2 - Apresentação dos Dados, Regressão Linear e Barras de Incerteza.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sumário\n",
    "\n",
    "- [Apresentação dos Dados](#Apresentação-dos-Dados)\n",
    "    - [Dados Pontuais](#Dados-Pontuais)\n",
    "    - [Texto dos Eixos e do Título](#Texto-dos-Eixos-e-do-Título)\n",
    "\n",
    "- [Regressão Linear](#Regressão-Linear)\n",
    "    - [Separação dos Dados](#Separação-dos-Dados)\n",
    "    - [Aplicação da Regressão](#Aplicação-da-Regressão)\n",
    "    - [Desenho da Regressão](#Desenho-da-Regressão)\n",
    "    - [Resultado da Regressão](#Resultado-da-Regressão)\n",
    "\n",
    "- [Barras de Incerteza](#Barras-de-Incerteza)\n",
    "    - [Dados Pontuais com Barras de Incerteza](#Dados-Pontuais-com-Barras-de-Incerteza)\n",
    "    - [Regressão Linear com Incertezas](#Regressão-Linear-com-Incertezas)\n",
    "    - [Resultado com Incertezas](#Resultado-com-Incertezas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-17T05:45:39.605782Z",
     "start_time": "2019-07-17T05:45:39.428740Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-17T05:45:39.612109Z",
     "start_time": "2019-07-17T05:45:39.607332Z"
    }
   },
   "outputs": [],
   "source": [
    "config_nb = {\n",
    "    'axes.spines.right': False,\n",
    "    'axes.spines.top':   False,\n",
    "    'axes.edgecolor':  '.4',\n",
    "    'axes.labelcolor': '.0',\n",
    "    'axes.titlesize': 'large',\n",
    "    'axes.labelsize': 'medium',\n",
    "    'figure.autolayout': True,\n",
    "    'figure.figsize': (8.1, 6.3),\n",
    "    'font.size': 16.0,\n",
    "    'grid.linestyle': '--',\n",
    "    'legend.facecolor': '1.0',\n",
    "    'legend.framealpha': 0.9,\n",
    "    'legend.frameon': True,\n",
    "    'savefig.transparent': False,\n",
    "    'text.color': '.0',\n",
    "    'xtick.labelsize': 'small',\n",
    "    'ytick.labelsize': 'small',\n",
    "}\n",
    "plt.style.use(['seaborn-darkgrid', 'seaborn-notebook', 'seaborn-bright', config_nb])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apresentação dos Dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os dados aqui são da relação de corrente e tensão em um resistor, dado pela equação teórica abaixo, que podem ser encontrados na página 'Resistor' da planilha 'Dados.xlsx' da [*Introdução*](1%20-%20Introdução.ipynb#Importando-os-Dados \"Introdução - Importando os Dados\").\n",
    "\n",
    "$$ I = \\frac{1}{R} V $$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-17T05:45:39.643440Z",
     "start_time": "2019-07-17T05:45:39.614324Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V</th>\n",
       "      <th>dV</th>\n",
       "      <th>I</th>\n",
       "      <th>dI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-3.065655</td>\n",
       "      <td>0.294275</td>\n",
       "      <td>-34.377359</td>\n",
       "      <td>3.155834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.697820</td>\n",
       "      <td>0.267610</td>\n",
       "      <td>-27.956811</td>\n",
       "      <td>3.067110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.692812</td>\n",
       "      <td>0.184774</td>\n",
       "      <td>-15.833063</td>\n",
       "      <td>1.646959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.471680</td>\n",
       "      <td>0.161785</td>\n",
       "      <td>-10.788397</td>\n",
       "      <td>0.936612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.615852</td>\n",
       "      <td>0.060667</td>\n",
       "      <td>-7.936452</td>\n",
       "      <td>0.716368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.043765</td>\n",
       "      <td>0.015124</td>\n",
       "      <td>-0.050024</td>\n",
       "      <td>0.114909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.717312</td>\n",
       "      <td>0.084274</td>\n",
       "      <td>7.430798</td>\n",
       "      <td>0.721983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.254987</td>\n",
       "      <td>0.129546</td>\n",
       "      <td>13.371898</td>\n",
       "      <td>1.357838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.353406</td>\n",
       "      <td>0.196184</td>\n",
       "      <td>21.558016</td>\n",
       "      <td>2.157432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.480765</td>\n",
       "      <td>0.267800</td>\n",
       "      <td>31.335690</td>\n",
       "      <td>2.895233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.380448</td>\n",
       "      <td>0.357523</td>\n",
       "      <td>33.322264</td>\n",
       "      <td>3.328871</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           V        dV          I        dI\n",
       "0  -3.065655  0.294275 -34.377359  3.155834\n",
       "1  -2.697820  0.267610 -27.956811  3.067110\n",
       "2  -1.692812  0.184774 -15.833063  1.646959\n",
       "3  -1.471680  0.161785 -10.788397  0.936612\n",
       "4  -0.615852  0.060667  -7.936452  0.716368\n",
       "5  -0.043765  0.015124  -0.050024  0.114909\n",
       "6   0.717312  0.084274   7.430798  0.721983\n",
       "7   1.254987  0.129546  13.371898  1.357838\n",
       "8   2.353406  0.196184  21.558016  2.157432\n",
       "9   2.480765  0.267800  31.335690  2.895233\n",
       "10  3.380448  0.357523  33.322264  3.328871"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados = pd.read_excel('Dados.xlsx', sheet_name='Resistor')\n",
    "\n",
    "dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dados Pontuais\n",
    "\n",
    "Para representar dados pontuais com <TT>pyplot</TT>, a função é [`scatter`](https://matplotlib.org/3.1.0/api/_as_gen/matplotlib.pyplot.scatter.html \"matplotlib.pyplot.scatter\"). O acesso no `DataFrame` é por nome da coluna, como se fosse um `dict` de <TT>Python</TT>."
   ]
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-17T05:45:39.875387Z",
     "start_time": "2019-07-17T05:45:39.645382Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
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\n",
      "text/plain": [
       "<Figure size 583.2x453.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dados['V'], dados['I'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Texto dos Eixos e do Título"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-17T05:45:40.104068Z",
