proba_estadistica.ipynb

{
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   "source": [
    "<a href=\"https://www.inove.com.ar\"><img src=\"https://raw.githubusercontent.com/InoveAlumnos/intro_analytics_python/main/images/PA%20Banner.png\" width=\"1000\" align=\"center\"></a>\n",
    "\n",
    "\n",
    "# Probabilidad y estadística\n",
    "\n",
    "Programa creado para mostrar ejemplos prácticos de los visto durante la clase\\\n",
    "v2.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "86driwTKyANt"
   },
   "source": [
    "Objetivos: \n",
    "* Conocer el entorno de trabajo en Colab y Notebooks (Habilitar, conectar, guardar, bloques de texto y código, ejecución).\n",
    "* Repasar las librerias de Numpy, Matplotlib y Seaborn.\n",
    "* Implementar con numpy los indicadores de probabilidad como la media, mediana y desviación estandar. \n",
    "* Comunicar visualmente la distribución de los datos a través de gráficos de frecuencia, distribución y caja (boxplot).\n",
    "* Comprender los outliers y su repercusión en la media, mediana y desviación estandar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "source": [
    "# Librerías a implementar\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IPO4G9mAtQdg"
   },
   "source": [
    "### Repaso NumPy"
   ]
  },
  {
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   "metadata": {
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      "displayName": "Johana Rangel",
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    "outputId": "16311c57-9de8-4e93-82f7-c30b507a4280"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
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     "execution_count": 5,
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   "source": [
    "# NumPy brinda una enorme variedad de formas rápidas y eficientes de crear \n",
    "# matrices y manipular datos numéricos homogéneos dentro de ellas.\n",
    "# Una matriz es una cuadrícula de valores, que permite almacenar multiples valores en una sola variable.\n",
    "# Una forma para inicializar las matrices es desde las listas de Python.\n",
    "# Haciendo uso del método array() de Numpy(np).\n",
    "# Fuente: https://numpy.org/doc/stable/user/absolute_beginners.html\n",
    "\n",
    "matriz = np.array([1, 2, 3, 4, 5, 6])\n",
    "\n",
    "\n",
    "# Indexación y corte (Acceder a elelmentos por partes o posición)\n",
    "matriz[2]"
   ]
  },
  {
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   "execution_count": 6,
   "metadata": {
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    "executionInfo": {
     "elapsed": 37,
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     "timestamp": 1674521892189,
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      "displayName": "Johana Rangel",
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    "id": "hR0CgYrDzF01",
    "outputId": "19485300-4f3e-46ca-ad5f-9b5f682d9364"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 6,
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   ],
   "source": [
    "# Se puede crear una matriz a partir de una secuencia de elementos, puede crear fácilmente una matriz llena de ceros.\n",
    "# Con el método zeros() de NumPy(np) ---> np.zeros(cantidad_elementos)\n",
    "matriz_ceros = np.zeros(5)\n",
    "matriz_ceros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
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    "executionInfo": {
     "elapsed": 31,
     "status": "ok",
     "timestamp": 1674521892189,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Se puede crear una matriz de unos como elementos.\n",
    "# Con el método: ones() de NumPy(np) ---> np.zeros(cantidad_elementos)\n",
    "matriz_unos = np.ones(3)\n",
    "matriz_unos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 29,
     "status": "ok",
     "timestamp": 1674521892191,
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      "displayName": "Johana Rangel",
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    },
    "id": "b5_o_52a0TR-",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3])"
      ]
     },
     "execution_count": 8,
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Se puede crear una matriz con un rango de elementos.\n",
    "# Con el método: arange() de NumPy(np) ---> np.arange(cantidad_elementos)\n",
    "matriz_range = np.arange(4)\n",
    "matriz_range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
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    "executionInfo": {
     "elapsed": 28,
     "status": "ok",
     "timestamp": 1674521892192,
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      "displayName": "Johana Rangel",
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    },
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    "outputId": "3a93ae14-b39e-4007-99cc-4ae9cef1e4e5"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  2.,  4.,  6.,  8., 10.])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
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    }
   ],
   "source": [
    "# También puede usar np.linspace() para crear una matriz con valores que están separados por la misma cantidad.\n",
    "# Con el método: linspace() de NumPy(np) ---> np.linspace(desde, hasta,cantidad_elementos)\n",
    "matriz_space = np.linspace(0,10,6)\n",
    "matriz_space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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    "executionInfo": {
     "elapsed": 25,
     "status": "ok",
     "timestamp": 1674521892193,
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      "displayName": "Johana Rangel",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7, 8])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Se pueden concatenar las matrices con el método: concatenate() de NumPy(np) ---> np.concatenate()\n",
    "matriz_a = np.array([1, 2, 3, 4])\n",
    "matriz_b = np.array([5, 6, 7, 8])\n",
    "\n",
    "matriz_concatenada = np.concatenate((matriz_a, matriz_b))\n",
    "matriz_concatenada\n",
    "\n",
    "# Si aparece el siguiente error:\n",
    "# TypeError: only integer scalar arrays can be converted to a scalar index\n",
    "# y tiene lo siguiente: np.concatenate(matriz_a, matriz_b)\n",
    "# Es porque el método concatenate() espera como parámetro una tupla con las matrices\n",
    "# a concatenar. Es por ello, que tiene doble (()), una del método y el otro de la tupla \n",
    "# con las matrices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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    "executionInfo": {
     "elapsed": 23,
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     "timestamp": 1674521892193,
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      "displayName": "Johana Rangel",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Con shape, es una herramienta que muestra una tupla(nro_filas, nro_columnas) que indican el número de elementos almacenados a lo \n",
    "# largo de cada dimensión de la matriz.\n",
    "matriz_concatenada.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
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    "executionInfo": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[14, 15],\n",
       "       [16, 17]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# COn reshape() se puede remodelar una matriz.\n",
    "# Por ejemplo, puede remodelar esta edad a una matriz con dos filas y dos columnas:\n",
    "edad = np.array([14,15,16,17])\n",
    "edad_reshape = edad.reshape(2,2)\n",
    "edad_reshape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "executionInfo": {
     "elapsed": 20,
     "status": "ok",
     "timestamp": 1674521892195,
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    {
     "data": {
      "text/plain": [
       "array([14, 15])"
      ]
     },
     "execution_count": 13,
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   ],
   "source": [
    "# Se pueden filtrar matrices.\n",
    "# Extraer los valores en la matriz que son menores a 16.\n",
    "menore_16 = edad[edad<16]\n",
    "menore_16\n"
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "CfNuNunB9NL_"
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   "source": [
    "### Ejemplo NumPy y Matplotlib\n",
    "\n",
    "Objetivo: Gráficar 1000 tiros de una moneda, cuya probabilidad de cara o ceca es de 1/2, que corresponde a 0.5\n",
    "\n",
    "1.   Obtener los valores a gráficar, tanto para cara y ceca con arrays de NumPy (np.ones).\n",
    "2.   Realizar una representación gráfica de tipo scatter con Matplotlib\n",
    "\n",
    "\n",
    "  \n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "executionInfo": {
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     "timestamp": 1674523551295,
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   "source": [
    "cantidad_tiros = 20\n",
    "probabilidad = 0.5\n",
    "\n",
    "# El 1 representa a las caras.\n",
    "# El 0 representa a las cecas.\n",
    "# Armar un array que represente las 10 veces que puede salir una cara(1)\n",
    "# Armar un array que represente las 10 veces que puede salir una ceca(0)\n",
    " \n",
    "caras = 1 * np.ones(int(cantidad_tiros * probabilidad))\n",
