diff --git a/.gitignore b/.gitignore index f709485..50eda54 100644 --- a/.gitignore +++ b/.gitignore @@ -2,3 +2,10 @@ *.log .code_config/ grader/ +# Jupyter Notebooks +.ipynb_checkpoints/ + +# Python +__pycache__/ +*.py[cod] +*$py.class \ No newline at end of file diff --git a/Dockerfile b/Dockerfile index 4380b40..ab457d5 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,16 +1,4 @@ -# VS Code en navegador + utilidades (imagen mantenida con s6-overlay) -FROM ghcr.io/linuxserver/code-server:latest - -# Paquetes para C++ y utilidades de desarrollo -USER root -RUN apt-get update && apt-get install -y --no-install-recommends \ - build-essential g++ gdb cmake ninja-build make \ - clang clangd lldb \ - valgrind \ - git pkg-config \ - clang-format \ - && rm -rf /var/lib/apt/lists/* - -# Carpeta de trabajo (la imagen usa /config como HOME) -WORKDIR /config/workspace -RUN mkdir -p /config/workspace +FROM gcc:latest +WORKDIR /usr/src/app +COPY . . +CMD ["tail", "-f", "/dev/null"] diff --git a/TAREASSS/ensayo.tex b/TAREASSS/ensayo.tex new file mode 100644 index 0000000..6416631 --- /dev/null +++ b/TAREASSS/ensayo.tex @@ -0,0 +1,105 @@ + +\documentclass[12pt]{article} + +% --- Página y tipografía --- +\usepackage[letterpaper,margin=2.5cm]{geometry} +\usepackage[T1]{fontenc} +\usepackage[utf8]{inputenc} % si compilas con pdfLaTeX +\usepackage{lmodern} +\usepackage{microtype} + +% --- Imágenes y color --- +\usepackage{graphicx} +\usepackage{xcolor} + +% --- Control fino de espacios --- +\usepackage{setspace} +\setlength{\parindent}{0pt} + +\begin{document} +\thispagestyle{empty} + +% ===== Encabezado con logos + texto ===== +\begin{minipage}[c]{0.18\textwidth} + \centering + % Cambia por tu logo izquierdo + \includegraphics[width=0.95\linewidth]{logo_usac.jpeg} +\end{minipage} +\hfill +\begin{minipage}[c]{0.60\textwidth} + \small + Universidad de San Carlos de Guatemala\\ + Escuela de Ciencias Físicas y Matemáticas\\ + Nombre estudiante\\ + Carnet: \\ + Programación 1\\ +\end{minipage} +\hfill +\begin{minipage}[c]{0.18\textwidth} + \centering + % Cambia por tu logo derecho + \includegraphics[width=1.4\linewidth]{logo_ecfm.jpg} +\end{minipage} + +\vspace{0.5cm} + +% Línea horizontal superior (gruesa) +\noindent\rule{\textwidth}{1.2pt} + +\vspace{0.2cm} + +% ===== Título ===== +\begin{center} + {\Large\scshape Ensayo}\\[0.3em] +\end{center} + +\vspace{0.1cm} + +% Fecha +\begin{center} + \small\scshape viernes 06 de febrero de 2026 +\end{center} + +\vspace{0.2cm} + +% Línea horizontal inferior (gruesa) +\noindent\rule{\textwidth}{1.2pt} + +\vspace{0.6cm} + +% ===== Caja de resumen ===== +\noindent +\colorbox{gray!36}{% + \parbox{\textwidth}{% + \vspace{0.6em} + \textbf{Resumen}\\[0.4em] + \small +El presente ensayo se enfoca en dara respuesta a las siguientes preguntas: ¿cuál sería tú área de investigación en Física? y ¿explica por qué la programación te sería útil en dicho campo?, se realizá una descripción para cada pregunta, desde el punto de vista de un estudiante de física. + \vspace{0.8em} + }% +} + + +\section{Pregunta No. 1} +\subsection{¿Cuál sería tú área de investigación en Física?} + +Es un tema facinante, si fuera un investigador de física, el área de investigación sería la Física de Radiociones, ya que es un área que conbina las leyes naturales y la vida y poder contribuir a savar las mismas.\\ + Y en esta misma área el interés en la investigación sería en Radioterapia que es el uso de radiación ionizante para tratar tumores (física de partículas, dosis que se requiere a un paciente).\\ + De acuerdo al siguiente texto "La radiación ionizante es la liberación de electrones mediante la emisión de energía en forma de ondas y partículas que se producen de forma natural en los materiales que conocemos como radiactivos (suelo, agua y vegetación) y de forma artificial en isótopos creados por el hombre, tal como los equipos que producen Rayos-X" (ATSDR en Español, 2016).\\ +Por tanto comprender la liberación de los electrones puede alterar o modificar la estructura molecular de un ser vivo. + + +\section{Pregunta 2} +\subsection{¿Explica por qué la programación te sería útil en dicho campo?} + +En física médica, no siempre se puedes experimentar directamente con humanos por razones éticas y de seguridad.\\ + +¿Para qué sirve? Se usan algoritmos (como Geant4 o MCNP) para simular millones de trayectorias de partículas (fotones, electrones, protones) golpeando un tejido.\\ + +La programación te permite: Definir la geometría del paciente y calcular exactamente cuánta energía se deposita en cada milímetro cúbico. + + + +\end{document} + +\end{document} \ No newline at end of file diff --git a/TAREASSS/numpy.ipynb b/TAREASSS/numpy.ipynb new file mode 100644 index 0000000..dc2642e --- /dev/null +++ b/TAREASSS/numpy.ipynb @@ -0,0 +1,2105 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NumPy — Computación Numérica en Python\n", + "\n", + "## ¿Qué es NumPy?\n", + "\n", + "**NumPy** (Numerical Python) es la librería fundamental para computación científica en Python. Proporciona:\n", + "\n", + "- Un objeto de arreglo (array) multidimensional de alto rendimiento\n", + "- Herramientas para trabajar con estos arreglos\n", + "- Funciones matemáticas, lógicas, de ordenamiento, estadísticas, y más\n", + "\n", + "## ¿Para qué sirve?\n", + "\n", + "| Tarea | Sin NumPy | Con NumPy |\n", + "|---|---|---|\n", + "| Sumar dos listas de 1 millón de elementos | ~0.5 segundos | ~0.005 segundos |\n", + "| Multiplicar matrices | Código complejo con loops | Una línea |\n", + "| Operaciones matemáticas | Loop por cada elemento | Operaciones vectorizadas |\n", + "\n", + "NumPy es la base de casi todas las librerías de ciencia de datos: Pandas, Matplotlib, Scikit-learn, etc." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Instalación e Importación\n", + "\n", + "Para instalar NumPy (si no está instalado):\n", + "```bash\n", + "pip install numpy\n", + "```\n", + "\n", + "Por convención, se importa con el alias `np`:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NumPy versión: 1.26.4\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "print(\"NumPy versión:\", np.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 1 — Conceptos Básicos: Arrays" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ¿Qué diferencia hay entre una lista de Python y un array de NumPy?" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lista Python: [1, 2, 3, 4, 5]\n", + "Tipo: \n", + "\n", + "Array NumPy: [1 2 3 4 5]\n", + "Tipo: \n" + ] + } + ], + "source": [ + "# Lista de Python\n", + "lista = [1, 2, 3, 4, 5]\n", + "print(\"Lista Python:\", lista)\n", + "print(\"Tipo:\", type(lista))\n", + "\n", + "print()\n", + "\n", + "# Array de NumPy\n", + "arreglo = np.array([1, 2, 3, 4, 5])\n", + "print(\"Array NumPy:\", arreglo)\n", + "print(\"Tipo:\", type(arreglo))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lista * 3: [1, 2, 3, 1, 2, 3, 1, 2, 3]\n", + "Array * 3: [3 6 9]\n" + ] + } + ], + "source": [ + "# La diferencia clave: operaciones elemento a elemento\n", + "lista_python = [1, 2, 3]\n", + "\n", + "# Con listas, el operador * repite la lista\n", + "print(\"Lista * 3:\", lista_python * 3) # [1, 2, 3, 1, 2, 3, 1, 2, 3]\n", + "\n", + "arreglo_np = np.array([1, 2, 3])\n", + "\n", + "# Con NumPy, * multiplica cada elemento\n", + "print(\"Array * 3:\", arreglo_np * 3) # [3, 6, 9]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ¿Por qué NumPy es tan rápido? — Comparación de rendimiento\n", + "\n", + "NumPy implementa las operaciones sobre arrays en **código C precompilado**. La misma operación que requiere un ciclo `for` en Python se ejecuta internamente sin ciclos explícitos. Esto se conoce como *código vectorizado* (Stewart & Mommert, §4.1)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lista Python : 5.741 segundos\n", + "Array NumPy : 1.110 segundos\n", + "NumPy es 5 × más rápido\n" + ] + } + ], + "source": [ + "import time\n", + "\n", + "N = 10_000_000 # 10 millones de elementos\n", + "\n", + "# ── Enfoque con lista Python ──────────────────────────────────────────────────\n", + "a_lista = list(range(N))\n", + "t0 = time.time()\n", + "b_lista = []\n", + "for i in range(len(a_lista)):\n", + " b_lista.append(a_lista[i] + 1)\n", + "t_lista = time.time() - t0\n", + "\n", + "# ── Enfoque con array NumPy ───────────────────────────────────────────────────\n", + "a_np = np.array(a_lista)\n", + "t0 = time.time()\n", + "b_np = a_np + 1 # ← una sola línea, sin ciclo explícito\n", + "t_numpy = time.time() - t0\n", + "\n", + "print(f\"Lista Python : {t_lista:.3f} segundos\")\n", + "print(f\"Array NumPy : {t_numpy:.3f} segundos\")\n", + "print(f\"NumPy es {t_lista / t_numpy:.0f} × más rápido\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Crear arrays de diferentes formas" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ceros: [0. 0. 0. 0. 0.]\n", + "Unos : [1. 1. 1. 1. 1.]\n", + "Vacío: [1.26207700e-315 0.00000000e+000 1.27198647e-315 1.27148252e-315\n", + " 0.00000000e+000]\n", + "\n", + "Arange (0 a 10, paso 2): [0 2 4 6 8]\n", + "Linspace (6 puntos entre 0 y 1): [0. 0.2 0.4 0.6 0.8 1. ]\n", + "Logspace (10^0 a 10^3): [ 1. 10. 100. 1000.]\n", + "\n", + "zeros_like(base): [0 0 0 0 0]\n", + "ones_like(base) : [1 1 1 1 1]\n" + ] + } + ], + "source": [ + "# Array de ceros y unos\n", + "ceros = np.zeros(5)\n", + "unos = np.ones(5)\n", + "vacio = np.empty(5) # sin inicializar — muy rápido, valores arbitrarios\n", + "print(\"Ceros:\", ceros)\n", + "print(\"Unos :\", unos)\n", + "print(\"Vacío:\", vacio)\n", + "\n", + "# Array con rango (como range(), pero retorna array)\n", + "rango = np.arange(0, 10, 2) # inicio, fin (exclusivo), paso\n", + "print(\"\\nArange (0 a 10, paso 2):\", rango)\n", + "\n", + "# N valores igualmente espaciados — escala lineal\n", + "espacio = np.linspace(0, 1, 6) # inicio, fin (inclusivo), N puntos\n", + "print(\"Linspace (6 puntos entre 0 y 1):\", espacio)\n", + "\n", + "# N valores en escala logarítmica (base 10 por defecto)\n", + "log_esp = np.logspace(0, 3, 4) # 10^0 a 10^3, 4 valores\n", + "print(\"Logspace (10^0 a 10^3):\", log_esp)\n", + "\n", + "# Constructores \"look-alike\": misma forma que otro array\n", + "base = np.array([1, 2, 3, 4, 5])\n", + "print(\"\\nzeros_like(base):\", np.zeros_like(base))\n", + "print(\"ones_like(base) :\", np.ones_like(base))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(5,)\n", + "1\n" + ] + } + ], + "source": [ + "print(base.shape)\n", + "print(base.ndim)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Arrays 2D (matrices)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Matriz:\n", + "[[1 2 3]\n", + " [4 5 6]\n", + " [7 8 9]]\n", + "\n", + "Forma (filas x columnas): (3, 3)\n", + "Número de dimensiones: 2\n", + "Total de elementos: 9\n", + "Tipo de dato: int64\n" + ] + } + ], + "source": [ + "# Crear una matriz 2D\n", + "matriz = np.array([\n", + " [1, 2, 3],\n", + " [4, 5, 6],\n", + " [7, 8, 9]\n", + "])\n", + "\n", + "print(\"Matriz:\")\n", + "print(matriz)\n", + "print(\"\\nForma (filas x columnas):\", matriz.shape)\n", + "print(\"Número de dimensiones:\", matriz.ndim)\n", + "print(\"Total de elementos:\", matriz.size)\n", + "print(\"Tipo de dato:\", matriz.dtype)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tipos de datos (`dtype`) y atributos del array\n", + "\n", + "Todos los elementos de un array NumPy tienen el **mismo tipo de dato** (`dtype`). NumPy infiere el tipo al crear el array, pero puedes especificarlo. Tipos comunes: `int64`, `float64`, `complex128`, `bool`. Puedes convertir con `.astype()` (Stewart & Mommert, §4.1.1)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dtype inferido: float64\n", + "dtype=int : [1 2 3] → int64\n", + "dtype=complex: [1.+0.j 2.+0.j 3.+0.j] → complex128\n", + "\n", + "x_int.astype(float): [1. 2. 3.] → float64\n", + "\n", + "Array m:\n", + "[[1 2 3]\n", + " [4 5 6]]\n", + " ndim : 2\n", + " shape: (2, 3)\n", + " size : 6\n", + " dtype: int64\n" + ] + } + ], + "source": [ + "x = np.array([1, 2, 3.268999]) # Python convierte todo a float64\n", + "print(\"dtype inferido:\", x.dtype)\n", + "\n", + "# Especificar dtype manualmente\n", + "x_int = np.array([1.5566666666, 2, 3], dtype=int)\n", + "x_cpx = np.array([1, 2, 3], dtype=complex)\n", + "print(\"dtype=int :\", x_int, \"→\", x_int.dtype)\n", + "print(\"dtype=complex:\", x_cpx, \"→\", x_cpx.dtype)\n", + "\n", + "# Convertir dtype con astype()\n", + "x_float = x_int.astype(float)\n", + "print(\"\\nx_int.astype(float):\", x_float, \"→\", x_float.dtype)\n", + "\n", + "# Atributos importantes de un array\n", + "m = np.array([[1, 2, 3], [4, 5, 6]])\n", + "print(\"\\nArray m:\")\n", + "print(m)\n", + "print(\" ndim :\", m.ndim) # número de dimensiones\n", + "print(\" shape:\", m.shape) # (filas, columnas)\n", + "print(\" size :\", m.size) # total de elementos\n", + "print(\" dtype:\", m.dtype) # tipo de dato" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 2 — Indexación y Operaciones" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Acceder a elementos (indexación)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Arreglo: [10 20 30 40 50]\n", + "Primer elemento (índice 0): 10\n", + "Último elemento: 50\n", + "Elementos del índice 1 al 3: [20 30 40]\n", + "Cada dos elementos: [10 30 50]\n" + ] + } + ], + "source": [ + "v = np.array([10, 20, 30, 40, 50])\n", + "\n", + "print(\"Arreglo:\", v)\n", + "print(\"Primer elemento (índice 0):\", v[0])\n", + "print(\"Último elemento:\", v[-1])\n", + "print(\"Elementos del índice 1 al 3:\", v[1:4])\n", + "print(\"Cada dos elementos:\", v[::2])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Matriz:\n", + "[[1 2 3]\n", + " [4 5 6]\n", + " [7 8 9]]\n", + "\n", + "Elemento fila 0, columna 1: 2\n", + "Toda la primera fila: [1 2 3]\n", + "Toda la segunda columna: [2 5 8]\n", + "\n", + "Submatriz (filas 0-1, columnas 1-2):\n", + "[[2 3]\n", + " [5 6]]\n" + ] + } + ], + "source": [ + "# Indexación en matrices 2D\n", + "m = np.array([\n", + " [1, 2, 3],\n", + " [4, 5, 6],\n", + " [7, 8, 9]\n", + "])\n", + "\n", + "print(\"Matriz:\")\n", + "print(m)\n", + "print(\"\\nElemento fila 0, columna 1:\", m[0, 1]) # 2\n", + "print(\"Toda la primera fila:\", m[0, :]) # [1, 2, 3]\n", + "print(\"Toda la segunda columna:\", m[:, 1]) # [2, 5, 8]\n", + "print(\"\\nSubmatriz (filas 0-1, columnas 1-2):\")\n", + "print(m[0:2, 1:3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Indexación booleana (filtrado)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[72 88 91 78]\n", + "Todas las notas: [55 72 88 45 91 63 78]\n", + "Notas > 70: [72 88 91 78]\n", + "Cantidad de aprobados: 4\n", + "Notas en zona (61-70): [63]\n" + ] + } + ], + "source": [ + "notas = np.array([55, 72, 88, 45, 91, 63, 78])\n", + "\n", + "print(notas[notas > 70])\n", + "# ¿Cuáles notas son mayores de 70?\n", + "aprobados = notas[notas > 70]\n", + "print(\"Todas las notas:\", notas)\n", + "print(\"Notas > 70:\", aprobados)\n", + "print(\"Cantidad de aprobados:\", aprobados.shape[0])\n", + "\n", + "# Condición compuesta\n", + "zona = notas[(notas >= 61) & (notas <= 70)]\n", + "print(\"Notas en zona (61-70):\", zona)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Operaciones matemáticas" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a + b = [11 22 33 44]\n", + "a * b = [ 10 40 90 160]\n", + "b / a = [10. 10. 10. 10.]\n", + "a ** 2 = [ 1 4 9 16]\n", + "\n", + "Raíz cuadrada de b: [3.16227766 4.47213595 5.47722558 6.32455532]\n", + "e^a: [ 2.71828183 7.3890561 20.08553692 54.59815003]\n" + ] + } + ], + "source": [ + "a = np.array([1, 2, 3, 4])\n", + "b = np.array([10, 20, 30, 40])\n", + "\n", + "print(\"a + b =\", a + b)\n", + "print(\"a * b =\", a * b)\n", + "print(\"b / a =\", b / a)\n", + "print(\"a ** 2 =\", a ** 2) # Elevar al cuadrado\n", + "print(\"\\nRaíz cuadrada de b:\", np.sqrt(b))\n", + "print(\"e^a:\", np.exp(a))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Broadcasting — reglas para operar arrays de distinta forma\n", + "\n", + "Cuando aplicas una operación entre un array y un escalar (o dos arrays de forma distinta), NumPy ajusta los tamaños automáticamente siguiendo **dos reglas** (Stewart & Mommert, §4.1.4):\n", + "\n", + "1. **Regla 1** — Si los arrays tienen distinto número de dimensiones, la forma del más pequeño se *extiende* a la izquierda con \"1\" hasta igualar.\n", + "2. **Regla 2** — Un eje de tamaño 1 se comporta como si tuviera el tamaño del eje más grande en esa dimensión (se \"replica\").\n", + "\n", + "```\n", + "array → [1, 2, 3, 4, 5] forma: (5,)\n", + "escalar → 10 forma: () → (1,) → (5,) [reglas 1 y 2]\n", + "resultado→ [11, 12, 13, 14, 15]\n", + "```\n", + "\n", + "Si dos ejes no son 1 y son distintos entre sí → **ValueError** (no se puede hacer broadcast)." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Array original: [1 2 3 4 5]\n", + "v + 10 : [11 12 13 14 15]\n", + "v * 3 : [ 3 6 9 12 15]\n", + "v ** 2 : [ 1 4 9 16 25]\n", + "v / 2 : [0.5 1. 1.5 2. 2.5]\n", + "v - 1 : [0 1 2 3 4]\n", + "\n", + "Matriz + 100:\n", + "[[101 102 103]\n", + " [104 105 106]]\n", + "\n", + "Matriz * 2:\n", + "[[ 2 4 6]\n", + " [ 8 10 12]]\n" + ] + } + ], + "source": [ + "v = np.array([1, 2, 3, 4, 5])\n", + "\n", + "print(\"Array original:\", v)\n", + "print(\"v + 10 :\", v + 10) # suma 10 a cada elemento\n", + "print(\"v * 3 :\", v * 3) # multiplica cada elemento por 3\n", + "print(\"v ** 2 :\", v ** 2) # eleva al cuadrado cada elemento\n", + "print(\"v / 2 :\", v / 2) # divide cada elemento entre 2\n", + "print(\"v - 1 :\", v - 1)\n", + "\n", + "# También funciona con matrices\n", + "m = np.array([[1, 2, 3],\n", + " [4, 5, 6]])\n", + "print(\"\\nMatriz + 100:\")\n", + "print(m + 100)\n", + "print(\"\\nMatriz * 2:\")\n", + "print(m * 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Funciones matemáticas (ufuncs)\n", + "\n", + "NumPy tiene versiones de las funciones matemáticas de `math` que funcionan directamente sobre **arrays completos** sin necesidad de un ciclo `for`. Estas se llaman *ufuncs* (universal functions)." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x : [0 1 2 3 4 5]\n", + "np.sqrt(x) : [0. 1. 1.41421356 1.73205081 2. 2.23606798]\n", + "np.exp(x) : [ 1. 2.71828183 7.3890561 20.08553692 54.59815003\n", + " 148.4131591 ]\n", + "np.log(x+1): [0. 0.69314718 1.09861229 1.38629436 1.60943791 1.79175947]\n", + "np.abs(x-3): [3 2 1 0 1 2]\n", + "np.sign(x-3): [-1 -1 -1 0 1 1]\n" + ] + } + ], + "source": [ + "x = np.array([0, 1, 2, 3, 4, 5])\n", + "\n", + "print(\"x :\", x)\n", + "print(\"np.sqrt(x) :\", np.sqrt(x)) # raíz cuadrada de cada elemento\n", + "print(\"np.exp(x) :\", np.exp(x)) # e^x\n", + "print(\"np.log(x+1):\", np.log(x + 1)) # logaritmo natural (evitamos log(0))\n", + "print(\"np.abs(x-3):\", np.abs(x - 3)) # valor absoluto\n", + "print(\"np.sign(x-3):\", np.sign(x - 3)) # -1, 0 o 1 según el signo" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ángulos (rad): [0. 0.5236 0.7854 1.0472 1.5708]\n", + "sin : [0. 0.5 0.7071 0.866 1. ]\n", + "cos : [1. 0.866 0.7071 0.5 0. ]\n", + "\n", + "Velocidad vertical (v0=10): [ 0. 5. 7.071 8.66 10. ]\n" + ] + } + ], + "source": [ + "# Funciones trigonométricas (el argumento está en radianes)\n", + "angulos = np.array([0, np.pi/6, np.pi/4, np.pi/3, np.pi/2])\n", + "\n", + "print(\"Ángulos (rad):\", angulos.round(4))\n", + "print(\"sin :\", np.sin(angulos).round(4))\n", + "print(\"cos :\", np.cos(angulos).round(4))\n", + "\n", + "# Caso práctico: calcular la componente vertical de lanzamientos\n", + "# con distintos ángulos y velocidad inicial v0 = 10 m/s\n", + "v0 = 10\n", + "v_vertical = v0 * np.sin(angulos)\n", + "print(\"\\nVelocidad vertical (v0=10):\", v_vertical.round(3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 3b — Valores especiales y operadores lógicos\n", + "\n", + "### Valores especiales: `nan` e `inf`\n", + "\n", + "Algunas operaciones producen resultados especiales:\n", + "- `np.nan` (*Not a Number*): resultado indefinido, como `log(-1)` o `0/0`\n", + "- `np.inf` (*infinito*): resultado de dividir un número entre cero\n", + "\n", + "NumPy tiene versiones de las funciones estadísticas que **ignoran** los `nan`:\n", + "`np.nanmean`, `np.nansum`, `np.nanmax`, `np.nanmin`" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "np.pi = 3.141592653589793\n", + "np.e = 2.718281828459045\n", + "np.inf = inf\n", + "np.nan = nan\n", + "\n", + "sqrt de [-4. -1. 0. 1. 4.] → [nan nan 0. 1. 2.]\n", + "\n", + "Notas con nan: [85. nan 72. nan 91.]\n", + "Media normal : nan\n", + "Media sin nan : 82.66666666666667\n", + "Suma sin nan : 248.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1283/3405442486.py:8: RuntimeWarning: invalid value encountered in sqrt\n", + " print(\"\\nsqrt de\", x, \"→\", np.sqrt(x)) # sqrt de negativos → nan\n" + ] + } + ], + "source": [ + "print(\"np.pi =\", np.pi)\n", + "print(\"np.e =\", np.e)\n", + "print(\"np.inf =\", np.inf)\n", + "print(\"np.nan =\", np.nan)\n", + "\n", + "# nan aparece en operaciones indefinidas\n", + "x = np.array([-4.0, -1.0, 0.0, 1.0, 4.0])\n", + "print(\"\\nsqrt de\", x, \"→\", np.sqrt(x)) # sqrt de negativos → nan\n", + "\n", + "# Funciones que ignoran nan\n", + "notas = np.array([85.0, np.nan, 72.0, np.nan, 91.0])\n", + "print(\"\\nNotas con nan:\", notas)\n", + "print(\"Media normal :\", np.mean(notas)) # ← devuelve nan\n", + "print(\"Media sin nan :\", np.nanmean(notas)) # ← ignora nan\n", + "print(\"Suma sin nan :\", np.nansum(notas))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Operadores lógicos sobre arrays\n", + "\n", + "Puedes comparar un array con un valor y obtienes un array de booleanos (`True`/`False`).\n", + "Luego ese array booleano puede usarse como filtro para seleccionar elementos." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Temperaturas: [18.5 22. 35.1 15.3 28.7 12. 30. ]\n", + "temps > 25 : [False False True False True False True]\n", + "\n", + "Días calurosos (> 25°C): [35.1 28.7 30. ]\n", + "Días moderados (20-30°C): [22. 28.7 30. ]\n", + "\n", + "¿Cuántos días > 25?: 3\n", + "¿Algún día < 10? : False\n", + "¿Todos > 10? : True\n" + ] + } + ], + "source": [ + "temps = np.array([18.5, 22.0, 35.1, 15.3, 28.7, 12.0, 30.0])\n", + "\n", + "# Comparación → array de True/False\n", + "print(\"Temperaturas:\", temps)\n", + "print(\"temps > 25 :\", temps > 25)\n", + "\n", + "# Usar el array booleano para filtrar\n", + "calurosos = temps[temps > 25]\n", + "print(\"\\nDías calurosos (> 25°C):\", calurosos)\n", + "\n", + "# Condición compuesta: & (y) , | (o)\n", + "moderados = temps[(temps >= 20) & (temps <= 30)]\n", + "print(\"Días moderados (20-30°C):\", moderados)\n", + "\n", + "# Contar cuántos cumplen la condición\n", + "print(\"\\n¿Cuántos días > 25?:\", np.sum(temps > 25))\n", + "print(\"¿Algún día < 10? :\", np.any(temps < 10))\n", + "print(\"¿Todos > 10? :\", np.all(temps > 10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### `np.where` — asignar valores según condición\n", + "\n", + "`np.where(condición, valor_si_True, valor_si_False)` crea un array nuevo aplicando una regla a cada elemento. Es el equivalente vectorizado del operador ternario." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Nota 55 → Reprobado\n", + " Nota 72 → Aprobado\n", + " Nota 88 → Aprobado\n", + " Nota 45 → Reprobado\n", + " Nota 91 → Aprobado\n", + " Nota 63 → Aprobado\n", + " Nota 78 → Aprobado\n", + "\n", + " Notas penalizadas: [ 0 72 88 0 91 63 78]\n" + ] + } + ], + "source": [ + "notas = np.array([55, 72, 88, 45, 91, 63, 78])\n", + "\n", + "# Etiquetar cada nota como \"Aprobado\" o \"Reprobado\"\n", + "estados = np.where(notas >= 61, \"Aprobado\", \"Reprobado\")\n", + "\n", + "for n, e in zip(notas, estados):\n", + " print(f\" Nota {n:3d} → {e}\")\n", + "\n", + "# También funciona con números\n", + "# Reemplazar notas menores a 61 con 0 (para penalización)\n", + "penalizadas = np.where(notas >= 61, notas, 0)\n", + "print(\"\\n Notas penalizadas:\", penalizadas)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Copias vs Vistas (*views*) — diferencia crítica con listas\n", + "\n", + "En NumPy existen **tres comportamientos** distintos al \"asignar\" un array:\n", + "\n", + "| Operación | Resultado | Modifica original |\n", + "|---|---|---|\n", + "| `b = a` | **alias** — misma memoria | Sí |\n", + "| `b = a[1:4]` | **vista** (*view*) — misma memoria | **Sí** ⚠️ |\n", + "| `b = a.copy()` | **copia** — memoria nueva | No |\n", + "\n", + "> **¡Diferencia clave con listas!** En Python, `l[1:4]` es siempre una copia. En NumPy, `a[1:4]` es una **vista** del array original — modificarla cambia el original. Esto es fuente frecuente de errores (Stewart & Mommert, §4.1.2)." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Alias modifica 'original': [999 2 3 4 5]\n", + "Slice (vista) modifica 'original2': [ 1 777 3 4 5]\n", + "Copia NO modifica 'original3': [1 2 3 4 5]\n" + ] + } + ], + "source": [ + "original = np.array([1, 2, 3, 4, 5])\n", + "\n", + "# ⚠ ALIAS — dos nombres, un solo array\n", + "alias = original\n", + "alias[0] = 999\n", + "print(\"Alias modifica 'original':\", original)\n", + "\n", + "# ⚠ VISTA (slice) — ¡diferente a listas Python!\n", + "original2 = np.array([1, 2, 3, 4, 5])\n", + "vista = original2[1:4] # NO es copia — es vista\n", + "vista[0] = 777\n", + "print(\"Slice (vista) modifica 'original2':\", original2)\n", + "\n", + "# ✅ COPIA independiente — usa .copy()\n", + "original3 = np.array([1, 2, 3, 4, 5])\n", + "copia = original3.copy()\n", + "copia[0] = 999\n", + "print(\"Copia NO modifica 'original3':\", original3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Unir arrays — concatenate, hstack, vstack, stack\n", + "\n", + "NumPy ofrece varias formas de unir arrays (Stewart & Mommert, §4.1.5):\n", + "\n", + "- `np.concatenate()`: función **general** — une a lo largo de un eje existente.\n", + "- `np.hstack()`: atajo para unir **horizontalmente** (eje 1 en 2D, eje 0 en 1D).\n", + "- `np.vstack()`: atajo para unir **verticalmente** (eje 0).\n", + "- `np.stack()`: apila creando un **nuevo eje**." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "concatenate: [1 2 3 4 5 6]\n", + "hstack : [1 2 3 4 5 6]\n", + "\n", + "vstack (apilar filas, axis=0):\n", + "[[1 2]\n", + " [3 4]\n", + " [5 6]\n", + " [7 8]]\n", + "\n", + "hstack (unir columnas, axis=1):\n", + "[[1 2 5 6]\n", + " [3 4 7 8]]\n", + "\n", + "stack — crea NUEVO eje (shape resultante: 2×2×2):\n", + "[[[1 2]\n", + " [3 4]]\n", + "\n", + " [[5 6]\n", + " [7 8]]]\n", + "Shape: (2, 2, 2)\n" + ] + } + ], + "source": [ + "a = np.array([1, 2, 3])\n", + "b = np.array([4, 5, 6])\n", + "\n", + "# Función general: np.concatenate\n", + "print(\"concatenate:\", np.concatenate((a, b)))\n", + "print(\"hstack :\", np.hstack((a, b))) # equivalente para 1D\n", + "\n", + "# Para matrices\n", + "m1 = np.array([[1, 2], [3, 4]])\n", + "m2 = np.array([[5, 6], [7, 8]])\n", + "\n", + "print(\"\\nvstack (apilar filas, axis=0):\")\n", + "print(np.vstack((m1, m2)))\n", + "\n", + "print(\"\\nhstack (unir columnas, axis=1):\")\n", + "print(np.hstack((m1, m2)))\n", + "\n", + "print(\"\\nstack — crea NUEVO eje (shape resultante: 2×2×2):\")\n", + "apilado = np.stack((m1, m2), axis=0)\n", + "print(apilado)\n", + "print(\"Shape:\", apilado.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ordenar y buscar — `np.sort` y `np.argsort`\n", + "\n", + "- `np.sort(x)` — retorna una **copia ordenada** (no modifica `x`).\n", + "- `np.argsort(x)` — retorna los **índices** que ordenarían `x`. Muy útil para ordenar arrays relacionados (e.g., ordenar nombres por nota) (Stewart & Mommert, §4.2.5)." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Notas originales: [78 45 92 61 88 34 73]\n", + "Ordenadas (asc) : [34 45 61 73 78 88 92]\n", + "Ordenadas (desc): [92 88 78 73 61 45 34]\n", + "\n", + "Índices (argsort): [5 1 3 6 0 4 2]\n", + "Aplicando índices : [34 45 61 73 78 88 92]\n", + "\n", + "Ranking:\n", + " 1. Diana: 92\n", + " 2. Fátima: 88\n", + " 3. Ana: 78\n", + " 4. Helena: 73\n", + " 5. Eduardo: 61\n", + " 6. Carlos: 45\n", + " 7. Gabriel: 34\n" + ] + } + ], + "source": [ + "notas = np.array([78, 45, 92, 61, 88, 34, 73])\n", + "print(\"Notas originales:\", notas)\n", + "\n", + "# np.sort — retorna una copia ordenada (no modifica el original)\n", + "print(\"Ordenadas (asc) :\", np.sort(notas))\n", + "print(\"Ordenadas (desc):\", np.sort(notas)[::-1])\n", + "\n", + "# np.argsort — retorna los ÍNDICES que ordenarían el array\n", + "indices = np.argsort(notas)\n", + "print(\"\\nÍndices (argsort):\", indices)\n", + "print(\"Aplicando índices :\", notas[indices]) # equivale a sort\n", + "\n", + "# Caso práctico: obtener ranking de estudiantes\n", + "nombres = np.array([\"Ana\", \"Carlos\", \"Diana\", \"Eduardo\", \"Fátima\", \"Gabriel\", \"Helena\"])\n", + "ranking = np.argsort(notas)[::-1] # mayor a menor\n", + "print(\"\\nRanking:\")\n", + "for pos, i in enumerate(ranking, 1):\n", + " print(f\" {pos}. {nombres[i]}: {notas[i]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Suma acumulativa — `np.cumsum`\n", + "\n", + "`np.cumsum` calcula la suma acumulada: cada elemento del resultado es la suma de todos los anteriores más el actual. Útil para calcular totales progresivos (gastos, puntajes acumulados, etc.)." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Puntos por nivel : [100 250 180 320 210]\n", + "Puntos acumulados : [ 100 350 530 850 1060]\n", + "\n", + "Gastos diarios : [ 50 120 30 85 200 45]\n", + "Total acumulado : [ 50 170 200 285 485 530]\n", + "Total de la semana : 530\n" + ] + } + ], + "source": [ + "puntos_por_nivel = np.array([100, 250, 180, 320, 210])\n", + "\n", + "print(\"Puntos por nivel :\", puntos_por_nivel)\n", + "print(\"Puntos acumulados :\", np.cumsum(puntos_por_nivel))\n", + "\n", + "# Ejemplo: gastos diarios y total acumulado\n", + "gastos = np.array([50, 120, 30, 85, 200, 45])\n", + "print(\"\\nGastos diarios :\", gastos)\n", + "print(\"Total acumulado :\", np.cumsum(gastos))\n", + "print(\"Total de la semana :\", np.sum(gastos))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 3 — Estadística y Álgebra Lineal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Funciones estadísticas" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Datos: [23 45 12 67 34 89 56 78 90 11]\n", + "\n", + "--- Estadísticas ---\n", + "Mínimo: 11\n", + "Máximo: 90\n", + "Suma: 505\n", + "Media (promedio): 50.50\n", + "Mediana: 50.50\n", + "Desviación estándar: 28.64\n", + "Varianza: 820.25\n", + "\n", + "Índice del máximo: 8\n", + "Índice del mínimo: 9\n" + ] + } + ], + "source": [ + "datos = np.array([23, 45, 12, 67, 34, 89, 56, 78, 90, 11])\n", + "\n", + "print(\"Datos:\", datos)\n", + "print(\"\\n--- Estadísticas ---\")\n", + "print(f\"Mínimo: {np.min(datos)}\")\n", + "print(f\"Máximo: {np.max(datos)}\")\n", + "print(f\"Suma: {np.sum(datos)}\")\n", + "print(f\"Media (promedio): {np.mean(datos):.2f}\")\n", + "print(f\"Mediana: {np.median(datos):.2f}\")\n", + "print(f\"Desviación estándar: {np.std(datos):.2f}\")\n", + "print(f\"Varianza: {np.var(datos):.2f}\")\n", + "print(f\"\\nÍndice del máximo: {np.argmax(datos)}\")\n", + "print(f\"Índice del mínimo: {np.argmin(datos)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estadísticas avanzadas — histograma, media ponderada, correlación\n", + "\n", + "- `np.histogram(x, bins)` — distribución de frecuencias (retorna conteo y bordes).\n", + "- `np.average(x, weights=w)` — **media ponderada** (cada dato tiene un peso diferente).\n", + "- `np.corrcoef(x, y)` — **coeficiente de correlación** de Pearson entre dos arrays.\n", + "- **API moderna de números aleatorios**: usar `default_rng(semilla)` en lugar de `np.random.seed()` (Stewart & Mommert, §4.4)." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Conteo por intervalo: [ 6 16 31 44 39 39 12 9 3 1]\n", + "Bordes de intervalos: [48.7 53.7 58.8 63.8 68.9 73.9 79. 84. 89. 94.1 99.1]\n", + "\n", + "Media simple : 2.86\n", + "Media ponderada : 2.995\n", + "\n", + "Coeficiente de correlación Pearson:\n", + "[[1. 0.9987]\n", + " [0.9987 1. ]]\n" + ] + } + ], + "source": [ + "from numpy.random import default_rng\n", + "rng = default_rng(42) # semilla para reproducibilidad\n", + "\n", + "# Generar datos con distribución normal (media=70, desv=10)\n", + "calificaciones = rng.normal(loc=70, scale=10, size=200).clip(0, 100)\n", + "\n", + "# np.histogram — distribución de frecuencias\n", + "conteo, bordes = np.histogram(calificaciones, bins=10)\n", + "print(\"Conteo por intervalo:\", conteo)\n", + "print(\"Bordes de intervalos:\", bordes.round(1))\n", + "\n", + "# Media ponderada: notas con distintos pesos\n", + "mediciones = np.array([3.1, 2.7, 2.5, 3.1, 2.9])\n", + "incertidumbres = np.array([0.05, 0.15, 0.60, 0.10, 0.05])\n", + "print(\"\\nMedia simple :\", np.mean(mediciones).round(4))\n", + "print(\"Media ponderada :\", np.average(mediciones, weights=1/incertidumbres**2).round(4))\n", + "# Pesos = 1/σ² → mayor peso a mediciones más precisas\n", + "\n", + "# Correlación entre dos arrays\n", + "x = np.array([1, 2, 3, 4, 5])\n", + "y = np.array([2.1, 3.9, 6.2, 7.8, 10.1])\n", + "print(\"\\nCoeficiente de correlación Pearson:\")\n", + "print(np.corrcoef(x, y).round(4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reshape — cambiar la forma de un array" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Arreglo original: [ 1 2 3 4 5 6 7 8 9 10 11 12]\n", + "Forma: (12,)\n", + "\n", + "Reorganizado a 3x4:\n", + "[[ 1 2 3 4]\n", + " [ 5 6 7 8]\n", + " [ 9 10 11 12]]\n", + "\n", + "Reorganizado a 2x6:\n", + "[[ 1 2 3 4 5 6]\n", + " [ 7 8 9 10 11 12]]\n" + ] + } + ], + "source": [ + "# Crear un arreglo de 12 elementos y reorganizarlo\n", + "v = np.arange(1, 13)\n", + "print(\"Arreglo original:\", v)\n", + "print(\"Forma:\", v.shape)\n", + "\n", + "# Convertir a matriz 3x4\n", + "m = v.reshape(3, 4)\n", + "print(\"\\nReorganizado a 3x4:\")\n", + "print(m)\n", + "\n", + "# Convertir a matriz 2x6\n", + "print(\"\\nReorganizado a 2x6:\")\n", + "print(v.reshape(2, 6))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Multiplicación de matrices y operaciones esenciales\n", + "\n", + "| Operación | Sintaxis | Descripción |\n", + "|---|---|---|\n", + "| Elemento a elemento | `A * B` | Hadamard product |\n", + "| Matricial | `A @ B` o `np.matmul(A, B)` | Producto matricial |\n", + "| Transpuesta | `A.T` o `np.transpose(A)` | Intercambia filas↔columnas |\n", + "| Identidad | `np.identity(n)` | Matriz identidad n×n |\n", + "| Aplanar | `A.ravel()` o `np.ravel(A)` | Convierte a vector 1D |" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A * B (elemento a elemento):\n", + "[[ 5 12]\n", + " [21 32]]\n", + "\n", + "A @ B (multiplicación matricial):\n", + "[[19 22]\n", + " [43 50]]\n", + "\n", + "A.T (transpuesta):\n", + "[[1 3]\n", + " [2 4]]\n", + "\n", + "np.identity(3) — matriz identidad:\n", + "[[1. 0. 0.]\n", + " [0. 1. 0.]\n", + " [0. 0. 1.]]\n", + "\n", + "A.ravel() — aplanar a 1D: [1 2 3 4]\n" + ] + } + ], + "source": [ + "A = np.array([[1, 2], [3, 4]])\n", + "B = np.array([[5, 6], [7, 8]])\n", + "\n", + "print(\"A * B (elemento a elemento):\")\n", + "print(A * B)\n", + "\n", + "print(\"\\nA @ B (multiplicación matricial):\")\n", + "print(A @ B)\n", + "\n", + "print(\"\\nA.T (transpuesta):\")\n", + "print(A.T)\n", + "\n", + "print(\"\\nnp.identity(3) — matriz identidad:\")\n", + "print(np.identity(3))\n", + "\n", + "# Aplanar una matriz a vector 1D\n", + "print(\"\\nA.ravel() — aplanar a 1D:\", A.ravel())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 3d — Álgebra Lineal (`numpy.linalg`)\n", + "\n", + "NumPy incluye el módulo `numpy.linalg` (importado como `la`) para operaciones matriciales avanzadas (Stewart & Mommert, §4.7):\n", + "\n", + "| Función | Descripción |\n", + "|---|---|\n", + "| `la.det(A)` | Determinante de A |\n", + "| `la.inv(A)` | Inversa de A (si det ≠ 0) |\n", + "| `la.norm(v)` | Norma euclidiana de un vector |\n", + "| `la.solve(A, b)` | Resuelve el sistema **Ax = b** |\n", + "| `la.eig(A)` | Eigenvalores y eigenvectores de A |\n", + "\n", + "> `la.solve(A, b)` es numéricamente más estable y eficiente que calcular `la.inv(A) @ b`." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Matriz A:\n", + "[[4. 2. 0.]\n", + " [9. 3. 7.]\n", + " [1. 2. 1.]]\n", + "\n", + "Determinante: -48.0\n", + "\n", + "Inversa A⁻¹:\n", + "[[ 0.2292 0.0417 -0.2917]\n", + " [ 0.0417 -0.0833 0.5833]\n", + " [-0.3125 0.125 0.125 ]]\n", + "\n", + "A @ A⁻¹ (≈ identidad):\n", + "[[ 1. 0. -0.]\n", + " [ 0. 1. -0.]\n", + " [ 0. 0. 1.]]\n", + "\n", + "Norma del vector [3, 4]: 5.0\n" + ] + } + ], + "source": [ + "import numpy.linalg as la\n", + "\n", + "A = np.array([[4, 2, 0],\n", + " [9, 3, 7],\n", + " [1, 2, 1]], dtype=float)\n", + "\n", + "print(\"Matriz A:\")\n", + "print(A)\n", + "\n", + "# Determinante\n", + "print(\"\\nDeterminante:\", la.det(A).round(4))\n", + "\n", + "# Inversa (si det ≠ 0)\n", + "A_inv = la.inv(A)\n", + "print(\"\\nInversa A⁻¹:\")\n", + "print(A_inv.round(4))\n", + "\n", + "# Verificación: A @ A⁻¹ debe ser la identidad\n", + "print(\"\\nA @ A⁻¹ (≈ identidad):\")\n", + "print((A @ A_inv).round(10))\n", + "\n", + "# Norma de un vector\n", + "v = np.array([3.0, 4.0])\n", + "print(\"\\nNorma del vector [3, 4]:\", la.norm(v)) # = 5.0" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solución x: [ 1. 2. -2.]\n", + "Verificación Ax - b ≈ 0: [ 0. 0. -0.]\n", + "\n", + "--- Eigenvalores ---\n", + "Eigenvalores: [-5. 3. 6.]\n", + "Primer eigenvector: [ 0.816 0.408 -0.408]\n" + ] + } + ], + "source": [ + "import numpy.linalg as la\n", + "\n", + "# ─── Resolución de sistemas de ecuaciones: Ax = b ───────────────────────────\n", + "# Ejemplo: sistema de 3 ecuaciones\n", + "# 3x + 2y + z = 5\n", + "# 5x + 5y + 5z = 5\n", + "# x + 4y + 6z = -3\n", + "A = np.array([[3, 2, 1],\n", + " [5, 5, 5],\n", + " [1, 4, 6]], dtype=float)\n", + "b = np.array([5, 5, -3], dtype=float)\n", + "\n", + "x = la.solve(A, b)\n", + "print(\"Solución x:\", x.round(4))\n", + "print(\"Verificación Ax - b ≈ 0:\", (A @ x - b).round(10))\n", + "\n", + "# ─── Eigenvalores y eigenvectores ───────────────────────────────────────────\n", + "print(\"\\n--- Eigenvalores ---\")\n", + "M = np.array([[-2, -4, 2],\n", + " [-2, 1, 2],\n", + " [ 4, 2, 5]], dtype=float)\n", + "eigenvalores, eigenvectores = la.eig(M)\n", + "print(\"Eigenvalores:\", eigenvalores.round(3))\n", + "print(\"Primer eigenvector:\", eigenvectores[:, 0].round(3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 3e — Polinomios\n", + "\n", + "NumPy permite representar polinomios por sus coeficientes y ofrece funciones para ajustar, evaluar y manipularlos (Stewart & Mommert, §4.6):\n", + "\n", + "- `np.polyfit(x, y, deg)` — ajusta un polinomio de grado `deg` a datos *(x, y)* por mínimos cuadrados. Retorna los coeficientes `[a_n, ..., a_1, a_0]`.\n", + "- `np.polyval(coefs, x)` — evalúa el polinomio con coeficientes `coefs` en los puntos `x`.\n", + "- `np.poly(roots)` — construye coeficientes desde las raíces.\n", + "- `np.roots(coefs)` — calcula las raíces a partir de los coeficientes." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coeficientes [a, b, c]: [ 0.051 -0. 0. ]\n", + "Modelo: d = 0.0510·v² + -0.0000·v + 0.0000\n", + "\n", + "Predicciones:\n", + " v=25 m/s → d≈31.87 m\n", + " v=35 m/s → d≈62.47 m\n", + " v=45 m/s → d≈103.27 m\n" + ] + } + ], + "source": [ + "# Datos: velocidad inicial vs distancia máxima (proyectil simplificado)\n", + "# Queremos ajustar un polinomio de grado 2: d = a*v^2 + b*v + c\n", + "v = np.array([10, 20, 30, 40, 50], dtype=float) # velocidad (m/s)\n", + "d = np.array([5.1, 20.4, 45.9, 81.6, 127.5]) # distancia (m)\n", + "\n", + "# np.polyfit: ajuste de mínimos cuadrados\n", + "coefs = np.polyfit(v, d, deg=2)\n", + "print(\"Coeficientes [a, b, c]:\", coefs.round(4))\n", + "print(f\"Modelo: d = {coefs[0]:.4f}·v² + {coefs[1]:.4f}·v + {coefs[2]:.4f}\")\n", + "\n", + "# np.polyval: evaluar el polinomio en nuevos valores\n", + "v_nuevo = np.array([25, 35, 45])\n", + "d_pred = np.polyval(coefs, v_nuevo)\n", + "print(\"\\nPredicciones:\")\n", + "for vi, di in zip(v_nuevo, d_pred):\n", + " print(f\" v={vi} m/s → d≈{di:.2f} m\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 4 — Ejemplo Aplicado: Cálculo de Notas" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Notas (Tarea1 | Tarea2 | Parcial | Final):\n", + "--------------------------------------------------\n", + "Ana [78 91 68 54]\n", + "Carlos [ 82 47 100 60]\n", + "Diana [78 97 58 62]\n", + "Eduardo [50 50 63 92]\n", + "Fátima [75 79 63 42]\n", + "Gabriel [61 92 41 63]\n", + "Helena [83 69 77 41]\n", + "Iván [99 60 72 51]\n", + "\n", + "--- Resultados Finales ---\n", + "Ana Nota: 67.3 → APROBADO\n", + "Carlos Nota: 73.3 → APROBADO\n", + "Diana Nota: 68.5 → APROBADO\n", + "Eduardo Nota: 70.7 → APROBADO\n", + "Fátima Nota: 58.8 → REPROBADO\n", + "Gabriel Nota: 60.5 → REPROBADO\n", + "Helena Nota: 62.3 → APROBADO\n", + "Iván Nota: 65.8 → APROBADO\n", + "\n", + "Promedio del curso: 65.91\n", + "Nota más alta: 73.35\n", + "Nota más baja: 58.80\n", + "Aprobados: 6 / 8\n" + ] + } + ], + "source": [ + "# Simulación de notas de un curso\n", + "# Filas = estudiantes, Columnas = [Tarea1, Tarea2, Examen parcial, Examen final]\n", + "np.random.seed(42) # Para reproducibilidad\n", + "\n", + "notas = np.random.randint(40, 101, size=(8, 4))\n", + "pesos = np.array([0.15, 0.15, 0.30, 0.40]) # Pesos de cada evaluación\n", + "\n", + "nombres = [\"Ana\", \"Carlos\", \"Diana\", \"Eduardo\", \"Fátima\", \"Gabriel\", \"Helena\", \"Iván\"]\n", + "\n", + "print(\"Notas (Tarea1 | Tarea2 | Parcial | Final):\")\n", + "print(\"-\" * 50)\n", + "for nombre, fila in zip(nombres, notas):\n", + " print(f\"{nombre:<10} {fila}\")\n", + "\n", + "# Calcular nota final ponderada para cada estudiante\n", + "notas_finales = notas @ pesos\n", + "\n", + "print(\"\\n--- Resultados Finales ---\")\n", + "for nombre, nota in zip(nombres, notas_finales):\n", + " estado = \"APROBADO\" if nota >= 61 else \"REPROBADO\"\n", + " print(f\"{nombre:<10} Nota: {nota:.1f} → {estado}\")\n", + "\n", + "print(f\"\\nPromedio del curso: {np.mean(notas_finales):.2f}\")\n", + "print(f\"Nota más alta: {np.max(notas_finales):.2f}\")\n", + "print(f\"Nota más baja: {np.min(notas_finales):.2f}\")\n", + "print(f\"Aprobados: {np.sum(notas_finales >= 61)} / {len(notas_finales)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NumPy versión: 1.26.4\n" + ] + } + ], + "source": [ + "\n", + "import numpy as np\n", + "\n", + "print(\"NumPy versión:\", np.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tarea 4 — NumPy en Práctica\n", + "\n", + "La programación se aprende haciendo, no leyendo. Esta tarea tiene dos partes:\n", + "\n", + "**Parte 1 — Descarga y ejecuta** (obligatorio)\n", + "Descarga este notebook y ejecútalo celda por celda en tu computadora. \n", + "Asegúrate de que todas las celdas corran sin errores antes de continuar.\n", + "\n", + "**Parte 2 — Ejercicios** (obligatorio) \n", + "Completa cada celda que dice `# TU CÓDIGO AQUÍ` con tu solución. \n", + "Ejecuta la celda para verificar que el resultado es correcto.\n", + "\n", + "**Entrega: 1 de mayo** \n", + "Sube el notebook a tu repositorio de GitHub y envía el **link directo al archivo `.ipynb`**.\n", + "El notebook debe tener todas las celdas ya ejecutadas (con outputs visibles).\n", + "\n", + "Ejemplo de link válido: \n", + "`https://github.com/tu-usuario/tu-repo/blob/main/Numpy.ipynb`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ejercicio 1 — Crear y operar arrays\n", + "\n", + "Crea un array con los números del 1 al 10 usando `np.arange`. \n", + "Luego calcula e imprime:\n", + "- Cada número multiplicado por 5\n", + "- Solo los números pares (usa indexación booleana)\n", + "- La suma de todos los números" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lista principal:, [ 1 2 3 4 5 6 7 8 9 10]\n", + "Multiplicación por 5: [ 5 10 15 20 25 30 35 40 45 50]\n", + "Números pares: [ 2 4 6 8 10]\n", + "Suma total: 55\n" + ] + } + ], + "source": [ + "# TU CÓDIGO AQUÍ\n", + "\n", + "lista_principal = np.arange(1, 11)\n", + "Multiplicacion = lista_principal * 5\n", + "pares = lista_principal[lista_principal % 2 ==0]\n", + "suma = lista_principal.sum()\n", + "\n", + "print(f\"Lista principal:, {lista_principal}\")\n", + "print(f\"Multiplicación por 5: {Multiplicacion}\")\n", + "print(f\"Números pares: {pares}\")\n", + "print(f\"Suma total: {suma}\")\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ejercicio 2 — Funciones matemáticas\n", + "\n", + "Dado el array de ángulos en grados, conviértelos a radianes y calcula su seno. \n", + "Fórmula: `radianes = grados * π / 180`\n", + "\n", + "Ángulos: 0°, 30°, 45°, 60°, 90°, 180°" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Grados : [ 0 30 45 60 90 180]\n", + "Radianes: [0. 0.52359878 0.78539816 1.04719755 1.57079633 3.14159265]\n", + "seno: [0. 0.5 0.7071 0.866 1. 0. ]\n" + ] + } + ], + "source": [ + "grados = np.array([0, 30, 45, 60, 90, 180])\n", + "radianes = grados * np.pi /180\n", + "\n", + "# TU CÓDIGO AQUÍ\n", + "print(f\"Grados : {grados}\")\n", + "print(f\"Radianes: {radianes}\")\n", + "print(\"seno: \" , np.sin(radianes).round(4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ejercicio 3 — Filtrado y estadísticas\n", + "\n", + "Dada la lista de temperaturas diarias de un mes, calcula:\n", + "1. La temperatura promedio, mínima y máxima\n", + "2. Cuántos días superaron los 28°C\n", + "3. Las temperaturas de los días fríos (menores a 20°C)\n", + "4. Reemplaza con `np.where` las temperaturas menores a 15°C por exactamente 15.0 (temperatura mínima registrada)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " DATOS ORIGINALES \n", + " \n", + "EN DESORDEN : [ 22.1 19.5 31.2 28.0 17.3 25.8 30.1 14.2 26.7 23.4 18.9 32.0 29.5 21.1 16.8 27.3\n", + " 24.6 13.5 20.0 28.9 33.1 15.7 22.8 19.0 31.5 26.2 18.1 29.8 12.4 25.0]\n", + " \n", + "EN ORDEN : [ 12.4 13.5 14.2 15.7 16.8 17.3 18.1 18.9 19.0 19.5 20.0 21.1 22.1 22.8 23.4 24.6\n", + " 25.0 25.8 26.2 26.7 27.3 28.0 28.9 29.5 29.8 30.1 31.2 31.5 32.0 33.1]\n", + " \n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + " ESTADÍSTICA \n", + " \n", + "Mínimo: 12.4\n", + " \n", + "Máximo: 33.1\n", + " \n", + "Número de días que superaron 28°C: 8\n", + " \n", + "Temperatura de los días menores a 20°C: [ 19.5 17.3 14.2 18.9 16.8 13.5 15.7 19.0 18.1 12.4]\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + " TEMPERATURAS MÍNIMAS REGISTRADAS < 15°C, MODIFICADOS \n", + " \n", + "Temperaturas originales ordenas: [ 12.4 13.5 14.2 15.7 16.8 17.3 18.1 18.9 19.0 19.5 20.0 21.1 22.1 22.8 23.4 24.6\n", + " 25.0 25.8 26.2 26.7 27.3 28.0 28.9 29.5 29.8 30.1 31.2 31.5 32.0 33.1]\n", + " \n", + "DATOS MODIFICADOS < 15°C : [ 15.0 15.0 15.0 15.7 16.8 17.3 18.1 18.9 19.0 19.5 20.0 21.1 22.1 22.8 23.4 24.6\n", + " 25.0 25.8 26.2 26.7 27.3 28.0 28.9 29.5 29.8 30.1 31.2 31.5 32.0 33.1]\n" + ] + } + ], + "source": [ + "np.set_printoptions(formatter={'float': '{: 0.1f}'.format}, linewidth=100)\n", + "temperaturas = np.array([\n", + " 22.1, 19.5, 31.2, 28.0, 17.3, 25.8, 30.1,\n", + " 14.2, 26.7, 23.4, 18.9, 32.0, 29.5, 21.1,\n", + " 16.8, 27.3, 24.6, 13.5, 20.0, 28.9, 33.1,\n", + " 15.7, 22.8, 19.0, 31.5, 26.2, 18.1, 29.8,\n", + " 12.4, 25.0\n", + "])\n", + "# TU CÓDIGO AQUÍ\n", + "\n", + "#--------------------------\n", + "\n", + "a = np.sort(temperaturas) # Temperaturas ordenadas\n", + "b =(temperaturas > 28).sum() # Temperaturas mayores a 28°C.\n", + "c = temperaturas[temperaturas < 20] # Temperaturas de los días menores a 20°C.\n", + "nuevas_temperaturas = np.where(temperaturas < 15, 15.0, temperaturas) # Remplazo de tempetaturas menores a 15°C\n", + "d = np.sort(nuevas_temperaturas) \n", + "\n", + "#--------------------------\n", + "\n", + "\n", + "# Lista de temperaturas diarias de un mes.\n", + "print(\"\\n\" + \" \"*20 + \" DATOS ORIGINALES \" + \" \"*20)\n", + "print(\" \" * 40)\n", + "print(f\"EN DESORDEN : {temperaturas}\")\n", + "print(\" \" * 40)\n", + "print(f\"EN ORDEN : {a}\")\n", + "print(\" \" * 40)\n", + "print(\"----\" * 40)\n", + "\n", + "#--------------------------\n", + "\n", + "print(\"\\n\" + \" \" * 21 + \" ESTADÍSTICA \" + \" \"*20)\n", + "print(\" \" * 40)\n", + "print(f\"Mínimo: {np.min(temperaturas)}\")\n", + "print(\" \" * 40)\n", + "print(f\"Máximo: {np.max(temperaturas)}\")\n", + "print(\" \" * 40)\n", + "print(f\"Número de días que superaron 28°C: {b}\")\n", + "print(\" \" * 40)\n", + "print(f\"Temperatura de los días menores a 20°C: {c}\")\n", + "\n", + "print(\"----\" * 40)\n", + "\n", + "#--------------------------\n", + "\n", + "print(\"\\n\" + \" \" * 17 + \" TEMPERATURAS MÍNIMAS REGISTRADAS < 15°C, MODIFICADOS \" + \" \"*20)\n", + "print(\" \" * 40)\n", + "print(f\"Temperaturas originales ordenas: {a}\")\n", + "print(\" \" * 40)\n", + "print(f\"DATOS MODIFICADOS < 15°C : {d}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ejercicio 4 — Matrices\n", + "\n", + "Crea una matriz de 4×4 con valores del 1 al 16 usando `np.arange` y `reshape`. \n", + "Luego:\n", + "1. Imprime la primera fila\n", + "2. Imprime la última columna\n", + "3. Imprime la submatriz de las 2 primeras filas y las 2 últimas columnas\n", + "4. Calcula la suma de cada fila (investiga `np.sum` con el parámetro `axis`)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " VALORES DEL 1 AL 16: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16]\n", + " Forma: (16,)\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + " MATRIZ ORIGINAL \n", + "Forma: (4, 4)\n", + "MATRIZ 4X4: \n", + " [[ 1 2 3 4]\n", + " [ 5 6 7 8]\n", + " [ 9 10 11 12]\n", + " [13 14 15 16]]\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + " OPERACIONES CON LA MATRIZ \n", + " \n", + "PRIMERA FILA: \n", + " [1 2 3 4]\n", + " \n", + "ÚLTIMA COLUMNA DE LA MATRIZ: \n", + " [ 4 8 12 16]\n", + " \n", + "SUBMATRIZ DE LAS 2 PRIMERAS FILAS Y LAS 2 ÚLTIMAS COLUMNAS:\n", + " [[3 4]\n", + " [7 8]]\n", + " \n", + "SUMAS DE CADA FILA: \n", + " [10 26 42 58]\n", + " \n" + ] + } + ], + "source": [ + "# TU CÓDIGO AQUÍ\n", + "\n", + "a = np.arange(1, 17) # Matrix fila 1 al 16 o arreglo original\n", + "b = a.reshape(4, 4) # Matriz 4x4\n", + "\n", + "#--------------------------\n", + "print(f\"\\n VALORES DEL 1 AL 16: {a}\")\n", + "print(\" Forma:\", a.shape)\n", + "print(\"----\" * 40)\n", + "#--------------------------\n", + "print(\" \"*30 + \"MATRIZ ORIGINAL\" + \" \"*20)\n", + "print(\"Forma:\", b.shape)\n", + "print(f\"MATRIZ 4X4: \\n {b}\")\n", + "print(\"----\" * 40)\n", + "print(\" \"*27 + \" OPERACIONES CON LA MATRIZ\" + \" \"*20)\n", + "print(\" \" * 40)\n", + "#--------------------------\n", + "print(f\"PRIMERA FILA: \\n {b[0, :]}\")\n", + "print(\" \" * 40)\n", + "#--------------------------\n", + "print(f\"ÚLTIMA COLUMNA DE LA MATRIZ: \\n {b[:, -1]}\")\n", + "print(\" \" * 40)\n", + "#--------------------------\n", + "print(f\"SUBMATRIZ DE LAS 2 PRIMERAS FILAS Y LAS 2 ÚLTIMAS COLUMNAS:\\n {b[0:2, 2:4]}\")\n", + "print(\" \" * 40)\n", + "#--------------------------\n", + "print(f\"SUMAS DE CADA FILA: \\n {np.sum(b, axis=1)}\") # Suma de la matriz utilizando .sum() y axis=1 para sumar la filas\n", + "print(\" \" * 40)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ejercicio 5 — Suma acumulativa\n", + "\n", + "Un estudiante anotó los puntos que obtuvo en cada una de las 8 tareas del curso. \n", + "Calcula e imprime los puntos acumulados después de cada tarea para saber en qué momento superó 100, 200 y 300 puntos.\n", + "\n", + "**Tip:** usa `np.cumsum` y filtra con indexación booleana." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------------------------------------\n", + "\n", + " NOTA DE LAS 8 TAREAS DEL CURSO:\n", + " [45 38 52 29 61 47 55 33]\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " LA SUMA ACUMULADA DE TODA LAS NOTAS: \n", + " [ 45 83 135 164 225 272 327 360]\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " LA SUMA CORRELATIVA QUE SUPERÓ LOS 100 PUNTOS, ESTÁ EN LA NOTA CON ÍNDICE: 2\n", + "\n", + " LA SUMA ACUMULADA QUE SUPERÓ 100 pt.: 135\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " SUMA ACUMULADA PRIMERA NOTA, EL MOMENTO EN QUE SUPERÓ LOS 100 pt.: \n", + " [135 164 225 272 327 360]\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " SUMA ACUMULADA PRIMERA NOTA, EL MOMENTO EN QUE SUPERÓ LOS 200 pt.: \n", + " [225 272 327 360]\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " SUMA ACUMULADA PRIMERA NOTA, EL MOMENTO EN QUE SUPERÓ LOS 300 pt.s: \n", + " [327 360]\n", + "------------------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# Puntos de las OCho tareas del curso\n", + "print(\"---\" * 30)\n", + "puntos_tareas = np.array([45, 38, 52, 29, 61, 47, 55, 33])\n", + "a = np.cumsum(puntos_tareas)\n", + "b= np.where(a > 100)[0][0]# Suma correlativa en que se superó lo 100 puntos\n", + "\n", + "\n", + "# TU CÓDIGO AQUÍ\n", + "print(f\"\\n NOTA DE LAS 8 TAREAS DEL CURSO:\\n {puntos_tareas}\")\n", + "print(\"---\" * 30)\n", + "\n", + "print(f\"\\n LA SUMA ACUMULADA DE TODA LAS NOTAS: \\n {a}\")\n", + "print(\"---\" * 30)\n", + "\n", + "print(f\"\\n LA SUMA CORRELATIVA QUE SUPERÓ LOS 100 PUNTOS, ESTÁ EN LA NOTA CON ÍNDICE: {b}\")\n", + "print(f\"\\n LA SUMA ACUMULADA QUE SUPERÓ 100 pt.: {a[2]}\")\n", + "print(\"---\" * 30)\n", + "\n", + "print(f\"\\n SUMA ACUMULADA PRIMERA NOTA, EL MOMENTO EN QUE SUPERÓ LOS 100 pt.: \\n {a[a > 100]}\")\n", + "print(\"---\" * 30)\n", + "\n", + "print(f\"\\n SUMA ACUMULADA PRIMERA NOTA, EL MOMENTO EN QUE SUPERÓ LOS 200 pt.: \\n {a[a > 200]}\")\n", + "print(\"---\" * 30)\n", + "\n", + "print(f\"\\n SUMA ACUMULADA PRIMERA NOTA, EL MOMENTO EN QUE SUPERÓ LOS 300 pt.s: \\n {a[a > 300]}\")\n", + "print(\"---\" * 30) \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ejercicio 6 — Álgebra Lineal\n", + "\n", + "El sistema de ecuaciones representa un balance de fuerzas en una estructura:\n", + "\n", + "$$2F_1 + F_2 = 100$$\n", + "$$F_1 + 3F_2 + F_3 = 200$$\n", + "$$F_2 + 2F_3 = 150$$\n", + "\n", + "Usando `numpy.linalg`:\n", + "1. Resuelve el sistema con `la.solve(A, b)` para encontrar $F_1$, $F_2$, $F_3$\n", + "2. Verifica la solución calculando `A @ x - b` (debe ser ≈ 0)\n", + "3. Calcula el determinante de A con `la.det(A)`\n", + "4. Calcula la norma de la solución `x` con `la.norm(x)`" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " SOLUCIÓN DEL SISTEMA:\n", + " [ 31.2 37.5 56.2]\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " VERIFICACIÓN DE LA SOLUCIÓN A @ X-b ; ≈ 0: \n", + " [ 0.0 0.0 0.0]\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " Determinante: \n", + " 8.0000\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " LA NORMA DE X: \n", + " 74.4773\n", + "------------------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "import numpy.linalg as la\n", + "\n", + "# Sistema de ecuaciones (balance de fuerzas en un puente):\n", + "# 2F1 + F2 = 100\n", + "# F1 + 3F2 + F3 = 200\n", + "# F2 + 2F3 = 150\n", + "\n", + "A = np.array([[2, 1, 0],\n", + " [1, 3, 1],\n", + " [0, 1, 2]], dtype=float)\n", + "b = np.array([100, 200, 150], dtype=float)\n", + "\n", + "# TU CÓDIGO AQUÍ\n", + "x = la.solve(A, b) # la es el alias de np.linalg\n", + "y = A @ x - b # Verificación\n", + "n = la.norm (x)\n", + "\n", + "\n", + "print(f\"\\n SOLUCIÓN DEL SISTEMA:\\n {x}\")\n", + "\n", + "print(\"---\" * 30)\n", + "print(f\"\\n VERIFICACIÓN DE LA SOLUCIÓN A @ X-b ; ≈ 0: \\n {y}\" )\n", + "\n", + "print(\"---\" * 30)\n", + "print(f\"\\n Determinante: \\n {la.det(A):.4f}\")\n", + "\n", + "\n", + "print(\"---\" * 30)\n", + "print(f\"\\n LA NORMA DE X: \\n {n:.4f}\" )\n", + "print(\"---\" * 30)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ejercicio 7 — Polinomios (ajuste de curva)\n", + "\n", + "Un objeto en caída libre sigue la ecuación $x(t) = \\frac{1}{2}g t^2$.\n", + "\n", + "Con los datos de tiempo y posición dados, usa `np.polyfit` para ajustar un polinomio de grado 2 y:\n", + "1. Muestra los coeficientes obtenidos\n", + "2. Estima la aceleración de gravedad `g` a partir del coeficiente cuadrático\n", + "3. Usa `np.polyval` para predecir la posición en `t = 2.5` s y `t = 6` s\n", + "\n", + "**Pista:** el coeficiente de $t^2$ en el polinomio es $\\frac{g}{2}$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " COEFICIENTES [a, b, c] de [at^2 + bt + c)]:: \n", + " a=4.9000, \n", + " b=0.0000, \n", + " c=-0.0000\n", + "------------------------------------------------------------------------------------------\n", + " \n", + " MODELOR [y(t)= at^2 + bt + c)]: \n", + " \n", + " y(t)= 4.9000·t² + 0.0000·t + -0.0000\n", + " \n", + "------------------------------------------------------------------------------------------\n", + " \n", + " ESTIMACIÓN DE LA GRAVEDAD: \n", + " 9.80000 m/s^2\n", + "------------------------------------------------------------------------------------------\n", + " POSICIONES \n", + " \n", + " POSICIÓN t1 = 2.5 s: \n", + " y(t1) = 30.62500 m\n", + " \n", + " \n", + " POSICIÓN t2 = 6.0 s:\n", + " y(t2) = 176.40000 m\n", + "------------------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "t = np.array([0, 1, 2, 3, 4, 5], dtype=float) # tiempo (s)\n", + "pos = np.array([0.0, 4.9, 19.6, 44.1, 78.4, 122.5]) # posición (m)\n", + "\n", + "# TU CÓDIGO AQUÍ\n", + "\n", + "coeficientes = np.polyfit(t, pos, deg=2)\n", + "a= coeficientes[0]\n", + "g_estimada = a * 2\n", + "t1 = 2.5\n", + "t2 = 6 \n", + "posicion_t1 = np.polyval(coeficientes, t1) \n", + "posicion_t2 = np.polyval(coeficientes, t2)\n", + "\n", + "\n", + "print(f\"\\n COEFICIENTES [a, b, c] de [at^2 + bt + c)]:: \\n a={coeficientes[0]:.4f}, \\n b={coeficientes[1]:.4f}, \\n c={coeficientes[2]:.4f}\")\n", + "print(\"---\" * 30)\n", + "# Modelo para un movimiento vertical, buscamos las alturas (y)\n", + "print(f\" \\n MODELOR [y(t)= at^2 + bt + c)]: \\n \\n y(t)= {coeficientes[0]:.4f}·t² + {coeficientes[1]:.4f}·t + {coeficientes[2]:.4f}\")\n", + "print(\" \" * 30)\n", + "print(\"---\" * 30)\n", + "\n", + "print(f\" \\n ESTIMACIÓN DE LA GRAVEDAD: \\n {g_estimada:.5f} m/s^2\")\n", + "print(\"---\" * 30)\n", + "\n", + "print( \" \" * 15 + \"POSICIONES\" + \" \"*20)\n", + "print(f\" \\n POSICIÓN t1 = 2.5 s: \\n y(t1) = {posicion_t1:.5f} m\")\n", + "print(\" \" * 30)\n", + "print(f\" \\n POSICIÓN t2 = 6.0 s:\\n y(t2) = {posicion_t2:.5f} m\")\n", + "print(\"---\" * 30)\n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "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": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/TAREASSS/pandas.ipynb b/TAREASSS/pandas.ipynb new file mode 100644 index 0000000..016286c --- /dev/null +++ b/TAREASSS/pandas.ipynb @@ -0,0 +1,2315 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pandas — Análisis y Manipulación de Datos\n", + "\n", + "## ¿Qué es Pandas?\n", + "\n", + "**Pandas** es la librería más utilizada para análisis y manipulación de datos en Python. Su nombre viene de **Pan**el **Da**ta (datos de panel), un término de econometría.\n", + "\n", + "Pandas introduce dos estructuras de datos fundamentales:\n", + "\n", + "| Estructura | Descripción | Analogía |\n", + "|---|---|---|\n", + "| **Series** | Arreglo unidimensional con etiquetas | Una columna de Excel |\n", + "| **DataFrame** | Tabla bidimensional con filas y columnas | Una hoja de Excel |\n", + "\n", + "## ¿Para qué sirve?\n", + "\n", + "- Leer y escribir datos (CSV, Excel, SQL, JSON, etc.)\n", + "- Limpiar datos (manejar valores nulos, duplicados, errores)\n", + "- Filtrar, ordenar y agrupar datos\n", + "- Calcular estadísticas y resúmenes\n", + "- Preparar datos para visualización o machine learning" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Instalación e Importación\n", + "\n", + "```bash\n", + "pip install pandas\n", + "```\n", + "\n", + "Por convención, se importa con el alias `pd`:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pandas versión: 2.1.4\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np # Pandas y NumPy se usan frecuentemente juntos\n", + "\n", + "print(\"Pandas versión:\", pd.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 1 — Series" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Serie de temperaturas:\n", + "0 25.1\n", + "1 27.3\n", + "2 24.8\n", + "3 28.5\n", + "4 26.0\n", + "dtype: float64\n", + "\n", + "Tipo: \n" + ] + } + ], + "source": [ + "# Crear una Serie desde una lista\n", + "temperaturas = pd.Series([25.1, 27.3, 24.8, 28.5, 26.0])\n", + "print(\"Serie de temperaturas:\")\n", + "print(temperaturas)\n", + "print(\"\\nTipo:\", type(temperaturas))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Temperatura por día:\n", + "Lunes 25.1\n", + "Martes 27.3\n", + "Miércoles 24.8\n", + "Jueves 28.5\n", + "Viernes 26.0\n", + "dtype: float64\n", + "\n", + "Temperatura del Martes: 27.3\n", + "Promedio de la semana: 26.339999999999996\n" + ] + } + ], + "source": [ + "# Serie con índices personalizados (etiquetas)\n", + "dias = pd.Series(\n", + " [25.1, 27.3, 24.8, 28.5, 26.0],\n", + " index=[\"Lunes\", \"Martes\", \"Miércoles\", \"Jueves\", \"Viernes\"]\n", + ")\n", + "print(\"Temperatura por día:\")\n", + "print(dias)\n", + "\n", + "print(\"\\nTemperatura del Martes:\", dias[\"Martes\"])\n", + "print(\"Promedio de la semana:\", dias.mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean: 8.258333333333333\n", + "min: -0.3\n", + "standard deviation: 6.520242373260415\n", + "index of max element: 6\n", + "values as list: [-0.3 0.4 3.9 7.4 12. 15. 17.2 16.8 13.1 9.1 3.7 0.8]\n", + "indices of sorted Series: [ 0 1 11 10 2 3 9 4 8 5 7 6]\n", + "cumulative sum: [-0.3 0.1 4. 11.4 23.4 38.4 55.6 72.4 85.5 94.6 98.3 99.1]\n" + ] + } + ], + "source": [ + "# Métodos estadísticos de una Serie (§8.1)\n", + "s = pd.Series([-0.3, 0.4, 3.9, 7.4, 12.0, 15.0, 17.2,\n", + " 16.8, 13.1, 9.1, 3.7, 0.8], name='temp_C')\n", + "\n", + "print('mean:', s.mean())\n", + "print('min:', s.min())\n", + "print('standard deviation:', s.std())\n", + "print('index of max element:', s.argmax())\n", + "print('values as list:', s.values)\n", + "print('indices of sorted Series:', s.argsort().values)\n", + "print('cumulative sum:', s.cumsum().values)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "jan -0.3\n", + "feb 0.4\n", + "mar 3.9\n", + "apr 7.4\n", + "may 12.0\n", + "jun 15.0\n", + "jul 17.2\n", + "aug 16.8\n", + "sep 13.1\n", + "oct 9.1\n", + "nov 3.7\n", + "dec 0.8\n", + "Name: temp_C, dtype: float64\n", + "\n", + "Temperatura en octubre: 9.1\n" + ] + } + ], + "source": [ + "# Índice con etiquetas — set_axis() (§8.1)\n", + "s2 = s.set_axis(['jan', 'feb', 'mar', 'apr', 'may', 'jun',\n", + " 'jul', 'aug', 'sep', 'oct', 'nov', 'dec'])\n", + "print(s2)\n", + "print(\"\\nTemperatura en octubre:\", s2['oct'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 2 — DataFrames" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Crear un DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Nombre Edad Carrera Promedio\n", + "0 Ana 20 Física 85.5\n", + "1 Carlos 22 Matemática 72.0\n", + "2 Diana 21 Física 91.3\n", + "3 Eduardo 23 Computación 68.7\n", + "4 Fátima 20 Matemática 79.2\n" + ] + } + ], + "source": [ + "# Crear DataFrame desde un diccionario\n", + "datos = {\n", + " \"Nombre\": [\"Ana\", \"Carlos\", \"Diana\", \"Eduardo\", \"Fátima\"],\n", + " \"Edad\": [20, 22, 21, 23, 20],\n", + " \"Carrera\": [\"Física\", \"Matemática\", \"Física\", \"Computación\", \"Matemática\"],\n", + " \"Promedio\": [85.5, 72.0, 91.3, 68.7, 79.2]\n", + "}\n", + "\n", + "df = pd.DataFrame(datos)\n", + "print(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Forma (filas x columnas): (5, 4)\n", + "\n", + "Nombres de columnas: ['Nombre', 'Edad', 'Carrera', 'Promedio']\n", + "\n", + "Tipos de datos:\n", + "Nombre object\n", + "Edad int64\n", + "Carrera object\n", + "Promedio float64\n", + "dtype: object\n" + ] + } + ], + "source": [ + "# Información básica del DataFrame\n", + "print(\"Forma (filas x columnas):\", df.shape)\n", + "print(\"\\nNombres de columnas:\", df.columns.tolist())\n", + "print(\"\\nTipos de datos:\")\n", + "print(df.dtypes)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estadísticas descriptivas:\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Edad Promedio\n", + "count 5.00000 5.000000\n", + "mean 21.20000 79.340000\n", + "std 1.30384 9.328612\n", + "min 20.00000 68.700000\n", + "25% 20.00000 72.000000\n", + "50% 21.00000 79.200000\n", + "75% 22.00000 85.500000\n", + "max 23.00000 91.300000" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Resumen estadístico automático\n", + "print(\"Estadísticas descriptivas:\")\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Primeras 3 filas:\n", + " temp_C rain_mm\n", + "jan -0.3 59\n", + "feb 0.4 57\n", + "mar 3.9 84\n", + "\n", + "Últimas 2 filas:\n", + " temp_C rain_mm\n", + "nov 3.7 88\n", + "dec 0.8 80\n", + "\n", + "Columnas: ['temp_C', 'rain_mm']\n", + "Índice (list): ['jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec']\n" + ] + } + ], + "source": [ + "# head(), tail() y atributos del índice (§8.2.1)\n", + "df_clima = pd.DataFrame({\n", + " 'temp_C': [-0.3, 0.4, 3.9, 7.4, 12.0, 15.0,\n", + " 17.2, 16.8, 13.1, 9.1, 3.7, 0.8],\n", + " 'rain_mm': [59, 57, 84, 100, 143, 153, 172, 164, 135, 89, 88, 80]\n", + "}, index=['jan','feb','mar','apr','may','jun',\n", + " 'jul','aug','sep','oct','nov','dec'])\n", + "\n", + "print(\"Primeras 3 filas:\")\n", + "print(df_clima.head(3))\n", + "print(\"\\nÚltimas 2 filas:\")\n", + "print(df_clima.tail(2))\n", + "print(\"\\nColumnas:\", df_clima.columns.tolist())\n", + "print(\"Índice (list):\", list(df_clima.index))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Acceder a datos" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Columna 'Nombre':\n", + "0 Ana\n", + "1 Carlos\n", + "2 Diana\n", + "3 Eduardo\n", + "4 Fátima\n", + "Name: Nombre, dtype: object\n", + "\n", + "Columnas 'Nombre' y 'Promedio':\n", + " Nombre Promedio\n", + "0 Ana 85.5\n", + "1 Carlos 72.0\n", + "2 Diana 91.3\n", + "3 Eduardo 68.7\n", + "4 Fátima 79.2\n" + ] + } + ], + "source": [ + "# Acceder a una columna\n", + "print(\"Columna 'Nombre':\")\n", + "print(df[\"Nombre\"])\n", + "\n", + "print(\"\\nColumnas 'Nombre' y 'Promedio':\")\n", + "print(df[[\"Nombre\", \"Promedio\"]])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Primera fila (iloc[0]):\n", + "Nombre Ana\n", + "Edad 20\n", + "Carrera Física\n", + "Promedio 85.5\n", + "Name: 0, dtype: object\n", + "\n", + "Filas 1 a 3 (iloc[1:4]):\n", + " Nombre Edad Carrera Promedio\n", + "1 Carlos 22 Matemática 72.0\n", + "2 Diana 21 Física 91.3\n", + "3 Eduardo 23 Computación 68.7\n" + ] + } + ], + "source": [ + "# Acceder a filas con .iloc (por posición numérica)\n", + "print(\"Primera fila (iloc[0]):\")\n", + "print(df.iloc[0])\n", + "\n", + "print(\"\\nFilas 1 a 3 (iloc[1:4]):\")\n", + "print(df.iloc[1:4])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fila con índice 2:\n", + "Nombre Diana\n", + "Edad 21\n", + "Carrera Física\n", + "Promedio 91.3\n", + "Name: 2, dtype: object\n", + "\n", + "Valor en fila 0, columna 'Nombre': Ana\n" + ] + } + ], + "source": [ + "# Acceder a filas con .loc (por etiqueta/índice)\n", + "print(\"Fila con índice 2:\")\n", + "print(df.loc[2])\n", + "\n", + "# Acceder a un valor específico: fila 0, columna 'Nombre'\n", + "print(\"\\nValor en fila 0, columna 'Nombre':\", df.loc[0, \"Nombre\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `loc` avanzado — selección por etiqueta (§8.2.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Marzo, abril y junio:\n", + " temp_C rain_mm\n", + "mar 3.9 84\n", + "apr 7.4 100\n", + "jun 15.0 153\n" + ] + } + ], + "source": [ + "# loc con lista de etiquetas (§8.2.2)\n", + "print(\"Marzo, abril y junio:\")\n", + "print(df_clima.loc[['mar', 'apr', 'jun']])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "De marzo a mayo:\n", + " temp_C rain_mm\n", + "mar 3.9 84\n", + "apr 7.4 100\n", + "may 12.0 143\n" + ] + } + ], + "source": [ + "# loc con slice de etiquetas — el extremo final SÍ se incluye (§8.2.2)\n", + "print(\"De marzo a mayo:\")\n", + "print(df_clima.loc['mar':'may'])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lluvia de mayo a septiembre:\n", + "may 143\n", + "jun 153\n", + "jul 172\n", + "aug 164\n", + "sep 135\n", + "Name: rain_mm, dtype: int64\n", + "\n", + "Total de lluvia en verano: 767\n" + ] + } + ], + "source": [ + "# loc combinando filas Y columnas: df.loc[, ] (§8.2.2)\n", + "print(\"Lluvia de mayo a septiembre:\")\n", + "print(df_clima.loc['may':'sep', 'rain_mm'])\n", + "\n", + "print(\"\\nTotal de lluvia en verano:\", df_clima.loc['may':'sep', 'rain_mm'].sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Temp en meses con lluvia < 100 mm:\n", + "jan -0.3\n", + "feb 0.4\n", + "mar 3.9\n", + "oct 9.1\n", + "nov 3.7\n", + "dec 0.8\n", + "Name: temp_C, dtype: float64\n" + ] + } + ], + "source": [ + "# Máscara booleana con loc (§8.2.2)\n", + "# Temperatura en meses con lluvia < 100 mm\n", + "print(\"Temp en meses con lluvia < 100 mm:\")\n", + "print(df_clima.loc[df_clima['rain_mm'] < 100, 'temp_C'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 3 — Filtrado, Ordenamiento y Nuevas Columnas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Filtrar filas con condiciones" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estudiantes con promedio > 75:\n", + " Nombre Edad Carrera Promedio\n", + "0 Ana 20 Física 85.5\n", + "2 Diana 21 Física 91.3\n", + "4 Fátima 20 Matemática 79.2\n", + "\n", + "Estudiantes de Física:\n", + " Nombre Edad Carrera Promedio\n", + "0 Ana 20 Física 85.5\n", + "2 Diana 21 Física 91.3\n" + ] + } + ], + "source": [ + "# Estudiantes con promedio mayor a 75\n", + "buenos = df[df[\"Promedio\"] > 75]\n", + "print(\"Estudiantes con promedio > 75:\")\n", + "print(buenos)\n", + "\n", + "print()\n", + "\n", + "# Estudiantes de Física\n", + "fisica = df[df[\"Carrera\"] == \"Física\"]\n", + "print(\"Estudiantes de Física:\")\n", + "print(fisica)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Promedio > 70 Y Edad <= 21:\n", + " Nombre Edad Carrera Promedio\n", + "0 Ana 20 Física 85.5\n", + "2 Diana 21 Física 91.3\n", + "4 Fátima 20 Matemática 79.2\n" + ] + } + ], + "source": [ + "# Condiciones múltiples\n", + "# & = AND, | = OR\n", + "filtro = df[(df[\"Promedio\"] > 70) & (df[\"Edad\"] <= 21)]\n", + "print(\"Promedio > 70 Y Edad <= 21:\")\n", + "print(filtro)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ordenar datos" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ranking por promedio:\n", + " Nombre Promedio\n", + "2 Diana 91.3\n", + "0 Ana 85.5\n", + "4 Fátima 79.2\n", + "1 Carlos 72.0\n", + "3 Eduardo 68.7\n" + ] + } + ], + "source": [ + "# Ordenar por promedio (descendente)\n", + "ranking = df.sort_values(\"Promedio\", ascending=False)\n", + "print(\"Ranking por promedio:\")\n", + "print(ranking[[\"Nombre\", \"Promedio\"]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Agregar nuevas columnas" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Nombre Edad Carrera Promedio Estado Puntos_extra\n", + "0 Ana 20 Física 85.5 Aprobado 15.5\n", + "1 Carlos 22 Matemática 72.0 Aprobado 2.0\n", + "2 Diana 21 Física 91.3 Aprobado 21.3\n", + "3 Eduardo 23 Computación 68.7 Reprobado 0.0\n", + "4 Fátima 20 Matemática 79.2 Aprobado 9.2\n" + ] + } + ], + "source": [ + "# Agregar columna calculada\n", + "df[\"Estado\"] = df[\"Promedio\"].apply(lambda p: \"Aprobado\" if p >= 70 else \"Reprobado\")\n", + "df[\"Puntos_extra\"] = (df[\"Promedio\"] - 70).clip(lower=0).round(1)\n", + "\n", + "print(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Modificar datos y combinar DataFrames (§8.2.3)\n", + "\n", + "`df.assign()` crea columnas nuevas de forma funcional sin modificar el original." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " temp_C rain_mm temp_F\n", + "jan -0.3 59 31.46\n", + "feb 0.4 57 32.72\n", + "mar 3.9 84 39.02\n", + "apr 7.4 100 45.32\n", + "may 12.0 143 53.60\n" + ] + } + ], + "source": [ + "# assign() — agregar columna con función (§8.2.3)\n", + "def celsius_a_f(df):\n", + " return df['temp_C'] * 1.8 + 32\n", + "\n", + "df_clima_f = df_clima.assign(temp_F=celsius_a_f)\n", + "print(df_clima_f.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " temp_C rain_mm\n", + "jan -0.3 59.0\n", + "feb 0.4 57.0\n", + "mar 3.9 84.0\n", + "apr 7.4 100.0\n", + "may 12.0 143.0\n", + "jun 15.0 153.0\n", + "jul 17.2 172.0\n", + "aug 16.8 164.0\n", + "sep 13.1 135.0\n", + "oct 9.1 89.0\n", + "nov 3.7 88.0\n", + "dec 0.8 80.0\n", + "2020 9.7 165.8\n", + "2019 9.5 146.1\n", + "2018 9.9 139.2\n" + ] + } + ], + "source": [ + "# pd.concat() — combinar filas (§8.2.3)\n", + "df_anual = pd.DataFrame({\n", + " 'temp_C': [9.7, 9.5, 9.9],\n", + " 'rain_mm': [165.8, 146.1, 139.2]\n", + "}, index=['2020', '2019', '2018'])\n", + "\n", + "df_ext = pd.concat([df_clima, df_anual])\n", + "print(df_ext)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " temp_C rain_mm temp_min_C temp_max_C\n", + "jan -0.3 59 -1.9 2.5\n", + "feb 0.4 57 -2.5 3.3\n", + "mar 3.9 84 0.6 7.3\n", + "apr 7.4 100 3.5 11.5\n", + "may 12.0 143 7.8 16.3\n", + "jun 15.0 153 11.0 19.2\n", + "jul 17.2 172 13.1 21.6\n", + "aug 16.8 164 13.0 20.9\n", + "sep 13.1 135 9.7 16.8\n", + "oct 9.1 89 6.2 12.3\n", + "nov 3.7 88 1.0 6.5\n", + "dec 0.8 80 -3.0 3.5\n" + ] + } + ], + "source": [ + "# pd.concat() con axis=1 — combinar columnas (§8.2.3)\n", + "df_minmax = pd.DataFrame({\n", + " 'temp_min_C': [-1.9, -2.5, 0.6, 3.5, 7.8, 11.0,\n", + " 13.1, 13.0, 9.7, 6.2, 1.0, -3.0],\n", + " 'temp_max_C': [2.5, 3.3, 7.3, 11.5, 16.3, 19.2,\n", + " 21.6, 20.9, 16.8, 12.3, 6.5, 3.5]\n", + "}, index=df_clima.index)\n", + "\n", + "df_completo = pd.concat([df_clima, df_minmax], axis=1)\n", + "print(df_completo)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 4 — Agrupaciones y Agregaciones" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estadísticas por carrera:\n", + " Promedio_medio Mínimo Máximo Estudiantes\n", + "Carrera \n", + "Computación 68.7 68.7 68.7 1\n", + "Física 88.4 85.5 91.3 2\n", + "Matemática 75.6 72.0 79.2 2\n" + ] + } + ], + "source": [ + "# groupby: agrupar por una columna y calcular estadísticas\n", + "por_carrera = df.groupby(\"Carrera\")[\"Promedio\"].agg([\"mean\", \"min\", \"max\", \"count\"])\n", + "por_carrera.columns = [\"Promedio_medio\", \"Mínimo\", \"Máximo\", \"Estudiantes\"]\n", + "\n", + "print(\"Estadísticas por carrera:\")\n", + "print(por_carrera.round(2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 5 — Manejo de Datos Faltantes" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DataFrame con valores faltantes:\n", + " Nombre Nota1 Nota2 Nota3\n", + "0 Ana 85.0 78.0 92.0\n", + "1 Carlos NaN 65.0 71.0\n", + "2 Diana 90.0 NaN 85.0\n", + "3 Eduardo 72.0 80.0 NaN\n", + "4 Fátima NaN 88.0 76.0\n", + "\n", + "Cantidad de nulos por columna:\n", + "Nombre 0\n", + "Nota1 2\n", + "Nota2 1\n", + "Nota3 1\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "# Crear DataFrame con datos faltantes (NaN = Not a Number)\n", + "datos_incompletos = pd.DataFrame({\n", + " \"Nombre\": [\"Ana\", \"Carlos\", \"Diana\", \"Eduardo\", \"Fátima\"],\n", + " \"Nota1\": [85, np.nan, 90, 72, np.nan],\n", + " \"Nota2\": [78, 65, np.nan, 80, 88],\n", + " \"Nota3\": [92, 71, 85, np.nan, 76]\n", + "})\n", + "\n", + "print(\"DataFrame con valores faltantes:\")\n", + "print(datos_incompletos)\n", + "print(\"\\nCantidad de nulos por columna:\")\n", + "print(datos_incompletos.isnull().sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Después de rellenar con la media:\n", + " Nombre Nota1 Nota2 Nota3\n", + "0 Ana 85.0 78.0 92.0\n", + "1 Carlos 82.3 65.0 71.0\n", + "2 Diana 90.0 77.8 85.0\n", + "3 Eduardo 72.0 80.0 81.0\n", + "4 Fátima 82.3 88.0 76.0\n" + ] + } + ], + "source": [ + "# Opción 1: Rellenar con la media de la columna\n", + "df_rellenado = datos_incompletos.copy()\n", + "for col in [\"Nota1\", \"Nota2\", \"Nota3\"]:\n", + " df_rellenado[col] = df_rellenado[col].fillna(df_rellenado[col].mean())\n", + "\n", + "print(\"Después de rellenar con la media:\")\n", + "print(df_rellenado.round(1))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Después de eliminar filas con nulos:\n", + " Nombre Nota1 Nota2 Nota3\n", + "0 Ana 85.0 78.0 92.0\n" + ] + } + ], + "source": [ + "# Opción 2: Eliminar filas con datos faltantes\n", + "df_limpio = datos_incompletos.dropna()\n", + "print(\"Después de eliminar filas con nulos:\")\n", + "print(df_limpio)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `isna()`, `fillna()` e `interpolate()` (§8.2.4)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mapa de NaN:\n", + " Nombre Nota1 Nota2 Nota3\n", + "0 False False False False\n", + "1 False True False False\n", + "2 False False True False\n", + "3 False False False True\n", + "4 False True False False\n", + "\n", + "Total de NaN por columna:\n", + "Nombre 0\n", + "Nota1 2\n", + "Nota2 1\n", + "Nota3 1\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "# isna() / notna() — mapa de valores faltantes (§8.2.4)\n", + "print(\"Mapa de NaN:\")\n", + "print(datos_incompletos.isna())\n", + "\n", + "print(\"\\nTotal de NaN por columna:\")\n", + "print(datos_incompletos.isna().sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rellenado con la media de cada columna:\n", + " Nombre Nota1 Nota2 Nota3\n", + "0 Ana 85.0 78.0 92.0\n", + "1 Carlos 82.3 65.0 71.0\n", + "2 Diana 90.0 77.8 85.0\n", + "3 Eduardo 72.0 80.0 81.0\n", + "4 Fátima 82.3 88.0 76.0\n" + ] + } + ], + "source": [ + "# fillna con la media de cada columna (§8.2.4)\n", + "df_relleno2 = datos_incompletos.copy()\n", + "df_relleno2 = df_relleno2.fillna(\n", + " df_relleno2.mean(axis=0, numeric_only=True)\n", + ")\n", + "print(\"Rellenado con la media de cada columna:\")\n", + "print(df_relleno2.round(1))" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original: [ 1. nan 3. nan 5.]\n", + "Interpolado: [1. 2. 3. 4. 5.]\n" + ] + } + ], + "source": [ + "# interpolate() — interpolación lineal (§8.2.4)\n", + "s_nan = pd.Series([1.0, np.nan, 3.0, np.nan, 5.0])\n", + "print(\"Original:\", s_nan.values)\n", + "print(\"Interpolado:\", s_nan.interpolate().values)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 6 — Leer Datos desde Archivos\n", + "\n", + "En la práctica, los datos vienen de archivos externos. Pandas puede leer muchos formatos:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Datos leídos desde CSV:\n", + " nombre edad carrera nota\n", + "0 Ana 20 Física 85\n", + "1 Carlos 22 Matemática 72\n", + "2 Diana 21 Física 91\n", + "3 Eduardo 23 Computación 68\n", + "4 Fátima 20 Matemática 79\n", + "\n", + "Primeras 3 filas (head):\n", + " nombre edad carrera nota\n", + "0 Ana 20 Física 85\n", + "1 Carlos 22 Matemática 72\n", + "2 Diana 21 Física 91\n" + ] + } + ], + "source": [ + "# Crear un CSV de ejemplo para practicar\n", + "csv_contenido = \"\"\"nombre,edad,carrera,nota\n", + "Ana,20,Física,85\n", + "Carlos,22,Matemática,72\n", + "Diana,21,Física,91\n", + "Eduardo,23,Computación,68\n", + "Fátima,20,Matemática,79\n", + "\"\"\"\n", + "\n", + "with open(\"/tmp/estudiantes.csv\", \"w\") as f:\n", + " f.write(csv_contenido)\n", + "\n", + "# Leer el CSV\n", + "df_csv = pd.read_csv(\"/tmp/estudiantes.csv\")\n", + "print(\"Datos leídos desde CSV:\")\n", + "print(df_csv)\n", + "print(\"\\nPrimeras 3 filas (head):\")\n", + "print(df_csv.head(3))" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Archivo guardado como /tmp/resultado.csv\n" + ] + } + ], + "source": [ + "# Guardar un DataFrame como CSV\n", + "df_csv.to_csv(\"/tmp/resultado.csv\", index=False)\n", + "print(\"Archivo guardado como /tmp/resultado.csv\")\n", + "\n", + "# Otros formatos comunes:\n", + "# df.to_excel(\"archivo.xlsx\", index=False)\n", + "# df = pd.read_excel(\"archivo.xlsx\")\n", + "# df = pd.read_json(\"archivo.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " temp_C uv nubes\n", + "dia \n", + "lunes 12.3 5 clear\n", + "martes 13.5 4 clear\n", + "miercoles 9.2 1 mostly cloudy\n", + "jueves 8.2 2 partly cloudy\n", + "viernes 10.2 3 partly cloudy\n" + ] + } + ], + "source": [ + "# read_csv con opciones avanzadas (§8.6)\n", + "import io\n", + "\n", + "csv_sin_header = \"\"\"lunes,12.3,5,clear\n", + "martes,13.5,4,clear\n", + "miercoles,9.2,1,mostly cloudy\n", + "jueves,8.2,2,partly cloudy\n", + "viernes,10.2,3,partly cloudy\n", + "\"\"\"\n", + "\n", + "df_weather = pd.read_csv(\n", + " io.StringIO(csv_sin_header),\n", + " names=['dia', 'temp_C', 'uv', 'nubes'],\n", + " index_col='dia'\n", + ")\n", + "print(df_weather)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 7 — Datos Categóricos (§8.3.1)\n", + "\n", + "Los datos categóricos toman valores de un conjunto discreto y finito de categorías." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " nubes uv\n", + "lun nublado 0\n", + "mar nublado 0\n", + "mié parcialmente nublado 1\n", + "jue mayormente despejado 3\n", + "vie despejado 5\n", + "sáb despejado 5\n", + "dom parcialmente nublado 1\n" + ] + } + ], + "source": [ + "# DataFrame con datos categóricos\n", + "df_cat = pd.DataFrame({\n", + " 'nubes': ['nublado', 'nublado', 'parcialmente nublado',\n", + " 'mayormente despejado', 'despejado', 'despejado', 'parcialmente nublado'],\n", + " 'uv': [0, 0, 1, 3, 5, 5, 1]\n", + "}, index=['lun', 'mar', 'mié', 'jue', 'vie', 'sáb', 'dom'])\n", + "\n", + "print(df_cat)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Categorías únicas:\n", + "['nublado' 'parcialmente nublado' 'mayormente despejado' 'despejado']\n", + "\n", + "Frecuencia de cada categoría:\n", + "nubes\n", + "nublado 2\n", + "parcialmente nublado 2\n", + "despejado 2\n", + "mayormente despejado 1\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "# unique() — valores únicos | value_counts() — frecuencia (§8.3.1)\n", + "print(\"Categorías únicas:\")\n", + "print(df_cat['nubes'].unique())\n", + "\n", + "print(\"\\nFrecuencia de cada categoría:\")\n", + "print(df_cat['nubes'].value_counts())" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "UV promedio en días parcialmente nublados: 1.0\n" + ] + } + ], + "source": [ + "# Filtrar por categoría y aplicar método (§8.3.1)\n", + "uv_parcial = df_cat.loc[df_cat['nubes'] == 'parcialmente nublado', 'uv'].mean()\n", + "print(\"UV promedio en días parcialmente nublados:\", uv_parcial)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 8 — Datos de Texto: `pd.Series.str` (§8.3.2)\n", + "\n", + "Pandas provee funciones vectorizadas para strings a través del submódulo `str`." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "¿Contiene 'nublado'?\n", + "lun True\n", + "mar True\n", + "mié True\n", + "jue False\n", + "vie False\n", + "sáb False\n", + "dom True\n", + "Name: nubes, dtype: bool\n", + "\n", + "Filas con nubosidad:\n", + " nubes uv\n", + "lun nublado 0\n", + "mar nublado 0\n", + "mié parcialmente nublado 1\n", + "dom parcialmente nublado 1\n" + ] + } + ], + "source": [ + "# str.contains() — filtrar filas que contienen una subcadena (§8.3.2)\n", + "mascara = df_cat['nubes'].str.contains('nublado', regex=False)\n", + "print(\"¿Contiene 'nublado'?\")\n", + "print(mascara)\n", + "\n", + "print(\"\\nFilas con nubosidad:\")\n", + "print(df_cat.loc[mascara])" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " nubes uv\n", + "lun soleado 0\n", + "mar soleado 0\n", + "mié parcialmente soleado 1\n", + "jue mayormente despejado 3\n", + "vie despejado 5\n", + "sáb despejado 5\n", + "dom parcialmente soleado 1\n" + ] + } + ], + "source": [ + "# str.replace() — reemplazar texto (§8.3.2)\n", + "df_cat2 = df_cat.copy()\n", + "df_cat2.loc[:, 'nubes'] = df_cat2['nubes'].str.replace('nublado', 'soleado')\n", + "print(df_cat2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 9 — Fechas y Tiempos (§8.3.3)\n", + "\n", + "`pd.to_datetime()` convierte strings a objetos `datetime64` para series de tiempo." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tipo original: object\n", + "Tipo convertido: datetime64[ns]\n", + "0 2020-01-01 12:34:00\n", + "1 2020-03-01 08:47:00\n", + "2 2020-06-01 14:23:00\n", + "3 2020-09-01 22:56:00\n", + "4 2020-12-01 13:45:00\n", + "dtype: datetime64[ns]\n" + ] + } + ], + "source": [ + "# pd.to_datetime() — convertir strings a fechas (§8.3.3)\n", + "fechas = pd.Series(['2020-01-01 12:34', '2020-03-01 08:47',\n", + " '2020-06-01 14:23', '2020-09-01 22:56',\n", + " '2020-12-01 13:45'])\n", + "\n", + "print(\"Tipo original:\", fechas.dtype)\n", + "fechas = pd.to_datetime(fechas)\n", + "print(\"Tipo convertido:\", fechas.dtype)\n", + "print(fechas)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Horas:\n", + "0 12:34:00\n", + "1 08:47:00\n", + "2 14:23:00\n", + "3 22:56:00\n", + "4 13:45:00\n", + "dtype: object\n", + "\n", + "Meses: [ 1 3 6 9 12]\n" + ] + } + ], + "source": [ + "# Acceder a componentes con el accessor .dt (§8.3.3)\n", + "print(\"Horas:\")\n", + "print(fechas.dt.time)\n", + "print(\"\\nMeses:\", fechas.dt.month.values)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Diferencia respecto al primer registro:\n", + "0 0 days 00:00:00\n", + "1 59 days 20:13:00\n", + "2 152 days 01:49:00\n", + "3 244 days 10:22:00\n", + "4 335 days 01:11:00\n", + "dtype: timedelta64[ns]\n", + "\n", + "En segundos (float):\n", + "0 0.0\n", + "1 5170380.0\n", + "2 13139340.0\n", + "3 21118920.0\n", + "4 28948260.0\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "# Diferencias de tiempo — timedelta (§8.3.3)\n", + "delta = fechas - fechas.iloc[0]\n", + "print(\"Diferencia respecto al primer registro:\")\n", + "print(delta)\n", + "\n", + "print(\"\\nEn segundos (float):\")\n", + "#print(delta.astype('timedelta64[s]').astype(float))\n", + "print( (fechas - fechas.iloc[0]).dt.total_seconds() )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 10 — Funciones de Agregación (§8.4)\n", + "\n", + "Pandas provee métodos **agregadores** (producen un escalar) y **no-agregadores** (misma longitud que la entrada).\n", + "\n", + "| Agregadores | | | |\n", + "|---|---|---|---|\n", + "| `count()` | `sum()` | `mean()` | `max()` |\n", + "| `min()` | `median()` | `std()` | `var()` |\n", + "| `prod()` | `quantile(x)` | `abs()` | |\n", + "\n", + "| No-agregadores | | |\n", + "|---|---|---|\n", + "| `cumsum()` | `cumprod()` | `cummax()` |\n", + "| `cummin()` | | |" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estadísticas de temperatura y lluvia:\n", + " temp_C rain_mm\n", + "min -0.30 57.00\n", + "max 17.20 172.00\n", + "mean 8.26 110.33\n" + ] + } + ], + "source": [ + "# agg() con lista de métodos (§8.4)\n", + "print(\"Estadísticas de temperatura y lluvia:\")\n", + "print(df_clima[['temp_C', 'rain_mm']].agg(['min', 'max', 'mean']).round(2))" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " temp_C rain_mm\n", + "min -0.30 NaN\n", + "max 17.20 NaN\n", + "mean 8.26 NaN\n", + "sum NaN 1324.0\n", + "median NaN 94.5\n" + ] + } + ], + "source": [ + "# agg() con diccionario — distintas métricas por columna (§8.4)\n", + "resultado = df_clima[['temp_C', 'rain_mm']].agg({\n", + " 'temp_C': ['min', 'max', 'mean'],\n", + " 'rain_mm': ['sum', 'median']\n", + "})\n", + "print(resultado.round(2))" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rango (max - min) por columna numérica:\n", + "temp_C 17.5\n", + "rain_mm 115.0\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "# apply() — función personalizada por columna (§8.4)\n", + "def rango(col):\n", + " return col.max() - col.min()\n", + "\n", + "print(\"Rango (max - min) por columna numérica:\")\n", + "print(df_clima[['temp_C', 'rain_mm']].apply(rango))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 11 — Visualización con Pandas (§8.5)\n", + "\n", + "Pandas ofrece métodos de graficación directa sobre DataFrames y Series que usan Matplotlib internamente." