[Numpy-svn] r4521 - in branches/numpy.scons: . numpy/doc/swig numpy/doc/swig/doc numpy/doc/swig/test numpy/fft numpy/lib numpy/random/mtrand numpy/random/tests

numpy-svn at scipy.org numpy-svn at scipy.org
Sun Dec 2 05:47:40 EST 2007 

Author: cdavid
Date: 2007-12-02 04:47:00 -0600 (Sun, 02 Dec 2007)
New Revision: 4521

Added:
   branches/numpy.scons/numpy/doc/swig/pyfragments.swg
Modified:
   branches/numpy.scons/
   branches/numpy.scons/numpy/doc/swig/doc/numpy_swig.html
   branches/numpy.scons/numpy/doc/swig/doc/numpy_swig.pdf
   branches/numpy.scons/numpy/doc/swig/doc/numpy_swig.txt
   branches/numpy.scons/numpy/doc/swig/numpy.i
   branches/numpy.scons/numpy/doc/swig/test/Makefile
   branches/numpy.scons/numpy/doc/swig/test/setup.py
   branches/numpy.scons/numpy/fft/fftpack.py
   branches/numpy.scons/numpy/lib/utils.py
   branches/numpy.scons/numpy/random/mtrand/mtrand.c
   branches/numpy.scons/numpy/random/mtrand/mtrand.pyx
   branches/numpy.scons/numpy/random/tests/test_random.py
Log:
Merged revisions 4486-4510 via svnmerge from 
http://svn.scipy.org/svn/numpy/trunk

........
  r4491 | wfspotz at sandia.gov | 2007-11-23 01:14:03 +0900 (Fri, 23 Nov 2007) | 1 line
  
  Added pyfragments.swg, which expands integer type-checking to accept numpy array scalars
........
  r4492 | wfspotz at sandia.gov | 2007-11-23 03:34:50 +0900 (Fri, 23 Nov 2007) | 1 line
  
  Updated documentation to include pyfragments.swg explanation
........
  r4493 | stefan | 2007-11-23 23:53:33 +0900 (Fri, 23 Nov 2007) | 3 lines
  
  Update SWIG typemaps to be C89 compliant (patch by Matthieu Brucher).
  Fix filenames in setup.py.  Fix tests in Makefile.
........
  r4494 | stefan | 2007-11-24 00:20:41 +0900 (Sat, 24 Nov 2007) | 2 lines
  
  Fix randint for negative interval.
........
  r4495 | wfspotz at sandia.gov | 2007-11-24 04:24:59 +0900 (Sat, 24 Nov 2007) | 5 lines
  
  Changed numpy.i so that helper macros and helper functions are now
  implemented as swig %fragments.  This allows me to implement the
  backward compatibility macros without duplicating them.
........
  r4496 | wfspotz at sandia.gov | 2007-11-24 04:30:04 +0900 (Sat, 24 Nov 2007) | 1 line
  
  Fixed some typos in the comments
........
  r4497 | wfspotz at sandia.gov | 2007-11-24 04:43:20 +0900 (Sat, 24 Nov 2007) | 1 line
  
  Changed #define to %#define so that they delay implementation
........
  r4498 | wfspotz at sandia.gov | 2007-11-24 07:39:37 +0900 (Sat, 24 Nov 2007) | 1 line
  
  Added %fragment 'NumPy_Fragments' for convenience
........
  r4499 | wfspotz at sandia.gov | 2007-11-24 11:04:19 +0900 (Sat, 24 Nov 2007) | 1 line
  
  Added new fragment dependency to unsigned long fragment
........
  r4500 | wfspotz at sandia.gov | 2007-11-24 11:19:50 +0900 (Sat, 24 Nov 2007) | 1 line
  
  Added min and max macros to Backward Compatibility fragments
........
  r4506 | jarrod.millman | 2007-11-26 14:08:55 +0900 (Mon, 26 Nov 2007) | 2 lines
  
  using identity testing for None
........
  r4507 | jarrod.millman | 2007-11-26 14:37:46 +0900 (Mon, 26 Nov 2007) | 2 lines
  
  replaced apply() with direct function call
........



Property changes on: branches/numpy.scons
___________________________________________________________________
Name: svnmerge-integrated
   - /branches/distutils-revamp:1-2752 /branches/multicore:1-3687 /trunk:1-4485
   + /branches/distutils-revamp:1-2752 /branches/multicore:1-3687 /trunk:1-4510

Modified: branches/numpy.scons/numpy/doc/swig/doc/numpy_swig.html
===================================================================
--- branches/numpy.scons/numpy/doc/swig/doc/numpy_swig.html	2007-12-02 09:43:04 UTC (rev 4520)
+++ branches/numpy.scons/numpy/doc/swig/doc/numpy_swig.html	2007-12-02 10:47:00 UTC (rev 4521)
@@ -5,7 +5,7 @@
 <meta name="generator" content="Docutils 0.4: http://docutils.sourceforge.net/" />
 <title>numpy.i: a SWIG Interface File for NumPy</title>
 <meta name="author" content="Bill Spotz" />
-<meta name="date" content="13 September, 2007" />
+<meta name="date" content="22 November, 2007" />
 <style type="text/css">
 
