[Numpy-svn] r3719 - in branches/multicore: . numpy/core numpy/core/src numpy/core/tests numpy/distutils numpy/distutils/command numpy/doc numpy/doc/swig numpy/f2py numpy/testing

numpy-svn@scip... numpy-svn@scip...
Wed Apr 18 13:32:51 CDT 2007


Author: eric
Date: 2007-04-18 13:32:47 -0500 (Wed, 18 Apr 2007)
New Revision: 3719

Modified:
   branches/multicore/
   branches/multicore/numpy/core/records.py
   branches/multicore/numpy/core/src/scalartypes.inc.src
   branches/multicore/numpy/core/tests/test_scalarmath.py
   branches/multicore/numpy/distutils/command/build_ext.py
   branches/multicore/numpy/distutils/system_info.py
   branches/multicore/numpy/doc/pep_buffer.txt
   branches/multicore/numpy/doc/swig/numpy.i
   branches/multicore/numpy/doc/swig/numpy_swig.html
   branches/multicore/numpy/doc/swig/numpy_swig.pdf
   branches/multicore/numpy/doc/swig/numpy_swig.txt
   branches/multicore/numpy/f2py/capi_maps.py
   branches/multicore/numpy/testing/numpytest.py
   branches/multicore/site.cfg.example
Log:
Merged revisions 3705-3718 via svnmerge from 
http://svn.scipy.org/svn/numpy/trunk

........
  r3706 | oliphant | 2007-04-11 21:02:44 -0500 (Wed, 11 Apr 2007) | 1 line
  
  Add example to buffer interface.
........
  r3707 | oliphant | 2007-04-12 03:37:38 -0500 (Thu, 12 Apr 2007) | 1 line
  
  Fixes to pep_buffer.txt
........
  r3708 | oliphant | 2007-04-12 03:38:23 -0500 (Thu, 12 Apr 2007) | 1 line
  
  Fix scalar creation function so it can take sequences.
........
  r3709 | oliphant | 2007-04-12 03:42:21 -0500 (Thu, 12 Apr 2007) | 1 line
  
  Add check for fix to allowing types to create arrays.
........
  r3710 | timl | 2007-04-13 00:49:41 -0500 (Fri, 13 Apr 2007) | 1 line
  
  remove unneeded semi-colon, add missing import, whitespace cleanups
........
  r3711 | oliphant | 2007-04-13 01:55:55 -0500 (Fri, 13 Apr 2007) | 1 line
  
  A few more changes to pep_buffer.txt
........
  r3712 | wfspotz@sandia.gov | 2007-04-13 12:04:02 -0500 (Fri, 13 Apr 2007) | 1 line
  
  Fixed typo in documentation
........
  r3713 | wfspotz@sandia.gov | 2007-04-13 15:38:36 -0500 (Fri, 13 Apr 2007) | 1 line
  
  Reformatted numpy.i routines descriptions
........
  r3714 | wfspotz@sandia.gov | 2007-04-13 15:44:24 -0500 (Fri, 13 Apr 2007) | 1 line
  
  Updated date, example function documentation and python links
........
  r3715 | rkern | 2007-04-14 23:38:59 -0500 (Sat, 14 Apr 2007) | 1 line
  
  Fix command dependencies for build_ext. This should fix a number of problems where the full 'build_src build_clib build_ext' chain of commands needed to be specified.
........
  r3716 | rkern | 2007-04-14 23:46:07 -0500 (Sat, 14 Apr 2007) | 1 line
  
  Add another place to check for configuration, namely ~/.numpy-site.cfg, to help automatic installation where one can't edit the source checkout. I've added the alternate name to make it hidden because that's just considerate.
........
  r3717 | cookedm | 2007-04-16 23:54:49 -0500 (Mon, 16 Apr 2007) | 1 line
  
  Sort module names when running tests
........
  r3718 | pearu | 2007-04-17 05:34:54 -0500 (Tue, 17 Apr 2007) | 1 line
  
  f2py: support for assumed shape arrays of fortran callback functions (will not work with python callback functions, for instance).
........



Property changes on: branches/multicore
___________________________________________________________________
Name: svnmerge-integrated
   - /branches/distutils-revamp:1-2752 /trunk:1-3704
   + /branches/distutils-revamp:1-2752 /trunk:1-3718

Modified: branches/multicore/numpy/core/records.py
===================================================================
--- branches/multicore/numpy/core/records.py	2007-04-17 10:34:54 UTC (rev 3718)
+++ branches/multicore/numpy/core/records.py	2007-04-18 18:32:47 UTC (rev 3719)
@@ -6,6 +6,7 @@
 import numerictypes as nt
 import types
 import os
+import sys
 
 ndarray = sb.ndarray
 
@@ -128,7 +129,7 @@
         except AttributeError:
             pass
         fielddict = nt.void.__getattribute__(self, 'dtype').fields
-        res = fielddict.get(attr,None)
+        res = fielddict.get(attr, None)
         if res:
             obj = self.getfield(*res[:2])
             # if it has fields return a recarray,
@@ -146,15 +147,15 @@
 
     def __setattr__(self, attr, val):
         if attr in ['setfield', 'getfield', 'dtype']:
-            raise AttributeError, "Cannot set '%s' attribute" % attr;
+            raise AttributeError, "Cannot set '%s' attribute" % attr
         try:
-            return nt.void.__setattr__(self,attr,val)
+            return nt.void.__setattr__(self, attr, val)
         except AttributeError:
             pass
-        fielddict = nt.void.__getattribute__(self,'dtype').fields
-        res = fielddict.get(attr,None)
+        fielddict = nt.void.__getattribute__(self, 'dtype').fields
+        res = fielddict.get(attr, None)
         if res:
-            return self.setfield(val,*res[:2])
+            return self.setfield(val, *res[:2])
         else:
             raise AttributeError, "'record' object has no "\
                   "attribute '%s'" % attr
@@ -187,7 +188,7 @@
 
     def __getattribute__(self, attr):
         try:
-            return object.__getattribute__(self,attr)
+            return object.__getattribute__(self, attr)
         except AttributeError: # attr must be a fieldname
             pass
         fielddict = ndarray.__getattribute__(self,'dtype').fields
@@ -232,7 +233,7 @@
             res = fielddict[attr][:2]
         except (TypeError,KeyError):
             raise AttributeError, "record array has no attribute %s" % attr
-        return self.setfield(val,*res)
+        return self.setfield(val, *res)
 
     def __getitem__(self, indx):
         obj = ndarray.__getitem__(self, indx)
@@ -240,10 +241,10 @@
             return obj.view(ndarray)
         return obj
 
