Module « scipy.ndimage »
Signature de la fonction convolve1d
def convolve1d(input, weights, axis=-1, output=None, mode='reflect', cval=0.0, origin=0)
Description
convolve1d.__doc__
Calculate a 1-D convolution along the given axis.
The lines of the array along the given axis are convolved with the
given weights.
Parameters
----------
input : array_like
The input array.
weights : ndarray
1-D sequence of numbers.
axis : int, optional
The axis of `input` along which to calculate. Default is -1.
output : array 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'.
cval : scalar, optional
Value to fill past edges of input if `mode` is 'constant'. Default
is 0.0.
origin : int, 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
-------
convolve1d : ndarray
Convolved array with same shape as input
Examples
--------
>>> from scipy.ndimage import convolve1d
>>> convolve1d([2, 8, 0, 4, 1, 9, 9, 0], weights=[1, 3])
array([14, 24, 4, 13, 12, 36, 27, 0])
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