 /*
@@ -302,7 +302,7 @@
 <tr class="field"><th class="docinfo-name">Institution:</th><td class="field-body">Sandia National Laboratories</td>
 </tr>
 <tr><th class="docinfo-name">Date:</th>
-<td>13 September, 2007</td></tr>
+<td>22 November, 2007</td></tr>
 </tbody>
 </table>
 <div class="contents topic">
@@ -319,20 +319,24 @@
 <li><a class="reference" href="#other-common-types-complex" id="id9" name="id9">Other Common Types: complex</a></li>
 </ul>
 </li>
-<li><a class="reference" href="#helper-functions" id="id10" name="id10">Helper Functions</a><ul>
-<li><a class="reference" href="#macros" id="id11" name="id11">Macros</a></li>
-<li><a class="reference" href="#routines" id="id12" name="id12">Routines</a></li>
+<li><a class="reference" href="#numpy-array-scalars-and-swig" id="id10" name="id10">NumPy Array Scalars and SWIG</a><ul>
+<li><a class="reference" href="#why-is-there-a-second-file" id="id11" name="id11">Why is There a Second File?</a></li>
 </ul>
 </li>
-<li><a class="reference" href="#beyond-the-provided-typemaps" id="id13" name="id13">Beyond the Provided Typemaps</a><ul>
-<li><a class="reference" href="#a-common-example" id="id14" name="id14">A Common Example</a></li>
-<li><a class="reference" href="#other-situations" id="id15" name="id15">Other Situations</a></li>
-<li><a class="reference" href="#a-final-note" id="id16" name="id16">A Final Note</a></li>
+<li><a class="reference" href="#helper-functions" id="id12" name="id12">Helper Functions</a><ul>
+<li><a class="reference" href="#macros" id="id13" name="id13">Macros</a></li>
+<li><a class="reference" href="#routines" id="id14" name="id14">Routines</a></li>
 </ul>
 </li>
-<li><a class="reference" href="#summary" id="id17" name="id17">Summary</a></li>
-<li><a class="reference" href="#acknowledgements" id="id18" name="id18">Acknowledgements</a></li>
+<li><a class="reference" href="#beyond-the-provided-typemaps" id="id15" name="id15">Beyond the Provided Typemaps</a><ul>
+<li><a class="reference" href="#a-common-example" id="id16" name="id16">A Common Example</a></li>
+<li><a class="reference" href="#other-situations" id="id17" name="id17">Other Situations</a></li>
+<li><a class="reference" href="#a-final-note" id="id18" name="id18">A Final Note</a></li>
 </ul>
+</li>
+<li><a class="reference" href="#summary" id="id19" name="id19">Summary</a></li>
+<li><a class="reference" href="#acknowledgements" id="id20" name="id20">Acknowledgements</a></li>
+</ul>
 </div>
 <div class="section">
 <h1><a class="toc-backref" href="#id1" id="introduction" name="introduction">Introduction</a></h1>
@@ -703,12 +707,65 @@
 </div>
 </div>
 <div class="section">
-<h1><a class="toc-backref" href="#id10" id="helper-functions" name="helper-functions">Helper Functions</a></h1>
+<h1><a class="toc-backref" href="#id10" id="numpy-array-scalars-and-swig" name="numpy-array-scalars-and-swig">NumPy Array Scalars and SWIG</a></h1>
+<p><a class="reference" href="http://www.swig.org">SWIG</a> has sophisticated type checking for numerical types.  For
+example, if your C/C++ routine expects an integer as input, the code
+generated by <a class="reference" href="http://www.swig.org">SWIG</a> will check for both <a class="reference" href="http://www.python.org">python</a> integers and
+<a class="reference" href="http://www.python.org">python</a> long integers, and raise an overflow error if the provided
+<a class="reference" href="http://www.python.org">python</a> integer is too big to cast down to a C integer.  With the
+introduction of <a class="reference" href="http://numpy.scipy.org">NumPy</a> scalar arrays into your <a class="reference" href="http://www.python.org">python</a> code, you
+might conceivably extract an integer from a <a class="reference" href="http://numpy.scipy.org">NumPy</a> array and attempt
+to pass this to a <a class="reference" href="http://www.swig.org">SWIG</a>-wrapped C/C++ function that expects an
+<tt class="docutils literal"><span class="pre">int</span></tt>, but the <a class="reference" href="http://www.swig.org">SWIG</a> type checking will not recognize the <a class="reference" href="http://numpy.scipy.org">NumPy</a>
+array scalar as an integer.  (Often, this does in fact work -- it
+depends on whether <a class="reference" href="http://numpy.scipy.org">NumPy</a> recognizes the integer type you are using
+as inheriting from the <a class="reference" href="http://www.python.org">python</a> integer type on the platform you are
+using.  Often, this means that code that works on a 32-bit machine
+will fail on a 64-bit machine.)</p>
+<p>If you get a <a class="reference" href="http://www.python.org">python</a> error that looks like the following:</p>
+<pre class="literal-block">
+TypeError: in method 'MyClass_MyMethod', argument 2 of type 'int'
+</pre>
+<p>and the argument you are passing is an integer extracted from a
+<a class="reference" href="http://numpy.scipy.org">NumPy</a> array, then you have stumbled upon this problem.  The
+solution is to modify the <a class="reference" href="http://www.swig.org">SWIG</a> type conversion system to accept
+<a class="reference" href="http://numpy.scipy.org">Numpy</a> array scalars in addition to the standard integer types.
+Fortunately, this capabilitiy has been provided to you.  Simply copy
+the file:</p>
+<pre class="literal-block">
+pyfragments.swg
+</pre>
+<p>to the working build directory for you project, and this problem will
+be fixed.  It is suggested that you do this anyway, as it only
+increases the capabilities of your <a class="reference" href="http://www.python.org">python</a> interface.</p>
+<div class="section">
+<h2><a class="toc-backref" href="#id11" id="why-is-there-a-second-file" name="why-is-there-a-second-file">Why is There a Second File?</a></h2>
+<p>The <a class="reference" href="http://www.swig.org">SWIG</a> type checking and conversion system is a complicated
+combination of C macros, <a class="reference" href="http://www.swig.org">SWIG</a> macros, <a class="reference" href="http://www.swig.org">SWIG</a> typemaps and <a class="reference" href="http://www.swig.org">SWIG</a>
+fragments.  Fragments are a way to conditionally insert code into your
+wrapper file if it is needed, and not insert it if not needed.  If
+multiple typemaps require the same fragment, the fragment only gets
+inserted into your wrapper code once.</p>
+<p>There is a fragment for converting a <a class="reference" href="http://www.python.org">python</a> integer to a C
+<tt class="docutils literal"><span class="pre">long</span></tt>.  There is a different fragment that converts a <a class="reference" href="http://www.python.org">python</a>
+integer to a C <tt class="docutils literal"><span class="pre">int</span></tt>, that calls the rountine defined in the
+<tt class="docutils literal"><span class="pre">long</span></tt> fragment.  We can make the changes we want here by changing
+the definition for the <tt class="docutils literal"><span class="pre">long</span></tt> fragment.  <a class="reference" href="http://www.swig.org">SWIG</a> determines the
+active definition for a fragment using a "first come, first served"
+system.  That is, we need to define the fragment for <tt class="docutils literal"><span class="pre">long</span></tt>
+conversions prior to <a class="reference" href="http://www.swig.org">SWIG</a> doing it internally.  <a class="reference" href="http://www.swig.org">SWIG</a> allows us
+to do this by putting our fragment definitions in the file
+<tt class="docutils literal"><span class="pre">pyfragments.swg</span></tt>.  If we were to put the new fragment definitions
+in <tt class="docutils literal"><span class="pre">numpy.i</span></tt>, they would be ignored.</p>
+</div>
+</div>
+<div class="section">
+<h1><a class="toc-backref" href="#id12" id="helper-functions" name="helper-functions">Helper Functions</a></h1>
 <p>The <tt class="docutils literal"><span class="pre">numpy.i</span></tt> file containes several macros and routines that it
 uses internally to build its typemaps.  However, these functions may
 be useful elsewhere in your interface file.</p>
 <div class="section">
-<h2><a class="toc-backref" href="#id11" id="macros" name="macros">Macros</a></h2>
+<h2><a class="toc-backref" href="#id13" id="macros" name="macros">Macros</a></h2>
 <blockquote>
 <dl class="docutils">
 <dt><strong>is_array(a)</strong></dt>
@@ -740,7 +797,7 @@
 </blockquote>
 </div>
 <div class="section">
-<h2><a class="toc-backref" href="#id12" id="routines" name="routines">Routines</a></h2>
+<h2><a class="toc-backref" href="#id14" id="routines" name="routines">Routines</a></h2>
 <blockquote>
 <p><strong>pytype_string()</strong></p>
 <blockquote>
@@ -900,11 +957,11 @@
 </div>
 </div>
 <div class="section">
-<h1><a class="toc-backref" href="#id13" id="beyond-the-provided-typemaps" name="beyond-the-provided-typemaps">Beyond the Provided Typemaps</a></h1>
+<h1><a class="toc-backref" href="#id15" id="beyond-the-provided-typemaps" name="beyond-the-provided-typemaps">Beyond the Provided Typemaps</a></h1>
 <p>There are many C or C++ array/<a class="reference" href="http://numpy.scipy.org">NumPy</a> array situations not covered by
 a simple <tt class="docutils literal"><span class="pre">%include</span> <span class="pre">"numpy.i"</span></tt> and subsequent <tt class="docutils literal"><span class="pre">%apply</span></tt> directives.</p>
 <div class="section">
-<h2><a class="toc-backref" href="#id14" id="a-common-example" name="a-common-example">A Common Example</a></h2>
+<h2><a class="toc-backref" href="#id16" id="a-common-example" name="a-common-example">A Common Example</a></h2>
 <p>Consider a reasonable prototype for a dot product function:</p>
 <pre class="literal-block">
 double dot(int len, double* vec1, double* vec2);
@@ -960,7 +1017,7 @@
 macro to perform this task.</p>
 </div>
 <div class="section">
-<h2><a class="toc-backref" href="#id15" id="other-situations" name="other-situations">Other Situations</a></h2>
+<h2><a class="toc-backref" href="#id17" id="other-situations" name="other-situations">Other Situations</a></h2>
 <p>There are other wrapping situations in which <tt class="docutils literal"><span class="pre">numpy.i</span></tt> may be
 helpful when you encounter them.</p>
 <blockquote>
@@ -997,7 +1054,7 @@
 </blockquote>
 </div>
 <div class="section">
-<h2><a class="toc-backref" href="#id16" id="a-final-note" name="a-final-note">A Final Note</a></h2>
+<h2><a class="toc-backref" href="#id18" id="a-final-note" name="a-final-note">A Final Note</a></h2>
 <p>When you use the <tt class="docutils literal"><span class="pre">%apply</span></tt> directive, as is usually necessary to use
 <tt class="docutils literal"><span class="pre">numpy.i</span></tt>, it will remain in effect until you tell <a class="reference" href="http://www.swig.org">SWIG</a> that it
 shouldn't be.  If the arguments to the functions or methods that you
@@ -1015,7 +1072,7 @@
 </div>
 </div>
 <div class="section">
-<h1><a class="toc-backref" href="#id17" id="summary" name="summary">Summary</a></h1>
+<h1><a class="toc-backref" href="#id19" id="summary" name="summary">Summary</a></h1>
 <p>Out of the box, <tt class="docutils literal"><span class="pre">numpy.i</span></tt> provides typemaps that support conversion
 between <a class="reference" href="http://numpy.scipy.org">NumPy</a> arrays and C arrays:</p>
 <blockquote>
@@ -1049,21 +1106,21 @@
 </blockquote>
 </div>
 <div class="section">
-<h1><a class="toc-backref" href="#id18" id="acknowledgements" name="acknowledgements">Acknowledgements</a></h1>
+<h1><a class="toc-backref" href="#id20" id="acknowledgements" name="acknowledgements">Acknowledgements</a></h1>
 <p>Many people have worked to glue <a class="reference" href="http://www.swig.org">SWIG</a> and <a class="reference" href="http://numpy.scipy.org">NumPy</a> together (as well
 as <a class="reference" href="http://www.swig.org">SWIG</a> and the predecessors of <a class="reference" href="http://numpy.scipy.org">NumPy</a>, Numeric and numarray).
 The effort to standardize this work into <tt class="docutils literal"><span class="pre">numpy.i</span></tt> began at the 2005
 <a class="reference" href="http://scipy.org">SciPy</a> Conference with a conversation between
 Fernando Perez and myself.  Fernando collected helper functions and
-typemaps from Michael Hunter, Anna Omelchenko and Michael Sanner.
-Sebastian Hasse has also provided additional error checking and use
-cases.  The work of these contributors has made this end result
-possible.</p>
+typemaps from Eric Jones, Michael Hunter, Anna Omelchenko and Michael
+Sanner.  Sebastian Hasse has also provided additional error checking
+and use cases.  The work of these contributors has made this end
+result possible.</p>
 </div>
 </div>
 <div class="footer">
 <hr class="footer" />
-Generated on: 2007-09-13 17:08 UTC.
+Generated on: 2007-11-22 18:34 UTC.
 Generated by <a class="reference" href="http://docutils.sourceforge.net/">Docutils</a> from <a class="reference" href="http://docutils.sourceforge.net/rst.html">reStructuredText</a> source.
 
 </div>

Modified: branches/numpy.scons/numpy/doc/swig/doc/numpy_swig.pdf
===================================================================
--- branches/numpy.scons/numpy/doc/swig/doc/numpy_swig.pdf	2007-12-02 09:43:04 UTC (rev 4520)
+++ branches/numpy.scons/numpy/doc/swig/doc/numpy_swig.pdf	2007-12-02 10:47:00 UTC (rev 4521)
@@ -60,1027 +60,1275 @@
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 %%EOF

Modified: branches/numpy.scons/numpy/doc/swig/doc/numpy_swig.txt
===================================================================
--- branches/numpy.scons/numpy/doc/swig/doc/numpy_swig.txt	2007-12-02 09:43:04 UTC (rev 4520)
+++ branches/numpy.scons/numpy/doc/swig/doc/numpy_swig.txt	2007-12-02 10:47:00 UTC (rev 4521)
@@ -4,7 +4,7 @@
 
 :Author:      Bill Spotz
 :Institution: Sandia National Laboratories
-:Date:        13 September, 2007
+:Date:        22 November, 2007
 
 .. contents::
 
@@ -387,6 +387,63 @@
 the user's complex type substituted for the first argument) should
 work.
 
