{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introdução à Programação em Python\n",
    "## Notebook 01 - IPython como ambiente de computação, cálculo e visualização\n",
    "## Carlos Caleiro, Jaime Ramos\n",
    "## Dep. Matemática, IST - 2016"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(actualizado em 18 de Setembro de 2019)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introdução ao ambiente IPython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O <a href=\"http://ipython.org\">IPython</a> (agora integrado no projecto <a href=\"https://jupyter.org\">Jupyter</a>) é um ambiente interactivo para a utilização livre da linguagem Python como ferramenta de cálculo e visualização, bem como para o desenvolvimento de (pequenos) programas na linguagem Python e sua prototipagem rápida. A versatilidade do ambiente IPython facilita a integração com outros ambientes e linguagens de programação.\n",
    "\n",
    "O IPython pode ser facilmente instalado através da distribuição Anaconda disponível em <a href=\"https://www.anaconda.com/download\">www.anaconda.com/download</a>. Basta descarregar o instalador adequado ao seu sistema operativo (Windows, <a href='#nota'>OSX</a>, Linux) para a versão corrente da linguagem Python (versão 3, que usaremos sempre ao longo deste curso) e executá-lo. Após a instalação, basta abrir o *Navigator* e começar a trabalhar. \n",
    "\n",
    "No modo *Jupyter Notebook*, que usaremos, a interacção dá-se através de um *interface* muito simples suportado por um *browser*, que por sua vez comunica com um *kernel* que disponibiliza um interpretador de Python. É neste modo que trabalharemos, desde já, sendo também muito útil para a produção de conteúdos de ensino/divulgação, como este texto.\n",
    "\n",
    "O ambiente IPython, simples e interactivo, é extremamente conveniente para a aprendizagem da programação (no caso em Python), nomeadamente a elaboração de pequenos programas e sua experimentação. Mais adiante, quando necessário, veremos como ultrapassar algumas limitações do ambiente IPython no contexto da programação em larga (maior) escala e introduziremos, nomeadamente, o ambiente *Spyder* também disponibilizado pela distribuição Anaconda."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O ambiente IPython surge-nos dividido em células, onde podemos escrever e avaliar expressões. Há também células de texto, como esta, onde podemos fazer anotações relevantes. O IPython providencia ainda um conjunto de extensões à linguagem Python, para interacção com o sistema operativo e com outros ambientes e linguagens, bem como mecanismos de introspecção e de controlo da computação, que iremos abordando ao longo do texto, e que se denominam de $\\textrm{magics}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## O IPython como ferramenta de cálculo e visualização"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cálculo numérico"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O Python disponibiliza à partida, para além de algumas funcionalidades básicas, várias operações numéricas que podemos utilizar: `+` (adição), `-` (subtracção), `*` (multiplicação), `/` (divisão), `**` (exponenciação), `//`\n",
    " (divisão inteira), `%` (resto da divisão inteira). Agrupamos expressões usando parênteses, como é usual. As expressões são avaliadas premindo **SHIFT+RETURN**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1+1-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(2*3)%5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.3333333333333335"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "7/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "7//3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10**3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(-1)*max(1,2,3)+min(1,2,3)+round(2.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333333"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/3+1/2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para podermos usar este ambiente como ambiente de cálculo e visualização, rico e apelativo, necessitamos de usar extensões à linguagem básica. A linguagem Python, como muitas outras linguagens de programação modernas, dispõe de mecanismos de modularização robustos. Um dos mais importantes é a existência de bibliotecas com extensões à linguagem básica, que servem os mais diversos fins. Estas extensões estão organizadas em pacotes de módulos, de que falaremos mais adiante. Existem vários módulos que dão suporte a mecanismos de cálculo e visualização gráfica que poderão ser bastante úteis, nomeadamente: Scipy e Matplotlib, ambas incluindo Numpy, que por sua vez inclui Math, e ainda Sympy, cuja utilização ilustramos de seguida.\n",
    "\n",
    "As extensões Numpy e Matplotlib estão incluídas na extensão Pylab, que pode ser carregada como se mostra abaixo. A instrução precedida de % é aquilo a que na terminologia do IPython se denomina $\\textrm{magic}$, uma extensão à linguagem Python que facilita a interacção com o sistema. Introduziremos, quando necessário, outras magias do IPython.\n",
    "\n",
    "Note-se que a extensão Pylab fornece um contexto essencialmente equivalente ao ambiente comercial <a href=\"http://www.mathworks.com/products/matlab/\">MATLAB</a>, profusamente utilizado em aplicações em diversas áreas da engenharia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "?pylab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Avaliando a expressão acima e percorrendo a informação na janela resultante podemos identificar as funcionalidades que passamos a ter disponíveis, algumas das quais ilustraremos de seguida. Poderá ser útil averiguar também os conteúdos de Numpy e Matplotlib, bem como da extensão Math que é incluída conjuntamente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.141592653589793"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note-se que o valor de $\\pi$ é aproximado, pelo que poderá haver pequenos (?) erros de cálculo. Tipicamente, os números reais em Python são representados com 53 dígitos de precisão."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "?pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cos(2*pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.49999999999999994"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sin(pi/6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.220446049250313e-16"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sin(2*pi/3)-sqrt(3)/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log(e**5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log2(1024)-log10(1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Veremos mais abaixo o que fazer caso pretendamos trabalhar com precisão arbitrária, ou mesmo com representações simbólicas, exactas mas menos eficientes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Vectores e matrizes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A manipulação numérica de vectores e matrizes é disponibilizada directamente pela extensão Pylab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array([1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arange(1,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 5, 7, 9])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arange(1,10,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4,)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shape(array([1,2,3,4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shape(array([[1,2,3],[3,4,5]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndim(array([[[1,1],[1,1]],[[2,2],[2,2]],[[3,3],[3,3]]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2, 2)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shape(array([[[1,1],[1,1]],[[2,2],[2,2]],[[3,3],[3,3]]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "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]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reshape(arange(100),(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 2.],\n",
       "       [1., 2., 3.],\n",
       "       [2., 3., 4.]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fromfunction(lambda i, j: i + j, (3, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zeros((3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ones((3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eye(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.39936009, 0.82021467, 0.57301195, 0.40932012],\n",
       "       [0.44970128, 0.87858468, 0.09337122, 0.64706307],\n",
       "       [0.51126101, 0.33821554, 0.18602321, 0.14068964]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand(3,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.56160006 0.11861895 0.00795708 0.7099636 ]\n",
      " [0.95610751 0.37245243 0.55466018 0.40657516]\n",
      " [0.55927415 0.79059928 0.25886881 0.22258423]]\n"
     ]
    }
   ],
   "source": [
    "print(rand(3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   1    2    3 ... 9997 9998 9999]\n"
     ]
    }
   ],
   "source": [
    "print(arange(1,10000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 6])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array([1,2,3])+array([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 6])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2*array([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4, 9])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array([1,2,3])*array([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2., 0., 0.],\n",
       "       [0., 2., 0.],\n",
       "       [0., 0., 2.]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eye(3)+eye(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1.],\n",
       "       [1., 1., 1.],\n",
       "       [1., 1., 1.]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ones((3,3))*ones((3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot(array([1,2,3]),array([4,5,6]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3., 3., 3.],\n",
       "       [3., 3., 3.],\n",
       "       [3., 3., 3.]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot(ones((3,3)),ones((3,3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1.],\n",
       "       [1., 1., 1.],\n",
       "       [1., 1., 1.]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot(ones((3,3)),eye(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 3, 5],\n",
       "       [2, 4, 6]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transpose(array([[1,2],[3,4],[5,6]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace(array([[2,0],[1,3]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "det(array([[1,0],[1,0]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0000000000000067"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "det(array([[2,3,2],[4,2,3],[9,6,7]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-4., -9.,  5.],\n",
       "       [-1., -4.,  2.],\n",
       "       [ 6., 15., -8.]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inv(array([[2,3,2],[4,2,3],[9,6,7]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 0, 1]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot(array([[2,3,2],[4,2,3],[9,6,7]]),array([[-4,-9,5],[-1,-4,2],[6,15,-8]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.],\n",
       "       [1.]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solve(array([[1,1],[1,-1]]),array([[2],[0]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3., 2.]), array([[ 0.        ,  0.70710678],\n",
       "        [ 1.        , -0.70710678]]))"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eig(array([[2,0],[1,3]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O significado e importância de operações matriciais como produtos, inversas, determinantes ou valores próprios é objecto de estudo da disciplina de Álgebra Linear."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Números complexos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1+1j)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complex(1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1+1j)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(0+1j)**2+1j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1-1j)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conjugate(1+1j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real(complex(1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imag(complex(1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4142135623730951"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(1+1j)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cálculo simbólico"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A extensão Sympy suporta uma miríade de funcionalidades para cálculo simbólico."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sympy import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esta extensão redefine várias das funcionalidades disponibilizadas pelas extensões anteriores, e introduz muitas outras. Discutiremos abaixo as mais relevantes. Note-se que pi é redefinida, enquanto e não. A versão simbólica do número de Napier é E."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 2.71828182845905 + e$"
      ],
      "text/plain": [
       "2.71828182845905 + E"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e+E"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{1}{2}$"
      ],
      "text/plain": [
       "1/2"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sin(pi/6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 0$"
      ],
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sin(2*pi/3)-sqrt(3)/2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "É sempre possível avaliar numericamente (e aproximadamente) uma expressão simbólica. Para tal recorre-se ao método evalf. Veremos mais tarde qual o significado exacto de um método."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 1.5707963267949$"
      ],
      "text/plain": [
       "1.57079632679490"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pi/2).evalf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A representação de números racionais como fracções exactas poderá ser particularmente útil."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{5}{6}$"
      ],
      "text/plain": [
       "5/6"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Rational(1,2)+Rational(1,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 0.833333333333333$"
      ],
      "text/plain": [
       "0.833333333333333"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(Rational(1,2)+Rational(1,3)).evalf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Manipulação simbólica de expressões"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A extensão Sympy permite fazer manipulação de simbólica de expressões, como é usual em álgebra. Os símbolos devem ser definidos como se mostra abaixo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "x,y = symbols(\"x\"),symbols(\"y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 2 x$"
      ],
      "text/plain": [
       "2*x"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x+x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 2 x y$"
      ],
      "text/plain": [
       "2*x*y"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x*y+y*x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left(x - y\\right) \\left(x + y\\right)$"
      ],
      "text/plain": [
       "(x - y)*(x + y)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factor(x**2-y**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left(x - y\\right) \\left(x + y\\right)$"
      ],
      "text/plain": [
       "(x - y)*(x + y)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x - y)*(x + y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle x^{2} - y^{2}$"
      ],
      "text/plain": [
       "x**2 - y**2"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expand((x - y)*(x + y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle x^{5} + 5 x^{4} + 10 x^{3} + 10 x^{2} + 5 x + 1$"
      ],
      "text/plain": [
       "x**5 + 5*x**4 + 10*x**3 + 10*x**2 + 5*x + 1"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expand((1+x)**5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left(x + 1\\right)^{5}$"
      ],
      "text/plain": [
       "(x + 1)**5"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factor(x**5 + 5*x**4 + 10*x**3 + 10*x**2 + 5*x + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 0$"
      ],
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x*x**2-x**3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{x + y}{x y}$"
      ],
      "text/plain": [
