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@emckiernan
Created March 20, 2018 04:12
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"# Tutorial 2. Discriminar amplitudes de forma simple y cómo obtener derivadas de los datos.\n",
"### Noel Isaías Plascencia-Díaz, Erin C. McKiernan, Marco Arieli Herrera-Valdez,\n",
"### Facultad de Ciencias, UNAM\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
],
"source": [
"# muestra las gráficas\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
],
"source": [
"import matplotlib.pyplot as plt\n",
"import scipy as sc\n",
"from __future__ import division, print_function\n",
"import numpy as np\n",
"import wave\n",
"import pandas as pd\n",
"import seaborn as sns"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"## Tabla de contenidos\n",
"[1. Introducción](#introduccion)\n",
"\n",
"[1.1 Extraer datos en formato .wav](#extract_wav)\n",
"\n",
"[1.2 Extraer características del muestreo](#extract_info)\n",
"\n",
"[2. Derivadas del voltaje](#derive)\n",
"\n",
"[3. Un filtro de amplitudes simple](#amp_filter)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"<a id='introduction'></a>\n",
"### Introducción (¡más formatos!)\n",
"Los datos obtenidos en el laboratiorio no siempre están codificados en formatos que se puedan analizar como números (por ejemplo.txt,.xls,.csv). En este caso, analizaremos un par electromiogramas (EMG) , los cuales fueron obtenidos colocando electrodos en el brazo de un voluntario, a quien se le pidió que levantara un peso mientras se realizó el registro.\n",
"\n",
"Estos archivos están en formato .wav, por lo que es necesario abrirlos a traves de la librería $\\textit{wave}$ para poderlos manipular:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
],
"source": [
"record1 = wave.open('Dec0.wav', 'r') "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"<a id='extract_wav'></a>\n",
"La librería $\\textit{wave}$ nos permite obtener los datos de la onda de la siguiente forma:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
],
"source": [
"frames = record1.readframes(-1) #toma todos los frames de \"audio\", es decir, todos los puntos que corresponden a una amplitud en el registro"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Es posible ver que *frames* tiene un formato de tipo *string*, es decir, interpretado como texto. Para que Python pueda interpretarlo como números, necesitamos hacer lo siguiente:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/dist-packages/ipykernel/__main__.py:1: DeprecationWarning: Numeric-style type codes are deprecated and will result in an error in the future.\n",
" if __name__ == '__main__':\n",
"/usr/local/lib/python3.5/dist-packages/ipykernel/__main__.py:1: DeprecationWarning: The binary mode of fromstring is deprecated, as it behaves surprisingly on unicode inputs. Use frombuffer instead\n",
" if __name__ == '__main__':\n"
]
}
],
"source": [
"waveData = np.fromstring(frames, 'Int16') #fromstring toma la variable \"frames\" y convierte sus elementos al tipo de variable 'Int16'\n",
" #es decir, números enteros"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"<a id='extract_info'></a>\n",
"Un último paso antes de analizar los datos, es saber el número de canales de registro y la frecuencia de muestreo del mismo para tener una escala de tiempo adecuada:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There is 1 channel.\n",
"The sampling rate is 44100 Hz.\n"
]
}
],
"source": [
"numChannels = record1.getnchannels() #número de canales\n",
"sampleRate = record1.getframerate() #frecuencia de muestreo\n",
"sampleWidth = record1.getsampwidth()\n",
"numFrames = record1.getnframes() \n",
"\n",
"print('There is %d channel.' % (numChannels))\n",
"print('The sampling rate is %d Hz.' % (sampleRate))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
],
"source": [
"# Obtiene la escala de tiempo \n",
"tiempo = np.arange(0,len(waveData))/(sampleRate)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"¡Ahora podemos graficar los datos!, puedes consultar un tutorial acerca de matplotlib en el siguiente link: http://mple.m-artwork.eu/tutorial"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
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},
"execution_count": 10,
"metadata": {
},
"output_type": "execute_result"
}
],
"source": [
"# Plot EMG signal\n",
"fig = plt.figure(figsize=(20, 5), dpi=100)\n",
"plt.figure(1)\n",
"\n",
"plt.title('Electromiograma')\n",
"plt.xlabel(r'tiempo [s]')\n",
"plt.ylabel(r'voltaje [$]')\n",
"\n",
"plt.plot(tiempo,waveData)\n",
"\n",
"plt.xlim(0,max(tiempo))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"<a id='derive'></a>\n",
"### Derivadas del voltaje\n",
"Una forma de detectar cambios de interés en el comportamiento en los datos es calcular las derivadas discretas entre puntos sucesivos del registro. Para nuestro caso, la derivada del voltaje con respecto al tiempo es un indicador de la rapidéz con la que cambia la actividad muscular a lo largo del registro."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
],
"source": [
"def derive_signal(x,dt):\n",
" \n",
" l1 = len(x)-1\n",
" der_s = [-((x[i] - x[i+1])/dt) for i in range(0,l1)]\n",
" return der_s"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
],
"source": [
"derivada_voltaje = derive_signal(waveData,1/sampleRate);"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: Some output was deleted.\n"
]
}
],
"source": [
"fig = plt.figure(figsize=(20, 5), dpi=100)\n",
"plt.figure(1)\n",
"plt.title('Electromiograma')\n",
"plt.xlabel(r'tiempo [s]')\n",
"plt.ylabel(r'$\\frac{dV}{dt}$ [$\\frac{$\\mu$V}{s}$]')\n",
"\n",
"plt.plot(derivada_voltaje)\n",
"#plt.xlim(0,max(tiempo))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"<a id='amp_filter'></a>\n",
"### Discriminar amplitudes\n",
"Es posible discriminar los datos considerando solamente las amplitudes que nos interesa estudiar. En el caso de los EMGs, las amplitudes que corresponden a potenciales de acción son se encuetran en el rango de $ 10^{2} \\mu V$. Una forma de extraer amplitudes en este rango para nuestro registro es utilizar la paquetería $\\textit{Pandas}$, que sirve para tabular y acceder a datos de forma rápida y práctica.\n",
"\n",
"Para hacer lo anterior, primero es necesario colocar los datos en forma de $\\textit{dataframe}$:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
],
"source": [
"df = pd.DataFrame({'Tiempo':tiempo,'Amplitud':waveData}) \n",
"df;"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Ahora podemos seleccionar las amplitudes que nos interesan y graficarlas:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f2613ecceb8>]"
]
},
"execution_count": 13,
"metadata": {
},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"
},
"execution_count": 13,
"metadata": {
},
"output_type": "execute_result"
}
],
"source": [
"filtered = df[(df[\"Amplitud\"]>400)] #selecciona amplitudes mayores a 400 microvolts\n",
"plt.plot(filtered['Tiempo'],filtered['Amplitud'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Las funciones presentadas en este tutorial pueden servir (entre otras cosas) como herramientas para preprocesar los datos y extraer sólamemte los fenómenos de interés. En el siguiente tutorial analizarenos la forma en la que fluctúan estos mismos datos para extraer información acerca del comportamiento global de la serie de tiempo."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (Ubuntu Linux)",
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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