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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "deepl_0.ipynb",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/ia35/4a5f07a68482ba4305b072d4efdc3f6a/deepl_0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4ciJeY6hSEPV"
},
"source": [
"# Apprentissage automatique, réseaux de neurones et apprentissage par renforcement\n",
"\n",
"Cette formation s'adresse aux informaticiens curieux du <font color='blue'>machine learning </font> et plus particulièrement du <font color='blue'>deep learning</font>.\n",
"\n",
"Copyright : BackProp - jboscher@gmail.com \n",
"\n",
"---\n",
"\n",
"note communune à tous les notebooks de cette formation : \n",
"- modifier le mode <font color='red'>Exécution</font> en GPU\n",
"- lisez la <font color='red'>Palette de commandes</font> pour apprendre à utiliser Jupyter\n",
"- les mots-clés sont expliqués dans un autre notebook <font color='blue'>sont en bleu</font>\n",
"- chaque notebook est associé à une présentation Powerpoint\n",
"\n",
"---\n",
"\n",
"## Tutoriel 0 - Python, Packages, Outils, GPU\n",
"\n",
"---\n",
"Au cours de cette leçon #0, sont présentés les langages, IDE, outils, utilisés au sein de cette formation concernant l'apprentissage automatique, les réseaux de neurones, l'apprentissage profond et l'apprentisssage par renforcement.\n",
"\n",
"Il n'est pas utile d'être un expert de chacun de ces outils, par contre il est bon de savoir qu'ils existent (généralement l'apprentissage est facile)\n",
"- Python\n",
"- Jupyter\n",
"- Anaconda\n",
"- NumPy\n",
"- Pandas\n",
"- Matplotlib\n",
"- seaborn\n",
" \n",
"###Python\n",
" [Python](https://www.python.org) est le seul langage que nous utiliserons.\n",
" \n",
" Python est un langage interprété. C'est en grande partie ce qui fait son intérêt mais il y a bien d'autres raisons qui justifient sa popularité, comme par exemple le nombre important de packages scientifiques, les libraries de visualisation, les frameworks (comme Django ), la facilité d'intégration avec le C ( [Cython](https://cython.org)) .\n",
" \n",
" Python est bien sûr gratuit et en open source. \n",
" \n",
" Les livres et tutoriels pour apprendre Python sont très nombreux. Voir une liste [ici](https://wiki.python.org/moin/BeginnersGuide/Programmers) et une autre [ici](https://www.guru99.com/best-python-books.html). \n",
" \n",
" La version courante de Python est la 3.7.x. Il n'y a pas de compatibilité totale avec l'autre version de référence, encore très utilisée, la 2.7. \n",
" \n",
" Seule la version 3.x est considérée dans nos tutoriels.\n",
" \n",
"Par ailleurs, avec Jupyter, nous utilisons IPtyhon qui est un terminal et une implémentation de Python (*Powerful interactive shell*)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "etASMYM1RSk4"
},
"source": [
"Il est difficile de faire une recommandation pour un livre d'apprentissage à Python car tout dépend de votre connaissance des langages et de vos attentes.\n",
"\n",
"Toutefois, étant donné que pour suivre facilement cette formation, il n'est pas nécessaire d'être fluent en Python mais plutôt en *Python for data analysis,* alors jetez un oeil à : \n",
"* Python for Data analysis (Wes McKinney) \n",
"** https://github.com/wesm/pydata-book\n",
"* Data Science from Scratch (Joel Grus)\n",
"** https://github.com/joelgrus/data-science-from-scratch\n",
"* Python Data Science Handbook (Jake VanderPlas)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "G1kD-mQFWORv"
},
"source": [
"Ne foncez pas vers la lecture de ces livres ! utilisez-les comme des dictionnaires, des références lorsque vous en aurez la nécessité."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1iHh05xLsonZ"
},
"source": [
"Concernant les IDE(Environnement de développement : Integrated Development Environment), ce n'est pas indispensable d'en utiliser pour la formation.\n",
"\n",
"Toutefois, si c'est important pour vous, alors il y a : \n",
"- Visual Studio (avec Python Tools for Visual Studio)\n",
"- PyCharm\n",
"- Spyder (avec Anaconda)\n",
"- PyDev (avec Eclipse) "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-69hzxDRXkPh"
},
"source": [
"###Jupyter\n",
"\n",
"Si vous lisez ces lignes, alors vous savez ce qu'est Jupyter.\n",
"\n",
"Pour exécuter une commande système dans Jupyter, précédez la commande de !"
]
},
{
"cell_type": "code",
"metadata": {
"id": "plsI_gAHXPm-",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "c1efb3b4-8628-4a3d-fbb4-7fa6b32dae74"
},
"source": [
"!python -V"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Python 3.7.10\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "A4wibsbNW39k"
},
"source": [
"Autre solution pour afficher la version de Python :"
]
},
{
"cell_type": "code",
"metadata": {
"id": "1wZA7_aUNC53",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "8d26792f-6020-42e9-a597-70383840fe6a"
},
"source": [
"import platform\n",
"print(platform.python_version())"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"3.7.10\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "vZlmyojXjEX9"
},
"source": [
"import sys"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Xed4FEyajG3w",
"outputId": "74dee6f7-e46d-46b4-add2-12e564b1e448"
},
"source": [
"sys.version_info"
],
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"sys.version_info(major=3, minor=7, micro=10, releaselevel='final', serial=0)"
]
},
"metadata": {
"tags": []
},
"execution_count": 18
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qRUvc-d4ZME5"
},
"source": [
"Jupyter utilise l'interpréteur IPython. \n",
"\n",
"La version Jupyter implémentée par Google Colaboratory est un petit peu différente de la version officielle. Elle est plus simple.\n",
"\n",
"Les fichiers créés par Jupyter ont l'extension .ipynb"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Zkdpzr6NaDZ8"
},
"source": [
"Pour importer un notebook stocké dans Github, alors Fichier/Ouvrir un notebook/GitHub/\n",
"\n",
"puis, par exemple : wesm/pydata-book"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Q6evLIDjbgXg"
},
"source": [
"Les commandes de Jupyter ne sont pas présentées ici, hormis : \n",
"- tab \n",
"- ?\n",
"- magic"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ClxAZydacvA8"
},
"source": [
"####tab "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OvQ4hFp-j3JL"
},
"source": [
"[![](https://raw.githubusercontent.com/BackProp-fr/meetup/master/images/LogoBackPropTranspSmall.png)](https://www.backprop.fr)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "i7ycBP9OdZNu"
},
"source": [
"La commande *tab* permet de présenter les variables, objets du namespace qui sont actuellement disponibles."
]
},
{
"cell_type": "code",
"metadata": {
"id": "MbYFmPgqYqA0"
},
"source": [
"une_pomme=2\n",
"une_orange=3"
],
"execution_count": 10,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "ncg7fNE8dyeQ"
},
"source": [
"Dans la cellule suivante, tapez *une* suivi de la touche *tab*\n",
"\n",
"une_orange\n",
"une_pomme \n",
"\n",
"s'afiche."
]
},
{
"cell_type": "code",
"metadata": {
"id": "PIvKJYf4c-b7",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "0a3e04ee-484a-4844-c233-ed08ebb30ac3"
},
"source": [
"une_orange"
],
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"3"
]
},
"metadata": {
"tags": []
},
"execution_count": 13
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lOuD5zeKehwb"
},
"source": [
"####?"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5TjS-3W7evpp"
},
"source": [
"La commande ? devant une fonction ou une variable présente la documentation associée\n",
"\n",
"? présente la définition\n",
"?? présente le code source"
]
},
{
"cell_type": "code",
"metadata": {
"id": "hBCWyHtwejI9"
},
"source": [
"?une_pomme"
],
"execution_count": 15,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "UPFjnyrmelKX"
},
"source": [
"une_pomme??"
],
"execution_count": 16,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "BJ7d7F_xgvFY"
},
"source": [
"####magic"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jpNWw5Khg1ZH"
},
"source": [
"Les magic commands sont spécifiques à IPython\n",
"\n",
"%matplotlib inline: pour que les graphiques soient affichés inline (dans le notebook)\n",
"\n",
"%reload_ext autoreload, \n",
"\n",
"%autoreload 2: recharge les modules avant exécution\n",
"\n",
"Ainsi, un module modifié, en parallèle, durant l'édition est rechargé\n",
"\n",
"Ces commandes commencent souvent les Notebooks"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FFTT53J7j-Hs"
},
"source": [
"Quelques exemples : "
]
},
{
"cell_type": "code",
"metadata": {
"id": "Ng14unXcf4eV",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "faea1600-3832-4b90-f5ea-7ba0bd4d4ace"
},
"source": [
"%hist"
],
"execution_count": 19,
"outputs": [
{
"output_type": "stream",
"text": [
"!python -V\n",
"import platform\n",
"print(platform.python_version())\n",
"import sys\n",
"sys.version_info\n",
"sys.version\n",
"sys.version()\n",
"sys.version\n",
"sys.version_\n",
"sys.version_info\n",
"une_pomme=2\n",
"une_orange=3\n",
"une_\n",
"une_orange\n",
"une_orange\n",
"une?\n",
"?une_pomme\n",
"une_pomme??\n",
"sys.version_info??\n",
"sys.version_info\n",
"%hist\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "jxMXR7-ygAt1",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "d662aa37-952b-4b70-8413-74a9c2d0af48"
},
"source": [
"%who"
],
"execution_count": 20,
"outputs": [
{
"output_type": "stream",
"text": [
"platform\t sys\t une_orange\t une_pomme\t \n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aBK0T5T0u4mz"
},
"source": [
"###Anaconda"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Nj21-ib_vVO9"
},
"source": [
"Si vous travaillez en local, ce qui n'est pas recommandé, sauf peut-être lorsque vous débutez, alors utilisez Anaconda\n",
"\n",
"[Anaconda](https://www.anaconda.com/distribution/) est un logiciel Open Source, orienté Data Science.\n",
"\n",
"Anaconda vous permet de gérer une centaine de packages Python (Tensorflow, NumPy, Pandas, ...)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "U-yrx9l1yWvB"
},
"source": [
"Pour ceux qui ne veulent qu'une version light d'Anaconda, il y a [Miniconda](https://docs.conda.io/en/latest/miniconda.html).\n",
"\n",
"Après avoir installé Anaconda, vous utiliserez Jupyter comme ici.\n",
"\n",
"Une fois Anaconda installé, créez des environnements virtuels pour vos besoins spécifiques. \n",
"\n",
"Par exemple, un environnement de travail avec Python 2.7 et un autre avec Python 3.x. ou un environnement pour TensorFlow et un autre pour fastai."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ip3PW61O017X"
},
"source": [
"Anaconda n'est pas détaillé ici car nous avons choisi de travailler avec Google Colab, mais l'outil est vraiment très bon, très intéressant, très facile d'utilisation."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IGHaX6AT3uG3"
},
"source": [
"###NumPy"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "87iZexZL3xWd"
},
"source": [
"Vous utiliserez [NumPy](https://www.numpy.org) tous les jours. \n",
"\n",
"Il faut donc en faire l'apprentissage."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IjkM373Q4QHl"
},
"source": [
"NumPy facilite les opérations sur les tableaux à N dimensions (des matrices, des tenseurs)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8pJU1YLh67BA"
},
"source": [
"2 livres sur le sujet : \n",
"- SciPy and NumPy par Eli Bressert\n",
"- NumPy Cookbook par Ivan Idris\n",
"\n",
"Il y aussi de nombreux tutoriels sur GitHub.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BTUWKk-eBE3L"
},
"source": [
"####Rapidité"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3jJrSwBWAYGx"
},
"source": [
"Pour vérifier la rapidité des calculs avec NumPy, ci-dessous un exemple tiré du livre SciPy and NumPy.\n",
"\n",
"Vous constaterez que NumPy peut être jusqu'à 50 fois plus rapide que la manière traditionnelle."
]
},
{
"cell_type": "code",
"metadata": {
"id": "GDMGm1GVkJKO"
},
"source": [
"import numpy as np\n",
"\n",
"# On crée un tableau de 10^7 éléments\n",
"arr=np.arange(1e7)"
],
"execution_count": 21,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "26wTcQ1XkeAa",
"outputId": "4ddea600-a8a1-4458-eaca-aeb3f60c3bde"
},
"source": [
"arr[:10]"
],
"execution_count": 23,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])"
]
},
"metadata": {
"tags": []
},
"execution_count": 23
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WLxZQVVzkn7o",
"outputId": "7dbb20e6-9e39-4a75-ba31-acb535e4b83b"
},
"source": [
"arr[:10].dtype"
],
"execution_count": 25,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"dtype('float64')"
]
},
"metadata": {
"tags": []
},
"execution_count": 25
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Az60l4UsvS7l"
},
"source": [
"# On convertit ce tableau en list\n",
"larr=arr.tolist()"
],
"execution_count": 28,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bOZp57klkhKI",
"outputId": "ced6577d-315e-4506-98a8-2f18975b65ea"
},
"source": [
"larr[:10]"
],
"execution_count": 29,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]"
]
},
"metadata": {
"tags": []
},
"execution_count": 29
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "hS3F_vuPk0m4",
"outputId": "5553d23f-8d6e-4f5b-88be-cc7469a60200"
},
"source": [
"type(larr[0])"
],
"execution_count": 33,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"float"
]
},
"metadata": {
"tags": []
},
"execution_count": 33
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "5rge1vMB-tmg"
},
"source": [
"# On crée une fonction qui prend en paramètre une liste et un scalaire et qui multiplie chaque élément de la liste par le scalaire\n",
"def list_times(alist, scalar):\n",
" for i, val in enumerate(alist):\n",
" alist[i]=val*scalar\n",
" return(alist)"
],
"execution_count": 34,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "5k4Ij9ji-X28",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "8627b7d3-6f6f-4333-9026-22930237b1e1"
},
"source": [
"# On mesure le temps de l'opération en broadcast sur array\n",
"%timeit arr*1.1"
],
"execution_count": 35,
"outputs": [
{
"output_type": "stream",
"text": [
"10 loops, best of 5: 25.1 ms per loop\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "lwXVoogg-e8W",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "cbdbd26c-80c4-48bb-cf1d-1a15d6a9735f"
},
"source": [
"# On mesure le temps de l'opération sur la liste\n",
"%timeit list_times(larr, 1.1)"
],
"execution_count": 36,
"outputs": [
{
"output_type": "stream",
"text": [
"1 loop, best of 5: 903 ms per loop\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NdcH7f-1VFIV"
},
"source": [
"####reshape"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "datmigKLVlrf"
},
"source": [
"Lorsqu'on réorganise un tableau, celui-ci reste le même en mémoire, ce qui fait qu'une modification dans l'un se retrouve dans l'autre\n",
"\n",
"Pour avoir une copie après reshape faire une copie avec numpy.copy"
]
},
{
"cell_type": "code",
"metadata": {
"id": "vDytqgkDF8OQ"
},
"source": [
"arr1d=np.arange(100)"
],
"execution_count": 37,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "yko2VtuLvUi-",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "78287e7f-65aa-4329-de0f-0025d2361c87"
},
"source": [
"arr1d"
],
"execution_count": 38,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n",
" 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
" 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
" 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
" 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
" 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])"
]
},
"metadata": {
"tags": []
},
"execution_count": 38
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ggyE7GCpGiuX"
},
"source": [
"arr2d=arr1d.reshape(10,10)"
],
"execution_count": 39,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "aeyDSSIhGjgX",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "0b9cf7a1-3b64-446e-b84a-2b269f50ce10"
},
"source": [
"arr2d"
],
"execution_count": 40,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
" [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],\n",
" [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],\n",
" [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],\n",
" [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
" [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],\n",
" [60, 61, 62, 63, 64, 65, 66, 67, 68, 69],\n",
" [70, 71, 72, 73, 74, 75, 76, 77, 78, 79],\n",
" [80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
" [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 40
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "vxgCKZWGVY1t"
},
"source": [
"arr1d[0]=100"
],
"execution_count": 41,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "2HZFfQI7VgnO",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "b0429291-7f36-4b72-c0f5-c0586c6398ef"
},
"source": [
"arr2d"
],
"execution_count": 42,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[100, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
" [ 10, 11, 12, 13, 14, 15, 16, 17, 18, 19],\n",
" [ 20, 21, 22, 23, 24, 25, 26, 27, 28, 29],\n",
" [ 30, 31, 32, 33, 34, 35, 36, 37, 38, 39],\n",
" [ 40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
" [ 50, 51, 52, 53, 54, 55, 56, 57, 58, 59],\n",
" [ 60, 61, 62, 63, 64, 65, 66, 67, 68, 69],\n",
" [ 70, 71, 72, 73, 74, 75, 76, 77, 78, 79],\n",
" [ 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
" [ 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 42
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "X1GwvjkcViDN"
},
"source": [
"arr2d[0][0]=200"
],
"execution_count": 43,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "bq77kNW-V4ky",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "18757b17-d831-4f07-9f2b-973da5eaeba9"
},
"source": [
"arr2d"
],
"execution_count": 44,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[200, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
" [ 10, 11, 12, 13, 14, 15, 16, 17, 18, 19],\n",
" [ 20, 21, 22, 23, 24, 25, 26, 27, 28, 29],\n",
" [ 30, 31, 32, 33, 34, 35, 36, 37, 38, 39],\n",
" [ 40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
" [ 50, 51, 52, 53, 54, 55, 56, 57, 58, 59],\n",
" [ 60, 61, 62, 63, 64, 65, 66, 67, 68, 69],\n",
" [ 70, 71, 72, 73, 74, 75, 76, 77, 78, 79],\n",
" [ 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
" [ 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 44
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "vSNNhJZtV6Sx",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "7060d1e4-49b6-413e-cfeb-ea12113bc598"
},
"source": [
"arr1d"
],
"execution_count": 45,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([200, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
" 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
" 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
" 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n",
" 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,\n",
" 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,\n",
" 91, 92, 93, 94, 95, 96, 97, 98, 99])"
]
},
"metadata": {
"tags": []
},
"execution_count": 45
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nyrkKbqCi_w-"
},
"source": [
"####indexation, slice\n",
"\n",
"Pour aller plus loin, voir [ici](https://zestedesavoir.com/tutoriels/582/les-slices-en-python/)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Hg3WdMRylg4I"
},
"source": [
"Pour créer une liste de 3*2 éléments"
]
},
{
"cell_type": "code",
"metadata": {
"id": "9ALdVS1sbqPq"
},
"source": [
"alist = [[1,3],[5,2],[6,4]]"
],
"execution_count": 46,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "uw-2sMh3l75a",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "c42a05ea-7c72-4f47-a8cf-28c16054f13f"
},
"source": [
"type(alist)"
],
"execution_count": 47,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"list"
]
},
"metadata": {
"tags": []
},
"execution_count": 47
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "vkgEsk9RjStW",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "cbea74d7-c88e-4ba1-8774-8dbf61b5a8f5"
},
"source": [
"alist"
],
"execution_count": 48,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[[1, 3], [5, 2], [6, 4]]"
]
},
"metadata": {
"tags": []
},
"execution_count": 48
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "iz3VhdBEktIS",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "18e7f97f-f282-42fb-a29f-a4dae4a74f97"
},
"source": [
"alist[2][1]"
],
"execution_count": 49,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"4"
]
},
"metadata": {
"tags": []
},
"execution_count": 49
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jvViy0ILl1ME"
},
"source": [
"et pour la convertir en array"
]
},
{
"cell_type": "code",
"metadata": {
"id": "CfjhoRjjk-KQ"
},
"source": [
"arr=np.array(alist)"
],
"execution_count": 50,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "BOM-4Hrfl6NG",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "75d72e27-5a03-4c31-e5e1-30c238a4f5d2"
},
"source": [
"arr"
],
"execution_count": 51,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[1, 3],\n",
" [5, 2],\n",
" [6, 4]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 51
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "UWzYcjo13GSA"
},
"source": [
"arr=np.arange(6).reshape(3,2)"
],
"execution_count": 52,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ttbFcg7B3KP8",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ec52946d-b8e8-4db3-8f95-bf21ae8a4ffb"
},
"source": [
"arr"
],
"execution_count": 53,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[0, 1],\n",
" [2, 3],\n",
" [4, 5]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 53
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BLPxOMN8n8S3"
},
"source": [
"Pour accéder à l'élément de la 2ème ligne, 2ème colonne (numérotation à partir de 0)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Nm-PUUc3mA-y",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "6733cd89-2e01-484a-8ddd-eb148d1d239b"
},
"source": [
"arr[2,1]"
],
"execution_count": 54,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"5"
]
},
"metadata": {
"tags": []
},
"execution_count": 54
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "v7ajuyKXoGCE"
},
"source": [
"Pour voir tous les éléments de la 2ème colonne"
]
},
{
"cell_type": "code",
"metadata": {
"id": "OzyhSLv5nrfr",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ce36f4a5-dda9-44a5-df20-85ab71be3ff2"
},
"source": [
"arr[:,1]"
],
"execution_count": 55,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([1, 3, 5])"
]
},
"metadata": {
"tags": []
},
"execution_count": 55
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "eDev-wMonx-7",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "3e09b456-c8c3-4280-82fc-b59ad6edd21c"
},
"source": [
"arr[2:]"
],
"execution_count": 56,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[4, 5]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 56
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dLC8jbVRraJ4"
},
"source": [
"Si on slice une liste dans l'intervalle [i,j], on commence au i-1 elt et on finit au j-1 elt"
]
},
{
"cell_type": "code",
"metadata": {
"id": "utZbG7T7ocJH",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "5c1a29a8-2df6-486d-f8c6-6d155f000d14"
},
"source": [
"l = [1, 2, 3, 4, 5, 6, 7, 8]\n",
"print(l[2:6])"
],
"execution_count": 57,
"outputs": [
{
"output_type": "stream",
"text": [
"[3, 4, 5, 6]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "y6gtoxr8r2jm"
},
"source": [
"Pour l'avant dernier élément :"
]
},
{
"cell_type": "code",
"metadata": {
"id": "lFZDwWDUrL0c",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "0b551c87-cca9-4c77-fbac-8c5af6ed7e60"
},
"source": [
"print (l[-1])"
],
"execution_count": 58,
"outputs": [
{
"output_type": "stream",
"text": [
"8\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "H3pJgxjYsbBv"
},
"source": [
"Pour sélectionner du 4ème elt à l'avant-dernier"
]
},
{
"cell_type": "code",
"metadata": {
"id": "KvronQ-SrNSr",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "4b5e75ed-fad2-4711-bdb4-6d5c6d8a5052"
},
"source": [
"print (l[3:-1])"
],
"execution_count": 59,
"outputs": [
{
"output_type": "stream",
"text": [
"[4, 5, 6, 7]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9hovn3oQtHD_"
},
"source": [
"Pour sélectionner du 2ème elt au 4ème elt avec un pas de 2"
]
},
{
"cell_type": "code",
"metadata": {
"id": "kW3zaTmVsGOo",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "3582cd88-5b66-4ffc-ba95-132669c25dd8"
},
"source": [
"print (l[1:6:2])"
],
"execution_count": 60,
"outputs": [
{
"output_type": "stream",
"text": [
"[2, 4, 6]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "4RjNzS2Ts7IN"
},
"source": [
"img1=np.zeros((20,20))+3"
],
"execution_count": 61,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "X6xF08w0wy10"
},
"source": [
"img1[4:-4,4:-4]=6\n",
"img1[7:-7,7:-7]=9"
],
"execution_count": 62,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "3pRlZ1P0wzhS",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 265
},
"outputId": "2f24b774-2aa9-46f2-c936-836b31d49a01"
},
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"imgplot = plt.imshow(img1)"
],
"execution_count": 63,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "o4Fwd1rt1RQ2"
},
"source": [
"####calcul matriciel"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ziM7ijkf5G8Y"
},
"source": [
"On peut le faire sur array"
]
},
{
"cell_type": "code",
"metadata": {
"id": "0NM4cApmxKwY",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "37861132-5efe-4451-ced8-ecb33a50076f"
},
"source": [
"a = np.array([[1., 2.], [3., 4.]]) ; a"
],
"execution_count": 64,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[1., 2.],\n",
" [3., 4.]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 64
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ii3pxFFUzhqk"
},
"source": [
"from numpy.linalg import inv\n",
"ainv = inv(a)"
],
"execution_count": 65,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "VmEr7--35OiG"
},
"source": [
"Calcul de l'inverse de a"
]
},
{
"cell_type": "code",
"metadata": {
"id": "ahy1eKfvzkcj",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "d42c05b3-fce2-4a1c-c664-e156e09f0fc6"
},
"source": [
"ainv"
],
"execution_count": 66,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[-2. , 1. ],\n",
" [ 1.5, -0.5]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 66
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "omB7LLEc5Sx6"
},
"source": [
"Produit matriciel : dot\n",
"\n",
"notez que le résultat est approximatif"
]
},
{
"cell_type": "code",
"metadata": {
"id": "YkohMpXyzqIM",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "4e930fe3-10d7-464f-a328-d4dbca5d28f8"
},
"source": [
"x=np.dot(a, ainv) ; x"
],
"execution_count": 67,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[1.0000000e+00, 0.0000000e+00],\n",
" [8.8817842e-16, 1.0000000e+00]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 67
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "gU7DUV-_zs35",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "a2100bfd-a7b6-41af-96d1-8b69af6dd3a4"
},
"source": [
"x=np.dot(ainv, a) ; x"
],
"execution_count": 68,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[1.00000000e+00, 0.00000000e+00],\n",
" [1.11022302e-16, 1.00000000e+00]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 68
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ILI8id7n44r_"
},
"source": [
"On peut aussi utiliser matrix"
]
},
{
"cell_type": "code",
"metadata": {
"id": "k1wMb6GE23f6"
},
"source": [
"am=np.matrix([[1., 2.],[3., 4.]])"
