{ "cells": [ { "cell_type": "markdown", "id": "b068e0cc-3bef-438b-9b18-30ae2d29cfae", "metadata": {}, "source": [ "# Práctica 8: Importación de datos desde archivos\n", "\n", "En el ambiente de programación MATLAB® es posible generar matrices y tablas de datos a partir de archivos en los formatos **.txt** y **.CSV**, este último formato se refiere a datos provenientes de un archivo de software de adquisición de datos o incluso de excell. El uso de archivos de datos funciona en sentido bidireccional, es decir, no solo es posible cargar los datos desde el archivo sino que tambien es posible crear un archivo de datos a partir de tablas generadas en MATLAB®, como las tablas de conversiones de unidades analizadas en las prácticas anteriores. \n", "\n", "Una característica importante de los archivos de datos que se pueden manipular en MATLAB® es el delimitador. Es importante que el archivo a leer separe los datos usando una coma, punto y coma o espacio, ademas de usar el tabulador para indicar el inicio de una nueva línea o renglón de datos, como se muestra en la {numref}`figura_datos`. Estas condiciones permiten la creación de una matriz en la que las columnas almacenan los datos de interés, importados desde el archivo de datos **.CSV** o **.txt**.\n", "\n", "\n", "```{figure} /images/formato_datos.jpg\n", ":height: 150px\n", ":name: figura_datos\n", "Formato de archivo de datos con delimitador **,**\n", "```\n", "\n", "Para hacer uso de los datos en un archivo es necesario cargarlos o importarlos al workspace de MATLAB®, la importación de los datos se lleva a cabo usando la instrucción `load()`, el argumento de entrada es el nombre del archivo en el que se encuentran los datos.\n", "\n", "## Importación de datos desde un archivo almacenado en el disco duro\n", "\n", "El primer método para cargar datos es usando un archivo que se encuentra en la misma carpeta de trabajo en la que se encuentran los scripts o programas que se estan utilizando como se muestra en la {numref}`figura_carpeta`. \n", "\n", "\n", "```{figure} /images/carpeta_archivos.jpg\n", ":height: 250px\n", ":name: figura_carpeta\n", "Ventana de usuarios que incluye al archivo de datos\n", "```\n", "\n", "Para poder cargar los datos del archivo y guardarlos en una variable tipo matriz o arreglo se usan las instrucciones:\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "2b6af66f-ed5b-4f71-9da7-d675269fae1b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
datos = 6×2 double\n",
       "         0    0.4500\n",
       "    0.1000    0.7500\n",
       "    0.2000    0.6500\n",
       "    0.3000    0.8100\n",
       "    0.4000    0.4800\n",
       "    0.5000    0.7900\n",
       "
" ], "text/plain": [ "datos = 6×2 double\n", " 0 0.4500\n", " 0.1000 0.7500\n", " 0.2000 0.6500\n", " 0.3000 0.8100\n", " 0.4000 0.4800\n", " 0.5000 0.7900\n" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "datos=load('valores.txt')" ] }, { "cell_type": "markdown", "id": "6fd27ecb-6657-43e8-a36d-736a6095d94a", "metadata": {}, "source": [ "Si el archivo se encuentra fuera de la carpeta de trabajo se puede escribir la ubicación compelta del archivo para la importación de su contenido:" ] }, { "cell_type": "code", "execution_count": 2, "id": "0ad77975-8c42-4dd9-9435-3c9f11fa3dc1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
datos = 5×2 double\n",
       "         0         0\n",
       "    0.0200    0.1000\n",
       "    0.0400    0.1600\n",
       "    0.0600    0.1930\n",
       "    0.0800    0.4190\n",
       "
" ], "text/plain": [ "datos = 5×2 double\n", " 0 0\n", " 0.0200 0.1000\n", " 0.0400 0.1600\n", " 0.0600 0.1930\n", " 0.0800 0.4190\n" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "datos=load('C:\\Users\\HP\\Documents\\MATLAB\\Voltajes.txt')" ] }, { "cell_type": "markdown", "id": "bb843122-b13d-4a2a-87cf-b0c8d5db9949", "metadata": {}, "source": [ "Una vez importados los datos, es posible manipular la matriz o arreglo que los contiene usando las funciones o comandos de MATLAB® vistos en prácticas anteriores, por ejemplo, es posible extraer los datos de cada culumna y almacenarlos en vectores, extraer un rango de datos solamente, hacer transpuesta, etc. Los comandos para para realizar algunas operaciones estadísticas comunes se reportan en la {numref}`Tabla_estadistica`, donde:\n", "\n", "* `mean(x)`, definido como $\\bar{x}=\\frac{\\sum^{n}_{i=1}x_i}{n}$, donde: $\\bar{x}$=promedio, $n=$total de datos.\n", "\n", "* `std(x)`, definida como $\\sigma=\\sqrt{\\frac{\\sum^{n}_{i=1}(x_i-\\bar{x})^2}{n}}$, donde: $\\bar{x}$=promedio y $n=$total de datos.\n", "\n", "\n", "```{list-table} Operaciones con conjuntos de datos.\n", ":header-rows: 1\n", ":name: Tabla_estadistica\n", "* - Parámetro estadistico\n", " - Instrucción de MATLAB®\n", "* - Promedio \n", " - `mean()`\n", "* - Desviación estandar\n", " - `std()`\n", "* - Máximo\n", " - `[renglon,columna]=max()`\n", "* - Mínimo\n", " - `[renglon,columna]=min()`\n", "```\n", "Por ejemplo, el cálculo de los datos estadísticos del ejemplo anterior se obienen con las instrucciones:" ] }, { "cell_type": "code", "execution_count": 3, "id": "b2c0ee9f-dae9-4291-b8af-bc1ef24472f8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Valor promedio de la columna dos:\n" ] }, { "data": { "text/html": [ "
ans = 0.1744
" ], "text/plain": [ "ans = 0.1744" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "Desviación estandar de la columna dos:\n" ] }, { "data": { "text/html": [ "
ans = 0.1552
" ], "text/plain": [ "ans = 0.1552" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "Valor mínimo de la columna dos:\n" ] }, { "data": { "text/html": [ "
ans = 0
" ], "text/plain": [ "ans = 0" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "Valor máximo de la columna dos:\n" ] }, { "data": { "text/html": [ "
ans = 0.4190
