diff --git a/.gitpod.dockerfile b/.gitpod.dockerfile
index e13c885f0..ef793fc9c 100644
--- a/.gitpod.dockerfile
+++ b/.gitpod.dockerfile
@@ -1,3 +1,4 @@
 FROM gitpod/workspace-full
 
 RUN npm i learnpack -g
+
diff --git a/project.es.ipynb b/project.es.ipynb
index da1f12ef6..28bc99fb2 100644
--- a/project.es.ipynb
+++ b/project.es.ipynb
@@ -28,7 +28,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": 28,
       "id": "frank-heath",
       "metadata": {},
       "outputs": [
@@ -415,7 +415,7 @@
               "[15335 rows x 37 columns]"
             ]
           },
-          "execution_count": 1,
+          "execution_count": 28,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -443,12 +443,27 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 29,
       "id": "developing-optimum",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "La casa con dirección en El Escorial es la más cara y su precio es de 8500000 USD\n"
+          ]
+        }
+      ],
       "source": [
-        "# TODO"
+        "import pandas as pd\n",
+        "\n",
+        "\n",
+        "indice_maximo_precio = ds['price'].idxmax()\n",
+        "\n",
+        "direccion = ds.loc[indice_maximo_precio, 'address']\n",
+        "precio = ds.loc[indice_maximo_precio, 'price']\n",
+        "print(f\"La casa con dirección en {direccion} es la más cara y su precio es de {precio} USD\")\n"
       ]
     },
     {
@@ -466,12 +481,32 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 30,
       "id": "lovely-oasis",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "La casa con dirección en Berlin, Coslada es la más barata y su precio es de 600 USD\n"
+          ]
+        }
+      ],
       "source": [
-        "# TODO"
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "\n",
+        "# Limpiar base de datos\n",
+        "ds = ds[ds['price'] > 0]\n",
+        "\n",
+        "indice_minimo_precio = ds['price'].idxmin()\n",
+        "\n",
+        "direccion = ds.loc[indice_minimo_precio, 'address']\n",
+        "precio = ds.loc[indice_minimo_precio, 'price']\n",
+        "\n",
+        "print(f\"La casa con dirección en {direccion} es la más barata y su precio es de {precio} USD\")\n"
       ]
     },
     {
@@ -491,12 +526,40 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 31,
       "id": "every-tiffany",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "La casa más grande está ubicada en Sevilla la Nueva y su superficie es de 249000.0 metros\n",
+            "La casa más pequeña está ubicada en Calle Amparo,  Madrid Capital y su superficie es de 15.0 metros\n"
+          ]
+        }
+      ],
       "source": [
-        "# TODO"
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "\n",
+        "# Limpiar base de datos\n",
+        "ds = ds[ds['surface'] > 0]\n",
+        "\n",
+        "# Encontrar el área máxima\n",
+        "indice_maximo_area = ds['surface'].idxmax()\n",
+        "\n",
+        "direccion_grande = ds.loc[indice_maximo_area, 'address']\n",
+        "area_grande = ds.loc[indice_maximo_area, 'surface']\n",
+        "\n",
+        "indice_minimo_area = ds['surface'].idxmin()\n",
+        "\n",
+        "direccion_pequena = ds.loc[indice_minimo_area, 'address']\n",
+        "area_pequena = ds.loc[indice_minimo_area, 'surface']\n",
+        "\n",
+        "print(f\"La casa más grande está ubicada en {direccion_grande} y su superficie es de {area_grande} metros\")\n",
+        "print(f\"La casa más pequeña está ubicada en {direccion_pequena} y su superficie es de {area_pequena} metros\")"
       ]
     },
     {
@@ -516,12 +579,70 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 32,
       "id": "exciting-accreditation",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "['Arganda del Rey' 'Boadilla del Monte' 'Fuenlabrada'\n",
+            " 'Las Rozas de Madrid' ' Madrid Capital' 'Villaviciosa de Odón' 'Pinto'\n",
+            " 'Valdemoro' 'Navalcarnero' 'Pozuelo de Alarcón' 'Torrejón de Ardoz'\n",
+            " 'Navalagamella' 'San Sebastián de los Reyes' 'Rivas-vaciamadrid'\n",
+            " 'Alpedrete' 'Móstoles' 'San Fernando de Henares' 'Coslada'\n",
+            " 'Becerril de la Sierra' 'Alcalá de Henares' 'Chinchón' 'Parla' 'Alcorcón'\n",
+            " 'El Escorial' 'Leganés' 'Pedrezuela' 'Majadahonda'\n",
+            " 'Villanueva de la Cañada' 'Villanueva del Pardillo' 'Torrelodones'\n",
+            " 'Moralzarzal' 'Mejorada del Campo' 'Aranjuez' 'Corpa' 'Getafe'\n",
+            " 'Velilla de San Antonio' 'Sevilla la Nueva' 'San Martín de la Vega'\n",
+            " 'Villalbilla' 'Collado Villalba' 'Alcobendas' 'El Molar (Madrid)'\n",
+            " 'Moraleja de Enmedio' 'Algete' 'Campo Real' 'Torrejón de la Calzada'\n",
+            " 'Colmenar Viejo' 'Valdemorillo' 'Fuente El Saz de Jarama' 'Tres Cantos'\n",
+            " 'Arroyomolinos (Madrid)' 'Griñón' 'Paracuellos de Jarama' 'Guadarrama'\n",
+            " 'Titulcia' 'Galapagar' 'Collado Mediano' 'Los Molinos'\n",
+            " 'San Lorenzo de El Escorial' 'Loeches' 'San Martín de Valdeiglesias'\n",
+            " 'Navas del Rey' 'Bustarviejo' 'Manzanares El Real' 'Carabaña'\n",
+            " 'Casarrubuelos' 'Cercedilla' 'Fresnedillas de la Oliva' 'Valdemaqueda'\n",
+            " 'Miraflores de la Sierra' 'Humanes de Madrid' 'Valdetorres de Jarama'\n",
+            " 'San Agustín del Guadalix' 'Camarma de Esteruelas' 'Torres de la Alameda'\n",
+            " 'Talamanca de Jarama' 'El Atazar' 'Hoyo de Manzanares'\n",
+            " 'El Boalo - Cerceda – Mataelpino' 'El Álamo' 'Valdilecha'\n",
+            " 'Valdeolmos-Alalpardo' 'Brunete' 'Redueña' 'Morata de Tajuña' 'Tielmes'\n",
+            " 'Gargantilla del Lozoya' 'Guadalix de la Sierra' 'Meco' 'Venturada'\n",
+            " 'Quijorna' 'Cenicientos' 'Torrejón de Velasco' 'Navacerrada'\n",
+            " 'Ciempozuelos' 'Santa María de la Alameda' 'Robledo de Chavela'\n",
+            " 'Orusco de Tajuña' 'Daganzo de Arriba' 'Villamanta' 'Anchuelo'\n",
+            " 'Valdaracete' 'Ajalvir' 'Los Santos de la Humosa' 'Villamanrique de Tajo'\n",
+            " 'Colmenar de Oreja' 'Villa del Prado' 'Fuentidueña de Tajo'\n",
+            " 'Colmenar del Arroyo' 'Belmonte de Tajo' 'Valdelaguna' 'Valdeavero'\n",
+            " 'Aldea del Fresno' 'Ribatejada' 'Batres' 'Pezuela de las Torres'\n",
+            " 'Colmenarejo' 'Serranillos del Valle' 'Cobeña' 'Soto del Real'\n",
+            " 'Cubas de la Sagra' 'El Berrueco' 'Villanueva de Perales'\n",
+            " 'Fresno de Torote - Serracines' 'Pozuelo del Rey' 'Pelayos de la Presa'\n",
+            " 'El Vellón' 'La Cabrera' 'Nuevo Baztán' 'Perales de Tajuña'\n",
+            " 'Villaconejos' 'Villarejo de Salvanés' 'Cadalso de los Vidrios'\n",
+            " 'Santorcaz' 'Madarcos' 'Garganta de los Montes' 'Puentes Viejas'\n",
+            " 'Torremocha de Jarama' 'Valdemanco' 'Braojos'\n",
+            " 'Lozoyuela-navas-sieteiglesias' 'Piñuécar-gandullas' 'Valdepiélagos'\n",
+            " 'Valverde de Alcalá' 'Villar del Olmo' 'Villamantilla'\n",
+            " 'Horcajo de la Sierra' 'Gascones' 'Zarzalejo' 'Brea de Tajo' 'Estremera'\n",
+            " 'Chapinería' 'Torrelaguna' 'Navarredonda' 'Patones' 'Ambite'\n",
+            " 'Navalafuente' 'Lozoya' 'Villavieja del Lozoya' 'Canencia'\n",
+            " 'Cabanillas de la Sierra' 'Buitrago del Lozoya' 'Fresno de Torote'\n",
+            " 'Robregordo' 'Pinilla del Valle' 'Rascafría' 'La Hiruela'\n",
+            " 'Montejo de la Sierra']\n"
+          ]
+        }
+      ],
       "source": [
-        "# TODO"
+        "import pandas as pd\n",
+        "\n",
+        "\n",
+        "poblaciones = ds['level5'].unique()\n",
+        "\n",
+        "print(poblaciones)"
       ]
     },
     {
@@ -537,12 +658,84 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 33,
       "id": "transparent-poetry",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "El dataset contiene valores nulos.\n",
+            "       Unnamed: 0  id_realEstates  isNew  realEstate_name  phone_realEstate  \\\n",
+            "0           False           False  False            False             False   \n",
+            "2           False           False  False            False             False   \n",
+            "3           False           False  False            False             False   \n",
+            "4           False           False  False            False             False   \n",
+            "5           False           False  False            False             False   \n",
+            "...           ...             ...    ...              ...               ...   \n",
+            "15330       False           False  False            False             False   \n",
+            "15331       False           False  False            False             False   \n",
+            "15332       False           False  False            False             False   \n",
+            "15333       False           False  False            False             False   \n",
+            "15334       False           False  False            False             False   \n",
+            "\n",
+            "       url_inmueble  rooms  bathrooms  surface  price  ...  level4Id  \\\n",
+            "0             False  False      False    False  False  ...     False   \n",
+            "2             False  False      False    False  False  ...     False   \n",
+            "3             False  False      False    False  False  ...     False   \n",
+            "4             False  False      False    False  False  ...     False   \n",
+            "5             False  False      False    False  False  ...     False   \n",
+            "...             ...    ...        ...      ...    ...  ...       ...   \n",
+            "15330         False  False      False    False  False  ...     False   \n",
+            "15331         False  False      False    False  False  ...     False   \n",
+            "15332         False  False      False    False  False  ...     False   \n",
+            "15333         False  False      False    False  False  ...     False   \n",
+            "15334         False  False      False    False  False  ...     False   \n",
+            "\n",
+            "       level5Id  level6Id  level7Id  level8Id  accuracy  latitude  longitude  \\\n",
+            "0         False     False     False     False     False     False      False   \n",
+            "2         False     False     False     False     False     False      False   \n",
+            "3         False     False     False     False     False     False      False   \n",
+            "4         False     False     False     False     False     False      False   \n",
+            "5         False     False     False     False     False     False      False   \n",
+            "...         ...       ...       ...       ...       ...       ...        ...   \n",
+            "15330     False     False     False     False     False     False      False   \n",
+            "15331     False     False     False     False     False     False      False   \n",
+            "15332     False     False     False     False     False     False      False   \n",
+            "15333     False     False     False     False     False     False      False   \n",
+            "15334     False     False     False     False     False     False      False   \n",
+            "\n",
+            "       zipCode  customZone  \n",
+            "0         True        True  \n",
+            "2         True        True  \n",
+            "3         True        True  \n",
+            "4         True        True  \n",
+            "5         True        True  \n",
+            "...        ...         ...  \n",
+            "15330     True        True  \n",
+            "15331     True        True  \n",
+            "15332     True        True  \n",
+            "15333     True        True  \n",
+            "15334     True        True  \n",
+            "\n",
+            "[14030 rows x 37 columns]\n"
+          ]
+        }
+      ],
       "source": [
-        "# TODO"
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "\n",
+        "# Comprobar si hay valores nulos\n",
+        "tiene_nas = ds.isnull().values.any()\n",
+        "\n",
+        "if tiene_nas:\n",
+        "    print(\"El dataset contiene valores nulos.\")\n",
+        "    print(ds.isnull())\n",
+        "else:\n",
+        "    print(\"El dataset no contiene valores nulos.\")\n"
       ]
     },
     {
@@ -558,12 +751,31 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 34,
       "id": "administrative-roads",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Dimensiones originales: (14030, 37)\n",
+            "Dimensiones después de eliminar NAs: (0, 37)\n"
+          ]
+        }
+      ],
       "source": [
-        "# TODO"
+        "import pandas as pd\n",
+        "\n",
+        "\n",
+        "original_shape = ds.shape\n",
+        "\n",
+        "ds_sin_nas = ds.dropna()\n",
+        "\n",
+        "nuevo_shape = ds_sin_nas.shape\n",
+        "\n",
+        "print(\"Dimensiones originales:\", original_shape)\n",
+        "print(\"Dimensiones después de eliminar NAs:\", nuevo_shape)\n"
       ]
     },
     {
@@ -579,12 +791,27 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 35,
       "id": "nuclear-belief",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "El precio medio de las propiedades en Arroyomolinos (Madrid) es: 299892.6923076923\n"
+          ]
+        }
+      ],
       "source": [
-        "# TODO"
+        "import pandas as pd\n",
+        "\n",
+        "arroyomolinos = ds[ds['level5'] == 'Arroyomolinos (Madrid)']\n",
+        "\n",
+        "# Calcular media\n",
+        "media_precio_arroyomolinos = arroyomolinos['price'].mean()\n",
+        "\n",
+        "print(\"El precio medio de las propiedades en Arroyomolinos (Madrid) es:\", media_precio_arroyomolinos)\n"
       ]
     },
     {
@@ -600,12 +827,33 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 36,
       "id": "sudden-message",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
       "source": [
-        "# TODO: Code"
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Filtramos los datos de Arroyomolinos\n",
+        "arroyomolinos = ds[ds['level5'] == 'Arroyomolinos (Madrid)']\n",
+        "\n",
+        "plt.hist(arroyomolinos['price'], bins=20, edgecolor='black')\n",
+        "plt.xlabel('Price')\n",
+        "plt.ylabel('Frecuency')\n",
+        "plt.title('Histograma de Precios en Arroyomolinos (Madrid)')\n",
+        "plt.show()\n"
       ]
     },
     {
@@ -614,7 +862,9 @@
       "id": "impressed-combination",
       "metadata": {},
       "source": [
-        "**TODO: Markdown**. Para escribir aquí, haz doble clic en esta celda, elimina este contenido y coloca lo que quieras escribir. Luego ejecuta la celda."
+        "Este histograma analiza la frecuencia, el rango de precios, y los outliers (casos atípicos) de las propiedades en Arroyomolinos (Madrid). Este tipo de análisis ayuda a entender mejor el mercado inmobiliario en la zona, ya que permite observar cómo se distribuyen los precios de las propiedades.\n",
+        "\n",
