Prejeto_1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Prejeto_1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/VGozzi/Intro_Python/blob/main/Prejeto_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xhe5oaZ8T7rJ"
      },
      "source": [
        "1)Usando integração numérica (o mêtodo da sua escolha) escreva um programa que resolva a equação diferencial para um valor de  k  e da temperatura inicial  C0  arbitrários. O resultado deve ser apresentado em forma de um gráfico da evolução da temperatura no equador em função do tempo para um intervalo de 50 anos.\n",
        "\n",
        "Chame a sua função para fazer o gráfico para  C0=20ºC  e alguns valores de  k  (p.ex. Figura abaixo).\n",
        "\n",
        "O programa deve utilizar funções, encapsulamento e generalização quando possível.\n",
        "\n",
        "Dica: Pode chamar e utilizar os módulos math, matplotlib, numpy e/ou scipy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FGkAK-t7PeMU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "ba585ad4-c24a-4bb4-8f29-be7434a3d1dc"
      },
      "source": [
        "\"\"\"\n",
        " Projeto 1 - Mudanças climáticas\n",
        " Alunos: Vitor Gozzi e Mariana Teixeira\n",
        " Grupo: 2\n",
        "\"\"\"\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "\n",
        "def eq_EDO(t,C,k) :                      \n",
        "  \"\"\"\n",
        "  Recebe um valor t referente ao tempo, um valor C referente à temperatura em graus Celsius e um valor k referente à regulamentação.\n",
        "  \n",
        "  Retorna a derivada da temperatura em relação ao tempo.\n",
        "  \"\"\"\n",
        "  return (1/10)*(C-20)*(22-C)*(C-26) - k\n",
        "\n",
        "\n",
        "def Euler(t,C0,k):\n",
        "  \"\"\"\n",
        "  Recebe uma lista t cujos elementos são os valores de um intervalo referente ao tempo decorrido, um valor C0 referente à temperatura inicial em graus Celsius e um valor k referente à regulamentação.\n",
        "  \n",
        "  Retorna uma lista de valores da temperatura em função do tempo de acordo com a função eq_EDO.\n",
        "  \"\"\"\n",
        "\n",
        "  C=[C0]    \n",
        "  dt = t[1]-t[0] # como os intervalos são regulares, basta identificar a diferênça entre o primeiro par.                                          \n",
        "\n",
        "  for i in range(1,len(t)) :                             \n",
        "    Ci = C[i-1]  + eq_EDO(t[i-1],C[i-1],k) *dt               \n",
        "    C.append(Ci)\n",
        "\n",
        "  return C\n",
        "\n",
        "\n",
        "def temp(C0,k) :\n",
        "  \"\"\"\n",
        "  Recebe um valor C0 referente à temperatura inicial em graus Celsius e um valor k referente à regulamentação.\n",
        "  \n",
        "  Plota um gráfico Temperetura x Tempo para os respectivos valores de k e C0 num intervalo de tempo de 50 anos.\n",
        "  \"\"\"\n",
        "  t = np.arange(0,50,0.1)\n",
        "  C = Euler(t, C0,round(k,2))\n",
        "  plt.plot(t,C,label=f'k = {round(k,2)}')\n",
        "\n",
        "  # legendas do gráfico\n",
        "  plt.xlabel('tempo [anos]')\n",
        "  plt.ylabel('temperatura °C')\n",
        "  plt.legend()      \n",
        "\n",
        "for ki in np.arange(-2,2.4,0.4) :\n",
        "    temp(20,ki)\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Noq2Dq6T-5e"
      },
      "source": [
        "2)Modifique o programa anterior para ser um módulo.\n",
        "\n",
        "Em um novo programa, chame o módulo para obter a solução da equação diferencial para um conjunto de valores variados de  k  e  C0  em uma \"rede\", isto quer dizer, percorrendo valores de  k∈[kmin,kmax]  e  C0∈[Cmin0,Cmax0]  em intervalos regulares.\n",
        "\n",
        "A temperatura de equilibrio ( Tfin ) é aquela que se mantem relativamente estável após um certo intervalo. Para determiná-la, escreva no seu programa um algoritmo que ache este valor e o tempo necessário para chegar nele (quantitativamente).\n",
        "\n",
        "Obtenha o valor da temperatura de equilibrio (asintótica) para cada ponto da configuração inicial  (k,C0)  e analise as soluções para as diferentes configurações, por exemplo gerando graficos que mostrem a temperatura final em função de  k  para alguns valores de  C0  ou qualquer outra relação relevante (justifique).\n",
        "\n",
        "Dica 1: o gráfico  Tfin  vs.  k  é chamado de diagrama de bifurcação e pode ser usado para responder algumas perguntas da parte 3.\n",
        "\n",
        "Dica 2: pode ser usado o gráfico de dispersão scatter a opção c=z com cmap do matplotlib site."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8PE7IJUMl4Ac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1da77b5d-aed6-4479-ba0b-9fd2178c867f"
      },
      "source": [
        "%%writefile Projeto.py\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "\n",
        "def eq_EDO(t,C,k) :                      \n",
        "  \"\"\"\n",
        "  Recebe um valor t referente ao tempo, um valor C referente à temperatura em graus Celsius e um valor k referente à regulamentação.\n",
        "  \n",
        "  Retorna a derivada da temperatura em relação ao tempo.\n",
        "  \"\"\"\n",
        "  return (1/10)*(C-20)*(22-C)*(C-26) - k\n",
        "\n",
        "\n",
        "def Euler(t,C0,k):\n",
        "  \"\"\"\n",
        "  Recebe uma lista t cujos elementos são os valores de um intervalo referente ao tempo decorrido, um valor C0 referente à temperatura inicial em graus Celsius e um valor k referente à regulamentação.\n",
        "  \n",
        "  Retorna uma lista de valores da temperatura em função do tempo de acordo com a função eq_EDO.\n",
        "  \"\"\"\n",
        "\n",
        "  C=[C0]    \n",
        "  dt = t[1]-t[0] # como os intervalos são regulares, basta identificar a diferênça entre o primeiro par.                                          \n",
        "\n",
        "  for i in range(1,len(t)) :                             \n",
        "    Ci = C[i-1]  + eq_EDO(t[i-1],C[i-1],k) *dt               \n",
        "    C.append(Ci)\n",
        "\n",
        "  return C\n",
        "\n",
        "\n",
        "def temp(C0,k) :\n",
        "  \"\"\"\n",
        "  Recebe um valor C0 referente à temperatura inicial em graus Celsius e um valor k referente à regulamentação.\n",
        "  \n",
        "  Plota um gráfico Temperetura x Tempo para os respectivos valores de k e C0 num intervalo de tempo de 50 anos.\n",
        "  \"\"\"\n",
        "  t = np.arange(0,50,0.1)\n",
        "  C = Euler(t, C0,round(k,2))\n",
        "  plt.plot(t,C,label=f'k = {round(k,2)}')\n",
        "\n",
        "  # legendas do gráfico\n",
        "  plt.xlabel('tempo [anos]')\n",
        "  plt.ylabel('temperatura °C')\n",
        "  plt.legend()   "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing Projeto.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LrmA57U7htPH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ec06fde-7e05-4eb8-c5e0-0c6488ccd517"
      },
      "source": [
        "%cat Projeto.py"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "import matplotlib.pyplot as plt\n",
            "import math\n",
            "import numpy as np\n",
            "\n",
            "\n",
            "\n",
            "def eq_EDO(t,C,k) :                      \n",
            "  \"\"\"\n",
            "  Recebe um valor t referente ao tempo, um valor C referente à temperatura em graus Celsius e um valor k referente à regulamentação.\n",
            "  \n",
            "  Retorna a derivada da temperatura em relação ao tempo.\n",
            "  \"\"\"\n",
            "  return (1/10)*(C-20)*(22-C)*(C-26) - k\n",
            "\n",
            "\n",
            "def Euler(t,C0,k):\n",
            "  \"\"\"\n",
            "  Recebe uma lista t cujos elementos são os valores de um intervalo referente ao tempo decorrido, um valor C0 referente à temperatura inicial em graus Celsius e um valor k referente à regulamentação.\n",
            "  \n",
            "  Retorna uma lista de valores da temperatura em função do tempo de acordo com a função eq_EDO.\n",
            "  \"\"\"\n",
            "\n",
            "  C=[C0]    \n",
            "  dt = t[1]-t[0] # como os intervalos são regulares, basta identificar a diferênça entre o primeiro par.                                          \n",
            "\n",
            "  for i in range(1,len(t)) :                             \n",