" ], "text/plain": [ "ans = 0.4190" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "disp('Valor promedio de la columna dos:')\n", "mean(datos(:,2))\n", "disp('Desviación estandar de la columna dos:')\n", "std(datos(:,2))\n", "disp('Valor mínimo de la columna dos:')\n", "min(datos(:,2))\n", "disp('Valor máximo de la columna dos:')\n", "max(datos(:,2))" ] }, { "cell_type": "markdown", "id": "fc1ce773-4882-41ea-8fef-c5e0f11550e0", "metadata": {}, "source": [ "Los datos importados se pueden graficar usando la instrucción `plot()`, de forma directa, o almacenando los valores en las columnas en arreglos unidimensionales como se muestra a continuación:" ] }, { "cell_type": "code", "execution_count": 4, "id": "16d8ac65-6c73-4e89-a85d-343d33c73ba7", "metadata": {}, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x=datos(:,1);\n", "y=datos(:,2);\n", "plot(x,y,'*-')% Gráfica de puntos, se especifica usando el caracter asterísco." ] }, { "cell_type": "markdown", "id": "9ac93e1c-cea0-4e60-b08e-a429dbe8317e", "metadata": {}, "source": [ "## Importación de datos desde un archivo disponible en internet\n", "\n", "Es posible importar datos desde archivos disponibles en repositorios de internet usando MATLAB®. Esta opción permite adquirir datos de diferentes orígenes y naturalezas, además de permitir almacenar información en la nube para utilizarla cuando sea necesario sin necesidad de almacenar grandes archivos en la computadora personal. GitHub es una excelente opción para almacenar ese tipo de archivos. El comando o función de MATLAB® que permite importar los archivos es `readtable()`. El argumento que se escribe entre paréntesis, es la dirección de internet o repositorio en el que está almacenado el archivo. Para leer directamente de internet el archivo sin necesidad de almacenarlo en la computadora y leer únicamente los datos alacenados en el se usan las siguientes instrucciones:" ] }, { "cell_type": "code", "execution_count": 5, "id": "d03ac896-e426-4011-b7b5-a8b5c0e0e5f8", "metadata": {}, "outputs": [], "source": [ "clear \n", "close all\n", "datos = readtable('https://raw.githubusercontent.com/LuisGerardo2204/Archivos_de_datos/main/ADC.txt');" ] }, { "cell_type": "markdown", "id": "5180be6b-ff27-4e3d-b5a3-aa41edbb87b4", "metadata": {}, "source": [ "Los datos han quedado almacenados en el la tabla con el mismo nombre. Una tabla de MATLAB® es una variable epecial que se utiliza para almacenar los datos descargados de internet. Para convertir la variable tabla en un arreglo o matriz se usa el comando `table2array()`, para convertir los datos importados en el punto anterior se usa la siguiente instrucción: " ] }, { "cell_type": "code", "execution_count": 6, "id": "155a2967-61ba-4736-990f-2b93ad32eed5", "metadata": {}, "outputs": [], "source": [ "V = table2array(datos);" ] }, { "cell_type": "markdown", "id": "c87c76b4-c1ef-4afe-812e-c17bc4f31bc0", "metadata": {}, "source": [ "Los datos descargados del repositorio de internet se han convertido en una matriz o arreglo de tantas columnas como las que tiene la tabla de datos original, Estos datos se pueden procesar como los obtenidos de la importación de un archivo almacenado en el disco duro. Los datos importados en el punto anterior son formas de onda capturadas por un sistema de adquisición de datos, la primera columna corresponde al tiempo y las siguietes tres columnas corresponden a los valores instantáneos de voltaje de las tres formas de onda. Para graficar la forma de onda almacenada en la segunda columna se usan las instrucciones: " ] }, { "cell_type": "code", "execution_count": 7, "id": "ca1d3260-b5cc-4c96-9783-78a036a1c725", "metadata": {}, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t=V(:,1);\n", "v1=V(:,2);\n", "plot(t,v1);\n", "grid\n", "ylabel('Voltaje [V]')\n", "xlabel('tiempo [s]')" ] }, { "cell_type": "markdown", "id": "0b414210-c274-4500-8bda-cf776087ccae", "metadata": {}, "source": [ "La segunda forma de onda se encuentra almacenada en la tercera columna, para graficarla se extrae del arreglo que contiene toda la información en forma condesada, para gráficar la segunda forma de onda se usan los comandos:" ] }, { "cell_type": "code", "execution_count": 8, "id": "2174a649-30d7-433e-a1ff-0e385a6bc46a", "metadata": {}, "outputs": [ { "data": { "image/png": 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1FQAAWIHFT08AAACtEBpgQXzUrDlaCkAQGkzB1fM1z+Meo3Wu0Jmu6rRjtNQVntnMiNBgDuadYqY/euUlGihPsle01JX2naFXBkdoMCseWgAAnREaAACAFEIDAACQQmjwR5aZZ+TNQDo8v2OZzkBzmh8bljnYLDMQyCA0+B2LTYbgarId4grivsDB5orFhgM3CA0AAEAKoQFAJ3gfCcCB0ADAV/giKMBiCA364dnTFQt0xgJDAIBOERp0xce8gFpmnyLKhzeu0Bl9EBpgCGZ/HlddAgDogdAAADD9ZzDQB6Ghy/gEDADgnwgN6AICEwyCQxFQgtDQVtzuuZ0+H/nnh3idjpqPNz3g/mDz25bKTGrzz854jHmC0AShAVCGlz0A5kJoAAAAUggNAABACqEBeuBkKgBYQJDO9zdhwoQffvhBfv1bbrklNzfXd/VoyDHPyAJnqXmB15zmBwYHm/vbtEBnPObnw3eDZzZN6P1JQ3V19U+kfffdd999953OFUJoPUGPpzC4wcEG3XB4eE/vTxqEEH/4wx9uvvlmmTXT0tJ8XQwAAJCk9ycNgwcP7tLK8fHxvisGAOAeF3hAa3p/0tDQ0HDddddJrpyTk+PTYgAAgDy9P2koLy+fOHFiYmLiqlWramtrdb53wyLIu2KozhiqGGjOUPt34PpyTsB3yFC7yQ/pHRqKiopeffXV3r175+fn33rrrampqZ988onONRgNTw2uGLAzBiwJmmDPmgg7SyEF12kYPXr0wYMHjx8/Pn/+/O+///6RRx4ZOnTookWLqqur9S8GAABIUnZxp7CwsAULFhQVFR08ePD222/fuXPnL37xi5SUlP3796sqCQAAuKH+ipBRUVGbNm2y2+1vvvlmaGjo448/npCQoLooAIAyTFwwLPWhwal3797XX399cHCw6kIsws8fdW6GT2d8cZv+fJqZg80VPx++JakPDVVVVU8++eTNN9985513nj59etasWceOHVNdlEX47fN4pwOnM9AKB5srfjtwa1NwRUiH2trazZs3b926taamRghx0003/eEPfxgwYICqegAAgHt6h4ampqb3338/Ozu7srJSCNG7d+/ly5ffe++9NptN50oAAECX6B0akpOT6+rqhBDjxo1btmxZZGSkzgUAAADP6D2noX///hs3brTb7Tk5OSQGGe5nEvnzPCMfjZ2WGofR6oGG2LkmpXdoCAwMDA8Pl1w5IyPj6aef9mk9psB8Ilc07wytdtUBP//WIh/9eYg/t1S4Pdh0rgTy9A4NX3/9dZdW/uqrr3xXjNnx0ALMi8cvzEjBX0/Mnz9fcs2qqqo+ffr4tBgAACBJ79AQFRXlmAgpuXJUVJRP6wEAAJL0Dg07d+7U+R4Bf+PnZ8oB+I76K0L6CUNdZ/famtSB68tVV/EPhuqMNwz1Um2clhpqBqVxKvGScQZiqMevoQ42qyI0ABowzvMm3GNPAd4gNAAAACmEBgAAIIXQAAAApKgPDS0tLVVVVRUVFUKIK1euqC6nE8yyoQO6odXokI+uTclsD8hQHBoef/zxhISElJSURx55RAgxevRo41832p8fWv48diVoOABDURkapkyZsn///rS0tFtuucWxZPjw4bt27XrmmWcUVgXACAhMgAEpCw1///vf7Xb77t27c3NznQvz8vKeeOKJd999t6WlRVVhgj/2BQCgI8pCQ35+fmRkZGxsbJvl06dPF0JcuHBBRVEAYGK824GvKQsNgYGB165da7/86tWrQoigIAXfpAVvdPps5bdPZ3RGc7TUY3QGXlIWGh555JHz589/+umnbZYvX77cZrOFh4crqQre4CS0K246Q9M8Q0tdoTPwKWWh4cYbbxw6dOivf/3rrKwsx5IjR46kpqa+9957CxYsUFUVAKA9PqKAg8qzANu3b587d+6BAwccP2ZmZgohFi9ePHPmzC7dTklJSe/evfv06aN9iQAA4J8UTx14+eWXGxoazp8/39jY+JOf/CQqKqqrt1BRUfHggw+uWrVq/PjxrtZJTEysr69vvSQ/P3/UqFGeVAwAgL9SP98wODh4wIABnm1bUlIyc+bMuro6N+tUV1fX19dnZGQkJCQ4F7b/qw2tmP3Cagb8ENIgLaUzmjNgS2FVZn+wGIfeoWHKlCmNjY379u0rLS2dNWuWmzW7d+8+adIkV/MbmpqaNmzY8MorrwwePPj8+fNubqe4uFgIkZmZGRER4U3lMqxxgQdDPbQM1VI6ozkDttQIJRltz1rjYIMm9J4I2djY2NDQ4Ph/g1vV1dXr169//vnnO7ydurq6119/feHChXl5ee7v8cyZMz169NAhMcANIzwRAyZSNj9adQlAB/T+pGHfvn2O/8TExHz00UfuV7777rsPHjy4ZMmS9r/q0aPH4cOHQ0JCampq3N9ISUnJgAEDZsyYcezYMSHEiBEjVqxYER3NAxIAgK5RP6