[Scipysvn] r5283  trunk/doc/source/tutorial
scipysvn@scip...
scipysvn@scip...
Sat Dec 20 05:53:53 CST 2008
Author: david.wardefarley
Date: 20081220 05:53:50 0600 (Sat, 20 Dec 2008)
New Revision: 5283
Modified:
trunk/doc/source/tutorial/ndimage.rst
Log:
Yet more markup cleanup, :obj: is now used where functions are introduced and currentmodule:: has been introduced so as to enable crossreferences to generated docs.
Modified: trunk/doc/source/tutorial/ndimage.rst
===================================================================
 trunk/doc/source/tutorial/ndimage.rst 20081220 11:21:30 UTC (rev 5282)
+++ trunk/doc/source/tutorial/ndimage.rst 20081220 11:53:50 UTC (rev 5283)
@@ 5,7 +5,6 @@
.. currentmodule:: scipy.ndimage

.. _ndimageintroduction:
Introduction
@@ 58,6 +57,8 @@
Filter functions

+.. currentmodule:: scipy.ndimage.filters
+
The functions described in this section all perform some type of spatial filtering of the the input array: the elements in the output are some function of the values in the neighborhood of the corresponding input element. We refer to this neighborhood of elements as the filter kernel, which is often
rectangular in shape but may also have an arbitrary footprint. Many
of the functions described below allow you to define the footprint
@@ 139,7 +140,7 @@
"constant" Use a constant value, default is 0.0 [1 2 3]>[0 1 2 3 0]
========== ==================================== ====================
The {"constant"} mode is special since it needs an additional
+The "constant" mode is special since it needs an additional
parameter to specify the constant value that should be used.
{The easiest way to implement such boundary conditions would be to
@@ 152,17 +153,17 @@
Correlation and convolution

 The :func:`correlate1d` function calculates a onedimensional correlation
+ The :obj:`correlate1d` function calculates a onedimensional correlation
along the given axis. The lines of the array along the given axis
are correlated with the given *weights*. The *weights* parameter
must be a onedimensional sequences of numbers.
 The function :func:`correlate` implements multidimensional correlation
+ The function :obj:`correlate` implements multidimensional correlation
of the input array with a given kernel.
 The :func:`convolve1d` function calculates a onedimensional convolution
+ The :obj:`convolve1d` function calculates a onedimensional convolution
along the given axis. The lines of the array along the given axis
are convoluted with the given *weights*. The *weights* parameter
must be a onedimensional sequences of numbers.
@@ 172,7 +173,7 @@
in the case of a correlation: the result is shifted in the opposite
directions.}
 The function :func:`convolve` implements multidimensional convolution of
+ The function :obj:`convolve` implements multidimensional convolution of
the input array with a given kernel.
{A convolution is essentially a correlation after mirroring the
@@ 186,7 +187,7 @@

 The :func:`gaussian_filter1d` function implements a onedimensional
+ The :obj:`gaussian_filter1d` function implements a onedimensional
Gaussian filter. The standarddeviation of the Gaussian filter is
passed through the parameter *sigma*. Setting *order* = 0 corresponds
to convolution with a Gaussian kernel. An order of 1, 2, or 3
@@ 195,7 +196,7 @@
implemented.
 The :func:`gaussian_filter` function implements a multidimensional
+ The :obj:`gaussian_filter` function implements a multidimensional
Gaussian filter. The standarddeviations of the Gaussian filter
along each axis are passed through the parameter *sigma* as a
sequence or numbers. If *sigma* is not a sequence but a single
@@ 216,11 +217,11 @@
prevented by specifying a more precise output type.}
 The :func:`uniform_filter1d` function calculates a onedimensional
+ The :obj:`uniform_filter1d` function calculates a onedimensional
uniform filter of the given *size* along the given axis.
 The :func:`uniform_filter` implements a multidimensional uniform
+ The :obj:`uniform_filter` implements a multidimensional uniform
filter. The sizes of the uniform filter are given for each axis as
a sequence of integers by the *size* parameter. If *size* is not a
sequence, but a single number, the sizes along all axis are assumed
@@ 238,15 +239,15 @@
Filters based on order statistics

