DOC: remove built separate swig docs.

author: rgommers <ralf.gommers@googlemail.com> 2010-09-04 09:52:44 +0000
committer: rgommers <ralf.gommers@googlemail.com> 2010-09-04 09:52:44 +0000
commit: 779d063af9c88152d7b1bc829410a4edb1ff4f88 (patch)
tree: e2e72b0ea77f6518fceb34cc9bdbc0a80d3a900b /doc
parent: 72665ab87449b9896dcb2db9aae36bb12c3daba7 (diff)
download: numpy-779d063af9c88152d7b1bc829410a4edb1ff4f88.tar.gz
5 files changed, 0 insertions, 1777 deletions
diff --git a/doc/swig/doc/Makefile b/doc/swig/doc/Makefile
deleted file mode 100644
index 9223f0481..000000000
--- a/doc/swig/doc/Makefile
+++ /dev/null
@@ -1,51 +0,0 @@
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-
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-
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-
-# PDF target
-.PHONY : pdf
-pdf: $(PDF_FILES)
-
-# Rule: %.tex -> %.pdf
-%.pdf: %.tex
-	$(LATEX) $<
-	$(LATEX) $<
-
-# Clean target
-.PHONY : clean
-clean:
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-	$(RM) *.pyc *.aux *.dvi *.log *.out *~
diff --git a/doc/swig/doc/numpy_swig.html b/doc/swig/doc/numpy_swig.html
deleted file mode 100644
index ed127f330..000000000
--- a/doc/swig/doc/numpy_swig.html
+++ /dev/null
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-<body>
-<div class="document" id="numpy-i-a-swig-interface-file-for-numpy">
-<h1 class="title">numpy.i: a SWIG Interface File for NumPy</h1>
-<table class="docinfo" frame="void" rules="none">
-<col class="docinfo-name" />
-<col class="docinfo-content" />
-<tbody valign="top">
-<tr><th class="docinfo-name">Author:</th>
-<td>Bill Spotz</td></tr>
-<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>1 December, 2007</td></tr>
-</tbody>
-</table>
-<div class="contents topic">
-<p class="topic-title first"><a id="contents" name="contents">Contents</a></p>
-<ul class="simple">
-<li><a class="reference" href="#introduction" id="id1" name="id1">Introduction</a></li>
-<li><a class="reference" href="#using-numpy-i" id="id2" name="id2">Using numpy.i</a></li>
-<li><a class="reference" href="#available-typemaps" id="id3" name="id3">Available Typemaps</a><ul>
-<li><a class="reference" href="#input-arrays" id="id4" name="id4">Input Arrays</a></li>
-<li><a class="reference" href="#in-place-arrays" id="id5" name="id5">In-Place Arrays</a></li>
-<li><a class="reference" href="#argout-arrays" id="id6" name="id6">Argout Arrays</a></li>
-<li><a class="reference" href="#argoutview-arrays" id="id7" name="id7">Argoutview Arrays</a></li>
-<li><a class="reference" href="#output-arrays" id="id8" name="id8">Output Arrays</a></li>
-<li><a class="reference" href="#other-common-types-bool" id="id9" name="id9">Other Common Types: bool</a></li>
-<li><a class="reference" href="#other-common-types-complex" id="id10" name="id10">Other Common Types: complex</a></li>
-</ul>
-</li>
-<li><a class="reference" href="#numpy-array-scalars-and-swig" id="id11" name="id11">NumPy Array Scalars and SWIG</a><ul>
-<li><a class="reference" href="#why-is-there-a-second-file" id="id12" name="id12">Why is There a Second File?</a></li>
-</ul>
-</li>
-<li><a class="reference" href="#helper-functions" id="id13" name="id13">Helper Functions</a><ul>
-<li><a class="reference" href="#macros" id="id14" name="id14">Macros</a></li>
-<li><a class="reference" href="#routines" id="id15" name="id15">Routines</a></li>
-</ul>
-</li>
-<li><a class="reference" href="#beyond-the-provided-typemaps" id="id16" name="id16">Beyond the Provided Typemaps</a><ul>
-<li><a class="reference" href="#a-common-example" id="id17" name="id17">A Common Example</a></li>
-<li><a class="reference" href="#other-situations" id="id18" name="id18">Other Situations</a></li>
-<li><a class="reference" href="#a-final-note" id="id19" name="id19">A Final Note</a></li>
-</ul>
-</li>
-<li><a class="reference" href="#summary" id="id20" name="id20">Summary</a></li>
-<li><a class="reference" href="#acknowledgements" id="id21" name="id21">Acknowledgements</a></li>
-</ul>
-</div>
-<div class="section">
-<h1><a class="toc-backref" href="#id1" id="introduction" name="introduction">Introduction</a></h1>
-<p>The Simple Wrapper and Interface Generator (or <a class="reference" href="http://www.swig.org">SWIG</a>) is a powerful tool for generating wrapper
-code for interfacing to a wide variety of scripting languages.
-<a class="reference" href="http://www.swig.org">SWIG</a> can parse header files, and using only the code prototypes,
-create an interface to the target language.  But <a class="reference" href="http://www.swig.org">SWIG</a> is not
-omnipotent.  For example, it cannot know from the prototype:</p>
-<pre class="literal-block">
-double rms(double* seq, int n);
-</pre>
-<p>what exactly <tt class="docutils literal"><span class="pre">seq</span></tt> is.  Is it a single value to be altered in-place?
-Is it an array, and if so what is its length?  Is it input-only?
