Problem.ipynb

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      "source": [
        "### Introducción a la Ciencia de Datos y el Machine Learning\n",
        "\n",
        "---- \n",
        "\n",
        "**Intrucciones:** \n",
        "\n",
        "----\n",
        "\n",
        "Acceda a la página del Instituto Nacional de Infromática del Perú (INEI) y diríjase a la sección de Microdatos https://proyectos.inei.gob.pe/microdatos/ , seleccionando:\n",
        "\n",
        "- Encuesta Nacional de Hogares (ENAHO), metodología actualizada\n",
        "- Condiciones de Vida y Pobreza - ENAHO\n",
        "- Año: 2022\n",
        "- Periodo: Trimestre 4\n",
        "\n",
        "Descargue el módulo 5, Empleo e Ingresos en su versión .csv, descomprima el archivo descargado y suba a su Drive el archivo \"Enaho01a-2022-500.csv\". Luego, haga lo mismo con el módulo Gastos en Alimentos y Bebidas (Módulo 601) y cargue el archivo .csv correspondiente. \n",
        "\n",
        "----\n",
        "\n",
        "1) Lea el diccionario inserto en el archivo .zip que ha descargado y determine cuáles son las variables con las que podría cruzar sus bases usando un Left Join. Justifique su respuesta en una celda de texto del Notebook de Google Colab. (2ptos)\n",
        "\n",
        "Nota: sí, se debe usar más de una variable de cruce y ninguna IA actual te podrá dar la respuesta &#x1F600;.  \n",
        "\n",
        "2) Cruce las bases usando las variables que determinó en la pregunta 1) y determine el número de filas y columnas con las que termina la tabla final. (4ptos)\n",
        "\n",
        "3) Cree una variable mensualizada del ingreso. Para ello, deberá identificar en la base de datos las variables P523 y P524A1 descritas en el diccionario. Usando la variable creada calcule la media, mediana, primer cuartil, tercer cuartil, mínimo, máximo y el número de datos con los que se han calculado dichos estadísticos.     (4 ptos)\n",
        "\n",
        "Haga un subset de la base datos, quédese con todas aquellas filas que tengan datos no missing para la variable mensualizada en la pregunta 3).\n",
        "\n",
        "5) Haga un histograma de la variable ingreso mensualizado. Muestre en el gráfico el valor de la mediana. (5ptos)\n",
        "\n",
        "6) Elija una variable continua del módulo \"Gastos en Alimentos y Bebidas\", cree un gráfico de dispersión entre ella y la variable  ingreso mensualizado. Ayuda: Para que ambas variables estén en la misma escala aplique la transformación logaritmo natural. (5ptos)\n",
        "\n",
        "\n"
      ],
      "metadata": {
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    },
    {
      "cell_type": "markdown",
      "source": [
        "#Solución:"
      ],
      "metadata": {
        "id": "sFHcSgzWdx-z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importamos las librerias a usar\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "mUORRlMFehLs"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Segun las indicaciones, las bases se tiene que subir al google drive y apartir de ahi llamarlas,\n",
        "# estos codigos solicitan acceso a los archivos de tu google drive.\n",
        "from google.colab import drive \n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "id": "RpXgtxINkkIm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8cb0d732-6c86-4f42-c5c2-7f8abc6a1fc6"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Importamos las bases*"
      ],
      "metadata": {
        "id": "pA2afgKaoY9Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Modulo 7 (601): Gasto en alimentos y bebidas}\n",
        "df_mod601=pd.read_csv('/content/gdrive/MyDrive/base_enaho/Enaho01-2022-601.csv',  sep=',' , encoding='latin-1') \n",
        "\n",
        "# Pasamos el nombre de todas las variables a minuscula para evitar posibles errores\n",
        "df_mod601.columns= df_mod601.columns.str.lower()  \n",
        "\n",
        "# Modulo 5: Empleo e ingresos\n",
        "df_mod5=pd.read_csv('/content/gdrive/MyDrive/base_enaho/Enaho01a-2022-500.csv', sep=',' , encoding='latin-1') \n",
        "\n",
        "# Pasamos el nombre de todas las variables a minuscula para evitar posibles errores\n",
        "df_mod5.columns= df_mod5.columns.str.lower() \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "11Md4RTuWWwN",
        "outputId": "77351bcc-f821-4212-f612-9cc123f1092d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-3bf09b70d51f>:10: DtypeWarning: Columns (221,226,231,239,244,249,254,259,264,269,274,279,284,289,294,299,304,309,314,319,324,329,334,339,346,349,352,355,358,361,364,367,372,374,376,378,380,382,384,386,388,392,393,394,395,396,400,491,492,493,494,941,942,943,944,945,946,947,948,949,950,1021,1024,1029) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df_mod5=pd.read_csv('/content/gdrive/MyDrive/base_enaho/Enaho01a-2022-500.csv', sep=',' , encoding='latin-1')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1) Lea el diccionario inserto en el archivo .zip que ha descargado y determine cuáles son las variables con las que podría cruzar sus bases usando un Left Join. Justifique su respuesta en una celda de texto del Notebook de Google Colab. (2ptos)**\n",
        "\n",
        "**Nota: sí, se debe usar más de una variable de cruce y ninguna IA actual te podrá dar la respuesta 😀.**"
      ],
      "metadata": {
        "id": "pqmGTdtXdujJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Respuesta:\n",