       "(x + y)/(x*y)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "together(1/x+1/y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "É possível avaliar uma expressão concretizando valores para os símbolos que contém, recorrendo ao método subs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 2 x$"
      ],
      "text/plain": [
       "2*x"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x+y).subs(y,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 22$"
      ],
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x**2-y).subs([(x,5),(y,3)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Matrizes revisitadas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Na extensão simbólica Sympy, as matrizes são agora representadas usando Matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}x + 1 & 2 x\\\\y + 2 & x + y\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[x + 1,   2*x],\n",
       "[y + 2, x + y]])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Matrix([[1,x],[2,y]])+Matrix([[x,x],[y,x]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "São introduzidas várias funcionalidades adicionais, e algumas operações são também redefinidas. Por exemplo, * passa a ser o produto de matrizes, em vez de dot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}x y + x & x^{2} + x\\\\2 x + y^{2} & x y + 2 x\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[   x*y + x,  x**2 + x],\n",
       "[2*x + y**2, x*y + 2*x]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Matrix([[1,x],[2,y]])*Matrix([[x,x],[y,x]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shape(Matrix([[1,x],[2,y]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}2 & y\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([[2, y]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Matrix([[1,x],[2,y]]).row(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & x\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([[1, x]])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Matrix([[1,x],[2,y]]).row(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1\\\\2\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1],\n",
       "[2]])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Matrix([[1,x],[2,y]]).col(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}\\frac{y}{- 2 x + y} & - \\frac{x}{- 2 x + y}\\\\- \\frac{2}{- 2 x + y} & \\frac{1}{- 2 x + y}\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[ y/(-2*x + y), -x/(-2*x + y)],\n",
       "[-2/(-2*x + y),  1/(-2*x + y)]])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Matrix([[1,x],[2,y]])**(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle - 2 x + y$"
      ],
      "text/plain": [
       "-2*x + y"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "det(Matrix([[1,x],[2,y]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{y/2 - sqrt(8*x + y**2 - 2*y + 1)/2 + 1/2: 1,\n",
       " y/2 + sqrt(8*x + y**2 - 2*y + 1)/2 + 1/2: 1}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Matrix([[1,x],[2,y]]).eigenvals()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(y/2 - sqrt(8*x + y**2 - 2*y + 1)/2 + 1/2, 1, [Matrix([\n",
       "   [-y/4 - sqrt(8*x + y**2 - 2*y + 1)/4 + 1/4],\n",
       "   [                                        1]])]),\n",
       " (y/2 + sqrt(8*x + y**2 - 2*y + 1)/2 + 1/2, 1, [Matrix([\n",
       "   [-y/4 + sqrt(8*x + y**2 - 2*y + 1)/4 + 1/4],\n",
       "   [                                        1]])])]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Matrix([[1,x],[2,y]]).eigenvects()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 2\\\\3 & 4\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1, 2],\n",
       "[3, 4]])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Matrix(2,2,[1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}0 & 1\\\\2 & 3\\\\4 & 5\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[0, 1],\n",
       "[2, 3],\n",
       "[4, 5]])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Matrix(3,2,range(6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}0 & 1\\\\1 & 2\\\\2 & 3\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[0, 1],\n",
       "[1, 2],\n",
       "[2, 3]])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Matrix(3,2,lambda i,j:i+j)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Derivação, primitivação e integração"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 2 x + 1$"
      ],
      "text/plain": [
       "2*x + 1"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff(x**2+x+5,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle - \\frac{2}{x^{3}}$"
      ],
      "text/plain": [
       "-2/x**3"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff(1/x**2,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\cos{\\left(x \\right)}$"
      ],
      "text/plain": [
       "cos(x)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff(sin(x),x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "É possível também definir nomes simbólicos para funções."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "f=Function(\"f\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 2 f{\\left(x \\right)} \\frac{d}{d x} f{\\left(x \\right)}$"
      ],
      "text/plain": [
       "2*f(x)*Derivative(f(x), x)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff(f(x)**2,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "g=Function(\"g\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{d}{d x} f{\\left(x \\right)} + \\frac{d}{d x} g{\\left(x \\right)}$"
      ],
      "text/plain": [
       "Derivative(f(x), x) + Derivative(g(x), x)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff(f(x)+g(x),x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle f{\\left(x \\right)} \\frac{d}{d x} g{\\left(x \\right)} + g{\\left(x \\right)} \\frac{d}{d x} f{\\left(x \\right)}$"
      ],
      "text/plain": [
       "f(x)*Derivative(g(x), x) + g(x)*Derivative(f(x), x)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff(f(x)*g(x),x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{d}{d g{\\left(x \\right)}} f{\\left(g{\\left(x \\right)} \\right)} \\frac{d}{d x} g{\\left(x \\right)}$"
      ],
      "text/plain": [
       "Derivative(f(g(x)), g(x))*Derivative(g(x), x)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff(f(g(x)),x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{x^{2}}{2}$"
      ],
      "text/plain": [
       "x**2/2"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integrate(x,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\operatorname{atan}{\\left(x \\right)}$"
      ],
      "text/plain": [
       "atan(x)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integrate(1/(1+x**2),x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle - x \\cos{\\left(x \\right)} + \\sin{\\left(x \\right)}$"
      ],
      "text/plain": [
       "-x*cos(x) + sin(x)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integrate(x*sin(x),x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{1}{2}$"
      ],
      "text/plain": [
       "1/2"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integrate(x,(x,0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 2$"
      ],
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integrate(sin(x),(x,0,pi))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 1$"
      ],
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integrate(E**-x,(x,0,oo))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Limites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 1$"
      ],
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "limit(sin(x)/x,x,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{\\pi}{2}$"
      ],
      "text/plain": [
       "pi/2"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "limit(atan(x),x,oo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "?limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\infty$"
      ],
      "text/plain": [
       "oo"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "limit(1/x,x,0,dir=\"+\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle -\\infty$"
      ],
      "text/plain": [
       "-oo"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "limit(1/x,x,0,dir=\"-\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Somatórios e séries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "?summation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 15$"
      ],
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summation(x,(x,0,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = Symbol(\"n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{n^{2}}{2} + \\frac{n}{2}$"
      ],
      "text/plain": [
       "n**2/2 + n/2"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summation(x,(x,0,n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 1$"
      ],
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summation(1/2**n,(n,1,oo))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle e$"
      ],
      "text/plain": [
       "E"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summation(1/factorial(x),(x,0,oo))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\infty$"
      ],
      "text/plain": [
       "oo"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summation(x,(x,0,oo))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "?series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 1 - \\frac{x^{2}}{2} + \\frac{x^{4}}{24} - \\frac{x^{6}}{720} + \\frac{x^{8}}{40320} + O\\left(x^{10}\\right)$"
      ],
      "text/plain": [
       "1 - x**2/2 + x**4/24 - x**6/720 + x**8/40320 + O(x**10)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series(cos(x),x, 0, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 1 + x + \\frac{x^{2}}{2} + \\frac{x^{3}}{6} + \\frac{x^{4}}{24} + \\frac{x^{5}}{120} + \\frac{x^{6}}{720} + \\frac{x^{7}}{5040} + \\frac{x^{8}}{40320} + \\frac{x^{9}}{362880} + O\\left(x^{10}\\right)$"
      ],
      "text/plain": [
       "1 + x + x**2/2 + x**3/6 + x**4/24 + x**5/120 + x**6/720 + x**7/5040 + x**8/40320 + x**9/362880 + O(x**10)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series(E**x,x,0,10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Números complexos revisitados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A extensão Sympy também disponibiliza a manipulação simbólica de números complexos. A constante imaginária é agora representada por $\\textsf{I}$, em vez de j."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "?I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle -1$"
      ],
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left(1 - i\\right) \\left(1 + 2 i\\right)$"
      ],
      "text/plain": [
       "(1 - I)*(1 + 2*I)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1+2*I)*(1-I)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 2$"
      ],
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im(1+2*I+3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 4$"
      ],
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re(1+2*I+3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "a,b=Symbol(\"a\"),Symbol(\"b\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\overline{a} - i \\overline{b}$"
      ],
      "text/plain": [
       "conjugate(a) - I*conjugate(b)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conjugate(a+b*I)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-I, I]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solve(x**2+1,x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Resolução de equações e inequações"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "?solve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solve(2*x+5-7,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "c=Symbol(\"c\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-b + sqrt(-4*a*c + b**2))/(2*a), -(b + sqrt(-4*a*c + b**2))/(2*a)]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solve(a*x**2+b*x+c,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-x*(a*x + b)]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solve(a*x**2+b*x+c,c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-I, I]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solve(x**2+1,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{a: 1, b: 2}"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solve((2*a-b,a+b-3),a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[pi/2, 3*pi/2]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solve(cos(x),x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle -\\infty < x \\wedge x < \\frac{1}{2}$"
      ],
      "text/plain": [
       "(-oo < x) & (x < 1/2)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solve(2*x<1,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left(-\\infty < x \\wedge x < -1\\right) \\vee \\left(1 < x \\wedge x < 3\\right)$"
      ],
      "text/plain": [
       "((-oo < x) & (x < -1)) | ((1 < x) & (x < 3))"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solve([x<3,x**2>1],x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "multiple generators [x, cos(x), log(x)]\nNo algorithms are implemented to solve equation x*cos(x) - log(x)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-134-b15b3c16e1e1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mcos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/sympy/solvers/solvers.py\u001b[0m in \u001b[0;36msolve\u001b[0;34m(f, *symbols, **flags)\u001b[0m\n\u001b[1;32m   1169\u001b[0m     \u001b[0;31m###########################################################################\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1170\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mbare_f\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1171\u001b[0;31m         \u001b[0msolution\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_solve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0msymbols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1172\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1173\u001b[0m         \u001b[0msolution\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_solve_system\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msymbols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/sympy/solvers/solvers.py\u001b[0m in \u001b[0;36m_solve\u001b[0;34m(f, *symbols, **flags)\u001b[0m\n\u001b[1;32m   1740\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1741\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1742\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\n'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnot_impl_msg\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1743\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1744\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'simplify'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNotImplementedError\u001b[0m: multiple generators [x, cos(x), log(x)]\nNo algorithms are implemented to solve equation x*cos(x) - log(x)"
     ]
    }
   ],
   "source": [
    "solve(x*cos(x)-log(x),x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como seria de esperar, há limites para aquilo que podemos fazer simbolicamente. Veremos mais abaixo como poderemos tentar obter soluções numéricas, aproximadas, para esta equação."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Controlo da precisão em cálculos numéricos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como a computação simbólica é bastante mais pesada e nem sempre é uma opção, por vezes é mesmo necessário trabalhar numericamente. Se e quando necessário podemos controlar a precisão dos cálculos envolvidos. Uma das formas mais expeditas de o conseguir consiste na utilização do módulo Mpmat da extensão Sympy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpmath import mp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pi: 3.14159~>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mp.pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpf('6.2831853071795862')"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2*mp.pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.141592653589793238462643383279502884197169399375105820974944592307816406286208998628034825342117068\n"
     ]
    }
   ],
   "source": [
    "mp.dps=100\n",
    "print(mp.pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.1415926535897932384626433832795028841971693993751058209749445923078164062862089986280348253421170679821480865132823066470938446095505822317253594081284811174502841027019385211055596446229489549303819644288109756659334461284756482337867831652712019091456485669234603486104543266482133936072602491412737245870066063155881748815209209628292540917153643678925903600113305305488204665213841469519415116094330572703657595919530921861173819326117931051185480744623799627495673518857527248912279381830119491\n"