-    def field(self,attr, val=None):
-        if isinstance(attr,int):
+    def field(self, attr, val=None):
+        if isinstance(attr, int):
             names = ndarray.__getattribute__(self,'dtype').names
-            attr=names[attr]
+            attr = names[attr]
 
         fielddict = ndarray.__getattribute__(self,'dtype').fields
 
@@ -304,7 +305,7 @@
             if issubclass(obj.dtype.type, nt.flexible):
                 formats += `obj.itemsize`
             formats += ','
-        formats=formats[:-1]
+        formats = formats[:-1]
 
     if dtype is not None:
         descr = sb.dtype(dtype)
@@ -370,7 +371,7 @@
 
     nfields = len(recList[0])
     if formats is None and dtype is None:  # slower
-        obj = sb.array(recList,dtype=object)
+        obj = sb.array(recList, dtype=object)
         arrlist = [sb.array(obj[...,i].tolist()) for i in xrange(nfields)]
         return fromarrays(arrlist, formats=formats, shape=shape, names=names,
                           titles=titles, aligned=aligned, byteorder=byteorder)

Modified: branches/multicore/numpy/core/src/scalartypes.inc.src
===================================================================
--- branches/multicore/numpy/core/src/scalartypes.inc.src	2007-04-17 10:34:54 UTC (rev 3718)
+++ branches/multicore/numpy/core/src/scalartypes.inc.src	2007-04-18 18:32:47 UTC (rev 3719)
@@ -1761,6 +1761,7 @@
         }
 
         arr = PyArray_FromAny(obj, typecode, 0, 0, FORCECAST, NULL);
+        if ((arr==NULL) || (PyArray_NDIM(arr) > 0)) return arr;
         robj = PyArray_Return((PyArrayObject *)arr);
 
  finish:

Modified: branches/multicore/numpy/core/tests/test_scalarmath.py
===================================================================
--- branches/multicore/numpy/core/tests/test_scalarmath.py	2007-04-17 10:34:54 UTC (rev 3718)
+++ branches/multicore/numpy/core/tests/test_scalarmath.py	2007-04-18 18:32:47 UTC (rev 3719)
@@ -32,6 +32,12 @@
                        val.dtype.char == valo.dtype.char, \
                        "error with (%d,%d)" % (k,l)
 
+    def check_type_create(self, level=1):
+        for k, atype in enumerate(types):
+            a = array([1,2,3],atype)
+            b = atype([1,2,3])
+            assert_equal(a,b)
+
 class test_power(NumpyTestCase):
     def check_small_types(self):
         for t in [N.int8, N.int16]:

Modified: branches/multicore/numpy/distutils/command/build_ext.py
===================================================================
--- branches/multicore/numpy/distutils/command/build_ext.py	2007-04-17 10:34:54 UTC (rev 3718)
+++ branches/multicore/numpy/distutils/command/build_ext.py	2007-04-18 18:32:47 UTC (rev 3719)
@@ -47,11 +47,10 @@
             return
 
         # Make sure that extension sources are complete.
-        for ext in self.extensions:
-            if not all_strings(ext.sources):
-                self.run_command('build_src')
+        self.run_command('build_src')
 
         if self.distribution.has_c_libraries():
+            self.run_command('build_clib')
             build_clib = self.get_finalized_command('build_clib')
             self.library_dirs.append(build_clib.build_clib)
         else:

Modified: branches/multicore/numpy/distutils/system_info.py
===================================================================
--- branches/multicore/numpy/distutils/system_info.py	2007-04-17 10:34:54 UTC (rev 3718)
+++ branches/multicore/numpy/distutils/system_info.py	2007-04-18 18:32:47 UTC (rev 3719)
@@ -58,7 +58,7 @@
 The file 'site.cfg' is looked for in
 
 1) Directory of main setup.py file being run.
-2) Home directory of user running the setup.py file (Not implemented yet)
+2) Home directory of user running the setup.py file as ~/.numpy-site.cfg
 3) System wide directory (location of this file...)
 
 The first one found is used to get system configuration options The
@@ -349,7 +349,9 @@
         defaults['src_dirs'] = os.pathsep.join(default_src_dirs)
         defaults['search_static_first'] = str(self.search_static_first)
         self.cp = ConfigParser.ConfigParser(defaults)
-        self.files = get_standard_file('site.cfg')
+        self.files = []
+        self.files.extend(get_standard_file('.numpy-site.cfg'))
+        self.files.extend(get_standard_file('site.cfg'))
         self.parse_config_files()
         self.search_static_first = self.cp.getboolean(self.section,
                                                       'search_static_first')

Modified: branches/multicore/numpy/doc/pep_buffer.txt
===================================================================
--- branches/multicore/numpy/doc/pep_buffer.txt	2007-04-17 10:34:54 UTC (rev 3718)
+++ branches/multicore/numpy/doc/pep_buffer.txt	2007-04-18 18:32:47 UTC (rev 3719)
@@ -73,7 +73,7 @@
 
    NumPy uses the notion of constant striding in each dimension as its
    basic concept of an array. With this concept, a simple sub-region
-   of a larger array can be described without copying the data.   T
+   of a larger array can be described without copying the data.   
    Thus, stride information is the additional information that must be
    shared. 
 
@@ -151,7 +151,7 @@
 
 ::
 
-    typedef int (*getbufferproc)(PyObject *obj, struct bufferinfo *view, int flags) 
+    typedef int (*getbufferproc)(PyObject *obj, PyBuffer *view, int flags) 
 
 This function returns 0 on success and -1 on failure (and raises an
 error). The first variable is the "exporting" object.  The second
@@ -161,59 +161,83 @@
 be called with NULL.
 
 The third argument indicates what kind of buffer the exporter is
-allowed to return.  It tells the exporter what elements the bufferinfo
-structure the consumer is going to make use of.  This allows the
-exporter to simplify and/or raise an error if it can't support the
-operation.
+allowed to return.  It essentially tells the exporter what kind of
+memory area the consumer can deal with.  It also indicates what
+members of the PyBuffer structure the consumer is going to care about.
 
-It also allows the caller to make a request for a simple "view" and
-receive it or have an error raised if it's not possible. 
+The exporter can use this information to simplify how much of the PyBuffer
+structure is filled in and/or raise an error if the object can't support
+a simpler view of its memory. 
 
-All of the following assume that at least buf, len, and readonly will
-be utilized by the caller.
+Thus, the caller can request a simple "view" and either receive it or
+have an error raised if it is not possible.
 