ehvZaWloCAACHE4MGDXV0a0maz2Ww2mVsrLi6+dOnSnDlzZs6cWV1dvW7dumnTpu3YsSMyMtLVJvHx8WLyy/Hx8aJVynEqLy93f4+uVnC/oZvf6ryhjwao/4aO337zzTeebdjVrdRu2GaYFj7YHCM12r7QvDMdHrcyGxqtM92eOujqUxM3j1D3N+ujg01zEyZM0PkedaAyNFRWVs6ePfvy5cvOJdeuXWtsbLx48eLp06eFEKtXr/b+XpYuXdqvX79hw4Y5fkxKSpo4cWJ+fr6br8Wy2+3dnjpot9s7+mV5Z59SuFrB/YblA9eXu/gMv5MNXf/Wsw19NED9N/y/37ZbzZqdafV/XY8Zx9luS7ZUekPtO3PDDTdYoqXlwmUH3DxC3d+sjx6/2mv9IuJ4F2oByi7u1NTUNHbs2LNnz9bX11dUVFy5cuX777+vrKz87rvvJk2apOEdjR8/3pkYhBAxMTGDBg06d+6chnfhPc1P+RtkPpdaTN3qkC8mtXGwueLnnfHFM5u2N4iuUhYaHBHs8OHDH3/88Q033PDLX/6yqKjoxIkTwcHBI0aM0Opeampqjhw5Ul1d3Wa54/QHAMBc/DyHKafstfPFF1/s27evY2JBTExMQUGBEKJnz57vvPPOypUrtbqXixcvZmZmbtu2zbmkoqKirKxs6NChWt0FAAB+QlloiIqKcs5kfPzxxy9duuT4/4033tjQ0NDp30S4UVhYmJWVderUKSFETExMampqfn7+oUOHhBAlJSVZWVkRERGOL+AGAADyVH7LZUVFRUtLixAiPDz88uXLjis9//jjj0KI5uZmj2+5srLywIEDzj+7eOGFF1JTU+fNmxcfHz958uTQ0NCtW7eGhYVpMQgYF59hQk+ca+8S2mVeykJDbGxsUFBQYmLi1q1bo6KiAgIC7r///pKSkmnTpgkh5K/FFB4ebrfbW3/xxNSpU+12+5gxYxw/hoWFZWdnnz592m632+32P/3pTx5ftVp/nT60uFJbVzFF1JVOO8PB5gpHlC9wsBmTyvmAH374Yc+ePW+66SYhxMqVK8+ePTt58uSzZ88uWrRIYVUAAF/rNKHqVgm6ROV1GiIiIpwXhbznnnvS0tLOnz/fq1cvvuQaAAADMtAVIXv27NmzZ0/VVQAAgI4pOz1RWlr60EMPtV9+6tSplJQUxwRJAABgHHqHhqv/1NLScubMmav/qqmp6fPPP6+qqnJ+E6Y1GHPynannGfmupWaf7sfBpjmfFk9ntGX2x6/x6X16wm63p6enO38cMmRI+3V69OgREhKiY1H+qGx+9MD1en99C/yTBZ7HSaiuGDChwqf0Dg1Dhgx5+OGHz5w5I4Q4fvz4rbfe2vq3iYmJaWlprb8qAgAAGISCiZBLliwRQpSWlv7ud7/bsmWL/gUAAAAPKJsIGRMT0/orIQAAgMHp/UnDPffc88MPP7hfJyAgwPH9VTAmDafamf2Ebht0RnPattRKJ+A52KCE3p80/ChH56okWexJxzOad8AyLe1wIJJPxx2uZpnOeIwOuMLDEKro/UnDvn37dL5Hs+PBDAAwCPVXhLxy5cr58+ebmppCQ0OjoqJUlwMAADqmODRMmzatqKio9ZJ58+Y98cQTquoBAGvjNCu8ofJbLsePH19UVPTQQw+99957+/fvf+edd372s5/l5uZu2rRJYVVOTA6SR6+gGy5KBiikLDT8/e9//+qrr958881ly5bFxMTceOONw4YNKygouP/++9esWaOqKieSeFfRMY/Ruq6iYx2Sye60Dl5SFhreeOONyMjI4cOHt1m+cOHC5ubmCxcuKKkKAAC4ovL0hBsBAQYtDAAAv6XstXn69OlVVVV2u73N8pycnICAgPDwcCVVAbAG5tkAvqAsNERHR0dGRk6ZMiU7O7uioqKysrK0tHTq1KlvvPHGvHnzVFWlCZ6tXKEzmqOlAPSk8izA4cOH4+Li8vLyUlNTx44dO3HixM8//3z+/PkLFixQWJWXTD3PyKd/i2XqzniDlnaIg81cTP0t3vyVqYYUX6dh165dNTU1Fy9ebGhoCAkJ+dnPfhYYGKi2JAAA0CG9P2mYOnVqaWlp6yXh4eExMTFDhgyJjo4mMQAAYFh6h4by8vKJEycmJiauWrWqtrZW53sHAAAe0zs0FBUVvfrqq717987Pz7/11ltTU1M/+eQTnWuwPPOeejQsHVrKXgNgfAomQo4ePfrgwYPHjx+fP3/+999//8gjjwwdOnTRokXV1dX6F2Mcpp5n5FMG6YyVJlJp2FKLTTEzwsFGS11RvmsgFP71RFhY2IIFC4qKig4ePHj77bfv3LnzF7/4RUpKyv79+1WVZCVWetIBAAee2ZRTf+HFqKioTZs22e32N998MzQ09PHHH09ISFBdFAAAaEt9aHDq3bv39ddfHxwcrLoQAADQAcXXaRBCVFVVrVq16oMPPmhoaOjRo8esWbMyMjJUFwUAANpS9klDbW3tSy+9lJycnJKS8t5778XFxR04cKCoqGjBggU9e/ZUVZUZGWHqlmG16YzFpphpSL4zHGyucLBJojOmpvcnDU1NTe+//352dnZlZaUQonfv3suXL7/33nttNpvOlQDoKhKqK3QGfkLv0JCcnFxXVyeEGDdu3LJlyyIjI3UuAAAAeEbv0NC/f/+nn356zJgxOt8vAFgeH/vD1/QODbt27dL5HgHAY5x0AFoz0J9cog2mC7ki+TzuTfd4qYBuONhcoTMGRGiAKfk0TpHV0BoHW4d8/Ypu3s5YG6EBAOAJXtf9EKEBAABIITT4NU4Zwk9wqAOaIDT4HM9WrliyM2oHZeSWGrk2k6KlrtAZ3yE06MGYZ/6MUJURatCQEYZjhBraM0JVRqgBOmBH+xShAQAASCE0AAAAKYQGAAAghdDgTuvZNMysaY1uQDc8DH2KlqJLCA0utZ9Nw/wahzZ94HLX3qOlrrTvQ9n8aCWVWAlBAR4jNACA+XgcK8mj8AahAQAASCE0AAAAKYQGAPBr1j5hwQQObREatHdtTSqHaYf8rTPMZ3RFh854c7CZ8SjlYIM+CA0AACsjTmmI0AAAkGLGz2CgLUIDAACQQmgAAKPgrTwMjtCAfzD4RCqFT6Z0xq8YeV+rRWcg/C00lJSUXLhwQXUV6DKereA/ONphZH4UGioqKh588MFPP/1UdSGAZniBAaAnfwkNJSUlDz74YF1dnepCAAAwK+uHhqamppdeeik9PT0yMlJ1LQAAmJj1Q0NdXd3rr7++cOHCvLw81bV0QpMLJjItro2B68sd/6EzbXCwac7ZEDrThiYHGyfjjMD6oaFHjx6HDx+eOXOm6kL0w0PLiVa4olVn6DA6xUFiJUGqC/A5m81ms9m6tEl8fPw//jf55fLycufy1v/vlM4bdnWrb775xpu784cNzVKnY+UOd6iP7lFtZ5wj1e0eJTcsmx89cH25c+Wy+dHetFRymOY6Sjv8v/uR6l+nhiZMmKDkfn3K+qHBA3a73fGfbk8djI6O/ufi8lb/71S5vht6slV0dLTudardsFz8Y9SuBXGtAAAXE0lEQVT63J3eG7bboT66R8WdueGGG8xQqgZ3J3EL5jtKO9zQ9e3oX6eWnC8lovV7UZOz/ukJANrihD3gtwgNBtXV52X/eR7XbaS0FNAcz2xmR2gwLvnZQ/42z0iH8dJS79e0AH1m7GvyZ1MmwsFmaoQG+BGegwDAG4QGAAAgxY/+eiI8PLz1XFYAgGf86nwKWuOTBn/Hg98y2JWAEw8HHyE0+BaXPgWceB63EiM/sxm5NrMjNPg1HlrQDQcbYAGEBgAWxwd+gFYIDQAAQAqhAdAYZ+4BWBWhQQovA+152ROrfmLs/aCs2hmP+dsFE+V53xkONnQVoUEWD63W6AYA+CFCAwAAkEJoAAAAUggNgHVw2giATxEaOsEMLPc8mEjlJy3t6jCZ7tcpDjZXPBimn3QGmiM0uMP7Ns35VUv9arAG5Ff979Jg/aoz0BahAULwtgMAIIHQAAAApBAaAACAFEKDr/CBv+ZoqSvm6oy5qtWTKTpjiiKduOSl5ggNPmHGw9SMNUOYc8eZsWYIdhwIDTALVc9W5npfBbPjVRkGR2gAOsHzOAA4EBoAAIAUQoPh8Hk4dMPB5gqdMRR2h3EQGozFy0/CmSoMeRxsrlh1XAp5f5V0dopBEBoAAIAUQgMAAJBCaAAAAFKCVBeADjDrR1v0E3rieIOF8UmDRXg/z8iqyuZHO/7DRKoOeTafkYOtQ86HIQdbhzw7bDjYDIXQYFA86cCwODjhsa4ePBxsRkNoAAAAUggNAABACqEBADrAqXSgPUKDD5nlSccsdQp9r0JorrmlA9eXG//sr7laKvQ9oW6izlj4YqDoFKHBt4z/0DJ+hYDl8TCEWRAaAACAFEIDAMBqTHcuzCwIDYCB8DSnOcu31PIDhKEQGuA5j5+trP00x1scmALzGeEBQgPU4NkKurHwwWbhocGYCA3wEM9WPkJjARgWoQEAAEghNHSO89OAcjwMASMgNAAAACmEBimcZnaFzmiIZrri6Az90RDNhGcIDQAAQAqhoRPkcQAAHAgNAABACqEBAABIITRACH3PwvjJGR9a6gqd0RwtdcVc1ZoCocFXOFgBABZDaAAAAFIIDQAAQAqhAQAASCE0GA6TIVyhM5rzpqXW3h1KRmftlnqDzhgHoQFwh2crAHAiNFgKr3AAAN8hNAAAACmEBgAAICVIdQFeuXr16vHjx2NjY/v06aO6FvX84dyEx2PUuTn+sC8sz7OdaJZDVH+WH6CfMHFoeOWVV9atW9fc3CyEuOOOO/74xz+GhYV1uGZiYmJ9fX3rJfn5+aNGjZK8I/2fBczyCmeWZwGz1OmNsvnRet6diR4Ult/7lm+pWer0E2YNDbt27XrxxRd///vf33vvvV9++eXcuXOfffbZnJyc9mtWV1fX19dnZGQkJCQ4F8bGxupYLNoyy4NZ/zrN0hkA/smsoWHjxo1jxoyZOnWqEGLYsGGLFi1atGhReXl5dHTb91vFxcVCiMzMzIiICAWFAgBgFaacCNnQ0HD27Nm0tDTnkrvuuksIUVhY2H7lM2fO9OjRg8QAAICXTPlJw7Fjx4QQffv2dS6x2WzBwcGnT59uv3JJScmAAQNmzJjh2GrEiBErVqxo/4FEa/Hx8c7/79u3T7O6jeSbb75RXYJO/GSk3gyzvLxcw0p8jR1qMRYe6YQJE1SXoD1ThoaWlhYhRFDQvxQfFBTU2NjYfuXi4uJLly7NmTNn5syZ1dXV69atmzZt2o4dOyIjI13dvt1u17xmA3KfnKzET0bq6TA7OKlncKYr2DN+Mkxh3ZG2filp/V7U1EwZGrpk6dKl/fr1GzZsmOPHpKSkiRMn5ufnP/PMM2oLAwDAXEwZGoKDg4UQjj+2dGpubu7evXv7lcePH9/6x5iYmEGDBp07d86nFQIAYD2mnAg5YsQIIcSlS5ecS5qamhoaGm666aY2a9bU1Bw5cqS6urrN8oAAUw4cAACFTPnaabPZEhISCgoKnEv27t0rhLjtttvarHnx4sXMzMxt27Y5l1RUVJSVlQ0dOlSfUgEAsAxThgYhxKOPPvrBBx9s3ry5paXls88+W716dVpammM2TWFhYVZW1qlTp4QQMTExqamp+fn5hw4dEkKUlJRkZWVFRERMnz5d8QAAY+ByUgDkmTU03H333U888cR//dd/JSQk3HfffYMHD161apXjV5WVlQcOHLhw4YLjxxdeeCE1NXXevHnx8fGTJ08ODQ3dunWrqwtOAwAAV7pdu3ZNdQ3GEh8f7w9/ctnh1TMtyU9G6ifDFH4zUj8ZpvCbkVrmlcWsnzQAAACdERoAAIAUQgMAAJBCaAAAAFIIDQAAQAqhAQAASCE0AAAAKYQGAAAghdAAAACkEBoAAIAUQgMAAJBCaAAAAFIIDQAAQAqhAQAASCE0AAAAKYQGAAAghdAAAACkEBoAAIAUQgMAAJBCaAAAAFIIDQAAQAqhAQAASCE0AAAAKYQGAAAghdAAAACkEBoAAIAUQgMAAJBCaAAAAFIIDQAAQAqhAQAASCE0AAAAKYQGAAAghdAAAACkEBoAAIAUQgMAAJBCaAAAAFIIDQAAQAqhAQAASCE0AAAAKYQGAAAghdAAAACkEBoAAIAUQgMAAJBCaAAAAFIIDQAAQAqhAQAASCE0AAAAKYQGAAAghdAAAACkEBoAAIAUQgMAAJBCaAAAAFIIDQAAQAqhAQAASCE0AAAAKYQGAAAghdAAAACk+EtouHr16tGjRy9cuKC6EAAAzMovQsMrr7xy8803