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Frequency domain filtering in python

Original signal (buried in 3 kHz noise):

Discrete Fourier Transform:

Get rid of offending noise spikes in frequency domain:


Cleaned signal (black) compared to original noisy signal.


And all this takes a few lines in python+scipy+matplotlib using scipy's signal processing module:


import scipy.signal as ss
import pylab

censor0 = pylab.array([[860,875], [2590,2615],[4330,4350],[5885,5893],[6885,6893]])
censor1 = len(x_orig) - censor0[:,[1,0]]
censor = pylab.concatenate((censor0,censor1))

x_orig = data['currents'][1][1][1][0] #The data
X_orig = ss.fft(x_orig)
X_filt = pylab.array(X_orig)
for n in range(censor.shape[0]):
X_filt[censor[n,0]:censor[n,1]] = 0
x_filt = pylab.ifft(X_filt)
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