 The :func:`minimum_filter1d` function calculates a onedimensional
+ The :obj:`minimum_filter1d` function calculates a onedimensional
minimum filter of given *size* along the given axis.
 The :func:`maximum_filter1d` function calculates a onedimensional
+ The :obj:`maximum_filter1d` function calculates a onedimensional
maximum filter of given *size* along the given axis.
 The :func:`minimum_filter` function calculates a multidimensional
+ The :obj:`minimum_filter` function calculates a multidimensional
minimum filter. Either the sizes of a rectangular kernel or the
footprint of the kernel must be provided. The *size* parameter, if
provided, must be a sequence of sizes or a single number in which
@@ 255,7 +256,7 @@
shape of the kernel by its nonzero elements.
 The :func:`maximum_filter` function calculates a multidimensional
+ The :obj:`maximum_filter` function calculates a multidimensional
maximum filter. Either the sizes of a rectangular kernel or the
footprint of the kernel must be provided. The *size* parameter, if
provided, must be a sequence of sizes or a single number in which
@@ 264,8 +265,8 @@
shape of the kernel by its nonzero elements.
 The :func:`rank_filter` function calculates a multidimensional rank
 filter. The *rank* may be less then zero, i.e., *rank* =1 indicates
+ The :obj:`rank_filter` function calculates a multidimensional rank
+ filter. The *rank* may be less then zero, i.e., *rank* = 1 indicates
the largest element. Either the sizes of a rectangular kernel or
the footprint of the kernel must be provided. The *size* parameter,
if provided, must be a sequence of sizes or a single number in
@@ 274,9 +275,9 @@
the shape of the kernel by its nonzero elements.
 The :func:`percentile_filter` function calculates a multidimensional
+ The :obj:`percentile_filter` function calculates a multidimensional
percentile filter. The *percentile* may be less then zero, i.e.,
 *percentile* =20 equals *percentile* =80. Either the sizes of a
+ *percentile* = 20 equals *percentile* = 80. Either the sizes of a
rectangular kernel or the footprint of the kernel must be provided.
The *size* parameter, if provided, must be a sequence of sizes or a
single number in which case the size of the filter is assumed to be
@@ 285,7 +286,7 @@
elements.
 The :func:`median_filter` function calculates a multidimensional median
+ The :obj:`median_filter` function calculates a multidimensional median
filter. Either the sizes of a rectangular kernel or the footprint
of the kernel must be provided. The *size* parameter, if provided,
must be a sequence of sizes or a single number in which case the
@@ 298,16 +299,16 @@