+        "Al observar la distribución de precios, podemos asumir también el nivel socioeconómico del sector, puesto que áreas con precios más altos suelen estar asociadas con un nivel de vida más elevado. y la identificación de outliers puede señalar propiedades excepcionales o casos de precios \"irreales\" en comparación con la media del mercado."
       ]
     },
     {
@@ -630,12 +880,48 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 37,
       "id": "numeric-commerce",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Precio promedio en Valdemorillo: 352767.3076923077\n",
+            "Precio promedio en Galapagar: 372108.6533333333\n",
+            "El precio promedio en Galapagar es mayor que en Valdemorillo.\n"
+          ]
+        }
+      ],
       "source": [
-        "# TODO"
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "# Limpieza de datos\n",
+        "ds.dropna(subset=['price'], inplace=True)\n",
+        "\n",
+        "Q1 = ds['price'].quantile(0.25)\n",
+        "Q3 = ds['price'].quantile(0.75)\n",
+        "IQR = Q3 - Q1\n",
+        "ds = ds[~((ds['price'] < (Q1 - 1.5 * IQR)) | (ds['price'] > (Q3 + 1.5 * IQR)))]\n",
+        "\n",
+        "# Filtrar datos\n",
+        "valdemorillo = ds[ds['level5'] == 'Valdemorillo']\n",
+        "galapagar = ds[ds['level5'] == 'Galapagar']\n",
+        "\n",
+        "promedio_valdemorillo = valdemorillo['price'].mean()\n",
+        "promedio_galapagar = galapagar['price'].mean()\n",
+        "\n",
+        "print(\"Precio promedio en Valdemorillo:\", promedio_valdemorillo)\n",
+        "print(\"Precio promedio en Galapagar:\", promedio_galapagar)\n",
+        "\n",
+        "if promedio_valdemorillo > promedio_galapagar:\n",
+        "    print(\"El precio promedio en Valdemorillo es mayor que en Galapagar.\")\n",
+        "elif promedio_valdemorillo < promedio_galapagar:\n",
+        "    print(\"El precio promedio en Galapagar es mayor que en Valdemorillo.\")\n",
+        "else:\n",
+        "    print(\"Los precios promedio en ambas localidades son iguales.\")\n"
       ]
     },
     {
@@ -653,12 +939,41 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 38,
       "id": "hourly-globe",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "El precio promedio por metro cuadrado en Valdemorillo es de 1317.95 USD/m²\n",
+            "El precio promedio por metro cuadrado en Galapagar es de 1606.32 USD/m²\n",
+            "Los precios promedios por metro cuadrado en Valdemorillo y Galapagar son diferentes.\n"
+          ]
+        }
+      ],
       "source": [
-        "# TODO"
+        "import pandas as pd\n",
+        "\n",
+        "ds = pd.read_csv('assets/real_estate.csv', sep=';')\n",
+        "\n",
+        "#  columna 'pps' \n",
+        "ds['pps'] = ds['price'] / ds['surface']\n",
+        "\n",
+        "precio_m2_valdemorillo = ds[ds['level5'] == \"Valdemorillo\"]['pps'].mean()\n",
+        "\n",
+        "precio_m2_galapagar = ds[ds['level5'] == \"Galapagar\"]['pps'].mean()\n",
+        "\n",
+        "# Imprimir los promedios de precio por metro cuadrado\n",
+        "print(f\"El precio promedio por metro cuadrado en Valdemorillo es de {precio_m2_valdemorillo:.2f} USD/m²\")\n",
+        "print(f\"El precio promedio por metro cuadrado en Galapagar es de {precio_m2_galapagar:.2f} USD/m²\")\n",
+        "\n",
+        "# Conclusión\n",
+        "if precio_m2_valdemorillo == precio_m2_galapagar:\n",
+        "    print(\"Los precios promedios por metro cuadrado en Valdemorillo y Galapagar son iguales.\")\n",
+        "else:\n",
+        "    print(\"Los precios promedios por metro cuadrado en Valdemorillo y Galapagar son diferentes.\")\n"
       ]
     },
     {
@@ -674,12 +989,41 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 39,
       "id": "common-drilling",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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1du3aCtXeo0ePMtdqu6qi/WnRokU6dOiQhg4d6lRjYGCgOnbseMbXd8iQIQoNDdWMGTMc2xYuXKj9+/e7fB3uhAkTFBsbq/j4ePXs2VM7duzQlClTNHjwYKd2N998s9P1xq7WXlXvtZMdOHBAQUFBql69utP23NxcSVJERMQp9y29Lycn57SPUdkq0h9Gjx7tdLt0BHvhwoXKy8tzu4bQ0FAtWrSozNfkyZMr9Jyuuuoqp1H1jh07SpKuu+46BQUFOW0/duyYSytWjBw50un67tIzdk78eefK74GMjAxt2LBBw4cPd6qxd+/eZX4WzJs3T4GBgbrjjjuctt9zzz0yxmj+/PmS5DjzaM6cOWecRK70rK7yzigA4B84pRyAR7z88ss677zzlJ2drTfffFPLly93mqV3+/btMsbooYce0kMPPVTuMTIzM3XOOeeUe9/u3bv18MMP6/PPP9fBgwed7iu9hrH0D7MWLVq4VfuuXbskSU2aNHHaHhISooYNGzruL1WnTp0yoaJmzZrauHHjaR9n27ZtMsbo3HPPLff+k0+lrejjnOicc84pM0nRtm3btHnzZsXGxpa7jysTnw0dOlRDhw5VTk6OfvjhB02fPl0zZ87UwIEDKzwzfWJiYpnJoc477zxJxy9R6NSpk9u1JyUluV1HqYr2p23btkn633XTJzvTaaY1atTQwIEDNXPmTD366KOSjp/Ce84555zymCcbNWqUrrrqKgUEBKhGjRqO66VPdvLr42rtVfVec1VpmM7NzXW6LONEroRyK7jbH4KCgsqc+pyUlKS7775bzz77rGbMmKFu3brp0ksv1XXXXefS6eSBgYFunUZ/Jid/yFJaQ926dcvdfvLPbFeOWRpcT9zXld8DpX2ovJ+zTZo00bp16xy3d+3apcTExDJ9olmzZk7Huvrqq/XGG2/on//8p+6//3716tVLgwcP1pVXXllmaTrz/6/99oVVLgBUjN8E7uXLl+upp57S2rVrlZGRodmzZ2vQoEFuHcMYo2eeeUavv/66du3apZiYGN12221OE7oAqBwdOnRwzFI+aNAgde3aVcOGDdPWrVtVvXp1x6jAvffeq759+5Z7jMaNG5e7vbi4WL1791ZWVpbGjRunpk2bqlq1atqzZ49GjBhR6cvWnMmpZiAu/UPrVEpKSmSz2TR//vxyj3HyqF1FH+dEJ44InVhHy5Yt9eyzz5a7z8l/NJ9OZGSkevfurd69eys4OFhvv/22fvjhB/Xo0eOUf3AWFxe7fPyTuVt7ec/faqX98d1331V8fHyZ+08cATyVG264QR999JG+//57tWzZUp9//rluu+22Mn/cn8q5557rUsA6+fWpjNqtVKtWLRUVFSk3N9cpJDVr1kyfffaZNm7cqO7du5e7b+kHVaWjnFb0z/K4+5ra7fZyv8/PPPOMRowYoTlz5uirr77SHXfcoUmTJmnVqlVlArrVTvWz6e/8zDrTvp78PRAWFqbly5dr6dKl+vLLL7VgwQJ98MEH+sc//qGvvvrKqfbSDwJiYmIsqweAZ/lN4D5y5IguuOAC3XjjjWVOgXPVv/71L3311Vd6+umn1bJlS2VlZZWZLRNA5QsMDNSkSZN00UUX6aWXXtL999+vhg0bSjo+iuvuSMvPP/+sX3/9VW+//bbTpEiLFi1yalf6GJs2bXLr+PXr15ckbd261XEMSTp27JjS09MrbWSoUaNGMsYoKSnJMXL7d1VkFKVRo0b66aef1KtXr0odhWnXrp3efvttZWRkSPrfCNXJs0+fahTzzz//LLME0q+//ipJjkmlrKq9PBXtT6Xr78bFxVW47/Tr10+xsbGaMWOGOnbsqLy8PF1//fUVOpY7XK3dU++1pk2bSjo+W/n555/v2H7JJZdo0qRJeuedd8oN3MXFxZo5c6Zq1qzpmGHfnf75d/paZfSHUi1btlTLli31f//3f/r+++/VpUsXvfrqq3rsscf+1nF9gau/B0r7WOmZBSfaunVrmbaLFy8u8wHOli1bnI4lSQEBAerVq5d69eqlZ599Vk888YQefPBBLV261On7mp6eroCAgEr7GQ/A+/jNNdz9+/fXY489dspZiwsKCnTvvffqnHPOUbVq1dSxY0enpT02b96sqVOnas6cObr00kuVlJSktm3bqnfv3lX0DICzW8+ePdWhQwc999xzys/PV1xcnHr27KnXXnvNEchOVN4M0aVKRw9OHCUxxuj55593ahcbG6vu3bvrzTff1O7du53uO90IS0pKikJCQvTCCy84tfvvf/+r7OxsXXzxxad/si4aPHiwAgMDlZqaWqYeY4wOHDjg9jFLw6krSyqVGjJkiPbs2aNp06aVue/o0aOO2arLk5eXp5UrV5Z7X+n1jqWnC5cGjeXLlzvaFBcX6/XXXy93/6KiIqc1oo8dO6bXXntNsbGxatu27d+u3V0V7U99+/ZVZGSknnjiCRUWFpa5/3R9vVRQUJBjpvbp06erZcuWTgHTKq7W7qn3WufOnSVJa9ascdp+4YUXKiUlRW+99Za++OKLMvs9+OCD+vXXX/Xvf//bMapfv359BQYGOvVP6fiSZSeryPusVGX0h5ycHBUVFTlta9mypQICAs64lJ+/cPX3QEJCglq1aqW3337bacm0RYsWKS0tzantgAEDVFxcrJdeeslp+3/+8x/ZbDbHqhPlDda0atVKksq8/mvXrlXz5s1dOtUfgG/ymxHuMxk7dqzS0tI0a9YsJSYmavbs2erXr59+/vlnnXvuuZo7d64aNmyoL774Qv369ZMxRikpKXryyScrdRkbAKd233336aqrrtL06dN166236uWXX1bXrl3VsmVL3XzzzWrYsKH27dunlStX6o8//tBPP/1U7nGaNm2qRo0a6d5779WePXsUGRmpTz75pNzrAl944QV17dpVbdq00ahRo5SUlKSdO3fqyy+/1IYNG8o9fmxsrMaPH6/U1FT169dPl156qbZu3apXXnlF7du3d3miqjNp1KiRHnvsMY0fP147d+7UoEGDFBERofT0dM2ePVujRo3Svffe6/Yxa9SooVdffVURERGODyBPd+3y9ddfrw8//FC33nqrli5dqi5duqi4uFhbtmzRhx9+qIULFzouDzhZXl6eLrzwQnXq1En9+vVT3bp1dejQIX322WdasWKFBg0apNatW0uSmjdvrk6dOmn8+PHKyspSdHS0Zs2aVSY4lEpMTNSUKVO0c+dOnXfeefrggw+0YcMGvf76647r2/9O7RVRkf4UGRmpqVOn6vrrr1ebNm10zTXXKDY2Vrt379aXX36pLl26lPkDvzw33HCDXnjhBS1dulRTpkyptOd0Ou7U7on3WsOGDdWiRQstXrzYsVRcqXfeeUe9evXSZZddpmHDhqlbt24qKCjQp59+qmXLlunqq6/Wfffd52gfFRWlq666Si+++KJsNpsaNWqkL774otw5DEo/8LnjjjvUt29fBQYG6pprrqn01/RUvv76a40dO1ZXXXWVzjvvPBUVFendd99VYGCgrrjiijPWUFRUpPfee6/c+y6//PIycyd4I3d+D0yaNEkXX3yxunbtqhtvvFFZWVl68cUX1bx5cx0+fNjRbuDAgbrooov04IMPaufOnbrgggv01Vdfac6cObrzzjsdHxo+8sgjWr58uS6++GLVr19fmZmZeuWVV1SnTh117drVcbzCwkLH2vYA/FjVTYhedSSZ2bNnO27v2rXLBAYGmj179ji169Wrlxk/frwxxphbbrnF2O1207FjR7N8+XKzdOlS06pVK3PRRRdVZemA3ytdAufHH38sc19xcbFp1KiRadSokWPpoR07dpgbbrjBxMfHm+DgYHPOOeeYSy65xHz88ceO/cpbniYtLc2kpKSY6tWrm5iYGHPzzTebn376qdwlsTZt2mQuv/xyU6NGDRMaGmqaNGliHnrooTI1n7xc1UsvvWSaNm1qgoODTe3atc3o0aPLLDPUo0cP07x58zLP9eTlc07nk08+MV27djXVqlUz1apVM02bNjVjxowxW7durdDjzJkzxyQnJ5ugoCCn1+NUxzDm+DJTU6ZMMc2bNzd2u93UrFnTtG3b1qSmpprs7OxT1l5YWGimTZtmBg0aZOrXr2/sdrsJDw83rVu3Nk899ZTTsj7GHP9+p6SkGLvdbmrXrm0eeOABs2jRonKXBWvevLlZs2aN6dy5swkNDTX169c3L730UoVrl2TGjBlT7vNwdVkwYyren5YuXWr69u1roqKiTGhoqGnUqJEZMWKEWbNmzSlf35M1b97cBAQEmD/++MOl9qXP4amnnjptu9O9b92p3cr32qk8++yzpnr16uUuEZWbm2smTpxomjdvbsLCwkxERITp0qWLmT59utNyZaX++usvc8UVV5jw8HBTs2ZNc8stt5hNmzaV6QdFRUXm9ttvN7GxscZmszktEaYzLAtWypXXdPjw4aZatWpl6vztt9/MjTfeaBo1amRCQ0NNdHS0ueiii8zixYvP+HqdblmwE+t0Z1mwk/tX6b4nL1tWXj871bJgJ+9b3vvRnd8Dn3zyiWnWrJmx2+0mOTnZfPrpp+X+/MzNzTV33XWXSUxMNMHBwebcc881Tz31lFN/WbJkibnssstMYmKiCQkJMYmJiWbo0KHm119/dTrW/PnzjSSzbds2A8B/2YxxYzYdH2Gz2ZwmTfvyyy91ySWXlPlEtqCgQIMHD9YHH3ygUaNGadq0adq6davjOpp169apbdu22rJlS5kZUgEAntOzZ0/t37/f7WuC/V3r1q0VHR2tJUuWeLoUr5Gdna2GDRvqySef1E033eTpcgCHQYMGOf5mBeC/zopTyg8fPqzAwECtXbu2zKyWpbP8JiQkKCgoyGnSitJlHnbv3k3gBgB4tTVr1mjDhg2aPn26p0vxKlFRUfr3v/+tp556SiNHjnR55nbASps3b9YXX3xxysspAPiPsyJwt27dWsXFxcrMzFS3bt3KbdOlSxcVFRVpx44djmtwSme7PXHWSQAAvMmmTZu0du1aPfPMM0pISNDVV1/t6ZK8zrhx4zRu3DhPlwE4NGvW7JTzUwDwL37zMe/hw4e1YcMGxyeF6enp2rBhg3bv3q3zzjtP1157rW644QZ9+umnSk9P1+rVqzVp0iR9+eWXko7PhNqmTRvdeOONWr9+vdauXatbbrlFvXv3ZqkGAIDX+vjjjzVy5EgVFhbq/fffV2hoqKdLAgAA/5/fXMO9bNkyXXTRRWW2Dx8+XNOnT1dhYaEee+wxvfPOO9qzZ49iYmLUqVMnpaamqmXLlpKOr+l6++2366uvvlK1atXUv39/PfPMM8xSDgAAAABwm98EbgAAAAAAvInfnFIOAAAAAIA3IXADAAAAAGABn56lvKSkRH/++aciIiJks9k8XQ4AAAAAwM8ZY5Sbm6vExMQzLjfp04H7zz//VN26dT1dBgAAAADgLPP777+rTp06p23j04E7IiJC0vEnGhkZ6eFqTq2wsFBfffWV+vTpo+DgYE+XA5SLfgpfQD+FL6CfwtvRR+ELvLmf5uTkqG7duo48ejo+HbhLTyOPjIz0+sAdHh6uyMhIr+ssQCn6KXwB/RS+gH4Kb0cfhS/whX7qymXNTJoGAAAAAIAFCNwAAAAAAFiAwA0AAAAAgAUI3AAAAAAAWIDADQAAAACABQjcAAAAAABYgMANAAAAAIAFCNwAAAAAAFiAwA0AAAAAgAUI3AAAAAAAWIDADQAAAACABQjcAAAAAABYgMANAAAAAIAFPBq4i4uL9dBDDykpKUlhYWFq1KiRHn30URljPFmWpYpLjFbuOKDZ6/7Qf1f8ptnr92jljgMqLvHf5wwAAAAAZ6MgTz74lClTNHXqVL399ttq3ry51qxZo5EjRyoqKkp33HGHJ0uzxOLN+/TIl1uVkZ1f5r6EqFBNGJisfi0SPFAZAAAAAKCyeXSE+/vvv9dll12miy++WA0aNNCVV16pPn36aPXq1Z4syzJ3fbCh3LAtSRnZ+Rr93jot2JRRxVUBAAAAAKzg0cB94YUXasmSJfr1118lST/99JO+/fZb9e/f35NlVbrS08VdOWk8dW4ap5cDAAAAgB/w6Cnl999/v3JyctS0aVMFBgaquLhYjz/+uK699tpy2xcUFKigoMBxOycnR5JUWFiowsLCKqm5In787S9Jkj3gzEE66/BRrdqeqQ5J0VaXBTgpfQ9583sJoJ/CF9BP4e3oo/AF3txP3anJZjw4Q9msWbN033336amnnlLz5s21YcMG3XnnnXr22Wc1fPjwMu0nTpyo1NTUMttnzpyp8PDwqigZAAAAAHAWy8vL07Bhw5Sdna3IyMjTtvVo4K5bt67uv/9+jRkzxrHtscce03vvvactW7aUaV/eCHfdunW1f//+Mz5RT1q1PVNZv67RQ2sCVFBiO2P7N4e3Z4QbVa6wsFCLFi1S7969FRwc7OlygHLRT+EL6KfwdvRR+AJv7qc5OTmKiYlxKXB79JTyvLw8BQQ4X0YeGBiokpKSctvb7XbZ7fYy24ODg73um3CinILjn2kUlNhUUHzqwG2TFB8Vqk6N4xQYcOZgDljB299PgEQ/hW+gn8Lb0UfhC7yxn7pTj0cnTRs4cKAef/xxffnll9q5c6dmz56tZ599Vpdffrkny6pUxSVGTy4sO1p/KhMGJhO2AQAAAMAPeHSE+8UXX9RDDz2k2267TZmZmUpMTNQtt9yihx9+2JNlVarV6Vnam1P+UmAniq4WrCcub8k63AAAAADgJzwauCMiIvTcc8/pueee82QZlsrMPXPYlqSh7eupoKhEK3ccUIekaEa5AQAAAMDHeTRwnw3iIkJdavfysh2O/ydEhWrCwGRGuwEAAADAh3n0Gu6zQYekaMVHuha6S+3Nztfo99ZpwaYMi6oCAAAAAFiNwG2xwACb7u/fVNLxWchdUbpOW+rcNBWXeGzVNgAAAADA30DgrgIpzWpLkmq7MdJtJGVk52t1epZFVQEAAAAArMQ13FVo4Z3dtf6PXGXm5mvbvly9tHTHGfdxddI1AAAAAIB3YYS7CgUG2NS5US1d1uocdWkc69I+rk66BgAAAADwLgRuD+mQFK2EqNBTXtdt0/HZyjskRVdlWQAAAACASkLg9pDAAJsmDEyWVHYytdLbEwYmsx43AAAAAPgoArcH9WuRoKnXtVF8lPNp4/FRoZp6XRvW4QYAAAAAH8akaR7Wr0WCeifHa3V6ljJz8xUXcfw0cka2AQAAAMC3Ebi9QOlkagAAAAAA/8Ep5QAAAAAAWIDADQAAAACABQjcAAAAAABYgMANAAAAAIAFCNwAAAAAAFiAwA0AAAAAgAUI3AAAAAAAWIDADQAAAACABQjcAAAAAABYgMANAAAAAIAFCNwAAAAAAFiAwA0AAAAAgAUI3AAAAAAAWIDADQAAAACABQjcAAAAAABYgMANAAAAAIAFCNwAAAAAAFiAwA0AAAAAgAUI3AAAAAAAWIDADQAAAACABQjcAAAAAABYgMANAAAAAIAFCNwAAAAAAFiAwA0AAAAAgAUI3AAAAAAAWIDADQAAAACABQjcAAAAAABYgMANAAAAAIAFCNwAAAAAAFiAwA0AAAAAgAWCPF3A2aq4xGh1epYyc/MVFxGqDknRCgywebosAAAAAEAl8WjgbtCggXbt2lVm+2233aaXX37ZAxVVjQWbMpQ6N00Z2fmObQlRoZowMFn9WiR4sDIAAAAAQGXx6CnlP/74ozIyMhxfixYtkiRdddVVnizLUgs2ZWj0e+ucwrYk7c3O1+j31mnBpgwPVQYAAAAAqEweDdyxsbGKj493fH3xxRdq1KiRevTo4cmyLFNcYjTx8zSZcu4r3ZY6N03FJeW1AAAAAAD4Eq+5hvvYsWN67733dPfdd8tmK/9a5oKCAhUUFDhu5+TkSJIKCwtVWFhYJXVWRGlt0775VQePHJU98NRtsw4f1artmeqQFF1F1QHHlfZTb34vAfRT+AL6KbwdfRS+wJv7qTs12YwxXjGc+uGHH2rYsGHavXu3EhMTy20zceJEpaamltk+c+ZMhYeHW10iAAAAAOAsl5eXp2HDhik7O1uRkZGnbes1gbtv374KCQnR3LlzT9mmvBHuunXrav/+/Wd8op5UcOyYlixerIfWBKig5Mwzkb85vD0j3KhyhYWFWrRokXr37q3g4GBPlwOUi34KX0A/hbejj8IXeHM/zcnJUUxMjEuB2ytOKd+1a5cWL16sTz/99LTt7Ha77HZ7me3BwcFe90040er0LElSQYlNBcWnD9wJUaHq1DiOJcLgMd7+fgIk+il8A/0U3o4+Cl/gjf3UnXo8OmlaqbfeektxcXG6+OKLPV2KJfYfLjhzo/9vwsBkwjYAAAAA+AGPB+6SkhK99dZbGj58uIKCvGLAvdLFVC87Kl+eu1LOYx1uAAAAAPATHg/cixcv1u7du3XjjTd6uhTLtK1fU5J0unHr+Ei7xv6jcdUUBAAAAACwnMeHlPv06SMvmbfNMieeIm6TnNbhLr1n4qXNOZUcAAAAAPyIx0e4zyb/ubqV4qNCnbbFR4Vq6nVtOJUcAAAAAPyMx0e4zyYpzWqrT4tztDo9S5m5+YqLCFWHpGhGtgEAAADADxG4q1hggE2dG9XydBkAAAAAAItxSjkAAAAAABYgcAMAAAAAYAECNwAAAAAAFiBwAwAAAABgAQI3AAAAAAAWIHADAAAAAGABAjcAAAAAABYgcAMAAAAAYAECNwAAAAAAFiBwAwAAAABgAQI3AAAAAAAWIHADAAAAAGABAjcAAAAAABYgcAMAAAAAYAECNwAAAAAAFiBwAwAAAABgAQI3AAAAAAAWIHADAAAAAGABAjcAAAAAABYgcAMAAAAAYAECNwAAAAAAFiBwAwAAAABgAQI3AAAAAAAWIHADAAAAAGABAjcAAAAAABYgcAMAAAAAYAECNwAAAAAAFgjydAFnk+ISozU7DigzN19xEaHqkBStwACbp8sCAAAAAFiAwF2F+j63XLsOFjhuJ0SFasLAZPVrkeDBqgAAAAAAVuCU8iqwePM+SdLenHyn7Xuz8zX6vXVasCnDE2UBAAAAACxE4LZYcYnR5Plbyr3P/P9/U+emqbjElNsGAAAAAOCbCNwWW52eVWZk+0RGUkZ2vlanZ1VdUQAAAAAAyxG4LZaZe+qwXZF2AAAAAADfQOC2WFxEaKW2AwAAAAD4BgK3xTokRSs+8tRh2qbjs5V3SIquuqIAAAAAAJYjcFssMMCm+/s3lXQ8XJ+o9PaEgcmsxw0AAAAAfobAXQVSmtWWJNU+aaQ7PipUU69rwzrcAAAAAOCHgjxdwNlk4Z3dtf6PXGXm5isu4vhp5IxsAwAAAIB/8vgI9549e3TdddepVq1aCgsLU8uWLbVmzRpPlwUAAAAAwN/i0RHugwcPqkuXLrrooos0f/58xcbGatu2bapZs6Yny7JM3+eWa9fBAsfthKhQTRiYzCnlAAAAAOCHPBq4p0yZorp16+qtt95ybEtKSvJgRdZYvHmfJGlvTr5OnDptb3a+Rr+3juu4AQAAAMAPefSU8s8//1zt2rXTVVddpbi4OLVu3VrTpk3zZEmVrrjEaPL8LeXeZ/7/v6lz01RcYsptAwAAAADwTR4d4f7tt980depU3X333XrggQf0448/6o477lBISIiGDx9epn1BQYEKCv53SnZOTo4kqbCwUIWFhVVWtztWp2fp4OGjkiR7QPmhOuvwUa3ansla3PCo0veQt76XAIl+Ct9AP4W3o4/CF3hzP3WnJpsxxmNDqyEhIWrXrp2+//57x7Y77rhDP/74o1auXFmm/cSJE5Wamlpm+8yZMxUeHm5prQAAAAAA5OXladiwYcrOzlZkZORp23p0hDshIUHJyclO25o1a6ZPPvmk3Pbjx4/X3Xff7bidk5OjunXrqk+fPmd8op6yOj1Lo99drUfbleihNQEqKDnzMmABNmlE5/q6u0/TKqgQOK6wsFCLFi1S7969FRwc7OlygHLRT+EL6KfwdvRR+AJv7qelZ1q7wqOBu0uXLtq6davTtl9//VX169cvt73dbpfdbi+zPTg42Ou+CaU6NY5TYGCQpGMqKLGpoNi1dbenrtitElugxg9IPnNjoBJ58/sJKEU/hS+gn8Lb0UfhC7yxn7pTj0cnTbvrrru0atUqPfHEE9q+fbtmzpyp119/XWPGjPFkWZWquMToaGFxhfadtiJdx4pKKrkiAAAAAEBV8Gjgbt++vWbPnq33339fLVq00KOPPqrnnntO1157rSfLqlTvrtypil4kX2KO7w8AAAAA8D0ePaVcki655BJdcsklni7DMruy8jy6PwAAAADAMzw6wn02qB/992ZP/7v7AwAAAAA8g8Btses7N5Br06SVFWA7vj8AAAAAwPcQuC0WGGBTWHBghfa9uVuSQoL4FgEAAACAL/L4Ndz+bnV6lvLcnKU8wHY8bLMkGAAAAAD4LgK3xTJz811qd3mrREWEBat+dLiu79yAkW0AAAAA8HEEbovFRYS61G5I+3rq3KiWxdUAAAAAAKoKw6gW65AUrfjIU4dum6SEqFB1SIquuqIAAAAAAJYjcFssMMCm+/s3laQys5WX3p4wMFmBARWdyxwAAAAA4I0I3FUgpVltSVLtk0a646NCNfW6NurXIsETZQEAAAAALMQ13FVo4Z3dtf6PXGXm5isu4vhp5IxsAwAAAIB/YoS7CgUG2NQhKVox1ez6bvtf+s+iX/Xd9v0qLjGeLg0AAAAAUMkY4a5Cizfv0/jP0nQor9Cx7aWl21UjPFiTB7fk1HIAAAAA8COMcFehOz/Y4BS2Sx3KK9St763Tgk0ZHqgKAAAAAGAFAncVcPWU8dS5aZxeDgAAAAB+gsBdBdbuOuhSu4zsfK1Oz7K4GgAAAABAVSBwV4H9hwtcbpuZm29hJQAAAACAqkLgrgJfb8l0uW1cROiZGwEAAAAAvB6B22KT5qVpwS97XWqbEHV8bW4AAAAAgO8jcFvoWFGJpq1Id7n9hIHJCgywWVgRAAAAAKCqELgt9O7KnXJ10vG7Us5jHW4AAAAA8CMEbgvtyspzuW2DmHALKwEAAAAAVDUCt4XqR7seopksDQAAAAD8C4HbQtd3biBXLsmOj7QzWRoAAAAA+BkCt4VCggJ0c7ekM7abeGlzJksDAAAAAD8T5OkC/N34AckKMMVS0W9l7qsWEqhnhlzAZGkAAAAA4IcI3FXg7j5NNW/eb7qvTxOt3nVI4SFBuqJNHV3YOIaRbQAAAADwUwTuKjT8wgb6Z49gT5cBAAAAAKgCXMMNAAAAAIAFCNwAAAAAAFiAwA0AAAAAgAUI3AAAAAAAWIBJ06pQcYnRmh0HlJmbrxr2YC3ask+7svLUoFa4HhiQrLCQQE+XCAAAAACoJATuKtT3ueXadbCgzPYV26R3V+1W7+Q4TbuhvQcqAwAAAABUNk4prwKLN++TJO3NyT9tu0Vpmbr5nR+roiQAAAAAgMUI3BYrLjGaPH+Ly+0XpWXq6LFiCysCAAAAAFQFArfFVqdnnXFk+2RPzEuzqBoAAAAAQFUhcFssM9e9sC1JOw/kWVAJAAAAAKAqEbgtFhcR6vY+DWqFW1AJAAAAAKAqEbgt1iEpWvGR7oXuBwYkW1QNAAAAAKCqELgtFhhg0/39m7rcvl39GqzHDQAAAAB+gMBdBVKa1ZYkBdhO384eFKAPbrmwCioCAAAAAFiNwF2FJg8+/7T3P39NKwWeKZUDAAAAAHyCRwP3xIkTZbPZnL6aNnX99Gtfc/+nG8vdnhAVqleva6N+LRKquCIAAAAAgFWCPF1A8+bNtXjxYsftoCCPl1Tpnv1qi5pKKjHl33/J+fGEbQAAAADwMx4/pTwoKEjx8fGOr5iYGE+XVKmOFZVo+spdp23z32936lhRSRVVBAAAAACoCh4fTt62bZsSExMVGhqqzp07a9KkSapXr165bQsKClRQUOC4nZOTI0kqLCxUYWFhldTrrvdW7lSw7fjQtj3gFEPckt77foeu79ygiqoCyip9D3nrewmQ6KfwDfRTeDv6KHyBN/dTd2qyGWNOnQItNn/+fB0+fFhNmjRRRkaGUlNTtWfPHm3atEkRERFl2k+cOFGpqallts+cOVPh4eFVUTIAAAAA4CyWl5enYcOGKTs7W5GRkadt69HAfbJDhw6pfv36evbZZ3XTTTeVub+8Ee66detq//79Z3yinvL29zv1wuIterRdiR5aE6CCklPPQr7mgRSFsgY3PKSwsFCLFi1S7969FRwc7OlygHLRT+EL6KfwdvRR+AJv7qc5OTmKiYlxKXB7/JTyE9WoUUPnnXeetm/fXu79drtddru9zPbg4GCv+yaUahJfwxGyC0psKig+deB+ctE2PTqoZVWVBpTLm99PQCn6KXwB/RTejj4KX+CN/dSdetwO3Onp6VqxYoV27dqlvLw8xcbGqnXr1urcubNCQ0PdPZyTw4cPa8eOHbr++uv/1nG8SdbRYy633Xkgz8JKAAAAAABVyeXAPWPGDD3//PNas2aNateurcTERIWFhSkrK0s7duxQaGiorr32Wo0bN07169d36Zj33nuvBg4cqPr16+vPP//UhAkTFBgYqKFDh1b4CXmbuAjXP4RoUIvr0AEAAADAX7gUuFu3bq2QkBCNGDFCn3zyierWret0f0FBgVauXKlZs2apXbt2euWVV3TVVVed8bh//PGHhg4dqgMHDig2NlZdu3bVqlWrFBsbW7Fn44U6JEUrtlqwpOIztn1gQLL1BQEAAAAAqoRLgXvy5Mnq27fvKe+32+3q2bOnevbsqccff1w7d+506cFnzZrlUjtfFhhg04OXNNex9LWnbZfSLFZhTJgGAAAAAH4jwJVGpwvbJ6tVq5batm1b4YL8UUqz2pKkkMBTv9y//JmrBZsyqqokAAAAAIDFXArckrRr1y699tprOnbs+CRgL7/8smVF+asfH0zR7T0blXvf3ux8jX5vHaEbAAAAAPyEy4H72muv1Zo1azRo0CAdO3ZMM2bMsLIuv/Xx+j3lbi9dDD11bpqKS7xmaXQAAAAAQAW5HLiLioo0bdo0jR07VmPGjLGyJr+1dtdBZWTnn/J+IykjO1+r07OqrigAAAAAgCVcDtyRkZGSpAEDBqh+/fr66aefLCvKX+0/XOBSu8zcU4dyAAAAAIBvcHkd7rFjx6qoqEhBQUF68MEHVb16dSvr8ksx1e0utXNn7W4AAAAAgHdyeYT70ksvVVDQ8Xxus9l05513WlWT32pbv6YSokJlO8X9NkkJUaHqkBRdlWUBAAAAACzg8gh3KWOMPv74Yy1dulSZmZkqKSlxuv/TTz+ttOL8TWCATRMGJmv0e+tk0/8mSpPkCOETBiYrMOBUkRwAAAAA4CtcHuEudeedd+r6669Xenq6qlevrqioKKcvnF6/Fgmael0bxUc5nzYeHxWqqde1Ub8WCR6qDAAAAABQmdwe4X733Xf16aefasCAAVbUc1bo1yJBvZPjtTo9S5m5+YqLOH4aOSPbAAAAAOA/3A7cUVFRatiwoRW1nFUCA2zq3KiWp8sAAAAAAFjE7VPKJ06cqNTUVB09etSKegAAAAAA8Atuj3APGTJE77//vuLi4tSgQQMFBwc73b9u3bpKKw4AAAAAAF/lduAePny41q5dq+uuu061a9eWzcZ1xwAAAAAAnMztwP3ll19q4cKF6tq1qxX1+LW3v9+plTsPakfmEVULCdQFdWuob/N4HTpayMRpAAAAAOBn3A7cdevWVWRkpBW1+K1nv9qippKe+mqrCor/F6h/ycjVzNW/O24nRIVqwsBklgYDAAAAAD/g9qRpzzzzjP79739r586dFpTjfybNS9Ob3+9yqe3e7HyNfm+dFmzKsLgqAAAAAIDV3B7hvu6665SXl6dGjRopPDy8zKRpWVlZlVacrztWVKJpK9IV7OLHGkaSTVLq3DT1To7n9HIAAAAA8GFuB+7nnnvOgjL807srd6rEuLePkZSRna/V6Vms0w0AAAAAPqxCs5TDNbuy8iq8b2ZufiVWAgAAAACoai6d7HzkyBG3Dupue39VPzq8wvvGRYRWYiUAAAAAgKrmUuBu3LixJk+erIyMU0/mZYzRokWL1L9/f73wwguVVqAvu75zA7l7FbZNx2cr75AUbUVJAAAAAIAq4tIp5cuWLdMDDzygiRMn6oILLlC7du2UmJio0NBQHTx4UGlpaVq5cqWCgoI0fvx43XLLLVbX7RNCggJ0yfnx+uoX12YdLw3nEwYmM2EaAAAAAPg4lwJ3kyZN9Mknn2j37t366KOPtGLFCn3//fc6evSoYmJi1Lp1a02bNk39+/dXYGCg1TX7lOeuaaOWE750qW0863ADAAAAgN9wa9K0evXq6Z577tE999xjVT1+59b31qi45PRtburSQCnJ8eqQFM3INgAAAAD4CbdnKYfrjh4r1qK0TNnPMOh/b9+mCgvhzAAAAAAA8CcuTZqGinliXlqltgMAAAAA+A4Ct4XS97u2Drer7QAAAAAAvoPAbaGwYNdeXlfbAQAAAAB8B0nPQn2Sa7vUrnaU3eJKAAAAAABVrUKBe8WKFbruuuvUuXNn7dmzR5L07rvv6ttvv63U4nxdnehqLrV7b9XvWrDJtbW6AQAAAAC+we3A/cknn6hv374KCwvT+vXrVVBQIEnKzs7WE088UekF+rIOSdGKjwx1qe3dH/6k4hJjcUUAAAAAgKriduB+7LHH9Oqrr2ratGkKDg52bO/SpYvWrVtXqcX5usAAm8Ltrr3EeceK9a9Z6y2uCAAAAABQVdwO3Fu3blX37t3LbI+KitKhQ4cqoya/cfRYsX77y/UZyL/YmKFjRSUWVgQAAAAAqCpuB+74+Hht3769zPZvv/1WDRs2rJSi/EVF1td+d+XOyi8EAAAAAFDl3A7cN998s/71r3/phx9+kM1m059//qkZM2bo3nvv1ejRo62o0Wf99tcRt/fZlcWa3AAAAADgD4Lc3eH+++9XSUmJevXqpby8PHXv3l12u1333nuvbr/9ditq9Fn5hcVu71M/OtyCSgAAAAAAVc3twG2z2fTggw/qvvvu0/bt23X48GElJyerevXqVtTn0/ZmH3V7n+s7N6j8QgAAAAAAVc7twF0qJCREycnJlVmLXzl6rFh7sgvc2uemrvUVElShpdEBAAAAAF7GpcA9ePBgTZ8+XZGRkRo8ePBp23766aeVUpivq8iEaYlRnE4OAAAAAP7CpcAdFRUlm83m+D/ObOcB9yc/q8g+AAAAAADv5FLgfuutt8r9f2WaPHmyxo8fr3/961967rnnLHmMqtSgVrhWbHN3L2NFKQAAAAAAD3D7guH09HRt21Y2SW7btk07d+6sUBE//vijXnvtNZ1//vkV2t8bPTDA/evbW9WpUfmFAAAAAAA8wu3APWLECH3//fdltv/www8aMWKE2wUcPnxY1157raZNm6aaNWu6vb+3CgsJVO/kOLf2SazJNdwAAAAA4C/cDtzr169Xly5dymzv1KmTNmzY4HYBY8aM0cUXX6yUlBS39/V2DWpVc7ltfKRdHZKiLawGAAAAAFCVKrQOd25ubpnt2dnZKi4udutYs2bN0rp16/Tjjz+61L6goEAFBf9baisnJ0eSVFhYqMLCQrce22rHikr07srfZA+U7AHHr80u/bc8DaLtKikuUol7LyFQaUrfQ972XgJORD+FL6CfwtvRR+ELvLmfulOTzRjj1kxdAwcOVFhYmN5//30FBgZKkoqLi3X11VfryJEjmj9/vkvH+f3339WuXTstWrTIce12z5491apVq1NOmjZx4kSlpqaW2T5z5kyFh3M6NgAAAADAWnl5eRo2bJiys7MVGRl52rZuB+60tDR1795dNWrUULdu3SRJK1asUE5Ojr7++mu1aNHCpeN89tlnuvzyyx2hXToe3G02mwICAlRQUOB0n1T+CHfdunW1f//+Mz7Rqvb4vM16f/VuScdHth9tV6KH1gSooMR2yn3G9W2i6zs3qKIKAWeFhYVatGiRevfureDgYE+XA5SLfgpfQD+Ft6OPwhd4cz/NyclRTEyMS4Hb7VPKk5OTtXHjRr300kv66aefFBYWphtuuEFjx45VdLTr1yD36tVLP//8s9O2kSNHqmnTpho3blyZsC1Jdrtddru9zPbg4GCv+ybUja6ugmLncF1QYiuz7UTfbM/Sjd3Ptbo04LS88f0EnIx+Cl9AP4W3o4/CF3hjP3WnHrcDtyQlJibqiSeeqMiuDhEREWVGw6tVq6ZatWq5PEruza7v3ECPfrnZrX3W7z6k4hKjwIBTh3IAAAAAgG9wKXBv3LhRLVq0UEBAgDZu3Hjatv60lvbfERIUoJu6NtB/v93p8j45+UVanZ6lzo1qWVcYAAAAAKBKuBS4W7Vqpb179youLk6tWrWSzWZTeZd+22w2t2cqP9GyZcsqvK83euiS5voqbZ8ys/Nc3iczN9/CigAAAAAAVcWlwJ2enq7Y2FjH/+G6Jwa11D/f/sHl9nERoRZWAwAAAACoKi4F7ssvv1xLlixRzZo19fbbb+vee+9lGS4XfbZhj8ttE6JC1SHJ9YnnAAAAAADeK8CVRps3b9aRI0ckSampqTp8+LClRfmL4hKj+T/vdbn9hIHJTJgGAAAAAH7C5Wu4R44cqa5du8oYo6efflrVq1cvt+3DDz9cqQX6stXpWcorLJa97ApnZVzZpo76tUiwvigAAAAAQJVwKXBPnz5dEyZM0BdffCGbzab58+crKKjsrjabjcB9AncmQHticEsLKwEAAAAAVDWXAneTJk00a9YsSVJAQICWLFmiuLg4SwvzB9FhIS61Cw0O4FRyAAAAAPAzLl3DXaqwsFDDhw93XM+N09uyL9eldvmFJVqdnmVxNQAAAACAquRW4A4ODtbs2bOtqsXv7Drg+gcTrL8NAAAAAP7FrcAtSZdddpk+++wzC0rxP3uzj7rclvW3AQAAAMC/uHQN94nOPfdcPfLII/ruu+/Utm1bVatWzen+O+64o9KK83XGxcuy7UEBrL8NAAAAAH7G7cD93//+VzVq1NDatWu1du1ap/tsNhuB+wSR9mCX2l1QJ4pJ0wAAAADAz7gduNPT062owy8Nan2OZm/484ztxlzUuAqqAQAAAABUJbev4S517Ngxbd26VUVFRZVZj1/5cM3vLrVbt/ugxZUAAAAAAKqa24E7Ly9PN910k8LDw9W8eXPt3r1bknT77bdr8uTJlV6grzpWVKJ5P+91qe3073aquMRYXBEAAAAAoCq5HbjHjx+vn376ScuWLVNo6P9m1k5JSdEHH3xQqcX5sndX7pSrETo7v4h1uAEAAADAz7h9Dfdnn32mDz74QJ06dZLN9r+Jvpo3b64dO3ZUanG+bFdWnlvtWYcbAAAAAPyL2yPcf/31l+Li4spsP3LkiFMAP9vVjw53q/2itH0WVQIAAAAA8AS3A3e7du305ZdfOm6Xhuw33nhDnTt3rrzKfNz1nRu41f6LjRmaNC/NmmIAAAAAAFXO7VPKn3jiCfXv319paWkqKirS888/r7S0NH3//ff65ptvrKjRJ1VkErTXl6frnj5NFRJU4cnjAQAAAABewu1k17VrV23YsEFFRUVq2bKlvvrqK8XFxWnlypVq27atFTX6pCcqMFptdHyyNQAAAACA73N7hFuSGjVqpGnTplV2LX5l5wH3Jk0r5e5kawAAAAAA71ShwF1cXKzZs2dr8+bNkqTk5GRddtllCgqq0OH8UoNa4Vqxzf393J1sDQAAAADgndxOyL/88osuvfRS7d27V02aNJEkTZkyRbGxsZo7d65atGhR6UX6ogcGJOvdVbvd2scm9ydbAwAAAAB4J7ev4f7nP/+p5s2b648//tC6deu0bt06/f777zr//PM1atQoK2r0SWEhgUppVnb5tNP5Z7ckJkwDAAAAAD/h9gj3hg0btGbNGtWsWdOxrWbNmnr88cfVvn37Si3O1428MEmLN2e63P4fTWtbWA0AAAAAoCq5PZx63nnnad++fWW2Z2ZmqnHjxpVSlL9Y+dt+t9pn5uZbVAkAAAAAoKq5HbgnTZqkO+64Qx9//LH++OMP/fHHH/r444915513asqUKcrJyXF8weZW67iIUIvqAAAAAABUNbdPKb/kkkskSUOGDJHNdjxQGmMkSQMHDnTcttlsKi4urqw6fVLnRrX00tLtLrVNiApVh6RoiysCAAAAAFQVtwP30qVLrajDL7Vv4HqAfujiZgoMcG9EHAAAAADgvdwO3D169LCiDr80dZlro9uSVLOa3cJKAAAAAABVze3AvXz58tPe37179woX40+KS4xe/Hqby+2ZMA0AAAAA/Ivbgbtnz55ltpVeyy3prL9uu9T32/arqMT19kyYBgAAAAD+xe1Zyg8ePOj0lZmZqQULFqh9+/b66quvrKjRJ32y/g+X29okJkwDAAAAAD/j9gh3VFRUmW29e/dWSEiI7r77bq1du7ZSCvN1ecdcH+k3km59b42m3dDeuoIAAAAAAFXK7RHuU6ldu7a2bt1aWYfzeW3r13Sr/aK0TB11I6QDAAAAALyb2yPcGzdudLptjFFGRoYmT56sVq1aVVZdPi85IdLtfZ6Yl6ZHB7W0oBoAAAAAQFVzO3C3atVKNptNxhin7Z06ddKbb75ZaYX5uqy8Y27vs/NAngWVAAAAAAA8we3AnZ6e7nQ7ICBAsbGxCg1llu0TxVR3f13tANuZ2wAAAAAAfIPbgbt+/fpW1OF/zJmbnGz5r/u1YFOG+rVIqPx6AAAAAABVyuVJ01auXKkvvvjCads777yjpKQkxcXFadSoUSooKKj0An3V/iMVey1S56apuKQCaR0AAAAA4FVcDtyPPPKIfvnlF8ftn3/+WTfddJNSUlJ0//33a+7cuZo0aZIlRfqiyBC3Tx6QkZSRna/V6VmVXxAAAAAAoEq5HLg3bNigXr16OW7PmjVLHTt21LRp03T33XfrhRde0IcffmhJkb5oxo+7K7zv3pz8SqwEAAAAAOAJLgfugwcPqnbt2o7b33zzjfr37++43b59e/3+++9uPfjUqVN1/vnnKzIyUpGRkercubPmz5/v1jG81e8Hj1Z430e/+EULNmVUYjUAAAAAgKrmcuCuXbu2Y4byY8eOad26derUqZPj/tzcXAUHB7v14HXq1NHkyZO1du1arVmzRv/4xz902WWXOZ267qvq1Qyr8L5ZRwo1+r11hG4AAAAA8GEuB+4BAwbo/vvv14oVKzR+/HiFh4erW7dujvs3btyoRo0aufXgAwcO1IABA3TuuefqvPPO0+OPP67q1atr1apVbh3HGz3QP/lv7W/EBGoAAAAA4Mtcntnr0Ucf1eDBg9WjRw9Vr15db7/9tkJCQhz3v/nmm+rTp0+FCykuLtZHH32kI0eOqHPnzuW2KSgocJoJPScnR5JUWFiowsLCCj+2FUb89zvZA4+HZXuA87+uyjp8VKu2Z6pDUnSl1wecrPQ95G3vJeBE9FP4AvopvB19FL7Am/upOzXZjDFupcDs7GxVr15dgYGBTtuzsrJUvXp1pxDuip9//lmdO3dWfn6+qlevrpkzZ2rAgAHltp04caJSU1PLbJ85c6bCw8PdelwAAAAAANyVl5enYcOGKTs7W5GRkadt63bgrmzHjh3T7t27lZ2drY8//lhvvPGGvvnmGyUnlz0lu7wR7rp162r//v1nfKJVre0jX6mg5H8j24+2K9FDawJUUGJz6zhvDm/PCDeqRGFhoRYtWqTevXu7PR8DUFXop/AF9FN4O/oofIE399OcnBzFxMS4FLjdXyy6koWEhKhx48aSpLZt2+rHH3/U888/r9dee61MW7vdLrvdXmZ7cHCw130T6sdW189/HnbaVlBiU0Gx64E7ISpUnRrHKTDAvZAO/B3e+H4CTkY/hS+gn8Lb0UfhC7yxn7pTj8uTplWVkpISp1FsXxUbEfq39rdJmjAwmbANAAAAAD7KoyPc48ePV//+/VWvXj3l5uZq5syZWrZsmRYuXOjJsipFVJh717KfKCEqVBMGJqtfi4RKrAgAAAAAUJU8GrgzMzN1ww03KCMjQ1FRUTr//PO1cOFC9e7d25NlVYrBbepo9oY/3d5vQIt4vTisDSPbAAAAAODjPBq4//vf/3ry4S11YeOYCu234Je9Ki4xBG4AAAAA8HFedw23vwgMsKmG3f2Xt8RI767cWfkFAQAAAACqFIHbQvklFVtxbVdWXiVXAgAAAACoagRuCwVWcL+6NcMqtQ4AAAAAQNUjcFvoSGHFRribxp9+8XQAAAAAgPcjcFukuIKnk0tSVt6xSqwEAAAAAOAJBG6LrE7PqvC+cRGhlVgJAAAAAMATCNwWyczNr9B+NcKC1SEpupKrAQAAAABUNQK3RUJNxdbRHtkliTW4AQAAAMAPELgt8sjCzW7vExYcoLH/aGxBNQAAAACAqkbgtsjBI4Vu79MhKZrRbQAAAADwEwRui0RXC3Z7n66NYy2oBAAAAADgCQRui8y+ravb+zSNj7CgEgAAAACAJxC4LRJdPcTtfVh/GwAAAAD8B4HbIqt2HHB7H9bfBgAAAAD/QeC2yMrf9rvVnvW3AQAAAMC/ELgtYtxs3zAmnBnKAQAAAMCPELgtUiPMvWu41/2erUnz0iyqBgAAAABQ1QjcFgm3u//STluRrmNFJRZUAwAAAACoagRui7z93U639ykx0rsr3d8PAAAAAOB9CNwWyckvqtB+u7LyKrkSAAAAAIAnELgtUj0ksEL71Y8Or+RKAAAAAACeQOC2SNOECLf3sUm6vnODSq8FAAAAAFD1CNwWOVbs/j4Xt0xQSBDfEgAAAADwB6Q7i5xfJ8qt9jZJzw9tbU0xAAAAAIAqR+C2yNpdWW6179ooWoEBNouqAQAAAABUNQK3RbbtO+LeDgF8KwAAAADAn5DyLBIU6N5odYNazE4OAAAAAP6EwG2RPsm13Wr/wIBkiyoBAAAAAHgCgdsizWOru9X+aEWmNQcAAAAAeC0Ct0XGz/3FrfbXvP69RZUAAAAAADyBwG2RvMISt9pvz3RzkjUAAAAAgFcjcHuJEkmH84s8XQYAAAAAoJIQuC1SLdj9fe76YH3lFwIAAAAA8AgCt0XCg91/aXdn5VlQCQAAAADAEwjcFvkrz71ruCWpuj3IgkoAAAAAAJ5A4PYiQ9rX9XQJAAAAAIBKQuD2IvWiq3m6BAAAAABAJSFwW6R/8zi32ocESB2Soi2qBgAAAABQ1QjcVrG51zwshOu3AQAAAMCfELgt8uPOg261z84v0ur0LIuqAQAAAABUNQK3F8nMzfd0CQAAAACASuLRwD1p0iS1b99eERERiouL06BBg7R161ZPllRpWtWp4fY+cRGhlV8IAAAAAMAjPBq4v/nmG40ZM0arVq3SokWLVFhYqD59+ujIkSOeLKtSPDOktVvtq4UEMmkaAAAAAPgRj87UtWDBAqfb06dPV1xcnNauXavu3bt7qKrKkZaR41b7I8eKNX9jhi5plWhRRQAAAACAquRV13BnZ2dLkqKjfX+k98fd+9zeZ+ys9Xr8yzQLqgEAAAAAVDWvWYuqpKREd955p7p06aIWLVqU26agoEAFBQWO2zk5x0eRCwsLVVhYWCV1uurlxb/JHnj8//YA4/Tv6bzz/W8KUrHu7tPUyvKAMkrfQ972XgJORD+FL6CfwtvRR+ELvLmfulOTzRhz5hRYBUaPHq358+fr22+/VZ06dcptM3HiRKWmppbZPnPmTIWHh1tdIgAAAADgLJeXl6dhw4YpOztbkZGRp23rFYF77NixmjNnjpYvX66kpKRTtitvhLtu3brav3//GZ9oVWsxcaHj//YAo0fbleihNQEqKLG5tP+4vk10fecGFlUHlFVYWKhFixapd+/eCg4O9nQ5QLnop/AF9FN4O/oofIE399OcnBzFxMS4FLg9ekq5MUa33367Zs+erWXLlp02bEuS3W6X3W4vsz04ONjrvgkFxWWDdUGJrdzt5dl5sMDrnhPODt74fgJORj+FL6CfwtvRR+ELvLGfulOPRwP3mDFjNHPmTM2ZM0cRERHau3evJCkqKkphYWGeLM3j6kdzijwAAAAA+DKPzlI+depUZWdnq2fPnkpISHB8ffDBB54sq1K4No5dvgCbOJ0cAAAAAHycx08p91cBkooruO/N3ZIUEuRVK7YBAAAAANzkNcuC+ZuKfJRgkzSqe5LGD0iu7HIAAAAAAFWMwG2Rkgrs8+yQVrq8zTmVXgsAAAAAoOpx3rIXeXLhFhWX+O9p9gAAAABwNiFwe5GM7HytTs/ydBkAAAAAgEpA4PYymbn5ni4BAAAAAFAJCNxeJi4i1NMlAAAAAAAqAYHbi9iDAtQhKdrTZQAAAAAAKgGB24vUqh6iwACbp8sAAAAAAFQCArcXSU6I9HQJAAAAAIBKQuD2Ite2r+fpEgAAAAAAlYTA7UVyjhV5ugQAAAAAQCUhcHsRZigHAAAAAP9B4PYSwQFihnIAAAAA8CMEbi/RNC6MGcoBAAAAwI8QuL1E+sECT5cAAAAAAKhEBG4vkV9Y4ukSAAAAAACViMDtJYqNVFxiPF0GAAAAAKCSELi9hDHSS19v83QZAAAAAIBKQuD2Im99t5NRbgAAAADwEwRuL3LoaKFWp2d5ugwAAAAAQCUgcHuZzNx8T5cAAAAAAKgEBG4vExcR6ukSAAAAAACVgMDtRRKiQtUhKdrTZQAAAAAAKgGB24tMGJiswACbp8sAAAAAAFQCAjcAAAAAABYgcHuR0e+t04JNGZ4uAwAAAABQCQjcXsRISp2bxlrcAAAAAOAHCNxeJiM7n7W4AQAAAMAPELi9EGtxAwAAAIDvI3B7IdbiBgAAAADfR+D2MqzFDQAAAAD+gcDtZR66uBlrcQMAAACAHyBwe5ma1eyeLgEAAAAAUAkI3F6GCdMAAAAAwD8QuL0ME6YBAAAAgH8gcHuR+Eg7E6YBAAAAgJ8gcHuRoR3qMWEaAAAAAPgJArcXaRBTzdMlAAAAAAAqCYHbi3D9NgAAAAD4DwK3l4itFsT12wAAAADgRwjcXiIiLITrtwEAAADAjxC4vcTRwhJPlwAAAAAAqEQeDdzLly/XwIEDlZiYKJvNps8++8yT5XhUYhTXbwMAAACAP/Fo4D5y5IguuOACvfzyy54swytMu6G9p0sAAAAAAFSiIE8+eP/+/dW/f39PluA1fv7jkHo0jfN0GQAAAACASuLRwO2ugoICFRQUOG7n5ORIkgoLC1VYWOipssplDzT/+3+Acfq3PNOW/6oLG9W0vC7gVErfQ972XgJORD+FL6CfwtvRR+ELvLmfulOTzRhz6hRYhWw2m2bPnq1Bgwadss3EiROVmppaZvvMmTMVHh5uYXUAAAAAAEh5eXkaNmyYsrOzFRkZedq2PhW4yxvhrlu3rvbv33/GJ1rVWkxc6Pi/PcDo0XYlemhNgApKTr30108P92FpMHhMYWGhFi1apN69eys4ONjT5QDlop/CF9BP4e3oo/AF3txPc3JyFBMT41Lg9qlTyu12u+x2e5ntwcHBXvdNKCguG5wLSmzlbi+1/o9cdW5Uy8qygDPyxvcTcDL6KXwB/RTejj4KX+CN/dSdeliH24ssStvr6RIAAAAAAJXEo4H78OHD2rBhgzZs2CBJSk9P14YNG7R7925PluUxH675Q8Ul5Z/hX1xitHLHAc3ZsEcrdxw4ZTsAAAAAgHfw6Cnla9as0UUXXeS4fffdd0uShg8frunTp3uoKs85XFCkVb8dUJfGMU7bF2zKUOrcNGVk5zu2JUSFasLAZPVrkVDVZQIAAAAAXODREe6ePXvKGFPm62wM26VW7jjgdHvBpgyNfm+dU9iWpIzsfN363jot2JRRleUBAAAAAFzENdxe53+niheXGKXOTdPpTh6//9OfOb0cAAAAALwQgdvLdG74v9PJV6dnlRnZPtmhvEK99PV2q8sCAAAAALiJwO1FaoQHq9MJy4Jl5p4+bJd66/t0RrkBAAAAwMsQuL3I5MEtFRjwv3W64yJCXdrvUF6hVqdnWVUWAAAAAKACCNxepHdyvNPtDknRqhHm2qLqro6GAwAAAACqBoHbi5w8Sh0YYNPILg1c2tfV0XAAAAAAQNUgcHuR8kapx/7jXNUIP/Uot03H1+TukBRtYWUAAAAAAHcRuL1IeaPUgQE2TR7cUrZy2pdumzAw2enabwAAAACA5xG4vUSQTaccpe7XIkFTr2ujhCjnQB4fFaqp17VRvxYJVVEiAAAAAMANQZ4uAMcl1Ag77Sh1vxYJ6p0cr9XpWcrMzVdcxPHTyBnZBgAAAADvROD2ErWqnXk28sAAmzqfsE43AAAAAMB7cUq5l8jOK/B0CQAAAACASsQIt5fIPFxY7vbiEsNp5AAAAADggwjcXiI4sGyIXrApQ6lz05SR/b/lwhKiQjVhYDITpQEAAACAl+OUci8RH+k8A/mCTRka/d46p7AtSXuz8zX6vXVasCmjKssDAAAAALiJwO0ltu47onkb/5R0/DTy1LlpMuW0K92WOjdNxSXltQAAAAAAeAMCt5cwkm6buV6T5qVpdXpWmZHtk9tmZOdrdXpWldUHAAAAAHAP13B7mdeWp6ugsMSltpm5pw7lAAAAAADPYoTbC729cpdL7eIiQs/cCAAAAADgEYxwe6EzXZltkxQfdXyJMAAAAACAdyJwezmbnAN46eJhEwYmsx434AbWtAcAAEBVI3B7uZrVQpR15JjjdjzrcANuY017AAAAeAKB28s9dHEzxUeFMSoHVFDpmvYnX6pRuqb91OvaELoBAABgCQK3l4uPClPnRrU8XQbgk860pr1Nx9e0750czwdZAAAAqHTMUu6lbDp+yisTowEVx5r2AAAA8CQCt5cyYmI04O9yda161rQHAACAFTil3EvdlXIu15UCf5Ora9Wzpj0AAIBnnbyiTOs6EZ4uqVIQuC1QXHKmlbTPrEFMtUqoBDi7dUiKVkJUqPZm55d7HTdr2gMAAHheeSvK1K9p191NPVhUJeGUcgtUxvWgjLgBf19ggE0TBiZL+t8a9qVY0x4AAMDzSleUOXnenX05x28v3rzPE2VVGgK3BfZmH63wvkyWBlSufi0SNPW6NoqPcv4QKz4qlCXBAAAAPOhMK8pI0uT5WyrlDGJP4ZRyC5R+GlNRjLgBlatfiwT1To53ui6INe0BAAA860wrykjS3pzjK8r46lLJBG4LLE77s0L72WzSqG5JjLgBFggMsPnsD2oAAAB/dDasKMMp5RZYtzu3QvsZI72+PF0LNmVUckUAAAAA4F3OhhVlCNwWKPmb+6fOTfPp6xQAAAAA4ExKV5Q53UV+8ZG+Pb8VgdvLGEkZ2fmVMtM5AAAAAHgrV1aUub9/U5+ed4fA7aV8+ToFAAAAAHDFqVaUqR15/HZKs9qeKKvSMGmal/K16xSKSwwzQAMAAABwW3kryrSuE6GFC+Z7urS/jcDtZWw6vj6wL12nsGBThlLnpjlN6Z8QFaoJA5OZcR0AAADAGZ28okxhYaEHq6k8nFLuhUqvY1i544DmbNijlTsOeO0kags2ZWj0e+vKrJ+3Nztfo99bx4zrAAAAAM5ajHB7kehqwXri8paSpK5Tvvb6EePiEqPUuWkq76MAo+Oj9alz09Q7OZ7TywEAAACcdRjh9iIPXdJcknxmxHh1elaZOk/EjOsAAAAAzmZeEbhffvllNWjQQKGhoerYsaNWr17t6ZI8Iq66/bQjxpJ3rdHt6kzqzLgOAAAA4Gzk8cD9wQcf6O6779aECRO0bt06XXDBBerbt68yMzM9XVqVqhEeLNnkUyPGrs6k7mszrgMAAABAZfB44H722Wd18803a+TIkUpOTtarr76q8PBwvfnmm54urUodyivUks37XGrrLSPGHZKilRAVWmaR+lI2Hb/23JdmXAcAAACAyuLRwH3s2DGtXbtWKSkpjm0BAQFKSUnRypUrPVjZ31Mj1P2X1SZpzoY/XWrrLSPGgQE2x4zqJ4fu0tsTBiYzYRoAAACAs5JHZynfv3+/iouLVbt2bafttWvX1pYtW8q0LygoUEFBgeN2Tk6OpONrtHnTOm2jujbQ80t3OG7bA4zTv6dyOL9A8RHBOpRXWO513DZJtSOPLwLvLc+3V5MYvTLsAk2ev0V7c/438h4fGar7+zdVryYxXlMrTq/0+8T3C96MfgpfQD+Ft6OPwhd4cz91pyabMcZjM3D9+eefOuecc/T999+rc+fOju3//ve/9c033+iHH35waj9x4kSlpqaWOc7MmTMVHh5ueb0AAAAAgLNbXl6ehg0bpuzsbEVGRp62rUdHuGNiYhQYGKh9+5yvXd63b5/i4+PLtB8/frzuvvtux+2cnBzVrVtXffr0OeMTrWotJi50/N8eYPRouxI9tCZABSWnP736zeHtlZNfeMoR45RmtU+zN1BxhYWFWrRokXr37q3g4GBPlwOUi34KX0A/hbejj8IXeHM/LT3T2hUeDdwhISFq27atlixZokGDBkmSSkpKtGTJEo0dO7ZMe7vdLrvdXmZ7cHCw130TRnZK0Kvf7XXaVlBiU0Fx+YHbJik+KlSdGscpMMCmPi3O0er0LGXm5isu4vjEY1wLjargje8n4GT0U/gC+im8HX0UvsAb+6k79Xg0cEvS3XffreHDh6tdu3bq0KGDnnvuOR05ckQjR470dGl/y/0D2+rV7750qW15E4wFBtjUuVEti6oDAAAAAFjN44H76quv1l9//aWHH35Ye/fuVatWrbRgwYIyE6n5op2TL1aD+88cuuOjQjVhYLL6tUiogqoAAAAAAFXB44FbksaOHVvuKeT+YOfki/Xk5z9K5n9LfsUGSFOub6/cgkJOFwcAAAAAP+UVgdvf3dW/lebN+1ObJvb1uusPAAAAAADWCPB0AQAAAAAA+CMCNwAAAAAAFiBwAwAAAABgAQI3AAAAAAAWIHADAAAAAGABAjcAAAAAABYgcAMAAAAAYAECNwAAAAAAFiBwAwAAAABgAQI3AAAAAAAWIHADAAAAAGABAjcAAAAAABYgcAMAAAAAYIEgTxfwdxhjJEk5OTkeruT0CgsLlZeXp5ycHAUHB3u6HKBc9FP4AvopfAH9FN6OPgpf4M39tDR/lubR0/HpwJ2bmytJqlu3rocrAQAAAACcTXJzcxUVFXXaNjbjSiz3UiUlJfrzzz8VEREhm83m6XJOKScnR3Xr1tXvv/+uyMhIT5cDlIt+Cl9AP4UvoJ/C29FH4Qu8uZ8aY5Sbm6vExEQFBJz+Km2fHuEOCAhQnTp1PF2GyyIjI72uswAno5/CF9BP4Qvop/B29FH4Am/tp2ca2S7FpGkAAAAAAFiAwA0AAAAAgAUI3FXAbrdrwoQJstvtni4FOCX6KXwB/RS+gH4Kb0cfhS/wl37q05OmAQAAAADgrRjhBgAAAADAAgRuAAAAAAAsQOAGAAAAAMACBG4AAAAAACxA4K4CL7/8sho0aKDQ0FB17NhRq1ev9nRJ8AMTJ06UzWZz+mratKnj/vz8fI0ZM0a1atVS9erVdcUVV2jfvn1Ox9i9e7cuvvhihYeHKy4uTvfdd5+Kioqc2ixbtkxt2rSR3W5X48aNNX369DK10MdRavny5Ro4cKASExNls9n02WefOd1vjNHDDz+shIQEhYWFKSUlRdu2bXNqk5WVpWuvvVaRkZGqUaOGbrrpJh0+fNipzcaNG9WtWzeFhoaqbt26evLJJ8vU8tFHH6lp06YKDQ1Vy5YtNW/ePLdrgX86Uz8dMWJEmZ+v/fr1c2pDP4WVJk2apPbt2ysiIkJxcXEaNGiQtm7d6tTGm37Pu1IL/I8r/bRnz55lfp7eeuutTm38vp8aWGrWrFkmJCTEvPnmm+aXX34xN998s6lRo4bZt2+fp0uDj5swYYJp3ry5ycjIcHz99ddfjvtvvfVWU7duXbNkyRKzZs0a06lTJ3PhhRc67i8qKjItWrQwKSkpZv369WbevHkmJibGjB8/3tHmt99+M+Hh4ebuu+82aWlp5sUXXzSBgYFmwYIFjjb0cZxo3rx55sEHHzSffvqpkWRmz57tdP/kyZNNVFSU+eyzz8xPP/1kLr30UpOUlGSOHj3qaNOvXz9zwQUXmFWrVpkVK1aYxo0bm6FDhzruz87ONrVr1zbXXnut2bRpk3n//fdNWFiYee211xxtvvvuOxMYGGiefPJJk5aWZv7v//7PBAcHm59//tmtWuCfztRPhw8fbvr16+f08zUrK8upDf0UVurbt6956623zKZNm8yGDRvMgAEDTL169czhw4cdbbzp9/yZaoF/cqWf9ujRw9x8881OP0+zs7Md958N/ZTAbbEOHTqYMWPGOG4XFxebxMREM2nSJA9WBX8wYcIEc8EFF5R736FDh0xwcLD56KOPHNs2b95sJJmVK1caY47/wRkQEGD27t3raDN16lQTGRlpCgoKjDHG/Pvf/zbNmzd3OvbVV19t+vbt67hNH8epnBxkSkpKTHx8vHnqqacc2w4dOmTsdrt5//33jTHGpKWlGUnmxx9/dLSZP3++sdlsZs+ePcYYY1555RVTs2ZNRz81xphx48aZJk2aOG4PGTLEXHzxxU71dOzY0dxyyy0u14Kzw6kC92WXXXbKfeinqGqZmZlGkvnmm2+MMd71e96VWnB2OLmfGnM8cP/rX/865T5nQz/llHILHTt2TGvXrlVKSopjW0BAgFJSUrRy5UoPVgZ/sW3bNiUmJqphw4a69tprtXv3bknS2rVrVVhY6NT3mjZtqnr16jn63sqVK9WyZUvVrl3b0aZv377KycnRL7/84mhz4jFK25Qegz4Od6Snp2vv3r1O/SUqKkodO3Z06pc1atRQu3btHG1SUlIUEBCgH374wdGme/fuCgkJcbTp27evtm7dqoMHDzranK7vulILzm7Lli1TXFycmjRpotGjR+vAgQOO++inqGrZ2dmSpOjoaEne9XvelVpwdji5n5aaMWOGYmJi1KJFC40fP155eXmO+86Gfhpk6dHPcvv371dxcbFTB5Kk2rVra8uWLR6qCv6iY8eOmj59upo0aaKMjAylpqaqW7du2rRpk/bu3auQkBDVqFHDaZ/atWtr7969kqS9e/eW2zdL7ztdm5ycHB09elQHDx6kj8Nlpf2qvP5yYp+Li4tzuj8oKEjR0dFObZKSksoco/S+mjVrnrLvnniMM9WCs1e/fv00ePBgJSUlaceOHXrggQfUv39/rVy5UoGBgfRTVKmSkhLdeeed6tKli1q0aCFJXvV73pVa4P/K66eSNGzYMNWvX1+JiYnauHGjxo0bp61bt+rTTz+VdHb0UwI34KP69+/v+P/555+vjh07qn79+vrwww8VFhbmwcoAwLddc801jv+3bNlS559/vho1aqRly5apV69eHqwMZ6MxY8Zo06ZN+vbbbz1dCnBKp+qno0aNcvy/ZcuWSkhIUK9evbRjxw41atSoqsv0CE4pt1BMTIwCAwPLzH63b98+xcfHe6gq+KsaNWrovPPO0/bt2xUfH69jx47p0KFDTm1O7Hvx8fHl9s3S+07XJjIyUmFhYfRxuKW0T5yuv8THxyszM9Pp/qKiImVlZVVK3z3x/jPVApRq2LChYmJitH37dkn0U1SdsWPH6osvvtDSpUtVp04dx3Zv+j3vSi3wb6fqp+Xp2LGjJDn9PPX3fkrgtlBISIjatm2rJUuWOLaVlJRoyZIl6ty5swcrgz86fPiwduzYoYSEBLVt21bBwcFOfW/r1q3avXu3o+917txZP//8s9MfjYsWLVJkZKSSk5MdbU48Rmmb0mPQx+GOpKQkxcfHO/WXnJwc/fDDD0798tChQ1q7dq2jzddff62SkhLHL+nOnTtr+fLlKiwsdLRZtGiRmjRpopo1azranK7vulILUOqPP/7QgQMHlJCQIIl+CusZYzR27FjNnj1bX3/9dZnLE7zp97wrtcA/namflmfDhg2S5PTz1O/7qaVTssHMmjXL2O12M336dJOWlmZGjRplatSo4TQTH1AR99xzj1m2bJlJT0833333nUlJSTExMTEmMzPTGHN86YN69eqZr7/+2qxZs8Z07tzZdO7c2bF/6TIMffr0MRs2bDALFiwwsbGx5S7DcN9995nNmzebl19+udxlGOjjKJWbm2vWr19v1q9fbySZZ5991qxfv97s2rXLGHN8iaMaNWqYOXPmmI0bN5rLLrus3GXBWrdubX744Qfz7bffmnPPPddpuaVDhw6Z2rVrm+uvv95s2rTJzJo1y4SHh5dZbikoKMg8/fTTZvPmzWbChAnlLrd0plrgn07XT3Nzc829995rVq5cadLT083ixYtNmzZtzLnnnmvy8/Mdx6CfwkqjR482UVFRZtmyZU7LKeXl5TnaeNPv+TPVAv90pn66fft288gjj5g1a9aY9PR0M2fOHNOwYUPTvXt3xzHOhn5K4K4CL774oqlXr54JCQkxHTp0MKtWrfJ0SfADV199tUlISDAhISHmnHPOMVdffbXZvn274/6jR4+a2267zdSsWdOEh4ebyy+/3GRkZDgdY+fOnaZ///4mLCzMxMTEmHvuuccUFhY6tVm6dKlp1aqVCQkJMQ0bNjRvvfVWmVro4yi1dOlSI6nM1/Dhw40xx5c5euihh0zt2rWN3W43vXr1Mlu3bnU6xoEDB8zQoUNN9erVTWRkpBk5cqTJzc11avPTTz+Zrl27Grvdbs455xwzefLkMrV8+OGH5rzzzjMhISGmefPm5ssvv3S635Va4J9O10/z8vJMnz59TGxsrAkODjb169c3N998c5kPEemnsFJ5/VOS0+9gb/o970ot8D9n6qe7d+823bt3N9HR0cZut5vGjRub++67z2kdbmP8v5/ajDHG2jF0AAAAAADOPlzDDQAAAACABQjcAAAAAABYgMANAAAAAIAFCNwAAAAAAFiAwA0AAAAAgAUI3AAAAAAAWIDADQAAAACABQjcAAAAAABYgMANAICfsNls+uyzz/72cR566CGNGjXq7xf0N+zfv19xcXH6448/PFoHAAB/B4EbAIBKNmLECNlsNtlsNoWEhKhx48Z65JFHVFRUZOnjZmRkqH///n/rGHv37tXzzz+vBx980LGtZ8+euvPOO8u0nT59umrUqOG4nZeXp/Hjx6tRo0YKDQ1VbGysevTooTlz5jgdq/S1sdvtOuecczRw4EB9+umnTseOiYnRDTfcoAkTJvyt5wMAgCcRuAEAsEC/fv2UkZGhbdu26Z577tHEiRP11FNPldv22LFjlfKY8fHxstvtf+sYb7zxhi688ELVr1/f7X1vvfVWffrpp3rxxRe1ZcsWLViwQFdeeaUOHDjg1O7mm29WRkaGduzYoU8++UTJycm65ppryoyqjxw5UjNmzFBWVtbfek4AAHgKgRsAAAvY7XbFx8erfv36Gj16tFJSUvT5559LOj4CPmjQID3++ONKTExUkyZNJEm///67hgwZoho1aig6OlqXXXaZdu7c6XTcN998U82bN5fdbldCQoLGjh3ruO/kU8p//vln/eMf/1BYWJhq1aqlUaNG6fDhw6ete9asWRo4cGCFnvPnn3+uBx54QAMGDFCDBg3Utm1b3X777brxxhud2oWHhys+Pl516tRRp06dNGXKFL322muaNm2aFi9e7GjXvHlzJSYmavbs2RWqBwAATyNwAwBQBcLCwpxGspcsWaKtW7dq0aJF+uKLL1RYWKi+ffsqIiJCK1as0Hfffafq1aurX79+jv2mTp2qMWPGaNSoUfr555/1+eefq3HjxuU+3pEjR9S3b1/VrFlTP/74oz766CMtXrzYKaCfLCsrS2lpaWrXrl2FnmN8fLzmzZun3Nxct/cdPny4atasWebU8g4dOmjFihUVqgcAAE8L8nQBAAD4M2OMlixZooULF+r22293bK9WrZreeOMNhYSESJLee+89lZSU6I033pDNZpMkvfXWW6pRo4aWLVumPn366LHHHtM999yjf/3rX47jtG/fvtzHnTlzpvLz8/XOO++oWrVqkqSXXnpJAwcO1JQpU1S7du0y++zevVvGGCUmJlboub7++uu69tprVatWLV1wwQXq2rWrrrzySnXp0uWM+wYEBOi8884rM6KfmJio9evXV6geAAA8jRFuAAAs8MUXX6h69eoKDQ1V//79dfXVV2vixImO+1u2bOkI25L0008/afv27YqIiFD16tVVvXp1RUdHKz8/Xzt27FBmZqb+/PNP9erVy6XH37x5sy644AJH2JakLl26qKSkRFu3bi13n6NHj0qSQkNDK/CMpe7du+u3337TkiVLdOWVV+qXX35Rt27d9Oijj7q0vzHG8WFDqbCwMOXl5VWoHgAAPI0RbgAALHDRRRdp6tSpCgkJUWJiooKCnH/lnhiEJenw4cNq27atZsyYUeZYsbGxCgiw/jPymJgYSdLBgwcVGxvr2B4ZGans7Owy7Q8dOqSoqCinbcHBwerWrZu6deumcePG6bHHHtMjjzyicePGOX3AcLLi4mJt27atzIh9VlaWUy0AAPgSRrgBALBAtWrV1LhxY9WrV69M2C5PmzZttG3bNsXFxalx48ZOX1FRUYqIiFCDBg20ZMkSlx6/WbNm+umnn3TkyBHHtu+++04BAQGOSdpO1qhRI0VGRiotLc1pe5MmTbRu3boy7detW6fzzjvvtHUkJyerqKhI+fn5p2339ttv6+DBg7riiiuctm/atEmtW7c+7b4AAHgrAjcAAF7g2muvVUxMjC677DKtWLFC6enpWrZsme644w798ccfkqSJEyfqmWee0QsvvKBt27Zp3bp1evHFF095vNDQUA0fPlybNm3S0qVLdfvtt+v6668v9/pt6fh11CkpKfr222+dto8ePVq//vqr7rjjDm3cuFFbt27Vs88+q/fff1/33HOPo13Pnj312muvae3atdq5c6fmzZunBx54QBdddJEiIyMd7fLy8rR371798ccfWrVqlcaNG6dbb71Vo0eP1kUXXeTUbu3aterTp0+FX1cAADyJwA0AgBcIDw/X8uXLVa9ePQ0ePFjNmjXTTTfdpPz8fEdYHT58uJ577jm98sorat68uS655BJt27btlMdbuHChsrKy1L59e1155ZXq1auXXnrppdPW8c9//lOzZs1SSUmJY1vDhg21fPlybdmyRSkpKerYsaM+/PBDffTRR+rXr5+jXd++ffX222+rT58+atasmW6//Xb17dtXH374odNjTJs2TQkJCWrUqJEGDx6stLQ0ffDBB3rllVec2s2ZM0f16tVTt27d3HotAQDwFjZjjPF0EQAAwDsYY9SxY0fdddddGjp0qEdr6dSpk+644w4NGzbMo3UAAFBRjHADAAAHm82m119/XUVFRR6tY//+/Ro8eLDHQz8AAH8HI9wAAAAAAFiAEW4AAAAAACxA4AYAAAAAwAIEbgAAAAAALEDgBgAAAADAAgRuAAAAAAAsQOAGAAAAAMACBG4AAAAAACxA4AYAAAAAwAIEbgAAAAAALPD/AOcObDXPYKI7AAAAAElFTkSuQmCC",
+            "text/plain": [
+              "<Figure size 1200x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
       "source": [
-        "# TODO: Código"
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "from scipy import stats\n",
+        "\n",
+        "ds = pd.read_csv('assets/real_estate.csv', sep=';')\n",
+        "\n",
+        "z_scores_price = stats.zscore(ds['price'])\n",
+        "outliers_price = ds[abs(z_scores_price) > 50000]\n",
+        "\n",
+        "cleaned_ds = ds.copy() \n",
+        "cleaned_ds.drop(outliers_price.index, inplace=True) \n",
+        "\n",
+        "plt.figure(figsize=(12, 6))\n",
+        "plt.scatter(ds['surface'], cleaned_ds['price'])\n",
+        "plt.ylabel('Superficie (m²)')\n",
+        "plt.xlabel('Precio (USD)')\n",
+        "plt.title('Relación entre Superficie y Precio (Outliers Eliminados)')\n",
+        "plt.grid(True)\n",
+        "plt.show()"
       ]
     },
     {
@@ -688,7 +1032,7 @@
       "id": "ahead-liquid",
       "metadata": {},
       "source": [
-        "**TODO: Markdown**. Para escribir aquí, haz doble clic en esta celda, elimina este contenido y coloca lo que quieras escribir. Luego ejecuta la celda."
+        "En algunas áreas, las propiedades con superficies similares pueden tener precios muy diferentes, lo que podría deberse a variaciones en el nivel socioeconómico del barrio, la antigüedad de la propiedad, o incluso la demanda del mercado en ese momento."
       ]
     },
     {
@@ -704,12 +1048,28 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 40,
       "id": "valid-honolulu",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "El número de agencias de bienes raíces en el dataset es: 1821\n"
+          ]
+        }
+      ],
       "source": [
-        "# TODO"
+        "import pandas as pd\n",
+        "\n",
+        "ds = pd.read_csv('assets/real_estate.csv', sep=';')\n",
+        "\n",
+        "numero_de_agencias = ds['realEstate_name'].nunique()\n",
+        "\n",
+        "print(f\"El número de agencias de bienes raíces en el dataset es: {numero_de_agencias}\")\n",
+        "\n",
+        "\n"
       ]
     },
     {
@@ -725,12 +1085,30 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 41,
       "id": "static-perry",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "La población con mayor cantidad de casas es  Madrid Capital con un total de 6228 casas.\n"
+          ]
+        }
+      ],
       "source": [
-        "# TODO"
+        "import pandas as pd\n",
+        "\n",
+        "ds = pd.read_csv('assets/real_estate.csv', sep=';')\n",
+        "ds = ds[ds['price'] > 0]\n",
+        "ds = ds[ds['surface'] > 0]\n",
+        "\n",
+        "poblacion_con_mas_casas = ds['level5'].value_counts().idxmax()\n",
+        "numero_de_casas = ds['level5'].value_counts().max()\n",
+        "\n",
+        "# población con mayor cantidad de casas y número \n",
+        "print(f\"La población con mayor cantidad de casas es {poblacion_con_mas_casas} con un total de {numero_de_casas} casas.\")\n"
       ]
     },
     {
@@ -746,12 +1124,64 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 42,
       "id": "binary-input",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "     Unnamed: 0  id_realEstates  isNew                    realEstate_name  \\\n",
+            "1             2       153867863  False  tecnocasa fuenlabrada ferrocarril   \n",
+            "3             4       152776331  False  tecnocasa fuenlabrada ferrocarril   \n",
+            "85           86       153152077  False            sinergical inmobiliaria   \n",
+            "94           95       153995577  False                    viviendas365com   \n",
+            "109         110       153586414  False    area uno asesores inmobiliarios   \n",
+            "\n",
+            "     phone_realEstate                                       url_inmueble  \\\n",
+            "1         916358736.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
+            "3         916358736.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
+            "85                NaN  https://www.fotocasa.es/es/comprar/vivienda/le...   \n",
+            "94        911226014.0  https://www.fotocasa.es/es/comprar/vivienda/le...   \n",
+            "109       912664081.0  https://www.fotocasa.es/es/comprar/vivienda/ma...   \n",
+            "\n",
+            "     rooms  bathrooms  surface   price  ... level4Id level5Id level6Id  \\\n",
+            "1      3.0        1.0      NaN   89000  ...        0        0        0   \n",
+            "3      3.0        1.0     86.0   89000  ...        0        0        0   \n",
+            "85     1.0        1.0     50.0  107000  ...        0        0        0   \n",
+            "94     3.0        2.0    120.0  320000  ...        0        0        0   \n",
+            "109    3.0        3.0    142.0  425000  ...        0        0        0   \n",
+            "\n",
+            "    level7Id level8Id accuracy          latitude          longitude zipCode  \\\n",
+            "1          0        0        1          40,28674           -3,79351     NaN   \n",
+            "3          0        0        0  40,2853785786438  -3,79508142135624     NaN   \n",
+            "85         0        0        1          40,35059           -3,82693     NaN   \n",
+            "94         0        0        0          40,31933           -3,77574     NaN   \n",
+            "109        0        0        0        40,3313411         -3,8313868     NaN   \n",
+            "\n",
+            "    customZone  \n",
+            "1          NaN  \n",
+            "3          NaN  \n",
+            "85         NaN  \n",
+            "94         NaN  \n",
+            "109        NaN  \n",
+            "\n",
+            "[5 rows x 37 columns]\n"
+          ]
+        }
+      ],
       "source": [
-        "# TODO"
+        "import pandas as pd\n",
+        "\n",
+        "ds = pd.read_csv('assets/real_estate.csv', sep=';')\n",
+        "\n",
+        "poblaciones_cinturon_sur = [\"Fuenlabrada\", \"Leganés\", \"Getafe\", \"Alcorcón\"]\n",
+        "\n",
+        "ds_cinturon_sur = ds[ds['level5'].isin(poblaciones_cinturon_sur)]\n",
+        "\n",
+        "# Subconjunto\n",
+        "print(ds_cinturon_sur.head())\n"
       ]
     },
     {
@@ -767,12 +1197,43 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 43,
       "id": "lyric-bunch",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
       "source": [
-        "# TODO: Code"
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "ds = pd.read_csv('assets/real_estate.csv', sep=';')\n",
+        "\n",
+        "poblaciones_cinturon_sur = [\"Fuenlabrada\", \"Leganés\", \"Getafe\", \"Alcorcón\"]\n",
+        "\n",
+        "ds_cinturon_sur = ds[ds['level5'].isin(poblaciones_cinturon_sur)]\n",
+        "\n",
+        "# Calcular la mediana \n",
+        "mediana_precios = ds_cinturon_sur.groupby('level5')['price'].median()\n",
+        "\n",
+        "# Trazar el gráfico \n",
+        "plt.figure(figsize=(10, 6))\n",
+        "mediana_precios.plot(kind='bar', color='skyblue', edgecolor='black')\n",
+        "plt.title('Mediana de Precios por Población en el Cinturón Sur de Madrid')\n",
+        "plt.xlabel('Población')\n",
+        "plt.ylabel('Mediana de Precios (USD)')\n",
+        "plt.xticks(rotation=45)\n",
+        "plt.grid(axis='y')\n",
+        "plt.show()\n"
       ]
     },
     {
@@ -781,7 +1242,9 @@
       "id": "sublime-newspaper",
       "metadata": {},
       "source": [
-        "**TODO: Markdown**. Para escribir aquí, haz doble clic en esta celda, elimina este contenido y coloca lo que quieras escribir. Luego ejecuta la celda."
+        "Este tipo de análisis es útil para entender las diferencias en el mercado inmobiliario entre estas áreas.\n",
+        "\n",
+        "Observamos que la mediana de precios varía entre las poblaciones, lo que refleja diferencias en el valor percibido de las propiedades, la demanda, o el nivel socioeconómico de cada área. Por ejemplo, si \"Getafe\" tiene una mediana de precios más alta que \"Fuenlabrada\", esto podría indicar que Getafe es un área más deseada o con propiedades de mayor valor en comparación con Fuenlabrada."