+NumPy Array Scalars and SWIG
+============================
+
+`SWIG`_ has sophisticated type checking for numerical types.  For
+example, if your C/C++ routine expects an integer as input, the code
+generated by `SWIG`_ will check for both `python`_ integers and
+`python`_ long integers, and raise an overflow error if the provided
+`python`_ integer is too big to cast down to a C integer.  With the
+introduction of `NumPy`_ scalar arrays into your `python`_ code, you
+might conceivably extract an integer from a `NumPy`_ array and attempt
+to pass this to a `SWIG`_-wrapped C/C++ function that expects an
+``int``, but the `SWIG`_ type checking will not recognize the `NumPy`_
+array scalar as an integer.  (Often, this does in fact work -- it
+depends on whether `NumPy`_ recognizes the integer type you are using
+as inheriting from the `python`_ integer type on the platform you are
+using.  Often, this means that code that works on a 32-bit machine
+will fail on a 64-bit machine.)
+
+If you get a `python`_ error that looks like the following::
+
+    TypeError: in method 'MyClass_MyMethod', argument 2 of type 'int'
+
+and the argument you are passing is an integer extracted from a
+`NumPy`_ array, then you have stumbled upon this problem.  The
+solution is to modify the `SWIG`_ type conversion system to accept
+`Numpy`_ array scalars in addition to the standard integer types.
+Fortunately, this capabilitiy has been provided to you.  Simply copy
+the file::
+
+    pyfragments.swg
+
+to the working build directory for you project, and this problem will
+be fixed.  It is suggested that you do this anyway, as it only
+increases the capabilities of your `python`_ interface.
+
+Why is There a Second File?
+---------------------------
+
+The `SWIG`_ type checking and conversion system is a complicated
+combination of C macros, `SWIG`_ macros, `SWIG`_ typemaps and `SWIG`_
+fragments.  Fragments are a way to conditionally insert code into your
+wrapper file if it is needed, and not insert it if not needed.  If
+multiple typemaps require the same fragment, the fragment only gets
+inserted into your wrapper code once.
+
+There is a fragment for converting a `python`_ integer to a C
+``long``.  There is a different fragment that converts a `python`_
+integer to a C ``int``, that calls the rountine defined in the
+``long`` fragment.  We can make the changes we want here by changing
+the definition for the ``long`` fragment.  `SWIG`_ determines the
+active definition for a fragment using a "first come, first served"
+system.  That is, we need to define the fragment for ``long``
+conversions prior to `SWIG`_ doing it internally.  `SWIG`_ allows us
+to do this by putting our fragment definitions in the file
+``pyfragments.swg``.  If we were to put the new fragment definitions
+in ``numpy.i``, they would be ignored.
+
 Helper Functions
 ================
 

Modified: branches/numpy.scons/numpy/doc/swig/numpy.i
===================================================================
--- branches/numpy.scons/numpy/doc/swig/numpy.i	2007-12-02 09:43:04 UTC (rev 4520)
+++ branches/numpy.scons/numpy/doc/swig/numpy.i	2007-12-02 10:47:00 UTC (rev 4521)
@@ -7,319 +7,407 @@
 #endif
 #include "stdio.h"
 #include <numpy/arrayobject.h>
+%}
 
 /* The following code originally appeared in
  * enthought/kiva/agg/src/numeric.i written by Eric Jones.  It was
  * translated from C++ to C by John Hunter.  Bill Spotz has modified
- * it slightly to fix some minor bugs, upgrade to numpy (all
- * versions), add some comments and some functionality.
+ * it slightly to fix some minor bugs, upgrade from Numeric to numpy
+ * (all versions), add some comments and functionality, and convert
+ * from direct code insertion to SWIG fragments.
  */
 
-/* Macros to extract array attributes.
- */
-#define is_array(a)            ((a) && PyArray_Check((PyArrayObject *)a))
-#define array_type(a)          (int)(PyArray_TYPE(a))
-#define array_numdims(a)       (((PyArrayObject *)a)->nd)
-#define array_dimensions(a)    (((PyArrayObject *)a)->dimensions)
-#define array_size(a,i)        (((PyArrayObject *)a)->dimensions[i])
-#define array_data(a)          (((PyArrayObject *)a)->data)
-#define array_is_contiguous(a) (PyArray_ISCONTIGUOUS(a))
-#define array_is_native(a)     (PyArray_ISNOTSWAPPED(a))
+/**********************************************************************/
 
+%fragment("NumPy_Backward_Compatibility", "header")
+{
 /* Support older NumPy data type names
 */
-#if NDARRAY_VERSION < 0x01000000
-#define NPY_BOOL        PyArray_BOOL
-#define NPY_BYTE        PyArray_BYTE
-#define NPY_UBYTE       PyArray_UBYTE
-#define NPY_SHORT       PyArray_SHORT
-#define NPY_USHORT      PyArray_USHORT
-#define NPY_INT         PyArray_INT
-#define NPY_UINT        PyArray_UINT
-#define NPY_LONG        PyArray_LONG
-#define NPY_ULONG       PyArray_ULONG
-#define NPY_LONGLONG    PyArray_LONGLONG
-#define NPY_ULONGLONG   PyArray_ULONGLONG
-#define NPY_FLOAT       PyArray_FLOAT
-#define NPY_DOUBLE      PyArray_DOUBLE
-#define NPY_LONGDOUBLE  PyArray_LONGDOUBLE
-#define NPY_CFLOAT      PyArray_CFLOAT
-#define NPY_CDOUBLE     PyArray_CDOUBLE
-#define NPY_CLONGDOUBLE PyArray_CLONGDOUBLE
-#define NPY_OBJECT      PyArray_OBJECT
-#define NPY_STRING      PyArray_STRING
-#define NPY_UNICODE     PyArray_UNICODE
-#define NPY_VOID        PyArray_VOID
-#define NPY_NTYPES      PyArray_NTYPES
-#define NPY_NOTYPE      PyArray_NOTYPE
-#define NPY_CHAR        PyArray_CHAR
-#define NPY_USERDEF     PyArray_USERDEF
-#define npy_intp        intp
-#endif
+%#if NDARRAY_VERSION < 0x01000000
+%#define NPY_BOOL          PyArray_BOOL
+%#define NPY_BYTE          PyArray_BYTE
+%#define NPY_UBYTE         PyArray_UBYTE
+%#define NPY_SHORT         PyArray_SHORT
+%#define NPY_USHORT        PyArray_USHORT
+%#define NPY_INT           PyArray_INT
+%#define NPY_UINT          PyArray_UINT
+%#define NPY_LONG          PyArray_LONG
+%#define NPY_ULONG         PyArray_ULONG
+%#define NPY_LONGLONG      PyArray_LONGLONG
+%#define NPY_ULONGLONG     PyArray_ULONGLONG
+%#define NPY_FLOAT         PyArray_FLOAT
+%#define NPY_DOUBLE        PyArray_DOUBLE
+%#define NPY_LONGDOUBLE    PyArray_LONGDOUBLE
+%#define NPY_CFLOAT        PyArray_CFLOAT
+%#define NPY_CDOUBLE       PyArray_CDOUBLE
+%#define NPY_CLONGDOUBLE   PyArray_CLONGDOUBLE
+%#define NPY_OBJECT        PyArray_OBJECT
+%#define NPY_STRING        PyArray_STRING
+%#define NPY_UNICODE       PyArray_UNICODE
+%#define NPY_VOID          PyArray_VOID
+%#define NPY_NTYPES        PyArray_NTYPES
+%#define NPY_NOTYPE        PyArray_NOTYPE
+%#define NPY_CHAR          PyArray_CHAR
+%#define NPY_USERDEF       PyArray_USERDEF
+%#define npy_intp          intp
 
-/* Given a PyObject, return a string describing its type.
- */
-char* pytype_string(PyObject* py_obj) {
-  if (py_obj == NULL          ) return "C NULL value";
-  if (py_obj == Py_None       ) return "Python None" ;
-  if (PyCallable_Check(py_obj)) return "callable"    ;
-  if (PyString_Check(  py_obj)) return "string"      ;
-  if (PyInt_Check(     py_obj)) return "int"         ;
-  if (PyFloat_Check(   py_obj)) return "float"       ;
-  if (PyDict_Check(    py_obj)) return "dict"        ;
-  if (PyList_Check(    py_obj)) return "list"        ;
-  if (PyTuple_Check(   py_obj)) return "tuple"       ;
-  if (PyFile_Check(    py_obj)) return "file"        ;
-  if (PyModule_Check(  py_obj)) return "module"      ;
-  if (PyInstance_Check(py_obj)) return "instance"    ;
-
-  return "unkown type";
+%#define NPY_MAX_BYTE      MAX_BYTE
+%#define NPY_MIN_BYTE      MIN_BYTE
+%#define NPY_MAX_UBYTE     MAX_UBYTE
+%#define NPY_MAX_SHORT     MAX_SHORT
+%#define NPY_MIN_SHORT     MIN_SHORT
+%#define NPY_MAX_USHORT    MAX_USHORT
+%#define NPY_MAX_INT       MAX_INT
+%#define NPY_MIN_INT       MIN_INT
+%#define NPY_MAX_UINT      MAX_UINT
+%#define NPY_MAX_LONG      MAX_LONG
+%#define NPY_MIN_LONG      MIN_LONG
+%#define NPY_MAX_ULONG     MAX_ULONG
+%#define NPY_MAX_LONGLONG  MAX_LONGLONG
+%#define NPY_MIN_LONGLONG  MIN_LONGLONG
+%#define NPY_MAX_ULONGLONG MAX_ULONGLONG
+%#define NPY_MAX_INTP      MAX_INTP
+%#define NPY_MIN_INTP      MIN_INTP
+%#endif
 }
 