Derivative filters can be constructed in several ways. The function
{gaussian_filter1d} described in section
+:func:`gaussian_filter1d` described in section
:ref:`ndimagefilterfunctionssmoothing` can be used to calculate
derivatives along a given axis using the *order* parameter. Other
derivative filters are the Prewitt and Sobel filters:
 The :func:`prewitt` function calculates a derivative along the given
+ The :obj:`prewitt` function calculates a derivative along the given
axis.
 The :func:`sobel` function calculates a derivative along the given
+ The :obj:`sobel` function calculates a derivative along the given
axis.
@@ 318,13 +319,17 @@
calculate the second derivative along a given direction and to
construct the Laplace filter:
 The function :func:`generic_laplace` calculates a laplace filter using
+ The function :obj:`generic_laplace` calculates a laplace filter using
the function passed through :func:`derivative2` to calculate second
derivatives. The function :func:`derivative2` should have the following
signature:
 {derivative2(input, axis, output, mode, cval, \*extra_arguments, \*\*extra_keywords)}
+ ::
+ derivative2(input, axis, output, mode, cval, *extra_arguments, **extra_keywords)
+
+
+
It should calculate the second derivative along the dimension
*axis*. If *output* is not None it should use that for the output
and return None, otherwise it should return the result. *mode*,
@@ 383,12 +388,12 @@
:func:`generic_laplace` by providing appropriate functions for the
second derivative function:
 The function :func:`laplace` calculates the Laplace using discrete
+ The function :obj:`laplace` calculates the Laplace using discrete
differentiation for the second derivative (i.e. convolution with
{[1, 2, 1]}).
 The function :func:`gaussian_laplace` calculates the Laplace using
+ The function :obj:`gaussian_laplace` calculates the Laplace using
:func:`gaussian_filter` to calculate the second derivatives. The
standarddeviations of the Gaussian filter along each axis are
passed through the parameter *sigma* as a sequence or numbers. If
@@ 401,13 +406,16 @@
generic Laplace function there is a :func:`generic_gradient_magnitude`
function that calculated the gradient magnitude of an array:
 The function :func:`generic_gradient_magnitude` calculates a gradient
+ The function :obj:`generic_gradient_magnitude` calculates a gradient
magnitude using the function passed through :func:`derivative` to
 calculate first derivatives. The function :func:`derivative` should have
+ calculate first derivatives. The function :obj:`derivative` should have
the following signature:
 {derivative(input, axis, output, mode, cval, \*extra_arguments, \*\*extra_keywords)}
+ ::
+ derivative(input, axis, output, mode, cval, *extra_arguments, **extra_keywords)
+
+
It should calculate the derivative along the dimension *axis*. If
*output* is not None it should use that for the output and return
None, otherwise it should return the result. *mode*, *cval* have
@@ 434,12 +442,12 @@
the *extra_arguments* and *extra_keywords* arguments.
The :func:`sobel` and :func:`prewitt` functions fit the required signature and
+The :obj:`sobel` and :func:`prewitt` functions fit the required signature and
can therefore directly be used with :func:`generic_gradient_magnitude`.
The following function implements the gradient magnitude using
Gaussian derivatives:
 The function :func:`gaussian_gradient_magnitude` calculates the
+ The function :obj:`gaussian_gradient_magnitude` calculates the
gradient magnitude using :func:`gaussian_filter` to calculate the first
derivatives. The standarddeviations of the Gaussian filter along
each axis are passed through the parameter *sigma* as a sequence or
@@ 461,10 +469,10 @@
also be written in C and passed using a CObject (see
:ref:`ndimageccallbacks` for more information).
 The :func:`generic_filter1d` function implements a generic
+ The :obj:`generic_filter1d` function implements a generic
onedimensional filter function, where the actual filtering
operation must be supplied as a python function (or other callable
 object). The :func:`generic_filter1d` function iterates over the lines
+ object). The :obj:`generic_filter1d` function iterates over the lines
of an array and calls :func:`function` at each line. The arguments that
are passed to :func:`function` are onedimensional arrays of the
{tFloat64} type. The first contains the values of the current line.
@@ 525,9 +533,9 @@
[51 56 62 65]]
 The :func:`generic_filter` function implements a generic filter
+ The :obj:`generic_filter` function implements a generic filter
function, where the actual filtering operation must be supplied as
 a python function (or other callable object). The :func:`generic_filter`
+ a python function (or other callable object). The :obj:`generic_filter`
function iterates over the array and calls :func:`function` at each
element. The argument of :func:`function` is a onedimensional array of
the {tFloat64} type, that contains the values around the current
@@ 708,26 +716,26 @@
transform. The parameter *axis* can be used to indicate along which
axis the real transform was executed.
 The :func:`fourier_shift` function multiplies the input array with the
+ The :obj:`fourier_shift` function multiplies the input array with the
multidimensional Fourier transform of a shift operation for the
given shift. The *shift* parameter is a sequences of shifts for
each dimension, or a single value for all dimensions.
 The :func:`fourier_gaussian` function multiplies the input array with
+ The :obj:`fourier_gaussian` function multiplies the input array with
the multidimensional Fourier transform of a Gaussian filter with
given standarddeviations *sigma*. The *sigma* parameter is a
sequences of values for each dimension, or a single value for all
dimensions.
 The :func:`fourier_uniform` function multiplies the input array with the
+ The :obj:`fourier_uniform` function multiplies the input array with the
multidimensional Fourier transform of a uniform filter with given
sizes *size*. The *size* parameter is a sequences of values for
each dimension, or a single value for all dimensions.
 The :func:`fourier_ellipsoid` function multiplies the input array with
+ The :obj:`fourier_ellipsoid` function multiplies the input array with
the multidimensional Fourier transform of a elliptically shaped
filter with given sizes *size*. The *size* parameter is a sequences
of values for each dimension, or a single value for all dimensions.
@@ 735,9 +743,14 @@
only implemented for dimensions 1, 2, and 3.}
+.. _ndimageinterpolation:
+
Interpolation functions

