From numpy-svn at scipy.org  Fri Mar  2 11:49:04 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Fri,  2 Mar 2007 10:49:04 -0600 (CST)
Subject: [Numpy-svn] r3568 - trunk/numpy/distutils/fcompiler
Message-ID: <20070302164904.893D239C36B@new.scipy.org>

Author: pearu
Date: 2007-03-02 10:49:00 -0600 (Fri, 02 Mar 2007)
New Revision: 3568

Modified:
   trunk/numpy/distutils/fcompiler/ibm.py
Log:
Applying info from Hans-Joachim Ehlers to improve xlf compiler support (on AIX using lslpp to determine compiler version).

Modified: trunk/numpy/distutils/fcompiler/ibm.py
===================================================================
--- trunk/numpy/distutils/fcompiler/ibm.py	2007-02-28 23:52:12 UTC (rev 3567)
+++ trunk/numpy/distutils/fcompiler/ibm.py	2007-03-02 16:49:00 UTC (rev 3568)
@@ -3,6 +3,7 @@
 import sys
 
 from numpy.distutils.fcompiler import FCompiler
+from numpy.distutils.exec_command import exec_command, find_executable
 from distutils import log
 
 class IbmFCompiler(FCompiler):
@@ -23,19 +24,18 @@
     def get_version(self,*args,**kwds):
         version = FCompiler.get_version(self,*args,**kwds)
 
-        if version is None:
-            # Let's try version_cmd without -qversion flag that
-            # xlf versions <=8.x don't have.
-            version_cmd =  self.version_cmd
-            orig_version_cmd = version_cmd[:]
-            if '-qversion' in version_cmd:
-                version_cmd.remove('-qversion')
-                version = FCompiler.get_version(self,*args,**kwds)
-                if version is None:
-                    version_cmd[:] = orig_version_cmd
+        if version is None and sys.platform.startswith('aix'):
+            # use lslpp to find out xlf version
+            lslpp = find_executable('lslpp')
+            xlf = find_executable('xlf')
+            if os.path.exists(xlf) and os.path.exists(lslpp):
+                s,o = exec_command(lslpp + ' -Lc xlfcmp')
+                m = re.search('xlfcmp:(?P<version>\d+([.]\d+)+)', o)
+                if m: version = m.group('version')
 
         xlf_dir = '/etc/opt/ibmcmp/xlf'
         if version is None and os.path.isdir(xlf_dir):
+            # linux:
             # If the output of xlf does not contain version info
             # (that's the case with xlf 8.1, for instance) then
             # let's try another method:



From numpy-svn at scipy.org  Sun Mar  4 05:47:13 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Sun,  4 Mar 2007 04:47:13 -0600 (CST)
Subject: [Numpy-svn] r3569 - trunk/numpy/distutils
Message-ID: <20070304104713.6381C39C0C3@new.scipy.org>

Author: timl
Date: 2007-03-04 04:47:08 -0600 (Sun, 04 Mar 2007)
New Revision: 3569

Modified:
   trunk/numpy/distutils/misc_util.py
Log:
add a delete flag to make_svn_version_py to allow the __svn_version.py to not be deleted

Modified: trunk/numpy/distutils/misc_util.py
===================================================================
--- trunk/numpy/distutils/misc_util.py	2007-03-02 16:49:00 UTC (rev 3568)
+++ trunk/numpy/distutils/misc_util.py	2007-03-04 10:47:08 UTC (rev 3569)
@@ -1304,7 +1304,7 @@
 
         return version
 
-    def make_svn_version_py(self):
+    def make_svn_version_py(self, delete=True):
         """ Generate package __svn_version__.py file from SVN revision number,
         it will be removed after python exits but will be available
         when sdist, etc commands are executed.
@@ -1326,10 +1326,12 @@
 
             import atexit
             def rm_file(f=target,p=self.info):
-                try: os.remove(f); p('removed '+f)
-                except OSError: pass
-                try: os.remove(f+'c'); p('removed '+f+'c')
-                except OSError: pass
+                if delete:
+                    try: os.remove(f); p('removed '+f)
+                    except OSError: pass
+                    try: os.remove(f+'c'); p('removed '+f+'c')
+                    except OSError: pass
+
             atexit.register(rm_file)
 
             return target



From numpy-svn at scipy.org  Mon Mar  5 01:04:06 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Mon,  5 Mar 2007 00:04:06 -0600 (CST)
Subject: [Numpy-svn] r3570 - trunk/numpy/testing
Message-ID: <20070305060406.6C3A839C096@new.scipy.org>