-/* Given a NumPy typecode, return a string describing the type.
- */
-char* typecode_string(int typecode) {
-  static char* type_names[25] = {"bool", "byte", "unsigned byte",
-				 "short", "unsigned short", "int",
-				 "unsigned int", "long", "unsigned long",
-				 "long long", "unsigned long long",
-				 "float", "double", "long double",
-				 "complex float", "complex double",
-				 "complex long double", "object",
-				 "string", "unicode", "void", "ntypes",
-				 "notype", "char", "unknown"};
-  return typecode < 24 ? type_names[typecode] : type_names[24];
-}
+/**********************************************************************/
 
-/* Make sure input has correct numpy type.  Allow character and byte
- * to match.  Also allow int and long to match.  This is deprecated.
- * You should use PyArray_EquivTypenums() instead.
+%fragment("NumPy_Macros", "header")
+{
+/* Macros to extract array attributes.
  */
-int type_match(int actual_type, int desired_type) {
-  return PyArray_EquivTypenums(actual_type, desired_type);
+%#define is_array(a)            ((a) && PyArray_Check((PyArrayObject *)a))
+%#define array_type(a)          (int)(PyArray_TYPE(a))
+%#define array_numdims(a)       (((PyArrayObject *)a)->nd)
+%#define array_dimensions(a)    (((PyArrayObject *)a)->dimensions)
+%#define array_size(a,i)        (((PyArrayObject *)a)->dimensions[i])
+%#define array_data(a)          (((PyArrayObject *)a)->data)
+%#define array_is_contiguous(a) (PyArray_ISCONTIGUOUS(a))
+%#define array_is_native(a)     (PyArray_ISNOTSWAPPED(a))
 }
 
-/* Given a PyObject pointer, cast it to a PyArrayObject pointer if
- * legal.  If not, set the python error string appropriately and
- * return NULL.
- */
-PyArrayObject* obj_to_array_no_conversion(PyObject* input, int typecode) {
-  PyArrayObject* ary = NULL;
-  if (is_array(input) && (typecode == NPY_NOTYPE ||
-			  PyArray_EquivTypenums(array_type(input), typecode))) {
-    ary = (PyArrayObject*) input;
+/**********************************************************************/
+
+%fragment("NumPy_Utilities", "header")
+{
+  /* Given a PyObject, return a string describing its type.
+   */
+  char* pytype_string(PyObject* py_obj) {
+    if (py_obj == NULL          ) return "C NULL value";
+    if (py_obj == Py_None       ) return "Python None" ;
+    if (PyCallable_Check(py_obj)) return "callable"    ;
+    if (PyString_Check(  py_obj)) return "string"      ;
+    if (PyInt_Check(     py_obj)) return "int"         ;
+    if (PyFloat_Check(   py_obj)) return "float"       ;
+    if (PyDict_Check(    py_obj)) return "dict"        ;
+    if (PyList_Check(    py_obj)) return "list"        ;
+    if (PyTuple_Check(   py_obj)) return "tuple"       ;
+    if (PyFile_Check(    py_obj)) return "file"        ;
+    if (PyModule_Check(  py_obj)) return "module"      ;
+    if (PyInstance_Check(py_obj)) return "instance"    ;
+
+    return "unkown type";
   }
-  else if is_array(input) {
-    char* desired_type = typecode_string(typecode);
-    char* actual_type  = typecode_string(array_type(input));
-    PyErr_Format(PyExc_TypeError, 
-		 "Array of type '%s' required.  Array of type '%s' given", 
-		 desired_type, actual_type);
-    ary = NULL;
+
+  /* Given a NumPy typecode, return a string describing the type.
+   */
+  char* typecode_string(int typecode) {
+    static char* type_names[25] = {"bool", "byte", "unsigned byte",
+				   "short", "unsigned short", "int",
+				   "unsigned int", "long", "unsigned long",
+				   "long long", "unsigned long long",
+				   "float", "double", "long double",
+				   "complex float", "complex double",
+				   "complex long double", "object",
+				   "string", "unicode", "void", "ntypes",
+				   "notype", "char", "unknown"};
+    return typecode < 24 ? type_names[typecode] : type_names[24];
   }
-  else {
-    char * desired_type = typecode_string(typecode);
-    char * actual_type  = pytype_string(input);
-    PyErr_Format(PyExc_TypeError, 
-		 "Array of type '%s' required.  A '%s' was given", 
-		 desired_type, actual_type);
-    ary = NULL;
+
+  /* Make sure input has correct numpy type.  Allow character and byte
+   * to match.  Also allow int and long to match.  This is deprecated.
+   * You should use PyArray_EquivTypenums() instead.
+   */
+  int type_match(int actual_type, int desired_type) {
+    return PyArray_EquivTypenums(actual_type, desired_type);
   }
-  return ary;
 }
 
-/* Convert the given PyObject to a NumPy array with the given
- * typecode.  On success, return a valid PyArrayObject* with the
- * correct type.  On failure, the python error string will be set and
- * the routine returns NULL.
- */
-PyArrayObject* obj_to_array_allow_conversion(PyObject* input, int typecode,
-                                             int* is_new_object) {
-  PyArrayObject* ary = NULL;
-  PyObject* py_obj;
-  if (is_array(input) && (typecode == NPY_NOTYPE ||
-			  PyArray_EquivTypenums(array_type(input),typecode))) {
-    ary = (PyArrayObject*) input;
-    *is_new_object = 0;
+/**********************************************************************/
+
+%fragment("NumPy_Object_to_Array", "header",
+	  fragment="NumPy_Backward_Compatibility",
+	  fragment="NumPy_Macros",
+	  fragment="NumPy_Utilities")
+{
+  /* Given a PyObject pointer, cast it to a PyArrayObject pointer if
+   * legal.  If not, set the python error string appropriately and
+   * return NULL.
+   */
+  PyArrayObject* obj_to_array_no_conversion(PyObject* input, int typecode)
+  {
+    PyArrayObject* ary = NULL;
+    if (is_array(input) && (typecode == NPY_NOTYPE ||
+			    PyArray_EquivTypenums(array_type(input), typecode)))
+    {
+      ary = (PyArrayObject*) input;
+    }
+    else if is_array(input)
+    {
+      char* desired_type = typecode_string(typecode);
+      char* actual_type  = typecode_string(array_type(input));
+      PyErr_Format(PyExc_TypeError, 
+		   "Array of type '%s' required.  Array of type '%s' given", 
+		   desired_type, actual_type);
+      ary = NULL;
+    }
+    else
+    {
+      char * desired_type = typecode_string(typecode);
+      char * actual_type  = pytype_string(input);
+      PyErr_Format(PyExc_TypeError, 
+		   "Array of type '%s' required.  A '%s' was given", 
+		   desired_type, actual_type);
+      ary = NULL;
+    }
+    return ary;
   }
-  else {
-    py_obj = PyArray_FromObject(input, typecode, 0, 0);
-    /* If NULL, PyArray_FromObject will have set python error value.*/
-    ary = (PyArrayObject*) py_obj;
-    *is_new_object = 1;
+
+  /* Convert the given PyObject to a NumPy array with the given
+   * typecode.  On success, return a valid PyArrayObject* with the
+   * correct type.  On failure, the python error string will be set and
+   * the routine returns NULL.
+   */
+  PyArrayObject* obj_to_array_allow_conversion(PyObject* input, int typecode,
+					       int* is_new_object)
+  {
+    PyArrayObject* ary = NULL;
+    PyObject* py_obj;
+    if (is_array(input) && (typecode == NPY_NOTYPE ||
+			    PyArray_EquivTypenums(array_type(input),typecode)))
+    {
+      ary = (PyArrayObject*) input;
+      *is_new_object = 0;
+    }
+    else
+    {
+      py_obj = PyArray_FromObject(input, typecode, 0, 0);
+      /* If NULL, PyArray_FromObject will have set python error value.*/
+      ary = (PyArrayObject*) py_obj;
+      *is_new_object = 1;
+    }
+    return ary;
   }
-  return ary;
-}
 
-/* Given a PyArrayObject, check to see if it is contiguous.  If so,
- * return the input pointer and flag it as not a new object.  If it is
- * not contiguous, create a new PyArrayObject using the original data,
- * flag it as a new object and return the pointer.
- */
-PyArrayObject* make_contiguous(PyArrayObject* ary, int* is_new_object,
-                               int min_dims, int max_dims) {
-  PyArrayObject* result;
-  if (array_is_contiguous(ary)) {
-    result = ary;
-    *is_new_object = 0;
+  /* Given a PyArrayObject, check to see if it is contiguous.  If so,
+   * return the input pointer and flag it as not a new object.  If it is
+   * not contiguous, create a new PyArrayObject using the original data,
+   * flag it as a new object and return the pointer.
+   */
+  PyArrayObject* make_contiguous(PyArrayObject* ary, int* is_new_object,
+				 int min_dims, int max_dims)
+  {
+    PyArrayObject* result;
+    if (array_is_contiguous(ary))
+    {
+      result = ary;
+      *is_new_object = 0;
+    }
+    else
+    {
+      result = (PyArrayObject*) PyArray_ContiguousFromObject((PyObject*)ary, 
+							     array_type(ary), 
+							     min_dims,
+							     max_dims);
+      *is_new_object = 1;
+    }
+    return result;
   }
-  else {
-    result = (PyArrayObject*) PyArray_ContiguousFromObject((PyObject*)ary, 
-							   array_type(ary), 
-							   min_dims,
-							   max_dims);
-    *is_new_object = 1;
-  }
-  return result;
-}
 