       ]
     },
     {
@@ -797,12 +1260,51 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 44,
       "id": "random-feeling",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Para la variable 'price':\n",
+            "Media: 223094.48\n",
+            "Varianza de muestra: 14921367508.05\n",
+            "\n",
+            "Para la variable 'rooms':\n",
+            "Media: 3.02\n",
+            "Varianza de muestra: 0.72\n",
+            "\n",
+            "Para la variable 'surface':\n",
+            "Media: 111.75\n",
+            "Varianza de muestra: 4263.05\n",
+            "\n",
+            "Para la variable 'bathrooms':\n",
+            "Media: 1.63\n",
+            "Varianza de muestra: 0.57\n",
+            "\n"
+          ]
+        }
+      ],
       "source": [
-        "# TODO"
+        "import pandas as pd\n",
+        "\n",
+        "ds = pd.read_csv('assets/real_estate.csv', sep=';')\n",
+        "\n",
+        "poblaciones_cinturon_sur = [\"Fuenlabrada\", \"Leganés\", \"Getafe\", \"Alcorcón\"]\n",
+        "\n",
+        "ds_cinturon_sur = ds[ds['level5'].isin(poblaciones_cinturon_sur)]\n",
+        "\n",
+        "# Calcular la media y varianza para las variables\n",
+        "variables = ['price', 'rooms', 'surface', 'bathrooms']\n",
+        "\n",
+        "for variable in variables:\n",
+        "    media = ds_cinturon_sur[variable].mean()\n",
+        "    varianza = ds_cinturon_sur[variable].var()\n",
+        "    print(f\"Para la variable '{variable}':\")\n",
+        "    print(f\"Media: {media:.2f}\")\n",
+        "    print(f\"Varianza de muestra: {varianza:.2f}\\n\")\n"
       ]
     },
     {
@@ -818,12 +1320,35 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 45,
       "id": "fifteen-browse",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "La casa más cara en Alcorcón está ubicada en Alcorcón con un precio de 950000 USD\n",
+            "La casa más cara en Fuenlabrada está ubicada en Calle de Paulo Freire, 5, Fuenlabrada con un precio de 490000 USD\n",
+            "La casa más cara en Getafe está ubicada en Getafe con un precio de 1050000 USD\n",
+            "La casa más cara en Leganés está ubicada en Avenida Reina Sofía, Leganés con un precio de 650000 USD\n"
+          ]
+        }
+      ],
       "source": [
-        "# TODO"
+        "import pandas as pd\n",
+        "\n",
+        "ds = pd.read_csv('assets/real_estate.csv', sep=';')\n",
+        "\n",
+        "poblaciones_cinturon_sur = [\"Fuenlabrada\", \"Leganés\", \"Getafe\", \"Alcorcón\"]\n",
+        "\n",
+        "ds_cinturon_sur = ds[ds['level5'].isin(poblaciones_cinturon_sur)]\n",
+        "\n",
+        "casas_mas_caras = ds_cinturon_sur.loc[ds_cinturon_sur.groupby('level5')['price'].idxmax()]\n",
+        "\n",
+        "# Dirección y el precio de cada casa más cara\n",
+        "for index, row in casas_mas_caras.iterrows():\n",
+        "    print(f\"La casa más cara en {row['level5']} está ubicada en {row['address']} con un precio de {row['price']} USD\")\n"
       ]
     },
     {
@@ -841,12 +1366,64 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 46,
       "id": "civic-meditation",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/tmp/ipykernel_653/487487773.py:12: SettingWithCopyWarning: \n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "  ds_cinturon_sur['normalized_price'] = ds_cinturon_sur.groupby('level5')['price'].transform(zscore)\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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ff79hsViMv//+2zAMw9i2bZshyRg0aJDDfL179zYkGSNGjLC3jRgxwpBk9OjRw2l5CQkJTm2ffvqpIcn46aefnPro37+/vS0lJcUoWbKkYbFYjHHjxtnbz58/bwQGBjqsk5SUFCMpKclhOefPnzeKFStmPPbYY/a25557zggNDTVSUlKc6roZScaAAQMMwzCMli1bGpGRkfbXlzauW7Zssc9fq1Yto2jRosbZs2ftbTt27DC8vLyMRx991Om1p7f+ypQpY0gyNmzYYG9bvny5IckIDAw0Dh8+bG9///33DUnGjz/+aG9zdf2n1X/w4EF7W/PmzY3mzZtnuD6++OILQ5IxatSoGy5v48aNhiRj3rx59rZBgwYZkoxffvnF3nbq1CmjYMGCDnWcOnXK8PPzM9q1a2ekpqba533vvfcMScZHH31kGIZh/Pbbb4YkY+HChRnWm57k5GSjaNGiRq1atRzePx988IEhyeH1f/zxx4aXl5fx888/O/Qxc+ZMQ5Kxfv36Gy6rV69eRnBwsHH69Gnj9OnTxt9//228+eabhsViMWrUqGGfL23Mly1b5vD80aNHG8HBwcZff/3l0D5s2DDD29vbOHLkiGEYhvHDDz8YkoyBAwc61WCz2RyWc+02lDYm176+S5cuGeXKlTPKli1rX/+dOnVy+oxyxY8//mhIMkqUKGHExcXZ29PeR++++65hGP8bkzvuuMO4cuWKfb5vv/3WkGS8/vrr9rZevXoZkoxhw4a5VEPa+3zVqlXG6dOnjX/++cf47LPPjMKFCxuBgYHGv//+a2zfvt2QZDz++OMOz33hhRcMScYPP/xgb0sbq8WLF9vbLl68aERFRRm1a9d2eu032zbHjh1rWCwWh+26WbNmRoECBRzaDMNxLK/ffl3dbgzj6nZ+/faZlJRkREZGGl27ds1wXRqGYRw6dMjw9vY2xowZ49D+xx9/GD4+Pg7tubWcXr16GWXKlLlhf++++64hyfjqq69uOF+a9MbP1dezZcsWQ5Ixe/Zsp36v3wav7fvaz5605ZcvX97hfZOVbeXaz2vDMIzatWsbdevWvek6ePTRRw1JRqFChYz77rvPeOedd4zdu3c7zZf2fXa99L5jMvqsS48Zn9WGcfV3o9atWxuGYRipqalGZGSkERsbaxw8eNCQZLz99tv25yUmJjpsb4ZhGAcPHjT8/f0d1vvkyZMNScYXX3xhb4uPjzcqVqyY4fts5syZTnVe/z7JTL9ARjhkHbeUadOmaeXKlU4/NWrUuOlzw8LC9Oeff2rfvn2ZXu73338vb29vDRw40KH9+eefl2EYWrp0qSRp2bJlkmTf+5rm2WefzbDvJ5980qnt2j2eiYmJOnPmjP3iL7/++qvT/I8//rj9/97e3qpXr54Mw1Dfvn3t7WFhYapcubIOHDjgMG/a+VQ2m03nzp1TSkqK6tWr57CcsLAwxcfH2w/vz6qRI0fqxIkTmjlzZrrTjx8/ru3bt6t3794KDw+3t9eoUUNt27bV999/7/Sc9NafJFWrVk3R0dH2xw0bNpR09TDb0qVLO7Vfu14yu/5dtWvXLj322GPq1KmTXn311XSXZ7VadfbsWVWsWFFhYWEOy/v+++9155132g8JlaSIiAinw41XrVql5ORkDRo0yOECZP369VNoaKj9cOq0PeDLly9XQkKCy69j69atOnXqlJ588kmH8/F69+7ttFd94cKFqlq1qqpUqaIzZ87Yf9IOd07v9IjrxcfHKyIiQhEREapYsaJefvllRUdHO+11LVeunGJiYpyW37RpUxUqVMhh+W3atFFqaqp++uknSdLixYtlsVg0YsQIp+Xf6IiI77//Xg0aNLCfLiFJISEh6t+/vw4dOqRdu3ZJuroN/fvvv9qyZctNX296Hn30URUoUMD++P7771dUVJR9m0gbk6efftrhvN27775bVapUSfcQ+hvt3UtPmzZtFBERoVKlSunBBx9USEiIvvrqK5UoUcJex/VHvzz//POS5LT84sWL248ekKTQ0FA9+uij+u2333TixIkMa7h2W4mPj9eZM2fUqFEjGYZhP/Xk9OnT+umnn/TYY485bOvSjcfS1e0mTUhIiMO1Dfz8/NSgQQOHz5L0fPnll7LZbOrevbvDezIyMlKVKlVy2iZyazk3k3ao97Xvw6zI6uvJql69ejm8b7KyrVz/PdO0aVOX6p09e7bee+89lStXTl999ZVeeOEFVa1aVa1bt3Y6PD4z0vusS09uf1an6dmzp9asWaMTJ07ohx9+0IkTJzI8Esjf39++vaWmpurs2bMKCQlR5cqVnb7/oqKidP/999vbgoKC1L9//wz77dOnz01rzWy/QHo4ZB23lAYNGqR76GjaL9s3MmrUKHXq1Em33Xab7rjjDrVv316PPPKIS2H+8OHDKl68uNMvIlWrVrVPT/vXy8tL5cqVc5ivYsWKGfZ9/bzS1XOqYmNj9dlnn+nUqVMO09I7t/f6XzoLFiyogIAAFSlSxKn9+vOg586dqwkTJmjPnj0Oh/5fW9fTTz+tL774Qh06dFCJEiXUrl07de/eXe3bt8/wdaWnWbNmatmypd566610g3Taekzv8LuqVatq+fLlThefSm/9SemvE0kOh+hd237+/Hl7W2bXvyvi4uLUpUsXlShRQvPmzXMIBmnnZs6ePVtHjx51OPT+2uUdPnzY/geEa12/vjJaj35+fipfvrx9erly5TRkyBBNnDhR8+fPV9OmTXXvvffar0eQkbTnV6pUyaHd19dX5cuXd2jbt2+fdu/erYiIiHT7un79picgIEDffPONpKu/ZJUrV04lS5Z0mi+998K+ffv0+++/33T5+/fvV/HixR3+EOSKjMbk2s+GO+64Qy+99JJWrVqlBg0aqGLFimrXrp169uyZ7vml6bl+XVssFlWsWNF+bumNtp0qVapo3bp1Dm0+Pj7prsMbmTZtmm677Tb5+PioWLFiqly5sv0X6bTPvus/6yIjIxUWFmavL03FihWdwnHaPaAPHTqkyMjIdGs4cuSIXn/9df33v/912Gal/20raUEps7fCdHW7SVOyZEmn11CoUCH9/vvvN1zOvn37ZBiG05imufaw+Nxczs2EhoZKki5dupSp510vq68nq67/XMjstpJ2PYlrFSpUyOn9lx4vLy8NGDBAAwYM0NmzZ7V+/XrNnDlTS5cu1YMPPpjhudw3k9H33vVy+7M6Tdq1KT7//HNt375d9evXd/i8ulbatTumT5+ugwcPOpybf+0pSYcPH073cyOjw/VLlCjh0gXcMtsvkB4COeCiZs2aaf/+/fr666+1YsUK/ec//9GkSZM0c+ZMhz3MuS2983+7d++uDRs26MUXX1StWrUUEhIim82m9u3bp3sBr/TO58voHL9rw94nn3yi3r17q3PnznrxxRdVtGhReXt7a+zYsfbzQyWpaNGi2r59u5YvX66lS5dq6dKlmj17th599FHNnTs3U693xIgRatGihd5///1Mnb+YkYyuLJvR63dlvWR2/buid+/eOnbsmDZv3mz/xTbNs88+q9mzZ2vQoEGKjo5WwYIFZbFY9OCDD2Z5ea6aMGGCevfubd8uBg4cqLFjx2rTpk2ZDmzpsdlsql69uiZOnJju9Ov/QJIeb29vtWnT5qbzpfdesNlsatu2rYYOHZruc9JCYE6rWrWq9u7dq2+//VbLli3T4sWLNX36dL3++uuKjY3NlRqude1eKVdl9AfRa+XkLcJSU1PVtm1bnTt3Ti+99JKqVKmi4OBgHT16VL17987xbeV6rnyWpMdms8lisWjp0qXp9hESEmLKcm6mSpUqkqQ//vhDnTt3ztRzr5XV15Mmo/dYampqun1n9+rj2Tlf/lqFCxfWvffeq3vvvVctWrTQ2rVrdfjwYZUpU+aGryk9OXFFdXd8Vqfx9/dXly5dNHfuXB04cOCGt5J988039dprr+mxxx7T6NGjFR4eLi8vLw0aNChb23ReuOo8bh0EciATwsPD1adPH/Xp00eXL19Ws2bNNHLkSHsgz+hLsUyZMlq1apUuXbrksJd8z5499ulp/9psNh08eNDhL9J///23yzWeP39eq1evVmxsrMPtWLJyqP3NLFq0SOXLl9eXX37p8NrTO2zXz89PHTt2VMeOHWWz2fT000/r/fff12uvvXbDIwCu17x5c7Vo0ULjx493ut1M2nrcu3ev0/P27NmjIkWK5PhtzXJi/Y8bN05LlizRl19+af+l9lqLFi1Sr169HK6Cn5iY6HRl5zJlyqRbx/Xr69r1eO1ekOTkZB08eNAp3FavXl3Vq1fXq6++qg0bNqhx48aaOXOm3njjjXRfT1r/+/btsx/OKF093P7gwYOqWbOmva1ChQrasWOHWrdubcr9nCtUqKDLly/fNNBXqFBBy5cv17lz5zK1l7xMmTIZvl/TpqcJDg7WAw88oAceeEDJycnq0qWLxowZo+HDh9/09lDXj7thGPr777/tR/hcO+bXjkla27V15IS0z759+/bZjw6Qrl707sKFC07L//vvv2UYhsN74q+//pIkpws3pvnjjz/0119/ae7cuXr00Uft7defSpP2nt+5c2emX4Pk+naTVRUqVJBhGCpXrlyO/kHI3ctp0qSJChUqpE8//VQvv/yy24Jqem70WVGoUCGnz0bp6p7O6/f6psfsbUWS6tWrp7Vr1+r48eMqU6aMChUqJOnqHSGu/UP19UdlZJaZn9U9e/bURx99JC8vLz344IMZzrdo0SK1bNlSs2bNcmi/cOGCw1F+ZcqU0c6dO50+N9L7/M2MnOoXtxbOIQdcdP2h2iEhIapYsaLDrS3Swt71X/Z33XWXUlNT9d577zm0T5o0SRaLxX6l27RzuqZPn+4w39SpU12uM+2XnOv3Flx7NWh3SW9Zv/zyizZu3Ogw3/XrzsvLyx4EsnJrkLRzya+/N2tUVJRq1aqluXPnOozBzp07tWLFCt11112ZXlZmuXv9r1q1Sq+++qpeeeWVDPcqeXt7Oy1v6tSpTntH7rrrLm3atEmbN2+2t50+fVrz5893mK9Nmzby8/PTlClTHPqdNWuWLl68aL/qf1xcnFJSUhyeW716dXl5ed1wXOvVq6eIiAjNnDlTycnJ9vY5c+Y4bTvdu3fX0aNH9eGHHzr1c+XKFcXHx2e4HHfo3r27Nm7cqOXLlztNu3Dhgv31d+3aVYZhpLu3+kZ77u666y5t3rzZYZuJj4/XBx98oLJly6patWqSnLchPz8/VatWTYZhuHSXiHnz5jkcKrxo0SIdP37c/tlTr149FS1aVDNnznQYu6VLl2r37t03vNODO6Rtm9dvJ2l7265f/rFjxxyuARAXF6d58+apVq1aGR6unt62aRiG0+0XIyIi1KxZM3300Uc6cuSIw7QbjaWr2012denSRd7e3oqNjXWqxzAMl26vaMZygoKC9NJLL2n37t166aWX0l2Xn3zyicPnU1Zl9F0sXQ2OmzZtcvjs+fbbb12+lVtubSsnTpywX0PiWsnJyVq9erXDKR4VKlSQJPs1LaSrnyOZPQLtemZ+Vrds2VKjR4/We++9l+E2LaX//bdw4UKnc+zvuusuHTt2TIsWLbK3JSQkOP0ekVk51S9uLewhB1xUrVo1tWjRQnXr1lV4eLi2bt2qRYsW6ZlnnrHPU7duXUnSwIEDFRMTI29vbz344IPq2LGjWrZsqVdeeUWHDh1SzZo1tWLFCn399dcaNGiQ/cu0bt266tq1qyZPnqyzZ8/ab3uWtufHlb86h4aGqlmzZnrrrbdktVpVokQJrVixwn6vX3e655579OWXX+q+++7T3XffrYMHD2rmzJmqVq2aw626Hn/8cZ07d06tWrVSyZIldfjwYU2dOlW1atVy2BvmqubNm6t58+Zau3at07S3335bHTp0UHR0tPr27Wu/7VnBggVveNibu7h7/ffo0UMRERGqVKmSPvnkE4dpbdu2VbFixXTPPffo448/VsGCBVWtWjVt3LhRq1atcjh/TpKGDh2qjz/+WO3bt9dzzz1nv+1ZmTJlHM6/jIiI0PDhwxUbG6v27dvr3nvv1d69ezV9+nTVr1/ffkGlH374Qc8884y6deum2267TSkpKfr444/l7e2trl27ZviafH199cYbb+iJJ55Qq1at9MADD+jgwYOaPXu20x6qRx55RF988YWefPJJ/fjjj2rcuLFSU1O1Z88effHFF/Z76eaUF198Uf/97391zz33qHfv3qpbt67i4+P1xx9/aNGiRTp06JCKFCmili1b6pFHHtGUKVO0b98+++kJP//8s1q2bOnwOXGtYcOG6dNPP1WHDh00cOBAhYeHa+7cuTp48KAWL15sPyy8Xbt2ioyMVOPGjVWsWDHt3r1b7733nu6++26XLpIVHh6uJk2aqE+fPjp58qQmT56sihUrql+/fpKujsn48ePVp08fNW/eXD169LDfyqls2bIaPHiw+1ZqOmrWrKlevXrpgw8+0IULF9S8eXNt3rxZc+fOVefOndWyZUuH+W+77Tb17dtXW7ZsUbFixfTRRx/p5MmTmj17dobLqFKliipUqKAXXnhBR48eVWhoqBYvXpzuubxTpkxRkyZNVKdOHfXv31/lypXToUOH9N1332n79u3p9u/qdpNdFSpU0BtvvKHhw4fr0KFD6ty5swoUKKCDBw/qq6++Uv/+/fXCCy/kyeW8+OKL+vPPPzVhwgT9+OOPuv/++xUZGakTJ05oyZIl2rx5szZs2OCW2sPCwjRz5kwVKFBAwcHBatiwocqVK6fHH39cixYtUvv27dW9e3ft379fn3zyif17+GZya1v5999/1aBBA7Vq1UqtW7dWZGSkTp06pU8//VQ7duzQoEGD7HuA27Vrp9KlS6tv37568cUX5e3trY8++kgRERFOf1TKDDM/q728vBwuXpqRe+65R6NGjVKfPn3UqFEj/fHHH5o/f75Tff369dN7772nRx99VNu2bVNUVJQ+/vjjbN9OLqf6xS0mh6/iDuQJ6d0e61rNmze/6W3P3njjDaNBgwZGWFiYERgYaFSpUsUYM2aMkZycbJ8nJSXFePbZZ42IiAjDYrE43Ibk0qVLxuDBg43ixYsbvr6+RqVKlYy3337b4TY6hnH1dhkDBgwwwsPDjZCQEKNz587G3r17DUkOtyFLu83J6dOnnV7Pv//+a9x3331GWFiYUbBgQaNbt27GsWPHMrx12vV9XHv7kRutJ5vNZrz55ptGmTJlDH9/f6N27drGt99+63QLnEWLFhnt2rUzihYtavj5+RmlS5c2nnjiCeP48eNOy7ierrnt2bXSbkmT3riuWrXKaNy4sREYGGiEhoYaHTt2NHbt2uUwz43WX5kyZdK9xV16taR3GxZX178rtz1Le43p/aTdTuX8+fNGnz59jCJFihghISFGTEyMsWfPnnRv7/P7778bzZs3NwICAowSJUoYo0ePNmbNmuVUh2FcvV1TlSpVDF9fX6NYsWLGU089ZZw/f94+/cCBA8Zjjz1mVKhQwQgICDDCw8ONli1bGqtWrXJad+mZPn26Ua5cOcPf39+oV6+e8dNPP6V727fk5GRj/Pjxxu233274+/sbhQoVMurWrWvExsYaFy9evOEyMnovXy+jMTeMq9vu8OHDjYoVKxp+fn5GkSJFjEaNGhnvvPOO0/b/9ttvG1WqVDH8/PyMiIgIo0OHDsa2bdsclnP9mOzfv9+4//77jbCwMCMgIMBo0KCB8e233zrM8/777xvNmjUzChcubPj7+xsVKlQwXnzxxZu+/rTt5NNPPzWGDx9uFC1a1AgMDDTuvvtup1t6GYZhfP7550bt2rUNf39/Izw83HjooYeMf//912EeV9dpmpt9/qaxWq1GbGysUa5cOcPX19coVaqUMXz4cCMxMdFhvrSxWr58uVGjRg3D39/fqFKlitPt99K7bdauXbuMNm3aGCEhIUaRIkWMfv36GTt27Ej3Flk7d+60b8cBAQFG5cqVjddee83pdWV2uzGM9L9zDMO124elWbx4sdGkSRMjODjYCA4ONqpUqWIMGDDA2Lt3b64vJzP9Gcb/vhPCw8MNHx8fIyoqynjggQeMNWvW2OfJ6LZnrr6er7/+2qhWrZrh4+PjNL4TJkwwSpQoYfj7+xuNGzc2tm7dmuFtzzK6rWN2tpWMblN2rbi4OOPdd981YmJijJIlSxq+vr5GgQIFjOjoaOPDDz90+t1h27ZtRsOGDe3fsRMnTszwtmc3u4Xr9fLKZ3VGtz17/vnnjaioKCMwMNBo3LixsXHjxnTrO3z4sHHvvfcaQUFBRpEiRYznnnvOWLZsmcvvs7RpWe0XyIjFMFy8CgYA02zfvl21a9fWJ5984nSLKgDIyJo1a9SyZUstXLjQ4bY8AAAgb+AcciCPuXLlilPb5MmT5eXlpWbNmplQEQAAAICcwDnkQB7z1ltvadu2bWrZsqV8fHzstwnr379/pm4bAgAAACBvI5ADeUyjRo20cuVKjR49WpcvX1bp0qU1cuRIvfLKK2aXBgAAAMCNOIccAAAAAAATcA45AAAAAAAmIJADAAAAAGACAjkAAAAAACbI9xd1s9lsOnbsmAoUKCCLxWJ2OQAAAACAfM4wDF26dEnFixeXl1fG+8HzfSA/duwYt4oCAAAAAOS6f/75RyVLlsxwer4P5AUKFJB0dUWEhoa6tW+r1aoVK1aoXbt28vX1dWvfyHmMn2dj/Dwb4+fZGD/Pxvh5NsbPszF+ni0z4xcXF6dSpUrZ82hG8n0gTztMPTQ0NEcCeVBQkEJDQ9mgPBDj59kYP8/G+Hk2xs+zMX6ejfHzbIyfZ8vK+N3stGku6gYAAAAAgAkI5AAAAAAAmIBADgAAAACACfL9OeQAAAAAkFWGYSglJUWpqanZ7stqtcrHx0eJiYlu6Q+5K238kpKSJEk+Pj7ZvrW2qYG8bNmyOnz4sFP7008/rWnTpikxMVHPP/+8PvvsMyUlJSkmJkbTp09XsWLFTKgWAAAAwK0kOTlZx48fV0JCglv6MwxDkZGR+ueff7Id5JD70sbvyJEjslgsCgoKUlRUlPz8/LLcp6mBfMuWLQ5/Gdq5c6fatm2rbt26SZIGDx6s7777TgsXLlTBggX1zDPPqEuXLlq/fr1ZJQMAAAC4BdhsNh08eFDe3t4qXry4/Pz8sh2ibTabLl++rJCQEHl5cfawp0kbv+DgYKWkpOj06dM6ePCgKlWqlOXxNDWQR0REODweN26cKlSooObNm+vixYuaNWuWFixYoFatWkmSZs+erapVq2rTpk268847zSgZAAAAwC0gOTlZNptNpUqVUlBQkFv6tNlsSk5OVkBAAIHcA6WNX2BgoLy8vOTr66vDhw/bxzQr8sy7IDk5WZ988okee+wxWSwWbdu2TVarVW3atLHPU6VKFZUuXVobN240sVIAAAAAtwqCMzLijvdGnrmo25IlS3ThwgX17t1bknTixAn5+fkpLCzMYb5ixYrpxIkTGfaTlJRkP8lekuLi4iRdPQHfarW6tea0/tzdL3IH4+fZGD/Pxvh5NsbPszF+no3xyz1Wq1WGYchms8lms7mlT8Mw7P+6q0/knuvHz2azyTAMWa1WeXt7O8zr6jaaZwL5rFmz1KFDBxUvXjxb/YwdO1axsbFO7StWrHDboSbXW7lyZY70i9zB+Hk2xs+zMX6ejfHzbIyfZ2P8cp6Pj48iIyN1+fJlJScnu7XvS5cuubW/vGbcuHH67rvv9PPPP7v8nEKFCumTTz7R3XffnYOV/c8999yj6tWra+zYsZl+btr4JScn68qVK/rpp5+UkpLiMI+rFwLME4H88OHDWrVqlb788kt7W2RkpJKTk3XhwgWHveQnT55UZGRkhn0NHz5cQ4YMsT+Oi4tTqVKl1K5dO4WGhrq1bqvVqpUrV6pt27by9fV1a9/IeYyfZ2P8PBvj59kYP8/G+Hk2xi/3