Author: timl
Date: 2007-03-05 00:03:54 -0600 (Mon, 05 Mar 2007)
New Revision: 3570

Modified:
   trunk/numpy/testing/numpytest.py
Log:
fix bug with test_suite being called incorrectly

Modified: trunk/numpy/testing/numpytest.py
===================================================================
--- trunk/numpy/testing/numpytest.py	2007-03-04 10:47:08 UTC (rev 3569)
+++ trunk/numpy/testing/numpytest.py	2007-03-05 06:03:54 UTC (rev 3570)
@@ -398,7 +398,7 @@
         mstr = self._module_str
         suite_list = []
         if hasattr(test_module,'test_suite'):
-            suite_list.extend(test_module.test_suite(level)._tests)
+            suite_list.extend(test_module.test_suite._tests)
         for name in dir(test_module):
             obj = getattr(test_module, name)
             if type(obj) is not type(unittest.TestCase) \



From numpy-svn at scipy.org  Mon Mar  5 01:06:21 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Mon,  5 Mar 2007 00:06:21 -0600 (CST)
Subject: [Numpy-svn] r3571 - trunk/numpy/testing
Message-ID: <20070305060621.43D0E39C096@new.scipy.org>

Author: timl
Date: 2007-03-05 00:06:12 -0600 (Mon, 05 Mar 2007)
New Revision: 3571

Modified:
   trunk/numpy/testing/numpytest.py
Log:
scratch that last one. my bad

Modified: trunk/numpy/testing/numpytest.py
===================================================================
--- trunk/numpy/testing/numpytest.py	2007-03-05 06:03:54 UTC (rev 3570)
+++ trunk/numpy/testing/numpytest.py	2007-03-05 06:06:12 UTC (rev 3571)
@@ -398,7 +398,7 @@
         mstr = self._module_str
         suite_list = []
         if hasattr(test_module,'test_suite'):
-            suite_list.extend(test_module.test_suite._tests)
+            suite_list.extend(test_module.test_suite(level)._tests)
         for name in dir(test_module):
             obj = getattr(test_module, name)
             if type(obj) is not type(unittest.TestCase) \



From numpy-svn at scipy.org  Wed Mar  7 14:12:30 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Wed,  7 Mar 2007 13:12:30 -0600 (CST)
Subject: [Numpy-svn] r3572 - trunk/numpy/linalg
Message-ID: <20070307191230.DE45A39C0A3@new.scipy.org>

Author: oliphant
Date: 2007-03-07 13:12:26 -0600 (Wed, 07 Mar 2007)
New Revision: 3572

Modified:
   trunk/numpy/linalg/lapack_litemodule.c
Log:
Fix problem 64-bit linalg.qr problem.

Modified: trunk/numpy/linalg/lapack_litemodule.c
===================================================================
--- trunk/numpy/linalg/lapack_litemodule.c	2007-03-05 06:06:12 UTC (rev 3571)
+++ trunk/numpy/linalg/lapack_litemodule.c	2007-03-07 19:12:26 UTC (rev 3572)
@@ -520,7 +520,7 @@
         int lda;
         int info;
 
-        TRY(PyArg_ParseTuple(args,"llOlOOll",&m,&n,&a,&lda,&tau,&work,&lwork,&info));
+        TRY(PyArg_ParseTuple(args,"iiOiOOii",&m,&n,&a,&lda,&tau,&work,&lwork,&info));
 
         /* check objects and convert to right storage order */
         TRY(check_object(a,PyArray_DOUBLE,"a","PyArray_DOUBLE","dgeqrf"));
@@ -531,7 +531,7 @@
                 FNAME(dgeqrf)(&m, &n, DDATA(a), &lda, DDATA(tau),
                               DDATA(work), &lwork, &info);
 
-        return Py_BuildValue("{s:l,s:l,s:l,s:l,s:l,s:l}","dgeqrf_",
+        return Py_BuildValue("{s:i,s:i,s:i,s:i,s:i,s:i}","dgeqrf_",
                              lapack_lite_status__,"m",m,"n",n,"lda",lda,
                              "lwork",lwork,"info",info);
 }
@@ -546,14 +546,14 @@
         int lda;
         int info;
 