    "cecas = 0 * np.ones(int(cantidad_tiros * probabilidad))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "executionInfo": {
     "elapsed": 437,
     "status": "ok",
     "timestamp": 1674523586879,
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      "displayName": "Johana Rangel",
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    "id": "qD5EPlDzR-fQ"
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   "outputs": [],
   "source": [
    "# x_tiros almacena la lista de números que van del 0 al 20\n",
    "x_tiros = range(cantidad_tiros)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "executionInfo": {
     "elapsed": 290,
     "status": "ok",
     "timestamp": 1674524026357,
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    "id": "xEi0-GjcSPAp"
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   "outputs": [],
   "source": [
    "# moneda almacena las dos matrices en una. Es decir, los 20 tiros\n",
    "moneda = np.append(caras, cecas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "executionInfo": {
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     "timestamp": 1674524125659,
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    "id": "Szq4V-4xSi1J"
   },
   "outputs": [],
   "source": [
    "# np.random.shuffle() se encarga de mezclar una lista (reorganizar el orden de los elementos)\n",
    "np.random.shuffle(moneda)"
   ]
  },
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     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# De matplotlib se utiliza el método scatter para graficar, necesita los valores para el eje x\n",
    "# y el eje y. Se necesita de Matplotlib el método show() para mostrar el gráfico.\n",
    "# fig almacena el espacio para dibujar, a partir del método figure().\n",
    "# ax almacena el espacio para configurar el gráfico.\n",
    "# El método scatter es para realizar un gráfico de dispersión, que necesita valores\n",
    "# para el eje x (x_tiros) y para el eje y(moneda)\n",
    "# Por lo tanto, el gráfico mostrará los 20 tiros Y que salió en cada jugada.\n",
    "# El parámetro color le asigna color a los puntos o línea del gráfico\n",
    "# plt.show() para mostrar la figura\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = plt.subplot()\n",
    "ax.scatter(x_tiros, moneda, color=\"darkorange\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "executionInfo": {
     "elapsed": 586,
     "status": "ok",
     "timestamp": 1674523698993,
     "user": {
      "displayName": "Johana Rangel",
      "userId": "05955916709715560742"
     },
     "user_tz": 180
    },
    "id": "E1W8YsDlY5_i",
    "outputId": "99f0b11f-bcda-41c0-8b33-f7bf28672eda"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_tiros, moneda)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VTtmWlqKTovI"
   },
   "outputs": [],
   "source": [
    "## Todo puede estar dentro de un bloque de código\n",
    "# cantidad_tiros = 20\n",
    "# probabilidad = 0.5\n",
    "# caras = 1 * np.ones(int(cantidad_tiros * probabilidad))\n",
    "# cecas = 0 * np.ones(int(cantidad_tiros * probabilidad))\n",
    "# x_tiros = range(cantidad_tiros)\n",
    "# moneda = np.append(caras, cecas)\n",
    "# np.random.shuffle(moneda)\n",
    "# plt.scatter(x_tiros, moneda)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "executionInfo": {
     "elapsed": 1006,
     "status": "ok",
     "timestamp": 1674528729153,
     "user": {
      "displayName": "Johana Rangel",
      "userId": "05955916709715560742"
     },
     "user_tz": 180
    },
    "id": "2nZwRWlpyANx",
    "outputId": "e5062afd-7ea2-4e29-dcf6-ae3d27a99eff"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Repaso  Matplotlib\n",
    "# Es una biblioteca completa para crear visualizaciones en Python.\n",
    "# El método hist() utiliza se utiliza para agrupar los datos en x, y contar la cantidad de valores en cada contenedor.\n",
    " # Fuente: https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.hist.html#matplotlib.axes.Axes.hist\n",
    "fig = plt.figure()\n",
    "ax = plt.subplot()\n",
    "ax.hist(moneda, color=\"darkviolet\")\n",
    "ax.set(xticks=np.arange(0, 2), yticks=np.arange(0,11)) # el método set configura los valores de los ejes\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "executionInfo": {
     "elapsed": 319,
     "status": "ok",
     "timestamp": 1674523649321,
     "user": {
      "displayName": "Johana Rangel",
      "userId": "05955916709715560742"
     },
     "user_tz": 180
    },
    "id": "eFfQLEdmYsSq",
    "outputId": "dddd303a-dc22-486a-f559-61ba262b3025"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(moneda)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ebOCNHvQdSB7"
   },
   "source": [
    "### Seaborn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "executionInfo": {
     "elapsed": 318,
     "status": "ok",
     "timestamp": 1674525959019,
     "user": {
      "displayName": "Johana Rangel",
      "userId": "05955916709715560742"
     },
     "user_tz": 180
    },
    "id": "yGkQNrsbyANx",
    "outputId": "50b65a91-d654-4328-c24b-a07a5932030a"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Es un gráfico de conteo que se puede considerar como un histograma a través de una variable categórica, en lugar de cuantitativa. \n",
    "# Fuente: https://seaborn.pydata.org/generated/seaborn.countplot.html\n",
    "sns.countplot(x=moneda)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "executionInfo": {
     "elapsed": 273,
     "status": "ok",
     "timestamp": 1674524683577,
     "user": {
      "displayName": "Johana Rangel",
      "userId": "05955916709715560742"
     },
     "user_tz": 180
    },
    "id": "1l6oP1jLcNea",
    "outputId": "d89f67f5-88b3-4851-cdc1-d4fe6b0b834a"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=x_tiros, y=moneda)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X9fsqU8xbYJi"
   },
   "source": [
    "### Indicadores de probabilidad: media, mediana y desviación estandar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "executionInfo": {
     "elapsed": 298,
     "status": "ok",
     "timestamp": 1674525323256,
     "user": {
      "displayName": "Johana Rangel",
      "userId": "05955916709715560742"
     },
     "user_tz": 180
    },
    "id": "eEOTxJGCfGD6"
   },
   "outputs": [],
   "source": [
    "# crear una matriz con valores que están separados por la misma cantidad\n",
    "y = np.linspace(0, 100, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 252,
     "status": "ok",
     "timestamp": 1674525443610,
     "user": {
      "displayName": "Johana Rangel",
      "userId": "05955916709715560742"
     },
     "user_tz": 180
    },
    "id": "KBNwt5OPtQdv",
    "outputId": "55377db4-776d-4696-9a9c-dca1bbca2c82",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Promedio: 50.00000000000001\n",
      "Mediana: 50.0\n",
      "Desvio: 29.157646512850626\n"
     ]
    }
   ],
   "source": [
    "# Los métodos:\n",
    "# mean() calcula promedio\n",
    "# median() calcula el valor central de los datos.\n",
    "# std() calcula la desviación estandar, que tan alejados están los datos de la media.\n",
    "print(\"Promedio:\", np.mean(y))\n",
    "print(\"Mediana:\", np.median(y))\n",
    "print(\"Desvio:\", np.std(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "executionInfo": {
     "elapsed": 288,
     "status": "ok",
     "timestamp": 1674525330415,
     "user": {
      "displayName": "Johana Rangel",
      "userId": "05955916709715560742"
     },
     "user_tz": 180
    },
    "id": "kp_J95dmtQdy",
    "outputId": "78bb1451-57ac-49f1-bd38-c164b4b3f46b",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribución uniforme:\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Distribución uniforme:\")\n",
    "plt.hist(y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "executionInfo": {
     "elapsed": 630,
     "status": "ok",
     "timestamp": 1674525823769,
     "user": {
      "displayName": "Johana Rangel",
      "userId": "05955916709715560742"
     },
     "user_tz": 180
    },
    "id": "QjWj3xYRtQd5",
    "outputId": "1d755b36-a6e2-4c55-9349-da3fa46b1129",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Promedio: 59.405940594059416\n",
      "Mediana: 50.505050505050505\n",
      "Desvio: 98.43232542030236\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mismo rango de datos en un array llamado x\n",
    "x = np.linspace(0, 100, 100)\n",
    "\n",
    "# Agragando un valor alejando del grueso de datos.\n",
    "# Para agregar el número 1000, debe tener el mismo formato de datos. Es decir, convertirlo en array. \n",
    "ruido = np.array(1000)  \n",
    "\n",
    "# Para agregarlo a los demás datos se implementa el método append()\n",
    "y2 = np.append(x, ruido) \n",
    "\n",
    "# Calculo de lo indicadores de probabilidad.\n",
    "print(\"Promedio:\", np.mean(y2))\n",
    "print(\"Mediana:\", np.median(y2))\n",
    "print(\"Desvio:\", np.std(y2))\n",
    "\n",
    "# Representación gráfica a través de un histograma\n",
    "plt.hist(y2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fQsUYcNBtQd8"
   },
   "source": [
    "\n",
    "\n",
    "Estos valores muy alejados del resto de los datos se los conoce como: \n",
    "\n",