-Output-only?  Input-output?  <a class="reference" href="http://www.swig.org">SWIG</a> cannot determine these details,
-and does not attempt to do so.</p>
-<p>If we designed <tt class="docutils literal"><span class="pre">rms</span></tt>, we probably made it a routine that takes an
-input-only array of length <tt class="docutils literal"><span class="pre">n</span></tt> of <tt class="docutils literal"><span class="pre">double</span></tt> values called <tt class="docutils literal"><span class="pre">seq</span></tt>
-and returns the root mean square.  The default behavior of <a class="reference" href="http://www.swig.org">SWIG</a>,
-however, will be to create a wrapper function that compiles, but is
-nearly impossible to use from the scripting language in the way the C
-routine was intended.</p>
-<p>For <a class="reference" href="http://www.python.org">python</a>, the preferred way of handling
-contiguous (or technically, <em>strided</em>) blocks of homogeneous data is
-with the module <a class="reference" href="http://numpy.scipy.org">NumPy</a>, which provides full
-object-oriented access to multidimensial arrays of data.  Therefore,
-the most logical <a class="reference" href="http://www.python.org">python</a> interface for the <tt class="docutils literal"><span class="pre">rms</span></tt> function would be
-(including doc string):</p>
-<pre class="literal-block">
-def rms(seq):
-    &quot;&quot;&quot;
-    rms: return the root mean square of a sequence
-    rms(numpy.ndarray) -&gt; double
-    rms(list) -&gt; double
-    rms(tuple) -&gt; double
-    &quot;&quot;&quot;
-</pre>
-<p>where <tt class="docutils literal"><span class="pre">seq</span></tt> would be a <a class="reference" href="http://numpy.scipy.org">NumPy</a> array of <tt class="docutils literal"><span class="pre">double</span></tt> values, and its
-length <tt class="docutils literal"><span class="pre">n</span></tt> would be extracted from <tt class="docutils literal"><span class="pre">seq</span></tt> internally before being
-passed to the C routine.  Even better, since <a class="reference" href="http://numpy.scipy.org">NumPy</a> supports
-construction of arrays from arbitrary <a class="reference" href="http://www.python.org">python</a> sequences, <tt class="docutils literal"><span class="pre">seq</span></tt>
-itself could be a nearly arbitrary sequence (so long as each element
-can be converted to a <tt class="docutils literal"><span class="pre">double</span></tt>) and the wrapper code would
-internally convert it to a <a class="reference" href="http://numpy.scipy.org">NumPy</a> array before extracting its data
-and length.</p>
-<p><a class="reference" href="http://www.swig.org">SWIG</a> allows these types of conversions to be defined via a
-mechanism called typemaps.  This document provides information on how
-to use <tt class="docutils literal"><span class="pre">numpy.i</span></tt>, a <a class="reference" href="http://www.swig.org">SWIG</a> interface file that defines a series of
-typemaps intended to make the type of array-related conversions
-described above relatively simple to implement.  For example, suppose
-that the <tt class="docutils literal"><span class="pre">rms</span></tt> function prototype defined above was in a header file
-named <tt class="docutils literal"><span class="pre">rms.h</span></tt>.  To obtain the <a class="reference" href="http://www.python.org">python</a> interface discussed above,
-your <a class="reference" href="http://www.swig.org">SWIG</a> interface file would need the following:</p>
-<pre class="literal-block">
-%{
-#define SWIG_FILE_WITH_INIT
-#include &quot;rms.h&quot;
-%}
-
-%include &quot;numpy.i&quot;
-
-%init %{
-import_array();
-%}
-
-%apply (double* IN_ARRAY1, int DIM1) {(double* seq, int n)};
-%include &quot;rms.h&quot;
-</pre>
-<p>Typemaps are keyed off a list of one or more function arguments,
-either by type or by type and name.  We will refer to such lists as
-<em>signatures</em>.  One of the many typemaps defined by <tt class="docutils literal"><span class="pre">numpy.i</span></tt> is used
-above and has the signature <tt class="docutils literal"><span class="pre">(double*</span> <span class="pre">IN_ARRAY1,</span> <span class="pre">int</span> <span class="pre">DIM1)</span></tt>.  The
-argument names are intended to suggest that the <tt class="docutils literal"><span class="pre">double*</span></tt> argument
-is an input array of one dimension and that the <tt class="docutils literal"><span class="pre">int</span></tt> represents
-that dimension.  This is precisely the pattern in the <tt class="docutils literal"><span class="pre">rms</span></tt>
-prototype.</p>
-<p>Most likely, no actual prototypes to be wrapped will have the argument
-names <tt class="docutils literal"><span class="pre">IN_ARRAY1</span></tt> and <tt class="docutils literal"><span class="pre">DIM1</span></tt>.  We use the <tt class="docutils literal"><span class="pre">%apply</span></tt> directive to
-apply the typemap for one-dimensional input arrays of type <tt class="docutils literal"><span class="pre">double</span></tt>
-to the actual prototype used by <tt class="docutils literal"><span class="pre">rms</span></tt>.  Using <tt class="docutils literal"><span class="pre">numpy.i</span></tt>
-effectively, therefore, requires knowing what typemaps are available
-and what they do.</p>
-<p>A <a class="reference" href="http://www.swig.org">SWIG</a> interface file that includes the <a class="reference" href="http://www.swig.org">SWIG</a> directives given
-above will produce wrapper code that looks something like:</p>
-<pre class="literal-block">
- 1 PyObject *_wrap_rms(PyObject *args) {
- 2   PyObject *resultobj = 0;
- 3   double *arg1 = (double *) 0 ;
- 4   int arg2 ;
- 5   double result;
- 6   PyArrayObject *array1 = NULL ;
- 7   int is_new_object1 = 0 ;
- 8   PyObject * obj0 = 0 ;
- 9
-10   if (!PyArg_ParseTuple(args,(char *)&quot;O:rms&quot;,&amp;obj0)) SWIG_fail;
-11   {
-12     array1 = obj_to_array_contiguous_allow_conversion(
-13                  obj0, NPY_DOUBLE, &amp;is_new_object1);
-14     npy_intp size[1] = {
-15       -1
-16     };
-17     if (!array1 || !require_dimensions(array1, 1) ||
-18         !require_size(array1, size, 1)) SWIG_fail;
-19     arg1 = (double*) array1-&gt;data;
-20     arg2 = (int) array1-&gt;dimensions[0];
-21   }
-22   result = (double)rms(arg1,arg2);
-23   resultobj = SWIG_From_double((double)(result));
-24   {
-25     if (is_new_object1 &amp;&amp; array1) Py_DECREF(array1);
-26   }
-27   return resultobj;
-28 fail:
-29   {
-30     if (is_new_object1 &amp;&amp; array1) Py_DECREF(array1);
-31   }
-32   return NULL;
-33 }
-</pre>
-<p>The typemaps from <tt class="docutils literal"><span class="pre">numpy.i</span></tt> are responsible for the following lines
-of code: 12--20, 25 and 30.  Line 10 parses the input to the <tt class="docutils literal"><span class="pre">rms</span></tt>
-function.  From the format string <tt class="docutils literal"><span class="pre">&quot;O:rms&quot;</span></tt>, we can see that the
-argument list is expected to be a single <a class="reference" href="http://www.python.org">python</a> object (specified
-by the <tt class="docutils literal"><span class="pre">O</span></tt> before the colon) and whose pointer is stored in
-<tt class="docutils literal"><span class="pre">obj0</span></tt>.  A number of functions, supplied by <tt class="docutils literal"><span class="pre">numpy.i</span></tt>, are called
-to make and check the (possible) conversion from a generic <a class="reference" href="http://www.python.org">python</a>
-object to a <a class="reference" href="http://numpy.scipy.org">NumPy</a> array.  These functions are explained in the