     "start_time": "2019-07-17T05:45:39.877005Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Relação da Corrente pela\\nTensão em um Resistor')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 583.2x453.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dados['V'], dados['I'])\n",
    "\n",
    "plt.xlabel('Tensão [V]')\n",
    "plt.ylabel('Corrente [mA]')\n",
    "plt.title('''Relação da Corrente pela\n",
    "Tensão em um Resistor''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regressão Linear"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Separação dos Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-17T05:45:40.307636Z",
     "start_time": "2019-07-17T05:45:40.106352Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f0f441824e0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 583.2x453.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "V, I = dados['V'], dados['I']\n",
    "\n",
    "plt.scatter(V, I, zorder=10, label='Dados Coletados')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aplicação da Regressão\n",
    "\n",
    "Uma das formas mais abrangentes de se fazer a regressão é com o pacote [`odr`](https://docs.scipy.org/doc/scipy/reference/odr.html \"scipy.odr\") do <TT>SciPy</TT>, feita por distância ortogonal dos dados, mas pode ser utilizada com mínimos quadrados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-17T05:45:40.315226Z",
     "start_time": "2019-07-17T05:45:40.309224Z"
    }
   },
   "outputs": [],
   "source": [
    "from scipy import odr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora, para a regressão, é preciso montar organizar os dados em uma instância de [`RealData`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.odr.RealData.html \"scipy.odr.RealData\") e usar isso para criar uma instância da [`ODR`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.odr.ODR.html \"scipy.odr.ODR\") com o modelo da regressão, que em todos os exemplos desse material será o [`odr.models.unilinear`](https://docs.scipy.org/doc/scipy/reference/odr.html#scipy.odr.unilinear \"scipy.odr.models.unilinear\").\n",
    "\n",
    "Para usar o método dos mínimos quadrados é só chamar o método [`set_job`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.odr.ODR.set_job.html \"scipy.odr.ODR.set_job\") com argumento `fit_type=2`. Depois é só rodar a regressão com o [`run`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.odr.ODR.run.html \"scipy.odr.ODR.run\"), que retorna um objeto [`Output`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.odr.Output.html \"scipy.odr.Output\") com várias informações, entre elas os coeficientes e suas incertezas, nos atributos `beta` e `sd_beta`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-17T05:45:40.323576Z",
     "start_time": "2019-07-17T05:45:40.317943Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coef. angular = (10.389236050041834+-0.4158465159985413) [mA/V -> kOhm^-1 -> kS]\n",
      "coef. linear  = (0.34999478441064824+-0.8623053486601878) [mA]\n"
     ]
    }
   ],
   "source": [
    "# regressão linear\n",
    "data = odr.RealData(V, I)\n",
    "odreg = odr.ODR(data, odr.models.unilinear)\n",
    "odreg.set_job(fit_type=2)  # muda para mínimos quadrados\n",
    "ans = odreg.run()\n",
    "\n",
    "a, b = ans.beta      # y = ax + b\n",
    "da, db = ans.sd_beta # incertezas de a e b\n",
    "\n",
    "# mostrando os coeficientes e suas incertezas\n",
    "print(f'coef. angular = ({a}+-{da}) [mA/V -> kOhm^-1 -> kS]')\n",
    "print(f'coef. linear  = ({b}+-{db}) [mA]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Desenho da Regressão\n",
    "\n",
    "Desenhar a reta da regressão é com a função [`plot`](https://matplotlib.org/3.1.0/api/_as_gen/matplotlib.pyplot.plot.html \"matplotlib.pyplot.plot\"), mas antes precisamos montar o rótulo que irá na legenda do gráfico. No código abaixo têm dois exemplos para o rótulo, um apenas textual e outro com os coeficientes da regressão.\n",
    "\n",
    "Qualquer tipo de curva no <TT>Matplotlib</TT> é feito com pontos que são ligados entre si, ou seja, são apenas segmentos de reta conectados. Para simular a continuidade em outras curvas, costuma-se fazer vários pontos na região em que se deseja desenhar. Podemos fazer isso com a função [`linspace`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html \"numpy.linspace\") do <TT>NumPy</TT>, em que o primeiro argumento é o começo dos pontos, o segundo é o final do intervalo e o argumento `num` é a quantidade de pontos igualmente espaçados nessa região.\n",
    "\n",
    "Por mais que isso não seja muito importante nesse caso, no código tem um exemplo de como fazer esse intervalo com 200 pontos igualmente espaçados, armazenado em `X`. Com esses pontos, é possível aplicar a função da curva nesse intervalo para encontrar a imagem desse intervalo, mas devido às técnicas de [vetorização](https://realpython.com/numpy-array-programming/\\#what-is-vectorization \"What is Vectorization?\") do `NumPy`, isso é feito como se fosse aplicar a operação em apenas um valor, mas a biblioteca realiza o laço implicitamente e retorna outro vetor em `Y`.\n",
    "\n",
    "Além disso, para diferenciar a regressão dos valores, ela foi desenhada em vermelho, com `color='red'`, e com 60% de transparência, com `alpha=0.6`. Depois do gráfico já desenhado, foi usado ainda a função [`legend`](https://matplotlib.org/3.1.0/api/_as_gen/matplotlib.pyplot.legend.html \"matplotlib.pyplot.legend\") para desenhar a legenda. Depois disso, é só colocar os nomes nomes do eixos e o título, como na seção [*Texto dos Eixos e do Título*](#Texto-dos-Eixos-e-do-Título \"Apresentação dos Dados - Texto dos Eixos e do Título\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-17T05:45:40.823668Z",
