journal/Readme.md

 # Journal de bord du Mooc / Mooc's logbook
 
-FR
-
-Espace réservé au journal de bord du Mooc
-
-EN
-
-Reserved for the Mooc's logbook
\ No newline at end of file
+Meiling
+-exprimen
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module1/exo2/fichier-markdown.md

-test
\ No newline at end of file
+# Partie 1
+
+## Sous-partie 1 : texte
+
+Une phrase sans rien
+
+*Une phrase en italique*
+
+**Une phrase en gras**
+
+Un lien vers [fun-mooc.fr](https://www.fun-mooc.fr/)
+
+Une ligne de `code`
+
+## Sous-partie 2 : listes
+
+**Liste à puce**
+
+- item
+    - sous-item
+    - sous-item
+- item
+- item
+
+**Liste numérotée**
+
+1.  item
+2.  item
+3.  item
+
+## Sous-partie 3 : code
+
+```
+# Extrait de code
+```
\ No newline at end of file

module2/exo1/toy_notebook_en.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1 On the computation of $\\pi$\n",
+    "\n",
+    "## 1.1 Asking the maths library\n",
+    "\n",
+    "My computer tells me that $\\pi$ is *approximatively*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.141592653589793\n"
+     ]
+    }
+   ],
+   "source": [
+    "from math import *\n",
+    "print(pi)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.2 Buffon's needle\n",
+    "\n",
+    "Applying the method of [Buffon's needle](https://en.wikipedia.org/wiki/Buffon%27s_needle_problem), we get the **approximation**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.128911138923655"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "np.random.seed(seed=42)\n",
+    "N = 10000\n",
+    "x = np.random.uniform(size=N, low=0, high=1)\n",
+    "theta = np.random.uniform(size=N, low=0, high=pi/2)\n",
+    "2/(sum((x+np.sin(theta))>1)/N)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.3 Using a surface fraction argument\n",
+    "\n",
+    "A method that is easier to understand and does not make use of the sin funciton is based on the fact that if $X \\sim U(0,1)$, then $P[X^2 + Y^2 \\le 1]=\\pi/4$ (see [\"Monte Carlo method\" on Wikipedia](https://en.wikipedia.org/wiki/Monte_Carlo_method)). The following code uses this approach:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "np.random.seed(seed=42)\n",
+    "N = 1000\n",
+    "x = np.random.uniform(size=N, low=0, high=1)\n",
+    "y = np.random.uniform(size=N, low=0, high=1)\n",
+    "\n",
+    "accept = (x*x+y*y) <= 1\n",
+    "reject = np.logical_not(accept)\n",
+    "\n",
+    "fig, ax = plt.subplots(1)\n",
+    "ax.scatter(x[accept], y[accept], c='b', alpha=0.2, edgecolor=None)\n",
+    "ax.scatter(x[reject], y[reject], c='r', alpha=0.2, edgecolor=None)\n",
+    "ax.set_aspect('equal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is then straightforward to obtain a (not really good) approximation to $\\pi$ by counting how many times, on average, $X^2 + Y^2$ is smaller than 1:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.112"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "4*np.mean(accept)"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +152,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-

module2/exo2/exercice.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14.113000000000001\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "data = [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",
+    "mean = np.mean(data)\n",
+    "print(mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "minimum = np.min(data)\n",
+    "print(minimum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "23.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "maximum = np.max(data)\n",
+    "print(maximum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "median = np.median(data)\n",
+    "print(median)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.334094455301447\n"
+     ]
+    }
+   ],
+   "source": [
+    "stDev = np.std(data, ddof=1)\n",
+    "print(stDev)"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +110,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-

module2/exo3/exercice.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ZaaKu1ui0R3/JJfRjFqMHFHv095PYiguBwI0G4VpbOIFsPOIRO3fu9HoIvsENW9j16EtK6KR3W+h//3t6zMpyJjg7P0+ipl0Nqhb6FSti7cE5Cb06PuyWR6+dgHRK6DlXwlElJeYxeiBW6NesUe4wBNKjt4FsPKLQp62w5DHf+x4QXajpOm7YQpzAk5OUYqg/Duo2JJb96zWLThdC6AcHdYrPJMHICIn90FDs6+rQzcqVsaEbURPGK48+GaH/8Y/j71rUnD9PaZJaj35ujv4nK6HXIoXeBrLxiMJpdYqHxwwNAXfdBfzoR958vxu2UAuaUYqlWugB/YJf6WB+HohWBsHIyLwj2xSe69BQ7AVL/O96oRvxGS9i9NPTseGK7GwgOztiKvThMPDpTwNf/arxe4SwC49ePFfXuQESE/p0hm44D4DQS/yJWCQT5KQotaAZhW96e2nSToibW+Gbw4eB4WH63slJZ4qQC9Gem4v32hmjFaHa0I2e0Hu5GrSgIGwq9CdO0B3IwYPG7xH/k9qj5zx5oU93eqVwLha10MvGIwrV1dVeD2GBY8fo0Suhd8MW588r4QA9j35qisIEao/eLaEXYZsbbwSmp5c5sk2jRULj45R9kpERH7rxk0cPAKtWZZgKfVsbPfb0xIeoBFqPXlz4zIR+bo6250To5nOfA/7sz+y/Px39YgGXhV42HlEIhUJeD2EBrz16N2wxNqa0hNPz6EX0SC30btnj97+nlM7t24GJCYZ5B6I3Zsv+1SmF4+OAyHr2m0e/ciU3FfpogUwAxl69+N+FRw/Q/6kV+uXLgawsOjaEHZwQ+mefBX7zG/vvD4TQ+63xSEuLM+lbydCuPko9Rnj0U1PefH+6bRGJkKAZdRIClNRKtz36cBg4cAB4+9tJeDlnC2UKUsGO0AuRE9+nDnMIxLJ/N/LotR59KDRu6dGLi5LROjNt6AYge6gnpQEKZ4k7HJFDv3Zt/PYSSa+cn6e7jVOn7M/3pKMxOLCEY/ThMPDWtwLf+pbXI/Eerz16J3n55fgsDCFkdoXezRh9SwuJztvepoiOWT0euwwOKqmBZh49oNjj3Dl6bZkmeuRGpolefZf8fPMYfVsbcNVVdDdk5tEXFNC2xUVBz6MHlMlpo8VSQGK26O8ngZ+fB7q77X0mEB59VpZzXd1T5exZEjavPPo1ekeRw3AOPPCA0uRYj7k5Ja3SK6F30haf+QzwhS/EviaEzCx0I4S+rMzd0I2Iz193XbzwpsLgILUCBOwLvXaxlCA31x2PXitupaXLDM/PuTlyULZtA+rqzD164cmrPXo3hF4t7tE+NZYEQuj9VLHx5El69Ercqqqq0v4d586R6D3+uPF73nhDySsPgi36+5UTVSCErKKCvFwjoV+9mk5kN0M3v/89xeZLS50X+vXrqferVehGiJ52sZTALY9eG6649NJCTEzor3s4cYLEXgh9by9V3tSirt1jFqMH4oW+tDR+ezk59L12qrmohf7ECev3A/E1f5zCVaH3U30XIfReTRuI1mrpZGSEHs3uWkTYZu1a74TeKVtEInRia094cVIXFcWnFAp6eylsA7gXupmdBRoaKGwDOB+6KSmhi9di9ejHxrpjxqdGZNxs2wa8+c30u174RpQ/AGiuobBQ8ehzcmgCVqAW+qIier8WMUY7cXpxJ52fv8Q8ej/htdC7gR2hFxOxV165+GP06tWgao9LCMXKlfr1XQBlsRTgnkff0kLfcd11yvgA5zx6vfouyQi9W6tBtR59QQG58nrHb3s73Z1t2ULHLmP64Rvt/yQWTakLmgnUQm8UTUzk2Ojupu1cfrl9jz4QQu+nxiNeC32O09PqOtj16Nes8dajd8oWIsNifl7534HkhT7d9oj2oF5