Proyecto_siniestros.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fmN9IszbuOkh"
      },
      "source": [
        "# Proyecto: Siniestro viales"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gS0Z5we2uOkp"
      },
      "source": [
        "La empresa bogotana de seguros SSeguro esta buscando identificar las causas de accidentalidad en las vias de Bogota, con el objetivo de tomar decisiones de mercadotecnia para reducir costos. Para esto se usaran datos de los siniestros registrados en bogota entre el año 2015 y 2020 y se hara un procesamiento de estos datos para su posterior analisis."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "piYcqDSduOkq"
      },
      "source": [
        "## Entendimiento del negocio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Valoracion del negocio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "El negocio no cuenta con información satisfactoria del comportamiento de sus clientes, es por esto que las inversiones en mercadotecnia se destinan a un publico general, por lo tanto, existe el interés de llevar a cabo una investigación que concluya con evidencia estadística sobre la  información relevante de las diferentes características  que involucran datos pertinentes de los  clientes que permita reforzar al negocio en puntos débiles que requieren una mejor atención para  obtener un ahorro de los  recursos económicos del negocio  y  a su vez ofrecer un mejor servicio. Es decir, los ingresos y egresos del negocio actual pueden ser potenciados identificando los factores que influyen en costos para el negocio."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nfjdm7OtuOkr"
      },
      "source": [
        "### Objetivos de la empresa"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sp1Bu-0LuOks"
      },
      "source": [
        "En general, La empresa busca identificar multiples relaciones con respecto a los siniestros ocurridos en Bogota. Estas relaciones son de utilidad para identificar como distribuir los publicos objetivos, es decir, identificar que tipos de seguros son mas aptos para que tipos de vehiculos y personas. Especificamente se busca contestar a las siguientes interrogantes para determinar que desiciones tomar para mejorar la rentabilidad del negocio."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LNasrOZiuOkt"
      },
      "source": [
        "- ¿Que clase de vehiculos presentan mayor accidentalidad?\n",
        "\n",
        "- ¿Como se comportan los accidentes en el tiempo?\n",
        "\n",
        "- ¿Que tipo de servicio y "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XUfH1B6OuOkt"
      },
      "source": [
        "### Metas del proyecto"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "En general, este proyecto es un primer estudio a todo lo que se busca por parte de la empresa, por lo tanto, inicialmente se quiere modelar el comportamiento de los accidentes a traves del tiempo. Despues, se tiene como objetivo poder encontrar si existe una correlación entre algunas variables, en especifico el sexo de los involucrados y el servicio del transporte (público o privado) contra la gravedad del siniestro. Es por esto que se busca contestar a las preguntas asociadas asociadas a variables categoricas de maximo 3 valores diferentes. Esto se logra realizando lo siguiente:\n",
        "\n",
        "- Caracterizacion de la tasa de accidentalidad en el tiempo, dicha tasa estara asociada a la distribuccion de Possion que modela la probabilidad de ocurrencia de siniestros.\n",
        "- Test de $\\chi^2$ y correlacion tetracorica para las variables de gravedad y sexo de los siniestros.\n",
        "- Test de $\\chi^2$ y correlacion tetracorica para las variables de gravedad y servicio publico o privado de los siniestros."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Planeacion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "La empresa nos dio acceso a una base de datos crudos, el primer paso a seguir es el analisis exploratorio de los datos, es decir, entender las caracteristicas datos entregados a traves de su visualizacion en crudo y en graficas. Habiendo entendido los datos se procede a prepararlos para el analisis estadistico, es decir, se hace una limpieza de los datos y se hace una seleccion de los datos necesarios. Finalmente, se hace el analisis estadistico dirigido a responder las preguntar planteadas por la empresa."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NOANcUXtuOku"
      },
      "source": [
        "## Entendimiento de los datos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "La empresa entrego un excel con 5 hojas denominadas **SINIESTROS**, **ACTOR_VIAL**, **VEHICULO**, **HIPOTESIS** y **DICCIONARIO**, cada una contiene informacion diferente sobre los siniestros y la hoja **DICCIONARIO** consiste de un listado de codigos para entender la informacion dada en las otras hojas.\n",
        "El archivo dado es tiene un peso de 48.6 MB, por lo tanto importar esta informacion a pandas a traves de la funcion *read_excel* toma un tiempo extenso. Para reducir tiempos, se guardo individualmente cada hoja en formato csv, el cual permite una rapida importacion de los datos."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Adquisicion de los datos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nEs2IqGHuOkv"
      },
      "source": [
        "Empezamos importando las diferentes librerias a usar para manejar los datos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "d4swX49juOkw"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zuZHraL1uOk0"
      },
      "source": [
        "Importamos los datos brindados por la empresa haciendo uso de la funcion *read_csv* que trae pandas, los datos seran almacenados en un diccionario para facilidad de manejo."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "--hB7l7wuOk1"
      },
      "outputs": [],
      "source": [
        "nombres = ['sin', 'actor', 'veh', 'hip']\n",
        "df_ = {nombres[i]:pd.read_csv('siniestros_viales_'+str(i+1)+'.csv', sep=';') for i in range(len(nombres))}\n",
        "Diccionario = pd.read_csv('diccionario.csv', sep=';')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Observamos el contenido de los dataframes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "sWKB_mtnuOk1",
        "outputId": "5f526934-9ce1-4213-f9d8-1a9291044d46"
      },
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              "      <th>CODIGO_ACCIDENTE</th>\n",
              "      <th>FECHA</th>\n",
              "      <th>HORA</th>\n",
              "      <th>GRAVEDAD</th>\n",
              "      <th>CLASE</th>\n",
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              "   CODIGO_ACCIDENTE       FECHA      HORA  GRAVEDAD  CLASE  CHOQUE  \\\n",
              "0           4401419  02/01/2015  13:00:00         2      4     NaN   \n",
              "1           4401420  01/01/2015  13:00:00         3      1     1.0   \n",
              "\n",
              "   OBJETO_FIJO                    DIRECCION  CODIGO_LOCALIDAD  DISENO_LUGAR  \n",
              "0          NaN  AV AVENIDA BOYACA-CL 49A 76                 6             1  \n",
              "1          NaN             KR 17A-CL 63B 02                19             1  "
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              "      <th>CODIGO_ACCIDENTADO</th>\n",
              "      <th>FECHA</th>\n",
              "      <th>CONDICION</th>\n",
              "      <th>ESTADO</th>\n",
              "      <th>EDAD</th>\n",
              "      <th>SEXO</th>\n",
              "      <th>VEHICULO</th>\n",
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              "      <td>4401419</td>\n",
              "      <td>2452497</td>\n",
              "      <td>02/01/2015</td>\n",
              "      <td>CONDUCTOR</td>\n",
              "      <td>ILESO</td>\n",
              "      <td>53</td>\n",
              "      <td>MASCULINO</td>\n",
              "      <td>4401419-1</td>\n",
              "    </tr>\n",
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              "      <td>PASAJERO/ACOMPAÑANTE</td>\n",
              "      <td>HERIDO</td>\n",
              "      <td>36</td>\n",
              "      <td>FEMENINO</td>\n",
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              "   CODIGO_ACCIDENTE  CODIGO_ACCIDENTADO       FECHA             CONDICION  \\\n",
              "0           4401419             2452497  02/01/2015             CONDUCTOR   \n",
              "1           4401419             2452499  02/01/2015  PASAJERO/ACOMPAÑANTE   \n",
              "\n",
              "   ESTADO EDAD       SEXO   VEHICULO  \n",
              "0   ILESO   53  MASCULINO  4401419-1  \n",
              "1  HERIDO   36   FEMENINO  4401419-1  "
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              "      <th>VEHICULO</th>\n",
              "      <th>CLASE</th>\n",
              "      <th>SERVICIO</th>\n",
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              "   CODIGO_ACCIDENTE       FECHA   VEHICULO  CLASE  SERVICIO  MODALIDAD ENFUGA\n",
              "0           4401419  02/01/2015  4401419-1    2.0       2.0       10.0      N\n",
              "1           4401420  01/01/2015  4401420-1    1.0       2.0        5.0      N"
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              "   CODIGO_ACCIDENTE       FECHA CODIGO_CAUSA\n",
              "0           4401419  02/01/2015          119\n",
              "1           4401420  01/01/2015          103"
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      ],
      "source": [
        "display(df_['sin'].head(2))\n",
        "display(df_['actor'].head(2))\n",
        "display(df_['veh'].head(2))\n",
        "display(df_['hip'].head(2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tq1SmDmqBC8M"
      },
      "source": [
        "Observamos que los dataframes provenientes de las hojas del excel contienen diferentes variables. La primera hoja **SINIESTROS** del excel genera un dataframe que contiene las siguientes variables:\n",
        "\n",
        "| Variable | Definición\t| Valores |\n",
        "| --- | --- | --- |\n",
        "| CODIGO_ACCIDENTE | Codigo asociado al siniestro. | Valor tipo int. |\n",
        "|FECHA| Dia, mes y año en el que ocurrio el siniestro. | Valor tipo str. |\n",
        "| HORA | Hora, con exactitud de minutos, en la que ocurrio el siniestro. | Valor tipo str. |\n",
        "| GRAVEDAD | Grado de gravedad del siniestro. | 1(Con muertos), 2(Con heridos), 3(Solo daños).|\n",
        "| CLASE | Tipo de siniestro ocurrido.  | 1(Choque), 2(Atropello), 3(Volcamiento), 4(Caida de ocupante), 5(Incendio), 6(Otro), 7(Autolesion). |\n",
        "| CHOQUE | Elementos involucrados en el choque. | 1(Vehiculo), 2(Tren), 3(Semoviente), 4(Objeto fijo).|\n",
        "| OBJETIVO_FIJO | Elemento inmovil contra el que ocurrio el siniestro. | 1(Muro), 2(Poste), 3(Arbol), 4(Barandas), 5(Semaforo), 6(Inmueble), 7(Hidrante), 8(Valla, Señal), 9(Tarima, Caseta), 10(Vehiculo estacionado), 11(Otro).|\n",
        "|DIRECCION| Direccion donde ocurrio el siniestro. | Variable tipo str. |\n",
        "|CODIGO_LOCALIDAD| Codigo asociado a la localidad donde ocurrio el siniestro. | 1(Usaquén), 2(Chapinero), 3(Santa Fe), 4(San Cristóbal), 5(Usme), 6(Tunjuelito), 7(Bosa), 8(Kennedy), 9(Fontibón), 10(Engativá), 11(Suba), 12(Barrios Unidos), 13(Teusaquillo), 14(Los Mártires), 15(Antonio Nariño), 16(Puente Aranda), 17(La Candelaria), 18(Rafael Uribe Uribe), 19(Ciudad Bolivar), 20(Sumapaz).|\n",
        "|DISENO_LUGAR| Tipo de lugar en el que ocurrio el siniestro. | 1(Tramo de via), 2(Intersección), 3(Via peatonal), 4(Paso elevado), 5(Paso inferior), 6(Paso a nivel), 7(Glorieta), 8(Puente), 9(Via troncal), 10(Lote o predio), 11(Cicloruta), 12(Ponton), 13(Tunel).|"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "La segunda hoja **ACTOR_VIAL** del excel, genera el dataframe que contiene las siguientes variables:\n",
        "\n",
        "|Variable|Definición|Valores|\n",
        "|---|---|---|\n",
        "|CODIGO_ACCIDENTE| Codigo asociado al accidente. | Valor tipo int. |\n",
        "|CODIGO_ACCIDENTADO| Codigo asociado al paciente accidentado. | Valor tipo str. |\n",
        "|FECHA|  Dia, mes y año en el que ocurrio el siniestro. | Valor tipo str. |\n",
        "|CONDICION| Clasificacion del tipo de actor que es el individuo en el siniestro. | CONDUCTOR, PASAJERO/ACOMPAÑATE, PEATON, CICLISTA, MOTOCICLISTA. |\n",
        "|ESTADO| Condicion del individuo involucrado en el siniestro. |  HERIDO, ILESO, MUERTO |\n",
        "|EDAD| Edad del individuo. | Variable tipo int. |\n",
        "|SEXO| Sexo del individuo. | MASCULINO, FEMENINO, SIN INFORMACION |\n",
        "|VEHICULO| Codigo asociado al vehiculo en el que se encontraba el vehiculo. | Variable tipo str, vacio si no se encontraba dentro de un vehiculo. |"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "La tercer hoja **VEHICULOS** del excel, produce un dataframe con las siguientes variables:\n",
        "\n",
        "|Variable|Definición|Valores|\n",
        "|---|---|---|\n",
        "|CODIGO_ACCIDENTE| Codigo asociado al accidente. | Valor tipo int. |\n",
        "|FECHA| Dia, mes y año en el que ocurrio el siniestro. | Valor tipo str. |\n",
        "|VEHICULO| Codigo del vehiculo involucrado en el siniestro. | Valor tipo str. |\n",
        "|CLASE| Tipo de vehiculo involucrado en el accidente. | 1(Automovil), 2(Bus), 3(Buseta), 4(Camion, Furgon), 5(Camioneta), 6(Campero), 7(Microbus), 8(Tractocamion), 9(Volqueta), 10(Motocicleta), 11(Maquina Agricola), 12(Maquina Industrial), 13(Bicicleta), 14(Motocarro), 15(Traccion animal), 16(Otro), 17(Motociclo), 18(No identificado), 19(Bus Articulado), 20(Bus Alimentador), 21(Tren), 22(Ambulancia), 23(Metro), 24(Cuatrimoto), 25(Remolque), 26(Semi-Remolque), 27(Bus Bi-Articulado).|\n",
        "|SERVICIO| Tipo de servicio prestado por el vehiculo involucrado. |0(Sin informacion), 1(Oficial), 2(Publico), 3(Particular), 4(Diplomatico).|\n",
        "|MODALIDAD| Modalidad de carga/pasajeros presentes en el vehiculo. |1(Mixto), 2(Carga - Extrapesada), 3(Carga - Extradimensionada), 4(Pasajeros - Colectivo), 5(Pasajeros - Individual), 6(Pasajeros - Especial escolar), 7(Pasajeros - Especial turismo), 8(Pasajeros - Especial asalariado), 9(Pasajeros - Especial Ocasional), 10(Pasajeros - Masivo), 11(Carga - Pesada), 12(Carga - Mercancia Peligrosa).|\n",
        "|ENFUGA| El vehiculo se encuentra en fuga del siniestro. | N(No), S(Si)|"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "La cuarta hoja **HIPOTESIS** del excel contiene las siguientes variables:\n",
        "\n",
        "|Variable|Definición|Valores|\n",
        "|---|---|---|\n",
        "|CODIGO_ACCIDENTE| Codigo asociado al accidente. | Valor tipo int.|\n",
        "|FECHA| Dia, mes y año en el que ocurrio el siniestro. | Valor tipo str. |\n",
        "|CODIGO_CAUSA| Codigo asociado a la hipotesis de la causa del sinietro. | Valor tipo int. |"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Finalmente, la ultima hoja **DICCIONARIO** contiene un diccionario que permite entender los codigos presentados en las hojas anteriores, por lo tanto el dataframe importado contiene lo siguiente:\n",
        "\n",
        "|Variable|Definición|Valores|\n",
        "|---|---|---|\n",
        "|HOJA| Hoja a la que esta asociado el codigo. |SINIESTROS, ACTOR_VIAL, VEHICULOS, HIPOTESIS|\n",
        "|CAMPO| Variable de la hoja a la que se encuentra asociada el codigo. |GRAVEDAD, CLASE, CHOQUE, OBJETO_FIJO, CODIGO_LOCALIDAD, DISENO_LUGAR, SERVICIO, MODALIDAD, CODIGO_CAUSA|\n",
        "|CODIGO|Numero dado para el campo a describir. |Valor tipo int.|\n",
        "|DESCRIPCION| Interpretacion del codigo dado en el respectivo campo. |Valor tipo str.|"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Continuando con el entendimiento de los datos, cabe notar que el dataframe con lo siniestros contiene un listado de los siniestros, identificando cada uno con un unico codigo de accidente. Lo interesante es que para un siniestro podemos encontrar multiples actores viales y vehiculos, es por esto que en los dataframes de actores viales y vehiculos cuentan con datos que repiten el codigo de accidente. Un ejemplo de esto lo encontramos asociado al codigo de accidente ***4401436***, para el cual tenemos los siguientes datos del siniestro, los actores viales y los vehiculos involucrados respectivamente:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CODIGO_ACCIDENTE</th>\n",
              "      <th>FECHA</th>\n",
              "      <th>HORA</th>\n",
              "      <th>GRAVEDAD</th>\n",
              "      <th>CLASE</th>\n",
              "      <th>CHOQUE</th>\n",
              "      <th>OBJETO_FIJO</th>\n",
              "      <th>DIRECCION</th>\n",
              "      <th>CODIGO_LOCALIDAD</th>\n",
              "      <th>DISENO_LUGAR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>4401436</td>\n",
              "      <td>02/01/2015</td>\n",
              "      <td>11:00:00</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CL 68B-KR 72A 02</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    CODIGO_ACCIDENTE       FECHA      HORA  GRAVEDAD  CLASE  CHOQUE  \\\n",
              "17           4401436  02/01/2015  11:00:00         2      1     1.0   \n",
              "\n",
              "    OBJETO_FIJO         DIRECCION  CODIGO_LOCALIDAD  DISENO_LUGAR  \n",
              "17          NaN  CL 68B-KR 72A 02                10             1  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<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",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CODIGO_ACCIDENTE</th>\n",
              "      <th>CODIGO_ACCIDENTADO</th>\n",
              "      <th>FECHA</th>\n",
              "      <th>CONDICION</th>\n",
              "      <th>ESTADO</th>\n",
              "      <th>EDAD</th>\n",
              "      <th>SEXO</th>\n",
              "      <th>VEHICULO</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>4401436</td>\n",
              "      <td>2452538</td>\n",
              "      <td>02/01/2015</td>\n",
              "      <td>CONDUCTOR</td>\n",
              "      <td>ILESO</td>\n",
              "      <td>33</td>\n",
              "      <td>FEMENINO</td>\n",
              "      <td>4401436-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>4401436</td>\n",
              "      <td>2452539</td>\n",
              "      <td>02/01/2015</td>\n",
              "      <td>CONDUCTOR</td>\n",
              "      <td>HERIDO</td>\n",
              "      <td>20</td>\n",
              "      <td>MASCULINO</td>\n",
              "      <td>4401436-2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>4401436</td>\n",
              "      <td>2452541</td>\n",
              "      <td>02/01/2015</td>\n",
              "      <td>PASAJERO/ACOMPAÑANTE</td>\n",
              "      <td>HERIDO</td>\n",
              "      <td>39</td>\n",
              "      <td>FEMENINO</td>\n",
              "      <td>4401436-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>4401436</td>\n",
              "      <td>2452542</td>\n",
              "      <td>02/01/2015</td>\n",
              "      <td>PASAJERO/ACOMPAÑANTE</td>\n",
              "      <td>HERIDO</td>\n",
              "      <td>63</td>\n",
              "      <td>MASCULINO</td>\n",
              "      <td>4401436-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>4401436</td>\n",
              "      <td>2452543</td>\n",
              "      <td>02/01/2015</td>\n",
              "      <td>PASAJERO/ACOMPAÑANTE</td>\n",
              "      <td>HERIDO</td>\n",
              "      <td>35</td>\n",
              "      <td>FEMENINO</td>\n",
              "      <td>4401436-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>4401436</td>\n",
              "      <td>2452544</td>\n",
              "      <td>02/01/2015</td>\n",
              "      <td>PASAJERO/ACOMPAÑANTE</td>\n",
              "      <td>HERIDO</td>\n",
              "      <td>43</td>\n",
              "      <td>MASCULINO</td>\n",
              "      <td>4401436-1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    CODIGO_ACCIDENTE  CODIGO_ACCIDENTADO       FECHA             CONDICION  \\\n",
              "39           4401436             2452538  02/01/2015             CONDUCTOR   \n",
              "40           4401436             2452539  02/01/2015             CONDUCTOR   \n",
              "41           4401436             2452541  02/01/2015  PASAJERO/ACOMPAÑANTE   \n",
              "42           4401436             2452542  02/01/2015  PASAJERO/ACOMPAÑANTE   \n",
              "43           4401436             2452543  02/01/2015  PASAJERO/ACOMPAÑANTE   \n",
              "44           4401436             2452544  02/01/2015  PASAJERO/ACOMPAÑANTE   \n",
              "\n",
              "    ESTADO EDAD       SEXO   VEHICULO  \n",
              "39   ILESO   33   FEMENINO  4401436-1  \n",
              "40  HERIDO   20  MASCULINO  4401436-2  \n",
              "41  HERIDO   39   FEMENINO  4401436-1  \n",
              "42  HERIDO   63  MASCULINO  4401436-1  \n",
              "43  HERIDO   35   FEMENINO  4401436-1  \n",
              "44  HERIDO   43  MASCULINO  4401436-1  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<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",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CODIGO_ACCIDENTE</th>\n",
              "      <th>FECHA</th>\n",
              "      <th>VEHICULO</th>\n",
              "      <th>CLASE</th>\n",
              "      <th>SERVICIO</th>\n",
              "      <th>MODALIDAD</th>\n",
              "      <th>ENFUGA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>4401436</td>\n",
              "      <td>02/01/2015</td>\n",
              "      <td>4401436-1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>4401436</td>\n",
              "      <td>02/01/2015</td>\n",
              "      <td>4401436-2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    CODIGO_ACCIDENTE       FECHA   VEHICULO  CLASE  SERVICIO  MODALIDAD ENFUGA\n",
              "30           4401436  02/01/2015  4401436-1    2.0       2.0       10.0      N\n",
              "31           4401436  02/01/2015  4401436-2    4.0       2.0       11.0      N"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "display(df_['sin'][df_['sin'].CODIGO_ACCIDENTE==4401436])\n",
        "display(df_['actor'][df_['actor'].CODIGO_ACCIDENTE==4401436])\n",
        "display(df_['veh'][df_['veh'].CODIGO_ACCIDENTE==4401436])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Esta observacion va a ser de suma importancia para la preparacion de los datos."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Preparacion de los datos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "De los datos, lo primero que notamos es que dos variables (**CLASE**), con diferentes definiciones, coinciden en nombre para diferentes dataframes, por lo tanto es necesario renombrar dichas variables:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "df_['sin'] = df_['sin'].rename(columns={'CLASE':'CLASE_SINIESTRO'})\n",
        "df_['veh'] = df_['veh'].rename(columns={'CLASE':'CLASE_VEHICULO'})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Posterior a esto, el objetivo principal en esta etapa es generar un unico dataframe que contenga la misma informacion que los anteriores, para esto usamos la funcion *merge* de pandas, almacenando toda la informacion en el dataframe ``` df ```. El merge de los datos se hara de forma *outer* entorno al codigo del accidente, la fecha, y el vehiculo involucrado:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "shwXihGBuOk4"
      },
      "outputs": [],
      "source": [
        "df = pd.merge(df_['sin'], df_['hip'], on=[\"CODIGO_ACCIDENTE\", \"FECHA\"], how=\"outer\")\n",
        "df = pd.merge(df, df_['veh'], on=[\"CODIGO_ACCIDENTE\", \"FECHA\"], how=\"outer\")\n",
        "df = pd.merge(df, df_['actor'], on=[\"CODIGO_ACCIDENTE\", \"VEHICULO\", \"FECHA\"], how=\"outer\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Veamos informacion sobre el nuevo dataframe creado"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ghqlr3JeuOk4",
        "outputId": "1bc634ce-27ed-463d-f446-fe2cf42754ca"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CODIGO_ACCIDENTE</th>\n",
              "      <th>FECHA</th>\n",
              "      <th>HORA</th>\n",
              "      <th>GRAVEDAD</th>\n",
              "      <th>CLASE_SINIESTRO</th>\n",