     "start_time": "2019-07-17T05:45:40.325629Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0f44096a20>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ougOF6sZG2rCTGBuJsZEYG4nV1chy8kTVar4XLiDUrh28D+QACbE7QeRkhoiIyCxUFfZdO+FcvxYIq/DfdTf8dwyKybMxF+NkBkAgEHtPKW5sbKQNO4mxkRgbibGR2CWNfD7Ib66Cbe/nUN3uqtV8b2ynz+AijBcAExERxTnpxx/hyl0AS0EBQi1aQMmZDDU1Te9hXRFeACyQmBg7t5/phY20YScxNhJjIzE2Evupke3L/XC/NBuWggIEet8G7+PTY24iI8LJDACXy6H3EAyPjbRhJzE2EmMjMTYSczmscK5fCzlvEQBAGZ8D3/BRgC3+rjCJvz0iIiIyOam0BHjjNdgPHkG4SRMoE6cg3KSp3sOKGk5miIiI4oj16LeQl+QCIR+CXbtBGX1/TK3mWx+8ABiAxSIhHDZcBkNhI23YSYyNxNhIjI1qoapwbN0Mx4b3AYsE/30j4L+1d8zfdv2Tui4A5pkZADabBX4/b/OrCxtpw05ibCTGRmJs9AseD+Tli2E7dAhqaiq8EybBdkNbwCSNeAEwgJQUt95DMDw20oadxNhIjI3E2Ohnlh9OIuFvs2E7dAihG26A56lnEG7ZylSNeGaGiIgoFqkq7J/uhnPdGiAYgn/gIPjvvAuwmO88RYP3ePPmzRgyZAi6du2KgQMHYtmyZQCAQCCAWbNmITs7Gz169MCsWbMQCAQaPGAiIiLT8/vhXLkMztUroTqc8E57BP67BptyIgM08MzMiRMn8PTTT+Mf//gHevXqhcOHD2Ps2LHo1KkTtmzZgkOHDmHDhg0IBoN44oknMH/+fMycOTNSY4+Y8nJF7yEYHhtpw05ibCTGRmJmbiQVFsKVtxCWM2cQbt4c3pzJUNMzLvk6MzVq0BSuRYsW2LVrF3r16oVwOIwLFy7AarUiISEBa9euxaOPPor09HRkZWXhySefxJo1ayI17ohSFJ4xEmEjbdhJjI3E2EjMrI1sB76sWs33zBkEbrkVnidn1jqRAczVqMHnoxITE1FSUoJOnTph8uTJGD9+PDIzM1FYWIi2bdtWf12bNm1QWFiI0tLShm4y4uq63YuqsJE27CTGRmJsJGa6RuEwHO+sh7xoIaRQCMq4B+EbNbbO1XzN1CgiFwAnJSXhiy++wJEjR/DQQw/B5XIBQPX/v/h/e71epKSk1Pnz3G4HEhJ+XuDnwoVKAEBaWkL1a5WVPng8fqSnJ8BqrZqTBQIhlJR4kJjorLHUdVFRBWw2S40ru8vLFShKoPrNzsxMgs8XRFmZF8nJLjidP6cpLCyHLNuRlCRXv1Za6kEwGEZGRmL1a16vHxUVPqSmumG3WwEAoVAYxcWVuuwTgIjt00+N4mmfovE+XdwpXvYp0u8TgLjbp2i8TzabJe72KdLvU2ZmUtztU63vU2kpgq/9HYFDR2BvdjVsTz4OV7Nmwn36qZEh96ke71NdIr5o3uzZs5Gfn49t27Zh69ataPaf4KdOncKAAQPw2WefITk5uc6f0diL5mVmJvFJ3QJspA07ibGRGBuJmaWRNf8o5MW5kMrLEexyE5SxDwCyLP5GxF+jqD01e9u2bRg/fnyN1/x+P5KTk5GZmYnjx49Xv56fn4+srCzhREYPPl9Q7yEYHhtpw05ibCTGRmJx30hVYd+6Ba7XX4VUWQnf0GFQciZrnsgAJmh0kQZ9zNS5c2d8++23WLFiBcaMGYO9e/di7dq1eO2115CVlYV58+ahXbt2AIB58+Zh2LBhERl0pJWVefUeguGxkTbsJMZGYmwkFteNPB7IK5bC9vVBqMnJ8E6YhHDrNlf8Y+K60S80+GOm/fv3489//jPy8/NxzTXXYPr06Rg4cCD8fj9eeOEFbNy4EYFAAIMHD8Z///d/w263C39mY58WS052mepNrw820oadxNhIjI3E4rWR5cxpyLkLYDlfhFDb66GMz4GaVL9PNOKtUV0fM/FBk4i/zxWjgY20YScxNhJjI7F4bGTb8ynktauAQBD+AQMbvAhevDXigyaJiIiMKhCAc90a2D/9BKpLhjJhMkIdO+k9qpjCyQwREZFOpKIiuHIXwHL6NMLNmsE7cSrUjNoXwaPL48dMREREOrB+fRDyiiWQPF4EembDN2I0oOG6UrPix0wCsmw31bLP9cFG2rCTGBuJsZFYTDcKh+HY+D4cH24G7DYoYx9AsGd2xDcT042uECczAJKSZNO84fXFRtqwkxgbibGRWKw2ksrLIC/Jg/XYUYSvyoCSMwXhZtdGZVux2qg+OJkhIiJqBJbj+XAtXgSprAzBjp2g3P8g4HaLv5GEOJkhIiKKJlWFfcd2ON99G1BV+O65F4F+AwBJ0ntkcYOTGYgfYEVspBU7ibGRGBuJxUwjRYG8chlsX30JNSkJyoSJCLW5vlE2HTONIoCTGaD66aJ0eWykDTuJsZEYG4nFQiNLwVnIixbAUliIUOs2UCZMhJqc0mjbj4VGkdKgB03Gi4sf/U61YyNt2EmMjcTYSMzojWx7P4P75b/CUlgIf9/+8D76RKNOZADjN4oknpkhIiKKlGAQzrfehH33LqiyE75JUxDsfJPeo4p7nMwQERFFgFRcBFfeG7D88APCV19dtZpvZqbewzIFTmYAeL1+vYdgeGykDTuJsZEYG4kZrZH18CHIyxZD8ngQ6NGzajVfh0PXMRmtUTTxcQZERET1FQ7D8cFGOLZ8AFgt8A0fhUD2LbztOgrqepwBLwAGkJrKRYtE2EgbdhJjIzE2EjNCI6miHK5/vgbH5k1Q09LgeXImAr1uNcxExgiNGgs/ZgJgt1v1HoLhsZE27CTGRmJsJKZ3I8v331Wt5ltSgmCHDlDuHw8kJOg6pl/Su1Fj4mSGiIhIK1WF/d8fw/n2OiCswj/4Hvj7DzTM2Riz4mQGQChknoWF6ouNtGEnMTYSYyMxXRr5fJBXr4Bt/z6oiYlQxk9E6PobGn8cGpnpOOIFwERERAKWcwWQcxfCcu4cQi1bQcmZBDUlVe9hmQovABZwu/W9fS4WsJE27CTGRmJsJNaYjWz798L18l9hOXcOgT63w/v4b2JiImOm44iTGQAJCU69h2B4bKQNO4mxkRgbiTVKo2AQznVrIC/JAwAoEybCd98IwBobF9aa6TjiNTNERES/IJVcgJy3CNYT3yPctCm8OVOgNmmi97DoMjiZISIiuoj1228gL8mFVFmJYPeboYwcAzjNc5YjFvECYAA2m8VUj0qvDzbShp3E2EiMjcSi0khV4diyCY5NGwGLBN+wkQjc8uuYve063o6jui4A5pkZIiKiykrIyxfDdvgw1LQ0eHMmI3xdC71HRRrxAmAAaWnGWrXRiNhIG3YSYyMxNhKLZCPLyRNI+Nts2A4fRqhdO1TOfCYuJjJmOo54ZoaIiMxJVWH/ZBecb70JhMLwD7oL/jsGARb+nh9rOJkhIiLz8fkgv7kKtr2fQ3W7oTyYg1C79nqPiuqJkxkAlZU+vYdgeGykDTuJsZEYG4k1pJH0449w5S6ApaAAoeuug5IzGWpaegRHZwxmOo54NxMREZmG7asv4Fy5DJLiQ+DXveEbOhyw8ff6WMDHGQikp5vnIqn6YiNt2EmMjcTYSOyKG4VCcLy9DnLuG5DCYSgPToBvxOi4nsiY6TiK33fxClitnNOJsJE27CTGRmJsJHYljaTSkqrVfL//DuGsLCiTpiLcpGkUR2cMZjqOOJkhIqK4ZT12FPLiRZAqKhD8VVcoo+8HZFnvYVGEcTIDIBAI6T0Ew2MjbdhJjI3E2EhM2EhV4di6GY4N7wOSBN+wEQj07hOzq/nWh5mOI14ATERE8cXjgbx8CWyHvoaakgLvhEkIt2qt96iogXgBsEBiIh8gJsJG2rCTGBuJsZHY5RpZTp+C+6U5sB36GqHrr4fnt78z7UTGTMcRJzMAXC6H3kMwPDbShp3E2EiMjcQuaaSqsH2yG+65L8JSVAT/HXfC+/DjUBMv/9t8vDPTccRrZoiIKLb5/XCuXQ37Z3ugul1QJk5AqENHvUdFjYiTGSIiillSYSFceQthOXMG4ebN4c2ZDDU9Q+9hUSPjBcAALBYJ4bDhMhgKG2nDTmJsJMZGYhaLBOnLLyGvWFK1mu8tt8J33wjAbtd7aIYRb8dRXRcA88wMAJvNAr/fPLew1QcbacNOYmwkxkYC4TDkje/DsnkzYLdBGfcggjf31HtUhmOm44gXAANISXHrPQTDYyNt2EmMjcTY6PKk8jK4Xp+PhJ3bEc68Cp4ZT3MicxlmOo54ZoaIiGKC9fgxyHmLIJWXA7f2hGfwCMDl0ntYZACczBARkbGpKuzbt8L5/rsAAN+998E1aihwvkLngZFRNPhjps8//xxjx45F9+7d0a9fP8yfPx+qqiIQCGDWrFnIzs5Gjx49MGvWLAQCgUiMOeLKyxW9h2B4bKQNO4mxkRgbXcTrhZy7EM5334aamAjvY08g0Lc/yit8eo/M8Mx0HDXozExxcTEee+wx/Nd//ReGDRuGkydPYtq0aUhLS0NBQQEOHTqEDRs2IBgM4oknnsD8+fMxc+bMSI09YhTFmJMsI2EjbdhJjI3E2KiK5cxpyHkLYSk8j1CbtlAmTISalAyAjbQwU6MGnZk5deoUbr/9dowcORJWqxWtWrXCwIEDsXfvXqxduxaPPvoo0tPTkZWVhSeffBJr1qyJ1Lgjqq7bvagKG2nDTmJsJMZGgO2zT6tW8y08D3+/AfA++kT1RAZgIy3M1KhBk5kuXbpgzpw51X/2+/3YsWMH2rVrh8LCQrRt27b679q0aYPCwkKUlpY2ZJNERBTPAgE4V6+AvGIZVJsN3snT4B8yFLDw5lu6vIhdAOzz+fDUU0/B4XBg8ODBmDNnDlwXXWX+0//2er1ISUmp82e53Q4kJPz8gKwLFyoBAGlpCdWvVVb64PH4kZ6eAKu16iAPBEIoKfEgMdFZ45kURUUVsNksNW5TKy9XoCiB6plrZmYSfL4gysq8SE52wen8OU1hYTlk2Y6kJLn6tdJSD4LBMDIyEqtf83r9qKjwITXVDbvdCgAIhcIoLq7UZZ8ARGyffmoUT/sUjffp4k7xsk+Rfp8AxN0+ReN9stkscbdPwvfpQjHwxt8R+v4ElGuugeXxx5B+XbPL7lNmZpLx90nH9+mnRvGyT3WJyArABQUF+M1vfgOHw4H58+dDkiT07NkTW7duRbNmVQfiqVOnMGDAAHz22WdITk6u8+c19grAyckulJV5G3WbsYaNtGEnMTYSM2Mj66GvIS9fDMnjRaBnNnwjRte5mq8ZG12peGtU18dmDT5vd/DgQYwcORLt27fHokWLkJqaipSUFGRmZuL48ePVX5efn4+srCzhREYP8fRmRwsbacNOYmwkZqpG4TAc778L14J/QAoEoIwZB9/YB4SPJTBVo3oyU6MGTWYKCgowdepUPPDAA5g1axbsFx18w4YNw7x581BYWIjCwkLMmzcPw4YNa/CAoyE5mYsuibCRNuwkxkZiZmkkVZTD9Y9X4fhwM8IZGfD85ikEs3tp+l6zNGoIMzVq0DUzy5YtQ0lJCf71r3/hX//6V/Xrt912G+bMmYMXXngBw4cPRyAQwODBgzF9+vQGDzgafvl5Pl2KjbRhJzE2EjNDI8t3x+FavAhSaSmCHTtBuf9BwK19+X0zNGooMzXiU7NR9TlcY28z1rCRNuwkxkZicd1IVWH/+CM431kPqCp8g4cg0G8AIElX9GPiulGExFsjPjWbiIj0pyiQVy2H7csvoCYlQZkwEaE21+s9KooDPDNDRERRZyk4C3nRAlgKCxFq1bpqNd+UVL2HRTGEZ2YEZNluqmWf64ONtGEnMTYSi7dGtr2fQV6zEvAH4O/bD/7B9wJWa4N+Zrw1igYzNeKSikCNhXmodmykDTuJsZFY3DQKBuF8cxXkZUugWixQJk6G/95hDZ7IAHHUKIrM1IhnZoiIKOKk4iLIixfBevIkwldfDW/OFKhZWXoPi+IUJzNERBRR1iOHIS/Ng+TxIHBzD/hGjgEcDvE3EtUTJzMQP/OB2EgrdhJjI7GYbRQOw7F5ExybNwFWC3yjxyKQfcsV33atRcw2akRmasTJDFD9QC66PDbShp3E2EgsJhtVVMC1LL/McpkAACAASURBVA/Wb76Bmp4Ob85khJtfF7XNxWSjRmamRrwAGKjxtFSqHRtpw05ibCQWa40sJ75Hwt9mw/rNNwi2b4/Kmc9EdSIDxF4jPZipEc/MEBFR/agq7Lt2wrl+LRBW4b97MPwD7ozKx0pEdeFkhoiIrpzPB3n1Ctj274OakABl/ESEbrhR71GRSXEyA8Dr9es9BMNjI23YSYyNxIzeyHKuAHLuQljOnUOoRUsoOZOgpqY16hiM3sgIzNSIjzMgIiLNbF/sg3PVckg+PwK39YFvyH2Ajb8XU/TV9TgDXgAMIDVV+2PnzYqNtGEnMTYSM2SjYBDOt96EvDgXAKBMmAjfsJG6TWQM2chgzNSI02kAdnvDl9aOd2ykDTuJsZGY0RpJJRcg5y2C9cT3CDdpAu/EqVCbNNF1TEZrZERmasTJDBERXZb1228gL8mFVFmJYNduUEbfDzideg+LqAZOZgCEQuZZWKi+2EgbdhJjIzFDNFJVOD78AI6NGwCLBN+IUQjc2tswt10bopHBmakRLwAmIqKaKishL18M2+HDUFNTq1bzbdFS71GRyfECYAG3mw9AE2EjbdhJjI3E9Gxk+eEkEl6aA9vhwwjdeCMqn/qdIScyPI7EzNSIkxkACQn8/FeEjbRhJzE2EtOlkarCvvvfcM97CdKFC/DfeRe80x4FEo25JD6PIzEzNeI1M0