Added '#ifdef SWIGPYTHON' protection

4 files changed, 38 insertions, 52 deletions

diff --git a/numpy/doc/swig/numpy.i b/numpy/doc/swig/numpy.i
index 9ffb3698a..f6e72c424 100644
--- a/numpy/doc/swig/numpy.i
+++ b/numpy/doc/swig/numpy.i
@@ -1,4 +1,6 @@
 /* -*- C -*-  (not really, but good for syntax highlighting) */
+#ifdef SWIGPYTHON
+
 %{
 #ifndef SWIG_FILE_WITH_INIT
 #  define NO_IMPORT_ARRAY
@@ -73,7 +75,7 @@ char* pytype_string(PyObject* py_obj) {
 /* Given a NumPy typecode, return a string describing the type.
  */
 char* typecode_string(int typecode) {
-  static char* type_names[24] = {"bool", "byte", "unsigned byte",
+  static char* type_names[25] = {"bool", "byte", "unsigned byte",
 				 "short", "unsigned short", "int",
 				 "unsigned int", "long", "unsigned long",
 				 "long long", "unsigned long long",
@@ -81,12 +83,13 @@ char* typecode_string(int typecode) {
 				 "complex float", "complex double",
 				 "complex long double", "object",
 				 "string", "unicode", "void", "ntypes",
-				 "notype", "char"};
-  return type_names[typecode];
+				 "notype", "char", "unknown"};
+  return typecode < 24 ? type_names[typecode] : type_names[24];
 }
 
-/* Make sure input has correct numeric type.  Allow character and byte
- * to match.  Also allow int and long to match.
+/* 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);
@@ -99,12 +102,12 @@ int type_match(int actual_type, int desired_type) {
 PyArrayObject* obj_to_array_no_conversion(PyObject* input, int typecode) {
   PyArrayObject* ary = NULL;
   if (is_array(input) && (typecode == NPY_NOTYPE ||
-			  type_match(array_type(input), typecode))) {
+			  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));
+    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);
@@ -112,9 +115,9 @@ PyArrayObject* obj_to_array_no_conversion(PyObject* input, int typecode) {
   }
   else {
     char * desired_type = typecode_string(typecode);
-    char * actual_type = pytype_string(input);
+    char * actual_type  = pytype_string(input);
     PyErr_Format(PyExc_TypeError, 
-		 "Array of type '%s' required.  A %s was given", 
+		 "Array of type '%s' required.  A '%s' was given", 
 		 desired_type, actual_type);
     ary = NULL;
   }
@@ -122,7 +125,7 @@ PyArrayObject* obj_to_array_no_conversion(PyObject* input, int typecode) {
 }
 
 /* Convert the given PyObject to a NumPy array with the given
- * typecode.  On Success, return a valid PyArrayObject* with the
+ * 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.
  */
@@ -130,7 +133,8 @@ 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 || type_match(array_type(input),typecode))) {
+  if (is_array(input) && (typecode == NPY_NOTYPE ||
+			  PyArray_EquivTypenums(array_type(input),typecode))) {
     ary = (PyArrayObject*) input;
     *is_new_object = 0;
   }
@@ -196,7 +200,8 @@ PyArrayObject* obj_to_array_contiguous_allow_conversion(PyObject* input,
 int require_contiguous(PyArrayObject* ary) {
   int contiguous = 1;
   if (!array_is_contiguous(ary)) {
-    PyErr_SetString(PyExc_TypeError, "Array must be contiguous.  A discontiguous array was given");
+    PyErr_SetString(PyExc_TypeError,
+		    "Array must be contiguous.  A non-contiguous array was given");
     contiguous = 0;
   }
   return contiguous;
@@ -365,7 +370,7 @@ int require_size(PyArrayObject* ary, npy_intp* size, int n) {
 /* Typemap suite for (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1)
  */
 %typemap(in) (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1)
-             (PyArrayObject* array=NULL, int is_new_object=0) {
+  (PyArrayObject* array=NULL, int is_new_object=0) {
   array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object);
   npy_intp size[1] = {-1};
   if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail;
@@ -379,7 +384,7 @@ int require_size(PyArrayObject* ary, npy_intp* size, int n) {
 /* Typemap suite for (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
  */
 %typemap(in) (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
-             (PyArrayObject* array=NULL, int is_new_object=0) {
+  (PyArrayObject* array=NULL, int is_new_object=0) {
   array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object);
   npy_intp size[2] = {-1,-1};
   if (!array || !require_dimensions(array, 2) || !require_size(array, size, 1)) SWIG_fail;
@@ -403,7 +408,8 @@ int require_size(PyArrayObject* ary, npy_intp* size, int n) {
 