     ]
    }
   ],
   "source": [
    "mp.dps=500\n",
    "print(mp.pi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O parâmetro `dps` controla a precisão de trabalho, no caso 100 ou 500 casas decimais significativas. É importante distinguir *precisão* de *exactidão* (*accuracy* em inglês): exactidão é o inverso do erro de cálculo, que é a diferença entre o valor calculado e o valor exacto (ou o seu logaritmo, dependendo das implementações).\n",
    "\n",
    "O controlo dos erros de cálculo é um assunto complexo, pois pequenos erros podem propagar-se por vezes de forma bastante invasiva. Vale a pena explorar as possibilidades oferecidas por esta extensão do Python, acessíveis através de completação por TAB como abaixo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"mpmethods.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vejamos, por exemplo, como podemos obter uma solução numérica para a equação não-elementar `x*cos(x)-log(x)==0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpf('1.3475798620233314552584182846207099276785226493535943506247392495225400905747579535928171658973488501381979031022252595554334302549182120963308798835760977059101353945868549451306814806749129977743512134206063929219345634123973737353947064792088527199092170903201049973375557998272607370418169628132094987634979460243980085010298890576971089442021776418437159996892451074988723179348906177897307264688001413012219897929773833092694387173064924984532873704752431660596342428427369357144625822520869874838')"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mp.findroot(lambda x:x*cos(x)-log(x),1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Geração de gráficos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<sympy.plotting.plot.Plot at 0x1189c7a58>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(1+x**2, (x, -5, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<sympy.plotting.plot.Plot at 0x118c51710>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(sin(x),(x,0,2*pi))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<sympy.plotting.plot.Plot at 0x118d59a58>"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(sin(x**2),(x,0,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<sympy.plotting.plot.Plot at 0x118f2ec88>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(x*cos(x)-log(x), (x, 10**(-3), 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O gráfico acima poderá ajudar-nos a confirmar a correcção da solução aproximada obtida mais acima usando `findroot` para a equação correspondente a `x*cos(x)-log(x)`.\n",
    "\n",
    "Podemos também desenhar o grafico de várias funções em simultâneo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<sympy.plotting.plot.Plot at 0x118e4c5f8>"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot((sin(x),(x,-pi,pi)),(x,(x,-2,2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Há várias outras primitivas gráficas disponíveis, nomeadamente para visualizar, das mais diversas formas, listas de valores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x119141e80>,\n",
       "  <matplotlib.patches.Wedge at 0x11914c390>,\n",
       "  <matplotlib.patches.Wedge at 0x11914c828>,\n",
       "  <matplotlib.patches.Wedge at 0x11914ccc0>,\n",
       "  <matplotlib.patches.Wedge at 0x119159198>,\n",
       "  <matplotlib.patches.Wedge at 0x119159630>],\n",
       " [Text(0.17207795223283862, 1.086457168210212, ''),\n",
       "  Text(-0.8899187180267096, -0.6465637441936393, ''),\n",
       "  Text(0.33991869870988073, -1.0461621663333946, ''),\n",
       "  Text(0.8899187028927927, -0.64656376502369, ''),\n",
       "  Text(1.0805159790822678, -0.2061194288462117, ''),\n",
       "  Text(1.0994572168309389, -0.03455182134657518, '')])"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pie([45,30,10,10,4,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar(range(10),[10,-7,20,3,15,-8,5,11,0,14])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3., 0., 2., 0., 0., 1., 0., 0., 0., 4.]),\n",
       " array([1. , 1.4, 1.8, 2.2, 2.6, 3. , 3.4, 3.8, 4.2, 4.6, 5. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist([5,5,1,1,1,5,2,2,3,5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A extensão Sympy.plotting adiciona várias outras funcionalidades úteis para geração de gráficos. No caso, importaremos apenas a funcionalidade plot3d."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sympy.plotting import plot3d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "?plot3d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADnCAYAAAC9roUQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOy9WWwk6XXv+Ys1I/eVTO57LSRrr9504dY8+Pp6pAfZkABDHgM9HhsQJE8bwhgzuPCdGd8xBhAgwPb4QYZhGwMJuA/2g18ECK3x9djWWFarq9eq6u7auO9MkpnMPSNjnYdGpJJkkkyyWNVV3fkDClKTwS9yifznifOd8z+C67p06NChQ4eng/hpP4AOHTp0+DzREd0OHTp0eIp0RLdDhw4dniId0e3QoUOHp0hHdDt06NDhKSIf8/tOaUOHDh06nBzhsF90It0OHTp0eIp0RLdDhw4dniId0e3QoUOHp0hHdDt06NDhKdIR3Q4dOnR4inREt0OHDh2eIh3R7dChQ4enSEd0O3To0OEp0hHdDh06dHiKdES3Q4cOHZ4iHdHt0KFDh6dIR3Q7dOjQ4SnSEd0OHTp0eIoc5zLWocOhuK6L4zjU63Usy0KWZURRRJIkRFFEFEUE4VCzpQ4dPpcIxwym7Fg7djiA67rYto1lWXv+v/e7ZqH1RNj71xHjDp8TDr3AO6LboW32i60gCOi6zsLCApZlEQqFCAQCBINBVFVt/I33b2lpie7ubgKBQEeMO3zWOfRC7qQXOhyL67pYlsXq6iqRSIRgMEi1WmV+fp5qtcrQ0BCSJFGr1chmsywvL2MYBpIkEQwGCQaDBAIBarUarusiip9sJViWhWmae87VEeMOn3U6kW6HQ/HE1ksdPHz4kFAoxM7ODpZlMTo6SjKZbES/nph6WJZFtVqlUqlQqVTIZDKIooiiKHvEOBgMomnanvN6/zyxFQQBSZIaeWNPnDti3OEZpRPpdmgfx3H25GkB8vk8W1tbFAoFLl68SCwWa/zuMOGTZZlIJEIkEgE+EeG+vr5GpFypVCgUCqyvr6PrOqIoNkTYE2S/399Yz7ZtlpeXAejr62tEzYdFxh1B7vAs0hHdDg0cx8GyLGzbbvxsZ2eHhYUFNE0jmUySTqf3CO5JEAQB13WRJIlwOEw4HN7ze9u2qdVqVCoVisUiGxsb6LoO0BBjXdfx+XwIgtCIrPd/QXjnEkURWZY7YtzhmaIjup9zvNt40zRxHKfxs0wmw9LSEpFIhMuXLxMIBJidneWYdNSReKJ7GJIkEQqFCIVCe37uOE5DjAuFAqVSiZ2dHQD8fv+eyDgQCDRE1Stn2/8YBEFobPx1xLjD06Yjup9TvBpby7K4f/8+Fy5cwHVd1tfXWV5eJplMcv369T25VkEQGsJ8Go4T3cMQRbEhrN7GW39/P47joOt6I2e8vb1NtVrFdd09YhwMBvH7/UiS1MhT37lzh+vXr+85T3OawouOO2Lc4azpiO7njGaxdRwHQRDI5XIsLS2xtrZGT08PL774YqPkq5nTiuaTwssBBwIBurq6Gj93XZdardbIG2ezWarVKo7joGkafr8f0zSpVqsEAoGGGHu57P21xq1yxp2Kig6npSO6nxNa1diapsnKygqVSgVBEHjllVeQ5cMvCVEUn2h6oV2OW0MQhIYYp1KpPX+n6zqlUont7W1WV1epVqvYto3P59sTGQcCAWRZPlKM6/U6qqo2ouiOGHdoh47ofsZpJbaGYbC4uEg2m2VoaIhgMMjIyMixa31a6YWzWkMQBPx+P4qi4PP5mJycBD55jQzDoFwuU61WWV9fp1KpYNs2qqoeEGNFUXBdl83NTYLBIMlkstOF16FtOqL7GcXLXTZHaF73WLFYZGRkhPPnzyOKIisrK22teRai+aykJ5oFUBAEfD4fPp+PZDLZ+LknxpVKhWq1yubmJpVKBcuyUBSl0dzhRcmeGEOn8aPD4XRE9zOGJ7YrKysEg0Gi0SiVSoX5+Xl0XWd0dJSpqalTfdBFUWwZ6ba71vMmLs1inEgk9vzOMAxmZ2cB2NraolKpYJomsizviYw9MfZoJca7u7skk0lUVe00fnwO6IjuZ4T9DQ3VapV6vc78/DyO4zA2NkY8Hn+sD/KzkF44C87iMaiqis/nIxaL7YmOTdNsVFPs7OywtLR0oCXa++dtVi4uLhKLxdB1vXFX0mn8+OzSEd3nnFYNDblcjo2NDRRFYWpqimg0euw6+zeJWrFfNF3XJZfLNaLo/ZtRwWBwz8bc09pIa4ezEK1Wr5miKMRisQMNJJZlNcQ4l8uxsrJCvV5veFasra0RCoUIBoP4fL7G33UaPz57dET3OaRVQwPA9vY2CwsLBINB0uk0oVCoLcH10gaSJB17nHfunZ0d5ufn8fv9XLhwoZHj9IRlY2OjsRnliXGtVgNoeO+ehmdJVNr5ovKQZZloNHrg/bBtm3feeQdVVcnn86ytrbVsid7vT9Gq8WNra4ve3t4DHhUdMX626Ijuc8T+GlvvZ5ubmywtLRGLxbh69Sp+v5/l5eW2UwEnKQUrFovcunWLUCjEpUuXCAaDOI6DYRgt85/NlQFelJfNZluWaQWDwWOF/yw4qxTHSUT3MLzUQW9v756f27a9x59iY2ODWq3WKIfb3/ghCAIrKyuk02ls28YwjJbn6TR+fPp0RPc5wBPbfD5PqVSip6cHx3FYW1tjdXWVrq4ubt68uee2VBTFPSmHozguV+sJ+8zMDLIsc/369T1GNMet7YlxpVJBVVV6enpwXZd6vd6IjNfW1qhUKjiOs0eMPY/esxbjJ5VeOCsO86dwHKchxqVSiUwm07iD0HWdpaWlPWLsfaHurzX2/rfT+PH06YjuM8z+GlvDMMhms9RqNdbX1+nt7eWll17aszvuIYrigV3ywzisKsFxHDY2NlhaWiKRSDA+Pk6lUmlbcPfTnNMVBAFN0xpGOs3PWdf1hrCsrKzs6SZzXRdZlimVSk9EjE/CkxTdwxBF8VB/irfffptgMLinJRpA07QDtcZHdeFtbm7S29vbyBl3ytvOlo7oPoO0amiwLIu1tTW2traYmJjgC1/4wpGCc5iQtnOsF0UvLy/T1dXFCy+8gKqqZLNZSqVSyzXO6sPoNTD4/f6WYryyskKtVjsgxvsj4/3evs2clVh+GqJ7GF6lQ3d3956f7/en2NnZoVarHXjdmsV4bW2N3t5eTNPEMIxO48cZ0xHdZ4hWDQ31ep3FxUV2d3dJp9Mkk0mGh4ePXeskoutFoLZts7q6yurqKul0+kAU/bjVB2fRTRYOh9E0jaGhIeAXYlypVCiXyw2fhVamN8eJ8Ul5lkT3sPf6KH+KZjHO5XKNL7Farcb8/HzLlmjoNH48Lh3RfQbYP6FBEITGhV+pVBgZGeHixYvUajWKxWJba55EdAGWl5fJZrP09fXx8ssvt6wuOGrD7TQlZ6dh/zmaI+P9PgueHaQX4Xli7PP5qFarZDKZxxLjZ0l0m8cgtcNRr9vbb79NPB5v5Nqb/Sn2b+IdJca5XK5x59GqvO3zSkd0P0W8qMLrSBIEgVKpxPz8PIZhMDY2tqev/3FSBq0wTbMhtv39/cemLI7acGv3Q/S0miOaTW/2R3j5fJ7Z2VlqtVpDjIGWdpDHCdmzIh6O45xJFO/VACeTyUNboveXBCqKsmcoqdeFl81mG00kzamy5saP5tK2z0tFRUd0PwWaGxo8gxVJkpifn0cQhEb32H4kSWq7IkGSpEMF0jAMlpaW2NraYnBwkJ6eHnp6eo7dlDosUj1JG/Cn3ZHmRXg+n2+PyU+zUXqlUmFra6tRFXCYGJ9Vo8dZrHNWonvYYzmsJdqrF2+eg+f5UxiGgW3bRKPRPV143jk+r40fHdF9ShzW0FAsFhtTEC5cuHCgRKiZk0a6+wW6Xq+zsLBALpdjaGiIL3zhC4iiyP3799ta91mxdnxcWj2GZqP0Zo4SY8MwWFtbIxaL7amXPeljOQuxPEvRPclzEAQBVVVRVfVAoHD79m26urowTZPt7W0WFxf3+FM0R8bN/s37Gz9M06RQKJBOpz8TjR8d0X3CtGpoAMhkMiwuLjYuuqtXrx671kk3x7xja7UaCwsL5PP5Pe5iJ133WfFeeJptwEeJ8e3bt/H7/ZRKJTY3N49tXjjsuZyFaJyV6Nq2fWZleK7rEo/HD5Q0egbynsH88vLyAX8K7zX0UhPZbJaurq5jGz+8oMYbhvos0hHdJ0SrCQ2u67KxscHy8jKJRILr168jyzLvvfdeW2ue5MPp1el+9NFHlEolRkdHmZycbLlGuxHsYaJZr9fZ2dlpOw/6ODwrUY2Xk+zq6trTlLK/eaGVGHseC5qmNa6Nx+WsRPes1oHDBVxRlJYt0ZZlNV673d1dVldXqdfrjbvE1dXVxmuoaVrjevRqjQH+4R/+gTt37vCd73znTJ7Dk6AjumeM12llGAaKoiAIArZts7a2xtraGt3d3Y26V+/4dvO07VIul5mdnaVQKDA8PMz09PSRH+x2I9j94uz58+7u7hKPx/fcejdHe6FQaM+H5NPmSbYBH9W8UK1WKZfLe8bOwy9y7M0eCycV4mcx0j3pY5JlmUgkciBK3d7eZmdnB1VV97x2zeVwwWCQarXK7u5uW34jnyYd0T0jmhsatre32d3dZWxsjOXlZTY3Nw8txTrLyK1UKjE3N4dhGAwPD1Ov1w8Uy7fipOmFZrEdHR3lwoULmKbZeC6HRXveB1oQhIYYq6r6qUSvT7s54jAxrlarPHjwAJ/P11JQvKjYu9U+7Hy2bZ+Z6J5VpHtW76vjOAQCAXp6evb83LbtRr69VCrx3e9+l7feegvHcbh79y5TU1O8/vrrbYnwysoKr732Gpubm4iiyDe+8Q2+/e1v7znGdV2+/e1v88YbbxAIBPjBD37AjRs3Tvx8OqL7mLRqaPBcuHZ2dhgcHOSVV155ou2qhUKBubm5hm9uIpHAtm0WFhba+vt20wv1ep1yucwHH3zA6OgoFy9ebBm9HiYwmUyG3d1dFEXZc/vojV5v5TXbimchWoazyccKgoCiKC0FxYuMm1+r5hxzsxif5YbcWeZ0T/gHQAmEvZHuYa503nXjXWd/9Vd/xXe/+12mpqa4evUq9+7dO/I6akaWZf70T/+UGzduUCqVuHnzJr/yK7/C1NRU45gf//jHzMzMMDMzw61bt/jWt77FrVu3TvYc6YjuqWnV0NAcASqKwosvvvhE85u7u7vMzc0hiiJjY2N7PFxPu+nWiuaNOFmWeeWVV049ecLn8x1w1PIsIcvl8p5dbkVR9uRAg8HgmUWoZ8FZiO5haxxmeGPbdqOSolmMvVv55plup7mLOMv0wpG4dQRnBsF5iOhsITvvILir6Npf4LYQ3ea8+VEUCgVSqRTT09NMT0+3/XB6e3sb12U4HGZycpK1tbU9ovvDH/6Q1157DUH4ZIhrPp9nY2PjwPV8HB3RPSH7JzQIgtAYh1Or1RgdHWV4eJjZ2dknIrjNXraqqnL+/PmWO7Un3XRrJbpeV1yxWGRsbIzz58/z7rvvnlpoDsvpHuY126oYv16vN7woHseF7FnxXjhp3lOSpJZ5z/X1dcrlMrIsHzBJbzWx4rDH/SQ25FxnF9e5j+Asg/0B2EsoQhnRXQHxMrL7EIcEhvofccVzB9aybbtt/+VisXjAQP6kLC4u8sEHH/Dyyy/v+fna2hqDg4ON/x4YGGj4VJyEjui2yf4JDYIgUCgUmJ+fx7btxm29F/GedHPsuA+wJ7a1Wo3V1VWmpqYO3L6flv2OZNVqlfn5eUqlEmNjY42Zao7jPNU63Vb1n5lMhlKp1GhTbTa+aW5iCIVCT7yS4iw4y1biQCBw6F3EUeVZ3uulKMpjRbqfVBIs4zgzmOYd+nvvYlTy4DxCFK8iOR/hIiBJ/w4bBUsYQGYX09VwlF/DVn655bonMb0vFouPtZFWLpf52te+xp//+Z8f+GJ7nMagZjqiewTNDQ2zs7N0dXURDofZ3d1lfn4eWZYZHx8/8CafpHMMjp7c4LouW1tbzM/PEw6HCYVCTE1NtZ2ragdPDD2xLZfLjI2NHah6OIvqg7PwXpBluWWbaiuvBfiko6w5RfE4tcbNfBqR7lHrtHosh91F7BfjpaUlTNNsjJ1vFuVW15rrGpj2Iwz7PqY9h+A8wLLvoEgTiM6HgICmnMdFAOEVXMHEFIaQhG4c+9+ACKoYxXVr2NIXcdRvHfrcTiK6hUKhZTdnO5imyde+9jV+67d+i69+9asHfj8wMLBncvbq6ip9fX0nPk9HdFvQqqHBMAy2t7d58OABfr+fycnJQyNNSZIOtDcehSfSzaLrGYcvLi4SjUa5du0afr+f995778xEw8M0TTY2NshkMoyPjx9aYnYWm0ZPisO8FryOsnK53Kik8Cb33rt3r+3qgKPO+zicpc3kSSLUw8TYE19BEH6RX7d3UQPr+IJbaP4VBGUGRQziuB8CLkF5GsvOIIkXAQVLvITo+JG1tzGdGKqQx7G2EKQXAANbeAFREDDcORBSoP2vR74GlmW1/dwKhcKp0guu6/K7v/u7TE5O8gd/8Actj/nKV77C9773Pb7+9a9z69YtotHoiVML0BHdPXhlX7Zt72loyGQyZDIZIpEIly9fJhAIHLnOSSOXZp8Ex3FYX1/f00DRPBvrpFH0UVQqFebm5igUCkQiEa5cufLEhfFpd6S16igrl8ssLS0xODh4oDrAi/Caxfgs7yr2c5b1te1uNh2G67pYwiZCeA0xvI0cvkfU1anb7+JiEhAuYrkPcI0BDNvEdc8j4sP1zyAIMpq7gi1soYo3cd0dqvUpwsEYpruFIJ5Hdu5iU0eWXgT7Aa44jOD/vxCEoz9PJ8npegY8J+VnP/sZ/+W//BcuX77MtWvXAPjOd77D8vIyAN/85jf58pe/zBtvvMHExASBQIDvf//7Jz4PdEQXaG0a7onfysoKqVSKgYGBRnviWePlVLe2tlhZWdljHN7q2MeNdD2xrdVqjI+P09PTQz6ff+L1sk/C2vE0eOVVraoDmm+7W1VSNAvyWfBptQE7rkHZWqBizVK2HmI6u1Stt7ACBUJcpFT7EJk0fjmNKl1FEfwYzjI2cfyageUs4pevYdvLuO4w2CGqThHHThLQ7iCKFo5zAdt+E8ftR5WS2OJlJIJYzkOghuz7nxHE/mMfa7vphce5tn7pl37p2L8XBIG/+Iu/OPU5PD7XonuY2K6srDR2JT0j7+Xl5ROlDNrFK/D+4IMP6OvrO3T8jsdJI93mD3W5XGZubg5d1xkfH2/YRmaz2TNPWTyvtFNJsbm52Ri0eefOncea5/Y02oANp0TRnKNgzVKz1ylbH1K25kgoUxStD1CECCGpC1kcgpqMpVaAUUTRpmTdJSRPUzI/QBb7CMrT2G4VRRqjbn+M4+4SkIcxhXeQ6UKT0rhcwzQUkDao2RFkR8QSblHXBwmoOWwniWH9PrJzgWCwPUFt5zVqHgX1LPO5FF2vVTebzTYqDizLatgdtvKWlWX5wMjrx8GyLJaXl1lfX0eWZaampvaYSR/GaSZCVCoVZmdnMQyD8fHxxnM+zZpHnavdx/M80qqS4u233+bixYsNAV5dXW0M19Q0bU+K4jCj9LNsahAEgZKVYdeco2CtULJmyBmPCMspitZdJHxEpS4cwSKuXMfBQBMnkQSTXWuWiDSBpdzDclUSylVsagTEIUxnDQsVv9RN0fw3ZCGJTwggCn2o0hUctwCcQxRtqvZH+KRJJOkDXDGCplwHqrgMoPk3sV0dm5tU6/8tldwvPHmb64ubDdJPgq7rT+RO9Kz5XIluc0ODYRgsLCwQCoUaDQ3Ndof7kWWZSqVyovO1ij5M02RpaYlMJtMQ99nZ2bbXPMmUX9d1uXPnDqZpNiLbw9Y8rRiapsnCwgKZTOZAM0MoFNrzwXlWRPesbumbPWZbzXPzxLiVUbr3Gtm2farHYrsWOXOFHWORkr3MsnYbRZco1+ZxselRRymba4SVQQQkguIVfBLsmvcISmny5l0c6iTVm1hOhYjyMrglDDtJROkjZ76FIkTxCRIICkH5BVxXRxavIlGl4swQkC6iW/+G6woElZdw3QqK9Eu47i6GG0ZyhqjbPwE0NHEAFxtR/g+E/P8nsdgv7uaOMkj3+Xzous7Gxsae0UGtyOfzz7S7mMfnQnRbNTRYlkWxWDzQ0noYJ72tl2V5Tx+7YRgsLi6yvb3N4ODgHnE/ynC81eM47thSqdSYjDA+Pk46nT7y+NNYNpqmyeLiIltbWwwNDfHiiy/u6ZbKZDLMz883uom8yMUwjMfePHoWhPsomkfhtKqkaPak8MYvVSqVQyspdLvClrFM3lpj03hExc5RMJeoOQUGtQtk6h+jkUATUiTkSVRBZdeaRxCClK01DLdIrzpNzc4RlaeRBQFD8KEJEbaMd5GFACE3iOHkkMxhXAU06QUkdKr2DAFpkLJ1C7CJyNdxXBG/9DKCYOIIo2hSkqr1JgJBfFIMxyngWsMgCSC+gCyI6PZtRKGbuPZHCIJy4PU6zCBd13Xu3LnTGMzaPDpo//y701YuPG0+06LbqqGheRyOoihtt7TKsnyqMjDbtllcXCSXyzE8PMzExMQBwTnpRIjDji0Wi8zNzWHbNuPj4ziO01ah+EnSC67rMjs7SyaTadwZCILQeD1jsdieC9+LYryhkZVKhffff5/mwZFe/3w77lrPer7uKJorKTwjovX1dSzL+kWzx+4imY1VsqxSkfO4Spm8tEavNMyuO4uMSq9vlLDcR0oYp2rvIAlJwGXLfkiPdJEd8yEBsZugHMV0akRFH5vGbQQEEopI3lokJl/EcqtEpetIIlTtNVRxEMM3R8GxSIpT1J0dfOJFZEEB6RI+IUjRehNRCOIXyxjWOkH5Kg4GkvgyigCms4BIDKQ5bLGKjxew7GVE8RLRwP+BKCaOfpGaEAQBSZLQNG1PJ5iXHvS+4NfW1vibv/kb/umf/glBEPjDP/xDpqen+dVf/dU9X3pH8Tu/8zv86Ec/oru7m48++ujA73/yk5/wa7/2a4yOjgLw1a9+lT/6oz9q+7k085kT3VYTGgRBIJ/PMz8/D9AYh/Pmm2+2/SE+ae0twMOHD6lUKg0nrsPOdZKUQSuBLBaLzM7O4jgO4+Pjjbxju2LaTnrBy3lXKhVUVd0TqR/1t81RjGdIPTU11TLqq9VqDWFqTlGcdcnWszBQ0nZtMsYmHxu32XG3Uao2j2p3CapBdCmP4eqMaRNULZG0cw7DrCG4KVRXZoWHRJxutsVH+IQoad8IeSNHt2+QorVMzdEJyzJr9Q+ISoO4bomochFN8KE7u2jiOcr2MpZbpUudJm88wC/14xOj6FWBeDBB3r4LuEhClry1SliawnB38Ik3UUQFy91Gls5Tsx/iUiMiX0e3PkQSBlDkbqr1CKoapmbfxaVK0ve/IUsXT/w6tarRFQQBTdPQNK2R1vmzP/szfvzjH/Mv//IvfPGLX+Tjjz8mn8+3Lbq//du/zeuvv85rr7126DGvvvoqP/rRj078HPbzmRHdwyY0ZLNZFhYWUFWVc+fOnTrn46ULjsPr6srn84yNjbVV+3qSSLdZSD13Mdd1GR8fP3Br1W7a4qj0grfht7Gx0Sib88af7+c4MWvO6baK+uAXhi5eZOwV63vDD5trqZ+KMcsZULWrrOrrrOprVOwiD6v32DVzRBWNrLnFoDiAXiuRlHvRJBVdDBAQ/czrHxORYuhiHgOdEe0CpqsTpRvdzmI7Kj47wlL9DoobxNZ3UYQAKakP06oTky6guxkKdp4+dYpN4z00IUVM7cVxz6MKGhVrEUtQQLDYNj9ApZ+qM48mDOOTolhuEU1KUbEf4lAjoVyhbL2FTA8BeQBXiiDhp+bMYQEhSaVqvYkr9uKyjSyOEFC+TED9D6d67U7quzA0NMSXvvQlvvSlL53oPF/84hdZXFw8xSM8Oc+96DZ/CG/fvs2VK1cA2NraYnFxkVAoxPT09KF1le1GPcelF8rlcsP0ZmxsDEEQiMViba0tSdKBESRHHZvP5xvTJlqJrcdJIt39x9m23aiu6O/vb9hTrq2tHXjNvDlV7eRajzvmMEMXb6PF8164ffs2tm3vqRJ4FvwWduq7LNfX2a7vMF9bYEVfI6lEmKvN06t2U3N2CUlRRv0j6I6ORphNZwXXddBEjbXaHEO+UWp2hQF1Ekm0KVg5utV+5vSPUAWNmBzAERz6fRdxMFHci5TNbcpilm6pi233I7AkwmYvLmFCpCk4BRwxgSKG2Kx/SFwepew8wCd0kVKuYrkVYkoXZfseVaeCX+knZ76FRISo3I9PuogqBtGdDBZdaFKEovUufmkMw/4YSeoiKP87HCqI4gtYrGCyjSpeJ6L9zqlfz5P6LjzJnO7Pf/5zrl69Sl9fH3/yJ39yIhezZp5b0W1VY6vrOqurq6yurhKPx7l27dqebq79eELaTgfLYdGoZxxumuYe05tcLtd2OqLdSDefz7O4uIht21y5cuXYfG27aYvm9IJt24065b6+vgNewJ64nub2/HFu6b2SLa+8aGxsrLHR0mwL2Ty5olmMmzemziK9YLk2WxT4/7Lvs17PMF9dRRLgYXUOn6iQVAJU7Roj/gFM1+Cc/zxFO0/Rsunxhfi4co+UnER0bRJSD3EtTNkukVYnWK0vY7smw9ogm/oqaXUQAYEeZRJZdMgYiyTkLlbrH+Pi0O+bAFemT7mMKxpo1hAhn58d8SFRqY+as4joqkTsAepmFdk+R0nOYckuqhsg495CEYJEpASulSCpXcZ2SyjCKIookrPuE5LG0M2PEAkSUaZw3Sqa/AUcJ4OBQ0hIUbb+FZAISxeoOz408ZfpCn7nsV7rk/ounKYttx1u3LjB0tISoVCIN954g1//9V9nZmbmVGs9d6LbSmxd12VlZYVyuUy5XD60m2s/JxHd/RdOs3F4cx61ee2zGJcOe31z+/v7qdfrbW2QtVuV4I0UWlxcbIhtqykX8Iuo+DTR5Fm3ATdXCTTXOO8fjbO2trbHLF0URer1eiNtcRwVS2e+usmGnuVBZZmsUWDTyLArl9acRp4AACAASURBVLiUG+JRZZEhfw8BSWXCP4YkuqzXt+hSU9yrPEIRFHp8cUQkJkPnqTt1epVRam6evJtnVAjzoPoxMTFJ0hejzzeGT1DYMTdQxThZK4PuVBjVJihYWVLyED5JRhTGCYgqq/WPUMUgOXuRulFlUJvCpkZauYZNmbobIKr0s2N+hCL7iYhhXEEjymVsu4ZojgN1ssoKkpMia7yLi0tUuIQrGITll4AiFnHC8iC75psIqITFNA5V/NJVwEWWrqLgo2S/gyuGSGj/C+IxLb7HcVLRfVKRbvOd15e//GV+7/d+j52dnbZq6/fz3ImubdsNQw7vFtgbh5NKpRgcHDyRW/xJN8eaBbCVw5jHSfO0rY71ziVJUsM3N5vNNqK542gnp+s4DqurqxSLRbq7uw8VW4/HEc6n1QZ82OQKr8XXm+X24YcfYlkWqqo2ouKa6rJNmdnaJvPVLSxH5/3iHOOBNCt6BlkQmQwP0KUmiRkBCpZOTEmQt0os62WmQsNs1HfoVpKogsSgbwRFdJmvLTGk9XG//AgBOB8cQXEThK0YNadGQEghSyJztTnGtDEW9UfE5C7iahTTNdAEhZX6I0RBQBQKbOhbDPnOU3PK9KjTlEtFBNWhSx1kuf4hkiCTUrqo2DkS8ic5+IQ8jSQ4ZM0ZInKavH0fG4O+wGUMp0RQvEG1lkMUfChOiF3hQwRTQbNDOHIe1RqkbteRxatokkzZvY+MH9eZxXbzhKVrmGTwSTfIr/57tOTAY73XcDJT9Scpupubm6TTaQRB4O2338ZxnEPr3o/juRNdz6dgcXHxwDic+/fv7/GFPY52Rdd1XXK5HJVKhaWlJS5cuHCgZ38/j1MGlsvlmJubQ5blA+c6SXnXUcd6YruyskI6nSYYDDI2Nnbsms9Kg8Np8Fp8HcfBdh3U/gQz5QwPi+sUavO8u72I5VpERZkcVS6IKeqSxaRvAAeHhBwjpvq5XVzkXKCXVbZJ2XEGtBR112RUG2C2toSMxDY5ClaJS6FRylaVCf8