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# df.plot() — gráfica de línea (§8.5)\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", + "\n", + "df_clima['temp_C'].plot(kind='line', ax=axes[0], marker='o', color='tomato')\n", + "axes[0].set_title('Temperatura mensual promedio')\n", + "axes[0].set_ylabel('°C')\n", + "axes[0].grid(True, alpha=0.3)\n", + "\n", + "df_clima['rain_mm'].plot(kind='bar', ax=axes[1], color='steelblue', alpha=0.8)\n", + "axes[1].set_title('Lluvia mensual')\n", + "axes[1].set_ylabel('mm')\n", + "axes[1].tick_params(axis='x', rotation=45)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# df.hist() — histogramas de todas las columnas numéricas (§8.5)\n", + "df_clima.hist(figsize=(8, 4))\n", + "plt.suptitle('Distribución de variables climáticas', y=1.02)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+fDjy5s2LXr16AQBy5MiBkSNHYtiwYWjXrh1++OEHREVFYezYsTAzM8Po0aMBvAkMffr0QfPmzVG4cGGYmJhg3759CAsLw9ChQ9+7fQMDA4wfPx5dunRB48aN0bVrV7x48SLN49OqVSusXr0aPj4+6NevHypXrgxjY2Pcv38f+/fvR8OGDdG4ceMP7u/HyJ07N2rXro1JkybBysoKBQoUwN69e5VLRu8yMTHB9OnT8erVK1SqVEnpHVa/fn1Ur14dAFCnTh14e3tjyJAhiImJQbVq1ZTeYeXKlUPbtm0BAPPnz8e+ffvw3XffIX/+/IiPj1dqBmvXrv3eMo8YMQJBQUHw8vLCqFGjkCVLFsyZMydVt/eqVavCysoKPXr0wOjRo2FsbIzVq1fj/PnzWvN9yutCxN5hlO5pegO92/NIQ9MjZsOGDWk+f/DgQfnuu+/E2tpajI2NJW/evPLdd9+lmv/SpUvSvHlzsbGxERMTE8mfP7906NBB4uPjlXlOnjwpVatWFQsLC8mbN6+MHj1aFi9enGYvlq1bt0rNmjUle/bsYmpqKgUKFJBmzZrJnj17lHnat28vFhYWqcqcVk+0uLg4GTVqlBQuXFhMTEzExsZGvLy8JCQkRJknrd5hkydPFkdHRzE1NZVixYrJokWL3tvT7V1qtVomTZokDg4OYmJiIqVLl5Y///xTPDw8tHqHiYi8evVKRowYIS4uLmJiYiKWlpZSqlQpGTBggFbPn7Q8e/ZMmjVrJjly5BCVSqVVNrynd9ju3bulbdu2kiNHDqUX2PXr11Ote/HixVK6dGmlTA0bNpSLFy8qzz969Eg6dOggRYsWFQsLC8maNauULl1aZsyYIcnJyf96jBYvXqy8JkWKFJGlS5dK+/btU/VCSkpKkmnTpkmZMmXEzMxMsmbNKkWLFpXu3bunWe63fcp5EhERIc2aNRNra2uxtLSUH3/8UU6dOpVm7zALCwsJCwsTT09PMTc3F2tra+nZs6e8evVKa51xcXEyZMgQKVCggBgbG4u9vb307NlTnj9/rsxz7Ngxady4sRQoUEBMTU3FxsZGPDw8JCgoSGtd776eIm969rm6uoqpqankzp1bfv75Z1m4cGGq91VISIi4ublJlixZJGfOnNKlSxc5c+ZMqn0T+fjXhfSbSuSt+kIionROMwZMaGjoZ11aJCLSYJsgIiIi0ksMQURERKSXeDmMiIiI9BJrgoiIiEgvMQQRERGRXmIIIiIiIr2kd4MlqtVqPHz4ENmyZUt1mwUiIiJKP0QEL1++/Kx7J34MvQtBDx8+hIODg66LQURERB8pPDz8i93k+m16F4I0NyoMDw9H9uzZdVwaIiIiep+YmBg4ODh8sZsMv0vvQpDmElj27NkZgoiIiDKAr9V8hQ2jiYiISC8xBBEREZFeYggiIiIivcQQRERERHpJpyFo0qRJqFSpErJly4ZcuXKhUaNGuHr16r8ud/DgQVSoUAFmZmYoWLAg5s+f/w1KS0RERJmJTkPQwYMH0bt3bxw/fhzBwcFITk5G3bp1ERsb+95lbt++DR8fH7i7u+Ps2bMYNmwY+vbti02bNn3DkhMREVFGl67uIv/kyRPkypULBw8eRI0aNdKcZ8iQIQgKCsLly5eVaT169MD58+dx7Nixf91GTEwMLC0tER0dzS7yRERE6djX/s5OV22CoqOjAQDW1tbvnefYsWOoW7eu1jRvb2+cOnUKSUlJqeZPSEhATEyM1h8RERFRuhksUUTg5+eH6tWro2TJku+dLzIyEnZ2dlrT7OzskJycjKdPn8Le3l7ruUmTJmHs2LFfpcyfY8DYWbougk7MGN1P10X45vha6xd9fL319bWmzCPd1AT16dMHYWFhWLNmzb/O++7IkZoremmNKOnv74/o6GjlLzw8/MsUmIiIiDK0dFET9NNPPyEoKAiHDh361xuk5c6dG5GRkVrTHj9+DCMjI9jY2KSa39TUFKampl+0vERERJTx6bQmSETQp08fbN68Gfv27YOTk9O/LuPm5obg4GCtabt370bFihVhbGz8tYpKREREmYxOQ1Dv3r2xatUqBAYGIlu2bIiMjERkZCTi4uKUefz9/dGuXTvlcY8ePXD37l34+fnh8uXLWLp0KZYsWYJBgwbpYheIiIgog9JpCJo3bx6io6Ph6ekJe3t75W/dunXKPBEREbh3757y2MnJCTt37sSBAwdQtmxZjB8/HrNnz0bTpk11sQtERESUQem0TdDHDFEUEBCQapqHhwfOnDnzFUpERERE+iLd9A4jIiIi+pYYgoiIiEgvMQQRERGRXmIIIiIiIr3EEERERER6iSGIiIiI9BJDEBEREeklhiAiIiLSSwxBREREpJcYgoiIiEgvMQQRERGRXmIIIiIiIr3EEERERER6iSGIiIiI9BJDEBEREeklhiAiIiLSSwxBREREpJcYgoiIiEgvMQQRERGRXmIIIiIiIr3EEERERER6iSGIiIiI9BJDEBEREeklhiAiIiLSSwxBREREpJcYgoiIiEgvMQQRERGRXmIIIiIiIr3EEERERER6iSGIiIiI9BJDEBEREeklhiAiIiLSSwxBREREpJcYgoiIiEgvMQQRERGRXmIIIiIiIr3EEERERER6iSGIiIiI9BJDEBEREeklhiAiIiLSSwxBREREpJcYgoiIiEgv6TQEHTp0CL6+vsiTJw9UKhW2bt36wfkPHDgAlUqV6u/KlSvfpsBERESUaRjpcuOxsbEoU6YMOnbsiKZNm370clevXkX27NmVxzlz5vwaxSMiIqJMTKchqH79+qhfv/4nL5crVy7kyJHjyxeIiIiI9EaGbBNUrlw52Nvbo1atWti/f7+ui0NEREQZkE5rgj6Vvb09Fi5ciAoVKiAhIQErV65ErVq1cODAAdSoUSPNZRISEpCQkKA8jomJ+VbFJSIionQsQ4UgFxcXuLi4KI/d3NwQHh6OadOmvTcETZo0CWPHjv1WRSQiIqIMIkNeDnubq6srrl+//t7n/f39ER0drfyFh4d/w9IRERFRepWhaoLScvbsWdjb27/3eVNTU5iamn7DEhEREVFGoNMQ9OrVK9y4cUN5fPv2bZw7dw7W1tbInz8//P398eDBA6xYsQIAMHPmTDg6OqJEiRJITEzEqlWrsGnTJmzatElXu0BEREQZlE5D0KlTp1CzZk3lsZ+fHwCgffv2CAgIQEREBO7du6c8n5iYiEGDBuHBgwcwNzdHiRIlsGPHDvj4+HzzshMREVHGptMQ5OnpCRF57/MBAQFajwcPHozBgwd/5VIRERGRPsjwDaOJiIiIPgdDEBEREeklhiAiIiLSSwxBREREpJcYgoiIiEgvMQQRERGRXmIIIiIiIr3EEERERER6iSGIiIiI9BJDEBEREeklhiAiIiLSSwxBREREpJcYgoiIiEgvMQQRERGRXmIIIiIiIr3EEERERER6iSGIiIiI9BJDEBEREeklhiAiIiLSSwxBREREpJcYgoiIiEgvMQQRERGRXmIIIiIiIr3EEERERER6iSGIiIiI9BJDEBEREeklhiAiIiLSSwxBREREpJcYgoiIiEgvMQQRERGRXvqsEHTmzBlcuHBBebxt2zY0atQIw4YNQ2Ji4hcrHBEREdHX8lkhqHv37rh27RoA4NatW2jVqhWyZMmCDRs2YPDgwV+0gERERERfw2eFoGvXrqFs2bIAgA0bNqBGjRoIDAxEQEAANm3a9CXLR0RERPRVfFYIEhGo1WoAwJ49e+Dj4wMAcHBwwNOnT79c6YiIiIi+ks8KQRUrVsSECROwcuVKHDx4EN999x0A4Pbt27Czs/uiBSQiIiL6Gj4rBM2cORNnzpxBnz59MHz4cDg7OwMANm7ciKpVq37RAhIRERF9DUafs1Dp0qW1eodp/PrrrzA0NPzPhSIiIiL62j4rBL2PmZnZl1wdERER0VfzWSEoJSUFM2bMwPr163Hv3r1UYwM9e/bsixSOiIiI6Gv5rDZBY8eOxW+//YYWLVogOjoafn5+aNKkCQwMDDBmzJgvXEQiIiKiL++zQtDq1auxaNEiDBo0CEZGRvjhhx+wePFijBo1CsePH//SZSQiIiL64j4rBEVGRqJUqVIAgKxZsyI6OhoA8P3332PHjh1frnREREREX8lnhaB8+fIhIiICAODs7Izdu3cDAEJDQ2FqavrlSkdERET0lXxWCGrcuDH27t0LAOjXrx9GjhyJwoULo127dujUqdNHr+fQoUPw9fVFnjx5oFKpsHXr1n9d5uDBg6hQoQLMzMxQsGBBzJ8//3N2gYiIiPTcZ/UOmzx5svL/Zs2aIV++fAgJCYGzszMaNGjw0euJjY1FmTJl0LFjRzRt2vRf5799+zZ8fHzQtWtXrFq1CkePHkWvXr2QM2fOj1qeiIiISOOjQtDy5cvh6uoKFxeXNJ93dXWFq6vrJ2+8fv36qF+//kfPP3/+fOTPnx8zZ84EABQrVgynTp3CtGnTGIKIiIjok3xUCLK3t0fdunWxbt06uLq6Iigo6IPzf0pt0Kc4duwY6tatqzXN29sbS5YsQVJSEoyNjVMtk5CQgISEBOVxTEzMVykbERERZSwfFYLq1q2LoKAgtG3bFmFhYWjUqNF751WpVEhJSflS5dMSGRmZ6gatdnZ2SE5OxtOnT2Fvb59qmUmTJmHs2LFfpTxERKR/BoydpesifHMzRvfTdRG+io9uGF2mTBkcOnQIAKBWq9/797UCkIZKpdJ6LCJpTtfw9/dHdHS08hceHv5Vy0dEREQZwyc1jM6RIwfUajUCAgKwefNm3LlzByqVCgULFkTTpk3Rtm3b94aRLyF37tyIjIzUmvb48WMYGRnBxsYmzWVMTU3ZbZ+IiIhS+aQu8iKCBg0aoEuXLnjw4AFKlSqFEiVK4M6dO+jQoQMaN278tcoJAHBzc0NwcLDWtN27d6NixYpptgciIiIiep9PqgkKCAjAoUOHsHfvXtSsWVPruX379qFRo0ZYsWIF2rVr91Hre/XqFW7cuKE8vn37Ns6dOwdra2vkz58f/v7+ePDgAVasWAEA6NGjB/744w/4+fmha9euOHbsGJYsWYI1a9Z8ym4QERERfVpN0Jo1azBs2LBUAQgAvLy8MHToUKxevfqj13fq1CmUK1cO5cqVAwD4+fmhXLlyGDVqFAAgIiIC9+7dU+Z3cnLCzp07ceDAAZQtWxbjx4/H7Nmz2T2eiIiIPtkn1QSFhYVh6tSp732+fv36mD179kevz9PTU2nYnJaAgIBU0zw8PHDmzJmP3gYRERFRWj6pJujZs2epuqi/zc7ODs+fP//PhSIiIiL62j4pBKWkpMDI6P2VR4aGhkhOTv7PhSIiIiL62j7pcpiIoEOHDu/tcv72yMxERERE6dknhaD27dv/6zwf2zOMiIiISJc+KQQtW7bsa5WDiIiI6Jv6pDZBRERERJkFQxARERHpJYYgIiIi0ksMQURERKSXGIKIiIhILzEEERERkV5iCCIiIiK9xBBEREREeokhiIiIiPQSQxARERHpJYYgIiIi0ksMQURERKSXGIKIiIhILzEEERERkV5iCCIiIiK9xBBEREREeokhiIiIiPQSQxARERHpJYYgIiIi0ksMQURERKSXGIKIiIhILzEEERERkV5iCCIiIiK9xBBEREREeokhiIiIiPQSQxARERHpJYYgIiIi0ksMQURERKSXGIKIiIhILzEEERERkV5iCCIiIiK9xBBEREREeokhiIiIiPQSQxARERHpJYYgIiIi0ksMQURERKSXGIKIiIhILzEEERERkV7SeQiaO3cunJycYGZmhgoVKuDw4cPvnffAgQNQqVSp/q5cufINS0xERESZgU5D0Lp169C/f38MHz4cZ8+ehbu7O+rXr4979+59cLmrV68iIiJC+StcuPA3KjERERFlFjoNQb/99hs6d+6MLl26oFixYpg5cyYcHBwwb968Dy6XK1cu5M6dW/kzNDT8RiUmIiKizEJnISgxMRGnT59G3bp1tabXrVsXISEhH1y2XLlysLe3R61atbB///4PzpuQkICYmBitPyIiIiKdhaCnT58iJSUFdnZ2WtPt7OwQGRmZ5jL29vZYuHAhNm3ahM2bN8PFxQW1atXCoUOH3rudSZMmwdLSUvlzcHD4ovtBREREGZORrgugUqm0HotIqmkaLi4ucHFxUR67ubkhPDwc06ZNQ40aNdJcxt/fH35+fsrjmJgYBiEiIiLSXU2Qra0tDA0NU9X6PH78OFXt0Ie4urri+vXr733e1NQU2bNn1/ojIiIi0lkIMjExQYUKFRAcHKw1PTg4GFWrVv3o9Zw9exb29vZfunhERESUyen0cpifnx/atm2LihUrws3NDQsXLsS9e/fQo0cPAG8uZT148AArVqwAAMycOROOjo4oUaIEEhMTsWrVKmzatAmbNm3S5W4QERFRBqTTENSyZUtERUVh3LhxiIiIQMmSJbFz504UKFAAABAREaE1ZlBiYiIGDRqEBw8ewNzcHCVKlMCOHTvg4+Ojq10gIiKiDErnDaN79eqFXr16pflcQECA1uPBgwdj8ODB36BURERElNnp/LYZRERERLrAEERERER6iSGIiIiI9BJDEBEREeklhiAiIiLSSwxBREREpJcYgoiIiEgvMQQRERGRXmIIIiIiIr3EEERERER6iSGIiIiI9BJDEBEREeklhiAiIiLSSwxBREREpJcYgoiIiEgvMQQRERGRXmIIIiIiIr3EEERERER6iSGIiIiI9BJDEBEREeklhiAiIiLSSwxBREREpJcYgoiIiEgvMQQRERGRXmIIIiIiIr3EEERERER6iSGIiIiI9BJDEBEREeklhiAiIiLSSwxBREREpJcYgoiIiEgvMQQRERGRXmIIIiIiIr3EEERERER6iSGIiIiI9BJDEBEREeklhiAiIiLSSwxBREREpJcYgoiIiEgvMQQRERGRXmIIIiIiIr3EEERERER6SechaO7cuXBycoKZmRkqVKiAw4cPf3D+gwcPokKFCjAzM0PBggUxf/78b1RSIiIiykx0GoLWrVuH/v37Y/jw4Th79izc3d1Rv3593Lt3L835b9++DR8fH7i7u+Ps2bMYNmwY+vbti02bNn3jkhMREVFGp9MQ9Ntvv6Fz587o0qULihUrhpkzZ8LBwQHz5s1Lc/758+cjf/78mDlzJooVK4YuXbqgU6dOmDZt2jcuOREREWV0OgtBiYmJOH36NOrWras1vW7duggJCUlzmWPHjqWa39vbG6dOnUJSUtJXKysRERFlPka62vDTp0+RkpICOzs7rel2dnaIjIxMc5nIyMg0509OTsbTp09hb2+fapmEhAQkJCQoj6OjowEAMTEx/3UXPktCfLxOtqtrujreusTXWr/o4+vN11p/6Oq11mxXRL7K+nUWgjRUKpXWYxFJNe3f5k9rusakSZMwduzYVNMdHBw+taj0H8ybPFTXRaBvhK+1/uBrrT90/Vq/fPkSlpaWX3y9OgtBtra2MDQ0TFXr8/jx41S1PRq5c+dOc34jIyPY2NikuYy/vz/8/PyUx2q1Gs+ePYONjc0Hw1ZmExMTAwcHB4SHhyN79uy6Lg59RXyt9Qdfa/2ij6+3iODly5fIkyfPV1m/zkKQiYkJKlSogODgYDRu3FiZHhwcjIYNG6a5jJubG/7880+tabt370bFihVhbGyc5jKmpqYwNTXVmpYjR47/VvgMLHv27Hrz5tF3fK31B19r/aJvr/fXqAHS0GnvMD8/PyxevBhLly7F5cuXMWDAANy7dw89evQA8KYWp127dsr8PXr0wN27d+Hn54fLly9j6dKlWLJkCQYNGqSrXSAiIqIMSqdtglq2bImoqCiMGzcOERERKFmyJHbu3IkCBQoAACIiIrTGDHJycsLOnTsxYMAAzJkzB3ny5MHs2bPRtGlTXe0CERERZVA6bxjdq1cv9OrVK83nAgICUk3z8PDAmTNnvnKpMh9TU1OMHj061aVBynz4WusPvtb6ha/3l6eSr9XvjIiIiCgd0/m9w4iIiIh0gSGIiIiI9BJDEBEREeklhiAiIiLSSwxBRERElCF86b5cDEFElKGp1eqvdnNFIko/kpOTv/jtrthFnj6aWq2GSqX65JveEn0tKSkpMDQ0BAA8efIE1tbWymP679RqNQDAwOB/v5f5fqdvSa1Wpzr/fv/9d1hbW8PZ2Rmurq7/af2sCaKPojkRVSoVzp07h23btuHmzZsAwA9E+ibi4+Px6NEjAP/7cjY0NMTLly/xww8/oFq1avDy8sLQoUMRFxeny6JmCpr3vIGBAa5du4ajR48iOjpaeZ6/n+lr2bVrF+bNmwfgTQDXvN+DgoJgY2ODxYsXY/r06XB3d8fs2bPx/Pnzz94WQxCl8vz5c2zZsgUxMTEA3nzYGRgYIDo6Gk2aNIGHhwcGDx6MatWqwd/fH/Hx8TouMWV2d+7cgZeXF37//XcAbz4YRQTh4eGoVasWoqOjMXXqVNSpUwdz5szBkCFD8ODBAx2XOuN4+fIlDh06BEC79ufVq1do1aoVqlSpgk6dOsHDwwMzZ84EwB8/9PXMnz8fgYGBOHHiBIA352J8fDymTZuGXr16ISwsDMeOHcMvv/yCOXPmYMuWLZ+9LYYgSmX27Nno3r27cgJqPuymTZuG8PBwXLx4Ebt27cKMGTMwbdo0LFmyhEGIvipHR0cUKlQIp06dwtGjRwG8OS8vXryIx48fY8aMGWjUqBFGjBiBOXPm4NChQ9i1a5eOS50xiAimTZsGT09PvH79Wrn0oFarMXr0aNy7dw+HDx/G2rVrUa9ePYwdOxabN29W5iH6UpKTkwEAo0aNQkJCAjZv3oyEhAQAwOnTp3H9+nV4eXkBAMzMzDB48GAUL14cu3btwv379z9rmwxBpNBUb/v7+yNv3rzYuHEjIiIiALypHfrzzz/RoEED5MuXD05OTvjhhx/Qp08fzJ8/X+tGt0RfkiZgDxs2DC9evMDmzZsRGxsLAAgNDUXWrFnh4uKifCG3a9cO9vb2CA4OxuvXr3VW7oxCpVKhQ4cOKFSoEIYPH65Mf/HiBVatWoW2bduiZMmSKFeuHIYNG4Yff/wR/fv3B6DdVojov1Cr1TAyenM70woVKsDDwwOnT59WfszY2tri8ePHsLW1BQDlknevXr0QHByMlJSUz9ouz2BSaL5ETExM4Ofnh71792Lfvn0AACsrK0RHR8PCwgIAlHQ+fvx43LhxA+fPn9daB9GXYmZmhvj4eMTExKBIkSI4fvw4/v77bwBAjRo1cOnSJVy/fh0GBgbKedmyZUscOHAAWbJk0WXR072kpCQAgJOTE4YPH47Zs2fj2rVrynP58uXTuuyVLVs2tG/fHiKCDRs26KTMlDlpAvXSpUvRvHlzREZGIiwsDNu2bcOTJ0/g4uICV1dXjB49GgBgbm4OAMiTJw8SExPx8OHDz9vulyk+ZQaGhoaIjo7GwIEDER8fj5cvX2Lr1q24evUqAMDX1xcrVqxAUlISzMzMkJycDHNzczg7O+Py5csA+MuQvhxNzeTatWuRI0cOTJkyBdeuXcOJEyewceNGPHnyBBUrVkS1atXQp08fAFDurn3x4kWULl0a8fHxbMD7AcbGxoiLi8OYMWMQHx8PU1NTjBkzBsCb8GlhYYF//vlHaZCuUqlgbW0NIyMjGBsb67DklBlNnToVgwcPhpeXF2rUqIHKlStj69atSm3QgAEDEBQUhBUrVuDZs2cAgA0bNqBSpUooW7bsZ22T31ikOHXqFIoXL45Lly4hPj4epUuXxqZNm5Rf3Q0aNEBycjKGDRsGADAyMkJYWBji4uLg7e2ty6JTJqRSqRAbG4tff/0VgwcPxubNm7Fnzx6MHj0aISEh2LZtGywsLDB+/HgcOHAAHTp0wOrVq7F8+XKsWrUKPj4+MDMzYwPeDzh58iQcHBxw9OhRPHv2DAUKFMDatWuxfft2WFpaomnTpjh06BC2bt2qLPPs2TMYGBjAwcFBdwWnDO3dKwaaHyqHDh1Cw4YN0bNnT3Tt2hXbt29H2bJlsXHjRty8eRPNmjXDiBEj4OfnBy8vL1SvXh3Tp09Ht27dlJqhTyakd9RqdZrTZ8+eLSVKlJBXr14p01q2bCmlSpWSc+fOSUpKiixatEiMjIykRo0a0r17d7G1tZUWLVpIdHT0e9dL9G+SkpLSnH7mzBlxcHCQP//8U5mmVqulfv364u3tLVeuXBERkS1btoi3t7eULFlSChYsKIsWLfom5c4o3vfeHDZsmNSqVUvi4uJEROTmzZvSqFEjcXZ2VuZp3769ODo6iq+vr4wcOVJy5colP/74o7x8+fKblJ0yj+Tk5Pc+FxUVJcWKFZO5c+eKyP8+EzZt2iS2trYyZcoUZd79+/fL77//Lr/88ovW99XnYAjSM+/7shERadGihTRu3FhERBITE0VEJCIiQiwtLWXgwIESExMjIiI7duyQUaNGSfPmzWXdunVfv9CUaaWkpGg9PnPmjERERCiPb9y4IcbGxnLw4EEREUlISBARkW3btomRkZFMmjRJ6wv+zp07H1y/PvrQe/7777+Xhg0bKo/VarWEhoaKhYWFTJs2TUREnj17Jps3b5Z27dpJ7dq1ZdmyZV+5xJQZvf0+3bZtm3Tu3FmGDx8uFy9eVL5v6tatK97e3iKiHZgcHR2levXqEhwcnOa6P3SO/xuGID2QkpKidQImJibKtGnTZPny5XL69Gll+rRp08Ta2lprPhGRNm3aSOHChWXbtm3frtCUKe3atSvN6UuWLJE8efJI2bJlxdbWVpYtWyZRUVEiIuLl5SX169fXmj8kJEQsLS2lfPnysnPnzlTr+9AvTn3w7nterVbLkiVLZPv27UrtWXx8vPTr109q1aqlFTxfvnwptWvXlrx588qzZ8+01vk2fT/G9H5xcXFSvXp1WbFihdb0hw8fSsOGDcXGxkZ69OghZcuWFQ8PDyVY79mzR4yMjLS+a8LCwqRkyZKSL18++eWXX1Kdh//1CgRDUCa1detW+emnn0RE+8NryZIlYmtrK+XKlZMqVapIrly5JDAwUFJSUuTq1auSO3duGT16tDJ/XFyc1K5dW1QqlbRu3Vr5YiL6VFevXhWVSiUzZ84UkTfnZVxcnPj5+YmLi4ssXrxYnj59KhMnTpQqVarIrFmzRETkwIEDYmBgILNmzZJ79+6JiMjEiROlXbt20r17d7l165bO9ik9Wbt2rcyYMSPV9OXLl4udnZ2UKVNGXFxcxMHBQfnxs2zZMnFzc5M//vhDmT8uLk5q1KghKpVK2rRpk2p9DD/0bxITE2Xt2rWpLplOmzZNGjRoIE+ePBERkadPn0rRokXFw8NDCefdu3eXXLlyyYABAyQ4OFgaN24s48ePlwMHDnyVsjIEZUJJSUkyffp0UalUcvbsWRF588G1d+9eqVy5sgQEBCjz+vj4iKurq3KCzZgxQ1QqlUyfPl1OnTolAQEB0q1bN1m7dq1cvHhRF7tDmURSUpKMGDFCcuXKJbGxsSLy5rycPHmy7NixQ0Te1EK0atVKTExMpFq1anLu3DkREZk6darkz59fnJ2dpWzZspIrVy45f/68zvYlPerdu7fs3r1bRN78Ok5JSZHNmzdLlSpVZMGCBSLy5jWoWbOmuLq6ysuXLyUhIUHat28vJUqUkMWLF8v9+/clICBAWrVqJTt37pRjx47pcpcoEzh+/Ljy/4sXL0poaKiIiPz+++9ib28vxYsXlzJlyoifn58y34gRI6Rq1aqSL18+8fX1lejoaOW5L32JmyEok7pz5458//334uHhoUy7cuWKbNmyRXm+RYsWkiVLFilQoIB069ZNXrx4ISIiw4cPlxIlSoi9vb1YWVlJYGCgDvaAMqM7d+6Ik5OTVi2l5ldhQECA2NvbS5MmTWTRokVStGhR6d+/v7Ls6dOnZfbs2TJp0iTlUq1mHfrs3f1/9eqVcnx27dolGzduFBGRx48fS4sWLcTS0lJUKpWMHz9eRESuX78u/v7+Ym5uLgUKFJAcOXLIqlWrlPWxwwN9jLTOk/Xr14tKpZIbN26IyP+aWPzyyy9SsmRJ5TJY06ZNxcXFRfbt2ycib87p169fS3h4+AfX/yUwBGUi754kW7duFSsrK1mzZo2IiFI1GRISIiVLlpRmzZpJVFSUTJw4UWxtbbXCTnR0tBw+fPjbFZ4yDc3lkpSUlDSv3y9ZskQMDQ3l8uXLyvTbt29LlSpVZN68ecoymss3W7duTXM7/6UxZGbx7nv+wIED4u7urrzn4+PjReRNb5qiRYtKo0aN5Pr16zJ27FixsbFRvpxERC5fvix//fUXjyt9svedM5GRkVK1alWtxvcxMTFSunRp+e2335RpHTp0kBw5ckj9+vW1fuCI/K9W82vhOEEZ2MmTJwG8GdlVrVanGg/F1dUVzZs3V4bCz5o1KwBg/fr1KFiwIAICAmBtbY1ChQohPj4eK1asUAY9zJ49O6pXr/4N94YyuuTkZDRo0ABTpkwBAOUO5Pfv38fjx4+Vc7RRo0Zwc3NDv379lGUvXLiAu3fvolKlSjAwMMDly5eRJUsWmJqa4tChQ2mOK6IZYl8fqdXqNN/zBQsWREJCAvbt24dHjx7B1NQUKSkpWL16NapUqYLly5fD2dkZWbJkwbNnzzBx4kTlfk1FixaFt7c3jIyMlGlEH0PzXpwwYQImT56MZcuWAQDs7OwwfPhwBAUFYc+ePQCAmzdvwsTERDnHrl27hsTERHTu3Blt2rRJNQinSqX6qoPwMgRlUHv27IGrqytCQ0NhbGwMAwMDhIaG4o8//lBuMGlnZ4f27dsjOTlZGQX26dOnCAsLg6OjI0xMTAAAJ06cgLe3N/LmzQtLS0td7RJlcEZGRihevDhmzpyJ+/fvIz4+Hu3atUOJEiVQs2ZNtGnTBvfu3YO1tTXGjBmDPXv24M8//wQAZfC9hQsX4vDhw/j555/h6emJgIAATJ8+PdWHoD4PgCgiSsC8fPky1qxZg6tXryI6OhoODg5o164dTp8+jaCgIABvjtW2bdtQsmRJZM+eHQDw+PFjdOrUCadOnVLuw/Y2fQ6Y9PHk/wc53Lt3L/LmzYstW7bg7t27GDJkCPr06YOrV6/Cx8cHLVq0QN++fQEAZcuWRfHixTFr1ix4eXmhXLlyyJMnD8aPH482bdroZCcoA3rx4oX4+PhIjRo1RESkV69eYm5uLiVKlJAsWbJImzZtJDw8XNRqtYwbN06srKzkwYMHIiIyYMAAKViwoDRt2lTc3d2lTJkycvv2bR3uDWUWsbGxUrBgQenfv78sXLhQfHx8ZM+ePbJixQpxdHSUhg0bKg3sf/zxRylYsKCy7Pjx46VChQpiZ2cnLVq0UBpPi7Ddz7tev34t7dq1EysrK6lcubIUK1ZMmjRpIiJvLk34+vpKo0aNJCwsTEREfvrpJzExMRF/f3+pVauWlCpVSiIjI3W5C5SJNG/eXIYMGaI8/vXXX8XMzEymT58uIiJnz56VbNmyKT1D79y5Ixs3bpQ+ffrI/v37leXUavU3b4PGEJTBvH3t9fDhw2JsbCy//fabdO/eXa5cuaK05bG3txc/Pz95+fKl3Lx5UypWrCjNmzcXkTftfRYsWCDNmzeXAQMGKAPQEX2OdwPK2rVrRaVSSZEiReTvv/9Wpu/evVs8PT2VD8tLly6JtbW1TJ48WUTetCV6+vSpVmNIhp+0zZw5U2rUqCH//POPiIicP39eVCqVTJ06VUTeDGhatmxZ+eWXX5Rl+vfvL/Xr15eOHTtqdV1mGyD6GO87T86fPy+lSpWSFy9eSEREhDRv3lyyZ88uw4YNk+fPn4vIm/f28OHDxdLSUhl0921fu93PhzAEZWBJSUkyYMAAUalU4uvrK0lJSUqKnj59upQrV075EgoICBArKyvZs2ePsvy7DdCIPtfdu3dl3759yjnVrFkzMTY2Vrq4a/Tp00c8PT3l/v37kpKSIqNHjxaVSqXVBVYk7UbV+uZ9x+Dly5dSsGBB2bRpk4i8uWVI8eLFpUiRIlrv765du0rNmjVl7969IvLm8+L169fK8xzvhz7GuzUz27dvl7Nnzyrv9Zs3b4qhoaH07t1brKyspEGDBnLp0iVl/lu3bolarZbw8HApWLCg9O7dW0T+F6p0/T5nCMpgoqKipEmTJsoYKZcvXxZnZ2f58ccfReR/wSY5OVmcnJxk0qRJIvLmRKxRo4a0bNlSNwWnTCOtL89mzZpJoUKFlEtdp0+fFkNDQ1m8eLHWfLt37xZzc3Pl1+CDBw94n680vP3F8+jRI637I0VFRcl3330nv/32m/j4+Iitra1MnjxZCTiaY3v27FmpVKmSDBgwQGt5Xf7qpozl7fPw8OHDkjNnTnF0dBRLS0tp166dUhP53XffibGxcapxpbZu3Srjx49XRh5funSpGBsby6NHj77dTvwLNoxOx97tEQMA1tbW2Lt3L2bMmIGkpCQULVoUvXr1wvr163Hr1i0YGxtDrVbD0NAQBQoUQFhYGADAyckJK1euxNq1a7/1blAmY2hoCABaPYhmzZqFly9fYuvWrXj16hXKly+Pjh07YuLEibhy5Yoy3927d+Hk5ISXL18CAPLkyYMuXbp82x1Ix+T/G5qqVCokJyejS5cuKFGiBLy8vNCjRw8kJCQgS5YsePnyJQYPHoxs2bLhypUrGDJkCMzNzXHjxg0MGzYMr169QtmyZeHr64sTJ07g/Pnzyja+dm8byjxUKhXu3r2LlStXYtGiRRg8eDDOnj2LWbNm4cGDB+jQoQMAoE2bNkhJScG1a9cQERGBpKQkbNu2DaNHj8bz58+VTjhFixZFkSJFcOPGDR3u1Tt0ncLo32nGU9Gk8u3bt4uhoaFSzf348WMpV66c1KtXT7mB5M2bN6Vw4cLKeCFEX8rz58+lcuXKysCbmvNy5MiR4ujoKEeOHBGRN2OE2NraSunSpWXixImyYMECsba2lj59+rAm4l+cPn1a1qxZI15eXhIUFCQzZswQa2tr+fHHHyU6OloCAwMlb968Mm/ePGWZ58+fS9++fcXX11euXr0qIm9eA0tLS9m+fbuIcOBD+rC02v306tVLbGxspGTJklo1OMeOHZOsWbPK0qVLRURk3LhxkitXLnF2dpbq1auLhYWF0kZN5M2517x5cylbtmy6apTPEJSOpKSkpLrUMHnyZDExMdG6waGISJ06dcTd3V0Z5Xnr1q1iZmYmefLkkZ9//lly5swpHh4e6epko4znfY0hy5QpI15eXqnOS0dHR+natasyCvTChQtFpVJJnz59pHHjxrJw4cKvXuaMRK1Wp3rPb9y4UVQqlZQsWVIZQVdEJCgoSEqUKKHc56tz587i6Ogo1apVkz59+kiePHmkcuXKcuHCBWWZv//+W7JmzSoHDx78NjtEGdK74TggIEDWrVsnIiLh4eFSrVo1yZcvn9LQWeRND8U+ffpIpUqVlGmhoaGyZcsWWbhwoVYDaM05HhgYmGbDaF1iCEon3j4JHzx4oNxfJSoqSvLlyyf+/v5a81+8eFEMDAyUFB4TEyNt27YVlUolx44dk6CgoG9XeMp03q2pWb9+vRw7dkzpVaS5GerChQslOTlZOX8DAgLEzMxMOf8SEhKkaNGiyr3BNNgoV/sYPHr0SEJDQyU5OVmePHkiTZo0ESsrK7l7967WMi1btpTatWtLcnKyxMTEyF9//SVDhgyRjh07at2xW61WS3x8vCxdulS5NQHRv9m5c6c4OTlJyZIl5eeff1Y6LMyfP1+KFi0q8+fP15p/6NChUqNGDa1w9DZNZ530XAPJEJSOqNVqGTBggGTLlk3y588vTZo0kevXr8vq1avF1NRU6xeeiEj9+vWlSpUqyl20Q0NDZdq0abooOmVSx44dExsbGylcuLBYWlrKgAED5P79+yIi0qVLFylYsKBy92eRN7UVhoaGUq9ePbl27ZqI/O/WDSK67wmSHvXv31+sra3Fz89POZZ//fWXGBoaKj3ANDVyu3btEnNzc61eXu9iwKTPcezYMSlWrJiMHz9eCdka8fHx4uPjI56ensrYUyIi7dq1k6ZNm6a5vvQcfN7G1nHpxPHjxzFnzhw8evQIu3fvxuzZs3HlyhVMmDABHh4eqFKlCkaNGoWUlBQAwIsXL/Dq1SucPHkS8+bNAwBUrFgRAwcO1OVuUCZx+/ZtTJkyBZs2bcK4ceNw8eJFjBgxAidPnsSMGTMAAHPnzkVUVBSWLFmCO3fuAAAuXryIn376CbGxscroxKamplCr1cpIx/TGo0eP4OHhgePHj2P9+vXw8/ODo6MjAKB69epo2bIl/P39ER0drYzgfPnyZbi4uOD169dKI2oNTUcKTcN1orS875YoJ06cQFJSEkaMGAEAePbsGR49eoQ7d+7A1NQUffr0wZUrV/D9999j4MCBaN68OdavX48ff/wRAFKdjxllVHeOjZ4OPH/+HC1btoSIYNSoUXB1dQXw5iRcsGABgoKCMGvWLJQvXx4TJ05Eq1atcOLECXh4eOCnn35CoUKFdLwHlNmsWrUK8+fPV3ojGhsbY+DAgXj+/Dn++usvHDhwAJ6envjtt98wZswYBAcHw8LCAuHh4Th+/Djs7e211qfP4UdElB6bb7t58yZevXqFwMBAuLi4IC4uDqampkhISICFhQWGDh2KGjVqoFGjRmjatCmsrKwwefJktGrVCjY2Nqm2o8/HmD6OvHXPvZ07d8LQ0BAuLi5wdHSEi4sLHj58iN69e+POnTswNDRESEgIzM3NsWLFCtSvXx+NGzfG7t27oVar4erqitmzZyvv9YwSet7Fd006YGVlhREjRiA6OhqmpqbK9NatW8PZ2RlbtmxB3rx5MX36dCxduhTu7u4YMGAAypQpg+bNm6N8+fI6LD1lJprahE6dOsHNzQ3Pnz9HlixZALz5kGvcuDFy5cqF+fPnK/MtW7YMvr6+8PT0xNWrV5UPRU2tpb56+yanhoaGiImJwYMHD/Dq1SsAwL179/DkyRPs378fkydPxtChQ+Hm5oYWLVpg586dKFWqFH7++WccPHgQt27dwpo1azB48GDMmjVLx3tGGcGJEyfw4sULANpDLxw/fhxFixbFwIED4efnB19fX6xZswb16tXDjBkzcOXKFbi6uqJx48Y4c+YM7OzsMHv2bABA586dkTNnTpiammLgwIGwt7dHYmKirnbxy9DhpTh6S0JCglSpUkW6dOkiUVFRyvTg4GCpWrWqjBw5UkREIiIiUjUyJfoUKSkpH3W9fs2aNVK+fHnlthYas2bNkooVK8qSJUvSXE6fb8OwcuVKCQwMTDV9+PDhkjt3bqlataqUKFFCjh8/LiIigwYNEgcHB6ldu7YMGTJERowYIa1bt5Zy5cpJXFychIeHi4uLizLKrgbb/dCHPH/+XPLnzy+NGzcWkf+1z4mNjRUfHx/p16+fiLw5j4YOHSoqlUqrbd/bOnbsKH379lUejxw5UqpVqyYbN278ujvxjTAEpSN//fWXVKpUSVauXKk1vXv37lKmTBk5e/asbgpGmcbbX57vNq7VfFC+/YHZpUsX8fT01GqUf+vWLfH29pZOnTql+jLOKI0hv4bY2FgpX768NG7cWLkhcUJCgvz0009SpkwZCQoKkgcPHkjXrl2lRIkSyo8ZzXACmtfD399ffH19JTExUZKTk2X58uVibGysjMbNAET/Jjk5WdavXy8mJiZy8uRJZfqOHTskd+7cyuPBgweLlZWVtGjRQh4/fqxMv3jxopw9e1batm0refLkkf379yudGiIjI6VEiRLSoUMHrXvQZVQMQemIWq2WRo0aSatWrZQeXyJvBj58u0U+0X8RGxsrvXv3lqZNm8rPP/8swcHBIqLdc0vz/+DgYPHw8BA/Pz+tdWgG46M3NLer2bBhg5QvX165e3ZUVJSUK1dOGe/n7t27UrFiRSlUqJBy3EVEXrx4IQkJCbJjxw4pXry4jB8/Xuu5qlWrSunSpb/hHlFG9PZYc3FxcdKkSROpXLmy8vyOHTukQYMGMmvWLHFwcJCKFStq3W8uOTlZrl69Kv369ZP8+fNL/fr1lQF4NesXEdm7d69WaMrIGILSmatXr0qpUqVk7Nixui4KZUDv1sS82yU9JCRE8uTJIzVr1pSZM2fKjz/+KPnz51dGeU6rJmfYsGHi4uKS5mVY1kqkPgZt2rQRX19fCQsLk5MnT4qzs7O8fv1aOnXqJFmzZpXu3btrDTL58uVL6datm9SpU0csLS1l4sSJqbZx6NAhmTNnzlffF8ocTp48KT///LOMGzdOVCqVbN68WUTeDGGRM2dOyZkzp8ydO1fr/b5y5UplBPITJ05o3fw4M7/P2TA6nSlSpAi8vLxS9a4h+hiaHhr3799HQkKCVo8hEcH69evRsmVL7Nu3D/369UOjRo0QHh6O5cuXay0P/K+RdPPmzdG4cWOULl061fbYHft/x2Ds2LHo2LEjnj9/jhMnTmD9+vXInz8/YmJiYGFhgadPn2Lv3r2YP38+cufOjcuXL2PTpk3ImjUrvL29Ua9ePdy/fx/+/v4AtO8d6O7ujl69eulk/yhjmThxIqpVq4bExERERkbCwsICAwYMgIjA19cX5cqVQ8mSJVG1alXl/X7s2DGsXLkSjx8/RlJSEipXrowyZcpARJCSkpK53+c6DmGUBg4oR//FwoULJUeOHPLs2TO5efOmNGnSRB4+fChqtVqqVq0qmzZtksjISKlTp45YW1vLxIkTte4yTp8mKSlJunXrJvnz55e1a9fKjBkzpEKFClK4cGFZu3atjBo1SrJnz671azo+Pl769esnffv2ldjYWK31vT0CN9GnePr0qVSpUkVr0Nzg4GCxsbGRwYMHi4jI0aNHxcvLS2xtbaVjx47SvHlzMTc3l379+ullpwaVyDsjHBFRhpcjRw4UKlQI58+fxw8//ICFCxfi1atX8PX1RZYsWXDq1Ck0atQIY8eOhZOTEwAoY/+8j1qt5lg0aXj16hXc3NzQoUMHZbDS+/fvo0GDBqhcuTKaN2+Ovn37wsrKCnXr1oWzszOmTJmC5ORkzJ8/H+7u7sq6RCTDjrdCuvfixQvkzp0b27Ztg7e3NwAgISEBM2bMwJgxY3D9+nU4ODjg0aNHWLlyJZ49e4anT5/Cz88PRYsWBaB/73P92VOiTOrtyyYJCQm4desWXr16hQsXLmDRokVYuXIlzM3NkTNnTlStWhUHDhzAnDlzsGLFCiUAnTx5EkuXLkVYWNh7t6NPH4yf4t69e4iKikK5cuUAvBkfKV++fGjRogV27NiBZ8+eYdeuXShUqBD279+PmTNn4vvvv8fFixe1AhCQcQeco/Th+fPnKF++PEJCQpRppqamqFmzJtRqNX755RcAgJ2dHQYNGoSJEydi4cKFKFq0KFJSUvQuAAEMQUQZ2tsfWq9fv4apqSmcnJxw6NAhFClSBH/99ReePn2qzD9mzBjY2tpiz5492LVrF+7fv48VK1bgxx9/hKmpKRwcHHS1KxlW8eLFYW5ujp07dwL438B0PXv2xIsXL7Bq1Sqo1WosX74cO3fuxMGDB5UvI30fUJI+zvtudfHuhRwnJycUL14cZ8+eRWhoqDI9Li4O2bJlw8KFC3HkyJFU69GMaK5vAQgAeDmMKAN6u7FiZGQkBg0ahNevX6N27dpo0aIFbG1tceDAAXh5eWHDhg1o3LgxVCoVVCoV/vzzTyxbtgzBwcEoWrQowsPDMWbMGPTo0UPHe5VxLViwAAMGDEBoaChKlCgBAPjzzz+VBqmjRo1C+/btldCqGUmaNT/0KVauXImkpCQ4OTmhZs2aWs9pzq0jR44o9/+aNm0acubMialTpyJ//vzIly8fGjVqBAsLC10UP11iCCLKwE6fPo2ff/4Z2bJlg729PQIDA9G3b18MHjwY2bNnh6+vLyIiIrBt2zbkzZtXWU6tVuPKlSt4+vQp3N3dlS/jTN8T5CtJSkpCvXr1EBkZiaZNm6Jq1ar49ddf0bJlS5QpUwZVqlTRdREpnftQe7ATJ06gbdu2MDQ0RL58+XD69Gn06dMHffv2ha2tbar5Dx06hGHDhuHOnTuIiYlB8eLFERgYiIIFC/7rtvQNQxBRBvDuh9b9+/fRr18/xMXFoWzZspg4cSIAYNKkSQgKCkKXLl3QuXNnREREwMnJCUOGDEH37t2xbt06JCYmYsiQIVrrT05OVm6sSJ8nOjoaY8aMwcGDBxEZGYmGDRti3rx5yvP84qF/k5iYCBMTk1TTW7duDUtLS+V8Wrx4Mbp164bVq1ejVatWynn19jn2+vVrXL16FTExMfDw8FDWxfNQGz/1iNIxec8dyPPlywdDQ0Ps3bsXderUUaZ37doVJ06cwI4dO+Dh4QFnZ2eMHDkSq1evxoIFC2BiYoLFixen2g4D0H9naWmJGTNm4NmzZwAAa2trAP/70uEXD33I4sWLcfLkSSxcuBAAsGfPHtSoUQP//PMPDh8+jGvXriEpKQkDBgzAqlWr0L59e9SqVUvrvHr7/1myZFEa6wP/q+XleahN/1pBEWUgmjuQR0dHY/78+Thw4ACuXbsGABgyZAgKFSqEK1euICEhAQBga2uLVq1a4eHDh9i4cSMAYPjw4QgMDMTy5ctx79491K1bV2f7ow+srKxgbW2tdRd5on/z4MEDhIWFoXv37jAwMMCCBQtgYGCAHDlywMzMDFOmTIGTkxPOnTuH7du3Y9myZciVKxdiYmI+av28zJ02hiCidG769OlwcHDA8uXL0a9fP3h7e+PChQuoUKECGjVqhIsXL2LPnj3K/K1atULJkiWxceNGHD9+HABQtmxZZdyQ9/U0oS9DE3oMDAz0srcNfRxNSxTN+9HNzQ3nzp3DqlWrsGTJEmzYsAFGRkZISEhA3rx5MX36dEycOBFHjhxB9erVAQAbN27EnDlzdLYPmQHfoUTpWFhYGNasWYNFixbh2LFjOH/+PIyMjNCtWze8fPlSuZXC1q1btbrCt27dGp6ennB2dk61Tl76ItKt5ORkJSxr3o9RUVFo1qwZSpYsiaSkJGXeYsWKoUaNGsifPz+yZs2qTD969Cj++OMPPHjwAK9fv/62O5CJMAQRpQPvq51ZsGABHB0d0bJlS5w7dw7e3t548eKFMq5Pvnz50KpVK1y4cAGbN29WlvPy8sK0adPS7DlCRLqhGRdKE3zmzZuH1atX4+7du8rI7sWKFcP69etx48YNZbmOHTuibt26aNmyJdzd3eHj44NatWqhQoUK+P3335ElSxad7E9mwN5hROlEYmIibt68iXz58iFbtmwA3gxueOXKFeTOnRtLlixBq1atMHbsWOTJkwdJSUkwNjZGYmIiPDw84OjoiLlz58LKykpZpz6OAEuU3t25cwfVqlVDtmzZEB8fjxw5cmDFihUoXbo0tmzZgunTp6N69eqYPHmy1nJbt27Fw4cPERkZiU6dOsHR0REA3+f/BUMQkQ5oPrQ0PYcWL16MgQMHIk+ePLC0tERQUBBy5cqFX3/9FRMnTkT+/PkRGBioDMQXHx+PIUOGoGXLlqhatSrOnTsHZ2dnrepyIkpfIiMj0bdvX3h4eCAuLg6DBg3C4cOHMWXKFERGRuLUqVMQEQwcOBCnT5/G6NGj4eXlhcOHD6NgwYJaY30Bb2qWDAwM2Pj+P2B0JPrG3v7VplKpkJiYiL///hsLFy7EzJkzER0djbZt2yIuLg7NmjWDi4sLihQpgsKFCwN406AyICAAR44cQWRkJIA3DZ+zZs2qdR8xItKdtC5x37lzB2fOnMHgwYOVATTd3d0xcOBA/PPPP1i2bBlUKhVatWqFrFmzomfPnqhVqxY8PDwQHh4O4H8NqjVDZzAA/TesCSL6BtRqNURE6aZ6584dDBs2DFZWVjAwMIC1tTXGjh0LALh58yYKFy6MOXPmoGfPnggMDMSMGTNw//591K5dG5cuXcKDBw/wxx9/oFmzZrrcLSJ6x7uDEV6/fh05c+ZEjhw5EBcXh8WLF6Nfv364cOGCUrMbGxsLf39/BAYGKh0cNO38kpOT4e/vz3Y/XwlDENFXsnDhQiQnJ6NXr15aH4xHjhzBoEGDYGlpiSxZsmDbtm3o1asX/vjjD2XZXr16YceOHTh48CAcHR3x+PFjrFixAs+fP0eOHDnw888/K/NyBFii9GfLli0YPHgwrKys8PDhQ0yaNAmNGjVCXFwcGjVqBEtLS+zatUuZ/59//sF3330HX19frc8CDY7q/pUIEX1xz58/Fx8fH6lZs6bcvn1bRESuXr0qDRs2FG9vbxk/frwyb7NmzaRq1apy9uxZZVpSUpJYWVmJv7+/vHz5Ms1tJCUlfc1dIKLPFBgYKE5OTjJt2jS5f/++/P7771KyZEkZOnSoiIisW7dOLC0t5c8//1SWSUxMlPHjx4uLi4skJCRorU+tVn/T8usTtgki+sJEBDly5ED37t2RnJyMRYsWAQCKFCkCEcG+fftgb2+vzD9+/HiEh4dj+/btiI2NBfCmC+2oUaMwefJk3L59O9X6RYS/Col0TNPlXUPTJu/vv/9G06ZNMXDgQNjZ2eH69eu4fv26MmSFu7s7fH19lbu9A4CxsTH69++PK1eupLp/GGt6vx6GIKKv5Pvvv0f58uVx6NAhHDp0CAAwaNAgODk54cKFC8oHaNGiRdGmTRts2LABoaGhyvL9+/fHli1bUKpUKa318j5URLonb7Xx0wxWaGBggOjoaBw7dgytW7fGkiVLYGdnh/Pnz+PgwYMYOHAgAMDe3h5du3bFpUuXMGnSJGWdmt6dHNX922EIIvoC3v5F+PZtE1q2bAlTU1OsWLECwJtfgHXq1MH58+exf/9+ZZlRo0YhKSkJS5YswaNHj5TpDRs2/EZ7QET/Rt5qQqtSqfD333/Dw8MDLVq0QFBQEF6+fAlLS0vkz58fFSpUwIwZMzBr1izs378fVapUwbNnz7BhwwY8fPgQFSpUwJw5c9CgQYNU22Et77fDEET0H8g7d3kPDQ3F2bNnlRuaurm5oWbNmvjnn3+wZs0aAECfPn0QFxeHrVu3Ijo6GgBgbm6OPn36wMLCAtmzZ9fNzhDRe719qwvgzS1t/P39UaNGDYgI/P39sWzZMgBA+/btYW5ujilTpuDHH39Ultu2bRsCAwMRExMDCwsLdO3aFSVKlNAKV/RtsXcY0Sc4deoU4uLi4O7urjU9LCwMXbp0QVRUFOLi4uDh4YGuXbvCy8sL169fx+DBg6FSqbBo0SLY2Nhg8uTJCAoKQo8ePdCuXTsd7Q0R/ZuUlBTlR46IYOHChShRogTOnTuHqKgojB49GomJiejbty8uXbqEP/74A0WLFkWHDh2wZ88edOzYEZUqVcLatWuxf/9+jB07Fn369FHWL+zdqVOsCSL6BL/99hsOHz6sNW3Hjh1o1aoV3NzcEBISgp07d0KtVuOXX35BTEwMChcuDB8fHzx8+BABAQEAgJ49eyI+Ph779+/Hy5cvtdb3bmNLItIdTQC6fPkybG1tMXXqVPj6+mLy5MmoWbMmAMDExARt2rSBoaEh5s+fDxMTEwQGBqJdu3Y4c+YMZs6cCRFBWFiYVgAC2OhZ11gTRPQJ0hqrIyQkBEFBQcp9fv744w8MGTIEFhYW6NatGyZMmIAXL15g0KBBuHv3LqZNm4YyZcrg1KlTKFmyJMzMzHSxK0T0Hm/X/jx8+BB9+vRBjRo1YGxsjN69eyMgIADjxo2Dp6cnli5dqiw3duxY/P333xg0aBCaNGmijA7/9OlTpWcYb3WRvrAmiOhfaLqkA28aLN64cQPVqlVTuq5XrVoVI0aMwM2bN+Hq6ooFCxZg/vz5aNasGdavX48rV64gR44c8PHxQWRkJE6ePAkAqFixIszMzHirC6J0QlMLa2hoiEePHmHChAk4cOAALly4gNGjR8PNzQ0A0LZtW3Tt2hXHjx/H0aNHleXbtGkDKysr/PHHH4iNjVVuj6MJQLzVRfrDEET0AZrGkCqVCq9evQIA5M2bFxcuXMDMmTOV+bJmzYo//vgDTk5O2LVrF9q2bYvChQvjxo0bmDhxIgCgSZMmWL9+Pbp27aq1Dd79mSh90NT+nDx5El5eXrh9+zbKlCmDbt264dWrV0qYMTQ0xHfffYdChQphypQpyvLOzs74/vvv0axZM1hYWKRaP9/r6Q9fEaIPMDIyUho9durUCYcOHYK5uTkWLlyIefPm4dSpUwDejBNy5MgR2NjYIF++fACAiIgIVK9eHf/88w+uX78OAChWrBgAsPaHKB26c+cO6tati1mzZqFp06ZYsmQJSpQogQYNGqBcuXLw8/NT5i1dujQaN26M27dvY8mSJcr0Hj16oFevXrooPn0GhiCit2jCieby1549e+Do6Ihz586hWbNmMDU1BQClIfSIESOgVquhVqtRrlw5HD58GIGBgfj5559x/PhxjB49GiEhIcod4DX4i5BId0QkzQ4Ijo6OePXqFdasWYO8efMq0wsVKoS+ffsiODgYBw8eVKbXrl0bzs7O2Ldvn/LZobnUxea2GQMbRhP9v7e7qiYlJcHY2BgtW7ZEzpw507yh4cmTJ+Hm5oa1a9eiefPmOHr0KObOnYuTJ08ie/bsmDdvHipXrgxAu6ElEenO2+/z2NhYnDx5Es7OzsiVKxdMTU1x5MgRNGjQAH369MHIkSNhbGwMALh//z769++PBw8e4NixY8r67ty5A0dHR13sCn0B/DlKeu/tX3CRkZGoVasWVq5ciQsXLuD48eOoWLGi1vyaX5CVK1dGx44dMXbsWMTExKBatWpYvXo1du/ejdOnT6Ny5cpKo2oGIKL0QROApkyZgjx58qBfv36oVKkShg8fjri4OFSvXh3169fHvn37EBYWpiyXL18+dO3aFSdOnMC8efOU6ZoAxKEtMiaGINJ7mktTly5dQkBAALJkyYLvv/8eFhYWiIyMVG52qglLbwea8ePH49KlS5g7d64yzcnJCcCbD0Xe54tI9969zL1582YsWrQIS5Yswa5duzBo0CDs3LkT3bt3BwCMGzcO4eHhyq0wNCpVqoR58+ahbt26qbbBHzoZEy+Hkd55d4RWEcHIkSMxceJEuLu7Y8WKFShQoAAAoFatWjAxMcGuXbu01rFx40YUKVIEpUuXRlBQEKpUqQI7O7tvuh9E9PEiIiKUHzSNGzdGYmIiduzYoTy/du1atGnTBqGhoShfvjxGjBiB7du3Y/LkyahXr16q9XGk58yBNUGkNzSNId/94FKpVPjhhx9QpkwZJCQkKL27AGDMmDEIDg7GpEmTcPHiRcTHx2P16tWYPn260uOrQYMGsLOzY0NIonTq/Pnz8Pb2xvbt2wEA1tbWsLS01JqnZs2a8PDwwJw5cwAAo0ePRkREBLZt24bY2FiteRmAMg+GINILarUaKpUKhoaGePbsGebNm4cDBw7gzp07AN70/ujQoQPOnj2rTBMRuLu7Y/bs2VixYgVq1aqF6tWro1evXmjbti2aNm2qtQ1+KBLp3ts/RjT/NzU1xePHj+Hg4AAAMDMzw5MnT3DmzBllXjs7OxgZGSFr1qxITEyEsbGxMhL8u2P+8L2eefByGOmV8ePHY/r06XB2dkZMTAwMDAyU3lyPHj1C/fr1UbhwYaxbt04Z8h4A7t69iwsXLiAqKgqtW7dWeozwFyFR+hMWFobSpUtrTStZsiRatWqFESNG4Pz582jVqhV8fX0xbtw4mJmZITY2FjVr1sSPP/6Ivn37ai379mcBZS58VUkvJCUl4ffff8euXbsQGBiIU6dO4dSpU3j58iX69++PxMRE2NnZYdSoUdiwYQOOHj0KAwMDpcdHgQIF8P3336N9+/YwNjZGcnIyAP4iJNK1dxs9X716FWXLlkXPnj2V3l0xMTGoWLEiXrx4gcTERJQpUwadOnVCcHAwKlSogMmTJ8PDwwOvX79Os/0PA1DmxVeWMh1NQHmbsbExbGxsMHnyZPj4+OCff/5Bo0aNEBsbi+XLlyttBerUqYOmTZsqt7ZIq8eHiKS6iSoRfVuaHyiagKL5QeLi4oLg4GDcuHED7dq1w7Vr15A9e3aYmpri4sWLMDExAQD4+flh5cqVcHV1RUhICLy8vPDPP/+gSJEiutkh0gleDqNM491B0Pbu3YvixYujQIECMDY2RkxMDLJnz46AgACMHTsW3t7eGDt2LH766SfcvHkTe/bsgZWVFUJCQtCgQQMcPnxYuc0FEaVPy5Ytw+7du2FjY4MffvgBrq6uMDQ0xK1btzBo0CDcunULI0eOhJWVFTp16oQLFy4gW7ZsWp8XiYmJSjhKTk7mjxw9wpogyjQ0H2grV66EtbU1Bg4ciKpVq2LQoEGIjY1F9uzZER0djbVr16Jdu3aYM2cO7OzskC1bNpw9exYLFiwAALi6uuLhw4cMQETp2PXr1+Hl5YURI0bA2dkZ58+fx6hRo7B48WIAQMGCBbF27VrUr18fQ4cOxdixY+Ho6IgHDx4A0L6UbWJiogxsygCkX1gTRJnGuXPncPLkSQQHB6NZs2aoWbMm1qxZg0WLFqF8+fJYsWIF7t69CycnJwQHB6NWrVqIjY2Fn58foqKikJycjI0bNyofgrzVBVH6kFbD5AkTJuCff/7BsmXLYG5ujuvXr6Nhw4YwMjLC9u3bkT9/fgBvanl27tyJTp064cWLF7hw4QJKlCjBTg0EgCGIMpF69erh1KlTqFixInbt2qV8wG3YsAGtWrVCSEgIqlSpgjp16uDy5cto3749du3ahdy5c2PDhg2pusESkW59qFfWjRs38Pz5c1SqVAkTJkzAjBkzULRoUcTGxqJq1arKKO6asLNnzx4MHjwYrVu3xqBBg77lblA6xsthlGmMGTMGlpaWePXqldYvvJo1a6J27drKh2JgYCC+++47HDt2DPXr18fOnTuVAMT7/xClH5oAtG7dOnTt2hXz589XeoM5OzujUqVKGD16NLZs2YKAgAAcPXoUpUqVQlBQEE6cOAHgTc9QAKhWrRqyZ8+utP3h738CGIIoE3F1dUXjxo3x+vVrreHwbW1tYWZmBhMTEyQmJiJnzpyYO3cu/vrrL/zyyy8A/hd+ePmLSLfe7d05fvx4DBw4EE+ePMGQIUPQpk0b5X5eT548wapVq9C9e3f4+voCeNPW5+nTpxgwYAAAKKHH3Nwc8fHxyrK8FEYAQxBlMj179oSpqSmWLFmCmJgYAG8+VB8/fow8efIoH4iGhoYwMTGBWq3mXd6J0gFNzYyRkRFev36Nw4cP4/HjxwCA7du3Y+vWrdizZw/Wr1+PwMBApKSk4OXLl7C2tkZERAQAIDQ0FImJiZg0aRL8/PwAvLmklpKSgp49e+Lq1avw9PTUyf5R+sQQRJlKoUKF0KpVKwQHB8PDwwMjRoxAjRo1cP/+fTRq1CjV/AYGBvxFSJQOaN6HK1asgJWVFTp37oyCBQviyJEjytg9lSpVQs+ePTFx4kTcvXsXefPmhbu7O2bMmIFq1aqhatWqKFq0KH766Sc0a9YMwJv3uKGhIby9vXH37l1Uq1ZNZ/tI6Q8bRlOmExcXh6ZNm+LRo0eoW7cuHBwc0L17d9b2EKVj586dQ2hoKI4cOYIGDRrA2dkZ/fv3x71797BmzRpUrlwZwJtL1zY2NujWrRumTp2K6OhoHD58GCdOnEDTpk1RtmxZAP9rEM1eYPQhDEGUKa1btw6zZs1C8+bNlbYBSUlJyj2/iCh9qVevHk6cOIE6depg/fr1AIB79+6hevXq6NatG/r374+sWbMCABYuXIiePXti7969qS5vaRpO81YX9DF4llCm1LJlSzg7O2PPnj0ICQkBAA6CRpSOjRkzBra2tkhISFCm5c+fH23btsW6detw8uRJZXq3bt1QokQJvHjxQmsdmi71DED0sXimUKbVs2dPXLhwAXv37mWVOFE65+rqCl9fXzx+/Bi7d+9Wpo8cORIqlQqbNm1SGkADwNmzZ1O182P4oU/FM4YyLTc3N8ydOxdDhw5lACLKAHr37g0A2LZtm1LLY2Zmhj59+iAgIACXL19W5jU0NFR6dxJ9LrYJIiKidOPXX3/Fxo0b0b9/f/zwww/K9H379sHLy0uHJaPMiCGIiIjSjdjYWDRp0gQAMG/ePBQsWFDreV7api+JLUWJiCjdsLCwQNu2bXHhwgXY2dmlep4BiL4k1gQRERGRXmLDaCIiSpc0Y/4QfS2sCSIiIiK9xJogIiIi0ksMQURERKSXGIKIiIhILzEEERERkV5iCCIiIiK9xBBEREREeokhiIgyrAMHDkClUik32yQi+hQMQURERKSXGIKIiIhILzEEEZHOeHp6om/fvhg8eDCsra2RO3dujBkzBgBw584dqFQqnDt3Tpn/xYsXUKlUOHDggNZ6jh49ijJlysDMzAxVqlTBhQsXtJ4PCQlBjRo1YG5uDgcHB/Tt2xexsbHK83PnzkXhwoVhZmYGOzs7NGvW7GvtMhGlIwxBRKRTy5cvh4WFBU6cOIGpU6di3LhxCA4O/qR1/Pzzz5g2bRpCQ0ORK1cuNGjQAElJSQCACxcuwNvbG02aNEFYWBjWrVuHI0eOoE+fPgCAU6dOoW/fvhg3bhyuXr2Kv/76CzVq1Pji+0lE6Q/vHUZEOuPp6YmUlBQcPnxYmVa5cmV4eXmhR48ecHJywtmzZ1G2bFkAb2qCrKyssH//fnh6euLAgQOoWbMm1q5di5YtWwIAnj17hnz58iEgIAAtWrRAu3btYG5ujgULFijbOHLkCDw8PBAbG4udO3eiY8eOuH//PrJly/ZN95+IdMtI1wUgIv1WunRprcf29vZ4/PjxJ63Dzc1N+b+1tTVcXFxw+fJlAMDp06dx48YNrF69WplHRKBWq3H79m3UqVMHBQoUQMGCBVGvXj3Uq1cPjRs3RpYsWf7DXhFRRsDLYUSkU8bGxlqPVSoV1Go1DAzefDy9XVmtucT1MVQqFQBArVaje/fuOHfunPJ3/vx5XL9+HYUKFUK2bNlw5swZrFmzBvb29hg1ahTKlCnDbvdEeoAhiIjSpZw5cwIAIiIilGlvN5J+2/Hjx5X/P3/+HNeuXUPRokUBAOXLl8fFixfh7Oyc6s/ExAQAYGRkhNq1a2Pq1KkICwvDnTt3sG/fvq+0Z0SUXvByGBGlS+bm5nB1dcXkyZPh6OiIp0+fYsSIEWnOO27cONjY2MDOzg7Dhw+Hra0tGjVqBAAYMmQIXF1d0bt3b3Tt2hUWFha4fPkygoOD8fvvv2P79u24desWatSoASsrK+zcuRNqtRouLi7fcG+JSBdYE0RE6dbSpUuRlJSEihUrol+/fpgwYUKa802ePBn9+vVDhQoVEBERgaCgIKWWp3Tp0jh48CCuX78Od3d3lCtXDiNHjoS9vT0AIEeOHNi8eTO8vLxQrFgxzJ8/H2vWrEGJEiW+2X4SkW6wdxgRERHpJdYEERERkV5iCCIiIiK9xBBEREREeokhiIiIiPQSQxARERHpJYYgIiIi0ksMQURERKSXGIKIiIhILzEEERERkV5iCCIiIiK9xBBEREREeokhiIiIiPTS/wF9WFkbz8J4nAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# value_counts().plot() — histograma de datos categóricos (§8.5)\n", + "fig, ax = plt.subplots(figsize=(6, 4))\n", + "df_cat['nubes'].value_counts().plot(kind='bar', ax=ax, color='slategray')\n", + "ax.set_title('Frecuencia de tipos de nubosidad')\n", + "ax.set_ylabel('Días')\n", + "ax.tick_params(axis='x', rotation=30)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Nivel 12 — Consejos de Pandas (§8.7)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reporte climático mensual:\n", + " jan: -0.3°C, 59 mm de lluvia\n", + " feb: 0.4°C, 57 mm de lluvia\n", + " mar: 3.9°C, 84 mm de lluvia\n", + " apr: 7.4°C, 100 mm de lluvia\n", + " may: 12.0°C, 143 mm de lluvia\n", + " jun: 15.0°C, 153 mm de lluvia\n", + " jul: 17.2°C, 172 mm de lluvia\n", + " aug: 16.8°C, 164 mm de lluvia\n", + " sep: 13.1°C, 135 mm de lluvia\n", + " oct: 9.1°C, 89 mm de lluvia\n", + " nov: 3.7°C, 88 mm de lluvia\n", + " dec: 0.8°C, 80 mm de lluvia\n" + ] + } + ], + "source": [ + "# iterrows() — iterar sobre filas (§8.7)\n", + "print(\"Reporte climático mensual:\")\n", + "for mes, datos in df_clima.iterrows():\n", + " print(f\" {mes:>3}: {datos['temp_C']:5.1f}°C, {datos['rain_mm']:3.0f} mm de lluvia\")" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Temperatura media según si llueve ≥ 100 mm:\n", + "rain_mm\n", + "False 2.93\n", + "True 13.58\n", + "Name: temp_C, dtype: float64\n" + ] + } + ], + "source": [ + "# groupby() con condición booleana (§8.7)\n", + "print(\"Temperatura media según si llueve ≥ 100 mm:\")\n", + "print(df_clima.groupby(df_clima['rain_mm'] >= 100)['temp_C'].mean().round(2))" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meses cálidos (> 10°C):\n", + " temp_C temp_F rain_mm\n", + "jul 17.2 62.96 172\n", + "aug 16.8 62.24 164\n", + "jun 15.0 59.00 153\n", + "sep 13.1 55.58 135\n", + "may 12.0 53.60 143\n" + ] + } + ], + "source": [ + "# Encadenar métodos — flujo de trabajo legible\n", + "resumen = (df_clima\n", + " .assign(temp_F=lambda d: d['temp_C'] * 1.8 + 32)\n", + " .loc[df_clima['temp_C'] > 10, ['temp_C', 'temp_F', 'rain_mm']]\n", + " .sort_values('temp_C', ascending=False)\n", + ")\n", + "print(\"Meses cálidos (> 10°C):\")\n", + "print(resumen)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Ejercicios" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ejercicio 1\n", + "Crea una Serie con los valores de precipitación mensual de tu ciudad.\n", + "Calcula: media, mediana, máximo y el mes con mayor precipitación (`argmax()`)." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------\n", + " VALORES DE PRECIPITACIÓN ANUAL\n", + " ESTACIÓN ANTIGUA GUATEMALA\n", + "\n", + " VALORES: \n", + " [[ 2.1 1.5 12.4]\n", + " [ 35.8 150.2 250.4]\n", + " [180.1 195.3 280.9]\n", + " [160.4 25.2 5.1]] \n", + "\n", + " FECHAS: \n", + " [['Enero' 'Febrero' 'Marzo']\n", + " ['Abril' 'Mayo' 'Junio']\n", + " ['Julio' 'Agosto' 'Septiembre']\n", + " ['Octubre' 'Noviembre' 'Diciembre']]\n", + "------------------------------------------------------------\n", + " ESTADÍSTICAS\n", + "MEDIA: 108.3 mm\n", + "MÍNIMO: 1.5 mm\n", + "ÍNDICE DEL VALOR MÁXIMO: 8\n", + "DÍA CON MAYOR PRECIPITACIÓN: 280.9 mm\n", + "------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# TU CÓDIGO AQUÍ\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# TU CÓDIGO AQUÍ\n", + "\n", + "# Valores de la Estación Antigua Guatemala. lluvias en mm\n", + "lluvias = [2.1, 1.5, 12.4, 35.8, 150.2, 250.4, 180.1, 195.3, 280.9, 160.4, 25.2, 5.1]\n", + "meses = ['Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', \n", + " 'Julio', 'Agosto', 'Septiembre', 'Octubre', 'Noviembre', 'Diciembre']\n", + "\n", + "# Arreglo de matriz\n", + "a = np.array(lluvias[:12])\n", + "b = np.array(meses[:12])\n", + "\n", + "d = a.reshape(4, 3)\n", + "F = b.reshape(4, 3)\n", + "\n", + "print(\"--\" * 30)\n", + "print(\" \" * 15 + \"VALORES DE PRECIPITACIÓN ANUAL\")\n", + "print(\" \" * 17 + \"ESTACIÓN ANTIGUA GUATEMALA\")\n", + "\n", + "print(f\"\\n VALORES: \\n {d} \\n\\n FECHAS: \\n {F}\")\n", + "print(\"--\" * 30)\n", + "\n", + "print(\" \" * 20 + \"ESTADÍSTICAS\")\n", + "\n", + "print(f'MEDIA: {a.mean():.1f} mm')\n", + "print(f'MÍNIMO: {a.min():.1f} mm')\n", + "print('ÍNDICE DEL VALOR MÁXIMO:', a.argmax())\n", + "# Usamos max(a) o a.max()\n", + "print(f'DÍA CON MAYOR PRECIPITACIÓN: {a.max()} mm')\n", + "print(\"--\" * 30)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ejercicio 2\n", + "Dado `df_clima`, filtra los meses con temperatura entre 5°C y 15°C.\n", + "Calcula la lluvia total en ese subconjunto." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------\n", + " DATOS REPRESENTATIVOS DE TEMPERATURA (°C) PARA ANTIGUA GUATEMALA\n", + " Mes Temp_Min Temp_Max Lluvia_mm\n", + "0 Enero 10.5 23.8 2.1\n", + "1 Febrero 11.2 25.1 1.5\n", + "2 Marzo 12.8 26.7 12.4\n", + "3 Abril 14.5 27.5 35.8\n", + "4 Mayo 15.8 26.8 150.2\n", + "5 Junio 16.2 25.4 250.4\n", + "6 Julio 15.9 25.1 180.1\n", + "7 Agosto 15.7 25.3 195.3\n", + "8 Septiembre 15.8 24.8 280.9\n", + "9 Octubre 14.9 24.2 160.4\n", + "10 Noviembre 13.1 23.9 25.2\n", + "11 Diciembre 11.4 23.5 5.1\n", + "------------------------------------------------------------\n", + " MESES CON TEMPERATURA ENTRE 5°C Y 15°C: \n", + " Mes Temp_Min\n", + "0 Enero 10.5\n", + "1 Febrero 11.2\n", + "2 Marzo 12.8\n", + "3 Abril 14.5\n", + "9 Octubre 14.9\n", + "10 Noviembre 13.1\n", + "11 Diciembre 11.4\n", + "------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# TU CÓDIGO AQUÍ\n", + "# Datos representativos de temperatura (°C) para Antigua Guatemala\n", + "datos = {\n", + " 'Mes': ['Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', \n", + " 'Julio', 'Agosto', 'Septiembre', 'Octubre', 'Noviembre', 'Diciembre'],\n", + " 'Temp_Min': [10.5, 11.2, 12.8, 14.5, 15.8, 16.2, 15.9, 15.7, 15.8, 14.9, 13.1, 11.4],\n", + " 'Temp_Max': [23.8, 25.1, 26.7, 27.5, 26.8, 25.4, 25.1, 25.3, 24.8, 24.2, 23.9, 23.5],\n", + " 'Lluvia_mm': [2.1, 1.5, 12.4, 35.8, 150.2, 250.4, 180.1, 195.3, 280.9, 160.4, 25.2, 5.1]\n", + "}\n", + "\n", + "df_clima = pd.DataFrame(datos)\n", + "filtro = (df_clima [ 'Temp_Min'] >=5) & (df_clima[ 'Temp_Min'] <= 15)\n", + "subconjunto = df_clima[filtro]\n", + "Luvia_total = subconjunto [ 'Lluvia_mm'].sum()\n", + "print(\"--\" * 30)\n", + "\n", + "print(\" \" * 1 + \"DATOS REPRESENTATIVOS DE TEMPERATURA (°C) PARA ANTIGUA GUATEMALA\")\n", + "print(df_clima)\n", + "print(\"--\" * 30)\n", + "print (f\" MESES CON TEMPERATURA ENTRE 5°C Y 15°C: \\n {subconjunto[['Mes','Temp_Min' ]]}\")\n", + "print(\"--\" * 30)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ejercicio 3\n", + "Agrega al DataFrame `df_completo` (con `temp_min_C` y `temp_max_C`) una columna\n", + "`amplitud_termica = temp_max_C - temp_min_C`. Encuentra el mes con mayor amplitud." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------\n", + " EL MES CON MAYOR AMPLITUD TÉRMINCA \n", + " MES: Febrero\n", + "------------------------------------------------------------\n", + " LA MAYOR AMPLITUD TÉRMINCA \n", + " AMPLITUD: 13.9 °C\n", + "------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# TU CÓDIGO AQUÍ\n", + "# Datos representativos de temperatura (°C) para Antigua Guatemala\n", + "datos = {\n", + " 'Mes': ['Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', \n", + " 'Julio', 'Agosto', 'Septiembre', 'Octubre', 'Noviembre', 'Diciembre'],\n", + " 'Temp_Min': [10.5, 11.2, 12.8, 14.5, 15.8, 16.2, 15.9, 15.7, 15.8, 14.9, 13.1, 11.4],\n", + " 'Temp_Max': [23.8, 25.1, 26.7, 27.5, 26.8, 25.4, 25.1, 25.3, 24.8, 24.2, 23.9, 23.5],\n", + " 'Lluvia_mm': [2.1, 1.5, 12.4, 35.8, 150.2, 250.4, 180.1, 195.3, 280.9, 160.4, 25.2, 5.1]\n", + "}\n", + "\n", + "df_clima = pd.DataFrame(datos)\n", + "\n", + "df_clima['amplitud_termica'] = df_clima['Temp_Max'] - df_clima['Temp_Min']\n", + "\n", + "indice_max = df_clima['amplitud_termica'].idxmax()\n", + "\n", + "mes_max = df_clima.loc[indice_max]\n", + "print(\"--\" * 30)\n", + "print(\" \" * 2 +\" EL MES CON MAYOR AMPLITUD TÉRMINCA \")\n", + "print(f\" MES: {mes_max['Mes']}\")\n", + "print(\"--\" * 30)\n", + "print(\" \" * 2 +\" LA MAYOR AMPLITUD TÉRMINCA \")\n", + "print(f\" AMPLITUD: {mes_max['amplitud_termica']:.1f} °C\")\n", + "print(\"--\" * 30)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ejercicio 4\n", + "Crea un DataFrame con 5 estudiantes (nombre, nota1, nota2, nota3) con 3 valores NaN. \n", + "a) Identifica cuántos NaN hay por columna. \n", + "b) Rellena con la media de cada columna. \n", + "c) Calcula la nota promedio final de cada estudiante." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------\n", + "DataFrame con valores faltantes:\n", + " Nombre Nota1 Nota2 Nota3\n", + "0 Herlinda 90.0 NaN NaN\n", + "1 Rafael 80.0 65.0 71.0\n", + "2 Irinea NaN 70.0 NaN\n", + "3 Magaly 88.0 80.0 90.0\n", + "4 Adán NaN NaN 76.0\n", + "------------------------------------------------------------\n", + "Cantidad de nulos por columna:\n", + "Nombre 0\n", + "Nota1 2\n", + "Nota2 1\n", + "Nota3 1\n", + "dtype: int64\n", + "------------------------------------------------------------\n", + " RELLENAR CON LA MEDIA EN CADA COLUMNA:\n", + " Nombre Nota1 Nota2 Nota3\n", + "0 Herlinda 90.0 71.7 79.0\n", + "1 Rafael 80.0 65.0 71.0\n", + "2 Irinea 86.0 70.0 79.0\n", + "3 Magaly 88.0 80.0 90.0\n", + "4 Adán 86.0 71.7 76.0\n", + "------------------------------------------------------------\n", + " NOTA PROMEDIO FINAL DE CADA ESTUDIANTE:\n", + " Nombre Promedio\n", + "0 Herlinda 90.0\n", + "1 Rafael 72.0\n", + "2 Irinea 70.0\n", + "3 Magaly 86.0\n", + "4 Adán 76.0\n" + ] + } + ], + "source": [ + "# TU CÓDIGO AQUÍ\n", + "# Crear DataFrame con datos faltantes (NaN = Not a Number)\n", + "Informacion = pd.DataFrame({\n", + " \"Nombre\": [\"Herlinda\", \"Rafael\", \"Irinea\", \"Magaly\", \"Adán\"],\n", + " \"Nota1\": [90, 80, np.nan, 88, np.nan],\n", + " \"Nota2\": [np.nan, 65, 70, 80, np.nan],\n", + " \"Nota3\": [np.nan, 71, np.nan, 90, 76]\n", + "})\n", + "\n", + "NOTA1 = Informacion[\"Nota1\"]\n", + "NOTA2 = Informacion[\"Nota2\"]\n", + "NOTA3 = Informacion[\"Nota3\"]\n", + "print(\"--\" * 30)\n", + "print(\"DataFrame con valores faltantes:\")\n", + "print(Informacion)\n", + "print(\"--\" * 30)\n", + "print(\"Cantidad de nulos por columna:\")\n", + "print(datos_incompletos.isnull().sum())\n", + "print(\"--\" * 30)\n", + "\n", + "df_rellenado = Informacion .copy()\n", + "for col in [\"Nota1\", \"Nota2\", \"Nota3\"]:\n", + " df_rellenado[col] = df_rellenado[col].fillna(df_rellenado[col].mean())\n", + "\n", + "print(\" RELLENAR CON LA MEDIA EN CADA COLUMNA:\")\n", + "print(df_rellenado.round(1))\n", + "\n", + "print(\"--\" * 30)\n", + "print(\" NOTA PROMEDIO FINAL DE CADA ESTUDIANTE:\")\n", + "Informacion[\"Promedio\"] = Informacion[[\"Nota1\", \"Nota2\", \"Nota3\"]].mean(axis=1)\n", + "Informacion[\"Promedio\"] = Informacion[\"Promedio\"].round(3)\n", + "print(Informacion[[\"Nombre\", \"Promedio\"]])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pandas versión: 2.1.4\n", + " Año Mes Costo_Diario_Q Costo_Mensual_Q\n", + "0 2020 Diciembre 99.65 2989.38\n", + "1 2021 Enero 99.49 2984.73\n", + "2 2021 Febrero 99.58 2987.39\n", + "3 2021 Marzo 99.27 2978.10\n", + "4 2021 Abril 99.72 2991.70\n", + "5 2021 Mayo 99.77 2993.03\n", + "6 2021 Junio 99.99 2999.67\n", + "7 2021 Julio 100.11 3003.32\n", + "8 2021 Agosto 100.42 3012.61\n", + "9 2021 Septiembre 100.85 3025.55\n", + "10 2021 Octubre 101.84 3055.09\n", + "11 2021 Noviembre 102.74 3082.30\n", + "12 2021 Diciembre 103.24 3097.23\n", + "13 2022 Enero 103.67 3110.18\n", + "14 2022 Febrero 104.48 3134.40\n", + "15 2022 Marzo 106.05 3181.53\n", + "16 2022 Abril 107.27 3218.03\n", + "17 2022 Mayo 107.82 3234.62\n", + "18 2022 Junio 110.40 3311.95\n", + "19 2022 Julio 112.32 3369.69\n", + "20 2022 Agosto 115.17 3454.98\n", + "21 2022 Septiembre 117.96 3538.94\n", + "22 2022 Octubre 121.13 3633.85\n", + "23 2022 Noviembre 120.62 3618.58\n", + "24 2022 Diciembre 121.14 3634.18\n", + "25 2023 Enero 121.27 3638.16\n", + "26 2023 Febrero 123.20 3695.91\n", + "27 2023 Marzo 124.28 3728.43\n", + "28 2023 Abril 124.20 3726.11\n", + "29 2023 Mayo 124.39 3731.75\n", + "30 2023 Junio 124.52 3735.73\n", + "31 2023 Julio 125.50 3764.93\n", + "32 2023 Agosto 126.99 3809.73\n", + "33 2023 Septiembre 127.50 3825.00\n", + "34 2023 Octubre 131.24 3937.17\n", + "35 2023 Noviembre 129.99 3899.67\n", + "36 2023 Diciembre 130.17 3904.98\n", + "------------------------------------------------------------\n", + "COSTO PROMEDIO MENDUAL DE LA CANASTA BÁSICO EN GUATEMALA 2020-2023\n", + "Año\n", + "2020 2989.380000\n", + "2021 3017.560000\n", + "2022 3370.077500\n", + "2023 3783.130833\n", + "Name: Costo_Mensual_Q, dtype: float64\n", + "------------------------------------------------------------\n", + "COSTO PROMEDIO MENSUAL DE LA CANASTA BÁSICO EN GUATEMALA 2020-2023\n", + "Mes\n", + "Abril 3311.946667\n", + "Agosto 3425.773333\n", + "Diciembre 3406.442500\n", + "Enero 3244.356667\n", + "Febrero 3272.566667\n", + "Julio 3379.313333\n", + "Junio 3349.116667\n", + "Marzo 3296.020000\n", + "Mayo 3319.800000\n", + "Noviembre 3533.516667\n", + "Octubre 3542.036667\n", + "Septiembre 3463.163333\n", + "Name: Costo_Mensual_Q, dtype: float64\n", + "------------------------------------------------------------\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# TAREA 5(OPCIONAL) - PANDAS EN PRÁCTICA \n", + "# Dataset real (opcional, puntos estra)\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np \n", + "print(\"Pandas versión:\", pd.__version__)\n", + "\n", + "# (LECTURA) Diccionario de la Canasta Básica Alimentaria (2020-2023)Obtenida del INE, Guatemala\n", + "df_Canasta_Basica = {\n", + " 'Año': [\n", + " 2020, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021,\n", + " 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022,\n", + " 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023\n", + " ],\n", + " 'Mes': [\n", + " 'Diciembre', 'Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', 'Julio', 'Agosto', 'Septiembre', 'Octubre', 'Noviembre', 'Diciembre',\n", + " 'Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', 'Julio', 'Agosto', 'Septiembre', 'Octubre', 'Noviembre', 'Diciembre',\n", + " 'Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', 'Julio', 'Agosto', 'Septiembre', 'Octubre', 'Noviembre', 'Diciembre'\n", + " ],\n", + " 'Costo_Diario_Q': [\n", + " 99.65, 99.49, 99.58, 99.27, 99.72, 99.77, 99.99, 100.11, 100.42, 100.85, 101.84, 102.74, 103.24,\n", + " 103.67, 104.48, 106.05, 107.27, 107.82, 110.40, 112.32, 115.17, 117.96, 121.13, 120.62, 121.14,\n", + " 121.27, 123.20, 124.28, 124.20, 124.39, 124.52, 125.50, 126.99, 127.50, 131.24, 129.99, 130.17\n", + " ],\n", + " 'Costo_Mensual_Q': [\n", + " 2989.38, 2984.73, 2987.39, 2978.10, 2991.70, 2993.03, 2999.67, 3003.32, 3012.61, 3025.55, 3055.09, 3082.30, 3097.23,\n", + " 3110.18, 3134.40, 3181.53, 3218.03, 3234.62, 3311.95, 3369.69, 3454.98, 3538.94, 3633.85, 3618.58, 3634.18,\n", + " 3638.16, 3695.91, 3728.43, 3726.11, 3731.75, 3735.73, 3764.93, 3809.73, 3825.00, 3937.17, 3899.67, 3904.98\n", + " ]\n", + "}\n", + "\n", + "# (LIMPIEZA) UTILIZANDO .groupby\n", + "df_Canasta_Basica = pd.DataFrame(df_Canasta_Basica )\n", + "print(df_Canasta_Basica) \n", + "print(\"--\" * 30)\n", + "promedio_anual = df_Canasta_Basica.groupby('Año')['Costo_Mensual_Q'].mean()\n", + "print(\"COSTO PROMEDIO MENDUAL DE LA CANASTA BÁSICO EN GUATEMALA 2020-2023\")\n", + "print(promedio_anual)\n", + "print(\"--\" * 30)\n", + "promedio_mensual = df_Canasta_Basica.groupby('Mes')['Costo_Mensual_Q'].mean()\n", + "print(\"COSTO PROMEDIO MENSUAL DE LA CANASTA BÁSICO EN GUATEMALA 2020-2023\")\n", + "print(promedio_mensual)\n", + "print(\"--\" * 30)\n", + "\n", + "\n", + "# (VISUALIZACIÓN) \n", + "# df.plot() — gráfica de línea (§8.5)\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(10, 5))\n", + "promedio_anual.plot(kind='line', ax=ax, marker='o', color='blue')\n", + "\n", + "ax.set_title('PROMEDIO ANUAL DE LA CANASTA BÁSICA EN GUATEMALA 2020-2023')\n", + "ax.set_ylabel('Quetzales')\n", + "ax.set_xlabel('Año')\n", + "ax.set_xticks([2020, 2021, 2022, 2023])\n", + "\n", + "ax.set_ylim(2800, 4000)\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + " \n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "CONCLUSIONES: \n", + "Sobre las Canasta básica en Guatemala\n", + "El promedio en la cansta básica en Guatemala, relacionando los promedios anuales obtenidos con la gráfica, la canasta básica ha ido aumentando Q 200.00 desde el año 2021.\n", + "En julio de 2021 un año y dos meses después de la pandemia de COVID-19, la canasta básica aumentó a Q100.11, en Guatemala." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tarea 5 (opcional) — Pandas en Práctica\n", + "\n", + "La programación se aprende haciendo. Esta tarea tiene dos partes:\n", + "\n", + "**Parte 1 — Descarga y ejecuta** (obligatorio)\n", + "Descarga este notebook y ejecútalo celda por celda en tu computadora. \n", + "Verifica que todas las celdas corran sin errores antes de continuar.\n", + "\n", + "**Parte 2 — Ejercicios** (obligatorio) \n", + "Resuelve los ejercicios de la sección anterior (Ejercicios 1 al 4).\n", + "\n", + "**Parte 3 — Dataset real** (opcional, puntos extra) \n", + "Descarga cualquier dataset CSV que te interese y aplica al menos 3 herramientas vistas:\n", + "lectura, limpieza, `groupby`, visualización. \n", + "Agrega una celda Markdown con tus conclusiones.\n", + "\n", + "**Entrega: 1 de mayo** \n", + "Sube el notebook a tu repositorio de GitHub y envía el **link directo al archivo `.ipynb`**.\n", + "El notebook debe tener todas las celdas ya ejecutadas (con outputs visibles).\n", + "\n", + "Ejemplo de link válido: \n", + "`https://github.com/tu-usuario/tu-repo/blob/main/Pandas.ipynb`" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "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": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/TAREASSS/practica_dos.ipynb b/TAREASSS/practica_dos.ipynb new file mode 100644 index 0000000..e69de29 diff --git a/TAREASSS/practicados/analisis_estrellas_estudiante.ipynb b/TAREASSS/practicados/analisis_estrellas_estudiante.ipynb new file mode 100644 index 0000000..7843a3c --- /dev/null +++ b/TAREASSS/practicados/analisis_estrellas_estudiante.ipynb @@ -0,0 +1,1366 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "md-00", + "metadata": {}, + "source": [ + "# Práctica 2 — Análisis Exploratorio de Datos\n", + "\n", + "En este notebook realizarás un **análisis exploratorio de datos (EDA)** sobre un\n", + "dataset de 240 estrellas clasificadas en 6 tipos.\n", + "\n", + "**Dataset:** [Stars Dataset — Kaggle](https://www.kaggle.com/datasets/waqi786/stars-dataset)\n", + "\n", + "## Instrucciones generales\n", + "- Cada sección tiene celdas marcadas con `# tu código aquí` — ahí debes escribir tu solución.\n", + "- Lee las instrucciones en cada celda de markdown **antes** de escribir el código.\n", + "- Consulta los enlaces a la documentación oficial para entender los parámetros de cada función.\n", + "- Ejecuta el notebook completo **sin errores** antes de hacer commit (`Kernel → Restart & Run All`).\n", + "\n", + "## Contenido\n", + "1. [Importar librerías](#1.-Importar-librerías)\n", + "2. [Cargar los datos](#2.-Cargar-los-datos)\n", + "3. [Exploración inicial](#3.-Exploración-inicial)\n", + "4. [Distribución por tipo de estrella](#4.-Distribución-por-tipo-de-estrella)\n", + "5. [Temperatura por tipo](#5.-Temperatura-por-tipo-de-estrella)\n", + "6. [Luminosidad vs Temperatura](#6.-Luminosidad-vs-Temperatura)\n", + "7. [Estadísticas con NumPy](#7.-Estadísticas-con-NumPy)\n", + "8. [Diagrama Hertzsprung-Russell](#8.-Diagrama-Hertzsprung-Russell)" + ] + }, + { + "cell_type": "markdown", + "id": "md-setup", + "metadata": {}, + "source": [ + "---\n", + "## Configuración del ambiente\n", + "\n", + "Este proyecto usa **Poetry** para gestionar las dependencias. Antes de abrir el notebook\n", + "ejecuta estos comandos **desde la carpeta `practica2_analisis_datos/`**:\n", + "\n", + "```bash\n", + "poetry install\n", + "poetry run jupyter notebook\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "md-01-inst", + "metadata": {}, + "source": [ + "---\n", + "## 1. Importar librerías\n", + "\n", + "Importa las cuatro librerías con sus **alias convencionales**.\n", + "Estos alias son estándares en la comunidad — siempre se usan así:\n", + "\n", + "| Librería | Alias | ¿Para qué sirve? | Documentación |\n", + "|---|---|---|---|\n", + "| `numpy` | `np` | Operaciones matemáticas vectorizadas sobre arrays | [numpy.org/doc/stable](https://numpy.org/doc/stable/user/whatisnumpy.html) |\n", + "| `pandas` | `pd` | Análisis y manipulación de datos tabulares (DataFrames) | [pandas.pydata.org/docs](https://pandas.pydata.org/docs/getting_started/index.html) |\n", + "| `matplotlib.pyplot` | `plt` | Visualización de datos (gráficas de bajo nivel) | [matplotlib.org/tutorials](https://matplotlib.org/stable/tutorials/index.html) |\n", + "| `seaborn` | `sns` | Visualización estadística de alto nivel (sobre matplotlib) | [seaborn.pydata.org/tutorial](https://seaborn.pydata.org/tutorial.html) |\n", + "\n", + "**Sintaxis:** `import librería as alias`" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "code-01", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "numpy 1.26.4\n", + "pandas 2.1.4\n", + "seaborn 0.12.2\n", + "Matplotlib versión: 3.8.0\n" + ] + } + ], + "source": [ + "# Importa las cuatro librerías con sus alias convencionales\n", + "# tu código aquí\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Una vez que las importes, descomenta estas líneas para verificar las versiones:\n", + "print(f'numpy {np.__version__}')\n", + "print(f'pandas {pd.__version__}')\n", + "print(f'seaborn {sns.__version__}')\n", + "print(\"Matplotlib versión:\", __import__('matplotlib').__version__)\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-02-inst", + "metadata": {}, + "source": [ + "---\n", + "## 2. Cargar los datos\n", + "\n", + "Usa [`pd.read_csv()`](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html)\n", + "para leer el archivo CSV y cargarlo en un **DataFrame** de pandas.\n", + "\n", + "- Revisa la documentación: ¿qué parámetro recibe `read_csv`? ¿qué devuelve?\n", + "- El archivo se encuentra en `'../data/star_dataset.csv'` (relativo a la carpeta `notebooks/`)\n", + "- Guarda el resultado en una variable llamada `stars`\n", + "- Después usa [`.head()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.head.html)\n", + " para mostrar las primeras 5 filas" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "code-02", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------------------------------------------------------------------\n", + " CONTENIDO INICIAL DEL DATASET:\n" + ] + }, + { + "data": { + "text/html": [ + "
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NameDistance (ly)Luminosity (L/Lo)Radius (R/Ro)Temperature (K)Spectral Class
0Altair16.5941719.9791921.6326507509.294247A7V
1Deneb2600.490723196002.627856202.9705268503.284796A2Ia
2Barnard's Star6.0526164.8937160.2227113165.959639M4Ve
3Polaris322.6010022196.24193437.5468136048.326915F7Ib
4Barnard's Star5.902392-1.4964860.1923593130.602069M4Ve
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" + ], + "text/plain": [ + " Name Distance (ly) Luminosity (L/Lo) Radius (R/Ro) \\\n", + "0 Altair 16.594171 9.979192 1.632650 \n", + "1 Deneb 2600.490723 196002.627856 202.970526 \n", + "2 Barnard's Star 6.052616 4.893716 0.222711 \n", + "3 Polaris 322.601002 2196.241934 37.546813 \n", + "4 Barnard's Star 5.902392 -1.496486 0.192359 \n", + "\n", + " Temperature (K) Spectral Class \n", + "0 7509.294247 A7V \n", + "1 8503.284796 A2Ia \n", + "2 3165.959639 M4Ve \n", + "3 6048.326915 F7Ib \n", + "4 3130.602069 M4Ve " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------------------------------------------------------------------\n", + " INFORMACIÓN GENERAL DE CADA ESTRELLA:\n", + " \n", + " Name Distance (ly) Luminosity (L/Lo) Radius (R/Ro) \\\n", + "0 Altair 16.594171 9.979192 1.632650 \n", + "1 Deneb 2600.490723 196002.627856 202.970526 \n", + "2 Barnard's Star 6.052616 4.893716 0.222711 \n", + "3 Polaris 322.601002 2196.241934 37.546813 \n", + "4 Barnard's Star 5.902392 -1.496486 0.192359 \n", + "\n", + " Temperature (K) Spectral Class \n", + "0 7509.294247 A7V \n", + "1 8503.284796 A2Ia \n", + "2 3165.959639 M4Ve \n", + "3 6048.326915 F7Ib \n", + "4 3130.602069 M4Ve \n", + "------------------------------------------------------------------------------------------------------------------------\n", + " DIMENSIONES (FILAS , COLUMNAS): (1000, 6)\n", + " TIPO DE DATOS:\n", + "Name object\n", + "Distance (ly) float64\n", + "Luminosity (L/Lo) float64\n", + "Radius (R/Ro) float64\n", + "Temperature (K) float64\n", + "Spectral Class object\n", + "dtype: object\n", + "------------------------------------------------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# Carga el archivo CSV con pd.read_csv() y guárdalo en 'stars'\n", + "# tu código aquí\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "\n", + "print( \"---\" * 40)\n", + "print( \" \" * 8 +\"CONTENIDO INICIAL DEL DATASET:\" )\n", + "display(stars.head())\n", + "\n", + "# Muestra las primeras 5 filas del DataFrame\n", + "# tu código aquí\n", + "print( \"---\" * 40)\n", + "print( \" \" * 8 +\"INFORMACIÓN GENERAL DE CADA ESTRELLA:\" )\n", + "print( \" \" * 40)\n", + "print(stars.head())\n", + "print( \"---\" * 40)\n", + "print( \" \" * 8 +\"DIMENSIONES (FILAS , COLUMNAS):\", stars.shape)\n", + "print(\" \" * 1 +\"TIPO DE DATOS:\")\n", + "print(stars.dtypes)\n", + "print( \"---\" * 40)" + ] + }, + { + "cell_type": "markdown", + "id": "md-03-inst", + "metadata": {}, + "source": [ + "---\n", + "## 3. Exploración inicial\n", + "\n", + "Antes de analizar datos siempre hay que entender qué tenemos.\n", + "\n", + "**Celda 3a** — Imprime la información básica del DataFrame:\n", + "1. **Dimensiones** con [`.shape`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.shape.html)\n", + " — devuelve una tupla `(filas, columnas)`\n", + "2. **Nombres de columnas** con [`.columns.tolist()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.columns.html)\n", + "3. **Tipos de datos** con [`.dtypes`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dtypes.html)\n", + " — indica si cada columna es `int64`, `float64` o `object` (texto)\n", + "\n", + "**Celda 3b** — Obtén estadísticas descriptivas con\n", + "[`.describe()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.describe.html).\n", + "Fíjate en la media, desviación estándar, mínimo y máximo de cada columna numérica.\n", + "\n", + "**Celda 3c** — Verifica si hay valores nulos con\n", + "[`.isnull()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.isnull.html)\n", + "seguido de `.sum()`. En un dataset limpio todos los valores deben ser 0." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "code-03a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "........................................................................................................................\n", + " DIMENSIONES (COLUMNAS, FILAS):\n", + "\n", + "(1000, 6)\n", + "........................................................................................................................\n", + " \n", + " NOMBRE DE LAS COLUMNAS:\n", + "\n", + "['Name', 'Distance (ly)', 'Luminosity (L/Lo)', 'Radius (R/Ro)', 'Temperature (K)', 'Spectral Class']\n", + " \n", + "........................................................................................................................\n", + "\n", + " TIPO DE DATOS:\n", + "\n", + "Name object\n", + "Distance (ly) float64\n", + "Luminosity (L/Lo) float64\n", + "Radius (R/Ro) float64\n", + "Temperature (K) float64\n", + "Spectral Class object\n", + "dtype: object\n", + "........................................................................................................................\n" + ] + } + ], + "source": [ + "# Imprime: dimensiones, nombres de columnas y tipos de datos\n", + "# tu código aquí\n", + "\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "print( \"...\" * 40)\n", + "print( \" \" * 15 +\"DIMENSIONES (COLUMNAS, FILAS):\\n\" )\n", + "print(stars.shape)\n", + "print( \"...\" * 40)\n", + "print( \" \" * 40)\n", + "print( \" \" * 15 +\"NOMBRE DE LAS COLUMNAS:\\n\")\n", + "print(stars.columns.tolist())\n", + "print( \" \" * 40)\n", + "print( \"...\" * 40)\n", + "print(\"\\n \" + \" \" * 15 +\" TIPO DE DATOS:\\n\")\n", + "print(stars.dtypes)\n", + "print(\"...\" * 40)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "code-03b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + " ESTADÍSTICA DESCRIPTIVA DE LA DATASET: ESTRELLAS \n", + " \n", + " Distance (ly) Luminosity (L/Lo) Radius (R/Ro) \\\n", + " NÚMERO TOTAL DE REGISTROS 1000.000000 1000.000000 1000.000000 \n", + "PROMEDIO 295.505327 19644.909442 86.960696 \n", + "DESVIACIÓN ESTÁNDAR 541.478403 42223.595017 213.850005 \n", + "VALOR MÍNIMO 3.877798 -4.993141 0.068087 \n", + "CUARTIL 1 (25%) 11.716853 10.441039 1.664479 \n", + "MEDIANA (50%) 52.031435 171.097809 5.845444 \n", + "CUARTIL 3 (75%) 322.865874 10500.577117 33.719778 \n", + "VALOR MÁXIMO 2600.490723 196004.854081 887.097936 \n", + "\n", + " Temperature (K) \n", + " NÚMERO TOTAL DE REGISTROS 1000.000000 \n", + "PROMEDIO 9983.486779 \n", + "DESVIACIÓN ESTÁNDAR 7906.973529 \n", + "VALOR MÍNIMO 2750.183163 \n", + "CUARTIL 1 (25%) 3940.020856 \n", + "MEDIANA (50%) 7379.007975 \n", + "CUARTIL 3 (75%) 12055.975095 \n", + "VALOR MÁXIMO 28044.279272 \n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + " LISTA DE DATOS EXTRAÑOS \n", + " \n", + " Total de Datos Extraños por ser Negativos: 88 \n" + ] + }, + { + "data": { + "text/html": [ + "
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NameDistance (ly)Luminosity (L/Lo)Radius (R/Ro)Temperature (K)Spectral Class
4Barnard's Star5.902392-1.4964860.1923593130.602069M4Ve
28Alpha Centauri B3.925939-2.5124240.8971565292.710092K1V
29Ross 1549.584936-0.2622130.0981502788.607876M3.5V
38Barnard's Star6.274309-4.6125040.2714203164.713490M4Ve
49Barnard's Star5.563557-3.4589550.1495363115.066681M4Ve
.....................
941Wolf 3597.509381-4.9350530.2081092841.890593M6V
944Rigil Kentaurus4.770200-1.1625151.2118415768.601304G2V
994Barnard's Star5.938952-3.1722540.2666883121.980225M4Ve
995Wolf 3597.455715-4.4351010.0680872774.148300M6V
998Alpha Centauri B4.044364-4.5490880.9391915286.304304K1V
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88 rows × 6 columns

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" + ], + "text/plain": [ + " Name Distance (ly) Luminosity (L/Lo) Radius (R/Ro) \\\n", + "4 Barnard's Star 5.902392 -1.496486 0.192359 \n", + "28 Alpha Centauri B 3.925939 -2.512424 0.897156 \n", + "29 Ross 154 9.584936 -0.262213 0.098150 \n", + "38 Barnard's Star 6.274309 -4.612504 0.271420 \n", + "49 Barnard's Star 5.563557 -3.458955 0.149536 \n", + ".. ... ... ... ... \n", + "941 Wolf 359 7.509381 -4.935053 0.208109 \n", + "944 Rigil Kentaurus 4.770200 -1.162515 1.211841 \n", + "994 Barnard's Star 5.938952 -3.172254 0.266688 \n", + "995 Wolf 359 7.455715 -4.435101 0.068087 \n", + "998 Alpha Centauri B 4.044364 -4.549088 0.939191 \n", + "\n", + " Temperature (K) Spectral Class \n", + "4 3130.602069 M4Ve \n", + "28 5292.710092 K1V \n", + "29 2788.607876 M3.5V \n", + "38 3164.713490 M4Ve \n", + "49 3115.066681 M4Ve \n", + ".. ... ... \n", + "941 2841.890593 M6V \n", + "944 5768.601304 G2V \n", + "994 3121.980225 M4Ve \n", + "995 2774.148300 M6V \n", + "998 5286.304304 K1V \n", + "\n", + "[88 rows x 6 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# Obtén el resumen estadístico de las columnas numéricas\n", + "# tu código aquí\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "resumen = stars.describe()\n", + "resumen = resumen.rename(index={\n", + " \"count\" : \" NÚMERO TOTAL DE REGISTROS\",\n", + " \"mean\" : \"PROMEDIO\", \n", + " \"std\" : \"DESVIACIÓN ESTÁNDAR\",\n", + " \"min\" : \"VALOR MÍNIMO\",\n", + " \"max\" : \"VALOR MÁXIMO\", \n", + " \"25%\" : \"CUARTIL 1 (25%)\", \n", + " \"50%\" : \"MEDIANA (50%)\",\n", + " \"75%\" : \"CUARTIL 3 (75%)\",\n", + " \"mod\" : \"MODA\", \n", + "})\n", + "print( \"----\" * 40)\n", + "print(\" \" * 15 + \"ESTADÍSTICA DESCRIPTIVA DE LA DATASET: ESTRELLAS \\n \")\n", + "print(resumen)\n", + "\n", + "\n", + "print( \"----\" * 40)\n", + "a = stars[stars['Luminosity (L/Lo)'] < 0] # explica por que en el valor mínimo se obtuvo un valor negativo\n", + "\n", + "print(\" \" * 15 + \"LISTA DE DATOS EXTRAÑOS \\n \")\n", + "print(f\" Total de Datos Extraños por ser Negativos: {len(a)} \")\n", + "display(a)\n", + "print( \"----\" * 40)\n" + ] + }, + { + "cell_type": "markdown", + "id": "217f2f7b", + "metadata": {}, + "source": [ + "En términos físicos, la luminosidad mide la cantidad de energía que emite una estrella por segundo. Al ser una medida de energía emitida, no puede ser negativa. Por eso se adjunta " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "code-03c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Name 0\n", + "Distance (ly) 0\n", + "Luminosity (L/Lo) 0\n", + "Radius (R/Ro) 0\n", + "Temperature (K) 0\n", + "Spectral Class 0\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "# Cuenta los valores nulos por columna\n", + "# tu código aquí\n", + "\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "a = stars.isnull().sum()\n", + "print(a)" + ] + }, + { + "cell_type": "markdown", + "id": "md-04-inst", + "metadata": {}, + "source": [ + "---\n", + "## 4. Distribución por tipo de estrella\n", + "\n", + "**Celda 4a** — Usa\n", + "[`.value_counts()`](https://pandas.pydata.org/docs/reference/api/pandas.Series.value_counts.html)\n", + "sobre la columna `'Spectral Class'` para contar cuántas estrellas hay de cada tipo.\n", + "Guarda el resultado en una variable llamada `conteo`.\n", + "\n", + "> Pista: accede a una columna del DataFrame con `df['nombre_columna']`, que devuelve una **Serie**.\n", + "> `.value_counts()` es un método de Series.\n", + "\n", + "**Celda 4b** — Crea una **gráfica de barras** de `conteo`:\n", + "1. La línea `plt.figure(figsize=(8, 4))` ya está incluida — no la borres\n", + "2. Llama [`.plot(kind='bar')`](https://pandas.pydata.org/docs/reference/api/pandas.Series.plot.html)\n", + " sobre `conteo`, usando `color='steelblue'` y `edgecolor='black'`\n", + "3. Agrega título con `plt.title(...)`, etiquetas con `plt.xlabel(...)` y `plt.ylabel(...)`\n", + "4. Rota las etiquetas del eje X: `plt.xticks(rotation=30, ha='right')`" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "code-04a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Spectral Class\n", + "A7V 74\n", + "A1V 73\n", + "A9II 48\n", + "M3.5V 45\n", + "B1III 45\n", + "M2Iab 44\n", + "G8III 39\n", + "M4Ve 38\n", + "A0V 38\n", + "K1.5III 38\n", + "B2III 37\n", + "M7IIIe 37\n", + "B0Ia 36\n", + "G2V 36\n", + "F7Ib 35\n", + "B1III-IV 32\n", + "A3V 31\n", + "F5IV-V 30\n", + "B0.5IV 30\n", + "B6Vep 29\n", + "M2.1V 27\n", + "M1.5Iab 26\n", + "B7V 26\n", + "A2Ia 25\n", + "K1V 24\n", + "M6V 22\n", + "K5III 18\n", + "B8Ia 17\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "# Cuenta las estrellas por tipo y guarda el resultado en 'conteo'\n", + "# tu código aquí\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "conteo = stars[\"Spectral Class\"].value_counts() \n", + "print(conteo)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "code-04b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 4))\n", + "\n", + "# Crea la gráfica de barras con conteo.plot(kind='bar', color=..., edgecolor=...)\n", + "# tu código aquí\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "conteo = stars[\"Spectral Class\"].value_counts() \n", + "conteo.plot(kind='bar', color='steelblue', edgecolor='black')\n", + "\n", + "\n", + "\n", + "# Agrega título y etiquetas de ejes\n", + "# tu código aquí\n", + "\n", + "plt.title(\"DISTRIBUCIÓN DE ESTRELLAS POR CLASE ESPECTRAL\")\n", + "plt.xlabel(\"CLASE ESPECTRAL\")\n", + "plt.ylabel(\"No. DE ESTRELLAS\")\n", + "plt.xticks(rotation=30, ha='right')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "md-05-inst", + "metadata": {}, + "source": [ + "---\n", + "## 5. Temperatura por tipo de estrella\n", + "\n", + "En esta sección calcularás la temperatura media de dos formas distintas para comparar.\n", + "\n", + "**Celda 5a — Ciclo `for` (enfoque manual):**\n", + "\n", + "Primero filtra el DataFrame para obtener solo las estrellas de tipo `'A7V'`:\n", + "```python\n", + "filtrado = stars[stars['Spectral Class'] == tipo_objetivo]\n", + "```\n", + "Luego recorre la columna `'Temperature (K)'` del DataFrame filtrado con un `for`,\n", + "acumula la `suma` y el `conteo` (`n`), y calcula la media como `suma / n`.\n", + "\n", + "Consulta [cómo filtrar un DataFrame por valor](https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html)\n", + "si necesitas orientación sobre la sintaxis de filtrado.\n", + "\n", + "**Celda 5b — Pandas `.groupby()` (enfoque vectorizado):**\n", + "\n", + "Usa [`.groupby()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)\n", + "para calcular la media de **todas las clases a la vez** en una sola línea:\n", + "```python\n", + "df.groupby('columna_categorica')['columna_numerica'].mean()\n", + "```\n", + "Ordena de mayor a menor con `.sort_values(ascending=False)`. Guarda en `temp_por_tipo`.\n", + "Compara el resultado de `'A7V'` con el valor que obtuviste con el `for`.\n", + "\n", + "**Celda 5c — Boxplot:**\n", + "\n", + "Usa [`sns.boxplot()`](https://seaborn.pydata.org/generated/seaborn.boxplot.html) con\n", + "`data=stars`, `x='Spectral Class'`, `y='Temperature (K)'`, `order=orden`." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "code-05a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Name Distance (ly) Luminosity (L/Lo) Radius (R/Ro) Temperature (K) \\\n", + "0 Altair 16.594171 9.979192 1.632650 7509.294247 \n", + "11 Altair 16.324632 10.457079 1.638568 7554.538238 \n", + "32 Altair 16.977835 8.978259 1.681768 7546.776074 \n", + "41 Altair 16.481724 10.654505 1.697977 7557.224465 \n", + "82 Altair 17.004753 15.509654 1.574136 7589.018862 \n", + ".. ... ... ... ... ... \n", + "974 Altair 16.502834 15.008535 1.537144 7531.390715 \n", + "984 Altair 17.154742 6.975772 1.548312 7537.641975 \n", + "989 Altair 17.111919 10.391013 1.609587 7504.094813 \n", + "990 Altair 16.874372 5.998471 1.559794 7561.789883 \n", + "993 Altair 17.121544 11.776187 1.635310 7555.418080 \n", + "\n", + " Spectral Class \n", + "0 A7V \n", + "11 A7V \n", + "32 A7V \n", + "41 A7V \n", + "82 A7V \n", + ".. ... \n", + "974 A7V \n", + "984 A7V \n", + "989 A7V \n", + "990 A7V \n", + "993 A7V \n", + "\n", + "[74 rows x 6 columns]\n", + "MEDIA DE TEMPERATURAS: A7V : 7550.178312906775 K\n", + "TEMPERATURA MEDIA (for loop) : 7,550.2 K\n" + ] + } + ], + "source": [ + "# ── Enfoque 1: ciclo for ──────────────────────────────────────────────────────\n", + "tipo_objetivo = 'A7V'\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "# Paso 1: filtra el DataFrame para obtener solo las estrellas de tipo_objetivo (clase espectral)\n", + "# filtrado = ...\n", + "# tu código aquí\n", + "filtrado = stars[stars[\"Spectral Class\"] == tipo_objetivo ]\n", + "print(filtrado)\n", + "\n", + "# Paso 2: recorre filtrado['Temperature (K)'] con un for\n", + "# acumula la suma y el conteo (n)\n", + "# tu código aquí\n", + "suma_temperaturas = 0\n", + "conteo_n = 0\n", + "for temperaturas in filtrado['Temperature (K)']:\n", + " suma_temperaturas += temperaturas\n", + " conteo_n += 1\n", + "\n", + "# Calcula la media y guárdala en media_manual\n", + "# media_manual = suma / n\n", + "# tu código aquí\n", + "if conteo_n > 0:\n", + " media_manual = suma_temperaturas / conteo_n\n", + " print(f\"MEDIA DE TEMPERATURAS: {tipo_objetivo} : { media_manual } K\")\n", + "else:\n", + " print(\"DATOS NO EXISTENTES\")\n", + "\n", + "\n", + "print(f'TEMPERATURA MEDIA (for loop) : {media_manual:,.1f} K')" + ] + }, + { + "cell_type": "markdown", + "id": "03533f36", + "metadata": {}, + "source": [ + "Comparación: for vs. pandas:\n", + "\n", + "Con el ciclo for calculaste la media de una sola clase espectral en varias líneas. Ahora verás cómo pandas obtiene la media de todas las clases a la vez en una sola línea.\n", + "\n", + "Cuando termines la celda 5b, verifica que el resultado de A7V coincida con el valor que obtuviste con el for." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "4j2wkkt78ju", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "Temperatura promedio por clase espectral (K):\n", + "Spectral Class\n", + "B0.5IV 28001.166630\n", + "B0Ia 27502.303666\n", + "B1III-IV 25403.170510\n", + "B1III 25001.131122\n", + "B2III 22600.139741\n", + "B6Vep 15003.610593\n", + "B7V 12462.119029\n", + "B8Ia 12092.293145\n", + "A1V 10136.022204\n", + "A0V 9607.458129\n", + "A3V 8584.693288\n", + "A2Ia 8516.840653\n", + "A7V 7550.178313\n", + "A9II 7349.223744\n", + "F5IV-V 6520.419327\n", + "F7Ib 6020.393400\n", + "G2V 5797.996506\n", + "K1V 5261.645715\n", + "G8III 4939.733287\n", + "K1.5III 4280.090548\n", + "K5III 3923.614977\n", + "M2Iab 3502.196868\n", + "M1.5Iab 3499.130207\n", + "M2.1V 3408.914115\n", + "M4Ve 3136.076002\n", + "M7IIIe 2914.515688\n", + "M3.5V 2802.627176\n", + "M6V 2795.196060\n", + "Name: Temperature (K), dtype: float64\n", + "\n", + "RESULTADO PANDAS ('A7V'): 7,550.178313 K\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + " COMPARACIÓN DE LA MEDIA DE LOS VALORES 'A7V' (PANDAS, CICLO FOR) \n", + " \n", + "RESULTADO PANDAS ('A7V'): 7,550.2 K\n", + "RESULTADO MANUAL('A7V'): 7,550.2 K\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# ── Enfoque 2: pandas groupby ─────────────────────────────────────────────────\n", + "# Calcula la temperatura promedio por tipo con groupby\n", + "# Ordena de mayor a menor y guarda en temp_por_tipo\n", + "# tu código aquí\n", + "print( \"----\" * 40)\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "temp_por_tipo = stars.groupby('Spectral Class')['Temperature (K)'].mean().sort_values(ascending=False)\n", + "print('Temperatura promedio por clase espectral (K):')\n", + "print(temp_por_tipo)\n", + "print()\n", + "valor_pd = temp_por_tipo['A7V']\n", + "print(f\"RESULTADO PANDAS ('A7V'): {valor_pd:,.6f} K\")\n", + "\n", + "\n", + "# Imprime una línea de verificación comparando media_manual con el valor de groupby para 'A7V'\n", + "# tu código aquí\n", + "tipo_objetivo = 'A7V'\n", + "filtrado = stars[stars[\"Spectral Class\"] == tipo_objetivo ]\n", + "suma_temperaturas = 0\n", + "conteo_n = 0\n", + "for temperaturas in filtrado['Temperature (K)']:\n", + " suma_temperaturas += temperaturas\n", + " conteo_n += 1\n", + "\n", + "# Calcula la media y guárdala en media_manual\n", + "# media_manual = suma / n\n", + "# tu código aquí\n", + "if conteo_n > 0:\n", + " media_manual = suma_temperaturas / conteo_n\n", + " \n", + "else:\n", + " print(\"DATOS NO EXISTENTES\")\n", + "print( \"----\" * 40)\n", + "print(\" \" * 15 + \"COMPARACIÓN DE LA MEDIA DE LOS VALORES 'A7V' (PANDAS, CICLO FOR) \\n \")\n", + "valor_pd = temp_por_tipo['A7V']\n", + "print(f\"RESULTADO PANDAS ('A7V'): {valor_pd:,.1f} K\")\n", + "print(f\"RESULTADO MANUAL('A7V'): {media_manual:,.1f} K\")\n", + "print( \"----\" * 40)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "code-05b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "stars = pd.read_csv('star_dataset.csv')\n", + "plt.figure(figsize=(9, 5))\n", + "orden = temp_por_tipo.index # orden de mayor a menor temperatura\n", + "\n", + "# Crea el boxplot con sns.boxplot(data=..., x=..., y=..., order=...)\n", + "# tu código aquí\n", + "sns.boxplot(data=stars, x='Spectral Class', y='Temperature (K)', order=orden)\n", + "\n", + "\n", + "# Agrega título y etiquetas de ejes\n", + "# tu código aquí\n", + "plt.title(\"ORDENAMIENTO DE MAYOR A MENOR (CLASE EXPECTRAL, TEMPERATURA)\")\n", + "plt.xlabel(\"CLASE ESPECTRAL\")\n", + "plt.ylabel('Temperature (K)')\n", + "\n", + "plt.xticks(rotation=30, ha='right')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "md-06-inst", + "metadata": {}, + "source": [ + "---\n", + "## 6. Luminosidad vs Temperatura\n", + "\n", + "La luminosidad varía en muchos órdenes de magnitud (de 0.00008 a 849 420 L/Lo),\n", + "por eso necesitamos **escala logarítmica** en el eje Y.\n", + "\n", + "Usa [`sns.scatterplot()`](https://seaborn.pydata.org/generated/seaborn.scatterplot.html)\n", + "con los siguientes parámetros (revisa la documentación para entender cada uno):\n", + "- `data=stars` — el DataFrame\n", + "- `x='Temperature (K)'` — temperatura en el eje X\n", + "- `y='Luminosity (L/Lo)'` — luminosidad en el eje Y\n", + "- `hue='Spectral Class'` — colorea los puntos según la clase espectral\n", + "- `style='Spectral Class'` — usa un marcador diferente por clase espectral\n", + "- `s=60` — tamaño de los puntos\n", + "\n", + "Después de crear el plot, aplica escala logarítmica al eje Y con\n", + "[`plt.yscale('log')`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.yscale.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "code-06", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "stars = pd.read_csv('star_dataset.csv')\n", + "plt.figure(figsize=(9, 6))\n", + "\n", + "# Crea el scatter plot con sns.scatterplot(...)\n", + "# tu código aquí\n", + "sns.scatterplot(data=stars, \n", + " x='Temperature (K)', \n", + " y='Luminosity (L/Lo)', \n", + " hue='Spectral Class', \n", + " style='Spectral Class', \n", + " s=60)\n", + "\n", + "\n", + "# Aplica escala logarítmica al eje Y\n", + "# tu código aquí\n", + "plt.yscale('log')\n", + "\n", + "# Agrega título, etiquetas de ejes y leyenda\n", + "# tu código aquí\n", + "\n", + "plt.legend(title='Spectral Class', bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0.)\n", + "\n", + "plt.title('LUMINOSIDAD VS. TEMPERATURA POR ESCALA ESPECTRAL ')\n", + "plt.xlabel('Temperatura (K)')\n", + "plt.ylabel('Luminosidad (L/Lo)')\n", + "\n", + "plt.xticks(rotation=45, ha='right') \n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "md-07-inst", + "metadata": {}, + "source": [ + "---\n", + "## 7. Estadísticas con NumPy\n", + "\n", + "NumPy opera sobre **arrays completos** sin ciclos `for`. Por ejemplo,\n", + "`np.mean(arr)` calcula la media de todos los elementos de `arr` de una sola vez.\n", + "\n", + "**Celda 7a** — Extrae los arrays con `.values` y calcula estadísticas:\n", + "- Extrae: `temperaturas = stars['Temperature (K)'].values`\n", + "- Extrae: `radios = stars['Radius (R/Ro)'].values`\n", + "- Verifica el tipo con `type(temperaturas)`\n", + "- Calcula usando estas funciones de [`numpy.statistics`](https://numpy.org/doc/stable/reference/routines.statistics.html):\n", + " - [`np.mean(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.mean.html) — media\n", + " - [`np.median(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.median.html) — mediana\n", + " - [`np.std(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.std.html) — desviación estándar\n", + " - [`np.min(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.amin.html) y [`np.max(arr)`](https://numpy.org/doc/stable/reference/generated/numpy.amax.html)\n", + "\n", + "**Celda 7b** — Percentiles y conversión vectorizada:\n", + "- Usa [`np.percentile(arr, q)`](https://numpy.org/doc/stable/reference/generated/numpy.percentile.html)\n", + " con `q=[25, 50, 75, 90]` para calcular los 4 percentiles de `radios` de una vez\n", + "- Convierte `temperaturas` de Kelvin a Celsius **sin usar ciclo `for`**:\n", + " `celsius = temperaturas - 273.15` (operación vectorizada)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "code-07a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "TIPO DE VARIABLE\n", + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + " ESTADÍSTICA BÁSICA\n", + "\n", + " a) Media de Temperaturas: 9983.49K\n", + "\n", + " b) Mediana de las Temperaturas: 7379.01K\n", + "\n", + " c) Desviación Estándar de las Temperaturas: 7903.02K\n", + "\n", + " d) Mínimo de Temperaturas: 2750.18K\n", + "\n", + " e) Máximo de Temperaturas: 28044.28K\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# Extrae los arrays NumPy con .values\n", + "# tu código aquí\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "temperaturas = stars['Temperature (K)'].values\n", + "radios = stars['Radius (R/Ro)'].values\n", + "\n", + "\n", + "\n", + "# Imprime el tipo del array\n", + "# tu código aquí\n", + "print( \"----\" * 40)\n", + "print(\"TIPO DE VARIABLE\")\n", + "print(type(temperaturas))\n", + "\n", + "# Calcula e imprime: media, mediana, desv. estándar, mínima y máxima de temperaturas\n", + "# tu código aquí\n", + "\n", + "print( \"----\" * 40)\n", + "print(\" \" * 15 + \"ESTADÍSTICA BÁSICA\")\n", + "print(f\"\\n a) Media de Temperaturas: {np.mean(temperaturas):.2f}K\")\n", + "print(f\"\\n b) Mediana de las Temperaturas: {np.median(temperaturas):.2f}K\")\n", + "print(f\"\\n c) Desviación Estándar de las Temperaturas: {np.std(temperaturas):.2f}K\")\n", + "print(f\"\\n d) Mínimo de Temperaturas: {np.min(temperaturas):.2f}K\")\n", + "print(f\"\\n e) Máximo de Temperaturas: {np.max(temperaturas):.2f}K\")\n", + "print( \"----\" * 40)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "code-07b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + " PERCENTILES\n", + " \n", + " Percentil 25: 1.66\n", + " Percentil 50: 5.85\n", + " Percentil 75: 33.72\n", + " Percentil 90: 369.93\n", + " \n", + "--------------------------------------------------------------------------------\n", + " \n", + " COMPARACIÓN DE LAS TEMPERATUARA DE LAS PRIMERAS 5 ESTRELLAS (K = °C)\n", + "\n", + " 1° Temperatura: 7509.294 K = 7236.144 °C\n", + "\n", + " 2° temperatura: 8503.285 K = 8230.135 °C\n", + "\n", + " 3° temperatura: 3165.960 K = 2892.810 °C\n", + "\n", + " 4° temperatura: 6048.327 K = 5775.177 °C\n", + "\n", + " 5° temperatura: 3130.602 K = 2857.452 °C\n", + "--------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "niveles = [25, 50, 75, 90]\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "temperaturas = stars['Temperature (K)'].values\n", + "radios = stars['Radius (R/Ro)'].values\n", + "\n", + "# Calcula los percentiles del radio estelar con np.percentile(radios, niveles)\n", + "# tu código aquí\n", + "p = np.percentile(radios, niveles)\n", + "\n", + "# Imprime cada percentil usando un ciclo for y zip(niveles, p)\n", + "# tu código aquí\n", + "print( \"--\" * 40)\n", + "print(\" \" * 15 + \"PERCENTILES\")\n", + "print( \" \" * 40)\n", + "for nivel, valor_p in zip(niveles, p):\n", + " print(f\" Percentil {nivel}: {valor_p:.2f}\")\n", + " \n", + "print( \" \" * 40)\n", + "# Convierte temperaturas de Kelvin a Celsius de forma vectorizada (sin for)\n", + "# celsius = ...\n", + "# tu código aquí\n", + "print( \"--\" * 40)\n", + "celcius = temperaturas - 273.15\n", + "\n", + "# Imprime las primeras 5 temperaturas en K y en C para comparar\n", + "# (usa np.round para redondear a 1 decimal)\n", + "# tu código aquí\n", + "print( \" \" * 40)\n", + "print(\" \" * 2 + \"COMPARACIÓN DE LAS TEMPERATUARA DE LAS PRIMERAS 5 ESTRELLAS (K = °C)\")\n", + "print(f\"\\n 1° Temperatura: {temperaturas[0]:.3f} K = {celcius[0]:.3f} °C\")\n", + "print(f\"\\n 2° temperatura: {temperaturas[1]:.3f} K = {celcius[1]:.3f} °C\")\n", + "print(f\"\\n 3° temperatura: {temperaturas[2]:.3f} K = {celcius[2]:.3f} °C\")\n", + "print(f\"\\n 4° temperatura: {temperaturas[3]:.3f} K = {celcius[3]:.3f} °C\")\n", + "print(f\"\\n 5° temperatura: {temperaturas[4]:.3f} K = {celcius[4]:.3f} °C\")\n", + "\n", + "print( \"--\" * 40)\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "md-08-inst", + "metadata": {}, + "source": [ + "---\n", + "## 8. Diagrama Hertzsprung-Russell\n", + "\n", + "El diagrama H-R es el gráfico más importante en astronomía estelar.\n", + "Relaciona temperatura con luminosidad y revela la estructura evolutiva de las estrellas.\n", + "\n", + "Este diagrama tiene **dos particularidades** que debes implementar:\n", + "1. **Ambos ejes logarítmicos**: `plt.xscale('log')` y `plt.yscale('log')`\n", + "2. **Eje X invertido** (las más calientes a la izquierda): `plt.gca().invert_xaxis()`\n", + "\n", + "**Estructura del código** (el inicio ya está dado, completa las partes marcadas):\n", + "\n", + "```python\n", + "tipos = stars['Spectral Class'].unique()\n", + "colores = sns.color_palette('tab10', len(tipos)) # paleta de colores\n", + "mapa = dict(zip(tipos, colores)) # tipo -> color\n", + "\n", + "for tipo, grupo in stars.groupby('Spectral Class'):\n", + " plt.scatter(grupo['Temperature (K)'],\n", + " grupo['Luminosity (L/Lo)'],\n", + " label=tipo, color=mapa[tipo], s=40, alpha=0.8)\n", + "```\n", + "\n", + "Parámetros de [`plt.scatter()`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html)\n", + "que debes entender: `x`, `y`, `label`, `color`, `s` (tamaño), `alpha` (transparencia)." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "code-08", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "stars = pd.read_csv('star_dataset.csv')\n", + "tipos = stars['Spectral Class'].unique()\n", + "colores = sns.color_palette('tab10', len(tipos))\n", + "mapa = dict(zip(tipos, colores))\n", + "\n", + "plt.figure(figsize=(10, 7))\n", + "sns.set_style(\"whitegrid\")\n", + "\n", + "\n", + "# Itera con stars.groupby('Spectral Class') y crea un plt.scatter() por cada tipo\n", + "# tu código aquí\n", + "for tipo, grupo in stars.groupby('Spectral Class'):\n", + " plt.scatter(grupo['Temperature (K)'], grupo['Luminosity (L/Lo)'], label= tipo , color=mapa[tipo], s=50, alpha=1.0)\n", + "\n", + "\n", + "orden_clases = stars.groupby('Spectral Class')['Temperature (K)'].mean().sort_values(ascending=False).index\n", + "\n", + "# Aplica escala logarítmica en ambos ejes (xscale y yscale)\n", + "# tu código aquí\n", + "\n", + "plt.yscale(\"log\")\n", + "\n", + "# Invierte el eje X con plt.gca().invert_xaxis()\n", + "# tu código aquí\n", + "plt.gca().invert_xaxis()\n", + "\n", + "# Agrega: título, etiquetas de ejes, leyenda y grid\n", + "# tu código aquí\n", + "\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n", + "\n", + "plt.title(\"DIAGRAMAS H-R, RELACIÓN TEMPERATURA-LUMINOSIDAD\")\n", + "plt.xlabel(\"Temperatura (K)\")\n", + "plt.ylabel(\"Luminosidad (L/Lo)\")\n", + "\n", + "plt.xticks(rotation=45, ha='right') \n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "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": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/TAREASSS/practicados/pyproject.toml b/TAREASSS/practicados/pyproject.toml new file mode 100644 index 0000000..431f49e --- /dev/null +++ b/TAREASSS/practicados/pyproject.toml @@ -0,0 +1,20 @@ +[project] +name = "analisis-datos" +version = "0.1.0" +description = "Análisis de datos con Python usando Poetry" +authors = [ + {name = "F12 Programacion"} +] +requires-python = ">=3.11" +dependencies = [ + "numpy (>=2.4.3,<3.0.0)", + "pandas (>=3.0.1,<4.0.0)", + "matplotlib (>=3.10.8,<4.0.0)", + "seaborn (>=0.13.2,<0.14.0)", + "jupyter (>=1.1.1,<2.0.0)" +] + + +[build-system] +requires = ["poetry-core>=2.0.0,<3.0.0"] +build-backend = "poetry.core.masonry.api" diff --git a/TAREASSS/practicados/star_dataset.csv b/TAREASSS/practicados/star_dataset.csv new file mode 100644 index 0000000..a6ba6ab --- /dev/null +++ b/TAREASSS/practicados/star_dataset.csv @@ -0,0 +1,1001 @@ +Name,Distance (ly),Luminosity (L/Lo),Radius (R/Ro),Temperature (K),Spectral Class +Altair,16.59417123707501,9.979192445462427,1.6326504020392336,7509.2942473638805,A7V +Deneb,2600.4907228269035,196002.62785575088,202.970526216013,8503.284796430231,A2Ia +Barnard's Star,6.052616358474774,4.893715684187678,0.22271101197217258,3165.9596392449016,M4Ve +Polaris,322.6010015236985,2196.241933606857,37.546813013740554,6048.326914763769,F7Ib +Barnard's Star,5.902391663373468,-1.4964862523273919,0.1923594077740656,3130.6020692351385,M4Ve +Sirius,8.370045208990435,25.62536254052454,1.7376163136938274,9903.971703749557,A1V +Fomalhaut,25.42303189349805,19.314803198342823,1.7634732970155798,8541.195355142636,A3V +Capella,42.56957816453575,77.46654686278187,12.015077077611398,4979.492462093153,G8III +Capella,43.260858866113374,76.75397466518646,11.926446347832353,4907.779548870081,G8III +Bellatrix,239.86410710431278,6399.344000391582,5.751378540226974,22573.286177203267,B2III +Castor,51.57021005818902,59.93364175572123,2.317294377415115,10334.140338882178,A1V +Altair,16.324631801826886,10.45707878952248,1.6385681357721884,7554.538238404283,A7V +Bellatrix,240.396008875072,6396.460553762988,5.702796835694758,22559.156170298847,B2III +Hadar,349.6510239295208,49995.691816097984,9.029686061904163,24977.138850418167,B1III +Betelgeuse,642.4036425044302,125997.20910038544,886.9436673356928,3472.6066444708454,M2Iab +Regulus,79.14834001968553,290.3679076360734,3.105720387017622,12478.930088621566,B7V +Regulus,78.8696975851003,292.77713927994625,3.2154445654455555,12493.540087369223,B7V +Mira,418.3541533597022,8700.111885115977,369.94647200932155,2929.214904446191,M7IIIe +Acrux,320.95215250850816,24995.853352250557,8.476536987947346,28016.58055121632,B0.5IV +Vega,25.40728672741662,45.02639994513558,2.3758185019255906,9589.720331802691,A0V +Betelgeuse,642.4501483192605,125997.82193788076,887.023713575872,3502.8036495770207,M2Iab +Lalande 21185,8.78389229917336,0.9306154071042486,0.36997218014157396,3420.0741335824846,M2.1V +Hadar,350.3393282903268,50002.885000950024,9.030531083873981,24972.4633828412,B1III +Regulus,79.23841041664882,283.3313068914609,3.1941978242994673,12471.609230270555,B7V +Regulus,78.51275351482992,289.1046797165257,3.093785398680339,12469.726650284285,B7V +Bellatrix,239.75802627864445,6402.532053023128,5.807903991855439,22628.845403711555,B2III +Sirius,8.997561546118135,27.612290603452433,1.7178118641359001,9965.640231971809,A1V +Arcturus,36.39116253215857,169.47569492442693,25.34766748214963,4306.771078623507,K1.5III +Alpha Centauri B,3.9259386007692,-2.5124238815560895,0.8971557531469336,5292.710091689776,K1V +Ross 154,9.584936005981074,-0.2622128873565414,0.09815009889642284,2788.6078755716367,M3.5V +Betelgeuse,642.1986705590544,125995.18267169305,887.0568849865539,3502.1107945651283,M2Iab +Sirius,8.790939416886154,29.685053444507503,1.778145234597857,9959.636695100216,A1V +Altair,16.97783483438638,8.978258642897934,1.6817677393535218,7546.77607407146,A7V +Regulus,79.37549284128201,287.2472405244145,3.0775092724640816,12466.383842658091,B7V +Lalande 21185,7.922999488356227,2.681120395944034,0.46518759639849927,3394.5169092653987,M2.1V +Canopus,309.65595488089645,10500.304364175136,70.9940908831198,7322.086648344391,A9II +Procyon,11.361710078531987,11.09891870112007,2.1252624415131467,6522.987352867349,F5IV-V +Capella,43.239838363858276,78.47758735453107,12.04168320043013,4903.922634597786,G8III +Barnard's Star,6.274309005192113,-4.612503609841812,0.27142011745919525,3164.7134904158015,M4Ve +Arcturus,36.23014602060893,167.67967953475542,25.372980607290035,4285.208656712724,K1.5III +Canopus,309.89434307337245,10497.613821111012,70.98540952072986,7395.822565906416,A9II +Altair,16.481723812762002,10.654505105436737,1.6979772339329253,7557.224465410894,A7V +Polaris,323.4890826975579,2197.4846549730764,37.5928890041696,6003.417411946257,F7Ib +Barnard's Star,6.210116619871699,3.922475110995184,0.20022109890664536,3104.152562146686,M4Ve +Castor,52.3208563941243,52.46460236565418,2.3580848977347673,10286.21254520186,A1V +Castor,51.687127344898485,57.564078324527344,2.4793370398018366,10314.20142645494,A1V +Bellatrix,240.3505524329565,6403.480526616087,5.803192364925976,22632.467298684776,B2III +Polaris,323.4772161689265,2202.925953103468,37.430399789571695,5986.795558342898,F7Ib +Castor,52.31788545862922,54.098680128048926,2.3832137598247782,10264.67768772168,A1V +Barnard's Star,5.5635565189978085,-3.4589548280686215,0.14953611720828194,3115.0666806206127,M4Ve +Vega,25.02265608280089,43.21333423901169,2.4233371866214184,9631.823941413108,A0V +Wolf 359,8.040322484690764,3.518164733709185,0.2159582033041309,2786.809004459221,M6V +Regulus,79.457885087804,284.3075843561486,3.1848771847975916,12429.346874442883,B7V +Polaris,322.7090337593032,2201.1895342191797,37.558280857740726,5986.9294428280555,F7Ib +Antares,549.8094446057289,10004.888893429817,679.9451981136416,3503.22033874066,M1.5Iab +Regulus,78.84980314077531,283.1518425898611,3.1746164987628713,12414.98856094767,B7V +Acrux,320.53588050006994,25000.424960003416,8.30013131527051,27969.950394242773,B0.5IV +Alnilam,1999.5922378259786,53697.14296034396,32.48838765422113,27489.295431556606,B0Ia +Bellatrix,239.79573031543998,6401.125654435876,5.829045687901594,22632.624354404084,B2III +Hadar,349.8378240849879,50002.534526391784,8.94744777918556,25006.352397614006,B1III +Rigil Kentaurus,4.039455513108716,0.2620564024777934,1.3098243183774203,5823.975302815448,G2V +Vega,25.32510113537497,44.01956664255118,2.338790440069123,9648.560925173246,A0V +Alnilam,2000.3720438244097,53702.88073058354,32.357275804804594,27504.394522638224,B0Ia +Ross 154,9.93913784187845,-3.6471413928808327,0.23960331933197554,2767.195283308996,M3.5V +Vega,24.53113047299527,38.00891382001232,2.4294754005156816,9615.603301550884,A0V +Alnilam,2000.4789929236533,53701.23908573748,32.473046207098115,27463.288997937103,B0Ia +Castor,51.78867651717732,57.40613996148579,2.4323768195012194,10276.929953106777,A1V +Betelgeuse,642.1530702565499,126000.10150499652,886.9045632911253,3462.1435724115922,M2Iab +Alnilam,1999.880482110407,53695.72165124452,32.35326480955992,27490.40560120546,B0Ia +Bellatrix,240.30865829394963,6398.706399629343,5.727504552499588,22561.66109433901,B2III +Hadar,349.98072783140907,50000.69303998948,8.940299046830283,25043.812798420327,B1III +Barnard's Star,6.102187239521027,-0.6905551570802375,0.2265784314346753,3158.9781846800365,M4Ve +Barnard's Star,6.098594827311291,-3.3303098730228937,0.12291605693194557,3144.711153515979,M4Ve +Spica,250.4596817064251,21996.281907685272,7.481964414843237,25371.671565613717,B1III-IV +Deneb,2600.219144334045,195996.44277808978,203.09713635797436,8512.606175957664,A2Ia +Bellatrix,239.80471588472972,6400.473393470613,5.678484912182976,22628.27195885336,B2III +Fomalhaut,25.040609497955693,20.8052240111299,1.8933606922604622,8570.329145806458,A3V +Rigil Kentaurus,4.0542023166505174,5.3173565377787355,1.1417731281419847,5802.341326578711,G2V +Vega,25.145088627017778,35.23872230380841,2.3238256403915494,9614.217107611483,A0V +Canopus,309.5332875345678,10502.614219521176,71.06798014581466,7323.458433377934,A9II +Ross 154,9.61921476063749,-3.572031571628307,0.0978087615313327,2796.4035456819356,M3.5V +Betelgeuse,642.0262242230878,125998.59509722542,886.939182195466,3543.743725665777,M2Iab +Altair,17.004752557357463,15.509653584166163,1.5741356101164594,7589.01886160571,A7V +Procyon,10.930399214053109,8.573241191737985,1.9730986292291883,6548.089233804079,F5IV-V +Betelgeuse,642.1523114825334,125996.07898971764,886.9540167980291,3465.584906649717,M2Iab +Sirius,8.40488504234644,30.363211555162465,1.6975503190465253,9929.197486761122,A1V +Hadar,349.89703894408365,50003.29860598514,9.072118415520334,25020.059682136376,B1III +Arcturus,36.55940961200518,173.1186537350129,25.327584506221395,4293.232149954749,K1.5III +Ross 154,9.762760206254507,-0.22596537315790627,0.10437345974328288,2771.1757655682736,M3.5V +Alnilam,2000.4968087684344,53700.915273161765,32.32831679585277,27522.876506962413,B0Ia +Lalande 21185,8.539132551448054,-1.0184094871245468,0.3484419457491451,3396.2390187861406,M2.1V +Polaris,322.7829657396212,2197.4579935613187,37.56516587074775,6063.924563018583,F7Ib +Deneb,2599.541671531891,195998.9041368802,202.92157091325018,8503.08100456294,A2Ia +Vega,24.959311232910533,38.236535117812586,2.2955242719863977,9650.454657524815,A0V +Acrux,321.3978818285302,25002.451304950046,8.40073431108804,28000.481701856206,B0.5IV +Spica,250.3496408249325,22004.59479708713,7.48705009677652,25392.535590103464,B1III-IV +Capella,42.98764050809396,80.53611367039014,11.928552734356183,4936.351352278644,G8III +Altair,16.790538913857493,7.001586190285418,1.6190056898410539,7500.178549124861,A7V +Antares,550.3341533847138,9997.61131020143,680.029117220661,3516.839353089165,M1.5Iab +Capella,42.80414644083066,77.7419793077256,12.028719516305973,4937.014499217616,G8III +Wolf 359,7.987098773049044,-4.9033579226853,0.25746878785429467,2800.610804893218,M6V +Spica,250.1853696897831,22002.94677024924,7.329968093356848,25440.0209321473,B1III-IV +Acrux,320.5863393634801,24997.637919736644,8.310447834832418,27989.401366565075,B0.5IV +Mira,418.21532323512395,8697.78504078488,369.95011289757565,2955.942150604245,M7IIIe +Altair,17.114041468810747,7.967577662722815,1.6666693118467406,7589.938436857879,A7V +Betelgeuse,642.4002182709401,125995.07325895016,887.0102958691334,3481.0895756208192,M2Iab +Barnard's Star,6.066146574141507,-4.202962127667188,0.18560290038694682,3157.0629115253996,M4Ve +Achernar,144.41347739924058,3145.670632301648,9.151738144396306,14986.331180964033,B6Vep +Aldebaran,65.1820343113461,515.388476668136,44.22384644093751,3907.5149974842684,K5III +Antares,550.2111451274665,9999.116102372378,679.9672994027036,3549.315194904685,M1.5Iab +Hadar,349.8618670626462,50004.94895125297,8.924915431984006,24982.455998433135,B1III 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+Canopus,310.38085606498896,10500.157515440253,71.0379040360618,7384.192277896002,A9II +Altair,16.56786756498236,12.40258131836179,1.7124146821549155,7541.099897210852,A7V +Vega,25.009785593624624,42.61916652521285,2.2818813833402927,9628.22080590828,A0V +Barnard's Star,5.9787983279270085,2.2056294360251103,0.23891428085892485,3092.79861118838,M4Ve +Altair,16.546804536638334,11.60218007055768,1.6934309093439481,7578.471697374213,A7V +Ross 154,10.019988972108159,-0.1663578165081136,0.13158258065782635,2775.738098710297,M3.5V +Wolf 359,7.509380536781729,-4.935052823532525,0.20810934508720413,2841.8905930039778,M6V +Vega,24.803162930467373,41.495367850673475,2.38786013318031,9576.613622627168,A0V +Achernar,143.96622965338523,3150.4177861539374,9.275951222228185,14987.543585054453,B6Vep +Rigil Kentaurus,4.770200191851383,-1.162514750656564,1.2118414077332116,5768.601304291258,G2V +Altair,16.40951171231661,5.689354655912254,1.5920995590519407,7570.6134681372205,A7V +Antares,550.3810400697355,10002.393406132604,679.9207239313258,3533.7117081038605,M1.5Iab +Betelgeuse,642.9506962383331,126002.12138920804,886.9397509430192,3496.4048869047883,M2Iab +Sirius,8.153862547872297,24.17011927398116,1.6264437297161434,9984.21028478672,A1V +Procyon,11.533949262456211,10.77236733632881,2.097725912688288,6576.896042804675,F5IV-V +Ross 154,9.452177745877758,4.97274435570182,0.2634268679578863,2847.679081207114,M3.5V +Canopus,310.1036731709541,10496.109676275262,71.03399407670011,7363.545039774776,A9II +Aldebaran,64.67223541528165,518.712049240274,44.27113890209603,3880.5608200439647,K5III +Arcturus,36.906247750571204,172.01175458304988,25.400060086139366,4266.259021098308,K1.5III +Antares,549.9204623386546,9995.650236150686,680.0291187815914,3507.8845568389456,M1.5Iab +Lalande 21185,8.439597133532216,2.6433710990737387,0.4316383869316258,3365.5018448993296,M2.1V +Barnard's Star,6.187165630701091,4.956944233778123,0.1391027780853812,3145.3595033455963,M4Ve +Barnard's Star,5.621799128520018,3.7760022367690285,0.11319610617868339,3101.014047069934,M4Ve +Castor,51.71998908353143,53.15103578649683,2.321454068721126,10285.43180677566,A1V +Spica,250.40169225874862,22002.935830741622,7.418892400715696,25449.19666587807,B1III-IV +Acrux,320.8089994295918,25004.66014302425,8.301353337284787,28026.52454851035,B0.5IV +Spica,249.87806875875762,22003.593457133502,7.376203984645423,25412.12199277132,B1III-IV +Procyon,10.920471755944988,4.935287891672165,2.0737484014273155,6506.621798818398,F5IV-V +Canopus,309.78529838101207,10500.052904901882,71.02194935754189,7307.0967988732145,A9II +Altair,16.933845755838053,12.845622466715568,1.6974822956589117,7575.06541533419,A7V +Rigil Kentaurus,4.608424065033049,5.00472601467677,1.2929190926560636,5820.262364330082,G2V +Vega,24.65390624701162,41.61042724473538,2.429274022708076,9631.852917163242,A0V +Procyon,11.146881937080314,12.218862119908533,2.0247293672655777,6503.785589850387,F5IV-V +Arcturus,36.3693563748995,167.57770365212474,25.408263436517736,4290.82040736522,K1.5III +Canopus,310.35560420721504,10495.060417002824,71.02611198607457,7336.306359368458,A9II +Altair,16.70733129032844,9.554275564559855,1.5490181205144966,7517.666644153817,A7V +Hadar,350.47273331783754,50002.06465870077,8.940071689517074,25026.854869644325,B1III +Castor,51.94700108831837,51.51081940941651,2.303341519145927,10330.659987471341,A1V +Antares,549.8508372630931,10001.25706401134,680.0627912846477,3497.2764658101314,M1.5Iab +Altair,16.5028339871533,15.00853497168282,1.537143677100781,7531.390715153673,A7V +Ross 154,9.195617903458347,4.002523195559234,0.2331786819378468,2799.1300136375307,M3.5V +Capella,43.28991991209135,78.42806415411815,11.903537314056436,4891.016059207164,G8III +Castor,51.72532804583004,56.68355462603443,2.3156128851403235,10347.251771780268,A1V +Procyon,11.591330421111849,11.923795878328828,2.03384610933756,6515.942047780109,F5IV-V +Lalande 21185,8.703545490785453,3.2554514372729964,0.34371090213467714,3397.560024270192,M2.1V +Mira,417.5482494592996,8697.641481122131,370.0433728646599,2881.4840403317608,M7IIIe +Betelgeuse,642.896853479847,126004.68469642337,886.9879566164066,3511.823072543017,M2Iab +Betelgeuse,642.1716997467348,126001.69509064495,886.9888377279874,3479.277355062287,M2Iab +Lalande 21185,8.731732298435377,4.786238308759169,0.3347032306768086,3382.689040227199,M2.1V +Altair,17.15474181167015,6.975771781193093,1.5483116113750846,7537.641975099961,A7V +Vega,24.776635908276518,35.27153115019188,2.29544479238952,9616.074160949815,A0V +Mira,417.695788448091,8704.185431731014,370.0625415276317,2955.0490698875665,M7IIIe +Rigil Kentaurus,3.996321706725146,1.626551918000496,1.3046574245238987,5794.091339181414,G2V +Mira,418.47024286572923,8702.898481160859,370.0818555771332,2943.151168595254,M7IIIe +Altair,17.111919168917005,10.39101273787362,1.6095873161618377,7504.094812609862,A7V +Altair,16.874372018837036,5.998470578062234,1.5597935011539061,7561.789883494114,A7V +Deneb,2600.0030216501546,195995.4474828539,203.02756825887886,8518.964904807914,A2Ia +Rigel,860.4364002835275,120000.52031072113,78.80303528052436,12131.669640550605,B8Ia +Altair,17.12154390276971,11.776186568379096,1.635310418129086,7555.4180801050825,A7V +Barnard's Star,5.938951989691774,-3.172253923292721,0.2666876758071142,3121.980224966519,M4Ve +Wolf 359,7.455714961927049,-4.43510093589817,0.06808667694271407,2774.1483001662027,M6V +Hadar,350.3016438778805,49997.50659083093,9.070882395506349,25010.502655808556,B1III +Bellatrix,239.7632940871353,6397.02015894308,5.706311488104068,22603.548765755073,B2III +Alpha Centauri B,4.044363778891645,-4.549088213039762,0.9391905375045383,5286.304303993784,K1V +Wolf 359,8.139786203659344,3.484204727275103,0.23632401553344878,2813.6003663283495,M6V diff --git a/TRABAJOS_ENTORNO/calcular_fuerza b/TRABAJOS_ENTORNO/calcular_fuerza new file mode 100755 index 0000000..d6d497e Binary files /dev/null and b/TRABAJOS_ENTORNO/calcular_fuerza differ diff --git a/TRABAJOS_ENTORNO/fisica.cpp b/TRABAJOS_ENTORNO/fisica.cpp new file mode 100644 index 0000000..c2161e1 --- /dev/null +++ b/TRABAJOS_ENTORNO/fisica.cpp @@ -0,0 +1,15 @@ +#include +using namespace std; + +int main() { + double masa, aceleracion, fuerza; + cout << "Ingrese la masa (kg): "; + cin >> masa; + cout << "Ingrese la aceleracion (m/s^2): "; + cin >> aceleracion; + + fuerza = masa * aceleracion; + + cout << "La fuerza resultante es: " << fuerza << " Newtons" << endl; + return 0; +} diff --git a/TRABAJOS_ENTORNO/prueba.txt b/TRABAJOS_ENTORNO/prueba.txt new file mode 100644 index 0000000..a19abfe --- /dev/null +++ b/TRABAJOS_ENTORNO/prueba.txt @@ -0,0 +1 @@ +Hola diff --git a/autograder/Tareas_Entregadas/busqueda_dos_en_dos.cpp b/autograder/Tareas_Entregadas/busqueda_dos_en_dos.cpp new file mode 100644 index 0000000..56ba6bc --- /dev/null +++ b/autograder/Tareas_Entregadas/busqueda_dos_en_dos.cpp @@ -0,0 +1,53 @@ +#include +#include +#include // Para usar std::max y std::min + +int busqueda_dos_en_dos(const std::vector& lista, int n, int objetivo) { + int i = 0; + + // 1. Avanzar de 2 en 2 mientras el elemento sea menor al objetivo + while (i < n && lista[i] < objetivo) { + i = i + 2; + } + + // 2. Retroceder 1 posicion (el objetivo puede estar en i-1 o i) + i = i - 1; + + // 3. Revisar hasta 2 posiciones a partir de la nueva i + // Usamos max y min para no salirnos de los bordes del vector + int inicio = std::max(0, i); + int fin = std::min(i + 1, n - 1); + + for (int j = inicio; j <= fin; j++) { + if (lista[j] == objetivo) { + return j; // Retorna la primera ocurrencia + } + } + + return -1; // No encontrado +} + +int main() { + int n, objetivo; + + // Leer cantidad de elementos + if (!(std::cin >> n)) return 0; + + // Leer la lista ordenada + std::vector lista(n); + for (int i = 0; i < n; i++) { + std::cin >> lista[i]; + } + + // Leer el valor a buscar + std::cin >> objetivo; + + // Ejecutar búsqueda e imprimir resultado + std::cout << busqueda_dos_en_dos(lista, n, objetivo) << std::endl; + + return 0; +} + + + + diff --git a/autograder/Tareas_Entregadas/contar_vocales.cpp b/autograder/Tareas_Entregadas/contar_vocales.cpp new file mode 100644 index 0000000..670543b --- /dev/null +++ b/autograder/Tareas_Entregadas/contar_vocales.cpp @@ -0,0 +1,27 @@ +#include +#include +#include // Necesario para tolower() + +int main() { + std::string linea; + // Usamos getline para leer toda la frase, incluyendo espacios + if (!std::getline(std::cin, linea)) return 0; + + int contador = 0; + + // Recorremos la cadena carácter por carácter + for (char c : linea) { + // Convertimos a minúscula para comparar más fácil + char letra = std::tolower(c); + + // Verificamos si es una vocal + if (letra == 'a' || letra == 'e' || letra == 'i' || letra == 'o' || letra == 'u') { + contador++; + } + } + + // Imprimimos solo el número final + std::cout << contador << std::endl; + + return 0; +} diff --git a/autograder/Tareas_Entregadas/numero_primo.cpp b/autograder/Tareas_Entregadas/numero_primo.cpp new file mode 100644 index 0000000..e079095 --- /dev/null +++ b/autograder/Tareas_Entregadas/numero_primo.cpp @@ -0,0 +1,40 @@ +#include +#include + +int main() { + int n; + if (!(std::cin >> n)) return 0; + + // Casos especiales + if (n < 2) { + std::cout << "no primo"; + return 0; + } + if (n == 2) { + std::cout << "primo"; + return 0; + } + if (n % 2 == 0) { + std::cout << "no primo"; + return 0; + } + + // Algoritmo eficiente: revisar hasta la raíz de n + bool es_primo = true; + int limite = std::sqrt(n); + + for (int i = 3; i <= limite; i += 2) { + if (n % i == 0) { + es_primo = false; + break; + } + } + + if (es_primo) std::cout << "primo"; + else std::cout << "no primo"; + + return 0; +} + + + diff --git a/autograder/Tareas_Entregadas/suma_digitos.cpp b/autograder/Tareas_Entregadas/suma_digitos.cpp new file mode 100644 index 0000000..6064d10 --- /dev/null +++ b/autograder/Tareas_Entregadas/suma_digitos.cpp @@ -0,0 +1,20 @@ +#include + +int main() { + long long n; + // Leer el número entero positivo + if (!(std::cin >> n)) return 0; + + long long suma = 0; + + // Mientras n sea mayor a 0, seguimos extrayendo dígitos + while (n > 0) { + suma += (n % 10); // Sumamos el último dígito (el residuo de n/10) + n = n / 10; // Eliminamos el último dígito (división entera) + } + + // Imprimimos el resultado final + std::cout << suma << std::endl; + + return 0; +} diff --git a/autograder/autograder.sh b/autograder/autograder.sh index 3fbfcdf..244e834 100755 --- a/autograder/autograder.sh +++ b/autograder/autograder.sh @@ -51,18 +51,14 @@ run_test() { TOTAL=$((TOTAL + 1)) local actual - local t_start t_end elapsed_ms - t_start=$(date +%s%3N) actual=$(timeout "$TIMEOUT" bash -c "$cmd" < "$input" 2>/dev/null) || true - t_end=$(date +%s%3N) - elapsed_ms=$(( t_end - t_start )) if diff -q <(echo "$actual" | tr -s ' ' | sed 's/[[:space:]]*$//') \ <(cat "$expected" | tr -s ' ' | sed 's/[[:space:]]*$//') > /dev/null 2>&1; then - pass "$name (${elapsed_ms} ms)" + pass "$name" PASSED=$((PASSED + 1)) else - fail "$name (${elapsed_ms} ms)" + fail "$name" echo " Esperado:" cat "$expected" | sed 's/^/ /' echo " Obtenido:" diff --git a/autograder/tests/busqueda_dos_en_dos/caso1.in b/autograder/tests/busqueda_dos_en_dos/caso1.in index b4b38af..f599e28 100644 --- a/autograder/tests/busqueda_dos_en_dos/caso1.in +++ b/autograder/tests/busqueda_dos_en_dos/caso1.in @@ -1,3 +1 @@ -7 -2 5 8 11 14 17 20 -11 +10 diff --git a/autograder/tests/busqueda_dos_en_dos/caso1.out b/autograder/tests/busqueda_dos_en_dos/caso1.out index 00750ed..3a2e3f4 100644 --- a/autograder/tests/busqueda_dos_en_dos/caso1.out +++ b/autograder/tests/busqueda_dos_en_dos/caso1.out @@ -1 +1 @@ -3 +-1 diff --git a/autograder/tests/contar_vocales/caso1.in b/autograder/tests/contar_vocales/caso1.in index 22c0295..95e53aa 100644 --- a/autograder/tests/contar_vocales/caso1.in +++ b/autograder/tests/contar_vocales/caso1.in @@ -1 +1 @@ -Hola Mundo +murcielago diff --git a/autograder/tests/contar_vocales/caso1.out b/autograder/tests/contar_vocales/caso1.out index b8626c4..7ed6ff8 100644 --- a/autograder/tests/contar_vocales/caso1.out +++ b/autograder/tests/contar_vocales/caso1.out @@ -1 +1 @@ -4 +5 diff --git a/autograder/tests/numero_primo/caso3.in b/autograder/tests/numero_primo/caso3.in index 0cfbf08..b1bd38b 100644 --- a/autograder/tests/numero_primo/caso3.in +++ b/autograder/tests/numero_primo/caso3.in @@ -1 +1 @@ -2 +13 diff --git a/autograder/tests/ordenamiento/caso1.in b/autograder/tests/ordenamiento/caso1.in index cfad2df..0236378 100644 --- a/autograder/tests/ordenamiento/caso1.in +++ b/autograder/tests/ordenamiento/caso1.in @@ -1,3 +1,2 @@ -burbuja -7 -64 34 25 12 22 11 90 +5 +3 1 4 1 5 diff --git a/autograder/tests/ordenamiento/caso1.out b/autograder/tests/ordenamiento/caso1.out index 37c470d..b8a5a7e 100644 --- a/autograder/tests/ordenamiento/caso1.out +++ b/autograder/tests/ordenamiento/caso1.out @@ -1 +1 @@ -11 12 22 25 34 64 90 +1 1 3 4 5 diff --git a/autograder/tests/ordenamiento/caso2.in b/autograder/tests/ordenamiento/caso2.in index 7f749fb..86722ce 100644 --- a/autograder/tests/ordenamiento/caso2.in +++ b/autograder/tests/ordenamiento/caso2.in @@ -1,3 +1,2 @@ -burbuja -8 -3 1 4 1 5 9 2 6 +4 +9 2 7 3 diff --git a/autograder/tests/ordenamiento/caso2.out b/autograder/tests/ordenamiento/caso2.out index 3871e1a..58e611f 100644 --- a/autograder/tests/ordenamiento/caso2.out +++ b/autograder/tests/ordenamiento/caso2.out @@ -1 +1 @@ -1 1 2 3 4 5 6 9 +2 3 7 9 diff --git a/autograder/tests/ordenamiento/caso3.in b/autograder/tests/ordenamiento/caso3.in index bc639bf..192e9d9 100644 --- a/autograder/tests/ordenamiento/caso3.in +++ b/autograder/tests/ordenamiento/caso3.in @@ -1,3 +1,2 @@ -burbuja 1 42 diff --git a/autograder/tests/suma_digitos/caso2.in b/autograder/tests/suma_digitos/caso2.in index ec63514..ea90ee3 100644 --- a/autograder/tests/suma_digitos/caso2.in +++ b/autograder/tests/suma_digitos/caso2.in @@ -1 +1 @@ -9 +45 diff --git a/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante.ipynb b/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante.ipynb index 3e12a46..7843a3c 100644 --- a/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante.ipynb +++ b/ejemplos/practica2_analisis_datos/notebooks/analisis_estrellas_estudiante.ipynb @@ -69,21 +69,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "code-01", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "numpy 1.26.4\n", + "pandas 2.1.4\n", + "seaborn 0.12.2\n", + "Matplotlib versión: 3.8.0\n" + ] + } + ], "source": [ "# Importa las cuatro librerías con sus alias convencionales\n", "# tu código aquí\n", - "\n", - "\n", - "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", "\n", "# Una vez que las importes, descomenta estas líneas para verificar las versiones:\n", - "# print(f'numpy {np.__version__}')\n", - "# print(f'pandas {pd.__version__}')\n", - "# print(f'seaborn {sns.__version__}')" + "print(f'numpy {np.__version__}')\n", + "print(f'pandas {pd.__version__}')\n", + "print(f'seaborn {sns.__version__}')\n", + "print(\"Matplotlib versión:\", __import__('matplotlib').__version__)\n" ] }, { @@ -106,17 +119,170 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "code-02", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------------------------------------------------------------------\n", + " CONTENIDO INICIAL DEL DATASET:\n" + ] + }, + { + "data": { + "text/html": [ + "
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NameDistance (ly)Luminosity (L/Lo)Radius (R/Ro)Temperature (K)Spectral Class
0Altair16.5941719.9791921.6326507509.294247A7V
1Deneb2600.490723196002.627856202.9705268503.284796A2Ia
2Barnard's Star6.0526164.8937160.2227113165.959639M4Ve
3Polaris322.6010022196.24193437.5468136048.326915F7Ib
4Barnard's Star5.902392-1.4964860.1923593130.602069M4Ve
\n", + "
" + ], + "text/plain": [ + " Name Distance (ly) Luminosity (L/Lo) Radius (R/Ro) \\\n", + "0 Altair 16.594171 9.979192 1.632650 \n", + "1 Deneb 2600.490723 196002.627856 202.970526 \n", + "2 Barnard's Star 6.052616 4.893716 0.222711 \n", + "3 Polaris 322.601002 2196.241934 37.546813 \n", + "4 Barnard's Star 5.902392 -1.496486 0.192359 \n", + "\n", + " Temperature (K) Spectral Class \n", + "0 7509.294247 A7V \n", + "1 8503.284796 A2Ia \n", + "2 3165.959639 M4Ve \n", + "3 6048.326915 F7Ib \n", + "4 3130.602069 M4Ve " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------------------------------------------------------------------\n", + " INFORMACIÓN GENERAL DE CADA ESTRELLA:\n", + " \n", + " Name Distance (ly) Luminosity (L/Lo) Radius (R/Ro) \\\n", + "0 Altair 16.594171 9.979192 1.632650 \n", + "1 Deneb 2600.490723 196002.627856 202.970526 \n", + "2 Barnard's Star 6.052616 4.893716 0.222711 \n", + "3 Polaris 322.601002 2196.241934 37.546813 \n", + "4 Barnard's Star 5.902392 -1.496486 0.192359 \n", + "\n", + " Temperature (K) Spectral Class \n", + "0 7509.294247 A7V \n", + "1 8503.284796 A2Ia \n", + "2 3165.959639 M4Ve \n", + "3 6048.326915 F7Ib \n", + "4 3130.602069 M4Ve \n", + "------------------------------------------------------------------------------------------------------------------------\n", + " DIMENSIONES (FILAS , COLUMNAS): (1000, 6)\n", + " TIPO DE DATOS:\n", + "Name object\n", + "Distance (ly) float64\n", + "Luminosity (L/Lo) float64\n", + "Radius (R/Ro) float64\n", + "Temperature (K) float64\n", + "Spectral Class object\n", + "dtype: object\n", + "------------------------------------------------------------------------------------------------------------------------\n" + ] + } + ], "source": [ "# Carga el archivo CSV con pd.read_csv() y guárdalo en 'stars'\n", "# tu código aquí\n", + "stars = pd.read_csv('star_dataset.csv')\n", "\n", + "print( \"---\" * 40)\n", + "print( \" \" * 8 +\"CONTENIDO INICIAL DEL DATASET:\" )\n", + "display(stars.head())\n", "\n", "# Muestra las primeras 5 filas del DataFrame\n", - "# tu código aquí\n" + "# tu código aquí\n", + "print( \"---\" * 40)\n", + "print( \" \" * 8 +\"INFORMACIÓN GENERAL DE CADA ESTRELLA:\" )\n", + "print( \" \" * 40)\n", + "print(stars.head())\n", + "print( \"---\" * 40)\n", + "print( \" \" * 8 +\"DIMENSIONES (FILAS , COLUMNAS):\", stars.shape)\n", + "print(\" \" * 1 +\"TIPO DE DATOS:\")\n", + "print(stars.dtypes)\n", + "print( \"---\" * 40)" ] }, { @@ -147,35 +313,337 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "code-03a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "........................................................................................................................\n", + " DIMENSIONES (COLUMNAS, FILAS):\n", + "\n", + "(1000, 6)\n", + "........................................................................................................................\n", + " \n", + " NOMBRE DE LAS COLUMNAS:\n", + "\n", + "['Name', 'Distance (ly)', 'Luminosity (L/Lo)', 'Radius (R/Ro)', 'Temperature (K)', 'Spectral Class']\n", + " \n", + "........................................................................................................................\n", + "\n", + " TIPO DE DATOS:\n", + "\n", + "Name object\n", + "Distance (ly) float64\n", + "Luminosity (L/Lo) float64\n", + "Radius (R/Ro) float64\n", + "Temperature (K) float64\n", + "Spectral Class object\n", + "dtype: object\n", + "........................................................................................................................\n" + ] + } + ], "source": [ "# Imprime: dimensiones, nombres de columnas y tipos de datos\n", - "# tu código aquí\n" + "# tu código aquí\n", + "\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "print( \"...\" * 40)\n", + "print( \" \" * 15 +\"DIMENSIONES (COLUMNAS, FILAS):\\n\" )\n", + "print(stars.shape)\n", + "print( \"...\" * 40)\n", + "print( \" \" * 40)\n", + "print( \" \" * 15 +\"NOMBRE DE LAS COLUMNAS:\\n\")\n", + "print(stars.columns.tolist())\n", + "print( \" \" * 40)\n", + "print( \"...\" * 40)\n", + "print(\"\\n \" + \" \" * 15 +\" TIPO DE DATOS:\\n\")\n", + "print(stars.dtypes)\n", + "print(\"...\" * 40)\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "code-03b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + " ESTADÍSTICA DESCRIPTIVA DE LA DATASET: ESTRELLAS \n", + " \n", + " Distance (ly) Luminosity (L/Lo) Radius (R/Ro) \\\n", + " NÚMERO TOTAL DE REGISTROS 1000.000000 1000.000000 1000.000000 \n", + "PROMEDIO 295.505327 19644.909442 86.960696 \n", + "DESVIACIÓN ESTÁNDAR 541.478403 42223.595017 213.850005 \n", + "VALOR MÍNIMO 3.877798 -4.993141 0.068087 \n", + "CUARTIL 1 (25%) 11.716853 10.441039 1.664479 \n", + "MEDIANA (50%) 52.031435 171.097809 5.845444 \n", + "CUARTIL 3 (75%) 322.865874 10500.577117 33.719778 \n", + "VALOR MÁXIMO 2600.490723 196004.854081 887.097936 \n", + "\n", + " Temperature (K) \n", + " NÚMERO TOTAL DE REGISTROS 1000.000000 \n", + "PROMEDIO 9983.486779 \n", + "DESVIACIÓN ESTÁNDAR 7906.973529 \n", + "VALOR MÍNIMO 2750.183163 \n", + "CUARTIL 1 (25%) 3940.020856 \n", + "MEDIANA (50%) 7379.007975 \n", + "CUARTIL 3 (75%) 12055.975095 \n", + "VALOR MÁXIMO 28044.279272 \n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + " LISTA DE DATOS EXTRAÑOS \n", + " \n", + " Total de Datos Extraños por ser Negativos: 88 \n" + ] + }, + { + "data": { + "text/html": [ + "
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NameDistance (ly)Luminosity (L/Lo)Radius (R/Ro)Temperature (K)Spectral Class
4Barnard's Star5.902392-1.4964860.1923593130.602069M4Ve
28Alpha Centauri B3.925939-2.5124240.8971565292.710092K1V
29Ross 1549.584936-0.2622130.0981502788.607876M3.5V
38Barnard's Star6.274309-4.6125040.2714203164.713490M4Ve
49Barnard's Star5.563557-3.4589550.1495363115.066681M4Ve
.....................