z5gxY9SoUTNnzqytrVVdkXoTJkxQXYJO/GSkfjJM4Tcj9ZNhCn8aqTVYPzTs2rXrxRdf/H//7/+dPn36nXfeKSkpefbZZ1UXBQCA+Vg/NGzcuHHMmDFTp04NCAgYNmzYokWLDhw4UF5errouAABMxuKhoaGh4ezZs2lpac4ld911lxCisLBQXVEAAJhSkOoCfOvYsWNCiL59+zqX2Gy24ODg06dPu9kqPj7e55UZgJ8MU/jNSP1kmMJvRuonwxT+NFILsHhoaGlpEUIEBf3LMIOCghobG11tYrfbfV4WAAAmZPHTEwAAQCsWDw3BwcFCiObm5tYLm5ubu3fvrqgiAADMyuKhYcSIEUKIS5cuOZc0NTU1NDTcdNNN6ooCAMCULB4abDZbQkJCQUGBc8nevXuFELfddpu6ogAAMCWLhwYhxKOPPvrBBx9s3ry5paXls88+W716dVpaWnR0tOq6AAAwmW7Xrl1TXYPP5ebm5uTkOGY2pKSkrFmzJiwsTHVRAACYjF+EBgAA4D3rn54AAACaIDQAAAAphAYAACCF0PB/rl69evTo0QsXLqguREuWHFSXlJSUWG/4lhxUp5qamo4ePVpaWqq6EC1ZclCSamtrCwsLa2trVReiJUsOqjUmQv7DK6+8sm7dOsdfWNxxxx1//OMfLfAXFvKDSkxMrK+vb70kPz9/1KhRelTpSxUVFVOmTFm1atX48eNV16IZmUFZbIdeuXLlueee2717t+PbZPr3779y5crk5GTVdXmlS4Oy2A5taGhYsmTJzp07HT/eeeedzz//vNmfcrs0KPPuUIt/YZWkXbt2vfjii7///e/vvffeL7/8cu7cuc8++2xOTo7qurwiP6jq6ur6+vqMjIyEhATnwtjYWB2L9YmSkpKZM2fW1dWpLkRLMoOy3g5dtmzZoUOH1q9fP3bs2G+//fa3v/3tnDlz9uzZExUVpbo0z8kPyno79IUXXigoKHj11VdHjx598uTJefPmLVmyZN26darr8or8oMy9Q6/h2rXJkyfPmjXL+eNf/vKXuLi4srIyhSV5T35Q77//flxc3Pnz53WszrcaGxvXrVs3dOjQX/3qV3Fxce+//77qijQgPyiL7dDGxsbBgwdv2LDBueS7776Li4vLy8tTWJWXujQoi+3Q5ubmIUOGrFmzxrkkJydn8ODBV69eVViVl7o0KFPvUOY0iIaGhrNnz6alpTmX3HXXXUKIwsJCdUV5q0uDOnPmTI8ePSIiIvSrz8fq6upef/31hQsX5uXlqa5FM/KDstgOra+vz87Ovvvuu51LevXqJYQw9WnjLg3KYjs0MDCwuLh41qxZziU//vhjQIC5X4y6NChT71BOT4hjx44JIfr27etcYrPZgoODT58+ra4ob3VpUCUlJQMGDJgxY4ZjqxEjRqxYscLUV9ru0aPH4cOHQ0JCampqVNeiGflBWWyHhoWFTZo0qfWSLVu2CCFSUlIUVaSBLg3KYjtUCGGz2Ww2W0tLS11d3a5du7Zs2TJ79myz5wb5QZl6h5p7J2nCMQspKOhf8lNQUFBjY6OiijTQpUEVFxeXlZXdfvvtGzZsWLFixVdffTVt2rSqqiqdavUBm80WEhKiugqNyQ/Keju0teLi4rVr144bN27kyJGqa9GM+0FZdYd+9NFHt9566/Lly/v165eenq66HG3IDMrUO5RPGiCWLl3ar1+/YcOGOX5MSkqaOHFifn7+M888o7YweMbCO/TEiROzZ8+Oi4tbvXq16lo00+mgrLpDhw0b9tlnn5WWli5fvvzee+/dvn37gAEDVBflLZlBmXqH8kmDCA4OFkI4/i7Rqbm5uXv37ooq0kCXBjV+/Hjn4SuEiImJGTRo0Llz53xdJHzEqjt0x44dGRkZw4cP37RpU2hoqOpytCEzKKvu0PDw8ODg4CFDhuTk5NTV1TlO0JidzKBMvUMJDWLEiBFCiEuXLjmXNDU1NTQ03HTTTeqK8pb8oGpqao4cOVJdXd1mudnPL/otq+7Q3NzcxYsXp6en5+XlWSYxyAzKeju0pqamoKCg9afxffr0CQkJOX/+vMKqvCQ/KLPvUHNU6VM2my0hIaGgoMC5ZO/evUKI2267TV1R3pIf1MWLFzMzM7dt2+ZcUlFRUVZWNnToUH1KhbYsuUO3bt26du3ahQsXrlixQnUtmpEclPV26JUrV+bPn79r1y7nksrKysuXL994443qivKW/KDMvkMJDUII8eijj37wwQebN29uaWn57LPPVq9enZaWZpa5rK64GVRhYWFWVtapU6eEEDExMampqfn5+YcOHRJClJSUZGVlRURETJ8+XfEAIM3aO7SysnLlypUDBw6MjIzc0crJkydVl+Y594Oy9g6NiopKS0vLzc3961//KoQoKSmZN29e7969p02bpro0z7kflKV2qOoLRRhFTk7OkCFD4uLi4uLiMjMzv//+e9UVacDVoN5+++24uLgPP/zQ8eP333//1FNPDR482LHmr3/966+++kpd1Vq6ePGiZS7u5NR+UNbeodu3b4/ryO9+9zvVpXnO/aCsvUOvXbt2+fLlp556yjnq9PT0//mf/1FdlLfcDMpKO5Tvnvg/V69e/fzzzwcOHGj2S6C3Jj+opqamU6dODRo0qGfPnvrUBp9ih1qM9XZoQ0PDyZMnY2Nj+/Tpo7oWzcgPyqQ7lNAAAACkMKcBAABIITQAAAAphAYAACCF0AAAAKQQGgAAgBRCAwAAkEJoAAAAUggNAABACqEBAABIITQA1nHixAnn/5988sknn3xSYTEynnzyyeTk5OTk5N/85jduVluyZIljtaysLN1qA9BekOoCAGhj8+bN//mf/2m32x0/Xr16VW09Mi5fvty3b9+ZM2f269fPzWr33HPPyJEjN23adPnyZd1qA9AeoQGwiHPnzrX+cd26daoq6ZKf/vSn99xzj/t1Ro4cOXLkyPfee6+xsVGfqgB0iNAAWMFnn3329ddfCyEKCgr69es3bNiwkydPCiGSkpKc6xw5cuTTTz8VQgwfPnz06NHO5Z9++mn37t0HDhy4d+/eL774IikpadKkSQEBAXa7ff/+/UKIO++8Mz4+vs36UVFRe/bsKS8vHz58+F133RUQ8C/nOg8dOlRUVGSz2drcV6dOnTr18ccf/+///m9iYuLPf/7ziIgIDzsCwAf4lkvACpYsWbJjx46mpqbw8PCJEycuW7bsscceE0Js3LhRCPHDDz/MmTPnxIkTt9xyS1BQUFFRUXJy8ssvvxwaGiqEyMjICAgIOHfuXE1NTUhIyHfffZeSkpKamrp8+fL+/ft/++23zc3N+fn5o0aNctxXRkZGc3PzuXPn+vTpExUV9eGHHw4cOPC1115zfBdwdXX1rFmzTp8+fcsttzQ2Np4+fTopKWnjxo0dfjl76yKFEC+99NL69evj4+N/+tOfFhUVXb16dcOGDcnJyc6VGxsbX3vtNV83E4ArTIQErOD555+///77hRBHjx5dtmxZm98uX778b3/729atW//85z//6U9/euutt/72t7+tXLnSucJHH330q1/96tNPP/3oo48eeuihw4cPb9q06cMPP/zggw8OHjwYEhKybdu21jd4/PjxKVOm7NixIycn5+233/76669XrVrl+NXTTz/91VdfvfXWW3/+85937NixefPmU6dOLVmypNMhXLlyJTc3d968eTt37ty4ceORI0ciIiKcNwvACAgNgMXV1tbu3r37gQceGDlypGNJYmLiL3/5y+3btzsnS4aEhMybN8/xf8fZhIyMjMjISCFEZGTkbbfdVltb2/o2+/btu2jRIsf/hwwZMnXq1N27dzc0NJSWlh49ejQzMzMxMdHx2+Tk5Iceemj//v1VVVXu67x06VJLS4vzjkJDQ3Nzc/Py8rzvAACtMKcBsLiioqKWlha73d767X55eXlzc/OJEyduv/12IcTw4cOdv3LMTujbt69zic1mazMD0Zk/HJKSkt54441jx47V1dUJIYYOHdpm5ddee+3LL790pBBX+vfvn5KSsmXLlnfffXfMP4WHh3swZAA+QmgALK65udnxb01NjXNhr169xo0bd9111zl+DArq2lOBzWZr/aNj85aWlqamJle/dZTh3iuvvPL222/v37///ffff++99wICAhYsWOD8CASAcoQGwOJ+8pOfCCGmTZs2ZcoUrW6zzfUSLl26JITo37+/4wOJH374ofVvHR8/OMpwLyAg4IEHHnjggQeuXr363//93zk5OWvXrk1LS2v9txsAFGJOA2AR3bp1E0K0tLS0WT5q1KiQkJB33nmn9cJnn3129OjR9fX1nt3XkSNHWieDv/zlL5GRkbGxscnJyQEBAXv27Gm98jvvvBMcHOz84wtXCgsLhw8fXlhYKIQIDAwcM2bMo48+KoT49ttvPSsSgOYIDYBFJCQkCCE2bNjguBiDU2Bg4Ny5c48ePfrMM8+Ul5dXV1fn5uZu3749NTU1JCTEs/tqaGiYO3duSUlJVVXVc889V1RUtHDhQiFEz54958yZs3///lWrVlVVVVVUVDz33HMff/zxggULAgMD3d9mYmJiYGDg0qVLjx49Wl9ff/LkyZdffvn66693/sklAOU4PQFYxM9//vOoqKi1a9cWFhZu2bKl9a9mz54dGBj48ssvv/vuu0KIoKCghx9++Nlnn/X4vpKTk3v16jV58mQhRFhY2H/8x384r+r4xBNP2Gy2DRs25OfnCyHCw8OXLl06ffr0Tm8zNDR048aNv/nNb2bMmOFYEh8fn5eXFxwc7HGdALTFxZ0AS6mvrw8ODm5zfUaHlpaW0tLSurq6m2++udP3/W5kZGR0795948aN1dXVVVVVQ4YMaX93LS0tZ86cCQwMjI2N7bAYhzYXd3Korq4uLS2NjY11XC2q9cpc3AlQi08aAEtxc8YhICAgNjZWw/uKiIhwdZnngICAIUOGaH6zANRiTgMAlb744ousrCz3F3HatGlTVlbWF198oVtVADrEJw0AuiYlJcWbsxut3XHHHY6rSDm+BcOV6667Ljw8fOzYsQMHDtTkfgF4hjkNAABACqcnAACAFEIDAACQQmgAAABSCA0AAEAKoQEAAEj5/8K6EFBVaAKmAAAAAElFTkSuQmCC" }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "figure\n", "t=V(:,1);\n", "v2=V(:,3);\n", "plot(t,v2);\n", "ylabel('Voltaje [V]')\n", "xlabel('tiempo [s]')\n", "grid" ] }, { "cell_type": "markdown", "id": "71f9700d-a44d-4ae7-801a-7e2bd6397c36", "metadata": {}, "source": [ "Finalmente, la tercera forma de onda, almacenada en la tercera columna se obtiene extreayendo y graficando los valores correspondientes:" ] }, { "cell_type": "code", "execution_count": 9, "id": "ba8223af-b162-476b-b7dc-6439ee08676c", "metadata": {}, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "figure\n", "t=V(:,1);\n", "v3=V(:,4);\n", "plot(t,v3);\n", "grid\n", "ylabel('Voltaje [V]')\n", "xlabel('tiempo [s]')" ] }, { "cell_type": "markdown", "id": "d5c9d7b7-72e0-4b21-8990-302c821363ed", "metadata": {}, "source": [ "Es posible graficar una subconjunto de datos en específico, extrayendo el subarreglo adecuado de los vectores de datos. Para graficar una porción de las tres formas de onda en una misma gráfica, en un intervalo de tiempo menor que el almacenado en el repositorio se usan las instrucciones siguientes:" ] }, { "cell_type": "code", "execution_count": 10, "id": "e1e046d8-fb2c-4eec-b140-6567bc9c897b", "metadata": {}, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "figure\n", "t=V(1000:10000,1);\n", "v1=V(1000:10000,2);\n", "v2=V(1000:10000,3);\n", "v3=V(1000:10000,4);\n", "plot(t,v1);\n", "hold on\n", "plot(t,v2);\n", "plot(t,v3);\n", "ylabel('Voltaje [V]')\n", "xlabel('tiempo [s]')\n", "grid" ] }, { "cell_type": "markdown", "id": "202cbc0f-a659-4c5f-97c9-858307bb94c8", "metadata": {}, "source": [ "Los datos estadísticos se pueden obtener de la misma manera que se hizo en el caso anterior:" ] }, { "cell_type": "code", "execution_count": 11, "id": "1cbeac32-cf73-41cd-ac7d-5b413e6e9bdb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Valor promedio del voltaje uno\n" ] }, { "data": { "text/html": [ "
