YOcC/sO2eTy9tfR7/RsT7NyQoPdBhyc3J9jU6HCwI0FDncORntLvHu+79fJQ1wLWrnIYaE/Qtsbh8U0JHl1f+np8U+m6gfYEa1c5HOr6/Ps40rOAhrrSY21qLD32XZ9TXd1dn1Nzg6vqc3p5x/yN53TgwIF785w8/0+tra3B/p9efvll1XM6uX3e9Jx8/0/f/OY37+mMAHBPZoSWOFedNx1E5AcACs65/36n2+zfv98NDa34906rRkTQ9eo7y/64518bDPK4IR+bz/n+eOzzrw2iWjNvOYnIsHNuv+92i16hi8haEXmw8j2AZwB8vNj7iwFPQadznKcN82JGOszJZimfctkC4C0RqdzPG865/1uVqmoUT0Gn8xOeNsyLGekwJ5tFr9Cdc//snNtd/trhnPvTahZWiyrbzOjuKttL6c6YkQ5zsuHHFg22bdsWuoQovJdhW/kwIx3mZMO0DNLpdOgSovAHm+N702m5MSMd5mTDgW6wZcuW0CVE4ZMr3LvPhxnpMCcbDnSDK1euhC4hCh1ruaryYUY6zMkmmoHe1tEJEQny1dbRCQBobGwMnEIc8r8LXUHtY0Y6Vc/pgfrgc+ReqsbBuZbFxcxnQXeCIKIVYOH6ip4j0azQa8G1a9dClxCFdQ2hK6h9zEiHOdlwoBs0NzeHLiEK0//KN7J8mJEOc7LhQDe4dOlS6BKi8KVmvpHlw4x0mJMNB7pBZ+e9f1NjJfgwx1WVDzPSYU42HOgGZ86cCV1CFJ55iCcl8GFGOszJhgPdYNeuXaFLiMJb53lAJR9mpMOcbDjQDXj4XB0e8tSPGekwJxsOdAMePleHhzz1Y0Y6zMmGA12jvHdZ5ZRty/UVK66q/JiRDnOyiWZP0aDKe5e9BaDr1X+3bA8b6x6qXFX5MSMd5mTDFbpB5WS5dHfMyY8Z6TAnGw50g7fPMy4N5uTHjHSYkw3TMhho515rGszJjxnpMCcbDnSD4Zl436hcTszJjxnpMCcbDnSDvnVcLWgwJz9mpMOcbDjQDXJFrhY0mJMfM9JhTjYc6AarV3G1oMGc/JiRDnOy4UA3qGdaKszJjxnpMCcbxmXw2zn++afBnPyYkQ5zsuFAN+hu4p9/GszJjxnpMCcbDnSDjy5ztaDBnPyYkQ5zsuFAN3hqKw+2r8Gc/JiRDnOy4UA3eDfNuDSYkx8z0mFONkzL4GgvVwsazMmPGekwJxsOdIM3UjyUpwZz8mNGOszJhgPd4HkebF+FOfkxIx3mZMOBbvBTHmxfhTn5MSMd5mTDgW5wrJerBQ3m5MeMdJiTDQe6wZspxqXBnPyYkQ5zsmFaBs928h13Debkx4x0mJMNB7rBBxcZlwZz8mNGOszJhmkZfLmFx5XQYE5+zEiHOdksaaCLyEER+VREzorI96tVVK2aKvC4EhrMyY8Z6TAnm0UPdBF5AMD/APDHAB4D8C0ReaxahdWijV/gakGDOfkxIx3mZLOUFfoTAM465/7ZOfc7AG8CeK46ZdWm63x/RoU5+TEjHeZks5SB/hCAz276ebp83YpVnOeffxrMyY8Z6TAnG3FucX/SiMgRAF93zv3H8s/HATzhnPvPt93uRQAvln98FMCniy83uFYAM6GLiABz8mNGOsyppMs5t8l3o1VLeIBpAA/f9HMHgAu338g599cA/noJj1MzRGTIObc/dB21jjn5MSMd5mSzlE0u/wTgERHpEZEGAEcB/KI6ZRERkdWiV+jOuXkR+S6AvwPwAIAfOedOV60yIiIyWcomFzjn3gXwbpVqicGK2HS0DJiTHzPSYU4Gi35TlIiIagt3/SciWiE40BVEZEpExkTklIgMha6nVojIj0QkJyIf33Rdi4j8UkQmy5cbQtZYC+6Q0w9EJFPuqVMi8mzIGmuBiDwsIn8vIp+IyGkReal8PXtKiQNd74Bzbg8/QnWLHwM4eNt13wfwvnPuEQDvl3++3/0Y/zYnAPjLck/tKb8fdb+bB/AnzrkvAfhDAN8pH06EPaXEgU6L5pz7NYDLt139HIDXy9+/DuAby1pUDbpDTnQb51zWOTdS/v5fAHyC0t7n7CklDnQdB+A9ERku7/lKd7bFOZcFSi9QAJsD11PLvisiH5U3yXAzwk1EpBtAP4APwZ5S40DX+Ypzbi9KR5b8jog8Hbogit5fAegFsAdAFsCfhy2ndohIE4C/BfCycy4fup6YcKArOOculC9zAN5C6UiT9PkuiUgbAJQvc4HrqUnOuUvOuQXnXALgf4I9BQAQkXqUhvn/cs797/LV7CklDnQPEVkrIg9WvgfwDICP7/5b97VfAHih/P0LAN4OWEvNqgyoskNgT0FEBMAPAXzinPuLm/6JPaXEHYs8ROSLKK3KgdKetW845/40YEk1Q0R+BuCrKB0R7xKA/wrg/wD4GwCdANIAjjjn7us3BO+Q01dR2tziAEwB+E+V7cT3KxF5CsA/AhgDUDkS+n9BaTs6e0qBA52IaIXgJhciohWCA52IaIXgQCciWiE40ImIVggOdCKiFYIDnYhoheBAJyJaITjQiYhWiP8PDqEsSGd9hX4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(a, histtype='bar', ec='black')\n",
+    "plt.grid(True, linestyle=':')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +91,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-