            "    Ci = C[i-1]  + eq_EDO(t[i-1],C[i-1],k) *dt               \n",
            "    C.append(Ci)\n",
            "\n",
            "  return C\n",
            "\n",
            "\n",
            "def temp(C0,k) :\n",
            "  \"\"\"\n",
            "  Recebe um valor C0 referente à temperatura inicial em graus Celsius e um valor k referente à regulamentação.\n",
            "  \n",
            "  Plota um gráfico Temperetura x Tempo para os respectivos valores de k e C0 num intervalo de tempo de 50 anos.\n",
            "  \"\"\"\n",
            "  t = np.arange(0,50,0.1)\n",
            "  C = Euler(t, C0,round(k,2))\n",
            "  plt.plot(t,C,label=f'k = {round(k,2)}')\n",
            "\n",
            "  # legendas do gráfico\n",
            "  plt.xlabel('tempo [anos]')\n",
            "  plt.ylabel('temperatura °C')\n",
            "  plt.legend()   "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2fW_AD1kauvR"
      },
      "source": [
        "import Projeto\n",
        "from matplotlib import cm\n",
        "\n",
        "def dicionario_Euler(C0_inicial,C0_final,dC,k_inicial,k_final,dk): \n",
        "  \"\"\"\n",
        "  Recebe um valor C0_inicial referente ao primeiro valor tomado para a temperatura inicial (em graus Celsius), um valor C0_final referente ao último valor \n",
        "  tomado para a temperatura inicial (em graus Celsius) e um valor dC referente ao espaçamento entre os valores tomados para a temperatura inicial. \n",
        "  Recebe também um valor k_inicial referente ao primeiro valor tomado para a regulamentação, um valor k_final referente ao último valor tomado para a regulamentação\n",
        "  e um valor dk referente ao espaçamento entre os valores tomados para a regulamentação.\n",
        "\n",
        "  Retorna um dicionário cujas chaves são os valores de C0 referentes ao intervalo dado e os valores correspondentes a essas chaves são dicionários.\n",
        "  Estes dicionários são aqueles cujas chaves são os valores de k referentes ao intervalo dado e os valores correspondentes a essas chaves são listas obtidas através da função Euler.\n",
        "  \"\"\"\n",
        "  t = np.arange(0,50,0.1)\n",
        "  dic = {}\n",
        "  for Ci in np.arange(C0_inicial,C0_final,dC):\n",
        "    dic[Ci] = {}\n",
        "    for ki in np.arange(k_inicial,k_final,dk) :\n",
        "      dic[Ci][round(ki,2)] = Projeto.Euler(t,Ci,ki)\n",
        "  return dic\n",
        "\n",
        "def grafico_temperatura_tempo(C0_inicial,C0_final,dC,k_inicial,k_final,dk):\n",
        "  \"\"\"\n",
        "  Recebe um valor C0_inicial referente ao primeiro valor tomado para a temperatura inicial (em graus Celsius), um valor C0_final referente ao último valor \n",
        "  tomado para a temperatura inicial (em graus Celsius) e um valor dC referente ao espaçamento entre os valores tomados para a temperatura inicial. \n",
        "  Recebe também um valor k_inicial referente ao primeiro valor tomado para a regulamentação, um valor k_final referente ao último valor tomado para a regulamentação\n",
        "  e um valor dk referente ao espaçamento entre os valores tomados para a regulamentação.\n",
        "\n",
        "  Plota o gráfico Temperetura x Tempo para os respectivos valores de k e C0 da rede dada como entrada num intervalo de tempo de 50 anos.\n",
        "  \"\"\"\n",
        "\n",
        "  for Ci in np.arange(C0_inicial,C0_final,dC):\n",
        "    for ki in np.arange(k_inicial,k_final,dk):\n",
        "      Projeto.temp(Ci,ki)\n",
        "\n",
        "def Temperatura_equilibrio(C0,k) :\n",
        "  \"\"\"\n",
        "  Recebe um valor C0 referente à temperatura inicial em graus Celsius e um valor k referente à regulamentação.\n",
        "  \n",
        "  Retorna um valor correspondente à temperatura de equilíbrio atingida num intervalo de 50 anos a partir dos valores de temparatura inicial e regulamentação dados.\n",
        "  \"\"\"\n",
        "  t = np.arange(0,50,0.1)\n",
        "  solucao_eq_dif = Projeto.Euler(t,C0,k)\n",
        "  \n",
        "  for i in range(len(solucao_eq_dif)):\n",
        "    if abs(solucao_eq_dif[-1] - solucao_eq_dif[i]) <= 0.01 : # como sabemos que o platô é atingido no ano 50, utilizamos o último valor da lista de soluções para a comparação.\n",
        "     \n",
        "      return solucao_eq_dif[i]\n",
        "          \n",
        "\n",
        "def Tempo_Temperatura_equilibrio(C0,k) :\n",
        "  \"\"\"\n",
        "  Recebe um valor C0 referente à temperatura inicial em graus Celsius e um valor k referente à regulamentação.\n",
        "  \n",
        "  Retorna um valor correspondente ao momento (em anos) no qual a temperatura de equilíbrio é atingida num intervalo de 50 anos a partir dos valores de temparatura inicial e regulamentação dados.\n",
        "  \"\"\"\n",
        "  t = np.arange(0,50,0.1)\n",
        "  T_eq = Projeto.Euler(t,C0,k).index(Temperatura_equilibrio(C0,k))\n",
        "    \n",
        "  return T_eq*0.1\n",
        "\n",
        "\n",
        "def dicionario_C0_k_Teq(C0_inicial,C0_final,dC,k_inicial,k_final,dk) :\n",
        "  \"\"\"\n",
        "  Recebe um valor C0_inicial referente ao primeiro valor tomado para a temperatura inicial (em graus Celsius), um valor C0_final referente ao último valor \n",
        "  tomado para a temperatura inicial (em graus Celsius) e um valor dC referente ao espaçamento entre os valores tomados para a temperatura inicial. \n",
        "  Recebe também um valor k_inicial referente ao primeiro valor tomado para a regulamentação, um valor k_final referente ao último valor tomado para a regulamentação\n",
        "  e um valor dk referente ao espaçamento entre os valores tomados para a regulamentação.\n",
        "\n",
        "  Retorna um dicionário cujas chaves são os valores de C0 referentes ao intervalo dado e os valores correspondentes a essas chaves são dicionários.\n",
        "  Estes dicionários são aqueles cujas chaves são os valores de k referentes ao intervalo dado e os valores correspondentes a essas chaves são as temperaturas de equilibrio respectivas.\n",
        "  \"\"\"\n",
        "  dic = {}\n",
        "  for Ci in np.arange(C0_inicial,C0_final,dC):\n",
        "    dic[Ci] = {}\n",
        "    for ki in np.arange(k_inicial,k_final,dk) :\n",
        "      dic[Ci][round(ki,2)] =Temperatura_equilibrio(Ci,ki)\n",
        "  return dic\n",
        "\n",
        "def grafico_Teq_x_k(C0_inicial,C0_final,dC,k_inicial,k_final,dk) :\n",
        "  \"\"\" \n",
        "  Recebe um valor C0_inicial referente ao primeiro valor tomado para a temperatura inicial (em graus Celsius), um valor C0_final referente ao último valor \n",
        "  tomado para a temperatura inicial (em graus Celsius) e um valor dC referente ao espaçamento entre os valores tomados para a temperatura inicial. \n",
        "  Recebe também um valor k_inicial referente ao primeiro valor tomado para a regulamentação, um valor k_final referente ao último valor tomado para a regulamentação\n",
        "  e um valor dk referente ao espaçamento entre os valores tomados para a regulamentação.\n",
        "  \n",
        "  Plota o grafico Temperatura de equilibrio x regulamentação para valores em rede dados como entrada. \n",
        "\n",
        "  obs: em alguns casos os graficos podem estar sobrepostos\n",
        "  \"\"\"\n",
        "  dic = dicionario_C0_k_Teq(C0_inicial,C0_final,dC,k_inicial,k_final,dk)\n",
        "  Ci = list(dic.keys())\n",
        "  for j in Ci :\n",
        "    ki = []\n",
        "    T_eq = []\n",
        "    for chave in dic[j].keys() :\n",
        "      ki.append(chave)  \n",
        "    for valor in dic[j].values() :\n",
        "      T_eq.append(valor)\n",
        "    plt.plot(ki,T_eq,label = f'C0 = {j}')\n",
        "\n",
        "\n",
        "  # legendas do gráfico\n",
        "  plt.xlabel('regulamentação')\n",
        "  plt.ylabel('temperatura °C')\n",
        "  plt.legend()      \n",
        "\n",
        "def grafico_dispersão(C0_inicial,C0_final,dC,k_inicial,k_final,dk) :\n",
        "  \"\"\"\n",
        "  Recebe um valor C0_inicial referente ao primeiro valor tomado para a temperatura inicial (em graus Celsius), um valor C0_final referente ao último valor \n",
        "  tomado para a temperatura inicial (em graus Celsius) e um valor dC referente ao espaçamento entre os valores tomados para a temperatura inicial. \n",
