{ "cells": [ { "cell_type": "markdown", "id": "continuous-certificate", "metadata": {}, "source": [ "
\n", " Datavisualisation\n", "
\n", "

Navigation dans la page

\n", "

\n", " Si c'est votre première visite dans ce TP, lisez attentivement chacun des points détaillé après ce paragraphe.
\n", " Si vous avez déjà commencer à travailler sur ce TP et que vous souhaiter vous déplacer rapidement dans une partie précise vous pouvez choisir la partie que vous souhaitez rejoindre ci-dessous. N'oubliez pas de réexecuter les case contenant des bibliothèques nécessaire au reste du code.
\n", "

\n", " Menu de navigation\n", " \n", "
\n", "

\n", "\n", "
\n", " Technologie jupyter\n", "

\n", " La technologie jupyter permet d'exécuter du code python par un simple clique sur Executer ci-dessus.
\n", " Les morceaux de code de cette page sont interprétées case par case. Pour savoir quelle case a été interprétée avant une autre, il suffit de repérer le numéro devant la case.
\n", " Une fois qu'une case a été interprétée (=exécutée), la page garde en mémoire les variables et fonctions lues
\n", " La plateforme propose quelques outils de purge de la mémoire : \n", "

\n", "

\n", "
\n", "

SAUVEGARDER VOTRE TRAVAIL

\n", "

\n", " Pour ne pas perdre votre travail pensez à le sauvegarder régulièrement. Par défault, la sauvegarde par un clic sur la disquette en haut à gauche de page, ou par le racourci clavier classique ctrl+S\n", " est une sauvegarde en local, sur le serveur de jupyter. Vous pouvez et devez très régulièrement sauvegarder votre travail sur votre support personnel de sauvegarde (clef USB, se l'envoyer par mail etc). Ce faisant vous disposerez d'un fichier .ipynb (IPYthon NoteBook) qu'il vous suffira de recharger pour avancer. Après le rechargement assurez vous que les fonctionnalités anciennement developpées et variables utilisées sont bien dans la mémoire de la page (en rééxecutant les cases, ou plus rapidement par Kernel > Restart & Run All.

\n", "

A NOTER : vous pouvez travailler sur le tp (et tout autre fichier .ipynb) hors connexion en installant une version local du notebook de jupyter. Il faut que votre machine possède un interpreteur de python et que vous soyez connecter à internet.\n", "

    \n", "
  1. Lancer un terminal
    2. \n", "
  2. Taper la commande suivante : pip install jupyterlab
    3. \n", "
  3. Une fois l'installation terminée portez votre attention sur les dernières lignes affichées dans votre terminal vous invitant probablement à taper une ligne de commande pour faire une mise à jour
    4. \n", "
  4. Pour lancer notebook de jupyter, taper dans votre termial : jupyter notebook
    5. \n", "
  5. Votre simulateur de serveur est lancé. Il ne faut pas fermer votre terminal, auquel cas votre simulateur de serveur s'interompera. Suivez le lien indiqué dans les dernières lignes de votre terminal pour vous diriger vers votre espace local. L'interface se présente comme celle que vous trouverez sur le web. Votre travail sera cependant toujours enregistré et jamais perdu même si vous le consultez après plusieurs jours
    6. \n", "
\n", "

\n", "
\n", "
" ] }, { "cell_type": "markdown", "id": "pressed-burden", "metadata": {}, "source": [ "
\n", " Retour sur les listes\n", "
\n", "

\n", "

\n", " Menu de navigation\n", " \n", "
\n", "

" ] }, { "cell_type": "markdown", "id": "proper-bradley", "metadata": {}, "source": [ "

Pour pouvoir réaliser des graphiques en python, nous avons besoin de spécifier des listes (de points). La fonction de base est la fonction range qui permet très rapidement d'énumeré des entiers (et non des nombres décimaux). Voici les trois utilisations possibles : \n", "

    \n", "
  1. range(n) qui donne la liste des entiers de 0 à n-1. Par exemple range(10) donnera [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
    2. \n", "
  2. range(a, b) qui donne la liste des entiers entre a et b-1. Par exemple range(3, 10) donnera [3, 4, 5, 6, 7, 8, 9]
    3. \n", "
  3. range(a, b, p) qui donne la liste des entiers entre a et b-1 par bond de p. Par exemple range(3, 10, 3) donnera [3, 6, 9]
    4. \n", "
\n", "\n", "En plus d'être une fonction range est un type en python. Pour s'en convaincre utilisons la fonction type qui renvoie le type d'une variable en python.\n", "

" ] }, { "cell_type": "code", "execution_count": 1, "id": "reflected-sample", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "type de '10' : \n", "type de '1.0' : \n", "type de '[10]' : \n", "type de 'range(10)' : \n" ] } ], "source": [ "#Le type 'int' pour les entiers\n", "x=10\n", "print(\"type de '10' :\", type(x))\n", "\n", "#Le type 'float' pour les réels\n", "x=1.0\n", "print(\"type de '1.0' :\", type(x))\n", "\n", "#Le type 'list' pour les listes\n", "x=[10]\n", "print(\"type de '[10]' :\", type(x))\n", "\n", "#Le type 'range' pour les listes organisées\n", "x=range(10)\n", "print(\"type de 'range(10)' :\", type(x))" ] }, { "cell_type": "markdown", "id": "nuclear-affairs", "metadata": {}, "source": [ "

Pour passer d'un type à l'autre on réalise un cast. Pour cela on utilise les fonctions qui portent le nom des types.

" ] }, { "cell_type": "code", "execution_count": 2, "id": "35105b30", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x = 10\n", "type de 'x' : \n", "type de 'float(x)' : \n", "x = 1.0\n", "type de 'x' : \n", "type de 'int(x)' : \n" ] } ], "source": [ "x=10\n", "print(\"x =\", x)\n", "print(\"type de 'x' :\", type(x))\n", "print(\"type de 'float(x)' :\", type(float(x)))\n", "\n", "x=1.0\n", "print(\"x =\", x)\n", "print(\"type de 'x' :\", type(x))\n", "print(\"type de 'int(x)' :\", type(int(x)))" ] }, { "cell_type": "markdown", "id": "11195a75", "metadata": {}, "source": [ "

Exercice

\n", "

Afficher la liste des entiers impaires entre $123$ et $456$

" ] }, { "cell_type": "code", "execution_count": 3, "id": "38f5f7c1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[123, 125, 127, 129, 131, 133, 135, 137, 139, 141, 143, 145, 147, 149, 151, 153, 155, 157, 159, 161, 163, 165, 167, 169, 171, 173, 175, 177, 179, 181, 183, 185, 187, 189, 191, 193, 195, 197, 199, 201, 203, 205, 207, 209, 211, 213, 215, 217, 219, 221, 223, 225, 227, 229, 231, 233, 235, 237, 239, 241, 243, 245, 247, 249, 251, 253, 255, 257, 259, 261, 263, 265, 267, 269, 271, 273, 275, 277, 279, 281, 283, 285, 287, 289, 291, 293, 295, 297, 299, 301, 303, 305, 307, 309, 311, 313, 315, 317, 319, 321, 323, 325, 327, 329, 331, 333, 335, 337, 339, 341, 343, 345, 347, 349, 351, 353, 355, 357, 359, 361, 363, 365, 367, 369, 371, 373, 375, 377, 379, 381, 383, 385, 387, 389, 391, 393, 395, 397, 399, 401, 403, 405, 407, 409, 411, 413, 415, 417, 419, 421, 423, 425, 427, 429, 431, 433, 435, 437, 439, 441, 443, 445, 447, 449, 451, 453, 455]\n" ] } ], "source": [ "L=[i for i in range(123,456,2) ]\n", "print(L)" ] }, { "cell_type": "markdown", "id": "general-equipment", "metadata": {}, "source": [ "

Il est possible de créer des listes de nombres pas forcément entiers de la manière suivante : \n", "

\n", " y=[fonction de x for x in valeur]\n", "
\n", "Dans ce cas la liste y aura dans chacune de ses cases la valeur de f(x) pour les différentes valeurs prises par x. Par exemples si\n", "\n", "
\n", " y=y=[5*x-5 for x in range(-9, 10, 2)]\n", "
\n", "alors \n", "y=[-50, -40, -30, -20, -10, 0, 10, 20, 30, 40]\n", "

" ] }, { "cell_type": "code", "execution_count": 4, "id": "swedish-elite", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-50, -40, -30, -20, -10, 0, 10, 20, 30, 40]\n" ] } ], "source": [ "y=[5*x-5 for x in range(-9, 10, 2)]\n", "print(y)" ] }, { "cell_type": "markdown", "id": "muslim-drawing", "metadata": {}, "source": [ "

\n", "Il est également possible de spécifier une condition à l'aide de if. Par exemple\n", "

\n", " y=[5*x-5 for x in range(-9, 10, 2) if(5*x-5!=0)]\n", "
\n", "affichera la même liste que précédement sauf $0$\n", "

" ] }, { "cell_type": "code", "execution_count": 5, "id": "found-float", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-50, -40, -30, -20, -10, 10, 20, 30, 40]\n" ] } ], "source": [ "y=[5*x-5 for x in range(-9, 10, 2) if(5*x-5!=0)]\n", "print(y)" ] }, { "cell_type": "markdown", "id": "resident-possession", "metadata": {}, "source": [ "

Exercice

\n", "

Afficher la liste des entiers impaires entre $123$ et $456$ qui sont multiple de $3$

" ] }, { "cell_type": "code", "execution_count": 6, "id": "random-elizabeth", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[123, 126, 129, 132, 135, 138, 141, 144, 147, 150, 153, 156, 159, 162, 165, 168, 171, 174, 177, 180, 183, 186, 189, 192, 195, 198, 201, 204, 207, 210, 213, 216, 219, 222, 225, 228, 231, 234, 237, 240, 243, 246, 249, 252, 255, 258, 261, 264, 267, 270, 273, 276, 279, 282, 285, 288, 291, 294, 297, 300, 303, 306, 309, 312, 315, 318, 321, 324, 327, 330, 333, 336, 339, 342, 345, 348, 351, 354, 357, 360, 363, 366, 369, 372, 375, 378, 381, 384, 387, 390, 393, 396, 399, 402, 405, 408, 411, 414, 417, 420, 423, 426, 429, 432, 435, 438, 441, 444, 447, 450, 453]\n" ] } ], "source": [ "L=[i for i in range(123,456,3) ]\n", "print(L)\n", "#Réponse : [123, 129, 135, 141, 147, 153, 159, 165, 171, 177, 183, 189, 195, 201, 207, 213, 219, 225, 231, 237, 243, 249, 255, 261, 267, 273, 279, 285, 291, 297, 303, 309, 315, 321, 327, 333, 339, 345, 351, 357, 363, 369, 375, 381, 387, 393, 399, 405, 411, 417, 423, 429, 435, 441, 447, 453]" ] }, { "cell_type": "markdown", "id": "stuck-nation", "metadata": {}, "source": [ "
\n", " La bibliothèque math\n", "
\n", "

\n", "

\n", " Menu de navigation\n", " \n", "
\n", "

" ] }, { "cell_type": "markdown", "id": "funny-publicity", "metadata": {}, "source": [ "

Comme n'importe quelle calculatrice, nous souhaitons pouvoir utiliser des fonctions comme le cosinus, la racine carrée, le logarithme etc. Pour pouvoir utiliser ces fonctionnalités dites avancées il faut les charger dans le programme. Autrement dit : pour calculer la racine carrée de $2$, il faut écrire un programme de calcul de la racine carrée. Fort heureusement d'autres s'en sont déjà occupé ! Le fait de charger cette fonction revient à aller chercher le code d'autre programmeur et de simplement l'utiliser. Toutes les fonctions mathématiques dont nous pourrions avoir besoin sont rangées dans une bibliothèque : math (original).

\n", "\n", "

\n", "Voici le fonctions que nous pouvons y trouver.\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MathématiquesPython
$|x|$abs(x)
$\\sqrt{x}$sqrt(x)
$e^x$exp(x)
$ln(x)$log(x)
$log(x)$log10(x)
$cos(x)$cos(x)
$sin(x)$sin(x)
$tan(x)$tan(x)
$\\pi$pi
$e(=e^1)$e
$x^y$pow(x, y)
$n!$factorial(n)
\n", "\n", "Il existe plusieurs maniènes de charger ces fonctions :\n", "

    \n", "
  1. Importer toute la bibliothèque\n", "
    \n", " import math\n", "
    \n", " qui s'utilise en appelant les fonctions précédées du nom de la bibliothèque x=math.cos(0)+math.exp(1).\n", "
    2. \n", "
  2. Importer des fonctions spécifiques en changeant leur nom (on parle d'allias) : pour cela il faut dire en python : \"importe depuis la bibliothèque XXX, les fonctions Y sous le nom y, Z sous le nom z, etc\" ce qui donne\n", "
    \n", " from math import cos as trigoDifficile, sin as trigoDifficile2, exp as UneFonctionDeTerminale\n", "
    \n", " et qui s'utilise comme d'habitude mais avec leur nouveau nom comme par exemple x=trigoDificille(0)+UneFonctionDeTerminale(1).\n", "
    3. \n", "
  3. Importer des fonctions spécifiques sans changer leur nom\n", "
    \n", " from math import cos, sin, exp\n", "
    \n", " et qui s'utilise comme d'habitude comme par exemple x=cos(0)+exp(1).\n", "
    4. \n", "
  4. \n", " Importer toutes les fonctions d'une bibliothèque\n", "
    \n", " from math import *\n", "
    \n", " et qui s'utilise comme dans le cas précédent.\n", "
    5. \n", "
\n", "

" ] }, { "cell_type": "code", "execution_count": 7, "id": "square-short", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.718281828459045\n" ] } ], "source": [ "import math\n", "x=math.cos(0)+math.exp(1)\n", "print(x)" ] }, { "cell_type": "code", "execution_count": 8, "id": "czech-craps", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.718281828459045\n" ] } ], "source": [ "from math import cos, sin, exp\n", "x=cos(0)+exp(1)\n", "print(x)" ] }, { "cell_type": "code", "execution_count": 9, "id": "pharmaceutical-major", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.718281828459045\n" ] } ], "source": [ "from math import cos as trigoDifficile, sin as trigoDifficile2, exp as UneFonctionDeTerminale\n", "x=trigoDifficile(0)+UneFonctionDeTerminale(1)\n", "print(x)" ] }, { "cell_type": "code", "execution_count": 10, "id": "responsible-yield", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.718281828459045\n" ] } ], "source": [ "from math import *\n", "x=cos(0)+exp(1)\n", "print(x)" ] }, { "cell_type": "markdown", "id": "extraordinary-noise", "metadata": {}, "source": [ "

Exercice

\n", "
    \n", "
  1. Donner une valeur approchée au dixième de $\\sqrt{2}$.
    2. \n", "
  2. Donner une valeur approchée au centième du nombre d'or $\\dfrac{1+\\sqrt{5}}{2}$.
    3. \n", "
  3. Donner une valeur approchée au dixième de $cos(6*6*6)$.
    4. \n", "
  4. Donner une valeur approchée au dixième de $sin(666)$.
    5. \n", "
  5. Donner une valeur approchée de $tan\\left(\\dfrac{\\pi}{2}\\right)$.
    6. \n", "
  6. Donner la valeur de $1983!$.
    7. \n", "
" ] }, { "cell_type": "code", "execution_count": 11, "id": "48dea0da", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Question 1 : 1.4142135623730951\n", "Question 2 : 1.618033988749895\n", "Question 3 : -0.7179850839697136\n", "Question 4 : -0.01764164581327013\n", "Question 5 : 1.633123935319537e+16\n", "Question 6 : 2708658145231884521851046419370163779878143255113928710317984868030029200552334349754298763607465516572970305674490350631476557540732094674462332478659703490901643312049415349640949021152597056044870095541948992615042417673014111656141364739786128007948967492982780003134831911425370753136252676561116166841812666304927981434374115623224767303483270207156766548203389716145765682544150968490006388020680375977176231850051712778101386147710547804853313329416341380152821487309756298429185095110100819439378342276323424657377141750458150853732785927260898926685692117673950624122973780359123541948444224417451307680824397571201393867781141723495525685078154822494858584643841361655426624638359650293769134524463919808675388851748430903759473600790496703422295615730017961776275469412771613357123203885739905750470560836815840366090616656411852713247418728864634195889854537683320084325684289004127137470739316906570077318215878367072167251905374801259806200924204304450275921706269909004604585012749183318781312368157837430280980207391149200278252114063109005141569773449371340593939729168719297238942528236609078858046180486005916991703225130067737782838153065605178320481952972375112785789419414450907827258765267298975108276007090097684183231381766362673800977404675432818816344619888581006972869225334819441438937815599799865302574540854689596517031383873209458587722516607491331260312301281902424568934078266109107956980319660970651459459918031876404692134605539045591950988131758144900398731465245200754673362711601811934105034535979622379106963103049600707706837645730723621480959233866353510800520655366949957925602503041727743155604935554975061727968100491818287994652799086134385765968564739118444981252812035122683872446721528169450073267634215325268573101173910858142669316652298758877686840606813260198272309292134432476965928802679798983337645106728643203951318292543679455929056666123614433684942624284362956738641345717180259019408430462703121010223038792177519323935172685220001429314110255095968749184973128032544051850902885236518768326859291872154732983036178847743468478067678712109156361888369795925875433624560871940936260717013269015954199269808568178854961407479278153773681799374104475288952736500471787588050245933564116660943312874659268313030011932726546236411833251519053297012759585637917664276774145981285431162034224758833975846347810961476555934983087783571443100061161473414914949281773035067971821865761608654818452786134147629717973263339981143093499648354419589238479318774987569408127345505241991791963977067296339674369109387781483866537148568336136707798970375409629219604999016570614630949805751571147017416100176070324053434711979645316789279902299774508956725212299890752199438016657293515445654427507597779616608096060319063564173673717220545745239818243997946336588046951125111348888273508932851182567524036687467653008800752239128230289489925818903412841276237658171804283396352128272489355112177125866230277894157147374011525669505747591489062249398340784360853720343725689367928517834187814261615329553138817393851480933202678605466599450087394421612827051010029925398132843717103389984817342088286632326453345640188192503232942023384522684337870086132743152207529060212270117694430412824881964650397951791973189405784809660479352534511168875393833472103228188705575637123202873292032434774195876909573355790507113166524353406343284094436668110495516656069703543942379099116854971302686563753960442286639923846476049434326198158836952553006014784824933903950925212462469772944871213564302075494503315210672405615672050726982094847793510772662139901530835411785347842280322618932832414758074105302234300540858385242729724962487621778873869107381718809433037345035495988926510639905831895728487774486466708978554323391762025795217091992769134048116765164644813894739038749426555519758036025522810128383793544341726797930378967822322352627271703718790368328655147627371585155938505282824594754397795322148177959640342191410693913935478368962177968541252590626090058747289298897906417072628413936771078024610512527876905101976958620404587526355754565167224353944293669486434633998322050517933840553698851646921621059456618706137239335466196130185851242756630577123187829922815779449168496876498319476088285084103237370212988507738195493524976856307714862325527596137340260690840375200531904464984431641996493394081777919911206049124420812763695986593937313502617214672551174414895221561154718496598041043697183847803231784778464081052801724373965308775109797747409225868722525833690477213871264192582858421595967394137501532645431567209128269636126321860350628998227919726176689037468822955876903562288945141333544086541345956968211061450002836474230198614440774370503386555022367110064505359176055275294525652403996108727650114950079467772408337041106067241778009611212470649752630805972351682125269650820698500366333383778648762834539429635998543156533961218682392147021176339923810231465941996913494184983138542340194687529584807336620006054809671421760595323754326322322327087614719900941186271559791907380061595655398070387605189062116284878198055072191567810144311085659125986892586762262277576811903828442315058550343298609493022164910080000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000\n" ] } ], "source": [ "print(\"Question 1 :\", sqrt(2))\n", "print(\"Question 2 :\", (1+sqrt(5))/2)\n", "print(\"Question 3 :\", cos(6*6*6))\n", "print(\"Question 4 :\", sin(666))\n", "print(\"Question 5 :\", tan(pi/2))\n", "print(\"Question 6 :\", factorial(1983))" ] }, { "cell_type": "markdown", "id": "molecular-nursing", "metadata": {}, "source": [ "

Application

\n", "

\n", "Si l'on souhaite afficher la liste des carrés des nombres réels entre $0$ et $1$ de $0.1$ en $0.1$ voici comment on peut procéder : \n", "

    \n", "
  • Il y aura $11$ valeurs à mettre au carré : $0$, $0.1$, $0.2$, $0.3$, $0.4$, $0.5$, $0.6$, $0.7$, $0.8$, $0.9$ et $1$. On part donc de la liste des entiers [x for x in range(11)].
    • \n", "
  • Pour chacun des entiers on divise par $10$ : [x/10 for x in range(11)].
    • \n", "
  • On met au carré : [(x/10)**2 for x in range(11)].
    • \n", "
\n", "

" ] }, { "cell_type": "code", "execution_count": 12, "id": "gothic-croatia", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.0, 0.010000000000000002, 0.04000000000000001, 0.09, 0.16000000000000003, 0.25, 0.36, 0.48999999999999994, 0.6400000000000001, 0.81, 1.0]\n" ] } ], "source": [ "print([(x/10)**2 for x in range(11)])" ] }, { "cell_type": "markdown", "id": "filled-microwave", "metadata": {}, "source": [ "

\n", " Autre exemple : on souhaite calculer le logarithme népérien (log) de $x^2+x-6$ pour $101$ valeurs équi-répartie dans l'intervalle $[-3 ; 3]$.\n", "

    \n", "
  • Puisqu'on aura $100$ valeurs on part de la liste range(101).
    • \n", "
  • Soit x une valeur de cette liste, si on réalise l'opération x/100 alors le nombre sera entre $0$ et $1$. Puisqu'on souhaite être entre $-3$ et $3$, il faut translater et dilater cette intervalle. Vite des maths : \n", "
    Soient $a < b$ des nombres réels \n", " $$a+(b-a)[0 ; 1]=[a ; b]$$\n", "
    \n", " Ainsi on réalise donc l'opération -3+(3-(-3))*x/100 soit plus simplement -3+6*x/100.\n", "
    • \n", "
  • \n", " Il y a un problème : la fonction $ln(X)$ n'est définie que lorsque $X>0$. Il faut donc rajouter la condition $x^2-x+6>0$.\n", "
    • \n", "
\n", " Au finale pour répondre à la question, on fait : \n", "\n", "
\n", " A=[-3+6*x/100 for x in range(101)]\n", "
\n", "
\n", " B=[x**2+x-6 for x in A if(x**2+x-6>0)]\n", "
\n", "
\n", " C=[log(x) for x in B]\n", "
\n", "

" ] }, { "cell_type": "code", "execution_count": 13, "id": "5ad7b969", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-1.6014697427849236, -0.6733445532637695, -0.19164488425483808, 0.13836966926477076, 0.3909604219052837, 0.5964159916001729, 0.7701082216960745, 0.9209200002578172, 1.0544513928823387, 1.1744616009515474, 1.2835969628815471, 1.3837912309017726, 1.476500629006039, 1.5628493230195084, 1.6437233904006503, 1.7198337296564667, 1.791759469228055]\n" ] } ], "source": [ "A=[-3+6*x/100 for x in range(101)]\n", "B=[x**2+x-6 for x in A if(x**2+x-6>0)]\n", "C=[log(x) for x in B]\n", "print(C)" ] }, { "cell_type": "markdown", "id": "confirmed-arthritis", "metadata": {}, "source": [ "

On peut bien sur simplifier cette rédaction

" ] }, { "cell_type": "code", "execution_count": 14, "id": "ranking-vaccine", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-1.6014697427849236, -0.6733445532637695, -0.19164488425483808, 0.13836966926477076, 0.3909604219052837, 0.5964159916001729, 0.7701082216960745, 0.9209200002578172, 1.0544513928823387, 1.1744616009515474, 1.2835969628815471, 1.3837912309017726, 1.476500629006039, 1.5628493230195084, 1.6437233904006503, 1.7198337296564667, 1.791759469228055]\n" ] } ], "source": [ "D=[log((-3+6*x/100)**2+(-3+6*x/100)-6) for x in range(101) if (((-3+6*x/100)**2+(-3+6*x/100)-6)>0)]\n", "print(D)" ] }, { "cell_type": "markdown", "id": "simple-soviet", "metadata": {}, "source": [ "

Exercice

\n", "
    \n", "
  1. Afficher la liste de $exp(-\\sqrt{x^2-1})$ pour $100$ valeurs équi-réparties sur $[-1 ; 2]$
    2. \n", "
  2. Afficher la liste de $exp(-\\sqrt{x^2-1})$ pour $1000$ valeurs équi-réparties sur $[-1 ; 2]$
    3. \n", "
  3. Afficher la liste de $exp(-\\sqrt{x^2-1})$ pour $1000$ valeurs équi-réparties sur $[-3 ; 2]$
    4. \n", "
  4. Afficher la liste de $exp(-\\sqrt{x^2-1})$ pour $n$ valeurs équi-réparties sur $[a ; b]$ pour des valeurs $n$, $a$, $b$ paramètrables
    5. \n", "
" ] }, { "cell_type": "code", "execution_count": 15, "id": "32f03f82", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a = -5\n", "b = 5\n", "Nombre de point = 101\n", "Liste des exp(-sqrt(-x²-1)) : [0.007454186295369829, 0.00824716839511264, 0.00912531881670917, 0.010097929695125062, 0.011175334467377665, 0.012369030135665829, 0.013691815092105568, 0.015157944763418274, 0.016783307728730015, 0.01858562544417071, 0.020584679297088126, 0.022802569440559448, 0.02526401076558019, 0.027996672507653544, 0.03103156942946612, 0.03440351437192287, 0.03815164436060757, 0.04232003558807346, 0.046958426740244315, 0.05212307569490473, 0.05787778217203295, 0.06429511932043125, 0.07145793178171235, 0.07946117847779095, 0.08841422835298215, 0.09844376158792309, 0.10969749564160027, 0.12234905880558083, 0.1366044980369552, 0.15271117641104964, 0.1709702705989308, 0.1917548811746938, 0.21553725090764667, 0.2429314805830074, 0.2747641753251676, 0.3121991675936521, 0.3569770777600332, 0.41193123921285013, 0.48230130643566904, 0.5801524071540504, 0.7526071631041998, 0.7526071631041998, 0.5801524071540498, 0.48230130643566865, 0.41193123921284985, 0.35697707776003296, 0.3121991675936519, 0.27476417532516734, 0.24293148058300712, 0.21553725090764642, 0.1917548811746938, 0.1709702705989308, 0.1527111764110496, 0.13660449803695515, 0.12234905880558078, 0.10969749564160022, 0.09844376158792305, 0.08841422835298209, 0.07946117847779088, 0.07145793178171225, 0.06429511932043125, 0.05787778217203295, 0.05212307569490473, 0.046958426740244315, 0.04232003558807344, 0.03815164436060755, 0.03440351437192287, 0.031031569429466093, 0.02799667250765352, 0.02526401076558017, 0.022802569440559427, 0.020584679297088106, 0.01858562544417069, 0.016783307728729997, 0.015157944763418247, 0.013691815092105543, 0.012369030135665806, 0.011175334467377665, 0.010097929695125062, 0.00912531881670917, 0.00824716839511264, 0.007454186295369829]\n" ] } ], "source": [ "#On fait la dernière question qui répond aussi aux autres\n", "a=float(input(\"a = \"))\n", "b=float(input(\"b = \"))\n", "while(a>=b) : \n", " print(\"Choisir a et b tel que a=0]\n", "Z=[exp(-sqrt(y**2-1)) for y in Y]\n", "print(\"Liste des exp(-sqrt(-x²-1)) :\", Z)" ] }, { "cell_type": "markdown", "id": "surgical-membership", "metadata": {}, "source": [ "
\n", " Graphiques en python\n", "
\n", "

\n", "

\n", " Menu de navigation\n", " \n", "
\n", "

" ] }, { "cell_type": "markdown", "id": "governmental-continent", "metadata": {}, "source": [ "

Comme pour les fonctions, il faut définir la fenêtre de graphique. Par chance, d'autres s'en sont occupés. Chargeons cette fonctionnalité : exécuter la case suivante.