              "      <th>CHOQUE</th>\n",
              "      <th>OBJETO_FIJO</th>\n",
              "      <th>DIRECCION</th>\n",
              "      <th>CODIGO_LOCALIDAD</th>\n",
              "      <th>DISENO_LUGAR</th>\n",
              "      <th>...</th>\n",
              "      <th>VEHICULO</th>\n",
              "      <th>CLASE_VEHICULO</th>\n",
              "      <th>SERVICIO</th>\n",
              "      <th>MODALIDAD</th>\n",
              "      <th>ENFUGA</th>\n",
              "      <th>CODIGO_ACCIDENTADO</th>\n",
              "      <th>CONDICION</th>\n",
              "      <th>ESTADO</th>\n",
              "      <th>EDAD</th>\n",
              "      <th>SEXO</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4401419</td>\n",
              "      <td>02/01/2015</td>\n",
              "      <td>13:00:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>AV AVENIDA BOYACA-CL 49A 76</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>4401419-1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>N</td>\n",
              "      <td>2452497.0</td>\n",
              "      <td>CONDUCTOR</td>\n",
              "      <td>ILESO</td>\n",
              "      <td>53</td>\n",
              "      <td>MASCULINO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4401419</td>\n",
              "      <td>02/01/2015</td>\n",
              "      <td>13:00:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>AV AVENIDA BOYACA-CL 49A 76</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>4401419-1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>N</td>\n",
              "      <td>2452499.0</td>\n",
              "      <td>PASAJERO/ACOMPAÑANTE</td>\n",
              "      <td>HERIDO</td>\n",
              "      <td>36</td>\n",
              "      <td>FEMENINO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4401420</td>\n",
              "      <td>01/01/2015</td>\n",
              "      <td>13:00:00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>KR 17A-CL 63B 02</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>4401420-1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>N</td>\n",
              "      <td>2452504.0</td>\n",
              "      <td>CONDUCTOR</td>\n",
              "      <td>ILESO</td>\n",
              "      <td>27</td>\n",
              "      <td>MASCULINO</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 21 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   CODIGO_ACCIDENTE       FECHA      HORA  GRAVEDAD  CLASE_SINIESTRO  CHOQUE  \\\n",
              "0           4401419  02/01/2015  13:00:00       2.0              4.0     NaN   \n",
              "1           4401419  02/01/2015  13:00:00       2.0              4.0     NaN   \n",
              "2           4401420  01/01/2015  13:00:00       3.0              1.0     1.0   \n",
              "\n",
              "   OBJETO_FIJO                    DIRECCION  CODIGO_LOCALIDAD  DISENO_LUGAR  \\\n",
              "0          NaN  AV AVENIDA BOYACA-CL 49A 76               6.0           1.0   \n",
              "1          NaN  AV AVENIDA BOYACA-CL 49A 76               6.0           1.0   \n",
              "2          NaN             KR 17A-CL 63B 02              19.0           1.0   \n",
              "\n",
              "   ...   VEHICULO CLASE_VEHICULO  SERVICIO  MODALIDAD  ENFUGA  \\\n",
              "0  ...  4401419-1            2.0       2.0       10.0       N   \n",
              "1  ...  4401419-1            2.0       2.0       10.0       N   \n",
              "2  ...  4401420-1            1.0       2.0        5.0       N   \n",
              "\n",
              "  CODIGO_ACCIDENTADO             CONDICION  ESTADO EDAD       SEXO  \n",
              "0          2452497.0             CONDUCTOR   ILESO   53  MASCULINO  \n",
              "1          2452499.0  PASAJERO/ACOMPAÑANTE  HERIDO   36   FEMENINO  \n",
              "2          2452504.0             CONDUCTOR   ILESO   27  MASCULINO  \n",
              "\n",
              "[3 rows x 21 columns]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 502226 entries, 0 to 502225\n",
            "Data columns (total 21 columns):\n",
            " #   Column              Non-Null Count   Dtype  \n",
            "---  ------              --------------   -----  \n",
            " 0   CODIGO_ACCIDENTE    502226 non-null  int64  \n",
            " 1   FECHA               502226 non-null  object \n",
            " 2   HORA                478759 non-null  object \n",
            " 3   GRAVEDAD            478759 non-null  float64\n",
            " 4   CLASE_SINIESTRO     478759 non-null  float64\n",
            " 5   CHOQUE              432936 non-null  float64\n",
            " 6   OBJETO_FIJO         12275 non-null   float64\n",
            " 7   DIRECCION           478759 non-null  object \n",
            " 8   CODIGO_LOCALIDAD    478759 non-null  float64\n",
            " 9   DISENO_LUGAR        478759 non-null  float64\n",
            " 10  CODIGO_CAUSA        478041 non-null  object \n",
            " 11  VEHICULO            478625 non-null  object \n",
            " 12  CLASE_VEHICULO      474966 non-null  float64\n",
            " 13  SERVICIO            457211 non-null  float64\n",
            " 14  MODALIDAD           188410 non-null  float64\n",
            " 15  ENFUGA              478605 non-null  object \n",
            " 16  CODIGO_ACCIDENTADO  502070 non-null  float64\n",
            " 17  CONDICION           502070 non-null  object \n",
            " 18  ESTADO              502070 non-null  object \n",
            " 19  EDAD                502070 non-null  object \n",
            " 20  SEXO                502070 non-null  object \n",
            "dtypes: float64(10), int64(1), object(10)\n",
            "memory usage: 80.5+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Observamos que el dataframe contiene una gran cantidad de elementos nulos, es por esto que debemos identificar que datos se pueden rellenar y cuales de deben descartar.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Retomando lo mencionado en el entendimiento de los datos, para un cierto siniestro encontramos diferentes actores viales y vehiculos involucradas, es por esto que en el nuevo dataframe encontramos multiples veces los diferentes codigos de accidentes, esto gracias al *merge* y la informacion resgitrada sobre los siniestro. Como en el ejemplo ya mostrado, veamos los datos asociados al codigo ***4401436***:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CODIGO_ACCIDENTE</th>\n",
              "      <th>FECHA</th>\n",
              "      <th>HORA</th>\n",
              "      <th>GRAVEDAD</th>\n",
              "      <th>CLASE_SINIESTRO</th>\n",
              "      <th>CHOQUE</th>\n",
              "      <th>OBJETO_FIJO</th>\n",
              "      <th>DIRECCION</th>\n",
              "      <th>CODIGO_LOCALIDAD</th>\n",
              "      <th>DISENO_LUGAR</th>\n",
              "      <th>...</th>\n",
              "      <th>VEHICULO</th>\n",
              "      <th>CLASE_VEHICULO</th>\n",
              "      <th>SERVICIO</th>\n",
              "      <th>MODALIDAD</th>\n",
              "      <th>ENFUGA</th>\n",
              "      <th>CODIGO_ACCIDENTADO</th>\n",
              "      <th>CONDICION</th>\n",
              "      <th>ESTADO</th>\n",
              "      <th>EDAD</th>\n",
              "      <th>SEXO</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>4401436</td>\n",
              "      <td>02/01/2015</td>\n",
              "      <td>11:00:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CL 68B-KR 72A 02</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>4401436-1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>N</td>\n",
              "      <td>2452538.0</td>\n",
              "      <td>CONDUCTOR</td>\n",
              "      <td>ILESO</td>\n",
              "      <td>33</td>\n",
              "      <td>FEMENINO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>4401436</td>\n",
              "      <td>02/01/2015</td>\n",
              "      <td>11:00:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CL 68B-KR 72A 02</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>4401436-1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>N</td>\n",
              "      <td>2452541.0</td>\n",
              "      <td>PASAJERO/ACOMPAÑANTE</td>\n",
              "      <td>HERIDO</td>\n",
              "      <td>39</td>\n",
              "      <td>FEMENINO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>4401436</td>\n",
              "      <td>02/01/2015</td>\n",
              "      <td>11:00:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CL 68B-KR 72A 02</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>4401436-1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>N</td>\n",
              "      <td>2452542.0</td>\n",
              "      <td>PASAJERO/ACOMPAÑANTE</td>\n",
              "      <td>HERIDO</td>\n",
              "      <td>63</td>\n",
              "      <td>MASCULINO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>4401436</td>\n",
              "      <td>02/01/2015</td>\n",
              "      <td>11:00:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CL 68B-KR 72A 02</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>4401436-1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>N</td>\n",
              "      <td>2452543.0</td>\n",
              "      <td>PASAJERO/ACOMPAÑANTE</td>\n",
              "      <td>HERIDO</td>\n",
              "      <td>35</td>\n",
              "      <td>FEMENINO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>4401436</td>\n",
              "      <td>02/01/2015</td>\n",
              "      <td>11:00:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CL 68B-KR 72A 02</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>4401436-1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>N</td>\n",
              "      <td>2452544.0</td>\n",
              "      <td>PASAJERO/ACOMPAÑANTE</td>\n",
              "      <td>HERIDO</td>\n",
              "      <td>43</td>\n",
              "      <td>MASCULINO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>4401436</td>\n",
              "      <td>02/01/2015</td>\n",
              "      <td>11:00:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CL 68B-KR 72A 02</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>4401436-2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>N</td>\n",
              "      <td>2452539.0</td>\n",
              "      <td>CONDUCTOR</td>\n",
              "      <td>HERIDO</td>\n",
              "      <td>20</td>\n",
              "      <td>MASCULINO</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6 rows × 21 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    CODIGO_ACCIDENTE       FECHA      HORA  GRAVEDAD  CLASE_SINIESTRO  CHOQUE  \\\n",
              "40           4401436  02/01/2015  11:00:00       2.0              1.0     1.0   \n",
              "41           4401436  02/01/2015  11:00:00       2.0              1.0     1.0   \n",
              "42           4401436  02/01/2015  11:00:00       2.0              1.0     1.0   \n",
              "43           4401436  02/01/2015  11:00:00       2.0              1.0     1.0   \n",
              "44           4401436  02/01/2015  11:00:00       2.0              1.0     1.0   \n",
              "45           4401436  02/01/2015  11:00:00       2.0              1.0     1.0   \n",
              "\n",
              "    OBJETO_FIJO         DIRECCION  CODIGO_LOCALIDAD  DISENO_LUGAR  ...  \\\n",
              "40          NaN  CL 68B-KR 72A 02              10.0           1.0  ...   \n",
              "41          NaN  CL 68B-KR 72A 02              10.0           1.0  ...   \n",
              "42          NaN  CL 68B-KR 72A 02              10.0           1.0  ...   \n",
              "43          NaN  CL 68B-KR 72A 02              10.0           1.0  ...   \n",
              "44          NaN  CL 68B-KR 72A 02              10.0           1.0  ...   \n",
              "45          NaN  CL 68B-KR 72A 02              10.0           1.0  ...   \n",
              "\n",
              "     VEHICULO CLASE_VEHICULO  SERVICIO  MODALIDAD  ENFUGA CODIGO_ACCIDENTADO  \\\n",
              "40  4401436-1            2.0       2.0       10.0       N          2452538.0   \n",
              "41  4401436-1            2.0       2.0       10.0       N          2452541.0   \n",
              "42  4401436-1            2.0       2.0       10.0       N          2452542.0   \n",
              "43  4401436-1            2.0       2.0       10.0       N          2452543.0   \n",
              "44  4401436-1            2.0       2.0       10.0       N          2452544.0   \n",
              "45  4401436-2            4.0       2.0       11.0       N          2452539.0   \n",
              "\n",
              "               CONDICION  ESTADO EDAD       SEXO  \n",
              "40             CONDUCTOR   ILESO   33   FEMENINO  \n",
              "41  PASAJERO/ACOMPAÑANTE  HERIDO   39   FEMENINO  \n",
              "42  PASAJERO/ACOMPAÑANTE  HERIDO   63  MASCULINO  \n",
              "43  PASAJERO/ACOMPAÑANTE  HERIDO   35   FEMENINO  \n",
              "44  PASAJERO/ACOMPAÑANTE  HERIDO   43  MASCULINO  \n",
              "45             CONDUCTOR  HERIDO   20  MASCULINO  \n",
              "\n",
              "[6 rows x 21 columns]"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[df.CODIGO_ACCIDENTE==4401436]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Observamos entonces que la informacion de dicho siniestro contiene la informacion de cada actor vial presente y la informacion relacionada a este. Con esto en mente podemos agrupar los datos por el codigo de accidente y rellenar datos generales que sean intrínsecos del siniestro. Por ejemplo, la hora en la que ocurrio el siniestro es especifica del siniestro e independiete del actor vial, es por esto que para todos los involucrados la hora debe ser igual."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Para cumplir el objetivo de completar datos podemos definir las siguiente funcion:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "JqHZ0OLXuOk5"
      },
      "outputs": [],
      "source": [
        "def fill_cols(df, cols, grupo):\n",
        "    \"\"\"\n",
        "    Llena los datos vacios de las columnas indicadas por el metodo ffill, \n",
        "    agrupando los datos por la caracteristica indicada.\n",
        "    Parametros:\n",
        "        df: Un dataframe de Pandas.\n",
        "        cols: Lista con cadenas de texto de los nombres de las columnas.\n",
        "        grupo: Cadena de texto con el nombre de la caracteristica por la cual agrupar.\n",
        "    Retorna:\n",
        "        Un dataframe en el que los datos vacios de las columnas indicadas se llenan por el metodo ffill.\n",
        "    \"\"\"\n",
        "    df[cols] = df.groupby(grupo)[cols].ffill()\n",
        "    return df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "La cual se encargan de llenar datos vacios. Esta funcion recibe como parametro un dataframe ```df```, las columnas ```cols``` a rellenar y la variable ```group``` que indica que informacion usar para agrupar los datos, y retorna el dataframe modificado."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "WaJr4bL2938v",
        "outputId": "061c4f4e-b676-4ccf-8b72-e1250f1cbfa1"
      },
      "outputs": [],
      "source": [
        "columnas = ['HORA', 'GRAVEDAD', 'CLASE_SINIESTRO', 'CHOQUE', 'OBJETO_FIJO', 'DIRECCION', 'CODIGO_LOCALIDAD', 'DISENO_LUGAR', 'CODIGO_CAUSA', 'ENFUGA']\n",
        "df = fill_cols(df, columnas, 'CODIGO_ACCIDENTE')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Con este cambio, obtengamos nuevamente informacion sobre el dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 502226 entries, 0 to 502225\n",
            "Data columns (total 21 columns):\n",
            " #   Column              Non-Null Count   Dtype  \n",
            "---  ------              --------------   -----  \n",
            " 0   CODIGO_ACCIDENTE    502226 non-null  int64  \n",
            " 1   FECHA               502226 non-null  object \n",
            " 2   HORA                502226 non-null  object \n",
            " 3   GRAVEDAD            502226 non-null  float64\n",
            " 4   CLASE_SINIESTRO     502226 non-null  float64\n",
            " 5   CHOQUE              434819 non-null  float64\n",
            " 6   OBJETO_FIJO         12551 non-null   float64\n",
            " 7   DIRECCION           502226 non-null  object \n",
            " 8   CODIGO_LOCALIDAD    502226 non-null  float64\n",
            " 9   DISENO_LUGAR        502226 non-null  float64\n",
            " 10  CODIGO_CAUSA        501469 non-null  object \n",
            " 11  VEHICULO            478625 non-null  object \n",
            " 12  CLASE_VEHICULO      474966 non-null  float64\n",
            " 13  SERVICIO            457211 non-null  float64\n",
            " 14  MODALIDAD           188410 non-null  float64\n",
            " 15  ENFUGA              502072 non-null  object \n",
            " 16  CODIGO_ACCIDENTADO  502070 non-null  float64\n",
            " 17  CONDICION           502070 non-null  object \n",
            " 18  ESTADO              502070 non-null  object \n",
            " 19  EDAD                502070 non-null  object \n",
            " 20  SEXO                502070 non-null  object \n",
            "dtypes: float64(10), int64(1), object(10)\n",
            "memory usage: 80.5+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Notamos entonces que las variables relacionadas directamente al siniestro ya no presentan datos nulos."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Ahora, un paso importante va a ser modificar las columnas **FECHA** y **HORA**, para generar una nueva columna que contenga esa informacion en una variable tipo *datetime*.\n",
        "\n",
        "Para esto debemos identificar primero como el tipo de variable que se encuentra almacenado en dichas columnas:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "CODIGO_ACCIDENTE        int64\n",
              "FECHA                  object\n",
              "HORA                   object\n",
              "GRAVEDAD              float64\n",
              "CLASE_SINIESTRO       float64\n",
              "CHOQUE                float64\n",
              "OBJETO_FIJO           float64\n",
              "DIRECCION              object\n",
              "CODIGO_LOCALIDAD      float64\n",
              "DISENO_LUGAR          float64\n",
              "CODIGO_CAUSA           object\n",
              "VEHICULO               object\n",
              "CLASE_VEHICULO        float64\n",
              "SERVICIO              float64\n",
              "MODALIDAD             float64\n",
              "ENFUGA                 object\n",
              "CODIGO_ACCIDENTADO    float64\n",
              "CONDICION              object\n",
              "ESTADO                 object\n",
              "EDAD                   object\n",
              "SEXO                   object\n",
              "dtype: object"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(str, str)"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "type(df.loc[0,'FECHA']), type(df.loc[0,'HORA'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "La fecha y hora estan almacenadas como objetos, en este caso, al obtener el tipo de los primero datos, dichos objetos son cadenas de texto. Por lo tanto la ruta que vamos a seguir es concatenar dichas cadenas y convertirlos a tipo *datetime* usando la funcion *to_datetime* que ofrece pandas. Estas variables seran almacenadas en la columna **FECHA** y eliminaremos la columna **HORA**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0        2015-01-02 13:00:00\n",
              "1        2015-01-02 13:00:00\n",
              "2        2015-01-01 13:00:00\n",
              "3        2015-01-01 13:00:00\n",
              "4        2015-01-02 11:15:00\n",
              "                 ...        \n",
              "502221   2020-11-09 11:11:00\n",
              "502222   2020-11-27 13:50:00\n",
              "502223   2020-12-06 02:20:00\n",
              "502224   2020-01-13 09:40:00\n",
              "502225   2020-06-06 02:31:00\n",
              "Name: FECHA, Length: 502226, dtype: datetime64[ns]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['FECHA'] = pd.to_datetime(df.FECHA + ' '+ df.HORA, format=\"%d/%m/%Y %H:%M:%S\")\n",
        "df.drop(columns=['HORA'], axis=1, inplace=True)\n",
        "df.FECHA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Por otro lado, la variable **OBJETO_FIJO** presenta valores vacios si el siniestro no ocurrio con algun objeto fijo, por lo tanto todos datos vacios de esta columna pueden considerarse bajo un nuevo valor 0 que indica un siniestro sin objetos fijos involucrados. De igual forma, los valores vacios de la columna **CODIGO_CAUSA** se llenan con ceros indicando que no se desconoce la hipotesis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [],
      "source": [
        "df['OBJETO_FIJO'].fillna(0, inplace=True)\n",
        "df['CODIGO_CAUSA'].fillna(0, inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Muchos de los siniestros no cuentan con informacion de los vehiculos involucrados, ya sea por que no aplica o porque este se encuentra en fuga. Como consecuencia de esto, los valores vacios de las columnas **CLASE_VEHICULO**, **SERVICIO** y **MODALIDAD**, se llenaran con 0 indicando que se trata de que esta caracteristica no aplica (ej. es un peaton) o si no hay registro de este por estado de fuga."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [],
      "source": [
        "df.loc[df.CONDICION=='PEATON', ['CLASE_VEHICULO', 'SERVICIO', 'MODALIDAD']] = 0\n",
        "df.loc[df.ENFUGA=='S', ['CLASE_VEHICULO', 'SERVICIO', 'MODALIDAD']] = 0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "De igual forma, para los ciclistas y los bicitaxistas las variables **SERVICIO** y **MODALIDAD** tampoco aplican o se desconocen."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [],
      "source": [
        "df.loc[df.CLASE_VEHICULO == 13, ['SERVICIO', 'MODALIDAD']] = 0\n",
        "df.loc[df.CLASE_VEHICULO == 28, ['SERVICIO', 'MODALIDAD']] = 0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Para la variable **CHOQUE**, se deben agrupar los datos por el codigo de accidente, y los datos vacios de esta variable se llenan con 5 si alguno de los elementos del grupo es un peaton y 0 si no, esto debido a que en los datos entregados, si el choque ocurria contra algun peaton o el siniestro no fue causa de un choque, la columna de choques no registra valor."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [],
      "source": [
        "condicion = lambda dfg: True if any(dfg=='PEATON') else False\n",
        "cond = df.groupby('CODIGO_ACCIDENTE')['CONDICION'].transform(condicion)\n",
        "df['CHOQUE'].fillna(cond.map({True: 5, False: 0}), inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Eliminamos los datos que despues del preprocesamiento contienen multiples valores vacios."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [],
      "source": [
        "df.drop(df[df['CONDICION'].isna()].index, inplace=True)\n",
        "df.drop(df[df.CLASE_VEHICULO.isna()].index, inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "La variable **CODIGO_CAUSA** contiene valores no numericos, de la siguiente forma:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CODIGO_ACCIDENTE</th>\n",
              "      <th>FECHA</th>\n",
              "      <th>GRAVEDAD</th>\n",
              "      <th>CLASE_SINIESTRO</th>\n",
              "      <th>CHOQUE</th>\n",
              "      <th>OBJETO_FIJO</th>\n",
              "      <th>DIRECCION</th>\n",
              "      <th>CODIGO_LOCALIDAD</th>\n",
              "      <th>DISENO_LUGAR</th>\n",
              "      <th>CODIGO_CAUSA</th>\n",
              "      <th>VEHICULO</th>\n",
              "      <th>CLASE_VEHICULO</th>\n",
              "      <th>SERVICIO</th>\n",
              "      <th>MODALIDAD</th>\n",
              "      <th>ENFUGA</th>\n",
              "      <th>CODIGO_ACCIDENTADO</th>\n",
              "      <th>CONDICION</th>\n",
              "      <th>ESTADO</th>\n",
              "      <th>EDAD</th>\n",
              "      <th>SEXO</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>801</th>\n",
              "      <td>4401761</td>\n",