ECBYJpZuI7Od+ZZ4+X5o8GXy2j9HQEIZdJ+5Ps2OuU3dBFiUeVe/Rp/ZTJUtKGSAo+ak6ZXrUBKv1GVxs+n0j5JV10sIQguCSVqeREdm1VlCECDlrAdOt0adOUnVyxOVz+EQFQ/CjiQHW6+8hCT5isk7dt0pQOo8oQYTrKIpIxV5BdLtx1E0qVAhY5yg587hmEtwwrmwiiyOU+BgwGQj+9+Tqw2eSW7csq+0us8cR3d/8zd/kJz/5CTs7OwwMDPDHf/zHDS355je/yd///d/zl3/5l8iyjN/v5+/+7u9OnTZ57kS3UChw+/ZtRkZGOHfu3J439qSR63HHu67L9vY2CwsL+P1+NE3j6tWrbW+Otds27ImuJ7aKonDx4sWWwn7aSgcPx3FYW1tjeXmZdDrd8HrY2to69Zrt8mkJdtmqs1DOcr+0yVJ1l7nSOg8qGa7U+rhbWmbAF0OVJAYDSUKyj4JVo19K82F5gRA+Km6Nmm4wSIyaqTMh9lKsVzEdkYDk4/3SLOP+XnJGjm41RVjSqDg1+n29fFR+REj0U5MrFK0SF4PjGK7B+cAFak4Z2/aR9HXzcfk+CTmOKJj4xCDnApPUnRqDvgvsWqtUnTJj/jiztY+IigliaoS0eg6/6KNgbyNbEapylqxVZNB3kS1jlpDcTViOYroJNMFHxvwQXBdZUckaK6TkMer2LjH5Mj5RRXd2cM0eStIqlluhS71E1riDJvYQVnqw3TSy4KMq3MN0dZKhGBXrQ0QS4Go4xghu9QLzc0PUalVmZmb2NHycptrkpBtpp/XS/du//dsjf//666/z+uuvn2rt/Tx3ohuJRBoF+fs5K9H17BwXFhYIh8MNO8dbt261XcLSbtuw67oUi0Wy2SzAoWLrcdpI13NNW1paoru7+1hjncP4tNMLx5HRyzwsbvGwtE3BqvKznXkEoGjXqNkG09EuSqbORTWF7cKFQD9Zq8BqNc/V6ADv5Bc4F0hjiiaXQqMIgs1GfZexQBf3y0tExQCaZCK6EsNOgnrVot/pYq2apS7UCeDjbm2FYV8vZapM+MdRRMiZeQa1BPcqMyiCQkoNU3cM0qRwERjyncdwS2SMPBOBLh5UP8InaPT6uojJafqlMWpOkYjUiyhYLOmLDGvjzNfuEpaShJwYQTlAl+Ajay2guxYRQWBZv0tSHsQQdohKowTEIHW3REy+QMFewXSr9KmTZIz38JFEdcIEpTSKoFG1l7FRUKUgW8a7BMVeRCrIQpKIPIntVlDES0iUqDlLRAI3udjznxHGJG7dukUymWw0LzR7Uuw3CDoqIj5JyVinI+0JIYrioZGmoiiPlV5wXZeNjQ0WFxeJxWIN4/D9x7dzERwXkXopCy+yDQQCDR/Px1m3GS9XvLa2xtLSEqlUihdffPGxmg1aNVJUq1UWFhYawxSDwSB+v//A+3SWG2mW47BUzvOguMVKtcCd/DoPilsMh2N8WNjgSjTNRr1AjxomrPqI2wH8ksz7+RVG/FEeGDuIJlyJ9pEWY0wE02zXi/RrSTJGkVK1yqVoHxu1HINaEhGBi8FhDNdkvrrJVKifh/YSUdFPOhChjzRBZPJWmaSbYr2WpS6YDIsJMuTolZIguoxoYygirOrrJJUYq2xA3WU80I/rCpzzX8BxTVLyEKrksqivMOAbYK72EZIrMxKYwHQN4nIvJTuLS4CgHGVTncO10hTcApoUo8s/jOnodKmTlK11ak6BAV+KVeN9ZPx0qUNE5BF8QgDdyQJJfFKMom8O0e6j5GZQxSgp9TqOWyMiX/vEI9cuEFN6yJpvAS4p5QqWaxJWXuVc+H9HFOSGiU8ikWjZ1ts8seIwTwrv+jmJgblpmo/tKfw0eO5E9yhO6gTmHd9sHJ5MJrl582bLN+8s2nVd1yWbzTI3N4ff72dqaopAIMDbb7/d1rrtRrpeBL2zs0Nvb2/bFR3H0VwRUavVmJubo1wuMzQ0hOM4lMvllt1lXtnWaaiYBg8LOzwo7LCY3+ad7Br+9feYKe+gihLdfj+W63I52kPVMXk5McxMeYuyZRJTLO7lMrwQH6Bg6tyIDVIzdWIE6Q6FeDe/ynS4h3d2F+hWIwwEYiSUIH5R5mFlk4CssVDbomrrXIoMYto2V8IjmK5Jyonh1xQeVTJMhwf5sLpAXAozEEySIIpfVFjXt/A7IfJuiTV9i0EnQVEsERZCyIJE3E4S0YLMVpdIKjEK9iK6XWcydB7drjHun8J0a6hiki4lwcPqfaJSDIEasqgyrJ3HdOsE9UHckEXerjOihFms3UYRfKSUXvxSiqQ8iuVW0YQhgrLChvGQmNxH3p5FRCatXsRwyqj18whBi7qjEBK72TLeRkAkKY9guw5R+SoiAn7pIqoQYsd8BxE/N+L/GUWMNa69VtHrUdUmXndiuVwmk8k0Novr9TqaphGNRg+U/jXzPO0zPHeie1Q+9aSRriiKZLNZ1tfX6e7uPlaYTpK+2C+6zVN8/X4/ly5dajRseN10p1l3P83Rus/no7+/n3PnDk5YPS2CIFCv11lZWSGfzzfG+3idgId1l21vb1MqlRqNDZ4Q77/F3KqWuV/IslzK88HuJjOFHYKKwsf5bW6merlXyNCrBAjKKpciPbiCy/3iNlOxFD/dWaTbFyTu0+j1RYlFfOwaNW7Ehngvt0pIVin5dAr1Khd8cQRkXoqNsWUUkFAJKirv5Be5HO5nrrZNjy9KWPFRtYOEZIXbxXl6fDG2zXV0y2BCTKCIPq6HJyjaFYJClLDi58PyIueDAyyUl0mpcfr9SeqOiV9Uma0tIiBhSSYL9ja9Tord+i5dTheyLeBKYfrlNPcrD/GLfsKyQsUqM+wfw3FNhrVJLLfGjqHTq6SYqX2IhERUiuCiMOSbwnEtItI4PlFitT5LQk5TdFexHJNhbRqTKmn1Go5bxRBqROU0q8b7iEiEpTg1u0ZUGkdAJCRdwScqZM07KEIAlzXqzjYxeQrD2SEsX2Uo8N8Rkica7/tJolNoPd0DPhHjd999F7/fT6FQYH19HV3XD7SK+/3+xuf2027vbofnTnTh6FHe7Yii1wCwtLSEqqpt5zdP4r/giaMntnNzcwSDwT1i2/x82uWwSNebqbawsEAikeDmzZvs7u6e2I7yKAzDoFgsUigUOHfuXFvObM3dZbZt88EHHzA5OUmhVOLB9ib3Z++zWinySC+DKPCwXsQvykT8KnXbZjreRdU2+WJ6mNlSDtWVKdkmCztrvJTqI2foXIv1YOIwFe6hZNd4WMzyQrKPn+0sMRFMogg212IDCILLaq3AmD/J7WqGlBNEkRw0SeV6dICKbXAzOsqj8gY1xyAia7yb3+BqZIC8WWnkeGVTIqn5+biyRNqMslbfRhYkzgX7qDsGl0Pn2TK2MVzwSyp3SjOM+HvYNXOk1S4isp+KUyPp62auMoMgCAwEg6zV1xlU+rBci25nAMGyKFsl/MSYqz4CwWFA6aPkFEmrA0iCSFo9jw+BlfojYk6Cqr1A3a0yqk2i2yX6fVMIOGBLJH1hluofIiGTUrsomhsk1FFAIKVcBtsl78yjuD4qzhp1K0+3MknZXiYkncMvBjHdMkG5n5L1ABudMe1/oMv3S3ve95PkYY/CSyX29vbuuc4sy2rUYWezWf7xH/+Rv/7rv6ZcLvP7v//7XLp0iV/+5V9mYmLiiNV/wXFmN67r8u1vf5s33niDQCDAD37wA27cuHHq5/Vciu5hHCe6zT4CfX19XLlyhdXV1bY3lE7a8FCtVrl16xahUIgrV66cicHy/i+c5k2/WCzGjRs3Gl14Z2FODnttHD0Pi5PsEldNk4e5LHP5HD/ZWCK3tYwgCdzNbnGju4c7u5uMReOEZJlpRcF0LJarZfpVH/+aWaZP9WMLEFM1BsIBCnWdc7Fu3s6u0BcI81Fxi7pl8EJqAFUUGU7F2aqXGfLFyZlVZitZrsV6Wa7sMhZMYNkm55UUqCJzlR2uxRL8LDdP2hcirKgMaAkiikbBqjIdHuTj0hqCIDAccFir5ZgOD2A6BiN0o8oy1brBgL+LdwszJJQQPgk00ceV0AS6W+dcYIyMkaFoVUioce6WH9GtxBEUjZTbTSoQpeiUSKu9bFtZdKfOxeAwK/omXUo3IclP0A4h2ZAx1hBdkU1rFUPU6aefnFgiYvYQ98epOSG6JD8L+kdISHQJabLmCmllGNutk1anUQWZvLWBLCQp2RvUnRJ96iQ5exbZjZNUezFdnZA8Ts68j+VW6fX1sGW+i0KYiJLCL08QVS4wFvjtA+/3Wfti7P9il2V5z5f5uXPn+OpXv8o3vvENfuM3foOPPvqIzc3NtkX3OLObH//4x8zMzDAzM8OtW7f41re+xa1bt079fD5TontYeqHZOHxgYKBR11utVs+0xAx+UWY2NzdHvV7nlVdeOVM3++ZWVm80ezQaPTDAEk4uuvvbYz1nsc3NzYaN44MHD9pa6/+Zn+OflhYoGQYfbG8iAKlQgEy5yIt9A1Qtk1d7B1mrlEhpAYpGnZlClmtdaUp1g8upNKbrcN0JsV4rkqlXiIoyb5dzjMl+Ngo5prQEiAJI0B1K8tbOKpPRFB9nN+n2hRgOxag7QSbDMvdLWwRlHw9L29Rsg/P+GLbj8kJsEMO1OB/sp2SXmS3nuBEb4FZ+nn5fjIQaYDI0gE+U2KjnGfB383F5FXDpFQMUjCqjWhrXdbkYGKHu6izVMkyF47xfekRQ1OjTYiSUBCPaABW7RlrpxaTGbHWDITfFvdojElKctJYgJAXRJJVMfRMIUHd1NvR1Jvyj7LibJOU0ETmE7uioSKwZMziOTYwo8/o9wlYcQyqTEEcIyBq6UyIs9VF2ttGtMoO+C6zoHxISUyTUfmxMErJK3ppHxyKOj9X6+wTEBJog4Jd6CYpxLLeKTziHKjlkzTki8jjnQ99oeafzaZgRFQoFurq6ePXVV3n11VdP9LfHmd388Ic/5LXXXkMQBF555RXy+TwbGxunbjl+LkW33fRCs3F4q6kQp/XIbUWzCEYiEa5evcrt27fPfHyIZ1v51ltvEYlEDlRYNHOa0T5ep9/Kygqrq6t7vqSajzuK/3dxgT/813+mLxQmHQxyKZFCEAWWS0WGfUF+urbCaDTGQilPUgtwLpakYppMRBO8u7XOYCTC+zubiMDFZIq0P8z5WIpcvca0ovKonEN0BXpkka1yhfNahFKpzLQcJV+pIToSAVHmzZ1lrsV7eVTapk+LEFJUYoof2XG5W95kLJTgg8IqtmNzMzGALIYY9ifJWxV61AQWFh+W1rkeHeROcZlBLUFY1jgfGETGYaayQZca4UF1FcuxmAz3YzoOl8Oj2NgMqP0okstMdZ2JQB93SjPIiEyGhzFdjX61h43yBioR/LLG/cocI1o/S7VF4nKMMf8QdccgqfSyYSxRsWsklRQPq/dIyV0YoktaHUZzfew6ORLyCLqYJeuU6BeHWLUfoDgaESuBKCaIi33k2cVBxS+FWa7fJSJ1ASUUIUI3fZhUicuTOFTIWcskhTAZ4wNcbHp9lzGdEgnlJa5Hv40stL7uzkp0T7I5ViwW22qNPw1ra2t7/HwHBgYaMxRPw3MpuofhlTPpun6scTicrq53f3VE8+39cSJ4HEcZsTTnhi3L4qWXXjr2PCet6bVtu9Gl1tvbyyuvvHIgN3fcmne3tvi/b98mrYUQgPc2N7ie7mGlkGc0FqdQLPBydx8btQqFmk5fMMy/ra9ws6uHTLXClWQ3CCC6ArIi8sF2hhtdPfw0s8xwKEJc9nFeixAKBVkqFzgf6eLDwiYx1U9AAlmQuOxPUbbqTCtxHuW3MHFQXYGPimWuR3oomnWmtBQ+RcVxY8R9Gm/nVpkIJZkpbxKSfFyMdKM7FqP+Lpb1LD7RR9UxmM9vcz06yIqeI0mQlBomKGkEJIW7pQW6fRFmzHWqts6NyCgVW+dS8ByGqxNXEqSUEHdL8/T5kqw6n6QKzvuHMDC4ELjIrrWD4UJEifJh+QEJOYoquYSkGEO+YXS3Rq86TtXOsmPkGfdHma3fRxQkxqRRNLGHHnGEmlNEsuIktTibxjxxKUXJzSC5KnF7mIqh43eHsGSdmmSSEDW2+BgEl17hPJbjkJKnUUQRV5ggIATZqL+PgMx/k/wfCUiHTyM5K9E9icdHPp9/YqLbSvwfZ8PuuRTdw55wrVZrTNYdGRk50jgcTj4brHkjrVlsD7u9PwleFL1f5JqrHoLBIFeuXOH27dttCXu7ous4DoZhcOvWLXp6eo70Xzgq0l0pFvmf/vG/EpQVJuJxqpbJcCTK3e0MXYEgd7YyCK7DWMBPRFEZ7hukUK/zUqqP93c2CcgKVctkR6/wUrqfsmXwanqILb1CWg1hO3A7v81UMMajQpbRUAxZErke60N3TR4Vs1xL9vDO7hq9WoigqjDu7yKsqGT1CpckjdvFTVREYpLErm4w7U9RN0xuhocouzoqGgP+GD/PLTIeTLFQzZBUQkyGe6nZBoP+FPdKq9iujSzC+4VFroQHyJolJkNDKKJAwfQx5vfzfnGeiOQnoBQpmhWmwyMYjslUcALdqVGwVOKOnzuVT7rWQrKMIIhMBc9huCYj2hi6U2GznmEiEONe9WNkQWJEGyAoxehS+zDdGiGhC58jM1ubJamk2DE/sYoc9p/HdHX61UtYlKmaFeJqL9vmQwQE0sogVdsizgiC46KYg8iOxAYziCgE0dHFHeLiMLqYI6ZcYiLwq3Spl468nk5avXDUOs9CY8TAwAArKyuN/15dXaWvr+/U6z2XorufSqXCwsIC5XIZWZbbHsFzUrzIeGNjg4WFBeLx+GOLrcd+c5rmet5AINDoijsJx4luc8WD4zjcvHnzgEnMfg4T3UK9zn/653+hVDMY6I7w05UVrqW7WSkVmYgmkCURnyhTrpa5t73DjZ4efrq2wkgkiuATuJ76xExko1LmarKHf9tYYSgUYbaYI6b6OBdLULMsBnxB7hV3CKoqc+U8RUPnRqoH03Z4KdmP7lpMhdLk7SqzpV1uJnt5M7vMeDCOpqpc9/Xj2Bab9TIXAnHuljP4RZmwJGE4NoNSkFrNYNrXy65ZoWhYjAb8vLU7z5CWIEuJfi1BRFTIVktcCqW4V17GxWU0ILJQznAx2Idu15kOjiKILttGgSEtxt3SPJIgMqAlyJslhrUeyuUC49oormCzrK8zoHVxrzIDuIz7BwGB8/4JwKFLHiIkK8zW5kjIcXJWBt3WGVfHqTglRrRJHLeO5drElU9ahkVE+nw9FK0dEnIfkiDTpU6jIrNhzCAJEqKUp+JmSagDWEKBuHwODT81u4hmhyg6GWyxRrzyAvp2PyuhlSOnepxV9cJJRDefzz8x0f3KV77C9773Pb7+9a9z69YtotHoY1lIPpei6wnqfuPw6elpbt26heM4Z57Id12XfD7P+vo6wJ4qgeP+rl2vBi9f7ImtpmmHlpi1c+t1VHmZl3+OxWLcvHmTjz/+uK0LvNWadcviP/7jP2GaNi/39VEyDV7u6eP2dgafJGHaDplymZf7+9H1Kq/2D5LVa4wGY5RNg8VigavdaeYLu1yMJ3Fdl5e7+skZNcyqQyoa5KcbK0zHU6zWK6QVP6lwhIppEozIvLWzylgkznu5DWzH4mZXP4oTYjyUZKdeYcSfoGDpzFV2uZns4155myE1giorXIsO4OIwV8kyGk5xr7ROAIUU6iebY2KcYrHCsBCnYFTJOzpXQgHeLS2TFPxojsK54AAhSSFrlhjVelmubaM7BlPhfmZLa4xo3SiixLnAKD5RYEnfJCQFWdBXqYsGF4UgebPIiK8fVZTo9w0TlGQeVmeJyCEqdpmSXWYyOE7NqTKqnUcSHEq2SrcSYK4+g4iA4khs1dfp1fqRBIEB30VUQWLLXAXXR9HeoWIWGPSdI2MuEJXSRJQIpqsTlvrYqc9goROV+tgw7iDjI6UN4DBITB7k5e5vUavqh0718ITYNM0z2cs4qe9Cu9UK+znO7ObLX/4yb7zxBhMTEwQCAb7//e+f6jwez6Xolstl7t+/j2VZe4zD4RfR6ElE97hcqtdsEAqFiMfjTE5OtrWuJ1DtPBZRFMnlcqyvr6OqKlNTU4dGne162u4XyOa8cDgc3hOln8R7d3/J2n/6r//MW0trXOvr5qcrKwyEw8QCGpdTXUiSyFa1yo2eXt5cW6PX5+PdzXWiPo2xeJy6bXMhnuThbpa0P8jd7BYCMBSN4rouL6X7qFoWr3T1cz+/Q8Uy8PsD/HxrlRdTveRNnZdSA1iuje24xDWNW9trTMVS/LyQIaZonI8mMZwAk+Fu5is5wqLGll1ldjfP9Vgvm3qJiVAKSRSYCvWDYHOvtMl0JM2DygaKIDIRTCLZMkPEKdVqdLkhDNfkfmmLSS3FO+YyXXKE/kCUgNxHUFJY03cISyFydpk5vcyVyBCz1XW6lRhxNUDUCWKWqyzUNpERQSiQ1fNcDAyTswqMaqOfWCjaFdJqDw8qcwjASKCHNX2FHrUXB5s+cQzRcT6Z+CDFKdo5NowSo9oEi/oDolIXKV8K8/9n701jLMvTMr/f2e855+77EntGrpGZkVt1VXbDjA1jY8vQFgyCxhJgoJFGghG2LNzYEgjJEvI3BG6kFjZWMx5Dt4GWuzXYPQMDnq6urKrMqqyszKyqrMzI2Lcbd4m7n/0cf0giHZmVS0RW0VB4Hik+xYlzz1H8z3Pf8/6f93kih5wwRdNbYhRa5FWNFftddCGBKZloUZacmCaIQgxxGkNS2HbvEpdK/JPsz6FIKkpSfWKqx17c/MbGBu12G0EQqNfrHyvV43tl6/g8sxtBEPi93/u9Fzr3k/CpJF2AqampJ2pF92RjBx05fZqReRiGbG1tsbKy8nDYIIoi3nvvvQNf4171+ryFs7u7S7vdxnVdTp48+dxX/Kf1fx/HftJttVosLCxgGMYTNcMH7f8+TrpfefMtNrs9LhUL9CyH6XiKUehzu97gTKXIvZ0mZwoF/DDicrXGWruJhEhON7iysc75UpkP2y3KxoMerCkrDzW8Z/NFXt1aI6fFKMVNpuNpYqJIYzTgYrbK9dYWuiSTiXnsWAMuFapYgc/n8uO0XAsdlZIe50pjjdPpInd6dUqxJBXdYOR7nE7HudHZIC5rLA3bdDyLl7I1Op7DxdQEgRAwEStiKCLv9bc4Fi/ynl1HjGAuXmFgWxzRyzTdHnpkIPghN3prTIopPow6ZKU4Y3oOT4iY0qssW5tYfkightzoL3LUqNESelSUIknFYBTa5OQ8C9YyQeRz1Ijx/nCRopJHBKb1GXRJpet1SYllBsGAbbfPpDzGWrRMKsxQ1Ur4kU9FVWl5W3iRiCnHuWfdJimlkQUfVTQpq1P42GSk40iiQ9NbJh7laUb3CByP8dgpnLBHUZ3ncurniElPzxZUVfURn4V79+6Ry+XQdf0hGT8p1WOPkJ822vv3Karnk8SnknQTicRTX+1f1Glsj3T3k+3jPgye531iEjN40IdaWFhAkiSy2Szj4+PPJVw4OEHuZa9du3YNRVGYm5t7ZvV8kE3F/Z/9R+/c4veuXKMYU+kFAbokUosbqL5AIZFgq92hosV4p14nJknk4jp+GHGhXGbk+XyuOs7t5g4hEW3X4lZ7h5er1Qeba8UKbhByIpnHjnzeb7W4WKrwRnOTMc0gIORCpoIgCWwMe5xKF3mtvkZGjaEpIgIC89kyo8Djcm6CpeEuIy9EMyXe6m1zLJZiadhixswRV1T6nsPxRJGr7RWyqkHHH9F2RrySn2Dg21xKT2OHDlk5RSlmcqO/Tlky2Rr10ASZU+kaVuAyJpaoO20CV8KUYrwzXKWAiYNFQjQ4Kpfx/JBjsSm2nB36uFQlhRv9e+TkJElVY1yrkZR1+sGAolLDC9OEbQAAIABJREFUjfrs2E2OGdO8N/gAXYgxbY7hhwlq6gQtZwch0knJae6M7pCVswTRAFWMMRM7hY9LRT1ByJAdd4OymmHZfo+QgGntCMPQpaycwLMdNNHElOOs2TcBgR/KfYGcOvncdbEfewXBns/C/kTe/akeu7u7rK+vP5LqsX+897AbaX9b6oVPGp9K0n0WZFl+Iaex/daH+Xz+iT4Mh5lIg6eTbrfbZWFhAVEUHyZC3L1792Ob6exHr9fj3r17WJbF2bNnn2u6fpj2QhiG/Pk7t/iTa9c5m0njShJ2v08lleRWo8n5aonlTodyPIEqCmiiiBsGLO32mDZivLq+RkXX0RSVyUSSuKbSdV0myymubK5TiyfYGPRxfJ9LlQqWHzBeTrJjDxnT4gwDl/W/kZJ92N7hWDKLJAi8lKvhhj73+m1OZvJ8Z2eFvGagKzJxWeNoIcfAd5g3S9yzWtiRT1rReaO1yulkiZY75Fy6hiIKtN0R05kcb7RWScoaCXXIrjvkfHoMO/Q4o4/R8waMCJg087zRXiSvxBFFH0VUOJ+cxok8TmsmLa9L0xkyqSd5z1ojHmgkPRktVMgEOfrDESWxQIDH0qjBifg47w7uICNyIj6JF2pU1SrDcIAppSmqad4fLJBTMvgMIISaOIYgCExpJ/AY0XAHpOQM96z3gYjJ2ASjYEhFnSEmyUjCEQwxxorzHgICsirRltZIhSVGgUNROc6M8TIzxmcOtCb341ntvWeleuxVxfV6neFwyGg0QlEUbNt+xArySefudrsv7KX7vcanknSfZ3pz2Gp0c3OTZrNJoVB4pvXhYRURj1fde2QrCMJH4uAPGpcOz650B4MB9+7dIwgCjh49ynvvvfdcwn3eOffDsiyu3l/id+4sI4kixZRC4HnMl8sMPZfvGx/jdqOJgEDLtqkPh1ysVQgch8vpNDudXU6mcuxYQ7ZGPY6YBjdbDY6aJk3X4UKmSCiCAJTSOa5sbnAsk+W9dp2cbjBuJBjYFmdyZT7stUjLOouDLj3PZj5bomlbnEkXCIH5VJlh6LLQb3MpX+XVxjJ5VSelKIypSYrxJB3P4kyiwsKwgRP4zKWL3Gg/6Ofagc/55DiiCFt2l1mzzNXdFRRRoqqZ7PojjqfKhFHE6cQEbuiyPGpyLJHlWvc+mihT09NIgsz55Ax+5DETGyMUPJatOkeNCqv2BkIUMStWsIKIiahIvz8gLiZJSwa3B0tkpSShYOGGHifMGZzQ5ahxDC+0aXoeGeIshUuII4FJvULba1FUqqiiSk07SkxUWLUXEASQhC6bboOaNkHLXyenTJKU4tjRCM0dw4sN6PodZvWXOZf4zw60Hh/Hi6gXZFkmnU4/0iJYXFwkFouhaRrD4ZC1tTVGoxFhGGIYBqZpoigKvV6PTqfz70n37woHbS/sJSjU63VyudzH9pl9EvYq0l6vx8LCAlEUMTs7+8TXoIMm9+4/734Mh0Pu37+PbduH9kbY+/xnke5e5bzZ7fNvG13mykWCKOL9eoOz1RKvrqwykUoiigITqSSmqtBzHKZTad7Y2OBIJsNb29tEQcB8KkVMVTilquyMhpyK6Sz2dgmikLLh07AtpnWD/qDPfCJD1/WQIpGcpnO1uc1RM8G77ToVPU7O0LF8H12Rebu1SVmPc6vTwA48zuXKSKHIZ/Pj2KHPsXgBJ/K4P+wwZ2b5bnOZvGIwlkhxPF7ClBXqTp8pM8+K1WHgO5xLV7nd3eKImUcRJU7Hx1ElgYXBDglR51ZvgyAKOBEv0/GGHI+XkQWBWX0MVRK4M1hnTM/y/mAFN/Q5mxxn4PucNqdAiEgGKWqpDO8Pl8jIcUTJp++PmDMmGfoWk0GNIHDphj5p4twePMhJqykFmn6TilZG9EMqwhRJXWfDWSOMJPphh3W3w1RsinV7nbRUIKOmcUIbQ82x6+2lURS5b99EQiIjZFGENGX9OP9R7p+9sOzykxqOCIIAXdfJZDKP5JFFUcRoNHq45n/rt36LpaUlfvAHf5BTp07xwz/8w/zoj/7ogT7j29/+Nr/yK79CEAR88Ytf5Nd+7dce+f1Xv/pVfvVXf5VarQY8SJD44he/+LHu6x8c6SqK8szgxv3ZYMVi8WEf9ZMmXHjwynTv3j1kWWZ2dvaZjf4XjeHZ87QdDofMzs4+ouQ4DJ6mvx0Oh9y7dw/f9ylPTPAb177NWm/ApYkqA8vhM+M1LN/nXKnEer9Hy7I4VSrw+toGF6ol+q7DK9UqdhhQCgzE0Ofa5hbnSiW+u77GRCJJMW5yOldEkkSWuh2OZvPcbjeIKyomIYQRJ80kw5HNKS3Bhm0xCgNc1ee17XUu5kusDXucShXQJAlTVokrClebG0wn0ryzu4Ud+LxcqGEFEhcTZXq+zVgsgyLBjfYWF7JVXmstU9YSTJgp0oqOIcssD3dJSCY77oD2YMTFTJUPB3WKkokhSuS0JIYsc6u3TlzWWLVadH2LC6kxml6fE+YYmiQRE1VSisaN3n0Sks5Qtmh6XaaELMPA5oQxgyxGdPzBgxTh0QoCMGOU2bBajBsldFEhHiaQwoiG10CIFBpWE0u0qUYFFsJ1UmKGsp7HjVxycpWGt04/dChpBh+ObqOJOjk5jiGlKKmTBJFHRp7GlDTWnTvoQYLPZ/4bFPHFteefFOk+racrCMJDK8hisci3vvUt/tE/+kf85V/+JR988MGB3xiDIOCXfumX+Iu/+AvGxsZ46aWX+PznP8+pU6ceOe4nf/In+fKXv/yx72cPn0rSfRapPK3SfTyIcc/OcXV19dCJwM/T3vb7fe7fv0+v16NarR5IPyhJ0oF70XsR4u+//z7dbpcjR45QKBQ+1kDI45Xu42SeSKX5H779/2BKCueKOd5e3eRIPsNba5tIgsCJSp6xRILj+RyN0YhzpSI3txvoskzCUOnaNvPlEju7u3z/2Dibgz4ZNUYIXNva4mKlzJ12k6lkClWSOJ8r40Y+H+62OVMo8HZjm5JuosqQReN4PE7PsTkdS3GzuQMChJ7HhjviQqZIz3X4TL5GSAQR5HWdNxobTJlpdtw+hCEXCg++MD6XS1F3+iSEGAlF42p7nTOpMrdbm5RjSY7G89ihz4l4mTuDbZxAwJJ8Fu0252I1FocNJvQ8aSXGKHA5Kqnc6C0jCSKSLrA+bHEyXqXl9jhhTqBLMl1/RE5JcW+4RjSMOJmocmewxphWQCBi1phGFxV23F3iUpquP2DV73PCnOS+s0RWyTAZq+GEHr7l0hU7WKFAPpJ5f/QBWqihiyAJKpPyEfwgoqzMoEoRG84SaTlL31/EiSymY7MM/C5xt8YPj/0MKfnwseKP45MYTjqs9NM0TS5dunTg469evcrs7OzDQNYvfOELfPOb3/wI6X7S+FSSLjy9Mnu8p7vnJ7C+vk65XP6Id+5h0yaeJdcaDAYsLCzgeR6zs7P0er0DLxpJkrBt+7nHua5Lu91ma2uL48ePc/LkyU9kge+Rruu63L9//6FB+d7O86998y/49gcLnC7l6DseL4/XcIKASjyOrilcX9vmXK3EldV1jmTTSKLIuVKJSIhY7fY4nsvz2to6eU1lp7FDStWYKxQYeR5TySS3W01iosxqv8eubXGuXGbgeFwolPEJmc8W6XoOy/0up5IprrbqjJsJDE1hTi9iqDLN0YjTis6N3R1kQSArq9Q9m7OJLCPH5TOZGjY+A9emopu8trPGmJGkH1jERJmzmQpW4HE5O8m63SWMBJJKjCutFY7GcyyPGuRUk5OJEl17SEY1WBg0GfkOeTXO1c4SM0aerj/iiFEirer0fJsTpsniaAsn9DibrPFOb5GCkkJWDSpkKadytN0uWSnPKHRYHzY4aU5wo3+XpBRn2ijjRR7jWo2W3wJipJUUtwYfkpEfDDeoospJ4zgBLuOhCYLDhrNBTkiwHiwTBAElv0BPGpASyphBjEA0KSoxVuy7hATM2f+YCeNgGvTvBQ7aG94b0DgsnmRk8yTLxj/7sz/jO9/5DseOHeO3f/u3H/mbF8GnlnSfhj31wp5T1sbGxjP9BA4aILn/+Mdfe4bDIQsLCziOw5EjRx72n4bD4aFMz5/1WrTnadtoNDBNk/Hxccrl8oGv+3nYGwK5f/8+09PTjxiU//6rb7HU6HCuUuJ2fQdNkrDCiL7jMD9WxnI9vn9qnPVuj7Ju0nVcFtodzlaKrHV7HMmlCYHPlKtsdNp0bJfpdJpX19Y4lc+zPuxTNeMkYxpDz+VENse1rU3Gkklutuo4YcC5QgkzUnmlUKZtjTiRzLPrWay121wsVbhS32AmkUJXNM5nKqiyxJY14JSR4FaviSwIFDSVtucwJZs4nsu8WcARA7qewEQiw6uNZQqagSiCJsq8nJvACjwuZSbYtHfZdR2mzTyvtZYoKgayEFHR0hSSJoPA4YQ5xrbTpuOPOJ8a583dRdKSTsVIMqWXSckaHX9ISS0Q4nNnuM2kkObt7l00QWEuMYYb+YxrZTpBj5hoUtKyvNtfIKekCAULAZET5jRu5HJEP4of2Wz6I9JinDvDu4SEHDcnaHpdquoYphwjESXQRY0l+0PCyCelpFkPljD9JCMvwgizlIJJCt0Ztre3DxQY+b3AQSVj3W73IwMbB8FBjGx+5Ed+hJ/6qZ9C0zS+8pWv8LM/+7P81V/91aE/az8+taT7tEpXEAR6vR6vv/461Wr1meYt8PHsHfca+ZZlPbGfepgq+mkbaY972r7yyiusr69/LJ/c/QiCgNXVVdbW1sjlch+xv/zTt9/jK//uKkcKWWRJ5XQxj+N5NB2X48U8372/ykw+w4fNITlDZyafwfJ8juWy3N5pkDN0bm43AJjMpYgieKX2YMrs5VKFu91deq5DJqbx+uY654slGsMR54plBEFAFSUSMZW36lscy2R5u9V60BooV4kpMsfTORr2iGkjTd9zWex3uVQs82Zjg9lkFl1RuJCrIIkCS/0OU4kc9/pNCCImZdi1bcakGL1ej+NyBo+QVbvPXLLAd5vLmJJCWTeJiQqXs1M4kcdcosqu02fT7VMyMlxpL2JICjPxLEUtxfF4mWHgMB4rIAghH/TrnIyXeat7H4BzqQmc0KeiZmgO2iTlNNVYkuu9JfJKklCw8cOAE+YEDi4nzBm8yKXueJS1DLcGD7wZjhpVdtwmmSiNLsaY1KfRRZUlawk/8jEllw9Hq1TVMrtenZxSIiuncUKLgjhDEA1o+ztMG8f50dx/yY3uuziO80hg5J5KYH/O3bPerD7JrLKnZa09jhedRjuIkc3+Dbxf/MVf5Etf+tKhP+dxfGpJ93H4vv+wsg2CgO/7vu87cFT6YYcp9jwfRqPRw8r2SQvx4wRZ7pHh3ivQfjI8jNJhv0/ufuypN1ZXV6lWq0xPTyNJ0iOL/Lv3VvijN25SMk26tsO9RpuTlRxbgxEnKgW8MOKViRpb/QGO42OkFK4srzNfLXK/3WEymSSmKZiKgiiJ3KzvMGXqvLa2TiYWo5gwGYsnSOt5dm2Hl4pV3mnUUQQRPwpZ6Xb5TLXKwHe5XBrDCj0Kik5clnhze5NT2TxX2mskFY3juTxuEHAqXWBl2CWvmuy6Fnd7LS7mK9xq15mIp0kqGifNHLIA950+xVicJbuHG/qcSubo/E1+mmvbTBFHQGBx0GEmluLq7goRcCZZRkHmjFYABKZiJXRZfKhyuD5awY8CLqYmGYUOF1PT2KFLTslSisW53l0hp8QJ8RlENuf0SdzQ4XR8hiDy2HEjKlqCdwf3gYhZo8KGXaemFYhJCkf0aXRJZsXexAslbMFhx2kyFRtj014jrWQpqhns0GZSS9ILGuz6I7JKkQ9GtxGA6dgEViQyo5/mJ0q/gC4ZyLLM5OTkI2tkb5Ch2+2ysbGB4zjIsvwIEZum+fBZO4wd4yeFFx2MeOmll7h37x5LS0vUajW+9rWv8Ud/9EePHLPfrPxb3/rWgS0AnoVPLenukch+sq3Varz88stcu3btwDrBw5CuZVl0Oh06nQ7Hjx8nn88/NyPssKS7f8OvWq1y+fLlj/SFRVE81Kbb/gdhv5dEoVB42ON+vHr+YKvBb/yf/5aMoTNTzGF7HrOFLDfXt8kZKm+tbqErMoWkiSrJvDxVY+C4XB6v8e72DpIg0BIFNnYavDRWpePYfKZaodntciLzYMrsTqPFhWqZV9fWGU8kQI9xNl9EkyUa1oiLpQpXtzZJqCpmTKFtWcznC3RHIz5XHqftWsRFjVo8yetb65zK5nm3/WDD7Wg6i+P7nEgVuNtroggyHdfmTrfJ2WSOJavHhJkiqWkklRiGInG9vUlW1VnzB/R8h0u5Gk1nwGmxBITUwhRaCDc62+REjR3Rw3J8LqWrDEKX88lJEALCKEdO03mru0JK1olJA5pOnwupcezA43R8CkEIaTg9koLGzd4SEHE0XmZxtM2k/sAg55jxgFzX7TqyoNEPh6wMNjlmjvP+YJmskuGIWaPZb1PQS3SCFqMwYEwyuTX4AFVQKGspQOKIPosEVNQjmJLKfesOiqDyhfI/w5CePqX4pEEGz/MeBo5ubW0xHA4JgoBYLIau6w8DSQ3D+J4ERb6ow5gsy3z5y1/mh37ohwiCgJ//+Z9nbm6O3/iN3+DSpUt8/vOf53d/93f51re+hSzLZLNZvvrVr37s6xWe8zrw9zbX2LZtlpaW2NzcZGxsjPHx8YfkdOXKFT772c8e6Dyu6/Luu+/y0ksvPfUYy7JYXFyk3+8Ti8WoVCqUSk83cd5