" ] }, { "cell_type": "code", "execution_count": 16, "id": "foster-nirvana", "metadata": {}, "outputs": [], "source": [ "from matplotlib.pyplot import *" ] }, { "cell_type": "markdown", "id": "adverse-civilization", "metadata": {}, "source": [ "

Pour faire un graphique rien de plus simple ! On utilise la fonction plot qui prend au moins deux paramètres : \n", "

    \n", "
  1. Le premier est la liste des points d'abscisses
    2. \n", "
  2. La seconde est la liste des points d'ordonnées
    3. \n", "
\n", "Voici un exemple : \n", "

" ] }, { "cell_type": "code", "execution_count": 17, "id": "afe742ec", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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9SrvQoHej+ZOTOF9Vx+ov8qweRSnlUF5Vy+s7crljSA/iI+1VXuaMBr0bjYjrwtjErrz6ebaWnSnlId78Io/y6joWTLJn3UFLNOjdbMHkZE6eq+Lv+7XsTCmrVdfV8+rn2aT2iWRIrwirx2k3GvRudkP/KPrHhGnZmVIe4L19Jyg6X818HzqaBw16txMR5k1K4mhROZ8d9a6rZyllJ5fKywb2CGeiTcvLnNGgbwdTh/ekZ0QIi7XsTCnLbPqqmIziCuZPTrJteZkzGvTtINDfj0euT2Rndhl7885YPY5SPmnx5szG8rIh9i0vc0aDvp3MHBNHeEgASzZrLYJS7S09p4z03DM8NjGRABuXlznjeyu2SGhwAN8dH89Hh0+RVVJh9ThK+ZQlW7Lo0jGQ+2xeXuaMBn07enhCoqPsLNvqUZTyGRnFFXx8uIgHxyfYvrzMGQ36dhQVFsz0Ub14e08BxeVVVo+jlE9YuiWTkEA/Hhwfb/UoltGgb2ePTUyitr6B5Wk5Vo+ilO0Vna/i3b2+U17mjAZ9O0vs1ok7BnfntR25WnamlJv9NS2b+gbDYxOTrB7FUhr0Fpg/KZlyLTtTyq3OV9Xyxo48vj20J727drR6HEtp0FtgWO/OjEtqLDurqdOyM6Xc4Y2djeVl8yf59tE8aNBbZr6j7Gy9lp0p1eaq6+r56+fZXN+nG4Njfae8zBkNeovc0C+KAd3DWLolk4YGLTtTqi29t/cExeXVLJjsW+VlzmjQW0REmD85iWNFFXx2rNjqcZSyjYYGw5ItmQzqGU5qn0irx/EIGvQWunPopbIzrUVQqq18cqSIzJJK5k9O9rnyMmc06C0U6O/HoxOT+CK7jD1adqZUm1iyJYveXTvwrcHdrR7FY2jQW2zm6N5EdAhkiVYYK9Vq6Tll7M49w2MTk3yyvMwZ/UpYrFNwAA+Oj+cfh4vI1LIzpVpl8ebG8rIZo3yzvMwZDXoP8NCEhMaysy16rl6pa3W8qJxPjhTx0IQEOgT5Wz2OR9Gg9wDdQoOZMaoX7+wppPi8lp0pdS2WbslylJclWD2Kx9Gg9xCPTUyirqGBZdtyrB5FKa9z6lwVf9tXyMzRcXTtFGT1OB5Hg95DJHTrxB2De7BqRy7lVbVWj6OUV1mWlk2DgUevT7R6FI+kQe9B5k1Koryqjje17Ewpl527WMvrO/P49pAePl9e5oxLQS8it4vIURHJEJGFLTwfLCJrHM/vFJGEZs/HiUiFiPy0jea2pWG9OzM+KVLLzpS6Cm/szKOiuo55Wl7m1BWDXkT8gReBO4CBwCwRGdhss0eBM8aYPsCzwDPNnv8j8EHrx7W/BTckU3S+mvf2FVo9ilIer7qunr+mZTOxr5aXXY4rR/RjgAxjTJYxpgZYDUxrts00YIXj9jrgZnF89lhE7gaygUNtMrHNTerbjQHdw1iyJYvaej2qV+py3kovoETLy67IlaCPBfKb3C9wPNbiNsaYOuAcECkiocAvgH+/3A5EZJ6IpItIeklJiauz25KI8NSt/cgoruC/PvjK6nGU8lgZxRX87v0jjEnsyoRkLS+7HHe/GPsb4FljzGU/8mmMWWqMSTHGpERFRbl5JM9326DuPDQ+nlc/z+b9gyetHkcpj1NZXcfjq3YTEujP8zNHaHnZFQS4sE0h0PTzxL0cj7W0TYGIBAARQCkwFpguIr8HOgMNIlJljHmhtYPb3a++PZD9Bef4+boDDOgeRlJUqNUjKeURjDH867sHySyp4LVHx9I9IsTqkTyeK0f0u4C+IpIoIkHATGB9s23WAw85bk8HNplGE40xCcaYBOBPwH9qyLsmKMCPF2ePJNBfeHzVHi7U6IXElQJYtSOX9/ad4Klb+5Hap5vV43iFKwa945z7k8BHwBFgrTHmkIgsEpGpjs1epfGcfAbwFPCNt2CqqxfbuQPPzRzBseJyfvXulxijV6JSvm1v3hkWbTjMTQOi+f4Nfawex2uIp4VHSkqKSU9Pt3oMj/LcJ8d59pNj/Mfdg5kzLt7qcZSyRFllDXc+vxU/P2HjDyYS0THQ6pE8iojsNsaktPScfjLWC/zgpj5M7hfFor8f5kDBWavHUard1TcYfrxmH6cranhp9igN+aukQe8F/PyEP90/nKiwYB5ftYczlTVWj6RUu/rzpuNsOVbCb6YOYkgv/WDU1dKg9xJdOgXx4uyRFJdX8ZO1+2ho8KxTbkq5y+ZjJTz36XHuHRnLrDF6QZFroUHvRYb37syv7xzIZ0dLePGfGVaPo5TbFZ69yI9X76V/TBhP3z1E3y9/jTTovcyccfFMG96TP35yjM+Pn7Z6HKXcpqaugSde30NtveEvs0fqVaNaQYPey4gIv7t3CH2iQvnh6r2cPHfR6pGUcounNx5mX/5Z/jBjqH5gsJU06L1Qx6AAXpoziuraep54fY9WGivbWb//BCu25/K96xO5fXAPq8fxehr0XqpPdCjPTB/Knryz/O6DI1aPo1SbOV5UzsK3DzA6oQu/uGOA1ePYgga9F7tzaE8enpDAsrQcNhw4YfU4SrVaRXUdC1btpmOQPy88MJJAf42otqBfRS/3r9+6jpFxnfnFugNkFF+2JFQpj2aMYeHbB8g+Xcnzs0YQE65lZW1Fg97LXSo/Cw705/FVu6ms1vIz5Z1WbMthw4GT/HRKfyYka1lZW9Kgt4EeER14fuYIMkoq+Nd3D2r5mfI6e/LO8PT7R7jlumgWTNKrRbU1DXqbuL5vN566pR/v7TvBqh25Vo+jlMtKK6p54vU9dI8I4b9nDMfPTz8U1dY06G3kiRv7cGP/KBZtaHz/sVKe7lJZWWmllpW5kwa9jfj5Cc/eP5zosBCeeF3Lz5Tne+7T42w9fppFUwcxOFbLytxFg95mOncM4qU5Iykpr+bHa7T8THmuz44W8+dNx5k+qhf3j9ayMnfSoLehob068+u7BrL5WAl/3qTlZ8rzFJy5wI/X7KN/TBi/nTZYy8rcTIPepmaPjeOeEbH86dNjbDlWYvU4Sn2tuq6xuqO+3rB4zigtK2sHGvQ2JSI8fc9g+kaH8qPVezlxVsvPlGf47YbD7C84x/+bMYyEbp2sHscnaNDb2KXys9p6w/e1/Ex5gL/tLWTVjjzmTUri9sHdrR7HZ2jQ21xyVCi/nz6Uffln+c/3tfxMWedYUTm/fOcgYxK68vMp/a0ex6do0PuAbw3pwSOpiSzflsP6/Vp+ptrfpbKyTsEBvPDACAK0rKxd6VfbR/zyWwNIie/CwrcPkFFcbvU4yocYY/jFugPkll7ghQdGEK1lZe1Og95HBPr78cIDI+kY5M+CVXu0/Ey1m2VpOWw8eJKfTenPuKRIq8fxSRr0PqR7RAjPzxxBVkkFC9/R8jPlfrtzy/jP949w68AY5k9Ksnocn6VB72Mm9OnGv9zWn7/vP8HK7Vp+ptzndEU1T7y+l9guHfjDjGH6oSgLadD7oMcnJ3PzgGj+Y+Nh9uSdsXocZUP1DYYfrd7LmQs1/GX2SCI6aFmZlTTofZCfn/DH+4YTEx7Ck6/voUzLz1Qb+9Mnx0jLKOW30wYzqKeWlVlNg95HRXQM5KXZozhdUcOPVu+lXsvPVBvZ9FURf96UwX0pvbhPy8o8gga9DxvSK4LfTB3E1uOnef7T41aPo2wgv+wCP1mzn4E9wlk0bbDV4ygHDXofN2tMb+4dGcvzm47z2dFiq8dRXqyqtp7vv76HBmN4ac5IQgK1rMxTaND7OBHh6buH0D8mjB+v2Uehlp+pa7Row2EOFp7jv2cMIz5Sy8o8iQa9okOQP3+ZPZI6R/lZdV291SMpL/POngLe2JnH/MlJ3DZIy8o8jQa9AiApKpQ/zBjK/vyzPL1Ry8+U6746dZ5/ffcgYxO78rPbtKzME2nQq6/dPrgHj01MZOX2XN7bV2j1OMoLlFfV8viqPYSFBPJnLSvzWC79rYjI7SJyVEQyRGRhC88Hi8gax/M7RSTB8fitIrJbRA46/ntTG8+v2tjPbx/A6IQuLHz7IMeKtPxMOWeM4efrDpBXdoEXHxhJdJiWlXmqKwa9iPgDLwJ3AAOBWSIysNlmjwJnjDF9gGeBZxyPnwbuMsYMAR4CXmurwZV7XCo/6xQcwIJVu6nQ8jPlxKufZ/PBl6f4xe39GZPY1epx1GW4ckQ/BsgwxmQZY2qA1cC0ZttMA1Y4bq8DbhYRMcbsNcZcKkA/BHQQkeC2GFy5T0x4CH+eNYKc05X84u0DWn6mviE9p4z/+uArpgyK4bGJWlbm6VwJ+lggv8n9AsdjLW5jjKkDzgHN+0i/A+wxxlQ334GIzBORdBFJLynRC1l7gvHJkfx0Sn82HjjJ8m05Vo+jPEhJeTVPvLGHXl068P+0rMwrtMsrJyIyiMbTOfNbet4Ys9QYk2KMSYmKimqPkZQLFkxK5pbronl64xF252r5mYK6+gZ++OZezl6o5S+zRxEeomVl3sCVoC8EmhZW9HI81uI2IhIARACljvu9gHeBB40xma0dWLUfPz/hv2cMp0fnEJ58Yw+lFd/4YUz5mD9+fIztWaX8x92DGdgz3OpxlItcCfpdQF8RSRSRIGAmsL7ZNutpfLEVYDqwyRhjRKQzsBFYaIxJa6OZVTu6VH5WWlnDj1bv0/IzH/bJ4SL+8lkmM0f3ZkaKlpV5kysGveOc+5PAR8ARYK0x5pCILBKRqY7NXgUiRSQDeAq49BbMJ4E+wK9FZJ/jV3Sbr0K51eDYCBZNHcTnGad57pNjVo+jLJBXeoGn1u5jUM9wfjN1kNXjqKsknvaOipSUFJOenm71GKoZYww/W3eAdbsLWDZ3NDf21+/XvqKqtp7pi7eRV3qBDT+YSFxkR6tHUi0Qkd3GmJSWntOPsSmXiAi/nTaYAd3D+MmafRScuWD1SKqd/PvfD/Fl4Xn+eN9wDXkvpUGvXNYhyJ/Fc0ZRr+VnPmPd7gLe/CKfx29I5paBMVaPo66RBr26KgndOvGH+4ZxoOAcv91w2OpxlBsdOXmeX717kPFJkfzLrf2sHke1gga9umpTBnVn/qQkVu3I4929BVaPo9zgfFUtj6/aTUSHQJ6fpWVl3k7/9tQ1+dmUxn6TX75zkKOntPzMTowx/Oyt/eSfuciLs0cSFaatJd5Og15dkwB/P16YNYLQ4EAeX7Wb8qpaq0dSbeTlrVl8dKiIX94xgNEJWlZmBxr06ppFh4fwwgMjyC27oOVnNrEzq5RnPjzKHYO78+j1iVaPo9qIBr1qlXFJkfxsSn/eP3iKv6blWD2OaoXi8iqefHMvcV078vvpQ7WszEY06FWrzZ+UxK0DY/jd+0dIzymzehx1DerqG/jBG3spr6rlpTkjCdOyMlvRoFetJiL8YcYwYrt04Ik39nBay8+8zh/+cYyd2WU8ffcQBnTXsjK70aBXbSKiQyB/mT2Ssxdq+dHqvVp+5kU+PlzE4s2ZzBoTx3dG9bJ6HOUGGvSqzQzqGcFvpw0mLaOUZz/W8jNvkFtayVNr9zE4Npx/u6v5FUKVXWjQqzZ13+je3JfSixf+mcGmr4qsHkddRlVtPY+v2oOfCC/NHkVIoL/VIyk30aBXbW7RtMEM7BHOT9bsJ79My8881b+9d4jDJ8/z7P3D6N1Vy8rsTINetbmQQH9emjOSBtNYflZVq+VnnmbtrnzWpOfz5I19uGmAlpXZnQa9cov4yE788b7hHCw8x7zXdrMnT6856wnOXahlyeZM/u97X5LaJ5KfaFmZTwiwegBlX7cOjOHXdw7k2Y+Pce9ftjGsd2ceSU3gjsE9CArQY4z2dLyonGXbcnh3TyEXa+uZkBzJczNH4O+nH4ryBXqFKeV2FdV1vL27gOXbcsg+XUl0WDDfHRfPrLFxdAvVwix3aWgwfHasmGVpOWw9fpqgAD/uHt6Thyck6oW9behyV5jSoFftpqHBsPlYCcu25bDlWAlBAX5MHdaTuakJDOoZYfV4tlFRXce69HxWbM8l+3QlMeGOb6xj4ojUb6y2pUGvPE5GcTnLt+Xw9u7GUwljErvySGoCt1wXo93n1yi3tJLl23J4K72Aiuo6RsR15uEJCXxrSA8C9Wtqexr0ymOdu1DLmvQ8VmzLpfDsRWI7d+DB8fHMHB1HREftW7kSYwzbMktZlpbNp18V4y/Ct4f2YG5qIsN7d7Z6PNWONOiVx6tvMHx8uIhladnszC6jQ6A/946MZW5qAn2iw6wez+NcrKnnb/sKWZ6Ww9GiciI7BfHA2DjmjIsnJjzE6vGUBTTolVc5dOIcy9NyeG//CWrqGpjYtxtzUxO4oV80fj7+LpETZy+ycnsuq3flcfZCLdf1CGduagJTh/XUT7b6OA165ZVKK6p5Y2cer+3Ipbi8msRunXhofDzTU3oTGuw77ww2xrA79wzL0nL48NApjDHcNrA7c1MTGJPYVXvjFaBBr7xcTV0DH3x5kmVpOezLP0tYcAAzUnrz8IQE4iLt+9H96rp6Nuw/yfJtORwsPEd4SAAzx8Tx3XHxWlmgvkGDXtnG3rzGI9v3D56k3hhuHhDD3NQEJiRH2ubItri8itd35PH6zjxOV1TTJzqUhyckcO/IWDoG+c5PMurqaNAr2zl1ropVO3J544s8yipr6B8TxsOpCdw9PJYOQd55rvpgwTmWpWXz9wMnqK033Ng/irmpiUzs280238SU+2jQK9uqqq1n/f4TLEvL4cjJ83TuGMgsx+mNnp07WD3eFdXVN/DhoVMsT8shPfcMnYL8mT6qFw9NSCApKtTq8ZQX0aBXtmeMYWd2GcvSsvn4cBEiwu2DuzN3QgKj4rt43BHxmcoa3tyVx2vbczl5roq4rh15aEICM1J6Ea7Xa1XX4HJBryf8lC2ICOOSIhmXFEl+2QVe25HL6i/y2HjgJENiI5ibmsC3h/YgOMDa0zpHT5WzfFs27+4tpKq2gQnJkSyaNpibBkRrwZhyGz2iV7ZVWV3HO3sLWZ6WTWZJJd1Cg5kzLo4HxsYRHdZ+HyqqbzBs+qqYZWnZbMssJTjAj3tGxPJwaoJeiFu1GT11o3xaQ4Nha8ZplqVl89nREoL8/bjTURMwpJf7ytTOV9XyVnoBK7blkFd2gR4RIXx3fDyzRsfRpVOQ2/arfJMGvVIOmSUVrNiWw7rdBVyoqSclvgtzUxOZMqjtytSyT1eyYlsOb6XnU1lTz6j4LsxNTWDKoO5aLqbcRoNeqWbOV9Wydlc+K7bnkF92sdVH28YYth5v/Knhn0dLCPQX7hrak4dTExjaq3PbL0CpZjTolXKivsHw6ZEilqXlsD2rlJBAx/nzCYn0737lMrULNXW8s6eQ5dtyyCiuoFtoELPHxjN7XPu+DqBUq4NeRG4HngP8gVeMMf/V7PlgYCUwCigF7jfG5Die+yXwKFAP/NAY89Hl9qVBr6xy5OR5VmzL4d29hVTXNZDaJ5K5ExK5acA3y9QKzlzgte25vPlFHuer6hgcG87cCYncOcz6d/Yo39SqoBcRf+AYcCtQAOwCZhljDjfZ5vvAUGPMAhGZCdxjjLlfRAYCbwJjgJ7AJ0A/Y0y9s/1p0CurlVXW8OYXje9xP3W+ivjIjjw4vvE97kdOnGdZWg7/OHwKEWHKoBjmpiaS4oHv1Ve+pbVBPx74jTFmiuP+LwGMMb9rss1Hjm22i0gAcAqIAhY23bbpds72p0GvPEVtfQMffnmK5dty2J17hgA/oa7BENHB8enb8fHEesGnb5VvaO0HpmKB/Cb3C4CxzrYxxtSJyDkg0vH4jma/N7aFAecB8wDi4uJcGEkp9wv09+OuYT25a1hP9uef5d29hfSLCeOeEd7bp6N8k0d8MtYYsxRYCo1H9BaPo9Q3DOvdmWF6aT7lpVx5U28h0LvJ/V6Ox1rcxnHqJoLGF2Vd+b1KKaXcyJWg3wX0FZFEEQkCZgLrm22zHnjIcXs6sMk0nvxfD8wUkWARSQT6Al+0zehKKaVcccVTN45z7k8CH9H49sq/GmMOicgiIN0Ysx54FXhNRDKAMhq/GeDYbi1wGKgDnrjcO26UUkq1Pf3AlFJK2cDl3nWjxRtKKWVzGvRKKWVzGvRKKWVzGvRKKWVzHvdirIiUALmt+CO6AafbaBwr2WUdoGvxRHZZB+haLok3xkS19ITHBX1riUi6s1eevYld1gG6Fk9kl3WArsUVeupGKaVsToNeKaVszo5Bv9TqAdqIXdYBuhZPZJd1gK7limx3jl4ppdT/ZscjeqWUUk1o0CullM15fdCLSFcR+VhEjjv+26WFbeJFZI+I7BORQyKywIpZL8fFdQwXke2ONRwQkfutmPVKXFmLY7sPReSsiGxo7xkvR0RuF5GjIpIhIgtbeD5YRNY4nt8pIgkWjOkSF9YyyfFvo05Eplsxo6tcWMtTInLY8W/jUxGJt2JOV7iwlgUictCRWZ87rr997YwxXv0L+D2w0HF7IfBMC9sEAcGO26FADtDT6tmvYR39gL6O2z2Bk0Bnq2e/lrU4nrsZuAvYYPXMTWbyBzKBJMf/N/uBgc22+T6w2HF7JrDG6rlbsZYEYCiwEphu9cytXMuNQEfH7ce9/O8lvMntqcCHrdmn1x/RA9OAFY7bK4C7m29gjKkxxlQ77gbjmT/JuLKOY8aY447bJ4BiGi/C7mmuuBYAY8ynQHk7zeSqMUCGMSbLGFMDrKZxPU01Xd864GYRkXac0VVXXIsxJscYcwBosGLAq+DKWv5pjLnguLuDxivaeSJX1nK+yd1OQKveNeOJgXe1YowxJx23TwExLW0kIr1F5ACNFzF/xhGUnsSldVwiImNoPBrIdPdg1+Cq1uJhvr7QvUNLF7T/ehtjTB1wDohsl+mujitr8RZXu5ZHgQ/cOtG1c2ktIvKEiGTS+BPyD1uzQ4+4OPiViMgnQPcWnvpV0zvGGCMiLX7nM8bkA0NFpCfwNxFZZ4wpavtpnWuLdTj+nB7Aa8BDxhhLjsTaai1KtTURmQOkAJOtnqU1jDEvAi+KyAPA/+F/Ltd61bwi6I0xtzh7TkSKRKSHMeakIwCLr/BnnRCRL4GJNP7Y3W7aYh0iEg5sBH5ljNnhplGvqC3/TjyMKxe0v7RNgYgEABFAafuMd1VcWYu3cGktInILjQcbk5ucrvU0V/v3shp4qTU7tMOpm6YXJn8IeK/5BiLSS0Q6OG53Aa4HjrbbhK5xZR1BwLvASmNMu36TukpXXIsH2wX0FZFEx9d7Jo3raarp+qYDm4zjVTMP48pavMUV1yIiI4AlwFRjjCcfXLiylr5N7n4bON6qPVr9CnQbvIIdCXzq+EJ8AnR1PJ4CvOK4fStwgMZXtw8A86ye+xrXMQeoBfY1+TXc6tmvZS2O+1uBEuAijecpp1g9u2OubwHHaHz941eOxxbRGCAAIcBbQAbwBZBk9cytWMtox9e+ksafSg5ZPXMr1vIJUNTk38Z6q2duxVqeAw451vFPYFBr9qcVCEopZXN2OHWjlFLqMjTolVLK5jTolVLK5jTolVLK5jTolVLK5jTolVLK5jTolVLK5v4/oD1blqMI1hIAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "X=[k/10 for k in range(-3, 4)]\n", "Y=[x**2 for x in X]\n", "plot(X, Y)" ] }, { "cell_type": "markdown", "id": "attempted-reflection", "metadata": {}, "source": [ "