        "Al analizar el diccionario y las bases de datos correspondientes al módulo 5 y al módulo 7 (601), se puede observar que el primero está desagregado a nivel de personas, mientras que el segundo se encuentra desagregado a nivel de hogares. Dado que cada individuo pertenece a un hogar, es necesario utilizar las variables comunes entre ambas bases, como el **\"año\", \"mes\", \"conglome\", \"vivienda\" y \"hogar\"**, para fusionarlas y así establecer una conexión entre los individuos y las características de sus hogares correspondientes, que se encuentran registradas en el módulo 7 (601). De esta forma, al momento de hacer el merge, se obtiene información correspondiente a empleo e ingresos de cada individuo con los Gastos de su hogar registradas en el módulo 7."
      ],
      "metadata": {
        "id": "PEkrkZRtNB8X"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2) Cruce las bases usando las variables que determinó en la pregunta 1) y determine el número de filas y columnas con las que termina la tabla final. (4ptos)**"
      ],
      "metadata": {
        "id": "hPrD2FoDeDHl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Nota: Antes de hacer el merge entre las bases de datos, es relevante recordar que el módulo 7 (601) está desglosado a nivel de artículos específicos de alimentos y bebidas por cada hogar. Sin embargo, como nuestro objetivo es hacer el merge entre la base de datos del módulo 5 (nivel de personas) con el módulo 7 (601), necesitamos agregar la información del modulo 7. De esta forma, sumamos las observaciones por cada hogar y asi calculamos el gasto total en alimentos y bebidas en los últimos 15 días (Variable p601c). De esta forma,podemos unir los datos del módulo 7 (601) agregados a nivel de hogares con los datos del módulo 5 que se encuentra a nivel de personas.*"
      ],
      "metadata": {
        "id": "kdgP9mtstZl6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Agregamos la variable \"p601c\" observaciones a nivel de hogar\n",
        "c_df_mod601 = df_mod601.groupby(['año','mes', 'conglome', 'vivienda' , 'hogar'])[['p601c']].agg(['sum'])"
      ],
      "metadata": {
        "id": "GKRYVfUCtJEE"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_merge = df_mod5.merge(c_df_mod601, \n",
        "                         left_on=['año',\"mes\", \"conglome\", \"vivienda\",\"hogar\"], \n",
        "                         right_on=['año',\"mes\", \"conglome\", \"vivienda\",\"hogar\"],\n",
        "                         how=\"left\", validate=\"m:1\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CJcyM1Hv1zzF",
        "outputId": "d6fbe53b-c66a-46ff-baf5-e47f32364d0a"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-54-225619d9f1f5>:1: FutureWarning: merging between different levels is deprecated and will be removed in a future version. (1 levels on the left, 2 on the right)\n",
            "  df_merge = df_mod5.merge(c_df_mod601,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1) y determine el número de filas y columnas con las que termina la tabla final.**"
      ],
      "metadata": {
        "id": "dadpRF2AZnYS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Obtenemos el número de filas y columnas\n",
        "filas = len(df_merge.axes[0])\n",
        "columnas = len(df_merge.axes[1])\n",
        "  \n",
        "# Print the number of rows and columns\n",
        "print(\"Número de filas: \" + str(filas))\n",
        "\n",
        "print(\"Número de columnas:\" + str(columnas))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e1ePCGtZ-0QX",
        "outputId": "7920a217-2bda-4cf6-d071-fa925dd27c18"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Número de filas: 21606\n",
            "Número de columnas:1186\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3) Cree una variable mensualizada del ingreso. Para ello, deberá identificar en la base de datos las variables P523 y P524A1 descritas en el diccionario. Usando la variable creada calcule la media, mediana, primer cuartil, tercer cuartil, mínimo, máximo y el número de datos con los que se han calculado dichos estadísticos. (4 ptos)**\n",
        "\n"
      ],
      "metadata": {
        "id": "pUMdPEnCeQR0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Antes de continuar con el análisis, examinamos la variable \"P523\":*\n",
        "\n",
        "\n",
        "\n",
        "> *p523) en su ocupacion principal, a ud. le pagan:?*\n",
        "\n",
        "\n",
        "> 1 = diario\n",
        "\n",
        "> 2 = semanal\n",
        "\n",
        "> 3 = quincenal\n",
        "\n",
        "> 4 = mensual\n"
      ],
      "metadata": {
        "id": "T_uBXYDobzWO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_merge['p523'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RJ8_t11CchrL",
        "outputId": "540dc699-2307-4d9b-be9c-7f077a52d55f"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     15186\n",
              "4     3981\n",
              "2     1932\n",
              "3      425\n",
              "1       82\n",
              "Name: p523, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Antes de crear la variable de ingreso mensual, es recomendable convertir la variable de ingreso (p524a1) a tipo float y corregir missings de esta manera, se evita posibles errores en el futuro y asegurarnos de que los cálculos sean precisos.*\n"
      ],
      "metadata": {
