[Scipy-svn] r5460 - trunk/scipy/ndimage
scipy-svn at scipy.org
scipy-svn at scipy.org
Tue Jan 13 03:33:08 EST 2009
Author: stefan
Date: 2009-01-13 02:32:53 -0600 (Tue, 13 Jan 2009)
New Revision: 5460
Modified:
trunk/scipy/ndimage/filters.py
Log:
Revert doc changes to ndimage/filters to avoid breaking templates.
Modified: trunk/scipy/ndimage/filters.py
===================================================================
--- trunk/scipy/ndimage/filters.py 2009-01-13 08:24:40 UTC (rev 5459)
+++ trunk/scipy/ndimage/filters.py 2009-01-13 08:32:53 UTC (rev 5460)
@@ -520,31 +520,19 @@
@docfiller
def convolve(input, weights, output = None, mode = 'reflect', cval = 0.0,
origin = 0):
- """
- Multi-dimensional convolution.
+ """Multi-dimensional convolution.
The array is convolved with the given kernel.
Parameters
----------
- input : array-like
- input array to filter
+ %(input)s
weights : ndarray
array of weights, same number of dimensions as input
- output : array, optional
- The ``output`` parameter passes an array in which to store the
- filter output.
- mode : {'reflect','constant','nearest','mirror', 'wrap'}, optional
- The ``mode`` parameter determines how the array borders are
- handled, where ``cval`` is the value when mode is equal to
- 'constant'. Default is 'reflect'
- cval : scalar, optional
- Value to fill past edges of input if ``mode`` is 'constant'. Default
- is 0.0
- origin : scalar, optional
- The ``origin`` parameter controls the placement of the filter.
- Default 0
-
+ %(output)s
+ %(mode)s
+ %(cval)s
+ %(origin)s
"""
return _correlate_or_convolve(input, weights, output, mode, cval,
origin, True)
@@ -871,40 +859,16 @@
@docfiller
def median_filter(input, size = None, footprint = None, output = None,
mode = "reflect", cval = 0.0, origin = 0):
- """
- Calculates a multi-dimensional median filter.
+ """Calculates a multi-dimensional median filter.
Parameters
----------
- input : array-like
- input array to filter
- size : scalar or tuple, optional
- See footprint, below
- 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).
- output : array, optional
- The ``output`` parameter passes an array in which to store the
- filter output.
- mode : {'reflect','constant','nearest','mirror', 'wrap'}, optional
- The ``mode`` parameter determines how the array borders are
- handled, where ``cval`` is the value when mode is equal to
- 'constant'. Default is 'reflect'
- cval : scalar, optional
- Value to fill past edges of input if ``mode`` is 'constant'. Default
- is 0.0
- origin : scalar, optional
- The ``origin`` parameter controls the placement of the filter.
- Default 0
-
+ %(input)s
+ %(size_foot)s
+ %(output)s
+ %(mode)s
+ %(cval)s
+ %(origin)s
"""
return _rank_filter(input, 0, size, footprint, output, mode, cval,
origin, 'median')
More information about the Scipy-svn
mailing list