La fonction plot crée un objet fenêtre graphique il faut demander à afficher cette fenêtre (NB: jupyter est très gentil, lorsqu'on ne lui indique pas d'afficher le graphique, il le fait tout seul. Pour s'en convaincre, remarquer le Out dans la marge de gauche).
\n", "Pour afficher la fenêtre graphique, on utilise la fonction show\n", "

" ] }, { "cell_type": "code", "execution_count": 18, "id": "injured-maximum", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "X=[k/10 for k in range(-3, 4)]\n", "Y=[x**2 for x in X]\n", "plot(X, Y)\n", "show()" ] }, { "cell_type": "markdown", "id": "caroline-luxembourg", "metadata": {}, "source": [ "

Quelques fonctionnalités supplémentaires

\n", "
    \n", "
  • grid(True) permet d'afficher une grille dans le graphique. Exemple : \n", "
    plot(X, Y)
    grid(True)
    \n", "
    • \n", "
  • On peut modifier la couleur de la courbre en ajoutant un paramètre à la fonction plot, comme dans l'exemple suivant : \n", "
    plot(X, Y, 'k')
    \n", " Les différentes couleurs prédéfinies sont : \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
    CouleurCode
    Bleu'b'
    Vert'g'
    Rouge'r'
    Cyan'c'
    Magenta'm'
    Jaune'y'
    Noir'k'
    Blanc'w'
    \n", "
    • \n", "
  • On peut préciser le style de la ligne parmi les choix suivant : \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
    StyleCode
    Ligne continue (par défaut)'-'
    Tiret'--'
    Pointillé':'
    \n", "
    plot(X, Y, ':')
    \n", " ATTENTION cependant ! Si vous voulez modifier la couleur et le style du trait, il faut le faire en même temps. De sorte que plot(X, Y, ':', 'r') est une erreur. On corrigera par\n", "
    plot(X, Y, 'r:')
    \n", "
    • \n", "
  • La fonction plot(X, Y) place par défaut des points (ceux de X et Y) et les relie par des traits (où d'autre style comme nous l'avons vu précédement). On peut changer le style des marques parmi ceux de la liste suivante : \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
    MarqueurCode
    Point (par défaut)'.'
    Cercle'o'
    Triangle vers le bas'v'
    Triangle vers le haut'^'
    Triangle vers la gauche'<'
    Triangle vers la droite'>'
    Carré's'
    Pentagone'p'
    Etoile'*'
    Plus'+'
    Croix'x'
    Losange'd'
    \n", " Il en existe quelque autres. Comme pour les styles de traits, il faut déclarer les marqueurs au même moment.\n", "
    plot(X, Y, 'rd:')
    \n", "
    • \n", "
  • On peut préciser l'épaisseur de la ligne en ajoutant à la fonction plot la paramètre linewidth.\n", "
    plot(X, Y, linewidth=5)
    \n", "
    • \n", "
  • On peut préciser les bords de la fenêtre de graphique par l'appel des fonction xlim et ylim qui prennent chacun deux paramètres : la plus grande et la plus petite valeur sur les axes respectifs.\n", "
    plot(X, Y)
    xlim(-1,1)
    ylim(-10, 10)
    \n", "
    • \n", "
  • On peut donner un titre au graphique par l'appel de la fonction title\n", "
    plot(X, Y)
    title(\"Graphique de ma fonction\")
    \n", "
    • \n", "
  • On peut déssiner plusieurs fonctions sur un même graphique (par l'appel de plusieurs plot différent). Dans ce cas, pour les disctinguer (en plus des couleurs), on peut légender les différentes courbes. Il y a pour cela deux choses à faire : \n", "
      \n", "
    1. Donner un titre à la courbe, en ajoutant un label dans plot.
      2. \n", "
    2. Demander l'affichage de ce label. Pour cela on appel la fonction legend sans paramètre.
      3. \n", "
    \n", "
    plot(X, Y, label=\"Fonction 1\")
    plot(X, Z, label=\"Fonction 2\")
    legend()
    \n", "
    • \n", "
  • On peut aussi labéliser les axes : xlabel pour les abscisses et ylabel pour les ordonnées.\n", "
    plot(X, Y)
    xlabel(\"Abscisses\")
    ylabel(\"Ordonnées\")
    \n", "
    • \n", "
\n", "\n", "

Voici un exemple qui résume tout.