REZuf3w/nmKtg//wyq2w3lwRyE2rXXe1REmnEyQ0RkYtKPP8KVuwCWggKErrsOyoRJUNMz9B4W0RXhBcAAbDaLqR6VXh9spA07ibGRWGM1sh34Es4VSyEpPgR+3Ru+ocNjZjVfHkdi8daorguAY+OoJSKiyAmF4Hj/HTi2bwMcdigPjEewew+9R0VUb7wAGEBaWoLeQzA8NtKGncTYSCyajaTSErhemwfH9m0IZ2bCM+PpmJzI8DgSM1MjnpkhIjIJa/5RyHmLIFVUIHjTr6CMGQfIst7DImowTmaIiOKdqsK+bQuc778HSBJ89w1H4LbbDbOaL1FDcTIDoLLSp/cQDI+NtGEnMTYSi2gjjwfyiqWwfX0QakoKvBMmIdyqdeR+vk54HImZqRHvZiIiilOW06cg5y6EpagIoeuvhzJ+ItTEy98RQmRkfJyBQHq6eS6Sqi820oadxNhILBKNbJ9+AvfcF2EpKoL/jjvhffjxuJrI8DgSM1MjfswEwGrlnE6EjbRhJzE2EmtQo0AAzrWrYd/zKVS3C0rOeIQ6dorc4AyCx5GYmRpxMkNEFCek8+fhylsIy+nTCF97Lbw5U6BmcDVfin+czAAIBEJ6D8Hw2EgbdhJjI7H6NLIePAB5xRJIXgWBXrfAN2wkYLdHYXTGwONIzEyNeAEwEVEsC4fheP9dOLZ9CNhtUEaOQbBHtt6jIoo4XgAskJhonsek1xcbacNOYmwkprWRVF4G1+vz4dj2IcKZV8Ez/bemmcjwOBIzUyNOZgC4XA69h2B4bKQNO4mxkZiWRtbjx+B+cTas+ccQ7NQZnhnPIHxNs0YYnTHwOBIzUyNeM0NEFEtUFfaPtsH53jsAAN+QoQj07c/VfMnUOJkhIooVXi/klctgO/AV1KQkKDmTEGrdVu9REemOFwADsFgkhMOGy2AobKQNO4mxkVhtjSxnz0DOXQBL4XmEWreBkjMJalKyTiPUH48jsXhrVNcFwDwzA8Bms8DvN88tbPXBRtqwkxgbif2yke3zPZDXrAQCQfj7DYB/8BDAYu5LHnkciZmpkbn/afiPlBS33kMwPDbShp3E2EisulEgAOfqFZCXL4VqtcI7aSr8Q4aafiID8DjSwkyNeGaGiMiApOIiuPLegOWHHxC+5pqq1XwzM/UeFpEhcTJDRGQ0Bw7APf91SB4vAj16wjdyTFyv5kvUUJzMACgvV/QeguGxkTbsJMZGdQiH4di0Af7tWyBBgjJmHILZvfQelSHxOBIzU6OIffD61VdfITv755UnA4EAZs2ahezsbPTo0QOzZs1CIBCI1OYiSlGMOS4jYSNt2EmMjWonVZTD9Y9X4djyAQIpafD85ilOZOrA40jMTI0iMpl59913MXnyZPj9/urXXnnlFRw6dAgbNmzAe++9hwMHDmD+/PmR2FzE1XW7F1VhI23YSYyNLmX5/ruq1XyPHkWwQ0ck/L//Rfja5noPy9B4HImZqVGDJzNz5szBwoUL8cQTT9R4fe3atXj00UeRnp6OrKwsPPnkk1izZk1DN0dEFD9UFfaPP4J7/lxIZWXwD74HypSHALd57kIhioQGT2YmTJiAtWvXomPHjtWvlZWVobCwEG3b/rwyZZs2bVBYWIjS0tKGbpKIKPYpCuQluXC+tRaq2w3vo0/AP+BOPpaAqB4afAFwkyZNLnnN4/EAAFwuV/VrP/1vr9eLlJSUOn+m2+1AQsLPT/u8cKESAJCWllD9WmWlDx6PH+npCbBaq+ZkgUAIJSUeJCY6azxgq6ioAjabpcY99+XlChQlUH0aLjMzCT5fEGVlXiQnu+B0/pymsLAcsmxHUpJc/VppqQfBYBgZGYnVr3m9flRU+JCa6obdbgUAhEJhFBdX6rJPACK2T+GwWv1z42WfovE+hULhGmONh32K9Pvk8wXjbp+u+H3ylMC+4J/AuXMId7gRxaMegOvqrBr7ZLNZYmufdHifMjOT4m6fIvk+/dQoXvapLhF7nMGnn36KRx99FPv370dpaSl69uyJrVu3olmzqqe4njp1CgMGDMBnn32G5OS6l+Bu7McZEBE1Ftu+zyGvXgH4A/Df3hf+e4YCVqvewyIyvLquAYrKMpIpKSnIzMzE8ePHq1/Lz89HVlaWcCKjh+Rkl/iLTI6NtGEnMdM2CgbhXLsa8tLFUC0WKBMnwz90eK0TGdM2ugJsJGamRlFbZ2bYsGGYN28e2rVrBwCYN28ehg0bFq3NNcjFp7eodmykDTuJmbGRdKEYct4bsJ48iXDTpvBOnAo1K+uyX2/GRleKjcTM1Chqezp9+nS88MILGD58OAKBAAYPHozp06dHa3NERIZkPXIY8tI8SB4Pgt1vhjJyDOB0ir+RiDSL2DUzkdTY18xkZibxOh0BNtKGncRM00hV4di8EY4PNgFWC3zDRiLQ61ZNdyuZplEDsJFYvDWq65oZTmaIiCKtogKu5YthPXIEaloavDmTEb6uhd6jIoppdU1mzPOBWh1k2W6qZZ/rg420YSexeG9kOXkCrrw3IF24gGD79lDGTQASEsTfeJF4bxQJbCRmpkaczABISpJN84bXFxtpw05icdtIVWHftRPO9WuBsAr/XXfDf8egei2CF7eNIoiNxMzUiJMZIqKG8vkgr1kJ2769UN1uKBMmIXTDjXqPisg0OJkhImoA6dw5uHIXwHLuHEItWkDJmQw1NU3vYRGZCiczEC+TTGykFTuJxVMj25f74Vy5DJLPj0Dv2+C7dxhga/i/VuOpUbSwkZiZGnEyA1Q/w4Iuj420YSexuGgUCsH57nrYd3wE1emAMj4Hwa7dI/bj46JRlLGRmJkaReVxBrHm4geMUe3YSBt2Eov1RlLJBbhefQX2HR8h3KQJvDOejuhEBoj9Ro2BjcTM1IhnZoiINLIe/RbyklxIFRUIdu0GZfT9XM2XyAA4mSEiElFVOD78AI6NGwCLBN/wkQj8+rZ63XZNRJHHyQwAr9ev9xAMj420YSexmGvk8UBevhi2Q4egpqbCO2ESwi1bRXWTMddIB2wkZqZGfJwBEdFlWH44WbWab3ExQjfcAOXBHKiJl19SnYiip67HGfACYACpqW69h2B4bKQNO4nFRCNVhf2TXXDPewlScTH8d94F70OPNdpEJiYa6YyNxMzUiB8zAbDbrXoPwfDYSBt2EjN8I78fzrWrYf9sT9VqvpMmINS+Q6MOwfCNDICNxMzUiJMZIqL/kAoL4cpbCMuZMwg3bw5vzmSo6Rl6D4uIBDiZARAKmWdhofpiI23YScyojWwHvoRzxVJIig+BW26Fb9jIiKzmWx9GbWQkbCRmpka8AJiIzC0UguP9d+HYvhWw26CMvh/B7j30HhUR/QIvABZwux16D8Hw2EgbdhIzUiOprBSu1+fDsX0rwpmZ8Mx8xhATGSM1Mio2EjNTI05mACQkcAVPETbShp3EjNLImn8U7hdnw3o8H8EuN8Ez8xmEm16t97AAGKeRkbGRmJka8ZoZIjIXVYV924dwbngPAOAbOgyBPn25mi9RDONkhojMw+OBvGIpbF8fhJqSUrWab6vWeo+KiBqIFwADsNkspnpUen2wkTbsJKZXI8vpU5DzFsJyvgihttdDGZ8DNSm50cehBY8jMTYSi7dGdV0AzDMzRBT3bHs+hbx2FRAIwj9gIPx3DQYsvGSQKF7wn2YAaWkJeg/B8NhIG3YSa9RGgQCcK5dBXrkMqs0G75SH4B88xPATGR5HYmwkZqZGPDNDRHFJKiqCK3cBLKdPI9ysGbwTp0LN4Gq+RPGIkxkiijvWrw9CXrEEkseLQHYv+IaPAux2vYdFRFHCyQyAykqf3kMwPDbShp3EotooHIZjw3twbN1StZrv2AcQ7Jkdve1FCY8jMTYSM1Mj3s1ERHFBKi+DvCQP1mNHEb4qA0rOFISbXav3sIgoQvg4A4H0dPNcJFVfbKQNO4lFo5HleH7Var7HjiLYqTM8M56J6YkMjyMxNhIzUyN+zATAauWcToSNtGEnsYg2UlXYd2yH8923AQC+IUMR6Ns/5lfz5XEkxkZiZmrEyQwRxSZFgbxyGWxffQk1KQnKhIkItble71ERkQ44mQEQCIT0HoLhsZE27CQWiUaWs2cg5y6ApfA8Qq3bQJkwEWpySgRGZww8jsTYSMxMjXgBMBHFFNvneyCvWVm1mm/f/lWL4Fmteg+LiKKMFwALJCaa5zHp9cVG2rCTWL0bBYNwrlkJeflSqFYrlElT4L/3vricyPA4EmMjMTM14mQGgMvl0HsIhsdG2rCTWH0aScVFcM97CfbduxC+5hp4Zv4Owc43RWF0xsDjSIyNxMzUiNfMEJGhWQ8fgrwsr2o13x494RsxGnCY51/SRCTGyQwRGVM4DMcHG+HYvAmwWeEbPRaB7Fti/rZrIoo8XgAMwGKREA4bLoOhsJE27CSmpZFUUQ55aR6s334LNT0d3pzJCDe/rpFGqD8eR2JsJBZvjeq6AJhnZgDYbBb4/ea5ha0+2EgbdhITNbJ8/x1cixdBKilBsEMHKPePBxLMs5IpwONICzYSM1MjXgAMICXFrfcQDI+NtGEnscs2UlXYd+6A+9W5kEpL4R98D5QpD5tuIgPwONKCjcTM1IhnZohIfz4f5FXLYftiP9TERCjjJyJ0/Q16j4qIYgQnM0SkK8u5AsiLFsDy448ItWwFJWcS1JRUvYdFRDGEkxkA5eWK3kMwPDbShp3ELm5k278XztUrIPn8CPS5Hb4h8bkI3pXicSTGRmJmasS7mYio8QWDcL7zFuw7P4bqdMA39gEEb+qq96iIyMD4OAOBugJRFTbShp3EMm1BuF6dC/vOjxFu2rRqNV9OZGrgcSTGRmJmahT1yczRo0cxbtw4dO3aFYMGDcLmzZujvUkiMijrN0eA//N/YD1xAsHuN8Mz/bdQs7L0HhYRxbioTmb8fj8eeeQRDBgwAHv27MGzzz6L3//+98jPz4/mZonIaFQVjg82wPXP1wFFgW/kaCjjxgNO8zwIj4iiJ6qTmU8//RRerxdTp06F3W5Hnz59cPvtt2P9+vXR3OwV8/mCeg/B8NhIG3aqRWUl5AV/h2PTRqipqfDN+C0Ct/bmYwnqwONIjI3EzNQoqncz5efno23btpAu+pdWmzZtcOjQoWhu9oqVlXn1HoLhsZE27FST5eQJuPLegHThAkLt2sH7QI4pF8G7UjyOxNhIzEyNojqZ8Xg8kGW5xmuyLMPrrTuw2+1AQsLPp58vXKgEAKSl/fwvwcpKHzweP9LTE2C1Vp1gCgRCKCnxIDHRWePR50VFFbDZLDVWQywvV6AogRoXSPl8QZSVeZGc7ILT+XOawsJyyLIdSUk/70tpqQfBYBgZGYnVr3m9flRU+JCa6obdXnV7aSgURnFxZczvU0ZGIiwWKa72KRrv08Wvxcs+1et9UlX4t2xDaPlyOKwSrGNGAPfcg0RJgs8XhN8fjL19isf3ifvEfYqhfapLVG/NXrRoET788EMsXry4+rVXXnkFhw8fxquvvnrZ72vsW7MzM5N4O7gAG2nDTqhazffNVbDt/Ryq2w3lwRyE2rWv/ms2EmMjMTYSi7dGut2a3aZNG3z33Xc1Xjt27Bjatm0bzc0SkU6kH3+Ee+6LsO39HKEWLeD57e9qTGSIiKIhqpOZ7Oxs2O12vP766/D7/dixYwc++ugjDB06NJqbJSId2L76Au6X58BSUIBA79vgfXw61LR0vYdFRCYQ9RWA8/Pz8dxzz+Hrr79GZmYmnn76adx55511fk88nRYjinuhEBzvvQ3HR9sBhx3KmHEIdu2u96iIKM7U9TETH2cAQJbtUJRAo24z1rCRNmbrJJWWQM5bBOv33yGclQVl0lSEmzSt83vM1qg+2EiMjcTirVFdkxk+aBJAUpIcV294NLCRNmbqZD36LeQluZAqKhD8VVcoo+8HfnH3Ym3M1Ki+2EiMjcTM1IiTGSK6MqoKx9bNcGx4H5Ak+IaNQKB3Hy6CR0S64WSGiLTzeCAvXwLboa+hpqTAmzMZ4Zat9B4VEZkcJzMQL8ZDbKRVPHeynPoBct4bsBQVIXTDDVAezIGaeOVP5Y3nRpHCRmJsJGamRpzMAAgGw3oPwfDYSJu47KSqsH36CeR1q4FgCP477oR/0N2ApX4rO8RlowhjIzE2EjNTo6iuMxMrLl7ymWrHRtrEXSe/H86VyyCvXgHV4YB36sPw331PvScyQBw2igI2EmMjMTM14pkZIqqVVFgIV95CWM6cQbh5c3hzJkNNz9B7WEREl+BkhoguYT3wFeQVSyApPgRuuRW++0YAdrvewyIiqhUnM6h6mijVjY20iflO4TAc770Dx/atgN0GZdyDCN7cM6KbiPlGjYCNxNhIzEyNuAIwEQEApLJSyItzYT2ej3DmVVAmTkX46mv0HhYREQAdn5odK1JT3XoPwfDYSJtY7WQ9fgzuF2fDejwfwc5d4JnxTNQmMrHaqDGxkRgbiZmpET9mAmC3W/UeguGxkTYx10lVYd++Fc733wUA+IYOQ6BP36iu5htzjXTARmJsJGamRpzMEJmV1wt5xVLYDh6AmpwMZcJEhFq31XtURERXjJMZAKGQeRYWqi820iZWOlnOnIactxCWwvMItWkLZcJEqEnJjbLtWGmkJzYSYyMxMzXiBcBEJmPb8ynktauAQBD+fgPgHzykQYvgERE1Bl4ALOB2O/QeguGxkTaG7hQIwLl6BeSVy6DabPBOngb/kKGNPpExdCODYCMxNhIzUyNOZgAkJDj1HoLhsZE2Ru0kFRXBPe8l2D/ZjXCzZvDM/B1CnTrrMhajNjISNhJjIzEzNeI1M0Rxzvr1warVfD1eBHpmwzdiNFfzJaK4wskMUbwKh+HY+D4cH26uWs13zDgEs3vpPSoioojjBcAAbDaLqR6VXh9spI1ROkkV5ZCX5MJ69CjCGRlQJk5BuNm1eg8LgHEaGRkbibGRWLw1qusCYJ6ZIYozlu+Ow7V4EaTSUgQ7doJy/4OA2zwrgRKR+fACYABpaQl6D8Hw2EgbXTupKuw7tsP96iuQysrgu+deKJOnGW4iw2NJjI3E2EjMTI14ZoYoHigK5JXLYPvqS6hJSVWr+ba5Xu9RERE1Ck5miGKcpeAs5EULYCksRKhV66rVfFNS9R4WEVGj4WQGQGWlT+8hGB4badPYnWx7P4O8ZiXgD8Dftx/8g+8FrMZ+uByPJTE2EmMjMTM14t1MRLEoGIRz/VrYd/0bquyE7/4HEex8k96jIiKKGj7OQCA93TwXSdUXG2nTGJ2k4iK45r8M+65/I3z11fDMeCamJjI8lsTYSIyNxMzUiB8zAbBaOacTYSNtot3JeuQw5KV5kDweBG7uAd/IMYAjtp6/wmNJjI3E2EjMTI04mSGKBeEwHJs3wbF5E2C1wDd6LALZtwCSpPfIiIh0x8kMgEAgpPcQDI+NtIlKp4oKuJblwfrNN1DT0+HNmYxw8+siv51GwmNJjI3E2EjMTI14ATCRgVlOfA9X3huQSkoQbN8eyrgJQIJ5PgcnIvoJLwAWSEw0z2PS64uNtIlYJ1WFfecOuOe/DKm0FP67B0OZ+khcTGR4LImxkRgbiZmpESczAFyu2LqAUg9spE1EOvl8kJfmwbnuTaiyC96HH4P/jkFxc30MjyUxNhJjIzEzNeI1M0QGYjlXADl3ISznziHUoiWUnElQU9P0HhYRkaFxMkNkELYv9sG5ajkknx+BPrfDd89QwMZ/RImIRHgBMACLRUI4bLgMhsJG2tSrUzAI57vrYf94B1SnA74x4xD8VbfoDNAAeCyJsZEYG4nFW6O6LgDmr30AbDYL/H7z3MJWH2ykzZV2kkouQM5bBOuJ7xFu0gTeiVOhNmkSxRHqj8eSGBuJsZGYmRrxAmAAKSluvYdgeGykzZV0sn77Ddwvzob1xPcIdu0Gz4yn434iA/BY0oKNxNhIzEyNeGaGqLGpKhwffgDHxg2ARYJvxCgEbu0dN3crERE1Nk5miBpTZSXk5YthO3wYampq1Wq+LVrqPSoiopjGyQyA8nJF7yEYHhtpU1cnyw8nq1bzLS5G6MYb4X0gB0hMbMTRGQOPJTE2EmMjMTM14t1MRNGmqrB/sgvOt94EQmH4Bw6Cf+AgwMJL1oiItOLjDATqCkRV2EibSzr5/XCuWArnmlVQHU54pz0C/6C7TT2R4bEkxkZibCRmpkb8mIkoSqQff4QrdwEsBQUIXXcdlAmToKZn6D0sIqK4E7FfDxctWoRHHnmkxmvnzp3DtGnT0K1bN/Tt2xfLly+P1OaIDM321RdwvzwHloICBH7dG94nZnAiQ0QUJQ2ezPj9fvztb3/DX/7yl0v+bsaMGWjZsiU++eQTzJ07Fy+99BJ2797d0E1GnM8X1HsIhsdG2vg8PjjeXgc59w1I4TCUByfAN2I0H0twER5LYmwkxkZiZmrU4AuA77//fmRlZSE9PR1nz57F3//+dwDA8ePHMWTIEOzZsweJ/7ljY/bs2SgoKMBf//rXOn8mLwCmWCSVlkBenAvrd8cRzsyEMmkqwk2v1ntYRERxoUEXAAeDQZSVldX6f6qq4uWXX8bcuXORkVHzFPrx48fRtGnT6okMALRp0wZHjx5twK5ER3KyS+8hGB4b1c2afxTuF2fDdfoEgjf9Cp6Zz3Aicxk8lsTYSIyNxMzUSHjue+/evcjJyan173bv3o0ml1l+vbKyErIs13hNlmV4vV7hoNxuBxISnNV/vnChEgCQlpZw0c/3wePxIz09AVZr1ZwsEAihpMSDxEQnXC5H9dcWFVXAZrPUWNq5vFyBogSqZ3qZmUnw+YIoK/MiOdkFp/PnNIWF5ZBlO5KSft6f0lIPgsEwMjJ+nqx5vX5UVPiQmuqG3W4FAIRCYRQXV+qyTwAitk9Op63658bLPkXkfbJIwKZNCK1dB78/BOuEcUjq3x9J/1nNNyb3KcrvE4C426dovE82myXu9inS71NmZlLc7VMk36eL/70dD/tUl4itM/PKK6/g4MGD1R8zbd68GX/+85+xdevW6q9Zu3YtcnNzsX79+jp/VmN/zJSZmcSPtgTYqBYeD+QVS2H7+iDUlBR4J0xCRs+b2EmAx5IYG4mxkVi8NdLlqdlt2rTBuXPnUFlZiYSEqtndsWPH0LZt22htkqjRWE6fgpy7EJaiIoSuvx7K+IlQE82zpgMRkZFEbeWu1q1bo2PHjpg9ezYURcFXX32FN998E8OGDYvWJustnmau0cJGP7N9+gncc1+EpagI/jvuhPfhx6snMuwkxkZibCTGRmJmahTVZUhfeeUVFBQU4LbbbsP06dMxc+ZM3HbbbdHcZL3Isl3vIRgeGwEIBOBcuQzyquVQHQ54pzwE/9331FjNl53E2EiMjcTYSMxMjfhsJsTf54rRYPZG0vnzcOUthOX0aYSvvRbenClQMy5dBM/snbRgIzE2EmMjsXhrpMs1M0TxwnrwAOQVSyB5FQR63QLfsJGA3Ty/8RARGR0nM0SXEw7D8f67cGz7ELDboNz/AII9svUeFRER/QInMxDfv07maySVl1Wt5pt/DOHMq6DkTEH4mmbC7zNbp/pgIzE2EmMjMTM14mQGQDAY1nsIhmemRtbjxyAvzoVUVoZg5y5Qxj4AuLStpGmmTvXFRmJsJMZGYmZqFNW7mWLFxSs/Uu1M0UhVYd++Fa7X5kOqqIDv3vugTJyieSIDmKRTA7GRGBuJsZGYmRrxzAwRAHi9kFcug+3AV1CTkqDkTEKoNRd4JCKKBZzMkOlZzpyGnLcQlsLzCLVuAyVnEtSkZL2HRUREGnEyg6oHcFHd4rWR7fM9kNesBAJB+PsNgH/wkBqL4F2peO0USWwkxkZibCRmpkZcNI/MKRCA8603Yf9kN1SXDOX+8Qh16qz3qIiI6DLqWjSPFwADSE11i7/I5OKpkVRUBPe8l2D/ZDfC11wDz8zfRWwiE0+dooWNxNhIjI3EzNSIHzMBsNuteg/B8OKlkfXQ15CXL4bk8SLQoyd8I8dEdDXfeOkUTWwkxkZibCRmpkaczJA5hMNwbNoAx5YPAJsVyphxCGb30ntUREQUAZzMAAiFzLOwUH3FciOpohzyklxYjx5FOCMDSs5khK9tHpVtxXKnxsJGYmwkxkZiZmrEC4Aprlm+Ow7X4kWQSksR7NARyrjxgNs8nyMTEcULXgAs4HY79B6C4cVcI1WFfcd2uF99BVJZGXz3DIEy5aGoT2RiQ9AFrQAAG1hJREFUrpMO2EiMjcTYSMxMjTiZAZCQ4NR7CIYXU40UBfKSXDjXr4PqdsP76BMI9B8ISFLUNx1TnXTCRmJsJMZGYmZqxGtmKK5YCs5CXrQAlsJChFq2qlrNNyVV72EREVEUcTJDccO273PIq1cA/gD8t/eF/56hgNU8tyYSEZkVLwAGYLNZTPWo9PowdKNgEM6318H+751QZSd8Yx9AsMuvdBmKoTsZBBuJsZEYG4nFW6O6LgDmmRmKadKFYsh5b8B68iTCTZvCO3Eq1KwsvYdFRESNiBcAA0hLS9B7CIZnxEbWI4fhfnE2rCdPItj9Znim/1b3iYwROxkNG4mxkRgbiZmpEc/MUOxRVTg2b4Tjg02A1QLfqDEI9Lq1Ue5WIiIi4+FkhmJLRQVcyxfDeuQI1PR0eHMmI9z8Or1HRUREOuJkBkBlpU/vIRieERpZTp6AK3chpJISBNu3hzJuApBgrNOoRuhkdGwkxkZibCRmpka8m4mMT1Vh37UTzvVrgbAK/6C74L9jED9WIiIyET7OQCA93Vi/3RuRbo18PsjLFsO5dg1U2QXvw4/BP/Auw05keCyJsZEYG4mxkZiZGvFjJgBWK+d0Ino0ks6dgyt3ASznziHUomXVar6paY0+jivBY0mMjcTYSIyNxMzUiJMZMiTbF/vgXLUcks+PQO/b4Lt3GGDj4UpERJfifx0ABAIhvYdgeI3WKBiE8723Yd/xEVSnA8r4HAS7dm+cbUcAjyUxNhJjIzE2EjNTI14ATIYhlVyAnLcI1hPfI9ykCZSJUxBu0lTvYRERkQHwAmCBxETzPCa9vqLdyHr0W7j/NgfWE98j2LUbPDOejsmJDI8lMTYSYyMxNhIzUyNOZgC4XA69h2B4UWukqnBs2QTX31+F5PXAN3wklAdzAGds/kPIY0mMjcTYSIyNxMzUiNfMkH4qKyGvWALboUNQU1PhnTAJ4Zat9B4VERHFGE5mSBeWH07ClfcGpOJihG64AcqDOVATL/95KBER0eXwAmAAFouEcNhwGQwlYo1UFfZPd8O5bg0QDMF/513wDxwEWOLjE08eS2JsJMZGYmwkFm+N6roAmGdmANhsFvj95rmFrT4i0sjvh3Ptatg/2wPV7YYyaQJC7TtEZoAGwWNJjI3E2EiMjcTM1Cg+fh1uoJQUt95DMLyGNpIKC+F+5W+wf7YH4ebN4XnqmbibyAA8lrRgIzE2EmMjMTM14pkZijrbgS/hXLEUkuJD4NZfw3ffCK7mS0REEcP/olD0hEJwvP8OHNu3AXYblAfGI9i9h96jIiKiOMPJDIDyckXvIRjelTaSykohL86F9Xg+wpmZUCZNRbjp1VEanXHwWBJjIzE2EmMjMTM14t1MFHHW/KOQF+dCKi9HsMtNUMY+AMiy3sMiIqIYxscZCNQViKpoaqSqsG/dAtdr8yFVVsJ333AoOZNNNZHhsSTGRmJsJMZGYmZqxI+ZKDI8HsgrlsL29UGoKSlVq/m2aq33qIiIyAQ4maEGs5w+BTlvISznixBqez2U8TlQk5L1HhYREZlEgz9mWr16NQYNGoRu3brh3nvvxZYtW6r/7ty5c5g2bRq6deuGvn37Yvny5Q3dXFT4fEG9h2B4l2tk2/Mp3K/8DZbzRfAPGAjvI4+beiLDY0mMjcTYSIyNxMzUqEFnZnbu3Ik5c+bgn//8Jzp37owdO3ZgxowZWLlyJW688UbMmDEDnTp1wquvvoojR47goYceQsuWLXHLLbdEavwRUVbm1XsIhndJo0CgajXfPZ9CdbugTJiMUMdO+gzOQHgsibGRGBuJsZGYmRo16MxMQUEBpk6dii5dukCSJNx+++1o3bo1vvjiCxw/fhxfffUVZs6cCYfDgS5dumDUqFFYs2ZNpMYeMcnJLr2HYHgXN5KKiqpW893zKcLNmsEz83ecyPwHjyUxNhJjIzE2EjNTI+GZmWAwCI/HU+vfjRw5EpIkVf/5xIkTOHr0KNq3b4/jx4+jadOmSExMrP77Nm3a4OOPP47AsCPL6eSlQyI/NbIePAB5xRJIXgWB7F7wDR8F2O06j844eCyJsZEYG4mxkZiZGgn3dO/evcjJyan173bv3o309HQAwOnTp/Hwww9j2LBh6NKlC9avXw/5F7fkyrIMr1d82svtdiAhwVn95wsXKgEAaWkJ1a9VVvrg8fiRnp4Aq7XqBFMgEEJJiQeJiU64XI7qry0qqoDNZqnxnIrycgWKEqi+dS0zMwk+XxBlZV4kJ7tqHASFheWQZTuSkn7en9JSD4LBMDIyfp6seb1+VFT4kJrqht1uBQCEQmEUF1fqsk8AIrZPCIeRuXMLsHEjwjbb/9/evUdFWe97HP/MlYcZBhFDzWx7gW2pQQIm1TqIcrwcLFumoOYFK9M6ugwp9x+Za9mqP1xL3aZyNF3dQDJpiyjZ2WcfdZtoRV5InWNuU7xllsb2AsrcZ57zByuKnfJDGeZ5Zp7P6z+ZCb7