        "id": "EtYtxrtpbcT4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_merge.loc[df_merge['p524a1']==\" \", 'p524a1'] = None\n",
        "df_merge.loc[df_merge['p524a1']==\"999999\", 'p524a1'] = None\n",
        "df_merge['p524a1'] = df_merge['p524a1'].astype(float)"
      ],
      "metadata": {
        "id": "bP56k2_RGRb5"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Creamos nuestra variable de ingreso mensual\n",
        "df_merge.loc[df_merge['p523']==\"1\", 'ingreso_mensual'] = df_merge['p524a1']*30  # Diario \n",
        "df_merge.loc[df_merge[\"p523\"]==\"2\", 'ingreso_mensual'] = df_merge['p524a1']*4   # Semanal\n",
        "df_merge.loc[df_merge[\"p523\"]==\"3\", 'ingreso_mensual'] = df_merge['p524a1']*2   # Quincenal\n",
        "df_merge.loc[df_merge[\"p523\"]==\"4\", 'ingreso_mensual'] = df_merge['p524a1']     # Mensual\n",
        "df_merge.loc[df_merge[\"p524a2\"]==\"1\", 'ingreso_mensual'] = None                 # No sabe, le ponemos missing"
      ],
      "metadata": {
        "id": "UFl46KzIAWGf"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Usando la variable creada calcule la media, mediana, primer cuartil, tercer cuartil, mínimo, máximo y el número de datos con los que se han calculado dichos estadísticos.**"
      ],
      "metadata": {
        "id": "FyawP-xOcWfZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Primer cuartil\n",
        "def q25(x):\n",
        "  return x.quantile(0.25)\n",
        "# Tercer cuartil\n",
        "def q75(x):\n",
        "  return x.quantile(0.75)\n",
        "\n",
        "# Estadisticos\n",
        "df_merge['ingreso_mensual'].agg(Media=('mean'), Mediana=('median'), Primer_cuartil=(q25) , Tercer_cuartil=(q75) ,\n",
        "                                Mínimo=('min'), Máximo=('max'), Número_de_datos=('count'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oKA4PxjWeaxb",
        "outputId": "c5492184-ca99-4fc8-8ee1-ed2f1e346e31"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Media               1501.081833\n",
              "Mediana             1200.000000\n",
              "Primer_cuartil       720.000000\n",
              "Tercer_cuartil      1900.000000\n",
              "Mínimo                10.000000\n",
              "Máximo             18000.000000\n",
              "Número_de_datos     6330.000000\n",
              "Name: ingreso_mensual, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4) Haga un subset de la base datos, quédese con todas aquellas filas que tengan datos no missing para la variable mensualizada en la pregunta 3).**"
      ],
      "metadata": {
        "id": "8y5CZ5VHeWts"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_merge_subset = df_merge[~df_merge['ingreso_mensual'].isna()]\n",
        "df_merge_subset.shape"
      ],
      "metadata": {
        "id": "XhcJoFWyX8fA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0a53f9df-abf5-400c-e567-20c377661576"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6330, 1187)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**5) Haga un histograma de la variable ingreso mensualizado. Muestre en el gráfico el valor de la mediana. (5ptos)**"
      ],
      "metadata": {
        "id": "qD5GYLDzeDQj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "valor= df_merge_subset['ingreso_mensual']\n",
        "\n",
        "plt.hist( valor, bins=35, color='c', edgecolor='k', alpha=0.7)\n",
        "plt.xlabel('Ingreso Mensualizado')\n",
        "plt.ylabel('Frecuencia')\n",
        "plt.title(r'Histograma')\n",
        "\n",
        "plt.axvline(valor.median(), color='red', linestyle='dashed', linewidth=1)\n",
        "min_ylim, max_ylim = plt.ylim()\n",
        "plt.text(valor.median()*1.1, max_ylim*0.9, 'Mediana: {:.0f}'.format(valor.median()), color=\"red\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "mUdBhL2mZGO4",
        "outputId": "fc9f3cf8-d617-4463-d69f-36fb27f4d009"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(1320.0, 1557.3600000000001, 'Mediana: 1200')"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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r1wKjRpVtFxmp/3BfuVI/uvHGG8BLLwH/G8FDejoweDDQvz+QkAC88op+ZtuD5OYCffvqA8WpU0Dv3vr975pZCABYuBDo0EHfZvx4YNw4ICnpn+12dvqwlZioDzWrVwOLF/+z/dIl/SnAAwcq/rpURH6+Png5Oekfp6QAWq3+NFkpe3t98PnfaV3ExelDYYcO/7QJCtKfpjt2rHrrIyKichltILKwsEDHjh2RdPeHHIA///wT3v/7qgl/f3+Ym5tjb+n/jQNISkpCWloaAgMDAQCBgYH4/fffkZmZKbWJjY2FWq0uE7YUxdxcP8PM3LzstpdeAn75Rf99Z6mpwOHD+nV3KyjQj9KsXas//dOokf76opdeAj77TN9mxQqgcWN9ePHz058W+rdrkNq0AV59FWjZEvD11X/fWuPGwPffG7br21cfhJo00Y8G1a+vP21V6r33gCee0I/y9O8PvPUW8M03hv3389OPIFWn6dMBD49/AlDpadl7b2/g5vbPNq227GlDMzN9qHrAaV0iIqo+ss4yy83NxYULF6THKSkpSEhIgJOTE7y8vDB16lQMGzYMXbt2RY8ePbBr1y788MMPOPC//6u3t7dHWFgYpkyZAicnJ6jVakycOBGBgYHo3LkzAKBXr15o3rw5hg8fjqioKGi1Wrz33nsIDw+HpaWlHN02Dq1aAX/9Vf42FxegXz/9CIsQ+t/r1zdsc+GCfjTkmWcM1xcW/nNq7exZ/UjI3f4XVO8rNxeYPRvYsUN/UXNxsf56pntHiFq3/ud3lQrQaIC7Qi82bdJft5OcrH/O4mL9BculHnsMOHfuwbVU1rx5+tNcBw4Ad90agoiIjJ+sgejEiRPo0aOH9HjKlCkAgBEjRiA6OhqDBg3CypUrERkZiUmTJsHPzw/ffvstunTpIu2zePFimJiYYMiQISgoKEBwcDA+/fRTabupqSm2b9+OcePGITAwELa2thgxYgTmzp1bex2ti0aPBiZM0P9e3n2iSmdA7dhR9l5GDxM033pLf2HxggX60R9ra+A//9EHrbvdO7KlUumv3QH0p6BCQ4E5c/SjV/b2+qCycGHV6/o3CxboA9GePYZhrfS0bEaGfpZZqYwM/XVHpW3uDnOAPsBlZf2zPxER1ShZA1H37t0hhHhgm9GjR2P06NH33W5lZYXly5ff9+aOAODt7Y2dO3dWuc5H0u+/A336AD/+qB8tulfv3voQolLpQ8W9mjfXB5+0NKBbt/KP0axZ2VNdR48+uK7Dh/Wn1QYN0j/OzS17Ifa/OXIE8PYG3n33n3WpqZV7jsqIigI+/BD46SfD64AA/QXnGo3+mqjSAKTT6a8NGjdO/zgwUH8fptL7QgHAvn36gHfvCBsREdUIWQMRyaioSD81/H4zr0xN9ae8Sn+/l52dfjTnjTf0H9xdugA5OfpAo1YDI0bo72e0cCEwdar+gur4eP1puAfx9dXPauvfXx/GZs78Z+Snonx99UFt40b9LLUdO4CtWw3bXL4M9Oypvyi8U6f7P1dCgv5nbi5w7Zr+sYWFPhACwMcf6+8xtGGD/nql0mt+6tXTLyqV/v5FH3ygr8vHR98nDw9g4EB922bN9AF0zBj9BepFRfrRuZAQfTsiIqpxRntRNRkBtdrwupt7vf++/sM9MvKfD/UdO/Qf+oB+Ovy33+qnl7dpo/+w/+ijBx9z0SL9DQ6feEIfioKD9bcCqIznntMHtQkT9KMyR47o67xbUZF+Vlp+/oOfq107/RIfrw897drpL+gutWKFfiTtP//RnxIrXRYs+KfNtGnAxIn6+wp17KgPV7t2GV5nFBOjn7bfs6f++bt0KXsrASIiqjEq8W/nrAgAoNPpYG9vj5ycHNmn4CcnJ+P5cePgMHkybCt4w8i89HRkL1mCzStW6L/+5ORJ/emZ+PjKBw4iIqI6oqKf3xwhIiIiIsVjIFIqX1/9fXt8feWuhIiISHa8qFqp7OyA7t3lroKIiMgocIRIqS5fBmbMMPwSUiIiIoViIFKqjAz9jQTv+Z43IiIiJWIgIiIiIsVjICIiIiLFYyAiIiIixWMgUipnZyAsTP+TiIhI4TjtXqm8vYHPP5e7CiIiIqPAESKlunULOHNG/5OIiEjhGIiU6uxZoGXLf77RnoiISMEYiIiIiEjxGIiIiIhI8RiIiIiISPEYiJRKpQIsLPQ/iYiIFI7T7pWqXTugoEDuKoiIiIwCR4iIiIhI8RiIlOrsWaB9e067JyIiAgORct26BZw6xRszEhERgYGIiIiIiIGIiIiIiIGIiIiIFI+BSKl8fIBvvtH/JCIiUjjeh0ipHB2B55+XuwoiIiKjwECkIEWFhUhNTQUAmP79N+p99x1yBwzAnfr177uPWq2Gi4tLbZVIREQkCwYihSjMyUHqxYuYOGcOLC0t0VSnw4ZjxzBp3z6cU6vvu5+zjQ02rF7NUERERI80BiKFuJOfj2IzM1gMGwaHhg1hd+ECcOwY7F58EQ5NmpS7zy2tFtc3boROp2MgIiKiR5qsF1UfOnQI/fv3h4eHB1QqFbZt23bftq+99hpUKhWWLFlisD4rKwuhoaFQq9VwcHBAWFgYcnNzDdqcPn0aTz31FKysrODp6YmoqKga6E3dYOXqCltPT1i7uQEArN3cYOvpWe5irdHIXC0REVHtkDUQ5eXloU2bNli+fPkD223duhVHjx6Fh4dHmW2hoaE4c+YMYmNjsX37dhw6dAhjx46Vtut0OvTq1Qve3t6Ij4/H/PnzMXv2bKxatara+0NERER1k6ynzPr06YM+ffo8sM3ly5cxceJE/PTTT+jXr5/BtrNnz2LXrl04fvw4OnToAABYtmwZ+vbtiwULFsDDwwMxMTEoLCzE2rVrYWFhgRYtWiAhIQGLFi0yCE5Kk2dnh2PduiHPzk7uUoiIiGRn1PchKikpwfDhwzF16lS0aNGizPa4uDg4ODhIYQgAgoKCYGJigmPHjkltunbtCgsLC6lNcHAwkpKScOPGjZrvhJHSenrig2XLoPX0lLsUIiIi2Rl1IPr4449hZmaGSZMmlbtdq9XC1dXVYJ2ZmRmcnJyg1WqlNm7/u16mVOnj0jblKSgogE6nM1geJaZFRVBnZcG0qEjuUoiIiGRntIEoPj4en3zyCaKjo6FSqWr9+JGRkbC3t5cWz0dsJKXh+fOI6d4dDc+fl7sUIiIi2RltIPr555+RmZkJLy8vmJmZwczMDKmpqXjzzTfRsGFDAIBGo0FmZqbBfsXFxcjKyoLmfzOkNBoNMjIyDNqUPtY8YBbVjBkzkJOTIy3p6enV2DsiIiIyJkZ7H6Lhw4cjKCjIYF1wcDCGDx+OUaNGAQACAwORnZ2N+Ph4+Pv7AwD27duHkpISBAQESG3effddFBUVwdzcHAAQGxsLPz8/ODo63vf4lpaWsLS0rImuERERkZGRNRDl5ubiwoUL0uOUlBQkJCTAyckJXl5ecHZ2Nmhvbm4OjUYDPz8/AECzZs3Qu3dvjBkzBitXrkRRUREmTJiAkJAQaYr+iy++iDlz5iAsLAzTp0/HH3/8gU8++QSLFy+uvY4SERGRUZM1EJ04cQI9evSQHk+ZMgUAMGLECERHR1foOWJiYjBhwgT07NkTJiYmGDJkCJYuXSptt7e3x+7duxEeHg5/f3/Ur18fERERip5yT0RERIZkDUTdu3eHEKLC7S9dulRmnZOTEzZs2PDA/Vq3bo2ff/65suU90lL8/DD0yBEUWFvLXQoREZHsjPYaIqpZJaamuFWvntxlEBERGQWjnWVGNcs9NRVzXnsN7qmpcpdCREQkOwYihbLJy0P7I0dgk5cndylERESyYyAiIiIixWMgIiIiIsVjICIiIiLFYyBSqGsaDVa88w6uPeDrS4iIiJSC0+4VSufkhJ0hIXKXQUREZBQ4QqRQ9XJy0H37dtTLyZG7FCIiItkxECmU2+XLePOdd+B2+bLcpRAREcmOgYiIiIgUj4GIiIiIFI+BiIiIiBSPgUihbltb41zr1rjNb7snIiLitHuluuzjg6lffSV3GUREREaBI0RERESkeAxECtU4MRE/tG6NxomJcpdCREQkOwYiIiIiUjwGIiIiIlI8BiIiIiJSPAYiIiIiUjxOu1eotMaNMXb7dvzt5iZ3KURERLJjIFKoIktLXPXykrsMIiIio8BTZgrl9tdfmDJjBtz++kvuUoiIiGTHQKRQ9XQ69NixA/V0OrlLISIikh0DERERESkeAxEREREpHgMRERERKR4DkUJlubhgw2uvIcvFRe5SiIiIZMdp9wp1w8UFX48fL3cZRERERoEjRAplnZuLdocPwzo3V+5SiIiIZMdApFAeaWmYO24cPNLS5C6FiIhIdrIGokOHDqF///7w8PCASqXCtm3bpG1FRUWYPn06WrVqBVtbW3h4eODll1/GlStXDJ4jKysLoaGhUKvVcHBwQFhYGHLvGfU4ffo0nnrqKVhZWcHT0xNRUVG10T0iIiKqI2QNRHl5eWjTpg2WL19eZlt+fj5OnjyJmTNn4uTJk9iyZQuSkpLw3HPPGbQLDQ3FmTNnEBsbi+3bt+PQoUMYO3astF2n06FXr17w9vZGfHw85s+fj9mzZ2PVqlU13j8iIiKqG2S9qLpPnz7o06dPudvs7e0RGxtrsO7//u//0KlTJ6SlpcHLywtnz57Frl27cPz4cXTo0AEAsGzZMvTt2xcLFiyAh4cHYmJiUFhYiLVr18LCwgItWrRAQkICFi1aZBCciIiISLnq1DVEOTk5UKlUcHBwAADExcXBwcFBCkMAEBQUBBMTExw7dkxq07VrV1hYWEhtgoODkZSUhBs3btz3WAUFBdDpdAbLo6TIwgJXPD1RdNfrQkREpFR1JhDdvn0b06dPxwsvvAC1Wg0A0Gq1cHV1NWhnZmYGJycnaLVaqY2bm5tBm9LHpW3KExkZCXt7e2nx9PSszu7ILq1JE7y6YwfSmjSRuxQiIiLZ1YlAVFRUhKFDh0IIgRUrVtTKMWfMmIGcnBxpSU9Pr5XjEhERUe0z+kBUGoZSU1MRGxsrjQ4BgEajQWZmpkH74uJiZGVlQaPRSG0yMjIM2pQ+Lm1THktLS6jVaoPlUdLwzz/xVbduaPjnn3KXQkREJDujDkSlYej8+fPYs2cPnJ2dDbYHBgYiOzsb8fHx0rp9+/ahpKQEAQEBUptDhw6hqKhIahMbGws/Pz84OjrWTkeMkGlxMexv3IBpcbHcpRAREclO1kCUm5uLhIQEJCQkAABSUlKQkJCAtLQ0FBUV4T//+Q9OnDiBmJgY3LlzB1qtFlqtFoWFhQCAZs2aoXfv3hgzZgx+/fVXHD58GBMmTEBISAg8PDwAAC+++CIsLCwQFhaGM2fOYNOmTfjkk08wZcoUubpNRERERkbWafcnTpxAjx49pMelIWXEiBGYPXs2vv/+ewBA27ZtDfbbv38/unfvDgCIiYnBhAkT0LNnT5iYmGDIkCFYunSp1Nbe3h67d+9GeHg4/P39Ub9+fURERHDKPREREUlkDUTdu3eHEOK+2x+0rZSTkxM2bNjwwDatW7fGzz//XOn6iIiISBmM+hoiqjmXGzbEW19+icsNG8pdChERkexkHSEi+dy2sUFSmzZyl0FERGQUGIiMwLVr1yp1J+zU1FQUP+TsMGetFgO//BLbhg/H9QfcfoCIiEgJGIhkdu3aNbw4Zgyu5+dXeJ+C/Hyka7Ww/99su6pwyMrCwC+/xIF+/RiIiIhI8RiIZKbT6XA9Px+WISGwrmAwuXH6NIpXrXroUSIiIiLSYyAyEtYaDWwr+H1pt65ereFqiIiIlIWzzIiIiEjxGIgUSufoiB3DhkGn4K8vISIiKsVTZgp1zd0dK999V+4yiIiIjAJHiBTK8tYtNE5MhOWtW3KXQkREJDsGIoVqkJKCJSEhaJCSIncpREREsmMgIiIiIsVjICIiIiLFYyAiIiIixWMgUqgSExPk29qixIR/AkRERJx2r1ApTZtiWFyc3GUQEREZBQ4PEBERkeIxECmUZ3Iylg8aBM/kZLlLISIikh0DkUJZFBTAKzkZFgUFcpdCREQkOwYiIiIiUjwGIiIiIlI8BiIiIiJSPAYihdI2aID3P/kE2gYN5C6FiIhIdrwPkULlqdX4tUcPucsgIiIyChwhUiiHv//Gfz7/HA5//y13KURERLJjIFIo58xMjFi6FM6ZmXKXQkREJDsGIiIiIlI8BiIiIiJSPAYiIiIiUjwGIoXKVavxyzPPIFetlrsUIiIi2XHavUJlNGiAjxculLsMIiIio8ARIoUyKyqCs1YLs6IiuUshIiKSXZUDUV5eHnbu3ImVK1di6dKlBktFHTp0CP3794eHhwdUKhW2bdtmsF0IgYiICLi7u8Pa2hpBQUE4f/68QZusrCyEhoZCrVbDwcEBYWFhyM3NNWhz+vRpPPXUU7CysoKnpyeioqKq2u1Hhvf584ju1Qve97yeRERESlSlU2anTp1C3759kZ+fj7y8PDg5OeHvv/+GjY0NXF1dMWnSpAo9T15eHtq0aYPRo0dj8ODBZbZHRUVh6dKlWL9+PXx8fDBz5kwEBwcjMTERVlZWAIDQ0FBcvXoVsbGxKCoqwqhRozB27Fhs2LABAKDT6dCrVy8EBQVh5cqV+P333zF69Gg4ODhg7NixVek+ERERPWKqFIjeeOMN9O/fHytXroS9vT2OHj0Kc3NzvPTSS3j99dcr/Dx9+vRBnz59yt0mhMCSJUvw3nvvYcCAAQCAL774Am5ubti2bRtCQkJw9uxZ7Nq1C8ePH0eHDh0AAMuWLUPfvn2xYMECeHh4ICYmBoWFhVi7di0sLCzQokULJCQkYNGiRQxEREREBKCKp8wSEhLw5ptvwsTEBKampigoKJBORb3zzjvVUlhKSgq0Wi2CgoKkdfb29ggICEBcXBwAIC4uDg4ODlIYAoCgoCCYmJjg2LFjUpuuXbvCwsJCahMcHIykpCTcuHHjvscvKCiATqczWIiIiOjRVKVAZG5uDhMT/a6urq5IS0sDoA8s6enp1VKYVqsFALi5uRmsd3Nzk7ZptVq4uroabDczM4OTk5NBm/Ke4+5jlCcyMhL29vbS4unp+XAdIiIiIqNVpVNm7dq1w/Hjx+Hr64tu3bohIiICf//9N7788ku0bNmyumuUxYwZMzBlyhTpsU6ne6RC0cWmTTHoxAncMeOdF4iIiKo0QvTRRx/B3d0dAPDhhx/C0dER48aNw7Vr17Bq1apqKUyj0QAAMjIyDNZnZGRI2zQaDTLv+XLS4uJiZGVlGbQp7znuPkZ5LC0toVarDZZHiTAxQbGFBYQJ77xARERUpU/DDh06oEePHgD0p8x27doFnU6H+Ph4tGnTploK8/HxgUajwd69e6V1Op0Ox44dQ2BgIAAgMDAQ2dnZiI+Pl9rs27cPJSUlCAgIkNocOnQIRXfdbyc2NhZ+fn5wdHSsllrrIo9Ll/DR6NHwuHRJ7lKIiIhkJ+vwQG5uLhISEpCQkABAfyF1QkIC0tLSoFKpMHnyZHzwwQf4/vvv8fvvv+Pll1+Gh4cHBg4cCABo1qwZevfujTFjxuDXX3/F4cOHMWHCBISEhMDDwwMA8OKLL8LCwgJhYWE4c+YMNm3ahE8++cTgdJgSWefno9WJE7DOz5e7FCIiItlV+AKS9u3bY+/evXB0dES7du2gUqnu2/bkyZMVes4TJ05II00ApJAyYsQIREdHY9q0acjLy8PYsWORnZ2NLl26YNeuXdI9iAAgJiYGEyZMQM+ePWFiYoIhQ4YY3BzS3t4eu3fvRnh4OPz9/VG/fn1ERERwyj0RERFJKhyIBgwYAEtLSwCQRmgeVvfu3SGEuO92lUqFuXPnYu7cufdt4+TkJN2E8X5at26Nn3/+ucp1EhER0aOtwoFo1qxZ5f5OREREVNdV6Rqi48ePSzc+vNuxY8dw4sSJhy6Kat41d3csmzUL1/43W5CIiEjJqhSIwsPDy70B4+XLlxEeHv7QRVHN0zk6YveQIdApeKYdERFRqSoFosTERLRv377M+nbt2iExMfGhi6Kap75xA72+/RbqB3x9CRERkVJUKRBZWlqWudkhAFy9ehVmvPNxneBy9SomzpkDl6tX5S6FiIhIdlUKRL169cKMGTOQk5MjrcvOzsY777yDZ555ptqKIyIiIqoNVRrOWbBgAbp27Qpvb2+0a9cOAJCQkAA3Nzd8+eWX1VogERERUU2rUiB67LHHcPr0acTExOC3336DtbU1Ro0ahRdeeAHm5ubVXSMRERFRjaryBT+2tra823MddsvGBr936IBbNjZyl0JERCS7Kgei8+fPY//+/cjMzERJSYnBtoiIiIcujGrWlYYN8c7atXKXQUREZBSqFIhWr16NcePGoX79+tBoNAbfa6ZSqRiI6gBVSQlMi4txx8wMwkTW7/glIiKSXZUC0QcffIAPP/wQ06dPr+56qJY0OncOS0JCMHnjRiQ3by53OURERLKq0tDAjRs38Pzzz1d3LURERESyqFIgev7557F79+7qroWIiIhIFlU6ZdakSRPMnDkTR48eRatWrcpMtZ80aVK1FEdERERUG6oUiFatWoV69erh4MGDOHjwoME2lUrFQERERER1SpUCUUpKSnXXQbUs1dcXI3fvRo6zs9ylEBERye6h5lsXFhYiKSkJxcXF1VUP1ZJic3Nc12hQzDuLExERVS0Q5efnIywsDDY2NmjRogXS0tIAABMnTsS8efOqtUCqGW5//YXpb74Jt7/+krsUIiIi2VUpEM2YMQO//fYbDhw4ACsrK2l9UFAQNm3aVG3FUc2pp9OhS2ws6ul0cpdCREQkuypdQ7Rt2zZs2rQJnTt3NrhLdYsWLZCcnFxtxRERERHVhiqNEF27dg2urq5l1ufl5RkEJCIiIqK6oEqBqEOHDtixY4f0uDQEff755wgMDKyeyoiIiIhqSZVOmX300Ufo06cPEhMTUVxcjE8++QSJiYk4cuRImfsSkXG67uqK9ZMm4Xo5I31ERERKU6URoi5duiAhIQHFxcVo1aoVdu/eDVdXV8TFxcHf37+6a6QakF2/Pv77yivIrl9f7lKIiIhkV6URIgBo3LgxVq9eXZ21UC2y1enQIj4eZ/z9kadWy10OERGRrKoUiErvO3Q/Xl5eVSqGao/mr78w8/XXMXnjRiQ3by53OURERLKqUiBq2LDhA2eT3blzp8oFEREREdW2KgWiU6dOGTwuKirCqVOnsGjRInz44YfVUhgRERFRbalSIGrTpk2ZdR06dICHhwfmz5+PwYMHP3RhRERERLXlob7c9V5+fn44fvx4dT4l1ZBCS0ukNW6MQktLuUshIiKSXZVGiHT3fP+VEAJXr17F7Nmz4evrWy2FUc1Kb9wY4Vu3yl0GERGRUajSCJGDgwMcHR2lxcnJCc2bN0dcXBxWrFhRbcXduXMHM2fOhI+PD6ytrdG4cWO8//77EEJIbYQQiIiIgLu7O6ytrREUFITz588bPE9WVhZCQ0OhVqvh4OCAsLAw5ObmVludREREVLdVaYRo3759BrPMTExM4OLigiZNmsDMrMq3Nirj448/xooVK7B+/Xq0aNECJ06cwKhRo2Bvb49JkyYBAKKiorB06VKsX78ePj4+mDlzJoKDg5GYmAgrKysAQGhoKK5evYrY2FgUFRVh1KhRGDt2LDZs2FBttdY1PufOYd6oUXh73TqkNG0qdzlERESyqlJ66d69ezWXUb4jR45gwIAB6NevHwD9dP+vv/4av/76KwD96NCSJUvw3nvvYcCAAQCAL774Am5ubti2bRtCQkJw9uxZ7Nq1C8ePH0eHDh0AAMuWLUPfvn2xYMECeHh41EpfjI1JSQls8vJgUlIidylERESyq9Ips8jISKxdu7bM+rVr1+Ljjz9+6KJKPfHEE9i7dy/+/PNPAMBvv/2GX375BX369AEApKSkQKvVIigoSNrH3t4eAQEBiIuLAwDExcXBwcFBCkMAEBQUBBMTExw7duy+xy4oKIBOpzNYiIiI6NFUpUD02WefoWk5p1latGiBlStXPnRRpd5++22EhISgadOmMDc3R7t27TB58mSEhoYCALRaLQDAzc3NYD83Nzdpm1arhes9X2BqZmYGJycnqU15IiMjYW9vLy2enp7V1i8iIiIyLlUKRFqtFu7u7mXWu7i44OrVqw9dVKlvvvkGMTEx2LBhA06ePIn169djwYIFWL9+fbUd435mzJiBnJwcaUlPT6/xYxIREZE8qnQNkaenJw4fPgwfHx+D9YcPH67Wa3KmTp0qjRIBQKtWrZCamorIyEiMGDECGo0GAJCRkWEQ0DIyMtC2bVsAgEajQWZmpsHzFhcXIysrS9q/PJaWlrB8hO/R85ePDyZv3Ii/7nkPiYiIlKhKI0RjxozB5MmTsW7dOqSmpiI1NRVr167FG2+8gTFjxlRbcfn5+TAxMSzR1NQUJf+7ENjHxwcajQZ79+6Vtut0Ohw7dgyBgYEAgMDAQGRnZyM+Pl5qs2/fPpSUlCAgIKDaaq1rCqytkdy8OQqsreUuhYiISHZVGiGaOnUqrl+/jvHjx6OwsBAAYGVlhenTp2PGjBnVVlz//v3x4YcfwsvLCy1atJC+L2306NEAAJVKhcmTJ+ODDz6Ar6+vNO3ew8MDAwcOBAA0a9YMvXv3xpgxY7By5UoUFRVhwoQJCAkJUewMMwBwuXoVQ9auxbejR+NaOac/SxUVFiI1NbVSz61Wq+Hi4vKwJRIREdWaKgUilUqFjz/+GDNnzsTZs2dhbW0NX1/faj/FtGzZMsycORPjx49HZmYmPDw88OqrryIiIkJqM23aNOTl5WHs2LHIzs5Gly5dsGvXLukeRAAQExODCRMmoGfPnjAxMcGQIUOwdOnSaq21rlHfuIF+mzYhdtCg+waiwpwcpF68iIlz5lTqvXW2scGG1asZioiIqM54qLsoarVaZGVloWvXrrC0tIQQwuCGjQ/Lzs4OS5YswZIlS+7bRqVSYe7cuZg7d+592zg5OSn6JoxVdSc/H8VmZrAYNgwODRtWaJ9bWi2ub9wInU7HQERERHVGlQLR9evXMXToUOzfvx8qlQrnz59Ho0aNEBYWBkdHRyxcuLC66yQZWbm6wrYStx0oqMFaiIiIakKVLqp+4403YG5ujrS0NNjY2Ejrhw0bhl27dlVbcURERES1oUojRLt378ZPP/2EBg0aGKz39fWt9AW4JI9sJydsGz4c2U5OcpdCREQkuyoFory8PIORoVJZWVmP9L17HiXXNRqsmTpV7jKIiIiMQpVOmT311FP44osvpMcqlQolJSWIiopCjx49qq04qjlW+fnw++03WOXny10KERGR7Ko0QhQVFYWePXvixIkTKCwsxLRp03DmzBlkZWXh8OHD1V0j1YDHLl3CguHDMXnjRiQ3by53OURERLKq0ghRy5Yt8eeff6JLly4YMGAA8vLyMHjwYJw6dQqNGzeu7hqJiIiIalSlR4iKiorQu3dvrFy5Eu+++25N1ERERERUqyo9QmRubo7Tp0/XRC1EREREsqjSKbOXXnoJa9asqe5aqBbdMTNDjqMj7pg91M3KiYiIHglV+jQsLi7G2rVrsWfPHvj7+8PW1tZg+6JFi6qlOKo5lx5/HC8dPCh3GUREREahUoHo4sWLaNiwIf744w+0b98eAPDnn38atKnO7zIjIiIiqg2VCkS+vr64evUq9u/fD0D/VR1Lly6Fm5tbjRRHNcfrwgW8+/rr+PCTT5DWpInc5RAREcmqUtcQCSEMHv/444/Iy8ur1oKodpgXFsIjPR3mhYVyl0JERCS7Kl1UXeregERERERUF1UqEKlUqjLXCPGaISIiIqrrKnUNkRACI0eOlL7A9fbt23jttdfKzDLbsmVL9VVIREREVMMqFYhGjBhh8Pill16q1mKo9lzx8kLEihW44uUldylERESyq1QgWrduXU3VQbXsVr16OPXkk3KXQUREZBQe6qJqqrscr13DC59+Csdr1+QuhYiISHYMRArldO0aXly5Ek4MRERERAxERERERAxEREREpHgMRERERKR4DEQKlatWY3+/fshVq+UuhYiISHaVmnZPj46MBg2wKDJS7jKIiIiMAkeIFMq8oADuaWkwLyiQuxQiIiLZMRAplFdyMlY9+yy8kpPlLoWIiEh2DERERESkeAxEREREpHgMRERERKR4Rh+ILl++jJdeegnOzs6wtrZGq1atcOLECWm7EAIRERFwd3eHtbU