{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y7JgKfzlPBrr"
      },
      "source": [
        "![taller_python](https://github.com/fifabsas/talleresfifabsas/blob/master/python/2_Numerico/fig/logo_fifa.png?raw=true)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "os_60KoG9612"
      },
      "source": [
        "# Taller de Python Capítulo 2:  Numérico y Laboratorio\n",
        "[Link al notebook en Google Colaboratory](https://drive.google.com/file/d/1yEKwDqog33Er6GNpxxXHSoz2Wh7HhIe4/view?usp=sharing)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w5EORpIB961-"
      },
      "source": [
        "## Nuestra motivación para hoy\n",
        "De la última clase, tenemos herramientas básicas de Python que tienen muchisimas aplicaciones. Hoy vamos a ver como se puede usar Python para analizar datos de una caida libre. Digamos que en su laboratorio adquirieron los datos de la posición de una pelotita en distintos tiempos, luego de haberla soltado a una altura de 100 metros. Ahora, quieren cargar estos datos a un programa de Python, visualizarlos, y obtener el valor de la gravedad $g$.\n",
        "\n",
        "Vamos a ver algunas herramientas que nos permitirán llevar esto a cabo."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jTt04ORN961_"
      },
      "source": [
        "---\n",
        "\n",
        "## _Bibliotecas_\n",
        "Ya hemos visto la vez anterior que Python tiene varias funciones que vienen \"de fábrica\" como `help()`, `print()` así también como operaciones básicas entre números como sumar, restar, etc; también vimos que nosotros podemos crear nustras propias funciones para que hagan lo que necesitemos usando la palabra clave `def nombre_funcion`. Si uno necesita siempre realizar las mismas operaciones, reutilizará la misma función en todos sus códigos.\n",
        "\n",
        "Por ejemplo: Supongamos que uno quiere calcular `cos(x)`. Python no viene por defecto con esa operación, uno debería crear un algoritmo (es decir, una serie de acciones) que calcule el valor del cos de x (cosa que puede ser no trivial), pero es _obvio_ que alguien ya lo hizo antes, alguien ya pensó el algoritmo, lo escribió, y lo utiliza diariamente, si esta persona subió su código a internet, todos podemos aprovechar y utilizarlo sin preocuparnos en cómo hizo esta persona!! Solamente hay que decirle a Python _donde_ es que está guardado esta función. **Esta posibilidad de usar algoritmos de otros es fundamental en la programación, porque es lo que permite que nuestro problema se limite solamente a entender cómo llamar a estos algoritmos ya pensados y no tener que pensarlos cada vez**.\n",
        "\n",
        "Vamos entonces a decirle a Python que, además de sus operaciones de fábrica, queremos ampliar nuestro abanico de operaciones matemáticas en todas las opciones que aparecen dentro de la biblioteca \"math\" (una biblioteca es, tal como sugiere el nombre, un archivo con un montón de funciones, objetos y demás).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AJQnKcwW962B"
      },
      "outputs": [],
      "source": [
        "import math  # Importo a mi programa todo lo que este contenido dentro del archivo \"math\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "swNFXCSM962D"
      },
      "source": [
        "Con esta linea Python entiende que queremos que traiga todo lo que está dentro del archivo \"math\"\n",
        "\n",
        "###### <span style='color:red'>OJO:</span> No todas las bibliotecas vienen instaladas por defecto, si queremos usar una muy rara es probable que tengamos que instalarla nosotros. Las que vamos a utilizar en el curso ya vienen instaladas por Google en Colaboratory o fueron instaladas automaticamente por Anaconda en su computadora.\n",
        "\n",
        "Macanudo, ahora que Python trajo esa biblioteca, nosotros podemos acceder a su contenido usando la sintaxis ```math.lo_que_haya_dentro_de_math``` por ejemplo, ```math.cos()```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iGzP4335962E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aebbc3ce-1efd-4f44-d32f-6cdf0c943825"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "math.cos(0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qvu3D0aN962F"
      },
      "source": [
        "Hay una infinidad de bibliotecas o librerías dando vueltas por internet. Muchas veces el problema que queremos solucionar se reduce simplemente a encontrar la librería que tenga la funcionalidad correcta.\n",
        "\n",
        "Ahora continuaremos usando una muy utilizada en nuestro ámbito, la querídisima NumPy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z_Zz58m6962G"
      },
      "source": [
        "## _NumPy_ : vectores, matrices y tablas de datos\n",
        "\n",
        "NumPy (NUMeric PYthon) es **LA** biblioteca para operar sobre vectores de números. Además de contener un nuevo [tipo de dato](http://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html) que nos va a ser muy útil para representar vectores y matrices, nos provee de un arsenal de [funciones de todo tipo](https://docs.scipy.org/doc/numpy/reference/routines.html).\n",
        "\n",
        "Vamos a empezar por importar la bliblioteca _numpy_. La sintaxis típica de eso era `import biblio as nombre`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ejBOu9HQ962H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2dedacfc-05ec-4602-f466-f93d62e22b5a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "El numero e = 2.718281828459045\n",
            "O el numero Pi = 3.141592653589793\n"
          ]
        }
      ],
      "source": [
        "import numpy as np  # con eso voy a poder acceder a las funciones de numpy a través de np.función()\n",
        "\n",
        "# Por ejemplo\n",
        "print(f\"El numero e = {np.e}\")\n",
        "print(f\"O el numero Pi = {np.pi}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Podemos calcular senos y cosenos de numeros igual que con math!\n",
        "print(np.sin(np.pi))  # casi cero! guarda con los floats!"
      ],
      "metadata": {
        "id": "8BjIa881S-i_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3a3b6a4-0911-4d45-dc0e-95f21642bf5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.2246467991473532e-16\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lSksN2iI962J"
      },
      "source": [
        "Todo eso está muy bien, pero lo importante de _numpy_ son los arrays numéricos, que van a ser como una lista de números pero con muchos esteroides. Los arrays numéricos nos van a servir para representar vectores (el objeto matemático de \"la tira de números\", no el físico) o columnas/tablas de datos (el objeto oriyinezco o de laboratorio).\n",
        "\n",
        "> (Ojo, los arrays no son vectores, aunque pueden comportarse como tales en muchos sentidos)\n",
        "\n",
        "La idea es que es parecido a una lista: son muchos números juntos en la misma variable y están indexados (los puedo llamar de a uno dando la posición dentro de la variable). La gran diferencia con las listas de Python es que los arrays de _numpy_ operan de la forma que todos queremos:\n",
        "1. Si sumamos o restamos dos arrays, se suman componente a componente.\n",
        "2. Si multiplicamos o dividimos dos arrays, se multiplican o dividen componente a componente.\n",
        "\n",
        "Veamos ejemplos usando la función `np.array` para crear arrays básicos."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WJ71p4br962K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b40573c2-c2bf-49c0-c460-40b119313905"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ARRAYS:\n",
            "[1 2 3 4] + [5 6 7 8] =  [ 6  8 10 12]\n",
            "[1 2 3 4] * [5 6 7 8] = [ 5 12 21 32]\n"
          ]
        }
      ],
      "source": [
        "array_1 = np.array([1, 2, 3, 4])\n",
        "array_2 = np.array([5, 6, 7, 8])\n",
        "\n",
        "print(\"ARRAYS:\")\n",
        "print(f\"{array_1} + {array_2} =  {array_1 + array_2}\")\n",
        "print(f\"{array_1} * {array_2} = {array_1*array_2}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "En el caso de las listas esto era muy molesto"
      ],
      "metadata": {
        "id": "EZaEuuZETcp7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lista_1 = [1, 2, 3, 4]\n",
        "lista_2 = [5, 6, 7, 8]\n",
        "\n",
        "print(\"LISTAS:\")\n",
        "print(f\"{lista_2} + {lista_1} = {lista_1 + lista_2}\")  # sumar concatena\n",
        "# print(lista_1 * lista_2) # esto ni siquiera se puede hacer!"
      ],
      "metadata": {
        "id": "rG7OAnYTTcLg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a7579d0a-478a-4cee-9fe9-35adade15d7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LISTAS:\n",
            "[5, 6, 7, 8] + [1, 2, 3, 4] = [1, 2, 3, 4, 5, 6, 7, 8]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a5yhuml3962L"
      },
      "source": [
        "Y al igual que con las listas, uno puede acceder a elementos específicos de un array con su índice:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RmKdobbT962M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63ee22e3-2c42-418e-ce3f-d9e6ea83b63d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 2 3 4\n",
            "8\n"
          ]
        }
      ],
      "source": [
        "print(array_1[0], array_1[1], array_1[2], array_1[3])\n",
        "\n",
        "# Y más o menos vale todo lo que valía con listas\n",
        "print(array_2[-1])  # agarro al último elemento de b"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_95ieRhV962M"
      },
      "source": [
        "También podemos iterar sobre ellos con _for_ , de las mismas formas que habíamos visto para iterar listas."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bcLrdFKg962N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3517353-2ce4-445f-f749-1282f48a3758"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "array_1:\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "\n",
            "array_2:\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n"
          ]
        }
      ],
      "source": [
        "# Aca itero sobre los ELEMENTOS de array_1\n",
        "print(\"array_1:\")\n",
        "for num in array_1:\n",
        "    print(num)\n",
        "\n",
        "print()\n",
        "\n",
        "# Aca itero sobre los INDICES de array_2\n",
        "print(\"array_2:\")\n",
        "for j in range(len(array_2)):\n",
        "    print(array_2[j])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vOQhszEc962N"
      },
      "source": [
        "#### Arrays de dos dimensiones\n",
        "\n",
        "Para crear *arrays* de dos dimensiones (o más), podemos aplicar la función `np.array` sobre una *lista de listas*:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y7rWNeSB962O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b3d0564-d8b3-462f-fa22-6feca9cf47a7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1 2 3]\n",
            " [4 5 6]\n",
            " [7 8 9]]\n"
          ]
        }
      ],
      "source": [
        "# Cada lista corresponde a una fila de la matriz\n",
        "\n",
        "M = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n",
        "\n",
        "print(M)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aca hacer M[0] dara la primer FILA de la matrix."
      ],
      "metadata": {
        "id": "E9IC_cd3gn2O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(M[0])"
      ],
      "metadata": {
        "id": "OHdX7Lt5gqBE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6feb64a-bbf8-428d-b3d9-770d2ad673c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 2 3]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Si queremos sacar la primer componente habra que hacer M[0, 0]"
      ],
      "metadata": {
        "id": "XPwPCK3TgxUF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(M[0, 0])"
      ],
      "metadata": {
        "id": "OWesiGfdgx12",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25f34830-6cdf-4f45-a7cf-fda80f667c94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c5CUGdHb962P"
      },
      "source": [
        "Para facilitar la vida del usuario _numpy_ viene con un montón de rutinas de creación de arrays típicos. En particular, matrices típicas como las identidades o de todos elementos iguales a 1 o 0 y arrays con cierta cantidad de elementos entre dos números (muy útil para crear dominios para gráficos).\n",
        "\n",
        "Veamos ejemplos de esos:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bUQe_D5K962P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "41669f6c-ceea-4e4a-c07c-f986e96bfb14"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Numero de puntos fijo: [0.         0.11111111 0.22222222 0.33333333 0.44444444 0.55555556\n",
            " 0.66666667 0.77777778 0.88888889 1.        ]\n"
          ]
        }
      ],
      "source": [
        "# N = 11 puntos en el rango [0, 1] con 1/(N-1) de espaciamiento\n",
        "\n",
        "equi_linspace = np.linspace(0, 1, 10)\n",
        "\n",
        "print(f'Numero de puntos fijo: {equi_linspace}')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#  Elijo el paso entre [0, 1), lo que me da\n",
        "#  {0, paso, 2*paso, ... n*paso} siempre que n*paso<1\n",
        "\n",
        "equi_arange = np.arange(0, 1, 0.1)\n",
        "\n",
        "print(f'Paso entre puntos fijo: {equi_arange}')"
      ],
      "metadata": {
        "id": "BPjEtNLzhHjz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf737f22-9fb3-4628-d199-399d4f8907c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Paso entre puntos fijo: [0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Otros ejemplos"
      ],
      "metadata": {
        "id": "phT_hxAsjEcj"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UFnRtdHb962P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7cd5735a-eb4d-4110-a4e6-f46fd7aec295"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Matriz identidad:\n",
            "[[1. 0. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 0. 1.]]\n",
            "\n",
            "Matriz de ceros:\n",
            "[[0. 0. 0. 0.]\n",
            " [0. 0. 0. 0.]\n",
            " [0. 0. 0. 0.]\n",
            " [0. 0. 0. 0.]]\n",
            "\n",
            "Matriz de unos:\n",
            "[[1. 1. 1.]\n",
            " [1. 1. 1.]]\n",
            "\n",
            "Identidad corrida:\n",
            "[[0. 1. 0. 0. 0.]\n",
            " [0. 0. 1. 0. 0.]\n",
            " [0. 0. 0. 1. 0.]\n",
            " [0. 0. 0. 0. 1.]\n",
            " [0. 0. 0. 0. 0.]]\n",
            "\n",
            "Matriz con diagonal cualquiera:\n",
            "[[1 0 0 0]\n",
            " [0 2 0 0]\n",
            " [0 0 3 0]\n",
            " [0 0 0 4]]\n"
          ]
        }
      ],
      "source": [
        "# Matriz identidad de 3x3\n",
        "identidad = np.identity(3)\n",
        "print(f'Matriz identidad:\\n{identidad}')\n",
        "print()\n",
        "\n",
        "# Matriz de todos 0 de 4x4\n",
        "ceros = np.zeros((4, 4))\n",
        "print(f'Matriz de ceros:\\n{ceros}')\n",
        "print()\n",
        "\n",
        "# Matriz de todos 1 de 2x3\n",
        "unos = np.ones((2,3))\n",
        "print(f'Matriz de unos:\\n{unos}')\n",
        "print()\n",
        "\n",
        "# Matriz con diagonal corrida de 5x5\n",
        "ojos = np.eye(5, k=1)\n",
        "print(f'Identidad corrida:\\n{ojos}')\n",
        "print()\n",
        "\n",
        "# Matriz con la diagonal que yo le doy\n",
        "diagonal = np.diag([1, 2, 3, 4])\n",
        "print(f'Matriz con diagonal cualquiera:\\n{diagonal}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UCsbhv9z962Q"
      },
      "source": [
        "Y antes de seguir, algo que siempre puede ser útil: los arrays tienen ciertas propiedades como su _shape_ (de cuánto por cuánto) y el _dtype_ (qué tipo de cosas tiene adentro). Podemos acceder a estos datos de la siguiente manera:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "grSCuJxV962Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e071d5ba-2585-4b32-cfe5-2bdf80efcb8a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "float64\n"
          ]
        }
      ],
      "source": [
        "# Array de 1000 elementos\n",
        "x = np.linspace(0, 10, 1000)\n",
        "\n",
        "# ¿De qué tipos son los elementos de x?\n",
        "print(x.dtype)  # Ojo, sin paréntesis"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Matriz llena de 0 de 40x100\n",
        "ceros = np.zeros((40, 100))\n",
        "\n",
        "# De qué tamaño es la matriz\n",
        "print(ceros.shape)"
      ],
      "metadata": {
        "id": "kCMWPo-gku6p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "43882b65-08ab-409b-80e8-d7e2f2c2f2ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(40, 100)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ccs3GpRG962T"
      },
      "source": [
        "---\n",
        "\n",
        "### Ejercicio 0\n",
        "\n",
        "Vamos a crear los valores de tiempo y posición que uno espera para una caida libre. Para esto, vamos a necesitar un array de valores 10 equiespaciados para el tiempo $t$, desde 0 hasta 3 segundos, y luego obtener la posición en metros mediante la ecuación:\n",
        "\n",
        "$$y(t) = y_0 - \\frac{g}{2}t^2$$\n",
        "\n",
        "Donde asumimos velocidad inicial nula, $g = 9.81 \\mathrm{\\frac{m}{s^2}}$ e $y_0 = 100\\; \\mathrm{m}$. Llamar las variables de tiempo $t$ e $y$ como ```t_teorico``` e ```y_teorico``` respectivamente.\n",
        "\n",
        "Mas adelante compararemos estos valores con otros medidos en el laboratorio.\n",
        "\n",
        "**Recordar que podemos aplicar operaciones matemáticas a los _arrays_**\n",
        "\n",
        "---\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6E7_LMjRqycH"
      },
      "source": [
        "## Llevando los datos a Python\n",
        "Muchas veces estamos acostumbrados a guardar los datos en hojas de Excel o en las Hoja de Cálculo de Drive. Python puede leer estas hojas utilizando las librerías adecuadas, pero es más sencillo, y es mejor práctica, exportar estos archivos en CSV para luego leerlos desde ahí.\n",
        "\n",
        "##### Por qué CSV\n",
        "* El formato **CSV (Comma Separated Values)** es abierto y un estándar a la hora de guardar datos de tipo fila o columna. Guardar los datos en Excel o Drive no está mal, pero se dificulta a la hora de pasarle los archivos a un/a colega, los CSV los puede abrir cualquiera! Inclusive, se pueden abrir Excel y Drive si se busca visualizarlo mejor.\n",
        "* Con pocos datos no hay problemas de _lag_ o *crasheos*, pero si son muchos datos: _\"Microsoft Excel dejó de responder\"_ .$^1$\n",
        "\n",
        "\n",
        "Acá una imagen de cómo descargar los archivos de Drive en CSV y de cómo se ven una vez en la compu. Es solo un archivo de texto con valores separados con comas, nada místico.\n",
        "\n",
        "![](fig/export_drive_calc_to_csv.gif)\n",
        "\n",
        "$\\scriptsize\\text{1. Para archivos muchos muuuchos datos inclusive los CSV se pueden quedar corto, y para estos casos existen formatos específicos que dependen de la naturaleza de los datos.}$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KmP_7wBqqycI"
      },
      "source": [
        "Una vez tenemos el archivo que descargamos podemos leerlo desde Python utilizando numpy. En este caso, la linea es\n",
        "```python\n",
        "data = np.loadtxt('nombre_del_archivo.csv', delimiter=',', skiprows=1, unpack=True)\n",
        "```\n",
        "\n",
        "Desglosemos esto por argumentos:\n",
        "1. El primer argumento es ubicación del archivo con la ruta respecto del archivo de Python. En este caso tenemos ambos archivos en la misma carpeta por lo que es solo el nombre.\n",
        "2. El segundo argumento, _delimiter_, recibe un _string_ que le indica a Python cómo están separados los valores en nuestro archivo. En este caso es por comas porque son archivos CSV.\n",
        "3. El tercer argumento es más opcional, y nos permite evitar que Python lea algunas lineas del archivo. En nuestro caso pusimos *1* ya que queremos evitar que lea la primera linea, que dice _\"VAR 1, VAR 2\"_.\n",
        "4. Por qué `unpack=True`\n",
        "\n",
        "    `np.loadtxt()` por defecto te da los valores exactamente como están en el CSV, por lo que en este caso nos daría 2 columnas de datos con 20 filas. Utilizando `unpack=True` trasponemos los datos para pasar las columnas a filas, de modo que la primera fila (`data[0]`) sean los datos de _VAR 1_ y la segunda fila (`data[1]`) sean los valores de _VAR 2_. Acá el código utilizando `unpack=True` y sin utilizarlo\n",
        "    \n",
        "    También podríamos importarlo sin usar el `unpack=True` y luego usar `data = np.transpose(data)`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7723e692-7056-47ce-b778-813257af42a4",
        "id": "NORDN2qbqycI"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data sin unpack:\n",
            " [[ 0.    1.93  1.  ]\n",
            " [ 1.58  1.67  1.  ]\n",
            " [ 3.16  1.3   1.  ]\n",
            " [ 4.74  0.63  1.  ]\n",
            " [ 6.32  1.26  1.  ]\n",
            " [ 7.89  2.38  1.  ]\n",
            " [ 9.47  2.91  1.  ]\n",
            " [11.05  2.36  1.  ]\n",
            " [12.63  3.93  1.  ]\n",
            " [14.21  4.8   1.  ]\n",
            " [15.79  4.83  1.  ]\n",
            " [17.37  5.17  1.  ]\n",
            " [18.95  6.21  1.  ]\n",
            " [20.53  7.94  1.  ]\n",
            " [22.11  9.12  1.  ]\n",
            " [23.68 11.09  1.  ]\n",
            " [25.26 12.57  1.  ]\n",
            " [26.84 14.28  1.  ]\n",
            " [28.42 17.1   1.  ]\n",
            " [30.   20.56  1.  ]]\n",
            "Primera columna:\n",
            "[0.   1.93 1.  ]\n"
          ]
        }
      ],
      "source": [
        "data = np.loadtxt('data_exp.csv', delimiter=',', skiprows=1)\n",
        "\n",
        "print(f\"Data sin unpack:\\n {data}\")\n",
        "\n",
        "print(f'Primera columna:\\n{data[0]}')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = np.loadtxt('data_exp.csv', delimiter=',', skiprows=1, unpack=True)\n",
        "\n",
        "print(f\"\\nData con unpack:\\n {data}\")\n",
        "\n",
        "print(f'Primera columna:\\n{data[0]}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63a1967a-d1e8-4f45-bf36-dd98f2749dc2",
        "id": "gsqqUxYSqycJ"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Data con unpack:\n",
            " [[ 0.    1.58  3.16  4.74  6.32  7.89  9.47 11.05 12.63 14.21 15.79 17.37\n",
            "  18.95 20.53 22.11 23.68 25.26 26.84 28.42 30.  ]\n",
            " [ 1.93  1.67  1.3   0.63  1.26  2.38  2.91  2.36  3.93  4.8   4.83  5.17\n",
            "   6.21  7.94  9.12 11.09 12.57 14.28 17.1  20.56]\n",
            " [ 1.    1.    1.    1.    1.    1.    1.    1.    1.    1.    1.    1.\n",
            "   1.    1.    1.    1.    1.    1.    1.    1.  ]]\n",
            "Primera columna:\n",
            "[ 0.    1.58  3.16  4.74  6.32  7.89  9.47 11.05 12.63 14.21 15.79 17.37\n",
            " 18.95 20.53 22.11 23.68 25.26 26.84 28.42 30.  ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uYoXx7Tc962T"
      },
      "source": [
        "---\n",
        "\n",
        "### Ejercicio 1\n",
        "\n",
        "Ahora vamos a cargar los datos guardados en el laboratorio, utilizando un .csv y las funcionalidades de Numpy. Guardar el tiempo y la posición en dos variables ```t_labo``` e ```y_labo```.\n",
        "\n",
        "Para cargar el archivo se pueden seguir los siguientes pasos:\n",
        "1. Descargar el archivo en este [link](https://raw.githubusercontent.com/fifabsas/talleresfifabsas/master/python/2_Numerico/data_caida_libre.csv). Pueden hacerlo con click derecho -> \"Guardar como...\"\n",
        "2. Importen el archivo data_caida_libre.csv a Python (miren el archivo para ver cuántas filas tienen que saltarse)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b3UEu1FY962T"
      },
      "source": [
        "---\n",
        "## Graficar funciones"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bCKr_oN7962U"
      },
      "source": [
        "A lo largo de nuestras carreras, y de la vida, nos encontramos con información que queremos **graficar**, como por ejemplo la posición en el tiempo de la pelota que cae. Vamos a ver que con la ayuda de la biblioteca [`matplotlib`](https://matplotlib.org/) esto no es nada complicado.\n",
        "\n",
        "Esta librería es muy grande, por lo que tiene divididas sus funciones en distintos módulos. Para lo que vamos a ver en esta segunda parte nos alcanza con la sublibrería ```pyplot```. Una de las formas de importar esta librería es:\n",
        "```python\n",
        "import matplotlib.pyplot as plt\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yz7M358E962U"
      },
      "source": [
        "Podemos usar esta librería en conjunto con ```numpy``` para graficar las funciones que querramos.\n",
        "\n",
        "Veamos un ejemplo, grafiquemos la función $f(x) = \\sin(x)\\sin(20x)$ entre $0$ y $2\\pi$ (no tienen por qué entender la función, es solo el logo de [Arctic Monkeys](https://c1.staticflickr.com/7/6126/5933658807_601e0b1751_b.jpg))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ehsyy74X962V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "78ead178-875c-4555-bc3f-9be5f1c252e4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Definimos la función\n",
        "def f(x):\n",
        "    return np.sin(x)*np.sin(20*x)\n",
        "\n",
        "# Definimos el dominio y la imagen\n",
        "x = np.linspace(0, 2*np.pi, 500)\n",
        "y = f(x)\n",
        "\n",
        "# Graficamos la función\n",
        "plt.plot(x, y)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wVzheD-v962V"
      },
      "source": [
        "Desglosemos el código que acabamos de escribir:\n",
        "* Primero escribimos la función que queremos graficar.\n",
        "* Luego, definimos nuestro dominio `x` y nuestra imagen `y`.\n",
        "* Y por último usamos la función de `plot` de `matplotlib.pyplot` para graficar `y` en función de `x`.\n",
        "\n",
        "**Aclaración 1: plt.show():**\n",
        "Si bien esta linea no es siempre necesaria en los notebooks (como google Colab), su propósito es \"cerrar\" la figura y mostrarla.\n",
        "\n",
        "**Aclaración 2:** `y` no es una función. Sino que es un _array_ que contiene los números que resultan de aplicarle `f` a cada uno de los valores de `x`. Lo que vemos arriba parece ser una _función continua_ pero en realidad es un conjunto de punto unidos con una línea. Para visualizar mejor esto podemos hacer un gráfico que muestre explícitamente los puntos que forman el gráfico:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wV8EmrWB962W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c271c18a-9580-4fe6-ff94-6393c1735e82"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(x, y, '.-')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UK7dME4T962W"
      },
      "source": [
        "#### Qué hace plt.plot()?\n",
        "Esta función que parece mágica lo único que hace es tomar los vectores que le damos como inputs y graficar un punto en cada par de coordenadas $(x,y)$. Además, por defecto une estos puntos con lineas rectas. Por lo tanto, para que la función pueda hacer su trabajo, es necesario que los _array_ ```x``` e ```y``` tengan la misma longitud. Para dejar claro este concepto veamos un ejemplo con pocos puntos escritos a mano:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X3vFYJl1962X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "73a40e09-398e-4550-f677-9d8275d4ed70"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "coords_en_x = np.array([0, 2, 6, 8,  10])\n",
        "coords_en_y = np.array([2, 4, 6, 14, 14])\n",
        "\n",
        "plt.plot(coords_en_x, coords_en_y, 'o:r')\n",
        "plt.grid()  # Esto me deja poner una grilla detrás!\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lglTUHTn962X"
      },
      "source": [
        "Vemos que los puntos que se grafican son los $(x,y)$ = $\\{(0,2);\\,(2,4);\\,(6,6);\\,(8,14);\\,(10,14)\\}$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JIYzGyRw962X"
      },
      "source": [
        "### Dar formato y estética a los gráficos\n",
        "En los gráficos anteriores cuando usamos `plt.plot` agregamos un término opcional para cambiar el color y la forma en la que se muestran los puntos que graficamos. Si usamos `help(plt.plot)` vemos que este argumento opcional es el _[fmt]_ y que toma distintos _strings_ con los que se puede cambiar la forma en que se ve el gráfico. La estructura general es\n",
        "```python\n",
        "fmt = '[color][marker][line]'\n",
        "```\n",
        "donde _marker_ hace referencia a la forma de los puntitos y _line_ hace referencia a la forma de la linea.\n",
        "\n",
        "Los _strings_ válidos son un montón y los pueden ver en la documentación usando el _help_ o en la [documentación online](https://matplotlib.org/3.2.1/api/_as_gen/matplotlib.pyplot.plot.html) de la función.\n",
        "\n",
        "Acá dejamos algunas de las opciones para que se den una idea:\n",
        "* **color:** 'r' (rojo), 'b' (azul), 'g' (verde), 'm' (magenta), etc\n",
        "* **marker:** '.' (puntito chiquito), 'o' (punto grande), '^' (triángulo), '*' (estrella), etc\n",
        "* **line:** '-' (sólida), '--' (de a rayas), ':' (de a puntos)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src=\"https://matplotlib.org/stable/_images/sphx_glr_named_colors_003_2_00x.png\" alt=\"colores_matplotlib\" width=\"600\" height=\"600\">"
      ],
      "metadata": {
        "id": "jBpAdMwPq28m"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Título, labels y grid\n",
        "Veamos también algunas funciones que nos permiten agregar información a la figura sobre la que estamos trabajando. Veamos un ejemplo con la función de los [Arctic Monkeys](https://qph.fs.quoracdn.net/main-qimg-69d69b537bb5e7e3f21e44c36781a10d)."