oAuKU0F+8SNsoKYkV+vl5mpjWC91EInSB9MKj51x/MnbVKtILveO3rQ2oqqKxFRYC1dXxQi/q3KjDUZdcooRu1GEbID1CX1VFk8VLyqP3U1GzU6fo0Suhd6OQlxA7tehpOXaMvCI3bs+NcMoW6pCN+nd1PFZP6GdmSOTcDt1ouyo5FbpRLxJSC72InGqFfmyMLjqce+PRi8ooWnG7+mpaSGck9Nu2Kc/1JmTHxii+rxV64dHrCf3MDIVcjIQ+kdCNEPqNG+2nWAYivXLKq0RtHbz26JuamtL+HXY9+upqb4XeKVuoc8fVvwth1yvkBcQulgK8a5+XlQUsXx5J2aPXE3rOYytXAtQUOz8/dpGQFx691g6C3t7XAcQfv3NzQGdnvNCfPQucOaO8praDQFSw1BN68byvL3WPPhymeZ/KSvLo7aZYBsKjn3diyZ8DcO690LuxeMxK6IeG6D1C6C9eNK7smE6csoXai9cKfV4eCZueRy9SK0UIRZxkbrfPA4C8vHDKQq8W7Usuoe+ZmootaCYQzUfMhD7dHr2RuGVl0S2I9vg9flzJuBHU1dGj2qvXWwB2ySV03I+N6Xv0glSFvreXwmHCowfsxekDIfR+YXhYOYmX8mSsmIgVoRtgcdtDLe7a0I04ifWybkQLQeHRi9Wgbnv0AC0SSjV0oxV6QH81KKBc+Pzo0RtNxqozbgRvehPtN3XmjZFHHw5TKCWdQi8ybkSMHrAXpw9EemV+fn7av2N+Hrj+evP6EsKbLyryLlzhRkNsIfTj4/qeukitFB49kH57nDkDbN4M3Hwz8PWvU+bJzp3O2GJwkHKfQ6F4j16cxNr6LkC80ANkDy+Efu3aHMc9eiBW6NUevQhl6YmiwCuPfu/enVi+PF7o1Rk3grw8YOvWWI9eXedGIH6/cCE5obcboxdhmqoqoLiYtr1kPPpZFwp8T07SIpSGBuP3iInYLVu882C77a6JTgG9zBM1x46R53rppe4JfWcneTavvQb89V9TDvTnPudAcReQF79mDZ3Mao9efZsuTmYhegAdD6tXKzYA3FskBMSe1FlZFxwR+mXLSNCthF7c4QwOkkdcXBy/vUQ9+rvvBm67zf77jSYgu7u7UVSk79Fv2BD//ro6oKmJwjqAfuhGfSFLp0ff3U32FIv07GbeCFtoy1CkSuA6TImDWX0iaxEevZdC369dvpkG1CeIXvimo4MOwFDI3SbQAPDkkzQJumED0N7uTCsn4dGXlsa3jFN79EDshe/UKSU+LxBdptKJSClUF3XNyppyJHRTUkLbtePRC6FfvZrESUtODt0R2p2/qa83d7S0GIUr+vv7DYVeHbYRvO99FJZ97jl6fu4c/a/qhU9q0U+30FdUKAuyNm6079FrjwknCFyMXhSwMjtZTp6kW73yctphbraMc5OREeVg1kuxFKmVgHtCL27qsrOBdeuotszEhDPNNgYG9HuDakM34jVBby+VC1DjRuhGbzVofr4zk7HaZf9WoRujxVJAYvM34TCV1Rgasp8mahau0Ar9xYvxGTeCm2+mi/zDD9NzvZIOdoQ+FAJWrdIfayKhG3Vlj02bSHesmuylozE44LLQu7FIyK7QX3qp+y3j1GzdujXt3zEyAlx2Gf2u9YouXqRiZqLnh9sevThhiouBixedmbtRe/RWoRutR68Veq/qu6xfvxLnz6fmfKhFOz+fLqpqoS8oUN4rQjfiIqlHIgvIuruV0Mkbb9gbr9Fk7NatW+OE/vhxupjoCX1WFvDxjwPPPEOLntR1bgRmQp+bS5lZJSUk9nok4tGLInoAefR2UizT0UYQcFnouQuusxB6q9CNWui9CN9E7FRFSoH5eTpBjIS+q4smJIXQi7Vsbnn0aqEfHU39MJyaorFrPXrRNs7IoxfdhvSE3ouuSgUFEVy8aO4x/sM/AL/+tfHf1UIvwjdC6PPyYkVs5UoSzp4eZzx6kckF2Bd6I48+EonECf2hQ/S4Y4f+tj75STquH300flUsQLFvIfBaoWeMXjMrpmpHM2ZmKKdf69ED1nH6QAj9TDq76kax69GvX++t0HeIlJc0IbxCI6EXJ6RXHr2Im5LQp75d4cELob9wQRH/cNhY6EXGjRehGz2hn5npjxmfltdeA/7u74B77zX2+rUhC7XQ69V3AUiYnPDo1Yd1qh59R0dHnNA3NdHFSs+jBygUec01FL4xCkeJ17RCD5A99KpWCuxohpgDVAu93Vz6QAi9G1gJ/dQUTdioPfrFXswrEokvmypi8kLotTF6IXDiYBRCn+7Fy3qhm5mZkO2uPUYID16EbgASf/WqWEARNnF8iAysVEM3X/0q8JGPJDZmPaHPywvHjE/L//7f9Hj0KPD66/F/n5qi7aoFzkzo1WLnlEcvLrapevQAxejV6bBNTZRdY9Z98pOfpHH09+uXXRavaW0B0N3SX/+18bYzM+m7zY5XdWqlYNUqOvasPHq9mj9O4KrQL3M6Z0gHK6EXJ7bXoZuysjLHtvXRjwIf+1jsa0LY16yhE1XrNZ89SwetmHTyYjIWUNL5UvXq1bnjep2EhMAXFsbWpBfHQ6pZN88+Czz9dGKxdT3v7dJLSXn1PPqjR4Ff/hK46y7ad3p9BvQWPtnx6LWfUZOoR19dTQ5GokKvveiVlZUtNHQfG6Nj59Ah6iplxgc+oIQiE/XoP/hBautohpUToCf0jNnLvAmER+9Ghyl1eqXeSSduq7wW+nL16pwUaW2lhUdqhNAXF+uHR/r7yfMV6XRehm7U400WEbpRe/SDg7GVKwH6fwsKYoU+MzM+LpuoR9/VRd702bP2P6Pn0W/cuBqAvtD/4z/S+7/yFeCGG0jotVVF0in0duyhFvpUm22Ul5cvCP3oKB3nFy8CVjXwCgpI7AFzj15P6O1gR+izs+N7ztrJpQ+E0LtR1Ex49JGIvmgJoV+/3rtl/089BTz6aJtj2xsYUApzCdRCX1QUL6Rnz8YeiG569KEQiasYn3q8Rrz0kn6oQqBeHCMESy90A8TWuxENR7ShgERi9BcuKALf2WnvM4C+0Pf0tAKIvyPt6gIee4y8+UsuAT70IbpIvfxy7PuMhH5ykv5mFrrRE0XA/rExMkIXlC1byHvt60usYqNW4JqammKEXtS+s1Ps9O676Ri7/PL4v+3YQed/soJq1SC8u5scSe2ahI0brVMsA5Fe6QYTqkWWeuGbkyfpAFi3zn2PfmYG+PSnadXgo49e6sg25+boBJucjM000gq9nkev9mKzssgubncSshO6CYeBP/kT4HOfM37PwACJVna2fss4teeqFnq91EogMY9e3ZM3VaHPz6cYvdajf+ABuhh9/vP0/L3vpc9qwzdGQi/GmU6PXl1S47LL6I7azgLw6Wk69jJ1llNohX7duthSFUa85S10PtTWxv/ts5+1Xx9eD6tjo6cnNrVSUFVFd2Bap0xNIDz6kNkMikOohV4vxfLUKcWDc1Poe3qA3bspGyA/H7hwwZn+ueqFQaIKI6AIZ1GRfuhG69ED3rSMs+PRv/yycqIbrc4UOfQAbX/FCnsevZnQX7xorzeoaK4OpC70a9aQcdRC398PfP/7wJ13ktABFJ649Vbg5z9X8tYB44qNANkuGaG369Gri+SJJAA7cXojccvPz48R+ldftY7PqzHyjDMyUisxoBX6116jVblf/SrQ2Bi/WEpgx6kJhNDnqouJpAk7Hv2lUWfaLaEfHSUP48QJyn++7jogEtGZ8k8C9cIgtacwMkJikJUV79GHwyQI2ri0G0I/Oxu7JN2O0IsCdVNTwOHD+u/Raxln5dFHInRx1BP6RMJ6QujXrEld6K+9thaZmbHH7quv0kXnjjti3/uhD9EK1BdfVF4bHKT9rt6uWvS1Qr98OYlednbsQio1iXj0WVnkzSYq9HqiXFdXtyD0b7xBXrgL/Xos0Qr900/Tz5e+RKmdw8PmQm92rHsm9IyxRxhjg4yxI6rXvswYO80YOxT9udnOl7kZowf8I/THj9MJ+fDDwHveQ8Lb3+/MmgK10Ks9+pER5cDSxuhFIwo/ePQFBUAoxE0P/meeUUo1aGPSgoGB2PxnUe9mbIyETP2d6pZx4bCxRw/YF/r8fGDXrsSEXu+kfuWVxria+SLMINY8CG66if6Xxx5TXtPLHTdbDQrQNkRtHD0S8eg3baIQzCWXkE3sTMgapRQ2NjYuCP3zz9OjH4ReG6Pv7aU7rXPngF/8Avh+p6MpAAAgAElEQVTCF4APfzj+c3aE3sv0yh8AuEnn9X/inL8p+vP/7HyZG41HJiaUE14r9HNz5PWKE9utPHpxUKgX7UxMOBPGMvPo1UJ/4YIyCSQmDr0Qeq1HzxiQnz9nePB3dVFZ2rvuopPJSOj1PHoRulmxIlbEhJAapVYCiR0bXV1UnK26mjxPu8W/9Dz62dnZuJr5J04o2VNqsrOB22+nlEtxkbcSer3c8RUrjMM2QGIevbgYiXTCVDz62dlZ5OTQ//nHP9I2RYMRL9F69L29dAytXk3742tf0z+mfO3Rc84PAEgx+c09xseVyRptjP70aZoMcduj1y77X7kSmJrKjEuNSwYh9Lm58UIvvCFtbFAIvRehG70DubAwbHjwi7DNe95Dt8V6Qh8O0+2yUehGHbYBlJr0YhLVidDNhg1UZ1+UE7Bibo5CR3oCp213ePy4srJSyxe/SHdnn/kMPeoJ/cqVykSnntCrm2PoIRqxqI+NyUlaOyBSmOfmSNTVNeLt5tJbebFFRWTXrVuNw0tuoif0diaIxfnohdCnUjbwzxhjHwfQDOAezrnuFANjbB+AfQCwbt061NfXAwA2bNiAgoICtLZSOtmqVauwbds2HDhwgAaWmYndu3ejpaUF41HFrqurw8DAAHp7e9HeXoCSkvW47jqGI0coqlRSUoKJicuxcuUwgNVobe0BUInm5mZMTk7i0KEVAK5EZuZp1Ncfj060XYehoUnU11PHgjVr1qCqqgqNjY0AqBDbzp070dTUtNDybteuXeju7l4oNbx161ZEIpGFsgZlZWUoLy9f6IV6+HAZgE3o7Hwdc3MjGB4uB+cb8eqrxxaWvG/fvh2zs7M4Hr1Pr6ioQGlpKZqjnRQKCwtRW1uLhoYGhKMu4549e9DWNoLly4uwdu00uruX4dSpfnR1daGv7y3YuhUYH4/g7Nk+AFvx8svHcNttW7B/fweAapw8+QquvvpqtLa2YnR0FBcv1mBiogA9PX3oiapVqvsJADZt2oTs7GwcOXIEZ89eAc6XIxxehoZoLdvCwjdjZAQL+wmghuF9fX340Y9WYf36bOTmjmPHjnw88UQBnnjiZWzfXrywn0ZGloHza1BaioX9ND1dieHhS3Hy5AWEQhHU17cs7KehoSlwvhENDcMAVuHkyT9gaCiC/Px81NXVobGxEW+8UQBgOyYmwmhv78RgdJZTu5/KyyvQ1bUBW7f2YXLyHIBaHD8O9Pdr91MbhoeHAQA1NTU4c2YSQAXOnDmBnp5MFBcXoyW6GCIzcwJjYwXYv38/OOc4cuRqvOMdyxf2EwDU1tZiZGQEp0714BOfKMd3vrMRP/jBJE6fzkJFxTAOHx6O2U+FhddgZGQZBgdPoL6+L2Y//fmfn0EoBJw+vWFhP4nzafPmzWhoaEBW1rXo6hoCsBbNzc147LEC/NM/VeMXv5hFTc0pvPLKCObmdqK0dAyDgxfR3t6OrKwN6Ooqx4ULc3j1VePz6cKFPQBmUV/fFHM+AUB9fT3y8nYByMb69WdRX98Rs59Ej4vdu3ejs9N4P9k/n2L308TEBLqikzCVlZUoLi7G+fPTGB0tRGtrJ664ogY9PRFs334G+/d3Ye/evbr7SZxPOTl7cObMRdTXvxJ3PkUiDJHIXuTkIO58ShnOueUPgEoAR1TPSwGEQHcEXwXwiJ3tbNu2jTvF9ddzfuWV8a8XFXF+992cA5z/z/