-section <a class="reference" href="#helper-functions">Helper Functions</a>, but hopefully their names are
-self-explanatory.  At line 12 we use <tt class="docutils literal"><span class="pre">obj0</span></tt> to construct a <a class="reference" href="http://numpy.scipy.org">NumPy</a>
-array.  At line 17, we check the validity of the result: that it is
-non-null and that it has a single dimension of arbitrary length.  Once
-these states are verified, we extract the data buffer and length in
-lines 19 and 20 so that we can call the underlying C function at line
-22.  Line 25 performs memory management for the case where we have
-created a new array that is no longer needed.</p>
-<p>This code has a significant amount of error handling.  Note the
-<tt class="docutils literal"><span class="pre">SWIG_fail</span></tt> is a macro for <tt class="docutils literal"><span class="pre">goto</span> <span class="pre">fail</span></tt>, refering to the label at
-line 28.  If the user provides the wrong number of arguments, this
-will be caught at line 10.  If construction of the <a class="reference" href="http://numpy.scipy.org">NumPy</a> array
-fails or produces an array with the wrong number of dimensions, these
-errors are caught at line 17.  And finally, if an error is detected,
-memory is still managed correctly at line 30.</p>
-<p>Note that if the C function signature was in a different order:</p>
-<pre class="literal-block">
-double rms(int n, double* seq);
-</pre>
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-<h2><a class="toc-backref" href="#id4" id="input-arrays" name="input-arrays">Input Arrays</a></h2>
-<p>Input arrays are defined as arrays of data that are passed into a
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-<p>1D:</p>
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-<h2><a class="toc-backref" href="#id5" id="in-place-arrays" name="in-place-arrays">In-Place Arrays</a></h2>
-<p>In-place arrays are defined as arrays that are modified in-place.  The
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-<p>Argout arrays are arrays that appear in the input arguments in C, but
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-<p>The C code to be wrapped for argoutview arrays are characterized by
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-<li><tt class="docutils literal"><span class="pre">(</span> <span class="pre">DIM_TYPE*</span> <span class="pre">DIM1,</span> <span class="pre">DIM_TYPE*</span> <span class="pre">DIM2,</span> <span class="pre">DIM_TYPE*</span> <span class="pre">DIM3,</span> <span class="pre">DATA_TYPE**</span> <span class="pre">ARGOUTVIEW_ARRAY3)</span></tt></li>
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-<li><tt class="docutils literal"><span class="pre">(</span> <span class="pre">DIM_TYPE*</span> <span class="pre">DIM1,</span> <span class="pre">DIM_TYPE*</span> <span class="pre">DIM2,</span> <span class="pre">DIM_TYPE*</span> <span class="pre">DIM3,</span> <span class="pre">DATA_TYPE**</span> <span class="pre">ARGOUTVIEW_FARRAY3)</span></tt></li>
-</ul>
-</blockquote>
-<p>Note that arrays with hard-coded dimensions are not supported.  These
-cannot follow the double pointer signatures of these typemaps.</p>
-</div>
-<div class="section">
-<h2><a class="toc-backref" href="#id8" id="output-arrays" name="output-arrays">Output Arrays</a></h2>
-<p>The <tt class="docutils literal"><span class="pre">numpy.i</span></tt> interface file does not support typemaps for output
-arrays, for several reasons.  First, C/C++ return arguments are
-limited to a single value.  This prevents obtaining dimension
-information in a general way.  Second, arrays with hard-coded lengths
-are not permitted as return arguments.  In other words:</p>
-<pre class="literal-block">
-double[3] newVector(double x, double y, double z);
-</pre>
-<p>is not legal C/C++ syntax.  Therefore, we cannot provide typemaps of
-the form:</p>
-<pre class="literal-block">
-%typemap(out) (TYPE[ANY]);
-</pre>
-<p>If you run into a situation where a function or method is returning a
-pointer to an array, your best bet is to write your own version of the
-function to be wrapped, either with <tt class="docutils literal"><span class="pre">%extend</span></tt> for the case of class
-methods or <tt class="docutils literal"><span class="pre">%ignore</span></tt> and <tt class="docutils literal"><span class="pre">%rename</span></tt> for the case of functions.</p>
-</div>
-<div class="section">
-<h2><a class="toc-backref" href="#id9" id="other-common-types-bool" name="other-common-types-bool">Other Common Types: bool</a></h2>
-<p>Note that C++ type <tt class="docutils literal"><span class="pre">bool</span></tt> is not supported in the list in the
-<a class="reference" href="#available-typemaps">Available Typemaps</a> section.  NumPy bools are a single byte, while
-the C++ <tt class="docutils literal"><span class="pre">bool</span></tt> is four bytes (at least on my system).  Therefore:</p>
-<pre class="literal-block">
-%numpy_typemaps(bool, NPY_BOOL, int)
-</pre>
-<p>will result in typemaps that will produce code that reference
-improper data lengths.  You can implement the following macro
-expansion:</p>
-<pre class="literal-block">
-%numpy_typemaps(bool, NPY_UINT, int)
-</pre>
-<p>to fix the data length problem, and <a class="reference" href="#input-arrays">Input Arrays</a> will work fine,
-but <a class="reference" href="#in-place-arrays">In-Place Arrays</a> might fail type-checking.</p>
-</div>
-<div class="section">
-<h2><a class="toc-backref" href="#id10" id="other-common-types-complex" name="other-common-types-complex">Other Common Types: complex</a></h2>
-<p>Typemap conversions for complex floating-point types is also not
-supported automatically.  This is because <a class="reference" href="http://www.python.org">python</a> and <a class="reference" href="http://numpy.scipy.org">NumPy</a> are
-written in C, which does not have native complex types.  Both
-<a class="reference" href="http://www.python.org">python</a> and <a class="reference" href="http://numpy.scipy.org">NumPy</a> implement their own (essentially equivalent)
-<tt class="docutils literal"><span class="pre">struct</span></tt> definitions for complex variables:</p>
-<pre class="literal-block">
-/* Python */
-typedef struct {double real; double imag;} Py_complex;
-
-/* NumPy */
-typedef struct {float  real, imag;} npy_cfloat;
-typedef struct {double real, imag;} npy_cdouble;
-</pre>
-<p>We could have implemented:</p>
-<pre class="literal-block">
-%numpy_typemaps(Py_complex , NPY_CDOUBLE, int)
-%numpy_typemaps(npy_cfloat , NPY_CFLOAT , int)
-%numpy_typemaps(npy_cdouble, NPY_CDOUBLE, int)
-</pre>
-<p>which would have provided automatic type conversions for arrays of
-type <tt class="docutils literal"><span class="pre">Py_complex</span></tt>, <tt class="docutils literal"><span class="pre">npy_cfloat</span></tt> and <tt class="docutils literal"><span class="pre">npy_cdouble</span></tt>.  However, it
-seemed unlikely that there would be any independent (non-<a class="reference" href="http://www.python.org">python</a>,
-non-<a class="reference" href="http://numpy.scipy.org">NumPy</a>) application code that people would be using <a class="reference" href="http://www.swig.org">SWIG</a> to
-generate a <a class="reference" href="http://www.python.org">python</a> interface to, that also used these definitions
-for complex types.  More likely, these application codes will define
-their own complex types, or in the case of C++, use <tt class="docutils literal"><span class="pre">std::complex</span></tt>.