        "  Recebe também um valor k_inicial referente ao primeiro valor tomado para a regulamentação, um valor k_final referente ao último valor tomado para a regulamentação\n",
        "  e um valor dk referente ao espaçamento entre os valores tomados para a regulamentação.\n",
        "\n",
        "  Plota o grafico de dispersão dos valores de temperature de equilibrio x regulamentação para cada valor de temperatura inicial \n",
        "\n",
        "  obs:muitos pontos estarão sobrepostos por isso fizemos um certo degrade para podermos identificar um pouco melhor\n",
        "  \"\"\"\n",
        "  from matplotlib import cm\n",
        "  dic = dicionario_C0_k_Teq(C0_inicial,C0_final,dC,k_inicial,k_final,dk)\n",
        " \n",
        "  colors = cm.rainbow(np.linspace(0, 1, len(dic))) \n",
        "  plt.figure(figsize=(15,9))            # formatação para cor\n",
        "  for i, dic_values in enumerate(dic.values()):               # enumera os valores respectivo para cada chave de C0\n",
        "    k = dic_values.keys()\n",
        "    T_eq = dic_values.values()\n",
        "    plt.scatter(k,T_eq,color=colors[i],s= (C0_final*3/dC) - ((C0_final - C0_inicial)/dC)*i)        # S é degrade do grafico \n",
        "\n",
        "  \n",
        "  plt.legend(dic.keys())                                      # as chaves do dicionario principal para suas respectivas cores de acordo com a enumeração\n",
        "  plt.xlabel('K')\n",
        "  plt.ylabel('temperatura Equilibrio (°C)')\n",
        "  plt.show()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "9dy5scX6q5WL",
        "outputId": "0e72a68f-35c4-436e-c9cc-ea6ddce0c05e"
      },
      "source": [
        "grafico_dispersão(20,25,0.5,-2,2,0.1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3UAAAIZCAYAAAABTxa1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzde3zU5Zn//9edTIAAiSRyCpMgglQSTmkSxK2ABBtQdFFJPdS02AqF/Za2KH5baV39aXdFtIti1a5f10OBaq0HFqzNIlaoCttKg8ETaBGIJiGEAMEcCZPM/fvjk2Agk8lMMhOS8H4+Hjwm+Rzuzz2CMFeu675uY61FREREREREuqeIMz0BERERERERaT8FdSIiIiIiIt2YgjoREREREZFuTEGdiIiIiIhIN6agTkREREREpBtTUCciIiIiItKNucI1sDEmCVgDDAEs8KS19hFjzB+ACxsvGwAcs9amhmseIiIiIiIiPVnYgjqgHrjdWvueMSYG2GGMecNae0PTBcaYlcCXYZyDiIiIiIhIjxa2oM5aWwKUNH5daYzZDbiBXQDGGANcD8wI1xxERERERER6unBm6k4yxowAvg682+zwVKDUWrunrfsHDhxoR4wYEZa5iYiIiIiIdHU7duw4bK0d5Otc2IM6Y0x/4BXgVmttRbNT3wZ+7+e+hcBCgOHDh5OXlxfWeYqIiIiIiHRVxpjPWzsX1u6XxpgonIDuOWvtumbHXcBc4A+t3WutfdJam2GtzRg0yGdAKiIiIiIictYLW1DXuGbuaWC3tfah005/E/jEWlsUrueLiIiIiIicDcKZqbsE+C4wwxizs/HX7MZzN+Kn9FJEREREREQCE87ul1sB08q574XruSIiIiIi0jN5PB6Kioo4fvz4mZ5K2PTp04fExESioqICvqdTul+KiIiIiIh0VFFRETExMYwYMQJntVfPYq3lyJEjFBUVcf755wd8X1gbpYiIiIiIiITK8ePHOffcc4MK6OoqoKLIee3qjDGce+65QWcilakTEREREZFuI5CArsEDu1+BrQ9A2UcQ2QsaTsCgcTDlDkjOhsjAqxs7VXsykMrUiYiIiIhIj1FbDk9fDK/+AEp3grcePDXOa+lO5/jTFzvXBauwsJDMzExSUlIYO3YsjzzyCABHjx4lKyuL0aNHk5WVRXm578FXr17N6NGjGT16NKtXr+7I2zyFgjoREREREekRGjyw9ptQ+hF4qnxf46lyzq/9pnN9MFwuFytXrmTXrl387W9/4/HHH2fXrl2sWLGCyy67jD179nDZZZexYsWKFvcePXqUe++9l3fffZft27dz7733thr8BUtBnYiIiIiI9Ai7X4HDn4L3hP/rvCec63avC278hIQE0tLSAIiJiSE5OZni4mI2bNjAzTffDMDNN9/M+vXrW9z7+uuvk5WVRXx8PHFxcWRlZbFx48bgJtAKBXUiIiIiItIjbH0APNWBXeuphm0PtP9ZBQUF5OfnM3nyZEpLS0lISABg6NChlJaWtri+uLiYpKSkk98nJiZSXFzc/gk0o6BORERERES6vboKpylKMA591L6umFVVVWRnZ7Nq1SpiY2NPOWeM6fTtFhTUiYiIiIhIt1dX4XS5DEZEVPBBncfjITs7m5ycHObOnQvAkCFDKCkpAaCkpITBgwe3uM/tdlNYWHjy+6KiItxud3APb4WCunbqTvtdiIiIiIj0dL1jnW0LguH1OPcFylrL/PnzSU5OZunSpSePz5kz52Q3y9WrV3P11Ve3uHfWrFls2rSJ8vJyysvL2bRpE7NmzQpuwq3QPnVBaPDAW2u8rMyvpXh2OREx9XgrXLj/J47bvx7NpfMi2r3fRV2F86t3bHB/sERERERExPkMPWics21BoAaPC+6z97Zt21i7di3jx48nNTUVgOXLl7Ns2TKuv/56nn76ac477zxefPFFAPLy8njiiSd46qmniI+P56677mLSpEkA3H333cTHxwf+cD+MtTYkA4VTRkaGzcvLO6NzqDlq+c6yGmp//inXxX7A1F3v07+8iqq4/ryTMpGXKiYQff+F/G5FX/rGB1ZD2+CBrWssa3JP8MXMYxDvgSNRDH9jAPNm92LKPNNlN0UUEREREelsu3fvJjk5udXzH70Ary4IrFlKVD+Y8zSMuyGEEwwRX+/TGLPDWpvh63pl6gLQ4IHvLKth2Ir/5c63/8DQx/8CO4qhph76urgg3c0Vi6dz34ob+M6yb/DS4/3aDMZqjlpuu+UE+/+//SRdd4jvfraTwUeOcmhUPG/dlMp9ewdz/nXn8/AzvQIOEpso6yciIiIiZ6PkbPjfXzn70Pnb1iCiFwwcA8lzO29u4aSgLgBvrfFS+/NPnYDuxy9hSiqhoTHDWVcPW/Yx9JMy7nwUbvn5uby1NpUZt7S+XLHBA7fdcoKSJz/mp++v47KfvYBpFiR+J93Nmz+7kUefnMttt4zlNy/1bjNIDGdpqIiIiIhIdxAZBd/9s7Ox+OFPfWfsovo5Ad1336DHfD5WUBeAlfm1XHfNBwx9/C+nBnRNGiympJKhj/+F7Bem8FD+hcygX6vjbV1j+eyX+7ln54tMmf+fLYJEs2Uf3/zkUfo83cA9v+zL1rUXcuktrWfrTikNvea00tB7J/Krigk8tji40tDmlPkTERERke4iOg7m/83ZWHzbA862BRFRTlOUwePgkjucDF1PCehAQV2b6iqgeHY5U3e975Rcnh7QNWmwsKOYS3d9wGOXf5O6in6tBkBr/qeOMd8qYsrtL/sNEqf86iXGvHIZa/5tBJfe0sf3Y8NQGto0bjgyfwoQRURERCTcIqOctXLjbjg7Pn8qqGtDXQVExNTTv7zKCZT8qa2nf3klEXH1J//g+Brvk7lHefCTdwIKEr/96TvccW0KdRXDfI4X6tJQCH3mT6WhIiIiInKm9ORgromCujb0jgVvpYuquP7Q1+UESq2JdlEVF4O3wtXqH5y6CqgbdoLhh0oCChLPO1hC3bATrQaJoS4NDXXmT6WhIiIiInImlJRaXt3o5e2/Wo4fhz59YNo/GeZcHkHCkOA/d3ZlCura0DsW3LlxvHPvRC5Id8OWfb6za5EG0t28lTKBxHvi6H1l6+OdqHNRHxsdUJBYH9uXE8d9B4nhKA0NZeavu5WGioiIiEj3Z63l2d972ZBr8Vqob/y4XVMLr22y5L7RwNWzDd//dgTGBBfcFRYWMm/ePEpLSzHGsHDhQpYsWcLRo0e54YYbKCgoYMSIEbz44ovExcW1uD8yMpLx48cDMHz4cF599dUOv18A/3V4AsDtX4/mpYoJHFw8HZsQ4wRwzUUabEIMBxdP55WKCSz9enSrY/WOBftBfw6cPwzS3S3HajYm6W6Kz0+AD/q3GtQFXRoa65SGtmZlfi3XxQaY+Yv9gIfya1sdq0WAuGUfpvw4pq7eed2yj6E/fok73/4DtT//lLfWev2/B5zM33WLq/nVjJ1k3/sSL8Ws5E/l9/FS7Eqy732JX83YyXWLq6k5Gvz+i3UVUFGE3/8+IiIiItK1Pft7L69utJzwfBXQNamvhxMeeHWjE/gFy+VysXLlSnbt2sXf/vY3Hn/8cXbt2sWKFSu47LLL2LNnD5dddhkrVqzweX90dDQ7d+5k586dIQvoQEFdQC6dF0H0/Rdy37QbOPjoddjMkdi4Ptg+Lud1xkgOPnod9027gegVF3Lpd/3/Z513bl+29z6Psh9O9Rsklv1wKn/vPYLvDuzrc5wWpaH+BFgaGmzmr+jy8laDoFAGiHBq5u+Z93/D92/8dy64+gmGXv9bLrj6Cb5/47/zzPu/YdiK/+U7y2po8Pj/T9I05uanvVz5o2omby1i+v4CJr9TxJU/qmbz096AxmiNgkQRERGRzlVSatmQa6mr839dXR1syLWUlAaXCEhISCAtLQ2AmJgYkpOTKS4uZsOGDdx8880A3Hzzzaxfv75d828vlV8GIDIKfreiL99Z9g1u+fm5fOuFKUzb9QH9yyupiovhrZQJvFIxgeifX8jv7u/bZunfkpwo0v74DS6ZtA9+PZdBv2lsmlJbD9EuyHBT9n+mUpA+mqff+Sfeu8n3gKEuDQ1lU5iuXhoK4Vnvp9JQERERkTPn1Y1evAHGaV7rXL/o5sh2PaugoID8/HwmT55MaWkpCQkJAAwdOpTS0lKf9xw/fpyMjAxcLhfLli3jmmuuadezT6egLkB94w0vPd6Pt9am8lD+hTx6+TeJiKvHWxFF4j0D+OnXo7n0scA+sPfqbVg1MYG7917JL9NzOfSb4cQXHabXsRpODOjL0aSB1PTqw92fzWbVxAR69W49oLj969H8qmICVyyeztBPylpmxE4rDf1pG6WhoWoKE+quoRDapjDhWO8XzqYwaggjIiIi0ra3/2pblFy2pr7euX7RzcE/p6qqiuzsbFatWkVs7Kkfzowxra7V+/zzz3G73ezbt48ZM2Ywfvx4Ro0aFfwETqOgLgiRUTDjlghm0I+6in5ffchuJfPlzzdHRxEVPZZF2weRnbKdqV/bSx+vh+MRUbxdMYp1H1zEqosGc2mi/9+iS+dF8NjiC7lvxQ3c+ShfBSfNsn4HfzjdKQ39+YVc+ljrmatQZv7C0TU0lJm/UGf9whEkKusnIiIiEpzjx8N7PYDH4yE7O5ucnBzmzp0LwJAhQygpKSEhIYGSkhIGDx7s81632w3AyJEjmT59Ovn5+QrqzqRQZEwuTXSRNyyBTcVX8vQnJzhcYxnY1/Dtr/XiF9e4iIxoO5sT6tLQUGX+unJpKIR+K4juUBranDJ/IiIi0hP16eN0uQzm+mBYa5k/fz7JycksXbr05PE5c+awevVqli1bxurVq7n66qtb3FteXk7fvn3p3bs3hw8fZtu2bfzsZz8LbgKtUFB3hkVGGK5IiuKKpPanXEJZGhrKzF9XLg0N9Xq/rl4a2jSuMn8iIiLSk037J8NrmwIrwXS5nOuDsW3bNtauXcv48eNJTU0FYPny5Sxbtozrr7+ep59+mvPOO48XX3wRgLy8PJ544gmeeuopdu/ezaJFi4iIiMDr9bJs2TJSUlKCfo8+30tIRpEzLlSloaHM/HXV0tBQZ/26emkoaL2fiIiInB3mXB5B7hsNAV0bYZzrgzFlyhSs9f1Z780332xxLCMjg6eeegqAb3zjG3z44YdBPS9QCup6oI5+uA5V5q8rl4aGer1fVy4N1Xo/EREROVskDDFcPdvw6kb/2xr07g1zLjckDAl+GUtXpKBOfApV5q8rloaGer1fVy8N1Xo/EREROZt8/9sRgJcNuRavPXUDcperKUNnGq/rGRTUSZs6+uG6K5aGhnq9X1ctDQWt9xMREZGzizGGW26K5IrLLK9u9PL2Xy3HjztNUab9k2HO5RE9JkPXREGddKquUhoayvV+0LVLQ7XeT1k/ERGRs1HCEMOimyPbtQ9dd6OgTrqdUGT+Qr3er6uWhmq9n7J+IiIi0vMpqJNurSMZmFCu9+vKpaFa76f1fiIiImejBq/l9eJ6Xtx3giN1lnN7G64f2YtZ7sD2g+5OFNTJWS1U6/2ga5aGar2f1vuJiIicjbYerOd7b9dQ12Cpavax5c0D9fSONPx2Wl+mDA0+FCosLGTevHmUlpZijGHhwoUsWbKEl156iXvuuYfdu3ezfft2MjIyfN6/ceNGlixZQkNDAwsWLGDZsmXtfYunUFAn0igU2ZeuWBqq9X5ovZ+IiMhZZOvBeq7fXE2tj+3qquqhqt5y/eZqXpzRL+jAzuVysXLlStLS0qisrCQ9PZ2srCzGjRvHunXrWLRoUav3NjQ0sHjxYt544w0SExOZNGkSc+bMCckG5ArqRMKkq5SGar1fI633ExER6fEavJbvvV3jM6BrrrYBvvd2DZ9+KyaoUsyEhAQSEhIAiImJITk5meLiYrKystq8d/v27VxwwQWMHDkSgBtvvJENGzYoqBPpyc6GrSC03i906/1EREQEXi+up661zwGnqWuwbCqu54qk9v1EtaCggPz8fCZPnhzQ9cXFxSQlJZ38PjExkXfffbddzz6dgjqRbqAnbgWh9X4dX+/X5FC9h9dqyzlm64kzLq6MjmOwSyk/ERE5+7y478Qpa+j8qaqHP+w/0a6grqqqiuzsbFatWkVs7JlfR6GgTuQsofV+jXrIej+Ao/X13FP5Eanenadk/l5LmcjOiFTuiRlHvEt/zYuIyNnjSF1gWbqT1x8P7noAj8dDdnY2OTk5zJ07N+D73G43hYWFJ78vKirC7XYH/Xxf9K+9yFlI6/2693o/cAK6+6q38PO3fGf+Di6ezr9n3sC/9ssMKrBT1k9ERLqzc3sHt4Th3D7BXW+tZf78+SQnJ7N06dKg7p00aRJ79uxh//79uN1uXnjhBZ5//vmgxmiNgjoRCZrW+zVzBtb7AdxT+ZET0PnJ/P30Ubjn0kH8Oi619YEaKesnIiI9wfUje/HmgfqASjD7u+CG83sFNf62bdtYu3Yt48ePJzXV+fd1+fLl1NXV8eMf/5iysjKuvPJKUlNTef311zlw4AALFiwgNzcXl8vFY489xqxZs2hoaOCWW25h7Nix7XmbLehfaBHpEK336/z1fofqPaR6dwaU+Zs4dQqH6sf6zbaFK+sHzk80S6ilmnr64SKBaIxRIxgREQmPWW4XvSMNVfVtl1X2iTTMdAf379qUKVOw1vfY1157bYtjw4YNIzc39+T3s2fPZvbs2UE9MxAK6kTkjNN6v0YBrPcDeK028MzftF0f8KfU2Xw/ZnCr44U66wdOMLe5fh8HK7dy0cc7T2b+nh+bytCYKcxwjVRwJyIiIRcZ4Wws3to+dU2iI+HZaX2D2s6gK1NQJyJditb7+V/vB3DMBpf5O2Zbvy7UWT9wAro1de8wc+MaZrSS+Vtz+Tzm9