      ],
      "metadata": {
        "id": "EgQ5ky3bihUg"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z2-xCQeY962Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "outputId": "0bfb8c80-fbf6-49f2-e955-e8accc3abb92"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Definimos la función\n",
        "def f(x):\n",
        "    return np.sin(x)*np.sin(20*x)\n",
        "\n",
        "# Definimos el dominio y la imagen\n",
        "x = np.linspace(0, 2*np.pi, 500)\n",
        "y = f(x)\n",
        "\n",
        "# Graficamos la función con linea sólida y le ponemos una etiqueta\n",
        "plt.plot(x, y, '-', label='Arctic Monkeys')\n",
        "\n",
        "# Agregamos título y etiquetamos los ejes\n",
        "plt.title('Logo de los Arctic Monkeys', fontsize=16)\n",
        "plt.xlabel('Eje de $\\hat x$')\n",
        "plt.ylabel('Eje de $\\hat y$')\n",
        "\n",
        "plt.grid()\n",
        "plt.legend(loc=\"upper right\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dphh2LbZ962Y"
      },
      "source": [
        "La mayoría de las funciones son bastante descriptivas. Veamos solo algunas aclaraciones:\n",
        "* El _fontsize_ en el ```plt.title``` es un argumento opcional y también se puede utilizar en el ```plt.xlabel``` o ```plt.ylabel``` por ejemplo.\n",
        "* El ```plt.grid()``` agrega una grid si no exite y la elimina si existe. Si se quiere forzar a que aparezca se puede utilizar ```plt.grid(True)```\n",
        "* El ```plt.legend()``` es el que permite que se muestren los labels asociados a las lineas (en este caso es el \"Arctic Monkeys\"). Esta función tiene algunos parámetros opcionales que pueden probar como el _fontsize_ o el _loc_. (ver ```help(plt.legend)```)\n",
        "* Podemos usar $\\LaTeX$ en los labels!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ooQ8-irSJPmC"
      },
      "source": [
        "Aca abajo dejamos una figura con los nombres de las distintas partes de una figura para que tengan a mano a la hora de googlear como cambiar cada parte. (fuente: [este libro hermoso](https://www.labri.fr/perso/nrougier/scientific-visualization.html))\n",
        "\n",
        "<img src=\"https://pbs.twimg.com/media/Cr5jxB-UkAAmvBn?format=jpg&name=medium\" alt=\"Milanesa\" width=\"600\" height=\"600\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "edwV5lcH962Z"
      },
      "source": [
        "---\n",
        "### Ejercicio 2\n",
        "\n",
        "En la misma figura:\n",
        "* Graficar los datos cargados en las variables ```t_teorico``` e ```y_teorico```. Etiquetar los datos como 'Teorico'.\n",
        "* Graficar los datos cargados en las variables ```t_labo``` e ```y_labo```. Etiquetar los datos como 'Laboratorio'.\n",
        "* Agregar la leyenda, etiquetar los ejes y poner\n",
        "un título (pueden usar $\\LaTeX$ si conocen la syntaxis)\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6QWAxhWX962Z"
      },
      "source": [
        "### Hacer ajustes\n",
        "\n",
        "Vamos a levantar nuevamente unos datos para graficarlos igual que lo hacíamos antes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4bUI5T2x962Z"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Importamos los datos\n",
        "data = np.loadtxt('data_exp.csv', delimiter=',', skiprows=1, unpack=True)\n",
        "\n",
        "# Los ponemos en variables\n",
        "tiempo = data[0]\n",
        "num_conejos = data[1]\n",
        "err_num_conejos = data[2]\n",
        "\n",
        "# O lo que es lo mismo\n",
        "tiempo, num_conejos, err_num_conejos = data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Graficamos los datos crudos\n",
        "plt.plot(tiempo, num_conejos, 'ok', label='Datos')\n",
        "\n",
        "# Labels, grilla, leyenda y show\n",
        "plt.xlabel(\"Tiempo\")\n",
        "plt.ylabel(\"Número de conejos\")\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "86bRsQNApOK9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "42671624-3c09-4cf2-aa8a-32d9f19745fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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R7Hp2FYCCgoIQERGB9evXGzTvjRs30Lt3byQnJ2P+/PlNrtPUHiB/f39UVFS02EBDqdVqZGRkICIiAk5OTiabty1ir6RjrwzDfknHXknXVnql0WiQk5OD0tJS+Pj4YMyYMZDJZCb9DHP2qqqqCp6envcMQHZzCEylUiEvLw87d+40+L1dunTBfffdhwsXLjS7jouLC1xcXBqNOzk5mWVDNte8bRF7JR17ZRj2Szr2Sjp77pVSqURcXByKi4t1Y3K5HKmpqVAoFCb/PHP0Sup8Vr8KTKp//OMfGD58OEJCQgx+b3V1NS5evAgfHx8zVEZERGT/lEolYmJi9MIPAJSUlCAmJgZKpdJKlZmH1QNQdXU1zpw5gzNnzgAACgoKcObMGRQWFurWqaqqwq5du7BgwYIm5xg/fjzee+893etXXnkF2dnZuHTpEo4ePYpp06ZBJpNh1qxZZv0uRERE9kij0SAuLg5NnRXTMBYfHw+NRmPp0szG6gHo5MmTGDZsGIYNGwYASEhIwLBhw7B8+XLdOjt27IAois0GmIsXL6KiokL3uri4GLNmzcKAAQMwY8YMdO/eHceOHUOPHj3M+2WIiIjskEqlarTn526iKKKoqAgqlcqCVZmX1c8BCg8PbzJx3m3RokVYtGhRs8svXbqk93rHjh2mKI2IiMghlJaWmnQ9e2D1PUBERERkXVLPkW1L59IyABERETm4sLAwyOVyCILQ5HJBEODv74+wsDALV2Y+DEBEREQOTiaTITU1FQAahaCG1ykpKSa/H5A1MQARERERFAoF0tLS4Ofnpzcul8uRlpZmlvsAWZPVT4ImIiIi26BQKBAVFQWVSqW7E3RYWFib2vPTgAGIiIiIdGQyGcLDw61dhtnxEBgRERE5HAYgIiIicjgMQERERORwGICIiIjI4TAAERERkcNhACIiIiKHwwBEREREDocBiIiIiBwOAxARERE5HAYgIiIicjgMQERERORwGICIiIjI4TAAERERkcNhACIiIiKHwwBEREREDocBiIiIiBwOAxARERE5HAYgIiIicjgMQERERORwGICIiIjI4TAAERERkcNhACIiIiKHwwBEREREDocBiIiIiBwOAxARERE5HKsHoCNHjmDq1Knw9fWFIAhIT0/XWz5v3jwIgqD3M3ny5HvOu2HDBgQEBMDV1RWjRo3CiRMnzPQNiIiIyN5YPQDV1NQgJCQEGzZsaHadyZMno7S0VPezffv2FufcuXMnEhISsGLFCpw+fRohISGYNGkSrl69auryiYiIyA61t3YBkZGRiIyMbHEdFxcXeHt7S54zOTkZCxcuxHPPPQcA2LhxI7766it8+umnWLJkSavqJSIiIvtn9QAkRVZWFry8vNC1a1c8+uijePPNN9G9e/cm1719+zZOnTqFpUuX6sbatWuHCRMmIDc3t9nPqK+vR319ve51VVUVAECtVkOtVpvom0A3lynnbKvYK+nYK8OwX9KxV9KxV9KZs1dS57T5ADR58mQoFAoEBgbi4sWLeP311xEZGYnc3FzIZLJG61dUVECj0aBnz5564z179sT58+eb/ZykpCQkJiY2Gj9w4ADc3Nxa/0V+JyMjw+RztlXslXTslWHYL+nYK+nYK+nM0ava2lpJ69l8AJo5c6bu9+DgYAwdOhR9+/ZFVlYWxo8fb7LPWbp0KRISEnSvq6qq4O/vj4kTJ8LDw8Nkn6NWq5GRkYGIiAg4OTmZbN62iL2Sjr0yDPslHXslHXslnTl71XAE515sPgD9Xp8+feDp6YkLFy40GYA8PT0hk8lQXl6uN15eXt7ieUQuLi5wcXFpNO7k5GSWDdlc87ZF7JV07JVh2C/p2Cvp2CvpzNErqfNZ/SowQxUXF+P69evw8fFpcrmzszOGDx+OQ4cO6ca0Wi0OHTqE0NBQS5VJRERENszqAai6uhpnzpzBmTNnAAAFBQU4c+YMCgsLUV1djVdffRXHjh3DpUuXcOjQIURFRaFfv36YNGmSbo7x48fjvffe071OSEjAxx9/jC1btuDnn3/Gn/70J9TU1OiuCiMiIiLHZvVDYCdPnsS4ceN0rxvOw5k7dy4++OAD/PDDD9iyZQtu3LgBX19fTJw4EW+88Ybe4aqLFy+ioqJC9/qpp57CtWvXsHz5cpSVleH+++/H/v37G50YTURERI7J6gEoPDwcoig2u/ybb7655xyXLl1qNLZ48WIsXry4NaURERFRG2X1AERERETSaTQaqFQqlJaWwsfHB2FhYU3eFoZaxgBERERkJ5RKJeLi4lBcXKwbk8vlSE1NhUKhsGJl9seok6CLior0mn/ixAnEx8fjo48+MllhRERE9H+USiViYmL0/v4CQElJCWJiYqBUKq1UmX0yKgDNnj0bmZmZAICysjJERETgxIkT+Otf/4pVq1aZtEAiIiJHp9FoEBcX1+Q5sw1j8fHx0Gg0li7NbhkVgM6dO4eRI0cCAD7//HMMGTIER48exdatW7F582ZT1kdEROTwVCpVoz0/dxNFEUVFRVCpVBasyr4ZFYDUarXuMvSDBw/iiSeeAAAEBQWhtLTUdNURERGR5L+t/BssnVEBaPDgwdi4cSNUKhUyMjIwefJkAMCVK1eafUo7ERERGae5px8Yux4ZGYBWr16NDz/8EOHh4Zg1axZCQkIAAHv37tUdGiMiIiLTCAsLg1wuhyAITS4XBAH+/v4ICwuzcGX2y6jL4MPDw1FRUYGqqip07dpVN75o0SK4ubmZrDgiIiICZDIZUlNTERMTA0EQ9E6GbghFKSkpvB+QAYx+FphMJsOdO3eQk5ODnJwcXLt2DQEBAfDy8jJlfURERARAoVAgLS0Nfn5+euNyuRxpaWm8D5CBjNoDVFNTgxdeeAGfffYZtFotgN8C0Zw5c7B+/XruBSIiIjIDhUKBqKgo3gnaBIzaA5SQkIDs7Gx88cUXuHHjBm7cuIE9e/YgOzsbL7/8sqlrJCIiov8lk8l05+CGh4cz/BjJqD1A//73v5GWlobw8HDd2JQpU9ChQwfMmDEDH3zwganqIyIiIjI5o/YA1dbWomfPno3Gvby8UFtb2+qiiIiIiMzJqAAUGhqKFStWoK6uTjd269YtJCYmIjQ01GTFEREREZmDUYfAUlNTMWnSJMjlct09gL7//nu4urrim2++MWmBRERERKZmVAAaMmQI8vPzsXXrVpw/fx4AMGvWLDz99NPo0KGDSQskIiIiMjWjAhAAuLm5YeHChaashYiIiMgiJAegvXv3IjIyEk5OTti7d2+L63bq1AlBQUHw9fVtdYFEREREpiY5AEVHR6OsrAxeXl6Ijo6+5/oymQxr1qzBSy+91Jr6iIiIiExO8lVgWq1W95gLrVbb4k9dXR0+/vhjrFmzxmyFExERERnL6HOAWuLs7Izp06fjhx9+MMf0RERERK1i9MNQ//nPf2L06NHw9fXF5cuXAQDr1q3Dnj17AADu7u5ITk42TZVEREREJmRUAPrggw+QkJCAKVOm4MaNG9BoNACArl27IiUlxZT1EREREZmcUQFo/fr1+Pjjj/HXv/5V7yFsI0aMwNmzZ01WHBEREZE5GBWACgoKMGzYsEbjLi4uqKmpaXVRREREROZkVAAKDAzEmTNnGo3v378fAwcObG1NRERERGZl1FVgCQkJiI2NRV1dHURRxIkTJ7B9+3YkJSXhk08+MXWNRERERCZlVABasGABOnTogL/97W+ora3F7Nmz4evri9TUVMycOdPUNRIRERGZlNH3AXr66afx9NNPo7a2FtXV1bqbJBIRETk6jUYDlUqF0tJS+Pj4ICwsTO+iIbK+Vt8I0c3NDW5ubqaohYiIyO7t3r0bL7/8MoqLi3VjcrkcqampUCgUVqyM7mbUSdDl5eV49tln4evri/bt20Mmk+n9GOLIkSOYOnUqfH19IQgC0tPTdcvUajVee+01BAcHo2PHjvD19cWcOXNw5cqVFudcuXIlBEHQ+wkKCjLmqxIREUmWm5uLmTNn6oUfACgpKUFMTAyUSqWVKqPfM2oP0Lx581BYWIhly5bBx8cHgiAYXUBNTQ1CQkLwhz/8oVEyrq2txenTp7Fs2TKEhITg119/RVxcHJ544gmcPHmyxXkHDx6MgwcP6l63b2+Wp34QEREB+O2w1yeffAJRFBstE0URgiAgPj4eUVFRPBxmA4xKBTk5OVCpVLj//vtbXUBkZCQiIyObXNa5c2dkZGTojb333nsYOXIkCgsL0atXr2bnbd++Pby9vVtdHxERkRQ5OTm4fv16s8tFUURRURFUKhXCw8MtVxg1yagA5O/v32TCtYTKykoIgoAuXbq0uF5+fj58fX3h6uqK0NBQJCUltRiY6uvrUV9fr3tdVVUF4LfDcGq12iS1N8x39z+peeyVdOyVYdgv6dgr6X5/2Ks5RUVFDt9Pc25XUucURCOSzIEDB7B27Vp8+OGHCAgIMPTtzRcjCNi9ezeio6ObXF5XV4fRo0cjKCgIW7dubXaeffv2obq6GgMGDEBpaSkSExNRUlKCc+fOwd3dvcn3rFy5EomJiY3Gt23bxpO8iYjons6ePYtly5bdc7033ngDwcHBFqjIMTXcnqeyshIeHh7NrmdUAOratStqa2tx584duLm5wcnJSW/5f//7X8MrRssBSK1WY/r06SguLkZWVlaLX+r3bty4gd69eyM5ORnz589vcp2m9gD5+/ujoqLCoM+6F7VajYyMDERERDTqG+ljr6RjrwzDfknHXklXV1eHwMBA/Pe//23yKIkgCPDz80N+fr7DnwNkzu2qqqoKnp6e9wxARh0Cs/QT39VqNWbMmIHLly/j8OHDBgeSLl264L777sOFCxeaXcfFxQUuLi6Nxp2cnMzyL7255m2L2Cvp2CvDsF/SsVfSLFiwAGvWrIEgCHohqOFiodTUVLi6ulqrPJtjju1K6nxGBaC5c+ca8zajNISf/Px8ZGZmonv37gbPUV1djYsXL+LZZ581Q4VERES/CQ0NxY4dO5q8D1BKSgrvA2RDjL42XKPRID09HT///DOA3y47f+KJJwzerVddXa23Z6agoABnzpxBt27d4OPjg5iYGJw+fRpffvklNBoNysrKAADdunWDs7MzAGD8+PGYNm0aFi9eDAB45ZVXMHXqVPTu3RtXrlzBihUrIJPJMGvWLGO/LhERkSTTpk3D9OnTeSdoG2dUALpw4QKmTJmCkpISDBgwAACQlJQEf39/fPXVV+jbt6/kuU6ePIlx48bpXickJAD4bS/TypUrsXfvXgBodMl9Zmam7jLCixcvoqKiQresuLgYs2bNwvXr19GjRw+MGTMGx44dQ48ePYz5ukRERAaRyWS81N3GGRWAXnzxRfTt2xfHjh1Dt27dAADXr1/HM888gxdffBFfffWV5LnCw8NbvKReyjnaly5d0nu9Y8cOyZ9PREREjseoAJSdna0XfgCge/fuePvttzF69GiTFUdERERkDkY9C8zFxQU3b95sNF5dXa07L4eIiIjIVhkVgB5//HEsWrQIx48fhyiKEEURx44dw/PPP48nnnjC1DUSERERmZRRAejdd99F3759ERoaCldXV7i6umL06NHo168fUlNTTV0jERERkUkZdQ5Qly5dsGfPHly4cEF3GfzAgQPRr18/kxZHREREZA5G3wcIAPr168fQQ0RERHbHqENg06dPx+rVqxuNr1mzBk8++WSriyIiIiIyJ6MC0JEjRzBlypRG45GRkThy5EiriyIiIiIyJ6MCUHOXuzs5OaGqqqrVRRERERGZk1EBKDg4GDt37mw0vmPHDgwaNKjVRRERERGZk1EnQS9btgwKhQIXL17Eo48+CgA4dOgQtm/fjl27dpm0QCIiIiJTMyoATZ06Fenp6XjrrbeQlpaGDh06YOjQoTh48CDGjh1r6hqJiIiITMroy+Afe+wxPPbYY6ashYiIyCI0Gg1UKhVKS0vh4+ODsLAwyGQya5dFFtSq+wARERHZG6VSibi4OBQXF+vG5HI5UlNToVAorFgZWZJRJ0ETERHZI6VSiZiYGL3wAwAlJSWIiYmBUqm0UmVkaQxARETkEDQaDeLi4iCKYqNlDWPx8fHQaDSWLo2sgAGIiIgcgkqlarTn526iKKKoqAgqlcqCVZG1tCoA3b59G3l5ebhz546p6iEiIjKL0tJSk65H9s2oAFRbW4v58+fDzc0NgwcPRmFhIQDghRdewNtvv23SAomIiEzBx8fHpOuRfTMqAC1duhTff/89srKy4OrqqhufMGFCk3eIJiIisrawsDDI5XIIgtDkckEQ4O/vj7CwMAtXRtZgVABKT0/He++9hzFjxuhtSIMHD8bFixdNVhwREZGpyGQypKamAkCjENTwOiUlhfcDchBGBaBr167By8ur0XhNTU2zyZqIiMjaFAoF0tLS4Ofnpzcul8uRlpbG+wA5EKNuhDhixAh89dVXeOGFFwD8X3L+5JNPEBoaarrqiIiITEyhUCAqKop3gnZwRgWgt956C5GRkfjpp59w584dpKam4qeffsLRo0eRnZ1t6hqJiIhMSiaTITw83NplkBUZdQhszJgxOHPmDO7cuYPg4GAcOHAAXl5eyM3NxfDhw01dIxEREZFJGf0ssL59++Ljjz82ZS1EREREFiE5AFVVVUme1MPDw6hiiIiIiCxBcgDq0qWL5Cu8+BwVIiIismWSA1BmZqbu90uXLmHJkiWYN2+e7qqv3NxcbNmyBUlJSaavkoiIiMiEJAegsWPH6n5ftWoVkpOTMWvWLN3YE088geDgYHz00UeYO3euaaskIiIiMiGjrgLLzc3FiBEjGo2PGDECJ06caHVRREREROZkVADy9/dv8gqwTz75BP7+/gbNdeTIEUydOhW+vr4QBAHp6el6y0VRxPLly+Hj44MOHTpgwoQJyM/Pv+e8GzZsQEBAAFxdXTFq1CgGMyIiItIxKgCtW7cO69evR3BwMBYsWIAFCxZg6NChWL9+PdatW2fQXDU1NQgJCcGGDRuaXL5mzRq8++672LhxI44fP46OHTti0qRJqKura3bOnTt3IiEhAStWrMDp06cREhKCSZMm4erVqwbVRkRERG2TUQFoypQpyM/PxxNPPIH//ve/+O9//4upU6fi//2//4cpU6YYNFdkZCTefPNNTJs2rdEyURSRkpKCv/3tb4iKisLQoUPx2Wef4cqVK432FN0tOTkZCxcuxHPPPYdBgwZh48aNcHNzw6effmroVyUiIqI2yOgbIcrlcvzP//yPKWtppKCgAGVlZZgwYYJurHPnzhg1ahRyc3Mxc+bMRu+5ffs2Tp06haVLl+rG2rVrhwkTJiA3N7fZz6qvr0d9fb3udcN9j9RqNdRqtSm+jm6+u/9JzWOvpGOvDMN+ScdeScdeSWfOXkmd0+gAZAllZWUAgJ49e+qN9+zZU7fs9yoqKqDRaJp8z/nz55v9rKSkJCQmJjYaP3DgANzc3Awt/Z4yMjJMPmdbxV5Jx14Zhv2Sjr2Sjr2Szhy9qq2tlbSeTQcgS1q6dCkSEhJ0r6uqquDv74+JEyea9M7WarUaGRkZiIiIgJOTk8nmbYvYK+nYK8OwX9KxV9KxV9KZs1dSn1xh0wHI29sbAFBeXg4fHx/deHl5Oe6///4m3+Pp6QmZTIby8nK98fLyct18TXFxcYGLi0ujcScnJ7NsyOaaty1ir6RjrwzDfknHXknHXklnjl5Jnc+ok6AtJTAwEN7e3jh06JBurKqqCsePH9fdgfr3nJ2dMXz4cL33aLVaHDp0qNn3EBERkWNp1R6ga9euIS8vDwAwYMAA9OjRw+A5qqurceHCBd3rgoICnDlzBt26dUOvXr0QHx+PN998E/3790dgYCCWLVsGX19fREdH694zfvx4TJs2DYsXLwYAJCQkYO7cuRgxYgRGjhyJlJQU1NTU4LnnnmvN1yUiIqI2wqgAVFNTgxdeeAH//Oc/dQ8+lclkmDNnDtavX2/QScMnT57EuHHjdK8bzsOZO3cuNm/ejL/85S+oqanBokWLcOPGDYwZMwb79++Hq6ur7j0XL15ERUWF7vVTTz2Fa9euYfny5SgrK8P999+P/fv3NzoxmoiIiByTUQEoISEB2dnZ2Lt3L0aPHg0AyMnJwYsvvoiXX34ZH3zwgeS5wsPDIYpis8sFQcCqVauwatWqZte5dOlSo7HFixfr9ggRERER3c2oAPTvf/8baWlpCA8P141NmTIFHTp0wIwZMwwKQERERESWZtRJ0LW1tU0eTvLy8pJ8/T0RERGRtRgVgEJDQ7FixQq953HdunULiYmJvNKKiIiIbJ5Rh8BSUlIwefJkyOVyhISEAAC+//57uLq64ptvvjFpgURE5Hg0Gg1UKhVKS0vh4+ODsLAwyGQya5dFbYhRASg4OBj5+fnYunWr7vESs2bNwtNPP40OHTqYtEAiInIsSqUScXFxKC4u1o3J5XKkpqZCoVBYsTJqSwwOQGq1GkFBQfjyyy+xcOFCc9REREQOSqlUIiYmptHVwSUlJYiJiUFaWhpDEJmEwecAOTk56Z37Q0REZAoajQZxcXFN3hqlYSw+Pl53/zmi1jDqJOjY2FisXr0ad+7cMXU9RETkoFQqld5hr98TRRFFRUVQqVQWrIraKqPOAfrPf/6DQ4cO4cCBAwgODkbHjh31liuVSpMUR0REjqO0tNSk6xG1xKgA1KVLF0yfPt3UtRARkQPz8fEx6XpELTEqAG3atMnUdRARkYMLCwuDXC5HSUlJk+cBCYIAuVyOsLAwK1RHbY1R5wABwJ07d3Dw4EF8+OGHuHnzJgDgypUrqK6uNllxRETkOGQyGVJTUwH8Fnbu1vA6JSWF9wMikzAqAF2+fBnBwcGIiopCbGwsrl27BgBYvXo1XnnlFZMWSEREjkOhUCAtLQ1+fn5643K5nJfAk0kZdQgsLi4OI0aMwPfff4/u3bvrxqdNm8Z7AxERUasoFApERUXxTtBkVkYFIJVKhaNHj8LZ2VlvPCAgACUlJSYpjIiIHJdMJkN4eLi1y6A2zKhDYFqttskbURUXF8Pd3b3VRRERERGZk1EBaOLEiUhJSdG9FgQB1dXVWLFiBaZMmWKq2oiIiIjMwqhDYGvXrsWkSZMwaNAg1NXVYfbs2cjPz4enpye2b99u6hqJiIiITMqoACSXy/H9999jx44d+OGHH1BdXY358+fzafBERERkF4wKQADQvn17PPPMM6ashYiIiMgijA5AV65cQU5ODq5evQqtVqu37MUXX2x1YURERETmYlQA2rx5M/74xz/C2dkZ3bt317tjpyAIDEBERERk04wKQMuWLcPy5cuxdOlStGtn9NM0iIiIiKzCqABUW1uLmTNnMvwQETkwjUbDuzWT3TIqwcyfPx+7du0ydS1ERGQnlEolAgICMG7cOMyePRvjxo1DQEAAlEqltUsjksSoPUBJSUl4/PHHsX//fgQHB8PJyUlveXJyskmKIyIi26NUKhETEwNRFPXGS0pKEBMTw4eWkl0wOgB98803GDBgAAA0OgmaiIjaJo1Gg7i4uEbhBwBEUYQgCIiPj0dUVBQPh5FNM/pO0J9++inmzZtn4nKIiMiWqVQqFBcXN7tcFEUUFRVBpVLxYaZk04w6B8jFxQWjR482dS1ERGTjSktLTboekbUYFYDi4uKwfv16U9dCREQ2zsfHx6TrEVmLUYfATpw4gcOHD+PLL7/E4MGDG50EzasAiIjaprCwMMjlcpSUlDR5HpAgCJDL5QgLC7NCdUTSGbUHqEuXLlAoFBg7diw8PT3RuXNnvR9TCggIgCAIjX5iY2ObXH/z5s2N1nV1dTVpTUREjkomkyE1NRVA44teGl6npKTwBGiyeUbtAdq0aZOp62jWf/7zH2g0Gt3rc+fOISIiAk8++WSz7/Hw8EBeXp7uNa9MIyIyHYVCgbS0NMTFxemdEC2Xy5GSksJL4MkuGP0wVEvp0aOH3uu3334bffv2xdixY5t9jyAI8Pb2NndpREQOS6FQICoqineCJrtlVAAKDAxsca/KL7/8YnRBLbl9+zb+9a9/ISEhocXPr66uRu/evaHVavHAAw/grbfewuDBg1ucu76+HvX19brXVVVVAAC1Wg21Wm2aL/C/8939T2oeeyUde2UY9ku6e/Xq7iuCtVottFqtReqyRdyupDNnr6TOKYhNncX2O2lpaXjooYcgl8sBQHf89+4P++6777B//368+uqrWLJkiREl39vnn3+O2bNno7CwEL6+vk2uk5ubi/z8fAwdOhSVlZX4+9//jiNHjuDHH3/U1d+UlStXIjExsdH4tm3b4ObmZrLvQEREROZTW1uL2bNno7KyEh4eHs2uJykApaen46WXXkJ6ejpCQkKaXW/Dhg04efKk2c4RmjRpEpydnfHFF19Ifo9arcbAgQMxa9YsvPHGG82u19QeIH9/f1RUVLTYQEOp1WpkZGQgIiKi0dVzpI+9ko69Mgz7JR17JR17JZ05e1VVVQVPT897BiBJh8Cio6Ph6+uLuXPn4syZM82uFxkZiaVLl5olAF2+fBkHDx40+BJ7JycnDBs2DBcuXGhxPRcXF7i4uDT5fnNsyOaaty1ir6RjrwzDfknHXknHXklnjl5JnU/yZfAjR47EkSNHWlwnLS0N3bp1kzqlQTZt2gQvLy889thjBr1Po9Hg7NmzvCkXERER6Rh0EnTDrqRhw4bpnYQsiiLKyspw7do1vP/++6atEL+dWLdp0ybMnTsX7dvrlzxnzhz4+fkhKSkJALBq1So89NBD6NevH27cuIF33nkHly9fxoIFC0xeFxEREdkno64Ci46O1nvdrl079OjRA+Hh4QgKCjJFXXoOHjyIwsJC/OEPf2i0rLCwEO3a/d+OrF9//RULFy5EWVkZunbtiuHDh+Po0aMYNGiQyesiIiIi+2RUAFqxYoWp62jRxIkTm7zlOgBkZWXpvV63bh3WrVtngaqIiIjIXhn1KAwiIiIie2bQHqB27drd87ESgiDgzp07rSqKiIiIyJwMCkC7d+9udllubi7effddh74LKBEREdkHgwJQVFRUo7G8vDwsWbIEX3zxBZ5++mmsWrXKZMURERERmYPR5wBduXIFCxcuRHBwMO7cuYMzZ85gy5Yt6N27tynrIyIiIjI5gwNQZWUlXnvtNfTr1w8//vgjDh06hC+++AJDhgwxR31EREREJmfQIbA1a9Zg9erV8Pb2xvbt25s8JEZERERk6wwKQEuWLEGHDh3Qr18/bNmyBVu2bGlyPUOf10VERERkSQYFoDlz5tzzMngiIiIiW2dQANq8ebOZyiAiIiKyHKMehUFERLZPo9FApVKhtLQUPj4+CAsLg0wms3ZZRDaBAYiIqA1SKpWIi4tDcXGxbkwulyM1NRUKhcKKlRHZBj4LjIiojVEqlYiJidELPwBQUlKCmJgYXqhCBAYgIqI2RaPRIC4uDqIoNlrWMBYfHw+NRmPp0ohsCgMQEVEbolKpGu35uZsoiigqKoJKpbJgVUS2hwGIiKgNKS0tNel6RG0VAxARURvi4+Nj0vWI2ioGICKiNiQsLAxyubzZm9YKggB/f3+EhYVZuDIi28IARETUhshkMqSmpgJAoxDU8DolJYX3AyKHxwBERNTGKBQKpKWlwc/PT29cLpcjLS2N9wEiAm+ESETUJikUCkRFRfFO0ETNYAAiImqjZDIZwsPDrV0GkU3iITAiIiJyOAxARERE5HAYgIiIiMjhMAARERGRw2EAIiIiIofDq8CIiKxEo9HwMnUiK2EAIiKyAqVSibi4OL0nt8vlcqSmpvJGhUQWwENgREQWplQqERMToxd+AKCkpAQxMTFQKpVWqozIcTAAERFZkEajQVxcHERRbLSsYSw+Ph4ajcbSpRE5FAYgIiILUqlUjfb83E0URRQVFUGlUlmwKiLHY/MBaOXKlRAEQe8nKCioxffs2rULQUFBcHV1RXBwML7++msLVUtE1LLS0lKTrkdExrH5AAQAgwcPRmlpqe4nJyen2XWPHj2KWbNmYf78+fjuu+8QHR2N6OhonDt3zoIVE1FboNFokJWVhe3btyMrK8skh6V8fHxMuh4RGccurgJr3749vL29Ja2bmpqKyZMn49VXXwUAvPHGG8jIyMB7772HjRs3Nvu++vp61NfX615XVVUBANRqNdRqdSuq19cwlynnbKvYK+kcuVcajQY5OTm6S8nHjBlzz0vJpfRr9+7dSEhIQElJiW7Mz88PycnJmDZtmtH1PvTQQ/Dz88OVK1eaPA9IEAT4+fnhoYceson/PR152zIUeyWdOXsldU5BbOrfQBuycuVKvPPOO+jcuTNcXV0RGhqKpKQk9OrVq8n1e/XqhYSEBMTHx+vGVqxYgfT0dHz//fctfk5iYmKj8W3btsHNza3V34OITC83NxeffPIJrl+/rhvr3r07FixYgNDQ0FbNu3r16maXv/baazY9P5Ejq62txezZs1FZWQkPD49m17P5ALRv3z5UV1djwIABKC0tRWJiIkpKSnDu3Dm4u7s3Wt/Z2RlbtmzBrFmzdGPvv/8+EhMTUV5e3uznNLUHyN/fHxUVFS020FBqtRoZGRmIiIiAk5OTyeZti9gr6RyxV7t378bMmTMb7UURBAEAsGPHjmb31LTUL41Gg379+unt+fn9/H5+fsjPz2/VTQub2sMkl8uxdu3aVu1hMjVH3LaMxV5JZ85eVVVVwdPT854ByOYPgUVGRup+Hzp0KEaNGoXevXvj888/x/z58032OS4uLnBxcWk07uTkZJYN2VzztkXslXSO0iuNRoOXX3652UvJBUHAK6+8gunTp7cYUprq17ffftts+GmYv7i4GMeOHUN4eLjR32HGjBmYPn263dwJ2lG2LVNgr6QzR6+kzmfzAej3unTpgvvuuw8XLlxocrm3t3ejPT3l5eWSzyEiIttnyKXkhoYUS16lJZPJWhWiiMh4dnEV2N2qq6tx8eLFZq+QCA0NxaFDh/TGMjIyeDydqA0xZ0jhVVpEjsHmA9Arr7yC7OxsXLp0CUePHsW0adMgk8l05/jMmTMHS5cu1a0fFxeH/fv3Y+3atTh//jxWrlyJkydPYvHixdb6CkRkYuYMKWFhYZDL5bpziX5PEAT4+/sjLCzM4LmJyHbYfAAqLi7GrFmzMGDAAMyYMQPdu3fHsWPH0KNHDwBAYWGh3n/lPfzww9i2bRs++ugjhISEIC0tDenp6RgyZIi1vgIRmZg5Q4pMJkNqaqpunt/PCwApKSk2e64OEUlj8+cA7dixo8XlWVlZjcaefPJJPPnkk2aqiIisrSGkxMTEQBAEvZOhTRFSFAoF0tLSmnxae0pKCp/WTtQG2PweICKipjSEFD8/P71xuVyOtLS0VocUhUKBS5cuITMzE9u2bUNmZiYKCgoYfojaCJvfA0RE1ByFQoGoqCizXUrOq7SI2i4GICKyawwpRGQMHgIjIiIih8MARERERA6Hh8CIyKw0Go3dPO6BiBwHAxARmY1SqWzyUvLU1FReTUVEVsVDYERkFkqlEjExMY2e2VVSUoKYmBgolUorVUZExABERGag0WgQFxfX7NPaASA+Ph4ajcbSpRERAWAAIiIzMORp7URE1sAAREQmZ86ntRMRmQIDEBGZnDmf1k5EZAoMQERkcuZ8WjsRkSkwABGRyTU8rR1AoxBkiqe1ExG1FgMQEZmFuZ/WTkTUGrwRIhGZjbmf1k5EZCwGICIyKz6tnYhsEQ+BERERkcNhACIiIiKHwwBEREREDocBiIiIiBwOAxARERE5HF4FRmTjNBoNLyMnIjIxBiAiG6ZUKhEXF6f3ZHW5XI7U1FTeSJCIqBV4CIzIRimVSsTExOiFHwAoKSlBTEwMlEqllSojIrJ/DEBENkij0SAuLg6iKDZa1jAWHx8PjUZj6dKIiNoEBiAiG6RSqRrt+bmbKIooKiqCSqWyYFVERG0HAxCRDSotLTXpekREpI8BiMgG+fj4mHQ9IiLSxwBEZIPCwsIgl8shCEKTywVBgL+/P8LCwixcGRFR28AARGSDZDIZUlNTAaBRCGp4nZKSwvsBEREZyeYDUFJSEh588EG4u7vDy8sL0dHRyMvLa/E9mzdvhiAIej+urq4WqpgckUajQVZWFrZv346srCyTXJ2lUCiQlpYGPz8/vXG5XI60tDTeB4iIqBVs/kaI2dnZiI2NxYMPPog7d+7g9ddfx8SJE/HTTz+hY8eOzb7Pw8NDLyg1dyiBqLXMebNChUKBqKgos94JmneaJiJHZPMBaP/+/XqvN2/eDC8vL5w6dQqPPPJIs+8TBAHe3t6SP6e+vh719fW611VVVQAAtVoNtVptYNXNa5jLlHO2VfbQq927d2PmzJmN7tfTcLPCHTt2YNq0aa3+nNGjR+t+12q10Gq1esuN7dXu3buRkJCAkpIS3Zifnx+Sk5NNUretsodty1awV9KxV9KZs1dS5xTEpu60ZsMuXLiA/v374+zZsxgyZEiT62zevBkLFiyAn58ftFotHnjgAbz11lsYPHhws/OuXLkSiYmJjca3bdsGNzc3k9VPbYdGo8GiRYtw/fr1Ztfx9PTEhx9+aJN7VHJzc7F69epml7/22msIDQ21YEVERK1XW1uL2bNno7KyEh4eHs2uZ1cBSKvV4oknnsCNGzeQk5PT7Hq5ubnIz8/H0KFDUVlZib///e84cuQIfvzxR8jl8ibf09QeIH9/f1RUVLTYQEOp1WpkZGQgIiICTk5OJpu3LbL1XmVnZyMiIuKe62VkZGDs2LFmrcXQXmk0GvTr109vz8/dBEGAn58f8vPzbTK8tZatb1u2hL2Sjr2Szpy9qqqqgqen5z0DkM0fArtbbGwszp0712L4AYDQ0FC9/3J9+OGHMXDgQHz44Yd44403mnyPi4sLXFxcGo07OTmZZUM217xtka326tq1a5LXs1T9Unv17bffNht+gN/uNF1cXIxjx44hPDzchBXaFlvdtmwReyUdeyWdOXoldT67CUCLFy/Gl19+iSNHjjS7F6c5Tk5OGDZsGC5cuGCm6sgR2fPNCnmnaSJydDZ/Gbwoili8eDF2796Nw4cPIzAw0OA5NBoNzp49a5N/iMh+2fPNCu05vBERmYLNB6DY2Fj861//wrZt2+Du7o6ysjKUlZXh1q1bunXmzJmDpUuX6l6vWrUKBw4cwC+//ILTp0/jmWeeweXLl7FgwQJrfAUdjUaD7OxsHDlyBNnZ2XySt52z55sV2nN4IyIyBZsPQB988AEqKysRHh4OHx8f3c/OnTt16xQWFurtqv/111+xcOFCDBw4EFOmTEFVVRWOHj2KQYMGWeMrAPjtXjEBAQGIiIhAcnIyIiIiEBAQAKVSabWaqPXs9WaF9hzeiIhMwebPAZJykVpWVpbe63Xr1mHdunVmqshwSqUSMTExzd4rxpb/UNK9WeJmhebQEN6auoljSkoKt0kiatNsPgDZO41Gg7i4uCaDnCiKEAQB8fHxiIqKsvk/mNQ8mUxml1dL2Wt4IyJqLQYgM1OpVHr/df17oiiiqKgIKpWq1X9A+UgDMoa9hjciotZgADIzS11ubM7nUREREbU1Nn8StL2zxOXGDecY/X5PU8M5RjzRmoiISB8DkJmZ+3Lje51jBADx8fG85J6IiOguDEBmZu7LjQ05x4iIiIh+wwBkAea8VwwfaUBERGQ4ngRtIQ2XG2dmZmLfvn2IjIzEuHHjWn2VFh9pQEREZDgGIAuSyWQYO3YsampqMHbsWJNcot5wjlFJSUmT5wEJggC5XG6SRxrY82X29lw7ERGZHg+B2TlLPdKg4VEe48aNw+zZszFu3Di7eZSHPddORETmwQDUBpj7eVT2fJm9PddORETmwwDURigUCly6dAmZmZnYtm0bMjMzUVBQ0OrwY8+X2dtz7UREZF48B6gNMccjDSz5KA9Ts+faiYjIvLgHiFpkz5fZ23PtRERkXgxA1CJ7vszenmsnIiLzYgCiFpn7UR7mZM+1ExGReTEAUYssdZm9Odhz7UREZF4MQHRP5r7M3pzsuXYiIjIfXgVGkjQ8ysMe76Zsz7UTEZF5MACRZOa4zL5BU4+qMCVz1k5ERPaHAYisTqlUIi4uTu+ePXK5HGvXroWLi4sVKyMioraK5wCRVbX0qIqZM2ciNzfXSpUREVFbxgBEViPlURX/+Mc/+KgKIiIyOQYgshopj6qoqKhATk6OBasiIiJHwABEVsNHVRARkbUwAJHV8FEVRERkLQxAZDVSHlXh6emJMWPGWLgyIiJq6xiAyGqkPKpi/vz5vGEhERGZHAMQWVVLj6rYsWMHQkNDrVQZERG1ZbwRIlldc4+q0Gq1+Prrr61dHhERtUEMQGQTmnpUhVartU4xRETU5tnNIbANGzYgICAArq6uGDVqFE6cONHi+rt27UJQUBBcXV0RHBzMPQlERESkYxcBaOfOnUhISMCKFStw+vRphISEYNKkSbh69WqT6x89ehSzZs3C/Pnz8d133yE6OhrR0dE4d+6chSsnIiIiW2QXASg5ORkLFy7Ec889h0GDBmHjxo1wc3PDp59+2uT6qampmDx5Ml599VUMHDgQb7zxBh544AG89957Fq6ciIiIbJHNnwN0+/ZtnDp1CkuXLtWNtWvXDhMmTGj2QZm5ublISEjQG5s0aRLS09Ob/Zz6+nrU19frXldVVQEA1Go11Gp1K76Bvoa5TDlnW8VeScdeGYb9ko69ko69ks6cvZI6p80HoIqKCmg0GvTs2VNvvGfPnjh//nyT7ykrK2ty/bKysmY/JykpCYmJiY3GDxw4ADc3NyMqb1lGRobJ52yr2Cvp2CvDsF/SsVfSsVfSmaNXtbW1ktaz+QBkKUuXLtXba1RVVQV/f39MnDgRHh4eJvsctVqNjIwMREREwMnJyWTztkXslXTslWHYL+nYK+nYK+nM2auGIzj3YvMByNPTEzKZDOXl5Xrj5eXl8Pb2bvI93t7eBq0PAC4uLnBxcWk07uTkZJYN2VzztkXslXTslWHYL+nYK+nYK+nM0Sup89n8SdDOzs4YPnw4Dh06pBvTarU4dOhQs3cJDg0N1Vsf+G03G+8qTERERIAd7AECgISEBMydOxcjRozAyJEjkZKSgpqaGjz33HMAgDlz5sDPzw9JSUkAgLi4OIwdOxZr167FY489hh07duDkyZP46KOPrPk1iIiIyEbYRQB66qmncO3aNSxfvhxlZWW4//77sX//ft2JzoWFhWjX7v92Zj388MPYtm0b/va3v+H1119H//79kZ6ejiFDhkj+TFEUAUg/liiVWq1GbW0tqqqquIv0Htgr6dgrw7Bf0rFX0rFX0pmzVw1/txv+jjdHEO+1hoMqLi6Gv7+/tcsgIiIiIxQVFUEulze7nAGoGVqtFleuXIG7uzsEQTDZvA1XlxUVFZn06rK2iL2Sjr0yDPslHXslHXslnTl7JYoibt68CV9fX72jQ79nF4fArKFdu3YtJsfW8vDw4L8gErFX0rFXhmG/pGOvpGOvpDNXrzp37nzPdWz+KjAiIiIiU2MAIiIiIofDAGRhLi4uWLFiRZM3XSR97JV07JVh2C/p2Cvp2CvpbKFXPAmaiIiIHA73ABEREZHDYQAiIiIih8MARERERA6HAYiIiIgcDgOQhW3YsAEBAQFwdXXFqFGjcOLECWuXZHNWrlwJQRD0foKCgqxdlk04cuQIpk6dCl9fXwiCgPT0dL3loihi+fLl8PHxQYcOHTBhwgTk5+dbp1gru1ev5s2b12g7mzx5snWKtbKkpCQ8+OCDcHd3h5eXF6Kjo5GXl6e3Tl1dHWJjY9G9e3d06tQJ06dPR3l5uZUqth4pvQoPD2+0bT3//PNWqth6PvjgAwwdOlR3s8PQ0FDs27dPt9za2xQDkAXt3LkTCQkJWLFiBU6fPo2QkBBMmjQJV69etXZpNmfw4MEoLS3V/eTk5Fi7JJtQU1ODkJAQbNiwocnla9aswbvvvouNGzfi+PHj6NixIyZNmoS6ujoLV2p99+oVAEyePFlvO9u+fbsFK7Qd2dnZiI2NxbFjx5CRkQG1Wo2JEyeipqZGt85LL72EL774Art27UJ2djauXLkChUJhxaqtQ0qvAGDhwoV629aaNWusVLH1yOVyvP322zh16hROnjyJRx99FFFRUfjxxx8B2MA2JZLFjBw5UoyNjdW91mg0oq+vr5iUlGTFqmzPihUrxJCQEGuXYfMAiLt379a91mq1ore3t/jOO+/oxm7cuCG6uLiI27dvt0KFtuP3vRJFUZw7d64YFRVllXps3dWrV0UAYnZ2tiiKv21HTk5O4q5du3Tr/PzzzyIAMTc311pl2oTf90oURXHs2LFiXFyc9YqyYV27dhU/+eQTm9imuAfIQm7fvo1Tp05hwoQJurF27dphwoQJyM3NtWJltik/Px++vr7o06cPnn76aRQWFlq7JJtXUFCAsrIyvW2sc+fOGDVqFLexZmRlZcHLywsDBgzAn/70J1y/ft3aJdmEyspKAEC3bt0AAKdOnYJardbbtoKCgtCrVy+H37Z+36sGW7duhaenJ4YMGYKlS5eitrbWGuXZDI1Ggx07dqCmpgahoaE2sU3xYagWUlFRAY1Gg549e+qN9+zZE+fPn7dSVbZp1KhR2Lx5MwYMGIDS0lIkJiYiLCwM586dg7u7u7XLs1llZWUA0OQ21rCM/s/kyZOhUCgQGBiIixcv4vXXX0dkZCRyc3Mhk8msXZ7VaLVaxMfHY/To0RgyZAiA37YtZ2dndOnSRW9dR9+2muoVAMyePRu9e/eGr68vfvjhB7z22mvIy8uDUqm0YrXWcfbsWYSGhqKurg6dOnXC7t27MWjQIJw5c8bq2xQDENmcyMhI3e9Dhw7FqFGj0Lt3b3z++eeYP3++FSujtmTmzJm634ODgzF06FD07dsXWVlZGD9+vBUrs67Y2FicO3eO591J0FyvFi1apPs9ODgYPj4+GD9+PC5evIi+fftaukyrGjBgAM6cOYPKykqkpaVh7ty5yM7OtnZZAHgStMV4enpCJpM1OsO9vLwc3t7eVqrKPnTp0gX33XcfLly4YO1SbFrDdsRtzDh9+vSBp6enQ29nixcvxpdffonMzEzI5XLduLe3N27fvo0bN27ore/I21ZzvWrKqFGjAMAhty1nZ2f069cPw4cPR1JSEkJCQpCammoT2xQDkIU4Oztj+PDhOHTokG5Mq9Xi0KFDCA0NtWJltq+6uhoXL16Ej4+PtUuxaYGBgfD29tbbxqqqqnD8+HFuYxIUFxfj+vXrDrmdiaKIxYsXY/fu3Th8+DACAwP1lg8fPhxOTk5621ZeXh4KCwsdbtu6V6+acubMGQBwyG3r97RaLerr621jm7LIqdYkiqIo7tixQ3RxcRE3b94s/vTTT+KiRYvELl26iGVlZdYuzaa8/PLLYlZWllhQUCB+++234oQJE0RPT0/x6tWr1i7N6m7evCl+99134nfffScCEJOTk8XvvvtOvHz5siiKovj222+LXbp0Effs2SP+8MMPYlRUlBgYGCjeunXLypVbXku9unnzpvjKK6+Iubm5YkFBgXjw4EHxgQceEPv37y/W1dVZu3SL+9Of/iR27txZzMrKEktLS3U/tbW1unWef/55sVevXuLhw4fFkydPiqGhoWJoaKgVq7aOe/XqwoUL4qpVq8STJ0+KBQUF4p49e8Q+ffqIjzzyiJUrt7wlS5aI2dnZYkFBgfjDDz+IS5YsEQVBEA8cOCCKovW3KQYgC1u/fr3Yq1cv0dnZWRw5cqR47Ngxa5dkc5566inRx8dHdHZ2Fv38/MSnnnpKvHDhgrXLsgmZmZkigEY/c+fOFUXxt0vhly1bJvbs2VN0cXERx48fL+bl5Vm3aCtpqVe1tbXixIkTxR49eohOTk5i7969xYULFzrsf4w01ScA4qZNm3Tr3Lp1S/zzn/8sdu3aVXRzcxOnTZsmlpaWWq9oK7lXrwoLC8VHHnlE7Natm+ji4iL269dPfPXVV8XKykrrFm4Ff/jDH8TevXuLzs7OYo8ePcTx48frwo8oWn+bEkRRFC2zr4mIiIjINvAcICIiInI4DEBERETkcBiAiIiIyOEwABEREZHDYQAiIiIih8MARERERA6HAYiIiIgcDgMQERERORwGICKyO/PmzUN0dLS1yyAiO9be2gUQEd1NEIQWl69YsQKpqangTeyJqDUYgIjIppSWlup+37lzJ5YvX468vDzdWKdOndCpUydrlEZEbQgPgRGRTfH29tb9dO7cGYIg6I116tSp0SEwrVaLpKQkBAYGokOHDggJCUFaWppueVZWFgRBwDfffINhw4ahQ4cOePTRR3H16lXs27cPAwcOhIeHB2bPno3a2lrd+8LDw7F48WIsXrwYnTt3hqenJ5YtW6a39+nXX3/FnDlz0LVrV7i5uSEyMhL5+fkW6RURGY8BiIjsXlJSEj777DNs3LgRP/74I1566SU888wzyM7O1ltv5cqVeO+993D06FEUFRVhxowZSElJwbZt2/DVV1/hwIEDWL9+vd57tmzZgvbt2+PEiRNITU1FcnIyPvnkE93yefPm4eTJk9i7dy9yc3MhiiKmTJkCtVptke9ORMbhITAismv19fV46623cPDgQYSGhgIA+vTpg5ycHHz44YcYO3asbt0333wTo0ePBgDMnz8fS5cuxcWLF9GnTx8AQExMDDIzM/Haa6/p3uPv749169ZBEAQMGDAAZ8+exbp167Bw4ULk5+dj7969+Pbbb/Hwww8DALZu3Qp/f3+kp6fjySeftFQbiMhA3ANERHbtwoULqK2tRUREhO78oE6dOuGzzz7DxYsX9dYdOnSo7veePXvCzc1NF34axq5evar3noceekjvxOzQ0FDk5+dDo9Hg559/Rvv27TFq1Cjd8u7du2PAgAH4+eefTf1ViciEuAeIiOxadXU1AOCrr76Cn5+f3jIXFxe9105OTrrfBUHQe90wptVqzVQpEdkSBiAismuDBg2Ci4sLCgsL9Q53mcrx48f1Xh87dgz9+/eHTCbDwIEDcefOHRw/flx3COz69evIy8vDoEGDTF4LEZkOAxAR2TV3d3e88soreOmll6DVajFmzBhUVlbi22+/hYeHB+bOnduq+QsLC5GQkIA//vGPOH36NNavX4+1a9cCAPr374+oqCgsXLgQH374Idzd3bFkyRL4+fkhKirKFF+PiMyEAYiI7N4bb7yBHj16ICkpCb/88gu6dOmCBx54AK+//nqr554zZw5u3bqFkSNHQiaTIS4uDosWLdIt37RpE+Li4vD444/j9u3beOSRR/D11183OrxGRLZFEHk7VSKiJoWHh+P+++9HSkqKtUshIhPjVWBERETkcBiAiIiIyOHwEBgRERE5HO4BIiIiIofDAEREREQOhwGIiIiIHA4DEBERETkcBiAiIiJyOAxARERE5HAYgIiIiMjhMAARERGRw/n/VpcLcN+T5VYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mgs3Wau4962a"
      },
      "source": [
        "Los datos parecen tener un comportamiento exponencial. Queremos ver que tan exponencial es su distribución realizando un ajuste. Realizamos un ajuste primero definiendo la función que queremos comparar a los datos, $f$, con los parametros libres que nos interesa determinar,\n",
        "$$\n",
        "\\text{variable dependiente} = f(\\text{variable independiente}, \\text{parametros a determinar}).\n",
        "$$\n",
        "\n",
        "En este caso, vamos a intentar con:\n",
        "$$\n",
        "f(t, A, C) = Ae^{Ct}\n",
        "$$\n",
        "\n",
        "Luego, para realizar un ajuste podemos usar la función ```curve_fit()``` que nos ofrece la librería/sublibrería ```scipy.optimize```. Veamos cómo funciona con un ejemplo:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KtuTMK_vDXh5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "81ca3e60-d1d8-442c-a15b-403ccea04288"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Parámetros óptimos para A y C (popt): [0.97349007 0.10119234]\n",
            "\n",
            "Matriz de covariancia de popt (pcov):\n",
            "[[ 4.92276135e-03 -1.89187395e-04]\n",
            " [-1.89187395e-04  7.51566964e-06]]\n"
          ]
        }
      ],
      "source": [
        "# Importo la función desde la librería\n",