              "      <td>2015-01-07 18:30:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>CL 60-KR 22B 55</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>ANT-139</td>\n",
              "      <td>4401761-1</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>2453250.0</td>\n",
              "      <td>MOTOCICLISTA</td>\n",
              "      <td>HERIDO</td>\n",
              "      <td>15</td>\n",
              "      <td>FEMENINO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72250</th>\n",
              "      <td>4431310</td>\n",
              "      <td>2015-12-05 05:10:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>CL 6A-KR 88D 60</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>ANT-139</td>\n",
              "      <td>4431310-1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>2516865.0</td>\n",
              "      <td>CONDUCTOR</td>\n",
              "      <td>ILESO</td>\n",
              "      <td>35</td>\n",
              "      <td>MASCULINO</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       CODIGO_ACCIDENTE               FECHA  GRAVEDAD  CLASE_SINIESTRO  \\\n",
              "801             4401761 2015-01-07 18:30:00       2.0              2.0   \n",
              "72250           4431310 2015-12-05 05:10:00       2.0              1.0   \n",
              "\n",
              "       CHOQUE  OBJETO_FIJO        DIRECCION  CODIGO_LOCALIDAD  DISENO_LUGAR  \\\n",
              "801       5.0          0.0  CL 60-KR 22B 55              19.0           1.0   \n",
              "72250     1.0          0.0  CL 6A-KR 88D 60               8.0           1.0   \n",
              "\n",
              "      CODIGO_CAUSA   VEHICULO  CLASE_VEHICULO  SERVICIO  MODALIDAD ENFUGA  \\\n",
              "801        ANT-139  4401761-1            10.0       3.0        NaN      N   \n",
              "72250      ANT-139  4431310-1             5.0       3.0        NaN      N   \n",
              "\n",
              "       CODIGO_ACCIDENTADO     CONDICION  ESTADO EDAD       SEXO  \n",
              "801             2453250.0  MOTOCICLISTA  HERIDO   15   FEMENINO  \n",
              "72250           2516865.0     CONDUCTOR   ILESO   35  MASCULINO  "
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[~df['CODIGO_CAUSA'].astype(str).str.isdigit()].head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Este termino *ANT* encontrado en **CODIGO_CAUSA** esta asociado a , por lo tanto, vamos a conservar el codigo de la hipotesis extrayendo la parte numerica de la cadena de texto."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {},
      "outputs": [],
      "source": [
        "df['CODIGO_CAUSA'] = df.CODIGO_CAUSA.astype(str).transform(lambda val: ''.join(filter(str.isdigit, val))).astype(int)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Por ultimo, las variable categoricas las convertimos a valores numericos para trabajar un analisis estadistico de estas. Para esto definimos la funcion ```categorias```, la cual genera un diccionario que nos permitira identificar cada valor categorico con un entero."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {},
      "outputs": [],
      "source": [
        "def categorias(df, col):\n",
        "    \"\"\"\n",
        "    Crea un diccionario asociando un valor entero a cada valor categorico de la columna,\n",
        "    en el dataframe df.\n",
        "    Parametros:\n",
        "        df: Un dataframe de Pandas.\n",
        "        col: Cadena de texto del nombre de las columna.\n",
        "    Retorna:\n",
        "        Un diccionario en el que cada nombre tiene asociado un entero.\n",
        "    \"\"\"\n",
        "    nombres = np.array(df[col].value_counts().index)\n",
        "    return {nombres[i]:i+1 for i in range(len(nombres))}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'CONDUCTOR': 1, 'MOTOCICLISTA': 2, 'PASAJERO/ACOMPAÑANTE': 3, 'PEATON': 4, 'CICLISTA': 5}\n",
            "{'ILESO': 1, 'HERIDO': 2, 'MUERTO': 3}\n",
            "{'MASCULINO': 1, 'FEMENINO': 2, 'SIN INFORMACION': 3}\n",
            "{'N': 1, 'S': 2}\n"
          ]
        }
      ],
      "source": [
        "variables = ['CONDICION', 'ESTADO', 'SEXO', 'ENFUGA']\n",
        "for variable in variables:\n",
        "    dic = categorias(df, variable)\n",
        "    df[variable].replace(dic, inplace=True)\n",
        "    print(dic)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Finalmente, el dataframe  resultante contiene las siguientes variables:\n",
        "\n",
        "| Variable | Definición\t| Valores |\n",
        "| --- | --- | --- |\n",
        "| CODIGO_ACCIDENTE | Codigo asociado al siniestro. | Valor tipo int. |\n",
        "|FECHA| Dia, mes, año, hora, minuto y segundo en el que ocurrio el siniestro. | Valor tipo datetime. |\n",
        "| GRAVEDAD | Grado de gravedad del siniestro. | 1(Con muertos), 2(Con heridos), 3(Solo daños).|\n",
        "| CLASE_SINIESTRO | Tipo de siniestro ocurrido.  | 1(Choque), 2(Atropello), 3(Volcamiento), 4(Caida de ocupante), 5(Incendio), 6(Otro), 7(Autolesion). |\n",
        "| CHOQUE | Elementos involucrados en el choque. | 0(No hubo choque), 1(Vehiculo), 2(Tren), 3(Semoviente), 4(Objeto fijo), 5(Peaton). |\n",
        "| OBJETIVO_FIJO | Elemento inmovil contra el que ocurrio el siniestro. | 0(No hubo choque contra un objeto fijo), 1(Muro), 2(Poste), 3(Arbol), 4(Barandas), 5(Semaforo), 6(Inmueble), 7(Hidrante), 8(Valla, Señal), 9(Tarima, Caseta), 10(Vehiculo estacionado), 11(Otro).|\n",
        "|DIRECCION| Direccion donde ocurrio el siniestro. | Variable tipo str. |\n",
        "|CODIGO_LOCALIDAD| Codigo asociado a la localidad donde ocurrio el siniestro. | 1(Usaquén), 2(Chapinero), 3(Santa Fe), 4(San Cristóbal), 5(Usme), 6(Tunjuelito), 7(Bosa), 8(Kennedy), 9(Fontibón), 10(Engativá), 11(Suba), 12(Barrios Unidos), 13(Teusaquillo), 14(Los Mártires), 15(Antonio Nariño), 16(Puente Aranda), 17(La Candelaria), 18(Rafael Uribe Uribe), 19(Ciudad Bolivar), 20(Sumapaz).|\n",
        "|DISENO_LUGAR| Tipo de lugar en el que ocurrio el siniestro. | 1(Tramo de via), 2(Intersección), 3(Via peatonal), 4(Paso elevado), 5(Paso inferior), 6(Paso a nivel), 7(Glorieta), 8(Puente), 9(Via troncal), 10(Lote o predio), 11(Cicloruta), 12(Ponton), 13(Tunel).|\n",
        "|CODIGO_CAUSA| Codigo asociado a la hipotesis de la causa del sinietro. | Valor tipo int. |\n",
        "|VEHICULO| Codigo del vehiculo involucrado en el siniestro. | Valor tipo str. |\n",
        "|CLASE_VEHICULO| Tipo de vehiculo involucrado en el accidente. | 0(Peaton), 1(Automovil), 2(Bus), 3(Buseta), 4(Camion, Furgon), 5(Camioneta), 6(Campero), 7(Microbus), 8(Tractocamion), 9(Volqueta), 10(Motocicleta), 11(Maquina Agricola), 12(Maquina Industrial), 13(Bicicleta), 14(Motocarro), 15(Traccion animal), 16(Otro), 17(Motociclo), 18(No identificado), 19(Bus Articulado), 20(Bus Alimentador), 21(Tren), 22(Ambulancia), 23(Metro), 24(Cuatrimoto), 25(Remolque), 26(Semi-Remolque), 27(Bus Bi-Articulado).|\n",
        "|SERVICIO| Tipo de servicio prestado por el vehiculo involucrado. |0(Sin informacion), 1(Oficial), 2(Publico), 3(Particular), 4(Diplomatico).|\n",
        "|MODALIDAD| Modalidad de carga/pasajeros presentes en el vehiculo. |1(Mixto), 2(Carga - Extrapesada), 3(Carga - Extradimensionada), 4(Pasajeros - Colectivo), 5(Pasajeros - Individual), 6(Pasajeros - Especial escolar), 7(Pasajeros - Especial turismo), 8(Pasajeros - Especial asalariado), 9(Pasajeros - Especial Ocasional), 10(Pasajeros - Masivo), 11(Carga - Pesada), 12(Carga - Mercancia Peligrosa).|\n",
        "|ENFUGA| El vehiculo se encuentra en fuga del siniestro. | 1(No), 2(Si)|\n",
        "|CODIGO_ACCIDENTADO| Codigo asociado al paciente accidentado. | Valor tipo str. |\n",
        "|CONDICION| Clasificacion del tipo de actor que es el individuo en el siniestro. | 1(CONDUCTOR), 2(MOTOCICLISTA), 3(PASAJERO/ACOMPAÑATE), 4(PEATON), 5(CICLISTA). |\n",
        "|ESTADO| Condicion del individuo involucrado en el siniestro. |  1(ILESO), 2(HERIDO), 3(MUERTO) |\n",
        "|EDAD| Edad del individuo. | Variable tipo int. |\n",
        "|SEXO| Sexo del individuo. | 1(MASCULINO), 2(FEMENINO), 3(SIN INFORMACION) |"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kLtfEYHvuOk_"
      },
      "source": [
        "## Analisis de datos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Empezamos este componente importando las librerias a usar para el analisis estadistico y la visualizacion de los datos."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {},
      "outputs": [],
      "source": [
        "#Paquetes para el analisis estadistico\n",
        "\n",
        "import scipy as sp\n",
        "from scipy import stats\n",
        "import statsmodels.api as sm\n",
        "from scipy.optimize import curve_fit\n",
        "from scipy.stats import poisson\n",
        "\n",
        "#Paquetes para la visualizaciones estadisticas e interactivas\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.graph_objs as go\n",
        "import plotly.express as px\n",
        "\n",
        "#Indicamos a pandas que las visualizaciones se contruiran en Plotly y estableciendo seaborn por defecto\n",
        "\n",
        "pd.options.plotting.backend = \"plotly\"\n",
        "sns.set()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Definimos un nuevo dataframe ```df_est```, en el cual eliminamos las variables que son cadenas de texto o sirven como identificacion. Esto para poder trabajar con las variables categoricas numericas."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "FECHA               datetime64[ns]\n",
              "GRAVEDAD                   float64\n",
              "CLASE_SINIESTRO            float64\n",
              "CHOQUE                     float64\n",
              "OBJETO_FIJO                float64\n",
              "CODIGO_LOCALIDAD           float64\n",
              "DISENO_LUGAR               float64\n",
              "CODIGO_CAUSA                 int32\n",
              "CLASE_VEHICULO             float64\n",
              "SERVICIO                   float64\n",
              "ENFUGA                       int64\n",
              "CONDICION                    int64\n",
              "ESTADO                       int64\n",
              "EDAD                        object\n",
              "SEXO                         int64\n",
              "dtype: object"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_est = df.drop(['CODIGO_ACCIDENTE', 'DIRECCION', 'CODIGO_ACCIDENTADO', 'VEHICULO', 'MODALIDAD'], axis=1)\n",
        "df_est.dtypes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Observemos la estadistica descriptiva asociada a los datos."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>FECHA</th>\n",
              "      <th>GRAVEDAD</th>\n",
              "      <th>CLASE_SINIESTRO</th>\n",
              "      <th>CHOQUE</th>\n",
              "      <th>OBJETO_FIJO</th>\n",
              "      <th>CODIGO_LOCALIDAD</th>\n",
              "      <th>DISENO_LUGAR</th>\n",
              "      <th>CODIGO_CAUSA</th>\n",
              "      <th>CLASE_VEHICULO</th>\n",
              "      <th>SERVICIO</th>\n",
              "      <th>ENFUGA</th>\n",
              "      <th>CONDICION</th>\n",
              "      <th>ESTADO</th>\n",
              "      <th>SEXO</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>501967</td>\n",
              "      <td>501967.000000</td>\n",
              "      <td>501967.000000</td>\n",
              "      <td>501967.000000</td>\n",
              "      <td>501967.000000</td>\n",
              "      <td>501967.000000</td>\n",
              "      <td>501967.000000</td>\n",
              "      <td>501967.000000</td>\n",
              "      <td>501967.000000</td>\n",
              "      <td>501967.000000</td>\n",
              "      <td>501967.000000</td>\n",
              "      <td>501967.000000</td>\n",
              "      <td>501967.000000</td>\n",
              "      <td>501967.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2017-11-26 00:33:18.273592064</td>\n",
              "      <td>2.584827</td>\n",
              "      <td>1.203079</td>\n",
              "      <td>1.438897</td>\n",
              "      <td>0.154247</td>\n",
              "      <td>9.297358</td>\n",
              "      <td>1.426480</td>\n",
              "      <td>147.693789</td>\n",
              "      <td>4.024390</td>\n",
              "      <td>2.299677</td>\n",
              "      <td>1.016649</td>\n",
              "      <td>1.542295</td>\n",
              "      <td>1.241667</td>\n",
              "      <td>1.197925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2015-01-01 01:05:00</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2016-06-30 11:15:00</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>112.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2017-11-23 15:30:00</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2019-04-16 08:30:00</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>145.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2020-12-31 22:34:00</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.526549</td>\n",
              "      <td>0.622049</td>\n",
              "      <td>1.289903</td>\n",
              "      <td>1.142773</td>\n",
              "      <td>5.008516</td>\n",
              "      <td>1.250823</td>\n",
              "      <td>79.832934</td>\n",
              "      <td>3.759828</td>\n",
              "      <td>0.906950</td>\n",
              "      <td>0.127951</td>\n",
              "      <td>1.033670</td>\n",
              "      <td>0.444330</td>\n",
              "      <td>0.453211</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               FECHA       GRAVEDAD  CLASE_SINIESTRO  \\\n",
              "count                         501967  501967.000000    501967.000000   \n",
              "mean   2017-11-26 00:33:18.273592064       2.584827         1.203079   \n",
              "min              2015-01-01 01:05:00       1.000000         1.000000   \n",
              "25%              2016-06-30 11:15:00       2.000000         1.000000   \n",
              "50%              2017-11-23 15:30:00       3.000000         1.000000   \n",
              "75%              2019-04-16 08:30:00       3.000000         1.000000   \n",
              "max              2020-12-31 22:34:00       3.000000         7.000000   \n",
              "std                              NaN       0.526549         0.622049   \n",
              "\n",
              "              CHOQUE    OBJETO_FIJO  CODIGO_LOCALIDAD   DISENO_LUGAR  \\\n",
              "count  501967.000000  501967.000000     501967.000000  501967.000000   \n",
              "mean        1.438897       0.154247          9.297358       1.426480   \n",
              "min         0.000000       0.000000          1.000000       1.000000   \n",
              "25%         1.000000       0.000000          6.000000       1.000000   \n",
              "50%         1.000000       0.000000          9.000000       1.000000   \n",
              "75%         1.000000       0.000000         13.000000       1.000000   \n",
              "max         5.000000      16.000000         20.000000      13.000000   \n",
              "std         1.289903       1.142773          5.008516       1.250823   \n",
              "\n",
              "        CODIGO_CAUSA  CLASE_VEHICULO       SERVICIO         ENFUGA  \\\n",
              "count  501967.000000   501967.000000  501967.000000  501967.000000   \n",
              "mean      147.693789        4.024390       2.299677       1.016649   \n",
              "min         0.000000        0.000000       0.000000       1.000000   \n",
              "25%       112.000000        1.000000       2.000000       1.000000   \n",
              "50%       121.000000        2.000000       3.000000       1.000000   \n",
              "75%       145.000000        6.000000       3.000000       1.000000   \n",
              "max       506.000000       28.000000       4.000000       2.000000   \n",
              "std        79.832934        3.759828       0.906950       0.127951   \n",
              "\n",
              "           CONDICION         ESTADO           SEXO  \n",
              "count  501967.000000  501967.000000  501967.000000  \n",
              "mean        1.542295       1.241667       1.197925  \n",
              "min         1.000000       1.000000       1.000000  \n",
              "25%         1.000000       1.000000       1.000000  \n",
              "50%         1.000000       1.000000       1.000000  \n",
              "75%         2.000000       1.000000       1.000000  \n",
              "max         5.000000       3.000000       3.000000  \n",
              "std         1.033670       0.444330       0.453211  "
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_est.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "El ojetivo ahora es poder obtener un analisis estadistico de los datos, dado que son variables categoricas, el poceso de analisis de correlaciones y de regresiones difiere del tratamiento a variables continuas. Un ejemplo lo observamos en las siguientes graficas y en la visualizacion de la matriz de correlacion."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\juan1\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
            "  self._figure.tight_layout(*args, **kwargs)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x238d467cb10>"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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AAAAApou7uH7llVf05JNP6h//+IcKCgp03HHH6YQTTtCgQYN04IEHqqCgwMs4AQAAAAAwVtzF9cSJEzV48GDdeeedOvLII5WXlydJ2rx5s2fBAQAAAACQCeJ+Wvj3v/99LV++XDNmzNAdd9yhDz/80Mu4AAAAAADIGHG/c/3kk09q5cqVevbZZ/Xcc8/piSeeUFlZmY499lhZluVljAAAAAAAGC2h77keMGCAJk2apL///e+67777tNtuu+m+++5TNBrVXXfdpXnz5qm2ttarWAEAAAAAMFJCxXXLRratH/zgB7rnnnu0ePFiXXvttdqyZYumTJmiQw89VBdddFGq4wQAAAAAwFhJFdfbKykp0dlnn63nn39ezzzzjE488UT985//TEVsAAAAAABkhIS/5zqW/fbbT/vtt58mT56cyt0CMJhtf/PMBZ69AAAAgGyWcHH93nvv6cknn9Q///lPrV+/XpZlqW/fvjrwwAP1y1/+UkOGDFFubq4XsQIwjG1bmvPKCtXWbVPPojxNPHl/v0MCAAAAfJFQcX377bdr9uzZys3N1dChQ7XffvspGo3q66+/1oIFCzRv3jyNHz9el156qVfxAjBMbd02bdrSyDvXAAAAyGpxF9fPPfecHn30UZ199tm65JJLVFRU1Ob12tpazZo1S7NmzdKQIUN01FFHpTxYAAAAAABMFPcDzZ566in9+Mc/1rXXXtuusJak4uJiXXPNNfrRj36kuXPnpjRIAAAAAABMFndxXVlZqeOOO67L9Y499lh98MEHroICAAAAACCTxF1c19XVqXfv3l2ut8MOO2jz5s2uggIAAAAAIJPEXVw7jqNgsOuPaAcCAUWjUVdBAQAAAACQSeIurgEAAAAAQMcS+iqu++67TyUlJTHXqampcRUQAAAAAACZJu7iun///lqxYkVc6+60005JBwQAAAAAQKaJu7h+7bXXvIwDAAAAAICMxWeuAQAAAABwKe7i+sorr9Tq1avbLPvggw/U2NjYZtn777+vAw88MDXRAQAAAACQAeIurl988cU2DyuLRCIaN26cPvnkkzbrRSIRvucaAAAAAJBVXN0WzvdZAwAAAABgwGeuN27cqN/+9rcaPXq0DjjgAJ1yyil69913O13/iy++0IUXXqgDDjhAo0aN0t13361IJJLGiAEAAAAAaMv34vqKK67Qe++9pxkzZujZZ5/V3nvvrfPOO0+fffZZu3Wbmpp03nnnSZKefPJJTZ06VX/+8581c+bMdIcNwEeObWtLk6O1tY2qa3Lk2P6cyhzb1saGsN7/dL02NYR9iYNctI2BXLTGkM5cmNDnrphyfHTG9BySP3f8yF+iOUkmxkS3SVcbiR4LpvYjmTa87jtii/uruLywatUqvfXWW5o7d66GDRsmSbr++uu1ePFivfDCC7rsssvarP/yyy/ryy+/1NNPP62ePXtqzz331IYNG/S73/1O48ePV25urh/dAJBGYcvSzGcqVFFZ1bKsvKxUE8YNVTCNH1UxIQ4TYjAlDhNiMCWOdMdgQp+7YnqMxOcO8blvM5kYaSOz20h2G8Tm+k8TlmUlvW1JSYkefPBB7bfffm32Z1mWamtr263/7rvvap999lHPnj1blh188MHasmWLPvroo6TjAJAZHNvWzHnL2lwEJKmiskoz5y1L219bTYjDhBhMicOEGEyJI90xmNDnrpgeI/G5Q3zu20wmRtrI7DaS3QZdS+id6/vuu08lJSVtlt1zzz3q1atXy8/bP1G8K8XFxTr88MPbLHv55Ze1atUqTZ48ud36X3/9tfr169dmWZ8+fSRJX331lcrLy+Nu+7uCwdgHUCBgt/kX/mI8zND8xzDLsqRv/86WqjHpaE5ubAi3uwg0q6isUn1jWL1C3t+QY0IcJsRgShwmxGBKHF7GYPKcjMX0GInPnWyOr7PfXRNtM5kYaSOz20h2G9OZUB/EnbH+/ftrxYoV7ZYtX7683bo77bRTUsH861//0nXXXaejjz5aRxxxRLvXt27dquLi4jbL8vLyJKnd920nwrYtlZQUxrVucXEo6XaQeoyH/4JBW8FgQMFvT2SpGJPO5uSa6vUxt6trCGtA/54x10kFE+IwIQZT4jAhBlPi8CoG0+dkLKbHSHzuZGt8sX53TbTNZGKkjcxuI9ltMoWf9UHcxfVrr73mZRxatGiRJk2apAMOOEB33HFHh+vk5+dr27ZtbZY1F9UFBQVJt+04UdXW1sdcJxCwVVwcUm1tgyIRJ+m2kBqMhxksy1I47Cgcjij87TjEMyZd/TGrszlZGMqJuV1hKKiamrouonbPhDhMiMGUOEyIwZQ4ko0h0+dk7BjMjpH43Omu8SU7J5NpM5kYaSOz20h2G9N5WR/E+0Zs3MX1/Pnz9eMf/7jlneJUeuKJJ3Trrbfq2GOP1e23397pg8n69evX7t3zdevWSZL69u3rKoZwOL4BiEScuNeF9xgPf9m2pWg0+s133n/73ItUjUlH+yjIC6q8rLTD25jKy0pVkBdMy/FgQhwmxGBKHCbEYEocXsZg8pyMxfQYic+dbI6vs+0SbTOZGGkjs9tIdptM4Wd9EPcN6ZMnT9ahhx6q3/72t1q2bFnKApg7d65uvvlmnXbaaZoxY0bMJ36PGDFCH374obZs2dKy7P/9v/+nwsJC7bXXXimLCYCZbMfRhHFDVV5W2mZ585MtbSc9J1IT4jAhBlPiMCEGU+JIdwwm9LkrpsdIfO4Qn/s2k4mRNjK7jWS3QdesaDS+56y///77WrBggV588UVt3LhRgwYN0kknnaSf/vSnbR5oloiVK1fq+OOP1xFHHKEbbrihzWv5+fnKy8vTpk2b1LNnT+Xm5qqxsVE/+clPtNtuu2nSpEn64osv9Jvf/EZnnHGGLrnkkqRikL7560Z1dezbHoJBWyUlhaqpqcvYv+J0J4yHGWzb0qwF72vTlkb16pGv35x7UFxjUlraI+brXc1Jx7ZV3xhWXUNYhaGgCvKCvlwETIjDhBhMicOEGEyJI9EYusucjMX0GInPne4Wn9s5mUybyeSQNjK7jWS3MZWX9UFXc7JZ3MV1s3A4rNdff10LFizQ3//+d1mWpSOPPFInnXSSDjnkkISCvP/++3XXXXd1+NrYsWM1duxYnXnmmXrsscd00EEHSfrmu7FvvPFGvfvuu+rZs6fGjRuniRMnynbxuHiK68zDeJjBr+LaJByLrchFq0zLRSrmZKb12UTk0J3ulL9UXSe7U04Ska39lui738V1ws9XDwaDOuqoo3TUUUdp48aN+utf/6rnnntO5557rvr3768TTjhBJ554YlxPDB8/frzGjx8fc53vPo18t9120x//+MdEwwYAAAAAwDOuvgSsV69eOv300/XMM89o4cKFOvHEE/Xcc8/pqKOOSlV8AAAAAAAYLyXfsF1dXa0lS5Zo6dKl+vLLL9WvX79U7BYAAAAAgIyQ8G3hzRoaGrRo0SK98MIL+sc//qFAIKCjjjpKDz/8sEaOHJnKGAEAAAAAMFpCxbXjOFq8eLFeeOEFvfrqq2poaNCQIUN03XXX6ac//al69Ijvg94AAAAAAHQncRfXN998s1566SXV1NSouLhYJ554osaNG8f3SwMAAAAAsl7cxfWf//xnjRw5UuPGjdORRx6p3NzcDtd755139OSTT+rOO+9MWZAAAAAAAJgs7uL6tdde6/RBZZs3b9b8+fP19NNP69NPP5Vt2xTXAAAAAICsEXdx3VFh/e9//1tPPfWUXnrpJW3dulW77babLr30Uv3sZz9LaZAAAAAAAJgs4aeF19XV6fnnn9dTTz2l5cuXKz8/X42NjZo2bZrGjh3rRYwAAAAAABgt7uL6ww8/1JNPPqm//vWvamho0MEHH6zf/e53OuiggzR69GjtsssuXsYJAAAAAICx4i6uTzjhBA0cOFAXXXSRfvKTn2innXaS9M3nrQEAAAAAyGZ2vCv269dPq1at0ptvvqmXX35Z1dXVXsYFAAAAAEDGiLu4/r//+z/df//92mGHHTRjxgyNHj1aF198sV599VVZluVljAAAAAAAGC3u28Ity9KoUaM0atQo1dbW6vnnn9f8+fN17bXXSpIef/xxhcNhHXzwwRTbAAAAAICskvDTwiWpuLhYp59+uk4//XR9/PHHmjdvnv7617/qlVde0Q477KBjjz1WU6ZMSXWsAAAAAAAYKe7bwjuz1157acqUKVq8eLHuuusu7b333nryySdTERsAAAAAABkhqXeuO5KTk6Njjz1Wxx57rBYsWJCq3QIAAAAAYLyEiuvly5frhRdekGVZ+vGPf6y99tqrzeuff/65brvtNv3973/Xz3/+81TGCQAAAACAseIurhcvXqyLL75YTU1NkqTZs2fr0Ucf1fDhw9XU1KR7771Xjz76qLZt26ZjjjnGs4ABAAAAADBN3J+5vv/++7XLLrto4cKFWrx4sUaMGKE777xTGzZs0Mknn6wHHnhAu+22m2bPnq3f//73XsYMAAAAAIBR4i6uKysrdeGFF2qPPfZQaWmprr76av3nP//RhAkT9Nlnn+nqq6/WggULdPDBB3sZL9CGZVmybb76DQAAAIC/4r4tfMuWLdp1111bft59990VDoe1bt06Pfvssxo4cKAnAQIdsSxL982rUE3tVvUoyNFpP9xTjhP1OywAAAAAWSru4tpxHAWDravn5ORIkiZNmkRhDV9srt+mTVsaFY1SVAMAAADwl+vvud5tt91SEQcAAAAAABnLdXFtWXzeFQAAAACQ3RL6nuv77rtPJSUlbZbdc8896tWrV5tllmXptttucx0cAAAAAACZIO7iun///lqxYkW7ZcuXL2+3Lu9mAwAAAACySdzF9WuvveZlHAAAAAAAZKyEbguXpJdeekmSdNxxx8lxHP3whz9s8/rxxx+vX//61ykJDgAAAACATBD3A80ikYguvvhiXX755XrjjTckSdFoVGvWrFFZWZkOPPBA9evXTw8//LA+//xzzwIGAAAAAMA0cb9z/fTTT2vx4sX6wx/+oKOPPrrNaxMnTtQ+++yjrVu36phjjtGTTz6pq6++OuXBAgAAAABgorjfuX7uued08skntyust5efn68TTzxRb731VkqCAwAAAAAgE8RdXH/yyScaPXp0l+sdcMAB3BYOAAAAAMgqcd8WHg6HFQqF2iwLBAL629/+pn79+rVZZttx1+wAAAAAAGS8uKvgvn37auXKle2W77rrrsrNzW35ecWKFerfv39qogMAAAAAIAPEXVyPGjVKTz31lBzH6XSdpqYmzZs3Tz/4wQ9SEhwAAAAAAJkg7uL6tNNO06effqpf//rXqqmpafd6fX29rrnmGn311Vc65ZRTUhokAAAAAAAmi/sz13vssYduu+02TZ48WUceeaRGjhyp3XffXZK0Zs0avfnmmwqHw/rd736nnXbayat4AQAAAAAwTtzFtST96Ec/0l577aWHHnpIr732ml599VVJUigU0pgxY3ThhRdqzz33TDqYBx54QG+++aYef/zxTtd5/vnnddVVV7Vb/uqrr2qXXXZJum0AAAAAAJKVUHEtffMO9rRp0yRJtbW1chxHvXr1ch3InDlzdPfdd2v48OEx11u+fLkOPPBAzZgxo83y3r17u44BAAAAAIBkJFxcb6+4uNh1AGvXrtUNN9ygpUuXttxmHsuKFSs0ePBglZaWum4bAAAAAIBU8P0LqT/44APl5OTo+eefV3l5eZfrL1++XAMHDkxDZAAAAAAAxMfVO9epMGbMGI0ZMyaudTdt2qS1a9fq3Xff1dy5c1VTU6OhQ4fqqquu0oABAzyOFAAAAACAjvleXCeisrJSkhSNRjVt2jRt3bpVs2bN0qmnnqoXXnhBO+64Y9L7DgZjv4kfCNht/oW/WsbBkizLUiBgy7aj/gaVhSzLavlP1jfLUjVHupqTpuDc0IpctOqOueA66T1y6E625S+e62S25aRZtvZbou/b/+uHjCquhw8friVLlqikpOSbX+Yl3XvvvTriiCM0f/58XXDBBUnt17YtlZQUxrVucXEoqTbgjWAgoGDQVq9eBX6HkrWCQVvBYEDBb09kqZgjicxJU3BuaEUuWnWXXHCdTC9y6E425C/R62Q25KQj2dpvib77JaOKa6n9U8FDoZB22WUXrV27Nul9Ok5UtbX1MdcJBGwVF4dUW9ugSMRJui2kRjAYkCSFIxGFw442bqxXNMo71+lmWZbCYUfhcEThb+dFPHOkq18I4pmTpuDc0IpctMq0XKRiTmZan01EDt3pTvlL1XWyO+UkEdnab4m+e9X3eP+YlVHF9VNPPaUZM2bo//7v/1RQ8M07lVu2bNF///tfjRs3ztW+w+H4BiASceJeF95pvnNB0W8+JhCJOHIciut0s21L0Wj0mz9sfJv+VM2RTJtnnBtakYtW3SkXXCfThxy6ky35S6SP2ZKT78rWfkv03a++G30zfiQSUVVVlbZu3SpJGj16tBzH0dVXX63Kykr95z//0cSJE9W7d2+dcMIJPkcLAAAAAMhWRhfXX331lUaNGqWFCxdKknbaaSfNnj1b9fX1OuWUU3T22WerR48eeuyxx5SXl+dztAAAAACAbGXUbeHTp09v8/Muu+yi5cuXt1m2zz776I9//GM6wwIAAAAAICaj37kGAAAAACATUFwDAAAAAOASxTUAAAAAAC5RXAMAAAAA4BLFNQAAAAAALlFcAwAAAADgEsU1AAAAAAAuUVwDAAAAAOASxTUAAAAAAC5RXAMAAAAA4BLFNQAAAAAALlFcAwAAAADgEsU1AAAAAAAuUVwDAAAAAOASxTUAAAAAAC5RXAMAAAAA4BLFNQAAAAAALlFcAwAAAADgEsU1AAAAAAAuUVwDAAAAAOASxTUAAAAAAC5RXAMAAAAA4BLFNQAAAAAALlFcAwAAAADgEsU1AAAAAAAuUVwDAAAAAOASxTUAAAAAAC5RXAMAAAAA4BLFNQAAAAAALlFcAwAAAADgEsU1AAAAAAAuUVwDAAAAAOASxTUAAAAAAC5RXAMAAAAA4BLFNQAAAAAALlFcAwAAAADgEsU1