Exercice done

module2/exo2/exercice.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[14.   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\n",
+      " 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.  12.4\n",
+      " 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\n",
+      " 14.4 15.7 14.  13.6 18.  13.6 19.9 13.7 17.  20.5  9.9 12.5 13.2 16.1\n",
+      " 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\n",
+      " 19.6 21.7 11.3 15.  14.3 16.8 14.   6.8  8.2 19.9 20.4 14.6 16.4 18.7\n",
+      " 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\n",
+      "  8.9 21. ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "x = 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",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14.113000000000001\n"
+     ]
+    }
+   ],
+   "source": [
+    "mean = np.mean(x)\n",
+    "print(mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "minimum = np.min(x)\n",
+    "print(minimum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "23.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "maximum = np.max(x)\n",
+    "print(maximum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "median = np.median(x)\n",
+    "print(median)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.334094455301447\n"
+     ]
+    }
+   ],
+   "source": [
+    "std = np.std(x, ddof=1)\n",
+    "print(std)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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xhMSWm5pl9MmeGTvirRtJcDl1CghzUdSNG/Yzej8GY998E3jwQaCvL9OT96NUvwc7H4gF7Au9iHjoZfS5uQOWGf0HH7DVo8ywU14JxMbDT49+xGf0steNwuLFi/3ehZtcv85E3q8bLhGxcJrRixT6lhbg6lVg4UKgoMCbVZW6utgAqhOPvrcXGBxUMn6OCOtGL6MvKxtjmtHzlbDMVl/s62PPKygwL68E7Gf0Ts6N69fjj4EVIz6jt9vaOBXI0C437yN8DWa/hF5ELNRCbzQWoc7oRcbirbeAT3+ararU3e1NLFpbmYg5EXruZftVO64VN70xBDXnz7P/5fhx42Oq/p+0g7F6GT3fnldC/4MfAN/7nv3nAyHI6DvM0qkUg/frDgJ+C72IWHR0sMG1tDTjC9WvjP7NN4F772VCc/myN9dIayswfTqb4Tk4qDxu5tHrVdwA/g3GtrR8ZCr0Z86wD8i0NGXBGC3q/0md0ff3sw8HtaCq4+HVYOyFC+yuwwkjXuiDxnvvxXfvS0Xq69n3MI+V8+Zder40wM6DwUH2HD8y+s98hu2bV428WltZRlpcHNvfRe3RazNmPX8e8G8w1qqO/swZ1k65osLYvlFPACsuZsd+aCh2GUEOjwelzE7TE3qnLRAuXmR3Hk4Y8dZNjtd7nyB//dfAgQP+bHvmzJn+bFiHujpmG/gl9F7G4vRplkVpuXGDCamRHcBnxRIiNqPv7QVOnAAWLGD7NzTkTVMzLtp6JYVG1o2R0IsYjNUTt7Ky0ZYZvZXQc+sGANLTmdi3tcX784ASjxs32P+sJ1dOzw03Qj/iM/pR2g5BPtPQ4F/zqvnz5wvZzquvWgt4XR1w553+Cb2XsXj2WUCvpTu/sI0yeu7PA+wi6+9nPeyTzeHDwJw5QF4e27f+fm+ucHWliRdC74d1c9ddtyWc0WvtKB4PrT8PKIOxRv484Ezoh4fZeTU05KyibcQ3NbvmdPg5iQwPM1/PL3Hja2gmm69/HTh2zPw59fX+Cr2XsWhpiV9gAlCEXq81L6D48wDzfLnYJ5s332S2DcCFlya1kZfao8/PZx4+/0Azs278aOT17rt7LDP6mTPtWzeAMiBrlNHfuOGd0Dc1se1Nn+7Mpx/xTc2CRHMz+6QN8wITANDebl650NnJTvqyMra03EintTVe6Ck1b80LxGb0gDj75q232EAswKyFrKxhdHcn/r5G1o3ao09PZ3cSfJwqaBl9Xp5xHT0vB540CbjjDmbX6e2jk4ye3+EYDcQCzs6LixeBadPYRDi79g2lsYujeEXKLjzS0MC++yX0hdp0Ikm0t8P09re+nmUceXn+ZfRexkJP6Lu7mVilpxtbN+qMHhAjbpQCBw8qQs+2O+Rpfxcz6waI/eBT+9lq/CqvHDcuy/DcPXuWZfOEMFGcORPQ66ThJKPn4zdeZfRuhL6/n3futPd8u6RsUzO/hX79+vVJ30ZfH/uyEvrycn8aeXG8jEVLC/tSo14uz2wwVnRG39vLLuzx45XHxo/PtVwFyw52hV5tZfmZ0evZFV/60ucNP/S4P88xsm/cZPRGs2IBZy0Q3Aq917YNIFjog9SDnQu9X+K2devWpG+DC7xZhlhXx4Tez4zeq1hQqp/R84obIFgZvV4WOzR0LelCr575qo5H0KybXbu2GjYtsyv02oyet0Ewyui9HIy9dIm13Ug5oR+2ajMnkIYGJm5+ZfQiJo9xoTfL6OvqmHXjZ0bvVSw6O9mtfGtr7ExJ7UpCVlU3gH92RVZWr2dCr1d1o/boAXtC79dgbFdXB/Lz2XHV4iSj11o3167FtijmqAdj/fLok1FDD6TwYOzlyyyTDfNgrF2h99u68YrWVmDyZObFqyfCqYXeaDBWL6P3Q+hzcgYTFnpK2SQpq/JKIDgZvZHAFRfrHy9eccOpqGDzEbQVS2bWjVEdvdce/a23ssFiO+W6ocjox+iN9PhEQwMTOL/EbcOGDUnfBq/dNbNuguDRexWLlhbjBSbMMvrubnaBFRcrj4maDaoVt9mzpyQ8GNvRwd43O5sJm9oxNRqM7elhIpmnM19LVMdGbSw2bNiAoqL4RGV4mCUoaqEfO5Yt5K2eLDc4yD7w+fgMfx63bvTq6K0GY53MjOVCz9fnvXLF+jWhyOi7vagb84iGBmDGDP8y+sOHDyd9G+3t7GQ2yuh7e9lJPXWqv0LvVSxaW9kt97hxxkKvl9Fz20Y9Hd4v66an52rCGb06M1dn9LzMVM+j55mvXt9BvwZjDx8+rJvRX7rE/i9tl02tfXP9OjB6dGwFS6IZfVYW+wCxcqG7u9mHJz8Odu2bUGT0fYJU9Ykn4tfKVEMpE/rp0/0T+rNnzyZ9G+3t7LbRSOjPnwduuYVZHSKF/k//FKisZAtGtLV5FwuzJeN4Vqc3GHv5MrN81PhlV/T1NSdF6Cll28vMZF8cfoejHbRU41cszp49q1slpfXnOVqh1/ufzAZj+Vq63PbSg7fIsKq84QOx/IMzpYReFAcPKo269Lh2jd2ijh4dfo/+1luNrRtu2wDsYhYxYSoSYbNB588HfvUrdmt76tQk6xfaQG3dqEss1VU3etbN5cushbEavzL6nJwBT4U+J4cJe1eXeUmhkT/P38NJLJ5/HvjRj5zts9G0fz3rxkjo7747dpU0vW6co0ez5OLGjfhYACweJSVM8I2wEw9u23DsCn0orBtRk4T4SW1EQwO7sP1YMo6zZMmSpG+jrc08o+cDsYC4jJ7P+nviCaCmhmX2hYWf9OS9uVg5tW6ClNHfc0+ZJ0KvLSm0qh03E3qnsXjrLZZsOUHPulmyZImudWMk9MuXA2+8oZzvehPAMjOZ5XPpUnxGD7DHjGwbTjKFPhQZPRW0+nRXl/lKQlzo/VgyjjM0NJT0bVhZN7y0EhAn9NoTubgYaG/3ZkEa7tE7HYzVE3q/Mvrc3KGEhd6s0kTraycjo6+rAz7+2P7zIxHWjkQ77X9oaMgwo9dreFpUBCxZAvzmN+x3Iztq7Fh2zPUy+qIib4SeWzccJ0I/4jP6TrM020OcZPR+Cf3+/fuTvo32diZgAwOxi09wtNZNb6/xSj1eoRX6khLg5EmDVSMcYmTdqIW+oICdG+r/0yij90Poz58/nnDVjVa0udBra+gB5Q7Hy4zeqdDzc0I7ELx//35HGT0AfPGLwIsvsp+NFlIZM4Yd/0QyeiuPPhHrZsRn9CKIRNiId9CFXgTt7UxIjWrH1dZNRgb7GhhI7j7pZfS9vd615rUqr8zIYMddXQDW0KCf0fth3eTmWnv0AwPmH8hGQm9m3RiJIuAsFgMD7IOzq8v+oj5m4qYdjO3sZPt6yy36z1+xgi0WfuWKcUbPH0tmRq8V+mnTWHWXXsKlJhTWTW5ubtK3wbNSu9aNXx797Nmzk76NtjYmpMweif3b4CCLw623Ko+JiMfAQLzQZ2QYKIxDjMorb9yIraXWVt74ad1oL+rZs6dZCv2qVcBPf2r8dydCr666Mcvo7cbiwgUWSz5JyA5GdsXs2bPjrJtjx1hLbaP+iLm5wEMPAb/+tfGH19ixzCbSE9TCQuNZsRw3Qp+ZCUyYwCwdM0IxGCtC6HkWYZbR8yoLPzP6OXPmJH0b7e2K0OsNQJaWxvqionqaaIWe0iLjFzhAndEbWTdA7B1OJMKyv0mawh+nsejrM5+YZvSa+MHY6aZCX1sLvPYaK001wu1grBfllfwu8dZb7ds3Rhn9nDlz4s7d998H7rnH/P24fWOW0etl8wDrJPpJi9oAK93g5dtqjx6wZ9+EIqMX0dTMjtAHwbp5yexKdciNG/oiw60bvQGtq1dZhqHGD6EvKQE+/tikR4NNhoZYDIqLza0bIHZAtrmZ/a69uJyeG//v/wHf+IazfdYT+r17f2Mq9D/5CfCtb7Hp/s3N+s8x8+iTPRjL217fcov9jN4oi33ppZfizt333mNllGYsWcL249gx44zeqADw8ceBBx4wf3+reLS0sFm62lnGt91mvQBJKIReBFzojS4WStntk99C7yX/5/8AP/hB7GOUsgukqEjfutGb/Seill4vo+/pSXyVhevX2YdGejqrlW5vV2YvmmX0erYN4PxDr77efJKeHma9bvQ8+NZWYNs24MknWSnhyy/rv6+T1rwFBeyaaW42F/r+fnsD9W4yerNKE21G/9571hl9Ziawdi1L6Jxm9HawaoOgtW04djL6UFg3GWazEDzCKqPv6GBTogsL/fXovezNf+WK0naZ09PDbJmsLH2hv3o1tg86IC6jV9tFxcVAX1/iZzb35wE24FpUxMYo1KtLcdQZvZHQO00CLlwwn6Snh/5iGyVxg8Wcqirg4YfZB/TDDwO//W38cyKR+JmdZkLPZ0U3NZnPBs3KsteH3UvrZvTo0TGDsX197P3tuJ6PPMK+Gwl9IlN6rM6NRIQ+FBl9SUlJ0rfR3c0yOyOhV1dYZGdbVzAkg9OngfvuW+vZ+7W2sgtVDR+IBfQX2zDK6EVbN4WFQF9flmVnv7o6Y6sCUEorOdyn7+lhF6Y6x1APxppl9E6E/uJF9l5OXqMn9GvXrtVt0zA4yAZgn3yS/f7gg2xykPZ5N24w20Dd5sBM6AEWj7w8/YZmHLvnhpcZ/dq1a2Osmw8+YP2p7GS8994L/Pf/zu7utHzyk8Bjj9nbNz2shF5bQ88pK0uRjF6URz9xornQ8+nuTjIVr9i2jXmMTz3lXVOzlpZ4oef+PGDfuhGx+IhW6Nk6qdYlhU8+6azShFfeqNsfcOxYN07LKy9cYHG223cc0L+ot2zZoiv027YxkauoUP6Hz3wGeOWV2Ofpee1mHj3AEgEj24ZjR+iHh1kcysqcZ/R64rZlyxbk5rL37e+3Z9tw0tKAf/gH/eqcadOATZvsvY8ebjP6yZNZO2wzQpHRi1h4pKuLVVAYCYda6AFxbRAoBb7/feDrX2f+6o0b3swGBYyFnmf0QbNutCdyXl6/ac/8gQFg/35WH22E2roBrNcG9dK66e5mXwsWWA+2qdHvXtmjO3v3Jz8Bnnoq9rE///N4+8aokZdVRm8l9HbiwT3x3Fx2bnV26ltQWoysm56eHhCi3JHaqbgRgTYWfX3Ac88px95I6EtKzNeGAEIi9CJwktED4togPPkk8MILwNtvs9vK3l7vlnlvaWEXuHoyhlrog2TdaOvo2XbNhf7tt9lFcviwsc1mZN3oCb3Xg7EXLrALu7w8caHX7h/AxPL4cWDlytjnff7zwO7dseevXkZfVMTeo63NWOitloqwEw/1BDxC7FfeWE375wOydipuRKAV+sOHWQK3cCGbsfv66/pCX1DAjoNZ9xPfrBtCyC8IIc2EkFrVY98hhFwmhByLfj1oZ2NjrdIGD+jqYtlEX59+z2i9jF6E0P/856z+ecoUduFNm+bNhCm+uMK4cbEettqjd1J140dGX1Y22lTo9+4FNm5kP2sHnTlG1o2IjP7CBSZqZWXOBmT1LurKyso46+bjj5kVorUhSkuBT3wC2LdPeUxP6NPS2AflhQvJzejVQg+wmNixb4wy+srKSgDseLW2AidPKtaVn2hbIDQ2spLOxkaWzD3zDHDXXfGv40UgZjalnxl9NQC9ytIfUUrvin69ovP3OET0uuH9PIzWmvRL6NUrGBUVAXV1reYvsMm1a2zAafLkWPvGyqMPknUzNNR6czUsPV57Dbj/ftba2GiNEq3AmVk3Xg/GXryoCH2iGf2BAwfixOD8eVaxocfDDzP/nmPW3+XSJX2P3o7Q2zk31L2TAPs+vVEWe+DAAQDsennnHXbdJlIW6RVazWhsZOdQWhowdy67ezeaG2pl3/jW1IxSegCAJ6Oo/QJGPfmAE29epUXbe1yERz88zMreeOUHy1C8aSrDLYuJE+OF3si6GR5mGb/2dt0voae0zfDkb2tjmdy99wLz5hn79HoefUtLfPsDQIkHX0JQrzLDyXnBM/rp0xMX+nPnzjkS+g0bWLvn06fZ70YTn8aMYcfdqL+LF0Kv7oYK2Bd6I3E7Fw1mcTHwxz8Gw58H4oX+8uX4mdVGFBfDNKkJYlOz/0YIORG1dpJfN2mTri5WXmYk9H549NrufIWF3nn0LS1M4MyEXpvRX7vGe8wk4D2MAAAgAElEQVTEvpeoCVPadrR5eQOGQv/660zks7PNM3qtR29m3XAh5dm80fJ5Tq0bPvPRbrmuUSarHYw1E/rSUuDb32YeMaXmQg/oC/1TT1lXoWjFrbOTVbWo0Vo3TjJ6M3ErKgIOHAiu0GsXljdD7+5aTbKsG7czmP4dwHcB0Oj3HwL4it4TCSGPA3gcAEpLS1FVVQUAWLBgAcaNG4ddu3YBAKZNm4Zly5Zh8+bNAICsrCxUVlZix44daI6az2vWrEFdXR2OHz+Os2cnoKzsE1ixgmD37t0AgLKyMnR03IdDh/ajr28Ofv3r9/Cd7zyAbdu24fr16xgYSEdv71dw6tRBfPjhSQBAJLIB587dQG1tDQBg5syZmD9/Pp5//nkAbLGU9evXY+vWreiIXn0bNmzA4cOHby6Bt2TJEgwNDd1sPTx79mzMmTPnZpuDrKzxyM5ehS1btqCnpweNjSUgZBX27t17M2tZvnw5Ojs7cTC6YkNFRQXKy8uxfft28NitXr0a1dXVGIi2mNy0aRNee+0EOjsL0djYjrNny3Hs2DkcOnQI77yzGJ/9bDauXs3Fjh2/R3PzI6ipeQ0rV67ET3+6HVlZ92Hz5t9g06ZNqKmpQWNjI2pr78a0abNw9OhpHD161JPjBAALFy5EQUEBdu/ejbfeuhv5+WMxMDAZ1dXVAIC8vLlob8fN4wQA69atQ21tLX7yk2KMHduJM2dKMHZsNg4enIif/ew53H577HG6eHEDxo4ddfM4nT8/Ds3Nn8fx4xdx9WobqqqO3DxOu3efwIULS/Hqq5cxfvwdqKr6BQA2QWft2rXYsmULrl8fQGfnoxgYoDhw4IDpcaqvvwfHj7+K3t4mZGY+hitXsrF7d+xx2rNnDy5evAgAWLFiBVpaWtDYOBU1NX9ERsZtmDJlCnbu3ImBgQFcvvwhRo+ehc2bNyMSieCPf1yGZ5659eZxAoBVq1ahoaEBR48eRU5OGk6f3ohf/rIbR450IiPjLKZOjcQcp7a2+wDMwL59O9HWdtXyOPHradGiRaiurkZT0xLs2XMZDz20CNu2bcPRo+n4p396GPfc04XCwuOorT2Js2e/jEjkY9TXp2Hfvn2ory/FBx98Ft3d6abXU2dnJa5ebUJV1e6Y62lgYABVVVXo6VmBGzcmo7FxF6qqLsccp55oZlJZWWl5nOxcT3rH6VB02aq5c+diypQpeOedUzh7diJqaj7CypUrcfRoEyZOPIK+vqsx15P2OAEAIV9Aff0w6uu3615Ply49jJyccXHXU8JQSi2/ANwKoNbp37Rfs2bNol7x9NOUPvFE/OMPPUTpjh2ULllC6Z49sX87f57SqVNjH/vc5yjdtcuz3dKlqYnS0lLl90uXKB03bsCT9372WUr/8i/Z9699TXn84Ycp3b6d/Tw0RGl6OqXDw+z3vXspve+++Pf64Q8pffJJT3bLkKefpvR//a/Yx771rUb613+t//zyckqPHVN+nzqV0o8+in9eXh6lXV3K7/X1lN5yC6Xf/Cal3/9+7HMvXaJ08mRKf/lLSh95RH+7g4OUpqVRGolY/kt06lR2blFK6ac+Rembb1q/hlJKZ8yg9PTp2Mc++OAD+n//L6X/5b8oj1VUUHr0qPl7vfIKi9W8eZTu3x//9298g9KsLHv7pcejj1L63HPK7zt2UJqTw64zSiltbIw9x40e0+OZZ+LPCUpZLCil9DvfoRSgtLXV5c57zAsvULpunfK73nE04itfoXTzZuO/33WX/rEGcITa0FijL1fWDSFkourXhwHUGj1XTZfdBtU2aGvT97rMPHq9pcVEePTa2zG9cke32PHo09PZZCgeD6NV7v2YMAUAV6+e1r2d/fhjFqc771Qe07NvenrYGIh6ZqcT60aPjAw2uGbVP3xwkA1s8/dxUnmjZ90cPHgwxqOnlNlBRtYN53OfY6V9R44YWzeJDGRqPforV4B169i+vflmvG0DsMH+jg5rO9DIuuHZeFERs8asSkBFobZuKPXWo/et6oYQ8iKAtwHcTghpIIR8FcD3CSEfEEJOALgPwNe93zVzrl/X97q6u5nQ65Ux6Qm9SI+ek58PDA6mW4qIHex49ECsN6hXcQM4H4x94w02kccJenX0Rh793r3A0qVMcDl6A7Lcl1Z77SzG7H/VCn1+PjtPLl0yFnrAXkXW5csslrzlgJPKG7M6en7uXr+ulEda8aMfsQ87vWObqNBrE6IrV9g0/6efBv7+7+MHYgG239OmWdfSW1WajBvHPuCDgvq84L2z7MbWyqP3rY6eUvoIpXQipTSTUjqFUvpzSumjlNI7KaWfoJR+nlLaZPU+AJBn1kzDIUZC7yajFyH06oNHCJCfHzFtpWwXI6FX19EDsV0AjTJ6p0L/+uvAjh3O9lcvY7nzzqm6x5KXVarRy+j1BiAJYY+dOxdfdZOWxs6R06fNhd5OEsAHYjnTpyeW0VdUVMQMxpoNxGqZMYMJcDI6NmpjceUKO+e+9CUm8r/8ZXxGD9irpTcSt4po0fwXvsDmoQQFtWbw0kq7+DUYK3RmbLaH/4GXQu+HXVFcTDyxb3hZ4YQJLHvlFR/qOnogtie9Vxl9XR37coJeLGbNmqR7LPfvBz772djH5s5lU+HVk+H4h50WLvRGa4OeOmWd0VvFg8+K5SSa0ZeXl8dk9E6EHjAW86lTrZfIM0PPupkwgd3JPP00+9DXE3o7lTdG1k159A0zMxPrNuk1WqG3a9sA1nX0oWhq1mZmTjnk+nVzjz7I1g0AUNruidBzkcvJYbft168zv7qzM/biUGcSXmX0dXWsXNXJa/RicfDgrrhj2dPD7kC0U8lLSpjA8LpxwLikcNw4ZtHoiURREbsDSjSj55OlOHZr6YeHmbWkLTXdvn17TAsEp0JvxIIF8Q3QnKC98+VCD7CsfvZsNktXix2hN7JueIVM0FDHwok/DwTYow8qI8m60ctYcnIGPBN6LnLcvunsZHMJ1FPmk2Hd1Nc779hot46eLw6jV+OutW+MhJ4/ZpTRAyxmRtjN6NVCP3EiOy+tmnlxcdP7/xLJ6M3Qxt0JRhk9f98TJ/T7xCeS0QcVdQsEpxl9Slg3GRmZ1k+ywfAwuxAGB2N7TgwMsGw2KytYQq938IqKErduIpHYKe9c6LX+PNuePevG7oSpzk729elPO7Nv9GIxdWoJentjmz0Z9fQG4gdktbNiOWZCX1QUv2auFjvnhlbo09KYuLntO15aWoqCAtxcZcpLoU8EbaVJU1PsOZRmoCSJZPSliXhNSSRZHv3wMPvK9EYmYxAq9AMDxdZPskF7O7t4S0riu/yNGsWyJLvWjV8e/e23T0hY6Nvb2f/LxYoLvbbiBlBOMEq9yej52qAzZiQu9A8/vDqu5NRM6BctYoPAfPBZOyuWw8XfqJGX1QVqJx5aoQfs2TdGQr969WpkZrIY9fQER+jVsbhxg51zo0ZZv27qVHYszTCLRRBJ1LoxEnrtDHovESr0nZ3ezK9va2P9SUpKYv0u9cIKdjN6vzz6pqbTCQu9NpOdMIHdUmsHYgHFuunuZieS3kXqROh5OV15eeJCX11dHeddcutGj3nzgL/8S9a2t6vL3LrJy9PPkIqKrIXeKqOnVL/3uJ1aeqMsls8W5ndgFy6wrNhv1OeG2raxoqjIuGU4x8i64bEIGokOxhp59MlqaAYIFvpIxJuPquvXmdBrPx3dCL1f1k12dm/CQq/NZM0yei4cRrYN4GzCFO9U6KScENCvox8YGIirRjDL6AG2TNw997BJO1euGAu9UbWG3Yze7NxoaWEfmNoPTTuVN0ZZLJ+OX1gInDnDjqOHVcmuUV8nvLTSDqNGseTCbKlII4HjsQga6sXBnQp9Xl685cxJ5lhFqIXernXjh9Dn5lovn2eFtqzQzKPnsTKybdg+Ocvoy8u9yejV+8dpaDAXekLY0oLDw8Bbb+l79OPGGQv9Jz8J3Hef+b5a2Xp6tg2QmHXDKSwEjh0Lhm0DuM/o+eLjZmM/ySopTBZc6CMRFgsnQk9IbGGEmmQNxAKChT4z05ujaUfoCwtjM/rBQZZZaCfP+OXRL1ky35OMXk/ojTz6GzfsCb2d7ovqRaAbGqxbBXD0YrFp06a4Y2mV0QPMkvn1r4Evf1nf3pg1iy23p8df/AW7GzDDKqM3Eno71o2RuG2KtpEsLGSrSgVF6LUZvV2hB4zXhuAYZbKbElnYNYmkp7Nz7/JlllA6FWcjnz6ZH3iCB2NN1tBygFro1X4Xb38AxFs3168zf0xbHeCXR3/x4geee/Rqodd69HasG7v9XQDFo8/KYhaInSXjAP1Y7NmzR9ejtxJ6gAniL36hv6DGhAnA975nb7/0cJvRT53KRMAMo4t6z549AIIn9G4zeoAdG7M2V0bWDY9FEMnJYXdtTrJ5jpFPH5qMfmjIZqNuC7jQa31ddUbPTy7uDerZNoB/1k1PT6PnGf2ECdZVN2YZPWDPvuntZdvmQjx9un37Rq+O/uLFizHHsqODlVpq/wfRWJ0bRotA5+crjdaMMBJ63iK3qAj48MNgCn1TkzOhLygwF3qrWASRRITeKKMPjdBT6p1HX1Jibt3wjo184oqZ0IuwbrQnck7OoKnQ9/Wxpl5Ga6QC8YOxRUVMIBsa3Fk3gD2hP38+dg1TJz69HY+eZ/PJKDNzglvrJi0tcV+6sJDdWZWV2d/fZJJM6yaZApcsuNA7qaHnhN66AdKtn2IDOx49EGvfBC2jX7bsk7pCv3078NBDTIwff5ytHmSENqMnhNk3p065q7oB7E2a0nYqTFToV6xYoSv0fmOWBLS1sfa88+bp/92OL613Ua9YsQKAMogcxIzeqdCbZfSUGnv0PBZBRGb0JgwPe5Oi8Tp6ra/rRuhFePR6J/LgYKuu0H/lK2yg8OOPgUOHgN/9zniChd6M0IkTgY8+ivfos7NZBv7xx4ln9Nre4+Xl9kss9U7mlpaWmGNpVXEjCrNz48c/Blat0rduAGtf2kjoW1paADChT08PRhwA9+WVgPmH3tAQS1C0y1oCSiyCSDI8+tBk9JGIuW9pFzOPXl3TrJ4d29oarIz+7NnDcULf1cXqzB99lP1/Y8cCy5YBL7yg/756XRsnTjT2t4uLgbNnzYXeTi09r6Hn2PXoKWX/n9ajP3ToUMyxDHpG39YG/Nu/sa6NRrj1pfmydYWFLAZ6AugHPAEYGmLXn145qxFmsTCbJMRjEURkRm8CIdSy2dN777GWp2Z4bd34UV6ZkzMYN5GE981Qe9Nf/apxL269qf/8llpP6Ll9Y2XdOM3oy8qYb69uHazH4CArS9PrixJE68Yoo//xj4HPf97cP3dr3XAKC4Nj2wDsw3lwUOl3n+7AhTW7uxlpNfScnBw23uWlRx8aoU9Pj5/EpOWf/5llsf/+78bP8VLo/SqvnD//HowaFSsGerPsli5ldyPHjsU+3t3NMmTtrEx+S22U0aelsdgZYVfo1R59Xh77wDEbOAaMT+S5c+fGCb1R+wOR6CUBdrJ5wH1GP3fuXADsGvjHf3S4w0mEELa/Fy448+cB8w89s9mgPBZBhB87LzP60Fg3doT+nXeAbdtY1vTNb8ZbPZTGVt2YefRq6yZog7FTpkyJa+SlJ/Tp6WxCkDar57aNtjJl4kTjpc2Ki5kgm2VjVkI/OMgEXTtByY5PbyT0U6ZMiTmWQcno9c6Nn/yEZfPaZfO0uM3op0Q/4caOBT71KYc7nGRyc9mdm1Ohd2vdTAnCp70B2dnsOnPTYDP0dfSUDpsKfWsrE7CVK4G332Y9x7WT47q6WDCys2M7MvK/Oc3os7OZb2xnNqhb9A7gzp0744TeaLHqL3+Z+fRqATZqzTtxIouLXmliUZG5bQNYC/2FC2wftT67HZ9er4YeYLFQH8ugCL32bq+7G3j2WetsHnA/GLtz507nOyqInBx3Qm+V0RsJfdBjMX68uzEUM+smFBl9WlrEVOjffZethMPthW3bgN/+NvY53LYBmHhmZCjC5EboCWHik8ys3uiT2k5GD7Ba7blzY2NhtHze5MnG1kxxsXUGYiX0eotAA/ZKLM0yltxcpXdIRkYwlo7TWjenTrEPIKtsHnBv3QSZZGT0I23REU5Ojjt/HjC3bkKR0aenE1Ohf+cd1myKM3Ysu/jVtzltbbGlg+qgubFugOT79HoCN2nSpJgl4wDzTnibNrFxC37nYdSD/c47gV279N/DK6HXWxs0Eetm0qRJN5s91dYGI5sH4s+Lc+fsT2Bya91McmP6CiKRjN6NdRP0WLjdvdAPxubkZFoKvdqXJCS+5as6owdifXp1rxtAyei5r28k9E58+kOHgB/+0N5zOXoHcOXKlboZvVGW8Od/zv4HfjdrlNETAsycqf8eY8dan5xWE6bMhN5tRr9y5UoA7Fh+8EEwBmKB+IyeL7ZiB7cZPY9FEMnNZfMwnNTQA+6tmyDHIlGhb2uLt4tDMxg7MNBrKPSRCBNRdUYPxHcC1BN6o4yeC31Hh+Lr6+GkxPKFF4CtW+09l6MncJs3b9b16I1OnowMNhD4jW+wE8LIozfjiSeA//E/zJ9jltEfPw78/vfAHXfE/81OX3q9XvQAiwXAjuWJE6md0fNYBJHcXDZ+4vVgrNF1GeRYFBXpt7+wQ04OK4jQXmehyegJoYZCf/o0Ey6teGl7e2uFXj3Rxsi6MbNtAGcZ/d69bEEIJ4O3erenkUgERUWKtUQpy+jNsqWlS4FPfIKVoBpl9Gbk5Vl733oTppqbgf/6X1nJ35NPAp/7XPzrCgpYDM06XxqdyJFoaVVJCcvogyL02gTAqdC7yegjXswoTBI5OWyuhKjB2CDH4umngaeecv96PfsmNEKflmYs9FrbhmM3o49EmOWgXo2HZ/RWQm/Xo29sZF9ZWWzQ0C56gyxpaWkxGX1bGzvhrdbh/OEPmdCfOKHv0SeKNqPv6mK+f14e+zD+q7/Sn/BECLugzSbEGZ3IadE3LC5mHRuDIvTa80KEdZNmtMp2AMjNZd9FDcYGPRaJ2Cx6Qh8a66awcJRjobfK6Lnf1dur9HPh2BV6uxn93r3AZz/LrIszZ6yfzzFabEMt9EallVrKylh2ffiw84zeDlqhP3+efaD86Efx/XO02Ok7brbARHExOw5BEXr1eTEwwNrzGvW20eLWugnqYhsA29+cHOcVUW4HY4Mci0TRq6UPTUbf399jKvRafx6wNxjb3h5v2wDOrBs7Hv3evcD99wO335640NfU1MQIvZO1J7/9baCiIjmLRmuF3qjnuh52hF6vjr6mpgaAMps3iIOxFy+y46O30LgebjN6HosgkpvLsnmn7aPdWjdBjkWihDqjj0SGdIW+s5PdFldUxP9t2jQmgnydYCOPXk/ovczoKWVCv3QpE/qzZ82fr0ZP6BsbG10LfX4+a4mQDEFMttDrZSyNjY0AlDuGoGT0WVmsidfwsDPbBnC/fB6PRRDhQu/mdYOD+uM3ZtZNkGORKCH36PUnTB0+DNx1l362l5nJLA2+2IxRHX0iQm/Hoz95kn0gTJ/uTUYPIE7o3U7A8BI/hJ5TXMyOrdU4hSh4f5e+PmcDsYD7wdggk5PjvLQSUMZv9OKRzNmgQSbUQl9UlK8r9O++a97XQ23fGHn0yc7ouW0DOBP6SIRlhNpb/lWrVsVMmDIrrRSJto7eSTsCt0K/atUqAOxYBiWb5/AkwKnQu7VueCyCiNuMHjCOh9kHXpBjkSh6Hn1orJvh4UFdoTcaiOWoK2+cePSZmeyroSFxj/611xShLytjAsjtJDO4L631NRsaGlxbN8kkmRm9UR19Q7Tt5axZwIMPOthZAfAkIBnWjd5F3WDVAtRHKiqAe+9191qzjN4oiw1yLBIl1Bl9f3+P7sl//Dhw993Gr1NX3jjx6AGWSZw/n1hGPzAAvPEGcN997PesLJZ52llVyejgHT16NKaOPhWE3iwWACvj/N73HOysAHg8nGb0OTnM3zeaV2Ak9DwWQWTdOmDDBnevNfrgM8tigxyLRDES+lBk9HzClHqyEaWsbM3Mn+bWDZ+Qo/ZwecC07Q84hYVs2rZZzbmVR//OO8yuUX9Y2LVvzD6leVUQpfbLK5ONesLU8LD1sVFjZVeM1EWg3Qi9mS8NjEyPPhHcWDdhxqjqJhQZfX5+HtLS2AXPaWtjQssnY+jBrRu+VqzaBjHz6AF2gnV0JGbdqG0bjt3KGyNxW7BgATIz2d86OtjsU7f+p5doF4EePdr