 /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
  */
-%typemap(in) (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) (PyArrayObject* temp=NULL) {
+%typemap(in) (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
+  (PyArrayObject* temp=NULL) {
   temp = obj_to_array_no_conversion($input, DATA_TYPECODE);
   if (!temp || !require_contiguous(temp)) SWIG_fail;
   $1 = (DATA_TYPE*) temp->data;
@@ -462,7 +468,7 @@ int require_size(PyArrayObject* ary, npy_intp* size, int n) {
 /* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1)
  */
 %typemap(in) (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1)
-             (PyArrayObject* array=NULL, int is_new_object=0) {
+  (PyArrayObject* array=NULL, int is_new_object=0) {
   array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object);
   npy_intp size[1] = {-1};
   if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail;
@@ -476,7 +482,7 @@ int require_size(PyArrayObject* ary, npy_intp* size, int n) {
 /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2)
  */
 %typemap(in) (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2)
-             (PyArrayObject* array=NULL, int is_new_object=0) {
+  (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, 1)) SWIG_fail;
@@ -500,7 +506,8 @@ int require_size(PyArrayObject* ary, npy_intp* size, int n) {
 
 /* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2)
  */
-%typemap(in) (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2) (PyArrayObject* temp=NULL) {
+%typemap(in) (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2)
+  (PyArrayObject* temp=NULL) {
   temp = obj_to_array_no_conversion($input, DATA_TYPECODE);
   if (!temp || !require_contiguous(temp)) SWIG_fail;
   $1 = (DIM_TYPE) temp->dimensions[0];
@@ -551,3 +558,5 @@ int require_size(PyArrayObject* ary, npy_intp* size, int n) {
  */
 /*%numpy_typemaps(complex long double, NPY_CLONGDOUBLE)
  */
+
+#endif /* SWIGPYTHON */
diff --git a/numpy/doc/swig/numpy_swig.html b/numpy/doc/swig/numpy_swig.html
index 5a8b0fe74..b8d6f9d9c 100644
--- a/numpy/doc/swig/numpy_swig.html
+++ b/numpy/doc/swig/numpy_swig.html
@@ -614,7 +614,8 @@ can be cast to a <tt class="docutils literal"><span class="pre">PyArrayObject*</
 <dd>Given a <a class="reference" href="http://numpy.scipy.org">NumPy</a> integer typecode, return a string describing the type.</dd>
 <dt><strong>int type_match(int actual_type, int desired_type)</strong></dt>
 <dd>Make sure input has correct <a class="reference" href="http://numpy.scipy.org">NumPy</a> type.  Allow character and
-byte to match.  Also allow int and long to match.</dd>
+byte to match.  Also allow int and long to match.  This is
+deprecated .  You should use <tt class="docutils literal"><span class="pre">PyArray_EquivTypenums()</span></tt> instead.</dd>
 <dt><strong>PyArrayObject* obj_to_array_no_conversion(PyObject* input, int typecode)</strong></dt>
 <dd>Given a <tt class="docutils literal"><span class="pre">PyObject*</span></tt>, cast it to a <tt class="docutils literal"><span class="pre">PyArrayObject*</span></tt> if legal.
 If not, set the python error string appropriately and return
@@ -651,19 +652,6 @@ python error string and return 0.</dd>
 <dd>Require the given <tt class="docutils literal"><span class="pre">PyArrayObject*</span></tt> to have a specified shape.
 If the array has the specified shape, return 1.  Otherwise, set
 the python error string and return 0.</dd>
-<dt><strong>static PyArrayObject *contiguous_typed_array(PyObject *obj, int typecode, int expectnd, int *expectdims)</strong></dt>
-<dd>This function tries to create a contiguous <a class="reference" href="http://numpy.scipy.org">NumPy</a> array of type
-typecode from an arbitrary python object <tt class="docutils literal"><span class="pre">obj</span></tt>.  This should
-work for any sequence object.  The argument <tt class="docutils literal"><span class="pre">expectnd</span></tt> is the
-expected number of dimensions, ignored if &lt;= 0.  The argument
-<tt class="docutils literal"><span class="pre">expectdims</span></tt> is an array of expected dimensions, ignored if &lt;=
-0.  This routine raises a <tt class="docutils literal"><span class="pre">ValueError</span></tt> exception if the
-underlying <tt class="docutils literal"><span class="pre">PyArray_ContiguousFromObject</span></tt> routine fails, if the
-array has a bad shape, if the extent of a given dimension doesn't
-match the specified extent.  If <tt class="docutils literal"><span class="pre">obj</span></tt> is a contiguous
-<tt class="docutils literal"><span class="pre">PyArrayObject*</span></tt> then a reference is returned; if <tt class="docutils literal"><span class="pre">obj</span></tt> is a
-python sequence, then a new <tt class="docutils literal"><span class="pre">PyArrayObject*</span></tt> is created and
-returned.</dd>
 </dl>
 </blockquote>
 </div>
@@ -671,8 +659,9 @@ returned.</dd>
 <div class="section">
 <h1><a class="toc-backref" href="#id12" 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>.  Nevertheless, <tt class="docutils literal"><span class="pre">numpy.i</span></tt> may still
-be helpful when you encounter them.</p>
+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.
+Nevertheless, <tt class="docutils literal"><span class="pre">numpy.i</span></tt> may still be helpful when you encounter
+them.</p>
 <blockquote>
 <ul>
 <li><p class="first">In some situations, it is possible that you could use the
@@ -719,7 +708,7 @@ result possible.</p>
 </div>
 <div class="footer">
 <hr class="footer" />
-Generated on: 2007-03-19 04:09 UTC.
+Generated on: 2007-03-23 12:04 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>
diff --git a/numpy/doc/swig/numpy_swig.pdf b/numpy/doc/swig/numpy_swig.pdf
index 97822c4d1..9167b5979 100644
--- a/numpy/doc/swig/numpy_swig.pdf
+++ b/numpy/doc/swig/numpy_swig.pdf
diff --git a/numpy/doc/swig/numpy_swig.txt b/numpy/doc/swig/numpy_swig.txt
index d3001da99..207f94e3d 100644
--- a/numpy/doc/swig/numpy_swig.txt
+++ b/numpy/doc/swig/numpy_swig.txt
@@ -304,7 +304,8 @@ Routines
 