Pe2atvmvmmWdQP2kKzMMykRDGx9QRjxPQ8uOQkXBMwX6cAETcMXXE42Q06iPumIL9OCUk2CLumIL5OP3SKFKOqTVBuWjeoUOHUFBQgJycHCxcuBB6vR47d+7EkiVLsHv37ub7VVRUoKSkBJWVla3+vlBfNC8hwcYL9bVCd6MB92z7BM4jxxC4pwtcM2Yi0OM+pcdSJT6XxNhIjI3E2Egs0hq1dt2cdr8GVVFRgbfeeguvvfYaJk6c2PzzxMREXL58GY2NjbBam7a72tpaJCUltfdPBl0kPdjBpj9zGtGlxXA2NMD3UHLT1Xwt2vkm1jvF55IYG4mxkRgbiWmpUbtOAK6qqsLixYuxdu3aFosMAPTt2xcDBw7EsmXL4HK5YLfbsWXLFowbN65dA3cESeI5H78jyzBVfQ7LO/8F3c2bkMePh+vZmVxkBPhcEmMjMTYSYyMxLTVq1ysz77zzDnw+H+bMmdPi54WFhcjPz0dRUREWL16MzMxMWK1WzJ8/H5mZme0auCPYbBJcLq/SY6iHywXpk49htB+FbLPBNX0G4h9NQ6OGtvy7xeeSGBuJsZEYG4lpqVG7lpmysrJWb+/WrRvWrVvXnj9BIab/6UdIJe9DX/dP+PsmwjV9BuTYTkqPRUREdFva+dwWCRkPHYBU/gng9cEzLBueJ8YCen4XKRERqRuXGYg/8hXxvF5EVVbAVP0VZCkK7qn58CU/3OIumm/URuwkxkZibCTGRmJaasRlBmj+PL4W6a5eQfSGD6G/cAGBHj3gzH8eckLC7+6n5UZ3gp3E2EiMjcTYSExLjfgeAtDiYklaYvjHcVjeXgb9hQvwPjIEjnmFt1xkAO02ulPsJMZGYmwkxkZiWmrEV2a0KBCAecffYN75v4DRAHfeJHgzHgN+89UURERE4YLLjMbobt6AtHEDDCdPQo6Ph3PG8wj0vF/psYiIiO4alxk0fWeFFujPnUV0aTF016/DN2AgXM9Ma/NF8LTSqL3YSYyNxNhIjI3EtNQoKN/NFGxaugRzSMgyTF/sRdSn2wBZhidnDDzZI/m2EhERhY3WvpuJJwADiIuL4Ev0u92QPipB1LYKyBYLnC/OgeffR93xIhPRjYKIncTYSIyNxNhITEuN+DYT0Px16JFGf/kSpOL3of/5Z/h794Er/1nIneLu6ndFaqNgYycxNhJjIzE2EtNSIy4zEcp4uAZRm8ugc3vgyRoGzxNPAQbtPLGJiEg7uMwA8Psj6MJCPh+itm+D6Yt9kKPMcOU/C9/Dqe3+tRHVqAOxkxgbibGRGBuJaakRTwCOILrr1yBt+BCG8+cR6N4dzhkzIXftqvRYRERE7cYTgAUsFrPSI7Sb4bsTsPx5KQznz8OXPhiOl18J6iITCY1CgZ3E2EiMjcTYSExLjbjMALBao5Qe4e7JMsw7/gfR766DzuOGe0Je0/VjooJ7TGHdKITYSYyNxNhIjI3EtNSI58yEs8ZGRH+8AYYTJyB37gxn/nMI/KGX0lMRERGFFJeZMKX//jyiN3wI3bVr8D/4IJxT8gGrVemxiIiIQo4nAAMwGvXh81XpsgxT9ZeIqqwA/AF4Ro2GZ+R/dPjVfMOqkYLYSYyNxNhIjI3EIq1RaycA85WZcOJ2Q9ryFxhrDkG2WOCaNgP+Bx5UeioiIiJF8QRgAJ07q//tGd3PP8OyegWMNYfg79ULjlf+FNJFJhwaqQE7ibGRGBuJsZGYlhrxlZkwYLQfQVTZRujcHnj/LRPuseMAIx86IiIigMuMuvn9iPqsEqa9VYDZBNe0fPhS05WeioiISFW4zABobHQrPcLv6OqvQ9pQDMO5swh07QrXszMR6NZdsXnU2EiN2EmMjcTYSIyNxLTUiJ9mUiHDqZOQPiqB7uZN+AalwpU3GZAkpcciIiJSDL/OQCA+XiUnSckyzH/fgej1a6FzOOB+egJc02aoYpFRTSOVYycxNhJjIzE2EtNSI77NBMBgUMFO53BA2vQRjMe/hdypU9PVfHv3UXqqZqpoFAbYSYyNxNhIjI3EtNSIy4wK6H+4AGnDh9BfuQJ/v35wTc2HHHP7l9OIiIjoV1xmAHi9fmX+sCzDuP9rSFs3Az4/PCNGwTM6B9Crb5tWrFGYYScxNhJjIzE2EtNSI54ArBSPB1EVm2E6eACyJRquKfnw9x+g9FRERESqxBOABWJiQvs16bq6OliK3obp4AEE7r8fjsI/qX6RCXWjcMVOYmwkxkZibCSmpUZcZgBER5tD9rcM/2eHZeUy6H/8Ed7HHodjbgHk+C4h+/t3K5SNwhk7ibGRGBuJsZGYlhrxnJlQCQRg/u/tMO/ZDZiMcD0zFb7BQ5SeioiIKOxxmQkBXUM9pNISGM6cRiDhHrhmzETg3h5Kj0VERBQReAIwAL1eh0CgYzIYTp+CVFoC3Y0b8CWnwDV5qiougnenOrJRJGEnMTYSYyMxNhKLtEatnQDMV2YAGI16eDxB/gibLMO0Zzei/voZAMD91Dh4hw4DdLrg/p0Q6ZBGEYidxNhIjI3E2EhMS414AjCATp0swf2FTiek4vcR9dmnkGNi4PzPufBmDQ/bRQbogEYRip3E2EiMjcTYSExLjfjKTJDpf7wIacMH0Nf9E/7EJLimz4Bsi1V6LCIioojFZSaIjAf2Q6r4C+D1wZM9Ap6cJ1R5NV8iIqJIwmUGwI0brvb9Aq8XUVvLYdr/NeRoCa5pz8L/UHJwhlOJdjfSCHYSYyMxNhJjIzEtNeKnmdpJd+UKokveh/7iRQTuuw/O/Och33OP0mMRERFFFH6dgUBrgVpj+PZY09V8L16Ed0gGHPMKI3aRudtGWsNOYmwkxkZibCSmpUZ8m+luBAIw/+2vMP99Z9PVfCdNgW9IhtJTERERaRKXmTuku9EAaeMGGE6dQqBLF7hmPI/AfT2VHouIiEizuMwAcLt9bbqf/uwZRJcWQ1dfD9/Ah5qu5mvRxuf429pI69hJjI3E2EiMjcS01Khd58zIsow1a9YgKysLqampyMvLw4EDB5pvv3z5Ml544QWkpaVh2LBh2LRpU7sH7ggNDc7W7yDLMFV9DsvaIugaGuB+Yixcz72gmUUGaEMjAsBObcFGYmwkxkZiWmrUrmWmoqIC27Ztw8aNG/HNN98gNzcXc+bMgdfrBQAUFBSgd+/e+Prrr7F69WqsXLkS1dXVQRk8mGJjo29/o8sFacOHiPp0G2Srtelqvtkjwvpqvnej1UbUjJ3E2EiMjcTYSExLjdr1NtP48eORk5MDi8UCl8uF+vp6xMbGQq/X48yZM7Db7XjvvfdgNpuRkpKC3NxclJeX47HHHgvW/EERFXXrDPpLP0Eqfh/6ujr4+yY2Xc03tlOIp1OH2zWilthJjI3E2EiMjcS01Eh4pD6fDw6H45a32Ww2WCwW7Nq1C/PmzYPBYMCqVatgMBhw5swZdO/eHTExMc33T0xMxL59+4I3fQcy1hyEVP4J4PHCMywbnjFPAgaD0mMRERHRvxAuMzU1NcjPz7/lbdXV1YiPj8fQoUNht9uxfft2FBYWYuvWrWhsbIQkSS3uL0kSnE7xe3gWixlWa1Tzv69dawQAdO5sbf5ZY6MbDocH8fFWGAxN75Z5vX5cv+5ATEwUoqPNzfe9cuUmjEZ9iy/dunHDBZfL2/w5/IQEG9xuHxqu3kDcjs9g+uoLwCoBc1/CzZ5JkCQTbLZfj6e+3gGfL4AuXX5d1pxOD27edCMuzgKTqWnx8fsDuHq1UZFjAppOAGtocCI2NrrFll5Xd+OOjumXRpF0TB3xOP22U6QcU7AfJwARd0wd8TgZjfqIO6ZgP04JCbaIO6ZgPk6/NIqUY2pN0K8AnJeXhzFjxqBnz55YsmQJdu/e3XxbRUUFSkpKUFlZ2ervUOoKwLqrVyCVFsPw/fcI3Htv09V8u3ZVZBYiIiL6VYddAXjlypVYunRpi595PB7YbDYkJibi8uXLaGxsbL6ttrYWSUlJ7fmTHUKSTDCc+Acsby+H4fvv4R38CBwvv8JF5jckyaT0CGGBncTYSIyNxNhITEuN2rXMpKeno6ysDHa7HT6fD6Wlpairq0N2djb69u2LgQMHYtmyZXC5XLDb7diyZQvGjRsXrNmDIxCAbc9ORL+3HjqPG+68SXBPngqYzeL/VkN++3If3R47ibGRGBuJsZGYlhq161TnzMxMvP7661iwYAGuXr2KAQMGoLi4GPHx8QCAoqIiLF68GJmZmbBarZg/fz4yMzODMnhQ3LyJ6I0lwIWzkDt3hjP/OQTu/4PSUxEREdEd0Oy3ZuvPnW26mu/164h+JBV1Y/MAq1X8H2pUQoItrL7NXCnsJMZGYmwkxkZikdaotXNmtPMh9F/IMkxf7kPUp1uBgAxPzhh4cnIAb0DpyVRNdCY5NWEnMTYSYyMxNhLTUiNtLTNuN6TNZTAe/gZyTAxc02bA/8d+0PtV9+KU6vzyMT9qHTuJsZEYG4mxkZiWGrXrBOBwY/pyL4yHv4G/V284ChfA/8d+ANDi+gJ0a2zUNuwkxkZibCTGRmJaaqSpV2Z8aYMhx8TCl5YOGDV16ERERBFLU/9Hl+M6wzckQ+kxiIiIKIg09TbT7TidHqVHUD02aht2EmMjMTYSYyMxLTXS7EeziYiIKHx02NcZRIq4OIv4ThrHRm3DTmJsJMZGYmwkpqVGXGaA5m8Qpdtjo7ZhJzE2EmMjMTYS01IjLjNEREQU1rjMAPD7tXNhobvFRm3DTmJsJMZGYmwkpqVGPAGYiIiIVI8nAAtYLGalR1A9NmobdhJjIzE2EmMjMS014jIDwGqNUnoE1WOjtmEnMTYSYyMxNhLTUiMuM0RERBTWuMwQERFRWOMJwACMRr2mvir9brBR27CTGBuJsZEYG4lFWiOeAExEREQRi8sMgM6drUqPoHps1DbsJMZGYmwkxkZiWmrEZYaIiIjCGpcZIiIiCmuqPAGYiIiIqK34ygwRERGFNS4zREREFNa4zBAREVFY4zJDREREYY3LDBEREYU1LjNEREQU1rjMAJBlGWvWrEFWVhZSU1ORl5eHAwcOKD2WKm3evBmjR49GWloaxo4di127dik9kmoVFxfjxRdfVHoM1Th16hSeeeYZpKamYvTo0di5c6fSI6mW3W5HRkaG0mOo0qFDhzBp0iSkp6dj+PDhWLNmDXiFkZZ27tyJJ598EqmpqRg5ciQ+/vhjpUfqeDLJ5eXl8ogRI+QLFy7IgUBALisrk9PT02WPx6P0aKqyb98+eciQIfLRo0flQCAg79mzR3744YflEydOKD2aqrjdbnnFihXyAw88IM+ePVvpcVTB7XbLw4cPl999913Z4/HIVVVV8qBBg+Ta2lqlR1Od7du3y2lpafKgQYOUHkV1rly5Ig8ePFguLy+XfT6ffObMGTk7O1veuHGj0qOpxrlz5+Tk5GS5urpalmVZPn78uJycnCwfPXpU4ck6Fl+ZATB+/HhUVlaiZ8+ecLvdqK+vR2xsLPR65vmtS5cuYebMmUhJSYFOp0NWVhb69u2LI0eOKD2aquTn5+Ps2bOYPHmy0qOoxv79++F0OjFz5kyYTCYMHToUWVlZqKysVHo0VVm+fDk++OADzJ07V+lRVOmHH35AVlYWJkyYAIPBgD59+mDkyJGoqalRejTV6NWrF7766is8+uijCAQCuHbtGgwGA6zWyP6eJqPSA4SKz+eDw+G45W02mw0WiwW7du3CvHnzYDAYsGrVKhgMhhBPqbzWOk2YMAE6na753+fPn8epU6fQv3//UI2nCqLn0qpVq9CtWzcUFRXhp59+CvF06nT69GkkJSW1eP4kJibi+PHjCk6lPtOnT8eCBQuwf/9+pUdRpZSUFCxfvrz53x6PB3v37sXTTz+t4FTqExMTg+vXr+Pxxx+H3+/H7NmzkZiYqPRYHUozy0xNTQ3y8/NveVt1dTXi4+MxdOhQ2O12bN++HYWFhdi6dWvEPwH+VVs6AcDFixcxe/ZsjBs3DikpKaEcUXGiRt26dQvxROrncDggSVKLn0mSBKfTqdBE6sTnTtu53W4UFhbCbDZj6tSpSo+jOjabDUeOHMGJEycwa9Ys9OnTB+PHj1d6rA6jmWUmIyMD3333Xav3MZvNAJredtq0aRP27t2ruWWmLZ0OHTqEgoIC5OTkYOHChSGaTD3a0ohaslgscLlcLX7mcrlgsVgUmojC2aVLlzBv3jyYzWYUFxfzeXQLBoMBBoMBKSkpyM3NxY4dOyJ6meFJIQBWrlyJpUuXtviZx+OBzWZTaCL1qqiowKxZs1BQUIBFixbxvCJqk8TERJw9e7bFz2pra5GUlKTQRBSujh07hgkTJqB///4oLi5GXFyc0iOpyueff45p06a1+JnH40FsbKxCE4UG/08EID09HWVlZbDb7fD5fCgtLUVdXR2ys7OVHk1VqqqqsHjxYqxduxYTJ05UehwKIxkZGTCZTFi3bl3zeQ5VVVV46qmnlB6NwsgvH0KYMmUK3nzzTZhMJqVHUp3k5GScPHkSZWVlCAQCOHjwICoqKpCbm6v0aB1KJ8v8gD4AbNmyBevXr8fVq1cxYMAALFq0CP369VN6LFWZPHkyjh49+rtzHwoLC297DomWFRUV4dixY1i/fr3So6jC6dOn8cYbb+Dbb79FQkICXn31VYwaNUrpsVRp//79eOmll3D48GGlR1GVFStWYP369b97WykzMxOrV69WaCr1OXz4MJYsWYLTp0+jR48eePnllzFy5Eilx+pQXGaIiIgorPFtJiIiIgprXGaIiIgorHGZISIiorDGZYaIiIjCGpcZIiIiCmtcZoiIiCiscZkhIiKisMZlhoiIiMIalxkiIiIKa/8Pf9z+54oipX8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 583.2x453.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rotulo = 'Regressão Linear'\n",