],
"execution_count": 69,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "82cq46k6n7-D",
"outputId": "0462db0d-5f1e-466c-9f6d-8d5ca85ddcb0"
},
"source": [
"am"
],
"execution_count": 71,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"matrix([[1., 2.],\n",
" [3., 4.]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 71
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "7pXqVUSt4dp-",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "b0d0d07a-02fb-4480-b41d-8f0790378abf"
},
"source": [
"aminv=am**(-1) ; aminv"
],
"execution_count": 72,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"matrix([[-2. , 1. ],\n",
" [ 1.5, -0.5]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 72
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "O-QDF7d84qB9",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ee72327d-2f25-467d-d36f-6ce7808f1cf1"
},
"source": [
"am*aminv"
],
"execution_count": 73,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"matrix([[1.0000000e+00, 0.0000000e+00],\n",
" [8.8817842e-16, 1.0000000e+00]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 73
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_3tKZ6s0Acgb"
},
"source": [
"###Pandas"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "efdSewthAeGx"
},
"source": [
"[Pandas](http://pandas.pydata.org) est une librairie Open Source qui s'appuie sur NumPy pour faciliter la présentation et l'analyse de données\n",
"\n",
"2 livres pour aller plus loin : \n",
"- Python Data Analytics With Pandas, NumPy, and Matplotlib de Fabio Nelli\n",
"- Pandas Cookbook de Theodore Petrou"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nPHLhY9MDa6Q"
},
"source": [
"Le plus intéressant avec Pandas, c'est la structure de données DataFrame"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZcYn4PqZF84x"
},
"source": [
"Pour montrer quelques exemples de l'utilisation de Pandas, nous allons travailler avec le jeu de données iris fourni par sklearn"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kjSTQv8izA4J"
},
"source": [
"Le jeu de données [Iris](https://fr.wikipedia.org/wiki/Iris_de_Fisher) connu aussi sous le nom de Iris de Fisher est un jeu de données multivariées présenté en 1936 par Ronald Fisher dans son papier \"The use of multiple measurements in taxonomic problems\" comme un exemple d'application de l'analyse discriminante linéaire. "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0pN-Si6a0Hy5"
},
"source": [
"Trois espèces d'iris dans la base : Iris setosa, Iris virginica et Iris versicolor\n",
"\n",
"Quatre caractéristiques ont été mesurées à partir de chaque échantillon : la longueur et la largeur des sépales et des pétales, en centimètres.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lHXYgSfc0f4Y"
},
"source": [
"![Texte alternatif…](http://intelligence-artificielle.agency/wp-content/uploads/2018/03/fleur_schema_coupe.gif)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "atrLuqcCDxpp"
},
"source": [
"from sklearn.datasets import load_iris\n",
"iris = load_iris()\n",
"data = iris.data\n",
"column_names = iris.feature_names"
],
"execution_count": 74,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "sr7gn7Ld7la0",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "efab9889-00fd-4143-d9c3-e9336d6d1f7e"
},
"source": [
"iris.target_names"
],
"execution_count": 75,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
]
},
"metadata": {
"tags": []
},
"execution_count": 75
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "noZ6-AzCE_ql",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "982d2e1e-c6e3-445c-b6af-cd78ddc571fd"
},
"source": [
"column_names"
],
"execution_count": 76,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['sepal length (cm)',\n",
" 'sepal width (cm)',\n",
" 'petal length (cm)',\n",
" 'petal width (cm)']"
]
},
"metadata": {
"tags": []
},
"execution_count": 76
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "qJUjjZnYD0sx"
},
"source": [
"import pandas as pd\n",
"df = pd.DataFrame(data, columns=column_names)"
],
"execution_count": 77,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "5Ax4Ude3D2Zp",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 419
},
"outputId": "4ad0e0de-5876-46db-b52a-f5df4cade484"
},
"source": [
"df"
],
"execution_count": 78,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sepal length (cm)</th>\n",
" <th>sepal width (cm)</th>\n",
" <th>petal length (cm)</th>\n",
" <th>petal width (cm)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.0</td>\n",
" <td>3.6</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145</th>\n",
" <td>6.7</td>\n",
" <td>3.0</td>\n",
" <td>5.2</td>\n",
" <td>2.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>146</th>\n",
" <td>6.3</td>\n",
" <td>2.5</td>\n",
" <td>5.0</td>\n",
" <td>1.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>147</th>\n",
" <td>6.5</td>\n",
" <td>3.0</td>\n",
" <td>5.2</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>6.2</td>\n",
" <td>3.4</td>\n",
" <td>5.4</td>\n",
" <td>2.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149</th>\n",
" <td>5.9</td>\n",
" <td>3.0</td>\n",
" <td>5.1</td>\n",
" <td>1.8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>150 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n",
"0 5.1 3.5 1.4 0.2\n",
"1 4.9 3.0 1.4 0.2\n",
"2 4.7 3.2 1.3 0.2\n",
"3 4.6 3.1 1.5 0.2\n",
"4 5.0 3.6 1.4 0.2\n",
".. ... ... ... ...\n",
"145 6.7 3.0 5.2 2.3\n",
"146 6.3 2.5 5.0 1.9\n",
"147 6.5 3.0 5.2 2.0\n",
"148 6.2 3.4 5.4 2.3\n",
"149 5.9 3.0 5.1 1.8\n",
"\n",
"[150 rows x 4 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 78
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "vVHDps_-Fpts",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"outputId": "3c36ff9e-c20a-46d2-fe7b-54c2b8aaf9e8"
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"source": [
"df.head()"
],
"execution_count": 79,
"outputs": [
{
"output_type": "execute_result",
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],
"text/plain": [
" sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n",
"0 5.1 3.5 1.4 0.2\n",
"1 4.9 3.0 1.4 0.2\n",
"2 4.7 3.2 1.3 0.2\n",
"3 4.6 3.1 1.5 0.2\n",
"4 5.0 3.6 1.4 0.2"
]
},
"metadata": {
"tags": []
},
"execution_count": 79
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "T7mbWTyqGtLm",
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"base_uri": "https://localhost:8080/",
"height": 204
},
"outputId": "209aba7c-e41c-4c87-9596-118777bafda4"
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"source": [
"df.tail()"
],
"execution_count": 80,
"outputs": [
{
"output_type": "execute_result",
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" <th>146</th>\n",
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" <th>147</th>\n",
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" <th>148</th>\n",
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" <tr>\n",
" <th>149</th>\n",
" <td>5.9</td>\n",
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],
"text/plain": [
" sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n",
"145 6.7 3.0 5.2 2.3\n",
"146 6.3 2.5 5.0 1.9\n",
"147 6.5 3.0 5.2 2.0\n",
"148 6.2 3.4 5.4 2.3\n",
"149 5.9 3.0 5.1 1.8"
]
},
"metadata": {
"tags": []
},
"execution_count": 80
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "915GmjtBGx3V",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "94a04643-218f-4688-fe9e-31ac31a21a3a"
},
"source": [
"df.index"
],
"execution_count": 81,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"RangeIndex(start=0, stop=150, step=1)"
]
},
"metadata": {
"tags": []
},
"execution_count": 81
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "caO8kB1GG1WR",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 297
},
"outputId": "c8d980e1-6b65-47e6-e574-481e7174e696"
},
"source": [
"df.describe()"
],
"execution_count": 82,
"outputs": [
{
"output_type": "execute_result",
"data": {
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"<div>\n",
"<style scoped>\n",
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" <thead>\n",
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" <th></th>\n",
" <th>sepal length (cm)</th>\n",
" <th>sepal width (cm)</th>\n",
" <th>petal length (cm)</th>\n",
" <th>petal width (cm)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>150.000000</td>\n",
" <td>150.000000</td>\n",
" <td>150.000000</td>\n",
" <td>150.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>5.843333</td>\n",
" <td>3.057333</td>\n",
" <td>3.758000</td>\n",
" <td>1.199333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.828066</td>\n",
" <td>0.435866</td>\n",
" <td>1.765298</td>\n",
" <td>0.762238</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>4.300000</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>5.100000</td>\n",
" <td>2.800000</td>\n",
" <td>1.600000</td>\n",
" <td>0.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>5.800000</td>\n",
" <td>3.000000</td>\n",
" <td>4.350000</td>\n",
" <td>1.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>6.400000</td>\n",
" <td>3.300000</td>\n",
" <td>5.100000</td>\n",
" <td>1.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>7.900000</td>\n",
" <td>4.400000</td>\n",
" <td>6.900000</td>\n",
" <td>2.500000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n",
"count 150.000000 150.000000 150.000000 150.000000\n",
"mean 5.843333 3.057333 3.758000 1.199333\n",
"std 0.828066 0.435866 1.765298 0.762238\n",
"min 4.300000 2.000000 1.000000 0.100000\n",
"25% 5.100000 2.800000 1.600000 0.300000\n",
"50% 5.800000 3.000000 4.350000 1.300000\n",
"75% 6.400000 3.300000 5.100000 1.800000\n",
"max 7.900000 4.400000 6.900000 2.500000"
]
},
"metadata": {
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},
"execution_count": 82
}
]
},
{
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"outputId": "7cce19e3-2d26-4de0-f328-ce800f2b0232"
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"source": [
"df.T"
],
"execution_count": 83,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <th>30</th>\n",
" <th>31</th>\n",
" <th>32</th>\n",
" <th>33</th>\n",
" <th>34</th>\n",
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" <th>...</th>\n",
" <th>110</th>\n",
" <th>111</th>\n",
" <th>112</th>\n",
" <th>113</th>\n",
" <th>114</th>\n",
" <th>115</th>\n",
" <th>116</th>\n",
" <th>117</th>\n",
" <th>118</th>\n",
" <th>119</th>\n",
" <th>120</th>\n",
" <th>121</th>\n",
" <th>122</th>\n",
" <th>123</th>\n",
" <th>124</th>\n",
" <th>125</th>\n",
" <th>126</th>\n",
" <th>127</th>\n",
" <th>128</th>\n",
" <th>129</th>\n",
" <th>130</th>\n",
" <th>131</th>\n",
" <th>132</th>\n",
" <th>133</th>\n",
" <th>134</th>\n",
" <th>135</th>\n",
" <th>136</th>\n",
" <th>137</th>\n",
" <th>138</th>\n",
" <th>139</th>\n",
" <th>140</th>\n",
" <th>141</th>\n",
" <th>142</th>\n",
" <th>143</th>\n",
" <th>144</th>\n",
" <th>145</th>\n",
" <th>146</th>\n",
" <th>147</th>\n",
" <th>148</th>\n",
" <th>149</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>sepal length (cm)</th>\n",
" <td>5.1</td>\n",
" <td>4.9</td>\n",
" <td>4.7</td>\n",
" <td>4.6</td>\n",
" <td>5.0</td>\n",
" <td>5.4</td>\n",
" <td>4.6</td>\n",
" <td>5.0</td>\n",
" <td>4.4</td>\n",
" <td>4.9</td>\n",
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" <td>4.8</td>\n",
" <td>4.8</td>\n",
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" <td>5.7</td>\n",
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" <td>5.1</td>\n",
" <td>5.7</td>\n",
" <td>5.1</td>\n",
" <td>5.4</td>\n",
" <td>5.1</td>\n",
" <td>4.6</td>\n",
" <td>5.1</td>\n",
" <td>4.8</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>5.2</td>\n",
" <td>5.2</td>\n",
" <td>4.7</td>\n",
" <td>4.8</td>\n",
" <td>5.4</td>\n",
" <td>5.2</td>\n",
" <td>5.5</td>\n",
" <td>4.9</td>\n",
" <td>5.0</td>\n",
" <td>5.5</td>\n",
" <td>4.9</td>\n",
" <td>4.4</td>\n",
" <td>5.1</td>\n",
" <td>...</td>\n",
" <td>6.5</td>\n",
" <td>6.4</td>\n",
" <td>6.8</td>\n",
" <td>5.7</td>\n",
" <td>5.8</td>\n",
" <td>6.4</td>\n",
" <td>6.5</td>\n",
" <td>7.7</td>\n",
" <td>7.7</td>\n",
" <td>6.0</td>\n",
" <td>6.9</td>\n",
" <td>5.6</td>\n",
" <td>7.7</td>\n",
" <td>6.3</td>\n",
" <td>6.7</td>\n",
" <td>7.2</td>\n",
" <td>6.2</td>\n",
" <td>6.1</td>\n",
" <td>6.4</td>\n",
" <td>7.2</td>\n",
" <td>7.4</td>\n",
" <td>7.9</td>\n",
" <td>6.4</td>\n",
" <td>6.3</td>\n",
" <td>6.1</td>\n",
" <td>7.7</td>\n",
" <td>6.3</td>\n",
" <td>6.4</td>\n",
" <td>6.0</td>\n",
" <td>6.9</td>\n",
" <td>6.7</td>\n",
" <td>6.9</td>\n",
" <td>5.8</td>\n",
" <td>6.8</td>\n",
" <td>6.7</td>\n",
" <td>6.7</td>\n",
" <td>6.3</td>\n",
" <td>6.5</td>\n",
" <td>6.2</td>\n",
" <td>5.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sepal width (cm)</th>\n",
" <td>3.5</td>\n",
" <td>3.0</td>\n",
" <td>3.2</td>\n",
" <td>3.1</td>\n",
" <td>3.6</td>\n",
" <td>3.9</td>\n",
" <td>3.4</td>\n",
" <td>3.4</td>\n",
" <td>2.9</td>\n",
" <td>3.1</td>\n",
" <td>3.7</td>\n",
" <td>3.4</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>4.4</td>\n",
" <td>3.9</td>\n",
" <td>3.5</td>\n",
" <td>3.8</td>\n",
" <td>3.8</td>\n",
" <td>3.4</td>\n",
" <td>3.7</td>\n",
" <td>3.6</td>\n",
" <td>3.3</td>\n",
" <td>3.4</td>\n",
" <td>3.0</td>\n",
" <td>3.4</td>\n",
" <td>3.5</td>\n",
" <td>3.4</td>\n",
" <td>3.2</td>\n",
" <td>3.1</td>\n",
" <td>3.4</td>\n",
" <td>4.1</td>\n",
" <td>4.2</td>\n",
" <td>3.1</td>\n",
" <td>3.2</td>\n",
" <td>3.5</td>\n",
" <td>3.6</td>\n",
" <td>3.0</td>\n",
" <td>3.4</td>\n",
" <td>...</td>\n",
" <td>3.2</td>\n",
" <td>2.7</td>\n",
" <td>3.0</td>\n",
" <td>2.5</td>\n",
" <td>2.8</td>\n",
" <td>3.2</td>\n",
" <td>3.0</td>\n",
" <td>3.8</td>\n",
" <td>2.6</td>\n",
" <td>2.2</td>\n",
" <td>3.2</td>\n",
" <td>2.8</td>\n",
" <td>2.8</td>\n",
" <td>2.7</td>\n",
" <td>3.3</td>\n",
" <td>3.2</td>\n",
" <td>2.8</td>\n",
" <td>3.0</td>\n",
" <td>2.8</td>\n",
" <td>3.0</td>\n",
" <td>2.8</td>\n",
" <td>3.8</td>\n",
" <td>2.8</td>\n",
" <td>2.8</td>\n",
" <td>2.6</td>\n",
" <td>3.0</td>\n",
" <td>3.4</td>\n",
" <td>3.1</td>\n",
" <td>3.0</td>\n",
" <td>3.1</td>\n",
" <td>3.1</td>\n",
" <td>3.1</td>\n",
" <td>2.7</td>\n",
" <td>3.2</td>\n",
" <td>3.3</td>\n",
" <td>3.0</td>\n",
" <td>2.5</td>\n",
" <td>3.0</td>\n",
" <td>3.4</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>petal length (cm)</th>\n",
" <td>1.4</td>\n",
" <td>1.4</td>\n",
" <td>1.3</td>\n",
" <td>1.5</td>\n",
" <td>1.4</td>\n",
" <td>1.7</td>\n",
" <td>1.4</td>\n",
" <td>1.5</td>\n",
" <td>1.4</td>\n",
" <td>1.5</td>\n",
" <td>1.5</td>\n",
" <td>1.6</td>\n",
" <td>1.4</td>\n",
" <td>1.1</td>\n",
" <td>1.2</td>\n",
" <td>1.5</td>\n",
" <td>1.3</td>\n",
" <td>1.4</td>\n",
" <td>1.7</td>\n",
" <td>1.5</td>\n",
" <td>1.7</td>\n",
" <td>1.5</td>\n",
" <td>1.0</td>\n",
" <td>1.7</td>\n",
" <td>1.9</td>\n",
" <td>1.6</td>\n",
" <td>1.6</td>\n",
" <td>1.5</td>\n",
" <td>1.4</td>\n",
" <td>1.6</td>\n",
" <td>1.6</td>\n",
" <td>1.5</td>\n",
" <td>1.5</td>\n",
" <td>1.4</td>\n",
" <td>1.5</td>\n",
" <td>1.2</td>\n",
" <td>1.3</td>\n",
" <td>1.4</td>\n",
" <td>1.3</td>\n",
" <td>1.5</td>\n",
" <td>...</td>\n",
" <td>5.1</td>\n",
" <td>5.3</td>\n",
" <td>5.5</td>\n",
" <td>5.0</td>\n",
" <td>5.1</td>\n",
" <td>5.3</td>\n",
" <td>5.5</td>\n",
" <td>6.7</td>\n",
" <td>6.9</td>\n",
" <td>5.0</td>\n",
" <td>5.7</td>\n",
" <td>4.9</td>\n",
" <td>6.7</td>\n",
" <td>4.9</td>\n",
" <td>5.7</td>\n",
" <td>6.0</td>\n",
" <td>4.8</td>\n",
" <td>4.9</td>\n",
" <td>5.6</td>\n",
" <td>5.8</td>\n",
" <td>6.1</td>\n",
" <td>6.4</td>\n",
" <td>5.6</td>\n",
" <td>5.1</td>\n",
" <td>5.6</td>\n",
" <td>6.1</td>\n",
" <td>5.6</td>\n",
" <td>5.5</td>\n",
" <td>4.8</td>\n",
" <td>5.4</td>\n",
" <td>5.6</td>\n",
" <td>5.1</td>\n",
" <td>5.1</td>\n",
" <td>5.9</td>\n",
" <td>5.7</td>\n",
" <td>5.2</td>\n",
" <td>5.0</td>\n",
" <td>5.2</td>\n",
" <td>5.4</td>\n",
" <td>5.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>petal width (cm)</th>\n",
" <td>0.2</td>\n",
" <td>0.2</td>\n",
" <td>0.2</td>\n",
" <td>0.2</td>\n",
" <td>0.2</td>\n",
" <td>0.4</td>\n",
" <td>0.3</td>\n",
" <td>0.2</td>\n",
" <td>0.2</td>\n",
" <td>0.1</td>\n",
" <td>0.2</td>\n",
" <td>0.2</td>\n",
" <td>0.1</td>\n",
" <td>0.1</td>\n",
" <td>0.2</td>\n",
" <td>0.4</td>\n",
" <td>0.4</td>\n",
" <td>0.3</td>\n",
" <td>0.3</td>\n",
" <td>0.3</td>\n",
" <td>0.2</td>\n",
" <td>0.4</td>\n",
" <td>0.2</td>\n",
" <td>0.5</td>\n",
" <td>0.2</td>\n",
" <td>0.2</td>\n",
" <td>0.4</td>\n",
" <td>0.2</td>\n",
" <td>0.2</td>\n",
" <td>0.2</td>\n",
" <td>0.2</td>\n",
" <td>0.4</td>\n",
" <td>0.1</td>\n",
" <td>0.2</td>\n",
" <td>0.2</td>\n",
" <td>0.2</td>\n",
" <td>0.2</td>\n",
" <td>0.1</td>\n",
" <td>0.2</td>\n",
" <td>0.2</td>\n",
" <td>...</td>\n",
" <td>2.0</td>\n",
" <td>1.9</td>\n",
" <td>2.1</td>\n",
" <td>2.0</td>\n",
" <td>2.4</td>\n",
" <td>2.3</td>\n",
" <td>1.8</td>\n",
" <td>2.2</td>\n",
" <td>2.3</td>\n",
" <td>1.5</td>\n",
" <td>2.3</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>1.8</td>\n",
" <td>2.1</td>\n",
" <td>1.8</td>\n",
" <td>1.8</td>\n",
" <td>1.8</td>\n",
" <td>2.1</td>\n",
" <td>1.6</td>\n",
" <td>1.9</td>\n",
" <td>2.0</td>\n",
" <td>2.2</td>\n",
" <td>1.5</td>\n",
" <td>1.4</td>\n",
" <td>2.3</td>\n",
" <td>2.4</td>\n",
" <td>1.8</td>\n",
" <td>1.8</td>\n",
" <td>2.1</td>\n",
" <td>2.4</td>\n",
" <td>2.3</td>\n",
" <td>1.9</td>\n",
" <td>2.3</td>\n",
" <td>2.5</td>\n",
" <td>2.3</td>\n",
" <td>1.9</td>\n",
" <td>2.0</td>\n",
" <td>2.3</td>\n",
" <td>1.8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4 rows × 150 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 ... 144 145 146 147 148 149\n",
"sepal length (cm) 5.1 4.9 4.7 4.6 5.0 5.4 ... 6.7 6.7 6.3 6.5 6.2 5.9\n",
"sepal width (cm) 3.5 3.0 3.2 3.1 3.6 3.9 ... 3.3 3.0 2.5 3.0 3.4 3.0\n",
