Réalisation de la partie 3 de l'exercice 2

parent 2950289d
{ {
"cells": [], "cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Module 2 - Exercice 2 (partie 3) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Enoncé"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"L'objectif de cet exercice est de réaliser des **tracés graphiques** des données de la partie précédente."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Réalisation de l'exercice"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"On commence par **récupérer les données de la partie précédente** et on les stocke dans un vecteur *numpy*:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"y = 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",
"x = np.linspace(0, len(y)-1, len(y))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dans un premier temps, on **trace les données** ci-dessus sous la forme d'un graphe:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
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NGL0+J7X9U5WsLDbOEU9Gf0Wza7i8WkRNcv6ushqDH4yHMJgIoyxIqNYk/RxIGCwQAJXRK4qCdInHUMJZummmIuvUTBavXs80fqIGfYxgEOgDtBrX06pt7W71dwFUBjfuMgQaANIlHpKsYCwVqZcUdmg9Ts1kEQuxuO/wUMulG8C9GzQZCZluejqjdyivbLt0U7MP21ADvTujv7qmEhRBkvWgb0XWcPMa0CZF5co1/QaXdAj0BV5ETVIw4pqM9b8Wf/jtc/g/v3XO9/Prg8GDOvoALcZMmjL63dkNCqjJWD3QO0g3dFbsqIHRd2r4yCvXM7jrwAAG4yHUZLQk2ZdzMfIq8SLihkoTQZL1wEWtAujoPAAIsQSEdGKqkgOjD3knY2mgB9QRhE7QpZt4WJekjI1kVLphGYJEmEWRF11r6IHmpZuFXAWzmXLjJ2oIGH0b8JXnr+HCsru+1yu4vq6eiLtVuuFFtZpkb79z2z+gdqUCMGn0nViPIi/i/FIe9x4c1FllIytlP3D3d5GQCNe7QYG6lJUrC2BIveIFAAghiHJsBxi93Zo31kC6ubpexI1jSYQ5BueXCo7PyZYEEAL0x0IYiFMbBMFUfUTRF1WNzdKasd2wm3TjM9ALoozVAo+NSg0bDaqHKOhNN2iYahGqNQm//61z+IfXFrb7ULYdlHHshkD/P15fwFNnl02P0QTkeL86BNpJo1/RGP2eVGcZ/euzWcgKcO+hISSj5u7MrSBbEhBi1S7TrIHRlwXRUa4AtK7YeBiMZcBItBO146K9djzGEc9k7NW1Eo6N9eHGsaQHo6+hXzNpo5JUpizoa2x0xqQ5C2p/MORWdePzOlnJV0ErdOd8snqrXXOr0LOBnm7Vd/NsUFlW8H/94zlcWnFmO4Aa3Bc3tIqPXSDdfPbZy/jy89dMj9GLeu8AdWy0Bw9duklGOqZLA2oiliHAXQcG6oHXw2HTLzJlAQeGVH+XnKs1r3kHkbUYmlFEOsDo3aQbN0bPixLms2XcMJLAzeMpT+mGSjZUkso6aPT034WqiLRmaDbiUEcfbkK6oZVUADCf9RforXbNrULPBvolPbh1P4t1Q7ok4Isnr+Gpcyuuz5nPlnXWUdkFa5EtC6aJQkA9cO7pi4K41GavFKoYSoQR5piOMvpXZzK4aTyFvmgIKYuUshVkSwLGUlEkI5yJ0Rd5Ua8dr7f91xm9sbSSIhpi2m9qVrNb88Y5grIgQXSoqJlNlyErwJHRJG6eSGG9KOjymxHZsqAnYXXpxqDRm6UbNdBnPBk9C1FWXJvujKAxBoDNisINejI2MDVrDZb1SULdz2LdQLe9Xttfqs+PJCNdf9Ojbf8Za5OQFshSUU5nbVas5tVmKcA4Sai96yFKMl6fzeHeQ4MAjLXcLdDo9bZ/ByMvqtFTIy/K6Es1DBrsDyhop3A74WTkFedUCcnp+7qiJWKPjCZw80QKABx1+myphkEtwIc5BskIh4ym0Uc4BiHDe1Kr4nRJQCrKOSZE6c3IT8J8MafGmFiIxZxPRi8EjL61WNrY3SPjAOgJIK+E1oymHd403tf1N718tQZJVpAtCSb7Avr5k1EOqWjINRm7R/No71ST0IXlAsqChHsOaoHeoplvBdRueNDQJASYpZs+S5NQRjP/sqITjN7JmlfzNXNcj6vrajnl4ZEEbtEDvV2+scpRtDu2wIv656fo06ZMrRd5R9kGaG5u7EKugqFEGIdHEv41eimoumkplmmg3wW6tBtoaZpXidpMuoS+KIfx/mjXM3rdx0RWTGZYRj1W7QZ11ujHbIy+vefGmmaLfHA4oR6fxVhrs5BkBblKDYPxEAbiIV26ofNirY6NRa1JKFty0ehDnWD09vF5MY3RO+1Ir66VMNoXQV80hP54CHv7o+6B3lAuSv1ujDc8imRUdfNMFwVH2QYwDmLxw+gr2DsQxf6hGOayPqWbmnlIeqvQ84F+t46MA+pGXl4OgNfTZRwaTmisrbvXwshcjZ2hNHAmo5ytGxRQA+NakccebWB2tGNzUs2ldH2R1lTd5Cs1KIraJDRocGys1mTICvQ6ehrw81URRV6EKCuOGn2Eay+jFyX1uJw6Y9Xjcwr0RRwZSeg/3zxhT8hWBAnVmmxh9Op6FA0WxRTJCIeSIGGtyDvW0APNkYClXBUT/THsH4xrubDGuj5l9EGgbxGW8r3D6L26C2fSJRwcjiMWYru+6oY2ugDmzlAaOPsiIZuRF6DuBNSuWFW66RSj5y2ldNEQA4Y4VwU1g4yhw3XQwOitlSaqRk1Q5EU9ge3E6KOh9lbdWNeBIu4p3ZRwZDSp/3zzRApX1kqm4zQ2S1GonvSClpS2SDfaDmcuU3Z0rjQeo58dzmKugn0DMewfiqNak7Gm1ed7gZ5zgXTTIixrGfFu16W9sNFAuqlJMhayFRwcjqsXc4eteVsNI6PPFI1NQjVwDEE0xDgO26ADR2gyVmf0HWgSAuo3FkIIYtzWGb3ZsTGs5y6M06Xo+9H1qN8cnJKxTFuted1KCnVGbzl/syUBuXINN4yaGb0kK7i8WrdCcA70YeRKanlln0ug50XZ0bkS8C/d5KtqHoBKN4C/yps6ow+qbraMmiTrY+O6Xa7wQr3qxjlwLOYqEGUFB4cTiIZYKEr9ROtGGLtBjYy+UFVngxJC0BcN2daDauU0GcsxBEwH2v4pKzRe1I26Qf2A5iooo1cU9aZfny5l7gZVGb09KFK0m9ELLiWFMZeqG5qIPWIK9H0AgHMG+SbrYOkwGA+jwIvIlWsOjL7+PFdG7zMZu6jV0O8dUKUbwF8tfWCB0AR4UYLsUee6VuD12vHdzOip7bCbdEPNzA4NJ/QTuBPyTZEX27JzyJYFsFpXp1GjN+qxfQ5zUq2MnhDSsZJCwCxZxDiy5WSs0ZqXSjHZsmCyKKag5aYZj0Dfbmve+g3PmoxV/2/V6Glp5eGRunRzcDiBWIg16fTGdaCgO5aljYqejKYwavbuyVh/st6SVlq5dyCGSS3Q+6m8cVuLrWLXBXpJVvDgp5/F352ac30OLa0cS0V2dXklvUCqNdnxc85qZmZUulGf2971uLZewol/9yQe+PQz+LdffwNPnl2G0KIBH+migLG+CMIcowcuQE3G6oFeGxNotLVd1gN9VH+sI01Coj3x1grphu5shjTpBnD3d6EDwp2CIkWzjL5ak0we+I3Au7BYajRmY/RrJYRYgv2DMdNzj4/3mQJ9zkG6oeshK7AnYw2B3y0ZG9YZvfe5Qbti9/bHEAuzGElGfJmbBYzeJ0qCiPUij2vr7otKK24ODid2OaM3D0K24nq6jGiI0aYqdSbQ027BsVQU3zqzhH/5l6fwdxeFBq/yh2xZwFBSdSnMWBi9bcCEYT1W8lWMJMOmi6sjjL5mN7CKcmTLDVPZsoAIxyAWZutt/yXntv8+A6NnGaJ35xoRDTFNDQf/377+Jv7Ff33V9/N16cZBl6ZGY0ZcXSviwFAcnEXTv2VvCmcX8vrfoze8AYt0Q2EN9MbP7l5H78/CejFXAccQjGq7xP1DMX8avSiDEFU+bCV2XaAva9vTksfFQoPNoeH47tboGwT6mXQJh4YTqkNhh4Zt0Ivw9z58C177vcdwZDSBTLV1U5UG46pcYfVgp/qrk0Pk8kZVr7ih6BSjj3AMCKlf1HFu652x2VK9DpwGtmxZQNlBo6flplmtWcp4LBQRjoUkK45WBE54Y2HDpJU3gl5m6sBiUzHONvnMWnFD8b6b96DAi3ju4hoA9TP3RTlT96ux89dWRx8xaPRu0o1Pe4zFXAXj/VFdStw/GPfVHet0TrQCuy7Q04ST18WyvFFFNMRgLBUFL8pdXWnihXxVRCKsMhCnypvr6bJufNUpIy/BIFeEOQbDiTDKtdYF+uFEGMMJM6MvVGsGjV4bOm1gict5HuOWQN8pjd6qxUZbkIzNGjpczcM2NI0+XGfOfVqTULZUc6y4AeoDwv2weklWMJsu601JfmCtPjKiLxoymbyJkoyZdMmUiKV46OgoBuIhfPPMIgDzOlAYf3aruiGkLvFYEfEp3Szmqtg7UJeW9g/FsLRRbXizdLKCaAV2XaCnjN4z0OfVRgZaadLJIdCdRL5S0xNB1oSWLCuYzZRxSGs66ZR0Y23xTkVDKLdoqhLt7By0BPoiL+qSjdWDHdBmxfZvB6OXHCtNvPoe/CBjYPTJCAeOIVoy1oHRR0Io8KKr/QFgkCt8nBvL+ar+HRvdG73AezQJ9UXNjH4hV0FNUnDDiJ3RhzkGP3piAt89t4KyICJbrtlyDsbPaGX08TALQtTcBusinfgtr1zcUGvoKQ4MxSHJip4fdAMv2mfntgK7LtBTRu/FJpY3qhhPRZvyreg2KIqCfLWGSS1hZWWJy/kqBFHGwWH1RhAL+2dtW4HVtKk/FmoJoxdEGQVexFA8jKF4yMLo6zXTNNDT9eBFCZmSsD2MvmZn9DFOvdi96tbPLeb1SiEnGAMcIQQD8TCy5RpKvIiwg5GXIMpYyVddK02aYfQz6/WpT76teT2ahFIWjf7Sir200oifuHMvKjUJ3zu/ipzF/gAAYmFW/zzWqhvaV+CWiAX8zdCVZAXLG1VMGMjDfp+VN4LDLq8V2HWBvuxDulnSvoROlhR2GpWahJqk6IHeKt1c1ypuDmk+K52aqlSzMvpYCOUWDMQ2Vo0MJSLIV0XUJDVg8qJsG7ZBzw/qQ28L9B0ZtuEQ6LVhIW5EpVCt4eOffxG/+w9vuv5d1W7YmIBUHSyNPjcU9OeFbMVVrmhmtzdjCGTzPv1dvJqErJ3MZxfzIAS4STMys+K+Q0MYT0XxzdMLes7GCmrzYF0LQL2xOE2WovAj3awVeIiyYpFutEDf4Obn5MvfCuy6QF9qIN3IsoKVfBXj/VHfGfRuBN3uukk39CKkTKNj0o1olW44lGvw7HvwA8rghxNhXWs2yhVJS9UNrVWnpZVW6UYdtuE/0F9dK+L12WxTx6xOVTIHN0oy3c7fv311HkVexHMX1x2fI0oyNipmyYI6WJZ4CfGw+f1osBNlxVWjb8YS4nq6hDCr5l8WWmDklYqFkK/W9DzaW4sbODyccAzSAMAwBB++fQLfv7iGtQLvGOgHPAL9jWNJ3LLX+SYC1HeiXoGeSlZG6WZCS8w2qrxxGpLeCuy6QF9uIN2sl9S77UR/54dAtwtvzOfw5vyG6TEa2Mf7o2Achm2sa74bI33qSR8N+b+YtwK61lQ+SMVCUAAUhRa1/RubhEo1/XPTJCydh0rlAKqZTlgDfYhpigB8+p8u4FNfPd3UMfOiw7CNkLsHuyQr+MoL1zCSDEOQZEy/vWp7Tq5Cu0Ht1rxF3s7ojVa9rhp9EyZvM+tl7B+KYXIw5pvRO/UTGI+vJin6c84t5nHrvn7Pv/cTd+7TX2OVboB65Y1VugGA//KL9+H3PnyL699Wm+m8zw1a1Wdk9BzLYKI/GjD6VkFn9C6VC7SGfrw/1tEh0O3Ev//WOfz+t86aHqNSzUA8pLb9W6SbdFFAPMwirvmeUEbf7ilTVne+lGY67mWl7AdGIy+6Nc+UBL1iw2jkxTFEPz9W9OY5SzKWY5u66c1mypjPlpvyhHHW6NVA78TWnz6/grlMBf/ux27FcCKMJ8/aJ4flHBqf6oze2ZqXYqvdoIDK6A8NJ7BvIOZbo29URw+o50e2JGAhV8EJD8YNACf2pXBYKzJwagCjN7Rk2HlX0Ajq3Fj3tajbH5jPqf2D8YYaPV+TA0bvBzqjF5xtEIwMbrcw+rUir9/AKKihWSoaUmuRLTe+tMWKtePSDWX0+oXcGn+XwXgYQ8l67bjuXKmXzhGT7rucryIWYm2NQs0y+oVcBbJSv8j9wEu6caq8+fLz17BvIIYfPTGO9908hmcvrNqO0dgVSzGQCOnJWJu/i6F23CkoAv5tmxVFreQ6OJzA5GC8eUbvMBCbfi/5qoizi2pt/q17vRk9IQQ/dsdeAM67FPqY0QqiGYQ51jNmLOaq6ItwJu8cAL586VVGH1TdNETJ0HpddghadUYf1WvHO2lVrCgKHn/hOs6nWxdQM0UBa0Xe1A9ApZtULKRPzjFivSiYuv+iHUpMC6KsmoZp5WspzdBkowGj/8Y8ffutAAAgAElEQVTpBbx4Je36+3qgD+lBLm2YDZp0aBIC1EA/3h+1NahEm9Do89W6ROSnzZ3CKxlrZfTnFvN46WoGv/Cug+BYBh84MY4iL+IFy5ro65Awd4MKooz1ooCkJbiZGL1bMtZneeVakUdZkHBwOI7JwRjSpXqTlhesvvxGGKW2txZVefLWBoweAH7qnkncMpHCbQ4yz4NHh/G+m8dsnbV+0Ui6WchVTLINxf7BONYKvKc9hCAGjN4XyoYLxEm+WdqoIswyGIqHm/KWbgV4UcJvfO00/t03z+K7M1uTKox