JCYm6p9//lFISIjT+cGvfLUzS30auro31dfXVxa5foG4Mffdkanl9OnTR/PmzXNq37t3rypWrJipvrLC399f3t7emc5fgYGBbs9sGfHx8ZGfn1+mlmcYhi5duqQCBQrIYrEoMTFRgYGBatasmdN7JO1I7ZvWkamqc8js2bNVtGhRh7+G1K1bV76+vlq9erW6du0q6eob6MiRI4qOjs6wL39/f/n7+zu1+/r65tiHVk72jZzH+Hk2xs+zMX6ejfHzbIyfZ2P8cl5qaqosFou8vLyczxXO4tXWLcb//ytLpvrI7LnKFotF7du31+zZsx3aIyIicuWc+LSr0Wd2Wemu6xtI++NGVqWNr6vSTjO49n1hsVjS3R5drcv0KxTYbDbNnj1bvXr1ko/P//4+ULBgQfXt21dDhgzRjz/+qG3btqlPnz6Kjo7mCusAAAAAcAP+/v6KjIx0+Onbt686d+7sMN+gQYPUokUL+2ObzaaxY8eqXLlyCgwMVM2aNbVo0SL79DVr1shisWj16tWqV6+egoKC1KhRI+3duzfDWrZs2aK2bduqSJEiKliwoJo3b65ff/3Vab7jx4+rQ4cOCgwMVPny5R2We+jQIVksFn3++edq3ry5AgICNH/+fJ09e1Y9evRQiRIlFBQUpOrVq+vTTz916Dc+Pl6PPvqoQkJCFBUVpQkTJjgt++OPP1a9evVUoEABRUZGqmfPnjp16tTNVnO2mR7IV61apSNHjuixxx5zmjZp0iTdc8896tq1q5o1a6bIyEiHw9oBAAAAAO4zduxYzZs3TzNnztSff/6pwYMH6+GHH9batWsd5nvllVc0YcIEbd26VT4+PunmuTSXLl1Sr169tG7dOm3atEmVKlXSXXfd5XQu/WuvvaauXbtqx44deuihh/Tggw9q9+7dDvMMGzZMzz33nHbv3q2YmBglJiaqbt26+u6777Rz5071799fjzzyiDZv3mx/zosvvqi1a9fq66+/1ooVK7RmzRqnPwhYrVaNHj1aO3bs0JIlS3To0CH7BcdzkumHrLdr185+tbrrBQQEaNq0aZo2bVouVwUAAAAAnuvbb79VSEiI/XGHDh0UHBx8w+ckJSXpzTff1KpVq+ynCZcvX17r1q3T+++/r+bNm9vnHTNmjP3xsGHDdPfddysxMTHd+3G3atXK4fEHH3ygsLAwrV27Vvfcc4+9vVu3bnr88cclSaNHj9bKlSs1depUTZ8+3T7PoEGD1KVLF4f+XnjhBfv/n332WS1fvlxffPGFGjRooMuXL2vWrFn65JNP1Lp1a0nS3LlzVbJkSYc+rv2DQvny5TVlyhTVr19fly9fdliP7mZ6IAcAAAAAuFfLli01Y8YM++Pg4GANHz78hs/5+++/lZCQoLZt2zq0Jycnq3bt2g5tNWrUsP8/KipKknTq1CmVLl3aqd+TJ0/q1Vdf1Zo1a3Tq1CmlpqYqISFBR44ccZjv+muFRUdHa/v27Q5t9erVc3icmpqqN998U1988YWOHj2q5ORkJSUl2e+wtX//fiUnJ6thw4b254SHh6ty5coO/Wzbtk0jR47Ujh07dP78efv54keOHFG1atWcXpO7EMgBAAAAIJ8JDg52uqK6l5eX09HJ194v+/Lly5Kk7777TiVKlHCY7/oLZ1970bK0i7hldG/1Xr166ezZs3r33XdVpkwZ+fv7Kzo6Oku3k7t+L//bb7+td999V5MnT1b16tUVHBysQYMGZarv+Ph4xcTEKCYmRvPnz1dERISOHDmimJgYt9/y7nqmn0MOAAAAAMh5EREROn78uEPbtXugq1WrJn9/fx05ckQVK1Z0+ClVqlSWl7t+/XoNHDhQd911l26//Xb5+/vrzJkzTvNt2rTJ6XHVqlVv2nenTp308MMPq2bNmipfvrz++usv+/QKFSrI19dXv/zyi73t/PnzDvPs2bNHZ8+e1bhx49S0aVNVqVIlVy7oJrGHHAAAAABuCa1atdLbb7+tefPmKTo6Wp988ol27txpPxy9QIECeuGFFzR48GDZbDY1adJEFy9e1Pr16xUaGqpevXplabmVKlWyX8U8Li5OL774ogIDA53mW7hwoerVq6cmTZpo/vz52rx5s2bNmnXTvhctWqQNGzaoUKFCmjhxok6ePGk/zDwkJER9+/bViy++qMKFC6to0aJ65ZVXHG53Vrp0afn5+Wnq1Kl68skntXPnTo0ePTpLrzWz2EMOAAAAALeAmJgYvfbaaxo6dKjq16+vS5cu6dFHH3WYZ/To0Xrttdc0duxYVa1aVe3bt9d3332ncuXKZXm5s2bN0vnz51WnTh098sgjGjhwoIoWLeo0X2xsrD777DPVqFFD8+bN06effnrT87dfffVV1alTRzExMWrRooUiIyOdbu329ttvq2nTpurYsaPatGmjJk2aqG7duvbpERERmjNnjhYuXKhq1app3Lhxeuedd7L8ejPDYmR0ifN8Ii4uTgULFtTFixcVGhrq1r6tVqu+//573XXXXdm6IT3Mwfh5NsbPszF+no3x82yMn2dj/HJPYmKiDh48qHLlyqV75fCssNlsiouLU2hoqMMeWniG68fvRu8RV3Mo7wIAAAAAAExAIAcAAAAAwAQEcgAAAAAATEAgBwAAAADABARyAAAAAABMwH3Ike/Ebox1aT4vm5dqqIbGbR4nm5cth6v6nxHRI3JtWQAAAADyLvaQAwAAAABgAgI5AAAAAAAmIJADAAAAAGACAjkAAAAAwFRLlizRp59+anYZuY5ADgAAAAAwzaZNmzRw4EBFR0ebXUqu4yrrAAAAAJAZ3zyXpadZDEOByVZZ/Hwli8X1J3Z8N1PL6d27ty5cuKAlS5ZkrkATnD17Vn379tWSJUtUtmxZs8vJdQRyAAAAAIApChcurD///NPsMkzDIesAAAAAcIvYuXOnOnTooJCQEBUrVkyPPPKIzpw5Y59+6dIlPfTQQwoODlZUVJQmTZqkFi1aaNCgQfZ5Pv74Y9WrV08FChRQZGSkevbsqVOnTtmnr1mzRhaLRatXr1a9evUUFBSkRo0aae/evQ61fP3116pTp44CAgJUvnx5xcbGKiUlRZJkGIZGjhyp0qVLy9/fX8WLF9fAgQNzduWYgEAOAAAAALeACxcuqFWrVqpdu7a2bt2qZcuW6eTJk+revbt9niFDhmj9+vX673//q5UrV+rnn3/Wr7/+6tCP1WrV6NGjtWPHDi1ZskSHDh1S7969nZb3yiuvaMKECdq6dat8fHz02GOP2af9/PPPevTRR/Xcc89p165dev/99zVnzhyNGTNGkrR48WJNmjRJ77//vvbt26clS5aoevXqObNiTMQh6wAAAABwC3jvvfdUu3Ztvfnmm/a2jz76SKVKldJff/2lqKgozZ07VwsWLFDr1q0lSbNnz1bx4sUd+rk2WJcvX15TpkxR/fr1dfnyZYWEhNinjRkzRs2bN5ckDRs2THfffbcSExMVEBCg2NhYDRs2TL169bL3M3r0aA0dOlQjRozQkSNHFBkZqTZt2sjX11elS5dWgwYNcmzdmIU95AAAAABwC9ixY4d+/PFHhYSE2H+qVKkiSdq/f78OHDggq9XqEHwLFiyoypUrO/Szbds2dezYUaVLl1aBAgXsofvIkSMO89WoUcP+/6ioKEmyH9q+Y8cOjRo1yqGWfv366fjx40pISFC3bt105coVlS9fXv369dNXX31lP5w9P2EPOQAAAADcAi5fvqyOHTtq/PjxTtOioqL0999/37SP+Ph4xcTEKCYmRvPnz1dERISOHDmimJgYJScnO8zr6+tr/7/l/68qb7PZ7LXExsaqS5cuTssICAhQqVKltHfvXq1atUorV67U008/rbfffltr16516NfTEcgBAAAA4BZQp04dLV68WGXLlpWPj3MULF++vHx9fbVlyxaVLl1aknTx4kX99ddfatasmSRpz549Onv2rMaNG6dSpUpJkrZu3ZqlWvbu3auKFStmOE9gYKA6duyojh07asCAAapSpYr++OMP1alTJ9PLy6sI5AAAAACQz1y8eFHbt293aOvfv78+/PBD9ejRQ0OHDlV4eLj+/vtvffbZZ/rPf/6jAgUKqFevXnrxxRcVHh6uokWLasSIEfLy8rLv4S5durT8/Pw0depUPfnkk9q5c6dGjx6d6fpef/113XPPPSpdurTuv/9+eXl5aceOHdq5c6feeOMNzZkzR6mpqWrYsKGCgoL0ySefKDAwUGXKlHHH6skzOIccAAAAAPKZNWvWqHbt2g4/o0eP1vr165Wamqp27dqpevXqGjRokMLCwuTldTUaTpw4UdHR0brnnnvUpk0bNW7cWFWrVlVAQIAkKSIiQnPmzNHChQtVrVo1jRs3Tu+8806m64uJidG3336rFStWqH79+rrzzjs1adIke+AOCwvThx9+qMaNG6tGjRpatWqVvvnmGxUuXNh9KykPYA85AAAAAGRGx3ez9DTDZtOVuDj5hobK4pVz+0bnzJmjOXPmZDj9yy+/zHBagQIFNH/+fPvj+Ph4xcbGqn///va2Hj16qEePHg7PMwzD/v8WLVo4PJakWrVqObWlnYuens6dO6tz584Z1plfEMgBAAAAAJKk3377TXv27FGDBg108eJFjRo1SpLUqVMnkyvLnwjkAAAAAAC7d955R3v37pWfn5/q1q2rn3/+WUWKFDG7rHyJQA4AAAAAkCTVrl1b27ZtM7uMWwYXdQMAAAAAwAQEcgAAAAAATEAgBwAAAADABARyAAAAAABMQCAHAAAAAMAEBHIAAAAAAExAIAcAAAAAuN2SJUtUsWJFeXt7a9CgQWaXkydxH3IAAAAAyITYjbFZep5hGLImW+Xr5yuLxeLy80ZEj8jS8k6cOKGxY8fqu+++07///quCBQuqYsWKevjhh9WrVy8FBQXdtI+RI0dqyZIl2r59e6aX/8QTT6hPnz4aOHCgChQokIVXkP8RyAEAAAAgnzlw4IAaN26ssLAwvfnmm6pevbr8/f31xx9/6IMPPlCJEiV077335tjyL1++rFOnTikmJkbFixfPseV4Og5ZBwAAAIB85umnn5aPj4+2bt2q7t27q2rVqipfvrw6deqk7777Th07dpQkXbhwQY8//rgiIiIUGhqqVq1aaceOHZKkOXPmKDY2Vjt27JDFYpHFYtGcOXMkSRMnTlT16tUVHBysUqVK6emnn9bly5clSWvWrLHvEW/VqpUsFovWrFkjSVq3bp2aNm2qwMBAlSpVSgMHDlR8fHzurpw8hEAOAAAAAPnI2bNntWLFCg0YMEDBwcHpzpN2yHy3bt106tQpLV26VNu2bVOdOnXUunVrnTt3Tg888ICef/553X777Tp+/LiOHz+uBx54QJLk5eWlKVOm6M8//9TcuXP1ww8/aOjQoZKkRo0aae/evZKkxYsX6/jx42rUqJH279+v9u3bq2vXrvr999/1+eefa926dXrmmWdyYa3kTRyyDgAAAAD5yN9//y3DMFS5cmWH9iJFiigxMVGSNGDAAHXs2FGbN2/WqVOn5O/vL0l65513tGTJEi1atEj9+/dXSEiIfHx8FBkZ6dDXtRdpK1u2rN544w09+eSTmj59uvz8/FS0aFFJUnh4uP25Y8eO1UMPPWR/bqVKlTRlyhQ1b95cM2bMUEBAQE6sjjyNQA4AAAAAt4DNmzfLZrPpoYceUlJSknbs2KHLly+rcOHCDvNduXJF+/fvv2Ffq1at0tixY7Vnzx7FxcUpJSVFiYmJSkhIyPBicTt27NDvv/+u+fPn29sMw5DNZtPBgwdVtWrV7L9ID0MgBwAAAIB8pGLFirJYLPbDxtOUL19ekhQYGCjp6oXXoqKi7Od3XyssLCzD/g8dOqR77rlHTz31lMaMGaPw8HCtW7dOffv2VXJycoaB/PLly3riiSc0cOBAp2mlS5d28dXlLwRyAAAAAMhHChcurLZt2+q9997Ts88+m+F55HXq1NGJEyfk4+OjsmXLpjuPn5+fUlNTHdq2bdsmm82mCRMmyMvr6mXJvvjii5vWVadOHe3atUsVK1bM3AvKx7ioGwAAAADkM9OnT1dKSorq1aunzz//XLt379bevXv1ySefaM+ePfL29labNm0UHR2tzp07a8WKFTp06JA2bNigV155RVu3bpV09fzwgwcPavv27Tpz5oySkpJUsWJFWa1WTZ06VQcOHNDHH3+smTNn3rSml156SRs2bNAzzzyj7du3a9++ffr6669v6Yu6EcgBAAAAIJ+pUKGCfvvtN7Vp00bDhw9XzZo1Va9ePU2dOlUvvPCCRo8eLYvFou+//17NmjVTnz59dNttt+nBBx/U4cOHVaxYMUlS165d1b59e7Vs2VIRERH69NNPVbNmTU2cOFHjx4/XHXfcofnz52vs2LE3ralGjRpau3at/vrrLzVt2lS1a9fW66+/fkvfp5xD1gEAAAAgE0ZEj8jS82w2m+Li4hQaGmo/1DsnRUVFaerUqZo6dWqG8xQoUEBTpkzRlClT0p3u7++vRYsWObUPHjxYgwcPdmh75JFH7P8PCwuTYRhOz6tfv75WrFjh6kvI99hDDgAAAACACQjkAAAAAACYgEAOAAAAAIAJCOQAAAAAAJiAQA4AAAAAgAkI5AAAAAAAmIBADgAAAACACQjkAAAAAACYgEAOAAAAADDV1q1bNWnSJNlsNrNLyVUEcgAAAAC4haxZs0YWi0UXLlwwuxRJ0unTp9WtWzfdcccd8vK6tSKqj9kFAAAAAIAnOf76iCw9zzAMWa3JSvD1k8Vicfl5UaNis7S8jRs3qkmTJmrfvr2+++67LPWR02w2mx555BGNGDFCbdu2NbucXEcgBwAAAIB8aNasWXr22Wc1a9YsHTt2TMWLF8+V5RqGodTUVPn43Dxuenl5admyZblQVd50ax0PAAAAAAC3gMuXL+vzzz/XU089pbvvvltz5sy54fzr169XixYtFBQUpEKFCikmJkbnz5+XJCUlJWngwIEqWrSoAgIC1KRJE23ZssX+3LRD4JcuXaq6devK399f69atk81m01tvvaWKFSvK399fpUuX1pgxY+zP++OPP9SqVSsFBgaqcOHC6t+/vy5fvmyf3rt3b3Xu3FnvvPOOoqKiVLhwYQ0YMEBWq9W9K8tEBHIAAAAAyGe++OILValSRZUrV9bDDz+sjz76SIZhpDvv9u3b1bp1a1WrVk0bN27UunXr1LFjR6WmpkqShg4dqsWLF2vu3Ln69ddfVbFiRcXExOjcuXMO/QwbNkzjxo3T7t27VaNGDQ0fPlzjxo3Ta6+9pl27dmnBggUqVqyYJCk+Pl4xMTEqVKiQtmzZooULF2rVqlV65plnHPr88ccftX//fv3444+aO3eu5syZc9M/LngSDlkHgFtU7MasnY/mDl42L9VQDY3bPE42r/SvpjoiOmvn5wEAgKuHqz/88MOSpPbt2+vixYtau3atWrRo4TTvW2+9pXr16mn69On2tttvv13S1eA8Y8YMzZkzRx06dJAkffjhh1q5cqVmzZqlF1980f6cUaNG2c8Dv3Tpkt59912999576tWrlySpQoUKatKkiSRpwYIFSkxM1Lx58xQcHCxJeu+999SxY0eNHz/eHtwLFSqk9957T97e3qpSpYruvvturV69Wv369XPn6jINe8gBAAAAIB/Zu3evNm/erB49ekiSfHx89MADD2jWrFnpzp+2hzw9+/fvl9VqVePGje1tvr6+atCggXbv3u0wb7169ez/3717t5KSkjLsd/fu3apZs6Y9jEtS48aNZbPZtHfvXnvb7bffLm9vb/vjqKgonTp1KqOX7nHYQw4AAAAA+cisWbOUkpLicBE3wzDk7++v9957z2n+wMBAtyz32nDtrj59fX0dHlsslnx1r3L2kAMAAABAPpGSkqJ58+ZpwoQJ2r59u/1nx44dKl68uD799FOn59SoUUOrV69Ot78KFSrIz89P69evt7dZrVZt2bJF1apVy7COSpUqKTAwMMN+q1atqh07dig+Pt7etn79enl5ealy5cquvlyPRyAHAAAAgHzi22+/1fnz59W3b1/dcccdDj9du3ZN97D14cOHa8uWLXr66af1+++/a8+ePZoxY4bOnDmj4OBgPfXUU3rxxRe1bNky7dq1S/369VNCQoL69u2bYR0BAQF66aWXNHToUM2bN0/79+/Xpk2b7Mt/6KGHFBAQoF69emnnzp368ccf9eyzz+qRRx6xnz9+K+CQdQAAAADIJ2bNmqU2bdqoYMGCTtO6du2qt956S7///rtD+2233aYVK1bo5ZdfVoMGDRQYGKiGDRvaz0EfN26cbDabHnnkEV26dEn16tXT8uXLVahQoRvW8tprr8nHx0evv/66jh07pqioKD355JOSpKCgIC1fvlzPPfec6tevr6CgIHXt2lUTJ05005rwDARyAAAAAMiEqFFZu1OJzWZTXFycQkND5eWVMwcrf/PNNxlOa9Cggf3WZwMHDnSY1rx5c4fD0q8VEBCgKVOmaMqUKelOb9GiRbq3VPPy8tIrr7yiV155Jd3nVa9eXT/88EOG9aZ3e7PJkydnOL8n4pB1AAAAAABMQCAHAAAAAMAEBHIAAAAAAExAIAcAAAAAwAQEcgAAAADIQHoXKwMk97w3COQAAAAAcB1fX19JUkJCgsmVIK9Ke2+kvVeywvTbnh09elQvvfSSli5dqoSEBFWsWFGzZ89WvXr1JF39q8OIESP04Ycf6sKFC2rcuLFmzJihSpUqmVw5AAAAgPzK29tbYWFhOnXqlKSr9822WCzZ6tNmsyk5OVmJiYk5dtsz5Jy08bty5YoSExN16tQphYWFydvbO8t9mhrIz58/r8aNG6tly5ZaunSpIiIitG/fPocbzL/11luaMmWK5s6dq3Llyum1115TTEyMdu3apYCAABOrBwAAAJCfRUZGSpI9lGeXYRi6cuWKAgMDsx3ukfuuH7+wsDD7eySrTA3k48ePV6lSpTR79mx7W7ly5ez/NwxDkydP1quvvqpOnTpJkubNm6dixYppyZIlevDBB3O9ZgAAAAC3BovFoqioKBUtWlRWqzXb/VmtVv30009q1qxZtg5zhjnSxq958+YKDAzM1p7xNKYG8v/+97+KiYlRt27dtHbtWpUoUUJPP/20+vXrJ0k6ePCgTpw4oTZt2tifU7BgQTVs2FAbN25MN5AnJSUpKSnJ/jguLk7S1ZXnjo3oWmn9ubtfZI+XzbXDf9Lmc3V+d+H94h5sf9mX2+/99JZ9oxoY27yL7c+zMX6ejfEzjzvCl81mU0pKiry9vd3SH3JX2vh5eXnJZrPJZrNlOK+r26jFMPGygWmHnA8ZMkTdunXTli1b9Nxzz2nmzJnq1auXNmzYoMaNG+vYsWOKioqyP6979+6yWCz6/PPPnfocOXKkYmNjndoXLFigoKCgnHsxAAAAAADo6gXfevbsqYsXLyo0NDTD+UwN5H5+fqpXr542bNhgbxs4cKC2bNmijRs3ZimQp7eHvFSpUjpz5swNV0RWWK1WrVy5Um3btuWQkzxk3OZxLs3nZfPSHefu0M7wnbJ5ZfzXLXcb1mCYU1vsN7tybfkZGdGxmtklZArbX/a5uq3kBFe2v/S2FeQNbH+ejfHzbIyfZ2P8PFtmxi8uLk5FihS5aSA39ZD1qKgoVavmGAKqVq2qxYsXS/rfRRROnjzpEMhPnjypWrVqpdunv7+//P39ndp9fX1z7E2fk30j8zIbrm1etlwN5Om9V1LzwB0IPfU9zPaXdbn5vr9RDRnVwbjmfWx/no3x82yMn2dj/DybK+Pn6viamgIaN26svXv3OrT99ddfKlOmjKSrF3iLjIzU6tWr7dPj4uL0yy+/KDo6OldrBQAAAADAnUzdQz548GA1atRIb775prp3767Nmzfrgw8+0AcffCDp6lUNBw0apDfeeEOVKlWy3/asePHi6ty5s5mlAwAAAACQLaYG8vr16+urr77S8OHDNWrUKJUrV06TJ0/WQw89ZJ9n6NChio+PV//+/XXhwgU1adJEy5Yt4x7kAAAAAACPZmogl6R77rlH99xzT4bTLRaLRo0apVGjRuViVQAAAAAA5CzzryQFAAAAAMAtiEAOAAAAAIAJCOQAAAAAAJiAQA4AAAAAgAkI5AAAAAAAmIBADgAAAACACQjkAAAAAACYgEAOAAAAAIAJCOQAAAAAAJiAQA4AAAAAgAkI5AAAAAAAmIBADgAAAACACQjkAAAAAACYgEAOAAAAAIAJCOQAAAAAAJiAQA4AAAAAgAkI5AAAAAAAmIBADgAAAACACQjkAAAAAACYgEAOAAAAAIAJCOQAAAAAAJiAQA4AAAAAgAkI5AAAAAAAmIBADgAAAACACQjkAAAAAACYgEAOAAAAAIAJCOQAAAAAAJiAQA4AAAAAgAkI5AAAAAAAmIBADgAAAACACQjkAAAAAACYgEAOAAAAAIAJCOQAAAAAAJiAQA4AAAAAgAkI5AAAAAAAmIBADgAAAACACXzMLgC4VdSds0WSdHzpCKdpdx48l9vlONh0X39Tlw8AAADcithDDgAAAACACQjkAAAAAACYgEAOAAAAAIAJCOQAAAAAAJiAQA4AAAAAgAkI5AAAAAAAmIBADgAAAACACQjkAAAAAACYgEAOAAAAAIAJCOQAAAAAAJiAQA4AAAAAgAkI5AAAAAAAmIBADgAAAACACQjkAAAAAACYgEAOAAAAAIAJCOQAAAAAAJiAQA4AAAAAgAkI5AAAAAAAmIBADgAAAACACQjkAAAAAACYgEAOAAAAAIAJCOQAAAAAAJiAQA4AAAAAgAkI5AAAAAAAmIBADgAAAACACQjkAAAAAACYgEAOAAAAAIAJCOQAAAAAAJiAQA4AAAAAgAkI5AAAAAAAmIBADgAAAACACQjkAAAAAACYgEAOAAAAAIAJCOQAAAAAAJiAQA4AAAAAgAkI5AAAAAAAmIBADgAAAACACQjkAAAAAACYwNRAPnLkSFksFoefKlWq2KcnJiZqwIABKly4sEJCQtS1a1edPHnSxIoBAAAAAHAP0/eQ33777Tp+/Lj9Z926dfZpgwcP1jfffKOFCxdq7dq1OnbsmLp06WJitQAAAAAAuIeP6QX4+CgyMtKp/eLFi5o1a5YWLFigVq1aSZJmz56tqlWratOmTbrzzjtzu1QAAAAAANzG9EC+b98+FS9eXAEBAYqOjtbYsWNVunRpbdu2TVarVW3atLHPW6VKFZUuXVobN27MMJAnJSUpKSnJ/jguLk6SZLVaZbVa3Vp7Wn/u7hfZ42Vz7cCPtPlcnT+7DB9fSVKqVzrL8/HOlRoy4i2bx72P2f6yL7fe+zda9o1qYGzzLrY/z8b4eTbGz7Mxfp4tM+Pn6hhbDMMwslVVNixdulSXL19W5cqVdfz4ccXGxuro0aPauXOnvvnmG/Xp08chXEtSgwYN1LJlS40fPz7dPkeOHKnY2Fin9gULFigoKChHXgcAAAAAAGkSEhLUs2dPXbx4UaGhoRnOZ2ogv96FCxdUpkwZTZw4UYGBgVkK5OntIS9VqpTOnDlzwxWRFVarVStXrlTbtm3l6+vr1r6RdeM2j3NpPi+bl+44d4d2hu+UzcuWw1VJtRb8KkmqHVHLadq2w+dzfPk3sqVjH43oWM3UGjKL7S/7XN1WcoIr29+wBsNyuSq4iu3PszF+no3x82yMn2fLzPjFxcWpSJEiNw3kph+yfq2wsDDddttt+vvvv9W2bVslJyfrwoULCgsLs89z8uTJdM85T+Pv7y9/f3+ndl9f3xx70+dk38i8zIZrm5ctVwK5JeXqYSvetnSWlZKa48u/kVR5eex7mO0v63Ljfe9KDRnVwbjmfWx/no3x82yMn2dj/DybK+Pn6viafpX1a12+fFn79+9XVFSU6tatK19fX61evdo+fe/evTpy5Iiio6NNrBIAAAAAgOwzdQ/5Cy+8oI4dO6pMmTI6duyYRowYIW9vb/Xo0UMFCxZU3759NWTIEIWHhys0NFTPPvusoqOjucI6AAAAAMDjmRrI//33X/Xo0UNnz55VRESEmjRpok2bNikiIkKSNGnSJHl5ealr165KSkpSTEyMpk+fbmbJAAAAAAC4hamB/LPPPrvh9ICAAE2bNk3Tpk3LpYoAAAAAAMgdeeoccgAAAAAAbhUEcgAAAAAATEAgBwAAAADABARyAAAAAABMQCAHAAAAAMAEBHIAAAAAAExAIAcAAAAAwAQEcgAAAAAATEAgBwAAAADABARyAAAAAABMQCAHAAAAAMAEBHIAAAAAAExAIAcAAAAAwAQEcgAAAAAATEAgBwAAAADABARyAAAAAABMQCAHAAAAAMAEBHIAAAAAAExAIAcAAAAAwAQ+ZhcA5CuHN2Q87fK5q/8mOs9TNj4x24s+FFwr230AAAAAyD3sIQcAAAAAwAQEcgAAAAAATEAgBwAAAADABARyAAAAAABMQCAHAAAAAMAEBHIAAAAAAExAIAcAAAAAwAQEcgAAAAAATEAgBwAAAADABARyAAAAAABMQCAHAAAAAMAEBHIAAAAAAExAIAcAAAAAwAQEcgAAAAAATEAgBwAAAADABARyAAAAAABMQCAHAAAAAMAEBHIAAAAAAExAIAcAAAAAwAQEcgAAAAAATEAgBwAAAADABARyAAAAAABMQCAHAAAAAMAEBHIAAAAAAExAIAcAAAAAwAQEcgAAAAAATEAgBwAAAADABARyAAAAAABMQCAHAAAAAMAEBHIAAAAAAExAIAcAAAAAwAQEcgAAAAAATEAgBwAAAADABARyAAAAAABMQCAHAAAAAMAEBHIAAAAAAExAIAcAAAAAwAQEcgAAAAAATEAgBwAAAADABARyAAAAAABMQCAHAAAAAMAEBHIAAAAAAExAIAcAAAAAwAQEcgAAAAAATEAgBwAAAADABARyAAAAAABMQCAHAAAAAMAEBHIAAAAAAExAIAcAAAAAwAQEcgAAAAAATEAgBwAAAADABARyAAAAAABMQCAHAAAAAMAEBHIAAAAAAExAIAcAAAAAwAR5JpCPGzdOFotFgwYNsrclJiZqwIABKly4sEJCQtS1a1edPHnSvCIBAAAAAHCTPBHIt2zZovfff181atRwaB88eLC++eYbLVy4UGvXrtWxY8fUpUsXk6oEAAAAAMB9TA/kly9f1kMPPaQPP/xQhQoVsrdfvHhRs2bN0sSJE9WqVSvVrVtXs2fP1oYNG7Rp0yYTKwYAAAAAIPtMD+QDBgzQ3XffrTZt2ji0b9u2TVar1aG9SpUqKl26tDZu3JjbZQIAAAAA4FY+Zi78s88+06+//qotW7Y4TTtx4oT8/PwUFhbm0F6sWDGdOHEiwz6TkpKUlJRkfxwXFydJslqtslqt7in8/6X15+5+kT1eNtf+zpQ2n6vzu8Y3wymGj58kKdXHeR6bry37i/bxzvJTvWXzuPcx21/2ufe9n7Vl36gGxjbvYvvzbIyfZ2P8PBvj59kyM36ujrHFMAwjW1Vl0T///