+.. currentmodule:: scipy.ndimage.interpolation
+
+
This section describes various interpolation functions that are
based on Bspline theory. A good introduction to Bsplines can be
found in: M. Unser, "Splines: A Perfect Fit for Signal and Image
@@ 754,13 +767,13 @@
functions. The following two functions implement the
prefiltering:
 The :func:`spline_filter1d` function calculates a onedimensional spline
+ The :obj:`spline_filter1d` function calculates a onedimensional spline
filter along the given axis. An output array can optionally be
provided. The order of the spline must be larger then 1 and less
than 6.
 The :func:`spline_filter` function calculates a multidimensional spline
+ The :obj:`spline_filter` function calculates a multidimensional spline
filter.
{The multidimensional filter is implemented as a sequence of
@@ 771,8 +784,6 @@
This can be prevented by specifying a output type of high precision.}
.. _ndimageinterpolation:

Interpolation functions

@@ 786,7 +797,7 @@
a *cval* parameter that gives a constant value in case that the {'constant'}
mode is used.
 The :func:`geometric_transform` function applies an arbitrary geometric
+ The :obj:`geometric_transform` function applies an arbitrary geometric
transform to the input. The given *mapping* function is called at
each point in the output to find the corresponding coordinates in
the input. *mapping* must be a callable object that accepts a tuple
@@ 840,7 +851,7 @@
{The mapping function can also be written in C and passed using a CObject. See :ref:`ndimageccallbacks` for more information.}
 The function :func:`map_coordinates` applies an arbitrary coordinate
+ The function :obj:`map_coordinates` applies an arbitrary coordinate
transformation using the given array of coordinates. The shape of
the output is derived from that of the coordinate array by dropping
the first axis. The parameter *coordinates* is used to find for
@@ 865,7 +876,7 @@
[ 1.3625 7. ]
 The :func:`affine_transform` function applies an affine transformation
+ The :obj:`affine_transform` function applies an affine transformation
to the input array. The given transformation *matrix* and *offset*
are used to find for each point in the output the corresponding
coordinates in the input. The value of the input at the calculated
@@ 879,15 +890,15 @@
shape and type.
 The :func:`shift` function returns a shifted version of the input, using
+ The :obj:`shift` function returns a shifted version of the input, using
spline interpolation of the requested *order*.
 The :func:`zoom` function returns a rescaled version of the input, using
+ The :obj:`zoom` function returns a rescaled version of the input, using
spline interpolation of the requested *order*.
 The :func:`rotate` function returns the input array rotated in the plane
+ The :obj:`rotate` function returns the input array rotated in the plane
defined by the two axes given by the parameter *axes*, using spline
interpolation of the requested *order*. The angle must be given in
degrees. If *reshape* is true, then the size of the output array is
@@ 899,7 +910,7 @@
Binary morphology