Dt9tlbW2N06ef7T8KD1oV7777LlEUUS6XmZycfOoXR71ep9/vH0gVce3aNebn51EU5WGMUDqdZmZm5hF/io2NDTzPY2pqiq1On3/+L/8Vq60OR6t53t9scH6yzM5gRDamEgQ+oqrhE/L+doMLExXe3tiibOromoKpapiaStd2SOoqb6xvUkmYDHyfvuPwmfEqlueTiKns2jYjz2MYetSHQy5UyrxT3+Z4NktMUZAFgUiAjUGfcjzO9eYWCUnGiGl4YcjpXJ5R4KNJIpvWgI1hj+PZHDfbO5zM5FgfdSnH4lTMBKPAw5QV3mlt4oUhpbjJ6rDLhVyZbXtATjVIqipD3yOhKLzZXsOUFBKawpbd52KmRse1SCoxItel69ukFI33nB1URHKyTCOyOKHlCeUIQ46hSiItd4ApqSyMtgmjgLlkhQ8HG0zEcsh+QMJMoosyW04LO3SRxIi212cuPsa90So5JcWkkccJPTRBoeE1aXodjpvj3B0tokUKaVUjEqCs5ImEED8KUEWRRWuZuGggSx6DYMCMPkXHb5CU0vyT3H/KXHweeFChXr9+/VC7//uxFx7QbrdZW1sjHo8zGo0+ktx7UHlmFEW89dZbz3wu9/CNb3yD1dVVfv3Xf/2Frv1vCU/9tvnUVrpbW1uIovjESvAweFala9s2i4uLD2VZp06dYmlp6VDV60Ey1ba2tlhcXCQMQy5fvvzcndjDJkK0221WVlYwTfOpyRZ75+xbDv/9n/4bFFHi8uw4fdvl8swY19e2MDWVLc+jObR4abpGz3Z4abxMu9tjJm6y6/tst3vMJE1u1RvMJAwansN8IYcoSbRtm7Ii8+baJrO5DB+0m+iSxMliATcMOJbJsjHoMx5PUB+O2HVs5ktF7rSaHM/mEAW4kCkydC22PZcjmQzf2Vojo8XQZBFJFHm5WMMKfD5bGGNl2GXoBMSTGq/WV5lNZFgddikqOjlVx5dFavkk7+5u44Q+SUXjSmONuVSBtdGIuUSJuKrQ8xxq6TTvdDaIiDiWyHHXajCjpXGkiLOJGoYi03KGJKME990WnhNwRE6wEnXJo6MqMKsVSagxmv6AtJxmGDi0ggFnIpO3e0uk5TgzZgkn9JmMlWl4uwho5LUU13sLpGQTVQyJgJPmDH7kMxObwbJ6tPwuWSXJkrOKE7qcMGbYsLcoK2UyagI7dCgqMlvOMlZo8XLyP3hIuPDx2wKCIKDrOqlUim63+1B2tVf1DofDJ9pB7v08Pt57WLObF9lI+7vCp5Z0JyYmnkpoB/WbhScbvexV0Z1Oh5mZmUdkWYfxX3jWsXvJE4uLi2SzWS5cuMDt27cPHAV/kGvodrt0u12iKHqm2Q38TcvCD/jS1/41N5frXJip8urdVaYKaVRP4lS5gKbINAdDirrG1aUNagmdhaaDLMDcWJmE7zOnaTSGI04VdBZ3O/hhQCVpsNEfcjRpYvsB89k0oyAkLijUUgneWN/gdLHAjZ06ZdNkIp3CC0JiisRCp01BN7jf6TDwHE7lc2w7NrPZB330S/kKduhzr9NmLlfg1a01UqpGUteIiTLfVx5nFHhczNbYsLo0rBGlRJar3To1I4Esi0ybGXIxg55ncz5d5d6gycB3yeUMrjRXGddTuJHHqUSJlKrRdi1mtAx1b8DQ8biYrfJGe4W8YlA1kxxXKyRklaY3oOAJiGLIPbfFUSHHW71lYpHEpJwiEGQy6HRdG1UwKMWyvNVdJKfEiQSPMIqYMydwI49jxgwRHhtOg7yS5P3BEn4UMJeYoBl1KMg5cmqCdJT4m7HfFQaBRUkrcHtwh6SUQJciTCnJ2fgF/uP8f/LI//+TnCLbfx5Jkh5J7t3Dnh3kYDD4yHhvPB5/+BwcxI+62+0yMTHxsa/9e4VPLek+C3vV62GnzBzHYXFxkd3dXWZmZh6RTe0/90EVCU8ixz1P24WFBVKp1EMz9DAMX2gi7UnY77+QTCY5ceLEgRIh/sXVOwwtl8/OjtN3XObKBbb6AzqWxYlakRuL65ytFdgd9DmZSSBqKkgyqbjOWyubnK0VeW15jVoySTFlMlfIocgSa70+p4oF3ms0iYkCliRieR5TcYPR0OJ0PEG7P0CLBHRJ4vX1DebLRd6v71Az4+TjcYq+T0yWudnYwRBlPtxtM/Bc5gtF2rb9MFttPlPGJ+CDTpMzuSLf2V7FlBTGkwmSisZsIkvHGnDczDCIfFZ6XS4VKnxnZ5mUrFGLJ5gy02Q1na7vcMws0vVHrPW6XMxV+W5zmbioMK7FGdNSFIw4u/6IippGkQVudrY5nSzxxu4iqiBxLjOGHXhM6nk6/pBkZDKhZ7jdXycrGQTY+HbIpJCm3x8wLeQhgmbgU9RS3OgvEhIxl6ixYm1RVnOkZYO4bGCIygO7yDBCEkVuDRYoqzmsoIspx5nWH/hBTMamCSOPDWeTaizFj5V+7CP//09yiuwg53ncDhIeVNuWZT10IBuNRly7du0jDmTxePyRKrjT6XD27NmPfe3fK3xqSfcgU2kHJd0wDLlz5w7tdvsjGtXH8XEUCfs9befn5x/xtD1MXtvTrsGyLBYWFhiNRhw9epRsNsutW7cO1Ir4315/j3/93hqzlRxvLW1iqBKzlQJTahpdK7DV6TObTXJrs0FMEkklFbrtHucnKoxcj89NjbPdH5DXdQQRrq9tcWG8zDubdY7kMmiSzIVyic6gT8PzOZLNcn27TsHQiaIIRRI5nc8zdBzOxpOstnbx/BBH9Xh9Y52z+QIfdLvUDBOFiIqmoasyb+9skdcN7uy26HsuF0pluo7HpUKFiIgzyQKCLHCzvcOpTJ43GutERBw30yQUjc8W09iBz1EzD2LE+50mZzIlXmusIv4NiatSnCOJBw5lY2oaU1P4oN9gJpbizd0Voijipew4o9Dj5UyKQWCTldPUjARvtlfIqSaCGDDwbM6nxrEij/nkJI7vsG2HTBhp7lubRFHIMaPM0qhOUUwgOD7VMEtMkNgY7OIh4YQuNwd1pvUyi8MVUkqC6ahITNQ5ahxhFAwYhBF50eT24MMH7RBjkrbXYTw2zi+O/yyq+NHn4pNSHXycqJ69/u+ex+6eC5/v+w837ur1Ovfv33+4cff1r3+d9fV1zp49e6iWBDzfd8FxHH7mZ36Gt99+m1wux9e//nWmpqZe6N7241NLus/CnpH58+C6LktLS4xGIxKJxHODLOFwaoc9It3d3WVhYQFVVZ8Yv3NYPF7p7lXo+6fI9u7jIP3fb9+4y7urDc5UMoSyRE7XScZjvLW4wfmpCq/fW6Ucj5FJGJyrlRlYQ3a9kBOlPN9dWGUsk6Tj2MRVhWPFHLbnM5lJcr/VIRXT2B4MaY8sztaKNGyHI7kHeuZLlTJDz+P+bofT5QLXtreoJEx8IrKGyVzCoGc75FWdD9sdnNAnFoVsug6n0mm2hj2Op3KYmsLQ84irKm/WN8jHDO65bXYdm5dKVdquxUv5KggRR+JZlCji9qDFVDzNO4MtrMDnlWKNnufwSm4MNwqY0DOkYxpvt7eYMFMsDltYvs/lwjj9wGHeKOISkFdTVPQ4b7RXySg6iiTQdi0uZsawQ5dzyUkiIWDH6XPETPN2dxWIOJEoc3dYpyLFUUWJk/FJDElm3W4SE3UiBRbdFsfjVe4PN0gJBkeVMpbnMB4U6Q0H+KJAPIyxJG0hDQUm9CIdr8+EVkGTVKb0IxiiwpK1jBO5fHHs82SVJxsh/W21F14U+wlUlmVSqdQjsrAoirAsi/n5eW7cuMEf//Ef8zu/8ztMTk7yjW9840DX+TzfhT/4gz8gk8mwsLDA1772Nb70pS/x9a9//WPf2z9I0n0eMbquy/LyMs1mk8nJSVKpFMVi8dBZZs9Dt9tlNBqxvLzMiRMnDmSxeBDsTa/tn1J7WoX+PNJ94+4qv/l//BWmJiOLAogiJ2oFRq7HpfECq40W2VgMF5H3ttucHiuy2X/QbggjeHmyRtuyafSHFHMZriytc7yY4267TdE0KSZMbN/nZCHHze0ddEHkw9YuduBxvJBn4Hqcr5Two5ALpTK7jsVyp8tELcV31zcZSySIBBhPJinETTqjEVlFY6Hfxw8D5CjiZtvimBGnNRxwMpnB1DQGvs+JdJ7X6+ukVY2B57JtDXilVGVn2ONsPEcspqGKEhnNZWGFAAAgAElEQVQtxhs76+S0GA1pSNMZcbkwRs+3uZQdI4pCREQKyQc93rikElckdtwHBjh24HEhPY4A7DgDjsWTXO+sEUYhZ9Il3u9uMW3mkAWRucQ4hiSzaXeISwYWPu/2NjidrHKtu0xaNjkRr+BEHtVYgbbXQRY0qkaB24NlYqJK0TAYeT7TagUhCik4OUxZYWvUJiKiFe7SY8iEWmLNXyatpPnxwuc5as48dR18kpXu96JNIQgChmHwEz/xE/zJn/wJv//7v8/4+PiBn82D+C5885vf5Dd/8zcB+PEf/3F++Zd/+cCZh8/Cp5Z0X8RTd4+kdnZ2mJyc5JVXXkEURer1+hMje56Eg1S6/X6fhYUFwjBE0zTOnz///Bs6BKIoYjAYcPXq1YdTak97YJ5FugvbTf7lv7vxoEXgOSw3Ohyv5Xjtw1XKCY2u51NMxalmU9iuz7Fyjvc3GiQVmVvrdQDGcqkHZuOTNUauy8vjVVY6XSzbQ0mKvL6yzny1xL1Wm4lUkshzKGkxYprC9c1tpjIpbmzVCYk4VsggCiKfHathBQHnCkUatsVGr8+lSplX19aoGAYQcjSdJR2L0XMdKsk0N9s7eGFATJa43W0zqel0RiEnjBSGpjIIfCZyVd6sb6KIAhUDVpstzmXLdFyLS7kasijQckZMmmneaKwjiyJTiRQL/Tan00WGvsOFdA1FEtkcdJiNZbnVq+OGAfPpMre6W0wYaWRB4HRiDFOW2XJ65JQUw8BladRiPlXlzc4iSSnGTCyH5TkcSVZou31UDGqxHFe7D35vyBJD3+JYvEoYhZw0ppBFWLObqKLBptdmGFhMiBlW2SGrpKjF8ox8m3yQY9ftYEcCWTdLytFZ3F0kkUhgmia6rj/yDH2Sle6L+CA86TyHUS/sWZke9B4O4ruw/5i9arvVapHPfzxDoE8t6T4Lj6dHeJ7HysoK9XqdycnJj4y6HqZl8Kxj9zwYXNdldnaWTCbDlStXPt7N7MPe4MTq6ipRFPHKK688d5E9jXTrnQH/1f/y5zR6Q05OFGn1LSbSOv1+j9PlNLtOgDdyMVSVK3dXOTdV5t52h1o6TuA5FDNpBEng9kadk5UCV+6vkjZiJAyVhKZxJJ9l6Lq8Ml7l9nYTPwwwFIXl3T7nagZb/T6ni3lUWUKTJDRF5u2tbY7ls7y5uYUowPFCjoSi8v1j4ww8l7lsgZY9ZHtkU02leXVjjaJukDFjnEjnSWoqHddhPmZwr9d+kINmmFzfbVDTdCzHYlYxiUkSfT/gpJnl/U7jgc9ttsRbrToziTRW4HM+U0WXZRrOkGPxPEuDDkPf5VK+wputNfJSjKSicipRJq4o1O0BRS2BFwa829lmPlPmSnuNuKhyOlPGCXymjQJtb4ApmEwaGW7010mJGs3hEMt3mUtW8aKAOXMKQQzZsnfJqBnuDDdwQ58zyQlu91fIqynG9eyDLxlRZrW/gY9MVk3z7uAeuqiRUmLYkce8eYpfmvgvcG2X4XBIv99ne3sby7Ie2aDyff8TGWT4JCvdg6RtA4xGoyfKIJ+Fg/guHOSYF8GnlnSft5G2l2e237fgcbLdf/xhjGkef4UZjUbcv3+f0WjE7OzsI/PacPAY9qdJ3fa0vEtLSxSLxYdTdwdZ3E8y0hnYLv/Tv7pCNZ1gupjh3eVt8qbCvcYQXVPJJ2V0VeGVo+MMHY/Pzo5zc72OAOzKDpudPhdnEjT7I86NlYkQOF0uYvkeC81dzk+UeW1pjUrSRFVkJtNJUobOwHGYyyS5vfXA8jGMYKPX56WxCi3L5lK5BJLIbDKDrsm8s13neD7L65sbEEWcKRXJqDFKioofhhxPZxkGHh8221wol3l184FmdyyZ4Ggq+1DedTyVo2mPaDk2F4tl3mptkREVVEliUjaICQK9wZBxNc6ubbHY73CpUOG1xipZWWMqleGYnCOhajScAWUthRD6vDdocTZT4rXmBjFBZj5bwQ58ps0cu96QjGwyYaZ5o7VKQTMJBY+R53IuM4YTupzUKoSRzy4ONT3Bzd46fhQyn6ryXm+NipYmq5hkFBNdVNhy2yCopBST691F8mqSEPuBnM2cwsdjVn/gy7vp7KCLJj8/9k+RRRnZkDEM45G8sv0bVO12G9u2aTQaxGKxRzS0j1fFz8Lftpfu49gjxsO2Rg7iu7B3zNjYGL7v0+12H1FbvCg+taQLTze9EUWRnZ2dJ/oWPAmHJd29Y/cPT8zOzj5xLPigyb17xz4+srs3RZbJZF4o3eLxStcPAv67P/w2V++uMz9dYmmryVhKwzAMVElCMWPc2WhyfqbKd++sMp5LIsoik7k0SVOjbzmkJbixtEUxZfJhvUXfdjg3WcHxfS5P17A8n9PFAi3bYqXV5dx4mSvLa0xn0ziuy6l8FlPX6NoO42MJ3lzfJKFpDD2P7cGAz4xX6NoOn6lUCQiZTqSIayrvbNc5kk6xMOgS9OBiuYzqy0xWUww8l5l4BlGCW40G88Ui39laJSaIzBWKxCSZU5kCu65FTTERJIG7wx7n8yVu7G4TE0ROpLLEPYVJ2aTZ6ZJFIy4qXG9tcyye4VZvGykSuJivsjsaUIslscKQvJJgzEzyRmuNgmYS4dPzHC5lxhiFLhdS40RCyLbdp2Smub67+sANzMiz4DQY09MYksrJxBiGpLBld9AEA1VSuNFbZVLPc9dtIwki84lJ7NDhVHyGIHKpOwHxUOW94TJBFHAmMcmdwTIFNc1/PfXTJOSnb9ru36DaM5epVCrYtv1QQ1uv1x9WxfslW4/Lth6ur0MqCJ6Gw5L3YSvQg/gufP7zn+cP//APuXz5Mn/6p3/KD/zAD/z/u9J9Enzff+iYFYvFDvT6DYdXJOyXmD0+PPGkcx+GdPeO3ZOXPWuK7KDXu78y/8qfv4k1cjldyXB7ZYdETKPjRCy0GhwrJgjDiMtHx7E8nzPjJba7AxrtHmcny7x+d425sSK7tsvZ8RKKLNGzHBKlPFeXN5gpZHh7dZsoCjlVK5LVdWayaXqOy1yxwGa/z65lk0skeH1lg6P5LO2RxblyCU2R2bVsJmoJrq5tkdQ0ujGHrcGAl8eq9FyXlytVbP+B6U0lneLa1jaTqST3u20s3+flWo2h5/K58hiDwGVcT5CKxXi7vs2x9INWQhiGnEnncKKQ7yuO0/MdKlqCgmFwY7fOTDzNotMjCANeLtToujYXVIOB75CMVPKyyuvNdZKiguQN6fkuL+VrjAKPS+kxQiFixx5QTqS4trtGRMhcqsz7/W0mjQyqKDGXHMOUVNaGTUxBJwTe7a1z3CzxXm8NTZQ5mxrDClzmEzNYoYUuGlRiKa517yMCx+NlFoabVLQ0mgAFfQJDUtlxWwiCxk9Vf5hx/eDWn2EYIsvyw8myx1N8nyXb2k/EhzUefxoOSt4v+nkH8V34hV/4BX76p3/6oYPg1772tRe5lY/gU+u9AA9UCFEUPXTk2tzcpFarkU6nWV1dPbBgenV1FUEQnmtO7HkeS0tLLC8vc/r0aSqVynO/+W7cuMHRo0cPJBN75513qFarrK6uoijKM//uoP4S6+vrBEHA5OQk//P/9Sb/6795m3JKQ4tppBMPnKV6QxvTUHh7cYsjlSyr7S6SAMdqRYIwJGVq7A5tZFHkTr2JF/hMlbIs7LS5NF2jbzvEJAHLdRl6Pgkjxvs7LU5W8nzYaKFJIkdL+QcpDq6LK4kgCGz0+vQclzO1Ije3dziWyyBKIoaiIEsirZFFytB4a2uLuKKQMmNs9PrMpZNEqkpcUfGikLZtUTAMrtY3KRomLj4dx+FypUbfd4jLKnbo07QsSnGDtxpb5LUYnhjRcx0ul2oMfBdDVnBCn4YzoqLHudbaICmrxGMqW6M+L+Wr2KGPJoiMrBGd0MUQZVb8LhERs3qK+26XsViSrKajiBKGpLLt9Bh4DrIM23aPM6kydwYbKIicMotEskhMUhgGFg13wISe4WZvFUWUmDCSrI6aHDGLaJKMLEgYokLd3aXrDUkpGltum0m9QNNrQCTwT8v/mB+r/IfPXRv7sbS0RDwef4Ron4c9v4W9qnivTWEYBolE4rlV8bNw8+ZNjh079ty+bqvV4ud+7uf467/+60Od/3uAf3jeC/Dg23llZYWNjQ2q1erDytayrEPbNT5rymx/b3hychLTND/S/3kaDiox27PPc12XkydPfmKz5HvRPv/7//0af3n1LjP5JC3LZafRJ6Zp3Fnc5sJslaHtcqKQQItp1FIJYjGFd5e3mJ8q8907qxQTBqVsghPlPI41IEDkwniF68ubxCQBI6bSHNmcqWbp2w4n0nHs4YiCqmLoKu9u1DldyXO73SGhKszks8xkMsRjCs2hxfFMlq3BgJ7jcrZa5O31bY7lsliez8VSBVkWaI4szuTz3Go1USWJWjLBYqfDhUqZoe/xmVKFUIDmaMRsKsOVrXXiikJaj7He7/NSuYIV+JxNZEES6AY+s4kMr9XXiUkS1XicpUGHc5nSA/PyXBVJFGnYQ+ZSRd5pbxNEAfO5MnfcLmOxBGZMZ16MY0gyW1aPgmgy8lzW7R5HpATvhD0UBM4kiwQCTKaz9AOLtJyiJGncGG4TlzTSmsq23eV0oooTeJxJTqCJEjtOj6ySoeUN6YyGzCWqvN1bRkZkPjXFyHeIIaOICrqY4oQ5fmjChQfP0mErxidVxdevX2dubu4hGT+rKn5Wr/gwBuYv4jD2d4lPNenuhT4+blR+2Bj2p6VH7K+g9/eG99QDn4SudzQasbCwgG3bJJNJZmZmPjHCjaKITqfDazfv8y9eXydpqExl4yQTEbqmsNMbcnayzLuLW5gxFUGM6LcGvHRsjIHt8rlj4+yOHCazaUIh4tZKnfNHKnzQHDBdkOi4NkfTceIJk/bIZjyf5vraNjlTJxKha9mcGyvRt23O5jP0BiOykowuy9zYqHOykOG723UyMY2pfIYjWgZTU9kZDplNZ6gPhnQdh3O1Em9ubDGTThNEEcfNBNl0kvpoxFw+z61GnSAKOZ7L836ryal8Hsv3ealQQZJEdqwhp3NF3mlsExIxE0+w0O9xIp3DCQIuZSuoisSONeR4Is+dXgs78LmQL/NWY52aniCpqpxN/39Khbyg44YhN3d3OJ8t81prBVUQuZCrYgcBx2WFnm+Tt0NKis71Xp2EqCCLEb3Q5WQsw8hzOa4UiJs6TXfAeCzPfauJFbicS43zVmeJmCBzKlXBDwNm9DLDwCItZ6jFUrzdXUQRJHJCjMZwh1mjyj+f+s9faK0EQfCJ6XRVVUXTtI8MMzypV7zfPnJ/VXzQnu6Leun+XeJTTbonT558IqEdxN1rPx7v6YZhyNra2sMdzcd7w4fdHHvSNTqOw/379x/ZhPvwww8PLO6Gp6si9vs7tIYB37nboRA3MOMa79zb5PzRKm/cWeVINYsgwLnpKhERK9tN5qcqvH5njbF8kvs7uwhEnJ4sY7s+k/k0250+RU2h2R3Sd33OTpW5vrrN6VoR1w+4OF4mBHb6Q6ZqZd5a3aKcjDMKPGzP51gmTihJlBNx2rZNTlGIiQLvrG9zNJPkne06aVXhSCFHMqZiair1wZDJeIqe47DYsTiRTnJlY4PxZIK4pnA2X0JXZLaHA2ZTaVa7PQaey4VyiWvbG0wkkkgizOfKGIrMWneXiViCrdGArutwsVTmSn2domYwlkxwKlUgrqjs2ENqehJBgButOudyJb6zs4IiCJzU00SyxGwiRy9wKKlJqkacN5rrFDSDUAjZdS0+kx1jGLhcSI8hiQJNZ0hRFFmwmnhRyDESvN1poEciY1qCmpgiGYsx9F1yaoqSGud6Z42MbKDIEbvegLl4jVHgcNKcRBUE6sM2Za3Af3vkC2jSi6Vaf1KqA3jyptZhe8V7plPPq4r/Pen+PcFhdxj3SHd/msKzctU+Dunu9YWbzeZHNuGel5O2H3sjxo/fa6fT4e7du8RiMcamj/E//s63sG2XM0drjByPz56aYLPdp5pO0u5bLGy1mZsqcXejyVgmhh+EvDxbo+942I7P/8vem8fYlaDXfb93t3fv2/e99mIVi8Uii2SzZ3oMyeOBEMUxIhsJYOkPAwGsMcaGYVgy4nHHgRPHkQJZEBArI9gKogiQkii2FMnROFakSEDGmu5mN/etuVWRtbyqeq/evr+73/xRqgLZTbKLbGpGPfEB6g9WXd631K1zv/d95zunmI7w4aMyi4UUt7erBGUf2YjKRDiKpsrstHvMJuM8abQZmxYLhRSP9pusFDNYtsPFiQKGa7PfGzKfi3Ntt0ouHGRgWziuy2opx9C0mEwmaAxHJCWFkCJzbbfKdCjAjdGIoCiykE6Q8KksxOPsdDsUAyFcD25Va5zPZ3lvt0zSrzIdjxH1qwQVmf3hkJIWxnZdbtVqXMjm+G6lTMgnMheLkZKChBSFuj6i4A+jKhLX6/ucTqS52dpGBN7KFBnZFlPBKH3bJK9EyIeCXG9VSCkae9aAtjHmS6kiA8fkQryE4IOmOTqoRjt7OJ7LaizP9dYeGTlAKBDiZCiPZDkMfBYpN0bMr7A2rJGTQ5RHLQzP4QQxulafeSmNKksMMImpYdZHNQzX4mxkgluDTUQEfnbmPyPjf33y+X4kPsCLV3wvX75MOBx+aVXs9/v/fXvhe4035UoviiL9fp9Lly6RyWQ+lRj8SRyS9NMm4J917KGyolKpPLMN9zReJYbn8NjDcxw6i3mex9LSEqLs57/8H38f07ApJkNcurvFfDHJ/XKNZCRAKRPDsh0WS2k299tMpmJs1FtIXZ1sMkyzP2Z5Ms3YsjlXSrPf6xOQRQqpGA/2GpwNRfhgbZtiLEI6ESYW1JAlH+V2j9lEjHuVBq7nMp2Os9XqsFLIYLkuy/EIniLjDD0mUhE+2NwhFw4xtCxs12G1mGdkmRTjcVrjMQnbIRnQuF6tMxFU2TV0JM/jZCqBA0znCjRNg7QSIB5QuVatciqV5sbuPorPx2o+h247zEfiNIwxWSVAWJK41WqyEEtwp13DdV2+lC8dKB8yE/Rtk5w/TD4U4lJth5SqHazX6mO+nCnQty2W/XH8mkLHMZnQolxt7R2s/caz3OlWyathNFHidCRHWFJoWSMKagxZ8HGnW2MhnGJTr+Pg8eXEBD1bZzUyiSBAx1IJS37uDcp4Liz5Ne6OKwSQSAl+UgQJi36Guk5SSPDnpTmWw5/P2vBNVrqfFz6fD0EQSKfTL62Kf/7nf57Lly8TCoVwXZczZ87wYz/2Y8f6u3warVaLH//xH2dzc5Pp6Wl+8zd/82jD7WmIosjKygpwYC377W9/+/Ve3xdZveA4zgvbCB988AHvvPPOZ4boHX6ksSyLr3zlK8fSwd65c4fJycljfazZ2tqi3W4zGAw+lXDxSWxubiLLMsVi8TPPe+3aNZaXlwFYX19nOByysLBAPB7Hdlze/ef/lvWdBhOZCIPRmFg0zH5nwH57wEQuzqOdBmfn8zzcrVNKRYmEVNrtNpF4jI39NqVUhNvb+4T8IpIiIQoCJ4sZhqaJPhwwQqQ9GBOPBtiotzk3k+dmucJUIkY8dEBSsiSy3eoSUhU22h08PHJhjf2hzuliBsf1UCQR3bGp9AZMxCNc262SCmo4PhhbFueLB5WwKst0DZ39wYCEX+JRv08+oNGyDBzX42Q0jAmEFIWR59LUdYqRMNdrVUrhMG1LZ2xbfDlfpNHvE/YrWIKPpj4mHwpxpb5HTFFRFIH98ZC303mGjkVAkvH5oGWMCcsKdzr7uJ7LfCDCutGj9CeyNEUQich+muaQoW3hYLM77nE6luV+vwrAxUSJvm0Qkfw4ONSHPVJqkNvDCpLPx3w4wfqwRkmN4pdENEEhJmv0nTF9S0cRBTZGdfJqlIHTR3ctviJO8zVjGkVRjrxoQ6EQ4XAYRVGOXZjcvHmT5eXlz7XC+yppDy/Dq6RY/OIv/iKqqrKwsMCtW7f4B//gH7yylv2b3/wmiUSCd999l5/7uZ+j3W7zT//pP/3UcaFQiMFgcNzTvvCN/4El3Y8++ogLFy48twXw9NJBNBplZmaGmzdv8s477xzrce/fv082m33pdorneezt7bG2tkYwGOTcuXOf2Y4ol8t4nncsQ+Zr167h9/vp9XrMz88/4yz23//Gv+MPLj1kMh/nwU4dTRGJRDREQWAiE2NkWmh+hfW9JrploWkK1XafmUyQxtihFA9hmDqiKOJTFLYbXRZLKa5t7FGIh2kNhkSDAebzSUaGheaX2Gp26Y11ggGF3U6fc1N5bu9WmUrGiAZUPDwkUeBxrUk0oFHu93E8l9lMnM1mh+V8GtfzUEQRw3Gp9AcUYyGuV/aJ+BUCfoXmaMTZXIb2cEAyEmHkWNSGQ4rRCFerFaKKgij66BgGi8EghucRkCQc0UfHtsiFQlxrVNEEkbimsjceciGdQ/dsNFFGFH00xiOCssTH3cbBskEqy532PqVAmIjixy9KhGWFnW4LR/Rh4lDVB6wmstzuVvEBF5IFRo5JWPRj49CxdNKqxpX2DgFBJqNplMcdpuUIrghRf5CYotK1RhiuzcgdsW/0WQpneTw6GP6di07QsYaEJQ1VkBg4Onk1xt9M/hDdP3GXOwyRPPwyDOOZhIZwOPzCaPVr166xurr6uapdx3G4efMmFy5ceO1zwIEU9N69e6yurn7msT/zMz/DO++8w1/5K3/ltR9vcXGR73znO+TzeSqVCl/96ld5+PDhp457U6T7A9teOLR3fJroPM+j2Wzy+PHjTy0dvEp09MuWKTzPo1ar8fjxYxKJBCdOnGA8Hh+7//tZBumO4xxVzzMzMywvLz/zPvxv//c1Lt3aZL6UQpQFlktpuqMhu40hK/M5Pri7xVQ2zqbRIR7UmCsmGBsWM9k4t57soikSG/ttdMfh5GSG/c6AUxNpbNfjrZkiXV2n3h1QiId578EWM5kY96sDYprKqVKasWkznYyx2eoQVvyMLIvH2werwdfLFXJBlbAqsxJII8sCm+0eE9EI9/YbWI7DYjbFo3qTU5kUjuvxVuHAF3evP2AxleRaZR/B5+HKEhvtDmezGUzH4e1cAReX/dGIiUiUO406gg8mI2EedzvMB0O0+30W/EEkwUfPcVkMx7jTrGO6DquZDFf39ykEQvhliTPxg6iehjFmOhBD92zudRoHw7T9LXzAaiKDhcdMKIbhOkxrCZKqxpXGLlFZRZNH1IwBZ2M5avqQlXCOiOKna+sshTS2Bk2GpsV5Lcz7zSeIPh/L0Qye7edsJIYkwKyWI6aoXOtuIPh8zAclPu7vkFdj/Nzsf4jZHSIIAj6f78iL9un8vqcTGra2to6i1Z/eLguHw2+kp/u9XgGGA7ObzztI29/fP0r8zefz1Gq15x6n6zpvvfUWkiTx7rvvvjbRf6FJ92X4JDG2223W1tZQVfVze9q+SJFwqBgIh8NHiRCNRuPYd8eXDdI+GZmezWY/tXb8hx8+4td+9yrhoJ/yfofOYMziTAbDcriwWMR0HM6fKFBtD+h2xxSTES59vM3SVJrHlQZhRaCQjeN6PhRF5O7WPrlkmFsbBx+Pi+kII8NiLhnEh4+350rU+0McyyOTC3FpbYe5TJz71TqJoMZSIYVu2cwm42y0OoRkBd1xubVb59xkjivbFSZiEeIBjZim4pdEtjo9CuEwT1odBqbF2WKW29V95pNxRJ/AmVQK2zLoWS5zsTj3m01022Yln+F2rcZ8LI7nwbl0FlURqQ6HzEfjVEYDhpbF+VyWa/UqSUkmIEtM+zVUfHR6AwpSANd1udesczZ1kDoh+uB8+qAvPBOKo7sWs6EEiu1wo1Uj6deoih5NY8yFZJ6WMeJsLE9QluhbBnk1woNeDcO1eStZ4P3mFgFRpqRFiIsqJwNpPM/HfCBLRFa43ikTFv3orknV6LEYyvB4VGM2kCHjDzF0TJZCJf7zE3+RiKxRc/svJcsXJTQc9kYbjQabm5sMh0Nu3759RMKv6rkA3x9P3uMO0n7kR36EarX6qe//7M/+7LGf1+Hf3pMnT/ja177GysoKc3Nzx/7/h/hCk+5xKt1ut8va2hqiKLK0tPRGPG0/SeidToe1tTUURWFlZeUZQn+VTLXnDdKezlJLpVJHQ7579+49Q9A3H+7y6//mCicmkmiawmBsMltMcuPhLrmkxvVHu/hlkWwqjKbIvHN6gsHY5PREgo1qC9P2CERkbjyusjpfYKPaYiYbR1MVgn4ZURK5V64xk4vzoFJH7RokIwEsx+WtuSK6afP2VIHqYIhnQyoU4NL6DvOZBB9X6yQDGsuFNK1un7lknN3eiJAo43ge17YrrJZyXNrapRgNkwuHSAcDaIrMdqdLVgvSGus0mm1W8xnudnoUwyEiqsaymiYgS+wNBpQCYTqGznqnzYV8jvd3d0gofqbiccKKn5Bfpjkek5M0JEXkUb/HmXSGO82DymY1mWJomWSCUYaDIXlBJab6uVKrkvKr7Ix7dEydi6k8u0aHs9HsQeqxqTMViHOrVT3wBU7luNQok1Q0on4/04E4KVVj7NqcCKTxSyJ3e1VSosqjYYO+Y3I+XmBr1GQhmCGtBhg6Fmklxq7epGWNKfoTXGo/wQf8zNJ/ynTgwF7wdSpUQRAIh8OEw+GjCu/y5cssLi4yGAzo9/uf8lw4JOJgMPjCKvT7Uel2u93nDr0+iT/6oz964c+y2SyVSuWovZDJZJ573OFC1OzsLF/96le5cePG//9I92VwHIeHDx+iKAoLCwvHWjh4lYUHy7Lo9/usra0BvNCk/FU0w8+L91lbW3umcn7esVt7Lf6r/+H3GOkm/okUd5+UWT1ZoNkdcnouy3A0ZKGQxBXgwVadMydyfHB3m2hARlMVCskoqViYar3Blxez3N6sIIoCzcGYSrnG6lyenWaPhUISRZGYjgQIxcI82Phn+VAAACAASURBVG2wWExx6dE2Ib9CMKDgeR4Xpg/ctt6eLrLfH4AN6XCQDx7vUIwEeNztE9c0Thcz6LbNZCzCTrdPUJJRJYkr23us5DNc3yyT0FTmM0lM22EplWS33ycmSsiiyPXdfc7kM3y3XCYkSZzKZrBch6VEkqahk1NDhFU/N6r7nEwmudPYx/U8TsdjmPj4SrbI2LWZCkVJBgJcq1fIaAEsHNqGzluZPE19yOlQEgGXnmWQkUNca1Rw8Yj6VW7V9slrIfyuyIlwkrQaYOCYLIYyODis95vMheJcae1iug5vJQrs6j1OhbNIpoWnyARlPx/3Kgwdk4lAgvebmwREmZTfj+QTORspIfh8nApO8LX0In8ueeLoOnhTUi+fz4eqqqiq+oxf7KFioN/vU6lUGAwGuK6LpmnPVMV+v/+Nmd38aZDuy3BobPPuu+/ya7/2a/zlv/zpBZN2u00gEMDv99NoNHj//ff55je/+VqP94UepHmeh2maz3xvMBiwvr5Or9cjn89z4sSJF/zvZ3H58mXOnz9/rF/2oanOoT/Cy37po9GIhw8fHsvIvNvtUi6XmZyc5NGjR0iS9EL/hbW1NaLRKJIS5L/9F/8Plu0QDPrp9McEAwo3Hu4RCamIskCjM+TcUoHO0CDkF+kNh/gEEcODnXqPsycK3HxcIRP1IygKkYBKIqwyNCwCqsL1jT2CfhlPgGZ/zEwqwNAVKMRDCIKI7ToMx2M2Gz0m4kEedwaEFIlg0I/rwVwmjm7byKLIbqtNbWhwqpTixs4+E/EITX2MXxI5mU0xtmxUWWJ/MGS/P2AqGePufp25ZJztfg/JB7PhEJKmEpRlmrpOfTgkFwlyp15nJhZjbzzAsm2+VCrSNy2CioxuW7R0nUwwwJX9CnG/H0ESaOgjLuYKdE2dkCyjiBJdUycoK1xvVvDwWE6mudOuUQyEkSUfmiChmBam4MNwbIauRc0xmNPCbJp9XDzOJ7PU9REJv0ZQlhk7NiFZ4VprB8u1ORXPcK+3T8YfxPEcApLCZCCG6dpYroMk+rjfrxKRVDzBomuN+Vp6kX9y6llC2NnZQRCEY6+lvwhXrlw5turgMCqn3+8/M7SDA/IulUpHVfHr3BAqlQq2bX+mFwrAD/3QD3Ht2rXPVWE3m03+6l/9q2xvbzM5Oclv/dZvkUgkuHr1Kr/8y7/Mr/zKr/DBBx/wjW9848jw6qd+6qf4yZ/8yZed9gdTvQAc/bI/6Wk7Ho+PjF6Og+vXr7O0tPRSN69DK8dms0koFGJ1dfUzK2PDMLhz586x5C+NRoO7d+8SDAZZWFh46YDg8ePHiJKfn/uV93m0UefMqQI3H+0xU0wgySKqX0ZWRHoDHdcxWK/0SUX9dA0bx3VZmsmhWxaJSICRcWD4/qTSoG/YnJ7NcWezykIpRXuskwipxILagVJBVbj2eId4KMDYteiODKbjGl3TpZiMIgpgWDaWY7PVHlAI+9nsj1ElgWjwgMjnM3E8QUASBTq6zl53wIlsguu7VdKhAJbnYjkOZwpZhpaFJkt0dYP9wZBSLMzN/RqZYADDc+kbBm+X8gwsi4AkoXsOzdGYbDjIlWqFiHKw1VYZDjifzTKwTSTbRfXLDFyHoKxws1nF8TxW/qTVMBEKI0oCIUkmoWr0LAPTcejYOrujPmeTGe50DpIzTsfTdC2DhKwh4DI0TVQffKx3EHyQ9avsWSNmAlEa1oCkP8RkMMrIsegPBqCKbIzazAYTbI2a2J7LhUSBj3t7ZPwRJoMRDMch5Q/xD0/+KKr4rKRre3sbRVHI5Y7vKPZJvCmp197eHt1ul2AwyGAwOFqt/6Ql5GdJuo57I/E8jx/+4R/mxo0bb0yz/wbxg6legAMifPz4Mf1+n7m5uaPhUrVaPXZUOrxckXAYYNlsNpmdnSWfz7O3t/fGMtUOV4I7nQ6qqh7r4vf5BP6X372JIol86ewU7d6IhWKK/e6A3shgYSbD7fUKy3MZGn2T+XyYQDBITLfQAgq31iucnMpw5X4ZTZEp5qLENJnTcwWGY5PzcwXWK00GukkiqPHhgzKnp7M8rjbJhVVSsSDt/oDJWJD1Ro9oQGWz3qGvm5yezFDtj1jMJ5EkkUg4jO06bDS7JDWZG7s1RAEy4QBt3WAhncRzPS6W8uiOw263z4lUgg+2dgjKMrGgSrU/4Fwxx8i0WIqEUYNBasMhM7EYl8p7+EWRfCzERqfDmWyGvmlyPpNDkYSDCjed4Xa9ju06LESjXGvUKYXD2J7LciJ9FP1zKpaiZY4pd/qcS2f548o2ouBjKZFCcUUupgr4BJiWQ2QiEa62KgQkGct1qIwHrCQyPBm2mQrGKAbD9A2dhBSgbg4Z2A5ZbL472kTAY0oJURsPmNUSxBWViFwgIEo8GTWwXR9xReNSc5u4HOC/PvUffYpw4c20F95kiyIUCj1ToT49tGs2m2xtbWGa5qfMbwKBwNHfk23bzyRlH+dxv0j4wpPu+vo66XSaU6dOPfPmHzcR+BDPI91PuoudOHECQRAYDAafK2ni6fNvbGwcBUvOzc1x586dY533f/+/7vKdDzc5MZPh/pMy4aDCRCnBlBZH0xQanQFz+QgPNuqIAkh+lfX1KhdOlegOdd46WcRyXGZzCXyij4ebdWayIS59vE08rBGLqOQTYZLhIAPd4OJ8iQe7NcamRTKscH1zn+XJNHudAZPJKNGgim7ZaIrM3XKNgCqz1ezRGxusTGbYaveYSkbBtVkOaCh+hSeNNhktwN1qHcfzKEU09oZjZhMRTNvifD6LIArs9QYsJA8qYc/zmIgEuF+psphOHigycllUWaI2HLGUSPGo2UK3bVYLWT7c2yMXDOLzwalk6sDNq9dlPhRh6Lo8bndYzWb5491tJMHHyUSKoKzwpWwEF1iOZgj6Ja7U9kioKj3LoGmMWdBC3O81mQslyAaCjByLqVCMJ/02fcPiZETju7Vt/KJIMRhG97mcieWQBB9nnBCK5/Fo2MTCo6sPWBs1KMkhmu4QySdwMV5Cd20uxCb5m7N/jqz6/JnEnyWp1/PO8/TQ7hCe52EYxlFrol6vMxqNjtZ8D1d+P6u367ruF45w4QeAdM+cOfNcmdWrGJN/8njXddne3mZnZ4dSqfSp5IlXSQR+3kVxeP7DZIvDlWDbto913m//4W12Kj1W5tMYDiTDKqGwxp17e6ws5fnozhbJiEI8HuL0XI7hsI8rSJxfLHLt3g6JaIBGb0SrN+LCUpHe0OTtpRLNdpfFfALD9djYbbN6osAH97fIxEIoskBCkwnGVHTL5eJsgoeVFiPTJB7SuLy+y+nJLPd26+RiQVKR4AEJ+2UeVBooPpFKp09HN1kpprmzV2MiESGm+YmHAyiyxFarQzYQZKc3YmTZzMeCrPeGZIMqri1wKplAU2R2O12mI1EqvQE9w+B8MccH5V0Sqp98LMxiIkFU9dPSdeYiMQzP4WGzxdlshj/eLR94BccSCD6YihSwPJdTsRQBReZq7WCY1tLHtA2dC9kcj7ttTkaTJAMBRrbFYjTJ7UaVgWszH1H47v42MeVA8uZ6Hm+nC7genIvlkUWBh70msiCxPezSMsesJnJc7VYIInIxVWLsWkwi0LNGDAyLpE/ho9Y2HvCT8RUSQ4+e0CMYDH6K1P4sVbrHrVBfNLRzHOdoJtPtdqnX6ziOczS0O/xSVRWfz0e/339jCdvfS3zhSfdFeB17R9u22dnZYWtri1wux5e//OXn3mlfldAPcbiltrm5+VxDneOQ+f/7wSO+9T9/h0hIxRPAcT1OLeQZjU3OLGSo1drkogFswcf6VovTiwXW9wYsTmuMxibnFgr4BB+dvs5sPs7V+7sU0xF2G11GhsnKXA7PdPjSyQl0y+ZUKU2zP2SnPuDUZJKPd5okggoto0lcVVjMxzEcjy/Pl3hUbTLSTYRYiA/Xdjg1keb+Zp1kSCMsg0+QOFXM8KTRQRMldMvhemOfs6Uslzd2yUYC5OMRMm6IgF9irzcgHQjgCT4etrqcTES5XG+i+mBCFsirfk7Go/QMk8lwGFES+bja4FQ2xXvlHcDjbD7HyLL4Ur6A7bksRpOIns29dotMIEhtNKRjGFzIZSkPeiwnUsRVlaFtsxSXuN3cZ2BZlEIR3quUyWpBDA6MzM9kipiey7l4HtfnstZrkVGD3G7V0B2bt9J5Ljd2yfgDzEWTWI7DUkSiZg4ICRoFQeb9xjaKIDAZivJk1GJaixFQFM4IYS7Eivx4epnBYMDu7i7D4RDP854xCbcs63NXqd8Pfe3zIIoi0WgUv9/PzMwMwWDwaGh3KGXb29vDMAwuXbrEjRs3ME2T69evc+rUqWMHWT6N3/qt3+If/+N/zP3797l8+fILZy+///u/z9/9u38Xx3H4+te/zrvvvvvar/MLT7ovi8k5bnvB8zxGoxHb29vk8/nPzCJ7VevIw7Xj9fX1l2adfdZHpXuPKvzeH95l9WSR/linWu8zO5Xm8o0tMkmV1sAioMrMzyQwbZcTE2n2W33yMZWd/S5jw2JuMsWj7TorJ3L0hgbnF4rgA9Uv4/Msbq9Xmc4nuPl4D8txKCUD+CWRLy2WMCyH0xMZOmOdnUaPfCzMlSdV4pqC43PRFImVYgLbhbdnC2w2Opimg+KDB/UBJ/JJrmzukQxqrExk0S2bmWSMnU6PoCQTVlWubVdZyqW4Va2higLLxYPljtl4jLauk9U0VNHHWmfITDzC9f2D1sRCJMjYsFmJR/Fsh+lQlERQ5epelXRAozYa0tJ13srn2O4OWYoliQcCDC2TQELmeq2K4dpktCDv7e1QDIUZ2CZxv8a5dI6xY3MhlWfkWDzptsnKCpfre9iex/l0jrutGqVgmKwWIKUGCEgS5WEX1SeTC0S4VCuTVFQEyUdTH7GayNPpdzgfK6FK4kHgpRJh7Npsdju8nZjg63PvIAnCM+J/13UZjUb0+32azSbNZpNOp/MMEYfD4aNq8Dh4U5XumyTvw2LE5/MRCAQIBALP6Gfn5uYIBoP8q3/1r/jWt77Fxx9/zC/+4i8ee5X/EKdPn+Z3fud3+MY3vvHS5/O3//bf5g//8A8plUpcvHiRH/uxH+PUqVOv9fq+8KT7IhynGj1cC15fX0cURYrF4rEkZq9ygXY6HUajEZVK5XNlne3stflH/92/YTg2mJlJsV/rU8ho9Ho9Ts7EMR0B3RiQz0S5dmeHUws5PryzSSoeJOAXmU/FUTWZRnvI8kyOBxs1BJ+PbCbCZrXNucUCteaYs7M5XM8lHZAJBEI8qnSZKyS4/rgCnsdcMYEkCLy9UMJ0HFYncnQMna16h+lMgpvlGlFVwfE6SIKPpXQYVxA4V0zT1A0E10cuGuaDtTITiQiN0RhREDg7eVCRfmW6RGusk/CrlJJRrmxVKMUirLfajEyL5WSUke1wsZjDdF0m8JEMaVzdqxJXVQaeQXOsczIWotxqMa8F0BQFE4+5cJSb+zVs1yWhBvhgd4fJSITtfpd8MEQpEmZs27yVytO2dPaHA6ZCUd7bK4MPllMpNrptpsJRfKbJuWiegCTzeNBCRkIWJT6q7zETinJXH2A5Dl/KHEQBXUwc3NwaxpBYSONBr47u2CR9MT5obCH6fKzG84wdi4Vwhv9m5T9Aes519rS9IRwMeScnJ5Fl+UjC9fRiw9NE/CIJ15+VSvcQx9HpJpNJFhYWuHjxIt/61rde+7GWlpY+85jLly8zPz/P7OwsAD/xEz/B7/7u7/570v0kDv1mX4Sn14LPnDlzFJfzpjAYDHj06BEAmqZx+vTp164mOt0Rv/q/vs90MY7kl1h/UiMaklnb6hIIKARDPsZji5Mncox1iy+dnaLeHpAOBdH8CluVNmeiET66s00xE8WvSpyazuL3S7R6Y87NF7j1qIIi+RibDs2Bwdn5HN2RwepMDp8kMCcKBxE+GxVm8wluPKngei6zhQSW5fLWbBHLdTiZjtE3dPYHNsuTaW5uVQn5Zfx+A8OymY1pDPoDllMxdDxkBOYyCT54XCYe0BAlH63hmLemCwxNiy9NFDBcB8e2KaoKd5tdQoqMLvjY7fY5k88chFtmM/hliZ5hMBONcmO/hotHKhPhTqNBIRhgXx+RkRXioohlWpyKxOg5Np2xwVQkxnd3yoiCj/lEnPpoyFI8hV8UOZ/Oo0kia502MhJD22J3POCUpnK9VcEviLyVOZCtfTk1wdg1MV2XlKpxqbaDBwepw60qUcnPZDjCfCiFOxzhuDAdSJL0q1xr7RIUFf7Jyo8SlY/3UdnzPERRPOqRPm2FaFnW0bCqXC5/SsJ1SMh/1kj3uCkW3ysv3cPZyyFKpRIfffTRa5/vC0+6rzq9fHqL7Om1YF3XX6tP+0mMx2PW19cZjUZHVotXrlzBtu1XtpwD0A2Lf/Sz3+bh2j6nlws8WqsQi/kJhTTmVAm/FmB7r8X0ZIrLN7bIZcIMdAs8j5MLOXTdRptwGA5NSqkwpu1wb32fM4sFLn9cZiofYzD2mM2GwbNxfDLThRTX13YJagoj02a/PeDciTy17pCVySyyIqHKEooscWuzwkQmxq2tCpbjMpOJoLs+Tk9k8Pl8nJvK43guG7U2U+k4H+/VkUWBXDzEbrfPbDxEp9NlIRbCJwp0DIuT6QSXN3aRRIFCLMRmp8dcPAyyzPlCCL8i0xnrZAoBPt5vYDoOZ4oZPtrZoxgJ07NM5hNxUn/SPjibSlPXx3R0i+lcnBv1GpLgYyIcYmAazAaCjAZ9TgbCqIrMRm+A7BOpjofUWyNWM1neq1SIKX7OpDOMbZuY7cPzICkFmIxG+W6ljCwITEejrPdaTAYj6I7D2USeiCLTNgwm1RiqLHKnXacUiNByhujtHhcSOXbHPZYjWf7G/NvMhZPHvj5elm0myzLxePyZ5Z2nJVz1ep2NjQ10XT8yzTkk4ldpTxziTzt94pM4rtnNy3wXnrd99kk8r3j7PKqJLzzpHhej0Yi1tTVM0+TEiROfukO+ikfCIZ5eGzZNkydPntBqtT5ltfgqaoenz+u6Hr/6a+8hCwLLC2merNfwqxLDkctutcHERIRapc3MZArPg/PLJYZjk9HYYmoixZWb28xMJtna7+NXJE7O5TAtm7lSgmZ3zGw+Qb3bpz+yWJhO8qDcYyITptkdsjyVRdNkBiOTqXSUa+t7aH4Z2/WotvuszuUoN7rMZWN4rs1UPEQsEuLjnTqJkMZatcnQsFiZyrJWaTKdjqFIEqsTOWRZZLvZZToeZ6vbxbAdlicy3N2rMRkNMTYM5iJBRJ/LQDeZiYTZG4wZW33OTea4tLVDJhQg5PdzIpkgEdTomyYr6TQtQ2e302e1kOX98g6yKDAVjxy8P9ksnucyrwaIhIM87HQISBJN5yA9eDWT5ma9RsLvp+BX0G2HQiBKezAgJMhMh6O8v7dDWFZQBI9mu8dKIs3QMrmQzBOQJRr6mJlAnJFjsj3scTqe5r3qgT3jW+n8QT83lkOTZfyWSyoe5XbnoJ/8n5RW+PPZ2Ve6Bl812+x5vguVSoXRaEQ4HD5a99V1HUmSnkn1/awNs++1EXqn0zlWevHLfBeOg1KpRLlcPvr3YYzX6+ILT7ovu+MIgsBwOGRzc5N+v3+URfY8vMrg7fDcjuPg8/nY2tqiUqkwPT3N4uLip57Tq5Du0zE8v/w/fYdv/9tbZDMaY9OjkI8RSwTRdYuZyQQP1/dJpSI8XN/H8zzy+RiN9pDF+SymaXPx9ASdoY4miJRyca7dLXNyNsNHd8v4ZYFMXCUXD3FqNkx3MOZELs5uu8/IsJnMBLi7OWIyHaY1HLFQTBIJaQx1k+l0jBsbe/g8D8+zqQ8Mzs7mub9TpxALkYgESNlBVL/EWrVJyC+z3x3SGo45N5vnypNdiokI4YDCciCD5peo9gZMxaL0TYPWSOdEIsTDzpiIXyGjCRQDBwO0TqfHVCCA4Xo8abQ5nU/z3sY2ouhjPp1E8vm4WMzj+eBsNosiidyq7pPQVB41mwxsm7OZFLcbDfKhgz6u6bhokkh52EMVRUqRKLcaNdKaRt2x6eg6p2Nx+qMhi/4goijQsUym1QiPe21Gts2FTJ7vVstIgo/lRBrJFpgKRvF8cDKaJub3c6lWxucDLS5zs14lLsp0+03S/gBvpyb4GydefSPsTQzBPM/D7/d/KqnhsD3R7/efaU88Ld8Kh8NH/dc3QbrH9T8Bjryk/7Rx8eJF1tbW2NjYoFgs8i//5b/kN37jN177fF940n0RTNPEMAxu3rzJ/Pz8p5YnPolXlYGJoki5XGZvb49isfip8MpPHntc0j1URvz273zEzRuPWZhNUGvpDPs6qVSEq9e3WFkusvakQSyikIgHiIRVBNHHxk6LiVyMG3fKBIMKWsBPrz9mshBEAN4+PUmzOyDqFwmFVbaqA07Nhbj88TZBVaaUjzOdSxAN+ekODZZKAcqNLgPdQhU87pcbFOIBtkyThCqRz8RwPIETeZEHew1s08UFLq/tcnoqy0frO2QiQaYyMUzL4VQxTbndI66q+PBxc7PKmaks76+X0SSR6VQY0RWYLWXQHZiKiciyyHq9xVI+xf16Ew+PpVwKb2ywnIhhjcdMBTRUReLRfpOkpvJIb9E1DC6U8ny8XyMf0AgKHik1Qjwc4nG7jc+FmKpxafegJdE1dUznIO59YFlczORw8GiMx+RjIe73W9iuy/lsjuv1KprPR1FTyKERkH30ej2m5BARVeFWo0ZEUuirBtXxgKVYirutfWbDcUqBCEPHYjWWpznoUDEM0v4w31z+4WNfe0/jTSwIvCjv73ntCcdxjtQT9XqdJ0+eHGlpDcM4WpH3+/2v9bxepXJ/E2Y3//pf/2v+zt/5O9Trdf7SX/pLrK6u8gd/8Afs7e3x9a9/nd/7vd9DkiR+6Zd+iR/90R/FcRz++l//60epLa+DL7z3guu6z1Sotm2zubnJ/v4+giBw+vTpYwmoXdflo48++kzJyaHV4t27d8nn8ywsLHxmxMlxkiYO8eGHH3LvXpP/47cfEQlrRJMBRFEglQ4xHJloqsLD9X1c10VSBdpdg9OnCzzeajBRjKMoEj4PHDx2q12K+Ri3H1WIR1UM12FsWKwulRgZB/KyoW4xGBlIisiT3RbL8znubFQJB/xkkyF8Ph+JaIDh2MS0DHZbA/q6zXwhwtp+j3RIYWA5hDWZiUwMyzmQn5VbXWrdAbOFJPd368znEmw2OiiiwMp0jrFpE1Bk2mOderdPQPGx1Rszk4qx0+tj2g5vzRTojg0iqh/P5zEwTMIBP1e290gHA9g+j9ZozPmJHI+bbZKaSlgWGZkmss/H9nhM37KZDAfYHI6YikWp6UNEn4+zuSxD20IRBcaOw/5wSCYY4E69hofHSjbD7XqNXDBITPUfxPH4FTqmQc/QcR2LsqlTDIXpmGOGtsVqMs3moEtMlIkIImPXRhJFaq5B09JZjWe52aniw2MlmeVeu8ZMJM4///J/TE57PZH/qxjVvAibm5ufkmS9Cg61tDdv3iSbzX4qteKwRfGi1IqnYRgGDx484OzZs5/5uH/rb/0tfvqnf/pzJ1X8KeEH13vh8G7qOM5RbPrExATvvPMO9+/ff+WP9S/DodViJBIhlUoxMTFxrEyp41TRh2qHx+sN7t7pcmI2g2HZbG00WTlb4vKVTUqlOJX9HslEkGQqRK3e4sRcnkePa4T8CpVql+5AZ2khz/pmnfmZNLbjMJML4Ph8SIbI/ESaax/vkEmGKJs2g5HOuVMlBiOTt5ZK6JbNfD6BJ/hYLzdZns3y4cfbqLJALKqRCAdZnomgWzbnZ0M0+kOawx7TmSBX16vENBkDFx8+lgpxLM/l7ZkCXcMk7PczlYnx4aMdsrEQQ8tkMDY5lY9i+SQuxhM4uIiCcLDltrFLVPPT0XVq/SGrEznK7R7L2TTRgMrIslhKp7hd3WdomhSiYW7uN5iKR9gcDtB8AheyaUa2xbIk0TdNRBuKIY33tv9EqZBK8KDZZCYaw3ZdVjM5wn6F5njMTDiOg8ODZovFZJJL1V1sz2MxEKQviJxJZAjKMkm/RkASud2uM7ItZjJxrjerBEWJmOzHsB0WtCj6eMSCHEYTRXZ7PXyuj7+/8A5ZNXSsa/RPC5+3RXGopZVl+Rl/2eelVhwmXDzdK366yv5e2zp+P/CFJ13XdV+4Rfaq/gsvQq/XO7JaPDQpv3///hvxXzAMg/X1dQaDAZFIjv/zd95jNLY5dbqE7bpcuDCFYdqsnCyw3+zTag7IZMJcub5FIR/k7v1d0skgqVTkYBFBk9gsN8klQzzerGPaDvlMkL3GkOWFHIZpcX6xgO15tPtj5ktJrt/bJZcMs1vvMtBNzi4W6A10zs5lGQxHTCdD+AMqj3YanJzKcPnBDookkEmFGJs2504UcByX87MFRpbFTqPLVCbGre0aAVlEVUXaI5P5dIj+YMhKMYlhmmD7mC1luLVbI6BIJKI25VaXlVKGSrfPSjFDSPMzNExmEjFu7e5jODapYIAPNspMJqKsjZpEVZWzxSwjy+ZsKkFjNEJwfczmUlytVJEFgclElHKnx2I6CbicjicQcWn2h2Qlhc54xEbX5HQqyR/Xqvh8sJrN0TMMLmbzOI7NpBwgHtS41WniAVFN41J1l2IozKatE5X9rCYzGK7DuVgWn+hjvddGFWXqjkHb1FmJZ7jd2Uf2CfxEqEioNeDK7hUkSXqmKnxdW8TXwZvqxX4Sz0utcBznyJ/3MBTWcZyjUM1DFcVxertvIqrn+4EvPOn6fD5M03xubPrrruse4lDxYFkWJ06ceOYX/Kp92k8e+7TZzezsLKXSDP/sF/6AaNjP5FSSj2+XmZvPcOtWGUWRyOQjaJrCxQvTjMcWb52Z4NFGFVN3EEWJaze3WT6V58adbTKpAJLoMF2IEo4E2dppyqlEUAAAIABJREFUUEhoPFivgc+jVIrzZKfF6YUc/bHB2RN5BNFHcKgQDatce7BLPKxQaTqMTIczJ3LUOkPOzOYQRIHliTSCJPKwXKeUiXLrcQXX85gvJtncb7NQTIEAF6bzeIKPaqfPyUKUB5UG4JEN+9nrGxQjKo1uj4VkhFBQZWQ5nCvluFetY9gO8aDGpcdlCrEwhmOTj4QoxiOMLIsLxTwtfYxhOmTTQd7bKCP5IBcOUtNNTmZTuN5Bxpoiiez1+5QiEaqDIR1d50Ixx9Vq5WDwlU0zNi1mQmHGls2EXyMkidyoVtEEgd5YomYaLMYTPBx0mQjHmIyEDwZo6TwD26Q+GlHQVC7XK9iuy1vZPFcaeyRklbloHMNxOBmR6Fo6aX+Qv5Cd4i+4oaNZw6HvwNNDq6erwkMy/tNQB3wv/RtEUSQSiTwTKvC0P2+9XqfX63HlyhUURflUqObTRNzv9/896X4/IAgCc3Nzz73Tvg7pHhqjP378mF6vx4kTJ0gmP62bfJVzP026h5V5uVw+MruxbZd/+Pd/k/v39phbSDHoG5w9W8LFx+xUEgSBR2v7LK8UuHJ1k2QiCLKALAlcWC0yHlu8vTrFRrmBjA/b9tjb77OyVODa7W3yuSjhkJ9QSAXBo9MdMZkKcH99H3xQyEXZrnZZnsuys99mKh0gEg1hOxAMKFxf2yMaUtmudWn3x5w9UeBxpclUNkYwoHBay6LIAhv7HTKRII+rLUaGxcpsjltPKkylojiOzUwsQCioMnZgMRRmr92jr5ssl0Jc364RVWVkyUdalUmFo5imxUouTc80qXTGlGJR3nu8jSIKlJJR6v0hS5kEg8GAxUiISCTMTq9PNhRkq9U9MMOZyPPB9g4BUWQxlyLhqiynUwwsi9lInGjAz41KlWRAoyFAczzmXC7HVq/DlBYkJPhwBB9JSWZ/0Gdk2QRU+Hc72wg+WEqmWe+0mA7HCCkKq4kcQVlmd9QnKqhMRGJ8uL9HXFFRZIGaPuR8LMPXLI3iZBHP844Mmw4rXFEUj4j4kIyq1SqDweCoKjwk4hdl6r0K3kSl+6Jh3HHw9KrvoT3k9PT0M05kjUbjyIlM13U++ugjfD4fuq4fbee9Ko7ruzA9PU04HEYURSRJ4urVq6/1eIf4wg/S4KB39LzXUalUGI/HR+t7n4WPPvqIWCx25JubzWZf+BFne3sbn893LHf7arXKcDgkGAzy5MkT0uk0MzMzSJKE53n8i3/2R9z/eJdQVOPOnTLhsIrkl2k2B6ysTtLpjEgkgziuh+O4DHWT7XKLUinE1s6AfC5CszdEUyVmZzKYlouqSVRqfUZjE1EW2W/0WTlV5PbDPXKpMOGoig8PWfbR7ev4fC7l5ggPKGTClGt9lmYzVFp9ktEA8UgA3bTRVJn7WzVczyMQUKi2B5w9kefWkwr5RIhcIoLreah+if3OgKFuMrZNOmOLM9NZbpX3CakKpWQEfD4SQZWBYWFYNmPHZrPRYbmU5uNKHUGAfDRAb2ySDijIiowgiCh+mSfNLrIP+o7F2HY4P5nn2m6FuOpnJpXA9TxCikxrrNMajUmENe7u1ylEwnRNg4FpcqGUp9Lvk9A0IqqfoWUd5Jc1agwsi5VkgjvtFkFJJh5U6eg6s5EILh6u7SDgsmeMGTk2UdVPRR8zG4mxM+5hug5fzhfpGDpBScEvCTT1MY5h8l9Mr3B+aRm/3w9wRJyO4+B53tHXITzPQxCEo9QCwzDo9/tHBjCaph35077OcsPdu3eZnZ19JQ/bT2I0GvHkyRNOnz792ueAAyN013UplUrP/bnjOGxvb/Pbv/3b/Pqv/zr5fB5d1/l7f+/v8df+2l97pce6f/8+giDwjW98g1/4hV94KelevXr1hXLTF+AHd5AGHPWAPonjVqOHVov9fp90On1ktfgyHOakHQfj8ZhyuUw6nebChQtHf2wAv/rL3+H3vn2T6dkUw6HO3FwSxS9jmC6Tkwlu3NimUIzz8GEV07KZmc9gGDarZyZoNNvMz8To9g0806M0m+T6rR2mJhM8eNJF80vMz2YxTIupUpxWZ8REJoJhu6w9rnN6Kc/ttQqxsIyqKsyVUoSDCv2hzmIpxsZOE8NyCKkil+81mS3EeFCukYoGmcjG0M2D6PadZo+QLONXZK6v73FqKsPNzQqiD6ZTIeLhCKcmAvR0k5lkDEkWebDbYCGf5MP1HVzP49REBst0OTd50Oo4U8yiyCJ392pEVZWabtHrjDiVi3OjvE9UFoiFVBJikFDAT3swIqGoZMIhrpcrTCWiPKg3GFs2F6cKNEdjzudzKJLIwDQJ+hWu7lWwXYdUMMD75TLpgMbYNvELIqcLBWw8zmdyCCKst9uEZIXNQZ+uYXAul+N6vYKEj7dyeXqGTlbRGDsWSZ9MWNX4qLKLB5xNpbm6X0P1CfzKV/4iFyZmnrk+Dq+1T15zruviuu5RNfz/sffmQZKlZ3nvLzPPObnv+1L7XtXb9DIj5ABkrqVwXFsQ3HtBIbANwghhm6uRdTWg0RBIsoWEsCHuDcI2EhcL4hopQGYTeNASMiMJTatnunt6me6ufc3KzKrKfc9z8pxz/yidJKu7uru6uwR45CcioyOqqk9+lXXO873f+z7v8xqnJUmSCAaDBINBSqUS586d6xWt+p24+tUDhx3PDRxXpHtcvgsPM5saGRnh/e9/P3/6p3/Kyy+/TLfbpdVqPfJ7HcV34TuBNwTp3g8Ps3e822oxFAoRDoePlJsSBOGhf2hDkaAoCoFA4B5t31/86TWuv7rO6EiI3b3afqvuiJ/l5RyzJxLk83XmTiQQRRG324ZkF7l1K8PQcIDrN/eF9sGwG001c+pkko6scvZEinKthc1iIRn3c+XGJqNDIe6s7GIy6cxMJlCULgNxL/lihVTASUeH3F6DSNDLlTsZwn4nJrOJaMBDLOSm3mpz0mEjW6rTlVVEunzr1ibxgJNCs4NgNnFiNE6zI/PMVIq9cg2vJBANuFnIlhiMCKzms7TlLmfH4hQbbc4OxTBZzEzHwzhtIlfWMviddtblMqVGi6dG4lzfyhH1uEgE3ChdDdEMq3tFbCYzkYCPxd0igwEPqzsFlK7GVMBFo9Fg1u/FIlgQnC4CLgevbGT2lQqRAFezOQZ9HrbrNQY8HlJeNw1l34eh0e1QUSHudnEll0PRNM4n4lzOZQhYbQx6vSiailMQKHY6JGxuUl4338rtT65IejysNmsMuT000Zhw+3EB1WqdIbOd/yM8gKPeJp1O4/F4eoWj+8GIbvthkHC73WZ5eRmPx7MvHxQEvF4vfr8fk8mEyWSi2+32THD6j+d3m+D8XSPdo5hC1ev1nhTU6Jz7TsFkMvG2t70Nk8nEe97zHn7mZ37mia73hibd+3WZGVaLKysrB6wWb9++fSyKBOOBaDQaTE5OYjabD7QRAlz6qyV+89e/jMdnIzEUYsgpYXda2dutMDERYnE+hyDum2JvpzOcPD3ATq7K8KAPVZMZGfDT6crs7TUZGgpy9doWToeEw2OjWmszOx2j1e5y/uT+qPWg104g4OLG7W2SMRcL601MZhgfjSDLXYaTATpKl7mhCLWOzGauzMnJOJde38TrtiFIFnTMXJgd2Nf6ej1UGi2kpkzEa+PSnS08doEuOg1Z5exYnFp7f+yPbgJJtOBz2XhleRuPw0pTVtip1Dk9HGM+k2ck5Cfkc9KRu8zGwyzvFdFVHadV4tLKNnG3nb1WGzBxfjRBrSPzzGCCjrYvTwu5HVxOZ5EsZsySxPpukQG3g6XdBgMOGwGHHaWrcSIYpNJVKDfapNwe/mpjCx2dqaCPdKvNkNeL12bltC2KQxTJ1Gu4LVYSbg+vZLYJO/ZnuJXabc5Eo+y1mpwJxfDarFQ6beZ8YYpyi2yzzpDdwUqniYrOs6ef4Senz/TSAltbW9TrdYAD0Wh/h9dhMJlMbG9vk06nGRsbIxKJHIiG+3PEJpOpV7QyVAGapvXypIZHb71eZ2FhobcRPGwNh+E7Yev4IFQqlSNN+H5S3wWAb37zmyQSCXZ3d3nrW9/K9PQ03/d9j9fMAm8Q0n2Qp+7dJGq4i9nt9nusFh+lOHa/8T6GImFsbIy5uTlMJlOvAGJg6U6W3//MXxEKOrG5bdy+vsXcU4NcvrRGKOJC0ixMjEdweOw0am1i54b2VQxWM+2OSKXSYe5kkuxGjVDASkduMzbsx2SxsFdoMDIY5Mq1LTxuG6JdoFCoc/pkimKpzmjShSBZGYiKOF02bi3mGBkMcG0+g6prjA2GUBSVp6b3px+cHosjqyrr2RIjqQCXbm1hkwRCAReZfJVTY3EaHZmJCGA20ZQ1UgGBqytZBAsEPDZy1TYTcR/re2VmEmF83540PBL2cXt7j3q7Qyro4VuLWwyGvOzmGkiChQujSUq1OtN+F5oooprMJP0eLi6nsQoWYgE364Uy07Ege40mJ2MRvA4rtY7M6ZiVdK1GuaMwEgpyNZPDYjaRcDsotloMOmw06zUmXE58LifpRhOHWUTVdS5vZxkP+rm+u4OiaXxPKtkbyS4IFqqdDkMuL/P5PG1V5XzcwdfSG/vRtM9PR1EYFm0EHS7Cbi+zwTDvObGfLzzMgKZer1OtVsnlciwtLaFpGg6HA4/H0yNiSZKo1WrMz8/j8/l4+umneyRnRMP9pHc3EfeTsdPpxOFwEI/HMZlMvPrqqwwODh7aZdYfFfenxe7G36StI+yT7lGUC0/quwD0fBYikQg//MM/zCuvvPI/Sfd+6Nfp9lst9ruL9eNRTG/6jcwPUyTcb7xPLlPm3/7852k1Osw+NUirJXP+mVEq5RYjw0HK1RaZjTq+M26uvLLG4HCATK5INOokEvXT6XQZG5NYXNzBabdSrXep5hqMjoVYXS+QiDqpVauMJN0IkkC1oTA5GuK1m2mcDhG7w8redoHTc0kK5SYnJ2IIkgWrKGC1i9xcyhIP789Wa8kKM6NR1rNFhhJ+LBYzT43FMQlmsvkqozE/N1eyqLrOWCrIUqbIaCKArOnMDURwO61UGi1CbicrO2XaiorTrHMxs0fYbaOpqjitEicGorQUhQujSSqtNqLJwoDPzcWlNJLFTDTgZnO3xGw8RL0jcyYZxWETKbc6nIhF2CiVqXVkzg7F+avVLeyCQNjrRLSYOZuMYzLB2UQMUbBwZyePiIWy0qWidJkJ+Xklt4sATHicyF2V0/4AmslEyuEm5HRwMb0NwKlohMvbOQJ2GzZRIO50E3M56eoaTwVjWEw6Nwr7BUCXy8WVwi6zgTDvO/Om+95HZrP5HglVv1F5Pp9ndXW153sQjUbx+XwoitKLXu93XePe67+u8a9BxqVSCU3TetaQkUik1yhkFOwqlQrpdBpZlpEk6YCEzW6391IZf5OkWy6X/0ZsHRuNBpqm4Xa7aTQafPnLX+aXfumXnuiabwj1gqqqh0aouq7zzW9+E6/XS6vVYmJi4oEdLJubm5jN5vtWTvvRarW4ffs2yWSS1dVVIpEIw8PDh94wiqJw7do1pidP8MkP/SHp9QKJoSCvX99iaDzC9lYJkwnG5xK0Wm2sNguaZqHdabGz16DdVhmbjLK0uMPkdIyt7TLBoJNQxI0iq1jtIptbRSyChUqtRaMlMzERYXF1j1jEhgZIonm/QKfoeNx2bi3v4nRIOF02cvkaJ2fibObKhP1OXG4biqJis4ksbxWwWMy0u12qjTanpuJcW84S9tmxieCw2/F5nJTqbXRdJ1ven0Z8ejzOtbUsQbcdURKQBAuJoJumoqCrOsVmk0ypzmDQyUqxjmA2EfHYyVSaDPv3R/R4nE5sdol8tYnZbGK7UqUhK5wdjnNlK4vbKhH0ODBhIuJxIKsacreLIFh4PbdL2Omg0pWpd2TOpvbVDS6LmfFIAJNFwCGJFFst8s0WAz4PlzNZbBYLIZeNrXqdhN1KR9NxiQIhh4MOOiqgmWChWGDA4yHfadJQFKbcbhYaVQI2G7PhCK2uglOU+PAz30fM9Xj5xv402MDAAH6/v1csq1ardDodrFZrjwQ9Hk+PBB8GRVFYWlqi3W4zNTWF1Wq9JyI2YOSIgV6e2MgVt1qt3j1vt9tJpVJP1Njx2muvMTc391Ab1P/23/4bN27c4OMf//hjvQ8c9F3w+XyH+i6srq7ywz/8w8D+7/5jP/ZjvPDCC0e5/H3/CG9Y0jWsFjc3Nzlz5swBq8X7waj6joyMPPDnAHZ3d7lx4wbxeJzx8fEHHr00TeOb37zIi/9lheU7WWZP7+dF7Q6JVkuhUmrg9DtYms8xNhVheTmP3SEQCLuRJJFA0EWzJSNZLWxsFKlWW6SGQqyt55mZS3BnMUs04iES9aCpOqLVwna2COhUGl1aHYWpySh3lncYSHpptRWskgmbzUK3Cw6nldXtCpqmE4q42cqVOTER5/W1HBG/i2TMR7erYreJbO+VqdVbmCUL+WqbU+Mxrq/mcNpEElEfuq4T9NhpyV2Urkarq7CaKzE7FOF2eheL2cRQfF/xMBb1YxEsmACTGbb2Suho1GSVdldjPOxmqVjDZRWIB/bzkgGnjabSpS0rmAQzt7N7DAV95Gp12t0uZ4fi3MzuEPO4iHvdKKqGaNLZKJYpywrjkSA3crsEnXZ0MxSaLU7Ho1TlDm5Jwm2zUu100HSdbLNOvtnkRCTE64U8OjDhdrHRbBAUJcJOO4qqYtY1KibIt9vMRcK8upNBNJv5nX/4QzwViR/hDr4XrVaL+fl5JEliYmLiviTU6XSoVqs9Imw2mwiCcCA10U+ChnfI2toaw8PDxGKx+w5PPSxPbFzDiLINPfHy8nJPQfQkjR2XL1/m7NmzDyXtz372s1QqFX7+53/+odf8W8IbWzLWD1VVWV9fJ5fLMTw8jNPpPLKRhyAIvWPc/dCfprDZbEdyGzKZTHz5D+6wk64zPB7mxuV1nG4bkstKYbfKmWdGqFXbTM/FqNUaxCJ2JLuDzfUCs6eSXH51jVDYjapr2GwSo2MR2h2F8+eGyGQrOCURSRK4cSPN6GiA5fkSbpdEKhkgEDDhclmp1NvMjcfYyBSpN2Vmp+LcWsqRSnhZz1RxO0T8Xom2rDA94GUnX8ZqMuNySFy9nWYsFeDmSg7QOTEZQ1Y0RpMhmh2F0agfQbKwuLHHeCrI5cUMqqYxNRSm0uhwYnBf/nV6KI4omFnOFfHbbGzkq9RaHWZTQW5lC7glgeFYkBjgtktUmm0GPTpYYClbJOa2srxboKvpzCaCbJdqTEWC+Jw2gg47dqvA0rcLcA5R4tL6NlGnjbIs01E1nh