1goKCcP78eYPnyMrKQmhoKNRqNRwcHBAWFobc3Nza7goREREZKaMORDdu3MCTTz4Jc3Nz/Pjjj0hMTMTChQvh6OgotYmKisLSpUuxcuVKHDt2DLa2tggODsbt27elNqGhoThz5gxiY2Oxfft2HDp0CGPHjpWjS0YjuXlz9D99GsnNm8tdChERkeyM+j5EH3/8MTw9PbFu3TppnY+Pj/S7EAJLlizBe++9hwEDBgAAvvjiC7i5uWHbtm0ICQnB2bNnsWvXLhw/fhwdOnQAACxbtgx9+/bFggUL4OHhUbudIiIiIqNj1CNE33//PTp06IDnn38erq6uaNeuHVavXi1tT0lJgVarRVBQkLTO3t4eAQEBiIuLAwDExcXBwcFBCkMAEBQUBBMTExw7dqz2OmNkHktJwfyXXsJjKSlyl0JERCQ7ow5EFy9exIoVK+Dr64uffvoJ48aNw6RJk7B+/XoAgFarBQC4ubkZ7Ofm5iZt02q1cHV1NdhuZmYGJycnqU15CgoKoNPpDJZHidWtW2h6+jSsbt2SuxQiIiLZGfUps5KSEnTo0AEfffQRAKBdu3b4448/sHLlSowYMaJGjx0ZGYk5c+bU6DGIiIjIOBj1CJG7uzua33PRb7NmzZCWlgYA0Gg0AICMjAyDNhkZGdI2jUaDzMxMg+3FxcXIysqS2pRnxowZyMnJkZb09PSH7g8REREZJ6MORE8++SSSkpIM1v3555/w9vYGoL/AWqPRYO/evdJ2nU6HY8eOITAwEAAQGBiI7OxsxMfHS2327duHkpISBAQE3PfYlpaWUKvVBgsRERE9moz6lNkbb7yBJ554Ah999BGGDh2KX3/9FatWrcKqVasAACqVCpMnT8YHH3wAX19f+Pj4YObMmfDw8MDAgQMB6EeUevfujTFjxmDlypUoKirChAkTEBISougZZhmPPYaFH32EjMcek7sUIiIi2Rl1IOrYsSO2bt2KGTNmYO7cufDx8cGSJUsQGhoqtZk2bRry8vIwduxYZGdno0uXLti1axesrKykNjExMZgwYQJ69uwJExMTDBkyBEuXLpWjS0Yj194eB559Vu4yiIiIjIJRByIAePbZZ/HsAz64VSoV5s6di7lz5963jZOTEzZs2FAT5dVZ6qwsdNm9G7/06gWdk5Pc5RAREcnKqK8hoprjotVi3EcfweUBtx4gIiJSCgYiIiIiUjwGIiIiIlI8BiIiIiJSPAYihcq3tcXJJ55Avq2t3KUQERHJzuhnmVHNuOrtjVkrV8pdBhERkVHgCJFCmdy5A+vcXJjcuSN3KURERLJjIFIon6QkfPPEE/C556tRiIiIlIiBiIiIiBSPgYiIiIgUj4GIiIiIFI+BiIiIiBSP0+4V6pKvL0IPHECenZ3cpRAREcmOgUih7pib81vuiYiI/oenzBRKk56O9yZOhCY9Xe5SiIiIZMcRIoWyvXkTAQcP4utx46r9uYsKC5Gamlrh9mq1Gi4uLtVeBxERUUUxEFG1KszJQerFi5g4Zw4sLS0rtI+zjQ02rF7NUERERLJhIKJqdSc/H8VmZrAYNgwODRv+a/tbWi2ub9wInU7HQERERLJhIKIaYeXqCltPzwq1LajhWoiIiP4NL6pWqOtubvj8rbdw3c1N7lKIiIhkxxEihcp2dsZ3L78sdxlERERGgSNECmWr0+HJ3bthq9PJXQoREZHsGIgUSvPXX3j7rbeg+esvuUshIiKSHQMRERERKR4DERERESkeAxEREREpHgORQhVYWSG5aVMUWFnJXQoREZHsOO1eof5q1AiTv/lG7jKIiIiMAkeIiIiISPEYiBSq0dmz2OLvj0Znz8pdChERkewYiBRKJQTMi4qgEkLuUoiIiGTHQERERESKV6cC0bx586BSqTB58mRp3e3btxEeHg5nZ2fUq1cPQ4YMQUZGhsF+aWlp6NevH2xsbODq6oqpU6eiuLi4lqsnIiIiY1VnAtHx48fx2WefoXXr1gbr33jjDfzwww/YvHkzDh48iCtXrmDw4MHS9jt37qBfv34oLCzEkSNHsH79ekRHRyMiIqK2u0BERERGqk4EotzcXISGhmL16tVwdHSU1ufk5GDNmjVYtGgRnn76afj7+2PdunU4cuQIjh49CgDYvXs3EhMT8dVXX6Ft27bo06cP3n//fSxfvhyFhYVydUl26Y0aIXzLFqQ3aiR3KURERLKrE4EoPDwc/fr1Q1BQkMH6+Ph4FBUVGaxv2rQpvLy8EBcXBwCIi4tDq1at4ObmJrUJDg6GTqfDmTNn7nvMgoIC6HQ6g+VRUmhlhbQmTVDIGzMSEREZfyDauHEjTp48icjIyDLbtFotLCws4ODgYLDezc0NWq1WanN3GCrdXrrtfiIjI2Fvby8tnp6eD9kT4+Jy5QomzpoFlytX5C6FiIhIdkYdiNLT0/H6668jJiYGVrU8kjFjxgzk5ORIS3p6eq0ev6aps7PRa+tWqLOz5S6FiIhIdkYdiOLj45GZmYn27dvDzMwMZmZmOHjwIJYuXQozMzO4ubmhsLAQ2fd8qGdkZECj0QAANBpNmVlnpY9L25TH0tISarXaYCEiIqJHk1EHop49e+L3339HQkKCtHTo0AGhoaHS7+bm5ti7d6+0T1JSEtLS0hAYGAgACAwMxO+//47MzEypTWxsLNRqNZo3b17rfSIiIiLjY9Rf7mpnZ4eWLVsarLO1tYWzs7O0PiwsDFOmTIGTkxPUajUmTpyIwMBAdO7cGQDQq1cvNG/eHMOHD0dUVBS0Wi3ee+89hIeHw9LSstb7RERERMbHqANRRSxevBgmJiYYMmQICgoKEBwcjE8//VTabmpqiu3bt2PcuHEIDAyEra0tRowYgblz58pYtfxuODtjc1gYbjg7y10KERGR7OpcIDpw4IDBYysrKyxfvhzLly+/7z7e3t7YuXNnDVdWt2S5ueGL11+XuwwiIiKjYNTXEFHNsc7LQ8vjx2Gdlyd3KURERLJjIFIoj9RURIaFwSM1Ve5SiIiIZMdARERERIrHQERERESKx0BEREREisdApFDFZmb429UVxWZ1bqIhERFRteOnoUKlPv44Ru3ZI3cZAICiwkKkVvLibrVaDRcXlxqqiIiIlIaBiGRVmJOD1IsXMXHOnErdOdzZxgYbVq9mKCIiomrBQKRQ3n/+idnjx2P2p58i9fHHZavjTn4+is3MYDFsGBwaNqzQPre0WlzfuBE6nY6BiIiIqgUDkUKZFRejfmYmzIqL5S4FAGDl6gpbT88Kty+owVqIiEh5eFE1ERERKR4DERERESkeAxEREREpHgORQl3x9saMNWtwxdtb7lKIiIhkx4uqFeqWrS3+6NhR7jKIiIiMAkeIFMopIwMvf/IJnDIy5C6FiIhIdgxECuV4/TqeX7MGjtevy10KERGR7BiIiIiISPEYiIiIiEjxGIiIiIhI8RiIFErn4IDdgwZB5+AgdylERESy47R7hbrm4YFlc+bIXQYREZFR4AiRQlncvg2vCxdgcfu23KUQERHJjoFIoTwvXsTywYPhefGi3KUQERHJjoGIiIiIFI+BiIiIiBSPgYiIiIgUj4FIoYRKhSJzcwiVSu5SiIiIZMdp9wp1sVkzDI6Pl7sMIiIio8ARIiIiIlI8BiKFanDxIpYMHYoGnHZPRETEQKRUlrdvo/G5c7DkjRmJiIiMPxBFRkaiY8eOsLOzg6urKwYOHIikpCSDNrdv30Z4eDicnZ1Rr149DBkyBBkZGQZt0tLS0K9fP9jY2MDV1RVTp05FcXFxbXaFiIiIjJTRB6KDBw8iPDwcR48eRWxsLIqKitCrVy/k5eVJbd544w388MMP2Lx5Mw4ePIgrV65g8ODB0vY7d+6gX79+KCwsxJEjR7B+/XpER0cjIiJCji4RERGRkTH6WWa7du0yeBwdHQ1XV1fEx8eja9euyMnJwZo1a7BhwwY8/fTTAIB169ahWbNmOHr0KDp37ozdu3cjMTERe/bsgZubG9q2bYv3338f06dPx+zZs2FhYSFH14iIiMhIGP0I0b1ycnIAAE5OTgCA+Ph4FBUVISgoSGrTtGlTeHl5IS4uDgAQFxeHVq1awc3NTWoTHBwMnU6HM2fOlHucgoIC6HQ6g+VRom3QAPMWLIC2QQO5SyEiIpJdnQpEJSUlmDx5Mp588km0bNkSAKDVamFhYQEHBweDtm5ubtBqtVKbu8NQ6fbSbeWJjIyEvb29tHh6elZzb+SVp1bjcK9eyFOr5S6FiIhIdnUqEIWHh+OPP/7Axo0ba/xYM2bMQE5OjrSkp6fX+DFrk8P16xjwxRdwuH5d7lKIiIhkV2cC0YQJE7B9+3bs378fDe46zaPRaFBYWIjs7GyD9hkZGdBoNFKbe2edlT4ubXMvS0tLqNVqg+VR4pyRgVcWLIDzPa8LERGREhl9IBJCYMKECdi6dSv27dsHHx8fg+3+/v4wNzfH3r17pXVJSUlIS0tDYGAgACAwMBC///47MjMzpTaxsbFQq9Vo3rx57XSEiIiIjJbRzzILDw/Hhg0b8N1338HOzk665sfe3h7W1tawt7dHWFgYpkyZAicnJ6jVakycOBGBgYHo3LkzAKBXr15o3rw5hg8fjqioKGi1Wrz33nsIDw+HpaWlnN0jIiIiI2D0gWjFihUAgO7duxusX7duHUaOHAkAWLx4MUxMTDBkyBAUFBQgODgYn376qdTW1NQU27dvx7hx4xAYGAhbW1uMGDECc+fOra1uEBERkREz+kAkhPjXNlZWVli+fDmWL19+3zbe3t7YuXNndZZWp+XZ2eFYt27Is7OTu5QqKSosRGpqaqX2UavVcHFxqaGKiIioLjP6QEQ1Q+vpiQ+WLZO7jCopzMlB6sWLmDhnTqVOeTrb2GDD6tUMRUREVAYDkUKZFhXB9uZN5NnZ4Y65udzlVMqd/HwUm5nBYtgwODRsWKF9bmm1uL5xI3Q6HQMRERGVwUCkUA3Pn8eSkBBM3rgRyXV0pp2VqytsK3HDzIIarIWIiOo2o592T0RERFTTGIiIiIhI8RiIiIiISPEYiIiIiEjxeFG1QqX4+WHokSMosLaWuxQiIiLZMRApVImpKW7Vqyd3GUREREaBp8wUyj01FXNeew3ulbzbMxER0aOIgUihbPLy0P7IEdjk5cldChERkewYiIiIiEjxGIiIiIhI8RiIiIiISPEYiBTqmkaDFe+8g2sajdylEBERyY7T7hVK5+SEnSEhcpdBRERkFBiIFKpeTg46/PwzTjz1FHLt7eUup1YUFRYitZK3GVCr1XBxcamhioiIyFgwECmU2+XLePOddzB540ZFBKLCnBykXryIiXPmwNLSssL7OdvYYMPq1QxFRESPOAYiUoQ7+fkoNjODxbBhcGjYsEL73NJqcX3jRuh0OgYiIqJHHAMRKYqVqytsPT0r3L6gBmshIiLjwVlmREREpHgMRAp129oa51q3