        "from scipy.optimize import curve_fit\n",
        "\n",
        "# Declaro la función con la que quiero ajustar con parámetros genéricos\n",
        "def f_ajuste(t, A, C):\n",
        "    exponencial = A*np.exp(C*t)\n",
        "    return exponencial\n",
        "\n",
        "# Utilizo curve_fit() para el ajuste\n",
        "popt, pcov = curve_fit(f_ajuste, tiempo, num_conejos, sigma = err_num_conejos)\n",
        "\n",
        "# Imprimo en pantalla los valores de popt y pcov\n",
        "print(f'Parámetros óptimos para A y C (popt): {popt}')\n",
        "print(f'\\nMatriz de covariancia de popt (pcov):\\n{pcov}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jPwdMZAGDoGW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "86c0ac04-df9f-44c5-8b1a-f33165546c97"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Graficamos los DATOS con el error asociado\n",
        "plt.errorbar(tiempo, num_conejos, yerr=err_num_conejos, fmt='ok', label='Datos',\n",
        "             capsize=2, capthick=1)\n",
        "\n",
        "\n",
        "# Graficamos el AJUSTE\n",
        "A, C = popt\n",
        "# Variables finas para evaluar la funcion en más puntos que los medidos\n",
        "x_ajuste = np.linspace(min(tiempo), max(tiempo), 1000)\n",
        "y_ajuste = f_ajuste(x_ajuste, A, C)\n",
        "\n",
        "plt.plot(x_ajuste, y_ajuste, '-r', label='Ajuste')\n",
        "\n",
        "\n",
        "# Labels, grilla, leyenda y show\n",
        "plt.xlabel(\"Tiempo\")\n",
        "plt.ylabel(\"Número de conejos\")\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L0-YsLe1962a"
      },
      "source": [
        "##### Vemos que arriba la función recibe cinco inputs (3 obligatorios y 2 opcionales):\n",
        "* `f`: **El primer argumento es la función _modelo_ con la que queremos ajustar.**\n",
        "    \n",
        "    La función modelo tiene que tener como primer parámetro la variable independiente (que corresponde por ejemplo al eje $\\hat x$). Luego se usan los otros parámetros para poner las constantes que queremos hallar (en nuestro caso `A` y `C`).\n",
        "\n",
        "\n",
        "* `xdata, ydata`: **Los otros dos argumentos son nuestros datos**\n",
        "\n",
        "    Estos son los datos crudos, los que usamos para visualizarlos normalmente.\n",
        "    \n",
        "* `sigma`: **El error asociado a cada valor en** `ydata`\n",
        "    \n",
        "    Es un parámetro opcional, para que el ajuste considere el error en la variable $y$.\n",
        "\n",
        "\n",
        "##### Outputs de `curve_fit()`\n",
        "* `popt`: **Parámetros Óptimos (`array 1D` de numpy)**\n",
        "    \n",
        "    Vemos que `curve_fit()` nos devuelve como primer parámetro una lista con los valores óptimos para nuestro ajuste. En este caso son dos porque en la función modelo pusimos dos constantes (`A` y `C`). Estos valores vienen ordenados del mismo modo que están en la función modelo por lo que `popt[0]` es el `A` óptimo y `popt[1]` es el `C` óptimo.\n",
        "\n",
        "\n",
        "* `pcov`: **Covarianza de los Parámetros (`array 2D` de numpy)**\n",
        "    \n",
        "    Esta es la matriz de covarianza de los resultados que nos da `popt`. Esta matriz nos da una idea de cuál es el error de este ajuste y de cuán ligados están estos errores entre sí. No es el objetivo del curso dar una clase de estadísitica, pero si conocemos que las variables son independientes podemos obtener el error de nuestros parámetros de ajuste como la raíz cuadrada de la covarianza. Para este ejemplo sería\n",
        "    ```python\n",
        "    err_A = np.sqrt(pcov[0,0])\n",
        "    err_C = np.sqrt(pcov[1,1])\n",
        "    ```\n",
        "    o\n",
        "    ```python\n",
        "    err_A, err_C = np.sqrt(np.diag(pcov))\n",
        "    ```\n",
        "\n",
        "**Una cosita más:** En este caso pusimos `var_x_ajuste = var_x` al momento de graficar, lo que implica que nuestro grafico del ajuste tiene 20 puntos (los mismos que `var_x`), pero como conocemos la función podríamos hacer un linspace del estilo `np.linspace(min(var_x), max(var_x), 1000)` para tener una mejor densidad de puntos. Esto esta bueno para hacer que los gráficos se vean menos acartonados, pero SOLAMENTE se puede usar a la hora de graficar."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ITpYWJC3962b"
      },
      "source": [
        "#### Barras de error\n",
        "\n",
        "* Si queremos poner barras de error a nuestro gráfico podemos usar ```plt.errorbar()```, que funciona de forma muy similar a ```plt.plot()``` pero nos permite dar un array con los errores o un error constante para todos.\n",
        "\n",
        "#### Guardar un gráfico\n",
        "Una vez tenemos un gráfico podemos guardarlo en el mismo directorio donde tenemos el archivo con el que estamos trabajando utilizando la linea\n",
        "```python\n",
        "plt.savefig('nombre_del_archivo.png')\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9RLsaOc1962b"
      },
      "source": [
        "---\n",
        "### Ejercicio 3\n",
        "\n",
        "Con los datos levantados en las variables ```t_labo``` e ```y_labo```, realizar el ajuste de la función:\n",
        "\n",
        "$$y(t) = a + b \\; t^2$$\n",
        "\n",
        "Donde $a = y_0$ y $b = -\\frac{g}{2}$.\n",
        "\n",
        "Agregar al gráfico del Ejercicio 3 este nuevo ajuste, junto con su label 'Ajuste'.\n",
        "\n",
        "Guardar la imagen, e imprimir la gravedad en pantalla con su error.\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "37lw9cFC962d"
      },
      "source": [
        "### Acá termina nuestra clase.\n",
        "\n",
        "Si aún hay tiempo abajo tenemos armado algunos párrafos sobre distintas cosas piolas que podemos hacer. La elección esta en ustedes. (Si, se convirtió en un elija su propia aventura, no se la esperaba nadie esa)\n",
        "\n",
        "* Pandas y DataFrames\n",
        "* Histogramas\n",
        "* Sistemas de ecuaciones diferenciales\n",
        "* Algebra Lineal\n",
        "* Calculo Simbólico\n",
        "* Integración Numérica\n",
        "* Funciones Locas\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HcgRMSPQ962d"
      },
      "source": [
        "---\n",
        "# Pandas y DataFrames\n",
        "\n",
        "[Pandas](https://pandas.pydata.org/) es una librería bastante utilizada para Análisis de Datos y tiene [demasiadas](https://pandas.pydata.org/docs/user_guide/index.html) cosas disponibles, demasiadas. Aca en el curso no vamos a cubrir casi nada, sólo veremos algunas cosas.\n",
        "\n",
        "Pandas, tal como NumPy y SymPy, trae un nuevo tipo de dato muy genial, los DataFrames. Recomendado es el [tutorial](https://pandas.pydata.org/docs/getting_started/index.html) de 10 minutos que aporta la misma página de Pandas, en los que hace un paneo sobre las cosas básicas de la librería. Hay un [ejemplo práctico](https://github.com/fifabsas/talleresfifabsas/blob/master/python/Extras/Pandas_ejemplo.ipynb) hecho por la FIFA en el que se trabaja un poco los datos de inscripción de una jornada anterior del cursito este.\n",
        "\n",
        "Los DataFrames, el nuevo tipo de dato introducido, son básicamente tablas de datos donde cada columna tiene un nombre descriptivo. Podemos operar con las columnas por su nombre, y también elegir filas como haciamos con arrays antes.\n",
        "\n",
        "\n",
        "Vamos a trabajar con un csv que pueden descargar de nuestro repositorio:\n",
        "\n",
        "[Datos de los jugadores](https://github.com/fifabsas/talleresfifabsas/blob/master/python/2_Numerico/datos_jugadores.csv)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dWDVUYHiscn9"
      },
      "outputs": [],
      "source": [
        "# Importamos pandas con un sobrenombre, como haciamos con numpy\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MLSXXan1sik_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b1d81be7-edd0-406c-d42b-e00497334a4f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "               Equipo Puesto          Jugador  Edad  Nacimiento dia_mes  \\\n",
              "0               Tigre    MED    Aaron Molinas    22    8/2/2000  02-ago   \n",
              "1            Banfield    DEF     Aarón Quirós    21  10/31/2001  31-oct   \n",
              "2               Tigre    DEF    Abel Luciatti    30   2/18/1993  18-feb   \n",
              "3               Velez    DEL     Abiel Osorio    20   6/13/2002  13-jun   \n",
              "4         San Lorenzo    DEL     Adam Bareiro    26   7/26/1996  26-jul   \n",
              "..                ...    ...              ...   ...         ...     ...   \n",
              "639       Atl Tucuman    DEF  Wilson Ibarrola    26    7/2/1996  02-jul   \n",
              "640     Sarmiento (J)    MED   Yair Arismendi    25    4/5/1998  05-abr   \n",
              "641             Union    MED  Yeison Gordillo    30   6/25/1992  25-jun   \n",
              "642       Atl Tucuman    DEF  Yonathan Cabral    30   5/10/1992  10-may   \n",
              "643  Estudiantes (LP)    DEF      Zaid Romero    23  12/15/1999  15-dic   \n",
              "\n",
              "     nro_mes   año  Altura_cm       Ciudad_nac   Pais_nac  \n",
              "0          8  2000      175.0              NaN  Argentina  \n",
              "1         10  2001        NaN              NaN  Argentina  \n",
              "2          2  1993      178.0            Morón  Argentina  \n",
              "3          6  2002      182.0              NaN  Argentina  \n",
              "4          7  1996      184.0         Asuncion   Paraguay  \n",
              "..       ...   ...        ...              ...        ...  \n",
              "639        7  1996      172.0              NaN   Paraguay  \n",
              "640        4  1998        NaN              NaN  Argentina  \n",
              "641        6  1992      176.0  Miranda (Cauca)   Colombia  \n",
              "642        5  1992      188.0  Isidro Casanova  Argentina  \n",
              "643       12  1999      193.0          Mendoza  Argentina  \n",
              "\n",
              "[644 rows x 11 columns]"
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_8b25ce48-69c3-415f-a42d-5089f04d6036 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 644,\n  \"fields\": [\n    {\n      \"column\": \"Equipo\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 28,\n        \"samples\": [\n          \"Barracas Central\",\n          \"Central Cba (SdE)\",\n          \"Gimnasia (LP)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Puesto\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"DEF\",\n          \"ARQ\",\n          \"MED\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Jugador\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 640,\n        \"samples\": [\n          \"Rodrigo Ins\\u00faa\",\n          \"Gustavo Canto\",\n          \"Ivo Mammini\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Edad\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 17,\n        \"max\": 41,\n        \"num_unique_values\": 25,\n        \"samples\": [\n          24,\n          33,\n          22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Nacimiento\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 601,\n        \"samples\": [\n          \"5/17/1990\",\n          \"8/29/1990\",\n          \"2/23/2001\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dia_mes\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 289,\n        \"samples\": [\n          \"05-jun\",\n          \"28-nov\",\n          \"01-jul\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nro_mes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 12,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          9,\n          11,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"a\\u00f1o\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1981,\n        \"max\": 2006,\n        \"num_unique_values\": 25,\n        \"samples\": [\n          1997,\n          1991,\n          2000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Altura_cm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.550231347128226,\n        \"min\": 156.0,\n        \"max\": 194.0,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          164.0,\n          183.0,\n          171.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ciudad_nac\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 206,\n        \"samples\": [\n          \"Salto\",\n          \"El Dorado\",\n          \"Labarden\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pais_nac\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"Espana\",\n          \"Argentina\",\n          \"Portugal\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Para cargar un csv usamos read_csv() de pandas.\n",
        "# El argumento es el nombre del archivo\n",
        "df = pd.read_csv('datos_jugadores.csv')\n",
        "\n",
        "# df es un \"DataFrame\", como si fuera una tabla de Excel cargada en Python.\n",
        "\n",
        "display(df) # Display es como un \"print\" especial para cosas que no son texto."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MXZyvrqczLUs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ee7538b-05e7-4282-a7c9-e1ae88937cee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(644, 11)\n",
            "Index(['Equipo', 'Puesto', 'Jugador', 'Edad', 'Nacimiento', 'dia_mes',\n",
            "       'nro_mes', 'año', 'Altura_cm', 'Ciudad_nac', 'Pais_nac'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "# Obtener cantidad de filas y columnas total:\n",
        "print(df.shape)\n",
        "\n",
        "# Obtener nombres de las columnas:\n",
        "print(df.columns)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pxc5IhQ4tN90"
      },
      "source": [
        "Nos gustaría operar de alguna manera con esta tabla, para hacer algun tipo de análisis de los datos.\n",
        "\n",
        "Preguntas posibles:\n",
        "- Cuál es la distribución de edad/altura/año de nacimiento?\n",
        "- Cuantos jugadores de cada País/Puesto/mes de nacimiento hay?\n",
        "- Hay dos jugadores que compartan cumpleaños en un mismo equipo?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IQ7ZnQ6VwwaM"
      },
      "source": [
        "## Cuál es la distribución de edad?\n",
        "\n",
        "Para esta pregunta vamos a armar un histograma de los valores de edad."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JF5HB7TTw4xw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a5bcec0-7834-4496-8ab0-35c2269c1360"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Para seleccionar una columna usamos:\n",
        "altura = df['Altura_cm']\n",
        "\n",
        "altura.hist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pMGWoi9Xxd-y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e107ba36-51fd-4248-c21c-2ef0c8e66e45"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Dejemos un poco más lindo el gráfico\n",
        "\n",
        "altura.hist(rwidth=0.9, edgecolor='black', color='lightblue')\n",
        "\n",
        "# Label de los ejes\n",
        "plt.xlabel('Altura [cm]', fontsize=13)\n",
        "plt.ylabel('Ocurrencias', fontsize=13)\n",
        "\n",
        "# Apagamos la grid que viene por defecto y despues colocamos la nuestra\n",
        "plt.grid()\n",
        "plt.grid(ls='--', axis='y', color='gray')\n",
        "\n",
        "plt.title('Distribución de altura de los jugadores')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3fjrpaOUx8ge"
      },
      "source": [
        "Como hacemos si queremos crear una columna nueva? Por ejemplo, el logaritmo de la altura?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "63Qh-KE6x7T0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d580fe9-d3a8-44a0-8478-d57bb7ae6c3a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df['Altura_log'] = np.log(df['Altura_cm'])\n",
        "\n",
        "# Dejemos un poco más lindo el gráfico\n",
        "df['Altura_log'].hist(rwidth=0.9, edgecolor='black', color='lightblue')\n",
        "\n",
        "# Label de los ejes\n",
        "plt.xlabel('log(Altura)', fontsize=13)\n",
        "plt.ylabel('Ocurrencias', fontsize=13)\n",
        "\n",
        "# Apagamos la grid que viene por defecto y despues colocamos la nuestra\n",
        "plt.grid()\n",
        "plt.grid(ls='--', axis='y', color='gray')\n",
        "\n",
        "plt.title('Distribución de altura de los jugadores')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pJ_DNmFVu3hd"
      },
      "source": [
        "## Cuántos jugadores de cada pais hay?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q4KT0Q2Vu7_C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "258ae242-7cf4-44e6-e173-8153f7aa3e49"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f43887eb430>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Podemos usar la función groupby de pandas para agrupar todas las filas\n",
        "# con el mismo país de nacimiento.\n",
        "\n",
        "df_gb = df.groupby('Pais_nac')\n",
        "\n",
        "display(df_gb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zjKOnIczvLLc"
      },
      "source": [
        "Vemos que esto asi solo no nos dice absolutamente nada. Una vez que tenemos agrupado por una columna nuestro DataFrame, podemos hacer cálculos como el promedio, la suma de los valores, la cantidad de veces que aparece una fila con el valor que agrupamos, el máximo, etc. A estas se las conoce como funciones de agregación."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qqy0Cb1lvY1X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "642ef7ec-68c4-40bc-ecbd-e03b4832e4dc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pais_nac\n",
              "Argentina    544\n",
              "Chile          5\n",
              "Colombia      22\n",
              "Ecuador        3\n",
              "Espana         3\n",
              "Mexico         1\n",
              "Paraguay      28\n",
              "Peru           2\n",
              "Portugal       1\n",
              "Uruguay       33\n",
              "Venezuela      2\n",
              "Name: Equipo, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "# Para contar la cantidad de filas seleccionamos una columna cualquiera y usamos 'count'.\n",
        "\n",
        "df_gb['Equipo'].count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m7_DkU3AwdP4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "33f908f8-475d-4289-ec07-6bb32c60db01"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pais_nac\n",
              "Argentina    194.0\n",
              "Chile        180.0\n",
              "Colombia     190.0\n",
              "Ecuador      176.0\n",
              "Espana       192.0\n",
              "Mexico       178.0\n",
              "Paraguay     188.0\n",
              "Peru         185.0\n",
              "Portugal     176.0\n",
              "Uruguay      190.0\n",
              "Venezuela    186.0\n",
              "Name: Altura_cm, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "# Si quisieramos el máximo de altura, por ejemplo:\n",
        "\n",
        "df_gb['Altura_cm'].max()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Smzezd51uyNC"
      },
      "source": [
        "## Hay dos jugadores que compartan cumpleaños en un mismo equipo?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y8nQrB--s_CL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ceb99e11-f9db-4abf-9142-4cc146796703"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Equipo      dia_mes\n",
              "Argentinos  01-dic     1\n",
              "            02-feb     1\n",
              "            04-jul     1\n",
              "            07-nov     1\n",
              "            08-ago     1\n",
              "                      ..\n",
              "Velez       25-feb     1\n",
              "            26-jun     1\n",
              "            28-abr     1\n",
              "            30-ene     1\n",
              "            31-ene     1\n",
              "Name: Puesto, Length: 622, dtype: int64"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Para responder esto, podemos contar cuantas veces se repite\n",
        "# el mismo valor de \"Equipo\" y \"dia_mes\". Para hacer esto, podemos aprovechar\n",
        "# a usar devuelta el groupby, y contar cuantas veces está esa combinaciones\n",
        "# de valores\n",
        "\n",
        "df_gb = df.groupby(['Equipo', 'dia_mes'])['Puesto'].count()\n",
        "\n",
        "display(df_gb)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T5RkU99ItxHE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c2f1376a-a3d6-4206-e4b9-4343d7329a27"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Equipo             dia_mes\n",
              "Argentinos         25-ene     2\n",
              "Atl Tucuman        10-may     2\n",
              "                   17-feb     2\n",
              "Banfield           01-feb     2\n",
              "Belgrano           29-ago     2\n",
              "Boca Juniors       04-jul     2\n",
              "                   22-feb     2\n",
              "                   23-jul     2\n",
              "                   24-abr     2\n",
              "Central Cba (SdE)  13-ene     2\n",
              "                   27-oct     2\n",
              "Colon              05-sep     2\n",
              "                   13-ene     2\n",
              "Def y Justicia     08-feb     2\n",
              "Estudiantes (LP)   29-mar     2\n",
              "Gimnasia (LP)      05-may     2\n",
              "Godoy Cruz         02-mar     2\n",
              "                   16-feb     2\n",
              "Lanus              22-mar     2\n",
              "River Plate        29-mar     2\n",
              "Tigre              07-feb     2\n",
              "Union              30-jul     2\n",
              "Name: Puesto, dtype: int64"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Ahora podemos filtrar usando esta sintaxis\n",
        "\n",
        "df_filtered = df_gb[df_gb > 1]\n",
        "\n",
        "display(df_filtered)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hyd3CN5Ey4TH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 896
        },
        "outputId": "fc395f53-00ca-46c6-fbec-84415f6dc729"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                Equipo Puesto               Jugador  Edad Nacimiento dia_mes  \\\n",
              "0                Tigre    MED         Aaron Molinas    22   8/2/2000  02-ago   \n",
              "67       Independiente    DEL       Braian Martínez    23  8/18/1999  18-ago   \n",
              "146           Belgrano    DEF            Erik Godoy    29  8/16/1993  16-ago   \n",
              "202           Platense    MED           Franco Diaz    22  8/28/2000  28-ago   \n",
              "219           Belgrano    MED   Gabriel Compagnucci    31  8/29/1991  29-ago   \n",
              "246     Def y Justicia    MED    Gonzalo Castellani    35  8/10/1987  10-ago   \n",
              "248  Central Cba (SdE)    DEF          Gonzalo Goñi    24  8/16/1998  16-ago   \n",
              "265            Newells    DEF       Guillermo Ortíz    30   8/9/1992  09-ago   \n",
              "279        Atl Tucuman    MED  Ignacio Maestro Puch    19  8/13/2003  13-ago   \n",
              "282           Platense    MED         Ignacio Schor    22   8/4/2000  04-ago   \n",
              "343    Rosario Central    DEF       Juan Cruz Komar    26  8/13/1996  13-ago   \n",
              "368       Talleres (C)    DEF       Julio Buffarini    34  8/18/1988  18-ago   \n",
              "410            Huracan    ARQ          Lucas Chaves    27   8/9/1995  09-ago   \n",
              "416              Velez    DEL          Lucas Janson    28  8/16/1994  16-ago   \n",
              "424         Argentinos    DEF        Lucas Villalba    28  8/19/1994  19-ago   \n",
              "442           Belgrano    ARQ      Manuel Vicentini    32  8/29/1990  29-ago   \n",
              "459              Tigre    DEF         Martín Ortega    23  8/20/1999  20-ago   \n",
              "467       Talleres (C)    DEF        Matías Catalán    30  8/19/1992  19-ago   \n",
              "487  Central Cba (SdE)    MED          Mauro Pittón    28   8/8/1994  08-ago   \n",
              "502         Argentinos    DEF         Miguel Torrén    34  8/12/1988  12-ago   \n",
              "504           Banfield    DEL        Milton Gimenez    26  8/12/1996  12-ago   \n",
              "545            Newells    MED           Pablo Pérez    37  8/10/1985  10-ago   \n",
              "571         Argentinos    DEL        Rodrigo Cabral    22   8/8/2000  08-ago   \n",
              "590          Instituto    DEL    Santiago Rodríguez    25  8/23/1997  23-ago   \n",
              "598              Union    ARQ      Sebastián Moyano    32  8/26/1990  26-ago   \n",
              "629            Huracan    MED       Valentín Burgoa    22  8/16/2000  16-ago   \n",
              "635              Velez    DEL            Walter Bou    29  8/25/1993  25-ago   \n",
              "\n",
              "     nro_mes   año  Altura_cm             Ciudad_nac   Pais_nac  Altura_log  \n",
              "0          8  2000      175.0                    NaN  Argentina    5.164786  \n",
              "67         8  1999      170.0                    NaN  Argentina    5.135798  \n",
              "146        8  1993      185.0           Buenos Aires  Argentina    5.220356  \n",
              "202        8  2000      180.0                    NaN  Argentina    5.192957  \n",
              "219        8  1991      178.0             Monte Buey  Argentina    5.181784  \n",
              "246        8  1987      183.0           Buenos Aires  Argentina    5.209486  \n",
              "248        8  1998      187.0             Pedro Luro  Argentina    5.231109  \n",
              "265        8  1992      183.0                Rosario  Argentina    5.209486  \n",
              "279        8  2003      180.0  San Miguel de Tucuman  Argentina    5.192957  \n",
              "282        8  2000      183.0           Buenos Aires  Argentina    5.209486  \n",
              "343        8  1996      190.0                Rosario  Argentina    5.247024  \n",
              "368        8  1988      172.0          Gral. Cabrera  Argentina    5.147494  \n",
              "410        8  1995      179.0        Martín Coronado  Argentina    5.187386  \n",
              "416        8  1994      171.0                    NaN  Argentina    5.141664  \n",
              "424        8  1994      177.0                    NaN  Argentina    5.176150  \n",
              "442        8  1990      187.0               Santa Fé  Argentina    5.231109  \n",