    "*   \"outliers\" (valores atípicos o ruido) los cuales debemos indentificarlos y extraerlos.\n",
    "*   Por otro lado, la \"mediana\" tiene mayor resilencia (se ve menos afetacada) por el ruido o valores atípicos.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x8SDRXq4tQd9"
   },
   "source": [
    "### Ejemplo: Frecuencia de un colectivo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 265,
     "status": "ok",
     "timestamp": 1674527447009,
     "user": {
      "displayName": "Johana Rangel",
      "userId": "05955916709715560742"
     },
     "user_tz": 180
    },
    "id": "Wkb77T6qjJrD",
    "outputId": "44090b43-2a6e-41f6-c2b7-e47735c7ab92"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datos recolectados los lunes: 43\n"
     ]
    }
   ],
   "source": [
    "lunes = [12, 13, 12, 19, 17, 14, 11, 20, 19, 15 ,11 ,14 ,16 ,14 ,13 ,15 ,16 ,20 ,15 ,19 ,20 ,15 ,15 ,16 ,11 ,17 ,18 ,15 ,11 ,14 ,11 ,12 ,14 ,13 ,13 ,10 ,12 ,11 ,18 ,12 ,16 ,13 ,11]\n",
    "martes = [11, 17, 11, 14, 16, 12, 13, 16, 16, 14, 13, 13, 15, 13, 17, 11, 14, 15, 11, 15, 14, 16, 11, 16, 12, 15, 15, 14, 12, 16, 13, 11, 16, 11, 12, 15, 15, 11, 11, 16, 12, 14, 12]\n",
    "miercoles = [21, 21, 19, 16, 18, 17, 21, 20, 15, 21, 17, 15, 19, 19, 20, 16, 19, 19, 16, 15, 20, 20, 18, 17, 22, 21, 19, 18, 21, 20, 19, 15, 17, 16, 17, 19, 15, 16, 22, 20, 20, 16, 16]\n",
    "jueves = [16, 12, 16, 15, 14, 18, 13, 18, 16, 14, 17, 18, 21, 14, 13, 18, 14, 19, 13, 21, 17, 18, 19, 22, 12, 20, 21, 14, 18, 21, 14, 21, 18, 14, 18, 19, 15, 21, 20, 21, 14, 21, 19]\n",
    "viernes = [12, 16, 12,  8,  9, 16,  9, 15,  9,  7, 15,  8, 10,  9,  8,  8,  8, 16,  9, 11, 12, 12, 12, 15, 13, 15, 10,  9, 16, 15, 15, 17, 15,  8, 14, 8, 7, 10, 15,  9,  8, 16, 17]\n",
    "sabado = [24, 34, 32, 20, 32, 27, 32, 22, 33, 21, 33, 27, 32, 26, 21, 34, 34, 26, 30, 26, 25, 30, 24, 34, 20, 23, 32, 28, 22, 21, 35, 23, 30, 27, 25, 23, 25, 28, 32, 27, 23, 23, 34]\n",
    "print(\"Datos recolectados los lunes:\", len(lunes))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iHhgN_catQeK"
   },
   "source": [
    "#### Frecuencia Lunes-Viernes\n",
    "\n",
    "\n",
    "*   Gráficar la distribución de los datos.\n",
    "*   Calcular los indicadores de probabilidad.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "executionInfo": {
     "elapsed": 410,
     "status": "ok",
     "timestamp": 1674528678017,
     "user": {
      "displayName": "Johana Rangel",
      "userId": "05955916709715560742"
     },
     "user_tz": 180
    },
    "id": "H1tRonSTnsr8"
   },
   "outputs": [],
   "source": [
    "# Agregar toda la información a una variable y tranformar en un dato tipo array.\n",
    "frecuencia = np.concatenate((lunes, martes, miercoles, jueves, viernes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 638
    },
    "executionInfo": {
     "elapsed": 1848,
     "status": "ok",
     "timestamp": 1674528131935,
     "user": {
      "displayName": "Johana Rangel",
      "userId": "05955916709715560742"
     },
     "user_tz": 180
    },
    "id": "vcyM1c0FtQeL",
    "outputId": "0d2a2202-a75d-41ee-80ef-622750a50f71",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datos ensayados: 215\n",
      "Promedio: 15.06046511627907\n",
      "Mediana: 15.0\n",
      "Desvio: 3.5882326760057266\n"
     ]
    },
    {
     "data": {
      "image/png": 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Pbty4oVKlSqlmzZq5ZnR+FF588UVVrlxZc+bM0Zw5c2QymVS2bFk1btxYFStWlPTbDNuLFy/W0qVLdePGDZUpU0adO3fWc88990izAADyzmQ2m81GhwAAAAAA4EFYKgcAAAAAYPEorwAAAAAAi0d5BQAAAABYPMorAAAAAMDiUV4BAAAAABavwJbKCQsLU4kSJWRlZSVra2stXrxY165d0wsvvKCEhAT5+vpq0qRJcnV1feB+QkJC5OvrW0CpAQAAAAAFKSEhQdu3b79re4Gu8zpr1iyVKlUq5/L06dPVuHFjDR8+XNOnT9f06dP18ssvP3Afvr6+Wrx4cX5HBQAAAAAYoFu3bvfcbuiw4fXr1ysiIkKSFBERoXXr1hkZBwAAAABgoQq0vA4ZMkTdunXTvHnzJElJSUkqU6aMJKl06dJKSkoqyDgAAAAAgEKiwIYNz507V15eXkpKStKgQYNUuXLlXNebTCaZTKaCigMAAAAAKEQK7Mirl5eXJMnDw0Nt2rTRvn375OHhoYsXL0qSLl68mOt8WAAAAAAA7iiQ8nrr1i2lpKTkfB8bG6uqVasqLCxMUVFRkqSoqCi1atWqIOIAAAAAAAqZAhk2nJSUpGeeeUaSlJWVpY4dO6p58+YKCgrS6NGjtXDhQvn4+GjSpEkFEQcAAAAAUMgUSHn18/NTdHT0Xdvd3d01a9asgogAAAAAACjEDF0qBwAAAACAvKC8AgAAAAAsHuUVAAAAAGDxKK8AAAAAAItHeQUAAAAAWDzKKwAAAADA4lFeAQAAAAAWj/IKAAAAALB4lFcAAAAAgMWjvAIAAAAALB7lFQAAAABg8SivAAAAAACLR3kFAAAAAFg8yisAAAAAwOJRXgEAxUZmZrbREYoFnmcAQH6wMToAAAAFxcbGSuPGxRodo8gbN66p0REAAEUQR14BAAAAABaP8goAAAAAsHiUVwAAAACAxaO8AgAAAAAsHuUVAAAAAGDxKK8AAAAAAItHeQUAAAAAWDzKKwAAAADA4lFeAQAAAAAWj/IKAAAAALB4lFcAAAAAgMWjvAIAAAAALB7lFQAAAABg8SivAAAAAACLR3kFAAAAAFg8yisAAAAAwOJRXgEAAAAAFo/yCgAAAACweJRXAAAAAIDFo7wCAAAAACwe5RUAAAAAYPEorwAAAAAAi0d5BQAAAABYPMorAAAAAMDiUV4BAAAAABaP8goAAAAAsHiUVwAAAACAxaO8AgAAAAAsHuUVAAAAAGDxKK8AAAAAAItHeQUAAAAAWDzKKwAAAADA4lFeAQAAAAAWj/IKAAAAALB4lFcAAAAAgMWjvAIAAAAALB7lFQAAAABg8SivAAAAAACLR3kFAAAAAFg8yisAAAAAwOJRXgEAAAAAFo/yCgAAAACweJRXAAAAAIDFo7wCAAAAACwe5RUAAAAAYPEorwAAAAAAi0d5BQAAAABYPMorAAAAAMDiUV4BAAAAABaP8goAAAAAsHiUVwAAAACAxaO8AgAAAAAsHuUVAAAAAGDxKK8AAAAAAItHeQUAAAAAWDzKKwAAAADA4lFeAQAAAAAWj/IKAAAAALB4lFcAAAAAgMWjvAIAAAAALB7lFQAAAABg8SivAAAAAACLR3kFAAAAAFg8yisAAAAAwOJRXgEAAAAAFo/yCgAAAACweJRXAAAAAIDFo7wCAAAAACwe5RUAAAAAYPEorwAAAAAAi0d5BQAAAABYPMorAAAAAMDiFWh5zcrKUkREhEaMGCFJio+PV2RkpNq0aaPRo0crPT29IOMAAAAAAAqJAi2v33zzjfz9/XMuf/DBBxo4cKDWrl0rFxcXLVy4sCDjAAAAAAAKiQIrr4mJidq4caN69OghSTKbzdq2bZvCw8MlSV27dtX69esLKg4AAAAAoBApsPL67rvv6uWXX5aV1W8PefXqVbm4uMjGxkaSVLZsWV24cKGg4gAAAAAACpECKa8bNmxQqVKlFBgYWBAPBwAAAAAoYmwK4kF+/vlnxcTE6Mcff1RaWppSUlL0zjvvKDk5WZmZmbKxsVFiYqK8vLwKIg4AAAAAoJApkCOvL730kn788UfFxMToo48+UmhoqD788EOFhIRozZo1kqQlS5YoLCysIOIAAAAAAAoZQ9d5ffnllzVz5ky1adNG165dU2RkpJFxAAAAAAAWqkCGDf9eSEiIQkJCJEl+fn4sjwMAAAAA+FOGHnkFAAAAACAvKK8AAAAAAItHeQUAAAAAWDzKKwAAAADA4lFeAQAAAAAWj/IKAAAAALB4lFcAAAAAgMWjvAIAAAAALB7lFQAAAABg8WyMDgAAACyT2WxWamqmrl9PV3JyutLSMpWVZc75srExydHRJufLxcVejo68tQAA5A9eYQAAgLKysnXhwi0lJKTo3LkUnT9/U1eupCojI/uh9uPsbKvY2HOqVctDdeuWUVhYefn5ueRTagBAcUJ5BQCgmLp+PU3Hj1/V8ePXdPLkdWVm/lZUnZxs5OPjrIoVXeXqaidXV3u5uNjJ0dFGVlYmWVubZG1tpczMbN2+nalbtzJ1+3aGrl1L06VLt3XtWqpmzNinW7cyJUkBAe5q3bqCwsMrKjy8ouztefsBAHh4vHoAAFCMpKSka9++y9q375IuXLglSXJ3t1fdumVUsaKLfHxKyNXVXiaTKU/7c3W1v2vbuHFNlZ1t1oEDl7V+/WmtW3das2Yd1NSpe+Tu7qCePaupX78aatLEV1ZWeXscAAAorwAAFHHZ2WYdPXpFe/Zc0vHjV2U2S76+zmrTpoICAtzl4eGQ57KaV1ZWJgUHl1ZwcGm98EIDpadnKSbmjL799pBmzz6ozz/fq8qVXfXcc/U0ZEiQnJ3tHunjAwCKHsorAABFVHp6lnbvvqht287r2rU0OTvbqkkTH9WpU0aeno4FmsXOzlpt21ZS27aVlJKSrqVLf9G0aXs1evQGjRu3VU8/XVvPPltPPj7OBZoLAFB4UF4BAChibt/O0E8/nVdcXKJSU7Pk51dSTzxRQdWqlbKIYbrOznbq27em+vatqW3bzunDD3dqwoQ4ffTRLv3jH3X06quh8vAo2HINALB8lFcAAIqI9PQsbd9+XrGx55SWlqXq1d3VpImv/PxKGh3tvkJDfbRgQWedPHlN77yzTZMm/awvvzygf/6zkZ57rp4cHW2NjggAsBBWRgcAAAB/T1ZWtuLiEjV58m7FxMSrYkUXPf10sHr1qm7RxfX3Kld205dfttXevf312GO+Gjt2swICvtLixcdkNpuNjgcAsACUVwAACrFff72uzz/fp5UrT8nDw0GDBweqd+/q8vIqYXS0vyQwsLSWLeumTZt6ycPDQd27RysiIkrx8clGRwMAGIzyCgBAIZScnKZFi45p1qxDysjIVq9e1TRwYK1Cc6T1zzRv7qedO5/SxIkttHbtadWsOVOTJ/+srKxso6MBAAzCOa8AABQiZrNZO3YkKibmjLKyzGrevJyaNfORra210dEeORsbK40Z01Ddu1fVyJHr9PzzMVqy5Lhmz26vcuWKRkkHAOQdR14BACgkrly5ra+/PqjVq39V+fIuGjWqjlq29CuSxfX3KlVy06pV3fXll+GKi0tUcPAsLVp0zOhYAIACRnkFAMDCZWebtW3beU2btk8XLtxSly7+6tOnukqVcjA6WoExmUwaPDhIu3f3l7+/q3r0iNbQoWt082a60dEAAAWEYcMAAFiw5OQ0LVnyi379NVlVq7qpY8fKcnGxNzqWYapWdVdsbB+NG7dV48dvV1xcopYs6aLKld2MjgYAyGcceQUAwEIdPpykadP2KiEhRZ07++vJJ6sX6+J6h52dtd599zGtXNld8fE31KDBt1qz5pTRsQAA+YzyCgCAhcnIyNKyZSc0f/4xlSrloBEjglW3bhmZTCajo1mUtm0raefOfvLzK6l27Rbpvfe2syYsABRhDBsGAMCCXL58W/PnH9WlS7fVtKmPWrb0k7U1nzXfT+XKbtq69UkNG/aDXn11s/bvv6SZM9vK3p63OABQ1PA/OwAAFuLgwSRFR/8iGxsr9etXQ/7+nMeZFyVK2Om77zooOLi0/vnPzTp7NkVLlnSRh4ej0dEAAI8QH+UCAGCwrKxsrV59SgsXHlOZMk4aPjyY4vqQTCaTxo4N0fffd9T27efVpMkcnThxzehYAIBHiPIKAICBUlLSNWvWIW3fnqiQkLIaOLCWXF2ZlOmv6tWrutavj9Tly7cVGvqdduw4b3QkAMAjQnkFAMAg586laMaM/Tp//qa6d6+qtm0rcX7rI9CsWTn99FMfubjYKSxsvmJizhgdCQDwCPAKCQCAAX6bWOiATCZp8OBABQZ6Gh2pSAkIKKUtW55UpUquat9+kZYu/cXoSACAv4nyCgBAATKbzVq37rQWL/5FPj7OGjYsWN7eJYyOVSR5eztr06Zeql27tLp3X6rZsw8aHQkA8Dcw2zAAAAUkIyNLUVEndOhQkurX91K7dhUZJpzPSpVy1Lp1PRUREaX+/VcpJSVDI0fWMToWAOAvoLwCAFAAbt7M0PffH9HZsylq06aCGjf2lslkMjpWsVCypJ1WrOimnj2XadSodTKbzRo1qq7RsQAAD4nyCgBAPrt8+bbmzDmsGzfSFRkZoJo1PYyOVOw4ONho4cLO6tEjWs88s15ms/TMMxRYAChMKK8AAOSj06eT9f33R2RlZdKAAbVUrlxJoyMVW3Z21lq4sLMiI6P1j3+sl0SBBYDChPIKAEA+2b//kpYuPSE3N3v17VtD7u4ORkcq9uzsrLVgwf8vsCaTGEIMAIUE5RUAgEfMbDZr8+YEbdgQrwoVXNSrV4AcHW2NjoX/+X2BfeaZ9XJystXAgYFGxwIA/AmmOAQA4BHKzjYrOvqENmyIV3Cwp/r1q0FxtUB2dtaaN6+TWreuoCFD1mjBgqNGRwIA/AnKKwAAj0hmZrbmzz+qPXsu6bHHfBURUUU2NrzUWioHBxtFRXVRaKi3+vRZoZUrTxodCQDwALyiAgDwCKSlZerbbw/r6NGratu2osLCyrMUTiFQosRvy+gEBXmqe/dobdx4xuhIAID7oLwCAPA33byZoVmzDik+/oa6dq2ikBBvoyPhIbi5OWjNmh6qVMlFnTtHaffuC0ZHAgDcA+UVAIC/4fr1NM2ceUCXLt1Sr17VFBxc2uhI+AtKl3bSDz9Eys3NXu3aLdKJE9eMjgQA+APKKwAAf9GlS7f01VcHlJKSoaeeqqmAAHejI+FvKFeupNas6aGMjGyFhy/UhQs3jY4EAPgdyisAAH9BQkKKZs48qKwsswYOrKXy5V2MjoRHoEYND61Y0U3nzqWoXbtFSk5OMzoSAOB/KK8AADykkyev65tvDsre3lqDB9dS2bIljI6ERyg01EcLF3bWvn2X1K3bUqWnZxkdCQAgyisAAA/l8OEkzZlzWG5u9ho8OFClSjkaHQn5oH37yvryy3CtX39GI0b8ILPZbHQkACj2bIwOAABAYbF790UtW3ZCvr7O6tOnuhwdbY2OhHw0YECgTp68rrff/klVqrjrtddCjY4EAMUa5RUAgDzYuvWc1q49LX9/V/XsWU12dtZGR0IBGDeuiU6evK7XX9+iypVd9eSTNYyOBADFFuUVAIAHMJvNWr/+jGJjz6lWLQ917VpF1tacdVNcmEwmffHFEzpzJlkDB65WuXIl9dhj5YyOBQDFEq++AADcR3a2WcuWnVRs7DnVr++lbt2qUlyLIXt7Gy1Z0kUVK7ooIiJKx49fNToSABRLvAIDAHAPmZnZWrTomHbvvqjHHvNVhw6VZGVlMjoWDFKqlKNWruwuKyuT2rdfpMuXbxkdCQCKHcorAAB/kJ6epblzj+jQoSt64okKCgsrL5OJ4lrc+fu7aenSCMXH31BExFKlpmYaHQkAihXKKwAAv3PrVoa++eaQTp26ri5d/NW4sY/RkWBBmjTx1ezZ7RUbm6BBg1YrO5sldACgoFBeAQD4n+TkNH399UElJt5Ur17VVKdOGaMjwQJFRlbT+PGP6fvvj+iNN7YYHQcAig1mGwYAQFJS0m3Nnn1Yt29nql+/GqpY0dXoSLBgr7zSSCdOXNe7725XjRoe6tevptGRAKDIo7wCAA59rnYAACAASURBVIq98+dv6ttvD0mSBgyoKR8fZ4MTwdKZTCZNmdJKx49f1dCha1S1qrtCQryNjgUARRrDhgEAxdrp08maNeugbG2tNGhQIMUVeWZra60FCzrJx8dZERFROnv2htGRAKBIo7wCAIqto0ev6NtvD6lkSTsNGhQoT09HoyOhkPH0dNKyZV2VkpKuLl2idOtWhtGRAKDIorwCAIqlvXsvad68oypTxkmDBtWSq6u90ZFQSNWq5am5cztq9+4LGjRotcxmZiAGgPxAeQUAFDvbtp1XVNQvqljRVf3715KTk63RkVDIdezor/Hjm2v+/KP6z3+2GR0HAIokJmwCABQbZrNZMTFntHlzgmrUKKVu3arKxobPcfFovPxyQx04cFlvvhmrWrU81K1bgNGRAKBI4RUbAFAsZGZma/jwH7R5c4Lq1i2jHj0CKK54pEwmk6ZPf0IhId566qmV2rPnotGRAKBI4VUbAFDk3b6docjIaH3xxX499pivOnWqLCsrk9GxUAQ5ONhoyZIucnd3UOfOS3Thwk2jIwFAkUF5BQAUadeupSo8fJGWLv1FkyeHKSysvEwmiivyj7e3s5YujdDly7fVrdtSpaVlGh0JAIoEyisAoMg6dy5FzZt/r23bzmnu3I569tl6RkdCMVG/fll9/XVbbd16TiNHrmMGYgB4BJiwCQBQJB07dkVPPLFQSUm3tXJld7VuXcHoSChmevasrv37L+s//9mm2rVL6/nn6xsdCQAKNcorAKDIiYs7r/btF8tkkjZu7KX69csaHQnF1L/+1VT791/Wiy9uVM2aHmrTpqLRkQCg0GLYMACgSFmz5pRatpyvkiXtFBvbh+IKQ1lZmTR7dnvVrOmhXr2W6/jxq0ZHAoBCi/IKACgyvvhinzp0WKwqVdwUG/ukqlZ1NzoSoJIl7RQdHSErK5O6dFmi5OQ0oyMBQKFEeQUAFHpms1mvvbZZw4b9oDZtKmjz5ifl7e1sdCwgR6VKblqwoJOOHbuqPn1WKCsr2+hIAFDoUF4BwAJkZvJG9q9KS8tUv34r9e672zVsWLCio7uqZEk7o2MBd2nZsrwmTw7TihUn9frrW4yOAwCFDhM2AYAFsLGx0rhxsUbHKHRu387QvHlHdfr0DbVqVV7e3k56551t9739uHFNCzAdcLeRI+to377LGj9+h4KCSqtPnxpGRwKAQoPyCgAolK5eTdWcOUd09WqqunWrqqAgT6MjAX/KZDJp8uQwHTqUpCFD1iggwF0NGjCpGADkBcOGAQCFTkLCDX355QGlpKTrqadqUlxRqNjZWWvRos7y8nJSRESUzp9PMToSABQKlFcAQKFy5MgVff31IdnaWmnIkCBVqOBidCTgoZUu7aSlSyP+N3JgqVJTM42OBAAWj/IKACgUzGazfvzxrObNOyovLycNGRIoT09Ho2MBf1nt2mX0zTfttW3beY0cuVZms9noSABg0SivAACLl5GRpUWLjmvDhngFBXlqwICacnZmRmEUft27B+ittxrr668PatKkXUbHAQCLxoRNAACLdv16mubNO6rz52+qdevyatLERyaTyehYwCPz5ptNtH//ZY0Zs0k1a3ooPLyS0ZEAwCJx5BUAYLHi429oxoz9SkpK1ZNPVlfTpr4UVxQ5VlYmzZrVToGBnurde7mOHbtidCQAsEiUVwCARdq9+6JmzTooe3trDR0aqIAAd6MjAfnG2dlOS5dGyMbGSp07R+n69TSjIwGAxaG8AgAsSna2WWvW/Kro6BOqUMFFQ4cGqnRpJ6NjAfmuYkVXLVzYWSdOXNOTTy5XVla20ZEAwKJQXgEAFuPmzQx9991hbdt2Xo0alVXfvjXk6GhrdCygwLRo4adPP22lVatO6dVXNxsdBwAsChM2AQAsQnz8DS1ceEw3b2aoU6fKqlfPy+hIgCFGjKitvXsvasKEOAUFlVa/fjWNjgQAFoHyCgAwlNls1vbtiVq79rRcXOw0ZEigvL2djY4FGOrjj8N0+PAVDR26RgEB7mrUyNvoSABgOIYNAwAMk5aWqYULj2vNml9Vtaqbhg8PprgCkmxtrbVgQSd5e5dQ165Lde5citGRAMBwlFcAgCEuXLipGTP26/DhJLVuXV69elWToyMDgoA7PD2dFB3dVdevp6lr1yilpmYaHQkADFUg5TUtLU09evRQ586d1aFDB02ePFmSFB8fr8jISLVp00ajR49Wenp6QcQBABhs795L+uKLA0pNzVL//jVZvxW4j6Cg0po9u7127EjU8OE/yGw2Gx0JAAxTIOXVzs5Os2bNUnR0tKKiorR582bt2bNHH3zwgQYOHKi1a9fKxcVFCxcuLIg4AACDZGRkadmyE4qK+kW+viU0YkSwKlZ0NToWYNG6dq2qt99uqtmzD+nDD3caHQcADFMg5dVkMqlEiRKSpMzMTGVmZspkMmnbtm0KDw+XJHXt2lXr168viDgAAAMkJt7U9On79fPPF9WkiY/696+lkiXtjI4FFAqvvx6qyMgA/d///aiVK08aHQcADFFg57xmZWWpS5cuatKkiZo0aSI/Pz+5uLjIxua385vKli2rCxcuFFQcAEABMZvN2rr1nGbM2K/U1Ez161dDbdpUkJUVw4SBvDKZTJo5s61q1y6tXr2Wae/ei0ZHAoACV2Dl1draWkuXLtWmTZu0b98+nTzJp4YAUNTduJGub789rLVrT6tqVTeNHFlb/v5uRscCCqUSJey0bFlXubraq0OHxUpIuGF0JAAoUAU+27CLi4tCQkK0Z88eJScnKzPzt5nzEhMT5eXFgvQAUFQcOXJF06bt1ZkzN9SxY2X16lVNTk62RscCCjVf35JasaKbrl9PU8eOS5SSwmSXAIqPAimvV65cUXJysiQpNTVVW7dulb+/v0JCQrRmzRpJ0pIlSxQWFlYQcQAA+Sg9/bdJmebNOyo3N3uNGBGs+vW9mE0YeERq1y6j+fM7af/+S+rde7kyM7ONjgQABaJAFtS7ePGixo4dq6ysLJnNZrVt21YtW7ZUlSpV9MILL2jSpEmqUaOGIiMjCyIOACCfnD17Q1FRvygpKVVNmvgoLMxP1tYsKQ48au3aVdann7bSyJHrNHp0jD75pBUfEAEo8gqkvFavXl1RUVF3bffz82N5HAAoAjIysrRhQ7y2bTuvkiXt1L9/TVWqxBI4QH56+uk6OnHimj74YKeqVHHX6NH1jY4EAPmqQMorAKDoOnMmWdHRJ5SUlKp69croiScqyN6elxegILz/fgudPHldL764QRUruigioqrRkQAg3zCWCwDwl2RkZGn16l81c+ZBZWaa9dRTNdSpkz/FFShAVlYmzZ7dXg0bllWfPisUF3fe6EgAkG8orwCAh3b6dLI++2yftm8/rwYNvDRyZG1VrswSOIARnJxsFR3dVV5eTurUaYlOn75udCQAyBeUVwBAnqWnZ2nVqlP6+uuDys42q3//murQobLs7a2NjgYUa15eJbRiRTelpmapbdtFSkq6bXQkAHjkKK8AgDw5evSKpkzZox07EtWoUVmNHFmbSZkAC1KzpqeioyN06tR1deiwmDVgARQ5lFcAwANdv56mefOO6Pvvj8rBwVqDBtVSu3aVZGfH0VbA0jRv7qd58zopLi5RPXpEKz09y+hIAPDIMKsGAOCesrPN2r79vDZujFd2ttSqVXk1buzNuq2AhevSpYpmzHhCQ4as0cCBq/Tttx1kZcUasAAKP8orAOAuCQkpWr78pBITb6pqVTe1a1dJ7u4ORscCkEeDBwfp0qVbGjt2szw8HDV5cphMJgosgMKN8goAyJGamqmYmHjFxSXK2dlWkZEBqlGjFG96gULolVca6dKl2/rww53y8nLS6683NjoSAPwtlFcAgMxms/bsuaT168/o5s0MNWxYVmFhfnJw4GUCKKxMJpMmTGihS5du6Y03YuXp6ainn65jdCwA+Mt4VwIAxVxCQopWrTqlhIQUlSvnrD59qsvHx9noWAAeASsrk774IlxJSakaNWqdPD0d1aNHNaNjAcBfQnkFgGLq5s0MrVt3Wnv2XFKJEraKiKii4GBPhggDRYytrbXmz++kJ55YqL59V8rd3UGtWlUwOhYAPDTKKwAUM1lZ2YqLu6CNG+OVkZGtxo291aJFOdnb85IAFFVOTrZatqyrmjf/Xl26ROmHH3qoSRNfo2MBwEPhnQoAFCOnTl3XqlWndOnSbfn7u6pt20ry9HQ0OhaAAuDu7qAffuihFi3mqV27RVq3LlING3obHQsA8ozF+gCgGLh+PU0LFhzVN98cUkZGtnr1qqa+fWtQXIFixtvbWTExPeXh4ajw8EXas+ei0ZEAIM848goARVhGRpa2bj2nLVvOSZIef9xPTZp4y9bW2uBkAIxSrlxJxcT0VPPm36tNmwXauLGXatXyNDoWAPwpjrwCQBFkNpt15MgVTZ26Vxs3nlVAgLv+8Y86atGiHMUVgCpWdFVMTE/Z2lqpVav5OnTostGRAOBPPfDI608//ZSnnTRuzKLXAGApLl++rdWrT+nEiesqXdpR/fvXVKVKrkbHAmBhqlRxV0xMT4WFzVfLlvO1fn2kAgNLGx0LAO7rgeX1tddey3X54sXfzotwc3PTtWvXJEleXl5av359PsUDAORVWlqmNm06q+3bE2Vra6W2bSuqYcOysrJi6RsA91a9uoc2buylli3vFNieCg6mwAKwTA8srzExMTnff/bZZ7p27Zqef/55OTo66vbt25o8ebLc3NzyPSQA4P7MZrP277+stWtPKyUlQ3XrllGrVuVVooSt0dEAFAIBAaW0adNvBTYsbL7WrYtUnTpljI4FAHfJ8zmvX3/9tV566SU5Ov42M6Wjo6NefPFFzZw5M9/CAQAe7Pz5m5o586CWLPlFrq72Gjo0SJ07+1NcATyUKlXctXFjLzk52SgsbL527DhvdCQAuEuey6uTk5P27duXa9v+/ftzyiwAoODcvp2hFStOasaMfUpKuq3Onf01ZEigfH2djY4GoJDy93fTpk295O5ur1at5mvDhjNGRwKAXPK8VM5zzz2noUOHKiwsTGXLllViYqI2bNigN998Mz/zAQB+JzvbrJ9/vqiYmDNKTc1Uw4Zl1bKlnxwcWPkMwN9XqZKbNm9+Uk88sUDt2i3SggWd1amTv9GxAEDSQ5TXiIgIBQYGas2aNbp48aIqVaqkkSNHqkqVKvmZDwDwP/HxN7Rq1SmdP39TFSqUVLt2leTlVcLoWACKGB8fZ23a1Ftt2y5Ut25LNWtWO/XpU8PoWACQ9/IqSVWqVKGsAkABS0lJ1/r1Z7RnzyWVLGmrbt2qKjDQQyYTswjDMmVmZsvGhqXk81t+Ps8eHo5av76nOndeon79Vujy5dt67rl6+fJYhQF/0wWD5xl/5qHK6/r16xUXF6erV6/KbDbnbJ8wYcIjDwYAxV12tlk7diRq48Z4ZWRkq2lTHzVvXk52dtZGRwMeyMbGSuPGxRodo8gbN65pvu7fxcVeq1Z1V9++K/X88zE6e/aGxo9vXiyX3+JvumDk9980Cr88f7Tx6aef6q233lJ2drZWr14tNzc3bdmyRS4uLvmZDwCKpXPnUjRjxn6tWfOrypVz1qhRtdW6dQWKK4AC5ehoqwULOmnUqDqaODFO/fuvVHp6ltGxABRTeT7yumjRIn311VcKCAjQ4sWL9eqrr6pjx46aOnVqfuYDgGIlLS1LMTFnFBeXqBIlbBUZGaAaNUoxRBiAYaytrfTpp61UrlxJvfrqZiUm3tTChZ3l5uZgdDQAxUyey2tycrICAgIkSba2tsrIyFBwcLDi4uLyLRwAFCdHjlzRypWndONGuho08FKrVuWZRRiARTCZTPrnP0Pk6+usIUPWqEmTOVq+vJsqV3YzOhqAYiTPw4bLly+v48ePS5KqVq2quXPnKioqSq6urvkWDgCKg/j4ZHXtGqV5847KyclGQ4YEqkOHyhRXABanf/9aWru2hy5cuKVGjb7T5s1njY4EoBjJc3kdPXq0rl27Jkl66aWXNHv2bE2cOFFjx47Nt3AAUJRlZWXr4493qWbNmVqz5le1bl1ew4YFqVy5kkZHA4D7evzx8tq2ra88PBzUqtV8zZp1wOhIAIqJPH+s36JFi5zva9eurbVr1+ZLIAAoDo4fv6pBg1YrNjZBbdtW1NSprTVr1kGjYwFAnlSt6q5t2/qqR49oDRy4Wnv3XtKECS1Y5gRAvnpgeY2Pj8/TTvz8/B5JGAAo6rKzzfrkk5/1z39ulr29tb75pp369avJhEwACh13dwetXt1dL764Uf/97y7t3n1R8+Z1VJkyJYyOBqCIemB5bdOmjUwmk8xmc643Vn+8fPjw4fxLCABFxIkT1zR48Gr9+ONZtW9fSTNmhMvHx9noWAAKqczMbMOPdNraWuuTT1qpYcOyGjFirerX/1aLF3dWw4behuYCUDQ9sLweOXIk5/tFixZp69atevbZZ+Xj46Nz585pypQpaty4cb6HBIDCLDvbrGnT9uiVVzbJxsZKM2e21YABtTjaCuBvsbGx0rhxsUbHyNG/fw3Nm3dMjRvPUdu2FVW/vleR+X9u3LimRkcAoIc45/Xjjz/WDz/8IAeH39b0qlixot5++22Fh4erW7du+RYQAAqzX3+9rsGDV2vDhniFh1fUjBlPyM/PxehYAPDIeXs7a/jwIC1Z8otWrDilU6eS1akTM6cDeHTyPNYkOztbCQkJubadO3dO2dnZjzwUABR2ZrNZX399QEFBX2vnzguaMeMJrVrVneIKoEhzcrJVnz7V1bp1eR0+nKTPP9+nhIQUo2MBKCLy/FHYwIEDNWDAAHXr1k1ly5ZVYmKiFi9erAEDBuRnPgAodJKT0/T002s1d+4RtWhRTrNmtVOFCqyJDaB4MJlMatrUV+XLu2jRomP66qsDCgvzU+PGPrKyKhrDiAEYI8/ldejQoQoICNDq1at16NAhlS5dWu+++66aN2+en/kAoFDZseO8nnxyuX79NVlvv91Ur74aImtrlo4AUPz4+ZXUiBHBWrbspNatO6Njx64qIqKK3N0djI4GoJB6qJMQmjdvTlkFgHvIzjbrgw/i9NprW+TjU0I//thbTZv6Gh0LAAzl6GiryMgA7dt3WStXntJnn+1V27aVVKdO6SIzmROAgvPA8jpt2jSNHDlS0m8TNt3P888//2hTAUAhkph4U/37r9TatafVvXtVzZgRzpEFAPgfk8mk2rVLq0IFF0VF/aLo6BM6fDhJHTpUlqurvdHxABQiDyyviYmJ9/weAPCbNWtOqX//VUpOTtdnn7XR8OHBHE0AgHtwc7PXgAE1tX17omJizmjq1L1q3bq8GjQoOkvqAMhfDyyv//rXv3K+f++99/I9DAAUFllZ2XrjjVi99952BQZ6Kiamp2rV8jQ6FgBYNJPJpNBQb1Wr5q7ly09q5cpTOnDgsjp18penp6PR8QBYuDzPItKoUaN7bm/cuPEjCwMAhcGVK7fVocNivffedg0ZEqQdO/pSXAHgIbi7O6hfvxrq3NlfFy/e0rRpe7Vu3Wmlp2cZHQ2ABcvzhE0ZGRn33MY6rwCKk717L6pr16VKSEjR9OlPaNiwYKMjAUChZDKZVLduGQUEuGvdutOKjT2n/fsv64knKqpmzVIMJQZwlz8tr3369JHJZFJ6err69u2b67rExETVrVs338IBgCWZM+ewhg5do1KlHLRpUy+FhvoYHQkACr0SJWzVpUsV1avnpZUrT2rhwmOqWNFFTzxRQd7ezkbHA2BB/rS8RkZGymw2a//+/erRo0fOdpPJJA8PD4WGhuZrQAAwWmZmtl55ZZP++99deuyxclqwoJO8vEoYHQsAihQ/v5IaNixYu3Zd0MaN8Zo+fb+Cgz0VFlaeWYkBSMpDee3ataskqXbt2vL398/3QABgSS5evKlevZZr48Z4PfdcPX3wQQvZ2lobHQsAiiQrK5MaNiyroCBPbdmSoG3bzuvQoSSFhHiraVMfOTraGh0RgIHyfM6rv7+/tmzZosOHD+vWrVu5rmOdVwBF0c6dieradakuX76t2bPbq1+/mkZHAoBiwcHBRq1bV1CDBmUVE3NGsbHntHPnBYWGeqtxY2/Z2+f5LSyAIiTP//LffvttrVq1SiEhIXJ0ZCpzAEVbVNRx9emzQmXKOCk29knVq+dldCQAKHbc3OzVrVtVNWvmq40b47Vp01nt2JGoxo191LChlxwcKLFAcZLnf/HLly/X0qVL5e3tnZ95AMBQZrNZkybt0ksvbVSjRt6Kjo5QmTKc3woARipTxkk9e1bTuXMp2rgx/n9HYxPUqFFZhYR4q0QJhhMDxUGey6u7u7tKliyZn1kAwFCZmdkaPTpGU6bsUffuVTV7dnvOrwIAC+Lj46w+fWro/PkUbdmSoM2bE/TTT+dVr14ZhYR4q1QpB6MjAshHeS6vgwYN0pgxYzRixAh5enrmus7Pz++RBwOAgpSSkq7evZdrxYqTGjOmgd5/v4WsrFhjEAAskbe3syIjq+ny5duKjU3Qzp0XtGNHoqpVc1doqLcqVHBhnVigCMpzeR03bpwkaePGjbm2m0wmHT58+FFmAoACde5cijp2XKx9+y5p2rTWevrpOkZHAgDkgaeno7p0qaKwsPKKi0vUzp0XdPToVZUt66R69bwUFOTJebFAEZLnf81HjhzJzxwAYIi9ey+qY8clunYtVcuXd1PbtpWMjgQAeEglS9opLKy8HnvMV/v2XVZcXKJWrjyltWtPq1YtD9Wr56Vy5Zw5GgsUcnwUBaDYWrPmlHr0iJarq722bHlStWuXMToSAOBvsLW1Vv36XqpXr4zOnUvRrl0XdeDAZe3Zc0mlSzuqfn0vBQd7Mp8BUEjlubxmZmZqzpw5iouL09WrV2U2m3Ou++677/IlHADkl7lzD6t//1UKDPTU8uVd5evLhHQAUFSYTCb5+paUr29JhYdX1IEDl/Xzzxe0evWvWrv2tGrW9FBQkKcqV3aVtbWV0XEB5FGey+t7772nbdu2qWfPnpo0aZJGjx6tuXPnqkOHDvmZDwAeuWnT9uiZZ9apefNyio7uKhcXe6MjAQDyib39b0dj69f3UmLiTe3adUEHDlzW/v2X5ehoo5o1PRQY6KHy5V2YqA+wcHkurz/88IPmzZsnHx8fffLJJxowYICaNWumt956S88++2x+ZgSAR8JsNuvdd7fr9de3qFMnf82b15GhYwBQjJQtW0IdOlRWeHhFnThxTQcOXNa+fZe0a9cFlSxpq5o1PRUU5CEfH86PBSxRnstramqqvL29JUkODg66ffu2/P39dejQoXwLBwCPitls1pgxG/XRR7vUr19NffVVuGxtrY2OBQAwgI2NlapVK6Vq1UopPT1Lx45d1YEDl7VzZ6K2bz8vd3d7Va9eStWrl1K5cpxWAliKPJdXf39/7d+/X8HBwQoMDNQnn3wiZ2dneXl55Wc+APjbMjOzNXz4D5o584CefbauJk0KY2gYAECSZGdnrcBATwUGeio1NVNHjlzRwYNJ2rEjUT/9dF7/r707D6u6TPz//zqsgiggsqgoCqLiSrmFa6LgggpKmlnqWI3NZ0pznMnU5quNbVPNTNtMi/mbbHHfwF0T9wXNNLVEAhUVZVEWF3Y45/eHM1zVuOXC+xx4Pq6r69Jz4PCSO+B+cb/f9+3q6qDz5wtUUFCqwEB3fvEJGOi2y+uMGTNkb3/ti3XatGl6+eWXVVhYqFdeeeW+hQOAu1VcXK7Ro9dq5coUzZoVplmzunEpGADgumrVclBoqI9CQ31UUlKu1NR8HT+eq6VLk3X5cqkcHe3UvLmHWrasp+bNPVS7NreeAFXpluX122+/1ZYtW/TCCy9UPta0aVPNmzdPb7/9thwcOG0HgHW6cqVUw4bFKSHhjN59t4+ef76j0ZEAADbC2dlBbdrUV5s29TVjxkN66qkNOn48V8nJuUpKypUkNWrkpuBgDzVv7qmGDWvzy1HgPrtl8/zkk080evTo6z7XtWtXffzxx/r444/veTAAuBv5+cUaMGC5DhzI1OefD9TYsW2MjgQAsFFOTvYKCvJQUJCHBg1qpoyMAqWk5CklJV/btqVr27Z0ubo6KDjYU82beygoyJ0NAYH74JblNSkpST179rzuc926ddOMGTPueSgAuBt5ecWKjFyqw4cvaPnyaEVHNzc6EgCgmjCZTGrY0E0NG7qpd+/GKigo04kT+UpNzdePP+bp8OELMpkkf/86lauyfn6urMoC98Aty+vVq1dVVlZWeb/rT5WXl6ugoOC+BAOAO5GbW6SIiGX6/vuLWrkyWlFRQUZHAgBUY7VrO6p9e2+1b+8ts9mi8+evKiUlT6mp+dqy5ay2bDkrNzdHNW/uoeBgTwUGuqtWLW67A+7ELb9yAgMDtWvXLvXr1+9/ntu1a5cCAwPvSzAA+LVycorUr99SJSXlKC4uWgMH8v0JAFB17OxM8vevI3//OurTp4muXi1Vamp+5cZP3313QXZ2JjVufG1VtkULT9Wv78KqLHCbbllef/Ob32jWrFkym83q16+f7OzsZDabtXnzZs2ePVvTpk2ripwAcFMXLxaqX7+lOn48V/HxMerfv5nRkQAANZybm1Pl7sVms0Xp6VeUkpKvlJQ8bd58Rps3n5Gnp7OCgz3VooWnAgLqysHBzujYgNW6ZXkdMmSILl68qBdffFFlZWXy8PBQfn6+HB0dNWnSJA0ePLgqcgLADV24UKi+fZcoJSVfq1cPU0REU6MjAQDwM3Z2JjVpUldNmtRV375NdPlyiVJSrt0ne/Bglvbvz5STk52Cgq5dXhwc7CE3NyejYwNW5bYuuB8/frxGjBihQ4cOKT8/Xx4eHnrggQfk5uZ2v/MBwE1lZxeob9+lOnEiX2vWDFPfvgFGRwIA4Jbq1nVWx46+6tjRC2h0ZwAAIABJREFUV2VlFTp16rJ+/DFPKSl5vziK59qqLJs+AbdZXiXJzc3thrsOA4ARsrIKFB6+RKdOXdLatcPVp08ToyMBAPCrOTraq0WLayXVYrEoK6uwsshu23ZW27adVd26TmrVqp5at/ZS48Z1ZGdHkUXNw1ZnAGxSdnaB+vRZrNOnL2v9+lj17t3Y6EgAANw1k8kkP7/a8vOrrV69/FVQUKaUlDwlJ+fq22+vXV7s5uZYWWQDAupSZFFjUF4B2Jz/7ir83+LaqxfFFQBQPdWu7Vi56VNJScV/LivO0eHDF3TgQJZcXR0qi2yzZu4UWVRrlFcANuXSpRL1779MP/6Yp7Vrh1NcAQA1hrOzvdq2ra+2beurrKxCqan5OnYsR99/f1EHD2bLzc1RbdvWV4cO3vLzq210XOCeo7wCsBlXr5Zq0KDlOnLkguLiYticCQBQYzk62iskxEshIV4qLzcrJSVPR45c0P79mUpMzJCPj6s6dPBWu3b1VacOuxajeqC8ArAJRUVlGjp0pfbty9DixUM0aFCg0ZEAALAKDg52lUW2sLBMP/xw7bLir78+rc2bTysw0F3t23srJKSeHB3tjY4L3DHKKwCrV1JSruHD47Vt21l9+eUgxca2MDoSAABWydXVUZ07+6lzZz/l5BTp8OELOnr0olauTNWGDQ4KDfVWp06+qlfPxeiowK9GeQVg1crKKjRq1Bpt2JCmuXP76/HHWxsdCQAAm+Dl5aLw8Cbq06ex0tIu68CBLO3bl6m9ezMUFOSuzp39FBzsySZPsBmUVwBWq6LCrLFj1ysuLlXvvx+up55qZ3QkAABsjslkUrNm7mrWzF1XrpTq4MEsffttlhYtSpa7u5MefNBXDz7oIzc37o2FdaO8ArBKZrNFTz+9UYsWHdebb/bSxIkPGh0JAACbV6eOk3r3bqyePf2VnJyrAweytHXrWW3fnq62bb3UrVtD+fqyUzGsE+UVgNWxWCyaPHmL5s37QTNnhmnq1C5GRwIAoFqxszNVbvJ08WKRvvkmU4cOZevIkYsKCnJXt26N1KxZXZlMXFIM60F5BWB1Xnllrz744JAmT+6ol1/uZnQcAACqtfr1XTRwYDM9/LC/DhzI1v79Gfryy2Nq2LC2evb0V8uWnpRYWAXKKwCr8s9/HtSsWXs0blwb/f3vD/PDEgCAKuLi4qiePRspLKyBjhy5oF27zmnx4mT5+LiqZ89Gat3ai82dYCjKKwCrsWBBkiZO3KKhQ4M0d25/fkACAGAABwc7Pfigr0JDffT99xe1c+c5LV+eoh070vXww40VElKPXy7DEJRXAFZh3bqTGjduvXr39tfixUPk4GBndCQAAGo0OzuT2rf3Vrt29XXsWK62bTurpUt/lJ9fbYWHN1bz5h6UWFQpyisAw+3ala7Y2FVq395bq1YNU61afGsCAMBamEwmtWnjpZCQejp69KK2bTurBQuOq0mTOoqMDFCjRnWMjogaghkiAEMdPpytwYNXqkmTOlq/frjq1nU2OhIAALgOOzuTOnTwVtu2Xjp4MFvbt6dr7tzv1batl8LDm8jTs5bREVHNUV4BGCY1NU/9+y9TnTpO+vrrEfLx4Vw5AACsnb29nTp39lP79t7avfuc9u7NUFJSrrp2baBevfzl7GxvdERUU1VSXjMyMjR16lTl5OTIZDJp5MiRGjdunPLz8/WHP/xB586dU6NGjfTuu+/K3d29KiIBMNj581cVEbFU5eUWbd36iJo0qWt0JAAA8Cs4O9srPLyJOnXy1datZ7Vnz3kdOXJBEREBateuPvfD4p6rkh1R7O3tNW3aNK1bt06LFy/WggULlJqaqjlz5igsLEybNm1SWFiY5syZUxVxABgsN7dIkZFLdfFikTZsiFVIiJfRkQAAwB2qW9dZ0dHN9fTTbVW3rpNWrkzVZ5/9oMzMAqOjoZqpkvLq4+OjNm3aSJLc3NwUGBiorKwsJSQkKCYmRpIUExOjzZs3V0UcAAYqKChVVNQKpaTkKz5+mDp18jM6EgAAuAcaNaqjp59upyFDApWTU6Q5c45o48Y0lZZWGB0N1USV3/Oanp6upKQkdejQQTk5OfLx8ZEkeXt7Kycnp6rjAKhCpaUVGj58lfbvz9SyZUMVHt7E6EgAAOAeMplMevBBX4WEeCkh4YwSEzOUlJSjQYMC1aKFp9HxYOOq9CDFgoICTZo0STNmzJCbm9vPnjOZTFwXD1RjFRVmjRmzTps2penTTyM1bFiw0ZEAAMB94uLioMGDAzV+fBs5Odlr4cLjWrr0RxUUlBkdDTasysprWVmZJk2apCFDhigyMlKS5OXlpezsbElSdna26tWrV1VxAFQhi8Wi555L0JIlyXr77d568sl2RkcCAABVoEmTunrmmfYKD2+s5ORcffjhd/rhh4tGx4KNqpLyarFY9NJLLykwMFDjx4+vfDw8PFxxcXGSpLi4OPXt27cq4gCoYrNm7dbHHx/Wiy920Z/+1NnoOAAAoArZ29upZ09/TZjQXh4ezlq2LEVLlyazCotfrUrK67fffqv4+HglJiYqOjpa0dHR2r59uyZMmKDdu3crMjJSe/bs0YQJE6oiDoAq9MEHB/XKK4l66ql2euONnkbHAQAABvHxcdVTT7VT375NlJycpw8//E7JyblGx4INqZINmzp16qTk5OTrPvf5559XRQQABli4MEmTJm1RTExzffxxBPe1AwBQw9nZmdSjRyO1aOGplStTtGhRsjp29FVkZIDR0WADqnTDJgA1x4YNpzR27Hr17u2vhQsHy8GBbzcAAOCa/67CduvWUN9+m6U5c47o228zjY4FK8dsEsA9l5h4XrGx8Wrbtr7i44epVq0qP5ULAABYOQcHO0VEBGjs2NYqKzMrLGyB3nnngCwWi9HRYKUorwDuqWPHLioqaoUaNHDThg2xcnd3NjoSAACwYs2auet3v+ugqKhATZmyTTExccrNLTI6FqwQ5RXAPXP69CVFRi6Tk5O9Nm16RL6+tY2OBAAAbICLi4NWrIjWe++Fa/36U3rggS+UmHje6FiwMpRXAPfEhQuFioxcpqtXy7Rx4yMKDPQwOhIAALAhJpNJkyY9qN27H5OdnUk9ey7SP/95kMuIUYnyCuCuXblSqkGDluvMmStavXqY2rf3NjoSAACwUZ07N9ChQ2M1YEBTTZy4RePGrVdhIWfCgvIK4C6VlJRr2LA4HTqUraVLh6hnT3+jIwEAABvn4VFL8fHD9Je/dNNXXx1T9+4LdepUvtGxYDDKK4A7VlFh1pgx65SQcEb//vcADR4cZHQkAABQTdjZmTRzZjetXj1MaWmX1LHjV0pIOG10LBiI8grgjlgsFj33XIKWLv1Rf/tbb40d28boSAAAoBqKigrSN988oQYNaqt//2X68MNDRkeCQSivAO7Iyy/v0ccfH9aLL3bRH//Y2eg4AACgGmve3FN7945W//5N9eyzCXruuc0qLzcbHQtVjPIK4Ff74IODmj17r558sq3eeKOn0XEAAEANULeus1atGqY//rGT/vWv7zRw4HLl5RUbHQtViPIK4FdZuDBJkyZtUUxMc33ySaRMJpPRkQAAQA1hb2+nv/3tYf373/21fftZde++QKdPXzI6FqoI5RXAbduw4ZTGjl2v3r39tXDhYDk48C0EAABUvfHj22njxkd0/nyBHnpogb79NtPoSKgCzDwB3JbExPOKjY1X27b1FR8/TLVqORgdCQAA1GB9+jTR7t2PycnJTr17L9a6dSeNjoT7jPIK4JaOHr2gQYNWqEEDN23YECt3d2ejIwEAAKhNm/pKTHxcLVvW05AhKzV37hGjI+E+orwCuKmUlDxFRCyVi4uDvv76Efn61jY6EgAAQKUGDdy0ffujiowM0G9/u0lvvLFPFovF6Fi4DyivAG7ozJnL6tdviSoqLNq8eYSaNfMwOhIAAMD/cHNz0qpVw/T44yGaMWOnpkzZJrOZAlvdcNMagOvKyipQv35LdelSqbZuHamQEC+jIwEAANyQo6O9vvhikLy9XfXuu9/qwoVCffbZADk62hsdDfcI5RXA/8jNLVJExFKdO3dFmzaN0AMP+BodCQAA4Jbs7Ez6xz8elq+vq6ZP36n8/BItWzaUjSarCS4bBvAzV66UatCgFUpOzlNcXIy6d29kdCQAAIDbZjKZNG1aV338cYTWrTupqKgVunq11OhYuAcorwAqFRWVKTp6pQ4cyNTixYMVEdHU6EgAAAB35JlnOuiLLwZp27az6t9/mfLzi42OhLtEeQUgSSorq9CIEau1bdtZzZs3UDExwUZHAgAAuCtPPNFaS5YM0TffZCo8fIkuXiw0OhLuAuUVgMrLzRozZp3Wrj2pDz/spyeeaG10JAAAgHsiNraF4uNjlJSUq759l1JgbRjlFajhKirM+s1v1mvx4mS99VYv/e53oUZHAgAAuKcGDgzUqlUxSk7OVUTEMuXmFhkdCXeA8grUYBUVZj355AbNn5+k117roRde6PI/b1NebjYgGQAAqGnu95wjIqLpf1Zgc9Sv31Ll5dXMe2BteW7HntFADWU2W/Tb327SF18c0+zZ3TVjxkPXfTsHBzu9/PLuKk5X87z8cnejIwAAYKiqmnPExgZr8eJktWnzmcaObV3jjtGx5TkHK69ADWQ2W/TMM5v02Wffa9asMP2//xdmdCQAAIAqERzsqZEjWyorq1BffnlMxcXlRkfCbaK8AjWMxWLR73+/WXPnHtVLLz2kWbO6GR0JAACgSrVo4amRI1soM7NQ8+cnqaSEAmsLKK9ADWKxWPTccwn65JPDmjati155pbtMJpPRsQAAAKpcy5b1NGJEC50/X6D584+rpKTC6Ei4BcorUENYLBZNnrxVH374nf70p056/fWeFFcAAFCjtWpVT7GxwUpPv6IFC5JUWkqBtWaUV6AGsFgs+uMft+n99w/qD3/oqLfe6k1xBQAAkNS6tZdiY1vo7NkrWrjwuE3vxlvdUV6Bas5stmjSpC16551vNWnSg/r73x+muAIAAPxEmzZeiolprrS0y1q+/EeZzRajI+E6KK9ANVZRYdbTT2/UP/95SH/8Yye9+24fiisAAMB1tG/vrQEDmur48TytXn1CFgsF1trUrEONgBqkrKxCY8as0+LFyZo1K0yzZnWjuAIAANxE164NVFRUru3b0+Xi4qCIiADmT1aE8gpUQ8XF5Ro5crVWrz6ht97qpRde6GJ0JAAAAJvQu7e/CgvLtXdvhlxdHdWjRyOjI+E/KK9ANVNQUKphw+L19den9a9/9dXvf/+A0ZEAAABshslk0sCBTVVUVKaEhDNycXFQx46+RseCKK9AtXL5comiolZoz57zmjdvgMaNa2t0JAAAAJtjMpkUE9NcxcUVWrv2pFxcHNS6tZfRsWo8NmwCqonc3CL17btEiYkZWrgwiuIKAABwF+zt7TRyZAv5+9fRihUpOnky3+hINR7lFagGMjML9PDDi3X06EWtXBmtkSNbGR0JAADA5jk62uuxx1rJy8tFixYl69y5K0ZHqtEor4CNO348Rw89NF8nTuRrzZrhGjw4yOhIAAAA1YaLi4OeeCJEtWs7av7848rNLTI6Uo1FeQVs2O7d59S9+8L/bOk+Sv36BRgdCQAAoNqpU8dJY8aESJLmzz+uwsIygxPVTJRXwEatXJmifv2WysvLRXv3jlanTn5GRwIAAKi26tVz0WOPtdLlyyVauPC4ysoqjI5U41BeARv0r38dUmxsvDp08Nbu3Y8pMNDD6EgAAADVXuPGdTRsWLDS069q5cpUWSwWoyPVKJRXwIaYzRZNm7ZDzz2XoCFDgrRly0h5e7saHQsAAKDGaN3aS5GRAUpKytWmTaeNjlOjcM4rYCNKSyv05JMbNH9+kp55poP++c++cnDg908AAABV7aGHGig/v0SJiRny8HBW164NjI5UI1BeARtw6VKJYmPjlZBwRq+91kPTp3eVyWQyOhYAAECNZDKZ1L9/U126VKING9Lk7u6sVq3qGR2r2mPZBrByJ0/mq1u3Bdq+PV3z5g3QjBkPUVwBAAAMZmdnUmxssBo1ctPy5SmcAVsFKK+AFdu+/ay6dJmvjIwCbdz4iMaNa2t0JAAAAPyHo6O9HnuslerUcdSCBceVl1dsdKRqjfIKWKm5c4+oX7+lql/fRfv2Pa7w8CZGRwIAAMAv1K7tqNGjQ2SxSPPnJ6moqNzoSNUW5RWwMmVlFXr++S367W83KTy8iRITRys42NPoWAAAALiB+vVd9OijLZWXV6KlS39URYXZ6EjVEuUVsCJZWQXq12+p3n//oCZP7qi1a4fLw6OW0bEAAABwCwEBdTVkSKBOnbqk9evTOAP2PmC3YcBK7N+foeHD45WbW6yvvhqkxx9vbXQkAAAA/AqhoT66eLFIu3efl4+Pi7p04Qide4mVV8BgFotFn356RD17LpKjo5327BlNcQUAALBRffs2UcuWntqwIU0nTuQbHadaobwCBiooKNW4ces1YcIm9e7trwMHxig01MfoWAAAALhDJpNJw4cHy8fHVUuX/qgLFwqNjlRtUF4Bgxw7dlFduszXV18d01/+0k3r18fKy8vF6FgAAAC4S05O147QcXCw08KFx1VYWGZ0pGqB8goY4Msvf1Dnzl/p4sUiff31CM2c2U329nw5AgAAVBfu7s4aNaqlLl8u1ZIl7EB8LzBbBqrQ5csleuKJtRo7dr06dfLToUNj1bdvgNGxAAAAcB/4+9dRdHSQTp++rLVrT7ED8V1it2GgiiQmntfo0Wt15sxlzZ7dXTNmdGW1FQAAoJpr185bFy4UaefOc/L2dlFYWEOjI9ksyitwn5WXm/Xmm/s1a9Zu+fvX0Y4do9StWyOjYwEAAKCK9OnTWDk5Rdq06bTq13dRcLCn0ZFsEss+wH2UkpKnnj0X6s9/3qURI1rqu+/GUlwBAABqGJPJpJiY5vLzc9Xy5SnKySkyOpJNorwC94HFYtGHHx5SaOjnSk7O08KFg7Vw4WB5eNQyOhoAAAAM4Ohor0cfbSV7e5MWLUpWSUm50ZFsDuUVuMfS0i4pMnKZnn02QT17+uvo0XEaNaqV0bEAAABgMA8PZ40Y0UI5OUVasSKVDZx+JcorcI+YzRZ98MFBtW07T4mJ5/XRR/20fn2sGjWqY3Q0AAAAWImmTd01YEAz/fhjnrZuPWt0HJvChk3APZCcnKunntqo3bvPqX//pvrkkwgFBLgbHQsAAABWqHNnX2VmFmjnznPy86ut1q29jI5kEyivwF0oKirT66/v01tvfaPatR01b94AjR3bRiaTyehoAAAAsFImk0mDBjXThQuFiotLlZdXLfn61jY6ltXjsmHgDq1Zc0Jt2szTq68masSIFjp2