-Assuming these data structures are compatible with <a class="reference" href="http://www.python.org">python</a> and
-<a class="reference" href="http://numpy.scipy.org">NumPy</a> complex types, <tt class="docutils literal"><span class="pre">%numpy_typemap</span></tt> expansions as above (with
-the user's complex type substituted for the first argument) should
-work.</p>
-</div>
-</div>
-<div class="section">
-<h1><a class="toc-backref" href="#id11" 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.  Sometimes, 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 for 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="#id12" 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 &quot;first come, first served&quot;
-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="#id13" 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.  These macros and routines
-are implemented as fragments, which are described briefly in the
-previous section.  If you try to use one or more of the following
-macros or functions, but your compiler complains that it does not
-recognize the symbol, then you need to force these fragments to appear
-in your code using:</p>
-<pre class="literal-block">
-%fragment(&quot;NumPy_Fragments&quot;);
-</pre>
-<p>in your <a class="reference" href="http://www.swig.org">SWIG</a> interface file.</p>
-<div class="section">
-<h2><a class="toc-backref" href="#id14" id="macros" name="macros">Macros</a></h2>
-<blockquote>
-<dl class="docutils">
-<dt><strong>is_array(a)</strong></dt>
-<dd>Evaluates as true if <tt class="docutils literal"><span class="pre">a</span></tt> is non-<tt class="docutils literal"><span class="pre">NULL</span></tt> and can be cast to a
-<tt class="docutils literal"><span class="pre">PyArrayObject*</span></tt>.</dd>
-<dt><strong>array_type(a)</strong></dt>
-<dd>Evaluates to the integer data type code of <tt class="docutils literal"><span class="pre">a</span></tt>, assuming <tt class="docutils literal"><span class="pre">a</span></tt> can
-be cast to a <tt class="docutils literal"><span class="pre">PyArrayObject*</span></tt>.</dd>
-<dt><strong>array_numdims(a)</strong></dt>
-<dd>Evaluates to the integer number of dimensions of <tt class="docutils literal"><span class="pre">a</span></tt>, assuming
-<tt class="docutils literal"><span class="pre">a</span></tt> can be cast to a <tt class="docutils literal"><span class="pre">PyArrayObject*</span></tt>.</dd>
-<dt><strong>array_dimensions(a)</strong></dt>
-<dd>Evaluates to an array of type <tt class="docutils literal"><span class="pre">npy_intp</span></tt> and length
-<tt class="docutils literal"><span class="pre">array_numdims(a)</span></tt>, giving the lengths of all of the dimensions
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-<dt><strong>array_size(a,i)</strong></dt>
-<dd>Evaluates to the <tt class="docutils literal"><span class="pre">i</span></tt>-th dimension size of <tt class="docutils literal"><span class="pre">a</span></tt>, assuming <tt class="docutils literal"><span class="pre">a</span></tt>
-can be cast to a <tt class="docutils literal"><span class="pre">PyArrayObject*</span></tt>.</dd>
-<dt><strong>array_data(a)</strong></dt>
-<dd>Evaluates to a pointer of type <tt class="docutils literal"><span class="pre">void*</span></tt> that points to the data
-buffer of <tt class="docutils literal"><span class="pre">a</span></tt>, assuming <tt class="docutils literal"><span class="pre">a</span></tt> can be cast to a <tt class="docutils literal"><span class="pre">PyArrayObject*</span></tt>.</dd>
-<dt><strong>array_is_contiguous(a)</strong></dt>
-<dd>Evaluates as true if <tt class="docutils literal"><span class="pre">a</span></tt> is a contiguous array.  Equivalent to
-<tt class="docutils literal"><span class="pre">(PyArray_ISCONTIGUOUS(a))</span></tt>.</dd>
-<dt><strong>array_is_native(a)</strong></dt>
-<dd>Evaluates as true if the data buffer of <tt class="docutils literal"><span class="pre">a</span></tt> uses native byte
-order.  Equivalent to <tt class="docutils literal"><span class="pre">(PyArray_ISNOTSWAPPED(a))</span></tt>.</dd>
-<dt><strong>array_is_fortran(a)</strong></dt>
-<dd>Evaluates as true if <tt class="docutils literal"><span class="pre">a</span></tt> is FORTRAN ordered.</dd>
-</dl>
-</blockquote>
-</div>
-<div class="section">
-<h2><a class="toc-backref" href="#id15" id="routines" name="routines">Routines</a></h2>
-<blockquote>
-<p><strong>pytype_string()</strong></p>
-<blockquote>
-<p>Return type: <tt class="docutils literal"><span class="pre">char*</span></tt></p>
-<p>Arguments:</p>
-<ul class="simple">
-<li><tt class="docutils literal"><span class="pre">PyObject*</span> <span class="pre">py_obj</span></tt>, a general <a class="reference" href="http://www.python.org">python</a> object.</li>
-</ul>
-<p>Return a string describing the type of <tt class="docutils literal"><span class="pre">py_obj</span></tt>.</p>
-</blockquote>
-<p><strong>typecode_string()</strong></p>
-<blockquote>
-<p>Return type: <tt class="docutils literal"><span class="pre">char*</span></tt></p>
-<p>Arguments:</p>
-<ul class="simple">
-<li><tt class="docutils literal"><span class="pre">int</span> <span class="pre">typecode</span></tt>, a <a class="reference" href="http://numpy.scipy.org">NumPy</a> integer typecode.</li>
-</ul>
-<p>Return a string describing the type corresponding to the <a class="reference" href="http://numpy.scipy.org">NumPy</a>
-<tt class="docutils literal"><span class="pre">typecode</span></tt>.</p>
-</blockquote>
-<p><strong>type_match()</strong></p>
-<blockquote>
-<p>Return type: <tt class="docutils literal"><span class="pre">int</span></tt></p>
-<p>Arguments:</p>
-<ul class="simple">
-<li><tt class="docutils literal"><span class="pre">int</span> <span class="pre">actual_type</span></tt>, the <a class="reference" href="http://numpy.scipy.org">NumPy</a> typecode of a <a class="reference" href="http://numpy.scipy.org">NumPy</a> array.</li>
-<li><tt class="docutils literal"><span class="pre">int</span> <span class="pre">desired_type</span></tt>, the desired <a class="reference" href="http://numpy.scipy.org">NumPy</a> typecode.</li>
-</ul>
-<p>Make sure that <tt class="docutils literal"><span class="pre">actual_type</span></tt> is compatible with
-<tt class="docutils literal"><span class="pre">desired_type</span></tt>.  For example, this allows character and
-byte types, or int and long types, to match.  This is now
-equivalent to <tt class="docutils literal"><span class="pre">PyArray_EquivTypenums()</span></tt>.</p>
-</blockquote>
-<p><strong>obj_to_array_no_conversion()</strong></p>
-<blockquote>
-<p>Return type: <tt class="docutils literal"><span class="pre">PyArrayObject*</span></tt></p>
-<p>Arguments:</p>
-<ul class="simple">
-<li><tt class="docutils literal"><span class="pre">PyObject*</span> <span class="pre">input</span></tt>, a general <a class="reference" href="http://www.python.org">python</a> object.</li>
-<li><tt class="docutils literal"><span class="pre">int</span> <span class="pre">typecode</span></tt>, the desired <a class="reference" href="http://numpy.scipy.org">NumPy</a> typecode.</li>
-</ul>
-<p>Cast <tt class="docutils literal"><span class="pre">input</span></tt> to a <tt class="docutils literal"><span class="pre">PyArrayObject*</span></tt> if legal, and ensure that
-it is of type <tt class="docutils literal"><span class="pre">typecode</span></tt>.  If <tt class="docutils literal"><span class="pre">input</span></tt> cannot be cast, or the
-<tt class="docutils literal"><span class="pre">typecode</span></tt> is wrong, set a <a class="reference" href="http://www.python.org">python</a> error and return <tt class="docutils literal"><span class="pre">NULL</span></tt>.</p>
-</blockquote>
-<p><strong>obj_to_array_allow_conversion()</strong></p>
-<blockquote>
-<p>Return type: <tt class="docutils literal"><span class="pre">PyArrayObject*</span></tt></p>
-<p>Arguments:</p>
-<ul class="simple">
-<li><tt class="docutils literal"><span class="pre">PyObject*</span> <span class="pre">input</span></tt>, a general <a class="reference" href="http://www.python.org">python</a> object.</li>
-<li><tt class="docutils literal"><span class="pre">int</span> <span class="pre">typecode</span></tt>, the desired <a class="reference" href="http://numpy.scipy.org">NumPy</a> typecode of the resulting
-array.</li>
-<li><tt class="docutils literal"><span class="pre">int*</span> <span class="pre">is_new_object</span></tt>, returns a value of 0 if no conversion
-performed, else 1.</li>
-</ul>
-<p>Convert <tt class="docutils literal"><span class="pre">input</span></tt> to a <a class="reference" href="http://numpy.scipy.org">NumPy</a> array with the given <tt class="docutils literal"><span class="pre">typecode</span></tt>.