" ] }, { "cell_type": "code", "execution_count": 19, "id": "radical-composition", "metadata": {}, "outputs": [ { "data": { "image/png": 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Hh+sHDeLCmBimBgf3TFPPzp3w9NPw8stGp/Wff0InI6Hq8+vZetZWfAf4MmbZGDy8e6/noM/1UTQliW61HXYgPz+fiy66iC+++IKgoCC++eabg/aZPHkys2fP3v9VWVnJlVdeyT333HPAfhdffDHvvvsuNTU1ByUCW86jaZrtRIQ/Kyu5eedOYn//nZM2b+az4mLOjo7m+8MOI+fII3lu+HCmhYT0XH9AaiosW2bUa4JOkwRAzfYaxCyMWz4OnyifnonDViLiUl+JiYnSWmpq6kGPteeJzEz5obT0gMd+KC2VJzIzbT5Ga/v27ZMjjjhCvv32WxER+fHHH+WII47o9vFERI466ihZtWpVh+dZuXJlp+fpyrVxZatXr3Z0CE5DX4tmHV2LbdXVcv/u3TLs99+F1avFNzlZzt6yRT4tKpJak6lnAsjLEznuOJH8fJGNG0WWL29+rtXnkC1MNd2PC1gr3fzcdbump850qe3QRgEBAfz+++/7t4877rgDtrvjiSee4KijjurwPEcfffQhn0fT3E1HQ+AvjInho6IiPigqYkN1NR7A38LDuTc+njnR0YT2UH/lfgsWwC+/wPz5sGsXZGTA6aeDpyfY+JmT91oeYhIGXj8QT3/H9Ef2uUThKo455hhHh6BpLqlpCPyyMWNQwBfFxVy8fTvD/Py4a/duxLrP88OHMzc6mgGtRiz1CH9/Y9Jck0WLjH99fY0kYaPyH8vZecNOwk8OZ+B1AzmEZa8PiU4Umqa5laTwcN4bNYpZW7cSCuSkpABQa7HwYEICF8TEMKKDEUs9YvduuP56WL7c2A4IMMpvPP20zYeoy6wj5ZwU/Ib5Meb9Mb02FLYtOlFomuYWRIS1VVW8XVDAh0VFVJnNVAHTg4N5JTGRSUFBvVN6PyMDEhKgf39QyriLqKuDkBDjMRuYa8xsnb0VS6OF8V+MxyvUsR/VfW7Uk6Zp7iW/vp6nsrIY99dfHL5+PW8VFDA5KIhQT08uBnbV1VFhMvVOknjrLUhMhC1bjLkR110Hf/xhTKKzYZ5Ek9JVpVRvqWbMh2MIGGnnux8b6DsKTdNcTp3ZzJclJbxdUMA3paVYgKNCQlicmEh/Hx+uSEvj83HjUJs2cYV1CHzLmdI9rrbW6JeYNQtycmDECPjss+bnFy7s0uGiz4pm+o7p+A/17+FAu0cnCk3TXELrpqUyk4lYX1/uiovj0v79SbT2OzyZlbU/KSTTSfmMnnDllUYRv5UrjSJ+DzzQ7UOVripF+SjCk8KdJkmAThSapjm5/Pp63iss5O2CAlJravDz8GBOVBSX9e/P38LD8WzVpGSPIfAHETH6HwCmTzfqNVksXRrR1FpNWg0p56UQMCKAyX9OdmjndWs6UWia5nTqLRZW7N3bZtPS3JiYnp/v0BXFxXDJJcaSpDNnGgsLHSJThYkts7bg4e3B2E/GOlWSAJ0oXMby5cv56quv9pf9OOmkkxwdkqb1qLaalgb5+HCntWmpoyJ8vSokBEpLjYTRA8QibLt4G3W76jjs+8Pwi/frkeP2JJ0oXERTfaiysjJuv/12nSg0t9FW09JZ1qalE9poWnKItDR46il45RVjuOsff9hUn8kWRUuLKPlvCSMWjiDs+LAeOWZP04nCxTz88MP885//dHQYmnZI6i0WvmzRtGQGjgwJ4bXEROZGRxPm7e3oEA+UlmaMYrrhBpg4sceSBEDM+TF4hXgRcWpEjx2zp/XZeRT1+fVsOH4D9QX1PXbMnlrhrq3jiAh33nknp5xyCpMnT+6xmDWtt4gIaysruWHHDgb89hvnpqaysbqaO+Li2H744fw2eTLzBg50niSxYQN8/rnx/ZlnGrOtJ07sscNXb62mdnctSikiT4vsnXke3dRn7ygyFmRQ8UsFmQsySVyY2CPH7IkV7to7zksvvcT3339PRUUF6enpXNvJKlia1tvaK8b3Q1kZoV5evF1QQIqzNi215a67IDPTSBKenhAW1mOHbtjbwJbTt+AV6sXUjVOdOkmAmyaKDTM2HPRYzNwYBl0/CHONmY0nbKRqTRVYIO/VPKo2VDFw3kAGXDaAhr0NpJyTcsBrJyVPsum8PbHCXXvHuemmm7jppptsvQSa1utaFuM7KjSUxzIzeSwrC5MIFpy8aanJb7/BmDFGUnjzTaNGUw+vIGlptJB6bioNBQ2M/Xis0ycJcNNE0Zn6zHoQ64ZYt3tAT6xwZ8txNM0ZJYWH89TQoZy2ZQsiQp0IUV5eXD1woHONWmpPbi7MmAH/+hc88QTExtrlNLtu20V5cjmj3hlFyLQQu5yjp7lloujoDsBUYcJUZjogUZjKTETMNDqSfKJ8bL6DaKnlynM33XQT33zzDTNnzjxgn6YV7pq89dZbB61wZ8txNM2Z1FssfFpczGt5efxUUYEHYAEujInhndGjnbdpqcnu3TB0KAwaBJ9+CklJdjtV0SdF5L6US+y/Yul/iW0FAp1Bn+vMzliQgVjkgMfELGQuyOz2MWtqapgzZw7PPPMMo0eP5v777+ehhx46YJ/bb7/9gP4JgCuuuIL77ruvS8fRNGexo6aG29PTGfTbb1y0bRs59fXMGzCAcC8v7o+P59uyMn4qL3d0mB17800YORI2bza2zzgDgoLsdrrIUyIZ8tgQhj451G7nsAe3vKPoSOXvlUhDq0TRIFT8VtHtY/bUCnf2WClP03pSg8XC8r17eS0vjx/Ky/FSilmRkVwzcCAewPnbtvHx2LFGyYywMPsX4+uupiJ+Z51lVHVN7JkBLe1pKGzAI8ADr2Av4u+Kt+u57KHPJYppG6Y5OgRNczm7a2tZnJfHkoICihobiff15ZEhQ7i8f//9K8S1LMYHvVCMr7suuwzy8+GbbyAiAu69126nqs+vJ2VuCuZqM55+nkz6bZJLdF631ucShaZptmm0WPiypITX8vL4tqwMD+AM693DSRERjinG110ti/gddRSUlR1yET9bZMzPoPKXSgDGfuYaI5zaYtdEoZSaCbwAeAJviMjjrZ6PA/4PCLPuc5eIfG3PmDRN61hmXR2v5+XxVkEB+Q0NxPr68lBCAlcOGMAge6wvbS/5+Uy8+WZ4/3249VZjNNMpp/RIET9b1OfXU/CGsViR8lKEHOkaI5zaYrdEoZTyBBYCfwdygL+UUitEJLXFbvcBy0RkkVJqDPA1kGCvmDRNa5vJYuHr0lJey8tjZWkpAKdGRHDNwIGcEhGBVw+WrOg1CxYQumULvPQSVFVBq8oI9rbtH9sQk7U/1IMendzb2+x5R3E4kC4iuwGUUh8Bs4CWiUKApjQbCuTZMR5N01rJqavjjfx83iwoIKe+ngE+PtwbH89VAwYQ7+d8VUxt4u9vrFENKIDFi43HN26Eiy/ulRDq8+up+Ll5gIw0CAVLCoi/Px7f/i50V2alRKTzvbpzYKXOAWaKyFXW7UuA6SJyQ4t9BgDfAuFAIHCiiKxr41jzgHkA0dHRU5YtW3bA86GhoQwfPtwu78OZmc3mg2Z/t5aenk5FRfdHdLmK6upqguw4rNGVdHYtzMBfwJfAHxh/rU0FzgCOxPU7Ln1KShg9fz5hmzejALOvL3uPPZZd111HQ0QvFN7LAZYBKwFTi8e9gNOAW+wfQluSkpLWicjUzvc8mKN/Ji4A3haRZ5RSRwLvKqXGiYil5U4ishhYDDBy5EiZMWPGAQfZtm1bn5zBXFVV1en79vPzY9Kkrk8gdDXJycm0/rnoq9q7Fvn19byZn8/r+flk1dfTz9ubOwcM4OoBAxji7zzLbnbbunWQkQFnnw3ffw9bt2L28sKzsZF+I0bQb84cu4ewL3UfG+ZsQHkpGk2tCn6aIDAzkGkzXG/kpT0TRS4wuMV2rPWxlq4EZgKIyO9KKT8gCiiyY1ya5nbaK8i3prKSScHBvJaXxxd792IGTggL4+lhw5gVFYWPK/Y9tOeeeyArC2bPhsJCuPZa1k+axLQNG4zhsHZWl1PH5pM3o3wUk3+b7FRrXh8qeyaKv4ARSqkhGAnifODCVvtkAScAbyulRgN+QM8sG9WZ/Hw4/3xYuhT6u85Uek1rS8uCfAr4tLiYS7dtI8TTk/zGRqK8vbl18GCuHjCAEc5ec6krfvkFxo6F8HB46y0IDDSGvH72GQD7kpPhqqvsHkZjaSObT96MqcLExJ8mulWSADuW8BARE3ADsArYhjG6KUUpNV8pdaZ1t9uAq5VSm4APgcvEXp0mrS1YYPyQzZ/fY4e053oUmtaRpPBwlo4ezVlbt3IZcE5KCvssFhIDAvhg9GhyjjySJ4cNc68kkZtr1GV63DrqftCgHi0FbisRIfX8VGrTaxn3xTiCJ7phM7iIuNRXYmKitJaamnrgA8cfL7JkifF9Q4Ox/e67xrafn4gx/ebAL29v4/niYmP/FSuM7fz8g87Xnh9//FGOP/54ee+99+TUU08Vk8m0/7mnnnpKljTFZPXOO+/InDlzpLGx0ebjtFRZWdlpTAddGze1evVqR4fgMLUmkyzJy5PD1qwRVq8WVq+W6WvXyrbqakeHZh87dzZ//+WXIh28z976uSj/tVyKPivqlXN1F7BWuvm56+jO7N63dasxM7O8HBoajKF0wcHw6KOHfGh7rkehaa0VNjSwKDeXRXl5FDU2kuDnR5CnJ2eazXxbV0d+QwOjAgMdHWbPeuMNuO46o+N6wgQ4/XSHhSIiVPxUQdjxYYQeFeqwOHqDeyaK5OTm7729D9weNgzmzDHGVvv5QX29UfvlyiuN56OiDty/C/0Xej0KrTdsqq7m+ZwcPigspEGE0yIimBEWxhPZ2awYNw61aRNXjRnjvAX5ukoEamqM/oc5c6CoyKj46mB77ttD1qNZTEyeSNjxYY4Ox67caMhDF1hHRPDHH8a/BQWHfMiW60gEBQXxzTffHLRP03oUTV+VlZUdrkfR3nG0vscswoq9e/nbxo1MXLuWZUVFXD1gAGmHH85/J0zAAu0W5HN5l15qVHkVMYr43XMPOLiUSM6LOWQ9msWAeQMIPc697yYAN+2j6GX79u2TI444Qr799lsRMfoYjjjiCLsfR/dRNHPXPorKxkZ5ITtbhv3+u7B6tQz+7Td5MjNTShsa2n2NW1wLi6X5+zfeEHnqKZF2+uo6Yo9rUfBhgaxWq2XLWVvEYrJ0/gInge6jcCy9HoXW0/bU1vJybi5v5OdTaTZzVEgIjw0dyllRUa5Zd6kriorgggvgttvg1FObm4WdQF1mHdsv3U7osaGM/mA0ytM1q8F2lU4UmuYkRIRfKip4PieH5Xv34qEU50ZHc0tsLIeHuG7l0S4LDTUWFnLC1fH84v0Y/d5owv8ejqdf3xlkohOFpjlYg8XCsqIins/JYV11NRFeXtwZF8f1AwcS66qF+boqNRWefNIYZOLrC7/+2rx+hBOoSa+hsbCR0KNDiTk3xtHh9DqdKDTNQYobGngtL4+FeXkUNDQwOiCA1xITubhfPwL62pDoPXvgv/+F7duNYa9OlCTqC+rZfPJmpEGYnj4dD183b/prg9skChFx2dWj7MXov9Kczdbqal7IzeW9wkLqLBZmRkRwS2wsJ4WH962f4b/+gsxMOOccOO00I1k42XBwU6WJLadsoaGggYk/TOyTSQLcJFH4+flRUlJCZGRk3/pF64CIUFJSgl9fabpwchYRVpaW8nxODt+XleHv4cFl/ftz06BBjHa3SXG2euABo9rrWWcZ9ZmcLElY6i1snb2VfVv3Me7LcYRM70P9RK24RaKIjY0lJyeH4uLeqSfoLOrq6jpMBH5+fsTGxvZiRFpr1SYT7xQW8kJODjtqaxnk48NjQ4Zw9cCBRHp7Ozq83vfjj0bTUng4vPlmcxE/J5T3ah7lq8sZ9c4oImdGOjoch3KLROHt7c2QIUMcHUavS05O7hNrTTiz9sp7f1dWhkmE1/PzKTeZODw4mA9Hj+bs6Gi83X14a3tycuDEE431q594AgYOdHREHRp0wyACxwUSfoKLz2zvAW6RKDTNUVqW954RFsbC3Fxu37WLRhEUcLZ1eOuRoX1g9m57duyAxESIjYUvvgAnX2Aqb3EeEadE4DfYTycJqz76p42m9Yyk8HA+Gj2a2Vu3Evv779yYno6XUtw+eDC7jziCpWPH9r0kkZ8Pxx9vlMZ5/XUYMwY2bTKeO/VUcOJS53lv5LHjmh3kPJ/j6FCcir6j0LRuMouwrKiIRzIzqTSbqTSbOSUigmVjxhDk1Yd/tebPb17r5ZFHoLQURo92dFSd2vvFXnZcs4OIUyIY+vhQR4fjVPQdhaZ1UaPFwpL8fEavWcOF27axz2wm2NOTe+Pi+Kuqyj0K8XWHv78x/+HVV8FigUWLjCJ+Dz4IPj6Ojq5D5T+Xk3p+KsFTgxn78Vg8vPVHY0v6amiajerMZl7NzWXEn39yRVoaQZ6ePJiQQLXFwhfjxvHw0KEss5b3Xl1W5uhwe5cI7N4NF17YnBQCAuCii4z5EU5MRMh4IAPfeF/GfzUez0DnHIXlSDpRaFonasxmns/OZtiff3Ldzp0M8PXlq/HjWTdlCv4eHu5b3ttWRUVwwgmwfj2EhIDJZKz1UldnbDv5mvRKKcYtH8dh3x2GT5Rz3/k4Sh9uSNW0jlWaTLySm8uzOTkUNzaSFBbGu6NHkxQWtn9i5x1xcQe9Lik83PUXC+qKsDBobISqqua1XubNM+o25ec7Orp2NextIPPhTIY+NhSvUC+8QvXHYXv0ldG0VkobG3kxJ4cXcnMpN5k4JSKCe+PjObqvjV7qyNatxlyIN94wivj99JPRP3H++c37LFzouPg6Yd5nZstpW6jeVE2/i/sRMrXvzrq2hU4UmmZV1NDAczk5LMzNpcpsZnZUFPfFxzPFyUpLOIWsLFi1CtLSnK6IX2csjRZSzk2ham0VYz8dq5OEDXSi0Pq83Pp6ns7O5rW8POosFs6LieGeuDjGBwU5OjTn8uefRhG/uXON+RC7d4OLXSOxCGlXplG6spTExYlEz452dEguQScKrc/KqK3liexs3srPxyzCJf37c1dcHCOdeEKYQ/3nP5CdDWefbdRncrEkAVCfXU/J1yUkLEhg4NXOXULEmehEofU5O2pqeCwri/cKC/EArhgwgDsGD2aIv7+jQ3M+q1fDYYcZ8yGWLHHqIn628Iv3Y9rWafj006ObukInCq3P2FpdzaNZWSwtKsLXw4N/DhzIv+PiGOTr6+jQnFNODpx0UnMRvwEDHB1RtxW8V0DtzloSHkzAt7/+/+4qnSg0t7euqopHMjP5fO9egjw9+ffgwfxr8GD6OflsYYfZvh1GjTKK+H35pVG3yYWVfFNC2uVphB4bijQKysd1Ot6dhU4Umtv6raKChzMzWVlaSpiXF/+Jj+em2Fgi+uI6ELZ67TX45z+NyXMTJsDMmY6OqNvq8+vhatiau5XAcYGM+3wcHj56jnF36EShuay21oL4obSUj4qLSa+tZXV5OVHe3jw6ZAjXDxpEaF8u1NcREaiuNlaYmzvXmDjnAkX8OrPr1l2QDh7BHoxfOV5PqDsE+sppLqvlWhAAj2Vm8kBGBiYRBvj48OywYcwbOJBAF+587RUXXmiU4fj+e2Pludtvd3REh6w+v57iz4wVLy2NFgdH4/p0otBcVlJ4OEuta0F4AaV79hDj7c2DCQlc3r8/fjpBtE+keZLcSScZdxEtH3Nh5jozGQsymh+wQOaCTBIXJjosJlenE4Xmsv6srORB61oQAGdERvLJ2LH49NWlRm1VWGg0Mf3733D66XD55Y6OqMdUb61m88mbadzbiDQIANIgFCwpIP7+eD3iqZv0b5TmcnbX1nJeSgpHrF/Plupqgjw9uRj4vbKSXysqHB2e8wsPN+4eamocHUmPqt5UzaakTZjKTSAHPidmIXNBpmMCcwM6UWguo6SxkX+lpzNqzRr+W1LCJf364akUK8aN40rou2tB2GLzZmNtiPp6Y72IH3807ircROXaSjYmbcTDzwO/eD+k8cBMIQ1CxW/6j4ju0k1PmtOrM5t5KTeXRzIzqTKbubx/f+YPGcJ7hYVc3r8/SeHhJHPgWhB9qsy3LXJyjM7qHTtg/Hi36ItoUrunlk0nbMI7wpvDfjgM/yHNM+yTk5OZMWOG44JzE3ZNFEqpmcALgCfwhog83sY+c4EHMW4WN4nIhfaMSXMdFhE+LCri3t27yayv59SICJ4YOpRx1hpDei2INuTnM/Hmm43Krnv2GFVezzuvuYhfYKCjI+xxfgl+DP73YPr/oz9+cX6ODsct2ZQolFI3A0uAKuANYBJwl4h828FrPIGFwN+BHOAvpdQKEUltsc8I4G7gaBEpU0rFdPudaG7lh7Iy/r1rF+urq5kUFMRbo0bxt76cAGy1YAGhW7bA/PlGosjOhnPOMeozuVmSKP+xHJ8BPgQkBpBwX4Kjw3Frtt5RXCEiLyilTgbCgUuAd4F2EwVwOJAuIrsBlFIfAbOA1Bb7XA0sFJEyABEp6mL8mptJ2bePO3bt4uvSUuJ8fXl31Cgu7NcPDzdqKrELf39j6VFAASxaZDzu6+vSRfzaU/ptKVtnbSX0+FAO++YwR4fj9pSIdL6TUptFZIJS6gUgWUQ+V0ptEJFJHbzmHGCmiFxl3b4EmC4iN7TYZzmwAzgao3nqQRH5po1jzQPmAURHR09ZtmxZV96j26quribIBUs9t6UE45Z1JeAPXAScDdhajcmdrkV3+JSUMOK554j69VcUYPb1Ze+xx7LruutoiIhwdHg963fgP0Ac8DQQ1v6uff3noqWkpKR1IjK1O6+19Y5inVLqW2AIcLdSKhjoiemOXsAIYAYQC/yklBovIuUtdxKRxcBigJEjR4runDK4Q0ddtcnEU9nZPJ2dTaMINw4cyP0JCUR2sR6TO1yLbktNhRkzjM7q337D7O2NZ2Mj/UaMoN+cOY6OrkcVf15M6n9SCZwQyGHfHoZ3RMc/J33656IH2ZoorgQmArtFpEYpFQl0NksnFxjcYjvW+lhLOcCfItII7FFK7cBIHH/ZGJfmokwWC2/k5/NgRgaFjY2cGx3NY0OHMkyvCdE1r70G119vFPErLITrrmP9pElM27AB8vMdHV2PEhFyX8oleEow41eOxztMF3fsLbYmCgHGAKcD84FAoLPhBX8BI5RSQzASxPlA6xFNy4ELgCVKqSggEdhtY0yaCxIRviwp4c7du9leU8MxoaF8MWwY00P0usU2EzFKboSEGCOa9u2DMWPgs88A2JecDFdd5dgYe5hYBOWhGLd8HABeIXpkf2+ydcLdK8CRGB/qYIx+WtjRC0TEBNwArAK2ActEJEUpNV8pdaZ1t1VAiVIqFVgN/FtESrr4HjQX8VdlJTM2bmTW1q1YRPh87Fh+mjhRJ4muOv98OOssI2GEhRkLC7lx6fT8JflsOmET5n1mvEK8dJJwAFuv+HQRmayU2gBgHcraaT+jiHwNfN3qsQdafC/ArdYvzU3tqa3lnj17+KioiGhvbxaOGMHVAwbgrWsy2a5lwb5TT3WrIn4dyVucx45rdhB+Urh1OJfmCLYmikbrvAgBUEpF0zOd2ZobK21s5JHMTF7OzcVTKe6Ni+OOuDhC9LoQXVNQYMyFuOMOOPNMuPRSR0fUK3JeziH9xnQiTotg7Cdj8fRzv2G+rsLW39gXgc+BfkqpR4BzgPvsFpXm0urMZl7OzeWRrCwqTKb9JTf02tTdFBFhNC01NDg6kl6T+2ou6TemEzU7ijFLx+iV6RzMpkQhIu8rpdYBJ2DcAM4WkW12jUxzORYRPioq4h5ryY2ZERE8OXQo4/U49q7btAkefxzeftuYNPfDD27fzNRSeFI4A68fyPDnh+PhrZOEo3XlfyAKqBGRl4G91tFMWh/1ZFbWAVVak8vKGP3nn1y0bRvh3t58N2ECKydM0Emiu/LzITkZdu40tvtAkhARSlaWICIEjAwgcWGiThJOot3/BaXUuBbf/we4E6MuE4A38J59Q9OcWdMypEvy8zljyxaSNm0iva6OuwYPZt2UKZzobrOBe8Mvv8CHHxrfz5wJu3bBuHEdv8ZNiAh77t3DllO3UPxJsaPD0VrpqOkpTil1sYjcBZyFUQhwPYCI5FlnZ2t91BEhIZwQFsYVaWn4KEWghwefjB3LzMhIR4fmuh55BHJzjXUiPD0hIMDREfUKEWHX7bvIeTaHAdcMIPrsaEeHpLXSbqIQka+VUmbrZoOIiFKqadSTe5Wh1Lrk5/JyrkxLY2dtLRMDA9m4bx93xsXpJNEd334LU6ZAZCQsWQJBQW5ZxK89IkL6TenkvpzLoBsHMfyF4ag+0MzmajpsABSRVdZvlymlXgPClFJXA98Dr9s7OM25VJlM3LBjB8dt3EijCE8NHUpOQwP3x8ezKC9PryzXVdnZcNpp8NRTxnb//kai6EOqN1aT92oesbfF6iThxGwd9fS0UurvQCUwEnhARL6za2SaU/m2tJSr09LIrq/npkGDODk8nEvT0lg2ZoyxWFBYGHNTU/dva+0QgZQUo+9h8GBYuRKOOcbRUTlM8KRgpqyfQuC4QJ0knJjNQwpE5DsR+beI3K6TRN9R1tjIFdu3c/Lmzfh7ePDzpEm8MGIEW2tqDkgKLZch1Trw6qtw2GHG8FeAE08Ev761KpvFZGHbpdso+thYfiZofJBOEk7O1hXu5gBPADEY8ygURgUOXaTHjS0vLua6nTspbmjg7rg4HoiPx8/afq6XIe0CEaishNBQuOACY+Lc2LGOjsohLI0Wtl20jeKPiwkcq7s6XYWtM7OfBM7Qk+z6hqKGBm7cuZNlxcUcFhjIV+PHMzlYD3LrtrlzYe9eY9JcWBjcfLOjI3IIS72FlPNSKPmihGHPDGPwrYM7f5HmFGxNFIU6Sbg/EeGDoiJu3rmTKrOZBQkJ3BkXp4v3dYfFAk3X7YwzjFLgfaCIX3ssjRa2ztlK6delDH9pOLE3xDo6JK0LbE0Ua5VSSzHWj6hvelBEPrNHUFrvy6mr49odO/iqtJTpwcG8NWoUYwJ104DN8vON8t9Llxrbc+bAnXfCrFnwj384NjYnoLwUgeMCiZoVxcB5Ax0djtZFtiaKEKAGOKnFYwLoROHiRIQ38vO5fdcuGkV4dtgwboqNxbOP/uXbbQsWGDOr58+HF14Af38wmRwdlcOZqk005DUQkBjAsCeGOTocrZtsHR7b2bKnmgvaXVvL1Wlp/FBeTlJYGK+PHKmXIu0qf3+oq2veXrTI+PLzg//9z3FxOVB9fj2p56cy8o2RbL98O3V76pi+czqeAX1nIqG7sXXUUzRwNZDQ8jUicoV9wtLsySzCSzk53LtnD55K8VpiIlcNGICHvovout274fbb4dNPob7eSBBnnw1PP+3oyBwmY0EGFb9UsOGYDZhKTYz+YLROEi7O1qanL4CfMWZkmzvZV3Ni2/bt48q0NH6vrOTUiAheTUxkcB8bx99jfvrJmF0dEgKNjUaSaGgwtvv3d3R0DlGfX0/BkgKwQGNRIyOXjCTm3BhHh6UdIlsTRYCI3GnXSDS7arRYeDI7m/kZGQR5evLe6NFcGBOjJzodiieegJwcGDoUrr0W5s2DxYuNju0+KmNBBtIgxoYXVP1VxYDLBjg2KO2Q2Zoo/quUOtW6BrbmYjZUVXFFWhobq6s5Nzqal0aMoJ9Pp0uea21ZuRIOP7y5iF9goPHVZOFCx8XmYHU5dRQuKWxeJNkEBUsKiL8/Ht/+enVDV2brAPmbMZJFnVKqyvpVac/AtENXZzZz7+7dTFu3joKGBj4bO5ZlY8fqJNFd2dnGmtVN/Q8xMQcmiT4sd1Eu66asQyxywONiFjIXZDooKq2n2DrqSU/LdTG/V1RwRVoa22tquKx/f54dNoxwb29Hh+V6RGDLFpgwwSji9+23cPTRjo7KaYhZSL8tndwXcvEM8Wxudmp6vkGo+K3CQdFpPcXWpieUUmcCx1k3k0Xkv/YJSTsU+6x3ES/m5jLY15dvJkzgZL3aXPe9+irccAOsX28U80tKcnRETsNUaSL1glRKvy4l9l+xDHtqGMpT93m5I1uHxz4OTAPetz50s1LqaBG5u4OXab3sf2VlXJ2Wxp66Ov45cCCPDR1KsJfNfwtoTUSgosKoy3ThhcbEuT5axK8jaVelUbqqlMRXExl4jZ5t7c5s/RQ5FZgoIhYApdT/ARtoXkNbc6AKk4nbd+3ijfx8Rvj78+PEiRwXFubosFzXOedASYlRxC80FG680dEROaWhjw9l4DUDCT9BVwx2d135czMMKLV+H9rzoWi2eDIri2nBwfvLeX+5dy+Xb99OqcnEvwcP5qGEBPz70FKaPaZlEb/Zs6GmxqHhOKvCDwsp+7aMkW+NxH+oP/5D9Uz+vsDWRPEYsEEptRpjLYrjgLvsFpXWrmnBwcxNTeX1xEReBv63dSuewMIRI7hu0CBHh+ea8vON5HD33ca/l1zi6IicjoiQOT+TjAczCD02FPM+M15Bulmzr7B11NOHSqlkjH4KgDtFpMBuUWntSgoP5/EhQzg7JQUBAjw8+HzcOE7SHdbdFxVlzKbW2mSuM5N2RRpFHxbR79J+jHxtJB6+uvR8X9JholBKTW71UI7134FKqYEist4+YWntWVlSwr927SLAw4Nqi4XbBg/WSaI71q2DRx+F9983Sm98p1f3bU/KOSmUflXK0MeHMviOwXo2fx/U2R3FM9Z//YCpwCaMpqcJwFrgSPuFprX2ck4ON6enM9TPj1KTiUssFhbl5ZEUFqaXIO2qkhJYswZ27dIjmjox+LbBDLhiANFzoh0diuYgHd4/ikiSiCQB+cBkEZkqIlOASUBubwSogcli4aadO7kxPZ0jQkIoM5n4ZOxYrgCWjRnD3NRUVpeVOTpM55ecbNxBAJx0EuzcqZNEO0q+LiH72WwAwpPCdZLo42xtaBwpIluaNkRkKzDaPiFpLVWZTMzaupWXcnO5LTaWMyIj+Xjs2P13EEnh4SwbM4a/qqocHKkLePJJeOopMFsLIOuquQcREXJeyGHLGVso/KAQS4Ol8xdpbs/WYQtblFJvAO9Zty8CNtsnJK1JVl0dp2/ZQuq+fbyWmMi8gW1PakoKD9dNT+356iuYPt3osH77bQgKAj18uE2WRgvpN6WT92oeUWdFMfrd0Xj46E5rzfY7isuAFIzigDcDqYBe9c6O/qqs5PB168iqq2PlhAntJgmtA9nZcNZZ8Iy1qy0mBgICHBuTkxKLsPXMreS9msfgOwcz9pOxeAbqhKoZOr2jUEp5AiutfRXP2T8k7dPiYi7Zto3+Pj78MHEiY3SFUtuJwMaNMGlScxG/o45ydFROT3koIs+MJHpuNAMu1+tHaAfq9I5CRMyARSnV5dnYSqmZSqk0pVS6UqrdCXpKqbOVUqKUmtrVc7gTEeHxzEzOSUlhYlAQf0yerJOELfLzmXjzzVBQAK+8AlOnGskCYMYM0GXV21X+czklX5cAMOi6QTpJaG2ytY+iGqOf4jtgX9ODInJTey+w3oksBP6OMf/iL6XUChFJbbVfMEZz1p9djN2tNFgsXLdjB28VFHBBTAxvjRyJn25Lt838+YRu2QLz58Njj4FSMH68o6NyegX/V0Da1WkETggkYmYEykPPj9DaZmui+AZjvWwBTECtDa85HEgXkd0ASqmPgFkY/RstLQCeAP5tYyxup6yxkbNTUlhdXs5/4uP5T0KCntRkC39/qKsDjMk9LFpkfPn5wfXXOzQ0ZyYWYc99e8h6LIuwE8IY+/FYnSS0DikRaf9JpbyAR4ErgEyM38c4YAlwj4g0dvDac4CZInKVdfsSYLqI3NBin8nAvSJytrVEyO0israNY80D5gFER0dPWbZsWVffp9PKxSjBW4CRKf/ehddWV1cTFBRkl7icnsWCT1kZwxYtIvrHH/EwmTD7+rL32GPZdd11NPTh2eod/lyYMP40+wk4HeNe3o1LNvXp35FWkpKS1olIt5r3O/sReQoIBoaISBWAUioEeNr63C3dOan1OB7AsxgjqjokIouBxQAjR46UGTNmdPe0TuXn8nJu2roVD6X4YexYjuliafDk5GTc5Vp0SX4+zJplFPEbMQJWr8bs44NnYyP9Royg35w5jo7QoTr6uRARdn66E//Z/sTeEuv2d6599nekh3WWKE4HEqXFbYeIVCqlrgO203GiyAUGt9iO5cDZ3MHAOCDZ+sPaH1ihlDqzrbsKd/NuQQFXpqUxzN+f/44fzzB/Xa7ZZlFREB5ulAUvLIRrr2X9pElM27DBSCLaQao2VOHh40Hg2EBGvDLC7ROE1rM6SxQibbRNiYhZKdV+m5XhL2CEUmoIRoI4H7iwxTEqgKim7Y6antyJRYT/ZGTwcGYmfwsL45OxY/Va1rb46y+jo/qDD4w+iFWrjMdnzQJgX3IyXHWV4+JzYsXLi9l20TaCpwYz6cdJOkloXdbZ8NhUpdQ/Wj+olLoY446iXSJiAm4AVgHbgGUikqKUmm9df7vPqTWbuTA1lYczM7myf3++mTBBJwlblZXB2rWwe7ejI3EZIkLWU1mkzEkhcFwgY5aOcXRImovq7I7in8BnSqkrgHXWx6YC/sBZnR1cRL4Gvm712APt7Dujs+O5ssKGBmZv3cqflZU8OXQotw/W5Zo79b//QW4u/OMfzUX8fH0dHZVLsDRY2HHdDgreKiB6bjSj3h6Fp78ebq11T4eJQkRygelKqb8BTWU2vxaR/9k9MjeSsm8fp23eTFFjI5+OHctZ0boSp02ee85IFBddZNRn0kmiU/X59XAzNKxooHZnLfH3x5PwYIIe/qodEltXuPsB+MHOsbilVaWlzE1JIdDTk58mTmSqXkmtYytWGCU3oqJgyRJdxK+Ldt22C7ZA1pNZHPa/w/Dw1kX9tEOnf4rsaFFuLqdt3swQPz/+nDxZJ4nOZGXBOefAs88a29HRxqQ6rVMiQs5LORR9WAQCBUsKaCxpd5qTpnWJThR2YBbhX+npXL9zJzMjIvh50iQG67UP2iYC660r6sbFwfffw0MPOTYmF9NY3si2i7aRflO6dYo6iFnIXJDp2MA0t6ETRQ+rNpmYvXUrz+fkcEtsLF+MH0+wlxtPfT1UCxfCtGmwaZOxfdxxoEeC2ax6SzVrJ66laGkRyksZRXYAaRAKlhRQX1Dv2AA1t6ATRQ/Kqavj2I0bWVlSwsIRI3hu+HA89cimg1ksUFpqfP+PfxjJYtw4x8bkonwH+eKX4EfU7KiDfpv1XYXWU3Si6CHrqqo4fP16dtXW8t/x47l+0CBHh+S85syB2bONhBESAtdeqzusu6B2dy1p16ZhabTgHeHNpORJ1O6uRRoOnAMrDULFbxUOilJzJ7pNpAcsLy7mom3biPb25rdJkxini5AdzGxuTgbnnWdUfdV3W10iIhS+V8jOf+4ED2P9iKDDjJ+1aRum7d9P1zfSepq+ozgEIsLTWVnMSUlhfGAgf06ZopNEW/Ly4PDD4bPPjO0LLoDLL9eJoguaOqy3/2M7QRODmLZ52v4koWn2pu8ouuDJrCymBQeTFB5Oo8XCP3fu5PX8fCYEBrJ64kT8dfNJ26KjoV8/vdLcIdh2wTZKvytlyMNDiLsrDuWpk6zWe/QdRRdMCw5mbmoqXxYXc8rmzbyen0+AhwfPDhumk0Rra9YYBftqa41RTF9/Daef7uioXIrFZMFcawZg6BNDmfzrZOLvjddJQut1+o6iC5LCw3kzMZGzUlIQINjTky/GjSMpPNzRoTmfykpjyGtGBowe7ehoXE7t7lq2XbSNgLEBjHpjFEETdDOT5jj6jqKL1lZXYwYswC2xsTpJtPTdd/B//2d8f+KJkJamk0QXiQgF7xawduJa9m3bR8Tf++5KfZrz0ImiCypMJp7NzsZHKe6Pj2dRXh6ry8ocHZZj5efD8cdDQQG88AK8+KIxwgl0Eb8uMlWYDuqwjjkvxtFhaZpueuqKW9PT2Wex8GpiItcMHEhSWBhzU1NZNmZM372zuPxy+OUXmD8f3n5bF/E7BI2ljZSu0h3WmvPRdxQ2qjaZ+KioiOnBwVwzcCBg9FksGzOGv6qqHBydA/j7G8NbV60yJs4tWmSMbuqrCbObLCYLBe8VICL4D/HniN1H6A5rzenoRGGjRXl51FgsPD98+AGPJ4WHc0dcnIOicgARY1nS3bvhwgubm5cCAox1I/bscWx8LqR2dy0bj93I9ku2U/5DOQBeofomX3M+OlHYoNZs5unsbE4MD+eI0FBHh+NYCxfC9OlQWGiU32hsNNawrqsztvv3d3SETq91h/WYj8YQfoK+E9Ocl/7zxQav5+dT1NjI/fHxjg7FMSwWY83qyEijiJ+vL0yYYCSLa6+FefNg8WKjY1vrVPq/0sl9IZfQY0MZ/e5o/OJ1CXrNuelE0Yl6i4Uns7I4LjSU48LCHB2OY8yebSSKH3807hquvtp4vKkkBxh3GppNIk+NxDvKm/i7dV+E5hp0oujE2wUF5DY0sGTUKEeH0rtaFvG74AKjiUnXZuoWi8lC5oJMlLci4b4EIk6KIOIkPT9Ccx26j6IDjRYLj2dlMT04mBP70mievDyYOhU+/dTYvuACo8lJJ4oua+qwzpyfSV1GHSLS+Ys0zcnoO4oOvFdYSEZdHS+PGIHqSx+SMTEwaJDRSa11S+uS4GM+GqMnz2kuS99RtMMswqNZWUwKCuLUiD7QTPDHH3DGGUYRPy8v+O9/4bTTHB2Vy6jPr2fD8Rv2Lz1at7uOtCvSjBnWm/QMa8216UTRjqVFRaTX1nJffHzfuJvYtw9SUiBTL53ZHRkLMqj4pYJdt+8CwH+YPxN/nMjE1RP1qCbN5elE0QaLCI9kZjI2IIDZUVGODsd+vvkGliwxvj/hBNi+Hfpap30PqM+vp3BJIVig6P0iCt4vACD0qFA9qklzCzpRtOHzvXtJranh3vh4PNz5buKVV4wvi8XY1gsLdUva1WlY6qzX0APKV5c7NB5N62k6UbQiIjycmckIf3/mxrhZu7KIMZKpqMjYXrLEKOjnoX8MuivlghRKvyptfsACRR8U7e+r0DR3oD8hWvmqpISN1dXcExeHp7vdTWRlGUNdX3jB2I6M1KXAu8FUbcJiMu4gGgsboVWxXDELmQt0X4/mPnSiaEFEWJCZSYKfHxf16+focHqGCPz5p/F9fDwkJ8NDDzk0JFclIhS+X8iakWvIf8MoV9JY1gjmVvs1CBW/VTggQk2zDz2PooXvyspYU1XFa4mJeLtLc8xLL8Ett8CGDXDYYXDUUY6OyCVVrati5007qfytkuCpwQRPCgZg2oZpDo5M0+xPJ4oWHs7MJNbXl0tdvQKq2WzUZoqKMhYWCgqC8eMdHZXLynwkkz3378E7ypuRb46k/2X9UR5u1iypaR3QicLqx/Jyfq6o4MXhw/F19buJWbOgvBx++gmCg+GKKxwdkcuxNFoQk+Dp70nw9GBib4kl/oF4vMO8HR2apvU6nSisFmRk0M/bm6sGDHB0KN1jMhkzqgEuucTYdrfO+F5S+n0p6TenE3laJMOeHEbEiRFEnNgHZudrWjvs+qezUmqmUipNKZWulLqrjedvVUqlKqU2K6X+p5RyyIIPv1dU8L/ycm4fPBh/V1nvOT+fiTffDAUFkJsLU6Y0F/E77zxjtTmdKLqkdk8tW+dsZfPfN2OpsxB6bB9fpErTrOyWKJRSnsBC4BRgDHCBUmpMq902AFNFZALwCfCkveLpyMOZmUR6eXGtdS1sl7BgAaFbtsD8+dCvHyQkQGCgo6NyWQXvFbBm9BpKV5Uy5NEhTEuZRtQZbjwrX9O6wJ5NT4cD6SKyG0Ap9REwC0ht2kFEVrfY/w/gYjvG06b1VVV8XVrKI0OGEOTlAi1x/v7GsqOAAli0yPjy84MvvnBoaK5GRLDUWPAM9CR4SjAx58Yw5LEh+MXq2kya1pKyV318pdQ5wEwRucq6fQkwXURuaGf/l4ECEXm4jefmAfMAoqOjpyxbtqzH4nwA47bmQyCox45qPz4lJQxbtIjon37Co7ERs48Pe487jl3XXUdDX6hy247q6mqCgrrwP5gOvASEAw/aJyZH6fK1cGP6WjRLSkpaJyJTu/Nap/gTWil1MTAVOL6t50VkMbAYYOTIkTJjxoweOe+W6mp+XruWB+LjOX3IkB45pl199RVUVMDw4bB6NWYfHzxNJvqNGEG/OXMcHZ1DJScnY8vPRWNJI3vu30Pea3l4hXsx9J9DGXD8ALeqEGzrtegL9LXoGfZMFLnA4BbbsdbHDqCUOhG4FzheRHq1QM6jWVkEeXpyc2xsb562+157zei8HjQIrr2W9ZMmMW3DBsjPd3RkLqEsuYyUOSmYKk0MumEQCQ8m4B2uh7tqWmfsmSj+AkYopYZgJIjzgQtb7qCUmgS8htFEVWTHWA6SVlPD0qIi7hg8mAhvJ/2wEIGPP4YZM4xV595+2+iwttZn2pecDFdd5cgIXYK5xoxngCeB4wIJmxFGwvwEgsbp5ghNs5XdRj2JiAm4AVgFbAOWiUiKUmq+UupM625PYXQNfKyU2qiUWmGveFp7NDMTPw8Pbh08uPOdHSUry5gT8eKLxnZEhC7i1wV1mXWkzE1h44yNiEXwifJh3GfjdJLQtC6yax+FiHwNfN3qsQdafH+iPc/fnt21tbxfWMiNsbHEONsaDCLGsqRHHmkU8fvxR5im6wl1hbnWTPZT2WQ9ngVA3N1xiElQPu7TD6FpvckpOrN72+NZWXgpxb+d8W6idRG/I45wdEROrz6/Hm6G+lX1mMpNbJ65mfrMeqLnRjPsqWH4xenhrpp2KPpcosiqq+PtggKuHjCAgc7SjGM2Q2kpREcbRfxCQ2HCBEdH5TIyFmTAFshckMnwZ4cTPDmY0f83mrDjwxwdmqa5BRevftd1T2VnI8AdcXGODqXZGWfAWWcZS5IGB8Oll+ryGzYq/6Wc/MX5IFDwVgGNZY2M+2ycThKa1oP61B1Ffn09r+flcWm/fsT7Obg5orHRKOKnFFx2mXFXoZODzSp+rSD76Wz2Lt+7/zGxGCvLJS5MdGBkmuZ++tQdxdPZ2TSKcHe8Q2oPNsvNhcmT4ZNPjO25c40lSnWisEnFHxVsOGYDZcllKK/mayYNQsGSAr1etab1sD6TKIobGng1L48L+/VjmL+/Y4Pp1w+GDYOQEMfG4SLMNWZyX80l62ljFFPI9BBGvTuKmHNiDvoJ1utVa1rP6zOJ4rmcHGotFu51VN/EL7/AzJlQU2M0OS1fDief7JhYXERDcQN7HtzDH/F/sPO6nZSuLEVEUErR/+L+VK6tRBoOrFWm16vWtJ7XJ/ooyhobeTk3l3OjoxnlqFLcjY2wezdkZ8PIkY6JwYUUvFPAjmt2YKmzEHlmJINvH0zoMaEH1GRquV61rumjafbTJxLFi7m5VJnN3NvbfRNffgmFhUaZjaQkSE1tXoVOO0jFbxV4R3oTMDKAoIlB9LukH7G3xhI4Sq+zoWmO5PZNT5UmE8/n5DArMpIJvV1u+M034Y03jGGvoJNEG8QsFH9ezPqj1rPh6A1kPWX0QwRNCGLk4pE6SWiaE3D7T65XcnMpN5m4rzfuJkRg6VLj7qFfP1iyxCji5+H2+bhbCt4pIPPhTGp31uI31I8RL4+g/2X9HR2WpmmtuPUn2D6zmWdycpgZEcHU3hhhlJVlTJZ7+WVjOzwcnK2WlIM17G2gabGsfVv24RXuxZiPxzB9x3QG/XMQnoEusma5pvUhbn1H8VpeHnsbG7nfnncTFgv8/jscfbRRxO/nn2HKFPudz0XV7Kgh+9lsCv+vkHFfjCPipAiGPDIE5a3catEgTXNHbntHUWc281R2NklhYRwVGtqzB8/Ph+OPNxYReuklOPZY2LTJeO7ww8FT/1XcpOK3CraetZU1o9ZQsKSAfhf3w3+YMY/Fw8dDJwlNcwFue0fxZkEBBQ0NfDB6dM8f/KGHjHkR8+fDk08a60ToIn4HsTRaSDk3BUuthbh74hh0wyB8+ztJIUZN02zmlomiwWLhiawsjg4JYUZYWM8d2N8f6uqatxctMr78/IwFhvqg+vx6Us9PZczSMXiFeFHwfwUULytmwqoJePh4MP7L8QSMDNB9D5rmwtyy6emdggKy6+u5Lz6+55o2Ghth1y648MLmDuqAALjoItizp2fO4YIyFmRQ8UsFW87YYsygvn4n5n1mGvIbAAieHKyThKa5OLdLFCaLhceyspgaHMzJERE9c9CcHGMRoZ9/NuozmUzGXURdnbHdv28O6azPr6fgrQKwQPXaaoImBTHxx4lM/nMyfvF6sSBNcxdu1/T0YVERu+vqeG748J67m+jfH0aNMvoiCgvh2mth3jxYvNjo2O4jLCYLFT9WUPxpMd6R3jSUNIC11JLyVviP8CfsuDCHxqhpWs9zq0RhFuGRzEwmBAZyRmTkoR3s559hwQKjeF9AAHz2mfH43//evM/ChYd2DhdRllxG4XuF7F2+F1OJCQ9/D6LOjmLvJ3v3F+WTRqPEd/z98brDWtPcjFs1PX1SXExabW3P9E2YTJCZaTQ79THmWjMlX5XsnxhX9GERxcuKiTgpgrGfjOXo4qPxDPZELK0qt+oS35rmltzmjsIiwsOZmYwOCODs6OjuHWT5cigqMpqVkpIgJaXP1GcyVZso/bqU4k+LKfmqBMs+C5P/nEzI4SEMeWQIw18Yjqdfc6d05e+6xLem9RVu8ym4Yu9etu7bx7ujRuHR3buJt99urvbq4dFnkkTlmko2Hr8RS50F7xhv+l3cj+izowmaZBRR9Ik6uAxJyxLfmqa5N7f4JBQRFmRmMszPj/NjYrryQnj/faPfoamIX1CQWxfxa9jbQMkXJRR/WkzoMaHE3xNP4IRABl47kKizogg9OhTlqWdLa5rWzC0SxTelpayvrubNkSPx6sqHfFYWXHkl3HmnMcs6PNx+QTpY/pv5FH5QSPmP5WAGvyF+RJxsDB/29PNk+HPDHRugpmlOy+UTRdPdRJyvLxf369f5CywWo/zGcccZRfx+/RUmTbJ/oL2sLquO8h/L6X+JMcdj74q91OfWE3dn3P5mJV1nSdM0W7h8ovihvJzfKyt5ZcQIfGy5m3jxRfjXv2DjRmMS3dSpdo/RHurz6+FmqF9Vv384ak16DXs/3Uvxp8VU/VUFQPjfwvEd5Mvo90fjGeipk4OmaV3m8oni4cxMBvj4cHlHs6NNJigpMfohrroKoqNdvohfxoIM2AKZD2WSuCiRoqVFpJ6fCkDwtGCGPj6UqLOj8B1kJBGvIJf/r9Y0zUFc+tPjl/JyksvLeW7YMPw6Ku192mlQVWU0OQUFGfWZXJDFZKE2rZayH8rIX5wPYvQ9xP8nnrCkMIY9N4zoOdH4xenyGZqm9RyXThQPZ2YS7e3NvIEDD36yoQG8vUEpY14EuNRoJku9hX0p+xCzEDItBEu9hV+jfsVcbT5gPxFjklviwkQG3zLYQdFqmubOXOeTs5U1lZWsKivjtsGDCWh9N5GdbfQ/LFtmbJ99tvHl5Ar+r4DtV21n7eS1/Bz8M+umrGPPfUZlWg9fD+LujWP4S8NRvi36GUxQsKSA+oJ6B0WtaZq7c9k7ioczM4nw8uL6tu4mBgyAceMgKqr3A+uEqcJE1YYqqjdUU72+GvM+M+M+GwdAwTsFVG+qJnhKMINvG0zQ5CCCpwbvf238XfGkXZ+2vxBfk6bSGYkLE3vzrWia1ke4ZKLYWFXFlyUlzE9IILhp9vSPPxpzIVasgMBA+PhjxwYJNBQ3UL2pmvATwlFKkf6vdHKeb64d5TPIh5DDQxARlFKMWz4Oz6CORybp0hmapvU2l0wUj2RlEeLpyY2DBh34RH4+5OXBiBF2PX/LVd1aVkrdl7KP4k+KqVpfRfX6aupzjOagI7KPwC/Wj7C/heEd7W3cKUwKxqffgaUxvII7/+9oWTojOTmZGTNm9Myb0jRNa4fLJYoG4NPiYu6JiyPsyy+huBiuuQaOPx62bIGORj8dIrEIpkoTu27bRcXPFWw5bQs+MT4MeWwIwRODqd5UTcZDGQSMDCD0uFCCJwcTNDkI7yhvAKLOiCLqDOdrDtM0TeuIXROFUmom8ALgCbwhIo+3et4XeAeYApQA54lIRkfHDEnP5rDdpUwdO5YdixeTWFUFV19tjGiyIUlYGiyYyk2Yykw0ljViKjfhP8yfgBEBNBQ3kP1UdvNzZSZM5Sbi7ooj5twYqtZWsX76+v3Hql5fjf9of0xlJgCiZkdxTOUxes6CpmluxW6faEopT2Ah8HcgB/hLKbVCRFJb7HYlUCYiw5VS5wNPAOd1dFxvSy3vPvQGZ4XF8PotzxHjE4X3tloCxwZiMVnIXJC5PxE0fdBHz40m9sZYGvY28Fv0bwcdM2FBAgn3JWCps5D7Ui5eYV54hRtfPv198AwwEpDfED9Cjgqh8s9KMIPyUYQnhROeZNSIatpP0zTNndjzT9/DgXQR2Q2glPoImAW0TBSzgAet338CvKyUUtK0Yk47RmetZPvZKzHjw8+sov/l/Rn11iiUpyLr8Sw8/D3wCvPCO9wbr3AvPPyMUcBeYV4kLEg44DmvMC/8hhoT1HxjfTmu9rh2zysmoXp9NVinMkiDXtVN0zT3Z89EMQjIbrGdA0xvbx8RMSmlKoBIYG/LnZRS84B5WJ+ciqIW3/qd1G1vJMnEEmBJixc0ABVA02Jrq5tefWj60z8umOAoRfOwJKkVqRpQtbeAgqxDP0OXRdHqWvVh+lo009eimb4WzUZ294Uu0ZguIouBxQBKqbV7xeKalfx6mFJqrYjoa4G+Fi3pa9FMX4tmSqm13X2tPWdm5wIta0rEWh9rcx+llBcQitGprWmapjkJeyaKv4ARSqkhSikf4HxgRat9VgCXWr8/B/ihs/4JTdM0rXfZrenJ2udwA7AKY3jsWyKSopSaD6wVkRXAm8C7Sql0oBQjmXRmsb1idkH6WjTT16KZvhbN9LVo1u1rofQf8JqmaVpHXLZ6rKZpmtY7dKLQNE3TOuS0iUIpNVMplaaUSldK3dXG875KqaXW5/9USiU4IMxeYcO1uFUplaqU2qyU+p9SKt4RcfaGzq5Fi/3OVkqJUspth0baci2UUnOtPxspSqkPejvG3mLD70icUmq1UmqD9ffkVEfEaW9KqbeUUkVKqa3tPK+UUi9ar9NmpdRkmw4sIk73hdH5vQsYCvgAm4Axrfa5HnjV+v35wFJHx+3Aa5EEBFi/v64vXwvrfsHAT8AfwFRHx+3An4sRwAYg3Lod4+i4HXgtFgPXWb8fA2Q4Om47XYvjgMnA1naePxVYCSjgCOBPW47rrHcU+8t/iEgD0FT+o6VZwP9Zv/8EOEF1tJCD6+r0WojIahGpsW7+gTFnxR3Z8nMBsACjblhdbwbXy2y5FlcDC0WkDEBEino5xt5iy7UQIMT6fSiQ14vx9RoR+QljBGl7ZgHviOEPIEwpNaCz4zpromir/Meg9vYRERNG0Y7IXomud9lyLVq6EuMvBnfU6bWw3koPFpGvejMwB7Dl5yIRSFRK/aqU+sNazdkd2XItHgQuVkrlAF8DN/ZOaE6nq58ngIuU8NBso5S6GJgKHO/oWBxBKeUBPAtc5uBQnIUXRvPTDIy7zJ+UUuNFpNyRQTnIBcDbIvKMUupIjPlb40TE4ujAXIGz3lHo8h/NbLkWKKVOBO4FzhSR+l6Krbd1di2CgXFAslIqA6MNdoWbdmjb8nORA6wQkUYR2QPswEgc7saWa3ElsAxARH4H/DAKBvY1Nn2etOasiUKX/2jW6bVQSk0CXsNIEu7aDg2dXAsRqRCRKBFJEJEEjP6aM0Wk28XQnJgtvyPLMe4mUEpFYTRF7e7FGHuLLdciCzgBQCk1GiNRFPdqlM5hBfAP6+inI4AKEcnv7EVO2fQk9iv/4XJsvBZPAUHAx9b+/CwROdNhQduJjdeiT7DxWqwCTlJKpWKsovJvEXG7u24br8VtwOtKqX9hdGxf5o5/WCqlPsT44yDK2h/zH8AbQERexeifORVIB2qAy206rhteK03TNK0HOWvTk6ZpmuYkdKLQNE3TOqQThaZpmtYhnSg0TdO0DulEoWmapnVIJwqtz1FKzbZWlh1l3Z6hlPrvIR5zoFLqk56JUNOci04UWl90AfCL9d8eISJ5InJOTx1P05yJThRan6KUCgKOwSjp0HKSZohS6ivrmgavKqU8lFKeSqm3lVJblVJbrJO1UEoNV0p9r5TapJRar5QappRKaFoDQCk1Vim1Rim10Vrzf4RSKtB6/E3W451n3XeKUupHpdQ6pdSqpkqeSqmbVPMaIx/17lXStAM55cxsTbOjWcA3IrJDKVWilJpiffxwjHUKMoFvgDnAHmCQiIwDUEqFWfd9H3hcRD5XSvlh/MEV0+Ic1wIviMj71pISnhizYfNE5DTrsUKVUt7AS8AsESm2Jo9HgCuAu4AhIlLf4rya5hD6jkLray7AWK8A679NzU9rrOsZmIEPMe46dgNDlVIvWUt0VyqlgjGSx+cAIlLXYi2QJr8D9yil7gTiRaQW2AL8XSn1hFLqWBGpAEZiFDH8Tim1EbiP5rVENgPvWysCm3r6ImhaV+g7Cq3PUEpFAH8DxiulBOMvfQG+sv7bkohImVLqMOBkjLuEucDNnZ1HRD5QSv0JnAZ8rZS6RkR+sK6VcSrwsFLqf8DnQIqIHNnGYU7DWK3sDOBea3lwnTA0h9B3FFpfcg7wrojEWyvMDsZoXjoWONxafdQDOA/4xVpx1UNEPsX4a3+yiFQBOUqp2bB/7faAlidRSg0FdovIi8AXwASl1ECgRkTewyjiOBlIA6Kt6yOglPK29m94YCy+tBq4E6OEfpBdr4ymdUDfUWh9yQUYS6S29CnGOuN/AS8Dw4HVGH/tjweWWD+4Ae62/nsJ8Jq1OmkjcC7QcgGcucAlSqlGoAB4FJgGPKWUslhfc52INCilzgFeVEqFYvw+Po+xbsR71scU8GIfXWxIcxK6eqymaZrWId30pGmapnVIJwpN0zStQzpRaJqmaR3SiULTNE3rkE4UmqZpWod0otA0TdM6pBOFpmma1qH/B5iuquExyb0tAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Coordonnes des points\n", "X=[x/10 for x in range(10+1)]\n", "Y=[sqrt(x) for x in X]\n", "Z=[x**2 for x in X]\n", "\n", "#Dessin\n", "plot(X, Y, 'cx-', linewidth=1.5, label=\"$x\\mapsto \\sqrt{x}$\")\n", "plot(X, Z, 'm^--', label=\"$x\\mapsto x^2$\")\n", "plot(X, X, 'r*:', label=\"$x\\mapsto x$\")\n", "\n", "#Dimension de la fenêtre\n", "xlim(0, 1)\n", "ylim(0, 1)\n", "\n", "#Titre et légende\n", "title(\"Comparaison entre $x^2$ et $\\sqrt{x}$ sur $[0; 1]$\")\n", "xlabel(\"Abscisses\")\n", "ylabel(\"Ordonnées\")\n", "legend()\n", "\n", "#Quadrillage\n", "grid(True)\n", "\n", "#Affichage\n", "show()" ] }, { "cell_type": "markdown", "id": "international-paris", "metadata": {}, "source": [ "

