I needed some spectral analysis functions, and finding none available,
wrote my own. I use matlab a lot, so I wrote them to be matlab
compatible. If you all think these look OK, I'm happy to submit them
for inclusion into MLab.
-------------------------------------------------------------------
"""
Spectral analysis functions for Numerical python written for
compatability with matlab commands with the same names.
psd - Power spectral density uing Welch's average periodogram
csd - Cross spectral density uing Welch's average periodogram
cohere - Coherence (normalized cross spectral density)
corrcoef - The matrix of correlation coefficients
The functions are designed to work for real and complex valued Numeric
arrays.
One of the major differences between this code and matlab's is that I
use functions for 'detrend' and 'window', and matlab uses vectors.
This can be easily changed, but I think the functional approach is a
bit more elegant.
Please send comments, questions and bugs to:
Author: John D. Hunter <jdhunter(a)ace.bsd.uchicago.edu>
"""
from __future__ import division
from MLab import mean, hanning, cov
from Numeric import zeros, ones, diagonal, transpose, matrixmultiply, \
resize, sqrt, divide, array, Float, Complex, concatenate, \
convolve, dot, conjugate, absolute, arange, reshape
from FFT import fft
def norm(x):
return sqrt(dot(x,x))
def window_hanning(x):
return hanning(len(x))*x
def window_none(x):
return x
def detrend_mean(x):
return x - mean(x)
def detrend_none(x):
return x
def detrend_linear(x):
"""Remove the best fit line from x"""
# I'm going to regress x on xx=range(len(x)) and return
# x - (b*xx+a)
xx = arange(len(x), typecode=x.typecode())
X = transpose(array([xx]+[x]))
C = cov(X)
b = C[0,1]/C[0,0]
a = mean(x) - b*mean(xx)
return x-(b*xx+a)
def psd(x, NFFT=256, Fs=2, detrend=detrend_none,
window=window_hanning, noverlap=0):
"""
The power spectral density by Welches average periodogram method.
The vector x is divided into NFFT length segments. Each segment
is detrended by function detrend and windowed by function window.
noperlap gives the length of the overlap between segments. The
absolute(fft(segment))**2 of each segment are averaged to compute Pxx,
with a scaling to correct for power loss due to windowing. Fs is
the sampling frequency.
-- NFFT must be a power of 2
-- detrend and window are functions, unlike in matlab where they are
vectors.
-- if length x < NFFT, it will be zero padded to NFFT
Refs:
Bendat & Piersol -- Random Data: Analysis and Measurement
Procedures, John Wiley & Sons (1986)
"""
if NFFT % 2:
raise ValueError, 'NFFT must be a power of 2'
# zero pad x up to NFFT if it is shorter than NFFT
if len(x)<NFFT:
n = len(x)
x = resize(x, (NFFT,))
x[n:] = 0
# for real x, ignore the negative frequencies
if x.typecode()==Complex: numFreqs = NFFT
else: numFreqs = NFFT//2+1
windowVals = window(ones((NFFT,),x.typecode()))
step = NFFT-noverlap
ind = range(0,len(x)-NFFT+1,step)
n = len(ind)
Pxx = zeros((numFreqs,n), Float)
# do the ffts of the slices
for i in range(n):
thisX = x[ind[i]:ind[i]+NFFT]
thisX = windowVals*detrend(thisX)
fx = absolute(fft(thisX))**2
Pxx[:,i] = fx[:numFreqs]
# Scale the spectrum by the norm of the window to compensate for
# windowing loss; see Bendat & Piersol Sec 11.5.2
if n>1: Pxx = mean(Pxx,1)
Pxx = divide(Pxx, norm(windowVals)**2)
freqs = Fs/NFFT*arange(0,numFreqs)
return Pxx, freqs
def csd(x, y, NFFT=256, Fs=2, detrend=detrend_none,
window=window_hanning, noverlap=0):
"""
The cross spectral density Pxy by Welches average periodogram
method. The vectors x and y are divided into NFFT length
segments. Each segment is detrended by function detrend and
windowed by function window. noverlap gives the length of the
overlap between segments. The product of the direct FFTs of x and
y are averaged over each segment to compute Pxy, with a scaling to
correct for power loss due to windowing. Fs is the sampling
frequency.