 The :func:`generate_binary_structure` functions generates a binary
+ The :obj:`generate_binary_structure` functions generates a binary
structuring element for use in binary morphology operations. The
*rank* of the structure must be provided. The size of the structure
that is returned is equal to three in each direction. The value of
@@ 923,7 +934,7 @@
Most binary morphology functions can be expressed in terms of the
basic operations erosion and dilation:
 The :func:`binary_erosion` function implements binary erosion of arrays
+ The :obj:`binary_erosion` function implements binary erosion of arrays
of arbitrary rank with the given structuring element. The origin
parameter controls the placement of the structuring element as
described in section :ref:`ndimagefilterfunctions`. If no
@@ 937,7 +948,7 @@
are modified at each iteration.
 The :func:`binary_dilation` function implements binary dilation of
+ The :obj:`binary_dilation` function implements binary dilation of
arrays of arbitrary rank with the given structuring element. The
origin parameter controls the placement of the structuring element
as described in section :ref:`ndimagefilterfunctions`. If no
@@ 970,15 +981,15 @@
[0 0 0 0 0]]
The :func:`binary_erosion` and :func:`binary_dilation` functions both have an
+The :obj:`binary_erosion` and :func:`binary_dilation` functions both have an
*iterations* parameter which allows the erosion or dilation to be
repeated a number of times. Repeating an erosion or a dilation with
a given structure *n* times is equivalent to an erosion or a
dilation with a structure that is {n1} times dilated with itself.
+dilation with a structure that is *n1* times dilated with itself.
A function is provided that allows the calculation of a structure
that is dilated a number of times with itself:
 The :func:`iterate_structure` function returns a structure by dilation
+ The :obj:`iterate_structure` function returns a structure by dilation
of the input structure *iteration*  1 times with itself. For
instance:
@@ 1018,7 +1029,7 @@
d dilation. Following functions provide a few of these operations
for convenience:
 The :func:`binary_opening` function implements binary opening of arrays
+ The :obj:`binary_opening` function implements binary opening of arrays
of arbitrary rank with the given structuring element. Binary
opening is equivalent to a binary erosion followed by a binary
dilation with the same structuring element. The origin parameter
@@ 1030,7 +1041,7 @@
number of dilations.
 The :func:`binary_closing` function implements binary closing of arrays
+ The :obj:`binary_closing` function implements binary closing of arrays
of arbitrary rank with the given structuring element. Binary
closing is equivalent to a binary dilation followed by a binary
erosion with the same structuring element. The origin parameter
@@ 1042,7 +1053,7 @@
same number of erosions.
 The :func:`binary_fill_holes` function is used to close holes in
+ The :obj:`binary_fill_holes` function is used to close holes in
objects in a binary image, where the structure defines the
connectivity of the holes. The origin parameter controls the
placement of the structuring element as described in section
@@ 1051,19 +1062,19 @@
using :func:`generate_binary_structure`.
 The :func:`binary_hit_or_miss` function implements a binary
+ The :obj:`binary_hit_or_miss` function implements a binary
hitormiss transform of arrays of arbitrary rank with the given
structuring elements. The hitormiss transform is calculated by
erosion of the input with the first structure, erosion of the
logical *not* of the input with the second structure, followed by
the logical *and* of these two erosions. The origin parameters
control the placement of the structuring elements as described in
 section :ref:`ndimagefilterfunctions`. If {origin2} equals None it
 is set equal to the {origin1} parameter. If the first structuring
+ section :ref:`ndimagefilterfunctions`. If *origin2* equals None it
+ is set equal to the *origin1* parameter. If the first structuring
element is not provided, a structuring element with connectivity
equal to one is generated using :func:`generate_binary_structure`, if
 {structure2} is not provided, it is set equal to the logical *not*
 of {structure1}.
+ *structure2* is not provided, it is set equal to the logical *not*
+ of *structure1*.
.. _ndimagegreymorphology:
@@ 1098,45 +1109,45 @@
Similar to binary erosion and dilation there are operations for
greyscale erosion and dilation:
 The :func:`grey_erosion` function calculates a multidimensional grey
+ The :obj:`grey_erosion` function calculates a multidimensional grey
scale erosion.
 The :func:`grey_dilation` function calculates a multidimensional grey
+ The :obj:`grey_dilation` function calculates a multidimensional grey
scale dilation.
Greyscale opening and closing operations can be defined similar to
their binary counterparts:
 The :func:`grey_opening` function implements greyscale opening of
+ The :obj:`grey_opening` function implements greyscale opening of
arrays of arbitrary rank. Greyscale opening is equivalent to a
greyscale erosion followed by a greyscale dilation.
 The :func:`grey_closing` function implements greyscale closing of
+ The :obj:`grey_closing` function implements greyscale closing of
arrays of arbitrary rank. Greyscale opening is equivalent to a
greyscale dilation followed by a greyscale erosion.
 The :func:`morphological_gradient` function implements a greyscale
+ The :obj:`morphological_gradient` function implements a greyscale
morphological gradient of arrays of arbitrary rank. The greyscale
morphological gradient is equal to the difference of a greyscale
dilation and a greyscale erosion.
 The :func:`morphological_laplace` function implements a greyscale
+ The :obj:`morphological_laplace` function implements a greyscale
morphological laplace of arrays of arbitrary rank. The greyscale
morphological laplace is equal to the sum of a greyscale dilation
and a greyscale erosion minus twice the input.
 The :func:`white_tophat` function implements a white tophat filter of
+ The :obj:`white_tophat` function implements a white tophat filter of
arrays of arbitrary rank. The white tophat is equal to the
difference of the input and a greyscale opening.
 The :func:`black_tophat` function implements a black tophat filter of
+ The :obj:`black_tophat` function implements a black tophat filter of
arrays of arbitrary rank. The black tophat is equal to the
difference of the a greyscale closing and the input.
@@ 1146,13 +1157,15 @@
Distance transforms