-/* Convert a given PyObject to a contiguous PyArrayObject of the
- * specified type.  If the input object is not a contiguous
- * PyArrayObject, a new one will be created and the new object flag
- * will be set.
- */
-PyArrayObject* obj_to_array_contiguous_allow_conversion(PyObject* input,
-                                                        int typecode,
-                                                        int* is_new_object) {
-  int is_new1 = 0;
-  int is_new2 = 0;
-  PyArrayObject* ary2;
-  PyArrayObject* ary1 = obj_to_array_allow_conversion(input, typecode, 
-						      &is_new1);
-  if (ary1) {
-    ary2 = make_contiguous(ary1, &is_new2, 0, 0);
-    if ( is_new1 && is_new2) {
-      Py_DECREF(ary1);
+  /* Convert a given PyObject to a contiguous PyArrayObject of the
+   * specified type.  If the input object is not a contiguous
+   * PyArrayObject, a new one will be created and the new object flag
+   * will be set.
+   */
+  PyArrayObject* obj_to_array_contiguous_allow_conversion(PyObject* input,
+							  int typecode,
+							  int* is_new_object)
+  {
+    int is_new1 = 0;
+    int is_new2 = 0;
+    PyArrayObject* ary2;
+    PyArrayObject* ary1 = obj_to_array_allow_conversion(input, typecode, 
+							&is_new1);
+    if (ary1)
+    {
+      ary2 = make_contiguous(ary1, &is_new2, 0, 0);
+      if ( is_new1 && is_new2)
+      {
+	Py_DECREF(ary1);
+      }
+      ary1 = ary2;    
     }
-    ary1 = ary2;    
+    *is_new_object = is_new1 || is_new2;
+    return ary1;
   }
-  *is_new_object = is_new1 || is_new2;
-  return ary1;
 }
 
-/* Test whether a python object is contiguous.  If array is
- * contiguous, return 1.  Otherwise, set the python error string and
- * return 0.
- */
-int require_contiguous(PyArrayObject* ary) {
-  int contiguous = 1;
-  if (!array_is_contiguous(ary)) {
-    PyErr_SetString(PyExc_TypeError,
-		    "Array must be contiguous.  A non-contiguous array was given");
-    contiguous = 0;
+/**********************************************************************/
+
+%fragment("NumPy_Array_Requirements", "header",
+	  fragment="NumPy_Backward_Compatibility",
+	  fragment="NumPy_Macros")
+{
+  /* Test whether a python object is contiguous.  If array is
+   * contiguous, return 1.  Otherwise, set the python error string and
+   * return 0.
+   */
+  int require_contiguous(PyArrayObject* ary)
+  {
+    int contiguous = 1;
+    if (!array_is_contiguous(ary))
+    {
+      PyErr_SetString(PyExc_TypeError,
+		      "Array must be contiguous.  A non-contiguous array was given");
+      contiguous = 0;
+    }
+    return contiguous;
   }
-  return contiguous;
-}
 
-/* Require that a numpy array is not byte-swapped.  If the array is
- * not byte-swapped, return 1.  Otherwise, set the python error string
- * and return 0.
- */
-int require_native(PyArrayObject* ary) {
-  int native = 1;
-  if (!array_is_native(ary)) {
-    PyErr_SetString(PyExc_TypeError,
-		    "Array must have native byteorder.  A byte-swapped array was given");
-    native = 0;
+  /* Require that a numpy array is not byte-swapped.  If the array is
+   * not byte-swapped, return 1.  Otherwise, set the python error string
+   * and return 0.
+   */
+  int require_native(PyArrayObject* ary)
+  {
+    int native = 1;
+    if (!array_is_native(ary))
+    {
+      PyErr_SetString(PyExc_TypeError,
+		      "Array must have native byteorder.  "
+		      "A byte-swapped array was given");
+      native = 0;
+    }
+    return native;
   }
-  return native;
-}
 
-/* Require the given PyArrayObject to have a specified number of
- * dimensions.  If the array has the specified number of dimensions,
- * return 1.  Otherwise, set the python error string and return 0.
- */
-int require_dimensions(PyArrayObject* ary, int exact_dimensions) {
-  int success = 1;
-  if (array_numdims(ary) != exact_dimensions) {
-    PyErr_Format(PyExc_TypeError, 
-		 "Array must have %d dimensions.  Given array has %d dimensions", 
-		 exact_dimensions, array_numdims(ary));
-    success = 0;
+  /* Require the given PyArrayObject to have a specified number of
+   * dimensions.  If the array has the specified number of dimensions,
+   * return 1.  Otherwise, set the python error string and return 0.
+   */
+  int require_dimensions(PyArrayObject* ary, int exact_dimensions)
+  {
+    int success = 1;
+    if (array_numdims(ary) != exact_dimensions)
+    {
+      PyErr_Format(PyExc_TypeError, 
+		   "Array must have %d dimensions.  Given array has %d dimensions", 
+		   exact_dimensions, array_numdims(ary));
+      success = 0;
+    }
+    return success;
   }
-  return success;
-}
 
-/* Require the given PyArrayObject to have one of a list of specified
- * number of dimensions.  If the array has one of the specified number
- * of dimensions, return 1.  Otherwise, set the python error string
- * and return 0.
- */
-int require_dimensions_n(PyArrayObject* ary, int* exact_dimensions, int n) {
-  int success = 0;
-  int i;
-  char dims_str[255] = "";
-  char s[255];
-  for (i = 0; i < n && !success; i++) {
-    if (array_numdims(ary) == exact_dimensions[i]) {
-      success = 1;
+  /* Require the given PyArrayObject to have one of a list of specified
+   * number of dimensions.  If the array has one of the specified number
+   * of dimensions, return 1.  Otherwise, set the python error string
+   * and return 0.
+   */
+  int require_dimensions_n(PyArrayObject* ary, int* exact_dimensions, int n)
+  {
+    int success = 0;
+    int i;
+    char dims_str[255] = "";
+    char s[255];
+    for (i = 0; i < n && !success; i++)
+    {
+      if (array_numdims(ary) == exact_dimensions[i])
+      {
+	success = 1;
+      }
     }
-  }
-  if (!success) {
-    for (i = 0; i < n-1; i++) {
-      sprintf(s, "%d, ", exact_dimensions[i]);                
+    if (!success)
+    {
+      for (i = 0; i < n-1; i++)
+      {
+	sprintf(s, "%d, ", exact_dimensions[i]);                
+	strcat(dims_str,s);
+      }
+      sprintf(s, " or %d", exact_dimensions[n-1]);            
       strcat(dims_str,s);
+      PyErr_Format(PyExc_TypeError, 
+		   "Array must be have %s dimensions.  Given array has %d dimensions",
+		   dims_str, array_numdims(ary));
     }
-    sprintf(s, " or %d", exact_dimensions[n-1]);            
-    strcat(dims_str,s);
-    PyErr_Format(PyExc_TypeError, 
-		 "Array must be have %s dimensions.  Given array has %d dimensions",
-		 dims_str, array_numdims(ary));
-  }
-  return success;
-}    
+    return success;
+  }    
 
-/* Require the given PyArrayObject to have a specified shape.  If the
- * array has the specified shape, return 1.  Otherwise, set the python
- * error string and return 0.
- */
-int require_size(PyArrayObject* ary, npy_intp* size, int n) {
-  int i;
-  int success = 1;
-  int len;
-  char desired_dims[255] = "[";
-  char s[255];
-  char actual_dims[255] = "[";
-  for(i=0; i < n;i++) {
-    if (size[i] != -1 &&  size[i] != array_size(ary,i)) {
-      success = 0;    
+  /* Require the given PyArrayObject to have a specified shape.  If the
+   * array has the specified shape, return 1.  Otherwise, set the python
+   * error string and return 0.
+   */
+  int require_size(PyArrayObject* ary, npy_intp* size, int n)
+  {
+    int i;
+    int success = 1;
+    int len;
+    char desired_dims[255] = "[";
+    char s[255];
+    char actual_dims[255] = "[";
+    for(i=0; i < n;i++)
+    {
+      if (size[i] != -1 &&  size[i] != array_size(ary,i))
+      {
+	success = 0;    
+      }
     }
-  }
-  if (!success) {
-    for (i = 0; i < n; i++) {
-      if (size[i] == -1) {
-	sprintf(s, "*,");                
+    if (!success)
+    {
+      for (i = 0; i < n; i++)
+      {
+	if (size[i] == -1)
+	{
+	  sprintf(s, "*,");                
+	}
+	else
+	{
+	  sprintf(s, "%ld,", (long int)size[i]);                
+	}    
+	strcat(desired_dims,s);
       }
-      else
+      len = strlen(desired_dims);
+      desired_dims[len-1] = ']';
+      for (i = 0; i < n; i++)
       {
-	sprintf(s, "%ld,", (long int)size[i]);                
-      }    
-      strcat(desired_dims,s);
+	sprintf(s, "%ld,", (long int)array_size(ary,i));                            
+	strcat(actual_dims,s);
+      }
+      len = strlen(actual_dims);
+      actual_dims[len-1] = ']';
+      PyErr_Format(PyExc_TypeError, 
+		   "Array must be have shape of %s.  Given array has shape of %s",
+		   desired_dims, actual_dims);
     }
-    len = strlen(desired_dims);
-    desired_dims[len-1] = ']';
-    for (i = 0; i < n; i++) {
-      sprintf(s, "%ld,", (long int)array_size(ary,i));                            
-      strcat(actual_dims,s);
-    }
-    len = strlen(actual_dims);
-    actual_dims[len-1] = ']';
-    PyErr_Format(PyExc_TypeError, 
-		 "Array must be have shape of %s.  Given array has shape of %s",
-		 desired_dims, actual_dims);
+    return success;
   }
-  return success;
 }
 