"petal length (cm) 1.4 1.4 1.3 1.5 1.4 1.7 ... 5.7 5.2 5.0 5.2 5.4 5.1\n",
"petal width (cm) 0.2 0.2 0.2 0.2 0.2 0.4 ... 2.5 2.3 1.9 2.0 2.3 1.8\n",
"\n",
"[4 rows x 150 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 83
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "DPgMWjviG_sF",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"outputId": "ad824b51-17ab-4712-e14b-5c9c361853ad"
},
"source": [
"df.sort_values(by='sepal width (cm)').head()"
],
"execution_count": 84,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sepal length (cm)</th>\n",
" <th>sepal width (cm)</th>\n",
" <th>petal length (cm)</th>\n",
" <th>petal width (cm)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
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" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>6.0</td>\n",
" <td>2.2</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>119</th>\n",
" <td>6.0</td>\n",
" <td>2.2</td>\n",
" <td>5.0</td>\n",
" <td>1.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>6.2</td>\n",
" <td>2.2</td>\n",
" <td>4.5</td>\n",
" <td>1.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>4.5</td>\n",
" <td>2.3</td>\n",
" <td>1.3</td>\n",
" <td>0.3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n",
"60 5.0 2.0 3.5 1.0\n",
"62 6.0 2.2 4.0 1.0\n",
"119 6.0 2.2 5.0 1.5\n",
"68 6.2 2.2 4.5 1.5\n",
"41 4.5 2.3 1.3 0.3"
]
},
"metadata": {
"tags": []
},
"execution_count": 84
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "5PbTBHSFHD2n",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "02342ccb-89dc-4646-a7d0-808ccbadc75c"
},
"source": [
"df['petal length (cm)'].head()"
],
"execution_count": 85,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 1.4\n",
"1 1.4\n",
"2 1.3\n",
"3 1.5\n",
"4 1.4\n",
"Name: petal length (cm), dtype: float64"
]
},
"metadata": {
"tags": []
},
"execution_count": 85
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "-rc4ScyhHQno",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 142
},
"outputId": "4509b263-b1be-42ca-8242-ed3c45cfeae7"
},
"source": [
"df[0:3]"
],
"execution_count": 86,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sepal length (cm)</th>\n",
" <th>sepal width (cm)</th>\n",
" <th>petal length (cm)</th>\n",
" <th>petal width (cm)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n",
"0 5.1 3.5 1.4 0.2\n",
"1 4.9 3.0 1.4 0.2\n",
"2 4.7 3.2 1.3 0.2"
]
},
"metadata": {
"tags": []
},
"execution_count": 86
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "YClRLV3wHaAS",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "2787a96c-8cf3-46db-d2d1-54170fdeb61b"
},
"source": [
"df.iloc[2]"
],
"execution_count": 87,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"sepal length (cm) 4.7\n",
"sepal width (cm) 3.2\n",
"petal length (cm) 1.3\n",
"petal width (cm) 0.2\n",
"Name: 2, dtype: float64"
]
},
"metadata": {
"tags": []
},
"execution_count": 87
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "1gnfNX2wHhkz",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 80
},
"outputId": "b8f36bcf-11a7-41af-beb1-4bfbe819c7f3"
},
"source": [
"df.iloc[1:2, 0:2]"
],
"execution_count": 88,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sepal length (cm)</th>\n",
" <th>sepal width (cm)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sepal length (cm) sepal width (cm)\n",
"1 4.9 3.0"
]
},
"metadata": {
"tags": []
},
"execution_count": 88
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "9UDm-RQLHwFS",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "dae0612e-1c09-4c59-8cd0-e4a219db6db3"
},
"source": [
"df.mean()"
],
"execution_count": 89,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"sepal length (cm) 5.843333\n",
"sepal width (cm) 3.057333\n",
"petal length (cm) 3.758000\n",
"petal width (cm) 1.199333\n",
"dtype: float64"
]
},
"metadata": {
"tags": []
},
"execution_count": 89
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BQrhgiZ3IBo3"
},
"source": [
"Pour aller plus loin, c'est [ici](https://pandas.pydata.org/pandas-docs/stable/getting_started/10min.html). "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GeL_tgs4NGOL"
},
"source": [
"###Matplotlib"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Z1oQGm-XP6u2"
},
"source": [
"[Matplotlib](https://matplotlib.org) a été importée précédemment\n",
"\n",
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1ZKaLSuebLOe"
},
"source": [
"Voir [ici](https://www.kdnuggets.com/2019/04/data-visualization-python-matplotlib-seaborn.html) et [ici](https://towardsdatascience.com/make-your-data-talk-13072f84eeac) pour d'autres infos"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "REVKyPigQLGK"
},
"source": [
"pylab est dans le package"
]
},
{
"cell_type": "code",
"metadata": {
"id": "kf3IvE1VN0eW",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 282
},
"outputId": "d2c3acc9-b54a-4da7-aeda-7b08d1b86526"
},
"source": [
"from pylab import *\n",
"plot([1,3,2,4])"
],
"execution_count": 90,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f70b4b8f790>]"
]
},
"metadata": {
"tags": []
},
"execution_count": 90
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RkSKcnQ3acRj"
},
"source": [
"Parce que nous avons tapé plus haut %matplotlib inline nous n'avons pas à écrire plt.show() pour que le graphique s'affiche"
]
},
{
"cell_type": "code",
"metadata": {
"id": "D7beeqYPPql7",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 312
},
"outputId": "02e4a3ab-0a2b-478c-9fe0-950b56cc84ab"
},
"source": [
"plt.title('Mon 1er graphique', fontname='Times New Roman')\n",
"plt.xlabel('axe des X')\n",
"plt.ylabel('axe des Y')\n",
"plt.grid(True)\n",
"plt.plot([1,2,2,4],[1,5,6,9],'g')"
],
"execution_count": 91,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f70b4af9f90>]"
]
},
"metadata": {
"tags": []
},
"execution_count": 91
},
{
"output_type": "stream",
"text": [
"findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "t304t8WlVtMq"
},
"source": [
"On peut faire des graphiques plus élaborés (mais est-ce important ?)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "UeL3ggF9QbsS",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 316
},
"outputId": "56be24b6-b0e1-4518-ee99-fa573347d04d"
},
"source": [
"import math\n",
"import numpy as np\n",
"t = np.arange(0,2.5,0.1)\n",
"y1 = np.sin(math.pi*t)\n",
"y2 = np.sin(math.pi*t+math.pi/2)\n",
"y3 = np.sin(math.pi*t-math.pi/2)\n",
"plt.plot(t,y1,'b*',t,y2,'g^',t,y3,'ys')"
],
"execution_count": 92,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f70b4a86410>,\n",
" <matplotlib.lines.Line2D at 0x7f70b4a903d0>,\n",
" <matplotlib.lines.Line2D at 0x7f70b4a90850>]"
]
},
"metadata": {
"tags": []
},
"execution_count": 92
},
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "v7LiZmTSWC61",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 316
},
"outputId": "3a0f67da-b5b8-4fe6-d083-22e6a62bda65"
},
"source": [
"plt.plot(t,y1,'b--',t,y2,'g',t,y3,'r-.')"
],
"execution_count": 93,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f70b49fd210>,\n",
" <matplotlib.lines.Line2D at 0x7f70b4a08510>,\n",
" <matplotlib.lines.Line2D at 0x7f70b4a08690>]"
]
},
"metadata": {
"tags": []
},
"execution_count": 93
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5d_NQAp3XJmu"
},
"source": [
"On peut aussi dessiner des histogrammes"
]
},
{
"cell_type": "code",
"metadata": {
"id": "V5XdE39_WHyV",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 367
},
"outputId": "3aa61b3e-90ef-447b-eaec-c28be21162e1"
},
"source": [
"pop = np.random.randint(0,100,100)\n",
"plt.hist(pop,bins=20)"
],
"execution_count": 96,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(array([8., 4., 3., 3., 2., 5., 5., 9., 6., 3., 4., 6., 2., 7., 6., 7., 7.,\n",
" 2., 4., 7.]),\n",
" array([ 0. , 4.95, 9.9 , 14.85, 19.8 , 24.75, 29.7 , 34.65, 39.6 ,\n",
" 44.55, 49.5 , 54.45, 59.4 , 64.35, 69.3 , 74.25, 79.2 , 84.15,\n",
" 89.1 , 94.05, 99. ]),\n",
" <a list of 20 Patch objects>)"
]
},
"metadata": {
"tags": []
},
"execution_count": 96
},
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sWmCD8UsXySa"
},
"source": [
"On peut même afficher notre DataFrame iris"
]
},
{
"cell_type": "code",
"metadata": {
"id": "_cvHJExGXQHs",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 291
},
"outputId": "e83a1d07-262f-4ef2-f5b1-c724909695b5"
},
"source": [
"df.plot(kind='bar', stacked=True)"
],
"execution_count": 97,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f70b479a350>"
]
},
"metadata": {
"tags": []
},
"execution_count": 97
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "h-AOmVzKYRJq"
},
"source": [
"Vous en savez désormais suffisamment pour vos besoins en machine learning"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SPyZ8Fzjb6oe"
},
"source": [
"###seaborn"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ACCUeQVSb88o"
},
"source": [
"[seaborn](https://seaborn.pydata.org) se positionne au dessus de Matplotlib.\n",
"\n",
"Les graphiques seaborn sont plus beaux que ceux faits avec Matplotlib mais seaborn étant de plus haut niveau, on ne peut pas tout faire avec seaborn, sauf à utiliser les API de Matplotlib\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "XfsZ9dR8X-zY"
},
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"sns.set(style=\"darkgrid\")"
],
"execution_count": 98,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "4Jpqaecbd_rk"
},
"source": [
"Pour afficher notre Dataframe iris"
]
},
{
"cell_type": "code",
"metadata": {
"id": "3pLOHABYc-5s",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 382
},
"outputId": "ff684d50-3a0c-423b-ee13-e1e3533bf559"
},
"source": [
"sns.relplot(data=df)"
],
"execution_count": 99,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x7f70b4088a10>"
]
},
"metadata": {
"tags": []
},
"execution_count": 99
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 496.975x360 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "-zNn6eDUdE20",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 365
},
"outputId": "a33f21e2-470c-405c-c418-c55ae5b10db7"
},
"source": [
"sns.relplot(kind=\"line\", estimator=None, data=df);"
],
"execution_count": 100,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 496.975x360 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kh5YTPhsg40K"
},
"source": [
"###Images"
]
},
{
"cell_type": "code",
"metadata": {
"id": "pvRuO8kLeRrb"
},
"source": [
"from PIL import Image, ImageFilter"
],
"execution_count": 101,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "bWFZEK0ejTws"
},
"source": [
"On va tout d'abord télécharger une image\n",
"\n",
"Son nom est 2.png"
]
},
{
"cell_type": "code",
"metadata": {
"id": "cokvMmUgikl4",
"colab": {
"resources": {
"http://localhost:8080/nbextensions/google.colab/files.js": {
"data": "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",
"ok": true,
"headers": [
[
"content-type",
"application/javascript"
]
],
"status": 200,
"status_text": ""
}
},
"base_uri": "https://localhost:8080/",
"height": 90
},
"outputId": "612c6be3-f4e6-4ed4-a282-74e5626057e8"
},
"source": [
"from google.colab import files\n",
"uploaded = files.upload()\n",
"for fn in uploaded.keys():\n",
" print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
" name=fn, length=len(uploaded[fn])))"
],
"execution_count": 105,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" <input type=\"file\" id=\"files-8a4e717a-735e-46ae-95a0-a2e558e8fcee\" name=\"files[]\" multiple disabled\n",
" style=\"border:none\" />\n",
" <output id=\"result-8a4e717a-735e-46ae-95a0-a2e558e8fcee\">\n",
" Upload widget is only available when the cell has been executed in the\n",
" current browser session. Please rerun this cell to enable.\n",
" </output>\n",
" <script src=\"/nbextensions/google.colab/files.js\"></script> "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Saving jbs.jpeg to jbs.jpeg\n",
"User uploaded file \"jbs.jpeg\" with length 19638 bytes\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "b6UOaCzuhS3V",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "3b32307b-6d6e-4d9f-b44d-cf1241aad9a5"
},
"source": [
"!ls"
],
"execution_count": 106,
"outputs": [
{
"output_type": "stream",
"text": [
"jbs.jpeg sample_data\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "wzs6SV7WqPDt"
},
"source": [
"path = \"jbs.jpeg\""
],
"execution_count": 109,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "JwA7Oq1Rky56",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 417
},
"outputId": "d7a6acfb-85e0-4e6e-98e2-10c6ff5e3c55"
},
"source": [
"from IPython.display import Image, display\n",
"display(Image(filename=path))"
],
"execution_count": 110,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/jpeg": 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cOjIUWwzMkcMaNPA5kUzZHN0hpUfRNaqq8JbBSat69eOlOa+zteNpqCuI4e+q49OtARhgp0U4EZgNFG61zhzbl/Rmt9pFzrwhve6GL/PirPN4Fco4BuTJwpuqr8bhJG5CCMl2NSTrKykjgyNme5x1AYqa1OwYTdjb0W6ljnenE9E1VqbsLT81G06H1jP+dfBaozrDh/Uo/emDdw4sBXlGPp/1up815H7TybuhLHj21R+9A9bF5S01GxrFdhiZHtLWgVRdrjcHK0t9XA8AssZ8pas07ma1m6kK6fTpepWrqb9VOG4UffafioZh67a4IO0Me+v5tPim40upsXIv7FyL+xNjkaREBrHC+N4q1woU/INpe01PBO8cnH5JnUP8K3nT6fL7JVqPs/VRWxo0Zj/ojYpDxs6P9FBah/DP0+qim10o7r8SY9XmLVaRxmMzevQFU6Bif3BJ1UUr+k9SQv4rxROYaslidgfqpI3UbLSjhsdqKfYZ80PNKHU7xJPa8Zz3uDWjEkp0DGuaaEtJ9ZWaEGmUlqRtoExtMTnH9wMjHWVADpIvdvBlYh95YPzDYhIzSMHMOvcmTWdpabovE6yhBM77y0fnHDKN48bwWB3kmnPcPWKltR0HMb9VZ7MNEcfxJ/1+4NrjoCzq3NMjtyAGgcJngoy0axqejHI0seNIKD2EtcMQQmwW6RtktGqZ3Ju6+j8upB74zkziJG4tPvRLADXSHLPgLTtY76Lj3faVWkEbkZJXhjBrcnQ2SrI9Bk1nqQij/mdsCZDEKMara/Ux90fy4enUGJV1lJH7tAXhE7rkR163dSEcLbrfFuzNztT26Qr0JFpbsGBRa9pa4aQVdslo8idMMmczs/RN8L+zYnTesWEt+h+awsT2dU5/9UJbPFmOwF6W98KBZpDOpDLTPlpovOqspdyNn/5X6+ramWOFpc48Z+9bFPL0yXfH0y+7NZ0joWnKnos/VXMI4+izX1qGE6HOx6kGtFGjADzFJY2SD8Qqq5C57LiF+0/Mr3g+V/iOKEPglmjYNGTiDfktB7VgzHemeVZmRhxbfqS6iM8nKSYr7SlrnR2Z7h10NEer0nOlaPfVZodJ8FmBkfur81V73PO1x4bMd9Ph5t+fcu7kA2jmn1tiutYHgDjHWuSauRansDaMDzhsFeC1NDyG5DJkA6a/7Qcqj0LiOWER7VmxtHWtIHUFyrvcs5xd1+O17TRzTUFRyjQ9tfNF/TPw8Vz2to52ngmyjnXHXnVb8Posi6NlohrUPiiF4fVFtbzK6VUenGxv0jFn6eOHUvY0WNWINjPk/Wk2IRMFGsaAPGtD9d26PerRJsYB8f7cFpa3QJHAdqw0bF0Ts9NbIw0e01CbKzqc3YfGo4VCvRYjoq/Eb0R48R0OXhEBqx3w3eNBH0nV7P8AalbFFDIJaVygOr3qa32hrWCO8aM0YJzjpcanho7Oasx1d3plTyL8Hj6oOaatOIKuXKotpdPBVjHOG5UOB4HSQtrhVzQsgTmS6t/jQs1Njr8ShPaath1N1uUP2ZAQ29nPa3U3UP8ANni1GBVJW3x0hgUMk8OJ9XQ7s9LEEzvu7tZ9RXhg/pLW1wQ8MElz/ppUpj7M27A4VaCsu0Y+twMlZxmmoTLdkGQTg/sRQH3cJe9wa0aSVQZR28NTJG8V4DgjaJIxJIRTPxAV80dO/CKPaf0T55nmSV5qXHzHHJ3OxWe2m9qwePfgscPSGwT50Op3RQ1g4hwQk40RNL41HYVZQRQ3NaLTiCsgTSpwO5B0bhNtAwITQ4UdsTLOI773U17eDJxnyDPidq6EI0vTI28VgDQs7ylodxIQcffuT7TaDV7tQ0AbPOZriOorG67rCFWddCsQ5cpTrBWEjSuO3tXHb2+hCKar7P8AFqvQy3on6bp0qN1a1bwAtNyVvFcqSge8LGNqfbZNub1/2U7I8XubQKtrGTjHqg4uV1oDGNHuCdFYCLTPoynqN/VOlme6SR2Jc79z1bnxHjR7UDZrRflbi+F2DmcBDrMJ2anRuoUGMjdEL1ayUTS5wN7YomsFGhg4C2WTKT/8Men+yMfIWavJM19Z1/uoSRPdE8aHMNChHb2eEs/5GYP/ALoZK2Rhx9SQ3T8Ve9XavvVps9R6pIcexFtkhfaaaCcxqo6fIR9CDNVT/wCec0n7srmk/dlc0n7srmk/dlc0n7srmk/dlc0n7srmk/dlc0n7srmk/dlc0n7srmk/dlc0n7srmk/dlc0n7srmk/dlc0n7srmk/dlc0n7srmk/dlc0n7srmk/dlc0n7srmk/dlc0n7srmk/dlc0n7srmk/dlc0n7srmk/dlc0n7srmk/dlc0n7srmk/dlc0n7srmk/dlc0n7srmk/dlc0n7srmk/dlc0n7srmk/dlc0n7srmk/dlc0n7srmk/dlc0n7srmk/dlc0n7srmk/dlf/8QAKxABAAECBAUEAgMBAQAAAAAAAREAITFBUWFxgZHB8BChsdEgQDDh8VBg/9oACAEBAAE/If8A3D0MsLFPaH1Unq89K8i7VPo89KwTmF2rD+bD5r2wZ/4+F90UvanxAZxCskHN7URvTgUjkWmBLEYvFRmbXVfYfVYnnAnxX0fcmlEZ50+2PpO6jCSCcRA1uWugNONbCAdKBA0wTssVxirDvz8GrY7V+WFbsCUn/CjYteJ80N107RQkHKT80qPXWd1SaYksbiSrBJIlQBgShYa+tq1wLbudO0wgZSE+9YDXybWq7cXHnRgBYEi9WNO66BgwFyc59De5izxM6HkF25n1WFSZJP8AgITnNgqbqdxtrJs2r341oLDDwbW93lUFvsewVM2HAT6S01qphmpttQLpQc0ihdOI1uRaLUIm9jyZplmhn3f181LeqwyGGd5qAME0JjGcal91kmCL1dQLJO00pQJJBJxadaPUDpFH2ffR4lWIVqFypk6z5b8P33GGY2kFGkszvS1MGLbn67FZzU3R7HHH8HNK6DyYiiEDSeSar3fQK5dMW8LQqIC4bfRqy14EmI5uaXm9HLWAwcBhzKX1GkvZpJNeMGc8hTsskGh4ULjOR7VPmjS3A0wQuNqkIXFZDuVotsmD9fuzBrDroiL0RrtAssCZOn+0LVoKwejkRe9WOeFyozuWv1Tlc1fqmLO6f1pkc6DHuFYMJhCPapgU2nAaRTfCAHQHX1GKCsQlInyNmSkmZ1iy/CjJzikbIkFkoiLAXs8623JMeCjakGdDUd/3IYmLa0AHyEegFz+B6zgZLVpQcxYBB/KgIbjrWDg5bN3ezbMqxZ0/XlSs0OSBIIzi/CmjhmVqlpseZ58ftIQErYipcwxiwnvV8CMtm2nRLrmkax6HnLT7fzz20m5PejMu5czmCPOmjCsUQAMVuda+1o4YfCOIlzWiDsYND9rix8/xsYsSBvTRBAOr+ZkR3SjkVwgxwGzzhRL6se2tF4W0KIoNkrdIP7M1kPqfipqJFyzH88gKNOIw0URQxDmRxQpJQA5o1pKIAriTlSLqaQTUHvXnNP2Lo/KvI3/lEgdMRGfQQvKaVGfDT4isQNZgEl2KAGuhjXIWtQ4SjMkodIqb8gfsRSI9tl+Xkb/yoJHnQR9/SD6B7qyLhOjH2+KtiDeRYS1FOCR3a5G9/SKz5dKPM9qP69X+oUf5tf5x90Plcn3QmigmXz0PmPLSpfF8UaXo/wAidFhSJgJWiMYBB+Xkb/zxnylQETCjUzOlMbJgdqT3lpKPf0/1D6r/AFj6r/WPqv8AWPqv9Y+q/wBY+q/1j6r/AFj6r/WPqv8AWPqv9Y+q/wBY+v5Fvhtqamz6BqSLVvHf8fI3rMXkBiI+6wybSL+qBCbBUn8kK46Vu+mAatIoFRTYrGlWJdJhW5QfRkJ5NCfdUtMb6FGEfgaFwXhQDgjw/QIgyMGu1AEAAUnDjixwnHh+DEqaqKAB2BNHolLXCqBD0BfrSWLVseHoqJHWGxDrvRqJRIlFIlwkvVWFc0lWewrE+irF04Fq/ocjFe3/AAolEtnwI9j1MpApxg+6p+gECq0EHxTcFxR7Vb+ml2xObvSMpcf71Py+6cmrBh0GsjxXZoZ4J1P5ExDSuI2pD8rA4VPMQDRSS9krgYWaMWqcBIEu7B7v8CEiJglWmCYU0XAYsLAEB0K3NxJ5isfYwup2rEsYFSOMVc3ioe9XcnnTemMGKCgkuTG2VTfg9isZKWzf0H5o9AwGZ6CRdngn64JNOBj6LeFgjLH7oQSYPowt2djA2DFvFGHAhW88feh4ngih1py0XgVY6bOPV2oW4cXfx1AiYkRxQSndeVGjaE4uheroKuoHRbcDTP0RcZpm+9SVzOqVP2KLIM3asaeF26SmeRG9MjEVEx3smDeLvmhBJf8AWbnOmIqkUWjStAGDhL0dnMQ48O/pBWIrsh8xXsv1hPgwrAARB3F+6XQAilFK2UcCcj4GdRPYBxxrYi8b40oMUIGcJXdfaj+TpFFuObati0l9vwIpmL3r39YPp8fo9YyLxk4d4vPEKgUAxHnbL1LLUDIkK7k5a48sfTN4IVEM24xWKnF/lH9/Qn5sPLejH60cVoSLj6z+q22Ap4MUG43gNPZXMCaqTlOJtT8mwMJ7ri6rR6yRnUAOVLAhotj8YqwRcMc6iBhWydvTZ0PaibxnmBjCKelnYfr8hceit8pAyaWHVgO5AXM9Ca91pvga2uI/W0YFq3/HWtXcHhrBMVe8RHaeykjpoTNjPia1ud1hd68FQ+lMwkPEj8CKMjIeAVrdUo+vSN8n0HPkhejhOGf3q+gc1htpS1aLXH4QakvT7Ki/qof36mCuBNH9gGrCeNO+KL21MxMQd1SD+At6LQsTym2qgmnQzmGi5TRvrW4/CXhLLMxGpm0Yy1OsBmkNXwWIEwXzCbVNFgLib/gzRcjNMcLhpC/4Q6joo+63ent6hR8E2FnmeiP2JFx6CpH8QIaMy9UjRSTBgqlorsO9W/ZakBJHaFlJNKkah2GXPkqEgijAAlX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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "-yLFB0DMpjVJ",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 417
