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numpy.correlate(a, v, mode='valid')[source] -
Cross-correlation of two 1-dimensional sequences.
This function computes the correlation as generally defined in signal processing texts:
1c_{av}[k]=sum_n a[n+k]*conj(v[n])with a and v sequences being zero-padded where necessary and conj being the conjugate.
Parameters: a, v : array_like
Input sequences.
mode : {?valid?, ?same?, ?full?}, optional
Refer to the
convolvedocstring. Note that the default is ?valid?, unlikeconvolve, which uses ?full?.old_behavior : bool
old_behaviorwas removed in NumPy 1.10. If you need the old behavior, usemultiarray.correlate.Returns: out : ndarray
Discrete cross-correlation of
aandv.See also
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convolve - Discrete, linear convolution of two one-dimensional sequences.
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multiarray.correlate - Old, no conjugate, version of correlate.
Notes
The definition of correlation above is not unique and sometimes correlation may be defined differently. Another common definition is:
1c'_{av}[k]=sum_n a[n] conj(v[n+k])which is related to
c_{av}[k]byc'_{av}[k] = c_{av}[-k].Examples
123456>>> np.correlate([1,2,3], [0,1,0.5])array([3.5])>>> np.correlate([1,2,3], [0,1,0.5],"same")array([2.,3.5,3.])>>> np.correlate([1,2,3], [0,1,0.5],"full")array([0.5,2.,3.5,3.,0.])Using complex sequences:
12>>> np.correlate([1+1j,2,3-1j], [0,1,0.5j],'full')array([0.5-0.5j,1.0+0.j,1.5-1.5j,3.0-1.j,0.0+0.j])Note that you get the time reversed, complex conjugated result when the two input sequences change places, i.e.,
c_{va}[k] = c^{*}_{av}[-k]:12>>> np.correlate([0,1,0.5j], [1+1j,2,3-1j],'full')array([0.0+0.j,3.0+1.j,1.5+1.5j,1.0+0.j,0.5+0.5j]) -
numpy.correlate()
2025-01-10 15:47:30
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