              "459        8  1999      183.0                    NaN  Argentina    5.209486  \n",
              "467        8  1992      172.0          Mar del Plata  Argentina    5.147494  \n",
              "487        8  1994      174.0                    NaN  Argentina    5.159055  \n",
              "502        8  1988      179.0               Santa Fé  Argentina    5.187386  \n",
              "504        8  1996      184.0            Grand Bourg  Argentina    5.214936  \n",
              "545        8  1985      179.0                Rosario  Argentina    5.187386  \n",
              "571        8  2000      170.0               Mercedes  Argentina    5.135798  \n",
              "590        8  1997      171.0               San Luis  Argentina    5.141664  \n",
              "598        8  1990      188.0                Mendoza  Argentina    5.236442  \n",
              "629        8  2000      175.0             Guaymallén  Argentina    5.164786  \n",
              "635        8  1993      176.0              Concordia  Argentina    5.170484  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3835f0f7-ccc8-4fae-9588-794f6ad21f01\" class=\"colab-df-container\">\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "    }\n",
              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Equipo</th>\n",
              "      <th>Puesto</th>\n",
              "      <th>Jugador</th>\n",
              "      <th>Edad</th>\n",
              "      <th>Nacimiento</th>\n",
              "      <th>dia_mes</th>\n",
              "      <th>nro_mes</th>\n",
              "      <th>año</th>\n",
              "      <th>Altura_cm</th>\n",
              "      <th>Ciudad_nac</th>\n",
              "      <th>Pais_nac</th>\n",
              "      <th>Altura_log</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Tigre</td>\n",
              "      <td>MED</td>\n",
              "      <td>Aaron Molinas</td>\n",
              "      <td>22</td>\n",
              "      <td>8/2/2000</td>\n",
              "      <td>02-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>2000</td>\n",
              "      <td>175.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.164786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>Independiente</td>\n",
              "      <td>DEL</td>\n",
              "      <td>Braian Martínez</td>\n",
              "      <td>23</td>\n",
              "      <td>8/18/1999</td>\n",
              "      <td>18-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1999</td>\n",
              "      <td>170.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.135798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>Belgrano</td>\n",
              "      <td>DEF</td>\n",
              "      <td>Erik Godoy</td>\n",
              "      <td>29</td>\n",
              "      <td>8/16/1993</td>\n",
              "      <td>16-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1993</td>\n",
              "      <td>185.0</td>\n",
              "      <td>Buenos Aires</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.220356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>202</th>\n",
              "      <td>Platense</td>\n",
              "      <td>MED</td>\n",
              "      <td>Franco Diaz</td>\n",
              "      <td>22</td>\n",
              "      <td>8/28/2000</td>\n",
              "      <td>28-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>2000</td>\n",
              "      <td>180.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.192957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>219</th>\n",
              "      <td>Belgrano</td>\n",
              "      <td>MED</td>\n",
              "      <td>Gabriel Compagnucci</td>\n",
              "      <td>31</td>\n",
              "      <td>8/29/1991</td>\n",
              "      <td>29-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1991</td>\n",
              "      <td>178.0</td>\n",
              "      <td>Monte Buey</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.181784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>Def y Justicia</td>\n",
              "      <td>MED</td>\n",
              "      <td>Gonzalo Castellani</td>\n",
              "      <td>35</td>\n",
              "      <td>8/10/1987</td>\n",
              "      <td>10-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1987</td>\n",
              "      <td>183.0</td>\n",
              "      <td>Buenos Aires</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.209486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248</th>\n",
              "      <td>Central Cba (SdE)</td>\n",
              "      <td>DEF</td>\n",
              "      <td>Gonzalo Goñi</td>\n",
              "      <td>24</td>\n",
              "      <td>8/16/1998</td>\n",
              "      <td>16-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1998</td>\n",
              "      <td>187.0</td>\n",
              "      <td>Pedro Luro</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.231109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>265</th>\n",
              "      <td>Newells</td>\n",
              "      <td>DEF</td>\n",
              "      <td>Guillermo Ortíz</td>\n",
              "      <td>30</td>\n",
              "      <td>8/9/1992</td>\n",
              "      <td>09-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1992</td>\n",
              "      <td>183.0</td>\n",
              "      <td>Rosario</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.209486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>279</th>\n",
              "      <td>Atl Tucuman</td>\n",
              "      <td>MED</td>\n",
              "      <td>Ignacio Maestro Puch</td>\n",
              "      <td>19</td>\n",
              "      <td>8/13/2003</td>\n",
              "      <td>13-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>2003</td>\n",
              "      <td>180.0</td>\n",
              "      <td>San Miguel de Tucuman</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.192957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>282</th>\n",
              "      <td>Platense</td>\n",
              "      <td>MED</td>\n",
              "      <td>Ignacio Schor</td>\n",
              "      <td>22</td>\n",
              "      <td>8/4/2000</td>\n",
              "      <td>04-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>2000</td>\n",
              "      <td>183.0</td>\n",
              "      <td>Buenos Aires</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.209486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>343</th>\n",
              "      <td>Rosario Central</td>\n",
              "      <td>DEF</td>\n",
              "      <td>Juan Cruz Komar</td>\n",
              "      <td>26</td>\n",
              "      <td>8/13/1996</td>\n",
              "      <td>13-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1996</td>\n",
              "      <td>190.0</td>\n",
              "      <td>Rosario</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.247024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>368</th>\n",
              "      <td>Talleres (C)</td>\n",
              "      <td>DEF</td>\n",
              "      <td>Julio Buffarini</td>\n",
              "      <td>34</td>\n",
              "      <td>8/18/1988</td>\n",
              "      <td>18-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1988</td>\n",
              "      <td>172.0</td>\n",
              "      <td>Gral. Cabrera</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.147494</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>410</th>\n",
              "      <td>Huracan</td>\n",
              "      <td>ARQ</td>\n",
              "      <td>Lucas Chaves</td>\n",
              "      <td>27</td>\n",
              "      <td>8/9/1995</td>\n",
              "      <td>09-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1995</td>\n",
              "      <td>179.0</td>\n",
              "      <td>Martín Coronado</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.187386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>Velez</td>\n",
              "      <td>DEL</td>\n",
              "      <td>Lucas Janson</td>\n",
              "      <td>28</td>\n",
              "      <td>8/16/1994</td>\n",
              "      <td>16-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1994</td>\n",
              "      <td>171.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.141664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>424</th>\n",
              "      <td>Argentinos</td>\n",
              "      <td>DEF</td>\n",
              "      <td>Lucas Villalba</td>\n",
              "      <td>28</td>\n",
              "      <td>8/19/1994</td>\n",
              "      <td>19-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1994</td>\n",
              "      <td>177.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.176150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>442</th>\n",
              "      <td>Belgrano</td>\n",
              "      <td>ARQ</td>\n",
              "      <td>Manuel Vicentini</td>\n",
              "      <td>32</td>\n",
              "      <td>8/29/1990</td>\n",
              "      <td>29-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1990</td>\n",
              "      <td>187.0</td>\n",
              "      <td>Santa Fé</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.231109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>459</th>\n",
              "      <td>Tigre</td>\n",
              "      <td>DEF</td>\n",
              "      <td>Martín Ortega</td>\n",
              "      <td>23</td>\n",
              "      <td>8/20/1999</td>\n",
              "      <td>20-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1999</td>\n",
              "      <td>183.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.209486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>467</th>\n",
              "      <td>Talleres (C)</td>\n",
              "      <td>DEF</td>\n",
              "      <td>Matías Catalán</td>\n",
              "      <td>30</td>\n",
              "      <td>8/19/1992</td>\n",
              "      <td>19-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1992</td>\n",
              "      <td>172.0</td>\n",
              "      <td>Mar del Plata</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.147494</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>487</th>\n",
              "      <td>Central Cba (SdE)</td>\n",
              "      <td>MED</td>\n",
              "      <td>Mauro Pittón</td>\n",
              "      <td>28</td>\n",
              "      <td>8/8/1994</td>\n",
              "      <td>08-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1994</td>\n",
              "      <td>174.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.159055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>502</th>\n",
              "      <td>Argentinos</td>\n",
              "      <td>DEF</td>\n",
              "      <td>Miguel Torrén</td>\n",
              "      <td>34</td>\n",
              "      <td>8/12/1988</td>\n",
              "      <td>12-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1988</td>\n",
              "      <td>179.0</td>\n",
              "      <td>Santa Fé</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.187386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>504</th>\n",
              "      <td>Banfield</td>\n",
              "      <td>DEL</td>\n",
              "      <td>Milton Gimenez</td>\n",
              "      <td>26</td>\n",
              "      <td>8/12/1996</td>\n",
              "      <td>12-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1996</td>\n",
              "      <td>184.0</td>\n",
              "      <td>Grand Bourg</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.214936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>545</th>\n",
              "      <td>Newells</td>\n",
              "      <td>MED</td>\n",
              "      <td>Pablo Pérez</td>\n",
              "      <td>37</td>\n",
              "      <td>8/10/1985</td>\n",
              "      <td>10-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1985</td>\n",
              "      <td>179.0</td>\n",
              "      <td>Rosario</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.187386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>571</th>\n",
              "      <td>Argentinos</td>\n",
              "      <td>DEL</td>\n",
              "      <td>Rodrigo Cabral</td>\n",
              "      <td>22</td>\n",
              "      <td>8/8/2000</td>\n",
              "      <td>08-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>2000</td>\n",
              "      <td>170.0</td>\n",
              "      <td>Mercedes</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.135798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>590</th>\n",
              "      <td>Instituto</td>\n",
              "      <td>DEL</td>\n",
              "      <td>Santiago Rodríguez</td>\n",
              "      <td>25</td>\n",
              "      <td>8/23/1997</td>\n",
              "      <td>23-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1997</td>\n",
              "      <td>171.0</td>\n",
              "      <td>San Luis</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.141664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>598</th>\n",
              "      <td>Union</td>\n",
              "      <td>ARQ</td>\n",
              "      <td>Sebastián Moyano</td>\n",
              "      <td>32</td>\n",
              "      <td>8/26/1990</td>\n",
              "      <td>26-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1990</td>\n",
              "      <td>188.0</td>\n",
              "      <td>Mendoza</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.236442</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>629</th>\n",
              "      <td>Huracan</td>\n",
              "      <td>MED</td>\n",
              "      <td>Valentín Burgoa</td>\n",
              "      <td>22</td>\n",
              "      <td>8/16/2000</td>\n",
              "      <td>16-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>2000</td>\n",
              "      <td>175.0</td>\n",
              "      <td>Guaymallén</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.164786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>635</th>\n",
              "      <td>Velez</td>\n",
              "      <td>DEL</td>\n",
              "      <td>Walter Bou</td>\n",
              "      <td>29</td>\n",
              "      <td>8/25/1993</td>\n",
              "      <td>25-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1993</td>\n",
              "      <td>176.0</td>\n",
              "      <td>Concordia</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.170484</td>\n",
              "    </tr>\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_filtered')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_filtered",
              "summary": "{\n  \"name\": \"df_filtered\",\n  \"rows\": 27,\n  \"fields\": [\n    {\n      \"column\": \"Equipo\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"Tigre\",\n          \"Independiente\",\n          \"Central Cba (SdE)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Puesto\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"DEL\",\n          \"ARQ\",\n          \"MED\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Jugador\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"Ignacio Maestro Puch\",\n          \"Lucas Janson\",\n          \"Ignacio Schor\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Edad\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 19,\n        \"max\": 37,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          34,\n          28,\n          22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Nacimiento\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"8/13/2003\",\n          \"8/16/1994\",\n          \"8/4/2000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dia_mes\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"02-ago\",\n          \"18-ago\",\n          \"10-ago\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nro_mes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 8,\n        \"max\": 8,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"a\\u00f1o\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1985,\n        \"max\": 2003,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          1988\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Altura_cm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.935071290647756,\n        \"min\": 170.0,\n        \"max\": 190.0,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          175.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ciudad_nac\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Grand Bourg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pais_nac\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Argentina\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Altura_log\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03315348678366484,\n        \"min\": 5.135798437050262,\n        \"max\": 5.247024072160486,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          5.1647859739235145\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Como hacemos un filtro un poco mas complicado?\n",
        "\n",
        "# Elijamos las personas que nacieron en Agosto, en Argentina y su altura es mayor que 150 cm\n",
        "\n",
        "df_filtered = df[(df['nro_mes'] == 8) & (df['Altura_cm'] > 150) & (df['Pais_nac'] == 'Argentina')]\n",
        "\n",
        "display(df_filtered)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sextO4P4y6i4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 896
        },
        "outputId": "2c8c3d1e-0a88-4c4b-8aed-ebc283c35474"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                Equipo Puesto               Jugador  Edad Nacimiento  \\\n",
              "0                Tigre    MED         Aaron Molinas    22   8/2/2000   \n",
              "67       Independiente    DEL       Braian Martínez    23  8/18/1999   \n",
              "146           Belgrano    DEF            Erik Godoy    29  8/16/1993   \n",
              "202           Platense    MED           Franco Diaz    22  8/28/2000   \n",
              "219           Belgrano    MED   Gabriel Compagnucci    31  8/29/1991   \n",
              "246     Def y Justicia    MED    Gonzalo Castellani    35  8/10/1987   \n",
              "248  Central Cba (SdE)    DEF          Gonzalo Goñi    24  8/16/1998   \n",
              "265            Newells    DEF       Guillermo Ortíz    30   8/9/1992   \n",
              "279        Atl Tucuman    MED  Ignacio Maestro Puch    19  8/13/2003   \n",
              "282           Platense    MED         Ignacio Schor    22   8/4/2000   \n",
              "343    Rosario Central    DEF       Juan Cruz Komar    26  8/13/1996   \n",
              "368       Talleres (C)    DEF       Julio Buffarini    34  8/18/1988   \n",
              "410            Huracan    ARQ          Lucas Chaves    27   8/9/1995   \n",
              "416              Velez    DEL          Lucas Janson    28  8/16/1994   \n",
              "424         Argentinos    DEF        Lucas Villalba    28  8/19/1994   \n",
              "442           Belgrano    ARQ      Manuel Vicentini    32  8/29/1990   \n",
              "459              Tigre    DEF         Martín Ortega    23  8/20/1999   \n",
              "467       Talleres (C)    DEF        Matías Catalán    30  8/19/1992   \n",
              "487  Central Cba (SdE)    MED          Mauro Pittón    28   8/8/1994   \n",
              "502         Argentinos    DEF         Miguel Torrén    34  8/12/1988   \n",
              "504           Banfield    DEL        Milton Gimenez    26  8/12/1996   \n",
              "545            Newells    MED           Pablo Pérez    37  8/10/1985   \n",
              "571         Argentinos    DEL        Rodrigo Cabral    22   8/8/2000   \n",
              "590          Instituto    DEL    Santiago Rodríguez    25  8/23/1997   \n",
              "598              Union    ARQ      Sebastián Moyano    32  8/26/1990   \n",
              "629            Huracan    MED       Valentín Burgoa    22  8/16/2000   \n",
              "635              Velez    DEL            Walter Bou    29  8/25/1993   \n",
              "\n",
              "                dia_mes  nro_mes   año  Altura_cm             Ciudad_nac  \\\n",
              "0    Esto es una prueba        8  2000      175.0                    NaN   \n",
              "67               18-ago        8  1999      170.0                    NaN   \n",
              "146              16-ago        8  1993      185.0           Buenos Aires   \n",
              "202              28-ago        8  2000      180.0                    NaN   \n",
              "219              29-ago        8  1991      178.0             Monte Buey   \n",
              "246              10-ago        8  1987      183.0           Buenos Aires   \n",
              "248              16-ago        8  1998      187.0             Pedro Luro   \n",
              "265              09-ago        8  1992      183.0                Rosario   \n",
              "279              13-ago        8  2003      180.0  San Miguel de Tucuman   \n",
              "282              04-ago        8  2000      183.0           Buenos Aires   \n",
              "343              13-ago        8  1996      190.0                Rosario   \n",
              "368              18-ago        8  1988      172.0          Gral. Cabrera   \n",
              "410              09-ago        8  1995      179.0        Martín Coronado   \n",
              "416              16-ago        8  1994      171.0                    NaN   \n",
              "424              19-ago        8  1994      177.0                    NaN   \n",
              "442              29-ago        8  1990      187.0               Santa Fé   \n",
              "459              20-ago        8  1999      183.0                    NaN   \n",
              "467              19-ago        8  1992      172.0          Mar del Plata   \n",
              "487              08-ago        8  1994      174.0                    NaN   \n",
              "502              12-ago        8  1988      179.0               Santa Fé   \n",
              "504              12-ago        8  1996      184.0            Grand Bourg   \n",
              "545              10-ago        8  1985      179.0                Rosario   \n",
              "571              08-ago        8  2000      170.0               Mercedes   \n",
              "590              23-ago        8  1997      171.0               San Luis   \n",
              "598              26-ago        8  1990      188.0                Mendoza   \n",
              "629              16-ago        8  2000      175.0             Guaymallén   \n",
              "635              25-ago        8  1993      176.0              Concordia   \n",
              "\n",
              "      Pais_nac  Altura_log  \n",
              "0    Argentina    5.164786  \n",
              "67   Argentina    5.135798  \n",
              "146  Argentina    5.220356  \n",
              "202  Argentina    5.192957  \n",
              "219  Argentina    5.181784  \n",
              "246  Argentina    5.209486  \n",
              "248  Argentina    5.231109  \n",
              "265  Argentina    5.209486  \n",
              "279  Argentina    5.192957  \n",
              "282  Argentina    5.209486  \n",
              "343  Argentina    5.247024  \n",
              "368  Argentina    5.147494  \n",
              "410  Argentina    5.187386  \n",
              "416  Argentina    5.141664  \n",
              "424  Argentina    5.176150  \n",
              "442  Argentina    5.231109  \n",
              "459  Argentina    5.209486  \n",
              "467  Argentina    5.147494  \n",
              "487  Argentina    5.159055  \n",
              "502  Argentina    5.187386  \n",
              "504  Argentina    5.214936  \n",
              "545  Argentina    5.187386  \n",
              "571  Argentina    5.135798  \n",
              "590  Argentina    5.141664  \n",
              "598  Argentina    5.236442  \n",
              "629  Argentina    5.164786  \n",
              "635  Argentina    5.170484  "
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              "      <th>Puesto</th>\n",
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              "      <th>Edad</th>\n",
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              "      <td>Aaron Molinas</td>\n",
              "      <td>22</td>\n",
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              "      <th>67</th>\n",
              "      <td>Independiente</td>\n",
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              "      <th>146</th>\n",
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              "      <td>1993</td>\n",
              "      <td>185.0</td>\n",
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              "      <td>Argentina</td>\n",
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              "    <tr>\n",
              "      <th>202</th>\n",
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              "      <td>MED</td>\n",
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              "      <td>22</td>\n",
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              "      <td>Argentina</td>\n",
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              "    <tr>\n",
              "      <th>219</th>\n",
              "      <td>Belgrano</td>\n",
              "      <td>MED</td>\n",
              "      <td>Gabriel Compagnucci</td>\n",
              "      <td>31</td>\n",
              "      <td>8/29/1991</td>\n",
              "      <td>29-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1991</td>\n",
              "      <td>178.0</td>\n",
              "      <td>Monte Buey</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.181784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>Def y Justicia</td>\n",
              "      <td>MED</td>\n",
              "      <td>Gonzalo Castellani</td>\n",
              "      <td>35</td>\n",
              "      <td>8/10/1987</td>\n",
              "      <td>10-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1987</td>\n",
              "      <td>183.0</td>\n",
              "      <td>Buenos Aires</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.209486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248</th>\n",