xm1+2z0RERFPmSnVZR8fTP3qK7nLMHqcmUZEpAwMRET3wZlpRETKwUCkUI0TE7EkJASTN25EcvPmcpdjlDgzjYhIORiIiP5FZWem5fI0GxFRncNARFSNeJqNiKhuYiAiqkY8zUZEVDcxEBHVgNq4AeS1a9eg0+kqtQ9PzRERlY+BSKHSGjfG2O3b8bebm9ylUBVcu3YNL44Zg+v5+ZXaj6fmiIjKx0CkUEWWlrjq5SV3GVRFOp0O1/PzYRkSAmuNpkL7VPXUXGVHojgKRUR1kaIC0fLlyzF//nxotVq0adMGy5YtQ6dOneQuSxZuf/2F0OXLERMejowGDeQuR/EqewPI1NRUFBcXw0GjqdFTc1UZieIoFBHVRYoJRJs2bcKUKVOwcuVKBAQEYMmSJQgODkZSUhJcXV3lLq/W1dPp0GPHDnw3fDgy5C5G4aoyM60gPx/pWi3sCwsrdayqBK8MnQ62L71UoZGo2hqFAjgSRUTVSzGBaNGiRRgzZgxGjRoFAFi5ciV27NiBtWvX4u2335a5OlKyqsxMu3H6NIpXrUJxcXGFj/MwwauNk1OFR6Iqex+m69evY+rs2bhZib4Axj0SxYBHVPcoIhAVFhYiPj4eM2bMkNaZmJggKCgIcXFxMlZG9I/KzEy7dfVqpZ+/NoLXw4Quv6lTYVfR/tfiSFRhYSEsLCwq3P5RC3jGHO5YG1UnRQSiv//+G3fu3IHbPTOq3NzccO7cuXL3KSgoQEHBP1dc5OTkAECl/8D/zc2bN3GnuBg3U1JQXMHrNPLS0yFKSpCXmgrzCh7n3n1yUlOh+9/PHFPTGjtObfSF+1Runzu3blX4b+3O7duVOo4uKQlFJiYofuIJWFdwBmNxcjKKfvgBBbm5sK5gXcW3bqHg1i0kJibi5s2bFdonKysLEfPmVSqoFBUWQpueDndvb5iZVeyfy4L8fFzOzIRnSAhsKng6vvDGDVzZtw9Hjx6FZyWuCatpVXnNAMDO1BRzZ8yAk5NTDVXG2h5FDg4ONdL30s9tIcSDGwoFuHz5sgAgjhw5YrB+6tSpolOnTuXuM2vWLAGACxcuXLhw4fIILOnp6Q/MCooYIapfvz5MTU2RkWF4+XBGRgY097lQdMaMGZgyZYr0uKSkBFlZWXB2doZKpaq22nQ6HTw9PZGeng61Wl1tz2vslNpvgH1XYt+V2m+AfVdi342t30II3Lx5Ex4eHg9sp4hAZGFhAX9/f+zduxcDBw4EoA84e/fuxYQJE8rdx9LSssw1EA4ODjVWo1qtNoo/nNqm1H4D7LsS+67UfgPsuxL7bkz9tre3/9c2ighEADBlyhSMGDECHTp0QKdOnbBkyRLk5eVJs86IiIhIuRQTiIYNG4Zr164hIiICWq0Wbdu2xa5du8pcaE1ERETKo5hABAATJky47ykyuVhaWmLWrFkVnqL8qFBqvwH2XYl9V2q/AfZdiX2vq/1WCfFv89CIiIiIHm0mchdAREREJDcGIiIiIlI8BiIiIiJSPAYiIiIiUjwGIhktX74cDRs2hJWVFQICAvDrr7/KXVKlREZGomPHjrCzs4OrqysGDhyIpKQkgzbdu3eHSqUyWF577TWDNmlpaejXrx9sbGzg6uqKqVOnlvky0QMHDqB9+/awtLREkyZNEB0dXdPde6DZs2eX6VfTpk2l7bdv30Z4eDicnZ1Rr149DBkypMyd0utivxs2bFim3yqVCuHh4QAerff70KFD6N+/Pzw8PKBSqbBt2zaD7UIIREREwN3dHdbW1ggKCsL58+cN2mRlZSE0NBRqtRoODg4ICwtDbm6uQZvTp0/jqaeegpWVFTw9PREVFVWmls2bN6Np06awsrJCq1atsHPnzmrv790e1PeioiJMnz4drVq1gq2tLTw8PPDyyy/jypUrBs9R3t/KvHnzDNoYW9//7T0fOXJkmT717t3boM2j+J4DKPe/e5VKhfnz50tt6uJ7bqBaviyMKm3jxo3CwsJCrF27Vpw5c0aMGTNGODg4iIyMDLlLq7Dg4GCxbt068ccff4iEhATRt29f4eXlJXJzc6U23bp1E2PGjBFXr16VlpycHGl7cXGxaNmypQgKChKnTp0SO3fuFPXr1xczZsyQ2ly8eFHY2NiIKVOmiMTERLFs2TJhamoqdu3aVav9vdusWbNEixYtDPp17do1aftrr70mPD09xd69e8WJEydE586dxRNPPCFtr6v9zszMNOhzbGysACD2798vhHi03u+dO3eKd999V2zZskUAEFu3bjXYPm/ePGFvby+2bdsmfvvtN/Hcc88JHx8fcevWLalN7969RZs2bcTRo0fFzz//LJo0aSJeeOEFaXtOTo5wc3MToaGh4o8//hBff/21sLa2Fp999pnU5vDhw8LU1FRERUWJxMRE8d577wlzc3Px+++/y9L37OxsERQUJDZt2iTOnTsn4uLiRKdOnYS/v7/Bc3h7e4u5c+ca/C3c/W+DMfb9397zESNGiN69exv0KSsry6DNo/ieCyEM+nz16lWxdu1aoVKpRHJystSmLr7nd2MgkkmnTp1EeHi49PjOnTvCw8NDREZGyljVw8nMzBQAxMGDB6V13bp1E6+//vp999m5c6cwMTERWq1WWrdixQqhVqtFQUGBEEKIadOmiRYtWhjsN2zYMBEcHFy9HaiEWbNmiTZt2pS7LTs7W5ibm4vNmzdL686ePSsAiLi4OCFE3e33vV5//XXRuHFjUVJSIoR4dN/vez8gSkpKhEajEfPnz5fWZWdnC0tLS/H1118LIYRITEwUAMTx48elNj/++KNQqVTi8uXLQgghPv30U+Ho6Cj1XQghpk+fLvz8/KTHQ4cOFf369TOoJyAgQLz66qvV2sf7Ke/D8V6//vqrACBSU1Oldd7e3mLx4sX33cfY+36/QDRgwID77qOk93zAgAHi6aefNlhX199znjKTQWFhIeLj4xEUFCStMzExQVBQEOLi4mSs7OHk5OQAAJycnAzWx8TEoH79+mjZsiVmzJiB/Px8aVtcXBxatWplcMfw4OBg6HQ6nDlzRmpz92tV2kbu1+r8+fPw8PBAo0aNEBoairS0NABAfHw8ioqKDGpu2rQpvLy8pJrrcr9LFRYW4quvvsLo0aMNvvD4UX2/75aSkgKtVmtQp729PQICAgzeYwcHB3To0EFqExQUBBMTExw7dkxq07VrV1hYWEhtgoODkZSUhBs3bkhtjP31yMnJgUqlKvN9j/PmzYOzszPatWuH+fPnG5warat9P3DgAFxdXeHn54dx48bh+vXr0jalvOcZGRnYsWMHwsLCymyry++5ou5UbSz+/vtv3Llzp8zXhri5ueHcuXMyVfVwSkpKMHnyZDz55JNo2bKltP7FF1+Et7c3PDw8cPr0aUyfPh1JSUnYsmULAECr1Zb7OpRue1AbnU6HW7duwdrauia7Vq6AgABER0fDz88PV69exZw5c/DUU0/hjz/+gFarhYWFRZkPBzc3t3/tU+m2B7WRs99327ZtG7KzszFy5Ehp3aP6ft+rtNby6ry7H66urgbbzczM4OTkZNDGx8enzHOUbnN0dLzv61H6HHK7ffs2pk+fjhdeeMHgizwnTZqE9u3bw8nJCUeOHMGMGTNw9epVLFq0CEDd7Hvv3r0xePBg+Pj4IDk5Ge+88w769OmDuLg4mJqaKuY9X79+Pezs7DB48GCD9XX9PWcgomoRHh6OP/74A7/88ovB+rFjx0q/t2rVCu7u7ujZsyeSk5PRuHHj2i6z2vTp00f6vXXr1ggICIC3tze++eYbo/jArg1r1qxBnz594OHhIa17VN9vKl9RURGGDh0KIQRWrFhhsG3KlCnS761bt4aFhQVeffVVREZG1rmvdCgVEhIi/d6qVSu0bt0ajRs3xoEDB9CzZ08ZK6tda9euRWhoKKysrAzW1/X3nKfMZFC/fn2YmpqWmXWUkZEBjUYjU1VVN2HCBGzfvh379+9HgwYNHtg2ICAAAHDhwgUAgEajKfd1KN32oDZqtdpowoeDgwMef/xxXLhwARqNBoWFhcjOzjZoc/f7W9f7nZqaij179uCVV155YLtH9f0urfVB/w1rNBpkZmYabC8uLkZWVla1/B3I/W9FaRhKTU1FbGyswehQeQICAlBcXIxLly4BqNt9L9WoUSPUr1/f4O/7UX7PAeDnn39GUlLSv/63D9S995yBSAYWFhbw9/fH3r17pXUlJSXYu3cvAgMDZayscoQQmDBhArZu3Yp9+/aVGQotT0JCAgDA3d0dABAYGIjff//d4B+R0n9cmzdvLrW5+7UqbWNMr1Vubi6Sk5Ph7u4Of39/mJubG9SclJSEtLQ0qea63u9169bB1dUV/fr1e2C7R/X99vHxgUajMahTp9Ph2LFjBu9xdnY24uPjpTb79u1DSUmJFBQDAwNx6NAhFBUVSW1iY2Ph5+cHR0dHqY2xvR6lYej8+fPYs2cPnJ2d/3WfhIQEmJiYSKeU6mrf7/bXX3/h+vXrBn/fj+p7XmrNmjXw9/dHmzZt/rVtnXvPa/yybSrXxo0bhaWlpYiOjhaJiYli7NixwsHBwWD2jbEbN26csLe3FwcOHDCYZpmfny+EEOLChQti7ty54sSJEyIlJUV89913olGjRqJr167Sc5ROw+7Vq5dISEgQu3btEi4uLuVOw546dao4e/asWL58uezTz998801x4MABkZKSIg4fPiyCgoJE/fr1RWZmphBCP+3ey8tL7Nu3T5w4cUIEBgaKwMBAaf+62m8h9DMivby8xPTp0w3WP2rv982bN8WpU6fEqVOnBACxaNEicerUKWkm1bx584SDg4P47rvvxOnTp8WAAQPKnXbfrl07cezYMfHLL78IX19fgynY2dnZws3NTQwfPlz88ccfYuPGjcLGxqbMNGQzMzOxYMECcfbsWTFr1qwan4b8oL4XFhaK5557TjRo0EAkJCQY/LdfOnvoyJEjYvHixSIhIUEkJyeLr776Sri4uIiXX37ZqPv+oH7fvHlTvPXWWyIuLk6kpKSIPXv2iPbt2wtfX19x+/Zt6Tkexfe8VE5OjrCxsRErVqwos39dfc/vxkAko2XLlgkvLy9hYWEhOnXqJI4ePSp3SZUCoNxl3bp1Qggh0tLSRNeuXYWTk5OwtLQUTZo0EVOnTjW4L40QQly6dEn06dNHWFtbi/r164s333xTFBUVGbTZv3+/aNu2rbCwsBCNGjWSjiGXYcOGCXd3d2FhYSEee+wxMWzYMHHhwgVp+61bt8T48eOFo6OjsLGxEYMGDRJXr141eI662G8hhPjpp58EAJGUlGSw/lF7v/fv31/u3/eIESOEEPqp9zNnzhRubm7C0tJS9OzZs8xrcv36dfHCCy+IevXqCbVaLUaNGiVu3rxp0Oa3334TXbp0EZaWluKxxx4T8+bNK1PLN998Ix5//HFhYWEhWrRoIXbs2FFj/