AAAAAAAuUVwDAAAAAOASxTUAAAAAAC4ZVVw/8MADOuOMM2KuU1NToyuvvFIjRozQgQceqBtvvFENDQ1pihAAAAAAgPaCfgfQbM6cObr77rs1fPjwmOtdeumlamho0OzZs1VbW6vf/OY3qq+v1+23356mSAH4zbFt1TeGVdfQpKJQjkJ5QdmO40scGxvCWlO93rc4yIV5sjEXmdBnU+ZKpjI9f6Yfg37kL9GcJBNjotukq41Ej4V0jE+ibUQtSw1NEdVvDasgP6hQTkBWNNplGyb2PR2a+/5lzQYVhoIK5aY+v/Hwvbheu3atbrjhBi1dulS77757zHXfe+89vf3221q4cKEGDhwoSbrpppt0/vnn64orrlDfvn3TEDEAP4UtSzOfqVBFZVXLsvKyUk0YN1TBFJwUMykOE2IwKQ4TZGMuMqHPmRCjyUzPH/G5bzOZGLO1jWQk2kbEsnTfs8v03orW9fcfXKqLTxiqQAr7YfrciVc68hsv328L/+CDD5STk6Pnn39e5eXlMdd99913VVpa2lJYS9KBBx4oy7L0z3/+0+tQAfjMsW3NnLeszclTkioqqzRz3jI5dnpOaSbEYUIMJsVhgmzMRSb0ORNiNJnp+SM+920mE2O2tpGMRNuIdlD4SdJ7y6t03/xlilpWSvph+tyJVzrymwjf37keM2aMxowZE9e6a9eu1U477dRmWW5urnr16qWvvvrKVRzBYOwDKBCw2/wLf7WMgyVZlqVAwJZtZ85f2LoLy7Ja/tO356JUzZGO5uTGhnC7k2ezisoq1TeG1Svk/WnNhDhMiMGkOEzQ3XNh8pyMJRNibGbi7xqm5y+b4+vsd9dE20wmxmxtIxmJtlG7NdKu8Gv23vIqNTQ5Ks4PuGoj2W1MlI78JsL8jG2noaFBubm57Zbn5eWpsbEx6f3atqWSksK41i0uDiXdDlIvGAgoGLTVq1eB36FkrWDQVjAYUPDbXwZTMUc6m5NrqtfH3K6uIawB/Xu6br8rJsRhQgwmxWGC7pwL0+dkLJkQ43eZ9LuG6fnL1vhi/e6aaJvJxJitbSQj0TbWraqOuf7WbWHttlOxqzaS3cZEX9ZsiPl6/Vb3+U1ERhXX+fn52rZtW7vljY2NKihIvrhynKhqa+tjrhMI2CouDqm2tkGRSOZ9yL+7CQa/+YtSOBJROOxo48Z6RTPosyHdhWVZCocdhcMRhb+dF/HMka7+mNXZnCwM5cTcrjAUVE1NXRdRu2dCHCbEYFIcJsjkXGT6nIwdg/kxNjPxdw3T89dd40t2TibTZjIxZmsbyUi0jfzc2OVZfm5q+mH63IlXQX7sfBXku8+v1PWcbJZRxXW/fv20aNGiNsu2bdumjRs3qk+fPq72HQ7HdxGLRJy414V3rObPQ0SlaDSqSMSR41Bcp5ttW4pGo9/8YePb9KdqjnS0j4K8oMrLSju8/ae8rFQFecG0zE8T4jAhBpPiMEF3z4XJczKWTIjxu0z6XcP0/GVzfJ1tl2ibycSYrW0kI9E2Qjm29h9cqveWt19//8GlCuXYKemH6XMnXunIbyLM+VBPHEaMGKGvv/5aq1ataln29ttvS5KGDRvmV1gA0sR2HE0YN1TlZaVtljc/ETJdXx1hQhwmxGBSHCbIxlxkQp8zIUaTmZ4/4nPfZjIxZmsbyUi0DSsa1cUnDNX+g9uu3/w0646+LsrUvqdDOvKbCCtq0L201157rdasWaPHH39ckhSJRFRdXa0ePXooPz9f0WhUp556qhobGzV16lTV19dr8uTJOuiggzRt2rSk241EHFVXx77tIRi0VVJSqJqauoz4K053l5MT0APPf6ANGxtUXJiri36+L+9c+8C2Lc1a8L42bWlUrx75+s25B8U1R0pLe8R8vas52fqdjGEVhoIq8PG7nf2Ow4QYTIrDBJmYi+4yJ2PJhBhN/l3D9Px1t/jczslk2kwmh9naRjISbSPZ77k2se/p0NyP5nylOr9dzclmRt8W/tVXX+nII4/UtGnTdMIJJ8iyLN1777268cYbddZZZykvL0/HHnusrrvuOr9DBZBGtuOoKMdWUc63Dzj06SJgO456hYIa0L+nb78MkwvzZGMuMqHPpsyVTGV6/kw/Bv3IX6I5SSbGRLdJVxuJHgvpGJ9E27CiURUEbRUUfbt+HO+Hmtr3dEi078nkNx5GFdfTp09v8/Muu+yi5cuXt1m2ww476A9/+EM6w2rDsizZtsW7pAAAAACAFkYV1yazLEv3zatQTe1W9SjI0Wk/3JMCGwAAAAAgieI6IZvrt2nTlka+8gkAAAAA0AbFtb55KFPv3l1/d1mPglxFo1EVF+aqV6/kv1cbqcF4mKF3z3wFg7aKC7/5zEpxccj1PuOdkyZJRb+7C3LRqrvkIpE52V367Cdy6E425C/R62Q25KQj2dpvib77xainhQMAAAAAkIky6nuuAQAAAAAwEcU1AAAAAAAuUVwDAAAAAOASxTUAAAAAAC5RXAMAAAAA4BLFNQAAAAAALlFcAwAAAADgEsU1AAAAAAAuBf0OwASRiKPq6rqY69i2pd69C1VdXSfHiaYpMnSG8TBPImNSWtoj5uvxzElTcCy2IhetMi0XqZiTmdZnE5FDd7pT/lJ1nexOOUlEtvZbou9e9b2rOdkSQ0pb7cZs25JlWbJty+9QIMbDRNk6Jtna746Qi1bZmIts7HOqkUN3yF972ZqTbO23RN/97jvFNQAAAAAALlFcAwAAAADgEsU1AAAAAAAuZdwDzZYuXaozzzyzw9d22WUXvfrqq2mOCAAAAACQ7TKuuN5///315ptvtln273//WxMnTtTFF1/sU1RIJ8e2tbEhrDXV61UUylEoLyjbcfwOCwCyFudlwDzMSyD9Mq64zs3NVWlpacvP9fX1mjZtmsaOHasTTzzRx8iQDmHL0sxnKlRRWdWyrLysVBPGDVUwml1fNwAAJuC8DJiHeQn4I+M/c33//feroaFB11xzjd+hwGOObWvmvGVtLhSSVFFZpZnzlsmxM/5wBoCMwnkZMA/zEvBPxr1zvb3q6mrNnj1bV155pXr16uVqX8Fg7BNNIGC3+Rfpt7Eh3O5C0ayiskr1jWH1CmX0IZ3RUj1HupqTpuDc0IpctOqOuehoTnJeTq3ueNykU7blr7PrJPMy+46F7dF3f/ue0TNr7ty56tGjh04++WRX+7FtSyUlhXGtW1wcctUWkremen3M1+sawhrQv2eaokFnUjFHEpmTpuDc0IpctOouuehsTnJe9kZ3OW78kg35i3WdZF62yoZjoTP03R8ZXVwvWLBAP//5z5Wfn+9qP44TVW1tfcx1AgFbxcUh1dY2KBLhYRB+KAzldPF6UDU1dWmKBt+VyBzpqnCOZ06agnNDK3LRKtNykeyc5LycWpl23JimO+XPzXWSedm9joVE0Xdv+h7vmz4ZW1x//PHHWr16tY4//viU7C8cjm8AIhEn7nWRWgV5QZWXlXZ4q1N5WakK8oKMjQFSNUcybSw5N7QiF626Uy466gfnZW90p+PGD9mSv876yLxslS3HQkfouz99z9ib8d99913tsMMO2muvvfwOBWliO44mjBuq8rLSNsubn37J10sAQHpxXgbMw7wE/JOx71x/+OGHGjx4sN9hIM2C0agmnlSu+saw6hrCKgwFVcD3NgKAbzgvA+ZhXgL+yNjiuqqqyvUTwpGZbMdRr1BQA/r3VE1NXdbe8gIApuC8DJiHeQmkX8YW1w899JDfIQAAAAAAICmDP3MNAAAAAIApKK4BAAAAAHCJ4hoAAAAAAJcorgEAAAAAcIniGgAAAAAAlyiuAQAAAABwieIaAAAAAACXKK4BAAAAAHCJ4hoAAAAAAJcorgEAAAAAcIniGgAAAAAAlyiuAQAAAABwieIaAAAAAACXKK4BAAAAAHCJ4hoAAAAAAJcorgEAAAAAcIniGgAAAAAAl4J+BwAAySosylcg0PXfCCMRR3VbtqYhIgAAAGQrimsAGSsQsDVl1ptdrnfLRaPSEA0AAACyGbeFAwAAAADgUtreuf7444/1xRdfSJL69++vIUOGpKtpAAAAAAA85Xlx/eyzz+q+++7Tl19+KUmKRqOyLEs77bSTxo8fr1/84hdJ7XfBggV68MEHtXr1au2666665JJLdNxxx6UydAAAAAAA4uJpcT19+nTNnj1b3//+93XmmWdqt912UyAQ0OrVq/XSSy/phhtu0IoVKzRlypSE9vvcc8/pN7/5jSZPnqzDDjtML774oq644gr169dP+++/f8r74di2NjaEtaZ6vYpCOQrlBWU7TsrbQXwYD/MwJkB24xwAxObYtuobw6praErbHGFeAunnWXH91ltvafbs2br22mt19tlnt3v91FNP1dy5c3XLLbdozJgxOuSQQ+LabzQa1e9//3udeeaZOu200yRJF110kd599129/fbbKS+uw5almc9UqKKyqmVZeVmpJowbqmA0mtK20DXGwzyMCZDdOAcAsfkxR5iXgD88e6DZ448/rqOPPrrDwrrZqaeequOOO05z5syJe78rV67UmjVrdPzxx7dZ/sgjj+jCCy9MNtwOObatmfOWtTkxSVJFZZVmzlsmx+Z5cOnEeJiHMQGyG+cAIDY/5gjzEvCPZ+9cf/DBB7r++uu7XO+4447TjTfeGPd+V65cKUmqr6/Xeeedpw8//FC77LKLLrroIo0ZMybpeIPB9ieajQ3hdiemZhWVVapvDKtXiG8zSxfGwzxejklHc7IdS7IsK6714tpfEpq/Zzue79vu7shFq+6YC66T3uuOx006mZg/P66TzEszj4V0oe/+9t2zmbVp0yb17t27y/V69eqlzZs3x73fLVu2SJKuueYaXXLJJZo0aZJefvllXXzxxXr00Uc1cuTIhGO1bUslJYXtlq+pXh9zu7qGsAb075lwe0gO42Eer8akszn5XduaIgoGA12uZ0lx7c+N4uKQp/vPJOSiVXfJBdfJ9Ooux41fTMqfH9dJ5mUrk46FdKPv/vCsuO7Tp48+++wzDR8+POZ6n376qfr16xf3fnNyciRJ5513nsaOHStJ2nvvvfXhhx8mXVw7TlS1tfXtlheGcmJuVxgKqqamLuH2kBzGwzzJjklXhW5nc/K7CoryFQ5HulwvKnl2bAQCtoqLQ6qtbVAkkt0PiiEXrTItF8nOSc7LqZVpx41pTMyfH9dJ5qWZx0K60Hdv+h7vmzSeFdeHHnqoHnvsMf385z9Xbm5uh+s0Njbqscce0xFHHBH3fvv27StJ2nPPPdssHzRokF5//fVkw1U43H4ACvKCKi8r7fDWmvKyUhXkBTvcDt5gPMzj5ZjEtV30m4ccxrOe18dGJOJw/H2LXLTqTrngOpk+3em48YNJ+fPjOsm8bGXSsZBu9N2fvnt2Q/oFF1ygr776ShMnTtS6devavf7VV1/pwgsv1Pr162M+9Oy79tlnHxUWFqqioqLN8hUrVmjXXXd1G3YbtuNowrihKi8rbbO8+WmLfJ1BejEe5mFMgOzGOQCIzY85wrwE/OPZO9c777yz7r33Xl122WUaM2aMhgwZop133lnBYFBr1qzRsmXLVFhYqD/84Q8J3Raen5+v888/XzNnzlTfvn01dOhQvfjiiy1f/ZVqwWhUE08q//a7CcMqDAVVwPcE+obxMA9jAmQ3zgFAbH7MEeYl4A9PHxU4cuRIvfDCC5o9e7befPPNltu2d955Z5177rk644wzVFpaGnsnHbj44osVCoV01113ae3atRo4cKDuueceHXTQQSnuwTdsx1GvUFAD+vdUTU1d1t5iYQrGwzyMCZDdOAcAsdmOo6IcW0U5335UMg1FLvMSSD/Pn8Pft29fXXPNNbrmmmtSut9zzjlH55xzTkr3CQAAAABAMjz/ErCGhgZt3bq109eXLVumX/ziF16HAQAAAACAZzwrruvq6nTFFVdo2LBhGjZsmC6//HI1NDS0vF5dXa3Jkyfrl7/8pT788EOvwgAAAAAAwHOeFdczZszQwoULdeyxx+rEE0/Uq6++qj/84Q+SpIULF+q4447T/PnzNWzYMD377LNehQEAAAAAgOc8+8z166+/rjPPPFOTJ0+WJA0dOlR333239thjD11//fXq06ePZsyYoR/96EdehQAAAAAAQFp49s71+vXrddhhh7X8PGbMGK1fv14333yzxo4dq4ULF1JYAwAAAAC6Bc/euW5sbFTPnj1bfi4uLpYk/fznP9dNN93kVbMAAAAAAKSd508Lb2ZZliTphBNOSFeTAAAAAACkRdqK62Y5OTnpbhIAAAAAAE95dlu4JFVVVenLL7+UJEUiEUnffBa7edn2+vfv72UoAAAAAAB4xtPi+pJLLmm3bPz48R2u+9FHH3kZCgAAAAAAnvGsuJ42bZpXuwYAAAAAwCieFddjx471atcAAAAAABjFswea7b333lq2bJlXuwcAAAAAwBieFdfRaNSrXQMAAAAAYJS0fxUXAAAAAADdTdq+iqsrfBUXAAAAACBTpf2ruDrDV3EBAAAAADKVp8X1+PHjteuuu3rZBAAAAAAAvvO0uP7BD36goUOHetkEAAAAAAC+44FmAAAAAAC4RHENAAAAAIBLnhXX06ZN0/e+9z2vdg8AAAAAgDE8+8z12LFjO33tk08+0SeffKKysjINHDgw4X2vXbtWo0ePbrd82rRpOuGEExLeHwAAAAAAbnj6QLNFixbp97//vU4++WSdfvrpkqTbb79ds2fPVjQalWVZOuWUU/Tb3/42of1+/PHHysvL06JFi2RZVsvyHj16pDR+mMmxbW1sCGtN9XoVhXIUygvKdhy/w8pqjAmQ3TgHAOZhXgLp51lx/c477+jSSy/V3nvv3fLu9D/+8Q89+uijGj58uKZMmaLPPvtMU6ZM0T777KMTTzwx7n2vWLFCu+++u/r06eNV+DBU2LI085kKVVRWtSwrLyvVhHFDFYxGfYwsezEmQHbjHACYh3kJ+MOzz1w/8sgjOvTQQ/XMM89o5MiRkqQ///nPsixL06ZN01577aUf/ehHOuuss/T0008ntO/ly5cndTs5Mptj25o5b1mbC4UkVVRWaea8ZXJsns+XbowJkN04BwDmYV4C/vHsneuKigrdeOONsr+dwI7jaMmSJRo0aFCbB50deOCB+tOf/pTQvlesWKGSkhKddtppWrlypXbbbTdddNFFHX4OO17BYOwTTSBgt/kX6bexIdzuQtGsorJK9Y1h9Qp5+kkHfIeXY9LVnJQkWWrz0ZBY68W1vyRwbmhFLlp1x1x0NIc4L6dWdzxu0inb8tfZdY15mX3Hwvbou79992xmbd68Wb179275efny5dqyZYsOOuigNuvZti0ngc9/hMNhffbZZxo0aJCuvfZaFRUV6cUXX9QFF1ygRx99tOVd8kTYtqWSksK41i0uDiW8f6TGmur1MV+vawhrQP+eaYoGkndjEu+c3NYUUTAY6HI9S4p7jieLc0MrctGqu+SisznJedkb3eW48Us25C/WdZJ52SobjoXO0Hd/eFZc77jjjvrqq69afl6yZIksy9LBBx/cZr2PPvpIpaWlce83GAxq6dKlCgQCys/PlyTtu+++qqys1COPPJJUce04UdXW1sdcJxCwVVwcUm1tgyIRHgbhh8JQThevB1VTU5emaCAlPyZdFbrxzElJKijKVzgc6XK9qOTZscG5oRW5aJVpuUh2TnJeTq1MO25M053y5+Y6ybzsXsdCoui7N32P900az4rrQw89VI899piOPPJIRSIRPfXUUyoqKtJhhx3Wss7GjRv12GOP6ZBDDklo34WF7TtXVlamN998M+l4w+H4BiASceJeF6lVkBdUeVlph7c6lZeVqiAvyNikmZdjEtd2USkaz4NZovHP8WRxbmhFLlp1p1x01A/Oy97oTseNH7Ilf531kXnZKluOhY7Qd3/67tkN6RMmTNBXX32lQw45RKNHj9aqVat0xRVXKC8vT5J07733auzYsdq0aZMuvPDCuPdbWVmpAw44QEuXLm2z/P3339egQYNS2geYxXYcTRg3VOVlbe90aH76JV8vkX6MCZDdOAcA5mFeAv7x7J3r/v37a8GCBXrqqae0YcMGHXHEEW0eODZ//nz169dP9957b5sHnHVl4MCB2mOPPXTTTTfpxhtvVElJiZ5++mn9+9//1rPPPutFV2CQYDSqiSeVq74xrLqGsApDQRXwvY2+YkyA7MY5ADAP8xLwh6ePCtxxxx01YcKEDl9btGhRy5PEE2Hbtu6//37deeed+vWvf63a2loNGTJEjz76qPbcc0+3ISMD2I6jXqGgBvTvqZqauqy95cUkjAmQ3TgHAOZhXgLp59tz+JsL661bt+q///2v9tprr7i33XHHHTVt2jSvQgMAAAAAICGefeZ61KhR+uijj9ose/TRR1VdXd1m2fLlyzV27FivwgAAAAAAwHOeFdfr169XU1NTy8+RSES/+93v2nw9FwAAAAAA3YFnxXVH4vrKHAAAAAAAMkxai2sAAAAAALojimsAAAAAAFyiuAYAAAAAwKW0F9eWZaW7SQAAAAAAPOXp91xPmDBBubm5bZaNHz9eOTk5LT9v27bNyxAAAAAAAPCcZ8U1310NAAAAAMgWnhXX06ZN82rXAAAAAAAYhQeaAQAAAADgkmfvXN97771xr2tZliZMmOBVKAAAAAAAeIriGgAAAAAAlzwrrj/++GOvdg0AAAAAgFH4zDUAAAAAAC559s71l19+mdD6/fv39ygSAAAAAAC85VlxPWbMGFmWFde6lmXpww8/9CoUAAAAAAA85Vlxfdttt8Usrjds2KAHHnhAmzdv1pAhQ7wKAwAAAAAAz3lWXJ9wwgmdvrZw4UL98Y9/1NatW3X55Zfr/PPP9yoMAAAAAAA851lx3ZHq6mpNnTpVr7zyivbZZx9Nnz5dgwYNSmcIAAAAAACkXNqK64ULF+rmm29WXV1dy7vVts3DygEAAAAAmc/z4rr53eq//e1vGjp0qKZNm6aBAwd63SwAAAAAAGnj6VvHf/3rX/WjH/1Ir7/+uq688ko9+eSTKS+sV65cqf3331/z589P6X4BAAAAAIiXZ+9cX3LJJXr11VdVXFysyZMna+DAgfrnP//Z6fojRoxIuI2mpiZNmjRJ9fX1bkLtkmPb2tgQ1prq9SoK5SiUF5TtOJ62ic4xHuZhTIDsxjkAiM2xbdU3hlXX0JS2OcK8BNLPs+J60aJFkqRNmzbpxhtvlCRFo9F2X8/VvOyjjz5KuI177rlHRUVF7oONIWxZmvlMhSoqq1qWlZeVasK4oQpGo562jfYYD/MwJkB24xwAxObHHGFeAv7wrLh+7LHHvNq1JOmdd97RU089pQULFuiII47wpA3HttudmCSporJKM+ct08STyvkLYBoxHuZhTIDsxjkAiM2POcK8BPzjWXF94IEHerVr1dbW6uqrr9aUKVO00047pWSfwWD7j59vbAi3OzE1q6isUn1jWL1Caf02s6zGeJjHyzHpaE62Y6nd3TCdrRfX/pIQCNht/s1m5KJVd8wF10nvdcfjJp1MzJ8f10nmpZnHQrrQd3/7npEza+rUqdp///11/PHHp2R/tm2ppKSw3fI11etjblfXENaA/j1TEgO6xniYx6sx6WxOfte2poiCwUCX61lSXPtzo7g45On+Mwm5aNVdcsF1Mr26y3HjF5Py58d1knnZyqRjId3ouz88K6732muv+N5R0jfvPH344YdxrbtgwQK9++67euGFF9yE14bjRFVb2/6haIWhnJjbFYaCqqmpS1kciI3xME+yY9JVodvZnPyugqJ8hcORLteLSp4dG4GAreLikGprGxSJZPdtduSiVablItk5yXk5tTLtuDGNifnz4zrJvDTzWEgX+u5N3+N9k8az4nrChAlxF9eJePbZZ7Vhw4Z2n7O+4YYbtHDhQj388MNJ7Tccbj8ABXlBlZeVdnhrTXlZqQrygh1uB28wHubxckzi2i76zUMR41nP62MjEnE4/r5FLlp1p1xwnUyf7nTc+MGk/PlxnWRetjLpWEg3+u5P3z0rridOnOjJfu+44w5t3bq1zbKjjz5al156qX7605+mtC3bcTRh3FDNnLesw6ct8jCI9GI8zMOYANmNcwAQmx9zhHkJ+Me3z1xXV1erd+/eCW/Xt2/fDpfvsMMOnb7mRjAa1cSTyr/9bsKwCkNBFfA9gb5hPMzDmADZjXMAEJsfc4R5CfjD00eprV69WjfffLNeffXVlmWLFi3SqFGjdOihh+qwww7TwoULvQwhJWzHUa9QUPsO3EG9QpyY/MZ4mIcxAbIb5wAgNttxVJRjq29xropy7LTMEeYlkH6evXO9evVqnXTSSWpsbNSQIUMkSStXrtSvf/1r9e7dW9dee60+++wzTZo0SX369NHw4cOTbmv58uWpChsAAAAAgIR5Vlzff//96t27t/70pz+ptLRUkvToo48qEonojjvuaPke7G3btumhhx5yVVwDAAAAAOAnz24L/8c//qHzzjuvpbCWpDfeeEN9+vRpKaylbx5GVlFR4VUYAAAAAAB4zrPiev369dp1111bfl69erW+/vprHXTQQW3W69Gjh+rquvd37QEAAAAAujfPiuvCwkLV1ta2/Pz222/LsiwdfPDBbdZbvXq1evXq5VUYAAAAAAB4zrPi+vvf/36bJ4E/99xzCgQCOvzww1uWRaNRPf300xo6dKhXYQAAAAAA4DnPHmj2q1/9SmeddZa+/vprOY6j9957TyeffLJ22GEHSdKSJUv0pz/9Sf/+97/16KOPehUGAAAAAACe8+yd62HDhumhhx5STk6ONm/erPPPP19TpkxpeX3SpElaunSppk6d2u5WcQAAAAAAMoln71xL0siRIzVy5MgOX5s1a5Z23313FRcXexkCAAAAAACe8+yd664MHTq0XWEdiUS0995764MPPvApKgAAAAAAEudbcd2ZaDTqdwgAAAAAACTEuOIaAAAAAIBMQ3ENAAAAAIBLFNcAAAAAALhEcQ0AAAAAgEsU1wAAAAAAuERxDQAAAACASxTXAAAAAAC4ZFRxbdu2xo4dq5KSEr9DAQAAAAAgbp4V10899ZSqq6u7XO+///2vLrnkEkmSZVmaNm2a+vfv71VYAAAAAACknGfF9dSpU/XFF1+0/Ow4jg444AB9/PHHbdbbtGmTXn31Va/CAAAAAADAc54V19FotN3P9fX1ikQiXjUJAAAAAIAvjPrMdbw2bNigq666SgcffLD2339/XXDBBfr000/9DgsAAAAAkKUysrieMGGCVq1apQcffFDz5s1Tfn6+zj77bDU0NPgdGgAAAAAgC2Vccb1p0ybtvPPOuuWWWzR06FANHDhQF198sdatW6fKykpP2nRsWxsbwnr/0/Xa1BCWY2dc2roVxsM8jAmQ3TgHALE5tq0tTY7W1jaqrslJyxxhXgLpF/Q7gET17NlTd955Z8vP1dXVmj17tvr166dBgwalvL2wZWnmMxWqqKxqWVZeVqoJ44Yq+J3PlcN7jId5GBMgu3EOAGLzY44wLwF/eFpc//3vf9dnn30m6ZunhVuWpddff73NO8yff/550vu//vrr9fTTTys3N1ezZs1SQUGB65i359h2uxOTJFVUVmnmvGWaeFK5bMdJaZvoHONhHsYEyG6cA4DY/JgjzEvAP54W1zNnzmy37J577mm3zLKspPZ/1lln6eSTT9acOXM0YcIEzZ07V/vss09S+woG298qs7Eh3O7E1Kyiskr1jWH1CmXcm/8Zi/Ewj5dj0tGcbMeK8/xhxbm/JAQCdpt/sxm5aNUdc8F10nvd8bhJJxPz58d1knlp5rGQLvTd3757NrPS8d3VzbeB33rrraqoqNATTzyhadOmJbwf27ZUUlLYbvma6vUxt6trCGtA/54Jt4fkMB7m8WpMOpuT37WtKaJgMNDlepYU1/7cKC4Oebr/TEIuWnWXXHCdTK/uctz4xaT8+XGdZF62MulYSDf67g/Piuudd97Zk/1WV1dryZIlOuaYYxQMfhO+bdsaNGiQ1q1bl9Q+HSeq2tr6dssLQzkxtysMBVVTU5dUm0gc42GeZMekq0K3szn5XQVF+QqHI12uF5U8OzYCAVvFxSHV1jYoEsnu2+zIRatMy0Wyc5Lzcmpl2nFjGhPz58d1knlp5rGQLvTdm77H+yaN5/eEbNiwQZZlqXfv3pKkbdu26ZlnntGnn36qwYMHa+zYscrNzY17f+vXr9cVV1yhhx9+WIcddpgkqampSR9++KHGjBmTdJzhcPsBKMgLqrystMNba8rLSlWQF+xwO3iD8TCPl2MS13ZRKRrPg1mice7PhUjE4fj7Frlo1Z1ywXUyfbrTceMHk/Lnx3WSednKpGMh3ei7P3339Ib0adOm6fDDD9df/vIXSd881Oycc87RLbfcohdffFE333yzTjnllIS+n3rPPffU6NGjdcstt+idd97RihUrdO2116q2tlZnn312SuO3HUcTxg1VeVlpm+XNT1vkYRDpxXiYhzEBshvnACA2P+YI8xLwj2fvXM+bN0+PPfaYzjzzTB111FGSpGeffVb//Oc/deqpp+r666/X2rVrdfrpp+uhhx7SpZdeGve+Z8yYoTvvvFOXX365Nm/erOHDh2vOnDnq379/yvsRjEY18aRy1TeGVdcQVmEoqIK8ICcmnzAe5mFMgOzGOQCIzY85wrwE/OFpcX366afruuuua1n2/PPPKycnR5dffrksy1K/fv10zjnnaM6cOQkV1z169NDUqVM1depUDyJvz3Yc9QoFNaB/T9XU1GXtLRamYDzMw5gA2Y1zABCb7TgqyrFVlPPtRyHTUOQyL4H08+y28E8++USjRo1q+bmxsVHvvfeehg4dqh49erQsHzx4sNasWeNVGAAAAAAAeM6z4rqpqUn5+fktP1dUVCgcDuvAAw9ss15DQ4NycmI/1RAAAAAAAJN5Vlzvsssu+uSTT1p+fuONN2RZlg499NA26y1dutSzr+0CAAAAACAdPPvM9bHHHqsHHnhAe+yxhxzH0dNPP63vfe97Gj58eMs6y5Yt09y5c3XWWWd5FQYAAAAAAJ7zrLg+//zz9c477+icc86RJBUUFGjatGktr59zzjlaunSpBg4cqPPPP9+rMAAAAAAA8JxnxXUoFNJjjz2md999V+vXr9eBBx6o3r17t7zeq1cv/epXv9J5552nwsJCr8IAAAAAAMBznhXXzba/DXx7d911V8v/r1y5UgMGDPA6FAAAAAAAPOHZA826Eg6HtXDhQp155pn60Y9+5FcYAAAAAAC45vk719+1evVqPf3005o/f742bNigwsJC/fznP093GAAAAAAApExaimvHcfTaa6/pz3/+s5YsWaJoNKrhw4fr2muv1Q9/+MM234cNAAAAAECm8bS4Xrt2rZ566inNmzdP69at02677aZf/epXevDBB3XppZdqxIgRXjYPAAAAAEBaeFZcX3TRRVq8eLFCoZCOOeYYjR07VsOGDdPmzZv1wAMPeNUsAAAAAABp51lx/X//938aPHiwrr76ah188MEKBAJeNQUAAAAAgK88e1r4TTfdpFAopPPPP1+HHHKIbrnlFn300UdeNQcAAAAAgG88e+f6F7/4hX7xi1/o008/1bPPPqvnn39ec+bM0YABA2RZlrZs2eJV0wAAAAAApJXn33M9cOBAXX311fr73/+umTNnasCAAQoEApowYYJOPfVUzZ07V9XV1V6HAQAAAACAZzwvrpsFAgGNGTNGM2fO1BtvvKGrrrpKmzdv1k033aTRo0enKwwAAAAAAFIuLd9z/V29e/fWOeeco3POOUfLli3T/Pnz/QgDAAAAAICUSNs7152pq6tTJBLxOwwAAAAAAJLme3G9YsUKzZs3z+8wAAAAAABImu/FNQAAAAAAmY7iGgAAAAAAlzKyuN64caN++9vfavTo0TrggAN0yimn6N133/U7LAAAAABAlsrI4vqKK67Qe++9pxkzZujZZ5/V3nvvrfPOO0+fffaZJ+05tq2NDWG9/+l6bWoIy7EzMm3dBuNhHsYEyG6cA4DYHNvWliZHa2sbVdfkpGWOMC+B9PPsq7jOPPPMuNb7+uuvE9rvqlWr9NZbb2nu3LkaNmyYJOn666/X4sWL9cILL+iyyy5LONZYwpalmc9UqKKyqmVZeVmpJowbqmA0mtK20DXGwzyMCZDdOAcAsfkxR5iXgD88+xNWNBqN67++fftq+PDhce+3pKREDz74oPbbb7+WZZZlybIs1dbWprQPjm1r5rxlbU5MklRRWaWZ85bxF8A0YzzMw5gA2Y1zABCbH3OEeQn4x7N3rh9//PGW/29oaFAoFGrz+kcffaS999474f0WFxfr8MMPb7Ps5Zdf1qpVqzR58uTkgpUUDLY/0WxsCLc7MTWrqKxSfWNYvUKepRDfwXiYx8sx6WhOtmN988e1eNaLa39JCATsNv9mM3LRqjvmguuk97rjcZNOJubPj+sk89LMYyFd6Lu/ffd0Zi1fvlyTJ0/WUUcdpYsuuqhleW1trU488USVlZXp7rvv1oABA5Ju41//+peuu+46HX300TriiCOS2odtWyopKWy3fE31+pjb1TWENaB/z6TaROIYD/N4NSadzcnv2tYUUTAY6HI9S4prf24UF4e6XilLkItW3SUXXCfTq7scN34xKX9+XCeZl61MOhbSjb77w7Pi+osvvtCZZ56p/Pz8dsVzTk6Orr76aj366KM69dRTtWDBAvXt2zfhNhYtWqRJkybpgAMO0B133JF0rI4TVW1tfbvlhaGcmNsVhoKqqalLul0khvEwT7Jj0lWh29mc/K6ConyFw5Eu14tKnh0bgYCt4uKQamsbFIk4nrSRKchFq0zLRbJzkvNyamXacWMaE/Pnx3WSeWnmsZAu9N2bvsf7Jo1nxfWDDz6oXr166c9//rN69+7d5rVQKKSzzz5bP/7xj3XSSSfpgQce0G9/+9uE9v/EE0/o1ltv1bHHHqvbb79dubm5ruINh9sPQEFeUOVlpR3eWlNeVqqCvGCH28EbjId5vByTuLaLfvN8h3jW8/rYiEQcjr9vkYtW3SkXXCfTpzsdN34wKX9+XCeZl61MOhbSjb7703fPbkhfsmSJzj///HaF9fZKS0t17rnn6q233kpo33PnztXNN9+s0047TTNmzHBdWHfGdhxNGDdU5WWlbZY3P23RdrLzgPUL42EexgTIbpwDgNj8mCPMS8A/nr1zvW7dOu2+++5drrfnnnsm9HVcK1eu1G233aYf/vCHuvDCC7V+fevnSvLz89WjR49kwu1UMBrVxJPKVd8YVl1DWIWhoArygpyYfMJ4mIcxAbIb5wAgNj/mCPMS8IdnxXXv3r21bt26LterqalRz57xP1Th5ZdfVlNTk1555RW98sorbV4bO3aspk+fnnCsXbEdR71CQQ3o31M1NXVZe4uFKRgP8zAmQHbjHADEZjuOinJsFeV8e7dlGopc5iWQfp4V1yNGjND8+fP14x//OOZ6CxYs0JAhQ+Le7/jx4zV+/Hi34QEAAAAAkDKefeb6jDPO0NKlSzV9+nQ1Nja2e33btm363e9+pzfeeEOnnXaaV2EAAAAAAOA5z9653m+//XTdddfptttu03PPPaeRI0dql112USQS0ZdffqmlS5eqpqZGl112mQ477DCvwgAAAAAAwHOeFdeSdNppp2mvvfbSI488oldffbXlHezCwkKNGjVK5557rsrLy70MAQAAAAAAz3laXEvSsGHDNGzYMElSdXW1gsGgiouLvW4WAAAAAIC08by43l6s77wGAAAAACBTefZAMwAAAAAAsgXFNQAAAAAALlFcAwAAAADgEsU1AAAAAAAuUVwDAAAAAOASxTUAAAAAAC5RXAMAAAAA4BLFNQAAAAAALlFcAwAAAADgEsU1AAAAAAAuUVwDAAAAAOBS0O8AACDTFBblKxD49m+TlrStKaKConwp2na9SMRR3Zat6Q8QAAAAaUdxDQAJCgRsTZn1piTJsiwFgwGFwxFFo22r61suGuVHeAAAAPABt4UDAAAAAOASxTUAAAAAAC5RXAMAAAAA4BLFNQAAAAAALmV8cf3AAw/ojDPO8DsMAAAAAEAWy+jies6cObr77rs9b8exbW1sCOv9T9drU0NYjp3Ract4jId5GBMgu2XCOcCxbW1pcrS2tlF1TY5xMZqeQ/Lnjh/5SzQnycSY6DbpaiPRY8HUfiTThtd9N1U6jvd4ZORXca1du1Y33HCDli5dqt13393TtsKWpZnPVKiisqplWXlZqSaMG6rgd752B95jPMzDmADZLRPOAabHSHzuEJ/7NpOJkTYyu41ktzFROvIbr4z808QHH3ygnJwcPf/88yovL/esHce2NXPesjaJl6SKyirNnLcsY/+yk6kYD/MwJkB2y4RzgOkxEp87xOe+zWRipI3MbiPZbUyUjvwmIiPfuR4zZozGjBmT0n0Gg+0TubEh3C7xzSoqq1TfGFavUEamMCMxHubxckw6mpPtWJJlWXGtF9f+4rV9u9Z2y2S1Wy+l7RouELDb/JvNumMuMvU6aXqMxOdONsfX2fUl0TaTiZE2MruNZLcxUTrymwjzM5YGtm2ppKSw3fI11etjblfXENaA/j29CgvfwXiYx6sx6WxOfte2poiCwUCX61lSXPuLV0ftBgPt40h1u5miuDjkdwjG6C65yOTrpOkxEp872RpfrOtkom0mEyNtZHYbyW5jonTkNxEU15IcJ6ra2vp2ywtDOTG3KwwFVVNT51VY+A7GwzzJjklXBWdnc/K7CoryFQ5HulwvKqX02GjTrvVNYR2ORL5pyMN2TRcI2CouDqm2tkGRiON3OL7KtFwkOycz4bxseozE5053jc/NdTLRNpOJkTYyu41ktzFROvIrxf9mCcX1t8Lh9r/8FOQFVV5W2uGtA+VlpSrIC3a4HbzBeJjHyzGJa7uoFI3nwRPROPcXr+3abbkVvKNYUt1uhohEnKzsd0e6Uy4y9TppeozE5042x9fZdom2mUyMtJHZbSS7jYnSkd9EdJ8Pg3nAdhxNGDdU5WWlbZY3P03Odsw/4LoTxsM8jAmQ3TLhHGB6jMTnDvG5bzOZGGkjs9tIdhsTpSO/ibCicb3tY65rr71Wa9as0eOPP570PiIRR9XVnd/24Ni26hvDqmsIqzAUVEFeMGMOuO6I8TBPomNSWtoj5v66mpPNinsWaMqsN7tc75aLRql2U9e3mcdr+3Yty1IwGFA4HGn3znWq2zVdMGirpKRQNTV1GfHXbi9lWi7czslMOC+bHiPxudPd4kvFdTLRNpPJIW1kdhvJbmMir/Pb1Zxsxm3hcbAdR71CQQ3o3zNjflHqzhgP8zAmaNbYFFFBUX67z59/VyTiqG7L1vQEBc9lwjnAdhwV5dgqysn9ZoFhvzyankPy544f+Us0J8nEmOg26Woj0WPB1H4k04bXfTdVOo73eGR8cT19+nS/QwAAfMuSNGXWW11+Fv6Wi0alJyAAAIA04TPXAAAAAAC4lPHvXANAtigsylcg0PXfRLnlGgAAIP0oriXZtqXeveP77rLi4pDH0SARjId5UjEm8c7JiBNVMBjocj3LUtxzPB4dtdtR0etFuzc8uKTL9W68YGRK201ExInG9QeAVOfGL+GII8uyOnxtW1NEhUX5kr75mrZgHHkxFdfJ9CKH7mRD/hKZk1J25KQj2dpvib77JeOfFg4AAAAAgN8y98/oAAAAAAAYguIaAAAAAACXKK4BAAAAAHCJ4hoAAAAAAJcorgEAAAAAcIniGgAAAAAAlyiuAQAAAABwieIaAAAAAACXgn4HYIJIxFF1dV3MdWzbUu/ehaqurpPjRNMUGTrDeJgnkTEpLe0R8/V45qQpOBZbkYtWmZaLVMzJTOuzicihO90pf6m6TnannCQiW/st0Xev+t7VnGyJIaWtdmO2bcmyLNm25XcoEONhomwdk2ztd0fIRatszEU29jnVyKE75K+9bM1JtvZbou9+953iGgAAAAAAlyiuAQAAAABwKSM/c7127VqNHj263fJp06bphBNO8CEiAAAAAEA2y8ji+uOPP1ZeXp4WLVoky2q9p75Hj/g+aA4AAAAAQCplZHG9YsUK7b777urTp09a2nNsWxsbwlpTvV5FoRyF8oKyHSctbaM9xsM86R4Tx7ZV3xhWXUOTr8eACcciuTAPuQBgAs5FQPplZHG9fPlyDRw4MC1thS1LM5+pUEVlVcuy8rJSTRg3VMFodj3e3gSMh3nSPSamHAMmxGFCDCbFYQJyAcAEnIsAf2TkA81WrFih6upqnXbaaTrkkEN0yimn6I033kh5O45ta+a8ZW1OTJJUUVmlmfOWybEzMn0Zi/EwT7rHxJRjwIQ4TIjBpDhMQC4AmIBzEeCfjHvnOhwO67PPPtOgQYN07bXXqqioSC+++KIuuOACPfrooxo5cmRS+w0G259oNjaE252YmlVUVqm+MaxeoYxLYcZiPMzj5ZiYPCdNiMOEGEyKwwTdPRcdzcntBQJ2m3+ROHLoTrblr7M52d3PRfHItmNhe/Td375n3MwKBoNaunSpAoGA8vPzJUn77ruvKisr9cgjjyRVXNu2pZKSwnbL11Svj7ldXUNYA/r3TLg9JIfxMI9XY2L6nDQhDhNiMCkOE3TnXHQ2JztSXBzyOJrujxy6kw35izUnu/O5KFHZcCx0hr77I+OKa0kqLGx/MikrK9Obb76Z1P4cJ6ra2vr27YRyYscRCqqmpi6pNpE4xsM8yY5JV7+kmz4nTYjDhBhMisMEmZyLZOfk9gIBW8XFIdXWNigS4aFJySCH7nSn/LmZk5l8LkqV7nQsJIq+e9P3eP/AnHHFdWVlpU4++WTNmjVLBx10UMvy999/X4MGDUp6v+Fw+wEoyAuqvKy0w1trystKVZAX7HA7eIPxMI+XY2LynDQhDhNiMCkOE3T3XMQbeyTiZHQ/TUAO3cmW/HXWx+5+LkpEthwLHaHv/vQ9427GHzhwoPbYYw/ddNNNevfdd/Xpp59q2rRp+ve//62LLroopW3ZjqMJ44aqvKy0zfLmpy3ydQbpxXiYJ91jYsoxYEIcJsRgUhwmIBcATMC5CPCPFY1m3vP4169frzvvvFOLFy9WbW2thgwZokmTJmn48OFJ7S8ScVRd3fntMa3fIxtWYSioAr4n0FeMh3kSHZPS0h4x95cpc9KEOEyIwaQ4TJCJuXA7J6VvHq5UUlKompq6rH23xC1y6E53yl8q5mQmnotSpTsdC4mi7970vas52RJDSltNkx133FHTpk1LW3u246hXKKgB/Xtm5YFqGsbDPOkeE9txVJRjqygn95sFPv2yYMKxSC7MQy4AmIBzEZB+GXdbOAAAAAAApqG4BgAAAADAJYprAAAAAABcorgGAAAAAMAlimsAAAAAAFyiuAYAAAAAwCWKawAAAAAAXKK4BgAAAADAJYprAAAAAABcorgGAAAAAMAlimsAAAAAAFyiuAYAAAAAwCWKawAAAAAAXKK4BgAAAADAJYprAAAAAABcorgGAAAAAMAlimsAAAAAAFyiuAYAAAAAwCWKawAAAAAAXKK4BgAAAADAJYprAAAAAABcorgGAAAAAMAlimsAAAAAAFyiuAYAAAAAwKWg3wEAAACkS2FRvgKBrt9biEQc1W3ZmoaIAADdBcU1AADIGoGArSmz3uxyvVsuGpWGaAAA3Qm3hQMAAAAA4BLFNQAAAAAALlFcAwAAAADgUsYX1ytXrtT++++v+fPn+x0KAAAAACBLZXRx3dTUpEmTJqm+vt7Tdhzb1saGsN7/dL02NYTl2BmdtozHeJgn3WPi2La2NDlaW9uouiaHYwAAAJeSubYmuk262kj0dxJT+5FMG1733VSJ9t2rfmf008LvueceFRUVedpG2LI085kKVVRWtSwrLyvVhHFDFYxGPW0b7TEe5kn3mHAMAACQWslcWxPdhjbMaiPZbUyUjvzGKzP/NCHpnXfe0VNPPaXp06d71oZj25o5b1mbxEtSRWWVZs5blrF/2clUjId50j0mHAMAAKRWMtfWRLehDbPaSHYbE6Ujv4nIyHeua2trdfXVV2vKlCnaaaedUrLPYLB9Ijc2hNslvllFZZXqG8PqFcrIFGYkxsM8Xo5JpszJQMBu8282IxetumMuOpqT28uYPluSZVlxrddVn1MtY3JoqGzLXzzHZzw5Sebamug2tGFWG8luY6J05DcR5mesA1OnTtX++++v448/PiX7s21LJSWF7ZavqV4fc7u6hrAG9O+ZkhjQNcbDPF6NSSbOyeLikC/tmohctOouuehsTnbE9D5va4ooGAx0uZ4lxd3nVDM9h6bLhvwlMiel2DlJ5tqa6Da0YVYbyW5jonTkNxEZV1wvWLBA7777rl544YWU7dNxoqqtbf9QtMJQTsztCkNB1dTUpSwOxMZ4mCfZMenqF4JMmpOBgK3i4pBqaxsUiThpbds05KJVpuUi2Tm5vUzpc0FRvsLhSJfrRSXOJxmmO+UvFXNSii8nyVxbE92GNsxqI9ltTJSO/Erx/7E144rrZ599Vhs2bNARRxzRZvkNN9yghQsX6uGHH05qv+Fw+xNOQV5Q5WWlHd46UF5WqoK8YIfbwRuMh3m8HJNMm5ORiMPx9y1y0ao75SLefhjf56gUjeeBNdH4+5xqxufQcNmSv0T6GCsnyVxbE92GNsxqI9ltTJSO/CYi4z6Ucscdd2jhwoVasGBBy3+SdOmll+rWW29NaVu242jCuKEqLytts7z5aXK2Y/4B150wHuZJ95hwDAAAkFrJXFsT3YY2zGoj2W1MlI78JsKKxvXnW7MNHjxY06ZN0wknnJDU9pGIo+rqzm97cGxb9Y1h1TWEVRgKqiAvmDEHXHfEeJgn0TEpLe0Rc3+ZNCeDQVslJYWqqanLiL/weolctMq0XLidk1Lm9Lm4Z4GmzHqzy/VuuWiUajd1fdttKmVKDk3VnfKXijkpJZaTZK6tiW5DG2a1kew2JvI6v13NyWYZd1u4H2zHUa9QUAP69+wWJ+xMx3iYJ91jYjuOinJsFeXkfrMgAy8CAACYJJlra6LbpKuNRH8nMbUfybThdd9NlWjfvep3tyiuly9f7ncIAAAAAIAslnGfuQYAAAAAwDQU1wAAAAAAuERxDQAAAACASxTXAAAAAAC4RHENAAAAAIBLFNcAAAAAALhEcQ0AAAAAgEsU1wAAAAAAuERxDQAAAACASxTXAAAAAAC4RHENAAAAAIBLFNcAAAAAALhEcQ0AAAAAgEsU1wAAAAAAuERxDQAAAACASxTXAAAAAAC4RHENAAAAAIBLFNcAAAAAALhEcQ0AAAAAgEsU1wAAAAAAuGREcR2NRrVlyxa/wwAAAAAAICmeFte//OUv9emnn7ZZ9re//U2bN29us2zZsmUaMWKEl6EAAAAAAOAZT4vrf//736qrq2v5ORKJ6LLLLtPnn3/uZbMAAAAAAKRV2m8Lj0aj6W4SAAAAAABPGfGZawAAAAAAMhnFNQAAAAAALlFcAwAAAADgki/FtWVZfjQLAAAAAIAngl43MHXqVBUVFUlqfZjZ9ddfr8LCwpZ1Ev2O6w0bNmj69OlavHixGhsbNWLECF1zzTUaOHBg6gIHAAAAACBOnr5zPWLECBUWFioajbYU1iNGjFBBQUHLsmg0qsLCQg0fPjzu/U6YMEGrVq3Sgw8+qHnz5ik/P19nn322GhoavOoKAAAAAACd8vSd68cffzzl+9y0aZN23nlnXXjhhdpzzz0lSRdffLF+9rOfqbKyUkOHDk15m45ta2NDWGuq16solKNQXlC246S8HcSH8TBPusfEsW3VN4ZV19DEMQAAQAf4fQlIP89vC0+1nj176s4772z5ubq6WrNnz1a/fv00aNCglLcXtizNfKZCFZVVLcvKy0o1YdxQBfnO7rRjPMyT7jHhGAAAIDaulYA/PC2u77333g6XW5algoIC7bjjjhoxYoT69euX1P6vv/56Pf3008rNzdWsWbNUUFDgJtx2HNtud2KSpIrKKs2ct0wTTyrnL4BpxHiYJ91jwjEAAEBsXCsB//hSXLcJIBjU+eefr1//+tcJ7/+ss87SySefrDlz5mjChAmaO3eu9tlnnyQilYLB9h8/39gQbndialZRWaX6xrB6hTLuzf+MxXiYx8sxyZQ5GQjYbf7NZuSiVXfMRUdzcnsZ02crzm8tsbruc6plTA4NlW356+z4NPFamW7Zdixsj77723dPZ9bHH3/c6Wvbtm3T2rVr9dJLL+mee+7RwIEDdfzxxye0/+bbwG+99VZVVFToiSee0LRp0xKO07YtlZQUtlu+pnp9zO3qGsIa0L9nwu0hOYyHebwak0yck8XFIV/aNRG5aNVdctHZnOyI6X3e1hRRMBjocj1LirvPqWZ6Dk2XDfmLNSdNvlamWzYcC52h7/7w7c9Wubm5+t73vqcLLrhANTU1+vOf/xxXcV1dXa0lS5bomGOOUTD4Tfi2bWvQoEFat25dUrE4TlS1tfXtlheGcmJuVxgKqqamLqk2kTjGwzzJjklXv7Bm0pwMBGwVF4dUW9ugSCS7b7MjF60yLRfJzsntZUqfC4ryFQ5HulwvKnE+yTDdKX9u5qSJ18p0607HQqLouzd9j/ePrUbcE3LooYdq/vz5ca27fv16XXHFFXr44Yd12GGHSZKampr04YcfasyYMUnHEA63H4CCvKDKy0o7vLWmvKxUBXnBDreDNxgP83g5Jpk2JyMRh+PvW+SiVXfKRbz9ML7PUbV8PWhX63E+yUzZkr/O+mjytTLdsuVY6Ah996fvRtyMn5+fr8bGxrjW3XPPPTV69Gjdcssteuedd7RixQpde+21qq2t1dlnn53SuGzH0YRxQ1VeVtpmefPTFnkYRHoxHuZJ95hwDAAAEBvXSsA/RrxzXVlZqT59+sS9/owZM3TnnXfq8ssv1+bNmzV8+HDNmTNH/fv3T3lswWhUE08q//Y7dcMqDAVVwPcE+obxME+6x4RjAACA2LhWAv7wvbj+8ssv9dBDDyV0S3ePHj00depUTZ061bvAtmM7jnqFghrQv6dqauqy9hYLUzAe5kn3mNiOo6IcW0U5ud8s4JcFAADa4PclIP08La6vu+66Tl/btm2b1q1bp2XLlmmHHXbQxRdf7GUoAAAAAAB4xtPieunSpR0utyxLBQUF2nHHHTV+/Hidfvrp6tGjh5ehAAAAAADgGU+L69dee83L3QMAAAAAYAQjnhYuSZs3b/Y7BAAAAAAAkpKWB5pVV1drzpw5WrRokdasWaNIJKKdd95Zo0eP1jnnnKPS0lL95je/0a677qpJkyalIyQAAAAAAFLG8+L67bff1q9//WtVV1drr7320iGHHCLbtrV69Wr96U9/0tNPP61zzz1Xr732mp577jmvwwEAAAAAIOU8La6//vprTZw4UQMHDtQTTzyhPfbYo83rq1ev1uTJk3XPPffo3HPP1cCBA70MBwAAAAAAT3j6mes//elP6tWrlx5++OF2hbUkfe9739N+++2naDSqrVu3ehkKAAAAAACe8bS4fu2113TmmWeqoKCgw9fXr1+vxx57TMcff7zefPNNL0MBAAAAAMAznhbXa9euVVlZWaevO46jyy67TCeffLLWrl3rZSgAAAAAAHjG0+K6qKhItbW1nb7ep08f/epXv9LGjRtVVFTkZSgAAAAAAHjG0+J633331f/+7/92ud5LL72kfffd18tQAAAAAADwjKfF9S9/+Uv99a9/1V/+8pdO13nmmWf00ksv6fTTT/cyFAAAAAAAPOPpV3EdccQR+uUvf6nrrrtOCxcu1FFHHaWdd95ZkvTFF1/ob3/7m5YsWaIzzjhDo0aN8jIUAAAAAAA842lxLUlTp07V4MGD9cADD2jx4sWyLEuSFI1G1a9fP91000066aSTvA4DAAAAAADPeF5cS9Ipp5yiU045Re+//76++uorSVL//v21zz77pKN5AAAAAAA8lZbiutm+++6r3XbbTdFoVMXFxelsGgAAAAAAz6SluP7000/10EMP6dVXX9WWLVskSYWFhTryyCN17rnnavDgwekIAwAAAAAAT3heXC9cuFDXXXedbNvWIYccol133VXBYFCrV6/Wa6+9ppdeekm33XabfvKTn3gdCgAAAAAAnvC0uP7000913XXX6fDDD9fNN9+snj17tnl9y5YtuuGGGzRlyhTtvffeGjhwoJfhAAAAAADgCU+/53r27NkaNGiQ7rrrrnaFtSQVFRXpf/7nf7TXXnvpT3/6k5ehAAAAAADgGU+L6yVLlujUU09VIBDoPADb1i9/+Uv94x//8DIUAAAAAAA842lxvW7dOu22225drrfLLruoqqrKy1AAAAAAAPCMp8V1cXGx1q1b1+V669atU+/evb0MBQAAAAAAz3haXB9wwAFasGBBl+vNnz9fBxxwgJehAAAAAADgGU+L67POOkuLFy/Wfffd1+k6d955p5YsWaKzzjrLy1AAAAAAAPCMp1/FNWzYMF1++eWaMWOGXnzxRf3gBz/QzjvvrGAwqDVr1uhvf/ubVq5cqWuuuUZDhw71MhQAAAAAADzjaXEtSRdccIHKysp077336uGHH27z2ve//3099NBDGjVqlNdhAAAAAADgGc+La0n6wQ9+oB/84AeqqanRmjVrFI1GtfPOO/MQMwAAAABAt5CW4rpZSUmJSkpKXO9n48aNmjFjhl5//XVt2bJFgwcP1pVXXqnhw4enIEoAAAAAABLjaXF93XXXxb2uZVm67bbb4lr3iiuuUFVVlWbMmKEddthBjz/+uM477zz95S9/0R577JFsuMgQjm1rY0NYa6rXqyiUo1BeULbj+B1WVkv3mDi2rfrGsOoamjgGAADoAL8vAennaXG9dOnSdsu++uor7bjjjsrJyWmz3LKsuPa5atUqvfXWW5o7d66GDRsmSbr++uu1ePFivfDCC7rsssvcBw5jhS1LM5+pUEVlVcuy8rJSTRg3VMFo1MfIsle6x4RjAACA2LhWAv7wtLh+7bXX2vwcDoe177776v7779c+++yT1D5LSkr04IMPar/99mtZZlmWLMtSbW2tq3hhNse2210oJKmiskoz5y3TxJPK+YtsmqV7TDgGAACIjWsl4J+0fuY63nenYykuLtbhhx/eZtnLL7+sVatWafLkyUnvNxiM/ZXfgYDd5l+k38aGcLsLRbOKyirVN4bVK5TWQzrreTkmHc1JE48Bzg2tyEWr7piLbnOdtOL8fcTqus+pljE5NFS25a+z49PEa2W6ZduxsD367m/fM35m/etf/9J1112no48+WkcccURS+7BtSyUlhXGtW1wcSqoNuLemen3M1+sawhrQv2eaooHk3Zh0NidNPgY4N7QiF626Sy6603VyW1NEwWCgy/UsKe4+p5rpOTRdNuQv1pw0+VqZbtlwLHSGvvsjo4vrRYsWadKkSTrggAN0xx13JL0fx4mqtrY+5jqBgK3i4pBqaxsUiXArjR8KQzldvB5UTU1dmqKBlPyYdPULa2dz0sRjgHNDK3LRKtNykeyc3F6m9LmgKF/hcKTL9aIS55MM053y52ZOmnitTLfudCwkir570/d4/9iascX1E088oVtvvVXHHnusbr/9duXm5rraXzgc3wBEIk7c6yK1CvKCKi8r7fBWp/KyUhXkBRmbNPNyTDrazuRjgHNDK3LRqjvlottcJ6NSNJ4HOkXj73OqGZ9Dw2VL/jrro8nXynTLlmOhI/Tdn777ckO6289ez507VzfffLNOO+00zZgxw3VhjcxgO44mjBuq8rLSNsubn37JwznSL91jwjEAAEBsXCsB/3j6zvWYMWM6LKTHjx/f4VdxLVq0qMt9rly5Urfddpt++MMf6sILL9T69a2fK8nPz1ePHj3cBw5jBaNRTTyp/NvvOA6rMBRUAd/b6Kt0jwnHAAAAsXGtBPzhaXF94IEHpuQJ4dt7+eWX1dTUpFdeeUWvvPJKm9fGjh2r6dOnp7Q9mMd2HPUKBTWgf0/V1NRl7S0vJkn3mNiOo6IcW0U53961wi8LAAC0we9LQPp5WlxvX+g2NDQoFGr75LaPPvpIe++9d0L7HD9+vMaPH5+S+AAAAAAASAXPP3O9fPlynXjiiZo9e3ab5bW1tTrxxBP1s5/9TCtXrvQ6DAAAAAAAPONpcf3FF1/ozDPP1Pr16zVgwIA2r+Xk5Ojqq6/Wxo0bdeqpp2rt2rVehgIAAAAAgGc8La4ffPBB9erVS3/5y1907LHHtnktFArp7LPP1rx585SXl6cHHnjAy1AAAAAAAPCMp8X1kiVLdP7556t3796drlNaWqpzzz1Xb731lpehAAAAAADgGU+L63Xr1mn33Xfvcr0999xTX3/9tZehAAAAAADgGU+L6969e2vdunVdrldTU6OePXt6GQoAAAAAAJ7xtLgeMWKE5s+f3+V6CxYs0JAhQ7wMBQAAAAAAz3haXJ9xxhlaunSppk+frsbGxnavb9u2Tb/73e/0xhtv6LTTTvMyFAAAAAAAPBP0cuf77befrrvuOt1222167rnnNHLkSO2yyy6KRCL68ssvtXTpUtXU1Oiyyy7TYYcd5mUoAAAAAAB4xtPiWpJOO+007bXXXnrkkUf06quvtryDXVhYqFGjRuncc89VeXm512EAAAAAAOAZz4trSRo2bJiGDRsmSaqurlYwGFRxcXE6mgYAAAAAwHNpKa63F+s7rwEAAAAAyESePtAMAAAAAIBsQHENAAAAAIBLFNcAAAAAALhEcQ0AAAAAgEsU1wAAAAAAuERxDQAAAACASxTXAAAAAAC4RHENAAAAAIBLFNcAAAAAALhEcQ0AAAAAgEsU1wAAAAAAuERxDQAAAACASxTXAAAAAAC4RHENAAAAAIBLFNcAAAAAALhEcQ0AAAAAgEsZX1w/8MADOuOMM/wOAwAAAACQxTK6uJ4zZ47uvvtuv8MAAAAAAGS5oN8BJGPt2rW64YYbtHTpUu2+++6et+fYtjY2hLWmer2KQjkK5QVlO47n7aJjjId50j0mjm2rvjGsuoYmjgEAADrA70tA+mVkcf3BBx8oJydHzz//vGbOnKk1a9Z41lbYsjTzmQpVVFa1LCsvK9WEcUMVjEY9axcdYzzMk+4x4RgAACA2rpWAPzLytvAxY8bonnvu0fe+9z1P23FsWzPnLWtzYpKkisoqzZy3TI6dkenLWIyHedI9JhwDAADExrUS8E9GvnPthWCw/YlmY0O43YmpWUVlleobw+oVIoXpwniYx8sxyZQ5GQjYbf7NZuSiVXfMRUdzcnsZ02dLsiwrrvW66nOqZUwODZVt+evs+DTxWplu2XYsbI+++9v37j2z4mTblkpKCtstX1O9PuZ2dQ1hDejf06uw8B2Mh3m8GpNMnJPFxSFf2jURuWjVXXLR2ZzsiOl93tYUUTAY6HI9S4q7z6lmeg5Nlw35izUnTb5Wpls2HAudoe/+oLiW5DhR1dbWt1teGMqJuV1hKKiamjqvwsJ3MB7mSXZMuvqFNZPmZCBgq7g4pNraBkUi2f2gGHLRKtNykeyc3F6m9LmgKF/hcKTL9aIS55MM053y52ZOmnitTLfudCwkir570/d4/9hKcf2tcLj9ABTkBVVeVtrhrTXlZaUqyAt2uB28wXiYx8sxybQ5GYk4HH/fIhetulMu4u2H8X2OStF4HugUjb/PqWZ8Dg2XLfnrrI8mXyvTLVuOhY7Qd3/6nn034yfAdhxNGDdU5WWlbZY3P22RrzNIL8bDPOkeE44BAABi41oJ+Id3rrsQjEY18aTyb79TN6zCUFAFfE+gbxgP86R7TDgGAACIjWsl4I+ML66nT5/ueRu246hXKKgB/XuqpqYua2+xMAXjYZ50j4ntOCrKsVWUk/vNAn5ZAACgDX5fAtKP28IBAAAAAHCJ4hoAAAAAAJcorgEAAAAAcIniGgAAAAAAlyiuAQAAAABwieIaAAAAAACXKK4BAAAAAHCJ4hoAAAAAAJcorgEAAAAAcIniGgAAAAAAlyiuAQAAAABwieIaAAAAAACXKK4BAAAAAHCJ4hoAAAAAAJcorgEAAAAAcIniGgAAAAAAlyiuAQAAAABwieIaAAAAAACXKK4BAAAAAHCJ4hoAAAAAAJcorgEAAAAAcIniGgAAAAAAl4J+BwAAAABkssKifAUCXb9nFYk4qtuyNQ0RAfADxTUAAADgQiBga8qsN7tc75aLRqUhGgB+4bZwAAAAAABcorgGAAAAAMAlimsAAAAAAFyiuAYAAAAAwCWKawAAAAAAXMrI4tpxHP3hD3/QYYcdpu9///v61a9+pdWrV/sdFgAAAAAgS2VkcX3fffdp7ty5uvnmm/Xkk0/KcRydf/752rZtmyftObatjQ1hvf/pem1qCMuxMzJt3QbjYZ50j4lj29rS5GhtbaPqmhzfjgETjkVy0TYGctEagwm5AOAfE85FQLbJuO+53rZtm/74xz9q0qRJOuKIIyRJd911lw477DD97W9/009+8pOUthe2LM18pkIVlVUty8rLSjVh3FAFo9GUtoWuMR7mSfeYmHIMmBCHCTGYEocJMZgShwkxAPAX5wHAHxn3J6yPP/5YdXV1GjlyZMuy4uJiDRkyRO+8805K23JsWzPnLWtzYpKkisoqzZy3jL8AphnjYZ50j4kpx4AJcZgQgylxmBCDKXGYEAMAf3EeAPyTce9cf/3115KknXbaqc3yPn36tLyWjGCw/YlmY0O43YmpWUVlleobw+oVyrgUZizGwzxejonJc9KEOEyIwZQ4TIjBlDjSPSe3FwjYbf41liVZlhXXel31OdUyJoeG8jV/PhxXne3HhHOR37J5LtF3f/uecTOroaFBkpSbm9tmeV5enjZt2pTUPm3bUklJYbvla6rXx9yuriGsAf17JtUmEsd4mMerMTF9TpoQhwkxmBKHCTGYEke652RHiotDCe8/nbY1RRQMBrpcz5Li7nOqmZ5D0/mRv3QfV7HmpAnnIlNk81yi7/7IuOI6Pz9f0jefvW7+f0lqbGxUKJRcIh0nqtra+nbLC0M5MbcrDAVVU1OXVJtIHONhnmTHpKtfLEyfkybEYUIMpsRhQgymxJHuObm9QMBWcXFItbUNikScroP1SUFRvsLhSJfrRaW0X1MyJYem8jN/qT6u3MxJE85FfsvmuUTfvel7vH8Uy7jiuvl28HXr1mnXXXdtWb5u3ToNHjw46f2Gw+0HoCAvqPKy0g5vrSkvK1VBXrDD7eANxsM8Xo6JyXPShDhMiMGUOEyIwZQ40j0nv6uxKaK8UO43FUQMkYijui1bk4rDtagUjeeBTtH4+uyFSMTheuaCL/nz4bjqbD8mnItMkc1zib770/eMuxl/r732UlFRkZYuXdqyrLa2Vh9++KFGjBiR0rZsx9GEcUNVXlbaZnnz0xZtJzsPWL8wHuZJ95iYcgyYEIcJMZgShwkxmBKH3zFYkq6f9ZamzHoz5n/Z+FlAIF38Pg8A2Szj3rnOzc3V6aefrjvuuEO9e/fWzjvvrP/5n/9Rv379dPTRR6e8vWA0qoknlau+May6hrAKQ0EV5AU5MfmE8TBPusfElGPAhDhMiMGUOEyIwZQ4TIgBgL84DwD+yLjiWpIuvfRShcNhTZkyRVu3btWIESP0yCOPKCcn9mdMkmU7jnqFghrQv6dqauqy9hYLUzAe5kn3mNiOo6IcW0U53z7Y0KdfFkw4FslF2xjIRWsMJuQCSLfGpogKivLN/mhCmphwLgKyTUYW14FAQFdddZWuuuoqv0MBAACAISxJU2a91eXnn2+5aFR6AgKQVfjQEwAAAAAALlFcAwAAAADgUkbeFp5qtm2pd+/4vrssm7+Q3USMh3lSMSaJzElTcCy2Ihetuksu4p2TESca15PALUu+zfGIE1UwGOhyPT9j7C7HjR/8OgbTfVwlep3M1mMqW/st0Xe/WNG4vpQPAAAAAAB0htvCAQAAAABwieIaAAAAAACXKK4BAAAAAHCJ4hoAAAAAAJcorgEAAAAAcIniGgAAAAAAlyiuAQAAAABwKeh3ACaIRBxVV9fFXMe2LfXuXajq6jo5Dl8N7jfGwzyJjElpaY+Yr8czJ03BsdiKXLTKtFykYk5mWp9NRA7d6U75S9V1sjvlJBHZ2m+JvnvV967mZEsMKW21G7NtS5ZlybYtv0OBGA8TZeuYZGu/O0IuWmVjLrKxz6lGDt0hf+1la06ytd8Sffe77xTXAAAAAAC4RHENAAAAAIBLFNcAAAAAALhEcQ0AAAAAgEsU1wAAAAAAuMRXccUhalmq3RrRulXVCuUFlR+0ZUWz69H2QCyObWtjQ1hrqterKJSjUF5QtuN42l59Y1h1DU1paQ8AYLZ0X4cAoCMU112IWJbue3aZ3ltR1bJs/8GluviEoQpQYAMKW5ZmPlOhisrWOVJeVqoJ44Yq6MEcSXd7AACzcV0AYApuC48h2kFhLUnvLa/SffOXKWpl3/fHAdtzbFsz5y1r8wuNJFVUVmnmvGVy7NSeYtLdHgDAbFwXAJiEM04MDU2RdoV1s/eWV6mhKZLmiACz1DeG2/1C06yiskr1jeGMbg8AYDauCwBMwm3hMdRvjX1Crt8aVkFRbpqiAcxT19DUxethFeWkbo6kuz0AgNm4LnStsSmigqJ8qYs75CMRR3VbtqYnKKCboriOoSA/dnq6eh3o7gpDOV28nto5ku72AABm47rQNUvSlFlvKdrF589vuWhUegICujFuC48hlBPQ/oNLO3xt/8GlCuUE0hwRYJaCvKDKyzqeI+VlpSrIS+0vNeluDwBgNq4LAExCcR2DFY3q4hOGtiuwm58WztdxIdvZjqMJ44a2+8Wm+Smtqf4alHS3BwAwG9cFACbhz3ldCESjuuSEoWpocrR1W1j5uUGFcviea6BZMBrVxJPKv/3e6bAKQ0EVePj9ouluDwBgNq4LAExBcR0HKxpVcX5Au+1UrJqaOoXDnKyB7dmOo16hoAb075mWOWI7jopy7NaH1PALFABktXRfhwCgI9wWDgAAAACAS0YX1w888IDOOOOMNsumTJmiwYMHt/lvzJgxPkUIAAAAAIDBt4XPmTNHd999t4YPH95m+fLlyzV+/HidfvrpLcsCAW+f2u3YtjY2hPVlzQYVhoIK5fI5HmB76Z4jjm1/+9m6JhWFchTis3UAkNWar0NrqtdzXQDgG+OK67Vr1+qGG27Q0qVLtfvuu7d5LRqN6pNPPtEFF1yg0tKOv3Yh1cKWpZnPVKiisqplWfMTKIM81AxI+xxhTgIAtsd1AYApjLst/IMPPlBOTo6ef/55lZeXt3nt888/V319vfbYY4+0xOLYtmbOW9bmZC1JFZVVmjlvmRzbuPQBaZXuOcKcBABsj+sCAJMY9871mDFjOv0M9YoVKyRJjz/+uN544w3Ztq3Ro0fr8ssvV48ePVy1Gwy2P/lubAi3O1k3q6isUn1jWL1CxqUwKwQCdpt/4Q8v50imzEmOxVbkolV3zEVHc3J73bHP6UYOE2fidSFdupqT0nbHkiVZsmKvbMW3z0yQzXOJvvvb94w626xYsUK2batPnz66//779fnnn+t3v/udKisr9ac//Ul2kn+dtG1LJSWF7ZZ/WbMh5nb1W8Ma0L9nUm0iNYqLQ36HkNW8miOdzck11etjblfX4N+c5FhsRS5adZdcdDYnO9Jd+uwnchg/k68LXkpkTm5riigYx/OJLCnufWaKbJ5L9N0fGVVcX3TRRTr11FNVUlIiSdpzzz1VWlqqX/ziF/rPf/7T7jbyeDlOVLW19e2WF+THTk9BflA1NXVJtQl3AgFbxcUh1dY2KBLhgSV+SXaOdHXx7mxOFoZyYm5XGEr/nORYbEUuWmVaLpKdk9vLtD6biBwmzsTrQiqkYk5K3xxT+aFchSMRqYuPn0eljMxVR7J5LtF3b/oe7x+eMqq4tm27pbBuVlZWJkn6+uuvky6uJSkcbj8ABXlBlZeVdni7UXlZqQrygh1uh/SJRBzGwEdezpFMm5Mci63IRavulIt4+9Gd+uwXchg/k68LXkuoX9FvHgzc1TrdLVfZPJfouz99z6ib8a+++mqdffbZbZb95z//kSQNGjQo5e3ZjqMJ44aqvKztk8mbn0DJVzwg26V7jjAnAQDb47oAwCQZ9c71Mccco4svvlj33nuvfvrTn2rlypW66aab9JOf/EQDBw70pM1gNKqJJ5WrvjGs+q1hFeQHVcB3JwIt0j1Htm+vriGswhBzEgCyGdcFAKbIqOL6yCOP1N13360HH3xQDz30kHr06KHjjz9ev/71rz1t13Yc9QoFNaB/T9XU1GXtLRZAZ9I9R2zHUVGOraKc3G8W8AsUAGQ1flcDYAKji+vp06e3W3bcccfpuOOO8yEaAAAAAAA6llGfuQYAAAAAwEQU1wAAAAAAuERxDQAAAACASxTXAAAAAAC4RHENAAAAAIBLFNcAAAAAALhEcQ0AAAAAgEsU1wAAAAAAuERxDQAAAACASxTXAAAAAAC4RHENAAAAAIBLFNcAAAAAALhEcQ0AAAAAgEsU1wAAAAAAuERxDQAAAACASxTXAAAAAAC4RHENAAAAAIBLFNcAAAAAALhEcQ0AAAAAgEsU1wAAAAAAuERxDQAAAACASxTXAAAAAAC4RHENAAAAAIBLFNcAAAAAALhEcQ0AAAAAgEsU1wAAAAAAuERxDQAAAACASxTXAAAAAAC4RHENAAAAAIBLwVTt6OOPP9YXX3whSerfv7+GDBmSql0DAAAAAGA018X1s88+q/vuu09ffvmlJCkajcqyLO20004aP368fvGLX7gOEgAAAAAAk7kqrqdPn67Zs2fr+9//vs4880zttttuCgQCWr16tV566SXdcMMNWrFihaZMmZKqeAEAAAAAME7SxfVbb72l2bNn69prr9XZZ5/d7vVTTz1Vc+fO1S233KIxY8bokEMOcRMnAAAAAADGSvqBZo8//riOPvroDgvrZqeeeqqOO+44zZkzJ9lmAAAAAAAwXtLF9QcffKCf/OQnXa533HHHadmyZck2AwAAAACA8ZIurjdt2qTevXt3uV6vXr20efPmZJsBAAAAAMB4SRfXffr00Weffdblep9++qn69euXbDMAAAAAABgv6eL60EMP1WOPPaZt27Z1uk5jY6Mee+wxHXHEEck2AwAAAACA8ZIuri+44AJ99dVXmjhxotatW9fu9a+++koXXnih1q9fH/OhZwAAAAAAZLqkv4pr55131r333qvLLrtMY8aM0ZAhQ7TzzjsrGAxqzZo1WrZsmQoLC/WHP/yB28IBAAAAAN1a0sW1JI0cOVIvvPCCZs+erTfffFOvv/66pG8K73PPPVdnnHGGSktLUxGnrxzb1saGsNZUr1dRKEehvKBsx/E7rKzFeJgn3WPi2LbqG8Oqa2jiGAAA8LsBACO4Kq4lqW/fvrrmmmt0zTXXpCIe44QtSzOfqVBFZVXLsvKyUk0YN1TBaNTHyLIT42GedI8JxwAAYHtcFwCYIunPXGcDx7Y1c96yNidrSaqorNLMecvk2KQvnRgP86R7TDgGAADb47oAwCRJv3O91157ybKsTl/Py8tT37599eMf/1gTJkxQIBBItinf1DeG252sm1VUVqm+MayiHE7a6cJ4mCfdY8IxAADYHtcFACZJurieMGFCzOI6Eonoyy+/1MMPP6xIJKLLL7882aZ8U9fQ1MXrYRXl5KYpGjAe5kn3mHAMAAC2x3UBgEmSLq4nTpwY13pDhw7VAw88kJHFdWEop4vXXX9kHQlgPMyT7jHhGAAAbI/rAgCTeH6fzB577KHNmzd73YwnCvKCKi/r+Gnn5WWlKsjjhJ1OjId50j0mHAMAgO1xXQBgEs+Ka8dx9Pnnn+uRRx75/+3de1wU5f4H8M/O7gILCKKCF0pFwNQU1F/esxBTy7TjPfNuXlIRu5xKvKWYaOeYl1TUVMw08wZGF7MyO1l4zKwM807q0SQVEBQFRJbZ3x+66y67sLDLzg7s5/16+TqnmWHmO/N95nnmmZ15BqGhoY7ajEMJooioQWFmlbZ+BEp+4kFazIf8SJ0TlgEiIjLGdoGI5MRht/OWLFmChIQEeHp6YvXq1Y7ajMOpdDpEDw6//01dLbw0Knjy24lOw3zIj9Q5YRkgIiJjbBeISC4c1rl++umnERISgi5duiAgIMBRm5GEIIqoqVEhqIEvcnLyoNWysnYm5kN+pM6JIIrwVgsPBqnhBRQRkUvjtQERyYHDOtetWrVCq1atHLV6IiIiIiIiItmwuXO9atWqci+rUCgQFRVV4W28//77SElJwZYtWwzTTp06hbi4OBw/fhy1atXCmDFjMGrUqAqvm4iIiIiIiKiyyLZzvXXrVixfvhyPPfaYYVpOTg7Gjh2LyMhIxMbG4vfff0dsbCy8vLwwcODACq2/IkRBwI0CLdKzs+CtUUPD93icivmQH6lzIgrC/XfrilgGiIiI1wZEJAs2d65Pnz5dmXEYXLt2DXPnzsXhw4fRuHFjk3k7d+6EWq3G/PnzoVKpEBwcjIsXL2LdunUO61xrFQrE70pFalqmYZp+BEqVTueQbVLpmA/5kTonLANERGSM7QIRyYXDv3NdUSdOnIBarcZnn32G8PBwk3m//PIL2rdvD5XqwT2Bjh074n//+x+ysrIqPRZREBCfeMyksgaA1LRMxCcegyjI7vBVa8yH/EidE5YBIiIyxnaBiOTE5l+uJ02ahJiYGLNfl+0VGRmJyMhIi/OuXr2Kpk2bmkzTj0R+5coV1KlTx+btqlTmle+NAq1ZZa2XmpaJ/EItamocNiYclcB8yI8jc1JVzkmlUjD5X1fGY/FAdTwWls5JY9Vxn6XGY1hxcmwXpGLtnASMypICUEBR9sKK8q2zKnDlc4n77tx9t7m2+f777zFlyhTDf4uiiD59+mDlypUIDg6ulOBKunPnDtzc3Eymubu7AwAKCwttXq8gKODn52U2PT277F/D8wq0CGrga/N2qWKYD/lxVE6q4jnp46NxynbliMfigepyLEo7Jy2pLvvsTDyG5SfndsGRKnJO3i0qhkqptLqcAij3OqsKVz6XuO/OUWm38nQ6Hc6fP487d+5U1irNeHh44O7duybT9J1qT09Pm9crijrk5uabTffSqMv8Oy+NCjk5eTZvlyqG+ZAfW3NirfGuSuekUinAx0eD3NwCFBe79uA5PBYPVLVjYes5aayq7bMc8RhWnBzbhcpQGeckcK9MeWjcoC0uBqy8fq4DquSxssSVzyXuu2P2vbw3nqrUczL16tVDRkaGyTT9f9etW9eudWu15gnwdFchPNTf4uNG4aH+8HRXWfw7cgzmQ34cmZOqdk4WF4ssf/fxWDxQnY5FefejOu2zs/AYlp+c2wVHq9B+6e79EGZtmep2rFz5XOK+O2ffq9TD+O3atcOvv/6K4uJiw7SffvoJQUFBqF27dqVvTxBFRA0KQ3iov8l0/QiU/MSDtJgP+ZE6JywDRERkjO0CEclJlfrleuDAgdiwYQNmzZqF8ePH49ixY9i0aRNiY2Mdtk2VTofoweH3v6mrhZdGBU9+O9FpmA/5kTonLANERGSM7QIRyYVdneuTJ08a3nkuLi6GQqHAyZMnkZ9v/g5Iu3bt7NkUAKB27drYsGED4uLi0L9/f/j7++PNN99E//797V53WQRRRE2NCkENfJGTk+eyj1jIBfMhP1LnRBBFeKsFeKvvD3DICygiIpfGawMikgO7OtclfzHW6XSYM2cOFAqFyTSFQoFTp05VeP3vvPOO2bSwsDDs2LGj4sESEREREREROYjNnevNmzdXZhxEREREREREVZbNnev27dtX+G90Oh3i4+Px/PPPw9/f3/ofEBEREREREVUBko4WLooi4uPjzT6nJXc6hQK5d4px5mI2bhUWQ2f02DtJTxQE3CjQ4vi5LNws0EIUqtSg99WS1DkRBQG3i0Rcyy1EXpHIMkBE5OJ4bUBEciD5aOFWv7EnM8UKBVYnHcPRsw++n9jmEX9MGRAGZRXbl+pAq1Agfleqyfcs9Z/bUDEfTiF1TlgGiIjIGNsFIpIL3tYrg85CxxoAjp7JxOrdx/gLtsREQUB84jGTxhMAUtMyEZ94jHepnUDqnLAMEBGRMbYLRCQnrHHKUFBUbNax1jt6JhMFRcUSR+Ta8gu1Zo2nXmpaJvILtRJHRFLnhGWAiIiMsV0gIjlh57oM+XfKrpCtzafKlVdQZGU+8yE1qXPCMkBERMbYLhCRnLBzXQZPj7JfSbc2nyqXl0ZtZT7zITWpc8IyQERExtguEJGcsHNdBo1aiTaPWP5kWJtH/KFRKyWOyLV5uqsQHmo5H+Gh/vB0ZwMqNalzwjJARETG2C4QkZywc10GhU6HKQPCzDrY+tHCFRyBUlKCKCJqUJhZI6ofEVQQRSdF5rqkzgnLABERGWO7QERywtt5Vih1OkwdEIaCIhF37mrh4aaCRi2wY+0kKp0O0YPDkV+oRV6BFl4aFTzdVWw8nUjqnLAMEBGRMbYLRCQXlda5Pn36NC5fvgwAaNCgAVq0aGG2jFKpxP79+xEQEFBZm5WEQqeDj4cSjer7ICcnD1otK2tnEkQRNTUqBDXwZT5kQuqcCKIIb7UAb7XbvQm8gCIicmm8NiAiObC7c52UlITVq1fj77//BgDodDooFArUr18fkyZNwpAhQ0yWDwwMtHeTRERERERERLJiV+f6nXfewaZNm9C6dWuMGjUKjRo1glKpxF9//YW9e/di7ty5OHv2LGbPnl1Z8RIRERERERHJjs2d64MHD2LTpk2IiYnBmDFjzOYPGzYMH3/8MRYsWIDIyEh07tzZnjiJiIiIiIiIZMvm0cK3bNmCnj17WuxY6w0bNgzPPPMMtm7dautmZEEUBNwo0OL4uSzcLNBCFDjIujMxH/IjdU5EQcDtIhHXcguRVySyDBARuTheGxCRHNj8y/WJEycwZ84cq8s988wziI2NtXUzTqdVKBC/KxWpaZmGafrPO6g4YrjkmA/5kTonLANERGSM7QIRyYXNt/Vu3ryJWrVqWV2uZs2auHXrlq2bcSpREBCfeMyksgaA1LRMxCce411RiTEf8iN1TlgGiIjIGNsFIpITm2ucgIAAnD9/3upy586dQ7169WzdjFPlF2rNKmu91LRM5BdqJY7ItTEf8iN1TlgGiIjIGNsFIpITmzvXXbp0webNm3H37t1SlyksLMTmzZsRERFh62acKq+gyMp8VthSYj7kR+qcsAwQEZExtgtEJCc2d64nTpyIK1euIDo6GhkZGWbzr1y5gpdeeglZWVllDnomZ14atZX5dn8mnCqA+ZAfqXPCMkBERMbYLhCRnNhc4wQGBmLVqlV4+eWXERkZiRYtWiAwMBAqlQrp6ek4duwYvLy8sGLFiir7WLinuwrhof4WHzcKD/WHp7sKEEUnROaamA/5kTonLANERGSM7QIRyYldozx06tQJn3/+OUaOHImCggJ8//33+Pbbb5Gbm4sXX3wRX3zxBTp06FBZsUpOEEVEDQpDeKi/yXT9CJQCK2tJMR/yI3VOWAaIiMgY2wUikhObf7n++++/4e/vj7p162L69OmYPn16ZcYlGyqdDtGDw5FfqEVegRZeGhU83VWsrJ2E+ZAfqXPCMkBERMbYLhCRXNj8y3X37t1x6tSpyoxFtgRRRE2NCi2Da6OmhpW1szEf8iN1TgRRhLdaQF0fN3irBZYBIiIXx2sDIpIDmzvXOp2uMuMgIiIiIiIiqrLseueaiIiIiIiIiOx45xoATp48icLCwnIt265dO3s2RWQgCgJuFGiRnp0Fb40aGr5X5XRS50QUhPvv1hWxDBAREa8NiEgW7Opcx8bGWl1Gp9NBoVC4zPvZ5FhahQLxu1JNPrmhHxFUxVcVnELqnLAMEBGRMbYLRCQXdnWu58yZg5CQkMqKhahMoiCYNZ4AkJqWifjEY4geHM671BKTOicsA0REZIztAhHJiV2d65YtWyIsLKyyYiEqU36h1qzx1EtNy0R+oRbeag4jICWpc8IyQERExtguEJGcsLahKiOvoMjKfK1EkZCe1DlhGSAiImNsF4hITti5pirDS6O2Mt+uBzHIBlLnhGWAiIiMsV0gIjmxuXO9efNmBAcHV2YsRGXydFchPNTf4rzwUH94urMBlZrUOWEZICIiY2wXiEhObO5c+/v7Q60u+24hAGRlZWHz5s22bobIQBBFRA0KM2tE9SOCcsAS6UmdE5YBIiIyxnaBiOTE5tt5vXv3xo4dOwwDmul0OkyePBmzZs3Cww8/bFguPT0dixYtwqhRo+yPllyeSqdD9ODw+9841sJLo4Inv2XpVFLnhGWAiIiMsV0gIrmw+ZdrXYnvBoqiiO+//x65ubl2B0VUFkEUUVOjQsvg2qipYeMpB1LnRBBFeKsF1PVxg7daYBkgInJxvDYgIjnggGZEREREREREdmLnmoiIiIiIiMhO7FwTERERERER2YnfJygHURBwo0CL9OwseGvU0HCQDKdiPuRH6pyIgnB/4JoilgEiIuK1ARHJgl2d68zMTPz9998AgOLiYgD3Pr2ln6ZfpirTKhSI35WK1LQH+6H/vIOqxKBu5HjMh/xInROWASIiMsZ2gYjkwq7O9dSpU82mTZo0yeS/dTodFAqFPZtxGlEQzCprAEhNy0R84jFEDw7nXVEJMR/yI3VOWAaIiMgY2wUikhObO9eLFi2qzDhkKb9Qa1ZZ66WmZSK/UAtvNV9blwrzIT9S54RlgIiIjLFdICI5sblz3b9//8qMQ5byCoqszNfCW+0mUTTEfMiP1DlhGSAiImNsF4hITngrrwxeGrWV+RwPTkrMh/xInROWASIiMsZ2gYjkxOYap1mzZmW+S+3u7o66devi2WefRVRUFJRKpa2bchpPdxXCQ/0tPm4UHuoPT3cVwPd4JMN8yI/UOWEZICIiY2wXiEhObO5cR0VFldm5Li4uxt9//40NGzaguLgYr776qq2bchpBFBE1KAzxiccsjkDJATKkxXzIj9Q5YRkgIiJjbBeISE5s7lxHR0eXa7mwsDC8//77VbJzDQAqnQ7Rg8Pvf1NXCy+NCp78dqLTMB/yI3VOWAaIiMgY2wUikguHv4jSpEkT3Lp1y9GbcShBFFFTo0JQA1/k5ORBq2Vl7UzMh/xInRNBFOGtFh4MUsMLKCIil8ZrAyKSA4d1rkVRxOXLl5GQkIDQ0FBHbYaIiIiIiIjI6RzWuV6yZAkSEhLg6emJ1atXO2ozRERERERERE7nsM71008/jZCQEHTp0gUBAQGVuu5r167hiSeeMJu+aNEiDBgwoFK3RURERERERGSNQzrXOp0OgYGBaNmyZZkjitvq9OnTcHd3x7fffmuy/ho1alT6tgBAFATcKNAiPTsL3ho1NBwkw6mYD/mROieiINwfuKaIZYCIiHhtQESyYFfnOjs7G1999RUUCgWefvpp+Pn5YdeuXXj33XeRm5sLHx8fjB8/HhMmTKiseAEAZ8+eRePGjSv9F3FLtAoF4nelWvy8g0qnc/j2yRTzIT9S54RlgIiIjLFdICK5EGz9wzNnzqB3796YP38+YmNj0bdvX+zZswdz5szBo48+igkTJqB58+ZYunQpduzYUZkx48yZMwgODq7UdVoiCoLZdxMBIDUtE/GJxyAKNh8+sgHzIT9S54RlgIiIjLFdICI5sfmX62XLluHhhx9GYmIifHx8EBcXhzfeeAP9+/fHokWLDMtNnz4du3btwvPPP18pAQP3frn28/PD8OHDceHCBTRq1AiTJ0+2+B52ealU5pXvjQKtWWWtl5qWifxCLWpqHP41M7qP+ZAfR+akqpyTSqVg8r+ujMfigep4LCydk8aq4z5Ljcew4uTYLkjF2jkJGJUlBaCAlVc1FeVbZ1XgyucS9925+25zbXP06FHExsbioYceAgC8/PLL+PTTT/H000+bLNevXz9ERUXZF6URrVaL8+fPIyQkBDExMfD29saePXswceJEfPDBB+jUqVOF1ykICvj5eZlNT8/OKvPv8gq0CGrgW+HtkW2YD/lxVE6q4jnp46NxynbliMfigepyLEo7Jy2pLvvsTDyG5SfndsGRKnJO3i0qhkqptLqcAij3OqsKVz6XuO/OYXPnOjc3F/7+/ob/1v//WrVqmSzn6emJgoICWzdjRqVS4fDhw1AqlfDw8AAAtGzZEmlpaUhISLCpcy2KOuTm5ptN99Koy/w7L40KOTl5Fd4e2Yb5kB9bc2Kt8a5K56RSKcDHR4Pc3AIUF7v24Dk8Fg9UtWNh6zlprKrtsxzxGFacHNuFylAZ5yRwr0x5aNygLS4GrLx+rgOq5LGyxJXPJe67Y/a9vDeebO5c63Q6qNUPKjTh/jstggTvtnh5me9caGgoUlJSbF6nVmueAE93FcJD/S0+bhQe6g9Pd5XFvyPHYD7kx5E5qWrnZHGxyPJ3H4/FA9XpWJR3P6rTPjsLj2H5ybldcLQK7Zfu3rW7tWWq27Fy5XOJ++6cfa/0nrAjPr1lLC0tDW3btsXhw4dNph8/fhwhISGVui1BFBE1KAzhof4m0/UjUPITD9JiPuRH6pywDBARkTG2C0QkJ3aN8LB69Wr4+fmZTFu5ciVq1qxp+O+cnBx7NmEmODgYTZo0MYxS7ufnh507d+L3339HUlJSpW4LAFQ6HaIHh9//pq4WXhoVPPntRKdhPuRH6pywDBARkTG2C0QkFzZ3rhs0aICzZ8+aTTtz5ozZsvXr17d1M2YEQcDatWuxZMkSvPLKK8jNzUWLFi3wwQcfoGnTppW2HZNtiiJqalQIauCLnJw8l33EQi6YD/mROieCKMJbLcBb7XZvAi+giIhcGq8NiEgObO5cf/fdd2bTbt26BZ1OBx8fH7uCsqZOnTomn/siIiIiIiIicia7P/x37tw5rF+/Hvv378ft27cB3BtwrHv37hg3bpzDfk0mIiIiIiIikgu7OtdffvklYmJioFQq0blzZzRs2BAqlQqXLl3Cd999h71792LhwoXo06dPZcXrFKIg4EaBFunZWfDWqKHhezxOxXzIj9Q5EQXh/rt1RSwDRETEawMikgWbO9fnzp3DjBkzEBERgbfffhu+vr4m82/fvo25c+di9uzZaN68OYKDg+0O1hm0CgXid6WafOJBPwKlytonDajSMR/yI3VOWAaIiMgY2wUikgubP8W1adMmhISEYNmyZWYdawDw9vbG4sWL0axZM3z44Yd2BeksoiAgPvGY2bcTU9MyEZ94DKIE3/SmB5gP+ZE6JywDRERkjO0CEcmJzTXOoUOHMGzYMCiVytJXLggYOnQo/vvf/9q6GafKL9SaVdZ6qWmZyC/UShyRa2M+5EfqnLAMEBGRMbYLRCQnNneuMzIy0KhRI6vLPfTQQ8jMtFzpyV1eQZGV+aywpcR8yI/UOWEZICIiY2wXiEhObO5c+/j4ICMjw+pyGRkZqFWrlq2bcSovjdrKfLsHW6cKYD7kR+qcsAwQEZExtgtEJCc2d67btm2L5ORkq8vt3r0bbdu2tXUzTuXprkJ4qL/FeeGh/vB0Z4UtJeZDfqTOCcsAEREZY7tARHJic+d69OjR+PHHH7F69epSl1myZAkOHTqE0aNH27oZpxJEEVGDwswqbf0IlPzEg7SYD/mROicsA0REZIztAhHJic238/7v//4Pr776KpYuXYo9e/agW7duCAwMhEqlQnp6Or755htcuHAB06dPR1hYWGXGLCmVTofoweH3v6mrhZdGBU9+O9FpmA/5kTonLANERGSM7QIRyYVdz8pMnDgRoaGhWLVqFTZs2GAyr3Xr1li/fj0ef/xxuwKUA0EUUVOjQlADX+Tk5EGrZWXtTMyH/EidE0EU4a0W4K12uzeBF1BERC6N1wZEJAd2v4jSrVs3dOvWDTk5OUhPT4dOp0NgYGCVHcSMiIiIiIiIqKIqbZQHPz8/+Pn5VdbqiIiIiIiIiKoMmwc0IyIiIiIiIqJ7+H2CchAFATcKtEjPzoK3Rg0NB8lwKuZDfqTOiSgI9weuKWIZICIiXhsQkSywc22FVqFA/K5UpKZlGqbpP++g0umcGJlrYj7kR+qcsAwQEZExtgtEJBd8LLwMoiAgPvGYSWUNAKlpmYhPPAZR4OGTEvMhP1LnhGWAiIiMsV0gIjlhjVOG/EKtWWWtl5qWifxCrcQRuTbmQ36kzgnLABERGWO7QERyws51GfIKiqzMZ4UtJeZDfqTOCcsAEREZY7tARHLCznUZvDRqK/P5yrqUmA/5kTonLANERGSM7QIRyQk712XwdFchPNTf4rzwUH94urPClhLzIT9S54RlgIiIjLFdICI5Yee6DIIoImpQmFmlrR+Bkp94kBbzIT9S54RlgIiIjLFdICI54e08K1Q6HaIHh9//pq4WXhoVPPntRKdhPuRH6pywDBARkTG2C0QkF+xcl4MgiqipUSGogS9ycvKg1bKydibmQ36kzokgivBWC/BWu92bwAsoIiKXxmsDIpIDPhZOREREREREZCd2romIiIiIiIjsxM41ERERERERkZ34znU5iIKAGwVapGdnwVujhoaDZDgV8yE/UudEFIT7A9cUsQwQERGvDYhIFti5tkKrUCB+VypS0zIN0/Sfd1DpdE6MzDUxH/IjdU5YBoiIyBjbBSKSCz4WXgZREBCfeMyksgaA1LRMxCcegyjw8EmJ+ZAfqXPCMkBERMbYLhCRnLDGKUN+odasstZLTctEfqFW4ohcG/MhP1LnhGWAiIiMsV0gIjlh57oMeQVFVuazwpYS8yE/UueEZYCIiIyxXSAiOWHnugxeGrWV+XxlXUrMh/xInROWASIiMsZ2gYjkhJ3rMni6qxAe6m9xXnioPzzdWWFLifmQH6lzwjJARETG2C4QkZywc10GQRQRNSjMrNLWj0DJTzxIi/mQH6lzwjJARETG2C4QkZzwdp4VKp0O0YPD739TVwsvjQqe/Hai0zAf8iN1TlgGiIjIGNsFIpILdq7LQRBF1NSoENTAFzk5edBqWVk7E/MhP1LnRBBFeKsFeKvd7k3gBRQRkUvjtQERyQEfCyciIiIiIiKyEzvXRERERERERHbiY+HlIAoCbhRokZ6dBW+NGhq+x+NUzIf8SJ0TURDuv1tXxDJARES8NiAiWWDn2gqtQoH4XalITcs0TNOPQKnS6ZwYmWtiPuRH6pywDBARkTG2C0QkF3wsvAyiICA+8ZhJZQ0AqWmZiE88BlHg4ZMS8yE/UueEZYCIiIyxXSAiOWGNU4b8Qq1ZZa2XmpaJ/EKtxBG5NuZDfqTOCcsAEREZY7tARHLCznUZ8gqKrMxnhS0l5kN+pM4JywARERlju0BEcsLOdRm8NGor8/nKupSYD/mROicsA0REZIztAhHJCTvXZfB0VyE81N/ivPBQf3i6s8KWEvMhP1LnhGWAiIiMsV0gIjlh57oMgigialCYWaWtH4GSn3iQFvMhP1LnhGWAiIiMsV0gIjnh7TwrVDodogeH3/+mrhZeGhU8+e1Ep2E+5EfqnLAMEBGRMbYLRCQX7FyXgyCKqKlRIaiBL3Jy8qDVsrJ2JuZDfqTOiSCK8FYL8Fa73ZvACygiIpfGawMikgM+Fk5ERERERERkJ3auiYiIiIiIiOxUJTvXoihixYoV6Nq1K1q3bo0JEybgr7/+cnZYRERERERE5KKqZOd69erV+Pjjj/H2229j+/btEEUR48ePx927dx2yPVEQcKNAi+PnsnCzQAtRqJKHrdpgPuRH6pyIgoDbRSKu5RYir0h0WhmQQ1nksTCNgcfiQQxSHgs57LM1cikfpZH7MZT78ZM7Hj8i11DlBjS7e/cuNm7ciNdffx0REREAgGXLlqFr16745ptv0KdPn0rdnlahQPyuVKSmZRqm6T/voNLpKnVbZB3zIT9S50QuZUAOccghBrnEIYcY5BKHq56TZZF7jIyveuPxI3IdVe622enTp5GXl4dOnToZpvn4+KBFixY4cuRIpW5LFATEJx4zqQwBIDUtE/GJx3jXUWLMh/xInRO5lAE5xCGHGOQShxxikEscrnpOlkXuMTK+6o3Hj8i1VLlfrq9evQoAqF+/vsn0gIAAwzxbqFTmlduNAq1ZZaiXmpaJ/EItamqq3CGsspgP+XFkTuR8TsohDjnEIJc45BCDXOJw1XOyLHKPkfFVHqVSMPlfOZD6nCzJcCwUgAKKshdWlG+dVYEcy4JUuO/O3Xd51IYVUFBQAABwc3Mzme7u7o6bN2/atE5BUMDPz8tsenp2Vpl/l1egRVADX5u2SRXHfMiPo3Ii93NSDnHIIQa5xCGHGOQSh6uek2WRe4yMr/L5+GicHYKB1OekJXeLiqFSKq0upwDKvc6qQk5lQWrcd+eocp1rDw8PAPfevdb/fwAoLCyERmPbgRRFHXJz882me2nUZf6dl0aFnJw8m7ZJFcd8yI+tObHWeMv9nJRDHHKIQS5xyCEGucThqudk2THIO0bGV3mUSgE+Phrk5haguFh0djgApD8nS1IqBXho3KAtLgasvN6tA2STS3vJsSxIhfvumH0v742nKte51j8OnpGRgYYNGxqmZ2Rk4JFHHrF5vVqteQI83VUID/W3+DhPeKg/PN1VFv+OHIP5kB9H5kTO56Qc4pBDDHKJQw4xyCUOVz0nyyL3GBlf5SsuFmUTk9TnZKl0gM7a4Gm6Cq6zCpBTWZAa9905+17lHsZv1qwZvL29cfjwYcO03NxcnDx5Eu3atavUbQmiiKhBYQgP9TeZrh/hURBds8A6C/MhP1LnRC5lQA5xyCEGucQhhxjkEoernpNlkXuMjK964/Ejci0KndXbWPKzbNkybN++HQsXLkRgYCAWL16My5cv44svvoBaXfbjN5YUF4vIzi79MRhREJBfqEVegRZeGhU83VWsDJ2I+ZCfiubE379GmeurKuekHOKQQwxyiUMOMcglDlc9J8si9xgZn/1UKgF+fl7IycmT3S92Up+TeiqVAC9vD8xec9DqL9cLJj+O3JvWHzWvCuRcFhyN++6Yfbd2ThpiqNStSmTatGnQarWYPXs27ty5g3bt2iEhIcGmjnV5CKKImhoVghr4umRBlRvmQ36kzokgivBWC/BW3x/Y0EkXeHIoizwWpjHwWDyIQcpjIYd9tkYu5aM0cj+Gcj9+csfjR+QaqmTnWqlU4o033sAbb7zh7FCIiIiIiIiIqt4710RERERERERyUyV/uSYiIiIiInJ1Xt4eUCqNfi9V3Pu2uae3h8nn14qLReTdviN9gC6GnWsAgqBArVrl+3aZK3+QXY6YD/mpjJxU5JyUC5bFB3gsHqgux4LtpLR4DO3jCsevIudksagz7XyVQgcdfGt6Wl1O1OkgKBTW16fTQVWO7TpSWWVBWyxCUUX2oyzFog5z1x0ymaZQKMwGsIud2KnKXVvZypl1QJUcLZyIiIiIiIhITuR7G4aIiIiIiIioimDnmoiIiIiIiMhO7FwTERERERER2YmdayIiIiIiIiI7sXNNREREREREZCd2romIiIiIiIjsxM41ERERERERkZ3YuSYiIiIiIiKyEzvXRERERERERHZi55qIiIiIiIjITuxcExEREREREdmJnWsiIiIiIiIiO7FzTURERERERGQndq4r4P3338fIkSOdHYbLu3HjBt566y088cQTaNu2LV544QX88ssvzg7LZV2/fh1vvPEGOnbsiDZt2mDixIk4d+6cs8NyOJZDyy5cuIA2bdpg9+7dzg7FaZKTk9G7d2+0atUKzz77LPbu3evskBxOFEWsWLECXbt2RevWrTFhwgT89ddfzg6rymB9UnlYB93jyufktWvX8Mgjj5j9q85lwlIf5dSpUxgxYgRat26NyMhIbN682UnROZalfZ89e7ZZ/iMjIyWJh53rctq6dSuWL1/u7DAIwGuvvYajR49i6dKlSEpKQvPmzTFu3DicP3/e2aG5pKioKFy8eBHr1q1DYmIiPDw8MGbMGBQUFDg7NIdiOTRXVFSE119/Hfn5+c4OxWk+/fRTzJo1C8OHD8eePXvQp08fQ1mpzlavXo2PP/4Yb7/9NrZv3w5RFDF+/HjcvXvX2aFVCaxPKgfroAdc+Zw8ffo03N3d8eOPPyIlJcXwr3fv3s4OzSEs9VFycnIwduxYNGzYEElJSYiKisK7776LpKQk5wTpIKX1z86cOYNJkyaZ5D8xMVGSmNi5tuLatWuYNGkS3n33XTRu3NjZ4bi8ixcv4uDBg5g3bx4ee+wxBAUFYc6cOQgICMDnn3/u7PBczs2bNxEYGIgFCxYgLCwMwcHBmDJlCjIyMpCWlubs8ByG5dCylStXwtvb29lhOI1Op8N7772HUaNGYfjw4WjYsCEmT56Mzp074+eff3Z2eA5z9+5dbNy4EdOmTUNERASaNWuGZcuW4erVq/jmm2+cHZ7ssT6pPK5eB+m5+jl59uxZNG7cGAEBAfD39zf88/DwcHZolaqsPsrOnTuhVqsxf/58BAcHY+DAgRgzZgzWrVvnnGArWVn7rtPp8Oeff6Jly5Ym+a9Vq5YksbFzbcWJEyegVqvx2WefITw83NnhuDw/Pz+sW7cOrVq1MkxTKBRQKBTIzc11YmSuydfXF0uWLEHTpk0BANnZ2di0aRPq1auHkJAQJ0fnOCyH5o4cOYIdO3bgnXfecXYoTnPhwgWkp6ejb9++JtMTEhLw0ksvOSkqxzt9+jTy8vLQqVMnwzQfHx+0aNECR44ccWJkVQPrk8rBOugBVz8nz5w5g+DgYGeH4XBl9VF++eUXtG/fHiqVyjCtY8eO+N///oesrCypQ610Ze37pUuXkJ+fjyZNmjglNpX1RVxbZGSkZM/ok3U+Pj548sknTaZ9/fXXuHjxImbOnOmkqAgA5syZg507d8LNzQ1r1qyBp6ens0NyGJZDU7m5uXjzzTcxe/Zs1K9f39nhOM2FCxcAAPn5+Rg3bhxOnjyJhx56CJMnT67W7cjVq1cBwCz3AQEBhnlUOtYn9mMdZMrVz8mzZ8/Cz88Pw4cPx4ULF9CoUSNMnjwZTzzxhLNDq1Rl9VGuXr1q+OFDLyAgAABw5coV1KlTx+HxOVJZ+3727FkAwJYtW/DDDz9AEAQ88cQTePXVV1GjRg2Hx8ZfrqlK++233zBjxgz07NkTERERzg7HpY0ePRpJSUno06cPoqKicOLECWeHJBlXL4fz5s1DmzZtzH6xdTW3b98GAEyfPh19+vTBxo0b0aVLF0yZMgWHDh1ycnSOox9fwc3NzWS6u7s7CgsLnRFSlebq9YktWAeZcuVzUqvV4vz587h58yaio6Oxbt06tG7dGhMnTqzW9XBJd+7csZh/ANW+DJw9exaCICAgIABr165FTEwMUlJSMGXKFIii6PDt85drqrK+/fZbvP7662jbti3effddZ4fj8vSPgcfFxSE1NRUfffQRFi1a5OSoHM/Vy2FycjJ++eUXvhsKQK1WAwDGjRuH/v37AwCaN2+OkydP4oMPPjB5RLM60b/HePfuXZN3GgsLC6HRaJwVVpXk6vWJLVgHmXPlc1KlUuHw4cNQKpWGfW/ZsiXS0tKQkJBQbevhkjw8PMwGr9N3qqvzk4UAMHnyZAwbNgx+fn4AgKZNm8Lf3x9DhgzBH3/84fDXfPnLNVVJH330EaKjo9GtWzesXbvWcDeOpJWdnY09e/ZAq9UapgmCgJCQEGRkZDgxMmmwHAJJSUm4fv06IiIi0KZNG7Rp0wYAMHfuXIwfP97J0Umrbt26AGD2KF5ISAguX77sjJAkoX/0tOQ5n5GRYTgmZB3rE9uwDjLn6uekl5eX2eBloaGhuHbtmpMikl69evUs5h9AtS8DgiAYOtZ6oaGhACDJaxH85ZqqHP2nJUaOHIlZs2ZBoVA4OySXlZWVhddeew0bNmxA165dAdz7FMrJkyer9TumAMuh3rvvvos7d+6YTOvZsyemTZuG5557zklROcejjz4KLy8vpKam4rHHHjNMP3v2LBo2bOjEyByrWbNm8Pb2xuHDhw37mZubi5MnT2LEiBFOjq5qYH1iO9ZB5lz5nExLS8Pzzz+PNWvWoEOHDobpx48fr9YDrZbUrl07bN++HcXFxVAqlQCAn376CUFBQahdu7aTo3OsN998ExkZGdi0aZNh2h9//AEAkpQBdq6pSrlw4QIWLlyIHj164KWXXjIZ8dDDw0OSgQrogaZNm+KJJ57AggULsGDBAvj6+uL9999Hbm4uxowZ4+zwHIbl8IHS7oDXrl272t8dL8nDwwPjx49HfHw86tati7CwMOzZswcHDx40aeSrGzc3N4wYMQLvvvsuatWqhcDAQCxevBj16tVDz549nR2e7LE+sQ/rIHOufE4GBwejSZMmmD9/PmJjY+Hn54edO3fi999/r3bfeC7LwIEDsWHDBsyaNQvjx4/HsWPHsGnTJsTGxjo7NIfr1asXpkyZglWrVuG5557DhQsXMH/+fPTp00eSUeTZuaYq5euvv0ZRURH27duHffv2mczr378/P8HhBEuXLsWSJUvw6quv4tatW3jsscewdetWNGjQwNmhOQzLIZVmypQp0Gg0WLZsGa5du4bg4GCsXLnS5BeU6mjatGnQarWYPXs27ty5g3bt2iEhIcHwHjqVjvUJOYKrnpOCIGDt2rVYsmQJXnnlFeTm5qJFixb44IMPzF7Zqc5q166NDRs2IC4uDv3794e/vz/efPNNw3gg1Vn37t2xfPlyrFu3DuvXr0eNGjXQt29fvPLKK5JsX6HT6XSSbImIiIiIiIiomuKAZkRERERERER2YueaiIiIiIiIyE7sXBMRERERERHZiZ1rIiIiIiIiIjuxc01ERERERERkJ3auiYiIiIiIiOzEzjURERERERGRnVTODoDk59y5c/j444+RkpKCq1evQqVSITQ0FM899xyGDBkClepesYmMjER6errJ3wqCAG9vb4SGhmLSpEl44oknzNYviiIiIyNx5coVJCUloWXLlgCAK1euIDIyEkOHDsXcuXMtxnb8+HEMHDgQcXFxGDRokMUYjPXu3RvLli2zGK8gCPDy8kJoaCief/559OvXr9T1DB06FEePHsWKFSvQq1cvs/kjR47Ezz//bPhvhUIBjUaDoKAg9OvXD8OGDTMcN6q6/vjjD2zevBlHjhxBdnY2AgIC0KlTJ0ycOBEPP/wwgHtlAQC2bNlSrnXu3LkTc+bMQbdu3bB27VqLy+Tn5yMhIQF79+7F5cuXoVarERoaioEDB2LQoEFQKBQAgMuXL6N79+5lbm/evHl44YUXyrvLAIBff/0V69evx9GjR5GXl4c6deqgc+fOmDx5smG/AWDlypVYtWoVzpw5AwA4fPgwRo0ahe7du2P16tVm6929ezdmzJiB/fv346GHHgIAPPLII5g6dSqio6MBADExMfjkk