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@emckiernan
Created March 20, 2018 04:17
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"# Tutorial 3 - Análisis en ventanas de tiempo\n",
"### Noel Isaías Placencia-Díaz, Erin C. McKiernan, Marco Arieli Herrera-Valdez,\n",
"### Facultad de Ciencias, UNAM\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"## Tabla de contenidos:\n",
"\n",
"[1. Introducción](#introduction)\n",
"\n",
"[2. Análisis en ventanas de tiempo](#timewindow)\n",
"\n",
"[3. Obtener la varianza en ventanas de tiempo](#variance)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"<a id='introduction'></a>\n",
"## 1. Introducción - Series de tiempo no estacionarias \n",
"\n",
"Una serie de tiempo es llamada estacionaria si su distribución de probabilidad no cambia con respecto al tiempo, esto quiere decir que su valor promedio y su varianza no cambian con respecto al tiempo, esto hace a los procesos estacionarios (o series de tiempo estacionarias), fáciles de predecir, un ejemplo de una serie de tiempo estacionaria en fisiología es el ritmo cardiaco en reposo de un corazón sano.\n",
"\n",
"En el análisis de datos fisiológicos es común encontrar series de tiempo no estacionarias, un ejemplo de esto lo podemos ver en la serie de tiempo del EMG que analizamos en el tutorial 2, recordemos que este registro fue obtenido colocando electrodos en el brazo de un voluntario, a quien se le pidió que levantara un peso mientras se realizaron las mediciones dela actividad muscular."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import wave\n",
"import pandas as pd\n",
"import scipy as sc"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/ext/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:3: DeprecationWarning: Numeric-style type codes are deprecated and will result in an error in the future.\n",
" app.launch_new_instance()\n"
]
}
],
"source": [
"record1 = wave.open('Dec0.wav', 'r') \n",
"frames = record1.readframes(-1) \n",
"waveData = np.fromstring(frames, 'Int16')\n",
"\n",
"numChannels = record1.getnchannels() #número de canales\n",
"sampleRate = record1.getframerate() #frecuencia de muestreo\n",
"sampleWidth = record1.getsampwidth()\n",
"numFrames = record1.getnframes() \n",
"\n",
"tiempo = np.arange(0,len(waveData))*(sampleRate)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"<a id='timewindow'></a>\n",
"### 2. Colocar los datos en ventanas de tiempo.\n",
"Una forma de estudiar computacionalmente procesos no estacionarios es dividir el registro original en intervalos de tiempo regulares y medir las propiedades estadísticas de los datos en cada uno de estos segmentos. A cada uno de estos subregistros se les llama $\\textit{ventanas de tiempo}$.\n",
"\n",
"A continuación implementaremos una función que divide un registro en ventanas de tiempo:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
],
"source": [
"def single_chan_wind(x,npoints): #los parámetros de esta función son el registro a analizar y el número de puntos en cada ventana de tiempo.\n",
" \n",
" single_chan_wind = []\n",
" tot_points = len(x) #Toma el número total de puntos de la serie\n",
" L = npoints\n",
" \n",
" nwind = tot_points//npoints #Define el número de ventanas como la parte entera del cociente entre el número total de puntos y el número de puntos por ventana de tiempo\n",
" \n",
" for h in range(0,nwind):\n",
" \n",
" t = x[h*L:L*(1+h)] #Separa en ventanas de tiempo\n",
" single_chan_wind.append(t) #Coloca todas las ventanas de tiempo en un arreglo\n",
" \n",
" return(single_chan_wind)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Aplicamos la función anterior a los datos, eligiendo ventanas de tiempo que contengan 50000 datos cada una:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
],
"source": [
"wind_waveData = single_chan_wind(waveData,50000)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"(55, 50000)"
]
},
"execution_count": 6,
"metadata": {
},
"output_type": "execute_result"
}
],
"source": [
"#la función generó 55 ventanas, cada una con 50000 datos:\n",
"np.shape(wind_waveData)\n",
"\n",
"# NOTA: Si queremos acceder, por ejemplo, a la primer ventana de tiempo tecleamos: wind_waveData[0]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.1337868480725624"
]
},
"execution_count": 7,
"metadata": {
},
"output_type": "execute_result"
}
],
"source": [
"50000/sampleRate # Cada ventana de tiempo equivale a 1.1338 s aproximadamente"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"<a id='variance'></a>\n",
"### 3. Calcular la varianza en cada una de las ventanas de tiempo\n",
"La siguiente función regresa un arreglo con la varianza calculada en ventadas de tiempo que tienen un tamaño predefinido."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
],
"source": [
"def variance_single_chan_wind(x,npoints):\n",
" \n",
" tot_points = len(x) #número total de puntos en el registro\n",
" nwind = tot_points//npoints #hace un cociente para obtener el número de ventanas de tiempo\n",
" wind_x = single_chan_wind(x,npoints) #Llama a la función que definimos anteriormente para reacomodar los datos en ventanas de tiempo\n",
" \n",
" v_single_chan_wind = [np.var(wind_x[w]) for w in range(0,nwind)] #Obtiene la varianza de cada una de las ventanas de tiempo anteriores.\n",
" \n",
" return(v_single_chan_wind)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
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},
"execution_count": 9,
"metadata": {
},
"output_type": "execute_result"
}
],
"source": [
"fig = plt.figure(figsize=(20, 5), dpi=100)\n",
"plt.figure(1)\n",
"\n",
"plt.title('Varianza del EMG en ventanas de tiempo')\n",
"plt.xlabel(r'ventana de tiempo')\n",
"plt.ylabel(r'varianza')\n",
"\n",
"plt.plot(variance_single_chan_wind(waveData,50000)/max(variance_single_chan_wind(waveData,50000)))\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Podemos ver que la varianza de los datos tiende a disminuir con el tiempo durante el registro, ¿puedes explicar qué relación tiene esto con la actividad muscular que realizaba el voluntario?."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (Anaconda)",
"language": "python",
"name": "anaconda3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.4"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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