simulacao_macro2.ipynb

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      "cell_type": "markdown",
      "source": [
        "# Simulação de um modelo Macroeconômico Moderno\n",
        "Considerando o seguinte modelo dado pelas equações:\n",
        "\\begin{equation}\n",
        "P_{t} = P^{e} + λ[Y_{t} - Y_{PE}]\n",
        "\\end{equation}\n",
        "\\begin{equation}\n",
        "Y_{t} = Y_{t-1} + β[m_{s,t} - P_{t}]\n",
        "\\end{equation}\n",
        "Onde $Y_{t}$ é o produto no tempo $t$, $Y_{t-1}$ é o produto no período anterior, $P_{t}$ é a expectativa de inflação no período $t$, $P^{e}$ é a expectativa de inflação no período anterior, $\\beta$ e $λ$ são sensbilidades e $Y_{PE}$ é o produto no ponto de equilíbrio. \n",
        "\n",
        "Primeiramente devemos resolver o modelo macro acima, significa encontrar o valor das variáveis endogenas do modelo em função das exogenas e seus parâmetros. \n",
        "\n",
        "Resolvendo o modelo encontramos a seguinte equação para $P_{t}$:\n",
        "\\begin{equation}\n",
        "P_{t} = \\frac{P^{e}}{(1+\\betaλ)} + \\frac{\\lambda Y_{t-1}}{(1+\\betaλ)} + \\frac{\\lambda \\beta m_{s,t}}{(1+\\betaλ)} - \\frac{\\lambda Y_{PE}}{(1+\\betaλ)}\n",
        "\\end{equation}\n",
        "\n",
        "E a seguinte equação para $Y_{t}$:\n",
        "\\begin{equation}\n",
        "Y_{t} = \\frac{Y_{t-1}}{(1+\\betaλ)} + \\frac{\\beta M_{s,t}}{(1+\\betaλ)} - \\frac{\\beta P_{t-1}}{(1+\\betaλ)} + \\frac{\\lambda \\beta Y_{PE}}{(1+\\betaλ)}\n",
        "\\end{equation}"
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      "source": [
        "# Importando as bibliotecas necessárias\n",
        "import pandas as pd \n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Configurando o plot para ele sair em alta definição \n",
        "plt.rcParams['figure.dpi'] = 100"
      ],
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        "id": "Zd4acrhFvIxX"
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      "execution_count": 2,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "# Definindo os parâmetros \n",
        "pe = 4 \n",
        "beta = 3 \n",
        "l = 0.05 \n",
        "ype = 500 \n",
        "ms = 4 "
      ],
      "metadata": {
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      "execution_count": null,
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      "source": [
        "#Criando matrizes e arrays para as variáveis \n",
        "# Array do Tempo\n",
        "Tempo = np.array(np.arange(0,200)) #Essa operação cria um array em uma operação que vai de 0 até o tempo final em um passso de um a um\n",
        "\n",
        "# Inicialmente criaremos uma matriz de zeros para cada componente da simulação, iremos preencher elas com suas operações no bloco seguinte\n",
        "\n",
        "# Matriz do pt\n",
        "pt = np.zeros([301,1],float)\n",
        "# Matriz do y \n",
        "yt = np.zeros([301,1],float)"
      ],
      "metadata": {
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      "execution_count": null,
      "outputs": []
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      "source": [
        "# Com as matrizes definidas, podemos preencher elas usando loops for \n",
        "\n",
        "# Loop geral para as operações, esse loop coordena que todas elas operem para cada valor do i\n",
        "for x in range(0,301):                                    \n",
        "  for i in range(0,300):                                   \n",
        "    pt[i] = 4                                                                                                                             #Configurando o nosso pt inicial para 4\n",
        "    pt_novo = np.copy(pt)                                                                                                                 #criando uma nova matriz copiada da matriz com o k inicial correto\n",
        "  for i in range(1,301):                                                                                                                  #Como o zero já esta definido, aqui iniciamos a partir do 1\n",
        "    pt_novo[i] = (pt[i-1]/(1 + beta*l)) + ((l*yt[i - 1])/(1 + beta*l)) + ((l*beta*ms)/(1 + beta*l)) - ((l*ype)/(1 + beta*l))               #Usando a nova matriz criada anteriormente e aplicando os novos valores \n",
        "    pt = np.copy(pt_novo)                                                                                                                 #voltando para a matriz pt\n",
        "# Loop para definir o y \n",
        " for i in range(0,70):                                   \n",
        "    yt[i] = 500                                           \n",
        "    y_novo = np.copy(yt)                                  \n",
        "  for i in range(1,70):\n",
        "      y_novo[i] = (yt[i - 1]/(1+(beta*l)))  + (beta*(ms[i])/(1+(beta*l)))  - (beta*pt[i - 1]/(1+(beta*l)))  + ((beta*l)*ype/(1+(beta*l)))                \n",
        "      yt = np.copy(y_novo)  "
      ],
      "metadata": {
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "# Transformando todas as nossas varíaveis calculadas antes em arrays \n",
        "pt = np.array(pt)\n",
        "yt = np.array(yt)"
      ],
      "metadata": {
        "id": "GdNSNcYkpJJl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Como nossos arrays são bidimensionais precisamos fazer um reshape de 2D para 1D\n",
        "# Usando o comando reshape \n",
        "pt = np.reshape(pt,301)\n",
        "yt = np.reshape(yt,301)"
      ],
      "metadata": {
        "id": "RctIHP5npQ0F"
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      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "# Criando o dataframe com um dicionário para as colunas já definidas\n",
        "data_modelo_macro = pd.DataFrame(index = Tempo)\n",
        "data_modelo_macro[\"Tempo\"] = pd.Series(Tempo)\n",
        "data_modelo_macro[\"Pt\"] = pd.Series(pt)\n",
        "data_modelo_macro[\"Yt\"] = pd.Series(yt)\n",
        "data_modelo_macro"
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              "     Tempo   Pt     Yt\n",
              "0        0  4.0  500.0\n",
              "1        1  4.0  500.0\n",
              "2        2  4.0  500.0\n",
              "3        3  4.0  500.0\n",
              "4        4  4.0  500.0\n",
              "..     ...  ...    ...\n",
              "195    195  4.0  500.0\n",
              "196    196  4.0  500.0\n",
              "197    197  4.0  500.0\n",
              "198    198  4.0  500.0\n",
              "199    199  4.0  500.0\n",
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              "      <td>4.0</td>\n",
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      "cell_type": "markdown",
      "source": [
        "## Como pode ser visto na tabela, o modelo é um modelo de Estado Estacionário. Agora vamos dar choques nele envolvendo política monetária. Isto é, em certo ponto alterar o $m{s,t}$ "
      ],
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      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "1.   Fazendo $m{s,t} = 5$ a partir do instante $t=2$\n",
        "\n"
      ],
      "metadata": {
        "id": "9VbYAzjeqJ0E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Definindo os parâmetros \n",
        "pe = 4 \n",
        "beta = 3 \n",
        "l = 0.5 \n",
        "ype = 500 "
      ],
      "metadata": {
        "id": "tNdLPXvvp9hX"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Criando matrizes e arrays para as variáveis \n",
        "# Array do Tempo\n",
        "Tempo = np.array(np.arange(0,70)) #Essa operação cria um array em uma operação que vai de 0 até o tempo final em um passso de um a um\n",
        "\n",
        "# Inicialmente criaremos uma matriz de zeros para cada componente da simulação, iremos preencher elas com suas operações no bloco seguinte\n",
        "\n",
        "# Matriz do pt\n",
        "pt = np.zeros([70,1],float)\n",
        "# Matriz do y \n",
        "yt = np.zeros([70,1],float)\n",
        "# Matriz do Ms \n",
        "ms = np.zeros([70,1],float)"
      ],
      "metadata": {
        "id": "Ds58i842qcvO"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Com as matrizes definidas, podemos preencher elas usando loops for \n",
        "\n",
        "# Loop geral para as operações, esse loop coordena que todas elas operem para cada valor do i\n",
        "for x in range(0,70):                                    \n",
        "  for i in range(0,70):                                   \n",
        "    pt[i] = 4                                                                                                                             #Configurando o nosso pt inicial para 4\n",
        "    pt_novo = np.copy(pt)                                                                                                                 #criando uma nova matriz copiada da matriz com o k inicial correto\n",
        "  for i in range(1,70):                                                                                                                  #Como o zero já esta definido, aqui iniciamos a partir do 1\n",