Les possibilités sont très nombreuses. Pour explorer les possibilités de graphique en python à l'aide de matplotlib, on pourra consulter la documentation ici : https://matplotlib.org/.

\n", "\n", "

Voici quelques exemples.

" ] }, { "cell_type": "markdown", "id": "working-ethiopia", "metadata": {}, "source": [ "

Le nuage de point

\n", " \n", "

Pour réaliser un nuage de point on utilise la fonction scatter.

" ] }, { "cell_type": "code", "execution_count": 20, "id": "tough-joseph", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "X=[1,2,3]\n", "Y=[-1,0,1]\n", "scatter(X, Y)\n", "show()" ] }, { "cell_type": "markdown", "id": "demographic-color", "metadata": {}, "source": [ "

On pourra en apprendre plus sur cette fonction ici

" ] }, { "cell_type": "markdown", "id": "dirty-agreement", "metadata": {}, "source": [ "

Les graphiques polaires

\n", "

La fonction plot utilise des données cartésinnes, c'est à dire des $x$ et $y$ classiques. il existe une autre manière de représenter les points du plan : les coodonnées polaires. Dans ce cas on précise la distance à l'origine et l'angle (en radian) formé avec l'axe des demi-abscisses positif. La fonction polar réalise alors un grpahique circulaire.