-On success, return a valid <tt class="docutils literal"><span class="pre">PyArrayObject*</span></tt> with the correct
-type.  On failure, the <a class="reference" href="http://www.python.org">python</a> error string will be set and the
-routine returns <tt class="docutils literal"><span class="pre">NULL</span></tt>.</p>
-</blockquote>
-<p><strong>make_contiguous()</strong></p>
-<blockquote>
-<p>Return type: <tt class="docutils literal"><span class="pre">PyArrayObject*</span></tt></p>
-<p>Arguments:</p>
-<ul class="simple">
-<li><tt class="docutils literal"><span class="pre">PyArrayObject*</span> <span class="pre">ary</span></tt>, a <a class="reference" href="http://numpy.scipy.org">NumPy</a> array.</li>
-<li><tt class="docutils literal"><span class="pre">int*</span> <span class="pre">is_new_object</span></tt>, returns a value of 0 if no conversion
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-<li><tt class="docutils literal"><span class="pre">int</span> <span class="pre">min_dims</span></tt>, minimum allowable dimensions.</li>
-<li><tt class="docutils literal"><span class="pre">int</span> <span class="pre">max_dims</span></tt>, maximum allowable dimensions.</li>
-</ul>
-<p>Check to see if <tt class="docutils literal"><span class="pre">ary</span></tt> is contiguous.  If so, return the input
-pointer and flag it as not a new object.  If it is not contiguous,
-create a new <tt class="docutils literal"><span class="pre">PyArrayObject*</span></tt> using the original data, flag it
-as a new object and return the pointer.</p>
-</blockquote>
-<p><strong>obj_to_array_contiguous_allow_conversion()</strong></p>
-<blockquote>
-<p>Return type: <tt class="docutils literal"><span class="pre">PyArrayObject*</span></tt></p>
-<p>Arguments:</p>
-<ul class="simple">
-<li><tt class="docutils literal"><span class="pre">PyObject*</span> <span class="pre">input</span></tt>, a general <a class="reference" href="http://www.python.org">python</a> object.</li>
-<li><tt class="docutils literal"><span class="pre">int</span> <span class="pre">typecode</span></tt>, the desired <a class="reference" href="http://numpy.scipy.org">NumPy</a> typecode of the resulting
-array.</li>
-<li><tt class="docutils literal"><span class="pre">int*</span> <span class="pre">is_new_object</span></tt>, returns a value of 0 if no conversion
-performed, else 1.</li>
-</ul>
-<p>Convert <tt class="docutils literal"><span class="pre">input</span></tt> to a contiguous <tt class="docutils literal"><span class="pre">PyArrayObject*</span></tt> of the
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-<tt class="docutils literal"><span class="pre">PyArrayObject*</span></tt>, a new one will be created and the new object
-flag will be set.</p>
-</blockquote>
-<p><strong>require_contiguous()</strong></p>
-<blockquote>
-<p>Return type: <tt class="docutils literal"><span class="pre">int</span></tt></p>
-<p>Arguments:</p>
-<ul class="simple">
-<li><tt class="docutils literal"><span class="pre">PyArrayObject*</span> <span class="pre">ary</span></tt>, a <a class="reference" href="http://numpy.scipy.org">NumPy</a> array.</li>
-</ul>
-<p>Test whether <tt class="docutils literal"><span class="pre">ary</span></tt> is contiguous.  If so, return 1.  Otherwise,
-set a <a class="reference" href="http://www.python.org">python</a> error and return 0.</p>
-</blockquote>
-<p><strong>require_native()</strong></p>
-<blockquote>
-<p>Return type: <tt class="docutils literal"><span class="pre">int</span></tt></p>
-<p>Arguments:</p>
-<ul class="simple">
-<li><tt class="docutils literal"><span class="pre">PyArray_Object*</span> <span class="pre">ary</span></tt>, a <a class="reference" href="http://numpy.scipy.org">NumPy</a> array.</li>
-</ul>
-<p>Require that <tt class="docutils literal"><span class="pre">ary</span></tt> is not byte-swapped.  If the array is not
-byte-swapped, return 1.  Otherwise, set a <a class="reference" href="http://www.python.org">python</a> error and
-return 0.</p>
-</blockquote>
-<p><strong>require_dimensions()</strong></p>
-<blockquote>
-<p>Return type: <tt class="docutils literal"><span class="pre">int</span></tt></p>
-<p>Arguments:</p>
-<ul class="simple">
-<li><tt class="docutils literal"><span class="pre">PyArrayObject*</span> <span class="pre">ary</span></tt>, a <a class="reference" href="http://numpy.scipy.org">NumPy</a> array.</li>
-<li><tt class="docutils literal"><span class="pre">int</span> <span class="pre">exact_dimensions</span></tt>, the desired number of dimensions.</li>
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-<div class="section">
-<h1><a class="toc-backref" href="#id16" 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">&quot;numpy.i&quot;</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="#id17" 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);
-</pre>
-<p>The <a class="reference" href="http://www.python.org">python</a> interface that we want is:</p>
-<pre class="literal-block">
-def dot(vec1, vec2):
-    &quot;&quot;&quot;
-    dot(PyObject,PyObject) -&gt; double
-    &quot;&quot;&quot;
-</pre>
-<p>The problem here is that there is one dimension argument and two array
-arguments, and our typemaps are set up for dimensions that apply to a
-single array (in fact, <a class="reference" href="http://www.swig.org">SWIG</a> does not provide a mechanism for
-associating <tt class="docutils literal"><span class="pre">len</span></tt> with <tt class="docutils literal"><span class="pre">vec2</span></tt> that takes two <a class="reference" href="http://www.python.org">python</a> input
-arguments).  The recommended solution is the following:</p>
-<pre class="literal-block">
-%apply (int DIM1, double* IN_ARRAY1) {(int len1, double* vec1),
-                                      (int len2, double* vec2)}
-%rename (dot) my_dot;
-%exception my_dot {
-    $action
-    if (PyErr_Occurred()) SWIG_fail;
-}
-%inline %{
-double my_dot(int len1, double* vec1, int len2, double* vec2) {
-    if (len1 != len2) {
-        PyErr_Format(PyExc_ValueError,
-                     &quot;Arrays of lengths (%d,%d) given&quot;,
-                     len1, len2);
-        return 0.0;
-    }
-    return dot(len1, vec1, vec2);
-}
-%}
-</pre>
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-addition to <tt class="docutils literal"><span class="pre">%ignore</span></tt>.</p>
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-macro to perform this task.</p>
-</div>
-<div class="section">
-<h2><a class="toc-backref" href="#id18" id="other-situations" name="other-situations">Other Situations</a></h2>
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-<li><p class="first">In some situations, it is possible that you could use the
-<tt class="docutils literal"><span class="pre">%numpy_templates</span></tt> macro to implement typemaps for your own
-types.  See the <a class="reference" href="#other-common-types-bool">Other Common Types: bool</a> or <a class="reference" href="#other-common-types-complex">Other Common
-Types: complex</a> sections for examples.  Another situation is if
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-example):</p>
-<pre class="literal-block">
-%numpy_typemaps(double, NPY_DOUBLE, long)
-</pre>
-</li>
-<li><p class="first">You can use the code in <tt class="docutils literal"><span class="pre">numpy.i</span></tt> to write your own typemaps.