-BUF_SIMPLE
-   The returned buffer will only be assumed to be readable (the object
-   may or may not have writeable memory).  Only the buf, len, and
-   readonly variables may be accessed. The format will be assumed to
-   be unsigned bytes.  This is a "stand-alone" flag constant.  It
-   never needs to be |'d to the others.
+All of the following assume that at least buf, len, and readonly
+will always be utilized by the caller.  
 
-BUF_WRITEABLE
-   The returned buffer must be writeable.  If it cannot be, then raise
-   an error.
+Py_BUF_SIMPLE 
 
-BUF_READONLY
-   The returned buffer must be readonly and the underlying object
-   should make its memory readonly.  If it cannot do that, then an
-   error should be raised if this flag is requested.
+   The returned buffer will be assumed to be readable (the object may
+   or may not have writeable memory).  Only the buf, len, and readonly
+   variables may be accessed. The format will be assumed to be
+   unsigned bytes .  This is a "stand-alone" flag constant.  It never
+   needs to be |'d to the others.  The exporter will raise an 
+   error if it cannot provide such a contiguous buffer. 
 
-BUF_FORMAT
-   The consumer will be using the format string information so make
-   sure that member is filled correctly.
+Py_BUF_WRITEABLE
 
-BUF_SHAPE   
-   The consumer can (and might) make use of using the ndims and shape
-   members of the structure so make sure they are filled in correctly.
+   The returned buffer must be writeable.  If it is not writeable,
+   then raise an error.
+
+Py_BUF_READONLY 
+
+   The returned buffer must be readonly.  If the object is already
+   read-only or it can make its memory read-only (and there are no
+   other views on the object) then it should do so and return the
+   buffer information.  If the object does not have read-only memory
+   (or cannot make it read-only), then an error should be raised.
+
+Py_BUF_FORMAT
+        
+   The returned buffer must have true format information.  This would 
+   be used when the consumer is going to be checking for what 'kind'
+   of data is actually stored.  An exporter should always be able
+   to provide this information if requested. 
+
+Py_BUF_SHAPE 
+
+   The returned buffer must have shape information.  The memory will
+   be assumed C-style contiguous (last dimension varies the fastest).
+   The exporter may raise an error if it cannot provide this kind 
+   of contiguous buffer. 
    
-BUF_STRIDES (implies BUF_SHAPE)
-   The consumer can (and might) make use of the strides member of the
-   structure (as well as ndims and shape)
+Py_BUF_STRIDES (implies Py_BUF_SHAPE) 
 
-BUF_OFFSETS (implies BUF_STRIDES)
-   The consumer can (and might) make use of the suboffsets member (as
-   well as ndims, shape, and strides)
+   The returned buffer must have strides information. This would be
+   used when the consumer can handle strided, discontiguous arrays. 
+   Handling strides automatically assumes you can handle shape. 
+   The exporter may raise an error if cannot provide a strided-only
+   representation of the data (i.e. without the suboffsets). 
 
-BUF_FULL
-   This is the same as BUF_OFFSETS | BUF_FORMAT 
+Py_BUF_OFFSETS (implies Py_BUF_STRIDES)
 
-Thus, the consumer simply wanting an contiguous chunk of bytes from
-the object would use BUF_SIMPLE, while a consumer that understands
-how to make use of the most complicated cases would use BUF_FULL.
+   The returned buffer must have suboffsets information.  This would
+   be used when the consumer can handle indirect array referencing
+   implied by these suboffsets.  
 
+Py_BUF_FULL (Py_BUF_OFFSETS | Py_BUF_WRITEABLE | Py_BUF_FORMAT)
+
+Thus, the consumer simply wanting a contiguous chunk of bytes from
+the object would use Py_BUF_SIMPLE, while a consumer that understands
+how to make use of the most complicated cases could use Py_BUF_INDIRECT.
+
+If format information is going to be probed, then Py_BUF_FORMAT must
+be |'d to the flags otherwise the consumer assumes it is unsigned
+bytes.
+
 There is a C-API that simple exporting objects can use to fill-in the
 buffer info structure correctly according to the provided flags if a
-contiguous chunk of memory is all that can be exported.
+contiguous chunk of "unsigned bytes" is all that can be exported.
 
 
 The bufferinfo structure is::
@@ -222,21 +246,22 @@
        void *buf;
        Py_ssize_t len;
        int readonly;
-       char *format;
+       const char *format;
        int ndims;
        Py_ssize_t *shape;
        Py_ssize_t *strides;
        Py_ssize_t *suboffsets;
+       int itemsize;
        void *internal;
-  };
+  } PyBuffer;
 
-Before calling this function, the bufferinfo structure can be filled with
-whatever.  Upon return from getbufferproc, the bufferinfo structure is filled in
-with relevant information about the buffer.  This same bufferinfo
-structure must be passed to bf_releasebuffer (if available) when the
-consumer is done with the memory. The caller is responsible for
-keeping a reference to obj until releasebuffer is called (i.e. this
-call does not alter the reference count of obj). 
+Before calling this function, the bufferinfo structure can be filled
+with whatever.  Upon return from getbufferproc, the bufferinfo
+structure is filled in with relevant information about the buffer.
+This same bufferinfo structure must be passed to bf_releasebuffer (if
+available) when the consumer is done with the memory. The caller is
+responsible for keeping a reference to obj until releasebuffer is
+called (i.e. this call does not alter the reference count of obj).
 
 The members of the bufferinfo structure are:
 
@@ -267,18 +292,19 @@
 shape
     an array of ``Py_ssize_t`` of length ``ndims`` indicating the
     shape of the memory as an N-D array.  Note that ``((*shape)[0] *
-    ... * (*shape)[ndims-1])*itemsize = len``.
+    ... * (*shape)[ndims-1])*itemsize = len``.  If ndims is 0 (indicating
+    a scalar), then this must be NULL.
 
 strides 
     address of a ``Py_ssize_t*`` variable that will be filled with a
-    pointer to an array of ``Py_ssize_t`` of length ``*ndims``
-    indicating the number of bytes to skip to get to the next element
-    in each dimension.  If this is not requested by the caller
-    (BUF_STRIDES is not set), then this member of the structure will
-    not be used and the consumer is assuming the array is C-style
-    contiguous.  If this is not the case, then an error should be
-    raised.  If this member is requested by the caller (BUF_STRIDES is
-    set), then it must be filled in.
+    pointer to an array of ``Py_ssize_t`` of length ``ndims`` (or NULL
+    if ndims is 0).  indicating the number of bytes to skip to get to
+    the next element in each dimension.  If this is not requested by
+    the caller (BUF_STRIDES is not set), then this member of the
+    structure will not be used and the consumer is assuming the array
+    is C-style contiguous.  If this is not the case, then an error
+    should be raised.  If this member is requested by the caller
+    (BUF_STRIDES is set), then it must be filled in.
 
 
 suboffsets
@@ -315,6 +341,12 @@
     the location of the starting pointer directly (i.e. buf would
     be modified).  
 
