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Multidimensional gradient magnitude using Gaussian derivatives.
Parameters
----------
input : array_like
The input array.
sigma : scalar or sequence of scalars
The standard deviations of the Gaussian filter are given for
each axis as a sequence, or as a single number, in which case
it is equal for all axes.
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 : str 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. 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-constant'
This is a synonym for 'constant'.
'grid-mirror'
This is a synonym for 'reflect'.
'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.
Extra keyword arguments will be passed to gaussian_filter().
Returns
-------
gaussian_gradient_magnitude : ndarray
Filtered array. Has the same shape as `input`.
Examples
--------
>>> from scipy import ndimage, misc
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure()
>>> plt.gray() # show the filtered result in grayscale
>>> ax1 = fig.add_subplot(121) # left side
>>> ax2 = fig.add_subplot(122) # right side
>>> ascent = misc.ascent()
>>> result = ndimage.gaussian_gradient_magnitude(ascent, sigma=5)
>>> ax1.imshow(ascent)
>>> ax2.imshow(result)
>>> plt.show()
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