8Z+7ef/IReb29XXlu2jPMvfMGxYeny85/T9x45Qs8feYSed3Wlvu2PfITzqirOb7451h6lpZzv20e/P/ssfd8f/0jPv/IVej4zE7utm2/m/M1vTn1MZuzZw/l112lfG9f93okJ2j/33EPPX3mFxv3EE7Hva22Nf/3b36bXqqo4f8c7Yt8v7H/rrZyvWKE/zueei7WZEWfP0vv+7d84Hxyk3//5n80/wznn/f303m9/O/b1trY2/v73cy5Olelpzhnj/P77jbc1N0f7raSE81CI87/92/j37NhB33fggPXY9Fi3jvNPfUp5/sUv0va2bKHv//Wv6Xljo/Ke736XXjt50nzbe/Zwvndv/OttbW2cc86vuYa2893vJjd2p/nUp8genHM+MkJj+8Y37H22vJzzO+/U/9vEBG3rwQfj/wagmdvQWKOfpLJuOOcDnPMI53wewEMAbCU9Odl45Nw5ul2PHRfF6FesoFs8behGr12aG+VotQtC1Mu6U0VMQpaXK3FazmNj9Hqhm6Ki2Fg54F3oJjt7Svd29ne/o3mFd7+bnl95JY1ZG75Rr4oViN97euJDN2JC8vXX9cM2gP0Yvci42bCBYrQrV9qbkDVaJDQ4OBgzGdvdTfvTLLSSmQn8x3/Q+RCJ6Mfbzeq72EF7npw+TSGdY8coyUBbJA9QMm+SXfYvvHNxvvhhIhaItYWoGaUXk9ejuNg4dJOuNoJAkumVjDH1NN5tAI4YvTddDA3FG2x2lmJ5BQVKd3s1585R/rw4cAB3hV4Iq15N9GQRQl9WRv/f7CylIc7NxU7GAorQ9/fHT8QCiQv9t79NdXYSQTsZC1ATaL2D/5lnaD9eey09X7aM0lTtrAYVv3OuH6MH6CJgJPR2Y/RqoWeM4vR2hN6oYqMYnzg2RMaNUYxe8KY3KcW40iH02mPjzBnKab/2WuD++6kpd0lJ7LklxmwVp7daDVpcTN9vVLHSbQIp9IyxxwE0AqhmjPUxxj4F4AHG2GHG2OsA3gbAZM2iglONRzgnoZ+cjF0sIlIrCwrogNYT+lWrYpcme+nRO1WeVwg9QN66elWs+vvE62fP6tfcTlTon3wyfsGOFXre28aNRTh/PjZbZX6eJmJvuklpxwbQhOzBg7HpbeoSxQK7vUGtPHo7Qs+YMsGfqtBv374dK1bQxTocVoTezKMX3H8/8OCD1GlJSzo8+rIyavA+MAD87GexE7EA3WVmZVkLvdFk7Pbt2wHQauBHH9VfOesFIr2Sc2eF3qjmjxPYybr5EOd8Lec8i3Nezjl/mHP+Mc75Ds75FZzzWznntko5cYcaj0xNKamCarEUQl9YaOzRr14d+5obQq+XdQOk7tFHInTBE6EbgMI3WqEX32fl0eflJSb0x46RyCfS51zPo8/PpyuF2h7t7TTOd70r9r3XXEPfefCg8ppohq0WdHUYx8ijB4xP0ERCN2Vlyr7dvJnSNq2OKSOhn52djSmlfOKEsrrZiuXLSRT1cuUrK+lC7qRHv24dcPXVlFIIxOf5h0KU0WPHo9cTNzHResUViTUbTzfqznS9vfpF8YzQqzsl8F3oJlmcajwyNKT8rjaaulXaihX6MXpt4SY3Oglpd6BWeJNlaIg8X7VHf/q0sl0hDpmZdIKPjpIXolf+AFBOZjvX44kJJZ0zkR4qeif1zMwZALH2EHHdqFO3wK5d9KgO36ibYQtWrFCWuZsJvROhmw0blOebN8eO3wgjoT9+/HiMI3D8uD1v3orPfIaW6idbQFbtEE1N0UVIHHOiqqZecoidFEu99QQAFjJm/Ib6bk8slrJb3cWs0U5ghN4pjITeKnQzNKQv9G6EbtQVGwsKgIwMnrJHrw5ZmHn0gOJJnD9P4zEK3XBur+KgOjyhbh9nxeysfh49ELsv1eWk1ZSUUOxXLfTaVbEAib5R3XG1V+tE6EYt9EKUrcI3Zie1GK/w6K3i83bIyVEuQsmg9ujFBV7U3RGVKj/96fjPiUVTZs5DunLH04UY6/Q03b3ZDdsAotGO/nFlVJffCVwV+tFRZ/4DO0JvFLrxSujVBzLVRJ93TOhLS+n/FYumjIR+dJTCIYCxRw/Y6zIlvHjGEvfotaGbyy6jgar3ZU8PhZJEYxQ111wDNDQowmPVMk7r0WdmKgtvUsm6mZkheycj9EYefUVFxcJ4+/tJSJzw6FNFfZ6coRswqHvnFBfHl+UFyKMfH489Z7UYTcZWJKKgLiLGOjOjrIq1i9nq2MB49DMzziz7V6dVmgm9OnQTidB7tULvRm9QPXErLmYph27UQs8YnXhGQi9uGY1WxQKJ1aQ/doxO7Kuusi/0nOt7b5ddRrPFWqGvrNSvv/KRj9Ax8L730faS7Q2akaF4pVqysuguzKocLRAr9AUFdBFNVuhLS0sXhP6118hmTnj0qaLn0dtpkibeoy7VoSYcph89cSs1a97qIWonoK9PCn0cc3PO1LqxG7qZnlYyQoaH6aTxwqPXC1dkZk456tEDSi79yAhdWNQiIjx6ozo3QGJC39FBE21XXGE/dBMO0z7QXvROnToEQF/o9bjhBuCRR4AXXqBOQnqhG0B5TevRi9fWrTOOWTNmfWyoUyvVbN5srzcoEC/0zc3NCxem6CJO33n02tCNGeLOSa9kOGAerhCrWP2GGOupU5QUIoVeQyRiUB4vQYaG6ETMyDD36AElfCMWS/khdANQgwknPHp1tonao9dmaYgYvZ3QjV2h37KFMi2GhuIXr+lhdCCLZhvaGL2R0APAJz4B/Pu/A//5n3QhTSR0I/6mFWgtqQh9sh49oIxX6JzfPPozZ+gcs1N3RsyHqKvKqklnSmG6EGMV+9gpofc0vdJJ5uedEfrhYTLYypXWQi88Cb8JfXFx6umVwpMV4Y3ychL6oaF4oVeHbrKz9cMZdoV+fp4O8upqJaXOTvhG2xhcUFRUgBUrlH15/jyNVTsRq+Xuu4FvfIN+17sobNxI+1cvzv9//y+t6DTDKiOrq4tspr3IbN5Mx5tVgwlAb2K6cKGBeV8fXaD1xu82OTl0RyYqwNrx5gFF6K08ej1xK0w2FzTNiIuzuGuTHr0Gp4R+aIjy4bWLD8bHaSeIdELAvx59ZWVRyh69uqsSQB59OEyiq+fRz86SOK1dqx/7tiv0vb1ksy1blEUydoTe6ECura2N2Zci48bMoxf81V8BR49SvF7LnXdSPn5eXvzfNm609pSt5m+6uih8pbWl8PDNqlgaeW+1tbULDczFOP2AOt30zBl78XnAOnRjdmdTq9cH0AeIsTrt0QdG6MVkXKoYCf3EhHJgJRK6sZs7nix6k7FTU32OefQCcfJ1duoLPUDCpxe2ARJvGVddTWKclZWa0Dc0NMTsSyGQdoQeoIuNXh6z6HaULHZCN3rhH2FrI3EDaLvZ2fGZKqKipwjf+CE+D8Smm4pVsXZIxaMXtvAbaqFX9yi2Q14eHZdmQr/o0ysB/a5PiTI0RLezyQi99jZYvcotXeh59Hl5FzE9Hf+9nNMB9J3vAP/tvwHf+57xdrVCL3LpIxFjoT9xwngVn12hVzeBzswkMbIzIWsUugmHwykJfbowC92Ew8ZCb+XFAsaLhETJXHH8+s2jn5pSVsXaQdjCKEZvJm5hux1cXEacy6IOvVFnLj0YMy6DoK2J5SSuV484f14/QyIRhoepuNXMTOwKRD2hV8foi4risyzUnkq6JoRmZ+MnrvLy6CAeG4vtiFVXp/xP2dnAc89RW7L8/NjPz8/HpxVq85rViOfhsDMevWg/B5DgHz1q/hnA3HsrLlZCNidP0n7Rlqtwm9xc4zUFjzxCf3v72+P/ZuXFAsZCL/CrR9/bS3F6ux59KER2tArdLKbJWGELzhML2wiMyiDMzChpvU6z6Dx6UdDMKnSjF6PXu8Vyo8uUnkf/lreQq6YO37S1kcj/zd/QRE99PZ0gP/pR/DZHR0m0taV5xUFi5NEDznj01dWKJ1NdTeO2Km5m5NHv2bMnzqM3yqF3E6PQzeQkFRC79lrqfKXFjtAbrQbds2cPAEXo/ebRCyfErkcPkD2SSa8UtvAb6rEmI/RmHn26LniuC71VXPrcOSXMosfkJOWuCqEfHcVCSz6r0I2e0CfSMi5Z9Hbg+Dg1LFVPyIpKeB/9KJ3gO3fSncu//mv8HIJeDfZQSPHWzYTeCY9eXalwyxa66FgVNzPy6Nva2hb2JefmOfRuYiT03/gGpak++KD+xcgqXAEYe/RtbW0AlOPXbx69EHq7Hj1AQp9MeqWwhd9QjzVZoddLxEhnVMF3Hv2f/AmJ3FNP6f9d5GuLGD3nyjbVQp+dTT/q0I2fPHrO6ZKuvvBpS54yBvz5n5Ow/u53sZ/XE3pAOQGNQjeAsdCLMZoJ/cQExWjVlQrtplhqq3gKhoeHUVREcwsTE/4Rer2yzULgb79dKbCm97mMjORCN6KNXWUlHQd2qla6gdajT0To9ZoACczCecN2Fmd4QHa2coGXHr0BZh795CQVq7p4kcqS3ntvbI1yQFkVKzx6QDHa+HhswSp1YTOvhT6+NK8Soxf09lIsXp3j/oEPUCz8X/4l9vNGQi8mZLUCIXKzAePQTUaGde64SCnTE3qrCVmzySYx3pMnaX/6Qej1PPovf5kuWP/4j8afY8w8XAFYx+jvuw9oafE+fCVQe/SM2S/LCyQfuvErjCmCLIXeADOP/pVXyKv72c+AffuA//N/qI1ctE8wAHOhV3v0gFLYbH5ev3Il4J1Hf9VVVEpQG7qpqIg9ubOzgbvuoiYc6nKviXr0GRlK3NfIowesm48IMVeHboqK6GJk5dEbeW81NTUL433tNXq0WizlBlqh7+igln13320dOy8oSC50U1NTA4Bs5PVktBq1R19Skli542RDN8IWfkTsu2SFfmIifk7LqtNWKvjKoz9wgATpbW8Dvvtd4O//Hvjtb2OXk6uFXt05KRIhgdIT+rEx+ruZ0KezJr1erZtQiFwcrUevd+D8j/9B8fdvf1t5bWCAXtMKuvDo1TF5QVFRbPlePayEvqOD9pHoByrYssXaozeajJ2YmFj4P1pa6NEvHr16jcVvf0vHkWjZZ4aVR2/kvU2YXR08RJwnk5OJhW2A5D16v9oCSN2jB+Lj9IHx6DMyuKlHf+AAUFuriLXoLqQWem2MHiChn5yk39VCLw4wo8VSQOIefaILq+bnKRSl3YFnznQhO1vfo9eybh3FhB95RLnQDQyQYGsX3Nx+O3UZ0vOIi4vJBmYt2ay6TB07RrnjWrGurk7eo+/q6vKl0Iv6/KKbWWcnHVPiYmpGsqGbLlFAx2eox5pIxg1gHqM38+j9aguA7JGXp19HyQqj1bEBEnrj0M3sLNDUBLz1rcprerW9h4aUMITaYOo6NwLh0Tsp9F/5Cl2M7GI0AQmQhy08+tlZEm8jD+G++yhv+5576LlRxcaqKpos1KsNXlJi7YHY8ei1LeMAe8XNjDx6ADGhm+XLze863EJ7bBw/TnVs7MTNkw3d+BURugGc9+gZU7qBLRZycuLDrHYJvNBnZhqHbpqb6R9Vp86KRTnqkq+iYFcoFBu6cUPoOaec9tdes98G0GgCsrKyEitXKvYQpV+NhHj7dpqc/tGPgOefj69zY4dvfpPuCswwEvrubqoYefgwoBc6tdNCz8ijr6ysXNiXk5N0N+KHSUhtWK+z036XpmQ9+ko/3MrokIpHX1hI8Wi91ecipVBvf/vVFgBpSbIdu4yEPjDplaGQsUf/hz/Q4+7dsa9rS74ODytlDLKySNjNhN4qdJNIHn1npzIhajfF10jciouLsXKlcsGw003+vvvIc77rLqqFnajXW11N9ePN0K4GvXgR+Oxn6bM/+xkVEbv33vjPiX1iNgdjdNErLi7G8uXKvvDL+a0+NmZmKCMo3UJf7Jd8Sg0ZGcp+S9SjNysJYebF+tUWAPDoo8BDDyX3WbWDqiYwHj