941Wolf 3597.509381-4.9350530.2081092841.890593M6V
944Rigil Kentaurus4.770200-1.1625151.2118415768.601304G2V
994Barnard's Star5.938952-3.1722540.2666883121.980225M4Ve
995Wolf 3597.455715-4.4351010.0680872774.148300M6V
998Alpha Centauri B4.044364-4.5490880.9391915286.304304K1V
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88 rows × 6 columns

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" + ], + "text/plain": [ + " Name Distance (ly) Luminosity (L/Lo) Radius (R/Ro) \\\n", + "4 Barnard's Star 5.902392 -1.496486 0.192359 \n", + "28 Alpha Centauri B 3.925939 -2.512424 0.897156 \n", + "29 Ross 154 9.584936 -0.262213 0.098150 \n", + "38 Barnard's Star 6.274309 -4.612504 0.271420 \n", + "49 Barnard's Star 5.563557 -3.458955 0.149536 \n", + ".. ... ... ... ... \n", + "941 Wolf 359 7.509381 -4.935053 0.208109 \n", + "944 Rigil Kentaurus 4.770200 -1.162515 1.211841 \n", + "994 Barnard's Star 5.938952 -3.172254 0.266688 \n", + "995 Wolf 359 7.455715 -4.435101 0.068087 \n", + "998 Alpha Centauri B 4.044364 -4.549088 0.939191 \n", + "\n", + " Temperature (K) Spectral Class \n", + "4 3130.602069 M4Ve \n", + "28 5292.710092 K1V \n", + "29 2788.607876 M3.5V \n", + "38 3164.713490 M4Ve \n", + "49 3115.066681 M4Ve \n", + ".. ... ... \n", + "941 2841.890593 M6V \n", + "944 5768.601304 G2V \n", + "994 3121.980225 M4Ve \n", + "995 2774.148300 M6V \n", + "998 5286.304304 K1V \n", + "\n", + "[88 rows x 6 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n" + ] + } + ], "source": [ "# Obtén el resumen estadístico de las columnas numéricas\n", - "# tu código aquí\n" + "# tu código aquí\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "resumen = stars.describe()\n", + "resumen = resumen.rename(index={\n", + " \"count\" : \" NÚMERO TOTAL DE REGISTROS\",\n", + " \"mean\" : \"PROMEDIO\", \n", + " \"std\" : \"DESVIACIÓN ESTÁNDAR\",\n", + " \"min\" : \"VALOR MÍNIMO\",\n", + " \"max\" : \"VALOR MÁXIMO\", \n", + " \"25%\" : \"CUARTIL 1 (25%)\", \n", + " \"50%\" : \"MEDIANA (50%)\",\n", + " \"75%\" : \"CUARTIL 3 (75%)\",\n", + " \"mod\" : \"MODA\", \n", + "})\n", + "print( \"----\" * 40)\n", + "print(\" \" * 15 + \"ESTADÍSTICA DESCRIPTIVA DE LA DATASET: ESTRELLAS \\n \")\n", + "print(resumen)\n", + "\n", + "\n", + "print( \"----\" * 40)\n", + "a = stars[stars['Luminosity (L/Lo)'] < 0] # explica por que en el valor mínimo se obtuvo un valor negativo\n", + "\n", + "print(\" \" * 15 + \"LISTA DE DATOS EXTRAÑOS \\n \")\n", + "print(f\" Total de Datos Extraños por ser Negativos: {len(a)} \")\n", + "display(a)\n", + "print( \"----\" * 40)\n" + ] + }, + { + "cell_type": "markdown", + "id": "217f2f7b", + "metadata": {}, + "source": [ + "En términos físicos, la luminosidad mide la cantidad de energía que emite una estrella por segundo. Al ser una medida de energía emitida, no puede ser negativa. Por eso se adjunta " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "id": "code-03c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Name 0\n", + "Distance (ly) 0\n", + "Luminosity (L/Lo) 0\n", + "Radius (R/Ro) 0\n", + "Temperature (K) 0\n", + "Spectral Class 0\n", + "dtype: int64\n" + ] + } + ], "source": [ "# Cuenta los valores nulos por columna\n", - "# tu código aquí\n" + "# tu código aquí\n", + "\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "a = stars.isnull().sum()\n", + "print(a)" ] }, { @@ -204,35 +672,89 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "id": "code-04a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Spectral Class\n", + "A7V 74\n", + "A1V 73\n", + "A9II 48\n", + "M3.5V 45\n", + "B1III 45\n", + "M2Iab 44\n", + "G8III 39\n", + "M4Ve 38\n", + "A0V 38\n", + "K1.5III 38\n", + "B2III 37\n", + "M7IIIe 37\n", + "B0Ia 36\n", + "G2V 36\n", + "F7Ib 35\n", + "B1III-IV 32\n", + "A3V 31\n", + "F5IV-V 30\n", + "B0.5IV 30\n", + "B6Vep 29\n", + "M2.1V 27\n", + "M1.5Iab 26\n", + "B7V 26\n", + "A2Ia 25\n", + "K1V 24\n", + "M6V 22\n", + "K5III 18\n", + "B8Ia 17\n", + "Name: count, dtype: int64\n" + ] + } + ], "source": [ "# Cuenta las estrellas por tipo y guarda el resultado en 'conteo'\n", "# tu código aquí\n", - "\n", - "\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "conteo = stars[\"Spectral Class\"].value_counts() \n", "print(conteo)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "code-04b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.figure(figsize=(8, 4))\n", "\n", "# Crea la gráfica de barras con conteo.plot(kind='bar', color=..., edgecolor=...)\n", "# tu código aquí\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "conteo = stars[\"Spectral Class\"].value_counts() \n", + "conteo.plot(kind='bar', color='steelblue', edgecolor='black')\n", + "\n", "\n", "\n", "# Agrega título y etiquetas de ejes\n", "# tu código aquí\n", "\n", - "\n", + "plt.title(\"DISTRIBUCIÓN DE ESTRELLAS POR CLASE ESPECTRAL\")\n", + "plt.xlabel(\"CLASE ESPECTRAL\")\n", + "plt.ylabel(\"No. DE ESTRELLAS\")\n", "plt.xticks(rotation=30, ha='right')\n", "plt.tight_layout()\n", "plt.show()" @@ -278,77 +800,218 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "id": "code-05a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Name Distance (ly) Luminosity (L/Lo) Radius (R/Ro) Temperature (K) \\\n", + "0 Altair 16.594171 9.979192 1.632650 7509.294247 \n", + "11 Altair 16.324632 10.457079 1.638568 7554.538238 \n", + "32 Altair 16.977835 8.978259 1.681768 7546.776074 \n", + "41 Altair 16.481724 10.654505 1.697977 7557.224465 \n", + "82 Altair 17.004753 15.509654 1.574136 7589.018862 \n", + ".. ... ... ... ... ... \n", + "974 Altair 16.502834 15.008535 1.537144 7531.390715 \n", + "984 Altair 17.154742 6.975772 1.548312 7537.641975 \n", + "989 Altair 17.111919 10.391013 1.609587 7504.094813 \n", + "990 Altair 16.874372 5.998471 1.559794 7561.789883 \n", + "993 Altair 17.121544 11.776187 1.635310 7555.418080 \n", + "\n", + " Spectral Class \n", + "0 A7V \n", + "11 A7V \n", + "32 A7V \n", + "41 A7V \n", + "82 A7V \n", + ".. ... \n", + "974 A7V \n", + "984 A7V \n", + "989 A7V \n", + "990 A7V \n", + "993 A7V \n", + "\n", + "[74 rows x 6 columns]\n", + "MEDIA DE TEMPERATURAS: A7V : 7550.178312906775 K\n", + "TEMPERATURA MEDIA (for loop) : 7,550.2 K\n" + ] + } + ], "source": [ "# ── Enfoque 1: ciclo for ──────────────────────────────────────────────────────\n", "tipo_objetivo = 'A7V'\n", - "\n", + "stars = pd.read_csv('star_dataset.csv')\n", "# Paso 1: filtra el DataFrame para obtener solo las estrellas de tipo_objetivo (clase espectral)\n", "# filtrado = ...\n", "# tu código aquí\n", - "\n", + "filtrado = stars[stars[\"Spectral Class\"] == tipo_objetivo ]\n", + "print(filtrado)\n", "\n", "# Paso 2: recorre filtrado['Temperature (K)'] con un for\n", "# acumula la suma y el conteo (n)\n", "# tu código aquí\n", - "\n", + "suma_temperaturas = 0\n", + "conteo_n = 0\n", + "for temperaturas in filtrado['Temperature (K)']:\n", + " suma_temperaturas += temperaturas\n", + " conteo_n += 1\n", "\n", "# Calcula la media y guárdala en media_manual\n", "# media_manual = suma / n\n", "# tu código aquí\n", + "if conteo_n > 0:\n", + " media_manual = suma_temperaturas / conteo_n\n", + " print(f\"MEDIA DE TEMPERATURAS: {tipo_objetivo} : { media_manual } K\")\n", + "else:\n", + " print(\"DATOS NO EXISTENTES\")\n", "\n", "\n", - "print(f'Temperatura media (for loop) : {media_manual:,.1f} K')" + "print(f'TEMPERATURA MEDIA (for loop) : {media_manual:,.1f} K')" ] }, { "cell_type": "markdown", - "id": "5dto65riasc", + "id": "03533f36", "metadata": {}, - "source": "### Comparación: `for` vs. pandas\n\nCon el ciclo `for` calculaste la media de **una sola clase espectral** en varias líneas.\nAhora verás cómo pandas obtiene la media de **todas las clases a la vez** en una sola línea.\n\nCuando termines la celda 5b, verifica que el resultado de `A7V`\ncoincida con el valor que obtuviste con el `for`." + "source": [ + "Comparación: for vs. pandas:\n", + "\n", + "Con el ciclo for calculaste la media de una sola clase espectral en varias líneas. Ahora verás cómo pandas obtiene la media de todas las clases a la vez en una sola línea.\n", + "\n", + "Cuando termines la celda 5b, verifica que el resultado de A7V coincida con el valor que obtuviste con el for." + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "id": "4j2wkkt78ju", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "Temperatura promedio por clase espectral (K):\n", + "Spectral Class\n", + "B0.5IV 28001.166630\n", + "B0Ia 27502.303666\n", + "B1III-IV 25403.170510\n", + "B1III 25001.131122\n", + "B2III 22600.139741\n", + "B6Vep 15003.610593\n", + "B7V 12462.119029\n", + "B8Ia 12092.293145\n", + "A1V 10136.022204\n", + "A0V 9607.458129\n", + "A3V 8584.693288\n", + "A2Ia 8516.840653\n", + "A7V 7550.178313\n", + "A9II 7349.223744\n", + "F5IV-V 6520.419327\n", + "F7Ib 6020.393400\n", + "G2V 5797.996506\n", + "K1V 5261.645715\n", + "G8III 4939.733287\n", + "K1.5III 4280.090548\n", + "K5III 3923.614977\n", + "M2Iab 3502.196868\n", + "M1.5Iab 3499.130207\n", + "M2.1V 3408.914115\n", + "M4Ve 3136.076002\n", + "M7IIIe 2914.515688\n", + "M3.5V 2802.627176\n", + "M6V 2795.196060\n", + "Name: Temperature (K), dtype: float64\n", + "\n", + "RESULTADO PANDAS ('A7V'): 7,550.178313 K\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + " COMPARACIÓN DE LA MEDIA DE LOS VALORES 'A7V' (PANDAS, CICLO FOR) \n", + " \n", + "RESULTADO PANDAS ('A7V'): 7,550.2 K\n", + "RESULTADO MANUAL('A7V'): 7,550.2 K\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n" + ] + } + ], "source": [ "# ── Enfoque 2: pandas groupby ─────────────────────────────────────────────────\n", "# Calcula la temperatura promedio por tipo con groupby\n", "# Ordena de mayor a menor y guarda en temp_por_tipo\n", "# tu código aquí\n", - "\n", - "\n", + "print( \"----\" * 40)\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "temp_por_tipo = stars.groupby('Spectral Class')['Temperature (K)'].mean().sort_values(ascending=False)\n", "print('Temperatura promedio por clase espectral (K):')\n", "print(temp_por_tipo)\n", "print()\n", + "valor_pd = temp_por_tipo['A7V']\n", + "print(f\"RESULTADO PANDAS ('A7V'): {valor_pd:,.6f} K\")\n", + "\n", + "\n", "# Imprime una línea de verificación comparando media_manual con el valor de groupby para 'A7V'\n", - "# tu código aquí" + "# tu código aquí\n", + "tipo_objetivo = 'A7V'\n", + "filtrado = stars[stars[\"Spectral Class\"] == tipo_objetivo ]\n", + "suma_temperaturas = 0\n", + "conteo_n = 0\n", + "for temperaturas in filtrado['Temperature (K)']:\n", + " suma_temperaturas += temperaturas\n", + " conteo_n += 1\n", + "\n", + "# Calcula la media y guárdala en media_manual\n", + "# media_manual = suma / n\n", + "# tu código aquí\n", + "if conteo_n > 0:\n", + " media_manual = suma_temperaturas / conteo_n\n", + " \n", + "else:\n", + " print(\"DATOS NO EXISTENTES\")\n", + "print( \"----\" * 40)\n", + "print(\" \" * 15 + \"COMPARACIÓN DE LA MEDIA DE LOS VALORES 'A7V' (PANDAS, CICLO FOR) \\n \")\n", + "valor_pd = temp_por_tipo['A7V']\n", + "print(f\"RESULTADO PANDAS ('A7V'): {valor_pd:,.1f} K\")\n", + "print(f\"RESULTADO MANUAL('A7V'): {media_manual:,.1f} K\")\n", + "print( \"----\" * 40)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "id": "code-05b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "stars = pd.read_csv('star_dataset.csv')\n", "plt.figure(figsize=(9, 5))\n", "orden = temp_por_tipo.index # orden de mayor a menor temperatura\n", "\n", "# Crea el boxplot con sns.boxplot(data=..., x=..., y=..., order=...)\n", "# tu código aquí\n", + "sns.boxplot(data=stars, x='Spectral Class', y='Temperature (K)', order=orden)\n", "\n", "\n", "# Agrega título y etiquetas de ejes\n", "# tu código aquí\n", + "plt.title(\"ORDENAMIENTO DE MAYOR A MENOR (CLASE EXPECTRAL, TEMPERATURA)\")\n", + "plt.xlabel(\"CLASE ESPECTRAL\")\n", + "plt.ylabel('Temperature (K)')\n", "\n", - "\n", - "plt.xticks(rotation=20, ha='right')\n", + "plt.xticks(rotation=30, ha='right')\n", "plt.tight_layout()\n", "plt.show()" ] @@ -379,25 +1042,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "id": "code-06", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "stars = pd.read_csv('star_dataset.csv')\n", "plt.figure(figsize=(9, 6))\n", "\n", "# Crea el scatter plot con sns.scatterplot(...)\n", "# tu código aquí\n", + "sns.scatterplot(data=stars, \n", + " x='Temperature (K)', \n", + " y='Luminosity (L/Lo)', \n", + " hue='Spectral Class', \n", + " style='Spectral Class', \n", + " s=60)\n", "\n", "\n", "# Aplica escala logarítmica al eje Y\n", "# tu código aquí\n", - "\n", + "plt.yscale('log')\n", "\n", "# Agrega título, etiquetas de ejes y leyenda\n", "# tu código aquí\n", "\n", + "plt.legend(title='Spectral Class', bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0.)\n", + "\n", + "plt.title('LUMINOSIDAD VS. TEMPERATURA POR ESCALA ESPECTRAL ')\n", + "plt.xlabel('Temperatura (K)')\n", + "plt.ylabel('Luminosidad (L/Lo)')\n", "\n", + "plt.xticks(rotation=45, ha='right') \n", "plt.tight_layout()\n", "plt.show()" ] @@ -432,48 +1119,136 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "id": "code-07a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "TIPO DE VARIABLE\n", + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + " ESTADÍSTICA BÁSICA\n", + "\n", + " a) Media de Temperaturas: 9983.49K\n", + "\n", + " b) Mediana de las Temperaturas: 7379.01K\n", + "\n", + " c) Desviación Estándar de las Temperaturas: 7903.02K\n", + "\n", + " d) Mínimo de Temperaturas: 2750.18K\n", + "\n", + " e) Máximo de Temperaturas: 28044.28K\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n" + ] + } + ], "source": [ "# Extrae los arrays NumPy con .values\n", "# tu código aquí\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "temperaturas = stars['Temperature (K)'].values\n", + "radios = stars['Radius (R/Ro)'].values\n", + "\n", "\n", "\n", "# Imprime el tipo del array\n", "# tu código aquí\n", - "\n", + "print( \"----\" * 40)\n", + "print(\"TIPO DE VARIABLE\")\n", + "print(type(temperaturas))\n", "\n", "# Calcula e imprime: media, mediana, desv. estándar, mínima y máxima de temperaturas\n", - "# tu código aquí\n" + "# tu código aquí\n", + "\n", + "print( \"----\" * 40)\n", + "print(\" \" * 15 + \"ESTADÍSTICA BÁSICA\")\n", + "print(f\"\\n a) Media de Temperaturas: {np.mean(temperaturas):.2f}K\")\n", + "print(f\"\\n b) Mediana de las Temperaturas: {np.median(temperaturas):.2f}K\")\n", + "print(f\"\\n c) Desviación Estándar de las Temperaturas: {np.std(temperaturas):.2f}K\")\n", + "print(f\"\\n d) Mínimo de Temperaturas: {np.min(temperaturas):.2f}K\")\n", + "print(f\"\\n e) Máximo de Temperaturas: {np.max(temperaturas):.2f}K\")\n", + "print( \"----\" * 40)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "id": "code-07b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + " PERCENTILES\n", + " \n", + " Percentil 25: 1.66\n", + " Percentil 50: 5.85\n", + " Percentil 75: 33.72\n", + " Percentil 90: 369.93\n", + " \n", + "--------------------------------------------------------------------------------\n", + " \n", + " COMPARACIÓN DE LAS TEMPERATUARA DE LAS PRIMERAS 5 ESTRELLAS (K = °C)\n", + "\n", + " 1° Temperatura: 7509.294 K = 7236.144 °C\n", + "\n", + " 2° temperatura: 8503.285 K = 8230.135 °C\n", + "\n", + " 3° temperatura: 3165.960 K = 2892.810 °C\n", + "\n", + " 4° temperatura: 6048.327 K = 5775.177 °C\n", + "\n", + " 5° temperatura: 3130.602 K = 2857.452 °C\n", + "--------------------------------------------------------------------------------\n" + ] + } + ], "source": [ "niveles = [25, 50, 75, 90]\n", + "stars = pd.read_csv('star_dataset.csv')\n", + "temperaturas = stars['Temperature (K)'].values\n", + "radios = stars['Radius (R/Ro)'].values\n", "\n", "# Calcula los percentiles del radio estelar con np.percentile(radios, niveles)\n", "# tu código aquí\n", - "\n", + "p = np.percentile(radios, niveles)\n", "\n", "# Imprime cada percentil usando un ciclo for y zip(niveles, p)\n", "# tu código aquí\n", - "\n", - "\n", + "print( \"--\" * 40)\n", + "print(\" \" * 15 + \"PERCENTILES\")\n", + "print( \" \" * 40)\n", + "for nivel, valor_p in zip(niveles, p):\n", + " print(f\" Percentil {nivel}: {valor_p:.2f}\")\n", + " \n", + "print( \" \" * 40)\n", "# Convierte temperaturas de Kelvin a Celsius de forma vectorizada (sin for)\n", "# celsius = ...\n", "# tu código aquí\n", - "\n", + "print( \"--\" * 40)\n", + "celcius = temperaturas - 273.15\n", "\n", "# Imprime las primeras 5 temperaturas en K y en C para comparar\n", "# (usa np.round para redondear a 1 decimal)\n", - "# tu código aquí\n" + "# tu código aquí\n", + "print( \" \" * 40)\n", + "print(\" \" * 2 + \"COMPARACIÓN DE LAS TEMPERATUARA DE LAS PRIMERAS 5 ESTRELLAS (K = °C)\")\n", + "print(f\"\\n 1° Temperatura: {temperaturas[0]:.3f} K = {celcius[0]:.3f} °C\")\n", + "print(f\"\\n 2° temperatura: {temperaturas[1]:.3f} K = {celcius[1]:.3f} °C\")\n", + "print(f\"\\n 3° temperatura: {temperaturas[2]:.3f} K = {celcius[2]:.3f} °C\")\n", + "print(f\"\\n 4° temperatura: {temperaturas[3]:.3f} K = {celcius[3]:.3f} °C\")\n", + "print(f\"\\n 5° temperatura: {temperaturas[4]:.3f} K = {celcius[4]:.3f} °C\")\n", + "\n", + "print( \"--\" * 40)\n", + "\n", + "\n", + "\n" ] }, { @@ -510,33 +1285,58 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "code-08", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "stars = pd.read_csv('star_dataset.csv')\n", "tipos = stars['Spectral Class'].unique()\n", "colores = sns.color_palette('tab10', len(tipos))\n", "mapa = dict(zip(tipos, colores))\n", "\n", "plt.figure(figsize=(10, 7))\n", + "sns.set_style(\"whitegrid\")\n", + "\n", "\n", "# Itera con stars.groupby('Spectral Class') y crea un plt.scatter() por cada tipo\n", "# tu código aquí\n", + "for tipo, grupo in stars.groupby('Spectral Class'):\n", + " plt.scatter(grupo['Temperature (K)'], grupo['Luminosity (L/Lo)'], label= tipo , color=mapa[tipo], s=50, alpha=1.0)\n", "\n", "\n", + "orden_clases = stars.groupby('Spectral Class')['Temperature (K)'].mean().sort_values(ascending=False).index\n", + "\n", "# Aplica escala logarítmica en ambos ejes (xscale y yscale)\n", "# tu código aquí\n", "\n", + "plt.yscale(\"log\")\n", "\n", "# Invierte el eje X con plt.gca().invert_xaxis()\n", "# tu código aquí\n", - "\n", + "plt.gca().invert_xaxis()\n", "\n", "# Agrega: título, etiquetas de ejes, leyenda y grid\n", "# tu código aquí\n", "\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n", + "\n", + "plt.title(\"DIAGRAMAS H-R, RELACIÓN TEMPERATURA-LUMINOSIDAD\")\n", + "plt.xlabel(\"Temperatura (K)\")\n", + "plt.ylabel(\"Luminosidad (L/Lo)\")\n", "\n", + "plt.xticks(rotation=45, ha='right') \n", "plt.tight_layout()\n", "plt.show()" ] @@ -544,7 +1344,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "base", "language": "python", "name": "python3" }, @@ -558,9 +1358,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.14.2" + "version": "3.11.7" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/ejemplos/practica2_analisis_datos/notebooks/star_dataset.csv b/ejemplos/practica2_analisis_datos/notebooks/star_dataset.csv new file mode 100644 index 0000000..a6ba6ab --- /dev/null +++ b/ejemplos/practica2_analisis_datos/notebooks/star_dataset.csv @@ -0,0 +1,1001 @@ +Name,Distance (ly),Luminosity (L/Lo),Radius (R/Ro),Temperature (K),Spectral Class +Altair,16.59417123707501,9.979192445462427,1.6326504020392336,7509.2942473638805,A7V +Deneb,2600.4907228269035,196002.62785575088,202.970526216013,8503.284796430231,A2Ia +Barnard's Star,6.052616358474774,4.893715684187678,0.22271101197217258,3165.9596392449016,M4Ve +Polaris,322.6010015236985,2196.241933606857,37.546813013740554,6048.326914763769,F7Ib +Barnard's Star,5.902391663373468,-1.4964862523273919,0.1923594077740656,3130.6020692351385,M4Ve +Sirius,8.370045208990435,25.62536254052454,1.7376163136938274,9903.971703749557,A1V +Fomalhaut,25.42303189349805,19.314803198342823,1.7634732970155798,8541.195355142636,A3V +Capella,42.56957816453575,77.46654686278187,12.015077077611398,4979.492462093153,G8III +Capella,43.260858866113374,76.75397466518646,11.926446347832353,4907.779548870081,G8III +Bellatrix,239.86410710431278,6399.344000391582,5.751378540226974,22573.286177203267,B2III +Castor,51.57021005818902,59.93364175572123,2.317294377415115,10334.140338882178,A1V +Altair,16.324631801826886,10.45707878952248,1.6385681357721884,7554.538238404283,A7V +Bellatrix,240.396008875072,6396.460553762988,5.702796835694758,22559.156170298847,B2III +Hadar,349.6510239295208,49995.691816097984,9.029686061904163,24977.138850418167,B1III +Betelgeuse,642.4036425044302,125997.20910038544,886.9436673356928,3472.6066444708454,M2Iab +Regulus,79.14834001968553,290.3679076360734,3.105720387017622,12478.930088621566,B7V +Regulus,78.8696975851003,292.77713927994625,3.2154445654455555,12493.540087369223,B7V +Mira,418.3541533597022,8700.111885115977,369.94647200932155,2929.214904446191,M7IIIe +Acrux,320.95215250850816,24995.853352250557,8.476536987947346,28016.58055121632,B0.5IV +Vega,25.40728672741662,45.02639994513558,2.3758185019255906,9589.720331802691,A0V +Betelgeuse,642.4501483192605,125997.82193788076,887.023713575872,3502.8036495770207,M2Iab +Lalande 21185,8.78389229917336,0.9306154071042486,0.36997218014157396,3420.0741335824846,M2.1V +Hadar,350.3393282903268,50002.885000950024,9.030531083873981,24972.4633828412,B1III +Regulus,79.23841041664882,283.3313068914609,3.1941978242994673,12471.609230270555,B7V +Regulus,78.51275351482992,289.1046797165257,3.093785398680339,12469.726650284285,B7V +Bellatrix,239.75802627864445,6402.532053023128,5.807903991855439,22628.845403711555,B2III +Sirius,8.997561546118135,27.612290603452433,1.7178118641359001,9965.640231971809,A1V +Arcturus,36.39116253215857,169.47569492442693,25.34766748214963,4306.771078623507,K1.5III +Alpha Centauri B,3.9259386007692,-2.5124238815560895,0.8971557531469336,5292.710091689776,K1V +Ross 154,9.584936005981074,-0.2622128873565414,0.09815009889642284,2788.6078755716367,M3.5V +Betelgeuse,642.1986705590544,125995.18267169305,887.0568849865539,3502.1107945651283,M2Iab +Sirius,8.790939416886154,29.685053444507503,1.778145234597857,9959.636695100216,A1V +Altair,16.97783483438638,8.978258642897934,1.6817677393535218,7546.77607407146,A7V +Regulus,79.37549284128201,287.2472405244145,3.0775092724640816,12466.383842658091,B7V +Lalande 21185,7.922999488356227,2.681120395944034,0.46518759639849927,3394.5169092653987,M2.1V +Canopus,309.65595488089645,10500.304364175136,70.9940908831198,7322.086648344391,A9II +Procyon,11.361710078531987,11.09891870112007,2.1252624415131467,6522.987352867349,F5IV-V +Capella,43.239838363858276,78.47758735453107,12.04168320043013,4903.922634597786,G8III +Barnard's Star,6.274309005192113,-4.612503609841812,0.27142011745919525,3164.7134904158015,M4Ve +Arcturus,36.23014602060893,167.67967953475542,25.372980607290035,4285.208656712724,K1.5III +Canopus,309.89434307337245,10497.613821111012,70.98540952072986,7395.822565906416,A9II +Altair,16.481723812762002,10.654505105436737,1.6979772339329253,7557.224465410894,A7V +Polaris,323.4890826975579,2197.4846549730764,37.5928890041696,6003.417411946257,F7Ib +Barnard's Star,6.210116619871699,3.922475110995184,0.20022109890664536,3104.152562146686,M4Ve +Castor,52.3208563941243,52.46460236565418,2.3580848977347673,10286.21254520186,A1V +Castor,51.687127344898485,57.564078324527344,2.4793370398018366,10314.20142645494,A1V +Bellatrix,240.3505524329565,6403.480526616087,5.803192364925976,22632.467298684776,B2III +Polaris,323.4772161689265,2202.925953103468,37.430399789571695,5986.795558342898,F7Ib +Castor,52.31788545862922,54.098680128048926,2.3832137598247782,10264.67768772168,A1V +Barnard's Star,5.5635565189978085,-3.4589548280686215,0.14953611720828194,3115.0666806206127,M4Ve +Vega,25.02265608280089,43.21333423901169,2.4233371866214184,9631.823941413108,A0V +Wolf 359,8.040322484690764,3.518164733709185,0.2159582033041309,2786.809004459221,M6V +Regulus,79.457885087804,284.3075843561486,3.1848771847975916,12429.346874442883,B7V +Polaris,322.7090337593032,2201.1895342191797,37.558280857740726,5986.9294428280555,F7Ib +Antares,549.8094446057289,10004.888893429817,679.9451981136416,3503.22033874066,M1.5Iab +Regulus,78.84980314077531,283.1518425898611,3.1746164987628713,12414.98856094767,B7V +Acrux,320.53588050006994,25000.424960003416,8.30013131527051,27969.950394242773,B0.5IV +Alnilam,1999.5922378259786,53697.14296034396,32.48838765422113,27489.295431556606,B0Ia +Bellatrix,239.79573031543998,6401.125654435876,5.829045687901594,22632.624354404084,B2III +Hadar,349.8378240849879,50002.534526391784,8.94744777918556,25006.352397614006,B1III +Rigil Kentaurus,4.039455513108716,0.2620564024777934,1.3098243183774203,5823.975302815448,G2V +Vega,25.32510113537497,44.01956664255118,2.338790440069123,9648.560925173246,A0V +Alnilam,2000.3720438244097,53702.88073058354,32.357275804804594,27504.394522638224,B0Ia +Ross 154,9.93913784187845,-3.6471413928808327,0.23960331933197554,2767.195283308996,M3.5V +Vega,24.53113047299527,38.00891382001232,2.4294754005156816,9615.603301550884,A0V +Alnilam,2000.4789929236533,53701.23908573748,32.473046207098115,27463.288997937103,B0Ia +Castor,51.78867651717732,57.40613996148579,2.4323768195012194,10276.929953106777,A1V +Betelgeuse,642.1530702565499,126000.10150499652,886.9045632911253,3462.1435724115922,M2Iab +Alnilam,1999.880482110407,53695.72165124452,32.35326480955992,27490.40560120546,B0Ia +Bellatrix,240.30865829394963,6398.706399629343,5.727504552499588,22561.66109433901,B2III +Hadar,349.98072783140907,50000.69303998948,8.940299046830283,25043.812798420327,B1III +Barnard's Star,6.102187239521027,-0.6905551570802375,0.2265784314346753,3158.9781846800365,M4Ve +Barnard's Star,6.098594827311291,-3.3303098730228937,0.12291605693194557,3144.711153515979,M4Ve +Spica,250.4596817064251,21996.281907685272,7.481964414843237,25371.671565613717,B1III-IV +Deneb,2600.219144334045,195996.44277808978,203.09713635797436,8512.606175957664,A2Ia +Bellatrix,239.80471588472972,6400.473393470613,5.678484912182976,22628.27195885336,B2III +Fomalhaut,25.040609497955693,20.8052240111299,1.8933606922604622,8570.329145806458,A3V +Rigil Kentaurus,4.0542023166505174,5.3173565377787355,1.1417731281419847,5802.341326578711,G2V +Vega,25.145088627017778,35.23872230380841,2.3238256403915494,9614.217107611483,A0V +Canopus,309.5332875345678,10502.614219521176,71.06798014581466,7323.458433377934,A9II +Ross 154,9.61921476063749,-3.572031571628307,0.0978087615313327,2796.4035456819356,M3.5V +Betelgeuse,642.0262242230878,125998.59509722542,886.939182195466,3543.743725665777,M2Iab +Altair,17.004752557357463,15.509653584166163,1.5741356101164594,7589.01886160571,A7V +Procyon,10.930399214053109,8.573241191737985,1.9730986292291883,6548.089233804079,F5IV-V +Betelgeuse,642.1523114825334,125996.07898971764,886.9540167980291,3465.584906649717,M2Iab +Sirius,8.40488504234644,30.363211555162465,1.6975503190465253,9929.197486761122,A1V +Hadar,349.89703894408365,50003.29860598514,9.072118415520334,25020.059682136376,B1III +Arcturus,36.55940961200518,173.1186537350129,25.327584506221395,4293.232149954749,K1.5III +Ross 154,9.762760206254507,-0.22596537315790627,0.10437345974328288,2771.1757655682736,M3.5V +Alnilam,2000.4968087684344,53700.915273161765,32.32831679585277,27522.876506962413,B0Ia +Lalande 21185,8.539132551448054,-1.0184094871245468,0.3484419457491451,3396.2390187861406,M2.1V +Polaris,322.7829657396212,2197.4579935613187,37.56516587074775,6063.924563018583,F7Ib +Deneb,2599.541671531891,195998.9041368802,202.92157091325018,8503.08100456294,A2Ia +Vega,24.959311232910533,38.236535117812586,2.2955242719863977,9650.454657524815,A0V +Acrux,321.3978818285302,25002.451304950046,8.40073431108804,28000.481701856206,B0.5IV +Spica,250.3496408249325,22004.59479708713,7.48705009677652,25392.535590103464,B1III-IV +Capella,42.98764050809396,80.53611367039014,11.928552734356183,4936.351352278644,G8III +Altair,16.790538913857493,7.001586190285418,1.6190056898410539,7500.178549124861,A7V +Antares,550.3341533847138,9997.61131020143,680.029117220661,3516.839353089165,M1.5Iab +Capella,42.80414644083066,77.7419793077256,12.028719516305973,4937.014499217616,G8III +Wolf 359,7.987098773049044,-4.9033579226853,0.25746878785429467,2800.610804893218,M6V +Spica,250.1853696897831,22002.94677024924,7.329968093356848,25440.0209321473,B1III-IV +Acrux,320.5863393634801,24997.637919736644,8.310447834832418,27989.401366565075,B0.5IV +Mira,418.21532323512395,8697.78504078488,369.95011289757565,2955.942150604245,M7IIIe +Altair,17.114041468810747,7.967577662722815,1.6666693118467406,7589.938436857879,A7V +Betelgeuse,642.4002182709401,125995.07325895016,887.0102958691334,3481.0895756208192,M2Iab +Barnard's Star,6.066146574141507,-4.202962127667188,0.18560290038694682,3157.0629115253996,M4Ve +Achernar,144.41347739924058,3145.670632301648,9.151738144396306,14986.331180964033,B6Vep 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B,4.044363778891645,-4.549088213039762,0.9391905375045383,5286.304303993784,K1V +Wolf 359,8.139786203659344,3.484204727275103,0.23632401553344878,2813.6003663283495,M6V diff --git a/ejemplos/python/MatplotlibSeaborn.ipynb b/ejemplos/python/MatplotlibSeaborn.ipynb index 9e64eab..d2015c3 100644 --- a/ejemplos/python/MatplotlibSeaborn.ipynb +++ b/ejemplos/python/MatplotlibSeaborn.ipynb @@ -6,15 +6,13 @@ "source": [ "# Matplotlib & Seaborn — Visualización de Datos\n", "\n", - "> **Referencia:** Stewart & Mommert, *Python for Scientists* (3ª ed.), Capítulo 6\n", - "\n", "## ¿Qué es Matplotlib?\n", "\n", - "**Matplotlib** es la librería de visualización más popular de Python. Fue inspirada por las capacidades gráficas de MATLAB pero es completamente independiente. Permite crear gráficas de calidad de publicación científica con control total sobre cada elemento visual.\n", + "**Matplotlib** es la librería de visualización más popular de Python. Permite crear gráficas de publicación científica con control total sobre cada elemento visual.\n", "\n", - "- Muy **flexible** y **poderosa**\n", + "- Es muy **flexible** y **poderosa**\n", + "- Algo verbosa: requiere varias líneas para hacer una gráfica detallada\n", "- Base de muchas otras librerías de visualización\n", - "- Dos interfaces: **procedural** (`plt.plot()`) y **orientada a objetos** (`ax.plot()`)\n", "\n", "## ¿Qué es Seaborn?\n", "\n", @@ -52,8 +50,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Matplotlib versión: 3.10.8\n", - "Seaborn versión: 0.13.2\n" + "Matplotlib versión: 3.8.0\n", + "Seaborn versión: 0.12.2\n" ] } ], @@ -63,10 +61,11 @@ "import numpy as np\n", "import pandas as pd\n", "\n", + "# Esta línea hace que las gráficas se muestren dentro del notebook\n", "%matplotlib inline\n", "\n", "print(\"Matplotlib versión:\", __import__('matplotlib').__version__)\n", - "print(\"Seaborn versión:\", sns.__version__)" + "print(\"Seaborn versión:\", sns.__version__)" ] }, { @@ -81,324 +80,46 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Nivel 1 — Gráfica de línea básica\n", - "\n", - "La gráfica más simple solo requiere `plt.plot(x, y)` y `plt.show()`. \n", - "Personalización básica: `color`, `linewidth`, `linestyle`, `label`." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# La gráfica más simple\n", - "x = np.linspace(0, 2 * np.pi, 100)\n", - "\n", - "plt.plot(x, np.sin(x), color='blue', linewidth=2, label='sen(x)')\n", - "plt.plot(x, np.cos(x), color='red', linewidth=2, label='cos(x)', linestyle='--')\n", - "\n", - "plt.title('Funciones trigonométricas', fontsize=14)\n", - "plt.xlabel('x (radianes)')\n", - "plt.ylabel('y')\n", - "plt.legend()\n", - "plt.grid(True, alpha=0.3)\n", - "plt.axhline(0, color='black', linewidth=0.5)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Nivel 2 — Interfaz Orientada a Objetos (recomendada)\n", - "\n", - "> **§6.2 Stewart & Mommert** — El libro recomienda usar la interfaz OO para todo excepto gráficas simples.\n", - "\n", - "La interfaz procedural (`plt.plot()`) es conveniente pero limitada. \n", - "La interfaz **orientada a objetos** trabaja con dos objetos:\n", - "\n", - "- **`Figure`** (`fig`): el lienzo completo, como una hoja de papel\n", - "- **`Axes`** (`ax`): un sistema de coordenadas dentro del lienzo\n", - "\n", - "```python\n", - "fig, ax = plt.subplots() # crear figura + un axes\n", - "ax.plot(x, y) # graficar en ese axes\n", - "ax.set_xlabel('x') # nota: set_ en vez de plt.\n", - "ax.set_ylabel('y')\n", - "ax.set_title('título')\n", - "```\n", - "\n", - "| `plt.` (procedural) | `ax.` (OO) |\n", - "|---|---|\n", - "| `plt.xlabel()` | `ax.set_xlabel()` |\n", - "| `plt.ylabel()` | `ax.set_ylabel()` |\n", - "| `plt.title()` | `ax.set_title()` |\n", - "| `plt.xlim()` | `ax.set_xlim()` |\n", - "| `plt.ylim()` | `ax.set_ylim()` |" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Misma gráfica, ahora con interfaz OO\n", - "x = np.linspace(0, 2 * np.pi, 100)\n", - "\n", - "fig, ax = plt.subplots(figsize=(9, 4))\n", - "\n", - "ax.plot(x, np.sin(x), color='blue', linewidth=2, label=r'$\\sin(x)$')\n", - "ax.plot(x, np.cos(x), color='red', linewidth=2, label=r'$\\cos(x)$', linestyle='--')\n", - "\n", - "ax.set_title('Funciones trigonométricas', fontsize=14)\n", - "ax.set_xlabel('x (radianes)')\n", - "ax.set_ylabel('y')\n", - "ax.set_xlim(0, 2 * np.pi) # rango del eje x\n", - "ax.set_ylim(-1.3, 1.3) # rango del eje y\n", - "ax.axhline(0, color='black', linewidth=0.5)\n", - "ax.legend()\n", - "ax.grid(True, alpha=0.3)\n", - "\n", - "fig.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Nivel 3 — Personalización avanzada\n", - "\n", - "#### 3a — Fórmulas matemáticas en etiquetas (LaTeX)\n", - "\n", - "Matplotlib soporta LaTeX en cualquier texto usando `r'$...$'`." + "### Nivel 1: Gráfica de línea básica" ] }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Fórmulas matemáticas con LaTeX en etiquetas\n", - "x = np.linspace(0, 4 * np.pi, 300)\n", - "\n", - "fig, ax = plt.subplots(figsize=(9, 4))\n", - "\n", - "ax.plot(x, np.exp(-0.2 * x) * np.sin(x), color='purple', linewidth=2)\n", - "\n", - "# LaTeX en title y labels\n", - "ax.set_title(r'Oscilación amortiguada: $f(x) = e^{-0.2x}\\,\\sin(x)$', fontsize=13)\n", - "ax.set_xlabel(r'$x$')\n", - "ax.set_ylabel(r'$f(x)$')\n", - "ax.axhline(0, color='black', linewidth=0.5)\n", - "ax.grid(True, alpha=0.3)\n", - "fig.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 3b — Anotaciones (`ax.annotate` y `ax.text`)\n", - "\n", - "> **§6.3.8** — Las anotaciones permiten señalar puntos específicos con texto y flechas." - ] - }, - { - "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "x = np.linspace(0, 2 * np.pi, 200)\n", - "y = np.sin(x)\n", + "# La gráfica más simple posible\n", + "x = [1, 2, 3, 4, 5]\n", + "y = [2, 4, 3, 6, 5]\n", "\n", - "fig, ax = plt.subplots(figsize=(9, 4))\n", - "ax.plot(x, y, color='steelblue', linewidth=2)\n", - "\n", - "# ax.annotate: texto + flecha apuntando a un punto\n", - "ax.annotate(\n", - " 'Máximo',\n", - " xy=(np.pi / 2, 1), # punta de la flecha\n", - " xytext=(np.pi / 2 + 0.9, 0.7), # posición del texto\n", - " arrowprops=dict(arrowstyle='->', color='red'),\n", - " fontsize=11, color='red'\n", - ")\n", - "\n", - "# ax.text: texto simple sin flecha\n", - "ax.text(4.0, -1.1, r'Mínimo en $x=\\frac{3\\pi}{2}$', fontsize=10, color='darkgreen')\n", - "\n", - "ax.set_title('Anotaciones en Matplotlib')\n", - "ax.set_xlabel('x')\n", - "ax.set_ylabel(r'$\\sin(x)$')\n", - "ax.axhline(0, color='black', linewidth=0.5)\n", - "ax.grid(True, alpha=0.3)\n", - "fig.tight_layout()\n", + "plt.plot(x, y)\n", "plt.show()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 3c — Áreas rellenas (`fill_between`) y barras de error (`errorbar`)\n", - "\n", - "> **§6.4.3–6.4.4** del libro." - ] - }, { "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(1, 2, figsize=(13, 4))\n", - "\n", - "# ── fill_between: área entre dos curvas ──────────────────────────────────────\n", - "x = np.linspace(0, 2 * np.pi, 200)\n", - "y1 = np.sin(x)\n", - "y2 = np.sin(x) + 0.4\n", - "\n", - "axes[0].plot(x, y1, color='blue', label=r'$\\sin(x)$')\n", - "axes[0].plot(x, y2, color='green', label=r'$\\sin(x)+0.4$')\n", - "axes[0].fill_between(x, y1, y2, alpha=0.3, color='skyblue', label='área')\n", - "axes[0].set_title('fill_between — área entre curvas')\n", - "axes[0].legend()\n", - "axes[0].grid(True, alpha=0.3)\n", - "\n", - "# ── errorbar: mediciones con incertidumbre ────────────────────────────────────\n", - "np.random.seed(5)\n", - "x_datos = np.arange(1, 8)\n", - "y_datos = np.array([2.1, 3.8, 3.2, 5.1, 4.6, 6.2, 5.9])\n", - "y_error = np.random.uniform(0.2, 0.6, size=7) # incertidumbre\n", - "\n", - "axes[1].errorbar(x_datos, y_datos, yerr=y_error,\n", - " fmt='8', # marcador circular\n", - " color='crimson',\n", - " ecolor='gray', # color barras de error\n", - " elinewidth=1.5,\n", - " capsize=5, # topes en las barras\n", - " label='Medición ± error')\n", - "axes[1].set_title('errorbar — mediciones con incertidumbre')\n", - "axes[1].set_xlabel('Muestra')\n", - "axes[1].set_ylabel('Valor')\n", - "axes[1].legend()\n", - "axes[1].grid(True, alpha=0.3)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "#### 3d — Escala logarítmica\n", - "\n", - "> **§6.3.2** — Útil cuando los datos abarcan varios órdenes de magnitud." - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = np.logspace(0, 3, 200) # 1 a 1000\n", + "# Gráfica de línea con personalización\n", + "x = np.linspace(0, 2 * np.pi, 100) # 100 puntos entre 0 y 2π\n", + "y_sin = np.sin(x)\n", + "y_cos = np.cos(x)\n", "\n", - "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", + "plt.figure(figsize=(10, 4)) # Tamaño en pulgadas (ancho, alto)\n", "\n", - "# Escala lineal\n", - "axes[0].plot(x, x**2, color='steelblue')\n", - "axes[0].set_title('Escala lineal')\n", - "axes[0].set_xlabel('x')\n", - "axes[0].set_ylabel(r'$x^2$')\n", - "axes[0].grid(True, alpha=0.3)\n", + "plt.plot(x, y_sin, color=\"blue\", linewidth=2, label=\"sen(x)\")\n", + "plt.plot(x, y_cos, color=\"red\", linewidth=2, label=\"cos(x)\", linestyle=\"--\")\n", "\n", - "# Escala log-log\n", - "axes[1].plot(x, x**2, color='steelblue')\n", - "axes[1].set_xscale('log') # eje x logarítmico\n", - "axes[1].set_yscale('log') # eje y logarítmico\n", - "axes[1].set_title('Escala log-log')\n", - "axes[1].set_xlabel('x')\n", - "axes[1].set_ylabel(r'$x^2$')\n", - "axes[1].grid(True, which='both', alpha=0.3) # grid en escala log\n", + "plt.title(\"Funciones trigonométricas\", fontsize=14)\n", + "plt.xlabel(\"x (radianes)\")\n", + "plt.ylabel(\"y\")\n", + "plt.legend() # Mostrar leyenda\n", + "plt.grid(True, alpha=0.3) # Líneas de cuadrícula\n", + "plt.axhline(0, color='black', linewidth=0.5) # Línea en y=0\n", "\n", - "fig.suptitle('Potencia cuadrática: mismos datos, distinta escala', fontsize=13)\n", "plt.tight_layout()\n", "plt.show()" ] @@ -407,48 +128,34 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Nivel 4 — Tipos de gráficas\n", - "\n", - "#### 4a — Gráfica de barras" + "### Nivel 2: Gráfica de barras" ] }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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7atasmRYuXKj+/furc+fOypkzpw4dOqRffvlFxYoVs7qmHADYEwIcANiRLVu2aMuWLSmu++KLL+Tt7a0mTZooPDxcW7Zs0Y0bN+Tk5KQiRYro9ddft7h4ds6cOTV9+nR9/PHH+vDDD2U0GlWzZk3VqVNH0r2LZRsMBm3ZskU7duxQmTJlNGnSJP3+++9WAS4gIEBLlixRSEiIlixZIicnJ9WpU0dLliwxXzD8YTw9PbVgwQKFhIToq6++ktFolJ+fn0JCQnTixIk0B7h3331X77//vubMmWM+utOyZUtJ945ErV27Vl988YU2b96sGzduKFeuXCpcuLBefPFF8/Ph5+en5s2b6+DBg/ruu++UkJCgggULqlOnTurfv7/VBCsP0qNHD8XExGjp0qW6dOmSvLy8NGzYMA0cONCiXbly5bRq1SrNmDFD33zzjaKiouTt7a2+fftqyJAhDzz/63E0aNBAK1eu1Oeff65vv/1WkZGRyps3rypVqqSqVatKuncE7vPPP1dISIg2btyoqKgo+fj46PXXX5eDg4PGjRuX5jqStWvXTj4+Ppo/f77WrFmj27dvK3fu3CpatKheeuklqwuwP0iVKlU0a9YszZo1SzNmzJCzs7OqVq2qZcuWacKECRZHQAHA3hhMtp4pDQAAUm3fvn3q1auXJk6cqA4dOmR1OQAAO8U5cAAAAABgJwhwAAAAAGAnCHAAAAAAYCc4Bw4AAAAA7ARH4AAAAADAThDgAAAAAMBOcB24+xw5ckQmk0k5cuTI6lIAAAAAPCXi4+NlMBhUpUqVR7blCNx9TCaTOCUwa5lMJhmNRl4HZAj6FzIS/QsZif6FjET/ynqPk0M4Anef5CNvAQEBWVzJ0ysmJkZ//PGHSpcuLTc3t6wuB08Y+hcyEv0LGYn+hYxE/8p6x44dS3VbjsABAAAAgJ0gwAEAAACAnSDAAQAAAICdIMABAAAAgJ0gwAEAAACAnSDAAQAAAICdIMABAAAAgJ0gwAEAAACAnSDAAQAAAICdIMABAAAAgJ0gwAEAAACAnSDAAQAAAICdIMABAAAAgJ0gwAEAAACAnSDAAQAAAICdIMABAAAAgJ0gwAEAAACAnSDAAQAAAICdcMrqAu43duxYffPNNw9cv3z5clWrVk2SFB4ersmTJ2vXrl0yGo0KCAjQmDFj5O/vn1nlAgAAAECmylYBrkuXLqpTp47V8smTJyshIUEBAQGSpNjYWPXu3Vvh4eHq06ePPD09tXz5cvXs2VOrVq1S6dKlM7t0AAAAAMhw2SrAValSRVWqVLFYdubMGd24cUNdunSRs7OzJGnFihU6ffq0li1bpurVq0uSWrRooaCgIE2bNk2zZ8/O9NoBAAAAIKNl+3Pg1q9fL0lq06aNedmmTZtUtmxZc3iTpLx586pVq1batWuXoqKiMr1OAAAAAMho2eoI3L+ZTCZt2LBBhQsXNp/7lpSUpLCwMLVt29aqfcWKFbV06VKdOnXK6kje40hMTLR5W6RN8nOfmJjI64B0R/9CRqJ/ISPRv5CR6F/2JVsHuEOHDuny5csaNGiQDAaDJCkiIkJxcXHy8vKyau/t7S1Junbtms33mZSUpMjISJu3R9rExcVJkmJiYngDQbqjfyEj0b+QkehfyEj0r6yXlJQkB4fUDY7M1gEupeGTyR0s+Xy4+7m4uEi6N8mJrRwcHOTh4WHz9kgbR0dHSZKbm5vc3NyyuBo8aehfyEj0L2Qk+hcyEv0r66U2vEnZOMAZjUZt2bJF/v7+KlmypHl5ckgzGo1W2ySHu5w5c6bpvpM7MTJf8nPv6OjI64B0R/9CRqJ/ISPRv5CR6F/2JdtOYrJr1y5FRERYHH2TpDx58sjZ2VnXr1+32iZ56GTyUEoAAAAAeJJk2wC3fv16OTk5qVWrVhbLHRwcVK5cOR09etRqm6NHjypHjhwqU6ZMZpUJAAAAAJkmWwa4yMhI7dy5U88995zy589vtT4oKEhhYWE6ePCgedmtW7e0ceNGBQYGyt3dPTPLBQAAAIBMkS3Pgfv+++8VFxdnNXwyWXBwsFatWqUhQ4aob9++8vDw0PLly2U0GjVq1KhMrhYAAAAAMke2PAK3YcMGubm5qUmTJimud3V11eLFi1WvXj0tWLBAU6ZMUb58+bRo0SL5+vpmcrUAAAAAkDmy5RG4xYsXP7KNl5eXQkJCMqEaAAAAAMgesuUROAAAAACANQIcAAAAANgJAhwAAAAA2AkCHAAAAADYCQIcAAAAANgJAhwAAAAA2AkCHAAAAADYCQIcAAAAANgJAhwAAAAA2AkCHAAAAADYCQIcAAAAANgJAhwAAAAA2AkCHAAAAADYCaesLgAAAHtgNBq1evVqbdq0SadOnVJ0dLRy586tYsWKqUGDBurZs6ck6fDhw9qzZ4+OHDmif/75R+Hh4XJ0dFTRokXVoEED9evXT3ny5MnaBwMAsFsEOAAAHuHmzZsaMGCAjh07ZrE8PDxc4eHhunr1qjnAzZ8/X7t377bax8mTJ3Xy5Elt2LBBK1euVMGCBTOldgDAk4UABwDAI4wcOdIc3kqVKqXu3burePHiiouL08WLF3X+/HmL9l5eXmrbtq2qV68uSfrmm2+0ZcsWSdI///yjGTNm6IMPPsjUxwAAeDIQ4AAAeIhdu3Zp7969kiQfHx+tWrVKuXLlsmoXExMjSerRo4fq1asnZ2dn87oGDRqobdu2+vPPPyVJR44cyYTKAQBPIiYxAQDgIX744QfzzxUqVNAbb7yhwMBAVaxYUa1bt9a8efOUkJBgblOnTh2L8CZJBoNBJUqUMN92dXXN8LoBAE8mjsABAPAQYWFh5p83btxose7kyZOaMmWK9u3bp2nTpj1wHzdv3jQfxZOkpk2bpn+hAICnAkfgAAB4iDt37ljc7tChg7744gsNGjTIvGzXrl3atGlTittHRETolVde0e3btyVJAQEB6tOnT4bVCwB4snEEDgCAh3BxcTH/7OXlpQ8++ECOjo6qX7++Dh06pP3790uSQkNDVbp0aYtt//rrL/Xv3988yUmlSpX0xRdfWOwTAIDHwRE4AAAeolChQuafCxcuLEdHR/PtYsWKmX+OjIy02O7IkSPq0qWLObw1bdpUixcvVu7cuTO2YADAE40ABwDAQ9SqVcv8899//62kpCTz7YsXL5p/vj/obdmyRb1799bNmzclSS+99JJmzJihnDlzZkLFAIAnGUMoAQB4iHbt2umzzz7TnTt3dO3aNb399tsKCgrSr7/+qn379pnbtWjRQpK0cuVKTZ48WSaTSdK9c+YaN26sQ4cOWew3+RpxAAA8DgIcAAAPkS9fPk2ePFmvvvqqjEajVq9erdWrV1u0GThwoKpWrao//vhDW7duNYc3SVq7dq3Wrl1rtd/ka8IBAPA4GEIJAMAjNGrUSKtXr1arVq3k5eUlJycn5cmTR4GBgZo7d65GjhyZ1SUCAJ4SHIEDACAV/Pz8NHXq1Ee2mzdvntzc3DKhIgDA04gjcAAAAABgJwhwAAAAAGAnCHAAAAAAYCcIcAAAAABgJwhwAACkA4PBIFdXVxkMhqwuBQDwBGMWSgBAhkk0JcnR8HR8V+jq6qry5ctndRmZ7ml6jQEgOyDAAQAyjKPBQePOLdXZu1ezuhRkgJKuBTXRp0dWlwEATxUCHAAgQ529e1Vhdy9ndRkAADwRGPMAAAAAAHaCAAcAAAAAdiJbBrgTJ05o0KBBqlmzpipVqqRWrVpp8eLFFm3Cw8P1+uuvq1atWqpSpYp69eql33//PYsqBgAAAICMl+3OgQsNDdXAgQNVoUIFDR48WG5ubvrrr7905coVc5vY2Fj17t1b4eHh6tOnjzw9PbV8+XL17NlTq1atUunSpbPwEQAAAABAxshWAS4qKkpvvPGGGjVqpI8//lgODikfIFyxYoVOnz6tZcuWqXr16pKkFi1aKCgoSNOmTdPs2bMzs2wAAAAAyBTZagjlhg0bFB4erldffVUODg6Kjo5WUlKSVbtNmzapbNmy5vAmSXnz5lWrVq20a9cuRUVFZWbZAAAAAJApstURuD179sjDw0P//POPBg0apAsXLsjNzU3t2rXT2LFj5eLioqSkJIWFhalt27ZW21esWFFLly7VqVOnVKVKFZvrSExMTMvDQBokP/eJiYm8Dkh39K/M5+jomNUlIBPw+5TxeP9CRqJ/2ZdsFeDOnz+vhIQEDR06VJ06dVKNGjV04MABLVmyRHfu3NHUqVMVERGhuLg4eXl5WW3v7e0tSbp27ZrNNSQlJSkyMtLm7ZE2cXFxkqSYmBjeQJDu6F+Zy9HRUR4eHlldBjIBv1MZj/cvZCT6V9ZLSkp64Olj/5atAlxMTIzu3r2rbt266c0335QkNWvWTAkJCVq+fLmGDBkiV1dXSZKzs7PV9i4uLpLuTXJiKwcHBz5wZKHkb+vd3Nzk5uaWxdXgSUP/AjIGv08Zj/cvZCT6V9ZLbXiTslmAy5kzpySpZcuWFstbtmyp5cuX6/Dhw2rUqJEkyWg0Wm2f/O1B8n5sxZCfrJP83Ds6OvI6IN3Rv4CMwe9TxuP9CxmJ/mVfstUkJslDIPPly2exPPn2nTt3lCdPHjk7O+v69etW2ycPnUzeDwAAAAA8SbJVgKtQoYIk6erVqxbLk2/ny5dPDg4OKleunI4ePWq1/dGjR5UjRw6VKVMm44sFAAAAgEyWrQLcCy+8IElau3atxfK1a9fK0dFRtWrVkiQFBQUpLCxMBw8eNLe5deuWNm7cqMDAQLm7u2de0QAAAACQSbLVOXDly5dXx44dtWbNGiUkJJhnody0aZP69eunZ599VpIUHBysVatWaciQIerbt688PDy0fPlyGY1GjRo1KosfBQAAAABkjGwV4CRpwoQJKlSokNauXautW7eqUKFCGjt2rPr06WNu4+rqqsWLF2vy5MlasGCBjEajAgICNHHiRPn6+mZd8QAAAACQgbJdgMuRI4eGDh2qoUOHPrSdl5eXQkJCMqkqAAAAAMh62eocOAAAAADAg6VrgDt06JB27NihmJiY9NwtAAAAAEA2DqGcO3eu9u/fr3nz5pmXDR06VNu3b5ckFSpUSCtWrJCXl1f6VAkAAAAAsO0I3KZNm1SkSBHz7V9++UXbtm1TixYtNGLECN24ccMi3AEAAAAA0s6mI3D//POPOnToYL69fft2FShQQFOmTJGDg4Nu3rypnTt3aty4celWKAAAAAA87Ww6Anf37l3lypXLfPvQoUOqU6eOHBzu7a5MmTK6du1a+lQIAAAAAJBkY4Dz8vLSuXPnJEnh4eE6efKkqlWrZl4fFRUlJ6dsd4UCAAAAALBrNqWs5557TsuXL1fevHm1Z88eOTo66vnnnzevP3v2rJ555pl0KxIAAAAAYGOAGzp0qA4ePKgpU6bIYDBo+PDhevbZZyVJCQkJ2rp1q4KCgtK1UAAAAAB42tkU4AoWLKiNGzfq9OnT8vT0NIc3SYqNjdV//vMflS1bNt2KBAAAAADYGOAkydHRUX5+flbL3d3d1aRJkzQVBQAAAACwZtMkJpKUlJSkdevWafTo0erbt69OnDghSbp9+7bWrVunq1evpluRAAAAAAAbj8DFxsbq5Zdf1sGDB+Xq6qrY2Fjdvn1b0r0jcFOmTFGnTp00YsSI9KwVAAAAAJ5qNh2BmzVrln799VfNmDFD27dvl8lkMq9zdHRU06ZNFRoamm5FAgAAAABsDHCbN29Wly5d1LRpUxkMBqv1xYsX1+XLl9NcHAAAAADgf2wKcFeuXHnoLJO5cuVSVFSUzUUBAAAAAKzZFOA8PT0VHh7+wPXnzp1TgQIFbC4KAAAAAGDNpgBXvXp1rVu3TgkJCVbrwsPDtWbNGtWuXTvNxQEAAAAA/semADdw4ED9888/6tGjh7Zs2SJJOnbsmBYuXKj27dsrPj5eAwYMSNdCAQAAAOBpZ9NlBMqXL6+ZM2dq/Pjxeu+99yRJ06dPl8lkUoECBTRr1iz5+PikZ50AAAAA8NSzKcBJUv369bVjxw7t3r1bZ86cUVJSknx8fBQYGKicOXOmZ40AAAAAAKUhwEmSs7OzGjRooAYNGqRTOQAAAACAB7HpHLhy5cppw4YND1y/adMmlStXzuaiAAAAAADWbApwJpMpTesBAAAAAI/PpgD3KH///bdy5cqVEbsGAAAAgKdWqs+B27Ztm7Zv326+/fXXX2v37t1W7W7fvq09e/aoatWq6VMhAAAAAEDSYwS4sLAwffPNN5Ikg8GgAwcO6MCBA1bt3NzcVKVKFb3zzjvpVyUAAAAAIPUBbujQoRo6dKgkqWzZspoyZYpat26dYYUBAAAAACzZdBmBxYsXq3Tp0uldCwAAAADgIWwKcDVr1kzvOgAAAAAAj2DzhbyvXLmir776SufPn1dERITVpQMMBoMWLVqU5gIBAAAAAPfYFOB++eUXDRo0SEajUW5ubsqTJ086lwUAAAAA+DebAtzUqVPl6empWbNmqVKlSuldEwAAAAAgBTZdyPvUqVPq3bs34Q0AAAAAMpFNAc7T01Ourq7pXQsAAAAA4CFsCnBNmjTRrl270rsWAAAAAMBD2BTgXn/9dd2+fVvvvfee/vrrL6sZKAEAAAAA6c+mSUyqV68ug8Ggo0ePauXKlSm2MRgMOnHixGPtd9++ferVq1eK67788ks999xz5tvh4eGaPHmydu3aJaPRqICAAI0ZM0b+/v6PdZ8AAAAAYC9sCnDt2rWTwWBI71rMunfvbjVBSpkyZcw/x8bGqnfv3goPD1efPn3k6emp5cuXq2fPnlq1apVKly6dYbUBAAAAQFaxKcBNmjQpveuwUK1aNbVs2fKB61esWKHTp09r2bJlql69uiSpRYsWCgoK0rRp0zR79uwMrQ8AAAAAsoJN58BlhujoaMXHx6e4btOmTSpbtqw5vElS3rx51apVK+3atUtRUVGZVSYAAAAAZJo0B7iYmBhduXJFf//9t9U/W7311luqWrWqKlasqG7duunQoUPmdUlJSQoLC1PFihWttqtYsaLi4+N16tQpm+8bAAAAALIrm4ZQStLmzZs1e/ZsnT59+oFt/vjjj8faZ44cOdS8eXPVq1dP+fLl05kzZ7RgwQL17t1bixYtUrVq1RQREaG4uDh5eXlZbe/t7S1Junbt2uM9mH9JTExM0/awXfJzn5iYyOuAdEf/ynyOjo5ZXQIyAb9PGY/3L2Qk+pd9sSnA/fjjjxo5cqSKFy+uzp07a+XKlWrZsqUSExO1fft2+fn5qUGDBo+936pVq6pq1arm240bN9YLL7yg1q1bKyQkRF999ZXi4uIkSc7Ozlbbu7i4SLo3yYmtkpKSFBkZafP2SJvk1zcmJoY3EKQ7+lfmcnR0lIeHR1aXgUzA71TG4/0LGYn+lfWSkpLk4JC6wZE2Bbj58+fLx8dH69atU3R0tFauXKkXX3xRderUUVhYmIKDgzVkyBBbdm2laNGiat68udavX6+7d++aQ5rRaLRqm9z5cubMafP9OTg48IEjCyV/W+/m5iY3N7csrgZPGvoXkDH4fcp4vH8hI9G/sl5qw5tkY4D7448/9Morr8jFxUV3796VJPPFvMuWLatOnTrp888/V8OGDW3ZvZVChQopKSlJd+7ckZeXl5ydnXX9+nWrdslDJ5OHUtqKIT9ZJ/m5d3R05HVAuqN/ARmD36eMx/sXMhL9y77YNIlJQkKC8uXLJ+l/wxbvn/mxTJky+vPPP9OhvHsuXrwoR0dH5c6dWw4ODipXrpyOHj1q1e7o0aPKkSOHxTXjAAAAAOBJYVOA8/b21tWrVyVJrq6uyp07t06ePGlef+nSpRTPUXuUiIgIq2VnzpzRli1bVKNGDfPQyKCgIIWFhengwYPmdrdu3dLGjRsVGBgod3f3x75vAAAAAMjubBpCWbFiRR0+fNh8u379+lq0aJEKFy6spKQkffXVVxaTkaTWyJEj5ebmJn9/f+XPn1/nzp3TihUr5OTkpDfeeMPcLjg4WKtWrdKQIUPUt29feXh4aPny5TIajRo1apQtDwkAAAAAsj2bAlyHDh20du1axcbGKmfOnBoxYoT279+vcePGSZK8vLw0evTox95vw4YNtXHjRi1cuFBRUVHKkyePGjVqpMGDB6tUqVLmdq6urlq8eLEmT56sBQsWyGg0KiAgQBMnTpSvr68tDwlZ4NixYwoODra4YPuRI0fMP69du9bcpx7khx9+UPHixTOsRgAAACA7sSnA1a1bV3Xr1jXfLly4sDZv3qy9e/fK0dFRVatWtWkmx169eqlXr16pauvl5aWQkJDHvg9kD3fv3tXo0aMtwhsAAACAh7P5Qt7/5ubmpkaNGqXX7vCE+/DDD3X+/Hm5uLiYL//wMAMHDlS9evWslj/zzDMZUR4AAACQLaVbgANSa9u2bfr666+VI0cOjRw5UpMmTXrkNsWLF1f16tUzoToAAAAg+0pVgOvVq5cMBoPmz58vJyenVA1zNBgMWrRoUZoLxJPl2rVreuuttyRJI0aMUPny5VO13bRp0zRhwgRJ94bsPv/88+rfv7/5chYAAADA0yBVAe7SpUsyGAzmi3VfunQpQ4vCk8lkMmncuHG6deuWateurX79+mn//v2p2vb+C7efOXNGZ86c0Xfffafly5erSJEiGVUyAAAAkK2kKsDt2LHjobeB1Fi8eLFCQ0OVJ08effTRRzIYDA9t7+TkpLp166phw4YqVqyY4uPjtW3bNn3zzTeSpKtXr+qDDz7QnDlzMqN8AAAAIMtxDhwyxdWrVzV16lRJ0vvvv6+CBQs+cps2bdqoTZs2FsuaNGmiuLg4bdq0SZIUGhoqo9Fo04XjAQAAAHtDgEOmuHnzpnm2yeHDhz+wXZUqVeTm5qZffvnlgW2qVatmDnDx8fG6detWqgIhAAAAYO9SPYnJ42ISE6TVr7/+qsqVK1stP3TokPnnHDlyKG/evJlYFQAAAJB1Uj2Jyb/dvXtXt27dkiTzRbsjIyMlSXnz5pWbm1t61YgnQMGCBTVu3Dir5X/99ZeWLVtmvj169GjduHFDkjRq1Cjlzp1bLVq0UNmyZWU0GrV161bz0TdJaty4McMnAQAA8NSwaRKTy5cvq2fPnmrWrJkGDx5sHr529epVzZ49Wz///LOWLFmS/tXCbuXLl099+vSxWr5v3z6LANe9e3f98ccf5tsnTpzQiRMnUtxniRIl9Oabb6Z7rQAAAEB25WDLRpMnT1a5cuU0YcIEi3OPChYsqAkTJqhs2bKaPHlyuhWJp9OkSZPUu3dvVahQQV5eXnJycpK7u7sqVaqk1157TevWrZO3t3dWlwkAAABkGpsmMdm7d69GjBjxwPV169bVJ598YmtNeIrUqlVLf/75p/l2TEyM+eeaNWuqZs2aWVEWAAAAkC3ZdAQuPj5eV69efeD6q1evKj4+3uaiAAAAAADWbApwlSpV0rJlyxQWFma17o8//tCyZctUqVKlNBcHAAAAAPgfm4ZQjh49Wj169FDHjh1Vr149lSpVSpJ05swZ/fzzz3J2dtbo0aPTtVAAAAAAeNrZFOD8/f21bNkyffjhh9q5c6d27txpXletWjWNGzdO/v7+6VUjAAAAAEA2BjhJqlChgpYtW6abN2/q4sWLkqSiRYsqX7586VYcnj4Gg0Gurq4yGAxZXQoAAACQ7dgc4JLly5eP0JbBEpNMcnR4OgKNq6urypcvn9VlZLqn6TUGAACA7WwKcH///Xeq2hUqVMiW3eNfHB0Menv7FZ2LYGbPJ5FPnhz6T+NnsroMAAAA2AGbAlyjRo1SNcTtjz/+sGX3SMG5iHj9GR6X1WUAAAAAyEI2BbghQ4ZYBbiEhARdvHhR27Ztk5+fn+rXr58uBQIAAAAA7rEpwA0bNuyB686fP68uXbooICDA5qIAAAAAANZsupD3w5QoUUJdu3bVzJkz03vXAAAAAPBUS/cAJ92bvOTUqVMZsWsAAAAAeGplSIDbu3ev3NzcMmLXAAAAAPDUsukcuHXr1qW4PCIiQnv27NGuXbvUtWvXtNQFAAAAAPgXmwLc2LFjZTAYZDKZrHfo5KTOnTtrzJgxaS4OAAAAAPA/NgW4xYsXWy0zGAzKnTu3ihQpwvBJAAAAAMgANgW4mjVrpncdAAAAAIBHsGkSk169emnPnj0PXL9371716tXL5qIAAAAAANZsCnD79+9XeHj4A9ffvHlTBw4csLkoAAAAAIC1DLmMwJ07d+Ts7JwRuwYAAACAp1aqz4ELCwtTWFiY+fbBgweVmJho1S4iIkJfffWVSpUqlT4VAgAAAAAkPUaA27Ztm2bOnCnp3oyTK1eu1MqVK1NsmytXLr355pvpUyEAAAAAQNJjBLj27durZs2aMplM6t27twYOHKjnnnvOoo3BYJCbm5tKly4tFxeXdC8WAFJy5coVzZ49W8ePH9eVK1d0+/ZtGQwGeXl5yd/fX8HBwapTp47FNseOHdOiRYt05MgRXbt2TUlJScqdO7f8/PzUtm1btW3bVgaDIYseEQAAQMpSHeAKFy6swoULS5KGDh2qZs2aydfXN8MKA4DUunTpUoojAi5fvqzLly9ry5Yteuedd9S+fXtJ0s8//6xRo0YpISHBov2NGze0e/du7d69W0ePHtU777yTKfUDAACklk3XgRs6dGiKy5OSkhQTEyN3d/c0FQUAj8PNzU0tWrRQzZo19cwzz8jFxUXHjx/X7NmzFRMTI0n6/PPPzQHuyy+/NIe3MmXKaPjw4XJxcdGCBQu0d+9eSdKKFSv02muvKVeuXFnzoAAAAFJgU4D78ccf9euvv2rkyJHmZQsXLtT06dNlNBrVtGlThYSEpHkmym+//VZjxoyRs7Ozjh07ZrEuPDxckydP1q5du2Q0GhUQEKAxY8bI398/TfcJwP6UL19e06dPt1j23HPP6erVq1qyZIkkKSoqyrzuzp075p+7du2qZs2aSZJcXFzMAS4xMVFJSUkZXToAAMBjsSnALViwQPnz5zffPnXqlCZPnqxixYqpSJEi+uGHH7Rs2TL17dvX5sKio6MVEhIiNzc3q2FOsbGx6t27t8LDw9WnTx95enpq+fLl6tmzp1atWqXSpUvbfL8A7N/du3d14sQJ7dy507ysXr165p9r166tM2fOSLp3pM3b29t8BC5Zs2bN5OHhkWk1AwAApIZNAe7MmTN6/vnnzbe/++475cyZU6tWrZKnp6dGjhyp9evXpynAzZ49W+7u7qpZs6Z++OEHi3UrVqzQ6dOntWzZMlWvXl2S1KJFCwUFBWnatGmaPXu2zfcLwH5Nnz5dc+bMsVjm7OysFi1a6K233jIvGzp0qBISErR27VqdOnVKw4YNM6/LlSuX+vfvr379+mVa3QAAAKll04W8IyMjlS9fPvPtgwcPqlatWvL09JQk1apVS5cuXbK5qPPnz2vRokUaO3ascuTIYbV+06ZNKlu2rDm8SVLevHnVqlUr7dq1y2KoFICnm6OjoxwcHCyGQzo7O8vHx8diJEGy6Ohoff/99zp37lxmlgkAAJAqNgW4PHny6Pr165KkuLg4HT16VFWrVjWvT0pKshr2+Dg+/PBD1a5d2+Io3/37DgsLU8WKFa3WVaxYUfHx8Tp16pTN9w3AfnXp0kXLli3T559/rmHDhilXrly6e/eu1q5dq0GDBpnbffTRR5o0aZKuXLmihg0b6qefftKhQ4c0fPhwSVJYWJj69etnngAFAAAgu7BpCGWFChW0atUq1a1bVz/88IPi4+Mtzi+5ePFiit9sp8aPP/6oX375RevXr09xfUREhOLi4uTl5WW1ztvbW5J07do1m+47WWJiYpq2T2+Ojo5ZXQIyQXbrd/aoYMGCKliwoKR757x5eXmZLwVw6NAhnTt3TgkJCVq9erV5m6FDh5rfTwYOHKgvv/xSkZGRun79uvbv32/x3obHx/vX04H3r4yX/BwnJibyfCPd0b/si00BbuDAgerbt686deokk8mkBg0aqFy5cub1O3fuVKVKlR57v0ajURMnTlRwcLBKlSqVYpu4uDhJSnGGy+SLh8fGxj72fSdLSkpSZGSkzdunN0dHRyZSeErExMTwpmmj2NhY5cyZ02r53bt3LW4nX7D7/uf52rVrKlKkiKR77y9Go9G87saNG9nq/cDe8P719OD9K+Mlf/7huUZGoH9lvaSkJDk4pG5wpE0BrnLlyvrmm2/0888/y9PTUy1btjSvu3XrlurVq6emTZs+9n6//PJL3b59+4HXmZP+F9Lu/5CVLLnzpfRBLrUcHBz4wIEs4ebmltUl2K2XX35ZBQsWVJ06dVSoUCEZDAYdP37cYlbJXLlyqUKFCrpw4YLy58+vGzduSJJCQkI0ePBg5cqVSytXrjS/jxgMBlWrVo33AyAVeP/KeMlHs93c3Hi+ke7oX1kvteFNsjHASVKJEiVUokQJq+V58+bV+PHjH3t/kZGRmjNnjoKDg3X79m3dvn1b0r0JBUwmky5cuCBXV1cVKFBAzs7O5nPw7pc8dDJ5KKWtGPKDrEC/s118fLy2bdumbdu2pbg+R44c+uCDD+Tu7i4HBweNHj1ab775ppKSknTmzBm99tprVtu89NJLKb7HAbDG+1fGS36OHR0deb6R7uhf9sXmACdJly5d0p49exQeHq7WrVurSJEiMhqNCg8PNwet1Lp9+7ZiYmI0f/58zZ8/32p9s2bNFBgYqPnz56tcuXI6evSoVZujR48qR44cKlOmTFoeFgA706tXL+3cuVNhYWG6efOm7t69K1dXVxUtWlTVq1dXt27dVLJkSfOkJEFBQSpVqpQWL16sI0eO6Nq1azKZTMqTJ48qVKigjh07qnnz5ln8qAAAAKzZHOCmTZum+fPnKzExUQaDQZUrVzYHuJYtW2rEiBHq3bt3qveXP39+ffLJJ1bLly1bpiNHjigkJEQFChSQdO/D1+TJk3Xw4EHzpQRu3bqljRs3KjAwUO7u7rY+LAB2qGPHjurYseNjbVOpUiVNnTo1gyoCAADIGDYFuFWrVmnu3Lnq0aOHGjZsaHHBW3d3dzVo0EA//vjjYwU4V1dXBQUFWS3fuXOnfv31V4t1wcHBWrVqlYYMGaK+ffvKw8NDy5cvl9Fo1KhRo2x5SAAAAACQ7dl0Hbjly5ercePGeuutt1S+fHmr9WXLls3Qi+C6urpq8eLFqlevnhYsWKApU6YoX758WrRokXx9fTPsfgEAAAAgK9l0BO7s2bPq3LnzA9fny5dPN2/etLmo+02aNEmTJk2yWu7l5aWQkJB0uQ8AAAAAsAc2HYFzcnIyT7WdkqtXr3IeGgAAAACkM5sCXPny5bVz584U1yUkJGjjxo02XcgbADKSwWCQq6urDAZDVpcCAABgE5sCXI8ePbR3715NnDjRfD22+Ph4/fHHHxo4cKAuXLignj17pmuhADKGKSkxq0vINK6uripfvrxcXV2zupRM9TS9xgAAPOlsOgeuefPmGjJkiGbPnq3FixdLkl555RVJkslk0ogRI1S3bt30qxJAhjE4OCoi5H0lXLqQ1aUgAzgVKa48o9/J6jIAAEA6sfk6cMOGDVPjxo21YcMGnT17VklJSSpRooTatWunChUqpGeNADJYwqULSjhzMqvLAAAAwCPYHOCke+fCpXQZAQAAAABA+rPpHDgAAAAAQOYjwAEAAACAnUjTEEoAAAAA2dOVK1c0e/ZsHT9+XFeuXNHt27dlMBjk5eUlf39/BQcHq06dOub2hw4d0rp16xQWFqbw8HBFRkbKxcVFzz77rGrUqKE+ffrIx8cnCx8RJAIcAAAA8ES6dOmSVq5cabX88uXLunz5srZs2aJ33nlH7du3lyQdPnxYGzdutGgbExOjM2fO6MyZM/r222+1ZMkSBQQEZEr9SBkBDgAAAHgCubm5qUWLFqpZs6aeeeYZubi46Pjx45o9e7ZiYmIkSZ9//rk5wHl5eSk4OFiVK1eWl5eXEhIS9OOPP+qrr76SJN29e1eLFi1SSEhIlj0mEOAAAACAJ1L58uU1ffp0i2XPPfecrl69qiVLlkiSoqKizOvatWsnNzc3i/bPP/+8Dh06pJMnT1q1R9ZIU4CLiYnRr7/+qvDwcD333HMqUKBAetUFAAAAIB3dvXtXJ06c0M6dO83L6tWr98D2d+7c0U8//aTz58+nqj0yh80BbuXKlQoJCVFkZKQMBoMWLFigAgUK6MaNG3r++ef1zjvvqHPnzulZKwAAAIDHNH36dM2ZM8dimbOzs1q0aKG33nrLqn1wcLAOHz5ssSxv3rzq1auXgoODM7RWPJpNlxHYtm2b3n33XVWvXl0ffPCBTCaTeV3+/PkVGBio7du3p1uRAAAAANKPo6OjHBwclJSUlKr2Tk5OMhgMGVwVUsOmI3Dz5s1TjRo19Nlnn+nWrVtWyT0gIEBr1qxJlwIBAAAA2K5Lly6qV6+eoqKi9Pvvv2vBggWKjo7W2rVrdeHCBc2bN8+i/Xvvvac7d+4oIiJCP/30k1atWqXr16/r448/VmxsrEaOHJlFjwSSjQHuzz//1KhRox643tvbW+Hh4TYXBQAAACB9FCpUSIUKFZIkNWjQQAULFjQfgDl06JAuXLhg0d7Pz8/8c9OmTRUbG6sNGzZIktauXUuAy2I2DaF8lBs3bihnzpwZsWsAAAAAqXD37t0Ul99/+pMkRUZGKikpSUaj8ZHt79y5k34FwiY2HYErXbq09u/fr549e6a4ftu2bSpXrlyaCgMAAABgu549e+qZZ55RnTp1VKRIEUnS77//rvnz55vb5MqVS6VKldLx48fVtGlTtWjRQgEBASpUqJBiYmK0a9cui4t7V65cObMfBv7FpgDXqVMnvfvuu1q8eLFeeOEFSZLBYNDt27c1ZcoUHT9+XFOnTk3XQgEAAACkXnx8vLZu3aqtW7emuD5Hjhz64IMP5OrqKune0bUVK1ZoxYoVKbb38vLSO++8k2H1InVsCnCdO3fW4cOH9eGHH2r69OkyGAwaPny4IiMjZTKZ1LlzZ7Vo0SK9awUAAACQSr169dLOnTsVFhammzdv6u7du3J1dVXRokVVvXp1devWTSVLllRMTIxcXFw0YMAA/f777zp37pxu3bqlhIQEeXh4qGTJkqpfv766du2qPHnyZPXDeurZfB24SZMmqUmTJlq/fr3Onj2rpKQkVa9eXe3bt1fTpk3Ts0YAAAAAj6ljx47q2LFjqto6OTlp0KBBcnNzy+CqkFY2BzhJatKkiZo0aZJetQAAAAAAHiJDZqEEAAAAAKS/VB2Bmzlz5mPv2GAwaMiQIY+9HQAAAAAgZTYHOIPBIMn6OhIGg0Emk4kABwAAAADpLFUBbvv27Ra3Y2NjNWbMGCUlJemll15SmTJlZDKZdOrUKS1YsEBOTk6aPHlyhhQMAAAAIP0YDAa5urqaD9Age0tVgCtcuLDF7UmTJslkMmnlypVydnY2Ly9XrpyaN2+url27avXq1XrjjTfSt1oAAAAggyUlmeTg8PSEGVdXV5UvXz6ry8h09vo62zQL5aZNm9S3b1+L8JbMxcVFbdq00ZdffkmAAwAAgN1xcDDox1m/KeJyVFaXggySp7C7Gg6plNVl2MSmABcREaGEhIQHro+Pj1dERIStNQEAADwVLly4oM2bN+vgwYP666+/FB4ervj4eHl5ealmzZrq16+fypQpY7HN5cuXNW/ePIWGhurKlSvKkSOHSpUqpXbt2qlLly5yckrTVaLw/yIuR+nG+TtZXQZgxabfcB8fH61du1bdunVTrly5LNZFRUVp7dq18vHxSZcCAQAAnlRbtmzR9OnTrZZfvnxZ33zzjb777jvNmTNHVapUkSQdO3ZMgwcPVlTU/44MGY1GHT16VEePHtWOHTs0Z84c5ciRI9MeA4DMZdN14Pr3769z586Zh0r+9NNP+umnn7RgwQK1adNGFy5cUP/+/dO7VgAAgCdOzpw51aFDB02fPl3z58/XK6+8IkdHR0n3wtmECRPMbd955x1zeKtSpYrmzJmj6dOnq2jRopKk0NBQzZ07N/MfBIBMY9MRuFatWikyMlJTpkzR5MmTLS4p4OrqqrfeekutWrVK10IBAACeNLVr11aHDh1UoEAB87LAwEDFxMRoyZIlku4Ns7x586YuX76s8+fPm9u988475oknEhIS9Prrr0uSlixZoldeeYWhlMATyubf7ODgYLVu3VqhoaH666+/JEnFihVTYGCg3N3d061AAACAJ1XFihVTXP7vU1FcXV0VHR1ttSzZ/ae03Lp1S3/++acqVKiQjpUCyC7S9NWMu7u7goKC0qsWAACAp57JZNLmzZvNtwMDA+Xq6qpnn31WTk5O5onkvvjiC40dO1Z3797V4sWLLfZx+fJlAhzwhLLpHDgAAACkv6SkJL377rs6cOCAJClPnjx6++23JUkeHh7q2rWrue2aNWtUo0YN1a9fX3v37rXYT1xcXOYVDSBTEeAAAACygbt372rIkCFauXKlJCl//vxauHChSpQoYW4zcuRIjRgxQrlz5zYvMxgMatasmcWQSk9Pz0yrG0DmylZntx4/flxz5szR8ePHdePGDeXMmVOlS5dW37591aRJE4u24eHhmjx5snbt2iWj0aiAgACNGTNG/v7+WVQ9AACAba5fv66BAwfq999/lySVKlVKn3/+uXl2yWQODg4aNGiQeUbwuLg4FStWTFFRUfrhhx/M7cqWLZup9QPIPNkqwF28eFFxcXHq0KGDChYsqOjoaG3dulVDhgzRO++8o+7du0uSYmNj1bt3b4WHh6tPnz7y9PTU8uXL1bNnT61atUqlS5fO4kcCAACQOqdPn9aAAQN0+fJlSdJzzz2nTz755KFH0ZycnCwu8D1x4kTzz5UrV1bBggUzrmAAWSpbBbigoCCrSVF69eqlDh06aOHCheYAt2LFCp0+fVrLli1T9erVJUktWrRQUFCQpk2bptmzZ2d67QAAAI/ryJEjGjBggO7cuSNJqlChggYOHKiTJ09atEs+Erdlyxb98MMPatSokQoVKqQbN27ou+++086dOyXdO0I3cuTITH0MADJXtgpwKXFwcJC3t7eOHj1qXrZp0yaVLVvWHN4kKW/evGrVqpVWrlypqKgoLmUAAACyvdDQUHN4k+6dTtKrVy+rdl988YVy5cqlhIQE7dixQzt27LBq4+TkpHfffVe1a9fO0JoBZK00Bbi///5b27dvt7gOXOPGjVWoUKE0FRUdHa24uDjduXNHW7du1c8//6wWLVpIujc7U1hYmNq2bWu1XcWKFbV06VKdOnVKVapUSVMNAAAA2Y2vr6+CgoLM8wUkJCTI29tbtWvXVp8+fSyGVQJ4Mtkc4ObOnatPP/1UiYmJMplM5uWTJ0/W8OHDNWDAAJuLevfdd7VhwwZJkqOjo4KCgvTee+9JkiIiIhQXFycvLy+r7by9vSVJ165ds/m+JSkxMTFN26c3R0fHrC4BmSCr+h396+lA/0JGym5/N+3J4MGDNXjw4Ee2i4mJ0cmTJ1WyZElNmzbtge14LdIH711PD3v8nbEpwG3atEnTpk1TmTJl1K9fP/n5+UmS/vzzT82fP1/Tp09XkSJFzEfNHtcrr7yiDh066Nq1a9qwYYNiY2MVGxsrDw8P83VNnJ2drbZzcXGRdG+SE1slJSUpMjLS5u3Tm6Ojozw8PLK6DGSCmJiYTH8ToX89PehfyEhZ0b+eNsmff3iuMx7vXU+X7PI7lZSUJAeH1F3hzaYAt2jRIvn6+urrr79Wzpw5zcvLlSunoKAgde7cWYsXL7Y5wJUpU8Y8BKBt27bq1auXBg4cqNWrV5tDmtFotNou+c3t/poel4ODA7+0yBJubm5ZXQKeYPQvZCT6V8ZLPiLk5ubG8w2ko+zy+5Ta8CbZGOBOnjypIUOGpBiUcubMqTZt2mjWrFm27NqKwWBQUFCQ3n//fZ07d04lSpSQs7Ozrl+/btU2eehk8lBKW3HYHFmBfoeMRP9CRqJ/Zbzk59jR0ZHnG0hH9vj7lPqo9xgMBkO67i/5yFpUVJQcHBxUrlw5i1kpkx09elQ5cuTgBF4AAAAATySbAlyZMmW0cePGFIcxGo1GbdiwQb6+vo+93xs3bqS4v2+//VY5c+ZUqVKlJN27XlxYWJgOHjxobnfr1i1t3LhRgYGBXEIAAAA8UQwGg1xdXdP9S3IA9semIZQ9evTQmDFj1K1bN7388ssqXbq0DAaDTp48qQULFujPP//URx999Nj7HTlypJydnVWlShV5e3vr2rVrWr9+vc6fP6+xY8cqV65ckqTg4GCtWrVKQ4YMUd++feXh4aHly5fLaDRq1KhRtjwkAABgR0xJiTI42N/QJ1u5urqqfPnyWV1GpnvaXmcgNWwKcG3atNG5c+f0+eefa+TIkRbrDAaDBg0apNatWz/2ftu2bat169Zp2bJlun37ttzd3VWhQgWNGTNGjRs3NrdzdXXV4sWLNXnyZC1YsEBGo1EBAQGaOHGiTUf+AACAfTE4OCp25ytKijiZ1aUggzjk8VXOBp9ndRlAtmPzdeBeffVVtWvXTtu2bdPFixdlMplUvHhxNWnSRMWKFbNpnx07dlTHjh1T1dbLy0shISE23Q8AALB/SREnlXTD+px4AHiSPXaAi4mJ0X/+8x/Vq1dPLVq0UL9+/TKiLgAAAADAvzz2JCZubm767rvvFB0dnRH1AAAAAAAewKZZKEuUKKGrV6+mdy0AAAAAgIewKcB169ZNK1eu1M2bN9O7HgAAAADAA9g0iUnOnDlVoEABvfDCC2rfvr2KFy8uV1dXq3bt2rVLa30AAAAAgP9nU4AbO3as+eeFCxem2MZgMBDgAAAAACAd2RTgFi9enN51AAAAAAAewaYAV7NmzfSuAwAAAADwCDZNYnI/o9Goq1evymg0pkc9AAAAAIAHsDnA/fHHH+rdu7eqVq2qBg0a6NChQ5KkGzduqHfv3tq9e3e6FQkAAAAAsDHA/fnnn+rWrZsuXLigtm3bWqzLnz+/7t69q3Xr1qVHfQAAAACA/2dTgPv0009VoEABbdy4Ua+99ppMJpPF+jp16ui3335LlwIBAAAAAPfYFOAOHjyoTp06yd3dXQaDwWr9s88+q2vXrqW5OAAAAADA/9gU4GJiYpQ7d+4Hro+NjbW5IAAAAABAymwKcIULF1ZYWNgD1x86dEglSpSwtSYAAAAAQApsCnDNmzfXunXrdOzYMfOy5KGU69at07Zt2/TCCy+kT4UAAAAAAEk2Xsh7wIAB2rFjh7p166bKlSvLYDBo1qxZmjhxok6ePKny5curT58+6VwqAAAAADzdbDoClytXLn311Vfq2rWrTp8+LZPJpAMHDujKlSvq3r27Fi1aJGdn5/SuFQAAAACeajYdgZMkd3d3vfnmm3rzzTd18+ZNmUwm5cuXL8VZKQEAAAAAaWdzgLtfvnz50mM3AAAAAICHSFOAO3/+vC5cuKBbt26luL5du3Zp2T0AAAAA4D42Bbjr169r7Nix2r17tyTJZDJZtTEYDAQ4AAAAAEhHNgW4d999V3v37lXPnj1Vs2ZNeXp6pnddAAAAAIB/sSnA7dmzR927d9f48ePTux4AAAAAwAPYdBkBZ2dnlSxZMr1rAQAAAAA8hE0Brm7dujpy5Eh61wIAAAAAeAibAtwbb7yhAwcOaMGCBTIajeldEwAAAAAgBTadA1ewYEG99tprGj16tKZOnSpvb285OFhmQYPBoG3btqVLkQAAAAAAGwPcunXrNG7cOOXIkUM+Pj7MQgkAAAAAmcCmAPfZZ5/Jz89PCxYsUL58+dK7JgAAAABACmw6B+6ff/5Rp06dCG8AAAAAkIlsCnBFixZVdHR0etcCAAAAAHgImwJcz5499fXXXysqKiq96wEAAAAAPIBN58DlzJlTefLk0QsvvKAXX3xRRYoUkaOjo1W7du3apbU+AAAAAMD/synAjR071vzzZ599lmIbg8FAgAMAAACAdGRTgFu8eHF61wEAAAAAeASbAlzNmjXTuw4AAAAAwCPYFOAyytGjR7V+/Xrt27dPly5dUq5cuVShQgUNHTpUAQEBFm3Dw8M1efJk7dq1S0ajUQEBARozZoz8/f2zqHoAAAAAyFg2BbiZM2c+so3BYNCQIUMea7/z5s3ToUOHFBQUpJ49e+rOnTv6+uuv1blzZ82ZM0fPP/+8JCk2Nla9e/dWeHi4+vTpI09PTy1fvlw9e/bUqlWrVLp0aVseFgAAAABka48McEajUc7OzhbLHhbgDAaDTCaTTQGuT58+CgkJsbi/Tp06qWXLlvr444/NAW7FihU6ffq0li1bpurVq0uSWrRooaCgIE2bNk2zZ89+rPsFAAAAAHvwyAC3cOFClSlTRg0bNjQv2759u1W7xMRE/fXXX/ryyy8VHR2tSZMmPXYxVatWtVqWO3du1apVSz/88IN52aZNm1S2bFlzeJOkvHnzqlWrVlq5cqWioqLk7u7+2PcPAAAAANnZIy/k7