+.. currentmodule:: scipy.ndimage.morphology
+
Distance transforms are used to
calculate the minimum distance from each element of an object to
the background. The following functions implement distance
transforms for three different distance metrics: Euclidean, City
Block, and Chessboard distances.
 The function :func:`distance_transform_cdt` uses a chamfer type
+ The function :obj:`distance_transform_cdt` uses a chamfer type
algorithm to calculate the distance transform of the input, by
replacing each object element (defined by values larger than zero)
with the shortest distance to the background (all nonobject
@@ 1182,7 +1195,7 @@
27:321345, 1984.
 The function :func:`distance_transform_edt` calculates the exact
+ The function :obj:`distance_transform_edt` calculates the exact
euclidean distance transform of the input, by replacing each object
element (defined by values larger than zero) with the shortest
euclidean distance to the background (all nonobject elements).
@@ 1209,12 +1222,12 @@
in arbitrary dimensions. IEEE Trans. PAMI 25, 265270, 2003.
 The function :func:`distance_transform_bf` uses a bruteforce algorithm
+ The function :obj:`distance_transform_bf` uses a bruteforce algorithm
to calculate the distance transform of the input, by replacing each
object element (defined by values larger than zero) with the
shortest distance to the background (all nonobject elements). The
 metric must be one of {"euclidean"}, {"cityblock"}, or
 {"chessboard"}.
+ metric must be one of "euclidean", "cityblock", or
+ "chessboard".
In addition to the distance transform, the feature transform can be
calculated. In this case the index of the closest background
@@ 1260,10 +1273,10 @@
[0 0 0 0 1 0]]
The result is a binary image, in which the individual objects still
need to be identified and labeled. The function :func:`label` generates
+need to be identified and labeled. The function :obj:`label` generates
an array where each object is assigned a unique number:
 The :func:`label` function generates an array where the objects in the
+ The :obj:`label` function generates an array where the objects in the
input are labeled with an integer index. It returns a tuple
consisting of the array of object labels and the number of objects
found, unless the *output* parameter is given, in which case only
@@ 1323,13 +1336,13 @@
There is a large number of other approaches for segmentation, for
instance from an estimation of the borders of the objects that can
be obtained for instance by derivative filters. One such an
approach is watershed segmentation. The function :func:`watershed_ift`
+approach is watershed segmentation. The function :obj:`watershed_ift`
generates an array where each object is assigned a unique label,
from an array that localizes the object borders, generated for
instance by a gradient magnitude filter. It uses an array
containing initial markers for the objects:
 The :func:`watershed_ift` function applies a watershed from markers
+ The :obj:`watershed_ift` function applies a watershed from markers
algorithm, using an Iterative Forest Transform, as described in: P.
Felkel, R. Wegenkittl, and M. Bruckschwaiger, "Implementation and
Complexity of the WatershedfromMarkers Algorithm Computed as a
@@ 1364,8 +1377,8 @@
[1 1 2 2 2 1 1]
[1 1 1 1 1 1 1]]
 Here two markers were used to designate an object (marker=2) and
 the background (marker=1). The order in which these are processed
+ Here two markers were used to designate an object (*marker* = 2) and
+ the background (*marker* = 1). The order in which these are processed
is arbitrary: moving the marker for the background to the lower
right corner of the array yields a different result:
@@ 1387,7 +1400,7 @@
[1 1 1 1 1 1 1]
[1 1 1 1 1 1 1]]
 The result is that the object (marker=2) is smaller because the
+ The result is that the object (*marker* = 2) is smaller because the
second marker was processed earlier. This may not be the desired
effect if the first marker was supposed to designate a background
object. Therefore :func:`watershed_ift` treats markers with a negative
@@ 1436,15 +1449,19 @@
of the input to \\constant{UInt8} and \\constant{UInt16}.}
+.. _ndimageobjectmeasurements:
+
Object measurements