        "    pt_novo[i] = (pt[i-1]/(1 + (beta*l))) + ((l*yt[i - 1])/(1 + (beta*l))) + (((l*beta)*ms[i])/(1 + (beta*l))) - ((l*ype)/(1 + (beta*l)))                #Usando a nova matriz criada anteriormente e aplicando os novos valores \n",
        "    pt = np.copy(pt_novo)  \n",
        "  # Loop para definir o ms\n",
        "  for i in range(0,1):\n",
        "    ms[i] = 4\n",
        "    ms = np.copy(ms)\n",
        "#fazendo o choque no Ms\n",
        "  for i in range(1,20):\n",
        "    ms[i] = 20\n",
        "    ms = np.copy(ms) \n",
        "  for i in range(20,70):\n",
        "    ms[i] = 3\n",
        "    ms = np.copy(ms)                                                                                                                #voltando para a matriz pt\n",
        "# Loop para definir o y \n",
        "  for i in range(0,70):                                   \n",
        "    yt[i] = 500                                           \n",
        "    y_novo = np.copy(yt)                                  \n",
        "  for i in range(1,70):\n",
        "      y_novo[i] = (yt[i - 1]/(1+(beta*l)))  + (beta*(ms[i])/(1+(beta*l)))  - (beta*pt[i - 1]/(1+(beta*l)))  + ((beta*l)*ype/(1+(beta*l)))                \n",
        "      yt = np.copy(y_novo)                                         "
      ],
      "metadata": {
        "id": "n186NTfsqedb"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Transformando todas as nossas varíaveis calculadas antes em arrays \n",
        "pt = np.array(pt)\n",
        "yt = np.array(yt)\n",
        "ms = np.array(ms)"
      ],
      "metadata": {
        "id": "I4R4EltNqx-8"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Como nossos arrays são bidimensionais precisamos fazer um reshape de 2D para 1D\n",
        "# Usando o comando reshape \n",
        "pt = np.reshape(pt,70)\n",
        "yt = np.reshape(yt,70)\n",
        "ms = np.reshape(ms,70)"
      ],
      "metadata": {
        "id": "5cy4Yh4rqyRr"
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      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Criando o dataframe com um dicionário para as colunas já definidas\n",
        "data_modelo_macro = pd.DataFrame(index = Tempo)\n",
        "data_modelo_macro[\"Tempo\"] = pd.Series(Tempo)\n",
        "data_modelo_macro[\"Pt\"] = pd.Series(pt)\n",
        "data_modelo_macro[\"Yt\"] = pd.Series(yt)\n",
        "data_modelo_macro['Ms'] = pd.Series(ms)\n",
        "data_modelo_macro"
      ],
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      "execution_count": 59,
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              "    Tempo       Pt        Yt    Ms\n",
              "0       0   4.0000  500.0000   4.0\n",
              "1       1  13.6000  519.2000  20.0\n",
              "2       2  21.2800  515.3600  20.0\n",
              "3       3  23.5840  504.6080  20.0\n",
              "4       4  22.3552  497.5424  20.0\n",
              "..    ...      ...       ...   ...\n",
              "65     65   3.0000  500.0000   3.0\n",
              "66     66   3.0000  500.0000   3.0\n",
              "67     67   3.0000  500.0000   3.0\n",
              "68     68   3.0000  500.0000   3.0\n",
              "69     69   3.0000  500.0000   3.0\n",
              "\n",
              "[70 rows x 4 columns]"
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              "  <div id=\"df-5e283b4c-e74f-4d20-b8c7-0245b752c3e0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Tempo</th>\n",
              "      <th>Pt</th>\n",
              "      <th>Yt</th>\n",
              "      <th>Ms</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>4.0000</td>\n",
              "      <td>500.0000</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>13.6000</td>\n",
              "      <td>519.2000</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>21.2800</td>\n",
              "      <td>515.3600</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>23.5840</td>\n",
              "      <td>504.6080</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>22.3552</td>\n",
              "      <td>497.5424</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>65</td>\n",
              "      <td>3.0000</td>\n",
              "      <td>500.0000</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>66</td>\n",
              "      <td>3.0000</td>\n",
              "      <td>500.0000</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>67</td>\n",
              "      <td>3.0000</td>\n",
              "      <td>500.0000</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>68</td>\n",
              "      <td>3.0000</td>\n",
              "      <td>500.0000</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>69</td>\n",
              "      <td>3.0000</td>\n",
              "      <td>500.0000</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>70 rows × 4 columns</p>\n",
              "</div>\n",
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              "\n",
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              "          document.querySelector('#df-5e283b4c-e74f-4d20-b8c7-0245b752c3e0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5e283b4c-e74f-4d20-b8c7-0245b752c3e0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.set_option('display.max_rows', None)\n",
        "display(data_modelo_macro)"
      ],
      "metadata": {
        "id": "0tMKARvRddOC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Fazendo o gráfico \n",
        "fig, ax = plt.subplots(figsize=(8,4))\n",
        "# Plotando os dados para os EUA\n",
        "ax.plot(data_modelo_macro['Tempo'],data_modelo_macro['Yt'],color='#006BA2') #Azul\n",
        "plt.title(\"Simulação Computacional do Modelo Macro com choque de política monetária\", loc='center')\n",
        "ax.set_ylabel(\"Produto\")\n",
        "ax.set_xlabel(\"Tempo\")\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "_isuvyZErFMP",
        "outputId": "43194c6d-ead8-42bb-9e19-de1f1d3f3422"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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KOZf8IrckNj/6OwwsSyyXfB674Hsaz8bvt/jtirDMSEXsSz3mkn6JH5y4N/4nzZG+IJTjfToIrExMy3df55LPtovZf7nO573OufU5po91Pp+Pf488NcZyubYLiWMR/3xGei7R/OjvcznOfbkem6998L8+JN/Dz4T5o12IIWpX8hw7gD+W09pOZJXbfrBn9Ph5sTY9m/zSQn7vvVLk+5k8w8z+x8zW4lMj1uF/HYD8z7uFxCZ1bPtZn+rnCP51PZHtX9c7w/wxX9cQuF5kZsvxPdM9QDQ2LNc+eT7HtFzrzSeWGtNEyznOCB+6jwCPmNkz+A+uU4ALx3joUAHT43kvI/UAbjfgZbuVmM3H55QtBT6Jf7P347/xnU8BX2LMD7C5A5iBTytZiu9xnYP/Zp/WF6KlwGvMbDfnXPLgLFUhrwGMnn9UjDFfM/ADEvBfOrrw+YfL8Af5a/H7Pq19PVrvclw+++ffgf/E9zZ8AZ8jOIzvId2Rviz/Et/rPAu4NdlrU0bRPvoFPh8vl9TriY/gavyX/8vw+aq/y7VQmd6nfTkCkfEo1zFRifNIpc5VSaN9DsXbVIf/mf6TIyyf1jm9UtsZ767Bf4H9Oj4Xfwt+39xG/sdfqZ+LaX6O1OHjjK+NsOwzI0yP+z/gaHw5tj/j90k9vkc61z7ZbhzLdg1MMZaasMFxwp/C313LuI1N4W9y5H0+31hPwueCvts5l/nWZmbJn5yiXq2DSHwLjDkYP9DhH51zmcGCYdBAXFTqbd8c69gPWJ/j23rcTfifdE/Hf0CPJr6tuxPz9o3NT9M++IFiAJi/MMqu+IEdkU0kXi/zlQ+S75ORTirH4isnnOxiA3nMbM/EcvHX7U7y9yL+YN6HbA9INJCnneL22/uAe5xz29SUDAFUsjct3zbuk2N68n0Vfw9kepfC/t6TwvYL+AEeP8QPQHn/GO07zsymJno094vNj/7W4XuC4j1yyeexDp9OVO+cK7TNpR5z23DOrTCzP+Dfh99325a2izuW8r5PI/nu61zy2Xaq+68Iy/DvkQPwAU6pXmTk5xLNj/4eZGaW6D3O9djtzmnBHmzbq7sMn3p41xipXblE7dqH2PnczBrxx3K8vGAp24nMtu1LBb46/H0h1qZDwqCr+Je2fN57ueTb1jHft+arJL0N+JJz7j9i03OdN8uhHJ8jy4ApeZwDc+7H8HlzAvB559xXY9NfnWv5AuQbS41pR+opGpOZvSXkBCZFOTel/Aw1lhfx37iSuTYfzeOx0Te1eGm4acCZieV+h/9gviDk5xBbPnpsrnUZiYugOOdW40/w/2ixUlpmdhB+EGM8iMzlV/gegc+Z2ZHJmSGvMRrp+yf8zyUfsVjpGDP7G/xo2e0qAqTg7HCyjpyD/zJ4a2zaMrZ/vc5m+57j6KSc/NDJta+b2P41/zP+J6FPWKJs2Qjv10j0GnwiMT3qhSlmvw2R6FEws1PwvywU4xZggZm9Pra+XfADK+LuxH+D/3jiOZ+F/6mroOcS0mTOwQ/IuGmM9tXjLwgUdz7+xB29H6K/H08st82+D3mr1wHvDcfKNsJzH6nNpR5zuXwe/2vYd0ZZptzv00i++zqXMbddpv1XiF/jf2X5ooVyVck2FugW4PXx82dICzkbH/Q9FVtuNv6LbbRca1guaRn+eGyKLfsuYLfEctfgj/ntLoRlZpPGSO/5E/5L4kfi28EPbEueI0vZTqQBP4AvelxTuL8OX9sc/D6aReyLcsi5/Ri+RzJKK8zXVnJ/yUjK55jZ7vgLkuf1cinH58g1+MpgJyRnmC/xFnW8RmMgkvsy+gLTmJied0nFEeQbS41povUcfwdoNbMb8N3qTfifMt6PP9lcMfJDS+Oc6zSza4GPma+7tww/UCifnKrf4QOHm8zsh/gBBB/GB5SZXkznXJeZnY8fZPKImf0S31NwKH5gzT/in/cyfGmiOfifUt9L7ny6T+M/sB40s8vJlkXqxAcdoz3fATM7GR/03Gdm1wB/wJeWORCfD7oJP3J9wMw+g9//95rZ1WRLub1AngXYC9QE3BXatS8+EPg9frBm5MfAD8zsOvxPRIfiv80me1Afwx90nwkHWh++x+SB8Bx/ambfxgcAHyJxEnTODZuvs3gT8JiZXYHPf94Pv6+2O8GEx/3VzH6KD/Sjn8Zfj3+df+2cuyfX48ZwM/7D/YrQ/oPxgWwyVzBfX8M/59vM7H/IlnJ7EZ/HHD2XdWZ2Eb6U221mdiPZ1+URsvmveXPOjZTWEHcT/heEL5uvu/lXfCD1HvwgmmVhXY+F9+VHw2v8AL63Z+8c6/wsfiDRw2Z2GT6ImYFPUzgu/D+Soo+5XJxz9zL2B39Z36cxee3rEZ5HvttOdf8Vwjn3XPjC/wXgfjO7Hn8uOAJfveSCAlf534SShOF12Yg/tvcE3hvrAY2u9vozMzscv18+RDbwiPsxPoi+LZz75uN/3Uvu+5/jS6L9IPSq/QH/xWa/MP0Esr+4JvfDgPla5j8E7jaz/wttPpPtzyNFbydmFf7cOw//c/378QMzz44NUPsRPmC+MuyjF8J+OBr4RI48+LE8CpwTnudzwCvOueSvnnm9b8Pn9n3Av4UOm5fxx0Xyl5uyKNPnyNfx5VxvNl9X+1FgMv7z5H34XPD1zrkeM3sKeL/59NaNwBPOuSfM7PfAp0Mg/TL+vbB7CU8V8oyl8uIKKG0x3m/4BPHL8T8dbMafuJ7Fj1h9VWLZF8hdyu11ieUWhuk7J6ZfyfblknbG96huxb8JfkC25NIZyXUmHnsS/sOkB/9N9N/wJ5ttSszElv1DmOfwo1M/EJu/Pz7Y24z/dv0jfKCyTTvCsm/DB43d+A+YG4H9C9jn7fieq8Xheffge5S/AsxKLHsq/pt2Lz5H8hfAnLH2a5i+CH9QJae/wLalu6LX8c34k/fGsB9+Qaz8U1i2Dv8BtS60/Tb