ans = 1.6800
" ], "text/plain": [ "ans = 1.6800" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "Valor máximo del voltaje dos\n" ] }, { "data": { "text/html": [ "
ans = 2.7432
" ], "text/plain": [ "ans = 2.7432" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "disp(\"Valor promedio del voltaje uno\")\n", "mean(v1)\n", "disp(\"Valor máximo del voltaje dos\")\n", "max(v2)" ] }, { "cell_type": "markdown", "id": "79bd2e7e-407e-4ae8-8c71-f972b5c19077", "metadata": {}, "source": [ "Una operación común en análisis de señales es la que consiste en centrar al rededor de cero las gráficas de los voltajes como función del tiempo. Este desplazamiento de las señales se logra restando el valor promedio de cada forma de onda, por ejemplo, para la forma de onda del voltaje dos se usan las siguientes instrucciones:" ] }, { "cell_type": "code", "execution_count": 12, "id": "fa3bbf28-d689-453a-ab0a-c095f10e66db", "metadata": {}, "outputs": [ { "data": { "image/png": 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QBwAhRIDeTzBu3LjvvvtOfvrhw4evWbNGvzyQodQZ017z8335FYuxUIdSY+HLlBoIX9sodD/SUFVV9SNp586dO3funN6RIEORzUCRGOZiIajA+UKlyFhwLaQ6A2F2BKPpfqRBCPHSSy8NGzZMZsr09HS9wwDwDi9UPvgKAbShe2mIj4/v1sR9+/bVLwwAAPCa7qWhvr7+iiuukJx49erVuoaBRfnaWUMAUJPu1zSUl5ePHz8+OTl56dKlNTU1ej8des7HD0ErRcGxUDASAMPoXhoKCwv/8Ic/9O3bNy8v77rrrktLS/vb3/6m95PCTnz8GIOPz74i1KxKaqbSm2pz7WvX+hhxn4ZRo0bl5+d/9tlns2bN+vbbbx9++OHExMSFCxdWVVUZ8OzwAi9U6JAvrxiqzbtqeYzky/NuOuNu7hQaGjp79uzCwsL8/PwbbrjhnXfeueWWW0aPHr1z507DMgAAAK+ZcEfIyMjIjRs3lpaWvvbaa8HBwY899lhCQoLxMQAAQLeYeRvpvn37Xn311YGBgSZmAJQ1aFW52RE65lNncKEU1j3TmVAaKisr586dO2zYsJ/+9KdHjhyZPn36gQMHjI+BDqm5TfrapUZQisrrnsrZfIdzB+UjY2HEHSGdampqNm3atGXLlurqaiHE0KFDX3rppYEDBxoWAJK4yEgdCo6FbxY4BQdCMBYq8Z2x0L00NDY2fvDBB8uWLTtz5owQom/fvkuWLLn33nsdDofeTw0AADSke2lITU2tra0VQtx2221PP/10RESE3s8IAAD0oHtpGDBgwIIFC8aMGaP3EwGA5tQ/5sxN1mEk3S+E9Pf3DwsLk5w4IyNjwYIFuuaBByrvH33nlKGT4jOreDzYkvprnfoJe0730vDNN990a+JTp07pFwZd4i2LCpzvHU/MijI7SMdYSdTha2Oh8vyqnE1DRvz1xKxZsySnrKysvOqqq3QNA+tyHmzwkS0T6mCVA1x0Lw2RkZHOCyElJ46MjNQ1DzrjCwfWAAA9oXtpeOedd/R+CgDG6zU/X9kTKAB0YuZtpAEv2P6IiO1n0EKsMhZWydkTVplHq+T0GqUB/6L+uVv1E2pC/dlUP6FW1J9T9RP6Dl8YC0oDAMACfOElWX2UBgAAIIXSAAAApJhQGlpaWiorKysqKoQQ33//vfEB0J61Lt6xVlobG7Sq3OwIOrLWamattN1lrbmzVtruMro0PPbYYwkJCaNHj3744YeFEKNGjeK+0YqwyvlCq+T0joXuXmWVnL7AF8bCKvNolZxeM+KOkC533nlnaWlpenr6uXPnzp8/L4QYMWLE9u3bAwICli5damQSAOiS7V8AgO4y7kjD119/XVpa+u67765Zs8b1YG5u7pw5c956662WlhbDkgAAAC8YVxry8vIiIiJiYmLaPD516lQhhPPAAwAAUJZxpcHf37+1tbX9483NzUKIgABDT5TAnRUv27Fi5i5ZcaasmFmGFefLipll2HW+LMq40vDwww+fPXu2qKiozeNLlixxOBxhYWGGJYE75wbJuVt4wfnZE+zTVWDvTdhyc2fjjcK40nDNNdckJiY++OCDOTk5zkf27t2blpb23nvvzZ4927AYaM9yG6TlAtuYjcfCxrMGXdl7zTH0pMCbb745c+bM3bt3O7/NysoSQvzyl7/MzMzU+6mbm5s/++yzmJiYq666Su/nArxm790NAKsz+kqCtWvX1tfXnz17tqGh4Uc/+lFkZKQBT7p+/fqVK1c2NTUJIW6++ebf//73oaGhBjwvAAB2YsIdIQMDAwcOHBgdHW1MY9i+ffuLL77461//+siRI2+88UZZWdmiRYsMeF4AFmXjE9KWY92xsG5yz3Q/0nDnnXc2NDTs2LHj+PHj06dP9zBl7969J0yYoPn1DRs2bBgzZsykSZOEEElJSQsXLly4cGF5eXlUVJS2T2RFll6tLXTzRBmMBXqOgYDedD/S0NDQUF9f7/y63qOqqqpVq1b95je/0fDZ6+vrjx07lp6e7nrk9ttvF0IUFBRo+CyWZtFdjEVjwyqsuIJZMbNnzg5kxfmyYmZJuh9p2LFjh/OL6Ojoffv2eZ74jjvuyM/Pf+qpp7R69gMHDggh+vXr53rE4XAEBgYeOXJEq6cAtGLRHU3r8jRLHyYBIE+JWyq1tLT4+fkJIeLj47W9NaTz7tRt7hwVEBDQ0NDg4afi4uJcX7tKjzpOnz6t4W8rL9f+gwq1TeiB1+ENS9gtbWZHzZDu3BPqsSL1nNfL0MjZYYv2QHJ21NxY3F9KbMPQ0nDmzJlHH3300qVLrkdaW1sbGhouXLjgfOv/wgsvGJmnM6WlpWZH6IJ2F2TodW2HIZeM9Ci8ehe1dDA76oVs658J1b1IyKtgRs8OW3QnujE76q2B5e4vJbYpEMb99URjY+Ott9567Nixurq6ioqK77///ttvvz1z5sy5c+cmTJig05MGBgYKIZx/bOnS1NTUu3dvnZ7RQmxwSNkGs+BkgxmxwSw4WX1GrJ7fxQYzYoNZaM+40uDsXHv27Pn000///d///a677iosLDx48GBgYODIkSN1elLnb7548aLrkcbGxvr6+qFDh+r0jNZi0ZPoTpYO356lZ8fS4duz2ezAFHZdi4wrDS+++GK/fv0iIiKEENHR0bt27RJC9OnT54033nj22Wd1elKHw5GQkOB8Lqf3339fCHH99dfr9IwAYBabvVDZbHbswbjSEBkZ6XA4nF8/9thjrnf/11xzTX19fXV1tU7P+8gjj3z44YebNm1qaWk5fPjwCy+8kJ6ert7ZLwAAVGfop1xWVFQ4/5whLCzs0qVLdXV1QogffvhBtLvsQEN33HHHnDlznn/++YSEhPvuuy8+Pn7p0qU6PRcAADZmXGmIiYkJCAhITk7esmVLZGSkn5/f/fffX1ZWNmXKFCFEeHi4fk+dnZ395ZdflpaWlpaW5ubm8sETwkZX6NhgRmwwC042mBEbzIIQotf8fKvPiNXzu9hmRlwM/eyJjz/+uE+fPs6LEJ999tljx45NnDjx2LFjCxcuNDIGbMM2pzxtMCM2mAUnG8yIDWbBHmw5EIbepyE8PNx1U8i77747PT397NmzV155JR9XbQpbrtAAAP2Y8CmXLn369ImOjqYxoIfsdwAQANRkXGk4fvz4Qw891P7xkpKS0aNHOy+QBAAAytK9NDT/U0tLy9GjR5sv19jY+MUXX1RWVro+CRPwKTY7TGLp2bF0eDux2UDYbHZ0v6ahtLT0nnvucX07ZMiQ9tOEhIQEBQXpnQQudlqJbfARi7a5uISxUIrzc6XNTuHrbLBRtKF7aRgyZMjPf/7zo0ePCiE+++yz6667zv1/k5OT09PTk5KS9I6BNtibALbk3LSt/lrFDkpZRvz1xFNPPSWEOH78+JNPPrl582YDnhEAAGjOuAsho6Ojt27datjTAYA8DuYDMnQ/0nD33Xd/9913nqfx8/Nz/0wp6MrSBy07wx4fsAe77qBizc6gFd2PNPwgR+8YgIJs2XWsuNO3YmYZdp0va7HZNq77kYYdO3bo/RToLputxFCHda+/s99GwVhADybcEfL777//+uuvy8rKKioqjH922A+7GNVY9LUKQJcM/ewJIcSUKVMKCwvdH8nOzp4zZ47BMQAAQHcZWhrGjh176tSphx566KGHHvL396+trZ07d+6aNWv69OmTmZlpZBLAdLwdB3zHsYlrzY6gDeNOT3z99denTp167bXXnn766ejo6GuuuSYpKWnXrl3333//8uXLDYvh4+z6QmXR07ecWFGBFdccedaaO2ullWenLd240vDHP/4xIiJixIgRbR6fN29eU1PT+fPnDUvi4+y0+kJBVlzBrJhZhhXny4qZfYqZH43tzs9PlSQAAGgr9t2ZZkfQhnEv1VOnTq2srCwtLW3z+OrVq/38/MLCwgxLAgAAvGDchZBRUVERERF33nnn9OnTH3zwwV69etXV1f3yl7/84osvZs2aZVgMX2bX84UAAGMYelJgz549sbGxubm5aWlpt9566/jx452NYfbs2UbGgC1Z61pIC0X1jlVm0Co5e8Iq82iVnD7O6Ps0bN++vbq6+sKFC/X19UFBQf/xH//h7+9vcAZfZu+LjJy9wSrzaJWcXrBWgbPxQAjGAlrT/UjDpEmTjh8/7v5IWFhYdHT0kCFDoqKiaAwAAFiF7qWhvLx8/PjxycnJS5curamp0fvpAACATnQvDYWFhX/4wx/69u2bl5d33XXXpaWl/e1vf9P7SQGVWegcCgC4M+JCyFGjRuXn53/22WezZs369ttvH3744cTExIULF1ZVVRnw7HCy0HlN2IP6q5z6CbWi/pyqnxBOxv31RGho6OzZswsLC/Pz82+44YZ33nnnlltuGT169M6dOw3LANtT/7IvHznM4AvzaBVWGQur5PRxJtyHMTIycuPGjaWlpa+99lpwcPBjjz2WkJBgfAwfxDYJAOgJM2/e3Ldv36uvvjowMNDEDAAAQJLR92kQQlRWVi5duvTDDz+sr68PCQmZPn16