   **int type_match(int actual_type, int desired_type)**
     Make sure input has correct `NumPy`_ type.  Allow character and
-    byte to match.  Also allow int and long to match.
+    byte to match.  Also allow int and long to match.  This is
+    deprecated .  You should use ``PyArray_EquivTypenums()`` instead.
 
 
   **PyArrayObject* obj_to_array_no_conversion(PyObject* input, int typecode)**
@@ -359,26 +360,13 @@ Routines
     the python error string and return 0.
 
 
-  **static PyArrayObject *contiguous_typed_array(PyObject *obj, int typecode, int expectnd, int *expectdims)**
-    This function tries to create a contiguous `NumPy`_ array of type
-    typecode from an arbitrary python object ``obj``.  This should
-    work for any sequence object.  The argument ``expectnd`` is the
-    expected number of dimensions, ignored if <= 0.  The argument
-    ``expectdims`` is an array of expected dimensions, ignored if <=
-    0.  This routine raises a ``ValueError`` exception if the
-    underlying ``PyArray_ContiguousFromObject`` routine fails, if the
-    array has a bad shape, if the extent of a given dimension doesn't
-    match the specified extent.  If ``obj`` is a contiguous
-    ``PyArrayObject*`` then a reference is returned; if ``obj`` is a
-    python sequence, then a new ``PyArrayObject*`` is created and
-    returned.
-
 Beyond the Provided Typemaps
 ============================
 
 There are many C or C++ array/`NumPy`_ array situations not covered by
-a simple ``%include "numpy.i"``.  Nevertheless, ``numpy.i`` may still
-be helpful when you encounter them.
+a simple ``%include "numpy.i"`` and subsequent ``%apply`` directives.
+Nevertheless, ``numpy.i`` may still be helpful when you encounter
+them.
 
   * In some situations, it is possible that you could use the
     ``%numpy_templates`` macro to implement typemaps for your own