scipy.ndimage.correlate¶

scipy.ndimage.
correlate
(input, weights, output=None, mode='reflect', cval=0.0, origin=0)[source]¶ Multidimensional correlation.
The array is correlated with the given kernel.
 Parameters
 inputarray_like
The input array.
 weightsndarray
array of weights, same number of dimensions as input
 outputarray or dtype, optional
The array in which to place the output, or the dtype of the returned array. By default an array of the same dtype as input will be created.
 modestr or sequence, optional
The mode parameter determines how the input array is extended when the filter overlaps a border. By passing a sequence of modes with length equal to the number of dimensions of the input array, different modes can be specified along each axis. Default value is ‘reflect’. The valid values and their behavior is as follows:
 ‘reflect’ (d c b a  a b c d  d c b a)
The input is extended by reflecting about the edge of the last pixel.
 ‘constant’ (k k k k  a b c d  k k k k)
The input is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter.
 ‘nearest’ (a a a a  a b c d  d d d d)
The input is extended by replicating the last pixel.
 ‘mirror’ (d c b  a b c d  c b a)
The input is extended by reflecting about the center of the last pixel.
 ‘wrap’ (a b c d  a b c d  a b c d)
The input is extended by wrapping around to the opposite edge.
 cvalscalar, optional
Value to fill past edges of input if mode is ‘constant’. Default is 0.0.
 originint or sequence, optional
Controls the placement of the filter on the input array’s pixels. A value of 0 (the default) centers the filter over the pixel, with positive values shifting the filter to the left, and negative ones to the right. By passing a sequence of origins with length equal to the number of dimensions of the input array, different shifts can be specified along each axis.
See also
convolve
Convolve an image with a kernel.