KN69epp5cqV9nPHW7RooVq1amny5MlasGCB+vTp4xCuJalBgwZq2bKlxo8fn26/I0eOVGxsrFP7ggULFBQU5P4XAgAAAADANRISEtSzZ09dvHhRoaGhGc5nWiBfsmSJ7rvvPnl7/2+vXmpqqiwWi7y8vLR8+XK1adNG58+fd9hLXqZMGQ0aNEiDBw9Ot9/09pCXKlVKZ86cueGKyAqr1aqVK1eqbdu28vXNeM8octe4zeNcms/L5qU7zt2hneE7ZfNywx5qSfrnlwwn1Vp6XpJU26eg07TTl5Oc2jLrSFD1LD93S8c+GtGxWrZryE1sf9nn6raSE1zZ/oY1GJbLVcFVbH+ejfHzbIyfZ2P8PFtmxi8uLk5FihS5aSA37ZD11q1b648//nBo69Onj6pUqaKXXnpJpUqVkq+vr1avXq2uXbtKkvbu3asjR44oOjo6w379/f3l7+/v1O7r65tjb/qc7BuZl9lwbfOyuS+QK+NDUywpyZIk73Tm8bImZ3/RKalZfmqqvDz2Pcz2l3Xue99nr4aM6mBc8z62P8/G+Hk2xs+zMX6ezZXxc3V8sxzI4+PjtXbtWh05ckTJyY5hYuDAgTd9foECBXTHHXc4tAUHB6tw4cL29r59+2rIkCEKDw9XaGionn32WUVHR+vOO+/MatkAAAAAAOQJWQrkv/32m+666y4lJCQoPj5e4eHhOnPmjIKCglS0aFGXArkrJk2aJC8vL3Xt2lVJSUmKiYnR9OnT3dI3AAAAAABmylIgHzx4sDp27KiZM2eqYMGC2rRpk3x9ffXwww/rueeey3Ixa9ascXgcEBCgadOmadq0aVnuEwAAAACAvChL97zZvn27nn/+eXl5ecnb21tJSUkqVaqU3nrrLb388svurhEAAAAAgHwnS4Hc19dXXl5Xn1q0aFEdOXJEklSwYEH9888/7qsOAAAAAIB8KkuHrNeuXVtbtmxRpUqV1Lx5c73++us6c+aMPv74Y6cLtQG5re6cLS7NZ/j4ytauhmot+FWWlIyvjp4pl8+5px94vNiNsWaXAAAAgDwuS3vI33zzTUVFRUmSxowZo0KFCumpp57S6dOn9cEHH7i1QAAAAAAA8qMs7SGvV6+e/f9FixbVsmXL3FYQAAAAAAC3giztIQcAAAAAANnj8h7yOnXqaPXq1SpUqJBq164ti8WS4by//vqrW4oDAAAAACC/cjmQd+rUSf7+/pKkzp0751Q9AAAAAADcElwO5CNGjEj3/wAAAAAAIPOydA75li1b9Msvvzi1//LLL9q6dWu2iwIAAAAAIL/LUiAfMGCA/vnnH6f2o0ePasCAAdkuCgAAAACA/C5LgXzXrl2qU6eOU3vt2rW1a9eubBcFAAAAAEB+l6VA7u/vr5MnTzq1Hz9+XD4+Wbq1OQAAAAAAt5QsBfJ27dpp+PDhunjxor3twoULevnll9W2bVu3FQcAAAAAQH6Vpd3Z77zzjpo1a6YyZcqodu3akqTt27erWLFi+vjjj91aIAAAAAAA+VGWAnmJEiX0+++/a/78+dqxY4cCAwPVp08f9ejRQ76+vu6uEQAAAACAfCfLJ3wHBwerf//+7qwFAAAAAIBbRpYD+b59+/Tjjz/q1KlTstlsDtNef/31bBcGAAAAAEB+lqVA/uGHH+qpp55SkSJFFBkZKYvFYp9msVgI5AAAAAAA3ESWAvkbb7yhMWPG6KWXXnJ3PQAAAAAA3BKyFMjPnz+vbt26ubsWACYa/uUfpi5/bJfqpi4fAAAAyG1Zug95t27dtGLFCnfXAgAAAADALSNLe8grVqyo1157TZs2bVL16tWdbnU2cOBAtxQHAAAAAEB+laVA/sEHHygkJERr167V2rVrHaZZLBYCOQAAAAAAN5GlQH7w4EF31wEAAAAAwC0lS+eQp0lOTtbevXuVkpLirnoAAAAAALglZCmQJyQkqG/fvgoKCtLtt9+uI0eOSJKeffZZjRs3zq0FAgAAAACQH2UpkA8fPlw7duzQmjVrFBAQYG9v06aNPv/8c7cVBwAAAABAfpWlc8iXLFmizz//XHfeeacsFou9/fbbb9f+/fvdVhwAAAAAAPlVlvaQnz59WkWLFnVqj4+PdwjoAAAAAAAgfVkK5PXq1dN3331nf5wWwv/zn/8oOjraPZUBAAAAAJCPZemQ9TfffFMdOnTQrl27lJKSonfffVe7du3Shg0bnO5LDgAAAAAAnGVpD3mTJk20fft2paSkqHr16lqxYoWKFi2qjRs3qm7duu6uEQAAAACAfCdLe8glqUKFCvrwww/dWQsAAAAAALeMLAXytPuOZ6R06dJZKgYAAAAAgFtFlgJ52bJlb3g19dTU1CwXBAAAAADArSBLgfy3335zeGy1WvXbb79p4sSJGjNmjFsKAwAAAAAgP8tSIK9Zs6ZTW7169VS8eHG9/fbb6tKlS7YLA3LLmUtJMlKsbumrgPXmR4ecupLolmUBAAAA8GxZusp6RipXrqwtW7a4s0sAAAAAAPKlLO0hj4uLc3hsGIaOHz+ukSNHqlKlSm4pDAAAAACA/CxLgTwsLMzpom6GYahUqVL67LPP3FIYAAAAAAD5WZYC+Q8//OAQyL28vBQREaGKFSvKxyfLtzYHAAAAAOCWkaX03KJFCzeXAQAAAADArSVLF3UbO3asPvroI6f2jz76SOPHj892UQAAAAAA5HdZCuTvv/++qlSp4tR+++23a+bMmdkuCgAAAACA/C5LgfzEiROKiopyao+IiNDx48ezXRQAAAAAAPldlgJ5qVKltH79eqf29evXq3jx4tkuCgAAAACA/C5LF3Xr16+fBg0aJKvVqlatWkmSVq9eraFDh+r55593a4EAAAAAAORHWQrkL774os6ePaunn35aycnJkqSAgAC99NJLGj58uFsLBAAAAAAgP8pSILdYLBo/frxee+017d69W4GBgapUqZL8/f3dXR8AAAAAAPlSls4hT3PixAmdO3dOFSpUkL+/vwzDcFddAAAAAADka1kK5GfPnlXr1q1122236a677rJfWb1v376cQw4AAAAAgAuyFMgHDx4sX19fHTlyREFBQfb2Bx54QMuWLXNbcQAAAAAA5FdZOod8xYoVWr58uUqWLOnQXqlSJR0+fNgthQEAAAAAkJ9lKZDHx8c77BlPc+7cOS7shrzh8smbz+PjJ0kKSTknWZNzuCC4U+zGWHnZvFRDNTRu8zjZvGxmlwQAAABkWpYOWW/atKnmzZtnf2yxWGSz2fTWW2+pZcuWbisOAAAAAID8Kkt7yN966y21bt1aW7duVXJysoYOHao///xT586d0/r1691dIwAAAAAA+U6W9pDfcccd+uuvv9SkSRN16tRJ8fHx6tKli3777TdVqFDB3TUCAAAAAJDvZHoPudVqVfv27TVz5ky98sorOVETAAAAAAD5Xqb3kPv6+ur333/PiVoAAAAAALhlZOmQ9YcfflizZs1ydy0AAAAAANwysnRRt5SUFH300UdatWqV6tatq+DgYIfpEydOdEtxAAAAAADkV5kK5AcOHFDZsmW1c+dO1alTR5L0119/OcxjsVjcVx0AAAAAAPlUpgJ5pUqVdPz4cf3444+SpAceeEBTpkxRsWLFcqQ4AAAAAADyq0ydQ24YhsPjpUuXKj4+3q0FAQAAAABwK8jSRd3SXB/QAQAAAACAazIVyC0Wi9M54pwzDgAAAABA5mXqHHLDMNS7d2/5+/tLkhITE/Xkk086XWX9yy+/dF+FAAAAAADkQ5kK5L169XJ4/PDDD7u1GAAAAAAAbhWZCuSzZ8/OqToAAAAAALilZOuibgAAAAAAIGsI5AAAAAAAmIBADgAAAACACQjkAAAAAACYgEAOAAAAAIAJCOQAAAAAAJjA1EA+Y8YM1ahRQ6GhoQoNDVV0dLSWLl1qn56YmKgBAwaocOHCCgkJUdeuXXXy5EkTKwYAAAAAwD1MDeQlS5bUuHHjtG3bNm3dulWtWrVSp06d9Oeff0qSBg8erG+++UYLFy7U2rVrdezYMXXp0sXMkgEAAAAAcAsfMxfesWNHh8djxozRjBkztGnTJpUsWVKzZs3SggUL1KpVK0nS7NmzVbVqVW3atEl33nmnGSUDAAAAAOAWpgbya6WmpmrhwoWKj49XdHS0tm3bJqvVqjZt2tjnqVKlikqXLq2NGzdmGMiTkpKUlJRkfxwXFydJslqtslqtbq05rT9394vsMXx8JR8/F+b7/3lcmNedbF45tDwf7yw/1Vs2NxaSNZnZjrxsXvKyedn/D8/jyvjx2Zp38f3n2Rg/z8b4eTbGz7NlZvxcHWOLYRhGtqrKpj/++EPR0dFKTExUSEiIFixYoLvuuksLFixQnz59HMK1JDVo0EAtW7bU+PHj0+1v5MiRio2NdWpfsGCBgoKCcuQ1AAAAAACQJiEhQT179tTFixcVGhqa4Xym7yGvXLmytm/frosXL2rRokXq1auX1q5dm+X+hg8friFDhtgfx8XFqVSpUmrXrt0NV0RWWK1WrVy5Um3btpWvr69b+0bWLRvUXbp86qbzGT5+Mu4dpOTFE6SU5Fyo7KqKqcG5tixXJTYpneXnfldikFtqGNGxmsvzjts8Tl42L91x7g7tDN8pm5f5e/iROa6M37AGw3K5KriK7z/Pxvh5NsbPszF+ni0z45d2pPbNmB7I/fz8VLFiRUlS3bp1tWXLFr377rt64IEHlJycrAsXLigsLMw+/8mTJxUZGZlhf/7+/vL393dq9/X1zbE3fU72jcyzpFhdDtiGdHVea+4Fcq/UvPde8TJSsvzcVDddGzIz29C1Ac7mZSOQe7AbjR+fq3kf33+ejfHzbIyfZ2P8PJsr4+fq+Oa5ky9tNpuSkpJUt25d+fr6avXq1fZpe/fu1ZEjRxQdHW1ihQAAAAAAZJ+pe8iHDx+uDh06qHTp0rp06ZIWLFigNWvWaPny5SpYsKD69u2rIUOGKDw8XKGhoXr22WcVHR3NFdYBAAAAAB7P1EB+6tQpPfroozp+/LgKFiyoGjVqaPny5Wrbtq0kadKkSfLy8lLXrl2VlJSkmJgYTZ8+3cySAQAAAABwC1MD+axZs244PSAgQNOmTdO0adNyqSIAAAAAAHJHnjuHHAAAAACAWwGBHAAAAAAAExDIAQAAAAAwAYEcAAAAAAATEMgBAAAAADABgRwAAAAAABMQyAEAAAAAMAGBHAAAAAAAExDIAQAAAAAwAYEcAAAAAAATEMgBAAAAADABgRwAAAAAABMQyAEAAAAAMAGBHAAAAAAAExDIAQAAAAAwAYEcAAAAAAATEMgBAAAAADABgRwAAAAAABMQyAEAAAAAMAGBHAAAAAAAExDIAQAAAAAwAYEcAAAAAAATEMgBAAAAADABgRwAAAAAABMQyAEAAAAAMAGBHAAAAAAAExDIAQAAAAAwAYEcAAAAAAATEMgBAAAAADABgRwAAAAAABMQyAEAAAAAMAGBHAAAAAAAExDIAQAAAAAwAYEcAAAAAAATEMgBAAAAADABgRwAAAAAABMQyAEAAAAAMAGBHAAAAAAAExDIAQAAAAAwAYEcAAAAAAATEMgBAAAAADABgRwAAAAAABMQyAEAAAAAMAGBHAAAAAAAExDIAQAAAAAwAYEcAAAAAAATEMgBAAAAADABgRwAAAAAABMQyAEAAAAAMAGBHAAAAAAAExDIAQAAAAAwAYEcAAAAAAATEMgBAAAAADABgRwAAAAAABMQyAEAAAAAMAGBHAAAAAAAExDIAQAAAAAwAYEcAAAAAAATEMgBAAAAADABgRwAAAAAABMQyAEAAAAAMAGBHAAAAAAAExDIAQAAAAAwAYEcAAAAAAATEMgBAAAAADABgRwAAAAAABMQyAEAAAAAMAGBHAAAAAAAExDIAQAAAAAwAYEcAAAAAAATEMgBAAAAADCBj9kFAEBGjr8+It32uie3yfDxla1dDdVa8KssKVa3LO/UpSSX5lt8TzW3LC89DcqF51jfAAAAyFvYQw4AAAAAgAkI5AAAAAAAmIBADgAAAACACUwN5GPHjlX9+vVVoEABFS1aVJ07d9bevXsd5klMTNSAAQNUuHBhhYSEqGvXrjp58qRJFQMAAAAA4B6mBvK1a9dqwIAB2rRpk1auXCmr1ap27dopPj7ePs/gwYP1zTffaOHChVq7dq2OHTumLl26mFg1AAAAAADZZ+pV1pctW+bweM6cOSpatKi2bdumZs2a6eLFi5o1a5YWLFigVq1aSZJmz56tqlWratOmTbrzzjvNKBsAAAAAgGzLU7c9u3jxoiQpPPzqbX+2bdsmq9WqNm3a2OepUqWKSpcurY0bN6YbyJOSkpSU9L9bF8XFxUmSrFarrFb33BopTVp/7u4X2WP4+Eo+fi7M9//zuDCvO9m8cnd5rrBZsv5R4C2bW2pIbztK9Ur/IB7Dx1eGt+/V////v+5g8XHttfjK223LvJ6X7da4tEfa67zR6+WzNe/i+8+zMX6ejfHzbIyfZ8vM+Lk6xhbDMIxsVeUmNptN9957ry5cuKB169ZJkhYsWKA+ffo4BGxJatCggVq2bKnx48c79TNy5EjFxsY6tS9YsEBBQUE5UzwAAAAAAP8vISFBPXv21MWLFxUaGprhfHlmD/mAAQO0c+dOexjPquHDh2vIkCH2x3FxcSpVqpTatWt3wxWRFVarVStXrlTbtm3l6+u+vXTInmWDukuXT910PsPHT8a9g5S8eIKUkpwLlV1VMTU415blqsQmpR0efxpw1OXnHgm+wy011C1TyKmt1oJfM5zf8PaV0fohWVbPlyXVPX9lPnMp6eYzSfq6fRW3LC896a2H/MjL5qU7zt2hneE7ZfNK/8iEYQ2G5XJVcBXff56N8fNsjJ9nY/w8W2bGL+1I7ZvJE4H8mWee0bfffquffvpJJUuWtLdHRkYqOTlZFy5cUFhYmL395MmTioyMTLcvf39/+fv7O7X7+vrm2Js+J/tG5llSrC4HbEO6Oq819wK5V2ree694GSkOj1MN19eHValuqSG9UGZJuXHQNiRZUq03nc9Vhov9uOs1pyejcJpf2bxsGb5mPlfzPr7/PBvj59kYP8/G+Hk2V8bP1fE19WRFwzD0zDPP6KuvvtIPP/ygcuXKOUyvW7eufH19tXr1anvb3r17deTIEUVHR+d2uQAAAAAAuI2pe8gHDBigBQsW6Ouvv1aBAgV04sQJSVLBggUVGBioggULqm/fvhoyZIjCw8MVGhqqZ599VtHR0VxhHQAAAADg0UwN5DNmzJAktWjRwqF99uzZ6t27tyRp0qRJ8vLyUteuXZWUlKSYmBhNnz49lysFAAAAAMC9TA3krlzgPSAgQNOmTdO0adNyoSIAAAAAAHLHrXHDWwAAAAAA8hgCOQAAAAAAJsgTtz2D54jdGGt2CTdV1+wCAAAAAMAF7CEHAAAAAMAEBHIAAAAAAExAIAcAAAAAwAQEcgAAAAAATEAgBwAAAADABARyAAAAAABMQCAHAAAAAMAEBHIAAAAAAExAIAcAAAAAwAQEcgAAAAAATEAgBwAAAADABARyAAAAAABMQCAHAAAAAMAEBHIAAAAAAExAIAcAAAAAwAQEcgAAAAAATOBjdgEAzBew5pDD4xbe8S4/95LvLjdX8z+nbjDN4mNTuKQzl5JkpFhzrAYAAAAgp7CHHAAAAAAAExDIAQAAAAAwAYEcAAAAAAATEMgBAAAAADABgRwAAAAAABMQyAEAAAAAMAGBHAAAAAAAExDIAQAAAAAwgY/ZBQAA3KPunC1ml5Cubb3rm10CAABAnsQecgAAAAAATEAgBwAAAADABARyAAAAAABMQCAHAAAAAMAEBHIAAAAAAExAIAcAAAAAwAQEcgAAAAAATMB9yAEgk8pe3pFznR/2z3hamUY5t1wAAADkOvaQAwAAAABgAgI5AAAAAAAmIJADAAAAAGACAjkAAAAAACYgkAMAAAAAYAICOQAAAAAAJiCQAwAAAABgAgI5AAAAAAAm8DG7AOBW85d3vNklAAAAAMgD2EMOAAAAAIAJCOQAAAAAAJiAQA4AAAAAgAkI5AAAAAAAmIBADgAAAACACQjkAAAAAACYgEAOAAAAAIAJCOQAAAAAAJjAx+wCAAB5y6lLSW7tb/PBc05tvvJWjWBp2+HzsirVaXqDcuFurQEAACAvYg85AAAAAAAmIJADAAAAAGACAjkAAAAAACYgkAMAAAAAYAICOQAAAAAAJiCQAwAAAABgAgI5AAAAAAAm4D7keUjsxlizSwDgghYr43Ks70A/74wnhmzJseUCAAAg97GHHAAAAAAAExDIAQAAAAAwAYEcAAAAAAATEMgBAAAAADABgRwAAAAAABMQyAEAAAAAMAGBHAAAAAAAExDIAQAAAAAwgY/ZBeDWtvngObf3WepSkgpYU28+o5EqP7cvHch/CljPZuv5ZS/vcGrztvhJwc1VOn6nUo1k5ycd9lfs4RipTKNsLTunjYgeYXYJAADAg7GHHAAAAAAAExDIAQAAAAAwAYEcAAAAAAATmBrIf/rpJ3Xs2FHFixeXxWLRkiVLHKYbhqHXX39dUVFRCgwMVJs2bbRv3z5zigUAAAAAwI1MDeTx8fGqWbOmpk2blu70t956S1OmTNHMmTP1yy+/KDg4WDExMUpMTMzlSgEAAAAAcC9Tr7LeoUMHdejQId1phmFo8uTJevXVV9WpUydJ0rx581SsWDEtWbJEDz74YG6WCgAAAACAW+XZ254dPHhQJ06cUJs2bextBQsWVMOGDbVx48YMA3lSUpKSkpLsj+Pi4iRJVqtVVqvVrTWm9eeufr1st94p/b7ydnufFh9fyZUbmvn4Of6LLLm6vk2QtlwfX1lyfeE5954xfG6wTeTSurb42K5ryd7r9bY4P9/b4uvw7/W89P/tefxz0d3fK57C3d9/yF2Mn2dj/Dwb4+fZMjN+ro6xxTAMI1tVuYnFYtFXX32lzp07S5I2bNigxo0b69ixY4qKirLP1717d1ksFn3++efp9jNy5EjFxsY6tS9YsEBBQUE5UjsAAAAAAGkSEhLUs2dPXbx4UaGhoRnOl2f3kGfV8OHDNWTIEPvjuLg4lSpVSu3atbvhisgKq9WqlStXqm3btvL1zf6eq3Gbx7mhKs+y7fB5t/fZadkehaScu/mMPn7y6/q8khdPkFKS3V7HreKyT7g5C/bxVXinx3Tu64+klNz9K7NL768sCvC9wR7ykKI5ttxrnbmU5PA4u6/355bOn73eFl+1iOivNac/UKrhPH4RBf5/r3qphtladk4b1mCY2SWYwt3ff8hdjJ9nY/w8G+Pn2TIzfmlHat9Mng3kkZGRkqSTJ0867CE/efKkatWqleHz/P395e/v79Tu6+ubY296d/Vt87r+MNH8z6pUt/dppFglayYCdkpy5uaHA0PmHHJlP0w9xXp1zHNTDr5fLJYbBPJcep1O6zObrzfVyPj5qYY13em2tBHO45+Lt/ovUzn53Yqcx/h5NsbPszF+ns2V8XN1fPPsyXnlypVTZGSkVq9ebW+Li4vTL7/8oujoaBMrAwAAAAAg+0zdQ3758mX9/fff9scHDx7U9u3bFR4ertKlS2vQoEF64403VKlSJZUrV06vvfaaihcvbj/PHAAAAAAAT2VqIN+6datatmxpf5x27nevXr00Z84cDR06VPHx8erfv78uXLigJk2aaNmyZQoICDCrZAAAAAAA3MLUQN6iRQvd6CLvFotFo0aN0qhRo3KxKgAAAAAAcl6ePYccAAAAAID8jEAOAAAAAIAJ8uxtzwB4hgLWs6Ys97JPpCnLzWlXkjO+FeCl6+4PjuypO2dLxhMvn3Spj+Mz17mpmv9XppEkKWpUrHv7BQAAeRJ7yAEAAAAAMAGBHAAAAAAAExDIAQAAAAAwAYEcAAAAAAATEMgBAAAAADABgRwAAAAAABMQyAEAAAAAMAGBHAAAAAAAE/iYXQAAZEVIyrn//WtNNrka5DdXklNdmu/UlUT3LnjnD5KkI1MOqGG5cPf2fTMd383d5QEAAPaQAwAAAABgBgI5AAAAAAAmIJADAAAAAGACAjkAAAAAACYgkAMAAAAAYAICOQAAAAAAJiCQAwAAAABgAu5DDgAeooD1rNklZEmLlXHOjb5+0gNS0x/j0r2PfKCf99X/hGzJ4eoAAADMwx5yAAAAAABMQCAHAAAAAMAEBHIAAAAAAExAIAcAAAAAwAQEcgAAAAAATEAgBwAAAADABARyAAAAAABMQCAHAAAAAMAEPmYXAADA9a4kp0qSLl1KMmX5BUxZ6v8ErDmk49tO5O5Ct4y46SxRo2JzoRAAAG4d7CEHAAAAAMAEBHIAAAAAAExAIAcAAAAAwAQEcgAAAAAATEAgBwAAAADABARyAAAAAABMQCAHAAAAAMAEBHIAAAAAAEzgY3YBAAB4qr+843Os739SEt3ST12fMLf0k+9885x5y+74rnnLBgDkKewhBwAAAADABARyAAAAAABMQCAHAAAAAMAEBHIAAAAAAExAIAcAAAAAwAQEcgAAAAAATEAgBwAAAADABARyAAAAAABMQCAHAAAAAMAEBHIAAAAAAExAIAcAAAAAwAQEcgAAAAAATEAgBwAAAADABARyAAAAAABMQCAHAAAAAMAEBHIAAAAAAEzgY3YByMcOb7jpLGUvJ7l9sQWscW7vE4A5CljPml2Caa4kp7qln1NXEl2bcecPTk1FCwQ4PD7eq60kKdXHV7r3CZ18spO8U6zZK7BMoxtO3nzw3E272HRf/0wvtvO/N+83pyz58g/Tli1J3rKpvrcU+80upbq4b2Zsl+o5XBUA3JrYQw4AAAAAgAkI5AAAAAAAmIBADgAAAACACQjkAAAAAACYgEAOAAAAAIAJCOQAAAAAAJiAQA4AAAAAgAm4DzkAAPnYX97xWX7uPynp38PckJ8k6beUi7KkJGe5f0nS/u9vODnZhfux1/l0faYXeyQT896WGpzp/iUpsUXZLD0vP4rdGGt2CTc1InqE2SUAuAWxhxwAAAAAABMQyAEAAAAAMAGBHAAAAAAAExDIAQAAAAAwAYEcAAAAAAATEMgBAAAAADABgRwAAAAAABMQyAEAAAAAMIGP2QUAAADkRwFrDqXbfmfwB7lbyPV8vKXOLVX/m9lSSqpLT/n6q+wtslTqnpvOs/ieatlbSDYNP/7HTecZ26W6a51981w2q8nY1kNxUunO2jqzv7yMFIdpS0oOzbHl5jUuj4UHit0Ya3YJkqS6c7ak316sbi5X4ixqVN5YR+7AHnIAAAAAAExAIAcAAAAAwAQEcgAAAAAATOARgXzatGkqW7asAgIC1LBhQ23evNnskgAAAAAAyJY8H8g///xzDRkyRCNGjNCvv/6qmjVrKiYmRqdOnTK7NAAAAAAAsizPB/KJEyeqX79+6tOnj6pVq6aZM2cqKChIH330kdmlAQAAAACQZXk6kCcnJ2vbtm1q06aNvc3Ly0tt2rTRxo0bTawMAAAAAIDsydP3IT9z5oxSU1NVrFgxh/ZixYppz57072eZlJSkpKQk++OLFy9Kks6dOyer1erW+qxWqxISEnT27Fn5+vpmu7/kS8luqCoPSbj5LKmJ7v+bUIKrf2eyeSklIUHJNi/l8b9NIT2Mn2dj/DyCzZb+2Bg2LxkJCbLYvGTJYB53SZSRo/27Is7N79EEW8rNZ8pJNkNKSJBsKZLNtfuQZ1eizYWZEsxdLym2uJvOc/bsWdc6i8+513Ip0aaEhARdSrTJy3BcsSkJN38N+YXLY5HHuJIf8komiM9gu71gNb8+P5PGPzP579KlS5Ikw7jx95jFuNkcJjp27JhKlCihDRs2KDo62t4+dOhQrV27Vr/88ovTc0aOHKnY2Pxzo3gAAAAAgGf6559/VLJkyQyn5+k95EWKFJG3t7dOnjzp0H7y5ElFRkam+5zhw4dryJAh9sc2m03nzp1T4cKFZbFY3FpfXFycSpUqpX/++UehoaFu7Rs5j/HzbIyfZ2P8PBvj59kYP8/G+Hk2xs+zZWb8DMPQpUuXVLx48RvOl6cDuZ+fn+rWravVq1erc+fOkq4G7NWrV+uZZ55J9zn+/v7y9/d3aAsLC8vROkNDQ9mgPBjj59kYP8/G+Hk2xs+zMX6ejfHzbIyfZ3N1/AoWLHjTefJ0IJekIUOGqFevXqpXr54aNGigyZMnKz4+Xn369DG7NAAAAAAAsizPB/IHHnhAp0+f1uuvv64TJ06oVq1aWrZsmdOF3gAAAAAA8CR5PpBL0jPPPJPhIepm8vf314gRI5wOkYdnYPw8G+Pn2Rg/z8b4eTbGz7Mxfp6N8fNsOTF+efoq6wAAAAAA5Ffc/BUAAAAAABMQyAEAAAAAMAGBHAAAAAAAExDIAQAAAAAwAYHcTQ4dOqS+ffuqXLlyCgwMVIUKFTRixAglJyebXRpcNGbMGDVq1EhBQUEKCwszuxzcxLRp01S2bFkFBASoYcOG2rx5s9klwUU//fSTOnbsqOLFi8tisWjJkiVmlwQXjR07VvXr11eBAgVUtGhRde7cWXv37jW7LLhoxowZqlGjhkJDQxUaGqro6GgtXbrU7LKQRePGjZPFYtGgQYPMLgUuGDlypCwWi8NPlSpVzC4LmXD06FE9/PDDKly4sAIDA1W9enVt3bo12/0SyN1kz549stlsev/99/Xnn39q0qRJmjlzpl5++WWzS4OLkpOT1a1bNz311FNml4Kb+PzzzzVkyBCNGDFCv/76q2rWrKmYmBidOnXK7NLggvj4eNWsWVPTpk0zuxRk0tq1azVgwABt2rRJK1eulNVqVbt27RQfH292aXBByZIlNW7cOG3btk1bt25Vq1at1KlTJ/35559ml4ZM2rJli95//33VqFHD7FKQCbfffruOHz9u/1m3bp3ZJcFF58+fV+PGjeXr66ulS5dq165dmjBhggoVKpTtvrntWQ56++23NWPGDB04cMDsUpAJc+bM0aBBg3ThwgWzS0EGGjZsqPr16+u9996TJNlsNpUqVUrPPvushg0bZnJ1yAyLxaKvvvpKnTt3NrsUZMHp06dVtGhRrV27Vs2aNTO7HGRBeHi43n77bfXt29fsUuCiy5cvq06dOpo+fbreeOMN1apVS5MnTza7LNzEyJEjtWTJEm3fvt3sUpAFw4YN0/r16/Xzzz+7vW/2kOegixcvKjw83OwygHwlOTlZ27ZtU5s2bextXl5eatOmjTZu3GhiZcCt5+LFi5LEd50HSk1N1Weffab4+HhFR0ebXQ4yYcCAAbr77rsdvgfhGfbt26fixYurfPnyeuihh3TkyBGzS4KL/vvf/6pevXrq1q2bihYtqtq1a+vDDz90S98E8hzy999/a+rUqXriiSfMLgXIV86cOaPU1FQVK1bMob1YsWI6ceKESVUBtx6bzaZBgwapcePGuuOOO8wuBy76448/FBISIn9/fz355JP66quvVK1aNbPLgos+++wz/frrrxo7dqzZpSCTGjZsqDlz5mjZsmWaMWOGDh48qKZNm+rSpUtmlwYXHDhwQDNmzFClSpW0fPlyPfXUUxo4cKDmzp2b7b4J5DcxbNgwpwswXP+zZ88eh+ccPXpU7du3V7du3dSvXz+TKoeUtfEDANzcgAEDtHPnTn322Wdml4JMqFy5srZv365ffvlFTz31lHr16qVdu3aZXRZc8M8//+i5557T/PnzFRAQYHY5yKQOHTqoW7duqlGjhmJiYvT999/rwoUL+uKLL8wuDS6w2WyqU6eO3nzzTdWuXVv9+/dXv379NHPmzGz37eOG+vK15/+vvXsPqqpq/wD+PYBcD4IIiljghUtoqIBggJMgOlyUQEsZhqupOKWSFxrDG469mqYlmOOFsXDGSSynVBSDiEQRUUFFMUnBQfDCHStRITms3x++7l8nIAG1Lb7fz8yeYa+119rPWfsMzuNarL14MaKiov7xmiFDhkg/3759G15eXnB3d0dSUtJzjo6epKvPj158pqam0NTURHV1tVp5dXU1zM3NZYqK6H/LvHnzcPjwYRw/fhyvvPKK3OFQF2hra8Pa2hoA4OzsjPz8fCQmJmLHjh0yR0ZPcvbsWdTU1MDJyUkqU6lUOH78OLZs2YLm5mZoamrKGCF1hbGxMWxtbVFaWip3KNQJAwYMaLOayN7eHt99991T982E/AnMzMxgZmbWqWtv3boFLy8vODs7Izk5GRoaXIAgt648P+oZtLW14ezsjKysLGkjsNbWVmRlZWHevHnyBkf0khNCYP78+di/fz+ys7MxePBguUOip9Ta2orm5ma5w6BO8Pb2RlFRkVrZjBkz8Nprr2HJkiVMxnuYxsZGXLt2DeHh4XKHQp3g4eHR5jWfV69ehZWV1VP3zYT8Gbl16xY8PT1hZWWFjRs3ora2VqrjrF3PUFFRgYaGBlRUVEClUkm7YFpbW0OpVMobHKlZtGgRIiMjMXr0aLi6uiIhIQH37t3DjBkz5A6NOqGxsVFtRqCsrAyFhYUwMTGBpaWljJHRk8ydOxd79uzBwYMHYWhoKO3bYGRkBD09PZmjoyeJi4uDn58fLC0tcffuXezZswfZ2dnIyMiQOzTqBENDwzb7NRgYGKBv377cx6EHiI2NRUBAAKysrHD79m3Ex8dDU1MTISEhcodGnbBw4UK4u7tj7dq1mD59Os6cOYOkpKRnsiKaCfkzkpmZidLSUpSWlrZZvsc3y/UMK1euVNuYwdHREQBw9OhReHp6yhQVtSc4OBi1tbVYuXIlqqqqMGrUKKSnp7fZ6I1eTAUFBfDy8pLOFy1aBACIjIzErl27ZIqKOmPbtm0A0OZ3YnJy8hP/PIjkV1NTg4iICFRWVsLIyAgjRoxARkYGJk6cKHdoRC+9mzdvIiQkBPX19TAzM8PYsWNx6tQpruTsIVxcXLB//37ExcVh9erVGDx4MBISEhAaGvrUffM95EREREREREQy4B85ExEREREREcmACTkRERERERGRDJiQExEREREREcmACTkRERERERGRDJiQExEREREREcmACTkRERERERGRDJiQExEREREREcmACTkRERERERGRDJiQExER/ZdCocCBAwfkDkMWnp6eWLBggXQ+aNAgJCQkPNd7PqvxXrFiBaKjo58+oBdQeno6Ro0ahdbWVrlDISKi54AJORERvXCioqKgUCigUCigra0Na2trrF69Gi0tLc/1vpWVlfDz8+t2++vXr0OhUKBfv364e/euWt2oUaOwatWqp4zw35Ofn98jktyqqiokJiZi2bJlAIDs7Gzpu9Pe4eXlJXPEXePr64tevXrh66+/ljsUIiJ6DpiQExHRC8nX1xeVlZUoKSnB4sWLsWrVKmzYsKHda//8889nck9zc3Po6Og8dT93797Fxo0bn0FE/0+lUv2rs6RmZmbQ19f/1+7XXTt37oS7uzusrKwAAO7u7qisrGxz7NixAwqFAu+//77MET/SlecZFRWFzZs3P+eIiIhIDkzIiYjohaSjowNzc3NYWVnhvffew4QJE5CamgrgUYISFBSENWvWwMLCAnZ2dgCAGzduYPr06TA2NoaJiQkCAwNx/fp1tX6/+uorDB8+HDo6OhgwYADmzZsn1f19CXVRURHGjx8PPT099O3bF9HR0WhsbHxi7PPnz8fnn3+OmpqaDq+5c+cOIiIi0KdPH+jr68PPzw8lJSVS/a5du2BsbIzU1FQMGzYMOjo6qKiowKBBg/Cf//wHERERUCqVsLKyQmpqKmpraxEYGAilUokRI0agoKBA6qu+vh4hISEYOHAg9PX14eDggJSUlH/8DH9dsr5r1652Z5sfz/jn5+dj4sSJMDU1hZGREcaNG4dz586p9VdSUoI333wTurq6GDZsGDIzM9vcszvjvXfvXgQEBEjn2traMDc3Vzvu3LmD2NhYLF26FNOmTeuwr+zsbLi6usLAwADGxsbw8PBAeXm5VH/o0CG4uLhAV1cXpqammDJlilTX3efZ3NyM2NhYDBw4EAYGBhgzZgyys7PV4goICEBBQQGuXbv2j2NBREQ9DxNyIiLqEfT09NRmwrOysnDlyhVkZmbi8OHDePjwIXx8fGBoaIicnBzk5uZCqVTC19dXardt2zbMnTsX0dHRKCoqQmpqKqytrdu937179+Dj44M+ffogPz8f+/btw08//aSWwHckJCREWmbfkaioKBQUFCA1NRV5eXkQQsDf3x8PHz6Urrl//z7Wr1+PnTt34pdffkG/fv0AAJs2bYKHhwfOnz+PSZMmITw8HBEREQgLC8O5c+cwdOhQREREQAgBAGhqaoKzszPS0tJw6dIlREdHIzw8HGfOnHnywAMIDg5Wm21OSUmBlpYWPDw8ADxaERAZGYkTJ07g1KlTsLGxgb+/v7Rsv7W1FVOnToW2tjZOnz6N7du3Y8mSJU893g0NDbh8+TJGjx7d4TW//fYbAgMD4enpiY8//rjD61paWhAUFIRx48bh4sWLyMvLQ3R0NBQKBQAgLS0NU6ZMgb+/P86fP4+srCy4urpK7bv7POfNm4e8vDzs3bsXFy9exLRp0+Dr66uWzFtaWqJ///7IycnpMH4iIuqhBBER0QsmMjJSBAYGCiGEaG1tFZmZmUJHR0fExsZK9f379xfNzc1Sm927dws7OzvR2toqlTU3Nws9PT2RkZEhhBDCwsJCLFu2rMP7AhD79+8XQgiRlJQk+vTpIxobG6X6tLQ0oaGhIaqqqtptX1ZWJgCI8+fPi/T0dNGrVy9RWloqhBBi5MiRIj4+XgghxNWrVwUAkZubK7Wtq6sTenp64ttvvxVCCJGcnCwAiMLCQrV7WFlZibCwMOm8srJSABArVqyQyvLy8gQAUVlZ2eFnnTRpkli8eLF0Pm7cOPHBBx+o3WfTpk1t2pWWlgoTExPx6aefdti3SqUShoaG4tChQ0IIITIyMoSWlpa4deuWdM0PP/zw1ON9/vx5AUBUVFR0GIefn5+wt7cXf/zxR4fxCiFEfX29ACCys7PbrXdzcxOhoaHt1nX3eZaXlwtNTU21cRFCCG9vbxEXF6dW5ujoKFatWvWPn4GIiHoezpATEdEL6fDhw1AqldDV1YWfnx+Cg4PVNkVzcHCAtra2dH7hwgWUlpbC0NAQSqUSSqUSJiYmaGpqwrVr11BTU4Pbt2/D29u7U/cvLi7GyJEjYWBgIJV5eHigtbUVV65ceWJ7Hx8fjB07FitWrGi3by0tLYwZM0Yq69u3L+zs7FBcXCyVaWtrY8SIEW3a/7Wsf//+AB6Nx9/LHi+ZV6lU+Pjjj+Hg4AATExMolUpkZGSgoqLiiZ/jr37//XdMnjwZkyZNwocffiiVV1dXY/bs2bCxsYGRkRF69+6NxsZGqf/i4mK8+uqrsLCwkNq4ubm1GZOujveDBw8AALq6uu3WL126FHl5eTh48CAMDQ2l8oqKCuk7olQqsXbtWpiYmCAqKgo+Pj4ICAhAYmIiKisrpTaFhYUdfne6+zyLioqgUqlga2urFs+xY8faLE/X09PD/fv3270/ERH1XFpyB0BERNQeLy8vbNu2Ddra2rCwsICWlvo/WX9N3ACgsbERzs7O7e5GbWZmBg2Nf///oNetWwc3Nze15LUr9PT0pCXTf9WrVy/p58f17ZU93jRsw4YNSExMREJCAhwcHGBgYIAFCxZ0aTM8lUqF4OBg9O7dG0lJSWp1kZGRqK+vR2JiIqysrKCjowM3N7dnttleR0xNTQE8+vttMzMztbq9e/di48aNSEtLg42NjVqdhYUFCgsLpXMTExMAQHJyMmJiYpCeno5vvvkGy5cvR2ZmJt544w3o6ek9dbx/f56NjY3Q1NTE2bNnoampqXatUqlUO29oaGjzGYmIqOfjDDkREb2QDAwMYG1tDUtLyzbJeHucnJxQUlKCfv36wdraWu0wMjKCoaEhBg0ahKysrE7d397eHhcuXMC9e/ekstzcXGhoaEibyD2Jq6srpk6dio8++qhN3y0tLTh9+rRUVl9fjytXrmDYsGGd6rsrcnNzERgYiLCwMIwcORJDhgzB1atXu9THwoULUVRUhAMHDrSZkc7NzUVMTAz8/f2lDfPq6uqkent7e9y4cUNtxvnUqVNqfXRnvIcOHYrevXvj8uXLauWFhYWYOXMm1q1bBx8fnzbttLS01L4fjxNyAHB0dERcXBxOnjyJ119/HXv27AHwaFVCR9+d7j5PR0dHqFQq1NTUtPnOmpubS9c9XuXh6OjYYV9ERNQzMSEnIqKXQmhoKExNTREYGIicnByUlZUhOzsbMTExuHnzJgBg1apV+Oyzz7B582aUlJTg3Llz+OKLLzrsT1dXF5GRkbh06RKOHj2K+fPnIzw8XFoS3hlr1qzBzz//rLbs2sbGBoGBgZg9ezZOnDiBCxcuICwsDAMHDkRgYODTDUQ7bGxskJmZiZMnT6K4uBhz5sxBdXV1p9snJydj69at2L59OxQKBaqqqlBVVSXtgG5jY4Pdu3ejuLgYp0+fRmhoqNqM8oQJE2Bra4vIyEhcuHABOTk50nvDH+vOeGtoaGDChAk4ceKEVFZXV4egoCB4enoiLCxMivXxUVtb225fZWVliIuLQ15eHsrLy/Hjjz+ipKQE9vb2AID4+HikpKQgPj4excXFKCoqwvr166XP353naWtri9DQUEREROD7779HWVkZzpw5g08++QRpaWnSdadOnZJWHRAR0cuFCTkREb0U9PX1cfz4cVhaWmLq1Kmwt7fHzJkz0dTUhN69ewN4tLQ6ISEBW7duxfDhwzF58mS13az/3l9GRgYaGhrg4uKCd955B97e3tiyZUuX4rK1tcW7776LpqYmtfLk5GQ4Oztj8uTJcHNzgxACR44cUVt6/qwsX74cTk5O8PHxgaenJ8zNzREUFNTp9seOHYNKpcJbb72FAQMGSMfjd61/+eWXuHPnDpycnBAeHo6YmBhpR3jgUeK8f/9+PHjwAK6urpg1axbWrFmjdo/ujvesWbOwd+9eaXl+WloaysvLceTIEbVYHx8uLi7t9qOvr49ff/0Vb7/9NmxtbREdHY25c+dizpw5AABPT0/s27cPqampGDVqFMaPH6+2S313n2dycjIiIiKwePFi2NnZISgoCPn5+bC0tJSuSUlJQWhoaI94LzwREXWNQoj/vhOFiIiIqIcRQmDMmDFYuHAhQkJC5A7nmaurq4OdnR0KCgowePBgucMhIqJnjDPkRERE1GMpFAokJSWhpaVF7lCei+vXr2Pr1q1MxomIXlKcISciIiIiIiKSAWfIiYiIiIiIiGTAhJyIiIiIiIhIBkzIiYiIiIiIiGTAhJyIiIiIiIhIBkzIiYiIiIiIiGTAhJyIiIiIiIhIBkzIiYiIiIiIiGTAhJyIiIiIiIhIBkzIiYiIiIiIiGTwfz0W200ozP43AAAAAElFTkSuQmCC",
+            "text/plain": [
+              "<Figure size 1200x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
       "source": [
-        "# TODO"
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "from scipy.stats import zscore\n",
+        "\n",
+        "ds = pd.read_csv('assets/real_estate.csv', sep=';')\n",
+        "\n",
+        "poblaciones_cinturon_sur = [\"Fuenlabrada\", \"Leganés\", \"Getafe\", \"Alcorcón\"]\n",
+        "\n",
+        "ds_cinturon_sur = ds[ds['level5'].isin(poblaciones_cinturon_sur)]\n",
+        "\n",
+        "# precios usando Z-score\n",
+        "ds_cinturon_sur['normalized_price'] = ds_cinturon_sur.groupby('level5')['price'].transform(zscore)\n",
+        "\n",
+        "# Gráfico\n",
+        "plt.figure(figsize=(12, 8))\n",
+        "\n",
+        "# Histograma de cada población\n",
+        "for poblacion in poblaciones_cinturon_sur:\n",
+        "    subset = ds_cinturon_sur[ds_cinturon_sur['level5'] == poblacion]\n",
+        "    plt.hist(subset['normalized_price'], bins=20, alpha=0.6, label=poblacion)\n",
+        "\n",
+        "# Título y etiquetas\n",
+        "plt.title('Histogramas Normalizados de Precios por Población en el Cinturón Sur de Madrid')\n",
+        "plt.xlabel('Precio Normalizado (Z-score)')\n",
+        "plt.ylabel('Frecuencia')\n",
+        "plt.legend(loc='upper right')\n",
+        "plt.grid(True)\n",
+        "\n",
+        "# Mostrar el gráfico\n",
+        "plt.show()\n"
       ]
     },
     {
@@ -855,7 +1432,7 @@
       "id": "precise-heavy",
       "metadata": {},
       "source": [
-        "**TODO: Markdown**. Para escribir aquí, haz doble clic en esta celda, elimina este contenido y coloca lo que quieras escribir. Luego ejecuta la celda."
+        "El gráfico muestra la distribución normalizada de los precios de las propiedades en las poblaciones de Fuenlabrada, Leganés, Getafe, y Alcorcón. Al normalizar los precios, podemos comparar las variaciones dentro de cada población sin que el efecto de las diferencias en los valores absolutos distorsione la comparación."
       ]
     },
     {
@@ -871,12 +1448,49 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 47,
       "id": "initial-liverpool",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "El precio promedio por metro cuadrado en Getafe es de 2066.31 USD/m²\n",
+            "El precio promedio por metro cuadrado en Alcorcón es de 2239.30 USD/m²\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/tmp/ipykernel_653/4059260355.py:9: SettingWithCopyWarning: \n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "  ds_cinturon_sur['pps'] = ds_cinturon_sur['price'] / ds_cinturon_sur['surface']\n"
+          ]
+        }
+      ],
       "source": [
-        "# TODO"
+        "import pandas as pd\n",
+        "\n",
+        "ds = pd.read_csv('assets/real_estate.csv', sep=';')\n",
+        "\n",
+        "poblaciones_cinturon_sur = [\"Fuenlabrada\", \"Leganés\", \"Getafe\", \"Alcorcón\"]\n",
+        "\n",
+        "ds_cinturon_sur = ds[ds['level5'].isin(poblaciones_cinturon_sur)]\n",
+        "\n",
+        "ds_cinturon_sur['pps'] = ds_cinturon_sur['price'] / ds_cinturon_sur['surface']\n",
+        "\n",
+        "# Calcular la media del precio por metro cuadrado\n",
+        "pps_getafe = ds_cinturon_sur[ds_cinturon_sur['level5'] == \"Getafe\"]['pps'].mean()\n",
+        "pps_alcorcon = ds_cinturon_sur[ds_cinturon_sur['level5'] == \"Alcorcón\"]['pps'].mean()\n",
+        "\n",
+        "\n",
+        "print(f\"El precio promedio por metro cuadrado en Getafe es de {pps_getafe:.2f} USD/m²\")\n",
+        "print(f\"El precio promedio por metro cuadrado en Alcorcón es de {pps_alcorcon:.2f} USD/m²\")\n"
       ]
     },
     {
@@ -891,12 +1505,60 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 48,
       "id": "accepting-airfare",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/tmp/ipykernel_653/3367897067.py:9: SettingWithCopyWarning: \n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "  ds_cinturon_sur['pps'] = ds_cinturon_sur['price'] / ds_cinturon_sur['surface']\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1600x1200 with 4 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
       "source": [
-        "# TODO"
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "ds = pd.read_csv('assets/real_estate.csv', sep=';')\n",
+        "\n",
+        "poblaciones_cinturon_sur = [\"Fuenlabrada\", \"Leganés\", \"Getafe\", \"Alcorcón\"]\n",
+        "\n",
+        "ds_cinturon_sur = ds[ds['level5'].isin(poblaciones_cinturon_sur)]\n",
+        "ds_cinturon_sur['pps'] = ds_cinturon_sur['price'] / ds_cinturon_sur['surface']\n",
+        "fig, axs = plt.subplots(2, 2, figsize=(16, 12))\n",
+        "poblaciones = [\"Fuenlabrada\", \"Leganés\", \"Getafe\", \"Alcorcón\"]\n",
+        "axes = axs.flatten()\n",
+        "\n",
+        "# Diagramas \n",
+        "for i, poblacion in enumerate(poblaciones):\n",
+        "    subset = ds_cinturon_sur[ds_cinturon_sur['level5'] == poblacion]\n",
+        "    axes[i].scatter(subset['surface'], subset['pps'], alpha=0.6, edgecolor='black')\n",
+        "    axes[i].set_title(f'{poblacion}')\n",
+        "    axes[i].set_xlabel('Superficie (m²)')\n",
+        "    axes[i].set_ylabel('Precio por Metro Cuadrado (USD/m²)')\n",
+        "    axes[i].grid(True)\n",
+        "\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "\n",
+        "plt.show()\n"
       ]
     },
     {
@@ -912,31 +1574,57 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 49,
       "id": "headed-privacy",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "9069040c8ca74cfb94d9707107ca41f4",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Map(center=[0.0, 0.0], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_out_t…"
+            ]
+          },
+          "execution_count": 49,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
       "source": [
-        "from ipyleaflet import Map, basemaps\n",
+        "import pandas as pd\n",
+        "from ipyleaflet import Map, Marker, Popup\n",
+        "\n",
+        "ds = pd.read_csv('assets/real_estate.csv', sep=';')\n",
+        "poblaciones_cinturon_sur = [\"Fuenlabrada\", \"Leganés\", \"Getafe\", \"Alcorcón\"]\n",
         "\n",
-        "# Mapa centrado en (60 grados latitud y -2.2 grados longitud)\n",
-        "# Latitud, longitud\n",
-        "map = Map(center = (60, -2.2), zoom = 2, min_zoom = 1, max_zoom = 20, \n",
-        "    basemap=basemaps.Stamen.Terrain)\n",
-        "map"
+        "m = Map(zoom=10, min_zoom=5, max_zoom=20, basemap=basemaps.OpenStreetMap.Mapnik)\n",
+        "\n",
+        "# Agregar ubicación\n",
+        "for index, row in ds_cinturon_sur.iterrows():\n",
+        "    population = row['level5']\n",
+        "    marker = Marker(\n",
+        "        location=(row['latitude'], row['longitude']),\n",
+        "    )\n",
+        "    popup = Popup(child=HTML(value=f\"<b>Población:</b> {population}\"))\n",
+        "    marker.popup = popup\n",
+        "    m.add_layer(marker)\n",
+        "    \n",
+        "# Mostrar el mapa\n",
+        "m\n",
+        "\n"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "id": "present-mistress",
+      "id": "542b7bf9",
       "metadata": {},
       "outputs": [],
-      "source": [
-        "## Aquí: traza la coordenadas de los estados\n",
-        "\n",
-        "## PON TU CÓDIGO AQUÍ:\n"
-      ]
+      "source": []
     }
   ],
   "metadata": {
@@ -955,7 +1643,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.11.3"
+      "version": "3.11.4"
     }
   },
   "nbformat": 4,