Module « scipy.ndimage »
Signature de la fonction rank_filter
def rank_filter(input, rank, size=None, footprint=None, output=None, mode='reflect', cval=0.0, origin=0)
Description
rank_filter.__doc__
Calculate a multidimensional rank filter.
Parameters
----------
input : array_like
The input array.
rank : int
The rank parameter may be less then zero, i.e., rank = -1
indicates the largest element.
size : scalar or tuple, optional
See footprint, below. Ignored if footprint is given.
footprint : array, optional
Either `size` or `footprint` must be defined. `size` gives
the shape that is taken from the input array, at every element
position, to define the input to the filter function.
`footprint` is a boolean array that specifies (implicitly) a
shape, but also which of the elements within this shape will get
passed to the filter function. Thus ``size=(n,m)`` is equivalent
to ``footprint=np.ones((n,m))``. We adjust `size` to the number
of dimensions of the input array, so that, if the input array is
shape (10,10,10), and `size` is 2, then the actual size used is
(2,2,2). When `footprint` is given, `size` is ignored.
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 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.
Returns
-------
rank_filter : 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.rank_filter(ascent, rank=42, size=20)
>>> ax1.imshow(ascent)
>>> ax2.imshow(result)
>>> plt.show()
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