8h8k2742w7D/jP2AG2baUz1H4urvd+AM8Kh+YWSa2jqPxB28Xvjfjr8D/G+N90YDPE12OP+hXhH3bPNp+SOy3RbH7zcDF+A+c7vDaL8ix3Lxc75cR3gMHh8f34AdpfR5fGzPXe/dc/PHZjx9d/j0SJZBG2Eb0ur5ujOW22w/4E+Q3w+vTj/+A/RSxUkthuRZ8XdL14fW5EV8b3JEoRYT/0vvd8Hr04z8Q7wQ+PNY+pMhjLra+T42x3JVsf25K83263foL3dejtH3Ubee7/yjgvD3a+WWENp5J9jy2MTz2uEKPxTBtL3ypu0344+dh4J05Hrs78Bv8eWodfozACSO8fp/EH4e9YT8dPsK2G/GfM0/Ensuf8Oebtjz2wzn481Iv/gvum9LeTvS6hOfwQNhHLwDn5lj2VfixFOvwn/2LyXH+Ir9SbjPxHQldxMrTMXLJ1rGOmTn4EoCb8IPErsEHa9udW3K0N9rm+xLTo3aPGbOQ8udI7Fj/Cj7G6gv7/Q/43t94ybkjw+vdF3+++F8zbgj7YxN+DMXMHK/Pds8nOS8xLe9YarSbhZXJDsh8GasngMPd9qOva5JlLzZyhHNurB4JEZEdkvmrtN2Dr1O7qLqtKQ/zV6bb2Tm3XQqTSDlNqJzjWuP8T0V/Jnu1OhEREREpwUTLOa4ZZvYpfLrAAmJVGURERESkeAqOd1zvwufy/IVRiv+LiIiISP6UcywiIiIiEijnWEREREQkUHAsIiIiIhIo55jMlWxm4we4iYiIiMj4NBVY5cqYF6zg2JuNL5ouIiIiIuPbXPzFhspCwbG3GeCll16ira2t2m0RERERkYSuri522203KPMv/QqOY9ra2hQci4iIiNQwDcgTEREREQkUHIuIiIiIBAqORUREREQCBcciIiIiIoGCYxERERGRQMGxiIiIiEhQ1eDYzBaamUvclsbmn21mi8ysK8xrTzx+npldbmbPm1mPmS0zswvNrKnyz0ZEREREdnTjoc7xk8BxsfuDsf9bgdvC7aIcj90PH+D/C/AccBBwGTAZ+FQ5GisiIiIiE9d4CI4HnXNrcs1wzn0LwMyOHWF+FDhHlpvZvsA5KDgWERERkQKNh5zjfcxslZktN7OrzGz3Etc3Ddg42gJm1mxmbdENmFriNkVEpAS9A0N8+bbH+ctLo56+RUTKrtrB8cPAGcCJ+N7ePYH7zayoYNXM9gY+BvxwjEUvADpjt5XFbE9ERNJx+5JVfP7mxzjvV49UuykiUuOqGhw75251zl3rnFvsnLsdeAfQDpxa6LrMbA4+xeJa59xlYyx+Eb6HObrNLXR7IiKSno1b+wB47OVNDA+7KrdGRGpZtXuOt+Gc6wCeAfYu5HFmNhu4B3gAODuP7fQ557qiG7C5mPaKiEg6egaGANjcO8CLG7dUuTUiUsvGVXBsZlOA+cDqAh4zB1gEPAqc6ZwbLk/rRESkXKLgGODxVR1VbImI1Lpq1zm+2MyOCfWKjwJuAIaAq8P8WWZ2GNme5IPN7DAzmxHmR4HxCnx1il3CY2ZV+rmIiEjxegayVTwXr9pUxZaISK2rdim3ufhAeCdgHfB7YIFzbl2Y/xHgS7Hl7wt/zwSuBI7HB857s/2gOitPk0VEJG09/dme48Uvq+dYRKqnqsGxc+4DY8xfCCwcZf6V+CBZRER2YNumVajnWESqZ1zlHIuISG3q7s+mVTzzymZ6+gdHWVpEpHwUHIuISNXFe46HneOpNZ1VbI2I1DIFxyIiUnXx4BhUsUJEqkfBsYiIVF1UrWJqSyMAi19W3rGIVIeCYxERqbqo5/iI3XcCVM5NRKpHwbGIiFRdFBy/Yd7OgNIqRKR6FByLiEjVRXWOX7f7TpjBK5t7WdvVU+VWiUgtUnAsIiJVF+Uc7zS5mX12aQOUdywi1aHgWEREqq479BxPaqzn4NntgFIrRKQ6FByLiEjVRT3HkxrrOWTOdECD8kSkOhQci4hI1UUD8iY11XOIeo5FpIoUHIuISNVlguPGBg6e7XuOn1zdweDQcDWbJSI1SMGxiIhU1cDQMEPDDvBpFXvuNIXJTQ30DQ7z7LrNVW6diNQaBcciIlJVPf2Dmf8nNdZTV2exQXnKOxaRylJwLCIiVRWlVAC0NNYDZIJjlXMTkUpTcCwiIlXV3Z+tVGFmANmKFS9rUJ6IVJaCYxERqarsYLz6zLQoOH58tXqORaSyFByLiEhVZcu4NWSmRWkVL2zYSmdPf1XaJSK1ScGxiIhUVa6e4+mtzcxtbwXgCdU7FpEKUnAsIiJV1RPLOY7LpFYoOBaRClJwLCIiVZWr5xhiFStUzk1EKkjBsYiIVFX86nhxh8yOKlYoOBaRylFwLCIiVRUFx61NybSK6EIgHTjnKt4uEalNCo5FRKSqsnWOt+053nfmNBrr6+jqHWDFpq3VaJqI1CAFxyIiUlXZUm7b9hw31tex/6xpgFIrRKRyFByLiEhV9QzkrlYBcMjsbGqFiEglKDgWEZGq6unPXa0C4GANyhORCqtqcGxmC83MJW5LY/PPNrNFZtYV5rXnWMcMM7sqLNNhZpeb2ZTKPhMRESnWSNUqIDsob7F6jkWkQsZDz/GTwK6x2xtj81qB24CvjPL4q4ADgeOBdwFvBn5UlpaKiEjqRqpzDNlybs+80kVvWE5EpJy2/5peeYPOuTW5ZjjnvgVgZsfmmm9m+wMnAkc45/4Upn0MuMXMPuWcW1WeJouISFoyOcdN2wfHu06bxIzWJjZ297NkTSev2W1GpZsnIjVmPPQc72Nmq8xseUiP2L2Axx4JdESBcXAnMAy8IdVWiohIWWTqHOdIqzCzzGWklXcsIpVQ7eD4YeAMfO/vOcCewP1mNjXPx88CXolPcM4NAhvDvJzMrNnM2qIbkO/2REQkZZk6xzl6joFMcKyKFSJSCVVNq3DO3Rq7u9jMHgZeBE4FLi/jpi8AvlTG9YuISJ5GyzkGOHh2NChPPcciUn7V7jnehnOuA3gG2DvPh6wBXhWfYGYNwIwwbyQXAdNit7kFN1ZERFIxWrUKyA7KU1qFiFTCuAqOQwm2+cDqPB/yINBuZofHpr0V/7weHulBzrk+51xXdAM2F9tmEREpzWh1jgEO3HUaZrB2cy+vbO6pZNNEpAZVu87xxWZ2jJnNM7OjgBuAISVSJcgAACAASURBVODqMH+WmR1Gtif5YDM7zMxmADjnluBLvV1mZq83s6OB7wL/q0oVIiI7htGukAcwubmR+Tv7oSHKOxaRcqt2z/FcfCD8NHANsAFY4JxbF+Z/BPgLcFm4f1+4/+7YOj4ILAXuAm4Bfg+cXfaWi4hIKjJpFSMMyAOlVohI5VR7QN4Hxpi/EFg4xjIbgdPSa5WIiFTSaKXcIofMaef6v65Qz7GIlF21e45FRKTG5dNzfLB6jkWkQhQci4hIVWXqHI/Sc7zfzDYAlq3fUpE2iUjtUnAsIiJVMzA0zNCwA0YekAew0+RmADp7+xkOy4uIlIOCYxERqZqe0GsMowfH0yY1AeAcbO4bKHu7RKR2KTgWEZGqifKNAVpGCY5bGusz8zd195e9XSJSuxQci4hI1UTBcUtjPWY26rLTQ+9xR4+CYxEpHwXHIiJSNdlLR4/caxxpbw3BsXqORaSMFByLiEjVRFfHa20au+x+e+g53qSeYxEpIwXHIiJSNT39BfQcT2oE1HMsIuWl4FhERKomW+N47OB4eqtyjkWk/BQci4hI1RSUcxylVajnWETKSMGxiIhUTTY4zj/nWD3HIlJOCo5FRKRqMsFxUyFpFboIiIiUj4JjERGpmqhahUq5ich4oeBYRESqprBqFSrlJiLlp+BYRESqJkqryKfO8XT1HItIBSg4FhGRqikorUID8kSkAhQci4hI1XT3F16tQqXcRKScFByLiEjVFFPneGv/IANDw2Vtl4jULgXHIiJSNZm0ijxKuU0Ll48G6FRqhYiUiYJjERGpmkJ6jhvq65ja4gNkpVaISLkoOBYRkarpKSDnGKA99B5rUJ6IlIuCYxERqZpCqlUATG9tBlTOTUTKR8GxiIhUTSF1jiHec6xLSItIeSg4FhGRqikk5xji5dz6ytYmEaltCo5FRKRquvsLS6vIXghEPcciUh4KjkVEpGoyPcd5lHKD2CWkNSBPRMqkqsGxmS00M5e4LY3NbzGzS81sg5ltMbPrzGxmYh1HmNldZtZhZpvM7HYzO7Tyz0ZERAqVTavIN+dYV8kTkfIaDz3HTwK7xm5vjM27BDgJOAU4BpgNXB/NNLMpwG3ACuAN4bGbgdvNrBERERnXCs45Vs+xiJRZfl/Vy2vQObcmOdHMpgFnAac55+4O084ElpjZAufcQ8B+wAzgi865l8IyFwKLgT2A5yr0HEREpAg9BeYcT5+k4FhEyms89BzvY2arzGy5mV1lZruH6YcDjcCd0YLOuaX4XuIjw6SngQ3AWWbWZGaT8AH1EuCFSj0BEREp3ODQMIPDDiggraJVaRUiUl7VDo4fBs4ATgTOAfYE7jezqcAsoN8515F4zNowD+fcZuBY4HSgB9gS1vU3zrnBkTZqZs1m1hbdgKlpPikRERlblFIB0JrngLx29RyLSJlVNTh2zt3qnLvWObfYOXc78A6gHTg1n8eHnuLLgT8AC4CjgSeA34Z5I7kA6IzdVhb/LEREpBjR1fEAWvK+Ql4IjtVzLCJlUu2e422EXuJngL2BNUCTmbUnFpsZ5gGcBswDznTOPRLykE/D90C/Z5RNXQRMi93mpvUcREQkP939vue4pbEeM8vrMeo5FpFyG1fBcag+MR9YDTwKDABvi83fF9gdeDBMagWGARdbTXR/xOfmnOtzznVFN3yFCxERqaBCK1VA9vLRfYPDmcF8IiJpqnad44vN7Bgzm2dmRwE3AEPA1c65TnzKxDfN7C1mdjhwBfBg6CEGuAOYDlxqZvub2YFhmUHgnoo/IRERyVuUVlFIcDyluZG60Mus3mMRKYdq9xzPBa7GV524Bl95YoFzbl2Yfz5wM3AdcB8+neLk6MGhesVJwCH43uT78bWQT3TOra7QcxARkSL09Bd2ARCAujrL9B7rEtIiUg5VrXPsnPvAGPN7gXPDbaRl7sD3IIuIyA6kmLQK8OXcNnb3s6m7rxzNEpEaV+2eYxERqVGZtIo8y7hFsoPy1HMsIulTcCwiIlUR9Ry3FpBWASrnJiLlpeBYRESqIpNzXGTPsa6SJyLloOBYRESqoruIahWgWsciUl4KjkVEpCqy1SoKC44zaRUKjkWkDBQci4hIVWSrVRSWc6yeYxEpJwXHIiJSFcVcBASUcywi5aXgWEREqiLTc1zogLzW6CIgCo5FJH0KjkVkRPc+u5a9F/6ae55ZU+2myARUbFrF9NZmQKXcRKQ8FByLyIiue+xFlq3fzNfufLLaTZEJKBqQ11pwKTffc7xJPcciUgYKjkVkRNEVyO5+Zg1b+nQ1MklXqTnH6jkWkXJQcCwiI+oMPXP9g8Pc9bRSKyRd3f3FplVkLx/tnEu9XSJS2xQci8iIOnuyvcU3P7Gyii2RiajUnuNh59jcq180RCRdCo5FZESdvdmfrX/7xMsMD6uXTtKTHZBXWHDc0lhPU4P/+OroUXAsIulScCwiI4r3HK/u6uEvKzdWsTUy0WRLuRWWVmFmuhCIiJSNgmMRGVEUeOw3sw1QaoWkq9ieY9AlpEWkfBQci0hOzjm6Qj7nB4/YE4Cbn3i5mk2SCabYnGPQVfJEpHwUHItITlv7BxkKOcZ/f7gPjv+0YgOrO7ur2SyZQLJ1jgtLqwCVcxOR8lFwLCI5RfnG9XXGXjtP4fV77ATALU+q91jS0a20ChEZhxQci0hOUY3jaS2NmBnvOmguoNQKSU9Pv9IqRGT8UXAsIjlFPXLTQhASBcd3LF1Nb+jxEynW4NAwgyFtp9CLgED2EtLqORaRtCk4FpGcorSKaSEIOWzudGZPm8TW/kHufXZtNZsmE0BP7AvWpKZi0iqaAQXHIpI+BcciklOUVhH9fL1taoVKuklpokoVAC0NxaRV+C9tSqsQkbQpOBaRnDp7t+05BnjXQXMAuPnJlTinq+VJ8aKe4+aGOurqrODHt2tAnoiUiYJjEckpk3Pc0pSZ9rZ9d6WlsZ4XNmzlqTWd1WqaTABRGbdi8o0hVspNl48WkZQpOBaRnJI5x+Dr0b711bMAuOlxpVZI8aKe49Yi8o0hVspNaRUikjIFxyKSU2eiWkUkk1qhvGMpQXemjFtpPceblFYhIilTcCwiOUU5x+3bBcd+UN6Dz69n/ZbeirdLJoaeEi4AAtn35ebeAQaHhlNrl4hIVYNjM1toZi5xWxqb32Jml5rZBjPbYmbXmdnMHOs5w8wWm1mvmb1iZpdW9pmITDzZnuPGbabvNn0yh86ZzrBz3PbUqmo0TSaAqFpFMWXcIDsgD6CrV3nHIpKe8dBz/CSwa+z2xti8S4CTgFOAY4DZwPXxB5vZJ4EvA/8NHAgcB9xe9laLTHDRQKf4gLyIUiukVKX2HDfW1zG5yadkqJybiKSpuGSvdA0659YkJ5rZNOAs4DTn3N1h2pnAEjNb4Jx7yMymA/8FnOScuyv28MWVaLjIRDZSzzH41Iov3/4Ety1ZxcDQMI314+F7tuxISq1WAb73eGv/oMq5iUiqxsMn2j5mtsrMlpvZVWa2e5h+ONAI3Bkt6JxbCqwAjgyTjsc/hzlmtsTMVprZNWa222gbNLNmM2uLbsDU1J+VyA4uV7WKyBF77MQuU5rp7BngD8teqXTTZAIotecYYPok1ToWkfQVHRyb2TFmdpOZPRduN5rZmwpczcPAGcCJwDnAnsD9ZjYVmAX0O+c6Eo9ZG+YB7BWew78DnwDeB8wA7jCz7X8LzroA6Izd9NuwSEJn77ZXyIurr6vjHQdGqRUvV7RdMjFEOcfFlnKDbN6x0ipEJE1FBcdmdjq+R7cb+Ha49QB3mdlp+a7HOXerc+5a59xi59ztwDuAduDUPFdRh+9d/rhz7nbn3EPA3wP7AG8Z5XEXAdNit7n5tlmkFgwPu8wgp2Qpt0jmUtJP6rulFC7bc1xCWkX4VUM9xyKSpmLPSp8D/s05d0ls2rfD4LgvAL8sZqXOuQ4zewbYG7gDaDKz9kTv8UwgylFeHf4+FVvHOjNbD+zOCJxzfUBfdN+s8EuXikxkm/sGiK4OPa1l+7QKgLfvtysNdcbTa7t49pUu9nlVWwVbKDu6bJ3jEnqOJ+lCICKSvmLTKvYCbsox/UZ8akRRzGwKMB8f9D4KDABvi83fFx/0Phgm/SH83Te2zAxgZ+DFYtshUuuifOPG+jpaRghe2iY1ccw+vrLib59UaoUUJtNzXEJaReYqeeo5FpEUFRscv0QsaI05LszLi5ldHHKX55nZUcANwBBwtXOuE7gc+KaZvcXMDgeuAB4M6RM4554BfgP8j5kdZWYHAT8FlgL3FPncRGpeVKmifVLjqL+svOMAn3d819OrR1xGJJd00iqUcywi6Sv2rPQNfBrFYcADYdrR+MF15xWwnrnA1cBOwDrg98AC59y6MP98YBi4DmjG1y/+aGId/4Cvh/zbsOy9wInOOVWFFylS5xj5xpEDdp0GwIpN3WVvk0ws2VJuKaRVqOdYRFJUVHDsnPu+ma0B/pXs4LklwPudc78pYD0fGGN+L3BuuI20TBe+HvJZ+W5XREaXqXE8Qr5xZM60VgBWbtpa9jbJxJK5Ql4ppdwyaRXqCxGR9BT9e5Zz7gZ8GoSITDAdmQuAjN5zPKfdB8cbu/vp6R9kUtN4uK6Q7AjSqHOsUm4iUg7FlnJbbmY75ZjebmbLS2+WiFTTaBcAiZve2pQZsLeqs6fs7ZKJIwqOW0v4QqW0ChEph2IH5M0Dcn3dbwbmFN0aERkXsgPyRu85NjPmht7jlzuVdyz5S6XnWKXcRKQMCvrKbmbvjt09wcw6Y/fr8RUsXkihXSJSRdkBeaP3HIPPO35u3WZe7lBwLPnL1Dkuoec4yjnepJ5jEUlRoWelX4e/Dl8yLW4AHxj/a4ltEpEqi3riprWM3nMMMKd9EoCCYylImj3HvQND9A4MjViTW0SkEAUFx865OgAzex44wjm3viytEpGqKqjnWGkVUoQ0qlW0tTRiBs75VKCWxklpNU9EalhROcfOuT0VGItMXPnmHEOsnJt6jqUAadQ5rquzzK8bGpQnImkpKtnLzL442nzn3H8U1xwRGQ/yvQgIxHqOO1StQvKXxhXyANpbG+no6Vc5NxFJTbFnpb9L3G8E9gQGgWWAgmORHVjmIiB5pFVkqlWo51gKkAmOm0rLE/a/bmxVz7GIpKbYK+S9JjnNzNqAK9GFQUR2eNEVx/IbkOeD41Wd3QwPO+rqrKxtkx3f0PAwA0PDALSW2HOcuUqeeo5FJCXF1jneTriM85eA/0xrnSJSHYX0HM9qm4QZDA471m3pLXfTZAKIeo0hrZ5jlXMTkfSkFhwH08JNRHZQQ8PDbOnzlQTyGZDXWF/HzKmhnJsqVkgeohrHAC0N6QTH6jkWkbQUOyDv48lJwK7Ah4BbS22UiFRPVxiMB/n1HAPMmTaJNV09rNzUzWt32+7K8iLbiHqOmxvqSk7DyV5CemCMJUVE8lNsstf5ifvDwDr8hUEuKqlFIlJVnSHIaGmspynPXr057a08+tJG9RxLXrJl3ErLN4bYVfK6+0pel4gIFD8gb8+0GyIi40M06n9aS369xhAv56bgWMaWxtXxIuo5FpG0pZ1zLCI7uKjnuL117HzjyFzVOpYCZK6OV+JgPMi+T1XKTUTSknfPsZldn++yzrmTi2uOiFRbZzE9x9N0CWnJX5o9x9MnKTgWkXQV0nPcGbt1AW8DXhebf3iY1pla60Sk4gq5Ol5EaRVSiCjnuLWp9Jzj9kzOsYJjEUlH3mcm59yZ0f9m9lXgGuAjzrmhMK0e+B4+cBaRHVQhNY4jUXC8UsGx5CHdnGP/PlXPsYikpdic438CLo4CY4Dw/zfDPBHZQWUH5BXQczzN1znu6h1gS58GRsnoojrHaVSriNc5ds6VvD4RkWKD4wZgvxzT9ythnSIyDmQG5BWQVtE2qYkpzT7QUWqFjCXVnOOQVjE47Ngau7iIiEixiv3afgVwuZnNB/4Ypr0B+GyYJyI7qGLSKsBXrFi6touXO7