09Kja1OnTo4ePCg4b/T09OxevVqpKSk4Pr16/D29kbr1q3x4osvon379ibbtebMmTOlLuvu7o46deogIiICr732Gry9vU3mX758GevXr0dKSgoyMjLg6+uLli1bYsSIEXj88cetbpuIiO759ddfsWnTJvz222/Izc01tL1jx45FcHCwYbmS13kKhQK+vr5o06YNXnnlFTRr1qzUbZRsv8rTTnz55ZfIysrCqFGjrO6DcTtX2e2Dvq0tTUREBN5//30A5m1sSSWPgx7btPLj1T6Z+PLLLzFjxgwEBwdj7NixCAoKwp07d3DgwAEsXLgQP/74I1avXm24mH/yyScxZcoUw99rtVpcunQJ69atw5QpU5CYmGhWmR08eBBZWVlo0qQJtm/fjgULFgAA6tevj86dO2Pv3r2YNWuWxc5ocnIyvLy80Lt3b8O0kjEY8/PzM/lv42W1Wi1ycnKwd+9eTJ8+HadOnbJYkZ4/fx5Hjx5F06ZNsX37doudawBo0aKF4aZAcXExbt68iR9++AGLFi3CL7/8guXLl0MQ+LBIVbV161YsXLgQHTp0wD//+U8EBATg4sWLSEhIwDfffIMPP/ywzIa7NElJSWjatCl++OEHXLlyBfXr1zeZr9PpMGnSJJw/fx4TJ05EaGgoCgsLkZKSgjlz5iAtLQ0zZ840+ZvJkycjIiLC4vaMO8PlcejQIYwfPx49evRAXFwcatSogUuXLmHjxo0YNGgQdu3ahYYNG5a5jv379+Ozzz7Dc889V6Ft6/n7+2PVqlUW56nVasP/z8zMxPPPP4+6devitddeQ/369ZGdnY1du3Zh9OjReO+999CzZ09ERERgx44dhr/7/vvvsWbNGqxatQr+/v4Wt1Ny3s2bN/Hjjz9iy5YtyM7OxvLlyw3zDh06hKioKNSrVw/jx49HcHAwsrOz8cUXX2DcuHEYPXq0Wc6oeirPDbmYmBj8/PPP+O677yyuIzIyEu3bt8c777xjMr28HQ5j33//PbZt24YTJ07g1q1bqFevHrp164bRo0eb1T2lbVev5EV6yZvMJYWHh2Pnzp2lzieyZN26dVi6dCkef/xxzJw5E/7+/rh48SK2bduG/v37Y9GiRXj22WcNy5e8zsvIyMDGjRsxevRofPnll6hdu3aFtl9Wu/DQQw+hbt26Ju3JiRMnMH/+fLz11lt49NFHDdMDAgIAOLZ9KLlNPR8fH5vWp8c2rWLYuSaDc+fOYcaMGejatSuWL19u0rl98skn0aFDB0ybNg179+41dG5r1aqF1q1bm6znscceQ3h4OHr37o3PPvvMrMOxe/dutGnTBl27dsWaNWsQExNj+NVn4MCBSElJQUpKilnnoKioCF988QV69+4NT09Pw3RLMZTG0rI9evSAv78/Nm3ahJ49e+L//u//zOINDAzESy+9hNdffx0XL15Eo0aNzNat/4XMWGRkJJo0aYK4uDh88cUXNncuyLl+/fVXxMXFYfjw4Zg1a5ZheocOHfDUU0+hX79+mDlzJnbv3l2h9Z47dw6///47NmzYgFdffRU7duzAK6+8Yrbtw4cPY+PGjejSpYthekREBARBwEcffYQJEyaYNP4NGzYs9zlhzdq1axEWFmbSeezQoQOefPJJ9OjRAx988EGpT5ro+fj4IC4uDp07d0adOnUqHIObm1u59mfnzp3Izc3FV199ZfJLco8ePTB48GBD57pWrVqoVauWYf758+cBAM2bNzf8slCSpXlPPvkkrl+/jr179yIvLw9eXl64du0apk2bhrZt2yI+Ph7u7u6G5Z9++mls2rQJixYtQmhoKAYPHlyRw0BVjKNuyAEV73AA955a2bZtG/r06YO5c+eiRo0a+PPPP7Flyxbs3r0bK1asQMeOHe3aZ+ObzCV5eXnZtW5yPf/5z3+wZMkSREdHY+rUqYbp7du3R79+/fDPf/4TMTExaNq0KUJDQwFYvs5r1aoVnnrqKXz11VcYPnx4hWIoq10A7j3FZLy9wsJCAEBISIhZHI5uHyxt015s0yqOP6ORwYYNGyAIAmJjYy3+atyrV68yH502pr9Lpv+FW+/mzZv49ttv0a1bN/Tp0wcFBQX49NNPDfOfeuop1KxZE59//rnZOg8cOICcnBwMGjSoAntVPlOnToW7uzu2b99uMr24uBjJycno1q0bnnrqKXh6eprcoSyPESNGoG7dumbrpqojISEBNWrUwGuvvWY2r1atWoiJiUH37t2Rn59fofUmJSXB19cXHTt2RK9evZCYmAitVmuyTGZmJoB7r1OUNGzYMLz66qtm51llysrKgk6nM5seEBCA2bNnm3T4S/Pqq68iPz8f8+bNc0CED2RlZUGhUKC4uNhkulKpxD//+U88//zzlb7NGjVqQKFQGHKwadMm5OfnY8GCBSYXIXpjxoxB69atsWbNGovHlaoH/Q25YcOGYePGjejbty86dOiAIUOGYNu2bXB3d7f5lx59h2Pq1KnYsGEDnn32WbRv3x6DBw/Gjh07EBERgZiYGKSlpRn+Ztu2bdi2bRvi4uKwZMkS9OjRAx07dsSIESOQlJSE4OBgvPLKK8jKyrJrv/U3mS3903d+iMpr1apVaNKkCaKioszmqdVqzJ8/H0qlEuvXry9zPb6+vo4KsUKqYvtQFWN2NnauyWD//v3o2LFjmY/M/Otf/zJ5JFun00Gr1Rr+FRQU4PTp05g+fTrUajX69Olj8veff/45iouL0bdvXzRo0AAdO3Y06ay6ubmhb9++2L9/P/Ly8kz+Njk5GaGhoWZ35UrGYPyvvGrUqIGwsDD8+uuvJtN/+OEHZGZmol+/fvDw8MAzzzyDTz75BHfv3i33ugVBQKdOnXDs2LEKxUTyoNPpkJKSgk6dOkGj0Vhcpnfv3oiKijJ5osIarVaLzz77DH369IFarUb//v2RmZlp9mho+/bt4enpiddeew2LFy/G4cOHcefOHQBA48aNMWHCBLNfg0VRtHg+lOx0lkdERASOHj2KkSNHIjExEX/99Zdh3uDBg/HUU09ZXUdwcDCio6Oxb98+fPHFFxWOAUCp57hxYx4REYE7d+5gyJAhSEhIwMmTJw373KVLl3K9F1ca42NaVFSE69evIzExEZ988gl69OhhyH1KSgqaN2+OevXqlbquZ555Bunp6Th16pTN8ZC8OeqGHFDxDodOp8PatWvx+OOPW7w57e3tjQULFiAnJwdbt26tcDxEjpCdnY3jx4+jW7dupd5ArlmzJjp37oz9+/cbphlfE969exd///034uLiUKdOHTzzzDMVjqO09tTSDW9rHN0+WIrVlnbfGNu0iuNj4QTg3i/KN2/eROPGjc3mlewQKhQKKJVKAPc6vMnJySbzVSoVWrZsiYSEBDRv3txk3u7du/HEE08YHmEdMGAA3njjDfz2229o27YtAGDQoEHYsmULvv32W/zjH/8AAOTk5OD777/H66+/bhafpRj0EhMT0apVK6v7D9wbGOnYsWNm8TZt2tSwjgEDBiAxMRFff/01+vbtW6716tddVFSEGzdu2PRYLDlPTk4OCgsLy3wszBb6GzcDBgwAcO91isaNG2P79u3o2bOnYbnatWtj/fr1iImJwYYNG7Bhwwao1Wq0bt0azz33HAYOHGg4H/VmzZpl8vi6nqenJ44ePVqhOF9++WXcunULiYmJhvcp69WrhyeffBJjxoxBkyZNyrWecePGYd++fXj77bfRsWPHCp0H6enpFt8jA4A333wT48aNA3DvMe233noLS5cuxb///W8A9zoOnTp1wgsvvFCuX9lL06NHD7NpderUwbBhwzBt2jTDtMuXL1scyNGY/rWS9PR0tGjRwuaYSJ70N+QiIyPLvCFXUnluvuo7HOPGjSt3h+PEiRO4evUqJk2aVOp6g4OD0axZM3z77bd4+eWXrcZRGn3HxhKlUunQp2yoetEPTBYYGFjmco0aNcL+/ftx8+ZNAJavCRUKBRYvXmzyOlB5War7AdNBwsrL0e3DmDFjzKYFBQXhq6++qtB6jLFNqzh2rgmA5UdOAeDixYsmF/rAvYpO/+tat27dDHfPL126hMWLF6Nu3boWB4A4ffo0Tpw4gREjRiA3NxcA0LFjR8Oj1vrOdbNmzfDoo4/i888/N3Su9+zZAwAW31k2jqGkkJCQcu0/cO+iwLjhz87Oxn/+8x9MmjTJEG9oaCgCAwOxY8eOCnWu9b+u8cKi6tF3XO29+1tSUlISgoKC0LBhQ0P5evrpp/H+++/j0qVLJoOEPfbYY/jmm2/w66+/IiUlBT///DN+//13HDlyBMnJydi4cSM8PDwMy0+dOtXigGYlO+Hl4ebmhvnz5yM6OhoHDhzATz/9hMOHD2PHjh3YvXs3li5dalZHWKJUKrFo0SL0798fsbGxWLlyZblj8Pf3x5o1ayzOKzkI0/DhwzFgwACkpKTg0KFD+Pnnn7Fv3z7s27cPY8eORUxMTLm3a2zNmjXw9/dHUVERdu/ejeTkZEybNs3sUXOdTmf1ywD6PPARuurJlhtyZd1AKrkcULEOx99//13uv0lJSSlnxJYdOXKk1P1477338PTTT9u1fnId+vrReNBKS0rWp8bXhDqdDtnZ2di7dy9ef/11FBQUYMiQIRWKQ1/3l2TLIGGObh9iY2PNzj/jawNbsE2rOHauCcC9UbU9PT3NPmtVv359JCYmGv47Pj4eZ8+eNfx3zZo1Db/qtmrVCo888ggGDhyICRMmYOfOnXBzczMsq1/PjBkzzEbl3rt3L2bOnGl4L2bgwIFYuHAhrl+/jtq1ayM5ORndu3e3eNfROAZ7XLt2zeSxl88++wxFRUVYuXKlWUcgPT0d586dK3VEVkvr9vDwQM2aNe2Ok6Tl6+sLLy8vwwWqJfn5+SgqKir3e13Xr1/HgQMHUFRUhHbt2pnN37FjB9544w2TaYIgoF27doblb968iWXLlmHbtm1ITEzEiBEjDMsGBgZWyjlhzN/fH4MGDTI8VvrTTz/hjTfewLx58/DUU0+VayT8kJAQTJ06FUuXLjXcMCsPNze3Cu2PRqNBjx49DL84XLx4ETNnzsQHH3yAAQMGoGnTpuVel17Tpk0NnaW2bdtCq9Xirbfegre3t8nAUYGBgWV+HhCA4dH6Bg0aVDgOkj9bbsiVdQNp8uTJhv9va4ejPBQKRYUfdS15w/jRRx9FbGysxWWtfVWAyJj+ZlB56lMvLy/D9ZWla8KIiAhkZGRg8eLFFp/2Kotx3W8vR7cPQUFBld72s02rOL5zTQaRkZFISUnB7du3DdP0F7X6f9Y6hyEhIZg2bRpOnTpl8umcu3fv4vPPP0fPnj2xefNmk38LFy5EYWGhybds+/btC6VSib179+LcuXP4448/HDKQmd7Nmzdx4sQJk45OUlIS2rRpYxbv2rVrIQgCtm3bVq51a7VaHD58GG3btrXpl0NyvscffxyHDx82jAJa0s6dO9GxY0ecOHGiXOv77LPPoNVqER8fb1a+2rVrh927dxve63/llVcsPurl6+uLt956C76+vvjzzz9t3reypKamonPnzibfkdbr2LEjxo0bh+vXryMnJ6fc6xw/fjxatmyJt99+G9evX6+0WIuLixEZGYkVK1aYzWvUqBFmz54NAJV2rGbPno2AgADMmzfPZBCoyMhI/PHHH2VejHz11VeoX78+H5+rpsp7Q07/GCtg3tYa/zO+SW1Lh0N/wXv58mWrf2N8cezp6Vnq+CL66SUfe/fy8ip1P+QyqBRVDbVr10br1q3x9ddfl3rT5/bt2zh48CAiIyOtrq9ly5bIzc1FTk4Ojh07hn//+98mrzAUFxdbHLCrMsmtfSjPcZBbzFUBO9dkMHHiRGi1WsyePdtig3rnzh2TwYxKM3r0aDRt2hQbN27E//73PwDAd999hxs3bmDo0KHo0KGDyb+BAweicePGJgOb+fj4oEePHvj666+xd+9eNGjQwK73Ja1Zu3YtioqKDI94/vHHHzh79iwGDBhgFm+3bt3QsWNHfPrpp4aBpcqyY8cOZGZm4oUXXnBY/ORYL774Im7cuGHyOSq9zMxMbNy4ESEhIeV6rBO49y5/69at8dRTT5mVryFDhiA7Oxv79u0DcK9j+NNPP+H33383W09GRgby8/Nt+iW2PBo3boyCggJs3rzZ4sXNhQsX4O/vX6H32JRKJd555x3cvn27wu+rWVtvQEAAkpKSLHb2L1y4AACVdqy8vb0xY8YM5ObmYsmSJYbpI0eONMyzVD98/PHH+Pnnn/HSSy/xu/fVWGXfkNOzpcPx6KOPol69evjmm29Mlrt27ZrhawR//fUXTp8+bfJuZZ06dZCRkWFxG1evXjUsQ+QoU6dOxYULF7B06VKzecXFxZg7dy7u3LmD8ePHW13XH3/8AV9fX/j5+eHPP/9EQkKCyQ2nK1eulDloV2WQW/tQnuMgt5irAj4WTgaPPPIIFi9ejBkzZmDAgAEYNGgQHnnkEWi1Whw9ehSJiYnIysqyWompVCrMnDkTY8aMwcKFC7Fu3TokJSWhdu3apX5D87nnnsOKFStw+PBhdOjQAcC9R8PHjx+PK1euYMCAAaWetNnZ2RY7HsC9C27jR2SMly0uLsb169fx9ddf44svvsCkSZMMyyYlJUGtVpf6Luk//vEP/Pe//8WXX35pGJDq9u3bhnWLooicnBykpKRgx44deO6558r1XirJU+vWrfHyyy9j+fLlOHfuHPr16wc/Pz+kpaUhISEBhYWFJh3vq1evYtOmTWbradq0Kby9vXH27FnMmTPH4rZ69OgBLy8vbN++Hc8++yxefPFFfPvttxg7diyGDRuGDh06QKPR4OzZs9i4cSNCQ0MNZVDv0qVLpZ4Tvr6+CAoKKtd++/r6Yvr06Zg7dy6GDRuGIUOG4OGHH8atW7ewb98+fPLJJ3j33XcrPJZAaGgooqKiLN6ssOTu3bul7g9wr+7SaDSYPXs2Ro4ciQEDBmDUqFFo3rw5RFHEkSNHsGnTJgwdOrRC4zBY07t3b3z88cf45JNP8MILLyAsLAwBAQF47733MG3aNEMcwcHBuHnzJvbu3Ys9e/Zg+PDhvNlWzb344ov45ptvsHz5ckyfPt1kni035IxNnToV48ePx9KlS80G+bTU4VAoFJg6dSpmz56NnTt3Gt45/fTTT/Hee+9h1KhROH78ODw8PDB27FjDutq3b481a9bg8uXLZo/FfvXVV1AqlRZfayGqLF27dkVMTAz+/e9/49SpUxg4cCACAgJw+fJlbNu2DadOnUJcXJzJ9+JLXhMWFBQgOTkZhw4dwmuvvQalUokuXbrA09MTb7/9NsaNG4cLFy7gyy+/xIsvvmgWw6lTp0r9RF1gYKDF97FLI7f2oTzHQW4xVwXsXJOJXr16oWXLlob3ONPT06HT6fDwww+jd+/eGDp0qMURxUvq1KkTevXqha+//hrJyck4ePAghg4dWupj0f/4xz+wcuVKbN++3dC57tSpE+rVq4fLly+bdR6MHThwAAcOHLA4r0aNGvjll18sLqtQKODj44MWLVpgxYoV6NWrFwCgsLAQe/bsQZcuXUp9DL5nz56IjY3F9u3bDbGdPHnS8Mu3QqGAl5cXmjZtinnz5mHw4MFlHC2qCiZPnowWLVpg69atWLhwIW7evIn69esjIiICkyZNMhlY69KlS1i0aJHZOgYNGgSVSgWlUlnqwD4ajQa9evXC7t27De/179ixA+vXr8d3332Hbdu2oaioCIGBgejTpw8mTpxoNmDJmjVrSn1/s3v37li9enW593vo0KFo1KgRNm/ejKVLl+LGjRvw8vJCWFgYPvzwQ8P5WlETJkzAvn37yvXLXWZmZpnfqE5OTkbz5s3RsmVLJCcn4/3338dHH32EzMxMKJVKhISEYObMmQ55tWT27NkYMGAA5s+fj127dkGhUKBjx45ITk7Gpk2b8MEHH+DKlSvw8fFBq1atsH79enTt2rXS4yB5qegNuYqwpcMxePBgnD17Fm+99RYOHz6MZ555Bm3atMEzzzyDjRs3AgCio6NRt25dw9+MGDECu3btwsiRIzFhwgSEhobizp07+O9//4stW7Zg/PjxZu9YGt9ktqRVq1Z8PYoqZOzYsWjTpg0+/PBD/Otf/0J2djb8/f3RpUsXxMXFmd0wLXlN6OnpiaCgIMNNYgCoW7cuVq9ejSVLlmDKlCnw9vbGmDFjMHXqVLPtW5qmN2PGDIuvbZVFTu1DeY+DnGKuChQ6Du1GREREVOkOHDiArVu34uTJk4Ybcp06dTK5IRcTE4Off/7Z7Bv3epGRkWjfvj3eeecdk+m///47PvzwQ/z2228mHY7Ro0eX+oTGDz/8gK1bt+L48eO4desW6tati8cffxweHh7YsmULIiIiEBcXZ3g/+vr161i9ejUOHDiAjIwMeHh4oEmTJhg6dCj69etnsu6RI0caPtdXmiNHjtg0yjIRUVXBzjURkYsRRbFcowJb+/wGEVUff/75J3bv3o3XX3+d704SyUBp34w3JggCz1eZYeeaiMjFxMTEmIzOX5ozZ85IEA0REREZu3z5Mrp37251ualTpyI6OlqCiKi82LkmInIxly9fLtfnsyr7e5lERERk3d27d8t1gzsgIMBkrARyPnauiYiIiIiIiOzEh/SJiIiIiIiI7MTONREREREREZGd2LkmIiIiIiIishM710RERERERER2YueaiIiIiIiIyE7sXBMRERERERHZiZ1rIiIiIiIiIjuxc01ERERERERkp/8HfEaG21Jb0g0AAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 1000x1000 with 20 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.pairplot(data= df_est.iloc[:,1:5])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "corr = df_est.corr(numeric_only=True)\n",
        "sns.heatmap(corr, cmap='coolwarm')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Con esto notamos que no existen muchas correlaciones significativas dentro del dataframe, y el proceso de generar una regresion lineal o multilineal no es muy fructuosa dado que las variables no presentan comportamientos de este tipo. Podemos empezar el analisis bajo series de temporales de la ocurrencia de siniestros, el resultado de esto se espera sea la caracterizacion de las ocurrencias bajo una distribucion de Poisson, con una tasa dependiente del tiempo."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Ajuste distribucion de Poisson"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Observemos como se comportan la cantidad de accidentes por dia para el año 2015:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "application/vnd.plotly.v1+json": {
              "config": {
                "plotlyServerURL": "https://plot.ly"
              },
              "data": [
                {
                  "alignmentgroup": "True",
                  "hovertemplate": "variable=FECHA<br>index=%{x}<br>value=%{y}<extra></extra>",
                  "legendgroup": "FECHA",
                  "marker": {
                    "color": "#636efa",
                    "pattern": {
                      "shape": ""
                    }
                  },
                  "name": "FECHA",
                  "offsetgroup": "FECHA",
                  "orientation": "v",
                  "showlegend": true,
                  "textposition": "auto",
                  "type": "bar",
                  "x": [
                    1,
                    2,
                    3,
                    4,
                    5,
                    6,
                    7,
                    8,
                    9,
                    10,
                    11,
                    12,
                    13,
                    14,
                    15,
                    16,
                    17,
                    18,
                    19,
                    20,
                    21,
                    22,
                    23,
                    24,
                    25,
                    26,
                    27,
                    28,
                    29,
                    30,
                    31
                  ],
                  "xaxis": "x",
                  "y": [
                    2444,
                    2641,
                    2631,
                    2468,
                    2584,
                    2577,
                    2544,
                    2409,
                    2703,
                    2729,
                    2855,
                    2547,
                    2720,
                    2835,
                    2645,
                    2979,
                    2758,
                    2803,
                    2803,
                    2803,
                    2819,
                    2414,
                    2714,
                    2899,
                    2947,
                    2619,
                    2860,
                    2757,
                    2321,
                    2504,
                    1650
                  ],
                  "yaxis": "y"
                }
              ],
              "layout": {
                "barmode": "relative",
                "legend": {
                  "title": {
                    "text": "variable"
                  },
                  "tracegroupgap": 0
                },
                "margin": {
                  "t": 60
                },
                "template": {
                  "data": {
                    "bar": [
                      {
                        "error_x": {
                          "color": "#2a3f5f"
                        },
                        "error_y": {
                          "color": "#2a3f5f"
                        },
                        "marker": {
                          "line": {
                            "color": "#E5ECF6",
                            "width": 0.5
                          },
                          "pattern": {
                            "fillmode": "overlay",
                            "size": 10,
                            "solidity": 0.2
                          }
                        },
                        "type": "bar"
                      }
                    ],
                    "barpolar": [
                      {
                        "marker": {
                          "line": {
                            "color": "#E5ECF6",
                            "width": 0.5
                          },
                          "pattern": {
                            "fillmode": "overlay",
                            "size": 10,
                            "solidity": 0.2
                          }
                        },
                        "type": "barpolar"
                      }
                    ],
                    "carpet": [
                      {
                        "aaxis": {
                          "endlinecolor": "#2a3f5f",
                          "gridcolor": "white",
                          "linecolor": "white",
                          "minorgridcolor": "white",
                          "startlinecolor": "#2a3f5f"
                        },
                        "baxis": {
                          "endlinecolor": "#2a3f5f",
                          "gridcolor": "white",
                          "linecolor": "white",
                          "minorgridcolor": "white",
                          "startlinecolor": "#2a3f5f"
                        },
                        "type": "carpet"
                      }
                    ],
                    "choropleth": [
                      {
                        "colorbar": {
                          "outlinewidth": 0,
                          "ticks": ""
                        },
                        "type": "choropleth"
                      }
                    ],
                    "contour": [
                      {
                        "colorbar": {
                          "outlinewidth": 0,
                          "ticks": ""
                        },
                        "colorscale": [
                          [
                            0,
                            "#0d0887"
                          ],
                          [
                            0.1111111111111111,
                            "#46039f"
                          ],
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      "source": [
        "accidente_hora = df.groupby(df_est[(df_est.FECHA.dt.year == 2015)].FECHA.dt.day).count().FECHA\n",
        "accidente_hora.plot(kind='bar')"
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        "Notamos un valor mas o menos constante, por lo tanto, la idea es modelar una tasa de siniestros diarios que dependa del mes, es decir que para cada mes contaremos con una tasa de accidentes diarion promedio. Para esto observemos primero como se comportan los siniestros diarios para el mes de enero y diciembre del año 2015."
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      "source": [
        "accidente_hora = df.groupby(df_est[(df_est.FECHA.dt.year == 2015) & (df_est.FECHA.dt.month==1)].FECHA.dt.day).count().FECHA\n",
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    {
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          },
          "metadata": {},
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      ],
      "source": [
        "accidente_hora = df.groupby(df_est[(df_est.FECHA.dt.year) & (df_est.FECHA.dt.month==12)].FECHA.dt.day).count().FECHA\n",
        "accidente_hora.plot(kind='bar')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Como esta cantidad se mantiene lo suficientemente estable, el siguiente paso es obtener un promedio de accidentes diarios por mes, esto se logra con la siguiente funcion:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {},
      "outputs": [],
      "source": [
        "def prom_año(df, año):\n",
        "    \"\"\"\n",
        "    Retorna el promedio de siniestros diarios para cada mes del año indicado,\n",
        "    tomando la infomacion del dataframe df.\n",
        "    Parametros:\n",
        "        df: Dataframe con la informacion.\n",
        "        año: Año al cual obtener los promedios para cada mes.\n",
        "    Retorna:\n",
        "        meses: Lista de los meses por su representacion numerica (ej. 2 para febrero).\n",
        "        prom: Lista de los promedios para cada mes en orden cronologico.\n",
        "    \"\"\"\n",
        "    prom = []\n",
        "    df_ = df[df.FECHA.dt.year==año]\n",
        "    meses = df_.FECHA.dt.month.value_counts().sort_index().index.values\n",
        "    for i in meses:\n",
        "        df_mes = df_[df_.FECHA.dt.month==i].FECHA\n",
        "        prom.append(df_mes.groupby(df_mes.dt.day).count().mean())\n",
        "    return meses, prom"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Con esto generamos un plot para el promedio de siniestros diarios por mes para cada uno de los años en el dataframe."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {},
      "outputs": [
        {
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",
            "text/plain": [
              "<Figure size 640x480 with 6 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axes = plt.subplots(nrows=3, ncols=2)\n",
        "años = df_est.FECHA.dt.year.value_counts().sort_index().index.values\n",
        "for i in range(3):\n",
        "    for j in range(2):\n",
        "        meses, prom = prom_año(df_est, años[i+j])\n",
        "        axes[i,j].plot(meses, prom)\n",
        "        axes[i,j].set_title(str(años[i+j]))\n",
        "axes[2,0].set_xlabel('Mes')\n",
        "axes[2,1].set_xlabel('Mes')\n",
        "axes[1,0].set_ylabel('Siniestros')\n",
        "plt.tight_layout()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Notamos un comportamiento recurrente en esta tasa, este comportamiento lo ajustaremos a una exponencial inversa, pero antes se debe hacer un promedio por años para cada mes, resultando con lo siguiente:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Promedio siniestros diarios')"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "total_meses = pd.DataFrame(np.array([prom_año(df_est, año)[1] for año in años]).T,index=[i for i in range(1,13)], columns=años)\n",
        "prom_año_total = total_meses.T.mean()\n",
        "sns.lineplot(prom_año_total)\n",
        "plt.xlabel('Mes')\n",
        "plt.ylabel('Promedio siniestros diarios')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Ahora si se hace el ajuste deseado, iniciamos definiendo la funcion a la que se le va a hacer el ajuste:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {},
      "outputs": [],
      "source": [
        "def exp_inv(x, a, b):\n",
        "    \"\"\"\n",
        "    Retorna los valores para la funcion f(x)= a - b*exp(-x), de acuerdo a los parametros a y b dados\n",
        "    en los valores del x dado.\n",
        "        Parametros:\n",
        "        x: Valores sobre el cual evaluar la funcion.\n",
        "        a: Parametro de la funcion.\n",
        "        b: Coeficiente de la funcion.\n",
        "    Retorna:\n",
        "        resultado: Resultado de evaluar la funcion para los valores de x y los parametros dados.\n",
        "    \"\"\"\n",
        "    resultado = a - b*np.exp(-x)\n",
        "    return resultado"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Y procedemos a realizar el ajuste por medio de la funcion `curve_fit` que incluye scipy:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "f(x) = 233.0360979962141 - 79.33673202569406*exp(x)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Promedio de siniestros por