},
"outputId": "1fe4614d-3725-4dfa-c92a-4f33b1d655aa"
},
"source": [
"from PIL import Image\n",
"display(Image.open(path))"
],
"execution_count": 111,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAGQCAIAAAAP3aGbAAEAAElEQVR4nOy9d3gc13U2fmdme9/FLnoHSIC9iKIaJUqU1RNHliI5LpITy1IcOU8U23HiJ/l96Z8Tf3FsOeVJ4sS2bFmWi+JQzbKoSnWKYicBkiAIAkTHYnvfnZnfH6/uycWiUISWpCjO+YMPuFjMzszOfe8p73mP9OlPf5oZZphhhp0PJp/rEzDMMMMMe69mAJZhhhl23pgBWIYZZth5YwZgGWaYYeeNGYBlmGGGnTdmAJZhhhl23pgBWIYZZth5YwZgGWaYYeeNGYBlmGGGnTdmAJZhhhl23pgBWIYZZth5YwZgGWaYYeeNGYBlmGGGnTdmAJZhhhl23pgBWIYZZth5YwZgfchNlmXGmK7rkiTJsqwoiiRJOrdT/rnEjd6M/+JnHFZ8RfzD2QfBycz+k7I/x2fR+w0zjMx0rk/AsDNrhULBZDLJsqyqqq7r+BloBZggsMAPmqaJf044RX8lIpcIKCKoMcZUVRV/RW/QNE3EO/EPy6AQZ2JglmGiGYD1ITer1YqVryiKpmnFYhE/l6FGmbc122Oi1wlcxD8no/+azWb8t8yVk2V59t9KkqRpGsFomU9nmGFkBmB9yE1VVXJVFEVRFIUxJkkSPKAyJ4vNAiAy8owoxsSLiqKIf0JAUygURN+NTPS8xD+0WCyaphFsVejqDfuwmQFYH3LTNM1isei6XigUisWi2WyWJKlYLAKAKAojcBHxSMQOCs0Af/RXpVKJzQQ+/IsPFQ+CPxQ/V7RCoSB+1nwenGEXuBmA9SE3WZYLhYKiKHa7XdO0fD6vaZrZbLbZbEwI2ci1gQckvgIrlUr4LYWEiCtNJpOIO4Q1ZbkwSnghVMQb4P2JKXb8LX0Qkm5n9X4Z9sE242n4kJvFYslkMsViUVXVQqEwMTExNjaWTCYZRysCDmBHPp9nMz0s/IzQj/4rhnhl7hVFjghCTSaT2Ww2m80ISPGD1Wq1Wq02m81ms1ksFpPJZLPZdF0HZiGThffDgzPMMJgBWB9yy2az8K1KpZLb7XY4HLlc7vjx47NDrdmviEgEzwsYxBgDwDEhhCzLPZGvxGYWImfXAcnMZrPD4XC5XG632+v1+v1+h8PhcDgqdCcM+zCYAVjn2GbTBcjLQLoHiAC8MJlM8IMokVQGAbpAViCwALOhVCrJsnz11Vd/9atfffDBB//7v/87Ho+XSiVKwyO3xQSsoVNChZEJwRpeR3VPxCaCM3qDeLE4PVVV4T1R+RKvF4vFdDodj8fhl+XzeUVRZFmGI1ZdXa2qalVVlcViCYVCNpstn8+7XK5MJoO7RLcRUSrOE3GrGHjSKdGLdKrSPLVRwz44JhmDVM+tIaSilJCYPAJylS22ssI/wYo+D6+qUCg4nU4QGkwmUzab9Xq9qVSqqqqqv7+/v79/fHw8kUjQ+YjZKMY9KRGhyj5aURRcgsViUVUVERyB0WzTdd1sNiMOlWXZ6XQmk0kc0Gw24yRxEPwgzeSsyrKM83G5XHV1dSaTacmSJS6XCxEozpZIGwSyOE86Z8qXSTNZGmKN0gCvD6YZgHWODeucyAe0z6uqajabifCJRaiqarFYxOIsq6PRghSDNVmWzWZzJpOBUwOvpFAoeDyeQqGA7Lssy7lcLpVKpdPpbDabTqdTqVQ8Hk+lUuRwwUToZIILhnMDylgsFlmWUYWc83p1XbdYLHD6cGmyLONneJGALcaY1WrN5/P4rSzLSK7h4/BZQCi8PxgMOhyOmpqaUChkNputVivqoTg+/C8mQJI+q/pJmwRMpF8Y9sExA7DOsWmaRjEgXgHooDomRn8IxzweT7FYzOVy2Ww2n8/n8/lisVgqlTKZDFaayWSyWCx2u93hcNhsNixvt9stSVIymXQ6naVSqVQqwc3RBcIBOR2Mg5SqqtlsNplMZjIZIFosFksmkyg14j3AC4pey0BttgH4bDZbLpeDd8YYu/baa48dOzY4OEgYhACW3E+CP7vdXigUVFUF6gGVAMeFQgHvkWXZ5/O1tLQ0NTV5PB7GGI5WFkHT+bCZ8a9hH2Qzcljn2Gh54wdxXeVyOfhB+Xw+kUgkEol8Pj85OVksFsmFoShSBDh4CmazGeDlcrmQA3K73QjWFEWB8wKUsVqtuq7jsEhCMY5ZqOIFAgG4UcCOQqGQSCRisVg6nR4fHwduEiFLElJa81mxWATiWK3WSy+9tL6+njE2PT2dSqXwt8BZXB2cR4fDkc1ms9ks424p0l4E5S6Xi/48kUjs379/7969uPDq6mrEjxaLBRhHtVFqbwTcM061pftg2AfKDA/rHBuwBm4C/BSAztTU1MTExOTkZDqdBpRIkgRkQdAnpr3obym7JMKZ0+lMp9NANKfT2dLS0tHR4XA40LUDd4kxBrYBwjGYmDya/V8go8lkyuVyo6OjfX19U1NTdFEL5IDIEbPZbKtWrVq1alU8HrdYLH19fe+88w7cLrwHoRzQBIBoNpuvu+669evXS5LkcDhefvnlF198sVgsIn5kjFmtVoou2Uzc9Hg8jY2NDQ0NgUDAZrNRQpBYHZJghsP1wTQDsM6xwX0gHyoWi01PT8fjcdCmGGMmkwluEVZXGdFcTH6XrTcYPBGgAw4Ix6S9vb2+vh6pH7TFABNnY5MYlFHSWjTKc8fj8RMnTgwODkYikfmul7wYUOFbWlr+4R/+4emnn0aJ8M033xwfHwdgidcLA+n0qquuWrZs2YoVK9ra2lauXPnkk0/+7d/+7cjICFUJxDMn1oWYk/J6vXV1dUAui8Ui8SIj3eQF0Nawc2sGYJ1j279/fyaTiUajiURCVVX4QVjPlD6HEQxRxp36Acu4EZKQj7dYLPBN6FB4MZvNejwet9sdDAabm5v9fr8+U0qBzcrviK6HGMlKPB8PDIpEIpOTkzt37pzzevGHlHFnjA0PDx86dOjhhx9WFGVkZOT555+H80ghIaWrAKkXXXRRV1cXeA8Oh6O2trazs/PFF1/cs2fPwMAAQl1EfOJ9Eyn4VCuQZbm5udnn81VXV/t8Ppy/kc/6IJuRwzrH1tfXh4QUWOAUzdHKET0mxnGHyFOiyEGZ+4M3IwsmSZLFYmGMFYtFXddLpZLJZCoWi+FweHR09MiRIy0tLUuWLKmpqcnn83BwzGZzNpuFX4YMPZsVFdIPIlnM5/P5fL6qqqoXX3wxn88Dm6i0RzEvAEjX9cOHDzscDniC9fX1y5cv7+npQZqcCa1F5FoODg6uXLkyn88DyI4fPz42Nqaq6k033aTr+sDAwM6dO8fGxhgvOJrNZjQqzg5pNU3r7+/Hf91ud2dnZ3t7u8PhEKsQFG6LymKIWxERw281cl5nxwzAOseGzItIFsWSKBQKc3KF6urqLBaL0+lEERBZZCTRUddvbm4OBoP5fH54eHhsbOzkyZPT09MnT56Mx+OUicfqzeVyoGWWSqX+/v5kMtnc3NzV1aVpGlJCQCvgwukuSLfbvWXLljfeeCOZTBJSAJFLpZLGhW4YY5OTkwjoAJFdXV09PT0OhyOTydCHAvIYj2fBKSVnDcA9PT1dKBTq6ur+7d/+zeFw7N69+5lnnjly5Mjw8PACIR6wWJKkXC63e/fuvXv3dnR0rFmzBq1ClKcD7AK7UWPN5/NIAlqtVsaY0UJ0dswArHNsIBkx3gys67rZbEbI5na7/X6/2+32eDw+n8/tdhM1SfwTuAPV1dX5fB60AzhTyD23tLTU1dV5vd4DBw4UCgWCHomTJ+EWqao6MTERjUZjsdiaNWuy2SxYBWazOZVKgUtxWtdlNpvb2trC4fDOnTslztIiOKa8m6ZpY2NjBw4cAOaaTCafzxcKhaanp3FWZZwPVVWTyeT09HRNTQ0FueQw6rqeTCZ/8pOfrFy58tZbb/3yl79Mrut85y/K7yCW7OvrC4fDDQ0N9fX1tbW18KFwbg6HA2EpbiPYZLlczujQPmtm3OhzbLRgsJIdDofX63W5XCtWrLDZbGB7E1eLuAjkcwHvZFkGxKD4BaYVfBb4JpIkZbNZxDJYvZQmw9pzOp1gXR09ejSRSKxataqhoQGEL7/fj789revSNC2ZTLa3t4fD4RMnTjCBEQrEBJ4qijI0NARvJZ/PO53OQqEQCoWmpqbg2QFxqG6I9HksFguFQhKvHsLxAdbjv319fX/zN39z+eWX/97v/R6OM9/5o0qL8NBisSiKksvlotFoNps9fPhwbW3t6tWr6+vrQXwDLZYyYlTo0HmbgWFn2gzAOse2adMm+hkiMG632263A4AImKhHT5rZRCLxbj68QedkLhL8xOZPcZPO5RmAa4A8TdPS6TSo8MVicXR0VFVVr9drMplcLlc+n1/EakTSymq1btiwYXJyEmxP+q0usGQHBgbcbremaQ6HAzhLiAaQok8nvxJpI7yT5CJwXVCGgEO0c+fOvr4+BNfz5dGpmEiHZYyB18oYGx0dnZ6erq+v7+joCIVCiEOB9dls1uFwEFYaefqzYwZgnWNrbGwUi3HYupEloa2bmktoAUuCnrouME6pOob3U6ENTc6UZ6FGH8aLZVQ7w3GSyeSzzz77kY98BIEh0l6ne2kADq/X297efuDAgTL2AONhaSqV8vl8wBoExbh8BF84lCQ0ZsuynEgkCH8lSUIeCmmmXC4HHIHTFIvFGGMIGOc8SV3X6cgIUcFNE3Nb/f39AwMDXV1dK1asgCoOY8xms2UyGapgnu7NMWxxZij8n2Oj4hf8BSxprFtR3w4BFEBNE3p0yXK5HPFOGWMQPJAkyW635/P5ZDKJrh0itTOhBxChKKppcFgKhUIymXzttdckSbJarYvIKJMfl8lk2tvbZzc/wrljjLndbrwCnj3ajHCGOqe5i+isadrExAShs67r2Wz22LFjAwMDw8PD0Wj05MmT+DjCKW2exkYYmG4656xheyiVSujlBpDput7b2/uLX/zi2LFj09PTCD9RwRAvzbAzbYaHdY6NYjfyjEReFaXGyZvA4hFDQsaYJEkulwsp56mpqWQyiUTM9PQ06vqMJ8uw+IFB0Hihyj0TOlTgNaDAl8lk7Hb76TYDI/uGCprL5aqtrR0dHSU/UeZN3aVSyev1Io+G0zCbzdFoFAehZkNAG1JUkiSl02nG0+Sqqk5NTb311ltM6HC67rrrGhoaYrHYtm3bEN/Nhyky13jAtRO1TRekaei3uq7v2LEjEAhs2bIlk8m0traOjY0FAgFqJDDsTJtxl8+xEXOK6OwETNjksZlbrdZ4PO52u8PhcD6fz+VyuVwunU6j3zCXy8ViMYQn5I9IglgVEzwa4r4zxjRNK5VKUHFAgx6qdaBQIiizWCzwU4BBcFsooy+SvwC7MhcazWQyNpstlUrZbLa2trbR0VGKOoELpVKpo6MDkEp1w5GREcjd4IbgnAGvEJ+hoh4R9wnUcHqapu3du9dkMgHFcrmcGGDS7QUS0a7AeCM6XRFlxyhUx9VFIpFnnnlmxYoVdrs9EAigVsgE7COkk4wWn0qbAVjn2OAyUNWJ8YgJPg4YQLlcLh6PP//888gf408ow4WFhKiNtPGIR07uFePlOaizI/WTy+VQnkMYCBeGyBM4CC1vgjYcFmlpMX1DDiB8JavVCjWufD4fDAZJRFAXBLyqq6vhASHhZTabd+3aBdAhULvjjjvQIYh37t+/v6enp1gs2u12/CGQjjFGLYeRSOT1118H8CHsJZkHNpPsSjlBlYvZE7+BcZRHURXXiMg6nU7v2rVrdHR03bp1gUAAriIAjhR7NE2z2+0GP6uyZgDWOTZit2szpTuxnECC13Xd6/ViDeD9+Fuxmg4FKKxJ+FnAO2AELU6AndVqhWsGPw75YxxZkiSI0litVjg1wAJiTlLxDiuTCQwyaWbvHukx6LoOHlk0GhWT/RaLpa6uTgxsDxw4AMVR+FZ0thCGB5d15cqVuGRUA3K5HEWslArUNC2RSOBkAN+QeQAAgfahKIrH43E6nbquFwqFbDaLq8PXQaQK3AFcMv6QcWgeHh6enJxsbW3t6ury+XyAeFGwzECripsBWOfYRHgS8ywUmCDMwSIERsjCdBnyVuAlUXBEbhoCOnhe4MSXSiUsQmiooxNYURSv14vYE04QFh7I3ERTymazLpdL07RMJgNHDKl9WZBVYNzRQ4SVzWbhdvl8PkpOAThCoZDH44FjparqyMjIvn37gBRMgONSqZTNZgGgqqpardZ169YxxhC6MsagnwOBLdwucpQYY7gQqjAUi8WmpqYVK1ZUV1fjlkIbB7MwotHokSNH+vv7Qd1CZEcKqIyzyYDIqCQeO3asv79/06ZNdXV10MCh5N0iOgQMW9gMwDrHRmRuNlMPExklBDJmsxlpZsZz57Igm0V+hM6FseAywEsqlUrBYLCqqgrTHGRZDgaDDQ0Nv/ZrvzY2NvbWW2+BhBUIBMDYphCVMQb/BUEQAklEgkShtFgs6JKBD4IzJOTCpYGjUCgUHA4HvQcuXnNzM2V5otFob28vYwxhL7w2p9NZX18vy7LD4QCznMgZKM8VCgXIYG3YsGHXrl0IV4F3FNgyIc8VDAZXrlyJz0VWC1cN6CkWiw6HY82aNatXrw6Hw319fSgUUNCN8BD80mw2S3USVVV37ty5ZMmSjo4OaCUSLfbsPUkXhhmAdY5NzNcwToZEEgTYkc/ns9ns5OQkFobCBcupDA/3B7kkArJAILBu3bp169Y1NTVRLpwo3Yqi9Pf3WyyWtra2YrE4NjYG/VKgJFSx8NGkkIVxD+vXrz9+/LimaU6nE7EbRvJowpBBZNx1zhqDEyTx7mvRo6ypqaHO6oMHD4bDYboJ8L/WrVsXDAYBYYyxbDaLJkq4k4jLkLNvaWkZHx8/duwYsFXstSaXbcmSJStWrHC73bhv8AFtNhviOMajVNyuYDDY2NgYj8cPHjw4MDCAU0J+nZL91A+EmLq3tzedTi9fvjwUCkEGdhHVVcMWNgOwzr3RGsayRNhSKpWmp6cnJycnJychyo5AQyxsARSw8ND8HAqFli1btmrVqtraWnQ1iyQmJsSeOJTP58tms+vWrYtEIplMZnJykghf5GQhIxaJRFwu1x/+4R/qup5KpXp7e1999dVYLIZoFP1DwFYqHYqhqyzLaCfGaRSLxZaWFug1a5rmcrluuOGGRx55hLLvjDGbzRYMBql3T5KkmpqaTCYTj8cdDgdlmojk2dnZOTExAdFRxouhhBdr1qxpamoKBAIaV/5CgAxGAjWE0wBqu91eLBY9Hs+ll17a3t4OTYhCoSAOyKC7CjcN0hGapnV3d0Npy0CripsBWOfYxIVdKBSmp6eHh4fD4TAo2ghAEIwgPwIXQOL9w5Qkbm5u/shHPrJ69WoxjsMbED0RjsDAroJqsCRJHo8nEAiEQqFwODw2Noayl0ho8Pl8CEvx6SdPnkSND4kh4GY2m52YmJienoYHRExxeDHIvpNbdPHFFyPNXygUgsHgb/3Wbz300ENAPUBwoVBA+gknkM/nM5kMyo6EGsVi0e12I7/e2Ni4YsWKt956C6kuRRhNFgwGu7q6bDYbDotgFp9FE7CJLcG490pRNtT+hoeHDx8+DH1B8RuUeVMBUOz48eMWiwVaY6lUyogKK2sGYJ17IyKSx+N54oknMpmM0+lkjAGqqBIHhwIAgfWGLPjq1avXrVu3evVqxpM1CA8pzFFn6gVLXBWPqnWIPQFt9fX1dXV1w8PDkUgE2AF0yGazOGxfX9/rr78OJ0ibOTnRYrE0NTU1NTUxxnw+39DQUCQSAfxZLJb6+vrPfOYzaMHTuYQ8Y8xkMjU0NKxcufLuu+/+7ne/yziIE6EJTh+Qmjpm6NMB4oyxfD7f2dk5Ojo6NDTEBB6/0+m86qqr4EYlk0nEtohhAXmoG+BfsBMAqUgg4meLxRIMBq+++uoTJ0709/dDb0vmil3E0ccmcfjwYV3X161bR81J6XSaco40AeQsPmIfHjMA69wb8kRut/uyyy77zne+4/P5kB6iQINmw8DjwJKTZbmqquruu+9uaGigPpj3fzJwxNra2tatW7ds2bJYLHbo0KGDBw8irfb222/v3LkTZ4J1ON9x6uvr77jjDrvdPj09ffTo0UOHDvX19Q0NDdGoCJJqkWW5paUln89jvGssFoNrQ2IScM2QmQJ4IZrTZzL+8cPGjRtHR0dBWMWZrF+/Hqph8K3i8Xg8HkfoigwXlMVAniA/CyOykemHT4fcGWQOd+zYMTo6ip0GXwpgjuhv0AVct26d0+lMpVJgpcH5BfK+/2/qwjRDIvncG1yJa6+91uVyffKTn8SOLXMpAuz8YHiiPojs+K233nr55ZcjhQTPqyIng8UPf6qqqioQCCC6HB8fHx8fD4VCyWQSVUuFS6TOeZxisej3+2tqaugqdF2/7LLLdu/e/cgjjwB38vk80tJ/8Rd/gTrg448/Dtb+X/zFX9jt9qamptraWqTnPB4PxpcRZ5XNmn2Nmzk4OEhukaZpDQ0NiqLs27dvaGgomUzCr6RMPMXjiqK43e5QKNTY2AgZLJA2oJwBNUEooyqKEo/HQcLAe2gmI46JP2GMLVu2bOXKlfDUQKNFoFp2zoa9dzM8rHNvSIHfddddjz32GLEBkPRBYR6Zabgbuq4vW7bsU5/6FMJG1P4rSFDUdZ34B5D0A7tCVVW32z0xMYHVCP7UAsex2+3pdPrgwYM4GhS1IpFIOBxOpVKlUsnv9zPG8vn82rVri8ViLBbLZDKbNm1CPNjW1vbHf/zHR48ePXr0KBUN4AE5HA7Mj4AUj8PhwOu4S2azefny5SB/RSIRj8eDG5hKpajjhwgHRKxljBWLRegXDg0NORyOSy+91O12Q7pPkiSv1wuQBZfCZrMtXbq0trb2jTfeCIfDlMbCeaJ1UZKkvr4+q9W6atUqqPTonJFv2KLNAKxzbNhpt2zZAscql8shWev1ehEcYXWBw7l8+fKVK1dedNFFWGm08KjDuSLnk0qlEOn4/X4EpMjiK4oSCATg0KGcR4mt2YYCJemjg/Y5NjYWi8WqqqqSyWQqlZJl+eKLLwZIlUolzKO2Wq2ZTGbFihU//OEPf/CDHxw6dOjYsWOpVIpqhel0ure3VxF0U81ms9Pp9Hq94Jo1NzdDPLqqqkrXdTg7QHzyhoC2utAkyHiOD1oRv/zlL9va2jo7O5GYi0QigJtisUhih8Fg8MorrxwdHT158uT4+Dj8X+Aj0SB6enoYY0uWLLFYLHDB0N4klm4Ne+9mANY5NrgMN910Uy6Xw5j1bDZrMplisRhkDJAV1nX9k5/85JIlS5CLQQo5kUhUVVUBOyp1PjIXh2CMIQ2ESmKpVEomkwRAyMcjSp3zOGCN19TUJJNJdP8ghYQCKA7Y3t5+0003mUwmENlVbiBktLS0fOUrX7FYLNu2bfvhD3+4e/duiU9/oCmE5Nqk0+nJyUl8tM1m6+zsxK+g79zd3Q3hUzRLwklUuJAp0UQoR4aorb+/v7+/3+/3X3nllR6PB04TCqyaprnd7nQ67XQ6u7q6WltbDx06NDo6GolEkK6C8CljrFAoHDx4MJfLXXrppdQKauSwFm0GYJ1jkyRpy5YtLpcLvWkonKGbL5FIIBZbs2bNLbfcUlVVhV42Eqjzer3IKEnzqALoi5LuxaJyOp2QWwA0SJIEcJQkKZPJQCMUGZw5D5LNZok9z7iDMzg4GI/H4Xq43e7Vq1eDwwW/yeVymc3mWCwWCASQJsPfXnfddZs3bx4cHNyxY8euXbuOHj06MjKCbh4qU1IOHvcNTlM2m12xYsVNN910ySWXHDt27PXXX4e3RWJbJMQqEl8ZbzlA1ikaje7YsWPNmjW1tbX4CL/fj9mRNpsNKUW73X7xxRefOHHi0KFDk5OTpNGK4xeLxaGhoYaGhmAwiPjdqBIu2oyk+zk2h8Px4IMP2u32J5988q677kJyF0l3XdedTudnPvOZ9vZ2uCF2u13iA3VkQb1T4SNkRCMSw2mdDyJTeCKUSkOGCBI3Xq8XTg1emY8biTK/LvRpHzlyBPBaKBRAy/ziF7/odDqTySQ0/JBK93g8sViM6oAQemeMIYzCn6fT6SNHjkSj0fHxcSTFMMUeXK2GhoaWlhZJku644w6wvYBH999//0MPPYQkFGlUUIVREoZ3UZyI4T2MMbPZvGnTppaWFlDbSU8GdDaEmVarNZlMHjhw4PDhw0zIwTNOJbnzzjvRJED8MsNO1wzAOktGCi0oP0mS5HK50un0Lbfccscdd/zTP/3T//k//wetMFSDW7du3U033VRVVYVXUFafL2sLppLYwkasJZ2rQTCh9ed08/TazNl8eFHiQnf0ucA46n0BLWB6enpqaiqVSmGp41RvvfXWlpYW8nFwSqCYAl/g3C06S61z5VV0NZvNZrfb/T//8z8PPPAAsv4AFJKUIEeS/hZxq8ZlraqqqtavX19TU4P7iXZIoBXOGZ+SyWT6+vp6enrgjRKRTZbl+vr6Sy+91OPxICBd3HVd4GbA/FkyuCTgQ6IVGbntdevW/fmf//mf/MmfYA1Qznvt2rV33HFHbW1toVBArgoK4vMdH/4ICJAkIAcNGQRKVqsV7Id8Pk8cJTJiwM93fLg8bOb8CI0rroDQRCRVu90OBnkymUR3UTKZxOtgURUKhf7+flAZyLtBdh+pInDTFpeboyARXhKUDtEVuGnTpldeeeXuu+8GTiE4hYo0YwyqrRBakAUlUoR+09PTb7/9diwWQ0YfsTk8YtxesPatVmtHRwe6IOGK2mw2kOyHh4dHR0eh5LOI6zKMGR7WWTPi+2B3BfGyqakpGo3+8z//M71BURSbzXb99devX78e1AFaQnCy5gvB4ECRpijjCRTEZZowukIWNJcZB6AyqJK4QBX9i+PoXLZF5sqcWLRI/IsDxAqFQiwWi0QiqVQKql4AU2oYWr9+/R133BGPx7HyAXxAW5UrUiApflr3GX8iC4Np6VdgWlit1kQiEYvFtm7d+thjj6GrnDGGtkSZD92g3YUxRvkyxlhTU9P69eu9Xi/Y7dTaCc9R51rv2WwWg2yPHz8uqtOUSqUbbrihurrayLsvzgzAOkumchkZhBKMMZT5nn76aVTK0XfmdrsfeOABkIzC4bDP54P/kkwmkcOeLycFbEJQhtQJ2EnU1kt/WOZJlWGT6ECJ/1IIQ8UyGNXysdTRyVxdXf3kk08iqQTvQ+aKMVi9drt948aNjY2N0Wg0nU7j+IVCoampacmSJYyL8xFJ6r3bbFombjvNrMctouaen/3sZ3//939P1QNkxEtclJUKpowxiJHl8/murq4rrrgin88jyUWNk5T5kgSN1v7+/p07d+JaEKFfdNFF3d3dRtJ9cWYA1lky7MAqF8m0Wq09PT0HDhwg10PX9S1bttx44400dUrkkRO7Z76dGa08SNngFVTx0Agy+68QfpLPJXolhERkCHYY14nHYqNcGHLhPp+vjpvX6/3EJz5BOlmEU7goxlgul3O5XOA3UC5JVdVAILB58+ampiafz4doaxFFAzYzwiU2PDVpo7WICqDpdPrHP/7xd77zHThNlIzDFyELo9WASiaT6aMf/ajdbmeMQecLPUYixNONZYxNTU3t378f0x5Xrly5evVquGyndV2GwQzAOkuGx5062iRJ2rZt28TEBKQy0RXY2NiIoEni8w4SiQQGcMHJWiAkJPajxJV5IZEMYoHVanU4HC6Xy+12u1wupPbpD5lQTCzzsJiQvVZVtVAoZDIZUCuxJuvr62tqalAZYIKD85nPfIZx2QO8iPNJp9NI6JCPA2cE8VQul/N4PLfffntbWxvS1e+/mkYXYrfbU6kUtCuQXcJ9iMfjFotleHj4D//wD48dO6YoCiRrqDebMJpx/Oru7r7iiivA/wJgAXDJFSUfDZ9uMplOnDiRy+VWrVqVz+fx/vd5XRemGTyss2Q6F9WFZkBvb+/U1BQYAxs3brztttvA0AHf0ufzIUjx+/3IGcuy7HK55psGyrieHNHKZVm22WwulysUClmtVrvdDo1Q8f3kEejCcFP6rWiMI4vdbrdarV6vl7L7mCcWjUbxicjrS5LU1taWzWZBOFD5mEVIepZKJUpyQ+qLRsBbLBa4fvCAFq4DzGkaHx4hBobAkXQ6jegbkTViZzQVqKra3Nz8yCOPfPvb3/7FL34BTGFC6p3xUY8IKgcHBxsbGxsaGiAJD/eZ7qrOBc4UPlRR1/XGxkZUe51OJxhep3VdhsEMD+vsGT3Hhw4d2r179x133HHNNdeofHAp1eBQYkcOmKJFxpj4tjkPDkwJBoMtLS1gZpuEIRQ6F8+i9hEmuFe0tiWhrl/2r8zlTOnjdEFBkFamxodliVdNfzg9PZ1KpUC8TKfTmP6AtD1U/W699VZ4H4lEAuqgp3WTqRpQ9jpS+1BGpLYenXMywPaCdKKmaZs2bUI3El0OE6CQ/urjH/84Re5illC8SyCywa/E0ZAWOK2LMozMAKzKG1Ld2MCRxwFRMJ/Pu1yucDj82muvffvb3/b5fBAhOa2Do/ZPGsT4FxJaDQ0NDQ0NIJfisJXSnPlwG90ipNvS6XQikfj85z8/ODgIiBfT/whmsZ0sX778oosuggqgzodXI2kIfgO1/shCTzv8a4krUK9cuRJuGikOGraAGTysChsxvEn7jVwMi8USj8ePHDly/fXXx2KxgYGBRRwfWRiVj03O5/P5fL66uvqyyy7r6ury+/0Kn4tjpHXfi2lcUQs/mEwml8tVVVX19a9/HVQyUENlLigIjxIEiBMnToCWhSRjKpVyuVzZbFbTNEz9okwWsXaplkqfi2S8yueknev78UE3A7AqbCiZgQXKGMN+S04WHuUrr7wS9Gi857QMawOMcHxWa2vrypUrsclT8kXi5MnKX+GHzoioQfwvq9Xa1dX1yCOPBINBKJpqmoZ2Iqo2apqWzWZ7e3t1Xc9ms7quI6iEUF8kEiFtVdRDdM6GU7lWNWqgExMTYK6c69twfpgBWBU2SqaiqwObJ57IUqnU19cXDAYRqcmyDP73aRlUfZGIKRQK1dXVbW1tgUAAisOIRrFmsAIrfX0fNqP0PJUOSbdnyZIlf/mXf4nNhgkt04wx6NhYLJaBgYGJiQkK5fBDqVRyuVz4piQ+1psJDDgk46memEgkkLaroK7Zh9UMwKq8wQNCmU/lkqHIUBw/fhzJCywStPWelolMLqfT2dbWRnObFWECGCX4K395HzqjlLkiGGMsnU5fc801X/nKV1AJ0QWlefQ2Ie4+duyY0+mkWM9ut8uyDPcKgouINPHt6HwcEeW8zGYzhHGkeSQ3DBPNeKArb8jIUletyqdC9Pf3I3ZAzxqKU6d7cJSlLBZLKpVaunSp2+0GLFILLn6mklblL+/DZVQKFIkdqJAEg0FJkj71qU/deOONYnVP5pNr8e/JkyfD4TDRGqamphifxU1xOuMIRW3eRIsHESybzSLAP0e34bwxA7AqbOCXQxsTfj6G2ZVKpSNHjkDHDiVzSZIWkcNCxgReW01NDcJAZHPRxYbEsMxHHFb+Cj90JnLNSETQarXGYjH4U3/0R38UDAZBngJHDBE3topisbhv3z6QSMxms9/v1/j4WMYYWLKMfxe6rqOMi88lRgiU/wyP+JRm3KAKm81my2azfr9fVdX29nbMXNF1fXx8PJlMapr2wgsv/Omf/umXv/zlL37xi48++ujpHl/mCpkQ1aRVRBwfqg8SbBm2gBE3jaI2vK5xPWVFUaqrq7/xjW+gLxKOFbUrobJx4sSJ0dFRi8WSTqe/+93vfvKTn/R4PP/8z//8zW9+0+Px4MjU1uNyuVAzoWoM2MKY9X1Ob8Z5YMYDvUgjzTxqPcFGqqoqCNxdXV1utxutvLIsDw8PY9dFKz/6nP/xH//xdD8XISGWCpYNnn5aafCw9JlCBYbNZ8SuImKBxIfowI1FJn7VqlW33XYbNUUSMxapK8bY4OAgSFiRSOTBBx/81a9+ddNNN+3cufPP//zPL7nkErwfdRLGWG1tLZWJESHG43Fm5Bzfgxk3aJFGXgyFYHjWkWdtamryer02my0UCnm9XsbY5OQkCoVQ1GWMXX755Rs3blzE5+LR9/v9+ERDvvJMGITbQdOFlNjnPvc5dHoynsBijMEFkyRpYGAALtL999/f19cny/L09PTv/d7vffWrX/2DP/iD3/3d3/V6vWAOZ7PZhoYG5DFzuZzOWw4xaOOcXvR5YMaD/r5M7B3T+Vz4YDAYCoUgaqzrem1tLWZMQcuN5mj+6Z/+6eI+FNmTQCBAyRGDb1Vxg9YgYwxd0NlsdsmSJX/913+NF+lXeDO++meffZYx9tOf/vTTn/40Wo4kSdq+fXtbW9vPf/7zr371q9/61rc+9rGPbdiwQdd16NaT6BDSWOfucs8bMwBrkUZpWqou4cmTZbm2tlZ8Q0dHRyQScblccK+wr95xxx2bNm2amJg43c/Fp4BdLfYGVvr6DGNMUKrAmOuPfexjbW1t1Kkj9l0qihKPx0+ePNnT0/Pnf/7n11xzDXYmiL5u3br113/917/3ve/19/frut7d3U0uOUWXiUTCoDWc0owmzMUb8XcAGciqQsUFvhXGPWUymT179qRSKUwALpVK119//b/927/t2bPnzTffPN0PxW6MvBXjIg1GsrbiRv06jDGTyZRKpSwWS7FY/K3f+q2vfe1rSFlSUwE8XFmWjx49+nd/93eXX3751772tX/913/9+c9/rqqqz+cDceHhhx/OZrM2my2RSIiTaFGLRLXE2HsWNuPuLNLEyjRjjIjm9fX1YnqVMTYwMAAqM5qWQ6HQv/zLv7z22muvvPLKInhYwCno0lBVywCsiht9s+jNpNk5N998s8fjoRgc+ELtgVNTU2NjY2+88cYf//EfL1u27Itf/CJjbHp6Gtx3jGgERw8Dx0TZBqSxzv6Vnl9mANbiTRdUdJH5djqdGF+MBxFULKvVesstt0BOt6Wl5eGHH96+fXtfXx9eWdyHYp1Q1GkkaytuyLVLXEmxrq4ORN+qqqo777wTvTtUUmS8vUHX9f3796Mj5/nnn9+/f/+1114bDAbtdjtEuOAaUxM1pRGAd7FY7Jxe9HlgBmAt0ih7hacW/Jrq6mryvMAqzGazHo/nuuuuu/baa30+35e+9KW+vr5cLoceDoi7n5YRRDLeuUZMesMqaJiLA1UGTdPC4TDoVIVC4Qtf+AJpMFDRFpuWJEnhcDgSiZRKpVwuBy2Nrq4u6BpCOYskHIg0J/HBiHC7DFvADD2sRRoeOIh84pm22+3Lly8/0zkIfK7X60WtHUFle3u71+uVZRmy7jRGwUiILNqIYIX9ADRRSACaTKZNmzZNT08DaPAM0PNQKpUuueSS9vZ2CDGCzBWNRgcHB9PpNOMZK8bLu5QJZYxZLJb169fDd4P8bKFQQHeE8VXCjLuwSIMnj2cOHOiqqqqzQC+AV5VKpcbGxkZHR2Ox2MTExP79+ycmJkwmE4rxuq6LvdCGLcIocCPpapRlQXy76KKL7HY7fCuS5cDzAPl29JACj9LptN/vX7VqFYYtSlx9n81UfIVDh6+PTsPoiC4zA7AWaToXz8WoFUVRQqHQWZBzsVgsmOtJ2zvUyoeGhlKpFAmQI0IxkvGLNgATOjcZY7lcDkEi7vztt9+OdmVN06DzxxhDZyho6/v376cBa+jNcrlczc3NGBwt84mHoq4G+A34UEo1EKKdsxvxATMDsBZpJMCG3ERVVdXZmYOSTCZB7UFaBOkws9ns8XjwoFutVqRUJD5Ty7BFGBHQFUVxOBy424x3Gqxdu9bn8+GG0y5FP2iatm/fvnA4jNgcg5GSyWRNTU1LS4vL5RInZRB5AtueWDgWoc0wmAFYizRd17Hlmkwmm81WX19fKBTOAkDU1dW1tLRUV1dXV1dXVVWFQqHW1tZly5Z1dnaiSoX2IIyiMXbmRZvZbKb4Gj03qVQKs1QlSXI4HPfddx8qfdC0ooE66PRkjO3btw/14nQ6bbFYAF5Wq7WlpYVYo+RDkWtMbBjyrXRDOlkwgzi6eMPjmM/nm5qaaKDTmY4KTSZTW1sbAg2i/8DJwg8SHwEtSRIms5/R8/mwGqi/brc7mUx6PB4QRzEshzFmNptvu+22733ve9FoFF89NcCTPt/o6OjBgwfXr18PIKPvJRgM1tbWjo2NEfWXXC02U9eUapEGYJEZHtbiDRkNq9UaCATwLC4wN7BShrZn0HnAm8c5IJTAno/GHTaTl2jYaZnL5YKXSlJ8GCiJ5nZN09xu96pVq5BZR94dyAWnG+5ST08PVDqQUIcjpmlaY2Ojw+EgvQ1JGNtD+u6U3jIASzTjgV6kQYktk8l0dnYyxhwOB5pdz/Sz5fV6m5qasDyi0Wg4HAaZS+KTQSHkBI1AzE04o+fzYbWtW7cODg7mcjlMzfnEJz7h9/vBCoZAkN1u37Rp0/bt2/EKlGcw/5kwS9O0vXv3BoNB5Kco6MMmp+s6NCEok0X4pQsTIQ0TzfCwFmmoZNvtdq/Xi3EDZ4f3dPDgwZdeegknQIV29JFQJR6KlzirM30+H1aDc4p5OYyxvXv3UjyI+5zP5y+//HLcfPr2oXKFdBWQKBKJ7NmzB7l5KC/jW6urq8NAMGQVaPZ12VemzZxKa5gBWIs0wAFEQiiBdRY+V5blI0eO7NmzB8FgNpvFfISz8NEXlEHqmjEGQdeRkZF4PA7CXTKZhMeEAWsoblD4RmPrkVnXNO3kyZNHjx6Fz4WxI/jzqqoqZB5B9aIIESdwFrz189EMwFqkwZcJhUKUuTg7O6HFYkkkErt3704kElge4lNuWKWMdBmRa4/H45OTk7jbdrsd4ZvJZPr0pz+NpgImaFLT1wEnK5PJHD16dGRkBA2nlAVD6hM+MvFFKXUFM2CrzAzAWqTpuu7z+TApk1SSz85Ho+//0KFDYLSDPH12PvrCMfTZID8FiOnv78crTqcTJb90On399de73W7qcEBrDuJxyqbLspxIJHbt2pXNZpFkBOPX4XAgusQ3CAQUAcv4WmebAViLtEKhEAwGGe+VOWutXshx2Gy2gwcPQsXUaME5Q0YxnSRJFotleHiYWO+oxmLe2mWXXQbfqlQqgUXV1NTU2toKVNL5wNRoNJpKpQBnGHGEw8KVY4ag+3sz4x4t0iwWCyjLpNZQ5syfIUP4ADty5AgpNxhWWUPDM4YSgSOSTqfRJMh4pw5jrFAo3HjjjWjPwh82NzevWrVq5cqV6JRmjCESVBQF9VzkrZAgk7gkP+WwxBjQiPRnmwFYi7SamhrKWZBgw1nwdPDol0olh8MxOjpKU6fO9OdegIaGAcyURFS4f//+bDYL7oKmaaAvrF69urm5WePTIWVZ9nq9Pp+vo6ODCf03QD1sMPi+6uvrkZ4vk20Qn6KzswueR2YA1iKtrq6OfH48vmenUEhM63Q6jfQK1EfP9OdeaIbiL2MMdT2knEZHR6empjKZjMvlKhaLGJIaDAbvuOMOypoPDg5OT0+n0+nu7m6kOOGsiYqAJpPJ5/NBjAgT7cskZJkBVfOYAVinMEqdwoFC2rWlpQVz65AuBWydnT5V1CJ1PnsCknLG1JxTmsQlj/E9nnJ0I7k8RIzCfT569Cj6CjAxFymqj33sY6DCM8Z0XR8ZGUHz4NVXX33llVdu2LDh8ssvv+KKK1wuF3T9JUn6+Mc/fvLkScajSxychuigdEgth8aGRGYA1imMgj48T2DNYNSgYeeRkSPMeFzGhDlds00MzcQYbXh4GBEcYkOwQL1e72WXXQZRf6fT2d/fj5Q8JGU6Ozs7OzuXLVvmdrvRCH3DDTdcfPHF+/fvB8uBtJLhs4ufCCfLKKqQGYB1aqM2MSRH/X7/IrTYDTvnBieLYr2F+WvzYUQ8Hh8eHqYhSaDgmc3mK6+80mq12my2dDqNyd7pdBrhJHEggEfLly//xCc+IcvyoUOHZFk2mUzomQcOUqqLzVTLOhM35Hw040acwtCCA7oTStE+n+9cn5RhizRkypFvEqWsZhuNHSQDdphMpj179pA8BnqVS6USYj3UAUulUjabraqqSiQS8NTo+YEqbD6f37lzJ0q9xIzHjojDSjN1+wwPi8wArFOb+EjBvTJyCuedUbUOgaHoas1pyH+TC0ahmSzLJ06cwBHgZEG1HeoLhDiMsampqVAohCIj5ICAaE6n02q17t+/Hz1VGEWBkykWi9S9KKo4GNl3MkOt4RSG7TGbzaIto66uDipuZ0EN2bAKWtlwLThHjLH5MGt2FAbUAJUhm80iLYDCi9lszmQycJcwpcJkMtXV1d15551utxsumMViee655956661cLqeqKhgSXq83l8uhd5oxhr4fJmTZxI82jBke1ilNLNlg7KBkzAU4Dw0OEYm3oEUG6DCnzdfEp+u6zWYbHBykA8JLon4duEh+v//WW28NBAJiadLn86G8KMvy3r17UaZEDgtemKIoTqeTEvBn8Hact2Z4WKcwSZLy+TwyFFQcFJnNhp0XduLEiXQ6ja4axlipVPL5fI2NjTRCoswWSBvl8/ljx45ddNFFGKoGUgImTSB74Ha777nnHsz6xnMCxlx7e7vb7ZZl+eDBg+Pj436/PxKJ2O12vAeAZbPZMpmMAVjzmeFhndrwiCuK4vP5xNqzYeeRORyOd955Z9euXfv379+7d++RI0e2b9++gIcFOrvEVY+JFQVvaHp6OhaLoQiDGHDDhg3f/OY3PR6Pqqp/8Rd/gbIyhLGQ2FJV1efzXXrppZ/85CdffPFFECPEE5D4ZFaMmCb5LZgumHie+kyr4B0TmffvPes/3/sX8Fhnq1Nglc35K2PhncLoqa2pqSHlKWP3O+/M5/N1dXVB4kLTtFQqBZ76fEsRXk8ZvYDWcC6XO3DgwLp16xhj4JHa7farr77a5XLF43FQHODNIZ+VTCYx0uKWW25xOBxPP/30fOeJagBybcQaEzmuZQXEMqIW/VBWF6LXy0YiitdYdiZlUjnv0earR813HPIu2cz7TDnEsrqHAVinMOC6LMt1dXWk1W147OedWa3WDRs2nDhxAsxyq9WKKTjzjW5E7Y/NWs9EPe/p6Vm5ciVoUxCQcbvdl19+ua7rgDBafsVi0el00pMzOTl57Nix+YASM9woNUZL95RIxGaCQhkQ0L+UyqA3i3Ij4js1PnyMzQU3853/fK/Pt17KfCjC3zKAph8MwDqFoaLkcrmcTmc+n6cAwQCs88sKhYLdbm9vb+/t7c1kMlar1Wq1LjBoFu1WbNaCkbjEVTKZnJycbG5uzuVyNBMM8yIzmQxcJGx1+XxekqRMJuPz+cbHx2Ox2AJaj3D6RO776fa3iwg7G3MXhp4y9018fXYousB/Zxti5IXf815g0QCsUxjau/x+P6n0VTxZYNhZMJPJlEql1q5dm8/ne3p6VFW95JJLwHWY8/1YYJRPYUJoRqu6r69vyZIlUEwmhIJrhuIy0Mpms+XzeeCj3W7v6elZQG7/rbfeAsmLqDM4QwpRxYSaaOLrjHtMs99J2ltlv5r9tjmBrMzRo9cpghM9IxH+xHmLZdCP18V4kPGuKfFFHNkArFNYqVTyeDxerxe6RWXC24adR+ZyuRhjGzZswDDnlStXsvldA+pkpm5zQi5AiSRJg4ODhUIBI9cymQy0GbCukD4n1waZqXQ6bbPZMJxtvpMk5Q8Ki2iaDt5Q5gTRcEM20xksA5T5Xqdf0SmV/cmcoMYYs9lscwIc9MJmIyDxy8pep82ADP8lgCs7JQOwTmGyLGMMPTZMg+N+nhpCwkgk4vf7V69e7XK5CIzmfH9ZDgsm+lyKoqRSqf7+/iVLlkiS5HK5UNcjdIOqjNPpTKfTjDGw2GmU9CkH3NJxmJCEJo+DIIz6t8WyGgWSumDi60zwWfAzAXTZrwBkZWjCGEsmkyLqiUAz5+vwKMuASbylZR4i9VSWAZwBWKcwi8Xi9/vpMV0giDDsg2xOpzORSDgcjnQ67fP5dF1Hc/J8OxCFKpKQBMDqEofi9Pb2NjU1mc3mfD5PIo5IdJrN5lwuh/qgzsd8gbWAMRZzfi4+Sww8CbPofCTBYyI/ruxXZdVAupzZHhn+pQGubCbWiLknMWQjZg+9WBbKlf1WFMYQf0tAXNY6AkXWOb6XOV+9AA3bI31VoM+USiWv12s2m9EgpnMxNiMkPO8MDTRwMYrFYqlUWjjpDjooJJLJF6N/NU0rFotWq/XEiROZTAZ/grgPDTo0yxazo4F0UJLRdX2Bz2VC3zXtkaKXxwS/qYyWIfpTsx0ZGNIas38lOjLi68Tm12dl2ctepE199ouSwKsq++3pribDw3rXsPUxxkjGW1EUq9XqcrmAXxJXfdPP1ghCw86hUcuhuIDpB0qKq6ra39+/YcMGrD16MzY8kgnEn4BaBbL7fJ9bhk2GlZkBWO8amiqQE6Vhf9BmowicMu6SQWv4sBvhCxwE+t4ZD2QkPpjrxIkTGzduRDc1+SnokWZcIRZuFx4kj8eDmHHOzz1d4uWFZgZgvWtosIBopMT7YyHJxozH5cIzkNRJs1h0oHRewoPfFI1GJycnq6qqKBYDSFEyiwmJJEmSUMOZLyrs7Ow8m5d53pkR2rxr0I3M5/OoH0ForbOzkx5BxhOW1DNh2IfYAFj6TDIBExJM4B+oqprNZnfv3g0fCu/RheE3lOTGRmi1Wv1+f3V19Xyfqxu2oBmA9a5ZrVakUUFWTqVSjY2N2DaZMFOTGX05F4xhyA2bC4AYn7eG1/v6+lKpFCakMp7AYsJjQwkvHKqpqel0T0YyTJIkg9ZApus6WliTyaTb7Xa5XCtXrjx58iSpd0u8ojFbPNewD59JkkQABOo5E7hRZf5UqVQaGRkJBAKMO1OU/0JREtVDapZYsmTJjh075vzc+fZCY4+EGYD1rkHOTdM0j8dTKpUuv/zyiy66aGxsTJKkUqlUhlkGYF0IBmk9sa6v6zq8KujtIcMFhsTo6OjSpUsRSOJpAYkBb7bZbCSpLMsyZqzOafMl4w3Aghkh4buGccrISoAsKnJ2adwARQGMF4DI7ZfO6fw4qhUwxrCcRGlwYt8wgaSHFSgexABiMnS8Y/gzybQjDU/VQESFjDO5AVX4Ld1YTdNQeqY9L5fLdXZ2UtsN/hZwpigKOqVprJx+KjkjegMYXkRsnv3lsll9zrONzUyiVepmVvBQhof1rpG7jqkqZrM5Go1S/yAeAswLoKwW/lDn1D4xR3v2DWeIUwWqogcNKwEQJvE5V+L+LxI1FtCHutBM5tMlpJnMTNxPyF3hbRB0fzfDYjLRageiUc6LIM9qtSqKEgwGY7EYjkBVRcpznRZeiJ9I3+NsGRlKaBDGiWhI7y8DtdkyL+Jhy15c4L+V2ssNwHrXsPuZTCasbYvFMj09Le45jDG8gbyVYrGYy+VSqVQikbDZbDU1NZLQtn6WzWQyQYMJyGsymQhk6bTx/MFrAHiRI4Y3sMo9WOe7SZIEZGfcS6WNqlgsIocFbwi6xoyT6Rlv9CP3XGwDpEE7dXV14XCYccCixDzRHUREkOav84DCKrr/hIDitTDuUs3XmjMnMOlCT6J4VoyHF2Uv6gLzY/b7T/s7mMsMwHrXMMaSnpJoNIonjNKl9EBgcJOmaZFIJBKJYF55fX09Zs+dq/MHDNntdpPJlE6nNWH4K7X7U+kKjhitH8b3UiM9R4ZNi36mH5C3AkfPZDJhopLVasW3L+qU0i3FzzQ5HC82NTXt27dPvPlw6KjZWJ9J+5rvPMFQxSNKGbf5PGV9/iYNEWHFS55vOpToe87+LHql4jGHAVj/a/SEmUymkZERdBHiASKHJZ/PY7Tv1NRUPp/Hb+mxO4dRIfIshUKhUCiMjIy4XK6amhq/3y/Lcj6fz+VypVIJeIqYEZEvYwy1MEqXGAbTeb8uIQ5+QIoTWn1oTkTSvba2VhQ8ILdFlHOhug1jrKWlhaJ4nbfvMEHmhY6zsM9LiXzGva0yN78swKQTKMtV4eRnx30ikIkgRXszvViGdGWpMSMkrLDRMsYuND4+jv3TYrHgoclkMvF4PBqNJpPJfD6Ph5XxpxBu13uRVTxDRt2OmqadOHECUwxkWXa5XA6HIxAIVFVVQTagVCqBayYJUw9w1YiIz8n5fwCNFE5EHweOEkYTKoqSyWSqqqqWLVvW0tIC0VEqaGCtimO+xIVdX1+PoFIWpt5TcPfePSxCqzL5o7KIcnYMKAtyFIyxYrEoCepUTMhtiQ4mHR/uZ1k6jM3M7VLZhxkhYcUN/fe4y8ViMRqN4lGgwVDIWGE9WywW6uNxOBw+n8/v9yMoOFchFRUBUZPSdR11q2QyGY1GR0ZGIGTudDq9Xq/b7fZ4PFarFSuETttAKzLksMqSR8jpAPGtVquqqq2trUuWLIG4u5j5pqCM2AziqmaMNTY2QnQbjxx8eUqhiqex8Hnqup7P58PhcDqdxjngo0XmqiwoMVCZu+x12oDFQE+alTuj31KoWHZ/yM0sg91KmQFY7xr557SHRCIRUdmj7OtMJpOhUCgUCiHsgod1Lk78XaPqEhLANGBKF4gX6XQ6kUhMTk4CcBsaGmpqapA6gQpKPp83JpjBJC52zGauSQSAiUQCEppbtmypqqqSOPGF8ewBsbeobb7MXWpoaPD7/alUSuKD45DCZzOrfnQy83nuFosln8/HYrFwOCwmYcvSWKdMKmkzpY3JccNNgLeOSxNhTuaTtMXXZW6IWuSKEq2Np/NdowyF+Arufi6Xc7vduVwOGyziwZaWlqqqKpfLhcoOdmNsvCKtBnEiq1z2URemEmQymeHhYafTWSwWC4WCy+USx4JSAoUJTjv+tlAo5PP5I0eOHDt2zOFwuFwuyEBj/LrMBxHjYRWT8cjg4AjYq+e7Ltw6CN1SvdJisWAYH50YeEM6Lw5g3WK3h6RU2d0TF56YIpkdm8xeJLSYRVygiIyqw/ivpmn4mcoX+CtxGP1HP/pRn8+HJ4SynAi6y2Ix8bbgh0KhsG7duqGhIWpitdvtcOdlQQhQFI2Y8z4T80a8J8ivLXxbxO+u7GbSVRDgslkb9pyFSDqsWG0gz46eCmkmMVDEPsI42jgJB/FfA7BObbT7ocpjtVrr6+uDwSDqRPR4wa+GyDfWM5Z0oVCo4CajC9qMyWQyk8kghnU4HJlMBlxHRVFyuRx+nu84yMWoqppKpVKp1MTEBJ4Yr9cbCoWCwSA8CIS9ABqHw0EYhAQfxcuzDWeF+QuSJOXzebfbnU6ngQLAcXpwQXDDKQHxsWDw7JZdvkgcK8sKM6EAwmZJ0IkxDi0SjQ+5AirpnBcy33VhTwoEAlu2bAkGg4y3zZ/uhmSxWNasWbN161ar1ZrNZqHfgKINpbHYTKyZ0yBDKMZf8JfF4EBMVxGUlPEYykBNtDk/t0zRlP6WVDBFIpgIfGU2+/si14G+I/G3BmCdwuDRiMQln89XV1dHrBlZEHIQn3tQH5gwMKpSRk1qCOIYY/D7dF1PpVL5fN7r9ZpMJmSy5nvgqHol8SIOUKNQKIyPj0uS5PV66+rqfD6fzWbTdd3tdhcKBQiT67qezWZJw3PO40uSZLfbsQgx+Qquk4jvOudVEDupVCrlcjksOZzhbI3w+RYSXsH7RVP41BnxSmm50vnQwpBnDjEt+xSc5LXXXltXV3dKdFvACoXC8uXL8WxQchqfi5ssCYKRC+x2+XyeaPQ4H1HBGe8hFNCFTlhZYIcxxjBWlkZg0A0X72fZ3Rb3CTYrhqD/ineS/hUBjs0FkcpMIXLCMgOwTmE6L/piIwVHmRT+aE9jQnoilUq53W58T0h5zCdQvQgDL4wJJWeQ13O5nMPhKBQK0IeDT2S1WucrJ5c9cLTR4YQlSUqn04cPHwZyVVdXB4NBl8tlt9slSYLSIdp6F1iruq5v27aNsMDj8Vx88cVEcVQUBdwxQAnuEn6wWCyJRELXdZvNBrgkR4nATtSzZoLnhenc+K8mKKMDB2m5ioQpAmvKAS2wwciyXCwWV65cOTw8jIPg2ThdzCoUCm63u6qqanx83Gaz4QiE0WKBcuHjYMBiR0fHkiVL7HY7biZtYKgPkKGFQ+Kpd8bzBshv4s35fB4S0sR6KQO+OT1WumnYz4huJt5kifuAoidFcbf45Za9hwn8WwOwTmGUwkDyoqamxuv1UsSH99AXgwcO7j3KzPBZ4J5U5HzE6ozO+zlKpZLFYsGaRHyhaRqWwXybszZzxiK9TZIkhIFmsxlHiMfjmUxmbGysurra7/d7PB6LxbJwAgvH8fv9xWIR6T9ZliORyNTUFGoUtOtSUpaiRVRjkSnD5YjAVLYPMyFNI94WNquOLvq55Iwga8Y46JArvYBTgwmD6G3QeXhOI8HfuwGS2tvbJyYm8LmMZ/ROC/4ATBL3LsHFk4QKoKIoDocDAb4INGVAj/CW/oRySVTMIQYMYdycHlYulxO5MmymNBN9IiFUMplEioD42BrnQrKZftm7rsNp3eUL00C3QREaZTXarnU+Txz5adxTu92+c+dOygoFg8HVq1dX6mSAg4y79/he6UUqrmu81WO+qp/Ge3HFJILGp5NLvImHiGnJZDKZTFoslqamptraWpvNRp0r85nJZMKqRvCoKMqxY8cuvvhi5JiRupY4F6y9vT0ejx87diyfz2ez2Y6ODvJqZd7LqQlGVyquCsb5REiNiR4BiLXaTI12VVWnpqbsdjum7FFyndw3NisqBNrS+SPaFWsd79Gw/7W2tu7YsQP7H+X1KEzGO+eDTvE4itCGRXkJ8Tv935DKZGLClDDGnRdy28WDS7NSUWTyTBoXmdvtZjODULwuhnjicTo7OxEiWCwWi8WC5wQ7PXYv0J4zmQyyEAZgncLoqdU0zePx2O12iL7TA03JY/wLB2Hfvn379+/HNnXbbbchOqvI+RDXhvYiAGU6nUaLBuhgtNrnc4Iony0+PWWNh5TNUfhM41wud+LEiWw229zcXFVVtYAHZzabnU4neS44VCKRiEQiNTU1WCFYlsQGOHjwYG9vL5LuJ0+eRPnS5XKBQYZQEeU5RKPix9EKISBDXIN/CWLyguVyuWKx6HQ64TXDr8Gtow2AngG6jfBkrVZrPB53uVyAg/n6VxYwuCotLS1ihCULU05wW3Dn1VmDc8quXeIVIeA7LmROn1REeQI1fea8wrLzZMIqoI1hvhyfOMdQPGBZkp7eQB40nZvYdCEWE9H/ZADWKQxQhVXU0NCAngyHw0F4gRtKrjJ4MdSGlk6nV6xYUSm0YrN2UXrW4SNomoYRxOTJz+cE6cKMYsbxi3ZmLHJiTsJdB2apqjo6OppOpzs6Ompra+djn0k8LYXCnyRJqP0PDw+73W6Hw6EIY2ksFsv27dv7+vpoqnsqlXI4HNAjIzeQCVEkOooIdgF82JNFNNeF4I4WKi1gdAIyxmw2G46m8mkR9HFl14WMeCqV8vl8iqIkk0mn07k4Ch5aoPHM4OaQOIxIGljYw6KHkHxJfPVlnhETYmeJ57BED3S2b8U4QtEfiu8RIUa8XWXPW5mjqguRO71HPAj5dIiyy75EWZYNwHrXdF3HAgMvAV88slHYzyVJGhgYGBsbI3ZlTU1NU1MTKv02my2dTiNTwDjnSNf1ZcuWgXaEbwg1bHACMLj8dHMf4oMivkLHAQObzUUCmn0cNqtaxHiwWVaiQvkcSFEqlRKJxMDAQCaTaWhowK8Q91EsgxwN0IoxhpumadrU1FQwGGxra8Pi1DirIJfLqVwyH4n/TCYj8+Y7uDxY0vBwiWZJbibeWXY5tAx0XQfpROdDuugPrVYr/FO4V7T+YfgslC+Q8yoUCrFY7KqrrsIFRiIR6C7oup7P5zG5Hkt34WyUpmn19fUQuaUsO9GyCHEWdq8YJwkz3kazMO+v7GlZ+DmZjVDzHXD2+xc41Ht5A+0B5SH5wke5cEyWZbgDgUCAQiEswkwmg3IYdmCZ89pjsVgulwNBCUEfik1gwaBN+o477sDKBH5RniKVSjmdzkpl4s+CUc5L5ZZMJkdGRvr7+2OxmNlspvIcY0xV1ZaWFuTRqVERiCPL8uHDhw8fPozQFU5cMpncuHGjy+WidAz09fHRlP3FdoKbj5oXmxk1kKvLhM4Sijo1gQ4Cqhf+i50G0TSxJQmwqDqBH5DN/J//+Z8HHnhgaGhIlmWv15vNZtEVKNZwiWQ7p9E20N3dDULfmf4GPxxm3KZ3DWhSXV3d0tLCOBmHMWaxWI4fPx6JRJAHQT1OkqRCoZDNZsfGxpqamspytMVi8ZprrhkaGvJ4PEh7EcES+yfjOcgysskH2TSuCYd1C2cnkUhMT09jrbpcLsazIZIkRaPR/fv3iwELeQqoPKZSKQRijDG73V4sFletWoU2l2PHjh08eBAfRI6YpmnV1dVtbW1VVVXwg/L5PEZsTU9PQ+RHEpLEMImnn8QcNtL8lH6OxWLYOcTQg0JmscqhKIrdbkdfjqqq3//+9+++++7W1laXy0XaWOCs4/QW+H5xQ6xW60UXXbR//366sWUOxSkdkwvNDA/rXZMkyeFwoAiIZCr222g0mslkYrEYOOWU5cHmGY1GLRYL/C9aEna7vbGx8bd/+7c7OztlWYYcJaXqsRIQQp5H+6oYasGVoOHYyWRyenoaa17lwq1vvvnmvn37sNrxVwAss9lsMpkSicS+ffui0SgpAiP1VigUEomEw+FAYMV4Vc5sNgeDwfr6ep/PxxiDz4uWg+7u7hUrVrS0tOBbo0JbsViE0h6hmJgooahc07RcLjc6OoqAUYyqqL5GYZTEWW/oXpicnPzGN74xOTnpcDhI6EIW+pwXiPcJmLq7u1Wu27dw6GcYMzwsslwu19TU5PF4KM2Bf10uV0tLCypKeF6RW0FEg3ADvE2EAyaTKZPJKIrS2dnpdDrhqVEHLB5lxB0UPpwXpvCGXvKSGA+7EonEiRMnYrGYLDTBQWAa7mSJz21HGgv3LZ1O9/X1hUKh+vr6QCCQTqdxr5C9gjMLrrzFYqmqqmprawuFQijCklQG6rCBQMDr9TY1NRWLxdHRUXwphHeoKuK7wFdArNHe3l68bWJioqGhQfyCiA+lzdXHR++0WCw//OEPf+d3fieRSBAiU/fSwnI92MDq6+tRapiPzKXPTwq7AM0ArHfNZrPV1tYCRJAXz2QyCD3QVIwVhccdLG0iRlOTCtJb8JsymYzf76duXrF+jyXh9Xozmcw5vuz3bBQiYbWLyoW4xomJCcbzvgB3VBsIOPC6qqooYloslmQymU6nM5nMlVdeidIbHDE0RQJ0PB5PTU1NTU0Nwi6ACHCQCTUyRVFsNpvJZFqxYgWln5hQEETcqgiN1lTYZYylUqlwOFxfX49cJNiY8P6oyoFLQIKc1MRyudzAwMCTTz558cUXiwVi/LuA3BD2NjxdNTU1J0+eZLMajA2bbUZI+K653W6SZGFcJAT15lKp5HQ6Ja4Z5PP57Ha73++vqalBbRsPH2V5wC3G6qI1xgQKDBy3EydOnNtLPi3TZnaEEUOdojAiUjAuXwlQ0zlZkTGGMhxqglSNHR4ePnjwYCaTQWSUyWQikQhunaIotbW1jY2NwWCQim6SQH/HYeGDEDVfJPJgn6AKJuEXnSTOuVAoDA0NYa6ExGv50OeQeMMwXrTb7V6vN5fLwafG/rRz587jx49T0RNbGoH1nEaunCzLDQ0NuqBwMOebK/EdfhjM8LDeNRryjAWGwBDugK7rWFpw8pctW8b45omF6nQ64Y4hjYr4ES0pEMlDt4RYlff7/T/72c9uvvlmchY+4EZFN7GJD2V+ndMOECbjJsBFRfqZ8dQ13pxOp3FzUBM0m809PT19fX04DqgegJgNGzZ4PB5VVVOpFPYPoAA8VpGMhtf1WWNuKekOp5jerGmaw+FwOp2pVApf0NTUVDQa9fl88MVUPiFJ4tpSdKjp6WnEqvCPMpmMxWJ5++23wc5H3h0fgWLCnPeTmFCSJKEthvExYsxwsua3Cw6wFC5oRa/gEfF6vRIn8ohuAp5v8K3dbnd7ezspEFGxSROkr8TCPGWvxLo4XIwVK1a8+eabfX19l1566fr16/1+fzKZxOolj4w4SpLAtAZGUD8QkYPI+8CapECG0j3IdiPgzeVy09PTSLug2zGRSIBhIMsyeFXBYBAKWSh05vN5p9OJHjqcHsSqxDgRqCTzsRckEYWbSX3/VquVfkacSHx6xhh6IX0+39KlS91uN1EfiAYBj4n4TeTWwWQ+wYyU1wkoiRQKKMnn8/X19QcPHkT4yRjr6+vbvHkz/CwCuNnYAU6WzKfPw++ORqN79+5dtWoV4x4o+s8XqKtQ9bO+vp64fvLMlhedNx4ZThbsggMsXdBso8cRbe5zcvwAZ06nk3FOo8PhwN6LhJfGZ6yWIcuc5nA44vE44zMIIpHIL3/5yxdeeOGqq67avHkzhLSQChFnTOkCpVOaSY+kNC2djNhWhhJBPB4fHx8fGRkZHR2dnJxMpVIgizGu1SfLcn19/ZYtWx566CHieXq93nA4DEp6XV3dihUruru7MZUnmUza7XY0fy0gROFwODZv3my1Wu12u9vt1nV9+/btR48epQuhawGqIr+O6129erXb7caCB8M2m82qqprL5bxeL2COigCyoPGGPoRSqYQiLAEcxfXpdBrIK0lSR0fH1NQU2o9RNBwZGamrq8OdpAwj3e0FvllN0w4dOtTZ2SlxqdIS126c04B3OPna2lqxvrnApxh2wQEWdn7a1fGU+3w+6uYXE5/0M7JOyWRyfHwc0n2UmiGYWHgbxPHHx8dra2v9fj9cNlCo8/n8Sy+9tGvXrpqamq6urqVLl0IQAs89dVpgeeOvNC7hROcA3Emn05OTk9FoNJFIjI2NjY2NZTIZ9KzA92FCF04ul3M6ndls9t57773nnnscDsfFF1/8Z3/2Z1NTUyBDIqDTNG1oaKi/v//ll1+2WCxLlixpbW3t6Ojw+XyJRGK+riNFUZCMZ4zl8/nh4eFAINDd3e12u8Ph8Pj4OGqplNdDMkjTNAg3BwKBWCwGbw5SNsAUNMEyxsBuKwljlnXeooTyCIlVQJEVQVw2mwWy48VSqdTU1JRIJHDOpVJpcHDQ4/Fgt8DWQjvcws+V2WweGxvDwHpsbGxBz4jiaMYYqHzkrjKjMji/XaCARY84ngyv14vflj1eOuc35HK5hoaGAwcOgNre0dGBcIB2eMb9nflaMaBU6fV64/H4JZdcks1mERalUimPx5PNZrGGjx8//vjjj3u93q6uLpvNhmqUyWSCR8MYQ886LXIcOZFIhMPh6enpVCqFkBNrFWERlcMkwYrFYigUuvPOO++66y6ISfT3969fv/7hhx9+8MEHn3/+eURhFKLKspxKpcxm85tvvrl7926Px3PHHXcsW7YM8DHb8vl8R0cHZaNMJlM4HPZ6vcFg0O12h0KhZDIZi8UikQiESc1m89KlS5EVCgQC4I7AjSKpLHhJqC3+9Kc/xT2k3D9u/tKlS1etWlVdXY07oGlaMpmUeJu62+0GCwwksmw2W11dDWcKiAmRQtQxIbhOVDIxjTXbZFm2WCy9vb24CnQXzX6iyFRBx8bv98O5Zu9NA+tCtgsOsGbvXcQULfstQEHmyqKhUAjJl2QyKXof0swm+/k+l9JYDodj6dKl5KPBI5AkyW63Z7NZhEX5fH7Hjh1UGhcrcTKfhoIOFaS3s9ksslQ09hXvx9+aBLlxpIe8Xu9tt912zz33BAIB8r/gF3R0dDz55JP/9V//9aUvfUmWZXTDgOGNsh0OlUgkMpkMfJn5rpfxBFyhUHA6nWhgAoyGQqGqqqp0Og26lt1uX758OeqD0HqOx+M+nw++LTjxkiRt3779jTfeePvtt9HZQyucIimiTTU1Na1cuXLt2rWXXnppR0cHQUOhUIhGo2ClwgfUNK2urg6HstlsYPkyxkp8GJoqjO1a4LkCoWFkZGRycrKmpua99DCgKxM3pKamZnh4+L24che4XXCAxfjDR/EgrXM2qzpDWze4Cz6fb3p6WtO0kydPdnd3iwcUF8ycH4rV4nA4MBJqYGAA+zmiD5rsADJRIpFAHl1c9li9Glc+Y4xBSA9A6XK5UIYnPe/6+vq2trbly5dXV1e3trbW1dWh4qlzEsDAwMDIyAiuy2KxjI6OOp3OycnJ+vr6z33ucxs2bPjCF76wd+9epH4QRJMbglrewgCNTBzwNxwOu91uhGyFQgHxoNvtRkOP2WyOxWK6rqN7XFXVUChUKpVwRS6X6/nnn/9//+//DQwMSHxardiGKbokwKahoaHR0dFnnnlG1/WVK1def/31d955p91uR8IoFouhPwElXaQjkRcDdyyfz2cyGUoIlP0w30OFJ+rYsWPV1dXEm5//MfzfM1dVtampCZ2Jhnu1sF1wgCVx6hA5RFQxJJ+fHlBx64YGcTgc1nV9enoasm1UKzxlxgFxTT6fv+SSS1577bW77roLqTSJqwKZzWZUwbPZLBwKJmTZiaQjFuZ0XU+n04wxh8ORSqU6OjpWrFjR1tbW2NjY2NhYXV3tdDpVrtJdVu9HQgcpsFgshrQOfIpIJBIKhdauXfvcc8/t2LHjySeffPHFF/v6+vBmBImQ3FW4VOZsM5vNVVVVoLxD2QqggA+F4h2cQWTiEPRR3ROYYjKZ9uzZ8y//8i9vv/02SPCiwKb4nTIOGRaLBVhGd+zQoUOHDh36z//8z02bNl177bVXX321wiXiQKYHo4oYc3CZkZWnB4Yehvm+aNwWq9U6ODi4YcMGaEkvgOnYwOgrrq+vF6srhs1nFyJgkYeFVwi86LknwKJ6opjhRvY3mUyauKImvX8B5MJKu+iii44fP/6JT3wC9B+Jz6TDB1H3j8470XRB6YXNVBPEZ1VVVV100UVXXnnl5ZdfbrVay3gGxNUuS/TgbVB/p35sjTcbo7Cg67rD4di0adM111zDGJucnNy3b9+ePXtOnDgxOTl56NAhwOt8CxK9JjgIXBj4MrIws0PhMgygMuGKsDHgVv/Xf/3Xt771LdwQKiyUBPlzMpm3K5PnpQsqMZIkJZPJZ5555plnnvH5fP/3//7fiy++2O/3Q89X5xoymHIGx5BiQ2kmp2RhpwkiOZlMBvIVC6hxQA0RjA18j/iBzawLG1ZmFyJgMeHJA8NTFRQmn3nmGYQtsiwHg8ENGzZgnTPG4CbgKZyamqqurkaWhxoPS3ycHwCFemipICjL8rXXXos0SqFQWL9+/Z133oklihLVf/7nf0IGkzGGWb5Y1ZC+AResubm5o6MD/lRrayv8Dkr80wWymXPDdUHpXOUK4kwYTUzuA0K2H/zgB5ShI3Sw2Wzd3d3d3d1Qg1rA4BCRV4KIUppJExOxFddIBLR4PP5Xf/VXzz33HBPcTDr/MrTShdFn4ou6QAeBM6goSiwW+9M//dM/+7M/++hHP6rzqcvglKlcoF3nNHdiYxFTFPcNzNV7772XeBjHjx//1a9+hZsZi8XcbvfCqn44GRwfrd10nkS1k3hdmOYzGnbBAVaZSQJ/ShHkrrDGUDzCOxHRoBwOfhM0rRCDoIgOvwC0A3BBJS45YjKZli5d+od/+IeYeBqNRp1O5w033JBOp4PBYDweh6YwUjaAJ4/HE41GrVYrhqGvXbu2q6sL+RHKrGGdI1VfkRtCjZCUsVpYhW4+g2M420mZzyDSACZHNpu9++67jx07hl+RV0iqBiRZ994NKIPvN5FI/PEf/7Esy7fccst8IW00GoUfDYZHiY+nn/NCytzq0vzK0bMNeOfxeIg9S5hehu+GsQsZsCgMFNMTOi8LYt3iSTXxAR7IE0ciESyeeDzucDjwMKl8Ph08LIfDgZiRRHhdLtdDDz20d+9epHsdDscVV1zhcrnAw8RCbWlpwafjwf3a1762ZMkSh8MhjkKiBxqXgChy4Zmpp2XIK0UiEcYXuf7eiEhlhji37IYv8H6/349UN4ZQDAwM4HVSxcAJ2Gy2YDAYCoVO1+MgDsrevXtxRY899tgNN9wwHxZ0d3c3NDTQQObt27eTOhDhyOwr0vnkmLKM4Wwr+21VVZXMR9QwQS6RvQfF0QvKLjjAKtuvREdAzBMxzh2Hx45cj67rXq8XkAF1AXRUoNgENx5Yg4gSmWmPx5NOp3fv3v3ss89imaEXBEqBTKDkiNxxTdNWrlzpcrlSqRQiAqKD44SxfiC8ebruxgIG4IZQFPlHizjOKVdsmUGBD6kfj8fzmc985vvf/74IlA6HAy3H2WzW7/cjan7vhrjP5XLV1dU9++yzDQ0Nf//3f0+M/9lWXV1dX18Pb1qW5eeff940azgFWZkLCRLZwudDqIdHAsNmaMsUn1LaCE/rej+sdsEBFpkulAJhJIQChwvpqmw263K56Il0u90gTKmqmk6noQIMNKGIknFZAkBbMpkMBAIvvfRSNpvFrLBcLrdy5cqWlhZJklKpFHRmOjs7I5EIWoIRHME7A2mb2FiUb0Lnx3wdRYs24CCKjyYucL6ITV4W+orey99KfJy9pmm5XO5P/uRPqqqqdu/eTf2VkiS1trauW7eusbFRkqQ33njjdM8HU0IVRenu7r711ls9Ho/X68WVzjZ83TTRg70HT4d+m0wm38v1MmHqJXVK6TPnR0hGs85MuxABixx7JlAZyMlyOp0o8+O5mc2lRowAnnokEqmrq0NdiToKNd79Aw6kzWbbv3//yMiIruuQDw8Gg5/85CdBU0THnKZpPp/vG9/4BrXjIAtL4llw9MQCIrXs6FylpCI3B4FnPB5/nxu7cqpJq2UmyzJ8KLDSdF3/xCc+cd9995UpydDPTU1Np3U+aEJEgrJQKKAtCWr0852/pmler5cKqfrMCVSilSX4MRx0YXTThTZ7aeYYJETThPVizcGwCxGwYLM3fzwWfr8f4pnU20H1KWgtQQMLeW74OGKbDn7IZrNutzuVSjHGqqqq/vIv/5J8rtWrV99zzz0YYAHKNVbO7//+7w8MDECrQJbllStXUrVRkiR0HSM2JDbAnCWz92mqqubz+bGxMVpLuNL5ktPzmUmYLPtePCzsB2Y+VTCTyXi9XhqwTLuFxCVuTjeH5XK5VFVFf6KiKIFAAN2a873/5MmT09PTPp8PKUjKgos/l10X/RYB9cJBMS4B4Ty+U5vNBtfMJEhpny/qQ2fNLjjAomdI3LIom4AslcSHhtKoG4nLwsiyXF1dHQgE8IfIcaCRmMbGoIkM/M/LLrvs+9//PpQDNE1rb2//4Q9/+LOf/QyBBlLmqVRq586dQ0NDaGFBz01rayukaeC7EWdHFXTyGEer+dR1F2G48Hg8jmOCYLmIuYoifQy2cGyo8hmu2WwWnUAYNE07gTJTheJ0PQ7gOwayFotF9AkiOznn+0dHRw8cOJBMJklMBk2Rc75f9IZ0XSfhrQWuV+cMBgIslFYkPjRQEwSpDfeK7IJTHKUiIB4LbLMUZ0mS5PF4AFIkF6dw3SLGGLAJ+yHjvTga793BRyB8s1gsdXV1S5Ys2bZtG962ZMmSH/zgBy+88ALWIbwJuDCvvPIK6YgzxiRJam5uprMqlUpwNBjPDYmRbGXTHA6HY82aNZs3b1b4uA2RQ0BgIcokzGnUdy1JEmlaiRsD3XDcZ8bTWNgYANaigyZCwOIWML4mHBkPwAL3rbOzU3SuVS58pnPCHbVJMT4qCQUTpDh1zvid7/h0jfCnsCHRf3GNuPMGA0u0C87D0gUNGSbUd3QuzAQXqVAoUBhSdgQ8T0jWoiUYDxm2bp3rZFksltWrVz/xxBPhcFhV1e7u7jfffHPv3r2ZTAaDzk0mE/7kmWeegR+BVYHnHqllnbfjYLmehWpRNBoFGK1ZswYKqzoX0gF0FgqFwcHBcDi8cJIL96TMnyWcFUmkOh8xj8Qf4B69ihWsfp6uNTU1rV+/fseOHeCLIB0JTCFnSiznrVu3bvny5TJXmPB4PFCnmS+mo22GDoX8He4M3iPmxc78FZ8fdsF5WGXfvSQwsGg7hRIDLSQ2M5DEm9FJS/4Xan/kBWC4i6Zp//zP/5xKpbZs2fLEE0+oqrp//35FUTB7FbW/ycnJXbt2UT5F4m03kIITF8Yp87gVMdA1VFWFyD3jvgC0jyH2QByrBc6Hmk6oHIafZVlOp9NgxgKSSnx2aSqVKhQKyNYlEolUKlUpNuwiLJ1O33TTTdddd10oFBLnG1GtRgQsSZIgGAsMCgQC0PlBa/ecVuYgK4oC0Q6xs5XN2l8Nu+AAC0bPASIyiWta4blE5ypjDJkF+itJqCfi8aKYwufzQVmY2nqsVuuxY8d6e3uvvvrqp556qrGxcdeuXSBDEPooirJ7926Zq/pidwVdiFQodaGgeRYeXHg6jDGQyBhvnSESPyZak+jKAscpc2NxFcVicWxsDDJY8OCwSq1Wq9vtxt1TVdXhcFit1vnEts6OTU9PX3vttX/7t397//33o21I7H8iLMa/IOih/xmIz7gbPqeJ7pXOpW9QX2Yz831nYZc6j+yCCwnLDLU/JpThZVkWiVeQ0MObRXfM4XAgZkTNG2kL5IxR5iuVSjt37pT4iIHx8fH9+/dj/gJjDEsxm83u27cPnEYqmeu6vnz5cqTwcRx9Zgv0GTUabkatPyqfOkFJN8StmqYtUGgjwJK4Xhjl+5DMtlgsNpvNarVCn6u6uhpSn0iQATd1QQP6LBu+Dgwc6+rqQrMnoQzjDww9J6gMIsyPx+PQyVnAyvJxeH6oLGgA1nx2QQMWIQIT+MQgBBGfAB5WWaoLIQwyDqA7RqNRmc/LA2yl0+mDBw/CXcrlcrt27SoWi36/PxqNYsEXi8UjR46kUilN0/CJ9Mii41rMW9GCP9OPLxQBieQFBU6or8Dde48IgqIEuQ8E9GjVxu3N5XLJZHJqagrVt2Aw2NLSgnG24PSfw6I+BBui0SiowogKQbiT+LggxvPxMjeTyRSPx/GVUWJ+vo8QHefx8XGJq7Oxmf3eBmCJdiECVtkTAMBShelVVMmGJJP4h+LCS6VSr7zyyuuvv07Psdfr/dKXvgQny+PxRCKR2traZDIZj8cHBwd1XU+n0xjrAN7pc889hziC4gu4aa2trbrQjE2l/bPw7IKoyRgDHDPeFWi321OpVDwez2Qy0Fy1Wq1U4JttlOOjOyZxChUqpKApAaoAx5FIZGpqav/+/Q0NDa2traFQaOEhF2fUwKQrFovhcBgzpROJBAl7YF/RNA0bDzV4AWLgHavCTJDZRt87QA1DKhEj4w1iBkBfkCFxQdkFB1iiHw7TuBILdaJgiULLZc7KtK7r2Wz2yJEjr776Kq23QqGwbNkyjWuDRCKRrq6u3/3d3x0cHHzqqaeQsU6lUuiLtlgsY2NjGLQFLEDijOJNRFI0mkGkTZ7R+yPSNZBm0nU9Ho8PDQ3F43GQXSnlt4D7gLh49usUVEIgzMTnblFeD6t3YGCgsbGxu7u7urr6zF3sAgZBZ8ZYOp1GKwIUU5mgAcmEMRM6V55BIwSynxLv95pt9Ie4S5OTk7gbijCvkBlJ91l2wQEWBSk6J9SQNDgkq1CoQgtbU1MTkMhmsyHtSvpHyWRy69atwBTosttstmXLlklCm0UymXS73atWrUKinTHmdDqhcmU2m3t6eoh4Ral9LAwaUYHo0m63q3yQRKVacFRVtVqtyFhhTjUUnMG9xswIk8k0NTWVSqVA2Wfcd8DSolUE5xSkIV3XobcjCXOu6Faj4Lhx48bBwcFYLEYin+SpEZGSMTY6Ojo8PNzc3Nzc3BwIBCwWCxJDNPFI53KAuNvgQ1RqbeucraqqaiKRgBtFzQ8I/+Epm4SZJrTHsFN1IEAELZPJ4GhHjhwplUqQsWdcDJLxkNOgYpFdcIDFZjnbJMzGeJXQbDY3NjY6nc5QKMT4PF48lyjwW63WAwcOMMbgcYCm4HQ66+rqsAKj0SjKRrlcDtpvVO9T+BgeOCyUotK53A10CMbHx+mRJdJzBeMCkCoAlEi7oHQgSVIqlZqYmJiamgIEsFMN8sRFkfrofJIGMEVRoD6YSqVOnjw5MDAQjUZJjwV+GX0piqKMj48fP37cbrcvWbIEo1UBCtg5GGPYKqgOUKn7IxKG4/E4fUF0yUgCLPr42IpQFdU07fjx4wgwK3T6H1q74AALy54I5djS8XTKXMxEluWGhgbUsJBsxtoWg8cDBw5giWKPLZVKwWAQGruFQgGqAyg4RiKRYDAIWhbjXbXotiEHhAlbut/vZ4yNjIw0NDQgwBQBq1IeBNSToTYFWZt0Og3HR+cyW5i+hfuz8NHA+QSm67q+gPYLqLagVq5evbqlpQWTZsbHx0knngm8LWpFGhgYGBoaam1t7e7uRps0HFVMPELJklDs/RtCeByNBheK9x+hsZgdPy2ThfnYuO2yLJ+uZs4FaBccYJFRmgA9fbROsOqcTidNRgDQkPyx3W6fnp4Oh8MoY2G3z2aztbW1aLYAowq1bVQG0R9HaRqdT5AHXrCZmTXoqU9MTNB8TcBHZRNYOlcFyOfzsVhseHg4kUgg5gJ3nzEGcUE02SwQlUgz53pgJNp8b4ZaocxVE6Cn2tXVFY1Gjx8/fvz4cSj2WK1Wain3+XzpdBpKLwcOHBgeHr7oooug4YcvqFgsQmKssreIAnDCaxGewMVni63i4f4jfhwcHGSM4ZE7VzSO88UuRMASay4IQOCci+wBWlQkzJDL5Rhn3+zZswdAo2kaesfMZnNdXR18N1TEXS4XOpnD4TB0HahqhhpTKpUiFXPisuu6Xl1dres68vFACl3oZanIHQD8TUxMxOPxiYkJZL79fj+Sd3BV7HY7cRrgaS5wQCw8Al9Qz+Y0Ul/ArQYOlkqlQCDgcrna29unpqaGhobgcMH1g4YBWOOapkUikTfffHPJkiUNDQ3BYFCbKRNWkfvDuM9Iw1DLsoe6rtOFiLoU791QRsSONTo6WigUfD5fBWWCPqx2QQMWca9SqRRmUjGuc0KYpXMKJZaWJEmFQuHgwYMIBqnrkDFWW1uLLAwedJPJtGXLlh/96Efbtm274447kNmhMjZjDHxxau8AXDLGsNrxccROlLjeyPt/oCXO1B8YGCiVSvl83ul0Njc3IwFHeuShUKivry8SiSQSCazPBQ6IvDtgCEO95nszLpliYbpqrFW/3+/3+xsaGkZGRgYHB6empiCDoWkaNF0xNzCbze7Zs2dkZGTNmjUej6eqqgpTuSqYnIZ/TXwx+o7I46b+LWlRzeeUETObzX19fczgLrw3uxABi2rSxMPEmgR2iGuPnkiScNM0LZlMhsNhgIgsy2AqWCwWv98vilJiDvvDDz8cj8eRCNO4FAw40/DLxGCQclg4GRI5oPcswsMqWwb0c0tLS19fn8ViAVQxxnK5HOb06bqeyWRisVg4HIbGzgJDH3B8eKPwF7xe7wKyLRh3jCBXnqUYha/G4/HY7fbq6upkMjkwMDAxMaFpGgq1IgEKYWxdXd2qVauIgFLBKiHtJel0WhFUQPEDTaVc3PFRAoakbU9PD+Q9FiaaGsYuTMASs1d4KCFMDDYjCb9QXQ+MB4QAyPggX45mNwzvQpKe8SWHvPsXvvCFsbExrDGR/wmpeLgMBEMUIiHzms/nU6kUMsqEm4vzIObcuqPR6LJlywKBALJUwCMksyGeo+s6qGHwNeb7aJw83uN0OpFNX2AlQysKAbLOx3MpwsAYxJ4mk8nn83k8nqampkKh0NPT09vbCz+XsvvIbY2OjkqSdOmll7JTFShPy5ANACOUdP3JwwUVQxamAZyuc4TUmyRJx48fn5ycdDgcXq93EVLUF5pdcAGzzoXTyCeHPwW3H+/Bc0lZG51zi0BZOnr0KOro4DSgPrVixQoaSox3Wq3Wffv2AWvGxsZUPlYHv0U8SNEEAKJUKjU0NCAUNZvNIyMjSHij2r24vbeMDEFuI92EsriMuEWxWKy/vx++FdGdqLaFtUpsD3G0X01NzQJujiSQTnWhR5K8WqCSzvWIgUFr1669/fbbr7jiirq6OiTXGGPIAMJfI6nSRdyiOU3nnQZIcYpVSJyey+XSefFkERsJVWyeeOIJj8fDeF2yUuf/YbULDrDmM1CB2Ezdj7L3IAueTCaxSECzxBpuamrC3FA4JpqmIcx0OByFQmFqagrZKJkPLibdYZnLrUiS5Ha7m5qa4NAxxlKpFIidjC/OCl5vJpMJhUJwrxgnFiEf53A4qqqqNm/e7HK5PB7PunXr6uvrUUuldB7BFtLthULB7/dHIpElS5ZQ6bMiRiSvYrFYX1+/efPmzZs3o6MAGUPMu4aHuAhl1AU+V+dFT3L68Cl4HZ+16OoteCSapu3cuROjlVBrrtT5f1jNAKx3DZq5Yg51TsDSdT0Wi2m8NRoRDcQGZD7XHgQITLLDEIrR0VEKBhEb0g9WqxVtd7qup9PppqYmhJCArenpaaScKfVeqeuFdiAQCsI4pVLJ6XR6PJ6lS5eGQqE/+7M/++pXv/oHf/AHkiS1t7dTjxv5VrhY8nFUVfX7/fX19RVEDcYYUNJisWBmrclkqqmpWbNmzfXXX7906VIEs4QaFQRKxgM9VVVB9IfDRX4lnOL3843YbLY33ngDj5Pf70eoW7nT/3CacYPeNXESH6XAxZ8peYGpyJIkURDU1tYmpqjQ5vL6668ripJKpdxu98mTJ0FQgGo7GjIAdqSejqizvb2dPlqW5ampqaamJuTCiVRZEUPmLhAI5PN5lNisVmtbW5vFYjlw4MBNN900OTnJGFMU5eqrr/7qV79aX1+PmIvQHAb3Bw7jsmXLgH2i4t37NJvNBs4tHNJkMon8UVVVVVVV1YYNG7LZbC6XQ0oIQz0q8rmMh89ms5mIo/Q6cuT4TheHWRBTfvzxxyGICC+VkomGzWeGh/WuQTKU/lvm5NNeSrlSGonMGGtsbNQ5+0FVVZvNhnq8pmlOpzOXy2EIDRLYSHxQshnCnljzVVVVCDMhYidJUjKZHBkZoeJABVcjmiWRnbFarZ2dnStXrnQ4HA899NDGjRsnJycp4EJ4CLeRUksmPnURC89isXR0dASDQYBypU6S8SKGxEfG2u12YCJ1C9lsNghpSbyBobKfjoH1OpeLwWOASovGNd0XYSaTaefOnejIcTgcxWIRhL6Knv6H0AzAetdUVRVbxubL4FLMSH6EJEl+v5/UkeCmjY+P42fERxDqRLqnbCwwyATwVurr66enpxljUGvAoUZGRgqFAj6ugiEhRIoZYz6fb+3atR6PJ5fLfe9737vvvvvgOCCllclkvvjFL6LTiPF4UOKkMOR3TCZTXV0dBgWS5moFzxMJIyTOVD41B0EoZRuBJhXXzyIeFhOYU7Iso9giCXJDp3tkWZZ/9rOfEeNP4j3VlT3/D58ZIeG7put6NBoNhUJwH8QyIpup8YBSEYIUSOKGQiFJGOSbz+fRuIPmZwANRq5i2eu6Dnaoruuf/exnly5dyhgDQ0LhM2ngrCmKkslkpqamrFYr5AEqxdOx2WyJRGJsbOzNN9+85pprbrnlll/84hf33nsvLT9EoE6nc9OmTQcOHCCSBxUK4YIFAoFAIEApPNyBCno6gD9gPfxT5N2cTidGflDLweKAYz6jhGYqlaJObI13g6KuIuo0nO5ecvDgwQMHDpAbC16IzWYzQsKFzQCsd03XdYh/0iM4+z0EWBKX/QX3mpCIcc3lVCoFoiO5APF4vLGxEYltFLxwhD179rS2tppMJrxOg6YlzhSDYkFNTQ3Ik5W63v7+/l/84hegkj322GOpVOrzn/884+V8ytx99rOf7e3thYqDxnXo8TaosHd1dXk8Ho1PooauIXqSKnKe5FLJXBsWbhRIpGCuEUWDaPqVMhCDcXC4nPjqid+w6Mt86aWXFD5pEV/9Auxcw8gMwHrX8CBOT09XV1eDEQNiJ34LSSwQlAKBwPHjxykP1dHRQWkdPNkWiwWRHYpoWOfT09M4GobBbNy4Ef7IgQMHLrnkko6ODvB9ampqwuEwkse09lRV7enpaWpqam9vR8VK5yxT6jckEhOdA+lGYalTNTCZTL7zzjs9PT0oeMFP3LZtG2GuznuAampqrrrqqsnJSfQSQqYCM7j8fn8oFII6BS4EOW/Ah3jrysxkMn3/+99HrzLST5qmhUIhFCi9Xi85sDofcUZ943B4cS1EDaOSq1ZR/WjcmUKhEI1G8Skkig1WMLKZRK9H7jIej2NYNNIF2HvQwsUYI62hWCz2yiuv4LlqbGxE9aayHuKH1QzA+l9DtoK4nWKnDnX2YacFrRQRwcqVK/P5vN/vn56exqgCxvd/8flLpVKQTvb5fC6Xy+VyUQz18ssv19XVmc1mp9MJEXHSh2I8Gi0UCrFYbHx8nAZkACUptaTz4a9ovUYWX+I8dVmWcVhN037+859j5A+lfiRJ6uvrI+osAq5SqfSRj3xk+fLly5YtQwGL4lksRUASfQpOBh6i0+mcL38cj8cPHz4M+TrEy1j2uByz2RwKhZYuXbps2bLm5mZoGeJURX9K5fKtuAR0dCqKQlSp928EH8hsspmUK5oLS+/HDkGyQngzmAqoJ5L3J0nSQw89RHlJeM2ko19ZD/HDZwZgvWvEuKGZw3BP6NEs8eE6LS0tqFjhgQPVE0uaUEYsOOLPE4kEllYul1u9ejXpIui6fvjw4bGxsY6ODlKzmd3zUSwWQdhpb29HxwwS20ANMINIuErno08lScpkMl6vt1Qq1dTUPPvss88++6zNZoMUKrIwCHP27dsnptXT6bTL5br77rvhK8ELI+IV3ApgInFHiZOFA8638NxuN4ApHo97PB4ADY38YoxNTEyMjIy89tprTqfT4XB0dna2tLR0dHR4PB4SEUObAdoJcIuQBV/As1uE4XqnpqbEF+nj6L+4z7FYbGhoiDoiuru7iVyK1vpYLAZQm5iYeOmllxgPBhEJSvOz/wwTzQCsGZbJZOLxeCAQIKTADo+nCitk1apVXV1d4AcxxlwuVz6fR8ocrcJMaKuG6bo+OTnpcrnAt2xtbUUNW+FTHiKRCEJLh8MBCU2dm8RFSvP5fDweHx0dra+vB9yofDKYxtuqVT74AGgrSZLD4WhqanK73X//93+/d+9eu92eTqcDgQB1Mlqt1v7+fkzQIwq7yWT61Kc+VV9fTwenxBbjvgaAgyazU4aLnNM577Cu65lMxuPxAJ11PrUB2A3UQ3CdSqXS6fTExMTLL7/sdruXLVu2YcOGjo4OaKUyxsD/IGI9HMPKDq1AtYTNnMolcVVYxgFLluXh4eE33ngDZ+L1etvb23Fv8S+Gj+Ar6+3tRZCo6zq6LxmfhGJUCU9pBmD9ryF6mp6erqqqop2T6kHYA5PJJCo74B9BJBO5CWIMiZ4LjgxPBz5Ra2trdXU1SkKMFxbhHVCajEgVhFnEjx8eHtZ1vb6+Hn4W5aFLfKgUUCObzXq9Xo/HU1dXl0wmH3zwwYMHD+KEEfMCHDH84sCBAyTErvOmwnvvvRfZMYSKiGEBVWI0iogJiSecPCha84VmmqZ5vd5EIuHz+RKJBCXd6GZie6B2QgTg2Wx2586dO3fuDIVCa9euXb16dXNzM64UH4o4MZvNVnbNq6qayWTE6A+oOlsaFI01SCAgH4etTuZ94EArs9n8wgsvIHUAuIfia2UF6T/EZgDWu0YkhkQiQfshYhyVz2WgOAvJFJnP1wH0YOXAx8EgHPHIiUTi//v//j8IBKdSqVAoFI1GfT6fpmlYEnheQSAU0xlweRgPLTVNGxsby2QyjY2NUN0k/4JYQkA30NaHhoZ++tOf7tu3T5ZlHBxDW4EpJpPp+PHjsVgMpTeCv0svvbShoQEMb/BCUSzDfFNqUsGbifBNZ7KwusONN9744osvhsNhABMuIZ1Oo98FKAzyusJVgIhJMD09/eKLL7755ptr165tbm5ev3493N5UKoVzqxTtA3c7k8moXG1VhC1Is0pCLwTwCN4ftGfxbSIM9Hg86XTaarU+88wzBw8eBOjjxlLmHt9aRU7+Q2wGYP2v6VxudGJiorW1VRJYVxSDSELPGnEXsEPCyceCcbvdU1NTYhCB96Blr7a2Np/Pk4QmdmxwSklJiuIjABa5WoiAwuEwXvT7/YowvpDxaBS9I5OTk9/97nePHz9O5xCLxdxutyKMC9q/fz9lkQBYxWLx7/7u72pra6urqyORSCaTQT2BlJol3qjEGIO/RhlxMfs25022WCxXXnml2WzeunWrpmmgOKGnkipxOH8mTHKnvmsC1rfeeuvNN998++23N27cuGLFCrfbDZSsVA4ImIIOdsZTexpv6sRTIQuyE5T0lCQJ2qHAYszZhe7z8PDwd77zHYjNQj+HXDAiixg5rIXNiJnfNTFhFA6HxRwWEzp+KeUMR4MY4XC1aL05nU5KPOOJh3ImRKMmJiaAfXi/y+UKhUJwMSiYokhQjHEkScrn8w6Hw2q1Tk5OHjt2LBwOU/mcnn6spXw+/6Mf/ejEiRPIKyHaQtIEUZ7JZIrH45FIhNIoWDZXX331FVdckc/nX3rppeuvv/6pp57q7Oy02+3wX4C5dHo//vGPn3/+eYiXUty6wKpTVTWfz2/cuPHKK68UB3zoM41gGmEUzfgj/MK19Pb2Pv30048++ig8rArGg7iTSO3ps9Q7yNcW9yTUZ6l+iqwc8gwI/bZt25bL5aDHbTKZoCqDx4bQsFLn/2E1w8N61+iZo1RRR0cHMgugBWDZ0x6LXZGmTqCMSCWqUCiEGqLGB96Y+DxUXdeRwsACM5vNd999t9frpbUqz1RShuFD4fEhXY1hgseOHQuFQrW1tUhp6bzonkqltm3btn//fkqHiaovtLAnJiaY4AMis/bggw/u27fv9ddfV1V1z549DzzwwFe/+tVf+7Vfu/HGGzdv3oz7g0mLjz322KFDh1RVffXVV+vr67u6ujo7OxsaGsgR0zl/SubTHDD2UVXVG264wWw2P/fccyB81dfX19bWBoNB0P1RB4S464kTJ0ZHR/P5POZQEMeyVCp5PJ5oNFosFr/5zW9eccUVV199NQh0iOOQ4UId43SlpkATQyBMDDuNqzPSexQu8ocTRgUG+EvfvsvlSiQSyWTymWeeYdxt9Hq9VKIh90quqCz9h9IMwHrXsNGhXGU2m6PRKDZtxoXVATciDX1OAzpAhw/latTR4F5hp6VwL5VK3XTTTTU1NQSF+JQFjk/0LuR60BKYSqWWLVsGUiJjLBwO19fX/+hHP4LrN+dxSKWPKAuor61bt+7tt9+Ox+NIzAE70un0T37yk5/+9KfBYPBP/uRP7rnnnmKx+OMf//jw4cOFQsHr9Waz2enp6ZdffvmFF16oqqpas2bN1VdfbeJzjMVb5/V6gTvxePyaa67x+/3t7e0gl6HaUOLTiSRe6Xc6ndPT0729vdu2bYOoBmMM4S0mfSDb/eyzzw4PD//Gb/xGIBAwm83pdLpUKkGPDFTy030kSqWS2PhJ5VdsMzR/V5KkZDJ58uRJ7G1oABCdr0gk4nK5vvGNb+DLcjqdiUQCXzoT3DeNd0qe7nleUGaEhO8aNjeVa+ym0+lYLEZZCcaR4pTPE/68pqaGkuj05/RcEmG6vr5+/fr1tJyk+bUDyYAgGMlntVpBSkwkEoODg4FAAEulurq6p6dncnJygVYe7O0ITuFE4JWLLrool8tRkUvXdbBYkSDPZDJf/vKX161b9/TTT2/ZsgUTZCHwAo095MVffvnlv/qrv/rOd77z8ssvo3mblnomk8nn83DQPB5Pd3d3bW0tChq4w1arFal3qnjG43Gz2bxhw4avfOUrt956a3d3N3JDwWAQrpbb7QZ87Nq162c/+9no6Ggmk8G54YMWgVaIQKFmowujwpEoJDmNUql0+PDhZ5999ujRowjwQQCmr1JRFIfDsW/fvldffRW+YTabdTqd1DcqfuNGlfCUZnhY75rESeFU+08mk7W1tbrQ3UpJ1lMezeVydXR04CEGJx6sUWRtKD666qqroNwmSpfAE5nv2RU3YaIsASZsNhvElWw2269+9auFG/rANSVpOkDAkiVLOjs7scJR8NJ1HXRzIALOfHR09Ld/+7d/53d+Z/Xq1b29vSBAUXBksVii0ajT6RweHu7v79d13efzQb6mpaXF6XSCLQVHA2EXbo5YIyO0AkUDYhKapm3YsGHFihVbt27dv39/KpXCvYrH43Dc7Hb74cOHp6en77vvvvr6+ng8DhnYRXguABHcH40LB8E1xq4AbNI07fjx40eOHIHOotvt9vl8CPaJ2m4ymR577DGK9PP5fF1dXVm6beFdyjAyA7DeNUoeUVUolUpFIpFAIECxCZ5aSmrMeRw83LIsb9q06dChQxhkT+kMJrAToJyJRDUmptB+e0pMpLQ0Pfeqqu7bt2/Lli12u/2Xv/xlMplE0mo+xRWqPzLGkL1ijP3mb/6myWSCrwSouvvuu1988UWwz1wuF0YE4v0/+tGPvF7vxo0bnU4nktNQdgcpBNDscDh0Xc9ms/v37z948KCiKE1NTTU1NS0tLfX19T6fD3Rw5NRpLhEl9RHtYhIt7pvL5TKZTLfffnt1dfW2bdsQQvr9/qmpKTQ2ggf74osv/sZv/Aa6FJA1W4STJcsyuMFExENqrKqqCt8aygW5XM7n8zEuqbZkyZIyku1LL720d+9eYBxmvqLPSeaS3Ow9+++GGYD1rmFLh3AwHKJcLjc6Oup2u2ertSnzy7zonHjZ2dkp8cIikuUoD1Gpq7u7GwEdYww/EP1qgdCAHDE6AYm3Fu7fvx+Zo5deeglNIfORoRhjqqrSCFUk77q7uxsbG8Uh9fl8fv369XV1dT/5yU+Gh4eTySTapzHOJ5VKxePxycnJhoYGxhgCWyTsND7WlLj4jA/7GBwcPH78+BtvvMEYczgcgUCgubk5FAq1tbU5HA7UIsVrtNvtSCQhkx2NRoFQV111VSgUeuqpp6ampvL5PBp3NE0rFos1NTWvvfaaruu/9Vu/BabIItAK3xHpbRCAwo2iLxGxIRqzGGOZTKa1tZUSkQiof/zjH6NrEg8GMJf2RU2YhrnA92UYzACsd02ayWaG655OpycnJ6urqxGwiPmsBQ5FxJwtW7a88MIL4kfQcyxJUm1tbYlPV0cUVkZimNMobpKEgrrEOZbRaPTw4cOYJwghhPkOaDabXS7XFVdcYbVaM5lMIBBYt24dMvGpVMrr9SJzxBirq6v7whe+8MILL7zyyiuZTIYwC5m4np4exDio4sPpoPuJK6K8O04eZ64oCmZfDw8PIxWFOc/I0MHtUhTF5XJdfvnll1566YkTJ5C5w6c7nc7ly5erqrp169ZYLOb3+zOZDP4EvU3vvPNOKBTavHmz1WqFv/leHgMyXAscT3BNiGUGx1DigwVbW1sBmrIsL1u2DCR4uvyXX3752LFjuCjoZ+C+EdtOfDxO6wwvTDMA613Dsqe0qNfrhas1MTHh8Xgoe423Lay7hMpgLpe76667tm/fDhRAVgh5FsrgYm0j/tIFIaoFgIaSKTgTYkKgfn/ixImtW7eiNIZIcz5sLRQKoVAIJ9PU1GQymUDF0DQNjX5QPdW5CPKNN964adOmhx9++MiRI2Cf4VfQgD9w4IDf77/99tuTySTp7TEB2ctOGDcEIEL8NWrNEe8k5myn0+nu7m7sGbgJgM5169YFg8F///d/TyQSUJeGUwa1jG3btq1atcpmsyEyPa3nAYCFkjF2LwCNrus4GuJWxtjFF19cKBR2795tt9uvvfZaxuehoVf0Jz/5CbgOOAJod0R503jDucwnmBi0hoXNqBK+a7Tysc6z2Sz6bIrFYm9vbyKRgJMlFnSQmVL5JBUxb4qnM51O7969u6WlhTCFyvy6rjudTp1LtSAFTmSfBfws+Ggyl2GiyiOCyueeew5zXoGwRKHQubidwkXjsCDdbjfGLAeDQQzXYHxqPBqScGLI3Tgcjvvvv/+b3/zmvffe29XVJbKHsD7/67/+S5Kk733vex/72MdIdqYM2SUucExZGwqO6EMlgUnPGHM6nQcPHoxGowSgcMQQr9XX13/pS1/yer0EduAW4DK//vWvT0xMSJxlBoiEu0eKZnOaLMvhcJh2FPhuiP4QOONXSK5ffvnln/3sZ++66y6n04mGUPBd/8//+T/xeFzkFXu9XuCdztvF6Xl4L7lLwwwP6xQGLOjv729tbQ0EAtg2sROiuoeSkN1uR9cLYwxtN5lMJhKJeL3ef/3Xf7355pvRCoffgmQIAdJKkbPh1ExMTFA5T9M0hCHw3cCJBSq53W6I53k8HmToGGMAtfnOh5BL1/Vly5YtXbp0fHx8eHj4+PHjO3bswAlYrdaf//znmNuIZalx2j2F0jgruH46H6uBljpk+pDGRpTa3t5+xRVXVFdXIzZEk7nMBxchONV13ev13n333d/97ncJaslls1qtv/zlL7/whS9AMAcaFYqgdzjf/QSgi1kCnQtLKFxQUCzUANDhXdpstnQ63d/f39vbiwtUuYIYGBuV+tIvQDMA6xQG0Rhd1ycmJsxmM0iSlIfCGkAd0OPxwM9CZRDyWM8///zHP/7xF1988fOf/zy6ZOBn+f1+eX4NlkUYjhaNRhnPhgBBJE6yx1r1+/1utxt8SxJFoK1+gYUEwCI2rNVqbW1traur27Rp06c//WnG2ODg4MjISDQa1XUdNC4mEPTpX/JbsYAptiJKRENDQ1dXV0tLSyAQAK/d5XKhXbFUKuEPgXeUEUPFYMuWLU8++STIHPgUfOjQ0NCOHTs2bNggcWO8zLpwfQPVA4rd8H4aIimWfTVNQ7kGb85ms36//4knnkAekFw5jPwRPXHDTtcMwDqFocbvdDojkUg+n1+2bJmiKLlcDt228Xgc3ko6nVb4WBeoWSFHMzU1lUgkLr/88l/96ld33nnnO++8Y7VarVbrfffdB5+rUpgly3IsFlOFcecU+FDE6vf7a2pqSOyUyotsJrLMeXxygnCZNAw5nU6PjY2BiKTrOrweGr3BuONGXh44bjg9eEANDQ0NDQ2rVq1aunRpMBhMpVJgUQBMcbcLhYLL5SJ+OSI7FDdxHFVVN2/efOTIkSNHjqAUi3uLP3z66afXrFkDgQSi8rJTETUJc+nNAEfGHVixAIpkP8L8fD4/Njb21ltv4SumuM/hcMh8IJOBWYszA7BOYaqqYofXdT2RSAwMDHR0dNjt9mQy6XA4kJPCSkgmk5hOjCQLYwxp+2PHjq1cubK+vv65557713/91x/+8Ie33HKL3+9XKyqJWyqVRkdHxfVACSnEU4FAANRwXZiygbcRsiywgHXe6QZXCxEfoLyhoSGfz2O6dalU8vl8qVQKcCNzGXir1epwOBwOB5TgA4FAY2NjXV2dx+MB7TsajRYKhXA4DCICglnADfFsJS6TgNAPpH8E6QDK3/iN3/j3f/93ZJqQ18e/qVRq375969evJ+qZMksxpswkSYLEjYjjcJ91rtMgJuCwVSAHZ7fb/+M//gPQjG8E+S8wxQx7P2YA1ikMPjztrrFY7OTJk2vWrAGf2+fzpdNp8I+whlOpFDZh8BULhcKbb74JLUqTyeRyue666y7KK1dQ/0jTNCKL6nx8g855lTU1NZjYCtdDFSaz0pJbOFRBto5opdTDhOrn0qVL29vbMaUKvXuiY0V5ZcYdq9HR0eHhYZPJ5HQ6a2tr6+vrkdsCfVzXdWSpkApEuhqsd/K8KEOv6zr0kfP5fHt7+5YtWx577DFAg8InsLpcrjfeeOPyyy9H9YD+fIEQWJIkVE7YTLlR0NzpPYT7SMPjmx0fH3/++eeJuwCIB1GD7nYFswEXlBmAdQqTJAnSayTpefLkyaqqqtWrV4OyBHYCohvqa6OkBvSISclX13WIJZELUCmuIFYXQRWln6uqqmpra6EUiI8z8YERYhWPwGsB3hZID8RpwDLGRUkC5xYVQOqOZIwh/UQf53A4XC6X1+v1+/00aIf8QZ0PgkdoSVEVnFZ4PbrQ1ofYE+FYJpO56KKL3njjjenpaW1ml/jY2NjAwEBraytRCugmzHdLS3y+tJjJ8nq95MZScwLjThb2IQhd6LoOXQomIJ3CpccMwFqcGYB1CkNOCgCk89noBw4ckGX585///DPPPBOPx8nVQq29VCodOXIECVdVVWtqapqamkDASSQSyCsxxtLpNOkrvH9LJBLkFIBvgSXU3NyMUAWrBQsJhU5KYMGIWDDn8WnSBDE5AG0Oh4PYpOhlQR4K3o3ERQhMJlNVVVVVVVUgEEChjXFHg5JNjDc2UYUOSSu8mVgLuDrR50ILAX5rs9k++tGPPvTQQ2C9iS2Kb731VltbG2hl7D207xHWaHxmqs6loql5iACL+CKqqvb09AC+iWalcxaLsuAMNMNOacaNO4WJlSC8glBi7969W7duvf3221VVHR4eHhoa0nV9amoql8s9+uijU1NTmUwGS85ms33uc59jjCE7QyNSKEA4LQOA4lC6rlMFCm4doiokodG4Z7PZCGXgjChcoZTNzCgjv0N1ek0Y4oILgQQVYIIWKhjqOmd7kYjd7PRQJpNZvXo1VrssjFATz4QcGUrMSzOV7cixkoVGdPLOcLRVq1bV1NRMTk46HA60E+FG7dmz57bbbkOtE6ixQFSo86lleD/eqShKXV0dJdfpbqh8MCWONjExAc6Xygcmon8QyTWx09Cw0zUDsBZvkGe65557urq60CaiKMoDDzwwOTk5NTUF0paqqolEAmLKgBhCDU1Qs3nvRgk1cXtnjEHMgJCFhshjsSFSI5eK8IhxpKDTENeSJDBUKaZjgsy8MktlUCwjEMcVBgIUZbJEhsHp3/tT3KLm5uapqSn06MBrA7iMjo62tLSgvCiWC2ebxlWAcJ64fCTOKaCj88d7sFXs3r0bTZqUL1MUhU6DXqzsJV84ZhDYFmlOp7NQKLz88stf//rXX3jhBUyv03X98ccfRx8faoXYYHt6epCL0XinK62E0/1cSRhgRz8jpCKHC4cNBAJYJ4g6KfUD+KA1VnYaAEFidZa4SVzMHu4b44pRZbVFaabR66VSKZvNQnta5gpTs2H3/Rsy9BdffDG1QMHTYYxpmgZ5e0LtBe6/JEnIr+m8qIrT9vl8GFDCZgEWEv/PPvtsPB4ntxGuNI3z0oUGLMMWYQZgLdKguuvxeA4cOPDwww///u///n/+53+ePHkS1EqaTwM5t6NHjy5ZsoTxJ5ja0BbBeJaEUQgEWMAUxlUf4MShZ42iKsaDWSY4Nfo8xrjYFpES4HEgHY5yoTSrlaTsIDJvvsM7M5nMiRMnND7W7H3e//kMO0RnZ+eKFSvIFSK/ZnR0FK7WAnN9YKhgMiGTxRiTJMnn8yEeZLOm5hSLxampqV27duFzieyG6Tgi9+0MXfuFYAZgLdKwgYNBiofy9ddf/8pXvgJOkMlkSiQSdrs9kUjk8/lwONzW1oaVjz9fNGVU4jIAIugUCgUEOETL9Pl81OwGFCM2E/lQGleVoCMj6BM7aQC+pGKOZZnL5dDHh8uZ008hLwwIhV68WCwmzrYpC0srYnAzdV3ftGkT0JbSRpIkTU5Oiq7fAl+BLMuQu2B8X8E9dLlcZa3U9LMsyz09PZQ1w9+azWY8KkjeUQ7OsMWZAViLNOSMSBoYD6jT6cQEBMxHYIwhdSXLciQSIc+C3JzFYZY+SzMLxX7irKqqGgwGdS6SA2wym80+nw8DLOAiETbhTFDh0riRE4HFRnw0NMQBDQnIyk5PjJKIH4s8Gg3OEmPbRdyEBQw429DQ0NbWBhxX+LiQaDSKbwFc+QXuvyRJmAxC8F0qlTCNTRSeppOHQ/rOO++AykDpKrTjiLvCwlwKwxY2I+m+SMMahsodcAqJnu7ubqvV2tzcfMMNN/zRH/2RruugO7z11lt+vz8cDhNGzFlKe48mBoaMMciuU0rIbDYjz6LrOnrfVFVNp9OZTOYXv/iFKJZArhb9V+dFetDNKVmO3kOPxxMKhXw+n81mQ48RcmdMWLoSJ4WLlwYWgiRJsVistrYWLxJgVXAN42jIW61fv763t1csYmazWXAd8MoCnBJULcQslSzLoVCIMQagJ1zG++HnHj16FNQW3DoE5sR4EKcoGYHh4swArEWaJEnJZNJisaDVWZZlpOHr6upKpVIkEnnuuecgRAlaaW9v7y233ALAev8PqzSzVggAgqxKoVAIBAKMEyZ1Pr4MPk5fX58kSejNFg8oxjVlGWXGefB4A9DN4/EsXbp06dKlXV1dbKaXRH8IUECLNdiS6D2kmEiMaisFWMQX0XUdrA4QuNDEjo5LDLL1eDxzeogwm83mdrtFtprZbA4Gg5og/iOeP5QwYrEY4+UIwBPiQcaYqqoQ9Tfcq/djBmAt0rAGEBWikRDqvXjEp6end+/ejdgECZT+/n6kVFSuQkfVq9MytMjgX0mSIFYDNwf9yYwxeEOgO0LtBCsTb4OgKLSuEBmRUJQ6S6OZkk1U2wJ4JZPJ3bt3792712w219bWrlq1qqOjA2OoQTElN0fnml+4UYlEYnR0FBUJm82WTCY7OzsTiQQT8nrksqHkh0EV0OfDZSJVR2/DNRb5dGhKk9fV1bW0tBw9elQS5lDE43FoOkPyTOJqqAioidFqs9nq6uqguxCNRtFCiJGLkDlOp9OAQpmrj+7duxcMEsabDZqbmxl3JE18lq3hXr0fMwBrkYb2GrfbDVUsi8Vy/Phx6G2CdZ3JZNBbh7FOU1NTkCuRhOk72oJafXMapZyIRqTrOhJqSJpAVQZLGukb4pGCWI/FBppFkY+qpvwU4wuMSKSMuwxlZ6LrOm7C4OBgf3+/1Wq9+eabP/e5z7W0tExNTcXj8XA4XCqVpqam8LegPkFcDJk1SHFVV1d3dXVRwqtUKmGaA6iwdrsdEvJUT4BqK+4zNSQRmxQoTMNWL7300uPHj0uCODUOS5cg3lXGI0q82eFw1NbWQsavUCisX7/+2muvBTb5/X5cApxHSZKKxeKhQ4fEmQA+n69SfVeGkRmAtUhD3hoVqEKhUFNT88ADDzzyyCOPP/44lrHK5XoZY9DMIq6WxKnki4gOQKQy8VHpmDdDpTGkVzweD7GudaGzBH4ZYhOkb3TeBoyzVfgcRomrOFAyng5IjEqdazNMTEwg8t26desvf/nLj370o/fff//mzZtxfxB7Tk5OKooSDoeLxeLo6OjAwMDQ0FA2m00kEkeOHGGMYZIYihjwhhBCut3uUqmE5m2fz1dTU1NVVeX3+4PBoKqq6H9CzxNOHidJdb0VK1YAvhmHM7iZYt2DGpJE9wc3GU3UsixfccUVH//4xwGOkOjCeeLrwH3Yu3evqO1F7PaKPG+GwQzAWqTBN0FrGOKRpqamb3/728Vicdu2bRaLBcMdcrkcbcL9/f3Nzc3kKcDVOl1GEuADvo/KVZ+wwkVteOp3w0nSWl2xYgXUx/E2hJCktQL3BAchuikS8HgnPotYXYVCYWpqChFoMpkEFD755JOPP/740aNHq6qqEGnCx9y1axfU36PRKK4C4qLhcFjhIq46F8bR+byv6elpi8XS29uLeTwAMkVRli1bdtFFF1111VWoxiIO3bdvH+AJ0TEwt76+fmBggHHAYgILVEyci2GaJElQzqqurh4bG1u+fPlnPvOZycnJe+6555/+6Z+qqqoKhUJXV9fBgwcJj9BfDcREGpGUfAyroBmAtUjD0sVi1jQtHo8/+OCD99xzz7e+9a0vf/nLv/rVr8AshVoTJBwOHjzY3Nw8Oww5LQM5gP4cUIihpMBBrDS4VNjhNd5+bLFYNm/enMlk9u/fj+ohzZJRVRUo5vV6vV5vTU1NMBjEiDP4VjSMj3AQsVsqlUITTCaTmZ6eHhsbi0ajZrP5E5/4xNe//vVwOLxz586JiYl4PI4zlLgSi8Y7k1BvxQRAipdVbpDBQIFP4/LK8Xj88OHDu3fvfvTRR+vr65ctW3b55ZdjlAbyXBqfw5rP51evXt3f3083kFo4y2houiB6BdYYY6ytra1QKNx9993ZbPa+++47dOjQZz/72UcffRTObFVVVSQSgV7gyZMnwRbGhgRF2QqqcRgGk6Bva9jpGggN5GRBFUBRlDvuuOPXfu3X/vIv//KRRx4BrRRhDrhLn/3sZ6kovrhyPol2kqqBxWJJJBIulwv5YKxqhU+7YjOTZVar9cSJEz/4wQ8w+pRGOXi93ptvvrm7uxtHxsHhPiA0AzLqgp4yvDCMcWaCbDki0Oeeew4rHwdJp9PgqdGFAFXhCoE+pgktR4yrRyCM1TQNbhr4GQMDAx6Pp76+HhiBj3Y6neCRIOVE+bjJyclvfOMbjKsm3H///Y2NjXDEiJMFZCHM8nq9DQ0NFDDu2rXr1ltvBTDBE9y3bx/kG8Ph8MDAgMvlGhgYuP/+++FVQdZ10VG/YQuY4WEt0rD5g4GFNA0Suv/93/+9fv36devW/eQnP8FqRMoDhNJEIhEMBukgiyjnIzsGNIF/JEkScv8yNzHQYzys07gsTEtLy0c+8pFnnnkGI5oBCpB/wDBUkVFFMSCtPUkwuJCMi1JhhRMJAKUAOE1IVMfjcfyh1Wqtrq5GAs7pdNIIRYAj48rUmqatWrXKYrEMDAxMTEz09PT09/dPT0+Pjo6ibYCIUcVicWRkBGFjWX7K5/MFg8Hp6Wn6r8xHJVLUpvHuTqAtgBVYyRj7h3/4B9woFFKKxeI999zzH//xH263OxgMejyew4cPj4yMIFZ1uVw0qtq04GAewxZhBmAt0nRdx7w5zErBGoAv8Nd//dd/93d/RxklGmNhsVjS6TS4PIx7EKf7ufgUxhhlzQmeqGNO4hoJcByo3ge3olgsXnbZZQMDAwcOHIDjg0AG1cayvDsugdL2OAfCL6xJuHX0uYxHWzIfM4GANJPJQBIrFAoB4nFYRVEQOItJJVBAQJu666678NFf+tKXfvnLX5Ln+Prrr1933XVerxdc0Pr6eijr01VThaGlpQXTMRCe052fM4cocbFA1Bkfe+yxd955hzGWyWTQlGOxWHbu3Pnrv/7rn/rUpxKJBKBz9+7dZrMZMz7IzzXQquJmANYiTedySBQrUU4qnU6/+uqrl1122VtvvaUJjcqoagE+gDJYOeBS6ZyCpOs6EGTOz8VSRGkSiIA1iUI+vCFQioBEOuey4z2MMcDW6tWrwaLSuDoo/B2TyZTNZqnbGVeHpcsEFjupTQAlAVWU/meMIfPt9/sxhha0eEzTwTvJk8La1riqJ4JcSZJyuZzT6bzmmmtKpdLExMS99977yiuvUFUhl8stXboU9CgmVAOcTmc6nYbHR+SGjo6Offv2QaeYol3cQ5C8cP+Jkg5A1HV9dHT0b/7mbywWSyQS8fl8pMmnKAoiTTi2yKwFg0HM7MHdloTWTsMqZcbdrLDhgX7hhRduuOEGRRjEgPWPCe+6wHiiuAPLHuleZIXmNMRZVBzEHg7EIYUZqNMh+67zHiCsZ4kTHVtbW1tbW4GYgNpYLEZK0ARk8IAymQxACv9l3MnS+LBiMBJAccKfd3R0XHPNNZ/61Keam5vr6+vtdrvf74/FYvhzXCOltHB6ILiiYQDZsU996lN1dXUmk+m+++5788034Q9ardZsNnvdddc98sgjUFKGfAIxUakIS51D3d3diqJEIpH29nbGGOAJQJzNZom7gNNAahJI/W//9m+4OrfbnU6n8ScyF0RkjFksFofD4Xa7aYw2jkOaPGf0YbsAzfCwKmwWiyWTyYyPj4MyipQH6RPE43GVD3mmeh9R1fEr7MxlrTNkSFRTzgWIQ5RReD2Tk5M+n8/tdmuzmmzoZ7vd/ju/8zt0EI2r3MXj8Ww229LSsnr16paWFkmSJiYmBgcHR0dHaTXScQBYQGR6BS5Gc3NzLpc7evQocttwZ9DhCOAeHR2trq5GPybOJ5FIeL3eZDKZy+UuvfTStWvXAom+9rWv/epXv5Jl2Waz4V4Fg8G//du/Xbp06T/+4z8mk0m4XbjPTqcTaEhJPZPJ5PV6NU3r7Oy87LLLAG3YMNAoA6OeBIoHd+7cuXXr1lQqBSSiJB15eWjMcjqdDocDYAeYo0jzjArpXJhmAFaFDY+y1Wp9/fXXKXhhfFdPJBIlPhkYtTxyVRgfvoLIZT7GAy0Mxr05cXkgbTw5OYlgBINI8QaU/zQunyC2syASpCB0cnJycnJyx44dUOZsa2urq6vr6upKpVKxWAzSz6T0AJaDxtXpKMWOX8XjcaTwaLgsypegwudyuYaGBnhkGEabSCQsFsuyZcuuuOIK+DJHjhz5q7/6K6fTiVgSB/m93/u9hoaGgYEBEx/squs6gkFcLBwfeKOo565evfrKK69sbm6mMFYWNJ1tNhs1kKPuKUnSt771rWw2S1eH8BNJSXhVlInDTUbUTAxbw86EGYBVYctms+h9AVcAGn6EPliQjDG4UYAYscCHopsyvyglRXbU+g/IIAcBCzKdTkejUbCoGFengi8G8EKeS+cCmFiuYq4K/wXDaHBwUOOyVk6n0+l0Qioep0qLn3LttHo9Hg/QihoMFUUZHBzEi5hmiEnUwWAQg7CuuuqqgYEBJNEYY9/+9rc1TctkMsAsXdc7Ozs/+tGPRiIRXdez2azH4xkeHh4dHW1sbKypqUF2H81SVC6UJOnGG29saGhAjhy3jjGGU8L9pE0C+80TTzyxfft2XCkQjTFms9kaGhpARmN8VCIuHzsNHDHGdxHK6BlWKTMAq8Jm4urpNpsNfXDIpGAJQWoGnguWASXRsXKQx9EXHLdFjTLIf9E4Gcb9OwAECvn19fUinZLCGZwJnCyQzk0mUzKZJAeB4jg0S2KR53I5yC0AMVEXs1qtdrvdzEc9a1xaHhUGVVXBnyIEhLQhY0yWZchjYQbt7bffXldXt3Xr1qeeeiqdTt98881TU1MPP/wwoCSRSID98MADDxBAOByOaDQ6PT392muv6bq+ZMmSjRs3QguMGsLxpdTU1EQiEZyAJDQJwA/N5/Ok64BKxfe+9z24b8AvfBxNUSV1MAqBKbun8mlpktHkfAbMAKwKG6Ib8LOIRw4DJMXjcVTNgCOHDh2itIjD4Vi1apXOOaVzHn9oaAgVesaYy+VaunQppfY13t8L4CgWi9PT03a7nUZRUMgGPC0UCuATaJx07na7EfJks1lx7gtWLCCMsjlYq+Pj4xJv3wFyQbMQ4llY84CDfD4PARyHw0H0V7vdjpTToUOHvvvd7zY3N2/fvl1RlGeeeaZUKh08eBB/ggi6VCrde++9W7ZsAYyioOnz+UZGRiA+0dvbe+LEifXr13d1dYVCoUwmQ25sPp9XVTUQCCB3DkYruVTE4JckyeVy/fSnP927d6/D4chms2gXp2+Tmnt03nKIa8cJMD5fA1+W4V5V3AzAqrBhZiqY1h6PBwPcFUHCbWpqqrm5uVQqgeX485//HPkjq9VaV1fX1tamcT3MOe3VV19FSKUoyvLly5uamiiWoU5AqNygShgOh10uF030pMQTZf0pUkN1zGazdXZ2SpIUDoeBsGhRhocFH4T6oqlKCC8sn88nk0n8lcfjQZwFjWCR1KqqKnoDkAsDZ1VRlKGhIUxLQ4nt1Vdffeqpp0wmE7RlTCbT7bfffu+992azWfw51GBQUYUPKMtyLpdDPXHLli0g7lJCHaF6LpdDWRCjuVEbwclQ98LXvvY1uiFIcqF+qvK5ZHT5+Bc+L91hmTcqaqevxmHYwmYAVoWNNlVVVdFQRusBkUgsFmtra2OMYRkg7MIWPTIyop9qijqUpPBbKDohhaxxbieVvZBExyc6nU6Jz4sXhRzgAVGVULwEkZHPGIP8UyaTgawCuSdi+EOlA/g+AAsQETRNczgc6AlHPginzTiG6oJQPdL20Wh07dq1QE9Zluvq6n7/938fvAfKFolEDVmWUV0tlUr79+/3+Xzr169HzCsLQwwxSYjxgR2Ibak04Xa733jjjUwmg/upCpMTZT7bWRfmZsM1I/cW94q8YwOtKm4GYJ0pw4Mutshh10WVTXyF8ThC1/Xh4eHW1tYFch8iNlHwuEDtHCVF8owod3O60YrNZnM4HFVVVYyxUqmUy+UymQzmaxBKyrxvmZgc6KlGq2MmkwFFEwr0yFUzjo80SUgTWPJOp/M3f/M3165di/Kf2WymaqPOmbqKoni9XiLow9czmUxHjhzp6uqCCtAC10sZd+wfVqv1kUceoZ5BxhhCV1wRRbXw2oC5KGsY6aqzYwZgVdjE/IXMdZMZ16WSZTkej1PqRMQybOA9PT3t7e0LtHTQWkUK/JTnowvzmSkYXERuBV4SUBKJagRWHR0dlM/KZDKJRCKRSKBxEqkizOPQONNV54xNJowF0zg1XIRgwP3AwEBbW5vb7WaMAapkQcQZ1+L1et1udzweJ1YqY2xsbCyTySB/x3gn02yjXgWwGQYGBsBHYdx5hCAizkdVVfjL1FRAuGykq86OGYBVYRNrQ8jgEkLhlUQiQa4BcitMCCKOHz9Oq3rO44PKRPl7xtfnfDs8UuZEO2AzeaTv3YilJQnGGMNUKyzdlStXrlixApd85MiRycnJY8eODQ0NwatijKEAR0QwxKRIWoMlj1c8Hk8gEKipqYFiH2iZyD2R4jNdBUqcTU1NsVgMWTmZy+BEIpHq6upT9scgfEZ4/vDDD5OjJ3PJVibsLuCR4rTFqqgBWGfHDMA6s4bkEe3AsiyjSoXfUkmO8f08FovF43GI0s1pSC3jzcgrUVJmTgNVQnQEFihBLmAU9EkzGe1OpzOTyTDGTCbT8ePHjx49GgwGW1paLrnkEtJFyGQyxWIRAxoSiQRKkKlUCkQw5ICQmSKGBFLXBArwrcDnJD0GqnhKktTR0XHw4EG4UaCSAgQJpufDLOTLdV1Pp9PJZPLJJ5/EoEnaUQBnlOxDaRWARcn1BXhzhlXWDMA6gwamQtneS3EcFdHBMsWLqqoODw+vXLlyvgUABhPWaj6fj0ajNTU1C1SjUPzCz2JBQDlNYTm4GFT7xwkgnkJJAYGVxWKJxWJTU1M7d+5sbGzcsGFDe3s7NdwNDQ2hdcbhcADOEHPJXN5LFzrAiRYLkgTI6KhRSFwHRuZCMXV1dXa7HUIOYKUCTOF/McGHnX1dCPrcbvejjz6KLwg1QfoUSZI8Hg+ACTR6up8I3nGQ07qfhi3ODMCqsOkzW/bACCc0QQlc4xLDkiT5/f6xsTEmCPj29fWBjTXn8X0+HxXUFEUZHR0NhUILxCOozVG2hXFX7nSvCwuY2AmMh0KAG8YRQedNdmazeXBwcGpqym63t7e3t7S0tLS0VFdXY1g8oR7dMYARDk5OHDE8EEoje2Xio24AXvivzWarqqoCbQr3FsQRlYu1z3ddEieRZjKZ5557rqqqClURSiwCrTBRAjAqxuBiedSws2AGYJ0Ro0wWRTe0niGxIPNGNqSTsXqxTkZGRhY4MrZ6lMMURcFgiwWE4jweD/qxZaFPZRFNuYibALgU4epcM17inAn8AE4WxGRUVT148OChQ4c8Hs/q1avr6upET03n6le6wFYjpABG4GypjRzcBdwxupO6rvv9fjRREpUBKq+kPjqnwUNUVfXtt98G7R6eGmJAxucJgjQL5hdhnMKl6I2k+1kzA7Aqb5LQ+p/P571e79jYGNYkCuSQ/QPJwOVyIQAhaYdEIjE0NFRXV4eSPInbISnj9XqxYLBKBwcHFT6JD6br+qpVq8TzwYqdzaU4LVP5TEASDkSGWwzlJC7kALcIrFQ6q3g8/uqrrzqdzoaGBlA3QCJFKl0SJteXlQVwdUQ1IKYrzoHqia2trXv27EGngSzLLpcLQqw0kGK+LwuA+PDDDytckIsoF7jtVVVVULYhjXxKxhvp9rNsBrHtDBrWnomLuDOhUk7vaWhoQFZL4dKguVxu165dQBZ0hGCt0vDkqqoqrFVVVSORCMqOZ+eKkGgnhrcuTLp/jxaLxaanp1OpFGMMySlc5umeCeS3yKfDzQFNzG63OxyOQCBAzNUFEnZQxD906FA4HKadhjJrmqY1NDRgjCOpjxl2Ds3wsM6gUdwnc4EXjY+BYrxwXl9fzwTSOfbwnp6eK6+8MhAIIGdM7X5YS52dndPT0wiacrlcNBr1er1nukoFX4acGkCkqJv8Hk2SpMnJSaTSFUUJBAJ1dXVWq/V0zwe8LcbZWCaTqaGh4bLLLmOMwaVyu90It5GkX+A87Xb7c889B74rOBPoQ8K0QbwCvig6E0/3VA2roBke1hkx0eURGwPhYYn6Cg6HAwNaGI990NF24MABCiFJoQGQsXz5cp3PfzeZTGNjY2dh54dvRRUDXVBDPi0DaSsWi01OTo6Pj4MSsYjjyHz+K/Lrmqa53e6urq4VK1YsW7ass7OzsbGRTnsBtEK/0c6dOynuJhE+k8nk9/uBXygdGtHfOTcDsM6UUQxYNlCTauH09K9evZrxHhHK5h44cIB4RiSsjvc3NjaScJWmaRjFfhauiNp6ZWEK6ekeBEl0ShKhxrcIwCUNYpnPOkTHj8plP1VVRScNlR3nNEVRnn322Xg8ns/n7XY7NCoQvfr9fuSqcrkcVPYN9+qcmwFYZ8oIsKiXjZY3ljoV1JYsWYJFQmJ7qOJj8VAkQmkjpIGppjY5OXkWmmwVbhTNLY4tieQ9yWwBGhao4s1nQE/kB3EyRIlC1yHyfac8T1VVn3rqKZnLsdKLZrMZox4Zb+eGzNnpnqdhlTUjh1V5I6jCfxU+CB5JK6orIahBPd7v94OgwLjcqCRJhw4d2rBhAzpXiJCNun5bW9vU1BScHeij19bWntGLIsF49AYyxih6Oq3jUP0B2TqorS8acBESYleAN4SSKEXQIt9iziP09vZOTEygzoi4D/l7r9fLOCOUuBFEOjPsXJkBWGfKaIUogugd7diMMwzQzRsMBuPxONhSWG+5XO7tt99esWKFJEno7AVpHuJZTU1NO3bsAK8ym8329vaeacCCQVa0sbHR6XS6XC4xGfceTeYyzdBdWETaXjwUiTTAwwKgI16muirtE3MeZMeOHfBhkVZHxhD3HOgMnBJ5G4adQzO2iwqbJgwixPoh4XBsziSajgq6pmmFQmHNmjUgJRLpQZblcDh87NgxCMhB0RxoZTKZWlpasITAe9yzZw9YQkQ4oqizUteFKwJCWSwWqAMuwt0gfCGW1sIlvPkMVyf6feS94siiRIzIUGeMEWtBlmUMSWWcfAvUg16NNGv+jQFY59wMwKqwzX6my9wr6oATCesNDQ0iaYvadPbs2UNeFcm3oxevubkZORpVVVOp1MTEBPTjydEoI5RW8NLej1t0Dk3XdWKoQsO+v79/bGwM2TTGU1eSJKFh07APoBmAdUasrIImApbMB7jjV3CLvF4vGp5R20JNUFGUnp6egYEBMbqEv2az2erq6uAp2Gw2SZKOHDli4sNZxS6cyl4XDiiqWVX2+GfOZmcSS6XSiy++CPEZBObYLZBuP9fna9jcZgBWhY0wQgQLeE8a73m22+20bMgP2rJlC9IlJj5mFc0627ZtQzWNcR8BMnKgyFMV/9ixYxQHSYIeZsUvzWQylRE1zhdTufI6UunRaPTVV18lHeR8Pg+i2SJYrIadNTMAq/Imuh70A+V9RcDSNC2fz0NNoa6urqWlBQRIXdeRgFcUZXx8/K233oJeCgpeaBPp7u72eDw0rW9qaqqvrw9BIqXMKuth4WgkB3h+JXQo6KPhXYODgxMTE8QvQZ6rqqrK7/cbLTgfWDMA68wakacYX/AIQAjI4HypqprJZC6++GKJD4+QJAkzk61W60svvYT2aRoWDfXx1atXy3zMcrFYfOmll8ShqkQureC1MK4ayhabLD9XhtsOBwpSNnv37sWeAQjGgIz6+no0Y5/r8zVsbjMA60yZxCUHYKSGbLfbqQyP1DgcIlVVOzo6Ojo6SE04n887nc5UKlUoFJ5//nlIlWqahpkuqVRq5cqVGh/CKknSwMDAiRMnEBViKWpnQKfpvYhMfQBN5tNhca+y2ezOnTvhKoJmoapqdXW1pmm5XO48AuILzQzAqryJ3hPjmEXMT3goACxwhRhjJpMJI0U3btyIN0DjHPUsRVH6+vp6enqQ4XI4HKqq+ny+hoaGYDBIWXaz2Xz06FEiji+CJHXK6wJx7Ezg4Jk2CPhAXR6KVydOnNB1nRoJNE1raWmhyann+nwNm9sMwKq8IcpAFxtjDJM+7XZ7JpORJKmmpoaYVtS4A1qQqqrLli2rqakh6gMpaiqK8sILL2BgF8AOkeMNN9zAuNqnJEl79+7FQECNK0ZV6qJkPi2ROhzPbRoL9wQhMFUAxSgVNDFS7EKIDcSXJOnFF19EeAh8LxQKoVCI9ALxlRn2ATQDsCpsxLGicXVYM1jtiqLAk9KF4fIS15BDC84ll1wC6qOu65jwnsvlNE1LJpNPPfUUqO3gYWua1tzc3N7eDoq2zWZLp9Pbt2/HB0GqvIKXVlb3rOCRF2EIS0lrDDEdkBqve71eopXqXBkVgJXP53fu3Ik6LCUZb7jhhuHhYZPJhHt+bq/OsPnMAKwKmy6MbsbiAa+K3BMMXyD3hPRP8vk8OJ9Lly5taGhAQAfBTGSFZVnu6enZuXOn0+lMJBJANFVVN27cGIvFKJf8zjvvYOC7LMsVX3hE4q/sYRd3JoQ1uFg0PEO1NZvNJpNJTdNEBgnVCsLhcG9vL7hXjDFZlqurqxljaMleXDO2YWfHDMCqsIkNtxpXEKUyealUwgh4SRizjpUGIMDMu2uuuQbcUUylpxRYqVR65ZVXpqamwHpH4Nnd3V1TU0Pxo67rECyF0kOlrqusrHnOMYuKFfgvNZzD0/T5fGB4IHWl8JE/aNgeHR2FO0Ze8NKlS3fv3m2z2RhjaCc8h5dm2AJmAFaFrYxoDv0AFASxyfv9fo2PERapUpSh1zStu7sbIlkoseOdOHg0Gn3qqacURclms5qmeTweRVE+8pGPICMDFOvp6Tl48CCmv5y7O3FmDTeTqP+4S6j3HTlyZPv27QcPHhweHna73V6vF9lDcn5TqRTyViTQ7vP5sK9g3gRNjTXsg2YGYFXY4H1QrIHeZsZT7Ha7nXhMME3TMIsBzgImoZdKpcsuuyyVSiGWxJAYkks+fPjw9u3bSSwBAFdfX49kWT6fLxaLzz//PCScKnhd+IGIGpU68uIM+wHFehhcqCjK0NDQww8//Nprrz399NNbt2612+3JZNLhcMh8giFyWBT3lUql2tpatGFKkgSX9pz7j4bNZwZgVdhEhQBZljOZDImo6LoeDAapTYdywMgHIzYkZlYoFFqyZAm5AEivIJNlt9tfffXVEydOoKEEQwAvvvhit9uNWMbhcITD4eHh4YqHNh8QtGIcsAD3jDGTyZRIJHp6eh5//HEoW1kslvHx8f/+7/+mWdn0V/39/fgTOGhtbW2RSIQUr9ACfY4vz7B5zACsChvpPTHG0GFDeSiEHhqXQhb1TCioMZvN8Xgc42TuvPPOYDAIfrbIXM9kMqqqPvroo6iCIfRbv379unXrIFKaSCRsNtsjjzwSjUYreGkiTn0QMIsMYjVHjhx56qmnEomE0+lkjMXj8erqaoznglgoPLJisbh//35UJDRNczgcfr8/n88jSITKqNGa84E1A7AqbDpXQ0ZuZWhoiBpxFEWBTDhxGjD0XCRkqaqKtsFcLuf3+2+66SbkjJEwBq5BQ4Yx9sMf/pBx6rmqqldffTUmgOEIpVLpkUceARMSbhE+EZ9yutdFelJAZMSt51B+k+4zHNJisfjKK68g6MZgC4fDkclktmzZQtJXuAnpdHpsbIymeNXV1fX29tIFGjrIH3AzAKvChv0ZEIMhCMRpQKCn8WHrJMM0pyHD0tHRcfPNNzPGSDAPC9JsNqdSqUgk8sQTT5AMltlsvvrqqzVNS6fTWM/hcPj55593u92AGDhfVqt10WV7cFz1D8BwdlK5gMf605/+FJ5sNpsF002SJJfLVV1dTfEdgsejR4/CvQWKnctrMOz0zfjCKmxEsFIUZXp6WuUjmrG6qquroQYDb2UB+Rf0u8myfNlll3V0dDDGpqenHQ4HlHyR8Mrlcvv373/nnXfQu6soyvLly5cvX+73+0GJVBRl165dr7/+OlpS0JyYTqcXfWnoGWKCg7O4Q71/wzA0XPW+ffsOHz4MdTAEzrjhS5YsgadJbHhFUd5++21COsOZOu/MAKwKGxGmzGbz5OQkNTlrmub1eh0OB5YQ45Lk8x0HpEfEg7fddpvH4wkEAvl8Xhd0SuEyvP766zt37gQbXtO0zf9/e2caJNdZnf/39r53z4xm30e7ZS2WZMvGlm2QNRiTmD0LCQkUiatIpYokZPtAgCpShFCVpEJSKYITEnCBseXCGGxjg/GGJUuWLCFZ+zKa0Yxmepae7um9e7rv/X/4cQ9XY2tizV+aRb7ng2s07um7vuc95znPec5dd01PT4dCIafTiW/66U9/OjAwgI+T8uWcL5BATzDsOX/P/6chGz09PR2Px5955hnUwWgkEArujTfeODk5qZnqifzAwEdlDjRaqPO3bW5mO6yrb5T8mNApWK/D4ejs7NQ0DRCdTX4WLEmzKPm6XK4/+IM/gCtEkCUTXDRNy2azP/3pTy9cuKBpWiKRePTRR0ulEmEF5fxyufzEE0+kUimlVKFQsIrbzMFEyn0xyMtMTU394Ac/cDgcyWRSbjvbQ2trK3xav9/vMIfXptPp4eFhUW5gNLRtS8hsh3WVTToEx8bGlFIC7mqa1tHRAWSuWWasX+57NE2rVCrgXJqmhcPhD3zgA+FwGBCK6Azoyuv1ptPpxx57bGRkhP/W1NSAoNFoomna1NTUQw89BGESUeYrvS5ht9Pus+ApYSgUorkyk8nIBiAtAS6Xa9u2bfwgXehOp3NoaAiHC3fX1m5fcmY7rKtvSFaJmqVSispdY2OjyCrIErrclzANASidZpHly5ffe++9kUiE/mfp/sEJlsvl//zP/6THkJlgJJXKlNwbGRl56qmnqtXq1NTUHFp25ELK5TLHXdgGnbGxsVdeeQVhaPqQJOo0DKOjo2PVqlVKqao5yJ74i88rE6q31ZCXnNkO6yobfiSTyeRyOVqX2czD4TBtz0K8mp1bkM/nfT6fKLszqXDLli333HMPX0iUBGsU8SwWJwUyxn9SOJMy/4kTJ/77v//b4/HMoSka3wSBYDHA1a+++uq+ffsikQj9AB6PJ5PJIHehlFqzZo2MO4tGo5AVqtXq0NCQoHgEmwt7FbZdqdkOa46mXTp6XjNn2PF7Ug+aaZVS1Wq1vb0dpqJSCmbD7GO4qII5zFmhIMSVSuWmm27asWNHKBRCUsYwjGKxCEVLKN3CtpdTUqaoeTwe37VrV6FQ4KxEDIdBDBzaYc5PpJppmHKDfB7XOUPGT3A6EVHgN8Q11kBMGi3F6/GHpVKJ28WBpKQAkKebxk04dOjQoUOHODduFE0CHELX9a6uLu6/FFvJnQcHBzkZ/u+CkzNsu1KzHdYcTTcHoEtbspA/33jjDZY64DcJXVtbm9frxZsopYiz5lCtczgcd95552233UbCaBhGKBSS0cQC0LDgCbiIJrByuXzy5MnHH3+cyTcYWRWrWlQfwNpyuRwwkDKHjBGgEdPJUGsiL9qJUU8lV6WjCL+MYyI85Hs4LrFhIBDA/3JcPJHT6fR4PNlslkKB3+8vFovxePzHP/4xfpDaaCgUSqfT8K3cbve2bdtisRjxJsQxjgVllD4EZsFe3VfCtnkwu6w7RxMEh2gCXnuhUOjv7y8Wi5lMJhaL5fP5crkciURaWlpaWlpkuUoENAfcmlX6rne9q6Wl5bHHHsvlcvl8HldCFimuJxKJQHNXZlyDKykUCufOnfunf/qnP//zP+/p6Tl27BgjLaTnUTMnPEuUJKII8CqGh4ddLteyZcvq6uoksCqVStlsllYk/I71RknvkbU5HJMIi6MTWHFnYIeQ7tXV1Y2MjLz44ounT5+m+cbaHgBpI5fL3XrrrbfeeivlCCKy6elp/Pjo6OjY2BgX4vf7fT7fgie2tl2p2Q5rjibeikXldDrz+fzw8PDIyAiLgYE3jLq54447EJaTXElks670uLLmly9f/qEPfeiRRx5hqbM4yZsE4SKW4VhEQD6fj1Qol8t96UtfeuCBBz772c8+/vjjuVwuFApBN/X7/cQmcAJIuIrFImEX+H0ikeC4sAe6uroaGxvr6uoymczk5CT8LyHEc8I4PmmRkSZquSfEbjg7ojPEP+ElJBKJb3/727lcTrybMofOQ6bNZDItLS3btm1Da9Tj8RBgIm0I4i6NTSJAZvuspWW2w5qjSUpIOY9h8fF4XCTYy+UyicyKFSva29vpwhXZOYk7rrTQRiiRzWadTufy5cs/+clP/uhHP4rH49VqNRwOI/wElpzJZBixJzAQ+amcfKVSefDBB0+fPv3FL35RM/XgnU4nzAndnJwoQ8OkhRvaKl8yODg4ODh48OBBt9sdDofr6urq6urC4TAAkwRWAGH4Fys3jZMh5CG/w8OirgcsNT09feDAgb179+KnqJzyhdDu8UHRaHTnzp2xWCyXy5FXkmz6fD6c1/HjxwH7HOakNdtbLTnTfv/3f3+hz2GpGtu12+3OZDIjIyPj4+O5XI4UJplMxmKxTCazdu3a3t5ewWWUUrLgrYj42zeSL74QvLlQKOzatWt4eDiVSjFQh/4bKKZg2CRohmXQtEzxqVQqjY2NX/nKVz7ykY+k0+k9e/b09fUZhgEKTvuL1cuIWjzuQzI4yZEFF4vFYoRdTDYTTy2R1ww8nnMmK0Q4bGJiolQqPfHEE8PDw3xnNptFXIHTECDP7/fffffdt9xyCzga7k9CTvp1Pve5z507d44Lb25u5p4sIDPDtjmY7bDmaJqpaVUul+PxeDweF+03ogNQnve///0w1IGEZYHJCr/STZ7cExeQzWZDoRDEq927dx8+fLhcLhN8VSqVurq6yclJtBzETwHGa+bQeZl2oev6zp07/+qv/mrDhg2aph0+fPjMmTPpdBquqdBEJSqhpCC9PupNCn+S+jmdTjSL6+rqQqEQSurCORAnji/z+Xzlcpmkb3Jysr+/f8+ePWD/gOu6rmcyGSkg4Ez9fv+GDRvuvPNOCcqA4UCpwPJLpdInP/lJZj4qpTo7O/lb22EtLbMd1tyNVRGPxy9evFgoFKSyLoHMAw88gAwmgQzYvMglk/7MQelF0kmCu2AwCOp8/vz5J554Ahg+FovF43HaD3EQ0nQtI12np6cDgQCokKZpwWAwk8l86lOf+trXvqaUcrvdqVTq/PnzBw8ehMmlTCo5fgo5ZunuNi4VfbdyPpQlBPP5fNFodNmyZdFoVGhQmtkuTugUiUR27979i1/8IpPJyJWSXaKywMlD2Xc6nZs3b7777ruVUmS+wHzsJWR/09PTY2Njf/iHf8hEj2Kx2NXVBar4//MC2Db/ZjustzDDwlAnWMA34ZKknuVwOE6fPp1IJIrFIuU50V9vb2/fsmVLV1cX6w3O1DU9Z46SSCRefvnlI0eOyIQrUa2CH8+ZK6VIlwxTRgIkDqcWi8U+9alP/fVf/zUOpVKpZLPZiYmJwcHBkZGRbDZLnEItUlkKpla3JXkZaJfP50un0+FwmBOT4Mvv90cikWAwSPJoGMbx48f37dvX398PHYSIEuqs1AGy2SwRa6VS2bp16913380n+ROZfMPP5ID79u372te+BtfB7/c3NjbCybimD8W2q262w/qViWNSl06IIe8D0CmXy5VKJRKJ5HI5p9N59uzZRCKRz+dBu0GRDcNYs2bNxo0bW1tbybkAgOZBGICz9fv9R48efeqppyYmJpQ5xgJyg2EYkDNpr+GsxHMppaSsNjU11dHR8eUvf/n+++/HNQjuppRKJBJDQ0MDAwNjY2OiEqFMgoLkxRwul8t5PB4ZQ231blYk6/z584zn4pMiuA4hi1iJuBWEi4Bry5Yt73vf+yhxlsvlQCAgXDAJXQGwnn766f/4j//gBMLhcENDg8My+8u2pWK2w5pphkX8G6OyTirkcrlSqZTP5ztx4kQ6nZapzmgztLW1bdiwoaOjo6GhoVKpAF2RzlzrheHxeHK5nM/nw+lMTU298MILb7zxhrXiZpgjpg1zRLthmefqMCeSKqV8Pl82m41Go3V1dR/+8Ifvu+++jRs3ihPRTAY8lIJCoTA1NTU1NZVOp3O5XC6XK5fLhUJBxpRqmgaaLjB5uVxmQCwJLNC+DOOYkV0SKBkm6wrn5XA4tm3btn37diqG8EullIGLJIjzer25XO6RRx55+OGHcbiRSKS+vp7ocpZ2TtsWodkO61c2Aw5XFmCFVT09PZ3P5yORiK7rhw4dIiuRbhVN01pbW3t7e6PRKCufoIb1MIdq4JUaGlgyyx4e6cWLF5955pmJiQnOkF9S2cQ7CLdA2nHkhJUlIpuenl61atVHP/rRD3/4w52dncpUoZA+G+t948/xRGNjYxMTE6Ojo5OTk+fPny8UCkzTkvBHqoewDSSIk+8EKXO73eBomqnPs3379m3btvEzmWCpVAqHw9bebGWZCfL1r3/9hRde4JuDwSDTQHRT99m2pWI2D+vXxpITciNbveQySOKdP39+fHyc9jTYiYVCobu7e+XKlVu3boX3yJ/APCBDmUMLzpUaDE8YDyRBoVBo5cqVTU1NY2NjAwMDJ06cmJqaCgaDNKkQLeJtiV/EUzgcjmKxGAwGYW8Wi8VwOOz1evfv33/s2DFY4+Rc3AFlodFa00MyNT4/PT0t3HfdHNdsveeAaCR6+FacPiltJpMBxqpWqx0dHffcc09jYyMaqsVi8cKFCy6Xa+XKlZJ9w5AQullTU9PAwADuWA7tMPs0r/Wjse0qmu2wLjEpwEkfCQu7sbGxr6/v4MGDLD9pPM7n8+vXr7///vvBXyQcUyZOpMxxUrPIt18VIzmNRqPpdLpYLIZCIWC1UCjk8/nWrFlz1113HTly5OWXX47H40RYBGKSBvJP1nwsFhMcCqdz6NCh1atX9/T0CA2dzyNarywTwLiH0uuHGj2fBAeU/6ssCbjorM8IuLLZLOx88rvPfOYz69evHxgYkHLBoUOHEEoMhUI1NTXMcJT+JyCwcDjMtGeHKT2oLEilbUvI7JTw1ybFQRaMx+NBxGpkZGTv3r2pVAp6ND1rFMi3bt26adMmXv1QKMTvNQs30mkOSb/WoDsM+Ewmg7wfCDQokiStnMO5c+eOHj3a39+fTqdJpqRsJ+x2zhlXZZjEKw4ktHWlFPmaurRMoUzCRywW+/SnP33PPfd861vfwnPBC5uRPOpm37gwS/Fc9ABChfX5fDt27HjPe94TCATQVoWucfz48Xg8zq1uamravHnzjGhRKcWTeve7322YbLJwOExKaMyp/9y2BTQ7wvqVSYbCf/1+f21tbW1t7aOPPkpnHGiOYRikS9PT08uXL9+6dStATz6fxytZ923NVBOfB6DEMAy8laQ5ULTgJcHeLBaLmqZ1dXX19PSk0+n9+/cfPnw4k8nI2crqdbvd8JUkO5NeYkqfuBh4TzMwLD7pcDhSqdRjjz129uxZIqx0Og3zU/6EOMhKIpXIiAguGAyWSqU777zzox/9aDQadTqd586dg+jg9XqHhoZow+YO33DDDVQ5CoUCfZH4YqfT2d/fb5j6PKKToc2pl9O2hbXr1mHpZuOeCNqxh4OtAHDwT5/PNzk5GQgEmIIVCoVisVhHR8eePXueeOIJZRbX4VjSeQOPfOvWreDu1tGBrAd1ad4xD3wfKmVWsF8kpZTZ0CM4tFIqFov19vbedddd4+PjFy9eHBwcvHjxYiKRYBmLPwKoEs/L/xLqpny5NcXj0OVyubW1devWrY2NjYODg6VSKRKJwIFQJpVBu5TsZpXcwVvFYrEVK1a0tLQcOnTo1ltvhfQAcFatVs+cOSM00fXr1yuluP90d0NMm5qaWrZs2alTp2QjcZjCFVJjudaPxraraNetw8IMw4A8rcw6IE3C1twnn8/X1dWVy+V0Ot3a2rpt27a9e/d+4xvfKJfLwWCQIrpujkR3Op301t588811dXXi9cDg50BbXygjAHG73S0tLc3NzVu3bgWZOnv27MDAwLlz55LJJG4Ljy9t21ayKH5HJhUyRmzdunWbNm36xCc+sXbtWlSuEonEK6+88vLLL9PNQ7xmrcmSw/r9fpLWQCAgHdTkrb29vbqup1IpHkqpVEokEsoklHR0dESjUa4LegeU1FwuFw6Hw+Hw6dOnefrisqHI2vngkrPr1mEJWdHn8xE6yQIThg6xEhKaLS0ttbW158+f/8IXviDKMNYp86xefNPKlStXrVoFwYfFbMVlloQhKSUMAxB0TdM2bdq0ceNGvE8qlZqYmMhms9VqNZ1OSy1CWSRGfT6f3+8HFWpra2ttbQ2Hw4ZhxOPxdDqNPBbDezZt2uRyufr6+nSLhIMy6aZEiF6vF9FnyGtwRHt7ewnTABYzmUxXV9f+/fuJtqLRaGtrq9Mc2gwKRqkEKpnP5ztz5oyVbsaFSzl4QZ+DbVdm163DQjZT0zTmNXg8HtQLdFPfEh1LTdNWr14di8VeeOGFgwcPAsqABElrG0GWZkrrhsPh2267jdq/eCtZxktlAUhCJ4COxIksbI/H09DQACNc2PAz6AhKKXJh/qRSqYAxCXOKjhmiOZfLlUgk6urq1FsNMRW9MMJhmX5arVZbW1snJyeFihUKhYaGhtLpNE5qxYoV0WhUOrFJGClQchq6ro+MjEhKKx2IS2uDsQ27bh2WLC3pLEH/E/SK8lNHR8fExMSePXvOnTtXKpXwaMDAtAeCFgvXgWVGDsXi4ct1UxtrYS/5ikz8lGZquvNLWAjwQsV30ITIH864TMHjvV4vVCk6gVCqktSP/+I+lMXfyZfIMC7+kFSUG5vP58fHx+kN9Pl8ra2tQgFta2tramoSZE14/Ow0qETkcrlEIiHuTzPbQoUyZtsSsuvWYQm8IukGy48aX3d3d7FYfPzxx6empoC0KPwZZv8HHkraBlkM5BptbW2iYKeUYm0sUTREHJZhyl0hjo574oaAB4kqg3gcvqFQKPj9fqJRSBIAUkIEpbbIgbi3wr2S7wFU4pOUDqDsS4EVHla5XO7p6cnlcsePH1dKOZ3Onp4e/CNSqKSQPHSquoFAYGhoSDrANVN+Hs+15LYZ265bh1WpVGisY2FEo9FyuZzL5ZqamtLp9COPPJLNZnFJ9Abyf0HfheIoNHeBh51OZ3t7OxRE4StJnLWEMBFO1VolwBnR9Ccxi0SXVtfG5zWzAQDGOVsCxFro6TgFyn+kYBKxvvkuwdSXRBXnhd4Din2lUqmlpcXr9b700kv4taamppqaGqI/KAvI3bCvTExMtLa26rqODCz1U4qDmqblcrlgMGh7qyVn163DQoQEbmc2mz1y5MjBgweJhliN8JIIoIgjqLsrpaBc0psiYYLDnHAViUQEwXWZxj6/5BaAcalGhbAirJwD4XNqlhZLZfYbAlQBcimlKAJCXyDq0S0CzSIwb00J+QyjwwyzsYmfKZV885vfJAHnl8R0kUhk1apVyNc4HA4osrDwCfFOnjy5Zs0alLCq5twwWhQo9QYCgSWEOdqGLZjDEvaNdVaKlUNImY9CHlGMLB7gFc3soRElNt5IXty2trZMJjM4OPid73wHepF0ogiNiMNZ+8s0c4QfkibKXNJAy6AwIDXEDoQMHJ04awmZ1SvJb97yYxIfiSKoZo7SIFuEJSAUU/Ajskvm8SAII80xM9wEZ2Kl40uIx2Zg/SVRUmdnp0yakMCNFwBm3JEjR4aHhx944IFUKmWY6vtKKfLWYrHIn0slQYrIhqV32rbFZgvmsMQ3CdGR9wY0xOv1snVD12aeCtOo+BMpirtcLhjeXq83EAj09PS43e5Tp059//vfV+akcsNU7J3DjuqwzObSTG70O7NpVrPwToX/Sa+fUN4FdcpkMm1tbbW1tVTrqOLNftP0S0efscFEo9GXXnpJoiF2soaGBrq4rSmqnI/L5fL5fCMjI8lk8t///d/hSejmCFtqiJVKhRdMwkbN7MpS80L0tW1utmAOS5SCrfuq0Hx0XaeRjXDGYQrdEvmTheFK3G53e3v79PT06OhoKpU6cODA6OiozCXG0ShLuepKp5MDtyuTebRIBrUviFndNLcF8oe1mYaYpVqthkIhyKjiyHhYl+PWojAhR0F9zO/3Nzc3HzhwQEAoArG6ujpgeHnKfImmaSTyAwMDnOG5c+daW1sNk/BFi7VSiigsFovJ6WmXzqx+Zz7ixW8L5rAk6p5R4ZZyD7iG6CKwNmSr9Hg8wWAwEAgMDg7+8Ic