    "# ou\n",
    "rotulo = f'Regressão: $y = ({a:.1f} \\pm {da:.1f})~x + ({b:.1f} \\pm {db:.1f})$'\n",
    "\n",
    "# monta os limites para desenho da reta\n",
    "X = np.linspace(min(V), max(V), num=200)\n",
    "Y =  a * X + b\n",
    "# e faz o gráfico dela atrá dos pontos\n",
    "plt.plot(X, Y, color='red', alpha=0.6, label=rotulo)\n",
    "\n",
    "# para exibir as legendas do gráfico\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resultado da Regressão"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-17T05:45:41.123042Z",
     "start_time": "2019-07-17T05:45:40.825757Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Regressão Linear da Corrente\\npela Tensão em um Resistor')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 583.2x453.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# desenho dos pontos\n",
    "plt.scatter(V, I, zorder=10, label='Dados Coletados')\n",
    "\n",
    "# desenho da regressão\n",
    "plt.plot(X, Y, color='red', alpha=0.6, label=rotulo)\n",
    "\n",
    "# legenda e textos\n",
    "plt.legend()\n",
    "plt.xlabel('Tensão [V]')\n",
    "plt.ylabel('Corrente [mA]')\n",
    "plt.title('''Regressão Linear da Corrente\n",
    "pela Tensão em um Resistor''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Barras de Incerteza"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dados Pontuais com Barras de Incerteza\n",
    "\n",
    "A função do <TT>pyplot</TT> para desenhar as barras de incerteza é [`errorbar`](https://matplotlib.org/3.1.0/api/_as_gen/matplotlib.pyplot.errorbar.html \"matplotlib.pyplot.errorbar\") com os argumentos `xerr=dx` e `yerr=dy`. O argumento `fmt` foi usado para fazer com que os dados fossem desenhados como pontos, que é o formato `'o'`. Além disso, os argumentos `elinewidth`, `capsize` e `capthick` controlam a grossura da barra de incerteza, o comprimento do topo da barra e a grossura desse topo, respectivamente. A cor dos pontos e das barras foi alterado para preto com `color='black'`. Novamente, esses desenhos foram colocados com ordem alta de desenho e com um rótulo para a legenda."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-17T05:45:41.335755Z",
     "start_time": "2019-07-17T05:45:41.125308Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ErrorbarContainer object of 3 artists>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 583.2x453.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# separa as colunas\n",
    "V, dV = dados['V'], dados['dV']\n",
    "I, dI = dados['I'], dados['dI']\n",
    "\n",
    "# desenha os dados com barras de incerteza\n",
    "plt.errorbar(\n",
    "    V, I, xerr=dV, yerr=dI,\n",
    "    fmt='o', elinewidth=1, capsize=3, capthick=1, color='black',\n",
    "    zorder=10, label='Dados Coletados'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regressão Linear com Incertezas\n",
    "\n",
    "A regressão com incertezas fica bem parecida com a da seção [*Aplicação da Regressão*](#Aplicação-da-Regressão \"Regressão Linear - Aplicação da Regressão\"), utilizando o pacote `odr`. A primeira diferença é que o `RealData` agora tem as incertezas de $x$ e $y$ em `sx` e `sy`. O problema com a regressão da seção anterior é que o método dos mínimos quadrados não é capaz de analisar as incertezas em $x$, então vamos deixar a `ODR` aplicar a regressão por distância ortogonal, como no código abaixo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-17T05:45:41.344089Z",
     "start_time": "2019-07-17T05:45:41.337326Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coef. angular = (10.349661586274507+-0.5546403384712124) [mA/V -> kOhm^-1 -> kS]\n",
      "coef. linear  = (0.3524272624345639+-0.21906887637354278) [mA]\n"
     ]
    }
   ],
   "source": [
    "# regressão linear com incertezas\n",
    "data = odr.RealData(V, I, sx=dV, sy=dI)\n",
    "odreg = odr.ODR(data, odr.models.unilinear)\n",
    "#odreg.set_job(fit_type=2)  # mínimos quadrados não suporta incertezas em x\n",
    "ans = odreg.run()\n",
    "\n",
    "a, b = ans.beta      # y = ax + b\n",
    "da, db = ans.sd_beta # incertezas de a e b\n",
    "\n",
    "# mostrando os coeficientes e suas incertezas\n",
    "print(f'coef. angular = ({a}+-{da}) [mA/V -> kOhm^-1 -> kS]')\n",
    "print(f'coef. linear  = ({b}+-{db}) [mA]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Note que os coeficientes $a$ e $b$ da regressão, tanto em seus valores quanto nas suas incertezas, são levemente diferentes dos da seção [*Aplicação da Regressão*](#Aplicação-da-Regressão \"Regressão Linear - Aplicação da Regressão\"), mesmo com os dados numéricos idênticos. A diferença aqui se deve, além do método de regressão diferente, mas também das incertezas dos dados, que agora estão sendo levadas em conta."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resultado com Incertezas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-17T05:45:41.618184Z",
     "start_time": "2019-07-17T05:45:41.345929Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Regressão Linear da Corrente\\npela Tensão em um Resistor')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 583.2x453.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rotulo = f'Regressão: $y = ({a:.1f} \\pm {da:.1f})~x + ({b:.1f} \\pm {db:.1f})$'\n",
    "\n",
    "# monta os limites para desenho da reta\n",
    "X = np.linspace(min(V), max(V), num=200)\n",
    "Y =  a * X + b\n",
    "\n",
    "# barras de incerteza\n",
    "plt.errorbar(\n",
    "    V, I, xerr=dV, yerr=dI,\n",
    "    fmt='o', elinewidth=1, capsize=3, capthick=1, color='black',\n",
    "    zorder=10, label='Dados Coletados'\n",
    ")\n",
    "\n",
    "# desenho da regressão\n",