0QMRSCAweArVvjMw02bYoP3ajfI1KVxEGkTa8cHyfvF0jdo3/mGeV3u0JvFLppaWmJCd3YEfrlyynro6fHOHSTKlqP/sknqe77xz9O3vrXv65fh9zOIqHZWbo4a8NKLdHAvDiv/SL06mND9D21K/R2Qjd6J7WwhR8R9kgmdAMYC71RCMvPtli3LvnwYuBDN0aTsZEI8Mc/xoZtBJs3Uw11cdLoCf3oqLFHzzmFHQoK9GPDiQj9b34DbNtGkzDt7dbvB8wXhCTq0QN0x3P33fS7G0IvQjH/8i/mJ7idQl5WB7KfhV44G4l49BMTyqptNeEwHfOLKUYPKHc4yYRuAP0LXzrDFX5lxQoKVekJfSDSKzMzma7Qv/46CYR6IlYgTiwRpx8e1vfojYQeIG/MqJRoKEReppXQj41ReOnWW+nOI9XQTVFRUYxHL5pM6NVO1/K1r9FCqhtusDeGRNAKfU8PXVDUk3F6WHUSAsij17vYFkXvZcUtrV+EXl0SQgi93ZIEhYXkZOgVRTOrwV6klxfrE5L16K1CN0bi5mdbpEIoFN80CQiQR798eSbOn49PUTxwgB7NhL6zUylopi41bCb0QnzMhB6wXg0K0ARoOAzccoszQl9TU7MwGct5Yt3kCwspXm4Vb08GrdB3d9sTXjuhG6MDWSyMER69HxZLAfEefWlpbGjQDDN7mAm9nxcJ5ebShTrR0LlZ6MbMo/ezLVJFr95NYIQ+HJ7F/LyS8y74wx+U2h5aLruMbnM6O2MLmgnsePR9fdZCb+XRP/MMfdfVV1P4pr9ffxmzFqMJyP379y/Ud5mcTEzo04me0FdVWX8uK4v+RyuPXu9A3r9/PwD/h24SybKwEjf19tUIW/iRnBwK2ySaEWV2t2cmbn62RapoyyBwbnx+OIHrMXogNjODc/LojSqS5uQA69fTiaZeFSsQtZ0nJpRaLQJ1zZhUhD4SAZ59Frj5Zrrt2raNXrfj1Rt59JzzhcUWY2P+Evq5OfqJRCi7x47QA/ZWg+qFbnj0Fu/SS+kESMfcQzKo0yvTIfR6JzVPZ6uzFFm7Vn8NhRVmoRuz9QR+tkWqaIU+nW0EAY+EXh2nP3WK0h+NKgECSoqlelWsoLiYROnsWTqg1N6G+jY7FaF/9VW6yLz73fQ8EaE3yrphjC0I/Zkz9L/5RegBskdfH4Wr7Aq9VaaJkffGojvt858HDh3SX+zlBcIWAwOUuZWI0JuFbsyW/TM/LCAw4OGHgR//OPHPWWXdGImbn22RKoEW+vx8OrLVQt/XR49mt+tC6EU+vDZ0A9CkoTZ+atejt2oC/cwz5MnfeCM9X7+eqkzaybwx2oF79+5dmHw8fJge/ST0Fy4o9eWdEnqjydi9e/cCUFYb+gUhxK2t9OhG6EbYwo+sWpVcobW8PHLAjC56Rh69n22RKoEW+nCY/ht16EYIvVn9kM2b6eIgimbpCf3Jk/H53U6Fbp55htIahQfOmP0JWaMd2NraurA9vwq96N3qZOhG70BuFUrqM7wQer/aIhUYM653YzYZG0RbCLRNk9JdrtlVoeec6g2rPXqx9N8sZUucYI2N9Kgn9L29en1ZlVBOskLf0UHpn+97X+zriQq91pMdHR1d8Ohff50e/Sb03d0URlm/3t5nk/XoR+3Wk3AZcdK1t9NxpK3YaUayWTd+tUWqGDkBZh59UG0BxDdNCpRHHwrFT8aePk072qwKnBD6P/4xtq46oAj93Fy80GdkKJ5VsumVjz9OJ/kHPhD7+rZtFLu1aoJjtWAKUDx6O1UR041W6MvL7dceTzZG71fEsv+5OQot6l2kjEjWow8qRkK/FBdMAfFlEAIl9EYx+vJy85St9etJbM6cIWFXT9apc3r1luaL8E0yHj3nwGOPUX187WpAuxOyRjuwtrZ2YWzDw7ScOhEhSRdaoU8k1THZ0E1tIuVAXUaIcaIFrLKzqSKjkRcLLD5bpIKRE2B28Q+qLYD4MgiBEvr5+QiWLYsP3VittMvMVG6btZNB6RT6lhZakfvhD8f/za7QG5XmHRkZQSikeH5+CNsA8UJvNz4PJB+6GbGzIMEjhD2SadJt1FnJzKP3sy1SQc8JELnjRnc2QbUFEC/0Zim3TuCq0M/OzsaU5gXsCT2geFRaoc/JUYyjJ/SFhUqTACOMhP6xx+hO4v3vj/9bRQV9nx2PPiuLsnbU9ERnOkX4xi9CL+w0Okp3UIkK/dRUbOtHNUbem7CFH0nWowfMJyDV21bjZ1ukgp7QW3mxQbUF4EOPnjH2CGNskDF2RPVaMWPsBcbY8eij7aIUokY8QDPOp0/bi02LE02dQy8QRjPy6K1SwvSEPhIBfvpTWiSlV3JDZN5YpVgaLRISiG37ReiFB3v0KHlciQi9uDvRrnwWGHn0fiYVoTeLS6u3vRTQu9tbjI3BncJ3Qg/gBwBu0rx2L4AXOeebALwYfW5JdnZ2jEc/NEQTXal49IAilnp1SG6/HfjkJ823LfLo1Qvx/vAH8mg/9CHjz23bZs+j19t5GzZsAOA/j14IvbiAJerRA8bhGytb+JFUhd4sdLPYbJEKehc9q3BFUG0BkI6tWEFNjAAfpFdyzg8A0AbL3gvgh9HffwhAk3yoTygUivHo7eTQC8yE3syjv/NO4MtfNt92Tk5sb1CAwjZ5ecB73mP8ua1bacWkKM2gh5G4FUQH61ehFxcwp4RexGP1PPoCvR3nE3JzaVLVboqpGqPQzcwMbVNvBbCfbZEK4qKndqasxC2otgAolHvTTVT6fH4+/R69SZtoU0o552cBgHN+ljFmWIKfMbYPwD4AKCkpQWHhOZw5k4tTp4bR0VEMIB/9/Qdx+PAybNu2DQeipSwzMzOxe/dutLS0YHx8HCMjywBcg1BoCPX1FEXatGkTsrOzo4sOVmN8/DTC4VI0NDQAoDuIXbt2obm5GZPReMLOnTvR19eH09EE/urqaoTD+QAK8MILDdi4cTXKyqrw059m4JprhnH48Ens3LkTTU1NmI66ILt27UJ3dzcikYsArsDLL4/iLW+ZQUe0O3ZZWRnKy8vR1NSEU6cuB2MrACxHY2MjZqOzs5xzlJaWYna2CMBaFBSM4fTpKRyP1mOuqKhAaWkpmpubAQCFhYWora1FQ0MDwuEwAGDPnj1oa2vDcDTHs6amBhMTEwtNlSsrK1FcXLzQwKGoqAg1NTXYv38/OOdgjGHv3r1obW1dyFnesaMWQCFOnODIzOQIh/swOlqwsHhl1apVhvupuzsTwBXo71Y4wLIAABW0SURBVL+AZcvOojdaZH/Tpk0AsgGsxpkzXWhvn8HmzZsX9tPU1BRuueUWy/0UCoXQHr3VWLNmDaqqqtAYXVyRk5NjuJ/6+/sBAFu3bkUkEtHdTwCQn5+Purq6mP1UWLgHl156EX/4wysAgO3bt2N2dtbWfpqZ2YSBgQLMzy+P2U+jo1dj+fJlqK8/ELefJicnUVFRYbmfamtrMTIyshDH3rBhAwoK7O2n8ejVp66uDgMDAzH7KTs7G0eO0PlVUlISs5/snk96+2lwsA/z85dh//7/wnXXvQVNTU1ob2cArkIoNIdjx96I208HDx5Efn6+rf20e/dudHZ2YjDaZSiR/ZTO88lsP23YUIqf/exyNDfP4/XXTwDYjNbWJqxfvzNuP6UM59zyB0AlgCOq52Oav4/a2c7mzZv5pz7F+bp1nHPO+Xe+wznAeV8ft8Vzz3E+Nhb/+p130nZ++Ut729EixnH6ND3/3e/o+X/+p/nnWlrofU89Zfye97+f8+3b419/6aWXOOec/+Vf0jZ6epIbezrIyqIxbdyY2OcOHKDPvfBC/N/Gxuhv3/xm/N+ELfzIsWOcv/Zacp/dt4/z0tL41//7f+d8zRr9z/jZFqkgzrEzZ5TXmpvptaef1v9MUG0hOHeOc8Y4v/9+zr/xDbLF+fP67wXQzG1orNFPslk3A4yxtQAQfRy086HMzMy40E0oBKxZY+9Lb7wxtqyBwCx0YwdtlykRtrjqKvPPiTkBoz64gHHoZlV0VnnjRppgTrRrTzoR4ZtEwjaAeejGKM0UUGzhR6qrgTe9KbnPGqWbmlVs9LMtUkFvAZlV6CaothCsXk3FHH/zG/+mV/4awB3R3+8A8LSdD+Xk5GDlSkrBm5ujjJs1a+JTDxPFaaE/doxi52a594D56keBUdbNtmgi/l13Ubs+u6tP3SBVoU90kZCwRdAoLKT1CNHIwAJmQh9UW+gdG1biFlRbqHn3u4HmZlqzwlj6dMBOeuXjABoBVDPG+hhjnwLwNQDvZIwdB/DO6HNLJiYmFjzy8XH7OfRWOC30HR3Ali3WDRbsCr3egazETs3LP3hBskJv1mDCzKMXtggaRvYwWw26lGxh5dEH1RZqbrmFHp9+mo6JdFVmtpyM5ZwbJRhen8wXCqE/f55CN5dfnsxWYnnnO4EPfpDCIMmg59G/853WnzNb5i6YmdHPw/cz6QjdpDurwI+o7aE+Bsw8+qCi5xSlO1yxGNixgzLuensTb9GYCK6ujFU32xgbc86jv+wyKj6W7GIctdBPTFD+vN1OOlb1XYzag2VmJpvwlH6SFfrsbLpDSVTo/WyLVDC64zMT+qDaQi90Y3XxD6ot1DCmNDRK5wXP5aJm+TF9XMfH/VWxUfQGBSh0Y4dkC3nt3r07sUG6SLJCz5ixPcxCN362RSokI/RBt0UioZug2kJL4IT+woULCx69WHnphEefKmqPXjQ3ccqjN5qMFbm4fiQ3lxaLJdNNyKxKIaB/MPvZFqlgFMoyE/qg2iKZ0E1QbaHlbW+LrdmVDly9N4pEIgsevUhh9JPQX7hAnapCIftNJpL16MfNPuQxGzZQvZpkJoaMhN7Mo/ezLVLBzKM3OqmDagsR1kskvTKottCSk0ONjdLZZ8X1IJhW6P0QutF69FVV9uP9hYVKlyw9FluzDQD45jeNK1BaYdZJCFh8tkgFI6E366oUVERYT+0EyMlYhR/8IL3bdzV0k5eXt3DwHz1Kj37y6KenKbXSbtgGSN6jd2RZc5rIzEx+YjsZj97PtkiFZEI3QbUFEH+umNX8AYJtCy3LltFPunBV6Ofm5pCZCeTn004uLvaHZyPGMDVFk7F2J2IBukMxEvpwmDxjPaEfGBhIfKCLgGRi9EG2BZDYZGxQbQHEC71VG8Eg28JtXBX6i9HykGJC1g/ePEAebGYmifzMTOIevVEJBOHF6h3MopBU0EgmdBNUW2Rm0sS22h7hMP0YCX1QbQHEV/O0CmsG2RZu46rQC0Sc3i9CD9CJJ2pDJ+LRFxZSeWMh6mqEuC22ZhupkEzoJsho7bGU49LaGP1SnKvwCleFfnn06BZC74eJWEFuLsXngcQ9eiBxL3ZTMk1IFwFC2NR1x4GlaQtAPy4NGAvcUrLFuXPmZUuCbAu3cX1lLOC/0A2gNB+xU8xMTbJCnx1Q17awkOw4NRX7uvDo9SacgmoLQD8uDRgLfZBtoQ7dzM0BBw4AZmuigmwLt3FV6EVDCL+GbgB7xczUmAm9WYxeNHcIGkaZJmLhmJ5tg2oLwDh0YyT0QbaFOnTT2EhrNW64wfj9QbaF23gSoxcevZ9CN+LESyRsAyTv0QcVM6FfSnYQJOrRB5nCQrrTi0SA55+nhYlvf7vXo1oauCr0WdFiy3736BPBjtDr3YGWlBh2X1zUGNnDqF8sEFxbAMZCb3TRC7It1E7A888DV1+t30hIEGRbuI2rQi9ibqtX0y38UvboN4tu5wEjGY8+qLYA4kM3VpOxQbaFOFe6u6nZxo03mr8/yLZwG1eFXjQU/uQngWefTW/95URxW+hFw+WgYST0Zh59UG0BJB66CbotAODJJ2nC3iw+DwTbFm7jSYy+qMj6au42OTmJFTMTyBh9LGb1XZaSHQTadRZLOUYvnIAnniANWEIVDjzH1aJmGUZFLXzA9u3A4GDiC3pycuKr8gnMsm6CmjqWjEcfVFsAsfbIzl7a6ZXqOld/+qfWvaKDbAu3cb2omV/5u78DXnop8c+Jqnx6ZRDMPPpdu3Yl/mWLgGRi9EG1BRB/h2M1GbsUbAFYh22AYNvCbVxvPBJErOq76Dkmzc3N6R2UR+TmUjXCRIQ+qLYA4oXeajI2yLZQr4K1I/RBtoXbuCr0kWSLnPucZAp5iYnpoMFYfPEqwDx0E1RbAPF3OFahmyDbQlz0tmwB1q+3fn+QbeE2/g2aLyJks41Y9AqbLeXJWCA+dLNUJ2OzsvyXiLEUcHUy1s8x+lQoLAT0SmebhW527tyZ3kF5iLZKIWDu0QfdFkCs0Js12wiyLbKygBdfBHbssPf+INvCbVxvPBJEjDx6IW569V36+vrSPzCP0AvdmHn0QbaFntCb3dkE2RYA8Na3KiVQrAi6LdzEk8YjQcMsdGPkxZ42azS7yDEK3SxVWwDU+PmVV4BDh8zDNkG2RaJIWziH683Bg4iZ0C/FuHRBAdDfH/va7OzStEVeHt3RffGLymsf/rB345EsTVwV+uUBPdMLC+mWfG6O4pACM6GvTrTWwiJC78K3VG2RkQHccw/d4Vx/PXDddeb9DoJsi0SRtnAOV4WeJVLofREh4rATE7H1e8zELWS1LHARow3dzM/TRdAodBNkWwDAgw/af2/QbZEI0hbO4UnjkaBhVprXSOjb29vTOygP0bYTNCsFAQTbFokibaEgbeEcMo/eAURNbVnIiygsBMJhJb10qTYGl0j8gieNR4KG8Oi19W7MMk3WrFmT3kF5iHY1qNXCsSDbIlGkLRSkLZzDk8YjQSOZ0rxVVVXpHZSHJCr0QbZFokhbKEhbOIcnjUeCRjJC39jYmN5BeYjWHlahmyDbIlGkLRSkLZxDxugdQDbbiCVRj14ikaSXlNIrGWM9ACYARACEOeemPWP83HgkFZLJuskJcFUrrdBbefRBtkWiSFsoSFs4hxN59G/jnA/ZeWNQi5qJ1Y96Hr0s5GXt0QfZFokibaEgbeEcrrrYU1NTbn6da4guU4mEbpqamtI/MI9I1KMPsi0SRdpCQdrCOVL16DmA5xljHMB3Oeff076BMbYPwD4AKCkpQX19PQBgw4YNKCgoQGtrKwBg1apV2LZtGw4cOEADy8zE7t270dLSgvGogtbV1WFgYAC9vb0AgE2bNiE7OxtHjhyB2P7mzZsXusdnZ2dj165daG5uXpgI3rlzJ/r6+hYKJlVXVyMUCi0szlizZg2qqqoWJoJycnKwc+dONDU1LSz42rVrF7q7u9EfLeiydetW5OWtQmfnIOrrO1BWVoby8nJcuLAM586dRnPzIOrq6tDY2IjZqOpxztHe3o7BwUEAwPbt2zE7O4vjx48DACoqKlBaWrrQZaewsBC1tbVoaGhAOBwGAOzZswdtbW0YHh4GANTU1GBiYgJdXV0AgMrKShQXF6OlpQUAUFRUhJqaGuzfvx+cczDGsHfvXrS2tmJ0dBQAUFtbi5GREfT09CS9nzZvrgOQj0OHTqC+vg+nT+8AsApHjjRjbm4ybj8JB8CN/RSJRNDR0QEAC/tJCEp+fn7cftq9ezc6Oztd20+Tk5NobW11ZT/5+XyKRCI4d+4c6uvrfbmfAPfOpzonuqhzzpP+AbAu+lgCoBXAHrP3b968mQeVbds4f//7lefz85wzxvmXvqT//pdeesmVcXlBJMI5wPn999Pzn/+cnh85ov/+INsiUaQtFKQtFAA08xS0OqXQDef8TPRxEMCTAK4ye39+fn4qX+drtKGbuTkqAbAUm0BnZAD5+cDICD23Ct0E2RaJIm2hIG3hHEkLPWMsjzFWIH4HcAOAI2afEbdZQWTFiliht6rv0t3dnf5BeciuXcDPf05VPa0mY4Nui0SQtlCQtnCOVDz6UgANjLFWAK8C+A3n/DmzDwS1wxRAHr26BIJZG0EAC/HIoPK3f0vtFR95xNqjD7otEkHaQkHawjmSnozlnHcBqHFwLIsabehmqS8S2rsXuOYa4IEHgLvvpteWqi0kEq9xNb0yyAsgEhX6rVu3pn9QHsIYcN99wKlTwPe/T68ZefRBt0UiSFsoSFs4h6tCz0WB8gBSWAhMTQGRCD23EvqIeGOAede7gCuvBDo7SfiNipcuBVvYRdpCQdrCOVwV+hmhfgFE3WUKsJ6MFbncQUZ49QDZwajB2FKwhV2kLRSkLZwjmMVnPMBo2X9AKzPb5rbbgMsvl3aQSLzE1Z6xy5Ytc/PrXCXR+i5lZWXpH5QPyMigGP2hQ8bvWSq2sIO0hYK0hXO4KvRB7TAFJC705eXl6R+UT9i5k36MWEq2sELaQkHawjlkUTOHSFToZcEmBWkLBWkLBWkL55AxeodIVOglEonELVwV+lAo5ObXuYpR+zwjoQ9y3Z9EkbZQkLZQkLZwDleFPjc3182vc5UVK+jRbtaNI6VHA4K0hYK0hYK0hXPIGL1DCOdD1LuxCt3IxscK0hYK0hYK0hbO4arQz8/Pu/l1rpKRQZ2V7Ap9kCt5Joq0hYK0hYK0hXO4ml4ZdFavBv7t34CmJiAcNl/2L5FIJG7B3Kw/8+Y3v5kfPHjQte9zm9ZW4Be/AF58Efiv/wJKS4Foh7U4wuEwMjPldRaQtlAjbaEgbaHAGDvIOU960sLV0E3Qb8VqaoB/+AegsZG6K0XbQurS2dnp3sB8jrSFgrSFgrSFc7gq9EFuPKKlsJBCOUaIJsYSaQs10hYK0hbOIRdMSSQSScCRjUc8Yvv27V4PwTdIWyhIWyhIWziHbDziEUGfr0gEaQsFaQsFaQvnkI1HPOL48eNeD8E3SFsoSFsoSFs4h4zRSyQSScBxVeiD3HgkUSoqKrwegm+QtlCQtlCQtnAOV4U+yI1HEqW0tNTrIfgGaQsFaQsFaQvnkEXNPKK5udnrIfgGaQsFaQsFaQvnkDF6iUQiCTiy8YhHFIpOJRJpCxXSFgrSFs7halGzuro6Lm/HJBKJJDEWVVGzyclJN7/O1zQ0NHg9BN8gbaEgbaEgbeEccmWsR4TDYa+H4BukLRSkLRSkLZxDTsZKJBJJwJExeo+Yn59HRoa8zgLSFmqkLRSkLRQWVYx+enraza/zNW1tbV4PwTdIWyhIWyhIWziHq0IvY24Kw8PDXg/BN0hbKEhbKEhbOIe8L5JIJJKA46rQ5+bmuvl1vqampsbrIfgGaQsFaQsFaQvnSEnoGWM3McY6GGMnGGP3Wr0/Eomk8nWBYmJiwush+AZpCwVpCwVpC+dIWugZYyEA3wbwLgBbAXyIMbbV7DOyY4xCV1eX10PwDdIWCtIWCtIWzpGKR38VgBOc8y7O+UUAPwXwXmeGJZFIJBKnyEzhs2UAelXP+wDs1L6JMbYPwL7o01nG2JEUvjNIrAYw5PUgfIK0hYK0hYK0hUJ1Kh9OReiZzmtxq684598D8D0AYIw1p5L0HySkLRSkLRSkLRSkLRQYYymtNE0ldNMHQN3rqxzAmVQGI5FIJBLnSUXo/wvAJsZYFWNsGYAPAvi1M8OSSCQSiVMkHbrhnIcZY38G4LcAQgAe4ZxbrVn+XrLfF0CkLRSkLRSkLRSkLRRSsoWrRc0kEolE4j6yBIJEIpEEHCn0EolEEnBcEfpESyUECcZYBWPsJcbYUcZYG2PsL6KvFzPGXmCMHY8+Fnk9VrdgjIUYY68xxp6JPl+StmCMrWSMPcEYOxY9PnYtYVt8Lnp+HGGMPc4YW75UbMEYe4QxNqheY2T2vzPGvhjV0g7G2I12viPtQp9MqYSAEQZwD+f8cgBXA/hs9P+/F8CLnPNNAF6MPl8q/AWAo6rnS9UW3wLwHOd8C4AakE2WnC0YY2UA/j8AdZzz7aDkjg9i6djiBwBu0rym+79HteODALZFP/PvUY01xQ2PfkmXSuCcn+Wct0R/nwCdzGUgG/ww+rYfAnifNyN0F8ZYOYBbAPyH6uUlZwvGWCGAPQAeBgDO+UXO+RiWoC2iZALIYYxlAsgFrclZErbgnB8AMKJ52eh/fy+An3LOZznn3QBOgDTWFDeEXq9UQpkL3+s7GGOVAK4E0ASglHN+FqCLAYAS70bmKv8M4G8AzKteW4q22ADgHIDvR8NY/8EYy8MStAXn/DSArwM4BeAsgPOc8+exBG2hwuh/T0pP3RB6W6USgg5jLB/ALwH8Jed83OvxeAFj7N0ABjnnB70eiw/IBFAL4Duc8ysBTCG4oQlTovHn9wKoArAOQB5j7KPejsq3JKWnbgj9ki+VwBjLAon8Tzjnv4q+PMAYWxv9+1oAg16Nz0WuBXArY6wHFMJ7O2PsUSxNW/QB6OOcN0WfPwES/qVoi3cA6Oacn+OczwH4FYBrsDRtITD635PSUzeEfkmXSmCMMVAc9ijn/JuqP/0awB3R3+8A8LTbY3MbzvkXOeflnPNK0HHwe875R7E0bdEPoJcxJqoSXg+gHUvQFqCQzdWMsdzo+XI9aC5rKdpCYPS//xrABxlj2YyxKgCbALxquTXOedp/ANwMoBPAGwDuc+M7/fIDYDfo1up1AIeiPzcDWAWaTT8efSz2eqwu2+U6AM9Ef1+StgDwJgDN0WPjKQBFS9gWfw/gGIAjAH4MIHup2ALA46C5iTmQx/4ps/8dwH1RLe0A8C473yFLIEgkEknAkStjJRKJJOBIoZdIJJKAI4VeIpFIAo4UeolEIgk4UuglEokk4Eihl0gkkoAjhV4ikUgCzv8POrp42B/GZpMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.plot([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], color='blue')\n",
+    "plt.xlim([0,100])\n",
+    "plt.ylim([0,25])\n",
+    "plt.grid(linestyle=\"dashed\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.hist([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], color='blue')\n",
+    "plt.xlim([0,25])\n",
+    "plt.ylim([0,25])\n",
+    "plt.grid(linestyle=\"dashed\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +78,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-

module2/exo4/exercice.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "# Useful links\n",
+    "\n",
+    "## For markdown:\n",
+    "- Page with good overview is [here in this MOOC](https://lms.fun-mooc.fr/courses/course-v1:inria+41016+self-paced/courseware/2bfe60a86fed4994b5493a220c38eb69/13f6fd96266746a0bd9d717a12f1f835/4?activate_block_id=block-v1%3Ainria%2B41016%2Bself-paced%2Btype%40vertical%2Bblock%4089c7893cb5aa4644b06569c65945d4d9)\n",
+    "\n",
+    "## For Python\n",
+    "- numpy documentation (used for statistics exercise) is [here on numpy.org](https://numpy.org/doc/stable/reference/generated/numpy.mean.html)\n",