+.. currentmodule:: scipy.ndimage.measurements
+
Given an array of labeled objects, the properties of the individual
objects can be measured. The :func:`find_objects` function can be used
+objects can be measured. The :obj:`find_objects` function can be used
to generate a list of slices that for each object, give the
smallest subarray that fully contains the object:
 The :func:`find_objects` function finds all objects in a labeled array and
+ The :obj:`find_objects` function finds all objects in a labeled array and
returns a list of slices that correspond to the smallest regions in
the array that contains the object. For instance:
@@ 1534,28 +1551,28 @@
one result, return their result as a tuple if *index* is a single
number, or as a tuple of lists, if *index* is a sequence.
 The :func:`sum` function calculates the sum of the elements of the object
+ The :obj:`sum` function calculates the sum of the elements of the object
with label(s) given by *index*, using the *labels* array for the
object labels. If *index* is None, all elements with a nonzero
label value are treated as a single object. If *label* is None,
all elements of *input* are used in the calculation.
 The :func:`mean` function calculates the mean of the elements of the
+ The :obj:`mean` function calculates the mean of the elements of the
object with label(s) given by *index*, using the *labels* array for
the object labels. If *index* is None, all elements with a
nonzero label value are treated as a single object. If *label* is
None, all elements of *input* are used in the calculation.
 The :func:`variance` function calculates the variance of the elements of
+ The :obj:`variance` function calculates the variance of the elements of
the object with label(s) given by *index*, using the *labels* array
for the object labels. If *index* is None, all elements with a
nonzero label value are treated as a single object. If *label* is
None, all elements of *input* are used in the calculation.
 The :func:`standard_deviation` function calculates the standard
+ The :obj:`standard_deviation` function calculates the standard
deviation of the elements of the object with label(s) given by
*index*, using the *labels* array for the object labels. If *index*
is None, all elements with a nonzero label value are treated as
@@ 1563,21 +1580,21 @@
used in the calculation.
 The :func:`minimum` function calculates the minimum of the elements of
+ The :obj:`minimum` function calculates the minimum of the elements of
the object with label(s) given by *index*, using the *labels* array
for the object labels. If *index* is None, all elements with a
nonzero label value are treated as a single object. If *label* is
None, all elements of *input* are used in the calculation.
 The :func:`maximum` function calculates the maximum of the elements of
+ The :obj:`maximum` function calculates the maximum of the elements of
the object with label(s) given by *index*, using the *labels* array
for the object labels. If *index* is None, all elements with a
nonzero label value are treated as a single object. If *label* is
None, all elements of *input* are used in the calculation.
 The :func:`minimum_position` function calculates the position of the
+ The :obj:`minimum_position` function calculates the position of the
minimum of the elements of the object with label(s) given by
*index*, using the *labels* array for the object labels. If *index*
is None, all elements with a nonzero label value are treated as
@@ 1585,7 +1602,7 @@
used in the calculation.
 The :func:`maximum_position` function calculates the position of the
+ The :obj:`maximum_position` function calculates the position of the
maximum of the elements of the object with label(s) given by
*index*, using the *labels* array for the object labels. If *index*
is None, all elements with a nonzero label value are treated as
@@ 1593,7 +1610,7 @@
used in the calculation.
 The :func:`extrema` function calculates the minimum, the maximum, and
+ The :obj:`extrema` function calculates the minimum, the maximum, and
their positions, of the elements of the object with label(s) given
by *index*, using the *labels* array for the object labels. If
*index* is None, all elements with a nonzero label value are
@@ 1606,7 +1623,7 @@
above.
 The :func:`center_of_mass` function calculates the center of mass of
+ The :obj:`center_of_mass` function calculates the center of mass of
the of the object with label(s) given by *index*, using the
*labels* array for the object labels. If *index* is None, all
elements with a nonzero label value are treated as a single
@@ 1614,7 +1631,7 @@
the calculation.
 The :func:`histogram` function calculates a histogram of the of the
+ The :obj:`histogram` function calculates a histogram of the of the
object with label(s) given by *index*, using the *labels* array for
the object labels. If *index* is None, all elements with a
nonzero label value are treated as a single object. If *label* is
@@ 1626,10 +1643,10 @@
.. _ndimageccallbacks:
Extending *ndimage* in C