5KUm13OJ+M79sztvcNa+Z383RUlXOpOH+1sYXVYiHhc2HBxJlv20OejcZxSgK3CwVUHZx2KzfKJQKiRFvXaKhdJhwOCrUqJ71BfmRyhtOh6JHuu35omsbGxga7u7tMTEw81CTpfpaMRjS8vr5Oo9HAbDZjt9up1Wo4HA6eeuqpBw5yPEqe2CBiI0IOBoOEQqGehNNo971fY4fL5bpnMzlqZ9vf1NSI7wTeEJGuIRg38qrJZJLBwUHMZjMXL17kmWeeOdIfslgssrOzc6h+725Fgt/v5+WXX+bNb37zQ6/7u7/xVf7k9y4yOhFFsklYBBM6Jor5OoGoh5uvbeINWOnI0Gp3GRj1omsCgZCTrqqj6Tr1Rof0VpGZE0lef32bWNxLtdHBbDYzORWhUmmiIaN0zXQ6GpJNZDNdYm42zuuLOdxuG/6AE7PZRCjg3J/aazaxW2yQ26tyYjrBjcUssbCbSns/VTCS9NJodjCbNFpdnbaiEw64mN8skAh7KDZbNNsKZyYS7FUaBDw2REmgLXdx2ERurO3gtImoJijVW5wZi/PaWpawx0Eq4qOjKGhKh0Z3//pup5XlnWLPkUxRNU6PxMjXmgScNmxWkVZHwSqYeD2Xp6tqBBwSuWaHlM9JttFCNJt5ajBGoVZH7ypYbFbqiorfYeN6dgcdOJmMcCO7S8Bhw2GVkAQLEee+KkFWNSTBzI3cLg5JxCKY2Gs2mYuEuV3MYzWbmfS4aXYVvE4XusVEsdXCarGwWi3TUVV+aGCYd8ZTPbLrP/4/SCZWKpVYXFwkEokwNDR0bN4Lmqaxvr5ONpslFArR7Xap1+vout4rkhmR8aOqFprNJgsLCwiCwPj4+D3GUcY4IUM50d/YUa/XURTlQGPHysoKzzzzzENPpZ/85Cc5deoUP/qjP/pYn8nfAN7Y6YV6vc6rr75KOBxmeHj4gAfDq6++yunTp480KqdarbK5uXmgq+ZuRUIkEundEEch3S//0VU+//9+nY7cQdPN1GttJk6kWLyVYeJEnFq9gc0u4XA6adQ7OL02Xr+eJhR1UCrJaLpObMBDu6WSHAigajoWi5lavc1OrkpqyM/8wi6hsIN6q0u7o3Dq1ADNZgeXy0pHVmm1FTQTrG0WmJ6Kcnt5B4ddxOtzIMsaQwN+ZEXFYjbRaCvk8jWGBvxcX8risFmQbBKlWpu5sTDlWgvJsp8O6CgaHreNW1tFnDYJp8tKrljj5FiMO1t7xPxu4iE3sqJitwpsF2sUqg2iITdL2SIJr53dZgdF1Tg3kaDSbOOxWfdVEB0Ft93Kq6vbWAULQZ+TrWKF6USIxd0CQZeDiViApqwgmMwUm03y9SYhh8RSpY7ZBEmvk81ag5TXjUUw45QkAk479Y5Mu9tF0TWW8kVGgj626zU63f1o90omS9TpZDTkp9PtYhUECu0WO7UaQUlgqdmAb9s/3szv4rfZ8NvtiCYzk4EgH/u+v4/l24RpEJyRAuiXiRlEZ7VaWV1dRZZlpqenH3t46WGoVCosLCwQDAYZGRk5QOTG6J7+9MTd0ajH4zn02dF1vSddu7tV/rCC3d08058n7m/s2Nrawm63P7Sx40Mf+hBvf/vbedvb3nZsn9Ux441NuoZz0WF51ddee42pqakjtTg2m00WFxc5c+YMmqYdsIpMpVL3RB4PI93L31jikx/4fYIRN1a3GZfTjdUhkdsuo5u67Gb3c1+xoSCba3lmnxqgVGrg9TqQbCLtloJJMLE0v0Mk7iK7U8dsBn/IQbncJply01XB69v3SSiVmoSiLm7eyhCPeSnX9iPR2bk45UqLoN8JZhOKoqKbYHkjz3AqwNJmHrPZRCrpZyNTYnwoQL3Vwmm3YnfaKddaeFw2bqzkECxmkjEva5kSE4Mhdso13DYBl0Og3elilSzs1DqUmzITqRDz6TyDUR87lTpdVWVmIEC53sLndmISLFQbHfweO5dXMzgkEZ/Xznaxymwqwka+RMjtIBH00JK7iIKFbKVGrlJnbiDC1c0sDlEg6HWyWaww5HWiouFzu3DbrBQaTTqyTFmWKXVkhj0O1uv7rc6zsRBblSoxtxO/00FX03CIAmvlCjv1BrOxEK9ld3BbJeyShZ1mk2G3E5Mo4paseO1WqnKHpqyAWWexVGTQ4+VzP/S/431Aft+4Xw0i3tnZoVKpYLVa8Xq9B3KxR7ENvR+63W7PvW5mZgan03mk/6frOs1m8wARy7KMzWY7EA2vrKzg8XgYHx8/smrhQXliA9euXePcuXO9zcpYQ6vV6s1w+8u//EsuX77Mz/3cz/G93/u9j/S5GHjuuef4sz/7MyRJYmxsjM985jOHpiu++MUv8uyzz6KqKj/90z/NBz/4waO+xRubdA2TmsPw+uuvMzAwcCRNnyzLXLt2jcHBwYcqEmCfdL/ne77n0KPQyu0M//7nP09pr0p0KMTG0i4z5wa5fT2NN2gjFPEhSSI2p0Sl3MLhtnLrehq7Q8Lpd7CTrTB3ZoCtzSLRmAerXUJTNbDAxnoeh1tgd7eF2WLG47dRKLSZmAzSljU8bjsWwUK90cHtsXHtZppwaF85UKt3mJ2Ns7ZZIBHzYbcL6DqIkoWtbBkTKoWajKJqTE9Eub26w2BiX/Fht4l43TYabQWLYGJ1u0i10WFuIsbN1RyxoJtapwM6jETd1NsyZnRkXafW6uJzCKwWm9glgYDXQbpQZWYgTKHRwu+04XPbacoKosXMar5ModbkzEic19azuO1W7HaRQq3BiYEIiqohCQKiYCZbqqB0FSqqRkNWeGowztV0FtFiZjjso9FRCLscCIKZtqwgmmCxVKbd7RJ22Mg02yTcTgpym05X5enBBNW2jM1iRpFlKrKMZJXYbTepdDqcjkW5trcDwNl4lNVKmWGvj49+7/cz5n+4UT3sn87m5+dxu92MjY31RksZudi7o06D8I5yYtvd3WVlZYXBwUESicQTT5YwOuCq1SpbW1vUajVEUezpeI213a/N+EHo77BbWlrCarUeKGQbEXF/Y8dv/dZv8YUvfAHYr8O8/e1v58Mf/vAjve+Xv/xlfuAHfgBBEPiFX/gFYD9l0Q9VVZmcnOQrX/kKqVSKCxcu8LnPfe6oo9ff2KQL+1XcwzA/P084HD7UKexuFAoFrl69SiKReKgiAfanPFy4cOGenX4vW+YXfvy3MJlgeDpOqynT6tSpVWR01YxJtLCXrTB7dojb19OkRoJomHC6JJxeB61GB4fTxp1b24iigMUmUMjXGZsJsbJcIJn04vY6MVtMCOJ+84PdIbK0ksdsBl/Qzl6hxdCQh0pVwe9z4PU7keUuNofI4soeZrMJk2iiUGpyYjrOzcUcXpdANOpDFAScLiulSgtN19mtNCjXWpyaTnB9KYvPY0MULQhmCwNxL225i8kE9Y5CJl9lJBngxmoOq2ghHHCxla8yELSjmkxYzSZEwURH1RFFga1Sg5bc5dRYnGvrWfwuO2bBRFfVmIgHkFUN87cf5Ey5hsMqki5X6XRVTgyEuZnZwyFaSIa8gAm/04aiarSVLnarwGtbOTx2KxbRzF69yYlEhNd3dnFbrZxMRKh3Olh0aCgyxWYbh8XEequFDoy67Kw0W9gFgYTfhQkT44EAQwEvKbcrTUp4AAAgAElEQVSHYZ+PYa8X7wMKUndDVVVWV1cpl8tMTU09sKvq7qizWq2iKAp2u71HdB6Pp3efttttFhYWMJvNTE1NPdb06fuhUqkwPz9/IN8sy/I9ygmLxXIgNfEw+Vi/muJhHXb9+Ef/6B/xhS98Aa/Xy97eHslk8rF/tz/+4z/mv/7X/8rv/d7vHfj6xYsX+chHPsKXvvQlAD7xiU8A8Pzzzx/lst896oW78TD/BfhrRYLJZDqyIgH+uiutn3Tr1Ra/9n/9PvVSnYnTg1z5+iLBhItKqYNFMDN5agC5ozA4EqJaaTE+FWF3p0613GTqTIrXvrXK6GSU9FaWQMhFOOalXmvi8wuU95r7o3MaCul0htmTKa69tkki6UNWNCYnIjhdNuqNDsl4kDtLObpdFZtDYOV6kWTCSXapid9nI5HwoWk6yYiL3F6JgNuK3WVjcTXP3GScS9c2sVsFEkkfAbed8cEQcrfL3HCUjqqytl1gcijMK7f2JzAMJAOsZQpMDYZQVJXTo1G6ikKx3mI86mO7VKcldzk9HuPaWg6/04alq+G1iQwHHdSrVcYDLnSLmWylSSzg5tp6DkXVODUc4/pGjoDTRsBjZzwSwKwqNBstRv1erDaJO9k9kgEP2+V9Le9TQ3Gup3dIeFwMhHy0u10mggF2G008opXxUIBvru0PkhwO+lkqlEl53ZhEM7OSwJjPQ9zn4UfNZjy6RtAqHYjqDKu/R4GhuU0mk5w/f/6hUaEhu3I6nb3Jvf3es+Vyma2tLTqdDrquoygKqVSKRCLxRKmJfhhysFqtxokTJw6kKSRJIhQKHXDfMpQTtVqNjY0N6vV6Ly3Q/7JYLHQ6He7cuYMoipw/f7635gc1drTbbX7913+dra0trFYrkiQ9EeEC/Of//J95xzvecc/Xt7e3D7gIplIpLl269ETvBW8g0n2Q09j93MDup0g4Ku72X1DkLp9872fJbRWZPJ2iWqkyNhdGUy20GwrhpJ+bl9YYn0vw2qVVrDaRxHCYUNjFxFyMZkPh1FMDpLdKNKotwjE3r11aIznsJZOp4/baGBoN0VVUxiYiFItNhgcDVOodMttl5k6luHJlg1jMw56mEw27icY8tNpdElE/6VwFMyY8Lhs3b2UJBSQKNQVV15keD6F04exsErmrMZrwI9lE5ld3GRkMcH0hg6KqTI9FyeWrTAwGEUULp8fiCKKFTL5Kwu9ha7dKvSUzEnWyutfA57Jhd5pJhb0EPQ5acpe5ZAQZlZVMkbFEgMWdIqqmMzMU5vbWHlGvHVSFQY8NuyRSrTWIue1YJZHbm3vEPTZ2W/L+eJ3hGJulCtOxID6nnYjbiV0SWN4ronV1Ai4nL69s4bPbEKT9aPdMan/o5A/OTTITDRN2OUi6XSiFPJ1Wk+np6Xs8WvtNxjOZDPV6HU3TegUxQ5lwWCqqPwJ96qmnHnqCehD6vWej0Si1Wo07d+7gdrvx+/00Gg3m5+dpt9tIknRgk3jU478xniqVSjE5OXmk/yuK4qHTIu7+7DqdDt1ul2g0SiwWe+h1zWYz165d49lnn+UHf/AHWVtbe+jGchTfhV/+5V9GEAR+/Md//J6fO4xPnjRVA28g0r0fRFG8J997P4+ER8Xd/guf+sifsHR9g0Dcza1XN7A7JVwBF8XdAtNnB6iWGwzPBGi2GoTCDgSbxMrtDFOnklz55iregBPRISKIZibmwrRbHU6dTVEqt3DYBMJhDzeubDI+E+PO5Q1E0czwWBRBEhgdC9NqKcxMRinX2hR3qyROpXj18gaBgJMuOmpX4+RsgmKpyuSwDywiFksbf8DO7aVd/F4r6VyZdkdldMjLXqHN5FAQh8vGjE3CahdZ2NhDspjZKTYoVVucmopz+U4at0MkGfUhmFVGIi5Uk8BozIpu1lndLjCRCvHqQhpV15kdiZDJVRmL+3HZrZwaimG3Cmzlq/gdNiyCyOpelemBMLez+f2pFGE35WaLYa8Vq1XEaZVwO2zcSu/R1TSCTgffWkmTCngoNFuomsbfnx5BM+m863vOMBkNEvW4GA748Nr/Oh2g6zrZbJaNtRWGhoaIz84cei9YLBa8Xu+B2oCRY6zVamSzWWq1Gpqm4XQ6eyRcqVR6mtujpLiOiv40xf3a2vvlWTs7O/c0Tng8nkOHRSqKwsLCAt1ulzNnzjxQz3sU9H92rVaLO3fu4HK5SCQSNJvN3gmg2+0eUE643W5sNhudTodf/dVf5aWXXuK3f/u3OXXq1JHe92G+C7/7u7/Ln//5n/PVr3710L95KpU6YFSVTqd7PgxPgjdMTldRlENzP7u7u1QqFSYmJo6sSLhfcexu9E/5/f9+7S948fcu4ovYcfs8iJKIrsNuukRkIMjt1zYJRNyoOlRKTWbOD1Iu1LG7RRSlS1dR0TQTO5k6iREP6Y0avqAT3Wyi1ZKZPZWi2VRw2EUaLYVatYXDY2NpcYexiSira3tYLCYSg0GaDZlE0kdX1TCZoC2rZHJlwmEHqxtlHHYRt99BbrfKybkElVobt1NCsIo0mzJOp8S1OxkkwYzdKVKotBlMONnYbeBzWxlM+NF0Ew67RKHapFxtIkomtvMNhhM+tvJVFE3jxFiUvUqDoMeJzSqgdFVsVon5rT1UTcPptJIr1ZgbjnBraxeAp8YTNGUFp1VE0TSqzQ6iWWV+t4pdFAh6HWwVqoyEvWyWq3itIlNhF6IgMBTyMZkIMx6PMhQJ9mRb94NRzHK5XIyNjR3LkdyQYe3s7JBOpzGbzYii2CNig/Ce5L3y+TzLy8skk0lSqdQjBQz9jRO1Wu0eLbGiKGSz2XvkkU8KXddJp9Nsb28zOTl5aNOHkTox1nbp0iU+8YlPIMsyk5OT/OzP/ixvectbiEYfvenkbnzxi1/k/e9/P1/72tcONJb0o9vtMjk5yVe/+lWSySQXLlzgs5/97FHTj2/8Qtr9SLdYLJLL5QgEAkdSJLzyyiucPXv2SCLxxcVFfD4ff/lHV/jj//gNrHYJLAKVYoPJM4Ms3UwzcSqFpoPNLoLJTK3cwhtycePVdWIDfirVNq2WzMBYkMJujXDCDSYzmqaidDWqZZlo0svyUh5/wIlmMlEuNTl5boBKqYXHZwOzmU5LRrCKLMxnSQ4E2M5WUDWNkbEwK2t7xGMOrDYbLqcds2imVG7hdFm5s7Q/hHFgMMDqZoGJ0TDbu1WCPgexiJd2R8FqFdjerbJXrJOIu1nNlAn5rJRb+62+wzEHXd1MwOvCbDHT6ig4nVauLm5jt4k4HBI7pTpzI1Fube7icdiYGgztj2G3ClRbMqV6i0jAxWtrWTwOK5IksFdtMBRwUOx0GQz7eGokgdMqMhj2MRTykgp4sYr7fyejHdZ49R+vjZcxTaHb7bK6ukqlUnloMetRcbengdPp7HVn9RfEut1ur032qMoEWZZZWFhA0zSmpqaeOAI10O12KRQKrKys9Lx5+/OwRtT+uIY2zWazF90eVWLW6XT4lV/5Fb7xjW/w0Y9+lHa7zdWrV/l7f+/v8Q/+wT94rHX0Y3x8nE6n0zt9vOlNb+I3f/M3D/guALz44ou8733vQ1VVfuqnfuqovgvw3UC63W73UIewdDrN4uIisViMsbGxh+bTrl69yuzs7ENvaF3XuXnzJq99Y4G/+NQ1AlEPkVQQuS1jdVjJbhYQrSK7mQpdRWNgKsr6wg4z5waplFq4vXasTolGtUUXhcx6hXDMRz7fQFFUhqeirC7vMD4TpSOrWMw6qq7RrCu4/Q5Wlop4vDYsVoFiscHcqRTr6wWiEQ9OjxVN1cGss5UuIlkFqk1lP/0wm+DWQpZE3IPZYuk1STSbMoJoZjNbplBqMDed4PXFLNHwvvSrI3c5PZuk3uxgs4k0Ox2K5To+j52FdBmbZMZuEyjWZUYSHrYK9f0JEzEfHVnFKlnIlmrky01GU0FurOUIuO3oFijWWpwZj1NudRgIeRgKOpHrVWaGk5yfm8DverwxMv1EbGg9ja8bm+9RDWIeBl3XyeVyrK+vMzIy8sBRT8bPH6ZM6JeIGY0Jhtn+5uZmLwI9LvRHoP0pkP48bLVavSeHfRTvX13X2dzcJJvNMj09feS23StXrvC+972PH/mRH+EDH/jAY89e+1vGdx/pGooEoyvm6aefPtJ1bty4wejo6AOH3RldPtnVAn/0axepl1sMTMZZn88yc2GEO69tEk75CUa9+4oIp5VKoY7NaWXh5jaCYCaY8JPZLDI4FWQ3Wyea8OHxO1G7GqJNJJsuYbOLZLL7pD08GWFlcZfxmRiVWguHXUCyW2g2Okg2gUy2gQlwuG3s7dUZGvWxvlElELATTfhAN+FyS5SrbdSuRqnaolBscOJEkpvzGbxuG2arBV3XGR0M0lZUBIuJVkclX6wTi3m4uZjDKllwe0R2y20mh0OU6x28Lhs+r4NWR0EUzaxlipSqLYaTHlZyVTx2Ec0M9ZbChZkkKiYGoz4mUiHCXieDER8xv6vXUmq1Wo8k2XsUNBoNFhYWEEWRUCjUO8a2Wq0DEbHb7X5kIjaKV06n84nSFHcfr42I3ZCJDQ0N4ff7j+1zaTQa3LlzB4/Hw9jY2EMj0Ad1sPXniUVRpF6vc+fOHfx+PyMjI0eKbtvtNp/4xCe4ePEin/rUp46sIvo7iu8e0r1bkeB2u7l69eqRSff27dskEolDd+VWq9UbvxPyRPm3P/HbaLrGwEScbkfF6rSylynTqLaxuqzkNovMXRjl1pUNogMBBKuA1SYgOsw0am18QS+bywUUpYsn5CKXLjF3bohb19JE4h58EQ9mkwmHy0ql0kIQzayt5el0uoxPx1mczzE6EWFru4jXayMUdVGvNbEIOvWGRqet4vDYyWZrTE3HWFjZQRQtpAYCyLJKKORCUfc3JUXV2NgqMjQUYHFt3xN2YCDI6maB8eEgqg7oXSwW0E0iNrvEcjpPs61wYirOzeUcQd9+Z5em6zx9YgCzxUwq4mMo6sVlM+ESdNqtRk/PaRCdw+FgZ2eHcrnM5OTksRqZqKrK2toaxWKRqampQ5tk+p267ibiB1kmHuXajwtN01hbWyOfzzMyMoKu6wcsHW0224GI2Gq1HnmjMLwY8vk809PTT5Re6Te2MU4VzWYTTdOIxWKEw+GHGqDDfrv++9//ft7xjnfw/ve//3/U6LYfb3zSNTpa8vn8gSKArutcvHjxSMY0sJ+n9fv997g2ra6uUiwWGR8fx2F18ZEf+4+s3EwzcjrJyo0M/riLekXGZIaRE0lURccTcFErt1C7KuVSk+JOjdR0kPRymdRomEKhjsdrJzkapt1SsDkl9nJVGvUOgiSwkykze3aQWzfS+INObC4bkmQhGPHQbikIopncTpW9nRqD4wFWVwrE4m6K5TadTpe5Uwmq1RaS1URHVpE7XWxOK+ubFVIpH7l8HbmrMj0VY3ltj6FUAKtdxGIyIdoE8oU6mCBXrNFqd5kcDbKwXsTnsSPaBexWgZNTCZwOK6mol6G4n2TEg9f1cO8ARVGoVqtkMhny+TwWi6Un+u8n48c9+htetKurq49VcDKE/8brbiI2ptIa1z4ucxrYN75ZWFggFov1jJvu/t3uTp20222sVuuBjeKwicCHNTkcFwz5WjAYJBqNHoiKjY3CWJ8xsbjT6fDxj3+cV199lU996lN/a8MivwN445OuYR+XTCYf2SOhH6urqzgcDmKx2AG1w9DQEIlEAkXu8ss/+WnSy7sMTERRFBVBEqhXWhSyZcJDQZZvbBMb9bO7XcNsMRMe8KF0FAIxLxaziMViobBXY3e7zMSZAeavpxkYj5DNlNE0ldmnhmi3FFweG62WgtJRaMkq25tFZk4PcPvmNm6vHdEu0m7JRJN2NM2Cx+uiLXeplJuEIh5u3kwTDLuQuxrVapvZE3G2M2U8HgnRakGRFSSrwF6hg44JLCbK1RYn5hLcmM/gckj4/BJOu5UTMwNEQh6SMS+pmJdo0IPZ/Pi5UGMul8PhYGxsDEmSekTcHzGJoniAiI9y9DfSFKIoMjExcWzHcVmWyefzrK+v9yYcGCbij7K++0FRFBYXFx/b+Kbdbh9QJvRvFE6nk2KxSLvdfiQvhqNA0zRWV1cplUrMzMwcmprr3yhqtRovv/wyn/zkJ3vrefe7381b3vKWI2l2/wfBG590NU27bxPEo5Du5uYmJpMJSZJYWVk5EBFomsZv/sLv87U/fJXBqQQbizkkm4gv6mV3u8jJN0/QqHawe2woskq1VEcza2RXywxMhkmvFBFtFhw+B11ZZXAyitbVsdolGg2ZcqFOKOHjzvUtBsbC5DJVVFVjZCZGvdIiHPOiAbqmI3e7bG0WCMdcbG5UsdlF/GEXmUyZ2ZNJqtU2LpcVm12i1ZJxuGzcurWNyWzCG3SSzVWYnYlzayGHyyUxPh7EZNLwewSCITs+l0jQb2NudppAIHBs0iHj1FCtVo+kHJBl+Z6j6/1UCYaXcj6f7zW7HBd0XWdra4tMJsP4+HivC6s/IjZaYR91o3jUItyjwLA8TafTvekQxvr6tbqP+35G5ByNRg+Nyg9Dq9XiYx/7GFevXuVjH/sY9XqdK1eu8KY3velYlAl/R/A/Sfeo2tvl5WUymQyBQIDx8XFEUexJ0T7/f3+Jv/jM14kMBrG77GiqhsliZmerSDgV4M6VdbwhF2ZRoLRbY/BkFLmhEox4MVnMqF0VTTezsZgjMRpkbWEXq11AclmpFtuMn4rRbnZx++yYLQKtpozdaeX2tS1iKT+FUoN2W2FgPMDWepnh0SCi1YoomBGsIuVyE7tDZGFxB13XGR4Ls7Kyx+h4hO1shUjYzamnBrBaBVJJP6mkn0Tch9dr732Gm5ubZDKZXnrGKOYYR1fDCetRo8deE8LGxn4TwreHIj4ODjv6m0ymngRoZGTkiYjkblSrVebn5wkEAkcqCvV7EhwWsfd3hzWbTebn57HZbExMTBxb+66xjsXFRVRVPSAx69/I+j0T+onY6XQ+8PNTVZWVlRWq1eojRc4XL17kueee45/+03/Ke9/73mOZq/Z3FG980n2Q09hRtLfNZpOlpSUajQYej4fZ2dme4YbJZOKlz7/Cb7/wB0h2CSwWyrtVZt40zvzldcZOD2AymZFsArKqUC3WCcYCzF/ZIhD1oqgq1VKT6XPDrN3JMTgZRZAs+yNPBDOlvTpun535mxmsdgGbx0q50GJoKkg+1yQYcuINuWk12nRRqJQURFGg3lCo19rMPLWfcojGvUgOEX/AycxsApfHRjLpJ5nyE4k8OB1gdAVFo9F7cn135xCNYs7dOdj76Uyr1WpvxPfo6OixEotR3NQ0jWg02qv+928Uj1Nsgv2ofGVlhUajwdTU1AMVLQ/DYURsjJlKpVLE4/Fj2yj6I+fR0dEjNRP0N00Y6zPMawwyNsxrjJzzo+TKm80m/+bf/Btu3LjBpz/9aSYnJ5/49/w7ju9u0r169SozMzOH5sj6i2QTExOYTKae45Hh8n/jG/P8+3f/Nr6Qm9BgELklY3fZya7nsQhmint1Ws0O0dEAudUS408NUi408QacuAMuOi0Zu9PG6nwGySbSqMs0qm2mzg2xcD3N4GQUHfZtHT12mo0OdqfE/OvbqF0Vd9BJYadBYsTD9lYdt9fK2Ewcj9fJyESUcNRDcsBPPOnDan00Qms0GiwuLiKKIuPj40cW3Pfb/VWrVSqVSk9n2n+s3tjYoNlsPjFp3Q1jrM3Ozs59W2z7c5wGEfcXc4zUxGG/m+F89aRR+WEwjuSBQACfz9fz1u2PiB/36G/MVrNarU8cOXe73QMbRb1eR5ZlTCYTAwMDBIPBB07CAHqF7Oeee453vetd/Kt/9a/eyNFtP767SffGjRuMjIwc6E8/rEim6zrdbpd0Ot17CAqbFT7/4a9QKzQYPJFk/fVtpt80zsKVDQIxL4Gkn3aricvvQu2AZBdZvZVBUVQGpuKs38kyfW6YhZtpQnEvsYEg3a6Gw2WjuFcDk4m9XJV6tcX0uSHmr6dJjYYoFur4gk6S414EycLcqVGGxmJ4/BKYld7DAPQeUK/Xe+RJrEZX1nHKtPoF/5lMhnK5jCRJvb77xx0JczeKxSKLi4uHRuUPW99hEbtBxIbGdHV19Tty3DeMxRuNBtPT04ceyQ/LYYuieKBYdxgR9+ec79dm+yQoFAosLi6SSqVwuVwHiNhsNt/TNGH4A3/0ox/l9u3bfPrTn2Z8fPxY1/R3HG980oX7e+reuXOHWCyG3+9H1/WeyfPdRTI4OEakmC3z/P/6q2iaSmg4QKvWxmI10yh16DQVBLtIIV1l5k1jzF/eIJjwYZEErHaRyECQdkPGapfIbBaoFOqEB4JsLu0wfXaY+WtbuLx2EqNhrA6JyVMpPAEXicEAsQEv1WaRarXC+Pj4Ax8gVVUPPKT1ev3AsdDr9R54SPu7m47L5Lof5XK5J7sz8p+GdMh43e3OdVSbxHa7zeLiIrquMzk5eSxjbYyIvVKp9Ay6JUk6sL4HpU6OCuOee5zI+WFELAgCa2tr+P1+RkdHjzWSNNqaO50OMzMzh54MjHvQWOMf/dEf8Yd/+Ic0m03e/OY38773vY+zZ88eS9vyT/3UT/Hnf/7nRCIRXn/99Xu+r+s6zz77LC+++CIOh4Pf+Z3f4ezZs8C+wc3HPvYxAH7xF3+Rn/iJn3ji9TwA3x2kK8vyoXZsS0tLeL1eJElicXERh8NxT5Gsn2wBWvU2H/3ffp2lK2vMvHmSWy8vExuNUCs3aTc7pE5EUWUdq0ui01JAA7mjk0+Xmf2eMW6/uk5kIEi91sZkgvP/yxw6kBwNkRyJEB8MEh8K7Ps1fBvG3Kmtra0nIkTjWGi8Go0GoihitVqpVqv4/X7Gx8eP1eS60+mwvLzcMyd5UGHF6GyqVCoHIva7idiIYI1TSTabPaAcOC4YkbOhizWZTAdSJ9VqFVmWD23RfRja7Tbz8/MIgsDk5OSxfeayLFOpVNjc3KRarSJJ0j3ytSfNEe/t7bG8vPzAUe13o16v8+EPf5jFxUX+5b/8l+Tzea5cucK73/1uLly48NhrMfD1r38dl8vFP/tn/+xQ0n3xxRf5jd/4DV588UUuXbrEs88+y6VLlygWi5w/f57Lly9jMpk4d+4cV65cOVaFy1347ibdxcVFCoUCkiT1CKG/SHZPp1FX5WPv+H9Yu7nJ4EySrqIhSgLlUp3SToXocJjlq/vfy20V0XUYnEnQanRITocQ7BZ8MSep8RjDkwmSQ7EDJHIYisUiy8vLvQjxODtyDD/Xdrvds9czCk3Gsf9xFAmwT4jpdJpMJsPo6CjhcPixHvT+uWFGxG5I92q1GsFg8DtW3e92u0xNTT0wcu5v0TU2CoOI+yNiY339x/3jtnWE/RPFwsLCAalWv87ZcBAzrBwfhYgNYx1d15mamjrSfaHrOt/4xjf44Ac/yM/8zM/wsz/7s8faeNGP9fV1/vE//seHku573vMe3vKWt/DOd74TgKmpKV566aXe61Of+tShP/cdwHfH5Ii7jcyN6vPOzg7hcJiZmRlUVUVVVUwm031vit967r9w6xvzJMZj3H55CbPFhCfmpZiucPatJ5BllX/4k9/H0FyKcCpAcjxCOBXEYjk4adUgkXQ6Ta1W61noGUTndDpptVosLS0BcOLEiSMN0Dwq+otNY2NjhEKhA2kGI79ZLpfZ3Nx8IIkchmKxyNLSEqFQ6NCxRY+Cu/1qZVnumXHH43Ha7TZXrlw5kDo5irTpMPSfKI5qINNvHm4I+I0cdq1Wo1AosLa2RrfbRRRFms0mXq/3WPxo+9GfFz558uSB+0UURYLB4AGC7yfivb29e1qwDVWC8Rnu7Oywurp6ZNUD7Hei/dIv/RKrq6v8yZ/8CcPDw8f2+z4qDpv2sL29fd+v/23gDUW6Bgy96fb2NsPDw3i9XkqlErIs78u0DoluDXz+3/0ZX/+DbzF+dpjISIihCwlS0wlOPT3H0HQKm/No0aDZbO7d2KlUCth/YGq1GpVKhZWVFUqlEpqmEQqFeoJ4I/p+Eui6Tj6fZ2VlhVgsxtNPP33PBmOMJrLZbD3SMaK5SqVCPp9ndXUVVVUPeMF6PJ5e55SmaZw6depYR4YbrlfpdJqxsbF7Iuf+1Mnq6uqBaM7YzB7UjFCr1Zifn8fr9XLhwoUnOlH0j9OJxWK90TalUolUKoUsy9y8efOAKYzxepz3NXx0BwYGmJqaOvIkh/sRca1W6xGxyWRCURQkSWJmZuZIPhK6rvO1r32N559/nn/xL/4F/+k//afvWHR7VNxv2sN3agrE4+ANRbqGPnF1dZVoNMozzzzTy8/t7e1x5coVTCZT78a/u8gE8Laf/H7e/n++tZefnJiYONSV/3EgCAI+n49ms0mz2WR8fJxwONwj4kwmc0DWZKzxUfKAhpuWJEmPPBqmP5rrn8ll5F+z2Sw3b95EURR8Ph+RSARZlrFarcfysBnubX6/n6effvrQyFkQhHvGwfRHc8aEhLu71gRB6HXCTU9PH9vf1EA+n7/vaJt+P12joHb3ZvYgVUe/j+6TjvuBg0RsPDNra2skEgnMZjObm5sPbZio1Wr84i/+Ipubm3zhC19gaGjoidZ0XLjftIdUKsVLL7104Otvectb/uYXyBssp3v79m3a7XbPXu+wIpmqqj1dqVFkMh5QY7xKuVy+5zh+HCiVSiwtLT0wb3s//avxgHq93kMfUENvXKlUjt2pC/46yopGowwMDBxQJPSnTvqla0f97GRZZnl5mVardV8p1aOiXxqWz+ep1+vY7XYikciB9uHjeJ/+/Oej6Jzv/gxVVb1HerW3t8f6+vqx++jCfq7/zp0799X0HtYw8R/+w39AlmWuX7/OP//n/5wPfehDx5pn/+IXv8izzz6Lqqr89E//NB/84AcPfP9f/+t/zZe+9CXW1t5MuDoAACAASURBVNYYGBhgd3eXcrkM7KeohoaGyOfzfP/3fz8vvPAC733ve3nllVcoFoucO3eOq1evAnD27FmuXLly7NK6Pnz3FNJUVX1gkewwdDod1tbWyOVySJLUi/j6j6xPcgw1uqYAJiYmHjlve/cDasiuDJLrdDrs7u729MbHuVG0Wq3eUMXJycn7koqxmfUrJvpzh16v955jf39u9bg9B4y1z8/P94xvdF1/7K66u9G/duPE8qTo96stlUrs7e1hMpl6TRSPIq87ytrT6fQjFfmq1SrPP/88mUyG8+fPs7q6yurqKi+//PKxyNRUVWVycpKvfOUrpFIpLly4wOc+9zlmZ2d7P/POd76Tl156iXw+j9Pp5MSJE/yTf/JPAPjABz5ArVbj537u5/jiF7+Iw+HgM5/5DOfPnwf2p/5+/OMfB+CFF17gXe961xOv+QH47iDd5557DpfLxfnz5zl37hxut/uhD3G/amB4eBhRFA/kNo0H1IhC+kX+DztSGwMwjW6349xVNU0jm82yurqKxWLBZDLdQ3JPIhnqN4953LUf5hpmNEuIokgul8Pr9TI+Pn6sao3+AuKDGgXuPlX0S8MeVEw0Zqu53W7GxsaOde13Nzn4fL5eesfQwuq6fs8U4qOSXqvV4vbt2480OkfXdf77f//vvPDCCzz77LO8613v+o7kbi9evMhHPvIRvvSlLwHwiU98AoDnn3/+0J9/85vfzEc/+lHe+ta3AvuSw3q9fuzrekx8d5DuwsIC3/rWt7h06RJXr15FlmVOnDjBuXPnuHDhAnNzc70HqFQqsb6+jsViYWJi4qHFoH41QqVS6Uma+qNhg+T6GxAGBgZIJpPHGsEZRu2KohzQxB6mz+3PbXq93of6D/T70Bq5sON8wOr1OktLS9TrdWw2G91uF5vN9v+3d+ZxUdXrH/8cGDZFZREEVIQBB3AQkMWlsiuYdrVcMkSqe6Pkl2YuaeWSltmiELkkV4vKXHspuePtupSFdq/FACapCMqOCirMwLAIDMM8vz/0fJuBGRhwUMR5v17zejlnzgzfc5x5znOe5fNolK7dSy1rRUUFrly50mG9WPWuOvULLl+jW1tbi9raWvj4+LSpkNZe+GkLNjY2rTY5qH8X+fK65oa4uVOgbszbMzpHLpdj+fLluHXrFhISEjQqAAzN/v37cfz4cWzZsgUAsGvXLkgkEmzatKnFvkVFRRg5ciSuXbvGzpNAIEBAQAAEAgGWLVuGqVOndtpa9eDRKBnz8vKCl5cX6zSpr69HRkYGUlJSsHnzZmRmZsLMzIw1Cnz22Wfw9vbW64epqxqB/+Ln5ubi9u3bMDExQUNDA3r37g0/Pz+D6pbywtl8CVjzW1ptSSa+iJ5vzeUTddqMHJ+Es7CwMEjCRh11EZZBgwYhICCAXaB4b7OiogJFRUVQKBQtKiba8iYVCgVycnKgUChalFK1B/WKBPVkYklJCWsP5jiOeYvt7arTBj8lQiqV6hynro76d1H9M/j467Vr15hTYG1tDQsLC5SVlcHOzk7v0j4iwk8//YSVK1firbfewssvv9zplQntqTBITExEeHi4xrEUFxfDxcUF+fn5CAsLw9ChQ+Hh4dFp6+0o3crTbYsDBw5g1apVmDhxIiwtLZGeno6ioiIWPwoKCkJwcDBsbW3b7Zny9baNjY1wdHREQ0MD5HI5u11VN3LtvR1V9z51TRNoz2fxba/8BaOxsRFEhKamJri5ucHFxcWgraS8YLm+M8R4b1O9Y009yaRu5NTvKoRCoUHHhgOaxlxdWLx5V50+3qY2tDU5GAq+Tr2srAzW1tZMrIZfoy6tjsrKSrz77ruQyWRISEhA//79Dbam1mhPeGHYsGHYvHmzTp3sV155Bc8++yzCw8M7b8Gt82iEF9ri+vXrsLOz0wgl8POiJBIJJBIJ0tPTUV1dDR8fH2aE/f39dSaQlEolCgsLIZVK4enp2SIpoX67yv9A+SQYb4hbU2riB2x2xrBGXk2LL7GztLRkRo43IPqsUReGHHWurXW4qakJjY2NsLa2hlAoRJ8+fQxmtDoiLK6tqw5Ai4YYExMTjSYHHx8fgzbFAJqjc9