+    "- matplotlib.pyplot is great for plotting and matplotlib has a great [documentation](https://matplotlib.org/) as well\n",
+    "    - a useful general tutorial [here](https://matplotlib.org/stable/tutorials/introductory/pyplot.html)\n",
+    "    - some information for [grid lines on this page](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.grid.html)\n",
+    "    - for histogram plots [take this](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "# Encountered problems \n",
+    "\n",
+    "## Exercise 3.2: had a weird error message when trying to execute the evaluation part:\n",
+    "\n",
+    "```\n",
+    "---------------------------------------------------------------------------\n",
+    "AssertionError                            Traceback (most recent call last)\n",
+    "<ipython-input-12-537d91e19818> in <module>\n",
+    "      4                         first_september_week[1:]):\n",
+    "      5     one_year = sorted_data['inc'][week1:week2-1]\n",
+    "----> 6     assert abs(len(one_year)-52) < 2\n",
+    "      7     yearly_incidence.append(one_year.sum())\n",
+    "      8     year.append(week2.year)\n",
+    "\n",
+    "AssertionError: \n",
+    "```\n",
+    "\n",
+    "worked on it together with Eva Diaz, trying to figure out what the problem was, but couldn't find any solution... "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With help of Eva understood that assertion error came from uncompleted year and the marked code line that said \"assert ...\", so deleted this line and worked through the rest of the code --> worked! Found strongest year 2009 and weakest year 2020."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
+  "hide_code_all_hidden": false,
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -16,10 +79,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-

module3/exo1/analyse-syndrome-grippal.ipynb

@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -28,13 +28,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\""
+    "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n",
+    "data_file = \"syndrome-grippal.csv\"\n",
+    "\n",
+    "import os\n",
+    "import urllib.request\n",
+    "if not os.path.exists(data_file): \n",
+    "    urllib.request.urlretrieve(data_url, data_file)"
    ]
   },
   {
@@ -364,7 +368,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,

module3/exo1/syndrome-grippal.csv

+# @source="rseau Sentinelles, INSERM, Sorbonne Universit, http://www.sentiweb.fr", @meta={"period":[198444,202221],"geo":["PAY","1"],"geo_ref":"insee","indicator":"3","type":"all","conf_int":true,"compact":false}, @date=2022-06-07T16:22:01+02:00
+week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name
+202221,3,13697,10259,17135,21,16,26,FR,France
+202220,3,19787,15756,23818,30,24,36,FR,France
+202219,3,17884,14079,21689,27,21,33,FR,France
+202218,3,30353,25089,35617,46,38,54,FR,France
+202217,3,36006,30373,41639,54,46,62,FR,France
+202216,3,49949,42836,57062,75,64,86,FR,France
+202215,3,100806,90824,110788,152,137,167,FR,France
+202214,3,155441,143891,166991,234,217,251,FR,France
+202213,3,191914,179558,204270,289,270,308,FR,France
+202212,3,166224,155035,177413,251,234,268,FR,France
+202211,3,122849,113306,132392,185,171,199,FR,France
+202210,3,87904,79741,96067,133,121,145,FR,France
+202209,3,50182,43958,56406,76,67,85,FR,France
+202208,3,30963,25942,35984,47,39,55,FR,France
+202207,3,34882,29446,40318,53,45,61,FR,France
+202206,3,46623,40398,52848,70,61,79,FR,France
+202205,3,62970,56043,69897,95,85,105,FR,France
+202204,3,72209,64804,79614,109,98,120,FR,France
+202203,3,74613,67144,82082,113,102,124,FR,France
+202202,3,55920,49511,62329,84,74,94,FR,France
+202201,3,57629,50699,64559,87,77,97,FR,France
+202152,3,54349,47029,61669,82,71,93,FR,France
+202151,3,41698,35359,48037,63,53,73,FR,France
+202150,3,38117,32497,43737,58,49,67,FR,France
+202149,3,40168,34716,45620,61,53,69,FR,France
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+199804,3,30005,23272,36738,51,39,63,FR,France
+199803,3,23900,18221,29579,41,31,51,FR,France
+199802,3,31143,23994,38292,53,41,65,FR,France
+199801,3,24809,17786,31832,43,31,55,FR,France
+199752,3,29039,21984,36094,50,38,62,FR,France
+199751,3,34924,27737,42111,60,48,72,FR,France
+199750,3,33527,26388,40666,58,46,70,FR,France
+199749,3,15978,11147,20809,27,19,35,FR,France
+199748,3,11507,7744,15270,20,14,26,FR,France
+199747,3,11059,7151,14967,19,12,26,FR,France
+199746,3,8073,4613,11533,14,8,20,FR,France
+199745,3,18202,13113,23291,31,22,40,FR,France
+199744,3,19770,14405,25135,34,25,43,FR,France
+199743,3,16707,11945,21469,29,21,37,FR,France
+199742,3,15340,10351,20329,26,17,35,FR,France
+199741,3,16933,11941,21925,29,20,38,FR,France
+199740,3,13292,8709,17875,23,15,31,FR,France
+199739,3,10189,6279,14099,18,11,25,FR,France
+199738,3,3231,1061,5401,6,2,10,FR,France
+199737,3,4542,1949,7135,8,4,12,FR,France
+199736,3,5131,2205,8057,9,4,14,FR,France
+199735,3,1842,0,3740,3,0,6,FR,France
+199734,3,3226,0,6717,6,0,12,FR,France
+199733,3,1799,0,4368,3,0,7,FR,France
+199732,3,2086,0,4411,4,0,8,FR,France
+199731,3,1386,0,3332,2,0,5,FR,France
+199730,3,2116,0,4919,4,0,9,FR,France
+199729,3,1973,0,4347,3,0,7,FR,France
+199728,3,1560,216,2904,3,1,5,FR,France
+199727,3,2540,754,4326,4,1,7,FR,France
+199726,3,5582,2699,8465,10,5,15,FR,France
+199725,3,4012,1090,6934,7,2,12,FR,France
+199724,3,4696,1824,7568,8,3,13,FR,France
+199723,3,4709,2136,7282,8,4,12,FR,France
+199722,3,2283,665,3901,4,1,7,FR,France
+199721,3,2108,664,3552,4,2,6,FR,France
+199720,3,3257,1519,4995,6,3,9,FR,France
+199719,3,4646,2436,6856,8,4,12,FR,France
+199718,3,5246,2764,7728,9,5,13,FR,France
+199717,3,14236,9906,18566,24,17,31,FR,France
+199716,3,12169,8111,16227,21,14,28,FR,France
+199715,3,9516,6192,12840,16,10,22,FR,France
+199714,3,13602,9659,17545,23,16,30,FR,France
+199713,3,18032,13345,22719,31,23,39,FR,France
+199712,3,17199,12707,21691,30,22,38,FR,France