RbiwX1PSUm573/7pfejio+PFwEBAcLe3l5YWVmJZs2aiY8++sggOBhj3x/U7/z8fNGrVy/h4uIizM3Nhbe3txgzZkyZ/4l9FN/zUp999pmwtrYW2dnZZfavq+/53VRCCFGjQ1BERERERo7XEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERmR6OhoODg4SI9nz56Ntm3b1ugxR44ciYEDB9boMYiMHQMR0SPsUf+gU6lUUKlUOHr0qMH6goICODs7Q6VS4cCBA/IUV03eeuutMt/tRETVj4GIiB7a3V/WWNs8PT2xbt06g3Vbt25FvXr1ZKqoetWrV69CX55KRA+HgYhIQbp3745JkyZh2rRpcHJygkajwezZsw3anDt3Dl26dIGVlRWaN2+OPXv2QKVSYdu2bQCAS5cuQaVSYdOmTejWrRusrKwQExMDAPj888/RrFkzWFlZoWnTpvj000+l5y0sLMSECRPg7u4OKysreHt7IzIyUtqelpaGAQMGoF69elCr1Rg6dCgyMjL+tU8jRozAxo0bcevWLWnd2rVrMWLEiDJt09PTMXToUDg4OMDJyQkDBgzApUuXpO2lI2oLFiyAu7s7nJ2dER4ebhD4Pv30U/j6+sLKygpubm74z3/+I21r2LAhlixZYnDMtm3bGrzGixYtQqtWrWBrawtPT0+MHz8eubm59+3fvafMSkfF7l4aNmwIALhz5w7CwsLg4+MDa2tr+Pn54ZNPPjF4vjt37mDKlClwcHCAs7Mzpk2bhnu/wamgoACTJk2Cq6srrKys0KVLFxw/fvy+NRI9ChiIiBRm/fr1sLW1xbFjxxAVFYW5c+ciNjYWgP7DcuDAgbCxscGxY8ewatUqvPvuu+U+z9tvv43XX38dZ8+eRXBwMGJiYhAREYEPP/wQZ8+exUcffYSZM2di/fr1AIClS5fi+++/xzfffIOkpCTExMRIH+QlJSUYMGAAsrKycPDgQcTGxuLixYsYNmzYv/bH398fDRs2xLfffgtAH6wOHTqE4cOHG7QrKipCcHAw7Ozs8PPPP+Pw4cOoV68eevfujcLCQqnd/v37kZycjP3792P9+vWIjo5GdHQ0AODEiROYNGkS5s6di6SkJOzatQtdu3at1OtvYmKCpUuX4syZM1i/fj327duHadOmVXj/q1evSsuFCxfQpEkTqYaSkhI0aNAAmzdvRmJiIiIiIvDOO+/gm2++kfZfuHAhoqOjsXbtWvzyyy/IysrC1q1bDY4xbdo0fPvtt1i/fj1OnjyJJk2aIDg4GFlZWZXqK1GdUitfIUtEshgxYoQYMGCA9Lhbt26iS5cuBm06duwopk+fLoQQ4scffxRmZmbi6tWr0vbY2FgBQGzdulUIIaRvO1+yZInB8zRu3Fhs2LDBYN37778vAgMDhRBCTJw4UTz99NOipKSkTJ27d+8WpqamIi0tTVp35swZAUD8+uuv9+1faV1LliwRPXr0EEIIMWfOHDFo0CBx48YNg29g//LLL4Wfn5/B8QsKCoS1tbX46aefpNfL29tbFBcXS22ef/55MWzYMCGEEN9++61Qq9VCp9OVW4+3t7dYvHixwbo2bdqIWbNm3bcPmzdvFs7OztLjdevWCXt7e+nxrFmzRJs2bcrsV1JSIgYNGiT8/f1Ffn7+fZ8/PDxcDBkyRHrs7u4uoqKipMdFRUWiQYMG0t9Jbm6uMDc3FzExMVKbwsJC4eHhYbAf0aOGI0RECtO6dWuDx+7u7sjMzAQAJCUlwdPTExqNRtreqVOncp+nQ4cO0u95eXlITk5GWFgY6tWrJy0ffPABkpOTAehPRyUkJMDPzw+TJk3C7t27pf3Pnj0LT09PeHp6SuuaN28OBwcHnD179l/79NJLLyEuLg4XL15EdHQ0Ro8eXabNb7/9hgsXLsDOzk6qz8nJCbdv35ZqBIAWLVrA1NS03NfnmWeegbe3Nxo1aoThw4cjJiYG+fn5/1rf3fbs2YOePXviscceg52dHYYPH47r169X+nneeecdxMXF4bvvvoO1tbW0fvny5fD394eLiwvq1auHVatWIS0tDQCQk5ODq1evIiAgQGpvZmZm8F4mJyejqKgITz75pLTO3NwcnTp1qtB7QVRXmcldABHVLnNzc4PHKpUKJSUllX4eW1tb6ffSa2BWr15t8GELQAoX7du3R0pKCn788Ufs2bMHQ4cORVBQEP773/9W+tj3cnZ2xrPPPouwsDDcvn0bffr0wc2bNw3a5Obmwt/fX7re6W4uLi7S7w96fezs7HDy5EkcOHAAu3fvRkREBGbPno3jx4/DwcEBJiYmZa7Hufv6o0uXLuHZZ5/FuHHj8OGHH8LJyQm//PILwsLCUFhYCBsbmwr196uvvsLixYtx4MABPPbYY9L6jRs34q233sLChQsRGBgIOzs7zJ8/H8eOHavQ8xIpGUeIiEji5+eH9PR0g4uZK3IxrZubGzw8PHDx4kU0adLEYPHx8ZHaqdVqDBs2DKtXr8amTZvw7bffIisrC82aNUN6ejrS09OltomJicjOzkbz5s0rVPvo0aNx4MABvPzyywYjPKXat2+P8+fPw9XVtUyN9vb2FToGoB9RCQoKQlRUFE6fPo1Lly5h3759APTB6urVq1JbnU6HlJQU6XF8fDxKSkqwcOFCdO7cGY8//jiuXLlS4WMDQFxcHF555RV89tln6Ny5s8G2w4cP44knnsD48ePRrl07NGnSxGD0y97eHu7u7gYBqbi4GPHx8dLjxo0bw8LCAocPH5bWFRUV4fjx4xV+L4jqIo4QEZHkmWeeQePGjTFixAhERUXh5s2beO+99wDoR0oeZM6cOZg0aRLs7e3Ru3dvFBQU4MSJE7hx4wamTJmCRYsWwd3dHe3atYOJiQk2b94MjUYDBwcHBAUFoVWrVggNDcWSJUtQXFyM8ePHo1u3bgancx6kd+/euHbtGtRqdbnbQ0NDMX/+fAwYMABz585FgwYNkJqaii1btmDatGlo0KDBvx5j+/btuHjxIrp27QpHR0fs3LkTJSUl8PPzAwA8/fTTiI6ORv/+/eHg4ICIiAiDcNakSRMUFRVh2bJl6N+/Pw4fPoyVK1dWqH8AoNVqMWjQIISEhCA4OBharRaAfhTOxcUFvr6++OKLL/DTTz/Bx8cHX375JY4fP24QSl9//XXMmzcPvr6+aNq0KRYtWoTs7Gxpu62tLcaNG4epU6fCyckJXl5eiIqKQn5+PsLCwipcK1FdwxEiIpKYmppi27ZtyM3NRceOHfHKK69Is8ysrKweuO8rr7yCzz//HOvWrUOrVq3QrVs3REdHSx/GdnZ2iIqKQocOHdCxY0dcunQJO3fuhImJCVQqFb777js4Ojqia9euCAoKQqNGjbBp06YK165SqVC/fn1YWFiUu93GxgaHDh2Cl5cXBg8ejGbNmkmn2O4Xou7l4OCALVu24Omnn0azZs2wcuVKfP3112jRogUAYMaMGejWrRueffZZ9OvXDwMHDkTjxo2l/du0aYNFixbh448/RsuWLRETE2Nw64F/c+7cOWRkZGD9+vVwd3eXlo4dOwIAXn31VQwePBjDhg1DQEAArl+/jvHjxxs8x5tvvonhw4djxIgR0mm1QYMGGbSZN28ehgwZguHDh6N9+/a4cOECfvrpJzg6Ola4VqK6RiXuPeFNRHSXw4cPo0uXLrhw4YLBhzsR0aOEgYiIDJTe5dnX1xcXLlzA66+/DkdHR/zyyy9yl0ZEVGN4DRERGbh58yamT5+OtLQ01K9fH0FBQVi4cKHcZRER1SiOEBEREZHi8aJqIiIiUjwGIiIiIlI8BiIiIiJSPAYiIiIiUjwGIiIiIlI8BiIiIiJSPAYiIiIiUjwGIiIiIlI8BiIiIiJSvP8HIKhg3ZYajl4AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**6) Elija una variable continua del módulo \"Gastos en Alimentos y Bebidas\", cree un gráfico de dispersión entre ella y la variable ingreso mensualizado. Ayuda: Para que ambas variables estén en la misma escala aplique la transformación logaritmo natural. (5ptos)**"
      ],
      "metadata": {
        "id": "CyaiDnWzeM-e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Aplicamos logartimos naturales*"
      ],
      "metadata": {
        "id": "JymZy_CKoG4Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Logaritmos\n",
        "ln_ingreso_mensual = np.log(df_merge_subset['ingreso_mensual'])\n",
        "ln_p601c = np.log(df_merge_subset[('p601c', 'sum')])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KuonRrmHbOvC",
        "outputId": "92d357c9-ff91-47ed-dfae-079e9d0aea42"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log\n",
            "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Graficamos*"
      ],
      "metadata": {
        "id": "1k34U3pqoJsB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(ln_ingreso_mensual, ln_p601c, color='blue', alpha=0.65)\n",
        "plt.title(\"Gráfico de dispersión\")\n",
        "plt.xlabel(\"Logaritmo natural del Ingreso mensualizado\")\n",
        "plt.ylabel(\"Logaritmo natural del Gasto en Alimentos y Bebidas\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "LAG2GyxkrznW",
        "outputId": "796c0d30-cdad-4feb-a4ae-bb1a356e710b"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}