+Extending :mod:`ndimage` in C
+
{C callback functions} A few functions in the {numarray.ndimage} take a callback argument. This can be a python function, but also a CObject containing a pointer to a C function. To use this feature, you must write your own C extension that defines the function, and define a python function that
+A few functions in the :mod:`scipy.ndimage` take a callback argument. This can be a python function, but also a CObject containing a pointer to a C function. To use this feature, you must write your own C extension that defines the function, and define a python function that
returns a CObject containing a pointer to this function.
An example of a function that supports this is
@@ 1745,7 +1762,7 @@
Functions that support C callback functions

The :func:`ndimage` functions that support C callback functions are
+The :mod:`ndimage` functions that support C callback functions are
described here. Obviously, the prototype of the function that is
provided to these functions must match exactly that what they
expect. Therefore we give here the prototypes of the callback
@@ 1773,7 +1790,6 @@
calculated valued should be returned in the *return_value*
argument.

The function :func:`generic_filter1d` (see section
:ref:`ndimagegenericfilters`) accepts a callback function with the
following prototype:
@@ 1788,7 +1804,6 @@
in the array passed through *output_line*. The length of the
output line is passed through *output_length*.

The function :func:`geometric_transform` (see section
:ref:`ndimageinterpolation`) expects a function with the following
prototype:
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