-For example, if you had a four-dimensional array as a function
-argument, you could cut-and-paste the appropriate
-three-dimensional typemaps into your interface file.  The
-modifications for the fourth dimension would be trivial.</p>
-</li>
-<li><p class="first">Sometimes, the best approach is to use the <tt class="docutils literal"><span class="pre">%extend</span></tt> directive
-to define new methods for your classes (or overload existing ones)
-that take a <tt class="docutils literal"><span class="pre">PyObject*</span></tt> (that either is or can be converted to a
-<tt class="docutils literal"><span class="pre">PyArrayObject*</span></tt>) instead of a pointer to a buffer.  In this
-case, the helper routines in <tt class="docutils literal"><span class="pre">numpy.i</span></tt> can be very useful.</p>
-</li>
-<li><p class="first">Writing typemaps can be a bit nonintuitive.  If you have specific
-questions about writing <a class="reference" href="http://www.swig.org">SWIG</a> typemaps for <a class="reference" href="http://numpy.scipy.org">NumPy</a>, the
-developers of <tt class="docutils literal"><span class="pre">numpy.i</span></tt> do monitor the
-<a class="reference" href="mailto:Numpy-discussion&#64;scipy.org">Numpy-discussion</a> and
-<a class="reference" href="mailto:Swig-user&#64;lists.sourceforge.net">Swig-user</a> mail lists.</p>
-</li>
-</ul>
-</blockquote>
-</div>
-<div class="section">
-<h2><a class="toc-backref" href="#id19" id="a-final-note" name="a-final-note">A Final Note</a></h2>
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-want.  Therefore, it is always a good idea to add a <tt class="docutils literal"><span class="pre">%clear</span></tt>
-directive after you are done with a specific typemap:</p>
-<pre class="literal-block">
-%apply (double* IN_ARRAY1, int DIM1) {(double* vector, int length)}
-%include &quot;my_header.h&quot;
-%clear (double* vector, int length);
-</pre>
-<p>In general, you should target these typemap signatures specifically
-where you want them, and then clear them after you are done.</p>
-</div>
-</div>
-<div class="section">
-<h1><a class="toc-backref" href="#id20" 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>
-<ul class="simple">
-<li>That can be one of 12 different scalar types: <tt class="docutils literal"><span class="pre">signed</span> <span class="pre">char</span></tt>,
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-<li>That support 41 different argument signatures for each data type,
-including:<ul>
-<li>One-dimensional, two-dimensional and three-dimensional arrays.</li>
-<li>Input-only, in-place, argout and argoutview behavior.</li>
-<li>Hard-coded dimensions, data-buffer-then-dimensions
-specification, and dimensions-then-data-buffer specification.</li>
-<li>Both C-ordering (&quot;last dimension fastest&quot;) or FORTRAN-ordering
-(&quot;first dimension fastest&quot;) support for 2D and 3D arrays.</li>
-</ul>
-</li>
-</ul>
-</blockquote>
-<p>The <tt class="docutils literal"><span class="pre">numpy.i</span></tt> interface file also provides additional tools for
-wrapper developers, including:</p>
-<blockquote>
-<ul class="simple">
-<li>A <a class="reference" href="http://www.swig.org">SWIG</a> macro (<tt class="docutils literal"><span class="pre">%numpy_typemaps</span></tt>) with three arguments for
-implementing the 41 argument signatures for the user's choice of
-(1) C data type, (2) <a class="reference" href="http://numpy.scipy.org">NumPy</a> data type (assuming they match), and
-(3) dimension type.</li>
-<li>Nine C macros and 13 C functions that can be used to write
-specialized typemaps, extensions, or inlined functions that handle
-cases not covered by the provided typemaps.</li>
-</ul>
-</blockquote>
-</div>
-<div class="section">
-<h1><a class="toc-backref" href="#id21" 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).
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-Fernando Perez and myself.  Fernando collected helper functions and
-typemaps from Eric Jones, Michael Hunter, Anna Omelchenko and Michael
-Sanner.  Sebastian Hasse and Georg Holzmann have 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: 2007-12-01 18:17 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.
