correlate1d#
- scipy.ndimage.correlate1d(input, weights, axis=-1, output=None, mode='reflect', cval=0.0, origin=0)[source]#
- Calculate a 1-D correlation along the given axis. - The lines of the array along the given axis are correlated with the given weights. - Parameters:
- inputarray_like
- The input array. 
- weightsarray
- 1-D sequence of numbers. 
- axisint, optional
- The axis of input along which to calculate. Default is -1. 
- 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. 
- mode{‘reflect’, ‘constant’, ‘nearest’, ‘mirror’, ‘wrap’}, optional
- The mode parameter determines how the input array is extended beyond its boundaries. Default is ‘reflect’. Behavior for each valid value 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. This mode is also sometimes referred to as half-sample symmetric. 
- ‘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. This mode is also sometimes referred to as whole-sample symmetric. 
- ‘wrap’ (a b c d | a b c d | a b c d)
- The input is extended by wrapping around to the opposite edge. 
 - For consistency with the interpolation functions, the following mode names can also be used: - ‘grid-mirror’
- This is a synonym for ‘reflect’. 
- ‘grid-constant’
- This is a synonym for ‘constant’. 
- ‘grid-wrap’
- This is a synonym for ‘wrap’. 
 
- cvalscalar, optional
- Value to fill past edges of input if mode is ‘constant’. Default is 0.0. 
- originint, 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. 
 
- Returns:
- resultndarray
- Correlation result. Has the same shape as input. 
 
 - Examples - >>> from scipy.ndimage import correlate1d >>> correlate1d([2, 8, 0, 4, 1, 9, 9, 0], weights=[1, 3]) array([ 8, 26, 8, 12, 7, 28, 36, 9])