+itemsize
+    This is a storage for the itemsize of each element of the shared
+    memory.  It can be obtained using PyBuffer_SizeFromFormat but an
+    exporter may know it without making this call and thus storing it
+    is more convenient and faster. 
+
 internal
     This is for use internally by the exporting object.  For example,
     this might be re-cast as an integer by the exporter and used to 
@@ -336,7 +368,7 @@
 interface call. The caller is responsible for the memory of the
 bufferinfo structure itself. 
 
-``typedef int (*releasebufferproc)(PyObject *obj, struct bufferinfo *view)``
+``typedef int (*releasebufferproc)(PyObject *obj, PyBuffer *view)``
     Callers of getbufferproc must make sure that this function is
     called when memory previously acquired from the object is no
     longer needed.  The exporter of the interface must make sure that
@@ -373,7 +405,8 @@
 
 ::
 
-    int PyObject_GetBuffer(PyObject *obj, struct bufferinfo *view, int flags)
+    int PyObject_GetBuffer(PyObject *obj, PyBuffer *view, 
+                           int flags)  
 
 This is a C-API version of the getbuffer function call.  It checks to
 make sure object has the required function pointer and issues the
@@ -381,7 +414,7 @@
 success.
 
 ::
-    int PyObject_ReleaseBuffer(PyObject *obj, struct bufferinfo *view)
+    int PyObject_ReleaseBuffer(PyObject *obj, PyBuffer *view)
 
 This is a C-API version of the releasebuffer function call.  It checks
 to make sure the object has the required function pointer and issues
@@ -395,45 +428,38 @@
 
 Return a memory-view object from an object that defines the buffer interface.
 
-A memory-view object is an extended buffer object that should replace
-the buffer object in Python 3K.  It's C-structure is::
+A memory-view object is an extended buffer object that could replace
+the buffer object (but doesn't have to).  It's C-structure is
 
+::
+
   typedef struct {
       PyObject_HEAD
-      PyObject *base;
-      struct bufferinfo view;
-      int itemsize;
-      int flags;
+      PyObject *base;      
+      int ndims;
+      Py_ssize_t *starts;  /* slice starts */
+      Py_ssize_t *stops;   /* slice stops */
+      Py_ssize_t *steps;   /* slice steps */
   } PyMemoryViewObject;
 
-This is very similar to the current buffer object except offset has
-been removed because ptr can just be modified by offset and a single
-offset is not sufficient for the sub-offsets.  Also the hash has been
-removed because using the buffer object as a hash even if it is
-read-only is rarely useful.
+This is functionally similar to the current buffer object except only
+a reference to base is kept.  The actual memory for base must be
+re-grabbed using the buffer-protocol, whenever it is needed. 
 
-Also, the format, ndims, shape, strides, and suboffsets have been
-added. These additions will allow multi-dimensional slicing of the
-memory-view object which can be added at some point.  This object
-always owns it's own shape, strides, and suboffsets arrays and it's
-own format string, but always borrows the memory from the object
-pointed to by base.
+The getbuffer and releasebuffer for this object use the underlying
+base object (adjusted using the slice information).  If the number of
+dimensions of the base object (or the strides or the size) has changed
+when a new view is requested, then the getbuffer will trigger an error. 
 
-The itemsize is a convenience and specifies the number of bytes
-indicated by the format string if positive.  
+This memory-view object will support mult-dimensional slicing.  Slices
+of the memory-view object are other memory-view objects. When an
+"element" from the memory-view is returned it is always a tuple of
+bytes object + format string which can then be interpreted using the
+struct module if desired.
 
-This object never reallocates ptr, shape, strides, subboffsets or
-format and therefore does not need to keep track of how many views it
-has exported.
-
-It exports a view using the base object.  It releases a view by
-releasing the view on the base object.  Because, it will never
-re-allocate memory, it does not need to keep track of how many it has
-exported but simple reference counting will suffice.
-
 ::
 
-    int PyObject_SizeFromFormat(char *)
+    int PyBuffer_SizeFromFormat(const char *)
 
 Return the implied itemsize of the data-format area from a struct-style
 description.
@@ -441,60 +467,70 @@
 ::
 
     int PyObject_GetContiguous(PyObject *obj, void **buf, Py_ssize_t *len,
-                               int fortran)
+                               char **format, char fortran)
 
 Return a contiguous chunk of memory representing the buffer.  If a
 copy is made then return 1.  If no copy was needed return 0.  If an
 error occurred in probing the buffer interface, then return -1.  The
 contiguous chunk of memory is pointed to by ``*buf`` and the length of
 that memory is ``*len``.  If the object is multi-dimensional, then if
-fortran is 1, the first dimension of the underlying array will vary
-the fastest in the buffer.  If fortran is 0, then the last dimension
-will vary the fastest (C-style contiguous). If fortran is -1, then it
+fortran is 'F', the first dimension of the underlying array will vary
+the fastest in the buffer.  If fortran is 'C', then the last dimension
+will vary the fastest (C-style contiguous). If fortran is 'A', then it
 does not matter and you will get whatever the object decides is more
-efficient.
+efficient.  
 
 :: 
 
     int PyObject_CopyToObject(PyObject *obj, void *buf, Py_ssize_t len,
-                              int fortran)
+                              char fortran)
 
 Copy ``len`` bytes of data pointed to by the contiguous chunk of
 memory pointed to by ``buf`` into the buffer exported by obj.  Return
 0 on success and return -1 and raise an error on failure.  If the
 object does not have a writeable buffer, then an error is raised.  If
-fortran is 1, then if the object is multi-dimensional, then the data
+fortran is 'F', then if the object is multi-dimensional, then the data
 will be copied into the array in Fortran-style (first dimension varies
-the fastest).  If fortran is 0, then the data will be copied into the
-array in C-style (last dimension varies the fastest).  If fortran is -1, then
+the fastest).  If fortran is 'C', then the data will be copied into the
+array in C-style (last dimension varies the fastest).  If fortran is 'A', then
 it does not matter and the copy will be made in whatever way is more
 efficient.
 
-The last two C-API calls allow a standard way of getting data in and
+::
+
+    void PyBuffer_FreeMem(void *buf)
+
+This function frees the memory returned by PyObject_GetContiguous if a
+copy was made.  Do not call this function unless
+PyObject_GetContiguous returns a 1 indicating that new memory was
+created.
+
+
+These last three C-API calls allow a standard way of getting data in and
 out of Python objects into contiguous memory areas no matter how it is
 actually stored.  These calls use the extended buffer interface to perform
 their work. 
 
