Created
October 5, 2010 01:38
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def xcorr(a, b=None, maxlag=None): | |
if b is None: | |
b = a | |
a = numpy.array(a) | |
b = numpy.array(b) | |
if maxlag is None: | |
maxlag = len(a) / 2 | |
# normalize a, b | |
na = numpy.linalg.norm(a) | |
nb = numpy.linalg.norm(b) | |
if na > 0: | |
a = a / na | |
if nb > 0: | |
b = b / nb | |
lags = range(-maxlag, maxlag + 1) | |
results = numpy.zeros(shape=(len(lags),)) | |
for i, lag in enumerate(lags): | |
if lag < 0: | |
lag = -lag | |
ta, tb = b, a | |
else: | |
ta, tb = a, b | |
part_of_a = ta[lag:len(tb)] | |
part_of_b = tb[0:len(part_of_a)] | |
print lag, len(a), len(b), part_of_a.shape, part_of_b.shape | |
assert len(part_of_a) == len(part_of_b) | |
results[i] = (part_of_a * part_of_b).sum() | |
return results, lags |
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