RkaG8TEAAEC3GFcaampqNm3atGXLlurqaiHE0KFDX3rppYEDBxoWwJcpfppfcz5y3YD6VB4INgp1+NpYWJrupaGxsfGDDz5YtmzZmTNnhBB9+/ZdsmTJvffe63A49H5quFN2f6E5la+FVHnHrTmVBwKq8Z3twup0Lw2pqam1tbVCiNtuu+3pp5+OiIjQ+xkBQIbv1BrfmVPoTffSMGDAgAULFowZM0bvJwLU5FNHFwDYm+6lYfv27Xo/BQCgS/RX9JyZf3IJwPbUPCquZirfxFhYC6XB/nzz7YWCeyIFI+nNB1c8eIH1xEIoDTbnm41B2VlWNph+lL0EzzkWvjMizjlVcyxgIZQGAAAghdIAAACkUBoAAIAUSgNsS6nTt0qFMZ5Ss69UGCMpeAGHb150ZWmUBjvz2Z2jUHL/qGAkn8VYAN6hNNgcO0eYjpUQsA1KAwAAkEJpgJ0pcoJGkRiAYG1Ez1AabItdA5SiyAqpSAz0mp/PVZBWRGmwMx/fIJWafaXCGE+p2VcqjMGUmnelwkASpQEAAEihNAAAACmUBgDwLVzYAa9RGuyJnYILi0Idpo+F6QHgxEBYF6UB0B0XfAllPpqZsWAJoCcoDbbFrkGwEABVsW1aFKUB0JHpb6yB9lqXp7FmwjuUBgAAIIXSYEO8h2iDBQLBagBowfKlobm5ef/+/efPnzc7iFo4X+hi+qIwPYA6TF8UpgdQh4lnKLh7tKUFmB2gR9avX79y5cqmpiYhxM033/z73/8+NDS0/WTJycl1dXXuj+Tl5d10000GpQQAwBYsXBq2b9/+4osv/va3v7333nu/+uqrmTNnLlq0aPXq1W0mq6qqqqury8jISEhIcD0YExNjbFgAACzPwqVhw4YNY8aMmTRpkhAiKSlp4cKFCxcuLC8vj4qKcp+suLhYCJGVlRUeHm5OUAAAbMGq1zTU19cfO3YsPT3d9cjtt98uhCgoKGgz5dGjR0NCQmgMPs6U07dcedchFos6GAt0l1VLw4EDB4QQ/fr1cz3icDgCAwOPHDnSZsqysrKBAwdOmzYtISEhISFh6tSp5eXlhmY1FnuB9ky86ooLvlTQa34+F98pgh2U1Vn19ERLS4sQIiDgsvwBAQENDQ1tpiwuLr548eKMGTMyMzOrqqpWrlw5ZcqUbdu2RUREdPbL4+LiXF/v2LFD0+AaOH36tOcJTsyKMrcYdZnQFO7LxLCEPRkINRejO68TGrZ+uhKavlF4YNZAn5gVNWhVucxi0TahHmOh5sYybtw4syNozxqlobGxsba21vVtWFiY/M8uXry4f//+SUlJzm9TUlLGjx+fl5f3xBNPdPYjpaWlXkc1RpvrNi7X9qoOU6iQ4XJtF4shCXs6Fuotxra6m7B1eVSv+flGzldUVJQiG4UH5sWTXTLaJdRrLBQcYveXEvf3opZmjdLw9ttvP/nkk65vDx06FBgYKIRaDpT0AAAY5UlEQVRw/rGlS1NTU+/evdv87NixY92/jY6OHjx48MmTJ3ULCwCAPVmjNNx4442rVq1yfRsYGDhy5EghxMWLF10PNjY21tfXDx061P0Hq6urS0pK4uPj21wI6edn1Ys5PON8oToYCw+4wkAdjAW6xRqvnZGRkWPd+Pv7OxyOhISEXbt2uaZ5//33hRDXX3+9+w9euHAhKytr69atrkcqKipOnDiRmJhoWHiowJT737EvVgHtTR2MhQ1YozR06JFHHvnwww83bdrU0tJy+PDhF154IT093Xlaq6CgICcnp6SkJDo6Oi0tLS8v76OPPhJClJWV5eTkhIeHT5061ez4euGFCoozfhVlo+gMY4HusnBpuOOOO+bMmfP8888nJCTcd9998fHxS5cudf7XmTNndu/e7fxAiueeey4tLS07OzsuLm7ixInBwcFbtmzp8G7TAADAA2tc09CZ7Ozs7Ozs9o9PmjTJeadIIURoaOiyZcuWLVtmbDQAsAYua4A8Cx9pAABYBRc02AOlwT7YJrtk2CJiLLpkzCIatErRGzoBFkVpsBWOMXpg8MJhLGAJRq6obBQ2QGkANMYZ4i7xQgVYFKUBPsSUuzUAgG1QGgAAgBRKg03wBloey0oReg8EAy2PsYAkSoN9cO5Wht5LiZ2jJFZXX8OI2wOlAdAYO0dYC2ss5FEaANjWiVlRZkcAbIXSAAAApFAaAJhGv0tAuLikuxgLyKA02AHbZHfpdHdhbuvULXovK8ZCHmMBSZQG+Bz2XwDgHUqDTfBCCADQG6UBAABIoTRYHhc0ANAEOxN0idJgB5yb8ILm+0d2uF7Q6SPEGAt1cHWwzVAaAM2wc1QHY9FdLDHIoDRYG++ovMONAgHAC5QGy+P9AQA18a7GfigNAMyk+WUNvFD1BEsPnlEaLIzNu4c0XICMRQ+xAFWgx2FLDoXaDKXB2tggvab5omMsvMZYAFZBaQAAAFIoDQAAQAqlAQDwL1xfAg8oDVbFhq0JTRYjY6EJxsJOes3PZyxsidIAaIAr73pIwwXIWPSEtgPBWNgPpQG+iz0aAHQLpQEAcBnOLKAzlAYL440y4I6XOkBvlAb4uh6+0vBCBbTBRmFjlAZLYpvUiiZHazjko5Wer9iMRc85L2Ds4VgwEHZFabAqtknYDKs0oD5KAwAAkEJpAAAAUigN1jNoVbnZEezG69O3XFyiDsYCMAClAb6uh6fSOROvrZ689jMWGvL6Wkjam71RGgCogld9e2AcbYzSYElskwAA41EaAACAFEoDIIRXJ2I5dwvA11AaAO9xnkgP3W1jvebn95qfz1jowYux0CkJFEFpAHjtVwhjAaiM0mA9J2ZFmR0BUAtVQw/eLVXGwt4oDQAAQAqlAQAASKE0AP/QrWu4uOBLV/KLl4HQG2MBd5QGQAivTsRy7lYn3V2wDIR+5JetszEwFrZHaQAAAFIoDQAAQAqlAfgXTsoCgAeUBqDbuP+gAWQKHCXPGINWlZsdAaqgNAD/QA9QB2OhDvmxYNR8AaUBgIXxQgUYidIAAACkUBqAy3CaHAA643Oloays7Pz582angNI89waugjRMlwNhWBIwFnDyrdJQUVExefLkoqIis4NAUV22ARqDYWSWM2OhDsbCR/hQaSgrK5s8eXJtba3ZQaA09n1AGydmRZkdAarwidLQ2Nj48ssv33PPPREREWZnAQDAqnyiNNTW1r766qvz5s3Lzc01OwsAAFYVYHYAI4SEhOzZsycoKKi6ulpm+ri4ONfXO3bs0C2Xl06fPm12hC7YIGF5eae3wPPwX9qywWLsuV7z8zs8Nu68R2GXY6H+MhRWCOlM2MOx0JWay3DcuHFmR9CeT5QGh8PhcDjkpy8tLdUvjCaiolQ/xWjxhOWDVpW3v7jBeYm4kbNm8cXYU63Lo3rNz+/kKToYoA6pvwyFNUKWi05zevgv45geoD33lxL396KWZrfTE42NjdVuzI4DS+JaSKANzxsFm4zvsNuRhrfffvvJJ590fXvo0KHg4GAT88Bm2DkC8GV2Kw033njjqlWrXN8GBgaaGAYAADuxW2mIjIyMjIw0OwXsgPs4KYKBUEf7seBekL7Gbtc0ADph52iKDusCNQIwC6UBkMULFXxZZ+s/24VPsdvpCc/CwsLU/3NKAADUxJEGoAO8eQKA9igNAFTHBSWAIigNQKdcr1W8aJmOsVCE+/JnLHwQpQGQwgkLs7QuT2PhK6L9QDA0vobSAHSMvaGyGBrALJQGAAAghdIAAACk+NZ9GoDu4lIvdTAWinDekZPh8E0caQA65Tp3zkl00zEEinAfCAbFB1EaAFgJL1SAiSgNAIDu4TPDfBalAQAASKE0AAAAKfz1BNAFDsOqg7FQAaPgyzjSAHjC/lEdjAVgOkoDAACQQmkAAABSKA0AAEAKpQEAAEihNAAAACmUBgAAIIXSAAAApFAaAACAFEoDAACQQmkAAABSKA0AAEAKpQEAAEihNAAAACmUBgAAIIXSAAAApFAaAACAFEoDAACQQmkAAABSKA0AAEAKpQEAAEihNAAAACmUBgAAIIXSAAAApFAaAACAFEoDAACQQmkAAABSKA0AAEAKpQEAAEihNAAAACmUBgAAIIXSAAAApFAaAACAFEoDAACQQmkAAABSKA0AAEAKpQEAAEihNAAAACmUBgAAIIXSAAAApFAaAACAFEoDAACQQmkAAABSbFIaysrKzp8/b3YKAADszA6loaKiYvLkyUVFRZ1NkJycHHe5goICIxNqa9y4cWZH6AIJNaF+SBJqQv2QJIRLgNkBeqqsrCwzM7O2trazCaqqqurq6jIyMhISElwPxsTEGJIOAAD7sHBpaGxsXLdu3fr16+Pj48+ePdvZZMXFxUKIrKys8PBwA9MBAGA3Fj49UVtb++qrr86bNy83N9fDZEePHg0JCaExAADQQ71aW1vNzuClxsbGpqamoKCg6urq1NTUVatWjR07tv1kjz322KlTp/7t3/7twIEDQoiRI0c+88wzUVFRnf3auLg4HUMDAHxSaWmp2RE0YOHTEw6Hw+FwdDlZcXHxxYsXZ8yYkZmZWVVVtXLlyilTpmzbti0iIqLD6e0xrgAAaM4apaGxsdH9UsewsDD5n128eHH//v2TkpKc36akpIwfPz4vL++JJ57QOCUAALZmjdLw9ttvP/nkk65vDx06FBwcLPmzbc5ZREdHDx48+OTJk1rmAwDAB1ijNNx4442rVq1yfRsYGCj5g9XV1SUlJfHx8W0uhPTzs/AVoAAAmMIapSEyMjIyMtKLH7xw4UJWVtasWbNmz57tfKSiouLEiRMTJkzQNCAAAPZnzzfcBQUFOTk5JSUl0dHRaWlpeXl5H330kRCirKwsJycnPDx86tSpZmcEAMBi7Fkazpw5s3v3buenUTz33HNpaWnZ2dlxcXETJ04MDg7esmVLaGio2RkBALAYC9+nAQAAGMmeRxoAAIDmKA0AAEAKpQEAAEihNPxLc3Pz/v37nZdPqhzA9JxeJykrKzMgdrdSGRPJA/m0jY2N+/fvP378uDqpjIzkmfxirKmpKSgoqKmpUSeVkZE86O7mXFNTU1RUpGskp24FMyxVZ9TZP+ulFa2tra2t69atGzJkSGxsbGxs7H/+539+++23agbwPNnw4cNjL7dv3z5zA7ucPn16xIgRH3zwgU55vEjVWSTDFqNk2kuXLi1YsCA+Pt455U9+8pNPP/1UjzzyqbqMpOCq+MMPPyxYsMCVZ/bs2bpu5jKpuoyk2qrobubMmYmJiXqE6UmwDlMpuBiN3EC0RWlobW1tfeedd2JjY19//fXm5uYvvvjilltuyc7OVjCA58nOnj0bGxv7u9/97i03Z8+eNTGwy7Fjx2655ZbY2FhdS0O3UnUWybDFKJ92/vz5I0aM2L17d3Nzc0VFxbRp04YPH3769GnNI8mn8hxJzVXx17/+9fDhw/fs2dPa2lpUVJSamjp79mw9Ismn8hxJwVXR5fXXX4+NjdW7NHQ3WIepFFyMRm4gmqM0tLa2tk6cOHH69Omub99+++3Y2NgTJ06oFsDzZB988EFsbKwxa578EmtoaFi5cmViYuLPfvYzvUuDZCrPkQxbjPJp4+Pj161b53rk3LlzsbGxubm5ZqXqMpKCq2JTU9OQIUOWL1/uemT16tXx8fHNzc1mpeoykmqrokt5eXlKSkpWVpbepaFbwTpLpeBiNHID0RzXNIj6+vpjx46lp6e7Hrn99tuFEAUFBUoF6HKyo0ePhoSEtPmUDRMDO9XW1r766qvz5s3Lzc1VJJXnSMYsRvm0dXV1y5Ytu+OOO1yPXHnllUIIPU6BS6bqMpKCq6K/v39xcfH06dNdj/zwww86fQaNZKouI6m2Kjq1tLQ8/vjjEyZM+MlPfqJOMA+pFFyMhm0geqA0iAMHDggh+vXr53rE4XAEBgYeOXJEqQBdTlZWVjZw4MBp06YlJCQkJCRMnTq1vLzcxMBOISEhe/bsyczM1COJd6k8RzJmMcqnDQ0NnTBhwoABA1yPbN68WQgxevRos1J1GUnNVdHhcPTp06elpaWmpmbLli2bN29+9NFH9egN8qk8R1JtVXRat25ddXX1okWLNE/Sk2AeUim4GA3bQPRAaRAtLS1CiICAyz67KyAgoKGhQakAXU5WXFx84sSJG264Yd26dc8888ypU6emTJlSWVlpVmAnh8MRFBSkeYaepPIcyZjF6PVaV1xcvGLFittuu+3aa6/VNpLXqdpHUnNVdNq3b9911123ZMmS/v3733PPPZpH8iJVZ5EUXBUPHz68YsWK559/Pjg4WNsYPQnmOZWCi9GwDUQP1viUS8hYvHhx//79k5KSnN+mpKSMHz8+Ly/viSeeMDeYtai8GA8ePPjoo4/Gxsa+8MILZmf5hw4jqbwMk5KSDh8+fPz48SVLltx7771vvvnmwIED1Yyk2mKsq6ubO3dudnZ2SkqKKQE61GUq1RajmpHkcaRBBAYGCiGamprcH2xqaurdu7dSAbqcbOzYsa61UAgRHR09ePDgkydPmhXYYFqlMmYxepF227ZtGRkZI0aM2Lhxo07v87qbqrNIKq+KYWFhgYGBQ4YMWb16dW1trfPEirmpOouk2qq4fPnyS5cuDRs27JNPPvnkk0+OHz/e2tr6ySeflJaWahupW8G6TKXaYjQskk440iBGjhwphLh48aLrkcbGxvr6+qFDhyoVwPNk1dXVJSUl8fHxbS6u0eOUrelLrEOapDJsMXY37Zo1a1asWHH//fc/88wz2ibxOlVnkdRcFaurqw8ePDhs2LCIiAjnI1dddVVQUNDZs2fNSuU5koKr4qlTp6qrq2fMmOH+4C9+8Yu77rrr+eef1zaVfDDPqRRcjEZuIHqwRkpdORyOhISEXbt2uR55//33hRDXX3+9UgE8T3bhwoWsrKytW7e6/reiouLEiROJiYlmBTaYJqkMW4zdSrtly5YVK1bMmzdP18bQrVQeIqm5Kn7//fezZs3avn2765EzZ85cunTpmmuuMSuV50gKropr164tcfNf//VfDoejpKTk2Wef1TZSt4J5TqXgYjRyA9GF2X/zqQTnTTleeeWV5ubmzz///JZbbpk5c6YiAfbt25ednf3FF190mXPGjBkpKSn5+fmtra3Hjh276667Ro0apdM97yQDu7tw4YIxN3eST9VhJMMWo2TaioqKxMTEcePGvXW5oqIizSNJpuoykpqr4syZM1NSUj7++GNXqtTUVF1vOdVlKs+RVFsV29i8ebMxN3fqVrD2qRRcjEZuIJqjNPzD6tWrXbf/zMrKMn78OgvgvMeZc7fiOee33347f/581519H3zwwVOnTpke2MWA0tDdVB1GMnIxyqR98803Yzvy5JNPmpWqy0hqroqXLl2aP3++K+0999zzv//7v+am8hxJtVWxDQNKgxfB2qdScDEavIFoq1dra6vZBztU4bz356BBg0JDQ1UO4HmyxsbGkpKSwYMH9+nTR7ekUknMokkqwxYjy1AT8oHr6+sPHToUExNz1VVXKZLKcyQfXxWF1dZG+bRGbiAaojQAAAApXAgJAACkUBoAAIAUSgMAAJBCaQAAAFIoDQAAQAqlAQAASKE0AAAAKZQGAAAghdIAAACkUBoA+zh48KDr67lz586dO9fEMDLmzp2bmpqampr6+OOPe5jsqaeeck6Wk5NjWDYA7QWYHQCANjZt2vS73/2utLTU+W1zc7O5eWRcunSpX79+mZmZ/fv39zDZ3Xfffe21127cuPHSpUuGZQPQHqUBsImTJ0+6f7ty5UqzknTLj3/847vvvtvzNNdee+2111773nvvNTQ0GJMKQIcoDYAdHD58+JtvvhFC7Nq1q3///klJSYcOHRJCpKSkuKbZu3dvUVGREGLEiBGjRo1yPV5UVNS7d+9Bgwa9//77X375ZUpKyoQJE/z8/EpLS3fu3CmE+OlPfxoXF9dm+sjIyL/+9a/l5eUjRoy4/fbb/fwuO9f50UcfFRYWOhyONs/VpZKSkk8//fT//u//kpOTb7zxxvDwcC+XCAAd8CmXgB089dRT27Zta2xsDAsLGz9+/NNPP/2LX/xCCLFhwwYhxHfffTdjxoyDBw8OHz48ICCgsLAwNTV17dq1wcHBQoiMjAw/P7+TJ09WV1cHBQWdO3du9OjRaWlpS5YsGTBgwN///vempqa8vLybbrrJ+VwZGRlNTU0nT5686qqrIiMjP/7440GDBr3yyivOT3auqqqaPn36kSNHhg8f3tDQcOTIkZSUlA0bNnT4ScHuIYUQL7/88qpVq+Li4n784x8XFhY2NzevW7cuNTXVNXFDQ8Mrr7yi98IE0BkuhATs4De/+c39998vhNi/f//TTz/d5n+XLFny+eefb9my5S9/+cuf/vSnP//5z59//vmzzz7rmmDfvn0/+9nPioqK9u3b99BDD+3Zs2fjxo0ff/zxhx9+mJ+fHxQUtHXrVvdf+Nlnn915553btm1bvXr166+//s033yxdutT5XwsWLDh16tSf//znv/zlL9u2bdu0aVNJSclTTz3V5Sx8//33a9asyc7OfueddzZs2LB3797w8HDXrwWgAkoDYHM1NTXvvvvuAw88cO211zofSU5Ovuuuu958803XxZJBQUHZ2dnOr51nEzIyMiIiIoQQERER119/fU1Njfvv7Nev38KFC51fDxkyZNKkSe+++259ff3x48f379+flZWVnJzs/N/U1NSHHnpo586dlZWVnnNevHixpaXF9UTBwcFr1qzJzc3t+RIAoBWuaQBsrrCwsKWlpbS01P3tfnl5eVNT08GDB2+44QYhxIgRI1z/5bw6oV+/fq5HHA5HmysQXf3DKSUl5Y9//OOBAwdqa2uFEImJiW0mfuWVV7766itnC+nMgAEDRo8evXnz5rfeemvMP4WFhXkxywB0QmkAbK6pqcn5b3V1tevBK6+88rbbbrviiiuc3wYEdG9X4HA43L91/nhLS0tjY2Nn/+uM4dn69etff/31nTt3fvDBB++9956fn9/s2bNdh0AAmI7SANjcj370IyHElClT7rzzTq1+Z5v7JVy8eFEIMWDAAOcBie+++879f52HH5wxPPPz83vggQceeOCB5ubm//mf/1m9evWKFSvS09Pd/3YDgIm4pgGwiV69egkhWlpa2jx+0003BQUFvfHGG+4PLlq0aNSoUXV1dd491969e92bwdtvvx0RERETE5Oamurn5/fXv/7VfeI33ngjMDDQ9ccXnSkoKBgxYkRBQYEQwt/ff8yYMY888ogQ4u9//7t3IQFojtIA2ERCQoIQYt26dc6bMbj4+/vPnDlz//79TzzxRHl5eVVV1Zo1a9588820tLSgoCDvnqu+vn7mzJllZWWVlZW/+tWvCgsL582bJ4To06fPjBkzdu7cuXTp0srKyoqKil/96leffvrp7Nmz/f39Pf/O5ORkf3//xYsX79+/v66u7tChQ2vXrr366qtdf3IJwHScngBs4sYbb4yMjFyxYkVBQcHmzZvd/+vRRx/19/dfu3btW2+9JYQICAj4+c9/vmjRIq+fKzU19corr5w4caIQIjQ09L//+79dd3WcM2eOw+FYt25dXl6eECIsLGzx4sVTp07t8ncGBwdv2LDh8ccfnzZtmvORuLi43NzcwMBAr3MC0BY3dwJspa6uLjAwsM39GZ1aWlqOHz9eW1s7bNiwLt/3e5CRkdG7d+8NGzZUVVVVVlYOGTKk/dO1tLQcPXrU398/JiamwzBObW7u5FRVVXX8+PGYmBjn3aLcJ+bmToC5ONIA2IqHMw5+fn4xMTEaPld4eHhnt3n28/MbMmSI5r8WgLm4pgGAmb788sucnBzPN3HauHFjTk7Ol19+aVgqAB3iSAOA7hk9enRPzm64u/nmm513kXJ+CkZnrrjiirCwsFtvvXXQoEGaPC8A73BNAwAAkMLpCQAAIIXSAAAApFAaAACAFEoDAACQQmkAAABS/h8hbKd0EkfAMAAAAABJRU5ErkJggg==" }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "figure\n", "v2=V(1000:10000,3)-mean(V(1000:10000,3));\n", "plot(t,v2);\n", "ylabel('Voltaje [V]')\n", "xlabel('tiempo [s]')\n", "grid" ] }, { "cell_type": "markdown", "id": "3373b908-9bc6-42c7-a59a-d201208fa00a", "metadata": {}, "source": [ "Se observa que la señal está centrada al rededor de cero Volts en vez de 1.68 Volts. \n", "\n", "En el caso de que sea conveniente guardar el archivo de datos en la computadora en la cual se está ejecutando MATLAB®, es posible guardar el archivo de interés usando el comando o instrucción `websave()`, colocando entre paréntesis el nombre asignado al archivo que se almacenará en el disco duro local, seguido de la dirección en internet del archivo del cual se desea obtener la información. El proceso para guardar los datos de las formas de onda del ejemplo anterior se consigue usando los siguientes comandos de MATLAB®:" ] }, { "cell_type": "code", "execution_count": null, "id": "fcc961e5-3a11-49a5-91c8-cc600e0144ef", "metadata": {}, "outputs": [], "source": [ "clear\n", "close all\n", "nombre_archivo = 'Datos_web.txt';\n", "websave(nombre_archivo,'https://raw.githubusercontent.com/LuisGerardo2204/Archivos_de_datos/main/ADC.txt');" ] }, { "cell_type": "markdown", "id": "d0c47359-96ed-46b9-9614-9cec64123330", "metadata": {}, "source": [ "La información contenida en el archivo que está disponible en el repositorio de GitHub se almacena en la carpeta de trabajo con el nombre 'Datos_web.txt', como se muestra en el video de abajo:\n", "\n", "
\n", "\n", "
\n", "\n", "Los datos almacenados en el archivo creado se pueden importar, procesar y/o graficar de la misma manera que se hace en el caso de los datos importados desde los archivos almacenados en el disco duro:" ] }, { "cell_type": "code", "execution_count": 14, "id": "d0986d53-e56d-424c-a40f-eb0d299b6148", "metadata": {}, "outputs": [ { "data": { "image/png": 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" }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "figure\n", "V2=load('Datos_web.txt');\n", "t=V2(500:4000,1);\n", "v1=V2(500:4000,2);\n", "v2=V2(500:4000,3);\n", "v3=V2(500:4000,4);\n", "plot(t,v1);\n", "hold on\n", "plot(t,v2);\n", "plot(t,v3);\n", "ylabel('Voltaje [V]')\n", "xlabel('tiempo [s]')\n", "grid" ] }, { "cell_type": "markdown", "id": "7f299884-e9c9-45a1-a493-8549381742e8", "metadata": {}, "source": [ "## Creación de archivos a partir de matrices o arreglos de datos\n", "\n", "Es posible hacer el proceso inverso al que se ha ilustrado con anterioridad, es decir, en vez de importar los datos desde un archivo, se puede crear un archivo de datos usando MATLAB®. El comando que se utiliza para tal fin es `fopen()`, con el nombre del archivo entre comillas, seguido de la palabra `wt` para indicar que será un archivo reescribible. Para ilustrar lo anterior usaremos la tabla de conversiones de centímetros a pulgadas y yardas de la práctica anterior:" ] }, { "cell_type": "code", "execution_count": 15, "id": "936de83b-3339-4c18-b721-6287a074b168", "metadata": {}, "outputs": [], "source": [ "clear\n", "clc\n", "cm=[1:1.5:33];\n", "% Se crea el vector con las equivalencias o factores de conversión\n", "% de centímetros a pulgadas pies y yardas.