 ::
 
-    int PyObject_IsContiguous(struct bufferinfo *view, int fortran);
+    int PyBuffer_IsContiguous(PyBuffer *view, char fortran);
 
-Return 1 if the memory defined by the view object is C-style (fortran = 0)
-or Fortran-style (fortran = 1) contiguous.  Return 0 otherwise.
+Return 1 if the memory defined by the view object is C-style (fortran = 'C')
+or Fortran-style (fortran = 'A') contiguous.  Return 0 otherwise.
 
 ::
 
-    void PyObject_FillContiguousStrides(int *ndims, Py_ssize_t *shape,
+    void PyBuffer_FillContiguousStrides(int *ndims, Py_ssize_t *shape,
                                         int itemsize, 
-                                        Py_ssize_t *strides, int fortran)
+                                        Py_ssize_t *strides, char fortran)
 
 Fill the strides array with byte-strides of a contiguous (C-style if
 fortran is 0 or Fortran-style if fortran is 1) array of the given
 shape with the given number of bytes per element.
 
 ::
-    int PyObject_FillBufferInfo(struct bufferinfo *view, void *buf, 
-                                Py_ssize_t len, int readonly, int infoflags)
+    int PyBuffer_FillInfo(PyBuffer *view, void *buf, 
+                          Py_ssize_t len, int readonly, int infoflags)
 
 Fills in a buffer-info structure correctly for an exporter that can
 only share a contiguous chunk of memory of "unsigned bytes" of the
@@ -685,10 +721,12 @@
 =========
 
 Ex. 1
-----------
+-----------
 
 This example shows how an image object that uses contiguous lines might expose its buffer.::
 
+::
+
   struct rgba {
       unsigned char r, g, b, a;
   };
@@ -711,8 +749,10 @@
 
 So what does ImageObject's getbuffer do?  Leaving error checking out::
 
-  int Image_getbuffer(PyObject *self, struct bufferinfo *view, int flags) {
+::
 
+  int Image_getbuffer(PyObject *self, PyBuffer *view, int flags) {
+
       static Py_ssize_t suboffsets[2] = { -1, 0 };
 
       view->buf = self->lines;
@@ -733,7 +773,7 @@
   } 
 
 
-  int Image_releasebuffer(PyObject *self, struct bufferinfo *view) {
+  int Image_releasebuffer(PyObject *self, PyBuffer *view) {
       self->view_count--;
       return 0;
   }
@@ -746,19 +786,21 @@
 chunk of memory (which will never be re-allocated while the object is
 alive) would do that.
 
-int myobject_getbuffer(PyObject *self, struct bufferinfo *view, int flags) {
+::
 
-    void *buf;
-    Py_ssize_t len;
-    int readonly=0;
+  int myobject_getbuffer(PyObject *self, PyBuffer *view, int flags) {
+
+      void *buf;
+      Py_ssize_t len;
+      int readonly=0;
         
-    buf = /* Point to buffer */
-    len = /* Set to size of buffer */
-    readonly = /* Set to 1 if readonly */
+      buf = /* Point to buffer */
+      len = /* Set to size of buffer */
+      readonly = /* Set to 1 if readonly */
+  
+      return PyObject_FillBufferInfo(view, buf, len, readonly, flags);    
+  }
 
-    return PyObject_FillBufferInfo(view, buf, len, readonly, flags);    
-}
-
 /* No releasebuffer is necessary because the memory will never 
 be re-allocated so the locking mechanism is not needed
 */
@@ -769,8 +811,9 @@
 A consumer that wants to only get a simple contiguous chunk of bytes
 from a Python object, obj would do the following:
 
+::
 
-  struct bufferinfo view;
+  PyBuffer view;
   int ret;
       
   if (PyObject_GetBuffer(obj, &view, Py_BUF_SIMPLE) < 0) {
@@ -790,8 +833,37 @@
   }
   
 
+Ex. 4
+-----------
 
+A consumer that wants to be able to use any object's memory but is
+writing an algorithm that only handle contiguous memory could do the following:
 
+::
+
+    void *buf;
+    Py_ssize_t len;
+    char *format;
+
+    if (PyObject_GetContiguous(obj, &buf, &len, &format, 0) < 0) {
+       /* error return */
+    }
+
+    /* process memory pointed to by buffer if format is correct */
+
+    /* Optional: 
+    
+       if, after processing, we want to copy data from buffer back
+       into the the object  
+ 
+       we could do
+       */
+
+    if (PyObject_CopyToObject(obj, buf, len, 0) < 0) {
+           /*        error return */
+    }
+   
+
 Copyright
 =========
 

Modified: branches/multicore/numpy/doc/swig/numpy.i
===================================================================
--- branches/multicore/numpy/doc/swig/numpy.i	2007-04-17 10:34:54 UTC (rev 3718)
+++ branches/multicore/numpy/doc/swig/numpy.i	2007-04-18 18:32:47 UTC (rev 3719)
@@ -965,9 +965,9 @@
  * Swig complains about a syntax error for the following macros
  * expansions:
  */
-/*%numpy_typemaps(complex float,       NPY_CFLOAT     )
+/*%numpy_typemaps(complex float,  NPY_CFLOAT , int)
  */
-/*%numpy_typemaps(complex double,      NPY_CDOUBLE    )
+/*%numpy_typemaps(complex double, NPY_CDOUBLE, int)
  */
 /*%numpy_typemaps(complex long double, NPY_CLONGDOUBLE)
  */