+199711,3,19988,15419,24557,34,26,42,FR,France
+199710,3,21903,17338,26468,38,30,46,FR,France
+199709,3,29860,24261,35459,51,41,61,FR,France
+199708,3,55445,47857,63033,95,82,108,FR,France
+199707,3,70876,62518,79234,122,108,136,FR,France
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+199705,3,95900,86095,105705,165,148,182,FR,France
+199704,3,96896,87079,106713,167,150,184,FR,France
+199703,3,143844,131917,155771,248,227,269,FR,France
+199702,3,211578,197395,225761,364,340,388,FR,France
+199701,3,280440,263783,297097,483,454,512,FR,France
+199652,3,388201,369211,407191,670,637,703,FR,France
+199651,3,640627,617388,663866,1106,1066,1146,FR,France
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+199649,3,411498,393335,429661,710,679,741,FR,France
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+199647,3,71980,63619,80341,124,110,138,FR,France
+199646,3,32138,26358,37918,55,45,65,FR,France
+199645,3,25551,20136,30966,44,35,53,FR,France
+199644,3,10753,7230,14276,19,13,25,FR,France
+199643,3,12971,9311,16631,22,16,28,FR,France
+199642,3,11929,8472,15386,21,15,27,FR,France
+199641,3,9918,6674,13162,17,11,23,FR,France
+199640,3,10765,7067,14463,19,13,25,FR,France
+199639,3,8377,5183,11571,14,8,20,FR,France
+199638,3,9470,5965,12975,16,10,22,FR,France
+199637,3,2925,829,5021,5,1,9,FR,France
+199636,3,1122,23,2221,2,0,4,FR,France
+199635,3,1674,93,3255,3,0,6,FR,France
+199634,3,1273,122,2424,2,0,4,FR,France
+199633,3,540,0,1246,1,0,2,FR,France
+199632,3,1453,176,2730,3,1,5,FR,France
+199631,3,3010,752,5268,5,1,9,FR,France
+199630,3,2992,706,5278,5,1,9,FR,France
+199629,3,1559,144,2974,3,1,5,FR,France
+199628,3,3109,541,5677,5,1,9,FR,France
+199627,3,4456,1595,7317,8,3,13,FR,France
+199626,3,2871,1138,4604,5,2,8,FR,France
+199625,3,2308,856,3760,4,1,7,FR,France
+199624,3,2490,1067,3913,4,2,6,FR,France
+199623,3,2819,1262,4376,5,2,8,FR,France
+199622,3,4005,1984,6026,7,4,10,FR,France
+199621,3,7042,4291,9793,12,7,17,FR,France
+199620,3,10619,7365,13873,18,12,24,FR,France
+199619,3,11898,8065,15731,21,14,28,FR,France
+199618,3,18338,13352,23324,32,23,41,FR,France
+199617,3,24954,19100,30808,43,33,53,FR,France
+199616,3,29313,23868,34758,51,42,60,FR,France
+199615,3,51703,43644,59762,89,75,103,FR,France
+199614,3,44414,36774,52054,77,64,90,FR,France
+199613,3,30622,25062,36182,53,43,63,FR,France
+199612,3,31818,25852,37784,55,45,65,FR,France
+199611,3,24427,19499,29355,42,33,51,FR,France
+199610,3,20910,16198,25622,36,28,44,FR,France
+199609,3,19594,15228,23960,34,26,42,FR,France
+199608,3,19046,14850,23242,33,26,40,FR,France
+199607,3,23041,18338,27744,40,32,48,FR,France
+199606,3,21810,17153,26467,38,30,46,FR,France
+199605,3,25275,20410,30140,44,36,52,FR,France
+199604,3,43359,36948,49770,75,64,86,FR,France
+199603,3,54691,48016,61366,94,82,106,FR,France
+199602,3,105606,96106,115106,182,166,198,FR,France
+199601,3,223830,209274,238386,386,361,411,FR,France
+199552,3,397122,378252,415992,688,655,721,FR,France
+199551,3,749988,725434,774542,1299,1256,1342,FR,France
+199550,3,604926,583100,626752,1047,1009,1085,FR,France
+199549,3,375500,358200,392800,650,620,680,FR,France
+199548,3,206446,193457,219435,357,335,379,FR,France
+199547,3,120472,110526,130418,209,192,226,FR,France
+199546,3,62380,55042,69718,108,95,121,FR,France
+199545,3,29791,24523,35059,52,43,61,FR,France
+199544,3,13168,9820,16516,23,17,29,FR,France
+199543,3,16188,12389,19987,28,21,35,FR,France
+199542,3,15178,11387,18969,26,19,33,FR,France
+199541,3,14980,10851,19109,26,19,33,FR,France
+199540,3,16245,11610,20880,28,20,36,FR,France
+199539,3,18499,13844,23154,32,24,40,FR,France
+199538,3,13123,9410,16836,23,17,29,FR,France
+199537,3,4895,2666,7124,8,4,12,FR,France
+199536,3,3659,1574,5744,6,2,10,FR,France
+199535,3,2496,644,4348,4,1,7,FR,France
+199534,3,1887,214,3560,3,0,6,FR,France
+199533,3,905,0,2007,2,0,4,FR,France
+199532,3,700,0,1699,1,0,3,FR,France
+199531,3,1946,92,3800,3,0,6,FR,France
+199530,3,1898,46,3750,3,0,6,FR,France
+199529,3,2148,687,3609,4,1,7,FR,France
+199528,3,3312,1430,5194,6,3,9,FR,France
+199527,3,4853,2683,7023,8,4,12,FR,France
+199526,3,4986,2911,7061,9,5,13,FR,France
+199525,3,4571,2520,6622,8,4,12,FR,France
+199524,3,8058,5054,11062,14,9,19,FR,France
+199523,3,13820,9801,17839,24,17,31,FR,France
+199522,3,14124,9999,18249,24,17,31,FR,France
+199521,3,11931,8655,15207,21,15,27,FR,France
+199520,3,18846,14311,23381,33,25,41,FR,France
+199519,3,26261,21488,31034,45,37,53,FR,France
+199518,3,39231,33258,45204,68,58,78,FR,France
+199517,3,88640,79343,97937,153,137,169,FR,France
+199516,3,140488,129439,151537,243,224,262,FR,France
+199515,3,181007,169110,192904,313,292,334,FR,France
+199514,3,248704,235144,262264,431,408,454,FR,France
+199513,3,209839,197191,222487,363,341,385,FR,France
+199512,3,122434,112497,132371,212,195,229,FR,France
+199511,3,81730,73346,90114,142,127,157,FR,France
+199510,3,49387,42703,56071,86,74,98,FR,France
+199509,3,39696,33259,46133,69,58,80,FR,France
+199508,3,38008,32225,43791,66,56,76,FR,France
+199507,3,30550,25479,35621,53,44,62,FR,France
+199506,3,37351,32007,42695,65,56,74,FR,France
+199505,3,27876,23218,32534,48,40,56,FR,France
+199504,3,27643,22818,32468,48,40,56,FR,France
+199503,3,24844,20391,29297,43,35,51,FR,France
+199502,3,23480,19249,27711,41,34,48,FR,France
+199501,3,26556,21916,31196,46,38,54,FR,France
+199452,3,22036,17495,26577,38,30,46,FR,France
+199451,3,26912,21750,32074,47,38,56,FR,France
+199450,3,28831,23470,34192,50,41,59,FR,France
+199449,3,21161,16559,25763,37,29,45,FR,France
+199448,3,18487,14456,22518,32,25,39,FR,France
+199447,3,15206,11578,18834,26,20,32,FR,France
+199446,3,14252,10525,17979,25,19,31,FR,France
+199445,3,12152,8448,15856,21,15,27,FR,France
+199444,3,11307,7688,14926,20,14,26,FR,France
+199443,3,11254,8002,14506,20,14,26,FR,France
+199442,3,14406,10653,18159,25,18,32,FR,France
+199441,3,14073,10341,17805,24,18,30,FR,France
+199440,3,15962,11850,20074,28,21,35,FR,France