bLzGjWtLcQUAAMAtOTjYaeTIlqpVy16LFiWrsLDM6EhWj/IK/EqnT19STEychgxZqVq17LV160h99VWU/Pz4bRkAAABuX506Tnr00Za6cqVUy5b9KLOZDZxuhvIK3KaSknK9/nqiQkI+09dfp+nNN3vpu+/G6eGHmxgdDQAAADaqUaM6Gjw4UKdOXdamTWlGx7Fq3PMK3IaEhNN69tkEJSfnavjwYL3zTh81aVLX6FgAAACoBkJDfZSZWaB9+zLl51dboaE+RkeySpRX4CaSknL04os7tHr1CQUFeWj9+lgNGNDM6FgAAACoZiIjmyo7u1Br1pxU/fou8vfnuMVf4rJh4Dqyswv0+99/rXbt5mn79rP661976vvvf0NxBQAAwH1hZ2fSI4+0UJ06TlqyJFlXrpQaHcnqUF6BnygsLNPrryeqefP/T3PmHNH//V+oUlOf1osvdlWtWlyoAAAAgPvH1dVRo0a1VHFxhZYsSVZ5udnoSFaF8gpIMpst+uKLH9Sy5b/10ku7FB7eRD/8MF4ffNBX3t6uRscDAABADeHrW1sxMc2Vnn5V69adksXCDsT/xVISajSLxaING07ppZd26dChbHXq5Kv58wepV6/GRkcDAABADdW6tZd69myknTvPyc/PVV26NDA6klWgvKLG2rr1jP78513as+e8mjatq/nzozRqVCvZ2ZmMjgYAAIAark+fxsrKKtTGjafl61tbAQGcdMFlw6hx9u49r759lyg8fInS0i7ro4/6KTn5KY0eHUJxBQAAgFUwmUwaNqy5PD2dtWRJsi5dKjE6kuEor6gxDh7MUlTUcnXrtkDff39R77zTR6mpT+l3vwuVk5O90fEAAACAn6lVy0GjRrVSeblFixcnq6yswuhIhqK8oto7fDhbsbHx6tjxS+3dm6E33uipEyee1uTJHeXi4mh0PAAAAOCG6td30fDhzZWRUaA1a07W6A2cuOcV1db+/Rl69dVErV59QnXqOGnWrDD94Q+d5O7ubHQ0AAAA4La1bFlPDz/cWNu2nVWDBm566KGauYET5RXVzo4dZ/Xaa/u0aVOaPD1r6S9/6aaJEx+Up2cto6MBAAAAd6RXr0bKzLyqTZvS5OvrqmbN3I2OVOW4bBjVgsVi0aZNaerVa5F6916s777L1ptv9tLp0xM0c2Y3iisAAABsmslkUkxMsOrXd9HSpT8qP7/Y6EhVjvIKm2axWLRqVaq6dp2v/v2X6eTJfL33XrhOnfqtpk7tojp1nIyOCAAAANwTzs72evTRljKbLVq0qOZt4ER5hU2qqDBr4cIkhYZ+oejoOF28WKRPPonQiRNPa9KkB+XqykZMAAAAqH68vFwUGxusrKxCrVp1okZt4MQ9r7ApZWUVWrjwuF5/fZ+Sk3Pl5VVLMTHN1a5dfZ0/f1VvvLHP6IjVzssvdzc6AgAAAH4iONhT4eFNtGXLGTVo4KZu3RoaHalKUF5hE0pKyvX55z/or3/dr1OnLql9e28tWTJER49ekJ2dyeh4AAAAQJXq0aOhMjOvavPm0/L1dVVQkIfRke47LhuGVSssLNP77x9UUNBcPfPM1/L2dtGqVcP03XdjNWJES4orAAAAaiSTyaTo6Oby9nbVsmU/Kje3+m/gRHmFVbpypVRvvbVfzZp9quef36KgIA9t2vSIEhMf15AhQTKZKK0AAACo2Zyc7DVqVEtJ0uLFySotrd4bOFFeYVXy84s1e/YeBQTM0Ysv7lBoqI+2b39U27ePUkREU0orAAAA8BOenrX0yCMtdOFCoeLjU6v1Bk5VUl6nT5+usLAwDR48uPKx/Px8jR8/XpGRkRo/frwuXbpUFVFgpXJzizRz5i4FBMzRrFl71KNHI+3b97g2bnxEvXo1NjoeAAAAYLWCgjzUr1+Ajh3L1a5d542Oc99USXkdPny45s6d+7PH5syZo7CwMG3atElhYWGaM2dOVUSBlbl4sVAzZuxUQMAcvfJKoiIiAnTo0FitWjVMXbo0MDoeAAAAYBPCwhqobVsvbdlyRikpeUbHuS+qpLx27txZ7u7uP3ssISFBMTExkqSYmBht3ry5KqLASmRlFWjq1O1q2vRT/fWv+xQVFaijR8dp2bJohYb6GB0PAAAAsCkmk0lDhwbJz89Vy5enKCenyOhI95xh97zm5OTIx+daSfH29lZOTo5RUVCFMjKuasqUrWrW7FP9/e8HFBPTXD/8MF6LFg1R27beRscDAAAAbJajo70effTaiRyLFiWrpKR6beBkFRs2mUymarMRT3m52egIVik9/YomTkxQs2af6v33D2rkyJZKShqvr76KUkiIl9HxAAAAgGrBw6OWRoxooZycIsXFpVSrDZwcjPrAXl5eys7Olo+Pj7Kzs1WvXj2jotxTDg52evnl3UbHsBqXL5do585zOnQoWxaL1KGDt3r0aKR69WppwYKku3rtl1/ufo9SAgAAANVHs2buioxsqo0b07Rjxzn17u1vdKR7wrDyGh4erri4OE2YMEFxcXHq27evUVFwHxQUlGnXrnP65ptMWSxSaKi3evZsJA+PWkZHAwAAAKq9rl39lJFxVdu2nZWfn6tatrT9xcIqKa9TpkzR/v37lZeXp169emnixImaMGGCJk+erGXLlqlhw4Z69913qyIK7rOionLt3XteiYkZKi83q0MHb/Xu7U9pBQAAAKqQyWTS4MGBunChSCtWpOq3v22n+vVdjI51V6qkvP7jH/+47uOff/55VXx4VIHS0golJmZo797zKi6uUJs2Xnr44cY2/wUCAAAA2Kr/buA0Z84RLVp0XE8/3c7oSHfFsMuGUT2Ul5v1zTeZ2rXrnAoLy9Wihaf69GksP7/aRkcDAAAAajx3d2eNHNlSX3xxTCtXpur113vJzs42N8ulvOKOWCwWHT16UVu2nNWlSyUKDHRXnz6N5e9fx+hoAAAAAH4iIKCu+vdvqvXrT+mtt/Zr2rSuRke6I5RX/GonT17S11+fVmZmgfz8amvo0EAFBnoYHQsAAADADXTu7KurV0tt+mhPyituW1ZWgTZvPqPU1Hy5uztr2LDmateufrU5oxcAAACorkwmk8LDm+jPfw4zOsodo7zili5fLtHWrWf13XcXVKuWvSIiAtSli58cHOyMjgYAAACghqC84oZKSyu0e/c57dlzXhaLFBbWQD17NpKLi6PR0QAAAADUMJRX/A+LxaLvv7+or78+oytXStW2rZf69m3CWa0AAAAADEN5xc+cP39VGzak6ezZK2rQoLYeeSRYTZrUNToWAAAAgBqO8gpJ0tWrpUpIOKPvvrug2rUdNXRokEJDvdmMCQAAAIBVoLzWcOXlZu3bl6EdO86pvNysbt0aqlevRnJ25n8NAAAAANaDhlKDnTx5SevWnVROTrFatPBUZGSAvLxcjI4FAAAAAP+D8loDXblSqk2b0vT99zny9HTW6NGtFBzsaXQsAAAAALghymsNYjZbtH9/prZuPauKCrN69/ZXjx6NOK8VAAAAgNWjvNYQZ89e0dq1J5WVVaigIHcNGtRM9epxiTAGWfphAAAO7klEQVQAAAAA20B5reYKC8u0efMZHTqUrTp1nDRiRAuFhNRjF2EAAAAANoXyWk1ZLBYdPXpRGzemqaioXGFhDdS7d2M5O9sbHQ0AAAAAfjXKazWUn1+stWtPKTU1X40auWns2ED5+tY2OhYAAAAA3DHKazViNlu0b1+Gtm49K0kaMKCpOnf2k50dlwgDAAAAsG2U12oiM7NAq1adUEZGgYKDPRQVFSh3d2ejYwEAAADAPUF5tXFlZRXati1de/eel6urox55JFitW3uxIRMAAACAaoXyasNOn76s+PhU5eWV6IEHfBQRESAXF4YUAAAAQPVD07FBZWUVSkg4q337MuTh4ayxY1urWTN3o2MBAAAAwH1DebUxZ85cVnz8CeXmFqtzZ1/16xcgJyeOvwEAAABQvVFebURZWYW2bDmrxERWWwEAAADUPJRXG3D27BXFx6cqJ6dYnTpdW211dma1FQAAAEDNQXm1YmVlFdq69az27s2Qu7uzxoxprcBAVlsBAAAA1DyUVyuVnn5FcXHXVls7dvRVRASrrQAAAABqLsqrlSkvN/9ntfW86tRx0hNPhCgoyMPoWAAAAABgKMqrFUlPv6L4+BO6eLFIDz7oo8jIADk7M0QAAAAAQDOyAuXlZm3bdlZ79lxbbX388RA1b85qKwAAAAD8F+XVYOfOXVV8fKouXCjSAw9cW22tVYthAQAAAICfoiUZpLzcrO3b07V79zm5uTnp8cdbqXlzT6NjAQAAAIBVorwa4Pz5q4qLu7baGhrqrf79m7LaCgAAAAA3QWOqQuXlZu3Yka5du87Jzc1Ro0e3UnAwq60AAAAAcCuU1yqSkXFttTU7u0gdOlxbbXVx4dMPAAAAALeD9nSfVVRcW23dufOcatd21GOPtVKLFqy2AgAAAMCvQXm9jzIyChQfn6qsrEJWWwEAAADgLtCk7oNrq63ntGvXObm6OmjUqJZq2bKe0bEAAAAAwGZRXu+xw4ez9emnR5WVVaj27etrwICmcnFxNDoWAAAAANg0yus9dOzYRXXq9JWcne316KMt1aoVq60AAAAAcC9QXu+hJk3q6u23e+v8+StydWW1FQAAAADuFTujA1Qnbm5Omjy5I8UVAAAAAO4xyisAAAAAwOpRXgEAAAAAVo/yCgAAAACwepRXAAAAAIDVo7wCAAAAAKwe5RUAAAAAYPUorwAAAAAAq0d5BQAAAABYPcorAAAAAMDqUV4BAAAAAFaP8goAAAAAsHqUVwAAAACA1aO8AgAAAACsHuUVAAAAAGD1KK8AAAAAAKtHeQUAAAAAWD3KKwAAAADA6lFeAQAAAABWj/IKAAAAALB6lFcAAAAAgNWjvAIAAAAArB7lFQAAAABg9SivAAAAAACrR3kFAAAAAFg9yisAAAAAwOpRXgEAAAAAVo/yCgAAAACwepRXAAAAAIDVo7wCAAAAAKwe5RUAAAAAYPUorwAAAAAAq0d5BQAAAABYPcorAAAAAMDqUV4BAAAAAFaP8goAAAAAsHqUVwAAAACA1aO8AgAAAACsHuUVAAAAAGD1KK8AAAAAAKtHeQUAAAAAWD3KKwAAAADA6lFeAQAAAABWj/IKAAAAALB6lFcAAAAAgNWjvAIAAAAArB7lFQAAAABg9Qwvrzt27FD//v0VERGhOXPmGB0HAAAAAGCFDC2vFRUVmj17tubOnau1a9dqzZo1Sk1NNTISAAAAAMAKGVpejxw5ooCAADVu3FhOTk6KiopSQkKCkZEAAAAAAFbI0PKalZUlPz+/yr/7+voqKyvLwEQAAAAAAGtkslgsFqM++IYNG7Rz50699tprkqS4uDgdOXJEM2fOvOH7dO3aVY0aNaqqiAAAAACAKnTu3Dnt27fvfx53MCBLJV9fX2VmZlb+PSsrS76+vjd9n+v9IwAAAAAA1Zuhlw23a9dOaWlpOnv2rEpLS7V27VqFh4cbGQkAAAAAYIUMXXl1cHDQzJkz9fTTT6uiokKxsbEKDg42MhIAAAAAwAoZes8rAAAAAAC3w9DLhgEAAAAAuB2UVwAAAACA1aO84pbmzZunqKgoDR48WFOmTFFJSYnRkfAL06dPV1hYmAYPHlz5WH5+vsaPH6/IyEiNHz9ely5dMjAhfup64/Xmm29qwIABGjJkiJ599lldvnzZwIT4qeuN13/9+9//VsuWLZWbm2tAMlzPjcbryy+/1IABAxQVFaW33nrLoHT4peuNV1JSkkaOHKno6GgNHz5cR44cMTAhfiojI0NjxozRoEGDFBUVpc8//1wScw5rdaPxsuU5B+UVN5WVlaUvvvhCy5cv15o1a1RRUaG1a9caHQu/MHz4cM2dO/dnj82ZM0dhYWHatGmTwsLCNGfOHIPS4ZeuN17du3fXmjVrtHr1ajVt2lSffPKJQenwS9cbL+napGD37t1q2LChAalwI9cbr8TERCUkJGjVqlVau3atnnrqKYPS4ZeuN15vv/22nn32WcXHx+v555/X22+/bVA6/JK9vb2mTZumdevWafHixVqwYIFSU1OZc1ipG42XLc85KK+4pYqKChUXF6u8vFzFxcXy8fExOhJ+oXPnznJ3d//ZYwkJCYqJiZEkxcTEaPPmzUZEw3Vcb7x69OghB4drG8CHhob+7AxsGOt64yVJb7zxhl544QWZTCYDUuFGrjdeCxcu1IQJE+Tk5CRJ8vLyMiIaruN642UymVRQUCBJunLlCvMOK+Lj46M2bdpIktzc3BQYGKisrCzmHFbqRuNly3MOQ4/KgfXz9fXVk08+qT59+sjZ2Vndu3dXjx49jI6F25CTk1P5A9/b21s5OTkGJ8LtWr58uQYOHGh0DNzE5s2b5ePjo1atWhkdBbchLS1NBw4c0DvvvCNnZ2dNnTpV7du3NzoWbmDGjBl66qmn9Oabb8psNmvRokVGR8J1pKenKykpSR06dGDOYQN+Ol4/ZWtzDlZecVOXLl1SQkKCEhIStHPnThUVFSk+Pt7oWPiVTCYTq0M24qOPPpK9vb2GDh1qdBTcQFFRkT755BM9//zzRkfBbaqoqNClS5e0ZMkSTZ06VZMnTxYnBVqvhQsXavr06dq+fbumT5+ul156yehI+IWCggJNmjRJM2bMkJub28+eY85hfW40XrY456C84qb27Nkjf39/1atXT46OjoqMjNShQ4eMjoXb4OXlpezsbElSdna26tWrZ3Ai3MqKFSu0bds2/e1vf+MHvxU7c+aM0tPTFR0drfDwcGVmZmr48OG6cOGC0dFwA76+voqIiJDJZFL79u1lZ2envLw8o2PhBlauXKnIyEhJ0sCBA9mwycqUlZVp0qRJGjJkSOU4MeewXtcbL8l25xyUV9xUw4YNdfjwYRUVFclisWjv3r0KCgoyOhZuQ3h4uOLi4iRJcXFx6tu3r8GJcDM7duzQ3Llz9dFHH8nFxcXoOLiJli1bau/evdqyZYu2bNkiPz8/rVixQt7e3kZHww3069dP+/btkySdOnVKZWVl8vT0NDgVbsTHx0f79++XdG2zraZNmxobCJUsFoteeuklBQYGavz48ZWPM+ewTjcaL1uec5gsXDeDW3j//fe1bt06OTg4KCQkRK+99lrlphewDlOmTNH+/fuVl5cnLy8vTZw4Uf369dPkyZOVkZGhhg0b6t1335WHh4fRUaHrj9ecOXNUWlpaOUYdOnTQ7NmzDU4K6frjNWLEiMrnw8PDtWzZMlYarMT1xis6OlozZszQ8ePH5ejoqKlTpyosLMzoqND1x6tZs2Z6/fXXVV5eLmdnZ82aNUtt27Y1OiokHThwQI8//rhatGghO7tra2BTpkxR+/btmXNYoRuN16uvvmqzcw7KKwAAAADA6nHZMAAAAADA6lFeAQAAAABWj/IKAAAAALB6lFcAAAAAgNWjvAIAAAAArB7lFQCAOxASEqLo6OjK/9LT042OdFNZWVmaNGmS0TEAALhjHJUDAMAdeOCBB3To0KHrPmexWGSxWCrP1QMAAHePn6oAANwD6enp6t+/v6ZOnarBgwcrIyNDc+fOVWxsrIYMGaL333+/8m3j4uI0ZMgQDR06VC+88IIkadq0adqwYUPl2zzwwAOVf77e66Snp2vgwIH685//rKioKD355JMqLi6WJJ0+fVq/+c1vNHToUA0bNkxnzpxRenq6Bg8eXPm+o0eP1rBhwzRs2DAdPHjwvn9+AAC4Ww5GBwAAwBYVFxcrOjpakuTv76/p06fr9OnTevPNNxUaGqpdu3bp9OnTWrZsmSwWi/7v//5P33zzjTw8PPTRRx9p4cKFqlevnvLz82/6cW70Og0aNNDp06f1j3/8Q6+++qqef/55bdy4UdHR0frTn/6kCRMmKCIiQiUlJTKbzcrJyal8TS8vL3322WdydnZWWlqapkyZohUrVtzXzxcAAHeL8goAwB2oVauW4uPjK/+enp6uhg0bKjQ0VJK0e/du7d69WzExMZKkwsJCpaWlqbi4WAMGDFC9evUkSR4eHjf9ODd6nQYNGsjf318hISGSpDZt2ujcuXO6evWqsrKyFBERIUlydnb+n9csLy/X7Nmzdfz4cdnZ2SktLe3uPhkAAFQByisAAPeIq6tr5Z8tFosmTJigUaNG/extvvzyy+u+r729vcxmsyTJbDarrKzspq+Tnp4uJyenn71/SUnJbeWcN2+e6tevr/j4eJnNZrVv3/623g8AACNxzysAAPdBjx49tHz5chUUFEi6tttvTk6OHnroIW3YsEF5eXmSVHnZcKNGjfTDDz9IkrZs2VJZXm/0Ojfi5uYmPz8/bd68WZJUWlqqoqKin73NlStX5O3tLTs7O8XHx6uiouIe/ssBALg/WHkFAOA+6NGjh06cOFG5Yurq6qq3335bwcHB+t3vfqcxY8bIzs5OrVu31l//+leNHDlSv//97zV06FD17NmzchX3Rq9zs52M33rrLc2cOVPvvfeeHB0d9d5778lkMlU+P3r0aE2cOFFxcXE/+1gAAFgzjsoBAAAAAFg9LhsGAAAAAFg9yisAAAAAwOpRXgEAAAAAVo/yCgAAAACwepRXAAAAAIDVo7wCAAAAAKwe5RUAAAAAYPUorwAAAAAAq/f/A7927Gnsv9AnAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig = plt.figure(figsize=(16,9))\n",
    "ax = fig.add_subplot()\n",
    "\n",
    "# set_style(\"color\"), agrega color de fondo \n",
    "sns.set_style(\"white\")\n",
    "\n",
    "# Se representará un histplot (histograma+línea)\n",
    "# kde, parámetro que si es True, calcula una estimación de densidad kernel para suavizar la distribución y mostrarla en el gráfico como una o más líneas. \n",
    "# ax, son los ejes preexistentes para la trama.\n",
    "ax = sns.histplot(x=frecuencia, kde=True, color=\"darkBlue\", ax=ax)\n",
