[Scipy-svn] r5460 - trunk/scipy/ndimage

scipy-svn@scip... scipy-svn@scip...
Tue Jan 13 02:33:08 CST 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')



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