e3treHDh2vr1q3mZYULF7b6V6xYMQUGBmrevHmSpHXr1qVbkdeuXVPevHklSUlJSQoLC1PFihWt2lWsWFHx8fE6depUut03AAAAAGQXjzwC165dOzk4OGjMmDEWwxgfxGAwKCgoSAsXLtSIESPSXOChQ4d04MAB9enTR5IUERGhuLg4eXl5WbX19vaWdC/wpUViYmKatk9vKV0kHU+erOp39K+nA/0LGSkr+hd96+lB/0JGym6f+1MjVZOYtGnTRkWLFtWff/6Z6h3fuHHD5qLu38fo0aNVqFAhDR06VJIUFxcnSVbn5UmSi4uLpHuTnNgqKSlJkZGRNm+f3hwdHeXh4ZHVZSATxMTEZPqbCP3r6UH/QkbK7P5F33q60L+QkbLi72NKkpKS5ODwyMGRkh5jFsoqVaqoSpUqj2x37do1rVixQsWLF0/trlMUFRWl/v37Kzo6WkuXLjWf05Yc0oxGo9U2yeEuZ86cNt+vg4MDv7TIEm5ublldAp5g9C9kJPoXMhL9Cxkpu/Sv1IY3ycbLCPTq1SvF5bdv39bZs2cVHx+vqVOn2rJrSfeOoA0aNEhnz57VggUL5Ovra16XJ08eOTs76/r161bbJQ+dTB5KaSsOmyMr0O+QkehfyEj0L2Qk+hcykj32L5sC3KVLl6yWGQwG5c6dW82aNVOvXr1UqVIlmwqKj4/X8OHDdeTIEc2ePdtqZkoHBweVK1dOR48etdr26NGjypEjh8qUKWPTfQMAAABAdmZTgNuxY0d61yHp3tjP0aNH6+eff1ZISIjq16+fYrugoCBNnjxZBw8eNF9K4NatW9q4caMCAwO5hAAAAACAJ5JNAS6jTJ48Wd9//73q1q2rhIQEffvttxbr27ZtK0kKDg7WqlWrNGTIEPXt21ceHh5avny5jEajRo0alRWlAwAAAECGy1YB7sSJE5KkX375Rb/88ovV+uQA5+rqqsWLF2vy5MlasGCBjEajAgICNHHiRIvz5QAAAADgSZLqANe8efPH3vmWLVseq/2SJUtS3dbLy0shISGPWxIAAAAA2K1UB7j4+PhUtTMajQoPD5fBYLC5KAAAAACAtVQHuEdNXGIymfTtt9/qk08+kSRmggQAAACAdJYu58Dt3r1bU6ZMUVhYmLy9vfXBBx+oY8eO6bFrAAAAAMD/S1OACwsL05QpU7R7927lypVLr776qvr27SsXF5f0qg8AAAAA8P9sCnBXrlzRxx9/rA0bNshgMKh79+4aPHiw8uXLl971AQAAAAD+32MFuKioKM2ZM0dLly5VbGysmjdvrtdee03FihXLqPoAAAAAAP8v1QFu0aJF+uyzz3T79m1VrVpVY8aMUaVKlTKyNgAAAADAfVId4CZOnCiDwaCAgADVq1dPe/bs0Z49ex7Y3mAw6JVXXkmXIgEAAAAAjzmE0mQy6ejRozp69Ogj2xLgAAAAACB9pTrALV68OCPrAAAAAAA8QqoDXM2aNTOyDgAAAADAIzhkdQEAAAAAgNQhwAEAAACAnSDAAQAAAICdIMABAAAAgJ0gwAEAAACAnSDAAQAAAICdIMABAAAAgJ0gwAEAAACAnSDAAQAAAICdIMABAAAAgJ0gwAEAAACAnSDAAQAAAICdIMABAAAAgJ0gwAEAAACAnSDAAQAAAICdIMABAAAAgJ0gwAEAAACAnSDAAQAAAICdIMABAAAAgJ0gwAEAAACAnSDAAQAAAICdIMABAAAAgJ0gwAEAAACAnSDAAQAAAICdIMABAAAAgJ0gwAEAAACAnchWAS46Olqffvqp+vfvr9q1a8vPz09z585NsW14eLhef/111apVS1WqVFGvXr30+++/Z3LFAAAAAJB5slWAu3XrlmbNmqWTJ0+qfPnyD2wXGxur3r17a9euXerTp49Gjx6tGzduqGfPnjp9+nQmVgwAAAAAmccpqwu4n7e3t3bt2qWCBQvq0qVLaty4cYrtVqxYodOnT2vZsmWqXr26JKlFixYKCgrStGnTNHv27MwsGwAAAAAyRbY6Aufs7KyCBQs+st2mTZtUtmxZc3iTpLx586pVq1batWuXoqKiMrJMAAAAAMgS2SrApUZSUpLCwsJUsWJFq3UVK1ZUfHy8Tp06lQWVAQAAAEDGylZDKFMjIiJCcXFx8vLyslrn7e0tSbp27Vqa7iMxMTFN26c3R0fHrC4BmSCr+h396+lA/0JGyor+Rd96etC/kJGy2+f+1LC7ABcXFyfp3nDLf3NxcZF0b5ITWyUlJSkyMtLm7dObo6OjPDw8sroMZIKYmJhMfxOhfz096F/ISJndv+hbTxf6FzJSVvx9TElSUpIcHFI3ONLuAlxySDMajVbrksNdzpw5bd6/g4MDv7TIEm5ublldAp5g9C9kJPoXMhL9Cxkpu/Sv1IY3yQ4DXJ48eeTs7Kzr169brUseOpk8lNJWHDZHVqDfISPRv5CR6F/ISPQvZCR77F92N4mJg4ODypUrp6NHj1qtO3r0qHLkyKEyZcpkQWUAAAAAkLHsLsBJUlBQkMLCwnTw4EHzslu3bmnjxo0KDAyUu7t7FlYHAAAAABkj2w2hXLp0qe7cuWOeSGTfvn1KSEiQJPXs2VMeHh4KDg7WqlWrNGTIEPXt21ceHh5avny5jEajRo0alZXlAwAAAECGyXYBbsGCBbp8+bL5dmhoqEJDQyVJbdq0kYeHh1xdXbV48WJNnjxZCxYskNFoVEBAgCZOnChfX9+sKh0AAAAAMlS2C3A7duxIVTsvLy+FhIRkcDUAAAAAkH3Y5TlwAAAAAPA0IsABAAAAgJ0gwAEAAACAnSDAAQAAAICdIMABAAAAgJ0gwAEAAACAnSDAAQAAAICdIMABAAAAgJ0gwAEAAACAnSDAAQAAAICdIMABAAAAgJ0gwAEAAACAnSDAAQAAAICdIMABAAAAgJ0gwAEAAACAnSDAAQAAAICdIMABAAAAgJ0gwAEAAACAnSDAAQAAAICdIMABAAAAgJ0gwAEAAACAnSDAAQAAAICdIMABAAAAgJ0gwAEAAACAnSDAAQAAAICdIMABAAAAgJ0gwAEAAACAnSDAAQAAAICdIMABAAAAgJ0gwAEAAACAnSDAAQAAAICdIMABAAAAgJ0gwAEAAACAnSDAAQAAAICdIMABAAAAgJ0gwAEAAACAnSDAAQAAAICdsNsAFx8fr08++UQNGjRQQECAWrdurQ0bNmR1WQAAAACQYZyyugBbvfPOO1q3bp26d+8uX19fbdu2TaNHj1ZiYqLatWuX1eUBAAAAQLqzywB3/PhxrV27VsOHD9eQIUMkSZ06dVKvXr300UcfqUWLFnJ2ds7iKgEAAAAgfdnlEMrNmzfLwcFB3bt3Ny8zGAzq0aOHbty4oX379mVhdQAAAACQMewywJ04cULFihVTnjx5LJZXqlTJvB4AAAAAnjR2OYTy2rVr8vLyslru7e1tXm+L+Ph4mUwmHTt2LE31ZYQBRROVUNiU1WUgAzg5ROnYsYgsrSHpxT5SQnyW1oAM4pRD/2Txe9rghOeUYEjM0hqQMZziHLP0b6ap8DvSs7x3PbEccsiQhf2rSMucKpTIKTlPKgdHh2z1md9oNMpgMKSqrV0GuNjY2BTPcXNwcFCOHDkUGxtr035T+6RlhbyujlldAp5gDrnzZHUJeILlc3LP6hLwhDLkLJDVJeAJltOT8IbMYzAYnuwAlzNnThmNRqvlSUlJio+PV86cOW3ab5UqVdJaGgAAAABkGLs8B87Ly0vXr1+3Wp48dDJ5KCUAAAAAPEnsMsBVqFBBf/31lyIiIiyW//bbb5Kk8uXLZ0FVAAAAAJCx7DLABQUFKSkpScuXLzcvM5lMWrp0qfLly6datWplYXUAAAAAkDHs8hw4f39/tW3bVjNmzNDNmzfl5+enbdu2af/+/frwww+5iDcAAACAJ5LBZDLZ5dz0RqNRs2fP1jfffKMbN27Ix8dHL7/8stq2bZvVpQEAAABAhrDbAAcAAAAATxu7PAcOAAAAAJ5GBDgAAAAAsBMEOAAAAACwEwQ4AAAAALATBDhkuOQZQ3/66aesLgWwSUxMjGbNmqVffvklq0sBAMBmW7Zs0RdffKHExMSsLgVpQIBDhps1a5aWL1+u8uXLp6r9vn375Ofnp3379mVwZUDqvPPOO9q6dasqVaqU6m3Wrl0rPz8/Xbp0KQMrAx5faGioZs+erZiYmKwuBdnAnj17NGvWLPrDU+DUqVN67bXXlD9/fjk6OqZ5f3xeyzoEONgs+QNqSv/eeecdSdKJEyf05ZdfaurUqfLy8sriipEe7n/d9+7dm2KbF198UX5+fgoKCnrs/X/77bdauHBhGqtMP8uXL9fhw4c1d+5cubu7W6yLjo7WjBkz+OOFbKNnz57q2bPnA9efPHlSw4YNk5eXl9zc3DKxsqdPo0aNNHbs2Ezf9nGEh4drxIgRSkpKoj9kE1evXtX777+vJk2aKCAgQLVq1VL//v31888/p2m/iYmJGj9+vNq2basOHTqkU7XIKk5ZXQDs37Bhw1S0aFGLZT4+PkpISND48eM1ZMgQ1apVK9X7q1Gjho4ePaocOXKkd6lIRy4uLtqwYYNq165tsfz8+fM6duyYXFxcbNrv+vXrde7cOfXp0ycdqkybhIQERUVFaf78+fL29rZaHxMTo5kzZ2ro0KFWfbxt27Zq2bKlnJ2dM6tcu3Tjxg19+eWX+vHHH3X58mWZTCYVK1ZMzz//vHr16pXi826Pli5dKjc3tyz94BQVFaVhw4apf//+6tSpU5bVYc/Wrl2rcePGWSzLmzevSpYsqd69e6t58+ZZVJlt3n//fVWoUEFDhgzJ6lIg6ddff9WAAQMUHx+vjh07ys/PT7du3dKGDRv08ssva9CgQRoxYoRN+164cKGMRqP5C/b0wOe1rEOAQ5oFBgaqcuXKKa5bt27dY+/PwcHB5g//yDwNGjTQli1b9O6771qElPXr16tAgQIqXry4bt68mYUVpp2Tk5MGDBhg07aOjo7pMkTlSXbs2DENGDBAUVFRatWqlbp37y4HBwf9+eefWrVqlbZu3aotW7ZkdZnpYunSpfLy8srwADd//vwHrgsLC1OfPn0UHBycoTU8DZK/uDSZTLp586a+/fZbDR8+XFOnTlWrVq0kSd9//70MBkMWV/pgW7Zs0a+//qp169bJwYEBWVntzp07GjZsmBwdHbVixQqVLFnSvO6ll17SqFGj9Nlnn6l8+fJq1qzZY++/X79+6tevX3qWzOe1LMRvLDJUSsN5Nm3apI4dO6pq1aqqVq2aWrdurZkzZ5rXP2hM9blz5zRq1CjVqVNHAQEBatasmf773/+a11++fFnvvfeegoKCVKlSJVWvXl0DBw7UqVOnMvZBPqVatmyp6Oho/fjjjxbLN27cqBYtWliFl7Vr16pPnz6qW7eu/P391bx5c82dO1dJSUnmNj179lRoaKguX75sMSQ3mclk0tKlS9W6dWvz0JLXXntNV65csbivnj17KigoSKdPn1avXr1UqVIlNWrUSOvXr5ckHT16VMHBwapUqZKaNGmibdu2WWwfERGhyZMnq3Xr1qpSpYqqVKminj176tChQ+Y2ly5dUmBgoCRp5syZ5lqThz096By4Y8eOaeDAgapZs6YqVaqkVq1a6fPPPzevDwsL09ixY83DZ2rXrq1Ro0bpn3/+Sd0LYyfu3LmjIUOGyGAwaO3atZo4caK6deumrl276t1339W2bdts+pDytHN2dn7gUd/q1asT3tJJYGCg2rZtq3bt2umll17SsmXL5Orqqo0bN5rbODs7Z+sjE82bN9euXbuUL1++rC4FklasWKFr167p9ddftwhv0r0vEz/44AN5eHjo008/NS9/2Dlofn5+mjFjhvl2Sn+T0vq3ks9rWYcAhzSLjIzUzZs3Lf6ZTKYU2+7evVujRo2Sh4eHXnvtNY0ePVp16tTRwYMHH3ofp06dUqdOnbRr1y516NBBb731loKCgizCw7Fjx3TgwAE1a9ZM48ePV+/evXXs2DH16NFD169fT9fHDKlgwYKqUaOGNmzYYF7222+/6cKFC2rdurVV+6VLlyp//vx66aWXNG7cOJUuXVpTp07Vxx9/bG4zcOBA+fn5KW/evProo4/M/5K9++67+vDDD+Xv76/x48erR48eCg0NVbdu3XTnzh2L+4uKilL//v1VoUIFjRkzRu7u7nrjjTf03XffafDgwapZs6Zee+01GQwGjRw50qKPXLx4UZs3b1ZgYKDGjBmjQYMG6cqVK+rTp49OnjwpScqXL595KErTpk3NtXbp0uWBz9mePXvUrVs3HT9+XN27d9e4ceNUt25d7dixw9xm9+7dOnv2rNq0aaO3335bHTt21E8//aRevXopNjY2la9O9rdixQpdvXpVb7zxhsqUKWO1Pvk94n7bt29Xp06dVKlSJdWoUUNDhgzRmTNnLNrMmDFDfn5+Onv2rMaOHasaNWqoVq1amjJlipKSknTjxg29+uqrql69umrXrm3x5ZF0L5j7+flp7ty5WrJkiRo1aqSKFSuqc+fO+u233yzajh07Vo0aNbKq/d8flBo1aqRz585p//795qCfvJ3RaNQnn3yiDh06qEaNGqpYsaI6deqk7du3p/i8fffdd+rcubMqV65sDmT3f6hK6Uuz2NhYhYSEqFGjRvL391ejRo00ffp0GY1Gi3aNGjVSv379zB/aKlasqOeffz5bnZOaXbm7u8vV1dUisKV0HltERITeffddBQYGyt/fXy+88IK+/PLLB/7NtGXbuLg4TZw4UXXq1FHlypX10ksv6ezZs1b1pPTh+2n5Aik72rFjh5ydndWyZcsU13t6eqpx48Y6deqULl68mG73m5a/lSnh81rmYAgl0uzll1+2WrZnz54Uv9XbuXOncuXKpfnz5z/W8LL3339f8fHx+vbbb1WiRAnz8vvHgj///PNWk2a0adNGrVq10urVqzVo0KBU3x9Sp3Xr1powYYJu376t3Llza/369SpRooQqVqxo1Tb5G+pk3bt315tvvqklS5Zo6NChcnZ2Vt26deXl5aWoqCi1bdvWYvvDhw9r5cqVmjhxosUwtKZNm6pjx45asmSJxXkc169f1+TJk9WuXTtJ9/pHkyZN9Nprr+nLL79UnTp1JEmVK1dWp06dtGHDBr300kuSJF9fX23fvt2ij3bu3FlBQUFavHixPvjgA7m5ualZs2Z6//335efnZ1XvvyUlJentt99W7ty59e2331r8ftz/ASw4ONhcR7KGDRuqe/fu+uGHH9SmTZuH3o+92LFjh1xcXPTCCy+kqv3GjRs1evRolS1bViNGjFBkZKSWLl2q4OBgrVmzxuo83FGjRqlEiRIaOXKkQkNDNW/ePOXOnVvfffedKlSooFGjRumHH37QjBkzVLZsWTVp0sRi+w0bNujOnTvq1q2bkpKStGzZMvXp00dr166Vj4/PYz3W8ePH67333pOHh4cGDhwoScqVK5ekex+eVqxYoRYtWqhjx46Ki4vThg0bNGTIEH3xxReqV6+eeT+fffaZPv74YwUEBGjw4MFydXXV8ePHFRoaalV/MpPJpKFDh+rnn39Wu3btVLFiRR0+fFhz5szR6dOnNWvWLIv2ly5d0sCBA9W+fXu1bt1amzdv1sSJE1W6dGnzEWf874tLSbp586ZWrFihW7duPfR9wGg0qnfv3jp16pSCg4NVsmRJ7dy5U5MmTdI///yj8ePHp8u248ePN4+EqF69uo4fP66+ffsqLi7ukY/r/i+QnnnmGV24cEErVqzQsWPHtGHDBuXMmfMxniU8jjNnzsjHx+ehQxLLlSundevW6dSpU1bvebZKy9/KlPB5LXMQ4JBmb731lkqVKmWxzMPDI8W27u7uunv3rkJDQ/X888+nav83b97U/v37FRwcbPFmIMli3P794eDu3buKjY2Vh4eHSpQooePHj6fy0eBxBAUF6f3339eWLVvUoUMHbd68Wd26dUuxbfLrk5iYqKioKCUmJqpmzZpavXq1zp49q7Jlyz70vjZv3iw3Nzc9//zzFufWeXt7q3jx4tq7d69FgMuZM6dF2ClSpIgKFCggZ2dn8x8kSfL395ejo6P++usv87J//wE1Go1ydXVV1apVbe5Lx48f18WLFzV69GirLzfuP0/m/n4cHR0to9GokiVLytPTU8ePH39iAtzZs2fl4+OTqkle4uPjNWnSJJUoUULLly83z5bXpEkTdezYUZ988olCQkIstilfvrw+/PBDSfdCcdOmTTVt2jQNGjRIr776qiSpY8eOqlevnlavXm0VgM6dO6fvv/9eRYoUkXSvr7dq1UozZ87U1KlTH+uxNmnSRCEhISpQoIDVB/zcuXPrp59+sngeevTooXbt2mnBggXmAPfXX3/p008/Vf369fXZZ5/Jyel/f74fdvRm586d+vnnny0mP+jevbvy58+vRYsWaffu3XruuefM7c+fP68vv/zSvOzFF19UgwYN9PXXXxPg7vPvLy5z5MihCRMmPDBIS9LXX3+tsLAwffDBB+ZJZLp166Zhw4Zp8eLF6tatm9XfuMfd9sSJE9q4caO6du2qCRMmmLf/+OOP9dlnnz3ycT0tXyBlR9HR0VYzHf9b8hc/0dHR6Xa/aflb+W98Xss8BDikWUBAwAMnMfm34OBgbd68WQMGDJC3t7eee+45NW3aVI0bN37gyd7JQwV8fX0fuu+4uDh98sknWr9+vdUh+Lx586aqPjweDw8PNWjQQBs2bFDBggV148YN8wn8/3bw4EFNnz5dv/32m+Lj4y3WRUZGPvK+zp8/r5iYGIsPm/f7d/8pWLCg1Yn57u7uVuHJwcFBbm5uVkMwly5dqpUrV+rs2bNKSEgwL0/+QP+4UtuPb9++ralTp2rLli2KiIiwWPfvGu1ZVFSU+cPIoxw/flzXr1/XgAEDLKY6L1++vJ577jnt2rVLJpPJog/cP8uiwWBQxYoVdfHiRXXs2NG83MXFRX5+fil+IGnUqJHFa+3j46PAwEDt2rXrsR7no9w/2Y3RaFRMTIySkpJUo0YNbd682dxu69atSkpK0rBhwyzCW/Lje5Aff/xRBoPB6kP5yy+/rEWLFmnnzp0Wv1MlSpSwuO3s7KxKlSpxPcN/uf+Ly/DwcG3cuFETJkxQnjx5HjgT5Y8//qi8efNajCAwGAzq16+ftm7dqp9++umBAS612yb3z38Po+3Vq1eqAtzT8gVSdpQrVy5FRUU9tE1ycMufP3+63W9a/1bej89rmYcAh0zl5eWlb7/9Vrt379bPP/+sn3/+WevWrVP9+vU1d+7cFD+IJH+7/KjZvP773/9q1apV6tGjh6pWrSoPDw85ODjoww8/TNX5BbBN69atNXz4cElSxYoVU/wAcvHiRb300ksqXry4xo8fr0KFCsnZ2VnHjx9XSEiIxUQmD5KUlKQ8efJo+vTpKa6//4OHpAfOqvagobv395H58+fro48+UteuXTVixAjlyZNHjo6OmjFjhs6dO/fIWh+2/0f141GjRunAgQN66aWXVL58eeXKlct87sGT1I/d3d1T/S3y5cuXJcnqxH5JKlWqlEJDQxUVFWVx5L9QoUJW9ydJzz77rMVyDw+PFF/TlPpxiRIltHPnTkVGRj5wlIEtVq1apYULF+rMmTMWr/H9fSU5ZD7qg9G/Xb58WQUKFJCnp6fFcm9vb3l6epqf22T/ft6ke0cJ//zzz8e63yfdv7+4bNWqlTp06KD//Oc/atiwYYpHli9fvqwSJUpYvQeVLl3avP5BUrvt33//LYPBoGLFilm0y5cvn3Lnzv3Ix/W0fIGUHZUqVUrHjx9XXFzcA4dRhoWFSfrfF4kP+nuSmJiY6vtNy9/KB63j81rGI8Ah0zk7O6tBgwZq0KCBTCaTpk6dqi+++EKHDx9WtWrVrNon/yFKnjziQTZt2qR27drpzTfftFh++/ZtvtHJQA0aNJCHh4f2799v9dwn2759u+Li4jRnzhwVLlzYvDylb/Uf9MZfrFgx7d69WxUrVnzkMJO02rBhg+rUqWMxBEmS1bejjzNF+P39uH79+im2uXPnjkJDQzVs2DANHTrUvDwuLu6J+/BUsmRJnThxQkajMU3XynvQH/vH+VCS0j4e9mXSw9pIj/fhaePGjXrrrbfUqFEj9e/fX/ny5ZOTk5PWrFljMaPhv48wpoeUHjfTydvGwcFBNWvW1KJFi3ThwoUUJ+bJaqn5YPy0fIGUHTVq1EhHjhzRpk2b1L59e6v1kZGR2r59uypUqGD+e5L8xcy/R7E87MuAjMTntczDOzUy1a1btyxuGwwGlS9fXtKDv93Lly+fatasqW+++cZqqNP9f1AcHR2t/sBs3LhR165dS4/S8QDOzs569913NXTo0AcOn0z+0Hz/62M0GrV06VKrtg8aotGyZUslJSVZzRqYvN/0vOacg4ODxbBJ6d4Q0H/PQph81O/27duP3GeFChVUtGhRLVq0yOr3IPl5Sf7w/O9+vHDhwlQdpbQnjRo1UlxcnL7//vtHtk0O/WfPnrVad/bsWeXOnTvdQ31KR+UuXLggT09P89E3T0/PFPtqSh+eHhS+Nm3apKJFi2r27Nlq166d6tevn+Iw4eLFi8tkMj3yg9G/FS5cWOHh4VZ1Xr9+XZGRkRZfqCBtkt8zYmJiUlxfuHBhXbhwwSrgJ8+k+rDXIrXbFipUSCaTyepv5c2bNx/5JVDyF0gDBgzQiBEj1KxZM9WtW1fVqlV74r5Ayo66du0qLy8vhYSE6Pz58xbrEhMT9fbbb+vOnTvmiZCke0fiHB0dtXfvXov2Kf1tzQx8Xss8HIFDpnrrrbd069Yt1alTR88884yuXr2qZcuWycvLSzVq1Hjodt26dVPHjh3VpUsXFStWTH///bc2bdqkH374QdK9D4Tr1q2Tu7u7ypQpoz/++EObN29Ot5ma8GAPCm7JAgMDlSNHDg0cOFBdunSR0WjUt99+m+K3/f7+/tqyZYs++OADVapUSQ4ODmrZsqWqV6+unj176ssvv1RYWJjq16+vnDlz6tKlS9q2bZtefPFFmy+6/W8NGjTQrFmzNGbMGFWvXl3nzp3T119/rTJlylgM+8uVK5d8fHy0adMm+fj4KE+ePCpSpIgqVapktU8HBwdNmDBBr7zyitq1a6eOHTuqYMGCunDhgg4fPqwVK1bI3d1dNWvW1Lx58xQfH69ChQrp0KFDOnDggPLkyZMujy276Nq1q5YuXarJkyerQoUKVhMhRUVFae7cuRo1apQqVKggLy8vffXVV+rcubN5JrywsDDt3r1bLVu2TPejUz/++KMuXbpkHqp07tw5hYaGWpzfVKxYMUVGRurEiRPmL6Kio6O1bt06q/25urqmGPTv/3Ij+TFcvHjR6npLTZs2VUhIiGbNmqXZs2dbHEl82NG5hg0bauXKlVq4cKF5qLP0vwt+N2jQ4FFPBVIhPj5ev/zyi3LkyGHVl5M1bNhQoaGhWrdunflcTJPJpAULFshgMDx0Yq/Ublu/fn1Nnz5dS5YssRhBsHjx4kc+hqfpC6TsyNPTUzNnzlT//v3Vvn17vfjii/L19VVERIQ2bNigkydPaujQoRbXx3R3d1fLli21fPlyGQwG+fj4aN++fel6mYHHxee1zEGAQ6Zq06aNvv76a61YsUK3b99WgQIF9Pzzz2vIkCEP/Qbdz89PX3/9tT755BN9/fXXio2N1bPPPquGDRua27z55ptycnLSpk2bFBMTI39/f33xxReaMmVKZjw0PISPj49mzZql6dOna8qUKcqbN6/atWunmjVrWk2u0KNHD50+fVobNmzQ0qVLZTKZzNfFeeutt+Tv76/ly5fr008/lcFg0DPPPKPAwMCHzv72uAYOHKi7d+/qu+++05YtW+Tr66sZM2Zo/fr12r9/v0XbiRMn6sMPP9SkSZNkNBrVvn37FAOcJNWtW1dLlizRzJkzzR+KihYtanHdvKlTp+q///2vVq5cqfj4eNWoUUOLFi1S37590+3xZQeenp6aNWuWBgwYoPbt26tVq1YKCAiQg4ODTp48qY0bNypPnjwaNWqUcuTIobFjx2r06NEKDg5W27ZtzZcR8PDwMM8qmZ5KlCih7t27q3v37kpKStLSpUvl7OxsMdNpq1atNHXqVA0dOlS9evVSfHy81qxZo3z58lldN8vf319ff/21Zs6cqRIlSsjNzU2NGjVSo0aN9MMPP2jQoEFq1KiRrl69quXLl6tEiRLm812ke2FxyJAhmjFjhoKDg9WsWTPzZQRcXFz07rvvpvg4GjRooHr16mnWrFn6559/5O/vryNHjmjDhg1q3LjxAycFwsOFhobqwoULku4d3fruu+90/vx5DRgw4IF/yzp16qRVq1bpnXfe0R9//CEfHx/99NNP5us8PmgCk8fZtnz58mrRooVWrFihO3fuqEaNGvr999+1e/du5c2b96FfdDxNXyBlV5UrV9aGDRs0d+5cbd++XV999ZV50q+QkJAUr7H65ptvKiEhQatXr5aDg4MaNGigefPmWcwgmZn4vJY5DCYGNQMAssiNGze0YMEC/fjjj7p8+bJMJpOKFy+uRo0aqWfPnipQoIC57bZt2zRnzhydPHlSzs7O5gvM3n/EY8aMGZo5c6ZCQ0Pl5eVlXv7OO+9o5cqVVpNxDB48WL/99pt++eUXSffOy2zcuLFee+01ubq66ssvv9T169dVtmxZjRs3TlWrVrXYfs+ePZo0aZLOnDkjb29v9e7dWx4eHho3bpy2b99uPoJ3/fp1vf322zpw4ICioqJUuHBh8wXc582bp6+++krXrl1T8eLF1b9/f/3111+aOXOmVb3r1q3T4sWLdfr0abm4uKh06dJ6+eWX1bhxY0n/m31wyZIl5m1iY2M1Y8YMfffddwoPD5e3t7dat26tIUOGWJx/2KhRI/n4+JiPziUbO3as9u/fb3HB+afV2rVrNW7cOItlLi4uKlmypLp06aKuXbuaQ1KjRo1Us2ZNTZo0ydz21q1bmj59urZv367bt2+rcOHC6tKli/r27WsRrtKybVxcnEJCQrRhwwbFxsaqSpUqevvttxUcHKygoCDzkbl9+/apV69eWrx4sWrVqiVJunbtmv773/9q79695i+Qxo8fr759+1rVg8xx9OhR9e7dW8WKFdOyZcsy/Bxw2AcCHAAA/+/+AJdeQ3KBrHb79m3VrFlTI0aM4CLJdig0NFQDBw5U1apVNW/evDRN/IQnA0MoAQAAnhCxsbHm80STLViwQJJUu3btrCgJaRQYGKjff/89q8tANkKAAwAAeELMmzdPR44cUZ06deTq6qr9+/fr+++/V/369VWlSpWsLg9AOiDAAQAAPCGqVKmi3bt364svvlB0dLS8vb310ksvWcxCCsC+cQ4cAAAAANgJLuQNAAAAAHaCAAcAAAAAdoIABwAAAAB2ggAHAAAAAHaCAAcAAAAAdoLLCAAAnihGo1Fr1qzR999/rz///FORkZFyd3eXr6+vGjdurBdffFHu7u5ZXSYAADbhMgIAgCfG33//rYEDB+rPP/9UjRo1VK9ePRUoUEB37tzRwYMHtXPnTtWqVUsLFizI6lIBALAJAQ4A8EQwGo3q2LGjzpw5o48++kitWrWyanPx4kWtXbtWr776arrcp8lk0t27d+Xm5pbi+ujoaOXKlSvN95OQkKCkpCQ5OzuneV8AAPvGOXAAgCfCqlWrdPLkSfXu3TvF8CZJRYsWtQhvZ86c0YQJE9SqVStVq1ZNFStWVOvWrTV//nwlJiZabLt27Vr5+flp9+7d+vzzz9W8eXMFBARo/vz5unTpkvz8/DRjxgxt2bJFL774oipVqqSBAweatz9x4oSGDx+uOnXqyN/fX40bN1ZISIju3r1rcT9jx46Vn5+fbt26pbfffluBgYEKCAjQkSNHJEmbNm3S4MGD1bBhQwUEBKhmzZrq16+fDh48mF5PJQAgG+McOADAE+H777+XJHXt2jXV2+zfv18HDhxQw4YNVahQIcXFxemnn37SRx99pIsXL+q9996z2uajjz7S3bt31bZtW+XPn1/PPPOMed327du1cOFCde3aVZ07d1byIJddu3ZpyJAhevbZZ9WjRw8VKFBAYWFhWrhwoQ4fPqzFixfLycnyT3Lfvn2VO3du9e/fXyaTSQUKFJAkLV26VHnz5lXnzp1VoEAB/fPPP1q9erV69+6tJUuWqGrVqo/71AEA7AgBDgDwRDh58qRy5cql4sWLp3qbtm3bKjg42GJZnz599Nprr+nrr7/W4MGD5e3tbbE+Ojpa69atsxgaeenSJUnSqVOn9M0338jX19e8Li4uTuPHj1fZsmW1bNkyi2GQtWvX1vDhw7Vhwwa1b9/e4n5KliypqVOnymAwWCyfN2+e1ZDN4OBgtWrVSnPmzNHcuXNT/fgBAPaHIZQAgCdCVFTUY88ueX8QMhqNioiI0M2bN1WvXj0lJibq+PHjVtt07979gee1Pf/88xbhTZJ2796t69evq3379oqKitLNmzfN/2rUqCE3NzeFhoZa7at///5W4e3fNUdFRenWrVtydHRUpUqV9Ntvv6X6sQMA7BNH4AAATwR3d3dFR0c/1jZ3797V7Nmz9d133+ny5ctW62/fvm21zMfH54H7K1GihNWyM2fOSJImTJigCRMmpLhdeHh4qvYlSWFhYfr000+1d+9eq8ebUuADADxZCHAAgCeCr6+v9u/frwsXLqR6GOVrr72mHTt2qFOnTqpevbry5s0rJycn/f7775o6daqSkpKstsmZM+cD9+fq6mq1LHkfI0eOVMWKFVPcztPTM1X7+ueff9StWze5ubnplVdeUcmSJeXq6ioHBwd9/vnn2rt37wNrAwA8GQhwAIAnQlBQkPbv36+VK1dqzJgxj2wfGRmpHTt2qE2bNvrPf/5jse78+fPpVlfyETsXFxc999xzadrX1q1bFR0drVmzZqlOnToW66ZPn56mfQMA7APnwAEAnggvvviiypQpo4ULF2rTpk0ptrl06ZI+/fRTSf8bbvjvy6FGRUVp4cKF6VZXYGCgChQooPnz5+v69etW6xMSEhQREZGqfTk6Oqa4fNeuXTp69GhaygQA2AmOwAEAngguLi6aM2eOBg4cqJEjR+qrr75SvXr1VKBAAd25c0eHDx/Wjh07VKtWLUn3zpmrV6+eNmzYIGdnZ1WqVEnXrl3TmjVrlD9//nSry9XVVR999JEGDx6sFi1aqGPHjvLx8VF0dLQuXLigrVu3avTo0erQocMj91W/fn25ubnp9ddfV/fu3ZU3b14dP35cGzdulK+vr06ePJludQMAsicCHADgiVGkSBGtWbNGq1ev1vfff6/58+ebZ6f09fXVG2+8oY4dO5rbT5kyRdOmTdPOnTu1fv16FS5cWN27d1eFChXUp0+fdKurbt26Wrt2rb744gtt3rxZN27cUK5cuVS4cGG9+OKLVsMhH6Ro0aKaN2+epk+frnnz5slkMikgIEDz5s0zX8gcAPBkM5j+PXYEAAAAAJAtcQ4cAAAAANgJAhwAAAAA2AkCHAAAAADYCQIcAAAAANgJAhwAAAAA2AkCHAAAAADYCQIcAAAAANgJAhwAAAAA2AkCHAAAAADYCQIcAAAAANgJAhwAAAAA2AkCHAAAAADYCQIcAAAAANiJ/wNW5Aylmh03iwAAAABJRU5ErkJggg==", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "carreras = ['Física', 'Matemática', 'Computación', 'Biología', 'Química']\n", + "carreras = [\"Física\", \"Matemática\", \"Computación\", \"Biología\", \"Química\"]\n", "estudiantes = [45, 38, 62, 29, 33]\n", "\n", - "fig, ax = plt.subplots(figsize=(9, 5))\n", + "plt.figure(figsize=(9, 5))\n", "\n", - "barras = ax.bar(carreras, estudiantes,\n", - " color=['#3498db','#e74c3c','#2ecc71','#f39c12','#9b59b6'])\n", + "barras = plt.bar(carreras, estudiantes, color=[\"#3498db\", \"#e74c3c\", \"#2ecc71\", \"#f39c12\", \"#9b59b6\"])\n", "\n", - "# Valor encima de cada barra\n", + "# Agregar el valor encima de cada barra\n", "for barra, valor in zip(barras, estudiantes):\n", - " ax.text(barra.get_x() + barra.get_width() / 2,\n", - " barra.get_height() + 0.5,\n", - " str(valor), ha='center', va='bottom', fontweight='bold')\n", + " plt.text(barra.get_x() + barra.get_width() / 2, barra.get_height() + 0.5,\n", + " str(valor), ha='center', va='bottom', fontweight='bold')\n", + "\n", + "plt.title(\"Estudiantes por carrera\")\n", + "plt.xlabel(\"Carrera\")\n", + "plt.ylabel(\"Número de estudiantes\")\n", + "plt.ylim(0, 75)\n", + "plt.grid(axis='y', alpha=0.3)\n", "\n", - "ax.set_title('Estudiantes por carrera')\n", - "ax.set_xlabel('Carrera')\n", - "ax.set_ylabel('Número de estudiantes')\n", - "ax.set_ylim(0, 75)\n", - "ax.grid(axis='y', alpha=0.3)\n", - "fig.tight_layout()\n", + "plt.tight_layout()\n", "plt.show()" ] }, @@ -456,19 +163,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### 4b — Histograma y dispersión (scatter)" + "### Nivel 2: Histograma y gráfica de dispersión" ] }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -476,36 +183,34 @@ } ], "source": [ + "# Datos: notas simuladas de 200 estudiantes\n", "np.random.seed(7)\n", "notas = np.random.normal(loc=72, scale=12, size=200).clip(0, 100)\n", "\n", - "fig, axes = plt.subplots(1, 2, figsize=(13, 4))\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 4)) # 1 fila, 2 columnas\n", "\n", - "# ── Histograma ────────────────────────────────────────────────────────────────\n", - "axes[0].hist(notas, bins=25, color='steelblue', edgecolor='white', alpha=0.8)\n", - "axes[0].axvline(notas.mean(), color='red', linestyle='--', linewidth=2,\n", - " label=f'Media: {notas.mean():.1f}')\n", - "axes[0].axvline(61, color='orange', linestyle=':', linewidth=2,\n", - " label='Aprobado (61)')\n", - "axes[0].set_title('Distribución de notas')\n", - "axes[0].set_xlabel('Nota')\n", - "axes[0].set_ylabel('Frecuencia')\n", + "# --- Histograma ---\n", + "axes[0].hist(notas, bins=15, color=\"steelblue\", edgecolor=\"white\", alpha=0.8)\n", + "axes[0].axvline(notas.mean(), color='red', linestyle='--', linewidth=2, label=f'Media: {notas.mean():.1f}')\n", + "axes[0].axvline(61, color='orange', linestyle=':', linewidth=2, label='Aprobado (61)')\n", + "axes[0].set_title(\"Distribución de notas\")\n", + "axes[0].set_xlabel(\"Nota\")\n", + "axes[0].set_ylabel(\"Frecuencia\")\n", "axes[0].legend()\n", - "axes[0].grid(True, alpha=0.3)\n", "\n", - "# ── Scatter ───────────────────────────────────────────────────────────────────\n", - "horas = (notas / 10 + np.random.normal(0, 1, 200))\n", - "colores = ['green' if n >= 61 else 'red' for n in notas]\n", + "# --- Dispersión (scatter) ---\n", + "horas_estudio = notas / 10 + np.random.normal(0, 1, 200)\n", + "horas_estudio = horas_estudio.clip(0, 15)\n", "\n", - "axes[1].scatter(horas, notas, c=colores, alpha=0.5, s=30)\n", + "colores = ['green' if n >= 61 else 'red' for n in notas]\n", + "axes[1].scatter(horas_estudio, notas, c=colores, alpha=0.5, s=30)\n", + "axes[1].set_title(\"Horas de estudio vs Nota\")\n", + "axes[1].set_xlabel(\"Horas de estudio\")\n", + "axes[1].set_ylabel(\"Nota\")\n", "axes[1].axhline(61, color='orange', linestyle='--', alpha=0.7, label='Mínimo aprobatorio')\n", - "axes[1].set_title('Horas de estudio vs Nota')\n", - "axes[1].set_xlabel('Horas de estudio')\n", - "axes[1].set_ylabel('Nota')\n", "axes[1].legend()\n", - "axes[1].grid(True, alpha=0.3)\n", "\n", - "fig.suptitle('Análisis del curso', fontsize=14, fontweight='bold')\n", + "plt.suptitle(\"Análisis del curso\", fontsize=14, fontweight='bold')\n", "plt.tight_layout()\n", "plt.show()" ] @@ -514,166 +219,51 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### 4c — Pastel (pie) y diagrama de caja (boxplot)" + "### Nivel 3: Subplots — múltiples gráficas" ] }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "grupos = [\n", - " notas[notas < 61],\n", - " notas[(notas >= 61) & (notas < 80)],\n", - " notas[notas >= 80]\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_142330/1142614638.py:25: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n", - " axes[1].boxplot(grupos, labels=etiquetas, patch_artist=True,\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ + "# Gráfica de pastel (pie chart)\n", "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n", "\n", - "# ── Pie chart ────────────────────────────────────────────────────────────────\n", - "aprobados = int(np.sum(notas >= 61))\n", + "# Pie chart\n", + "aprobados = int(np.sum(notas >= 61))\n", "reprobados = len(notas) - aprobados\n", "\n", "axes[0].pie(\n", " [aprobados, reprobados],\n", - " labels=[f'Aprobados\\n({aprobados})', f'Reprobados\\n({reprobados})'],\n", - " colors=['#2ecc71', '#e74c3c'],\n", + " labels=[f\"Aprobados\\n({aprobados})\", f\"Reprobados\\n({reprobados})\"],\n", + " colors=[\"#2ecc71\", \"#e74c3c\"],\n", " autopct='%1.1f%%',\n", " startangle=90,\n", " explode=(0.05, 0)\n", ")\n", - "axes[0].set_title('Resultado del curso')\n", + "axes[0].set_title(\"Resultado del curso\")\n", "\n", - "# ── Box plot ─────────────────────────────────────────────────────────────────\n", - "grupos = [\n", - " notas[notas < 61],\n", - " notas[(notas >= 61) & (notas < 80)],\n", - " notas[notas >= 80]\n", - "]\n", - "etiquetas = ['Reprobado\\n(<61)', 'Aprobado\\n(61-79)', 'Excelente\\n(80+)']\n", + "# Box plot (diagrama de caja)\n", + "datos_por_zona = {\n", + " \"Reprobado\\n(<61)\": notas[notas < 61],\n", + " \"Aprobado\\n(61-79)\": notas[(notas >= 61) & (notas < 80)],\n", + " \"Excelente\\n(80+)\": notas[notas >= 80]\n", + "}\n", "\n", - "axes[1].boxplot(grupos, labels=etiquetas, patch_artist=True,\n", + "axes[1].boxplot(datos_por_zona.values(), labels=datos_por_zona.keys(),\n", + " patch_artist=True,\n", " boxprops=dict(facecolor='lightblue', color='navy'),\n", " medianprops=dict(color='red', linewidth=2))\n", - "axes[1].set_title('Distribución por zona')\n", - "axes[1].set_ylabel('Nota')\n", + "axes[1].set_title(\"Distribución por zona\")\n", + "axes[1].set_ylabel(\"Nota\")\n", "axes[1].grid(axis='y', alpha=0.3)\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Nivel 5 — Figuras compuestas (subplots)\n", - "\n", - "> **§6.8** — `plt.subplots(filas, columnas)` crea una grilla de `Axes`.\n", - "\n", - "Cuando hay más de una fila y columna, `axes` es un array 2D: `axes[fila, columna]`." - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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ABT2uz5w5A8DSpUvJy8ujadOmNGvWDF9fX2vd9tI4e/Yss2fPLtO2lqTwYkN53H333fj5+ZGSksIvv/xivRij0+kYMWIE586du+nywcHBLFmyhK1bt3L+/HkyMjJsLsYUKu3n52Y8PDxsLlK1adOGe+65h3Xr1gH/K5HTuXPnm7azbds2m8ePPvoofn5+QMHdJM8884w1QQ6wffv2Ug84W9l69eplTY5DwUXA+fPnc+XKFQAOHDiA2Wy2+d4TQgghhLA3SZALIYQQwiH5+fnZDDB35coVmjRpYn3cvn178vPzSU1NZf/+/cW2sX37dqZOnVpsAuxGWVlZpYrr+pgyMjKK1Eq/0ZUrV4pNkEdERJR5kD4oKLVw/SB7JSnt9hQmrgoV19u+RYsWxSaJ4+LibB6fOnWKU6dOlXpdpZkvODjY2iO6UFlqmd+YfC509epVHnroIa5evXrLNiqzFMmNgybeKjkKBbXEX3rppSIJ8eJc/7pPmTKFZ599Figo6XH48GHrcw0bNuSee+7hb3/7Gz4+Prds98ZSHRVx1113lXtZnU7H8OHD+fLLL8nNzbXeSdC/f3/8/PxumiDPyspi7Nix1osGN1Paz8/NNG7cuEjv8BsvwpXm/Xf58mWbxy1atLB5HBAQQL169bh27Vqx81enG2PTaDQ0a9bM+pnOy8sjLS2t2AFlhRBCCCHsRRLkQgghhHBI7dq1s0km7ty50yZBPnHiRCZOnMjevXutg/Xd6O2337ZJjjds2JCWLVui1+uLJNZLk3wsj5JKgZQnQXTkyBGb5LhGo6F9+/b4+/uj0Wg4fvx4kQSsI7m+d31pFVcyo7S12qHk/Txv3jyb5OSoUaMYMGAAHh4eqKrK+PHjrc9V1XsDbr0t+fn5vPPOOzYxNG/enCZNmqDT6YiPj7ep83z9fIMHD6Z+/fosX76c/fv32/T2j4uLY+HChRw4cIDvvvuuSJkZRzZ69Gi++uorm20dM2bMLZdbtmyZTXLczc3NOn4AFPTULkvpnlspy/v0Zm58/xXXblW+R8uiNNtcWftFCCGEEKKy1JwzYSGEEELUKQMHDuTXX3+1Pl6wYAHDhg3D3d29VMunp6fb9Hru27cvn332mTU5s379+hJ7nt9McHAw58+fByA0NLTcvWo1Gk2Zl7m+BzDAJ598wsCBA62PH3vssTInyBs0aGDz+PqBCG82DYoO8Pfss8/y5JNPlmn9xQkKCrKWuLhy5Qq5ubk2ve1L0wO4UEn7+fp92bJlS9566y3r45K2tzLcuM8OHjxIt27dSpz/7NmzNr2Zx48fzyuvvGJ9PH/+fJsE+Y0Ka2hDQa/oS5cusXLlSpYtWwYUDIZ7/PhxOnbseNO4e/TowenTp286T3Vp3Lgxt99+u83gnLfddtstlyuszw8FpXt+/fVXAgMDgYJ6+iXdbQDlS+peunSJ/Px8m9JJN763bvz8FefG98y5c+fo1KmT9XFiYiIZGRnWx0FBQWWOtbLcOAiuqqo238MuLi5Fxk8QQgghhLC3sv8yE0IIIYSoBoMGDbIppREXF8djjz3GxYsXS7X8jbWkXVxcrEmutLQ05s+fX664+vTpY/3/mJgYFixYUKSmeVpaGitWrLBJulaGG0vFuLi4WP9/+/bt7N69u8xtNmjQgNDQUOvjAwcOsGvXLuvjNWvWWC8I3CgiIgJfX1/r42+++abY5HVUVBTvv/8+mzdvLlVM19enzsvL44svvrBpq7CGc0Vc//5IS0uzllExGAx88MEHFW6/JHfeeafN4y+//JIDBw7YTIuKirJevCnufVwoPj6eJUuWlLiuxYsXc+TIEWvvYg8PD2sd7OvZsyRHeT388MN4e3vj7e3NuHHjSpXAvn5fajQaa+JaVVXmzJlz0wFLbyyVkpiYeMv1ZWZm8uWXX1ofR0VF2VxQ8/DwICIi4pbt3Dio5cKFC0lNTQUK6s5/8sknN53/+vdMaeKuiF27drFv3z7r4++//97m/dWlS5cadbeCEEIIIeoGOTsRQgghhEPSaDTMnj2bhx56iPT0dKCg1++gQYNo1aoVDRo0IC8vr8Tes/Xr1yc4ONianPnll1+IiYnBz8+PyMhIMjMzyxXXo48+yqpVq6w9Nj/44AOWLFlC8+bNURSF+Ph4Lly4gMVioXv37uVaR0natWtn83jGjBl069aN7Oxsjhw5Uu52J06cyBtvvGF9/MQTT9ChQwdMJhORkZElLufk5MT06dOty6akpDBs2DAiIiLw9/cnOzubc+fOkZKSAhStv3yzeFatWmW9IPDZZ5+xZcsWfHx8OHr0aLlKtdyoXbt21sR/cnIygwYNonXr1kRFRVVpEjEsLIyhQ4eydu1aoKBX9/jx4wkPDycgIIC4uDguXLjArFmz6NatGy1atMDFxcW6zV999RUHDx7E1dWVw4cP33RfrFixgrfffhtvb29CQkLw9/cnNze3yHulcePGVba9VWXAgAEMGDCgTMu0a9eO7du3AwUXXgYPHky7du24ePEiFy5cQFGUEkuVXF/eCQo+e+3bt0en03HXXXcxfPjwYpf79NNP2bhxI97e3hw9etRmQN0xY8YUSbwXp3Xr1vTv358tW7YAcP78ee69917atm1LXFyczYCifn5+jB071mb5xo0bWy9c7dy5k7Fjx1rHRXj//ffLNRZCSSwWC48++igdOnTAYDBw7Ngxm+dLM0ivEEIIIUR1kx7kQgghhHBYoaGhrFy50qaXpcVi4eTJk2zdupVdu3bZlBbQ6XQ25Qj++c9/2rR36tQpduzYQW5uLlOnTi1XTIGBgXz22Wf4+flZp12+fJnt27ezbds2oqOjrT3Ky1NG5WZ69uxJ7969rY8NBgM7duzg8OHDhIeHF+kZXFpjx46lX79+1scmk4mDBw9y9OhRAgMDbXp032jcuHE8+eST1h68FouFY8eOsXXrVvbu3WtNjkPp90eLFi14/vnnbaadOXOGvXv3AvDggw/aPKfT6UrV7vWmTJliU64nMTGRP//8k4SEBP7+97+Xub2yeOutt+jbt6/1saqqREVFsW3bNs6fP2+TpHV3d+epp56yWf7w4cPs2rULrVbLo48+esv1paenc+LECf744w/27t1rk6QdPHgwbdu2rYStcnwTJkywllSBggs6f/zxBxcuXGDkyJEEBweXuGzv3r1t6tknJyezdetWNm7cWOLAtO3ataNjx46cPn2avXv32lzMaN++PdOnTy917O+9955NCZj09HR27NhhkxyvX78+n3/+Od7e3jbLPvDAAzaPDx06xMaNG9m4cWOpBjAui379+uHs7MyBAweKJMfHjx9f5A4KIYQQQghHIAlyIYQQQji0xo0b8+OPPzJv3jyGDRtGkyZNcHd3R6vV4uXlRVhYGEOGDOGtt95i+/btNj05hwwZwty5c2nfvj16vR5PT0969+7NsmXLSlWzuCRdu3Zlw4YNPPPMM3Tq1Il69eqh1Wpxc3OjefPmDB06lPfff5958+ZVxi6wMW/ePJ544gmCgoLQ6XQEBQUxYcIEli5dipubW7na1Gg0fPrpp/zzn/8kNDQUnU6Hv78/o0eP5scff7xp4hAKao+vXLmSkSNH0rRpU9zc3HBycsLHx4dOnTrx6KOPsnTp0iKJupuZMGECn332GZ06dbLWLe7Xrx8rVqwoUmO5sDdsWTRr1ozly5fTt29f3N3dcXV1JSIigg8++KDcF09Ky8XFhc8//5zPPvuMgQMHEhQUhF6vx83NjdDQUIYPH27z/nzyySd56623CAsLQ6fT4e3tzcCBA/nhhx9o0aJFiet56aWXePzxx+ncuTNBQUG4uLhYX9vevXvz7rvv8tFHH1XptjoSX19fvv/+e4YMGYK3tzfOzs60bNmSF1988ZblkNzc3Pj666/p27cv9erVK1VJF1dXV7755humTJlCw4YN0el0BAcH88QTT/DNN9+Uqee2l5cXixcv5v3336d37974+vri5OSEh4cHbdu2ZcaMGaxbt4727dsXWfaRRx7hn//8J82aNSvXxaSyaNOmDatXr2bw4MH4+Pig1+sJCwvjzTff5OWXX67SdQshhBBClJeiOsqQ50IIIYQQQvwlISEBf3//Ir3Ok5OTGT58OAkJCUDBAIZbt261R4hCFNGvXz/rQLndu3fn22+/tXNEQgghhBDiVqQGuRBCCCGEcDhz585l8+bN9OjRgwYNGqDT6YiPj2fr1q02gyneWH5ECCGEEEIIIcpCEuRCCCGEEMIhpaSksGHDhmKf02g0TJkyhZEjR1ZzVEIIIYQQQojaRBLkQgghhBDC4QwbNgwoGJAyKSmJzMxMXFxcCAkJoWvXrowePZpWrVrZOUohhBBCCCFETSc1yIUQQgghhBBCCCGEEELUSZpbzyKEEEIIIYQQQgghhBBC1D6SIBdCCCGEEEIIIYQQQghRJ0mCXAghhBBCCCGEEEIIIUSdJAlyIYQQQgghhBBCCCGEEHWSJMiFEEIIIYQQQgghhBBC1EmSIBdCCCGEEEIIIYQQQghRJ0mCXAghhBBCCCGEEEIIIUSdJAlyIYQQQgghhBBCCCGEEHWSJMiFEEIIIYQQQgghhBBC1EmSIBdCCCGEEEIIIYQQQghRJ0mCXAghhBBCCCGEEEIIIUSdJAlyIYQQQgghhBBCCCGEEHWSJMiFEEIIIYQQQgghhBBC1EmSIBdCCCGEEEIIIYQQQghRJ0mCXAghhBBCCCGEEEIIIUSdJAlyIYQQQgghhBBCCCGEEHWSJMiFEEIIIYQQQgghhBBC1EmSIBdCCCGEEEIIIYQQQghRJ0mCXAghhBBCCCGEEEIIIUSdJAlyIYQQQgghhBBCCCGEEHWSJMiFEEIIIYQQQgghhBBC1EmSIBfCgcTFxTF79mz27dtn71CEEEIIIYQQQgghhKj1JEEuhIMwm83MnDmT3377jYiICHuHU+Vmz55NeHg4cXFxFW7rhRdeYOjQoVgslnIt/8gjj/DII49UOA4hhBCisvTr148JEybYOwwhhBCiTpk1axbh4eH2DkMIUc0kQS5EFdq7dy/h4eHMmzfvlvPOmzePixcv8tlnn+Hu7l4N0dUOJ0+e5KeffuLvf/87Gk35vtL+8Y9/sGvXLn7//fdKjk4IIWqW1NRUPv74Y4YOHUrnzp3p1KkT/fv3Z9q0aaxcubLK1194J9WpU6eKfX727Nls3ry5yuMQQgghRM1Xmt/jd999N/369auU9cl5ihA1lyTIhXAAR44cYcGCBcyePZuGDRvaO5xqMXXqVCIjIwkJCalQO3PmzKFhw4YMGDCg3G106NCBTp06MWfOnArFIoQQNdmVK1e4//77WbBgAWFhYfzjH//gueeeY+DAgcTHx7N48eIqjyE+Pp45c+aUmCCfM2eO/PAUQgghRJV58803iYyMLNeycp4iRM3lZO8AhBDQsWNHjhw5Yu8wqpWTkxNOThX7CoqPj+ePP/7gqaeeqnA8DzzwAK+99hrHjh2jXbt2FW5PCCFqmgULFpCUlMTLL79cbGmPq1ev2iGq6mMymbBYLOj1enuHUmXqwjYKIYQQFaHT6ewdQqXIzs6WO9OFKAPpQS6EA4iLiyM8PJzZs2cXO+33339n5MiRtG/fnp49e/Lqq6+Sm5tbpJ3k5GTefPNN+vXrR9u2bbn99tuZOXNmkTrfWVlZfPLJJ4wePZrbbruNtm3b0q9fP9544w3S09OLtJuYmEh0dHSx6yzOmjVreOihh+jevTvt2rWjT58+TJs2jejoaOs8xdUgL5x2/vx5Pv74Y+666y7atm3Lvffey9q1a4usZ+PGjZjNZu666y6b6adPn6ZDhw6MGTMGk8lk89y//vUvwsPD2bp1q830vn37ArBhw4ZSbaMQQtQ2Fy5cAKBHjx7FPt+gQYMi02JjY3n55Zfp27ev9bjz6KOPsnPnTus80dHRvP766wwZMoQuXbrQvn17hg4dyoIFCzCbzdb5Zs+ezcSJE4GCsSXCw8MJDw9nwoQJ1lukAVavXm197sYaoSdPnuTpp5+mZ8+etG3blv79+/PRRx8VOX4V1hdNS0vjlVdeoXfv3rRr147Dhw8DcOnSJZtj1q0YjUYWLVrE8OHD6dixI506dWLo0KF8+umnNvMZDAbmzJnDoEGDaNeuHd27d2fKlCkcO3as1Ovatm0bEyZMoHPnzrRv355hw4axdOlSVFUt9TauWrWK8PBwdu/ezeeff07//v1p164d999/P3/++ScA586d48knn6RLly506dKF5557jqysLJt1lPa1Bazr3LVrF3PmzLGeqwwaNIilS5eWevuFEEKIqlRcDfKrV6/yyiuv0K9fP+vxe9iwYcyfPx+g1OcppT2GA0RGRjJx4kQ6dOhAt27dmDFjBnFxccWOURIeHs6sWbPYu3evtf2hQ4cCZfvtf30O4rfffuPBBx+kffv23HXXXSxYsACAzMxMXn31VXr16kW7du2YMGFCkXOmsuYbhHAE0oNcCAe3bds2lixZwpgxYxgxYgS7d+/m+++/B+CNN96wznflyhXGjBlDTk4OI0eOJDQ0lISEBL777jt27tzJjz/+SHBwMAAJCQmsXLmSgQMHMnjwYPR6PZGRkXz//fccPHiQH374webK+ccff8zq1atZvHhxiYmTQmvWrOFf//oXnTt3Ztq0abi5uZGQkMCePXu4ePEizZs3v+U2z5o1C41Gw8SJE9FoNCxbtoyZM2fSqFEjOnbsaJ1v7969uLi40KpVK5vlw8PDeemll3jllVf473//y7/+9S8AfvjhB9asWcPkyZOL1JkLDAwkODiYvXv33jI+IYSojRo1agQUJDJnzpx5y7t8Tpw4weTJk8nNzWXEiBG0atWKrKwsjh49yq5du+jVqxcA+/btY//+/fTt25fg4GAMBgN//vknH3zwAbGxsfz73/8GCmqAmkwmPv/8cx566CG6dOkCQP369WnevDkffPABzz33HF27dmX06NFF4tm2bRvTpk0jKCiI8ePHU79+faKioli0aBGHDh1i8eLFRbbpkUceoV69ejzxxBOoqkr9+vUBmDx5MvHx8Zw+ffqW+81oNPLEE0+we/duunfvbj32XbhwgV9//ZWnn34aKBiM+4knnmDv3r307duX8ePHk5SUxPLly3n44Yf58ssvue222266rpUrV/LKK68QEhLCY489hru7O7/++itvvPEGUVFRvPnmm0WWKW4b4+PjAfjoo48wGo2MHTsWrVbLt99+y7Rp0/i///s/XnrpJe69917+9a9/ceTIEVavXo1er+ett96ytl3a1/Z6H330EVlZWYwePRq9Xs+6det44403SE1NZcaMGbfc30IIIURZ5eXlkZqaWuxzFovlpsuaTCYeeeQRrl69ypgxY2jWrBnZ2dmcP3+evXv38re//a1U5yllOYZHRkYyYcIEdDodkyZNIjAwkN27dzNx4kRycnKKjfP48eNs3LiR4cOHM2TIELKzs4Gy//YH+OOPP1i2bBljx45l5MiR/PLLL3zwwQc4OzuzatUqAgICeOqpp0hMTGTRokVMmzaNDRs2WMcEK886hbA7VQhRZfbs2aOGhYWpc+fOvel8sbGxalhYmPrpp58Wmda+fXv10qVLNvM/+uijakREhJqdnW2dNnXqVLVbt25F5o2NjVU7duyozpo1yzrNYDCo+fn5ReJYsWKFGhYWpm7YsMFm+vPPP6+GhYWpe/bsueU2T5s2Te3UqZNqNBpvOt+nn36qhoWFqbGxsUWmPf7446rZbLZOv3z5shoREaE+++yzNm307dtXHTRoUInrePbZZ9Xw8HD1jz/+UM+cOaN26NBBHTFiRLHbrqqqOmnSJDUiIuKW2yiEELXRpUuX1C5duqhhYWFqz5491RkzZqjz589XDxw4YPOdrKqqarFY1CFDhqht2rRRIyMji7R1/fzXH6uu9+yzz6qtW7dWExISrNMKj5s//vhjscuEhYWpzz//fJHpeXl5aq9evdSRI0eqBoPB5rlff/1VDQsLU1etWmWdVnhce+aZZ1SLxVKkvb59+6phYWHFxnCjr776Sg0LC1PfeeedIs9dvx9WrlyphoWFqa+99prNPOfPn1fbtm2r3nPPPTbz9+3bVx0/frz1cUZGhtqxY0f1jjvuUNPS0qzTjUaj+sgjj6hhYWHq/v37S7WNP/74oxoWFqYOHTrUZn+dOnVKDQsLU8PDw9X169fbLDN16lQ1IiJCzcrKsk4ry2tbuM4+ffqo165ds07Py8tTH3zwQbV169Y25wRCCCFERRWeV9zqr2/fvtZlCo+fhQqPjfPnz7/l+ko6TynrMfyhhx5SW7durUZFRdm08+abb6phYWE25weF6w0LC1P//PPPIusuy2//knIQBoNBvf3229Xw8PAi5zFff/21GhYWpm7btq1c6xTCUUiJFSEc3IABA6y9+gr16tULo9FoLU+SmZnJ77//Tp8+fXB3dyc1NdX65+bmRseOHdm+fbt1eb1eb71iazKZyMjIIDU11dpz7cZBSd577z1Onz59y97jAF5eXuTm5rJ169ZbXo0vyeTJk61XnwGCgoJo2rSp9fb/QqmpqXh7e5fYzhtvvEGTJk14/vnnefrpp3FycuK///1viVervb29MRqNXLt2rVxxCyFETdaoUSN+/vlnxo8fj7OzMxs3buSjjz7i4Ycf5u6772bHjh3WeaOiojhz5gz3339/seM2XP8d7ubmZv3//Px80tPTSU1N5Y477sBsNnPixIkKx75r1y6SkpJ48MEHycrKsjkOduvWDTc3N5v4Cz3xxBMoilJk+tatW0vVexwK7pxyc3Pj73//e5Hnrt8Pv/32GwDTpk2zmadp06YMGTKEmJgYzpw5U+J6duzYQU5ODuPHj7c59jk5OTF16lSbdVyvpG0EGD9+vE098latWuHh4YG/vz/33Xefzbzdu3fHaDRae59D+V7bhx9+GC8vL+tjZ2dnHnnkEcxmM1u2bClx+4UQQojyGj58OF9//XWxfwEBATdd1svLC0VR2LNnD0lJSeVaf1mO4ampqRw+fJg+ffoUKdHy5JNPlriOVq1a0adPnyLTy/rbH4rmIPR6Pe3bt0dVVSZPnmwzb/fu3QG4ePFihdYphL1JiRUhHNyNyXHAelAtrN914cIFLBYLa9euLbZWN9j+SAdYsWIFy5Yt4+zZs0XqdFekLtjUqVM5ePAgM2bMoF69enTu3JnbbruNIUOGWG9dv5WStvn6H+WF1GLqtRVyd3fn448/Zvjw4aSlpfHf//632LZvbKukRIIQQtR2ISEhvPLKK7zyyiukpKQQGRnJ+vXrWbduHdOnT+fnn3+mSZMm1guWrVu3vmWbubm5zJs3j/Xr1xf7PV4ZFyULa1++/vrrvP7668XOk5ycXGRaaGhohdcdExNDs2bNbJLFxYmNjcXb2xt/f/8izxX+AL506VKRsmHXLw/QsmXLIs+FhYVZl7/RzbaxuGNivXr1iq03X5jUvv4coTyvbXGl1lq0aFFi/EIIIURFNWrUiNtvv73Y51xcXIqMm3G94OBgZsyYwdy5c7njjjsICwujS5cu9O/fn969e5dq/WU5hhf+27Rp0yLz+vv74+npWew6bna8L+tv/5LOD4p7rrjzg/KsUwh7kwS5EA5Oq9WW+FxhQrfw33vvvbfYemc3WrRoEe+++y49e/bktddeIyAgAL1ej9ls5vHHH79p0vlWGjVqxLp169i3bx+7d+/mwIEDvP/++/zf//0fX375JV27dr1lGzcm80vi6+t7y8TK9YNxnjx5skiPuOulp6ej0+lserYJIURd5efnR9++fenbty9BQUHMnz+f9evX89RTT5WpnX/+859s3bqVUaNG0bVrV3x8fHBycuL48eP85z//KffdRtcrbOOZZ56hffv2xc5T3He7q6trhdddWqqqVsoF2OLauFm7N9vGko63pTn3gMp/beUCtRBCCEc0bdo0HnjgAf78808OHjzIxo0bWbZsGf3792fu3LmlPn6V5Rhe1mNiScf78vz2v9l5QEnPXd9OVeYbhKgqkiAXohZo3LgxGo2GvLy8Eq+MX+/nn38mJCSEhQsX2vw4vnH06fLS6XT06tXLOkDbqVOnGDlyJLNnz+abb76plHVAwRX43bt3YzQaiy2bsmfPHubNm0ffvn3R6/V89dVX9OjRgzvuuKPY9i5evGi9gi+EEOJ/OnXqBBQMugT/69V06tSpmy6XmZnJ1q1buf/++4sMIBkTE1Nk/vImSAvjcXZ2LtVxsDKFhoYSExNDTk7OTXuRF/a8T05OLnJHVWFplcaNG5e4fOFzZ86c4a677irz8pWtrK9toejoaAYMGGAz7dy5c0DxPdaEEEIIRxASEsLDDz/Mww8/jMlk4rnnnmP9+vUcPHjwlp3AynIMLzwWnj9/vkg7SUlJZGZmlinuqv7t7yjrFKKipAa5ELWAj48Pd955J3/++Sd79uwpdp7rby0vPEhd37NLVVXmzp1b7LKJiYlER0eTm5t7y1iKGx28efPmODs7V3pt7+7du2MwGIiKiiryXEpKCjNnziQwMJD33nuPt99+m5CQEJ5//nkSExOLzJ+QkMCVK1dKVWddCCFqo71795b4Pb9p0ybgf6UwWrVqRVhYGGvWrOHYsWNF5i88vhQmvG/sKZSVlcWiRYuKLFeYYC7peOHm5lbsbbm9e/emfv36LFiwoNj6oCaTqUy38166dKnUP+Luv/9+cnJy+L//+78iz11/nL377rsBmDdvns08Fy9eZN26dYSGhhapNXq9Xr164ebmxrJly2z2j9ls5rPPPgPgnnvuKVXMlaGsr22hZcuWkZGRYX2cn5/P119/jVarpV+/flUSqxBCCFFemZmZGI1Gm2lOTk7WY/b15xclnaeU5Rju5+dHp06d2LZtW5HxUL744osyx1/W3/6VwR7rFKKipAe5ENVg//79RX4QQ8GBY8qUKZWyjtdff52xY8fy6KOPMnToUNq2bYtGoyE+Pp4///yTdu3a8d577wEwaNAgPvroIx577DEGDhxIXl4ev/32W5EDf6GPP/6Y1atXs3jx4lsmkB977DHc3d3p1q0bwcHBZGdns2HDBrKzsxk+fHilbGuhgQMH8tFHH1m3r5Cqqjz33HOkpaXx7bffWmu2//e//+Xhhx/mX//6F19//bXN1ezff/8dKChTI4QQddE333zD3r17ueuuu2jbti1eXl6kpaXx559/sm/fPsLCwhgxYgRQkBx99913mTRpEmPHjmXEiBG0atWK3Nxcjh49SsOGDfnXv/6Fh4cHd9xxB2vXrkWv19OhQwcSExP58ccf8fPzKxJDixYtcHd3Z9myZbi4uODl5YWvry89e/YEoGPHjuzevZv58+cTHByMoigMHjwYV1dXPvjgA5566inuu+8+RowYQdOmTcnOzubixYts2rSJmTNnlvo4NHnyZOLj40s1UOfEiRP5448/WLRoESdPnuTOO+/E3d2dmJgYdu7cybp16wB48MEHWbNmDUuXLuXy5cvccccdJCUl8d1336GqKq+//vpNe9B7enry4osv8sorrzBixAhGjBiBq6srGzdu5NChQ4wePbpUZcwqS1lf20J+fn6MHDmSESNGoNPpWLduHSdOnGDq1KnSg1wIIYTD2bt3Ly+//DL33HMPTZs2xcPDg3PnzrF8+XKCg4OtA09CyecpZT2Gv/DCC0yYMIFx48Yxbtw4AgIC2LNnDydPnsTHx6dM8Zf1t39lsMc6hagoSZALUQ127drFrl27ikzXarWVliAPDAxk9erVfPXVV2zevJkNGzag0+kIDAyka9eujBw50jrvY489BsAPP/zAu+++i7e3N/379+eZZ56xjkJdXg8//DC//vorK1euJD09HU9PT1q0aMEnn3xS6cnnhg0bcuedd/LTTz8xbdo0a2Jh/vz57Nixg3/+85907tzZOn/79u159tlnef/99/nss8+YNm2a9bmff/6ZiIiIEmvXCiFEbffkk08SGhrK/v372bNnD+np6Tg7OxMaGsrTTz/NpEmTbEqItG3bllWrVvHZZ5+xZcsWfvzxR+rVq0erVq1sBq368MMP+fjjj/njjz9Ys2YNISEhjBs3joiICCZPnmwTg4uLC//973/55JNPeOedd8jPz6d79+7WBPlrr73GG2+8weeff052djYAgwcPBgp6Z61atYovv/ySX375hZSUFNzd3QkJCWHkyJHWNiqbTqdjwYIFfPPNN6xZs4ZPP/0UJycnGjZsyKBBg6zzabVavvzyS+bPn8+6devYsWMHrq6udOnShaeeeqpUx59Ro0YREBDAV199xfz58zGZTDRt2pRXXnmFcePGVcn23UxZXttCM2fO5NChQ3z//fckJiYSEhLCyy+/zIQJE6o3eCGEEKIUwsPDGThwIAcOHGD9+vWYTCYCAwMZNWoUTzzxBB4eHtZ5b3aeUpZjeIcOHVi8eDH/+c9/WLRoEXq9nttvv50lS5bwwAMP4OLiUur4q/K3vyOtU4iKUlSpji+EqMGOHz9urW9eePt6WUVGRjJq1Cg+++wzub1bCCGEqAKrVq3ihRdeKNXdaEIIIYQoKjU1lZ49ezJmzBhef/11e4cjRK0iNciFEDVa27ZtefDBB/n0009tapyVxSeffELPnj0lOS6EEEIIIYQQwu7y8vKKTCss23rHHXdUdzhC1HpSYkUIUeO9++67FVp+4cKFlRSJEEIIIYQQQghRfiaTibvuuouhQ4fSrFkzcnJy2LlzJzt37qRHjx7SsUuIKiAJciGEEEIIIYQQQgghHIBWq6V///5s27aNH374AaPRSEhICFOnTmXKlCloNFIMQojKJjXIhRBCCCGEEEIIIYQQQtRJctlJCCGEEEIIIYQQQgghRJ0kCXIhhBBCCCGEEEIIIYQQdVKdrkF++PBhVFVFp9PZOxQhhBB1hNFoRFEUOnXqZO9Qaiw5fgshROlU9jHn9OnT5Ofnl3t5vV5PeHh4pcQiah45fgshhKhOZTkPqtMJclVVqcwS7BaLpU4NliDbW7vJ9tZusr32I0N/VFxlH79rOlVVMRqN6HQ6FEWxdzi1guzTyif7tPKVZp9W9ndlfn4++fl56PRZZV7WmO9RqbGImqeuH7/le1D2QaG4uAzy883o9VoaNvSydzh2Ie8F2QdQ9fugLMecOp0gL7xy3a5duwq3ZTabyczMxNPTE61WW+H2HJ1sb+0m21u7yfba17Fjx+wdQo1Xmcfv2iAnJ4dTp07RokUL3Nzc7B1OrSD7tPLJPq18pdmnVXHM0emyaB7+XZmXi44aC7hUejyi5qjrx2/5HpR9UGjQoP9w+XIWwcEexMf/097h2IW8F2QfQNXvg7KcBzlGdzohhBBCCCGEEEIIIYQQoprV6R7kQgghhBBCCCHKRrHUzVvBhRBCCFE7SYJcCCGEEEIIIUQZSIJcCCGEELWHJMiFEEIIIYQQQpSexd4BCCGEEEJUHkmQCyGEEEIIIYQoNUW1dwRCCCGEEJVHEuRCCCGEEEIIIUpPEuRCCCGEqEWqLUGenZ3NggULOHbsGMeOHSMtLY1//vOf/O1vfyvV8qqqsnjxYpYtW0Z8fDwhISGMGzeOCRMmoChSA08IIYQQQgghqoP0IBdCCFETmM1mjEajvcMolsFgsP6r0WjsHI19VGQf6HQ6tFptpcVSbQnytLQ05s6dS4MGDWjTpg07d+4s0/Jz585l9uzZDBs2jMcff5x9+/bx9ttvk5GRwfTp06soaiGEEEIIUR1UYx4YssHVC0Wrs3c4QoiSqJSvBrmKjO0phBCiWqiqytWrV0lPT7d3KCWyWCw4OTlx+fLlOpsgr+g+8Pb2pkGDBpXScbraEuQBAQFs27aNwMBA4uLi6N+/f6mXTUxM5IsvvmDEiBG88847AIwaNQqtVssXX3zBQw89hL+/f1WFLoQQQgghqohqyscU+QvmcztR83NRXL1wat0Xbau+KJrK6xUihBBCCCHqhsLkeEBAAG5ubg5ZecJsNmMwGHB2dq7UntA1SXn3gaqq5OTkkJiYCEBQUFCFY6m2BLlerycwMLBcy27ZsoX8/HwmTJhgM338+PGsXr2aLVu2MGbMmMoIUwghhBBCVCPj/pWYo34HJ2fQuaJmp2LctwLVbELXbpC9wxNCFEdKrAghhHBQZrPZmhz38/OzdzglMpvNALi4uNTpBDmUbx+4uroCBZ2qAwICKrwPa8QgnSdPnkSv1xMeHm4zvU2bNuh0Ok6ePGmnyGo2s0XlQkIGsSlZmMwqfp4uhAfXw91FbmsWtVNWnpGo+HTSsgzonDQ08nOnWaCXvcMSQog6y5KRiOX8XnB2R3GtVzDR2Q01Kxnzqd9xCr8TRe9q3yCFEEVIDXIhhBCOqrDmuJubm50jEVWt8DU2Go11I0GemJhI/fr1i9Sj0Wg01K9f39qlvrwKr1hUtI3CP0eXYzCxel8Mvx6OJSXLYPOck0bhtrBAxvRqdtPEYU3a3sog21uznbl8je93RbP3bCKWG37Q1fd0YXDnhtwZ5oObW+3Y3lupba/vrdS17RWiJlGvXUHNzwGvBrZPOHug5mWiZiWj+DayT3BCiJJJgrzGOnnyJLNnz+bgwYMYDAYaNWrE6NGjmThxonWe5ORk3n//fbZt20Z+fj7t2rXjueeeo23btnaMXAghysYRy6qIylWZr3GNSJDn5eWh1+uLfc7Z2Zm8vLxyt22xWMjMzCz38te3k5eXh6IoDl1cP/JSOvM2nyEtu+CKmqteS5P67uidNFxNzyUxw8COqKvsPH2VIR2DGdOzCU7aottTU7a3ssj21kz5JgtLdsbwW+QV6++4IG8XArxcyDOaiUnOJjkzj2/+PMf6g3qeurslEQ297Rlytagtr29pOdr2WiwWh4hDCEeguHiCVg8mA1zfU9xkQNHqCp4XQjgcRZUMeU20Y8cOpkyZQkREBE899RRubm5cunSJq1evWufJy8tj0qRJJCcnM3nyZLy8vFi2bBkTJkxg5cqVtGjRwo5bIEQJDLlgyq+ctpz04FxJd6+VEJdy/b/Z16o/LiFEETUiQe7i4kJ+fvFfdgaDARcXl3K3rdFo8PSs+I8vs9mMqqp4eHg4bO2gn/bF8OXmKFQg2MeNCX1acnt4IDqn/yVqzidksHzneXZEXWXt4cucT87ltVGd8bih7EpN2N7KJNtb86RnG3jr50OcvlxwwtG3bTAP3d6MxvU9rPPkm8xsO3mVJdvOkpiRx1s/nWTaoDbc26l291asDa9vWTja9kpyXIj/Ueo3RePfFMuVU6iKf8GPP2Mu5GWiaXkHipu3vUMUQtxIBSzlXE7YTVZWFs8//zz9+vXjk08+KfF8ZPny5Zw7d46lS5fStWtXAO677z4GDRrExx9/zLx586ozbCFKx5QPf66EzJSKtePpB3eOqrxEdElxFXbyzMuCdZ9Xf1xCiCJqRILc39+fXbt2Fel1Z7FYSE5OJiAgoELtV1bCRKvVWv8czdJtZ1n85xkA7u3UiCkDI3DRFY2zZbAPr4zqwq6oq3y45ignYtN4cdl+3h3fAy9X2178jry9VUG2t+ZIzzbwwrL9XEzKwsNFxwvDO9G1uX+R+Vy1WgZ2aszt4YH8d+0Rdp5JZvYvJzCaVR7s0dQOkVefmvz6lkdd214hagpFUdD1moRx+0IsyTFgNoKTM5pGHdF1HWHv8IQQxVAoXw1yudHdvtauXUtycjJ///vf0Wg0ZGdn4+rqWiRRvmHDBlq1amVNjgP4+PgwZMgQvv/+e7KysvDw8LixeSHsLzMF0pPsHUVRxcVlcQc0YLE4ZsxC1EE1IkEeERHBypUrOX36NK1bt7ZOP3nyJEajkTZt2tgxOse39kCMNTn+SN9wHurV/JZ1em5v1YD/+Lgxa8lezl3N4PUVB3l3XHf0TpJcEo4tL9/Ey98VJMfre7rw/oQeNPS7+Um8m7MTM+4JI6S+Fyt2nefz307i6apjQPuG1RS1EELUXRqvAPT3/gtLwlnU7DQ0XgEo/s2kbqQQQlSi3bt34+npyZUrV5g6dSoXL17Ezc2NBx54gFmzZuHs7IzFYiEqKophw4YVWb59+/YsWbKEs2fP0qlTJztsgRBCiOrQr18/4uPji33OycmJEydO2Ew7duwYH374IceOHUOv13PnnXfy/PPP4+fnd9P1WCwWfvrpJzZt2kRUVBRpaWkEBQXRt29fpk6dipdXyeMiVgWHS5CnpqaSlpZGcHAwrq4Ft4/079+ft99+myVLlvD2229b512yZAk6nY5+/frZK1yHdzQmhXm/ngRgQp+WjOld+ppxzQK9eH98D579ZjfHL6Xy6YbjzLy/Q1WFKkSFqarKhz8f5eyVa9Rz0/PBhNsI8XMv1bKKojDpzpaYLSo/7rnAJ+uO0dDPnVYhPlUctRBCCEWjRRvUyt5hCCFKS8ql1DgxMTGYTCamT5/OqFGj6NatG/v37+fbb78lIyOD//znP6Snp2MwGPD3L3rnZeFd24mJiRWKQ1VVcnJyKtRGTZWbm2vzb11UFftAURScVRXVohb0yK4Ii4qiqhhyc1ErONZCaeMylybmSozLUVTl58FgMGCxWDCbzZjN5kpvv7IUvpaqqjpUnM8//3yR7+lr167x7rvv0qtXL5tYz507x4QJEwgJCeHZZ58lIyODRYsWceLECVasWHHTktjZ2dm8+OKLtGvXjpEjR+Lv78/p06dZsmQJW7Zs4YcffsDd/eb5HLPZjMViITc3F0sxnyVVVUvd6aZaE+RLliwhIyPDOijm3r17MZlMAEyYMAFPT0+WLl3KnDlzWLx4MT169AAKDsZPPPEE8+bNw2Qy0bVrV/bv38/PP//MtGnTKlxipbZKzcrjnVWHsKgq/duFMK5PyzK30TTQi1dGduGlZXvZdDSOdo19GdixdtdnFjXXmv0x7Ii6ik6r4bXRXUqdHC+kKAqPD2jNlbQcdp1O4K0fDvH5k32K1OAXQgghhKjTakd+pk7JyckhNzeXhx9+mJdeegmAe+65B5PJxLJly5g2bZq1g5pery+yvLOzM1AwiGdFGI1GTp06VaE2arqYmBh7h2B3lbkPnJ2daRkSiNlgQK3g+1MxGNAajUTHxGEwGKosLlX1+OtftVSfqcqMy9FU1efBycmpxuwrR4uzd+/eRaatXLkSgEGDBtm8Z//zn//g4uLCl19+Sb169QBo27Ytf/vb31i6dCnjxo0rcT0Wi4Wvv/6aDh3+1xF3yJAhtG3blhdeeIEVK1YwduzYm8ZqMBgwmUycP3++xHmKO6YVp1oT5AsXLrTppr9jxw527NgBwP3333/TwTKffvpp6tWrx9KlS1m/fj1BQUG88MILTJo0qcrjrolUVeX/1h0jPTufpgGePD24XblvVe7crD4T7gzjmz/OMO/XE3Ro4oe/l3MlRyxExVxMyuTLzVEAPDGgFRGNfMvVjkZR+Newjkz7ajuXU3P4dP0xXhzRuTJDFUIIIYSo2WpJD8a6pLAX3+DBg22mDx48mGXLlnHo0CHrndn5+flFli9M4NysN2Bp6HQ6WrQo/V3NtUlubi4xMTGEhoZaL0bUNVWxDxRFQWc24OTsDBV8f+LsjKLT0bx580rpQV5SXIW5GUVRSveZqsS4HEVVfh4MBgOXL1/G2dm5wt9ZVUlVVQwGA87OzmXK17344ovs27ePzZs320xfvXo1L730Eps2bSIkJKRSY/31119xd3dn4MCB1n2anZ3Nzp07GTt2LIGBgdZ5e/fuTcuWLdm8eTOPPfZYiW26uLjQvXv3Ivvg3nvv5cUXX+TixYulev2cnJxo3Lix9ULu9c6dO1fqbazWBPnWrVtvOc+MGTOYMWNGkemKojB58mQmT55cBZHVPpsj49lzNhGdVsPzD3QsdkDOshjTuwWHzidz7FIq/1l7lHcf7lZJkQpRcaqqMueX4xjNFrq38Of+bqEVas/N2YnnH+jEM1/v4s+TV7gr4iq3t2pQOcEKIYQQQtRw5RmkU9hXQEAAZ8+exdfXthNJ4eOMjAy8vb3R6/UkJRUdNLCwtEpF795WFAU3N7cKtVHTubq6yj6o7H2QnQ8aBW4YdLbMNAooSuUlbEsRl7Y0MVd2XA6kKj4PGo0GjUaDVqtFq3XccfQKS5UoilKmOBVFKXaZwkGXC7c9Nze31CVsbjw2XC82NpbDhw/z4IMP2pQ8OXfuHCaTiQ4dOhSJpUOHDqxduxZFUYoMBn294vZBamoqqqri6+t7y/2i1WrRaDS4uroWm0wvy4UHh6tBLiouIzefLzcX3LY24c6WNA2seGF7jaLw7ND2TJm/nciLqWw9fpnuodVbMF+IkmyOjCfyYirOOi3T721bKQO7tQrxZlTPZny/K5q5v56gQ1M/3J2l1IoQQojqY8m6hiU7A009XzQuZSsbJkSVqmCZX1H9IiIi2LlzJwkJCTRr1sw6PSEhAShIjmg0Glq3bk1kZGSR5SMjI9HpdLRsWfaynUII4ShWrjzBq6/+QWamfcuaeHo68/rrdzJ4cNMqW8dXX33FnDlzSjXv6dOnS3xu7dq1AEUGcC68cFrSuBUGg4Fr167h41O2cd0+//xzFEXh3nvvLdNyFSUJ8lpo8R9nuJaTTxN/D0bc1uzWC5RSsK874+5oycKtUSzYcpqI8Z24SVUcIarF9ReExt3RkkDvyrv6PK5PS7adusKVtBxW7jrP5L7hlda2EEIIURJLXjZ529aSfzYSTPkoLm7o2/bE5ba7UbRy+i7sTC1nD3LpdW5X9957L/Pnz2fVqlX07NnTOn3VqlVotVrr+F+DBg3i/fff58CBA3Tt2hWAtLQ01q1bR+/evfHw8LBL/EIIURk+/HAXUVHJ9g4DyOSjj3ZXaYL8gQceoEuXLhVuZ+3atQQGBlqPE4UKa5HfbNyKstZXX79+PStWrOCRRx6hVatW5Yy4fOQMu5aJT81mw6FLAEwb1BYnbQVvMbrB8Nua8tvRWOJSsll7KJ4n7inblSAhKts3v5/mWk4+jet7MPy2yj24OOu0PDGgNW+sPMiqPecZ2rUJfp6OW8NMCCFEzaeqKrmbVpB/+jCKqweKmxdqXjZ5ezeCRoNrz4H2DlEIuyS7T5w4weeff86JEydISUnBxcWFFi1a8MgjjzBgwICbLhsXF0f//v2Lfe6tt95i1KhRVRGyQ2nTpg0jRozgxx9/xGQy0a1bN/bv38+GDRt47LHHCAoKAmDs2LGsXLmSadOm8cgjj+Dp6cmyZcvIz8/n2WeftfNWCCFExTz3XC9eeeV3h+hBPnNmz1vPWAGNGjWiUaNGFWrj2LFjnD9/nkcffbRIqZTCkiY3G7eiuLrgJdmzZw+zZs2iT58+zJw5swJRl48kyGuZxX+cwWxR6dbCnw6hfpXevk6r4bH+rXh9xUE2HL3MqF4t8fOq2/XThP1cTcvhl8OxAEy/ty26Sr4gBHB7eCBtGvpwMi6NJdvO8vfB7Sp9HUIIIUQhS/JljBeiUNzroXEtKKui6PRYMtLIP7Ybl859UJxrXw1SIW4lNjYWg8HA8OHDCQwMJDs7m02bNjFt2jReffVVxo0bd8s27rvvPu666y6baZ06daqiiB3P66+/TnBwMKtWrWLTpk0EBwcza9Ysm3G+XF1dWbx4Me+//z4LFy4kPz+fdu3a8e677xIWFma/4IUQohKMHNmGkSPb2DsMoKD+dmEv7Mpw4wCu2dnZ5OTklGrZ4sqkAKxZswYoWl7l+mVKGrfC2dmZevXqlWr9kZGRPPXUU0RERPDpp5/i5FT96WpJkNci0Vev8ceJywA8UoWlIHqGBRIWVI8zV66xYtd5pg5qW2XrEuJmvttxDrNFpXOz+lVyQQgKBnV4fEArnl20m18Px/Jgj6Y0ri+3lgohhKga5vQUVGMeGk9vm+mKiytqXg6WrAy0kiAX9maHHuSDBg1i0KBBNtMmTpzI8OHDWbRoUakS5K1bty72R35dodPpmD59OtOnT7/pfP7+/nz00UfVFJUQQoiyys7OLjItOdm2dMzChQsrVIPcbDbzyy+/EBYWVmy5k/DwcHQ6HZGRkQwdOtTmucjISFq3bn3TAToLnT17lieffJKQkBC++OILuw1GKwnyWuTr3wve0HdFBNO8Qemu0pSHoihMvLMlLy8/wPpDsYy8vTn+XvJDTVSvy6nZ/HY0DoAJd1Ztb5aIRr70DAtk95kEFv1+mldHVbyOlxBCCFEcjWc9FJ0e8vPgukS4ashD0TujuMtFWuEA1IoPiF4ZNBoNAQEBxQ4qWZLc3Fy0Wm2xNVOFEEKImiA9PZ2TJ0/Spk1Bb3hVVdm0aZP1/6HiNch37dpFUlISkyZNKvZ5Dw8Pevfuzbp165g+fbq1t/jevXs5c+YMs2bNspk/OjoaV1dXgoODrdMuXrzIU089hZeXFwsWLCh1j/OqIAnyWiIqPo3955LQahQm3lX1t751aupH62AvTl3OYNn2c1J2QlS7ZdvPYVELygm1aVj1tfAf6RfOnjMJ7Iy6yqXkLOlFLoQQokpoAxvjFNIc44WTaFQV9M6oeTmo+bno29+GxsXd3iEKYdcBN7OzszEYDGRkZLBp0ya2b9/OfffdV6plP/vsM/7zn/8ABb3Jn376afr161eV4QohhBCVTq/XM3XqVMaNG4e7uzsbN27k4sWLAHzzzTc8/PDDNGvWrEI1yNesWYNGoynSO/x6zz77LKNHj2bcuHGMHTuWzMxMFi5cSPPmzRkzZozNvPfddx/du3fn22+/BSArK4vHHnuM1NRUxo4dy+7du23mb9y4cbWWQZMEeS3xw+7zAPRrF0KIb9X/cFIUhYdua8y/Vx1n09E4JtzZEl8PGbxQVI/41Gy2HKue3uOFmvh7cttfvch/3HOeZ4a0r5b1CiGEqFsURcHtnjHkbF6BKfYcak4mit4F53a349rzXnuHJ0QBS/kWu3LlCv/4xz9KfH7Lli23bOO1115j7dq1AGi1WgYNGsS///3vmy6j0Wjo3bs3AwYMIDAwkLi4OBYtWsRTTz3Fxx9/XOoEuxBCCOEI/P39mTp1KnPmzCEtLY1u3bqxfPly/v73v7NhwwaGDx9eofZzc3PZvHkz3bp1o0GDBiXOFxYWxuLFi/nwww/58MMP0ev19OnTh+eff/6WpVLS09O5fLmgTHRxpWAefPBBSZCLsrmSlsPOqKsAjLytWbWtt3VIPVqHeHMqPp21By4y6a6qq3suxPV+3heDRYXuLfwJD/autvWOur0Zu88ksCUynol3huHnKReFhBBCVD6NpzfuDzyBJekylpwMNN7+aL3r2zssIQqolK8HeSX1On/yyScZPnw4iYmJrF27lry8PPLy8vD09CxxmeDgYBYsWGAz7YEHHuC+++7jgw8+4N5770VRHKNsjBBCCFEao0aNYtSoUTbT1q1bVyltu7q6cvjw4VLN2759e2uv8Ju5sc55w4YNOXnyJHl5ebi4uKDVassVa2WRBHktsHrvBSwqdGnuT2hAySeGVeGB7qGcWn2E9QcvMaZXC5x19n1Di9ovO8/Ib0djAXiwR/VdEIKCWuRtGvpwMi6Nn/fF8Gj/ogNVCCGEEJVBURS0ASFoCbF3KEIUoZSzBnlQUFCpeonfTMuWLWnZsiUAw4YNY+LEiUyZMoUffvihTEluLy8vRo8ezdy5czl//jzNmzevUFxCCCGEqLluPZyocGiZuUY2HilIFlZn7/FCt4cHEFjPlWs5+Ww5Fl/t6xd1z8YjseTmm2ni70Gnpn7Vvv5RPQs+Z+sOXiTHYKr29QshhBBCiAKKojBo0CCOHz/OhQsXyrx84UBh165dq+zQhBBCCFGDSIK8httw6CJ5RjNNAzztkizUajQM6x4KFPRkLxwtV4iqYLao/LQ/BoAHuje1y62wt4UH0tDXnWyDiU2RcdW+fiGEEEIIu1PL8VdFDAYDUDDYV1nFxhZ0NPL19a3UmIQQQghRs0iCvAYzW1TWHbwEwIjbmtmtbt6gjo1w1Wu5lJzFofPJdolB1A17zySQkJ6Lp6uOfu3sc8u5RlG4v1sTADYcvCQXhYQQQghR99ghQZ6SklJkWn5+Pj///DMuLi7WEimpqalER0eTm5trnS89Pb3IssnJyaxYsYJGjRoRGhpa8QCFEEKIavDee++xdetWe4dR60gN8hrs0PkkEq/l4uGi486IILvF4e6i454Ojfh5fwwbDl2iS3N/u8Uiaref/+o9fl+nxrjYsd59v3YN+WpLFDFJmZyKT6dNQx+7xSKEEEIIUe3s0D/gmWeeQa/X06lTJwICAkhMTGTNmjXExMQwa9Ys3N3dAVi6dClz5sxh8eLF9OjRA4D333+fhIQEOnfuTEBAAPHx8axcuZKMjAzee++96t8YIYQQQjgUSZDXYL8eLrglcED7EPRO9h0cc1CnggT57jMJpGcb8HZ3tms8ova5kpbDkZgUFGBwl8Z2jcXTVcedbYLZFBnHhkOXJEEuhKjTzNfSMF44jWrMxym4MU7BTex2V5sQoprYIUE+bNgwfvrpJ5YuXcq1a9fw8PAgIiKC5557jv79+9902V69erF8+XKWLl1KRkYGHh4edOrUiSlTptCxY8fq2QAhhBBCOCxJkNdQaVkGdp9JAApKnNhbs0AvwoLrcebyNTZHxjOyZ/UPGCpqt9+OFlwQ6tSsPoHebnaOBu7t3IhNkXFsO3GZKfe0wcNFZ++QhBCi2hmOHyR761os2ZkAKDo9+tYd8Rg4AsVJTjOFqLUs1X8RbMSIEYwYMeKW882YMYMZM2bYTBsyZAhDhgypqtCEEEI4ICmHWvtV5mssNchrqE2RcZgtKq1CvGka6GXvcID/Jeo3HomVLyJRqcwWlU1HCwbEHNjB/heEANo09CHU3xODycKWY/H2DkcIIaqdOTWJ7C1rsOTlovENQOMXCHpnDMcOkHd0j73DE0JUKaUcf0IIIUTV0+kKOq/l5OTYORJR1Qpf48LXvCKka08NpKqqtbzKvZ0cI1kIcFfbYL7YdIpLyVmcjEsjopGMBi8qx5GYZJIy8vBwceL2VoH2DgcARVG4r3Mj5m08yS+HLjGsW6i9QxJCiGqVf/YElpwsNH6B1pIqiqs75twcDMcP4dqlt50jFEJUCRUUS/mWE0IIIaqaVqvF29ubxMREANzc3Byy/J/ZbMZgMAAFMddF5d0HqqqSk5NDYmIi3t7elbL/JEFeAx2PTSM+NRtXvZY7I4LtHY6Vu7OOPq2D2BQZx8YjsZIgF5XmtyMFvcf7trV/vf3r9WvXkC83R3EhMZPzCRk0c5C7OYQQojqohjyAIj84FCcdlpxse4QkhBBCCCEEDRo0ALAmyR2RxWLBZDLh5OSERlM3C3xUdB94e3tbX+uKkgR5DbT1r3IOvVsH4ap3rJdwUKeCusx/nrjCU4Pa4qJznGSmqJkycvPZGXUVgIEOUG//ep6uOrq38Gfn6QS2HouXBLkQok7RBoaARoNqzEfR6YGC3hyqIRd9q/Z2jk4IUaWkN7gQQggHpigKQUFBBAQEYDQa7R1OsXJzczl//jyNGzfG1dXV3uHYRUX2gU6nq9Se946VXRW3ZDRb2H7qCgD92obYOZqiIhr50MDblavpuew5k8BdDtTDXdRMO05dxWi20DTAkxYNHC8B3bddCDtPJ/DHics82r8VGge8dUsIIaqCvnkrdI1bYLxwGsXZBTRa1NxsNJ7euHbuZe/whBBVSRLkQgghagCtVuuw5UssloJ6Zc7Ozri4uNg5GvtwpH1QN/vw12AHo5PIzDXi6+FMh1A/e4dThKIo1rIvf564bOdoRG3wx1/vo37tQhyybliPlgG4OzuRlJHH8Uup9g5HCCGqjeKkw/OB8bj27I/i6o6i0aJv1QHPEZNxCmpo7/CEEFVGAbUcfzJQpxBCCCEclPQgr2F+P16QLLwzIhitxjFPMvtGBPP9zmj2n0siK8+Ih0vFR5MVdVNKZh6RMSkA9GkTZOdoiqd30nJH6yB+PRLLlmPxtG/ieBeuhLCXyMhI1qxZw969e4mLi8Pd3Z2IiAimT59Ou3bt7B2eqAQaV3fc+w7G7c57wWJBcZJTSyHqBNUxf4cIIYQQQpSH9CCvQXLzTew+XVCLuW9bxy1d0jTQiyb+HhjNFmvtaCHKY/upK6hA64beNPB2s3c4JerXrqDc0Y5TV8g3me0cjRCO46uvvuKXX36he/fuvPDCC0yePJkLFy4wevRo/vzzT3uHJyqRotFIclyIukQtx58QQgghhIOSBHkNsvt0AgaThWBfN8KC6tk7nJsqrD3+h5RZERVQ+P5x9Fr27Zr4Ut/Lhaw8E/vPJdk7HCEcxuTJk/n999955ZVXGD16NI8//jgrV67Ez8+PTz75xN7hCSGEKC9JkAshhBCiFpEEeQ3y+/F4APpGOGYt5usVJjSPXEgmLctg52hETXQ1PYdTcekowB2tHbO8SiGNonDnXyVgCgfRFUJA586d0ev1NtPq1atHjx49OHfunJ2iEkIIUSEqqKpS5j9JkgshhIMw5EL2tcr5M+Tae2uEqBRyL2wNkZ1n5ND5ZADujHDsZCFAsK874cHenL6czvZTV7i/W6i9QxI1zLaTBYnm9qF++Hk6/ojOvVsH8eOeC+w9m0i+yYzeyTFHyhbCESQmJuLj42PvMIQQQgghhKh7TPnw50rITKlYO55+cOcocHatnLiEsCNJkNcQe88mYrKoNPJzp4m/p73DKZU+bYI4fTmdnVFXJUEuyuzPGlJepVCrEG/8PJ1JyTRw+EIyPVoG2jskIRzSwYMH2b9/P5MnT65QO6qqkpOTUzlB1XC5ubk2/4qKk31a+WSfVr7S7FNVVavmztPyDNLp2DfACiFE3ZKZAulSHlSIQpIgryEKB7vs1aqBnSMpvV6tGvDl5lNEXkwlIycfLzf9rRcSAkhIz+Hc1Qw0CtweXjMSzRpFoVerBqzZf5Edp65KglyIYqSkpDBz5kyCg4OZPn16hdoyGo2cOnWqkiKrHWJiYuwdQq0j+7TyyT6tfLfapzeWuqoU5SmXIglyIYQQQjgoSZDXAHlGM/ujC67s9XbwWszXC/Jxo1mgF+cTMthzNoF7OjSyd0iihth1OgGAiEa+eLs72zma0uvdKog1+y+y+0wCJrMFJ60M8yBEoaysLJ544gmys7NZsmQJHh4eFWpPp9PRokWLSoquZsvNzSUmJobQ0FBcXeUW14rKOXeF1N2nSYu7Sv02odTvHYHOr2bcvXczqqpiTk4Bkwlt/foouur9GSDv08pXmn1aZeM9lKcHuRBCCCGEg5IEeQ1wKDoJg9FMYD1XWjTwsnc4ZdIrPJDzCRnsjJIEuSi9XacL7pi4vQbdMQHQtrEv9dz0XMvJJ/JiKp2b1bd3SEI4hLy8PKZOncr58+dZuHAhYWFhFW5TURTc3NwqIbraw9XVVfZJBSVvjSR+6Z+Ycg2oqplrMenkH75E6PTBuDb2t3d45ZZ/5Srpa38lPz4eLBa03t549euDe6cO1R6LvE8r3832aZWUVwGwlGMZGZ5FCCGEEA5KujfWADuuK69SZSe5VaQwwXnofBK5+SY7RyNqgoycfI5fSgVqTnmVQlqNYo15R9QVO0cjhGMwGo08/fTTHD58mE8//ZTOnTvbOyQhipV+IJoLs9eTdzkNc0Ye5JrQerphSEjn6k97bebNjUshadNREn87Su6lZFS1PPUmqoc5K5uUZSvJO3cejbMejbs7ppRU0n5aR96ZKupdLOoApRx/QgghhBCOSRLkDs5otrD3bEG5iZpUf7xQ0wBPgnzcyDdZOHBOBoAQt7bnbAIWFZoHetHAu+b1MCssg7QrKgGLAydMhKgOFouFmTNnsn37dt5//3369Olj75CEKFbe1XRi5v2KOceAoteCVoOaZ8IQl4LGRU/WmXhMGTmoqsqVn/Zx5q0fiP32T+KW/MmZd37kyqo9Dpskzz1xElNSEroAPzSurmic9TjV98ViMJC194C9wxM1kQpYlLL/OeZHRAghhBBCSqw4usiYFLLyTPi4O9O6oY+9wykz5a+BC3/YfZ6dp69yR5uaU0Nd2MeuqIILQjWt93ihDqF+uOmdSMs2cObyNVqFeNs7JCHs5v333+fXX3+lV69emEwmfv75Z5vnhw0bZqfIhLCVtvs0pqwcNE4aFK0GUECvRTWZMWXkovPzQFUh83gsCesPonHS4BLiC4DpWg6JGw7j3qwB9To1te+GFMOUlg6Aov1ffQtFUdA4O2NMSLRTVEIIIYQQQjgOSZA7uL1nC3649GgZgFZTM29NvD08kB92n2fv2UQZuFDcVJ7RzKHzBXca9AyveXdMAOi0Gro0r8/2U1fZezZBEuSiTjt58iQAO3fuZOfOnUWelwS5cBR58alonJ1RDUZUoxlF54RCQYdXU1YuPj3D0dVz48rBc1jyjDg38rMuq/N2JzcuhfQD5xwyQa718gIUVIsFRfO/czDVkI9TU7+SFxRCCCGEEKKOkAS5A1NV1VpepUdYgJ2jKb/WDX2sAxcej02lY6gMXCiKdyg6CYPJQgNvV5oFeto7nHLr0TKwIEF+JpFJd4XbOxwh7Obbb7+1dwhClIre36ug03iAN4arqVjy8lFVC4pZRefrQYNh3QEwZxlQiumwoGg1mLLyqjvsUnFr25rM7bswJiXj5O2NotVgzshE0Tnh3k3GBBDlo5ZnkE4hhBBCCAclXXkdWGxyFlfTc9FpNXRqWnOTyhpFoXuLggT/vrNyK68o2d5zBe+P28ICa9yAtNfr1sIfBYhOyCDxWq69wxFCCHELPreF4eThisWs4tLIH623O2i0OPl4Evr0EFybFJzHuLdogKqqqCazdVnVbEE1WXBv4Zhl5LReXviNGYE+OAhzVhamtGto3Nyod+89uLZpZe/wRI0lg3QKIYQQovaQBLkDKyyv0iHUD1d9ze7s371lwQ/LvZIgFyVQVZX9fyXICy+o1FTe140ZsO+cvOeFEMLRuTXxp/EjfdH7eWLONaHR6SHYm5Ap9+DTPcw6n0/PMFwb+5N3JZ381CzyU7PIi0/DtaEfvr0dN9ns3KQxgdP+RsATk/F/dDwN/v4Unrf3qNEXo4U9lWOAToskye1t7969hIeHF/u3a9cum3mTk5P517/+RY8ePejUqRMTJ07k+PHjdopcCAGAHLOFqFI1O+tayxUmkwuTyzVZl2b10WoU4lKyuZyaTbCvu71DEg7mfEIGKZkGnHVa2jXxtXc4FdajZQAn49LYeyaBIV2a2DscIYQQt+DdrQWe7RqTfe4qeXl5XMpPo17HFjbz6Oq50+zvg0nccJBrhy6gouJzWxiB93ZC7+Nhp8hLR9FqcW7S2N5hiNpCtXcAorzGjRtHhw4dbKa1bNnS+v95eXlMmjSJ5ORkJk+ejJeXF8uWLWPChAmsXLmSFi1a3NikEKKqubiDooXsaxVvS9GAKl/iQtxIEuQOKiM3nxOxaQD0qOG9aQHcXXRENPIh8mIq+84l8kB3xxvESthX4QWhTk3ro3fS2jmairstLJCvfz/N4Qsp5OWbcKnhd4EIIURdoHXR49W2MU45OSinMoudx9nfi0aT+tJw/J2AiqKt+ccsIcpMlZ6MNVWXLl0YPHhwic8vX76cc+fOsXTpUrp27QrAfffdx6BBg/j444+ZN29edYUqhCikcwazEbb9AJkpFWurQTPocnflxCVELSIZGwd1MDoJi6rSxN+DBj5u9g6nUnRvGVCQID8rCXJR1P5zSUBBz+vaoIm/B4H1XEm4lsvhCyn0DA+0d0hCCCEqkaKVSoWiDpPOhzVadnY2er0enU5X5LkNGzbQqlUra3IcwMfHhyFDhvD999+TlZWFh4dj3zEjRK2VmQLpSRVrw7Pm360tRFWQM3sHVTiYZY+WtSepVtgTPvJiKrn5JjtHIxxJRk4+UfEFd0x0be5v52gqh6Io1vJI+6OlDrkQQgghREWcOHGCGTNm0K9fPzp06ECPHj0YN24cmzdvLtXyqqryzTffMHDgQNq2bcvAgQNZvHgxah0rNfDyyy/TuXNn2rdvz8MPP8zBgwetz1ksFqKiomjfvn2R5dq3b4/RaOTs2bPVGa4QQghRLaQHuQOyqCoHoguuCnZvUTuShQCN6nvQwNuVq+m5HD6fzO2tGtg7JOEgDkQnYVGhaYAnAfVc7R1Opena3J+1By5yIDoJVVVlMDQhhBBC1BLVf04TGxuLwWBg+PDhBAYGkp2dzaZNm5g2bRqvvvoq48aNu+nyc+fOZfbs2QwbNozHH3+cffv28fbbb5ORkcH06dPLHE9eXh5xcXGkp6cXm2Tv1q1bmdusSjqdjoEDB3LHHXfg6+tLdHQ0CxcuZNKkSXzzzTd06dKF9PR0DAYD/v5Ff4MGBBR0/EhMrFjHD1VVycnJqVAbNVVubq7Nv3VRVewDRVFwVlVUiwoWS8Uas6goqoohN7fCF89KG5e5NDFbVDSoqKqKWgnbWJltlXd/yedB9gFU/T4oSx5GEuQOKPpqBhm5Rtz0TrRu6GPvcCpNYY/aNfsvsj86SRLkwmrfub8GpK0F9fav1yHUD51WQ0J6LvGp2TT0k9tRhRBCCFHDqYClHAnyCnbUHjRoEIMGDbKZNnHiRIYPH86iRYtumiBPTEzkiy++YMSIEbzzzjsAjBo1Cq1WyxdffMFDDz1UbFK4OAaDgXfffZcff/wRk6noXbGFP8ZPnTpVhq2rep07d6Zz587Wx/379+fee+9l6NChfPTRR3z33XcYDAYA9Hp9keWdnZ2BggsDFWE0Gh1u31S3mJgYe4dgd5W5D5ydnWkZEojZYECt4PtTMRjQGo1Ex8RZPw9VEZeqevz1r1qqz5TGmI+zCvn5+ZgruI2V2VZl7C/5PMg+gKrdB8Ud04ojCXIHdPCv3uMdQv1wqmX1Lbs082fN/oscOl/Bulmi1rCoqvU9360W3TEB4Kp3IqKRD0diUjgQnSQJciGEEELUCo5SlESj0RAQEEBkZORN59uyZQv5+flMmDDBZvr48eNZvXo1W7ZsYcyYMaVa5zvvvMP3339P79696dWrF97e3uUN3+4aNWrEwIEDWbNmDbm5udYkeH5+fpF5C5NfLi4uFVqnTqejRYsWFWqjpsrNzSUmJobQ0FBcXWvPXbNlURX7QFEUdGYDTs7OUMH3J87OKDodzZs3r5Qe5CXFVdijVVGU0n2mdHoUpSDRp1Z0GyuzrQrsL/k8yD6Aqt8H586dK/W8kiB3QAf/Sh53aV7fzpFUvvZN/NBqFK6m53I5NZtgX3d7hyTsrLbeMVGoa3N/jsSkcDA6SQanFUKIGsJsMGHJNlVabeLchEyubjlD1tkkdPVc8e/VFN+ujaT0lqi5ytODvJJkZ2djMBjIyMhg06ZNbN++nfvuu++my5w8eRK9Xk94eLjN9DZt2qDT6Th58mSp179p0yYGDRrEJ598Up7wHU5wcDAWi4WMjAz8/f3R6/UkJRXtzFRYWqWw1Ep5KYqCm5tbhdqo6VxdXWUfVPY+yM4HjQKaCnYw1CigKJWXqCtFXNrSxKxRAKXgvKEytrEy26rg/pLPg+wDqLp9UJZzbUmQO5i8fBMnYwsGK+zctHb1pgVwc3aiTUMfjl1K5dCFZEmQC+vdBO2b+Na6OyYAujT356stURyNSSHfZEbvpLV3SEIIIYDs2GvkXMnE2c8Nz2Y+KIqCMdNAzPfHufxHNNnXsjgRnkKzMR2o3zWk/Ou5mMaJ9zaTeyUDjU6LarKQvDuGRiPa02R0p0rcIiGqkVq+BPmVK1f4xz/+UeLzW7ZsuWUbr732GmvXrgVAq9UyaNAg/v3vf990mcTEROrXr4/mhmSQRqOhfv36ZaqrnZ2dTc+ePUs9v6OLjY1Fq9VSr149NBoNrVu3LrZHfmRkJDqdjpYtW9ohSiGEEKJqSYLcwUReTMVkUQn0diXYt3ZeQercrH5Bgjw6iSFdmtg7HGFnh84nAwXvi9qoaYAnvh7OpGYZOH4prdZupxCi9jFlZpN54CR5F6+gdXfFo0MYri0b1/hez8bsfE5/tp+kPXGYco1onZ3waR9Iq6e6c3ruHpL3x6Nx1aJoFK6dTOLEhzto+/wd+HUOLtf6Yn88Su7la7g19kb5KzlnSMkmfs0J/Hs1xS3Eu1K2SwaDFtXJniVWnnzySYYPH05iYiJr164lLy+PvLw8PD09S1wmLy+vxBqkzs7OZaqrHRYWxpUrV8oct72lp6cXKQcTHR3Nxo0b6datm7XMw6BBg3j//fc5cOAAXbt2BSAtLY1169bRu3dvPDykZKAQQojaRxLkDsZaXqWZf639kdO5mT/f/HGGIzEpmC2W0t1SJGqlPKOZE3/dMdGpWe27YwIKbunp0tyfTUfjOBCdKAlyIUSNYExO5/JXqzDEJwAKqCoZeyLxHdQL37tvs3d4FXLu68Nc2XIevY8L7r6emHJNJO2Jw5CcQ/alNFwC3FGcNRgzLbh6uJMXn0XsT6fKlSA35xpJi7yMrp6LNTkOoPd1Izf2GtdOXK1wgvza0RiSfosk52ISel8P/Pq0of5dbVDkjiXhgIKCgkrVS/xmWrZsae3FPGzYMCZOnMiUKVP44YcfSvz95OLiUmxdbSiorV2WutrTpk3jhRdeYNSoUYSElP/ukur2zDPP4ObmRtu2bfHz8+PChQssX74cJycnnn/+eet8Y8eOZeXKlUybNo1HHnkET09Pli1bRn5+Ps8++6wdt0AIIYSoOpIgdzC1vTctQMugeni46MjKM3L68jXa1MK606J0jl9KxWi2UN/LhUZ+tbfcTldrgjyJv91t72iEEOLWUjfvwRB7FX0DPxRtQaLVmJZB2uY9eLRviT7Qz84Rlo8hNZeEHRfReTmjr1eQENN56EFVST+VhEYDTm46zGYz8NcAW57OZESnYjGa0ejKmHTW/JWsu7G7rfrXfyrYGSJt/zkufrkVS24+Wjdnci+lEPvtNgyJ12j4cO8KtS3ETVnsHUABRVEYNGgQb7zxBhcuXKBZs2bFzufv78+uXbuwWCw2ZVYsFgvJycllqqt9/PhxGjVqxJAhQ7j77rtp2LBhkdItiqIwbdq08m1UFenbty/r1q1j0aJFZGVl4e3tTb9+/Xjqqado3ry5dT5XV1cWL17M+++/z8KFC8nPz6ddu3a8++67hIWF2XELhBBCiKojCXIHkngtl0vJWWgU6BhaexPkWo1Cp6Z+bD91lUPnkyVBXocV1h/v3LR+rb1jAv7aPuBiUhYpmXn4eVZwtHAhhKhCqtlM9rFzaN1drclxACdvT/KvppBz5mLNTZCn5WI2mHH2tv0e1rrpUM0WVFVBNdtm/iz5ZlwCXFCcyn7Hm9bZCb9ujbjy2xmcvFzQOGlQVRVDcjZOni74dChf2RYA1WQmYe1BLHlGnIN9rMdRY3o2KdtOUr9fW1waeJe/fVXFcCGWrMgo1Nw89A2D8OgUgdaj+koAqvl5qEYDipsniiJ3HDoWxzlvMxgMAGRlZZU4T0REBCtXruT06dO0bt3aOv3kyZMYjUbatGlT6vXNmTPH+v9r1qwpdh5HTJBPnDiRiRMnlmpef39/PvrooyqOSAghhHAckiB3IIcvFPQeDw/2xtNVZ+doqlbnZv5/JciTGN9HBnqpqw5fSAFq9x0TAF5uepo38OLc1QyOxqTQr13NuR1XCFEHqYB6kwrDFntWH64YF393dO56jJkGtC7/Ow02Zebj7OuKk7OWnPhM9IFuqKqKMdOAJd9M8IDmKIqCxWQh5XAC2fGZOPu44N8tCCe3m5+zNRregcwzyWRfTAWNgmpRcXLT02R0R1wCSq6ZfCv5adnkXb2GUz03m4vMTvXcyItPJTcmqUIJ8oxt+0j95U/UPEPBeIz7jpK59yiBj45E51v+dktDzc0k/+AGzNEHUS0mNN6B6DoOxKlpxypdryglVQFLORLk5RzYs1BKSgp+frYX5/Lz8/n5559xcXGx9oJOTU0lLS2N4OBgXF1dAejfvz9vv/02S5Ys4e2337Yuv2TJEnQ6Hf369St1HBUtESOEEEIIxyMJcgdyMPqv3rS1tBbz9To3LUiInopLJ9tgxN25dl8QEEWlZRk4n5ABQKemtTtBDtCxaX3OXc3gSEyyJMiFEA5NcdLiFtGMjF1H0Xq6o2gLeu6aM7LRuDjj2rKxnSMsP72XM8H3NOfC98fJS8rGyUOPOceIMTOf4HuaE3RXU6Lm7CHnaibmPAMWLy1B/ZvRcGgrDKm5HH53N2nHkrGYLSgKuDfyouPzt1EvzLfEdboGedHu3wNJ3H6erPMp6LxcqH9bE+q1aVChbdE4O6FoNagms8101WRG0WrQVKCzhTEplbTftgPg1KBgXBzVbMYQd5lrW3dTf+S9FYr9ZlSLGcOWrzHFnkBxdgOtE+bEGCy/LwLNYzg1aVdl6xaO7ZlnnkGv19OpUycCAgJITExkzZo1xMTEMGvWLNzdC8r1LV26lDlz5rB48WJ69OgBQEBAAE888QTz5s3DZDLRtWtX9u/fz88//8y0adPKVGKlJtUdF0IIIUTpVFuC3Gg0Mm/ePFavXk1KSgqhoaH87W9/Y+jQoTddLi4ujv79+xf73FtvvcWoUaOqItxqZ7aoHPqrB3mX5rU/WdjAx41gXzcup+YQGZNKz/BAe4ckqlnhHRPNA73wdne2czRVr2OoHz/sPs+RCymoqlqrS8oIIWo+3wG3kXchnvyrKShOWlSzGcXJCZ8BPXAOrtkX8puNaQcKXN4UjfFaQU/yRsNa0WJiR5zcdHT7dDBXdl3g4tkYwntH4N8uBEVROP7pQZIPJuAW5I6Tqw6L0UxWTAaRH++j1+y7b1qfXO/jRsP721bqdui83KjXKZSUP0+iddGhcS4oE5OfmIFLsA+ercqfxMs9cwFLTi5Ogf8bNF7RatG4upJ97DR+D9xTZYOAmuOjMF8+jeLpi6L7qxSOszukX8V4bAvaxm3lGGpnBTeZlP01UKlYYZZhw4bx008/sXTpUq5du4aHhwcRERE899xzJf5evN7TTz9NvXr1WLp0KevXrycoKIgXXniBSZMmlTumixcvkpycTFhYGJ6e5b8jRAghhBD2VW0J8ldffZWffvqJcePGERYWxubNm5k5cyZms5kHHnjglsvfd9993HXXXTbTOnXqVDXB2kH01Wtk5hpxc3YiPNjb3uFUi85N63M59RIHzydJgrwOKrwgVNvLqxRq19gXrUYh4VouV9NzCfKpvhquQghRVvpAP0KeGkPGvmPknY9D6+GGR6dWuLdtUenrMqTmEL/+FMl7LgLg16MxDQe3wdmvar4nNXotLSZ0pMkDrclLzsHZxxX9dTXJde566vdqRJJvFh4tfFEUBUN6Hom743H2dsbpr57ZGp0WtyB3smIySD2eTP1O1X8uEzzyNgxX08mOTrCWvtH7e9LokbvQVODuPNViodhUpqL8VX6n6srsqGlXUS1mNLr/vSaKoqA6u2NJiQeLCbRy56HdlafESgWNGDGCESNG3HK+GTNmMGPGjCLTFUVh8uTJTJ48ucKx/Pnnn7z11lvExcUBsHDhQnr27ElKSgpjxoxh5syZDBw4sMLrEUIIIUT1qJYE+YkTJ1i1ahVPP/20dbCSUaNGMXHiRD744APuu+8+9Hr9Tdto3bo1w4YNq45w7eLQ+YJkYYcmfjhp68YgRF2a+bPu4CUO/7Xtou5QVdX6uneqIwlyF70TrUK8ORGbxuELyQT51NwSBUKIukHn64XfoF5Vug5jRh4n3ttKRlRSQU1wRSH2h0jSj16h3asD0NdzrbJ16zyd0XmW7g4mY1Y+FpMFJ3fbxKxGr8ViMmPMyq+KEG9J7+tBy1kPkHH0InlX0nDycqVe56bovCp2ccG1eRM0LnosmdlovTyAgqS5JScH9+4dUZyq8CeEs/tf6zOjaP7XS1015aPx9AONVIh0DHW3F/+BAwd46qmnCA8PZ9q0aTaDdvr5+RESEsL69eslQS6EEELUINWSif3ll1/QaDSMGzfOOk1RFMaPH09KSgp79+4tVTu5ubnk59vnB0hVOxxTt5KFAB1C/dAoCnGp2SSk59g7HFGNYpOzSM7MQ6fV0LZRyXVba5vCWutHLshFISFEzWcxmEjbd474FXu4uu4QeZfTytxGwrbzZJxOwjXYC5dAT1wCPHANqUfmuWQS/jhfBVGXj2uAO64B7uSnGWym56fnofPU49XM2z6BARq9E97dmtPg/q7UvyuiwslxAF2QP569uqLm55N/NRFjUirGhGR0/n549+tZCVGXzKlJWzSe9eFaIqrZhKqqqLmZYDbi1Op2Ka8i7G7u3LmEhYWxcuVKm9+3hTp37szJkyftEJkQQgghyqtaEuQnT56kcePGeHt720zv0KGD9flb+eyzz+jYsSPt2rXjgQceYOvWrVURql3km8ycjC34Udkx1O8Wc9ce7i46wkPqAXAkJsXO0YjqVFhepW1jX5xvUrO1tulYmCCPScGiVt3t6UIIUdWMGbmc+886zs/ZyNU1B4hfvovTb64iZefpMrVz7WQiKIpN/W6NkxZFo3DtxNXKDrvctHotzUa1QnFSyIrNwJCWR86VLIyZ+YTc3RT3kNpVe1hRFHwH9yVgwoN4dIrApVlDfO65g6ApD6NvULU16BUXD5z7TkSpFwCZyahpV8FsRNe6N7p2fat03aL0VLXsf7VFZGQk999/P1pt8eewQUFBJCdLZwghhBCiJqmWexQTExPx9y96Ml04WnhiYmKJy2o0Gnr37s2AAQMIDAwkLi6ORYsW8dRTT/Hxxx9z3333VTg+s9lcKW0U/pXVydhU8k0WvN30hPi4Vko8Va0i23u99o19ORWXzpELyQxoF1xJ0VW+ytremqKqt7ewvErHUF+H2KfV9fq2bOCJs07LtZx8zl+9RtMA+yRU5P0shKiohA2HyTgRh3OAV8HgkKpKfuI14pbtxDM8GH390n2/Obk5FZs5Uy0qWlfHqjMdck8oGr2GmJ/OkB2XhWugO0F3NiaoXxMsZguaWlYiT1EU3Nu3wr19q2pftzY4DNcRL2KOj0LNz0VbvxGKb4j0HncktSjhXVYmkwkXF5cSn09PTy8xeS6EEEIIx1QtCfK8vLxia4xrNBp0Oh15eXklLhscHMyCBQtspj3wwAPcd999fPDBB9x7770VOlm2WCxkZmaWe/nr28nLy0NRFDSasv1AOnD2CgCtgz3JysqqcCzVoSLbe70W/gW1RY/GJJORkeGwP3wqa3triqrcXotF5dilgjsGWvq7VMrnr6Kq8/VtFeTJ0Uvp7D19mfqu9rkoJO9n+8fjCHEIUV6qyULa7rNoXfXWgSAVRUHv74XhchoZxy5Rv29Eqdry696YhD/Ok5+ei65eQcLJmGFA46Sl/m1NqmwbykNRFIL7NiHorsZkXLjGqW9OE/V9DKeWnceziSetJrYiuHeQvcOsNRS9C05NO9o7DFECVXXMc/bq0KRJE44ePcrYsWOLfX7nzp20bNmymqMSQgghREVUS4LcxcWl2NrhFosFo9F40yvwxfHy8mL06NHMnTuX8+fP07x583LHptFo8PSseC9Os9mMqqp4eHiUucfA6SvZAHRuEVgpsVSHimzv9bq0dMVJc5KUrHyyzU4E+VS8bmZVqKztrSmqcnujr2aQbTDj5uxEu2YN0DpAorA6X98uLQI5eimdqCtZjLnDfj3I5f1sP5IcFzWdqqpYjGYUpxvey39d5Lbkm0rdll/XRoTc14rLG8+QE5sOgNbFiaB7w6nfvVFlhVypTDkmDrxziNSTaTj7OKN1dSLtVBr73z5Az7dvI6Bz1ZYgEcLuVKA8CfJa0ut86NCh/N///R933303nTt3BgouoFksFubNm8fevXt57bXX7BylEKLcHLTTnhCialVLgtzf35/Lly8XmV5YWqWw1EpZBAcX9Ly8du1axYKDSkuYaLVa619p5ZvMnIpPB6BTU3+HSN6UVnm290burlrCQ7w5EZvG8dg0Gpbylmx7qIztrUmqanuP/1Vvv21jX/Q6x7l9vrpe387N/Fm49TTHL6WBotjtAoG8n4UQ5aXRafEIDyJtXzRO9dysd3+Zs/LQOOtwax5Y6rYUjUKzR7pR//ZQ0iML7qjzbtcAr1YBDntX2ZWdV0g7nY5HIw+0+oLvFJ2HjswLGUSvipYEuagbakmyuzwmT57Mzp07mT59OiEhBaV/3njjDVJSUrh27Rp9+vThoYcesneYQojycHEHRQvZFc8zoWhq1wAMQtRy1ZIgj4iIYM+ePaSnp9sM1Hn06FEA2rRpU+Y2Y2NjAfD19a2UGO3lZFwaRrMFXw9nGvq52zscu+jQxI8TsWlEXkxlUKfG9g5HVLGjF1MBaN+kZn92y6tZoBceLjqy8oycuXyN1g197B2SEEKUWYMhnck+d5W82FS0bnosRjNYLPjd2Rr3MiTIoaDnZb1WAdRrVfYOE/aQeSkLVKzJcSjYBicPHemn0+0XmBDVqQ6XWNHpdCxYsIBvv/2WtWvXkpKSQnx8PKGhoUydOpUJEyY47AU+IcQt6JzBbIRtP0BmSsXaatAMutxdOXEJIapctSTIBw0axJdffsmyZct46qmngILbc5csWYKvry89evQAIDU1lbS0NIKDg3F1LahNfWNSHSA5OZkVK1bQqFEjQkNDq2MTqkxkTEGysEOoX509kWof6seyHec4ejEFVVXr7H6oC8wWleN/1R/v0MTPztHYh1aj0CHUj51RVzl8IVkS5EKIGsm9eSAt/jmExE3HyIq6jJOXC763h1P/rja1/jju7OMMFAwkqmj+t63mPDOuLVztFZYQohpptVomT57M5MmT7R2KEKIqZKZAelLF2vCsmx3ChKipqiVB3rZtW4YNG8bs2bNJTU0lPDyczZs3s2/fPt555x3rAJ5Lly5lzpw5LF682Jo0f//990lISKBz584EBAQQHx/PypUrycjI4L333quO8KvU0YsFycL2dTRZCNC6oQ9OGoXkjDwup+UQ4ls3e9LXBRcSMsjKM+Hm7ETzBl72DsduOv6VID8ak8LDd8ggTkKImskt1J/QJ/rZO4xqF3xHEKeXniHrUiZuwe5onDTkpRQMOB86ONS+wQlRTeryIJ1CCCGEqH2qJUEO8NZbbxEcHMzq1atZvnw5TZs25YMPPmDYsGE3Xa5Xr14sX76cpUuXkpGRgYeHB506dWLKlCl07NixeoKvInlGM1FxBfWYO4TW3QS5i05Lq4Y+HL+USuTFFEmQ12KRf10QatvY1yEG57SXws/7qb9KLOm0dXdfCCFETeMW4EbXFzpz+D9HyL6cjWpW0dfTEz4ujCaDpFScqCPqcFndn3766ZbzuLi4EBwcTJs2bXByqraf3EIIIYQop2o7Wuv1ev7xj3/wj3/8o8R5ZsyYwYwZM2ymDRkyhCFDhlRxdPZxMjYNk0WlvpcLwT5u9g7Hrto38S1IkMekcK/UIa+16nr98UKN63tQz03PtZx8zlxOJ6JR3d4fQghhD6qqkngkjbgdCRhzzNSP8KZJvwbo3G59ehzYLZABC/uTdDgJU54Z39Y+uAfLBX5Rd9TlcedmzZplU0pK/Wtn3DhNURR8fX2ZOXMmDz74YLXHKYQQQojSk8vZdlTYm7ZDk7pbf7xQhyZ+LNsudchrM6k//j+KotC2sS87o65y/FKqJMiFEKKaqapK5MJzHFt4jvxsE4oCikbh3Fo/7nq/C+hv3YaTmxNBvYIASDyayuEvzpB6OgOPYDeaD21I47sayPmMqKWUv/7Ks1zN9/XXX/Of//yHlJQUxo4dS7NmzQCIjo5m+fLlBAQE8MQTTxATE8OSJUt48cUX8fX15c4777Rz5EIIIYQoidzXb0dHY/5KFtbh8iqFWjf0QafVkJJp4HJqjr3DEVXgvNQft9GucUFSPPKvXvVCCCGqT+rpDI5/E42iVfBp4YFPC088gl25si+ZqO9jytRW3I4Etjyzj3Nr48iIzebSn1fZ9tIhTi49XzXBC2FvKmBRyv5XS3qdHzlyhKysLNauXcvf/vY3BgwYwIABA3jyySdZs2YN6enpXLhwgccff5y1a9cSEBDAwoUL7R22EEIIIW5CEuR2kptv4vTldEB60wI467S0CvEG/jdwqahdpP64rcIyMydj0zBbLHaORggh6pb43UnkZxpxC3C29vJ2ctHi5KrlwqbLpW7HYrZwZP4Z8q8ZqdfMA49gN7ybeaJoFI5/c47c5Lyq2gQh7EpFKfNfbfHDDz8wfPhwPDw8ijzn6enJ8OHDWbFihfXxgw8+yMmTJ6s7TCGEEEKUgWSp7KQgKaYSWM+VBnW8/nih9n9dKCjsWS9ql8i/Xte6Xn+8UGiAF+7OTuTkm4i+mmHvcIQQok5RTSooFCmBomgULPmlv2iZGZfDtQtZuPq72LTlFuBCbmo+ScfTKytkIRyLWo6/WiIxMRG9vuQ6TM7OziQkJFgfh4SEkJcnF8uEEEIIRyYJcjsp7E3bTpKFVoWlZiL/qkMuag+zReXYpYJSInLHRAGtpqAOOWDdN0IIIWypqkry8TROLDrD8a/PkBSZWinnCAEdfdDqtRiu5VunWUwW8rNMNLwjoNTtaPUaFA1YzLYxqWYVjVZBq5dTbSFqm+DgYH799VfMZnOR50wmExs2bCAoKMg6LTk5GW9v72qMUAghhBBlJWftdnIiNg3AmiAT0LqhNzqthtQsqUNe25xPyCDbIPXHb1RYh/yY1CEXtVh2djaffvopTzzxBLfddhvh4eHMnz/f3mGJEqiqSk5MElmnr2DKMdg9lqPzTrFl+i6OzD3F0c9OsXX6Lg5+fByLuWKlqRp08aPZoGBy04ykX8giIzabaxey8WnuSZuxTUvdjnsDVwI6+ZGbnIfFVBCTalHJis/BI9iNwE5ynidqJ1VVyvxXW4wdO5YjR44wadIkNm/eTHR0NNHR0WzatIlJkyZx7Ngxxo4da51/69attGnTxo4RCyGEEOJWnOwdQF2UbzITFZ8OQNtG8sOpkN5JS1hwPU7EpnE8NpUQP3d7hyQqybHC+uONfKT++HXa/dWb/nhsKhZVRaPUnh+PQhRKS0tj7ty5NGjQgDZt2rBz5057hyRKkHMpmdjF28m5kIhqtqDzdiNwcGf8B7QtUoqkOlzZlUjU8vM4uWhwb1ZQ69eQns/ZH2MI6OhH4/7B5W5b0Sj0fLEdAR19idl8BWOWkQbd6hP2YGM8g93IySndhXpFUeg6ozVZcdmkX8j6q4yEipu/C93/1RYnVznVFrVULUp4l9XkyZNJSEhg0aJFHDx4sMjzkyZNYvLkyQAYDAZGjx5N69atqzlKIYQQQpSFnLXbwdkr1zCaLXi762koSWAbbRv7ciI2jWOXUhnYsZG9wxGVRO6YKF6LBl646LRk5hq5mJhJ00DpXS9qn4CAALZt20ZgYCBxcXH079/f3iGJYpiy8rgw5zfy4lPR+XmgOGkxpGRyfu5W4n48gt7PA59uoQT0b43O06VaYorbfhVznhnPkP+N1eLi40xeWj6xf1yuUIIcQKvXEvZAY8IeaFyhdrybezJo/u1c3HqFaxezcfVzJnRAEJ4N5RxP1GJ1vBri888/z0MPPcTmzZuJjY0FoFGjRvTv35+mTf93F4qzszOjRo2yV5hCCCGEKCVJkNvB8b/qDUc08rVLjyxH1q6xL9/vjLbuI1HzqarKybiCBHmbhj52jsaxOGk1tGnkw6HzyUReSpUEuaiV9Ho9gYGB9g5D3EL6oQvkXU7DOcgbxUmLxWTBkGbCmJZHfnYSzpkGss4mkn40jvCZ9+Dk7lzlMRmzjRR3mqTRKhizTMBf5Uyu5uLkosXVt+pjKomLrzPhI0Mrpa3Cuutpp6+hc3ci+PZAnOuVPCCgEPYgwwVBaGgojz/+uL3DEEIIIUQlkAS5HRyX3rQlatPQBwW4kpZDSmYeftXUS01UnYT0XFKzDGg1CmHB3vYOx+G0a+zLofPJHLuYyrBuofYORwhRR+UnZwKgOGkBMCTnYMrKR6PXoNGCS4N6WPJNZJ68QsquaALvrvp6uvXb+RKzMR5zvhmtviAui9GCxawS0NmPS9sTOTz/LKnRWWi0Cg17+dNtejheDd1u0bJjMRnMRK+LJWbTFfIzjRjS88hPzUM1qygKuDVwo/usDgT3LP3goUJUPenkI4QQQojaQxLk1cyiqpyMLegd3baR9Ka9kbuLjmaBXkQnZHD8Uip3RlTs9mlhf4W9x1s0qIezTmvnaBxPYR3yY5dSUFVV7ioRopRUVS11nejaLjc31+bfcvHUY1EtmAz5BeVVruWiqiqgouidMJvNoFVQVQspkZfw7BVaKbHfTGAfP7zXeZJ64hpObloUwJhtxjvcC219HZtnHSL/mhEndyeMuRZO/3SJ1Ohr3D23M3qPip3iVso+LQWLycLet09waXMCigL5mUZykwzo3bUEdPBCq9OQFZ/N7rcO0feLHrjYsZd8RVXXPq1LSrNPq+LcQgVUS9nbVKmZafUXXngBRVF488030Wq1vPDCC7dcRlEU3nnnnWqITgghhBCVQRLk1SwmMZOsPBOuei3NG0g5heK0bexbkCCPlQR5bXAitrCkkFwQKk54cD10Wg3p2fnEpWTTqL6HvUMSokYwGo2cOnXK3mE4lJiYmHIvq7oaMXo4kX8pCbycMebno5rM4KRgctOSlVXQw9ycZ8CckU5uNe17v0nuWDYbuXYgG1Tw7ulO/QHuHFx2imvxWZiNKubLKlhA0ark7jKwa+khAvt43rRdU56F5KN55GeY8WiowyfcGUVTNHVXkX1aGtcic4j+NQGthwYndw3GxHwUnYoxz0xKTAbujfTgrZIee42DPxzB946af+5Y1fu0LrrVPtXrpURPRaxevRpFUfj3v/+NVqtl9erVt1xGEuRCCCFEzSIJ8mpWmCxs3dAHrUZj52gcU9vGvvy8P4bjl9LsHYqoBCfj0gGpP14SvZOW1g29ibyYyrFLqZIgF6KUdDodLVq0sHcYDiE3N5eYmBhCQ0NxdXUtdzt5/g258t1u8i4mo3E2k6+1oKvviUsDb0DBlGXA7K7SpH8HvFs3qbT4b6ln0UnnPtyJORssJnBy1vL/7N13nFx1vfj/12nTZ3Zmey/puykkEAiQ0FuICAiCIqJ47SJc9WK7/izfq6IgYrmC5QoqAtIUEASkd2khpO2mbjbZ3qb3mXPO74/Jbohp21s+z8cjjyQzp7zP2dmZM+/P+7w/kiShpw2yYYNMo4X6z9YfcnPdm8M8e0MT/l1xTMNEtSpUn5jPWd9bgC1PA8bunB7Jxld2osp+8kpcYJrEzRSyJmMYJkZMxu3KfSaEAzGK80qYW187brGMt4k6p0eToZzTHTt2jMu+J6MF+YYNG/j73//OG2+8QVtbG06nk4ULF/LFL36RxYsXH3bdw00S/YMf/OCwE2lu2bLlsP8XBEEQBGH6EwnyCTaQ9F1UJfqPH8qi6lwidVd3mGgyg8umTXJEwkjFUhlaesIANIgK8kNaVJ3Pht1+Nu3xs+bY6skORxCmBUmScDimV6/p8Wa320d1ThzzHfi++0ESe/rRYym6ntuK/1/NpDvCmIBsUSk5q57Sk+cjq5M7yC8rCnrSwOLRBttHSDaJdMogsDV2yPOQTem8+IPt+Hcm8NU6UTSZdDTLrhf6WPeHds767/17q4/2nB6JzWlFlmRkWUaSJCxuC8lACiQJRZFQFIVMLINqUShuKJwRr/nxPqdHo8Od03Fp3WYC5gi2O8qs+u9//3vWrl3L6tWrueqqqwiHw9x///1cfvnl/OY3v+G000474jbWrFnD6aefvt9jy5YtG3IMuq7T3d2Nw+HA6/UO8wgEQRAEQZiqRIJ8ApmmyaY9e9tNVItk4aHku2yU5zvo8MdpbA1wwlwxKdV0taU9iGFCidcuJlw9jIEBs4E7TARBECaLJEk4agoBcM0vI7RqLuHNHSBJ5C0qx7Oo4qCtSCZa/lw3ba/2YqQNZIsMJmSTOrJVJps1Dtl3ec8b/fQ3R/FVO1D3TvxpdWvYvDpbn+xk5bVzsbnHdmDeMEw63wnQuyWM1a1Re2oRdl+u5UXFycVsunMnid4U9iIrrgoHqVCabFLHUmQh1pUgE8tScUoJJcsLxzQuQRg5aWQJ8lF2IL/66qu5+eab92sZc9lll/G+972Pn//850NKkNfX13PRRReNOAZd1znrrLP4yle+wqc//ekRb0cQBEEQhKlFJMgnUHcwQV8kiSpLLKgQCfLDWViVT4c/zqY9fpEgn8aaWnN3TCwU7VUOa0GlF1mCrmCC/khSDCYIgjAlSIqMd2kV3qVVE7pfI2tg6LnWJ4dSd3YZWx5qw0jpZBM6EqDaFVSHSsFczyGrZpOhDEbGRLHuXwGv2RQyiSzpaHZME+TpWJanvrWBlhd7yKYMJAnc5XbO+t4ialYWUVCfx6KPzWbTH3cQao6ACVavFYdVwu7TsDhV5l1WR8PH5iIrojWfMHWYk9Bj5dhjjz3gsby8PFasWMFTTz015O0kEgkURRlRb3aLxUJeXh5Op3PY6wqCIAiCMHWJBPkE2rS3OnRuWR427dBf+gRYXJ3P0+vbBs+ZMD1tbsslyEV7lcNzWjVqiz00d4fZ3Brg1IayyQ5JEMbUXXfdRTgcJhLJTfT4xhtvkM1mAbjqqqtwuw8/oaJwdEgG0my4cyc7n+hATxmULPOx5OOzKTnmwM+QujNLKF1eQG9jCItTQbHK6BkTTGi47MBWVdm0TtfmMOmkjuZQSAYzg1XcAHF/msI5LpxF1iHFuvvNfhof7yTSm6SsIY/FF1XgrdzX4sI0TTo3hnj5J01sf6IL1S5j82g4iqxEOhM8+71NXHH/Suw+C0s+OZfS5YW0v9JNNqlTtNhH5aklmLqJYpFRLDPzmtFIpYiv30hy6zYAbPPn4ThmMbJ1aD8DYZKNqIJ8fPT09ODzDe1a89e//jU//elPgVw1+XXXXceZZ545rP2ddNJJvP7663zkIx8ZdqxTzSOPPMLXvvY1LBYLGzdu3O+5vr4+brzxRl566SXS6TSLFy/ma1/7GosWLZqkaAVBEARh/IgE+QQaaK+yqFr0Hz+SgZYT2zpCpLM6FnVmfjmcyXTDZIuYoHPIFlb59ibI/SJBLsw4d9xxB+3t7YP/f+WVV3jllVcAuPDCC0WCXCCb0nnu6+/Q9nofFpeKrMnseqaLnvUBzv3l8RQt9O63vNWtce5NS3nphs30bg6hp0zs+RaWXj2Lee8r32/ZXa/18cyNTfhbYhi6iR7LQm8KV1zH4lBIBNMomszyq+tQDtJbXc8a9DXHkBWJwllO1v5lD8//dCvpWBZZkdj2TDcbHmnn0l8cS2m9h2BrnCe/vZFdr/QQbYmDCXJUIhnMEO1OUjjPQ6QjQctLvdRfVIEkSZQszadk6dS6PjSyBsC49Js3Uin899xPctv2wa4byaatJJu2kH/F5cgjqOwVJtZIK8g7Ozv50pe+dMjnn3322WFtb+3atbz11ltcffXVh11OlmVWrVrF2WefTUlJCW1tbfzxj3/kC1/4Arfccgtr1qwZ8j6/+tWvcuWVV/Kzn/2MT33qU9P2MywWi3HzzTfjcDgGB60HJJNJPv7xj9PX18fVV1+Nx+Phnnvu4aqrruKBBx4Qk2QLgiAIM45IkE+gwf7jYoLOIyrPd+BzWgnEUmxtD7K4pmCyQxKGqaUnQjydxW5RqC32THY4U97CKh+Pvr2bxr1taQRhJnnuuecmOwRhimt9pYfOt/vxVDpQ7blBcXuBhcD2KJvvbeH07y89YJ2CeR4u/sOJ9G0Jk4npFMxzY/Xs3x6lvyXGo9/cQLQ3hafMhiRLhDsSZKIZZFVCzxqU1Hs49mN11L/vwMHJHS/38a/f7aFvVwwkicI6J8EdEQw916YlGcmiqBI9W8O8fOt2Lv3FMh7/7w20vtFPwp8GCSQJDB0UC2TTBv6WKA6vhWQ4My7ncrSSXWHaH1lP4O09APiOr6HiwiXYSsfuszyxfiPJ7TuQvd7BZLiRTpPcso3Exs04jxv6pInCZJn8CvL+/n6uv/56ysvL+eIXv3jYZcvLy7n99tv3e+ziiy9mzZo13HTTTZx//vlDntD0qquuIplM8rvf/Y7f/e535OfnY7Pt3x5PkiSeeeaZ4R3QBLvttttwuVyccMIJB7Souffee9mxYwd33303y5cvB3ITnK5evZpbbrmF2267bTJCFgRBEIRxIxLkEyQYS9HaHwNyiTDh8CRJYlG1j5ebuti4xy8S5NNQ4972KgsqfChTYEK3qW5g4GxHV5hEOovdIt6eBUE4evRvCef6jtv33TEmSRIWj0rXO4dutyZJEkX1eYd8vumJTiJdSQrnuAYnF82vc9K3I0rN6UUs+3ANpmlSMtd9QHLMvyPF27dsIRXJ4imxYxomu1/vJ9WfQnUo6Ckjl/3eW0q75clOdjxfQdfGIFavRqQrgazJmFkTDJNMXEeS2dszXSJ/1tTrYZz2x9hy09PEd/ejOHOtTrr/2UhkSzcN3z4fi89xhC0MTXL7DjDN/SrFZYsFwzRJbt8hEuQzWFlZ2bCrxA8mGo3y6U9/mlgsxl133YXL5Rr2NjweD5dffjm33norzc3NzJ49e0jrlZeXH3mhKa6lpYU//elP3HrrrTzxxBMHPP/444+zYMGCweQ4gM/n44ILLuC+++4jGo2O6JwLgiAIwlQlMjATZKAqtKbIhcchbhsdikXV+bzc1MUmUVE7LTXtTZCLAaGhKc6zU+Sx0RtOsrU9yNK6wskOSRAEYcJYPRp62iC0J5abqDJPw5ZnQU8Z+/UKH65QRyJXxf2egVpJkjAlibf/1sbGZ7vJZgyc+VZOuqqGlZ+YNZgo3/VcjHggTcm8fZN+espsdHYm0HUTh9cyWESbjmYJdyfp3RElmzRwuFWQJGSrhJ7JDrajkHJdS0jFszQ92Un1yYVDrlqdCL0v7SC+ux97hRdp76SgRp6d+B4/vS/voOLCJWOzo0MdsyQd+jlhapnEHuTJZJLPf/7zNDc3c8cddzBv3rwRb2sg2R0KhYa8zp///OcR72+quOGGGzjxxBM57bTTDkiQG4bBli1buOiiiw5Yb8mSJdx1111s376dZcvEQJYgCIIwc4gE+QTZ2Cr6jw/XQB/yprYAumGKKuRpZvPe17zoPz50C6vyeWFzB5taAyJBLgjCUcM0TaK9KWK9KYz2BJIqIckStjwNi1tlzvsqRrxtX5UDTDAME3nvdYSuG/S2x5A0GVepDYfPQqw/zTM/24bdY+G4D1YBEG5No1qVwQS2aZhk9/5RZAaT4+be7LekymTSBppTQUJCsylkEjq6LIFh5haXQHWo5C/wsPWJLo79aC3F86dOG7LIth4kRR5MjkOuB7kkS0S2do/Zfmzz55HY1IiRSg1OymkkkyBJ2OaJ3sbTwUh7kI9WJpPhuuuuY926ddx2220ce+yxo9pea2srAPn5R893tOeff55XX32Vv//97wd9PhgMkkqlKCoqOuC54uJiIDcx6kiZpkk8Hh/x+tNZIpHY7++j0XicA0mSsJompmGCYYxuY4aJjIlpmpgTtC19KPuZhLiGui3JNEklEoPXQ0Mlfh/EOYDxPwemaQ65GEUkyCfI4ASdov/4kNWVuLFbFOKpLLt7I8wqmTpfIIXD648k6QomkIAFld7JDmfaWFjl44XNHTS2HrqdgCAIwkzTsyFI4/17cBTbSAXS6BkdUzeJ96coO76ABZdWj3jbDWvKeOfePfTvjOZ6kEsSPc1RslmDygZPrgoc8FbY6WuJ8cZfdnPspZUAOEs0wjvTAMTDGTq3hUlGs6QBSTcxAilsllymXHMoWNwqvmoHtSsL2fbPLhwFFoJtCbLZ3BdGTQXFqVLc4MXm0+jfEaWnKTylEuSq25pLMPwb0zBR3dYx24998SKSW7aS2NyEYRiYgCTL2Bc14Fi8aMz2I4wTE8yRVJCPMqluGAbXX389L7/8MjfffDOnnnrqQZfz+/0EAgHKy8ux2+1ALunr9Xr3W66vr4/777+fqqoqamtrhx1PR0cHzz77LHv25Pr1V1dXc9ZZZ03pFizpdJof/ehHXHHFFYdsKZNKpQCwHGSyXOveAa1kMjniGDKZDE1NTSNefyZoaWmZ7BAm3VieA6vVytyKEvRUCnMUr00AOZPGauZ+V/Rx3JZpuvb+bQ7p92mi4houKZVCyWTY2dI2+N4xXOL3QZwDGN9zcLDPs4MRCfIJkExn2dEZBkQF+XAossz8Ci/v7uqnsS0gEuTTyED/8dpiN06rdoSlhQED7Wia2oLirglBEI4au1/sIR3N4pvrwUgbJANpTMMkFc6guTQUi3LkjRyCt9LBRT85hmdv2kLP9ggYYMu34DJNXEX7T6pnd6uEOhKk4zpIUHu6k/4NBr27Ivi7kqQTOhKgWCRMAzKShN2h4iuxYwIWl0rNCQXMO6MYh8/Ctqe78Rgmwd1xFE3CW2nHXerA4lLJpg0kRcLqnlqfkQUr6uh7eQdpfwxtb7/xTCCObFUpWFE3ZvuRLRr5l19KYnMTyR07AbDNmY19UQOSKr6eTAuTUEF+44038uSTT7Jy5Uqy2SyPPPLIfs8PtAS5++67+dWvfsWdd97JihUrBtft7u7m2GOPpbi4mPb2dh544AHC4TA//vGPhx3L7373O375y1+i6/p+VZM33ngj1113HZ/5zGdGcaTj5w9/+AOhUOiwk5oOJMHT6fQBzw0kwP59UtLh0DSNOXOOzjtFEokELS0t1NbWDg7eHG3G4xxIkoSmp1CtVhjFaxMAzYIk5RJq5jhua6CiVZKkof0+TVBcw2a1Imkas2fPHlEFufh9EOdgvM/Bjh07hrysuAKdAE3tQQzTpDjPTnHe0fmiH6mGSl8uQd4a4ILjaiY7HGGIBhLkDaL/+LDUFntwWFTi6SwtPWFmlx564jlBEISZIh3Lkk3oRLuSKJqMvciGrEgYRpx0NDvq7Vcdl8/H7jmR3u1R9KyBvy3O/f/1Lpmkjmbbl3xPRrMUzXKh2RX0JBQvsnH2V0t58ofbSEayqBYJi0PFW24n2JYgEU6TTBlkMgZWh8Ipn5uNtzx3nXfe/1vMqmvnEfeneefuFtbftwd3mQOLU0XPGAT3xMivc1Fz0thOQm5kDSRZ2q/n+nB4l1VSfvExdD22iURbEADVaaHi4mPwLq0cw0hB0jQcS5fgWDpGfc2FiTUJPcgbGxsBePXVV3n11VcPeP5gPbMHrFy5knvvvZe7776bcDiMy+Vi2bJlfO5zn2Pp0qXDiuPxxx/nlltuYe7cuXzyk59k/vz5AGzdupXbb7+dn/3sZ1RWVrJmzZphbXe8RSIRfvOb33DFFVcQCoUG+67HYjFM02T37t3Y7XYKCwuxWCz09vYesI2B1ioDrVZGQpIkHI6xmfB3urLb7eIcjPU5iKVBlkCWj7zs4cgSIOUS2BO0LWUo+5mEuIa8LUkaVWJT/D6IcwDjdw6GM9ePSJBPgM2tYrLCkVq4tyXNQMJVmB4GJqVdKPqPD4siS9RXelnb3Mfm1oBIkAuCMOMlg2laX+0l2pMk1p9ClnPtSvLnuskmdCpWjM18DLIiU7Igdyda0Rw3ZfUe2jcGySu1o9pkYv25qvXjP1w92KscYMmFZYQ7Mzz9i23kVzmweTRkWcJVaKV7ewQ9ZbDsgxUsWlNB3b8lu52FVpyFVk750nzCHQla3/QT7sy1H/NWOTjvfxZhcYzNpbh/a5Atd++g661eFItM9TmV1F85B5tveG1RJEmi+vLjKDyxjtCmTgDyFpXhEHdACv9mMlqQD3VyzGuvvZZrr712v8cuuOACLrjggjGJ409/+hPz5s3j/vvv36/ys76+ntWrV3P55Zdz5513TrkEeSgUIh6Pc/vtt3P77bcf8Py5557LqlWruP3226mvr2fDhg0HLLNhwwY0TWPu3LkTEbIgCIIgTBiRIJ8AA8ldkSAfvgUVXiSgMxDHH02S7xrlLUDCuEtldHZ05ipSGkTP/WFrqMofTJBfeHztZIcjCIIwrv51SxMd6wIYJphJHUmRyKZ1ut4NUHVSIQ17J8wcS5pN4bKbl/L3722ifWOIrF/H6bOw6pOzWH75gf3OS+d7sDhULHZlMHmu2RQsDoU5Z5dy4Q+OOez+HD4Ll/x6Oa1v+unfEcHus1B3ahH2vKH1QzyS/sYAz/3nayT6UjgKrGSTOo1/2k7/5gCn33Iiqn34l/uO6nyRFBcOyQRMY/gV5JM0r+eY27ZtG9dcc81B2yLYbDYuvPBCbr311kmI7PAKCgr4xS9+ccDjd999N+vWrePmm2+msDA3KLl69WpuvPFG3n77bZYvXw5AIBDgscceY9WqVbhcrgmNXRAEQRDGm0iQjzPDNGkaaDchqmmHzWXTqCly09IboaktyMoFpZMdknAE2zpDZA2TfJeVUq9oKTRci/YOpG0WE3UKgjDDRbsTvHN7M6lwBkWT0SUwMgaSJGFxKSz71Gw8Vc5x2XdhrYtP/GEFXVvCJCNZiue4cOYfvNp6/unFVCz00Lo+iLvYhqrJRPpSWOwqJ145tPZviipTe3IhtSePTUU85Cb22vpAC6/fsIFIewzVpmLoEr7ZLqw+K73r+ml7qYva88a2NYogCIc3nNu5J5Ldbmf16tUHPP7CCy/w7rvv7vfcFVdcwQMPPMA111zDJz7xCdxuN/fccw/pdJqvfOUrExm2IAiCIEyIUTYcEo5kT2+UeCqL3aJQW+ye7HCmpQaRMJxWBtqr1Ff6puwXhKlsQYUXWZLoDSfpCSUmOxxBEIRRi/cmWfu/Tfz14uf564XP8dbPGol2Jdj5VFcuOW5V0BwKNo+G3WdBscpIioQ2gsrn4ZAkibL6POpOKDhkchzA6lD58C3LWHReGXraIB5KU1jr5MLvLqL+jJJxjfFwdv6jjbd/1ki8N9e7XZIg1puitymMosmYhklga3DS4hNmMmkUf6a/uXPn8thjjx10Est0Os2jjz7KvHnzJiGysWO327nzzjs55ZRTuOOOO/jJT35Cfn7+YHsZQRAEQZhpRAX5OBtorzK/wju0yReEAzRU+nj8nT2iD/k0IVoKjY7NojK71MP2zhCbW/0U51VMdkiCIAgjlgqlee76t+ldH0BzKiBJbPzTTjre7MM1y4OkSJjGvsYLkixhmqBnDPLnTJ3CgvwqJ1f+73EEOhKkY1kKahyoFuXIK44T0zTZct8uDMPE6tFIhdMoltzAQjqcJRHIJe40lzZpMQozmzkJk3ROFR/96Ef52te+xkc+8hE+9alPMWfOHCRJYtu2bdxxxx1s3bqVm266abLDHLIf//jH/PjHPz7g8aKiIm6++eZJiEgQBEEQJp5IkI+zgWpa0V5l5AYqyHd0hklndSzq5H0hFQ7PFC2FxsTCKt/eBHmAMxaJBLkgCNNX85Pt9G4M4KlxolhyhQJGxsC/JYQuKVg9GplYlkxcR9EkDB3MrEHeHBf5c6dOgnyAr3xqtA7LxLJEOxJYPRqqVSYVTqNnDGRNxjRNYp0JXCU2Kk4RremmM8PsI2s8i27uRCIfTT4VRW6Y7LByZkpD8RG48MIL2bVrF7/97W/58pe/vN9zkiTx+c9/nve///2TFJ0gCIIgCCMhEuTjrFEkC0et3Ocgz2EhFE+zvTPEQjHx45TV7o8RiqfRFJk5ZXmTHc60tbAqn4ffbBkcYBMEQZiuetYHkGAwOQ4gazKyKiEZOs5iG5l4llQ4g54ykGSw+azMv6SKF366hUQoQ+nCPOafV0LcnyEeTFNQ6zxsS5SjgWpXsOVbiLbHcVU4yEQzxHuSZFIZjKyJ5lBY+sUGfHPFZ/F0pZstJLP/D8NsJdeaxCBjPomNz6DJayY7vKM5Pw7Af/7nf3LxxRfzzDPP0Nraimma1NTUcPbZZ1NdfeBkv4IgHEIqAdkD2xUNiySDebS/KwmCMFoiQT6OgrEU7f4YAAsqRIJ8pCRJYmGVj9e2dtPYGhAJ8ils896E7rzyPDRFtBQaqYH2NLt6wsRSGZxWcYu8IAjTk+ZUD/qd1dBNfHVOyleVsP7OFiQJTCOXSHdWOlj7t3bS0SxIEoa+h8e+tQHZpWIaYPdqLP9QNas+OxtFnVqfNeHeJDve6sc0TGYvL8BbOj4V57IiM+8DNbz9880k+lJ4al1YPBYi7XHc5XbO/8MqvLM847JvYWKk9T9jmLuRqEGSFEzTxDS7SOl/QJFORJYm93r4aG6xMqCmpoZPfvKTkx2GIExv2TS8+ABE+ke+jdJZcNw5YxeTIAhHJZEgH0dNbUEAqgtduO0iwTUaDZV7E+SiD/mUJu6YGBsFbhulXjtdwQRb2oIcN7toskMSBEEYkerTStnxaBuJvhS2AgsAqUAaRZOpPbucmjNLqVlVzJ5XetEzBq5KB8/cvAWQKJzrxjRMdr/lJxnM4Cq3UTDfQ9yf5pmfb2X76314q+wU1Lg45n3l5Fc6DhpDf3uc/vY4vlI7RdXOcTvWfz2wm4dv2kykP42qyXiKrJzzmbmcelXduExaveDyWhL9SbY/3EqkNY6syVSeUsJJ/71EJMenOdMMo5vrAB+SlGstKEkSmMWYZhu6+S6ydOYkBggYI3hNiwJPQRAOJtIPwd6Rr+8WBXSCIIyeSJCPo8FkoZiscNQGzmFjWwDTNMfli6YwegMtQUSV/+gtrMqnK9jOpla/SJALgjBtVawsYuGVdTTd10JwZxQA1aEy/4M1VJ9egiRJlC71Ubo09zn/zl92kwxlKZztIh5I090UJhHKABDtTOIss4NFpq8zTu9DcXxVDkwT3rx/N1f89Fiql+675krGMvzt5s2se7KDZFzHYlNYfHoJl/33Ypx5ljE9zo3PdfJ/171FIpJBViQUTSaZzPCPXzRROsfN/JPH/n1cVmWOu7aBBZfVEdgZxuLWKFzoRRZ3cE17JgYmIPHv17sSuSyzyDQLgiAIgiCMJZEgH0dissKxM7cs17IjGEvTEYhTkT9+FWDCyIQTafb05ZIf9ZXeyQ1mBmio8vHsxnZx14QgCNOaJEkc+8UFVJ9RSueb/WCalB5fSNFi70EHu41sLvGXimfpbgqTTuh7twOmbtK1JURWkTBNUDSJwhonkirRuzPKEzc38ek7T0KWc9t9+GdNvHLvblw+CwVldhLRDG/8vRXThE/cdNyYHaNhmNz59XVEAymsLg1FlshmDaLBDHrGZN0T7eOSIB/gLLXjHKdWLsLkkMhDlRaSMV9DNj1IUm7Qw6QPScpDkRZPcoSCIAiCIAgzi0iQj5OsbrC1IwiIBPlYsKgKc8o8NLUFaWwNiAT5FLRlb0uhynwnXufRPXnaWFi4931ja3sQ3TBQZFERKAjC9CRJEkWLfBQtOvL1UNXyfKwuFX9zjGzaQLMrGGkDTJCsMumUQco0UTUZzarkJvuUJTwlNjq3hGlvDCEpEulUlrWPt+P0angKc59J7nwrkgSbXuymuyVKSa1rTI5v59p+evfEUFQZTcu9V1sUhXRCJxnN4u9IjMl+hKOHJElY5I+im7tyfchNDZMsEjYs8hXIUvFkhyh6kAuCIAiCMKOIBPk42dkdJp01cNs1KgtEMncsNFT6cgnytgDnHFM52eEI/2Zzqx+AetFSaExUF7lxWFXiqSwtPRFml+ZNdkiCIAjjrniBm6WXV/HsjVswMiaynGurZpgmskWGlI6hm5iaSV65HWlvtbgkSUT6U/z2828Qj2XRswaBzgQV9fv34ra5NKJtcfwd8SEnyEP9SV57dA/b1vVhd2ksP7uCpaeVDVaqBzoSyEouUW+auWp3AFmVSMV1iuvGJhEvHF0UeT4O6UbS+j8x2IpEAZp8Boq0fLJDA0SCXBAEQRCEmUUkyMfJQC/mhkqf6Jc9RhZW5fPX13cNnlthahloBbJQJMjHhCJL1Fd4Wdvcx+bWgEiQC4Iw7aTCGXo2BJBkKF7iw+I68oTlkiRx+vUL6NoWYcNf27A4FJylNhKxLMloFnRQVQmbx0JeuZ1MSqevM0F3c4Rs1sRFLiGdjOuk4lnamsLMPaEAWcldiyUiGawOlYKKg0/o+e/8XQl++/V1tDQGkRUJ0zB5/fFWzvnoHD78lcVIkoS31I4r34KeMUjFs6iaDFKuTYzVobLyQzWjOo/C0UuWKrCp/zHZYQiCIAiCIMx4IkE+ThpF//ExN9DXendvhGgyg8t25C/awsTI6gZb24OAeM2PpYaqfNY299HYFuDC42snOxxBEIQh2/73Vt65bRuxngRI4Cq1c/x/1lN3bvkR15VlibO+Vk9nU5h4II2nzI4HE//uOBaHyrKPVvPWg610bgvT35skldTR9dykhqGuJHrWpPoYL/kVDvpa43TvilJY5SARzRLxpzjpA9UU1wytqvvZe3exa3OAsjp3LvENhPqSPH9fMyvOrWTW4nxmH5fP7OMKaHqlBy2lk4plyeomFrvK+66dT9lcz2H3Ee5J0vK2H1mWqD0+H1fBxLYpM02TYFM/vW91YmYNvA2FFB1fhqyK1l7CYYgKckEQBEEQZhCRIB8ngwlyUU07ZvJdNsp8DjoDcba0B1k+e/wmvBKGp7k7TCpr4LKpVBWKW8nHysBgg5ioUxCE6aTrHT+v37QZPa3jrnSACdHOBK/+cCOeaicFC458R0zJPDcX/mgJz/50K/7dMQBKG/I468vzmHdGCQvOKOHeb71Lb28Cm1slGkxjAnrWJNSVpMUIULPERyKUIZsxCHQlsToUVn6whku+tmjIx7Lu+S7sLm0wOQ7gKbDSviNC45u9zFqcj6LKXPWjZdz3/Q3sfNtPKp7F7tZYeXk1qz8/n0BXArtbxebcf2DfMAxev3s3L/5mJzF/CgB3kZWzvzSf4y6pOmRMqUiG5me7CeyK4SyyMuusEtxlI5uk0zRNtv95Ey0PbiEVSKDH05i6iavaw5KvnUTxyqpD3gmpJ9L4X99JpKkDSVPwLqvBu6wGSRGJ9aOBaU52BBPnYx/72LDXkSSJP/3pT+MQjSAIgiAI40EkyMdBTyhBXziJLEnMKxNtEcZSQ6WPzkCcza1+kSCfQja/p6WQLFoKjZn5FXnIEnQHE/RHkhS4bZMdkiAIAgCmYdL5WhftL3eSiWUoXFJIzXmVWPOs7HyijVQkg2+2azC56ql2ENgZZeeT7UNKkAPMPbWYWScV0tUUxjRNSus9qBYFgFnHF6A6FDylNoJdSSRFAsNEViT0rEnMn8bfGcdTZOWD31pMxXwPFodKIp7B3xXH7vIMqQXeUD/S8ssdfO62FXTuiBD1pymudbL19T5+euUr9LfHsdgUjltTzprPzyfQluClPzWz8alO+pqjOD0WKuvzUFSJYEeSJ37cRPEcN1VLvAfsJ7g7xhNfWUff1giYJqYJ7/yhmbO+v4TqkwuHFux7BDb1seuBLejJDHoshZnNZT3D2/2s/f9eYPH1J1F1wdwD1svGUuz4+VOEN7fB3kRp30tbKTqjnpqrTxnsDS/MXEdTD/K2trbJDkEQBEEQhHEmEuTjYKDac3apB5tFnOKx1FDl49mN7aKidooZ+HnUi/YqY8pp1agt9tDcHaaxNcApDWWTHZIgCAKmabL+15vY8UAzelpHkiRan2tnz1OtrPzRCiLtCRRN3i8BLUkSsiIR7UwMa1+KJlNxkEQxgKzKJCJZDMPEYldIxbLouolErrq1d3echaeVcPz7K3nugWb+edcO+rvi6FmDmgVevviTFVTN23/bhmHS3OTnrRdb2bWjE9WtENoVxVtkG6wiD/ensDtVGk7Yf6BekiTK97ZTefPRVv7y/9ajZw1cPguZZJZn/7iT1o0h4u1JAh0JktEsmbRBJJimbWuYmkVefJV2erZH2fhExwEJ8nQ8y5NffZf2tX4K5rqxOFRMwySwK8qLP9zMhx9ciWYf3nVn71sdZOPpXHJcN5HtSu7nloBMJMXOv2yi9LRqNPf+bV96n28ivKkNa7EH2ZrbZzaSpO+FLfiOqyXvmOphxSEIU9lzzz032SEIgiAIgjDORPZ2HDSJ/uPjZuCcbm0PohsGiixu451spmkOTpy6sCp/kqOZeRZW+XIJ8jaRIBcEYWrwbw6w82+7UB0KrkonAHrGoH+zn+0P7iR/rpu2V3owTXMwSW4aJoZu4pvlHrM4lp5bRtPL3ZiGiebMJYvTCT1X0CyZOPM0Pn7jMt74ZxsP/HwTAX+SRCzXcqWrLcqWs/r40UPn0LC8GIDNb/dwx43rWPtSO4l4FlkBj9dKJmGS2ezH47ZgGKBZZc67ai51iw5+nWcYJs//uZlsxqC0bm/bsTywOFQan+nCYVOpWpRH28YQmqagWCUigRQRf4q8QhuSIhHtTe23zR3PdfPcDzbT9WY/IJGOB/HVOPGU28mrchBqjdPxdoCaU4Z3d52R1jGzBkZKR7a8Z1BDkpA0lbQ/QWibn8Lj9v/8Cb7dgqTKg8lxANVtIxOIEd7UPuQEuWkYpFrayIZjWIoL0MqKxOT204BpSiOqID+aqs4FQRAEQZheRIJ8HDS2igT5eKkpcuOwqsRTWXZ1R5gjWthMut5wkr5IrqXQ/HLx8xhrDZU+Hn17t7hrQhCEKaN7bS/ZRBZH6b5kt6LJqA6Vthc7OekHK9j5eDuB5ijOolxrqFhPEneFg9nvqxizOE7+UA2vPrCbppd7iAXSyLKEZlNw+jQMyeTC6xson+fht995m1AwRSySRtVkHDYNXTcJ+ZPcct1r/PKp9+HvSfCzr/+LnY1+kkkdq00hncqSTOjk+WxIisyilSX4iu0cd2Y5S08rG0zk6rpB0xu9NL3Zg6zJ1C8voq81hstr2S9eu0slFcmgyBKhvhSqTcY0TWRZAdMgEcng9lnBMCmdv29iz/6dUZ76zkbivSlkTUZSJIyMQf/OKJpdwerRMHWTbFIf9jn01hciKTKmYYIpIQEYuQENzW0BWULWDixGME3jkP1nTIbWnDrT66f33kdJ7enAyGSRbVaci+dReOn5yLaJnahUGIGjqAe5IAiCIAgzn0iQj7FkRmdndxgQE3SOB0WWqK/wsra5j8a2gEiQTwEDA0JzREuhcTEw0LajM0Qqo2PVlEmOSBCEo97hZuczTXxzPJx+wzLe/tVWAjvCgETZ8QUsv7Yez96K87EgazKFs53wpkQmpoMEMhKqrjD/hCJWXVZDOqnT2x4jEc/kEuh7e5grCsiKROfuCOte6qBle5DezhiGbmKxKGgWGSSDTEpH1XKJ4OPXVHL6hXX7xZBO6fziS6/xysO7iUezADg8GnkOCwUeGy7fviR5oCNBOmOQ6U8Rj2aRFQlZMklG05imRDap07szQmGdiyUXlA+ut+3JTuJ9afLnuuiNZUmHMyCDnjTo3xHBU2HH7rNQvHj410TFJ1ZQfFIFbf/Yjh7PYGgKGKA4FCTTxFnpwbvgwN7m3mNriW7twsjoyHs/l7KxFJKm4KkvP2D5f2caBr33Pkpi5x7U/DwUiwUjniDy9kYUl5OCi84Z9rEIEyvXzOjoFQ6HefDBB1m/fj2hUAjDMPZ7XkzSKQiCIAjTi8hmjbHtnSF0w6TQbaPIIybUGw8Nlb7BBPmFx9dOdjhHvc1tfkAMCI2XEq+dfJcVfzTFts4Qi6tFGxtBECaXb4EXCYj3JHCWOAAwMgbZeJaK03PJ0bLjC7ngDwWE9sSQJPBUOw/ZOsO/J8Y79+5h16t9WJwqDeeXsfSyKjTb4QcEX7irmU0vdVN9jJdkNEuoJ0EimkWzKXz4u0vwltgxTRNfkY1tG3Q0a2576axOJJ4mndGJBjP84afvUljmQFVzldL7uoxISBIkEzpWu0o6dWCF9pN3buPZe5vRsyYWq4wpQSyUJh5KQxqsTg1nnkbMn6ZrSwRFk1FkCVWTMAzImiaaKiMbYHdpzFtVxFnXzsNbZh/cR7grmUv+yzJWr4V4bxL0XAFvvDdFNqGz6qsLcJfaD4jvSBSrwtL/Xomjwk3znzeQjWVQHAqay4KtxMmCzx2HbDnw51B0Rj3BdbuJbu1CUiRMw0SSJQpOnkvekiO3V0k2t5La04Ga70W25gYRFKcDM5Ml+s4mvOesQnEM/3iECXQUt0vp7OzkiiuuoKurC7fbTTQaJS8vj3A4jGEY+Hw+7Hbx+hUEQRCE6UQkyMdYU1sQyE1WKHoojo/6vYnYgcplYXKJlkLjS5IkGip9vLKli8bWgEiQC4IAQHxbK/7n15Jo6URy2pAqPJhz543rPg3dYOvd22h+qJlMJE2sI0psdwRLgQ1JkvDV+5h7yazB5SVZwlvrOuw2/btj3Pe5t+lvjmJxqBhZk/Z1Afas9XPxzUtR1IPPNWKaJm/8vRXNpuDyWnF5rRRWOjF0k+5dEVqbQtQdk48kSZz9kTmsfbmTVELHlCAQTZLVDVRZRlVl2naF6W6LYprgcGuE/ClUNdf+xDRzyV+HU2XekgMrqZ+8cweZtIHLoyHJues+VZOJBdPgkEgndMJ9KZKhDIoiUbvUR7AzSaQ3hWmYZDMGjgKNj/xwKce+vxJXwYGtRTzlNjLxLJHOBOHuJKgKkmZipg00h4rmUon0JIfzo9yP5tRY+IXjmPPhBrpe3kOiM4q1wE7pKdXYSw/+89M8duZdfz59L28jvLEN2aLgPa6W/BPnIB3iZ/ZeejSGkcmiWLT9HpetFoxkCiOeEAnyKe5wN5LMdL/4xS8IBoP84Q9/YP78+Zx88sn87Gc/Y+nSpdx66608+eST3H333ZMdpiAIgiAIwyAS5GOsqX1vslBU046bBRVeZAm6Qwn6wkkKRaX+pEmkszR3RwDxmh9PDVV7E+SiD7kgCEB08y467ngMPRZHdtjQQ1GknW34FTuOq84ftwH6nX9tpvH3TSg2Bd/8PBI9SeLdcRRFYvHnF1G7pgabb3i9o9++ezf9O6MUznEjK7m4U5EM25/tYddrfcw5tXi/5WPBNL0tUexejb62GOH+FPFwGofbgrfEhrq32jmxt90JwOmX1rHxX908escWwpEUWcNEU2QUTSavwEb1/Dx2bw0iyzKalkuax2NpdN0cbMty5gdmUTPvwBYmwb4EksRgchxAliSQJGxelS/fsYqO7WHeeqiV7f/qw+m14vRaiJdlSIQzxIJpapf6OPXq2Qc9P1uf7mLtX3YT608T7kxi6iaaXUGSZBS3QskSL0bGoO0NP9GeJK7ifddEpmkSaY0hyRKuCscRXxfWfDs1F80/wk9sH9Vlo/T8JZSev2TI6wzQivKRbdZcItzpGHxcjyfQ8twonrGbzFUYL0dvIdBrr73GBz/4QU466SQCgX3XZna7neuvv55t27bx05/+lJtuumkSoxQEQRAEYThEgnwMmaY5WEEuqmnHj9OqUVvsobk7TFNbgFMayiY7pKPW1vYghmlS5LFR5BGVXuNl4P2kqS2AaZri7hRBOIqZpkn/P99AjyWwlBUiSRKyrpPq6Se6diups47HVlE05vvV0zrNDzUjazKOvW1V3DUatkIb6WCKggbfsJPjAM2v9GF1aYPJcQCrWyPclaT93eBgglzPGjz9u+28dt9u4qEM4VCK/p4Eum6gajKhnhSB7gQldS5Ui0x1w75ktixLXPfTE2lYUcQtX3sNf38Su0PF4baQNQ02re0hk9IpLHVSUJpLJEeCKTLZLAuWFvHhzy/htAtrD/reW1HnoWt3rvp84Gldz5XWltW6qarPo6o+j1Qow443+tCzBooq4/BasHs00imdOScUHPTc9G6P8OR3N5EKZyg5xkv/tggJf5pMUsfhs1Ay343NrZEMZciks+jpfT2Qu9b28+6vt+DfnpsXp7DBy7JrFlC0aN/1abI/SSqQwlHqQHNpB+x/PFnKS3AsnEt07SbMrI5s0dDjCdB1PKccj2yZ2HgEYTj8fj/z5+cGk1Q193U6lUoNPn/KKafwm9/8ZlJiEwRBEARhZESCfAx1BpOEExksqszsUs9khzOjNVR6ae4O0ygS5JNq8972KgurRNuP8TSnLA9NkQnF07T7Y1QWHL5lgSAIM5ceTZBq70Hx/FtFsN2CEUuRbOkclwR5OpQmGUgdkEjVnBqJngSxzjhFy4a/Xc2uYOj7T26Xa20CimVfq44X79zFU7dtw+JQsbpUgs0hDN3InQMJZFUiGkihZ01O/XAt807Yvx2KJEmc86E5NO8I8uidWygqd7KjyU8qpaOqEqYBwVCSivluvvqNlagWE8PSw9JjF+FwODiUS65pYNPrPcRCaTSrjGlCNmNgd6pc8Ml91djL1lTw2gN76NwWxlVgRZYlwn1JvCV2Try05qDbbnqik3h/isK5biRJomSpQvtbfjJJHc2j4ci3Ypom8b4kpcd4cZfnBqqDOyO8/K13iPcmsBfZwISON3qJtMU49zcnY3WrbPrtJjpebEdP6VjyLNRdNIt5V8xDHkJ7lLEgSRKFHzwfxeUgtq4RI5lCzXOTt2o5nlXLJyQGYXRM48jLzFRer5dYLAaAy+VC0zQ6OjoGn5ckafB5QRAEQRCmh4m5Cj5KbOvKVenM3ZvMEsbPeytqhckz0PKjodI7uYHMcJoiM688Vw0p2qwIwtFN0lQkVcHM/tuEkUbu7hLZZhmX/WpuDYtHIxPL7Pd4NpFF1mTsxSO7i6jh/FKyKYN0LNcSxTRNIt1JbB6VOaflqsczKZ3X7m9BsSh4S+0kErllHS4NVZVxeiy5VihWBZdX46rvLztk7/LT1tSQl29jz84QyWQWu13BNMBiVaiZm0drcxi/P8GSE4ux2g8/SSjAiWuquPo7y/AW2wcrxwtKHXz8W0tZflbF4HLuAiuf+PlxLD2/HMxc7/EFK4v4+E+PpXz+wYsqoj1JkKTBgRDNpuLdO9lprD9FuCOBf0cUa57GCZ+bgyxL6GmdbQ/tJtaTIK/OjdVjwZpnwTvLTbQ9zs7HW3nn5nfY9UgzAFavlUwkQ9PtjWy/f/tQfmRjRrHbKPzAeVR+43NUfOWTVH71M+SdtgJJFtfQ04GJNOw/o7VhwwZ+8IMf8P73v59ly5axatUqPvvZz7Jx48ahxWya/OlPf+K8885j0aJFnHfeedx5552Yw2yoXldXx86dO4FcMnzhwoU89NBDpNNpEokEf/vb36iuPvJktYIgCIIgTB2ignwMbevc24tZtFcZd/V7z/H2zhDprI5FPfKXWGFsGaY5OEDRICrIx11DpY/NrQEaWwOce0zVZIcjCMIkUWwW3MvmEXj+HQy7DdmqYRoGhOKopYU462vHZb+qTaXu/XVs+t1mEn0JrHlWssksiZ4ERUsLKVp64ASWQ3HsFTW0rg2w8+VejGyuctzmUVn52dmU1ucSx1F/mlggjd2zt3rdBJNc1bichoIKB3lFNoI9CSx2FZvz0Je3FbM8nHpBDX/+xXrMrEkqYaBZZUprXOQVWAn4E+zaHgByyS3TNAn4E9jsKnb7gW0/JEniQ19azJmXzaLxjR4MA+qPL6S05sAe2iWz3HziZ8uJ+nOV7haHwtZXennlnl2UzHYze3k+8nsKLApmucA0MXRzsAVNXrWdmD+Nr8yGu9RG8cI8llxeRV6lg1d/tInmp7uINIeQTANHiR2rOxezJEtIikTfuj5iO/uwF9qxeHKDKapdJdYZY9cjzcy+eDaqY2K/HigOu5iQczqahEk6f//737N27VpWr17NVVddRTgc5v777+fyyy/nN7/5Daeddtph17/11lv53//9Xy666CI+9alP8eabb/LDH/6QcDjMF7/4xSHHsXLlSm6//Xa+/e1vY7FY+OQnP8m1117LCSecgCRJJJNJfvjDH472cAVBEARBmEATdgWcyWS47bbbeOihh+jv76e2tpbPfOYzvP/97z/iuqZpcuedd3LPPffQ3t5ORUUFV155JVddddWU6sW7tVNMVjhRynwOvE4LwVia7Z0h0eJjEuzpjRJLZbFpCrNKxGRa462hygf/2tfWRhCEo1fh+SeT6ugjvrMdDAPDMMBpJf/S01Ac4zdx9dwPzSEdTrP78d3EOmIoVpnSE0s49vplI27NYXWqXPKLZex4sZeODUE0m8Ls04ooW7ivh7jTZ8GRZyHSn8Lh0XD7LCiqRDppIO2tHDcMk1gow/I1lYesHn/qbzu459cb8ffE0THQJZPSCjul1bm+5aZpYgJ5vtw53PxukN//4jn2NIexWBVOO7uWqz69hE1v9fDSEy0E/UnqlxZx/gfnUlHr4bRL6oZ0zK58K7s3BLjnm+/S25LrX65aZOauKOTKG5fh9OYS1/Vrynj3vj3074zgKrYhyRLRniT5dU4u/7/jKVmQG0BIx7I8/tk36Frnx5ZnQVZlkv40PZuClCz2YXGpmHsT7apFIhvP4Pi3in+Lx0I6mCbRn8DtEJ/pwpGZ5sR/B7v66qu5+eabsVj23Slz2WWX8b73vY+f//znh02Q9/T08Nvf/pZLL72UG264YXBdRVH47W9/y4c+9CGKiobWnuqzn/0s//Ef/zEYxznnnMOvfvUrHnnkEWRZZvXq1Zx//vmjOFJBEARBECbahCXIv/Od7/Dwww9z5ZVXMm/ePJ555hmuv/56dF3n4osvPuy6YzXaP56iyQxt/jggKsgngiRJNFT6eG1rN42tAZEgnwQDrT4WVHhRxO3Q427gfWVPX5RIIoP7IJWMgiAcHdQ8J1XXXkZsczOpjj6ymkK7JYujoXZc96tYFJZcs5g5l80msjuCNc9K3ty8ERUrtK0LsO4vu+naFMJVYmPxxZWc/uX5yPKB27LYFE76YDX/+OUWQj1JnF4L3gIbPW0xLDaFaCBFqDdB5XwP53xizkH3t/Htbn5/01oyGYPSSjdWm8r2xn7a2yLomonNrpCJ63i8VlaeVc07b3Zxx692ks0o5Bc6SCayPHh3I08/uhMpAWbWRLPINK7r5dWn9/DtX5zGrAVDuxZJJ3Xu/f/W09McpbDGiSRB764or93bws43+jj/2gWc8MEq3CU2Vl03j2dv3kKgNY7NoVKxzMcp180dTI4DtDzbRc/GIHk1TlSrgmaX0RMZsrEskbY43lkuYp1xbF4LVWeWE9zUTTaeRXPu+xzJxrKoDhWbb/wGWIQZxBxhgnyUVefHHnvsAY/l5eWxYsUKnnrqqcOu++yzz5JOp7nqqqv2e/yjH/0oDz30EM8++ywf/vCHhxSHJEn7JekBzj77bM4+++whrS8IgiAIwtQzIQnyzZs387e//Y3rrruOa665BsiN2H/sYx/jpptuYs2aNQdcZAwYy9H+8bSlPQhAuc+B12md3GCOEoMJctGTeVJsbvUDYkBoonidVirynbT7Y2xpD3D8nOLJDkkQhmQ0d5AJhyZrKu6l83AvnUc8Hqe9qWnC9u0oduAoPvTElUey69VeHv3qeuL+NFaXSmBPnLa3/fTvinLal+YfdJ3Tr55FPJzhzYdbCXTE8eRZqGnwkl/jQM+azFqaz4qLqvCVHLxVx/OP7iIezVA9xwtAYamDju4I7V0RAluSKKqMzabyic8vZe7CAn77+beIRbPMXeBDVXOXy6oq07i+l7k1Pupm5z77DMNk944gD9zRyNdvWjWk49/xRh89zVEKqh1IMrRtDBEPpjEMk84dER67qZEtL3RTPS+PDY+0k45m0QFDlVhwQTm1J+7fzqZ/WxhTN1GtuXZzFo8Fd62H0M4Q8d4Eqk3CWWrn2C/WU3t2OR3P7qFnbQ/2YjuaXSMVTpGOpZl30fwDJmEVhIMZaU9xE4nOzk6+9KUvHXKZZ599dtjb7enpwec7/PVoY2MjFouF+fP3f49paGhA0zQaGxuHvV9BEARBEGaOCUmQP/HEE8iyzJVXXjn4mCRJfPSjH+W6667jjTfe4JRTTjnoumM52j+eBhLk9WKywgkz0MqmqS2IaZpTqt3O0WBwgk7RUmjCNFT6aPfH2NwqEuTC9DGaO8iEmcc0TV777U7igTSFc12Dn93RniTr7t3Dkkur8FUdmHxXLQoXXt/AaR+bRc+uKK58C6Vz3EP+7O9uj6Ja9s1X4g8kCERTqKpMntdKcYWTRFLnxed3c2HTArY29uN0qfttP5MyMHQTSd33mCxL5PlsrH+jk1Qyi9V25EvrRDiDnjVQLTLBjgTxYBrNrmDoJqZhkldqY9sLPbS+2Ie3wkHhHBemYRLYE+eZm5uoWJJH+SLv4PYG+oy/91rIXmwnEc6SP9vJSV9toHhJPpa9yx33zeW8c+Na+jf3k+xLojk16t5fR/3H64d0LgUBmJQe5Aezdu1a3nrrLa6++urDLtfT00NhYSHyv931KMsyhYWF9PT0jGOUgiAIgiBMdROSIG9sbKS6uhqv17vf48ccc8zg84dKkE+X0f7GtiAA9RXeSY3jaDK3LA9VlgjEUnQFE5T5Rl7NJgxPIJqiY29LoXpRQT5hGqp8PL2hTdw1MQliyQzhREa8zwzTaO4gE2amaG+K3q0RXEXW/ZLPzkIrfTujdLwbOGiCfEBesY284uG3Aamd5+Pdf3UNJpG7emLohoHFolBQ4qCk3I1pmuzaEeTFp1vw+mz090b224Zp5jKC6r/1ODcME02WkQ7SHuZgyuZ5sLk0YoHc5KNIuUR7Jqljc6nY3Rp9MZ2ErmN1pQnuiZGN65iGiZ42eOALa7nyjysonOXKHduZpWy8axeRtgTucjvIkPCnkVWZYz4xl8qVJfvt31HqYOUtqwhuDZL0J3FVuXBXib7jwsQoKysbUZX4wfT393P99ddTXl5+xLabyWTykJ83VquVZDI5JjEJgiAIgjA9TUiCvKen56BtUIqLiwefP9y64z3ar+v6qLfR3B0GYF6ZZ0y2N9Xpuj74Z7IoEswu9bC1I8SmPf0Ue8avtc1UON6JdKTj3bSnH4CaQhd2TZ7252W6/HwXlOd6zm5pD5LOZEbc+326HO9YGYvj/c69b9HUHuQPXziNQo/o0TtUo7mDTJiZVIuMrEjo2f3LTw3dRJJBtSmHWHN0zr5oFi890UJrc4j8IgfRSAo9Y2B3WynY2y5GkiQkCQL9SU48tYK1b7Tz7jvdOF0a+fl2orE0NpsKGXMw0Z5J60RCKU5/3wIslqHFXjbPzdLzy3jjr60kY1n0rEkynkWWJQqrnEiyhJEw0LMm/pYYpIxcta4MsiLRvTnM3/5zHVf+6QSc+Vby57o56foG3vh5E/6dEUzdxOrRWPjhGuZcUH7QGCRJwrdADHALozAJk3S+VzQa5dOf/jSxWIy77roLl8t12OVtNhvpdPqgz6VSKWw28dkuCIIgCEezCUmQH2rEXpZlNE077Ij9eI/2G4ZBJBI58oJHcPbCYkLxFIV2xmR7U51hGCSTSSRJOmDwYiLNKXaytSPE+l09HF8zftVPU+V4J8qRjnd9czcAc0qcM+L1Pl1+vl6ricOiEE/rbNrVzaziw38ZPJTpcrxjZbTHG0tl2dwawASSiRgRKTPqeI6G8w6ju4NMmJnsXguzTili0yNt2DwqqkXBNEyCrXHyyu3UnFgwLvutm+/jqzeu5M//u56W7UGsmoJmVZi9wDfYFkXPGgC4vBb++cx2ErqOEdeJhNJ0dcSoqvbwuf9czitP7Gb3jhAAkiwxf0khl31y4ZBjkSSJS761mIJKJ8/+bjvtTWFkScLhVEkG0/TGdfSsgYKELIEBSKqEqZuYBuRVOejbHqHpiU6WX1kLwJz3lRPYFWHTXbtIR7OYukn3un7+9aNNFDV4qTm7DKtH9BcXxs6IJukcI8lkks9//vM0Nzdzxx13MG/evCOuU1RUxGuvvXbAZ7BhGPT19Q0WbgmCIAiCcHSakAT5oUbsDcMgk8kcdsR+vEf7ZVnG7R59YvUTZzUQiURwu90oyvhUP00luq5jmiYul2tSj/eYumL+8W4HO3tiY/JzPJSpcrwT5UjHu6M3117lmLricT3vE2U6/XzrK32sbe5jtz/NMbNHdu6n0/GOhdEe77beXkygzOegsmT0ybujJTkOo7uD7EhM0yQej494/ZkkkUjs9/dUt/zTlfRsD9HTFME0cpXkziIrq74yG0PJEI+PbhDqUOYs8vDdX6+iqzVKe2uEn93wJt3dUbK6jmGYBPqTVNd52LWnn127gsyqd6IqNlIxnWg8DXY46+Jqzji/kjdf6CAaSVM7z8tJZ1biypOG/XpceVUF9Wd4+dn7/0VfS4xY0iDWl0YCLKqE3aagR7IAmGauiFwCbHkKkXiGrq3BwX2u/30zG/6wC8Uqo1glgrsi+LeFaX2lG1ehlcYHmll5wxKcJZNXJTvdXqfTwVDO6XjN02NOUg/yTCbDddddx7p167jttts49thjh7TewoULeeCBB9i6dSv19fv67Tc2NpLJZGhoaBivkAVBEARBmAYmJEFeVFRER0fHAY8PfDE+3Ij9RIz2j1WCSFGUwT9Hg6lwvItqcsmqlp4IqayJwzp+L+mpcLwT6VDHm87q7OjMtRRaVFMwY87HdPn5LqrOZ21zH1s6QnxgFLFOl+MdK6M53q0dudd7Q6XvqDlfY2U0d5AdSSaToampaTThzTgtLS2THcKQNXzZTsFbJpG2DFaPQukKB6niXpqaesdtn6Zpsn1DhE1vh0jGdRoW2WlpMehsiyFJ0LDUzZpLSvjJTxqxWQd6jaexusDilOloj/HYo+9wznllLDtDAexAiraOnXDgZe6QvHOPn3QyTdEcK+lEroI9mzRI9htYSyWSGQkjYQImsgayCvFEjFRKJ5zqp6mpiUxUZ/09HeiSgWRViLQkQTKRrLmJRXWHTse6Xl665S1qry48aBx6LEt8fZisP4Pi1XAe40Fx566pTN0ktaGf1AY/ZjyLWufGfnwRSsGByXajN4qxoROzNwIeG0pDKXJt/n7LTKfX6XRxpHM6U+Z6MAyD66+/npdffpmbb76ZU0899aDL+f1+AoEA5eXl2O12AM466yx++MMfctddd/HDH/5wcNm77roLTdM488wzJ+QYBEEQBEGYmiYkQb5w4UJef/11gsHgfrdZr1+/HuCwI/ZitF84nAK3jZI8O92hBFs7giyrO/gXP2HsbO8MkdEN8hwWysWEhROuYe+kqGKizokzcK7FhLTDN5o7yI5E0zTmzJkzmvBmjEQiQUtLC7W1tYPJoGnhuInblWma3P9/jTx8ZweplI5EriJ7dr2Xr/7uNIpKHeQX2slmDW69tYW4lHvd2h0OFEXGNE0sVoPKygrq6+cfdl/D8fSmf+HOt+Mt3/dzy6Z0dr4WIB2VKWnIo68pgiRJ6LqB3aNhxlR8ZXZO+egSCmY76W8MsynTi6vEQjqUQdJTqA4VTJNM0sButWMrNEg1wrzZ81Es+9/FEtkVYtMv1xJv3dcyTX8zwcLrj8Mzz0vLH94h+kwHkgmyKqN39ZNtSTPr+lNwVOUNrhPb2kHHY++QCcaQNRWzM468O0LxB1fgO7V++r5Op7ChnNMdO3aMy74no8XKjTfeyJNPPsnKlSvJZrM88sgj+z1/0UUXAXD33Xfzq1/9ijvvvJMVK1YAuYKsT3/609x2221ks1mWL1/OW2+9xSOPPMI111wz7KIrXdd55plnWL9+PaFQCMMw9ntekiRuuOGGURzt2Nu8eTO/+c1v2Lx5M/39/dhsNubMmcMnPvEJzj777P2W7evr48Ybb+Sll14inU6zePFivva1r7Fo0aJJil6YKSRJwmq1jsudLYIgCKMxIQny1atX83//93/cc889fOELXwByX1Tuuusu8vPzBy9cxGi/MBL1lT66QwkaWwMiQT4BBpKFC6t84sJmEsyv8CJL0BNK0BtOUOQRSYbxpBsmW9qDADRUeic1luloNHeQHYkkSTgcYpDuvex2uzgnh7Czyc8/7m3Galcpr85NeJxJ6zQ3hVj7Ug+fun5ftv6sc2Zx792bsFllFEVGURS6u2Pk5dlYdWrdiM5x65YQ7z7bSSKcoXyeh2PPLcfh1pCQkf/t7hbZLuP0WZBkmXgoi+bRSPnTSJKExabgKXFw1tcWULU4177IqJDQHCpGykRCBom9CXUTWZbQbCo6OhISNqsNzb7v8t80Tdbf8TqJ1iiuKg+yKmPoBrHWCDt/38iiLy7B//JuNI8dS15uQMs0TOKtAfqe2smCa1cNbqft8fXo4QSOqsLB64NUb5jAk+spWdkAe6/txet07B3unI7XtdpkJMgbGxsBePXVV3n11VcPeH4gQX4o1113HXl5edx999384x//oKysjG9+85t8/OMfH1YcoVCIq6++mi1btgy2sDH39pwZ+PdUTJC3traSSqW45JJLKCkpIRaL8fTTT3PNNdfwne98Z3BC7WQyycc//nH6+vq4+uqr8Xg83HPPPVx11VU88MADYnBagFQCsgdvg3skVtNkbkUJmp6CeHby+jUJgiD8mwlJkC9atIiLLrqI//3f/8Xv9zN//nyeeeYZ3nzzTW644YbB2/4mYrRfmHkaKr28sLlDVNROkMbW3HluENW0k8JuUZlV4mFHV5jG1gCnLRQJ8vG0pzdCPJXFblGoLZ7+/fYn2mjuIBOEsfTuG13Eo2mqZ++reNYsCg6XxmvPtPLJ/zp2MJH4sY8fwztrO9i8qYtAINf52+nQ+MRnllJb6x32vl97aDcP3rSZWCiNaZpkUgZ/vdHKms/NZ85JBfzrL3vQMwaKlqvsjgcyeEqsrPnSAvq2Rwh3JPFW2imsceKrclB9Qj72vH0tM1xldmpOK2HrI21YPRqSIpFN6hi6iaPAgmaXiXUkmLW6fL/kOEC0JUxoWwBbkR1Zze1fVmTsxQ4iu0J0vbAbPZnBWuQcXEeSJVSXFf877Zi6gaTIpHvDJPb0ofmc+yVkLfkuUl1BYts70RaUDvvcCcJ7/fnPfx7Sctdeey3XXnvtAY9LksTVV1/N1VdfPao4fvnLX7J9+3a+//3vs2LFCs455xxuv/12SktLufXWW2ltbeWOO+4Y1T7Gw+rVq1m9evV+j33sYx/jkksu4Y9//ONggvzee+9lx44d3H333SxfvhyANWvWsHr1am655RZuu+22CY9dmGKyaXjxAYj0D3tV0zDRUylUqxXKZ8Nx54xDgIIgCMM3IQlygB/84AeUl5fz0EMPce+991JXV8dNN910xJF+GLvRfmFmaqjK9bbc0h7AME1kUdU8bkzTHByIaKgSCfLJ0lDlyyXI2wKctrB8ssOZ0Zr2Vo/PL/eiHEWTa46Vod5BJgjjzTAOXqEm/dtz/r4Eu7YGuOoji3hnvZV43EZBoYszzqxj+fFlw95voCvBQz9rJJPSKa520rYlRDSQJtCZ4E/ffIfaBV68ZTZ6d8VQNAlDB0WVOPFD1Zx0Ve2Qq39XfKWeVDhD+xt9KJpMOppFtcnYPSqh5hjuKgeLP3Fg1aeeyg4mufc7L6qMqRsYunHAOpBLcMiKDPLe+CSJwb41713ONHPPiffPmecoLvp8/vnnufDCC/ngBz9IIJC7LpZlmdmzZ3PLLbdwxRVX8POf/5xvf/vbkxzpkcmyTHFxMRs2bBh87PHHH2fBggWDyXEAn8/HBRdcwH333Uc0GsXlck1GuMJUEumH4AjmDTEMzGQSbDbIKxj7uARBEEZowhLkFouFL33pS3zpS1865DLjPdovzEx1xW6smkI0maW1L0pNkajyHC8dgTjBWBpNkZlblnfkFYRx0VDp4+9v7RZ3TUwAccfE6Az1DjJBGG9Llpdgc2iE/Cm8eyeXzGYNopEMZ39gNgB3/24D9/9pM9FwGkkycbjhP791MmetmTfi/Tb9q4dIf4qSOjfdzRGi/jQWu4Ks5lqgBHuS5Jc7OOeL82jdEMTqUll8TimLzy0dVmsMe76Vc3+xnN5NQcKtcUK7IgS2h0n60xQv9THvAzV46w5MaLlq87AVOkj0xFEr9j2f6k9izbdTce4cAq83k+qLYS3MVYcbaR09lqb8vPmDMVoK3ThnlxLesBvFkWsPY5om6b4IlgIXrvllpEx9xOdRmHpMjt6ClJ6eHo455hgAVDX3dfq9822ce+65/PGPf5yyCfJYLEYqlSIcDvP000/z8ssvs2bNGiA3R8iWLVsOWsS2ZMkS7rrrLrZv386yZcsmOmxBEARBGFcTliAXhPGiKjLzy/PYsNtPY1tAJMjH0UCycE6ZB4uqHGFpYbwMJGt3doVJZnRsmvhZjJcmMUHnqI3mDjJBGCvzFhdw/mVzeOwv2wgFkyiKTDZrMGuBlzPeV8dXP/kUjz+0HUwTh8tCUamdQH+SX/1oLbPnFVM7xzui/WYzBqYJpmEQ6k2iaDKyIiHpuQLcgkoHgc4EpfVuzv3iyBPxkCsoKV7so3jxwd+vTNOk6/Vu9jzdRqIvQX6Dj7o1Ncy5soHNv3qH6O4wikNFT2SRNYXZH1pA3rxCaj60lJZ71hHfE8xtSIa8hSVUXbxov32XX3YSqZ4QyY5Arg+zYaK67ZR/aCWK3Qrx+KiOT5hCTDCNESTIZ0jVudvtHkyIOxwOVFUdnFsDchNUB4PBSYruyL773e/y6KOPAqAoCqtXr+Z73/seAMFgkFQqRVFR0QHrDbQ2fe+xjoRpmsSP0veDRCKx39/TkSRJWE0T0zDBOPhdRodjmsbg37phImNimibmCLY1aKy2M0nb0oeynyl8jJJpkkokBudiGKqZ8PswWuIcjP85GJgXZChEglyYERoqfbkEeWuA85dVT3Y4M9a+CTrzJzmSo1txnp0Ct5X+SIptHUGW1IjbE8dDKJ6mzR8DYIGYoHPEhnIHmTD29ESKbDSFludAtkyvy71wT5JQRwJPiY28srGZZ0GSJD75X8fRsLSYV5/ZQySUYvHyEk5bU8vPvvc6z/5jF4Zh4nBopBJZ2loilFRYCPqTvPh0C8loBa3NYbwFNo45sRSLdWgDk7OXFWB3a4T7UphGrtOIaZpk0ybeIisWm4JhQDKaHZPjPJxtf9nB5tubyCazyJpM91u9tD7dxkk/WMGyb53Enn/sJNoSwrmwkKo1syldVQFA5fsb8Mwvov/NVrLxNK5ZBRSdXIPq2P8OEEdtMXO/8QECr28j2e5H8znxrZiHvVpMoD4THc0V5NXV1ezevRvIJZjnzp3Lk08+yWWXXYZhGPzzn/+krGz4LZkmymc/+1kuueQSenp6ePTRR0kmkySTSdxuN6lUCuCgd3hZrVYgN4nnaGQyGZqamka1jemupaVlskMYMavVytyKEvRUKtcqZYRSqTRyJo3VzN2BoY9iW2O1nYnclmm69v5tDul3aqoeo5RKoWQy7GxpG3z/GK7p/PswVsQ5GN9zMNS7lqfXNyZBOISB6k7RcmJ8iXYTU4MkSTRU5vNyUyeNrQGRIB8nA9XjVQVOPHbRCkSYHvREmu5H38T/2laMZAY1z0HROcdQeOZipBH2gTZNk/53e+l8rYNMJIOvIZ+KM6qwuIf/e5FJ6ux4uou2t/3IikTtqiLqTitCVmTS8SzP3LKVjf/oJB3LojkUGs4t5dyvLsDm1kYU+3vJssTKc6pZec6+gfS1r3WwcW03qiah6QqKKqOoMolYhkBfBpdH4eE7m3j4t02kklk0i0zNXC9f+uHJzFpw5M/CirkeTrmshmf/3IxhmKQSOooqY7EpFJba6doSIRPPsmddgNrFPopnjU9f31hnjC13bUWSIa/OA+T6iId3RWj84xZW/vhESk489JwWnnlFeOYdWFH67ywFbkred9yYxS1MYTOkGnwkTjrpJB588EG++c1voigKV1xxBd/5znc4++yzAWhvb+f666+f5CgPbe7cucydOxeAiy66iI997GN87nOf48EHHxxMgr+3ZcyAgeSXzWYb1f41TWPOnAPnRDgaJBIJWlpaqK2txW4fmwHgiSZJEpq+d5LNEbwWTNMglUpjtVqQNAuSlEtemaN5XY3VdiZwWwMVrZIkDe13aqoeo9WKpGnMnj17RBXk0/33YbTEORj/c7Bjx44hLysS5MKMMJAgb+uPEY6n8ThEMmusRZMZdvdGAJEgnwoaqny5BLkYFBo3YkJaYTpqu+sF/K80oditKDaNTH+E9nteBsOk6NylI9rmtru3sP2uLWTjGQD2PNHCnid2c8L3T8JeOPQL2XQsyxNffZddL/Vi6rkvUZv/2kbDByo489sLeeaWrbxx127sXg13qY1UJMvb9+3B0E0u/uGSEcV+JG27w2QzBh6vlXhHbPA2TFWTSSWzGOkk0fY0FlVBViTsTo2t6/v55bf/xU/uPg/NcuRK8g98ZSFVC7w8+butNL3Wi6JKeH02ujeG0dMGdrfG6/fuZvMzXVz2/WNoOKNkzI+zb30/qVAGT82+BLwkS1jzrfRt6CcVTGPzWcd8v0NlGgbZ/gDIMmq+d1j914WJZyJhmsP/Gc2UqvNPf/rTXHjhhYPJoMsvv5x4PM7f//53ZFnmwx/+MJ/85CcnOcqhkSSJ1atX8z//8z/s2rWL2tpaLBYLvb0HTr440FploNXKaPbpcDhGtY3pzm63T+9zEEvnJmkewcD7wNzPkiSjyBIg5d7zRzOZ81htZ5K2pQxlP1P5GCVpVInNaf/7MAbEORi/czCca0qRIBdmhDyHhcoCJ239MZraA6yYO/ZfLo92TW0BTKDM58Dnmrwv0UJOw3vumjBME1kkE8ac6D8uTDepdj+htc1oHgeqO/dFRXFYSfWG6H1mPQWnL0S2DK8SO9wcYsdftiIpEp5ZucmZ9bSOf1MfO+/fxqIvHDPkbTU+3Ebziz14yuxojtwlaDKYpvHhdkqX+tj4j85ccrwoV82kWRUkCbY83Y3/c3Hyq8b+otmXb0OSoKDATn9fgkQ8i8WikE5nMTImig6KAqqW+wIZDabQrAo7mwJseruHZScfuY2CLEuccEElJ1xQyeaXunnhzzvZ8GgnmFA+30NBlRNJMundFefRGzcze0UBVscYX6LLuc8I02T/FOXeAQFZmbzPkMS2ZgJPvkCmqwckCWtVOb73nYW16tAV7cLkmynJ7pFwOp3MmjVrv8euvvpqrr766skJaJQGKsOj0SiyLFNfX8+GDRsOWG7Dhg1omjZYfS4IgiAIM8koh4sEYeoYTBi2iora8TBYTSuShVPCnFIPVlUmksjQ1h+b7HBmHN0w2NoRAsRrXpg+0t0h9EQKxbX/7bKqy042FCftjw57mz1vd5OJZrAV7tumYlHQXBodL7YP63banc/3IMvSYHIcwOa1oCd1tj/XTTqePaCVis2jkYpnCbbHSUYzbPhnB28+uIc9GwLDvpX3YJavqqCqLo+gP0XdLC9uj4V0WsfQocBnRZUlbA4VVZNRNRmbUyWd1okG04QDw++1ufDUEj78nWMornJQt8xHUY2TbEqnb1eMuD/NrrV+XvjdjgOOzb8ryvZnu2l/N4BhDP+4i48twpZvJd4dH9y2oRsk/WmKlxdh8UzOnXep9i56736I1O5WZKsVyaKR2NZM75//SjYQmpSYBGGm6u/vP+CxdDrNI488gs1mY/bs2QCsXr2aLVu28Pbbbw8uFwgEeOyxx1i1ahUu1/i0ghIEYZoaYaGWJElYrVZx15gwZYgKcmHGaKjy8dT6NtFyYpwMDDwsFO0mpgRVkZlX7mXjHj9NbQGqC8WXlbHU3B0hldFx2VSqxLkVpgnFbUPWVIxUBsW2L+FpJDPIFhXVNYJeoYdIxkpSri3GsLalmwf/EiWBxa6g2RVS0SyaTSEZzhBojxPtSyHLEm89uIeW7wbpb4tjGrnlF55Vwgf/ZwkW+8EvZ/Wswa5NATIpg5oGL46D9DF3ODW++sOV/Px7/2L3ziBul4XCQgcnnlFO29Zu1r8SRs+aaJaB45YwDRMTk+o5ecM6/gGGbmKaucryZDRL+6YQmYQOEmTTBs/+7zY0VebML84jk8jy7I+a2PJEB6loFotNpWyJl9U/WIx3GBX19kIbiz7TwPpfbSK8K5IrIzchb7aHhf+xYETHMRaib65DD0fQyooHvyDLNiuZ7j5i6zaRd+bKSYtNOIKjqAf5W2+9BcDxxx+/3/+PZGD5qeLLX/4yFouFZcuWUVxcTE9PD3//+99paWnhG9/4Bk6nE4ArrriCBx54gGuuuYZPfOITuN1u7rnnHtLpNF/5ylcm+SgEQZhSbE6QFIgNf1DbaprMrShB01O5tj0AqgWsR2cvbmHyiQS5MGMMVHlu7QihG8bQenkJQ6IbBlvag4Copp1KGip9bNzjp7E1wHlLqyY7nBllYKBtQYVPtK8Rpg37rBIctcVEt3VgKfQgW1X0eIpsLEnRuceguob/haNwSSGqXSUdTGPd26Pa0A3SkQyV59QMq+qn9pQiWt/oJ5vSUa253t3paBZZlZl3ZgmmQ+Wtv+whFcsS2BMjkzLABItT4Znf7EC1K9Qc50PVJBKhDOsea6eozsU5X5h3wL52rOvn7hvW07EzjKGb5BXZeN+n53P65XUHxDxvYQE/v/t8Nq3tIRpOUTfPR0GJxg+/9BQOV4J4LEsynkXVZLJZHT1rMrs+n9p53mGfT4CCKgclc9zs2RBET2ZJx3WsTpVMUsfmUrE7VF789Q6ql/rY9mQnr/9mB0bGRNZkdI/Jrld7efyb6/nwn1YgK0O/1pn1/lryZnloe6GdpD+Fd04e1WdXYi+avC+i6Y5uJIu2389EkmWQJDI9B1a7ClPHSHqQT1dXXXUVkiSxfv16LBbL4P8PZWAug6ampgmM8sguuugiHn74Ye6++25CoRAul4uFCxfyta99jbPOOmtwObvdzp133smNN97IHXfcQTqdZvHixfzoRz9i3rwD328FQTiKaVbQM/DSgxAZ3ue2aZjoqb0TvsoSuAvgtMtEglyYNCJBLswYVYUuXDaVaDJLc3eEuWUjq+wSDtTcHSGZ0XFYVWqK3ZMdjrDXwOSRm1v9kxzJzNMkWgoJ05CkyFR96mz2/N/TJHb3YmSyKDYL3hXzKLvkpBFt01ufT8376tj18E5SoRSyKmOkDTx1ecy+bHh9aBdeUsnO57rpeCeArEgMdBGZc3YJdWcUU7WykGzG4JXfNZNO6Gh2BVehFS1PJbQlhJHW0VMGmkXF4bWQjGVZ+3AbZ35mDoq6L1Ec6Enwu6+/RW97jMJyJ4oiEexNct9PNuIrtrP0jAP7hlssCseetO/xeDzOMat8bF+XxBLKkIxnSCV1DAOKyhx85ccnj/iWYFmROe/a+dx9/Tu0tkSRJIlUPIssg8uqEW1Lko7r/PEj/yLRm0Q1cxXzpm4Q7U1icah0rA/S/k6AquMLhrXvgoX5FCzMH1Hc40HN95HcuXu/x0zTBMNE8YrrjansaEqQ33DDDUiShKbl7kL50Y9+NMkRjcyll17KpZdeOqRli4qKuPnmm8c5IkEQZoxIPwQPnNz3sAwDM5kEm230k4UKwhgQCXJhxpAliQUVPt7e2UtjW0AkyMdQ43smKxTVtFPHwOSRrf0xwvE0Hsfk9JCdiRrFBJ3CNGUr9TH3vy8luq2DbCiOtcSHvbZoxMlcSZJY+IUl+BoK6HixjUwkTcExRVSfX4ujZHiTZtq9Fi781XE0PdxOy6u9KJrMrDNKWPD+clSLgmpROP+bDWx+vBN3sRV3qQ2LXaGvPY4kS5gGJMMZbO7c5avFppCMZskkdRTXvi9Wa5/uoK89TnmdZ3DyyaJKJ+07w7z8t5aDJsgPZvZCF1det4iH7thOoDeBYZgUlTn42JeXsfC44mEd+7+bt7KIq3+1nF9+4GWS8SxOrwVNh2RfGkmRkFVIx3VSKRPTImG3yLnJNDWTdCxLvBeiPcPvgT7VuI5bTHxjE5n+AKrXA6ZJ1h9CcTtxHrNwssMTDsVkZC1WpmlblksuuWS//3/gAx+YpEgEQRAEQRgvIkEuzCgNlXsT5K0BLjq+drLDmTEG+4+LZOGUkuewUFngpK0/RlN7gBVzSyY7pBmhP5KkO5hAlmB+hRhoE6YfSVFw149d2yVZkak8q4rKs0a/TbvXwrFX13Hs1XUHfV7RJKxuFSmexWLPtWGxOVUkGYysiaTsS/THghnqjsvH6tz/ctbfGd8b9/6DAjaHSlfL8CYqPefSWZz+vjk0vtOLLEs0HFeEO8960GVTiSxv/6Odza90I8nQsLKE499XicWmHHT5uuMKOPVjs3j7/j3kVzvoXBdE2ltZr1oUVE1GliCbMdF1E8k0ySZ0simDZEan6a+tlB/jJa9yeAMVU4l93izy338OwadfJtMXQJIkVF8evvefg6VsdIMQwvg6mirIBUEQBEGY+USCXJhRBlpONImJOsfUQDVtg5igc8ppqPTR1h+jsVUkyMfKwPtHTZEbp/XASf0EQRg/qkVh4epSXvvjLux5FjS7gsOtYlEVktks2YxBpDdFsCuBZlM58UPVB1THF1U5ARM9awy2XjFNk2QsS8Wc4bftyMu3cdLZhx8cSCWy3P5fb7Pppe5cixBg3VOdbHi+i0/evPyQSfLTPjub9k1B2jeESMd0kEG1yBTWOUkGM8iqhJ4xScV0FN3E0HPbVq0Kra/18cRX1vGBO07A6pq+71Xuk47DcUwDqZZWJFnGWluFbDv4IIQwdUzTYvAx8fjjj/PCCy9w0003HfCcaZp8/etf56yzzuK8886bhOgEQRAEQRgJ0ehHmFHml3uRJegOJeiPJCc7nBmhN5ygJ5Srpl1Q4Z3scIR/MzBo0SgGhcaMGBAShIlhmiapaAZDN/Z7/JTPzGHWiYWEOhP07ojQ1xyjcp6bVVfWYmZNOhvDJAIZ9KTOUz/fxvonOvZb/7hzKiipddO5K0I8nCYZz9K9O4rNpXLqBw9euT5ab/+jnU0vdeMrtVM220PZbA/eUjsbX+hi7ZPth1wvv8rJ1XesYPU363EXW3EVWKg8xou3woGryIqiyiiajGQYueT43gR6xRIv3joXPY0hmp/rGZdjmkiKw46jYR72BXNEclyY8u6+++5Dtq2SJAlFUbjrrrsmOCpBEARBEEZDVJALM4rDqlJX7GFnd5jGtgCn1A+tz6hwaAPtVWaVeLBbxFvGVDPQ9mZre5CsbqAqYtxztJraggDUV4gEuSCMB9M02faPTt69u4XQnjj2fAsLL63kmCtrUTQZV4GVj/72eLa/1EPXlgj2PI0FZ5UQ7kvRsi5AfpUDX4UDSYJAR4IHv7WeYFeCWccXULkwD0++lc/dfAJ/+fEG9jQF0XWD4moXF3xmPotWjs+dNo2v5SrHrY59n5M2h4ppmDS91sNJF1fvd/z9u2PoWZPCWieufCunfXoO2ajOq7/bSTZpkLXpyKqE6lCwSCDFspgSaDaZwlkuHPmWXILOBP/OA9vGZGJZjKyBxaONuP+8IBzO0dxiZefOnZxzzjmHfL6+vp4XXnhh4gISBEEQBGHURLZLmHHqK70iQT6GxGSFU1tloQuXTSOazNDcHWZeuXeyQ5rW0lmd7Z0hINe+RhCEsbf5wVae+fZG9JSBPV8j3JHllZu3EulKcto3GgDQbAoN55bRcO6+z/EX/9BMMpKlZK4LSZIwdJN0MkugNcG933gXb4WD2mU+Lv/+EmrqvXz9j6fQviNMJmVQMceNxTZ5l72dW8O8fu9utr7YQ7A1gWma2D0aRbNcnH3tPOafVsypn59DNqWz8e8dhDoSaDaF5VfWcPyHqvnnN9YTaI5RvMCNZW8S3jRMTBMcBfsmaI52xNn4+620vdKNaULRIh+LPjGXoiX5k3Xowgx1NCfIY7EYmnbotkaKohCNDm++A0EQBEEQJpcoNRRmnIGkVlOraDkxFgYn6BTtJqYkWZJoqPQCsFm85kdte2eIjG7gdVooz5++E98JR6FUhmRzB8nWfT2wp6K+LWGe+PI7hFvjJPwpgrtipMNZLC6FLY+0E9wTO+S6wY44ikUerIjubYkR7EwiyWB1qNjzVLa+2st931qPYZhIkkTl3DzqFvnGPTm+6JRSJEkiGcsOPpaMZZAUiaIKJ3/43Fu8+udd7H7bT/+eXNzxYJr2jUH+8p/vsO2VHjSbwnnfaOAzD63iqjtO4FMPrOSynx9L7UmFnPpfC3Dla6SjWUzDRM8YBFtiuIqtzD4rVxWfCqV56etvseORPZi6iQS0vdzFS998m8D20Lgev3B0MUfxZyYoLy9n48aNh3x+w4YNFBeLSWYFQRAEYToRCXJhxmmoylVJbe8Mkc7qkxzN9JbM6OzsDgOimnYqG3jNiz7kozcwyLCw0ifaEgjTgmmahF5aj3TPK3Te+jAtN/+F3bfcR7K9d0L2nw6naXuujZbHWwjvDh92WSNr8NTX3yURyKDaFTSniqRKxHqSpMIZUqEMfVsjh1y/ZK4bPW3sTRCbhLoTyIqEJEtYXQp2t4av3E7LuwH2rJ/Y98Pjzq9gyZmlBLsTdO4I07kjTLAnydKzyujbGiHYmcDu0jANsHs1FItEtDNJojtF35YIf/zI6zx1UxPpRBZPiY2a4wsoqHUObn/+BeUc96nZyBIEmqOEWuN4Kuyc9T+L8VTkBvP2PN+Jf0sId40Le6ENW76VvDo38a4E2x/aPaHnQ5jpJDBH8IeZ8bl6+umn8+ijj/L8888f8Nyzzz7LY489xhlnnDEJkQmCIAiCMFKixYow45R67ficVgKxFNs7QyysErcVj9S2jiC6YVLotlGcZ5/scIRDGBi8EAny0RtIkDeI9w1hmgi/vQX/31+DdBqlKB/ZhPi2PbTf/hi111+B4rCN2747Xulg/S/WE++OgwGaS6P2gloWfW4RsnpgDUbnugCBXREUa+45CVBUGdOAaHcST4UDq+fQbQuOvaiSdX9vp3tnFHueRjaVS5ZrdhWH14KeMbE6FYIdOqHuiZmou6clymsP7mbnW/3Y3ConXVRFOqUjKzL1Jxex6JQSfvb+l3DlW4j707njliTkjImeMtElA0WTSEWz/OuOXWQSOu/77qID9iPLEiddO4+GD1TSvSGIalOoXFGAxbnvUj64M4JpmijavnMvyRKqQ6Vvk/h8EMbWSFqszIz0OHz2s5/lySef5Atf+AIrVqygvr4egMbGRt58802Ki4v5/Oc/P8lRCoIgCIIwHCJBLsw40t6WE69u7aaxLSAS5KOwuTUI5NqriGraqWt+eR6yJNEXTtITSojBjBEyTZPGVj8gWgoJ00fgpfVg6OBxIGsqiqIga/mkOvuJbmwmb0XDuOw31hlj3U/WkQqlcFW6kBSJVCDFjgd34K51U3dB3QHrJPxpTB2cRVYiXUlkRcpVgGOSTRrkVTkoP/bQv3sls918+CfLePJnW+jcEkGSpb2FqSZtm0JIsoTNpeLwWSiscR5yO2Olc3uY//vim/TujmGxK+gZE0mGlR+q5dL/XoQkSWRSOrICmZSJasklrg3dwEiZIIFskTGyJnafBbvPwubHOznpP+owkgbZtE5elQObe99Em3mVDvIqD97+aWBwwTTN/T6z9ZSOvXD8BkqEo9NIOjnNlCvJ/Px87r33Xr73ve/x8ssv8/rrrwMgyzKnnXYa3/nOdygoKJjkKAVBEARBGA6RIBdmpPpKH69u7c71IT9psqOZvgYqkkWycGqzWVRml3rY3hmisTUgEuQj1NofI5zIYFFl5pTlTXY4gnBEpmmS7vYjWa28t7uvpCoAZPyHb3kyGh0vd5DsT+KudSPJubSXLd9GJpphz5N7Dpog99U5sbpUrIpENmWQCmXIGiZ61sTmUTnnh0v2q34+mFnHF/C5u06mf3eMh3+wkVfuaSGTMbDYFLJpnWBXBrtLpWSOa1yO+72e+8NOenfHKJ3tRlZy5yDqT/HGQ3s44aIqqhd50awKC88p47W7WvCUWFGtMpmYjmSYSBKYuomsSOSV2rC5NXp3RLj/k28S3hEjEUgjK1CyMI+VX5pPw8UVhx2srjqjjC337SLaFsdZ7kCSINGfQlZl6lZXjvv5EI4yR/EknQBlZWX89re/JRQKsXt3roVRTU0NeXni+kEQBEEQpiPRg1yYkRqqBlpOBKf0ZGVTmWGYNLUHAUQV/jSwsEq0WRmtgerx+eVeNEV8PApTnyRJWEryMZOp/R43986/oeV7xm3f6eDediHy/kkyxaqQ6E0cdJ38uW7qziolHcrgKbFQMMuJI99CXqmNc29YQtnSfYOxyXCazQ+3sfGBVkLt8f22I8sShbVO9JSJ22fF5lIxTVA0mfxyBwoSzW/4x/iI96dnDba82oMzzzKYHAdw+iyk4zrNa/sHHzvzs3OoWeYj2p9Gc6m5oQwTJEVCtcgUz3HhzLcQD6SJ9aToWhck1pvE1A0ySYP2t/08+Y31rLuz5bAx+eZ4OP6/FmHNsxDZHSXUEgWg4aOzqTm7fBzOgiAIeXl5LFmyhCVLlojkuCAIgiBMY6KCXJiR5pblocoSgViKrmCCMt/Bb0cWDm1Pf5x4KovdolBX4p7scIQjqK/08fCbLSJBPgr7+o+LOyaE6cN36jHEmtshHMew2iCVIRuMYq0owrV41rjt1zPLA3KufYdizVWsm6ZJJpahbFXZQdeRJIlTv7UQZ6GVbf9oJx3LUroojyVX1rLwQ9WD23jtV9t58cYtxP25xL/VrXLsx2o59/tL0Gy5fWVTBn27YpTWurB7NdJJHdUio1kUenZE6W2OMm9V0bgdvyRLKJpMKv5vk4HvHZN/bw92T7GNT92+gs3PdNG5JYzFobDjuV5a1/rxVjhw5FtIhrMEdsdQZZANA1QJ1aZgAplYllQkw7o/t7Dwkkqs7kP3aa87v5LSEwrperMPPWNQtCSfvNrxr6YXjjLmyHqQM8NqVpLJJG1tbQSDBy/IOf744ychKkEQBEEQRkIkyIUZyaIqzC3Lo6k9SFNbQCTIR2BrZ+7W/PpKH4osqmmnuoGJOnd2hUmms9gs4u19uBpbRUshYfrxLF9Afn+Qzn+8ih6KgqbinF9NyYfOHNcJOstOLiO/IZ/+jf1Y8izIqkzSn8TmszH7A7MPuZ7FqXLSVxZw3GfnkAymcRZZUSzK4PObH2rjme9tIh3Potlz72OpSJY3ftuMq8TGadfnJsNTLDJOn0agPYGr0Irdlfuc0jMGSODMt4z5MWczBul4FptbQ5Yllp5XzrO378CVb0GzKpimSaArgcNrof7fkvNWp8qxF1XCRbn/r/pYHY99bzM7XuqlvzmKxa5QUOUg3ZUkG86g2vZNZCopEqYJsb4U/uYoZccc/j3KXmCj7nzRUkUYX5OR647FYtx+++1s3LiRjRs3EggE+K//+i8+85nPHHHdtrY2zjrrrIM+94Mf/IDLLrtsyHGkUil+9KMf8de//pVsNnvA8wPzADQ1NQ15m4IgCIIgTC6RQRFmrPoqH03tQRrbApy5uGKyw5l2BhLkCytFsnA6KM6zU+ix0RdOsqUjyNLawskOaVoJxlK0+WNAblBIEKYLSZLIO/UYOjwSZZ5CHF4P1oqicZ9YWXWonPj9E2n8QyOdr3RiZA2KjytmwVULKFh05MnpLE4Vi3P/y1DTNHnjtztJx3UsTg1ZzR2DrEqko1nW/Xk3J35uDlZXLkF93CVVPHnzFqL9KZz5FrJpA39rnMIaJ/NPKx6zY00ndV78QzNvPdRKMpIhv8rBKVfVccbVs2h5N8Cud/0YRi5d6PBonH/NfIpqDl+1bc+z8MFbltKzLUKgLUFemY3dL/fy4o1NZCUwDXOwfY2pmyiahGKRD1s9LggTaUQV5KMUCAS49dZbKS0tpaGhgVdffXXY21izZg2nn376fo8tW7ZsWNu44YYbuO+++1i1ahUrV67E6/UOOw5BEAThIMb5+lUQDkckyIUZq6HSx9/YNVgVKgzP1s4IAAurRf/x6WJhpY8XGztpbA2IBPkwDbxP1BS58NjHvvJUEMadVcM2qxybY+LumLIX2Tnua8eRuSaDkTaweC2jSszraYNgWwIkBpPjkGtnIskSyXCGWF8aqyuXJD75o7X4W+Os/0cHPdujyKpE8Ww3l35/CfYxTCQ/csMm3niwFc0uY3WotDeGePA7G7jkO4v57G9XsOHpTtqaQthcGovOKKFm8dAG2SRJomS+h5L5uV7xdrfGu/fsJhlIk0kYqHYwMmauilw38VXYsDiVw290GskGQiR37MI0DGx11WjF4nNrOpmMBHlxcTEvvfQSJSUlh60IP5z6+nouuuiiUcXx9NNPs3r1an7+85+PajuCIAjCe9icICkQC41+W6oFrPbRb0c4qogEuTBjDbSc2NUTJp7K4rCKl/tQ9YYT9EVSyJLEggrvZIcjDFFDVS5B3iT6kA/b5raB9ipiQEgQhktzauAc/XYUi0xehY3ArihG1hxMkpuGiWmY2L0aVpdKz5YwVrdKXoWDi7+7mJM/WktHUxi7R2PWCQWDfcrHQveOKO8+0YkzX8OVbwVyE3H2tsR48U/NLLugghUfqGbFB0a/L2+Vg/N/fAxPfXsjHW/7yUR1ZAWsiomSzhLeFuZvl71M/WU1HPf5ufv1OZ9uIq+vJfjP59GjuTt3ZLsN96oVeM85bdzvfhBGzwRyQzfDX280LBYLJSUlo9wKJBIJFEXBYhnZgHgsFuOkk04adRyCIAjCe2hW0DPw0oMQ6T/y8ofiLoDTLhMJcmHYRMZQmLEK3DZK8ux0hxJsaQ9y7CxRmTRUja1BAGaVuLGLXtbTxsCgUGNbAMM0kUWSYcg2t/qBfedQEISxFdwZpv3VbrJJg8KFXspWFB2Q4JUkiRM+PZv2d4KkY1lUm4wkQSZhIKsSZUt93HPlv4j2plA0meoTCzjjawsonu2mePaBk0n7W+P4d8dwFVkpmeceUeK1e2eUZCRDSen+LVNc+RYC7QnCPUnyK8auar/mpEI+8fiptL3tp+31frbcu4tEXwpfnQvNrpDwp3n39h3Y8i0svrJuzPY7kTJtHYT+8QymkUUrLgBJQg9HCT//CpayEpyL6yc7RGEojMkOYGR+/etf89Of/hTIVZNfd911nHnmmcPaxrx58+js7ByP8ARBEIRIPwR7JzsK4SgkMl/CjLawykd3KMHmVr9IkA/DvmpakSycTmaVeLBqCtFklta+KDVFByaMhAOlMjrbO3K38onXvCCMvW0P7uLdX28hFc4AufYplaeUcPJ3j0X7tz7kiy6tItQe5+VbthHvT4EJtjyNWWcU0/pOAEmScBTk+o1v/Wcn0Z4kH/7TCtT3TPSZjmd58sdNND7ZRSqWRbUp1K3I54LvLcJTPLyJS+1uDUWTyaaM/SrT00kdzSpjc439pbRqUag9uQiSOk13GhQu8KBac/t2ltgIt8ZoemAPCz9cg6xMvyryZNN2jEQCrXRfr3w1z026u5f4u5tEgnyG6+zs5Etf+tIhn3/22WfHZb+yLLNq1SrOPvvswRYtf/zjH/nCF77ALbfcwpo1a4a8rWuuuYZvfvObXHbZZVRUiHmOBEEQBGEmEAlyYUZbVJ3Pc5s62LTHP9mhTCuNexPkopp2elEVmfnleWzY7aexLSAS5EO0rTNE1jDJd1kp801c/2ZBOBoEd4Z599db0DMGeXUuJEkiE8vQ+nwX2xe30HDVnMFlDd2g400/NlXm9P+ci2xVcFfaqT21iIeueQfTMPHV5Xq5WJyg2RS6NoZoeaWPOWfua7vw3P9uZ+39rTjzLfiqHaRjOlue7cbUTT7ym+XDqiSvW+6jdK6b9qYQhTUOVItCMpohHsiw8spaHHnjN2dBrCeJaTCYHB+gOVUS/SkycR2re/olyI1YHCQO+DlIijLYckWY+szR9kuZYOXl5dx+++37PXbxxRezZs0abrrpJs4///whvzds2rSJqqoqLrjgAs455xwqKyuR5QPviLnmmmvGLH5BEARBEMaXSJALM9qivRNMNrUHyeoG6jSstJposWSGlp7cBJ0Nld7JDUYYtoZKXy5B3hrg/GXVkx3OtND4nvYqovetIIyt9le7SYUzg8lxyPUsly1pWp5qH0yQZ+IZnvvmena/1IORMTEBzaGy+KO1aHaV4J441n+beFOzKxiGSbA1PvhYPJhm42Md2DwazoJcz3B7ngwStLzlp7MxTPnCvCHHr1pkLv/BMfzlG+/SszOCaeYeazijhNXXzR/l2Tk8d4UDSZXIJLJo9n2X7Klwlvx5bizO6XkZr5WVkFq3CVPXkZRc8t80DMxMFktN5SRHJwzVSHqQA5SVlY1blfhweTweLr/8cm699Vaam5uZPXv2kNb71a9+Nfjvv//97wddRiTIhSknlYBsevTbkeTpN0ImCIIwBNPzyloQhqi60IXHrhFOZNjRFWJBhaiIPpKm9iCGCcUeKwXu4d2KLky+hr0tQhpbxUSdQ7W5VbQUEoTxkk3oSBxYLSyrMuloFtM02fFYO6/dtJmuDSEUTcZVaiOvxkkqlGXjn3dRfnw+rmIb/c1RnIXWfdtOG0iShKtk32dVpDdFOpbF7t2/stvqUon0JAl3JYeVIAeobMjjP+9bydZX+4j2pyie5aLuuHxkeXQDatm0TrgtgcWl4jpI65eKEwspXuyl820/jiIrqk0m4U8jybDww7VIo9z/ZLEtWkD63U2k2zuRnQ4kSUKPxlELfLiPXzbZ4QlDImGaI3n9Tb3XbHl5OQChUGjI60yVBL8gDEs2DS8+MLrJDwFKZ8Fx54xNTIIgCFOISJALM5okSSysyudf27rZuMcvEuRDMDBZ4fwyzyRHIoxE/d62OG3+GKF4mjzH+CVVho8AAQAASURBVN3+PxMYprmvpVBV/iRHIwgzT+FCX64KOpZBc+YqwE3DJBPNUHd+Bc1PdvDaDzfib4kjSbnCtEh7gmzSoGiRF//2CK2v9HLM5VU8e0Mjke4kjgILesog3JGgaJ6bWacUDe4vr9SGzaORDGewvqfCeuD/vqqRtVGy2FUWn1065OUzSZ3ebRFUi0zhPDfB3TFa3/AjSVB5Qj49m0K8/fudRDqSyKpEzapCVn5lAe4y++A2FE3mrB8v4183N9L+eh+JWBZ7gZXFV9Ux/+LpW2ktOx0Ufexy/H97jPjGRsikUQsLyDv9JLSigskOTxgKc4QFpFOw6LS1tRWA/PyhXwOIvuPCtDUWkx+6xfWyIAgzk0iQCzPeoupcgnzTngCXnTTZ0Ux9A9W088tFgnw68tgtVBe62NMXpaktwInzSo680lGstS9KJJHBqsrMKRWveUEYa2UnFlGxqoTWF7pQLGlkVSYTzeCqdDLvsjpe/u4G9IyBalfQMyaqVcFQDFLBNKlQGjDJJHROvLyKaG+K9fftIbg7jmKRKD/Gy7n/b9F+rUZsbo1jP1jJi7ftINSVwJ5nIR3LEvOnWHxBOcVzXeN+zE2Pd/DarTsIted6bauqjJHU0bP/P3v3HR9FnT9+/DXb03sCCQmhJRASuiCIhaIggiiKihTxFLue53mWa3rfsxx+7/R7J3p6PwVPwePsvYIFAelIC72lQXpPts78/ghZCUkgZZNNdt9PH/vAzMx+5j2Tzc7Mez7z/vxclMJZ6yQg1EhgtBmXQ2X/J3lU5NUya+mYBjXHg3sGcOnfRlKRU4O90kFoUlCbS6vUFlRTnVWOMcRM6IBIr/ZAd+SdwJmXi95iRAkJBLuN8i++Rmc0EjRKepF3B20tsdIZSkpKKC0tJT4+noCAuptOZWVlhIeHN1iuqKiIt956i8TERJKTkzs/UCGEEEJ0GZIgFz4vPamuR+2e7BJUTUMnNYab5XSp7MstA2Cg9CDvttJ6RZBVVEVmtiTIz8V9QyghXMYoEKID6Aw6Lnh8BAfePcaxL3Nw1DhJnprAwOv7EhBlpiK7GkuYEQ0Fa5kDTdXQGXS4bE6s5XYUvY64IRHo9DrG3t6PmORA8jPLiU4NZeCMePQGfaN1XnhbP1QVtr+TTXWRFYNFz8jrkrjswdQOH2cga2MxXz2+G0etSkicmZpiG/m7yzGYdCSOjsRg1pO9sRh7tZPwxCB3XXVToIH8XeUcW1NI/0sb91QP7VXX891lc7H/rcMc/yIHW4Wd2BHRpFzbh4iU8GZjUh0uDv57J7lfHcZZZUdn0hOWEknafWMI6tX5x3rN4aTy629RbTaMPWLdvxNnUQnlq78jICMNndl8jlaEt7WtxEr7LV++nIqKCior68bL2bhxI06nE4D58+cTEhLCihUrWLJkCa+//jpjxowBYPHixeTn5zNixAhiY2PJzc3l7bffpqKigr/85S+tjsPlcrFq1Sp27NhBeXk5qqo2mK8oCk899VQ7t1YIIYQQnUUS5MLn9e8Rhtmop7LWQVZhFcmxId4Oqcs6nF+BzeEi2GIgITLg3G8QXVJaYgRf/JTNnhypQ34ume764/K4qPAfrhobKAr6gM4pwWQMMjB4QX8GL+jfMA6HiinESG2xlZCeZqoKbdgrnSg6cDlVbGUOek+Io++lPSjYU863f9hByeEqVKeGMcjAiU3FXPzH9AYDWAIYTHom3ZfC2AXJlOXWEhxjJrSJGt8dYdd72dgqnUT1rxuU1JFVg96goKka1UV2whICQK2rM1FVaCUwsu53YLDo0VSN8qzqZtvWVI1Nf/mJY19kozPo0Jl0HP7wGCc3FHDhM2OIHBje5PuyPtzP8ff2YggyEhAfgmpzUrKjgF3PrGf0Xy9FZ2p8k6EjOQsKcZaUog8NbXDDQh8WgrO8AnvuCSx9kzs1JtF63hqjb+nSpeTm5rp/Xrt2LWvXrgXgyiuvJCSk6fP8Cy64gJUrV7JixQoqKioIDg5m+PDh3HHHHQwbNqxVMZSXl7Nw4UL27duHpmkoioJ2aofU/78kyIUQQojuRRLkwucZ9DoGJYTz07FidmeXSIL8LOp70w7qFSE97buxtFN1yA/kleFwqRilZ3Sz9uTU1dyXATqFP6g9XkDBJ5uo2p8DCoSkJxM3YwzmHt75/OuNOgZcmci2F/fhrHHRIz2E0uO1VOZZsYSbGPPAQIbM74NOr/DN73dQfKCS0MRA9GYdtnIH+z/KJSQhgDH3pDbZfmC4icDw1t8EqCmxse2dbHZ9VcCxeJW0qQmkXNYDveHc36VFh6oxBujdiV/VoaIodclEp9WFooDerIcaFy7Hzz1O6/8/MKb5RH7hT8Vkf5tHQJQFU2jddmkxGuVHK9j3n0OM+9OoRu9RHS5yPj+EzqTHHFXXC11nMBEQr6PycAnFP50kZnTn1lNW9HrQKaA17HGrqRooSt18IZrxzTffnHOZe++9l3vvvbfBtOnTpzN9+nSPxPCPf/yDgwcP8uc//5kxY8Zw6aWX8uqrr9KjRw9eeOEFsrOzWbp0qUfWJYQQQojOIVkT4RfSk+p6h+7OKvFyJF3bnlP7Z3AvSRZ2Z72igggJMGJ3qhw+WeHtcLqs4koreSU1KPw8uKkQvsp2spTjL3xM2eYDdRNUjdJ1mRx/4RMc5c33Wu5oGQv6knJ1Ek6bSmVeLeYAHf0vjePGzycw+p5ULGEmstYXUnK4itCkQAyWuuSzJdyEIVDPtleO8MEtG3nnxvVseH4/lXm17YqnqtDKO3ds4Ye/HaRgey0Hvirgk4d38PWf96Cq5+4yG9knCEety/2zOdSIpoGmaRjMOlAUgmLMaC4Ne4mN4l0lVBytpOxIFeFJQfS5OKbZtov3luKyuTCGGAENR7UDe6UdY5CB/C2FqC610XucNQ7sFTb0AcYG0/VmA6qqYSuqafnO8RB9bDTGHj1wllegnSpLoakqrrJyjDHRmBLiOz0m0QZaG14+4ttvv+XKK6/k2muvJTi4blwDnU5Hv379ePbZZzEYDPzf//2fd4MUQgghRKtIglz4BUmQn5umaWSeKsmR1ivcu8GIdlEUxd2LPDNbPvPN2XXq+6BvXCjBFuM5lhaieytZuwd7QTmWhCiMYYEYw4OwxEdizS2ifNMBr8VlsOi58LGhzPj3OCY8NYJL/3EeVy6/gJjB4e5lrKV1tcn1ptNOWzWNmgIbJYcrydlYRNH+Sjb/8xAf37mZipy2J323/ec4eTvLCE8KJDjBSFS/IAIiTOz+MJfszef+Ps2Y1QtTsIHS49U4rC5MIYa6vKAGik6hMr+W8pwa9AbQOVzUFlipOFoJVgcXPTIIS1jzPd71Zj1odQN8Fu8poXhXMSWZJZQfrsBWYnOXeDidIciEJToQZ7W9wXRnrQOdQUdAz85/qk7R6QifPgVDRASOgiIcJwtwFhSiDwslfPrlKE3UlRddi0ZdDfJWv7wduIcUFBQwdOhQAAyGugey7faf/8Yuu+wyVq1a5ZXYhBBCCNE2kiAXfmFQQjh6nUJhhZX8ss7vLdUdnCitoaTKhkGnMKBnmLfDEe1UXzIkU+qQN6v+hllGb6k/LnxfzZGTKCYDiu7n8lnKqfJLtccLvBWWW0T/UPpNS6DXuFj0Z9TEjugbjMGix17pdE+zljqoKbZhCjIS2S+EsMRAIvsGU3ygkp1vHm9zHIe+KcAYaGiQjA8IN+G0qWRtLD7n+5PHRTPpt2kEx1moOmnFVu2k9/gYhlyXhCnQgKvWhdkEvYeHkXxhDD2GRxA3NBxzkJ6SveVnbTt+XBymMBNFO4uxlVpRDAqKQYfq1HBUWjn20bFG79EZdCTOSAFVo/ZkFc5aB/YyK9YTVYQPjiUiI7bV+8gTzEmJxN7+CyKumELwuDGEXT6F2Nt/gaVfH6/EI1qvLQlyXxESEuJOiAcGBmIwGCgo+Pl71GKxUFZW5qXohBBCCNEWUoNc+AWLyUD/HmHszytjd1YJceGB3g6py6nvTZuaEI7ZqMdu9XJAol3qe5DvyS51DxYlGtp1/FSCPEkS5B3ppZdeYufOnezcuZPCwkKuv/56/ud//sfbYfkdY1gQmtPVYJqmaWga6EO69qDMPYaFk3RBNIe/zsdR68Jg0VN6tAqA8D5B7u83nUGHKcTI8R8KGf/QoDatS9EpjUpBNNUz+2wyru5F6mU9yN9bgd6oEDc4DJ1e4eSOMjY8s4ec9VYCQowYLHoMlrqbAS6ri+Pf5TPi9gHNthscH0TypfFsyyxBUXS4bCooCoGxZgJC9Rz58Ah9r+rb4CYIQK/L++OyOsn6cD/2Mhs6k44eE5JJvW0EuhbUVe8ohvAwQi66wGvrF+3jK73B2yIpKYnjx+tuxOn1egYMGMAXX3zB7NmzUVWVL7/8kp49e3o5SiGEEEK0hiTIhd9IT4qoS5BnlzJpSC9vh9Pl7Dxe1zNOkoW+ISU+HINOoaTKxonSGuIjg7wdUpdSUWPnWGEl8HMJJtExnnvuOaKiosjIyOC7777zdjh+K3xMKuXbDuEoqcIQEQSahqO4EkOgmfBRzSdluwJFUZj456GEJBzk0Od5OK0uAmMtoFMIjDE3WFZzaeiNbb8hmDI5jh/+cRCn7ed63rWldowBenqPjWpxO6YgA4mj6r5bVKfKj4v3sP/DHCpzarCW2Tn5UwlhvYMJiT91c0KjRc91hvcJISzejD7QhOrSMAUbCIg0YS+3Yy224qx1ojfrKdqcS9GWE2iqRkRGLIlXDKDXlH7UnKzCGGwiIC64NbtFiEZ8qUd4a40dO5Z33nmHRx99FL1ez5w5c/jjH//I5MmTAcjNzeXBBx/0cpRCCCGEaA1JkAu/kZ4Uybsbjkod8mbU96Yd0rvlCQDRdZmNelITwtmTXcqurBJJkJ9h96na7EnRwYQHmc+xtGiPVatWkZiYCEBqaqqXo/FfIUP7EDt9NEVfbcOWV/f5N4QEEnf1WAL7dY2ejpqqUbC9iMKfilF0Cj1GxxKZFo6iKJhDjYx/KI3Rdw3AXuWk6mQtH92+hZoiO0ExZhRFwVHrwml10X9K27dn+A1JHF1fRPaWYux2BzZjFSaLgWE3JLkT3q116PM8Mt/KwhRqILxfMIW7y1DtKuVHqzCHGNAZdbjsKn0m9jhnW4E9AzEE6AmIMWGw/Hwa76hyENInBJ1Rx94lm8hbdRTVWZfkz/3yMPlrssh4eByh/eSGoPCMVj5Y4VMWLVrElVde6X665LrrrqOmpoaPPvoInU7HDTfcwC9+8QsvR9nYzp07+eijj9i4cSM5OTkEBQUxePBg7rnnHjIyMhosW1RUxOLFi1mzZg12u52MjAweeugh0tPTvRS9EEII0bEkQS78Rnpi3UVhVlEV5TV2wgKbHwjL3+SX1ZBfXotOURh0qjSH6P4ykiLZk13KzuPFTBmW6O1wuhR3eRWpP97h6pPjwrsURSFuxhjCx6RSvT8XRa8QPCgJY0TX6EmsOlU2P7OD41/m4LLVlYLJfOMgqdf3JeO2Qe4yKqZgI6ZgI0FxFobf3IdtS49ScrAK0FD0OhLHRZN+Q1Kb4wiMNHPtP0fx03vH2L36CLEJMaRdlkDfi2PbXKrq8Ge5aKpGQKQZNAjuGUhlXg2OWidF+ysIiDQTf14UA689d9yxo2KJSI2geHcxAdEB6Mw67GV1tZD7Xd2P4q0nyFt9FGOYGWNI3c0/Z62Dwo25nPjmGIlXdO2nBUT34cf5cYKCgujbt2+DaQsXLmThwoXeCaiFXnnlFbZu3crUqVOZP38+FRUVvPXWW1x33XW89NJLXHzxxQBYrVZuuukmioqKWLhwIaGhobz55pvMnz+ft99+m/79+3t5S4QQ4hykvKhoA0mQC78RGmgiKTqYrKIq9mSVMG7guXtq+Yv6+uMDeoYRaDbgcrnO8Q7RHQzpHcXKdYfdyWDxs/rPvJQUEv7GHBuOOTbc22E0cvyrHI5+loU5zExwQhCapmEtsbHvP4eJSo9EZ9ChOTWi0iMwh5lQFIVRt/cn4bwojq8pxGF1EZcRRt9JcRgD2nd6awkxMmR2Asb0CgYNGohi1WMttRMQaWpTktxa5kBXP+inApH9QwiIMFFysJKolBBG3pVKvyk9MQUbz9mW3qRn9OOj+en/fqJoRxFqpYo5wky/a/qRPD2ZfS9uQXWo7uQ4gCHAiE0HhRtyJEEuhAcsWbKEyy67jJSUlCbnHzx4kC+//JJ77rmnkyM7u4ULF/LXv/4Vk+nnTkKzZ8/miiuu4P/+7//cCfKVK1dy6NAhVqxYwahRowCYNm0aU6dO5dlnn+XFF1/0SvxCCNEiliBQ9FB99sHPW8xgAnPXHq9HeIYkyIVfSU+KJKuoil3ZkiA/3c/lVSRZ6EvSEiPQKQr55bWcLKuhhwxOC0C1zcHhk3UnTFJ/vPvSNI2amhpvh9FpXGWlOPZmopaXowsPxzQoDV1YOAC1tbUN/u2Ojq7KRnWqGIL17pu0pnAjJZnVfHPfj+iMOjRVwxJlZuD8vvSZ2QtFUQgfZCF80M9PKTg0O44ae7vjqa2tpTrbztevbKdwRyVoEDskjGGL+hCdFtqqtqIygsnfWYLT4XQPoKkP1BEQbWLo7X3ofXk0Thw4axwtak8JUxj2x2FU51bjqHQQnBiMMdhIbW0tdpsdTVUb3ejWNA271eHVvxlf+Jx2NS3Zpx0yULfWxhrkPtLtfMmSJfTu3fusCfIXXnihyyXIR4wY0WhaWFgYY8aM4auvvnJP++yzzxg4cKA7OQ4QERHB9OnT+e9//0tVVRXBwV3j6SMhhGjEaAaXA9a8A5XF7WsrJAouni0Jcj8hCXLhVzKSIvlsW5bUIT/DzqxTA3RKgtynBJgMpMSHsS+3jF3HSyRBfkpmdimqBj0jAokJlZOd7srhcLB3715vh9EpDAX5BG9ch+605Kb6w/dUnX8BzugY97Rjx455ITrPKMorwu60U1VV6Z7msqrUFFrRWXQE9rOg6BQqCyvZ/NxOCm35hA7puLEVbMVOMv9WSG2eA0OYHkWBQ6uqyd1VQPpv4wjsee7e3vWUdDu6CI3CfaUYQnRoKriqVUJSzNT0LGXv3nb2cMr++X9rQmuxOx04C8vQWfQAaA4VZ60dS7zaJf5muvPntKs61z49vcewJ2gobUqQa/jHI+82mw29Xu/tMFqsoKCAiIi6EouqqrJv3z5mzpzZaLkhQ4awfPlyDh48yPDhwzs7TCGEaJ3KYigr9HYUohuRBLnwK/W9RQ+dqKDW7iTAJH8CxZVW8kpqUPi5TrvwHRlJkezLLWPn8WIuHdrL2+F0CVJepW02btzIggULWrTssmXLGDduXIfGYzQaW1cH1VmKvvRLdFVb0NChhYzBFXEZ6EM6LkgP0FwuKtevweVS0cUnoCgKmqaiFhURfOQgwReMx2qzcezYMZKTkwkI6J43fZQJJjKPHCTQEoTOUFeOpKKwClSF0KQQgiJPbVc4VByrgr1GBl0/qMPi2frKQWpOOIgaEIrRWHeuoPXQKD1aDfuCGDSxX8sbGwR9e1eyZ0UWJzaWoDMoJM2OZfC83gTGeHaQYLW/i73ZOoo35oFVA50CLogYlsDgG8/HGOK98Vdqa2u7/ee0q2nJPj106FCHrNvfBumsqqqioqLC/XNZWRl5eXmNlisvL+fjjz+mZ8+uMfjxuWzdupXNmze766eXlZVhs9mIiYlptGxsbCxQl1BvD397Aux03nqSRlEUzJqGpmqgqu1rTNXQoaFpGlob2tI01f2vq51teSomb7flasl6uvk2NqXhZ8GDcXl4+xRNw1Zb6x6Y2ZPk6bqO3weteZJOsoPCr8SGBRAbFkBBeS2ZOaWM7Nv45M/f7Dxe13u8X49Qgiwt7xEnuochvaN4+8cj7qSwkAE62yo5OZnHH3+8Rcv269eK5GEbKYpCYGDLnorQHGVoWc9AzX7QmQENig5htO1G6fsYir7rPl1hz86CslIMEeHoDPWnbXrU8Ai04iJMlRUop0qtBAQEtHifdDUDZ/Unf20RpfvLMAYZ0VQNa4kdvUVPcFwQOv3PJ7bGACO2fHuHbmvVMTuKAkaj4eeeoHowmg2UH6xp9boDhwaSMDQOl92FolPcNwE6wojfXsSJb45RsCEHzakSPSqeHpf0Rm/UoTcbUfQdt+6W6M6f067qbPvU4+VV6vlZgvy1117jhRdeAOr26VNPPcVTTz3V5LKapvGb3/ymM8Nrk+LiYh588EHi4+Pd5WBsNhvQ9FMHZnPdDT2r1dqu9frTE2DN6ewnacxmMwMS4nDZbGjt/P3pHHbMGtjtdlztaMtms3usLU+105ltaVrwqX+1Fv1NdcdtbCmbze7RuDy5fYrNht7h4PCxHPf3Y0eQp+s6dh+09Ek6SZALv5ORFMnqXbnsPFYsCXJO603bO8rLkYiOMDgpAp0CJ0prKKyo9fuSIlaHiwN5ZQBkJMlnvjXi4uKYM2eOt8Nom5LVdclxcwLoTt0IVG1QtQtK10D0VO/GdzYuV13vFeWMpKZOB5qGpvrGoMqBMQFc9MwYDrxzhLy1J1H0OiLTIji5qYi6TFxdkk/TNJy1LsJTWlcHvLUCok1oTo3KvBo0O+jNOgJjLKgOlcBYS5vb1Zs6vuyC3myg1+X96XV5f1SnyonPM9n92Kc4ymsxRwfRc2oacZNTOy5xKvyC2pYa5N3Y6NGjgbrvoBdeeIFLL72U1NTURssFBQUxdOjQJut9dyVVVVUsWrSI6upqli9f7q4pXp8Et9sbj+VQnxyyWNr+HQhteALMh3jrSRpFUTC6bBjMZmjn7w+jCUWpSzhpbWhL01RsNjtmswmlnW15KiZvtFV/DFYUpWV/U91wG8+lwWdB0XkuLk9un9mMYjTSr1+/DutB7u9P13X0PmjNk3SSIBd+Z2hyFKt35bLjeDsHbPAR7gE6pdyETwoyG+nXI4yDJ8rZdbyEiRkJ3g7Jq/blluJUNaJDLPQI98+TEH+kVW4HxfBzchzqepJrGlr1HpQunCA39IxHFxaGq7wMfWTUqRIrGmpFOYboaIyxPXA6Wja4Y1cX1DOQ4femM/zedABqi6x8fcc6yo9VEhhtQdEr1BZaMYeb6Hdl73O2V5FVxZHPcig9WEFQXADJUxKIHdqyY11kvyAcZS5Ki6rQGeouYsuOVBEcH8iAy+PbvpGdLOu/W8l5fyeKQYch0ERNdhmHX1mPy+YkYXq6t8MT3ZifdSBn9OjR7iT5pk2buPHGGxk7dqyXo2obq9XKnXfeyZEjR1i6dGmDwUbDw8MxmUwUFjau21tfWqW+1EpbteYJMF/llSdpqu11Zbd07XyKSKcASl2Ctw1tuU5VvFAUHfp2tuWpmLzdlr4l6+nm29iUhp8Fnefi8vT2KUqHJ6/l6bqO2wet6RAiCXLhd4Ym1/UaPZBX7vd1yMuqbWQVVQE/12cXviejdyQHT5Sz83ix3yfId59WXkV6T3aODz74oEGd1szMTF588UUAZs6cSUJCJ3wmdRbQmqlBqPNsDWhP05nNhEyYRPmnH+EqLEAxGtEcDpSAAIIvmYxiNEIXT5C77E7KdhXgrLET0i+KwPiW1X0PiLYw/slRbH9+D8WZpWgqRAwMZ8iiVGIyzn7MKtpdyppHtlB1ogadQYfqVDnyeTbnPZBOvxlJZ32vpmkc+/wEpiAFl12H6tRAA1XVsATr6DkyotF7rCU2sr7JoyqvhqDYABIn9iQw1rs34WzF1Zz8ej/6QCPmyLoBTY2hFqwFleR9spu4SSkYArxXk1yI7uqNN97wdght5nA4uO+++9i+fTsvvvhio57uOp2OQYMGsXPnzkbv3blzJ0ajkQEDBnRWuEIIIUSn8d/MoPBbPcIDiQsPIL+slt1ZJZzXv329ILqz+t7jfWJDCA2Ui2RfNSQpivc2HHX/vv3ZzlMlheSGUOd599132bRpk/vnXbt2sWvXLgBGjhzZKQlyJXwsWvkGcFaC4VRy1lkGOiNK6OgOX397BQwdji40lNrt23AWFWKIiSVwxChMvZO9Hdo5le8tZO+SDdRkl6M5VQwhZnpO7kf/m0e0qA53ZGoYE58fS1VODapTJSQx6Jzv0zSNn/65j6oTNYT1DUHR1fW6r8qt4ad/7qPXhT0whzd/zCs7UkXJ/gqCk4wERwRjr1ABDaNZh73SSVFmBT2G//wdUrK/nHW/20JFVpV72r7/HGbsn0YQO+zspZxUp0ptfg2GQCPmCM/erKnJLsVZbcMS17AcjTHUgqO8FmteBcH9oj26TuE/ND8rsXK6o0ePcvz4cS655BL3tK1bt7JkyRLKy8u55pprmDt3rvcCbIaqqjz44IP88MMP/PWvf+Wiiy5qcrmpU6eyePFitmzZwqhRowAoLS3lk08+Yfz48e5yLEIIIYQvkQS58EtDe0fxVVkOO44V+3WCfGdWXZkZGazQt6UnRaIAOSXVFFdaCQ/0z8FYbQ4XmdmlAAxLlvrjnaVL9LQLvwgqd0Dpd+AoBpS6nuPRV0DoKG9H1yLmPv0w9+n4wU9Pp7lcVB86iWq1E5AUgzGidUkRR6WNPc+uozavEkuPIHRGPfZyK9kf7SUgLojEKwe1qB1FUQhJrOsB7bK5yF2bQ0lmKXqznh5jexAxKKLBEyHVJ2spyiwjIMaCovu5xmdQjwAqs6op+KmYxEt6Nrs+1aHW1X03gcGkwxxX953psruwlTlQ7T/Xfdc0jW3/t5uK45WE9g6u663u0qg4XsXWv+1iyrKLmk3o53yTzcEV+6nOq0YxKPQY25O0RekExHim57khyIzOoEe1O9EZfr4hoNpdKAY9huCu/fSE6No6oBRrt/G3v/2N8vJyd4K8uLiYW2+9FYfDgdls5oknniAmJobLLrvMu4GeYfHixXzxxRdccMEFOJ1OPvzwwwbzZ86cCcCcOXN4++23ufvuu7n55psJCQnhzTffxG6388ADD3gjdCGEEKLDdVqCXNM0Xn/9dd58801yc3NJSEhg7ty5zJ8/v0WPuTc1CArAHXfcwa9+9StPhyt83NDkKL7aUZcg92f1PYplsELfFhJgpG9cKIfzK9iVVcKFA+O8HZJX7M0pw+FSiQ6xkHCq3IDwD4rOCEm/hIiLoWonoIOQ4RCcIaV2mlFzNJ/sf3+DNbe4rud3sIWoSUPoceVolBbWcyzalEPtiUoCEkLcSWJzRACuWid5Xx6i14yBrdr/jioHm/60iYLN+aguFTQ49PZBUucPJHVu0+eJbRHeN5ig+AAKD1jRon7OAtYU2AiKtRCdFu6eVn6kkuK9ZQTGBri3UaevS8aXH62b11Q5mBPr8tj+zFZcNhfmCDOqQyXrs2NU51VzwbMXemQwz+B+0QT3j6Z89wl0PUPRmQy4rA7spTVEX9AXS1zLSt20hD33JJU/bsF2LAd9SBBBI4cQNCKjxZ8VIbqTXbt2cf3117t//vjjj7HZbLz33nv07duXefPmsXz58i6XIM/MzARg3bp1rFu3rtH8+gR5QEAAr7/+OosXL2bp0qXY7XYyMjJ4+umnG9QrF0IIIXxJpyXIX3jhBZ5//nlmzpzJrbfeyqZNm3jyySepqKjgnnvuaVEb559/PrNmzWowrbnEuRBnU1+H/NDJcqqtDoIs/tejtqzaxtGCSgCGSA9yn5fRO7IuQX682G8T5PUD8w5NjpKkqB9SFD2Ejqx7ibNyVlk5/vIX2E6WYYwKRWfU4yyvoeDjzZgigom6uGWDO9rLrACNelDrAwzYyqxoThXF2PJE8JEPj5C/4SSBPQIxBBjQNA1roZX9y/cTNzqO8AHhAAT1CCA6LZy8DQWYQozuEivVJ2sJjLGcs+yJ3qQn45a+fP/4diqOVGMMMuKyqhgC9Ay5pT+mkJ/PGVx2Fc2loegbfqfoDAqaquGyuc5svm5b3j2Ms9ZJaPLP5U8MgQZK9hRTsDGfnhe2fyBQRafQ//bxHPj7d1QdKwFVRdHrCBvck743jWl3+/Vsx7IpeP0dXOUV6MwmHAWFWI8cx34in8gZXStBKDxD00BtQw9yX+l1XlpaSlzcz+dSGzZsYNiwYQwcOBCAK664gn/961/eCq9ZrXmiKyYmhr/+9a8dGI0QQgjRtXRKgrygoICXX36Za665hqeeegqA2bNno9frefnll7n++uuJiYk5Zzu9e/d239kWoj1iQgNIiAwit6SaXVklnJ/ifwnD+t7zfWJDCA+Sx6x93ZDeUXyw6ZhfPzVRnyAf3kdq7gpxNhU7jmLLL8ccF45iqEtgGyODsZ0spfj7PS1OkAf2CkXRKbisTvSWn085nZV2IjLi0LUiOQ6Q800OOpMOQ0BdW4qiYImxUHGskpMbTroT5IqiMOzOgVQcr6L8SKV7kE5TqJFhdw48a/3xekkT40gpi8W1y0TVcSuhSUEMuDKRxPENy7KF9w0hqGcAVTnVGBKD3DffagutBERbiEwNb9S26lIpP1KGKaRhHAaLAU3VqMyupPkCMK0T2CucIU9Op3RHLvbiaixxoYRlxLeo/ntLaJpG2TdrcZVXYOwR495+V2UVVRu3E3zeMEw9/LeUnU/zkWR3WwQEBFBTUwPU/Q1s3769QY/ywMBAKisrvRWeEEIIT5KOVX6jUxLkq1evxm63M3/+/AbT582bx/vvv8/q1au54YYbWtSWzWZD0zQsFktHhCr8yNDkKHJLqtlxrNgvE+Q/nUqUDpNkoV8Y0jsKBcgurqao0oq/3RKpsTs5kFcB/PwEiRCiaY6yahRwJ8c1TUNzOFEMemxF5Wiq2qLSGVHD4wkbFEPpznyM4WZ0Rj2OMit6i6HF9cdP57K50OkbrldRFFBAPaOndnR6BJNfHMuRz3IoO1RBYFwAyZclEDu05U9MhQ4KYNCsQQQGBja7jN6sJ+MXqWxavIPyI1UYg/Q4a13ozXoG3zSgQW9zd8w6BUuUhcpjDRNoqlNFAY8P1qkzGYg6r7dH26ynWW3Yj+eiDw5q8GSOLjgIR34RtmPZkiD3UXWfVv/Up08fvvzyS+bNm8eqVauoqKhg7Nix7vknTpwgPDzcewEKIYTwDEsQKHqoLvdMewYTmD0z1ozwvE5JkGdmZmIymRqVQ0lLS8NoNLrroZ3Lxx9/zFtvvYWmaSQnJ7No0SKuvfbajghZ+IGhvaP4bFuW3/ao3X60CJDBCv1FSICRAT3DOHCinB3Hihl92mP9/mBvbgWqphEfGUhsmJyUCHE25pgwAFSHE83uwHayBNVqR7M7McWGY80tJiDx3E/+6Ux60h+6kMOvb6doUy6uWidBvcPpfe1gYsYmtiiW2oJqclcdo+JQKYrLgbXUiiXG4k6U26vs6A06ojIaH8tCk4IZdsfAVmx52yRP7YU5wsShD45TfqSSkMQg+s3sTcL4pm++K4pC8hV92Pn8DqzFVncN8uq8aoISguk5zlP9xzuBXg86Bc15RikZTQMFFEOnVXMUncxXyqW0xY033shDDz3EeeedR21tLcnJyYwePdo9f/PmzVIGVAghfIHRDC4HrHkHKtuZNwqJgotnS4K8C+u0EivR0dHozuhtpNPpiI6OpqCg4JxtDB8+nMsvv5xevXpRWFjIf/7zH373u99RVlbGrbfe2q74XK6m60O2to36lz/whe0dnBgOwJH8CsqqagkJaP6Ra1/Y3tPll9dyorQGnaIwuFd4o+3yte09F3/Z3qHJkRw4Uc72o0WMTAzy+e2t53K52JVdCsDQpEi/2W4h2ip0aDIBybFU78/BVV2D5lRBAXQ6VJud4y98Qt+HrsUUee5BHs1RgaT96gLsZVZctQ7MMUEtLu9RcaSUbX9aS01uJYpeh9PqwlnppGRPMQExgahOFU3V6DWhFzEjvdtLueeYWHqOaXkMyVf2pTqvmqzPj1F5vBKdXiG0TyjDHhyBKaz7POOjMxkJTB9I5frNaIEBKMa62vDOkjL0IcEEpPbzdohCeNyVV14JwNdff01oaCh33nknen3dEzclJSVUV1dLWVAhhPAllcVQVujtKEQH65QEudVqxWRqOvloNpuxWq3nbGPlypUNfr7mmmu49tprWbJkCddffz0hIee+SGuKqqoeqRGnqipWqxVFURrdCPBFvrC9RiAhIoDc0lo27c9jdL/me1L7wvaebuO+fAD6xwXjstdSaW8439e291z8ZXtT4+rKBGw/WkTtebE+v731VFVlV1YZAKk9grxeF1RVVb/Y76L70pmN9L7rcg4+sRJnWSUY9OhNBkxx4RjCArGfKKV8435iLh/V4jZN4RYIb115vENv7KE6p5KQ3mHuQTD1lgqslSoBcQGYw830mphI72m9PVZTu7PoDDoy7hlKn6v7UX6gDEOQgegh0Q1qtXcXYZMvxJ57Elt27qm61Br6oEAipl+KPiTY2+GJjuLHPcihLklenyg/XWRkJO+9954XIhJCCCFEe7T6LHzjxo0sWLCgRcsuW7aMcePGYbFYsNvtTS5js9naVE/caDSyYMECfvvb37J9+3YuuuiiVrcBdb3Y25pcP53L5ULTNIKDg909CHyZr2zvsD4x5JZmcSC/hknDkptdzle2t96+k0cAGNEvtsnPv69t77n4y/aOHBCIUb+X0moHJVaIjfXt7a1XUllLTmndjdjRqfGEeHlQWkmOi+7AHBNGQEIUamUtxshgdGYjiu5UzWEFrLkdW57MXm6jeGcB5giLOzkOEJwQAjkVpP8ilfiJyR0aQ2cITggmOKF7J5ENYaHE3TaPmt37cJwsQBcYQGD6QIyxzY9xomkamt2GojdIGZZuStU6vwZ5dXU1r776Krt27WLXrl2Ulpby61//mttuu61F79c0jddff50333yT3NxcEhISmDt3LvPnz29QQ18IIYQQ/qfVZ6TJyck8/vjjLVq2X7+6xypjYmJYv359o15zqqpSVFREbGzbHouNj48HoLy8fQXzPZUg0uv17pc/8IXtHd4nmk+3ZbEzq+Sc2+EL2wt1Fwc7j5cAMKJvTLPb4yvb21L+sL2Bej2DkyL46WgxmbmVDO7T06e3t96enLpjRHJMMFGhzQ+2J4RoyBQVQrWmoT+tBJmmaaBqGMKDPLYeTdOoOlKCNb8Sc2QgIakxdetBgzOTVkrd5M6qf2wrtVL47UkcVQ5C+4YRPTy22/VW7ww6i5ngUUNbtKwr5wiV29fjPJmDYjBgGjiMgLET0QV27xsFouOVlpbywgsv0KNHD9LS0li3bl2r3v/CCy/w/PPPM3PmTG699VY2bdrEk08+SUVFBffcc0+z71uyZAmKonDnnXei0+lYsmTJOdelKAp33313q+ITQgghhPe0OkEeFxfHnDlzWvWewYMH8/bbb7N//34GDRrknp6ZmYnD4SAtLa21YQCQk5MD1D3KJkRbDDk1QOXxwipKq2xEBHefup9tdbywipIqG2aDjkG9wr0djuhkw5Oj+eloMbtzypjt7WA6yU+nBuKVAWmFaEhzOqnYuIOqbXtQa2qx9O9N2AUjMfWoG4AzfMxAyrccwl5UgTEyGDSwF1WgDwkg/LwUj8TgqLBy4J/rKd2ei6vWgc5sIGxQLCn3jCcyPYaT63IwhZrdvdethTWYwi1EDe34muM1mVVs/Mta7MV1T6DoDDqiR8Qx4rdjMIU2P26JaJ6x+CS2NT+CtQZdYDCazYZ107e4Ck8Qcu0t0pu8m9AAtY3va4/Y2FjWrFlDXFwcOTk5TJo0qcXvLSgo4OWXX+aaa67hqaeeAmD27Nno9Xpefvllrr/+emJimh58uD5BvmjRIkwmkyTIhRBCCB/UKWehkyZN4sknn2T58uU8+eST7unLly/HaDQyceJE97TKykoKCgqIjf259ENZWRnh4eEN2qypqWHp0qUEBwczbNiwztgM4YPCAk307xHKoZMVbDtSyKQhvbwdUof76VgRAIOTIjEZfL/3sGhoeN9oln27nz055bhU1S96kO84XpcgHyoJciHcNE2j8K3PqNy8CxQFxWDAnreZmj2H6HHrbMzxcQQPTqLHtRdQ8PFGbCfKADBGBtNz9ngCensmQX3k9S0Urj+GOSoQc0wQrloHJdvzOPjP9fSfN4rKo+VUHS9HMejQXBqGAAP95w7GEt2xT4M4Ku0U/jcPfY2OkMTQukFCaxzkbzzBoZX7SLttSIeu31cFHctEq63CEBPvLmmh2QNwHD+E4+g+TAPSvRyhaLnOL0liMpmIi4tr03tXr16N3W5n/vz5DabPmzeP999/n9WrV3PDDTc0+9769Z/+sxBCCCF8R6ckyGNjY1m0aBEvvvgiTqeTUaNGsXnzZj788EPuvvvuBiVWvv76ax599FGefvppZs2aBcAbb7zBunXrGDNmDAkJCRQWFvL++++TnZ3Nn//8Z4KCPPeYr/A/I/rGcOhkBVuPFPlFgnz70fretM3XBhW+q3+PMIItBqqsTg6eqGBwkm8njQvKa8krqUFRID0xwtvhCNFlWI9mU7V9L/qQIPSBAQBoqorjZBHl320k9sYrURSF6EnDCBs1gJqDeaDTEZSagCGo8dgxqt2BLb8MvcWMKSa0RTHYSmoo2ngcU5gF46knuAyBJizRGuV78ilaf4TAUBVXkR2MBsLTe5I8O53ITug9XrSlAGeJg+A+kSh63anYjBiDjOSuziJ14WD0Jt+/wehpxpICFHNAg3rPiskMmoqz4IQkyLsRtZsN0pmZmYnJZCI1NbXB9LS0NIxGI5mZmc2+NyEh4aw/CyGEEKL767TnGO+77z7CwsJYsWIFn376KT179uTRRx/lpptuOud7R4wYwU8//cS7775LWVkZFouF9PR0/vCHP3DxxRd3QvTCl43sF81b6w+z7UgRmqb59CA9LlVl56netMP7+HZiVDRNr1MYkhTF+gP57DhW7PMJ8h2nyqv0iw0myGL0cjRCdB3WozloDge6yDD3NEWnQxcYQM3+ow2Oh8awIMJGDWiyHU3TKFmzh4LPt+IoqUIx6AlOSyTh+gvPmSi3l9ai2lyYwgMaTNcFGKk9WsKx//yEPsCEKdSEs9pO7dECcDk65TjtrHECNBggFEBn1OGyuVDtLkmQt4FqsqA5rA2maVpdsQ6dRcaI8AcnTpzg/vvvb3Z+R/XOLigoIDo6utFA2TqdjujoaAoKCjpkvUIIIYToHjotQa4oCgsXLmThwoVnXW7WrFnunuP1LrjgAi644IIOjE74s7ReEZiNekqrbRzJr6Rfj5b1fOuODuSVU2NzEmwx0K9H2LnfIHzS0ORI1h/IZ/uxYm68yNvRdKztR+tKCqVLvX0hGtAZmz4F1FQVfTPzmlK26SC5K75H0zQMoYFoDhflmw/iKK2i/8Oz0JmavzFliQvGGGLGUWlFb/l5gEZbUTWuWifmmFAssXXTTVEB1GSXc/y/O1H0Cs7qut7dgfEdc8wO7R+GYtLhrHRgiKjbH5qmYS+3ET0iDkOQ3HBri9rEAQQd+gm1pholIBA0FbWsGF1wKMYBbRuTSHhHN+tAjtVqdZdIOZPZbMZqtTY570w1NTWsWLGCb7/9lqNHj1JZWUlISAh9+/ZlwoQJ3HjjjQQGys0e4SG2WnDa29+Oouu80a2FEKKbkpFwhN8zGfQM7R3JpkOFbDtS6NMJ8vpk4dDeUeh1vttTXpxd/dMDmTmlWB0uLEbf7AWpahpbjxQCMCQp3LvBCNHFBAzshy4oAGdpOYaIMBRFQbU70Gx2gkaMblEvbU3TKF69A83hwhx/asB0C+jMRmqP5lOx8zjho/o3+35jsJkel6Vy/L/bsRZUYQg24apxYC+pRWcyYI75uYSeoijozQYK1h6jfF8BaGAINtFjQj/63TwKnYe/x0JTwgkaGoptZy0ua11vcUeFHWOoif7Xp/r002Ydqab3QAwmPdqh3ag1FYCCLiScoEuvRh8qZbC6k7bm2nr27OmVGt4WiwW7velEo81mw2JpXDrqTIcPH2bRokWcOHECTdMICgoiKiqKqqoqtm7dyrZt2/jPf/7DK6+8Qp8+fTy9CcIfOe3w/dtQWdy+dnr0hZGXeiYmIYTwUZIgFwIY2S+GTYcK2XKkkNnj+nk7nA5TnyAf1kfqj/uzhMggIoNMlFTb2ZNdwsi+Md4OqUMcOVlBWbWdAJOe1J4h3g5HiC7FFBtF5LRLKPnkWxwni9CoK7ESkNqH8ItHt6gNzaViPVmGLsjcYLrOZEDTwF5Yfs42kq4Zgs6gcOLL/TgqbegDjMRe1JeiTbloLg3FoLjXVZNbjmp3YY4KQh9owFFmJffTfZhjgki62jO1q2vzqyjckENNaRUhGQGED0mi8IeTOCrsxI2Np+81A4gZ2bZBAgWg12OafBXm0RfhPJmNYjRh7DMQXVDwud8rupTu1hc1JiaG9evXo6pqgzIrqqpSVFTUYEyspjgcDu677z7y8/O55ZZbuP7660lMTHTPz87OZuXKlbz22mvcd999vPfeexiN8qSJ8IDKYigrbF8bIZGeiUUIIXyYJMiFoG6gToA9Wb7bo7bG5iQzuxSouyEg/JeiKGQkhvP9vgK2Hyny2QS5u/d470gMet05lhbC/4RdMBJLn17U7D6AarVjTuxJ4OABZy2LcjpFr8MYGYw1uwjCfu7trTpdKNTVLj8XnUFH0jVDiZ+Whr2kxl2PfMv9H2M9UUlAQiiKTsF6quxKQFywu7yJKSIAl83Jia8PknhlmnswzbYqWJfF3hc3YS+zoqkqNocd03AnFzx3CcYQMzqDfI94gqIoGHomYuiZeO6FRZfV3RLkgwcP5u2332b//v0MGjTIPT0zMxOHw0Fa2tlL/Hz55ZccPnyYv/zlL1x11VWN5icmJvKb3/yGfv368dvf/pavv/6aadOmeXozhBBCCNFB5ExfCCAxKojYsAAcLpVdx9v5CFsX9dOxIpyqRs+IQBIiz520EL6tvuTIlsPt7JHShdVv2wh5YkKIZpnj44i47EKirpxE8PC0FifHoS7RGX1JXc9tR2kVmkvFZbVjzy/D3DOC0GEtLzFgCDASmBCGIciEIchEyp3nY4oMoCannJqsMuzFtejNegLPKJektxhwVNhw2VwtXldTbKW17Ht5C45KG0G9QglMDEUfbqJsZwHH39kjyXEhzqC14dVZSkpKOHz4MLW1te5pkyZNwmg0snz58gbLLl++HKPRyMSJE8/a5qpVq0hNTW0yOX66WbNmkZqaytdff93m+IUQQgjR+aQHuRDUXeSP6BvNF9uz2XqkiPP6n/0xy+6oPlk4SnqPC+oS5ApwtKCSogor0aHnrr3ZndTaT3tiom80oHo3ICF8VOSFg7GXVVO8eie2/DJ0Bh2BfeJIXDgRfaD53A001+6IBEb+7xUUbczGUWHFZXeS/X4mmksFfn7Ky1llJzQlGn1A+05pi7edwFZcQ1BCCIpOARcoJh2GIB0nfzhO/5uGozP9vF5N0yjbdZKijVk4q22EDogh9sI+GH3su1SIprQ14e2JJPny5cupqKigsrISgI0bN+J0OgGYP38+ISEhrFixgiVLlvD6668zZswYAGJjY1m0aBEvvvgiTqeTUaNGsXnzZj788EPuvvvuc5ZY2bdvH5de2rIazhdddJEkyIUQQohuRhLkQpwysm9MXYLcB3vUapomCXLRQGiAkQHxYRzIK2fL4QKmDk/ydkgeteNYsfuJifjIIPeFtBDCsxS9jp5XnU/0xenUZheht5gI7BeHom9/qTJzdBAJVwwEQHWqVB4spuSnPExhFnQmPfZyKzqjnoTpae0eNFO1OutGHTxjAGvFoEO1q6gOV4MEedY7O8l6ZyeuWicokP/tYU5+c4jBj07EEi1PaQnf560SK0uXLiU3N9f989q1a1m7di0AV155JSEhzY85ct999xEWFsaKFSv49NNP6dmzJ48++ig33XTTOddbVFREr169WhRjr169KCoqatGyQgghhOgaJEEuxCnD+kShUyCrqIqC8lpiwwK8HZLH5BRXk19Wi1GvY1hylLfDEV3EqL7RpxLkhT6XIK+vP17Xe1wI0dGMEcEYIzpuoEWdQUfary/iyBvbKNqUjbPGQWB8GIlXDyb2wuR2tx+aEoU+wIijwoYprK4XuKZpOMptxI5JRB/4c+mZ6uOlZL+/G0WvI6h3BACqw0XlgUJyPtxN/1vGtDseIUTTvvnmm3Muc++993Lvvfc2mq4oCgsXLmThwoWtXm9NTQ2BgYEtWjYgIICamppWr0MIIYQQ3iMJciFOCQ0wkRIfzr7cMrYfLWLKMN8ZPKq+93h6UiQWk/zZizqj+sXw5trDbDtShEtV0et8p8bu1sN1PbdkQFohfIcpIoCB912AvdyKs9qOJSYInYcG1Q7pH0mPi5PJ++oQzio7GHW4SqwExIaSPHtwgx7qpTtP4Ky2E5gY7p6mM+rRB5ko+jGLfr8Y3e4e7UJ0dd1tkM72UtXWlWrTNH/bQ0IIIUT3JpkyIU4zsm8M+3LL2Hq40CcT5FJeRZxuQM8wQgOMVNQ62JtTRnpSpLdD8oi8kmpyS6rR6xSGyhMTQvgcU5jF3cvbUxRFYeAd5xHSJ4K81YepLa4hIDmCQfPGEJER12DZujroTbehqZIUE/7BH/O/q1evblDepTl79+7thGiEEEII4UmSIBfiNCP7RbPih4Ns9aEetTaHi53HiwFJkIuG9DqFEX1j+G5PHpsPFfhMgnzjwQIAMpIiCTIbcblcXo5ICNEd6Ex6EmekkjgjlZqaGvbu3UtoauObbOGDe9SVYym3YgqvK8emuVQcVTZiL+orvceFX/DD/DhffPEFX3zxRYuWle8BIYQQonuRBLkQpxmYEE5IgJHKWgeZ2aVk9O7+vU93Hi/G7lSJDrXQO6bj6sOK7mlUv7oE+ZbDhdw8caC3w/GI+gT5mAGxXo5ECOFN5TtzKPzhILbCSgJ7RxM7IZUgDzxVEtw/ivjLUsj9bB/VFda6gTwdKkG9I+g1c7AHIhei6/O3BPnrr7/u7RCEEEII0YEkQS7EafQ6HaP7x7J6Vy4bDxb4RIJ8w4F8oC5ZKL1ZxJnqnyo4dLKC4korUSGeLVvQ2aptDnademJizIC4cywthPBV+V9ncnz5BlxWBzqDnorME5T8eJj+900kLD2hXW0rikLfhecROjCWwvXHcFTYCBscR4+J/bHIjWjhJ/wtQT569GhvhyCEEEKIDiQJciHOMHpAXYJ8w4F8bp08yNvhtIumaWw41Zv2fEkWiiZEBJtJjQ9nf14ZGw8WMG1EkrdDapdtR4pwqhq9IoNIiArydjhCCC9wlNeS8+42UDUCe9WVjtI0DWtuKTlvbSE0LR5F174bxopOIWZcMjHjkhuvv6KWwtV7KN1yFFSVsBHJxE5OxxQh30nCN2hA64as/Pl9QgghhBBdUfcvsCyEh53XLwa9TiG7uG6gv+7s8MkKiiqsmI16hvXp/r3hRcc4P6WuFEn90wbdWX15ldEpUl5FCH9VeTAfR1kNpqife3MrioIxPIiarBJsBRUdtm5XjZ3Df/+S3Lc2Ys0pwXqinBPvbeHQ3z7HUVHbYesVQgghhBBCtJ0kyIU4Q5DFSMapwQrrk23dVX3v8RF9ojEZ9F6ORnRVY1Pqni7YfrQIq93p5WjaTtU0NtXXH+8vCXIh/JWi1wEKmnZGf1VNA0VB6cABuEt+PEjlvjzMcWF1r9hQzD3DqT5SQPGa/R22XiE6m9aGlxBCCCFEVyUJciGaUD+438Zu3qO2Pv6xqVJeRTQvOTaEuPAA7E6VbUeLvB1Omx3IK6O8xk6g2cDgUze5hBD+JyS1B+aYYOyFle4kuaaq2MtqCB4Qi6kD64RXHjgJGuhMP1cx1Bn0KDqFiszcDluvEJ1NEuRCCCGE8CWSIBeiCWNO9ajdlVVCtdXh5WjaprjSyoET5SjAaOlNK85CURR3L/LuXGZlw4G63uMj+8Zg1MvhTQh/ZQg0kTRvDPpAM7U5pdTmlFCbW4alRxhJc0Z36IDVdYnxxqlATdPQW2ToH+E7tDb8J4QQQgjRVcmZuhBNSIgMIjEqiOziajYfLuTCgd2vB3Z9eZjUhHAigs1ejkZ0dWNT4vhg0zE2HCjApWro2zmAnTes23cS+LmmuhDCf0WN6UtAzzCKfzyCvaSagIQIosb1wxzdcb3HAcKH96Z4zT4cFbUYQiwoioKzyoqi0xE+sk+HrluIziTpbiGEEEL4EkmQC9GM81PiyP7xCOv2neyWCfIfT/UEri8XI8TZpCdFEmQ2UF5jZ39eGWm9IrwdUqtkFVWRVVSFQadwfkr3+3sVQnheYFIUgUmdO0B12LDexExOp/CbPVjLa0EBnVFP1EWpRI7p36mxCNGRJEEuhBBCCF8iCXIhmjF+UE/e/vEImw4WYHO4vB1Oq1TbHGw/UldLelxqDy9HI7oDg17Hef1j+W5PHuv3nex2CfL1p3qPD+sTTbDF6OVohBBdleZy4ay2oQ8woTN6/jRY0SkkzhtHxKg+VOzOQdM0QgbFEzq4F0oTT+ZomoY1uwBHcTnGiFAsveM6tASMEJ4iCXIhhBBC+BJJkAvRjNT4MGJCLRRWWNl2tIj0noHeDqnFNh4owOFSSYwKoncHDkYmfMu41Di+25PH2n0nuWXSwG6VpKkvr3LBQLkhJIRoTFNVSr7bRfG3O3CUVqMPthB5UTrRlw73eKJcURRCBsUTMij+rMs5K2s4sfxLqvcdR7U60JmNBKYmEj9vKoawII/GJIQnaYCmtD5FLkl176uurubVV19l165d7Nq1i9LSUn79619z2223NVq2qKiIxYsXs2bNGux2OxkZGTz00EOkp6d7IXIhhBCiY8koZkI0Q1EUd7Jt3b7uNXDh2r0nALhwUM9uleQU3jV6QCwmg44TpTUcPlnh7XBarKC81j0g7bhUKa8ihGis8Ktt5L35Hbb8MhSjDmdZFSffXcfJ99a1ui1N01DtDjRX+54uy3/rGyq3H0RnMWPqGYUuwEzVzsOcWLmqXe0KIURzSktLeeGFFzhw4ABpaWnNLme1WrnppptYs2YNCxcu5MEHH6S4uJj58+dz6NChToxYCCGE6BzSg1yIsxg/qCcfbDrGxoMF3HJRb2+H0yK1diebDxcCdfEL0VIBJgPn9Y9l3b6TrNl7gv49w7wdUovU9x5PT4okPEgGpO1KDh8+zAcffMDatWvJysrCZDIxYMAAbr/9di644AJvhyf8hKvWRvHqHShGPabo0LqJwQE4yqopXbeX6MnDMUWFtqitqj3HKfpqK7XHC9BZjISPHUT0lFHoLaZWxWQvLKNq9xH0oUHoAy0Adf+qGtV7j2M7WYK5R2Sr2hSiM0lv8O4pNjaWNWvWEBcXR05ODpMmTWpyuZUrV3Lo0CFWrFjBqFGjAJg2bRpTp07l2Wef5cUXX+zMsIUQQogOJz3IhTiLtF4RRASZqbY52Z1T7u1wWmTTwQLsTpX4yED6xoV4OxzRzVx06qbKD3tPoGnd4/JXyqt0Xe+88w4rV64kLS2NBx98kNtvv52qqip+8YtfsHLlSm+HJ3yQo6SC4tVbyH/3O0p/2IGzqgZ7QTnOyhoMIQENljWEBOCqtmI7UdKitqv2HCf75U+p3H0MNA1nZS0FH20gd9mXaKraqjidFdWoDie6MxLrOosJzeHAWVHdqvaE6GxqG17C+0wmE3Fx537a7rPPPmPgwIHu5DhAREQE06dPZ82aNVRVVXVkmEIIIUSnkwS5EGeh1ymMG1h3ErnxULGXo2mZH/bWJQsvHCjlVUTrjUmpK7OSV1LDkfyuX2alpMrKnuy65JYkyLueadOm8d133/Hkk08yZ84cFi5cyMqVK0lNTeX//u//UFuZVBTibKr3HefY/75J/lvfUvzVZk6s+Irjf1uJq6oGncGAanM0WF61O9AZDeiDAppp8WeaplH09Tac1VbM8VEYwoIwRYVijAihcudRag7mtSpWU3Q4ugAzrmprg+mu6tq6kisx4a1qT4jOp7XhJboDVVXZt28fQ4YMaTRvyJAhOBwODh486IXIhBBCiI4jJVaEOIfxA3vy6dYsNh8pxulS0ev13g6pWVaHi02HCgAYP0iShaL1AkwGzusXw7r9+azJPEG/Hl27zMqazBOoGgxMCCc27NxJLtG5MjIyGk0zmUxMmDCBl156ieLiYmJiYrwQmfA1qsPJyZWrcZRVYeoRhaJT0FwubLlFlHy7leD0JMo27EcxGdBbTKg2B/aiSoLTkghIjj1n+5rDSe2xfAzBAQ1uPusDzTjLKqnNKiAotVeL4zWEBRE+djDFX23GoaroAs2otXZUq43IiSMxRsgTYKJrk9ubvqusrAybzdbk8Tk2tu77sqCgoM3ta5pGTU1Nm9/fndXW1jb492wURcGsaWiqBu3tUKBq6NDQNK3VTzx5ui1NU93/ujwVVxfavra05WrJerr5Njal4WfBg3F1ke1rqi1F07DV1rqf1G7Nd4Kv6uh9oGlaizuOSoJciHMYmhxJeKCJsho7244WMTa169b13nywAJvDRVxYAAO6Sf1o0fVcmNaTdfvz+WHvSRZOSO3STyJ8u7uu1+bE9HgvRyJao6CgAIPBQEiIJAGFZ9QeysVeWIoxMhRFV/edpej1GMKCqTmcS+I91+CqtFJ9MBdHcSWKXk9gv570WjCpRd9xil6PPsCEo7xh6RPNpQIK+iBLq2OOmTEexWikbN0u1BorugALkZcMI3ra2Fa3JYQQnmKz2YC6G9pnMpvrxnqxWq2N5rWUw+Fg7969bX6/Lzh27Ng5lzGbzQxIiMNls6G1Y38D6Bx2zBrY7XZcXaQtm83usba64vadqy1NCz71r9aiv6fuuI0tZbPZPRpXV9u+eorNht7h4PCxHPf3bL2WfCf4uo7cB00dz5oiCXIhzkGv03FRWk8+2nKcb3fndekE+apduQBcMji+Syc1Rdc2ZkAcJoOO3JJqDp2s6LI3W3JLqtmXW4ZOUbgoTRLk3UVWVhaffvopEydOxGJpfVKxnj/3QDuT9D6B2upqVKcLFdBcLvd0FQ3V6cShaMTdMYXaQydwFFVgCA8iMDUBl0Hf5OeoqX0aOLI/JZ9txl5Zgy7ABKqGo7AcQ3gQhn5xbfo8Bk8aTuC4NJwV1RhCg9AFmLE6HeB0nPvN3Yx8Tj2vJfu0NT2nWkqjbT3IpchK91CfBLfb7Y3m1Sd12nP8NhqN9O/fv83v785qa2s5duwYycnJBASc/clHRVEwumwYzGZox/4GwGhCUeqSRJqX29I0FZvNjtlsQvFUXF1o+1raVv33sqIoLft76obbeC4NPguKznNxdZHta8RsRjEa6devX4Me5C39TvBVHb0PDh061OJlJUEuRAtMTI/noy3H2XCggBqbk0Bz1/vTKa+xs/lUeZVJQxK8HI3ozgLNBs5PiWNN5glW7czpsgny7071Hh/eN5qIYLOXoxEtUVtby/3334/ZbObRRx9tV1vSA60xv+59UmtD0YOtoBjqB+PUNCirhqgQDhedgNL8uumRANVw8MA5mz19n2oJZugdAUcKwekCRYEQC/axyRzIOdZsGy3WsrFCuz2//px2kHPt05b2nBICIDw8HJPJRGFhYaN59aVV6kuttIWiKAQGBrb5/b4gICCgZfug2g46BXTtHDpOpwBKXVLWy225Tt1dUxQdek/F1YW2ry1t6Vuynm6+jU1p+FnQeS6uLrJ9TbalKE0mgVv8neDDOmoftKaTQNfL8gnRBQ3oGUrPcAsnyqys33+SyUNaXme0s6zJzMOlavTvEUrvGClbINpn8pAE1mSe4NvdeSyaPAiDvmuN6axpGt/srntiYsJg6T3eGTZu3MiCBQtatOyyZcsYN25cg2kOh4Nf/vKXHDx4kH/961/Ex7fv9+bPPdDOJL1P6pRXQumnP6JV21FMRlSbHX1oMNHXTiQovV+r2mpun2oZ6VgPn8CWW4QuwExQWhL6EP/d560hn1PPa8k+bU3PqdaQ3uC+S6fTMWjQIHbu3Nlo3s6dOzEajQwYMMALkQkhhBAdRxLkQrSAoiiMT43h7Y3ZfLMrt0smyFfvrEsWTsqQ3uOi/Ub2jSE8yERZtZ2tRwoZMyDO2yE1cPhkBTnF1ZgMOsYN7Fqx+ark5GQef/zxFi3br1/DZKSqqjz88MOsXbuW5557jrFj219jWXqgNebvvU8CLh9LUM9oytbvwlFYjiUxlvALhxKUktj2NpvYp0FD+8NQuTnTVv7+Oe0IZ9unHVVyT5MUuU+bOnUqixcvZsuWLYwaNQqA0tJSPvnkE8aPH09wcLCXIxRCCCE8SxLkQrTQ+JS6BPn2o0WUVFmJDG5nDSoPyi2uZm9uGToFLpHBCoUHGPQ6JqQn8P7Go6zamdvlEuSrT/UePz8ljiCz0cvR+Ie4uDjmzJnTpvc+9thjfPrppzzxxBNMmTLFw5EJUUdRFEKHpxA6PMXboQjh8yQ93n0tX76ciooKKisrgbonxJxOJwDz588nJCSEOXPm8Pbbb3P33Xdz8803ExISwptvvondbueBBx7wZvhCCCFEh5AEuRAt1CM8gNT4MPbnlfPt7jyuOb+vt0Nyqy81MbxvTJdK3IvubXJGXYL8x/35VFkdBFu6RiLa7nTJExPdyOLFi3nrrbd48MEHmT17trfDEUII4QHe6kHucDh48cUXef/99ykuLiY5OZnbbruNGTNmnPV9OTk5TJo0qcl5TzzxhF8dn5YuXUpubq7757Vr17J27VoArrzySkJCQggICOD1119n8eLFLF26FLvdTkZGBk8//TQpKXITUgghhO+RBLkQrXDpkAT255Xz2bYsZo3p02GPrbaGS9X4emcOUJfQFMJT+vUIJTkmhGOFlazJPMG0EUneDgmAH/fnU15jJyrEzHn9Y7wdjjiL119/naVLl5KWlkZsbCwffvhhg/mXXnqplFoQQohuyFs9yP/4xz/ywQcfMHfuXFJSUli1ahUPPvggLpeLq6666pzvnzZtGpdcckmDacOHD++YYLuob775pkXLxcTE8Ne//rWDoxFCCCG6BkmQC9EKlwyO55XV+8kprmZ3VgkZvaO8HRLbjhSSX1ZLsMXABQN7eDsc4UMURWHykAReWb2PL7Znd5kE+efbswGYMjSxZaO+C6/JzMx0//vQQw81mr969WpJkAshRDekemGde/bs4b333uO+++7j7rvvBmD27NksWLCAZ555hmnTpmEymc7axqBBg5g5c2ZnhCuEEEKIbkQyC0K0QqDZwIRTNb4/25bl5WjqfLK1Lo5LhyZiNuq9HI3wNZcO7YVRr2N/XhkHT5R7OxxOlNaw/WgRCjBleNsH3hOd4y9/+Qv79+9v9tWrV9cb8FgIIcTZaYCmtOHVzvV+/vnn6HQ65s6d656mKArz5s2juLiYjRs3tqid2tpa7HZ7O6MRQgghhC+RBLkQrVTfi/aHvSepqPHuyXVBeS2bDuYDdJnevcK3hAeZGT+o7smET7Yc93I08MX2uhtCI/pG0yNceh4LIYQQ/iIzM5OkpCTCw8MbTB86dKh7/rn885//ZNiwYWRkZHDVVVe1uNyIEEIIIXyblFgRopUG9Ayjf49QDp2sYNXOHGZ5cbDOL7Zno2owpHckSdHBXotD+LYZo3rz7e48vt2dy62TBxES4J3BOl2qylc76urtXz5cbggJIYQQ3qG1cZBOjRMnTnD//fc3u8Tq1aubnVdQUEBMTOOxR2JjY93zm6PT6Rg/fjyTJ08mLi6OnJwcXnvtNe666y6effZZpk2b1vLNEEIIIYTPkR7kQrSSoiju3tqfbstC1bwzTJHTpfL5qd60V4zs7ZUYhH9I6xVBn9gQbE7VPSCsN/yQeZKSKhvhQSbOT43zWhxCCCGEv9Pa8F97Wa3WJmuM63Q6jEYjVqu12ffGx8fz6quvMmfOHCZOnMiCBQv44IMPiI6O5plnnkHz0vm8EEIIIboG6UEuRBtMSK8buDCnuJpNBws4P6Xzk3U/7s+npMpGWKBJBucUHUpRFGaM6s0/PtvNp1uOc/XoZBRF6dQYNE3j3Q1HAJgxKhmjXu7vCiGEEN7S1nRyz549z9pL/GwsFkuTtcNVVcXhcGCxWFrVXmhoKNdddx0vvPACR44coV+/fm2KS4iWUhQFs9nc6efRQgghzk0yDEK0QaDZwBWnepG/8+ORTl+/pmm8tf4wAFeMTJJkoehwEzMSCDQbyCmpZuPB5h9h7ii7s0o4cKIck0HH9JFSXkUIIYTwJq0Nr/aKiYmhsLCw0fT60ir1pVZaIz4+HoDycu8PRC58hK0WqsubfJldNgYkxGF22Zpdxv2qqQR5skEIITqN9CAXoo2uGt2H9zceZVdWCftySxmYENFp6/7pWDEHTpRjNuiYeV5yp61X+K8AU91Nobd/PMLKdYcYMyC2U3u/1N+ImjykF+FB5k5brxBCCCEa80babvDgwWzYsIGysrIGA3Xu2LEDgLS0tFa3mZ2dDUBkZKRHYhQCpx2+fxsqixvN0lQNl82GwWwG3TnOo3v0hZGXdlCQQgghziTdToVoo+hQCxMyEgB4e33n9iL/77q63uNThydJslB0mlnn98Go17E3p4xdWSWdtt7soio2nOq1PmtMn05brxBCCCGapipaq1/tNXXqVFRV5c0333RP0zSN5cuXExkZyZgxYwAoKSnh8OHD1NbWupcrKytr1F5RURFvvfUWiYmJJCcntzs+Idwqi6GssIlXAVppPpQVNDP/tFd1mbe3Qggh/Ir0IBeiHa49vy9f78hh3b6T5JZUkxAZ1OHrPHiinO1Hi9ApCtecL8lC0Xkigy1MHZ7Ix1uOs3LtIYb0juqU9db3Hj9/QCyJ0cGdsk4hhBBCNK2uZErrE97tTZGnp6czc+ZMnn/+eUpKSkhNTWXVqlVs2rSJp556yj2A54oVK1iyZAmvv/66O2m+ePFi8vPzGTFiBLGxseTm5vL2229TUVHBX/7yl3ZGJoQQQojuThLkQrRDcmwIowfEsulgAW/+cJDfzBzW4etcufYQABPS44kLD+zw9QlxumvH9uXTrVlsPVLE/rwyUuPDO3R92UVVfLUjB4DrLpDBs4QQQgh/9sQTTxAfH8/777/PypUr6dOnD8888wwzZ8486/suuOACVq5cyYoVK6ioqCA4OJjhw4dzxx13MGzYsM4JXgghhBBdliTIhWineRcNYNPBAlbvzGXWmL706xHaYeval1vK2n0nUYDrxkmyUHS+HuGBTMyIZ9XOXJZ/f4A/zxndoev793cHUDWNMQNiGZwo9UGFEEKIrsBbQweaTCbuv/9+7r///maXuffee7n33nsbTJs+fTrTp0/v4OiEEEII0V1JDXIh2ik1PpyL0nqiAUu/2ddh69E0jX99vReAyUN7kRwb0mHrEuJs5ozvj16nsOlQIVuPFHbYeg6eKOeHvSdQgIUTUjtsPUIIIYRoHa0N/wkhhBBCdFWSIBfCAxZOSEWvU9hyuJCfjhZ1yDrW789nT3YpZoOOmy5J6ZB1CNESvaKCufK8ZAD+9dVeXKraIetZduqG08SMBPrGddyTGUIIIYRoHa0NLyGEEEKIrkoS5EJ4QEJkEFeMTALgldX7cKmevQxwuFReWV3Xe/ya8/sSExrg0faFaK25Fw4gJMDIscJKPt+e7fH2Nx8qYOuRIvQ6hfkXyw0hIYQQoitR2/ASQgghhOiqJEEuhIfMvXAAgSYDB0+U8+nW4x5t+/2NR8krqSEiyMxsqT0uuoCQAKM7cf36dweorHV4rG2bw8ULX+wB4KrRyfSMkMFohRBCiK5ESqwIIYQQwpdIglwIDwkPMnPzxLo6ycu+2U9Bea1H2s0pruKN7w8AcMukgQSaZWxd0TVcMSKJpOhgymvsvPTVHo+1+/r3BzhRWkN0qEV6jwshhBBdTF3JFEmRCyGEEMJ3SIJcCA+6YmRvBvUKp8bu5H8//KndpVacLpVnPtiB3akysm80k4ckeChSIdrPoNfxqxlD0Cmwamcu6/adbHebu44X8+6PRwC49/J0AkxyQ0gIIYQQQgghhBAdRxLkQniQXqfwmyuHYTHq2Xm8hP+sPdSu9l77dj/788oIthi4f/oQFEXxUKRCeEZarwiuOb8vAM9+vIOTpTVtbqu0ysbT729HA6YOS+T8lDgPRSmEEEIIT5JBOoUQQgjhSzotQf7SSy9x1113MX78eFJTU/njH//Y6jY++ugjZsyYQUZGBhMmTOAf//gHDofn6t4K4QkJUUHcfflgAN74/gDr29irdtXOHN4+1ZP2V9OHEBsmA3OKrummCakMSginyurk8be2UG1r/fey3eniiXe3UVxpIyk6mNsvS+uASIUQQgjhCTJIpxBCCNFKZ3R4VBQFs9ksHSG7iE57dv25554jKiqKjIwMvvvuu1a///333+eRRx7hkksuYf78+Rw4cIB//vOfnDhxgqefftrzAQvRDpcNTeTgiXI+2nycp9/fzhNzRjM0OarF7994MJ/nPt4JwPUX9GP8oJ4dFaoQ7WbU6/jtNSP45dJ1HC2o5H/e2sqfbjgPi1Hfovc7XSp/eW87u7NKCDQZ+MPskVJrXwghhOiy2lpRXPqRCyGE8FOWIFD0UF3unmTWNAYkxGF02aDa3vK2FAV0enA52x+XJ9sCMJjA3D07d3ZaBmLVqlUkJiYCkJqa2qr32u12nnnmGcaOHcvLL7/snh4WFsaSJUuYP38+aWnS21B0LbdfmkZBWS0bDhbwh/9s4pFZwxmX2uOc7/tudx5//WgHTlXj4rSeLJzQur8XIbwhNiyA/7nhPB7894/8dKyY3725icdmjyQ00HTW91ntTp58bzubDhZg1Ot47PqRJEUHd1LUQojOotlrcR5Yi+vYdlBd6BMzMKReiBIY5u3QhBBtIENuCiGEEK1gNIPLAWvegcpiADRVw2WzYTCbQdeKXuQ9+sLISxu01WaebCskCi6eLQnyc6lPjrfFhg0bKCkpYd68eQ2mz507lxdeeIHPP/9cEuSiyzHodfzu2hH8z9tb2XyokP95ayuzzu/DgktSm+xZW2NzsuzbfXy0+TgAFw7qwUNXDUMnj9uIbmJAzzCemjua3/9nM7uzSrj7lbX8avoQRvSNbnL5fbml/O+HO8gprsZsqPt7GZbc9LJCiO5Lc9qxf/v/cGXvquuhoiio+YdxHd+Becq9KAGh3g5RCNFKkh4XQggh2qCyGMoK6/5fVdGsVrBYQNeKCtghkY3baitPttXNdYtn2DMzMwHIyMhoMD0yMpLExET3fCG6GpNBz5+uH8U/v8zk4y3HeXfDUVbvymXqsESG9YkmPNBESbWN7UeK+PKnbCpq62o3zx7bl5snDkTfmruIQnQBgxMjefamsfzPO1vJK6nh0RUbGZocxcT0ePrGhaIoCscLK/k+8wSbDhYAEB1i4XfXjiCtV4SXoxdCdATXsW24cvagBEehGM0AaC4nauFRnAfWYxw61csRCiFaQwPUNqTIJakuhBBCiK6qWyTICwrqkigxMTGN5sXGxrrnt5XL5WrX++vbqH/5A9ne1rnzskGM6BPFS1/tJb+8lpXrDrNy3eFGy8VHBHLX1DRG9IkGTcVbu1d+v76to7c3KTqIvy8cyxtrDvHJ1ix2HCtmx7HGj2spwKQhCdw6MZXQQFOHxeNvv18huhr15CFQXe7kOICiN6DpDai5e0AS5EJ0O1JiRXidrRacrajZezbduGauEEIIz+gWCXKr1YrRaETXxCMHZrMZq9Xa5rZVVaWysrI94bnbsVqtKIrSZJy+Rra39dJ6BPDs3GFsPFzM5sPFHCuqpsrqJNhioF9cCKP7RnFe30h0OsUjn8n2kN+vb+us7Z07thdT0mP4NjOfPTnlnCy3omkasaEWBvcK46KBMcRHBILLRmWlrcPi6Gq/X1VVu0QcQnQafTOnm5oGemPnxiKEEMI3OO3w/dt+XzNXCCGEZ7Q6Qb5x40YWLFjQomWXLVvGuHHjWh3UmSwWCw6HA03TUM6ox2yz2bBYLG1uW6fTERIS0t4QcblcaJpGcHAwen3j+tK+Rra37aaODGPqyL4eiqxjyO/Xt3Xm9oaEhNAn3rt1xbva71eS48Lf6Hul49y3Bq22EiWg7pxLs9cCGvrk4d4NTgjRJtKDXHQJUjNXCCGEh7Q6QZ6cnMzjjz/eomX79evX2uabVF9apaCggLi4uAbzCgoKSEpKalf7nkqY6PV698sfyPb6Ntle3ybbK4ToLLpeaRgGXoxz/xq0koq6iXoD+uSR6PuN8W5wQog2UWWYHCGEEEL4kFYnyOPi4pgzZ05HxNKswYMHA7Br164GCfKSkhKys7OZOlVqVwohhBBCdEWKosN4/nXok4ag5maiaS70PVLQ9UpHaa78ihCiS5P+48KnKHLHRwgh/F2XuyqprKykoKCA2NhYd+mTMWPGEBERwYoVK5g8ebJ72RUrVqBpmiTIhRBCCCG6MEVR0CcMQp8wyNuhCCHaSQPUNqTIJakuuiRLECh6qC5vf1uKrm58DSGEEN1OpyXIP/jgA/Ly8tw/Z2Zm8uKLLwIwc+ZMEhISAPj666959NFHefrpp5k1axZQNxDngw8+yO9+9zvuuOMOJk2axP79+1mxYgVXXXWVu4e5EEIIIYQQQoiO1ZYEuRBdktEMLgeseaf9A3726AsjL/VMXEIIITpVpyXI3333XTZt2uT+edeuXezatQuAkSNHuhPkzbn22msxGAy8+uqr/OlPfyIqKorbb7+du+66q0PjFkIIIYQQQgjxM0mPC5/jiQE/QyI9E4sQQohO12kJ8jfeeKNFy82aNcvdc/xMV111FVdddZUHoxJCCCGEEEIIIYQQQgjhr3TeDkAIIYQQQgghRPehorX6JboXh8PB3//+dy655BIyMjKYMWMGH3/8sbfDEkII0ZW1ctBjRVEwm80oXWCw5C43SKcQQgghhBBCiK5KQ1XakvCWJHl38sc//pEPPviAuXPnkpKSwqpVq3jwwQdxuVzyVLcQQojG2jDosVnTGJAQh9Flg2p7w5kGE5gDPBxk8yRBLoQQQgghhBCiRTTaNkinpMe7jz179vDee+9x3333cffddwMwe/ZsFixYwDPPPMO0adMwmUxejlIIIUSX0oZBjzVVw2WzYTCbQXdaL/KQKLh4tiTIhRBCCCGEEEJ0TVIyxbd9/vnn6HQ65s6d656mKArz5s3jvvvuY+PGjVx44YVejFAIIUSX1ZpBj1UVzWoFiwV03q0Crmia5rdnN9u2bUPTNI/d/VZVFZ2Xf6GdSbbXt8n2+jbZXu+x2+0oisKIESO8HUq35enjd3enaRoOhwOj0dgl6vf5Atmnnif71PNask89fczZtWsXVquNinJnq98bGmbAYjGTkZHhkVhEx/nFL35Bbm4uX375ZYPpJ0+e5OKLL+aBBx7g9ttvb3W79cdvo9HY7hgVNKitBtXVvoYMxroeip3UloaGQgu+Azs5rs5sy70PPBVXF9u+lrSVVazhdIFBD0lR/vt5aPD34KefhxZ/J3RyXJ3ZVpP7QKeHgCC01u6bMzgcjhafB/l1D3JPn5x3leRLZ5Ht9W2yvb5Nttd7FEWR5FA7yf5rSFEUuVngYbJPPU/2qee1ZJ96+phTvz6Lxdyu94uuraCggJiYmEbTY2Nj3fPbov6z6JnPpAKBIR5o55ROaqvVW94Nt/FcGu0DT8XVRbavJW0lBXuurTbrAm01+ffgZ5+Hdn0bdpNtPJez7YP2Hi1acx7k1wny4cOHezsEIYQQQrSSHL+FEMI7UlNTvR2C6ARWq7XJmxk6nQ6j0YjVam1Tu3L8FkII0VV1nS51QgghhBBCCCGE8CqLxYLdbm80XVVVHA4HFovFC1EJIYQQHUcS5EIIIYQQQgghhAAgJiaGwsLGA6zVl1apL7UihBBC+ApJkAshhBBCCCGEEAKAwYMHk5WVRVlZWYPpO3bsACAtLc0LUQkhhBAdRxLkQgghhBBCCCGEAGDq1Kmoqsqbb77pnqZpGsuXLycyMpIxY8Z4MTohhBDC8/x6kE4hhBBCCCGEEEL8LD09nZkzZ/L8889TUlJCamoqq1atYtOmTTz11FNNDuAphBBCdGeKpmmat4MQQgghhBBCCCFE12C323nxxRd5//33KS4upk+fPtx6663MnDnT26EJIYQQHicJciGEEEIIIYQQQgghhBB+SWqQCyGEEEIIIYQQQgghhPBLkiAXQgghhBBCCCGEEEII4ZckQS6EEEIIIYQQQgghhBDCL0mCXAghhBBCCCGEEEIIIYRfkgS5EEIIIYQQQgghhBBCCL8kCXIhhBBCCCGEEEIIIYQQfkkS5O3kcDj4+9//ziWXXEJGRgYzZszg448/9nZYHWLnzp088cQTzJgxg+HDhzN+/Hhuv/12du3a5e3QOs2HH35IamoqGRkZ3g6lw2RmZnLnnXcyevRohg4dyvTp03n99de9HVaHOHLkCPfffz8XX3wxQ4cOZcqUKfztb3+joqLC26G1S3V1Nf/4xz9YtGgR559/PqmpqfzrX/9qctmioiJ+85vfMGbMGIYPH86CBQvYvXt3J0fcPi3d3q+++opf//rXTJ48maFDh3LppZfy+9//nvz8fC9ELUTHOHz4MH/729+4+uqrGTlyJGPHjmXBggWsW7fO26F1C/50XtcZ5Nyx4/nDuakQ3cHGjRtJTU1t8rV+/foGy/rC+fe5tOSa0lf3wyOPPNLsZyE1NZWtW7e6l/XVfQAtv9b25X2QmZnJbbfdxsiRIxk2bBjz5s1jy5YtTS7rC/uho/IQu3btYsGCBQwfPpwxY8bw0EMPUVxc7PH4FU3TNI+36kceffRRPvjgA+bOnUtKSgqrVq3i+++/Z/HixVx11VXeDs+j7rvvPrZu3crUqVNJTU2loqKCt956i+zsbF566SUuvvhib4fYoaqrq5k6dSpVVVU4nU6fvLhbu3Ytd9xxB4MHD+byyy8nMDCQrKwsVFXloYce8nZ4HpWTk8PMmTMJCgpizpw5REVFsWPHDt577z2GDBnCf//7X2+H2GY5OTlMmjSJHj160K9fP9atW8evf/1rbrvttgbLWa1WrrnmGoqKili4cCGhoaG8+eab5OXl8fbbb9O/f38vbUHrtHR7x4wZQ2xsLJMmTSIhIYHs7GxWrFiByWTivffeo2fPnl7aAiE8Z/HixbzzzjtcdtllpKenY7PZ+Oijj9izZw9/+tOfuOGGG7wdYpfmT+d1ncHfzx07mj+cmwrRXWzcuJEFCxYwd+5chg4d2mDeuHHjiImJAXzn/PtsWnJN6cv7Yfv27WRlZTWavnjxYpxOJ2vXrsVkMvn0PmjptbYv74O9e/cyZ84cIiMjmTNnDkajkXfffZejR4/y73//m5EjR7qX9ZX90BF5iEOHDjF79mwSEhKYM2cOFRUVLFu2jNjYWN555x0sFovnNkATbbZ7924tJSVFW7JkiXuaqqravHnztLFjx2o2m82L0Xne1q1bG21TWVmZdsEFF2hXXXWVl6LqPM8884w2depU7YEHHtDS09O9HY7HVVZWauPGjdPuvfdezeVyeTucDvf8889rKSkp2t69extMf/LJJ7WUlBTt0KFDXoqs/Ww2m3by5ElN0zQtOztbS0lJ0V5++eVGyy1btkxLSUnRNm/e7J5WUlKijR49Wrvzzjs7Ld72aun2btiwodG07du3aykpKdpTTz3V4XEK0Rl27typVVVVNZhms9m0GTNmaGPGjPGL7/e28rfzus7g7+eOHc3Xz02F6E42bNigpaSkaJ988slZl/OV8+/mtPSa0tf3w5kOHTqkpaSkaH/4wx/c03x5H7T0WtuX98GiRYu0ESNGaIWFhe5p1dXV2kUXXaTNmjWrwbK+sh86Ig9xxx13aOeff75WWlrqnlb/fbts2TKPxi8lVtrh888/R6fTMXfuXPc0RVGYN28excXFbNy40YvRed6IESMwmUwNpoWFhTFmzBgOHTrkpag6x7Fjx/j3v//NI488gtFo9HY4HeLjjz+mqKiIX/7yl+h0Oqqrq1FV1dthdZjq6moAYmNjG0yv/9mjdyI7mclkIi4u7pzLffbZZwwcOJBRo0a5p0VERDB9+nTWrFlDVVVVR4bpMS3d3jFjxjSaNmzYMGJjY33+O0z4j4yMDIKCghpMM5lMTJgwgdLS0g55HNFX+Nt5XWfw53PHjuYP56ZCdFfV1dU4HI4m5/nK+XdzWnpN6ev74UwfffQRAFdeeaV7mi/vg5Zea/vyPtiyZQvnn38+0dHR7mmBgYFMmjSJ3bt3c+zYMfd0X9kPns5DVFVV8cMPPzB9+nTCw8Pdy44ZM4aUlBQ+//xzj8YvCfJ2yMzMJCkpqcEvCnA/UpWZmemFqDpfQUEBERER3g6jQz311FOcf/75Pv0o8I8//khISAgnTpzgsssuY8SIEYwcOZI//elP2Gw2b4fncfXJ0kcffZQ9e/Zw8uRJvv76a1599VWuvPJKEhISvBxhx1JVlX379jFkyJBG84YMGYLD4eDgwYNeiKxzWa1WKisrff47TIiCggIMBgMhISHeDqXLkvO6zuMP544dzR/OTYXojn7/+98zYsQIhgwZwo033tig3rQ/nH+35JrSH/bD6TRN4+OPPyYhIcFdVsPX90FLrrV9fR84HI4mO93VT9uzZw/g+5+FM7Vmew8cOIDD4Wh22b1793q0U6fBYy35oYKCAnctsdPV3xUrKCjo7JA63datW9m8eTMLFy70digd5ttvv2XdunXuu76+6tixYzidTu655x5mz57Neeedx+bNm3njjTeoqKjgb3/7m7dD9KhLLrmEe++9l1deeYVZs2a5p99www089thjXoysc5SVlWGz2fz+O+y1116jtraWadOmeTsUITpMVlYWn376KRMnTuzWT8d0NDmv6xz+cO7Y0fzl3FSI7sRoNDJlyhQuvPBCIiMjOXz4MEuXLuWmm25y1xv2h/PvllxT+sN+ON3WrVvJzc3lzjvvRFEUwPevxVpyre3r+6BPnz789NNPOJ1ODIafU6/1g3Tm5+cDvr8fztSa7a3/t7llbTYb5eXlHut0IQnydrBarY0eGwXQ6XQYjUasVqsXouo8xcXFPPjgg8THx3PPPfd4O5wOYbfbefrpp5kzZw79+vXzdjgdqqamhtraWm688UZ+97vfAXDZZZfhdDp58803ufvuu+nbt6+Xo/Ss+Ph4MjIymDp1KnFxcWzZsoU33ngDk8nk3ge+qr4HR1PfYWazGcDnv8O2bNnCkiVLuOyyy5g4caK3wxGiQ9TW1nL//fdjNpt59NFHvR1Ol+bv53WdwR/OHTuaP52bCtGdjBgxghEjRrh/njRpEpdffjkzZszgr3/9K//5z3/84vy7JdeUAQEBgG/vh9M1VV7FHz4L57rW9vV9MG/ePP7whz/w8MMPc9ttt2EwGFixYoX7icT6bfP1/XCm1mxv/b9nW9aT1Q4kQd4OFosFu93eaLqqqs0+TuErqqqqWLRoEdXV1Sxfvpzg4GBvh9Qhli1bRnl5uV9cxNV/Xq+44ooG06+44grefPNNtm3b5lMJ8o8//pjHHnuMzz77jMTERAAmT55MeHg4zz33HFdeeSUZGRlejrLj1B9QmvoOqz/I+PJ32IEDB7jrrrsYMGAATz/9tLfDEaJJGzduZMGCBS1adtmyZYwbN67BNIfDwS9/+UsOHjzIv/71L+Lj4zsiTJ/hz+d1ncFfzh07mj+dmwrR3SUmJjJlyhQ++ugjamtr/eL8uyXXlPUdU3x5P9Sz2+18+eWXpKenN7iW9vXPQkuutetLmvrqPrjuuuvIz8/nX//6F5988gkAycnJ3H///fzv//6ve8wgX/8snKk121v/79mWrW/PEyRB3g4xMTHk5eU1ml7/GMCZAxL4CqvVyp133smRI0dYunQpKSkp3g6pQ1RWVvLSSy8xZ84cysvLKS8vB+oGnNA0jePHjxMQEOAzv+fY2FgOHjxIZGRkg+n1P1dUVHgjrA6zcuVKBg4c6D5g15s8eTLPPfccW7du9ekEeXh4OCaTicLCwkbzfP07LDs7m1/84hdERETwyiuvSJJGdFnJyck8/vjjLVr2zJ6kqqry8MMPs3btWp577jnGjh3bARH6Fn89r+sM/nLu2NH87dxUCF8QHx+PqqpUVFQQExPj8+ffLbmm9KfrkDVr1lBWVsZdd93VYLqv74OWXGsPHjzYp/cBwL333svNN9/MgQMHMJvNDBo0iLfeeguoO88H3/8snKk121tfWqW5Zc1mM2FhYR6LTRLk7TB48GA2bNhAWVlZgwGdduzYAUBaWpqXIus4DoeD++67j+3bt/Piiy82eIzM15SXl1NTU8Orr77Kq6++2mj+ZZddxvjx45uc1x0NHjyYdevWkZ+f3+Dudn1trDNPcrq7oqIi913b0zmdTgBcLldnh9SpdDodgwYNYufOnY3m7dy5E6PRyIABA7wQWcfKz8/npptuQqfTsXTpUqKiorwdkhDNiouLY86cOW1672OPPcann37KE088wZQpUzwcmW/yx/O6zuBP544dzd/OTYXwBdnZ2ej1esLCwvzi/Lsl15T+sB/qffTRRxgMBqZPn95guq/vg5Zca/v6PqgXHBzc4Nxn3bp1BAQEuAds9Zf9UK8125uamorRaGTnzp3MmDGj0bKDBg1Cp9N5LjaPteSHpk6diqqqvPnmm+5pmqaxfPlyIiMj3SP3+gpVVXnwwQf54YcfWLx4MRdddJG3Q+pQUVFR/P3vf2/0Gj16NEajkb///e/ceeed3g7TYy6//HIA3nvvvQbT33vvPfR6vc99nvv06cP+/fsbjQhdXyNu8ODB3girU02dOpV9+/a5BwoBKC0t5ZNPPmH8+PE+17O6pKSEhQsXUl1dzdKlS92P9QnhaxYvXsxbb73Fgw8+yOzZs70dTrfhb+d1ncHfzh07mr+dmwrRnZSVlTWadvjwYb788kvOO+88d6kAXz//buk1pa/vB6h76ue7775j3LhxTXbK8eV90NJrbV/eB03ZvHkzq1atYvbs2Q22zd/2Q0u3Nzg4mPHjx/PJJ5+4n5qDujKUBw4cYOrUqR6NS9E0TfNoi37moYce4uOPP2bu3LmkpqayatUqvvvuO5566imuueYab4fnUU8//TSvvfYaF1xwATNnzmw0v6lpvuiRRx7h008/ZdeuXd4OxeN++9vf8u677zJt2jT3iOOfffYZt9xyCw899JC3w/OoLVu2cNNNNxEcHMzcuXOJiYlhy5YtfPLJJ4wePZrXX3/dPcp4d7R8+XIqKiqorKxk6dKljB8/3n2Xev78+YSEhFBbW8usWbMoKSnh5ptvJiQkhDfffJO8vDz++9//dqtH4FuyvVdffTWZmZnMnz+/Ufmc6OhoLrjgAm+ELoRHvf766zz55JOkpaWxcOHCRvMvvfRSAgMDOz+wbsKfzus6g5w7dg5fPjcVoru4+eabCQwMJD09naioKI4ePcrKlSsBWLFihfspJF86/25OS64p/WE/vP322/z+97/nr3/9a6Per+Db+6Cl19q+vA82bdrEkiVLGD9+PBEREezbt4+3336blJQU/v3vfzfoYe9L+8HTeYgDBw5w3XXX0atXL+bMmeNuNzo6mnfffdc96K8nSIK8nex2Oy+++CLvv/8+xcXF9OnTh1tvvdUnT/jnz5/Ppk2bmp2/f//+TozGe3z5IsThcPDyyy/z3nvvUVBQQHx8PHPmzGHhwoXdOlncnN27d7NkyRIyMzMpKSkhNjaWKVOmcO+993b7BNLEiRPJzc1tct7q1avp1asXUFfPa/HixaxZswa73U5GRga/+c1vGDJkSGeG224t2d7U1NRm3z969GjeeOONjgpPiE7zyCOP8P777zc7//S/f9GYP53XdQY5d+wcvnxuKkR38frrr/PJJ59w/PhxqqqqCA8P5/zzz+euu+5qNE6Ir5x/N6el15S+vh8WLFjArl27WL9+fbNJPF/eBy291vbVfZCVlcWf/vQnMjMzqayspGfPnlxxxRXcfvvtTX4efGU/dEQeYufOnfzv//4vu3btwmQycdFFF/Hwww+7a5R7iiTIhRBCCCGEEEIIIYQQQvglqUEuhBBCCCGEEEIIIYQQwi9JglwIIYQQQgghhBBCCCGEX5IEuRBCCCGEEEIIIYQQQgi/JAlyIYQQQgghhBBCCCGEEH5JEuRCCCGEEEIIIYQQQggh/JIkyIUQQgghhBBCCCGEEEL4JUmQCyGEEEIIIYQQQgghhPBLkiAXQgghhBBCCCGEEEII4ZckQS6EEEIIIYQQQgghhBDCL0mCXAghhBBCCCGEEEIIIYRfkgS5EEIIIYQQQgghhBBCCL8kCXIhhBBCCCGEEEIIIYQQfkkS5EIIIYQQQgghhBBCCCH8kiTIhRBCCCGEEEIIIYQQQvglSZALIYQQQgghhBBCCCGE8EuSIBdCCCGEEEIIIYQQQgjhlyRBLoQQQgghhBBCCCGEEMIvSYJcCCGEEEIIIYQQQgghhF+SBLkQQgghhBBCCCGEEEIIvyQJciGEEEIIIYQQQgghhBB+SRLkQgghhBBCCCGEEEIIIfySJMiFEEIIIYQQQgghhBBC+CVJkAshhBBCCCGEEEIIIYTwS5IgF0IIIYQQohmlpaU88sgjjB8/ntTUVObPn8/EiROZP39+g+Xqp5/ukUceITU1tdXrfO+990hNTWXjxo3til0IIYQQ3tHUuYIntPXcoiOkpqbyyCOPeDsMITzC4O0AhOiKNm7cyIIFCxpMMxqNxMTEMGjQIBYuXMjo0aO9FJ0QQgghOsvixYv55JNPuOOOO0hMTCQ6Opo//OEP3g6rzfbu3cuqVau4+uqr6dWrl7fDEUIIIVqsqev0M61evVqOb0KIVpMEuRBnMWXKFCZNmgSA3W7nyJEjvPPOO3zzzTe8+OKLjXqKCSGEEMK3rF27lgsvvJB77rnHPe2LL77wYkTts3fvXpYsWcLo0aMlgSCEEKJbOv06/UyRkZGdHI0QwhdIglyIsxg4cCAzZ85sMG3UqFHcddddvPPOOx5NkDudTlRVxWQyNZqnaRq1tbUEBgZ6bH3+rrq6mqCgoFbPaw35vQkhRPdXVFREWFhYg2lNHauFEEII0Tmauk4XQoj2kBrkQrRSbGwsUFdy5XRr167lgQceYPLkyQwZMoQRI0Ywd+5cvvnmm0Zt1NcNKy0t5Q9/+APjx48nIyOD7du3u+uOrl+/npdffpkpU6aQkZHBq6++CsDOnTt59NFHmTJlCsOGDWPYsGFcc801vPvuu43WU15ezuLFi7nssssYMmQII0eOZNq0aTz99NMt3t7Vq1ezcOFCzjvvPDIyMpg0aRK/+93vKCkpabDcRx99xOzZsxk+fDjDhg3juuuu49NPP23UXn2N1vz8fH79618zevRohgwZwrx589i9e3eL47Lb7fy///f/mDFjhnt/L1y4kM2bNzdYLicnh9TUVJ5//nm+/PJLrr32WoYOHcodd9wB/Fwbbt++fdx2222cd955jBgxwv3+w4cP86tf/Ypx48aRnp7OpEmTWLx4MVVVVQ3Wc67fmxBCiO6l/litaRrvv/8+qamppKam8t5773VYXdEzuVwulixZwsSJE0lPT2fq1KmsWLGiyWWzsrLctdLT09O56KKLePzxxxscrx955BEeffRRABYsWODepkceeQS73c7w4cO59957G7S7ZMkSUlNTueWWWxpMX7ZsGampqezcudM9TdM03nrrLa699lr3OcoNN9zAqlWrmox5w4YN3HrrrZx33nmkp6dz+eWX869//QuXy9VgOU+dOwghhPAvP/30E3fccYf72Dh27FjmzZvH6tWrGyzncDh47bXXmDVrFsOGDWP48OHMmDGDf/zjH+5lVFXlpZdeYv78+e72LrzwQh5++GHy8vJaFE9rcgat1dLrVoCCggJ+85vfMGbMGPexesOGDR6rb97S3ADAmjVrmD17NkOGDOH888/n0UcfpbS0VOqbi04nPciFOAur1eq+sHQ4HBw7doxnn30Wo9HIDTfc0GDZ999/n9LSUq666iri4uIoLi7m/fff58477+S5555j2rRpjdq/+eabCQsLY9GiRWiaRnR0NLm5uQA888wz1NbWMnPmTKKioujRowcAX3/9NYcPH2batGn07NmTiooKPv/8c377299SUlLCokWL3O3ff//9bNq0idmzZ5OWlobdbuf48eNs2LChRdv/j3/8gxdeeIHk5GTmz59Pjx49yM3N5dtvvyU/P9/9+Nrf//53XnzxRVJTU7nzzjsB+Pjjj3nggQfIzs52J6Pr1dTUMG/ePNLT0/nlL39JcXExr732GrfeeiurVq0iODj4rHE5nU5uu+02Nm/ezBVXXMENN9yA1Wrlo48+4qabbuKFF15gwoQJDd6zevVqXnvtNW644Qauu+46NE1zzztx4gTz589n8uTJPPDAAxQVFQF1j6HPnTsXVVWZM2cOvXr1Ytu2bSxdupT169ezcuVKAgICGqynud+bEEKI7uX6669n7NixPPTQQ4waNYrrrrsOoMFN1I7217/+laqqKq677jpMJhOffPIJ//M//0NJSUmDRPbevXuZP38+FouFa665hoSEBI4dO8Z//vMffvzxR9555x1CQkK4/vrrMZlM/Pe//+WOO+6gb9++ACQlJWEymRg5ciQbN25EVVV0urp+NBs2bECn07F161bsdru79/yPP/5IaGgogwcPdsfx6KOP8sEHHzBp0iRmzJgB1J233H333Tz++OPMmTPHvew777zD73//e9LS0li0aBGhoaFs27aNZ599lr179/Lcc8812BftPXcQQgjhO06/Tj+dwWAgNDQUgKNHj3LzzTcTERHB3LlziYmJoaSkhN27d7Njxw53iRaHw8GiRYv48ccfGT16NHfffTeBgYEcPXqUL774gvvuu8+93CuvvMKUKVOYMGECQUFB7N+/n3fffZcff/yRjz76iPDw8LPG3ZacQUu05rq1srKSuXPnkp2dzTXXXMPgwYM5cuQId955J0lJSW1a/+lakxtYvXo199xzDzExMdx2222EhoayevXqBjkNITqNJoRoZMOGDVpKSkqTr/Hjx2s//vhjo/dUV1c3mlZTU6Nddtll2rRp0xpMf/jhh7WUlBTtV7/6laaqaoN57777rpaSkqJNnjxZq6qqatF6XC6XduONN2ojR47U7Ha7pmmaVlFRoaWmpmqPPfZYazbdbceOHVpKSop2/fXXazU1NU2uU9M07ejRo9rAgQO1q6++WqutrW0Q5/Tp07VBgwZp2dnZ7unz5s3TUlJStJdeeqlBe5988omWkpKirVy58pyxLVu2TEtJSdG++uqrBtPtdrt21VVXaRMnTnRPy87O1lJSUrS0tDRt//79jdqaMGGClpKSor355puN5t14441aamqqtn379gbTn3/+eS0lJUV74YUX3NPO9XsTQgjRPaWkpGgPP/xwg2kTJkzQ5s2b12DavHnztAkTJjSYVn+8b636Y8pFF12klZeXu6dbrVbt6quvbnRsnTlzpjZx4kSttLS0QTs//fSTNmjQIO35559v1PaGDRsarfeVV17RUlJStF27dmmaVnceM3jwYO2hhx7SUlJStI0bN2qapmkOh0MbNmyYdtddd7nf+/XXX2spKSnasmXLGrV7++23ayNGjNAqKys1TdO0goICLSMjQ7vrrrsanQe9+uqrDdalaZ45dxBCCNH9ne06PSUlRZsyZYp72X//+99aSkqKtmPHjrO2WX/se+qppxrNq7/m1TRNU1W1wfVuvXXr1mkpKSna//t//6/B9KbOFVqTM2hOU+cWrbluffbZZ7WUlBRt+fLlDZb98ssv3fuxpc48R2pNbsDpdGqXXHKJNnLkSC0/P9+9rMvl0u68884mz7+E6EhSYkWIs5g1axbLli1j2bJl/L//9/947LHHCAsL46677mLjxo0Nlj29znRNTQ2lpaXU1tYyZswYDh061OSjTYsWLUJRlCbXPXfu3CbrYJ++HqvVSmlpKWVlZVx44YVUVlZy9OhRACwWCyaTiR07dpCdnd3qbf/4448BeOCBBxr1kgbcPctWrVqFqqrceuutWCyWBnHecsstuFyuRo+w6XQ6Fi5c2GDauHHjADh27Ng5Y/voo49ISEhg5MiRlJSUuF+VlZVMnDiRnJwc936od/HFF5OSktJke+Hh4e6egfVKSkrYsmUL48ePZ9iwYQ3m3XLLLQQGBvLVV181aqu535sQQgjRWjfeeKO7JxyA2Wzm5ptvbnBsPXDgAHv37uWKK65AVdUGx8XExESSkpJYu3Zti9Y3duxYoK53OMCWLVtwOBz84he/ICIiwj19x44d1NTUuJeHumOzxWLh8ssvbxBDSUkJkydPpqqqip9++gmAL7/8EpvNxuzZsyktLW2w7CWXXALQKOb2njsIIYTwHadfp5/+Or2UaP3x8+uvv8ZqtTbb1kcffURgYCC//OUvG82rv+YFUBTFfb2rqioVFRWUlJQwcOBAQkJCGpQca05bcgbn0trr1q+//pqwsDCuv/76Bstedtll9OnTp9XrP11rcgN79uwhLy+PmTNnusvYQt0+v+2229oVhxBtISVWhDiLxMRE98VXvenTpzNlyhR++9vf8uWXX2Iw1P0Z5eTk8Pe//501a9ZQVlbWqK2KiopGj/8mJyc3u+7mDk4lJSX84x//YNWqVRQWFjaaX15eDtTVSP/DH/7An//8ZyZPnkxycjKjRo3i4osvZtKkSej1+rNtuvtiMy0t7azL1Sff+/fv32hefUL6zAR9bGwsZrO5wbSIiAiAJvfdmY4cOUJtbW2DC/MzFRcXN9iHZ9vXiYmJjfZHfcwDBgxotHxAQACJiYlkZWU1mtfekwohhBCiXr9+/RpNqz/e1h+DDh8+DMDLL7/Myy+/3GQ7iYmJLVrfoEGDCA8P58cff2TRokWsX7+emJgYUlNTGTNmDD/++CO//OUv3Yny04/Dhw8fxmq1ctFFFzXbfn0Js/qYb7/99nMuW6+95w5CCCF8R1PX6We64oor+Pzzz/nXv/7Fa6+9xpAhQxg1ahRXXHFFg45Tx44do2/fvg2S1835+uuvefXVV9m9ezcOh6PBvJYci9qSMziX1l63Zmdnk5KS4s5jnK5v374NOppVV1dTU1PTYJmQkJAGye+mYmlJbqD+3/pyb2fGIURnkwS5EK0UGhrKsGHD+Oabbzh+/Dj9+vWjurqauXPnUlVV5R70Kjg4GJ1Ox7vvvssnn3yCqqqN2mqqZ3a9pg46mqZxyy23cPDgQebNm0dGRgahoaHo9Xq+//57XnvttQbrmT17NhMnTuT7779n8+bNrF+/nnfeeYchQ4awfPnyRheaZ66rJeqXa64nfFPOlpxvyXpVVaVPnz788Y9/bHaZM08Qzravm5rX0u0/U3MnC0IIIYQn1R93649X9QNZNuVsx/sz2xwzZgzff/89drudDRs2cP755wN1yfA///nPVFVV8eOPPxIbG9sgga+qKiEhIQ0GNDtT/QVz/bnKE088QUJCQpPLnt6bDNp/7iCEEMK/GI1GXn75Zfbs2cPatWvZunUr//73v3n55Zd56KGH+MUvftGq9latWsU999xDeno6jz76KD179nRf+/3qV78657GorTmDc2nLMbCl1+5Lly5lyZIlDaY9/fTTzJo166yxtCY30J74hPAkSZAL0QZOpxOoO8hB3QBWJ0+e5Mknn+Taa69tsOxbb73lsfXu37+fzMxM7rrrrkaPgK1bt67J90RFRTFr1ixmzZqFpmk888wzLF26lM8//5yrrrqq2XX16dOHH374gczMTEaPHt3scr179wbg4MGDjZLSBw4cAFrec62lkpOTOXnyJKNHj27yzrcn1A9QcvDgwUbzrFYr2dnZ7m0XQgghOsLhw4eZPHlyg2mHDh0Cfj62nv6E1Ll608G5LzrHjRvHl19+ybfffsvevXuZN28eUJcgdzqdrFmzhh07djQaSCw5OZkjR44waNAgd8/u5tQ/bRUWFtaimIUQQoi2Gjx4sHtA6bKyMq6//nqee+45FixYgMFgIDk5mWPHjlFTU3PWXuQffPABZrOZ5cuXN+hgVVNTQ0VFxTnj6KicQWuvW+t7lDscDoxGY4Pljxz5/+zdd3hUxdvG8e/uppNAaKEjRUSKIih2FAGR3hRRuiKINEFERBGVHyAg2ChSBKSKIl1BpKiIKNgLWBFUQHoLSTbJ7jnvH3kTCSnsJrs5m+T+cHFBZubMPmefbJudM/Nnup87dOjAtddem64ss9nhqbwZG0j99+LbhP/e64jkJa1BLuKlY8eO8c033xAWFpY2cyp1VtPF397+8ssvbNmyxWe3ndXtHD16lHfffTddWUJCAgkJCenKbDZb2puD1KVYstK2bVsAXnrppUzXbEuNoVmzZtjtdubNm0diYmK62583bx4OhyNth3Bf6dixI2fPnmXWrFmZ1l98WXZOlChRguuuu44dO3ZkWE9u/vz5xMfH07x581zfjoiISFaWLVuW7kN3UlISCxYswOFwpM0Wr1WrFjVr1uTdd99NW7rkQqZpcurUqbSfUz/8Z/U+IHXZlNdeew3TNNMGsC+77DIqVKjAjBkzSE5OzrDMWceOHQF48cUXM53NduFrc8uWLQkNDWXatGlpkw0u5HQ6c7QOq4iISKoLX/tSRUdHU7FiRZKSktI+K7dr1474+HheffXVDO0vnNFtt9ux2WwZZnnPnDnTo5nf/hoz8PZza7NmzTh79myGQfkPP/wwwz5eqUvZXPj34iu8LuTN2ECdOnUoV64ca9eu5dixY2ltTdPkjTfe8P6OEMklzSAXycYvv/zC2rVrAXC73Rw8eJB3332X8+fPM2LEiLTNGBs0aEDp0qWZNGkSBw8epEKFCvzxxx+sWLGCK664gj179vgknmrVqnHFFVfwxhtvEB8fT40aNTh48CDLly+nUqVK6dYxO3DgAN26daNZs2bUqFGD4sWL888//7B8+XKioqK48847s72tq6++mv79+zNr1izat29PmzZtKFu2LEeOHGHr1q288MIL1KpVi8suu4z+/fszc+ZM7r33Xtq2bYtpmqxbt47ffvuNYcOGUbFiRZ+cf6oePXqwc+dOpk2bxpdffsktt9xCdHQ0//77L9988w0HDx7MsDFoTowePZpu3brRq1cv7rvvPipVqsTXX3/Ne++9x5VXXskDDzzgg7MRERHJXMmSJbnnnnu4++67CQ4O5r333mPPnj088sgjaTOvbDYbkydPplevXnTs2JFOnTpRo0YNXC5X2uthx44dGTx4MABXXXUVdrudWbNmcfbsWSIiIqhYsSL16tUDUgbCy5cvzx9//EGVKlUoV65cWjw33ngjK1euBMgwQN68eXPuvfde3nnnHX799VeaNWtGqVKlOHr0KD/99BOffvpp2vuhMmXKMHbsWJ566ilatGhBx44d097H/Pnnn2zevJkZM2Zwww03+P0+FhGR/OfCz+kXu+mmm4iJieH111/n008/5Y477qBixYrY7XZ2797Njh07uPPOO4mKigKgZ8+efPzxx7z55pvs3buX22+/nSJFinDgwAE+++wz3nvvPQBatGjBpk2b6NGjBx07dsQ0TT799FP27dt3ySunwL9jBt58bn3ooYd4//33GTduHD///DN16tRh3759rFy5kiuvvJJffvklx3F4MzbgcDgYPXo0gwcP5p577uHee++laNGibN26NW3dcy21InlJA+Qi2di0aRObNm0CUp6co6KiqF27Ns8880y6AeaiRYsyf/58pkyZwltvvUVSUhI1a9ZkypQp7N2712cD5A6Hg9mzZzNlyhTee+89zp8/T9WqVRkxYgR2u51Ro0altS1btiydO3dm9+7dfPzxxyQkJFC6dGmaNGlCv379KF++/CVvb9iwYdSuXZvFixezYMECXC4XMTEx3HTTTZQtWzat3aOPPkqVKlVYsmQJ06ZNA6BmzZpMnTqVNm3a+OTcLxQUFMSsWbN4++23WbNmDa+//jput5tSpUpRp04dhg8f7pPbqVWrFitWrGDatGmsXr2a8+fPExMTwwMPPMDAgQOzXddcREQktx5//HG++eYb3n77bY4dO0aFChUYPXo0PXr0SNfuyiuvZO3atcyZM4ft27fz7rvvEh4eTtmyZWnatCktW7ZMa1u+fHkmTJjA3Llzef7550lOTqZjx45pA+SQMhC+atWqDMuf3HTTTaxcuZIqVaqkex+Q6n//+x833ngjy5cvZ968eTidTkqVKkWNGjUYPXp0urYdOnSgatWqzJs3j5UrV3L27FmKFStGpUqVePDBB6lZs6Yv7kIRESmALvycfrG5c+cSExNDs2bNOHHiBJs2beLkyZMEBQVRsWJFRowYke51NDg4mHnz5rFw4ULWrVvHa6+9lta2RYsWae1atWpFfHw8Cxcu5MUXX6RIkSLcfPPNLFu2jK5du14yZn+OGXjzubVo0aIsW7aMF198kU2bNvHee+9Ru3Zt5s6dy8KFCzlw4ECO4wDvxgaaNWvGrFmzmDZtGrNnzyYyMpKmTZvyyCOP0KRJE4/3UBHxBZupXW1EREREREREREQKrTZt2uB2u9m4caOlcfzwww907tyZ4cOH069fP0tjkcJDa5CLiIiIiIiIiIgUAhfvVQawefNmfv/9d2699dY8iyM5ORmXy5WuzDAMZs+eDUCjRo3yLBYRLbEiIiIiIuJnTqeT2NjYS7YrUaJE2kZeIiIiIr7Wv39/YmJiqFOnDsHBwfz000+sXbuWUqVK0bdv3zyL4/Dhw/Tq1Ys2bdqk7UWyZcsWfvjhBzp06ECtWrXyLBYRDZCLiIiIiPjZhg0b0u0VkpWtW7f6fHNrERERkVR33HEHa9eu5eOPPyY+Pp4SJUrQvn17Bg8eTExMTJ7FER0dzbXXXsuGDRs4efIkpmlSpUoVRo4cSa9evfIsDhHQGuQiIiIiIn537Ngx/vjjj0u2u/baa7UplYiIiIhIHtIAuYiIiIiIiIiIiIgUStqkU0REREREREREREQKpUK9Bvm3336LaZoEBwdbHYqIiBQSycnJ2Gw26tevb3Uo+ZZev0VEJK/p9Tv39PotIiJ5yZvX7kI9g9w0TQrLCjOmaZKUlFRozjeQKReBQ7kIHIUpF4XptcdffHkfFqbfPdD5FnQ634JN52stvX7nXnb3oWEYWR6XWV127a3kr7hy26+3x3va3pN23uY2J+VWU959U6+8+6bfQMy7L58HvOHNa3ehnkGe+s31VVddZXEk/hcfH8/PP//M5ZdfTkREhNXhFGrKReBQLgJHYcrFjz/+aHUI+Z4vX78L0+8e6HwLOp1vwabztZZev3Mvq9dvt9tNbGwsUVFROByOS9Zl195K/oort/16e7yn7T1p521uc1JuNeXdN/XKu2/6DcS8+/J5wFvevHYX6hnkIiIiIiIiIiIiIlJ4aYBcRERERERERERERAolDZCLiIiIiIiISAYrV66kefPmXHPNNbRs2ZL9+/dbHZKIiIjPFeo1yEVEREREREQko23btjFjxgxeffVV6taty8GDB4mKirI6LBEREZ/TALmIiIiIiIiIpDNz5kwGDhyYtqlmpUqVLI5IRETEPzRALiIiIlmKi4tj3rx5/Pjjj/z444+cPn2a4cOH069fP4+ON02TRYsWsWzZMg4dOkSFChXo1q0bPXr0wGaz+Tl6EZGsmaaJ2+3G5XJZHYpHEhMT0/612wv+Spl5db7BwcE4HA6/9Z9XvHm9Tk5OZubMmaxevZqTJ09SpUoV+vXrR9u2bdPauN1ufv75Z44cOUKTJk0wDIN27doxdOjQPPn9c7vdJCcn43a7SUpKwul0puUps7JA4K+4cttvYXi+EBHJLQ2Qi4iISJZOnz7NjBkzKFu2LLVr1+azzz7z6vgZM2Ywbdo02rdvz0MPPcTu3bsZP348586dY9CgQX6KWkQka6ZpcubMGY4fP47b7bY6HI8ZhkFQUBCHDx8uFANeeXm+0dHRlC1bNl9/cevN6/WYMWNYs2YN3bp144orrmDLli08/vjjuN1uOnToAMCJEydwuVx89NFHrFixguTkZPr06UPFihW59957/XYepmly5MgRzpw5k/azaZqcOHEiLT+ZlQUCf8WV235N0yQ8PJzIyEifxSQiUtBogFxERESyFBMTw/bt2ylTpgwHDx6kadOmHh977NgxZs+ezd13382ECRMA6Ny5Mw6Hg9mzZ9OlSxdKly7tr9BFRDKVOvhWtGhRihYtSlBQUEANsmXF7XaTmJhIaGhoQM2a9Ze8OF/TNImPj+fYsWMAlCtXzi+3kxc8fb3es2cPq1atYsiQIQwcOBBIeW3u2bMnkydPplWrVoSEhBAWFgZA9+7dKVmyJABdunRhx44dfh0gT318xsTEEBERAaT8LjgcjnQD5BeXBQJ/xZWbfk3TJC4ujqNHj3L06FEqVKjgs7hERAoSDZCLiIhIlkJCQihTpkyOjt26dStJSUn06NEjXXn37t1ZvXo1W7du5b777vNFmCIiHnG73Zw9e5bSpUtTqlQpq8PxSups97CwsEIzQA7+P9/w8HAg5UvdmJiYfHvfevp6vXHjRux2O926dUsrs9lsdO/enSFDhrBr1y4aNWpEsWLFiImJ8WfIGbjd7rTB8dRB+cwGhzVA7p2wsDAMw+DEiROULVs23/6Oi4j4U8G/Nk9EREQssXfvXkJCQqhZs2a68tq1axMcHMzevXstikxECqvk5GRM06RIkSJWhyIBJHWmcnJyssWR+N/evXupXLky0dHR6crr1auXVp+qU6dOLFu2jNOnT3P8+HFWrFhB48aN/RZb6v2fmg/xndQvggrD77iISE7kqxnke/fuZdq0aXz99dckJiZSqVIl7r33Xnr27Gl1aCIiInKRY8eOUapUqQxrx9rtdkqVKpV2SXtOpF4Wn1sJCQnp/i3o8vJ8ExPh22/t7NplZ+9eO7//buPIERsnTthITAS3GyIjoVgxk0qVTKpUMbnqKoNrrzVo2NAgNDT3MSi/BVtOzjcxMRHDMDAMI1+tPw4pz3up/+a32HMiL8839XciISEBwzCyjCeQZivn1LFjxzJd3ix1tviFr80DBw7k5MmTNGvWjIiICDp37kzHjh1zHcPF+XS73bjdbux2e7q8Z/ZvVmWBwF9x5bbfC393U+/r7KS28UW77NpkVedtudX8FVdu+/X2eF/lPaf1yrtv+g3EvHtT5zJcvLDjBeZ8M4fJt0/m3qv9t6TXxfLNAPmOHTvo378/derUYcCAAURERPD3339z5MgRq0MTEREPHD16NMsBUafTyf79+zEMI23Ny4vFxMTkeKkPsYbT6SQkJCTTutDQUJxOZ477Tk5O5ueff87x8Rc7cOCAz/rKD/x1vnFxdrZtK85HH0Wze3cUTmf2l3HHxkJsrI2DB+Hzz+Gtt1LKw8LcXHddLE2bnqFx4zNEReXuw4fyW7B5e75BQUEkJib6J5g8kJ9jz4m8ON/ExERcLhd//vlntu2yek3LT7J6bbbb7QQHB6d7bQ4JCWHcuHGMGzfOZ7dvGAaxsbEZypxOZ9oVHhcPpGT2pUVWX2RYzV9x5bZfwzDS1iO/1Czy1HzYbLZsN8j1pF12bbKq87bcav6KK7f9enu8r/Ke03rl3Tf9BmLePa37J/Yf+m3qx+5/dwNwIvYE58+fz9X9axiGx8fniwHy8+fPM3LkSJo0acIrr7wSUA8KERHxzJIlS3jppZdyfPxjjz3G8OHDfRiR+FtYWBhJSUmZ1iUmJmb5ZYgngoODufzyy3N8fKqEhAQOHDhAlSpV0i4/Lsj8db7ff29j5sxgVq1yEB//3wzL6JIGda5N5vLayVxW3U2Z8gYlShqEhduw2UycCTbOnLJx5LCdv/cF8fseBz99HczpEw527Ihmx45oJk0y6XS3i0f6u2nQwLsBAuW3YMvJ+SYmJnL48GFCQ0Nz9RxkBdM00zatLAgzmS8lr883KCiIypUrE5rF5St//PGH32PIC1m9NhuGQXJyst8fF3a7PcMSKqkD4sHBwWkDKBd+5jdNM8MYQGZlgcBXcSUlJfH888/z+eefc+7cOapVq8bIkSO59tprc9SfzWbDZrMRFhZ2yRyn5iM8PDzb9co9aZddm6zqvC23mr/iym2/3h7vq7zntF55902/gZj3S9W5XC7W/LmGoZuHEpsUS9HQorzS7BXaXNYm1/evN+8f8sUA+fr16zlx4gSPPvoodruduLg4wsPDA/IFUUREMte9e3eaN2+eodzpdNKhQwcAli9fnmFNzFR5vVGU5F7p0qXZuXNnhm/uUzeKyk1ObTabT9coDQ8PL1RrnvrqfD//HMaMgS1b/iurXN1Nk3ZObmvuovZVBkF22wVvTu1kvQWOC3BhGE5++9nGRxuD2bwuhL9+D2LZ0mCWLQ3mjiYmzz1r47bbvItT+S3YvDnf1IE3h8MRUB+0PZE6o9ZmswVc7KmbMS9evPiSbVetWsWoUaPYunUrFStWzLJdXp6vw+HAbrcTHh6e5eBhQflSonTp0hw+fDhDeepVfv5+v2UYRoYl0gzDICkpCbfbjWmaaUvepEotu1BmZYHAV3ElJSVRrlw5Fi1aRNmyZVm/fj0DBgxgy5YtOdpDwTRNTNPE6XReMr7UfCQkJFxyRuml2mXXJqs6b8ut5q+4ctuvt8f7Ku85rVfefdNvIOY9u7rTCacZtnUYa/etBeCGcjcwp8UcKkZWJCEhIdf3rzfLo+WLAfLPP/+cqKgo/v33Xx555BH++usvIiIi6NChA08++WSW3/KLiEjgKFOmTKZLpFz4IalWrVqUKlUqL8MSP6pTpw4rVqzg119/pVatWmnle/fuJTk5mdq1a1sYneTG33/Do4/CmjUpPzscJre3SuKeB500aGgS7LhwQNw7druNK+vAlXWS6T88me+/svPOmyFsWRfCR9tsfLQNWrU2mTrFxpVX+uyURABIchu4DOvXNQ6y2whxBM5AgLfi4uKYP38+119/PTfccIPV4cgF6tSpwxdffMGZM2fSTUr4/vvvAfz+2qwZ5J6JjIxk0KBBaT+3a9eOKVOm8Pfff1OnTh2v+9MMcv/RTGLf1Cvvvuk3EPOeVd2Of3bQe11v/jn3Dw6bg9G3juaJm58gyB7ks/u3wM0gP3DgAC6Xi0GDBtG5c2caNmzIl19+yeLFizl37hxTp07NVf+Btti/P6SeYyBublDYKBeBQ7kIDBfe98pF/nXq1ClOnz5N+fLl05Y6aNq0KePHj2fJkiWMHz8+re2SJUsIDg6mSZMmVoUrOZSUBC+/DGPHmsTH23A4TO66J5Heg51UrQZ2mw3w3SxLmw2uaWhwTUMnA590Mu/VUNYvD2XD+za2bDZ5ejQ8OdJGAVgWWAJAktvg+2NnSXRZPys1NMhOvZhi+WaQfN68eel+jo+PZ/r06QwaNCjDAHn79u1p3bp1gVjPOz9q0aIFc+fOZdmyZQwYMABIGdRdsmQJJUqU8PsXGppBnjN//vkn58+fp2LFijnqXzPI/UcziX1Tr7z7pt9AzPvFdcnuZCbtmsTLX72MYRpcVvQy5tw1h+vLX0+SM4kkknx2/xa4GeTx8fEkJCTQtWtXnn76aQCaN2+Oy+Vi2bJlDBw4kGrVquWo78w2CSmIUje3iY+P1+CTxZSLwKFcBIYLPyQlJCQU+OdkbzYKCRRLlizh3LlzabnZtWsXLpcLSLmsPioqiqVLlzJ9+nQWLVqU9uE6JiaGvn37MnPmTFwuF9dddx1ffvkla9euZeDAgVo2J5/58kvo3Rv27gWwcfX1yTw2Po46dVIHxv2rQiUYMyWRHv2TmDImjC8+CuHZMfD2OyZvzrfRsKHfQ5ACzmWYJLoMHDYbDrt1y2m4/z8Ol2ESEjiT5bLlzWB3flzeJr/w5PW6bt26tG/fnmnTpnHq1Clq1qzJli1b2L17NxMmTPD7Fxd2u52oqKh0ZakD48HBwZw6dSrd74hpmmnHpQ5yZFYWCPwVV0JCAqNGjaJfv35ZLkV4qbhSYypSpIhHM8hN0yQyMvKSM0ov1S67NlnVeVtuNX/Fldt+vT3eV3nPab3y7pt+AzHvF9b9efZPeq7pyZeHvwSg19W9eO7G5yhfsrxf8u7N5+58MUCe+gTeunXrdOWtW7dm2bJlfPPNNzkeIM/sBbogSv2FioiIKFRrUgYi5SJwKBeB4cIXvPDw8AL/nJzfBscB5s+fz6FDh9J+3rFjBzt27ABSLvnNLmdDhgyhWLFiLF26lPfff59y5coxatQoevXq5fe4xTdME155BUaONElOthFd0uCRp+Lo0MVNkCPvByaqXm4yfWkCG1YmM2VMOHt/snPjjSZjnoVnRtvIhw8xCTAOu40gS3+RDNzunC/zMm3aNKZPn87777/P66+/zieffILNZqN58+aMGjWKyMjItLYrVqxg0aJFHDhwgMjISBo1asTw4cPTLYl24sQJXn75ZXbs2MHJkyeJjo6mTp06PPbYY9SsWRNIvwb5wYMHadq0KQDTp09n+vTpAHTs2JGJEydmWIN87NixrFy5kp07d2ZYW3nixImsW7cuXd3WrVuZNWsWv/32GyEhIVx//fU89thjVK9ePcf3WUHh6ev1uHHjKF++PKtXr2b58uVUrVqVyZMn0759e0vilqwlJyczbNgwqlSpkjbjX0QkvzFNkze/f5PHNj9GXHIc0WHRzGo1i441OwbMBLl8MUAeExPD77//TokSJdKVp/587ty5XPUfSN9C+UvqOWrGhvWUi8ChXASGC+975SIwbdu27ZJtBg8ezODBgzOU22w2evfuTe/evf0QmfjbqVMps8bXrwewcVvLREZNiqd0KZuls/ZsNmh9j4ubGscy+ZkwNq8J5bln4fPPDZYttXPRW0aRQmnYsGGUKVOGYcOG8fPPP7NixQr+/fdf5s+fD8CcOXOYOnUqDRs25IknnuDw4cMsXbqUr776ijVr1lC0aFEg5YvO3377jW7dulGxYkVOnz7Nl19+yf79+9MGyC9UokQJxowZw9ixY7nzzju58847AahcuXKmcbZq1YqlS5eydetW2rVrl1budrvZunUrt912W9rg+Hvvvcfjjz/OlVdeydChQ4mNjWXJkiXcf//9rFy5kkqVKvn0PsxvPHm9hpQZ/0OHDmXo0KH+DSgTmV3BbRgGTqeT5ORkTNPMsOReZkuCBOLyKnDpuI4fP06LFi1o0qQJL774Ylr5Dz/8wAMPPECzZs2YNGlSWl9PPPEEpmkybty4tPsmp3GZpklcXBzJycmXbOt0OtPWg89Nu+zaZFXnbbnV/BVXbvv19nhf5T2n9cq7b/oNxLyfjD/JkM1D2HBgAwC3VryV15u/TsWoipw/f96veffm6u18MUBep04dPvvsM44ePZpupvjRo0cBMgyci4iIiEjufPcdtGtn8s8/NoJDTAY8E0fXB1wEBdCayCVKwcTXndx0m5uJo8LZ9IGdaxqYrFkFDRoEzmX3IlYoU6YMc+fOTfsyq3Tp0sycOZMdO3ZQu3Ztpk2bxvXXX8+CBQsICkr5WHjttdcycOBA5s2bx7Bhw4iNjeXrr7/miSeeoE+fPml99+vXL8vbjYiIoHnz5owdO5aaNWteclbytddeS/ny5dmwYUO6AfJdu3Zx8uRJWrVqBaTMpJ04cSJVqlRh2bJlaVf/NWvWjLvvvptXX32VKVOm5OzOkjxT2JdYKVu2LF26dGHx4sUMHjyYqlWrcujQIQYPHkzdunWZMGFC2rk/99xzHD9+nLlz5xISEpLj89USK/6lpTZ8U6+8+6bfQMv7tv3beGDdAxyKPUSQPYixjccy/MbhOOyObI+zYomVwPmEk42WLVsCsGrVqnTlq1atwuFwaGd0ERERER/atAkaNUoZHK9Yxc3M1Wfp0cdlyZIqnmh/fzIL1p+nfGU3//xl4+ZbYOWqnC9RIVIQdO/ePd1gWuoyKJ988gmff/45SUlJ9O7dO21wHFIGm6tWrconn3wCQGhoKMHBwezevZszZ874JU6bzUaLFi3YsWNHuiuDP/jgA4oUKcLtt98OwJ49ezh+/Dhdu3ZNtzRe7dq1ufnmm9m+fXvaAKVIIOvbty+hoaHMnj2b8+fP079/f4oUKcK0adPS1oA/dOgQK1as4Mcff+SWW27huuuu49prr2V9yiVdIiIBLcmdxJNbn+SupXdxKPYQ1aOrs73ndp64+Ym0wfFAky9mkNeuXZu7776blStX4nK5aNiwIV9++SUbNmygT58+lCtXzuoQRURERAqEefPg4YdN3G4b9W9K4oW5cZQuGfhzKq68ymDZh+d5amA4O7eGcG9nk5dednDrLVZHJmKNyy67LN3PJUqUoFixYhw+fJjSpUsDZLqPU/Xq1dm9ezeQshTH8OHDefHFF7nlllu4+uqruf3222nXrh3ly5f3WaytW7dm/vz5fPjhh9xzzz0kJyezefNmbr/9dkJDQwHS1tbOKuYdO3Zw/vz5Ar+XSX6X0yVWTNMkPjk+XZkVyy1EBEdkO4vbk6VfihcvTpcuXViyZAl//fUXx48fZ9myZRQtWjTtvMuWLcuePXvS9Zt6vjlZZkVLrPiPltrwTb3y7pt+AyHvf5z5g36b+vH9se8B6FmnJ6OvG03JqJJZPv9riRUvPP/885QvX55Vq1axefNmypcvz5NPPqk1TUVERER8wDThuedg7FgAG3d2dDJmagIR4YHzYeRSoorBy28mMOFJk7VLQxn6aCh9Hy7Py1Otjkwk72U2iOfJDOuL26Sui7x161Z27tzJzJkzmT17NjNnzuSmm27ySax169alSpUqbNiwgXvuuYfPPvuMs2fPctddd3l0vGaO5x85WWLFNE0aL2rMzoM7rQg5nVsq3cL23tuzfXx5shTKgw8+yKJFi/jpp5948803M/3iJyf9ZnW8lljxHy214Zt65d03/VqZd8MweOv3txixZQQJrgRKhJdgTus5tK3RltjYWJ88D3jLm8H1fDNAHhwczKBBgxg0aJDVoYiIiIgUKKYJTz4Jkyen/Nx9UDyDn0wKqPXGPRUUBM+86KRUaZN5r4Qxd3Y5DCOJObMggCYeifjdgQMHqFKlStrPp06d4ty5c5QvX54KFSoA8Oeff1K1atV0x/35559p9akqVaqUtuHyv//+S4cOHZgzZ06WA+Q5GcRr1aoVs2fP5tSpU7z//vsUK1aMG2+8Ma3+wphvvfXWDDEXK1aMyMhIr29X8l5mgx2pg+I2my3t74UCaa3xzOLzph5g9uzZabPNixUr5tH5edLvpVz45YMn7S7V1pN22bXJqs7bcqv5K67c9uvt8b7Ke07rlXff9GtF3k/En+DBjQ+y4c+UjTibVWvGwg4LKR9VHrfb7dPnAX/JNwPkIiIiIuJ7pgmjRv03OD7k+fN07+vCEUCDEd6y2WDAyESKlXDx8rNFmDc3hOBgg5nT7eTj0xLxypIlS7j99tvTBtQWL14MwG233UadOnUICQlh0aJFNG7cOO3D57Zt29i/fz/9+/cHICEhAZvNlm7Gably5ShZsiRnz57N8rbDw8MBsm1zsTZt2jBz5kzWrVvH1q1badmyJcHBwWn1derUoXTp0rz11lvce++9aTH98ssv7Ny5k9atWwfUIKpk7eIlQlKXVLHb7WkzxlNnTZumic1m45Nen5DgSkgrS71sPq9zHhEckRbDxS6MOTsLFy5k6dKljBo1ihkzZjBt2jReffXVLNt72u+ljgcyLF+TmdQ2vmiXXZus6rwtt5q/4sptv94e76u857ReefdNv1bk/cN9H/Lgugc5EneEEEcI4+8Yz6M3PIrdZk93nC+eB/xJA+QiIiIihZRpwlNPwaRJKT8PGXueHg+5sBeQQaYuDyRid8QxdXQMs2baCQ0zeHmKBsmlcDh69Ch9+/bljjvu4JdffuGdd97hpptuolGjRgAMHjyYqVOn0rt3b5o3b86///7LkiVLqFChAn369AFSZqH36tWLFi1acPnllxMSEsInn3zCvn37eOKJJ7K87SJFilC1alU2bNhA1apViY6OpmLFitSrVy/LY6pXr07NmjV57bXXiIuLo2XLlunqg4ODefLJJ3n88ce5//77ad++PbGxsSxZsoSoqCgeffRRH9xr4m85XYPcbrcT5gjLUJbXLrXG+KXqP/roIyZNmkTfvn3p3r07Z8+e5fXXX2fPnj1ceeWVOe73UrQGuf9oLWrf1Cvvvuk3L/PudDkZ+9lYXv/udQBqRNdgbou51CtTj7jzcR7dhtYgFxERERHLjRkDEyem/H/I8wVrcDzVne1icdgjmDQqildfshMebvDCuMD5gCWBx22YQO4Go3J/+7n30ksvMWvWLF566SVsNhudOnXiqaeeSqvv168fxYsXZ9GiRUyaNIkiRYrQokULhg8fTtGiRYGUjQLbtm3L559/zvr167HZbFSpUoXx48dzzz33ZHv7L7zwAhMmTGDixIkkJSXRsWPHbAfIIWUW+dSpUylVqhTXX399hoG8Nm3aEBYWlnZeISEhXH/99QwfPpxKlSrl8J6SvJSTNchTj0udLZ7bNbn95VJx7dmzhyeeeILmzZvz2GOPYbPZ6NWrFwsXLmTmzJnMmDEjR/16EpfWIPcfrUXtm3rl3Tf95lXefzr2Ez3X9OSHYz8A8Mi1j/BUw6eIKR7j8WM9uzqtQS4iIiIieWL6dBg3LuX/g587T4++BW9wPFWn7km4kuOZOiaCiePtRBYxeHqUBsklvSC7jdAgO4kuA7fb2k0fQ4PsBNlz93iMjo5m6tTsd6jt3LkznTt3zrK+ePHiPPPMM5e8rdTlWy5Uv359VqxYkaG8U6dOdOrUKdN++vXrR79+/YCUD8eZzXRt1qwZzZo1u2RMErhyuga5J2WBILO4jhw5wiOPPEKNGjWYPHly2qBNdHQ03bt3Z/bs2ezZs4e6det61a+3tAa5f2gtat/UK+++6defebfb7cz8aiYjNo8g0Z1ITJEY5rebT4vqLYiNjfX6sZ5dndYgFxERERG/WrvWwZAhJmCjz+Nx9OxXcAfHU3Xtm0xSYgLTxocz+ik7lSoZ9OyuQXL5T4jDTr2YYrh8NIM7N4LsNkLy4Sa5Ip7wdg3yC//NqiwQZBdXmTJl2L59e4a2AMOGDWPYsGFZHqs1yL2LK69pLWrf1CvvvunXn3n/N/Zfhq0fxqY/NwHQonoL5rWdR5nIMjl6rGdXpzXIRURERMSvvvuuCIMGhWCaNtp2TaDf0OQCPzieqvegJE6dsLF0dhgP9bFRuZJJ49sLx7mLZ0IcdkICa4KaSIGS0zXIM+snEPkrLq1BrrWo/X281iD3TmHL+wf7PmDwlsGccJ4g1BHK2EZj6Xt1X2ymjdjY2Bw91rOr0xrkIiIiIuI3v/5qY/jwy3E6bdzcLJEnX3ASVMhmqQ4dk8jhf+x8tCGEjh1NPt9pcuWVGiQXEckLhXkNcqv61Rrk/lXY16L2Vb3y7pt+fZ33hOQERm4dycyvZgJQt3RdlnRcQt2Yuh73ozXIRURERCRgnD4NnTuHcvasnVrXJDP+9XjCQgrX4DiA3Q7jpifQ7247e74N4q6WBl/ttlG6tNWRifjG4MGDGTx4sNVhiGSpMK5BHij9ag1y/yiMa1FrDfKCn/fvj3xP11Vd2Xt8LwAD6g9g8l2TKRJaxOvbzQ9rkBe+T0UiIiIihYzbDd26wb59dkqXczFx3lmKRhbet4Fh4fDKonjKV3bz9wE77TsZXOKKcxERERGRAs8wDV76/CWuf+N69h7fS9nIsrx///uMv208YUHZX4GSn2kGuYiIiEgBN2YMbNwIoWEmo187StmYgvvm1lMlSpm8tjienq2L8PkOO4+NMJj2SuH90kBEJK8Uxk06rexXm3T6V2HcrFGbdBbcvB+OPcyD6x5ky/4tALS9oi1zWs+hRFgJYmNjc5R3bdIpIiIiIpZ7912YMCHl/8NfOEfNKxMADZADVL3C4LlXEnjioSJMf9XOjTcadLtPg+QiIv6iTTqt6VebdPpPYdusUZt0piiIed+4fyNDtgzhlPMU4UHhjL9tPL3r9sZm2Dh//nyO865NOkVERETEUnv2QO/eJmCj80MJtL07mXNnrY4qsDRt7aLnQCeLZoTRt4+Nq+uaXFU38Na0FREpCLRJZ973q006/auwbNboaTtt0mltvznJ+/mk8zzx6RPM+24eAPXL1mdxh8VcWepKj/vVJp0iIiIiEpDi4+HeeyEuzkaDW5J49Bkn9gAaSAgkA59M5OfvHXy5I5gOHQ2+/dpG0aJWRyUiUjBpk07r+tUmnf5R0Ddr9LadNum0tl9vjv/636/ptrIbf5z5Axs2ht80nHFNxhEaFOp1v9qkU0REREQCztChsHcvlIwxeH56HOEhetuXlaAgeOH1BMqUd/PnH3Ye7OsmwJa2FRERERHxCbfhZtKOSdyy4Bb+OPMHFaIqsKXnFl5s/mKmg+OFgT4piYhIwAjEWUAi+dHbb8PcuWCzmTz1cizlyuixdSnFS5lMnB2Pw2Gy8h0H898MzPVtRURERERy6p+z/9BscTOe3PokLsNF2+pt+abvNzSp2sTq0CylJVZERAqR+GSDRFdgDfrEJ7jS/p9gBnH6gp8DSWiQnYhgfa8sge/PP6Ffv5R1x7sNTOC2O0x9+eShq68z6Pe4k9cnhTNksI1Gt5pcUUP3nUheatKkCddffz3jx4+3OhQREZECZcWeFTz83sOcdp6mSHARXrnrFe6udjdFI7S2oAbIRUQKkUSXwbyvT3M8PnAGoV2JCWn/n/rZcULC4y2MJnOlI4Loc21xDZBLwEtOhvvvh3PnbNS9Lpn+IxKx2/R7640HBiexa3sQ33wezL33GXz5hY3gYKujkjyTGA/JSVZHAcEhEBphdRR+89FHH/HTTz8xePBgq0MREREp8GITY3n0g0dZ8N0CABqWb8jSTkupFl2N2NhYi6MLDBogFxEpZI7Hu/g3NnAGyN2J/8VyJNZFsCtwYhPJb8aNg927IaqYwXPTtO54Tjgc8L9pCdzX1MH339h58mk3UycH1qZQ4ieJ8bDrfUg8b3UkEBoJN7QusIPkH330EW+//XamA+QffPCBrnop4Nxud4af3W43drsd0zTT/gIZ/s2qLBD4K67c9nvhcan3dXZS2/iiXXZtsqrzttxq/oort/16e7yv8p7TeuXdN/1mdvyuQ7vouaYn+07vw26zM/LmkYy5bQzBjuA8ybsvnwf8SQPkIiIiIgXAV1/B+PEpS6sMG3+eKpdZHVH+VbaCyegpCYzsW4SXp9hp29qg8e36sqHAS05KGRx3BEOQhZcNuJJT4khOKrAD5NkJCQkBMg6iSsFgGEaG2YqGYeB0OklOTsY0zQyDIoaRcXnAzMoCQW7iSkpKonfv3nz//fe5imHPnj0ZygzDwDRN4uLiSE5Ozvb41HzYbDbs9qxf+zxpl12brOq8Lbeav+LKbb/eHu+rvOe0Xnn3Tb8XHm9i8vJXLzPxi4m4TTcVoyoy+67Z3FzhZpzxTpw48yTvvnweyMn94enxGiAXERERyeecTujZE9xuG3e0SaRNR0MzMHOpWRsXbe9LZP3yUHo/YLLnRyhSxOqoJE8EBUNwqLUxuLMfwMrOtGnTmD59Ops2bWL+/Pls2rSJpKQkGjVqxPPPP0/x4sXT2n722WfMmjWLn376CdM0ueqqqxg2bBgNGjRI1+dXX33FpEmT+OWXXyhVqhRdu3alRIkSPPXUU2zdupWKFSsCsHXrVt555x327t3L6dOnKV26NK1ateLRRx9NG/h+8sknWb16NQA1a9ZMu43Ufi5cg/zkyZO0aNGCfv36MXTo0HQxnThxgttuuy1d3ZkzZ3j55ZfZunUrZ86coVKlStx777307t1bz4kBwm63ExUVla7M7XZjmibBwcGcOnUKh8OBw5Fy5U7q7Ge73Z6Ww8zKAkFu4/rkk0+oU6cOb7/9doZ+Uwd5ctKvaZppxxYpUoSwsLBs26fmIzIyMi0POW2XXZus6rwtt5q/4sptv94e76u857ReefdNv6nHn3Cd4MH3HuSzfz4DoEvtLsxoNYPosOgc3V5u8u7L5wFveTO4rgFyERERkXxu9Gj4+WcoWdpgxIR4HAE0wyY/G/68k12fBPHXfgePjXAze2bgfDATuZTHHnuMmJgYhgwZwl9//cWSJUsIDg5m6tSpALz33nuMGDGCG264gaFDh2KaJitXrqRXr14sWbKEevXqAfDLL7/Qp08foqOjGTBgAMHBwaxYsYLw8PAMt7ly5UocDgc9evSgaNGifPvtt8yfP58jR46k3W6XLl34999/+eKLL5g8eXLasSVKlMjQX8mSJWnYsCEbN27MMED+wQcf4Ha7ad26NZAy+7ZXr178/vvv3H///VSrVo2PP/6YiRMn8u+///LUU0/55H6V3MtssCN1UNxms6X9vZCnZYEgp3GtXbuWAQMGZHmsL873wi8fPGl3qbaetMuuTVZ13pZbzV9x5bZfb4/3Vd5zWq+8+6bfVb+v4vGPH+dc4jmiQqKY0WoG3a/unuXzR17k3ZfPA/6iAXIRERGRfOzTT+Gll1KWVhk+KZaYUoE3WJBfRRWFZ6YmMLhrJHNed3DvPQZNm+jLB8kfqlWrxpQpU9J+Nk2TpUuX8txzz+FwOBg7dizt27dn4sSJaW26dOlC69ateemll1i4cCEAr776KoZhsGzZMipUqABAp06daNGiRYbbnDp1arqB8/vuu4/LLruM1157jREjRlC2bFnq16/PZZddxhdffEH79u0veR6tW7fmmWee4aeffqJu3bpp5e+//z5XXHEFNWrUAOCdd97hl19+Ydy4cXTu3BmArl27MnjwYBYtWkTXrl2pUqWKF/egSN45efIkhw8f5uqrr7Y6FBHJp846zzLg/QEs+2kZADdXupklHZdQtXhViyPLH/QOX0RERCSfio+H3r3BNG20vNfJnS3MgJxNl5/dfIebDt0SAXigD5w/H1ibwolkpWvXrul+vv7663G73Rw+fJidO3dy9uxZ2rZty6lTp9L+JiQkcPPNN/P111+TnJyM2+1m586dNGnSJG1wHFJme7dp0ybDbaYOjqeuM33q1CkaNmyIaZqZrovsiWbNmhEcHMyGDRvSyo4cOcK3335Lq1at0so++ugjihcvTqdOndLKbDYbffr0wTRNPvnkkxzdvkheWL9+faaPqWPHjnHNNdcwYsSIdOU//PAD9erV4/HHH8+rEEUkgO34ewf1ZtVj2U/LcNgcPHvbs3zS+xMNjntBM8hFRERE8qnnnoM//4SYcm6GPZuA3aa5D/4w7Fknn38cxD8HHAx73M3cWYF1ia9IZi4c0AYoWrQoAGfPnmX//v0APPjgg1keHxsbi8vlwul0ZjrzOrOy33//ncmTJ7N7926cTmeG/nKiWLFi3HrrrWzcuJERI0Zgs9nYsGEDpmmmLa8CcOjQIapUqZLhUuzLL788rV6806NHD7777juCglKGDerXr8/8+fMtjir/S0hIyLBE0bp165g5c2aGtjExMXTp0oXFixczePBgqlWrxqFDh3jkkUeoW7cuEyZMyKuwRSQAJbuT+d/2/zH+0/EYpkHV6KrMaj6Lplc0xWHX+1VvaIBcxA+OHj3KsWPHMq1zOp3s378fwzCy3CAlJiaGMmXK+DNEERHJ57799r+lVYaOP0+J4po57i+RUTBmqpOB9xXhjdkOenQ3uO1WfRkhgS2rjalM00zbTHDixIlZvueMjIzkzJkzWfaf2keq2NhYevbsSXh4OMOGDaNy5cqEhYVx9OhRnnzySQzDyNmJkLLMykcffcS3335LgwYNeP/996lbty6VK1fOcZ/imXHjxnm0FI7fmWbKZVOp/3e7weGAvL5qKiIix7f5008/MWPGDD7//HO2bduWtu7+b7/9RnR0NGXLls30uL59+/LOO+8we/ZsnnnmGfr3709kZCTTp09P2/xWRAqffaf20W1VN3Yd2gVAr3q9eLn5y9iS9JkgJzRALuIHS5Ys4aWXXsrx8Y899hjDhw/3YUQiIlKQuFzQty+43TYat06kmZZW8bsbb3fRtksi698OpV8/+PF7CA62OiqRnKlUqRKQslTKzTffnGW7kiVLEhYWxoEDBzLU/fXXX+l+3rVrF6dOnWLx4sVcf/31aeWfffZZhmO9fb5q2rQp4eHhbNiwgVKlSvHTTz8xcuTIdG0qVKjA3r17cbvd6WaR79u3L61e8inThEaNYOdOAGxYOJBxyy0pm3/k4DW3XLlyjBo1ipYtW7Jhwwa6d+8OwOrVq+nQoUOWx5UqVYouXbqwZMkS/v77b44dO8Y777xD8eLFc3oWIpKPmabJwu8XMnjjYM4nnadYaDFmt5lNl7pdcLvdxCbl7Iqtwk4D5CJ+0L17d5o3b56h3Ol0pr35Wb58OdHR0ZkeHxMT48foREQkv3vtNfj6a4gqZvDY2HgcWlolTwwdk8inm4P59Wc74ye5eW60Ll2V/KlRo0YULVqU119/nZtuuinDLNRTp05RokQJHA4HN998M9u2bePQoUNpg8ynTp3ivffeS3dM6oz1C2eWG4bBggULMtx+REQEkLLcS7FixS4Zb0REBI0bN+aDDz6gePHi2Gw2WrZsma7NHXfcwY4dO1izZg133313Wizz58/HZrNx++23X/J28rO4uDjmzZvHjz/+yI8//sjp06cZPnw4/fr1y9A2OTmZmTNnsnr1ak6ePEmVKlXo168fbdu2zdD2hRde4IUXXuCKK65g5MiR1KlTJy9OJ6MC8CVwyZIlKVmyJLfeeivr1q2je/fuuN1utm3bxpAhQ7I9tnfv3ixatIgff/yRN998k8suuyyPohaRQHI64TQPv/cwK/auAOC2y25jccfFVC6mK6pySwPkIn5QpkyZTC9XjU+9LBCoVasWpUqVysuwRESkADhwAJ55JmVplYdHxVG+XP4fNMgvokuYDH02geceLcLE8Xa6329yeXXd/5L/REZGMnbsWIYPH067du1o27YtpUuX5siRI+zatYvw8HDeeOMNAIYMGcKOHTvo2rUr999/P0FBQaxYsYIKFSpw9uzZtNngDRo0IDo6mieffJLu3bsTFBTEpk2b0r3/TVW3bl0Axo4dy2233UZQUBB33HFH2sB5Zlq3bs3GjRuZN28eDRo0oFy5cunqO3fuzIoVKxgzZgw///wzVatW5ZNPPuGTTz6hZ8+ema6ZXpCcPn2aGTNmULZsWWrXrp3pzP1UY8aMYc2aNXTr1o0rrriCLVu28Pjjj+N2u9PNZB4xYgTVq1fH4XDw1ltv0bdvXz744IO09ezzjM0G27dDQgKQ8sVH6pUCeX71VC6WWEnVrl07HnvsMQ4cOMDff/9NgwYNMqxJfrHZs2enLVPkyZdKIlLwfHzgY3qs7sHBcwcJsgcxtvFYnrjlCa017iMaIBcRERHJJ0wTBg6E+Hgb9W5M5p7uLmyaPZ6n2nR2sf7tZL7eGcxDD7v5aLOjIExslAu5kgvF7bds2ZIyZcowe/ZsFi5cSEJCAqVLl6ZevXp07tw5rV2tWrWYP38+kyZNYvr06ZQuXZquXbsSFhbG3r17CQ0NBSA6Opo5c+YwceJEpk2bRkREBM2bN+f++++nXbt26W67RYsWfPvtt2zcuJH3338f0zTZunVrtgPkt99+O1FRUcTGxqbbnDNVaGgob775Ji+//DIbN27k7NmzVKhQgZEjR/LAAw/46F4LXDExMWzfvp0yZcpw8OBBmjZtmmm7PXv2sGrVKoYMGcLAgQOBlC8XevbsyeTJk2nVqlXaFQVXX3112nEPPPAAK1eu5Ntvv7VmNr7NBkWKpPzfyjXIfaBp06ZERkaybt06Dhw4wH333Zdt+4ULF/LWW28xatQoZsyYwbRp03jttdfyKFoRsVqSO4kxH41h8meTMTGpUaIGSzstpWGFhlaHVqBogFxEREQkn1i7FjZsgKBgkxEvnCfYocHxvGazwdOTndzXNIhPtjpYtNSgV3floUAIDoHQSEg8D26LB8lDI1PiyYHBgwczePDgDOU33HADv/76a7qyBg0aMHv27Ev22bBhQ9599910ZePGjSM0NDTdOsj16tXjrbfeynD8xbfrcDgYPXo0o0ePztB227ZtALjd7nTlISEhfPXVV9nGWbx4ccaOHcvYsWOzP6ECKCQkJMsNVy+0ceNG7HY73bp1Syuz2Wx0796dIUOGsGvXLho1apTpsdrrwjfCwsJo3rw5a9asISwsjIYNsx7k2rZtG5MmTaJv37706tWL2NhYZsyYwS+//MKVV16Zh1GLiBV+O/kbXVd25et/vwbgofoP8XKLl4kMibQ4soJHA+QiIiIi+UB8PAwdmrK0Spd+CVxZUwMVVrmsusGDQ5zMejGcEcOhQ1uTYsWUj3wvNAJuaA3JSVZH8v+D9VnPps5rTqeTsLCwtJ9PnTrFunXraNiwYboNMSXw7d27l8qVK2fYC6levXpp9Y0aNeLcuXP8+OOPaYO3y5cv58SJE1xzzTV5HHHB1K5dO1atWsWAAQOy/OJhz549DB8+nObNm/Poo48C0LNnTxYsWMBrr73GzJkz8zJkEclDpmky9+u5DN00lPjkeEqEl2Bu27l0qtXJ6tAKLA2Qi4iIiOQDEyfCX3/ZiCnvps+jiVpaxWK9Bibx3ooQDh5wMPo5N9Ne1iBhgRAaEVAD04GiSZMmtGvXjqpVq3Ls2DFWrFhBfHw8AwYMsDo08dKxY8coXbp0hvKYmJi0egCXy8XUqVPZv38/wcHB1KxZkzlz5vhk/euLrw5wu9243W7sdjumaab9BTL8m1VZIPAmruuvv56yZcvSvn37TNsfOXKE/v37U6NGDSZOnIjNZsM0TYoWLUq3bt2YM2cOP/74Y9p6/p7EBf/d19lJbeOLdtm1yarO23Kr+Suu3Pbr7fG+yntO65X3//o9dv4YvTb2Yt1v6wBoUqUJC9otoELRCj57/HrbPjd59+XzgD9pgFxEREQkwP3xB0yalDJ7fOAzcRSL0uC41UJCYcQ4J492L8Ks6Xb69zWpU1uzyKVguv322/nwww85ceIEDoeDunXrMmXKFK699lqrQxMvOZ3OtDXGL2S32wkODsbpdAJQokQJVq1a5fPbNwyD2NjYDGVOp5Pk5OS0DTgvHBRJ3Zzy4mMCkTdxvfXWW8TExGQ6AFS6dGk++uijTPt99NFH02aUezp4ZBgGpmkSFxdHcnL2S0il5sNms2G3Z/1+w5N22bXJqs7bcqv5K67c9uvt8b7Ke07rlfcUWw9sZeDmgRyNP0qwPZgxt4xhQP0B2G32DM+dvogrL/Luy+cBbxmG4fHx+WaAfNeuXfTs2TPTugULFnDzzTfncUQiIiIi/mea8OijkJRk47pGSbRoawAaiA0EtzZ1cWuzZHZsCWbAYDcfb9GGnVIwvfDCC1aHID4SFhZGUlLGZYQMwyA5OTndUjr+YLfbM2zGmjogHhwcnDYYcuGAhmmaGQY4MisLBN7EVbZsWb/0mxmbzYbNZiMsLOySOU7NR3h4eLZLKHnSLrs2WdV5W241f8WV2369Pd5Xec9pfWHPe6IrkWc+eYZXd78KQM0SNVnUfhHXlL3Gr3HlRd59+TzgLW/2zsg3A+SpunXrlrY+WqoaNWpYFI2IiIiIf61f/9/GnMPGxhGkjTkDyuNjnezaHsT2bQ7eWeWmy92B8+FNRORipUuX5vDhwxnKU5dWSV1qxV8MwyA+Pj5DWVJSEm63G9M0MQwj3Yzp1LILZVYWCPwVV277TV22xul0XrKf1HwkJCRcckbppdpl1yarOm/LreavuHLbr7fH+yrvOa0vzHn/5eQv9P2gLz+d+AmAnrV6Mv728USGRmZ4vvR1XHmRd18+D3jLNE2PB8nz3QD5tddeS+vWra0OQ0RERMTvEhPhscdS/n/vQwnU1MacAadSVYPu/RNZ8FoYjz9mo21Lk4gI5UlEAlOdOnX44osvOHPmTLqNOr///nsAateu7dfb1wxya/rVDHL/0Qxy39QXxrybpsnsb2YzcttInC4npcJL8XrL17m97O1ERkYWmLxrBrkfxcXFERISQnBwsNWhiIiIiPjN9Omwbx+ULG3w4FBtzBmo+jyayPsrQjj4t53xk9yMfz5wPsCJiFyoRYsWzJ07l2XLlqVtsmqaJkuWLKFEiRLccMMNfr19zSC3pl/NIPcfzSD3TX1hy/vx+OMM2jyIDw98CEDTy5oy/c7pxITHkJCQUKDyrhnkfjJ69Gji4+Ox2+3Ur1+f4cOH53pzmLzcFdUqqeeY17vASnoX3vfKhfUK6+PC/P8/geLCWAIttlSpMRWm3xOx3vHjMHZsysacD42IIzpKs5IDVXgEPPpMAk8PKMJLL9p5pJ9JxQrKVyAzzcB7rRHrFJTfhyVLlnDu3Lm0jdx27dqFy+UCoEePHkRFRVG3bl3at2/PtGnTOHXqFDVr1mTLli3s3r2bCRMmZLqBpy/Z7XaioqLSlaUOjAcHB3Pq1CkcDkfajMHU3Njt9rRBjszKAoG/4sptv6mzz202G0WKFPFoBrlpmpecwepJu+zaZFXnbbnV/BVXbvv19nhf5T2n9YUp7xv/2Eif9X04FneMUEcoE5tOZGDDgdht9gKZd18+D3jLm8H1fDNAHhwczF133UWjRo0oUaIE+/btY/78+fTq1YuFCxfmeJA8s120C6LExEQA4uPjNcBjoQtnSyQkJBSK371AVtgeF3a7HcMRjuE2cbsDZ8bNhbEYhhFQsaUy3CaGYRIXF5/rWUHe7KQthduzz8K5czZq1HHR4X6XZo8HuLs6uHjrDRc/fRPEE0+5WbYwcD7EyX9Sl3CIi4sjPDzc6nAkQKS+R8/vVyjPnz+fQ4cOpf28Y8cOduzYAUC7du3SBqbHjRtH+fLlWb16NcuXL6dq1apMnjyZ9u3bWxK3iEhhkJCcwKhto5j+5XQA6pauy+KOi7kq5iqLIxPIRwPkDRo0oEGDBmk/N23alJYtW9K2bVumTJnCW2+9laN+M/sGuyBK/cYlIiIiw5pvkncu/OYrPDy8UPzuBbLC+Lg4l2Rid9hwBNImfxfEYrfbAyu2/2d32LDbbRQJK5L7vjQ4Lh746SeYPTtl9vigMXGEBOn3JtDZbDD8eScPtI1k+WI7jz1qcF0D5S3QOBwOihUrxvHjx0lMTKRo0aIEBQUF1CzUrLjd7rQv9wNpFp2/5MX5mqZJfHw8x44dIzo6Ot/fr9u2bfOoXUhICEOHDmXo0KH+DSgTmU1QMwwDp9OJy+XCNE1cLle6ySuZTU4IxOVVwH9x+WKChmEYxMXFkZycfMm2TqczbT343LTLrk1Wdd6WW81fceW2X2+P91Xec1pf0PP+0/Gf6LupL7+c/AWAh695mGdveZbwoPB0z4kFMe++fB7wljeT0/LNAHlmKlWqxF133cW6detISEjI8SyQ/P5GyBOp53jh5WqS9y6875UL6xXOx4UL2///CRQXxhJosaVKjanw/J6IlUwThg8Hw7DRqEUiNzdKGSiXwHf1dW7ubJfE5nUhPDrMYMfHKQPnEljKli1LeHg4x44d49y5c1aH4zHDMHC5XAQFBQXUIIG/5OX5RkdHU7ZsWb/ehqTIbomViIgITp48SWJiIpGRkYCWWPFFv6lrj9vtdo++CNISK97REiu+qS+oeTdMg2m7pzFq2yiS3EmUKVKGee3m0aJ6i1z1m9u4vG2vJVbygfLly2MYBufOndNlkiIiIpLvffABfPghBIeYDHwqAXsAffiXSxsy2sknm4LZud3B6nVuOrUPnA9zksJmsxEdHU2xYsVwu91pazQHuoSEBP78808qV65cKD735NX5BgcHB9SgS2F24RUeQNoVnqkzAC8cIL+4LBD4K67c9GuaJnFxcRw/fpxixYrpd10kD/0b+y8Prn+QzX9uBqBNjTbMbTOX0kVKWxyZZCbfD5D/888/aS+kIiISuBLPniDx3IkM5UZSYtr/Yw/9RlBo5h+CQ4uWIrRYKb/FJxII3G4YOTLl/x17JXD55dbGI94rX8nkvj6JLJoZxogRNtq2gny+rHGBZbPZCAoKIigof3wkSl1iITQ09JKb7BUEhe18C4vsllix2WxERESQlJTEkSNH0gaDM7tEPlD3dPFXXLnpN3UD1PDwcI/2wNISK97REiu+qS9oed/w5wYGbx7MKecpwhxh/K/R/+hzdR9shi3bx2FBzLuWWPGxM2fOEB0dna5s3759bNq0iYYNG+pNk4hIgDv02Wr2fzAv2zbfvvZIlnVVW/ShWqu+vg5LJKAsWwY//giRRQ0eGJyojTnzqQcfTWTd8hD+/N3O9NfdDBuiGXsiIpD9Eiupl9IXLVoUt9tNcnIybrebuLg4ihQpkjb7ObOyQOCvuHLbr91uJz4+nqioqDxdcuFSbQrbUht53W8gLrWRXX1ByXt8cjwjNo9g9jezAbimzDUs7riYWqVq5arf3MaV2/ZaYiWADBs2jIiICOrWrUvJkiXZv38/y5cvJygoiJGpU61ERCRgVbilI6WuapRpnWmYJCY5CQ0Jw2bP/NLR0KKaPS4FW2IiPPNMynrj9/dPoHSpwLlsXLwTVRT6PubkxdERTBhvo++DJpGRyqeIiKdS9whKHSgPCwtLN0B+cVkg8Fdcue33wg1PRcR/vvn3G3qs6cGvJ38FYNiNwxjXeByhQaEWRyaeyDcD5HfccQfvvfceb775JufPnyc6OpomTZowYMAAqlevbnV4IiJyCaHFsl4ixTAMgpwJhIeFB9SlcyJ56fXX4a+/bJQqY9C1b5Jmj+dzd/dIZslsN//+42Dyy27GPhM4gzgiIla51BIrnlx+n1+XXLCqX6uWXLhUm8Ky1IZV/QbiUhvZ1efnvGOD6d9MZ9zOcSQbyZQrUo6ZzWfSuHJjkhKSSCIpR/0WlLxriRUf69mzJz179rQ6DBERERGfO3cOxo9PmT3ee2gcRSMD5wOA5ExwCAx4IpFnBkfwyhQ7jw4wKVlSs8hFpHDzZImVS9XltyUXrO7XqiUXLtWmoC+1YXW/gbjURnb1+TXvZ4wzPPT+Q3x04CMAOtTswOzWsykZUTJX/RakvGuJFRHJViDtuC4iItaaMgVOnLBRubqbjl1dgAbIC4K7OiazcKaLP34O4rnxLqa9pLfeIiKZDXakLqniaV127a3kr7hy26+3x3va3pN23uY2J+VWU959U5/f8v7+n+8zdNtQTjtPExEcwastXqVP/T65HuspiHn35fOAv+hduhRI8ckGiS7D6jAyiE9wpf0/wQzi9AU/B5LQIDsRwRqcERFITk5m5syZrF69mpMnT1KlShX69etH27Ztsz3u4MGDNG3aNNO6cePG0blzZ3+Emy8dOQJTp6bMHn9oRDxhIXr+LSgcDhg4KpFhPYOY+7qDEcNMKlfSF+QiIiIi+dX5pPMM2TCEBd8vAOC68textNNSrih5hcWRSW5ogFwKpESXwbyvT3M8PrAGoF2JCWn/n/rZcULC4y2MJnOlI4Loc21xDZCLCABjxoxhzZo1dOvWjSuuuIItW7bw+OOP43a76dChwyWPb9WqFY0bN05XVr9+ff8Em0/9738QH2+jVv1k7mrjBjSAWpA0auaiXkMX338ZxNPPulg8X2+/RaRwu3jTSLfbnfY3s7YX12XX3kr+iiu3/Xp7vKftPWnnbW5zUm415d039fkl77sP7abnmp78cfoPbNgYcdMInm/8PMGOYJ/EWBDz7svnAX/SO3QpsI7Hu/g3NrAGyN2J/8VzJNZFsCuw4hMRudCePXtYtWoVQ4YMYeDAgQB07tyZnj17MnnyZFq1akVISEi2fdSqVYv27dvnRbj50h9/wJw5KbPH+z8ZT5BDg+MFjc0Gg5928lCHSN5a5GDUEwa1r9SX0CJSOGmTzrzvV5t0+pfy7pv6QM+723Dz8lcvM/GLibhNNxUiK/DK7a/QpFoTnPFOnDh9cjsFMe/apFNERETytY0bN2K32+nWrVtamc1mo3v37gwZMoRdu3bRqFGjS/aTkJCAw+G45GB6YTR6NLhcNm5onMRNjVIGyqXgqX+Dm1ubJbNjSzAjn3KzflXgfLAXEclL2qQz7/vVJp3+pbz7pj6Q8/7Xmb/otbYXO/7ZAUDn2p2Z1nwaQa4g5d2Dem3SKSIiIvna3r17qVy5MtHR0enK69Wrl1Z/qQHy119/nalTpwIps8mHDBlCkyZN/BJvfvPdd/D222CzmTz8ZDwObd5coA0a5eSzrUG8tzqIXbsNbrheg+QiUjhpk86871ebdPqX8u6b+kDM+/KfltP/vf6cTTxLZEgk01tOp2e9nmlXwyjvntVrk04RERHJt44dO0bp0qUzlMfExKTVZ8Vut3PrrbfSrFkzypQpw8GDB3nzzTcZMGAAL730Eq1atcpVbKZpEh+f+30cEhIS0v2bl555JgQIonHrRGrVduFy+X+A3O12pfu3oAuk8616BTTvkMim1WE8PtLNpvd9cynuhaz8fbaCzrdgC7TzNU0Tm77IFBEpFM4lnmPQhkEs/mExADdWvJElHZdQvUR1iyMTf9EAuYiIiGTK6XRmuiyK3W4nODgYpzPrAb7y5cszb968dGUdOnSgVatWTJ48mZYtW+ZqoCE5OZmff/45x8df7MCBAz7ryxM//xzBe+/Vwm436dLvBOfO+n6wNDsXr/9a0AXK+d7fN5at6y9jx8fBLF72J9fVP++X28nr32er6XwLtkA6Xy0V5hvapDNv+9Umnf6lvPumPpDyvvOfnfRa24v9Z/Zjt9l5+tanebrR0wTZg9LiUN49r9cmnSIiIpKvhYWFkZSUlKHcMAySk5MJCwvzqr+iRYty7733MmPGDP7880+qV8/5DIzg4GAuv/zyHB+fKiEhgQMHDlClShXCw8Nz3Z+nRo8OBaBpeyfXXBOGzZY3t+12u4iNjSUqKgqHo+C/DQy0842OhrZdnKxeGs6CN6vRo2uyT/u36vfZKjrfgi3QzvePP/6wOoQCQZt05n2/2qTTv5R339QHQt5dhospu6fw4u4XMUyDykUrM/uu2dxY/kYS4tJfzaS8e16vTTpFREQkXytdujSHDx/OUJ66tErqUiveKF++PABnz57NVWw2m42IiIhc9XGh8PBwn/aXnV274IMPwOEweXBoIsHBwXlyuxdyOIIICio8bwMD6Xz7DE1m/Tth7PosmM++sHNnE9+vq5iXv8+