+/* Combine all NumPy fragments into one for convenience */
+%fragment("NumPy_Fragments", "header",
+	  fragment="NumPy_Backward_Compatibility",
+	  fragment="NumPy_Macros",
+	  fragment="NumPy_Utilities",
+	  fragment="NumPy_Object_to_Array",
+	  fragment="NumPy_Array_Requirements") { }
+
 /* End John Hunter translation (with modifications by Bill Spotz)
  */
 
-%}
-
 /* %numpy_typemaps() macro
  *
- * This macro defines a family of typemaps that allow pure input C
- * arguments of the form
+ * This macro defines a family of 23 typemaps that allow C arguments
+ * of the form
  *
  *     (DATA_TYPE IN_ARRAY1[ANY])
  *     (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1)
@@ -329,6 +417,10 @@
  *     (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
  *     (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2)
  *
+ *     (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY])
+ *     (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
+ *     (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3)
+ *
  *     (DATA_TYPE INPLACE_ARRAY1[ANY])
  *     (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1)
  *     (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1)
@@ -337,12 +429,18 @@
  *     (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
  *     (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2)
  *
+ *     (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY])
+ *     (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
+ *     (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_ARRAY3)
+ *
  *     (DATA_TYPE ARGOUT_ARRAY1[ANY])
  *     (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1)
  *     (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1)
  *
  *     (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY])
  *
+ *     (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY])
+ *
  * where "DATA_TYPE" is any type supported by the NumPy module, and
  * "DIM_TYPE" is any int-like type suitable for specifying dimensions.
  * In python, the dimensions will not need to be specified (except for
@@ -442,18 +540,22 @@
 
 /* Typemap suite for (DATA_TYPE IN_ARRAY1[ANY])
  */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+	   fragment="NumPy_Macros")
   (DATA_TYPE IN_ARRAY1[ANY])
 {
   $1 = is_array($input) || PySequence_Check($input);
 }
-%typemap(in)
+%typemap(in,
+	 fragment="NumPy_Fragments")
   (DATA_TYPE IN_ARRAY1[ANY])
   (PyArrayObject* array=NULL, int is_new_object=0)
 {
-  array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object);
   npy_intp size[1] = { $1_dim0 };
-  if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail;
+  array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE,
+						   &is_new_object);
+  if (!array || !require_dimensions(array, 1) ||
+      !require_size(array, size, 1)) SWIG_fail;
   $1 = ($1_ltype) array_data(array);
 }
 %typemap(freearg)
@@ -465,18 +567,22 @@
 
 /* Typemap suite for (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1)
  */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+	   fragment="NumPy_Macros")
   (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1)
 {
   $1 = is_array($input) || PySequence_Check($input);
 }
-%typemap(in)
+%typemap(in,
+	 fragment="NumPy_Fragments")
   (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1)
   (PyArrayObject* array=NULL, int is_new_object=0)
 {
-  array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object);
   npy_intp size[1] = { -1 };
-  if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail;
+  array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE,
+						   &is_new_object);
+  if (!array || !require_dimensions(array, 1) ||
+      !require_size(array, size, 1)) SWIG_fail;
   $1 = (DATA_TYPE*) array_data(array);
   $2 = (DIM_TYPE) array_size(array,0);
 }
@@ -489,18 +595,22 @@
 
 /* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1)
  */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+	   fragment="NumPy_Macros")
   (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1)
 {
   $1 = is_array($input) || PySequence_Check($input);
 }
-%typemap(in)
+%typemap(in,
+	 fragment="NumPy_Fragments")
   (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1)
   (PyArrayObject* array=NULL, int is_new_object=0)
 {
-  array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object);
   npy_intp size[1] = {-1};
-  if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail;
+  array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE,
+						   &is_new_object);
+  if (!array || !require_dimensions(array, 1) ||
+      !require_size(array, size, 1)) SWIG_fail;
   $1 = (DIM_TYPE) array_size(array,0);
   $2 = (DATA_TYPE*) array_data(array);
 }
@@ -513,18 +623,22 @@
 
 /* Typemap suite for (DATA_TYPE IN_ARRAY2[ANY][ANY])
  */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+	   fragment="NumPy_Macros")
   (DATA_TYPE IN_ARRAY2[ANY][ANY])
 {
   $1 = is_array($input) || PySequence_Check($input);
 }
-%typemap(in)
+%typemap(in,
+	 fragment="NumPy_Fragments")
   (DATA_TYPE IN_ARRAY2[ANY][ANY])
   (PyArrayObject* array=NULL, int is_new_object=0)
 {
-  array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object);
   npy_intp size[2] = { $1_dim0, $1_dim1 };
-  if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2)) SWIG_fail;
+  array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE,
+						   &is_new_object);
+  if (!array || !require_dimensions(array, 2) ||
+      !require_size(array, size, 2)) SWIG_fail;
   $1 = ($1_ltype) array_data(array);
 }
 %typemap(freearg)
@@ -536,18 +650,22 @@
 
 /* Typemap suite for (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
  */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+	   fragment="NumPy_Macros")
   (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
 {
   $1 = is_array($input) || PySequence_Check($input);
 }
-%typemap(in)
+%typemap(in,
+	 fragment="NumPy_Fragments")
   (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
   (PyArrayObject* array=NULL, int is_new_object=0)
 {
-  array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object);
   npy_intp size[2] = { -1, -1 };
-  if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2)) SWIG_fail;
+  array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE,
+						   &is_new_object);
+  if (!array || !require_dimensions(array, 2) ||
+      !require_size(array, size, 2)) SWIG_fail;
   $1 = (DATA_TYPE*) array_data(array);
   $2 = (DIM_TYPE) array_size(array,0);
   $3 = (DIM_TYPE) array_size(array,1);
@@ -561,18 +679,22 @@
 
 /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2)
  */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+	   fragment="NumPy_Macros")
   (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2)
 {
   $1 = is_array($input) || PySequence_Check($input);
 }
-%typemap(in)
+%typemap(in,
+	 fragment="NumPy_Fragments")
   (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2)
   (PyArrayObject* array=NULL, int is_new_object=0)
 {
-  array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object);
   npy_intp size[2] = { -1, -1 };
-  if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2)) SWIG_fail;
+  array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE,
+						   &is_new_object);
+  if (!array || !require_dimensions(array, 2) ||
+      !require_size(array, size, 2)) SWIG_fail;
   $1 = (DIM_TYPE) array_size(array,0);
   $2 = (DIM_TYPE) array_size(array,1);
   $3 = (DATA_TYPE*) array_data(array);
@@ -586,18 +708,22 @@
 
 /* Typemap suite for (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY])
  */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+	   fragment="NumPy_Macros")
   (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY])
 {
   $1 = is_array($input) || PySequence_Check($input);
 }
-%typemap(in)
+%typemap(in,
+	 fragment="NumPy_Fragments")
   (DATA_TYPE IN_ARRAY3[ANY][ANY][ANY])
   (PyArrayObject* array=NULL, int is_new_object=0)
 {
-  array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object);
   npy_intp size[3] = { $1_dim0, $1_dim1, $1_dim2 };
-  if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3)) SWIG_fail;
+  array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE,
+						   &is_new_object);
+  if (!array || !require_dimensions(array, 3) ||
+      !require_size(array, size, 3)) SWIG_fail;
   $1 = ($1_ltype) array_data(array);
 }
 %typemap(freearg)
@@ -610,18 +736,22 @@
 /* Typemap suite for (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2,
  *                    DIM_TYPE DIM3)
  */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+	   fragment="NumPy_Macros")
   (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
 {
   $1 = is_array($input) || PySequence_Check($input);
 }
-%typemap(in)
+%typemap(in,
+	 fragment="NumPy_Fragments")
   (DATA_TYPE* IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
   (PyArrayObject* array=NULL, int is_new_object=0)
 {
-  array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object);
   npy_intp size[3] = { -1, -1, -1 };
-  if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3)) SWIG_fail;
+  array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE,
+						   &is_new_object);
+  if (!array || !require_dimensions(array, 3) ||
+      !require_size(array, size, 3)) SWIG_fail;
   $1 = (DATA_TYPE*) array_data(array);
   $2 = (DIM_TYPE) array_size(array,0);
   $3 = (DIM_TYPE) array_size(array,1);
@@ -637,18 +767,22 @@
 /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, 
  *                    DATA_TYPE* IN_ARRAY3)
  */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+	   fragment="NumPy_Macros")
   (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3)
 {
   $1 = is_array($input) || PySequence_Check($input);
 }
-%typemap(in)
+%typemap(in,
+	 fragment="NumPy_Fragments")
   (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* IN_ARRAY3)
   (PyArrayObject* array=NULL, int is_new_object=0)
 {
-  array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object);
   npy_intp size[3] = { -1, -1, -1 };
-  if (!array || !require_dimensions(array, 3) || !require_size(array, size, 3)) SWIG_fail;
+  array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE,
+						   &is_new_object);
+  if (!array || !require_dimensions(array, 3) ||
+      !require_size(array, size, 3)) SWIG_fail;
   $1 = (DIM_TYPE) array_size(array,0);
   $2 = (DIM_TYPE) array_size(array,1);
   $3 = (DIM_TYPE) array_size(array,2);
@@ -667,30 +801,36 @@
 
