Last active
April 13, 2023 08:53
-
-
Save hyperconcerto/950bbb9d9d4014d893e5 to your computer and use it in GitHub Desktop.
Matplotlib realtime audio FFT
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
#!/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() | |
this is great but how would you apply a Hanning window to the fft plot?
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Windows 7 64bit
Python 3.4.4 x86
numpy 1.10.2 x86
matplotlib 1.5.1 x86
Pyaudio 0.2.9 x86