              "      <td>Central Cba (SdE)</td>\n",
              "      <td>DEF</td>\n",
              "      <td>Gonzalo Goñi</td>\n",
              "      <td>24</td>\n",
              "      <td>8/16/1998</td>\n",
              "      <td>16-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1998</td>\n",
              "      <td>187.0</td>\n",
              "      <td>Pedro Luro</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.231109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>265</th>\n",
              "      <td>Newells</td>\n",
              "      <td>DEF</td>\n",
              "      <td>Guillermo Ortíz</td>\n",
              "      <td>30</td>\n",
              "      <td>8/9/1992</td>\n",
              "      <td>09-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1992</td>\n",
              "      <td>183.0</td>\n",
              "      <td>Rosario</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.209486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>279</th>\n",
              "      <td>Atl Tucuman</td>\n",
              "      <td>MED</td>\n",
              "      <td>Ignacio Maestro Puch</td>\n",
              "      <td>19</td>\n",
              "      <td>8/13/2003</td>\n",
              "      <td>13-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>2003</td>\n",
              "      <td>180.0</td>\n",
              "      <td>San Miguel de Tucuman</td>\n",
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              "      <td>5.192957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>282</th>\n",
              "      <td>Platense</td>\n",
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              "      <td>Ignacio Schor</td>\n",
              "      <td>22</td>\n",
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              "      <td>04-ago</td>\n",
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              "    <tr>\n",
              "      <th>343</th>\n",
              "      <td>Rosario Central</td>\n",
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              "      <td>Juan Cruz Komar</td>\n",
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              "      <td>8/13/1996</td>\n",
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              "    <tr>\n",
              "      <th>368</th>\n",
              "      <td>Talleres (C)</td>\n",
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              "      <td>Julio Buffarini</td>\n",
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              "      <td>1988</td>\n",
              "      <td>172.0</td>\n",
              "      <td>Gral. Cabrera</td>\n",
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              "    <tr>\n",
              "      <th>410</th>\n",
              "      <td>Huracan</td>\n",
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              "      <td>Lucas Chaves</td>\n",
              "      <td>27</td>\n",
              "      <td>8/9/1995</td>\n",
              "      <td>09-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1995</td>\n",
              "      <td>179.0</td>\n",
              "      <td>Martín Coronado</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.187386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>Velez</td>\n",
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              "      <td>Lucas Janson</td>\n",
              "      <td>28</td>\n",
              "      <td>8/16/1994</td>\n",
              "      <td>16-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1994</td>\n",
              "      <td>171.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.141664</td>\n",
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              "    <tr>\n",
              "      <th>424</th>\n",
              "      <td>Argentinos</td>\n",
              "      <td>DEF</td>\n",
              "      <td>Lucas Villalba</td>\n",
              "      <td>28</td>\n",
              "      <td>8/19/1994</td>\n",
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              "      <td>8</td>\n",
              "      <td>1994</td>\n",
              "      <td>177.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.176150</td>\n",
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              "    <tr>\n",
              "      <th>442</th>\n",
              "      <td>Belgrano</td>\n",
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              "      <td>Manuel Vicentini</td>\n",
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              "      <td>8/29/1990</td>\n",
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              "      <td>1990</td>\n",
              "      <td>187.0</td>\n",
              "      <td>Santa Fé</td>\n",
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              "    <tr>\n",
              "      <th>459</th>\n",
              "      <td>Tigre</td>\n",
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              "      <td>Martín Ortega</td>\n",
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              "      <td>8</td>\n",
              "      <td>1999</td>\n",
              "      <td>183.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Argentina</td>\n",
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              "    <tr>\n",
              "      <th>467</th>\n",
              "      <td>Talleres (C)</td>\n",
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              "      <td>Matías Catalán</td>\n",
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              "      <td>8</td>\n",
              "      <td>1992</td>\n",
              "      <td>172.0</td>\n",
              "      <td>Mar del Plata</td>\n",
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              "      <td>5.147494</td>\n",
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              "      <th>487</th>\n",
              "      <td>Central Cba (SdE)</td>\n",
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              "      <td>Mauro Pittón</td>\n",
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              "      <td>8/8/1994</td>\n",
              "      <td>08-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1994</td>\n",
              "      <td>174.0</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>5.159055</td>\n",
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              "    <tr>\n",
              "      <th>502</th>\n",
              "      <td>Argentinos</td>\n",
              "      <td>DEF</td>\n",
              "      <td>Miguel Torrén</td>\n",
              "      <td>34</td>\n",
              "      <td>8/12/1988</td>\n",
              "      <td>12-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1988</td>\n",
              "      <td>179.0</td>\n",
              "      <td>Santa Fé</td>\n",
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              "      <td>5.187386</td>\n",
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              "    <tr>\n",
              "      <th>504</th>\n",
              "      <td>Banfield</td>\n",
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              "      <td>Milton Gimenez</td>\n",
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              "      <td>8</td>\n",
              "      <td>1996</td>\n",
              "      <td>184.0</td>\n",
              "      <td>Grand Bourg</td>\n",
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              "      <td>5.214936</td>\n",
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              "      <th>545</th>\n",
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              "      <td>Pablo Pérez</td>\n",
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              "      <td>8/10/1985</td>\n",
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              "      <td>1985</td>\n",
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              "      <th>571</th>\n",
              "      <td>Argentinos</td>\n",
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              "      <td>2000</td>\n",
              "      <td>170.0</td>\n",
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              "    <tr>\n",
              "      <th>590</th>\n",
              "      <td>Instituto</td>\n",
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              "      <td>25</td>\n",
              "      <td>8/23/1997</td>\n",
              "      <td>23-ago</td>\n",
              "      <td>8</td>\n",
              "      <td>1997</td>\n",
              "      <td>171.0</td>\n",
              "      <td>San Luis</td>\n",
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              "      <td>5.141664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>598</th>\n",
              "      <td>Union</td>\n",
              "      <td>ARQ</td>\n",
              "      <td>Sebastián Moyano</td>\n",
              "      <td>32</td>\n",
              "      <td>8/26/1990</td>\n",
              "      <td>26-ago</td>\n",
              "      <td>8</td>\n",
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              "      <td>188.0</td>\n",
              "      <td>Mendoza</td>\n",
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              "      <td>5.236442</td>\n",
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              "      <th>629</th>\n",
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              "      <td>Valentín Burgoa</td>\n",
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              "      <td>2000</td>\n",
              "      <td>175.0</td>\n",
              "      <td>Guaymallén</td>\n",
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              "      <td>5.164786</td>\n",
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              "      <th>635</th>\n",
              "      <td>Velez</td>\n",
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              "      <td>29</td>\n",
              "      <td>8/25/1993</td>\n",
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              "      <td>1993</td>\n",
              "      <td>176.0</td>\n",
              "      <td>Concordia</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>5.170484</td>\n",
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              "<div id=\"df-68bbb508-5cc5-4773-bcb4-510521259123\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-68bbb508-5cc5-4773-bcb4-510521259123')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-68bbb508-5cc5-4773-bcb4-510521259123 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_3dff2d31-bd33-42c2-9a53-5afb3c10e03b\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_filtered')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_3dff2d31-bd33-42c2-9a53-5afb3c10e03b button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_filtered');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_filtered",
              "summary": "{\n  \"name\": \"df_filtered\",\n  \"rows\": 27,\n  \"fields\": [\n    {\n      \"column\": \"Equipo\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"Tigre\",\n          \"Independiente\",\n          \"Central Cba (SdE)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Puesto\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"DEL\",\n          \"ARQ\",\n          \"MED\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Jugador\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"Ignacio Maestro Puch\",\n          \"Lucas Janson\",\n          \"Ignacio Schor\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Edad\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 19,\n        \"max\": 37,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          34,\n          28,\n          22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Nacimiento\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"8/13/2003\",\n          \"8/16/1994\",\n          \"8/4/2000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dia_mes\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"Esto es una prueba\",\n          \"18-ago\",\n          \"10-ago\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nro_mes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 8,\n        \"max\": 8,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"a\\u00f1o\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1985,\n        \"max\": 2003,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          1988\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Altura_cm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.935071290647756,\n        \"min\": 170.0,\n        \"max\": 190.0,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          175.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ciudad_nac\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Grand Bourg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pais_nac\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Argentina\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Altura_log\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03315348678366484,\n        \"min\": 5.135798437050262,\n        \"max\": 5.247024072160486,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          5.1647859739235145\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Si quiero acceder a la fecha de nacimiento, del primer jugador de los que tenemos filtrados, como puedo hacer?\n",
        "\n",
        "# Primero el numero de fila, luego la columna a la que queremos entrar\n",
        "df_filtered.loc[0, 'dia_mes'] = 'Esto es una prueba'\n",
        "\n",
        "display(df_filtered)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z9ebY5arG7r1"
      },
      "source": [
        "## Lambda functions\n",
        "En Python existe otra forma de declarar funciones además de la que vimos anteriormente, que es utilizando la palabra `lambda`. La función `promediar_dos_numeros` que vimos más temprano se escribiría de la siguiente forma\n",
        "\n",
        "```python\n",
        "promedio = lambda num1, num2: (num1 + num2) / 2\n",
        "\n",
        "```\n",
        "\n",
        "Los `lambda` son funciones \"anónimas\", y fueron introducidas a Python para código que sigue un estilo de programación llamado \"funcional\". Generalmente, la idea no es asignarla a una variable, sino usarla directamente dentro de otra función. Por ejemplo, la función `filter`:\n",
        "\n",
        "```python\n",
        "pares = list(filter(lambda x: x % 2 == 0, range(10)))\n",
        "```\n",
        "\n",
        "Lo introducimos por completitud, por si se la encuentran leyendo el código de otra persona, pero lo recomendado es definir funciones \"normales\" con `def`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TCo6ZF4g962g"
      },
      "source": [
        "---\n",
        "\n",
        "# Histogramas\n",
        "\n",
        "Una cosita más que nos va a ser útil a la hora de dejar el Oriyin sin instalar es poder hacer histogramas. Con _pyplot_ eso lo podemos obtener de la función _hist_.\n",
        "\n",
        "Recordemos que en un histograma dividimos una serie de datos en rangos y contamos cuántos de nuestros datos caen en cada rango. A esos rangos se los llama _bins_.\n",
        "\n",
        "_hist_ toma como argumentos un array de números, en cuántos _bins_ queremos dividir a nuestro eje x y algunas otras opciones de color como constante de normalización y color de las barras.\n",
        "\n",
        "Hagamos un histograma simple de un set gaussiano. Para eso, creemos datos sintéticos usando ```random.normal``` de _NumPy_ . Esto de crear datos lo hacemos acá a modo de ejemplo, en la vida real uno importaria algun dataset de las formas que ya hemos visto."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KQj6HJf1962h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 604
        },
        "outputId": "d57e77cf-407a-404c-cf35-04a223442b96"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "n:\n",
            "[  2.   0.   2.  12.  24.  55. 101. 190. 245. 279. 291. 291. 220. 137.\n",
            "  77.  46.  18.   6.   1.   3.]\n",
            "\n",
            "bins:\n",
            "[ 42.26532864  47.92479703  53.58426543  59.24373383  64.90320223\n",
            "  70.56267062  76.22213902  81.88160742  87.54107581  93.20054421\n",
            "  98.86001261 104.51948101 110.1789494  115.8384178  121.4978862\n",
            " 127.1573546  132.81682299 138.47629139 144.13575979 149.79522819\n",
            " 155.45469658]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Distribución gaussiana centrada en 100, con 15 de desviación estándar\n",
        "data = np.random.normal(100, 15, 2000)\n",
        "\n",
        "n, bins, patches = plt.hist(data, bins=20, edgecolor='black', facecolor='royalblue', alpha=0.75)\n",
        "# n la variable n se encuentran los datos del histograma\n",
        "# bins es un vector con los bordes de los rangos de datos\n",
        "# patches no nos interesa en general\n",
        "\n",
        "# OBS1: Si no saben qué cantidad de bines elegir, pueden dejarle la\n",
        "# eleccion a matplotlib, usando: bins='auto'\n",
        "\n",
        "# OBS2: Si quieren forzar bines específicos, pueden pasarle un array o lista\n",
        "# con los comienzos de cada bin. Ej:\n",
        "#\n",
        "#    Esto da bines de ancho 0.1:\n",
        "#    bins = [1, 1.1, 1.2, 1.3, 1.4, 1.5]\n",
        "#\n",
        "#    Indistintamente pueden usar cosas que ya vimos para generar ararys:\n",
        "#    bins = np.arange(1, 1.6, 0.1)\n",
        "\n",
        "print(f'n:\\n{n}\\n')\n",
        "print(f'bins:\\n{bins}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K7PJtxW0962h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "6b23945c-252a-4d53-96e1-c18da6e002d1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Si lo quisieramos normalizar el area hay que agregar una opcion mas que es density=True como para\n",
        "# que entienda que queremos ver la \"densidad de probabilidad\", forma cheta para decir: normalizar el area.\n",
        "\n",
        "n, bins, patches = plt.hist(data, bins=20, edgecolor='black', facecolor='royalblue', alpha=0.75, density=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M3zTg3Fy962h"
      },
      "source": [
        "Y ya que estamos, para concientizar acerca de los peligros a la hora de la elección de *bins*, graficamos algunos histogramas superpuestos."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q2C6GQa1962h",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e71749f-fe25-4e18-bb64-5e0fc944fe11"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "n, bins, patches = plt.hist(data, bins=100, density=True, edgecolor='black', facecolor='royalblue', alpha=0.75)\n",
        "n, bins, patches = plt.hist(data, bins=20 , density=True, edgecolor='black', facecolor='purple'  , alpha=0.5)\n",
        "n, bins, patches = plt.hist(data, bins=2 , density=True, edgecolor='black', facecolor='indianred'  , alpha=0.3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "os9gI8VL962h"
      },
      "source": [
        "### Ejercicio 6\n",
        "\n",
        "La función `randn` que usamos nos brinda números aleatorios distribuidos de manera gaussiana (distribución normal).\n",
        "\n",
        "1. Con lo mostrado arriba, cree un histograma con una distribución normal (igual que como hicimos ya) y superpongale una curva teórica de una gausiana.\n",
        "\n",
        "    __AYUDA__: Busquen \"plot normal distribution python\", en particular busquen la librería `scipy`.\n",
        "\n",
        "2. Haga el mismo ejercicio de recién pero con números aleatorios distribuidos de manera _uniforme_\n",
        "\n",
        "3. Lo mismo pero con números con una distribución pareto, o la distribución que prefieran.\n",
        "\n",
        "_Info:_ https://docs.scipy.org/doc/numpy-1.14.0/reference/routines.random.html"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jkcydFNN962i"
      },
      "source": [
        "---\n",
        "## Resolución de un sistema de ecuaciones diferenciales ordinarias (ODE)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CmCFnDGi962i"
      },
      "source": [
        "Vamos con un caso simple y conocido por la mayoría: el infaltable problema del péndulo. Arrancamos desde donde sabemos todos. (Donde sabemos todos? Si no saben de donde salió esto no se hagan problema, es solo algo físico, no lo podemos evitar)\n",
        "\n",
        "$$ \\frac{d^2\\theta}{dt^2} + \\omega^2 \\theta = 0$$\n",
        "\n",
        "con $\\omega^2 = \\frac{g}{l}$\n",
        "\n",
        "Para resolver numéricamente este problema, proponemos utilizar la función `odeint` de la biblioteca `scipy.integrate` (Otra opción podría ser la función `solve_ivp`, para problemas de valores iniciales), y esa función utiliza un método no taaaaan distinto al [método de Euler](https://es.wikipedia.org/wiki/M%C3%A9todo_de_Euler) para la solución de ODE's, (inserte conocimientos de Cálculo Numérico aquí, sí, dale, cursala antes de recibirte) por lo que, para un problema con derivadas de segundo orden debemos armar un sistema de ecuaciones de primer orden.\n",
        "\n",
        "Sea $\\phi = \\dot{\\theta} \\rightarrow \\dot{\\phi} = \\ddot{\\theta}$\n",
        "\n",
        "Nos queda entonces"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "upfUBhg0962i"
      },
      "source": [
        "$$\n",
        "\\begin{align}\n",
        "\\dot{\\theta} = \\phi \\\\\n",
        "\\dot{\\phi} = -\\omega^2 \\theta\n",
        "\\end{align}\n",
        "$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dDaJEPfA962i"
      },
      "source": [
        "o bien"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Zgo72_G962i"
      },
      "source": [
        "$$\n",
        "\\begin{equation}\n",
        "\\begin{pmatrix}\n",
        "\\dot{\\theta} \\\\\n",
        "\\dot{\\phi}\n",
        "\\end{pmatrix}\n",
        "=\n",
        " \\begin{pmatrix}\n",
        "  0 & 1  \\\\\n",
        "  -\\omega^2 & 0\n",
        " \\end{pmatrix}\n",
        "\\begin{pmatrix}\n",
        "\\theta \\\\\n",
        "\\phi\n",
        "\\end{pmatrix}\n",
        "\\end{equation}\n",
        "$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U2iDy-Ds962i"
      },
      "source": [
        "o también\n",
        "\n",
        "$$ \\dot{\\vec{X}} = A \\vec{X} $$\n",
        "\n",
        "Escencialmente, nuestro `odeint` va a intentar resolver ese sistema para distintos $t$ mientras le hayamos dado un valor inicial de donde comenzar. Nada demasiado extraño. Así que lo que la función va a necesitar es una función que tome como primer argumento $\\vec{X}$, segundo argumento $t$ (por ser la variable independiente) y luego los demás parametros que necesite (en este caso sean $g$ y $l$) y opere para obtener $\\dot{\\vec{X}}$."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J8BJtVwJ962i"
      },
      "outputs": [],
      "source": [