+199439,3,15878,11643,20113,28,21,35,FR,France
+199438,3,15366,11100,19632,27,20,34,FR,France
+199437,3,5949,3561,8337,10,6,14,FR,France
+199436,3,2806,1133,4479,5,2,8,FR,France
+199435,3,4556,2358,6754,8,4,12,FR,France
+199434,3,3459,1444,5474,6,2,10,FR,France
+199433,3,4272,977,7567,7,1,13,FR,France
+199432,3,4786,1613,7959,8,2,14,FR,France
+199431,3,5027,1580,8474,9,3,15,FR,France
+199430,3,2100,520,3680,4,1,7,FR,France
+199429,3,1188,0,2384,2,0,4,FR,France
+199428,3,1581,316,2846,3,1,5,FR,France
+199427,3,713,0,1459,1,0,2,FR,France
+199426,3,990,80,1900,2,0,4,FR,France
+199425,3,5431,3222,7640,9,5,13,FR,France
+199424,3,6288,3723,8853,11,7,15,FR,France
+199423,3,6440,1052,11828,11,2,20,FR,France
+199422,3,845,0,2449,1,0,4,FR,France
+199421,3,2197,395,3999,4,1,7,FR,France
+199420,3,2217,852,3582,4,2,6,FR,France
+199419,3,1483,406,2560,3,1,5,FR,France
+199418,3,3705,1940,5470,6,3,9,FR,France
+199417,3,2288,1064,3512,4,2,6,FR,France
+199416,3,5156,3176,7136,9,6,12,FR,France
+199415,3,6952,4678,9226,12,8,16,FR,France
+199414,3,4671,2784,6558,8,5,11,FR,France
+199413,3,3343,1641,5045,6,3,9,FR,France
+199412,3,4305,2412,6198,7,4,10,FR,France
+199411,3,5399,3327,7471,9,5,13,FR,France
+199410,3,4548,2529,6567,8,4,12,FR,France
+199409,3,5635,3251,8019,10,6,14,FR,France
+199408,3,9539,6652,12426,17,12,22,FR,France
+199407,3,14639,11188,18090,25,19,31,FR,France
+199406,3,17308,13395,21221,30,23,37,FR,France
+199405,3,18489,14290,22688,32,25,39,FR,France
+199404,3,23712,19048,28376,41,33,49,FR,France
+199403,3,29882,24817,34947,52,43,61,FR,France
+199402,3,47402,41415,53389,82,72,92,FR,France
+199401,3,118119,108182,128056,205,188,222,FR,France
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+199350,3,627639,607097,648181,1094,1058,1130,FR,France
+199349,3,898045,873419,922671,1565,1522,1608,FR,France
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+199346,3,76034,68003,84065,133,119,147,FR,France
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+199344,3,17832,14015,21649,31,24,38,FR,France
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+199342,3,19187,14893,23481,33,26,40,FR,France
+199341,3,18285,14384,22186,32,25,39,FR,France
+199340,3,13805,10348,17262,24,18,30,FR,France
+199339,3,14783,11173,18393,26,20,32,FR,France
+199338,3,10492,7329,13655,18,12,24,FR,France
+199337,3,7023,4503,9543,12,8,16,FR,France
+199336,3,4361,2445,6277,8,5,11,FR,France
+199335,3,5589,3066,8112,10,6,14,FR,France
+199334,3,2984,1209,4759,5,2,8,FR,France
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+199329,3,3714,1668,5760,6,2,10,FR,France
+199328,3,1774,484,3064,3,1,5,FR,France
+199327,3,2574,918,4230,4,1,7,FR,France
+199326,3,3048,1450,4646,5,2,8,FR,France
+199325,3,8348,5260,11436,15,10,20,FR,France
+199324,3,6624,3747,9501,12,7,17,FR,France
+199323,3,7424,4302,10546,13,8,18,FR,France
+199322,3,3432,1849,5015,6,3,9,FR,France
+199321,3,5942,3670,8214,10,6,14,FR,France
+199320,3,5137,3157,7117,9,6,12,FR,France
+199319,3,4593,2725,6461,8,5,11,FR,France
+199318,3,5027,3075,6979,9,6,12,FR,France
+199317,3,8111,5138,11084,14,9,19,FR,France
+199316,3,24932,19468,30396,43,33,53,FR,France
+199315,3,22181,17147,27215,39,30,48,FR,France
+199314,3,38266,30865,45667,67,54,80,FR,France
+199313,3,62987,54506,71468,110,95,125,FR,France
+199312,3,63493,55956,71030,111,98,124,FR,France
+199311,3,80548,72628,88468,140,126,154,FR,France
+199310,3,127910,117688,138132,223,205,241,FR,France
+199309,3,184150,171378,196922,321,299,343,FR,France
+199308,3,222932,209051,236813,389,365,413,FR,France
+199307,3,248494,234936,262052,433,409,457,FR,France
+199306,3,286939,271830,302048,500,474,526,FR,France
+199305,3,255890,241516,270264,446,421,471,FR,France
+199304,3,193107,181261,204953,337,316,358,FR,France
+199303,3,147854,137256,158452,258,240,276,FR,France
+199302,3,90269,82164,98374,157,143,171,FR,France
+199301,3,75288,67244,83332,131,117,145,FR,France
+199253,3,56622,49107,64137,99,86,112,FR,France
+199252,3,49588,42568,56608,87,75,99,FR,France
+199251,3,41156,35024,47288,72,61,83,FR,France
+199250,3,40100,34699,45501,70,61,79,FR,France
+199249,3,39146,33847,44445,69,60,78,FR,France
+199248,3,32392,27499,37285,57,48,66,FR,France
+199247,3,26276,21797,30755,46,38,54,FR,France
+199246,3,22758,18462,27054,40,32,48,FR,France
+199245,3,22023,17804,26242,39,32,46,FR,France
+199244,3,27619,22945,32293,48,40,56,FR,France
+199243,3,24969,20704,29234,44,37,51,FR,France
+199242,3,29980,25261,34699,52,44,60,FR,France
+199241,3,24091,19953,28229,42,35,49,FR,France
+199240,3,17593,13885,21301,31,25,37,FR,France
+199239,3,13535,10301,16769,24,18,30,FR,France
+199238,3,9837,7106,12568,17,12,22,FR,France
+199237,3,7480,5130,9830,13,9,17,FR,France
+199236,3,5155,2958,7352,9,5,13,FR,France
+199235,3,6383,3687,9079,11,6,16,FR,France
+199234,3,2953,922,4984,5,1,9,FR,France
+199233,3,4046,1549,6543,7,3,11,FR,France
+199232,3,4963,2215,7711,9,4,14,FR,France
+199231,3,4233,1733,6733,7,3,11,FR,France
+199230,3,3091,1145,5037,5,2,8,FR,France
+199229,3,3586,1583,5589,6,2,10,FR,France
+199228,3,4179,2133,6225,7,3,11,FR,France
+199227,3,5568,3134,8002,10,6,14,FR,France
+199226,3,5886,3229,8543,10,5,15,FR,France
+199225,3,5304,2805,7803,9,5,13,FR,France
+199224,3,5928,3482,8374,10,6,14,FR,France
+199223,3,3914,1855,5973,7,3,11,FR,France
+199222,3,4899,2641,7157,9,5,13,FR,France
+199221,3,5059,2889,7229,9,5,13,FR,France
+199220,3,4308,2226,6390,8,4,12,FR,France
+199219,3,2982,1181,4783,5,2,8,FR,France
+199218,3,3887,1777,5997,7,3,11,FR,France
+199217,3,3614,1216,6012,6,2,10,FR,France
+199216,3,5460,2934,7986,10,6,14,FR,France
+199215,3,8642,5029,12255,15,9,21,FR,France
+199214,3,11346,7536,15156,20,13,27,FR,France
+199213,3,11693,8096,15290,20,14,26,FR,France
+199212,3,11752,7834,15670,21,14,28,FR,France
+199211,3,11689,7575,15803,20,13,27,FR,France
+199210,3,19224,12971,25477,34,23,45,FR,France
+199209,3,26629,20275,32983,47,36,58,FR,France
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