Real time microphone analysis. Real time plot of signal and FFT using numpy, matplotlib and pyaudio

plot_mic_fft.py

import pyaudio
import numpy as np
import time
import wave
import matplotlib.pyplot as plt


# open stream
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 44100

CHUNK = 2048 # RATE / number of updates per second

RECORD_SECONDS = 20


# use a Blackman window
window = np.blackman(CHUNK)

x = 0

def soundPlot(stream):
    t1=time.time()
    data = stream.read(CHUNK, exception_on_overflow=False)
    waveData = wave.struct.unpack("%dh"%(CHUNK), data)
    npArrayData = np.array(waveData)
    indata = npArrayData*window
    #Plot time domain
    ax1.cla()
    ax1.plot(indata)
    ax1.grid()
    ax1.axis([0,CHUNK,-5000,5000])
    fftData=np.abs(np.fft.rfft(indata))
    fftTime=np.fft.rfftfreq(CHUNK, 1./RATE)
    which = fftData[1:].argmax() + 1
    #Plot frequency domain graph
    ax2.cla()
    ax2.plot(fftTime,fftData)
    ax2.grid()
    ax2.axis([0,5000,0,10**6])
    plt.pause(0.0001)
    print("took %.02f ms"%((time.time()-t1)*1000))
    # use quadratic interpolation around the max
    if which != len(fftData)-1:
        y0,y1,y2 = np.log(fftData[which-1:which+2:])
        x1 = (y2 - y0) * .5 / (2 * y1 - y2 - y0)
        # find the frequency and output it
        thefreq = (which+x1)*RATE/CHUNK
        print "The freq is %f Hz." % (thefreq)
    else:
        thefreq = which*RATE/CHUNK
        print "The freq is %f Hz." % (thefreq)

if __name__=="__main__":
    p=pyaudio.PyAudio()
    stream=p.open(format=pyaudio.paInt16,channels=1,rate=RATE,input=True,
                  frames_per_buffer=CHUNK)

    plt.ion()
    fig = plt.figure(figsize=(10,8))
    ax1 = fig.add_subplot(211)
    ax2 = fig.add_subplot(212)

    for i in range(0, RATE / CHUNK * RECORD_SECONDS):
        soundPlot(stream)

    stream.stop_stream()
    stream.close()
    p.terminate()