Modified: branches/multicore/numpy/doc/swig/numpy_swig.html
===================================================================
--- branches/multicore/numpy/doc/swig/numpy_swig.html	2007-04-17 10:34:54 UTC (rev 3718)
+++ branches/multicore/numpy/doc/swig/numpy_swig.html	2007-04-18 18:32:47 UTC (rev 3719)
@@ -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="4 April, 2007" />
+<meta name="date" content="13 April, 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>4 April, 2007</td></tr>
+<td>13 April, 2007</td></tr>
 </tbody>
 </table>
 <div class="contents topic">
@@ -363,6 +363,7 @@
 <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
@@ -453,19 +454,19 @@
 <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 python 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 python 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>
+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
@@ -623,12 +624,12 @@
 </blockquote>
 <p>These are typically used in situations where in C/C++, you would
 allocate a(n) array(s) on the heap, and call the function to fill the
-array(s) values.  In python, the arrays are allocated for you and
+array(s) values.  In <a class="reference" href="http://www.python.org">python</a>, the arrays are allocated for you and
 returned as new array objects.</p>
 <p>Note that we support <tt class="docutils literal"><span class="pre">DATA_TYPE*</span></tt> argout typemaps in 1D, but not 2D
 or 3D.  This because of a quirk with the <a class="reference" href="http://www.swig.org">SWIG</a> typemap syntax and
 cannot be avoided.  Note that for these types of 1D typemaps, the
-python function will take a single argument representing <tt class="docutils literal"><span class="pre">DIM1</span></tt>.</p>
+<a class="reference" href="http://www.python.org">python</a> function will take a single argument representing <tt class="docutils literal"><span class="pre">DIM1</span></tt>.</p>
 </div>
 <div class="section">
 <h2><a class="toc-backref" href="#id7" id="output-arrays" name="output-arrays">Output Arrays</a></h2>
@@ -688,7 +689,7 @@
 </pre>
 <p>We could have implemented:</p>
 <pre class="literal-block">
-%numpy_typemaps(Py_complex , NPY_DOUBLE , int)
+%numpy_typemaps(Py_complex , NPY_CDOUBLE, int)
 %numpy_typemaps(npy_cfloat , NPY_CFLOAT , int)
 %numpy_typemaps(npy_cdouble, NPY_CDOUBLE, int)
 </pre>
@@ -696,9 +697,9 @@
 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 python 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>.
+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
@@ -745,57 +746,161 @@
 <div class="section">
 <h2><a class="toc-backref" href="#id12" id="routines" name="routines">Routines</a></h2>
 <blockquote>
-<dl class="docutils">
-<dt><strong>char* pytype_string(PyObject* py_obj)</strong></dt>
-<dd>Given a <tt class="docutils literal"><span class="pre">PyObject*</span></tt>, return a string describing its type.</dd>
-<dt><strong>char* typecode_string(int typecode)</strong></dt>
-<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.  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
-<tt class="docutils literal"><span class="pre">NULL</span></tt>.</dd>
-<dt><strong>PyArrayObject* obj_to_array_allow_conversion(PyObject* input, int typecode, int* is_new_object)</strong></dt>
-<dd>Convert the given <tt class="docutils literal"><span class="pre">PyObject*</span></tt> to a <a class="reference" href="http://numpy.scipy.org">NumPy</a> array with the given
-typecode.  On Success, return a valid <tt class="docutils literal"><span class="pre">PyArrayObject*</span></tt> with the
-correct type.  On failure, the python error string will be set and
-the routine returns <tt class="docutils literal"><span class="pre">NULL</span></tt>.</dd>
-<dt><strong>PyArrayObject* make_contiguous(PyArrayObject* ary, int* is_new_object, int min_dims, int max_dims)</strong></dt>
-<dd>Given a <tt class="docutils literal"><span class="pre">PyArrayObject*</span></tt>, 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 <tt class="docutils literal"><span class="pre">PyArrayObject*</span></tt> using the
-original data, flag it as a new object and return the pointer.</dd>
-<dt><strong>PyArrayObject* obj_to_array_contiguous_allow_conversion(PyObject* input, int typecode, int* is_new_object)</strong></dt>
-<dd>Convert a given <tt class="docutils literal"><span class="pre">PyObject*</span></tt> to a contiguous <tt class="docutils literal"><span class="pre">PyArrayObject*</span></tt>
-of the specified type.  If the input object is not a contiguous
+<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
+performed, else 1.</li>
+<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
+specified type.  If the input object is not a contiguous
 <tt class="docutils literal"><span class="pre">PyArrayObject*</span></tt>, a new one will be created and the new object
-flag will be set.</dd>
-<dt><strong>int require_contiguous(PyArrayObject* ary)</strong></dt>
-<dd>Test whether a <tt class="docutils literal"><span class="pre">PyArrayObject*</span></tt> is contiguous.  If array is
-contiguous, return 1.  Otherwise, set the python error string and
-return 0.</dd>
-<dt><strong>int require_native(PyArray_Object* ary)</strong></dt>
-<dd>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.</dd>
-<dt><strong>int require_dimensions(PyArrayObject* ary, int exact_dimensions)</strong></dt>
-<dd>Require the given <tt class="docutils literal"><span class="pre">PyArrayObject*</span></tt> 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.</dd>
-<dt><strong>int require_dimensions_n(PyArrayObject* ary, int* exact_dimensions, int n)</strong></dt>
-<dd>Require the given <tt class="docutils literal"><span class="pre">PyArrayObject*</span></tt> 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.</dd>
-<dt><strong>int require_size(PyArrayObject* ary, int* size, int n)</strong></dt>
-<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>
-</dl>
+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>
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+</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">
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+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>
+</ul>
+<p>Require <tt class="docutils literal"><span class="pre">ary</span></tt> to have a specified number of dimensions.  If the
+array has the specified number of dimensions, 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_n()</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>, an array of integers representing
+acceptable numbers of dimensions.</li>
+<li><tt class="docutils literal"><span class="pre">int</span> <span class="pre">n</span></tt>, the length of <tt class="docutils literal"><span class="pre">exact_dimensions</span></tt>.</li>
+</ul>
+<p>Require <tt class="docutils literal"><span class="pre">ary</span></tt> 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 <a class="reference" href="http://www.python.org">python</a> error string
+and return 0.</p>
+</blockquote>
+<p><strong>require_size()</strong></p>
+<blockquote>
+<p>Return type: <tt class="docutils literal"><span class="pre">int</span></tt></p>
+<p>Arguments:</p>
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+<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">npy_int*</span> <span class="pre">size</span></tt>, an array representing the desired lengths of
+each dimension.</li>
+<li><tt class="docutils literal"><span class="pre">int</span> <span class="pre">n</span></tt>, the length of <tt class="docutils literal"><span class="pre">size</span></tt>.</li>
+</ul>
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+string and return 0.</p>
+</blockquote>
+</blockquote>
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 </div>
 <div class="section">
@@ -808,7 +913,7 @@
 <pre class="literal-block">
 double dot(int len, double* vec1, double* vec2);
 </pre>
-<p>The python interface that we want is:</p>
+<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;
@@ -818,7 +923,7 @@
 <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 python input
+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),
@@ -948,7 +1053,7 @@
 </div>
 <div class="footer">
 <hr class="footer" />
-Generated on: 2007-04-10 15:20 UTC.
+Generated on: 2007-04-13 20:43 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/multicore/numpy/doc/swig/numpy_swig.pdf
===================================================================
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 <<
-/Size 301
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-/Info 300 0 R
-/ID [<8AEBC1FCCADD8D71CBEF5E0E60CFF503> <8AEBC1FCCADD8D71CBEF5E0E60CFF503>]
+/Size 339
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+/ID [<6D5220E93E8808964213440D85A6832B> <6D5220E93E8808964213440D85A6832B>]
 >>
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-132230
+141278
 %%EOF