NFFT must be a power of 2
Refs:
Bendat & Piersol -- Random Data: Analysis and Measurement
Procedures, John Wiley & Sons (1986)
"""
if NFFT % 2:
raise ValueError, 'NFFT must be a power of 2'
# zero pad x and y up to NFFT if they are shorter than NFFT
if len(x)<NFFT:
n = len(x)
x = resize(x, (NFFT,))
x[n:] = 0
if len(y)<NFFT:
n = len(y)
y = resize(y, (NFFT,))
y[n:] = 0
# for real x, ignore the negative frequencies
if x.typecode()==Complex: numFreqs = NFFT
else: numFreqs = NFFT//2+1
windowVals = window(ones((NFFT,),x.typecode()))
step = NFFT-noverlap
ind = range(0,len(x)-NFFT+1,step)
n = len(ind)
Pxy = zeros((numFreqs,n), Complex)
# do the ffts of the slices
for i in range(n):
thisX = x[ind[i]:ind[i]+NFFT]
thisX = windowVals*detrend(thisX)
thisY = y[ind[i]:ind[i]+NFFT]
thisY = windowVals*detrend(thisY)
fx = fft(thisX)
fy = fft(thisY)
Pxy[:,i] = fy[:numFreqs]*conjugate(fx[:numFreqs])
# Scale the spectrum by the norm of the window to compensate for
# windowing loss; see Bendat & Piersol Sec 11.5.2
if n>1: Pxy = mean(Pxy,1)
Pxy = divide(Pxy, norm(windowVals)**2)
freqs = Fs/NFFT*arange(0,numFreqs)
return Pxy, freqs
def cohere(x, y, NFFT=256, Fs=2, detrend=detrend_none,
window=window_hanning, noverlap=0):
"""
cohere the coherence between x and y. Coherence is the normalized
cross spectral density
Cxy = |Pxy|^2/(Pxx*Pyy)
The return value is (Cxy, f), where f are the frequencies of the
coherence vector. See the docs for psd and csd for information
about the function arguments NFFT, detrend, windowm noverlap, as
well as the methods used to compute Pxy, Pxx and Pyy.
"""
Pxx,f = psd(x, NFFT=NFFT, Fs=Fs, detrend=detrend,
window=window, noverlap=noverlap)
Pyy,f = psd(y, NFFT=NFFT, Fs=Fs, detrend=detrend,
window=window, noverlap=noverlap)
Pxy,f = csd(x, y, NFFT=NFFT, Fs=Fs, detrend=detrend,
window=window, noverlap=noverlap)
Cxy = divide(absolute(Pxy)**2, Pxx*Pyy)
return Cxy, f
def corrcoef(*args):
"""
corrcoef(X) where X is a matrix returns a matrix of correlation
coefficients for each row of X.
corrcoef(x,y) where x and y are vectors returns the matrix or
correlation coefficients for x and y.
Numeric arrays can be real or complex
The correlation matrix is defined from the covariance matrix C as
r(i,j) = C[i,j] / (C[i,i]*C[j,j])
"""
if len(args)==2:
X = transpose(array([args[0]]+[args[1]]))
elif len(args==1):
X = args[0]
else:
raise RuntimeError, 'Only expecting 1 or 2 arguments'
C = cov(X)
d = resize(diagonal(C), (2,1))
r = divide(C,sqrt(matrixmultiply(d,transpose(d))))[0,1]
try: return r.real
except AttributeError: return r
-------------------------------------------------------------------
I wrote a little test code comparing the output of matlab's equivalent
functions. Basically, I compute the psd or cohere in matlab and
python and do the rms difference on the resultant vectors
RMS cohere python/matlab difference 0.000854587104587
RMS psd python/matlab difference 0.00210783306638
I am not sure where these differences are arising, but they are quite
small. I'm going to keep trying to track them down.
For corrcoef, the answers are the same past 8 significant digits.
Hope this helps!
John Hunter