[Scipy-svn] r5459 - in trunk/scipy: ndimage special
scipy-svn at scipy.org
scipy-svn at scipy.org
Tue Jan 13 03:25:01 EST 2009
Author: stefan
Date: 2009-01-13 02:24:40 -0600 (Tue, 13 Jan 2009)
New Revision: 5459
Modified:
trunk/scipy/ndimage/filters.py
trunk/scipy/special/orthogonal.py
Log:
Apply documentation patch.
Modified: trunk/scipy/ndimage/filters.py
===================================================================
--- trunk/scipy/ndimage/filters.py 2009-01-13 08:12:45 UTC (rev 5458)
+++ trunk/scipy/ndimage/filters.py 2009-01-13 08:24:40 UTC (rev 5459)
@@ -520,19 +520,31 @@
@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)s
+ input : array-like
+ input array to filter
weights : ndarray
array of weights, same number of dimensions as input
- %(output)s
- %(mode)s
- %(cval)s
- %(origin)s
+ 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
+
"""
return _correlate_or_convolve(input, weights, output, mode, cval,
origin, True)
@@ -859,16 +871,40 @@
@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)s
- %(size_foot)s
- %(output)s
- %(mode)s
- %(cval)s
- %(origin)s
+ 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
+
"""
return _rank_filter(input, 0, size, footprint, output, mode, cval,
origin, 'median')
Modified: trunk/scipy/special/orthogonal.py
===================================================================
--- trunk/scipy/special/orthogonal.py 2009-01-13 08:12:45 UTC (rev 5458)
+++ trunk/scipy/special/orthogonal.py 2009-01-13 08:24:40 UTC (rev 5459)
@@ -1,8 +1,3 @@
-#!/usr/bin/env python
-#
-# Author: Travis Oliphant 2000
-# Updated Sep. 2003 (fixed bugs --- tested to be accurate)
-
"""
A collection of functions to find the weights and abscissas for
Gaussian Quadrature.
@@ -14,30 +9,44 @@
Many recursion relations for orthogonal polynomials are given:
-a1n f_n+1 (x) = (a2n + a3n x ) f_n (x) - a4n f_n-1 (x)
+.. math::
+ a1n f_{n+1} (x) = (a2n + a3n x ) f_n (x) - a4n f_{n-1} (x)
+
The recursion relation of interest is
-P_n+1 (x) = (x - A_n) P_n (x) - B_n P_n-1 (x)
+.. math::
-where P has a different normalization than f.
+ P_{n+1} (x) = (x - A_n) P_n (x) - B_n P_{n-1} (x)
+where :math:`P` has a different normalization than :math:`f`.
+
The coefficients can be found as:
-A_n = -a2n / a3n
+.. math::
-B_n = ( a4n / a3n sqrt(h_n-1 / h_n))**2
+ A_n = -a2n / a3n
+ \\qquad
+ B_n = ( a4n / a3n \\sqrt{h_n-1 / h_n})^2
- where
- h_n = int_a^b w(x) f_n(x)^2
+where
+
+.. math::
+
+ h_n = \\int_a^b w(x) f_n(x)^2
+
assume:
-P_0(x) = 1
-P_-1(x) == 0
+.. math::
+
+ P_0 (x) = 1
+ \\qquad
+ P_{-1} (x) == 0
+
For the mathematical background, see [golub.welsch-1969-mathcomp]_ and
[abramowitz.stegun-1965]_.
-Functions:
+Functions::
gen_roots_and_weights -- Generic roots and weights.
j_roots -- Jacobi
@@ -66,7 +75,11 @@
Mathematical Functions: with Formulas, Graphs, and Mathematical
Tables*. Gaithersburg, MD: National Bureau of Standards.
http://www.math.sfu.ca/~cbm/aands/
+
"""
+#
+# Author: Travis Oliphant 2000
+# Updated Sep. 2003 (fixed bugs --- tested to be accurate)
# Scipy imports.
import numpy as np
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