Modified: branches/multicore/numpy/doc/swig/numpy_swig.txt
===================================================================
--- branches/multicore/numpy/doc/swig/numpy_swig.txt	2007-04-17 10:34:54 UTC (rev 3718)
+++ branches/multicore/numpy/doc/swig/numpy_swig.txt	2007-04-18 18:32:47 UTC (rev 3719)
@@ -4,7 +4,7 @@
 
 :Author:      Bill Spotz
 :Institution: Sandia National Laboratories
-:Date:        4 April, 2007
+:Date:        13 April, 2007
 
 .. contents::
 
@@ -41,6 +41,7 @@
 
     def rms(seq):
         """
+	rms: return the root mean square of a sequence
         rms(numpy.ndarray) -> double
         rms(list) -> double
         rms(tuple) -> double
@@ -134,19 +135,19 @@
 The typemaps from ``numpy.i`` are responsible for the following lines
 of code: 12--20, 25 and 30.  Line 10 parses the input to the ``rms``
 function.  From the format string ``"O:rms"``, we can see that the
-argument list is expected to be a single python object (specified by
-the ``O`` before the colon) and whose pointer is stored in ``obj0``.
-A number of functions, supplied by ``numpy.i``, are called to make and
-check the (possible) conversion from a generic python object to a
-`NumPy`_ array.  These functions are explained in the section `Helper
-Functions`_, but hopefully their names are self-explanatory.  At line
-12 we use ``obj0`` to construct a `NumPy`_ 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.
+argument list is expected to be a single `python`_ object (specified
+by the ``O`` before the colon) and whose pointer is stored in
+``obj0``.  A number of functions, supplied by ``numpy.i``, are called
+to make and check the (possible) conversion from a generic `python`_
+object to a `NumPy`_ array.  These functions are explained in the
+section `Helper Functions`_, but hopefully their names are
+self-explanatory.  At line 12 we use ``obj0`` to construct a `NumPy`_
+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.
 
 This code has a significant amount of error handling.  Note the
 ``SWIG_fail`` is a macro for ``goto fail``, refering to the label at
@@ -305,13 +306,13 @@
 
 These are typically used in situations where in C/C++, you would
 allocate a(n) array(s) on the heap, and call the function to fill the
-array(s) values.  In python, the arrays are allocated for you and
+array(s) values.  In `python`_, the arrays are allocated for you and
 returned as new array objects.
 
 Note that we support ``DATA_TYPE*`` argout typemaps in 1D, but not 2D
 or 3D.  This because of a quirk with the `SWIG`_ typemap syntax and
 cannot be avoided.  Note that for these types of 1D typemaps, the
-python function will take a single argument representing ``DIM1``.
+`python`_ function will take a single argument representing ``DIM1``.
 
 Output Arrays
 -------------
@@ -374,7 +375,7 @@
 
 We could have implemented::
 
-    %numpy_typemaps(Py_complex , NPY_DOUBLE , int)
+    %numpy_typemaps(Py_complex , NPY_CDOUBLE, int)
     %numpy_typemaps(npy_cfloat , NPY_CFLOAT , int)
     %numpy_typemaps(npy_cdouble, NPY_CDOUBLE, int)
 
@@ -382,9 +383,9 @@
 type ``Py_complex``, ``npy_cfloat`` and ``npy_cdouble``.  However, it
 seemed unlikely that there would be any independent (non-`python`_,
 non-`NumPy`_) application code that people would be using `SWIG`_ to
-generate a python 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 ``std::complex``.
+generate a `python`_ 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 ``std::complex``.
 Assuming these data structures are compatible with `python`_ and
 `NumPy`_ complex types, ``%numpy_typemap`` expansions as above (with
 the user's complex type substituted for the first argument) should
@@ -436,77 +437,196 @@
 Routines
 --------
 
-  **char* pytype_string(PyObject* py_obj)**
-    Given a ``PyObject*``, return a string describing its type.
+  **pytype_string()**
 
+    Return type: ``char*``
 
-  **char* typecode_string(int typecode)**
-    Given a `NumPy`_ integer typecode, return a string describing the type.
+    Arguments:
 
+    * ``PyObject* py_obj``, a general `python`_ object.
 
-  **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.  This is
-    deprecated .  You should use ``PyArray_EquivTypenums()`` instead.
+    Return a string describing the type of ``py_obj``.
 
 
-  **PyArrayObject* obj_to_array_no_conversion(PyObject* input, int typecode)**
-    Given a ``PyObject*``, cast it to a ``PyArrayObject*`` if legal.
-    If not, set the python error string appropriately and return
-    ``NULL``.
+  **typecode_string()**
 
+    Return type: ``char*``
 
-  **PyArrayObject* obj_to_array_allow_conversion(PyObject* input, int typecode, int* is_new_object)**
-    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``.
+    Arguments:
 
+    * ``int typecode``, a `NumPy`_ integer typecode.
 
-  **PyArrayObject* make_contiguous(PyArrayObject* ary, int* is_new_object, int min_dims, int max_dims)**
-    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.
+    Return a string describing the type corresponding to the `NumPy`_
+    ``typecode``.
 
+  **type_match()**
 
-  **PyArrayObject* obj_to_array_contiguous_allow_conversion(PyObject* input, int typecode, int* is_new_object)**
-    Convert a given ``PyObject*`` to a contiguous ``PyArrayObject*``
-    of the specified type.  If the input object is not a contiguous
+    Return type: ``int``
+
+    Arguments:
+
+    * ``int actual_type``, the `NumPy`_ typecode of a `NumPy`_ array.
+
+    * ``int desired_type``, the desired `NumPy`_ typecode.
+
+    Make sure that ``actual_type`` is compatible with
+    ``desired_type``.  For example, this allows character and
+    byte types, or int and long types, to match.  This is now
+    equivalent to ``PyArray_EquivTypenums()``.
+
+
+  **obj_to_array_no_conversion()**
+
+    Return type: ``PyArrayObject*``
+
+    Arguments:
+
+    * ``PyObject* input``, a general `python`_ object.
+
+    * ``int typecode``, the desired `NumPy`_ typecode.
+
+    Cast ``input`` to a ``PyArrayObject*`` if legal, and ensure that
+    it is of type ``typecode``.  If ``input`` cannot be cast, or the
+    ``typecode`` is wrong, set a `python`_ error and return ``NULL``.
+
+
+  **obj_to_array_allow_conversion()**
+
+    Return type: ``PyArrayObject*``
+
+    Arguments:
+
+    * ``PyObject* input``, a general `python`_ object.
+
+    * ``int typecode``, the desired `NumPy`_ typecode of the resulting
+      array.
+
+    * ``int* is_new_object``, returns a value of 0 if no conversion
+      performed, else 1.
+
+    Convert ``input`` 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``.
+
+
+  **make_contiguous()**
+
+    Return type: ``PyArrayObject*``
+
+    Arguments:
+
+    * ``PyArrayObject* ary``, a `NumPy`_ array.
+
+    * ``int* is_new_object``, returns a value of 0 if no conversion
+      performed, else 1.
+
+    * ``int min_dims``, minimum allowable dimensions.
+
+    * ``int max_dims``, maximum allowable dimensions.
+
+    Check to see if ``ary`` 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.
+
+
+  **obj_to_array_contiguous_allow_conversion()**
+
+    Return type: ``PyArrayObject*``
+
+    Arguments:
+
+    * ``PyObject* input``, a general `python`_ object.
+
+    * ``int typecode``, the desired `NumPy`_ typecode of the resulting
+      array.
+
+    * ``int* is_new_object``, returns a value of 0 if no conversion
+      performed, else 1.
+
+    Convert ``input`` 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.
 
