numpy.correlate#
- 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 [1]:
\[c_k = \sum_n a_{n+k} \cdot \overline{v}_n\]with a and v sequences being zero-padded where necessary and \(\overline v\) denoting complex conjugation.
- Parameters:
- Returns:
- outndarray
Discrete cross-correlation of a and v.
See also
convolve
Discrete, linear convolution of two one-dimensional sequences.
scipy.signal.correlate
uses FFT which has superior performance on large arrays.
Notes
The definition of correlation above is not unique and sometimes correlation may be defined differently. Another common definition is [1]:
\[c'_k = \sum_n a_{n} \cdot \overline{v_{n+k}}\]which is related to \(c_k\) by \(c'_k = c_{-k}\).
numpy.correlate
may perform slowly in large arrays (i.e. n = 1e5) because it does not use the FFT to compute the convolution; in that case,scipy.signal.correlate
might be preferable.References
Examples
>>> 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:
>>> 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 (\(\overline{c_{-k}}\)) when the two input sequences a and v change places:
>>> 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])