\n", "\n", "conversion= [0.393701 0.0328084 0.0109361];\n", "\n", "% Se crean los vectores que almacenan la conversión\n", "[factor_conversion,longitud]=meshgrid(conversion,cm);\n", "\n", "% En la matriz llamada equivalencias se almacenan las \n", "% conversiones, una vez que se aplica la multiplicación por el\n", "% conjunto de factores correspondiente\n", "equivalencias=(factor_conversion.*longitud);\n", "\n", "% Tabla de conversión de centímetros a pulgadas, pies y yardas\n", "tabla=[cm;equivalencias']; % Se agrupan los datos convertidos \n", "%en una matriz que será la tabla de datos" ] }, { "cell_type": "markdown", "id": "18bc3190-b817-4f7e-bcbf-0c7e3d0e9881", "metadata": {}, "source": [ "Para almacenar los datos en un archivo, en vez de desplegar la tabla en la ventana de comandos se usan las siguientes instrucciones:" ] }, { "cell_type": "code", "execution_count": 16, "id": "45bde901-9310-4901-b61e-807c888113b3", "metadata": {}, "outputs": [], "source": [ "% En vez de desplegar en pantalla la tabla, se crea un archivo con los datos \n", "% con el nombre \"Tabla_de_longitudes.txt\"\n", "archivo= fopen('tabla_de_longitudes.txt', 'wt');\n", "fprintf(archivo,\" Tabla de equivalencias de distancias \\n\");% Se usa el caracter especial \"\\n\"\n", "fprintf(archivo,\"centímetros pulgadas pies yardas \\n\");% para indicar cambio de línea\n", "fprintf(archivo,\" %6.3f %6.3f %6.3f %6.3f \\n\",tabla);\n", "fclose(archivo);" ] }, { "cell_type": "markdown", "id": "5180e5c1-6a80-4871-a03a-4c91549a2493", "metadata": {}, "source": [ "El archivo con los datos tiene el nombre 'tabla_de_longitudes.txt' y se encuentra almacenado en la carpeta en la cual se está trabajando. Los datos almacenados en dicho archivo se pueden importar de la misma manera que se hace con los datos de la web o de archivos **.txt** provenientes de sistemas de aquisición de datos o señañles. \n", "Una práctica usual es omitir los encabezados, es decir el título y los rótulos de las columnas para poder acceder a la información almacenada, además, se incluye una coma para separar las columnas como se ilustra en los comandos siguientes:" ] }, { "cell_type": "code", "execution_count": 17, "id": "bc783bb6-f36b-4958-8e10-2cb20de090e6", "metadata": {}, "outputs": [], "source": [ "archivo= fopen('tabla_datos.txt', 'wt');\n", "fprintf(archivo,\"%6.3f,%6.3f,%6.3f,%6.3f,\\n\",tabla);\n", "fclose(archivo);" ] }, { "cell_type": "markdown", "id": "99782c8a-dcee-4e21-86eb-12cd70266fb9", "metadata": {}, "source": [ "En el video de abajo se ilustra la creación de los dos tipos de archivos, uno con encabezados y un segundo sin ellos y usando comas como delimitador de columnas.\n", "\n", "
\n", "\n", "
" ] }, { "cell_type": "markdown", "id": "9b191679-0100-4520-87f7-8a78658d6392", "metadata": {}, "source": [ "## Ejercicio de la práctica 8\n", "\n", "1. Genere un archivo con nombre **Tabla_de_barometro.txt** que contenga la tabla de alturas contra presión barométrica del ejercicio 7 de la práctica 7.\n", "\n", "2. Importe el archivo **\"tabla_temperaturas.txt\"** desde internet usando el link:\n", " \n", " 'https://raw.githubusercontent.com/LuisGerardo2204/Archivos_de_datos/main/tabla_temperaturas.txt' \n", " \n", "Guarde la información descargada en un archivo llamado **\"Datos_temperatura.txt\"**. El archivo **\"Datos_temperatura.txt\"** contiene los datos de temperatura de cinco sensores tomados en un intervalo de cero a 20 segundos, la primera columna del archivo contiene el instante de tiempo en el cual se registra la temperatura de los sensores uno al cinco (columnas dos a la seis).\n", "\n", "3. Cargue los datos del archivo **\"Datos_temperatura.txt\"** en una matriz llamada $D$ y determine lo siguiente:\n", "\n", " a) Calcule la media y la desviación estándar para cada columna de temperaturas, omita el calculo de estos parámetros para la columna uno, dado que la columna uno del arreglo con los datos (matriz $D$) tiene los valores de tiempo.\n", "\n", " b) Encuentre el valor máximo y mínimo de cada columna, usando las instrucciones `max()` y `min()`. Determine el tiempo en el cual suceden esos valores máximos y mínimos de temperatura, sabiendo que el primer areglo entregado por el comando `[valor,localidad]=min(x)` almacena las localidades en las que ocurren dichos valores extremos.\n", "\n", " c) Convierta a grados Farenheit los datos de la tercera columna.\n", "\n", "4. Importe el archivo **\"aceleracion.txt\"** desde internet que se encuentra en el link:\n", "\n", " 'https://raw.githubusercontent.com/LuisGerardo2204/Archivos_de_datos/main/aceleracion.txt'\n", "\n", " Use el comando `readtable()` para importar los datos y alacenarlos en una matriz $A_c$ y realice lo siguiente:\n", "\n", " a) Almacene los datos de cada columna en tres vectores $X$, $Y$ y $Z$.\n", "\n", " b) Multiplique los valores de cada uno de los tres vectores $X$, $Y$ y $Z$ por el factor de conversión $3.3/4095$.\n", "\n", " c) Genere un vector `t=[0:1/2000:(1/2000)*(max(size(X))-1)]`, donde $X$ es el vector generado en el punto aterior.\n", "\n", " d) Grafique $X$ contra $t$ usando el comando `plot(t,X)`\n", "\n", " e) Guarde en un vector llamado $Zm$ los datos del vector $Z$ menos su promedio usando el comnado `mean()` Grafique el vector $Zm$ contra el vector de tiempo generado en el punto c), usando la instrucción `plot(t,Zm)`.\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "d8414979-3acb-472a-b29b-4502f8e61b4d", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "MATLAB Kernel", "language": "matlab", "name": "jupyter_matlab_kernel" }, "language_info": { "file_extension": ".m", "mimetype": "text/x-matlab", "name": "matlab" } }, "nbformat": 4, "nbformat_minor": 5 }