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-<div class="document" id="testing-the-numpy-i-typemaps">
-<h1 class="title">Testing the numpy.i Typemaps</h1>
-<table class="docinfo" frame="void" rules="none">
-<col class="docinfo-name" />
-<col class="docinfo-content" />
-<tbody valign="top">
-<tr><th class="docinfo-name">Author:</th>
-<td>Bill Spotz</td></tr>
-<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>6 April, 2007</td></tr>
-</tbody>
-</table>
-<div class="contents topic">
-<p class="topic-title first"><a id="contents" name="contents">Contents</a></p>
-<ul class="simple">
-<li><a class="reference" href="#introduction" id="id1" name="id1">Introduction</a></li>
-<li><a class="reference" href="#testing-organization" id="id2" name="id2">Testing Organization</a></li>
-<li><a class="reference" href="#testing-header-files" id="id3" name="id3">Testing Header Files</a></li>
-<li><a class="reference" href="#testing-source-files" id="id4" name="id4">Testing Source Files</a></li>
-<li><a class="reference" href="#testing-swig-interface-files" id="id5" name="id5">Testing SWIG Interface Files</a></li>
-<li><a class="reference" href="#testing-python-scripts" id="id6" name="id6">Testing Python Scripts</a></li>
-</ul>
-</div>
-<div class="section">
-<h1><a class="toc-backref" href="#id1" id="introduction" name="introduction">Introduction</a></h1>
-<p>Writing tests for the <tt class="docutils literal"><span class="pre">numpy.i</span></tt> <a class="reference" href="http://www.swig.org">SWIG</a>
-interface file is a combinatorial headache.  At present, 12 different
-data types are supported, each with 23 different argument signatures,
-for a total of 276 typemaps supported &quot;out of the box&quot;.  Each of these
-typemaps, in turn, might require several unit tests in order to verify
-expected behavior for both proper and improper inputs.  Currently,
-this results in 1,020 individual unit tests that are performed when
-<tt class="docutils literal"><span class="pre">make</span> <span class="pre">test</span></tt> is run in the <tt class="docutils literal"><span class="pre">numpy/docs/swig</span></tt> subdirectory.</p>
-<p>To facilitate this many similar unit tests, some high-level
-programming techniques are employed, including C and <a class="reference" href="http://www.swig.org">SWIG</a> macros,
-as well as  <a class="reference" href="http://www.python.org">python</a> inheritance.  The
-purpose of this document is to describe the testing infrastructure
-employed to verify that the <tt class="docutils literal"><span class="pre">numpy.i</span></tt> typemaps are working as
-expected.</p>
-</div>
-<div class="section">
-<h1><a class="toc-backref" href="#id2" id="testing-organization" name="testing-organization">Testing Organization</a></h1>
-<p>There are three indepedent testing frameworks supported, for one-,
-two-, and three-dimensional arrays respectively.  For one-dimensional
-arrays, there are two C++ files, a header and a source, named:</p>
-<pre class="literal-block">
-Vector.h
-Vector.cxx
-</pre>
-<p>that contain prototypes and code for a variety of functions that have
-one-dimensional arrays as function arguments.  The file:</p>
-<pre class="literal-block">
-Vector.i
-</pre>
-<p>is a <a class="reference" href="http://www.swig.org">SWIG</a> interface file that defines a python module <tt class="docutils literal"><span class="pre">Vector</span></tt>
-that wraps the functions in <tt class="docutils literal"><span class="pre">Vector.h</span></tt> while utilizing the typemaps
-in <tt class="docutils literal"><span class="pre">numpy.i</span></tt> to correctly handle the C arrays.</p>
-<p>The <tt class="docutils literal"><span class="pre">Makefile</span></tt> calls <tt class="docutils literal"><span class="pre">swig</span></tt> to generate <tt class="docutils literal"><span class="pre">Vector.py</span></tt> and
-<tt class="docutils literal"><span class="pre">Vector_wrap.cxx</span></tt>, and also executes the <tt class="docutils literal"><span class="pre">setup.py</span></tt> script that
-compiles <tt class="docutils literal"><span class="pre">Vector_wrap.cxx</span></tt> and links together the extension module
-<tt class="docutils literal"><span class="pre">_Vector.so</span></tt> or <tt class="docutils literal"><span class="pre">_Vector.dylib</span></tt>, depending on the platform.  This
-extension module and the proxy file <tt class="docutils literal"><span class="pre">Vector.py</span></tt> are both placed in a
-subdirectory under the <tt class="docutils literal"><span class="pre">build</span></tt> directory.</p>
-<p>The actual testing takes place with a <a class="reference" href="http://www.python.org">python</a> script named:</p>
-<pre class="literal-block">
-testVector.py
-</pre>
-<p>that uses the standard <a class="reference" href="http://www.python.org">python</a> library module <tt class="docutils literal"><span class="pre">unittest</span></tt>, which
-performs several tests of each function defined in <tt class="docutils literal"><span class="pre">Vector.h</span></tt> for
-each data type supported.</p>
-<p>Two-dimensional arrays are tested in exactly the same manner.  The
-above description applies, but with <tt class="docutils literal"><span class="pre">Matrix</span></tt> substituted for
-<tt class="docutils literal"><span class="pre">Vector</span></tt>.  For three-dimensional tests, substitute <tt class="docutils literal"><span class="pre">Tensor</span></tt> for
-<tt class="docutils literal"><span class="pre">Vector</span></tt>.  For the descriptions that follow, we will reference the
-<tt class="docutils literal"><span class="pre">Vector</span></tt> tests, but the same information applies to <tt class="docutils literal"><span class="pre">Matrix</span></tt> and
-<tt class="docutils literal"><span class="pre">Tensor</span></tt> tests.</p>
-<p>The command <tt class="docutils literal"><span class="pre">make</span> <span class="pre">test</span></tt> will ensure that all of the test software is
-built and then run all three test scripts.</p>
-</div>
-<div class="section">
-<h1><a class="toc-backref" href="#id3" id="testing-header-files" name="testing-header-files">Testing Header Files</a></h1>
-<p><tt class="docutils literal"><span class="pre">Vector.h</span></tt> is a C++ header file that defines a C macro called
-<tt class="docutils literal"><span class="pre">TEST_FUNC_PROTOS</span></tt> that takes two arguments: <tt class="docutils literal"><span class="pre">TYPE</span></tt>, which is a
-data type name such as <tt class="docutils literal"><span class="pre">unsigned</span> <span class="pre">int</span></tt>; and <tt class="docutils literal"><span class="pre">SNAME</span></tt>, which is a
-short name for the same data type with no spaces, e.g. <tt class="docutils literal"><span class="pre">uint</span></tt>.  This
-macro defines several function prototypes that have the prefix
-<tt class="docutils literal"><span class="pre">SNAME</span></tt> and have at least one argument that is an array of type
-<tt class="docutils literal"><span class="pre">TYPE</span></tt>.  Those functions that have return arguments return a
-<tt class="docutils literal"><span class="pre">TYPE</span></tt> value.</p>
-<p><tt class="docutils literal"><span class="pre">TEST_FUNC_PROTOS</span></tt> is then implemented for all of the data types
-supported by <tt class="docutils literal"><span class="pre">numpy.i</span></tt>:</p>
-<blockquote>
-<ul class="simple">
-<li><tt class="docutils literal"><span class="pre">signed</span> <span class="pre">char</span></tt></li>
-<li><tt class="docutils literal"><span class="pre">unsigned</span> <span class="pre">char</span></tt></li>
-<li><tt class="docutils literal"><span class="pre">short</span></tt></li>
-<li><tt class="docutils literal"><span class="pre">unsigned</span> <span class="pre">short</span></tt></li>
-<li><tt class="docutils literal"><span class="pre">int</span></tt></li>
-<li><tt class="docutils literal"><span class="pre">unsigned</span> <span class="pre">int</span></tt></li>
-<li><tt class="docutils literal"><span class="pre">long</span></tt></li>
-<li><tt class="docutils literal"><span class="pre">unsigned</span> <span class="pre">long</span></tt></li>
-<li><tt class="docutils literal"><span class="pre">long</span> <span class="pre">long</span></tt></li>
-<li><tt class="docutils literal"><span class="pre">unsigned</span> <span class="pre">long</span> <span class="pre">long</span></tt></li>
-<li><tt class="docutils literal"><span class="pre">float</span></tt></li>
-<li><tt class="docutils literal"><span class="pre">double</span></tt></li>
-</ul>
-</blockquote>
-</div>
-<div class="section">
-<h1><a class="toc-backref" href="#id4" id="testing-source-files" name="testing-source-files">Testing Source Files</a></h1>
-<p><tt class="docutils literal"><span class="pre">Vector.cxx</span></tt> is a C++ source file that implements compilable code
-for each of the function prototypes specified in <tt class="docutils literal"><span class="pre">Vector.h</span></tt>.  It
-defines a C macro <tt class="docutils literal"><span class="pre">TEST_FUNCS</span></tt> that has the same arguments and works
-in the same way as <tt class="docutils literal"><span class="pre">TEST_FUNC_PROTOS</span></tt> does in <tt class="docutils literal"><span class="pre">Vector.h</span></tt>.