rZd6YulCkjy1SrSCE4bm1qoKHOGBx2dHT3M6W5sPetiEhSscHxp4A1wL/ir4wHsBr4OvCNFNolIlWSHZBXWJAxJwqOO1XOTUaX6TlOoZSbmdHe2sT6LX109PQzd/rkktcpIrWt2IuADANfA75mZm1hmgbiiUwAUVpFITnHECvnprQKGUM2raL0nGPw5dzWb+lTxQoRSUVJZyYz2wXYN/y/1Dm3PpVWiUjVdBSZVqFybpKv7OWjS+85Bl0IRETSVdTgOTObbGY/wadS3Bduq83scjNrTbOBIlJZUc5xIQPyINtzrHJuMpYo5ziNOsegS0iLSLqKrSzxTeAY4CSgPdzeE6Yp51hkB5ZJqyg0OG735dx0lTwZS5rVKiBbsaJDaRUikoJig+P3Amc55251znWF2y3Ah4H35bsSM1toZi5xWxqb32Jml5rZBjPbYmbXmdnMEda1k5mtDOtoL/J5idS8zt5iq1X4gVBKq5CxZOscp5RWEb7IbVJahYikoNjguBVYm2P6K2FeIZ7EV7yIbm+MzbsE3zt9Cr5XejZw/QjruRxYXOC2RSRmYGiY7pAPWvCAvNBzvKarl8Gh4dTbJhNH2j3H8QuBiIiUqtjg+EHgQjNriSaY2STgS2FeIQadc2tit/VhfdOAs4BPOufuds49CpwJHGVmC+IrMLNz8KkdFxf5fESEbL4xFN5z/KqpLdTXGcPOsXZzb9pNkwkkW8otrZxjXUJaRNJTbHD8CeBoYKWZ3WVmdwEvAUcB5xW4rn3MbJWZLTezq8xs9zD9cKARuDNa0Dm3FFgBHBlNM7MDgC8C/wDk1V1lZs1m1hbdgKkFtllkQoryjSc3NdBQX9jpob6ujl3bQt6xUitkFOnnHDcDqJSbiKSiqODYOfc4sA9wAfBYuH0W2Mc592QBq3oYOAM4ETgH2BO438ymArOAfudcR+Ixa8M8zKwZuBr4tHNuRQHbvQDojN1WFvBYkQmr2HzjSFTOTRUrZDQ9qeccq+dYRNJT8G9aZtYILAXe5Zy7rJSNO+dujd1dbGYPAy8CpwL5XGbrImCJc+4XBW76InzFjchUFCCLFF2pIqILgUg+Us85Vp1jEUlRwT3HzrkBoGXMBYsQeomfAfbGX566KUfliZlhHsBbgVPMbNDMBoG7wvT1ZnbhKNvpi1XZ6AI2p/pERHZQmQuAtBTbc6xybjK2KDhOq85xVMpNaRUikoZic44vBT5jZumc2QIzmwLMx19c5FFgAHhbbP6+wO5kB/29FzgUOCzc/jlMf1Noo4gUIOo5jnriCqVybpKPslWrUM+xiKSg2OD2CHzQ+nYzexzYGp/pnDs5n5WY2cXATfhUitnAhcAQcLVzrtPMLge+aWYbgS7gO8CDzrmHwnaWJda3c/h3SY5cZREZQ6d6jqUCsnWO071CXlfvAMPDjro6S2W9IlKbij0zdQDXpbD9ufgBdTsB64DfAwucc+vC/PPxFSiuA5qB24GPprBdEcmhszetnON8hgxIrcqWcku359g5HyAX+8uHiAgUGBybWR3waeDVQBNwN7DQOVfUJ6Fz7gNjzO8Fzg23fNa3CFCXgUiRMj3HJVer2IpzDjMdjrKtoeFhBsJFYtJKq2hurGdSYz09A0Ns6u5TcCwiJSk05/hzwFeALcDLwMdRbq/IhBHlbLYX23McguPu/qFM/rJIXNRrDOkFxxDPO9b7TkRKU2hw/A/AR51zJzjn/hZ/aecPhh5lEdnBZUq5FXjp6EhrU0MmSFHeseTS0x8PjtMb061ybiKSlkKD2t2BW6I7zrk7AYcfTCciO7hS0yoA5rar1rGMLOo5bmqoS3XgnMq5iUhaCg2OG4DexLQB/GWeRWQHlx2QV/whPUfBsYyiZ8BXqmhNsdcYVM5NRNJT6NnJgCvNrC82rQX4gZllyrnlW8pNRMaXUq+QByrnJqNLu8ZxJHMJafUci0iJCg2Of5pjWqGXbhaRcarUAXmgcm4yukyN45TKuEWmtzYDsEk9xyJSooKCY+fcmeVqiIhUXxo5x/FybiJJ6jkWkfFOVSZEBIC+gSH6Bn392WKrVYB6jmV0UbWKNCtVgHKORSQ9Co5FBIDO3mxQMbWl+MBl7vQQHCvnWHIoW8+xSrmJSEoUHIsIAB3dfjDe1JZG6uuKPzVEPcevbO6lf3BojKWl1kTVKtIOjqdPUik3EUmHgmMRAbI9x+0l5BsD7DylmaYGf2pZ3aXUCtlWpuc45QF56jkWkbQoOBYRoPSr40XMjNnTQjk31TqWhCg4Ll+dY10+WkRKo+BYRIB0KlVEotSKlQqOJaGnbKXcorSKvjGWFBEZnYJjEQHiV8crrecYdJU8GVn3QHmrVXT3DynXXURKouBYRIBsfdhSc45B5dxkZNlSbun2HE+b1IiZ/1+D8kSkFAqORQRIt+d4brvKuUlu5apWUV9Xx86T/VXy1mggqIiUQMGxiACxnOOWFHqOlVYhIyhXnWOAXcMvFmu6elNft4jUDgXHIgLEqlWkmXOsnmNJyJZySzfnGGDW1BZAJQRFpDQKjkUEyNY5TqdaRbaUm3Ou5PXJxFGutAqAXcP7brW+lIlICRQciwiQvXhCewo9x7PDz9t9g8Ns2KrSWpIVDchrLUPP8a5tIThWz7GIlEDBsYgA6aZVNDfWs/MUPzhKeccS113WnGMFxyJSOgXHIgLEr5BXeloFxCtWKFCRrMxFQMoRHLdpQJ6IlE7BsYgA6eYcQ7zWsXqOJauc1SpmtYUBefpCJiIlUHAsIjjnMj3HaeQcg8q5SW49ZbpCHsTTKjQQVESKp+BYROgZGGJgaBhIJ+cYVM5NcsuWcitHWoUPjrv7h9gcLmojIlIoBccikrkASJ0ZU5rT6dGLyrmtVM+xxJSzlNvk5kamhpx5DcoTkWIpOBaRTEpFW0sjZpbKOpVWIblEPcflKOUG2d5jDcoTkWJVNTg2s4Vm5hK3pbH5LWZ2qZltMLMtZnadmc2MzT/UzK42s5fMrMfMlpjZedV5NiI7rrQH40GsWoWCYwmGhofpH/TpO+XoOYb4oDy970SkOOX56l6YJ4HjYvcHY/9fArwTOAXoBL4LXA8cHeYfDrwCnA68BBwF/MjMhpxz3y1zu0UmjI7udAfjQbbneGN3Pz39g2W5XLDsWKJeYyhfcKwLgYhIqcbDp9Wgc25NcqKZTQPOAk5zzt0dpp0JLDGzBc65h5xzP0k8bLmZHQmcjA+kRSQP5eg5bp/UxKTGenoGhljV2cP8Xaamtm7ZMUVXx4PyVKsAXQhEREo3HnKO9zGzVWa23MyuMrPdw/TDgUbgzmhB59xSYAVw5CjrmwZsHG2DZtZsZm3RDdCnttS07AVA0us5NjNVrJBtRD3HTQ111NWlk9ueFF0IRLWORaRY1Q6OHwbOAE4EzgH2BO43s6nALKDfOdeReMzaMG87ZnYU8H7gR2Ns9wJ8mkZ0W1lk+0UmhKhaRZo9x5C9EMjKTQqOpbyVKiLqORaRUlU1rcI5d2vs7mIzexh4ETgVKOjMZmYHAb8BLnTO/W6MxS8Cvhm7PxUFyFLDOkNN2LRqHEfmtPtART3HAuW9AEhk1lQ/IG+NgmMRKVK1e463EXqJnwH2BtYATWbWnlhsZpiXYWYHAHcBP3LO/Vce2+lzznVFN2BzKk9AZAfV0e17jttT7jme2z4ZUMUK8aKc44r0HCutQkSKNK6CYzObAswHVgOPAgPA22Lz9wV2Bx6MTTsQuAf4qXPucxVtsMgEoZ5jqYQoraK1DFfHi0Q5xxu7++mLVccQEclXVdMqzOxi4CZ8KsVs4EJgCLjaOddpZpcD3zSzjUAX8B3gQefcQ+HxBwF3A7eH5aJc5CHn3LrKPhuRHVcm57ilPDnHL3eoF08qk1YxY3ITTQ119A8Os2ZzD3vMmFK2bYnIxFTtnuO5wNXA08A1wAZgQSywPR+4GbgOuA+fTnFy7PHvA3bB1zleHbs9UonGi0wUmWoVKfccz53ug+MVm7amul7ZMXVXIK3CzJg1VakVIlK8ag/I+8AY83uBc8Mt1/yFwMLUGyZSY6I6x2leBARgn13aAJ9zvLl3gKkp90zLjqUS1SrAXyVvxaatGpQnIkWpds+xiIwDHWUq5TZjcjMzQ/WApWs7U1237HgyaRVlzDkGlXMTkdIoOBaRsqVVAOw/axoAT61WcFzrKpFzDLoQiIiURsGxSI1zztEVVasoQ9rDASE4XqKe45rX01+ZtAr1HItIKRQci9S4rf2DDA07IP20Csj2HC9Zo+C41mV7jsscHLdpQJ6IFE/BsUiNi1IqGuqM1qb0f+6Oeo6fUnBc86LguBzvs7hZbbpKnogUT8GxSI3LDsZrwsxSX3/Uc7x8/RZ6dVGGmla5nuOQc6zgWESKoOBYpMZ1lqlSRWRW2ySmTWpk2DmefaWrLNuQHUN3hXOO127uZWh4uKzbEpGJR8GxSI3LVKpoSb9SBfiLMhwwqx1QakWty5ZyK29axcypLZjB0LBj/Za+sm5LRCYeBcciNS7qOW5vLd8FOvaf5S8GokF5ta1SFwFpqK9jlyk+71ipFSJSKAXHIjWus7e8PccA+89UOTeBngpcPjoSVazQoDwRKZSCY5EaV66r48UdsGtIq9CFQGpapS4CAj7XHVTOTUQKp+BYpMaV8+p4kf1n+rSKZ9Z1MTikAVK1qlLVKiBW61g9xyJSIAXHIjUuU62iDFfHi+wxYwqTGuvpHxzm+Q1byrYdGd+inOPWpgoEx9PUcywixVFwLFLjopzj9jL2HNfVGfvN1MVAal0l0yrUcywixVJwLFLjOrrLn3MMqlgh0B0NyKtgz7EG5IlIoRQci9S4TLWKMvYcQ/ZKeQqOa1elSrlBbECegmMRKZCCY5EaV4mcY0AXApHqpFV09uCcK/v2RGTiUHAsUuOiahXtrZXpOV66tlPBSg0aGh6mf9BX4/UtigAAIABJREFUKqlItYqQVtEzMERX+HVERCQfCo5Falxnb9RzXN7geO9dptJQZ2zpG2RlR3dZtyXjT2/oNYbK5By3NjXQFn4NUcUKESmEgmORGjY87DK9auUekNdYX8c+r9KgvFrVEw+OK9BzDBqUJyLFUXAsUsM29w0QZTiUe0AeZC8jrbzj2hMFx00NddTXVeajZ9ZUDcoTkcIpOBapYVG+cVNDHS0V6M1TObfa1dNfuavjRcbjhUA29w5kyieKyPhU/iHDIjJuRZUqynkBkLioYoWC49rTnSnjVrmPnfF2IZDHX97Em751O509A8xobWL+LlOZv/NU5u88xf8N93dtm0RdnVW7uSI1S8GxSA3L1Dgucxm3SFSxQmkVtaeqPcfjIDjeuLWPv71sUebXmo3d/Wx8cQOPvLhhu2Xn7TSZb/zd6/i7Q3fDTEGySKUpOBapYdmr41Wm53jfmW2YwYatfazb3MsuU1sqsl2pvkpeACQS9RxXe0De4NAwH7jifpav38K8nSZz73kn0NHTz7L1m1m2bjPL1m/x/6/fzIsbt/LChq2898f3cuIBs/nOKUew9y5tVW2/SK1RcCxSwzJl3MpcqSLS2tTAvBlTeH7DFpas7RwXwfELG7bw1TuepHdgiF2mNrPLlJZwa87+ndrClObK7KOJKnsBkMoFx7PaxkfO8b/f9BfuWLqa1qZ6fv3hY9l9xmR2ZzKHzJm+3bLd/YNc9Lsn+NqdT3LbU6s46Ms38dnjD+Izxx/IpCZ9ZItUgo40kRoW/cRb7hrHcfvPmsbzG7bw1OpO3rz3zIptN5cb/rqCM3/xQGY/jGbfmW186Ii9OP31e7LHjCkVaN3EkgmOKxjgjYe0iv/90/N8/c6nALji9KM4dO6MUZdvbWrgP991GB86Yi/+37V/5I6lq7nw1sX8/JHlfOeU1/OOA+dUotkiNa2q1SrMbKGZucRtaWx+i5ldamYbzGyLmV1nZjMT69jdzH5rZt1m9oqZfd3MFPSL5CEzIK+1cr2iB4S84yVrq5d33DcwxMev/SMnX3YvnT0DLJi3M1856TDOf8v+nH7Enpyw/2xeu9sMdpvemqni8fTaLj5/82PM++INHPOt2/nxA89m9p+MrRo9x1Faxabu/m0uQlIpj63cyD9d9SAAnz3+QE597by8H/vqmW3cfu7buOaf3szsaZNYvn4L7/z+3Zx82SJWbNxaphaLCIyPnuMngeNi9wdj/18CvBM4BegEvgtcDxwNYGb1wG+BNcBRwK7Az4AB4N/L3XCRHV12QF5le46hehUrlq3bzPt/ch+PvrQRgE8fdwBfPuk1NNbn7itwztHR089vFq/kZ39cxqJn13Lfc69w33Ov8LFrH+HdB8/lQ0fsxQkHzB5xHQI9/f7U3lqBq+NFprc20dxQR9/gMGu6epi3U+V6/Ndv6eVvf7SInoEhTjxgNv910mEFr8PMOOW1e3DiAbO58JbFfGvREm7460vc+tQqPvi6Pfn4sfvlTM0QkdKMh+B40Dm3JjnRzKYBZwGnOefuDtPOBJaY2QLn3EPA24EDgOOcc2uBx8zsC8BXzWyhc07dOiKjyA7Iq1zPcTUvBHLtn1/kn3/5IF29A+w0uZmffugo3nnQ3FEfY2ZMb23mjAXzOWPBfF7atJWrHnmen/9xOU+t6eSaP7/INX9+kWmTGjlm75m89dWzeOurZ3HQ7HZVGojJ9hxX7mPHzJjVNokXN26taHA8ODTMqT+5jxc3bmX+zlP55RlvLOnCJ1NbGrn45MM5Y8FenHvNH7nvuVe4/MHnuPzB5zh2n5mcd+x+nHTw3IpdXEVkohsPwfE+ZrYK6AUeBC5wzq0ADgcagTujBZ1zS81sBXAk8FD4+3gIjCO3A98HDgT+UpmnkL+tfQOZ/DORanvg+XVA5apVQLbn+OWObrp6+mmrwLZ7B4b41+v/xPfufwaAo/fahf89803MnT654HXtNn0yn327HyD155c28vM/LufqR1/glc293Pj4Sm58fCUAu0xpzgTKb913FvN3nppKsDw4NExHTz+buvvZ2N3n/27197sHBukdGKJnYGibv72DQ/QMDDI8nF1PvCnR/3Vm1NcZDXV1NNRl//d//f36OsssV2+x/8N0MzCiv9H6jfue86fpSqZVAJnguJJ5x5/+9aPc88xaJjc18Jt/OZbprc2prPeg2dNZdN7b+cPydXx70VKu/+sKFj27lkXPrmXeTpM59037ctZRe6e2vbjBoWFWdfbw0qatrOzoZsPWPrp6B7a5bY793zeYTWOJv++j/+rMaGqoo6m+LvG3nqaGOhrr6misNxrr62io9/cbovt1/q8BdXWWeb/VmWGQ+T9c/BPnHM6Bi/6HcD9MzzUt9jjI3o/+L1byDDDWOWFH+Hp9yJzpnHzY7tVuRqqqHRw/DJwBPI1PifgScL+ZHQTMAvqdcx2Jx6wN8wh/1+aYT2yZ7ZhZMxA/e0wtpvHF6O7//+3de3xU5Z3H8c8vl8kFQsLFBBAQ5KJFUBRRVLxXxdZWa1urratt1bq1fbnbXesLbCtau2t366rtynbbalfrqrtq1a5FW+ul1SKKCF6Rm9wFwj0JuWfmt3+cMzBEkMTMzJkk3/frdV6TOc+5PPPLSeY3zzzPc+Lc/PRb2TqdSIdUZXHWiIrSGEP6lbCxtpEl1bUcN3JQRs+3bkc9n/3FC7yxfgcAM8+ewA8/fRQFXewCYWZMHjGQySMG8m8XTmbRuh08v2wTzy/bxEvvV7NlVzP/u3AN/7twDQAD+xQxoDRGRWmM8uJCKkpjVJTEKC+OUVFaSGlhAbta2qhtbKW2qWVPwtHcRm1TKzVhQlzbdODBg7ksWzecSRqS5Rkr7p+/kjtfCIbO/OaykzhiSEVaj29mTBtdybTRlazbUc9/vLiUX85dzupt9Xz3iYXMeupNLpk8iglDKzi4vJSh5SUMLS9hSHnpPu+CmUg4OxpaqK5rZFNtI9V1TVTXNbF+RwPrdtazdns963Y2sLGmkYR3JS2UnurSKaOUHKeTuz+d8vQtM3sVWANcBGTyP9lMgkQ864oL87nm5HFRnFpknyrLirngyOFZPecnBpezsbaRxZtqMpocuztXPjCPN9bvYFDfIv77smmcM35o2s+Tn5fHsYcM5NhDBnL9WUfQ0hbn1dVbdyfL81ZtZVt9M9vqm9N2zrLiQgaUxuhfGmNAaREVJTH6FhVQXJhPcWE+JYX5FBfs+bmoIJ+C/D3tUPvKc+IJJ55w2hKJ8NGJJxLh4571CQ+39cTufYJ1iZTWuj2tcMnnpbF8rj3t8LTFoCOyOWPF0uoavvHQKwD8YPrEjCcMw/v34dbzj+HGc4/kwQWr+Omfl/D2hp3cM2/FPrcfUBpjaHkpB5UVsbOhleq6RjbXNdGW6FjSW5ifx8EVJQyv6ENlWTH9igvpV1xIWfiYuhQV5GNGyu9/71bXeMJpjSdoiSdoaUs+xmmNOy3xOM1tCdoSCVrjTls8QWsiQWs8QVs82K81kQhbfYPrLLFXC3DwfM83F8GHitRvMpItzHu1Ooffduwp//A3IKnH7Kz2f3MHirof4MNIRz+qZLr1+bhDMtvAEYWoW4734u47zWwZMAb4ExAzs4p2rcdVBAPwCB+Pa3eYqpSy/bkVuD3leRmw/mNXvBPKiguZ/aXjs3EqkZw1fnA5zy/blPFBeb97ax3PLNlIrCCPud+Zzriq7NxMIVaQz8ljqjh5TBWzPnUU9c2tvL91FzWNLexsbGFnY+uenxtaqWlqoaGljbKiQvqVhAlHUbuEo6SQ/iVF9A9bnzX4r2Oy2XJ898sraGqNc8a4wdz0qaMyfr6kklgBV5w4lq+fMIY/L6/m9++s54OdDWyoaWRDTSMf1DTQ1BoP7srX0AIbP3yM/qUxBvcroaqsmKqyYg6uKGV4/z4MTz72L6WqTLe1lt4hp5JjM+sLjAbuB14nmHXiTOC3YflhwAiCvsmEj98zs0p33xyuOwuoBfbbsdfdm4HdTTgaNCOSXdmYsaKxpY3vPLYAgO+eOT5rifG+9Ckq1KwCEUm2HGf6LnnxRIKHXl8NwLdPPSySJNLMOH3cYE4ft3evwuSMKxtqGtmws4HNu5qoKIlRVVbC4H7FVJYVEyvIbl9wkVwWaXJsZrcBTxJ0pRgK3AzEgYfcvcbM7gFuN7PtBAnvvwPzwpkqAJ4hSILvN7PrCfoZ/wiYHSbAIpKDkslxJmes+Mlzi1m9rZ5hFaXMPHtCxs4juW1wWXa6Vby0YjMf7GygvKSQT43PrRt1JGdc6V9alPY+0CI9UdQtx8OAh4CBwBbgr8BUd98Sln8HSBC0HBcRzERxTXJnd4+b2XkEs1PMA+qB+4Abs/UCRKTzkjcCWbVtF02t8X0OFOqKNdt3cesz7wBw2+cm00e3fu61dvc5znC3igcWrALgC5MOoSjLM3KISHpFPSDv4gOUNwHfCpf9bbMG+FSaqyYiGVRZVkz/0hg7GlpYtrk27V0Ornv8dZpa45w2toqLjjkkrceW7iXZ57i6rol4IpGRuYCbW+M8umgtAF+ZMirtxxeR7NKIDhHJOjPb07ViY/vZGrvmuaUbeXTRWvLzjJ99YYrGFPRylWXFmEHCnS27MtPb7unFH7CzsYWh5SWcMqYyI+cQkexRciwikUh2rXivujZtx2yNJ7j2kdcAuObkcUzUILheryA/j8q+wTzemRqU9+CC1QBcMnmU7lIn0gPor1hEIpG8jXQ6Z6yY/eJSFm+qYVDfIm7+dPam0pLclsl+x7WNLTz5TjAT6JenjEz78UUk+5Qci0gk9sxYkZ5uFdW1jcya8yYA//yZozNyC13pngb3y9yMFY+/uY6m1jiHV/Xj6GED0n58Eck+JcciEonxQ4LkeNnmOtriiS4f74YnF1Hb1Mrk4QP4+gmju3w86TkyeSOQ5CwVXz52lPq3i/QQSo5FJBLDK/pQGsunNZ5g5dZdXTrW/NVb+fW89wG466Lj1O9T9jIkQy3Hm2obeW5pcDPWLx+rWSpEegq9g4hIJPLybHe/4650rUgknG8/Mh+Ay48/lKmjDkpL/aTnyNRd8h5euJqEO1NHDmL0QWVpPbaIREfJsYhEJh23kb731fd5bc02yooL+fH5x6SratKDZKrl+IHX9nSpEJGeQ8mxiETmE12czm1TbSPXPf46ALPOPXL3wCuRVIMz0Od4xZZa5q/ZRn6e6UYzIj2MkmMRicz4Lt4I5NsPz2dHQwtHDxvAtacdns6qSQ+yeyq32kbcPS3HTM5t/MnDhlClD2UiPYqSYxGJTLLleEl1LQ0tbZ3a99FFa/jtG2spyDN+fekJFObr35nsW7JbRVNrnJrG1i4fz915MJylQreLFul59G4iIpEZPaiMIf1KqG9p4/L755JIdKxVb9uuZr71cDAIb8bZE5ik+WXlI5TECigvKQTSMyhv4brtLK2upaQwnwuOHN7l44lIblFyLCKRKcjP4+ErTiFWkMeji9Zy45w3OrTfdx5bwOa6JsYPLuf750zMcC2lJ0jnoLxkq/FnJw6jrLiwy8cTkdyi5FhEIjVtdCW/umQqAP/0x3f4zavvf+T2T737AffPX0meBd0pigrzs1FN6ebSNSgvnkjwUNjfWLNUiPRMSo5FJHKXHT+aG86eAMCVD77CSyuq97ldbWMLVz/0CgB/f/rhHD9ScxpLx6Sr5fgvy6vZWNtI/9IY08cPTUfVRCTHKDkWkZxwy3mT+PykEbTGE3zuV3/h/S11H9rm+icWsn5nA6MHlXHLeZMiqKV0V7tnrOhiy3HydtFfPPoQYgX61kKkJ1JyLCI5IS/P+M1lJzF5+AC21TfzmV+8wM6Glt3lLyzbxC/mLgfgnq+cQGmsIKqqSjeUbDneVPfxk+Om1ji/fWMtoFkqRHoyJccikjNKYwX839Wnc3BFKe9tquGiX79IWzxBfXMrVz44D4BvnjyOU8dWRVxT6W7S0XL81LsfUNPYyvD+pUw7tDJdVRORHKOmFxHJKUMrSnny6tOZdscf+NOSjVz76GsUFeSxcusuhvcv5cefPTrqKko3NDgNfY7/65UVAFwyeRR5eZaWeolI7lHLsYjknKOHD+CBy6dhBj9/aRl3vrAEgF9eMpV+JbGIayfd0ZAuzlbxwc4Gnnp3AwBXnDAmbfUSkdyj5FhEctIFR43gX84/Zvfzy48/lOnjD46wRtKdJbtV7GxsobGTd2OEoNU44c4pYyoZV9Uv3dUTkRyibhUikrOuO3M8Ta1x3li/g9svPDbq6kg3VlESo6ggj+a2BNV1TYwc2LfD+yYSzj3zgi4VV504NlNVFJEcoeRYRHKWmfGDc4+MuhrSA5gZQ8pLWL2tnve31nUqOX5u6UZWb6unoiTG5yeNyGAtRSQXqFuFiIj0CqeOCWY5ueP59zq1391hq/GlU0ZRoikERXo8JcciItIrfO+cieTnGXPe/YD5q7d2aJ8tdU08/uY6AK48UQPxRHoDJcciItIrjK3sx6XhzTtmPfVmh/a5f/5KWuMJphwykKOGDchk9UQkRyg5FhGRXuMH048kP8/4w+INzFu55SO3dXd+9XJwV8YrNX2bSK+RM8mxmc0wMzezO1PWjTazx81si5nVmtnDZlbVbr9xZvY7M9sabvNXMzs9+69ARERy3eiDyvjq8aOBA7cev7xyC0uqaymN5XPx5JFZqJ2I5IKcSI7NbApwNfBWyro+wDOAA2cAJwEx4EkzS6337wlm3TgDmAy8CfzezAZnp/YiItKdfH/6RAryjD8t2chf39+83+2SrcYXTx6pm8+I9CKRJ8dm1hd4ALgK2JFSdBIwEviqu7/t7m8DlwPHEiTCmNkgYCzwY3d/y92XAzOAUmBC1l6EiIh0GyMH9uXrYTeJWXP23Xpc09jCwwvXAHDlCZrbWKQ3iTw5BmYDc9z92XbriwhajZtT1jUBCWBa+HwbsBS4zMz6mFkBQQv0ZuD1jNZaRES6re+dM5HC/DyeX7aJvyyv/lD5gwtW0dga54gh5UwdNSiCGopIVCJNjs3sYuAYYOY+il8B6oF/MbPSsJvFbUA+MATA3R34JHA0UEeQPP8DMN3dd+zjmMnzFplZv+QClKXxZYmISI4bMaDP7qnZbpzzBsHbyR53vxzMbXzliWMxs6zXT0SiE1lybGbDgZ8CX3H3pvbl7r4F+CLwGWAXUANUAAsJWo+x4D/WbIKW4pOB44AnCPolD/mI088Mj5dc1qfnVYmISHdxw9kTiBXk8eKKzbywbNPu9QvXbWPhuu3ECvL4mymHRlhDEYlClC3Hk4FKYKGZtZlZG3AqcG34PN/dn3H30eF2g9z9b4CDgZXhMc4AzgMudve57r7Q3a8BGgn6J+/PrUB5yjIsEy9QRERy17D+ffjGiUF/4hvnvLm79TjZanzhUSMY2LcosvqJSDSiTI6fAyYCk1KWBQSD8ya5ezy5obtvdfedZnYGQaL8f2FRafiYaHfsBB/x2ty92d1rkwtBlwwREellZp49gaKCPOau3MKzSzZS39zKA6+tAuAq3RFPpFeKLDl29zp3fyd1IehjvC38GTP7mplNDec7vhR4BLjD3ZeGh5lHMMPFfWZ2VDjn8U+AUcCcCF6WiIh0I0MrSvnbaeOAoPX4kUVrqW1q5dBBfTltrGYEFemNCqKuwAEcRtAFYgCwGvgn4I5kobtvNbPp4frngULgXeB8d+/YvUFFRKRXm3H2BH45dzmvrN7Kym27ALjihDHk5WkgnkhvZO1H6PZG4YwVNTU1NfTr1y/q6oiISJZd99jr/NvziwHIzzPW/vBChlaUHmAvEcmm2tpaysvLAcrDbrEZkQvzHIuIiETq+rPGUxrLB+DTRxysxFikF1NyLCIivV5lWQm3fHoS/YoLmXGWbrAq0pupWwXqViEiIiKS69StQkREREQky5Qci4iIiIiElByLiIiIiISUHIuIiIiIhJQci4iIiIiElByLiIiIiISUHIuIiIiIhJQci4iIiIiElByLiIiIiISUHIuIiIiIhJQci4iIiIiECqKuQC6prc3YbbpFREREpAuylaeZu2flRLnMzA4G1kddDxERERE5oGHu/kGmDq7kGDAzA4YCdVk6ZRlBMj4si+fsiRTHrlMM00NxTA/FsesUw/RQHNMjE3EsAzZ4BhNYdasAwgBn7BNIe0EuDkCdu6svx8ekOHadYpgeimN6KI5dpximh+KYHhmKY8Z/HxqQJyIiIiISUnIsIiIiIhJSchyNZuDm8FE+PsWx6xTD9FAc00Nx7DrFMD0Ux/TolnHUgDwRERERkZBajkVEREREQkqORURERERCSo5FREREREJKjkVEREREQkqOs8zMvmVmq82sycxeNbPjoq5TLjOzU8zsSTPbYGZuZhe0Kzcz+6GZbTSzRjN71szGRlXfXGVmM83sNTOrM7PNZvaEmR3WbptiM5ttZtvMbJeZ/dbMqqKqc64xs2+a2VtmVhsu88zs3JRyxe9jMLMZ4d/2nSnrFMsDMLObwrilLktSyhXDDjCzg83sv8M4NZrZ22Z2bEq53mMOIMxp2l+Lbmazw/Judy0qOc4iM/sScDvBtCbHAG8CfzSzykgrltv6EMTpW/spvx64Fvhb4HigniCmxdmpXrdxKjAbmAqcBRQCz5hZn5Rt7gA+A3wx3H4o8FiW65nL1gMzgMnAscDzwO/M7IiwXPHrJDObAlwNvNWuSLHsmHeBISnLtJQyxfAAzKw/MBdoBc4FxgP/COxI2UzvMQc2hb2vw7PC9Y+Ej93vWnR3LVlagFeBu1Ke5xHctnpG1HXrDgvgwAUpzw3YCFyXsq4caAIujrq+ubwAB4XxPCUlbi3AF1K2OTzcZmrU9c3VBdgOXKH4fazY9QWWAZ8E/gzcGa5XLDsWv5uAN/ZTphh2LIY/Bl76iHK9x3y8uN4JrAjj1y2vRbUcZ4mZxQhanJ5NrnP3RPj8hKjq1c2NAgazd0xrCD6EKKYfrTx83B4+TiZoTU6N5RJgLYrlh5hZvpldTPDNxjwUv49jNjDH3Z9tt16x7LixYZezlWb2gJmNCNcrhh3zWWCBmT0SdjdbZGZXpZTrPaaTwlznUuDXHmTC3fJaVHKcPYOAfKC63fpqgj8+6bxk3BTTTjCzPIJP9nPd/Z1w9WCgxd13tttcsUxhZhPNbBfB3Z7+E/icuy9G8euU8IPFMcDMfRQrlh3zKvBVYDrwTYJE7iUzK0Mx7KhDCWK3HDgH+DnwMzO7PCzXe0znXQBUAPeGz7vltVgQdQVEJOtmAxPYu3+idMxSYBJBy/sXgPvM7NRoq9S9mNlw4KfAWe7eFHV9uit3fzrl6Vtm9iqwBrgIaIymVt1OHrDA3W8Iny8yswkE/Yvvi65a3doVwNPuviHqinSFWo6zZysQB9qP0KwCNmW/Oj1CMm6KaQeZ2V3AecDp7r4+pWgTEDOzina7KJYp3L3F3Ve4++vuPpNgsOjfofh1xmSgElhoZm1m1kYwSOfa8OdqFMtOC1vmlgFj0PXYURuBxe3WvQcku6foPaYTzOwQgjEEd6es7pbXopLjLHH3FuB14MzkuvDr7TMJ+ixK560i+ONKjWk/ghHFimmKcDqiu4DPAWe4+6p2m7xOMGI7NZaHEbxJKJb7lwcUofh1xnPARIIW+OSyAHgg5WfFspPMrC8wmiDh0/XYMXOBw9qtG0fQAg96j+msrwGbgTkp67rltahuFdl1O8HXsAuA+cDfEwzo+a9Ia5XDwn/4Y1JWjTKzScB2d18bzo36fTNbTvCP7BZgA/BE9mub02YDXwbOB+rMLNnXq8bdG929xszuAW43s+1ALfDvwDx3fyWaKucWM7sVeJpgIEkZQTxPA85R/DrO3euAd1LXmVk9sC3ZB16xPDAzuw14kiCRG0owRWgceEjXY4fdAbxsZjcADwPHAd8IF9zd9R7TMWFj39eA+9y9Lbm+216LUU+X0dsW4NsE/8yaCQZUHB91nXJ5IUg+fB/LvWG5AT8k+HTfRDAidlzU9c61ZT8xdOCrKdsUEyTR2wnm8nwMGBx13XNlAe4BVod/u5vDa+0sxS8tsf0z4VRuimWHY/Y/BElaM8Ec3P8DjFYMOx3H84C3w/eP94Cr2pXrPaZjcTw7fE/5UGy647VoYcVFRERERHo99TkWEREREQkpORYRERERCSk5FhEREREJKTkWEREREQkpORYRERERCSk5FhEREREJKTkWEREREQkpORYRERERCSk5FhGJkJn5AZaboq6jiEhvUhB1BUREerkhKT9/ieBWtYelrNuV3eqIiPRuajkWEYmQu29KLkBNsGqvdReb2Xtm1mRmS8zsmuS+ZjYybF2+yMxeMrNGM3vNzMaZ2RQzW2Bmu8zsaTM7KGW/e83sCTObZWZbzKzWzP7TzGIp2xSZ2c/MbHN47r+a2ZSsBkdEJAJKjkVEcpSZfYWgJfl7wCeAG4BbzOzydpveDPwIOAZoAx4E/hX4O+BkYEx4nFRnhsc8DbgEuBCYlVL+r8DngcvD464A/mhmA9Lz6kREcpOSYxGR3HUz8I/u/pi7r3L3x4A7gKvbbXebu//R3d8DfgpMBm5x97nuvgi4Bzi93T4twNfd/V13nwPcCFxrZnlm1gf4JvBdd3/a3RcDVwGNwBWZerEiIrlAfY5FRHJQmKCOBu4xs1+lFBUQdL9I9VbKz9Xh49vt1lW22+dNd29IeT4P6AsMB8qBQmBustDdW81sPkFrs4hIj6XkWEQkN/UNH68CXm1XFm/3vDXlZ9/POn1TKCLSAfpnKSKSg9y9GtgAHOruK9otq9JwiqPMrCTl+VSCmTHWAe8TdLs4KVloZoXAFGBxGs4tIpKz1HIsIpK7ZgE/M7Ma4A9AEXAs0N/db+/isWMEXTZ+BIwk6N98l7sngHoz+znwEzPbDqwFrgdKCfovi4j0WEqORURi24CCAAAAs0lEQVRylLvfbWYNwHeBnwD1BH2J70zD4Z8DlgMvEiTdDwE3pZTPIPh28X6gDFgAnOPuO9JwbhGRnGXufuCtRESkxzCze4EKd78g6rqIiOQa9TkWEREREQkpORYRERERCalbhYiIiIhISC3HIiIiIiIhJcciIiIiIiElxyIiIiIiISXHIiIiIiIhJcciIiIiIiElxyIiIiIiISXHIiIiIiIhJcciIiIiIiElxyIiIiIiof8H/xnJbF3mJVEAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Fazendo o gráfico \n",
        "fig, ax = plt.subplots(figsize=(8,4))\n",
        "# Plotando os dados para os EUA\n",
        "ax.plot(data_modelo_macro['Tempo'],data_modelo_macro['Pt'],color='#006BA2') #Azul\n",
        "plt.title(\"Simulação Computacional do Modelo Macro com choque de política monetária\", loc='center')\n",
        "ax.set_ylabel(\"Índice de Preços\")\n",
        "ax.set_xlabel(\"Tempo\")\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "hJnV9kWKtlsy",
        "outputId": "575578fe-6abb-49ef-8e10-32c4bda8d5fe"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Política Gradualista "
      ],
      "metadata": {
        "id": "0Oi_F527CTGO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Criando matrizes e arrays para as variáveis \n",
        "# Array do Tempo\n",
        "Tempo = np.array(np.arange(0,70)) #Essa operação cria um array em uma operação que vai de 0 até o tempo final em um passso de um a um\n",
        "\n",
        "# Inicialmente criaremos uma matriz de zeros para cada componente da simulação, iremos preencher elas com suas operações no bloco seguinte\n",
        "\n",
        "# Matriz do pt\n",
        "pt = np.zeros([70,1],float)\n",
        "# Matriz do y \n",
        "yt = np.zeros([70,1],float)\n",
        "# Matriz do Ms \n",
        "ms = np.zeros([70,1],float)"
      ],
      "metadata": {
        "id": "xg8Qh5bmCWNF"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Com as matrizes definidas, podemos preencher elas usando loops for \n",
        "# Definindo os parâmetros \n",
        "pe = 4 \n",
        "beta = 3 \n",
        "l = 0.5 \n",
        "ype = 500 \n",
        "# Loop geral para as operações, esse loop coordena que todas elas operem para cada valor do i\n",
        "for x in range(0,70):                                    \n",
        "  for i in range(0,70):                                   \n",
        "    pt[i] = 4                                                                                                                             #Configurando o nosso pt inicial para 4\n",