+yec2o1+wYIHzHRVETg6z/jIyrOcRaDGKRyRibGMFORaJYJTRm334hzUWQMjr6DMzM2M6SgIsY7QayefWzbVr8S14uc/d0WEs9EBi1s0rrwDLl8c+ZjejN/qUHhed7VRUxBbzHj3afo12MlELvRPbBrD2pY3q6MclY+aXR+TmsoorJ9k8xyiL5Re0Xj16kGORCG4y+rDGAgh5HX17e7sroS8uZhkVF0Q1GRnMbrhyRb8sr7DQur+5mXXz8cds4PUzn4l93Il1o3fwdkV7CRcVsT7nQbBtgMSF3k1Gv8uor3IAyMlhQu9kIJbjxpcOciwSwejcMLMrwhoLIOSDsQBcCT3ALrQjR/QX1SgpYbfXRhm91u7RYpbR79gBPPRQ/PJ7M2cm7tEDitAHYSAW8Efog0wyMvpU9KXNrJtUiwUQcqHPzs52LfRlZUzo9XzS4mKWdRsJvZltA5h79L/9LfDww/GPT5jADsz16+bvbXTwpkUVtLCQNfIKitBz+2hwUJzQT3OyEcHk5LDj40bo3WT0QY5FIrj50AtrLACmWTduKGtiUxqiwdjCwsI4ob9yxd4gJBd6vYy+uNg4oy8stCf0ehl9czMr/Vy6NP5vhNizb4zEbdmyZQCCZ90AyqQpp2u3uq2j57EIIrm5rKLLrXXjVNyCHItEcDMYG9ZYAMxOzs1Vzo+hIdYV1s0atHawFHpCyC8IIc2EkFrVY6MJIa8RQj6KfrdVj9DS0pKQdaOtoecUF7MPDLcZvZFH//LLrAe70UVpp/LG6ETmiyoUFbGKoqBk9IBi34jK6IO8wAQ/9qKsmyDHIhFkLOJR2zfJtvPsZPTVAB7QPPZ3APZRSmcA2Bf93Ra83JHjxLoBjD36SMT7jP63v2VL+BmRSEbPKSpi9eqpLPRBJieHZVluGsi5sW7Cipu7m7BTXKxYv8m+NiyFnlJ6AIDWiV4F4Lnoz88BWG1rY2lpCXn0gHFGD+gL/SOPAH/7t+bvrefRd3Sw2bBmU/LtDMgaHcCsaJ1hURH7PUhCn5fHSlm7u531vHcr9Fl6NZcBITeXtYN2c0vtJosNciwSgfed0mImcGGNBWf2bOC999jPvgu9AeMppU0AEP1eaudFY8aMcS30U6eyi82p0E+YwDJvM/Qy+ldeYSWVZmWZiWT0lZWVABShD5pHf/Ysi7mT3uNu+9HzWASRnBx3tg3gLqMPciwSwU1GH9ZYcO6/n03GBJJ/Z5Mk61+BEPI4gMcBYPTo0fjgg7dw9Woxjh0bxKhRpejtLcVLL/0Ct9wyDcuWLbvpy2VlZaGyshI7duxAc3MzAOCWW76KpqZTqKo6CABYuHAhCgoKcOpUI4BP49ixNzFjxgJUV1cDAPLy8rBx40Zs27YN16P3SOvWrUNtbS1OnjwJAFi8eDFaW/Nw+XIuqqp+g5kzZ2L+/Pn4wQ+u4E/+5DK2bm3E+vXrsXXrVnREP6E2bNiAw4cPo7a2HmfPPoaPPrqASGQI+/fvBwDMnj0bc+bMwUsvvYQ33rgDjY1TAdyGLVu2oCfaGnLMmDEoKirC++9nIC1tEbq7L6G2thMHD7L/raKiAuXl5di+fTsAoLS0FKtXr0Z1dTUGot3UNm3ahD179uBitIfzihUr0NLSgkOHDgEA5s6diylTpmDnzp0AgEmTJmHlypXYvHkzIpEI0tLSsGnTJtTU1Nzs/b1q1SoMD4/CCy+cQVraBBw71oJx48bdrGmeNs34OF26dB3t7Y/i2rUO1NXV4fjx4zHH6eLFHLz66jsYHh6FRYsW3TxOXV1d+Ju/+RvL45SRkYF9+/YBwM3j9PzzzwNgA/1Gx+lsdCBlyZIlGBrSP078/Fy7dm3MccrJ+Qqys5tQVfV7AMDy5cvR2WnvOL3//nRcujQWkcjtMccpPX0N+vszUFX1Utxxam9vx6xZs2wdp4aGhpv9YBYsWGD7OPHrac2aNbrHaffu3QCAsrKymONk93rSO0579+7BuXP3YuvWP8Qcp/b2RzE0FMHrr8cfp507d6K4uNjWcaqsrMSBAwdwLtoUy8lxSvb1ZHScrl3Lx+9/vwZDQ5n41a+2obf3flRX79Q9TglDKbX8AnArgFrV72cATIz+PBHAGTvvM23aNFpdTemjj1JKKaV1dZTeeiu1TV0dpcPD8Y//x39QClDa2mr/vdR8+CGlt9+u/D44SGlhIaVXrli/Nj+f0o4O47//679S+ld/Ff/4z372M0oppb/5DaVTpzrc4SSzZAml69ZRWlnp7HXDw5SmpVE6NKT/97vvpvTIkfjHeSyCSFsbpc3N7l67ZQulX/xi/OMvvsjiq0eQY5EIJ09Sescd8Y8XFlLa3q7/mrDGQs2MGZS+/z6lR49SWlFh/DwAR6gNjTX6cmvdvAzgsejPjwHYafeFauvGrm3DmT5df2Fqbt24XbBCa91cuMAGeMePt36t25WEOGPHArfcYn9fRcCtG6dlzGlpzN9X97NXMxIHY4uL3a/NKwdjFeRgrD7cvkn2xDE75ZUvAngbwO2EkAZCyFcB/BOA+wkhHwG4P/q7JSUlJQkJvRHFxWzmqlsR0Qp9XR1QXm7vtUaDTBwjceO3Y/fey6p7gkRuLht7cDNfxayDpVEdvSe3pgHEjbiFNRZ61wmlbGKe0ZhrWGOhZulStqhRspMgS4+eUvqIwZ+WON1Yf39/0oQ+P9/5oiPOmkoAABHuSURBVMUcbR2910Kvd1HX1dVhzJgxSEszb6HsB3zClJvVnswGZI1OZh6LsGF0bpgJfVhjMWoUOy8oVa5To0V5OGGNhZr77gO+9CU2S9bvOnrP6OnpiRN6L1rzTpiQWHmitryyvt6Z0LspKeQDYEGEt4x2k9GbWVkjMRaJ4CajD2sssrLYXbc6obKybcIaCzXFxcCcOcAf/hDM8krXqIX+yhVvMvoJE4APPnD/+uxsZivwvhN1dfanvNtpzTvSfOm8PPZdVEYfVmRTs1i08Ui188GIpUuB3/0uREKfn5+fFOsGiO8u6QRC2IXHF0QR4dEvXLjQ+Y4KIjeXzSZ2M7htJfR6fmyQY5EIbgZjwxoLIP7csPrAC3Ms1Nx/P1vuNDTWTXp6OvLy2AU/OOit0CcKH5CNRFjQ7U6ScSv0BXxFlACSm+vOtgGMhZ7SkRmLROCxUC+dCZgLXFhjAcRfK1ZCH+ZYqPnUp1hSFZqM/saNGyCE2TedncET+t5etnbr6NH2s1m3Hj2flBJEcnPd2TaAsdDz7nx6d15BjkUiZGay2dzaWddmAhfWWADx54aVdRPmWKjJygL+7M9G+MxYPQoLWTOftjb3NcpewzP6CxectaQNo0d/xx368xXsYCT0RqWVYYfHQ70mcqp69NprJVXjoMcXvxjbGsZrhAp9dvRKLyxkPvjYsYl5617CSyyd+PMAy+ibmoz/biT0ZW4bqAhgxQr25QYjoTf7wAtyLBKF3/GpExozgUuFWHCskqAwx0LLF7+Y3PcXat1wz62wkE3ICYptAyjWjRuhd5PRL1q0yPlOjgDcCH1YYwHo3/GZCX3YY+FkMDbMsRCNUKFvbW0FEFyh7+tzVkMPWAu9UQsE3igqbLgR+rDGAtCPh5nAhTkWTgdjwxwL0QivoweCLfROaugB94OxYcVM6EPeXlwXvUQgVb1pp4OxEu8QXl4JKELvxaxYr+CrKjkVejuDsXoXdR6flRQy3GT0YY0F4DyjD3ssnGT0YY6FaIQK/ejoqiEFBayMMWgZ/YULTPB5N0w7uPXoN27c6HwnRwBuhD6ssQD07/jMBC6VYmEl9GGOhWiECn1bWxsAZdWmoAl9ba0zfx5wL/Tbtm1ztqERgpGVZSb0YY0F4HwwNuyxUJ8b586Z96gKcyxEI1Toh4aGAARb6J3YNoB7j56v0BM23NTRhzUWgHPrJsyx0CZF+/eziUJGhDkWovFtMBYIltDn5rrL6MM4YSoRjOKRanHgyMFYBfWHXnc3cPw4m/4vST6+ePRc6IM0GJuTA7S3uxP6nh6l86UWI4Fbt26d850cAbjx6MMaC8B5Rh/2WPAPvYMHgXvuUTql6hHmWIhGqND3Rpu+FxayAc8gZTV8X5wKfVoauxvo7tb/u5HA1dbWOtvQCMGN0Ic1FoDzwdhUicXrr5vbNkC4YyEaX4S+qChYtg2g9CJx6tEDxj59JGK8VNrJkyedb2gEwNeM1d7hmNXRhzUWQLyVRan5eEXYY6EW+sWLzZ8f5liIxhePfv584IUX/NiyMTk57E7DzbJ+Rr70wID5UmlhJD09fsUuIHU9er1JQql2TnD4eAX35z/9ab/3KHXwpddNRgZw110it2xNTg6zbdxcgGZrgxqJ22KrdGYEo2ffmAl9mGPhdNp/mGPBz4u33wbuvtvcnwfCHQvRCBV6EuA0pqAAmDnT/WudVppkZPjSIVoIToU+lWJhJfRhjsWoUczW+8MfrP15INyxEI1Qoe9IZsPlBPnCF4Bnn3X3WjeThPbt2+duYyMAPaE386XDHAuns0HDHIu0NJbF79pl7c8D4Y6FaORHZpScHPdVQGa140GqLBKF04w+zMjFNmLJzwdOnZL+vGiECn1OSM9wN9bNTLc+0QjASOj5/AktqRQLK6EPcywAdq2Ul9tbqjPssRCJUOtmlN2FWEcYboR+/vz5yd0pH3Ga0Yc5FtyX5uWmVkIf5lgA7Nyw488D4Y+FSIQK/bVr10RuThhuhP75559P7k75iN6YhVkdfZhjwX1pPqHOrBILCHcsALbo/AMP2Htu2GMhEunRe0B+PnD1avzjqexLS49egX/wFRRIj/7ll/3eg9TEl4VHwoabjL7QyLAOAU6FPsyxAGIHZK2EPuyxcIKMhXf40tQsbLgR+vXr1yd3p3zEqdCHORZAbDyshD7ssXCCjIV3CBX6sPaXdjMzduvWrcndKR/RKzc1q6MPcywAdn489xzw8MPA177GZoUaEfZYOEHGwjuEevTDw8MiNycMNxOmgjx5LFGcZvRhjgXAJgedOwesWQNUVQHjxhk/N+yxcIKMhXfIwVgPkIttxCIHY2P57nf93gNJqiPUuhkzZozIzQnDjUe/YcOG5O6UjzgV+jDHwikyFgoyFt4hVOi7jVbnGOG4EfrDhw8nd6d8xEjojerowxwLp8hYKMhYeIdQoe/r6xO5OWGYefRGFRZnz55N7k75iNOMPsyxcIqMhYKMhXf4svBI2Bg1is18pDT28VT1paVHL5EEC6FCH9YJEHxVJa0zZSZuS5YsSf6O+YRToQ9zLJwiY6EgY+EdQoWealPeEKHn05uJ29DQUPJ3yiec9qMPcyycImOhIGPhHUKFvlNvxDIkOBX6/fv3J3+nfIILvfpzPVVj4RQZCwUZC+9IqI6eEPIxgE4AwwCGKKXzvNipkYj0pRUyMoDMzNjB6FSNhUQSBLyYMHUfpbTVzhNzc3M92Fww0cvozVogzJ49O/k75SP8g8+O0Ic9Fk6QsVCQsfAOodZNqgm9mbjNmTMn+TvlI+o7nOFhtvCGUfPSsMfCCTIWCjIW3pGo0FMAewghRwkhj1s9OaxNzQDnQv/SSy8lf6d8RC30PA6E6D837LFwgoyFgoyFdyRq3dxLKW0khJQCeI0QcppSekD9hOgHwOMAUFJSgqqqKgDAggULMG7cOOzatQsAMG3aNCxbtgybN28GAGRlZaGyshI7duxAc3MzAGDNmjWoq6vD8ePHAQALFy5EQUEBdu/eDQAoKyvDokWLUF1dDQDIy8vDxo0bsW3btpsfMuvWrUNtbS1OnjwJAFi8eDEyMjJurjg/c+ZMzJ8//+bqNoWFhVi/fj22bt16s8nShg0bcPjw4ZsTOpYsWYLh4TF45ZUT6Ow8jdmzZ2POnDmor+/Evn0nMDjYg7Vr12LLli3o6ekBAEQiEezduxfnzp0DACxfvhydnZ04ePAgAKCiogLl5eXYvn07AKC0tBSrV69GdXU1BgYGAACbNm3Cnj17cPHiRQDAihUr0NLSgkOHDgEA5s6diylTpmDnzp0AgEmTJmHlypXYvHkzIpEI0tLSsGnTJtTU1KCxsREAsGrVKjQ0NODo0aMJHScgC7/61T5Mn96M2bM/g8zMO1BVtVn3OPFjI+I4DQ0N3Rzk48eJC8ro0aPjjlNlZSUOHDgg7Di1traipqZG2HEK6vU0NDSE1tZWVFVVBfI4AaKvpwShlHryBeA7AL5p9pyysjIaVp56itIf/jD2sUWLKP3DH/Sf/5//+Z/J3ykfWbqU0j172M9NTZSWlho/N+yxcIKMhYKMhQKAIzQBfXZt3RBCRhFCCvjPAJYBqDV7TUlJidvNBR6n1s3atWuTv1M+UlgIRBMS0xp6IPyxcIKMhYKMhXck4tGPB/AmIeQ4gEMAdlFKXzV7QSp69Ea9brZs2ZL8nfKRVavYYhuAdWll2GPhBBkLBRkL73Dt0VNKzwGocPKasC48AjChr6+PfcxM4Li3GFbWrQO+9S3g5Ek2ccpM6MMeCyfIWCjIWHiHbGrmEXqLj6TyJKHsbLZs3r/8S2rHQSIJAkKFfuzYsSI3JxSnHn1lZWXS98lvvvY14Ne/BpqajHvRA6kRC7vIWCjIWHiH7HXjEU6F/sCBA/p/CBHjxwOrVwP/+q/mGX0qxMIuMhYKMhbeIVTo+/v7RW5OKHqLj5i1QOD1vmHnySeBPXvMhT5VYmEHGQsFGQvvkB69R0iPXp+77gIWL5ZxkEj8RKjQFxUVidycULTWTSQCDA4ae9PLly8Xs2MB4JlngLlzjf+eSrGwQsZCQcbCO7zoXmmbsJdXqoV+YIC16k0z+CgN83iFlqVL2ZcRqRQLK2QsFGQsvENoRt+lt4J2SMjPj1031sq24T04JDIWamQsFGQsvEN69B6Rns6Enc/xkP68RCIJCkKFPi8vT+TmhKO2b6yEvqLC0aTiUCNjoSBjoSBj4R1ChT475CmuVuiN+twAQHl5uZidGgHIWCjIWCjIWHiHUKFva2sTuTnhOMnoeV9siYyFGhkLBRkL75AevYforaokkUgkfiNU6DMzM0VuTjhOMvrS0lIxOzUCkLFQkLFQkLHwDqFCX1xcLHJzwikoAPbuBRobzdsfAMDq1avF7VjAkbFQkLFQkLHwDqFCf+3aNZGbE843v8lEfs4c4NFHzYWer8MpkbFQI2OhIGPhHUJnxkYiEZGbE878+cBLLwFDQ8C77wIZJtHlCxJLZCzUyFgoyFh4h1ChTxUyMoB77/V7LyQSiYRBKJ+zL4B58+bRI0eOCNtekIlEIkgzaoSTYshYKMhYKMhYKBBCjlJK57l9vdAodnR0iNxcoNmzZ4/fuxAYZCwUZCwUZCy8Qy484hMXL170excCg4yFgoyFgoyFd8j7IolEIgk5so7eJ1asWOH3LgQGGQsFGQsFGQvvECr0g4ODIjcXaFpaWvzehcAgY6EgY6EgY+EdQoW+u7tb5OYCzaFDh/zehcAgY6EgY6EgY+Ed0qOXSCSSkCO0jp4Q0gngjLANBpuxAFr93omAIGOhIGOhIGOhcDultMDti0XPjD2TSNF/mCCEHJGxYMhYKMhYKMhYKBBCEpppKq0biUQiCTlS6CUSiSTkiBb6KsHbCzIyFgoyFgoyFgoyFgoJxULoYKxEIpFIxCOtG4lEIgk5QoSeEPIAIeQMIaSOEPJ3IrYZFAghUwkhfySEnCKEnCSEPBl9fDQh5DVCyEfR7yV+76soCCHphJD3CSE10d9TMhaEkGJCyDZCyOno+fHpFI7F16PXRy0h5EVCSE6qxIIQ8gtCSDMhpFb1mOH/Tgj5dlRLzxBCltvZRtKFnhCSDuDfAHwOwCwAjxBCZiV7uwFiCMA3KKV/AuBTAJ6I/v9/B2AfpXQGgH3R31OFJwGcUv2eqrH4CYBXKaV3AKgAi0nKxYIQMhnA/wdgHqV0DoB0AOuROrGoBvCA5jHd/z2qHesBzI6+5qdRjTVFREa/AEAdpfQcpXQAwFYAqwRsNxBQSpsope9Ff+4Eu5gng8XguejTngOQEishE0KmAFgBYLPq4ZSLBSGkEMAiAD8HAErpAKW0HSkYiygZAHIJIRkA8gA0IkViQSk9AOC65mGj/30VgK2U0n5K6XkAdWAaa4oIoZ8M4JLq94boYykHIeRWAHcDeBfAeEppE8A+DACU+rdnQvkxgL8FoF5AOBVjUQagBcB/RG2szYSQUUjBWFBKLwP4AYCLAJoA3KCU7kEKxkKF0f/uSk9FCD3ReSzlSn0IIfkAtgN4ilKakkttEUJWAmimlB71e18CQAaAewD8O6X0bgDdCK81YUrUf14F4DYAkwCMIoRs9HevAosrPRUh9A0Apqp+nwJ2W5YyEEIywUT+eUrpb6IPXyWETIz+fSKAZr/2TyD3Avg8IeRjMAvvs4SQLUjNWDQAaKCUvhv9fRuY8KdiLJYCOE8pbaGUDgL4DYCFSM1YcIz+d1d6KkLoDwOYQQi5jRCSBTaQ8LKA7QYCQggB82FPUUr/WfWnlwE8Fv35MQA7Re+baCil36aUTqGU3gp2HvyBUroRqRmLKwAuEUJujz60BMCHSMFYgFk2nyKE5EWvlyVgY1mpGAuO0f/+MoD1hJBsQshtAGYAsO7nTClN+heABwGcBVAP4GkR2wzKF4DPgN1anQBwLPr1IIAxYKPpH0W/j/Z7XwXH5c8A1ER/TslYALgLwJHoubEDQEkKx+LvAZwGUAvgVwCyUyUWAF4EG5sYBMvYv2r2vwN4OqqlZwB8zs425MxYiUQiCTlyZqxEIpGEHCn0EolEEnKk0EskEknIkUIvkUgkIUcKvUQikYQcKfQSiUQScqTQSyQSSciRQi+RSCQh5/8Hq0g4QLDFyY0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig1, ax1 = plt.subplots(1,1)\n",
+    "ax1.plot(np.linspace(0,len(x)-1, num=len(x)), x, linestyle=\"-\", linewidth=1, color=\"b\", marker=None)\n",
+    "ax1.grid(linestyle=\"--\", color=\"gray\", linewidth=0.8)\n",
+    "ax1.set_xlim([0, len(x)])\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "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": [
+    "fig2, ax2 = plt.subplots(1,1)\n",
+    "ax2.hist(x, facecolor=\"b\", edgecolor=\"black\")\n",
+    "ax2.grid(linestyle=\"--\", color=\"gray\", linewidth=0.8)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +203,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-