dia')"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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Yhr86EuCvJe1osU9PR5DkxwfZa/y4oecHoG+nWEIC/cgvqWL7AVn2LoRo+ExmC8s2HuLf8/7gt50nUIE+HWOY/Y/LuGlwgkvnW15IVKMA/jYsEYDvfk0nx0eHvyT58UEG+75egW55Pj+dliHJ1bu9++47DSFEw6eqKptTc5j6wSYW/ZpBldFCfJMwptzRk/uv70K0m37vXsjg7k3p1DoCY/Xwl+p7w1+S/Pig6mEvV1R3/itDk5uj1SgcOFLE4ZxStz2vEEK4S+aJEl74fBvvfb8HQ3ElEaF67hvdian/15PE5o08HZ6doijcNaoDen8tB48W++Qm1JL8+BiT2ULJSSPgnjk/1SJC9fRsf2rZuxQ9FEI0IIWlVXz4v73M+jSFtKPF+PtpuP7yeGbf15d+XeLQnGfzUU+LDg/kb0Ntw1+L1qaTU+hbw1+S/PiY/JIqwFaBOdjJtSQu5opeLQD4Y28OJeVGtz63EEI4W5XJwvfrM5k8byO/784GbFv7zL6vL9dfHo/eX+vhCC9scFJTOrayDX99/OM+nxr+kuTHxxiKKwDbkJfi5ncjCU3DiG8Sitli5dcdsuxdCOGdrKrKxt3ZTJn3B9+vz8RospLYrBHT7+zFfdd2ItJNi0kuleZU8UO9n5YDR4pYs+2Yp0Nymzq99c/JyWHr1q0YjaffvVutVioqKkhJSeG1115zWoDCuew1ftw45FVNURSu6NmCD/63lzXbjnLVZS3RaSX/FkJ4j9TMAt77bicZx0sAW720W4Ym0LtDjNvfUDpD4/BAbhmawMKfD/Dt2jS6JkQRE+75Sdmu5nDys3z5ch5//HHMZrP9P1pVVfvnbdq0cW6EwqnsNX489M6kd8cYvlmTRlGZka3787isU6xH4hBCCEfkFJazZF0mm/bmAKD31zK6XytG9m6Bn65+D29dzJDkZqTsy2Xf4SI++TGVx8cm18t5Ss7k8Nvu9957j86dO/Pdd99x4403cv3117Ns2TKeeOIJtFotU6ZMcUWcwkk82fMDoNNqGJLcDIBVW2V/GSFE/ZZXVMH8ZalMnbeJTXtzUBTbXJkX/9GXa/q19vrEB04Nf13dEX8/DfsOF7F2e8Mf/nK45yczM5NXX32VTp06cdlllzF//nwSEhJISEjAYDDw3nvvMWDAAFfEKpygusaPO5e5n21IcjP+9/sh0o+VkHmihPgmYR6LRQghzie/uJL/bTzE+l0nsJzaBqJ7YjT3XNeFiCAdZnPDqlYfEx7ILUMS+XzlAb5dk07XNlE0bsDDXw73/Gg0Gho1stUraNWqFRkZGVhPbVkwaNAg0tLSnBuhcCpPD3sBNAr2p09H23DXKtldWAhRjxSWVrHw5/1MnreRX3ccx2JV6RwfydRxPZn09yTaNKs/9XqcbWiPZrRrEU6VycLHDbz4ocPJT5s2bdi2bZv9c6PRyL59+wAoKSmpMQla1C9mi5XCUttSd08Ne1W74tR+X5tTcykuq/JoLEIIUVxWxRerDvDUextZve0YZotKh5bh/Pv2Hky6NYmEBpz0VNMoCvdc3QF/nW34qyGvynV42Ovvf/87M2bMoLy8nMcee4y+ffsyefJkbr75ZhYuXEjnzp1dEadwgqLSKlQVdFqFsGB/j8YS3ySMxGaNSDtWzJrtxxgzUCbKCyHcr6TcyPI/DrN621GMp4ayEps34oaBbejopt3W65OYiCBuGpLAl6sO8s2aNLrGRxLdAIe/HO75ueWWW5g6daq9h+fZZ5+lqqqK559/HrPZzNSpU50epHCOM4e86sNM/uren7U7jmNqYOPnQoj6razCxKJf03nq3Y0s33wYo9lKm6ZhTLo1icm39/DJxKfa8J7Nade8EVVGCx//tA+1AQ5/1anOz+23327/vGXLlvz0008UFhYSGRnptMCE8xk8vNLrbD3aNSYiVE9haRUp+3Lp1yXO0yEJIRq48koTP285ws9bjlBptADQKi6UGwbG07VNlFfW6nE2jaJw99UdmTF/M6lZhfy68zhDkpp5OiynqlXyc/z4cRo3boyfnx/Hj//1GGD1uaZNmzonOuFU9mXu9aT6qE6rYWhyM777LYOVKUfo2zlWfvEIIVyiosrMqpQjrNh8hPIqMwDNG4dww8B4ktpGy++es8RGBnHj4AS++uUgX69Oo0t8ZL3Ykd5ZapX8DB8+nK+//ppu3boxbNiwi36TpKamOiU44VyGkvrV8wO2ehk/bDjEoexS0o+XkOgDkwqFEO5TaTSzetsxfvoji5OVtqSnaXQwYy6Pp0f7xvViCkB9dUXP5qTszyXtaDGf/rSPibcmNZgksVbJz+zZs2nRooX984by4n1Nfev5AQgN8qdv51jW7zrBqpQjkvwIIZyiymRh7fZj/PhHFqXlJsDWm3H95a3p0yEWjUb+jl2MRqNwz6nhrz2HCvlt53EGN5Dhr1olPzfccIP98xtvvNFlwQjXyq8HBQ7P54qezVm/6wQp+/IoGFrpNZsCCiHqH5PZwtodx/lxYxbFJ20Lc2LCA7nu8tZc1ikWrUb2E3REXGQQNw5qw9er004Nf0XVq9GDuqpV8rNlyxaHbtq7d+86BSNcx6qqFJTWv2EvgJaxobRvEc7+I0Ws2X6MmwYneDokIYSXMVusrNt5nP9tzDpdzywsgOsGtKZflzjZRPkSjOjVgpT9uaQfK+GT5fuY+LfuXj8CVKvkZ9y4cefdxLT6a6DGMUfm/BQVFTF37lzWrl1LWVkZ7du3Z9KkSfTq1avGdaqqcu+992I0GlmwYIH9eFVVFS+++CLLly+nsrKSYcOGMXXqVFl5dpbiMiNmi4pGUYgI1Xs6nHNc0as5+4/Yimpd2781/n7ev1+OEML1zBYrv+/OZumGTPJLbElPRKiea/u35vJuTSTpcYLTw19b2JNZwLpdJxjU3bsXNtUq+fnss8/snx8/fpzp06dz0003cdVVV9G4cWOKiopYvXo1X331Fc8++6xDAUycOJG8vDzmzp1LVFQUCxYsYPz48SxevLjGDvGffvop69evp0+fPjUeP3PmTFJSUnjzzTfx9/dnxowZPPLIIyxcuNChOBq66ho/EaH+9bLbN6ltNFFhAeSXVLIpNYeB3bz7B0sI4VoWq5U/9uTww4ZM8opsv98ahfgzul9rBnVvip+u/v2e82ZNooK5YVA8365J5+vVB+kSH+nVUxRqlfycmXCMGzeOu+66i0mTJtW4pkePHgQEBPDxxx9z9dVX1+rJs7Ky2LBhA1988QU9e/YEYPr06axbt46lS5fy6KOPArB//37efvttkpKSajw+JyeHJUuW8N5779l7iubOncuoUaPYvn07ycnJtYrDFxiKKwCIqqdLFbUaDcN6NuPbNemsSjnK5V2beH23qhDC+axWlc2pOXy/4RA5BeUAhAX5cXXfVgxJbia9xi50Ze+WbN2fR8Zx2/DXY7d47/CXw6nxrl276Nev33nPJScnc+DAgVrfKyIignnz5tG1a1f7MUVRUBSFkpISwDas9fjjj/PII48QHx9f4/Fbt24FoG/fvvZj8fHxxMbGOjxPqaGrjyu9zjawW1P8dRqO5JZx4EiRp8MRQtQjVlVly75cnp6/mXlL95JTUE5IoB+3DEngpfv7M7JPS0l8XKx6+Eun1bA7o4D1f57wdEh15nCF57i4ONatW0f//v3PObd8+XJatmxZ63uFhYUxePDgGsdWrFhBVlYWU6ZMAeCVV14hJiaGO+64g8mTJ9e4Nicnh4iICPT6mnNYYmJiyM7OrnUc56NrYF2m1RMAG0cEXvC1aU+Nj2s9ME4eHqpnQLcmrNl2jF+2HaVzmyi3x+AsnmzHhkTa0Tm8uR1VVWXr/jwW/5bBkdwyAIIDdFzVtxUjercgUF+njQrqxJvb0VlaxoVy4+A2fLM6ja9/SaN7YrTDw1/1oR0d/q65++67mTlzJrm5uQwdOpSIiAgMBgPLly9n7dq1zJ07t87BbNu2jcmTJzNy5EiGDBnCb7/9xtKlS/nhhx/O27VWUVGBv/+5G3Tq9Xqqquq+U7hGoxAREVznx9dHxafqXLRs0qhWry0szDPDYzcNb8eabcfYtj8Pk6oQExnkkTicxVPt2NBIOzqHN7WjqqpsSc3h8+X7yDhWDEBQgI4xgxK4blACwYF+HovNm9rRFW4b1ZHtBw0cPFLEwpUHeXr8ZXUa/vJkO9ZpV3ez2cy7777LsmXL7MebNGnCnDlzuOqqq+oUyKpVq3j88cfp0aMHc+bMoaCggClTpjBz5kxiY2PP+5iAgAD7BqtnqqqqIjCw7o1qtaqUlJTX+fH1UbbhJABBfgqFhSf/8jqtVkNYWCAlJRVYLO7fbDRMr6VzfCR7MgtYtPoAfx/e1u0xOIOn27GhkHZ0Dm9qR1VV+TOjgO9+TSfjuG36Q4C/lpG9WzCqbytCAv0wVhoxVp77u9/VvKkdXe3uqzvw9IebSEnNYemvaQx0YPWXq9oxLCyw1r1JdeovvOOOO7jjjjvIyMiguLiYiIgIWrduXZdbAbBw4UKef/55Ro0axUsvvYS/vz8//vgjeXl5TJkyxT4EZjQasVqtJCcns2zZMuLi4igqKsJoNNboAcrNzf3LhKm2zA1ol3FVVck7NeE5IkRfq9dmsVg91gbDezRnT2YBa7cd49p+rdH7e+84vifbsSGRdnSO+t6OqYcKWLw+k7Sjtp4efz8Nw3s0Z9RlLQkNsv2Orw/x1/d2dIe4iCCuvzyeRb9msPDnA3RoGeFwGRVPtuMlDZaeuRS9rr744gtmzZrFuHHjmDp1qr3rbMSIEfTo0aPGtXPmzCE7O5s5c+YQExNDz549sVqtbN261T4JOzMzk5ycHCm0eIayChNGk+0bLDKs/tX4OVu3xChiwgPJLapg497sBrebsBCiptJyI+99v4fUrEIA/HS2TY+v6tuKRsHnTm0Q9cOoy1qSsj+PrOxSPl2+j0dv7uY1q788OmsrMzOT2bNnM2LECCZMmIDBYCAvL4+8vDxUVaVVq1Y1PoKDgwkICKBVq1bodDpiY2O55pprmDZtGps2bWLXrl1MnDiRPn36nLMs3pdV1/hpFOyPn67+96JoFIVhPZsD8EvKUXshTSFEw/TtmnRSswrRaRWG92zOixP68ffhbSXxqee0Gg3jr+mIVqOwKz2fjXsubaGRO3k0+VmxYgUmk4mVK1dy+eWX1/h4/vnna3WPWbNm0a9fPx566CHGjx9PmzZteOONN1wcuXexL3OvZ9taXMjlXZug99dyzHDS/m5QCNHwHMouYcOpJdNPjE3m9hHt6mUVenF+zRuHcN3ltjI0X6w8SFFZ3RcbuZOiytvqc1gsVgoK/npSsLdZsfkwX69Oo0/HGO6/vssFr9XpNEREBFNYeNLjY9qf/3yAX7YdJSkxmkdu7ubRWBxVn9rRm0k7Okd9bUdVVXnx820cPFpM386x/OPazp4O6YLqazt6mtli5fnPtpKVU0pSYjQP39T1gsNfrmrHyMjgWk94drjn55133iE9Pd3hoITneEOBw/MZ1tM212dnmoHcwoa1+k4IASn78zh4tBh/nYabZUNjr6XTarjn1PDXjjQDf+zN8XRIF+Vw8vP+++9z9OhRV8QiXKR6zo83DXuBbS+Zrm2iUIFfth7zdDhCCCcymS18szoNsE2c9eZ9ogS0iAnh2gGtAfhi5QGK6/nwl8PJT2JiIpmZma6IRbiIt/b8gG23d4D1fx6nosrs4WiEEM7y85Yj5JdUEhGq56rLWnk6HOEEV/dtRcuYEE5Wmvlsxf56vVjF4aXuQ4cOZe7cuaxbt4727dsTFFSzAq+iKDz44INOC1BcOsOp5Cfay3p+ADrHRxIXGUR2QTm/785m+KlVYEII71VUVsX/fs8C4OYhCV5dy0ucVj38NevTFLYfNLApNYe+neI8HdZ5OZz8vPXWWwBs2LCBDRs2nHNekp/6paLKTPmpHhNvG/YC27L34T2b8/nKA6zaepShPZqh8ZI6EkKI8/vu1wyqTBbaNA3jsk6XVpBW1C8tY0MZ3b8136/P5POfD9CxVWS9LFngcPKzb98+V8QhXKR6yCs4QEeAv/s2AHSm/l3i+O63dHIKytmTWUBXL97wVAhfd+bS9rHD28qbmQbomn6t2HYgjyO5ZSxYsZ8Hb+hS74ofXlKdn/T0dHbs2MHhw4edFY9wMoOXTnY+U6Bex8Butn1jVqYc8XA0Qoi6UlWVr1YdRAX6dooloVkjT4ckXECnPV38cNuBPLbsy/V0SOeoU/Lzv//9j4EDBzJ69GjGjh3LlVdeycCBA1myZImTwxOXKt8+38e7dyEe1rM5CrA7o4AT+Q2nBpMQviRlfx4Hqpe2D5Gl7Q1Zy9hQrulnm8i+8OcDlJx0/0a0F+Jw8rN69WqeeOIJEhMTmT17NvPmzeO5556jTZs2TJ48mbVr17ogTFFX3rzS60wx4YF0T4wGYLUsexfC68jSdt8zun9rmjcOpqzCxMKf93s6nBocTn7effddRo0axccff8wNN9zAwIEDuemmm/j0008ZNWoU77//viviFHXUEIa9qtmXve8+QXmlLHsXwpvI0nbfYxv+6oRGUUjZX7+GvxxOfg4cOMANN9xw3nM33HCDTIiuZxpKzw9Ax1YRNIsOpspoYf2pCZNCiPqvqKyK/208tbR9sCxt9yWt4kK5+tTw14IV+ykprx/DXw4nPxERERQXF5/3XFFREf7+9W9Jmy+rru7sjTV+zqacWvYOsG7XcQ9HI4Sore9+zaDKeGppe2dZ2u5rru3fmmanhr8+//mAp8MB6pD89OvXj7feeovs7Jpb1584cYK3336bAQMGOC04cWmMJot9kllDGPYC6NUhBo2icCzvJHlFFZ4ORwhxEbK0XfjpNNxzdUc0isKWfblsTvX83l8OJz8TJ06koqKCkSNHcueddzJp0iTuvPNOrrzySsrLy5k0aZIr4hR1UN3ro/fXEhzgnTV+zhYS6Efb5rblsTvSDB6ORghxIbK0XVSLbxLGVX1bAvDpT/s8vveXw8lP48aNWbx4MePGjaOiooLdu3dTUVHBuHHjWLx4Mc2aNXNFnKIO7ENeYQH1rsDUpUhqa1v1tVOSHyHqta2ytF2c4boB8TSNDqa03MSiNWkejaVO3QFRUVE88cQTzo5FOJl9snMDGfKqlpQYzder09h/uIjySjNBDaRXS4iGxGS28M0aWdouTvPTafjHtZ2Y/2MqreJCPRpLnf5q7Nu3jw8++IDNmzdTXFxMVFQU/fr145///CctWrRwdoyijgwNNPmJjQyyb3a6OzOfPh1lAqUQ9c3PW45gKJal7aKmlrGhPHdfXyIigiks9FzBWoeHvTZv3swtt9zCpk2bGDRoEHfddRf9+vXjt99+Y8yYMRw4UD9mcouaw14NjQx9CVF/ydJ2Ud853PPz6quv0rNnT95//330er39eGVlJffeey8vv/wyH374oVODFHXTUIe9wDb0tXzTYXal52OxWtFqLmmbOiGEE333m21pe3wTWdou6ieH/2Ls37+fu+++u0biAxAQEMD48ePZunWr04ITl6a656chFDg8W0KzMEIC/ThZaSbt6PnrTgkh3C8ru5QNu2xL22+7Qpa2i/rJ4eSnSZMmHD169LznCgoKiIyMvOSgxKUzW6wUltqWEjaEAodn02o0dG0TBciSdyHqC1VV+XLVAVnaLuo9h5Ofp556ijfeeIOlS5ditVrtxzds2MDrr7/OlClTnBqgqJvC0ipU1ba3Smhww6y6XT3vZ0davocjEUKALG0X3sPhOT/PPvssRqORJ598ksmTJ9O4cWOKioqorKxEVVUeeugh+7WKorB3716nBixq5/SeXvoG2+3cJT4SrUYhp6CcE/knaRIV7OmQhPBZsrRdeBOHk58bb7zRFXEIJ8tvQLu5/5VAvY4OLcPZc6iQnWn5kvwI4UGytF14E4eTnzN7dkT9Vd3z0xDn+5wpqW1j9hwqZEeagVGXtfR0OEL4JFnaLryNrA9uoOwFDht413P3RNuk54NHiyirMHk4GiF8kyxtF95Gkp8GyheGvQCiGwXSvHEwqgp/psvEZyHcTZa2C28kyU8Dle8jPT9w5qovWfIuhDvJ0nbhrST5aYCsqnp6a4tGgR6OxvW6J9qSnz8z8jFbrBe5WgjhLLK0XXgrp2yHbTabKSsrIzw83Bm3E5eouMyIxaqiURTCQxtmjZ8zxTcJIyzYn5KTRvYfKaJzaym0KYSrOWNpu6qqgHrqi/NecZGvz3fofNec9+YO31tVFaxGDaqpEtXswButWj2/s7jzuepGVf0Az67OdTj5MZvNvPfee7Rq1Yprr72WTZs28cgjj1BSUkKfPn144403aNRIuj49qXrIKyJU7xN7XmkUhe4JUazbdYIdBw2S/DQwqmpFtZhRTSZQLWC1olotoFrBarZ9feo4VovtQz3zmlPHalxnPn1N9ePUs685615n3E+1Wk9dX3381B9xVT39B736D55qrX4hZ3x+5jEVlVPnVE4/tsZ9ah5Tz3Psgp8DxQqoVmvNP43nTT7Us85z3mssVpV/a6woEaDbr6F0//nuc4F7e6kiTwfQIChohv8ftB/usQgcTn7eeOMNPvroI3sl5+eee47w8HAefPBBPv74Y1599VWeffZZpwcqas9QUgE0/MnOZ0pKjGbdrhPsTDNw2xVtUWTSpVupqhXMJlRTJZirUE1Vp/6tRDVXgan6WCWqyXje6zAbz3N9FYWqDGU6g7PTDi2grf4xs8r/kXCAoqBodR5NhR1OfpYtW8bEiRO5/fbbSU9P5+DBg7z44ouMGTOG8PBwXn75ZUl+PMyXJjtX69Q6Ej+dBkNxJccMJ2neOMTTIdVrqsWMerIQ1Vh+KtmoPCMROTspqap57jzXY65y/4vQaEHRgkYDGi2KRguK7XM0WhT755pT1517DYrGdqzGNZoa961xrzPvf+Z1igZQQLF9KGd8zvk+t/+L7bEKKGhOXQdgO2a7b/U1Z1x/6l8Fzrr3qced9Zw6nYawRsGUFJdjttr+5ChUZy7Vz3nq8+p7cvax019/syaNLftyaRETykM3dUVjj+msNx3nvc+515yO5Txq80bmnGtq+eanNo8745BOqyE8IpiionLMjgx7XYo6vZGr32/+dDoNjaIbUVh40nMxOPqA3NxcunfvDsDatWvRaDQMGjQIgLi4OEpLS50boXCYrxQ4PJPeX0vHVhHsSs9nx0GDzyc/qrkKa2k+apmhxr/WMgNqWT7qySJcNgSh06P46U//6xeAcvYxnR7FL8D+9V9drwsIJDyyEcUllZityulER2n4w7nOpNVp8I8IRqs96dhclfPIyi5l5d5yVEL454ge6EJ9Z5qDotOg8dOj6MwoSG9XXSlaz//8Opz8xMTEcPToUXr16sXq1avp2LGjfSf37du3ExcX5/QghWMMPlLj52xJbaPZlZ7PzjQDo/u39nQ4LqUay7GWGlBPJTTWsnzU0tP/qpW1eBOi9UPRB4Of/lSyEQA6/xpJSXWSovj5gy7gVHJy5vVnJzN+Tk1MNDoN2qBglCoNirveaYu/pKoqX/5y0L60PVGWtgsv5XDyM3r0aF544QWWLl3K1q1befrppwF4/vnn+fLLL7n//vudHqRwjH3Yy8eSn+4J0cB+Mo6XUHzSSCMv3c1eVVXUylLUsnxbgmPvvbF9bS0zgLHi4jfyC0ATGo0SEoUmJNr2eajtcyU0GiUgVOZGCYds3Z/HgSNFsrRdeD2Hk59//etfBAUFsWXLFiZNmsRtt90GwJ9//sk999zDAw884PQgRe2pZ9b48aE5P2Bb3dYqLpSs7FJ2pRkY2L2pp0M6L1W1opYXn+qpOWNY6ozeG8zGi95HCQg9ldhEoYRGn5HoRKEJjQb/IEluhNPIru2iIXE4+VEUhQkTJjBhwoQax7/66iunBSXqrqzChNFkGx7wxV9OyYnRZGWXssPDyY+1rICKkkyqjh/FXJxXM8Epy7ctj74IJSjcltScmeCEnNF746d3wysRwkZ2bRcNSZ2KHBYUFDB//nw2b95MSUkJERER9OrVi7vuuouoqChnxygcUL2haaMQf/x0np9U5m7dE6NZsj6TPYcKMJkt+Onct7u0tSQXU8ZmzBlbsBqyKL7QxYoGJTjiVG9NNJrQKFuvTWhjW6ITEomi9XNX6EJcULHs2i4aGIeTn+zsbG699VYKCgpISkqiU6dO5OXl8fHHH7NkyRL++9//Ehsru/p6in2llw/2+gC0jA0hIlRPYWkVqVmFdEuIdunzWYtzMGVssSU8+VmnTygKfhFxqEGnEpmQ6JrDU0HhtiXWQngB2bVdNDQOJz+vvPIKOp2OH3/8kRYtWtiPHzlyhHvuuYfXXnuNF1980alBitrzld3c/4qiKCQlRrNm+zF2HDS4JPmxFmefkfAcPuPJNWibdkTXpjcBib2IatqEwsKT7qsHIoQLZGWXsv7Uru1jZdd20UA4nPysX7+eKVOm1Eh8AFq0aMGDDz7Iyy+/7LTghON8daXXmbqfSn52puejqqpTJv1ai06cTngKjpw+cUbCo2vdA01gGGBboi2EtztzaftlsrRdNCAOJz8Wi4WIiIjznouMjKSsrMyh+xUVFTF37lzWrl1LWVkZ7du3Z9KkSfTq1QuABQsWsGDBAk6cOEHLli255557uOmmm+yPr6qq4sUXX2T58uVUVlYybNgwpk6daq895GsMPj7sBdCxVTh6Py2FpVUczimjVVxone5jKTqO2Z7wHD19QtGgbdbpdMITULf7C1Hfnbm0/RZZ2i4aEIeTn/bt27N06VJ7Veczff/997Rr186h+02cOJG8vDzmzp1LVFQUCxYsYPz48SxevJgtW7YwZ84cnnvuOZKSkti4cSPTp0+nUaNGXHHFFQDMnDmTlJQU3nzzTfz9/ZkxYwaPPPIICxcudPSlNQi+PuwF4KfT0jk+km0H8th+MM+h5MdSeEbCU3hmwqNF28zWw+PXuidKgG9XkBYNnyxtFw2Zw8nPAw88wPjx4ykuLubqq6+mcePG5OXlsWzZMtavX88bb7xR63tlZWWxYcMGvvjiC3r27AnA9OnTWbduHUuXLiU4OJhJkyZx7bXXArahtS+++IINGzZwxRVXkJOTw5IlS3jvvffsPUVz585l1KhRbN++neTkZEdfntfzxX29zicpMZptB/LYmZbPmIFtLnitpfDYGQnPsdMnFC3a5p3wi7f18EjCI3xJ9dL28BB/WdouGhyHk58BAwbw4osvMmfOHH777Tf78ejoaGbPns2IESNqfa+IiAjmzZtH165d7ccURUFRFEpKSnj00Uftx00mEytXriQ9PZ2HHnoIgK1btwLQt29f+3Xx8fHExsayZcsWn0t+yivNlFeZAd/u+QHolhCFAmTllFJQUnnOu1ZLwTHMGZsxZ27BWnj89AlFi7Z5Z/za9EbXKlkSHuGTaixtHyJL20XD43Dys3HjRkaNGsX1119PRkYGxcXFNGrUiDZt2jg8sTQsLIzBgwfXOLZixQqysrKYMmWK/VhKSgrjxo3DarVy0003MXz4cABycnKIiIhAr69Z7C0mJobs7GxHX5rXqx7yCgn0I8C/TiWcGoywYH/aNAsj/VgJO9PzGZLUFOuZPTxFZyQ8Gi3aZqcSntY9bPtdCeHDzlza3rez7NcoGh6H/0I+/PDDPP3001x33XUkJDh3Aty2bduYPHkyI0eOZMiQIfbj8fHxLF68mD///JPZs2cTERHBE088QUVFBf7+5+7fpNfrqaqquqRYdF64WqeozPaaoxsF1Dl+7anddrX1YNfdS9WjbWPKs7PQ/vkD5QcPYy06cfqkRotfiy74JfTBL74HGicnPA2pHT1J2tE5HGnHQ9kl9qXtd1zZDn8/6fWpJt+PzlEf2tHh5CcsLIyAAOcPqaxatYrHH3+cHj16MGfOnBrnoqKiiIqKokOHDhQUFPDWW2/x6KOPEhAQgNF47h5IVVVVBAYG1jkWjUYhIsL73v2Xm3IAaNI45JLjDwure/t5kqqqGHOzOJm6kcuz1nN5o2yoBGsloNURFN+d4I79CWrXG22A6/+PvbUd6xtpR+e4WDuqqsrXX2xHBQYlN6NP12buCczLyPejc3iyHR1OfiZMmMBzzz1HZmYmHTp0ICgo6Jxrevfu7dA9Fy5cyPPPP8+oUaN46aWX7L05v/32G02bNiUxMdF+bfv27TEajRQVFREXF0dRURFGo7FGD1Bubu4lVZm2WlVKSsrr/HhPOXyiBICwQB2FhSfrdA+tVkNYWCAlJRVYLN5RnE9VVSz5RzClb8aYvhlr0ekhT7OqIdXUlNikgbTrNxhFH4QZKKkAKurWRrXhje1YH0k7Okdt23FLag57MvLx12kYc3l8nX+PNFTy/egcrmrHsLDAWvcmOZz8zJgxA4DXXnsNoMY8n+qCcqmpqbW+3xdffMGsWbMYN24cU6dOrXG/119/ndatWzN37lz7sZ07dxIeHk50dDQ9e/bEarWydetW+vXrB0BmZiY5OTkOJ2Bn88aqvHlFFQBEhgZccvwWi7Vet4GqqljzD2PO2IIpcwtqcc7pk1oduuZd0bXpzeLMEJZvM3B5cRMStAHg5tdU39vRW0g7OseF2tFktvDlqoOAbWl7eLC/tPlfkO9H5/BkOzqc/Hz22WdOe/LMzEz7CrEJEyZgMBjs5wICArj33nuZOHEiPXr0YODAgWzatImPPvqIJ598Eo1GQ2xsLNdccw3Tpk1j9uzZBAYGMmPGDPr06UNSUpLT4vQW+cW25Kchr/RSzUaMO5ZhSvsDteSshKdFN1vhwZZJKP627tQufgUs32ZgZ7oBq1VFo5HS/EKcz8qUo7K0XfgMh5MfRVHo1KkTwcHnzpcoKSlh3bp1tb7XihUr7EvYV65cWePcDTfcwIsvvojJZOKDDz7gpZdeomnTpkyfPp1bbrnFft2sWbOYPXu2ffn7oEGDmDZtmqMvq0Fo6DV+1KqTVKz4D5bsA7YDWr8zEp7u9oTnTO1ahBOo11FabiLjRImU5xfiPIrLqlj6+yFAlrYL36Coqqo68oCOHTvy9ddf061bt3PO/fHHH/zjH/9g165dTgvQEywWKwUF3jXWbTRZuP/VXwF449GBhAT61ek+Op2GiIjgerchp7WsgIqf5tqqLvsHEtD/Dtuy9PMkPGd77/vdbE7N5Zp+rbhpsHtK9NfXdvQ20o7OcbF2/PjHVNbtOkF8kzCm/l9P2bz0L8j3o3O4qh0jI4OdO+fnqaee4sQJ29JHVVWZOXMmISHnFn87dOgQ0dHO30VbXFx1jZ8Afy3BAQ2rxo+l8DgVP85BPVmAEhRO4NWT0Ea2uPgDT0lKjGZzai470gxuS36E8Baya7vwRbVKka688kpUVeXMTqLqr6s/NBoNSUlJvPDCCy4LVvy1M3dzd8Yu5vWFJSeN8h+eRz1ZgKZRHEHXT3Mo8QHomhCFRlE4lnfSPilcCGH7Pf6V7NoufFCtugiGDRvGsGHDABg3bhwzZ850eoFDcWkMJQ1vvo85awcVq94BixFNTBsCRz1Wpx3UgwP8aNu8EfuPFLEjzcCIXo4lT0I0VFv357H/SBF+Og03S6+o8CEOl1dcsGDBOYnP7t27+fnnnykpKXFaYMIxZ/b8NASmfb9R8fMbYDGibdGNoGueqlPiUy2prW04dsdBw0WuFMI31Ni1vU/LBvO7Q4jacDj5yc3NZdy4cbzzzjuArUDhLbfcwiOPPMLIkSM5ePCg04MUF1c95yfay3+BqapK1falVP42H1QrunYDCLzyERQ//cUffAFJibbk58CRIsorzc4IVQivdubS9qv7ytJ24VscTn5eeeUVMjMz6dq1K1arlffee4/+/fuzZMkSEhMTefXVV10Rp7gIQwNY5q5arVT9vhDjlkUA+CddQ8Dge1E0lz6BOzYyiCZRQVisKrsz8y/5fkJ4s+KyKv4nS9uFD3M4+Vm/fj1PPfUUAwcOZNu2bRgMBv7v//6PDh06cO+995KSkuKKOMVFePuwl2oxUbn6XUx7fgEU9P1vR9/nFqdO3u5+qvdnR5oMfQnf9t1vGVQaLcQ3CZVd24VPcjj5KS8vJy7O9sPy22+/4e/vT9++fQHw9/fHwbJBwgnMFuvpHd29sOdHNZZT8eOrmDO2gEZLwPD78e8ywunPUz309Wd6Phar1OgQvqnG0vbh7WRpu/BJDic/rVu3JiUlBZPJxIoVK+jTpw96vW0+xg8//EDr1q2dHaO4iMLSKlQVdFoNocH+F39APWItL6J86QtYTuwDvwACr5qEX8JlLnmuhGZhhAT6cbLSTNrRYpc8hxD12TlL25vL0nbhmxxOfu677z7eeust+vXrx5EjR7j77rsBuPnmm/nhhx8YP36804MUF3bmkJc3vYuzFmVT/v1zWPOPoASGEXTtZHTNOrns+bQaDV3bRAEy9CV8U4osbRcCqMPeXqNHj6ZJkyZs3bq1xgaivXv35pFHHmHQoEHOjlFcRPVk5+iwS1sR5U6W3Awqlr+GWlmKEhZL0NWT0ITFuPx5k9tGs3FPNjvS8rl1WFuXP58Q9YXRZOGr6l3bZWm78HF1WkbTs2dPevbsCUBVVRX+/v489dRTTg1M1F71Mndv+WVmPrKLipVvgdmIJro1gVdNRBMY5pbn7hwfiVajkFNQzon8kzSJOneDXiEaoh/WZZBXVGHbtb1vS0+HI4RHOTzsBZCRkcG//vUv+vTpQ3JyMnv37uWZZ55hwYIFzo5P1II37eZuOrCBiuX/AbMRbbPOBI1+ym2JD0CgXkeHluEA7EyTJe/CNxSVVfHNqgOAbWl7gH/D2v9PCEc5nPykpqZy8803s2fPHq699lr76i6tVsvs2bNZvHix04MUF3a6wOHFdzj3JOPOn6hc+wGoFnSJfQkc9VitdmV3tqS2jQHYcTDP7c8thCcsWptORZWZNk3DZGm7ENQh+XnppZfo0qULP/30E5MnT7YnP9OmTePmm2/ms88+c3qQ4sIMxbbNOuvrsJeqWqnc+CVVm74GwK/rlQQM/QeK1jPvPrsn2iY9HzxWTFmFySMxCOEuqYcK+G3HcQBuHyFL24WAOiQ/O3bs4K677kKn051TgO7qq6/m0KFDzopN1IJVVSkosdX4qY/DXqrFTOWaeZj+XAGAvu+tBPQbi6LUacTVKaIbBdK8cQiqaqv5I0RDlXqogP/8dxcqMLRnc9q2CPd0SELUCw7/BdLr9VRWVp73XFFREf7+3lVnxtsVlxmxWFU0ikJ4aP1qe9VYQcXy1zCn/QGKloCh/8C/21WeDguApLa23p/tsuRdNFB7DhXw+n93YTRb6Z4YxUO3JHk6JCHqDYeTnwEDBvDGG2+QnZ1tP6YoCidPnmT+/Pn079/fqQGKC6ue7BwZpker8VxvytmsFSWU/+8lLMf2gE5P4KhH8Wtbf743qre62J2Rj9nie9WejSYLS38/xJ5DBZ4ORbjA7sx83vjvLkxmK90Tonjk5u74+8n+XUJUc3jSxRNPPMGtt97KqFGj6NChA4qi8OKLL5KZmYmqqsydO9cVcYq/YJ/vU4+GvKwluZT/OAe1JBclIJTAUY+hjWnj6bBqiG8SRliwPyUnjew/XETn+EhPh+RWS38/xLKNWQBc2acFNw1OQKetP8mzqLs/M/J5c9GfmC1WkhKj+eeYLvjp5P9WiDM5/BPRpEkTvv/+e+68805UVaVly5aUl5czevRovvvuO1q0aOGKOMVfqG81fiyGQ5R//5wt8QltTND1U+td4gOgURS6J/hmteeCkkp+3nLE/vWKzUd48fNtGIoqPBiVcIZd6QbeXLQLs8VKcttoHrhBEh8hzqdOy20iIiJ47LHHnB2LqIP6VOPHfHQPFSvfBFMlmqgWBF41CU1QuKfD+ktJbaNZt+sEO9MM3HZFW6fuIF+fLVmXiclsJbF5I0b1acn8ZalkHC9h5sdbuPvqjvRs39jTIYo62JFm4J3Ff2K2qPRs15gJ13eW3jwh/kKtkp8lS5YwePBgIiIiWLJkyUWvHzNmzCWGJWrLUE96fkxpf9hq+FgtaJt2JHDkwyj+QR6N6WI6tY7ET6fBUFzJsbyTNI8J8XRILnckt4wNf9p29L51aCIJzRrRMjaE97/fQ/rxEt5e/CfDezbnb0MTpcfAi2w/kMc7S3Zjsar06hDDP67tJImPEBdQq+Tn3//+N9988w0RERH8+9//vuC1iqJI8uNG1T0/0R5Mfox//kzVxi8A0LXpQ8DQ+1C0fh6Lp7b0flo6topgV3o+O9IMPpH8fLs2DRXo1SGGhGa2Hb2jGwXy1O09WPxbBj9tOswvW4+SdrSY+8d0JjaifiewArbuz+O9722JT5+OMdx3bad6tfhBiPqoVsnPL7/8QuPGje2fi/pBVdUaO7p74vmNm7/FuPNHAPw6X4G+/20ereHjqKS20fbkZ3T/1p4Ox6X2HCpgd0YBWo3CTYNrzsPSaTXcMjSR9i3D+fB/qWTllPLMx1u466oO9OkY66GIxcWk7Mvl/R/2YLGqXNYplntHd5TER4haqFXy06xZs/N+LjyrtMKE0Wxbph0Z6t7kR7Waqfz1Y8wHNwDg3/tm/JOu8bp5M90TooH9ZB4vofikkUbB9atWkrNYVZVvV6cBMDS52V/26HRLiGbm3b2Z98MeDhwt5r3v97Avq5C/D28rS6XrmS37cnn/+z1YVZW+nWMZf40kPkLUVp0mPG/YsIE1a9ZQUVGB1VqzRoqiKMyePdspwYkLq+71aRTi79b5GaqpiopVb2M5sgsUDQGD7sav/UC3Pb8zRYTqaR0XyqHsUnalGRjYvamnQ3KJP/Zkczi3jEC9lmsHtL7gtZFhATxxWzLfrz/Est8PsXbHcdKOFfPPMV1oEhXsnoDFBW1OzWHeD3uxqir9u8Rxz9Ud0Wi8642HEJ7kcPIzf/58Xn75ZfR6PZGRkee80/e2d/7ezBPzfayVpVT89BrWvAzQ+hN4xQPoWiW57fldISkxmkPZpexooMmP0WThu98yALi6bytCgy7eu6XVaLhxUBvatwjng6V7OJp3kmc/SWHcle3o36WJq0MWF/DHnmw++N9eVBUGdI3j7qsk8RHCUQ4nPwsXLuTaa6/l+eefl60sPMzg5mXu1tI8Kn58FWtxNuiDCRr1GNrYRLc8tyt1T4xmyfpM9hwqwGiyNLjhnV+2HqWgpIqIUD0jejlWh6tzfCQz7+nDB0v3kppVyIf/SyU1q5A7RrRH79+w2skbbNydzYfLbInPwG5NuPOqDrJRqRB14PBYicFg4Oabb5bEpx5wZ4FDS/4Ryr9/HmtxNkpwJEHXTW0QiQ9Ay9gQIkL1GE1WUrMKPR2OU5VVmPjfqUrONw5qU6fELjxEz6RbkxhzeTyKAhv+zGbWZykcyytzdrjiAjb8eYIPT/X4DE5qKomPEJfA4eSnU6dOHDx40BWxCAfZh71c3PNjPp5K+Q+zUcuL0EQ0J2jMdLQRDWd4SFEUkk7t9bWzgVV7/mFDJhVVZlrEhNCvc1yd76PRKFx3eTxP/D2ZRiH+HDecZNanKfy28ziqqjoxYnE+63YdZ/6yVFRgSHIzxl3ZXhIfIS6Bw8nPlClTmD9/Pt999x3p6ekcP378nA/hHqd7fgJd9hymjC1U/PgqmCrQxrUj6LrJaIIjXPZ8npLU1pb87EgzNJg/5rmF5azZdgyAW4YmOGVeSIdWETxzTx+6tInEaLbyyU/7+OB/e6moMl/yvcX5/bbzOJ/8uA8VGNqjGeNGtpPER4hL5PCcn7Fjx2K1WpkyZcpfTm5OTU295MDExRlcXOPHuHc1VesXACq61j0JGDYBRdcwhzs7tAxH76elqMxIVk4prePCPB3SJVv0awYWq0rn+Ei6xEc57b5hQf7865buLN90mO9+zeCPPTlknijln9d3pmVsqNOeR8DaHcf4bPl+AIb3bO5T27AI4UoOJz+zZs2SH756oLzSbH+37exhL1VVqdj0X6q2/gCAX8ch6Af8H0oDriHip9PSJT6SrQfy2HHQ4PXJT/rxYrbsy0UBbhmS4PT7axSFq/u2ol3zcN77YTc5BeU899lWxg5PZEhyM/kd4QRrth1lwc8HABjRqwV/H54o7SqEkzic/Nx4442uiEM4qHrIKyTQz6mrblSrBcOP71G5YxUA/j3H4N/jep/4pds9MdqW/KQZGDOw/u1EX1vqGQUN+3eJc2lvTGLzRsy8uw/zl6WyI83Agp8PkHq4iLtGdSAooE5lxAS2FXqfr7QlPlf2acHfhkriI4QzycamXsoV21qoVjMnf34b06HtoCjoB/wf/p2GOu3+9V23hCgU4HBOGQUllUS6qYSAs+04aODA0WL8dBpuGOT6JC4k0I+Hb+rKyi1H+HZtOin7cjl0ooR/julCfBPv7kHzhJUpR/hylW1RyajLWnLLkARJfIRwMtnY1EsZiisA5w55mQ5swHRoO4rWj6ARD6Bpmey0e3uDsGB/Epo1Iu1YMTvT8xma7H1buVisVr5dmw7YhkrclcApisLIPi1JbB7Oe9/vxlBcyewFW7llaCIjejWXP9619POWI3z1iy3xubpvK24a3EbaTggXkI1NvZQravyY9q4BIGLw31Hb9MRstl7kEQ1P98Qo0o4Vs+OgwSuTn992niC7oJyQQD+u7tvK7c/fpmkYM+/uzcc/7mPrgTy++uUg+7IKueeajoQE+rk9Hm+yfNNhvlljG64c3b8VNwyUxEcIV6nVDNZmzZrZixo2a9bsnI/Y2FiCg4PtXwvXy3dydWdLbgZWwyHQ+hHafbhT7umNktrakvzUrEIqjd61fLuiysz362zbWFw3oLXH5twEBfjxwA1duH1EO3RahR1pBp75eDNpx4o9Eo83+OmPLHvic92A1pL4COFiDi/fMZvNvPXWWyxduhSATZs2MWDAAPr168edd95JcbH8gnMHZ/f8mFJtvT7+CX3QBvnucuWmUUE0Dg/AbLGyJ9O7qj2v2HyYknITMRGBDPFwr5WiKAzv2Zyp43oRExFIfkkVLy7cxk9/ZGFtIHWUnGXZxkP2ocrrL49njCQ+Qricw8nPG2+8wbvvvktJSQkAzz33HOHh4UyePJnDhw/z6quvOj1IcS5nbmqqVp3ElLYJAH3nYZd8P2+mKArdvbDac2FpFcs3Hwbg5sEJ6LT1oyxBq7hQZtzVmz4dY7CqKt+uTec/3+6ipNzo6dDqhaUbMln0q623bszAeK6/PN7DEQnhGxz+Dbls2TImTpzI7bffTnp6OgcPHuSf//wn//d//8djjz3G6tWrXRGnOEOVyUJJuQlwTs+P6eDvYDGiiWyONq5h7Nd1KZKrk590A1ard/RSfL8+E6PJSkLTMHq2b+zpcGoI1OuYcF1n7hzVHj+dhj8z8pk5fzP7D3tXz5qz/bA+k8XrMgHbvmvXDZDERwh3cTj5yc3NpXv37gCsXbsWjUbDoEGDAIiLi6O0tNS5EYpzFJwa8grw1xKkv7R5Haqq2ic6+3UaJt3tQNsW4QTqdZSWm8g4UeLpcC7qWF4Z63bZtpX527D6WQ9GURQGJzVj+v/1oklUEEVlRl7+cjtLN2R6TYLpLKqqsmRdBkvW2xKfm4ckMLp/a88GJYSPcTj5iYmJ4ejRowCsXr2ajh07EhkZCcD27duJi6v75omids6s8XOpf+gsJ/ZjLToOOj1+if2cEZ7X02k1dG1j+572hqGv/65NR1WhR7vGtG0e7ulwLqh5TAjT7+xF/y5xqCosXpfJq1/voLisytOhuYWqqixel8kPGw4B8LehiR5ZlSeEr3M4+Rk9ejQvvPAC48ePZ+vWrdx0000APP/887z55ptce+21Dt2vqKiIp59+mkGDBtGjRw/Gjh1LSkqK/fyiRYu49tprSUpKYuTIkcybNw+LxWI/X1hYyKRJk+jduzd9+vThmWeeoaKiwtGX5VUMJc7bzb16orNf234o/q7bINXbVO/yvuNg/U5+9mUVsjM9H42icNNg76hKHeCv497RnRh/TUf8/TSkZhUy4+Mt7D1U4OnQXEpVVb77LYP//X4IgL8PS2TUZS09G5QQPsrhMZN//etfBAUFsWXLFiZNmsRtt90GwJ9//sk999zDAw884ND9Jk6cSF5eHnPnziUqKooFCxYwfvx4Fi9ezO7du5kxYwbTp0+nX79+7N69m+nTp2M0GnnooYcAeOSRR6ioqOCTTz6hpKSEqVOnUl5ezksvveToS/MazqrubC0vxpxpSzT9OvpOJefa6JoQhUZROGY4SV5RBY3D619iaFVV+/LowclNaRIV7OGIHDOgaxPim4Tx7ve7OZZ3kle/2sHo/q257vLWaBvYPnKqqvLften8tMk2KX3s8LaM6N3Cw1EJ4bscTn4URWHChAlMmDChxvGvvvrK4SfPyspiw4YNfPHFF/Ts2ROA6dOns27dOpYuXcoff/zBmDFjuPXWWwFo2bIlmZmZfPvttzz00ENs376dzZs38+OPP5KQYNu88dlnn+Xee+9l4sSJxMbGOhyTN3BW8mM6sA6sFjQxbdBGS9f7mYID/GjXohH7DhexI83AiF717w/V5tQcDmWXovfXeu1k2abRwUz/v158seogv+08ztLfD7H/SBETrutMRKje0+E5haqqfLsm3b4a7/YR7Rjes7mHoxLCt3n07VVERATz5s2ja9eu9mOKoqAoCiUlJTz++OOMHz++xmM0Go29llBKSgqNGze2Jz4Affr0QVEUtm7d6p4X4QHVw16XUuBQVa2YUtcC4N/Jt5e3/5Xu9Xjoy2S28t2pJdJXX9aSRsH+Ho6o7vz9tNx1VQf+cV0n9P5aDhwpYsb8zfyZke/p0C6Zqqp8vTrNnvjcMVISHyHqA49uuxwWFsbgwYNrHFuxYgVZWVlMmTLF3htUrbS0lC+//JKBAwcCkJOTQ5MmTWpc4+/vT3h4OCdOnLik2HS6+tvtXnCq5yc2MqjOcZqy/kQtNaDogwho1xfl1H202pr/+rKeHWL4enUaB44UYTRbHaqY7Op2XJlyBENxJRGheq7u37pef7/W1uXdmpLYPJy3Fu3icE4Zr32zk2sHtOaua7t45fejqqp8vvIgP285AsBdV3VgmIcSH/m5dg5pR+eoD+3o0eTnbNu2bWPy5MmMHDmSIUOG1Dh38uRJHnjgAaqqqnjyyScBqKiosG+7cSa9Xk9VVd1Xj2g0ChER9XP+hMlspfDUypiEVpFEhNat9yd75W8AhHYfRmTjiHPOh4XVvzku7hYREUzzmBCO5paRkV3GwDpUTXZFO5aVG1l6arXQHVd1JC6m4eycHhERzGuPDWH+0j0s25DJ0g2HWLYxiyZRwbSMC6VFrO2jZWwozWJC0PtpPR3yeamqyrzFf/LzZlvi89At3bmyb2vPBoX8XDuLtKNzeLId603ys2rVKh5//HF69OjBnDlzapzLy8tjwoQJHD16lI8++ojmzW3vngICAjAaz60UW1VVRVBQUJ1jsVpVSkrK6/x4V8otLEdVwU+nQTWZKSw86fA9rKX5lKedWlGXMLDGPbRaDWFhgZSUVGCx+N7GpmfrlhDF0dwy1u84SpfW4bV+nCvb8ctVBymrMNGscTA9E6Pq9D1Q3906NIE2cSEsWLGfojIjx/LKOJZXxsY/T/foKkDj8ECaNg6mWXQwTaODadbY9m+Av+d+tVlVlc+W72f11qMowD2jO9KnfWOP/j/Jz7VzSDs6h6vaMSwssNa9SZf0G6K0tJTc3FxatGiBVqtFq63bu7CFCxfy/PPPM2rUKF566aUavTnp6ence++9WK1WPv/8c9q2bWs/FxcXx6pVq2rcy2g0UlRURExMTN1e1Cn1dUfznALbMv7IsAAsFhVwvEBc1Z41oKpom3ZEDY0972u1WKz1tg3cqVubKH7cmMXONANVRrPDq5Cc3Y6GogpWbrHNH7llSAJWq9pgiwQmt21Mrw4xqFote9PyOJJTxvH8k5wwnOSY4SQnK83kFlWQW1RxzrysqDA9TaKDaRplS4Zs/wYRFODaneWtqsqCFfv5dcdxFODuqzsyoEuTevOzJD/XziHt6ByebMc6JT+bNm1izpw57N69G0VR+Pbbb/nggw+Ii4vj3//+t0P3+uKLL5g1axbjxo1j6tSpNYr2HTlyhDvvvJOwsDA++uijc+b39O7dmzlz5pCVlUWrVrbVSps3bwY4Z75QQ2Hf0yusbithVKsZ0z7bkJdfJ1nefjGJzRoREuhHWYWJtKPFtG957hChO333WwZmi0rHVhF0bRPl0VjcQVEUIhsF0qVNFB3OaHtVVSktN3HccJLj+Sdt/xpOcjy/nJKTRvJLqsgvqWJ3Rs3aQY1C/E8nRNHBNI0Koml0MKFBlz5h3Nbjs4/fdp6w9fhc05EBXZtc9HFCCPdzOPnZuHEj9913H8nJyTz++OP2IaoOHTrwxhtvEBsby913312re2VmZjJ79mxGjBjBhAkTMBhOv3sLCAhgypQpGI1G5s6di06nIy8vz36+cePGdO/enR49evDYY48xc+ZMysvLefrppxkzZkzDXeZu3829bmOl5kPbUcuLUALD0LXq4czQGiSNRqFbQhS/785mR5rBo8nPoewS/tibA9gqA9fHbSzcRVEUwoL9CQv2p0Ormv8nZRU1k6IT+eUcN5yksLSK4jIjxWVGUrNq7isWGuRH06jgU71FQfbkqFGwf63a2aqqfPLTPtbvOoGiwL2jO9Gvs1S7F6K+cjj5ef311xk+fDj/+c9/MJvNvPLKKwDcf//9lJeX8+2339Y6+VmxYgUmk4mVK1eycuXKGucGDBhg78W5/vrrz3ns/v37URSFt956i2eeeYY777wTvV7PqFGjmDx5sqMvy2sYim3DXnWt8VO9vN2v/SAUbb2Z8lWvJSVG25KfgwZuHdb24g9wAVVV+Wa1raBh386xtIoL9Ugc3iAk0I92LcJp1yK8xvHySjMnCk4lRIZye3JkKK6ktNzE/vIi9h8pqvGYIL3uVCIUZO8xahIVTGSY3p4UWa0qH/+Yyobd2SgK3HdtJ/p2ksRHiPrM4b9+qampPPjggwDnvCMaMGAAn376aa3vdf/993P//fc7GkINUVFRvPHGG5d0D29yetjL8eTHWpyN5dgeQMGv4xDnBtaAdY6PRKtRyCms4ET+SY9UUv4zI599h4vQaRVuHOQd21jUN0EBOhKaNiKhaaMax6uMltNJ0aleouOGk+QWVVBeZSbtWDFpx4prPEbvr7X1EEUFU1Zhsm8x8o/rOtGnY8PsdRaiIXE4+QkNDa0x/HSmEydOEBoq70hd6fSwl+PJj/FUr4+2ZTc0odHODKtBC9Tr6NAqgj2ZBexMy3d78mOxWvl2TToAV/RsQXQdhzzF+en9tbSOC6N1XM2SASazheyCijPmE51KigorqDJayDxRSuaJUgA0isL913emV4dLW2ghhHAPh5Of4cOH89prr9GuXTs6deoE2HqAsrOzee+9986pzyOcx2pVKSix1fiJdjD5Uc1GTPvXAeAv+3g5LCkxmj2ZBew4mOf2zSg3/JnNMcNJggN0XNNftiFxFz+dlhYxIbSICalx3Gyx2noBTyVEuYUV9OkYQ7cEeUMhhLdwOPmZNGkSO3fu5G9/+xvR0bYf9okTJ5KdnU2TJk2YOHGi04MUNkVlVVisKlqNQniIY6u9zBlboOokSkgU2hbdXBRhw9U9MYrPV8LBY8WUVZgICXTtkulqVUYLi9fZtrG4tn9rgl28VFtcnE6roVm0rbaQEMI7OZz8NGrUiG+//ZYlS5bwxx9/UFRURGhoKOPGjePGG28kMFC65F2lesgrIlSPRuPYSh9j6hoA/DoMRmlgO2a7Q3SjQJo3DuFoXhm70g307+KeJcwrthymuMxIdKMAhvaQPaGEEMIZ6rTcx9/fn7/97W/87W9/c3Y84gLsu7k7ONnZkn8Ea04aKFr8OgxyRWg+IaltFEfzytiRlu+W5Kf4pJGfNtkKGt40OAG/BrB/lxBC1Ae1Sn6WLFni0E3HjBlTh1DExVT3/Dg638d0qtdHF98DTVC4s8PyGUmJjfnf71nszsjHbLGic/GmfD+sz6TKaKF1XCi9O8pEWiGEcJZaJT9nV22uXuKuquo5x0CSH1cxFDu+0ks1VmA6+DsAfjLR+ZK0bhJKo2B/ik8a2X+4iM7xkS57rhP5J/l1x3EAbh2WiMaHCxoKIYSz1Sr5+eWXX+yfp6am8sQTT/DAAw9w1VVXERMTQ2FhIatXr+bNN9/khRdecFmwvq4uw16mtD/AVInSKA5t046uCs0naBSF7olR/LbzBDvSDC5Nfv67Nh2rqpKUGO3xLTWEEKKhqVXy06xZM/vnDz/8MA888AD33Xef/VhsbCxjx47FaDTyyiuvMHjwYOdHKhyu8aOqKqbU1YBtebsvb4fgLN0To23Jz0EDt13R1iVteuBIEdsPGlAUuGlIgtPvL4QQvs7hSQvp6en2+j5na9OmDUePHr3koMS5VFU9Xd25lsmPNS8Da/4R0Prh126AK8PzGZ1aR+Kn05BfUsmxvJNOv7+qqnyzxraNxaDuTWU5tRBCuIDDyU/r1q1ZunTpec99/fXXtGvX7pKDEucqrTBhNFtRgMhaDnsZ99p6fXQJfVACQi5ytagNvZ+WTqc20tyRZrjI1Y5L2Z9HxvES9H5axlwe7/T7CyGEqMNS9wcffJBHH32UQ4cOMXToUCIiIjAYDPz888+kpaXxwQcfuCJOn1fd69MoxL9Wq4zUyjLM6baNYaWis3N1bxvNzvR8dqQZGN2/tdPua7ZYWbTWto3FlX1a0MjBQpZCCCFqx+HkZ+TIkbz99tu88847vP7666iqikajITk5mU8++YRevXq5Ik6fl+/gSi/TwQ1gMaGJaokmRuaNOFP3hGhgP5nHSyguq3JakrJm+zFyiyoIC/Z3+xYaQgjhS+pU5HDYsGEMGzaMqqoqiouLCQ8Px9/f39mxiTMYHFjppaoqpr2nKjrLRGeniwjV0zoulEPZpexMz2dQ96aXfM/yShNLNxwCYMzl8QT41+lHUwghRC1cUpU2vV5PTEyMJD5ucLrA4cW3D7Gc2Ie1OBv8AvBL7Ovq0HxSUqJtX7udTpr38+MfhymrMNEkKoiB3d2zdYYQQvgqqZfvJRwZ9jKdmujs17Y/ir/steYKSW1tyc+ezAKMJssl3augpJKVKUcAuGVIIlrZe00IIVxKfst6idoOe1nLizBnbgOkorMrtYgJITJMj9FsJTWr8JLutfi3DExmK+1ahNM9McpJEQohhPgrkvx4idoWODTtXweqBU1sItqoFu4IzScpikJ3Jwx9Hc4p5ffd2QD8bWiizM8SQgg3kOTHC5RXmqioMgMQfYGeH9VqxZS6FpDl7e5QPe9nR5qhxj53jvh2TRoq0KdjDG2ahjkxOiGEEH+lTktKMjMzeeONN9i8eTMlJSVERETQq1cvHnjgARITE50do8+rHvIKCfRD76/9y+ssR3ehluWDPhhdm97uCs9ndWgZjt5PS1GZkaycUlrHOZa87M7MZ8+hQrQahRsHSzkCIYRwF4eTn7S0NP7+97+j1WoZNmwY0dHR5OXlsWbNGtauXcu3335LQoL8Inem2g55GauXt7cfiKKTFXiu5qfT0iU+kq0H8thx0OBQ8mO1qnyz2lbQcHjP5sSEy8R0IYRwF4eTnzlz5tC8eXMWLFhAaGio/XhpaSl33nknr732Gm+99ZZTg/R19j29LjDkZS01YDm8CwD/DkPcEZbAttHp1gN57EgzMGZgm1o/buOebI7mlRGo1zm1SrQQQoiLc3jOz5YtW7j//vtrJD4AoaGh/OMf/2DLli1OC07Y1Kbnx7TvV0BF26wTmvA4N0UmuiVGoQCHc8ooOPX/dDFGk4XvfssAYHT/VoQE+rkwQiGEEGdzOPnR6XTo9ecv5+/v74/RaLzkoERNhovU+FEt5lPJjyxvd7ewIH8SmjUCar/qa2XKEQpLq4gK03NFz+auDE8IIcR5OJz8dO3alS+++OKc1S2qqvL555/TpUsXpwUnbC427GXO2oZaUYISFI6udbI7QxNgr82zIy3/oteWlBtZtjELgBsHJeCn++sJ7EIIIVzD4Tk/jz76KGPHjuW6665j1KhRNG7cmLy8PJYvX05mZiYff/yxK+L0aRcb9rLv49VhEIpG9oRyt6S2jVn0awapWQVUGs0X3Jdr6YZDVBottIwN4bLOsW6MUgghRDWH/1J27dqVDz/8kFdffZW33noLVVVRFIUuXbrwwQcf0Lu3LLF2piqThdJyE3D+5MdadALL8VRQFPw6DHZ3eAJoGhVE4/AA8ooq2ZNZSM/2jc97XU5hOWu3HwNsBQ01UtBQCCE8ok7dBH379uXbb7+loqKCkpISwsLCCAyUpbquUD2JNlCvJUh/7n+X8VRRQ22L7mhCZGsET1AUhaTExqxMOcLONMNfJj+L1qZjsap0bRNFp9aRbo5SCCFEtVolP1u2bKFTp04EBwfXajWX9P44z5l7ep299YFqNmI6sB4A/07D3B6bOC0pMcqW/KQbsFrPrfacdqyYlP15KArcMkTqYAkhhCfVKvkZN24c33zzDd26dWPcuHEoimKf8Fz9+Zn/pqamujRoX5J/gQ1NzRmboeokSmg02uYy0dyT2rYIJ1Cvo7TcRMaJEjq0irCfU1WVb9akATCgaxOax4R4KkwhhBDUMvn57LPP7FWbP/vsM5cGJGq60GRne0XnDkNQNLJNmyfptBq6tolkc2ouOw4aaiQ/2w4YSDtajL9Oww0OFEIUQgjhGrVKfvr06XPez4Xr2Ze5N6o5p8piyMKamw4aLX7tB3oiNHGWpMRoNqfmsjPNwN+vaAuA2WLlv7/atrEY2acFEaHnr5ElhBDCfWqV/CxZssShm44ZM6YOoYjz+asCh6ZUW6+PrnVPNEGN3B6XOFfXhCg0isIxw0lyC8uJiAhm7fZj5BSUExrkx1WXtfJ0iEIIIahl8vPvf/+7xtfVE2/PLHR45mRcSX6cxz7sdcacH9VYgengRgD8ZKJzvREc4Ee7Fo3Yd7iI7QcNtGgazuJT21hcNyCewPOs1hNCCOF+tfpt/Msvv9g/T01N5YknnuCBBx7gqquuIiYmhsLCQlavXs2bb77JCy+84LJgfY3ZYqWotAqo2fNjStsI5io04U3QNmnvqfDEeXRPjLYlPwcMmK1QWm4iNjKIwUlNPR2aEEKIU2qV/DRr1sz++cMPP8wDDzzAfffdZz8WGxvL2LFjMRqNvPLKKwweLMX2nKGgtAoV8NNpCAuybX6pqiqmvasB2z5eZy9/F56VlBjN16vT2H+4kLRjxQDcPDgBnVYmpAshRH3h8G/k9PR0OnXqdN5zbdq04ejRo5cclLDJL6oAatb4seakYS04Clp//NoN8GR44jxiI4NoEhWExapiNFlo27wRPdpFezosIYQQZ3A4+WndujVLly4977mvv/6adu3aXXJQwsZwnmXuxuqJzgmXoeiDPRKXuLCkxNPJzt+vaCu9c0IIUc84PAPzwQcf5NFHH+XQoUMMHTqUiIgIDAYDP//8M2lpaXzwwQeuiNMnnV3gUK0ssxU2BPw7DfVYXOLC+ndtwi9bjzKkZwvaNg/HbLZ6OiQhhBBncDj5GTlyJG+//TZvv/02r7/+OqqqotFoSE5O5pNPPqFXr16uiNMnVa/0ij7V82M6sA4sZjTRrdA0jvdkaOICmkUH8/6TQ4iKDKGoqNzT4QghhDhLndbeDhs2jGHDhlFVVUVxcTHh4eH4+/s7Ozafl39GjR9Vtdo3MZWJzvWfVqOR/yMhhKin6lx4JD09nQ0bNpCXl8cdd9zBkSNH6NChAyEhsm+Rs5y5qanlWCpqcQ74BeKX2NfDkQkhhBDey+EJz1arlWnTpjF69Ghmz57Nhx9+iMFg4J133mHMmDFkZ2c7dL+ioiKefvppBg0aRI8ePRg7diwpKSnnXJeVlUVSUtI5q8mqqqp45pln6NevH8nJyUyaNImCggJHX1a9Y7WqFJ6q8RPdKMBe0dmvbX8Uv3P3+RJCCCFE7Tic/LzzzjssXbqU5557jg0bNtirPD/xxBNYrVZee+01h+43ceJEtm/fzty5c1m0aBEdO3Zk/PjxZGRk2K9JT0/nnnvuoaKi4pzHz5w5k/Xr1/Pmm2/y6aefkpGRwSOPPOLoy6p3isqqsFhVtBqFMKUc86FtAPjJRGchhBDikjic/CxatIhHHnmEm266ifDwcPvxjh078sgjj7Bhw4Za3ysrK4sNGzYwc+ZMevXqRXx8PNOnTycmJsa+nP7999