        "    pt_novo = np.copy(pt)                                                                                                                 #criando uma nova matriz copiada da matriz com o k inicial correto\n",
        "  for i in range(1,70):                                                                                                                  #Como o zero já esta definido, aqui iniciamos a partir do 1\n",
        "    pt_novo[i] = (pt[i-1]/(1 + (beta*l))) + ((l*yt[i - 1])/(1 + (beta*l))) + (((l*beta)*ms[i])/(1 + (beta*l))) - ((l*ype)/(1 + (beta*l)))                #Usando a nova matriz criada anteriormente e aplicando os novos valores \n",
        "    pt = np.copy(pt_novo)  \n",
        "  # Loop para definir o ms\n",
        "  for i in range(0,70):\n",
        "    ms[i] = 4\n",
        "    ms = np.copy(ms)\n",
        "#fazendo o choque no Ms\n",
        "  for i in range(1,20):\n",
        "    ms[i] = 20\n",
        "    ms_novo = np.copy(ms)\n",
        "  for i in range(20,21):\n",
        "    ms_novo[i] = 19\n",
        "    ms = np.copy(ms_novo)  \n",
        "  for i in range(21,22):\n",
        "    ms_novo[i] = 18\n",
        "    ms = np.copy(ms_novo)\n",
        "  for i in range(22,23):\n",
        "    ms_novo[i] = 17\n",
        "    ms = np.copy(ms_novo)\n",
        "  for i in range(23,24):\n",
        "    ms_novo[i] = 16\n",
        "    ms = np.copy(ms_novo) \n",
        "  for i in range(24,25):\n",
        "    ms_novo[i] = 15\n",
        "    ms = np.copy(ms_novo)\n",
        "  for i in range(25,26):\n",
        "    ms_novo[i] = 14\n",
        "    ms = np.copy(ms_novo)\n",
        "  for i in range(26,27):\n",
        "    ms_novo[i] = 13\n",
        "    ms = np.copy(ms_novo)\n",
        "  for i in range(27,28):\n",
        "    ms_novo[i] = 12\n",
        "    ms = np.copy(ms_novo)\n",
        "  for i in range(28,29):\n",
        "    ms_novo[i] = 11\n",
        "    ms = np.copy(ms_novo)\n",
        "  for i in range(29,30):\n",
        "    ms_novo[i] = 10\n",
        "    ms = np.copy(ms_novo)\n",
        "  for i in range(30,31):\n",
        "    ms_novo[i] = 9\n",
        "    ms = np.copy(ms_novo)  \n",
        "  for i in range(31,32):\n",
        "    ms_novo[i] = 8\n",
        "    ms = np.copy(ms_novo)\n",
        "  for i in range(32,33):\n",
        "    ms_novo[i] = 7\n",
        "    ms = np.copy(ms_novo)\n",
        "  for i in range(33,34):\n",
        "    ms_novo[i] = 6\n",
        "    ms = np.copy(ms_novo)\n",
        "  for i in range(34,35):\n",
        "    ms_novo[i] = 5\n",
        "    ms = np.copy(ms_novo) \n",
        "  for i in range(35,36):\n",
        "    ms_novo[i] = 4\n",
        "    ms = np.copy(ms_novo) \n",
        "  for i in range(36,70):\n",
        "    ms_novo[i] = 3\n",
        "    ms = np.copy(ms_novo)                                                                                                                          #voltando para a matriz pt\n",
        "# Loop para definir o y \n",
        "  for i in range(0,70):                                   \n",
        "    yt[i] = 500                                           \n",
        "    y_novo = np.copy(yt)                                  \n",
        "  for i in range(1,70):\n",
        "      y_novo[i] = (yt[i - 1]/(1+(beta*l)))  + (beta*(ms[i])/(1+(beta*l)))  - (beta*pt[i - 1]/(1+(beta*l)))  + ((beta*l)*ype/(1+(beta*l)))                \n",
        "      yt = np.copy(y_novo)"
      ],
      "metadata": {
        "id": "ratSg9p4CWPs"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Transformando todas as nossas varíaveis calculadas antes em arrays \n",
        "pt = np.array(pt)\n",
        "yt = np.array(yt)\n",
        "ms = np.array(ms)"
      ],
      "metadata": {
        "id": "LLffGWBQCfWe"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Como nossos arrays são bidimensionais precisamos fazer um reshape de 2D para 1D\n",
        "# Usando o comando reshape \n",
        "pt = np.reshape(pt,70)\n",
        "yt = np.reshape(yt,70)\n",
        "ms = np.reshape(ms,70)"
      ],
      "metadata": {
        "id": "QHnAvqY1Ci17"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Criando o dataframe com um dicionário para as colunas já definidas\n",
        "data_modelo_macro_grad = pd.DataFrame(index = Tempo)\n",
        "data_modelo_macro_grad[\"Tempo\"] = pd.Series(Tempo)\n",
        "data_modelo_macro_grad[\"Pt\"] = pd.Series(pt)\n",
        "data_modelo_macro_grad[\"Yt\"] = pd.Series(yt)\n",
        "data_modelo_macro_grad['Ms'] = pd.Series(ms)\n",
        "data_modelo_macro_grad"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "614c3352-5c90-489d-b962-b4814761a924",
        "id": "OfPu57BOClLO"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Tempo         Pt          Yt    Ms\n",
              "0       0   4.000000  500.000000   4.0\n",
              "1       1  13.600000  519.200000  20.0\n",
              "2       2  21.280000  515.360000  20.0\n",
              "3       3  23.584000  504.608000  20.0\n",
              "4       4  22.355200  497.542400  20.0\n",
              "..    ...        ...         ...   ...\n",
              "65     65   2.999999  499.999997   3.0\n",
              "66     66   2.999999  500.000000   3.0\n",
              "67     67   3.000000  500.000001   3.0\n",
              "68     68   3.000000  500.000001   3.0\n",
              "69     69   3.000000  500.000000   3.0\n",
              "\n",
              "[70 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a929d667-c137-4636-97a0-12dcf7691443\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>Tempo</th>\n",
              "      <th>Pt</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>13.600000</td>\n",
              "      <td>519.200000</td>\n",
              "      <td>20.0</td>\n",
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              "      <td>20.0</td>\n",
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              "      <td>20.0</td>\n",
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              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>66</td>\n",
              "      <td>2.999999</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>67</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>500.000001</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>68</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>500.000001</td>\n",
              "      <td>3.0</td>\n",
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              "      <th>69</th>\n",
              "      <td>69</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>3.0</td>\n",
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              "  </tbody>\n",
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              "<p>70 rows × 4 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Fazendo o gráfico \n",
        "fig, ax = plt.subplots(figsize=(8,4))\n",
        "# Plotando os dados para os EUA\n",
        "ax.plot(data_modelo_macro_grad['Tempo'],data_modelo_macro_grad['Yt'],color='#006BA2') #Azul\n",
        "plt.title(\"Simulação Computacional do Modelo Macro com choque de política monetária gradualista\", loc='center')\n",
        "ax.set_ylabel(\"Produto\")\n",
        "ax.set_xlabel(\"Tempo\")\n",
        "plt.savefig('Produto_gradualista.jpg')\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "outputId": "b0f227ff-31f0-421d-c6cc-d7b409192a01",
        "id": "Sc0_VnUaCnjR"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Fazendo o gráfico \n",
        "fig, ax = plt.subplots(figsize=(8,4))\n",
        "# Plotando os dados para os EUA\n",
        "ax.plot(data_modelo_macro_grad['Tempo'],data_modelo_macro_grad['Pt'],color='green') #Azul\n",
        "plt.title(\"Simulação Computacional do Modelo Macro com choque de política monetária gradualista\", loc='center')\n",
        "ax.set_ylabel(\"Índice de Preços\")\n",
        "ax.set_xlabel(\"Tempo\")\n",
        "plt.grid(False)\n",
        "plt.savefig('Precos_gradualista.jpg')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "outputId": "a27511aa-b2a5-415d-ea23-c220f0a8d4f9",
        "id": "_wipuzcTCpTJ"
      },
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plotando os dois gráficos em conjunto \n",
        "fig, ax = plt.subplots(figsize=(8,4))\n",
        "# Plotando os dados para os EUA\n",
        "ax.plot(data_modelo_macro['Tempo'],data_modelo_macro['Yt'],color='#006BA2',label = 'Política de Choque') #Azul\n",
        "ax.plot(data_modelo_macro_grad['Tempo'],data_modelo_macro_grad['Yt'],color='red',label = 'Política Gradualista') #Vermelho\n",
        "plt.title(\"Simulação Computacional do Modelo Macro em termos de política monetária\", loc='center')\n",
        "plt.legend(loc = 'upper left', borderaxespad = 0,bbox_to_anchor=(1.02, 1))\n",
        "ax.set_ylabel(\"Produto\")\n",
        "ax.set_xlabel(\"Tempo\")\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "-oCfF6CmYsxi",
        "outputId": "e0b578d9-46aa-4241-eb70-15204c995544"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Índice de Preços \n",
        "#Plotando os dois gráficos em conjunto \n",