 /* Typemap suite for (DATA_TYPE INPLACE_ARRAY1[ANY])
  */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+	   fragment="NumPy_Macros")
   (DATA_TYPE INPLACE_ARRAY1[ANY])
 {
-  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),DATA_TYPECODE);
+  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
+						 DATA_TYPECODE);
 }
-%typemap(in)
+%typemap(in,
+	 fragment="NumPy_Fragments")
   (DATA_TYPE INPLACE_ARRAY1[ANY])
   (PyArrayObject* array=NULL)
 {
+  npy_intp size[1] = { $1_dim0 };
   array = obj_to_array_no_conversion($input, DATA_TYPECODE);
-  npy_intp size[1] = { $1_dim0 };
-  if (!array || !require_dimensions(array,1) || !require_size(array, size, 1)
-      || !require_contiguous(array) || !require_native(array)) SWIG_fail;
+  if (!array || !require_dimensions(array,1) || !require_size(array, size, 1) ||
+      !require_contiguous(array) || !require_native(array)) SWIG_fail;
   $1 = ($1_ltype) array_data(array);
 }
 
 /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1)
  */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+	   fragment="NumPy_Macros")
   (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1)
 {
-  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),DATA_TYPECODE);
+  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
+						 DATA_TYPECODE);
 }
-%typemap(in)
+%typemap(in,
+	 fragment="NumPy_Fragments")
   (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1)
   (PyArrayObject* array=NULL, int i=1)
 {
@@ -704,12 +844,15 @@
 
 /* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1)
  */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+	   fragment="NumPy_Macros")
   (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1)
 {
-  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),DATA_TYPECODE);
+  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
+						 DATA_TYPECODE);
 }
-%typemap(in)
+%typemap(in,
+	 fragment="NumPy_Fragments")
   (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1)
   (PyArrayObject* array=NULL, int i=0)
 {
@@ -723,30 +866,36 @@
 
 /* Typemap suite for (DATA_TYPE INPLACE_ARRAY2[ANY][ANY])
  */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+	   fragment="NumPy_Macros")
   (DATA_TYPE INPLACE_ARRAY2[ANY][ANY])
 {
-  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),DATA_TYPECODE);
+  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
+						 DATA_TYPECODE);
 }
-%typemap(in)
+%typemap(in,
+	 fragment="NumPy_Fragments")
   (DATA_TYPE INPLACE_ARRAY2[ANY][ANY])
   (PyArrayObject* array=NULL)
 {
+  npy_intp size[2] = { $1_dim0, $1_dim1 };
   array = obj_to_array_no_conversion($input, DATA_TYPECODE);
-  npy_intp size[2] = { $1_dim0, $1_dim1 };
-  if (!array || !require_dimensions(array,2) || !require_size(array, size, 2)
-      || !require_contiguous(array) || !require_native(array)) SWIG_fail;
+  if (!array || !require_dimensions(array,2) || !require_size(array, size, 2) ||
+      !require_contiguous(array) || !require_native(array)) SWIG_fail;
   $1 = ($1_ltype) array_data(array);
 }
 
 /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
  */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+	   fragment="NumPy_Macros")
   (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
 {
-  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),DATA_TYPECODE);
+  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
+						 DATA_TYPECODE);
 }
-%typemap(in)
+%typemap(in,
+	 fragment="NumPy_Fragments")
   (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
   (PyArrayObject* array=NULL)
 {
@@ -760,18 +909,21 @@
 
 /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2)
  */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+	   fragment="NumPy_Macros")
   (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2)
 {
-  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),DATA_TYPECODE);
+  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
+						 DATA_TYPECODE);
 }
-%typemap(in)
+%typemap(in,
+	 fragment="NumPy_Fragments")
   (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2)
   (PyArrayObject* array=NULL)
 {
   array = obj_to_array_no_conversion($input, DATA_TYPECODE);
-  if (!array || !require_dimensions(array,2) || !require_contiguous(array)
-      || !require_native(array)) SWIG_fail;
+  if (!array || !require_dimensions(array,2) || !require_contiguous(array) ||
+      !require_native(array)) SWIG_fail;
   $1 = (DIM_TYPE) array_size(array,0);
   $2 = (DIM_TYPE) array_size(array,1);
   $3 = (DATA_TYPE*) array_data(array);
@@ -779,37 +931,43 @@
 
 /* Typemap suite for (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY])
  */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+	   fragment="NumPy_Macros")
   (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY])
 {
-  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),DATA_TYPECODE);
+  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
+						 DATA_TYPECODE);
 }
-%typemap(in)
+%typemap(in,
+	 fragment="NumPy_Fragments")
   (DATA_TYPE INPLACE_ARRAY3[ANY][ANY][ANY])
   (PyArrayObject* array=NULL)
 {
+  npy_intp size[3] = { $1_dim0, $1_dim1, $1_dim2 };
   array = obj_to_array_no_conversion($input, DATA_TYPECODE);
-  npy_intp size[3] = { $1_dim0, $1_dim1, $1_dim2 };
-  if (!array || !require_dimensions(array,3) || !require_size(array, size, 3)
-      || !require_contiguous(array) || !require_native(array)) SWIG_fail;
+  if (!array || !require_dimensions(array,3) || !require_size(array, size, 3) ||
+      !require_contiguous(array) || !require_native(array)) SWIG_fail;
   $1 = ($1_ltype) array_data(array);
 }
 
 /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2,
  *                    DIM_TYPE DIM3)
  */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+	   fragment="NumPy_Macros")
   (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
 {
-  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),DATA_TYPECODE);
+  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
+						 DATA_TYPECODE);
 }
-%typemap(in)
+%typemap(in,
+	 fragment="NumPy_Fragments")
   (DATA_TYPE* INPLACE_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3)
   (PyArrayObject* array=NULL)
 {
   array = obj_to_array_no_conversion($input, DATA_TYPECODE);
-  if (!array || !require_dimensions(array,3) || !require_contiguous(array)
-      || !require_native(array)) SWIG_fail;
+  if (!array || !require_dimensions(array,3) || !require_contiguous(array) ||
+      !require_native(array)) SWIG_fail;
   $1 = (DATA_TYPE*) array_data(array);
   $2 = (DIM_TYPE) array_size(array,0);
   $3 = (DIM_TYPE) array_size(array,1);
@@ -819,12 +977,15 @@
 /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, 
  *                    DATA_TYPE* INPLACE_ARRAY3)
  */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY,
+	   fragment="NumPy_Macros")
   (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_ARRAY3)
 {
-  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),DATA_TYPECODE);
+  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),
+						 DATA_TYPECODE);
 }
-%typemap(in)
+%typemap(in,
+	 fragment="NumPy_Fragments")
   (DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3, DATA_TYPE* INPLACE_ARRAY3)
   (PyArrayObject* array=NULL)
 {
@@ -843,7 +1004,8 @@
 
 /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY1[ANY])
  */
-%typemap(in,numinputs=0)
+%typemap(in,numinputs=0,
+	 fragment="NumPy_Backward_Compatibility,NumPy_Macros")
   (DATA_TYPE ARGOUT_ARRAY1[ANY])
   (PyObject * array = NULL)
 {
@@ -859,11 +1021,14 @@
 
 /* Typemap suite for (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1)
  */
-%typemap(in,numinputs=1)
+%typemap(in,numinputs=1,
+	 fragment="NumPy_Fragments")
   (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1)
   (PyObject * array = NULL)
 {
-  if (!PyInt_Check($input)) {
+  npy_intp dims[1];
+  if (!PyInt_Check($input))
+  {
     char* typestring = pytype_string($input);
     PyErr_Format(PyExc_TypeError, 
 		 "Int dimension expected.  '%s' given.", 
@@ -871,7 +1036,7 @@
     SWIG_fail;
   }
   $2 = (DIM_TYPE) PyInt_AsLong($input);
-  npy_intp dims[1] = { (npy_intp) $2 };
+  dims[0] = (npy_intp) $2;
   array = PyArray_SimpleNew(1, dims, DATA_TYPECODE);
   $1 = (DATA_TYPE*) array_data(array);
 }
@@ -883,11 +1048,14 @@
 
 /* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1)
  */
-%typemap(in,numinputs=1)
+%typemap(in,numinputs=1,
+	 fragment="NumPy_Fragments")
   (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1)
   (PyObject * array = NULL)
 {
-  if (!PyInt_Check($input)) {
+  npy_intp dims[1];
+  if (!PyInt_Check($input))
+  {
     char* typestring = pytype_string($input);
     PyErr_Format(PyExc_TypeError, 
 		 "Int dimension expected.  '%s' given.", 
@@ -895,7 +1063,7 @@
     SWIG_fail;
   }
   $1 = (DIM_TYPE) PyInt_AsLong($input);
-  npy_intp dims[1] = { (npy_intp) $1 };
+  dims[0] = (npy_intp) $1;
   array = PyArray_SimpleNew(1, dims, DATA_TYPECODE);
   $2 = (DATA_TYPE*) array_data(array);
 }
@@ -907,7 +1075,8 @@
 
 /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY])
  */
-%typemap(in,numinputs=0)
+%typemap(in,numinputs=0,
+	 fragment="NumPy_Backward_Compatibility,NumPy_Macros")
   (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY])
   (PyObject * array = NULL)
 {
@@ -923,7 +1092,8 @@
 
 /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY])
  */
-%typemap(in,numinputs=0)
+%typemap(in,numinputs=0,
+	 fragment="NumPy_Backward_Compatibility,NumPy_Macros")
   (DATA_TYPE ARGOUT_ARRAY3[ANY][ANY][ANY])
   (PyObject * array = NULL)
 {
@@ -960,14 +1130,14 @@
  * The follow macro expansion does not work, because C++ bool is 4
  * bytes and NPY_BOOL is 1 byte
  */
-/*%numpy_typemaps(bool, NPY_BOOL)
+/*%numpy_typemaps(bool, NPY_BOOL, int)
  */
 
