Detrending Data in Python with Numpy

While continuing my quest into the world of linear data analysis and signal processing, I came to a point where I wanted to emphasize variations in FFT traces. While I am keeping my original data for scientific reference, visually I want to represent it emphasizing variations rather than concentrating on trends. I wrote a detrending function which I’m sure will be useful for many applications:detrend_fft

def detrend(data,degree=10):
	detrended=[None]*degree
	for i in range(degree,len(data)-degree):
		chunk=data[i-degree:i+degree]
		chunk=sum(chunk)/len(chunk)
		detrended.append(data[i]-chunk)
	return detrended+[None]*degree

However, this method is extremely slow. I need to think of a way to accomplish this same thing much faster. [ponders]

UPDATE: It looks like I’ve once again re-invented the wheel. All of this has been done already, and FAR more efficiently I might add. Simply:

import scipy.signal
ffty=scipy.signal.detrend(ffty)

Now I’m looking into scipy.signal.triang()