Z4aVGCnrJ+IiARiylAXL87ox/ferqGuwZ5Sitnf5WTonp3WN+iNx7uynvNORETo+ev9AAaY4DJ/A0zrf9WHOusHsLl+HzM3rvGb+Zv5KGy+0s1lUaP8jgXK+omISPCmDHXx6bdi2FRczx/2neBIneXcPoYbzu/FTLerx2TomiioE5Eeqyeu9wO4KjqO11ICy/y9nTKBf46Oa3WsUGb9wAnADlZuZUYAmb/Nl07FDvAfkIUr6yciIj1fZIThiqSodm8u3p0oqBMR8aMrrvcb7IpiZ0QqBwPI/L0fkcp8P+WSocz6AZRQy0Uf7wwo83fRx+9Tckktw+jb6nihzvo1p3JOERHpKRTUiYgEqKus9wO4J2Yc/555Az/1k/n7VeYN3NNvnN9xQpn1A6gmiMzf0UqqaP26UGf9mo+rck4RkbNDWX09W2pqqPB6OScigul9+zKoB27R0/PekYhIFxWq9X4A8S4X/9ovk3suHUTq1ClMbZb5eztlAu9HpHJPv7b3lgtl1g+gH0Fk/uJj6Ofnn6FQZ/1A5ZwiImeL8oYG7iorY1ttLRGAx1qijGH5kSNcEh3Nvw0aRFxkZNDjFhYWMm/ePEpLSzHGsHDhQpYsWcJLL73EPffcw+7du9m+fTsZGRk+7x8xYgQxMTFERkbicrnIy8vr4Dt1KKgTETkDOrreD5zA7tdxqRyqH8ufUmdzzNYzwLiYEx3XZvDVXKiyfgAJRPP82NSAMn/bx07kJlpvChPKrF+TcJRzfumtYM+Xm4n76G36lldSExdD+bhpjD5nBudEdPA3WUREglbe0MC3ioo43NBwyr8MDY37y71TU8O3iop4OTEx6MDO5XKxcuVK0tLSqKysJD09naysLMaNG8e6detYtGhRm2Ns2bKFgQMHBvXcNucV0tGaMcYkAWuAIYAFnrTWPtJ47sfAYqAB+JO19mfhmoeISE832BXVZkdKf0KV9QMwxjA0ZkpAmb+h/af4zYaFMusH4Snn/PzEHnrnPkD6aVk/0l+ldPF0js2+g/N6jfY7hoiIhNZdZWUtArrm6oHDjZm8x4YODWrshIQEEhISAIiJiSE5OZni4mKysrI6NukOCmemrh643Vr7njEmBthhjHkDJ8i7Gphora0zxrT/k4iIiIREqLJ+ADNcI1lz+Txm+sn8bbp8HvNcI/2OE8qsH4S+nPNLbwW9cx9gSCtZvyGflFEKfDnnoaAydtZaqmo/48sDb2PLyzBxgzhn2DT6R1+gklARkTaU1dezrba2zdqNemBbTQ1l9fXtXmNXUFBAfn4+kydPDvgeYwwzZ87EGMOiRYtYuHBhu559urAFddbaEqCk8etKY8xuwA38AFhhra1rPHcoXHMQEZHgdDTrB84/WPN6T2XzlW42T5vKRbved8oj42PYPnYiQ/tPYV4AzUhCmfWD0Jdz7vlyM+ltZP2GPP4XdkzfQsaAq/0/s1F9QzWFOx9i0Mt/wf35YWyEwXgt1SOepyB7OkmpS3FF9gtoLBGRs9GWmhr8bwz0lQhj+EtNDdfFBl8qX1VVRXZ2NqtWrSI2iPu3bt2K2+3m0KFDZGVlMWbMGKZNmxb080/XKWvqjDEjgK8D7wK/AqYaY+4DjgP/11r7dx/3LAQWAgwfPrwzpikiIiFijOGyqFHYuJGUXFJLFfX0x8VNQW4bEKqsH4S+nDPuo7cDyvoN+OhtmNJ2UGet5fP8/+D8h9dh9h7F/OMwpsYDfaPo97Wj9P3iMPtv9TIy/W5l7EREWlHh9eKxrfy9fJp6a6nweoN+hsfjITs7m5ycHObOnRvUvW63G4DBgwdz7bXXsn379u4R1Blj+gOvALdaayuMMS4gHrgYmAS8aIwZae2p//WttU8CTwJkZGQE9jsjIiJdijGmzY6Ubd0fiqwfhL6cs295ZUBZv35HK9qcG8Chmg9JfP4NzF/2Y0qrmpVzNmDyiqHwSxIH/5lDydcypN+EgMZUExcROdvERkQQZczJpij+uIwhNiLQvJ7DWsv8+fNJTk5m6dKlQd1bXV2N1+slJiaG6upqNm3axN133x3UGK0Ja1BnjInCCeies9auazxcBKxrDOK2G2O8wECgLJxzERGR7ilUWb9Ql3PWxMUElPWrjg8seDr8xUYG//XzUwO6Jg0WU1pFr79+zuHC1xkypu2gTk1cRORslNm3L/cfORLQtV5rmd43uB88btu2jbVr1zJ+/HhSU1MBWL58OXV1dfz4xz+mrKyMK6+8ktTUVF5//XUOHDjAggULyM3NpbS0lGuvvRaA+vp6brrpJi6//PLg3mArwtn90gBPA7uttQ81O7UeyAS2GGO+BvQCDodrHiIi0jN0NOsHoS3nLB83DdJfbTPrd2xcYGU1sQX74dMj/ss5/3GY2P37YIz/scLVxEVEpKsb5HJxSXQ079TU+F0Z7QIuacdG5FOmTMG2kgVsCtiaGzZsGLm5uQCMHDmS999/P6jnBSqcmbpLgO8CHxpjdjYe+wXwDPCMMeYj4ARw8+mllyIiIuEQynLO0efMoHTxdIb4yfqVLp7O6NjMgObm7RUJNSf8X1Ttwdu77T2VwtHEBVTOKSLdw78NGuRzn7omLmBgZCT/NmhQZ08tbMLZ/XIr0Nq/it8J13NFRET8CVU55zkRsRybfQelwBAfWb/SH06nbvYdDA0w2DkydAjDY/tAWXXrF8X25vCQoZzXxlihbuICKucUke4jLjKSlxMTuausjG21tUTgNEVxGYMXuCQ6mn8bNCjojce7sk7pfikiItLVhKKc87xeo/lyzkPsuHQLAz5+m35HK6iOj+XYuGmMjs0MOKADODFyOvaqDZg1+dDgoxtbZAT2qjF4Rk5vc6xQN3FROaeIdDdxkZE8NnQoZfX1/KWmhgqvl9iICDL79mVgO/el68p63jsSERHpROdExJIRd3XAGa/WpPRJ5f0fzWbie8VEfHwI6psFdq4IvGOH8P6PZpPSZ2KbY4W6iUs4yjlVyikinWGQy9Wufei6GwV1IiIiXUCsiWHgxIXkP2tIfWoTJvcTzJFa7LnR2Nlj2LlgJoMn/IBYE9PmWKFu4hLqck6VcoqIhJaCOhERkS4iKTKRc1L/L39fOZP6W/+X3rXV1EX3w5X0DVJ6TwgooIPQN3EJZTlnuEo5rbVU1X7GlwfexpaXYeIGcc6wafSPvkCbtYucxay1lFBLNfX0w0VCkOunuwsFdSIiIl1IrInh4j6XwOhL2j1GqJu4hLKcMxylnPUN1RTvXMXAl7fg/rwMG2EwXkvNiN/zRXYm7tRbcUX2C2gsEekZrLVs9Zbymi2iAg+RGBqwxBLFVSaRKRFD2hXcFRYWMm/ePEpLSzHGsHDhQpYsWcJPf/pT/vjHP9KrVy9GjRrFs88+y4ABA1rcv3HjRpYsWUJDQwMLFixg2bJloXi7CupERER6olA2cQllOWeoSzmttRTnP0zSwy9h9h7F/OMwpsYDfaPo+7WjJH1RRuGtMDz9Fz3yp/Mi0pK1lme8e8izRzjBqY2nDlPH83Y///BWcEvE6KD/XnC5XKxcuZK0tDQqKytJT08nKyuLrKws7r//flwuF3fccQf3338/DzzwwCn3NjQ0sHjxYt544w0SExOZNGkSc+bMISUlpcPvWUGdiIhIDxWqJi6hLOcMdWfOqtrPSHj+dcxf9mNKq5qVczZg8oqh8EsSBm+kKuUGYvpeENCYItK9bfWW+gzompzAS549wte8sUyNHBrU2AkJCSQkJAAQExNDcnIyxcXFzJw58+Q1F198MS+//HKLe7dv384FF1zAyJEjAbjxxhvZsGGDgjoREREJv1CWc4a6M2fFF5sZ9teCUwO6Jg0WU1pF1F8/p+yLzcSMCSyos9ZSVfExng/XEXG0DG/8IKLGz6V/7Fhl+0S6OGstr9miVgO6Jifw8potYoptXxkmQEFBAfn5+UyePPmU48888ww33HBDi+uLi4tJSko6+X1iYiLvvvtuu559OgV1IiIi0qZQlXOGujNn7/2fwqdH/Jdz/uOwc92Ytserr6+i8rU7GPD466d15lzDscWziLnqAVyu/gHNTUQ6Xwm1VOAJ6NoKPJRQ2649S6uqqsjOzmbVqlXENtsy4b777sPlcpGTkxP0mB2hoE5EREQCEopyzlB35nT1joaaE/4vqvbg6tOnzbGstU5A9+Pf+ezMORet+roAACAASURBVOCT33EMGHD1Y8rYiXRR1dQTSWD/f0ZiqKaNcnAfPB4P2dnZ5OTkMHfu3JPHf/vb3/Laa6/x5ptv+vw7wu12U1hYePL7oqIi3G530M/3RUGdiIiIdJpQd+bsnTARYvtAWXXrF8X2pvfQ1DbHqqr4mAGPv+63M+eAx1+nasYuYmLHBjQ/UDmnSGfqh4sGWsncn6YBS78gwyFrLfPnzyc5OZmlS5eePL5x40YefPBB3nrrLfr29Z35mzRpEnv27GH//v243W5eeOEFnn/++aCe3xoFdSIiItKpQtmZs8+oq7BXJWPWvAcNPtbQREZgr0qmz6ir2hzL8+G6gDpznvjgFZgSWFCnck6RzpVANLFEcZi6Nq+NJYoEooMaf9u2baxdu5bx48eTmur8sGj58uX85Cc/oa6ujqysLMBplvLEE09w4MABFixYQG5uLi6Xi8cee4xZs2bR0NDALbfcwtixgf+AyB8FdSIiItLpQtWZ0/Tqi/cnt2PfW4r5uBTqmwV2rgjs2CHYn9xORK+2P7hFHC0LqDNn5NGygOamck6RzmeM4SqTyPN2v99mKb2I4CqTGPT/e1OmTMHalj/4mT17ts/rhw0bRm5u7inXtXZtRyioExERkW4tcuIN2NVR2CcfgtzdcKQGzu0Ls5Nh4VIiJ8xtexDAGz8ooM6cDfGDAhovHOWcKuUUaduUiCH8w1vR6rYGvYggw5zLlIghZ2B24aGgTkRERLo3YzATs2HVbLj971BbDtFxkHQRRLXdIKVJ1Pi5kL6mzc6cvSZkBzReqMs5VcopEhhjDLdEjOZr3lhes0VU4CESQwOWWKK4yiQyJaL9Wxl0RQrqREREpGeIioaRgW2F4Ev/2LEcWzyLAZ/8rtXOnMcWz2JATGAbBYeynFOlnCLBMcYwNXIoU+wQSqilmnr642Io0T3y/xEFdSIiIiI4HwJjrnrACY6asmHNOnMe+6GTDQv0A2EoyznD1ZlTpKczxrRrH7ruRkGdiIiISCOXqz8Drn6MqsxdnPjwFSKPltEQP4heE7IZEJMS1E/4Q1nOGY7OnF8NXgu73oSjByB+GKRc5mQ9RaTbUFAnIiIi0owxhphzxgYfHJ0mlOWcoe7MCYC1sOX/wf0rYUdRszV6ifDz2yFzEfTAMjWRnkhBnYiIiEgYhLKcM9SdOQEnoLv5DmixRm8vfHIHrAZm/Evg44l0MRW2ko+8u9lr9+OhnihcjDLnMy4imVgT064xCwsLmTdvHqWlpRhjWLhwIUuWLOGnP/0pf/zjH+nVqxejRo3i2WefZcCAAS3uHzFiBDExMURGRuJyucjLy+vo2wTA+NpnoavJyMiwoXrDIiIiIp3J2YagZTln/yDKOSu//Ij+37oG46eU02aOpOqVDYGtqfPUwuwJTgDXWmnojFHwpw+D6iAqEm67d+8mOTm5zesKvcVs8b5DA14sX/0ZNxgiiSAzYipJEe6gn19SUkJJSQlpaWlUVlaSnp7O+vXrKSoqYsaMGbhcLu644w4AHnjggRb3jxgxgry8PAYOHOj3Ob7epzFmh7U2w9f1ytSJiIiIhFEoyjlD3ZmTXW86JZf+1ujlFcEnm2F8EBsla32edAEVtpIt3neop6HFOYulnga2eN/hGnNl0Bm7hIQEEhISAIiJiSE5OZni4mJmzpx58pqLL76Yl19+uWNvIkgRnfo0EREREQnayVLOR7+DzRyJjeuD7eNyXmeM5Nij3wmqMydHDwS0Ro/DRYGNZy1sfsLJ/mVeB1csdl5nT3COd4PKMOk5PvLupsHHpuPNNeDlI+8nHXpOQUEB+fn5TJ48+ZTjzzzzDFdccYXPe4wxzJw5k/T0dJ588skOPb85ZepEREREuoFQduYkflhAa/QYmBjYeFqfJ13IXrv/lJJLXyyWvXY/32BSu55RVVVFdnY2q1atIjY29uTx++67D5fLRU5Ojs/7tm7ditvt5tChQ2RlZTFmzBimTWv//ppNFNSJiIiIdBOh6sxJymVOl0t/a+oyEmHMjLbH8tQ6HTRb2UOPkkpYsRKmfk/r86RTeGgjC33yOk/7xvd4yM7OJicnh7lz5548/tvf/pbXXnuNN998s9Ufsrjdzjq+wYMHc+2117J9+/aQBHUqvxQRERE520RFO9sWJMQ4AVxzkcY5vuz2wIKwYNbnBctTC++/BluedF49tcGPIWedqADzVlFEBT22tZb58+eTnJzM0qVLTx7fuHEjDz74IK+++ip9+/re7Ly6uprKysqTX2/atIlx48YFPQdfFNSJiIiInI0yF8HqB5wul3F9oI/LeZ0xyjmeuSiwcUK9Pg+0Rk86ZJQ5H4P/cmSDYZQ5P+ixt23bxtq1a9m8eTOpqamkpqaSm5vLj370IyorK8nKyiI1NZV/+Ren3PjAgQPMnu00GyotLWXKlClMnDiRiy66iCuvvJLLL788+Dfog8ovRURERM5Gxjjr3KbeDJ9scYKugY0ll8GUSYZ6fR5ojZ50yLiIZD5r2Oez+2WTSCIYFzEm6LGnTJmCry3hmgK30w0bNozc3FwARo4cyfvvvx/0MwOhTJ2IiIjI2Swq2tm2IHOh8xrsurem9Xmnl3E2CWZ9HgS+Rs9zPLh5ylkj1sSQGTEVF5EtMnYGg4tIMiOmtnsD8q5IQZ2IiIiItF8o1+dBeNfoyVkjKcLNNZFXcqEZfXLtXBRRXGhGc03kle3aeLwrU/mliIiIiHRM5iKnJHLFSifgqq13Si4zEp2ALtD1eRCeNXqgjdHPQrEmhm9ETmr3tgXdiYI6EREREemYUK3Pg9Cv0bPWWaN3/0onA1hT74yfnuhkGDMXOfMX6cYU1ImIiIhIaDStz+uIUO6hB2q6ImcFrakTERERka4jlGv01HRFzhIK6kRERESkawnVHnpqunJWs9ZSWbOHLw6+wN6i/+KLgy9QWbPH55YEgSosLCQzM5OUlBTGjh3LI488AsBdd93FhAkTSE1NZebMmRw4cMDn/atXr2b06NGMHj2a1atXt3sepzMdeVOdJSMjw+bl5Z3paYiIiIhIZ/LUdmyN3pYnnY3L/a3P6+OC3MedLR2CnZsar3S63bt3k5yc3OZ19Q3VFBxYjaf+S7z2xMnjEaYXUa5zGDHsZlyR/YJ+fklJCSUlJaSlpVFZWUl6ejrr168nMTGR2NhYAH7961+za9cunnjiiVPuPXr0KBkZGeTl5WGMIT09nR07dhAXFxfQ+zTG7LDWZvialzJ1IiIiItI1dXQPvaamK/4EuzG6tbD5CZg9ATKvc4LGzOuc7zc/4ZyXM8paS8GB1dR5jpwS0AF47QnqPEcoOLCmXRm7hIQE0tLSAIiJiSE5OZni4uKTAR1AdXU1xkfznddff52srCzi4+OJi4sjKyuLjRs3Bj0HX9QoRURERER6plA3XQE1XukGqmo/w1P/JeBt5QovnvpjVNXuJabvBe1+TkFBAfn5+UyePBmAO++8kzVr1nDOOeewZcuWFtcXFxeTlJR08vvExESKi4vb/fzmlKkTERERkZ4p1Bujq/FKt1BesaNFhu50XnuC8or2L++qqqoiOzubVatWnczS3XfffRQWFpKTk8Njjz3W7rHbQ0GdiIiIiPRcoWq6Amq80k146itDel2L+zwesrOzycnJYe7cuS3O5+Tk8Morr7Q47na7KSwsPPl9UVERbre7XXM4ncovRURERKTnCuXG6EcPOJuX+1Nb7zxDzpgoVwzH/SfqTl4XLGst8+fPJzk5maVLl548vmfPHkaPHg3Ahg0bGDNmTIt7Z82axS9+8QvKy8sB2LRpE/fff3/Qc/BFQZ2IiIiI9Hyh2Bi9qfGKv26awTZeaVJxFI4dgQHnQmx8++coxMWmU127328JZoTpRVysz0aSfm3bto21a9cyfvx4UlNTAVi+fDlPP/00n376KREREZx33nknO1/m5eXxxBNP8NRTTxEfH89dd93FpEmTALj77ruJjw/N77W2NBARERERCYSn1uly6a/xyoxR8KcPA88CFn4Gty2ALX+HmhPQtxfMuAge+i9Ian8Tj54qkC0NrLXsLfpP6jxH8N0sJYLeUQMZlfgvPrtUdgXa0kBEREREJBxC3Xil8DO45CJY/zYcrYHj9c7rf7/lHC/8LPTv4SxgjGHEsJvpHTWQCNPrlHMRphe9owYyYti8LhvQtYfKL0VEREREApW5yNm2YMVKpylKbb1TcpmR6AR0wTReuW0BHDjmu5PmgWOw9AfwUsvW+NI2V2Q/RiX+C1W1eymvyMNTX0mUK4a42Az6R4/qUQEdKKgTEREREQlcqBqvVBx1Si79ddLcvB0qyiE2LjRzP8sYY4jpe0GH9qLrLhTUiYiIiIgEq6ONV44dcdbQ+VNzAo4dVlAnbdKaOhERERGRzjbgXKcpij99e8GAgZ0zH+nWFNSJiIiIiHS22HjInNSy4UqTSON0wVSWTgKgoE5ERERE5Ex4+CkYNsB3J81hA5xtDdqr4ih8scd5PZt5amHvW/DxBufVU9uh4QoLC8nMzCQlJYWxY8fyyCOPAHDXXXcxYcIEUlNTmTlzJgcOHPB5f2RkJKmpqaSmpjJnzpwOzaW5sO1TZ4xJAtYAQwALPGmtfcQYcw/wA6Cs8dJfWGtz/Y2lfepEREREpEcq/Mzpcrl5+1f71F02GVY+2b596nr4vneB7FMHgLXw8Xr4YB2YCGjwQGQUWC9MmAtjr3Ga3gSppKSEkpIS0tLSqKysJD09nfXr15OYmEhsbCwAv/71r9m1a9fJDcib69+/P1VVVe16n/72qQtno5R64HZr7XvGmBhghzHmjcZzD1tr/yOMzxYRERER6fqSLnC2Lagod5qiDBjY/pLLpn3vmm+TcLze2ffu3Ytg2/YeEdgF5OP18MF/Q0OzZjT1Dc7rB//tvI67NuhhExISSEhIACAmJobk5GSKi4tJSUk5eU11dXWnb5kQtvJLa22Jtfa9xq8rgd2AO1zPExERERHptmLjYPjojq2hC2Tfu7OBp9bJ0DXU+T7fUOec9xzv0GMKCgrIz89n8uTJANx5550kJSXx3HPP8ctf/tLnPcePHycjI4OLL76Y9evXd+j5zXXKmjpjzAjg68C7jYd+ZIz5wBjzjDHG559cY8xCY0yeMSavrKzM1yUiIiIiIgLB7XvX032x3Sm59MdEQOH2dj+iqqqK7OxsVq1adbLs8r777qOwsJCcnBwee+wxn/d9/vnn5OXl8fzzz3Prrbeyd+/eds+hubAHdcaY/sArwK3W2grgP4FRQCpQAqz0dZ+19klrbYa1NmPQoEHhnqaIiIiISPcVzL53Pd3xY84aOn8aPFDbvgDX4/GQnZ1NTk4Oc+fObXE+JyeHV155xee9brdTuDhy5EimT59Ofn5+u+ZwurAGdcaYKJyA7jlr7ToAa22ptbbBWusF/gu4KJxzEBERERHp8bTv3Vf6DHCaovgTGQXRwZe6WmuZP38+ycnJLF269OTxPXv2nPx6w4YNjBkzpsW95eXl1NU5JaGHDx9m27Ztp6zF64iwBXXGWR34NLDbWvtQs+MJzS67FvgoXHMQERERETkraN+7rwy/yOly6Y/1QlLwuaVt27axdu1aNm/efHJrgtzcXJYtW8a4ceOYMGECmzZtOrnVQV5eHgsWLACcjpYZGRlMnDiRzMxMli1bFrKgLpxbGkwB3gE+BJr+q/4C+DZO6aUFCoBF1toSf2NpSwMRERERkTb46n4JX+171wO6Xwa8pcFH/93Y/dJHs5TI3jDh2nZ1v+wsXWZLA2vtVsDXjwr87kknIiIiIiLtkHSBE7iFct+77mrsNc6rz33qrv3qfA8Rzn3qRERERESkM4Vy37vuzBgnE3fh5VD4d6cpSnScU3IZ1edMzy7kOmVLAxERERER6USh2PfO64XnnoOMDBgyxHl97jnneHcRFQ0jp8HYq53XHhjQgTJ1IiIiIiJyOq8X5s6FP/8ZqqudY4cOwaJF8PLL8MorEKH8UFeh3wkRERERETnV739/akDXpLoa3ngDXnjhzMxLfFJQJyIiIiIip3r44ZYBXZPqanjoId/n5IxQUCciIiIiIqcqLPR/vqioc+YRChVH4Ys9zmsHFRYWkpmZSUpKCmPHjj25H12TlStXYozh8OHDPu9fvXo1o0ePZvTo0axevbrD82miNXUiIiIiInKqpCRnDV1rEhM7by7tVbwPnlkOn+aDKwrqPTAmDb7/c3CPbNeQLpeLlStXkpaWRmVlJenp6WRlZZGSkkJhYSGbNm1i+PDhPu89evQo9957L3l5eRhjSE9PZ86cOcTFdbw7qTJ1IiIiIiJyqttug379fJ/r1w+WLu3c+QSreB/ceRN8tB08J6C22nn98F3nePG+dg2bkJBAWloaADExMSQnJ1NcXAzAbbfdxoMPPogxvrbqhtdff52srCzi4+OJi4sjKyuLjRs3tu/9nUZBnYiIiIiInOrb34ZvfrNlYNevH2RlwY03npl5BeqZ5VBbA9jTTljn+LP3d/gRBQUF5OfnM3nyZDZs2IDb7WbixImtXl9cXExSUtLJ7xMTE08GhB2l8ksRERERETlVRASsW+d0uXzoIWcNXWKik6G78cauvZ1BxVGn5LJFQNfEwifvORu0t3Mfv6qqKrKzs1m1ahUul4vly5ezadOmdk+5o7rw74aIiIiIiJwxERFw002QlwcHDzqvN93UtQM6gGNHnDV0/rii4JjvZiZt8Xg8ZGdnk5OTw9y5c9m7dy/79+9n4sSJjBgxgqKiItLS0jh48OAp97ndbgqbNaApKirC7Xa3aw6n6+K/IyIiIiIiIkEYcK7TFMWfeg8MGBj00NZa5s+fT3JyMksb1xWOHz+eQ4cOUVBQQEFBAYmJibz33nsMHTr0lHtnzZrFpk2bKC8vp7y8nE2bNjFr1qyg5+CLgjoREREREek5YuPhwq8DvhuWgHG6YLaj9HLbtm2sXbuWzZs3k5qaSmpqKrm5ua1en5eXx4IFCwCIj4/nrrvuYtKkSUyaNIm7776b+Pj4oOfgi7G2tVrTriMjI8Pm5eWd6WmIiIiIiMgZtHv3bpKTk9u+sKn7ZYtmKQai+8J9z7d7W4PO4Ot9GmN2WGszfF2vTJ2IiIiIiPQs7pFO4DZ+MkT1guh+zuuEi7t8QNce6n4pIiIiIiI9j3sk/Ot/OV0ujx121tC1s9tlV6egTkREREREeq7YuB4bzDVR+aWIiIiIiEg3pqBORERERESkG1NQJyIiIiIi0o0pqBMREREREQlAYWEhmZmZpKSkMHbsWB555JFTzq9cuRJjDIcPH/Z5f2Rk5Mn97ebMmROyealRioiIiIiI9DxeL/z+9/Dww1BYCElJcNtt8O1vQ0T7clsul4uVK1eSlpZGZWUl6enpZGVlkZKSQmFhIZs2bWL48OGt3h8dHc3OnTvb+45apUydiIiIiIj0LF4vzJ0LixbBjh1w6JDzumgRZGc759shISGBtLQ0AGJiYkhOTqa4uBiA2267jQcffBBjTMjeRqAU1ImIiIiISM/y+9/Dn/8M1dWnHq+uhjfegBde6PAjCgoKyM/PZ/LkyWzYsAG3283EiRP93nP8+HEyMjK4+OKLWb9+fYfn0ETllyIiIiIi0rM8/HDLgK5JdTU89BDcdFO7h6+qqiI7O5tVq1bhcrlYvnw5mzZtavO+zz//HLfbzb59+5gxYwbjx49n1KhR7Z5HE2XqRERERESkZyks9H++qKjdQ3s8HrKzs8nJyWHu3Lns3buX/fv3M3HiREaMGEFRURFpaWkcPHiwxb1utxuAkSNHMn36dPLz89s9j+YU1ImIiIiISM+SlOT/fGJiu4a11jJ//nySk5NZunQpAOPHj+fQoUMUFBRQUFBAYmIi7733HkOHDj3l3vLycurq6gA4fPgw27ZtIyUlpV3zOJ2COhERERER6Vluuw369fN9rl8/aAzIgrVt2zbWrl3L5s2bT25NkJub2+r1eXl5LFiwAIDdu3eTkZHBxIkTyczMZNmyZSEL6oy1NiQDhVNGRobNy8s709MQEREREZEzaPfu3SQnJ7d9YVP3y9ObpfTrB1lZ8Mor7d7WoDP4ep/GmB3W2gxf13fddyIiIiIiItIeERGwbh08+SSkp8OQIc7rk092+YCuPdT9UkREREREep6ICKfDZQe6XHYXPStEFREREREROcsoqBMRERERkW6jO/QE6Yj2vD8FdSIiIiIi0i306dOHI0eO9NjAzlrLkSNH6NOnT1D3aU2diIiIiIh0C4mJiRQVFVFWVnampxI2ffr0ITHIffQU1ImIiIiISLcQFRXF+eeff6an0eWo/FJERERERKQbU1AnIiIiIiLSjSmoExERERER6cYU1ImIiIiIiHRjCupERERERES6MQV1IiIiIiIi3ZiCOhERERERkW5MQZ2IiIiIiEg3pqBORERERESkG1NQJyIiIiIi0o0pqBMREREREenGFNSJiIiIiIh0YwrqRERERESkdRVH4Ys9zqt0Sa4zPQEREREREemCivfBM8vh03xwRUG9B8akwfd/Du6RZ3p20kzYMnXGmCRjzBZjzC5jzMfGmCWnnb/dGGONMQPDNQcREREREWmH4n1w503w0XbwnIDaauf1w3ed48X7zvQMpZlwll/WA7dba1OAi4HFxpgUcAI+YCbwRRifLyIiIiIi7fHMcqitAexpJ6xz/Nn7z8SspBVhC+qstSXW2vcav64EdgPuxtMPAz+j5Z8SERERERE5kyqOOiWXrX5Ut/DJe1BR3pmzEj86pVGKMWYE8HXgXWPM1UCxtfb9Nu5ZaIzJM8bklZWVdcIsRURERESEY0ecNXT+uKLg2OHOmY+0yW+jFGNMInAjMBUYBtQCHwF/Av7HWutt6wHGmP7AK8CtOCWZv8ApvfTLWvsk8CRARkaGMnoiIiIiIp1hwLlOUxR/6j0wQK0xuopWM3XGmGeBZ4ATwAPAt4EfAn8GLge2GmOm+RvcGBOFE9A9Z61dB4wCzgfeN8YUAInAe8aYoR1/KyIiIiIi0mGx8XDh1wHTygXG6YIZG9eZsxI//GXqVlprP/Jx/CNgnTGmFzC8tZuNMQZ4GthtrX0IwFr7ITC42TUFQIa1VrlbEREREZGu4pZfOF0uWzRLMRDd19nWQLoMf2vqSpu6VTZnjEkxxgyy1p6w1n7m5/5LgO8CM4wxOxt/ze7ohEVEREREJMzcI+G+52H8ZIjqBdH9nNcJFzvHtU9dl+IvU/co8Bsfx88F/hW4yd/A1tqttJ6zbbpmRBvzExERERGRM8E9Ev71v5wul8cOO2voVHLZJfkL6i6w1r59+kFr7TvGmP8M45xERERERKSriI1TMNfF+Su/jPFzro0epyIiIiIiItIZ/AV1n/laA2eMuQLYF74piYiIiIiISKD8lV/eCvzJGHM9sKPxWAbwT8BV4Z6YiIiIiIiItK3VTJ21dg8wHngLGNH46y1ggrX2H50xOREREREREfHPX6YOa20d8GwnzUVERERERESC1Gqmzhgz1Rjzh8Zfl3bmpERERERERCQw/jJ1K4FpjV+/A0wK/3REREREREQkGP6Cur8A9wJe4K+dMhsREREREREJSqtBnbX2Z8aYC4FIa+2uTpyTiIiIiIiIBKitRimfdtZEREREREREJHj+GqX80Rjzz8aYKB/nRhpjfmmMuSW80xMRERERERF//GXqfgAsBVYZY44CZUAfnP3q9gKPWWs3hH2GIiIiIiIi0ip/a+oOAj8DfmaMGQEkALXAP6y1NZ0yOxEREREREfHL75q6JtbaAqAgrDMRERERERGRoLW6pk5ERERERES6PgV1IiIiIiIi3VhA5ZfGmF7A1xq//dRa6wnflERERERERCRQbQZ1xpjpwGqcNXUGSDLG3GytfTu8UxMREREREZG2BJKpWwnMbNqI3BjzNeD3QHo4JyYiIiIiIiJtC2RNXVRTQAdgrf0H0GJDchEREREREel8gWTq8owxTwG/a/w+B8gL35REREREREQkUIEEdf8HWAz8pPH7d4DfhG1GIiIiIiIiErA2gzprbR3wUOMvERERERER6UJaDeqMMS9aa683xnwI2NPPW2snhHVmIiIiIiIi0iZ/mbolja9XdcZEREREREREJHitBnXW2hJjTCTwW2ttZifOSURERERERALkd0sDa20D4DXGnNNJ8xEREREREZEgBNL9sgr40BjzBlDddNBa+5PWbxEREREREZHOEEhQt67xl4iIiIiIiHQxgWxpsNoY0wsYg9MF81Nr7Ymwz0xERERERETa1GZQZ4yZDfw/YC9ggPONMYustf8T7smJiIiIiIiIf4GUXz4EZFprPwMwxowC/gQoqBMRERERETnD/Ha/bFTZFNA12gdUhmk+IiIiIiIiEoRWM3XGmLmNX+YZY3KBF3HW1F0H/L0T5iYiIiIiIiJt8Fd++c/Nvi4FLm38ugzoE7YZiYiIiIiISMBaDeqstd/vzImIiIiIiIhI8PyVX/7MWvugMeZRnLLLU2jzcRERERERkTPPX/nl7sbXvM6YiIiIiIiIiATPX/nlHxtfV3fedERERERERCQY/sov/4iPsssm1to5YZmRiIiIiIiIBMxf+eV/dNosREREREREpF38lV++1ZkTERERERERkeD5K7980Vp7vTHmQ3x3v5wQ1pmJiIiIiIhIm/yVXy5pfL2qMyYiIiIiIiIiwfNXflnS+Pp5501HREREREREguEvUweAMaaSr8ovewFRQLW1NjacExMREREREZG2tRnUWWtjmr42xhjgauDicE5KROT/b+/ew+Ouy4T/v++ZpDm0DbRFaElpWc5UfhykyqkC4oGTiIgou09FBGUP5VEXlqO7i+4+LiAL+4D4rLKCyGFFWDywiAc8gRVhLYKoLQgIxZaWQ1tJ2qZpMvP5/TEJDSXJfNPOJJnk/bquiFoCzQAAIABJREFUXJ3Dd2bu9Jtpes/n/ty3JEmSsimb1PWVUkrAtyLiEuDC6oQkSZKk4ZBSYm3HU6xpe5iu7nbq6yYzpeVAJjXtRumzfEm1IEv55fv6XM0Bc4ENGR63E3ATsAOl8s3rUkpXR8Q/U1rtKwIvAqenlJ7fgtglSZK0hboL63j2+a/S1f0KxbQRgA0bYV3HM9TXbcPOO36YuvzEEY5SUha5DMec0OfraKCdUlJWTjdwbkppDqVyzQURMQe4IqW0b0ppf+Bu4B+3KHJJkiRtkZQSzz7/VTq7Vr2a0PUqpo10dq3i2edvolSkJWm0y7Kn7iNb8sQ93TN7O2i2R8QSoDWltLjPYRPpZwaeJEmSqmdtx1N0db9CqXCqP0W6uv/E2o6nmdy823CGJmkLZCm/vGaw+1NKH8/wHDsDBwAP9Vz/LHAa8ArwtgxxSpIkqULWtD38uhW6zRXTRta0LTKpk2pAlvLLRuBNwJM9X/tTGm3wcM/XoCJiEnAn8MmUUhtASulTKaWdgFuBswd43FkRsSgiFr300ktZvhdJkiRl0NXdXtHjJI2sLN0v9wXmpZS6ASLii8DPUkp/Ve6BEVFPKaG7NaX0jX4OuRW4B7hk8ztSStcB1wHMnTvXEk1JkqQKqa+bzIbBF+pePU7S6JdlpW4K0HfQ+KSe2wbVM9PuemBJSumqPrfv3uewE4HHs4UqSZKkSpjSciC5mDDoMbmYwJSWucMUkaStkWWl7jLgkYj4CRDA4cBnMjzuMOBDwG8i4tGe2y4GzoyIPSntzF0KlF3xkyRJUuVMatqN+rpt6OxaRf/NUnLU123LpKZdhzs0SVsgsrSqjYjpwEE9Vx9KKa2salSbmTt3blq0aNFwvqQkSdKYVppTdxNd3X96TdOUXEygvm5bdt7xNOfUSaNIRDycUup3+XzAlbqImJ9SuqXn6q4ppW/3ue/slNK1FY5TkiRJw6QuP5FdZ/4VazueZk3bIrq626mvm8yUlrlMatqV0k4aSbVgsPLLc4DepO7zlDpg9joDMKmTJEmqYRHB5ObdHFsg1bjBGqXEAJf7uy5JkiRJGgGDJXVpgMv9XZckSZIkjYDByi/3iojHKK3K7dpzmZ7ru1Q9MkmSJElSWYMldXsPWxSSJEmSpC0yYFKXUlo6nIFIkiRJkoZusD11kiRJkqRRzqROkiRJkmqYSZ0kSZIk1bDBGqUAEBG7A5cCc4DG3ttTSnbAlCRJkqQRlmWl7ivAvwPdwNuAm4BbqhmUJEmSJCmbLEldU0rpR0CklJamlD4NHF/dsCRJkiRJWZQtvwQ6IyIHPBkRZwPLgUnVDUuSJEmSlEWWlbpPAM3Ax4EDgfnAh6sZlCRJkiQpm0FX6iIiD3wwpfR3wFrgI8MSlSRJkiQpk0FX6lJKBWDeMMUiSZIkSRqiLHvqHomIu4A7gHW9N6aUvlG1qCRJkiRJmWRJ6hqBVcBRfW5LgEmdJEmSJI2wskldSsl9dJIkSZI0SpVN6iLiK5RW5l4jpXRGVSKSJEmSJGWWpfzy7j6XG4GTgOerE44kSZIkaSiylF/e2fd6RHwNWFi1iCRJkiRJmWUZPr653YHtKx2IJEmSJGnosuypa+e1e+pWAhdULSJJkiRJUmZZyi8nD0cgkiRJkqShK1t+GRE/ynKbJEmSJGn4DbhSFxGNQDOwXURMAaLnrhagdRhikyRJkiSVMVj55V8CnwR2BB5mU1LXBlxb5bgkSZIkSRkMmNSllK4Gro6I/51S+vwwxiRJkiRJyihLo5TPR8Q+wBxKw8d7b7+pmoFJkiRJksrLMtLgEuBISkndPcCxlIaPm9RJkiRJ0gjLMnz8/cDbgZUppY8A+wHbVDUqSZIkSVImWZK6jpRSEeiOiBbgRWCn6oYlSZIkScqibPklsCgitgX+g1IXzLXAL6oalSRJkiQpk0GTuogI4NKU0p+AL0bE94CWlNJjwxKdJEmSJGlQgyZ1KaUUEfcA/1/P9WeHIyhJkiRJUjZZ9tT9KiLeXPVIJEmSJElDlmVP3UHA/4qIpcA6ICgt4u1b1cgkSZIkSWVlSeqOrnoUkiRJkqQtUrb8MqW0lNIIg6N6Lq/P8jhJkiRJUvWVTc4i4hLgAuCinpvqgVuqGZQkSZIkKZssK24nAe+htJ+OlNLzwORqBiVJkiRJyiZLUrcxpZSABBARE6sbkiRJkiQpqyxJ3e0R8SVg24j4GPBD4D+qG5YkSZIkKYuy3S9TSv8aEe8E2oA9gH9MKd1b9cgkSZIkSWVlGWkA8BugiVIJ5m+qF44kSZIkaSiydL/8KPA/wPuA9wMPRsQZ1Q5MkiRJklRelpW684ADUkqrACJiGvAAcEM1A5MkqVaklFhBB+voZiJ1zKCJiBjpsCRJ40SWpG4V0N7nenvPbZIkjWspJRYWX+DutIw2usgTFEi0UM+7YybzcjuY3EmSqi5LUvcU8FBEfJvSnroTgcci4hyAlNJV/T0oInYCbgJ26HncdSmlqyPiCuAEYCPwNPCRlNKftvo7kSRpGKWUuKH4JIvSKjZSfM19L9PJf6Zn+H2xjTNyu5vYSZKqKstIg6eBb9Ezpw74NvAMpQHkgw0h7wbOTSnNAQ4GFkTEHOBeYJ+U0r7A74GLtjB2SZJGzMLiC/0mdL02UmRRWsXC4gvDHJkkabzJMtLgM1vyxCmlFcCKnsvtEbEEaE0p/aDPYQ9Sar4iSVLNSClxd1o2YELXayNF7k7LmJcsw5QkVU/ZpC4i5gKfAmb3Pb5npS2TiNgZOAB4aLO7zgC+nvV5JEkaDVbQQRtdmY5to4sVdLAjzVWOSpI0XmXZU3crpQ6Yv4EyH0n2IyImAXcCn0wptfW5/VOUSjRvHeBxZwFnAcyaNWuoLytJUtWso5s82Vbe8gTr6K5yRJKk8SxLUvdSSumuLXnyiKinlNDdmlL6Rp/bTwfeDbw9pZT6e2xK6TrgOoC5c+f2e4wkSSNhInUUyParqUBiYqZft5IkbZksv2UuiYgvAz8COntv7Juk9SdKmweuB5b07ZAZEccA5wNHpJTWb1HUkiSNoBk00UI9L2/6tTigFuqZQdMwRCVJGq+yJHUfAfYC6tlUfpmAQZM64DDgQ8BvIuLRntsuBq4BGoB7ezaNP5hS+qshxi1J0oiJCN4dM/nP9MygzVImkOPdMdMmKZKkqsqS1L05pbTnUJ84pbQQ+t1wcM9Qn0uSpNFmXm4Hfl9sG3CswQRyzI1pzMvtMALRSZLGkyxJ3QMRMSeltLjq0UiSVCMigjNyu7NHsYW70zLa6CJPUCDRQj3vjpnMyznKQJJUfVmSuoOBRyPiGUp76gJIQxlpIEnSWBQRvDU/nXlpB1bQwTq6mUQd02kymZMkDZssSd0xVY9CkqQaFhHOoZMkjZhcuQNSSkuBnYCjei6vz/I4SZIkSVL1lU3OIuIS4ALgop6b6oFbqhmUJEmSJCmbLCtuJwHvAdYBpJSeByZXMyhJkiRJUjZZkrqNKaVEaTYdETGxuiFJkiRJkrLKktTdHhFfAraNiI8BPwS+XN2wJEmSJElZlO1+mVL614h4J9AG7An8Y0rp3qpHJkmSJEkqq2xSFxGXp5QuAO7t5zZJkiRJ0gjKUn75zn5uO7bSgUiSJEmShm7AlbqI+Gvgb4BdIuKxPndNBn5e7cAkSZIkSeUNVn75n8B3gUuBC/vc3p5SWl3VqCRJkiRJmQyY1KWUXgFeAf58+MKRJEmSJA1Flj11kiRJkqRRyqROkiRJkmqYSZ0kSZIk1TCTOkmSJEmqYSZ1kiRJklTDTOokSZIkqYaZ1EmSJElSDTOpkyRJkqQaZlInSZIkSTXMpE6SJEmSaphJnSRJkiTVMJM6SZIkSaphJnWSJEmSVMNM6iRJkiSphpnUSZIkSVINM6mTJEmSpBpmUidJkiRJNcykTpIkSZJqmEmdJEmSJNUwkzpJkiRJqmEmdZIkSZJUw0zqJEmSJKmGmdRJkiRJUg0zqZMkSZKkGmZSJ0mSJEk1zKROkiRJkmqYSZ0kSZIk1TCTOkmSJEmqYSZ1kiRJklTDTOokSZIkqYaZ1EmSJElSDTOpkyRJkqQaZlInSZIkSTWsbqQDkCRtuUIx8f3l3dz+h42s6kxMawg+sMsEjm6tI5+LkQ5PkiQNA5M6SapRC1d2c/r96+ksJNZ2b7r9R89305APbjy8mXnT/WdekqSxzvJLSapBC1d284Efr2NV52sTOoC13bCqM/GBH69j4cru/p9AkiSNGVVL6iJip4j4SUQsjojfRcQnem4/ped6MSLmVuv1JWmsKhQTp9+/no7C4Md1FOD0+9dTKKbhCUySJI2Iaq7UdQPnppTmAAcDCyJiDvBb4H3A/VV8bUkas76/vJvOQrZErbOQ+MFyV+skSRrLqpbUpZRWpJR+1XO5HVgCtKaUlqSUnqjW60rSWHf7Hza+ruRyIGu74evPbKxuQJIkaUQNy566iNgZOAB4aAiPOSsiFkXEopdeeqlaoUlSzVnVObRyylUbLL+UJGksq3pSFxGTgDuBT6aU2rI+LqV0XUppbkpp7hve8IbqBShJNWZaw9BGFUxrdLSBJEljWVWTuoiop5TQ3ZpS+kY1X0uSxosP7DKBSRknFUyqgw/+2YTqBiRJkkZUNbtfBnA9sCSldFW1XkeSxpujW+toyGdbfWvMB+9qdVadJEljWTVX6g4DPgQcFRGP9nwdFxEnRcQy4BDgOxHx/SrGIEljTj5XGizelB/8uKY8fOXwZvI5yy8lSRrLqvbxbUppITDQ/yS+Wa3XlaTxYN70Om4/aiKn37+ezsJrB5BPqiut0H3l8GbmTXeVTpKksW5Yul9KkirvkGl1XNc+iTf9oJHt/pin5aUc2/0xz5t+0MiX2idxyDQTOkmSxgN/40tSDVq/OnHhB4s8vU0ikWe3X2+qxewqwJXfLfKt2xKXfT1H81TLLyVJGstcqZOkGlPoopTQtSSKeUib7a1LeSjm4emWUuJX6BqZOCVJ0vAwqZOkGrPwpsTT2ySKZWotinXw9DaJhTc7fFySpLHMpE6SaszttxXJmqYl4I7bitUMR5IkjTCTOkmqIZ1t8Mem9LqSy4GkPDzXmOhsq25ckiRp5JjUSVIN6WyjbNnl5op1mNRJkjSGmdRJUg1paIFcd/nj+sp1lx4nSZLGJpM6SaohDS2wU0cQhWzHRwFmbQiTOkmSxjCTOkmqMR84NUfWyXMBnHKq/9RLkjSW+ZtekmrMvNOCXV+JsmWYuW7YtS2Y9yGHj0uSNJaZ1ElSjcnXw2Vfz7FrW5Ar8LpSzChArgC7tgeX3ZYjXz8ycUqSpOFhUidJNah5avBv9+Q5/7g8u7QF9R2Q74L6DtilLTj/uDz/9p08zVNdpZMkaawbYmNsSdJoka+HI84IjjgjT2dbaWxBQ4udLiVJGm9M6iRpDDCZkyRp/LL8UpIkSZJqmEmdJEmSJNUwkzpJkiRJqmEmdZIkSZJUw2yUIkkCYNnKIld8v4sfrOpiQz7RWAjeNa2e846uZ+Z0PwOUJGm0MqmTpHEupcTFt2zky50bKOag+IZN993SVeA/797ARxsa+Zf5E4hw7p0kSaONH71K0jh34Y0bua57A90NUKx/7X3FeuhugOu6N3DhjRtHJkBJkjQokzpJGseeW1bkhsIGimXqNop1cENhA88tKw5PYJIkKTOTOkkaxy65qYtixt8ExRx8+uau6gYkSZKGzKROksaxhdH1upLLgRTrS8dLkqTRxaROksapzjbY2JCG9pgJic62KgUkSZK2iEmdJI1TnW0wYcPQullO2BAmdZIkjTImdZI0TjW0wG6/qieXsaIy11U6vqGlunFJkqShcU6dJI1TDS1w1B/rWVTcQJaelrkivP2P2ZO6ZSuL/J8fb+C+hnV0NxSp68xxROdE/v6oRoeZS5JUQf5WlaRx7Ni/zXHofzeS6x78uFw3HPrfjRxzTvlfGyklzvnaeo5Yupz737wS9m2nfq91sG879795JUcsXc45X1tPSkPbzydJkvpnUidJ49jeJ8NxyyYw7xuN1HXyulLMXBfUdcK8bzRy3PIJ7P2+8s95zi0d3LPfizRM6yLykMtD5Hr+zEPDtC7u2e9FzrmlozrflCRJ44zll5I0juXr4bQfBvGOBva5uJ6H53Xx9NwuNjYkJnQGuy6q58CF9cyeleND95aOH8xzy4rctccqGiYUiQE+Nowc5CaUjvvbZY3Mmunni5IkbQ2TOkka55qmwJkPwpJv5Jh1eQMv3t1Arh6KXbD9PnDY52Hv95VP6AAuun0DDSd0DZjQ9YpcacXu4js2cMvfNlfmG5EkaZwyqZMkka+HfT5Y+upsK301tDDkTpeLWteRA7IMSkgJftm6DjCpkyRpa5jUSZJeY0uSOSglgoXmIvmMo+8ioNBYfDWBlCRJW8akTpJUEZ1tkF+fI6XsK3X59blMSZ3jESRJGphJnSSpIhpaYPZ3J7LsTe2Zjo+A2d+bSMNxAx+TUuLc2zr49i6raHhzqTVnfZQSwvtp54il9Zz4k2lceWoTERmXCCVJGmP8eFOSVBENLfDOJY10rq4nlZlmnorQuaqedy1pHHSVzvEIkiSVZ1InSaqYY/82x95XT6O4MTdgYpeKUNxYOm6wYea94xFyGccjPLesTCYpSdIYZVInSaqYvU+GE55oYrd/2p7OVfWkAhQLPYlcAVKhtEK32z9tzwm/bxp0mPlFt2+gYerQxiNIkjQeuadOklQxm4aZN7PfCY38z7EbeO7YdaWumOtzzLpnIm/5XmOmYeaOR5AkKRuTOklSRb12mHkzL36tecjDzKs9HsFumpKkscSkTpJUcVs7zLxa4xHspilJGov8OFKSVFUNLdAyc2gDxnvHI2RNq14dj1DmNeymKUkai0zqJEmjTjXGI9hNU5I0VpnUSZJGpUqORwC7aUqSxi6TOknSqFTJ8QhQ6qaZMr72pm6a2XW2Qduy0p+SJA0nG6VIkkalSo5HqFY3zUIX3HdTkSsf2cBTb+2JbV2O3RZO5NwDGjnitNygcUmSVAkmdZKkUasS4xGgOt00169OzL+wg1+fuYqGw7vIAfmeTprPHNjOx1bXs9+CadxyWRPNU+2kKUmqHpM6SdKotrXjEWBTN81lb2rPdPyr3TSP6//+QhfMv7CDxZ98kYaexiu9aVvvnw3Tulj8yReZf+H23PGFZlfsJElVU7U9dRGxU0T8JCIWR8TvIuITPbdPjYh7I+LJnj+nVCsGSdLYsiXjEXofV8lumvfdVOTXZ2brpPnrM1dx381D76TpHj1JUlbVbJTSDZybUpoDHAwsiIg5wIXAj1JKuwM/6rkuSVJVVbKb5pWPDK2T5lWPZOukWeiCH19f5Piz13HQwmUc+cyzHPSzZRx/9jp+fH2RQlemp5EkjTNVK79MKa0AVvRcbo+IJUArcCJwZM9hXwV+ClxQrTgkSYKebppXNPHf/7Q9S/52FQ1Tu0iUSi1799p1rq5n73+bxglPDdxNs7MNnnrrOnJk35/35GHr6GxrHnSFsbRHbz0vnL+Unf+ijTfkC6QURCS6jlrGPyxvYYcFs7nlsmb36EmSXmNY9tRFxM7AAcBDwA49CR/ASmCHAR5zFnAWwKxZs6ofpCRpTKtUN80t6qTZPHgnzdIevfVsvPQJdp/cQf7VFcDSEIZ8rsDus9bw8qUbmH/hntzxhYnu0ZMkvarqSV1ETALuBD6ZUmqL2PRbMKWUIqLfsUEppeuA6wDmzp2bdbSQJEkDqkQ3zYaWLeukOdgq3X03FXnh/KWbJXSvlc/BdpM7ePL8pdx3814cdUb2HRRb2mBGklQbqprURUQ9pYTu1pTSN3pufiEiZqSUVkTEDODFasYgSVJfW9tNs6EFdvvZRJ4ZQifN3X8+kYZTBz7mykc62Pkv2gZM6F6NPQc7t7Zx1SMdHMXEQY/dNEOvg+XHrSE3uZtiWx2t353CuQc0OUNPksaQqiV1UVqSux5YklK6qs9ddwEfBi7r+fPb1YpBkqTBbOnK1bkHNPKx1fU0TBu8WUpvJ81zDmgc8JjONlh+3BrekC9keu36fJFlx6yhs21imRl67s+TpPGimt0vDwM+BBwVEY/2fB1HKZl7Z0Q8Cbyj57okSTXjiNNy7Hd9tk6a+90wjSM+NPCv2842yE3uJqVsyVUCci3dA446eM3+vFlraKwvkM9BXT6Rz0FjfWl/3sZLn2D+hevtqClJY0DVkrqU0sKUUqSU9k0p7d/zdU9KaVVK6e0ppd1TSu9IKa2uVgySJFVDvh5uuayJOf93ezpX1ZMKUCz0JHIFSIXSCt2cq7fnlkubyu7RK7bXMcAW89cJoNhWN+gMvRfOX8p2GfbnvXD+UmfoSdIYMCzdLyVJGmuapwZ3fKGZ+25u5KpHNvDkYZs6ae7+84mcc0AjR1xbft9aQwu03jOFrrctI58rX4LZVcgx83tTaDi+//ursT8P3KMnSaOZSZ0kSVsoXw9HnZHjKJrpbGve1HRlkKYo/Tn3gCb+YXkLu89aM2gyVijCs8tb+OcDmvq9vxr788A9epI02lVzT50kSeNGQwu0zNyyxitHnJZjh8/N5uX2JgoDVEMWivByexM7XDF7wD16ld6fB+7Rk6RaYFInSdIIK+3Ra2bCRXvy5HNT2NCVp1AMuotBoRhs6Mrz5HNTmHDxntxyafOAZY6V3p8H1d+j5/48Sdp6ll9KkjQKlPboTeS+m/fiqkc6WHbMGnIt3RTb6pn5vW355wOayu7Rq/T+PHCGniTVApM6SZJGiU179CbS2TZx0x69QZKuzVVqfx44Q0+SaoXll5IkjUJbukevUvvzoDZn6FnOKWk8MqmTJGkMqdT+PKidGXqFLvjx9UWOP3sdBy1cxpHPPMtBP1vG8Wev48fXF23eImnMs/xSkqQxphL786A2Zuj1lnOuv/j3HDJ/GTNy7eRJFAhWvGMyn1sxk2sX7GE5p6QxzaROkqQxqBL782B0z9DrLeeccsXDHNu8jIj0aglSHYnWfBsnty7hwSvamX/egdzxhYk2YJE0Jll+KUnSGDdWZ+jdd1OR9Rf/noObl5Hvk9D1ygH5SBzcvIz1F//ecQuSxiyTOkmSNKDRPEPvykc6OGTGsrLPGZE4ZMYyrnqko+zruj9PUi2y/FKSJA1qNM7Q6y3nnJFrL/sJdQ6YkWvPPG7B/XmSao1JnSRJKms0ztDLTe4mT7aVvzzp1XLO/pK64dif19nGpr+3LSiFlaSBWH4pSZKGZDTM0Ost5yyQbcWsQJQdt1CN/XmWc0oaDq7USZKkYdG7P2/+hXvy5PlL2bm1jfp8kURpD11XIcezy1vY4YrZmWbotd4zhRVvn0xrvm3QT6mLwIri5LLjFg6ZP5T9eXs4bkHSqGFSJ0mShk2lZuhBqZzzcytmcnLrEhgkGUsp+MWKmZxfZtxCJffnVbuc01JOSX2Z1EmSpGFVqRl6R5yW49oFe/DgFe0cvFniBKUVupSCB9fPpPnSPTji2sHHLVRqfx5sKuc8tqecc3M5gJ5yzjsvnsx9N+/HUWcMnlIWukrPe+UjHSw/bg25yd0U2+po/e4Uzj2giSNOy5YMSxp7TOokSdKI2ZqVplfLOc87kDsvnswhMzYrcSxO5hcrSgldlnELBYK6DIlduf15UPlyzmqXcrryJ9U2kzpJklSzNpVz7sdVj+zxunLO84cwbqFS+/MqXc5ZrVJOV/6kscOkTpIk1bRKjVuoxP48qHw5ZzVKOW3iIo0tjjSQJEljxtaMW2j+lz14cP1MCinYfGBBESj03Z83jOMWrnykg0NmDKWUs2Pw1+uz8ndy6xJa823URSIC6mLTyt+UKx5m/oXrhzx2obMN2paV/pQ0PEzqJEnSuNe7P2/NeQdy5/K9WV5ooTsFKUF3CpYXWrhz+d6sOf/A7OMWipNflxxu7jXlnP0kdVteyjnwcdWYyec8PmlkWX4pSZJEZfbn9arkuIVKd+asVhOXjoue4L3vX8ze61bSUOimM1/HknnTuWL1HK5dsKelnFIVmdRJkiT1GG3jFirdmbNaTVx2vPwXnNb+SxpXd5FPpTgndXVySOcfOCD/R266fDXzLzhkyPP4emO2M6c0OMsvJUmS+rGl+/OgcuWclSzlhK1b+evPfTcV6bjoCU5r/yVN3RtfTehefXxKNHVv5LS1v6TjoicylXKC5ZzSULlSJ0mSVAWVKuesZGfOSq/8XflIB+99/2IaV3cNuFKQAxq7uzhx6mKuemTPQUs5obrlnK76aawyqZMkSaqSSpRzVqqUEyo7k6+3lHPvdStft0K3uXxKzFm3ki8OUsoJ1SnndB6fxgPLLyVJkobBlpZzVrIzJ5RW/n6xYiYpDb7K1bvyd06ZJi4Nhe5M30dDoXvQUk6ofDnn+tWJUxas44qjHuU9n/0m1+x/G/++821cc8BtvOez3+SKox7llAXrWL86WzmqNFq5UidJkjTKVbIzZ6WbuHTm65jU1Vn2dTvzdYOWckJlyzmr3cQlpcTStR38aX032zbXMXtSExF299TIMKmTJEmqAZXqzNm78jf/vAO58+LJHDJjGTNy7eRJFAhWFCfzixWlhC5LE5clh03nkM4/DFqCWYhg8cTpA5ZyQuXLOTdf9ds8Sey76nfJRVO57+b9OeqM8kVs3V2JGx5Yyc/3WkZs00WxJchFIr1Yz2GPz+SMQ6dTV29yp+FlUidJklRjtrbRRyWbuFyxeg4H5P/Yb+IEpZW/DXX1fHv1HM4bpInL1pRz9vd3UY0mLutWF/nL3z3BpINeYF7XH9lzzQuvNnF5YtIOPHzQOu5/8E986Y17MnHq0HY52cRFW8OkTpIkaRyqXBOXPbnp8tWctvaXNHZ3vWaVrRDBhrp6bpr0Zpou3LNsE5dKlXNWq4nLmd9exp+d+gdOeumx13yvk+jkoA3PsF8Xfgd3AAASL0lEQVTdMr45dz/OvG0it86fZRMXDRsbpUiSJI1zW9vE5fkLDuGS7qN5YOourGqcyNr6BlY1TuSBqbtwSffRPH/hIWWbuLxazjlxOoUye9NeU845QFJX6SYuP72pwMRTnuWklx+jqWuAJi5dGznp5V8z8ZRn+enNhUFfs28Tl5M/cwe37XAt38pdzW3Tr+Xkz9yx1U1cOtugbRmDfk8aO1ypkyRJ0hbbVMq5P1c9sidf3KyU87whNHGpVDlnNZq4XP38K5xQXEpjd/lyzoOKz3HN8n14O1P7Pe7VJi6XPcAFi79D6wMrSUCumCjmgj0ef5Z3zn6Uyy87nvkXHpq5iYsrf+OXSZ0kSZK2SqWauFSqnLMaTVyeP2Qte659IVM5515rV3LdwWvpbJs6aBOXCxZ/h9alK8kVNz1nvudy69KVXMB3OOuiaZmauPQd2n7Kex/j0Gcfp3lDJ+unN/DAZ/biirZ9Hdo+hpnUSZIkqWK25j/+r3bmvOAQLrloKidOXcycdStfbUayeOJ0vr16Dk0X7lm2nLPSTVw25mJI5ZwbczFoE5dT3vsYrQ+8NqHrK1dMtC5dycl7P1a2iUs1Vv5c9astJnWSJEkaNSpVzlnpJi6dLzXQuVf2cs7OlxpoeHM/9/U0cTn02ccpt1suAfOefZxryzRxqfTKXzVX/cCVv2owqZMkSdKoUolyzkqu+jW0wG4/m8Qjb2/lbZ1Pli3n/FXDTHb/+SQaTn39/b1NXJo3dA64Stcriql03CCjG6CyK3/u96tNJnWSJEkatbZmNaeyTVwaOf+PB3DItGfLlnPe8tz+fO6AxgG/n2J7Het3aKCYi1dX0vqTcsH6xgaKKwdu4lLplb/h2O/31sW/ZtKataydMomffWY/9/tVgEmdJEmSxqxKNnGZtWAWn7voSM6v/+mA5Zyf6z6SWV+cVbaJywOf2Ys9Hn920NcMYOHOezHzxsGbuFRy5a+a+/0+df/Xmf6Fn8LDy2F9NzTXsduBrRy74Eg+e9kHh7Tf77f/lfh/3+/kfw5tp2ubIvWv5HjLA5P5m6Mb2Of9Me5W/UzqJEmSNC5sfROXJuZfuB9/89dT+cCMxziouJTGQjcb8nU8lJvN7Sv2ZdYXd+KWS5vKN3Fp25d3zn70dathvQq54PnZ07mzbd9Bm7hUcuWvmvv9PnX/15n+v+8gVrRDoefZO7vhJ39g+uMv8anPwxkZVv461sDFCzpZ+HdLOfhdS/lEWkpL21raWibx8Mmz+ejS2cz78Gz+5QsNNE0p8030o1ZX/kzqJEmSpAxK5ZzN3Hfz7lx1w05cf9g6Cs1F8utz7P7ziXzugMYhNXG5/LLjuYBSmWOitJKWckEAz8+ezuVzjqfpovJNXCq18lfN/X7Tv/DT1yZ0vQqJWNHO9C/8lJNvm1d2v99FCzbwxL8s5u+K93PMR2+g5b6noKMLmuo56cjd+d6/foQb/+VwLlowhyu/2jik/X5XL9zIMwethW264ZU6/uyhSXxi3oSa2O9nUidJkiRltKmcs5nOtuZNqzr9NEUZ7DluuayZ+RceylkXTeP9ez/GYb0dJhsbWLjzXtzZti9NF5Vv4gKVW/mr1n6/ty7+dankcvOE7tXgEjy8nCMWP8a1x7xjwJW/3/5XYuF5S7m468ecdNQ/w4p2ovc5N3TTctdv+MCiS6j70T9w6XkT+e2de7DfqYPv0Vu/OnH6xzt56ew/MP89/8Nhz206Dz8/eS/++em38MWP7MKN1zRsUafP4WJSJ0mSJG2BSjZx+XxvE5eV9cy8cWhNXCq18let/X6T1qwt7aEbTEc3k9a0k5sy8Mrf//3hBo46/mnefeqXX5vQ9cZUSKTn2znhk1/moa+9kas/P4sbTh24dLXQBad/vJOpn/0FVy79Fq2XPETc8wSs7oCpTexx3J6869RH+D+ffS+nf/wQvvaVbCt/I8GkTpIkSRoBlWriUsmVv2rs91s7ZRI015X20A2kqY61UyZTbBt4v9//HNnO5asfp/7Bpa9L6HpFIVH/i6Uc/qclXHDkgXS2NQ263+/ljz/NlUu/xcyP30787kXoLpbubO8k/uOXzHzgOf7+Gvjwx7fnvpv3Ltvpc6SMzqgkSZKkcaShBVpmbv3K33k/2Z9vfvoUTl15Nu8tfoJTV/5vvvXpUzjvJ/tzx7UTy5YQHnFajqZL9+TyOcezfPZ0irmgkAuKlJK5Yk9Cd/mc42m6bE+O+FD5/X4/m7MfHNgK+QFeOx9wYCv3zdmXmd+bMmBSl9+xkzc+/lSmVb83Ln6afGsnnW0DH3b1wo3M3+WXtN74c+K3fRK6Xt1F4rcv0Hrjz/mLXX7JNQs3Dv66I8iVOkmSJGkMqOjQ9grv9zt2wZFMf/yl1zdLyQdpxmRWLjiy7H6/CSlRl49Mq351daXjB9vv98zhr3Do0seJux+HQrH/AwtF4u7Hmbfgca556yt0tjWOyq6YJnWSJEnSGDPa9vt99rIP8qnPs2lOXUc3NNXB3FZW/s2RfPbwD5bd7zf54Ym88o5tmXlgK/zkD/03XulZ9XtlyrZM/tFEGt7Rf1ydbZDfeQOTVrdD24bBv4m2TiataSe/84ZBO32OpKoldRFxA/Bu4MWU0j49t+0HfBGYBDwL/K+U0iCLopIkSZKGWzX2+51x0TTef9s8Dl/8GJPWtLN2ymTum7Nv5pW/t73Swnd22p85J/2OePwl6GfVjxmTSSftw9077c9RrwycfTW0QOrKsbEINE+Azo6BX3hiPRu7IRVzozKhg+qu1N0IXAvc1Oe2LwN/l1K6LyLOAM4D/qGKMUiSJEnaCls7iPv1K3/vIDelm2JbPTM/nX3l78wFdXzsh2/hA/s+wOyLIPfN37521e/AVgon7cNz++7C/T98C//xNwOnOg0tsO3jzTz4njfyZ3tOg18OMHIhH7DHdvx87zcy5a5mGt615X8P1VS1pC6ldH9E7LzZzXsA9/dcvhf4PiZ1kiRJ0phWiZW/1tbgxA2tXJL/EJ/e9xZ23qaR9Of7Ex1dpKZ6oqGOpbN35NP5+Zy4oZXW1sGbwnxyymSub3wz7ztkZxqee4V4Ye3r9/vtMInOQ2dze+Ob+cSUyVv43VffcO+p+x1wIvAt4BRgp2F+fUmSJEkjaGtW/s76SJ78Lftywdpz2O/4hzn2xV/R0rmetoZmvrv9m/j1dw7k6EnTOfMj+bLPdcKpE7j0Bzvyrx/7Sy5+8Qp4ajXx+5dhfRc015P22I6021T+9aN/yUuLduSED07YsqCHQaQ08KT4rX7y0krd3X321O0FXANMA+4CPp5SmjbAY88CzgKYNWvWgUuXLq1anJIkSZJqx4oXEt/+XoGFT3fQWd9NQ1cd83Zt4sRj8szYYfAVur6ef7mbY59bxqF1j/H3d36JGU8ug1xAMfH87jvx2ZPP4oHCvnx3p5nsuN3I9piMiIdTSnP7vW84k7rN7tsDuCWl9JZyzzN37ty0aNGiygcoSZIkaVx7eUOBsx97kd9uu54dNq5m+/VreKF5Ki9OmMI+f2rm2n23Z7vG8it/1TZYUjes6WZEbJ9SejEicsDfU+qEKUmSJEkjYrvGPLe9ZQYvdXfzw9VvYE1HkSlNOd45tZnt6mpjAlw1Rxp8DTgS2C4ilgGXAJMiYkHPId8AvlKt15ckSZKkrN5QV8efbz9KZxaUUc3ul38+wF1XV+s1JUmSJGm86X9kuyRJkiSpJpjUSZIkSVINM6mTJEmSpBpmUidJkiRJNcykTpIkSZJqmEmdJEmSJNUwkzpJkiRJqmEmdZIkSZJUw0zqJEmSJKmGmdRJkiRJUg0zqZMkSZKkGmZSJ0mSJEk1zKROkiRJkmpYpJRGOoayIuIlYOlIx9GP7YCXRzoIeR5GAc/B6OB5GHmeg9HB8zDyPAejg+dh5FXyHMxOKb2hvztqIqkbrSJiUUpp7kjHMd55Hkae52B08DyMPM/B6OB5GHmeg9HB8zDyhuscWH4pSZIkSTXMpE6SJEmSaphJ3da5bqQDEOB5GA08B6OD52HkeQ5GB8/DyPMcjA6eh5E3LOfAPXWSJEmSVMNcqZMkSZKkGmZSNwQRcUVEPB4Rj0XENyNi2wGOOyYinoiIpyLiwuGOcyyLiFMi4ncRUYyIATsJRcSzEfGbiHg0IhYNZ4zjwRDOg++FKoqIqRFxb0Q82fPnlAGOK/S8Fx6NiLuGO86xqNzPdkQ0RMTXe+5/KCJ2Hv4ox7YM5+D0iHipz8/+R0cizrEsIm6IiBcj4rcD3B8RcU3POXosIt403DGOBxnOw5ER8Uqf98I/DneMY11E7BQRP4mIxT3/P/pEP8dU9f1gUjc09wL7pJT2BX4PXLT5ARGRB74AHAvMAf48IuYMa5Rj22+B9wH3Zzj2bSml/W3lWxVlz4PvhWFxIfCjlNLuwI96rveno+e9sH9K6T3DF97YlPFn+0xgTUppN+DfgMuHN8qxbQj/vny9z8/+l4c1yPHhRuCYQe4/Fti95+ss4N+HIabx6EYGPw8AP+vzXvinYYhpvOkGzk0pzQEOBhb0829SVd8PJnVDkFL6QUqpu+fqg8DMfg57C/BUSukPKaWNwG3AicMV41iXUlqSUnpipOMY7zKeB98L1Xci8NWey18F3juCsYwnWX62+56b/wLeHhExjDGOdf77MgqklO4HVg9yyInATankQWDbiJgxPNGNHxnOg6ospbQipfSrnsvtwBKgdbPDqvp+MKnbcmcA3+3n9lbgj32uL+P1J1XVl4AfRMTDEXHWSAczTvleqL4dUkorei6vBHYY4LjGiFgUEQ9GhInf1svys/3qMT0fBr4CTBuW6MaHrP++nNxT5vRfEbHT8ISmPvw9MHocEhG/jojvRsQbRzqYsayn3P4A4KHN7qrq+6GuUk80VkTED4Hp/dz1qZTSt3uO+RSlZdZbhzO28SLLOchgXkppeURsD9wbEY/3fJKljCp0HrSVBjsPfa+klFJEDNTOeHbP+2EX4McR8ZuU0tOVjlUaZf4b+FpKqTMi/pLSyulRIxyTNBJ+Ren3wNqIOA74FqUSQFVYREwC7gQ+mVJqG87XNqnbTErpHYPdHxGnA+8G3p76nwexHOj7aeDMntuUUblzkPE5lvf8+WJEfJNSqY5J3RBU4Dz4XqiAwc5DRLwQETNSSit6SjheHOA5et8Pf4iIn1L6BNGkbstl+dnuPWZZRNQB2wCrhie8caHsOUgp9f37/jLwuWGIS6/l74FRoG9ykVK6JyL+X0Rsl1J6eSTjGmsiop5SQndrSukb/RxS1feD5ZdDEBHHAOcD70kprR/gsF8Cu0fEn0XEBOBUwG5zwygiJkbE5N7LwLsoNfbQ8PK9UH13AR/uufxh4HUrqBExJSIaei5vBxwGLB62CMemLD/bfc/N+4EfD/BBoLZM2XOw2V6V91Da46LhdRdwWk/Xv4OBV/qUjGuYRMT03j29EfEWSv//90OmCur5+70eWJJSumqAw6r6fnClbmiuBRoolfMBPJhS+quI2BH4ckrpuJRSd0ScDXwfyAM3pJR+N3Ihjy0RcRLweeANwHci4tGU0tF9zwGlfUXf7DlHdcB/ppS+N2JBj0FZzoPvhWFxGXB7RJwJLAU+ABClMRN/lVL6KLA38KWIKFL6RX5ZSsmkbisM9LMdEf8ELEop3UXpl/vNEfEUpQYGp45cxGNPxnPw8Yh4D6XtEquB00cs4DEqIr4GHAlsFxHLgEuAeoCU0heBe4DjgKeA9cBHRibSsS3DeXg/8NcR0Q10AKf6IVPFHQZ8CPhNRDzac9vFwCwYnvdDeE4lSZIkqXZZfilJkiRJNcykTpIkSZJqmEmdJEmSJNUwkzpJkiRJqmEmdZIkSZJUw0zqJEkaQESs7XP5uIj4fUTMHsmYJEnanHPqJEkqIyLeDlwDHJ1SWjrS8UiS1JdJnSRJg4iIw4H/AI5LKT090vFIkrQ5h49LkjSAiOgC2oEjU0qPjXQ8kiT1xz11kiQNrAt4ADhzpAORJGkgJnWSJA2sCHwAeEtEXDzSwUiS1B/31EmSNIiU0vqIOB74WUS8kFK6fqRjkiSpL5M6SZLKSCmtjohjgPsj4qWU0l0jHZMkSb1slCJJkiRJNcw9dZIkSZJUw0zqJEmSJKmGmdRJkiRJUg0zqZMkSZKkGmZSJ0mSJEk1zKROkiRJkmqYSZ0kSZIk1TCTOkmSJEmqYf8/yZB8JuYD+IYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HoIFM2xtfVfQ"
      },
      "source": [
        "3.1)O que acontece com a temperatura de estabilização se a desregularização $k$ (negativo) for menor que $\\sim -0.6$ ?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "IQGUFy6qeOQL",
        "outputId": "2cb211c2-11a3-406b-d1f4-59237a8bef4c"
      },
      "source": [
        "grafico_temperatura_tempo(20,23,1,-0.6,-1.0,-0.1)\n",
        "\n",
        "for i in dicionario_C0_k_Teq(20,23,1,-0.6,-1.0,-0.1).keys() :\n",
        "  j = dicionario_C0_k_Teq(20,23,1,-0.6,-1.0,-0.1)[i] \n",
        "  for z in j.keys() :\n",
        "    print(f'para temperatura inicial = {i} e para k = {z}, temos que temperatura de equilibrio é igual a : {j[z]}')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "para temperatura inicial = 20 e para k = -0.6, temos que temperatura de equilibrio é igual a : 26.2206857171979\n",
            "para temperatura inicial = 20 e para k = -0.7, temos que temperatura de equilibrio é igual a : 26.254116809527755\n",
            "para temperatura inicial = 20 e para k = -0.8, temos que temperatura de equilibrio é igual a : 26.286169620588545\n",
            "para temperatura inicial = 20 e para k = -0.9, temos que temperatura de equilibrio é igual a : 26.31891120349182\n",
            "para temperatura inicial = 21 e para k = -0.6, temos que temperatura de equilibrio é igual a : 26.218146464889863\n",
            "para temperatura inicial = 21 e para k = -0.7, temos que temperatura de equilibrio é igual a : 26.253984535976542\n",
            "para temperatura inicial = 21 e para k = -0.8, temos que temperatura de equilibrio é igual a : 26.287775076549853\n",
            "para temperatura inicial = 21 e para k = -0.9, temos que temperatura de equilibrio é igual a : 26.319969053944355\n",