    "ax.set_title(\"Frecuencia Lunes-Viernes\", fontsize=16)\n",
    "ax.set_ylabel(\"Cantidad\", fontsize=12)\n",
    "ax.set_xlabel('Frecuencia')\n",
    "\n",
    "print(\"Datos ensayados:\", len(frecuencia))\n",
    "print(\"Promedio:\", np.mean(frecuencia))\n",
    "print(\"Mediana:\", np.median(frecuencia))\n",
    "print(\"Desvio:\", np.std(frecuencia))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-UoHQs8JqIsO"
   },
   "source": [
    "### Gráfico de caja (Boxplot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 639
    },
    "executionInfo": {
     "elapsed": 428,
     "status": "ok",
     "timestamp": 1674528300411,
     "user": {
      "displayName": "Johana Rangel",
      "userId": "05955916709715560742"
     },
     "user_tz": 180
    },
    "id": "cEfaJJH0tQeV",
    "outputId": "6e4d8fc3-6999-41c1-9e9a-f8af81b5a36c",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datos ensayados: 215\n",
      "Promedio: 15.06046511627907\n",
      "Mediana: 15.0\n",
      "Desvio: 3.5882326760057266\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns.boxplot() Método de Seaborn para el gráfico de caja\n",
    "fig = plt.figure(figsize=(16,9))\n",
    "ax = fig.add_subplot()\n",
    "ax = sns.boxplot(x=frecuencia, ax=ax)\n",
    "ax.set_title(\"Grafíco boxplot: Frecuencia Lunes-Viernes\", fontsize=16) # Agrega título al gráfico\n",
    "ax.set_xlabel('Frecuencia') # Identifica al eje x con nombre.\n",
    "\n",
    "print(\"Datos ensayados:\", len(frecuencia))\n",
    "print(\"Promedio:\", np.mean(frecuencia))\n",
    "print(\"Mediana:\", np.median(frecuencia))\n",
    "print(\"Desvio:\", np.std(frecuencia))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "deXTd1RftQea"
   },
   "source": [
    "#### Frecuencia Lunes-Sábados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1674528874344,
     "user": {
      "displayName": "Johana Rangel",
      "userId": "05955916709715560742"
     },
     "user_tz": 180
    },
    "id": "H6Uy_lTyseLl"
   },
   "outputs": [],
   "source": [
    "# Transformando los datos del sabado a un array.\n",
    "sabado = np.array(sabado)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 638
    },
    "executionInfo": {
     "elapsed": 390,
     "status": "ok",
     "timestamp": 1674528926222,
     "user": {
      "displayName": "Johana Rangel",
      "userId": "05955916709715560742"
     },
     "user_tz": 180
    },
    "id": "1AKFT1PptQeb",
    "outputId": "a3c059d7-49f7-43cd-d2db-9168ca0955e1",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datos ensayados: 258\n",
      "Promedio: 17.124031007751938\n",
      "Mediana: 16.0\n",
      "Desvio: 5.965674100376816\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Agregar a la variable frecuencia los datos del sábado.\n",
    "frecuencia2 = np.append(frecuencia, sabado)\n",
    "\n",
    "fig = plt.figure(figsize=(16,9))\n",
    "ax = fig.add_subplot()\n",
    "    \n",
    "sns.set_style(\"white\")\n",
    "        \n",
    "ax = sns.histplot(x=frecuencia2, kde=True, color=\"darkgreen\", ax=ax)\n",
    "ax.set_title(\"Frecuencia Lunes-Sabados\", fontsize=16)\n",
    "ax.set_ylabel(\"Cantidad\", fontsize=12)\n",
    "ax.set_xlabel('Frecuencia')\n",
    "\n",
    "print(\"Datos ensayados:\", len(frecuencia2))\n",
    "print(\"Promedio:\", np.mean(frecuencia2))\n",
    "print(\"Mediana:\", np.median(frecuencia2))\n",
    "print(\"Desvio:\", np.std(frecuencia2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TlOOu_0htQee"
   },
   "source": [
    "##### Otra forma de verlo con el gráfico de caja (Boxplot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 639
    },
    "executionInfo": {
     "elapsed": 482,
     "status": "ok",
     "timestamp": 1674528508764,
     "user": {
      "displayName": "Johana Rangel",
      "userId": "05955916709715560742"
     },
     "user_tz": 180
    },
    "id": "GVVU5qb8tQef",
    "outputId": "24ac1b2a-5afa-4f8f-b240-49eeb4ee8498",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datos ensayados: 258\n",
      "Promedio: 17.124031007751938\n",
      "Mediana: 16.0\n",
      "Desvio: 5.965674100376816\n"
     ]
    },
    {
     "data": {
      "image/png": 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d2nTTTWsPH3231q1b5957702STJgwIRMmTCgeW3No47vdeeedueyyy3LyySenZ8+e2WSTTVKpVHLUUUcVn1mrj4ULFxZn41z19mT1n41q06ZNvb4+4t2//L77scuWLcsrr7yS5cuXp1KprPbnbdOmTZ3PKCZJ3759M3bs2FRVVeW44457X2Ou7pC9RYsWpVWrVsXhwjX3Xd1nsj6oHXfcca0nuFn1eV+wYEGWLVu2xpO/1LweCxcuXO3z+X688sormT17djp16rTWMWuseobYmuezZv1cuHBhWrVqlY033rjec1i5cmXOOOOMzJ07N4MGDcr222+fZs2a5eGHH864cePe17r/Qbw7FJPyZ1ywYEGS5MILL8yFF15YPH6bbbZJkmy33XYZP358brvttpx33nlZunRpdt555wwdOjTdunXLU089Vfwx5t2HODdv3jwHHHBA7R8Vnn322Zx88sm5+uqrM3HixCTJiBEj8vjjj2fw4MHp2LFjPvWpT+W5557LpZdeWjxv9XnfjBkzJj/4wQ8ycODAdO3aNS1atMicOXPyzW9+s3Z58+bNq33suzVu3LhYPxYtWrTG7UvNe+7Tn/50vv/97+emm27KJZdckjfffDM77rhjBg0alD59+hSPBfiwiEVgnWrcuHH22GOPPP7443XOZtm4cePstNNOSd75bNPqrG6v4gMPPJA999yzzufE/tXnm9amdevWdU6Os6qaX+xqfvl7t/nz568xIN6t5hfnVR/bpEmTbLbZZlmyZEmqqqrWOMa7f7lcuXJlzj///Gy55ZZ58803c/HFF6/2ZCD/aszVadWqVRYtWlScdbTmhDCrBsL6sOo6UPPHhx/+8IervX9NILZu3Tp/+9vf1rrsZs2aJSnPsrq6+Nt2221z7bXXrnY57dq1W/sPsYrWrVtn0aJFWbJkSb2D8cUXX8wf/vCHjBo1Kocddljt9TV7nj6omufi3XvdkvK5qK+adfbcc8/NnnvuWdzepEmT2v/u0aNHevTokaVLl+b3v/99rrvuugwYMCCPPPJIOnXqVPtHpfro0qVL9tprr0yZMiXJO/H6yCOP5Jvf/Gads6auepKdGvV53zzwwAM57LDDMnDgwNr7PPHEE3UeUxN/Ne+dGsuXLy+e01atWhX3q3nsu99zHTt2zPXXX5/ly5fnD3/4Q2655ZacffbZmTx58nr5zljgk8nZUIF17tRTT82rr76aq6+++gMva8mSJcVhXDV7EN6PvfbaK7/85S/XeGbTz33uc2nTpk1xRsSnn346s2fPTrdu3f7lGC+99FKeffbZ2ssrVqzIgw8+mJ133jmNGjVK8+bN06lTpzz44IN1DrmrOYHFu8e45ZZb8vTTT2f06NG5/PLL84tf/CI/+tGP3vOYq9OtW7esXLmy+BqTn/zkJ2nSpEnt3ryasFrT2STXpb333jtvv/123njjjey0007Fv5pDWvfaa6/Mmzcvjz766BqXVbN3691R+dprrxUnDdl7773z8ssvp3nz5qsdc03xvSZ77bVXKpVK7rnnnno/pua5fndkLVu2LD/5yU/e09hrsrrnIlnzH3L+le233z7t2rXL3/72t9U+Zx06dCge07Rp0+y555459dRTs3jx4syaNSstW7YsHpu8c2jpuw9Br7FixYrMmDGjNtaWLl2aFStWFNuM+++/f7Xzrs/7pj7boK222ipbb711fvazn9W5/uc//3mdw3KTZI899shvfvObvPHGG7XXvfHGG/nlL3+52u1L48aN06VLl5x11llZuXJl7Rl0AdYFexaBdW7PPffMueeem9GjR+cvf/lLDj/88Gy77bZ5++23M3369DzwwANp3rx5vb5Dce+9985tt92WcePGZeedd84TTzyRhx566H3PbdCgQfn1r3+dY445Jqeffnr+7d/+LXPmzMmUKVNy9dVXZ6ONNsrgwYMzcuTIDB06NP369cucOXNy7bXX5rOf/WyOPPLIfzlGmzZtcs4552TQoEHZbLPNctddd2X69Ol1Tnt/1llnZcCAARkwYECOPfbYLF68ONdff31atmyZk08+OUnyP//zP7nhhhty5plnpmvXrkmSY489NldeeWX22GOPOmfRrM+Yq+rVq1d22223XHzxxXnllVey44475te//nXuueeeDBgwoDaKar4o/oc//GH69++fxo0bp3379mnatGkuuOCCTJo0KX/605/e60tRL927d0/fvn0zePDgnHTSSbW/xM+ePTu//vWvM3To0Hzuc59Lv379cs899+Tcc8/Naaedll122SVvvvlmpkyZkhNPPDE77LBDevXqlU9/+tP593//9wwaNChLly7N+PHj07x58zpjHnrooZk4cWJOOumknHLKKenQoUOWLl2amTNn5tFHH82NN96YT33qU/X+GXr06JE+ffrkyiuvzEsvvZQePXrUnjV43333Tffu3YvH1MTXNddck0aNGqVx48b53ve+956eu7fffnu132e63XbbpWPHjunWrVtuueWWtG7dOptttll+/OMfv++vRKmqqsrFF1+cgQMHZtmyZTn44IPTunXrzJ8/P88880y22WabnHzyybnrrrvyu9/9Lr169crWW2+dV199Nbfccku22GKLte4te+GFF3Lqqaemb9++6datWzbffPPMnTs39957b/7617/m4osvTvLO4ZtdunTJd7/73WyxxRZp3bp17rvvvjUeTVCf983ee++dSZMmpbq6Otttt11+/vOfF39gaNSoUc4888xcdNFFGTFiRA455JC8+OKLufXWW9OyZcs69x04cGB+9atf5aSTTso3vvGNVFVV5bbbbstbb72VM888M8k7e5Dvvvvu7L///tl2223z1ltv5c4770yLFi1qtwUA64JYBNaLb3zjG9l1113z/e9/P2PGjMmrr76apk2b5nOf+1wOOeSQHHPMMXW+MmFNzjzzzLz22mu544478vbbb6dbt24ZP3589t9///c1r2233TYTJkzItddem9GjR2fx4sXZcsst65yw5Oijj87GG2+c//zP/8zAgQPTokWL9OrVK8OGDSvCYnX+7d/+LaeeemquueaaTJ8+Pe3atcuYMWPqfIVFr169csstt+TGG2/M2WefnSZNmqRbt24ZNmxYttxyy7zxxhs599xz07Vr15x++um1jxs+fHh+97vf5dxzz82ECRNq9/rVZ8xVNWrUKLfeemvGjBmT8ePHZ+HChWnXrl1GjBhR5xC+Dh06ZNCgQbn77rtzzz33ZOXKlXnkkUey7bbbZuXKlR/K9/6tzVVXXZU777wz9913X8aNG5emTZumXbt26dmzZ+1nxJo0aZL//M//zA033JAJEybkxhtvzKabbppdd9219hDJTTbZJOPGjcsVV1yRs88+O1tttVUGDhyY//7v/85TTz1VO17Nsm699dbcfffdmTVrVpo3b57PfOYz2Xfffevs7auvMWPG5LbbbsukSZPy/e9/v3YPWs13Sa6qadOmufHGG3PppZfm/PPPT6tWrXLkkUdmm222yUUXXVSvMRcuXJizzjqruP64447LyJEjc9VVV+Vb3/pWLrvssjRr1ixHHnlkunfvXu/lr2qfffbJD37wg4wbNy4XXXRRlixZkrZt22aXXXbJIYcckuSddek3v/lNxowZkwULFtS+RldfffVaD9HdbrvtcsIJJ+S///u/89BDD+XVV19N8+bN06FDh4wdOzYHHXRQ7X1Hjx6db33rW7nkkkuy8cYb5+CDD86FF16YAQMGFMutz/vmoosuSqVSqT0suVevXhk9enTx2n3lK1/J4sWLc8cdd+SnP/1pdtxxx4wePTrnnXdenft16NAhd955Z6655poMHz48lUolu+yyS37wgx/U7oHdbrvtsvHGG+emm27KvHnz0qJFi+y000757ne/u9bPXAN8UFWVyv//pmUAPhZOOOGELF++PHfddVdDTwUA2ID5zCIAAAAFsQgAAEDBYagAAAAU7FkEAACgIBYBAAAorPWrM7p375527dqtr7kAAACwHs2ePTtPPvnkam9bayy2a9cuEydOXCeTAgAAoGEdccQRa7zNYagAAAAUxCIAAAAFsQgAAEBBLAIAAFAQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFMQiAAAABbEIAABAQSwCAABQEIsAAAAUxCIAAAAFsQgAAEBBLAIAAFAQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFMQiAAAABbEIAABAQSwCAABQEIsAAAAUxCIAAAAFsQgAAEBBLAIAAFAQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFMQiAAAABbEIAABAQSwCAABQEIsAAAAUxCIAAAAFsQgAAEBBLAIAAFAQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFBo39ATgk+aKK67IX/7yl4aexsfS/PnzkyRt2rRp4JnwYWnfvn1GjBjR0NMAgE8ksQjr2V/+8pc8+cwzWdaqVUNP5WOnyaJFSZJpr73WwDPhw1DzegIADUMsQgNY1qpVXtl774aexsfOZlOmJInn9mOi5vUEABqGzywCAABQEIsAAAAUxCIAAAAFsQgAAEBBLAIAAFAQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFMQiAAAABbEIAABAQSwCAABQEIsAAAAUxCIAAAAFsQgAAEBBLAIAAFAQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFMQiAAAABbEIAABAQSwCAABQEIsAAAAUxCIAAAAFsQgAAEBBLAIAAFAQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFMQiAAAABbEIAABAQSwCAABQEIsAAAAUxCIAAAAFsQgAAEBBLAIAAFAQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFMQiAAAABbEIAABAQSwCAABQEIsAAAAUxCIAAAAFsQgAAEBBLAIAAFAQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFMQiAAAABbEIAABAQSwCAABQEIsAAAAUxCIAAAAFsQgAAEBBLAIAAFAQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFMQiAAAABbEIAABAQSwCAABQEIsAAAAUxCIAAAAFsQgAAEBBLAIAAFAQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFMQiAAAABbEIAABAQSwCAABQEIsAAAAUxCIAAAAFsQgAAEBBLAIAAFAQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFMQiAAAAhQ06FidPnpzJkyc39DQAAPiY8XsmJI0begIfxP33358kOeywwxp4JgAAfJz4PRM28D2LAAAArBtiEQAAgIJYBAAAoCAWAQAAKIhFAAAACmIRAACAglgEAACgIBYBAAAoiEUAAAAKYhEAAICCWAQAAKAgFgEAACiIRQAAAApiEQAAgIJYBAAAoCAWAQAAKIhFAAAACmIRAACAglgEAACgIBYBAAAoiEUAAAAKYhEAAICCWAQAAKAgFgEAACiIRQAAAApiEQAAgIJYBAAAoCAWAQAAKIhFAAAACmIRAACAglgEAACgIBYBAAAoiEUAAAAKYhEAAICCWAQAAKAgFgEAACiIRQAAAApiEQAAgIJYBAAAoCAWAQAAKIhFAAAACmIRAACAglgEAACgIBYBAAAoiEUAAAAKYhEAAICCWAQAAKAgFgEAACiIRQAAAApiEQAAgIJYBAAAoCAWAQAAKIhFAAAACmIRAACAglgEAACgIBYBAAAoiEUAAAAKYhEAAICCWAQAAKAgFgEAACiIRQAAAApiEQAAgIJYBAAAoCAWAQAAKIhFAAAACmIRAACAglgEAACgIBYBAAAoiEUAAAAKYhEAAICCWAQAAKAgFgEAACiIRQAAAApiEQAAgIJYBAAAoCAWAQAAKIhFAAAACmIRAACAglgEAACgIBYBAAAoiEUAAAAKYhEAAICCWAQAAKAgFgEAACiIRQAAAApiEQAAgIJYBAAAoCAWAQAAKIhFAAAACmIRAACAglgEAACgIBYBAAAoiEUAAAAKYhEAAD6hxo8fn06dOuX2229fb2NOmzYt3dhxZogAABD/SURBVLt3z7Rp09bbmHfffXc6deqUCRMmrLcx582blxNPPDHz5s1bb2N+2MQiAAB8Ql1zzTVJktGjR6+3Mc8777y88cYbOf/889fbmP/xH/+RJLn00kvX25g333xzfv/732fcuHHrbcwPm1gEAIBPoPHjx9e5vD72Lk6bNi1///vfkyTPP//8etm7ePfdd6dSqSRJKpXKetm7OG/evEyaNCmVSiX333//Brt3sXFDT+CDmD9/fubPn5+TTjqpoacC9TZt2rQ0+v8bLGDNGr39dqZNm2YbDzSIadOmpU2bNg09jXWqZq9ijdGjR+eUU05Zp2Oed955dS6ff/75mTx58jods2avYo1LL700Rx111Dod8+abb87KlSuTJCtXrsy4cePy7//+7+t0zHXBnkUAAGC9qNmrWOP5559f52NWVvkj/aqX14Wf/vSnWbZsWZJk2bJl+clPfrLOx1wXNug9i23atEmbNm1yxx13NPRUoN5OOumk/PYf/2joacBH3spmzdJh++1t44EG4aiGdWOHHXaoE4yf//zn1/mYVVVVdQKxqqpqnY/Zt2/fTJw4McuWLUuTJk1y6KGHrvMx1wV7FgEA4BPonHPOqXP53HPPXedjjho1qs7l73znO+t8zFUP/xw5cuQ6H/OMM85Io0bvpFajRo1y+umnr/Mx1wWxCAAAn0Cnnnpqncvr+vOKSdKhQ4fssMMOSd7Zq9ihQ4d1PubRRx9duzexqqpqnX9eMUnatm2bww8/PFVVVenfv3/atm27zsdcF8QiAAB8QtXsXVwfexVrjBo1Ki1btlwvexVr1OxdXB97FWucccYZ2W233TbYvYrJBv6ZRQAA4P079dRTiz2M61qHDh3y5JNPrtcxjz766Bx99NHrdcy2bdvme9/73nod88NmzyIAAAAFsQgAAEBBLAIAAFAQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFMQiAAAABbEIAABAQSwCAABQEIsAAAAUxCIAAAAFsQgAAEBBLAIAAFAQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFMQiAAAABbEIAABAQSwCAABQEIsAAAAUxCIAAAAFsQgAAEBBLAIAAFAQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFMQiAAAABbEIAABAQSwCAABQEIsAAAAUxCIAAAAFsQgAAEBBLAIAAFAQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFMQiAAAABbEIAABAQSwCAABQEIsAAAAUxCIAAAAFsQgAAEBBLAIAAFAQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFMQiAAAABbEIAABAQSwCAABQEIsAAAAUxCIAAAAFsQgAAEBBLAIAAFAQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFMQiAAAABbEIAABAQSwCAABQEIsAAAAUxCIAAAAFsQgAAEBBLAIAAFAQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFMQiAAAABbEIAABAQSwCAABQEIsAAAAUxCIAAAAFsQgAAEBBLAIAAFAQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFBo39AQ+iP79+zf0FAAA+BjyeyZs4LF42GGHNfQUAAD4GPJ7JjgMFQAAgNUQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFMQiAAAABbEIAABAQSwCAABQEIsAAAAUxCIAAAAFsQgAAEBBLAIAAFAQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFMQiAAAABbEIAABAQSwCAABQEIsAAAAUxCIAAAAFsQgAAEBBLAIAAFAQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFMQiAAAABbEIAABAQSwCAABQEIsAAAAUxCIAAAAFsQgAAEBBLAIAAFAQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFMQiAAAABbEIAABAQSwCAABQEIsAAAAUxCIAAAAFsQgAAEBBLAIAAFAQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFMQiAAAABbEIAABAQSwCAABQEIsAAAAUxCIAAAAFsQgAAEBBLAIAAFAQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFMQiAAAABbEIAABAQSwCAABQEIsAAAAUxCIAAAAFsQgAAEBBLAIAAFAQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFMQiAAAABbEIAABAQSwCAABQEIsAAAAUxCIAAAAFsQgAAEBBLAIAAFAQiwAAABTEIgAAAAWxCAAAQEEsAgAAUBCLAAAAFMQiAAAABbEIAABAQSwCAABQEIsAAAAUxCIAAACFxg09AfgkarJoUTabMqWhp/Gx02TRoiTx3H5M1LyeAEDDEIuwnrVv376hp/CxNX/+/CRJmzZtGngmfFi8XwCg4YhFWM9GjBjR0FMAAIB/yWcWAQAAKIhFAAAACmIRAACAglgEAACgIBYBAAAoiEUAAAAKYhEAAICCWAQAAKAgFgEAACiIRQAAAApiEQAAgIJYBAAAoCAWAQAAKIhFAAAACmIRAACAglgEAACgIBYBAAAoiEUAAAAKYhEAAICCWAQAAKAgFgEAACiIRQAAAApiEQAAgIJYBAAAoCAWAQAAKIhFAAAACmIRAACAglgEAACgIBYBAAAoiEUAAAAKYhEAAICCWAQAAKAgFgEAACiIRQAAAApiEQAAgIJYBAAAoCAWAQAAKIhFAAAACmIRAACAglgEAACgIBYBAAAoiEUAAAAKYhEAAICCWAQAAKAgFgEAACiIRQAAAApiEQAAgELjtd04e/bsHHHEEetrLgAAAKxHs2fPXuNtVZVKpbIe5wIAAMAGwGGoAAAAFMQiAAAABbEIAABAQSwCAABQEIsAAAAU1vrVGXxyjBgxIr/61a+y+eab56c//WmSZOHChTnnnHMye/bstGvXLtdee21atWrVwDPlo2h168/111+fCRMmZLPNNkuSDBkyJPvss09DTpOPsJdeeinnnXdeFixYkKqqqhx11FE58cQTbYeotzWtQ7ZF1Nfbb7+d4447LkuXLs2KFSvSp0+fDB48ODNnzsyQIUOycOHCdOrUKaNGjUrTpk0berp8BK1pHRo+fHieeuqpfPrTn06SXHnllenYsWMDz7Z+fHUGSZKpU6emefPmOf/882t/2R81alQ23XTTnHbaabn11luzaNGiDBs2rIFnykfR6taf66+/Ps2bN8/Xv/71Bp4dG4K5c+dm3rx56dSpU954440ceeSRufHGGzNx4kTbIeplTevQz372M9si6qVSqWTx4sVp0aJFli1blmOPPTYXXnhhvvvd7+bAAw/Ml7/85YwcOTIdOnTIscce29DT5SNoTevQj370o+y777456KCDGnqK75nDUEmS7LHHHsVf6x955JEcfvjhSZLDDz88Dz/8cENMjQ3A6tYfeC+22GKLdOrUKUnSsmXLbL/99pkzZ47tEPW2pnUI6quqqiotWrRIkixfvjzLly9PVVVVnnjiifTp0ydJ0r9//zzyyCMNOU0+wta0Dm3IxCJrtGDBgmyxxRZJkrZt22bBggUNPCM2ND/84Q9z6KGHZsSIEVm0aFFDT4cNxKxZs/LnP/85u+yyi+0Q78u716HEtoj6W7FiRQ477LB88YtfzBe/+MV85jOfySabbJLGjd/55NZWW23ljxCs1arrUM126Jprrsmhhx6ayy+/PEuXLm3gWdafWKReqqqqNvi/jLB+ffWrX80vfvGLTJ48OVtssUWuvPLKhp4SG4A333wzgwcPzgUXXJCWLVvWuc12iPpYdR2yLeK92GijjTJ58uT8+te/znPPPZd//OMfDT0lNjCrrkN//etfM2TIkDz44IO57777smjRotx6660NPc16E4us0eabb565c+cmeeezIDUnB4D6aNOmTTbaaKM0atQoX/nKV/J//+//begp8RG3bNmyDB48OIceemgOPPDAJLZDvDerW4dsi3g/Ntlkk3Tv3j3PPvtsXnvttSxfvjxJ8vLLL2fLLbds4NmxIahZh6ZMmZItttgiVVVVadq0aY444ogNajskFlmj3r17Z9KkSUmSSZMmZb/99mvgGbEhqfkFP0kefvjh7Ljjjg04Gz7qKpVKLrzwwmy//fY5+eSTa6+3HaK+1rQO2RZRX6+88kpee+21JMmSJUvy+OOPZ4cddkj37t3z0EMPJUnuv//+9O7duyGnyUfY6tah7bffvnY7VKlUNrjtkLOhkuSdU4k/9dRTefXVV7P55ptn0KBB2X///XP22WfnpZdeyjbbbJNrr702m266aUNPlY+g1a0/Tz31VKZNm5YkadeuXS699NLaz57Bqn73u9/luOOOS3V1dRo1eufvmEOGDMnOO+9sO0S9rGkd+ulPf2pbRL1MmzYtw4cPz4oVK1KpVHLQQQflm9/8ZmbOnJlzzjknixYtSseOHXP11Vf76gxWa03r0Ne+9rW8+uqrqVQq6dChQy655JLaE+F81IlFAAAACg5DBQAAoCAWAQAAKIhFAAAACmIRAACAglgEAACgIBYB2CB07Ngxhx12WO2/WbNmNfSU1mrOnDkZPHhwQ08DAN43X50BwAaha9eueeaZZ1Z7W6VSSaVSqf1+PQDgg/N/VQA2SLNmzUqfPn1y3nnnpW/fvnnppZcyfvz4HHnkkTn00ENz3XXX1d530qRJOfTQQ9OvX78MGzYsSTJ8+PA8+OCDtffp2rVr7X+vbjmzZs3KwQcfnIsuuihf/vKXc8opp2TJkiVJkhkzZuSkk05Kv3790r9//7z44ouZNWtW+vbtW/vYY489Nv3790///v3z9NNPr/PnBwA+qMYNPQEAqI8lS5bksMMOS5Jsu+22GTFiRGbMmJHvfOc76dKlS377299mxowZuffee1OpVHLGGWdk6tSp2XTTTXPzzTfnrrvuymabbZaFCxeudZw1LWfrrbfOjBkzMmbMmFx22WU566yz8tBDD+Wwww7L0KFDc9ppp+WAAw7I22+/nZUrV2bBggW1y9x8883z3e9+N82aNcv06dMzZMiQTJw4cZ0+XwDwQYlFADYIG2+8cSZPnlx7edasWdlmm23SpUuXJMljjz2Wxx57LIcffniSZPHixZk+fXqWLFmSgw46KJtttlmSZNNNN13rOGtaztZbb51tt902HTt2TJJ06tQps2fPzhtvvJE5c+bkgAMOSJI0a9asWOby5ctz6aWXZtq0aWnUqFGmT5/+wZ4MAFgPxCIAG6zmzZvX/nelUslpp52WY445ps597rzzztU+dqONNsrKlSuTJCtXrsyyZcvWupxZs2aladOmdR7/9ttv12ued9xxR9q0aZPJkydn5cqV2Xnnnev1OABoSD6zCMDHQs+ePXPfffflzTffTPLO2UgXLFiQHj165MEHH8yrr76aJLWHobZr1y5//OMfkySPPvpobSyuaTlr0rJly2y11VZ5+OGHkyRLly7NW2+9Vec+r7/+etq2bZtGjRpl8uTJWbFixYf4kwPAumHPIgAfCz179szf//732j2CzZs3z1VXXZUdd9wxp59+ek444YQ0atQoX/jCF3LllVfmqKOOysCBA9OvX7/svffetXsp17SctZ1pddSoURk5cmTGjh2bJk2aZOzYsamqqqq9/dhjj82gQYMyadKkOmMBwEeZr84AAACg4DBUAAAACmIRAACAglgEAACgIBYBAAAoiEUAAAAKYhEAAICCWAQAAKAgFgEAACj8P1pKNYSXSpBzAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16,9))\n",
    "ax = fig.add_subplot()\n",
    "ax = sns.boxplot(x=frecuencia2, ax=ax, color=\"darkcyan\")\n",
    "ax.set_title(\"Grafíco boxplot: Frecuencia Lunes-Sabados\", fontsize=16)\n",
    "ax.set_xlabel('Frecuencia')\n",
    "\n",
    "print(\"Datos ensayados:\", len(frecuencia2))\n",
    "print(\"Promedio:\", np.mean(frecuencia2))\n",
    "print(\"Mediana:\", np.median(frecuencia2))\n",
    "print(\"Desvio:\", np.std(frecuencia2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Fq5bXui3tQeh"
   },
   "source": [
    "#### Frecuencia solo sábados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 638
    },
    "executionInfo": {
     "elapsed": 1044,
     "status": "ok",
     "timestamp": 1674529069645,
     "user": {
      "displayName": "Johana Rangel",
      "userId": "05955916709715560742"
     },
     "user_tz": 180
    },
    "id": "9npVanEVtQei",
    "outputId": "63915edd-b136-44ac-ee47-308793ea7f8f",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datos ensayados: 43\n",
      "Promedio: 27.441860465116278\n",
      "Mediana: 27.0\n",
      "Desvio: 4.627030418330738\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16,9))\n",
    "ax = fig.add_subplot()\n",
    "    \n",
    "sns.set_style(\"white\")\n",
    "\n",
    "ax = sns.histplot(x=sabado, kde=True, color=\"darkred\", ax=ax)\n",
    "ax.set_title(\"Frecuencia Sábados\", fontsize=16)\n",
    "ax.set_ylabel(\"Cantidad\", fontsize=12)\n",
    "ax.set_xlabel('Frecuencia')\n",
    "\n",
    "print(\"Datos ensayados:\", len(sabado))\n",
    "print(\"Promedio:\", np.mean(sabado))\n",
    "print(\"Mediana:\", np.median(sabado))\n",
    "print(\"Desvio:\", np.std(sabado))\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "vscode": {
   "interpreter": {
    "hash": "97e12f1ac9f26099b54b9e7d4ebc9834a88f2d401bac5ba802dc7f7f5939f7f3"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}