        "from scipy.integrate import odeint\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FZEzyKKf962i",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e41dcc31-155d-43d3-c52e-cc626496285c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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BQCAQCAzC5/Ph9XrNHoZl8Hq9uFwuWlpa8Pl8vfqs2+3G6XT2ewxCERIIBAKBQGdkWaa0tJTa2lqzh2IpZFkmIyODoqKiPtXqS0hIICMjo191/oQiJBAIBAKBzqhKUFpaGlFRUaJAbwC/309DQwMxMTFnLXp4OrIs09TURHl5OQCZmZl9HoNQhAQCgUAg0BGfzxdUgpKTk80ejqXw+/20tbURERHRK0UIIDIyEoDy8nLS0tL67CYTwdICgUAgEOiIGhMUFRVl8kgGHuo57U/clVCEBAKBQCAwAOEO0x4tzqlQhAQCgUAgEAxahCIkEAgEAoGgR3z00UdIkjSgst+EIiQQCAQCgaBLFi5cyPe+9z3N9ytJEq+99prm++0LQhESCAQCweDC7wdvi9mjEFgEoQgJBh/rH4F/fxn2vCQWw8GKtxkOrIS3vg9bnzN7NAIjaayCZy6E/8uFvS+bPRpLc9NNN7F27Voef/xxJElCkiQKCwsB2LZtG+eddx5RUVHMnj2bgwcPdvrs66+/zrRp04iIiGDkyJE88MADtLe3A5CTkwPA1VdfjdPpZNKkSQAcPXqUK6+8kvT0dGJiYjj//PN5//33df+eoo6QYHCx/y1Y84Dy/MBbEJEAk66DqV+FzEmmDk2gM7522PcK7H8DjqwBb1PHe0kjYeRC04YmMIimavjblVC2R/n/pVugZDcsug8c/W/V0BtkWabZ27uWEloQ6Xb2ONPq8ccf59ChQ0yYMIGf/exnAOzbtw+A//3f/+Xhhx8mNTWVO+64g1tuuYWNGzcCsH79em644QZ+97vfMW/ePI4ePcrtt98OwP3338+nn35KWloaf/nLX1i6dClNTcpcbGhoYPny5fziF78gPDycv/3tb1x++eUcPHiQ4cOHa30qgghFSDB4aCiDN7+jPB+xAKqOgOckbHla+bvoJzD/LnPHKNCPlT+Abc93/B+fBbEZUPwpvP5t+ObHEB5r2vAEOtNc06EERafB2Mth659h42NQugc+/2eITDRuOF4f4+57z7DjqXz2s2VEhfXs0h8fH09YWBhRUVFkZGQAcODAAQB+8YtfsGDBAgDuueceLr30UlpaWoiIiOCBBx7gnnvu4cYbbwRg5MiR/PznP+eHP/wh999/P6mpqUBHewyPxwPA5MmTmTx5cvD4P//5z3n11Vd54403uPPOO7U5AV0gXGOCwYEs43zru9BUBekT4cv/he/tga+8rCyIAOsfVu4YBQOPmuOw/e/K8znfg9vXKvL/6muQMBzqTsDq+8wcoUBPmmvhb1dB6W6ISoEb34TLHoFr/gyuSDi6Bp6+ECoPmz1S26C6s6CjvYXa7mLXrl387Gc/IyYmJvh32223UVJSErT+dEVDQwN33XUXY8eOJSEhgZiYGPbv38+JEyd0/S7CIiQYFORUfoCj+H1whsM1z4ArXHkjbzHkLoI/zVPuCj/9Myy429zBCrTn49+B7IORF8KSBzpeD4+BK34Pf7tCiRUad6VwkQ00WhvgH5+Dkp0QlawoQWljlPcmfh5SRikxgzUF8MZ34JZ3DBlWpNvJZz9bZsixTj+uFrjd7uBz1dXm9/sBRaF54IEH+NznPnfG5yIiIrrd51133cXq1av57W9/S15eHpGRkXz+85+nra1NkzF3h1CEBAOfqsOMP/kv5fmSByBtbOf3JUmxErz8Ndj8FMy+E9yRhg9ToBMN5bDjH8rzeSvOfH/kAjjva4qbRLjIBh6fPgsnt0FkEtzwBqSP6/x+5iS4eSU8PglOfKxYhVLydR+WJEk9dlGZSVhYGD5f72KZpk2bxsGDB8nLy+t2G7fbfcZ+N27cyE033cTVV18NKAqVGpytJ8I1JhjY+Lw4X/8GLrkN/4gFMOPrXW837iqIHw5NlbDrX4YOUaAzn/wB2ltg6HmQM6/rbZb8TLjIBiKyDLv+rTxffD9kTOh6u4QsyF+qPN/xd2PGZhNycnLYvHkzhYWFVFZWBq0+Z+O+++7jb3/7Gw888AD79u1j//79/Pvf/+bHP/5xp/2uWbOG0tLSYHHG/Px8XnnlFXbu3MmuXbu4/vrre3S8/iIUIcHAZte/cJTspM0Zje+y30N33Y2dLpj1TeX5x78Hv/HZHAIdaK5V3J2gWIO6y5YJj4Ern1Seb30Ojn9syPAEOlO2Fyr2gzNMudk5G1O/qjzu/Bf4+t7Ac6Bx11134XQ6GTduHKmpqT2K11m2bBlvvfUWq1at4vzzz+eCCy7g0UcfJTs7O7jNww8/zOrVq8nOzmb+/PkAPPLIIyQmJjJ79mwuv/xyli1bxrRp03T7birWt8sJBP1h32sAHElbTn5c5tm3nfpV+OhXUH0UDrwN467Qf3wCffn0WWj1QOpYGHXJ2bcdMR+mfAV2/kNxpWXPNmaMAv3Y/R/lcdTFEJlw9m1HLVOyyRrL4dB7kGd8/I4VGTVqFJs2ber02k033dTp/ylTpiDLcqfXli1bxrJl3Z/Dyy+/nMsvvxy/3x/MGsvJyeGDDz7otN23vvWtfoy+ZwiLkGDg0lIHBesAKEk479zbh8fA+bcqzzc+rpjVBfalrQk++aPyfO73u7cGhjL5C8rjoXeFVdDu+H1K0VSASV849/ZON0z+ovJcuMcGFUIREgxcDq8Gvxc5ZRQNEeewBqnM/LqSWXZyK5zYdO7tBdZlxz+UmK+E4TDhmp59ZvhspchmUxUUbdZ1eAKdKVwP9SWKPPOX9Owzqnvs8Crls4JBgVCEBAOX/W8C4B+1vOefiUmDKV9Snm/8nQ6DEhiCz6ukzAPM/o4SA9YTnC7FRQKKe1RgX3a/qDyOv7qjXMa5SB0FWReA7MehutUEAx6hCAkGJt4WOKL0qJFH90IRApj1bUCCQ+9AdYH2YxPoT9FmqCtSUqanfqV3n1V/LwfeFu5Ru9LWBJ+9oTzviVsslGk3AODY9U8h/0GCUIQEA5OCtdDWALFDkDOn9u6zKXmQPUd5fvSDs28rsCbH1iqPeYt6XxMqb5GSZVRTABUHtB+bQH8OvQNt9YpbNGtm7z47/ioIi0WqKSC54eA5NxfYH6EICQYmAbcYYy7tPmX6bIxUeuiowdYCm3HsI+VxxILefzY8tqO6tHCP2RPVLTbxup4FyYcSFg0TlIrI2VVrNR6YwIoIRUgw8PD74GCgTP7Yy/q2jxFKXQsK14MBBb0EGtLiUSoJQ4dC21tU99jBldqMSWAcjZVBtziTruvbPgLusczaT5Xfk2BAIxQhwcCjaLOSLRSR0OHi6i1DpoE7WskeKv9M0+EJdOb4x0pfsaSRimukL4wO1Bw6uQ08InvIVux7FfztkDkFUkf3bR9DpyMn5+GS25COb9B0eALrIRQhwcBj/1vK46iLldogfcEVBtmzlOfCPWYvCgLujL64xVRiM5SWHKDEmwjsg+oW622QdCiShD97rvK0SJTRGOgIRUgwsJBlOBBQhPrqFlMZIeKEbIkaH9RXt5jKGDV7TLjHbENrvVIDDGDclf3alRwIspZEPamzkpOTw2OPPdbt+4WFhUiSxM6dOw0bU28RipBgYFG2F2qPgysSchf1b19qnNDxjeBr7//YBPrTUN7hysyZ3799jb5UeSxYq1xgBdbn5DaQ/UoD5fih/dqVnKVYhKXS3dDWqMXoBiVZWVmUlJQwYUI3DW8tgK0UoXXr1nH55ZczZMgQJEnitddeO+v2H330EZIknfFXWlpqzIAFxqNm+eReBGFR/dtXxkQlzqjVAyU7+zsygRGo1ruMSRCd3L99pY5W4ox8bXBkTf/HJtCfoi3KY9aM/u8rfhhN7iQkfzsUb+3//gYpTqeTjIwMXC7rtja1lSLU2NjI5MmTefLJJ3v1uYMHD1JSUhL8S0tL02mEAtPRyi0G4HBCjhInEIw7EVgbrdxioJRdENlj9kJ1Y/W2dlA3VMUEgq1PfKLJ/uzIwoULufPOO7nzzjuJj48nJSWFn/zkJ52arDY1NXHLLbcQGxvL8OHDefrpp4PvCdeYxlxyySU8+OCDXH311b36XFpaGhkZGcE/R2/rSgjsQUsdlO5Vnuf1sLfQuRBxQvZBljsKKY5YqM0+xwTcY4dXiTIKVsfvh6JPledaWISA6uhRypMTH2uyv07IsuJyM/qvD9Wy//rXv+JyudiyZQuPP/44jzzyCM8++2zw/YcffpjzzjuPHTt28M1vfpNvfOMbHDxon2KU1rVVaciUKVNobW1lwoQJ/PSnP2XOnO5TqltbW2ltbQ3+7/EoNSS8Xi9er1ezMan70nKfgx2paCsuZOT44bSHJ8Bp57hP5zprNm5APvEJ7c0NPe9ZNAgx/TddU4C77gSyw037kPOC8u8X6VNwuSKQmmvwlh+E5Lz+77OfmH6erUrFAdytdcjuKNqTR/db/l6vl6oYRRGSiz6lvbUZHH27ZHq9XmRZxu/341cV6rZGHL8a1q8x9gX/PcVK0chekJWVxcMPP4wkSeTn57N7924effRRvva1rwGKkeKOO+4A4O677+bRRx9lzZo15OfnB79vp+8egmpZUs9Pr7+P348sy3i9XpxOZ6f3ejpHBrQilJmZyVNPPcV5551Ha2srzz77LAsXLmTz5s1Mmzaty8889NBDPPDAA2e8vmrVKqKi+hlz0gWrV6/WfJ+DlfzSNxkHnJIy2LryTFdGn861LLPMFU9Eex2bX36Sqtgx/R/oAMes33R25YdMAaqiRrLxfe1cmfPChpLUfpTd7zxPcdJszfbbX8Ta0RlV/pXh2Xz87iptdhoxlDZnFGHeRj5+5Slqo0b2aTcul4uMjAwaGhpoa2tTXvQ2kaDNKHuFp74e3L4eb9/e3s60adOor+9IGJg8eTKPPPIINTU1+P1+Ro0aFTQaAKSmplJcXIzH46GhoQFQQltCtzmd0P33hra2Npqbm1m3bh3t7Z2TWpqamnq0jwGtCI0ePZrRozsKas2ePZujR4/y6KOP8ve//73Lz9x7772sWLEi+L/H4yErK4ulS5cSFxen2di8Xi+rV69myZIluN19rHUj6ITzpf9ACWRMv5TlF3Q0Wu3vuXZ6X4d9rzArow3/gl42cB1EmP2bdr7yMgCJ065i+Tzt5ORwroetR5mSJjNpqfnyN/s8WxXnm+9AESRNvoTlC/svJ/U8O7Nnw7H3mTvchX9G3/bb0tJCUVERMTExREREKC/KsYp1xmDi3FG9ajvkcrlwu92drn+RkUr/vri4OBwOB7GxsZ3eD/1MTEwMANHR0V1eQ2VZpr6+ntjYWKQ+tENqaWkhMjKS+fPnd5zbAGdTvDp9x14f1ebMmDGDDRu6rxQaHh5OePiZ7g+3263LoqPXfgclgcwuZ9b5OLs4p30+17kXwr5XcJ7Y2OV+BZ0x5Tft90OgArAz7yJt5ZR1Hmx9BmfpTkvJX6wdp3FSiQ9yZs/SVk4BRchZvBnnnG/3aRc+nw9JknA4HJ1jVJ2xGg1SX7Zs2dJp3Fu2bCE/Pz/4+1O/Wyinf98zvnsA1R3W1T56gsPhQJKkLudDT+fHoIsa3rlzJ5mZmWYPQ6A1nhLwnATJAZmTtd23Wk+o+FNobdB23wJtKNurtEMJi4Gh07Xdt7q/kt2inpRVaayCqiPK82HnabprtbAiJz7pU6DxQODEiROsWLGCgwcP8q9//YsnnniC7373u2YPSzNsZRFqaGjgyJEjwf8LCgrYuXMnSUlJDB8+nHvvvZeTJ0/yt7/9DYDHHnuMESNGMH78eFpaWnj22Wf54IMPWLVKI/+xwDqc2q48po6F8Bht952Yo/Ssqj2hLIb5i7Xdv6D/qOUNsmf3va1KdyTlQnicUk+qYr9SX0pgLYoD9YNSRkNUkqa7ljOngDMcGiug6iikmB8wbzQ33HADzc3NzJgxA6fTyXe/+11uv/12s4elGbZShLZu3cqFF14Y/F+N5bnxxht5/vnnKSkp4cSJE8H329ra+MEPfsDJkyeJiopi0qRJvP/++532IRggqN3Gh3YdBN9vRsyHHf9QLrhCEbIexwPpzSP6WU26KxwOGDJFKaFwcrtQhKxIsH6QNmnznXCFK1bBEx/DiU2DUhFyu9089thj/PGPfzzjvcLCwjNeC60ZlJOT06nmkBWxlSK0cOHCs57Q559/vtP/P/zhD/nhD3+o86gEluBkwCKklyKUM09RhETfIWtSukd51NotpjJkmqIIndoO02/U5xiCvhOsKK1NIcUzGH5BhyI07av6HENgGoMuRkgwAPH7O1xjel0I1bijsn2isJ7VaKqGuiLlefp4fY6hKtiq5VFgHXzeDrnopQhlB8omnBCd6AcitrIICQRdUn1MqSrtioC0cfocIzlfiRNoa4DaQqUHlcAalAWqiSfmQES8PscYElCEyj4DbzO4I/U5jqD3lO6G9haITNSv4OWw8wFJWWvqyyA2XZ/jWJCPPvrI7CHojrAICeyPejeYMUn7QFkVpwvSxirPVTeMwBqU7FYe9YzdiR8G0akg+4T8rYbqFhs2Q4nn0oPIhA5ro7AKDTiEIiSwP8FAaZ3cYirqhVZcCK2FKo+MSfodQ5I6rEJqPJrAGugZKB3K8FnKYz8UIasHDdsRLc6pUIQE9kfv+CCVoCK0V9/jCHqHEYoQdMQJnRKKkKXQO1BaRd1/H+LE1MJ+PW35IOg56jntT3FRESMksDftbR2uEb0yxlSERch6eFugMtDlWu+0dmERsh51xYFCqk4D5v8E5bF8v5Iw0Qs3nNPpJCEhgfLycgCioqL61E5iIOL3+2lra6OlpaVXlaVlWaapqYny8nISEhLOaLjaG4QiJLA35fvA1woRCfoHMKsxAp5iJVNJ48Jtgj5QcQD87RCZBHFD9D2WeqGtOqwE5+sVmC3oOao1KGNirzuq95rkPHC4lYSJuhNKcH4vyMjIAAgqQwIFWZZpbm4mMjKyT8phQkJC8Nz2FaEICexNaCFFve+wIuIhIRtqjyuZSnoU7xP0jqBbbKL+8o9O6agwfmonjFyg7/EE50aV/5Cp+h/L6YbU0crcL/us14qQJElkZmaSlpaG1+vVZ4w2xOv1sm7dOubPn99r95bb7e6XJUhFKEICe3Nyh/Kod3yQSsZERREq3SMUISsQqggZwZBpAUVou1CErED5fuVRr/pRp5M2TlGEyvfBmL51onc6nZpcvAcKTqeT9vZ2IiIiTGsiLIKlBfZGtQgN0Tk+QEUNyBVxQtbAqEBplaEiTshSlH+mPKqlLfQmPVCnrOwzY44nMAShCAnsS2u9EiMC+gdKqqgBkyJzzHz8fnMsQgCndhhzPEH3tDUq1llQmi0bQVrA8lQuFKGBhFCEBPbl1E5AhrhhENu/YLkeo15wKw4oGWsC86gthLZ6peJ3Sr4xxxwyBZCUlh4NFcYcU9A16k1QTDpEJxtzTNUiVHkY2luNOaZAd4QiJLAvJTuVxyFTjDtmfJYSNO33dizEAnNQrUFpY/WrKH464bGQMkp5LuoJmYsaH5Q6xrhjxg2F8HilwnjlIeOOK9AVoQgJ7IuqiKRPMO6YkgTpAatQmXCPmYrRbjGVYGHFncYeV9AZVRHSq79gV0iSiBMagAhFSGBfKgKF9FJHGXtcUVjRGqjnP3OyscdVA3PVQo4CczA6UFpFVbzK9xl7XIFuCEVIYE9kGSoCpmkjTeMgFCGrYJZFKGW08lghXCOmYoZFCIRFaAAiFCGBPakvhdY6kBxKxVcjCWaO7VEUMoHxNFYprRXAuBoyKqkBRajyEPh9xh5boNBUDfUlynNVHkahuuJF5tiAQShCAnuixgcljQRXuLHHTh0DDhe01Cq9jgTGUxroL5c0UglgNpKE4eCKUFq7qOnbAmNR53/8cIiIM/bYqivOcxKaa4w9tkAXhCIksCfB+CCD3WKgKF7qcYV7zBzMcosBOJwd6foVIk7IFMyKDwIlazQ+S3ku3GMDAqEICeyJGqiaYnCgtIpqHheZY+ZgpiIEIXFCooSCKZQHzrsZihCEBEwLRWggIBQhgT0x0yIEIQHTu805/mDH6NYap6P+7kTAtDmYFSitEgyYFpljAwGhCAnsiXonbnSgpIrIHDMPb3NHMTuzLEJqyQZhETIeWTbXNQai1cYAQyhCAvvRWAlNVYBknmtMvQDXFEKLx5wxDFYqDiqVfSOTIDbTnDGoFqHKQyJz0GgayqG5WskYNc01rrrG9gv5DwCEIiSwH+pdeMJwCIsyZwxRSRA7RHku7gqNpfKw8pg6Wqn0awZJI5XMwbaGjjR+gTGo8y1pJLgjzBlDcr4i/1aP0ndOYGuEIiSwH2bHB6mo7pGqI+aOY7BRFVCEjK4fFYrTDUm5ynOROWYswfggk9xiAK6wDmuUyByzPUIREtgPs1prnE5yIIVatVAIjEE930Z1nO+OYJyQUIQMJRgfZFKgtIpotTFgEIqQwH4EA6VNtgipF2JhETKWoEXIbEVIjRMSipChWMEiBKLVxgBCKEIC+2EV11hywDUiFCHj8Puh6qjy3GyLULCWkFCEDEOWO26ETLcIicyxgYJQhAT2orkWGkqV52ZljKioFonqY6LnlFHUnwJvkxKomphj7lhSQ4oqiswhY6grUgLUnWFKsLSZqBahykPQ3mbuWAT9QihCAnuh1o+JG2p8j6HTic8CZzj42kTPKaNQ44MSc5SAZTNJyQckpd9UY6W5YxksqG6xlFHmyz8+C8LjwN/e4a4V2BKhCAnshWoWN9saBOBwdLjHKoV7zBBUN6TZ8UEA7khIzFaeizghYzC7kGIoUkgdM5EwYWuEIiSwF1aJD1JRU7hFnJAxBDPGTEydD0X0HDMWqwRKq6g3QtVHzR2HoF8IRUhgL8xurXE6wcwxcUdoCFbJGFMJxgmJnmOGYJXUeZXgjZBQhOyMUIQE9kK94FjGIiRqCRmK6oI0O2NMJVVYhAzD7+9cVdwKqAHbQhGyNUIREtiH1gaoO6E8t8pCKO4IjcPb3NHOwDIWoZCeYwJ9qT8F7S1KxmD8cLNHoyBc4wMCoQgJ7IN6sYlOU3p9WQE1VqX+lKKoCfSj6iggQ0Q8RKeYPRoF1TJVXwItdeaOZaCj3mwk5oDTZepQgqgxQk2VQv42RihCAvsQDJS2iDUIIDIRogIXZXFXqC+h8UFmNVs9nYj4jua7Ik5IX6qPKY9qjzcrEB6r3JiBsArbGFspQuvWrePyyy9nyJAhSJLEa6+9ds7PfPTRR0ybNo3w8HDy8vJ4/vnndR+nQCesFiitIlptGIPV4oNUgj3HRJyQrqiZWckWUoRAuMcHALZShBobG5k8eTJPPvlkj7YvKCjg0ksv5cILL2Tnzp1873vf49Zbb+W9997TeaQCXai0WKC0imi1YQxW6DrfFaLnmDFUqRYhkytKn05yYDwihd62WMTR2jMuueQSLrnkkh5v/9RTTzFixAgefvhhAMaOHcuGDRt49NFHWbZsmV7DFOiFVS1CInPMGKzSdf50UkQXekNQFQ2rKUKqq05YhGyLrRSh3rJp0yYWL17c6bVly5bxve99r9vPtLa20traGvzf4/EA4PV68Xq9mo1N3ZeW+xzQ+Npw1RQiAd74HOjFedP7XEsJI3ABcuUh2gexPHU9z7KMq+pwQP4jeiV/vZGS8hT5Vxw0RP6Dcu2Q/biqCwLyzzZE/j09z+r891cexjeYZKIRev6ee7rPAa0IlZaWkp6e3um19PR0PB4Pzc3NREZGnvGZhx56iAceeOCM11etWkVUVJTmY1y9erXm+xyIRLeUslj20+4IY+W67X0KltXrXMe0nGQR4Cs/xMq337ZOIK9J6HGew721XNxaj4zEu1sO4ncUaH6MvhLm9XAJQO0J3n3rNfyOMEOOO5jWjsi2Spb6WvFLTt7ZuBtZ2mfYsc91nmObT3IR0F5+kHfE/O8zevyem5qaerTdgFaE+sK9997LihUrgv97PB6ysrJYunQpcXHaNfn0er2sXr2aJUuW4Hab3DzQBkhH3of94EzJY/mll/bqs7qfa18b8oEf4/K3sHz+NIjN1P4YNkDP8ywd3wh7gYThXHzZVZruu9/IMvKhe5DaGrj4gnG698EbjGuHVLAO9oGUNIJLLr3ckGP2+Dx7m+HA/xLma2L5hRdAVLIh4xso6Pl7Vj0652JAK0IZGRmUlZV1eq2srIy4uLgurUEA4eHhhIeHn/G62+3WZdHRa78DDo9SSFFKGtnn86XbuXa7leab1cdw1xVCkkWKvZmELue5VrEASSn51pwvSSOhdDduTxFkjjfkkINq7agrBEBKzjP8O5/zPLvdEDcMPMW4645DfIZxgxtA6PF77un+bJU11ltmzZrFmjVrOr22evVqZs2aZdKIBH2mOuAKsVqgpIoImNYXK3Wd74qkEcqjWutGoC1WrCEUisgcszW2UoQaGhrYuXMnO3fuBJT0+J07d3LihGItuPfee7nhhhuC299xxx0cO3aMH/7whxw4cIA//OEPvPjii3z/+983Y/iC/hBcCEeYO47uELVE9MVqXedPR1XQa6wTuzSgEPNfoCO2UoS2bt3K1KlTmTp1KgArVqxg6tSp3HfffQCUlJQElSKAESNG8Pbbb7N69WomT57Mww8/zLPPPitS5+1IjcUtQuoFWnSh1werdZ0/nURhEdKVKosWU1RJErXE7IytYoQWLlyILMvdvt9V1eiFCxeyY8cOHUcl0B2/D2oKledWVYSEa0w/2tug5rjy3Go1hFTU32W1sAhpjt8XciNkUUVIVdCEa8yW2MoiJBikeE6Crw0cbogbavZoukY1jdceVy7cAu2oKQDZB2Ex1s3IU102tcfB127uWAYadcXK/HeGQfwws0fTNUHX2DE4y826wJoIRUhgfdS77MQccDhNHUq3xGYoF2rZL+JEtEa1siXnWrdGS+wQcIaDvx08xWaPZmBRHdJ13qrzPyEbJAd4G6G+1OzRCHqJUIQE1qfaoj2GQpGkjrtC4R7TFqvHBwE4HMqFGkSckNZYPWMMwBWmKEMg3GM2RChCAutj9YwRlWAXeqEIaYpVu86fTjCFXlgENUVttmrVQGkV0XzZtghFSGB9rJ4xphKMExALoaZYPVBWJRgwLSxCmhJstmrxGyHRfNW2CEVIYH2sXkxRRdQS0Yeg/C1+IVRT6NUMR4E2qPPJ6oqwOv+FImw7hCIksDayHBIsbZcL4XFzxzGQ8LZA/SnluRqDY1WERUh7fO0diqXlXWMB+QuLsO0QipDA2jSUK5kYkgMSLN7DS71Q159SLuCC/lMbKJAaFmv9ZpahMUIihVobPMXg9yoZeXEWTZ1XUS1W1QXg95s7FkGvEIqQwNqod9fxWUpmhpWJSlIu2NBxARf0D9UakJhj3dR5lfgskJzQ3ixSqLWiKjR13uKXq4ThSq0zX6sooWAzLP7LEgx67JIxBsqFWrUKiTgRbQgqQtmmDqNHuEIK/olaUtpQbZOMMVBqHKnrlHCP2QqhCAmsjV0yxlTUC7ZQhLQh1CJkB0SckLYEA6VtMv9FwoQtEYqQwNrYoZhiKMIipC22U4RELSFNsZNFCIQibFOEImQSnmYvdaIl1blRFxSrZ4ypCEVIW4KKkE3kLy6E2lJtN4uQKKpoR2zVfX4g8a9Pi3lku5OPW3Zxw6wcZuUmI1k9GNQM7FJDSOUctWSOlDfwxAeHaWv3kxYbTmpsOGmxEYzOiGVyVoJhw7QFsmw/i1BQ/l1bhGqb2thRVMuO4zVsP1FLSV0z10wfxq1zRxLmEvelnQhNnbd6DSEVdZ3qZv63eH08/3Eh/9x8nMSoMBaOSmXB6DSmZCXgdIj13yyEImQSh8oa8MsS7+4r4919ZYxMjeYrM7O5Zvow4iPdZg/PGjRVQ0ut8tw2F8Ic5bGmULmQB5RbWZb5z6dFPPDmZzR7fV1+9DsX5fH9JaOEQqzSWKGUTkCChCyzR9MzghahzopQTWMb3/zndjYdqzrjI7959yAvbS3mp1eMZ/6oVCNGaQ/qTihNbJ3hEDfU7NH0DLXfWO2JTvO/3efn5e3FPLr6MKUepbRGUXUzu4vr+N0HR0iIcrN0XDo/vmwccRFi/TcaoQiZxMPXTmQMRRSF5/DGrhKOVTTys7c+49n1x3j1W3NIj4swe4jmo15MYjMhLMrcsfSUhCxAUi7gjZUQk0pdk5d7X93Nyj1KSvXcvBQWj02joqGVivpWTtW2sOFIJb/74AgVDW08eNUEcXcIHXfV8cPAFW7qUHqMqgi31CqKfFQSdc1evvrcZvae9AAwMiWaKcMTmDY8EYck8cjqQxyrbOSG57Zw8fgMfnL5OIYmRJr2FSxDVUjGqNVT51Xihyk1z9pboKEMYjP45FgV//vqHo5WNAIwNCGS7y7KR5Lgo0MVrD9UQW2Tlxe3FnOiuonnb55BhNtp8hcZXAhFyESGRsNty8fxv5eN59UdJ3nqo6OcrG3m9r9t5T9fnyUmg90yxkC5YMcNVeqI1BSyvdrFnf/czqm6FlwOibuXjea2eSNxnKbo/HPzcX7y2l7+teUE1Y2tPP7FqUL+dnOLgaKwx2RAQynUFNDgjOPG57aw96SH5OgwXrjtAkZnxHb6yGWTM3l09SH+tuk47+4r5dPCalZ+d564GbJD1/nTcboVZaj2BNQUcrQlhlue/5SmNh8JUW7uvDCPr1yQHZzb156XRbvPz8ajVXzrn9v55Fg13/33Dp68fhoup02UvwGAONMWICbcxVcvyOaF22aSEOVmV3EdP3p5N/Jgr05rpxpCoQQu3HUlh7jl+U85VddCTnIUr3xzNl9fkHuGEgTw5ZnZ/OHL0whzOnhvXxk3PrcFT4vX4IFbDDvVEAoloLi3lh/h5r9sYWdRLQlRbv5x68wzlCCAuAg3918+nre+PZf8tBiqGttY8eJO/P5BPv9rA61q7KQIQ9A91lZZwLf+uZ2mNh8zRySx7ocXcuu8kWfc4LicDhaMSuXpG6YH5/+PX9sr1n8DEYqQhchOjuYP10/D6ZB4fecp/rh2kNeisEuPsdMJLNwfbvqU2iYv44fE8dZ35jFpWMJZP3bxhEz+essMYsNdbC6o5pv/2D64F0M7WoQgqLi//sFGPi2sITbCxd9vmcnYzLizfmxsZhxPfXU6kW4nG49U8fT6QZ55ZldFODDetZs/5UBpPSkxYTzxpannjP2ZnZvC7740BYcE//60iN+uOmjEaAUIRchyzM5L4adXjAfg/947yPuflZk8IhOxWw0hlcCFu63iGC6HxG+vnUxMeM+80LNyk/nX7RcQ7nKw4UhlMK5oUGK31HmVgCLkqC0gOszJX2+ZwcRh8T36aG5qDPdfPg6A3753kN3FtXqN0vqoFqEEmylCCTkA1J48jCTB41+cSloP3ZwXT8jkF1dPBODJD4/yt02FOg1SEIpQhCzIVy/I5isXDEeW4bv/3sHhsnqzh2QONlWEPJFKhstwRznfvij/nJaA05kwNJ47FihxEQ++/RmNre2aj9EW2NQiVOLIBGC4VMYfvzKdacMTe/X5L5yfxSUTMmj3y3znXzsGsfwD/fpsZhEqdWYAkOWo4DsX5TMnL6VXn//SjOHctXQUAL965wDl9aKBs94IRcii3H/5eGaNTKaxzceDb+83ezjG09oAjeXKcxvFCMmyzB93KenxI52VfPPCvgV6fmNhLsMSIympa+HJDwdhcTZvC3hOKc9tpgj9aa/izhztruhTOrwkSTz0uYlkxkdQWNXE/W/s03qI1qe5BlrrlOcJw80dSy9o8fr41aZmAHLdVXxnUX6f9vOtC/OYnJVAU5uP3605rOUQBV0gFCGL4nY6+NU1E3E6JNYeqmDb8Wqzh2QsasZYVDJE9MytYAXe3lPCi0eVYMhUuRK33LeA5wi3k/suU1wkz6w/xrGKBs3GaAtqTwAyhMUovwGbsP5wBa8UhgEQ76uGtsY+7SchKoxHvzAFSYKXthXz7t5B5iKtCbjFolMhLNrcsfSC5z8uZGOVMt4UXwVOf9/mvyRJ3HPxGAD+taVo8M1/gxGKkIXJTo7m2ulKN+uHVx0yeTQGY0O3WFVDK/e9vo8q4mhzRCEhQ21Rn/e3ZFw6C0en4vXJ/PTNzwZX4HRofJBNCkz6/DK/eHs/HmJocgbcof3oOXbByGS+Pl+xKD66+tDgyiKzYXxQc5uPZ9cfo4J42h0Ryvyv6/v8n5WbzIWjU/H5ZRE4rTNCEbI4d16Uh9sp8fHRKjYdPbMq7YDFhhljz24ooLqxjTEZcbhSzt5qoydIksT9l48nzOlg3aEKVg2mwHkbZgy9tK2IA6X1xEe6CUsNuES7abXRU76xIJeYcBcHy+r54EC5BqO0CapFyEby/9eWE1Q2tDEsMQpnUo7yoqrQ9ZEfXTIGSYKVe0rZcaKm/4MUdIlQhCzOsMQovni+4iN/ZPXBwWMVqLVXoGRjazv//ERZ9FYsGYVDjWvq54VwREo0t85T9vWzNz+jpZv2HAMOmwVKN7a289uA1fbbF+XhSgkoQv1svhof5ebLFyjz/w8fHRlE899eFqHWdh9/WqeUO/nmwjwkdd3qZ/PlMRlxXDNN8Qo89M6BwSN/gxGKkA341oV5hLkcfFpYw/rDlWYPxxiCC6E9AiVf3FqEp6WdESnRLB6brmkX+jsvyiMzPoKTtc28tK243/uzBTZThP607hgV9a1kJ0dxw6ycDgW+pn8WAYCvzR1BmMvB9hO1bC4YJLGCNrMIvbStmDJPKxlxEVwzfWjI/O+//FcsGUWYy8GWgmo+PDiIrIIGIhQhG5ARH8FXZioLwsOrDw2OuwLVImSDO8J2n5/nNiqWn1vmjlAqR2uoCEWFufjaXMUq9LdNhYND/jaqIVTd2MYz6xTLzz0Xj1G6yIc23+wnabERwVjBP3w0SIqs2sgi5PX5+WNALl9fMJJwl7NDgeunawxgSEIkN8/OAeDX7xzEN5hixQxCKEI24RsLc4l0O9lVVDvwYwVkOUQRsr5F6L19ZRRVN5MY5ebzATO2looQKD2JIt1ODpU18MmxAW4VkGVbWYRe2lZEs9fHhKFxXDxBqSET/N1qoAgBfH1+Lg4J1h2qYN8pjyb7tCyh898GFqHXd56iuKaZlJiwYBhDUIHTaP5/c2Ee8ZFuDpbVs2rfIMsgNAChCNmE1NhwbpitTK5HBrpVqKFc6d4sOZQGhhZGluVgK4SvXpBNZFigj1CoIqSBrOIj3Vw9TSnUOOCrzTZWgrcRkCAhy+zRnBW/X+aFzcpF+6sXZCOpGW6JIRYhDeQ/PDmKyycPAeBP6/oXd2Z5GspC5r+15e/zy/whUOfrtnkju5j//bcIgRIr9tULlN/UC1u0Ua4FHQhFyEZ8fX4uEW4H+0552H6i1uzh6IdqTo4bqnRztjBbj9ewq6iWMJeDr87K6XgjPguQoK0BmrTJ9rthlrIQrvqsjFO1zZrs05KoAeZxQ8EVbu5YzsHHR6sorGoiNtwVVFQAiBsGSNDeDI0VmhzrGwuVAOx3PyujfACLP6g82GD+v72nhGOVjSREufnyBSHWK1URbq6GVm06A3zh/CwkCdYfrqSoukmTfQoUhCJkI5Kiw1g+USnf/9+tfa9PYXlsFB+kxoZ8bupQUmNDLtruCIgLXBg1Mo+PyYjjgpFJ+Pwy/9yszZ2mJVHPlw0qiqty+Ny0oUSFhfSTc4WFyF8bWY3JiGPx2DRkGdacGsBLt43ig/4UaIx9y5wRnfsJhsdCZJLyXCP5ZyVFMS9fqVT+L2EV0pQBPJsGJl84TzEVv7nr1MDtQaReCC0eH1RQ2cjq/UptHzXFvRMaxwkB3BiwOv1rS9HATaW3SQ2hMk9LsLbT9TO7GGuCdgGzKt9YmAfApxUSVY1tmu3XUtgkY2zfqTr2nfIQ5nQErbWd0CiFPpTrZyjr/4tbi/H6/Jrtd7AjFCGbMWNEEjnJUTS2+Vi5p8Ts4eiDTQIl/7zhGLIMF41JIy8t9swNgoqQdjEdS8alkxkfQXVj28CVv00CpV/8tAifX+b8nERGZ3Qhf40DpgGmZycyfkgsPlli5Z4BGjRbW6g8Wtwi9PK2kwAsHpdGQlTYmRuov18NFeFFY9NJiQmnsqGVNfsHUYFVnRGKkM2QJIlrz1PvCgaoe8wGNYSa23y8ul1ZCG+d240LRweLkMvp4MszlfPy100D1D1mg9R5n18Ouieun9nN7zSoCGkrp6umKC6313ad0nS/lsEGFiGvz8/rO5X5rxY8PINg5ph28nc7HVx3nnK8F7YM0PXfBIQiZEOumTYMhwSfFtYMzGZ8NogRen9/GY1tPoYlRjIrt5umoBpnjqh8ccZwwpwOdhXVsrOoVtN9WwIbWIQ+OljOqboWEqLcXDIhs+uNQjPHNOTyiRk4kNld7OFI+UCc/9aPEVp7sIKqxjZSYsKYPyq16410cI0BwRT99YcrRNC0RghFyIZkxEewIDD5/jvQKg37/R2NSi1sEVLvBq+cMqQjZfp0dLAIAaTEhHPZJOXiO+BS6b0t4AlYOiysCKkp89dOH0aE29n1Rjq4xgCSY8IZk6Ck5L+6Y4DNf1871Clzy8oWoZe3K+f9qilDcTu7uYzq4BoDpZTCvPwUZBn+86mwCmmB7RShJ598kpycHCIiIpg5cyZbtmzpdtvnn38eSZI6/UVERBg4Wv34wvmKe+zlbcW0D6SgufoS8HvB4erIurEYNY1tfHRQSYm+asrQ7jdUF8K6YmjXNrBVTdV9d28pzW0DKGi6rgiQISwGorqxtJlMcU0THwRaHXxpxlmU9VBFyK/tHJ2RqihCr+04NbC60nuKQfaBMxxiMsweTZfUNLaxZr8i/2umn6XOWahrTOO6b+rv7sWtRSJoWgNspQj95z//YcWKFdx///1s376dyZMns2zZMsrLu6+0HBcXR0lJSfDv+PGBEVdx0Zh0kqLDKK9vZe0hbeqUWAL17jl+GDi6udM2mbf3lNDulxmXGUd+ehdBsirRqeCOAuTABV47pg1PYGhCJE1tvoHVf6gmJD6sO0ubybz4aRGyDHPykhmZGtP9hnHDQHKCr00pEqgh4xNlYiNcnKxtHlj9x4LyzwKHNS9Pb+4+RZvPz7jMOMZmxnW/oVpLrL1ZKRKrIYvHppMSo6z/A77TgAFY85fWDY888gi33XYbN998M+PGjeOpp54iKiqK5557rtvPSJJERkZG8C89Pd3AEetHmMvB1VMVa8SAMo/aIFA61C12ViQp5K5Q22rAkiRx2WTFPfbW7gEUNGvx+BBZlnlzt5Ktd91556h67HQpRQFBc/dImBMuGa+sZQPKPWZx+YNihYdzWINAqSWlVsbXWv4uB5+frvz+/i1qCvUb2yhCbW1tbNu2jcWLFwdfczgcLF68mE2bNnX7uYaGBrKzs8nKyuLKK69k3759RgzXENSF+IMD5VTUt5o8Go2weKB0cU0TnxbWIElwxbkUIQhxj2ivrF4+STn+BwfKB05NqTprx4ftL6mnoLKRcJeDxWN7cFOlU8A0dGSPrdwzgNyjFs8YO1Jez67iOlwO6dw3QqBL5pjK5wOK2PrDldQ1eTXf/2DCde5NrEFlZSU+n+8Mi056ejoHDhzo8jOjR4/mueeeY9KkSdTV1fHb3/6W2bNns2/fPoYN61qbb21tpbW1Q6nweJQGh16vF69Xux+buq/+7HNkcgSThsWxu9jDK9tOcMucHI1GZx7OqgIcgC9uGH6NzrcW51rltUCQ5IycRFKiXOfcpyNuGE7AV31cs++jMio1kuykKI5XN/He3lNcPqmb7CWD0OI8O6sLA/Ifqvn50oI3dynyn5+fQphDPud3dcYNU75PVYHmv+dJQ6IZlhBBcW0L7+w5abr8tcBZrf387ytd/Z5f/FRRaOfnpxAf7ji3/OOzAvI/qvn3yU4MZ1RaDIfKG3h370k+N/Us8YoWRsv1ubt9nwvbKEJ9YdasWcyaNSv4/+zZsxk7dix/+tOf+PnPf97lZx566CEeeOCBM15ftWoVUVFRmo9x9erV/fp8vktiN07+teEgGXWfaTQq85h9bAepwM7CGoo9KzXdd3/PNcA/dzoBiRFSJStXnnt8uWWNTABO7f+E7c3afh+A0ZEOjuPgufd34Szeofn++0J/zvO8wt0kAduOVlBSpf356g+yDC8H5J/ZXsLKled2SY4ub2EMULx3Izs9YzQdz5r332d8tIPiWgfPrLaO/PvDvMJdJAHbj1VzqsYa8ld/z34Z/rNNkX+2XNqj+T+qopWxQPGejez0jNV8bCPDJA7h5G8f7iGiZJfm+zcSLdbn02lq6ll5AdsoQikpKTidTsrKOgcdlpWVkZHRs+wCt9vN1KlTOXLkSLfb3HvvvaxYsSL4v8fjISsri6VLlxIXd5bAuF7i9XpZvXo1S5Yswe3ue2PBaZ4WXv6/dRQ2SEyfexHpcfbOinP9/scATF5wOZOyZmqyT63O9YHSeko2bcLtlLj7i4uJizz3vqT9Xnjl3wyN9pGxfHmfj90duaX1rHpyEwc9TuZeuKhHY9ILLc6z69BdAEy98EqmZk7RcHT950BpPeWfbCLM5eD7X7ioc2+pbpB218Obr5IVKzNEI/mHnudxnjbee2wjB+scnD/vws797mxIqPynDJlq6lhO/z1vOFJF3SfbiI908YMvLSbcde7IEmlPA7zxClmxfs3kH0p+WQPv/v5jDnmczLtoEbER1m5S2xVarc9doXp0zoVtFKGwsDCmT5/OmjVruOqqqwDw+/2sWbOGO++8s0f78Pl87Nmzh+Vn+UGGh4cTHn7mYuJ2uzUXkhb7zUp2M3V4AjtO1PLhoarOHdDthq8dPEogsislFzQ+3/0912/tVZTwi8akkRzXQ+tgslId2VFXhEOH38/4YYnkpcVwpLyBDw9XB+MGzKTP59nbAo1KBoxbB/n3l1X7lezMhaNSSYyJ7NmHdJS/2+0mPyMqOP9X7ivn1nkjNT2GoXibg/J3peZZRv7q73n1AUX+yycOISayhwpnSi4AjtoTusz/sUMTyE2N5mhFI+uO1HCVTd1joM81tqf7s02wNMCKFSt45pln+Otf/8r+/fv5xje+QWNjIzfffDMAN9xwA/fee29w+5/97GesWrWKY8eOsX37dr7yla9w/Phxbr31VrO+gi5cPF6xiL27z+a9hzwnAzVEwiDGWtl9fr/MmzsVV8hZawedjhosWV8K7doHtEuSFCyuaPvssbpA9lNYDEQmmjuW05BlmbcDvd0u7U0sjhr0XVcMfn0Cmq+crATtvmf3+a8GlIfFWk7+fr/M6kCD3WXje7E2qbXEPMXg0z4GRpIklk9Ufo8DtvegAdhKEfrCF77Ab3/7W+677z6mTJnCzp07effdd4MB1CdOnKCkpOPHUFNTw2233cbYsWNZvnw5Ho+Hjz/+mHHjxpn1FXRhWUAR+uRYNTV27kgdrCFkvRoinxZWc6quhdhwFxeOSev5B6OSwRWJUktInzTnywLZYxsOV9pc/oHMmvgsy9UQOlhWz7GKRsJcDhb1JFtMJW6IUhzU71WKherAksD833a8hsoGG2ePhmaMWUz+O4trqahvJTbcxezclJ5/MCYdXBEg+3Wb/2qLl7WHKgZO9qjBWOtq0wPuvPNOjh8/TmtrK5s3b2bmzI44ko8++ojnn38++P+jjz4a3La0tJS3336bqVPN9TvrQU5KNGMyYvH5Zd63c0fiWuumzr63TzmvS8and99SoSskSbdWCyp5aTGMyYil3S/b2yoQLJ1gvdT5lYHaQQtGpfYoNiiIw9lRS0aHFGqAoQmRTBgah1+GD/bbuLiehWsIqfNq4Zg0wnoQGxQkdP5r3GpHZWxmLDnJUbS2+wdWcVUDsZ0iJOiaiycod4XiQqg9siyzer9yXpeO60PZ/6B7RL/Cl5cH3CNv7baxedzC8g+6xSb2IUVdZ0UYYMlY5Xe56jMb3wgFm+1aSxGSZZlV+/rgFlPRef5LksTFAavQO3tsvP6biFCEBgiqIrTucCUNdjWPWrSY4uHyBoqqmwlzOZiX3wuzuEpCoAKxjhdCNU7o46OV9nWPWLSY4qGyBo5WNBLmdHDR2F64RVUS9CuqqLI0cIFef7iCpja7zn9rWoSOVjRSUKnIf0F3nebPRrw6//W7EVo+UVn/PzhQPnCKaxqIUIQGCKPTYxmREk1bu5+P7GoerbFmew01SHJObjLRvXGLqBhgEchOjmbi0Hj8MrxvV6uARS1CqjVo/qgU4vqSnhxUhPTrczgmI5ZhiZG0tvtZf7hSt+PoikWrSq8OuBtn5yX3LT3dAIvwxKHxDE2IpNnrY+0hm67/JiIUoQGCJEnBoOl399rUPKpeCNVMC4ugxl0tHtfHTDYDFCEg2PLBtk0Yg4rQOXp4GYyajbO8L24x0LXNhookSUG3rerGsR0WtQi/H1CE1PW11xgw/5XsMWV8K4V7rNcIRWgAobrHPjxQTovXZubR9rZgDSErWQTK61vYWVQL0LPeUl0RtAjo2xx3UcBts+FIpQ3l39qRVWWhC+GR8gaOlDcQ5nRooAjrZxECWDJOVYTLaPf5dT2W5rR4oKVWeW6h+V/bCrtPepCkjvnVa3TsNxjKJQFFfc3+MvvNf5MRitAAYtLQeDLjI2hs87HxiM3M455iQFZSzaP74IfXiQ8PlCPLMHlYfN+rdqsxAvWnFIVPJ8YPiSM9LpymNh+bC6p1O44uqKnF7iil5IBF+DBgXZs5MqlvbjEIcY2cVIqG6sT5OYkkRLmpafKy9XiNbsfRBdVtFJkI4THmjiWEPTVKGv+04YmkxfZz/nv0lf+UYQnB9X+DXd2jJiEUoQGEw2Fj91hofJCFaois/ky5EPbZGgQQk9ZRS0S1eumAJElcNCZgFbBbGYXQ+CALyV91M144uo/WAICYDKVIqOzTVf4up4OLAjWubOceq7VmoPzuauW32KdsMZWY9A751+tX9NThkILrlEij7x1CERpgqIrQ6v02M49bMFC2uc3HhiNKWf0+u0VAubDH6585BrAocCFcc6AcWZZ1PZamhBbTtAieFi+fFiqWtYt6U0TzdByOEPnr6x5T44RW7y8V8u8ndc1ejngURWhJX8pmqDgcHbWkdJ7/F45RrOkfHaywl/xNRihCA4zzcxKJj3RT2+QNxrbYAgsWU9x4pJIWr5+hCZGMyYjt387UAGAdM0cA5uSlEO5yUFzTzOHyBl2PpSkWVIQ3HK6k3S8zMiWanJTo/u3MoID5+aMU+RdVN3OgtF7XY2lKnfXk/9HBCvyyRH5aNCP6K38DUugBZo1U5H+y1mbz32SEIjTAcDkdzA/UurCVedSCF0I1W2zJuHSk/rprDLoQRoY5mZ2rxNissVOVYQvKX40P6lVLle4wIHMMICrMFax1tdpOZRRUBcFCFiE1bX5Jf9ziKgak0IMy/2cF5r9ts0dNQChCA5CFozrMo7ahxlrF1Px+OZg226/4IBWDFCGAi8Z2ZA/ZBosVU/T7ZT4MzJ9+ucVUgm0W9HWNQUf22KrPbBQnaDFFuK3dz4YjVQAsHqtB8oZBmYPQEc/2oVCEeoxQhAYgC0YrE3ffKQ/lnhaTR9NDLLYQ7iqupbJBabI4Y0RS/3cYb0wKLXRcuLcdr7FPE1aL1ZDZe6qOyoZWYsJdnJ+jgfwNqC6tsmhsOpIEe096KK2zyfwPKsLWsAhtPV5NY5uPGLfM+My4/u/QINcYdChCW4/X4GnRvuP9QEQoQgOQlJhwJg2LB+CjQzawCnlboCFw92qRC6HqFlswOrV3TRa7w0CLkBrT5JeVjtSWp70NPIFsGotcCFW3wty8FI3kb5wipMz/BADW2UH+3mZoDIzTIq4xdd6MTZBxODTIYjRw/g9PjiI3NRqfX2b9IZFG3xOEIjRAUd1ja+3gHlNTit1REKXB3bcGvB9Im1/Sn2yxUNSFUOdaIipq8bc1djCPB2tIRVimhpTqVtDELQad5a9jLSkVtSeWLRRh1UoSFqvUEbIA6ro5NkGjzKuEkFpCfv2zeYPuMTvFiZqIUIQGKAsDC/i6wxXWT6MPTZ21QA2ZkrpmDpbV45DoW5PFrgitJaJjLRkVtZ7Q2oPleC0v/5D4IAvIv6K+lV3FdQAsHK2R/KNTwRkOyIbIX/3drrfD/A9mjFlj/pfWtXCgtB5JgjHxGilCsUNAcoKvDRr0j91TFfiPDlbg94s0+nMhFKEByuRhCSRGualvaWf7iVqzh3N2LBYoq7oTJmclkBAVps1OQ2uJ6Jw5AjAlK4Gk6DA8Le1ss3qVYYvFh6lNiycMjSOtr9XET8fhMKyEAiiV0OMj3Xha2tlVXKv78fqFxTLG1Kalk4bGE93HYuJn4HRB3FDluQHusfNykogJd1HZ0MreU3W6H8/uCEVogOJ0SME0est3o7dYs811Ab/6/HyN3TQGxgk4HVLQmmH5NFrLKUKBbLH+VJPuCgMDZl1OB3MDafSWd49bTP6qO3FB4PxphkEp9ABhLgdz85Txf3jA2vKXCtcztOYTQzLqukMoQgMY9UL4oeUXQuvcEbb7/Kw/rJyv+Vq5xVQMVIQAFo2xSTd6C1UV9vr8QYugJvWDQjHQIgQ2ihOyUMaYMv+VG6F5+Rr3vEswprq4ilpl2upxQo5tz3Fe4R9wHH7PvDGYdmSB7szPT0WSYH+JhzIrp9FbyDW2q7gOT0s78ZFuJgcy7zTDwBR6UDKeHJLSQf1UbbMhx+wTFpL/1sIa6lvbSY4OY3Ig80oz4o1VhNWEid0n66hqaDXkmH3CQjdCO4pqqW9pJzHKzcShWs9/4yyCAAsDFs1dxbXWln9g/ssmyr/XitC7777Lhg0bgv8/+eSTTJkyheuvv56aGovHIgwykkPSaC1tHrfQQqhaA+bmpeByanyfYGBRNYD4KDeTsxIAglYuS2KhGkKqG3nB6FRt0qZDSTCm35xKWlwEYzPjkGXYcMTCadQWlP+8/FScmsvfWEU4PS6C8UMU+VvZKijVFQM2U4TuvvtuPB4PAHv27OEHP/gBy5cvp6CggBUrVmg+QEH/WGj1dhu+9o4sGguYxoPxAVq7xcDwhRA64pzWWbWeiM8bIn/zLUKq/BdqHR8EHYq+Qa4xCHGPWfVGqL0N6kuU5wN+/hsvfzWN3rLu8bYmpKbA2mQnRaigoIBx48YB8PLLL3PZZZfxy1/+kieffJJ33nlH8wEK+ocaJ7ThcKU106jrS5SUcocbYvrR4VkDapva2B3IsJk3SuNASTitlohP+/13wfzA99hwpBKfFdNoPSdB9iup5SbXECr3dKRNq4GmmhIMljWmlgx0XNDXHbZoGrWFakhV1Ley96Ryk695fCCE3AgVgUGd4dU4IcvO/4BS6HVEQoTGrshe0GtFKCwsjKamJgDef/99li5dCkBSUlLQUiSwDpOGKWnU9a3tbLdiGnUwUHaYkmJsIhuOVOKXYVR6DJnxkdofIDYTHC7wt3fcBevM5GEJxEa4qGv2BpU8SxGaMWgB+QNMHBpPUrRGZRNCic1Uasn4vR2V1HVmenYi0WFOKhva+KzEgutzqFvc5BpCqlt8wtA4UmPDtT9A3DBAgvZmaDTGQjt5WAKx4S5qm7zsPWnBNPqA/JvCdLjx6AW9Xnnmzp3LihUr+PnPf86WLVu49NJLATh06BDDhg3TfICC/uF0SMwPpIFaMnvMQhkjqvtAF7M4gMPZUUvIIPeYy+lgTq4ifzUbxlLUWidQuiNbSKdFuVMtGWPcI2EuB7MD1i1LxolYaf6rbtFROrhFAVxhijIMHUUkdcbldDA7T8l+s2ScYOA8NIdpnKHXS3qtCP3+97/H5XLx0ksv8cc//pGhQ5WJ/c4773DxxRdrPkBB/1GbsG44YsGJELwjNPdCKMsy6/RKmw8l3tiAWej4PpbsO2WRGjJ+vxyiCOkofxPiRCwdJ2SR0gk+f8f8X6BVNfGuMDhgHjp+z1a+ETLbIuTq7QeGDx/OW2+9dcbrjz76qCYDEmjPnMAd4b5THqoaWkmO0cHs21dCy+ubyKGyBso8rUS4Hdp0G++OhGxgvWEWAeiwcOwoqsXT4iUuQqtyuRpgEUXoQGk9lQ2tRIU5mTZcx35X8cbWkoEORWjbiRrqmr3ER1pJ/tawCO4urqW2yUtshIupgUxLXYjPgqLNhs5/NWFi+4kaGlrbiQnv9WVfPwI3BLazCAEcPXqUH//4x3zpS1+ivFyJRn/nnXfYt2+fpoMTaENabARjMmKRZdh4tMrs4XTGIqnzaln9C0YmE+F26ncgg1PoAbKSohiZonSj/viI1eSvWgTMvRCqboNZI5O16TbfHQnG1pKBgPxTVflbzCpgkRpSalalLmUzQjGwurTK8OQohidF4fXJbD5mtflvDYtQryW+du1aJk6cyObNm3nllVdoaGgAYNeuXdx///2aD1CgDapVYIPV/MQWWwg1b6txOiaYxqFD/paLE7CIRVD3+CAVEy6E0Dl7zFKoNwQm3wipYQO6ukXBAvPfmopws90UoXvuuYcHH3yQ1atXExbWkVlx0UUX8cknn2g6OIF2zM3vSKOXDUrdPCeyDIFiWmZeCJva2tlSUA3oHB8Apl0I54dcCC0jf78PPKeU5yZeCJvbfGwpVOQ/T8/4MDC8urDK/JA4EUvK38T5X9/iZUegMbVhirDB8lcVPEspwu1tQfnbziK0Z88err766jNeT0tLo7LSYtqmIMiMnCTCXA5O1bVwtKLR7OEoNJRDewtIjo5sGhPYfKyaNp+foQmRjEyJ1vdgwaJ6xYbVkgHF5ed2ShRVN3O8qsmw456V+hKllIDDBbHm1ZDaUlhNW7tB8g9VhA1USGaOTMLtlCiusar8M00bxifHqmn3y2QnR5GVFKXvwULbrBgo/1m5yTgdEscqGjlplXY7npOAjOyKoNUVZ+pQeq0IJSQkUFJyZg2UHTt2BDPIBNYjMszJ+TlKEKhl3GOqVSQ2E5zmBXB2ZIulIOldyyRuiKL4+dqgoUzfY4UQHe5ierYif8vcFap3xXFDldICJrH+kOoWMUL+gTXS2wRN1foeK4SoMFcwCHy9VeKEVPeQyfJX10PdrUHQUT6jrR5aavU/nnrYkN6Jllv/44aaXkOq14rQF7/4RX70ox9RWlqKJEn4/X42btzIXXfdxQ033KDHGAUaYbk0Soukzm4wIm1axemG2CHKc4PdY0HzuFXS6C0SH2ZI2ryKOwJi0pXnBtWSUQnGiVhF/hbJGFMVw7l5Bsg/LKqjgrZp7jGrrP/mN1tV6bUi9Mtf/pIxY8aQlZVFQ0MD48aNY/78+cyePZsf//jHeoxRoBFq24BPjlVZo92GBYqplda1cLi8AUmC2bkGpXCaFDCpBsxuOlpFW7sF5G8BRbjM08LBMqWtxpw8g+RvQi0p6IgT3HS0inZLzH/zSyecqm3mWEUjDklxHxmCSfJX2+1stEq7DXX9jze/EHOvFCFZliktLeV3v/sdx44d46233uIf//gHBw4c4O9//ztOp3nmTcG5GZcZR3J0GI1tvmBwoKlYIHV+Y0hbhYQoHdoqdIUJzTehs/y3n7BAuxULWIRUa9CkYQnGyd+kgNmJQ+OJj3RT39rOrmILtFuwgCKsWoMnZyUYV1/JpIQJy7XbsKtFSJZl8vLyKC4uJisri+XLl3PdddeRn5+v1/gEGuJwSMHiipZIo7bAhXDDkY76IYZhQi0Z6Cz/DVYwj9eabxFU58F8I+JDVEyoLg1Kux3V6inkr6DGy80zZf4baxFyOR1Bq9cGK8SJBSyCtlOEHA4H+fn5VFVZrCiToMdYqp5Erbk1ZGRZ7lCEjLwQmmQRgo7vaYmAWZMtAn6/bGx8mIpJKfTQIX9LtNsx+UbI75f5OFBgdq6R8k/IVh4NVoSgozyEJeIEa23qGgP41a9+xd13383evXv1GI9AZ9QFf3dxLXVNXvMGIsum9xk7WFZPRX0rkW5nMKPKEExyjUCHIrzHCvI3uYbUZyUeqhrbiA5zMnV4gnEHDrpGTLgQBgKCd5yopaG13fDjB/H7TXeNf1biodoM+ZsUIwQdlk+13YZp+P3B+W87ixDADTfcwJYtW5g8eTKRkZEkJSV1+hNYm4z4CPLTYvDL8PFRE60CLbVKCimYdkegWgNmjEgi3GVgfJtJtWQAMuMjyU2Nxi/DpmMmyr+xEtqbAQniTJJ/wCo2KzcZt55tFU7HRIvQ8OQospOjaPfLfGJmu53GCvC1osjfnLIrqlXccPmb5BoFyE6Otka7jYZS8HtBcppaQ0ql19J/7LHHePrpp3nuued46qmnePTRRzv96c2TTz5JTk4OERERzJw5ky1btpx1+//+97+MGTOGiIgIJk6cyMqVK3Ufo9WxhHtEvQhEpSgppSZgWFuF0wnWEmmAZuODli1RRkG1hsRmgMugIOXTUBVhQ+PDoONC2FILLR5jj03H9zU1TiRYQ2aIefIPuAcNl7+qCDfXQGuDsccmZP03c/53qiFmfhPYXo/gxhtv1GMcPeI///kPK1as4KmnnmLmzJk89thjLFu2jIMHD5KWlnbG9h9//DFf+tKXeOihh7jssst44YUXuOqqq9i+fTsTJkww4RtYg/n5qfxlY6G5AdMmp863tvvYXKDGBxi8ELojlVoijRXKeYgy1pI6Jy+F5z8uNPdCaLJbpMXb0VbD0PgQgPBYiExULoR1RRAx3tDDz8tP4Z+bT5g7/03uMdbc5uPTQuUmxHD5R8RBRDy01CnyTxtr6OHn5aXwwuYT1lCETe4xqNIne6DP5+Pll1/mwQcf5MEHH+TVV1/F5/NpPbYzeOSRR7jtttu4+eabGTduHE899RRRUVE899xzXW7/+OOPc/HFF3P33XczduxYfv7znzNt2jR+//vf6z7Wc+JvJ7K1ApqMN0+q5faVdgsmtdswuZjatuM1tHj9pMaGMzo91vgBmBgncMHIJJwOieNVTRRVm9RuweSF8NNAW43M+AhyU3Vuq9EVJrrHZuWm4JDgaEUjJXUmtVswOWNsi9nyNzFOcHZA/kfKG0yUv/mlE0LptUXoyJEjLF++nJMnTzJ69GgAHnroIbKysnj77bfJzc3VfJAAbW1tbNu2jXvvvTf4msPhYPHixWzatKnLz2zatIkVK1Z0em3ZsmW89tpr3R6ntbWV1tbW4P8ej2K69nq9eL3aBZdKr97B0gOv0ZbuwTv725rttye4JZialcCWwho+OlDG9TOM/zE6qgtwAr7Yofg1PK9docotVH7rDpYDMHtkEu3txgcNOuOG4Ti1HV91oe7f/3QinDBlWDzbTtTy0YEyvni+NjE6XZ3n7nBUHzdM/l2xVpV/ronyL93dJ/n35jx3RZRLqSm0q7iOjw6U8flpxsfoOGpU+Q8zRf7rDirtbc4m//6e57OhyH8PvqoCw79/lBsmDI1jd7GHtQfKuMZU+Q/V9Tz3dJ+9VoS+853vkJubyyeffBIMjq6qquIrX/kK3/nOd3j77bd7u8seUVlZic/nIz09vdPr6enpHDhwoMvPlJaWdrl9aWlpt8d56KGHeOCBB854fdWqVURFaRfLMq7aSz5QtHcTe2v1UR7PRqpPApy8vHEfCZV7DD/++ce2MgTYd9JDgUFxW6tXrw4+f3u3E5CIaSxm5Urj78rGV3vJAwp3rmdvpfFWsVS/Kv+9xFXs1nTfoee5O2Yc20omsLeojkIT4vbe2aXIP8pTxMqVxlvlJtS0kwsc27GWz8r7Fizak/PcHemyA3Dw33V7iCrd1ef99JWZR7eRAewpquW4ifKPqT+3/PtznrtjQo0vRP7GNxzOkB3sxsF/1+8h0gT5X3B0O+nA7hM1nGhSzq8e57mpqWcW714rQmvXru2kBAEkJyfzq1/9ijlz5vR2d5bj3nvv7WRF8ng8ZGVlsXTpUuLitOuQK285CavfJjteYvjy5Zrtt6cMLa7j7T9tpqApjKXLFuIyMmsCcP75YaiDcbOWMXbUJboey+v1snr1apYsWYLb7aamqY3iTz4C4I6rLyQjLkLX43eF49OTsOodRiS5TJF/xola3n1mC4VN4Sy7eCFOR/+bHp5+ns+G65nfADB+ziWMy1vc72P3hqqGVk5uWgvAt665iOSYcEOPD+DYfBzeX0VuspucXsq/N+e5O1IKq1n1560UNIdz8cULcWgg/97gevqXAEyYcwnjcy8y9NgV9a2cCsj/G9csIjm662BtLc5zdzg2FyryT+m9/LUguaCaVc9tpbA5gosvXmC8/P/0IAAT5y5ndNYc3c6z6tE553h6u+Pw8HDq6+vPeL2hoYGwMP2i/1NSUnA6nZSVde7YXVZWRkZG1xp1RkZGr7YH5fuFh5+5MLrdbk2F1J6UA4Cz/hSSxsLvCVOzk4mPdFPX7GV/eVOwM7VheJQaEq7kEWDQ91dl+OnxSmQZ8tNiyEo2IT4IIHkEAA5PMQ4T5D89J5mYcBe1zV4OVTQxaViCZvvu0VzxKFY4I+Wvsvm44hYbmxlHRmKMoccOEpT/yT7Lvz9r0vkjUokKc1LT5OVwZTMThsb3aT99IqSGlDnyV64J44fEkZFw7vggrdd+AALrf3/k3x9mjEwl0u2kqrGNo1UtjBui3U3+OTlN/nLg++txnnu6v16bAS677DJuv/12Nm/ejCzLyLLMJ598wh133MEVV1zR64H2lLCwMKZPn86aNWuCr/n9ftasWcOsWbO6/MysWbM6bQ+K+a277Y0kWETKhKJqYHK5/bbGjiBxE4LlgmmzRmeLhWJisCwo5fYvGKnI3/A02pY65Q9MCZZV+8sZXjYhFBOriwOEuRzMGmlSu4XmGqV0BJgi//VmVBM/HZPnf5jLwcyRilfH8CrjTdXgDbisLFBVGvqgCP3ud78jNzeXWbNmERERQUREBHPmzCEvL4/HH39cjzEGWbFiBc888wx//etf2b9/P9/4xjdobGzk5ptvBpRij6HB1N/97nd59913efjhhzlw4AA//elP2bp1K3feeaeu4+wRgR+A1FJnSi0RCCm3b/SFUK0oHB4PkQmGHlqWZfPqB4WiXgCaqxXF0ATmmSV/dfGPTIIwYzN2ZFk2r35QKGrWUEMZeFtMGUJHPRmDL4Sq8hedqpSSMJBQ+Zs7/wNtNhpKzZN/nkn1hNSb/+g0cBsfltAVvXaNJSQk8Prrr3P48OFgkPLYsWPJy8vTfHCn84UvfIGKigruu+8+SktLmTJlCu+++24wIPrEiRM4HB263ezZs3nhhRf48Y9/zP/8z/+Qn5/Pa6+9Zo0aQmExtDpjCPc1mFJLBDrK7avl1mPCDSpsZWKPseNVTRTXNON2SswckWz48YNExCuKYGudohikjTF8COqFcNvxGprbfESGGVRd28TU+WOVjZyqayHM5WDGCBMr4UcmgjsavI3KjUGK/uvn6agWkU8LDZa/ianTh8oaKK9vJdzlMLatzulEJYE7SrGMeE5CsvEJM4r897OloJoWr48It1Hyt1YNIeiDIqSSn59vStf5O++8s1uLzkcffXTGa9deey3XXnutzqPqG81hKYQ3NygLQ7rxitDw5CiGJ0VxorqJzceqWDQ2/dwf0gITF0L17nd6diLRRil+3ZGQBWVqUTXjFaGRKdEMiY/gVF0LWwqrWTDKIFeBicUUVWvAedmJxi38XSFJivwrDih3yCYoQrmp0WTGR1BilvxNcYsp83/myGTz5R+fBZUHlfXQBEVoVHoMabHhlNe3su14DXOMspCa3Gy3K3p0JTi9Fs/ZeOSRR/o8mMFGU1gyCc2FpvmJQbEKvLD5BOsPVxqnCJloEVhnhfgAlfgsKNtrSlFFAEmSmJufwotbi9lwuMK4C6FqGjdhIVTdAKbGh6nEBxQhk+a/JEnMM0X+5l0Ig25xM92iKgkBRcikODFJkpibl8IrO06y4UilcYqQyVXlu6JHitCOHTt6tDNJMjYFz+40hwV+eCYFTIPSjdjwcusmVZX2+vxsCjSanG8FRUhVBE1ShEBpL/Di1mJj4wRMWgi9Pj+fBBpNqm5hUwlWFx5s8lctwsbO/9C2OvNGWUARMrG6vMrc/IAidLiSH11s0EHtahH68MMP9R7HoKRJVYRMnAizTiu3nhlvQPCiSa6xXcV1NLS2kxjlZryR6aLdYXLmECgBk5IEB0rrKfe0kGZETSWTLIK7imqtJX8Tu5CrmCJ/k2IETW+rczomttlQUQOm956qo6axjcRuaippisl95rrC2Cp6gk50KELmTYT4SHewhoxhd4Um3RFsPKLcDc7JSzG8gFiXWGAhTIoOY8IQpYaMYfI3ySKkfr/ZQv5BkqLDgkqhYVZhk+b/+pBsQUt4L9Tvb6IinBYXwej0WGQZNh41eP7bPVh669atvPjii5w4cYK2trZO773yyiuaDGww0BwWyFoycSKAkka6s6iWDYcrue48nX+c7a1QX6I8N3gh3GAltxhYwiIAMH9UCntO1rHucAXXTNe5roe3BRqVgobGXwiVQFlLxIdAh2vIRIswKPFye0962HC4ks9N01n+rQ1KHSEwXBG2RNp8KCbXElKZm5/CwbJ6Nhyu5LJJQ/Q9WGs9tNQqz+1sEfr3v//N7Nmz2b9/P6+++iper5d9+/bxwQcfEB9vYHXSAUDQItRYAW0mdQGnwzy68Uglfr+s78HUGkLuKIgyLn29qR12FytF/CwRKAsdF8L6UmhvO/u2OqIqhusPGyj/sBglhdwg6pq87CyqBWC+UUHB50JVBOtPgc/4xqMqqmKw/kglsqy3/AMX/Yh4iDDOPVnd2MbeU4H5bxVFWL0R8pwEn/GNf1U66kkZIP9ac+R/LnqtCP3yl7/k0Ucf5c033yQsLIzHH3+cAwcOcN111zF8uHWCn+yA1xmNHBYo8a9eIExg6vBEosKUcuv7S3Uu7hgaH2SgefpQnYRfhry0GIYkGFvErVuiU8AVCcjBliNmMHV4ItFhTqob2/isRGf515kj/41HKy0o/1RwhoPsVy6GJjE9O5FIt5OK+lYOlp3ZPklTTAqU3nhEaaszOj3WmDionhCTAQ43yD5FGTaJC0YkE+ZycLK2maMVOhd3tWCgNPRBETp69CiXXnopoLS9aGxsRJIkvv/97/P0009rPsABjVpLAkzNHAtzGdhuwaSJcLBWuehaxiwOAfkHXBEmmsfDXA5m5SrnZe0hnasMmxQou/ag8r0s4xYFcDhCMgfNk3+4yxlst7D+kM7zv9ac0gmWc4uBIv/4ocpzE+UfGeZkZqC4qHHzP1vf4/SSXitCiYmJwaarQ4cOZe/evQDU1tb2uOW9oAPZAhdCCDGP611u34QLoSzLHKhTFCFLXQjBMnFCC0YZJX/jA6VlWWZd4HstGG0x+VsghRo63EXr9Ja/CRmDSlsdC/QX7AoLBEwDwRpS63RXhKyXMQa9UIRUhWf+/PmsXr0aUKo2f/e73+W2227jS1/6EosWLdJnlAMY2SILoToRPi2oobFVR3+1CRfC49VNVLdKSluNkSa2VegKiwRMqgUmtx3XWf4mXAiV0hAthLscwTtfy2CRC6EaN6W2W9ANE+Z/sK2K02FuW52uiDc/cxA65P/JsSpj5G9X19ikSZOYOXMmEydODLas+N///V9WrFhBWVkZ11xzDX/+8591G+iAxQK1ZABGpEQzPCmKtpCig7pggml8QyBtfvrwBKLCTG6rcToWuRDmBOTv9ck6y9/4C6Fq7p8xIsnctgpdYYGimgD5aTGkx4XT2u5na2GNfgcyYf6vD8j/vJxE4/qp9ZQE80MjQJF/ZnwEre1+thRU63cgk1yj56LHitDatWsZP348Dz30EGPHjuXGG29k48aN3HPPPbzxxhs8/PDDJCaa2MTOpsgWsQhIkhS0CunqJzYhRkitH2SZbJFQLFBdWGW+Ee4xE+Sv/p4NayHRG9RYCZPlr7RbULMHjZC/cYrwhwctLH+LeAQkSQqGDRiz/tvUNTZv3jyee+45SkpKeOKJJygsLGTBggWMGjWKX//615SWluo5zoGLRSYCdCwUHx0q1yeN0uftyI4x6ELo9fnZFLjDmZNrMbM4WEr+qntsnV4B87528ASyYwyyCLV4fcE7XMukzYdiIfmrivBHB3W6EHpboKFMeW5Q1liL1xdsq7JwdJohx+wVFgiWV1Hj53RThNqalFIxYF+LkEp0dDQ333wza9eu5dChQ1x77bU8+eSTDB8+nCuuuEKPMQ5oghah+hJTa8kAzMpNJszpoKi6mYJKHdIoPaeUVGFnGEQbsyjtLKqlsdVHtEtmXKYFyuqfTmgtEb+OvvkeMDs3GZdDoqCykaJqHRIf6k8pqcLOMIgxpsHv5oJqWtv9ZMZHkJ8WY8gxe4V6QbCA/Ofnp+KQ4GBZPadqm7U/QKcaYsbEam06VhWU/6h0C8u/rhj8flOHMicvBadD4kh5Ayd1kX9A2QuPg4gE7fffD/rVYiMvL4//+Z//4cc//jGxsbG8/fbbWo1r8BCdCq4IlFoy5tUSAYgOd3H+CMW9qctdQWgNIYcx3V0+OqhUMR4dL1ujrcLpxGaCwwX+dqWwoplDiXAzbbgif12yh9S73rihhsk/NG3eEm0VTic2I0T+JaYOJTE6jClZCYBOVqG6kPgQg2Shyn/haIvKP24oSA7wtXZYS0wiPtIdlL8u2WOh8YEWk0WfV6N169Zx0003kZGRwd13383nPvc5Nm7cqOXYBgedasmYbx4Pusd0WQhNiA84oHyPcYk6V0ztKw4nxAXK2pscMA0dZRT0WQgDv+9E42qIqAqdJd1ioMjfIiU0AC4MuI/UGwhNMSFQXv0eC0ZZ0C0G4HQrN0Ngifmvaxq9mjpvMbcY9FIROnXqFL/85S8ZNWoUCxcu5MiRI/zud7/j1KlTPPPMM1xwwQV6jXNgY5HMMejwo+uSRmlwxkBpXQuflXiQJBibYFFFCEICZs2Xv6owfHykinafxqZ6g+V/qraZI+UNOCSLBsqrWChOSJ3/G49U0tausfwNvhEqqGyksKoJl0NiTp4F4wNVgvI/bu446Jj/G45U2n7+94YeK0KXXHIJ2dnZPPHEE1x99dXs37+fDRs2cPPNNxMdHa3nGAc+FuhCrRKaRrlZ6zTK4B2hMRPhw8Dd4KSh8cS4DTlk3wjKv9DUYQBMGBpPYpSb+tZ2dgR6c2mGwQuhelc7JSuB+CgL/wBURdjkFGqA8UPiSIkJp7HNx9ZCred/iGvcAFRr0Pk5ScRGWFn+1gmYnqjO/5b2YG8+zbBoxhj0QhFyu9289NJLFBcX8+tf/5rRo0frOa7BhUWqC0PnNHrNzeMGm0Y/PKCMf+EoC1sDwFIp9E6HFMweU8+fZgTlb4xrTI1zs6xbTMUitYQAHA6JhYHsoQ81n//Glk74KCQ+yNJYyCPgdEjM1SuNfiBYhN544w2uvPJKnE6LFaQaCMRb50IIHQuH5hPBwDuC1nYfG44oaeAXWn0htJAiBLBorOIe+UBzRci4hbDd5w/K3/qKkHUswkCIIqTX/Ndf/pZPmw/FYvLXLU5oIChCAh2x0B0hwOxAGuWxCg3TqP2+jvRZA0zjnxbU0NTmIzU2nLEZFkybD8ViitCCUak4HRIHSusprtFI/r52Q2tI7Siqpb6lnfhIN5OHJeh+vH5hoRghgHl5qcE0as3mv8E1pCyfNh+KhTwCAPMDCRO7T9ZR1dCqzU5NqCHVG4QiZAUsVEsEIC7CzfThGqfR15cqKcIOV0eWhI6o1owLR6daM20+lNBgaZNriQAkRIUxPVuRv2ZWofqSgPzdEJOhzT7PwvufKYvuhaOVi7qlsVAtGYD4KDfThicA8JFW899z0tAaUpZPmw8l1COgRyHbXpIWF8HYzDhkWcMyGsEaUtGG1ZDqDUIRsgKdasmYW0tERa0yqlkavXq3GzcEnPr3+1LjGy60ulkcQuTvhQZrVGhfNEY5b2v2a6QIBc3ixtSQWr1fUYQWjTWmcGO/iBsSUktGh7T1PqC6k9ZqFSekWjvihxkif8unzYeilk9oa4BmHfu89QJ1/r+v2fwPiQ+1oGIqFCErEFpLxmJ+4o+PVtLaroGVKhgfoH+gbEFlIwWVjbidEnPzLR4oDYpiGDdUeW4R94gaJ7TpaJU23egNjA84VtHAsYpGXA4pqNBbGqc7RP7WmP8XBtPoNSqjYWANIdukzauERUFUYJ2yiHts8TjlBmLtwQqN13/rZYyBUISsQzCF1hoTYVxmHGmx4TS1+fjkmAZptOodgQELoZrtZPm02VAsFieUmxpDdnIUbSFBx/3CQEVItWJdMDKZOLvI30K1ZADGZsaSHhdOc0ivtn5h4IXQNmnzoVhs/k8aGk9abDgNre1s1mT9t26gNAhFyDpYbCF0OCSWBO4KVu3TwF1jYOqsrdxiKsE4IWvIX5IkLgqYxz/Qwjxu4EL4ftAtZif5q3FC1rgRkiSJhaPUKtMauMcNLJ1gm7T5UCymCDkcUtCtrM6nfiEUIUGPsFBRLZWl45Wg1tWfleH39zOILzRGREcaQ+5gLhxjwwuhRRZCgEVjlIVwzYFyDeRvzIWwprGNrceVOIvFdogPUrFY5ih0KBKa1BMzyDXW2NrOJrukzYeitp2pscaNEMCScYE4oc/KkPsbxG1Ce5XeIBQhq2CholoqF4xMIibcRXl9K7uKa/u3M4NqiGw8Ukmbz09WUiS5qTaqeG5BRWjGCEX+lQ2t7DlZ17+dGXRH+NGhcnx+mTEZsWQlRel6LE2xWC0ZgDn5KbgcEscqGzlW0dC/nRnkGvvoYAVt7X5ykqOsnzYfigXn/+zcFCLdTk7VtbDvlKd/OwvOf+P6DPYGoQhZBQsuhOEuZ/CucNVn/TCP+v2G3RGobrGLRqdZP202FAsuhGEuB/MDVbnX9CeN3sAaQmqWi63cYmC5WkKglNGYlasEG7/bH/e4329YDTF1nMvGZ9hs/ucojxZxjQNEuJ3B+b+6P+t/e1tHNrRwjQnOSmhRLQvUklBR3WP9ihNqrFBSgyVHR3aMDvj9ckf9IDu5xaCzImyBWjIqF6nusf7ECRhUQ6it3R+sH2Mrtxh0jhGy0Py/eIIir/f29mP+N5SCrw0kp67zv7XdF0yUWDZB/1pVmqK6xixSS0hlsRZxQp5iQAZXJERbM4tXKEJWIW4YIEF7CzRYo5YIKHECbqfE0YpGjpT30Tyu3uXGZoIrTLvBncaOolrKPK3EhLu4YKQN0mZDsWAtIVAL0sG+Ux5K61r6thODaghtKaimobWdlJhw61eTPh21loy3CZqqzB1LCEvHZSBJsKu4jpO1zX3biRr3Ej9M1xpiHx+poqG1nbTYcKbYTv6BG+G2BmjSuNltP7hoTBqOwPw/1Vf5h85/i1rphCJkFVxhIbVErGMeVczj/TSPql21dTaLv7NHMb8uGptGhNtmPfFCawlZKGAyJSacqVkJQD+qTBsUHxTMFhuTZv1q4qfjCu+ouG4h91hqbDjnZyuVgPtsFVLXs0R940Pe3dvhFrOd/N0RHdZSC63/yTHhwSrzfbYKGdxsty8IRchKWDBzAGCpmkb/WV8XQv0ngizLvBNYCC+ZoH8LD12wYJwQdFRn7rN7zABFSJbloKKuFoOzHRaME4IO99i7fVWEavTPGPT55WA18Yvt5hZTCc5/a63/qnuszzfCtcbcCPcHoQhZiWAtmUJTh3E6aj2hHSdqKff0wT1iQOq8arqPCnPaq35IKAkhcQIWQl0I1x+uxNPi7f0ODFCEDpbVc7K2mXCXg7l51oxDOCcWqyWkosbbfHq8mor6PjThNKB0wqeF1VQ3thEf6WbGCOv1suoRFr0RVtf/T45VUW/R+d9fhCJkJSw6EdLjIpgccI+s7otVwIDU+ZUBt9hFY2zoFlOx6B3hqPQY8tNiaPP5WbWvD/I34EKoNlmdm5dCZJhd5W9Ni9DQhEgmD4tHlvtoFVa/j46usfcCyRyLx6bjdtr0smZRi/DI1BhGpkbj9cl9a8JtUOmU/mDTX8wAxaIXQghxj/XpQqivaVSW5aAidOlEm7rFwLILoSRJXD5Z6YX35q5Tvd+BAXeEK/cELoR2dYuBJUtoqCzrj3tMZ9eYLMvBdWnZeDvL31rV5UNZomaP9cU9JixCgl6RYE2LEHQsMB8freydeVSWdY8R2nvSQ3FNM5Fup72qyZ6ORRUhgMsmKQrmxiOVVDe29fyDBtQQOlxWz2clHtxOiYvH2zQ+BCDeuvJXz+umo1XUNfVi/vu8gfRpdLMI7T3p4WStMv/nj7KpWxwsPf+XjlfT6Mt714TX5zWshlh/sI0iVF1dzZe//GXi4uJISEjga1/7Gg0NZ0/nXrhwIZIkdfq74447DBpxH1AXirpi5QJiIXJTYxiZ0gfzaGMleBuV5zpZhN4OcYvZ1i0CITEixeDXoOOzhoxMjWH8kDja/XLvrAIG1BB6fadipVowKpXEaP3KM+iORWsJgSL/0emxtPvl3mUP1RWD7AdnOETrc5Py7j5l/i8cnWpftzhYtpYQwNSsRIYmRNLQ2h5satwjPKd0l78W2EYR+vKXv8y+fftYvXo1b731FuvWreP2228/5+duu+02SkpKgn+/+c1vDBhtH4nNVC4Ysq9Di7YIkiSxJHBX8Naukp5/UDXzxg5RUkQ1RskWU8ZzyUQbWwMA4oZ01BKqt04tIZU+ucd0riEkyzKv71LmyhVT9CvWZwhqLaFWDzTXmDuWLlCzsd7pjSIcjA8brlsNKVUxt222mIpFa8mB0oT1yinK/H9tZy+uTcGwiGG61hDrL9YdWQj79+/n3Xff5dlnn2XmzJnMnTuXJ554gn//+9+cOnX2RTkqKoqMjIzgX1xcnEGj7gMOp2UDJgGunKxcaD44UE5tUw/dIzWFyqNOZvF9pzwcr2oiwu2wV7f5rnA4Oy6GFpS/Gn/1SUFVz7MHdY4P2FFUS1G1ki242G5tNU4nLApiAjEuFowTURWNdYcraGztocVa50DpI+X1HK1oxO2U7FdN/nQsWktO5aqpytg+OtiL9d8G8UEA+pX51JBNmzaRkJDAeeedF3xt8eLFOBwONm/ezNVXX93tZ//5z3/yj3/8g4yMDC6//HJ+8pOfEBXVfTPG1tZWWls7UkQ9HqXZnNfrxevtQ+pgN6j7On2fzvjhOKqP0V51DHnYBZodTwvyUyMZkxHLgdJ6XttRzJdnnNvV5ag6hhPwxw/Hp+H5U3k7YA2Yn59CmEPuUkbdnWsr4ozPwlFTqMh/yHnn/oCBZMS6mZIVz86iOt7cdZIbLui8uHV1nh3VBYr844bpIv9XtyvxJ0vGpuGWupa/nXAmZONoKKO98ihy6oQutzHr95ybHEF2UhTHq5t4f18Jy3tggXVUKfL3xWXh12G8r+9Q5D9rZBKRTm3PiRnn2Rk/DIenWJn/GVMNO25PGJEUwdiMWPaX1vPGzmK+dH4P1v/qwnPOfz3Pc0/3aQtFqLS0lLS0ztq+y+UiKSmJ0tLuzbTXX3892dnZDBkyhN27d/OjH/2IgwcP8sorr3T7mYceeogHHnjgjNdXrVp1VgWqr6xevbrT/5M9EjnA0a1rOHAyQfPj9Zcx4RIHcPL8h5+RWLnnnNtPPrGBHOBQRSsHV67UdCyyDC/tdAISmd4SVq48u3Xw9HNtRaZ4JLKBw5+u4VCR9bpnj3BK7MTJP9btJ6V6b5fbhJ7nKcc/Jhs4WNbMIY3l75Ph1W2K/DNai1m50nrZVr1lWqOLLODgJ6s4UuA+67Zm/J7zIhwcx8Gzq3dC0bl74k0r3EQWcKC0iSMay98vwz93KPLPlstZqfH+VYw8z1MbnQwHDm1ezeHjkYYdt6eMCpfYH1j/4yvOvf5PPb6J4fRs/utxnpuamnq0namK0D333MOvf/3rs26zf//+Pu8/NIZo4sSJZGZmsmjRIo4ePUpubm6Xn7n33ntZsWJF8H+Px0NWVhZLly7V1K3m9XpZvXo1S5Yswe3uWPAcHx+GDz8kPyWMkcuXa3Y8rTi/vpU3f7uOwgYYc/4CRqZGn3V75z+fgSrIO38xuZO0/T4HSuup+GQTYS4H3/vCRcSEd/1z7u5cWxHH+s9g3TpGp0WQZ0H5T/e08Npv11FQLzF59oUMTehYrLs6z85/PA3VkH/+IvImavt91h+upOGT7SRGufnuFxfbt35MCI61u2DDx4xJj2BUN/I38/ecX9bAmt9/zP46JzPmX0hKTPhZt3c+/wTUwOgLljJqrLby//hoFdWfbCM2wsUPr1+keaC0GefZsW4PrN/ImPRI8i04/6d5Wnjjt+s4Vi8xadaFDEs8u7Lm/MeflPk/YxF5E4z/PasenXNhqiL0gx/8gJtuuums24wcOZKMjAzKyzsHj7W3t1NdXU1GRs8D5GbOnAnAkSNHulWEwsPDCQ8/c3K73W5dJsMZ+00eAYCjrgiHBS/aQ5LczM9P4cODFby5p4y7lo0++wcCPmJXSi5o/H3e2qNkrywclUpizLnvnvSSoaao8vcUW1L+w5LdzMhJYnNBNe99VsHXF5w5jzqd50AxNVfySO3lv1eR/2WThhAVcfYLsm1IHgmA01OE8xzny4zf87hhiUzJSmBnUS1v7C7rUv6dCPQZ1EP+r+5UkiSumDyE2CjtEzFUDD3PSer8t+b6n5XsZtbIZD4+WsXKfeV868K8s38guP7nnVP+epznnu7P1Fuo1NRUxowZc9a/sLAwZs2aRW1tLdu2bQt+9oMPPsDv9weVm56wc+dOADIzLVx0LyFHebRgsJzK56YpAb2v7jiJ33+WNE9fu5I+C5oXU2tt9/Hfbcq+rz3Puj1seo2Fa4moBLPHdp8je0zHGkItXl+wCaiazTIgSMxRHtUkAwvyhUBsyH+2FiGfLc3b2wwNgVR79XtpRF2zN5i9NqDmf6I12+yEogZNv7rj5Nnl397Wsf5rLH+tsYUteezYsVx88cXcdtttbNmyhY0bN3LnnXfyxS9+kSFDlEXw5MmTjBkzhi1btgBw9OhRfv7zn7Nt2zYKCwt54403uOGGG5g/fz6TJk0y8+ucHXUi1JeAtw99vQxgybh0YiNcnKxt5pOCqu439BQrpQCcYR2dtTXivX1lVDe2kREXwYV27S3WFRauJaRyyYQMnA6JvSc9FFQ2dr9haA2hWG1Tm9/fX0Zjm49hiZHB7tgDgmB14SLLyv/yyUOICnNyrKKRbcfPkuavFlINi4VIbWX01u5TtLb7GZUew+Rh8Zru21RCq4tbVP4XT8ggzOXgSHkD+06dxfVUVwTI4I6CaGuv0bZQhEDJ/hozZgyLFi1i+fLlzJ07l6effjr4vtfr5eDBg8HgqLCwMN5//32WLl3KmDFj+MEPfsA111zDm2++adZX6BlRycoPByzXfFElwu0MVhp+ZftZakrU6FdD5IXNyr6/cH4WrgEQGxIkNjOkllAv6jUZSHJMOHMCjU3//elZ7lw71RDRNn5DLaJ4xeQhSJKk6b5NJW6Iojj6vUoxOgsSE+4KllL496dnWaNUq3ZiNmgsoxe3BqzB07MGlvxjrV1LDCAuwh1sufH62WoK1RQoj4k5mstfa2xzBUlKSuKFF16gvr6euro6nnvuOWJiOrJqcnJykGWZhQsXApCVlcXatWupqqqipaWFw4cP85vf/MbadYRA+cFYuNWGyjUB99g7e0poauumpkiwhlCOpsc+WtHAJ8eqcUgdZvoBg8VrCal8ZaZy5/rvLUU0t3Vz56pTDZHapjY+OqjEDF5p9yKKpxNaS8wG7rG3d5d033JHHb/G8j9UVs+uolpcDinophkwOF2WriWkorqjX995Cl934RE6rf96YBtFaFAR9BMXmjqMszE9O5HhSVE0tvmCnZ/PQKeu4//arFxgLxydxpAE66WY9hsbxAktGpvOsMRI6pq93d8V6qQIvbDlBF6fzLjMOEZnxGq6b0ugXjgsfCGcnp3IyNRomr0+3trdjeVSp/n/362KFeqiMWmkxg6QIPlQbBAntHB0GglRbsrrW9l4pLLrjYKKsD7FNLVEKEJWJMH6E0GSJD43Tblz6dY9VhNiGteIFq+PlwJF9K6fae1qpX3GBoqQ0yFxwyxFrs9/XNh10GRQEdJO/m3tfv76cSEAX5s7QrP9WoqgRbjQ1GGcDUmS+KIaNN2de0yHqtJen59XdyjrzYAKkg5Fnf8W9giEuRxcFbDG/nlDQdcbCYuQoF8kWt81BvC5qYoLZ8ORSgq7CprVYSK8u7eU2iYvQ+Ij7N1p/mwEFWFry/+687KIcDs4UFrP5oLqMzcI7TOlEW/vOUWZp5W02PBg9tqAI5g5Zm35f27aMFwOiZ1FtRwsrT9zgxrtLUIfHiinsqGNlJhwFg6kJIlQbJA5DHDLnBE4JFh7qOLs8heKkKBP2ORCODw5igtHpyLL8Kd1R8/cQAfT+AsBt9gXzh+O02HtALw+Y4M7QoCEqDCuDijDqpWmE9WBO8UkbSw3sizz7HplnzfOziHMNUCXr0TrW4QAUmLCWRTo79alVUgHRVgtmfG5aUMHRAHNLrGBRRiU9f+SCUrQ/NPrjnV+U5aFRUjQT2xiEQKCBbVe2lZMSV1zxxttjdBYoTzXaCIcLqtnS2E1Toc08IKkQ7GJRQDgxtnKb3XVZ2Wcqg2Rf3trRw0hjeT/ybFq9p3yEOF2cP2MAeoWBVvECKmo8/DVHcW0eEOC5ls80BxIrdfINXakvIE1+5W6RNdOH6bJPi2Jjdb/2+YrBUDf2HWS0rqQci/NNdAaSK23eMNVEIqQNVEtKM3V0NqFydFCnJeTxMwRSXh9Ms+sC/EVq5M4Ih4iEzQ51gtblDuki8akkRGvXyVZ01EvhJ5ipSiZhRmTEceskcn4/DIvbCnueKP2BEoNkWjNaoj8eYNy1/n56cNIjA7TZJ+WRJV/Qxm09axXklnMz09laEIkNU1e/vFJyIVbVeIikyBcm4D23605jF9W6pjlpw/AIHkVdf33nFSKklqYKVkJzAis/3/ZGLr+FyqPMRkQpn2PTq0RipAViYjrKEBmg7sC1Sr0wpbjVDW0Ki/Wausfrmls46VtAzxIWiUmXaklJfstbx4HxU0F8OK2YoKZ9KFuMQ1qiBytaOD9/eVIkhKbMKCJTITwQJFAi8vf5XTwnUXK/P/jR0dpbA1cuDUOlD5UVh+sZP69xfma7NOyxKSDM1wpRuspPvf2JvP1gFXohc0nOkop2MgtBkIRsi42iRMCmJefwqRh8bR4/Tyn3hVonDr5xAdHqG9pZ0xGLAvyB2iQpIok2aLVgsrisWlBq8D2qoDSE1pMTQOeC2SmLBqTzsjUmHNsPQCwSZwQKEHTOclRVDW2dVgFNA6UfnzNYWQZLh6fwfghA6iSdFc4HB21pCyuCINSxiQvLYb61nb+FbDadyhC1k+dB6EIWRcb+YklSQpahf728XE8LV5NU+ePVzXy908KAfif5WNxDNQg6VASA1aPmoKzb2cBXE4HXw2k0n94ykG7z6/pHWF1YxsvB0om3DpvgFuDVBLtcyPkdjr4/pJRAPxp3THqmryaBkofLK1n5R6lVtF3B7o1SMUmCRMADofE7fMUq9BzGwppa9d2/huBUISsSjBzwPoTAWDJ2HRGpSt3BX/fdFxT19hv3juI1yczLz+F+aMGuDVIRc20qra+IgTwpfOHkxDpprRZ4u+bizTNGPv7puO0eP1MGBrHzBFJ/d6fLbCRRRDg8klDGJ0eS31LO8+sP6bpjdDjaw4hy7B8YgZjMy3eGUArbFBLLpQrpw4hLTacUk8Lb+46JRQhgUbYbCI4HBLfXKhYhf68oQC/eiFUa2L0kR0nanh7dwmSpFiDBg3BC6E9FKH4KDd3LVXu1h//4AjtVYF02sT+KUKFlY38ce0RAG6bN3Jg9ZU6GzZosxOKwyGxYqliFXpuYwHt1YXKG/2c/5+d8rByTymSBN9dNKp/g7QTNrsRDnc5uWlODgBPfnQEv1CEBJpgoxRqlcsmZTI8KYrqxlZ86kLYj4kgyzIPrTwAKL3NBs3dINjOIgRw7bShZMfINLa2dyjC/ZT/Pa/spsXrZ3ZuMlcM1AKKXRF0jRaaOozesHRcOpOHxdPU1o6vWhuL0ONrDgGwfGLmwGyn0h02Co1Q+fLMbFJiwjle4YHaQJC3UIQE/SI0WLqr9gUWxOV0cOdFeSTjwe1rRkbqCPrrA6s/K2NLYTXhLgc/WDqI7gah84XQJvJ3OCSuHeEjQ6olTG5Dlpz9ihH515YiPjlWTaTbya8+N2nwWIOgc4yQTeQvSRI/WDqaROoJ9wfS/uP7Pv93F9fy3r4yJAm+t2iQxAap2ChGUCU+0s1Dn5tIplSFAx9+Z5iSPm8DhCJkVdQLSFsDNHXRvsCiXDt9GJ8boeRQV0hJNPqcfdqP1+fnV+8q1qCvzR1BZvwAbK56NuKzQHJAezPUd9PU1oJkxcCt45QLdwkptMl9k39JXTO/XLkfgLuWjWZ4svVrkWhKfBYgBeZ/ldmj6THz8lO4eKhSQqPWmYzf2bemqLVNbXzrhe0AXDF5yMCuG9QVqkW4oQxaG8wdSy9YMi6dr+Qr87/In0ZTu9/kEfUMoQhZFXdEhzZt4S70pyNJEt+drhS7K/Cl8pPX9/ZpP4+uPsSxikaSosO4Y2GulkO0B64wiA9Uz7WRewTgi3lKLZlj7Sk8u+HYObY+E1mW+d9X99LQ2s7U4QncFKhTNKhwR0Cs0r7ATvKXJIk7JrsAOOJN5jfvHez1Ptp9fu58YQdF1c0MT4rigSvGaz1M6xOZGFJLrtDUofSWG8YqitDR9hR+827v5W8GQhGyMjb0EwPENCl9h4rlVF7ZfpKXt/WuKNif1h7lDx8pvcv+d/lY4iLcmo/RFtjQPA4Q2ajI/4Sczu/WHOZQWe+qo7++8xQfHCgnzOngN9dMGrg95c6FzTLHVLIdSmudIjmVp9Ye5cWt3XSn74Zfv3uADUcqiXQ7efqG6SREDeAq4mcjSUlJp7r3NxNmEhWY/0VyKs9/XMjHRytNHtG5EYqQlbFRUcVOBMY7dISS5fWT1/dyrKJn5t1/bj7OQ+8oLrEfXTyGawZyT6FzYcOAaQApcOGWE0fQ4vVz7VOb2NJVd/ou2FlUy0/f3AfAty/KG3wukVBsVFSxE4ELd0a2Mv//55U9bDraM/feaztO8kygse7D101mTMYgSpA4HZsqQurvNSVrNAB3/3c31Y3WbhUkFCErY9MLoToRZkydyqyRyTS1+bjjH9v47JTnrB97fedJfvya4kr75sJcvjEYXWKh2NQipMr/igvnMG14AnXNXr7y7Galvkg3yLLMcxsKuPapj6lt8jJhaNzgdImGYqPmq50IrFcXnH8+l03KpN0vc8c/tlFQ2XjWj+09WcePXt4NwLcuzGX5xEzdh2ppbK4IXTR7BsMSIzlZ28zFj63jo4Pl5o7rLAhFyMrYdiIoC7cjaQSPfXEKKTFhHCpr4NIn1nPPy7spr2/ptLmnxct/Pj3Bihd3Ictww6xs7l422oyRWwubKsJS4MIdm5nHC7ddwLLx6bT5/Hz7Xzt4et1R5NOyoOqavXzjH9v52Vuf4fXJXDIhgxduuwC3c5AvTzZ1jVGluLWlpFx+e+1kpmQpyvD1z3zC0+uOUlHf2mnzI+UN/M+re/j8Ux/T2u7nwtGprFgi5r991/9CACJTc3n2xvPITY2mvL6Vm/7yKT9+bQ9NbWc2kvX6odXEwGqXaUcWnJukwB2xnSaCrx3q1BoS2aTHRfDat+bwq3cO8NbuEv79aRFv7jrFrfNG0uL1selYFXtP1uEPXBs/N3UoP718/OBKle4OmxVVBHD5mpGaAjEBiSOIcDv5w5en8+Dbn/GXjYX8cuUBXt95ipSYcOIi3cRFuFh/uJIT1U24nRL/u3wsN87OEfIH2xVVBKCtCeoDlr/kXCLcTp654Tw+98eNFFU388uVB/j1uwe5cHQqi8ams2pfKR8erAh+fHp2Io99cergjQsLxYa1pGiuheYa5XliNmPCY3n7O/P41TsHeP7jQv7xyQk2HqnikgkZnKxtprimmaLqJsrrXbizy7lyWt/LLfQHoQhZGfWOwHNSWWDCbJBC7ClWuiY7w4NZb8MSo/j99dO4aXY1P3/rM3YV1/H4msOdPjYiJZpLJmSwYsmowdFLrCeoC2FTFbR4IML68RJRrQHzd1RycLxOh8T9l49naEIkv1i5n31duEiHJUby5PXTmJyVYOBoLY6qCNcVKzcYThss1+pFOyI+mPWUGhvOyu/M481dJby4tYidRbW8v7+c9/crvxVJUprpfm3uCC4YmSSUYBV1/a8rBm+LkklodVQ3blQKhCvxfRFuJz+9YjyLxqZx9393U1DZGEyGCeVkXbORI+2EDWbWICYqSVlQWuqUBSZ9nNkjOjfBrtPDlS7KIZyXk8Sr35zD67tO8tK2YoYmRDIrN5kLRiYPvjpBPSEiTlEomqoU+WdOMntE5yS6LaAIdVFR9tZ5I7loTBpHKxqpa/biafbiafES5nLw5RnZxEcN0uzA7ohJV24ofK3KDYYdqvRWBy5wSSMVDSdAbISb62cO5/qZwzlcVs9L24r5+GgV04YncNOcEYxIiTZpwBYmOgXCYqGtXlEwUm3gLgz2mMs54615+am89735/GndUTwtXrISoxiWGEVmnJv9Wzdy7ZwzP2MUQhGyMpKkLCindigLjC0UoULlsZvS+g6HxNVTh3H11EGcDdYbEkcEFKECeyhCqkWomx5jI1NjGJkaY+CIbIzDocyjykPKvLKFIhRw4yd1H+ienx7LvYOpb2BfkSQlTrB0t3JebaEIFSqP3az/8VFufnjxmE6veb1einZhqiVwkEcj2gC7xQnZrNme5bFZwHTQNaZB13kB9osTqgqxCAn6j90Cpm26/gtFyOqoE6HqTJ+qJalSOoWTnGfuOAYKNkuh73CNCUVIE+wWMB+0CAlFSBOCipBN5C8UIYEuJNvMItQD07igF9jMIhTdGsgAstlCaFnU+W+XGyH1d5os5r8mBOe/TdZ/oQgJdMFOplG/v2PBFguhNtjJIuDzEtkWSJ0XrjFtUC2rdlCEvM1KUDcIi5BW2Gr990HtCeW5UIQEmqJaVtQUeitTf0rplu5wdcQ2CPqH6mKqK4Z2a5epx1OMAz+yK6RhsKB/hF4I/Rbv5K1aA8ID2Y6C/qPKv/YE+LzmjuVceE6B36us/3FDzR5NrxCKkNVRU+jB+oW11LvWxBx71DyxA7EZ4IoE2Q91vWteaTTSWUonCPpIQrZyYWlv7ihUaFVC44NELSBtiFHnf4i1xaqoVuuE4eBwmjuWXiJWK6ujptBDR40Oq6IGSov4IO2QJNu4x6TA+OSEHHMHMpBwujqsgur8sioiY0x7HI4ON7PF5z+VgSK5NkyUEYqQHbBLCr06PhtOBEtjl4Dp2kJA6Tov0JBgnJDFFSGRMaYPiTaZ/8H40Hxzx9EHhCJkB+ySQh9MnRcWIU2xSc8h6RzF1AR9xC6ZY9UiUUIX7JI5VqVahOwnf6EI2QG7pNALRUgfbGIRUmOEhGtMY2yjCAV+n8IipC12yRxT1/8UYRES6IEdJoKvvcNiIVxj2mKHGCFZhtpAjJBwjWmLHVxj3hYlsxFEjKDW2GH9b2/rqH5uw/VfKEJ2IDSF3mteh96zUnsc/O3gioDYIWaPZmAR6hqTZVOH0i2NlUhtjchIkJBl9mgGFuqFpabQuinUNYWArDQJjU4xezQDC1URqilUavVYkZpCJbPNHQ2xmWaPptcIRcgORCVBeCCF3qrukdCK0iJ1WlsShoPkAG8TNJSbPZquCVirWtyJijIs0I7YTHBHKRcaq/YcC87/ESJ1Xmvih4HDDb42pVaPFQmND7Kh/MUVyw5IEiRb3DwajA8S8QGa4wqDuGHKc6vKP5A62xAuCilqjiR1WIWt6h4LZowKt5jmOJwdCQhWnf82jg8CGylCv/jFL5g9ezZRUVEkJCT06DOyLHPfffeRmZlJZGQkixcv5vDhw/oOVC+sXktINFvVl2SLXwgrDwFQHyHcorpgdflXixpCumL1OCEb1xACGylCbW1tXHvttXzjG9/o8Wd+85vf8Lvf/Y6nnnqKzZs3Ex0dzbJly2hpadFxpDqRZPHMkWANCXtOBMuTMkp5DCgclkO1CEXYLz7AFqjzyqo3QqKGkL5YXRGycQ0hANv0QXjggQcAeP7553u0vSzLPPbYY/z4xz/myiuvBOBvf/sb6enpvPbaa3zxi1/Ua6j6YPUU+mBVWWEa1wXV5GxZRUgZV0O4UIR0weqZY1UhMYIC7QkGTFs0RtTGNYTARhah3lJQUEBpaSmLFy8OvhYfH8/MmTPZtGmTiSPrI1a+I/C2dPTBEhYhfUgdrTxaURHyeYMLtHCN6YSVu9C3t3bMf2ER0ofg+m9BRai5FhorlOc2Xf9tYxHqLaWlpQCkp6d3ej09PT34Xle0trbS2toa/N/j8QDg9XrxerVLXVX31eN9xg3HDeA5ibfJA+5IzcbSbyoO40ZGDo+lPSweNDxPWtDrc21F4kfgBuSaQtqbG8AVbvaIOqg8jNvfjuyOosWdaO/zbFVC539jHWCh33PlUWX+h0XTHp5oufnfVyy1bsQOU+Z/9THa29oslZkllR3EBcjRabQ7I3stfz3Pc0/3aaoidM899/DrX//6rNvs37+fMWPGGDQieOihh4JuuFBWrVpFVFSU5sdbvXp1zzaUZZY7o3D7mlj/xt+pjxym+Vj6SkbtNmYCtc4U1r3zjtnD6ZYen2srIsssd0Ti9jez/vW/WlL+da40kCR7n2cLc4kzmjBfI5vf+RdEDbfMeU6v28EFQJ0zmbUWnv99xQrnWfK3czkSkreJNW/8i1Z3gtlDCjKseiPTgSopiY0rV/Z5P3qc56amph5tZ6oi9IMf/ICbbrrprNuMHNk3U2tGhpLGW1ZWRmZmR9xCWVkZU6ZM6fZz9957LytWrAj+7/F4yMrKYunSpcTFxfVpLF3h9XpZvXo1S5Yswe129+gzzrJRULKT+ROGIY9ertlY+otj0xEogPgRU1m+3DrjUunLubYizvJxcGob88dlII+1znl2fHwYCiBmxDQA259nq+IsHwsntzJ7TDrvnrDOeXZsPg7HIC57iiXnf1+x3LpxfDjUHmfx1Bzk4bPNHk0Qx9pdcByS8s7vk/z1PM+qR+dcmKoIpaamkpqaqsu+R4wYQUZGBmvWrAkqPh6Ph82bN5818yw8PJzw8DPdDm63W5fJ0Kv9JudCyU5cdYVghYmpEogPcaSMwmGlcZ2GXjI0jNTRcGobrppj1pJ/IJNJSh0NngFwnq1KSj6c3KrMf0Zb5zzXKUUeHSl5lp7/fcUy5zklH2qP46otgNwFZo+mgxolbtWRNrpf8tfjPPd0f7YJlj5x4gQ7d+7kxIkT+Hw+du7cyc6dO2loaAhuM2bMGF599VUAJEnie9/7Hg8++CBvvPEGe/bs4YYbbmDIkCFcddVVJn2LfpJk0cyxYDE1ewbK2YZg5thBc8dxOoEAbtmmqbO2ITD/Jaul0FeJGkKGkBoIEamw2PwfADXkbBMsfd999/HXv/41+P/UqVMB+PDDD1m4cCEABw8epK6uLrjND3/4QxobG7n99tupra1l7ty5vPvuu0RE2LQFgLrQWC1zRFSVNgYrZo7JcrCGkJycDwUWzGoZKIR2oU8zdyidEDWEjEGd/xUHzB1HKH6/7WsIgY0Uoeeff/6cNYTk0xpSSpLEz372M372s5/pODIDCdYSstDFprUeGsqU56KGiL4EiyoeVhYgK/R0ayiH1jqlF1rSSMBCv82BRuCOW6q2kCLUqXSGmP+6YkWLUH2J0gPR4epoA2JDLLCSCnpMsAt9MbQ1mjsWFfVuICoFIhNMHcqAJzFHWXC8TeA5afZoFFTrVGKOtVL6ByIBi4vUXI27veEcGxtE5SGQ/RARDzHp595e0HfUGyHPSWipO/u2RqEWUkzMAacF4qj6iFCE7ER0sqJwgHXuCgaAf9g2ON0dyrBV3GPqONRFWqAf4TEQqxSsjGktM3kwAVQ3Tdo4S9W2GZBEJkBsIAO6wiLzf4Cs/0IRshtpY5VHq/iJRddpYwkGTFukebA6Dpt2nbYdgXkW3dp9UVhDKd+vPKYaV+ttUGO1OKFKoQgJzEBVhMo/M3ccKsE7AqEIGYLVmq8Ki5CxBOZZTEuJyQMJELQIjTV3HIOFVIvdCAuLkMAUgoqQVSaC6DpvKFbLHAtahIQiZAiBeRZjGYtQ4IZMWISMIWgRskpoxMCwCAtFyG6odwSqSdpMZLnjjkBkjBmDlbrQtzVB3QnluVCEjCGgCFnCNdbWBDVKMUVhETIIK2WOtbdCbWD+2/xGWChCdiMtMBE8xdDSs/LhutFQBi21Suq0cI0Zg1qro6FM6fpsJqoSHJUMUUnmjmWwELQIlSk3ImZSeRCQITIJovXpECA4DdUiVHcCWk3OHKwuUDIGw2JtnzEoFCG7EZkYkjlgsnusbJ/ymJQL7khzxzJYiIgLZg6ZHjAt4oOMJyEb2eHC5W9VbobMpFxkjBlOVBJEB4pImV1hPjQ+1ObyF4qQHUmziHtMVYTSx5k7jsGGVdxjQUXI3vEBtsIVFlQ8JXX+mUVFYP1JE/FBhmKVOKEBEh8EQhGyJ1aJE1IDJdMnmDuOwYZVMseERcgU5DTlxkOqMHv+ByxCIlDaWKxSQmWAZIyBUITsSXAiWMQilCYsQoZilcwxkTFmCkFFqNwqFiERKG0oVrEIqccXFiGBKVjBNeZr75gIwjVmLFZwjfl9HXeEA2AhtBNy2ngAJDNribU2dGQMpQpFyFCCmWMmWoT8figbOB4BoQjZEfWOoKEMmqrNGUP1UfC1gjsaEnLMGcNgRbXAVBdAe5s5Y6grgvYWcIZBgn2bLdoR1SJE1RGl6akZqDdB0alK6x+BcaiKUM1xpYSBGdQUgLcRXBEDonSKUITsSHgsxA9XnptlFSrbqzymjbVGF/TBRGymkrIq+zpanBiN6hZLzgOH05wxDFZiMmh1xiDJfvOsAhWitYZpRKcoJSuQOwKWjaZ0j/KYNhacLnPGoCHiCmZXzG61ETSLjjfn+IMZSTLfPSYyxsxDkvBEZinPzcocU2/ARHygOagKqFkdBlRFKGOiOcfXGKEI2ZU0k/3EwdR5oQiZgtmZYyJjzFRMV4SCPcaERcgUzG6+qnoE0oUiJDAT9U7MLNdYuVCETMXsLvTBjBGhCJlBUBEyK3MsmDovAqVNIdh81aTMsdKAIpRh/0BpEIqQfQmaRvcbX2q/xdORMSJM4+ag3hGa4Rr1+6Fkt/I8Y5LxxxfgiTDRItTi6ahqLSxC5mCmRaipukP+A+RGWChCdiV1NCBBczU0lBt7bNUKFTtE9JgyC9U3X75faX5oJNVHAxkjkSJGyCTqI4ciI0FjhfHzX7VCxGQoLX8ExhPMHCswPnNQVb4ThkNEvLHH1gmhCNkVdyQkjVCeG11YsVy01jCdhGyISAC/13j3aMku5TFjosgYMwmfIxySRir/qPEaRqFaIYU1yDxi0pT5L/s76nkZRTBQeuBYg4UiZGfMihMSgdLmI0kwZIryvGSnscdWj5c52djjCjqhFlY03D1WEdJsVWAOkmReYcVgoPTAiA8CoQjZm9A4ISNRU+fThCJkKqoicmqnscdVLUJCETKVYGFFoxWhclFDyBKYFScUtAgJRUhgBcxotSHLwiJkFTKnKI+qYmIEsiwUIYvQoQgZ7BoLWoRExpipmGER8nk7jicsQgJLENqF2KjMMc9JaK0Dh0ukTpuN6hor26csUEZQUwgtdUprDXEhNBVZvRGpOGic/Jtrob5Eea5aJATmoM6/UgMV4crD4GtTKtsPoNY6QhGyM8n5ikLS6lEUFCNQrUEpo8AVZswxBV2TOALC/7+9O4+Ook77Bf7tJekkZCEkkI0EEoiENQZiWBVkkV24jvj6DgqMXme54QzLjFdgDqAjEHFeEURGwJlxuUccfR1BXBBiBEaQBEKMgISQsJgEApHFrJClu+4fv3Q3eQkhS1dVd9X3c05OV3r51ZPq7clveSpInPNNqV5Be29QWH/A5KXMPql5QdGAt7/4Yrp6Rpl92nsDAqM0s2LIY9n/Ebp+TrlzTt46LKahUytp5y/RI7O384R3SpVatydCnCipPoMBiGhcuaHUhGkOi7kPg9H5PlRqeIzzg9yHb7A41x8AXMhRZp+XGxMhDQ2LAUyEPJ9jnpBCEyY5P8i9OFaOKTRPiImQewlTeOWYPeHm+989RA0RlxeyldmfxipK2zER8nT2wnoXv1Nmf2U82apbsU+YVmLlGCdKux+lE6HiI+IyOkWZ/VHLopLF5YWjyuxPY+cYs2Mi5Om63ycu7R9Qcmqoc55sk4mQe7AnQpdPANYGefdVcQGouSLmpbF0gnuwD1EokQjVVjr/EbJ/7pC6HD1CR+VfMFN5WVQyNxg1t1CCiZCnixoiXpgVJUC5zBOmr5wGbA1igm5glLz7otbpEidWcDTclH8Zrb03qGtfwMtH3n1R69iru1eUADeuy7uvC0cBSEBQDBAQLu++qHXCB4gVnDVXxYpOOdknSof0Brz95N2XwpgIeTqLv7N3puSwvPu6dVjMYJB3X9Q6RqNzmErueUIcFnM/PkEiMQGchU7lUtLY69w9Wd79UOuZLc7pEXIPj2l0ojTAREgbujeO18s9PGafh8JhMfei1Kk2mAi5J6XmCXF+kHu6dXhMThqdKA0wEdIG+weT3D1CRd827m+ovPuhtlHqVBtMhNyTY8GEjEuoJemWHiHOD3IrSiVCGp0oDTAR0gZ7IlT6PdBQK88+aquA0mNiu8dwefZB7WOfMH3puHwTpisvN1YUNmjyP0KPFjNMXP54UL59XDsL3LgGmCyaOuu4JthXjpV+L1+F8foboqo0oMn3PxMhLQiOBfxCRYVZueaJlBwGJCvQOQYI6i7PPqh9QnqLCsMNN4CrBfLsw/66Cr0H8O4kzz6ofaJTAIMJ+LkI+LlYnn3Ye4MiEllR3t10iRNzxRpuyjc8WnZSfP77dgECIuTZh4qYCGmBweDsFSrOkmcfPzYOi8WMkKd9aj+j8ZbhkVx59mFPhOzzkch9WAKcw5VFh+TZR3HjsDvnB7kfoxGIHCy25RoeO9/Y2xidosmFMkyEtMJRT0imeUI/Nn7AcljMPTnORJ8rT/v2djk/yD31aPwHRa7hMa4Yc2/250WuU22c/0Zc9rxfnvZVxkRIKxwTpo+4vrBWQ62zhDt7hNyT3KfasM8PYyLknnqOEpf2nltXqqt2Drl0Z4+QW5LzVBvWBuc/wrFMhFS1evVqjBgxAn5+fujcuXOrHjNv3jwYDIYmP5MmTZI3ULVEDhbzBCpLgfIS17Z9MVeMP/uFAqHxrm2bXMPRI3QMsFld23bNNaC8SGyHa2/FiCbEDANgEEVPq35ybdsXvxPzQwIigSAWUnVL9kTop3zgZoVr2y7NBeoqAZ/OmlwxBnhQIlRXV4dZs2bhd7/7XZseN2nSJJSWljp+3n//fZkiVJm3n/NLytXL6O3d7T2Ga3J8WBNC4wEvP6C+Grha6Nq27cMi9kmZ5H58g531hFw9POaYH8Rl827Lv1tjYU3J9eedPPdvcdlzlJiPpEEe81e98MILWLRoEQYObFtGarFYEB4e7vgJDg6WKUI34Jgw7eJEyD4Bk8Ni7stocvYKufqLsDBDXMaOdm275FqOeUIuHh4raRxuYf0g9xYl04Rpjc8PAgCz2gHIbd++fejWrRuCg4MxduxYrFq1CiEhIXe8f21tLWprnbV4KipEN2N9fT3q611Xo8HelivbNEQMhhmArSgLVle1a7PCXJQJA4D6qBTAhfEqRY5j7Y6MsaNhKvoWttN7YE180mXtmgvTYQDQEPsgpBaOoV6Os9rudJwN3YfCfHgrpPMH0eCq50CSYC45LJ7/iMEtPv9a42mvZ2NEEkwnd8BWku26z39rnfPzP3q4LJ//ch7n1rap6URo0qRJeOSRRxAbG4szZ85g2bJlmDx5Mg4dOgSTydTsY9LS0vDCCy/cdv2ePXvg5+f6E82lp6e7rC2/2mpMAIDSY/jysx2wGTte7yOw5kc8WFuBeqMPvsgpBgwyn9hVRq481u4oqMYHYwDYCvdi12c7IRk7/vb2q72MCdfOwgYTdp++gYYzX9z1MVo/zu7ifx5nS301JgFA2Q9I3/nfqDd3vN6TX+1PmFD9E2wGE3blXoTt2N2ff63xlNdzl6o63A+g9uy32POFa56nLlWncX99DWrNAfjyyFnAcN4l7TZHjuNcU1PTqvupmggtWbIEa9eubfE+eXl5SEhIaFf7jz/+uGN74MCBGDRoEHr16oV9+/Zh3LhxzT5m6dKlWLx4seP3iooKREdH46GHHkJgYGC74mhOfX090tPTMWHCBHh5ebmmUUmC9ONaGKvLMDkxHFL0sA43aTzyJpAPmHqOwJSp01wQpPJkOdbuSLJB2rAJ5uoyTBnQGVLPBzrcpDH7H8BJADFD8dD0X7R4X90cZ5W1dJyl0tdguFqIh/oGQoqf2OF9GU58JJ7/iERMmjazw+15Eo97PdeNhvRfL8G3/jqmjEoCAjte+NB4IA8oALx6Pyjb57+cx9k+onM3qiZCf/jDHzBv3rwW7xMXF+ey/cXFxSE0NBSFhYV3TIQsFgssFstt13t5ecnyZnB5u9EpwKnPYC7NAeJcMKZbIgo0GnuOgNETPgxaINdz6FZ6jwe+3wbz2a+B+OZf421ybi8AwBg/odXPvy6Osxto9jj3GAlcLYS5JAvo54IvrlIx8dYYneLx7//28pjXs1dnoFs/4PJxeF08DIQ82vE2i8R8Q2PcA7I//3Ic59a2p2oi1LVrV3Tt2lWx/ZWUlODq1auIiNBeiXCHxkTIJROmJck58bLHyI63R/KLF4kQCr8CJq7uWFsNtc4VI73Hdzw2kl+PkUDOO66bMM8TrXqW+PHA5eNA3qfAwA4mQg21zu8RjS+U8JhVY0VFRcjNzUVRURGsVityc3ORm5uLqqoqx30SEhKwfft2AEBVVRWeffZZZGZm4vz588jIyMCMGTPQu3dvTJzY8S5jt9X9lpVjHS2seO0sUF0GmLydJdzJvcU9CBiMwE+nxLmnOqIoUyzH9w9j/SBPYV85djFXnCi5I25cBy41FtJkIuQZ+j4sLgvSxYlSO6LkiKgf5x+m+fpxHpMIrVixAklJSVi5ciWqqqqQlJSEpKQkZGc7K2nm5+ejvLwcAGAymXDs2DE8/PDDuOeee/D0009jyJAh+Oabb5od+tKMyHsBs69IYDpaT8LeGxQ1BPDy6XBopAC/Ls5kuKCDkw8LvxKXvcayfpSn6Bwt6slI1o7XE8v7DLA1AN36A8E9XBMfySsyCQjsLv6BOfN1x9py1A+6X/Pvf49JhN5++21IknTbz5gxYxz3kSTJMefI19cXu3fvRllZGerq6nD+/Hls3boVYWFh6vwBSvHyBRKmiO3jH3WsLXv9oB6sH+RR4huHseyJTHvZ6wdxWMyz9Gwcxu5oPaET/xKXAx7pWDukHIMB6DtdbOd92rG2zjXWD9LoaTVu5TGJELXBwFni8sS/Ona6Bfs8AxZS9Cy9J4jLs/vFOH97VFwEyn4AYBDDbeQ5XFFYseon4Nx+sc1EyLP0axwey/8CsLazNk9djXN+mIYLKdoxEdKiXuPEeWGqLgHnD7SvjdLvgevnAaOXs2I1eYbwQUCnbqJ73N6r11b23qCowUCnOxcgJTdkX9hQkg3UVravjbxPAMkmhlq6uG7lLikgeijQqStws9w5vNVWxVmArR4IjNLF889ESIvM3kC/GWL7RDuHx46+Iy77TgN8XFc/iRRgNALxjb1C7Z0nZB9W47CY5+kSB4TEA9Za4Ph/t6+NEx+LywEt144iN2Q0AQlTxXZ7h8duPa2GxucHAUyEtMs+PHbyk7YPj9RVA8c+FNtD5rk0LFKIPYFpTyJkbQDO7m3aDnkOgwFI/pXYPvKPtq8eLb/gHFbr/79cGxspw7567NTnbZ8eIUliojygi/lBABMh7eoxAgiIFN2jbZ00e+JjoK4SCI4FXFCdmFTQq3EZ/ZV84PqPbXvshaPidePTmWUTPFXifwJmH1FTpiT77ve/1ckdACQgZjgQ1F2O6EhuPe8HfILE6uHirLY9tjBDfG54BzgTKo1jIqRVRpNzkmNbu8dzGofFhswVwyzkeXyDncvoC9vYK+RYNv8gYNL06Qi1y68L0L/x/Z/997Y91rFajMNiHsvsDfRpXD3c1uGxzE3icvAc3UyL4LecltmHx/J3tX7S5KUTYrWA0QzcO1u+2Eh+9mX0p9pwAsaGOuB447Aoh8U8231Pi8sTHwM111r3mGvnRI+gweicZ0ie6dZl9K0dHr18UtQfMhiBob+RLzY3w0RIyyISxaTJhptirLg17L1BfaYA/t3ki43k12+m+EA7kyGqRLfG0bfFasFO3cTjyXNFDREVwa21QO621j3mB1GZH7EP8P3v6XqNBbw6AeXFrS+ua+8N6vuwropoMhHSMoPBeb6Z1gyP1dUAxz4Q25wk7flC44GkJ8X2l0sBm63l+9+sAPavFdtjngMs/vLGR/IyGIDkxl6h7FZOmuZqMe3w8nWuHm3N8FhVmXORzPBU+eJyQ0yEtG5AYyJ0Zq8oktaSk5+ISbKdY1hETyse/BPg7Q9czLl7KYVvNwI1V4CQ3sDgucrER/IaOEtMer12xlkg8U5+yheTq41eQIILzlxP6rMPj+W8A1SUtnzfI38DrHXivHI6qx3HREjrQnuLomiSVfxX2JKjb4vLwZwkrRkBYcCoRWL7q+dFr19zKi8Bh14X2+NWACYvRcIjmVn8gcT/ENstvf9tNiDjz2K711gx2Zo8X9+HxfBozVXg42fuvJS+/oZIhADd9QYBTIT0wd49vm+Ns+vzfyo7BRRnAgYTkPSEcrGR/IanAkHRQMUF4NCm5u+z7yWgvkb8N6iTJbO6kfyUuDz1uUh4m5O+HDj1GWDyBh74o3KxkbzM3sCjb4u5Que/Ab5Z1/z9jn0okqWgGCBhuqIhugMmQnqQ9AQw9Ldie8fvgNO7m95efBh4r3GFWZ/JQEC4svGRvLx8gfHPi+0Dr97+ZXilAMh5V2xP+LMuKsnqSlh/IHqYOJP8x78WBRNvdfhNZ2/gjL/qblhE80J7A9MaE6B9a24/B11DHZD5V7E97Le6LJnBREgPDAZgYhow8DHxYfjhHODHQ6I7/Jt1wD8mAeVFQHBPYPwLakdLchjwCyAqWZx/LOPPQPUVoLwEuFII7Fkuhk7vmew8YSdpy4NLAZNFzBP66zAg5/+JydP5XwK7/q+4z9jlwKBZ6sZJ8kh8XBTZlGzAv/63KKdwsxw4+BqwIRH46ZSYS2ZfXKEz+kv99MpoBGb+Vbz4C3YD2/4DiBjkPKfMgF8A09brpoCW7hgMwKQ04O8TgNz3xE+T243OXiPSnrgxwG+/AXb8H+BCNrBzvqgXVZItvhwHzwHu/4PaUZKcpvyX6P2/dgZ4e5pYVl9bIW7zDwOm/EW3n//sEdITkxcw621ROr+2XCRBZl/g4deBX/xdt28C3YhOaVoWweQNWIIA/3BgzFKgW4JqoZECuvYBnt4jhj9NFnFm8voaMTl66joOiWqdxR+Y9ZZ435f9IJKg0D7i83/hcV0X0GSPkN54+wH/+U8xPNZQC0zfwC9APZm+AZi0ViTFRpPa0ZDSjCZg5AIxDPrlcwAM4p8jrhLUh4hE8U/vD9vFcFnvCVwhDCZC+uTbGZi7U+0oSC1ePmpHQGrreg/w5Ha1oyA19HtY/JADU0EiIiLSLSZCREREpFtMhIiIiEi3mAgRERGRbjERIiIiIt1iIkRERES6xUSIiIiIdIuJEBEREekWEyEiIiLSLSZCREREpFtMhIiIiEi3mAgRERGRbjERIiIiIt1iIkRERES6ZVY7AHcnSRIAoKKiwqXt1tfXo6amBhUVFfDy8nJp29QUj7UyeJyVweOsDB5nZch5nO3f2/bv8TthInQXlZWVAIDo6GiVIyEiIqK2qqysRFBQ0B1vN0h3S5V0zmaz4eLFiwgICIDBYHBZuxUVFYiOjkZxcTECAwNd1i7djsdaGTzOyuBxVgaPszLkPM6SJKGyshKRkZEwGu88E4g9QndhNBrRvXt32doPDAzkm0whPNbK4HFWBo+zMniclSHXcW6pJ8iOk6WJiIhIt5gIERERkW4xEVKJxWLBypUrYbFY1A5F83islcHjrAweZ2XwOCvDHY4zJ0sTERGRbrFHiIiIiHSLiRARERHpFhMhIiIi0i0mQkRERKRbTIRUsmnTJvTs2RM+Pj4YOnQoDh8+rHZImpKWlob77rsPAQEB6NatG2bOnIn8/Hy1w9K8l156CQaDAQsXLlQ7FE26cOECnnjiCYSEhMDX1xcDBw5Edna22mFpitVqxfLlyxEbGwtfX1/06tULL7744l3PV0Ut+/e//43p06cjMjISBoMBO3bsaHK7JElYsWIFIiIi4Ovri/Hjx6OgoECR2JgIqeCDDz7A4sWLsXLlSuTk5CAxMRETJ05EWVmZ2qFpxv79+5GamorMzEykp6ejvr4eDz30EKqrq9UOTbOOHDmCLVu2YNCgQWqHoknXr1/HyJEj4eXlhV27duHkyZN45ZVXEBwcrHZomrJ27Vq88cYbeP3115GXl4e1a9fi5ZdfxsaNG9UOzaNVV1cjMTERmzZtavb2l19+Ga+99ho2b96MrKwsdOrUCRMnTsTNmzflD04ixaWkpEipqamO361WqxQZGSmlpaWpGJW2lZWVSQCk/fv3qx2KJlVWVkrx8fFSenq6NHr0aGnBggVqh6Q5zz33nDRq1Ci1w9C8qVOnSk899VST6x555BFp9uzZKkWkPQCk7du3O3632WxSeHi49Je//MVx3c8//yxZLBbp/ffflz0e9ggprK6uDkePHsX48eMd1xmNRowfPx6HDh1SMTJtKy8vBwB06dJF5Ui0KTU1FVOnTm3yuibX2rlzJ5KTkzFr1ix069YNSUlJePPNN9UOS3NGjBiBjIwMnD59GgDw/fff48CBA5g8ebLKkWnXuXPncOnSpSafH0FBQRg6dKgi34s86arCrly5AqvVirCwsCbXh4WF4dSpUypFpW02mw0LFy7EyJEjMWDAALXD0Zx//vOfyMnJwZEjR9QORdPOnj2LN954A4sXL8ayZctw5MgR/P73v4e3tzfmzp2rdniasWTJElRUVCAhIQEmkwlWqxWrV6/G7Nmz1Q5Nsy5dugQAzX4v2m+TExMh0rzU1FScOHECBw4cUDsUzSkuLsaCBQuQnp4OHx8ftcPRNJvNhuTkZKxZswYAkJSUhBMnTmDz5s1MhFzoww8/xHvvvYdt27ahf//+yM3NxcKFCxEZGcnjrFEcGlNYaGgoTCYTLl++3OT6y5cvIzw8XKWotGv+/Pn47LPPsHfvXnTv3l3tcDTn6NGjKCsrw+DBg2E2m2E2m7F//3689tprMJvNsFqtaoeoGREREejXr1+T6/r27YuioiKVItKmZ599FkuWLMHjjz+OgQMH4sknn8SiRYuQlpamdmiaZf/uU+t7kYmQwry9vTFkyBBkZGQ4rrPZbMjIyMDw4cNVjExbJEnC/PnzsX37dnz99deIjY1VOyRNGjduHI4fP47c3FzHT3JyMmbPno3c3FyYTCa1Q9SMkSNH3lYC4vTp0+jRo4dKEWlTTU0NjMamX40mkwk2m02liLQvNjYW4eHhTb4XKyoqkJWVpcj3IofGVLB48WLMnTsXycnJSElJwfr161FdXY1f/epXaoemGampqdi2bRs++eQTBAQEOMaZg4KC4Ovrq3J02hEQEHDbvKtOnTohJCSE87FcbNGiRRgxYgTWrFmDxx57DIcPH8bWrVuxdetWtUPTlOnTp2P16tWIiYlB//798d1332HdunV46qmn1A7No1VVVaGwsNDx+7lz55Cbm4suXbogJiYGCxcuxKpVqxAfH4/Y2FgsX74ckZGRmDlzpvzByb4ujZq1ceNGKSYmRvL29pZSUlKkzMxMtUPSFADN/rz11ltqh6Z5XD4vn08//VQaMGCAZLFYpISEBGnr1q1qh6Q5FRUV0oIFC6SYmBjJx8dHiouLk/70pz9JtbW1aofm0fbu3dvsZ/LcuXMlSRJL6JcvXy6FhYVJFotFGjdunJSfn69IbAZJYrlMIiIi0ifOESIiIiLdYiJEREREusVEiIiIiHSLiRARERHpFhMhIiIi0i0mQkRERKRbTISIiIhIt5gIEZHHmjdvnjKVZ4lIs3iKDSJySwaDocXbV65ciQ0bNoA1YYmoI5gIEZFbKi0tdWx/8MEHWLFiRZOTjvr7+8Pf31+N0IhIQzg0RkRuKTw83PETFBQEg8HQ5Dp/f//bhsZsNhvS0tIQGxsLX19fJCYm4qOPPnLcvm/fPhgMBuzevRtJSUnw9fXF2LFjUVZWhl27dqFv374IDAzEL3/5S9TU1DgeN2bMGMyfPx/z589HUFAQQkNDsXz58ia9UdevX8ecOXMQHBwMPz8/TJ48GQUFBYocKyJqPyZCRKQZaWlpePfdd7F582b88MMPWLRoEZ544gns37+/yf2ef/55vP766/j2229RXFyMxx57DOvXr8e2bdvw+eefY8+ePdi4cWOTx7zzzjswm804fPgwNmzYgHXr1uFvf/ub4/Z58+YhOzsbO3fuxKFDhyBJEqZMmYL6+npF/nYiah8OjRGRJtTW1mLNmjX46quvMHz4cABAXFwcDhw4gC1btmD06NGO+65atQojR44EADz99NNYunQpzpw5g7i4OADAo48+ir179+K5555zPCY6OhqvvvoqDAYD+vTpg+PHj+PVV1/FM888g4KCAuzcuRMHDx7EiBEjAADvvfceoqOjsWPHDsyaNUupw0BEbcQeISLShMLCQtTU1GDChAmO+UP+/v549913cebMmSb3HTRokGM7LCwMfn5+jiTIfl1ZWVmTxwwbNqzJBO7hw4ejoKAAVqsVeXl5MJvNGDp0qOP2kJAQ9OnTB3l5ea7+U4nIhdgjRESaUFVVBQD4/PPPERUV1eQ2i8XS5HcvLy/HtsFgaPK7/TqbzSZTpETkTpgIEZEm9OvXDxaLBUVFRU2GwVwlKyurye+ZmZmIj4+HyWRC37590dDQgKysLMfQ2NWrV5Gfn49+/fq5PBYich0mQkSkCQEBAfjjH/+IRYsWwWazYdSoUSgvL8fBgwcRGBiIuXPndqj9oqIiLF68GL/5zW+Qk5ODjRs34pVXXgEAxMfHY8aMGXjmmWewZcsWBAQEYMmSJYiKisKMGTNc8ecRkUyYCBGRZrz44ovo2rUr0tLScPbsWXTu3BmDBw/GsmXLOtz2nDlzcOPGDaSkpMBkMmHBggX49a9/7bj9rbfewoIFCzBt2jTU1dXhgQcewBdffHHbsBsRuReDxLKsREQtGjNmDO69916sX79e7VCIyMW4aoyIiIh0i4kQERER6RaHxoiIiEi32CNEREREusVEiIiIiHSLiRARERHpFhMhIiIi0i0mQkRERKRbTISIiIhIt5gIERERkW4xESIiIiLdYiJEREREuvX/AbLrRxoQQt+fAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "def ecdif(X, t, g, l):\n",