" ] }, { "cell_type": "code", "execution_count": 21, "id": "tropical-yugoslavia", "metadata": {}, "outputs": [ { "data": { "image/png": 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9TafTwWAwQCQSQaPRIDc3l7uArBSZTAa1Wg21Wh1+TCh7OTk5ib6+PkgkknB5h7WINLY4fVAmLixkUqkUVVVVMBgMOH36NMrLy6FWqyGXy/Hyyy+nfu5zn7vjySef3MEYu5WuEdNCRF8E8EUgtDuJkKXyAcZYJhGNT/djeiuAjgUHiXLW/ypbJoyx3yO0q5PBGBsB8FUARxljWxByZg0C+Pj0y98J4BOMMT8AF4DbaBnrvenyBH95+umnNYcPH2ZAyMTv6OiAz+fDwYMHMTP0fj0IBoOYmJgIV39LTk6GVqvFnj17Im7DsVZIpVKoVCqoVCqUlpbC5XJBp9PhwoUL8Hg8UKlU0Gq1C1Z9ixSz04vUBSyVmajVaigUCjQ1NWFqagpFRUUQi8V46KGHUqqrqw/ce++9rYyxNxBR/wqm8SRjTIWQM7YVr17DG44N6VNZCxQKxUczMzO/+89//jM9Pz8fQMh0b2pqgkqlQnFx8br6AiwWCwYHB2EymZCeng6tVouMjIw1W4KtVUSt3++HwWAIL+PUajXy8/ORnMwtZASv+0EtyrLkeOR91Ut6fTAYRHt7O4gIFRUV4S+WxsZGvO1tbzMYDIbbvF7vcW4T3GBsOEtltWGMiVUq1U/37dt325/+9CelcPHabDacP38epaWl0Gq16zI3v9+P4eFhDA8PIyEhAfn5+aioqFh3R+dqIpFIkJWVhaysLAQCAeh0uvANnZ+fj6ysrIiFNGSpLN2PIxKJUFlZif7+fpw+fRq7du1CfHw8ampq0NjYqH7jG9/4p7S0tAdMJtOPIprYBiUmKjNgjMWpVKq/f+xjH9v9jW98I0m4WHU6Hbq6ulBdXQ25XL7m83I4HOjv74fRaEROTg727NmDuLi4a7/xOkMsFiM7OxvZ2dlwOBwYHBxET08PsrKyUFBQsKK/STBIsLp8S1r+zIQxhqKiIqSkpOD06dPYsWMHlEolMjMzce7cubT3v//9X9NoNKXTuUOvqeVATFSmmd7heflLX/rSjk9/+tMJQMjx2dPTg4mJCezfv3/NfRTCDonf70dhYSG2bt0adTtM60VSUhK2bt2KLVu2YGRkBGfOnIFCocDmzZuRmJi45HGm3H4ECVAkrGzHSaVSYffu3WhqakJxcTGys7MRHx+P//3f/1X827/923uff/75BMbYh5e4M3RdEBMVhBqYq1Sq2gceeGD7nXfeGQeE2l+0trZCKpVi7969a3ozOxwOdHV1wePxoKysDKmpqWt27o2GWCxGfn4+8vLyYDAY0NTUhNTUVGzevHlJlovFFQruW66lMpOkpCTs378f58+fh81mQ2lpKUQiEX75y1/KExIS3vb000/HM8beR6+RGi2veVFhjKWoVKpTP/jBD0pvv/12GRDyXTQ2NkKr1aKwsHDN5uJ2u9Hd3Q2LxYLS0tJZ264xFocxBo1GA7VajdHRUTQ0NCArKwubNm1adCvdLETTLsOnMh9SqRS7d+9GR0cH2tvbw76uhx9+OCUxMfHoY4899hxj7K1E5I/oRBuA17QtzRhL0mg0DZ/85Ce3v+c975EBoZiJs2fPIjs7e80ExefzobOzE2fOnEF6ejoOHToUE5QVwhhDTk4Ojhw5AqlUipMnT+LKlSsIBudffQh5PwsFvy333Nu2bYNMJkNLS0s4b+hLX/pS8i233HKzRqP5C2NsfWMQ1oDXrKgwxhJUKtWJ73//+5s/8YlPsDNnzsBut+Ps2bMoKCjAfK1FeUNE6O/vx6lTpxAfH4/Dhw8jOzv7ut7NWStEIhGKiopw8OBB+Hw+1NXVYXz86sL5Fk6WigBjDGVlZUhKSkJzczMsFguam5vx0EMPiT/0oQ8dUalUT013obxuua4/3EJM7/K88s1vfnPbBz/4QZlarUZJSQlqa2uRl5eH7OzsVZ+Dw+FAQ0MDHA4HDh06hMLCwpgTdhWQSqXYsmUL9u3bh9HRUTQ3N89KkjQ7I/epzMeWLVsQHx+P+vp67NixA3K5HN/5zneSb7vttjep1er/YdfxN8dr7ipmjElVKtU/v/zlL++444474oDQ8uPKlSsoLi5Gf38/PB7Pqp2fiHDlyhU0NjairKwM27Zti4rw+euduLg47Nq1C5mZmaivrw9bLYKlIo/n+39gs9lgNBqRn5+P3t7e8FLo4YcfTnnLW97yVrVa/TOuJ4wiXnNXs1qt/u9PfOITNXfffXc8EHLKnjt3DoWFhcjOzkZaWhrOnDmDvXv3co8FcTgcaG1thUKhwKFDh9Y9xP+1SFZWFtLT09He3o6xsTFM2qWQx0sgEfP7frXZbGhubsbOnTshl8tx+fJltLS0YMeOHWCM4ec//7l8cnLytrS0tK7rMUDuNWWppKWl3bV37963fPWrX00EQtvG586dm7XkUavVKCsrw5kzZ7hZLILvpLGxEaWlpdi2bVtMUNaRuLg41NTUIDMzE71DY0iJWz1BAV5dCrW1tYUqo4lEePLJJxU5OTlfkclkN15jyA3Hayb3RyKRHCotLf1LY2NjWkJCAogonMdTUFBw1esNBgM6Ozsjtli8Xi+am5uRkpKC0tLSDbPUCQaDswo3eb1eBINBEBGCwWB4ucgYg1gsDtdhiYuLQ1xc3IbxD33gV2cwPmnFg2/UYPv27RGJ/XyCIkBE6OjogEQiQVlZGQBgbGwMu3fvNoyOju5dYRJiVLIxrvAIYYzlZ2Vl/ekf//hHmlDr4/Lly0hMTJxXUACEt3QjWQpFQ77QYhARXC4XLBYLLBYLbDYb3O5Qgz7G2FXFmiQSCRhj4UMmkyEYDCIYDMJsNocLMnk8nrAPISEhIVyQSaFQRE2DeYEptx85qlTI5fJZeTzLZTFBAV7dbj537hxGR0eRnZ2NrKws/PnPf1bfcsstLzHGdiynAmE0c92LynS07EvPPPOMOicnB0DoG8JisWDPnj2LvjcSYVnvfKH5CAQCMBqNMJlMsFgs8Hg8SExMDBdIKiwsRHx8/JK2tHt7e5Gbm7voa4RSlRaLBRMTE+jr6wufU6lUIj09HWlpaetq1ZidPhRkJM2bx7NUriUoAowxVFdXo6GhAUlJSVAqlaipqcGDDz6Ye8899zzPGLvpegjnv65FhTEmUqvVf/n2t7+dv3fvXgChfJru7m4cOHBgSTfPcoVlvfOF5iJUf9PpdHC5XFCpVEhPT0dRUdGqWw0ikQhJSUlISkoK+6xmWkejo6O4cOEC5HI5tFrt+lR9m1FLZb48nmuxVEERkEql2LVrF86dO4d9+/YhPj4eH/jAB2Tnz5/f+cQTT3wfwL2Rfqb15roWlYyMjG+/613v2vORj3xEBgAejwctLS3YtWvXsi7epQpLIBBAS0sL4uLi1jxfaCZOpxMjIyPQ6/Xh6m/l5eVca5CsFMYYEhMTkZiYiKysLBARrFYrdDpduOqbVqtFdnb2qmdi+wNB2Nz+WcmE8+XxLPTls1xBmXmObdu2oampCfv27YNYLMb3v//9lJaWlg/L5fJmm832VMQfbj1Z7x4hq3UkJye/++DBgya/309ERIFAgE6dOkV6vZ5Wil6vp9raWnK73Vc953Q6qa6ujgYGBlY8fiQEAgEaHR2lhoYGOnXqFA0ODpLH41m1861W3x+n00l9fX1UW1tLjY2NZDAYVq2r48SUm/I//zf6n1NXrnouGAzSxYsX6cyZM+Tz+a563mq10rFjx8hqta74/H19fXT+/Pnw57NarbRp06YJADsoCu6hlR4bw0W/TBhj+WlpaY/89a9/TRWLxSAitLe3h03slbLQdrPT6cTZs2dRVlYGoUrcWuHxeHD58mWcOHECZrMZ27dvx4EDB5CXl7fuS6+VkJCQgKKiIhw+fBibNm3CyMgITpw4gf7+fvj9fHPxLC4hRP/qvxNjDFu3bkVWVhbOnDkDn88Xfm6lFspcCgsLwRjDlStXAAByuRz/+Mc/0jUazXOR9BVab647UWGMMbVa/afHH388XXC29ff3g4hQVFQU8fhzhcVut+PcuXOorKxERkZGxOMvFbfbjba2Npw+fRpxcXE4dOhQ1CxxeMAYQ2pqKnbs2IH9+/fD5/Ph5MmT6OrqmnWDR8KryYQLL4Vzc3PDzcW8Xi83QQFCn7GioiJcqBwAiouL8bWvfU2t0WgeiWjwdeS686kolcpPvfWtb9185MgRBgBmsxmjo6PYv38/t0Q9wdqpr68HAOzcuXPNKur7fD709PTAYDBg8+bN1305SSBUgX/z5s0oLi7G4OAgTp06hby8PBQUFEQUV/JqMuHiFp1QsrK+vh7BYBA1NTXcdvREIhF27dqFhoYGyOVyxMfH49/+7d/ifve7371ZIpEc8fv9J7icaA25riwVxliBQqH4ykMPPSQHQo7T9vZ27Nixg3sEa2JiIgKBUM2dtYi9CAQC6O3txalTp5CUlITDhw8jKyvruheUmYhEIhQWFuLQoUPw+/2oq6vD8PAwaIUBnEItlaXUp01MTITf74dIJOL+/x0XF4fy8vJwxC1jDL///e/TMjIyntiIy6DrRlSEZc/vfve7dKFXzOXLl5GTk8N9SeB0OtHU1ITdu3dj27ZtXEP650JEGBkZQV1dHYgIhw8fRn5+/oaJWF0NJBIJtmzZggMHDsBqtaKurg5Go3HZ4yy1loqw5NmzZw/Kyspw9uxZbkswAbVajbi4OIyMjAAA8vLycP/996vUavWjXE+0Blw3V6ZSqbz7bW97W8mhQ4fCyx6TycTFjzITl8sV9qEoFIpVyRUScLvdOHfuHCYmJnDgwAGUlJTEcoZmIJPJsG3bNtTU1KCvrw9tbW3LcuZanD6IGJASt7AXYK4PRavVYtOmTTh79ix3x3F5eTl6e3vDUc133nlnXElJyS1isfgGridaZa4LUWGMFSoUii/PXfZUVVVxXR4IZSa3b98+q24sb2EhIgwPD+P06dMoLCxEVVXVhtzJWSsSExOxZ88eKJVKnDp1aslWi9DuVCRaXhxKVlYW8vPz0dzcvOKl13xIpdJ5l0EqleoJxtiG8cBveFGZjpr90xNPPJEuVFFfjWUPEeH8+fMoLCyE0Jx9JryEZaZ1cvDgwVhZySXCGEN+fj727NmD3t7eJVktZqcPygWq6F9rlyc3NxcpKSno6uriMn+Bucug3NxcfP3rX1dpNJqfcz3RKrLhRSU1NfXT73jHO4oPHjwYXvaYzWbuy57Lly8jKSlp0XyXSIVFp9Ph9OnTKCgowI4dO9Y8ZP16ICEhAXv37g1bLRaLZcHXWpzeebeTl7ptXFZWBpvNhtHRUR5TDyMsg1wuFwDgjjvukJWUlBwVi8Wv43qiVWJDiwpjLC0pKemLP/jBD2YteyorK7kue4QExK1bt17ztSsRFiJCd3c3rly5ggMHDkCj0UQ65dc0gtVSU1ODtra28Lf+XEKN2WcvK5cThyIkCPb29i4qXstFWAYJnRgZY3jqqadSVSrVrzdC4ewNLSpqtfpbDzzwgHI1lz1WqxU9PT3YuXPnkoVqOcLi9/vR3NwMj8eDvXv3xnwnHBHyeEZHR3Hp0qWr/B8hUXnVUllJYJuQINja2hp2sPJgvmXQbbfdlp6cnPwRbidZJTasqDDGclJSUt7xwQ9+UAqESjVOTExwXfYICYg7d+5c9lJkKcLicrnQ0NAAlUqF7du3v6a3iVcLoR8PYwznzp2btRVscXqhTAiJeCSRsklJSSgvL0dTU1M4dokHwjJI8A199atfTUlKSrqfMRZdRWnmsGEjarVa7Y8eeuihNGGLtaura9GM0uUSDAbR1NSErVu3rtjyWSy72WQyoa2tDRUVFfM6ftcLn88Hi8UCu90ervrm8XjChZcEpqamUFtbC2B2Qae4uDgkJCQgJSUFCoUiKrbAhbYZIyMjaGhoCGWpxyXA4Q0gNVHKJfRepVJhamoKFy5cQFVVFZd5S6VS5OXl4cqVK9i8eTNSU1Nx1113KR566KF7AHyLy0lWgQ1ZTpIxVlZZWXmypaUlnTEGq9WKjo4OrqH4PT09CAQCKC0tjXisuaUpjUYjLl68iN27dy+r7y9vaLrswMTERFhIJBIJFAoFUlJSZlV+i4uLm/W3ra2txQ033AAgJMBC1Te32w2XywWbzQar1QoASElJgVKphEqlQkpKynp81DBmsxmtra0oKK3AjQ+fwZfeuAklGOeSy0PTJUpzc3O5VfoLBAKoq6vDgQMHIJPJ4HQ6UVJSYhgbG9tMRFYuJ+HMhrRUtFrtzx555JF04SLv7OzE1q1buQmKzWbD+Pg4Dh48yGW8mRbLpk2b0NfXh717965LacVAIICJiQnodDqYTCbI5XKoVCps3rwZKSkpK/obikQiJCQkQCjVOfd8U1NTMJvNuHTpUrhQlEajWZeqb6mpqdi5cyeeP9EIADCND2PnLXu45PIICYINDQ1IS0vj4h8Ti8XYtGkTenp6UF5ejsTERHzlK19R3nffffcD+EzEJ1gFNtwinjG2t7S0dNuBAwcAABMTExCJRNyamAeDQbS1taGyspLrBa9Wq6HVatHa2orq6uo1FRQiwuTkJJqbm8Mh7dnZ2Thy5Ah27tyJvLw8yOXyVckjEovFUCqVKCwsxJ49e3Do0CGkp6djZGQEtbW1uHDhAmw2G/fzLoZcLoczIbTDVlGSz7XcZ1xcHLZs2YILFy5wGzM3NxcTExPhLeaPfexjsqSkpPczxjK5nYQjG0pUGGNMo9H8/Cc/+Uk6ELpZurq6wtXJedDb2wuVSsU969hoNEKn02HHjh04f/78qjYsE/D7/RgYGEBdXR0GBwdRUFCAG264Adu2bUNGRsa6OIbFYjG0Wi2qqqpwww03QKVSobOzE6dOncLo6OiCPY95MmIw4Sd1g9imTULc1AimpvjWm87KykIwGJy3zepKYIxhy5Yt4UA7iUSC73//+6kajeYHjLF4xtg5xlgbY+wiY+z+ed4fxxj7A2OslzF2ljFWwGViC7ChREUikbzp4MGDedu2bQMQChZLTk7mtk632WzQ6XTYvHkzl/EEJicncfHiRezZswfZ2dmrliskEAgE0NPTg5MnT8Lr9WLv3r2orq5Genp6VGU1i0QiaLVa7NmzB9XV1bBYLDhx4gSGhoa4hr/PxGaz4Wt/aoTDB3znXTtQs2sXmpqa4HA4uJ6noqICXV1ds1qsRoJGo4HD4QgL4Dve8Q5xenr6vwDIAfA6IqoEUAXgZsbY3jlv/ygAMxEVA/ghgO9ymdRCrHfpuaUeAJhGo+kWyjUGg0E6fvw4OZ1O4kEgEKC6ujqyWCxcxhNwOBx07Nixq+a5WGnKlRIIBGhgYICOHTtGPT09JJTSXA1Wq5ykx+Ohjo4Oqq2tpbGxMa6lJK1WKz32l5ep8At/oy//+UL4cZPJRLW1teT1ermdi4hodHSUmpqauI03MTFBZ8+eDf/+8ssvBzMzM/9Gr94jiQDOA9hDs++dfwLYN/2zBMAEpjdpVuPYSJbKwX379qUL5RqHhoagUqnmdQ6uhNVY9ggJiFVVVVfNk3cSok6nQ11dHZxOJw4ePIji4uKo2M5dLjKZDOXl5di9ezf0ej3q6+thNpsjHtdms6GxqQl/GZZBmSjDZ/9lS/i51NRUbNq0CefPn+dqIfFeBqWnp4OIYDKZAACve93rmEKh2M0Yy2aMtQIwAHiJiM7OeWs2gGEAICI/ACuAVYtj2DCikpWV9fUvf/nLacCrbURLSkq4jO12uzE2NsZ12UPTCYhFRUULOpF5CIvQAXF4eBh79+5FWVnZdZEzlJCQgKqqKlRWVuLixYu4dOnSigPLhDiUMVkuWkZs+MLNpVDMyfnJyclZlQTBiooKXL58mZuvaMuWLejp6QEQ8rV84QtfSFWpVPcQURVCS6HdjLFtXE62QjaEqDDG8jMyMsp37twJIBT3kZqayi2kvbu7GyUlJVwdl0tJQAQiExadTof6+npotVrU1NREXfc/HqSkpODAgQOIi4vDqVOnlm21CIJSUl6Jh2sHsCNPiXfuzJn3tauRIBgXFwetVovBwUEu4ymVSvj9/rAP6LbbbpNIJJL3M8biicgC4DiAm+e8bRRALgAwxiQAFAAmuUxoHjaEqGg0mi/813/9V9hcu3LlCrdwfIfDAavViqysLC7jActLQASWLyx+vx/nz5/H8PAw9u/fv6SmVxsZxhg2bdqEnTt34uLFi+jq6lrSMmVmpOyvzoxj0uHFA2/ZtmD9lNVKENy0aRMGBgbmLcUwPDyMG2+8EVu3bkV5eTkefvjhq15TW1sLhUKBqqoqVFVVoampCf39/eHP+N73vjc5KSnpA4yxBABvADDX3HoewIemf34ngGPEc503l9Vy1vA6ACRkZ2frhd4rNpuNGhoaluzcuhZCbxleOBwOOn78+Iqcfktx3jqdTjpx4sS69RcSWC1H7bWY2Y9nsb/xzL48F0etVzlnF8Nut9Px48fn7fezUvr6+qirq+uqx8fGxqi5uZmIQtd2SUkJXbx4cdZrjh8/Tv/v//2/8O/BYJCOHTtGXq+X2traqLy8nMRisQdAB4D7KHTffB3ArdM/xwP4XwC9AM4BKKJVvGej3lKJj49/z+23354kkYSCf3laKRaLBT6fDyqVist4RITW1lZs3759RX6Na1ksJpMJZ86cQXl5+Zr3F4oWhH48OTk5aGhomHcreKaFkpKSgvue67jKObsYSUlJKCwsxKVLl7jNu6CgAOPj41f9v2ZmZqK6uhpAaKlXVlZ2zeUXYwy5ubkYHh5GRUUFOjo68PrXv94G4ENE9HUAIKL7iOj56Z/dRPQuIiomot1EdIXbB5uHqBeV1NTUz3784x9PAkJmv8lk4lYNrbOzk2vg3MDAAFJSUiJKEFxIWIaGhtDR0YE9e/ZEVQLiepGdnY3KykqcO3duVvnIucmBz54fRdOgeV7n7GLk5eXB4XCsqKD2fIhEonC4/UIMDAygpaUFe/bsueq506dPo7KyEm9605tw8eJF5OXlzeokcO+992ZkZmb+J5fJRkhUiwpjrLSwsFCdl5cHABgdHeXWlsJoNEIqlUJoOBYpDocDg4ODXERqrrD09fVhfHwc+/fvX9cExGhDqVRi37596OzshE6nu0pQrC4fvv33TlTlLuycXQjGGKqqqnDx4kVuBa5zcnIwOTkJp9N51XN2ux3veMc78KMf/eiqtIHq6moMDg6ira0Nn/rUp/DWt74VMpkMycnJYcf1TTfdBIlEcmM01LKNalHRaDT3fPaznw1/LQ8NDUEQmEi5fPkylwxkILTsaWtrw/bt2yEs0yJFEJba2loYjUbU1NRwG/t6Ij4+Hnv37kVXVxfOnDkzK9v4hy91Y9LhxTfeurBzdjESEhJQWFiIixcvcpkrYwylpaXo7u6e9bjP58M73vEOvP/978fb3/72q94nl8vD5TeOHj0Kn8+HiYkJ5Ofnh3eVRCIRPvrRjyYlJia+j8tkIyBqRYUxJmaM3XrLLbeIgJBZK5PJuAS7mc1mxMXFcasQNzAwALlczn1ZMjU1haSkJLjdbu59Zq4n3G43gsFguDQAAFwas+Hx0wN4/548bMteeUBjXl4eXC4Xt2WQWq2G1WoNL22JCB/96EdRVlaGe+65Z9736HS68DLn3LlzCAaDSE9PR3p6OqxWa/jauOOOOxIVCsWnuUw0AqL5q2/PkSNHJILDc3BwkJtzkqez1+fzYWBgAIcOHeIynsDg4CAmJiawf/9+TExMzFvoaa1wu92w2WwIBALhw+v1YnR0FCKRCFKpFHK5fF1KYQpLnl27diE+Ph5nzpyBSCTCfc/1Lss5uxBCOYPGxkYcPnx41tJ7eHgYt99+O/R6PRhjuPPOO/HpT8++p4kIn/70p/Hiiy8iMTERjz32GPLz8zEwMIAtW7agvr4ev/vd77B9+/ZwcadvfetbGBoaAgB8/OMfx5/+9Cf87Gc/g0QiQUJCAp5++unwPLKzszE6OoqCggJkZWUhLS1NxRhTEREfFVwBUSsqGo3m/e973/vC2cgTExMoLy+PeFyXywWHw4G0tLSIxwJCxZyKioq4Lk0mJiYwNDSE/fv3QyQSLVpBjjculwtWqxUWiwUWiwUulwtxcXFQKBSQSCQQi8UQiUQgIjidTgSDQXi93nDj9OTkZCgUCiiVSiiVylUVmvkqtu3evRsPPnMSTYNefPcd26/ZfXApJCYmhss1zAxmlEgkePDBB1FdXY2pqSns3LkTb3jDG2bFJ/39739HT08Penp6cPbsWXziE59AfX09Tp48iZKSEhw8ePCaMTd33XUX7rrrrnmfy8rKwoULF1BQUAAAuO2225IfeOCBWwD8T8QffIVEragwxm55/etfDyC0DEhOTuYS8SqUAODh7HW5XDAYDDh8+HDEYwk4nU5cuHABe/funZW7s5rC4nA4MDw8jPHxccTHx4dFIS8vDwkJCfP+rYaGhq5KkyAi2O12WK1WGAwGdHd3IxAIIDs7G7m5uVznvFAJSA+J8UwfYZNChLdW8Km+BgCbN29GQ0MDsrKywv8vmZmZyMwMlTSZuR08U1See+453H777WCMYe/evbBYLDAajdBoNBgfH484cFFYHvv9fkgkErz97W9P+MUvfvGviInKbBhjxQcPHkwQdjp0Oh2X8nxCchcvEeju7sbmzZu5hfcvloAI8BUWIoJOp8OVK1fAGENeXh4OHToUkcXFGENKSgpSUlKQkxPabRGWSefOnYNMJkNxcTHS0tIiEvXFasr+8KVumF0+fP/Nm9Ha2hIueh0pMpkMmZmZGBgYwKZNm656fqHt4NHR0VnWTU5ODkZHR1FWVoa2tjYu0dBqtRpGoxGZmZnC7mMZYyyOiFa/aM88RKWjVqFQvPMDH/hAOAtPr9dz6YVjMBiQkZHBJXvXbrfDZrOFv6kihYjQ0tKyaAIiEHkSYjAYxMDAAE6cOIGJiQlUVVVh//79yMnJWZXdJZlMhsLCQhw6dAhbtmzBwMAATp06hbGxsRVlBC8mKDOdszftKEFKSgouX77M66Ng06ZNGBoausppvth28EIkJSUBAJc6LlqtFjqdDkBI2I8ePSoFcEPEA6+QqBSVpKSk9916660SIOQkZIxxWZvzdPYKgXO8ih6Njo5CIpFcMwERWLmw2Gw2nDp1Ci6XC/v378f27dvDF/daoFQqsXPnTuzcuRMGgwFnz54Nl0hcCosJChHhq893QJEgDTtny8rKMDExwaV0AhDyoRQUFKCvry/82LW2g7OzszE8PBz+fWRkJGydzNwSjoTU1FSYzeawSN92223KrKysD13jbatG1IkKYyxVLpdrBQtAr9dzWfp4PB54vV4u9Ujtdju8Xi8yMjIiHgsICWdPTw+EinZLYTnCEgwG0d3djdbWVlRWVqKsrGxdm5YlJiaiqqoKRUVFOHPmzJIqvV2rjcafW0bROGDGF95UGnbOCgFs7e3t3Prx5OfnY3x8HH6/f0nbwbfeeisef/xxEBHOnDkDhUIRtm61Wi30en3ENVwYY2FhAYCDBw8iEAjcyNapzF/UiYpUKj367ne/O/z1ycufwmscgO+WtBA4t3Xr1lVpWGaz2VBfXw8iwsGDB7nX3o0EtVqNgwcPwmQy4dy5cwtaLdcSFJvbh2+92IWqXCXetXO2pZecnIycnJwFl0Ef+chHoFarFxT0uRnC3/jGN8LWh7AdfOzYsfDzL774In7+85/j5z8P9VM/evQoioqKUFxcjDvuuAOPPvpoeGyxWAyFQsHFkpq5BJrumigBUBnxwCthNbMVV3JkZ2e/3NbWRkREfr+fWzbsmTNnyGazRTyO1+ulY8eOcStzODw8TOfPn49ojPmym4PBIPX09NCJEye4l8gk4p+lrNfr6dixYzQ6Ojrr8ZnZxgvxtec7qOALf6P24fk/ZzAYpJMnT5LJZLrquRMnTlBzczOVl5fP+965GcJEoZKXx48f53INjI6OXpWVvBJ8Pt+s/5OnnnoqmJ6e/h1ah3s4qiyV6ebT27dv3w4glJ/DI4M4EAjA5XJxiaAdHh5Gbm4uF1+K3+9f9rJnPuZaLESEjo4O2O32qLNOFkKwWgYGBmbVCrlW58DOcRt+2zCA9+3Ow/ac+T8nYyxcQY7mLDUOHz687JglmUwGhUKBiYmJZb1vPoSdm0iRSCSIj48PO37f9KY3MZlMdrWTZw2IKlEBsKWioiK8FOS16zMxMYGMjIyIhYCIwqLCg/7+fuTm5nIp/ygIy+nTp9Hc3AyRSMS9d9FqI5VKsWfPHhiNRly4cOGagkJEuO+5kHP2c29cPHI2JSUFSUlJ0Ov1y57X3AxhIFTKgIeTVSKRQCaTcdkF0mg04c+nVCqRkJCgZIyteQh2VF1xjLGdhw8fDl9BVquVSxYxL3+KxWJBUlISlyAur9eLkZERFBYWRjyWgEqlgkwmw+TkJIqLi6OqHcdSEYvF2Lx5M4aHh5GRkbGoY11wzn7+5tIlRc6Wlpbi8uXLy3KMzpchDIRuWofDwaVo+Ux/SCSkpqbOqli3Y8cOBmB7xAMvk6gSlaysrNfv2bMnDgjtWASDwYhjJ4hC1cd5hOXzzJLu6enBpk2buFW8JyJcuHABSqUSO3bsWNW+QquJzWZDS0sL9u/fD5fLFV4KXfW6aedsZa4S7961NMsxISEBGRkZs7Z4r8VCGcKMsXAgW6QIu0CRIpfLZ3V7PHLkiFIqle6KeOBlElWiEgwGd+/YsQNAKDSfR5Mwq9UKuVwe8TJAECce28gejwdGo5HbMgoIRfcSEcrKyri3/1grZvpQhJiW8fFxjI2NXfXaH77UjYkpN87+9D9QUTH/lzER4e6770ZxcTEqKipw/vx5lJSU4MqVK0uubr9QhjAQyrvhYWEkJCTA7/dHnIkuEokgEonC9V9qamokGo3mpognuNx5rPUJF4IxJpZIJGnCcsdisUTV0oeXOAGhkO7CwkJuyxOz2Qyj0YiKiorwmBtNWOZzyorFYtTU1ODy5ctwu93h1wrO2RvzZHjxyZ8vOObMZL5f/vKX+MQnPgGZTAaVShUWg/e+973Yt28fLl++jJycHPz617+etSX8pz/9Cdu2bUNlZSXuvvvuWRnCvMQACP1/GQyGiMeZaa1UVFTA7/dXRzzoMomm3J/NM6umWSyWcP5IJJhMpnAGZyTwzD8aGxvjln8UCATQ3t6OnTt3XiVSa5ndHAmL7fJIpVKUl5ejra0Nu3fvBoCwc/ahf70BVuPVVozAfMl84+PjKCwsxPnz55GVlYXf//73i85tsQxh4FUxiDSHJz09HTqdLuJxlEolLBYL0tLSkJiYiPj4+BTGmIyI+PRfXQLRZKnsPHLkyCwnbaRboUQEj8fDpR+OwWDgUht3fHwcarWamy9F+IZdaLs82i2WpWwbq9VqxMXFYWRkJOyc/c+bS5GatLhzdqFkvsTEREil0ln+h5XCy8mqUChgtVojHkcQFYFpZ+2aNheLGlHJzMy8ac+ePfEAPyety+XiUinO5XJBIpFw2frlmX9kNpthMpmuGd0brcKyFEERKC8vR3tnD771Qicqc5V4zxKdswvBK+9GoVDAZrNF3IFQJpPB5/NFHLKfkpISbuIOAEeOHFFIpdKaiAZdJlGz/CGiPUKrAl5OWp5+GR7xMi6XC0TEJQhvsWXPfKx0KeTz+WA2m8NFm4RyjVNTUzhx4gSSk5OhVCqhUCiQmpq6ZAtsOYIChJZBTQ4lJh3j+PW/7lpSzdnFkvnUajUuXbqEYDAYkZ+MMYa0tDQuTvzk5GTY7faIrn3h7x8IBASflFStVt8E4BcRTW4ZRIWlwhgTicXiDCHln5cY8IpzmZyc5BLZyys5Egg1lM/Ozl6WQC3VYqHpSnuNjY1oaGiA0WhEYmIitm7diiNHjuCGG25ASkoKDh06hOLiYkilUoyNjaGurg6tra3XXFYsV1AE7j1agc8dSINKvLTM5sWS+UQiEdLS0rjk3ahUKkxORt5FVKlUclkCzVxKVVVVIRAIrKmzNipEBUBWXl5e2H7k4U8BQuLEY5xIvz0EeDl7A4EAxsbGVhQ4dy1hERIQBwcHUVxcjMOHD6O8vDzst5lpFYlEIigUCuTl5aGiogI33HADsrKycPHiRZw7d27Wjs3M8WcKyj/+8Q9s2bIFxcXF+M53vnPV6x977DGoVCpUVVXh4J5diDd2oq+vD0R0zZ2bxZL5AL7+EB5tUnmOI4hKYmIiJBJJ5BfvMoiW5U9mfn5+2G52uVwR97chIrjd7oidtD6fD2KxOOLtX7/fD7fbzaV+yfDw8KyyhstlvqVQMBgM9xeqrKxckRgzxqBWq8M7IqdPn0ZxcTFycnLAGLtKUAKBAD75yU/ipZdeQk5ODmpqanDrrbde1YP6Pe95D37605+Gf29ubobJZLrmzg1jDI888siCz6tUKnR1zW07vHwSExPn7eWzXJRK5aLNxpYzn5mWk1wuB2MsiYgizwVYAtFiqWTm5+eH736PxxPx9qfb7V6wvupy4GU1GY1GLrtHRBSOc4mEmRaLw+HAmTNnEAgEuCUgCgmCExMTaGlpgcViuWrJc+7cORQXF6OoqAgymQy33XYbnnvuuWuOvWnTplmFklaKWCxGQkLCLMfmShCKiEXqBJfJZPB6vRE7a+Pj42dZidN+JD4lCpdAVIiKRCLJys/PD5smwWAw4i3XaHP28tqSNpvN3NphqNVqlJSU4Pjx48jOzkZpaSnXBESpVIodO3ZAKpWivr4eO3bsmOVDWWjLdy7PPPMMKioq8M53vhPDw8NQKpXweDxcdrJ4ZQnP3cpdKQkJCcuqhjcfcXFxs/42eXl5ErzWRCUtLa0kKytLBCBilRbgsfQB+ImKzWbjMs7Q0BC38P5AIID+/n5s2rQJAwMDq7LdbLPZMDExgby8PFy5cmXZ/79vfvObMTAwgPb2drzhDW/Ahz4UqpKYk5ODkZGRiOfHyznKS1TmWhkrYa6oFBQUJADIinBqSyYqRCUuLq4gKyv0mb1eL5dvYV6iYrfbI94CDgaDCAQCEcfdBAIBmM1mbmUsL1++DI1Gg7KyslWJY5npQ9m+fTsYY7OEYLEtX4H09PTwUvhjH/sYmpubw+/lISpzk/BWCi9xio+Pj/j/YO6SPy8vLyEhIYFfotk1iApRIaIcYauPlxjwGEf4Vo10ScBzKRZpewsBIXBOaDfBO0Buvm3jbdu2obe3N/xNXFNTg56eHvT398Pr9eLpp5/GrbfeOmuc8fHx8M/PP/+80IICMpkMYrEYXm9k0ecz4zoigceyBeBjqQAhYREC8jIzM6FUKosjHnSJRIWo+P1+tRBcxsNJC/ARFb/fzyWKltcSitc4RIT29nZUVVXNEihewrJQHIqQx9Pe3g4gVKDopz/9Kd74xjeirKwM7373u1FeXo777rsPzz//PADgxz/+McrLy1FZWYkf//jHeOyxx8Lj8Vpy8AiRF7o2RgovUZlp8WRmZkIqlfIJ414CUbGlLBaL44Slgcvl4mKp8BAnXnOZmpriEpFrtVq5FNweGxtDenr6vMu6SJMQrxXYplar0dfXB5vNBrlcjqNHj+Lo0aOzXvP1r389/PO3v/1tfPvb3573XIIYROoAl8vlmJqa4lJzh4gisiTn+kNWSnx8fDhNJTMzE4FAIPKuZUtk3S0VxphYJpOFxY1XAiAPHwavuQjb25Ei3IiR0t/fv6g4RdJXaCmRsps2bcKVK1eWNef54Lnjwts6iGQMHnOZKU5paWnw+/0Ld6jjzLqLCoAMlUoVjqbl6QuJFLfbzW0pFuk4fr8/XIQnEux2O8Ri8TWDC5crLMsJvVepVDCbzRH7MZKTk7nUdo2Li+N2I/PeuVkpM8WJMQYpj3X8EokGUUlMTk4O24tCo+lI4OUL4eU05jEfm83GJShtYmJiyUuxpQrLcnN5GGNIT0+P2MpgjEEikURcJImnHyPScXj5ZiQSSbgCHACIxeI1K1h8TVFhjP2GMWZgjHXMeKyKMXaGMdbKGGtijO2efpwxxn7MGOtljLUzxqpnvOc/GGPnGWPvmXMKycwbLtI1KRC6iXnUK+HlNOaBUHkuUpbr7F1KrtBKkgNXI89lpfBYtgD8rAwezBWn5XQrZIzdzBi7PH0ff2H6sXLG2GnG2G8ZY4vqxlIslccA3Dznse8BuJ+IqgDcN/07ALwJQMn0cSeAn01PKBlADYDdAN43Z6yrRIVHPVke265C+ngk8BI4n8/HJX5nJRbPQsKyUkEB+PlDpFIpl9qukdZDAULb07zaq0YKY2yuxcOWIizTvbceQehe3grgvYyxrQDuAXArgCYA/7LYGNe8e4moDoBp7sMAhKtIAUCo6fcWAI9PN0k7A0DJGMsEwGa8by4SqVQa/rA8BIGHMEXbXHikLkQyzlxhCQaDKxYUIFRMyG63L/t9cxGLxVwEgQfz3Mjrxty5TP+fL+VC3A2gl4iuTJegfBqh+1qM0P0bxKv387ys1HnxGQD/ZIz9YHqi+6cfzwYws//BCIBsImpijF1ASOW+P3cOEokkPMlIi+YA/CwVHoIQDAa5WU08/i6RIGzd1tbWwu1248Ybb1zxkkwkEnGxVNxuNxfrgEc0rMfj4eI4tlqtEV/DXq931mfq6+vLABAP4FoTnO8e3gPgYQAvAOgBsHC1caxcVD4B4D+I6BnG2LsB/BrA6xd7AxF9G8B8AQfM6XQm19bWAgAcDgdOnz4d0Q0UCATg8XggjLlSnE4nTCZTRBZCMBiEy+WKeC4ulwvj4+MRObGJCA6HI6K5BAKBsDOyqakpYqHj8X/EGONSFyXSuQgCx0Msa2trIxIVj8cDn8839zOt+EImohaExGVJL77mAaAAQMeM360A2PTPDIBt+udfAHjvjNddBpB5jbEr3v72txuExtJnz54lu91OkWC32+ncuXMRjUFE1NLSQmazOaIxPB4PnTp1KuK5XLp0icbHxyMeJ5LG6kKzdJvNRi+99NJVTeGXQzAY5NLkvbu7m4aHhyMeh8dchoaGqLe3NyrmMj4+TpcuXQr/XlxcbAAgoWvf6/sA/HPG718E8MVrvW/msdKvmTEAR6Z/fh1CJhEAPA/g9uldoL0ArEQ0Pt8AM/DPzN/gsaU2M+9hvcfhtUXI03ewknEEp+yuXbuQkpICiUQSUUi/3W7nUrCKl6+JB7yWujygOUv3QCDAACxlndgIoIQxVsgYkwG4DaH7eslc05ZmjP0ewA0AMhhjIwC+CuAOAA8zxiQA3Ajt9ADAiwCOAugF4ATw4SXMwe/z+cJ3HQ9nFy+HGY9dAbFYPCteYKXwiMcAXq22vpwdoLmCIhBJSD+v4lc8dtd4iQEPfyAvaI5PJhgMgpZwUxCRnzF2F4B/IrRc+g0RXVzOua8pKkT03gWe2jnPawnAJ5czAQC+mTcLD+uA19Yej7gDXt9cKSkpXPwGwlbuUm/ohQRFYKXCYrFYuBStslqt4UzrlcIrHsnj8XARSh7MFcqlCMqM176IkIGwIqJBVj0ulyv8gXkIAo/YBYBf+DaPz8SrXkdGRsaS22teS1AElhvST0SYnJyE0D1hpRCnZnG8crx4iBMPCxu4OsaKiNZs3z0aRMVoNBrD8+AR6szLOkhISOAWaRnpZ5JKpfD7/RFfdHK5HE6n85p1SJYqKALLERaLxRLuEhgJPAqkA3xzvCJNHF2N0h9EBJ/PF/kafImsu6gQkc/lcoW/xnmFOotEooitA16WCq/ckqSkpIgDxhhjyM/Px8DAwIKvWa6gCCxVWK5cucKlhAOvFiw8C4NFKgg8l2LCODabDSKRKPLydktk3UUFAAKBgFcQgNVI/V4pvOaSkpLCrWQhjxiI3NxcjI6OLprLs1xBEbiWsFitVrjdbi61S3jWD+ZRmW/ujstKWI3Kh9PxTQt3sudMVIiKWCyemJiYABBdGaNCy4RIWa3m2ytFLBajrKwMbW1ts5ZTkQqKwELCEgwG0draGq5XGyk8O1lGavHw8oWslqj4/f7IG0cvkagQFZFINCrUIo0mUREu/EgvGJ6iYjKZuFzAWq0WEokk3BKDl6AIzCcs3d3dyMrK4pJt7ff7uThYiQiBQCBi/0401VYGZsfvjI+PY2pqKvJGSUskKkTF5/MNjI2FrLPVKFITCYmJiRHnc/AqriyVSpGYmMhFoABg+/bt6O3txcjICFdBEZgpLIODgzCZTCgu5lN/eXx8PNwXORKmpqYi7pYA8PXv8N5BGh4e9kxNTb22LBWTydQzNjYWivnntHPDq0YGLyuDVyuIvLy8WW0tIkEqlWLr1q1oaWnB5s2buQqKgNAG9cKFC6isrOT2/zs8PMyl/1E0LaEAPtvbPp9vluU1MDDgxKuVBFadqBAVr9c7OjQ0FG5GyyMALikpKeJ2lgA/P8Zy4kMWQ6VSYWJigkuUrs1mw8WLF7Fnzx709PRgZGSEa+o+EaGnpwcmkwnV1dVoamriIvQOhwNExCXM32g0cumjxEucnE5nxNvSc5dQg4ODPgDXSpfhRlSICoDxwcHB8FqFxxIoKSmJSxo6L1HRaDRcREUkEl3VhGslzPShqNVq7N+/HwaDAY2NjVxu/KmpKdTX18Pv92Pv3r3Iysri1leor6+Py5Z0MBjkVqZTqFwfCbzqEM8VlWm/2WtSVMIOBx6NmYSm2ZHu3sTFxXFpmi2TycAY4+LnKSgowODg4IrnNJ9TViaTobq6Gnl5eWhoaMDly5dX9LdzOp3o6OjA+fPnUV5ejrKysrBPiUdfIa/XC5PJBK1Wu6L3z8RkMnFpzibcxJGOw2sJNbe1zPSX4prFqURF3x8AowMDA+H/EblcDqvVGnEsg1AHNdIck8TERDidzojNba1WC71ej/z8yPo6yWQypKenY3R0FDk5Oct677V2ebRaLVQqFYaHh3H69GkkJiZCrVZDqVQiJSXlqm9Rv98Pm80Gi8UCvV6PYDCIwsJClJeXz3uTRdpXqK+vDwUFBVx8Mzqdjos48XLS8vTvzGwj7PV63cvJ/YmUqBAVIvJlZmbaHA6HKikpCQqFAkNDQxGPKyxdIhWV9PR0TExMcBGVjo6OiEUFALZs2YL6+nqoVKol35hL3TYWi8UoKChAfn5+uMG60ABMYGpqCrW1tRCJRJDL5VAqlaioqFjS32ilwmK1WjExMYEDBw4s6fWLQUQwGo3hNqqRMDk5ifT09IjHsVgsXHbHrFZr+HN1dHRAIpFciHjQZRAVogIAYrG4ubW1ddOBAwe47bgoFIpZvXhXikajwaVLlyIWg+TkZHi9Xi6xCDKZDKWlpWhvb0dNTc01X7+SOBTGGBQKxVXfwkSE2tpa3HDDDSuZOoDlC0swGERbWxuqqqq4lBcwmUxQKBRcarEYjUaUlpZGPA6Pnttz426ampoCJpPplYgntwyixacCvV7/8rlz53wAv7iO5ORkLsWVU1JS4HK5uJRTyMvL42KFAaEeuSKRKBzAthC8A9sYY1yWH8vxsXR3d0Or1XIJnAOAgYEBLhbj1NQUEhISuHRd4OGknRt3U1dXZ3E6nWcjGnSZRI2o+P3+prq6OovwO4+4junObFxC7YWt3EjJzs7G6Ogot63b7du3o7u7e8Gbkreg8GYpwmK1WmE0GrkFznk8Htjtdi75R3q9notfhpeTdq5fprGxMQCgNeKBl0HUiAqAi62treFfeC2BUlNTYTLN7TCyfDQaDZciSVKpFBkZGVzGAkLLoPnyeIDoFxSBxYRFyBeqrKzkVlVtcHAQ+fn5UeXsNZvN3IPwfD4fbDabm4ic13gbV6JGVIjI6/F4bE5n6PPzjA/R6/URj5OWlsYt76aoqAh9fX3crBWtVguZTIa+vlfTOzaKoAjMJyxEhPb2dmRnZ3Nb9gQCgRXtms2HEGrAo1SBXq9fcjvaxZhpqVy8eBFSqbRj8XfwJ2pEBQDEYnFLW1sbAH6WCi8xEHY5eAhdUlISEhMTuQTDCVRUVGBychKDg4MbTlAEZgqL2+3GpUuXIJFIIi4XOZP+/n7k5ORE3K8bCFkpPITA6/UiGAxyT45samoKmkym4xFPcJlElajo9fqXGxsbZzlreVSzF+JeIiU3N5ebk7W0tBRdXV3crBWRSIRdu3ZhYGAAZ86c2XCCIiAIy4kTJ+ByuRaMd1kJPp8Pw8PDXKJxgVD+EQ+Lh5eVYrfb5zppzQ6H40zEAy+TqBIVn8/XeOLEifDdz0sMtFotFx+GSqWCyWTikneTmJiI9PR0jIyMRDyWgMPhQCAQQHx8PBc/0nogxI8IbVF5ONkFenp6UFRUxGUbeWpqCiKRiEs5S55+mZnO3nPnzgWxxk5aIMpEBUBHc3Nz+KtbpVJxWSKo1Wou4zDGkJWVdc0t3KWyefNm9PX1cdmqFpY8NTU1OHDgAHQ6HXp7e6Omt+9SEHwowWAQ+/btw9atW7nkCgGhUHqDwcAlsxl41dkbKcFgEHa7nYtVaTAYwvE/LpcLVqvVSUSRx1Qsk6gSFSLy+v3+fsHhyMvJKpVKIZFIIs4nAoD8/PyI8m5mIpPJkJOTg56enmu/eBHm+lDEYjFqampgt9u5JQiuNna7HfX19UhISMC2bdvAGOOSKyRw4cIFlJaWctlB8vv9MBqNXKyLiYkJZGRkRLzECwaDmJqaCju0X3nlFRDRPyKe4AqIKlEBALPZ/Nu//OUvHiAkBmKxmIsY8NoSjo+PR1JSErflRVFREYxG44odwAs5ZUUiEaqqqsIJgrysK94QEXp7e9Hc3Izy8nJs3rx51g3GQ1hGR0chEom4iAAAjIyMIDs7m4tA8XL2TkxMID09Pfy3e+qpp0x6vf7JiAdeAVEnKk6n8y9PPfVU2JEiJOFFSmZmJpeQfeDVLWEeCDd/W1vbsp3SS9nl0Wq14eVQtFktgnXi9Xpx8ODBBfsARSIsHo8H3d3d2L59O48pg4i4ReMK/Y941HOZGYQXDAZRW1vrB3A64oFXQNSJChGNjY+P24Rvbl5O1sTERBARhDiYSEhNTRUCiyIeCwilAWRlZeHy5ctLfs9yto1lMhl27tyJ3NxcNDQ0cC/GtFwCgcAs62Tr1q3XdJ6uRFiICG1tbSgrK4NMJuMxdYyPjyM1NZVLbIrRaERaWlrEFg8RhS0VAGhuboZYLG4iojXr9TOTqBMVAPB6vX/6+9//TkAopsPtdnPZceGZd1NWVobOzk4uYwFAcXExJicnl5QKsNI4FMFqMZvNOHHiBPr7+7n8XZeK1+vF5cuXUVdXh0AgsKh1Mh/LFZahoSFIpVJuy55gMIju7m5s2bKFy3i8nL1Cvo8gzH/605/sY2Nj/xPxwCskKkVlcnLyD0888UT47lKr1TAajRGPm5WVhfHxcS7f0kJxn8nJyYjHAkI7Szt37sSFCxcWrVgXaWCbTCbD9u3bsX//fvj9fpw8eRLt7e2wWCyrYr0IW8TNzc1oaGhAXFwcDh8+jC1btqxoa3epwjI5OYmhoSFUVFREMv1ZDA0NQaPRcGuR6nK5uITmz92SfvbZZ13BYPCfEQ+8QqKm9MEc2s6fP+8XCvhqtVoMDg5GXD1dLBYjIyODWxKYkHNz4MABLgFaCQkJqKqqQlNTEw4cOHBV1CfPSFmZTIaSkhJs2rQJRqMRPT09cDqdSEtLg1KphEKhQEpKyrI/l1Ci0WKxwGq1wmQyITU1FYWFhUhNTeWW3QwsXDbB6XSivb0de/fu5RKTAoR2fPr7+3Hw4EEu4w0NDXHb3tbr9dizZw+AkPXjdDqHiSjyAs0rJCpFhYgoOzv7+KlTp9534403IjU1Fa2trSCiiC/KgoICXLhwgYuopKSkICkpiZtIASF/TVFREc6fP4+amprw512t0HuRSASNRgONRgOfzweLxRKu4ma32yEWi6FUKiGXyyGRSCAWiyESieDz+TA+Po5gMAiv1wur1QqbzQYiQkpKCpRKJXJzc7Ft2zZuN/ZMFhIWv9+PpqYmVFZWRlwzdib9/f3Izc2NuD8QEBLekZERHDp0KOKx3G53uHQqADz33HM+q9X6u4gHjoCoFBUAGBsbe/zpp58+euONNyoZY0hNTeXiKRe+fW02G5cktdLSUpw7dw4ajYZbOHlubi6mpqbQ0dGBbdu2YWpqak1yeaRSKVQqFVQqVfgxn88XFgyPx4NgMIhAIIBAIACr1QqxWAyJRIK8vLyw8KwVc4VFKpWiubkZBQUFXMoaCHi9XoyMjODw4cNcxtPpdFCpVNzyj2Z+oT3xxBMWh8PxbMQDR0DUigqA2hdffNEnWCd5eXkYHBzksv1WVFSE/v5+VFZWRjxWQkIC1Go1+vr6uNX7AEJLq/b2drS2tsJsNqOmpmZdcnmEUg1z/+68qp1FiiAsp0+fRnx8PFQqFfLy8rie4+LFiygpKeFmcfX396OqqorLWENDQ9i9ezeAUIby8PCwnYj47EaskKh01AIAEXkCgcCpEydOAAg5Rm02G3w+X8Rjq9VqmM1mbjEbmzdvxujoKJc+QwKMMRQUFGB8fBxpaWlcuuhdr2RkZEAsFsNqtXJJ8JuJXq+H1+tFdnY2l/HMZjOkUimXnkVWqxXx8fFhx/Hvfvc7r9vt/lXEA0dI1IoKAIyPj3/3wQcfnARCN1lOTg6X7nyMMZSUlCwrLmQxxGIxKioq5i2UtFJsNhvOnz+PgwcPgjGGjo6ODZXHs1YEAgE0NjYiKysLO3bs4JYrBISWfpcuXeLaWbGzs5PblvTMIDwiwk9+8hOrxWL5JZfBIyCqRQXAucbGRpuwbZubm4vh4WEuN1dWVhYsFguXhmNAyMGanp7OJdJ2plNWLpejoqICYrEYjY2NaxpXEu24XC40NDRAq9Vi06ZNXHOFgFAl+pKSEi5byEAo4S8uLo5L2Ui/3w+TyRRe/jU3N8PhcLQRUeQ1TyMkqkWFiMjpdD7yP//zPx4g1NgrKSkJZrM54rEZY+GaJrzgsQyab5eHMYatW7ciMzMT9fX1XKKCNzpmsxlnzpxBWVnZrAAyXsKi1+vh8/m4LXuICF1dXdz8UKOjo8jKygpbUD/84Q/NY2Nj3+EyeIREtagAwNTU1G8effRRm2CdFBUV4cqVK1zGVqvV8Hg8XGq2AKFlUGVlJVpbW1dUXOpa28a5ubmoqKjA2bNnuRTh3qgMDw/jwoUL2LNnz7yO+0iFxev14tKlS6ioqOC27BkbG4NSqeTiS5mbfzQ1NYVXXnnFCWDNq7zNR9SLChGZ3W73yVdeCbUuSU1Nhcvl4pK5DPAPt1cqlcjMzMSFC8vr37TUOJTU1FTs3bsXXV1d6OzsjLgy3kbC5/Ph/Pnz0Ol02L9//6IFklYqLMFgEE1NTSgrK+O27AkGg+jp6eHmS5mcnERKSkp4fv/93//t8Xg8jxBRVFwMUS8qADA+Pv61Bx54YAJ4dVekv7+fy9ipqakQi8Vcv/k3bdoUjsBcCssNbEtISAhH3J48eZJL3dxoR6/X49SpU1Cr1di1a9eSYjxWIiwdHR3IyMjgFswIhKJctVotlyREYHaD+mAwiIcffthmsVh+xmVwDmwIUSGiCz09PaO9vb0AQr1z9Ho9l4ppwKvWCq/dFcYYqqqqMDIyck2xWmmkrLCDVV1djQsXLqCrq4vb3yOa8Pl8aGlpwcDAAPbt24ecnJxlLUmWIyyDg4Pwer0oKSmJdNph/H4/BgYGuBXvdjgc8Pv94ZyhF154gbxe7z+JyMLlBBzYEKICAEaj8Wvf+973rEAotDwnJ4ebtZKcnMytf7OAWCzGrl27Fk0Q5BF6n5KSMstq4bU7tt4I5RFOnToFlUqF3bt3r3g5shRhERIQd+zYwc2PAgBdXV0oKCjgEt4PhDo1zhSob3zjGxM6ne4bXAbnxIYRFb/f/9fnn39+StheLiwsxPDwMJdgOADYunUr+vv7uflqgNkJgnO3gnnm8ohEIhQXF2P//v2wWq2oq6uDXq/fkOJCRBgcHERdXR2ICIcPH162dTIfiwmLkIC4a9curnlKk5OTsFqtKCgo4DLe1NQUHA5HuFJcW1sbhoaGrhARn4ArXhDRhjmSkpI+etddd1lpmv7+frp06RLxwmg0UkNDAwWDQW5jEhGNjIxQfX09+f1+IiKyWq107NgxstlsXM8j4HA46Pz583Ty5EkaHR3l/nmIiI4fP851PL/fTwMDA3T8+HHq6Oggj8fDdXwBvV5PtbW15Ha7iYjI5XLR8ePHaXJykut5fD4fHT9+nOx2O7cxz549SxMTE+HfDx06NAFgN0XBvTnz2DCWCgA4HI7H/vjHP04KUbV5eXnQ6/Vwu91cxs/IyEBSUhLXZRAQ8gFlZ2fj3LlzsFgsq54cmJiYiB07dmDHjh2YnJxEbW0turu7uf2deGK323Hx4kXU1dXB7XZj3759KC8v51apbS4zLZapqSmcPXsW5eXlXBMQgdCyJz8/n8sWMoBwQzyhultdXR16enraiegclxNwhNEGM5GlUumt73jHOx57+umnU4HQ/r/RaOSSHAiEHGunTp3C7t27ufR0mUlnZyeuXLmCgwcPcomqXCo+nw9jY2OzKqFptdqItkxra2txww03rOi9drsder0e4+PjEIvFyM/Ph1ar5dYreSmMjo6ipaUFVVVV3POFJicncfnyZezbt4+Lf4aI0NDQgO3bt0Mul4OIUFlZOXnhwoUDFG1LH0R3lvK8+P3+v9bW1o52dnamlpWVITMzE319fVd1Z1spEokE27ZtQ1tbG/bu3cvNaWez2aDT6VBUVITOzk7U1NSsSp2R+ZBKpcjPz0d+fj7sdjt0Oh2am5sRCASg0WigVqshl8tXbT5C+QS9Xg+j0Yj4+HhotVrs3LmTa82TpeJ2u9Hb24uSkhL09fVBpVJx2+71+/24cOECdu/eze3aMRgMSEhICJfqeO6554IGg+GVaBQUYANaKgDAGDtw0003Pf/yyy+nAaE0/MHBQezatYvbOdrb2yGXy7k42eY6ZQcHBzE6OoqamhpuuwIrwev1Qq/XY2JiIlzEWy6Xh4syxcfHIy4ubt6YkLmWChHB7/fD7XbD7XbDarXCarViamoKYrEYCoUiXKtlLWuuzMXlcoWXPEKzus7OznkryK2ECxcuIDk5GYWFhRxmG/q71tXVoaamBomJiQgEAtiyZctEX19fFRFFZd+VDWepAAAR1WdmZl46c+bMwb1790KlUqG3t3dWx/tI2bp1K06dOoW0tLSIijnNt8uTn58PsViMhoYG7Nq1i9u6e7nIZDLk5uaGyxoGAoFwKcjR0VF4PB643e5w/ItEIgFjDIwxOBwO1NfXh4s2Cc8LqfhyuRwlJSVITk5e02XNYphMJrS1taGioiLsm7hWacrloNPpYLfbsW3bNi7zBUI9htLT08NL8ccee8w3NTX1x2gVFAAba/dn5gFgW3V19YSws2E2m6mhoWFBz/lKsNlsdPz48fBOwXK51i6PyWSiY8eOkdFojGSaa0IwGCSfz0der5fcbje98sor5PV6wzta0c7Q0BCdOHGCHA7HvM/P3RVaLlarlY4fP8511yoQCMy6/lwuF+Xk5BgApFIU3IMLHdHxFbICiKhjbGzs1D/+8Y8gEMq5iYuLw9jYGLdzpKSkoKysDE1NTavS6Cs1NRX79u1DZ2cnt0C+1YIxBolEAqlUiri4OIhEonAHyWiGiHDx4kWMj48vmi8USRKi1+vF+fPnUV1dzXXXqqenB9nZ2WHr6cc//rHb5XI9QkSRp+mvJuutapEcAPI3b95sFL4tPR4PHTt2bMXfNgvR09NDra2tS379cuNQ/H4/NTU1UWtrKwUCgZVOc03hHaeyGni9Xjp9+jR1dnYuOVZnuRZLIBCg+vp6Gh8fj2SqV2GxWKiuri58PZjNZtJqteMAEikK7r3Fjg1rqQAAEQ2azeYnf/CDH7iAkI+gtLQU7e3tXM+zadMmBAKBJVkTK4mUFYvFqK6uRlJSEurr67mWpXytMjk5ifr6euTl5aG0tHTJOzHLtVg6OjqgUqm4JiAGg0G0tbWhsrIy7I/693//d4vD4fgiEUV9MZ0NLSoAYDQaP//QQw+NC6UhMzMzIRKJuDYkZ4yhsrISo6OjiyYIRhJ6zxhDcXExtm/fjvPnz6O3t1ewxmIsA2FL9/Lly9i9ezeysrKWPcZShWVgYAA+n49rwXMglN+j1WrDGwR///vfg8eOHWubmpr6LdcTrRIbXlSIyGMwGN797ne/2yTsQmzfvh3d3d1cm5FfK0GQVy6PUqnEwYMH4fP5YlbLMpmcnMSpU6eQnJyMffv2RRS8eC1hmZiYwPDwMKqqqrgmIFqtVhiNxrBQWa1W3HnnnRN6vf422iDfMhteVACAiJrHx8d/973vfS+8DBJaXPAkPj4e1dXVaGxsnFXSkXejL7FYjLKyMpSXl8esliXg9/vR0dERtk4KCwu5dUKcT1jMZjM6Ojq4JyDOt+z5+Mc/brFYLJ8jIh23E6026+3U4XUAiFOr1X2dnZ1hZ1dTUxONjIzM6wiLhMnJSTp+/Dg5nc5VTw70+/106dIlqq2tpbGxsVVJDlwJ0eCoDQQCNDAwQMeOHaMrV66s2t9mpvPWYrHQ8ePHF9yajoTOzk66fPly+PcXXnghoNFojmM6SHWjHBsyonYhGGM7t2/f/n8tLS1pYrEYXq8X9fX12L9/P7cwbIHJyclwS47du3eveqMvl8uFy5cvw263o6ysLBy8tV5EkvsTKUSE8fFxdHd3Q6PRoLi4eNUjkw0GAzo6OgAAe/bs4R6waLVa0d7ejgMHDkAkEsFisWDbtm2G0dHRStpIVgquk+WPABE163S6x7773e86gVeXQUIfZp5IpVIEAoFw/MZqI9RmqaysRF9fH86cORMOrX8tYTQacerUKRiNRuzduxdlZWVrkuoQFxc3K3KYJ4FAAK2trbOWPXfeeafFarXeu9EEBdiguT+LwRiTqdXqS7W1tZvKysoAhLb9pFIpt8LDM30oXq83nEDGO6t5MUwmE7q6uiCRSFBUVIT09HSuDsNrsZaWChFBp9PhypUr4S+KtezYaDab0dbWhl27dsHpdHLNFSIiNDc3Q6VShavj/+1vfwt+7GMfq9Xr9a+nDXiDXneiAgCMsR3btm17ubm5OU0mkyEYDOLs2bPIz89f0RbjTOZzyppMJrS3t6OmpmbN83jMZjOuXLkCh8OB/Px8ZGdnr4nltBai4vF4MDw8jOHhYWRkZKCwsHDN279OTk5e9aXBMwlR2KXcvn17+HyVlZWG0dHRCiLSR/wB1oP1duqs1pGenv6lD37wg1bBeef1eun48eNktYYLxy2bxZyyZrN5XfN4XC4XdXV10bFjx6i1tZVMJtOqOnVXy1EbCARIr9dTY2Mj1dbWUl9fH3m93lU517UYHBykEydOkNPpvOq5SHOFiIjGx8epvr4+HDXr9Xpp9+7dpoSEhFsoCu6hlR7rPoFV+2AAU6vVzz788MMu4T8xkgTBpezyuFwuOnnyJF25cmXZ4/MiGAySXq+npqYmOnbsGLW0tND4+Dj3xD+eouL1eml4eJgaGxvXTBQXIxgMUkdHB507d458Pt+Cr4tEWOZLQPzIRz5izcjI+CpFwf0TyXFdLn8EGGNxKpWq+fe//33ZTTfdJAJC/WN6e3uxb9++JafkLycOJRAIoK2tDWKxGNu3b1/XtP9gMAiz2QydTgej0YiEhARotVpkZGQgMTExIh9MJMsfIsLU1BSMRiN0Ol24WJQQRbqWvqG5+Hw+NDc3IzU1FZs3b77mXFayFPJ6vWhoaEB1dXU4avaRRx5xf/3rX3/JYDC8hTb4TXldiwoAMMa0Wq32fH19fabQgKm3txcOh2NJJShXEthGROjr64Ner8euXbu4b2evlKmpKej1ephMJjgcDshkMiiVSigUinBLzqXe0EsVlWAwCLvdDovFAovFAqvVikAggOTkZKSnp0Or1a5L9bf5sNvtaGpqwubNm5fle1uOsASDQZw5cwZFRUXhfKETJ07Qu971ri6j0VhNRNFXSHiZXPeiAoTiV0pKSv7Z3NycnpKSAiJCS0sLUlNTF63QFWmkrF6vR2dn56xvpGhC6CMt3PBClLBMJkNcXFy44JJQAU4kEoExBpFIhKamJlRXV4OIEAgEwgWdhMPj8YTbp6SkpISFS6FQrGu1u4UwGAy4dOkSduzYsaL6wUsVlvb2diQkJIQblg0MDGD//v3j4+Pju4iIX92OdeQ1ISoAoFAoPlhTU/Pw//3f/6WKRCIEAgGcPn0amzdvDlf/mgmv0PupqSk0NzejoKAA+fn562raLwUigtfrnVckgsFgeN08Pj6O7OzssMjMFaH4+PhwpbhoJhgMoru7G5OTkxFbldcSlv7+fphMJlRXV4MxBrvdjp07d052d3cfpSisir9i1tups5aHSqX68Wc/+9mwp9XtdlNtbe1VOza8Q+99Ph+1tbVRQ0PDqoR3rwfREKYfKWazmWpra6m7u5ubU3gh5+3g4CA1NDSEHeaBQID+5V/+xaxQKD5MUXBv8DzWfQJr+mEBkUqlqnvyySfDe5Rzm0mtZi6P0Wik48ePU39/f9Tk8KyUjSwqgUCAOjs7qa6ublX+n+cKy/DwMJ06dWrWTtLnP//5KZVK9ShFwX3B+1j3Caz5BwbkKpWq64UXXgjvsTqdTjp+/DiNjIysanIg0fVjtWxUUbFYLHTixAnq7u5e1Sp7grAMDg7SyZMnZwnKD3/4Q6dKpToGQExRcE/wPtZ9AuvyoYF0lUrV89JLL4WvKp1OR88///yqZDXPh2C1DAwMbEirZaOJykzrJJIAyOXQ2dlJf/3rX2lqair82M9+9jO3SqU6BSCOouBeWI3jukooXCpENGk0Gg+8//3vH5o2gXHp0iXs2bMHPT09MJlMqz6HjIwMHDx4EFarFadOnVq0olyMlUNEGB0dRV1dHUQiEQ4cOLAmO3FClcDq6mo0NzfD4/Hgscce89x3333tRqPx9UTEr4JYlPGa2f2ZD8ZYlkajOfeVr3wl+/bbb0dKSkq42dS2bduQkZGxJvOw2+3o7OyE3+/H1q1b17Ql6kpZz9IHS8VgMKCrqwtKpRKbN2+OqM3rchgaGsLw8DB2794NqVQKg8GARx99lH72s5+1GwyGA0R0denA64jXtKgAAGMsV6vVNvzhD3/IPnz4MANCbTHPnj2LsrKyebebVwuz2YzOzk7ExcWhtLR03ZqMLYVoFhWLxYJLly6ty99xYGAA4+PjqKmpCSd2Pv74497//M//vKzX6w8Q0XVfH/Q1LypAyGJRq9X1Tz75ZN7rX/96ERAKDGtsbER2dja3FpZLZb2+YZdDNIrKelp8RKH+Qi6XC9XV1eEyk7/4xS889913X7vBYLjxerdQBGKiMg1jTK1Sqeofe+yxwqNHj4qB9c3jISKMjY2hp6cHSqUSRUVFURWVGy2iQkQwmUy4cuUKPB4PSktL12zZKuDz+dDU1IS0tLRZ+UIPP/yw65vf/GbLtA/FtaaTWkdiojIDxli6SqWq/9GPflT0vve9Twqsfx4PEcFoNOLKlSsIBoMoKiqCWq1e9/7E6y0qgUAAo6OjGBgYQFJSEoqKipCamrrm8xAipmfmCxER7r//fuejjz56zmg03nw9O2XnIyYqc2CMyVUq1V9vv/326u9973vJws0r5PGsNDeEB1NTUxgYGMDExAS0Wi3y8/PXtNrcTNZLVKxWKwYHBzE5OYnMzEwUFBSs2/LQYDDg4sWLqK6uDl8TTqcTt912m+Xs2bN/NhgMdxKRf10mt47ERGUeGGMilUr1w4qKig/++c9/ThVyf+b7VloPAoEAxsbGMDQ0BMYYMjMz1zzbd61EhYhgt9uh0+mg0+kgk8mQn58PjUazbnlFC1mvIyMjeOMb32gaGxv7itlsfnRdJhcNrHegzHIPALkAjgO4BOAigE9PP/59AF0A2gH8GYBy+vECAC4ArdPHz2eMdQOAJgDfm+9ccrn89pKSkone3l4S8Hg81NDQQF1dXVERtOZwOKivr4/q6+vpxIkT1NnZSWazedXntprBb4FAgIxGI124cIGOHz9OZ8+epcHBQe49sleC3++n5uZmamlpmRWRW19fH8zKytKJxeKDdPU1Gw/gHIC26Wv2/unH7wLQC4AAZNDs69I645q9b8ZztwE4D+Azc88TLce6T2DZEwYyAVRP/5wCoBvAVgD/AkAy/fh3AXyXXhWVjgXG+gOABAAPAihd4DU7tVrt2Msvvxy+ggKBAF24cIHOnDkTFRe6gNfrpZGRkXDVt9bWVhofH59VXYwXvEXF6XReNXedTse9Yl0k2Gw2qquro76+vlmi/atf/cqjUqkuA8ij+a8hBiB5+mcpgLMA9gLYMX19DswjKn9bYKy/ABADeFoYM9qO1a+QzBkiGgcwPv3zFGOsE0A2Ef3fjJedAfDOJQwnQuhbIojQf/x852tmjFW/973vfem//uu/iu++++54kUiEbdu2QafToaGhAZs3b0Z2dnZkH4wDUqkU2dnZyM7ORjAYhMlkgl6vR19fH7xeL5KSkqBUKsOHTCZb8zkSEdxud7hgk8VigcvlQnx8PFJTU7Fp0yYoFIqoKplAFFrujI6OoqKiIuwQ9vv9uPvuu6eeeeaZM0aj8W20wJYxhdTAPv2rdPogImoBsNzPKryYsMA1u95sOFGZCWOsACG1PzvnqY8gZIUIFDLGWgDYAHyZiE5OP/4rAA0AjhNR50LnISIdY2zXt771rd83NTW97te//rVCJpNBq9UiLS0NHR0dGBsbQ0VFRdRUeROJRMjIyAhvrxIRHA4HLBYLDAYDenp6wkKTnJx8VS2UuLi4Fbf09Pv94RosM2uy2Gw2uN1uxMfHh4UtLy8PCQkJUSUiM7Hb7WhtbUVaWhoOHjwY/puYzWa8+c1vNnd3d//CaDR+aVo4FoQxJgbQDKAYwCNENPeancs+xlgbgDEAnyWii9OPP4vQkv0JitJAug3rqGWMJQM4AeCbRPTsjMf/C8AuAG8nImKMxSFkJk4yxnYiZD6WE9GyO3Exxlh6evoXMzMz7/3jH/+YJvQVAgCdTofOzs6osVqWgiA0Dodj1s0/sygTELKAxGIxGGPhokwGgwEZGRlhkzcQCIQrvYnF4lniJPyckpKC+Pj4qBWQmRARrly5gpGRkVnWCQDU1tbShz70oQmTyfTJqamp/13OuIwxJUI+v08RUcf0YwMAdhHRxPTvcgBBIrIzxo4CeJiISvh8sjVgvddfKzkQMh//CeCeOY//K4DTABIXeW8tQv+BkZx/h1qt7v3GN77hmLnm93g81NTUROfOnYsqX0skBINB8ng85HK5yOFwkN1uJ5vNRi+//DLZ7XZyOp3kcrnI6/VGheOaB1NTU3Ty5Em6ePHiLJ+O3W6nj33sY1a1Wn0OC/hPlnIAuA8h60P4fQAzfCrzvH7R56PtWPcJrOA/hAF4HMCP5jx+M0I7Qqo5j6swXbcCQBGAUQBpHOYhU6lUD27fvn1yZlN4olA/l2PHjq1ZGYX1YKOVPlgKwWCQent7qba2lkwm06znamtrg/n5+UalUvkpLLNh+vQ1qJz+OQHASQC3zHh+lmgA0OLVVcRuAEPLPed6Hus+gWVPGDiIkJOqHa9uuR1FaGtuGHO2jgG8A6FtvFaEtuLezHk+1Wq1uu+b3/zmVVZLc3Mz1dfXk9lspuuN60lUgsEg6XQ6OnHixLzWyR133GFVq9WNAPJpZddIBYCW6Wu2A9NbxADuBjACwI+Q7+RX04/fNX3NtiG06bB/Jeddr2PD+lSiCcaYLCMj4ztZWVm3//GPf0yf2bPZYrGgs7MTEolkzXsArybrHabPC5PJhM7OTiQkJKC0tHRWhHJdXR3dfvvtk1ar9QGLxfITit0sSyImKhxhjO1Uq9V//I//+I/Mz33ucwkzd0+MRiO6urogl8uxZcuWqMw8Xg4bXVSmpqbQ2dkJIkJZWdmsZE2n04l77rnH9uc//7nHYDC8k4gG1m+mG5D1NpWutwNAnEql+lFJSYnxhRdeCMx0XgaDQRodHaXjx4/TxYsX161HMA826vLH6XRSS0sLnTx5kiYmJmY95/f76Te/+Y03JyfHoFQqP4MN5MeIpiNmqawSjLF8rVb7cFZW1oFHHnkkY+/eveHngsEghoaG0N/fj9zcXBQWFq44JmS92GiWitfrRU9PDyYmJrB582Zotdrw1jYR4fnnnw/ee++9pqmpqf81GAxfJqLVryl6nRITlVWGMbZNq9X+bOvWrVt/+tOfzopt8fv96O/vx8jICDQaDQoLC6OmBei12CiiYrPZ0N/fD7PZjKKiIuTm5s6Kkzl16hQ++clPThgMhlqdTvcfRDSyjtO9LoiJyhrBGDug0Wh+fuTIkZwf/OAHytzc3PBzwWAQY2Nj6O/vR0JCQrg2SDQHiUWzqBAR9Ho9rly5AsZYuAbNzL/nhQsXcNddd012d3df0Ol0nyCirnWc8nVFTFTWEMYYk0gkt6Snpz/8zne+M/3++++Xp6enz3qNUMXM4XAgNzcXubm5Udl7OBpFxeVyYWhoCGNjY0hLS0NRUdFVLWsHBgZwzz33mBsaGob0ev2/0bXD5WMsk5iorAOMMXFSUtKHkpOT77/llltSPv/5zyuEht0CHo8HIyMjGB4eRkpKCvLz85Genh411ku0iEogEIDRaMTg4CB8Ph9yc3ORnZ0dLjot0NjYiG9+85ums2fPThgMhk8Hg8F/UuziXxViorKOMMbEEonkFpVK9bXi4uKc++67L+Omm26aJRxEoRqsQ0NDMJvNUCqV0Gq1UKvVV904a8l6iorH44Fer4der4fdbodKpUJeXt5VNXx9Ph+effbZwDe+8Q3z5OTkhfHx8fsA1MfEZHWJiUqUwBjbnpmZeV98fPyRf//3f5d/+MMfjpu7NCIimM1m6HQ6GAwGxMXFQavVrnnVN2DtRWVqago6nQ56vR5EBI1GA61Wi5SUlKust6GhIfzsZz9zPP744w6/3/+MwWD4DhENrdlkX+PERCXKYIylyuXyDycmJt61c+fOlHvvvTfjhhtumHfZ43A4wjea3++HWq2GRqOBXC5f9S3q1RYVv98Pi8UCnU6HiYkJJCQkQKvVQqPRzBs46PP58PzzzwcffPDByYGBAb3ZbP6+2+3+X3oNVbGPFmKiEqWwkIrUZGZmfl4kEh06evSo9LbbblMeOnRoXsetz+eDXq+H0WiEzRaq6qBQKKBQKKBUKrkLDU9R8fv94YJNFosFU1NTEIlEUCqVUKvVUKlU887d4XDg5ZdfxlNPPTVZV1fnJ6Ln9Hr9Q0R0mcvEYqyImKhsAKZrwhzJysq6PRAIvK66ulr6gQ98IP1Nb3oTW6gtRSAQgNVqDd+sgtDI5XIolUooFAokJCQgLi5uRe0+ViIqgUAAHo8HTqczPK+pqSmIxeLwvJRKJVJSUhac09jYGJ5//nn/E088Ye7r63MGg8G/GQyGJwGcI6LAsj9IDO7ERGWDMW3BVKSnp79HJpO9S6vVKt7//vfL3/rWt8Zt2rRp0fcGAgHYbLawyLhcLng8HiG9YFZBpZk/CwWaRCJR+N/Tp09jz549CAaD4fBsoeLb3GJPXq8XQKgaXVxcHBITE8NW1GICAoT8SO3t7XjmmWecf/zjHx1TU1Pjdrv9SZvN9gwR9XH7w8bgRkxUNjiMsazExMS3pqam3i6RSIqqq6vZkSNHFDU1NdKqqqol9wUiolnlH2f+HAgEZuV2BINBTExMhAPKBKGZWfFtpjjJZLIlb4WbzWa0tLTg7Nmz7hMnTkx1dHQQgHadTvc/gUDg70RkjuDPFWMNiInKdQRjTAagXCaT1ajV6psCgUB1XFycoqqqCocOHVLu3r1bWlVVxaX8Ag+fislkwvnz5wUBsXV2drJAIDDBGDs7Pj7+ChE1A+iOLWs2FjFRuc5hjEkBlEskkl0ajeb1wWBwp0wmU+Tn51N+fr44Pz8/Pi8vLzErK4tlZWUhMzNzQcfoTK4lKoLjeGxsDOPj4xgbGwsODg46BgcHPUNDQ8GhoSEEAoEJAGd1Ot1MAQny/Pwx1p6YqLwGmRaaLIR6KGXKZLLs1NTUEplMVkBE2X6/P0MsFsfFx8eLVSpVMCEhAVKplEmlUuFfZrfbk2Uy2ZTP5yO/3w+fz0d2u50ZjUbm8/n8gUDALZFIDIyxEbfbPTA5OdkTDAbHEGqvMg5gPCYg1ycxUYmxINPikwEgDqFi45LpQ4pQryT/nMMFYCK2XHltExOVGDFicGX5AQoxYsSIsQgxUYkRIwZXYqISI0YMrsREJUaMGFyJiUqMGDG4EhOVGDFicCUmKjFixOBKTFRixIjBlZioxAjDGMtljB1njF1ijF1kjH16+vE/MMZap48BxljrjPd8kTHWyxi7zBh744zHb2OMnWeMfWbtP0mM9WT9KifHiEb8AO4lovOMsRQAzYyxl4joPcILGGMPArBO/7wVwG0AyhHKJXqZMbZ5Okz/NgA1AJ5kjCUTkX2tP0yM9SFmqcQIQ0TjRHR++ucpAJ0AsoXnpwtEvRvA76cfeguAp4nIQ0T9AHoB7BZeLgw74+cYrwFiohJjXhhjBQB2AJjZbOsQAD0R9Uz/ng1geMbzI3hVhJ4F0ASgaVqgYrxGiC1/YlwFYywZwDMAPkNEthlPvRevWimLQkS/BfDbVZhejCgnJioxZjFd7uAZAE8S0bMzHpcAeDuAnTNePgogd8bvOdOPxXgNE1v+xAgz7TP5NYBOInpoztOvB9BFRCMzHnsewG2MsTjGWCGAEgDn1ma2MaKVmKUSYyYHAHwQwIUZ28ZfIqIXEdrNmbX0IaKLjLE/AriE0M7RJ2MFmmLEijTFiBGDK7HlT4wYMbgSE5UYMWJwJSYqMWLE4EpMVGLEiMGVmKjEiBGDKzFRiREjBldiohIjRgyu/H9c10Z8xCpPBAAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "T=[pi/6, pi/4, pi/3]\n", "R=[1, 2, 3]\n", "polar(T, R)\n", "show()" ] }, { "cell_type": "markdown", "id": "iraqi-square", "metadata": {}, "source": [ "