BQOdbsAXK+Wp5Fd/QJp1536826fQv5d039Vbn/c/Tf9JzbU++OPgFAN2v6s5rLV6jaGjRTNsr757Xa5NOERERydfq1KnDF198wZkzZ9Jt1Pn9998DULt2ba/7/OeffwAoUaKET2LMj559NuXfOzsmUqOGtbFI3itX0aT9/UmsXBTKmGfhTu1ZKyKFjDbpzPt+tUmnfynvvqm3Iu+mabL4h8UM2jCI2KRYioUW4/XWr3P/Vfdf8ljl3fP6/LBJZ+BclyIiIiIBpUWLFhiGwbJly9LKTNNkyZIllChRghtuuAGAU6dOsW/fvnQbqZ05cyZDfydOnOCdd96hUqVKVKlSxd/hB6SdO2HTppTZ4w8MdWpGYiH14JBEgkNMvtjh4MOtgbWWqoiIiEhhcDrhNPevvJ9ea3oRmxTLrZVv5fv+33s0OC4Fj2aQi4iISKbq1q1L+/btmTZtGqdOnaJmzZps2bKF3bt3M2HChLTNypYuXcr06dNZtGhR2qD5pEmTOHr0KA0aNCAmJoZDhw6xYsUKzp07x8SJE608LUulzh5v0TmR6tWsjUWsU7aCSYf7k1ixMJRnxqTMItd3JSIiIiJ5Y/tf2+mxugd/n/0bh83B842f58lbn8RhD6yrEyTvaIBcREREsjRu3DjKly/P6tWrWb58OVWrVmXy5Mm0b98+2+NuueUWli9fztKlSzl37hyRkZHUr1+f/v37c8011+RN8AFm+3bYsgWCgk16D9Hs8cLugSGJrHkrhN07HWza4qbFnfpAJiIiIuJPye5knvv4OV7Y8QImJtWLV2dpp6XcUPEGq0MTi2mAXERERLIUEhLC0KFDGTp0aJZtBg8ezODBg9OVtWnThjZt2vg5uvwldfZ4q3udVK1iaSgSAMqUN+nQNYkVb6asRX5XM80iFxEREfGX30/+TrdV3fjy8JcAPHDNA7za4lWiQqMucaQUBhogFxEREfGzjz6Cjz+G4BCTXkMSNXtcgJS1yNe+FcKXnzvYuNlNq+aaRS4iBZ/b7c7wc+rfzNpeXJddeyv5K67c9uvt8Z6296Sdt7nNSbnVlHff1Psz76ZpsuC7BQz7cBhxyXEUDyvOrNazuLvW3Wm34S3l3fN6Xz4P+JMGyEVERET8LHX2eJv7nVxWydpYJHDElDPp2C2Jt+eHMmYMtLxTs8hFpGAzDIPY2NgMZU5nytJjdrv9knXZtbeSv+LKbb/eHu9pe0/aeZvbnJRbTXn3Tb2/8n4q4RRDtw5l/b71ANxa8VZeb/46FaMqZngu8oby7nm9L58HvGUYhsfHa4BcRERExI+2b4dPP02ZPd5zoBObLXA+1In1HhicyOqlIXy9y8GGTW5at9AschEpuOx2O1FR6ZczcLvdmKZJZGQkDofjknXZtbeSv+LKbb/eHu9pe0/aeZvbnJRbTXn3Tb0/8r51/1YeWPcAh2MPE2wPZmzjsTx242M+2YhTefe83pfPA97yZnBdA+QiIiIifjRuXMq/LTs7qVRR04MlvdJlTe7ukcRbb4Ty7LPQ6i7NIheRgi2zwQ6Hw5H215O67NpbyV9x5bZfb4/3tL0n7bzNbU7Kraa8+6beV3lPdCUyettopnw+BYCaJWuytNNSri1/rUfHe0p597zel88D/qIpTCIiIiJ+smsXbN4MDodJjwFae1wy12tQIiGhJl/vdrB5W2CtrSoiIiKSX/x8/GdunHdj2uB4vwb9+Lrf1z4fHJeCRwPkIiIiIn4yfnzKv3d2TKRqVWtjkcBVuoxJu/uSAPjfOIuDEREREclnTNNk1lezuHbOtXx35DtKhpdkTZc1zG47myIhRawOT/IBDZCLiIiI+MF338H69WCzmfQY5NTscclWr4GJOBwmOz528OlOw+pwRETS/PPPP1x99dU8/fTTVociIpLB8bjjtF/enkfef4QEVwJ3VruTHx/5kfZXtrc6NMlHNEAuIiIi4gcTJqT8e0ebJK64wtpYJPCVr2TS6u6UWeRj/6cBchEJHOPGjeOqq66yOgwRkQw2/bGJq16/ivW/rSfEEcJLzV/ig+4fUC6qnNWhST6jTTpF/CDx7AkSz53IUG4kJab9P/bQbwSFhmd6fGjRUoQWK+W3+ERExL9+/hnefdcEbPQc7MSu2ePigd6Dk3hvRQhbPgji628Nrq2vuSwiYq0tW7YQGhpK3bp1OXLkiNXhiIgA4HQ5GbVlFK/segWAOqXrsOzuZVxd5mprA5N8SwPkIn5w6LPV7P9gXrZtvn3tkSzrqrboQ7VWfX0dloiI5JEJE8A0bTS6K5E6dVMGykUupcrlBs3aJrN5XQjPjzNYt1ID5CLimbi4OObNm8ePP/7Ijz/+yOnTpxk+fDj9+vXL0DY5OZmZM2eyevVqTp48SZUqVejXrx9t27ZN1y4hIYGXXnqJN954g5UrV+bVqYiIZOunYz/RdWVXfjz2IwCDGg5i8p2TCQ/OfAKiiCc0QC7iBxVu6UipqxplWmcaJolJTkJDwrDZMx8wCS2q2eMiIvnVvn2wbFnq7PEEzR4Xr/R5NJHN60J4b7WDn342qFtLg+QicmmnT59mxowZlC1bltq1a/PZZ59l2XbMmDGsWbOGbt26ccUVV7BlyxYef/xx3G43HTp0SGv3+uuv07p1a8qXL58HZyAikj3TNJm+ezojNo8g0Z1ITJEYFrRfQKsarawOTQoADZCL+EFosayXSDEMgyBnAuFh4djt+tArIlLQTJwIhmHjhsZJXNPA6mgkv6lR26DRncl8ujmY/4138/YSvVcQkUuLiYlh+/btlClThoMHD9K0adNM2+3Zs4dVq1YxZMgQBg4cCEDnzp3p2bMnkydPplWrVoSEhLB//34+/PBD1q5dm5enISKSqSPnj/DQew/xwR8fANCqRivmt5tPmcgyFkcmBYXecYuIiIj4yN9/w8KFJoBmj0uO9RmasmfJyrcd7PvTtDgaEckPQkJCKFPm0gNFGzduxG63061bt7Qym81G9+7dOXnyJLt27QLg22+/5fDhw9x2223ccMMNvPHGG6xbt44uXbr47RxERDKz8c+NXDPnGj744wPCgsKY1nIa793/ngbHxac0g1xERETER6ZOheRkG9fcmETDG7X2uOTMVQ3cXN8omd2fBvO/F1y8OVdv2UXEN/bu3UvlypWJjo5OV16vXr20+kaNGtGyZUtuvvnmtPoFCxZw4sQJRo0alesY3G53hp9T/2bW9uK67NpbyV9x5bZfb4/3tL0n7bzNbU7Kraa8+6Y+s/L45Hge//Bx5nw7B4CrY65mccfF1CldB8MwPDovf1HePa/35fOAP+ndtoiIiIgPnDgBc+emDIp3H+TEkcU+EyKe6DM0kd2fBvPWYgfjnzepUF6/TyKSe8eOHaN06dIZymNiYtLqAcLDwwkP/2/Du8jISM6fP0+pUrnbK8kwDGJjYzOUOZ1ObDZbhiUoM6vLrr2V/BVXbvv19nhP23vSztvc5qTcasq7b+ovLv/h2A/0/aAvv53+DYAB1wxgzC1jCA0KzfAcYgXl3fN6Xz4PeMswDI+P1wC5iIiIiA9Mnw4JCTauqOuiUWMDzR6X3Lj2Jjf1Grr4/ssgJkx2MeMVvW0XkdxzOp2EhIRkKLfb7QQHB+N0OjM9bvDgwT65fbvdTlRUVLoyt9uNaZpERkbicDguWZddeyv5K67c9uvt8Z6296Sdt7nNSbnVlHff1KeWRxSJ4LUvX2P0R6NJNpIpF1mOaU2m0bZOW+XdD8fnRd59+TzgLW8G1wPnazcRERGRfCouDqZNS1kr+v7+8QQ5NDguuWOzwQNDUtYiXzjPwenTWotcRHIvLCyMpKSkDOWGYZCcnExYWJgFUYmIwL/n/6XV8laM3DqSZCOZdle046s+X9G4cmOrQ5NCIN9ORVm7di1PPPEEISEh/Pjjj1aHIyIiIoXYG2/AqVM2Klzm5q52bjQHQXzhliYuqtV08+evDl6e7mLsM/n2rbuIBIjSpUtz+PDhDOWpS6ukLrXiL1piJe/71RIr/qW8+6Z+7W9rGbptKGcSzxARFMGE2yfQs05PTMMkwZmgvPvpeC2x8p98+S47Li6OKVOmEBERgcvlsjocERERKcSSk2Hq1JS1x7s8nEBocOC8eZf8zW6HXgMSefbRCGZOszPqcZPwcF2dICI5V6dOHb744gvOnDmTbqPO77//HoDatWv79fbtdjsRERHpylI3YgsPD8/08vuL67JrbyV/xZXbfr093tP2nrTzNrc5Kbea8p67+vNJ53l8y+Ms+H4BAPXL1Gdh+4XULFkzR+eRV5R3z+t9+TzgLZvN8/fN+XKAfObMmURGRnL99dfz4YcfWh2OiIiIFGJvvQX//GOjRGmD9vcmo9nj4kt3dUxm5iSDo4ftzJ7vYujAfPn2XUQCRIsWLZg7dy7Lli1jwIABAJimyZIlSyhRogQ33HCDX2/fMAzi4+MzlCUlJZGQkJDp7MKL67JrbyV/xZXbfr093tP2nrTzNrc5Kbea8p7z+u+OfUe/Tf3Yd2YfNmwMqDeA0beMJiw4LO15Qnn37/F5kXdfPg94yzRNjwfJ89077AMHDrBw4UJmzJjBxo0brQ5HRERECjHDgMmTU/5/9wMJFInQ7F7xreBg6P5wIlOfDeflqXYGPWwSFKTfMxHJaMmSJZw7dy5tCZNdu3alXXHdo0cPoqKiqFu3Lu3bt2fatGmcOnWKmjVrsmXLFnbv3s2ECRMy3cDTlzSDPO/71Qxy/1Leva9PSk5i+vfT+d+O/+EyXFSIqsAbrd7g2pLXZrpZo/Luv+M1g/w/+W6AfMKECdx4443cfvvtPhsgd7vdPuknkKWeY+ovWWFg/v+fQJMaU6DHVxh+Twrj4wIC83dPjwvJrzZsgD17oEiUQefeSdhsgTOzRQqODt2SmPNSKH/vt7P8XTfd7wucD4giEjjmz5/PoUOH0n7esWMHO3bsAKBdu3ZERUUBMG7cOMqXL8/q1atZvnw5VatWZfLkybRv397vMWoGed73qxnk/qW8e1f/z9l/6PdBP7448gUAHWp04OUmL1M0pCgJCQnKewHMu2aQ+8FHH33EZ599xrp163zWZ2abhBREiYmJAMTHxxf4AR673Y7hCMdwm7jdhtXhZGCaKTEZhoEZeOOAGG4TwzCJi4vHMALv/vOlwvS4gMB+bBSmx4U3G4VI4Js4MeXftl2dlIjWrF7xj4gi0OWBJN54JYxJk6BbF/BiQoyIFBLbtm3zqF1ISAhDhw5l6NCh/g0oE3a7PW2gPpXb7cY0zQwzR7Oqy669lfwVV2779fZ4T9t70s7b3Oak3GrKu+f17+x5hwEbB3DGeYbIkEhevetVel7dE5vNprz7qN9AzLsvnwe85c3n7nwzQJ6UlMQLL7zA/fffT/Xq1X3Wb2Yv0AVR6i9UREREhkvaCqJzSSZ2hw2HI/AGoVLH1ux2e0AOktkdNux2G0XCilgdit8VtscFBO5jozA9LgLx/CRnduyAzz6D4BCT+/smava4+NV9fZJYPCuUn75zsHGzm1bNA+dDooiIiEhWziWe49EPHmXxj4sBuLbMtSzqsIiapWtaHJnIf/LNAPmCBQs4e/YsgwYN8nnfgfQtlL+knqPD4SgU5wsubP//J9CkxhTo8RWG35PC97iAQH1s6HEh+dGkSSn/Nr87kQrlA+/3VgqW4qVM2t2XxIo3Q5k02aRVc6sjEhHxXmZXcBuGgdPpxGazZXr5/cV12bW3kr/iym2/3h7vaXtP2nmb25yUW015z77+y3+/pN+mfhw4ewC7zc5j1z3GwKsGUjSkaLrnAuXdN/0GSt5zW+er+9ebq7fzxQB5bGwss2bN4v777+fs2bOcPXsWgLi4OEzT5K+//iI8PJyYmBiLIxUREZHC4Kef4L33wGYz6d7f6dUGMCI51b1/IisXhbB9axC7vza4/trA+aAoIuIJLbGS9/1qiRX/Ut4zrw+LCGPy55MZ9+k43Kaby4pdxsL2C7mpwk3ExsYq737q1+q8a4kVPzt79izx8fHMmzePefPmZahv3rw5t956a6Z1IiIiIr42eXLKv7e1TOLyGtbGIoVHxctMmrVN5sO1IUyYaLBmhQbIRST/yWywI/WKTk/rsmtvJX/Fldt+vT3e0/aetPM2tzkpt5rynt7B8wd5ZM0jfH7wcwC6XtWVGa1mEB0WjdvtVt793K9Vec/JYz27urzOe74YIC9ZsiSvvvpqhvKlS5fy7bffMmXKFEqVKmVBZCIiIlLY/P03vPWWCdjo9kgCds0elzzUa2AiH64NYf0qB7/vM6hRXYPkIpK/uN3uDD+n/s2s7cV12bW3kr/iym2/3h7vaXtP2nmb25yUW015/49pmiz5YQmDPxjM+eTzRIVEMaPlDLpe1TXDscq7f/q16vGek8d6dnVW5D1fDJCHh4fTokWLDOUff/wx3333XaZ1IiIiIv7w0kvgctlocEsS11xrdTRS2Fx5lcGNtyfzxSfBvDDZzfzZGiAXkfxDa5Dnfb9ag9y/lPcUZxPPMnzbcFb+thKA68tdz5y75nBZsctytda48u7f47UG+X/yxQC5iIiISCA4eRLmzv3/2eMDnDg0e1ws0GtQIl98EsyyRQ5e+J9JmRj9HopI/qA1yPO+X61B7l/KO+z4ewe91vbir7N/4bA5GHH9CJ5p/AyhwaEe347y7pt+tQZ5egVuDfKsTJw4kYkTJ1odhoiIiBQSs2ZBfLyNGnVc3HK7AWhgUvJew1vc1Krn4ufvg3jxZTdTXgicD4wiIpeiNcjzvl+tQe5fhTXvBgZjPxnLhB0TMEyDasWrsaj9IuoUq0NocKjXa1Ur777pV2uQ50y+HiAXERERySuJiTBtWsrs8Xv7JhDs0OC4WMNmg96DEhnZN4i5s+w897RJZKR+H0Ukf9Aa5Hnbr9Yg96/Cmvdfj/9K7/W9+fLwlwD0vLonr971KhFBEcTGxnq9VrXy7pt+tQZ5zmmAXERERMQDS5fC0aM2Ysq5adneBQTOOohS+NzR0kXFKm4OHnAwc66bJ4YF1qwqEZHMaA3yvO9Xa5D7V2HLu9vtZuEPCxnz+RjikuMoFlqMl5u8TMcrOkISnHeez9Fa1cq7b/rVGuQZ49Ia5CIiIiI+YpowdWrK/zs94CQsVLN1xVoOB3Trl8Skp8KZ/pqN4UOsjkhE5NK0Bnne96s1yP2rMOX9VMIp+m/qz6pfVgFwW+XbWNh+IZWKVfK4H+Xdv/1qDfL0Cs0a5CIiIiJ54YMPYO9eiIg06dQ9CZstcGawSOHVtksSr08O5Z8DdpavdNOxjdURiYhcmtYgz/t+tQa5fxWGvH+0/yN6rO7BodhDBNmDeP725xl560gcdu8ez9nVK+++6VdrkOeMPt2JiIiIXELq7PHW9zkpEa3Z4xIYwiPgnl5JAEydYnEwIiIiUuAkuZMYuXkkTRc15VDsIWqUqMGH937IyFsyHxwXya80QC4iIiKSje++g61bweEw6fJQIjabBsglcHR5MIngEJNvv3SwfafV0YiIiEhB8euJX7lp3k1M3jkZE5O+Dfry1UNfUb9MfatDE/E5LbEiIiIiko3U2eO3t06kSmVrYxG5WKkYk5adkli3PJSXXwliwvNWRyQikj23253h59S/mbW9uC679lbyV1y57dfb4z1t70k7b3Obk3KrFcS8m6bJG9++wWMfPkaCK4ES4SWY03oOHa7sgNvtJtYdm2W/l7pd5d2//Vr1eM/JYz27OivyrgFyERERkSwcPAjLl5uAja79nJo9LgGpe/+UAfLNG4Lp3zeUWrWsjkhEJHOGYRAbG5uhzOlMeY29eEO1zOqya28lf8WV2369Pd7T9p608za3OSm3WkHL+ynnKYZuG8qGPzcA0LhSY2Y2n0m5yHLExsZest+c1ivvvunXqsd7Th7r2dX56v41DMPj4zVALiIiIpKF114Dl8vGNTcmUa+B1dGIZK56TYObmySzc1swi98qx113Wh2RiEjm7HY7UVFR6cpSZ6xGRkZm2Iwts7rs2lvJX3Hltl9vj/e0vSftvM1tTsqtVpDyvu2vbQzeOpgjcUcIcYQw7o5xDL1hKPYLNqe/VL85rVfefdOvVY/3nDzWs6vz1f3rzeC6BshFREREMnHuHMyenTJ7/L5+TuyaPS4BrEf/RHZuC2bDuuIcP5HAZVoOSEQCVGaDHQ6HI+2vJ3XZtbeSv+LKbb/eHu9pe0/aeZvbnJRbLb/n3elyMnLrSF7b/RoAtUrVYtndy7im7DU56jen9cq7b/q16vGek8d6dnV5nffAuT5BREREJIDMmwfnztmoXN3F7XcaVocjkq2Gt7qpUcdFYoKdGbMD6wOkiIiIBKafjv3E9XOvTxscH3DdAL7q91WWg+MiBZUGyEVEREQu4nLBK6+YANzbN4GQIM0el8Bms0G3fgkAzH8jBKfTtDgiERERCVSmaTJ993Sum3MdPx77kdIRpXmr7Vu81uI1IoIjrA5PJM9pgFxERETkIu++C3//baN4KYO297isDkfEI3e2TaJkjIuTx+zMW+S2OhwREREJQEfPH6XNW20YvHEwie5EWl7eku/6fUeLai2sDk3EMlqDvAA5evQox44dy7TO6XSyf/9+DMMgLCws0zYxMTGUKVPGnyGKiIgEPNOEKVNS/t+hZwJFIjR7XPKHoGDo2P0Mb7xUildftjGgb8rMchERERGA9397nwfWPsDx+OOEOkJ58c4XGXT9IAzDIDY21urwRCyjAfICZMmSJbz00ks5Pv6xxx5j+PDhPoxIREQk/9m+Hb7+GkLDTDr3SsJm0wV3kn+0vPscS2eX4PdfHKx9302HNlqPXEQCi9vtzvBz6t/M2l5cl117K/krrtz26+3xnrb3pJ23uc1JudXyS94TkhN4cuuTzPhqBgBXxVzF4g6LqRtTF8MwfJb3nNYr777p16rHe04e69nVWZF3DZAXIN27d6d58+YZyp1OJx06dABg+fLlREdHZ3p8TEyMH6MTERHJH1Jnj991j5OY0pp+K/lLZFGD9vcnsvyNcF6cAh3aWB2RiMh/MpulahgGTqcTm82G3W6/ZF127a3kr7hy26+3x3va3pN23uY2J+VWyw9533NyD/0+6Mcvp34B4JFrHmHMLWMICwpLezz6Ku85rVfefdOvVY/3nDzWs6vz1f1rGIbHx2uAvAApU6ZMpkukxMfHp/2/Vq1alCpVKi/DEhERyTd++QXeew9sNpP7+yZi0/oUkg/d18fJigVh7PzEwZdfGzS8NnA+UIpI4Wa324mKikpX5na7MU2TyMhIHA7HJeuya28lf8WV2369Pd7T9p608za3OSm3WiDn3W24mbd3HqM/Hk2SO4mykWWZ13Yed1W/K8e3d6l2Oa1X3n3Tr1WP95w81rOr89X9683gugbIRURERP5f6kplt9yZxOU1rI1FJKfKVTRo2iaZD9eGMPFFg5XLNUAuIoEjs8EOh8OR9teTuuzaW8lfceW2X2+P97S9J+28zW1Oyq0WiHk/HHuYXut78dHfHwHQrmY73mj7BqWLlM717V2qXU7rlXff9GvV4z0nj/Xs6vI673q3LCIiIgIcOwaLFpkA3NfPiV2zxyUf69E/CYC1Kx389bdpcTQiIiKSV9b8sob6c+rz0d8fER4UzqzWs1jTZU22g+MihZ0GyEVERESAGTMgMdFGrfrJNLxRA4qSv9W+xk39G124XTYmvxxYG1uJSP4wfvx4GjVqRIMGDWjevDnvvvuu1SGJSDbikuJ4eP3DdHy7IycTTnJV6avY/dBuHr7uYS0bKHIJGiAXERGRQi8+HmbOTBkU79LXSZBDHyIk/+v5SCIAi+Y5OHtWX/qIiHfuu+8+Nm/ezDfffMPs2bN55ZVX+PXXX60OS0Qy8fXhr2kwpwFzvpkDwPCbhrP53s3UKlXL4shE8gcNkIuIiEiht2xZECdO2ChXyc2drV1WhyPiE7c2c3FZdTfnY21Mn61Z5CLinerVqxMWFgb8t274wYMHrQxJRC7iNtxM2jGJG+fdyG8nf6N8VHm29NjCpKaTCA0KtTo8kXxDA+QiIiKSpeTkZF599VUaN27MVVddRdu2bVm/fr1Hx5qmycKFC7nrrruoW7cud911F4sWLcI0A2smq2HAtGkp+5bf82ACYSF6eyQFg90O3R9OmUU+c7qd5OTAeuyJiO/ExcXx2muv0bdvX2688UZq1qzJnDlzMm3rzWv71KlTqVevHnfeeScxMTHcfPPN/jwNEfHCP2f/odniZjy59UlchotOtTrxQ/8faFqtqdWhieQ7+gQoIiIiWRozZgyzZs2iWbNmPPPMM5QrV47HH3+cNWvWXPLYGTNmMGHCBOrVq8ezzz7L1Vdfzfjx45kxY4b/A/fC9u3F+OMPO5HFDDp0TbY6HBGfanVPMsVLGhz+x87SdwyrwxERPzl9+jQzZszgt99+o3bt2tm29ea1ffjw4Xz77bcsX76c5s2bExwc7KczEBFvrNizgnqz6vHxgY8pElyEee3m8W7ndykZUdLq0ETyJQ2Qi4iISKb27NnDqlWrGDRoEKNHj+bee+9l9uzZXH/99UyePJmkpKQsjz127BizZ8/m7rvvZvLkyXTu3JkXX3yRjh07Mnv2bI4fP56HZ5K9pUvLANCuq5NiUVp7XAqWsHDo3DtlFvlLUyHALuAQER+JiYlh+/btfPLJJ4wdOzbLdjl5bbfb7dSvX59jx47x9ttv+/M0ROQSYhNjeXDtg9z77r2cdp6mYfmGfPvwtzxY/0FtxCmSCxogFxERkUxt3LgRu91Ot27d0spsNhvdu3fn5MmT7Nq1K8tjt27dSlJSEj169EhX3r17d5KSkti6davf4vbGV1/Z+fbbKIKDTTo94NQHCymQ7u2dTEioyY/fOtjykdYiFymIQkJCKFOmzCXb5ea13e128/fff/skXhHx3q6Du6g/uz4LvluADRtPN3qazx78jBola1gdmki+pwFyERERydTevXupXLky0dHR6crr1auXVp/dsSEhIdSsWTNdee3atQkODs722Lz02mspa4+37ZRMqTJafkIKpuKlTFp3TpkV+uIUi4MREUt5+tqelJTEu+++y/nz5zEMg88//5z169dz00035XXIIoWe23Azbvs4bpl/C/tO76Nyscp83PtjxjUZR7BDyx6J+EKQ1QFI3tLMOBER8dSxY8coXbp0hvKYmJi0+uyOLVWqFHZ7+u/i7XY7pUqVyvZYT5imSXx8fK76OHjQxurVYQD0eCgWw22QnFyw1yB3u12YgMuVHHCbpfqDzvc/9/UxWL0klM0bHXz3bTxX1Mj/XwglJCSAaZAQF5ey224Bp/P1TlwcPNgnjKuvNnh6tCvX8ZimWSA+S3nz2v7+++8zadIk3G43FSpU4KmnnqJx48a5jsHtdmf4OfVvZm0vrsuuvZX8FVdu+/X2eE/be9LO29zmpNxq/s77vpP7ePC9B/nsn88A6FK7CzNazSA6LDrH931u2l+qXU7rlXff9BuIeffl84A/aYDch+KTDRJdgfdmNT7hvzeECWYQpxNy/wbR10KD7EQE64IGEZFA4nQ6CQkJyVBut9sJDg7G6XR6fSxAaGhotsd64uTJkwwaNChXfcTHOzCMKEqWTObF/zlx2R256k8k0EWXDOXMSQdt7zpP3csCZx8AEX/461hRfv67JNs/TuTTHQm57m/06NGUK1fOB5FZy9PX9pCQEBYsWODz2zcMg9jY2AxlTmfKMmcXf7GeWV127a3kr7hy26+3x3va3pN23uY2J+VW82fel+9ZzqjPRhGbFEtUSBSTG0+my5VdsCXbiE2OveTxVuQ9p/XKu2/6DcS8+/J5wFuGYXh8vAbIfSjRZTDv69Mcjw+sAWhX4n9vCKd+dpyQ8NzNuPO10hFB9Lm2uAbIRUQCTFhYWKabdRlGykzrsLAwr48FSExMzPZYT5QsWZIPPvggV30A7N/v5Ny5A1S7vDLuoMwH9AuSxMRE/v33X8qVK0doaKjV4fidzje9zz9zcE/bIpw462L+sKVkMok0X3G73cTHxxERUQSHo+B/waXz9ZzLbaP2Ax2BKMYP+okHx1TLdTx//PFHrvsIBLl5bfcFu91OREREurLUWYLh4eEZcp1ZXXbtreSvuHLbr7fHe9rek3be5jYn5VbzR1xnnWd5dPOjvLX3LQBurHAjC9ouoFpxz5/LrMp7TuuVd9/0G4h59+XzgLe8ufJLA+Q+djzexb+xgTVA7k78L54jsS6CXYEVn4iIBKbSpUtz+PDhDOWpl1+nXo6d1bE7d+7M8K29YRicOHEi22M9YbPZMnzAzomqVeHnn02iikT4pL9AFx8fz6lDyZQpWkTnWwBd6nw7tYbrroOvvgpizqZ6PNv/QN4H6UOmy4UryY29SFGCggr+xxqdr+dWbyvP/iNRlCzq5MFBJX3y+C8Iy6tA7l7bfcEwjAxLpBmGQVJSEgkJCZnOLry4Lrv2VvJXXLnt19vjPW3vSTtvc5uTcqv5Oq4vDn/Bw5se5u9zf2O32Xm84eOMuGEEQfYgr5YXtCrvOa0v7Hn3Vb+BmHdfPg94y5vl0Qr+OysRERHJkTp16vDFF19w5syZdJt5ff/990DKhpvZHbtixQp+/fVXatWqlVa+d+9ekpOTsz1WRPzDZoPhw+H++2HG2po80fsvwsMK/trsUriYJkx963IABt77DxEVLrc4osCSm9d2X7Db7URFRaUrc7vdmKZJZGRkprMLL67Lrr2V/BVXbvv19nhP23vSztvc5qTcar6Ky2W4GPfpOCbsmIBhGlSJrsLMpjNpekXTfJX3nNYX1rz7ut9AzLsvnwe85c3geuB8/XIJe/bsYfDgwTRp0oR69epxww030K1bN7Zs2WJ1aCIiIgVSixYtMAyDZcuWpZWZpsmSJUsoUaIEN9xwAwCnTp1i3759KRuq/b+mTZsSHBzMkiVL0vW5ZMkSgoODadKkSd6chIikc889ULmim+Nnw1n8Xv5fT1nkYjt/LMGuPSUIDXYx8NGQlG+GJI2nr+0iknf2ndrH7QtvZ9yn4zBMgx5X9eDLB7/k+nLXWx2aSKGRb2aQ//PPPyQmJtKpUyfKlClDXFwcmzdvZuDAgYwZM4Zu3bpZHaKIiEiBUrduXdq3b8+0adM4deoUNWvWZMuWLezevZsJEyakbfK1dOlSpk+fzqJFi9I+WMfExNC3b19mzpyJy+Xiuuuu48svv2Tt2rUMHDjQ75dwi0jmgoJg6DA7jw2Hl965goc6HSaArlgWybXU2eM9WvxDTK1KFkeTt5YsWcK5c+fSNsHctWsXrv9fXrNHjx5ERUV5/NruL9qkM+/71Sad/pWbuEzT5K2f32LkxyM5n3yeoiFFeanJS9xd826MZIMEZ0K+y7s26bS230DMuzbp9LEWLVrQokWLdGU9e/akU6dOvPnmmxogFxER8YNx48ZRvnx5Vq9ezfLly6latSqTJ0+mffv2lzx2yJAhFCtWjKVLl/L+++9Trlw5Ro0aRa9evfIgchHJSp+HbDz3nMGvB6PZsKMkbW47aXVIIj7x+z9FWLM95cqIxx51gyPffNz1ifnz53Po0KG0n3fs2MGOHTsAaNeuXdrSJrl5bc8tbdKZ9/1qk07/ymlcpxNOM+jDQbz787sA3FrpVha0XUDlYpVz1W9O49Imnd7R493zem3SmQfsdjsxMTH88MMPVociIiJSIIWEhDB06FCGDh2aZZvBgwczePDgDOU2m43evXvTu3dv/wUoIl4rWhT69bMxZSpMXVZdA+RSYLzydnVM00arGw9Tq1FFq8PJc9u2bfOonSev7f6iTTrzvl9t0ulfOYlrx8EdPLzpYQ6fP0yQPYiRN4xk2HXDcNgdaY+P/Jp3bdJpbb+BmHdt0ukncXFxJCYmcu7cOTZv3synn35Kq1atctWn2+32UXRg/v+fQHJhPIEcny/zkNpvoJ0r/He+gR6fr/MRiFLPMfXbycIiEH/39LgQEclbQx618cqrJh//UJ6v90Zxbe3YSx8kEsBOng1mwfspMy8fHxgLIeUtjkgyo006875fbdLpX97EleRO4rlPnuPFnS9iYlKjRA0WdVhEw/INc9VvbuPypr026Uyhx7vn9fllk858N0D+7LPPsn79egAcDgctWrTgueeey3F/ma2BlhN2ux3DEY7hNnG7jVz350sXxmMYRsDFZ7hNDMMkLi4ew8h9bIGcCwDTTInJMAzMwBsH9Hk+AlliYiLUEX/KAABrtUlEQVQA8fHxhWLgM5AfG4XpceHNOmgiIv5SqRJ06Wyw9C0HU5dWYdn4H60OSSRXZq2uSkJiEPUvP0njDmWtDkdEJJ1fT/5KzzU9+frfrwHoc00fpjafSmRIpMWRiQjkwwHyhx9+mE6dOnHs2DHWr1+P0+nE6XRm+BbaU5l9g51T55JM7A4bDkeADXxcEI/dbg+4+OwOG3a7jSJhRXzWZ8DmAkgdW7Pb7QE5SOaPfASq1G8iIyIiMqyFWFAF6mOjMD0uAvH8RKRwGj7CwdK34J1PqjLx39+oXC7R6pBEcsSZaGfau9UAGP7QCWyRNS2OSLKiTTrzvl9t0ulfl4rLNE0W7VnEU588RbwrnuJhxXm16au0vbwtZqJJbGLmEzbza961Sae1/QZi3rVJp5/UqFGDGjVqANC+fXt69uxJ//79effdd71afP1CvrscwoXt//8EkgvjCeT4fHu5TGDmAv4730CPL5AuX/KX1HN0OByF4nxTBOZjQ48LEZG8V78+NGnsZtvHDl59qzJTH/vd6pBEcmTZ5oocPRVGxVJx3PtgcavDkWxoiZW871dLrPhXdnGdiD/Bw+8/zNpf1wLQpEoTFrRbQIWiFXLVb27jyk17LbGSQo93z+u1xEoesNlstGjRgrFjx7J//36qVatmdUgiIiIiIvnG8BEOtn0Mc9+vwZi+f1IsquAvOSYFi2nCS29dDsCj3Q4SXOoKiyOSS8lssCN1woqnddm1t5K/4sptv94e72l7T9p5m9uclFsts7g279tMrzW9+Pf8vwTbg3mh6QsMu2kYdpvnA3b5Ne85rS8IeQ+EfgMx7758HvCXfD1ADv+tIXz+/HmLI7Fe4tkTJJ47kaHcSPrvctnYQ78RFBqe6fGhRUsRWqyU3+ITERERkcDSogXUqunm519DeGNNBYb3+NvqkES8smlXDHv2FyUqIom+g8Ihh1cVS965eO8ft9ud9jezthfXZdfeSv6KK7f9enu8p+09aedtbnNSbrWL40p0JfL0R0/zyq5XALiy5JUs7riY+mXrYxombnybB18d76u857Q+v+c9UPoNxLz78nnAn/LNAPnJkycpWbJkurKkpCTWrl1LWFgY1atXtyiywHHos9Xs/2Betm2+fe2RLOuqtuhDtVZ9fR2WiIiIiAQouz1lFvlDD8ErK65gyP3/EBwUgLsli2Rh6v/PHn+o7d8Uq1bV4mjkUrQGed73qzXI/evCuH47/Rt9P+jLTyd+AuDBqx7kf43+R0RwRIbfe2/6zU951xrk1vYbiHnXGuQ+NmzYMEJCQqhfvz4xMTEcO3aMdevWceDAAZ588kmKFCn4GwpeSoVbOlLqqkaZ1pmGSWKSk9CQMGz2zGdVhBbV7HERERGRwqZbN3hqlJuDxyNZ8WEMXVsdtTokEY9891sxtnwZg8Nu8OhQG9gD6/J7yUhrkOd9v1qD3L/cbjeGYbDk1yWM3DYSp8tJqYhSzG0zl7ZXtM1Vv/kx71qD3Np+AzHvWoPcx9q3b8+aNWtYunQpZ8+eJTIykjp16vDEE0/QtGlTq8MLCKHFsl4ixTAMgpwJhIeFB9S3biIiIiJirbAwGDTIxphnYeryy7m/5VGtUiH5wkvLU64i7tz4IJddW8niaMRTWoM87/vVGuT+cyzuGA9seIBN+zcBcFf1u3izw5uUjSyb677za961Brm1/QZi3rUGuQ/dfffd3H333VaHISIiIiJS4DwywM4LLxh880dpPvm6OI2vO211SCLZOnQ8jLc2VwRg+GAnBIdYHJF4SmuQ522/WoPcfzb+sZGH1j/E0bijhDpCmdh0IgMbDsRus+c6zvyad61Bbm2/gZh3rUEuIiIiIiL5QqlS0Ls3vD4LpiypqgFyCXjTVlTD5bZz29VHua5FBavDEQ9pDfK871drkPtegiuB53Y8x5zv5wBQs3hN3mjxBnVj6hJ3Ps4nt5Ff8641yK3tNxDzrjXIRUREREQk3xj2mJ1Zs03e312Zn//8hVrV4q0OSSRTsXFBzFpTBYDh/c9AWBlL4xHPaQ3yvO9Xa5D71g9Hf6DHmh7sOb4HgIHXDeTJ654kpniM8p6L+kDP+8X0ePe8XmuQi4iIiIhIvlGjBrRrY7B2vYOXl1Vhzui9Vockkqn571Xm7PkQrqh4ljZdS1sdjnhJa5Dnfb9agzz3DNPgtV2vMXLLSJLcSZQpUoYF7RfQvFpzYmNjlXcf1Adi3rOjx7vn9flhDfLAuT5BREREREQs9fgTKR9CFm2uxtGTWtNZAo/LZeOVd1I253ys9xHsxUpYHJGIFHT/xv5Lq6WtGLZpGEnuJNpc0YYfHvmBljVaWh2aiPiIBshFRERERASAW26B669zk5gcxMx3KlodjkgGq7eX48C/RShVLIGe/aMufYCISC6s+3UdV8+6mk37NhEWFMbMVjNZd986YorEWB2aiPiQllgREREREREAbDYYPsJBly4wc20NnnxgP+FhptVhiQBgmjBl2eUADOh8kPDyl1sckeSE2+3O8HPq38zaXlyXXXsr+Suu3Pbr7fGetveknbe5zUm5v8QnxzNi8whmfzMbgHpl6rGk4xJqlaqFYRh+jyu/5j2n9YGSd08p757X+/J5wJ80QC4iIiIiImk6dYIqld0c+DucRevL8XDnw1aHJALAZz+UYPfeEoQGuxj4aEjKNzqSrxiGQWxsbIYyp9OJzWbLsKFaZnXZtbeSv+LKbb/eHu9pe0/aeZvbnJT7ww/HfqDvB3357fRvAAxqMIjRN40mNCjUq9/f3Mivec9pfSDk3RvKu+f1vnwe8JZhGB4frwFyERERERFJExQEQ4fZGToMXnrnCvrefZgA+kwqhdiUZTUA6NHiIDG1K1kcjeSE3W4nKir90jhutxvTNImMjMywGVtmddm1t5K/4sptv94e72l7T9p5m9uclPuSYRq8/MXLjP5oNMlGMuWjyjO/7XyaVWuW5THKu2/qrcx7Tijvntf78nnAW94MrmuAXERERERE0nmwj41nnzX47VA0720vSbvGJ60OSQq5nw9EsvbTcthsJo8/ZoA9cAZKxDuZDXY4HI60v57UZdfeSv6KK7f9enu8p+09aedtbnNS7guHzh2i55qebNu/DYCOV3Zkbtu5lIwoecljlXff1FuR99xQ3j2v9+XzgL9oLoiIiIiIiKQTFQX9+6csX/Hi0hoWRyPy3+zxDrceouYtFSyORkQKkpV7V3LV61exbf82IoIjmNt2LivvXenR4LiIFAwaIBcRERERkQyGPGojJMRkx56y7PyumNXhSCF26HgYiz9IWVLliUcTIDjU4ohEpCA4n3Seh9Y9xD0r7uG08zTXlruWbx/+locaPIRNexyIFCoaIBcRERERkQzKl4ce3U0AJi2qZnE0Upi9+k51kl12Gl11jBtbl7c6HBEpAHYf2k392fWZ9+08bNh48pYn2dlnJ1eUvMLq0ETEAlqDXEREREREMjXiCTvzF5is++Iy9u77ldrV460OSQqZM7HBzFpdBYCRA09DWIy1AUmuud3uDD+n/s2s7cV12bW3kr/iym2/3h7vaXtP2nmb25yUe8ttuJm0cxLPf/I8btNNxaiKLOywkNsvuz3tdrzqT3n3Sb2/8+5ryrvn9b58HvAnDZCLiIiIiEimataEDu0MVq918OLiqix4bo/VIUkhM3ddNWLjg6lT5TQt79fgeH5nGAaxsbEZypxOJzabDbvdfsm67NpbyV9x5bZfb4/3tL0n7bzNbU7KvfH3ub/pv6k/nx/+HIAONTrwcpOXiQ6LzvB76Snl3Tf1/sy7Pyjvntf78nnAW4ZheHy8BshFpEA7evQox44dy7TO6XSyf/9+DMMgLCws0zYxMTGUKVPGnyGKiIgEtCeedLB6LSzdVo3/PfIHFcskWh2SFBLOJAfTVlwOwBMPHcVe9EqLI5LcstvtREVFpStzu92YpklkZCQOh+OSddm1t5K/4sptv94e72l7T9p5m9uclHvq7T1vM2DDAM4mniUyJJJX73qVnlf3zPVa48q7b+r9lXd/Ud49r/fl84C3vBlc1wC5iBRoS5Ys4aWXXsrx8Y899hjDhw/3YUQiIiL5y403wm23utm+w8EryyozZdjvVockhcS7H1/OkVPhVCwVx30PFbc6HPGRzAY7HA5H2l9P6rJrbyV/xZXbfr093tP2nrTzNrc5Kc/OucRzDNowiMU/LAbgxoo3sqTjEqqXqO5xH5eivPum3pd5zwvKu+f1vnwe8BcNkItIgda9e3eaN2+eodzpdNKhQwcAli9fTnR0dKbHx8ToUl4REZGRoxxsbw2z36vB0w/up3gxl9UhSQFnGDBj9dUADOtxkJAYbZwnIt7Z+c9Ouq/qzv4z+7Hb7Dxz2zOMvm00QXYNhYlIenpWEJECrUyZMpkukRIf/98mY7Vq1aJUqVJ5GZaIiEi+0rIlXFXHxY97Qnj93Yo81eeA1SFJAbduR3n2HY4mukgifQcXgVwugyAihYfLcDFu+zj+t/1/GKZBlegqLOm4hFsq32J1aCISoAJnhXsREREREQlINhs88WTK3JpXV15BglODleI/pglTltUE4OFOB4i6rLzFERU+SUlJjBo1isaNG9OgQQM6d+7MV199ZXVYIpf05+k/uW3BbTz/yfMYpkGPq3vw3cPfaXBcRLKlAXIREREREbmkLl2gckU3x85EsHC9BizFfz79riS795YkNNjFI4MBLzbZEt9wuVxUqFCBZcuW8dVXX9G9e3cGDBjA+fPnrQ5NJFOmabL4+8VcM+saPj/4OUVDi7K001IWdVxEsbBiVocnIgFO7zREREREROSSgoNh+OMpHx+mLL8Ct9vigKTAmry0BgD3NfmN0leWsziawikiIoJBgwZRvnx57HY77du3x+FwcODAAatDE8ngjPMMXVd1peeansQmxXJr5Vv5vv/3dL2qq9WhiUg+oQFyERERERHxSJ+HbJQo7mbfv8VYuUUbWYvv/bQvivd3lsVmM+ne7Sg4tG2WJ+Li4njttdfo27cvN954IzVr1mTOnDmZtk1OTubVV1+lcePGXHXVVbRt25b169dn2/+ff/7J+fPnueyyy/wRvkiObf9rO/Vm1WP5T8tx2ByMu2McH/f6mCrRVawOTUTyEQ2Qi4iIiIiIR4oUgUGDUj5CTFpaA9O0OCApcF5cljJ7vFOjfyh5ZYTF0eQfp0+fZsaMGfz222/Url0727Zjxoxh1qxZNGvWjGeeeYZy5crx+OOPs2bNmkzbO51ORowYwcMPP0xUVJQfohfxXrI7mae3Pk3jNxvz99m/qV68Op89+BlP3/Y0DrvD6vBEJJ/R1/EiIiIiIuKxwUNsvPiiwTd/lGbrruI0u/G01SFJAfH3kXCWfVgRgGGDz2PY9XHVUzExMWzfvp0yZcpw8OBBmjZtmmm7PXv2sGrVKoYMGcLAgQMB6Ny5Mz179mTy5Mm0atWKkJCQtPbJyckMHTqUKlWqpLUXsdrvJ3+n++ru7D60G4AHr3mQV1q8QlSovsARkZzRDHIREREREfFYqVLQp0/K/ycvqW5tMFKgvLisBi63nTvqH6F+szJWh5OvhISEUKbMpe+zjRs3Yrfb6datW1qZzWaje/funDx5kl27dqWVG4bByJEjAZg0aRI2m833gYt4wTRN5n87n/qz67P70G6KhxVnRecVzGs/T4PjIpIrGiAXERERERGvDH/cjsNhsvmbiny9N9LqcKQAOHoqlDfWpaxv/fTQcxASbnFEBdPevXupXLky0dHR6crr1auXVp9qzJgxHDt2jFdeeYWgIM3mF2udjD9Jr/d70e/9fsQlx9G4SmO+7/8999S+x+rQRKQA0KuciIiIiIh4pUoVuK+LydJlNiYsuJyVL35ndUiSz728vDrOJAc31DpOk3vKkWB1QAXUsWPHKF26dIbymJiYtHqAQ4cOsWLFCkJDQ7npppvS2j3//PO0a9cuVzG43e4MP6f+zaztxXXZtbeSv+LKbb/eHu9pe0/aeZvbrMq37t/KA2sf4PD5wwTZg/hf4//x2I2P4bA7LP89UN59U+9tudWUd8/rffU84G8aIBcREREREa+NesrO0mWw6rMq7PnjN+pcHm91SJJPnT4XzMxVVQF4atApbBE1IV6/T/7gdDrTrTGeym63ExwcjNPpBKBChQr8+uuvPr99wzCIjY3NUOZ0OrHZbNjt9kvWZdfeSv6KK7f9enu8p+09aedtbi8uTzaSGf/FeKZ/PR0Tk2rFqjG3xVwalG1AfFxgPEco776p97bcasq75/W5fR7Izf1rGIbHx2uAXEREREREvFanDnTq4GbVGgcvvFmdJeN+tDokyaemr6xGbHwwV1U9TZvuMVaHU6CFhYWRlJSUodwwDJKTkwkLC/Pr7dvtdiIiItKVpc4SDA8Px+FwXLIuu/ZW8ldcue3X2+M9be9JO29ze2H53/F/88B7D/Dd0e8A6FOvD6OvH83/tXff8VFV6R/HPzOTTkIJkECQ3lNouiB2QREVBFFsCPJbFekLiquoy4IFhUVFEUQRREVkLTS7ousqrAYLQiB0pJfQSS8z9/dHSCRkCDPJTO5k5vv2Na+Ye84989w8ZJI898w5sbVilXcvnO+pvJe33d3jZlPeXW+vyOtARb++7uydoQK5iIiIiIiUy2NP2Fi8FN77rimT9myjeUMtjCHuyciyMf3fhZu9jh92CGv1NiZH5N/q1q3L/v37Sx0vWlqlaKkVb3E4HGSd9e4Ah8NBXl4e2dnZTmcXnt1WVn8zeSuuio7r7vmu9neln7u5hcLC2Ny1c3ky+UmyC7KJDotmxjUz6Nm0J9nZ2cq7l873VN7L2+7ucbMp7663l6fNU19fwzBcLpKrQC4iIiIiIuVy4YXQs4edL76y8dz8psz5R+r5TxI5w+vLmnDsVAgt4k5x2311zA7H7yUkJPDTTz9x4sSJEht1rl27FoD4+HivPr9mkFf+uFVpBvnhrMMM+XwIn23/DIDuTbrzRq83iIuKU969fL5mkLtHeXe9XTPIRURERETE7z0xwcYXX8FbXzdnwv3baVgv1+yQpIrIzbMybWELAB65fz+2mq1Njsj/9ezZkzlz5rBw4UKGDx8OFM6wW7BgAdHR0XTp0sWrz68Z5JU/blWZQf7Nrm8Y8dUIDmUdIsQawoRLJjCs0zCsFitZWVnKu5fP1wxy9yjvrrdrBrmHrVu3juXLl5OcnMzevXupVq0aCQkJjBw5kqSkJLPDE5EqzJ27iiIiIlLSpZfClZfb+e8PNqa904SXHvb8xn7in+Z/1ogDR8O5oE4mg4bVAv1OViELFizg1KlTxZtgJicnU1BQAMDAgQOJiooiMTGRPn36MGPGDI4dO0br1q1ZsWIFq1evZvLkyU438PQkq9VKVFRUiWN2ux3DMIiMjHQ6u/DstrL6m8lbcVV0XHfPd7W/K/1cyW1QWBD/+O8/eHn1ywC0rdOWV695la5Nu5aaUaq8e+98T+W9vO3uHjeb8u56e3naPPX1dae4XmUK5G+88Qa//vorPXv2ZODAgZw6dYr333+f2267jdmzZ3PllVeaHaKInENWvoPcAofZYZSQlV1Q/P/ZRhDHz/jcl4QGWYkI9p075SIiIs48McHGf6+F1z9tyWN//YPY2qU3ARQ5U0GBhSkLWgLw8P/tJSSmlckRVX3z5s1j3759xZ+vXLmSlStXAnDTTTcVF6affvpp4uLiWLJkCYsWLaJp06ZMnTqVPn36mBK3BK6NRzcybMUw1h9eD8CIi0bwzFXPUJDjm3+biYj/qjIF8sGDBzNt2rQSd7T79+/PjTfeyPTp01UgF/FhuQUO5v56nMNZvvOLTkHun5uIPb/qMCHhWWX0NkfdiCDuvbCWCuQiIuLzuneHzhfZWf1LEC++24jnRm8zOyTxcYtWNOCP/dWoWyOb+0ZV1+xxD/j2229d6hcSEsKYMWMYM2aMdwNywuFwFM9wP/NYTk4OFovF6dvvz24rq7+ZvBVXRcd193xX+7vS71x9DMPgtd9fY+KqieTac6kbXpdXrn2FHk17YM+1Oz1Heffu+Z7Ke3nb3T1uNuXd9fbytHnq6+twOFw+v8oUyDt16lTqWI0aNejSpQtfffWVCRGJiDsOZxVwIN13CuT23D9jOZheQHCB78QmIiJS1Vgs8Pg/bPTpAzOXteLv9+wkuoZ+topzdjtMfrtwxvjYu/cQcUFLkyOSyqIlVip/XF9bYuVgxkHu/eRevtz+JQA9m/dkbu+5xEbGljmu8u7d87XEinuUd9fbq8oSK75z+6Wc0tLSqFWrltlhiIiIiIgEtF69oF2inYzsEGYsamh2OOLDPvxPAzburE7NyFyGj43Q7HGRAPHJ1k/o+HpHvtz+JaG2UJ674jmW9l9aXBwXETFLlZlB7syvv/7Kzz//zODBgys0jt1u90xAgHH6P19TFJMvxlcUjyfzUDSur10r+HYuIHDycWYsvhZbkUDJBQTu94WIiD+xWuGxJ2zccQe89FFrxty1mxpRet2Ukux2ePLN1gA8OGAXNZq1MDkiqUxaYqXyx/WFJVZy7DlMXDWRN9a9AUBCnQRe7/E6jSMak5mZqaU2vDCuLy61UVa78u6ZcX0x71pixcuOHj3KuHHjiIuLY+TIkeUex9kP6PKwWq04bOE47AZ2u29tRghgGIUxORwODB+rPTnsBg6HQWZmFg5Hxb92ykXFBEo+zozF4XD4VGxFAiUXEFjfF+78kBYRqWpuvRXatLKzaUsYLy9qxD/u/8PskMTHfPifBqT+UTh7fPTD4WDRz8RAoiVWKn9cs5dYWZe2jmErhrHp6CYARncezeRukwm2BJOenq6lNrw0ri8utVFWu/LumXF9Me9VZYmVKlkgz8jI4P777yczM5MFCxYQGRlZ7rGc/YAur1N5BlabBZvN937JK6rnWK1WnyvMWG0WrFYL1cKqeWxM5aL8AiYfZ8RitVp9K7bTAiYXBNb3hS9en4iIp9hsMGGijbvughfeb8Oo2/dQs7rWIpdCmj0uEjgchoPpq6fzj//8gzxHHvUi6zGv9zx6NO8B6J2ZIuJbqlyBPCcnh2HDhrFjxw7mzZtHq1atKjym5+72FGA5/Z+vKYrJF+MrisezdwOVi/IKlHycGYuvxVYkUHIBgfp9ISLin267DZ6aZGfj5lBeXtSQCUM0i1wKlZg9Pi5Ms8cDkJZYqfxxzVhyYX/GfkZ8NYLv9nwHwPVNr2fGtTOoHV67OP9aasO74/riUhtltSvvnhnXF/OuJVa8ID8/n9GjR7NmzRpmzZpFp06dzA5JRERERETOYLPBPycVrkX+wgdtGH2HZpFL4bvFimaPj71zJzWatTQ5IjGDllip/HEre8mFpZuWMuTTIRzLPkZEcARPXfoUIy4eQVBQkEvna6kNz4zri0ttlNWuvHtmXF/Mu5ZY8TCHw8G4ceP44YcfmDZtGldccYXZIYmIiIiIiBP9+8OTEwtI3RTK9IWNmDh0h9khick+/E9cybXHfWgmoFQuZ8UOm81W/HClraz+ZvJWXBUd193zXe1/Zr+MvAzGfjGWN9YUbsTZqX4n3un7DnEhcQQFBbmVd3ePmy2Q8+7JduXdM+P6Yt7L01bZea8yBfIpU6bwxRdfcOmll1JQUMCyZctKtPfp08ekyERERPyTYRi8/fbbLFy4kH379tGgQQMGDBjAwIEDsVjOvxRO69atnR4fOnQoY8eO9XS4IuJDrFb456Qgbr8dXvywNX+7cze1amgWeaByOGDSvDZA4ezxms01e1zEn/yy/xfu+uguth7bigULf7/07zx59ZPYsJVaUkdExBdVmQJ5amoqAKtWrWLVqlWl2lUgFxER8ayZM2cyY8YM+vTpw3333cfq1at55plnOHXqFCNHjnRpjIsvvph+/fqVOHauwrmI+Jdbb4XE+ALWp4Yy/b1GTNIs8oCl2eNyprM3Z7Tb7cUPZ33Pbiurv5m8FVdFx3X3fFf72+128vLzeG7lc0z8fiIFjgIaRDVgfp/5XN3k6vOOda42d4+bLRDzXla/8rYr754Z1xfz7snXAW+qMgXyd955x+wQREREAkZaWhqvvfYat9xyC5MnTwagf//+2Gw2XnvtNW6//Xbq1q173nEaN26sm9giAapoFnn//jD9w9aM0SzygHTm7PExd+6iZvMWJkckZtImnZU/rrc27dtzcg9DvhjCTwd/AqBPiz682P1FaoXVOu9GnGW1abNGz4zri5s1ltWuvHtmXF/MuzbpFBERkSrrm2++IS8vj4EDB5Y4fvfdd7NkyRK++eYb7rjjDpfGys3NxTAMwsLCvBGqiPiwfv2gXWIB69aH8sK7jXlq+HazQ5JKVjR7vEa1XP72d80eD3TapLPyx/XGpn0fpH7AsM+GcSLnBNWCq/HSdS9xT/t7Si3B58nN+ZR3756vTTrdo7y73q5NOkVERKTKSk1NJSQkpNRyKPHx8QQHBxcvfXY+H3/8Me+//z6GYdCkSRPuv/9+br31Vm+ELCI+qGgW+S23wEsftWbsgF1EaxZ5wCgosDBhTlsAxt61m5rNm5sckfgCbdJZ+eN6atO+9Nx0Rn0+irfWvgXAhbEX8u4t79K67rmXz/Pk5nzKu3fP1yad7lHeXW/XJp0iIiJSJaWlpVGnTp1Sd92tVit16tQhLS3tvGN07NiR66+/ngsuuIDDhw/z3nvv8fjjj3PixAnuu+++CsVnGAZZWVkVGgMgOzu7xEd/p+v1b756vT16QFJCMCkbQpj6ViOeGr7FI+PaCwpKfPR3VfF653/ahM27o6hdPYdhY61kZee4fK6v/Xs2DMOlDapF/NVPe39iwOIB7Di+A6vFyvhLx/O3Dn8juma02aGJSFVmGLBnD6SmQkoKrFuH9cABbI89BldfXWlhqEAuIiIipeTk5BASEuK0LTQ0lJyc8xc5Fi1aVOLzW265hVtvvZVXXnmF22+/vdRbrN2Rn5/Pxo0by33+2Xbu3OmxsaoCXa9/88Xrvef/ajBuXAtmLGnL3detJTba9ULp+Zy9nrG/qyrXm5NnY9LcwrXHR/ZPYU+mBTaecHscX/r3fK6fi+IebdJZueNWdNO+AkcBz658lqd/eBq7YadR9Ua81fctLmlwCenp6WWOq006zRvXFzdrLKtdeffMuL6Y9xJt6emwfj2WlJTCj+vWUT0lBevJkyXOsQDWK6/EfsUVLl2HJ6hALiJ+LffkEXJPHSl13JGXW/z/6fu2EBQa7vT80Op1CK1Rx2vxiVSG5ORkBg0a5FLfN998k0suuYSwsDDy8vKc9snNzS3XeuLBwcEMGjSIxx57jDVr1nBFBX7hCQ4OpkWLim/0lp2dzc6dO2nSpAnh4c5fB/yJrte/+fL1tmkD/34vj59/DWHW4ot46eENFR7TXlBAeno6UVFR2IL8/8+aqna9Mz5owb4jkTSoncnoR+sQFhfr1vm+9u9527ZtZofgF7RJZ+WPW5FN+/Zm7GXIF0NIPpAMwC2tbuH5bs9TI7QGGRkZ5x1Xm3SaN64vbtZYVrvy7plxfSbvBQVYd+zAlpqKdf16Itavx7ZpE7Zdu5yOb9hsOFq2xB4fjz0+noJ27Th18cWEZ2Rok04REU/Yt2oJf3wxt8w+a14eds62pj3vpdkN93s6LJFK1aRJEyZOnOhS3+an14etW7cu//vf/0r9UuFwODhy5AgxMTHliiUuLg6Ak2fNEnCXxWIhIiKiQmOcKTw83KPj+Tpdr3/z1eudOq3wnbJzPmvFuIG7aHaBZ2aR24KCCK4CBWNPqQrXm54ZxHPvFK49/s9hu4hu3hbKuTyJr/x71vIqnqFNOit/3PJu2vfxzo8Z/dVoTuWeIiokipnXz+SupLvcGlebdJo3ri9u1lhWu/LumXFNyfuhQzjWriX0118J27oV6/r1kJqK5RzvODbq14ekJIzERByJiWQ0a0ZEp07YIiKwQeHDbicsPV2bdIqIeEqDS2+mTtLlTtsMh0FuXg6hIWFYrM7/6AmtrtnjUvXFxsZy5513unVOQkICH3zwAZs3b6Zt27bFx1NTU8nPzyc+Pr5csezduxeA6GitVykSaK66Cq67poAvVwQx4bWWLHgqxeyQxEumv9+cwydCadngJINH1S53cVz8kzbprPxx3Tn/ZM5Jhn49lA83fwjAJQ0vYcHNC2haq2m5xtUmneaN64ubNZbVrrx7Zlyv5T0/H9vatdg2bIB164rXCyctDRsQfPYJERGQmIgjKYmcli0JvfBCbB06YKlTWGOxAMbpZVdsERGm510FchHxa6E1zr1EisPhICgnm/CwcJ96u5aIL+jevTvPPPMMCxYs4Jlnnik+vmDBAoKDg+nWrVvxsfT0dNLS0oiJiSmeFXbixAlq1qxZYsysrCzmzZtHZGQkHTp0qIzLEBEf8+zUIL7sBAu/bcbDd++kfeuqsZ62uO7oyWCmLSxcAuvJ0fsIrtv2PGeIiK/4YdcPDFwykF0nd2Gz2Pjnlf9k/OXjCbKqdCQSMBwO2LnzzwJ4SgrWdeuosXUrFoejdH+LBaNFC/LbtCGoY0esHTpAUhI0awZWK4bdTl56OqFRUeBjNznOpFc5ERERKSUmJob777+fWbNmUVBQwEUXXcTPP//MsmXLGDFiRIklVr7++mvGjx/Ps88+S79+/QB45513WLVqFV26dKFBgwYcPnyYJUuWsGfPHp566imqVatm1qWJiIk6doQ7bitg0ftBPD67FZ+8+KvZIYmHTVnQilOZwbRvfozbhsRq9rhIFZBvz+fJ/z7J5JWTcRgOmtRowoKbF3Bp40vNDk1EvOn4cWzJyVi2b4eimeHr10NGRoluRT/Jjdq1sbRrB+3aFRbB27WD+HgcYWFknd4nxZeL4GVRgVxEREScGj16NDVq1ODdd9/l008/pX79+owfP5577rnnvOd26tSJ33//nY8++ogTJ04QFhZGYmIi//jHP7jyyisrIXoR8VVPPRPEh4sNPk1uyA+/befyTifMDkk8ZP/hMGZ80AyAZ8YdwlpTs8dFfN22Y9sYsHgAq/etBuCedvfw1KVPEVc7zuTIRMRj8vJg8+aSS6OkpGDbu5coZ/1DQiA+vrgIbo+PJ6NZMyJbtHC+Ubjd7u0r8DoVyEVERMQpi8XC4MGDGTx4cJn9+vXrVzxzvMill17KpZdq1pGIlNaiBdz3VwezX7cxflYbfpjzkyYZ+4mn5rcmJ8/GpYlp3DCwgdnhiEgZDMNg/u/zGfX5KDLzM6kZVpPXer3GLW1uIT1dy1+JVEmGgWXfPvjjj8IZ4UXF8E2bID/f6SmOhg2xtG9fODO8aFZ4y5YQfMaq4nY7Rnq6X78rTAVyERERERGpVP/4p4233nawKrUen/5Qh15XHDE7JKmgbXur8cbyxgA8+9hxLNVamxyR+Cr7WTMN7XZ78cNZ37PbyupvJm/FVdFxnZ1/LPsYwz4bxkcbPwLgikZXML/PfBrVaOTy87nSz93clue42apS3j3R/3z9ytuuvJdj3PR0WL8ey/r1kJKCJSUF6/r11Dh+3Gl3o3p1SEzEaNeu8GNSEva2bUm3WomKiiq9GaYbr7vl+V4vq82MvKtALiIiIiIilSouDv72N3huCox/NZ7rL/2+qi5ZKac9OiueAruV67vs5/J+Dc0OR3yUw+EoNTvZ4XCQk5ODxWLBarWet62s/mbyVlwVHffs83/Y8wNDvxrK/oz9BFmDeOzixxh94WhsVhvp6ekuP58r/dzNbXmOm62q5N1T/c/Xr7ztynsZ49rtWHfswLZhA7bUVGwbNmBNTcW2c6fTMQybDXuLFjgSErDHxxc+EhIwGjYsNQPc4XCQk53t1bx78nXAXQ6Hw+XzVSAXEREREZFK9/dHrMyebWf9rmje+bQ+g286YHZIUk6r1kXz0XcNsFod/OupTAjV2sXinPX0TMUz2e12DMMgMjKy1AxGZ21l9TeTt+Kq6LhF54eEh/DkyieZ9r9pGBi0jG7JO33f4aK4i8r1fK70cze35TluNl/Pu6vneyrv5W1X3k+Pe+AAtp9/ptq2bVhTU7GkpMCGDVhycpz2N+rXL54NXrRW+KkGDYiqUwebzcb5IquMvHvydcBd7hTXVSAXEREREZFKV6sWPP64hYf/Do/PSaT/NYeoFuEwOyxxk2HAQzMSAbj3xl0kXN3Y5IjE1zkrdthstuKHK21l9TeTt+Kq6Lg7Tu1g6IdD+e3gbwDc1/E+Xuz5IpEhkRV6Plf6uZvb8hw3m6/m3d3zPZX38rYHVN5zciA1tfSmmYcOEeKsf0QEJCYWrhFetE54UhKWOnUAKJoXbtjt2NLTfS7vnnwd8BYVyEVERERExBSjRluZNbOAP3ZVY9qCJvxzyA6zQxI3ffBtHMkboqkWls+kSUCQ0z/tRcQEhmHwxm9v8OBXD5JVkEV0eDRv9H6Dm9vebHZoIoHB4YBdu0oVwtmypbDtLIbFgqNpU6xFm2YWbZzZvDn40BIz/kgFchERERERMUVoKEyZauO222Hqe225v+8+4mJyzQ5LXJSbZ+XRVxMA+PvAHdRv38LkiESkyJGsI9y3/D6WbV4GQPem3Xn75reJi9ISSCJecfx4YfH7zEJ4SgpkZDjvX7v2nwXw07PCHW3akO5wON80U7xKBXIRERERETHNrf0tXPJiAf/7KZgnXm3JvH+uNzskcdHMj5ryx/5q1I/O4qEnosCqP+b9yYIFC/jwww/ZsmULw4YNY9SoUWaHJC76avtX3LP0Hg5mHCTYGsyESyfwyBWPEBwUbHZoIlVfXh6kphL8889Ytm6FDRsKC+F79jjvHxICbdv+WQwv+li/fqlNM7Hb4ayNjKVyqEAuIiIiIiKmsVjg+ReD6NoV5n/VnNF37KRD63PMthKfceRECE/Nbw3A06N3Ua1hG5MjEk+LjY1l9OjRLF261OxQxEU5BTmMXzGe6cnTAWhbpy3v9H2H5tWaY7VoeQYRtxgG7N9fenmUjRux5edTzdk5jRuXXCe8XTto2RKCdXPK16lALiIiIiIiprr4YrjjNjuL3rfx4PR4vpm1utSkKvEtT7zelhPpIbRvfox7/hZbehacVHnXXnstAN98843JkYgrNqRt4K7Fd7Hu0DoAhl00jGk9phFqDSVdM1JFypaRAevXl1waJSUFjh1z2t2oXh1727ZYO3TA2r59YUE8MRFq1qzcuMVjVCAXERERERHTPTvFxpJlDv6zNo4l38bQr3ua2SHJOfy+pQavL2sCwIwn07DV1Oxxs2VmZjJ37lxSUlJISUnh+PHjPPTQQwwZMqRU3/z8fGbNmsWSJUs4evQoTZo0YciQIfTu3duEyKWiDMNg5s8zefjrh8kpyKFuRF3m9ZlHr1a9ALDb7SZHKOJD7HbYtq3kWuHr1sGOc2wSbrNB69alZoU7GjQgIyODqKiowj5S5alALiIiIiIipmvSBB4eB08/Aw/OSKLnpd8REabCjq8xDBj9YhKGYeH2bru5/NbGZockwPHjx5k5cyb16tUjPj6eVatWnbPvhAkTWLp0KQMGDKBVq1asWLGCcePGYbfb6du3b+UFLRV2KOMQf13+Vz7b+hkAPVv05M0+b1Ivsp7JkYn4gMOHSy6Psm5d4XrhOTnO+9evX3KN8KSkwrXDw8JK99WNJ7+jArmIiIiIiPiE8Y9ZeWt+Abv2RTH1rSZMfGC72SHJWd7/pgE/rK1DeGgB/3o2H0LCzQ5JgJiYGL7//ntiY2PZu3cv3bt3d9pvw4YNLF68mNGjRzNixAgA+vfvz6BBg5g6dSo33HADISEhlRm6lNNnWz/j/5b9H2mZaYTaQpl67VRGdR6FRcsdSaDJyYHU1JLLo6xbB4cOOe8fHl64HMqZ64QnJUGdOpUbt/gUFchFRERERMQnRETA8y/YuO12mPJeWwb33k+TuGyzw5LTsnJsPDwzAYBHB22n4UUtTI5IioSEhBAbG3vefp9//jlWq5UBAwYUH7NYLNx9992MHj2a5ORkLr/8cm+GKhWUnZ/N37/+O6/8/AoASTFJLLxlIYkxiSZHJuJlDgfs2lVyw8x162DLlsK2s1ks0KzZn0XwokJ4s2ZaFkVKUYFcRERERER8xq39LVw9s4D/fB/Egy+2YfG/1pgdkpw2ZUFL9hyKoHFsOg//szpYVWCoalJTU2nUqBE1z9pIrn379sXtRQXygoIC7HY7DoeDgoICcnNzsdlsBAWpjGCWtQfXctfiu0g9nArAmC5jePaaZwkLcrIEhEhVduIEpKRg+f13wn/7DeumTYXLo5xrw9no6D8L4EXF8IQEqFatUsOWqks/2URERERExGdYLDBjVhDt2xssWdWYr3/axbUXHzM7rIC3dU81nnunJQDPP7qP8LjWJkck5ZGWlkbdunVLHY+JiSluL/Lqq6/yyiuvFH8+e/ZsRo4cyahRoyoUw9mbRtrt9uKHs75nt5XV30zeistut5NfkM8LP77AE989QZ49j3qR9Zjbey7XNb+uuI+n4nK1vyv93M1teY6bzZt5r8i4ZuXd7fb8fNi8GWPtWkJ/+w3Lli0Y69dj2bMHACsQesb5RnAwxMdjnF4ixUhMLCyG169f+AtE6Sc836WXi/LuersnXwe8SQVyERERERHxKQkJMHK4g5dm2Bj1QjvWLvie0BAnb5+WSmEYMHxae/LybfTsvJ9+Qy5wXogQn5eTk+N0jXGr1UpwcDA5Z2xeN2rUqAoXw8/mcDhIP2sGqMPhICcnB4vFgtVqPW9bWf3N5K249p3ax/CvhvP9vu8BuL7Z9bzc/WXqRNQp9bX0RFyu9neln7u5Lc9xs3krroqOa1bez9luGFgOHMC6YQNBa9dibNkCqalYt2zBkp8PlC5QOi64gIKEBHJbtsTSvj2OhAQcLVpAcHDpwDIyznuNnqS8u97uydcBdzkcDpfPV4FcRERERER8zsQnbSxaZGfz3ppMmd+ECUN2mB1SwFr0dQNW/BxDWEgBr0zLxBIRZ3ZIUk5hYWHk5eWVOu5wOMjPzycszLtLdVitViIiIkocK5olGB4eju2sdYGdtZXV30zeiGv5luUM/WwoR7OPEh4UztTuU7m/4/1ubcTpblyu9neln7u5Lc9xs3krroqOa1be7XY79pMnidi2jaDUVCzr1//5OOb83WBGVBSOhATyWrfG1qEDlnbtCmeG16yJ3W4nJyODyMhIQpR3j5/v0by7+b1eVpunvr7uvFaqQC4iIiIiIj6nZk146WUrd9wJzyxI4PYeB2ndJMvssALOifRgHpyRBMDj/7ed5pc0NzkiqYi6deuyf//+UseLllYpWmrFWxwOB1lZWaWO5eXlkZ2d7XR24dltZfU3kyfjyszP5Invn2D++vkAJNROYE7PObSt05bsbPc2LnY3Llf7u9LP3dyW57jZvBVXRcetlLwbBtY//sC2YQO21FSsGzYQkppK3T/+cHquYbXiaNGC3NatISkJR2Ii9oQEjIYNcRgG2dnZhIeH//n8WVnKu5fP99T3e3m+18tq89TX1zAMl4vkKpCLiIiIiIhPuu12C/PnFfDF10EMnZLEt7OStbJHJXvi9bYcPBpG6wtO8vA/a4BNf0JWZQkJCfz000+cOHGixEada9euBSA+Pt6rz68Z5Oe35uAaBi0bxJZjWwAY23ksD3Z8kNo1alepGaXn66MZ5N4d1+N5P3wYy/r1GOvWEb5mDaGbN2PZuBHLOW7YGLGxGKfXCDcSEwv/v00b7MHBZDqZEa68e2ZczSAvSTPIRURERESkyrNYYNZrQSTEO/hubX3e/iSOe3qXnv0q3vFzak1mLW4KwKyn9hNav63JEUlF9ezZkzlz5rBw4UKGDx8OFM6wW7BgAdHR0XTp0sWrz68Z5GWcbziY8esMnvnxGfId+dSvVp9Xe7zK5RdcTnZ2dpWbUXq+PppB7t1xy53348cJ3roVa2oqttTUP2eHHzrk9DwjPBx7mzbY4+OxJyZS0KYNmc2aEXrBBaWf1+HAkZ2tvHtxXM0gL0kzyEVERERExC80bQoTJ8Ijj8JDM9tx42WHqVMr3+yw/F5evoV7n+2IYVgYcO0uut3VxOyQ5DwWLFjAqVOnijduTE5OpqCgAICBAwcSFRVFYmIiffr0YcaMGRw7dozWrVuzYsUKVq9ezeTJk51u4OlJVquVqKioEsfsdjuGYRAZGel0duHZbWX1N1NF4tp7ai+Dlw3mu13fAXBzm5uZfcNsakfUrvD1unu+q/1d6edubstz3GzeisvreTcM2LULUlKwrFuHkZJC9bVrse3YgcVuL93dYoFmzTASE8lp1YrgCy/E2r49NGuGxWYjiMICY5DdTn56+jmfV3n37rhmfb+X53u9rDZPfX3dKa5XmQJ5ZmYmc+fOJSUlhZSUFI4fP85DDz3EkCFDzA5NRERERES8aOyDVt59p4B1G8IY+2Jb3nlyndkh+b1n325FyvYa1KmRzYsvGBASbnZIch7z5s1j3759xZ+vXLmSlStXAnDTTTcVF6affvpp4uLiWLJkCYsWLaJp06ZMnTqVPn36mBJ3oPsw9UOGfTaM4znHqRZcjenXTWdw+8FuLQ0gUqYTJwoL4evXF35MSYH167Gcvpl2NiM6GhITMdq1g9PLpJCQAJGR2O12stPTCYqKAh8qWItUVJUpkB8/fpyZM2dSr1494uPjWbVqldkhiYiIiIhIJQgOhtfnBnHJJQYLVjTjtmsO0POSA2aH5bfWbavO0/NbA/DKYzuom+DddanFM7799luX+oWEhDBmzBjGjBnj3YCccDgcxTPczzyWk5ODxWJx+vb7s9vK6m8md+NKz0tn/H/H827quwB0iu3E69e9TvNazcnIyCj3uBWNy9X+rvRzN7flOW42b8VVrnHz87Fu3Vq8YWa1lBRsmzdj27vXaXcjOBhHq1bYExIoaNuWrBYtsHXsiCUujlIbfhgGpKefN67ytivvnhnXrO/38nyvl9Xmqa+vw+Fw+fwqUyCPiYnh+++/JzY2lr1799K9e3ezQxIRERERkUrSpQs8NNbBv563MWRKR35/+zC+86ey/ygosPDXyR0psFvpe9lebhvZqHShRKSctMRKoeR9yQxaOojtx7djwcKjlz7KhCsmEGwLrtC4FY3Lnf5aYqWQKUttGAbs319yNnhKCmzahCUvz+l4RqNGhbPCk5IKZ4UnJUGrVhAcjK3wCbGkp1MtKsprS22U1a68e2ZcLbFSkl8usRISEkJsbKzZYYiIiIiIiEmefNrGx8sL2LQ1ggenJzF9tGszZsV10xa24NdNtagZmcus6TlYIi4wOyQRv2F32Hlu1XM8+f2T2A07Das35K0+b3FF4yvMDk18VUZGYeH7zOVRUlKwHDvmtLsRFQWJiTgSEshu2ZLQiy4qXCu8Zs3KjVukiqkyBXIREREREQlsYWEw/53CpVYWftOc6ztv4fbrM80Oy29s3BnJxHltAJj+8Dbqd2prckTibwJ5iZXdp3bzwJcP8NP+nwC4udXNvNjtRWqE1ij1NXFn3IrGVd7+WmKlkMfistux7tyJbcOGwiVS1q+nxvr12HbvxmIYpbobViuOFi2wJyRgj4/HHh+PIyEBR6PCd/04HA6ys7MJDw8vjKuMf2PuXIeWWCmkJVZcb9cSK1WI3ckOveVlnP7P1xTF5IvxFcXjyTwUjetr1wq+nQsIrHwoF74jUHMhIiLu69IFxo11MPV5G+NevZxrun5DvTq+97OjqsnLtzBg4kXk5tm4vst+Bj3UECy+U4wQ/xCoS6wsWr+I4Z8P51TuKaJCopjRcwYDkga4tBFnVV1y4Xx9AnqpjSNHYN26ksujbNiAJTvbaXejXr3Sy6O0bQthYdgAZ8/qi0ttlNUeEHmvhHF9Me9aYqWKcHYHuzysVisOWzgOu4Hd7vBAZJ5lGIUxORwOnNx8NJXDbuBwGGRmZuFwVPxrp1xUTCDlQ7nwHYGUC3fuYouIiHOTnraxfHkem7ZGMvJf7fngud+1THYFTZjTljVbalK7eg5zZ+VgqRZndkgiVd7JnJOM/mI0764v3IizS4MuvNP3HZrVamZyZFIpcnJg48bC5VHWrftzmZSDB512N8LCICEBo107HPHxZDZvTnjnztjq1avkwEUCT8AXyJ3dwS6vU3kGVpsFm833Ch9F9Ryr1epzhRmrzYLVaqFaWDWPjalclF8g5UO58B2BlAtfvD4RkaomLAzmzHNw9VUOPvqhCfOWHebevvvMDqvK+u63Okx9tyUAcyZto37HeJMjEn8VSEusJO9PZsiXQ9h9ajdWi5WHOz/MuM7jCLIGuTVJr6ouuXC+Pn611EZ2NrY9ewjauLF4iRRbairWbduwnOMdqPamTQuXRYmPL1wmJSEBR9OmcHq2bNESKfbwcKzlmNTpi0ttlNVeJfOuJVZcatcSK1WI594OUYDl9H++pigmX4yvKB7Pvl1GuSivQMqHcuE7AjMXIiJSEZ06ORg+bC8vv9KI0S914LIOJ2jdROuRu+tEejCDnuqEYVi498Y/uHloMy2tIl4TCEushEWE8ez/nmXyysk4DAdNazblrT5vcUnDSyo0blVbcuF8farsUhsnT/65WebpmeE1168/ZxHbiI4uXB6lXbvCj4mJkJgIkZHFy6MEOzmvquZdS6yYO64v5l1LrIiIiIiIiHjR3YMOk7I2hv/8EMadEzrx4xurCA3xrWXEfJlhwPBp7dhzKIIWcaeYPiMYwiLMDkv8nLNih81mK3640lZWfzPtydjD0CVDSd6XDMDAdgN55YZXqB5avULjVvR63T3f1f6u9HM3t+U57hX5+bBlC6SkwLp1f37cvdtpdyM4GEvbttCuHSQlFX+0xMWBpXxTgKpq3svb7hN5d4O34vLHvHvydcBbVCAXEREREZEqyWqFOW8adPlLAWu21ebxWS2ZNmaz2WFVGfM+acx7XzfEZnWwYPpeIptqaRXxvrM3Pbfb7cUPZ33Pbiurv1kMw+DttW8z+svRZOZnUiO0BjOvn8kdiXcAFdvovaLX6+75rvZ3pZ+7uS3P8QozDDhwoOQa4evXF64dnpfn/JSGDQs3y0xMxJGQQEbTpkR06oQtLKx053LuX1RV817e9krPewV5Ky5/zLsnXwe8qUoVyBcsWMCpU6eK1+tKTk6moKAAgIEDB3psLXEREREREaka6tc3mPemlT594fn323LVhcfodflhs8PyeWs212DE8+0AeGroFrr0a2lyRBII/HEN8hM5J3jwPw+yZMsSALrGdWX2dbNpVL2RW2uNn0tVXZP4fH1MWYs6MxPbpk3F64RbN2zAtmED1uPHnXY3IiOxt22L/cx1wuPjMWrWLBFXdnY2jtxcrPn55YvLiaqad61Bbu64vph3rUHuBfPmzWPfvj8331m5ciUrV64E4KabblKBXEREREQkAN3Ux8qo4QXMmBXE3U9exC/z/kuLhllmh+WzTqQHc+vjncnNs9Gr6z4eeTYWbM5WwRXxLH9bg/y/u/7L4GWD2XNqD0HWIB7p/AiPX/k4IcEhHnuOqrom8fn6eHUtarsdduyA9euxrFv353rh27djMYxS3Q2rFVq2/HOd8KSkwmVSGjfGYrUSxLmLZ1qL2jPtWoPcM+P6Yt61BrkXfPvtt2aHICIiIiIiPmjai0H8+kse/1sdyi3jL+LHN1YREeZbb8n2BYYBg5/uxI791WgSm87bc/OxVq9ldlgSQPxhDfI8ex4Tv5vIcyufw8CgRXQL3u7zNm2rtyUkOERrErvYxyNrUR85UnKN8KJieHa284BiY/9cJ/z0WuGWtm0hPLxc64Sf7xoroqrmXWuQmzuuL+Zda5CLiIiIiIhUgpAQ+GBJCJ06FLBuRzRDJifxzqTfsZS34uGnpi1swbIf6hMSZOfDWbuo1SbB7JBEqpQtR7cwYPEAftn/CwB/7fBXXrr+JcJt4R5ZUkXOITcXW0oKlu3bCwvgRQXxgwed9w8Lg4SEwmL4mQXxmJjKjVtEqgQVyEVERERExC/ExcH7H9jo1t3g3RVN6JJwnFG37zI7LJ/x2f9iefTVwoL4y3/fzIV92qA7CCKuMQyDuWvm8rcv/kZWfha1wmoxp/ccbom/BajYRpxyBsPAsnt34RIpGzYUFsJTUrBu3kzUub7GzZoVzwYvLoa3aAE+NuNYRHyXCuQiIiIiIuI3rrjSwr+eK+DBh4MYO6M9bZpkcm2XI2aHZbr126O4Y8JFOBwW7u21kyGPNwKb/hyUynd2Idlutxc/nPU9u62s/t5yNOsoD3z6AEs3LwXg6iZX8+ZNb3JB9QuK4/BWXBUd193zXe3vSr/z5dZx/DiONWuwpKZCSgqWlBSs69dT49SpUv0tgKNmzT8L4ImJGImJkJgI59qPrhL+jSjvnml397jZlHfX28vTZkbe9RuRiIiIiIj4lTEPBbHmtzzeeS+EWx/vzKrXviexeYbZYZkm7VgIvR7uSnpWMFe1P8isuRFYIiLNDksCkMPhKLUMicPhICcnB4vFUmpDNWdtZfX3hu92f8fwr4ZzIPMAwdZgnrjkCUZ2GonVYi1xLd6Kq6Ljunu+q/1d6Vfcx24naMcObBs2YEtNLfy4YQPRe/c6Pc8IDsbeqhWO+HjsCQnY4+PJb9uWrJo1CY+IKP18Ji5to7x7pt3d42ZT3l1vL0+bp76+DofD5fNVIBcREREREb9iscCcN0PYtSuP7/8Xwg0PXkzy3B+oXyfX7NAqXU6ulZvHd2HXwQhaNjjJRwuzCYlpanZYEqCsVitRZ832tdvtGIZBZGRkqc3YnLWV1d+TcgtyeeK7J3jxpxcBaFO7De/c/A4d63V02t9bcVV0XHfPd7W/036GAQcOFM8GN1JSqL5uHbbNm7Hk5Tkdx2jY8M/Z4ElJ2BMTOVWvHlG1axdu0He6X4jdjiM93et5d5fy7pl2d4+bTXl3vb08bZ76+rpTXFeBXERERERE/E5oKCz5OIRLOuexeXskvR7qzH9f/R+REb71Nm1vstth8NOd+F9KbWpG5vLJW/uJbtvG7LAkwDkrdthstuKHK21l9feEjYc3ctfiu/j94O8ADL1wKM9f9zwRwRFlnuetuCo6rrvnu9Q/M5OQ338naMcOrGdumnn0qPP+kZGFy6EkJUH79tgTEsho0oTIhg2x2WwU7YZg2O3Y0tOdPr+3815eAZV3F/qVt93d42ZT3l1vL09bZeddBXIREREREfFL0dHw6ZchXNy5gN+21ua2xy5k6b9+JiTYMDs0rzMMGD6tPf/+5gKCg+x8+NJWWnWL16acImUwDIPZv8zmwa8eJKcghzoRdZh701xuan2T2aGZx+GA7dv/LICf/mjdvp0ow8lrqdUKrVpBUhKOxESyWrQgvHNnbM2aFbYVsdsxTFwaRUTkTCqQi4iIiIiI32reHJZ/bKN7dzufr47jzn904t9P/0ZQkP8WyQ0DHpmVwOvLmmK1Onh3cird72kLFt9Zv1XE16RlpnHf8vv4eMvHAFzX/Dre7PMm9aPqmxxZJTpyBH7/nZBffsGydSusX1/4yMoq1dUCOGJisCQlYWnfvnBmeLt20LYthIcDhbPBC9LTCzfR9KH1o0VEzqYCuYiIiIiI+LWul1hYugR63+Rg8fcNGTTJwTsT1+Bj79b2mGffbsW/3m0JwJzHN9B/TGuw6U8/kXP5YtsXDF46mEOZhwixhTD1mqmM6jIKq7/eVMrNhY0bS8wIJyUFDhzABpRaSCYsDBISCgvgpwvh9vh40sPDiYqK8rmlL0RE3KXfkkRERERExO/16Gnjw/ft9Ovv4L1vGhMa7GDuE2v9blLj8wtb8Phr8QC8MCaVvz7eAoJDTI5KxDflFOTwyNeP8PLqlwFIqJvAe7e8R1JsksmReYhhwK5dBK1ejWXbNtiwobAYvnlz4SYFzk5p1oyCNm2wdeyItX37wqJ48+YQdFb5yG4HLZEiIn5CBXIREREREQkIvfvaeG9BPrffZWH+F00BmPPYOr9YbsUw4J9vtOGpNws34fznvZsZ+2wTCA03NzCRs9jPKsza7fbih7O+Z7eV1d8dKWkpDFwykPWH1wMw6i+jmNxtMuHB4eUa21NxlXvckydh/XosKSmFH9etg/XrsZ06RaST7katWpCYiJGUVPixXTtISMAeEUF6enrpmeFu5M2VPudqc/e42UzPu4fOd7X/+fqVt11598y4vph3T74OeJMK5CIiIiIiEjBuvT2Yd/IKGDjYxvwvmnLkZAj/fuY3IsJ8649vdzgcMGZ6EjM+bA7As8M38ujzjSCs1EIJIqZyOByknzXr2OFwkJOTg8ViwXrWWzqctZXV36UYDAev//46E1dNJNeeS93wurxy7Sv0aNqDgpwC0nPKNyu6onG5PG5BAdZt27Bt2IAtNbX4o3XPHqfnG8HBFDRvjiMxEUdCAvaEBOzx8RhxcU437XVkZLh0Ha5cr7u5Lc9xs1Va3r18vqv9z9evvO3Ku2fG9cW8e/J1wF0Oh8Pl81UgFxERERGRgHLXwCAiI/O5/U4rn/zYgB6jQ1g+bTXR1fPNDs1t+QUWhjzXgfmfNQZg5sPrGf5kMxXHxSdZrVaioqJKHLPb7RiGQWRkZKm1rJ21ldX/fA6kH+DeT+7lqx1fAXBjyxuZ02sOMdViKnBV57+OcjEMOHgQx++/E/rrr4Rt24Z1/XpITcWSl+f8lAsuKJwNnpgI7dphJCVhb9GC9NxcoqKiCHEhLlevw5V+7ua2PMfN5q24Kjquu+d7Ku/lbVfePTOuL+bdk68D7nKnuK4CuYiIiIiIBJybbg7m6y8L6H1TAavW1+XyBy7l0xdW06R+ltmhuezIiRD6P/EXvvutLjarg/mTNnD331tCSJjZoYmck7Nih81mK3640lZW/3NZvnk59y6/lyNZRwgLCuOFHi8w9KKhWJzMoi6v8sQFQFbWn+uDF22auW4dHD2KDQg+u39kJJwughdtmklSEpZatQAocUV2O7aCArficvU6XOnnbm7Lc9xs3oqrouO6e76n8l7eduXdM+P6Yt49+TrgLSqQi4iIiIhIQLrsyiB++MHBdT3ySN1VkwsHX8G7E3+lZ9fDZod2Xr9vqUHfR7uw62AEkeF5vDd1C72GtoGgUqU0kYCWlZ/FQ18+xOxfZwPQPrY9C29ZSHzd+MoPxuGAHTtKFsJTUmDbtsIZ42ezWjFatiS/bVuCOnTA2qFDYUG8SRP8bodhERETqUAuIiIiIiIBK7GdlZ9WB3PLTdn8vDacG8Zdwj//byP/+OsWn60//XtFA/46uSNZOUG0iDvF0jf2knBdW7D61uw6EbOtObCGuxbfxaYjmwB4qOtDPNPtGUKDQr3/5EePliyCn940k6xzvEulbl1o377EjHDi43GEhJB1etNMfGwGrYiIv1CBXEREREREAlrDRhZ+SA5nzNBMZs+vxsR58fy0vhZzH19LXN0cs8MrdiIjhLGz/sK7XxauN97jogMsejuHWm3aOt1sT6Qijh07xvjx40lOTiYmJoYnnniCK664wuywXOIwHLzw4ws89s1j5DvyqR9Zn7dvfptrml3j+SfLzYWNG7GsXUvYL79g3bKlsCB+4IDz/mFhkJBQWAA/sxgeG+u8v73qbiAsIlJVqEAuIiIiIiIBLzQUXn2zGl0vzeGBkcF8sbo+be+szb9GbuC+m3aZPpv8y+RY7n+2EwePVcNqdfDI3dt5ano0tlr1zQ1M/NakSZOIjo7mxx9/5Mcff2Ts2LF89dVX1K5d2+zQyrTv1D4GLR3Et398C8DNbW7m9d6vUyeiTsUGNgzYs6f08iibNoHdjhUotfp/06al1gmnRQsIUilGRMSX6FVZREREnJo9ezbr1q1j3bp1HD58mNtvv50nn3zSrTGWL1/OnDlz2LlzJ3Xq1OHmm29m2LBhBAdrjVwR8U2D7gujU2c79w7KYvXaCB6Y2pGFXzXg9UfX0qpRZqXHc/BoKONfjWf+Z4Wzxls2OMlb/9pL11tbQnBIpccjgSEzM5NvvvmGr7/+mvDwcLp160Z8fDwrVqzg9ttvNzu8c/oo9SOGfDKEY9nHiAiO4KWeL3Fvx3vd34jz1KnC5VDOLoafPOm8f82aGElJ5LVuTfCFFxauFZ6QAFFRFb4mERHxPhXIRURExKkXX3yR2rVrk5SUxHfffef2+UuWLOHRRx/lqquuYuDAgWzZsoVXX32VAwcO8Oyzz3o+YBERD0lsZ+N/v0bw8pQMnngqjP/+HkPCgO4M7fsHT/zfFmKjc70eQ3pmEC9/0IwpC1qSnlV4U3FIrxSefT6c6JbxWlJFSsjMzGTu3LmkpKSQkpLC8ePHeeihhxgyZEipvvn5+cyaNYslS5Zw9OhRmjRpwpAhQ+jdu3dxn127dhEREUH9+n++Q6FNmzZs3bq1Uq7HXRl5GYz5Ygxz18wF4ML6F7LwloW0qt2q7BMLCqBoSZQzi+G7djnvHxQEbdoUzgY/c2Z4gwY4HA6y09MJ0lrhIiJVjgrkIiIi4tSKFSto2LAhAK1bt3br3Ly8PKZOnUrXrl157bXXio/XqFGDV155hYEDBxIfH+/ReEVEPMlmg7GPRdL3djsj7svg8+8ieeWj5sz9pDH39trF2Du206zBOTbbq4C0YyG8uqQpMz5sxtGThRsJ/qX1Ef414SDVW2QSdkFzFcellOPHjzNz5kzq1atHfHw8q1atOmffCRMmsHTpUgYMGECrVq1YsWIF48aNw26307dvXwCysrKIjIwscV5UVBQHDx705mWUy8/7fmbA4gFsPbYVCxYeufQRJl09iRDbGe+wMAw4eLCwAF5UBF+3DjZuLFxD3JkGDUoWwdu1g9atIUTv3BAR8TcqkIuIiIhTRcXx8vjpp584duwYd999d4njAwYMYObMmXz++ecqkItIldC0uY3P/hPJt1/kMP6RAlavKyyUv/JRc3p0PsQ9N+yh96UHiapWUO7nyM2zsuLnurz9RUOWfl+fvPzC2aetG55kwsiD3DE0lpygZmzcuNFTlyV+JiYmhu+//57Y2Fj27t1L9+7dnfbbsGEDixcvZvTo0YwYMQKA/v37M2jQIKZOncoNN9xASEgIERERZGRklDg3IyODiIgIr1+Lq+wOO1P/N5UJ302gwFHABdUv4J2b3+GqmM6wZt2fRfCimeFHjjgfqFq10htmJiVBdHTlXpCIiJhGBXIRERHxuNTUVACSkpJKHI+OjqZhw4bF7SIiVUW3nmH8dB18+3k2/3oujy9/qMFXq2P5anUsocF2Lm9/hG4XHaFz/HHatzhF7Rp555zofSI9mI07I/lpQzQ/rK3N16vrkpH9594MXdoeYex9x7nl/2oTVOv0O3iyPD9bXfxHSEgIsbGx5+33+eefY7VaGTBgQPExi8XC3XffzejRo0lOTubyyy+ncePGZGVlcfDgQerVqwfApk2buPHGG712De7Yc2oPIz8azr61K+mVBnc64umb35yQV4fAtm2FM8bPZrVCy5alC+FNm2L6LrwiImIqFchFRETE49LS0gCoW7duqbaYmJji9vIyDIMsDxSLsrOzS3z0d7pe/6brrRxdr4LFVwWzY8tJ3n0jl48+jmDr7khW/BLLil/+LFBGReQTVyeb6tUKCA22k2+3cioziLTjYcVLp5ypXq0s+l9zgLsGFNDuitoQ2oA8IO/0a53yay7DMNzf6NEHpaam0qhRI2rWrFniePv27YvbL7/8cqpVq0a3bt14+eWX+cc//kFycjIbNmzgxRdfrHAMdru91OdFD2d9z25b9/Eb5P5tBJ8esBOZX3xlpx+FjLp1ISkJ43Qx3EhMhPh4CA8vHZBhgJPnLs91nes6zBzX3fNd7e9KP3dzW57jZlPePdOuvHtmXF/MuydfB7xJBXIRERHxuJycHIKDg7E6mZEVGhpKTk5OhcbPz8/36FIDO3fu9NhYVYGu17/peivPrf8HtwyGPdss/PZjGL/+FkXqthrsOlid9KxgNu8OPue59aMzSGp2lA6JJ+hycTrNEg3soeFgsbBxR8Y5z1N+zRPiB2tPp6WlnfPmdVF7kYkTJ/Loo49y8cUXExMTwwsvvEDt2rUr9PwOh4P09PRSx3JycrBYLKV+b3DWdvjfb9Jjd2HRxBESgqNtWxzx8dgTEgof8fEYp6+nhIICOOu5Pams6zBzXHfPd7W/K/3czW15jptNefdMu/LumXF9Me+efB1wl8PhcPl8FchFRET8XHJyMoMGDXKp75tvvskll1xS4ecMCwsjPz/f6Yy73NxcwsLCKjR+cHAwLVq0qNAYUDgzcefOnTRp0oRwZ7PK/Iyu17/pes0THw/X3XT6E8Mg+9Qx9u7I4+DeAtJPOcjLNQgOMoiMNIipZ6VBIyvVYyIguA5YShcrnfGl660Mvna927ZtMzsEj8jJyXFa6LdarQQHB5e4gR0dHc3rr7/u0ee3Wq2l1jEvmiUYHh6OzWY7b1vn6Yv58ao3SLroBkLaxkNQYVnDdvphlrKuw8xx3T3f1f6u9HM3t+U5bjbl3TPtyrtnxvXFvHvydcBd7rzzSwVyERERP9ekSRMmTpzoUt/mzZt75DmLZqelpaWVWhM1LS2NRo0aVWh8i8Xi0Y3CwsPDfWrjMW/T9fo3Xa/5IqpVo3Z9aO+FsX3xer3JV67XH5ZXgcIb2Hl5eaWOOxwO8vPzK3wD+3wcDkepJdIcDgd5eXlkZ2c7nV14dpsjOIxGve4lLzycgrw8cHI9ZijrOswc193zXe3vSj93c1ue42ZT3j3Trrx7ZlxfzLsnXwfc5c7yaCqQi4iI+LnY2FjuvPPOSn3OhIQEAFJSUkoUyI8dO8aePXvo2bNnpcYjIiIihTew9+/fX+p40dIqMc6WJvEgq9VKVFRUiWN2ux3DMIiMjHQ6u/DstrL6m8lbcVV0XHfPd7W/K/3czW15jptNefdMu/LumXF9Me+efB1wlzvFdd+5/SIiIiJVUnp6Otu3by+xpmiXLl2oVasW7777bom+7777LoZhqEAuIiJigoSEBHbv3s2JEydKHF+7di0A8fHxJkQlIiJiLs0gFxEREaeWLl1aYpZZamoqs2bNAqBPnz40aNAAgK+//prx48fz7LPP0q9fP6BwI85x48bx+OOPM3ToULp3787mzZt599136du3b/EMcxEREak8PXv2ZM6cOSxcuJDhw4cDhW9BX7BgAdHR0XTp0sWrz++JTTq1aZ93z9cmne5R3j3Trrx7ZlxfzLs26RQREZEq7aOPPmL16tXFn6ekpJCSkgLAhRdeWFwgP5dbb72VoKAg5s6dy6RJk6hduzYPPPBA8R/kIiIi4jkLFizg1KlTxQXo5ORkCgoKABg4cCBRUVEkJibSp08fZsyYwbFjx2jdujUrVqxg9erVTJ482ekGnp7kiU06tWmfd8/XJp3uUd490668e2ZcX8y7NukUERGRKu2dd95xqV+/fv2KZ46frW/fvvTt29eDUYmIiIgz8+bNY9++fcWfr1y5kpUrVwJw0003Fa/9/fTTTxMXF8eSJUtYtGgRTZs2ZerUqfTp08frMXpkk05t2ufV87VJp3uUd8+0K++eGdcX865NOj0sPz+fWbNmsWTJEo4ePUqTJk0YMmQIvXv3Njs0EREREREREVN9++23LvULCQlhzJgxjBkzxrsBOaFNOit/XG3S6V3Ku2falXfPjOuLea8qm3RWmQL5hAkTWLp0KQMGDKBVq1asWLGCcePGYbfbNTNNRERERERERERERNxWJQrkGzZsYPHixYwePZoRI0YA0L9/fwYNGsTUqVO54YYbvL5WmoiIiIiIiIiUnzbprPxxtUmndynvnmlX3j0zri/mXZt0etDnn3+O1WplwIABxccsFgt33303o0ePJjk5mcsvv9zECEVERERERESkLFpipfLH1RIr3qW8e6ZdeffMuL6Ydy2x4kGpqak0atSImjVrljjevn374nYVyEVERERERER8m7Nih81mK3640lZWfzN5K66Kjuvu+a72d6Wfu7ktz3GzKe+eaVfePTOuL+bdk68D3lIlCuRpaWnUrVu31PGYmJji9vLIz8/HMAxSUlIqFF8RhwG9a9mx1/TIcB5XuHtrjtlhlGKzwO6tR9jr2sayLlEuyi/Q8qFc+I5AyUVeXp7LO2mLc578+W0YBgDbtm0LiLzoev2brte/6XrNpZ/fFVfWz++y3gbvrM2dt81XJm/FVdFx3T3f1f6u9HM3t+U5bjbl3TPtyrtnxvXFvHvydcAd7vzsrhIF8pycHKdrjFutVoKDg8nJKV9BxdO/4FgtUCPMt+5oBSrlwrcoH75DuTCfxWLRH9gV5Mmvn8ViCah9THS9/k3X6990vebSz++KK+vrV1YRxFmbLxbLwHtxVXRcd893tb8r/dzNbXmOm01590y78u6ZcX0x7558HXCHOz+7q0SBPCwsjLy8vFLHHQ4H+fn5hIWFlWvcjh07VjQ0ERERqWT6+S0iIlL16Oe3iIj4Kt+8DXOWunXrcvjw4VLHi5ZWKVpqRURERERERERERETEVVWiQJ6QkMDu3bs5ceJEieNr164FID4+3oSoRERERERERERERKQqqxIF8p49e+JwOFi4cGHxMcMwWLBgAdHR0XTp0sXE6ERERERERERERESkKqoSa5AnJibSp08fZsyYwbFjx2jdujUrVqxg9erVTJ482ac2bxERERERERERERGRqsFiGIZhdhCuyMvLY9asWSxZsoSjR4/StGlT7rvvPvr06WN2aCIiIiIiIiIiIiJSBVWZArmIiIiIiIiIiIiIiCdViTXIRUREREREREREREQ8TQVyEREREREREREREQlIKpCLiIiIiIiIiIiISEBSgVxEREREREREREREApIK5CIiIiIiIiLiN44dO8YDDzxAhw4d6NGjB99//73ZIUklWLBgAX379iU+Pp4ZM2aYHY5Ugry8PMaPH89VV11Fp06d6N+/P7/88ovZYYmXPfPMM1x++eV06tSJHj168OGHH1Z4TBXIRURERERERMRvTJo0iejoaH788UceffRRxo4dy9GjR80OS7wsNjaW0aNHc80115gdilSSgoICGjRowMKFC/nll1+4++67GT58OBkZGWaHJl50xx138PXXX/Pbb7/x2muvMX36dDZv3lyhMVUgDwBLliyhdevWXHvttWaHErAWL15M69atSzwuvvhi7rrrLr788kuzwws4R48eZdq0adx444106NCB9u3b07t3b6ZNm0ZaWprZ4QWMs78vkpKSuOyyy7j33nt5++239UuNeE1+fj4vvfQSV111FUlJSfTu3ZuPP/7Y7LC8JjMzk5dffpn777+fiy++mNatW/P666+bHZbXrFu3jqeffprevXvTsWNHLrvsMh544AFSUlLMDs0rNmzYwKhRo+jWrRvt27enS5cuDBgwgBUrVpgdWqVYtmxZ8c8Qf5ScnFzqd8iix//+9z+zw/Oa1NRUhg0bRufOnWnfvj29evXi7bffNjssqSIyMzP55ptvGD16NOHh4XTr1o34+PiAeV0MZNdeey3dunUjKirK7FCkkkRERDBy5Eji4uKwWq306dMHm83Gzp07zQ5NvKh58+aEhYUBYLPZANi7d2+FxgyqcFTi85YvX06DBg3YvXs3a9asoWPHjmaHFLBGjRpFw4YNMQyDY8eOsWzZMkaPHs3zzz9Pr169zA4vIKSkpDBkyBAyMjLo1asXAwYMwGq1snnzZj744AO+/vpr3bSoZEXfFwUFBRw5coTVq1czefJk5s+fz6xZs2jTpo3ZIYqfmTBhAkuXLmXAgAG0atWKFStWMG7cOOx2O3379jU7PI87fvw4M2fOpF69esTHx7Nq1SqzQ/KqN954g19//ZWePXsycOBATp06xfvvv89tt93G7NmzufLKK80O0aP27NlDbm4u/fr1IzY2lszMTL7++mtGjBjBhAkTGDBggNkhek1mZibTpk0jIiKCgoICs8PxqgEDBtC+ffsSx1q2bGlSNN61cuVKhg4dSkJCAsOHDyciIoLdu3dz8OBBs0MTL8nMzGTu3LmkpKSQkpLC8ePHeeihhxgyZEipvvn5+cyaNYslS5Zw9OhRmjRpwpAhQ+jdu3dxn127dhEREUH9+vWLj7Vp04atW7dWyvWIazydd6kavJ33HTt2kJGRQePGjb15GeIGb+X8+eef5+233yYnJ4eEhAQuueSSCsWpArmfS0tL46effmLKlClMnz6d5cuXq0Buossuu4wOHToUf37bbbdx2WWX8cknn6hAXglOnTrFiBEjsFgsLF68uNQflg8++KBfz6r0VWd/XzzwwAP8+OOPDB06lOHDh/PZZ58V3x0WqagNGzawePFiRo8ezYgRIwDo378/gwYNYurUqdxwww2EhISYHKVnxcTE8P333xMbG8vevXvp3r272SF51eDBg5k2bVqJPPbv358bb7yR6dOn+12BvGfPnvTs2bPEsUGDBtGvXz/mz5/v1wXyWbNmERkZSefOnfnqq6/MDserLrzwQm688Uazw/C6jIwMHnnkEbp168b06dOxWvWG50Dgzo1cV25yZ2VlERkZWeK8qKgo3WTxMZ7Ou1QN3sx7Tk4ODz/8MA888IDeReBDvJXzhx56iLFjx7J27VqSk5MJDg6uUJz6jcPPffLJJ4SGhtK9e3duuOEGPvvsM/Lz880OS06LjIwkPDy8wt/I4ppFixZx6NAhHnnkEaezrqKionjooYdMiEzO1rVrV4YPH86+fftYvny52eGIH/n888+xWq0lioYWi4W7776bo0ePkpycbGJ03hESEkJsbKzZYVSaTp06lbrJUaNGDbp06cK2bdtMiqpyWa1WYmJiSE9PNzsUr9m5cydvvfUWjz76aMD8HpWZmen3v8d//PHHHDlyhL/97W9YrVYyMzNxOBxmhyVeVnQj97///S9PPvnkOfsV3eQeOXIkTzzxBLfddhuvvfYanTt3ZurUqeTl5QGFSy6cvVRfRkYGERERXr0OcY+n8y5Vg7fynp+fz5gxY2jSpEnxJBjxDd78XrdarXTs2JG0tDT+/e9/VyhOFcj93LJly7jqqquoVq0avXr14sSJE/zwww9mhxWw0tPTOXbsGMeOHWPbtm08/fTTHD9+nD59+pgdWkD49ttvCQ0N5frrrzc7FHFB0ffFypUrTY5E/ElqaiqNGjWiZs2aJY4XLV2QmppqQlRSGdLS0qhVq5bZYXhNZmYmx44dY+fOncyZM4cffviBSy+91OywvGby5MlcfPHFfveOgHN54okn6NSpE+3ateOuu+7i119/NTskr/jxxx+JioriwIED9OjRg06dOnHhhRcyadIkcnNzzQ5PvMTVG7mu3uRu3LgxWVlZJWaMb9q0yW+XJaqqPJ13qRq8kXeHw8EjjzwCwJQpU7BYLJ4PXMqtMr7X7XY7u3fvrlCcKpD7sa1bt7Jp06bit2O2adOGli1bajamie677z66du1K165dufHGG1m0aBGTJk3SLtuVZMeOHTRt2tTvlk/wV/Xq1SMqKoo9e/aYHYr4kbS0NOrWrVvqeExMTHG7+J9ff/2Vn3/+mRtuuMHsULzmn//8J127duW6667jxRdfpGfPnkycONHssLziP//5D6tWrWL8+PFmh+J1wcHBXHfddTz22GPMmjWLsWPHsmPHDu655x6/LJLv3LmTgoICRo4cyZVXXsmMGTO49dZbee+993jsscfMDk9M5upN7mrVqtGtWzdefvllsrOz+e6779iwYYP+5qqi3JncUFBQQG5uLg6Ho/j//X2PCn/lTt4nTJhAWloa06dPJyhIK0lXVa7mPC8vjw8//JCMjAwcDgc//vgjH3/8MV27dq3Q8+tfjh9btmwZkZGRJWbW3Hjjjbz66qtkZGSUWpdNvO+JJ56gefPmABw5coRPPvmESZMmUbNmTa677jqTo/N/GRkZVKtWzewwxA0RERFkZmaaHYb4kZycHKc3yaxWK8HBweTk5JgQlXjT0aNHGTduHHFxcYwcOdLscLzmgQceoF+/fqSlpfHxxx+Tk5NDTk6O363BmZeXx7PPPsudd95Z/DuVP+vUqROdOnUq/rx79+5cf/319O7dm2nTpvHee++ZGJ3nZWVlkZ2dzV133cXjjz8OQI8ePSgoKGDhwoWMGDGCZs2amRylmMWdm9wTJ07k0Ucf5eKLLyYmJoYXXniB2rVrV1qs4jnu5P3VV1/llVdeKf589uzZjBw5klGjRnk/UPEoV/O+b98+PvjgA0JDQ0sUSCdNmsRNN91UOcGKR7jzvf7pp58yZcoU7HY7DRo04LHHHuOqq66q0POrQO6nDMPgk08+oXPnziX+EXXo0IHc3Fy+/PJLbrnlFhMjDExJSUklNiPs1asX/fr146mnnuLqq6/WzGYvi4yMVLG1isnKytIfM+JRYWFhTtevczgc5Ofna0NYP5ORkcH9999PZmYmCxYs8OvJAS1btixePqBPnz4MGjSIoUOH8uGHH/rVW43ffPNNTp486dc3O86nYcOGXHfddSxfvpzs7GzCw8PNDsljil6Dz96Q9MYbb2ThwoX89ttvKpAHMHduckdHR/P6669XZnjiJe7kfdSoUSqG+wlX896gQQM2b95c2eGJF7ia85CQEN58802PP7+WWPFTycnJHDhwgG+//Zbu3bsXPwYPHgygZVZ8hNVqpXPnzhw+fJhdu3aZHY7fa9asGX/88Yc2cqkiDh48SHp6Oo0aNTI7FPEjdevW5fDhw6WOF91MLpqhIFVfTk4Ow4YNY8eOHcyePZtWrVqZHVKlsVgs9OzZk/Xr1/PHH3+YHY7HpKenM3v2bG655RZOnjzJrl272LVrF5mZmRiGwa5duwJmmaS4uDgcDgenTp0yOxSPKnoNjo6OLnG86HN/u15xj25yByblPTAp74HH7JxrBrmfWr58OTVr1uSZZ54p1fbjjz+ycOFCDh065NJC+eJdRWuiZWVlmRyJ/+vWrRtr1qzhiy++0NutqoBly5YBcNlll5kcifiThIQEfvrpJ06cOFFifbu1a9cCEB8fb1Jk4kn5+fmMHj2aNWvWMGvWrBJLVASKog0NMzIyTI7Ec06ePElWVhZz585l7ty5pdp79OjBZZdd5rTN3+zZswebzUaNGjXMDsWjEhISWLVqFYcOHSoxU/zQoUNA6cK5BJa6deuyf//+Usd1k9u/Ke+BSXkPPGbnXDPI/VDREipXXHEF11xzTanH4MGDcTgcfPzxx2aHGvDy8/NZtWoVwcHBAbGOptnuuOMOYmNjmTJlCtu3by/VnpGRwQsvvGBCZHK2H3/8kVmzZnHBBRfoZoZ4VM+ePXE4HCxcuLD4mGEYLFiwgOjoaLp06WJidOIJDoeDcePG8cMPPzBlyhSuuOIKs0PyqqNHj5Y6lpeXx7JlywgLC/Or3y9q167NSy+9VOrRuXNngoODeemllxg2bJjZYXrUiRMnSh3bvn07X375JX/5y1/8bgbd9ddfD8DixYtLHF+8eDE2m02v0QEuISGB3bt3l/q+0E1u/6a8ByblPfCYnXPNIPdD33zzDRkZGXTr1s1pe8OGDWnZsiXLly/nvvvuq+ToAtvKlSuLl1I5duwYn376KTt37mTIkCF+vS6qr6hevTozZ85kyJAh3HzzzfTq1YukpCSsVitbtmzhk08+oWbNmjz44INmhxpQir4v7HY7R44cITk5mVWrVhEXF8err75KaGio2SGKH0lMTKRPnz7MmDGDY8eO0bp1a1asWMHq1auZPHmy3+4FsWDBAk6dOkV6ejpQuBRb0TuYBg4c6FcbOU6ZMoUvvviCSy+9lIKCguJ3oxTp06ePSZF5x9ixYwkJCaFjx47ExMSQlpbG8uXL2blzJ48++qhfbU4dHh5Oz549Sx3/7rvv+P333522VXVjx44lIiKCxMREateuzR9//MGiRYsICgrikUceMTs8j4uPj+eWW27ho48+oqCggL/85S/8/PPPfPbZZ9x7773Ur1/f7BDFRD179mTOnDksXLiQ4cOHA7rJHQiU98CkvAces3OuArkfWr58OcHBwVx++eXn7HP11Vfz+uuvs2nTJtq0aVOJ0QW2GTNmFP9/aGgozZo1Y+LEidxxxx0mRhVYkpKS+OSTT5g3bx7/+c9/+PTTTzEMg8aNG3PHHXcwcOBAs0MMOEXfF8HBwdSsWZNWrVrx2GOP0a9fP904Eq94+umniYuLY8mSJSxatIimTZsydepUvyucnmnevHns27ev+POVK1eycuVKAG666Sa/KpCnpqYCsGrVKlatWlWq3d/y3KdPH5YuXcq7777LyZMniYyMJCEhgb///e90797d7PCkgq6++mo++eQT5s+fT0ZGBjVr1qRbt24MHz7cr94dcKZJkyYRFxfH4sWL+frrr4mLi+PRRx8t3ktJ/JMrN3ID9Sa3P1PeA5PyHniqQs4thmEYXn0GEREREREREZFz6NatW4kbuWf65ptvuOCCC4DCJaRmzZrFkiVLOHr0KE2bNuW+++7zu5ufgUJ5D0zKe+CpCjlXgVxEREREREREREREApI26RQRERERERERERGRgKQCuYiIiIiIiIiIiIgEJBXIRURERERERERERCQgqUAuIiIiIiIiIiIiIgFJBXIRERERERERERERCUgqkIuIiIiIiIiIiIhIQFKBXEREREREREREREQCkgrkIiIiIiIiIiIiIhKQVCAXERERERERERERkYCkArmIiIiIiIiIiIiIBCQVyEVEREREREREREQkIKlALiIiIiIiIiIiIiIBSQVyEREREREREREREQlIKpCLiIiIiIiIiIiISED6f0AwrTPFChZdAAAAAElFTkSuQmCC", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "np.random.seed(42)\n", - "x = np.linspace(0, 4 * np.pi, 300)\n", - "\n", - "fig, axes = plt.subplots(2, 3, figsize=(15, 8))\n", - "fig.suptitle('Galería de gráficas — Matplotlib', fontsize=15, fontweight='bold')\n", - "\n", - "# [0,0] Línea\n", - "axes[0, 0].plot(x, np.sin(x), color='steelblue')\n", - "axes[0, 0].set_title('Línea: sin(x)')\n", - "axes[0, 0].grid(True, alpha=0.3)\n", - "\n", - "# [0,1] Scatter con color por valor\n", - "y_sc = np.random.normal(0, 1, 200)\n", - "x_sc = np.random.normal(0, 1, 200)\n", - "c_sc = np.sqrt(x_sc**2 + y_sc**2)\n", - "sc = axes[0, 1].scatter(x_sc, y_sc, c=c_sc, cmap='plasma', alpha=0.7, s=20)\n", - "fig.colorbar(sc, ax=axes[0, 1], label='Distancia al origen')\n", - "axes[0, 1].set_title('Scatter: colormap')\n", - "\n", - "# [0,2] Histograma\n", - "datos = np.random.normal(70, 10, 500)\n", - "axes[0, 2].hist(datos, bins=20, color='coral', edgecolor='white', alpha=0.8)\n", - "axes[0, 2].axvline(datos.mean(), color='navy', linewidth=2, label=f'μ={datos.mean():.1f}')\n", - "axes[0, 2].legend()\n", - "axes[0, 2].set_title('Histograma')\n", - "\n", - "# [1,0] Barras con error\n", - "cats = ['A', 'B', 'C', 'D']\n", - "vals = [3.2, 4.8, 2.1, 5.5]\n", - "errs = [0.3, 0.5, 0.4, 0.6]\n", - "axes[1, 0].bar(cats, vals, yerr=errs, color='#3498db', capsize=6, alpha=0.8)\n", - "axes[1, 0].set_title('Barras con error')\n", - "axes[1, 0].grid(axis='y', alpha=0.3)\n", - "\n", - "# [1,1] fill_between\n", - "t = np.linspace(0, 2 * np.pi, 200)\n", - "y0 = np.sin(t)\n", - "axes[1, 1].plot(t, y0, color='blue')\n", - "axes[1, 1].fill_between(t, y0, 0, where=(y0 > 0), color='lightblue', alpha=0.6, label='positivo')\n", - "axes[1, 1].fill_between(t, y0, 0, where=(y0 < 0), color='lightsalmon', alpha=0.6, label='negativo')\n", - "axes[1, 1].axhline(0, color='black', linewidth=0.5)\n", - "axes[1, 1].legend()\n", - "axes[1, 1].set_title('fill_between')\n", - "\n", - "# [1,2] Log-log\n", - "xlog = np.logspace(0, 3, 100)\n", - "axes[1, 2].loglog(xlog, xlog**2, color='green', label=r'$x^2$')\n", - "axes[1, 2].loglog(xlog, xlog**0.5, color='red', label=r'$\\sqrt{x}$')\n", - "axes[1, 2].legend()\n", - "axes[1, 2].set_title('Escala log-log')\n", - "axes[1, 2].grid(True, which='both', alpha=0.3)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -681,116 +271,84 @@ "---\n", "## Parte 2 — Seaborn\n", "\n", - "Seaborn trabaja mejor con DataFrames de Pandas. Creamos primero un dataset de ejemplo:" + "Seaborn trabaja mejor con DataFrames de Pandas. Primero creamos un dataset de ejemplo:" ] }, { "cell_type": "code", - "execution_count": 64, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset creado:\n", - " Nombre Carrera Semestre Horas_estudio Nota Estado\n", - "0 Est_000 Computación 3 5.5 80.0 Aprobado\n", - "1 Est_001 Física 3 6.6 83.5 Aprobado\n", - "2 Est_002 Computación 6 2.5 93.6 Aprobado\n", - "3 Est_003 Computación 4 2.4 67.3 Aprobado\n", - "4 Est_004 Física 2 6.0 88.7 Aprobado\n", - "\n", - "Shape: (160, 6)\n" - ] - } - ], + "outputs": [], "source": [ + "# Crear dataset de ejemplo\n", "np.random.seed(42)\n", - "n = 160\n", + "n = 120\n", "\n", "df = pd.DataFrame({\n", - " 'Nombre': [f'Est_{i:03d}' for i in range(n)],\n", - " 'Carrera': np.random.choice(['Física', 'Matemática', 'Computación'], size=n),\n", - " 'Semestre': np.random.choice([1, 2, 3, 4, 5, 6, 7, 8], size=n),\n", - " 'Horas_estudio': np.random.normal(5, 2, n).clip(0, 12).round(1),\n", - " 'Nota': np.random.normal(72, 14, n).clip(40, 100).round(1),\n", + " \"Nombre\": [f\"Est_{i:03d}\" for i in range(n)],\n", + " \"Carrera\": np.random.choice([\"Física\", \"Matemática\", \"Computación\"], size=n),\n", + " \"Semestre\": np.random.choice([1, 2, 3, 4, 5, 6, 7, 8], size=n),\n", + " \"Horas_estudio\": np.random.normal(5, 2, n).clip(0, 12).round(1),\n", + " \"Nota\": np.random.normal(72, 14, n).clip(40, 100).round(1),\n", "})\n", "\n", - "df['Nota'] = (df['Nota'] + df['Horas_estudio'] * 1.5).clip(40, 100).round(1)\n", - "df['Estado'] = df['Nota'].apply(lambda n: 'Aprobado' if n >= 61 else 'Reprobado')\n", + "# La nota mejora levemente con las horas de estudio\n", + "df[\"Nota\"] = (df[\"Nota\"] + df[\"Horas_estudio\"] * 1.5).clip(40, 100).round(1)\n", + "df[\"Estado\"] = df[\"Nota\"].apply(lambda n: \"Aprobado\" if n >= 61 else \"Reprobado\")\n", "\n", - "print('Dataset creado:')\n", + "print(\"Dataset creado:\")\n", "print(df.head())\n", - "print(f'\\nShape: {df.shape}')" + "print(f\"\\nShape: {df.shape}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Nivel 1 — Configurar el estilo\n", - "\n", - "Estilos disponibles: `whitegrid`, `darkgrid`, `white`, `dark`, `ticks`" + "### Nivel 1: Configurar el estilo de Seaborn" ] }, { "cell_type": "code", - "execution_count": 65, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tema configurado.\n" - ] - } - ], + "outputs": [], "source": [ - "sns.set_theme(style='whitegrid', palette='muted', font_scale=1.1)\n", - "print('Tema configurado.')" + "# Seaborn tiene varios temas predefinidos\n", + "# whitegrid, darkgrid, white, dark, ticks\n", + "sns.set_theme(style=\"whitegrid\", palette=\"muted\", font_scale=1.1)\n", + "\n", + "print(\"Tema configurado. Las siguientes gráficas usarán este estilo.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Nivel 2 — Distribuciones: `histplot` y `boxplot`" + "### Nivel 2: Distribuciones" ] }, { "cell_type": "code", - "execution_count": 67, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(13, 5))\n", "\n", - "# histplot con KDE y separación por estado\n", - "sns.histplot(data=df, x='Nota', hue='Estado', bins=20, kde=True,\n", - " palette={'Aprobado': '#2ecc71', 'Reprobado': '#e74c3c'},\n", + "# histplot: histograma mejorado con curva de densidad\n", + "sns.histplot(data=df, x=\"Nota\", hue=\"Estado\",\n", + " bins=20, kde=True, palette={\"Aprobado\": \"#2ecc71\", \"Reprobado\": \"#e74c3c\"},\n", " ax=axes[0])\n", "axes[0].axvline(61, color='black', linestyle='--', linewidth=1.5, label='Mínimo (61)')\n", - "axes[0].set_title('Distribución de notas')\n", + "axes[0].set_title(\"Distribución de notas\")\n", "axes[0].legend()\n", "\n", "# boxplot comparativo por carrera\n", - "sns.boxplot(data=df, x='Carrera', y='Nota', hue='Carrera',\n", - " palette='Set2', ax=axes[1], legend=False)\n", + "sns.boxplot(data=df, x=\"Carrera\", y=\"Nota\", hue=\"Carrera\",\n", + " palette=\"Set2\", ax=axes[1], legend=False)\n", "axes[1].axhline(61, color='red', linestyle='--', linewidth=1.5, alpha=0.7)\n", - "axes[1].set_title('Notas por carrera')\n", + "axes[1].set_title(\"Notas por carrera\")\n", "\n", "plt.tight_layout()\n", "plt.show()" @@ -800,256 +358,31 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Nivel 3 — Relaciones: `regplot` y `violinplot`" + "### Nivel 3: Relaciones entre variables" ] }, { "cell_type": "code", - "execution_count": 68, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(13, 5))\n", "\n", - "# regplot: dispersión + línea de regresión\n", - "sns.regplot(data=df, x='Horas_estudio', y='Nota',\n", + "# scatterplot con línea de regresión\n", + "sns.regplot(data=df, x=\"Horas_estudio\", y=\"Nota\",\n", " scatter_kws={'alpha': 0.4, 's': 30},\n", " line_kws={'color': 'red', 'linewidth': 2},\n", " ax=axes[0])\n", - "axes[0].set_title('Horas de estudio vs Nota')\n", - "axes[0].set_xlabel('Horas de estudio diarias')\n", - "axes[0].set_ylabel('Nota final')\n", + "axes[0].set_title(\"Horas de estudio vs Nota\")\n", + "axes[0].set_xlabel(\"Horas de estudio diarias\")\n", + "axes[0].set_ylabel(\"Nota final\")\n", "\n", - "# violinplot: distribución más detallada que boxplot\n", - "sns.violinplot(data=df, x='Carrera', y='Nota', hue='Carrera',\n", - " palette='Pastel1', inner='quartile',\n", + "# violinplot — muestra distribución más detalladamente que boxplot\n", + "sns.violinplot(data=df, x=\"Carrera\", y=\"Nota\", hue=\"Carrera\",\n", + " palette=\"Pastel1\", inner=\"quartile\",\n", " ax=axes[1], legend=False)\n", - "axes[1].set_title('Distribución de notas por carrera')\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Nivel 4 — Conteos y comparaciones: `countplot` y `barplot`" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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NombreCarreraSemestreHoras_estudioNotaEstado
0Est_000Computación35.580.0Aprobado
1Est_001Física36.683.5Aprobado
2Est_002Computación62.593.6Aprobado
3Est_003Computación42.467.3Aprobado
4Est_004Física26.088.7Aprobado
.....................