    "plt.plot(X, Y, color='red', alpha=0.6, label=rotulo)\n",
    "\n",
    "# legenda e os textos\n",
    "plt.legend()\n",
    "plt.xlabel('Tensão [V]')\n",
    "plt.ylabel('Corrente [mA]')\n",
    "plt.title('''Regressão Linear da Corrente\n",
    "pela Tensão em um Resistor''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Próxima Parte - [Escala Logarítmica](3%20-%20Escala%20Logarítmica%20e%20Equação%20Característica.ipynb#Escala-Logarítmica \"Escala Logarítmica\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Ignore o código abaixo, é pra carregar os estilos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-17T05:45:41.623841Z",
     "start_time": "2019-07-17T05:45:41.619885Z"
    }
   },
   "outputs": [
    {
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       "<link href='http://fonts.googleapis.com/css?family=Alegreya+Sans:100,300,400,500,700,800,900,100italic,300italic,400italic,500italic,700italic,800italic,900italic' rel='stylesheet' type='text/css'>\n",
       "<link href='http://fonts.googleapis.com/css?family=Source+Code+Pro:300,400' rel='stylesheet' type='text/css'>\n",
       "<style>\n",
       "\n",
       "@font-face {\n",
       "    font-family: \"Computer Modern\";\n",
       "    src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n",
       "}\n",
       "\n",
       "\n",
       "#notebook_panel { /* main background */\n",
       "    background: rgb(245,245,245);\n",
       "}\n",
       "\n",
       "div.cell { /* set cell width */\n",
       "    width: 900px;\n",
       "}\n",
       "\n",
       "div #notebook { /* centre the content */\n",
       "    background: #fff; /* white background for content */\n",
       "    width: 1000px;\n",
       "    margin: auto;\n",
       "    padding-left: 0em;\n",
       "}\n",
       "\n",
       "#notebook li { /* More space between bullet points */\n",
       "margin-top:0.8em;\n",
       "}\n",
       "\n",
       "/* draw border around running cells */\n",
       "div.cell.border-box-sizing.code_cell.running {\n",
       "    border: 1px solid #111;\n",
       "}\n",
       "\n",
       "/* Put a solid color box around each cell and its output, visually linking them*/\n",
       "div.cell.code_cell {\n",
       "    background-color: rgb(256,256,256);\n",
       "    border-radius: 0px;\n",
       "    padding: 0.5em;\n",
       "    margin-left:1em;\n",
       "    margin-top: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "div.text_cell_render{\n",
       "    font-family: 'Alegreya Sans' sans-serif;\n",
       "    line-height: 140%;\n",
       "    font-size: 125%;\n",
       "    font-weight: 400;\n",
       "    width:600px;\n",
       "    margin-left:auto;\n",
       "    margin-right:auto;\n",
       "}\n",
       "\n",
       "/* Formatting for header cells */\n",
       ".text_cell_render h1 {\n",
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       "    font-weight: 200;\n",
       "    font-size: 50pt;\n",
       "    line-height: 100%;\n",
       "    color:#CD2305;\n",
       "    margin-bottom: 0.5em;\n",
       "    margin-top: 0.5em;\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".text_cell_render h2 {\n",
       "    font-family: 'Fenix', serif;\n",
       "    font-size: 22pt;\n",
       "    line-height: 100%;\n",
       "    margin-bottom: 0.1em;\n",
       "    margin-top: 0.3em;\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".text_cell_render h3 {\n",
       "    font-family: 'Fenix', serif;\n",
       "    margin-top:12px;\n",
       "    font-size: 16pt;\n",
       "    margin-bottom: 3px;\n",
       "    font-style: regular;\n",
       "}\n",
       "\n",
       ".text_cell_render h4 {    /*Use this for captions*/\n",
       "    font-family: 'Fenix', serif;\n",
       "    font-size: 2pt;\n",
       "    text-align: center;\n",
       "    margin-top: 0em;\n",
       "    margin-bottom: 2em;\n",
       "    font-style: regular;\n",
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       "\n",
       ".text_cell_render h5 {  /*Use this for small titles*/\n",
       "    font-family: 'Alegreya Sans', sans-serif;\n",
       "    font-weight: 300;\n",
       "    font-size: 16pt;\n",
       "    color: #CD2305;\n",
       "    font-style: italic;\n",
       "    margin-bottom: .5em;\n",
       "    margin-top: 0.5em;\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".text_cell_render h6 { /*use this for copyright note*/\n",
       "    font-family: 'Source Code Pro', sans-serif;\n",
       "    font-weight: 300;\n",
       "    font-size: 9pt;\n",
       "    line-height: 100%;\n",
       "    color: grey;\n",
       "    margin-bottom: 1px;\n",
       "    margin-top: 1px;\n",
       "}\n",
       "\n",
       ".CodeMirror{\n",
       "        font-family: \"Source Code Pro\";\n",
       "        font-size: 90%;\n",
       "}\n",
       "\n",
       ".warning{\n",
       "    color: rgb( 240, 20, 20 )\n",
       "    }\n",
       "</style>\n",
       "\n",
       "<script>\n",
       "    MathJax.Hub.Config({\n",
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       "                           equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
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       "                        tex2jax: {\n",
       "                            inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
       "                            displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
       "                            },\n",
       "                        displayAlign: 'center', // Change this to 'center' to center equations.\n",
       "                        \"HTML-CSS\": {\n",
       "                            styles: {'.MathJax_Display': {\"margin\": 4}}\n",
       "                            }\n",
       "                        });\n",
       "    MathJax.Hub.Queue(\n",
       "                      [\"resetEquationNumbers\", MathJax.InputJax.TeX],\n",
       "                      [\"PreProcess\", MathJax.Hub],\n",
       "                      [\"Reprocess\", MathJax.Hub]\n",
       "                     );\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "\n",
    "with open('.estilo.css', 'r') as estilo_arq:\n",
    "    estilo = estilo_arq.read()\n",
    "\n",
    "HTML(estilo)"
   ]
  }
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