        "fig, ax = plt.subplots(figsize=(8,4))\n",
        "# Plotando os dados para os EUA\n",
        "ax.plot(data_modelo_macro['Tempo'],data_modelo_macro['Pt'],color='#006BA2',label = 'Política de Choque') #Azul\n",
        "ax.plot(data_modelo_macro_grad['Tempo'],data_modelo_macro_grad['Pt'],color='red',label = 'Política Gradualista') #Vermelho\n",
        "plt.title(\"Simulação Computacional do Modelo Macro em termos de política monetária\", loc='center')\n",
        "plt.legend(loc = 'upper left', borderaxespad = 0,bbox_to_anchor=(1.02, 1))\n",
        "ax.set_ylabel(\"Índice de Preços\")\n",
        "ax.set_xlabel(\"Tempo\")\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "s3EcJtxUZmGy",
        "outputId": "6d620cc7-7ce2-4bfa-f139-c739e30aa19c"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Fazendo com o $P^{e}$ sendo igual a $P_{t-2}.$"
      ],
      "metadata": {
        "id": "Srlwet9tj9bE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Criando matrizes e arrays para as variáveis \n",
        "# Array do Tempo\n",
        "Tempo = np.array(np.arange(0,70)) #Essa operação cria um array em uma operação que vai de 0 até o tempo final em um passso de um a um\n",
        "\n",
        "# Inicialmente criaremos uma matriz de zeros para cada componente da simulação, iremos preencher elas com suas operações no bloco seguinte\n",
        "\n",
        "# Matriz do pt\n",
        "pt = np.zeros([70,1],float)\n",
        "# Matriz do y \n",
        "yt = np.zeros([70,1],float)\n",
        "# Matriz do Ms \n",
        "ms = np.zeros([70,1],float)"
      ],
      "metadata": {
        "id": "MqvTfD7wkJ_R"
      },
      "execution_count": 139,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Com as matrizes definidas, podemos preencher elas usando loops for \n",
        "# Definindo os parâmetros \n",
        "pe = 4 \n",
        "beta = 3 \n",
        "l = 0.5 \n",
        "ype = 500 \n",
        "# Loop geral para as operações, esse loop coordena que todas elas operem para cada valor do i\n",
        "for x in range(0,70):                                    \n",
        "  for i in range(0,70):                                   \n",
        "    pt[i] = 4                                                                                                                             #Configurando o nosso pt inicial para 4\n",
        "    pt_novo = np.copy(pt)                                                                                                                 #criando uma nova matriz copiada da matriz com o k inicial correto\n",
        "  for i in range(1,3):                                                                                                                  #Como o zero já esta definido, aqui iniciamos a partir do 1\n",
        "    pt_novo[i] = (pt[0]/(1 + (beta*l))) + ((l*yt[i - 1])/(1 + (beta*l))) + (((l*beta)*ms[i])/(1 + (beta*l))) - ((l*ype)/(1 + (beta*l)))                #Usando a nova matriz criada anteriormente e aplicando os novos valores \n",
        "    pt = np.copy(pt_novo)\n",
        "  for i in range(3,70):                                                                                                                  #Como o zero já esta definido, aqui iniciamos a partir do 1\n",
        "    pt_novo[i] = (pt[i-1]/(1 + (beta*l))) + ((l*yt[i - 1])/(1 + (beta*l))) + (((l*beta)*ms[i])/(1 + (beta*l))) - ((l*ype)/(1 + (beta*l)))                #Usando a nova matriz criada anteriormente e aplicando os novos valores \n",
        "    pt = np.copy(pt_novo)  \n",
        "  # Loop para definir o ms\n",
        "  for i in range(1,20):\n",
        "    ms[i] = 20 \n",
        "    ms_novo = np.copy(ms)\n",
        "#fazendo o choque no Ms\n",
        "  for i in range(20,70):\n",
        "    ms_novo[i] = 3\n",
        "    ms = np.copy(ms_novo)                                                                                                               #voltando para a matriz pt\n",
        "# Loop para definir o y \n",
        "  for i in range(0,70):                                   \n",
        "    yt[i] = 500                                           \n",
        "    y_novo = np.copy(yt)                                  \n",
        "  for i in range(1,70):\n",
        "      y_novo[i] = (yt[i - 1]/(1+(beta*l)))  + (beta*(ms[i])/(1+(beta*l)))  - (beta*pt[i - 1]/(1+(beta*l)))  + ((beta*l)*ype/(1+(beta*l)))                \n",
        "      yt = np.copy(y_novo) "
      ],
      "metadata": {
        "id": "zEFDul-okKM9"
      },
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Transformando todas as nossas varíaveis calculadas antes em arrays \n",
        "pt = np.array(pt)\n",
        "yt = np.array(yt)\n",
        "ms = np.array(ms)"
      ],
      "metadata": {
        "id": "bpLSvMP_kKP6"
      },
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Como nossos arrays são bidimensionais precisamos fazer um reshape de 2D para 1D\n",
        "# Usando o comando reshape \n",
        "pt = np.reshape(pt,70)\n",
        "yt = np.reshape(yt,70)\n",
        "ms = np.reshape(ms,70)"
      ],
      "metadata": {
        "id": "y499MYy8kKSw"
      },
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Criando o dataframe com um dicionário para as colunas já definidas\n",
        "data_modelo_macro_p2 = pd.DataFrame(index = Tempo)\n",
        "data_modelo_macro_p2[\"Tempo\"] = pd.Series(Tempo)\n",
        "data_modelo_macro_p2[\"Pt\"] = pd.Series(pt)\n",
        "data_modelo_macro_p2[\"Yt\"] = pd.Series(yt)\n",
        "data_modelo_macro_p2['Ms'] = pd.Series(ms)\n",
        "data_modelo_macro_p2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "uHc3ETM1kKVf",
        "outputId": "2bd9de9b-0446-45d9-bc70-37159f3be272"
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Tempo       Pt        Yt    Ms\n",
              "0       0   4.0000  500.0000   0.0\n",
              "1       1  13.6000  519.2000  20.0\n",
              "2       2  17.4400  515.3600  20.0\n",
              "3       3  22.0480  509.2160  20.0\n",
              "4       4  22.6624  501.2288  20.0\n",
              "..    ...      ...       ...   ...\n",
              "65     65   3.0000  500.0000   3.0\n",
              "66     66   3.0000  500.0000   3.0\n",
              "67     67   3.0000  500.0000   3.0\n",
              "68     68   3.0000  500.0000   3.0\n",
              "69     69   3.0000  500.0000   3.0\n",
              "\n",
              "[70 rows x 4 columns]"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>Tempo</th>\n",
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              "      <td>3.0</td>\n",
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              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>66</td>\n",
              "      <td>3.0000</td>\n",
              "      <td>500.0000</td>\n",
              "      <td>3.0</td>\n",
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              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>67</td>\n",
              "      <td>3.0000</td>\n",
              "      <td>500.0000</td>\n",
              "      <td>3.0</td>\n",
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              "    <tr>\n",
              "      <th>68</th>\n",
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              "      <td>500.0000</td>\n",
              "      <td>3.0</td>\n",
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              "      <th>69</th>\n",
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              "<p>70 rows × 4 columns</p>\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Fazendo o gráfico \n",
        "fig, ax = plt.subplots(figsize=(8,4))\n",
        "# Plotando os dados para os EUA\n",
        "ax.plot(data_modelo_macro_p2['Tempo'],data_modelo_macro_p2['Yt'],color='#006BA2') #Azul\n",
        "plt.title(\"Simulação Computacional do Modelo Macro com choque de política monetária ($P_{t-2}$)\", loc='center')\n",
        "ax.set_ylabel(\"Produto\")\n",
        "ax.set_xlabel(\"Tempo\")\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "4Z2OSt21kKYR",
        "outputId": "ac00b8de-b09b-477d-ccbf-e0460003eb47"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Fazendo o gráfico \n",
        "fig, ax = plt.subplots(figsize=(8,4))\n",
        "# Plotando os dados para os EUA\n",
        "ax.plot(data_modelo_macro_p2['Tempo'],data_modelo_macro_p2['Pt'],color='green') #Azul\n",
        "plt.title(\"Simulação Computacional do Modelo Macro com choque de política monetária ($P_{t-2}$)\", loc='center')\n",
        "ax.set_ylabel(\"Índice de Preços\")\n",
        "ax.set_xlabel(\"Tempo\")\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "z4dCaP0ckKau",
        "outputId": "ef8d890e-95cf-4a35-97b4-f4336e640aba"
      },
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Gráfico conjunto para as duas economias \n",
        "#Fazendo o gráfico \n",
        "fig, ax = plt.subplots(figsize=(8,4))\n",
        "# Plotando os dados para os EUA\n",
        "ax.plot(data_modelo_macro['Tempo'],data_modelo_macro['Yt'],color='green',label = \"$P_{t-1}$\") #Verde\n",