 
-  **int require_contiguous(PyArrayObject* ary)**
-    Test whether a ``PyArrayObject*`` is contiguous.  If array is
-    contiguous, return 1.  Otherwise, set the python error string and
+  **require_contiguous()**
+
+    Return type: ``int``
+
+    Arguments:
+
+    * ``PyArrayObject* ary``, a `NumPy`_ array.
+
+    Test whether ``ary`` is contiguous.  If so, return 1.  Otherwise,
+    set a `python`_ error and return 0.
+
+
+  **require_native()**
+
+    Return type: ``int``
+
+    Arguments:
+
+    * ``PyArray_Object* ary``, a `NumPy`_ array.
+
+    Require that ``ary`` is not byte-swapped.  If the array is not
+    byte-swapped, return 1.  Otherwise, set a `python`_ error and
     return 0.
 
+  **require_dimensions()**
 
-  **int require_native(PyArray_Object* ary)**
-    Require that a numpy array is not byte-swapped.  If the array is
-    not byte-swapped, return 1.  Otherwise, set the python error string
+    Return type: ``int``
+
+    Arguments:
+
+    * ``PyArrayObject* ary``, a `NumPy`_ array.
+
+    * ``int exact_dimensions``, the desired number of dimensions.
+
+    Require ``ary`` to have a specified number of dimensions.  If the
+    array has the specified number of dimensions, return 1.
+    Otherwise, set a `python`_ error and return 0.
+
+
+  **require_dimensions_n()**
+
+    Return type: ``int``
+
+    Arguments:
+
+    * ``PyArrayObject* ary``, a `NumPy`_ array.
+
+    * ``int* exact_dimensions``, an array of integers representing
+      acceptable numbers of dimensions.
+
+    * ``int n``, the length of ``exact_dimensions``.
+
+    Require ``ary`` 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(PyArrayObject* ary, int exact_dimensions)**
-    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.
 
+  **require_size()**
 
-  **int require_dimensions_n(PyArrayObject* ary, int* exact_dimensions, int n)**
-    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.
+    Return type: ``int``
 
+    Arguments:
 
-  **int require_size(PyArrayObject* ary, int* size, int n)**
-    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.
+    * ``PyArrayObject* ary``, a `NumPy`_ array.
 
+    * ``npy_int* size``, an array representing the desired lengths of
+      each dimension.
 
+    * ``int n``, the length of ``size``.
+
+    Require ``ary`` to have a specified shape.  If the array has the
+    specified shape, return 1.  Otherwise, set the `python`_ error
+    string and return 0.
+
+
 Beyond the Provided Typemaps
 ============================
 
@@ -520,7 +640,7 @@
 
     double dot(int len, double* vec1, double* vec2);
 
-The python interface that we want is::
+The `python`_ interface that we want is::
 
     def dot(vec1, vec2):
         """
@@ -530,7 +650,7 @@
 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, `SWIG`_ does not provide a mechanism for
-associating ``len`` with ``vec2`` that takes two python input
+associating ``len`` with ``vec2`` that takes two `python`_ input
 arguments).  The recommended solution is the following::
 
     %apply (int DIM1, double* IN_ARRAY1) {(int len1, double* vec1),

Modified: branches/multicore/numpy/f2py/capi_maps.py
===================================================================
--- branches/multicore/numpy/f2py/capi_maps.py	2007-04-17 10:34:54 UTC (rev 3718)
+++ branches/multicore/numpy/f2py/capi_maps.py	2007-04-18 18:32:47 UTC (rev 3719)
@@ -307,6 +307,10 @@
             i=i+1
             if d not in ['*',':','(*)','(:)']:
                 ret['cbsetdims']='%s#varname#_Dims[%d]=%s,'%(ret['cbsetdims'],i,d)
+            elif isintent_in(var):
+                outmess('getarrdims:warning: assumed shape array, using 0 instead of %r\n' \
+                        % (d))
+                ret['cbsetdims']='%s#varname#_Dims[%d]=%s,'%(ret['cbsetdims'],i,0)
             elif verbose :
                 errmess('getarrdims: If in call-back function: array argument %s must have bounded dimensions: got %s\n'%(`a`,`d`))
         if ret['cbsetdims']: ret['cbsetdims']=ret['cbsetdims'][:-1]

Modified: branches/multicore/numpy/testing/numpytest.py
===================================================================
--- branches/multicore/numpy/testing/numpytest.py	2007-04-17 10:34:54 UTC (rev 3718)
+++ branches/multicore/numpy/testing/numpytest.py	2007-04-18 18:32:47 UTC (rev 3719)
@@ -453,8 +453,11 @@
                 continue
             if os.path.basename(os.path.dirname(module.__file__))=='tests':
                 continue
-            modules.append(module)
+            modules.append((name, module))
 
+        modules.sort()
+        modules = [m[1] for m in modules]
+
         self.test_files = []
         suites = []
         for module in modules:

Modified: branches/multicore/site.cfg.example
===================================================================
--- branches/multicore/site.cfg.example	2007-04-17 10:34:54 UTC (rev 3718)
+++ branches/multicore/site.cfg.example	2007-04-18 18:32:47 UTC (rev 3719)
@@ -4,6 +4,9 @@
 # packages will use all sections so you should leave out sections that your
 # package does not use.
 
+# To assist automatic installation like easy_install, the user's home directory
+# will also be checked for the file ~/.numpy-site.cfg .
+
 # The format of the file is that of the standard library's ConfigParser module.
 #
 #   http://www.python.org/doc/current/lib/module-ConfigParser.html



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