/s8CLEETZxIrpv1NRzrG7cL3Im6oLOFaVNNo9bMN6IqccGpisqEkyfvcf3sKffe+i63OyJQH9sRA4ltGTbVljoDcEtGSkPk5wZaOKsZS9yqIZRm9k8TPNBnpbHb36f6tG/1+ev4ZYiMXPvOMAAOCBo8NIRjg8dXbZ9LysQcKioDr10kZFl+oojDdAd43e3853RjPJo4EegK+ErCDKCLH1m78RlAhQRr9vIOa68zBi/1Ac3/nUwziglQ8b8YETE/jiJ+5t+Dfc0Kgii06WsmJcywM5DTGncJq01QrsvkBvuFs66ZzLGxXsSUXAMKSj5ZWZkoCf++LL+MbpRSTCLCotKCkE1I5HCuMJRDXvVCyEVDRkC6TpkoARS71wu+1oAXVragz0/T40+jNzORR5EW8tbEByqc5JG5qEwhyDviiHTEnQJStjF6Qq3ajvt5yv2korAZW11STF9f2MMAYzv7NBAZUh2xh9yM7o00Ue3zi9iI/dsw/9WtCOcCymjo/iu+dWTMfo5ELpZeRlSsa6dsb6k/Wur9dLdid1a97Ggd6rG7TPwOjPLmz4YvPtRoRjTbmYy6sF/PbfncFXfziL5Y0qljaqjoye5qO8Op8FhwR9K7CrA71T5Y3aFUsnCXWmvLIsiPjY517AmfkN/MU/uwv3HR5qWTeocaoS9dgHVIYcD7MIsYxmDFV/Q1lWkCkJNuOmaAemTNUk80WtJ2M9Tv4fXFoHoH63V9eKjs/Jls1zPoc0v5uipeoGUIM+tUpezfO20krAOCDc3xBoQE18z6RLDZ5dh5N0E2ZUJ0bjbvTMfA6CJOPH79hneu77bx3HelHAqZl6WWe2pCbZo4adgrkb1LyDoFOmQixxLVn02/Y/mymDZQj2DcZ0Z0m/HuzWnQ0FvREt5aq4ul7CiQYVN52A1bb5O28u47+fmsfvfP1N3P8fnkamJDgGej+kRtguCwRCyJcJIauEkLcMj/0+IWSBEHJa+++DLT+yTaJk0ASdGX0V4/3qlxDtUDL20koR19ZL+MOPnMCHb9+LvmioZYze2AFqZPQblZoui6jSTf3kylXUiUPWDsBoiPHV5r4V8Bbppi/CgcBboz95eV03mTpjKSOlyJRqtklCmZKAIl8Dy9RN24B6E06mJECQZFdGD/jL3yxkKwizDO7cP4BZHw6FFGqgNwc42p1pzKnQ2utDFhni0Zv2IMwyeNIg3zhZGRhLDK3JWPp+boZmQBOMPl3GvoEYQiyDkaRqF+2P0dt3NhT0HH75WgaAWlGz3VCrbuprsZyvYjAewpO/8W782x+9Ce+/dQzvu3nM9jo/MqVTyW0r4OcvfgXABxwe/zNFUe7U/vtOaw9r8yjzUt3PxBLoFUXRu2KB5ia6bwW0pJBqdH1RDpUWGXmlS3UWT7s8AVWjp/pmKqr6mdAtflqrobdO0aHjBNsJK2NhGIIo585y8tUaTs/l8PF37EcizOLN+Zzj8zIl3tTwQ43N6NARYxCjyVjaLOUU6JsZEL6gabKHhhOYy/gbAg1Qrxv7JZiMcKbhI3OZMiIco1veGp/30LERPPHWsm6Wpfr9mCUYY8drwqGkMBnlXEsrAf+Jejp/GFC/18kBf7X01pu/EfEwC5YhePmamnRuVHHTCURC5qqb1bzqfnp8vA//8pEb8PmfvxfHx/tsr/NTeKBeH9tQdaMoynMAMi1/5zahJIj6pCCrdJMr18CLdQbnx7eiFbCWFKqMvjV/2zhJac0i3dCtovXGR4cU2zX69ht5OVUVxDniynJeupKGJCt497FR3Lqv35HRK4qCbMnSDZoII1sSTENHKPqiIRSrou4X4yTdNMXocxXsG4zhwFAcRV407bLcIMkKapLi2iRklG7msmVMDsYcGffPvGM/FnIV/LcfzgLQfG4sjH7Ag9EDanJ6wKGxiIJjGbAMaShjXV8v6ZYaALBvMIZ5H+WmXv4uxnLYkWREv7a3E1bpZiXP6yTOC/WeEfeL3+umtxVs5S/+K0LIG5q0M9iyI9oiyoKkTwqyMnprF6Rf7XGrsLb990U5iEpratYzJQGEqO3WVo2+Lt2YtUGq69s0eo5tajLQZmBNxgJAIkRcT/7nL68jFmJx98EB3DHZj3NLedN0KEAtqRUk2eQhPpQII10SVEMzh5FxgiRjVqsS2TKjz1awtz+mWz43M0nIqWY6GeFM5+5cpqL/bSseu2UMDx4dxmeeuohsSbDlKgC1C7lP9/qxv9+n3nsMvzp11PN4o5yZBJyayeJjn3tBJxq5spr4PmiQlyYHY1jw7cHuzmLp93fr3lTLfdo3A2t55XK+irG+xoE+GebAEHfpRlGUtnXGbq41DPgcgD8AoGj//wyAX3J6IiHkkwA+CQCjo6OYnp7e5Fua8Y3LAso1BT97szlYZQtljIfUgP7G+YuY5q/rvzu9ql48C1fOYTr9NhRFAQHw9uWrmCbzLTkuJ7y2or7vG6dfw8ZVFstz6hf95DPPoT+ytRP3zUs8khwQA4+Lc8v6+q5kyuhTSpiensac9v7PnHwRB1MsXtJKOy+ceRWL4fr7V4oVZEW07DtywspaBaJsfo8II2F2ec3xfZ98o4xj/QxePPkDsHm1jPS/fftZHEzVA8NaWQ1AK7NXMT2tjpDMLQvgRRlXFtbAMeb3W5pVP//3T18EAXDutZdw0VLad1E7V154+RUs97sHoZqsYLXAo7axghWtpv2Jk6ewsdf70ioKqrwzd71+zABQLBYhlDmkeUU/5mtrJUyEONfv5YNjMl68UsNvfeVZrG6IKCcF23OjjIQCgMsXzmF6/W3z7wDIAKYX3Y+XUSRcm5nD9LRqu/DtqwJOzdTwf/z1ND52YxhXc2rgKyxdxfS0ursQsgLWizU8+fSziLDu5/nyWgWCZD/visUipqenQWoqgekTc209N/0iu85joyBhenoakqxgvcCjkl3G9HRj4SPGAecuX8d0eMn2u5omrS7MXse015exCWwq0CuKovdeE0L+PwD/6PHcLwD4AgAcP35cmZqa2sxb2vAX51/ARqWGqalHTI+Lzz6Jm45M4ocrMxjbdwBTUzfpv1t7dQ547Q28/93v0t3kIs/8E8b37cfU1M0tOS4n5M8sAq+/jgfvvw9H9/Qh+/o8/vLcGdx29zscJ+U0g6/Nn8IYX8SxPUm8vVIAXV/h+0/hxoN7MTV1AuHL6/iPr7+M4yfuxP1HhvHqk2+DuXAZH37flKl2+a9mXsFCroqpqYe3dExe+IvzLyDMMZiaul9/7M9fewJlJo6pqXebnruYq2D5iWfwK1M3YurhIzicLuE/n5lGePwYpu47oD/v9FwOeO55PHDP7ZjSkmCriTn87cU3kBM53DHZj6mp+/TnZ1+fx1+dP4MSl8JoXwnve8+jtuPkLq0Dr72ME7erVVJuuL5eAp6axoN33owP374Xv/v8E0iOHcTU1DHPdVjJV4FnnsatN9+IqXce1B+fnp7Gwb39OLuwgampKWyUa6g88RTuP3EMU+8+4vr3Liln8V9fvA5ZAW678Yjt/SfeOom1+Q286x13456DzW/A+158GkN7RjA1dQcA4LnCOeDiNTy7oOAPfu4BbFxcA146jQ8/8k4cG1P16Y2BBfz9pdO44bZ7cXSPXbOm+OyFFzDAms8JuhZTU1PYe/FFzBYy+NC7bsPUbRNNH3ur8WTmTbydX8HU1BSWN6pQnnoa9912HFP3H2z42qEfPoO+oUFMTd1l+12+WgOeego33XgUUw+7f9ebwab2CIQQ42r/TwDecntuu5Cr1GxbIEVRdCvWRJi1NUzRmnOjHhnh2I5p9HR7StvOtzpJCKgPmdjTF8GaloyVZQUFg0Zvk25KPIYSEVuDSiTUgbVwkG7iIeKYjD2plVU+dGwEgDq8oT8WwhuWhCyVD4YsGj2grk/S0opOR8ZdXi26aqv10YqNZ4MCqh4dC7PY0+dvCHR9qpKzdJO3dNruH7KX6xnxr993o/59OzUUeTk2+kHUkoBcK/K6xPSlk9dwfZ0ep1m6AdCw7b+RLk1LY3dCIhYwSzcrHgl9J6g9Lc7XvXH6Wqvhp7zybwC8COA4IWSeEPLLAP6IEPImIeQNAI8C+NctP7IGyJVrNl23WpOhKEA8rPpM0NFpFNmyAI4x1wtbEyvtgJNGD7Qu0A8nwtiTiqLAi6gIEkqCCFmpB/h6WZf6fqr9gT0YxJpsmPrT717Exz//YlPH61QnnOCc6+hPXl7HaF8ExzWGSAjB7ZP9eMOSkM04BHrjv92ahDIlwWZmRuG3ImveYkl7cDjuqzuWBgrXZKxmxkbdDmkDkhv64yH85o8cBwCMOny3tMRy8/4ujOncWCtUcfNEHz502wS+8vx1vDGfw0R/1FS/77dpqtGwjeFEGP2xUMObXadg7IzVba59Bvr+mN1gkIK3xIlWouHtXVGUn3V4+EstP5ImoCgK8pUaBG2wBF2Ykj78mEUiwtqqbrLlGgYs9cJWptIOCBYvjz6LVe5WkNHG59HSu7UCD0Y7T2iAr99Y1PdbL/KOU+6jIaapCVOvXMvgtZksapJsmlrkBTdGX+RrECVZ9x+RZQXPX17Hu28cNX1ft0/24/Pfv4pqTdKDSt3fxTnQOyVjKdyYmF9Gv5CtgBBgQuvN2D8U9/TkofCy5k1GOFRrMmqSrHfa7ndJxhrxz+47gNFkGFPH99h+N9gCRm8O9DyOj/fhf3nvUXz7zSU8fWEV9x8xS1yjyQjCLNOwaapRMvZfvecofvod+3dEIhaod8aqDXc00PurBkpFQ7i67tz055Wg3yq6sjO2UpP02nSjfEMrRmIhFglL5QIAx9FiEQtTaQesc1JbxehlWUG2rDF6LdCvFqp1n5uYeXwefTxdFBzHpalGXv7XYiZdgigrTTk2OjH6OGf3YL+yVkS6JOBdNwybnnvbvgGIsoJzS3n9sUxZQIglJpsDo0GXV9u/m3Tjl9Ev5CrY0xfRv9sDQ3Es56sN11Fn9A4dofT4SryIuWwZqSinyzJeYBmCD5yYMLFqivH+KMIcY/O68QvrznetwGM0GcFN4yl88LZxADCVVgJqLf0+H770jYZtTA7GcdeBHVPYp9+cBUnGSp4HyxBbT4obUjHOtY7euvNvJboy0Bv9XYzboDqj52wlaoDzVPhOjIyzT1Vq3CHnBxuVGmRFZa+jeqDn686V2vtwLINEmNUfT1sMzSgoa/PT8FOtSVjUylWvp/37uzjW0WsxzLgeVLI4usecrL5jv6rTGgetZLWRcUbG1xfl9AHPVkZvtENw23JHfRreLWTNQ6APDsehKI3lirpG78zoAfXGN5ep+GLzjfDz9x/EP/zqA5sOIlFD/qZak5Cvivo59+vvPQZC7N8VAF8DSNpVUtguGMuyV/JVjCYjrsPErUhFQ66lxF4unltF96yuAaZAb2CBdOhIPMwiGeEcG6asjSHRTgyBFmUQAnDayZB06dxtFmmDNk17B1bzVZOhGUWfNmRZ1fAlfbKUEdEQA1mp70C8YDTvasbfxU26AcyNJIs59Sayt9+sy46nohhJRnDGkJA1GppRMAxxlSv8SDf1oTTea7G4YbakpfXuDQdMSO6B3rjjm8uW9XGPW0Eiwm0pmRk1ECLamEcD/U3jKfzTpx7GzzlUnewbaFxLrw5gab1c0S7QXRhfk9Uaep+yDaBq9JWa5DigRpduut3UjG9RPM1V6p2HRkZfNjB6J+nGjdF3pBuUZXTGyTIEUXbr0o0xCTmcCINlCNaKvH7zM273qVWxPkLQhdED/tw8ZwwsnlZc+IGXdGNk9EsbFbAMsbX9E0Jwx2S/I6O3gloiWBl9mGP0ADve73yR+rGwlmUFS7kq9g0aA70qXzS6+XlX3dR3fPPZyo5IQkYMsh7trN5jaBK6aTzlKBlNDsawXhQ8G/Ha1Q3aLtQZvYTVPI89PhOxgNHB0r6bt3bQtxIdXd0c3xp/lw0DozfqXVZGbwz0iqKoydiEVaPvDKO3nshRjmw5GZvRfG6GEmEwDMFIMozVPG+aLkVBrYrpLsBJo68zlcbrcV0LZGOpCGYzTTB6R+lGDfTG73IpV8VYn/OW+O6Dg7i0WsQTb6lmXhmHblDAmIC069s0+LtX3TT2d1kr8hAkGZMGRj+SDCMWYhuam9U1egfpRju2a+slCKLs2hXbSRjtMayM3gt0t0OrU6xoZzdou2CSbgrONtdu8PK7aWfVTUdX16/ZUyPkKkbpxoHRhzlduqHvSbdLNkbfYP5jK+B0Ise5VjB69bNTvX1PX1TV6LX1MQ7boH4h6wXqc+PA6Jvw559Jq0nCOyYHfGv0sqxAlBUHrxv1/8bd2dJGFRMOVq8A8EsPHsad+wfwqa++jtdms8g6SDdA/WbmNAQ6qeVxrOPeKAghCDeYJES1ZyOjJ4TgwFC8YS19o6obADivJZwnd0CgNxKiZgI9XV+3Gc5eYwR3KuguLF+pIVeuNSXdWEudjeB3S9WNjxkOvmBOxho0em17GI+oVTeyUh92ndVeY6266Ux5pV2uiHGkBYFeveCoW+FoX0RPxhqTkYC6ZVQZPXWudNLo/fu7XE+XcGgkgcMjCcymy74GdFirjyh0jd4i3Uy4VMTEwiy++Il7MZaK4lcefxW5Ss1zCLRVulEfCzW8QKMNSMCCPgTafEM6MBxvuMvxuqjpnFQa6Fuh0W8VVkZPiPswcSMSYfXzlVxGCvJtlCvaBXpzpjfzZqQbL096PRnb7Yy+VYF+o1JDiCUIs4yZ0fMGRm9JeNLuyQEHRt+JgdjWLy8W2rp0ky4JSEY4PVjs6YtgTSuvTFmYqprtr2HdxdBMPSb/A8JnM2UcGIrjwHAcgiS7bs2N0AO95aKOsmregt60qZ200/AGipFkBF/5xXdAVhQoCjDk4L5IA1GfQ+34TeN9uGP/gOfxqna07muxaGmWoqCM3msHS+UxL+nmwlIBQL3DdDsR1dZCURSsFXkMxcO+eifi2tq7zY6tJyC7KBmrXcs04e63WQowDtpx1+i3pTO2lWgVb96oCBiIh201qSVDHT116aO6fU5n9A7STScYfVukG7NksacvgnRJQKbEmypuAIN0o7WuOyXO/CZja5KM+WwFh4YTeu20n8obtzphQghS0fp3mSkJJjtpNxwZTeKLv3AvhhJh3DRhH0hx++QADo8kbDd3APjjn7oDf/rxOz3/vjqIxYPRZytIRe3yz4GhOKo12WQbbYWXdBMLsWAIUOBVy22n76rTiHBqRVZNUtQaep92wfQ6tHapU+i5im5i9CEzo29Ko6fSjUOJpeBRibVVbNa9clNokUSPXFn1cZFlxVx1w4uIh1kwDNEHLFBtkA5NtjVMNWBtrYBTSWGMI8gXWxvoR1NRKIqaxLOyjFQsBFFWMJepOMo2QHPdoJKs4OBwXLelnUmX8cAN3sfrVVVApSWgbiftNGDZinsPDeHU//4+x67Jx24Zw2O32Cf9+EWE8x7EovrQ22UVYwLSbVvvJVnQqU/5qtiSGvpWwCjrNRPoaYNW2aWUuJ0lhe0C3UHP6oy+CY3eKxlb2y3JWLRmPmuuXMNALKQFB7NGT08sq3STKztLN1GN0bcqUewEwaHFO8YR3c9ks6A+NxS0O3Y2U7Z1UlImcW29aHqNEX5HxlEvl0MjCUz0xxBmGb0KxwtenX9UWgKMcwP8SRbtao1vlKhXm6XsgTxh2U06gY7Pczt2ukvYvwNkG8A8iKWZQE8Hnbj1jHjtbHYq6tJNBWGO8dW1TBELsQixzoN26oy+y5OxgHv2vRnkKmrjU8piEFQWVEYP1CsXaNNU1sG5ElAZveKzSWizcKwdD0H3M7GiJsl46Woaf/TEBTx9fsX2ewrqc0NBLz6joRkFZRKzmbKjPg/UGX2lUaDXgvrBoThYhmByKIYZH7X0bslYQDN70s6NpQ1V+57wwejbCa/RioqiqIzeIY9Ad5NOw+kp1CYhL8dG9W/sFEYfMZCA5hi9+rqySx19O0sK2wUaiBc3KhhPRZsiGqpM6Wxs1k5G31HpBlDv7H59IdywURZwy0QKvCiZDJPKgqSfWFYmkS0L6ItwtgSSsSa2Xd15vCRjIGwOvDG27u9C5RdBlPFv/vsZPHN+VZ8ZOpaK4PkbR3WzLwpFUZB2YfQAbMlYyhBrkuK6/rq/SwON/vp6GbEQq1/sh4YTvhwbvaUbTk/oLuaqCLHEsamrk/Bi9NNvr6HIi7h90p7QpeeeW6UJQIdAu59vlKjshIoboL7bWy2ovQOjPq/hEMsgzDGuN712Gnm1C1RmUpTmZBsKqxJBIUgSWIb4tlNoBh2/jbbCmteL0dOLrC9ilW7szVJAc7NBNwvnqhv1/8bKm4srBXzj9CLuv2EY//nn7sFnfuoOrOR5PHdpzfY3y4LaFzDkwOiBOoPXfzaUGDpZFAP+yyvpEGjKZA4OxzGTLjWUv7zYm1m6qWAsFbX55XcaboxeURT8P9+7iMnBGH7sjr2231Ppxo3FAnXpxg1UepzcAV2xQP06ocTKL6MH1JuWe3mlu13zToXxWJspraQwFh4Y0ciueSvovHSzRX8XQZRRFiQMxEKat3O9KarE2xl9ycDondrkI02UFG4WvGh354s5ODbSIQa/OnUDPnBiHD9+516MJMP42itzsMLJgz3Csbo0ZdPoDT83km78aPTG2aCHhhMoC5LeGu+Gmod0Y0rG5qo2j5vtgBujn764hjPzG/i1R486fhbruecEvsFFTRn9TuiKBeokgJYUNhPo42EWZbeqmzbKFe2CcffhZ1asFVaCStFOK4gdp9E//sJ1/PGTFxy1a6CerR6Ih5CKhiBIss4Uy4Ko66PxMAtCjNJNzbHMrhMDwgVRtpWPOfm7rGgToqh1bohl8NG7J/H0+VVbqV7aIdADdfnGLt3UGb171U3j8kpJVjCbLpssaQ8YKm+84FUn3B8LoVqTwYsSFjcq267PA87NdCqbv4R9AzF87O5Jx9fFQz6SsQ2MvPqiHDiG+E5Itxt1Rq/mT/Y0yejdCF47E5DtgjEYu3klecFIaozoKUb/n6Yv47PPXsEvfeUVx8XY0AzN+rU6eqDeZVbiJcS1bTMhBMkwZ6q6sZZWAkbf8fYxekfpRou7VkZPiJlxf/ze/RBlBV9/zTy83OhzYwRlWrZkrCHwO1kUA+qNhWOIJ6NfzlchSDIOGgJ9vZbeX6B3arSh0tJGpYaVfHVHBDinZrrvX1zDmbmcK5sHVFvoCMc00Oglz5LCj949id9+//G26LWbgc7oqXST9H8jjodZj2Rs+7pB2wWWIboTbTPNUhRuVsVOcaJV6LxG7xHolzYqWMnzePjYCF68ksZPfe5Fvc2cgjY+9cdCevCiLN/I6AF1C10ydMY6STd13/E2MnoPa15roB9OREyB8OieJO45OIivvTpn0sCtPjcU1FEwZWn7j4ZY/RhGHSyKjc/zYvS04uaQQbrZNxADy5CGTVNeVTf0xnR1rYSapPiqoW83rM10Rjb/k/c4s3kKJ5tsIxpJN+84NIT/+ZEGjQkdBL1O5rNqSaE1B+QFJydZinZ2g7YT9Hj3bEq64ZCv1Gw5Ld6hOq9V2FHSzeuzqsf4v3n/cXzlF+/DYq6Cn/zcCyZWpQ/41urogbr8URLqjB5Qk2JFXoQoychXRVtpJWD0HW8zo3fwugGAokm6qTpuBX/63v24ulbCqZms/pjO6JPO0k2/w2elwd+N0QN0nKD7WlDWfsAQ6MMcg30DsYbmZp5VN9pN++1lte2/mW7DdsE6Pu/laxmcbsDmKeIRdxYLtLfKqx2gx7qQrWA0GWmqpDAR5lwtELqxjh6o5/bcJpR5oT9mlpwp2nlObIN0494k9PpsFhGOwU3jKTx0bAR/8JETWNqo4spafcZizqDR1w2CRNS0+bFGRp/UBoRTxu+cjO2MRu9PuuEdkzsfun0CiTCLrxqSsumSgLA2OcoIWgXg1MSRiobAMsSzwSPSYJzg9XQJYZaxSSu08sYLng1T2oJc0AK9l89Np0AZPWVeb2jDTj50+0TD1ybC7iwWUL1uuim4UUYvykpTiVhAvem55Su6MRkLGBn9JjR63QbBHAudijZahY6uLkMaM/oT+/r1D0tHkxmn9egdrrGwzlDz1ZrOnuKGwJfUBoS7NUsB7S+vdLPm5RiCCMeYpKwVl5b5RITDj92xF99+Y0kfHJIpqvYHVmb10bv24dMfvc1R4+6LhTCsede7QXUp9GD062XsH4rZtOODw3FcX2/g2NigYQoA3l5WHRvdnCs7Cd2fX7tBzWbKJoLhhUTEncUC7dVj2wEj02w20HuVV3ZjMhZQ40afNuCoWbgZm+0ajZ6Bu0YviDLeXNjAXQZHQSoPGL29Nyo1EKJWJdAF26jUTNOlKBJhDsWqqN8cnDX65pKxn5u+go/+p+d9PRfw1qXpeD9A/fzpkuDagPErDx9BTZLxmafeBmD3uaEYTITxM/cdcPwbg/EQ9jSy5m2k0WfKtiHQgJqQzRvW2gk1uk1nnax569JNhGN8WeC2G9aKrJl02Xe5YzzszmLp3+ym4BY1JI6bZvRhzqO8UgIhQIjdGUlnv4hwbMNryQ3GwgMj2jmApaOBnngw+gvLefCibJr2noqGMBAPmao5NiqqoRnDENMWyDhdioKWdWVdnCuB5hn9N87BWhgAAB+8SURBVE4v4LXZnO9+gEa+47RDjtagu2nTR/ck8YkHDuGrr8zhrYUNZMqCa5mkG373gzfj0x+93fM5Vl26/jkkfP77V3B5tYDDI/ZATwOgV+WNn2RsSZAw0d9cW3m7YCUBc5myb0uCxslY76qbnQYTo2+ysz0ZYSFo0qoVNAG5E77vZpCMco6Gdn5glJyNaGSLsRV01AKBIe7llafnVP3zrgPmlnLrtB5qaAaoASMWYpGvivpMyrhJo1e3jFnd0Gxr5ZWrhaquIV9fL+HEvsbDlr10aWodDNSbpbzKtX79vcfwP15fwO9/8yzSRaHp9vhjY30NnxMNMbb5nk+dXcYffuc8ZtJlvOemPfjku4/YXkc9d7w6n73WIsIxCLMMBEneVIKrHTCSAElWMJ+t4IO3NdbnAY3FeiVj23hRtwMsQxBiCWrSJjR66mApiAhzZnLSqPpop+I/fPQ2vcSyWbhKN9IuScYyhLgG+tdncxhLRWza7IGhuFmjr9TQb2DmtFSppI8RNFbdWKQbBzmgmWTsC5fT+r+NCWIv6BqkQ6WJUbpZ0RwbvbaD/bEQfvv9x/HqTBazmXJb5I2YRbp5+Woan/zLUwixDB7/pfvw5X/+Dsc8QrzBJCFADfQMgWNtOCFEvwB2QlcsYNToJSzmKhBlxbd0k9QqvtzQbdINAES142020Cd0T3rn8XndNHSE4saxPhwZTW7qtW7J2F2l0btJN6/PZnHn/gHbFu7AUBzz2QpELWBulAWd0QPqohk1+rhBo09GOIiygpU8jxBLbBUqQP3k9VNe+YNL6+iPhUCIWu/tB3SSUCsYPaA2UN26Vx2y4WY3vBVELP4u57Rxdn/zL+7HIzeOur4uYWBtbnDqJzCCVt7shK5YwDxDl5INY1mpF+IeyVhZ7r6B2ECdFDVbaZLQp0zZrzGn0uPdDr3R0xILeVHaHXX0dGqOFZmSgOvpskmfpzgwFIcoK7pHeU7T6CloOzHV6BMWjR5QtdWBuL1CBVCTQIQ0ZvSKouDk5TU8dGwEk4MxXG1QYULhnYzl9BvfSkG9GQ055BGMYBmC3//xW0GIeSh1qxDlWFO+YjFXQYRjXI3QKOI+PNgbXdSU6eyErljAzOipfOiX0SfCLGqS4qhL67u8LtLogbrM2TSj97Bt7rZcRSsQ4VhEQ4w9GSvKbVuLjmr0bsnY03NqI9BdDjM8KYOiibBcuWbS2lNRDutFwZHRUyYxn6042h+ox0R8jRO8vFrESp7HQ0dHUKyKuOpXuvFoEjJJN/kq9vT5c2x8x6EhPPtbU+0J9JbyysWcOru1UbLMD6NXTZvct+m6dLPDGD1fkzGTKSPE+veeMRqb2XTpWpeWFGpByM0Uzw31tXBK8vceowfg6Em/azpjGaIOtRAthmWvz+bAMgS3TdqTm3o1R6asjg6s1kzSTb8no1f/PZ8tOxqaUURDrC6xuOHk5XUAwENHR3BkNIFr641teQHvBKRaXyxBkhWtht7/BXRoJOFrOHOzsFbdLG5UfAXemA8jr0amTXSnNp7aWYy+qjH6ycG4b+8ZncU63Pi60ZoXUHd7qajzvGEveOVvhC7V6LcKJ2Oz3aPRa9eINRicnsvhpvE+U8UMxUR/DCGWYDZTRqEqQlFgScZaNHpj1U2EZrdFV0YPUPMqb0Z/8tI6Dg3HsX8ojiMjqi0vdZv0QqOqG0Dd5azk+R3S9s+gUpP0m9hiruIrOcowRDOvcmf0tUYavbYeO4bRh+qMfjbtv7QSaMxigS4M9CGmadkG8LZtbuTLv1uRinKm8kpJa6zcJVU36v+LhmAgywpOz+ZsZZUULEMwOaiWWOYqtCvWnIzNV2oo8hJCLDEFkoTB98aphp4iwnkPCKej/R48OgIAerbdj3zj1Q2qZ9+rqmPjZpzwWo0ox0JWoOvLqwUeEz7tCOJhdYfihkYa/bE9SewfijU1g7Od0H2QNEZ/oIkhIHrOwovRdxmTPTScwK17G5cUW6HP0HU4N7q1vHKr6NcIKoUXIWwFOqvRa/836vQLuQoKvIgTHifQ/qE4ZtPluqGZUaOPcZAVYK3A23YESYNe7yXdNNLoT8/lUBIkPHyMBnq1YejKegkPaMHfDd4avXp8qwUehaq46U67VsI4ZWqjXIOiwHEAthMSmuWEGxpV3XzigUP4hXcd2jHNMzQAreZ5bFRqODhkbxRzg56zcGD01Vp3MvrPfPwO+FArbaivhYt0k+iudWgFUrGQqaCj3S6e28PoDcZmqwW1msarSebAUExj9PZAT9nfcr5i6ooF6uPYAHhKN04DJoz4waV1MAR41xE1qI+nooiHWV+M3usLpHNcr6yqf2cz02paDeOUqUXNItqvwVg8zDXU6L1a3Qkh2z4+0Ah607ukfT/NSTfeteNA9wX6zX4/sZA6BMhZuum+foJWwJqMbbcvf8cbpgBz9+SqpnN7aX8HhxLYqNQwq7kj9scMGr0WLJdyVVugN/reeEs33kZeJy+t4bbJAd36lxCCwyMJX7X0fjT6S6uaNe8O6AjV2/5rMhY3mgv0iQYaffcZeanHemlF/X6aGevnVYXUjQOxtwKGIYiHWBfppn2OjTsZqZhqf0JzYV7zlFuBbWL0hkCvjcjzMvCnTOqN+Q0AsNXRA8DSRtXmJGe0LHayP6CIhNylm3SRx+m5HB45ZpZojowmcXXdB6P3cOejgf4yZfQ7SbqpSVjMqbstv52q8Yi3Rs9L3uWVOw16oNe+H7/NUkDjBCTQfXX0W0HcxfunnePzdjJS0RAkWdGvl3qc2E2B3sjoC1WwDPHs8qRM6s0Fh0CvMfpKTbIxelarBAGc7Q8ovJKxT59fhawAP3LruOnxwyMJzGcrDT1yvBm9Jt1oO4PNTJRvNYxzYxdyav9BzKGj2AmJMOuow1J0Wxckp41WLAsShhNhU86nERolIIHuk262gqQLCWjnQOydjLqxmSrf8G3O23TWvVL7v5HRrxV4jCS9PdIpk7q4UkAizJpODGPQTziUZ1Jm5a3Ru5dXPnF2GfsGYrrtAMUNowkoiv85qV7SzVy2jFiIRd8mvK1bDV2jFyUs5SpNDQBRPdi9NPruK6WjN75m9Hmgrks73fi8HE13K+IuJKBnGb3F2KzdvvzbwuhNGn2Bbzh3MRnhMJwIQ1bs1TPG2ZVxh0BJWZh31Y0zoy/yIk5eWsf7bx23VYIcGfFXYqknWRyYbIRjEGIJFEXV53dCtQkNbBVB0rti/SIRZj1NzWqSfQDLTgcNQgebkG0ANY+jTplyYLG17myY2grc5sb2cjIWqFsVt7u8suNnWtLyha/meV8mSZRRWWusk6ZBI/YTRg/0nuPzGEc/+um3VyFIMt5/65jtd4dpiWWDhCz9Ap2qTQghunyzmZFk7YDR5G0xV8G+JgJ9POI+YALoPukGqN/4mknEUrg1kOmMvoc0ejVRbz43REm1f+62m38rQAkqraXf9qobQsiXCSGrhJC3DI8NEUK+Swi5pP3f7kbmgmSEs2j0vK9uO8qorElVjmX0YO7UWZuIsOiLcuA8AoxbHf2TZ1cwnAjj3kNDjp9jLBVpWHnDa7Xjbmydyjc7oVkKqEs360UBBV5saqRfIuw+YAJoXEe/E0FZ92YCvZXUUPSidJNwSMa2OwG5k2HV6HdCHf1XAHzA8tjvAHhaUZRjAJ7WfvaFZLR+8ouSjHTJH6OnF5pT9QxtnTd2wurvFwl5llYCzlOVeFHCsxdW8dgtY67+JodHEg0rbwRRdvSip6CBfieUVgJ1BkslqWakG3qjtQ4uoVDr6Lvrog5vIdDHI3YWC3Sv181WkAjb58a2OwG5k0Glm5zO6LdZulEU5TkAGcvDPwHgce3fjwP4iN83TEY43ao4UxKgKMCoDzZbl27sQZsmNpwY/S8+eAi/+diNnn+bMnqjSdkLl9Mo8iLeb6m2MeLIaBLXGtgVN6od74vsLOmGygm0a6+5ZKz38JFuq6MHDNJNkxo9QBvIHBh9Dwa4eMQ+Q7ce3HpnZ0MxEA9hJBnGm/PqZD2vDvpWYLNlHmOKoiwBgKIoS4SQPX5fqHqwq3exeg29D+nGRaMH6oHeidE/2MCiAKh7jhhHeT15dhnJCIcHjg67vu7ISAK5cs11UDfgI9DvMOmGulDSG1hTGr1Hk5CiKF0r3YQ5ZlNdy8kIp3d+G0FLCndC8r1TUMsr1QYh+rnbLVfsZBBC8MANIzh5Oa1eG3repj03vbbX8xFCPgngkwAwOjqKSj6LlZKM6elpnF5VA8L8pbOYXrvg+XeyVXUhMsuzmJ5eNv2uVlIvppkrlzDNX2/6GOdn1BvP088+h3iIQFYUfPtMGbcMsXjx5A9cX1daU4//75/6AY4NOn9Bc4tVSDX18xpRLBYxPT2NUk692S1eOY/p7MWmj73VEGV1VzOTLoElwLnXXsQFnwHpivZ9PvfiDzHfb16PmvZ3F2avY3p60fQ7uhY7EZViFcMRBc899/2mX1vMVbGet3/3V67zYGF/HNjZa7EVLM+ru/ennplGRCtMWCiq1/TlixcwXbhse81uXQuKUamG9aKAv/rHZ3Exq+52Xn35JVyKtJ4AbDbQrxBCJjQ2PwFg1e2JiqJ8AcAXAOD48ePK4ckJLF5ex9TUFJZ/OAu89ibe/8i7MOlj0LU8Oo933zhqS95+c/U0Xl9dwD13nMCUz+HNRsy9NANceAv33v8u7OmL4tRMFoUnX8DPP3o7pu7Y6/q6ofkc/uzU8zh8/ASmbrFX5gDA1+ZPoV8qYmrqEdPj09PTmJqawnT+LJ5fvI4ffeRdm5IH2gH2e9+BJCvYNxDDex591PfrolfTwGsv4aZb77CZvRWqNeCpp3DTsaOYsgwXp2uxEzF0NIcSL+FdN7jv7NzwRPoNXL+wavtsT2XfRCK94viZd/JabAVzkev4u4tncfd9D+jX71sLG8DJk7jr9hOYcpBId+taUNyYq+BLbz0DfuAQDg4AOHcejz7ykK7ftxKb3TN9E8AntH9/AsA3/L4wGa1r9GuFxj43RnzsnknH5/brGv3mtj1060i1U+ptcqfDxCsj9Db3Lfi7DMbDYBmyI5wrKehkpWZkG8A4bMOegKxJKqPvNunm9smBTQV5QJWyHJOxtd5rEnKS9fg2yxU7HXsHYjgymsAPLq3X8xXbNWGKEPI3AF4EcJwQMk8I+WUAnwbwGCHkEoDHtJ99oU8rOVMUBasFHgPx0JbLzOgd0Op14xd6oNcW+3paHRvXKBFJyzqdSugoGg2B/rn7D+DxX7yv6ak97QQ9lmYHgFAPdi8jr24L9FtBIsLqurQRvTgn1cnN06uZsFfw8NERvHwtra/LtiVjFUX5WZdfvXczb5iMclAUdSL8aqHakmqT1BYZvdHIC1D16f1DjcfGeRlXUTTy8hhORvDQsZ3D5gFjoN8ko3dommp3VcFORCKinuuqD1P9UuvFblB6rRh3OEIPNo5Z8dCxUTz+4gxevJJGmGXaZtO9DZ2xalAu8qIv+wM/GEtFQAhcK18awcroZ9JlHBpuPGQiHqIsxcOxscFA7J0IeuH5nSxF4cnoJXWNQr3E6OmsVIeywl6Vbkq8g3TTY2thxDuPDIFlCM7M59q62+34CtMtXKEqYjXvryu2ET5w6zi++WsPYcKnna4VlF3xojordSZd8tUgo89J3UWOjUDdBsHvZCkKL0bfbg1yJ8IpuAGq102vBbdkxH5uBIFelZ3v3D8ARWmvrNnxFaZ144VqDWtFf12xjcCxDG6bbH6WJQVlsLwoY70ooCRIOOSzAiYRsXf8GdGNjo3UlrhZ6YZlCKIhpsGwje5ai63ALVnPi3LPJSCprGpci14bwOKGh7QKtXZeG9sm3SzmqhBEuSWMfqugDJavSZjNqI1CB0f8zQdV/Uw8jLy6sEmI+t00G+gB51Z3oHeTsQBslTe9KN045bN60QrCCQ9pQ412FaOnWzjqpbIThm0YGf31ddVf/qBPb5N4uMFA7C6Vbvoi3KbqeeMR1tHBslvLK7eChEtVFt+Fu7ytwumm14s3fyfcuX8AyQjX1nOi45MuqHRDvVR2gseLsY5+PlsGQ+CrgQtwduUzohv9XfYPxT3lKC+4Mnqp90rp9Lmx1mRsrfeqbsLatK6iYzK2t9bCihDL4LFbxpAtC217j44Hehuj3xGBvp6MvZ4uY99gzHdwdvMzoejGQP97H74Fkqw0fqID4g6+40Bvsjddl7YxernnSgoJIeoEMmOgr/XeOeGGP/rJ29FO56OOB3q6naWMfkdo9HR8Xk3GTMZfaSVFIsKhtL67NHqWIQ17CNzgtsNptw3rTkTSNRnbe9INQCeQGaQbSQK3hXNtN6Hd9t0dP9vCHIMIx6BQFRELsU0NXG4XjIzeb2klRSLMunbGyrKijs/rIblCzVkEDVOAsa/AKRnbe3JF3EICetEKYruwLatMdfo9qciOsGoNsQSEqLbJuXKtaUbvVkdPJ+j0Eot11+h7by0iHIsQa9alqSVtLwY4tRTZXEffS+fDdmJbVpmy+J2gzwOqfhjlWLy9rJqZNTMImp68soOm3Yuj0tymKvUiowc0YzOnBGSPafSAJt3w5jr6XtzZbAe2J9BTRt8C+4NWIRJicFFzrTzks4YeAJJ0e15zD269FOgTLlOVejEZC9h16V6uNLHmb3rR3G27sK2MfickYikiHINsWR1A0pRG72Fs1osJyHiYAy/KECXzgPBaD0o3gHNwA3rr5k+RsFRkXU+Xd8yufrdjmwK92oizswK9yrDGU9GmLINprbRTQrYXWWzCZYcjiDIIAbgeq7CIW3XpHpwXS2G86RV5EW8ubOCdhzfn9R+gOWxvMnYHBXpaYtmMPg8Y7Fc9K016Z5sed2sSktQO4Z2QfO8kkhHW2bGxx7xuALMv1KmZLCRZwf1HgkDfCWxvMnYH2B9QUEbfTMUN4DxQgaKXGb218qYbG8dagXg4kG4o4mEW1Zoq6710NY0QS3D3Qe8pbgFag21Oxu4cRk8vvGbntiY9NHq97b+HLmq3tv9u9PxpBay6dC9b89JrpVyT8NLVNO6YHDANZAnQPgTJWA00+98so9c9xx1qx3vSgz1g9CZYk7G9bM1Lr5XVPI835jfwziND23xEvYNtuZ1+5K59SIRZjCR3TqCnVsXNavROAxUoelK6cRgCDXSnFUQrYJ1X0NN19BoJeO7iWqDPdxjbcrbtG4jhnz94eDve2hWRTSdjnY2rgB6to9fXI5BuAPXGR3VpQJ15APTWOUFBScCzb6+CYwjuOTi4zUfUO+i9s80F8TCHkWQYfU16sMe9yit7sHY87sLoaz3L6M3lpr3cMEVlvZeupnHH/kCf7ySCldbwa48exU/dM9n061iGIBZyHj7Si23/9b4Cu5FXLwZ6Y7lpKhoKkrFQh9DcH+jzHUUQ6DUcHkngcBPWB0a4zY0VelCPpfNmrUZvPSvdWMpve7u8sh5ugkapzqL3zrY2QG2KsSdje7HqJswxCLOMqRsU6OFkrEXKqnfG9p50Qxl9oM93Hr135bUBbsM2erHqBqAOlgGjB+xzY3u56oZq9LdP9uvrEqAz6L2zrQ1IhLkgGWuA6mDpUHXTY+sAGJKxPE3G9t7sXIpEmEMizOLhY6PbfSg9h+C22gIkIizWirzt8V6UbgA6NzaoowfsDXW8trNheszcDVALF7796w9jvH/nWJ/0CnrvymsD1ClTzg1TvWjkZXVsBHpXurE21PX6+LxDI4mm3GEDtAa9e8a1EMmIi3TTo3JFMsLaqm56tY6+PjdWhCjJWC/yPanPB9heBNJNC+CajJWk3gxuYQ6ZUsX0WK/W0dOqm798aQZ//vQl5Ksibt2b2uajCtBrCAJ9C0DHxcmyYtJee1WuSDhp9D0a6FmG4PBIAkVexI/cOo5Hj+/Bu28c2e7DCtBjCAJ9C0BLxSo1yVQ21qvBLR4xV90oigJBkhHpwZseAHzvNx8BAXoyARtgZ6A3r7wWw21ubK9WmlgZvSgrUBQg1KOBnmVIEOQDbCt688prMZIRZ2OzXpVu4mEOZU3KAnq3cSxAgJ2C4MprAeJhZ2teXpR7ssLC6tgYBPoAAbYXwZXXArgxer6HGT1QNzar9WiHcIAAOwXBldcCUI0+qDRRUR8QbvZg78WbXoAAOwHBldcCWI2rKASxN7sg9bZ/bT161fMnQICdguDKawFcx+f1bNUN3eGYNfpevOkFCLATsKU6ekLIdQAFABIAUVGUe1txUN0G1/LKXtXodelGY/RBMjZAgG1FKxqmHlUUZb0Ff6drkbA4FFL0rEZvGJ8H1KWbXq2jDxBguxFceS2A29zYnpVu3Bh9EOgDBNgWbPXKUwA8RQg5RQj5ZCsOqFuRiHC2gdiqdNN7lqwJS3llIN0ECLC92Kp086CiKIvk/2/v7mKsuOswjn+fs2dZWBa71AJpgRYa6GJrxBpKaGnMSdsIrUa8xKQNURNvjGJjNDS98t4YvVATUl/wJa2mtpXUtmmtnlCjaUVe6iqsIGBZXrq0TSnQAIv9eTGznOPZgu3uYeYw83ySzZ4ZzjL/+WXn2Tm/eZNmA89J2h0RW5rfkP4B+CLArFmzqNfrk1xkZ6q8c5b9Bw9Rr79+ft7ps+c4eniYen1k3PtPnjxZ2FqMplfEDu7eQ33032x7NQn8l3ds4/i+8X/4ilyL98u1aHAt2mdSQR8Rh9PvI5IeB5YDW1resxHYCDAwMBC1Wm0yi+xYV+18gb7+qdRqtwDJjbzOPfMUi65fQK12w7j31+t1ilqLiKD6u6eZM+9aarUlnNh5GLZvZ+WK5SyaPWPc+4tci/fLtWhwLdpnwp+lJU2XNGPsNfAJYLBdA7vctD58ZOwAZBlPKZRE39QqI28lj1ds9OjL18Yy6wSTSaE5wB8l7QReAn4bEc+0Z1iXn+k9Xf9zHv2Zkp87XrthFk8PHuXE6VFfMGWWswm3biJiH7C0jWO5rPX2VDn1+tvnp8t+APLzty/kiR2H+dXWYarpLXrLWguzvHnLa5O+KS2tm5KfUviRef0su24mm/50gNPpXSy7u3xPdrM8lDOFLoHpPdXzl/yD9+gBPrdyIa+88TZPDR4Fyl0Lszx5y2uTvp4uTp09R0T6sA33pVl10xyuuWIqOw++CZT3041Z3rzltUlvT5WI8TfyKnO4VbsqrLttAZDUQXLrxiwP5U2hNmu9sdkZt24AWHvLtUzr7ip9Hczy5K2vTfpaHrbhHn3iit5u7rv1Oub2T8t7KGal1Y67VxpNd7BsedhGWc+jb7Zh9RK+vmog72GYlZaDvk1anzLlq0EbKhVRwf15s7x4d7NNWnv0bt2YWadwCrXJWI/+/B79f5JevYPezPLmFGqTsT36sdMrz4y6R29mncEp1CbjWje+YMrMOoRTqE16u1taN+7Rm1mHcAq1SbWrwtTuyvgLpkp8ZayZdQanUBv19VR5Yc9rPL59mGMnkoduOOjNLG8+j76N7luxgJ/++QD3/3InkNyWt1Lx+eNmli8HfRutv2sxX75jEX87dJz60DGmTfHevJnlz0HfZpWKWDq/n6Xz+/MeipkZ4B69mVnhOejNzArOQW9mVnAOejOzgnPQm5kVnIPezKzgHPRmZgXnoDczKzhFRHYLk04AQ5ktsLNdBbyW9yA6hGvR4Fo0uBYNAxExY6I/nPWVsUMRsSzjZXYkSVtdi4Rr0eBaNLgWDZK2Tubn3boxMys4B72ZWcFlHfQbM15eJ3MtGlyLBteiwbVomFQtMj0Ya2Zm2XPrxsys4DIJekmrJQ1J2itpQxbL7BSS5kv6g6Rdkv4uaX06/0pJz0nak36fmfdYsyKpS9J2SU+m06WshaR+SY9K2p3+ftxa4lrcn24fg5IeljS1LLWQ9CNJI5IGm+ZdcN0lPZBm6ZCkVe9lGZc86CV1Ad8D7gZuBD4r6cZLvdwOcg74WkR8CFgBfCld/w3A8xGxGHg+nS6L9cCupumy1uK7wDMRsQRYSlKT0tVC0lzgK8CyiPgw0AWspTy1+AmwumXeu657mh1rgZvSn/l+mrEXlcUe/XJgb0Tsi4izwCPAmgyW2xEi4khEbEtfnyDZmOeS1GBT+rZNwGfyGWG2JM0DPgk81DS7dLWQ9AHg48APASLibES8SQlrkaoC0yRVgV7gMCWpRURsAd5omX2hdV8DPBIRZyJiP7CXJGMvKougnwscbJoeTueVjqQFwM3Ai8CciDgCyR8DYHZ+I8vUd4BvAO80zStjLa4HjgE/TttYD0maTglrERGHgG8BrwBHgOMR8SwlrEWTC637hPI0i6DXu8wr3ak+kvqAXwNfjYi38h5PHiR9ChiJiL/mPZYOUAU+BvwgIm4GTlHc1sRFpf3nNcBC4BpguqR78x1Vx5pQnmYR9MPA/KbpeSQfy0pDUjdJyP8iIh5LZ78q6er0368GRvIaX4ZWAp+WdICkhXeHpJ9TzloMA8MR8WI6/ShJ8JexFncB+yPiWESMAo8Bt1HOWoy50LpPKE+zCPq/AIslLZQ0heRAwuYMltsRJImkD7srIr7d9E+bgXXp63XAb7IeW9Yi4oGImBcRC0h+D34fEfdSzlocBQ5KGkhn3Qn8gxLWgqRls0JSb7q93ElyLKuMtRhzoXXfDKyV1CNpIbAYeOn//m8Rccm/gHuAfwL/Ah7MYpmd8gXcTvLR6mVgR/p1D/BBkqPpe9LvV+Y91ozrUgOeTF+XshbAR4Gt6e/GE8DMEtfim8BuYBD4GdBTlloAD5Mcmxgl2WP/wsXWHXgwzdIh4O73sgxfGWtmVnC+MtbMrOAc9GZmBeegNzMrOAe9mVnBOejNzArOQW9mVnAOejOzgnPQm5kV3H8BlsWNNpoMa/oAAAAASUVORK5CYII=\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",
"fig, ax = plt.subplots()\n",
"ax.plot(x, y)\n",
"ax.set_xlim([0, 100])\n",
"plt.grid()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dans un second temps, on trace un **histogramme** représentant la répartition des données:"
]
},
{
"cell_type": "code",
"execution_count": 35,
"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": [
"fig, ax = plt.subplots()\n",
"ax.hist(y, edgecolor='k')\n",
"plt.grid()"
]
}
],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3", "display_name": "Python 3",
...@@ -16,10 +129,9 @@ ...@@ -16,10 +129,9 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.3" "version": "3.6.4"
} }
}, },
"nbformat": 4, "nbformat": 4,
"nbformat_minor": 2 "nbformat_minor": 2
} }
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