zd3dl50abVwYvT/Pnnn7C0tMS2bduwePFi/OMf/zDoRaCtyoRvv/0Ws2bNgkgkgpmZGSoqKnD8+HGIxWIN3YTo6GgkJCSgoKCA/Z9UVFSgR48esLCwQHl5OUaNGoWkpCSNJNx9xmh020NjYyMuXLjADPH58+chEAgQGBiIwMBABAcHQygU4tChQ3B1dcXAgQNZjaM+qN8K8vHh5kkwXsSkqqoKIpHIoEMPgb+8TysrK606DLwBUffk+G6w5jHs5qhLI3ZGTLupqYnJcQ4YMIBVTrQl9qMvhhQW12bkVCoVFAoFHBwc4OrqCmtra4Odn/aGKvg1ZmRkYM2aNcjLy2NDL+fPn4/IyEiDrEufyoTt27dj//79THh95syZWLFiBRYvXoydO3ciKysLHMdBKBQiKioKn3/+OXvvb7/9htmzZzPxqoULFyI6Otoga+8gj0ZM11CYmZkxAztnzhw2iTU9PR0SiQQLFy7EhQsX4O3tjSeffBJBQUEQCAR6lzyZmJiwllc+McHXRMrlchQVFaG2thY9evSAg4MDFAoFFAqFQcRS9B2X07wtt/kab926pdGEwBvipqYmXL58GZaWlggKCjKoqA7wl9h6//79MXz48BbnWz3OzguQm5mZaYR3tA1sBP6qerh16xa8vLwMUutsamoKGxsb2NjYsCaHpqYmeHh4oK6uDgUFBR3WSGhOVVUVsrKy4OjoiODgYL2cACLCiRMn8OGHH2Lp0qV48cUXYWJiwiZKGIrU1FR4enpCKBQCACIjI7V6om5ubvjhhx80tgUGBqKqqorlKiIiIjBixAiNfR577DFcuHDBYOvtTIxGVw/4KoWwsDBYWVnh999/R0pKCqytrZk3/OWXX7LyKr5kLTAwUO8fj5mZGUxMTFBWVgZ7e3sEBwczA6KujdCzZ08NA6Jv7LX5uJzmXVP6YGZm1iJRp67fcOXKFZZEtLS0RHV1dZv6DfrCi/aYmJi0muTTlUzkDTGfTGwu9sPXIzs4OCAkJMSgt9Xq515Xk4N6eV3zC1pbFwve86+srISvr6/eyVuZTIalS5eirq4OP/74I0scAmDNGYZCm/aBRCJpsd+BAwfw66+/QiQSYcOGDRg4cGCX0k0wBEaj205GjhyJI0eOsOfTpk3DtGnTANz58mdlZUEikeDQoUN4//330dTUBD8/PwQHByM4OBg+Pj4tEmn8+HilUgmxWMx+NAKBoIU2Al88f+PGDeTk5ICI2hQxr6r6a1xOcHCwQTuIzM3NQUSQSqUYNGgQ+vfvz6oRmus3qNc463uxUJd17Khal7m5Ofr27cskIdXFfmQyGbKysqBUKtGnTx9wHIeKigqDXSzq6uqQlZUFKyurVs+9No0E/mKh3iLefAQRLx3p4uKC4OBgvS6kRIT//Oc/+Pjjj7F8+XJERkZ2ug6BPpUJkyZNwgsvvAALCwskJCQgKioKv/zyS5fSTTAERqPbTlr7zzY1NYWvry98fX0RHR3NmizOnj2L1NRUrFu3jomtBAUFwd/fHykpKfD19cXkyZPb7GriM8/W1tZwcXEBoBl7LSoqYnFN3gDLZDImQG3IcTmA5qhzde+zuRqXeqdVSUmJ3hMveC0Ge3v7e1YxU4fjODYZVyaTwdPTE05OTjovFupGrj13FsXFxSgtLYWXl1eHdFubXywAsDXy56axsZGJEJWXl7ep1SGVSrF48WIolUqcPHlSbyWxe0WXJoI66hec1157DUuXLmXv7Sq6CYbAmEi7z/DlX/Hx8diyZQuEQiGqq6sxaNAg5g0HBgYyr6sjKBQK5Ofn4+bNm8y48CI6vLd5L7FWQ4w6V08wyeVyjbbhnj17oqKigl0sDFnrDOgvLK4u9sNXTKi3X+sqC+OVzNSnZxgSXnR9wIABcHFxaTGCqHlXHX8RPnLkCNasWYMVK1ZgxowZ99VbVCqVEIlE+Pnnn9G/f3+EhIRg9+7dEIvFbJ/S0lIW4jh06BA+/fRTpKSkPAjdBENgTKR1FTiOg4ODA6ysrJCRkQEnJyeoVCrk5uZCIpHgxIkTWLNmDW7fvg2xWMwMsa+vr17lYurjch5//HEIBAJ2Oy2Xy1knnlKpbCGio49x4IVvnJ2d7yn2qZ5g4lEoFCgqKkJeXh67WPBhEfWOuo7SXmFx9TsLvlZVVxNCr1692OywmpoavSsH2oNSqWRxc3WdXisrK1hZWTGvVb1Msby8HHPnzkVxcTFMTEwwc+ZMgw/abKsUbP369diyZQtUKhXEYjEcHBwwa9YsiMVicBzH5Ff5Dko+Lr99+3YAgJ2dHd5//302eWLlypVd3eC2itHT7aIoFApkZGRAIpEgNTUVFy9ehKWlJYYNG8YMsVAoZEavvr4eeXl5aGhogJeXV5veoTYvDmh5y8//OHnRHo7jWhW+6Si1tbXIzs7WGKXEH5e64lp7hdZ5qqqq2AReQ3ufTU1NuH79OgoLC5nX3JbYT3vhpSPbM2ONiHD48GHExsZixYoV8PPzw9mzZ5Gfn48PPvigw2tRR59SsOTkZIwYMQI9evTAl19+iVOnTuH7778H0OX0EgyJsU73YYeIUFlZibS0NEgkEqSlpSE/Px/Ozs6wtLREaWkptmzZAk9Pzw57n/wtv7rsJW+c6uvrWezT0DW3fHOJl5dXm/XIurQRdLXkNjU1IS8vD3K5vFPi2kqlEjk5Oairq4OPjw9rvGlN7IdfZ1s6GPzn8GVm3t7eenv6t27dwttvvw0zMzP861//MogKmjbaK1Jz7tw5zJs3D2fOnAFgNLraMBrdLsyff/6JqKgoeHp6YsCAAfjjjz9QWVkJLy8vJvLj7+/fYS9LJpPh8uXL6NWrFysBq6+vZ3KIvAHpaJZfKpUiJyfnnoV1dA26NDc3R01NDZycnODh4WFw7QB+cKO+3qd67FUul7Px77rEfnjB8/a0NxMRDh48iLi4OKxatQrTpk3r1Nhte0RqAGDevHlwcnLCe++9B6DLidQYEmNMtzvi6OiIgwcPsoJz4I7nlZmZiZSUFOzZswdLly4Fx3EICAhgbc1eXl6t3l7zo85VKhUCAgI02qbVtRukUinL8qv387elOaAu/G2IGWK8gDofQ21oaEB2djYaGxvh4uKC27dvIzU1lSWUeAPX0YsRv34A7WoAsbCwgIODA/M6dYn9WFlZoa6uDmZmZvD399e7RfjmzZt4++23YWVlheTkZINPTdZGe8q5vvvuO6Snp+P06dNs28MiUmNIjEb3IUa9mJ1HIBDA398f/v7+mD17NogINTU1OHv2LCQSCWJjY3HlyhXY29sjKCgIQUFBGD58OJycnNDY2Ig///yTdUxpuyXlOI4lbpqXhMnlcly/fh3V1dVaxyIBd8p9rl+/rvPz74W2hMX5W365XI4bN26grq6uXROR1ZscDCFcrn4u+/Xrxz6/oKAAffv2BREhMzOz1fAJcOf8HzhwAGvXrsVHH32EqVOn3rfKBH1KwQDg5MmTWL16NU6fPq1xjvl9hUIhxowZg3PnznV7o/tQhxf27duHVatWISsrC6mpqUwhvjm6sqsFBQWIjIyETCZDYGAgdu3aZfC21a4IL+aSmpqKlJQUpKWlIS8vD0qlEmFhYYiMjERgYOA9aQI0H4tUXV2NhoYGWFtbw9XVFTY2NgZNxnVEWFy9SYJfpy5VOF6PgU/0GVK4HPirjM3MzIwJvvDoCp+cPHkSwJ24qrOzM+Lj4w0+6r2tyoTa2lr069cP9vb2cHR0RE1NDfbv3w+xWIyYmBh8++23aGxsRGNjI06fPo3Bgwez93ZBkRpD0j1jullZWTAxMcHs2bOxdu1arUa3texqREQEpk2bhsjISLz++uvw9/fHnDlzHsCRPFhiY2Px66+/YsGCBSgtLUVqairOnTvHtGn5+PCQIUPaHb/l5QVramrg4eHBWpv5mOa9jkVqampi0zm8vb3vWVhcmypcfX09mpqa4OLiAicnJ70kG9vz93hpSpFIpLfRbGxsRHx8PI4ePQorKyvI5XJYWlri0KFDBpulpk9lwhdffIEffvgBubm5kMvlcHR0xIULFzBnzhwcO3YMly9fRlhYGCQSCXx9fQEArq6uOHLkSFcUqTEk3dPo8owZM0an0dWVXV22bBkcHBxw48YNCASCFvs9SsjlcvTu3buFV1tfX49z584xbzgzMxPW1tYsNhwcHKxTA1ZdaUxXoulexyLxTQLOzs4YOHCgwRNlvDyinZ0d+vbtyzr/1Adxqks2dkSDOSsrq93e840bN/Dmm2/Czs4OGzZsYDWrVVVVHZLg1IU+lQlPP/00Vq1ahVGjRkGpVMLJyQllZWWIjY3V2Fd9v0eERzeRpktoQyqVwsbGBlVVVZgxYwZyc3NRVlaGioqKFh1WycnJWLRoEXuenZ2NxMRETJ06Fa+88gpOnz7NSp22b9+OgICA+3NwBkJXmZalpSVGjRrFfii8xkJaWhpSUlKQmJiI4uJiuLq6MpGfoKAglJSUIDs7G0OGDGk10aRt5Lv6KJpr164xA9fcG87NzYVCoWDVGYaE954rKiowZMgQVmZmY2OjMTWEL6/Lz89nambq69QlUENELLbdnhZhlUqFxMRExMfHY82aNXjmmWc0Pt/Q44P0EalR30cgEKBPnz6QSqW4fv06Ro4cqfHeh1mkxpB0eaP71FNP4caNGy22r169GlOmTGnz/bqyq/z22NhYjB07Flu3bkVISAhiY2Px6aefauwfGhqKjIwMAGC9+uPHj2evf/bZZw9Sof6+wXEc+vbtiwkTJmDChAkA/tJvlUgk+PHHH7FgwQIoFAqMHj0at27dQk1NDfz8/PSO37Y2Fkkul6O4uBg1NTVM9pIX4TaUuHtFRQUuX77cpoCMQCCAra2thsHUJlDDt1/zxrixsRFZWVno3bt3u/QkSktL8eabb8LBwQGnT5/uUOt1e9GnMqGt31dr731U6fJGl08WdBRd2dW+ffuisrISSUlJOHXqFAoLC+Ht7Y3Dhw+3MLrq7N+/HxMmTDC40v/DiomJCTw8PODh4YH//e9/mDdvHt544w1kZ2dDIpFg27ZtuHDhAhPF4ePD7Wni4NXWCgsL0bt3bwQGBkKlUjEDd/XqVRYf7uhYpMbGRuTk5KC+vr7D3rM2NTP1krDLly+joaEBNjY2MDc3Z2Gd1tapUqmwe/dubNq0CTExMZg4cWKXqkzg9xkwYACUSiXkcjns7Oz0rmp4FOn2Md3WhDamT5+Oo0ePora2Fq+//jr8/PywYsUKVFRU6PxbYWFheOutt/Dss88CuDOL6ffff4eFhQXGjh2L2NhYg47UeZggIp2301VVVUwEPjU1FXl5eejXr59GfFhbA4C+wuL3MhaJb0Jwc3MzeMcd8NeUX14Ap66uTqNbTV1Eh2+/NjU1RUlJCRYsWABnZ2esW7fOIKLq2pDJZJgxYwYKCwvh5uaGvXv3wtbWVuO3U15ejjFjxsDJyQk9e/ZkojmbN29GfHw8FAoFVCoV5HI5Tp06BTMzM7z44otITU1FSUkJxo4di5ycHIOL/3Rhumci7dChQ5g/fz7KyspgY2ODgIAAnDhxAiUlJfi///s/HD16FAAQEBCArKwsEBFsbW1ZfeX8+fMxd+5cuLm5YdiwYfjuu+/g5OSk0+iWlpbCz88PJSUlLItfWloKc3NzREREIC0tDQ4ODkhPT9d6+2dqaoqhQ4cC+CuDCzyapWt8Ta1EImGJOqlUCpFIxOLDcrkcV65cwdSpUzs0Tr2tsUhWVlYoLCyEqakpvLy8DH7O+fl75eXlrQrgqK+zqqoKMTExyMzMRGVlJf75z38iOjoaXl5enTaNd8mSJbCzs8OyZcsQGxuLiooKdrd39OhRLFy4EPX19QgPD8f69euxaNEibN++HQUFBbC0tIS3tzfq6+vh6uqKxMRE1qyzevVqbN26FQKBAJ9//jkLST0idE+jawi8vLxw6tQpODs7o7S0FGPGjGHdRs3ZuHEjMjMz8fXXX2ts57+0I0eOxOuvv44pU6ZoDVHo6jM3lq7doampCZcuXUJycjISEhJQXV0NV1dXeHl5MW/Y29v7nmpk+QaJa9euQSqVwszMTENgvS09Wn3hBXYcHBzadcG4du0aFixYABcXFzz//PO4dOkS0tLSEBcXBzc3t3telzba8xvg8ff3x/79+zF48OCuMHm3K2I0urpYvHgx7O3t2VVeJpMhLi5O674jR45ETEwMQkND2Tb+S5qcnIy4uDgolUr89NNPWr+02owuERlL15oRGxuLAQMG4MUXX9QQgZdIJLh8+TJsbW1ZpURISEi7Bl/evn0bWVlZ6NmzJyvTUhcG5xskOjoWSaVSIT8/HxUVFe0S2FGpVNi5cye++uorfPbZZxg3btx9i93a2NigsrKSPbe1tW01xJaamoqoqChkZmbCxMTEGGLTjtHo6kIqlSIiIoKVPu3btw92dnZIT09HQkICE/IoLCzE448/jqtXr2p4LWFhYTh9+jR8fHwQEBCAhIQEDBw4UOuXVpu4R3l5OUaOHInc3FwAwNWrVzFhwgRcvHjx/pyAhwxeBJ6fTcfHDN3d3TVE4JvXHatUKhQXF+PmzZttDp3k48NyuZzV5eozFqmyshLZ2dlwdnaGq6ur3kbz6tWrmD9/PoRCIeLi4gxe+gW0XgUUFRWlt9HlnYwdO3awkrDS0lI4OTlBoVBg1qxZ8PDwwMqVKw1+DA8ZRqN7rxjiS1tSUqIh7vHzzz+jd+/eGDVqFHJzcyGTyTBlyhSkpqbiySefZAkNdTIyMjBnzhxUVVXB1NSUJTQAdIua4Y7Ai8CnpKQgNTUVZ8+eRX19PROBt7a2xqlTp7B06VK4u7t3KDaqayR9nz59YG1tjYqKCtTV1WHIkCF6V7aoVCps374d33zzDdatW4exY8c+kLIqfcMLVVVVGDNmDN59911Mnz5d62edOnUKa9eubTHR9xHEaHQ7k47ExPg42PPPP8/CC8uXL0d1dTXy8/MRGhqqkdDg4YXEBw8ejJKSEgQFBSErKws2NjbG2JoaDQ0NSElJwSeffIJLly5h0KBBICIEBgYyj7ijBpinsbER169fR1FREYsD6zsWqbi4GPPmzYNIJEJcXJzBdX7bgz4hNoVCgQkTJmDSpElYuHChxmv8mB0iwqJFi2Bpack60h5hHt2OtPvB5MmTsWPHDixbtgw7duzQ2rTRXNzjzJkzWLJkCTiOQ2hoKPbv34+kpCQMHz4cU6ZMwXPPPYcxY8a0MLoikYj928XFBY6Ojqx6w8hfWFhYQCAQYPLkyTh+/DhMTExQWVnJYsMHDx5EQUEB+vfvz4xwUFAQ7O3t9fI2lUolcnNzUVdXh+HDh8PKyqrNsUg3b96Et7c3vv/+e2zbtg3r1q1DWFhYp3m3ukrBmrNu3Tr07NkTH3zwAXr37s0chqSkJLz22mvo06cP7O3tcfbsWUilUjZGh7+Teumll1BWVsakOhMSEjrleLoNRNTaw4gelJeXU1hYGHl6elJYWBhJpVIiIkpLS6Po6GgiIjpz5gz5+vqSn58f+fr60pYtW9j78/LyKCQkhExMTCg8PJzq6+uJiMjGxqbVvyuRSMjb25uampqIiCgqKopEIhENHTqUFi5cyD7HiHaampqooKCA9uzZQ4sWLaInnniChg4dStOnT6fY2Fj6+eefqby8nGprazUeRUVFdPLkScrJyaGampoWr6s/ampq6ObNm3TlyhWaMWMGubq6kqOjI0VHR9PWrVupoaGh045v8eLFFBMTQ0REMTExtGTJEq379ezZU+v26dOn0549e4iIaPbs2fTFF190zkK7JzrtqtHo3mfGjh1LYrG4xePw4cPUp08fjX1bM7olJSUkEono999/JyKiY8eOkVAoJA8PD/r444/p5Zdfpg8//JDtX19fTxEREeTh4UHDhw+ngoIC9tqaNWvIw8ODRCIRHT9+3LAH/JChUCjo3Llz9OWXX9LMmTMpMDCQgoOD6bXXXqN169bR3//+d9q9ezdJpdJWja36o6qqijZs2ED+/v6UnJxMNTU1dObMGdq4cSO7YHYGIpGISkpKiOiv74s2tBldlUpF9vb21NjYSEREv/32G40fP77T1toNMRrdhwF9fyRyuZyGDRtGe/fuJSIipVJJQqGQ8vLyqKGhgfz8/Gjbtm30zDPPsPds3ryZZs+eTUREe/bsoYiICCIiyszMJD8/P6qvr6f8/HwSCoWkVCo78zAfKlQqFVVVVdEnn3xCTk5ONH78eBKLxRQaGkrvvPMOJSYmUl5enk6P9+LFixQaGkoLFiygmpqa+7p2fS/ipqamFBQURCNGjKBDhw4REVFZWRl5eHiwfYqLi0ksFnfeYrsfOu2qMabbhdAnNqxQKPDcc8/h5ZdfZhnk1NRUeHp6wsrKCubm5pgxYwa++eYbjB49mr0vKSkJq1atAgCEh4dj3rx5ICIkJSUhMjISFhYWcHd3h6enJ1JTUx8lCb5W4cewm5iY4Pz583BwcGATHngR+K+//hq3bt2Cp6cna2v29/fHnj17sGvXLmzcuBGjR4/ulNhta1U1+qJtZI62sjWjYI2BaM0iP4CrwyONPrHhXbt2kUAgIH9/f/aIi4uj6OhoCg0NJV9fX+rfvz+JRCKqrq5mny0Wi+nq1avsuVAopLKyMpo7dy7t2rWLbZ85cybt27fvPh1x90GpVFJmZiZt3bqVZs+eTYMGDaLp06dTbW3tA1uTvndO6kRFRdG+ffuM4YV7xxhe6M7s3buXGWUiop07d9K8efM09hkyZEgLo1teXk5vvPGGhtEdP348ubi4kIeHB0vCqLNu3Try8fGhoUOHUlhYGBUWFrLXYHolGAAAB6lJREFUTExM2IVg0qRJhjzEhw6VSvWgl0DvvPOORiJt8eLFLfaRyWQs4VpWVkaenp6UmZlJRETh4eEaibTNmzffp5V3C3Ta1c5R0DByX2mPBB8AnRJ8TU1N+O9//4uNGzfi0qVL2LNnDy5duqTxOcOGDUN6ejrOnz+P8PBwLFmyhL1mZWWFjIwMZGRkMDGfR5XOvBWXyWQYN24cBg8ejHHjxmltxElOTsaxY8ewevVqWFhYYPny5Uxs6dlnn0WvXr0QEBCA4cOHw9fXF/7+/ggNDcWyZcvYOJ5PP/0U69evh6enJ6RSaXcapfNgac0iP5Drg5F209jYSO7u7pSfn88SaRcvXtTYZ9OmTRqJtOnTpxMR0cWLF1kibf/+/WRlZcUSaWvWrKE1a9bo/Lt//PEHPfbYY+y5rtIjI4ZF31IwHqlUSra2tizUwYcQjHQqRk+3OyMQCLBp0yY8/fTT8PHxQUREBMRiMVauXMk8zujoaEilUnh6emL9+vWsY0gsFiMiIgJDhgzB/PnzMXr0aCbu0taIlW+//VZDrq++vh7BwcEYOXIkVq5cCS8vL3h6emrtTtq+fTscHBwQEBCAgIAApnEBADt27MDgwYMxePBg7NixwyDnqDuRlJSEqKgoAEBUVBQOHz7c6v5G4f0uRmsW+YFcH4w8MPSJDfPs2rWLRowYodGAcf36dSIiunLlCgkEAvrll1+Y583HCXm2bdtGc+fObfG5UqmU3N3dSSqVkkwmI3d3d5LJZIY4vG5De+q5iYhCQ0Pp3//+N3tubKK5Lxg9XSNto++IlZMnT2L16tU4cuSIhoQfv295eTkcHR0hlUphbm6OyMhIJCUl6bWGEydOYNy4cbCzs4OtrS3GjRuH48eP3+ORPXw89dRT8PX1bfHQ9zzylJaW4sKFC3j66afZtpiYGGRnZyMtLQ0ymazV8VRGDI+xTtcIIyQkBDk5OUyTIDExEbt379bY59y5c5g9ezaOHz8OR0dHtl1dWyIrKws1NTUsIaNtiiwAHDhwAL/++itEIhE2bNiAgQMHap1A+yhOkW1tNmC/fv2YyExpaanG/0Nz9u7di+eee45NOgHAJi9bWFjg1Vdfxdq1aw23cCNtYvR0jTD0iQ0vXrwYNTU1mD59OgICAjB58mQAQFZWFmsKWLVqFfz8/JjRBVpm8ydNmoTCwkKcP38eTz31FItRUjPVu5ycHMTFxemMDS9atIjFhUUikYbwj6mpKXuNX2d3gG+iAaCziYZnz549eOGFFzS2lZaWArhzrg8fPgxfX9/OW6yRlrQWe3gAcRAj3YDmhfRtVUEolUrq3bs3ERHt3r2bZs2axbb36tWLNmzYoDM2rE58fDy9+uqr7PnDVk2xd+9eGjJkCHEcR2lpaTr3+/7778nKyorMzMzIw8ODNdEcPnyYHBwcyNPTkyIiIujy5cvk4uLSQt+Bb6IRi8X00ksvaTTRGDEYxuYII/cPfUrY+E4pIqKDBw/SiBEjiOhOIs3NzY1kMhkdP36cLC0tmVFpy3iPGjWKfvzxR/b8YTO6ly5douzsbPrb3/6m0+hq09ngL0RGVbAuhTGRZuT+oU+YIj4+HmKxGP7+/oiPj2carXZ2dnj//fcREhKCqKgohISEwM7ODkDr8d2ioiIUFBQgLCyMbVMvYWurrKor4OPjAy8vr1b34XU2hEKhRpKSiPDLL78wAXt9SsmMPBiMiTQjncLEiRMxceJEjW0fffQR+3dMTAxiYmK0vnfmzJmYOXMm9u3b12JAp65Or8TERISHh2sMkFQXcvHz84OFhQWcnZ21zp8jIrz55ps4evQoevToge3btyMwMBDAnbjpJ598AgB47733WPz5QaAt0SiRSCCVSmFjY8MmJT+qCciHAaPRNdJl0beEDbhjdDdv3qyxjd9XKBTiiSeeQFhYGHbu3Kn1/ceOHUNOTg5ycnIgkUgwZ84cSCQSyGQyfPjhh0hPTwfHcQgKCsLkyZO1TmDQh9ZUwVpLiPEQtZygxXGczu1GuiCtxR6MD+PjQT5wxynIB+AOwBzAnwDEWvbzAlCIuzP/7m6zBWBx9999AeQAeArARR1/6ysAL6g9vwzAGcALAL7StV8nHfcpAME6XhsF4ITa83fvPjgA5QAE2vYzPrrOwxjTNdJlISIlgHkATgDIArCXiDI5jvuI4zj1GrAXACTSXWtzFx8A6RzH/QkgGUAsgNxW/lx/AFfVnl+7u03X9gdFGoDBHMe5cxxnDiASwJG7x54MgJ9KGgWgfZ0URu4LxvCCkS4NER0FcLTZtpXNnq/S8r7fAAxV38ZxnFsrf0rbvTi1st3gcBz3HIB/AXAA8B+O4zKI6GmO41wAbCGiiUSk5DiOvxCZAthKRJl3P2IpgESO4z4BcA7At52xTiP3htHoGjFyh2sABqo9HwCg5O72Mc22n+qMBRDRIQCHtGwvATBR7XmLC9Hd7fkAhnfG2owYDmN4wYiROxwB8DJ3h5EA5ERUijse5XiO42w5jrMFMP7uNiNGOoTR0zXySMBx3B7c8Vj7chx3DcAHAMwAgIgScMdznIg7cd/bAF69+5qM47iPcSeWCgAfEZHs/q7eSHeC08w9GDFixIiRzuT/ARNmFa/UxRjaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<sympy.plotting.plot.Plot at 0x1192e7518>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot3d(x+y,(x,-1,1),(y,-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<sympy.plotting.plot.Plot at 0x1192e7cc0>"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot3d(y**2*sin(x),(x,0,2*pi),(y,0,20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<sympy.plotting.plot.Plot at 0x107db93c8>"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot3d(sin(x*cos(y)),(x,0,2*pi),(y,10,20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Há extensões que permitem visualizar informação de outros tipos. No caso abaixo, importaremos da extensão Matplotlib.pyplot apenas a funcionalidade plot, a que chamaremos dataplot para que não seja redefinida."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.pyplot import plot as dataplot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "?dataplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x119c78860>]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataplot([1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x119ea0b38>]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataplot([1,-2,3,-4,5],\"ro-\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x119f5b780>]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataplot([1,5,3,4,2,6],\"g*--\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11a240588>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataplot([1,2,3,4,5], \"go-\", label=\"line 1\", linewidth=2)\n",
    "dataplot([2,9,0,4], \"rs\",  label=\"line 2\")\n",
    "axis([-2, 8, -2, 10])\n",
    "legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Veremos mais adiante, quando necessário, como produzir conteúdos gráficos mais elaborados, nomeadamente animações."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outros ambientes de computação baseados em Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pelas suas características, que estudaremos, a linguagem Python vem ganhando uma crescente importância para experimentação e desenvolvimento de aplicações, nomeadamente científicas, vindo a ser adoptada por empresas e universidades, pequenas e grandes, pelo mundo fora.\n",
    "De referir, por serem relevantes a jusante, as seguintes ferramentas baseadas em Python:\n",
    "- <a href=\"http://http://www.sagemath.org\">SageMath</a> (também integrado no projecto Jupyter) é um ambiente livre que integra ferramentas computacionais para matemática, à semelhança de sistemas proprietários como o \n",
    "<a href=\"https://www.wolfram.com/mathematica/\">Mathematica</a>\n",
    "- <a href=\"https://pymol.org/2/\">PyMOL</a> é uma ferramenta de modelação e visualização biomolecular de crescente importância para engenheiros nas áreas da biotecnologia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sumário"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* O IPython é um ambiente de programação interactivo muito simples, apropriado à experimentação rápida de programas em Python, e portanto adequado à sua aprendizagem\n",
    "* Munido das extensões adequadas o ambiente IPython pode ser utilizado como poderosa ferramenta de cálculo numérico e simbólico, e de visualização gráfica, extremamente útil em aplicações científicas, matemática e engenharia\n",
    "* A extensão Pylab fornece um ambiente de computação essencialmente equivalente ao popular sistema proprietário <a href=\"https://www.mathworks.com/products/matlab.html\">MATLAB</a> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bibliografia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Introdução à Programação em Mathematica* (3a edição): J. Carmo, A. Sernadas, C. Sernadas, F. M. Dionísio, C. Caleiro, IST Press, 2014.\n",
    "\n",
    "*Think Python: How to think like a computer scientist*: A. Downey, Green Tea Press, 2012.\n",
    "\n",
    "*Introduction to Computation and Programming Using Python* (revised and expanded edition): J. V. Guttag, MIT Press,  2013.\n",
    "\n",
    "*The Art of Computer Programming*: D. E. Knuth, Addison-Wesley (volumes 1--3, 4A), 1998.\n",
    "\n",
    "*Learning Python* (fifth edition): M. Lutz, O'Reilly Media,  2013.\n",
    "\n",
    "*Programação em Python: Introdução à programação utilizando múltiplos paradigmas*: J. P. Martins, IST Press, 2015.\n",
    "\n",
    "*Introdução à Programação em MatLab*: J. Ramos, A. Sernadas e P. Mateus, DMIST, 2005. \n",
    "\n",
    "*Learning IPython for Interactive Computing and Data Visualization*: C. Rossant, Packt Publishing,  2013.\n",
    "\n",
    "*Programação em Mathematica*: A. Sernadas, C. Sernadas e J. Ramos, DMIST, 2003."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='nota'></a>\n",
    "##### Nota\n",
    "\n",
    "No sistema operativo OSX há por vezes diferenças na codificação de caracteres. Caso ocorra um erro do tipo\n",
    "\n",
    "`ValueError: unknown locale: UTF-8`\n",
    "\n",
    "é necessário editar manualmente o ficheiro `.bash_profile` juntando-lhe as duas linhas seguintes:\n",
    "\n",
    "`export LC_ALL=en_US.UTF-8`\n",
    "\n",
    "`export LANG=en_US.UTF-8`"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