Le diagramme en batôn

\n", "

La fonction bar permet de réaliser des diagrammes en batôn.

" ] }, { "cell_type": "code", "execution_count": 22, "id": "hydraulic-cosmetic", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "X=[1, 2, 10]\n", "H=[50, 20, 75]\n", "bar(X, H)\n", "show()" ] }, { "cell_type": "code", "execution_count": 23, "id": "hawaiian-matrix", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "X=[1, 2, 10]\n", "H=[50, 20, 75]\n", "bar(X, H, width=0.1)\n", "show()" ] }, { "cell_type": "code", "execution_count": 24, "id": "ranking-experiment", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "X=[1, 2, 10]\n", "H=[50, 20, 75]\n", "bar(X, H, width=0.1)\n", "xticks(X, ['Jeune', 'Adulte', 'Vieux'], rotation=-45)\n", "for i in range(len(X)) : text(X[i], H[i], str(H[i])+'%')\n", "title(\"Répartition\")\n", "show()" ] }, { "cell_type": "markdown", "id": "regional-italy", "metadata": {}, "source": [ "

On pourra en apprendre plus sur la fonction bar par ici

" ] }, { "cell_type": "markdown", "id": "designed-burton", "metadata": {}, "source": [ "

Dimensionnement de la figure

\n", "

Pour dimensionner une figure, il suffit d'utiliser figure(figsize=(hauteur, largeur)).

" ] }, { "cell_type": "code", "execution_count": 25, "id": "exact-tuning", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "figure(figsize=(2, 5))\n", "\n", "X=[1, 2, 10]\n", "H=[50, 20, 75]\n", "bar(X, H, width=0.1)\n", "xticks(X, ['Jeune', 'Adulte', 'Vieux'], rotation=-45)\n", "for i in range(len(X)) : text(X[i], H[i], str(H[i])+'%')\n", "title(\"Répartition\")\n", "show()" ] }, { "cell_type": "code", "execution_count": 26, "id": "secret-concord", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "figure(figsize=(5, 2))\n", "\n", "X=[1, 2, 10]\n", "H=[50, 20, 75]\n", "bar(X, H, width=0.1)\n", "xticks(X, ['Jeune', 'Adulte', 'Vieux'], rotation=-45)\n", "for i in range(len(X)) : text(X[i], H[i], str(H[i])+'%')\n", "title(\"Répartition\")\n", "show()" ] }, { "cell_type": "markdown", "id": "angry-female", "metadata": {}, "source": [ "

Le multiplot

\n", "

Il est possible de réaliser plusieurs graphique à l'aide de la fonction subplot qui prend en paramètre le nombre de ligne, le nombre de colonne et le numéro du graphique considéré.

" ] }, { "cell_type": "code", "execution_count": 27, "id": "danish-charleston", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "figure(figsize=(10, 2))\n", "\n", "subplot(121)\n", "X=[1, 2, 10]\n", "H=[50, 20, 75]\n", "bar(X, H, width=0.1)\n", "xticks(X, ['Jeune', 'Adulte', 'Vieux'], rotation=-45)\n", "for i in range(len(X)) : text(X[i], H[i], str(H[i])+'%')\n", "title(\"Répartition\")\n", "\n", "subplot(122)\n", "X=[1,2,3]\n", "Y=[-1,0,1]\n", "scatter(X, Y)\n", "\n", "show()" ] }, { "cell_type": "code", "execution_count": null, "id": "hired-stereo", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "hide_input": false, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 5 }