        "ax.plot(data_modelo_macro_p2['Tempo'],data_modelo_macro_p2['Yt'],color='red',label = \"$P_{t-2}$\") #Vermelho\n",
        "plt.title(\"Simulação Computacional do Modelo Macro com choque de política monetária\", loc='center')\n",
        "ax.set_ylabel(\"Produto\")\n",
        "ax.set_xlabel(\"Tempo\")\n",
        "plt.legend(loc = 'upper left', borderaxespad = 0,bbox_to_anchor=(1.02, 1))\n",
        "plt.grid(False)\n",
        "plt.savefig('Produto_P-1_P-2.jpg')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "iXV4EOYIkKfz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "outputId": "bb54e69b-51be-4bb0-b628-a0d5fcfaa486"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Gráfico conjunto para as duas economias \n",
        "#Fazendo o gráfico \n",
        "fig, ax = plt.subplots(figsize=(8,4))\n",
        "# Plotando os dados para os EUA\n",
        "ax.plot(data_modelo_macro['Tempo'],data_modelo_macro['Pt'],color='green',label = \"$P_{t-1}$\") #Verde\n",
        "ax.plot(data_modelo_macro_p2['Tempo'],data_modelo_macro_p2['Pt'],color='red',label = \"$P_{t-2}$\") #Vermelho\n",
        "plt.title(\"Simulação Computacional do Modelo Macro com choque de política monetária\", loc='center')\n",
        "ax.set_ylabel(\"Índice de Preços\")\n",
        "ax.set_xlabel(\"Tempo\")\n",
        "plt.legend(loc = 'upper left', borderaxespad = 0,bbox_to_anchor=(1.02, 1))\n",
        "plt.grid(False)\n",
        "plt.savefig('IndicePrecos_P-1_P-2.jpg')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "smginGOGkKi8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "outputId": "cc70f307-034f-4780-96c8-1fb06ea5fa0e"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Choque de Política Fiscal"
      ],
      "metadata": {
        "id": "70X5HSdKouZd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Criando matrizes e arrays para as variáveis \n",
        "# Array do Tempo\n",
        "Tempo = np.array(np.arange(0,70)) #Essa operação cria um array em uma operação que vai de 0 até o tempo final em um passso de um a um\n",
        "\n",
        "# Inicialmente criaremos uma matriz de zeros para cada componente da simulação, iremos preencher elas com suas operações no bloco seguinte\n",
        "\n",
        "# Matriz do pt\n",
        "pt = np.zeros([70,1],float)\n",
        "# Matriz do y \n",
        "yt = np.zeros([70,1],float)\n",
        "# Matriz do Ms \n",
        "ms = np.zeros([70,1],float)"
      ],
      "metadata": {
        "id": "ZqEwEaiNpMFg"
      },
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Com as matrizes definidas, podemos preencher elas usando loops for \n",
        "pe = 4 \n",
        "beta = 3 \n",
        "l = 0.5 \n",
        "ype = 500 \n",
        "alpha = 2\n",
        "g = 10 \n",
        "# Loop geral para as operações, esse loop coordena que todas elas operem para cada valor do i\n",
        "for x in range(0,70):                                    \n",
        "  for i in range(0,70):                                   \n",
        "    pt[i] = 4                                                                                                                             #Configurando o nosso pt inicial para 4\n",
        "    pt_novo = np.copy(pt)                                                                                                                 #criando uma nova matriz copiada da matriz com o k inicial correto\n",
        "  for i in range(1,70):                                                                                                                  #Como o zero já esta definido, aqui iniciamos a partir do 1\n",
        "    pt_novo[i] = (pt[i-1]/(1 + (beta*l))) + ((l*yt[i - 1])/(1 + (beta*l))) + (((l*beta)*ms[i])/(1 + (beta*l))) - ((l*ype)/(1 + (beta*l)))                #Usando a nova matriz criada anteriormente e aplicando os novos valores \n",
        "    pt = np.copy(pt_novo)  \n",
        "  # Loop para definir o ms\n",
        "  for i in range(0,70):\n",
        "    ms[i] = 4\n",
        "    ms = np.copy(ms)                                                                                                               #voltando para a matriz pt\n",
        "# Loop para definir o y \n",
        "  for i in range(0,10):                                   \n",
        "    yt[i] = 500                                           \n",
        "    y_novo = np.copy(yt)                                  \n",
        "  for i in range(10,11):\n",
        "      y_novo[i] = (yt[i - 1]/(1+(beta*l)))  + (beta*(ms[i])/(1+(beta*l)))  - (beta*pt[i - 1]/(1+(beta*l)))  + ((beta*l)*ype/(1+(beta*l)) + (alpha*g)/(1+(beta*l)))                \n",
        "      yt = np.copy(y_novo)  \n",
        "  for i in range(11,70):\n",
        "    y_novo[i] = (yt[i - 1]/(1+(beta*l)))  + (beta*(ms[i])/(1+(beta*l)))  - (beta*pt[i - 1]/(1+(beta*l)))  + ((beta*l)*ype/(1+(beta*l)))                \n",
        "    yt = np.copy(y_novo) "
      ],
      "metadata": {
        "id": "jX1QlkwXpM6M"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Transformando todas as nossas varíaveis calculadas antes em arrays \n",
        "pt = np.array(pt)\n",
        "yt = np.array(yt)\n",
        "ms = np.array(ms)"
      ],
      "metadata": {
        "id": "jcQyRk8_rHzw"
      },
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Como nossos arrays são bidimensionais precisamos fazer um reshape de 2D para 1D\n",
        "# Usando o comando reshape \n",
        "pt = np.reshape(pt,70)\n",
        "yt = np.reshape(yt,70)\n",
        "ms = np.reshape(ms,70)"
      ],
      "metadata": {
        "id": "Dqh9r450rKCt"
      },
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Criando o dataframe com um dicionário para as colunas já definidas\n",
        "data_modelo_macro_g = pd.DataFrame(index = Tempo)\n",
        "data_modelo_macro_g[\"Tempo\"] = pd.Series(Tempo)\n",
        "data_modelo_macro_g[\"Pt\"] = pd.Series(pt)\n",
        "data_modelo_macro_g[\"Yt\"] = pd.Series(yt)\n",
        "data_modelo_macro_g['Ms'] = pd.Series(ms)\n",
        "data_modelo_macro_g"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "7186ab24-8f71-4167-8b53-eec5b96d0b40",
        "id": "XDDQfNarrMYT"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Tempo   Pt     Yt   Ms\n",
              "0       0  4.0  500.0  4.0\n",
              "1       1  4.0  500.0  4.0\n",
              "2       2  4.0  500.0  4.0\n",
              "3       3  4.0  500.0  4.0\n",
              "4       4  4.0  500.0  4.0\n",
              "..    ...  ...    ...  ...\n",
              "65     65  4.0  500.0  4.0\n",
              "66     66  4.0  500.0  4.0\n",
              "67     67  4.0  500.0  4.0\n",
              "68     68  4.0  500.0  4.0\n",
              "69     69  4.0  500.0  4.0\n",
              "\n",
              "[70 rows x 4 columns]"
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "      <th>1</th>\n",
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              "      <td>500.0</td>\n",
              "      <td>4.0</td>\n",
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              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>66</td>\n",
              "      <td>4.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>67</td>\n",
              "      <td>4.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>68</td>\n",
              "      <td>4.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>4.0</td>\n",
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              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>69</td>\n",
              "      <td>4.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>4.0</td>\n",
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              "  </tbody>\n",
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              "<p>70 rows × 4 columns</p>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Fazendo o gráfico \n",
        "fig, ax = plt.subplots(figsize=(8,4))\n",
        "# Plotando os dados para os EUA\n",
        "ax.plot(data_modelo_macro_g['Tempo'],data_modelo_macro_g['Yt'],color='#006BA2') #Azul\n",
        "plt.title(\"Simulação Computacional do Modelo Macro com choque de política fiscal\", loc='center')\n",
        "ax.set_ylabel(\"Produto\")\n",
        "ax.set_xlabel(\"Tempo\")\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "outputId": "c454cf37-507c-4932-abb9-00ae16750fae",
        "id": "EusqzYHUrRlk"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Fazendo o gráfico \n",
        "fig, ax = plt.subplots(figsize=(8,4))\n",
        "# Plotando os dados para os EUA\n",
        "ax.plot(data_modelo_macro_g['Tempo'],data_modelo_macro_g['Pt'],color='green') #Azul\n",
        "plt.title(\"Simulação Computacional do Modelo Macro com choque de política fiscal\", loc='center')\n",
        "ax.set_ylabel(\"Índice de Preços\")\n",
        "ax.set_xlabel(\"Tempo\")\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "outputId": "b8e667b3-ddcb-48bb-8596-3bd6b6a63036",
        "id": "dhB99ss8rcxC"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}