Compute Normalized Cross-Correlation in Python
I have been struggling the last days trying to compute the degrees of freedom of two pair of vectors (x and y) following reference of Chelton (1983) which is:
degrees of freedom according to Chelton(1983)
and I can't find a proper way to calculate the normalized cross correlation function using np.correlate,
I always get an output that it isn't in between -1, 1.
Is there any easy way to get the cross correlation function normalized in order to compute the degrees of freedom of two vectors?
python numpy correlation cross-correlation
asked Nov 22 '18 at 18:04
Daniela Belén RisaroDaniela Belén Risaro
284
add a comment |
I have been struggling the last days trying to compute the degrees of freedom of two pair of vectors (x and y) following reference of Chelton (1983) which is:
degrees of freedom according to Chelton(1983)
and I can't find a proper way to calculate the normalized cross correlation function using np.correlate,
I always get an output that it isn't in between -1, 1.
Is there any easy way to get the cross correlation function normalized in order to compute the degrees of freedom of two vectors?
python numpy correlation cross-correlation
asked Nov 22 '18 at 18:04
Daniela Belén RisaroDaniela Belén Risaro
284
add a comment |
I have been struggling the last days trying to compute the degrees of freedom of two pair of vectors (x and y) following reference of Chelton (1983) which is:
degrees of freedom according to Chelton(1983)
and I can't find a proper way to calculate the normalized cross correlation function using np.correlate,
I always get an output that it isn't in between -1, 1.
Is there any easy way to get the cross correlation function normalized in order to compute the degrees of freedom of two vectors?
python numpy correlation cross-correlation
asked Nov 22 '18 at 18:04
Daniela Belén RisaroDaniela Belén Risaro
284
I have been struggling the last days trying to compute the degrees of freedom of two pair of vectors (x and y) following reference of Chelton (1983) which is:
degrees of freedom according to Chelton(1983)
and I can't find a proper way to calculate the normalized cross correlation function using np.correlate,
I always get an output that it isn't in between -1, 1.
Is there any easy way to get the cross correlation function normalized in order to compute the degrees of freedom of two vectors?
python numpy correlation cross-correlation
python numpy correlation cross-correlation
asked Nov 22 '18 at 18:04
Daniela Belén RisaroDaniela Belén Risaro
284
asked Nov 22 '18 at 18:04
Daniela Belén RisaroDaniela Belén Risaro
284
edited Nov 22 '18 at 22:31
Daniela Belén Risaro
asked Nov 22 '18 at 18:04
Daniela Belén RisaroDaniela Belén Risaro
284
asked Nov 22 '18 at 18:04
Daniela Belén RisaroDaniela Belén Risaro
284
asked Nov 22 '18 at 18:04
Daniela Belén RisaroDaniela Belén Risaro
284
284
add a comment |
add a comment |
1 Answer
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Nice Question. There is no direct way but you can "normalize" the input vectors before using np.correlate like this and reasonable values will be returned within a range of [-1,1]:
If a and b are the vectors:
a = (a - np.mean(a)) / (np.std(a) * len(a))
b = (b - np.mean(b)) / (np.std(b))
c = np.correlate(a, b, 'full')
answered Nov 22 '18 at 18:12
seraloukseralouk
5,93722341
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Nice Question. There is no direct way but you can "normalize" the input vectors before using np.correlate like this and reasonable values will be returned within a range of [-1,1]:
If a and b are the vectors:
a = (a - np.mean(a)) / (np.std(a) * len(a))
b = (b - np.mean(b)) / (np.std(b))
c = np.correlate(a, b, 'full')
answered Nov 22 '18 at 18:12
seraloukseralouk
5,93722341
add a comment |
Nice Question. There is no direct way but you can "normalize" the input vectors before using np.correlate like this and reasonable values will be returned within a range of [-1,1]:
If a and b are the vectors:
a = (a - np.mean(a)) / (np.std(a) * len(a))
b = (b - np.mean(b)) / (np.std(b))
c = np.correlate(a, b, 'full')
answered Nov 22 '18 at 18:12
seraloukseralouk
5,93722341
add a comment |
Nice Question. There is no direct way but you can "normalize" the input vectors before using np.correlate like this and reasonable values will be returned within a range of [-1,1]:
If a and b are the vectors:
a = (a - np.mean(a)) / (np.std(a) * len(a))
b = (b - np.mean(b)) / (np.std(b))
c = np.correlate(a, b, 'full')
answered Nov 22 '18 at 18:12
seraloukseralouk
5,93722341
Nice Question. There is no direct way but you can "normalize" the input vectors before using np.correlate like this and reasonable values will be returned within a range of [-1,1]:
If a and b are the vectors:
a = (a - np.mean(a)) / (np.std(a) * len(a))
b = (b - np.mean(b)) / (np.std(b))
c = np.correlate(a, b, 'full')
answered Nov 22 '18 at 18:12
seraloukseralouk
5,93722341
answered Nov 22 '18 at 18:12
seraloukseralouk
5,93722341
answered Nov 22 '18 at 18:12
seraloukseralouk
5,93722341
answered Nov 22 '18 at 18:12
seraloukseralouk
5,93722341
5,93722341
add a comment |
add a comment |
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