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Matplotlib realtime audio FFT
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#!/usr/bin/env python | |
# encoding: utf-8 | |
## Module infomation ### | |
# Python (3.4.4) | |
# numpy (1.10.2) | |
# PyAudio (0.2.9) | |
# matplotlib (1.5.1) | |
# All 32bit edition | |
######################## | |
import numpy as np | |
import pyaudio | |
import matplotlib.pyplot as plt | |
class SpectrumAnalyzer: | |
FORMAT = pyaudio.paFloat32 | |
CHANNELS = 1 | |
RATE = 16000 | |
CHUNK = 512 | |
START = 0 | |
N = 512 | |
wave_x = 0 | |
wave_y = 0 | |
spec_x = 0 | |
spec_y = 0 | |
data = [] | |
def __init__(self): | |
self.pa = pyaudio.PyAudio() | |
self.stream = self.pa.open(format = self.FORMAT, | |
channels = self.CHANNELS, | |
rate = self.RATE, | |
input = True, | |
output = False, | |
frames_per_buffer = self.CHUNK) | |
# Main loop | |
self.loop() | |
def loop(self): | |
try: | |
while True : | |
self.data = self.audioinput() | |
self.fft() | |
self.graphplot() | |
except KeyboardInterrupt: | |
self.pa.close() | |
print("End...") | |
def audioinput(self): | |
ret = self.stream.read(self.CHUNK) | |
ret = np.fromstring(ret, np.float32) | |
return ret | |
def fft(self): | |
self.wave_x = range(self.START, self.START + self.N) | |
self.wave_y = self.data[self.START:self.START + self.N] | |
self.spec_x = np.fft.fftfreq(self.N, d = 1.0 / self.RATE) | |
y = np.fft.fft(self.data[self.START:self.START + self.N]) | |
self.spec_y = [np.sqrt(c.real ** 2 + c.imag ** 2) for c in y] | |
def graphplot(self): | |
plt.clf() | |
# wave | |
plt.subplot(311) | |
plt.plot(self.wave_x, self.wave_y) | |
plt.axis([self.START, self.START + self.N, -0.5, 0.5]) | |
plt.xlabel("time [sample]") | |
plt.ylabel("amplitude") | |
#Spectrum | |
plt.subplot(312) | |
plt.plot(self.spec_x, self.spec_y, marker= 'o', linestyle='-') | |
plt.axis([0, self.RATE / 2, 0, 50]) | |
plt.xlabel("frequency [Hz]") | |
plt.ylabel("amplitude spectrum") | |
#Pause | |
plt.pause(.01) | |
if __name__ == "__main__": | |
spec = SpectrumAnalyzer() | |
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