 /* ***************************************************************
  * On my Mac, I get the following warning for this macro expansion:
  * 'swig/python detected a memory leak of type 'long double *', no destructor found.'
  */
-/*%numpy_typemaps(long double, NPY_LONGDOUBLE)
+/*%numpy_typemaps(long double, NPY_LONGDOUBLE, int)
  */
 
 /* ***************************************************************

Copied: branches/numpy.scons/numpy/doc/swig/pyfragments.swg (from rev 4507, trunk/numpy/doc/swig/pyfragments.swg)

Modified: branches/numpy.scons/numpy/doc/swig/test/Makefile
===================================================================
--- branches/numpy.scons/numpy/doc/swig/test/Makefile	2007-12-02 09:43:04 UTC (rev 4520)
+++ branches/numpy.scons/numpy/doc/swig/test/Makefile	2007-12-02 10:47:00 UTC (rev 4521)
@@ -11,9 +11,9 @@
 # Test target: run the tests
 .PHONY : test
 test: all
-	testVector.py
-	testMatrix.py
-	testTensor.py
+	python testVector.py
+	python testMatrix.py
+	python testTensor.py
 
 # Rule: %.i -> %_wrap.cxx
 %_wrap.cxx: %.i %.h ../numpy.i

Modified: branches/numpy.scons/numpy/doc/swig/test/setup.py
===================================================================
--- branches/numpy.scons/numpy/doc/swig/test/setup.py	2007-12-02 09:43:04 UTC (rev 4520)
+++ branches/numpy.scons/numpy/doc/swig/test/setup.py	2007-12-02 10:47:00 UTC (rev 4521)
@@ -16,21 +16,21 @@
 # _Vector extension module
 _Vector = Extension("_Vector",
                     ["Vector_wrap.cxx",
-                     "vector.cxx"],
+                     "Vector.cxx"],
                     include_dirs = [numpy_include],
                     )
 
 # _Matrix extension module
 _Matrix = Extension("_Matrix",
                     ["Matrix_wrap.cxx",
-                     "matrix.cxx"],
+                     "Matrix.cxx"],
                     include_dirs = [numpy_include],
                     )
 
 # _Tensor extension module
 _Tensor = Extension("_Tensor",
                     ["Tensor_wrap.cxx",
-                     "tensor.cxx"],
+                     "Tensor.cxx"],
                     include_dirs = [numpy_include],
                     )
 

Modified: branches/numpy.scons/numpy/fft/fftpack.py
===================================================================
--- branches/numpy.scons/numpy/fft/fftpack.py	2007-12-02 09:43:04 UTC (rev 4520)
+++ branches/numpy.scons/numpy/fft/fftpack.py	2007-12-02 10:47:00 UTC (rev 4521)
@@ -35,9 +35,11 @@
              work_function=fftpack.cfftf, fft_cache = _fft_cache ):
     a = asarray(a)
 
-    if n == None: n = a.shape[axis]
+    if n is None:
+        n = a.shape[axis]
 
-    if n < 1: raise ValueError("Invalid number of FFT data points (%d) specified." % n)
+    if n < 1:
+        raise ValueError("Invalid number of FFT data points (%d) specified." % n)
 
     try:
         wsave = fft_cache[n]
@@ -110,7 +112,7 @@
     different n's."""
 
     a = asarray(a).astype(complex)
-    if n == None:
+    if n is None:
         n = shape(a)[axis]
     return _raw_fft(a, n, axis, fftpack.cffti, fftpack.cfftb, _fft_cache) / n
 
@@ -155,7 +157,7 @@
     within numerical accuracy."""
 
     a = asarray(a).astype(complex)
-    if n == None:
+    if n is None:
         n = (shape(a)[axis] - 1) * 2
     return _raw_fft(a, n, axis, fftpack.rffti, fftpack.rfftb,
                     _real_fft_cache) / n
@@ -174,7 +176,7 @@
     within numerical accuracy."""
 
     a = asarray(a).astype(complex)
-    if n == None:
+    if n is None:
         n = (shape(a)[axis] - 1) * 2
     return irfft(conjugate(a), n, axis) * n
 
@@ -192,7 +194,7 @@
     within numerical accuracy."""
 
     a = asarray(a).astype(float)
-    if n == None:
+    if n is None:
         n = shape(a)[axis]
     return conjugate(rfft(a, n, axis))/n
 
@@ -200,14 +202,14 @@
 def _cook_nd_args(a, s=None, axes=None, invreal=0):
     if s is None:
         shapeless = 1
-        if axes == None:
+        if axes is None:
             s = list(a.shape)
         else:
             s = take(a.shape, axes)
     else:
         shapeless = 0
     s = list(s)
-    if axes == None:
+    if axes is None:
         axes = range(-len(s), 0)
     if len(s) != len(axes):
         raise ValueError, "Shape and axes have different lengths."

Modified: branches/numpy.scons/numpy/lib/utils.py
===================================================================
--- branches/numpy.scons/numpy/lib/utils.py	2007-12-02 09:43:04 UTC (rev 4520)
+++ branches/numpy.scons/numpy/lib/utils.py	2007-12-02 10:47:00 UTC (rev 4521)
@@ -323,7 +323,7 @@
 
     elif inspect.isfunction(object):
         name = object.func_name
-        arguments = apply(inspect.formatargspec, inspect.getargspec(object))
+        arguments = inspect.formatargspec(*inspect.getargspec(object))
 
         if len(name+arguments) > maxwidth:
             argstr = _split_line(name, arguments, maxwidth)
@@ -338,7 +338,7 @@
         arguments = "()"
         try:
             if hasattr(object, '__init__'):
-                arguments = apply(inspect.formatargspec, inspect.getargspec(object.__init__.im_func))
+                arguments = inspect.formatargspec(*inspect.getargspec(object.__init__.im_func))
                 arglist = arguments.split(', ')
                 if len(arglist) > 1:
                     arglist[1] = "("+arglist[1]
@@ -374,7 +374,7 @@
         print >> output, "Instance of class: ", object.__class__.__name__
         print >> output
         if hasattr(object, '__call__'):
-            arguments = apply(inspect.formatargspec, inspect.getargspec(object.__call__.im_func))
+            arguments = inspect.formatargspec(*inspect.getargspec(object.__call__.im_func))
             arglist = arguments.split(', ')
             if len(arglist) > 1:
                 arglist[1] = "("+arglist[1]
@@ -402,7 +402,7 @@
 
     elif inspect.ismethod(object):
         name = object.__name__
-        arguments = apply(inspect.formatargspec, inspect.getargspec(object.im_func))
+        arguments = inspect.formatargspec(*inspect.getargspec(object.im_func))
         arglist = arguments.split(', ')
         if len(arglist) > 1:
             arglist[1] = "("+arglist[1]

Modified: branches/numpy.scons/numpy/random/mtrand/mtrand.c
===================================================================
--- branches/numpy.scons/numpy/random/mtrand/mtrand.c	2007-12-02 09:43:04 UTC (rev 4520)
+++ branches/numpy.scons/numpy/random/mtrand/mtrand.c	2007-12-02 10:47:00 UTC (rev 4521)
@@ -1,4 +1,4 @@
-/* Generated by Pyrex 0.9.5.1a on Wed Aug 29 00:25:24 2007 */
+/* Generated by Pyrex 0.9.5.1a on Fri Nov 23 17:16:35 2007 */
 
 #include "Python.h"
 #include "structmember.h"
@@ -2438,7 +2438,7 @@
   if (__pyx_1) {
 
     /* "/home/stefan/work/scipy/numpy.patch/numpy/random/mtrand/mtrand.pyx":603 */
-    __pyx_3 = PyLong_FromUnsignedLong((rk_interval(__pyx_v_diff,((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state) + __pyx_v_lo)); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 603; goto __pyx_L1;}
+    __pyx_3 = PyInt_FromLong((((long )rk_interval(__pyx_v_diff,((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state)) + __pyx_v_lo)); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 603; goto __pyx_L1;}
     __pyx_r = __pyx_3;
     __pyx_3 = 0;
     goto __pyx_L0;

Modified: branches/numpy.scons/numpy/random/mtrand/mtrand.pyx
===================================================================
--- branches/numpy.scons/numpy/random/mtrand/mtrand.pyx	2007-12-02 09:43:04 UTC (rev 4520)
+++ branches/numpy.scons/numpy/random/mtrand/mtrand.pyx	2007-12-02 10:47:00 UTC (rev 4521)
@@ -598,9 +598,9 @@
         diff = hi - lo - 1
         if diff < 0:
             raise ValueError("low >= high")
-    
+
         if size is None:
-            return rk_interval(diff, self.internal_state) + lo
+            return <long>rk_interval(diff, self.internal_state) + lo
         else:
             array = <ndarray>_sp.empty(size, int)
             length = PyArray_SIZE(array)

Modified: branches/numpy.scons/numpy/random/tests/test_random.py
===================================================================
--- branches/numpy.scons/numpy/random/tests/test_random.py	2007-12-02 09:43:04 UTC (rev 4520)
+++ branches/numpy.scons/numpy/random/tests/test_random.py	2007-12-02 10:47:00 UTC (rev 4521)
@@ -9,5 +9,11 @@
     def test_zero_probability(self):
         random.multinomial(100, [0.2, 0.8, 0.0, 0.0, 0.0])
 
+    def test_int_negative_interval(self):
+        assert -5 <= random.randint(-5,-1) < -1
+        x = random.randint(-5,-1,5)
+        assert N.all(-5 <= x)
+        assert N.all(x < -1)
+
 if __name__ == "__main__":
     NumpyTest().run()

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