            "para temperatura inicial = 22 e para k = -0.6, temos que temperatura de equilibrio é igual a : 26.220302537241984\n",
            "para temperatura inicial = 22 e para k = -0.7, temos que temperatura de equilibrio é igual a : 26.254475704363166\n",
            "para temperatura inicial = 22 e para k = -0.8, temos que temperatura de equilibrio é igual a : 26.286994114379674\n",
            "para temperatura inicial = 22 e para k = -0.9, temos que temperatura de equilibrio é igual a : 26.32106674733223\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "K8NEzILDfGOg",
        "outputId": "cd37051c-140a-4bb3-9be1-e21134e0a375"
      },
      "source": [
        "grafico_Teq_x_k(20,23,1,-0.6,-1.0,-0.1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "VUhE3iB-pTHs",
        "outputId": "932ce572-0ed1-4949-d665-f2804b34217e"
      },
      "source": [
        "grafico_dispersão(20,23,1,-0.6,-1.0,-0.1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NK0qP7EfnjdF"
      },
      "source": [
        "R: A partir da análise dos gráficos acima podemos concluir que a temperatura de equilíbrio fica estável em torno da temperatura 26.2ºC e 26.3ºC para qualquer valor de temperatura inicial, variando apenas 0.03ºC aproximadamente de acordo com a variação de k."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "foVI57r7fb4C"
      },
      "source": [
        "3.2) O que acontece com a temperatura de estabilização se a regularização $k$ (positivo) for maior que $\\sim 1.8 $ ?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "29aDlwwgehcr",
        "outputId": "3237b0b5-0598-4694-d590-3d377ca49fe2"
      },
      "source": [
        "grafico_temperatura_tempo(20,23,1,1.8,2.1,0.1)\n",
        "\n",
        "for i in dicionario_C0_k_Teq(20,23,1,1.8,2.1,0.1).keys() :\n",
        "  j = dicionario_C0_k_Teq(20,23,1,1.8,2.1,0.1)[i] \n",
        "  for z in j.keys() :\n",
        "    print(f'para temperatura inicial = {i} e para k = {z}, temos que temperatura de equilibrio é igual a : {j[z]}')\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "para temperatura inicial = 20 e para k = 1.8, temos que temperatura de equilibrio é igual a : 19.109575306369063\n",
            "para temperatura inicial = 20 e para k = 1.9, temos que temperatura de equilibrio é igual a : 19.074436628756523\n",
            "para temperatura inicial = 20 e para k = 2.0, temos que temperatura de equilibrio é igual a : 19.040204617899434\n",
            "para temperatura inicial = 20 e para k = 2.1, temos que temperatura de equilibrio é igual a : 19.009875901661967\n",
            "para temperatura inicial = 21 e para k = 1.8, temos que temperatura de equilibrio é igual a : 19.108250946933385\n",
            "para temperatura inicial = 21 e para k = 1.9, temos que temperatura de equilibrio é igual a : 19.075497772425987\n",
            "para temperatura inicial = 21 e para k = 2.0, temos que temperatura de equilibrio é igual a : 19.04064317592387\n",
            "para temperatura inicial = 21 e para k = 2.1, temos que temperatura de equilibrio é igual a : 19.00982840390089\n",
            "para temperatura inicial = 22 e para k = 1.8, temos que temperatura de equilibrio é igual a : 19.107973409259028\n",
            "para temperatura inicial = 22 e para k = 1.9, temos que temperatura de equilibrio é igual a : 19.074474523145735\n",
            "para temperatura inicial = 22 e para k = 2.0, temos que temperatura de equilibrio é igual a : 19.042105764127594\n",
            "para temperatura inicial = 22 e para k = 2.1, temos que temperatura de equilibrio é igual a : 19.007533473736526\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "nFbSVUeVhRaQ",
        "outputId": "78ac0b72-bce8-44a5-915d-e96519ce3a73"
      },
      "source": [
        "grafico_Teq_x_k(20,23,1,1.8,2.1,0.1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "fSrWOVLEpL2r",
        "outputId": "1563d0d0-f586-41c2-d03f-e6e986ebc46a"
      },
      "source": [
        "grafico_dispersão(20,23,1,1.8,2.1,0.1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ca9nVBZJl6qt"
      },
      "source": [
        "R: A partir da análise dos gráficos acima podemos concluir que a temperatura de equilíbrio fica estável em torno da temperatura 19ºC e 19.1ºC para qualquer valor de temperatura inicial, variando apenas 0.03ºC aproximadamente de acordo com a variação de k."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FDd9JFrgfnhx"
      },
      "source": [
        "3.3) Se $k$ for mantido em 0, o que acontece com a temperatura final para um valor de temperatura inicial $C_0 = 22ºC$ ? e se este valor flutuar em 0.1º para cima?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "RdwHFpb2n0Bk",
        "outputId": "60b198cf-a36c-4e8f-e470-fc89981c1a4e"
      },
      "source": [
        "grafico_temperatura_tempo(22,22.2,0.1,0,1,1)\n",
        "print(f'Temperatura de equilibrio para a flutuação ={Temperatura_equilibrio(22.1,0)}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Temperatura de equilibrio para a flutuação =25.991237012162824\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IUO5FjgToEnY"
      },
      "source": [
        "R: se k for mantido a 0 para temperatura inicial de 22ºC a temperatura de equilibrio se mantem em 22ºC, porem ao flutuar a temperatura inicial 0.1ºC para cima a temperatura de equilibrio muda para aproximadamente 26ºC\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j1wkHHNJPjoq"
      },
      "source": [
        "4.a)Qual a temperatura no equador no momento da transição de  k=−0.5  a  k=−0.6 ?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G2VNc6L8PiRb"
      },
      "source": [
        "import Projeto\n",
        "\n",
        "def Temperatura_mod(C0,k1,k2,t_mudança) :\n",
        "        Intervalo1 =round(k1,2)\n",
        "        Intervalo2 =round(k2,2)\n",
        "        t1 = np.arange(0,t_mudança,0.001)\n",
        "        t2 = np.arange(t_mudança,50,0.001)\n",
        "        C1 = Projeto.Euler(t1, C0,k=Intervalo1)\n",
        "        C2 = Projeto.Euler(t2, C0,k=Intervalo2)\n",
        "        plt.plot(t1, C1, label='k = -0.5')\n",
        "        plt.plot(t2, C2, label='k = -0.6')\n",
        "        plt.xlabel('tempo [anos]')\n",
        "        plt.ylabel('temperatura °C')\n",
        "        plt.title('Predições de transição')\n",
        "        plt.legend()\n",
        "        plt.show()    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dMJW4vAqz1PP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "outputId": "4251e87b-9ff4-499d-b65c-deb12353b5e9"
      },
      "source": [
        "import Projeto\n",
        "\n",
        "Temperatura_mod(21,-0.5,-0.6,5)\n",
        "print(\"temperatura de equilibrio depois do relaxamento =\",Temperatura_equilibrio(21,-0.6))\n",
        "print(\"temperatura durante a transição =\",Temperatura_equilibrio(21,-0.5))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "temperatura de equilibrio depois do relaxamento = 26.218146464889863\n",
            "temperatura durante a transição = 21\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MBv4jZx_o5-h"
      },
      "source": [
        "R:No momento de transição de k = -0.5 para -0.6 temos que a a temperatura no ano 5 é igual a 21ºC."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1lWIPyxhTWDP"
      },
      "source": [
        "4.b)Qual a tendência da temperatura (ou seja, desce ou sobe) 5 anos após a mudança?\n",
        "\n",
        "\n",
        "R: Como pode ser observado pelo gráfico acima após 5 anos a tendência da temperatura é subir até que alcance os 26° onde irá permanecer constante."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WUgR1rMUUIdp"
      },
      "source": [
        "4.c) Qual o valor mínimo de  k  a partir desse momento (10 anos após o começo) para que a temperatura do equador não aumente nos próximos 50 anos?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "jQQKoKQgqzAz",
        "outputId": "3b4a56da-fde9-4924-8c8a-90f243d1b50e"
      },
      "source": [
        "grafico_temperatura_tempo(21,22,1,-0.5,-1.6,-0.1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ab7gDrOuRCdi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "2a8c4625-4dce-4a21-8f7e-a9a213337831"
      },
      "source": [
        "grafico_dispersão(21,22,1,-0.5,-3,-0.1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1neMqVL3rOBt"
      },
      "source": [
        "R: Podemos perceber que o menor valor k para que a temperatura de equilíbrio não aumente é o próprio valor de relaxamento k = -0.6"
      ]
    }
  ]
}