/n5ptvplGjc/evysnJYcmSJUybNo1evXrRrVs35s6dy5YtW9i+fbujL61eqZ7sHBGqx3JwHahWtHHt0EY293BkQgghhHdzOPkxGAx07NjxvOdiY2MpKSmp9b0iIiKYN28eXbt2tR9TFAVFUez3WbVqFS+88AJPPfXUOY/funUrAH37np4DEx8fT2xsLFu2bKl1HPVR9XyfxmH+mFJ/BcCv4xAPRiSEEEI0DA4nP61ateLXX38977nNmzfTqlXtd64OCwtj8ODBNVaKrVixgqysLAYOHAjAt99+y6hRo877+JycHCIiItDr9TWOx8TEODz3qL6pXunVRX8M9WQBij4EXbyUERBCCCEulcOrve68806efvppTCYTQ4fallxnZWWxadMm5s+ff84O8I7Ytm0bkydPZuTIkQwZMuSi11dUVJx3ib1er6eqqqrOcQDodJ7di6l6snNH458A+HcchF+A6yc6a0/tQaWVvaguibSjc0g7Ooe0o3NIOzpHfWhHh5OfW265hYKCAt59912+/PJLVFVl4sSJ+Pn5ce+99zJ27Ng6BbJq1Soef/xxevTowZw5c2r1mICAAIxG4znHq6qqCAwMrFMcABqNQkSEZ7eOKC43EakpJbIsHYCYflfj58aYwsLq3n7iNGlH55B2dA5pR+eQdnQOT7Zjner8TJgwgdtvv51t27ZRXFxMWFgY3bt3rzEB2hELFy7k+eefZ9SoUbz00ku1LpgYFxdHUVERRqOxxmNyc3OJjY2tUyxgW2ZeUuLZyrwnDGX01x9EQUXXogtlShgUnnT582q1GsLCAikpqcBikW0Z6kra0TmkHZ1D2tE5pB2dw1XtGBYWWOvepDoXOQwJCWHQoEF1fbjdF198waxZsxg3bhxTp051qCpuz549sVqtbN26lX79+gGQmZlJTk4OvXv3vqS4PLkfk6qqFBWfpG9IGgC6DkPdHo/FYpU9qZxA2tE5pB2dQ9rROaQdncOT7ehw8lNcXMwbb7zBtm3bzruyS1EUVq1aVat7ZWZmMnv2bEaMGMGECRMwGAz2cwEBAYSGhl7w8bGxsVxzzTVMmzaN2bNnExgYyIwZM+jTpw9JSUkOva76pLTcRAfNIUI1lRAUjq5VkqdDEkIIIRoMh5Of6dOn88svvzBw4EA6dOhwSU++YsUKTCYTK1euZOXKlTXO3XDDDbz44osXvcesWbOYPXs2Dz30EACDBg1i2rRplxSXp+WXVDJAfwAA/w6DUTRaD0ckhBBCNByKWl2iuZZ69erFpEmT6jyx2RtYLFYKClw/v+av7Nr2J/Epr2JFIfS2V9GERLrtuXU6DRERwRQWnpRu3Usg7egc0o7OIe3oHNKOzuGqdoyMDK71nB+H15kFBwfTvLlUGXYl/SFblexj+gS3Jj5CCCGEL3A4+bn99tv56KOPOHnScz0jDZlqrqJxvm1rjpzoPh6ORgghhGh4HJ7zc8cdd7B48WIGDx5MfHz8OfV0FEXh008/dVqAvsacvhk/tQqDJQSanH8bESGEEELUncM9P08//TSZmZnExMQQEBCAqqo1PqxWGQe9FMa9qwHYUNWO6PAgD0cjhBBCNDwO9/ysXr2aSZMmcd9997kiHp9myTuENS8Ts6phU1Uiw8Jcv52FEEII4Wsc7vnx9/enS5curojF55lSbb0+O40tOakGECXJjxBCCOF0Dic/119/PV9++aUMbzmZaizHlPYHAOur2hMS6IfeX+r7CCGEEM7m8LBXaGgo//3vfxk2bBjdunUjOLjmZpuKojB79mynBegrTAd/B7ORquBYMgpiaB0tvT5CCCGEKzic/Hz33Xc0atQIgN27d59z3pG9uYSNqqqY9q4B4HhELziiENVIkh8hhBDCFeo04Vk4lyXnINbCY6Dz54CuA5An832EEEIIF6nzru4lJSXs2LGD0tJSIiMj6dq1KyEhIc6MzWdU9/r4JfQlp8B2THp+hBBCCNeoU/Izb9483nnnHSorK+3H/P39mTBhAg8++KDTgvMF1ooSzBlbAPDrNIz8ZXkAREvyI4QQQriEw8nPokWLmDt3LjfffDPXXXcd0dHR5OXl8f333/PWW2/RtGlTbrjhBlfE2iCZD6wHqxlN43i0jVuTX3IEQIa9hBBCCBdxOPn55JNPGDt2LDNmzLAfa9OmDZdddhkBAQF89tlnkvzUkqpaMaauBcCv4xCqjBZKy02A9PwIIYQQruJwnZ+srCyuuOKK854bPnw4GRkZlxyUr7Ac24takgv+gfgl9CW/xDaMGKjXEhTg5+HohBBCiIbJ4eQnNjaW48ePn/fc0aNHZdKzA+wTndsOQPHT25OfqLDACz1MCCGEEJfA4eRn2LBh/Oc//2HXrl01ju/cuZM333yTYcOGOS24hsx6shBz1nYA/DoNBSC/2Jb8yJCXEEII4ToOz/l5+OGH+f3337n11ltp1qwZ0dHRGAwGjh07RkJCApMmTXJFnA2Oad+voFrRNmmPNqIZAIbi6p4fSX6EEEIIV3E4+QkJCeG///0vixYtYsuWLRQXF9O1a1fuuecebrzxRgIC5A/3xahWiy35Afw6DrUftw97Sc+PEEII4TIOJz/jx4/n3nvv5bbbbuO2225zRUwNnvnwDtSThSgBoejie9qPVw97SfIjhBBCuI7Dc362bdsm+3ddIvtE5/YDUbSnV3VV9/zInB8hhBDCdRxOfgYOHMgPP/yAyWRyRTwNnrUkF8vR3YCCX8ch9uNmi5Wi0ipA5vwIIYQQruTwsJder+eHH37gp59+IiEhgaCgoBrnFUXh008/dVqADY3pVFFDbYsuaMJi7McLSipRAX+dhtAgqfEjhBBCuIrDyU92djbJycn2r1VVrXH+7K/FaarFhGn/OqDmRGc4Pd8nMixAhhWFEEIIF3I4+VmwYIEr4vAJ5swU1MpSlOBIdC271zhnkPk+QgghhFs4lPzs2rWLY8eO0apVKzp16uSqmBos+0TnDoNRNNoa52SllxBCCOEetUp+SkpKmDBhAjt27EBVVRRFITk5mVdffZUmTZq4OsYGwVqcgyX7ACga/DoMOud8vhQ4FEIIIdyiVqu9Xn/9dfbu3cvDDz/MvHnzeOqpp8jIyODpp592dXwNh1aHog/Br9NQNMER55yWAodCCCGEe9Sq52fNmjVMnDiRO++8E4BBgwYRGxvL448/Tnl5+TkrvsS5NCFRhNz51l+eN8i+XkIIIYRb1KrnJy8vj86dO9c4dtlll2GxWDhx4oRLAvMlVqtKodT4EUIIIdyiVsmP2WzG39+/xrFGjRoBUFVV5fyofExRWRUWq4pWoxAeovd0OEIIIUSD5nCF57NJXZ9LVz3kFRGqR6ORGj9CCCGEK11y8iMF+S6d7OklhBBCuE+t6/zMnDmTkJAQ+9fVPT7Tp08nODjYfly2t3Cc1PgRQggh3KdWyU/v3r2Bc4e4zndchsEcZ5AaP0IIIYTb1Cr5kS0tXEtq/AghhBDuc8lzfsSlqx72ipaeHyGEEMLlJPnxMFVVT/f8hAd6OBohhBCi4ZPkx8NKyk2YzFYUIDJUavwIIYQQribJj4dVD3mFh+rRaeW/QwghhHA1+WvrYfYhL5nvI4QQQriFJD8eli8bmgohhBBuJcmPh0mBQyGEEMK9PJ78FBUV8fTTTzNo0CB69OjB2LFjSUlJsZ/fuHEjN954I927d2fUqFEsW7asxuOrqqp45pln6NevH8nJyUyaNImCggJ3v4w6MxRXADLsJYQQQriLx5OfiRMnsn37dubOncuiRYvo2LEj48ePJyMjg/T0dCZMmMDAgQP57rvvuOWWW3jyySfZuHGj/fEzZ85k/fr1vPnmm3z66adkZGTwyCOPePAVOUYKHAohhBDuVeu9vVwhKyuLDRs28MUXX9CzZ0/AtlfYunXrWLp0Kfn5+bRv357HHnsMgISEBPbu3cuHH35Iv379yMnJYcmSJbz33nv06tULgLlz5zJq1Ci2b99OcnKyx15bbcmmpkIIIYR7ebTnJyIignnz5tG1a1f7MUVRUBSFkpISUlJS6NevX43H9O3bl61bt6KqKlu3brUfqxYfH09sbCxbtmxxz4u4BOWVJiqqLABEyrCXEEII4RYe7fkJCwtj8ODBNY6tWLGCrKwspkyZwuLFi4mLi6txPiYmhoqKCgoLC8nJySEiIgK9Xn/ONdnZ2ZcUm07n+rywsMwIQGiQH8GBfi5/vtrQnqo1pJWaQ5dE2tE5pB2dQ9rROaQdnaM+tKNHk5+zbdu2jcmTJzNy5EiGDBlCZWUl/v7+Na6p/tpoNFJRUXHOeQC9Xk9VVVWd49BoFCIiguv8+Nraf6wEgNioYLc8nyPCwmSrDWeQdnQOaUfnkHZ0DmlH5/BkO9ab5GfVqlU8/vjj9OjRgzlz5gC2JMZoNNa4rvrrwMBAAgICzjkPthVggYF1b1SrVaWkpLzOj6+trGNFAEQE+1NYeNLlz1cbWq2GsLBASkoqsFisng7Ha0k7Ooe0o3NIOzqHtKNzuKodw8ICa92bVC+Sn4ULF/L8888zatQoXnrpJXtvTpMmTcjNza1xbW5uLkFBQYSGhhIXF0dRURFGo7FGD1Bubi6xsbGXFJPZ7Ppv7NxC2zL3yDC9W57PERaLtd7F5I2kHZ1D2tE5pB2dQ9rROTzZjh4fuPziiy+YNWsWt99+O3Pnzq2RxPTq1YvNmzfXuP6PP/6gR48eaDQaevbsidVqtU98BsjMzCQnJ4fevXu77TXUlb3AoUx2FkIIIdzGo8lPZmYms2fPZsSIEUyYMAGDwUBeXh55eXmUlpYybtw4du3axZw5c0hPT2f+/PksX76ce++9F4DY2FiuueYapk2bxqZNm9i1axcTJ06kT58+JCUlefKl1YpBavwIIYQQbufRYa8VK1ZgMplYuXIlK1eurHHuhhtu4MUXX+Sdd97hlVde4dNPP6V58+a88sorNZa/z5o1i9mzZ/PQQw8BMGjQIKZNm+bW11FX0vMjhBBCuJ+iqqrq6SDqG4vFSkGBaycgVxkt/HPurwC89a+BBAXUj6XuOp2GiIhgCgtPypj2JZB2dA5pR+eQdnQOaUfncFU7RkYG13rCs8fn/Piq6srOgXpdvUl8hBBCCF8gyY+HGGTISwghhPAISX48RPb0EkIIITxDkh8PsU92luRHCCGEcCtJfjykuudHhr2EEEII95Lkx0MMxbbqzjLsJYQQQriXJD8eIsNeQgghhGdI8uMBZouV4jLbhqwy7CWEEEK4lyQ/HlBQUokK+Os0hAZJjR8hhBDCnST58QDDGUNeiqJ4OBohhBDCt0jy4wGyp5cQQgjhOZL8eEC+7OYuhBBCeIwkPx5Q3fMjy9yFEEII95PkxwOkwKEQQgjhOZL8eIBBavwIIYQQHiPJj5tZrSqFpVWA9PwIIYQQniDJj5sVlVVhsapoNQrhIXpPhyOEEEL4HEl+3Kx6yCsyTI9GIzV+hBBCCHeT5MfNpMaPEEII4VmS/LiZQWr8CCGEEB4lyY+bna7xE+jhSIQQQgjfJMmPm0mNHyGEEMKzJPlxM6nxI4QQQniWJD9upKoqBTLnRwghhPAoSX7cqKTchMlsRQEiQ6XGjxBCCOEJkvy4UfVk5/BQPTqtNL0QQgjhCfIX2I0MxRWADHkJIYQQniTJjxtVr/SKlpVeQgghhMdI8uNG+bLSSwghhPA4SX7cSJIfIYQQwvMk+XEjGfYSQgghPE+SHzdRVVUKHAohhBD1gCQ/blJeZabSaAEgUnp+hBBCCI+R5MdNquf7hAX5offTejgaIYQQwndJ8uMmJrMVgCZRwR6ORAghhPBtOk8H4Cvim4Rx37WdiG8S5ulQhBBCCJ8myY+baDQK/TrHeToMIYQQwufJsJcQQgghfIokP0IIIYTwKZL8CCGEEMKnSPIjhBBCCJ8iyY8QQgghfEq9Sn7ef/99xo0bV+PYunXruOmmm0hOTubaa6/lf//7X43zVVVVPPPMM/Tr14/k5GQmTZpEQUGBO8MWQgghhBepN8nP559/zuuvv17j2NatW7nvvvtISkriv//9L/fffz9PP/00S5YssV8zc+ZM1q9fz5tvvsmnn35KRkYGjzzyiHuDF0IIIYTX8Hidn5ycHGbMmMGmTZto3bp1jXMfffQR3bp1Y/r06QAkJCRw+PBh3njjDcaMGUNOTg5Llizhvffeo1evXgDMnTuXUaNGsX37dpKTk939coQQQghRz3m852fPnj34+fnxww8/0L179xrnsrKy6NmzZ41jnTp14tixYxw/fpytW7cC0LdvX/v5+Ph4YmNj2bJli+uDF0IIIYTX8XjPz7Bhwxg2bNh5z8XExHDixIkax44ePQpAfn4+OTk5REREoNfrz3lcdnb2JcWl03k8L/QIrVZT419RN9KOziHt6BzSjs4h7egc9aEdPZ78XMj111/PlClT+OGHH7j66qs5ePAg8+fPB8BkMlFRUYG/v/85j9Pr9VRVVdX5eTUahYgI396ANCws0NMhNAjSjs4h7egc0o7OIe3oHJ5sx3qd/IwZM4Zjx44xffp0nnrqKZo0acJ9993HzJkzCQ0NJSAgAKPReM7jqqqqCAyse6NarSolJeWXErrX0mo1hIUFUlJSgcVi9XQ4Xkva0TmkHZ1D2tE5pB2dw1XtGBYWWOvepHqd/AA8+OCD3H///RgMBho3bsy6devQarU0bdqUuLg4ioqKMBqNNXqAcnNziY2NvaTnNZt9+xvbYrH6fBs4g7Sjc0g7Ooe0o3NIOzqHJ9uxXic/CxcuJDMzk+nTp9uTmRUrVpCcnExwcDA9e/bEarWydetW+vXrB0BmZiY5OTn07t27zs+r0ShERsqwl7h00o7OIe3oHNKOziHt6BzObkeNRqn1tfU6+UlISOCFF16ga9eu9OrVix9//JEffvjBPu8nNjaWa665hmnTpjF79mwCAwOZMWMGffr0ISkpqc7PqygKWm3tG7Ehkgl9ziHt6BzSjs4h7egc0o7OIROe/0K/fv145plneOedd8jJySExMZF3332XPn362K+ZNWsWs2fP5qGHHgJg0KBBTJs2zVMhCyGEEKKeU1RVVT0dhBBCCCGEu0jfnRBCCCF8iiQ/QgghhPApkvwIIYQQwqdI8iOEEEIInyLJjxBCCCF8iiQ/QgghhPApkvwIIYQQwqdI8iOEEEIInyLJjxBCCCF8iiQ/QgghhPApkvwIIYQQwqdI8iMAKCoq4umnn2bQoEH06NGDsWPHkpKS4umwvFpmZibJycl89913ng7FKy1ZsoSrr76arl27cs011/DTTz95OiSvYzab+c9//sPQoUNJTk7m9ttvZ8eOHZ4Oy6u8//77jBs3rsax1NRU7rjjDpKSkhg2bBifffaZh6LzHudrx9WrV3PTTTeRnJzMsGHDeOmll6isrHRLPJL8CAAmTpzI9u3bmTt3LosWLaJjx46MHz+ejIwMT4fmlUwmE48//jjl5eWeDsUrff/990ydOpXbb7+dZcuWMXr0aPv3qKi9d999l2+//ZZZs2axZMkS4uPjuffee8nNzfV0aF7h888/5/XXX69xrLCwkLvvvpuWLVuyaNEiHnzwQebMmcOiRYs8E6QXOF87pqSk8NBDDzFixAgWL17MjBkz+PHHH3nmmWfcEpMkP4KsrCw2bNjAzJkz6dWrF/Hx8UyfPp2YmBiWLl3q6fC80ptvvklISIinw/BKqqryn//8h//7v//j9ttvp2XLlvzzn/+kf//+bN682dPheZVVq1YxevRoLr/8clq1asW///1vSktLpffnInJycrj//vuZM2cOrVu3rnHum2++wc/Pj2effZaEhARuuukm7rrrLubNm+eZYOuxC7XjV199xWWXXcb9999P69atGTx4MI899hhLly7FaDS6PDZJfgQRERHMmzePrl272o8pioKiKJSUlHgwMu+0ZcsWvv76a1588UVPh+KVMjMzOXbsGNdee22N4x999BETJkzwUFTeKSoqijVr1nD06FEsFgtff/01/v7+dOjQwdOh1Wt79uzBz8+PH374ge7du9c4l5KSQp8+fdDpdPZjffv25dChQxgMBneHWq9dqB3vuecennrqqRrHNBoNJpOJsrIyl8emu/gloqELCwtj8ODBNY6tWLGCrKwspkyZ4qGovFNJSQlPPvkk06ZNo0mTJp4OxytlZmYCUF5ezvjx49m7dy/Nmzfnn//8J8OGDfNwdN5l6tSpPProowwfPhytVotGo+HNN9+kZcuWng6tXhs2bNhffq9lZ2fTrl27GsdiYmIAOHHiBNHR0S6Pz1tcqB07depU42uTycQnn3xCly5diIyMdHls0vMjzrFt2zYmT57MyJEjGTJkiKfD8SozZ84kOTn5nF4LUXvV7/qeeuopRo8ezfz58xkwYAAPPPAAGzdu9HB03iUtLY3Q0FDefvttvv76a2688UYef/xxUlNTPR2a16qsrMTf37/GMb1eD0BVVZUnQvJ6ZrOZJ598koMHDzJjxgy3PKf0/IgaVq1axeOPP06PHj2YM2eOp8PxKkuWLCElJUXmSV0iPz8/AMaPH88NN9wAQMeOHdm7dy8ff/wx/fr182R4XuPEiRNMmjSJTz75hF69egHQtWtX0tLSePPNN3nnnXc8HKF3CggIOGdOSnXSExQU5ImQvFpZWRn/+te/2Lx5M2+99RbdunVzy/NKz4+wW7hwIQ8//DBDhw7lvffes7+bEbWzaNEi8vPzGTJkCMnJySQnJwMwY8YM7r33Xg9H5z1iY2MBzhlaSExM5OjRo54IySvt3LkTk8lUYy4fQPfu3cnKyvJQVN4vLi7unNVy1V9Xf++K2snNzbWXX/joo4/OmX7hStLzIwD44osvmDVrFuPGjWPq1KkoiuLpkLzOnDlzzqlRMXLkSB555BGuu+46D0XlfTp37kxwcDA7d+6091gAHDhwQOaqOCAuLg6A/fv313g3feDAgXNW3oja6927N1999RUWiwWtVgvAH3/8QXx8PFFRUR6OznsUFxdz5513UlZWxueff0779u3d+vyS/AgyMzOZPXs2I0aMYMKECTVWLAQEBBAaGurB6LzHX73ri4qKkneEDggICODee+/l7bffJjY2lm7durFs2TI2bNjAJ5984unwvEa3bt3o2bMnTz31FDNmzCAuLo4lS5awceNGvvzyS0+H57VuuukmPvzwQ6ZOncq9997Lrl27+OSTT9xWn6aheOGFFzhy5AgffvghkZGR5OXl2c9FRkbaE0tXkeRHsGLFCkwmEytXrmTlypU1zt1www2yZFu43QMPPEBgYCCvvfYaOTk5JCQk8Oabb3LZZZd5OjSvodFoePfdd3n99deZPHkyxcXFtGvXjk8++eScZcei9qKiovjwww95/vnnueGGG2jcuDFPPvmkfX6auDiLxcKPP/6IyWTizjvvPOf8L7/8QvPmzV0ag6KqqurSZxBCCCGEqEdkwrMQQgghfIokP0IIIYTwKZL8CCGEEMKnSPIjhBBCCJ8iyY8QQgghfIokP0IIIYTwKZL8CCGEEMKnSPIjhPBK48aNo3379vz973//y2see+wx2rdvz7///W83RiaEqO8k+RFCeC2NRsOOHTvIzs4+51x5eTlr1qzxQFRCiPpOkh8hhNfq1KkTer2e5cuXn3NuzZo1BAYGyr5qQohzSPIjhPBaQUFBDB48+LzJz48//siVV16JTnd6C0Or1cq8efMYMWIEXbp04corr2TBggU1Hnf48GHuv/9+LrvsMrp3786tt97Kr7/+6vLXIoRwH0l+hBBe7eqrrz5n6KusrIzffvuN0aNH17h25syZvPHGG1x33XW89957jBo1itmzZ/P2228DtuRowoQJVFRU8PLLL/POO+8QHh7OP//5T7Kystz6uoQQriO7ugshvNqQIUMIDAxk+fLl3HXXXQCsXLmSqKgoevbsab8uMzOTb775hokTJ/KPf/wDgMsvvxxFUXj//fe57bbbMJvNZGRk8MADDzB48GAAunXrxltvvYXRaHT7axNCuIb0/AghvFpAQADDhg2rMfS1bNkyrrrqKhRFsR/7448/UFWVYcOGYTab7R/Dhg2jqqqKrVu3Eh0dTWJiItOnT+epp55i6dKlWK1WJk+eTNu2bT3x8oQQLiA9P0IIr3fVVVfx0EMPkZ2djV6vZ+PGjfzrX/+qcU1RUREA11xzzXnvkZOTg6IozJ8/n3fffZeVK1eyZMkS/Pz8uOKKK3jmmWdo1KiRi1+JEMIdJPkRQni9QYMGERwczPLlywkKCqJ58+Z06dKlxjVhYWEAfPrppwQHB59zj6ZNmwIQGxvLzJkzmTFjBvv27WP58uV88MEHREREMGPGDNe/GCGEy8mwlxDC6/n7+3PFFVewYsUKfvrpp/P27vTq1QuAwsJCunbtav8oKCjgP//5D0VFRWzfvp3+/fuza9cuFEWhY8eOPPbYY7Rr147jx4+7+2UJIVxEen6EEA3C1VdfzYQJE9BoNEybNu2c8+3bt+e6665j+vTpHDt2jC5dupCZmclrr71G8+bNad26NWazmYCAAJ588kkefvhhoqOj+f3330lNTeX//u//PPCqhBCuIMmPEKJB6N+/P2FhYTRp0oSEhITzXvPCCy/w/vvv89VXX5GdnU1UVBRXX301//rXv9BqtWi1WubPn8+rr77K888/T0lJCa1bt+bZZ5/lxhtvdPMrEkK4iqKqqurpIIQQQggh3EXm/AghhBDCp0jyI4QQQgifIsmPEEIIIXyKJD9CCCGE8CmS/AghhBDCp0jyI4QQQgifIsmPEEIIIXyKJD9CCCGE8CmS/AghhBDCp0jyI4QQQgifIsmPEEIIIXyKJD9CCCGE8Cn/D/K+ciHG722fAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "x = np.array(prom_año_total.index.values)\n",
        "(a, b), pcov = curve_fit(exp_inv, x, prom_año_total.values)\n",
        "print(f\"f(x) = {a} - {b}*exp(x)\")\n",
        "y = exp_inv(x, a, b)\n",
        "plt.plot(prom_año_total, label='Datos')\n",
        "plt.plot(x, y, label='Ajuste')\n",
        "plt.xlabel('Mes')\n",
        "plt.ylabel('Promedio de siniestros por dia')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Este ajuste lo podemos aplicar para cada mes del año, y con esto obtenemos las tasas de siniestros diarios para cada uno de los meses, ademas esta tasa es el parametro de la distribucion de Poisson que determinara las probabilidades de ocurrencia de siniestros."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x23883688510>"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "mes = ['Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', 'Julio', 'Agosto', 'Septiembre', 'Octubre', 'Noviembre', 'Diciembre']\n",
        "k = lambda x: exp_inv(x, a, b)\n",
        "for i in range(12):\n",
        "    l = k(i+1)\n",
        "    x = np.arange(160, 260, 1)\n",
        "    y = poisson.pmf(x, mu = l)\n",
        "    plt.scatter(x, y, label=mes[i])\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x238839ed990>"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "for i in range(12):\n",
        "    l = k(i+1)\n",
        "    x = np.arange(160, 270, 1)\n",
        "    y = poisson.cdf(x, mu = l)\n",
        "    plt.scatter(x, y, label=mes[i])\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Test de chi-cuadrado y correlacion tetracorica"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Por otro lado, todas las variables presentes en el dataframe consisten de valores categoricos, es por esto que es ideal considerar tests de $\\chi^2$ en matrices de contingencia y correlaciones de tetracoricas para dichas variables. Como se consideran dos variables categoricas, este tipo de test se hace bajo la siguiente hipotesis nula y alterna:\n",
        "- $H_0$: Las variables son independientes entre si.\n",
        "- $H_1$: Las variables son dependientes entre si.\n",
        "\n",
        "Ademas, la correlacion tetracorica, usualmente denominada $r_{tet}$, utilizada para medir la tasa de relacion para datos binarios, nos indica que tan asociadas estan las propociones para dos variables. Para dos variables **A** y **B**, descritas por la matriz de contingencia:\n",
        "\n",
        "\n",
        "|  | B | |\n",
        "| --- | --- | ---  |\n",
        "| A | a | b |\n",
        "|  | c | d |\n",
        "\n",
        "La correlacion esta dada por\n",
        "\n",
        "$r_{tet} = \\cos\\left({\\frac{\\pi}{1+ \\sqrt{\\frac{bc}{ad}}}}\\right)$."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        " A partir de esto definimos las siguientes funciones:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {},
      "outputs": [],
      "source": [
        "def tetrachoric_corr(crosstab):\n",
        "    \"\"\"\n",
        "    Calcula la correlacion tetracorica para una matriz de contingencia dada\n",
        "    Parametros:\n",
        "        crosstab: Una matriz de contingencia como dataframe.\n",
        "    Retorna:\n",
        "        Un valor flotante con el valor de la correlacion en la matriz.\n",
        "    \"\"\"\n",
        "    [A, B], [C, D] = crosstab.values\n",
        "    coef = 1 + (B*C / (A*D))\n",
        "    return np.cos(np.pi/coef)\n",
        "\n",
        "def chi_test(crosstab):\n",
        "    \"\"\"\n",
        "    Evalua un test de chi cuadrado con hipotesis nula de independencia entre las variables,\n",
        "    para la matriz de contigencia dada, y retorna el chi cuadarado, el p-value, y las propociones\n",
        "    de las variables dadas.\n",
        "    Parametros:\n",
        "        crosstab: Una matriz de contingencia 2x2 como dataframe.\n",
        "    Retorna:\n",
        "        chi2: Chi cuadrado resultante del test.\n",
        "        p_value: P-value resultante del test.\n",
        "        prop_1: Proporcion entre la segunda y la primera fila la primera columna.\n",
        "        prop_2: Proporcion entre la segunda y la primera fila la segunda columna.\n",
        "    \"\"\"\n",
        "    chi2, p_value, gdl, expected = stats.chi2_contingency(crosstab)\n",
        "    prop_1, prop_2 = crosstab.iloc[1,:]/crosstab.iloc[0,:]\n",
        "    return chi2, p_value, prop_1, prop_2\n",
        "\n",
        "def Chi_test(df, variable_1, variable_2, alpha=0.05):\n",
        "    \"\"\"\n",
        "    Evalua un test de chi cuadrado con hipotesis nula de independencia entre las variables,\n",
        "    para la matriz de contigencia nx2 dada, y retorna una cadena de texto indicando el resultado\n",
        "    del test y las proporciones de las variables.\n",
        "    Parametros:\n",
        "        df: Dataframe con los datos.\n",
        "        variable_1: Variable para introducir en la matriz de contingencia.\n",
        "        variable_2: Variable para introducir en la matriz de contingencia\n",
        "        alpha: Significancia del test.\n",
        "    Retorna:\n",
        "        resultado: Cadena de texto con toda la informacion de los tests.\n",
        "    \"\"\"\n",
        "    nl = \"\\n\"\n",
        "    crosstab = pd.crosstab(df[variable_1], df[variable_2], margins=False)\n",
        "    chi2, p_value, prop_1, prop_2 = chi_test(crosstab)\n",
        "    if abs(p_value)>alpha:\n",
        "        return f\"Existen evidencia estadistica para no rechazar la hipotesis nula (p-value = {p_value}, chi2 = {chi2}) de independencia entre las variables {variable_1} y {variable_2}.\"\n",
        "    elif abs(p_value)<=alpha and crosstab.shape[0]==int(2):\n",
        "        corr = tetrachoric_corr(crosstab)\n",
        "        if prop_1<prop_2:\n",
        "            return f\"Existe evidencia estadistica para rechazar la hipotesis nula (p-value = {p_value}, chi2 = {chi2}) de independecia.\\n Ademas, una mayor proporcion de la variable {variable_1} con valor {crosstab.iloc[1,:].name} lo tiene el valor {crosstab.columns[1]} de la variable {variable_2} con una correlacion de {corr}.\"\n",
        "        else:\n",
        "            return f\"Existe evidencia estadistica para rechazar la hipotesis nula (p-value = {p_value}, chi2 = {chi2}) de independecia.\\n Ademas, una mayor proporcion de la variable {variable_1} con valor {crosstab.iloc[1,:].name} lo tiene el valor {crosstab.columns[0]} de la variable {variable_2} con una correlacion de {corr}.\"\n",
        "    elif abs(p_value)<=alpha and crosstab.shape[0]!=2:\n",
        "        resultado = ''\n",
        "        alpha = alpha / sp.special.comb(crosstab.shape[0], 2)\n",
        "        dummies = pd.get_dummies(df[variable_1])\n",
        "        for series in dummies:\n",
        "            crosstab = pd.crosstab(dummies[series], df[variable_2], margins=False)\n",
        "            chi2, p_value, prop_1, prop_2 = chi_test(crosstab)\n",
        "            corr = tetrachoric_corr(crosstab)\n",
        "            if abs(p_value)>alpha:\n",
        "                resultado += f\"Existe evidencia estadistica para no rechazar la hipotesis nula (p-value = {p_value}, chi2 = {chi2}) del valor {crosstab.index.name} de la variable {variable_1}. {nl}\"\n",
        "            elif prop_1<prop_2:\n",
        "                resultado += f\"Existe evidencia estadistica para rechazar la hipotesis nula (p-value = {p_value}, chi2 = {chi2}) y encontramos que una mayor propocion del valor {crosstab.index.name} de la variable {variable_1} la tiene el valor {crosstab.columns[1]} de la variable {variable_2} con una correlacion de {corr}. {nl}\"\n",
        "            else:\n",
        "                resultado += f\"Existe evidencia estadistica para rechazar la hipotesis nula (p-value = {p_value}, chi2 = {chi2}) y encontramos que una mayor propocion del valor {crosstab.index.name} de la variable {variable_1} la tiene el valor {crosstab.columns[0]} de la variable {variable_2} con una correlacion de {corr}. {nl}\"\n",
        "        return resultado\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Las cuales son de gran utilidad ya que realizan dicho test y nos indican las propociones entre las variables.\n",
        "\n",
        "\n",
        "En este caso consideraremos unicamente los datos de **SEXO** y **SERVICIO** contra **GRAVEDAD**, sin tener en cuenta los valores sin informacion para sexo y servicio, y los valores diferentes de publico o privado para el servicio. Esto lo almacenamos en los siguientes dataframes:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {},
      "outputs": [],
      "source": [
        "df_sexo = df_est[~(df_est.SEXO == 3)]\n",
        "df_servicio = df_est.loc[(df.SERVICIO == 2) | (df.SERVICIO == 3)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Estas elecciones son debido a que dichas variables descartadas, como se observa en las siguientes figuras, no son fuente de informacion o no aportan una cantidad significativa de datos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
        "sns.heatmap(ax=axes[0], data=pd.crosstab(df_est.GRAVEDAD, df_est.SEXO, margins=False), cmap='coolwarm')\n",
        "sns.heatmap(ax=axes[1], data =pd.crosstab(df_sexo.GRAVEDAD, df_sexo.SEXO, margins=False), cmap='coolwarm')\n",
        "fig.tight_layout()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
        "sns.heatmap(ax=axes[0], data=pd.crosstab(df_est.GRAVEDAD, df_sexo.SERVICIO, margins=False), cmap='coolwarm')\n",
        "sns.heatmap(ax=axes[1], data =pd.crosstab(df_servicio.GRAVEDAD, df_sexo.SERVICIO, margins=False), cmap='coolwarm')\n",
        "plt.tight_layout()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Bajo estos nuevos dataframes procedemos a ejecutar los test para ambas variables **SEXO** y **SERVICIO** contra **GRAVEDAD**:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Existe evidencia estadistica para no rechazar la hipotesis nula (p-value = 0.17436090026696632, chi2 = 1.8450440700196005) del valor 1.0 de la variable GRAVEDAD. \n",
            "Existe evidencia estadistica para rechazar la hipotesis nula (p-value = 0.0, chi2 = 14783.32269231122) y encontramos que una mayor propocion del valor 2.0 de la variable GRAVEDAD la tiene el valor 2 de la variable SEXO con una correlacion de -0.6428735551965102. \n",
            "Existe evidencia estadistica para rechazar la hipotesis nula (p-value = 0.0, chi2 = 14640.530291719224) y encontramos que una mayor propocion del valor 3.0 de la variable GRAVEDAD la tiene el valor 1 de la variable SEXO con una correlacion de 0.6420714820123188. \n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(Chi_test(df_sexo, 'GRAVEDAD', 'SEXO'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Existe evidencia estadistica para no rechazar la hipotesis nula (p-value = 0.0445179611106451, chi2 = 4.036807563751175) del valor 1.0 de la variable GRAVEDAD. \n",
            "Existe evidencia estadistica para rechazar la hipotesis nula (p-value = 2.591093787609529e-186, chi2 = 847.4613134334024) y encontramos que una mayor propocion del valor 2.0 de la variable GRAVEDAD la tiene el valor 3.0 de la variable SERVICIO con una correlacion de -0.14736877320069522. \n",
            "Existe evidencia estadistica para rechazar la hipotesis nula (p-value = 5.936655314934605e-189, chi2 = 859.6044983323969) y encontramos que una mayor propocion del valor 3.0 de la variable GRAVEDAD la tiene el valor 2.0 de la variable SERVICIO con una correlacion de 0.14691609536669523. \n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(Chi_test(df_servicio, 'GRAVEDAD', 'SERVICIO'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Con esto concluimos que, en proporción, los hombres presentan mayores accidentes en cuanto a la gravedad de tipo \"daño material\" y por el contrario las mujeres en proporción presentan mayor accidentalidad de tipo \"herido\". Es por esto que para la empresa resulta favorable dedicar mayor publicidad a seguros por daño material a un publico masculino y mayor publicidad a seguros por heridas a publico femenino. También podemos concluir que el servicio privado tiene mayor proporcion en accidentes con heridos en comparación con el servicio público, y el servicion publico presenta mayor proporcionalidad en accidentes con daño material. Por lo tanto, al igual que el sexo, la publicidad para vehiculo particular se debe enfocar mas a seguros por daño por heridas."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Autores"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Este es un proyecto final realizado para el modulo 1 \"Análisis y visualización de datos con Python\" del diplomado modular \"Machine learning and data science\" ofrecido por la Universidad Nacional de Colombia.\n",
        "\n",
        "Autores: Diego M. Lopez; Juan A. Guzman."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "El dataframe preprocesado es exportado usando la siguiente funcion:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def exportar_csv(df, nombre):\n",
        "    \"\"\"\n",
        "    Exporta el dataframe indicado como un archivo de tipo csv.\n",
        "    Parametros:\n",
        "        df: Dataframe a exportar como csv.\n",
        "        nombre: Cadena de texto con el nombre del archivo generado\n",
        "    Retorna:\n",
        "        El texto 'Procedimiento exitoso' si se ejecuto correctamiente.\n",
        "    \"\"\"\n",
        "    df.to_csv(nombre+'.csv')\n",
        "    return 'Procedimiento exitoso'\n",
        "\n",
        "exportar_csv(df, 'siniestros_viales')"
      ]
    }
  ],
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