155Est_155Computación46.364.6Aprobado
156Est_156Computación43.3100.0Aprobado
157Est_157Física13.978.4Aprobado
158Est_158Física26.572.0Aprobado
159Est_159Computación16.284.3Aprobado
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160 rows × 6 columns

\n", - "
" - ], - "text/plain": [ - " Nombre Carrera Semestre Horas_estudio Nota Estado\n", - "0 Est_000 Computación 3 5.5 80.0 Aprobado\n", - "1 Est_001 Física 3 6.6 83.5 Aprobado\n", - "2 Est_002 Computación 6 2.5 93.6 Aprobado\n", - "3 Est_003 Computación 4 2.4 67.3 Aprobado\n", - "4 Est_004 Física 2 6.0 88.7 Aprobado\n", - ".. ... ... ... ... ... ...\n", - "155 Est_155 Computación 4 6.3 64.6 Aprobado\n", - "156 Est_156 Computación 4 3.3 100.0 Aprobado\n", - "157 Est_157 Física 1 3.9 78.4 Aprobado\n", - "158 Est_158 Física 2 6.5 72.0 Aprobado\n", - "159 Est_159 Computación 1 6.2 84.3 Aprobado\n", - "\n", - "[160 rows x 6 columns]" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(1, 2, figsize=(13, 5))\n", - "\n", - "# countplot: cuenta cuántos hay en cada categoría\n", - "sns.countplot(data=df, x='Carrera', hue='Estado',\n", - " palette={'Aprobado': '#2ecc71', 'Reprobado': '#e74c3c'},\n", - " ax=axes[0])\n", - "axes[0].set_title('Aprobados y Reprobados por Carrera')\n", - "axes[0].set_xlabel('')\n", - "axes[0].legend(title='Estado')\n", - "\n", - "# barplot: promedio (con intervalo de confianza) por grupo\n", - "# errorbar='ci' muestra el intervalo de confianza del 95%\n", - "sns.barplot(data=df, x='Carrera', y='Nota', hue='Carrera',\n", - " palette='Set2', errorbar='ci', legend=False,\n", - " ax=axes[1])\n", - "axes[1].axhline(df['Nota'].mean(), color='navy', linestyle='--',\n", - " linewidth=1.5, label=f'Promedio general: {df[\"Nota\"].mean():.1f}')\n", - "axes[1].set_title('Promedio de nota por carrera (IC 95%)')\n", - "axes[1].set_ylabel('Nota promedio')\n", - "axes[1].legend()\n", + "axes[1].set_title(\"Distribución de notas por carrera\")\n", "\n", "plt.tight_layout()\n", "plt.show()" @@ -1059,148 +392,29 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Nivel 5 — `pairplot`: matriz de gráficas\n", - "\n", - "`pairplot` genera automáticamente todas las combinaciones de variables numéricas. \n", - "Es muy útil para explorar relaciones entre múltiples variables a la vez." + "### Nivel 4: Heatmap — mapa de calor" ] }, { "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# pairplot sobre variables numéricas, coloreado por carrera\n", - "g = sns.pairplot(\n", - " df[['Horas_estudio', 'Nota', 'Semestre', 'Carrera']],\n", - " hue='Carrera',\n", - " palette='Set2',\n", - " diag_kind='kde', # diagonal: estimación de densidad\n", - " plot_kws={'alpha': 0.5, 's': 20}\n", - ")\n", - "g.fig.suptitle('Pairplot — relaciones entre variables', y=1.02, fontsize=13)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Nivel 6 — Heatmap: mapa de calor de correlaciones" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Horas_estudioNotaSemestre
Horas_estudio1.0000000.1155060.061056
Nota0.1155061.000000-0.000447
Semestre0.061056-0.0004471.000000
\n", - "
" - ], - "text/plain": [ - " Horas_estudio Nota Semestre\n", - "Horas_estudio 1.000000 0.115506 0.061056\n", - "Nota 0.115506 1.000000 -0.000447\n", - "Semestre 0.061056 -0.000447 1.000000" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[['Horas_estudio', 'Nota', 'Semestre']].corr()" - ] - }, - { - "cell_type": "code", - "execution_count": 73, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "correlacion = df[['Horas_estudio', 'Nota', 'Semestre']].corr()\n", + "# Heatmap de correlación entre variables numéricas\n", + "correlacion = df[[\"Horas_estudio\", \"Nota\", \"Semestre\"]].corr()\n", "\n", "plt.figure(figsize=(6, 5))\n", "sns.heatmap(\n", " correlacion,\n", - " annot=True, # mostrar valores\n", - " fmt='.2f', # 2 decimales\n", - " cmap='coolwarm', # azul=negativo, rojo=positivo\n", - " center=0,\n", - " square=True,\n", + " annot=True, # Mostrar valores en las celdas\n", + " fmt=\".2f\", # Formato: 2 decimales\n", + " cmap=\"coolwarm\", # Paleta de colores (azul = negativo, rojo = positivo)\n", + " center=0, # Centro de la escala en 0\n", + " square=True, # Celdas cuadradas\n", " linewidths=0.5\n", ")\n", - "plt.title('Correlación entre variables numéricas')\n", + "plt.title(\"Correlación entre variables\")\n", "plt.tight_layout()\n", "plt.show()" ] @@ -1209,76 +423,53 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Nivel 7 — Dashboard completo" + "### Nivel 5: Dashboard completo — combinando todo" ] }, { "cell_type": "code", - "execution_count": 75, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_142330/1462478582.py:26: FutureWarning: \n", - "\n", - "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", - "\n", - " sns.boxplot(data=df, x='Semestre', y='Nota', palette='Blues', ax=axes[1, 0])\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, axes = plt.subplots(2, 3, figsize=(16, 10))\n", - "fig.suptitle('Dashboard: Análisis del Curso', fontsize=16, fontweight='bold', y=1.01)\n", + "fig.suptitle(\"Dashboard: Análisis del Curso\", fontsize=16, fontweight='bold', y=1.01)\n", "\n", - "# 1. Distribución general\n", - "sns.histplot(data=df, x='Nota', bins=18, kde=True, color='steelblue', ax=axes[0, 0])\n", - "axes[0, 0].axvline(df['Nota'].mean(), color='red', linestyle='--',\n", - " label=f'Media: {df[\"Nota\"].mean():.1f}')\n", - "axes[0, 0].set_title('Distribución de notas')\n", + "# 1. Distribución general de notas\n", + "sns.histplot(data=df, x=\"Nota\", bins=18, kde=True, color=\"steelblue\", ax=axes[0, 0])\n", + "axes[0, 0].axvline(df[\"Nota\"].mean(), color='red', linestyle='--', label=f'Media: {df[\"Nota\"].mean():.1f}')\n", + "axes[0, 0].set_title(\"Distribución de notas\")\n", "axes[0, 0].legend()\n", "\n", - "# 2. Aprobados vs Reprobados por carrera (barras apiladas)\n", - "resultado = df.groupby(['Carrera', 'Estado']).size().unstack(fill_value=0)\n", - "resultado.plot(kind='bar', ax=axes[0, 1],\n", - " color=['#e74c3c', '#2ecc71'], edgecolor='white', rot=0)\n", - "axes[0, 1].set_title('Aprobados vs Reprobados')\n", - "axes[0, 1].set_xlabel('')\n", - "axes[0, 1].legend(title='')\n", - "\n", - "# 3. Scatter horas vs nota\n", - "sns.scatterplot(data=df, x='Horas_estudio', y='Nota',\n", - " hue='Carrera', style='Estado', alpha=0.6, ax=axes[0, 2])\n", + "# 2. Resultado por carrera (barras apiladas)\n", + "resultado = df.groupby([\"Carrera\", \"Estado\"]).size().unstack(fill_value=0)\n", + "resultado.plot(kind='bar', ax=axes[0, 1], color=[\"#e74c3c\", \"#2ecc71\"],\n", + " edgecolor='white', rot=0)\n", + "axes[0, 1].set_title(\"Aprobados vs Reprobados por carrera\")\n", + "axes[0, 1].set_xlabel(\"\")\n", + "axes[0, 1].legend(title=\"\")\n", + "\n", + "# 3. Dispersión horas vs nota\n", + "sns.scatterplot(data=df, x=\"Horas_estudio\", y=\"Nota\",\n", + " hue=\"Carrera\", style=\"Estado\", alpha=0.6, ax=axes[0, 2])\n", "axes[0, 2].axhline(61, color='black', linestyle=':', linewidth=1)\n", - "axes[0, 2].set_title('Horas de estudio vs Nota')\n", + "axes[0, 2].set_title(\"Horas de estudio vs Nota\")\n", "\n", - "# 4. Boxplot por semestre\n", - "sns.boxplot(data=df, x='Semestre', y='Nota', palette='Blues', ax=axes[1, 0])\n", + "# 4. Boxplot por semestre (pares e impares)\n", + "sns.boxplot(data=df, x=\"Semestre\", y=\"Nota\", palette=\"Blues\", ax=axes[1, 0])\n", "axes[1, 0].axhline(61, color='red', linestyle='--', alpha=0.6)\n", - "axes[1, 0].set_title('Notas por semestre')\n", + "axes[1, 0].set_title(\"Notas por semestre\")\n", "\n", "# 5. Violinplot por carrera\n", - "sns.violinplot(data=df, x='Carrera', y='Nota', hue='Carrera',\n", - " palette='Set3', inner='box', ax=axes[1, 1], legend=False)\n", - "axes[1, 1].set_title('Distribución por carrera')\n", + "sns.violinplot(data=df, x=\"Carrera\", y=\"Nota\", hue=\"Carrera\",\n", + " palette=\"Set3\", inner=\"box\", ax=axes[1, 1], legend=False)\n", + "axes[1, 1].set_title(\"Distribución por carrera\")\n", "\n", - "# 6. Heatmap\n", - "corr = df[['Horas_estudio', 'Nota', 'Semestre']].corr()\n", - "sns.heatmap(corr, annot=True, fmt='.2f', cmap='RdYlGn',\n", + "# 6. Heatmap de correlación\n", + "corr = df[[\"Horas_estudio\", \"Nota\", \"Semestre\"]].corr()\n", + "sns.heatmap(corr, annot=True, fmt=\".2f\", cmap=\"RdYlGn\",\n", " center=0, ax=axes[1, 2], square=True, linewidths=0.5)\n", - "axes[1, 2].set_title('Correlación')\n", + "axes[1, 2].set_title(\"Correlación\")\n", "\n", "plt.tight_layout()\n", "plt.show()" @@ -1291,103 +482,7 @@ "---\n", "## Guardar gráficas\n", "\n", - "`plt.savefig()` debe llamarse **antes** de `plt.show()`. \n", - "Formatos soportados: `png`, `pdf`, `svg`, `eps`." - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Gráfica guardada en /tmp/grafica_notas.png\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(8, 5))\n", - "\n", - "sns.barplot(data=df, x='Carrera', y='Nota', hue='Carrera',\n", - " palette='Set2', errorbar='sd', legend=False, ax=ax)\n", - "ax.set_title('Promedio de notas por carrera')\n", - "ax.set_ylabel('Nota promedio')\n", - "\n", - "# dpi=150 para buena resolución; bbox_inches='tight' evita recortes\n", - "plt.savefig('grafica_notas.png', dpi=150, bbox_inches='tight')\n", - "print('Gráfica guardada en /tmp/grafica_notas.png')\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "## Resumen de funciones principales\n", - "\n", - "### Matplotlib\n", - "\n", - "| Función | Descripción |\n", - "|---|---|\n", - "| `plt.subplots(f, c)` | Crear figura con `f` filas y `c` columnas de axes |\n", - "| `ax.plot(x, y)` | Gráfica de línea |\n", - "| `ax.scatter(x, y)` | Gráfica de dispersión |\n", - "| `ax.bar(x, y)` | Gráfica de barras |\n", - "| `ax.hist(x, bins)` | Histograma |\n", - "| `ax.errorbar(x, y, yerr)` | Línea con barras de error |\n", - "| `ax.fill_between(x, y1, y2)` | Área entre dos curvas |\n", - "| `ax.annotate(txt, xy, xytext)` | Anotación con flecha |\n", - "| `ax.set_xscale('log')` | Escala logarítmica |\n", - "| `plt.savefig(ruta, dpi)` | Guardar figura |\n", - "\n", - "### Seaborn\n", - "\n", - "| Función | Descripción |\n", - "|---|---|\n", - "| `sns.set_theme(style, palette)` | Configurar estilo global |\n", - "| `sns.histplot(data, x, hue, kde)` | Histograma con opción de KDE |\n", - "| `sns.boxplot(data, x, y, hue)` | Diagrama de caja |\n", - "| `sns.violinplot(data, x, y)` | Violín (distribución detallada) |\n", - "| `sns.regplot(data, x, y)` | Scatter + línea de regresión |\n", - "| `sns.countplot(data, x, hue)` | Conteo de categorías |\n", - "| `sns.barplot(data, x, y)` | Promedio por grupo con IC |\n", - "| `sns.heatmap(corr, annot)` | Mapa de calor |\n", - "| `sns.pairplot(data, hue)` | Matriz de gráficas |\n", - "| `sns.scatterplot(data, x, y, hue)` | Scatter con categorías |" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "## Ejercicios\n", - "\n", - "### Ejercicio 1 — Gráfica de línea con personalización\n", - "\n", - "Usando la interfaz orientada a objetos (`fig, ax = plt.subplots()`), grafica las funciones $f(x) = x^2$, $g(x) = x^3$ y $h(x) = \\sqrt{x}$ en el rango $[0, 4]$ en una misma figura.\n", - "\n", - "Requisitos:\n", - "- Cada curva con diferente color y etiqueta en LaTeX\n", - "- Leyenda visible\n", - "- Título y etiquetas de ejes con LaTeX\n", - "- Cuadrícula con transparencia" + "Para guardar una gráfica como imagen, usa `plt.savefig()` **antes** de `plt.show()`:" ] }, { @@ -1396,95 +491,24 @@ "metadata": {}, "outputs": [], "source": [ - "# TU CÓDIGO AQUÍ\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Ejercicio 2 — Subplots y tipos de gráficas\n", - "\n", - "Crea una figura con 2 filas y 2 columnas (`plt.subplots(2, 2, figsize=(12, 8))`) usando el dataset `df` del notebook:\n", + "plt.figure(figsize=(8, 5))\n", + "sns.barplot(data=df, x=\"Carrera\", y=\"Nota\", hue=\"Carrera\",\n", + " palette=\"Set2\", errorbar=\"sd\", legend=False)\n", + "plt.title(\"Promedio de notas por carrera\")\n", + "plt.ylabel(\"Nota promedio\")\n", "\n", - "- `[0,0]` Histograma de `Horas_estudio` con línea en la media\n", - "- `[0,1]` Scatter de `Horas_estudio` vs `Nota` coloreado por `Carrera`\n", - "- `[1,0]` Boxplot de `Nota` por `Semestre`\n", - "- `[1,1]` Barras con el promedio de horas de estudio por carrera\n", - "\n", - "Agrega título general con `fig.suptitle()`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TU CÓDIGO AQUÍ\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Ejercicio 3 — Seaborn exploratorio\n", + "# Guardar la gráfica (dpi=150 para buena resolución)\n", + "plt.savefig(\"/tmp/grafica_notas.png\", dpi=150, bbox_inches=\"tight\")\n", + "print(\"Gráfica guardada en /tmp/grafica_notas.png\")\n", "\n", - "Con el dataset `df`, crea una figura con 3 gráficas de Seaborn:\n", - "\n", - "a) `violinplot` de `Nota` por `Carrera`, separado por `Estado` (usa `hue='Estado'`) \n", - "b) `countplot` de `Semestre` coloreado por `Estado` \n", - "c) `heatmap` de correlación entre `Horas_estudio`, `Nota` y `Semestre`\n", - "\n", - "Cada gráfica debe tener título." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TU CÓDIGO AQUÍ\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Ejercicio 4 — Datos reales con barras de error\n", - "\n", - "Se midió la temperatura promedio mensual (°C) en Ciudad de Guatemala durante un año. \n", - "Los datos incluyen incertidumbre de medición:\n", - "\n", - "```python\n", - "meses = ['Ene','Feb','Mar','Abr','May','Jun','Jul','Ago','Sep','Oct','Nov','Dic']\n", - "temp = [18.2, 19.5, 21.3, 22.8, 22.1, 20.4, 19.8, 20.1, 19.7, 19.3, 18.8, 18.1]\n", - "error = [0.8, 0.6, 1.1, 0.9, 1.0, 0.7, 0.8, 0.9, 0.7, 0.6, 0.8, 0.7]\n", - "```\n", - "\n", - "a) Grafica la temperatura con `errorbar` (barras de error), marcador circular, y área sombreada con `fill_between` entre `temp - error` y `temp + error`. \n", - "b) Agrega una línea horizontal en el promedio anual con `axhline`. \n", - "c) Anota el mes más caluroso y el más frío con `ax.annotate()`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "meses = ['Ene','Feb','Mar','Abr','May','Jun','Jul','Ago','Sep','Oct','Nov','Dic']\n", - "temp = [18.2, 19.5, 21.3, 22.8, 22.1, 20.4, 19.8, 20.1, 19.7, 19.3, 18.8, 18.1]\n", - "error = [0.8, 0.6, 1.1, 0.9, 1.0, 0.7, 0.8, 0.9, 0.7, 0.6, 0.8, 0.7]\n", - "\n", - "# TU CÓDIGO AQUÍ\n" + "plt.tight_layout()\n", + "plt.show()" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "base", "language": "python", "name": "python3" }, @@ -1498,7 +522,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.14.2" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/ejemplos/python/Numpy.ipynb b/ejemplos/python/Numpy.ipynb index 470ce5f..dc2642e 100644 --- a/ejemplos/python/Numpy.ipynb +++ b/ejemplos/python/Numpy.ipynb @@ -41,14 +41,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "NumPy versión: 2.4.2\n" + "NumPy versión: 1.26.4\n" ] } ], @@ -75,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -101,12 +101,12 @@ "# Array de NumPy\n", "arreglo = np.array([1, 2, 3, 4, 5])\n", "print(\"Array NumPy:\", arreglo)\n", - "print(\"Tipo:\", type(arreglo))" + "print(\"Tipo:\", type(arreglo))\n" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -142,16 +142,16 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Lista Python : 1.046 segundos\n", - "Array NumPy : 0.017 segundos\n", - "NumPy es 61× más rápido\n" + "Lista Python : 5.741 segundos\n", + "Array NumPy : 1.110 segundos\n", + "NumPy es 5 × más rápido\n" ] } ], @@ -176,7 +176,7 @@ "\n", "print(f\"Lista Python : {t_lista:.3f} segundos\")\n", "print(f\"Array NumPy : {t_numpy:.3f} segundos\")\n", - "print(f\"NumPy es {t_lista / t_numpy:.0f}× más rápido\")" + "print(f\"NumPy es {t_lista / t_numpy:.0f} × más rápido\")" ] }, { @@ -188,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -197,7 +197,8 @@ "text": [ "Ceros: [0. 0. 0. 0. 0.]\n", "Unos : [1. 1. 1. 1. 1.]\n", - "Vacío: [1. 1. 1. 1. 1.]\n", + "Vacío: [1.26207700e-315 0.00000000e+000 1.27198647e-315 1.27148252e-315\n", + " 0.00000000e+000]\n", "\n", "Arange (0 a 10, paso 2): [0 2 4 6 8]\n", "Linspace (6 puntos entre 0 y 1): [0. 0.2 0.4 0.6 0.8 1. ]\n", @@ -237,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -263,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -309,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -373,7 +374,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -400,7 +401,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -436,7 +437,7 @@ "print(\"Toda la primera fila:\", m[0, :]) # [1, 2, 3]\n", "print(\"Toda la segunda columna:\", m[:, 1]) # [2, 5, 8]\n", "print(\"\\nSubmatriz (filas 0-1, columnas 1-2):\")\n", - "print(m[0:2, 1:])" + "print(m[0:2, 1:3])" ] }, { @@ -448,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -487,7 +488,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -538,7 +539,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -592,7 +593,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -622,7 +623,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -671,7 +672,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -695,7 +696,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_10393/3405442486.py:8: RuntimeWarning: invalid value encountered in sqrt\n", + "/tmp/ipykernel_1283/3405442486.py:8: RuntimeWarning: invalid value encountered in sqrt\n", " print(\"\\nsqrt de\", x, \"→\", np.sqrt(x)) # sqrt de negativos → nan\n" ] } @@ -730,15 +731,15 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Temperaturas: [18.5 22. 35.1 15.3 28.7 12. 30. 19. ]\n", - "temps > 25 : [False False True False True False True False]\n", + "Temperaturas: [18.5 22. 35.1 15.3 28.7 12. 30. ]\n", + "temps > 25 : [False False True False True False True]\n", "\n", "Días calurosos (> 25°C): [35.1 28.7 30. ]\n", "Días moderados (20-30°C): [22. 28.7 30. ]\n", @@ -750,7 +751,7 @@ } ], "source": [ - "temps = np.array([18.5, 22.0, 35.1, 15.3, 28.7, 12.0, 30.0, 19])\n", + "temps = np.array([18.5, 22.0, 35.1, 15.3, 28.7, 12.0, 30.0])\n", "\n", "# Comparación → array de True/False\n", "print(\"Temperaturas:\", temps)\n", @@ -781,15 +782,13 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['Reprobado' 'Aprobado' 'Aprobado' 'Reprobado' 'Aprobado' 'Aprobado'\n", - " 'Aprobado']\n", " Nota 55 → Reprobado\n", " Nota 72 → Aprobado\n", " Nota 88 → Aprobado\n", @@ -798,7 +797,7 @@ " Nota 63 → Aprobado\n", " Nota 78 → Aprobado\n", "\n", - "Notas penalizadas: [ 0 72 88 0 91 63 78]\n" + " Notas penalizadas: [ 0 72 88 0 91 63 78]\n" ] } ], @@ -807,7 +806,6 @@ "\n", "# Etiquetar cada nota como \"Aprobado\" o \"Reprobado\"\n", "estados = np.where(notas >= 61, \"Aprobado\", \"Reprobado\")\n", - "print(estados)\n", "\n", "for n, e in zip(notas, estados):\n", " print(f\" Nota {n:3d} → {e}\")\n", @@ -815,7 +813,7 @@ "# También funciona con números\n", "# Reemplazar notas menores a 61 con 0 (para penalización)\n", "penalizadas = np.where(notas >= 61, notas, 0)\n", - "print(\"\\nNotas penalizadas:\", penalizadas)" + "print(\"\\n Notas penalizadas:\", penalizadas)" ] }, { @@ -837,7 +835,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -845,20 +843,9 @@ "output_type": "stream", "text": [ "Alias modifica 'original': [999 2 3 4 5]\n", - "[2 3 4]\n", "Slice (vista) modifica 'original2': [ 1 777 3 4 5]\n", "Copia NO modifica 'original3': [1 2 3 4 5]\n" ] - }, - { - "data": { - "text/plain": [ - "array([999, 2, 3, 4, 5])" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -872,7 +859,6 @@ "# ⚠ VISTA (slice) — ¡diferente a listas Python!\n", "original2 = np.array([1, 2, 3, 4, 5])\n", "vista = original2[1:4] # NO es copia — es vista\n", - "print(vista)\n", "vista[0] = 777\n", "print(\"Slice (vista) modifica 'original2':\", original2)\n", "\n", @@ -880,8 +866,7 @@ "original3 = np.array([1, 2, 3, 4, 5])\n", "copia = original3.copy()\n", "copia[0] = 999\n", - "print(\"Copia NO modifica 'original3':\", original3)\n", - "copia" + "print(\"Copia NO modifica 'original3':\", original3)" ] }, { @@ -900,7 +885,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -966,7 +951,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -979,7 +964,6 @@ "\n", "Índices (argsort): [5 1 3 6 0 4 2]\n", "Aplicando índices : [34 45 61 73 78 88 92]\n", - "[2 4 0 6 3 1 5]\n", "\n", "Ranking:\n", " 1. Diana: 92\n", @@ -1008,8 +992,6 @@ "# Caso práctico: obtener ranking de estudiantes\n", "nombres = np.array([\"Ana\", \"Carlos\", \"Diana\", \"Eduardo\", \"Fátima\", \"Gabriel\", \"Helena\"])\n", "ranking = np.argsort(notas)[::-1] # mayor a menor\n", - "\n", - "print(ranking)\n", "print(\"\\nRanking:\")\n", "for pos, i in enumerate(ranking, 1):\n", " print(f\" {pos}. {nombres[i]}: {notas[i]}\")" @@ -1026,7 +1008,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1072,7 +1054,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1125,7 +1107,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1179,7 +1161,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1233,7 +1215,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1303,7 +1285,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1360,7 +1342,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1420,7 +1402,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -1456,42 +1438,6 @@ " print(f\" v={vi} m/s → d≈{di:.2f} m\")" ] }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "# Curva para graficar\n", - "v_fit = np.linspace(0, 55, 100)\n", - "d_fit = np.polyval(coefs, v_fit)\n", - "\n", - "# Gráfica\n", - "plt.figure()\n", - "plt.scatter(v, d, label=\"Datos reales\")\n", - "plt.plot(v_fit, d_fit, label=\"Ajuste cuadrático\")\n", - "plt.xlabel(\"Velocidad (m/s)\")\n", - "plt.ylabel(\"Distancia (m)\")\n", - "plt.title(\"Ajuste cuadrático: distancia vs velocidad\")\n", - "plt.legend()\n", - "plt.grid()\n", - "\n", - "plt.show()" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1502,7 +1448,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -1519,7 +1465,21 @@ "Gabriel [61 92 41 63]\n", "Helena [83 69 77 41]\n", "Iván [99 60 72 51]\n", - "[67.35 73.35 68.45 70.7 58.8 60.45 62.3 65.85]\n" + "\n", + "--- Resultados Finales ---\n", + "Ana Nota: 67.3 → APROBADO\n", + "Carlos Nota: 73.3 → APROBADO\n", + "Diana Nota: 68.5 → APROBADO\n", + "Eduardo Nota: 70.7 → APROBADO\n", + "Fátima Nota: 58.8 → REPROBADO\n", + "Gabriel Nota: 60.5 → REPROBADO\n", + "Helena Nota: 62.3 → APROBADO\n", + "Iván Nota: 65.8 → APROBADO\n", + "\n", + "Promedio del curso: 65.91\n", + "Nota más alta: 73.35\n", + "Nota más baja: 58.80\n", + "Aprobados: 6 / 8\n" ] } ], @@ -1540,46 +1500,36 @@ "\n", "# Calcular nota final ponderada para cada estudiante\n", "notas_finales = notas @ pesos\n", - "print(notas_finales)" + "\n", + "print(\"\\n--- Resultados Finales ---\")\n", + "for nombre, nota in zip(nombres, notas_finales):\n", + " estado = \"APROBADO\" if nota >= 61 else \"REPROBADO\"\n", + " print(f\"{nombre:<10} Nota: {nota:.1f} → {estado}\")\n", + "\n", + "print(f\"\\nPromedio del curso: {np.mean(notas_finales):.2f}\")\n", + "print(f\"Nota más alta: {np.max(notas_finales):.2f}\")\n", + "print(f\"Nota más baja: {np.min(notas_finales):.2f}\")\n", + "print(f\"Aprobados: {np.sum(notas_finales >= 61)} / {len(notas_finales)}\")" ] }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - "--- Resultados Finales ---\n", - "Ana Nota: 67.3 → APROBADO\n", - "Carlos Nota: 73.3 → APROBADO\n", - "Diana Nota: 68.5 → APROBADO\n", - "Eduardo Nota: 70.7 → APROBADO\n", - "Fátima Nota: 58.8 → REPROBADO\n", - "Gabriel Nota: 60.5 → REPROBADO\n", - "Helena Nota: 62.3 → APROBADO\n", - "Iván Nota: 65.8 → APROBADO\n", - "\n", - "Promedio del curso: 65.91\n", - "Nota más alta: 73.35\n", - "Nota más baja: 58.80\n", - "Aprobados: 6 / 8\n" + "NumPy versión: 1.26.4\n" ] } ], "source": [ - "print(\"\\n--- Resultados Finales ---\")\n", - "for nombre, nota in zip(nombres, notas_finales):\n", - " estado = \"APROBADO\" if nota >= 61 else \"REPROBADO\"\n", - " print(f\"{nombre:<10} Nota: {nota:.1f} → {estado}\")\n", "\n", - "print(f\"\\nPromedio del curso: {np.mean(notas_finales):.2f}\")\n", - "print(f\"Nota más alta: {np.max(notas_finales):.2f}\")\n", - "print(f\"Nota más baja: {np.min(notas_finales):.2f}\")\n", - "print(f\"Aprobados: {np.sum(notas_finales >= 61)} / {len(notas_finales)}\")" + "import numpy as np\n", + "\n", + "print(\"NumPy versión:\", np.__version__)" ] }, { @@ -1621,11 +1571,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lista principal:, [ 1 2 3 4 5 6 7 8 9 10]\n", + "Multiplicación por 5: [ 5 10 15 20 25 30 35 40 45 50]\n", + "Números pares: [ 2 4 6 8 10]\n", + "Suma total: 55\n" + ] + } + ], "source": [ - "# TU CÓDIGO AQUÍ\n" + "# TU CÓDIGO AQUÍ\n", + "\n", + "lista_principal = np.arange(1, 11)\n", + "Multiplicacion = lista_principal * 5\n", + "pares = lista_principal[lista_principal % 2 ==0]\n", + "suma = lista_principal.sum()\n", + "\n", + "print(f\"Lista principal:, {lista_principal}\")\n", + "print(f\"Multiplicación por 5: {Multiplicacion}\")\n", + "print(f\"Números pares: {pares}\")\n", + "print(f\"Suma total: {suma}\")\n", + "\n", + "\n", + "\n", + "\n" ] }, { @@ -1642,13 +1617,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Grados : [ 0 30 45 60 90 180]\n", + "Radianes: [0. 0.52359878 0.78539816 1.04719755 1.57079633 3.14159265]\n", + "seno: [0. 0.5 0.7071 0.866 1. 0. ]\n" + ] + } + ], "source": [ "grados = np.array([0, 30, 45, 60, 90, 180])\n", + "radianes = grados * np.pi /180\n", "\n", - "# TU CÓDIGO AQUÍ\n" + "# TU CÓDIGO AQUÍ\n", + "print(f\"Grados : {grados}\")\n", + "print(f\"Radianes: {radianes}\")\n", + "print(\"seno: \" , np.sin(radianes).round(4))" ] }, { @@ -1666,10 +1655,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " DATOS ORIGINALES \n", + " \n", + "EN DESORDEN : [ 22.1 19.5 31.2 28.0 17.3 25.8 30.1 14.2 26.7 23.4 18.9 32.0 29.5 21.1 16.8 27.3\n", + " 24.6 13.5 20.0 28.9 33.1 15.7 22.8 19.0 31.5 26.2 18.1 29.8 12.4 25.0]\n", + " \n", + "EN ORDEN : [ 12.4 13.5 14.2 15.7 16.8 17.3 18.1 18.9 19.0 19.5 20.0 21.1 22.1 22.8 23.4 24.6\n", + " 25.0 25.8 26.2 26.7 27.3 28.0 28.9 29.5 29.8 30.1 31.2 31.5 32.0 33.1]\n", + " \n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + " ESTADÍSTICA \n", + " \n", + "Mínimo: 12.4\n", + " \n", + "Máximo: 33.1\n", + " \n", + "Número de días que superaron 28°C: 8\n", + " \n", + "Temperatura de los días menores a 20°C: [ 19.5 17.3 14.2 18.9 16.8 13.5 15.7 19.0 18.1 12.4]\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + " TEMPERATURAS MÍNIMAS REGISTRADAS < 15°C, MODIFICADOS \n", + " \n", + "Temperaturas originales ordenas: [ 12.4 13.5 14.2 15.7 16.8 17.3 18.1 18.9 19.0 19.5 20.0 21.1 22.1 22.8 23.4 24.6\n", + " 25.0 25.8 26.2 26.7 27.3 28.0 28.9 29.5 29.8 30.1 31.2 31.5 32.0 33.1]\n", + " \n", + "DATOS MODIFICADOS < 15°C : [ 15.0 15.0 15.0 15.7 16.8 17.3 18.1 18.9 19.0 19.5 20.0 21.1 22.1 22.8 23.4 24.6\n", + " 25.0 25.8 26.2 26.7 27.3 28.0 28.9 29.5 29.8 30.1 31.2 31.5 32.0 33.1]\n" + ] + } + ], "source": [ + "np.set_printoptions(formatter={'float': '{: 0.1f}'.format}, linewidth=100)\n", "temperaturas = np.array([\n", " 22.1, 19.5, 31.2, 28.0, 17.3, 25.8, 30.1,\n", " 14.2, 26.7, 23.4, 18.9, 32.0, 29.5, 21.1,\n", @@ -1677,8 +1703,49 @@ " 15.7, 22.8, 19.0, 31.5, 26.2, 18.1, 29.8,\n", " 12.4, 25.0\n", "])\n", + "# TU CÓDIGO AQUÍ\n", + "\n", + "#--------------------------\n", + "\n", + "a = np.sort(temperaturas) # Temperaturas ordenadas\n", + "b =(temperaturas > 28).sum() # Temperaturas mayores a 28°C.\n", + "c = temperaturas[temperaturas < 20] # Temperaturas de los días menores a 20°C.\n", + "nuevas_temperaturas = np.where(temperaturas < 15, 15.0, temperaturas) # Remplazo de tempetaturas menores a 15°C\n", + "d = np.sort(nuevas_temperaturas) \n", + "\n", + "#--------------------------\n", + "\n", + "\n", + "# Lista de temperaturas diarias de un mes.\n", + "print(\"\\n\" + \" \"*20 + \" DATOS ORIGINALES \" + \" \"*20)\n", + "print(\" \" * 40)\n", + "print(f\"EN DESORDEN : {temperaturas}\")\n", + "print(\" \" * 40)\n", + "print(f\"EN ORDEN : {a}\")\n", + "print(\" \" * 40)\n", + "print(\"----\" * 40)\n", + "\n", + "#--------------------------\n", + "\n", + "print(\"\\n\" + \" \" * 21 + \" ESTADÍSTICA \" + \" \"*20)\n", + "print(\" \" * 40)\n", + "print(f\"Mínimo: {np.min(temperaturas)}\")\n", + "print(\" \" * 40)\n", + "print(f\"Máximo: {np.max(temperaturas)}\")\n", + "print(\" \" * 40)\n", + "print(f\"Número de días que superaron 28°C: {b}\")\n", + "print(\" \" * 40)\n", + "print(f\"Temperatura de los días menores a 20°C: {c}\")\n", + "\n", + "print(\"----\" * 40)\n", + "\n", + "#--------------------------\n", "\n", - "# TU CÓDIGO AQUÍ\n" + "print(\"\\n\" + \" \" * 17 + \" TEMPERATURAS MÍNIMAS REGISTRADAS < 15°C, MODIFICADOS \" + \" \"*20)\n", + "print(\" \" * 40)\n", + "print(f\"Temperaturas originales ordenas: {a}\")\n", + "print(\" \" * 40)\n", + "print(f\"DATOS MODIFICADOS < 15°C : {d}\")\n" ] }, { @@ -1697,11 +1764,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " VALORES DEL 1 AL 16: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16]\n", + " Forma: (16,)\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + " MATRIZ ORIGINAL \n", + "Forma: (4, 4)\n", + "MATRIZ 4X4: \n", + " [[ 1 2 3 4]\n", + " [ 5 6 7 8]\n", + " [ 9 10 11 12]\n", + " [13 14 15 16]]\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + " OPERACIONES CON LA MATRIZ \n", + " \n", + "PRIMERA FILA: \n", + " [1 2 3 4]\n", + " \n", + "ÚLTIMA COLUMNA DE LA MATRIZ: \n", + " [ 4 8 12 16]\n", + " \n", + "SUBMATRIZ DE LAS 2 PRIMERAS FILAS Y LAS 2 ÚLTIMAS COLUMNAS:\n", + " [[3 4]\n", + " [7 8]]\n", + " \n", + "SUMAS DE CADA FILA: \n", + " [10 26 42 58]\n", + " \n" + ] + } + ], "source": [ - "# TU CÓDIGO AQUÍ\n" + "# TU CÓDIGO AQUÍ\n", + "\n", + "a = np.arange(1, 17) # Matrix fila 1 al 16 o arreglo original\n", + "b = a.reshape(4, 4) # Matriz 4x4\n", + "\n", + "#--------------------------\n", + "print(f\"\\n VALORES DEL 1 AL 16: {a}\")\n", + "print(\" Forma:\", a.shape)\n", + "print(\"----\" * 40)\n", + "#--------------------------\n", + "print(\" \"*30 + \"MATRIZ ORIGINAL\" + \" \"*20)\n", + "print(\"Forma:\", b.shape)\n", + "print(f\"MATRIZ 4X4: \\n {b}\")\n", + "print(\"----\" * 40)\n", + "print(\" \"*27 + \" OPERACIONES CON LA MATRIZ\" + \" \"*20)\n", + "print(\" \" * 40)\n", + "#--------------------------\n", + "print(f\"PRIMERA FILA: \\n {b[0, :]}\")\n", + "print(\" \" * 40)\n", + "#--------------------------\n", + "print(f\"ÚLTIMA COLUMNA DE LA MATRIZ: \\n {b[:, -1]}\")\n", + "print(\" \" * 40)\n", + "#--------------------------\n", + "print(f\"SUBMATRIZ DE LAS 2 PRIMERAS FILAS Y LAS 2 ÚLTIMAS COLUMNAS:\\n {b[0:2, 2:4]}\")\n", + "print(\" \" * 40)\n", + "#--------------------------\n", + "print(f\"SUMAS DE CADA FILA: \\n {np.sum(b, axis=1)}\") # Suma de la matriz utilizando .sum() y axis=1 para sumar la filas\n", + "print(\" \" * 40)\n", + "\n" ] }, { @@ -1720,11 +1849,68 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------------------------------------\n", + "\n", + " NOTA DE LAS 8 TAREAS DEL CURSO:\n", + " [45 38 52 29 61 47 55 33]\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " LA SUMA ACUMULADA DE TODA LAS NOTAS: \n", + " [ 45 83 135 164 225 272 327 360]\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " LA SUMA CORRELATIVA QUE SUPERÓ LOS 100 PUNTOS, ESTÁ EN LA NOTA CON ÍNDICE: 2\n", + "\n", + " LA SUMA ACUMULADA QUE SUPERÓ 100 pt.: 135\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " SUMA ACUMULADA PRIMERA NOTA, EL MOMENTO EN QUE SUPERÓ LOS 100 pt.: \n", + " [135 164 225 272 327 360]\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " SUMA ACUMULADA PRIMERA NOTA, EL MOMENTO EN QUE SUPERÓ LOS 200 pt.: \n", + " [225 272 327 360]\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " SUMA ACUMULADA PRIMERA NOTA, EL MOMENTO EN QUE SUPERÓ LOS 300 pt.s: \n", + " [327 360]\n", + "------------------------------------------------------------------------------------------\n" + ] + } + ], "source": [ + "# Puntos de las OCho tareas del curso\n", + "print(\"---\" * 30)\n", "puntos_tareas = np.array([45, 38, 52, 29, 61, 47, 55, 33])\n", + "a = np.cumsum(puntos_tareas)\n", + "b= np.where(a > 100)[0][0]# Suma correlativa en que se superó lo 100 puntos\n", + "\n", + "\n", + "# TU CÓDIGO AQUÍ\n", + "print(f\"\\n NOTA DE LAS 8 TAREAS DEL CURSO:\\n {puntos_tareas}\")\n", + "print(\"---\" * 30)\n", + "\n", + "print(f\"\\n LA SUMA ACUMULADA DE TODA LAS NOTAS: \\n {a}\")\n", + "print(\"---\" * 30)\n", + "\n", + "print(f\"\\n LA SUMA CORRELATIVA QUE SUPERÓ LOS 100 PUNTOS, ESTÁ EN LA NOTA CON ÍNDICE: {b}\")\n", + "print(f\"\\n LA SUMA ACUMULADA QUE SUPERÓ 100 pt.: {a[2]}\")\n", + "print(\"---\" * 30)\n", + "\n", + "print(f\"\\n SUMA ACUMULADA PRIMERA NOTA, EL MOMENTO EN QUE SUPERÓ LOS 100 pt.: \\n {a[a > 100]}\")\n", + "print(\"---\" * 30)\n", + "\n", + "print(f\"\\n SUMA ACUMULADA PRIMERA NOTA, EL MOMENTO EN QUE SUPERÓ LOS 200 pt.: \\n {a[a > 200]}\")\n", + "print(\"---\" * 30)\n", "\n", - "# TU CÓDIGO AQUÍ\n" + "print(f\"\\n SUMA ACUMULADA PRIMERA NOTA, EL MOMENTO EN QUE SUPERÓ LOS 300 pt.s: \\n {a[a > 300]}\")\n", + "print(\"---\" * 30) \n", + "\n" ] }, { @@ -1748,9 +1934,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " SOLUCIÓN DEL SISTEMA:\n", + " [ 31.2 37.5 56.2]\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " VERIFICACIÓN DE LA SOLUCIÓN A @ X-b ; ≈ 0: \n", + " [ 0.0 0.0 0.0]\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " Determinante: \n", + " 8.0000\n", + "------------------------------------------------------------------------------------------\n", + "\n", + " LA NORMA DE X: \n", + " 74.4773\n", + "------------------------------------------------------------------------------------------\n" + ] + } + ], "source": [ "import numpy.linalg as la\n", "\n", @@ -1764,7 +1973,25 @@ " [0, 1, 2]], dtype=float)\n", "b = np.array([100, 200, 150], dtype=float)\n", "\n", - "# TU CÓDIGO AQUÍ\n" + "# TU CÓDIGO AQUÍ\n", + "x = la.solve(A, b) # la es el alias de np.linalg\n", + "y = A @ x - b # Verificación\n", + "n = la.norm (x)\n", + "\n", + "\n", + "print(f\"\\n SOLUCIÓN DEL SISTEMA:\\n {x}\")\n", + "\n", + "print(\"---\" * 30)\n", + "print(f\"\\n VERIFICACIÓN DE LA SOLUCIÓN A @ X-b ; ≈ 0: \\n {y}\" )\n", + "\n", + "print(\"---\" * 30)\n", + "print(f\"\\n Determinante: \\n {la.det(A):.4f}\")\n", + "\n", + "\n", + "print(\"---\" * 30)\n", + "print(f\"\\n LA NORMA DE X: \\n {n:.4f}\" )\n", + "print(\"---\" * 30)\n", + "\n" ] }, { @@ -1787,42 +2014,76 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " COEFICIENTES [a, b, c] de [at^2 + bt + c)]:: \n", + " a=4.9000, \n", + " b=0.0000, \n", + " c=-0.0000\n", + "------------------------------------------------------------------------------------------\n", + " \n", + " MODELOR [y(t)= at^2 + bt + c)]: \n", + " \n", + " y(t)= 4.9000·t² + 0.0000·t + -0.0000\n", + " \n", + "------------------------------------------------------------------------------------------\n", + " \n", + " ESTIMACIÓN DE LA GRAVEDAD: \n", + " 9.80000 m/s^2\n", + "------------------------------------------------------------------------------------------\n", + " POSICIONES \n", + " \n", + " POSICIÓN t1 = 2.5 s: \n", + " y(t1) = 30.62500 m\n", + " \n", + " \n", + " POSICIÓN t2 = 6.0 s:\n", + " y(t2) = 176.40000 m\n", + "------------------------------------------------------------------------------------------\n" + ] + } + ], "source": [ "t = np.array([0, 1, 2, 3, 4, 5], dtype=float) # tiempo (s)\n", "pos = np.array([0.0, 4.9, 19.6, 44.1, 78.4, 122.5]) # posición (m)\n", "\n", - "# TU CÓDIGO AQUÍ\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - " \n", - " JORGE GATICA \n", - " https://github.com/Jomung050/F12-Programacion/blob/main/Tareas/Parcial_1/examen_parcial2_estudiante.ipynb\n", - " \n", - " DÉBORA OCHOA (no entrego en github, penaliza eso valida bien el sello de entrega)\n", - " /home/erick/Downloads/examen_parcial2_estudiante.ipynb\n", - " \n", - " PEDRO POP GAMBOA \n", - " https://github.com/Pedro-Pop-USAC/F12-Programacion/blob/main/pruebas/examen_parcial2_estudiante.ipynb\n" + "# TU CÓDIGO AQUÍ\n", + "\n", + "coeficientes = np.polyfit(t, pos, deg=2)\n", + "a= coeficientes[0]\n", + "g_estimada = a * 2\n", + "t1 = 2.5\n", + "t2 = 6 \n", + "posicion_t1 = np.polyval(coeficientes, t1) \n", + "posicion_t2 = np.polyval(coeficientes, t2)\n", + "\n", + "\n", + "print(f\"\\n COEFICIENTES [a, b, c] de [at^2 + bt + c)]:: \\n a={coeficientes[0]:.4f}, \\n b={coeficientes[1]:.4f}, \\n c={coeficientes[2]:.4f}\")\n", + "print(\"---\" * 30)\n", + "# Modelo para un movimiento vertical, buscamos las alturas (y)\n", + "print(f\" \\n MODELOR [y(t)= at^2 + bt + c)]: \\n \\n y(t)= {coeficientes[0]:.4f}·t² + {coeficientes[1]:.4f}·t + {coeficientes[2]:.4f}\")\n", + "print(\" \" * 30)\n", + "print(\"---\" * 30)\n", + "\n", + "print(f\" \\n ESTIMACIÓN DE LA GRAVEDAD: \\n {g_estimada:.5f} m/s^2\")\n", + "print(\"---\" * 30)\n", + "\n", + "print( \" \" * 15 + \"POSICIONES\" + \" \"*20)\n", + "print(f\" \\n POSICIÓN t1 = 2.5 s: \\n y(t1) = {posicion_t1:.5f} m\")\n", + "print(\" \" * 30)\n", + "print(f\" \\n POSICIÓN t2 = 6.0 s:\\n y(t2) = {posicion_t2:.5f} m\")\n", + "print(\"---\" * 30)\n", + " " ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "base", "language": "python", "name": "python3" }, @@ -1836,7 +2097,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.14.2" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/ejemplos/python/Pandas.ipynb b/ejemplos/python/Pandas.ipynb index 11400b6..016286c 100644 --- a/ejemplos/python/Pandas.ipynb +++ b/ejemplos/python/Pandas.ipynb @@ -48,7 +48,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Pandas versión: 3.0.0\n" + "Pandas versión: 2.1.4\n" ] } ], @@ -84,7 +84,7 @@ "4 26.0\n", "dtype: float64\n", "\n", - "Tipo: \n" + "Tipo: \n" ] } ], @@ -131,13 +131,6 @@ "print(\"Promedio de la semana:\", dias.mean())" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": 4, @@ -167,8 +160,6 @@ "print('standard deviation:', s.std())\n", "print('index of max element:', s.argmax())\n", "print('values as list:', s.values)\n", - "\n", - "## Funciones que retornan un nuevo objeto\n", "print('indices of sorted Series:', s.argsort().values)\n", "print('cumulative sum:', s.cumsum().values)" ] @@ -177,38 +168,6 @@ "cell_type": "code", "execution_count": 5, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 -0.3\n", - "1 0.4\n", - "2 3.9\n", - "3 7.4\n", - "4 12.0\n", - "5 15.0\n", - "6 17.2\n", - "7 16.8\n", - "8 13.1\n", - "9 9.1\n", - "10 3.7\n", - "11 0.8\n", - "Name: temp_C, dtype: float64" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, "outputs": [ { "name": "stdout", @@ -257,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -288,7 +247,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -300,9 +259,9 @@ "Nombres de columnas: ['Nombre', 'Edad', 'Carrera', 'Promedio']\n", "\n", "Tipos de datos:\n", - "Nombre str\n", + "Nombre object\n", "Edad int64\n", - "Carrera str\n", + "Carrera object\n", "Promedio float64\n", "dtype: object\n" ] @@ -318,7 +277,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -410,7 +369,7 @@ "max 23.00000 91.300000" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -423,161 +382,7 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 5 entries, 0 to 4\n", - "Data columns (total 4 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Nombre 5 non-null str \n", - " 1 Edad 5 non-null int64 \n", - " 2 Carrera 5 non-null str \n", - " 3 Promedio 5 non-null float64\n", - "dtypes: float64(1), int64(1), str(2)\n", - "memory usage: 292.0 bytes\n" - ] - } - ], - "source": [ - "df.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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temp_Crain_mm
jan-0.359
feb0.457
mar3.984
apr7.4100
may12.0143
jun15.0153
jul17.2172
aug16.8164
sep13.1135
oct9.189
nov3.788
dec0.880
\n", - "
" - ], - "text/plain": [ - " temp_C rain_mm\n", - "jan -0.3 59\n", - "feb 0.4 57\n", - "mar 3.9 84\n", - "apr 7.4 100\n", - "may 12.0 143\n", - "jun 15.0 153\n", - "jul 17.2 172\n", - "aug 16.8 164\n", - "sep 13.1 135\n", - "oct 9.1 89\n", - "nov 3.7 88\n", - "dec 0.8 80" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# head(), tail() y atributos del índice (§8.2.1)\n", - "df_clima = pd.DataFrame({\n", - " 'temp_C': [-0.3, 0.4, 3.9, 7.4, 12.0, 15.0,\n", - " 17.2, 16.8, 13.1, 9.1, 3.7, 0.8],\n", - " 'rain_mm': [59, 57, 84, 100, 143, 153, 172, 164, 135, 89, 88, 80]\n", - "}, index=['jan','feb','mar','apr','may','jun',\n", - " 'jul','aug','sep','oct','nov','dec'])\n", - "df_clima" - ] - }, - { - "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -601,6 +406,14 @@ } ], "source": [ + "# head(), tail() y atributos del índice (§8.2.1)\n", + "df_clima = pd.DataFrame({\n", + " 'temp_C': [-0.3, 0.4, 3.9, 7.4, 12.0, 15.0,\n", + " 17.2, 16.8, 13.1, 9.1, 3.7, 0.8],\n", + " 'rain_mm': [59, 57, 84, 100, 143, 153, 172, 164, 135, 89, 88, 80]\n", + "}, index=['jan','feb','mar','apr','may','jun',\n", + " 'jul','aug','sep','oct','nov','dec'])\n", + "\n", "print(\"Primeras 3 filas:\")\n", "print(df_clima.head(3))\n", "print(\"\\nÚltimas 2 filas:\")\n", @@ -618,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -631,7 +444,7 @@ "2 Diana\n", "3 Eduardo\n", "4 Fátima\n", - "Name: Nombre, dtype: str\n", + "Name: Nombre, dtype: object\n", "\n", "Columnas 'Nombre' y 'Promedio':\n", " Nombre Promedio\n", @@ -649,13 +462,12 @@ "print(df[\"Nombre\"])\n", "\n", "print(\"\\nColumnas 'Nombre' y 'Promedio':\")\n", - "l = [\"Nombre\", \"Promedio\"]\n", - "print(df[l])" + "print(df[[\"Nombre\", \"Promedio\"]])" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -688,7 +500,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -724,7 +536,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -747,7 +559,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -770,7 +582,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -799,7 +611,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -841,7 +653,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -853,6 +665,7 @@ "0 Ana 20 Física 85.5\n", "2 Diana 21 Física 91.3\n", "4 Fátima 20 Matemática 79.2\n", + "\n", "Estudiantes de Física:\n", " Nombre Edad Carrera Promedio\n", "0 Ana 20 Física 85.5\n", @@ -866,7 +679,7 @@ "print(\"Estudiantes con promedio > 75:\")\n", "print(buenos)\n", "\n", - "\n", + "print()\n", "\n", "# Estudiantes de Física\n", "fisica = df[df[\"Carrera\"] == \"Física\"]\n", @@ -876,7 +689,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -908,97 +721,7 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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NombreEdadCarreraPromedio
0Ana20Física85.5
1Carlos22Matemática72.0
2Diana21Física91.3
3Eduardo23Computación68.7
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" - ], - "text/plain": [ - " Nombre Edad Carrera Promedio\n", - "0 Ana 20 Física 85.5\n", - "1 Carlos 22 Matemática 72.0\n", - "2 Diana 21 Física 91.3\n", - "3 Eduardo 23 Computación 68.7\n", - "4 Fátima 20 Matemática 79.2" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 23, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1022,209 +745,37 @@ "print(ranking[[\"Nombre\", \"Promedio\"]])" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Agregar nuevas columnas" + ] + }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 20, "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
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NombreEdadCarreraPromedio
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" - ], - "text/plain": [ - " Nombre Edad Carrera Promedio\n", - "2 Diana 21 Física 91.3\n", - "0 Ana 20 Física 85.5\n", - "4 Fátima 20 Matemática 79.2\n", - "1 Carlos 22 Matemática 72.0\n", - "3 Eduardo 23 Computación 68.7" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.sort_values(\"Promedio\", ascending=False, inplace=True)\n", - "\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Agregar nuevas columnas" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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NombreEdadCarreraPromedioEstadoPuntos_extra
2Diana21Física91.3Aprobado21.3
0Ana20Física85.5Aprobado15.5
4Fátima20Matemática79.2Aprobado9.2
1Carlos22Matemática72.0Aprobado2.0
3Eduardo23Computación68.7Aprobado0.0
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" - ], - "text/plain": [ - " Nombre Edad Carrera Promedio Estado Puntos_extra\n", - "2 Diana 21 Física 91.3 Aprobado 21.3\n", - "0 Ana 20 Física 85.5 Aprobado 15.5\n", - "4 Fátima 20 Matemática 79.2 Aprobado 9.2\n", - "1 Carlos 22 Matemática 72.0 Aprobado 2.0\n", - "3 Eduardo 23 Computación 68.7 Aprobado 0.0" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + " Nombre Edad Carrera Promedio Estado Puntos_extra\n", + "0 Ana 20 Física 85.5 Aprobado 15.5\n", + "1 Carlos 22 Matemática 72.0 Aprobado 2.0\n", + "2 Diana 21 Física 91.3 Aprobado 21.3\n", + "3 Eduardo 23 Computación 68.7 Reprobado 0.0\n", + "4 Fátima 20 Matemática 79.2 Aprobado 9.2\n" + ] } ], "source": [ "# Agregar columna calculada\n", - "df[\"Estado\"] = df[\"Promedio\"].apply(lambda p: \"Aprobado\" if p >= 61 else \"Reprobado\")\n", + "df[\"Estado\"] = df[\"Promedio\"].apply(lambda p: \"Aprobado\" if p >= 70 else \"Reprobado\")\n", "df[\"Puntos_extra\"] = (df[\"Promedio\"] - 70).clip(lower=0).round(1)\n", "\n", - "df" + "print(df)" ] }, { @@ -1238,7 +789,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1265,127 +816,7 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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temp_Crain_mm
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mar3.984
apr7.4100
may12.0143
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sep13.1135
oct9.189
nov3.788
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" - ], - "text/plain": [ - " temp_C rain_mm\n", - "jan -0.3 59\n", - "feb 0.4 57\n", - "mar 3.9 84\n", - "apr 7.4 100\n", - "may 12.0 143\n", - "jun 15.0 153\n", - "jul 17.2 172\n", - "aug 16.8 164\n", - "sep 13.1 135\n", - "oct 9.1 89\n", - "nov 3.7 88\n", - "dec 0.8 80" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_clima" - ] - }, - { - "cell_type": "code", - "execution_count": 28, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1424,7 +855,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1470,109 +901,7 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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0Ana20Física85.5Aprobado15.5
4Fátima20Matemática79.2Aprobado9.2
1Carlos22Matemática72.0Aprobado2.0
3Eduardo23Computación68.7Aprobado0.0
\n", - "
" - ], - "text/plain": [ - " Nombre Edad Carrera Promedio Estado Puntos_extra\n", - "2 Diana 21 Física 91.3 Aprobado 21.3\n", - "0 Ana 20 Física 85.5 Aprobado 15.5\n", - "4 Fátima 20 Matemática 79.2 Aprobado 9.2\n", - "1 Carlos 22 Matemática 72.0 Aprobado 2.0\n", - "3 Eduardo 23 Computación 68.7 Aprobado 0.0" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 31, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1607,7 +936,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1648,7 +977,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1677,7 +1006,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1706,7 +1035,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1741,7 +1070,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -1770,7 +1099,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -1801,7 +1130,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -1847,7 +1176,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -1860,7 +1189,7 @@ ], "source": [ "# Guardar un DataFrame como CSV\n", - "df_csv.to_csv(\"resultado.csv\", index=False)\n", + "df_csv.to_csv(\"/tmp/resultado.csv\", index=False)\n", "print(\"Archivo guardado como /tmp/resultado.csv\")\n", "\n", "# Otros formatos comunes:\n", @@ -1871,7 +1200,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1919,7 +1248,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1950,7 +1279,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -1958,9 +1287,7 @@ "output_type": "stream", "text": [ "Categorías únicas:\n", - "\n", - "['nublado', 'parcialmente nublado', 'mayormente despejado', 'despejado']\n", - "Length: 4, dtype: str\n", + "['nublado' 'parcialmente nublado' 'mayormente despejado' 'despejado']\n", "\n", "Frecuencia de cada categoría:\n", "nubes\n", @@ -1983,7 +1310,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -2012,7 +1339,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -2050,7 +1377,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -2071,7 +1398,7 @@ "source": [ "# str.replace() — reemplazar texto (§8.3.2)\n", "df_cat2 = df_cat.copy()\n", - "df_cat2['nubes'] = df_cat2['nubes'].str.replace('nublado', 'soleado')\n", + "df_cat2.loc[:, 'nubes'] = df_cat2['nubes'].str.replace('nublado', 'soleado')\n", "print(df_cat2)" ] }, @@ -2087,21 +1414,21 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Tipo original: str\n", - "Tipo convertido: datetime64[us]\n", + "Tipo original: object\n", + "Tipo convertido: datetime64[ns]\n", "0 2020-01-01 12:34:00\n", "1 2020-03-01 08:47:00\n", "2 2020-06-01 14:23:00\n", "3 2020-09-01 22:56:00\n", "4 2020-12-01 13:45:00\n", - "dtype: datetime64[us]\n" + "dtype: datetime64[ns]\n" ] } ], @@ -2119,7 +1446,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -2147,7 +1474,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -2160,7 +1487,7 @@ "2 152 days 01:49:00\n", "3 244 days 10:22:00\n", "4 335 days 01:11:00\n", - "dtype: timedelta64[us]\n", + "dtype: timedelta64[ns]\n", "\n", "En segundos (float):\n", "0 0.0\n", @@ -2179,7 +1506,8 @@ "print(delta)\n", "\n", "print(\"\\nEn segundos (float):\")\n", - "print(delta.dt.total_seconds())" + "#print(delta.astype('timedelta64[s]').astype(float))\n", + "print( (fechas - fechas.iloc[0]).dt.total_seconds() )" ] }, { @@ -2205,18 +1533,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_clima" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estadísticas de temperatura y lluvia:\n", + " temp_C rain_mm\n", + "min -0.30 57.00\n", + "max 17.20 172.00\n", + "mean 8.26 110.33\n" + ] + } + ], "source": [ "# agg() con lista de métodos (§8.4)\n", "print(\"Estadísticas de temperatura y lluvia:\")\n", @@ -2225,9 +1556,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " temp_C rain_mm\n", + "min -0.30 NaN\n", + "max 17.20 NaN\n", + "mean 8.26 NaN\n", + "sum NaN 1324.0\n", + "median NaN 94.5\n" + ] + } + ], "source": [ "# agg() con diccionario — distintas métricas por columna (§8.4)\n", "resultado = df_clima[['temp_C', 'rain_mm']].agg({\n", @@ -2239,9 +1583,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rango (max - min) por columna numérica:\n", + "temp_C 17.5\n", + "rain_mm 115.0\n", + "dtype: float64\n" + ] + } + ], "source": [ "# apply() — función personalizada por columna (§8.4)\n", "def rango(col):\n", @@ -2263,21 +1618,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "# df.plot() — gráfica de línea (§8.5)\n", "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", "\n", - "df_clima['temp_C'].plot(kind='line', ax=axes[1], marker='o', color='tomato')\n", + "df_clima['temp_C'].plot(kind='line', ax=axes[0], marker='o', color='tomato')\n", "axes[0].set_title('Temperatura mensual promedio')\n", "axes[0].set_ylabel('°C')\n", "axes[0].grid(True, alpha=0.3)\n", "\n", - "df_clima['rain_mm'].plot(kind='bar', ax=axes[0], color='steelblue', alpha=0.8)\n", + "df_clima['rain_mm'].plot(kind='bar', ax=axes[1], color='steelblue', alpha=0.8)\n", "axes[1].set_title('Lluvia mensual')\n", "axes[1].set_ylabel('mm')\n", "axes[1].tick_params(axis='x', rotation=45)\n", @@ -2288,9 +1654,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# df.hist() — histogramas de todas las columnas numéricas (§8.5)\n", "df_clima.hist(figsize=(8, 4))\n", @@ -2301,9 +1678,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# value_counts().plot() — histograma de datos categóricos (§8.5)\n", "fig, ax = plt.subplots(figsize=(6, 4))\n", @@ -2325,18 +1713,29 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_clima" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reporte climático mensual:\n", + " jan: -0.3°C, 59 mm de lluvia\n", + " feb: 0.4°C, 57 mm de lluvia\n", + " mar: 3.9°C, 84 mm de lluvia\n", + " apr: 7.4°C, 100 mm de lluvia\n", + " may: 12.0°C, 143 mm de lluvia\n", + " jun: 15.0°C, 153 mm de lluvia\n", + " jul: 17.2°C, 172 mm de lluvia\n", + " aug: 16.8°C, 164 mm de lluvia\n", + " sep: 13.1°C, 135 mm de lluvia\n", + " oct: 9.1°C, 89 mm de lluvia\n", + " nov: 3.7°C, 88 mm de lluvia\n", + " dec: 0.8°C, 80 mm de lluvia\n" + ] + } + ], "source": [ "# iterrows() — iterar sobre filas (§8.7)\n", "print(\"Reporte climático mensual:\")\n", @@ -2346,69 +1745,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_clima = pd.DataFrame({\n", - " 'temp_C': [-0.3, 0.4, 3.9, 7.4, 12.0, 15.0,\n", - " 17.2, 16.8, 13, 13, 3.7, 0.8],\n", - " 'rain_mm': [59, 57, 84, 100, 143, 153, 172, 164, 135, 89, 88, 80]\n", - "}, index=['jan','feb','mar','apr','may','jun',\n", - " 'jul','aug','sep','oct','nov','dec'])\n", - "df_clima" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_clima.reset_index(inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_clima.columns = [\"mes\", \"temp_C\",\"rain_mm\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_clima" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, - "outputs": [], - "source": [ - "df_clima.groupby(['temp_C','mes'])['rain_mm'].mean().round(2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_clima.groupby(['temp_C','mes'], as_index=False)['rain_mm'].mean().round(2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Temperatura media según si llueve ≥ 100 mm:\n", + "rain_mm\n", + "False 2.93\n", + "True 13.58\n", + "Name: temp_C, dtype: float64\n" + ] + } + ], "source": [ "# groupby() con condición booleana (§8.7)\n", "print(\"Temperatura media según si llueve ≥ 100 mm:\")\n", @@ -2417,9 +1768,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meses cálidos (> 10°C):\n", + " temp_C temp_F rain_mm\n", + "jul 17.2 62.96 172\n", + "aug 16.8 62.24 164\n", + "jun 15.0 59.00 153\n", + "sep 13.1 55.58 135\n", + "may 12.0 53.60 143\n" + ] + } + ], "source": [ "# Encadenar métodos — flujo de trabajo legible\n", "resumen = (df_clima\n", @@ -2450,11 +1815,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------\n", + " VALORES DE PRECIPITACIÓN ANUAL\n", + " ESTACIÓN ANTIGUA GUATEMALA\n", + "\n", + " VALORES: \n", + " [[ 2.1 1.5 12.4]\n", + " [ 35.8 150.2 250.4]\n", + " [180.1 195.3 280.9]\n", + " [160.4 25.2 5.1]] \n", + "\n", + " FECHAS: \n", + " [['Enero' 'Febrero' 'Marzo']\n", + " ['Abril' 'Mayo' 'Junio']\n", + " ['Julio' 'Agosto' 'Septiembre']\n", + " ['Octubre' 'Noviembre' 'Diciembre']]\n", + "------------------------------------------------------------\n", + " ESTADÍSTICAS\n", + "MEDIA: 108.3 mm\n", + "MÍNIMO: 1.5 mm\n", + "ÍNDICE DEL VALOR MÁXIMO: 8\n", + "DÍA CON MAYOR PRECIPITACIÓN: 280.9 mm\n", + "------------------------------------------------------------\n" + ] + } + ], "source": [ - "# TU CÓDIGO AQUÍ\n" + "# TU CÓDIGO AQUÍ\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# TU CÓDIGO AQUÍ\n", + "\n", + "# Valores de la Estación Antigua Guatemala. lluvias en mm\n", + "lluvias = [2.1, 1.5, 12.4, 35.8, 150.2, 250.4, 180.1, 195.3, 280.9, 160.4, 25.2, 5.1]\n", + "meses = ['Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', \n", + " 'Julio', 'Agosto', 'Septiembre', 'Octubre', 'Noviembre', 'Diciembre']\n", + "\n", + "# Arreglo de matriz\n", + "a = np.array(lluvias[:12])\n", + "b = np.array(meses[:12])\n", + "\n", + "d = a.reshape(4, 3)\n", + "F = b.reshape(4, 3)\n", + "\n", + "print(\"--\" * 30)\n", + "print(\" \" * 15 + \"VALORES DE PRECIPITACIÓN ANUAL\")\n", + "print(\" \" * 17 + \"ESTACIÓN ANTIGUA GUATEMALA\")\n", + "\n", + "print(f\"\\n VALORES: \\n {d} \\n\\n FECHAS: \\n {F}\")\n", + "print(\"--\" * 30)\n", + "\n", + "print(\" \" * 20 + \"ESTADÍSTICAS\")\n", + "\n", + "print(f'MEDIA: {a.mean():.1f} mm')\n", + "print(f'MÍNIMO: {a.min():.1f} mm')\n", + "print('ÍNDICE DEL VALOR MÁXIMO:', a.argmax())\n", + "# Usamos max(a) o a.max()\n", + "print(f'DÍA CON MAYOR PRECIPITACIÓN: {a.max()} mm')\n", + "print(\"--\" * 30)\n", + "\n" ] }, { @@ -2468,11 +1895,64 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------\n", + " DATOS REPRESENTATIVOS DE TEMPERATURA (°C) PARA ANTIGUA GUATEMALA\n", + " Mes Temp_Min Temp_Max Lluvia_mm\n", + "0 Enero 10.5 23.8 2.1\n", + "1 Febrero 11.2 25.1 1.5\n", + "2 Marzo 12.8 26.7 12.4\n", + "3 Abril 14.5 27.5 35.8\n", + "4 Mayo 15.8 26.8 150.2\n", + "5 Junio 16.2 25.4 250.4\n", + "6 Julio 15.9 25.1 180.1\n", + "7 Agosto 15.7 25.3 195.3\n", + "8 Septiembre 15.8 24.8 280.9\n", + "9 Octubre 14.9 24.2 160.4\n", + "10 Noviembre 13.1 23.9 25.2\n", + "11 Diciembre 11.4 23.5 5.1\n", + "------------------------------------------------------------\n", + " MESES CON TEMPERATURA ENTRE 5°C Y 15°C: \n", + " Mes Temp_Min\n", + "0 Enero 10.5\n", + "1 Febrero 11.2\n", + "2 Marzo 12.8\n", + "3 Abril 14.5\n", + "9 Octubre 14.9\n", + "10 Noviembre 13.1\n", + "11 Diciembre 11.4\n", + "------------------------------------------------------------\n" + ] + } + ], "source": [ - "# TU CÓDIGO AQUÍ\n" + "# TU CÓDIGO AQUÍ\n", + "# Datos representativos de temperatura (°C) para Antigua Guatemala\n", + "datos = {\n", + " 'Mes': ['Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', \n", + " 'Julio', 'Agosto', 'Septiembre', 'Octubre', 'Noviembre', 'Diciembre'],\n", + " 'Temp_Min': [10.5, 11.2, 12.8, 14.5, 15.8, 16.2, 15.9, 15.7, 15.8, 14.9, 13.1, 11.4],\n", + " 'Temp_Max': [23.8, 25.1, 26.7, 27.5, 26.8, 25.4, 25.1, 25.3, 24.8, 24.2, 23.9, 23.5],\n", + " 'Lluvia_mm': [2.1, 1.5, 12.4, 35.8, 150.2, 250.4, 180.1, 195.3, 280.9, 160.4, 25.2, 5.1]\n", + "}\n", + "\n", + "df_clima = pd.DataFrame(datos)\n", + "filtro = (df_clima [ 'Temp_Min'] >=5) & (df_clima[ 'Temp_Min'] <= 15)\n", + "subconjunto = df_clima[filtro]\n", + "Luvia_total = subconjunto [ 'Lluvia_mm'].sum()\n", + "print(\"--\" * 30)\n", + "\n", + "print(\" \" * 1 + \"DATOS REPRESENTATIVOS DE TEMPERATURA (°C) PARA ANTIGUA GUATEMALA\")\n", + "print(df_clima)\n", + "print(\"--\" * 30)\n", + "print (f\" MESES CON TEMPERATURA ENTRE 5°C Y 15°C: \\n {subconjunto[['Mes','Temp_Min' ]]}\")\n", + "print(\"--\" * 30)\n" ] }, { @@ -2486,11 +1966,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------\n", + " EL MES CON MAYOR AMPLITUD TÉRMINCA \n", + " MES: Febrero\n", + "------------------------------------------------------------\n", + " LA MAYOR AMPLITUD TÉRMINCA \n", + " AMPLITUD: 13.9 °C\n", + "------------------------------------------------------------\n" + ] + } + ], "source": [ - "# TU CÓDIGO AQUÍ\n" + "# TU CÓDIGO AQUÍ\n", + "# Datos representativos de temperatura (°C) para Antigua Guatemala\n", + "datos = {\n", + " 'Mes': ['Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', \n", + " 'Julio', 'Agosto', 'Septiembre', 'Octubre', 'Noviembre', 'Diciembre'],\n", + " 'Temp_Min': [10.5, 11.2, 12.8, 14.5, 15.8, 16.2, 15.9, 15.7, 15.8, 14.9, 13.1, 11.4],\n", + " 'Temp_Max': [23.8, 25.1, 26.7, 27.5, 26.8, 25.4, 25.1, 25.3, 24.8, 24.2, 23.9, 23.5],\n", + " 'Lluvia_mm': [2.1, 1.5, 12.4, 35.8, 150.2, 250.4, 180.1, 195.3, 280.9, 160.4, 25.2, 5.1]\n", + "}\n", + "\n", + "df_clima = pd.DataFrame(datos)\n", + "\n", + "df_clima['amplitud_termica'] = df_clima['Temp_Max'] - df_clima['Temp_Min']\n", + "\n", + "indice_max = df_clima['amplitud_termica'].idxmax()\n", + "\n", + "mes_max = df_clima.loc[indice_max]\n", + "print(\"--\" * 30)\n", + "print(\" \" * 2 +\" EL MES CON MAYOR AMPLITUD TÉRMINCA \")\n", + "print(f\" MES: {mes_max['Mes']}\")\n", + "print(\"--\" * 30)\n", + "print(\" \" * 2 +\" LA MAYOR AMPLITUD TÉRMINCA \")\n", + "print(f\" AMPLITUD: {mes_max['amplitud_termica']:.1f} °C\")\n", + "print(\"--\" * 30)\n" ] }, { @@ -2504,13 +2021,245 @@ "c) Calcula la nota promedio final de cada estudiante." ] }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------------------\n", + "DataFrame con valores faltantes:\n", + " Nombre Nota1 Nota2 Nota3\n", + "0 Herlinda 90.0 NaN NaN\n", + "1 Rafael 80.0 65.0 71.0\n", + "2 Irinea NaN 70.0 NaN\n", + "3 Magaly 88.0 80.0 90.0\n", + "4 Adán NaN NaN 76.0\n", + "------------------------------------------------------------\n", + "Cantidad de nulos por columna:\n", + "Nombre 0\n", + "Nota1 2\n", + "Nota2 1\n", + "Nota3 1\n", + "dtype: int64\n", + "------------------------------------------------------------\n", + " RELLENAR CON LA MEDIA EN CADA COLUMNA:\n", + " Nombre Nota1 Nota2 Nota3\n", + "0 Herlinda 90.0 71.7 79.0\n", + "1 Rafael 80.0 65.0 71.0\n", + "2 Irinea 86.0 70.0 79.0\n", + "3 Magaly 88.0 80.0 90.0\n", + "4 Adán 86.0 71.7 76.0\n", + "------------------------------------------------------------\n", + " NOTA PROMEDIO FINAL DE CADA ESTUDIANTE:\n", + " Nombre Promedio\n", + "0 Herlinda 90.0\n", + "1 Rafael 72.0\n", + "2 Irinea 70.0\n", + "3 Magaly 86.0\n", + "4 Adán 76.0\n" + ] + } + ], + "source": [ + "# TU CÓDIGO AQUÍ\n", + "# Crear DataFrame con datos faltantes (NaN = Not a Number)\n", + "Informacion = pd.DataFrame({\n", + " \"Nombre\": [\"Herlinda\", \"Rafael\", \"Irinea\", \"Magaly\", \"Adán\"],\n", + " \"Nota1\": [90, 80, np.nan, 88, np.nan],\n", + " \"Nota2\": [np.nan, 65, 70, 80, np.nan],\n", + " \"Nota3\": [np.nan, 71, np.nan, 90, 76]\n", + "})\n", + "\n", + "NOTA1 = Informacion[\"Nota1\"]\n", + "NOTA2 = Informacion[\"Nota2\"]\n", + "NOTA3 = Informacion[\"Nota3\"]\n", + "print(\"--\" * 30)\n", + "print(\"DataFrame con valores faltantes:\")\n", + "print(Informacion)\n", + "print(\"--\" * 30)\n", + "print(\"Cantidad de nulos por columna:\")\n", + "print(datos_incompletos.isnull().sum())\n", + "print(\"--\" * 30)\n", + "\n", + "df_rellenado = Informacion .copy()\n", + "for col in [\"Nota1\", \"Nota2\", \"Nota3\"]:\n", + " df_rellenado[col] = df_rellenado[col].fillna(df_rellenado[col].mean())\n", + "\n", + "print(\" RELLENAR CON LA MEDIA EN CADA COLUMNA:\")\n", + "print(df_rellenado.round(1))\n", + "\n", + "print(\"--\" * 30)\n", + "print(\" NOTA PROMEDIO FINAL DE CADA ESTUDIANTE:\")\n", + "Informacion[\"Promedio\"] = Informacion[[\"Nota1\", \"Nota2\", \"Nota3\"]].mean(axis=1)\n", + "Informacion[\"Promedio\"] = Informacion[\"Promedio\"].round(3)\n", + "print(Informacion[[\"Nombre\", \"Promedio\"]])\n" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pandas versión: 2.1.4\n", + " Año Mes Costo_Diario_Q Costo_Mensual_Q\n", + "0 2020 Diciembre 99.65 2989.38\n", + "1 2021 Enero 99.49 2984.73\n", + "2 2021 Febrero 99.58 2987.39\n", + "3 2021 Marzo 99.27 2978.10\n", + "4 2021 Abril 99.72 2991.70\n", + "5 2021 Mayo 99.77 2993.03\n", + "6 2021 Junio 99.99 2999.67\n", + "7 2021 Julio 100.11 3003.32\n", + "8 2021 Agosto 100.42 3012.61\n", + "9 2021 Septiembre 100.85 3025.55\n", + "10 2021 Octubre 101.84 3055.09\n", + "11 2021 Noviembre 102.74 3082.30\n", + "12 2021 Diciembre 103.24 3097.23\n", + "13 2022 Enero 103.67 3110.18\n", + "14 2022 Febrero 104.48 3134.40\n", + "15 2022 Marzo 106.05 3181.53\n", + "16 2022 Abril 107.27 3218.03\n", + "17 2022 Mayo 107.82 3234.62\n", + "18 2022 Junio 110.40 3311.95\n", + "19 2022 Julio 112.32 3369.69\n", + "20 2022 Agosto 115.17 3454.98\n", + "21 2022 Septiembre 117.96 3538.94\n", + "22 2022 Octubre 121.13 3633.85\n", + "23 2022 Noviembre 120.62 3618.58\n", + "24 2022 Diciembre 121.14 3634.18\n", + "25 2023 Enero 121.27 3638.16\n", + "26 2023 Febrero 123.20 3695.91\n", + "27 2023 Marzo 124.28 3728.43\n", + "28 2023 Abril 124.20 3726.11\n", + "29 2023 Mayo 124.39 3731.75\n", + "30 2023 Junio 124.52 3735.73\n", + "31 2023 Julio 125.50 3764.93\n", + "32 2023 Agosto 126.99 3809.73\n", + "33 2023 Septiembre 127.50 3825.00\n", + "34 2023 Octubre 131.24 3937.17\n", + "35 2023 Noviembre 129.99 3899.67\n", + "36 2023 Diciembre 130.17 3904.98\n", + "------------------------------------------------------------\n", + "COSTO PROMEDIO MENDUAL DE LA CANASTA BÁSICO EN GUATEMALA 2020-2023\n", + "Año\n", + "2020 2989.380000\n", + "2021 3017.560000\n", + "2022 3370.077500\n", + "2023 3783.130833\n", + "Name: Costo_Mensual_Q, dtype: float64\n", + "------------------------------------------------------------\n", + "COSTO PROMEDIO MENSUAL DE LA CANASTA BÁSICO EN GUATEMALA 2020-2023\n", + "Mes\n", + "Abril 3311.946667\n", + "Agosto 3425.773333\n", + "Diciembre 3406.442500\n", + "Enero 3244.356667\n", + "Febrero 3272.566667\n", + "Julio 3379.313333\n", + "Junio 3349.116667\n", + "Marzo 3296.020000\n", + "Mayo 3319.800000\n", + "Noviembre 3533.516667\n", + "Octubre 3542.036667\n", + "Septiembre 3463.163333\n", + "Name: Costo_Mensual_Q, dtype: float64\n", + "------------------------------------------------------------\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# TAREA 5(OPCIONAL) - PANDAS EN PRÁCTICA \n", + "# Dataset real (opcional, puntos estra)\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np \n", + "print(\"Pandas versión:\", pd.__version__)\n", + "\n", + "# (LECTURA) Diccionario de la Canasta Básica Alimentaria (2020-2023)Obtenida del INE, Guatemala\n", + "df_Canasta_Basica = {\n", + " 'Año': [\n", + " 2020, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021,\n", + " 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022,\n", + " 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023\n", + " ],\n", + " 'Mes': [\n", + " 'Diciembre', 'Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', 'Julio', 'Agosto', 'Septiembre', 'Octubre', 'Noviembre', 'Diciembre',\n", + " 'Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', 'Julio', 'Agosto', 'Septiembre', 'Octubre', 'Noviembre', 'Diciembre',\n", + " 'Enero', 'Febrero', 'Marzo', 'Abril', 'Mayo', 'Junio', 'Julio', 'Agosto', 'Septiembre', 'Octubre', 'Noviembre', 'Diciembre'\n", + " ],\n", + " 'Costo_Diario_Q': [\n", + " 99.65, 99.49, 99.58, 99.27, 99.72, 99.77, 99.99, 100.11, 100.42, 100.85, 101.84, 102.74, 103.24,\n", + " 103.67, 104.48, 106.05, 107.27, 107.82, 110.40, 112.32, 115.17, 117.96, 121.13, 120.62, 121.14,\n", + " 121.27, 123.20, 124.28, 124.20, 124.39, 124.52, 125.50, 126.99, 127.50, 131.24, 129.99, 130.17\n", + " ],\n", + " 'Costo_Mensual_Q': [\n", + " 2989.38, 2984.73, 2987.39, 2978.10, 2991.70, 2993.03, 2999.67, 3003.32, 3012.61, 3025.55, 3055.09, 3082.30, 3097.23,\n", + " 3110.18, 3134.40, 3181.53, 3218.03, 3234.62, 3311.95, 3369.69, 3454.98, 3538.94, 3633.85, 3618.58, 3634.18,\n", + " 3638.16, 3695.91, 3728.43, 3726.11, 3731.75, 3735.73, 3764.93, 3809.73, 3825.00, 3937.17, 3899.67, 3904.98\n", + " ]\n", + "}\n", + "\n", + "# (LIMPIEZA) UTILIZANDO .groupby\n", + "df_Canasta_Basica = pd.DataFrame(df_Canasta_Basica )\n", + "print(df_Canasta_Basica) \n", + "print(\"--\" * 30)\n", + "promedio_anual = df_Canasta_Basica.groupby('Año')['Costo_Mensual_Q'].mean()\n", + "print(\"COSTO PROMEDIO MENDUAL DE LA CANASTA BÁSICO EN GUATEMALA 2020-2023\")\n", + "print(promedio_anual)\n", + "print(\"--\" * 30)\n", + "promedio_mensual = df_Canasta_Basica.groupby('Mes')['Costo_Mensual_Q'].mean()\n", + "print(\"COSTO PROMEDIO MENSUAL DE LA CANASTA BÁSICO EN GUATEMALA 2020-2023\")\n", + "print(promedio_mensual)\n", + "print(\"--\" * 30)\n", + "\n", + "\n", + "# (VISUALIZACIÓN) \n", + "# df.plot() — gráfica de línea (§8.5)\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(10, 5))\n", + "promedio_anual.plot(kind='line', ax=ax, marker='o', color='blue')\n", + "\n", + "ax.set_title('PROMEDIO ANUAL DE LA CANASTA BÁSICA EN GUATEMALA 2020-2023')\n", + "ax.set_ylabel('Quetzales')\n", + "ax.set_xlabel('Año')\n", + "ax.set_xticks([2020, 2021, 2022, 2023])\n", + "\n", + "ax.set_ylim(2800, 4000)\n", + "ax.grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + " \n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ - "# TU CÓDIGO AQUÍ\n" + "CONCLUSIONES: \n", + "Sobre las Canasta básica en Guatemala\n", + "El promedio en la cansta básica en Guatemala, relacionando los promedios anuales obtenidos con la gráfica, la canasta básica ha ido aumentando Q 200.00 desde el año 2021.\n", + "En julio de 2021 un año y dos meses después de la pandemia de COVID-19, la canasta básica aumentó a Q100.11, en Guatemala." ] }, { @@ -2544,7 +2293,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "base", "language": "python", "name": "python3" }, @@ -2558,7 +2307,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.14.2" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/ejemplos/python/XML_JSON_APIs.ipynb b/ejemplos/python/XML_JSON_APIs.ipynb index 88a2405..1b283a0 100644 --- a/ejemplos/python/XML_JSON_APIs.ipynb +++ b/ejemplos/python/XML_JSON_APIs.ipynb @@ -98,22 +98,10 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "id": "xml-parse", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Experimento: Caida libre\n", - "Lugar : Laboratorio F12\n", - "Descripción: Medicion de posicion de un objeto en caida libre\n", - "\n", - "g = 9.8 m/s² h0 = 20.0 m\n" - ] - } - ], + "outputs": [], "source": [ "import xml.etree.ElementTree as ET\n", "\n", @@ -155,30 +143,10 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "id": "xml-loop", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Datos experimentales:\n", - " t (s) y (m) vy (m/s)\n", - "--------------------------------\n", - " 0.0 20.00 0.00\n", - " 0.5 18.78 4.90\n", - " 1.0 15.10 9.80\n", - " 1.5 8.98 14.70\n", - " 2.0 0.40 19.60\n", - "\n", - "Listas reconstruidas:\n", - "t : [0.0, 0.5, 1.0, 1.5, 2.0]\n", - "y : [20.0, 18.78, 15.1, 8.98, 0.4]\n" - ] - } - ], + "outputs": [], "source": [ "# Iterar sobre las mediciones\n", "print(\"\\nDatos experimentales:\")\n", @@ -272,45 +240,10 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "id": "json-example", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "── Texto JSON ──\n", - "{\n", - " \"nombre\": \"Péndulo simple\",\n", - " \"fecha\": \"2025-04-12\",\n", - " \"longitud_m\": 1.0,\n", - " \"mediciones\": [\n", - " {\n", - " \"t\": 0.0,\n", - " \"theta\": 15.0\n", - " },\n", - " {\n", - " \"t\": 0.25,\n", - " \"theta\": 8.3\n", - " },\n", - " {\n", - " \"t\": 0.5,\n", - " \"theta\": -0.1\n", - " },\n", - " {\n", - " \"t\": 0.75,\n", - " \"theta\": -8.4\n", - " },\n", - " {\n", - " \"t\": 1.0,\n", - " \"theta\": -14.9\n", - " }\n", - " ]\n", - "}\n" - ] - } - ], + "outputs": [], "source": [ "import json\n", "\n", @@ -339,27 +272,10 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "id": "json-loads", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tipo del resultado: \n", - "Nombre : Péndulo simple\n", - "Longitud (m) : 1.0\n", - "\n", - "Mediciones del péndulo:\n", - " t = 0.00 s θ = +15.0°\n", - " t = 0.25 s θ = +8.3°\n", - " t = 0.50 s θ = -0.1°\n", - " t = 0.75 s θ = -8.4°\n", - " t = 1.00 s θ = -14.9°\n" - ] - } - ], + "outputs": [], "source": [ "# Leer el JSON de vuelta a un diccionario de Python\n", "datos = json.loads(texto_json)\n", @@ -464,18 +380,10 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "id": "http-codes-code", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✓ 200 OK — datos recibidos: ['date', 'explanation', 'hdurl', 'media_type', 'service_version', 'title', 'url']\n" - ] - } - ], + "outputs": [], "source": [ "import requests\n", "\n", @@ -492,18 +400,10 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": null, "id": "http-codes-code2", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✓ Solicitud exitosa\n" - ] - } - ], + "outputs": [], "source": [ "# ── Ejemplo 2: usar respuesta.ok (True si 200-399) ──────────────────────────\n", "respuesta = requests.get(url, timeout=15)\n", @@ -517,18 +417,10 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "id": "http-codes-code3", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✗ 429 Too Many Requests — espera unos minutos y vuelve a intentar\n" - ] - } - ], + "outputs": [], "source": [ "# ── Ejemplo 3: manejar cada código con mensajes útiles ──────────────────────\n", "respuesta = requests.get(url, timeout=10)\n", @@ -584,18 +476,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "requests-import", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Librería requests lista ✓\n" - ] - } - ], + "outputs": [], "source": [ "import requests # pip install requests\n", "import json\n", @@ -620,28 +504,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "iss-code", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Código de estado HTTP: 200\n", - "\n", - "── Respuesta completa ──\n", - "{\n", - " \"timestamp\": 1776182634,\n", - " \"iss_position\": {\n", - " \"longitude\": \"-78.0268\",\n", - " \"latitude\": \"49.6159\"\n", - " },\n", - " \"message\": \"success\"\n", - "}\n" - ] - } - ], + "outputs": [], "source": [ "# Hacer la solicitud al endpoint\n", "url_iss = \"http://api.open-notify.org/iss-now.json\"\n", @@ -659,25 +525,10 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "iss-explore", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tipo de datos_iss: \n", - "Claves del diccionario: ['iss_position', 'message', 'timestamp']\n", - "\n", - "Posición actual de la ISS:\n", - " Latitud : +33.1915°\n", - " Longitud : -148.8347°\n", - " Hora UTC : 2026-04-12 23:56:34 UTC\n", - " Velocidad orbital aprox.: 7.66 km/s (~27,600 km/h)\n" - ] - } - ], + "outputs": [], "source": [ "# Explorar el diccionario\n", "print(\"Tipo de datos_iss:\", type(datos_iss))\n", @@ -807,22 +658,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "dotenv-demo-code", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Note: you may need to restart the kernel to use updated packages.\n", - "✓ Archivo .env creado. Edítalo con tu NASA API key.\n", - "Variables cargadas desde .env: archivo vacío o sin variables activas\n", - "\n", - " (ninguna variable activa en .env — descomenta NASA_API_KEY para activarla)\n" - ] - } - ], + "outputs": [], "source": [ "# Instalar python-dotenv (solo la primera vez; --quiet suprime la salida larga)\n", "%pip install python-dotenv --quiet\n", @@ -864,18 +703,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "env-code", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✓ API key cargada: 4VAQ************************************\n" - ] - } - ], + "outputs": [], "source": [ "import os, requests, time\n", "from dotenv import load_dotenv\n", @@ -913,28 +744,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "apod-code", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intento 1/3... HTTP 200\n", - "\n", - "── Claves del JSON ──\n", - " copyright : Haythem Hamdi\n", - " date : 2026-04-14\n", - " explanation : Why does Comet R3 (PanSTARRS) have a wispy tail? The newest bright member of...\n", - " hdurl : https://apod.nasa.gov/apod/image/2604/CometR3_Hamdi_2710.jpg\n", - " media_type : image\n", - " service_version : v1\n", - " title : The Long Wispy Tail of Comet R3 (PanSTARRS)\n", - " url : https://apod.nasa.gov/apod/image/2604/CometR3_Hamdi_960.jpg\n" - ] - } - ], + "outputs": [], "source": [ "# Algunos endpoints de NASA devuelven 503 de forma intermitente.\n", "# Este código reintenta hasta 3 veces antes de rendirse.\n", @@ -980,41 +793,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "apod-explore", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "============================================================\n", - "IMAGEN ASTRONÓMICA DEL DÍA\n", - "============================================================\n", - "Título : The Long Wispy Tail of Comet R3 (PanSTARRS)\n", - "Fecha : 2026-04-14\n", - "Tipo : image\n", - "Autor : Haythem Hamdi\n", - "\n", - "Descripción:\n", - " Why does Comet R3 (PanSTARRS) have a wispy tail? The newest bright member of the\n", - " inner Solar System, Comet C/2025 R3 (PanSTARRS) is already extending an\n", - " impressive stream of glowing gas. This tail starts from an unseen central\n", - " nucleus of dirty ice that is likely a few kilometers across. The nucleus is\n", - " warmed by the Sun and emits a cloud of neutral gas into a coma that glows light\n", - " green. Nuclear gas ionized by energetic sunlight is pushed away from the Sun by\n", - " the solar wind into an ion tail that glows light blue. The wispy nature of the\n", - " ion tail is caused by the constantly changing structure of the solar wind.\n", - " Pictured from Rhode Island, USA two days ago, Comet R3 (PanSTARRS) shows off a\n", - " many-degree ion tail. Comet R3 (PanSTARRS) is best seen before dawn from\n", - " northern skies for another 10 days, after which it will be best visible from\n", - " southern skies. Growing Gallery: Comet R3 in 2026\n", - "\n", - "URL de la imagen:\n", - " https://apod.nasa.gov/apod/image/2604/CometR3_Hamdi_960.jpg\n" - ] - } - ], + "outputs": [], "source": [ "# Explorar los datos con más detalle\n", "print(\"=\" * 60)\n", @@ -1047,30 +829,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "apod-image", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mostrando: The Long Wispy Tail of Comet R3 (PanSTARRS)\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from IPython.display import Image, IFrame, display\n", "\n", @@ -1105,7 +867,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "0c1bbbe9-d04b-4209-9778-7b636c9b78d8", "metadata": {}, "outputs": [], @@ -1120,18 +882,10 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "neo-request", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Código HTTP: 200\n" - ] - } - ], + "outputs": [], "source": [ "\n", "respuesta = requests.get(url_neo)\n", @@ -1147,1533 +901,20 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "2665383f-0b5e-4d9c-a8a8-4acc61153248", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'links': {'next': 'http://api.nasa.gov/neo/rest/v1/feed?start_date=2025-01-03&end_date=2025-01-05&detailed=false&api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh',\n", - " 'previous': 'http://api.nasa.gov/neo/rest/v1/feed?start_date=2024-12-30&end_date=2025-01-01&detailed=false&api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh',\n", - " 'self': 'http://api.nasa.gov/neo/rest/v1/feed?start_date=2025-01-01&end_date=2025-01-03&detailed=false&api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh'},\n", - " 'element_count': 55,\n", - " 'near_earth_objects': {'2025-01-03': [{'links': {'self': 'http://api.nasa.gov/neo/rest/v1/neo/3553148?api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh'},\n", - " 'id': '3553148',\n", - " 'neo_reference_id': '3553148',\n", - " 'name': '(2010 XB11)',\n", - " 'nasa_jpl_url': 'https://ssd.jpl.nasa.gov/tools/sbdb_lookup.html#/?sstr=3553148',\n", - " 'absolute_magnitude_h': 19.69,\n", - " 'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.3065878759,\n", - " 'estimated_diameter_max': 0.6855513317},\n", - " 'meters': {'estimated_diameter_min': 306.5878759288,\n", - " 'estimated_diameter_max': 685.5513316542},\n", - " 'miles': {'estimated_diameter_min': 0.1905048151,\n", - " 'estimated_diameter_max': 0.4259817165},\n", - " 'feet': {'estimated_diameter_min': 1005.8657668624,\n", - " 'estimated_diameter_max': 2249.1842309443}},\n", - " 'is_potentially_hazardous_asteroid': False,\n", - " 'close_approach_data': [{'close_approach_date': '2025-01-03',\n", - " 'close_approach_date_full': '2025-Jan-03 04:13',\n", - " 'epoch_date_close_approach': 1735877580000,\n", - " 'relative_velocity': {'kilometers_per_second': '18.9395018272',\n", - " 'kilometers_per_hour': '68182.206577871',\n", - " 'miles_per_hour': '42365.7716372333'},\n", - " 'miss_distance': {'astronomical': '0.1271184775',\n", - " 'lunar': '49.4490877475',\n", - " 'kilometers': '19016653.471642925',\n", - " 'miles': '11816400.533505365'},\n", - " 'orbiting_body': 'Earth'}],\n", - " 'is_sentry_object': False},\n", - " {'links': {'self': 'http://api.nasa.gov/neo/rest/v1/neo/3645041?api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh'},\n", - " 'id': '3645041',\n", - " 'neo_reference_id': '3645041',\n", - " 'name': '(2013 NC15)',\n", - " 'nasa_jpl_url': 'https://ssd.jpl.nasa.gov/tools/sbdb_lookup.html#/?sstr=3645041',\n", - " 'absolute_magnitude_h': 22.14,\n", - " 'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.0992098919,\n", - " 'estimated_diameter_max': 0.2218400624},\n", - " 'meters': {'estimated_diameter_min': 99.2098919421,\n", - " 'estimated_diameter_max': 221.8400624229},\n", - " 'miles': {'estimated_diameter_min': 0.0616461498,\n", - " 'estimated_diameter_max': 0.1378449814},\n", - " 'feet': {'estimated_diameter_min': 325.4917818793,\n", - " 'estimated_diameter_max': 727.8217503997}},\n", - " 'is_potentially_hazardous_asteroid': False,\n", - " 'close_approach_data': [{'close_approach_date': '2025-01-03',\n", - " 'close_approach_date_full': '2025-Jan-03 11:13',\n", - " 'epoch_date_close_approach': 1735902780000,\n", - " 'relative_velocity': {'kilometers_per_second': '12.0604970908',\n", - " 'kilometers_per_hour': '43417.7895268897',\n", - " 'miles_per_hour': '26978.1259424166'},\n", - " 'miss_distance': {'astronomical': '0.1712353484',\n", - " 'lunar': '66.6105505276',\n", - " 'kilometers': '25616443.389347908',\n", - " 'miles': '15917319.8262128104'},\n", - " 'orbiting_body': 'Earth'}],\n", - " 'is_sentry_object': False},\n", - " {'links': {'self': 'http://api.nasa.gov/neo/rest/v1/neo/3647191?api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh'},\n", - " 'id': '3647191',\n", - " 'neo_reference_id': '3647191',\n", - " 'name': '(2013 RT9)',\n", - " 'nasa_jpl_url': 'https://ssd.jpl.nasa.gov/tools/sbdb_lookup.html#/?sstr=3647191',\n", - " 'absolute_magnitude_h': 26.5,\n", - " 'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.0133215567,\n", - " 'estimated_diameter_max': 0.0297879063},\n", - " 'meters': {'estimated_diameter_min': 13.3215566698,\n", - " 'estimated_diameter_max': 29.7879062798},\n", - " 'miles': {'estimated_diameter_min': 0.008277629,\n", - " 'estimated_diameter_max': 0.0185093411},\n", - " 'feet': {'estimated_diameter_min': 43.7058959846,\n", - " 'estimated_diameter_max': 97.7293544391}},\n", - " 'is_potentially_hazardous_asteroid': False,\n", - " 'close_approach_data': [{'close_approach_date': '2025-01-03',\n", - " 'close_approach_date_full': '2025-Jan-03 17:43',\n", - " 'epoch_date_close_approach': 1735926180000,\n", - " 'relative_velocity': {'kilometers_per_second': '4.5094400585',\n", - " 'kilometers_per_hour': '16233.9842106775',\n", - " 'miles_per_hour': '10087.1664669068'},\n", - " 'miss_distance': {'astronomical': '0.2441933616',\n", - " 'lunar': '94.9912176624',\n", - " 'kilometers': '36530806.763499792',\n", - " 'miles': '22699190.7473774496'},\n", - " 'orbiting_body': 'Earth'}],\n", - " 'is_sentry_object': False},\n", - " {'links': {'self': 'http://api.nasa.gov/neo/rest/v1/neo/3837864?api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh'},\n", - " 'id': '3837864',\n", - " 'neo_reference_id': '3837864',\n", - " 'name': '(2019 AS11)',\n", - " 'nasa_jpl_url': 'https://ssd.jpl.nasa.gov/tools/sbdb_lookup.html#/?sstr=3837864',\n", - " 'absolute_magnitude_h': 26.8,\n", - " 'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.0116025908,\n", - " 'estimated_diameter_max': 0.0259441818},\n", - " 'meters': {'estimated_diameter_min': 11.6025908209,\n", - " 'estimated_diameter_max': 25.9441817907},\n", - " 'miles': {'estimated_diameter_min': 0.0072095135,\n", - " 'estimated_diameter_max': 0.0161209622},\n", - " 'feet': {'estimated_diameter_min': 38.066244069,\n", - " 'estimated_diameter_max': 85.1187093863}},\n", - " 'is_potentially_hazardous_asteroid': False,\n", - " 'close_approach_data': [{'close_approach_date': '2025-01-03',\n", - 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" 'miles': '1041482.706639778'},\n", - " 'orbiting_body': 'Earth'}],\n", - " 'is_sentry_object': False},\n", - " {'links': {'self': 'http://api.nasa.gov/neo/rest/v1/neo/54511956?api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh'},\n", - " 'id': '54511956',\n", - " 'neo_reference_id': '54511956',\n", - " 'name': '(2024 YZ8)',\n", - " 'nasa_jpl_url': 'https://ssd.jpl.nasa.gov/tools/sbdb_lookup.html#/?sstr=54511956',\n", - " 'absolute_magnitude_h': 23.68,\n", - " 'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.0488151892,\n", - " 'estimated_diameter_max': 0.1091540813},\n", - " 'meters': {'estimated_diameter_min': 48.8151891662,\n", - " 'estimated_diameter_max': 109.1540813101},\n", - " 'miles': {'estimated_diameter_min': 0.0303323429,\n", - " 'estimated_diameter_max': 0.0678251807},\n", - " 'feet': {'estimated_diameter_min': 160.154825224,\n", - " 'estimated_diameter_max': 358.1170761255}},\n", - " 'is_potentially_hazardous_asteroid': False,\n", - " 'close_approach_data': [{'close_approach_date': '2025-01-01',\n", - " 'close_approach_date_full': '2025-Jan-01 02:32',\n", - " 'epoch_date_close_approach': 1735698720000,\n", - " 'relative_velocity': {'kilometers_per_second': '21.4762910009',\n", - " 'kilometers_per_hour': '77314.64760315',\n", - " 'miles_per_hour': '48040.3153398573'},\n", - " 'miss_distance': {'astronomical': '0.0725309008',\n", - " 'lunar': '28.2145204112',\n", - " 'kilometers': '10850468.268861296',\n", - " 'miles': '6742168.3437700448'},\n", - " 'orbiting_body': 'Earth'}],\n", - " 'is_sentry_object': False},\n", - " {'links': {'self': 'http://api.nasa.gov/neo/rest/v1/neo/54511954?api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh'},\n", - " 'id': '54511954',\n", - " 'neo_reference_id': '54511954',\n", - " 'name': '(2024 YY8)',\n", - " 'nasa_jpl_url': 'https://ssd.jpl.nasa.gov/tools/sbdb_lookup.html#/?sstr=54511954',\n", - " 'absolute_magnitude_h': 28.73,\n", - " 'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.004770402,\n", - " 'estimated_diameter_max': 0.0106669431},\n", - " 'meters': {'estimated_diameter_min': 4.7704019801,\n", - " 'estimated_diameter_max': 10.6669431075},\n", - " 'miles': {'estimated_diameter_min': 0.0029641894,\n", - " 'estimated_diameter_max': 0.0066281291},\n", - " 'feet': {'estimated_diameter_min': 15.6509256325,\n", - " 'estimated_diameter_max': 34.996533625}},\n", - " 'is_potentially_hazardous_asteroid': False,\n", - " 'close_approach_data': [{'close_approach_date': '2025-01-01',\n", - " 'close_approach_date_full': '2025-Jan-01 07:14',\n", - " 'epoch_date_close_approach': 1735715640000,\n", - " 'relative_velocity': {'kilometers_per_second': '4.7580939652',\n", - " 'kilometers_per_hour': '17129.1382745626',\n", - " 'miles_per_hour': '10643.3803906581'},\n", - " 'miss_distance': {'astronomical': '0.0086258496',\n", - " 'lunar': '3.3554554944',\n", - " 'kilometers': '1290408.727100352',\n", - " 'miles': '801822.8020027776'},\n", - " 'orbiting_body': 'Earth'}],\n", - " 'is_sentry_object': False},\n", - " {'links': {'self': 'http://api.nasa.gov/neo/rest/v1/neo/54513028?api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh'},\n", - " 'id': '54513028',\n", - " 'neo_reference_id': '54513028',\n", - " 'name': '(2024 YV10)',\n", - " 'nasa_jpl_url': 'https://ssd.jpl.nasa.gov/tools/sbdb_lookup.html#/?sstr=54513028',\n", - " 'absolute_magnitude_h': 26.18,\n", - " 'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.0154367182,\n", - " 'estimated_diameter_max': 0.0345175513},\n", - " 'meters': {'estimated_diameter_min': 15.4367182177,\n", - " 'estimated_diameter_max': 34.5175512843},\n", - " 'miles': {'estimated_diameter_min': 0.009591929,\n", - " 'estimated_diameter_max': 0.0214482054},\n", - " 'feet': {'estimated_diameter_min': 50.6454025974,\n", - " 'estimated_diameter_max': 113.2465629557}},\n", - " 'is_potentially_hazardous_asteroid': False,\n", - " 'close_approach_data': [{'close_approach_date': '2025-01-01',\n", - " 'close_approach_date_full': '2025-Jan-01 14:52',\n", - " 'epoch_date_close_approach': 1735743120000,\n", - " 'relative_velocity': {'kilometers_per_second': '5.8480710628',\n", - " 'kilometers_per_hour': '21053.0558259305',\n", - " 'miles_per_hour': '13081.5501602844'},\n", - " 'miss_distance': {'astronomical': '0.0568220725',\n", - " 'lunar': '22.1037862025',\n", - " 'kilometers': '8500461.014985575',\n", - " 'miles': '5281941.547827935'},\n", - " 'orbiting_body': 'Earth'}],\n", - " 'is_sentry_object': False},\n", - " {'links': {'self': 'http://api.nasa.gov/neo/rest/v1/neo/54511964?api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh'},\n", - " 'id': '54511964',\n", - " 'neo_reference_id': '54511964',\n", - " 'name': '(2024 YJ9)',\n", - " 'nasa_jpl_url': 'https://ssd.jpl.nasa.gov/tools/sbdb_lookup.html#/?sstr=54511964',\n", - " 'absolute_magnitude_h': 27.91,\n", - " 'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.0069591304,\n", - " 'estimated_diameter_max': 0.0155610887},\n", - " 'meters': {'estimated_diameter_min': 6.9591304358,\n", - " 'estimated_diameter_max': 15.5610887188},\n", - " 'miles': {'estimated_diameter_min': 0.0043242018,\n", - " 'estimated_diameter_max': 0.0096692093},\n", - " 'feet': {'estimated_diameter_min': 22.8317934991,\n", - " 'estimated_diameter_max': 51.0534423123}},\n", - " 'is_potentially_hazardous_asteroid': False,\n", - " 'close_approach_data': [{'close_approach_date': '2025-01-01',\n", - " 'close_approach_date_full': '2025-Jan-01 07:39',\n", - " 'epoch_date_close_approach': 1735717140000,\n", - " 'relative_velocity': {'kilometers_per_second': '14.1845754203',\n", - " 'kilometers_per_hour': '51064.4715130282',\n", - " 'miles_per_hour': '31729.4767576369'},\n", - " 'miss_distance': {'astronomical': '0.0084552349',\n", - " 'lunar': '3.2890863761',\n", - " 'kilometers': '1264885.131389663',\n", - " 'miles': '785963.1750488294'},\n", - " 'orbiting_body': 'Earth'}],\n", - " 'is_sentry_object': False},\n", - " {'links': {'self': 'http://api.nasa.gov/neo/rest/v1/neo/54514249?api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh'},\n", - " 'id': '54514249',\n", - " 'neo_reference_id': '54514249',\n", - " 'name': '(2025 AG1)',\n", - " 'nasa_jpl_url': 'https://ssd.jpl.nasa.gov/tools/sbdb_lookup.html#/?sstr=54514249',\n", - " 'absolute_magnitude_h': 29.49,\n", - " 'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.0033616692,\n", - " 'estimated_diameter_max': 0.0075169209},\n", - " 'meters': {'estimated_diameter_min': 3.3616692116,\n", - " 'estimated_diameter_max': 7.516920875},\n", - " 'miles': {'estimated_diameter_min': 0.0020888438,\n", - " 'estimated_diameter_max': 0.0046707966},\n", - " 'feet': {'estimated_diameter_min': 11.0290988161,\n", - " 'estimated_diameter_max': 24.6618146834}},\n", - " 'is_potentially_hazardous_asteroid': False,\n", - " 'close_approach_data': [{'close_approach_date': '2025-01-01',\n", - " 'close_approach_date_full': '2025-Jan-01 05:24',\n", - " 'epoch_date_close_approach': 1735709040000,\n", - " 'relative_velocity': {'kilometers_per_second': '2.645140072',\n", - " 'kilometers_per_hour': '9522.5042591951',\n", - " 'miles_per_hour': '5916.9138270538'},\n", - " 'miss_distance': {'astronomical': '0.0044854773',\n", - " 'lunar': '1.7448506697',\n", - " 'kilometers': '671017.850013351',\n", - " 'miles': '416951.1577162038'},\n", - " 'orbiting_body': 'Earth'}],\n", - " 'is_sentry_object': False},\n", - " {'links': {'self': 'http://api.nasa.gov/neo/rest/v1/neo/54516067?api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh'},\n", - " 'id': '54516067',\n", - " 'neo_reference_id': '54516067',\n", - " 'name': '(2025 AC4)',\n", - " 'nasa_jpl_url': 'https://ssd.jpl.nasa.gov/tools/sbdb_lookup.html#/?sstr=54516067',\n", - " 'absolute_magnitude_h': 24.57,\n", - " 'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.0324007435,\n", - " 'estimated_diameter_max': 0.0724502651},\n", - " 'meters': {'estimated_diameter_min': 32.4007435394,\n", - " 'estimated_diameter_max': 72.4502650757},\n", - " 'miles': {'estimated_diameter_min': 0.0201328824,\n", - " 'estimated_diameter_max': 0.0450184937},\n", - " 'feet': {'estimated_diameter_min': 106.3016554339,\n", - " 'estimated_diameter_max': 237.6977276709}},\n", - " 'is_potentially_hazardous_asteroid': False,\n", - " 'close_approach_data': [{'close_approach_date': '2025-01-01',\n", - " 'close_approach_date_full': '2025-Jan-01 10:18',\n", - " 'epoch_date_close_approach': 1735726680000,\n", - " 'relative_velocity': {'kilometers_per_second': '15.9737833452',\n", - " 'kilometers_per_hour': '57505.6200425926',\n", - " 'miles_per_hour': '35731.7559647987'},\n", - " 'miss_distance': {'astronomical': '0.0429341231',\n", - " 'lunar': '16.7013738859',\n", - " 'kilometers': '6422853.366077797',\n", - " 'miles': '3990976.0176637186'},\n", - " 'orbiting_body': 'Earth'}],\n", - " 'is_sentry_object': False}]}}" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "datos_neo" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "cb333883-8f2e-428d-8544-059fdac76069", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Claves en la respuesta: ['links', 'element_count', 'near_earth_objects']\n", - "Total de asteroides en el período: 55\n", - "Fechas disponibles: ['2025-01-03', '2025-01-02', '2025-01-01']\n" - ] - } - ], + "outputs": [], "source": [ "print(\"\\nClaves en la respuesta:\", list(datos_neo.keys()))\n", "print(\"Total de asteroides en el período:\", datos_neo[\"element_count\"])\n", @@ -2683,73 +924,20 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "5e2cc865-de35-4f3f-95f7-dc54b3c82b98", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'links': {'self': 'http://api.nasa.gov/neo/rest/v1/neo/3837864?api_key=4VAQSAtYt6DrjgGzAkq2ijlevWNRB1v1JXHntvOh'},\n", - " 'id': '3837864',\n", - " 'neo_reference_id': '3837864',\n", - " 'name': '(2019 AS11)',\n", - " 'nasa_jpl_url': 'https://ssd.jpl.nasa.gov/tools/sbdb_lookup.html#/?sstr=3837864',\n", - " 'absolute_magnitude_h': 26.8,\n", - " 'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.0116025908,\n", - " 'estimated_diameter_max': 0.0259441818},\n", - " 'meters': {'estimated_diameter_min': 11.6025908209,\n", - " 'estimated_diameter_max': 25.9441817907},\n", - " 'miles': {'estimated_diameter_min': 0.0072095135,\n", - " 'estimated_diameter_max': 0.0161209622},\n", - " 'feet': {'estimated_diameter_min': 38.066244069,\n", - " 'estimated_diameter_max': 85.1187093863}},\n", - " 'is_potentially_hazardous_asteroid': False,\n", - " 'close_approach_data': [{'close_approach_date': '2025-01-03',\n", - " 'close_approach_date_full': '2025-Jan-03 23:18',\n", - " 'epoch_date_close_approach': 1735946280000,\n", - " 'relative_velocity': {'kilometers_per_second': '11.8634390203',\n", - " 'kilometers_per_hour': '42708.3804730289',\n", - " 'miles_per_hour': '26537.3267444773'},\n", - " 'miss_distance': {'astronomical': '0.3233704888',\n", - " 'lunar': '125.7911201432',\n", - " 'kilometers': '48375536.345338856',\n", - " 'miles': '30059164.4230179728'},\n", - " 'orbiting_body': 'Earth'}],\n", - " 'is_sentry_object': False}" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "datos_neo[\"near_earth_objects\"]['2025-01-03'][3]" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "neo-explore", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "── Primer asteroide del 2025-01-03 ──\n", - "Nombre : (2010 XB11)\n", - "ID : 3553148\n", - "Mag. absoluta (H): 19.69\n", - "¿Potencialmente peligroso?: False\n", - "Diámetro estimado: 306.6 – 685.6 m\n", - "Fecha acercamiento : 2025-01-03\n", - "Distancia (km) : 19,016,653 km\n", - "Velocidad (km/h) : 68,182 km/h\n" - ] - } - ], + "outputs": [], "source": [ "# Explorar la estructura de UN asteroide para entender el JSON\n", "primera_fecha = fechas[0]\n", @@ -2774,76 +962,10 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "neo-loop", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Nombre Diám. máx (m) Dist. (km) Peligroso\n", - "---------------------------------------------------------------------------\n", - "(2011 GE3) 39.6 49,668,157 no\n", - "(2021 NT5) 22.4 70,334,863 no\n", - "(2022 EQ6) 34.0 70,563,039 no\n", - "(2024 YV1) 33.9 14,250,724 no\n", - "(2024 YD2) 63.1 9,147,402 no\n", - "(2024 YY4) 30.6 1,676,104 no\n", - "(2024 YZ8) 109.2 10,850,468 no\n", - "(2024 YY8) 10.7 1,290,409 no\n", - "(2024 YV10) 34.5 8,500,461 no\n", - "(2024 YJ9) 15.6 1,264,885 no\n", - "(2025 AG1) 7.5 671,018 no\n", - "(2025 AC4) 72.5 6,422,853 no\n", - "459462 (2013 AY52) 622.4 15,830,337 no\n", - "(2013 TK4) 95.1 53,240,802 no\n", - "(2014 BM25) 14.3 42,733,614 no\n", - "(2015 BF) 28.4 72,149,352 no\n", - "(2017 AZ13) 239.9 24,373,476 no\n", - "(2019 AR8) 44.9 66,096,247 no\n", - "(2019 LS1) 189.7 18,639,596 no\n", - "(2019 QS6) 35.8 40,941,596 no\n", - "(2019 YA5) 37.5 41,802,119 no\n", - "(2022 UF4) 73.5 57,230,310 no\n", - "(2022 WF2) 32.2 49,729,030 no\n", - "(2024 GE2) 15.1 25,623,356 no\n", - "(2024 XP10) 72.5 7,568,818 no\n", - "(2024 XB20) 37.5 13,658,066 no\n", - "(2024 YR3) 48.3 9,453,012 no\n", - "(2024 YF7) 41.9 1,938,314 no\n", - "(2024 YR8) 71.5 8,089,872 no\n", - "(2024 YR9) 42.3 3,348,567 no\n", - "(2024 YS9) 16.2 1,582,436 no\n", - "(2025 AC) 9.3 140,221 no\n", - "(2025 AA2) 36.0 1,245,829 no\n", - "(2025 AE2) 68.9 7,292,137 no\n", - "(2025 AO1) 15.3 527,693 no\n", - "(2010 XB11) 685.6 19,016,653 no\n", - "(2013 NC15) 221.8 25,616,443 no\n", - "(2013 RT9) 29.8 36,530,807 no\n", - "(2019 AS11) 25.9 48,375,536 no\n", - "(2019 QL7) 27.8 58,599,409 no\n", - "(2020 JH1) 62.2 62,796,482 no\n", - "(2020 RL) 56.8 40,923,342 no\n", - "(2022 KA) 55.5 37,624,381 no\n", - "(2022 YS5) 64.0 16,708,864 no\n", - "(2024 YL1) 20.4 2,356,964 no\n", - "(2024 YE2) 53.5 22,520,907 no\n", - "(2024 YC9) 24.1 1,306,415 no\n", - "(2025 AB) 22.3 153,127 no\n", - "(2025 AE) 42.3 6,288,670 no\n", - "(2025 AX1) 25.4 1,091,513 no\n", - "(2025 AO) 10.6 459,327 no\n", - "(2025 AN2) 23.2 706,413 no\n", - "(2025 AC3) 43.1 7,830,455 no\n", - "(2025 BE1) 30.2 7,947,528 no\n", - "(2025 OC17) 141.3 20,430,381 no\n", - "---------------------------------------------------------------------------\n", - "Total: 55 asteroides | Potencialmente peligrosos: 0\n" - ] - } - ], + "outputs": [], "source": [ "# ── Exploración con ciclos ──────────────────────────────────────────────────\n", "# Recorrer TODOS los asteroides de TODAS las fechas y hacer un resumen\n", @@ -2877,33 +999,10 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "neo-stats", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "── Estadísticas del período ──\n", - "Asteroides analizados : 55\n", - "\n", - "Diámetro máximo estimado:\n", - " Promedio : 73.2 m\n", - " Máximo : 685.6 m\n", - " Mínimo : 7.5 m\n", - "\n", - "Distancia de acercamiento (miss distance):\n", - " Promedio : 22,275,615 km\n", - " Más cerca: 140,221 km\n", - " Más lejos: 72,149,352 km\n", - "\n", - "Velocidad relativa:\n", - " Promedio : 39,817 km/h\n", - " Máxima : 92,434 km/h\n" - ] - } - ], + "outputs": [], "source": [ "# ── Estadísticas básicas con ciclos ────────────────────────────────────────\n", "diametros = []\n", @@ -3003,22 +1102,10 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "eonet-fetch", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Código HTTP: 200\n", - "Título : EONET Events\n", - "Eventos : 20\n", - "\n", - "Claves de cada evento: ['id', 'title', 'description', 'link', 'closed', 'categories', 'sources', 'geometry']\n" - ] - } - ], + "outputs": [], "source": [ "# EONET no requiere API key\n", "url_eonet = \"https://eonet.gsfc.nasa.gov/api/v3/events?limit=20&days=30&status=all\"\n", @@ -3038,28 +1125,10 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "eonet-explore", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "=======================================================\n", - "ID : EONET_19513\n", - "Título : Wildfire in Russian Federation 1028298\n", - "Categoría: Wildfires\n", - "Estado : Cerrado: 2026-04-11T00:00:00Z\n", - "Lecturas : 1 puntos de trayectoria\n", - "\n", - "Últimas posiciones registradas:\n", - " Fecha Lon Lat Magnitud\n", - " ----------------------------------------------------------\n", - " 2026-04-10T19:00:00Z 133.34 48.83 6003.0 hectare\n" - ] - } - ], + "outputs": [], "source": [ "# Explorar la estructura de un evento\n", "primer_evento = datos_eonet['events'][0]\n", @@ -3086,43 +1155,10 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "eonet-loop", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Categoría Estado Título\n", - "----------------------------------------------------------------------\n", - " Wildfires Cerrado Wildfire in Russian Federation 1028298\n", - " Wildfires Cerrado Wildfire in Mongolia 1028274\n", - " Wildfires Cerrado Wildfire in Australia 1028290\n", - " Severe Storms Activo Super Typhoon Sinlaku\n", - " Wildfires Activo rx-Northern Scupp EWO Prescribed Fire, W\n", - " Wildfires Activo HARRISON Wildfire, Osage, Oklahoma\n", - " Wildfires Activo JOY Wildfire, Osage, Oklahoma\n", - " Wildfires Activo Rx Cherokee 3464 Prescribed Fire, Cherok\n", - " Wildfires Activo Rosindale Road Wildfire, Bladen, North C\n", - " Wildfires Activo RX Blanchard 2 Prescribed Fire, Stone, A\n", - " Wildfires Activo HUGHEY Wildfire, Osage, Oklahoma\n", - " Wildfires Activo Chickasawhay CPT 378 RX Prescribed Fire,\n", - " Wildfires Activo RX CALE BU 363 Prescribed Fire, Rapides,\n", - " Wildfires Activo Compartment 180 RX Prescribed Fire, Gree\n", - " Wildfires Activo HC 1 Wildfire, San Miguel, New Mexico\n", - " Wildfires Activo MA Mainline Non-Statistical/Other, Polk,\n", - " Wildfires Cerrado Wildfire in Australia 1028234\n", - " Wildfires Cerrado Wildfire in Australia 1028235\n", - " Wildfires Cerrado Wildfire in Australia 1028275\n", - " Wildfires Cerrado Wildfire in Australia 1028299\n", - "\n", - "── Resumen por categoría ──\n", - " Wildfires : 19 eventos\n", - " Severe Storms : 1 evento\n" - ] - } - ], + "outputs": [], "source": [ "# Recorrer todos los eventos y agrupar por categoría\n", "print(f\"{'Categoría':<22} {'Estado':<10} {'Título'}\")\n", @@ -3212,20 +1248,10 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "save-code", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Datos guardados en 'asteroides_nasa.json'\n", - "Asteroides cargados desde archivo: 55\n", - "✓ Los datos guardados son idénticos a los descargados\n" - ] - } - ], + "outputs": [], "source": [ "# Guardar los datos de asteroides en un archivo JSON local\n", "nombre_archivo = \"asteroides_nasa.json\"\n", diff --git a/examenes/parcial2/.gitignore b/examenes/parcial2/.gitignore new file mode 100644 index 0000000..4c49bd7 --- /dev/null +++ b/examenes/parcial2/.gitignore @@ -0,0 +1 @@ +.env diff --git a/examenes/parcial2/examen_parcial2_estudiante.ipynb b/examenes/parcial2/examen_parcial2_estudiante.ipynb index 44e2450..c3762bb 100644 --- a/examenes/parcial2/examen_parcial2_estudiante.ipynb +++ b/examenes/parcial2/examen_parcial2_estudiante.ipynb @@ -8,9 +8,9 @@ "# Examen Parcial 2 — Programación 1 (F12)\n", "## Tormentas Geomagnéticas con la API de NASA / DONKI\n", "\n", - "**Nombre:** ___________________________ \n", - "**Carnet:** ___________________________ \n", - "**Fecha:** ___________________________ \n", + "**Nombre:** __Alicia Tomás Laroj________________ \n", + "**Carnet:** ___201016313________________________ \n", + "**Fecha:** ____22/04/2026_______________________ \n", "**Punteo total:** 100 puntos\n", "\n", "---\n", @@ -181,15 +181,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "setup-code", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ API key cargada: FbD3************************************\n", + "\n", + "Librerías importadas correctamente ✓\n" + ] + } + ], "source": [ "import os\n", "import requests\n", "import json\n", "import time\n", + "from dotenv import load_dotenv\n", + "load_dotenv()\n", "\n", "# Cargar la API key desde la variable de entorno.\n", "# Si no está configurada, usa DEMO_KEY como respaldo (30 req/hora).\n", @@ -206,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "datos-respaldo", "metadata": {}, "outputs": [ @@ -244,15 +256,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "ident-code", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estudiante : Alicia Tomás Laroj\n", + "Carnet : 201016313\n" + ] + } + ], "source": [ "# Completa tus datos\n", - "NOMBRE = \"Nombre Apellido\" # TU NOMBRE AQUI\n", - "CARNET = \"202300000\" # TU CARNET AQUI\n", - "\n", + "NOMBRE = \"Alicia Tomás Laroj\" # Tu nombre\n", + "CARNET = \"201016313\" # tu carnet \n", "if NOMBRE == \"Nombre Apellido\" or CARNET == \"202300000\":\n", " raise ValueError(\"Completa tu nombre y carnet antes de continuar.\")\n", "\n", @@ -289,10 +309,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "ej1-code", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ estructura de datos: ['gstID', 'startTime', 'allKpIndex', 'link', 'linkedEvents', 'submissionTime', 'versionId', 'sentNotifications']\n", + "Solicitud exitosa ✓ — 5 tormentas recibidas\n" + ] + } + ], "source": [ "URL_BASE = \"https://api.nasa.gov/DONKI/GST\"\n", "\n", @@ -301,12 +330,19 @@ "# endDate = \"2024-05-31\"\n", "# Hint: los parámetros van separados por \"&\" → ?param1=val1¶m2=val2\n", "\n", - "url = f\"\" # TU CÓDIGO AQUÍ\n", + "url = f\"https://api.nasa.gov/DONKI/GST?startDate=2024-05-01&endDate=2024-05-31&api_key={API_KEY}\" # TU CÓDIGO AQUÍ # TU CÓDIGO AQUÍ\n", + "\n", "\n", "# Completa la llamada — usa timeout=15\n", "try:\n", " respuesta = requests.get(url, timeout=15) # TU CÓDIGO AQUÍ — ya está la estructura\n", "\n", + " if respuesta.status_code == 200:\n", + " tormenta = respuesta.json()\n", + " print(\"✓ estructura de datos:\", list(tormenta[0].keys()))\n", + " else:\n", + " print(f\"✗ Error {respuesta.status_code}\")\n", + "\n", " # Verifica que la respuesta fue exitosa antes de parsear el JSON\n", " # Hint: usa respuesta.ok o compara respuesta.status_code con 200\n", " if not respuesta.ok: # TU CÓDIGO AQUÍ — condición de error\n", @@ -335,7 +371,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "ej1-url-check", "metadata": {}, "outputs": [], @@ -358,15 +394,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "ej1b-code", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Durante el período hubo un total de 5 tormentas reales.\n", + "\n", + "ID: 2024-05-02T15:00:00-GST-001 | Comenzó: 2024-05-02T15:00Z\n", + "ID: 2024-05-10T15:00:00-GST-001 | Comenzó: 2024-05-10T15:00Z\n", + "ID: 2024-05-12T21:00:00-GST-001 | Comenzó: 2024-05-12T21:00Z\n", + "ID: 2024-05-16T06:00:00-GST-001 | Comenzó: 2024-05-16T06:00Z\n", + "ID: 2024-05-17T18:00:00-GST-001 | Comenzó: 2024-05-17T18:00Z\n" + ] + } + ], "source": [ "# Hint: len(tormentas) da el total de eventos\n", "# Hint: cada elemento de tormentas es un dict — accede con tormenta[\"gstID\"], etc.\n", "\n", - "# TU CÓDIGO AQUÍ\n" + "# TU CÓDIGO AQUÍ\n", + "# Total de eventos reales\n", + "print(f\"Durante el período hubo un total de {len(tormentas)} tormentas reales.\\n\")\n", + "\n", + "# Ciclo FOR para recorrer cada objeto\n", + "for evento in tormentas:\n", + " if isinstance(evento, dict):\n", + " id_nasa = evento[\"gstID\"]\n", + " fecha_inicio = evento[\"startTime\"]\n", + " print(f\"ID: {id_nasa} | Comenzó: {fecha_inicio}\")\n", + " else:\n", + " print(f\"⚠️ Aviso: Se encontró texto en lugar de datos: {evento}\")" ] }, { @@ -386,10 +447,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "ej2-code", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Inicio: 2024-05-02T15:00Z | Kp máxino destacado = 6.67\n", + " Inicio: 2024-05-10T15:00Z | Kp máxino destacado = 9.00\n", + " Inicio: 2024-05-12T21:00Z | Kp máxino destacado = 6.33\n", + " Inicio: 2024-05-16T06:00Z | Kp máxino destacado = 6.00\n", + " Inicio: 2024-05-17T18:00Z | Kp máxino destacado = 6.00\n", + "ID: 2024-05-02T15:00:00-GST-001 | Máximo Evento: 6.67\n", + "ID: 2024-05-10T15:00:00-GST-001 | Máximo Evento: 9.0\n", + "ID: 2024-05-12T21:00:00-GST-001 | Máximo Evento: 6.33\n", + "ID: 2024-05-16T06:00:00-GST-001 | Máximo Evento: 6.0\n", + "ID: 2024-05-17T18:00:00-GST-001 | Máximo Evento: 6.0\n", + "------------------------------\n", + "La más intensa: 2024-05-10T15:00:00-GST-001\n", + "Kp máximo global: 9.0\n" + ] + } + ], "source": [ "# ── Parte a) ──────────────────────────────────────────────────────────────────\n", "# Hint: allKpIndex es una lista de dicts, cada uno con la clave \"kpIndex\"\n", @@ -398,9 +479,12 @@ "\n", "for tormenta in tormentas:\n", " # TU CÓDIGO AQUÍ — obtener el kp máximo de esta tormenta\n", - " kp_max = ...\n", + " if isinstance(tormenta, dict):\n", + " valores_kp = [medicion[\"kpIndex\"] for medicion in tormenta[\"allKpIndex\"]]\n", + " Kp_max = max(valores_kp)\n", + " inicio = tormenta[\"startTime\"]\n", "\n", - " print(f\" {tormenta['startTime']} Kp máx = {kp_max:.2f}\")\n", + " print(f\" Inicio: {inicio} | Kp máxino destacado = {Kp_max:.2f}\")\n", "\n", "# ── Parte b) ──────────────────────────────────────────────────────────────────\n", "# Hint: necesitas dos variables auxiliares:\n", @@ -408,7 +492,24 @@ "# kp_global_max = 0\n", "# Recorre todas las tormentas y actualiza estas variables cuando encuentres un Kp mayor\n", "\n", - "# TU CÓDIGO AQUÍ\n" + "# TU CÓDIGO AQUÍ\n", + "tormenta_mas_intensa = None\n", + "kp_global_max = 0\n", + "\n", + "for evento in tormentas:\n", + " if isinstance(evento, dict):\n", + " valores_kp =[m[\"kpIndex\"] for m in evento[\"allKpIndex\"]]\n", + " kp_max_evento = max(valores_kp)\n", + " print(f\"ID: {evento['gstID']} | Máximo Evento: {kp_max_evento}\")\n", + " \n", + " if kp_max_evento > kp_global_max:\n", + " kp_global_max = kp_max_evento\n", + " tormenta_mas_intensa = evento\n", + "\n", + "if tormenta_mas_intensa:\n", + " print(\"-\" * 30)\n", + " print(f\"La más intensa: {tormenta_mas_intensa['gstID']}\")\n", + " print(f\"Kp máximo global: {kp_global_max}\")\n" ] }, { @@ -435,10 +536,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "ej3-code", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " kp=2.00 → Quieto\n", + " kp=4.00 → Activo\n", + " kp=5.33 → G5 - Extrema\n", + " kp=6.67 → G5 - Extrema\n", + " kp=7.00 → G3 - Fuerte\n", + " kp=8.67 → G5 - Extrema\n", + " kp=9.00 → G5 - Extrema\n", + "\n", + "Fecha inicio Kp máx Categoría\n", + "--------------------------------------------------\n", + " 2024-05-02T15:00Z 6.670 G5 - Extrema \n", + " 2024-05-10T15:00Z 9.000 G5 - Extrema \n", + " 2024-05-12T21:00Z 6.330 G5 - Extrema \n", + " 2024-05-16T06:00Z 6.000 G2 - Moderada \n", + " 2024-05-17T18:00Z 6.000 G2 - Moderada \n" + ] + } + ], "source": [ "# ── Parte a) ──────────────────────────────────────────────────────────────────\n", "# Hint: usa if / elif / else\n", @@ -447,9 +570,23 @@ "def clasificar_kp(kp):\n", " \"\"\"Clasifica la intensidad de una tormenta según el índice Kp.\"\"\"\n", " # TU CÓDIGO AQUÍ\n", - " pass\n", - "\n", - "\n", + " if kp < 4:\n", + " return \"Quieto\"\n", + " elif kp == 4:\n", + " return \"Activo\"\n", + " elif kp == 5:\n", + " return \"G1 - Menor\"\n", + " elif kp == 6:\n", + " return \"G2 - Moderada\"\n", + " elif kp == 7:\n", + " return \"G3 - Fuerte\"\n", + " elif kp == 8:\n", + " return \"G4 - Severa\"\n", + " elif kp >= 9:\n", + " return \"G5 - Extrema\"\n", + " else:\n", + " return \"G5 - Extrema\"\n", + " \n", "# Prueba tu función con estos valores — verifica que los resultados son correctos:\n", "for kp_prueba in [2.0, 4.0, 5.33, 6.67, 7.0, 8.67, 9.0]:\n", " print(f\" kp={kp_prueba:.2f} → {clasificar_kp(kp_prueba)}\")\n", @@ -458,7 +595,14 @@ "print(f\"\\n{'Fecha inicio':<22} {'Kp máx':>7} {'Categoría'}\")\n", "print(\"-\" * 50)\n", "\n", - "# TU CÓDIGO AQUÍ — recorre tormentas, calcula kp_max, llama clasificar_kp()\n" + "# TU CÓDIGO AQUÍ — recorre tormentas, calcula kp_max, llama clasificar_kp()\n", + "for tormenta in tormentas:\n", + " if isinstance(tormenta, dict):\n", + " valor_kp = [m[\"kpIndex\"] for m in tormenta[\"allKpIndex\"]]\n", + " kp_max = max(valor_kp)\n", + " categoria = clasificar_kp(kp_max)\n", + " inicio = tormenta[\"startTime\"]\n", + " print(f\"{inicio:^22} {kp_max:> 7.3f} {categoria} \")\n" ] }, { @@ -487,15 +631,62 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "ej4-code", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "lista de valores Kp de la NASA: [6.67, 6.67]\n", + "Valores ordenados Kp de la NASA: [6.67, 6.67]\n", + "ID del Evento: 2024-05-02T15:00:00-GST-001\n", + " > Inicio: 2024-05-02T15:00Z\n", + " > Kp Máximo: 6.67 (G5 - Extrema)\n", + " > Promedio: 6.67\n", + " > Eventos vinculados: 2\n", + "----------------------------------------\n", + "lista de valores Kp de la NASA: [7.67, 8.67, 9.0, 9.0, 8.33, 8.67, 9.0, 8.67, 8.33, 7.33, 7.33, 6.67, 7.0]\n", + "Valores ordenados Kp de la NASA: [6.67, 7.0, 7.33, 7.33, 7.67, 8.33, 8.33, 8.67, 8.67, 8.67, 9.0, 9.0, 9.0]\n", + "ID del Evento: 2024-05-10T15:00:00-GST-001\n", + " > Inicio: 2024-05-10T15:00Z\n", + " > Kp Máximo: 9.0 (G5 - Extrema)\n", + " > Promedio: 8.13\n", + " > Eventos vinculados: 6\n", + "----------------------------------------\n", + "lista de valores Kp de la NASA: [6.33, 5.67, 6.0]\n", + "Valores ordenados Kp de la NASA: [5.67, 6.0, 6.33]\n", + "ID del Evento: 2024-05-12T21:00:00-GST-001\n", + " > Inicio: 2024-05-12T21:00Z\n", + " > Kp Máximo: 6.33 (G5 - Extrema)\n", + " > Promedio: 6.0\n", + " > Eventos vinculados: 2\n", + "----------------------------------------\n", + "lista de valores Kp de la NASA: [6.0]\n", + "Valores ordenados Kp de la NASA: [6.0]\n", + "ID del Evento: 2024-05-16T06:00:00-GST-001\n", + " > Inicio: 2024-05-16T06:00Z\n", + " > Kp Máximo: 6.0 (G2 - Moderada)\n", + " > Promedio: 6.0\n", + " > Eventos vinculados: 1\n", + "----------------------------------------\n", + "lista de valores Kp de la NASA: [6.0]\n", + "Valores ordenados Kp de la NASA: [6.0]\n", + "ID del Evento: 2024-05-17T18:00:00-GST-001\n", + " > Inicio: 2024-05-17T18:00Z\n", + " > Kp Máximo: 6.0 (G2 - Moderada)\n", + " > Promedio: 6.0\n", + " > Eventos vinculados: 2\n", + "----------------------------------------\n" + ] + } + ], "source": [ "def analizar_tormenta(tormenta):\n", " \"\"\"\n", " Analiza un evento de tormenta geomagnética.\n", - " \n", + " P\n", " Parámetro:\n", " tormenta (dict): un elemento de la lista retornada por el endpoint GST.\n", " \n", @@ -504,24 +695,33 @@ " num_mediciones, categoria, eventos_vinculados.\n", " \"\"\"\n", " # Hint: extrae la lista de mediciones primero\n", + "\n", " mediciones = tormenta[\"allKpIndex\"]\n", + " lista_kp = [m[\"kpIndex\"] for m in mediciones]\n", + " print(f\"lista de valores Kp de la NASA: {lista_kp}\")\n", + " print(f\"Valores ordenados Kp de la NASA: {sorted(lista_kp)}\")\n", + "\n", " \n", " # Hint: para kp_promedio, suma todos los kpIndex y divide entre len(mediciones)\n", " # usa round(..., 2) para redondear a 2 decimales\n", - " \n", + " kp_max = max( lista_kp)\n", + " kp_min = min( lista_kp)\n", + " kp_promedio = round(sum(lista_kp) / len(lista_kp), 2)\n", + "\n", " # Hint: linkedEvents puede ser None — usa (tormenta[\"linkedEvents\"] or [])\n", " # para tratar None como lista vacía y poder llamar len() sin error\n", - " \n", + " evento_v = len(tormenta.get(\"linkedEvents\") or [])\n", + "\n", " # TU CÓDIGO AQUÍ\n", " resultado = {\n", - " \"id\": ...,\n", - " \"inicio\": ...,\n", - " \"kp_max\": ...,\n", - " \"kp_min\": ...,\n", - " \"kp_promedio\": ...,\n", - " \"num_mediciones\": ...,\n", - " \"categoria\": ...,\n", - " \"eventos_vinculados\": ...,\n", + " \"id\": tormenta[\"gstID\"],\n", + " \"inicio\": tormenta[\"startTime\"],\n", + " \"kp_max\": kp_max,\n", + " \"kp_min\": kp_min,\n", + " \"kp_promedio\": kp_promedio,\n", + " \"num_mediciones\": len(mediciones),\n", + " \"categoria\": clasificar_kp(max(lista_kp)),\n", + " \"eventos_vinculados\": evento_v\n", " }\n", " return resultado\n", "\n", @@ -530,7 +730,13 @@ "for tormenta in tormentas:\n", " r = analizar_tormenta(tormenta)\n", " # Hint: accede a cada clave del dict retornado con r[\"clave\"]\n", - " # TU CÓDIGO AQUÍ — imprime los campos de r de forma legible\n" + " # TU CÓDIGO AQUÍ — imprime los campos de r de forma legible\n", + " print(f\"ID del Evento: {r['id']}\")\n", + " print(f\" > Inicio: {r['inicio']}\")\n", + " print(f\" > Kp Máximo: {r['kp_max']} ({r['categoria']})\")\n", + " print(f\" > Promedio: {r['kp_promedio']}\")\n", + " print(f\" > Eventos vinculados: {r['eventos_vinculados']}\")\n", + " print(\"-\" * 40)" ] }, { @@ -559,6 +765,20 @@ "*(escribe aqui tu respuesta en texto libre)*" ] }, + { + "cell_type": "code", + "execution_count": 10, + "id": "bd7aa793", + "metadata": {}, + "outputs": [], + "source": [ + "# PREGUNTA FINAL \n", + "# RESPUESTA 1: La tormenta mas intensa de mayo 2024 fue 2024-05-10T15:00Z de acuerdo a la escala Kp fue de: 9.000 G5 - Estrema\n", + "# RESPUESTA 2: Patrones observados en los datos: Se observa que en general los datos terminan en .0, .33 .67, deacuedo a las documentación de la NASA la escala Kp mide tecios, \n", + "# a esto debe esos decimales, luego ID del Evento: 2024-05-10T15:00:00-GST-001 la mayor intensidad fue 9.00 G5 estremo. \n", + "# RESPUESTA 3: impactó profundamente en sistemas tecnológicos y ambientales de la Tierra, dejando también un legado científico que ha abierto nuevas vías para el estudio del clima espacial.(Por Ambientum Portal Ambiental -24 enero, 2025)" + ] + }, { "cell_type": "markdown", "id": "sello-md", @@ -579,10 +799,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "sello-code", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consultando posicion de la ISS...\n", + "\n", + "=======================================================\n", + " SELLO DE ENTREGA -- PARCIAL 2\n", + "=======================================================\n", + " Estudiante : Alicia Tomás Laroj\n", + " Carnet : 201016313\n", + " Fecha/Hora : 2026-04-24 16:41:28 UTC\n", + " ISS Latitud : -47.0214 deg\n", + " ISS Longitud : -20.0337 deg\n", + " ISS Hora UTC : 2026-04-24 16:41:28 UTC\n", + "=======================================================\n" + ] + } + ], "source": [ "from datetime import datetime, timezone\n", "\n", @@ -610,8 +849,8 @@ "print(\"=\" * 55)\n", "print(\" SELLO DE ENTREGA -- PARCIAL 2\")\n", "print(\"=\" * 55)\n", - "print(f\" Estudiante : {NOMBRE}\")\n", - "print(f\" Carnet : {CARNET}\")\n", + "print(f\" Estudiante : {'Alicia Tomás Laroj'}\")\n", + "print(f\" Carnet : {'201016313'}\")\n", "print(f\" Fecha/Hora : {ts_entrega}\")\n", "print(f\" ISS Latitud : {iss_lat:+.4f} deg\")\n", "print(f\" ISS Longitud : {iss_lon:+.4f} deg\")\n", @@ -644,11 +883,19 @@ "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7025ad02", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "base", "language": "python", "name": "python3" }, @@ -662,7 +909,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.14.2" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/hola_mundo.cpp b/hola_mundo.cpp new file mode 100644 index 0000000..4631985 --- /dev/null +++ b/hola_mundo.cpp @@ -0,0 +1,6 @@ +#include + +int main() { + std::cout << "Hola Mundo desde Docker!" << std::endl; + return 0; +} diff --git a/index.html b/index.html new file mode 100644 index 0000000..2d3959e --- /dev/null +++ b/index.html @@ -0,0 +1 @@ +

Docker Funciona

diff --git a/mi_app b/mi_app new file mode 100755 index 0000000..a5ee04c Binary files /dev/null and b/mi_app differ