        "    theta, phi = X\n",
        "    omega2 = g/l\n",
        "    return [phi, -omega2 * theta]\n",
        "\n",
        "g = 9.8\n",
        "l = 2\n",
        "X0 = [np.pi/4, 0]  # Inicio a 45 grados con velocidad = 0\n",
        "t = np.linspace(0, 10, 101)\n",
        "solucion = odeint(ecdif, X0, t, args = (g,l))\n",
        "\n",
        "plt.plot(t,solucion[:,0], label = 'theta')\n",
        "plt.plot(t,solucion[:,1], label = 'phi')\n",
        "plt.title('Resolucion del pendulo')\n",
        "plt.xlabel('Tiempo')\n",
        "plt.ylabel('Valores')\n",
        "plt.grid(True)\n",
        "plt.legend(loc = 'best')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ertoUgzD962j"
      },
      "source": [
        "Si quieren ver otro ejemplo que esto, recomendamos el [resuelto](https://github.com/fifabsas/talleresfifabsas/blob/master/python/Extras/Fisica2/oscilador_forzado.py) que hizo un Ex-FIFA del oscilador con forzante sin aproximación y comparado con la solución analítica (sí, esa que sin aproximación no buscó nadie). También recomendamos, a cuento de esto, un pasito más en resolución de ODE's que es una [simulación](https://github.com/fifabsas/talleresfifabsas/blob/master/python/Extras/Fisica1/simulacion.ipynb) de uno de los ejercicios de F1, donde se arma una animación y una visualización más interactiva con la biblioteca `ipywidgets` de tantas otras posibles."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X--JkuZr962j"
      },
      "source": [
        "### Ejercicio\n",
        "1. Resuelvan el oscilador armónico amortiguado con el parámetro $\\gamma$\n",
        "2. Vuelvan a todas esas guías que nadie terminó, busquen los ejercicios con asterisco, métanlos en Python y aprecien el poder del cálculo numérico."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O-H8jqXf962j"
      },
      "source": [
        "---\n",
        "\n",
        "## Un poco de algebra lineal con *NumPy*\n",
        "\n",
        "NumPy trae muchas funciones para resolver problemas típicos de algebra lineal usando a los arrays como vectores. El que nos interesa en general es el de autovalores y autovectores y el sistema de ecuaciones lineales, pero empecemos con un ejemplo más fácil:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QNs8tiWP962j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a5053e49-df92-40d4-9b84-18822a89a6de"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.7320508075688772\n",
            "1.7320508075688772\n",
            "True\n"
          ]
        }
      ],
      "source": [
        "# cargamos el módulo de algebra lineal\n",
        "from numpy import linalg\n",
        "v = np.array([1, 1, 1])\n",
        "w = np.array([2, 2, 2])\n",
        "z = np.array([1, 0, 1])\n",
        "\n",
        "norma =linalg.norm(v)  # la norma 2 / módulo del vector v\n",
        "print(norma)\n",
        "print(np.sqrt(v[0]**2 + v[1]**2 + v[2]**2))  # calculado a mano == sqrt(3)\n",
        "print(norma == np.sqrt(3))  # y numpy sabe que son lo mismo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tlvBi-C1962j"
      },
      "source": [
        "Ahora si, usemos los vectores que creamos recién para crear una matriz y digamosle a _NumPy_ que calcule los autovectoresy autovalores de esa matriz:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gN8V4XrM962k",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b9dde2c5-3ef4-452f-8fdb-16f2586d2639"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Los autovalores: [ 3.41421356e+00 -1.23150120e-16  5.85786438e-01]\n",
            "\n",
            "Los autovectores: [[ 4.39732612e-01  7.07106781e-01 -3.03890631e-01]\n",
            " [ 8.79465224e-01 -1.09165123e-16 -6.07781262e-01]\n",
            " [ 1.82143212e-01 -7.07106781e-01  7.33656883e-01]]\n"
          ]
        }
      ],
      "source": [
        "matriz = np.array([v, w, z], dtype=np.float64)\n",
        "#eig devuelve una tupla de arrays con los autovalores en un array 1D y los autovec en un array 2D\n",
        "eigens = linalg.eig(matriz)\n",
        "\n",
        "autvals, autvecs = eigens\n",
        "\n",
        "print('Los autovalores:', autvals)\n",
        "print()\n",
        "print('Los autovectores:', autvecs)\n",
        "\n",
        "#se terminó el problema"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8raIhKR9962k"
      },
      "source": [
        "Y para un sistema de ecuaciones del tipo $Ax = b$:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rOJc7Tr9962k",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b158e8dc-7e6f-436f-f6f8-d2e499ca17d9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.67857143 0.07142857 0.03571429]\n"
          ]
        }
      ],
      "source": [
        "mat = np.array([[1, 2, 5], [2, 5, 8], [4, 0, 8]], dtype=np.float64)\n",
        "b = np.array([1, 2, 3])\n",
        "x = linalg.solve(mat, b)  # resuelve el sistema A*x = b\n",
        "print(x)\n",
        "\n",
        "# se terminó el problema"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HpYidTEj962k"
      },
      "source": [
        "Por supuesto, también se puede hacer producto matriz con vector, y... oh si, se pueden calcular *inversas*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4p7XCsP8962k",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "821688ab-3210-476b-fb19-59fa84d9d835"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1. 2. 3.]\n",
            "[[-1.42857143  0.57142857  0.32142857]\n",
            " [-0.57142857  0.42857143 -0.07142857]\n",
            " [ 0.71428571 -0.28571429 -0.03571429]]\n"
          ]
        }
      ],
      "source": [
        "print(np.dot(mat,x))\n",
        "print(linalg.inv(mat))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3G_G--wB962k"
      },
      "source": [
        "---\n",
        "\n",
        "### Ejercicio\n",
        "\n",
        "Parecen incrédulos. Fabriquen entonces la matriz\n",
        "$$\n",
        "\\begin{equation}\n",
        "A =\n",
        "\\begin{pmatrix}\n",
        "  0 & 1 \\\\\n",
        "  1 & 0  \n",
        "\\end{pmatrix}\n",
        "\\end{equation}\n",
        "$$\n",
        "\n",
        "cuyos autovalores son $\\lambda_1 = 1$ y $\\lambda_2 = -1 $, hallen sus autovalores y autovectores (a mano y con Python) y calculen $Ax$ con\n",
        "$$\n",
        "\\begin{equation}\n",
        "x =\n",
        "\\begin{pmatrix}\n",
        "1\\\\0\n",
        "\\end{pmatrix}\n",
        "\\end{equation}\n",
        "$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vAPW4KK4962l"
      },
      "source": [
        "---\n",
        "### Ejercicio\n",
        "\n",
        "Inventen una matriz de 5x5 (con el método que quieran y ¡que no sea la identidad!) y averigüen si es invertible. Si están prestando atención, saben que para resolver este problema les conviene ver la documentación de numpy.linalg.\n",
        "\n",
        "*Ayuda*: ¿Es necesario calcular la inversa? ¿Se acuerdan algo de álgebra lineal?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CIyR9IN1962l"
      },
      "source": [
        "---\n",
        "\n",
        "# Cálculo simbólico"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eYBAxJJm962l"
      },
      "source": [
        "Ahora no sólo nos vamos a emancipar del *Oriyin*, sino también del bendito *Wolfram Alpha* (Disclaimer: si bien la librería que se usa es muy buena, todavía no llega a tener el nivel de software como Mathematica, Maple, etc).\n",
        "Así es, Python nos va a permitir hacer cálculos simbólicos, como las integrales que nunca supimos calcular. Todo eso y más, en el paquete **SymPy**:\n",
        "\n",
        "Hemos dicho que en general no es una buena práctica, pero este es uno de los pocos casos para los cuales se justifica importar toda la librería."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kuI4A5Bi962l"
      },
      "outputs": [],
      "source": [
        "from sympy import *\n",
        "from sympy.abc import *"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W1olTVDy962l"
      },
      "source": [
        "Les daremos ahora un breve tour por algunas de las funciones que nos ofrece *SymPy*. Así como *numpy* nos introdujo el array como tipo de variable, las expresiones algebraicas en *sympy* son del tipo *symbols*. Estas pueden representar números enteros, reales, funciones, etc.\n",
        "\n",
        "A diferencia de las librerías anteriores, en las cuales usualmente escribimos los comandos dentro de un archivo a ejectutar todo junto (*script*); a modo de demostración utilizaremos *SymPy* de forma *interactiva*. Esta es posiblemente la manera en la que usaron *Wolfram Alpha* o *Mathematica* (para quienes lo hayan hecho)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CxxEcAOg962l"
      },
      "source": [
        "Vimos aquí que una cantidad de variables se definieron como *symbols* de distintos tipos.\n",
        "Veamos ahora algunas de las posibilidades que tenemos:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X5XqCDBK962l",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "outputId": "3351a1fb-e32a-4c96-ad25-19ac3000a5b3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "x**2 + 2*x*y + y**2"
            ],
            "text/latex": "$\\displaystyle x^{2} + 2 x y + y^{2}$"
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "expand( (x + y)**2 )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_3BpC6FA962m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 38
        },
        "outputId": "9092b056-42b7-4497-f283-859f3ec15b22"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(x - 1)*(x + 1)*(x**2 - x + 1)*(x**2 + x + 1)"
            ],
            "text/latex": "$\\displaystyle \\left(x - 1\\right) \\left(x + 1\\right) \\left(x^{2} - x + 1\\right) \\left(x^{2} + x + 1\\right)$"
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "factor( x**6 - 1 )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HY6jOTy1962m"
      },
      "source": [
        "Utilizando variables simbólicas de tipo integer, podemos hacer algunas sumatorias, por ejemplo, la famosa $$\\sum_{k=0}^{m}k$$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NdPN4ENj962m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "3f0656af-a0a7-4578-ad1c-817f555651e5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "m*(m + 1)/2"
            ],
            "text/latex": "$\\displaystyle \\frac{m \\left(m + 1\\right)}{2}$"
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "Sum(k, (k,0,m) ).doit().factor()  #el comando .doit() evalúa la suma"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Q3uw70F962m"
      },
      "source": [
        "O incluso algunas series, como $$ \\sum_{n=1}^{\\infty}\\frac{1}{n^2} $$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kwmMpZqV962m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "4272016a-e3de-4cc4-c3b6-f471f5fb58be"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pi**2/6"
            ],
            "text/latex": "$\\displaystyle \\frac{\\pi^{2}}{6}$"
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "Sum(1/n**2, (n, 1, oo)).doit()  # el infinito se escribe como oo (dos o minúscula)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D2cvgvFz962n"
      },
      "source": [
        "---\n",
        "Posiblemente si queremos algo de cálculo simbólico, sea para calcular derivadas e integrales que nos molesten. Veamos algunos ejemplos $\\dfrac{\\text{d}x^n}{\\text{d}x}$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kxObFxZm962n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 38
        },
        "outputId": "4505ffcc-6488-465c-8f47-bee2316985e2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "n*x**(n - 1)"
            ],
            "text/latex": "$\\displaystyle n x^{n - 1}$"
          },
          "metadata": {},
          "execution_count": 58
        }
      ],
      "source": [
        "diff(x**n , x).simplify()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K53CSEud962n"
      },
      "source": [
        "$$ \\frac{d}{dx}\\big(\\ \\sin(\\tan(8x))\\ \\big)$$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lVIREd40962n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 38
        },
        "outputId": "52652548-cbdb-4b3d-b11d-85305bec1d9c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8*tan(8*x)**2 + 8)*cos(tan(8*x))"
            ],
            "text/latex": "$\\displaystyle \\left(8 \\tan^{2}{\\left(8 x \\right)} + 8\\right) \\cos{\\left(\\tan{\\left(8 x \\right)} \\right)}$"
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "diff(sin(tan(8*x)), x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ndyUqaL5962o"
      },
      "outputs": [],
      "source": [
        "out = diff(sin(tan(8*x)), x).simplify()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "45pXhy2R962p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 62
        },
        "outputId": "3a6e6b20-849d-4e41-853c-fbb74624d631"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8*cos(tan(8*sqrt(y)))/cos(8*sqrt(y))**2"
            ],
            "text/latex": "$\\displaystyle \\frac{8 \\cos{\\left(\\tan{\\left(8 \\sqrt{y} \\right)} \\right)}}{\\cos^{2}{\\left(8 \\sqrt{y} \\right)}}$"
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "# Podemos hacer sustituciones de simbolos por otros simbolos!!\n",
        "out.subs(x, sqrt(y))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "98T7KC_p962p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bbdd4d20-eef8-4686-e4be-89f0ce238e9d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8*cos(tan(64))/cos(64)**2 = -36.5316624851842\n"
          ]
        }
      ],
      "source": [
        "# Hasta podemos sustituir por valores numéricos y evaluar la funcion!!\n",
        "print(f\"{out.subs(x, 8)} = {out.subs(x, 8).evalf()}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xf8_cHFd962p"
      },
      "source": [
        "Si alguno desconfia, puede verlo desde [WolframAlfa](https://www.wolframalpha.com/input/?i=%288+%2B+8*tan%2864%29**2%29*log%288%29*cos%28tan%2864%29%29+%2B+sin%28tan%2864%29%29%2F8)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TqZ6zIq-962p"
      },
      "source": [
        "---\n",
        "Otro ejemplo! Derivadas parciales: $\\dfrac{\\partial^2}{\\partial y \\partial x}\\left( x\\sin{y} \\right)$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ljf-qGec962p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 38
        },
        "outputId": "0f3dc6db-b757-4d78-893f-beadb4cecbee"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cos(y)"
            ],
            "text/latex": "$\\displaystyle \\cos{\\left(y \\right)}$"
          },
          "metadata": {},
          "execution_count": 63
        }
      ],
      "source": [
        "diff(x*sin(y), x, y)  # qué pasa si en lugar de x,y ponemos y,y?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aOF7nn_m962q"
      },
      "source": [
        "También se pueden hacer integrales indefinidas o definidas con dominio infinito/acotado, obviamente luego se podría reemplazar en la expresión final por algun valor numérico, si así se quisiera\n",
        "$$\\int \\dfrac{1}{1+x^2}\\text{d}x$$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ySbc_4Sc962q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 38
        },
        "outputId": "ffbf749c-f5c0-445f-bb2e-321adcb4406e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "atan(x)"
            ],
            "text/latex": "$\\displaystyle \\operatorname{atan}{\\left(x \\right)}$"
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "integrate(1/(1+x**2), x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vIiA9_y9962q"
      },
      "source": [
        "$$\\int_{-\\infty}^{+\\infty} \\dfrac{\\sin(x)}{x}\\text{d}x$$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RFle8Vrl962q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 38
        },
        "outputId": "e26d90d6-8c2c-4e9b-9e09-3fbb23736c56"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pi"
            ],
            "text/latex": "$\\displaystyle \\pi$"
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "integrate( sin(x)/x, (x,-oo,oo) )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vJPWRHlX962q"
      },
      "source": [
        "---\n",
        "Algo importantisimo y fabuloso de Sympy (algo que sorprendentemente NO tiene Mathematica ni Matlab) es que a cualquier expresión, podemos envolverla en un `latex()` para que devuelva la misma expresión que renderizó, pero en el formato de $\\LaTeX$. Derecho para mandar al informe o trabajo sin tipearla."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z9-WrUrC962q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "a13bac5b-0743-40fa-eec8-b058bf6bca65"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8*tan(8*x)**2 + 8)*log(x)*cos(tan(8*x)) + sin(tan(8*x))/x"
            ],
            "text/latex": "$\\displaystyle \\left(8 \\tan^{2}{\\left(8 x \\right)} + 8\\right) \\log{\\left(x \\right)} \\cos{\\left(\\tan{\\left(8 x \\right)} \\right)} + \\frac{\\sin{\\left(\\tan{\\left(8 x \\right)} \\right)}}{x}$"
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "out = diff(sin(tan(8*x))*log(x), x)\n",
        "out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2jbmPoSA962q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8794f09-f583-47ad-b1f4-e301900bb571"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Normal: (8*tan(8*x)**2 + 8)*log(x)*cos(tan(8*x)) + sin(tan(8*x))/x\n",
            "LaTeX: \\left(8 \\tan^{2}{\\left(8 x \\right)} + 8\\right) \\log{\\left(x \\right)} \\cos{\\left(\\tan{\\left(8 x \\right)} \\right)} + \\frac{\\sin{\\left(\\tan{\\left(8 x \\right)} \\right)}}{x}\n"
          ]
        }
      ],
      "source": [
        "print(f\"Normal: {out}\")\n",
        "print(f\"LaTeX: {latex(out)}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-RLymUU6962r"
      },
      "source": [
        "### Ejercicio 9"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mgtwvL9L962r"
      },
      "source": [
        "Calculen la siguiente integral:  $$ \\int_{-\\infty}^{+\\infty} e^{-x^2} \\text{d}x $$ (opcional: verificar el resultado a mano!)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q-Q3z26J962r"
      },
      "source": [
        "---\n",
        "# Integración numérica\n",
        "Siempre es útil tener a mano una rutina de integración numérica. Puede ser para el caso en el cual la integral analítica sea muy complicada o no estándar. También es fundamental para poder integrar datos obtenidos experimentalmente, sin asumir alguna función que los modele. Para ambos casos, el paquete relevante será `scipy.integrate`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RC3J0M8Q962r"
      },
      "source": [
        "### A partir de una función predefinida\n",
        "En este caso, la función a importar es `quad`, porque usa un método de [cuadraturas](https://en.wikipedia.org/wiki/Numerical_integration#Quadrature_rules_based_on_interpolating_functions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SgaxatwB962s"
      },
      "outputs": [],
      "source": [
        "from scipy.integrate import quad"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uNMiXoEJ962s"
      },
      "source": [
        "Definamos una función para ser integrada. A modo de ejemplo, calcularemos\n",
        "\n",
        "$$\\int_a^b \\dfrac{\\text{e}^{-x^2 / 2}}{\\sqrt{2\\pi}}\\text{d}x$$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lYJ_LgaS962s"
      },
      "outputs": [],
      "source": [
        "def integrando(x): return np.exp(-x**2 / 2)/np.sqrt(2*np.pi)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XG-C7bdB962s"
      },
      "source": [
        "Ahora, llamamos a `quad` para integrar esta función entre $a=-1$ y $b=1$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ySfZTQwC962s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c49932f-2115-4674-b0f0-4e5a904d0d30"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.682689492137086 7.579375928402476e-15\n"
          ]
        }
      ],
      "source": [
        "a = -1\n",
        "b = 1\n",
        "integral, error = quad(integrando, a, b)\n",
        "print(integral, error)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vjN2Vtlx962s"
      },
      "source": [
        "Podemos, con este método, incluso obtener la primitiva numéricamente. Por ejemplo\n",
        "\n",
        "$$F(x) = \\int_a^x \\dfrac{\\text{e}^{-t^2 / 2}}{\\sqrt{2\\pi}}\\text{d}t$$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sAEC3Y9E962s"
      },
      "outputs": [],
      "source": [
        "# Definimos este dominio\n",
        "x = np.linspace(-5,5,100)\n",
        "\n",
        "# Ahora obtenemos la primitiva\n",
        "prim = np.zeros(x.size)\n",
        "\n",
        "for i in range(x.size):\n",
        "    prim[i], error = quad(integrando, a, x[i])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fklWsmsQ962s"
      },
      "source": [
        "Veamos la gráfica para este dominio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ms1tun5v962s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "0294a7d1-03ed-4beb-93e4-af13688735fb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f4386bb67a0>"
            ]
          },
          "metadata": {},
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure()\n",
        "\n",
        "plt.plot(x, integrando(x), 'b', label = 'integrando')\n",
        "plt.plot(x, prim, 'r', label = 'primitiva')\n",
        "plt.grid(True)\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gJe4qqAN962t"
      },
      "source": [
        "---\n",
        "# Funciones especiales"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "66Niqv2P962t"
      },
      "source": [
        "Tal vez en algún momento de la vida nos encontremos con funciones más exóticas que el seno, coseno y exponenciales, funciones que las conocemos de nombre pero de graficarlas ni hablemos. Por suerte, la biblioteca *scipy* cuenta con abanico muy grande de estas funciones con nombre propio. En la documentación (bloque de ayuda del spyder) podemos encontrar la lista completa de funciones, buscando `scipy.special`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LtHMxZRT962t"
      },
      "outputs": [],
      "source": [
        "import scipy.special as sp"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8l__cNHR962t"
      },
      "source": [
        "Empecemos graficando un ejemplo común en probabilidad, la *función error*:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sLUtQHSu962t",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "879bec23-3bfa-4a90-de61-52d1d5b3b4e6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "x = np.linspace(-5,5,100)  # generamos el dominio\n",
        "y = sp.erf(x)  # llama a la función error de la librería\n",
        "\n",
        "plt.plot(x,y, label='función error')\n",
        "plt.grid(True)\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w1MvwHrI962t"
      },
      "source": [
        "Dentro de las muchas funciones que nos ofrece la librería, otro ejemplo importante en la física son las *funciones de Bessel* $J_{\\nu}(x)$. Hay una función para cada valor del índice $\\nu$ (que llamamos orden). En la librería, se llaman con el comando `jv(i,x)`, donde la primer entrada corresponde al orden $\\nu$.\n",
        "Como demostración, veamos ahora algunos de sus gráficos:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QDA3Sseq962t",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24471f45-e684-41cb-8597-bfb997b5c987"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Funciones de Bessel')"
            ]
          },
          "metadata": {},
          "execution_count": 75
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "num = 500\n",
        "r = np.linspace(0,10,num)\n",
        "\n",
        "for i in range(5):\n",
        "    y_i = sp.jv(i,r)  # para cada i, grafica la función de orden i\n",
        "    plt.plot(r,y_i,label='orden {}'.format(i))\n",
        "    plt.legend(loc='best')\n",
        "plt.grid(True)\n",
        "plt.xlim(0, 10)\n",
        "plt.title('Funciones de Bessel')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vD1okFNq962u"
      },
      "source": [
        "### Ejercicio 8"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HYCHhzoQ962u"
      },
      "source": [
        "1. Busquen en la documentación la función *gamma*, y grafiquenla en el intervalo $\\left[-5,5\\right]$\n",
        "2. (Opcional) Googleen a ver qué es lo tan importante de esa función."
      ]
    }
  ],
  "metadata": {
    "anaconda-cloud": {},
    "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": "3.9.16"
    },
    "vscode": {
      "interpreter": {
        "hash": "a42cd95ca9a57c6387dbbfdd995a278478871c13a93fa5b980ac93bb6d23c0e2"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}