I just realized that NumPy uses the time domain algorithm for correlation.
So it would be much easier to modify the correlation functions in SciPy
than in NumPy.
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Did you check out my implementation? I was able to modify the Numpy correlate
function just fine.
https://github.com/numpy/numpy/compare/master...bringingheavendown:maxlag
On Jun 21, 2015, at 1:53 PM, Mansour Moufid mansourmou...@gmail.com wrote:
I just realized that NumPy uses the time
Mansour Moufid mansourmou...@gmail.com wrote:
The cross-correlation of two arrays of lengths m and n is of length
m + n - 1, where m is usually much larger than n.
He is thinking about the situation where m == n and m is much larger than
maxlag.
Truncating the input arrays would also throw
On 17/06/15 04:38, Honi Sanders wrote:
I have now implemented this functionality in numpy.correlate() and
numpy.convolve(). https://github.com/bringingheavendown/numpy. The files that
were edited are:
numpy/core/src/multiarray/multiarraymodule.c
numpy/core/numeric.py
I will also repeat what I said in response on Github (discussions at:
https://github.com/scipy/scipy/issues/4940,
https://github.com/numpy/numpy/issues/5954):
I do want a function that computes cross-correlograms, however the
implementation is exactly the same for cross-correlograms as
Hello,
There is a simple solution.
The cross-correlation of two arrays of lengths m and n is of length
m + n - 1, where m is usually much larger than n.
If you need to compute the cross-correlation with a bound on the lag
of k, then truncate the longer array to length k - n + 1.
That is,
def
I have now implemented this functionality in numpy.correlate() and
numpy.convolve(). https://github.com/bringingheavendown/numpy. The files that
were edited are:
numpy/core/src/multiarray/multiarraymodule.c
numpy/core/numeric.py
numpy/core/tests/test_numeric.py
Please look over the code, my
I am learning numpy/scipy, coming from a MATLAB background. The xcorr function
in Matlab has an optional argument maxlag that limits the lag range from
–maxlag to maxlag. This is very useful if you are looking at the
cross-correlation between two very long time series but are only interested in