/LBQKtGuUy+VwOGzVAuYQ5CaUtK/oPK1vszJ7Ca3DF94hxrbBLGviWelYIoyl+xoQStf1LVu2rFix4uTJk0yLEFd1ubZKuBGa2f0jKMHExARMLp4CM5xlfJkyNxIpFCildF0fGBiQzm0ZVQ+lTmoI8E4dFvkt6wtjO6zFaQucEkoGwcsHHqTrOog4TWGhUAiSDkxopVQmkxkeHh4dHYU9iLIKn+cHmVvHtizJYLVavdIIS/JBoTUi83TVb8giNx4Knrqnp6elpYU7nM/nR0ZGRkZG0AKjYzESiWzevJkkulAokOBDc7ucwxLal7IMtZ+eno7H48rSCVStVru7u2WuvWYy1wWH4ikjfIqKTjqdrprDGeWTYPDpdBohHWXCam8Jsdm2eGwhHRZp2gzeAC8ruzGAOt3IyWRycHBwYGBgamqK1EMpRUglVGxq6vgpdnWOxQdALq70PK0AFtloMplsaWm5qjdjaRgJO4QDHp+u6+FwOBQKdXR0jI+P9/X15XK5rq6uLVu2wIdSSvn9fsOUtJeZN282CY644cJ+IPUjjuOtaGlpkchIXarDxdNnD5OIbHJy0lqukeipUqmkUimwMIFQZ1QhbFtstpAYFj9IuofHgcbJD6Ojo4xThm1AFMbgcsMUbwqFQlNTU7g2gA9libZ0c3LnnDkHAm04zJkFY2Nj69evf6eB7oJw4z74gf4kHHpTUxMsNvT2Ll68yLYhwTLEhcthWAKQUZGUiophGIjJ8BvmCbLPaZZRifLPfD7PvLJAIJDP551OJyz5GbU/6SVilo+ocUn12cbdF6ctpMOSsZc+nw/1pVKpdPbs2eHhYTAL3nJ2P83CFbL2zU5NTYlKrwihMGm9as6nUyZEwldd0XkKx11YFOzYV/+OLG5zWYY8Qx0A1aKWx50hZ9d1PZVK8VeBQIA6r5o1vFJKCenBac5MNMzp9kJed7vdN9xwA52JfFgz2erCg4XokEgkKCvTJCQUMI4l3hBmP6ct25Lj0nnUti0qczkuM+xIfm9c2r5gmOKQVk6NZhFvNCw8aemPUSZNiVfH7/ejjF4qlXK5XCqVQhJAUAynKWgp9RrDIhGnLGpTwFVKKQmvhHxgLcBbgfMrNacpfIzLY346fG4hLkL11swu6OvPmMYopBPDLPzJ87W+SPKYkCSVMuvsN0e2FmUp/JXL5ZqampGREYYJFovFaDSaTCbhf0nPuTLxUKfT2dfXJ8+a3mlSVMIuh8MRj8f37t0Lv1TTNOahEWcB9uP15P3RTNJZdV7k+W2bxX697Wim8U9patEu5TerS6ekyGcMS5eZVW6F0U+grZqm5fP5qakpKDDiPnBwTlNTfP6u/m0YxXvNnLCiaVqpVDpx4sSmTZuAzKzU7es4T5QiLKAPXM15eFgul2vt2rV+v//ixYtdXV2Eb0BayvJayuaq6/rx48eRVGQXCYfDmqZ1dHR0dHREIpGGhoaVK1f+yZ/8ye7du3HBjGiDEsxFEUvqlmEZQld+BwbXi8p+3dglj0c6V5QFfZQXQtrcZYlKkZgJK6VSiR+KxSIcApllgpOi3AZDR1nEJ/lfiy0aN0zSKfR3zvzIkSM33nijZsraKTMiuL53YHKuhoYGTdNw1sa8NA8Hg8Hly5d7PB6kF0jSZ4Ry4lkMwzh//rwMnc7n8ytWrDh79uzhw4ePHTuGC4tEIt3d3YODg0NDQzg+qorhcLipqQl0QoJ9ecmVJe6zbaHMNUOPjd8aJgvGGnbxX1wSUi3T09OZTCafz2sW9RXNIhElKBKJA/wD6cjXLDwscZqLzWEJBizRE4218Xic7hBrpnkdb79WsQqetVR4r6kZhgFmv3z58nw+z7bHzNQZUT//HBsbE6msmpqatra2z3zmM6+99tqTTz4pmenY2BhE/Pr6+sOHDwsJvlQqoZKmLGmEw1Z/X0zmAo2umoYWEnAShECEPnBPRBk094EXoNRB0frNeSJfYpidFiLDoluUduXD2qKUrHWYkzg1c8ICnPuzZ892d3dz7dasYYFP91oaVLhoNArKoyw359qZpOG8eJqmQRKesbfJzjo2NsZIMbfbnUwmly9fHgqFurq6HnjggQMHDvT391+8eDESieDyPB7P6tWrz5w5Q4ORYRjxeDwcDre2ttJTIXuqzX1fJOZ67bXX5B+apRJnfULW38vcJ34PCiBwhmYZGMVnrPuVMl1VtVplGpVhEmTUIg5PcN/S/IhjGhgYoG2Nz8zIUK4/I8qIxWLsYej8zc8j4/Vgq6heOuPDenr8cPHixXK5HAqFeIdramqGh4ehv9x222233377nj179u7dS2t9IBCgo0g3NftpiWest7z2ukVdZx6u17ZZzCGBj2BJVuhdWWIfHJNSStrHCLUIwSRNsDZnATcQhUnO6HK5YCrL79Wlk1cWm+mWplxhOaTT6aGhISvl4vo21mpHR4dkwcalclrXyHAldI/yUonolXxGPEulUhkbGyNFyGazDocjEong6QzDiEQibrd7+/btn/70p6HLl0ql06dP5/N5xLzkzbxw4UI2m7U27lhvgm0LaC7d0sorL4H4LMHareimZjZJKNN5WT9s/XNl0fwWdiguzPmmEQDCRZ6nS397NuPSiCVJdePx+KpVq0Q8HthusZ3/1TLqLV1dXYlEwmFpCJ+HQweDQcAKw9SWecv5j+yX4+PjTL2GYb9s2TIyRBoSlVK6rjc0NHzoQx9KJpO6rj/wwAMCgEDi5wmOjo7W1tZGIhEr0vpO2JkWuf1KN/bND8PqembsZlXL/DgpzcxwVeLppB6szKHB8k+rrzQsnVyLyniJlbk4dYusaF9fH/swr/ssNO6FNcMkkenm6HbDYurSWpvDFFbXdR0hQ5G+2LhxYyQSQY4Viav5iThEIEgaaHTTlPnO8BROnDiBX6NKaBhGQ0ODCNLzcvKwlFI1NTV1dXXLly/nq4jCxBc7HI5kMhmPx6HLy6GtSPw8XLttM2zROYilYpqmjY6OXrhwgTWsmTz7hT6vmaZZRIrxLxRPCCUkuVMWAp2u636/PxqNjo+PNzQ0INxaW1u7adMmlIWnpqZQNL5cJ/P8XJdVkojg/cSJEyKCrGkajc3OWQfkNDc3y6hEzcJ/Vkrpul4sFtPpdCqVkunfmtnwoFtUWO0a4ryZ7bDmaLzcR48eFWBOxN0XlbGWCEDQMqbTgBiKfhcRF4N6QuNLOp32+XypVAoQ+qabbkJqCpQaTFok2OffZlRmoYkODQ0ppSD6KaVqa2ur5jSgy30PMw2VWVqRCBpU1zCMQqGQTCaTyWQmkykWi1SZhHanm0rZ83LRttkOa65mGEYwGBwaGnr99ddl0M5Cn9RbmGbKhIKvZbNZmL1MioV8VCwWRaLaac7CYh16PB6Hw7F+/fobb7yRPmdy5AVvSCDSETzLMIyLFy9KTxgeKhwO8+FZHFY0Gq2aM6iFBiyeSCiEuVwumUyOj4/ncjlpbJREe3E++uvS7J1hjqbrOhOkd+/e3dzcvGzZMk3TAOAX+tQuMRYhGtOGYQwMDNCf7HA4vF5vMBj0+/10QSpLW6hhGB6Ph4Bi/fr1d9xxBx9geiOt6WSOCxhkOczJXaBap0+f5vdcoNPpDIVC+N9Z+GJyN5TpBB2WAXEOi3o9TdRAeOFwOBAIUGyBrmj7rPmxxbW6lpCREDHOYP/+/ffffz+50kKf10yz6nw6nc5UKoWIq2SvTqeTITTRaJSkj94UJt80NDTceeedhFQ+ny+fz8Oh42IXsMig6zpjpSWVGxkZsRJQlFKRSIQPzxJhaRYuu1yOTLrWL+20VUrRcFYqlcLhsGhpSdHctmtttsOao0EIQrfrzJkzaIeLMsHiMd0UVIBnxOKUNIrAoVgsZrPZRCLh8XhQRAgEAm63u66u7r3vfS+rV2KQYrEIfdTtdlsnQi/UpREVJpPJXC6nmR2p/LKurk5SvMsVoCkmSoDJxQrnxrhURYsaa6VSKRQK1WoVCW+i1Hm76ne42RjWHA0oxzAM6kcvvfQSjUoLfV4zjRAAZkDVHPAh5TCPx4PwsWEYxWIxk8mcO3euv7+fyuB9991HkMKH8/m8z+cLBALJZJKYYgGLDBLfkZSJhpqgddDc/08KAq7HWifFT4myttCq1aWz6XRdz2Qyk5OTyWQym83O24W/w812WHM0h8NRKBSi0Shv9sTExC9/+UtI2DIuVEYrLmC+YBgG6l1KqWKx2NzcDJ5F2AX4IiVCVmMymTx16lRTU5PU7Mm5UOOrVqv4uCsd53F1rVKpgKAR1Z44cUKKfTCq3G732rVrCX8MwxBqqBCYcVKpVAofx4NDgR4tCmWhE4pHsxIP+Uw2mx0eHp6cnJRgzdrIJaqEVg6ETYOYs9kOa44GLD0yMhKJRHh3jx49Suctq4juWQjWCxuJSL+n3+9vaGjo6upatWqVy+VijBAlQv6plMrlctFo1OPxfPvb337ooYeUUg6HgwUM4k44lslkYEgs1HW5XK5sNhsKhVKplMvlGhkZIdSSgbutra1+v7+9vT0SieDUqOsht+10OtldGHJhZTaoOTH4oWvpus7XchRiMbfbnU6nlWVAr51CztlsDGvuRrs/qpWVSmV8fHzfvn07d+5UJhqCSuf80MEvZ1b1MZwsdTGv1zs6Ojo5OSlUBqKScDjMLK9oNPr888+PjY198pOfbG9vh9rO/FG/38+SXsAUWIqDjAWhBooan8fjYaAho3rq6urq6upQtYWZQfWTpzMwMMB+I0jf3Bgq0HEzmUwkEmlpafH7/aL3gIfi9rI3wJ6/Frflujetp6dnoc9hSRp5hM/ny+VyRFU00/7Gb/zGunXrULw0Lm1gWhCzrkZptGKdT09PJxKJeDxO/42madJzF4vFEOF0Op11dXV/8Rd/0djYmE6nL1y4gNZwMBjM5XL4voW6NC7K7/cfOHDg5z//uXQLaZqWy+X8fj81XOkQrFarN954Y29vr1IqEAhks9lyufyJT3xCxiYSoM3tivBKMod8zZo1sVhseHiYuC8cDhPJapoWiUTy+fzVvhnvFLNTwjkatIZCoeDz+aARglsdOHBAYBF0xxnkuVDnKQ6LLV00OZiq3dTU1NbWFo1GQVtIlKLRqDIJXMVicWRk5B/+4R/Onj0rALzT6czlcoFAYAG9lW7KauN2JX5Bu40cPJfLwX4AROd5wZglvHr99deB83TLwOe5dbDTtUPZ1Ol0njx58o033ggEAk1NTZQpYOG6XC6aB67BLXlHmO2w5miCVtCXBxjk8XhGR0f37t0rOn+wKxcWdEehRRJDsj+Xy5XL5arVanNzc3d3dygUIhIpl8tTU1PIaWiaRuPOxMTEgw8+iBpnMpkMBAJyExbquiBnkLtNTExI6QBqPg4LCAkMTtO0cDhMzgitTNO0n/3sZxJyGubk3bmdj2GOmHY6nfl8HorpyZMnDx8+HIlEOjo6KGJyzgtbr1jSZjusORrvNxRKKT9Rjzty5Mgbb7xB2x3UgQVMCYV7ZS3PEwBKGuj1eru6utra2kDlvF6vLGxhZg4PD//zP//z8PBwKBQqFAqBQCCdTi9s7yThldvtHhsbI/NymtN5Rc5BWPsI1MDwgPV6+vTpU6dOOSwjBZRZEp3D82LKGS6SsRcIOheLxddff/3o0aPpdLq+vh58bRH2nC4Vsx3WHA1aQ6VSCQaD+Xze6ph0XX/55ZdHR0cJTzKZDLKrC2LEgLQHvlmeQZmaPz6fr76+vqOjA0xdhIBkTqrb7T516tSLL75IPDI9PY1W8kJdF+cATRRevmEqFGnmjG6CRDqovF4vM1Zhorhcrn379mUyGWWy2CVanJvDkuGMpVKJxJCGAbxnuVyenJzcs2dPNpuNRqOLUEZpqZh94+ZogCOkG8zpkxoTU+2efvppnJrf79ctKqzKjHpktc9IrMBTRBBGM3UBZ8+/dMv4PESdAKEEYldKicSryMhIWYDDRSKRzs5O8DiAHrwVCZTL5bpw4cIrr7wiIyYXNiVENGJwcHAGhVX0SLnhMgJ2586dt99+O3SzYrH4/PPPk9tKPsitm1v+bpg6WRjxnchbcoaapvX39585c8bpdHZ1dRGOKVMVktHlumWAgLK0BElHOjuQWpTicfNg78RrvqYGUO3xeCYmJg4dOlRXV5fNZg1z7CibrXAIeBdFrsQwZ6xZoxtcDxnHLDu/w5xFJkkfi5A8hVXq9XqJ9WaJjNrb28GG+RKWMdgWfvPQoUNjY2NKKdbY1b5/b9dkqxgdHdXMpmVuHeg7tFh0KQKBwH333bdjxw4ehN/vf/bZZycnJ+ehWse5McOFECyZTPb39z///PPd3d033HADQRmVTe48OJc1pZW2CqWU2+3mIQpB/x1ltsO6ygYVAJr7/v37z507F4lEdFPsTZrUxLNYBZscpnA+FSWYnDQkC3t+lkMbFhU6ftbN2QrWjhNlTuF+SyuVSitWrKirqwMPIu0yzD4eNIgPHDiAC5sDX+lqGV7Y5/P19/dLmU+Z2CIuHspoIBD4+Mc/vnHjxvHxcRLDTCbz3e9+l5LCPJyn0zSPx8OuQ7njJz/5yXPPPdfU1LR58+bm5mY+j5OV7iJuPo9vRmw1N77YUjfbYV1lw2WQMI6Njf385z+njC2okCDZfB4mtHTGsKlWq9V8Pi9Fet7L/1PDRDOV5PCY1M5FvEk3R4RIlvGWxkk2NzfjW9FHJmzh6kKh0NGjRxOJhNfrZdDkgpike/F43Lp0uZNKKZ/Pl81mly9f/nu/93stLS1gc9yKAwcOMFhzHugFIGjsIlaJcITqy+XyoUOHvve977nd7p07d0ajUeJuNjyiY+NS8fG3+RyvV7OZ7lfZwCDcbjdMpbGxsd27d/f29oJlUM+WSQfKVPgdGxtjyosVdSKJKxaLq1evLhaLQvV6y+Py+tJAw/eQzTkt08mkmQaq9+UugXW1evXqgYGByclJYkZd1yExcRWHDh163/vel8lkFgpJERJWPp+Xi9UutTvuuOPWW28VcEpuyOHDh+F5zoPKs9XRWMVq0um0VFq9Xu+LL7742muvrVu3bu3atel0enx8PJ1Og4cSLUoNge+RcPKanvwiNNthXWUTAIVgqlqtnjlzpr6+fsOGDcFgMJvN8s5JjgbUig6vQObyZvNG8nLPjrvjkoRpJf/FhcFuVaam8Ow7M33OuVxu7dq1p0+fRhrYCp34fL7Tp0/fcccdC6st43A40um0FWJXpgDOsmXLent7V69eDQOLya9w98+fP//yyy9z/+chwhIvo5kkLPYDIiyXy+Xz+Xis2Wz29ddff/3117ds2bJ+/fpcLjcwMDA1NUWcyz5kLSaoRTkp/VqbnRJeZWMDLBaL9LiAtr766qunT5+W+XrWqp/U5vkkhTypFlEgHxwclKkHsxwXD9jU1NTY2NjR0bFs2bKWlpba2lryEWGuq1kxLLwS518qlT7wgQ90dnbi7wSJr1ar2Wx2dHR0YQtVhmFMTk7KpfFfXdfr6+t37NixevVqocgppUqlUigUmp6e/vrXv85dJQe/1idZNaep84AIqClT4rDK5XI2m6UvQtO0SqWyb9++H/zgB0ePHq2rq9uwYUNLS0u1WmXHkstR71QMy46wrrJRwEbcTqZel0qlffv2NTc3x2IxNEzAJqAOUBuS4F9gCyIdXdcnJiai0Whzc/Ms7ygbuMPhWLlypcfjIURiikQmk6ETxSqZcrnzZ3UxzN3v9585c+Yv//Ivv/rVrw4NDeVyOdY80Q00tAX0WfhNHJbcN0QHu7u7CUkoVvAgMpnMj3/847Nnz6p5jE2c5tRhZcJPSikiJhnwE4lEKpUKYSzzQUql0oULF86cOeN2u2+44Ybbb799YGBAhLckTHsHtvjYEdZVNpaNDEAXrlMikXj66aczmQwaIySDFN0p5Om6Ts7Cy20FZQzDGBkZSSQSxAUOU9JXuF18JhAIrFu3zu/3+3y+YrFIsun3+9evX09zL4ebHbwHlhbBv3w+PzQ09KUvfel973tfLBZDFIGepLm5Kr72zWMsWNUyD0KZLlg35QZnTPrgNCYmJhBsIbHq6Oj44Ac/2NnZKd9Dhg7LbGRk5OGHHxZpqv+T2nZVTLB2NgmpYFoLLzIjVsrHuCR+c+zYsYceeuj06dNer7e7u7umpkaZ5AYJEp2mdL0y66f84LD03jveNPAY41Zc6/twtcxWa5gno0sjFAp97GMfo4t4enqaxn0cxODgINGQz+djB2b7Fc4nbckrV66kBKmUKhaLfKBarcZisRtuuEHa5UD9fT4fQdz4+Pjx48cp6ouDe8vzlJoUi9ztdjc0NHzkIx8BI9u9e/f3v/99kLh77733pptuulKaJdwIwxyGikORnM5hESYmNWZ2vG4Z58UHwKpeffXV/fv3e73ed73rXZs3b4Y6gOx6JBLJ5XJsDNDH/vZv/xbVF6s7WCpZldxnn8/X2NjY0NBA0yLROjBlsVhEAggnLpslPtowTdBMZar0GJZpjIvcbIc1T6ZpGtlfJBL5zd/8zdbWVmg1ECDxUMlkcmBgQOa1OE3NZf4vCjbhcLihoYF5MMQpLpcrFovt3LnT6/UODw9TRw+Hw7QEsfFOT0+fOXMGtROgtFnOU5ki6CRT2Wz2Yx/7WHd39/T09NjY2NDQUDweP3XqVHd394YNG65Uw54iI5QIIjXNomylzHCDriZlsvOJSa2ri8hlz549Pp/vzjvvVEqlUilIGDi+QqEQDodxfPF4/O///u/Pnz+vm4qjcpn/H490vk0z9SSklzscDi9btoyQmXuICJfD4SC+Nixy9fKzlQDIrVhC98F2WPNkKDdBXm9qavrQhz5kGIbf73c6nfTBhcPhXC4nersEC9KyK8x1kNe2trbW1lZc2C233LJp0ybDMAYHB6empojXSDrozSaQKRaLr732mnzhLNmQZvLIpAkxGo2m02mv17t8+XLE/AzDiEajU1NTsFvfvuFofD4fflOSF8OiHSY5C16MkFDYsxJ4Al37fD7IljBsK5UKGoTKlEuempr62te+xvhIghG32w2EtFTCK6UU0CdbiBU3IPRubGz0+XyhUAjuPjdEUFEcHHsA+5ncYYEUFvjy3rYtjTjwOjC32w130TCM4eHhp556SlnCLmIoKlydnZ2NjY0sKumSYz1TUdI07fz589DNt2zZ0tHRIWAWzAaB8Fmc4CMej2fVqlVUCWcB3aUOxa7rcDhCoRACxKVSiaOTtOKCr/Q+sHLIN0lnpDGbIikgmtBWCccA4MRlG4ZB/BgIBGiEisViRIXsCkhN6LpeKpV27dq1Z88eUnKwxWKxiHdbQgsVXYpAIEAUCTDPqMRyudzX19fX13fkyJF0Ol2pVEKhEGgA/h0sXymVTqcFwqM0pJZatdGOsObPCKkAVvx+/7Zt226++WYheSJSqky0a3R0dGRkBH4jrx2f9Hq9uVyO+IseNBr6enp6tm3b1traCuYKdMVWjFNAWOLFF1/MZDKz0BqEa0rsA0gveWt7e3tPTw+OQ7MoHFyR0VKTzWbpPSZL1TRtcHCwr69vbGwM4b1gMBgOh2tra++44w7hlFkxZmWWJrjAcrkcCoUot/l8PoKLp59++hvf+Aa7BcW4VCpVX1+fzWYBfZYMdmOWWZSl8shbIUIRxF94n9bW1rq6ukAgkM/nU6mUUE+tHRfKJAnPT/3hqphNa5gnI6nJ5/OU8yqVyu7du0OhkIDoINyFQkEp5ff7ly1bVq1WE4kEkLMUepi1pUwFQajeHo8HUKmxsRE42eFwyJBEcChytzVr1pw6dQqC0luep8sczKfMTK1YLFKZEpo+f44KwpW24wk853a7fT5fqVRKJBLDw8N79+6VHjoWZ7FYTKVSAwMD27Ztw0GTokqTndwWCAEIdbnd7mAwSCT1zDPPfPOb3ySk8ng8DGRrbGycmJjwer3QcZfKQhXkThjz5MUMSeRNQKSbgPTcuXPnz5/3+XzRaLSurm7ZsmUUZxKJBO5JVPCVUqAEC32Jb8tshzVPppsKpcBJLMtnnnnG7Xa3t7drZldNMBgslUrkLJ2dnR6PZ3h4WPoTlcmkV6YHlHY/h8MxMTEBMdXpdKbT6VgsxqRP1rPT6cxkMg0NDRMTE319fZeT6ALkAlwTmhjICCAIuBLLBmT3iu4DX55KpZYtWxaPx/1+/6uvvnr27NkZsLoyYwqOksvlaLITXI/7yQXCXZK8khjz1KlT//Vf/wUbS/LiUqmUyWQAuYLB4Cyp8SK0GewE/DWPe3p6muiSJBo1Lm5UMplMJBJsMH6/f+PGjYTzyWSS/UZbUgqoSyMevg5MajHCl+H9++lPf3ru3DkJy/FKRDH4snXr1vFWSeDDl4AZ847yA7LilNVksJXQtaanp4PB4PT0dENDQyAQkLdf6m6ybwO0VatVwdeseRNsA+tWL6QEKahLGU6uXWpVnEkoFJqamvJ4PA8//PDg4KDDFMBRJnFBiJ3CwBB2EquUmgBag4Kg45vy+fzJkye/+MUvZjKZQCDAEflO7iROzcoaX1o2w6cbZlsl1zjjBlqTvlwut2fPnt27d/PK0Q0OAkiQxQsDXKhMn8iDRmdCsnKBOK0vs2YO6JUyrrKMEZDzl5dBN81aB5jd7Ahrga1UKj377LNut7unp4dKEMwj4hp6zW688cYLFy5MTk7CkGR+BMvP5XIlEolIJCI+wtqnokyXpMz3w+l0RiKRtra2gYEBIYiLOwPJBgsHpqWDRCQEiOD4HuJEwzBQgib8wccp8w2WoEmzNMGJD3r++eel5Zu8lWSZTAfHR07KQQ1TJI8lms1mXS4XgZLH44nH4w0NDZQmHnzwQWZ8sT0srUjqmhph1+TkJDNxpY6xdu1anhE7gexebCRwKSrmqF2hBxqXmjI5E5pl4LaVN2t9Day0O87N+oNxKbVV/ms7rAU2gotnn332zjvvvPnmm8vlciaTCYVCpVIJl8G40La2Nq/XG4/Hk8nksmXLcrkc4YamaSxyiJS4EmpwotInz5tdNxAItLS0xONxlIWhU1jHUzOQAmYA3gcsjLCO94/f4yXB/pXFJ5KEWl9Tef9IQFwu1+uvv07rifgUkhq32w3iVldXl0gkVq9eTdxUNYXDWDP5fB6xY90UJm5qaqpUKv39/d/4xjdOnToVjUYpL8A+XUJZzzU1mcwmdAd+PnLkCP83HA5TYSTXhtGGzBEogaZptJ1ZnZS4J3BGZb4bHFRoKwLDVd+krTrDZhRD5Fi2w1pgY7cpl8uvvPJKIpG48847+aegocFgUNd1v9/f2NgYi8WSyeTg4CBvA5mjlX4p5TOJxnkteF2qpoqD1+vt6ek5duwYX66UwkMBBgk3SnyigFZkmhxFgh1KTuzA4XDYKo765kSgUql4vd6+vr79+/fLSy9ZjGE27kBY3759O4EnR+co+ERqCEBUdD7m8/n9+/f/4Ac/+OUvf4nQPgUBstR5f7CL1HBSDnMuibV0CJggIKAym+Tr6+sbGhoymczU1JRSqqOjo7OzkwG0b46J2CEMU2KX92SGAKEyQzA5kPxSfuYFfrM7sx3WAhuSTCAvR48eTaVSO3bsiEajkIZApuinoUnQ5/O1tbWdPHkykUjEYjEJMWDo4MJIiwT5cljksXRT3w7P4nA4KAKWSqWOjo76+vpYLOb3+9Pp9KlTp4aGhgxTrBnnQtAHH5V8Qb75xIkTxWKRputgMLhmzRpJBq2vHed24MABfmYaK97KYQryBAKBSCRy11131dXVUfKrmlL0knFI37Xb7U4mk6FQaNeuXd/73veSyWRtbS1NS/Dp5csX5gEvMpNbgYvhlQBkICi2gkpQmoeGhsbHx9EdU0olk8lqtdrQ0MC+4rYYeWI6nc5msyD6AiYoC9teigZsfurSZFBO1Zo/yi9th7XARmlfmWHzwMDAT37yk/vvv58xwsokE7DhwNXy+Xz333//448/jsYb79/U1JRhGHyV6H9XzckFEn5LaFZTU7N27dozZ854vd5CocArG4lESqVSOBxeu3bt8uXLjxw5cujQoVwuV1tbm81mneagMCE9AMCHQqF0Oj08POxwOFKplFKqra3tzd7qVyG9y/Xcc8/F43Gp8YlDgelKhvvBD34wGo2WSiU8DqiWtdTAf4PBILW/f/u3f3vmmWeKxWIsFkun02B/wpOAEjH/D3cRGrm8NVlzXKrZzVPjFQInZbdg5IrD1KEcGRmhyUmZ2LwAlI2NjS0tLTB45b0yDCOfz09NTeXzeQnPHeYoFs1iyqKdK+iY+FDbYS2wQZ+hZMYc6fHx8ccff3zHjh3Nzc2aptF6Aq0ZPD6TyUxOTv7xH//xQw89NDY2RuzjdDrr6upArFnhM1Bqw1IWBHhqb28HyQLd5wXy+XyTk5OpVCocDi9fvryhoaG/v394eJhx0HybcAiq1WpNTU1zc3N/fz9fCz61cuXKdDo940o5h7Nnzx4+fBgCLViJ8N1JHKLR6N13363reiaTcZojMDRNEw0JZZEbhYX0L//yLwcPHsSVZ7NZgrJMJoPC1NIiiF5rI6eWbcxpzjSbkcLLP9kwlFLk4/gR7dKRcUKIw9EMDw9fuHDBsNBTOSIAWTgcphOWPirxdA7T+KdhMYHtbYe18MbOA/0qHA6Xy2UmFDz77LMf//jHSccymUxTU1MmkwFf8Pv94+PjTU1NX/rSl7785S8jpHfDDTesWbOGgIXZYhKYzHgLhZQQCARWr169d+/ecDgsTf8uc/BqLpdDz7OhoaGtrQ0GOcgR/ExlIqMej+fs2bO8ZBy3vb392LFjMy6Tg7700kuRSARf4/V6EVkmdiuVSj09Pe9///uVUhD6gcwgLpCNCu2DoGxsbOwLX/jC+Pi4dNswcNth8mZJUW1vJUbEZI27MdnJNFN+Xln6qKrmkFoBGZQZWKlL/Rf0CKnhWN+9YrFYKpUY060sdWQ5hzcjVtY/x+wHucDGciXZYTeDCDo+Pv7CCy/I48/n88JcJ7M7duxYOp3+yle+0tvbu3nz5t/93d+VwXZkQA5THYmNUYg5ZIUESrFYbN26dShwgqnLSyyuQTNnkfIW8jFldh3qui4SEbzo69at+9W75XCQuHHQQqHwox/9SNf1dDqNtA7hmDKV7W699db3vOc9mqbBSocDqZmTHAkkdYtm1p49e/7u7/4OOdZIJAJznZOXcNKqXGibskxp0t9KuVQiccnOpOTHG2X1cRIWWR2Q1AHFJ+oWrf0ZR1FvSgZnmPgpOZwdYS1Sczqdx48fz+fzvb29kUgEeU9IWCIn8uSTTxqGgVDB0aNH29raQCh0s+1uFiUZcgGHw1FfX5/L5YaHh/v6+vL5fH19fSQSoUmIIyKwRS3SGpzjakOh0IEDBygdAN+uWLEimUxCQRTsrFwuT0xMXLx40eFwgFJRkyIJLRQKvb293d3daFfRX2KYoldKqZqaGmjxqFwppXbt2vXYY4+l0+lwOJzNZicmJurr66lOzONTsm2+zXZYi9RY6mfOnKlWq729vTBiSHYETcCVwJSZnJy09rFLYDLL97NxRSKRrq6uUqk0MjKSz+f7+vqUUuFwmAod8V0wGIT5RSFcWcKrycnJEydO+P1+Tqmpqam2tra/v1+a/gRGNQyDtiROjGogHdrvfe97m5ubYYQJr5oqEh2ChUKhrq4un8/ncrnp6envfOc7P/zhDwHsxsfHa2trBdKah1GDti2g2Q5rkRppUTgc7u/vf/jhh3/7t38bNVF8FkkifAi4WjD9yKQkLMcvzHIUMqxQKNTd3a1pWjweJ6+EdCNVRaWUeK5gMMiwL1xqJpMRIFbX9e3bt4+MjEB3sOKySimmbNC5Ddc0mUy2tbXt3LmzqampXC5LfAQa5TRni+H70ul0TU1NJpP56le/un//fuF8QXMl5Zx9OLZt14HZDmuRGi348Bhyudx3v/vdj3zkI7W1tdCymAstdZZMJqOUQqtPmeEVCdrl4Bvp3aGuXFNTg38ZHR0VMEKzjP9Ev4VOGmWpZFPbNgxjampq06ZNgUAALQT+FvK91+sltQROymaz4XDY4XCsWrVq+/btcCYobgqXmhYfzWwPhMj+7LPPfutb34rH4wg8IWgXDofRGqXufqXN2LYtLbMd1iI1QCIBoXK53MMPP9zb27ty5cpIJEJxDZwoGAwWCoV8Pg/0QyYFZ2928pE0CYJMoyHjcDgSiQQVOlGGEEqh4KlCyELfxuv11tfXr1u3bnh4WBpoPR4P/djQ6JHxyufzDLNYvXr17bffDr/BZQ4QoxrIb4gfuXbDML7yla889dRTOKZqtQqX1eFwMLmPOgCiDvPwdGxbKLMF/BapUTSEQ4TjMAwDeaPt27d3dXVRv2eJ4lA2bdpUX18PDE9ZrTLr6D0RdRMJXX6fyWQuXrx48eJF6AtA5jO6baRUR1DjcrnuvfdeZdJcrfmgUgp5BmEnkLf+ilbjclEyJwfkQHDTDMNIp9OpVGrXrl3PPfccmNfU1BTIl0gA8re6KW1oO6zr22yHtUgN7RSY62ReSilSvHA4fNddd3V2dmqahmIMEHhnZ2dXVxcjhfEC+uWn75HHVcwJ1WSXkBtY86lUanBwcHR0FHLTW3arEmRVKpWurq53v/vdFy5cmJ6eBjsX2g6EWBwTaaxuCnuBr/MNErXhs3Rdz+VyJ0+e/Md//EfIDURtfr+fgA53KZQiHKgwP2y7Xs12WEvPiIbuvvvuTZs2VatVICGGxNx8882a2dYAj2mWL7FmnRLmKDMBBLd64YUXNIuiixDBRLlhenq6trb2Yx/7GKR5HKt2efXkarUK6kQMJV5SmUphHOjcuXOPPvroK6+8YtMUbLOajWEtMaNdLpfLPffccw6HY82aNXgrNH/j8XhnZyduSDKvt/weQdOVSfYTfSLpsEf9SqB96UAWmp+u67W1tUzZwls5zE7py52/2+1Op9M+n88wha4IoCQEq1arx44d+/znPw+SZTss26xmO6wlZiDudJwggHfLLbeA+yBi19nZCWSOVszlqoTVajUSicRiMaUUaFe5XE6n0zQMi0yN6Li7zLnE0tuhlNI0LRwOr1ix4uLFi9bGNO3yg1qtBGiQcsPsti0UCoODg//7v//72muvkajaTcu2zTDbYS0xo0Ga6KNcLv/85z8fGhr6m7/5m7NnzxqGEQ6Hm5qaxsfHdV1H7OVyWeHY2NiePXsqlcrKlSvXr18fCASi0eiyZcuUUolEIpvN0tJI6zWmzJk6iLcwSQxeVTKZhDsK4DXL+WezWXoJ5XIQt0kmk08++eT//M//1NTU0KCrTPGmq3sDbVvSZjusJWZkSaRUmqbV1NScO3fuc5/73Gc/+9nu7u4dO3bINMNSqSSjw95sN998s1Lq5MmThw8f7uvrczqdNTU1sVisubnZ7XaDXk9OTlKwQyyJuIk4TiRxPB4PKjdSMVQmTf8tTWoCFAR8Pt/o6OhTTz31s5/9jIYeerxRHxXUzDbbMBt0X3pWKBTgguMXGPFUKpV+67d+6/Of//yyZctKpdLp06cJwS7XnYNXSqfThw8fhixKCilkCESKg8FgU1MT/lF6X48dO5ZIJHAlK1as2Lx5cyqVkgZAUPzLpYQAVVK+/MlPfvLII48kEgnYGPQS6uYchBnDLGyzzY6wlp7R4ifDGsCbgsHgo48+evLkyT/90z99z3ve09TUNDk5Sb3vLb+EpCwYDK5fv3716tUMTITtxUwaRIusmu7KpL/DTQXJqlarSF/R5SNtOpc7rtDiX3zxxaeeeioej3MmNEVns1m/38+lKVMj8OrfQduWrNkR1hIzcCVRp5MWGeEoeL3ebdu2fetb3xoaGmKe4FuatFKLHI1mTqnAA4KUQYmSsiDHOnjw4OTkJBFQT09PY2NjIBCgPmgdLHa5Q/f19T344IPHjx8XgQeZxCOyuQ5z1vQs2aVt70CzHdZ1Yi6XK5fLBQKBQqEQi8Xcbvef/dmfLV++nDqgEDLxDjTBRKPRbDarTP4n9AJJxKTep5ujBHAr09PTfX19IyMjHK6hoWHt2rUw8l2W6ecyV0IU6/v6+s6fP//888+fP38e1yZEfMfSGZVu28Ka7bCuE5Noi5bpXC7X3NxcLBb/6I/+6N5776UbGdm8kZER9I6npqZ8Pl8oFEKzBUU9XJXoruHLNHPoAzx4URzma8vlMmKkcKkkUtN1Ha2rl156adeuXX19feFw2O12T05OaqaUpcwgsDX2bHs7Zjus68TAj2jokSE3hUJBKdXe3n7ffffdc889kUhEmULdNAzzM4OwYMaLqDYehHKeyIcK2Yr2RpGjBCxnAgVCz9Vq9cyZM7/4xS9ee+21sbEx/ha/Rt6qlCLpU0pZdZxts20Wsx3W9WNAPzgp8TgSwvj9/s985jM33XRTY2NjsVj0+XwM2pEoibZEwijBxSTOAloSgrv8X6IqkesDXzt+/PjXv/71ZDKJhxIXGQqFmBaF28JhGfYYLtvettkO6zoxWmeYnRUKheBeEhahX6yUcrlc7e3tvb29t912WzAYjMViSGjNALwNy8imGT8rpXTTmEOHevL09HQikThx4sT+/ftPnDgxODgIdwFkSoI16X+WCdIQIGwMy7a3abbDuk4MBoC1N7BaraKyAPwUCATgLkDX3Llz5+/8zu/U19cjpYAuILP8JIxSpq+RRkIAe8B7wxwJceDAgSeffPLkyZMyplyqlsosLOIxQbhkfCb+VLqvF+7m2bZkzHZY14kJBwp0ibROBuTgtkSGQSml63okEmlvb7/llls2bNjQ2tqKZBVRj7KMS1FK4WJoh85ms6lU6sSJE8ePHz979uzJkydh1XM4ZYmnIMcXi0XOTUZj6uYwarJIssXLMfJts81qtsO6Tkwm0RvmOELSQ2lIpruY1j9J/UCjoDtt3Lhxy5Ytq1evbmhoaGxsDIVCeJOJiYmJiYnh4eGRkZFTp0798pe/pBeHTJCjE3wRcIkaH8AWXlIoqZJjMsEMvJ+QcCFvn21LxGyHZdtMI1ZCxo9SoF3Cs22RmN2aY9slRv4oiBI/2KC4bYvEbIdl21uYYQ4rn73Jxjbb5tlsh2XbJSbC7TLOS80qymqGsgAAAONJREFUF2ObbfNpNlvPtrcwzWILfS622fZrsyMs22aaZs5PlX8u7PnYZpuYHWHZ9tYmjC3bbFs8ZkdYtl1iInZsHSdhQ++2LRKzIyzbLjER6uOf1mnPttm24GY7LNtss23JmO2wbLPNtiVjtsOyzTbblozZDss222xbMmY7LNtss23JmO2wbLPNtiVjtsOyzTbblozZDss222xbMuay2y9ss822pWIue7ySbbbZtlTMjrBss822JWN2eGWbbbYtGbMjLNtss23JmMtuxLfNNtuWimn/+q//utDnYJttttn2tuz/ASsOxvcGZ7KKAAAAAElFTkSuQmCC\n",
"text/plain": [
"<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=400x400 at 0x7F70B0AA3DD0>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "x3XdkQYZqD9i"
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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