-<tt class="docutils literal"><span class="pre">TEST_FUNCS</span></tt> is implemented for each of the 12 data types as above.</p>
-</div>
-<div class="section">
-<h1><a class="toc-backref" href="#id5" id="testing-swig-interface-files" name="testing-swig-interface-files">Testing SWIG Interface Files</a></h1>
-<p><tt class="docutils literal"><span class="pre">Vector.i</span></tt> is a <a class="reference" href="http://www.swig.org">SWIG</a> interface file that defines python module
-<tt class="docutils literal"><span class="pre">Vector</span></tt>.  It follows the conventions for using <tt class="docutils literal"><span class="pre">numpy.i</span></tt> as
-described in the <a class="reference" href="numpy_swig.html">numpy.i documentation</a>.  It
-defines a <a class="reference" href="http://www.swig.org">SWIG</a> macro <tt class="docutils literal"><span class="pre">%apply_numpy_typemaps</span></tt> that has a single
-argument <tt class="docutils literal"><span class="pre">TYPE</span></tt>.  It uses the <a class="reference" href="http://www.swig.org">SWIG</a> directive <tt class="docutils literal"><span class="pre">%apply</span></tt> as
-described in the <a class="reference" href="numpy_swig.html">numpy.i documentation</a> to apply the provided
-typemaps to the argument signatures found in <tt class="docutils literal"><span class="pre">Vector.h</span></tt>.  This macro
-is then implemented for all of the data types supported by
-<tt class="docutils literal"><span class="pre">numpy.i</span></tt>.  It then does a <tt class="docutils literal"><span class="pre">%include</span> <span class="pre">&quot;Vector.h&quot;</span></tt> to wrap all of
-the function prototypes in <tt class="docutils literal"><span class="pre">Vector.h</span></tt> using the typemaps in
-<tt class="docutils literal"><span class="pre">numpy.i</span></tt>.</p>
-</div>
-<div class="section">
-<h1><a class="toc-backref" href="#id6" id="testing-python-scripts" name="testing-python-scripts">Testing Python Scripts</a></h1>
-<p>After <tt class="docutils literal"><span class="pre">make</span></tt> is used to build the testing extension modules,
-<tt class="docutils literal"><span class="pre">testVector.py</span></tt> can be run to execute the tests.  As with other
-scripts that use <tt class="docutils literal"><span class="pre">unittest</span></tt> to facilitate unit testing,
-<tt class="docutils literal"><span class="pre">testVector.py</span></tt> defines a class that inherits from
-<tt class="docutils literal"><span class="pre">unittest.TestCase</span></tt>:</p>
-<pre class="literal-block">
-class VectorTestCase(unittest.TestCase):
-</pre>
-<p>However, this class is not run directly.  Rather, it serves as a base
-class to several other python classes, each one specific to a
-particular data type.  The <tt class="docutils literal"><span class="pre">VectorTestCase</span></tt> class stores two strings
-for typing information:</p>
-<blockquote>
-<dl class="docutils">
-<dt><strong>self.typeStr</strong></dt>
-<dd>A string that matches one of the <tt class="docutils literal"><span class="pre">SNAME</span></tt> prefixes used in
-<tt class="docutils literal"><span class="pre">Vector.h</span></tt> and <tt class="docutils literal"><span class="pre">Vector.cxx</span></tt>.  For example, <tt class="docutils literal"><span class="pre">&quot;double&quot;</span></tt>.</dd>
-<dt><strong>self.typeCode</strong></dt>
-<dd>A short (typically single-character) string that represents a
-data type in numpy and corresponds to <tt class="docutils literal"><span class="pre">self.typeStr</span></tt>.  For
-example, if <tt class="docutils literal"><span class="pre">self.typeStr</span></tt> is <tt class="docutils literal"><span class="pre">&quot;double&quot;</span></tt>, then
-<tt class="docutils literal"><span class="pre">self.typeCode</span></tt> should be <tt class="docutils literal"><span class="pre">&quot;d&quot;</span></tt>.</dd>
-</dl>
-</blockquote>
-<p>Each test defined by the <tt class="docutils literal"><span class="pre">VectorTestCase</span></tt> class extracts the python
-function it is trying to test by accessing the <tt class="docutils literal"><span class="pre">Vector</span></tt> module's
-dictionary:</p>
-<pre class="literal-block">
-length = Vector.__dict__[self.typeStr + &quot;Length&quot;]
-</pre>
-<p>In the case of double precision tests, this will return the python
-function <tt class="docutils literal"><span class="pre">Vector.doubleLength</span></tt>.</p>
-<p>We then define a new test case class for each supported data type with
-a short definition such as:</p>
-<pre class="literal-block">
-class doubleTestCase(VectorTestCase):
-    def __init__(self, methodName=&quot;runTest&quot;):
-        VectorTestCase.__init__(self, methodName)
-        self.typeStr  = &quot;double&quot;
-        self.typeCode = &quot;d&quot;
-</pre>
-<p>Each of these 12 classes is collected into a <tt class="docutils literal"><span class="pre">unittest.TestSuite</span></tt>,
-which is then executed.  Errors and failures are summed together and
-returned as the exit argument.  Any non-zero result indicates that at
-least one test did not pass.</p>
-</div>
-</div>
-<div class="footer">
-<hr class="footer" />
-Generated on: 2007-04-06 21:21 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>
-</body>
-</html>
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author	rgommers <ralf.gommers@googlemail.com>	2010-09-04 09:52:44 +0000
committer	rgommers <ralf.gommers@googlemail.com>	2010-09-04 09:52:44 +0000
commit	779d063af9c88152d7b1bc829410a4edb1ff4f88 (patch)
tree	e2e72b0ea77f6518fceb34cc9bdbc0a80d3a900b /doc
parent	72665ab87449b9896dcb2db9aae36bb12c3daba7 (diff)
download	numpy-779d063af9c88152d7b1bc829410a4edb1ff4f88.tar.gz