-        TRY(PyArg_ParseTuple(args,"lllOlOOll",  &m, &n, &k, &a, &lda, &tau, &work, &lwork, &info));
+        TRY(PyArg_ParseTuple(args,"iiiOiOOii",  &m, &n, &k, &a, &lda, &tau, &work, &lwork, &info));
         TRY(check_object(a,PyArray_DOUBLE,"a","PyArray_DOUBLE","dorgqr"));
         TRY(check_object(tau,PyArray_DOUBLE,"tau","PyArray_DOUBLE","dorgqr"));
         TRY(check_object(work,PyArray_DOUBLE,"work","PyArray_DOUBLE","dorgqr"));
         lapack_lite_status__ = \
         FNAME(dorgqr)(&m, &n, &k, DDATA(a), &lda, DDATA(tau), DDATA(work), &lwork, &info);
 
-        return Py_BuildValue("{s:l,s:l}","dorgqr_",lapack_lite_status__,
+        return Py_BuildValue("{s:i,s:i}","dorgqr_",lapack_lite_status__,
                              "info",info);
 }
 
@@ -778,7 +778,7 @@
         int lda;
         int info;
 
-        TRY(PyArg_ParseTuple(args,"lllOlOOll",  &m, &n, &k, &a, &lda, &tau, &work, &lwork, &info));
+        TRY(PyArg_ParseTuple(args,"iiiOiOOii",  &m, &n, &k, &a, &lda, &tau, &work, &lwork, &info));
         TRY(check_object(a,PyArray_CDOUBLE,"a","PyArray_CDOUBLE","zungqr"));
         TRY(check_object(tau,PyArray_CDOUBLE,"tau","PyArray_CDOUBLE","zungqr"));
         TRY(check_object(work,PyArray_CDOUBLE,"work","PyArray_CDOUBLE","zungqr"));
@@ -788,7 +788,7 @@
         FNAME(zungqr)(&m, &n, &k, ZDATA(a), &lda, ZDATA(tau), ZDATA(work),
                       &lwork, &info);
 
-        return Py_BuildValue("{s:l,s:l}","zungqr_",lapack_lite_status__,
+        return Py_BuildValue("{s:i,s:i}","zungqr_",lapack_lite_status__,
                              "info",info);
 }
 



From numpy-svn at scipy.org  Mon Mar 12 13:07:28 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Mon, 12 Mar 2007 12:07:28 -0500 (CDT)
Subject: [Numpy-svn] r3573 - trunk/numpy/core/tests
Message-ID: <20070312170728.15DF339C032@new.scipy.org>

Author: stefan
Date: 2007-03-12 12:07:19 -0500 (Mon, 12 Mar 2007)
New Revision: 3573

Modified:
   trunk/numpy/core/tests/test_regression.py
Log:
Add test for ticket #449.


Modified: trunk/numpy/core/tests/test_regression.py
===================================================================
--- trunk/numpy/core/tests/test_regression.py	2007-03-07 19:12:26 UTC (rev 3572)
+++ trunk/numpy/core/tests/test_regression.py	2007-03-12 17:07:19 UTC (rev 3573)
@@ -608,7 +608,7 @@
         todivide = N.array([2.0, 0.5, 0.25])
         assert_equal(N.subtract.reduce(tosubtract), -10)
         assert_equal(N.divide.reduce(todivide), 16.0)
-        assert_array_equal(N.subtract.accumulate(tosubtract), 
+        assert_array_equal(N.subtract.accumulate(tosubtract),
             N.array([0, -1, -3, -6, -10]))
         assert_array_equal(N.divide.accumulate(todivide),
             N.array([2., 4., 16.]))
@@ -622,6 +622,12 @@
         self.failUnlessRaises(AssertionError,N.convolve,[],[1])
         self.failUnlessRaises(AssertionError,N.convolve,[1],[])
 
+    def check_multidim_byteswap(self, level=rlevel):
+        """Ticket #449"""
+        r=N.array([(1,(0,1,2))], dtype="i2,3i2")
+        assert_array_equal(r.byteswap(),
+                           N.array([(256,(0,256,512))],r.dtype))
+
     def check_string_NULL(self, level=rlevel):
         """Changeset 3557"""
         assert_equal(N.array("a\x00\x0b\x0c\x00").item(),



From numpy-svn at scipy.org  Wed Mar 14 11:48:04 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Wed, 14 Mar 2007 10:48:04 -0500 (CDT)
Subject: [Numpy-svn] r3574 - trunk/numpy/doc/swig
Message-ID: <20070314154804.8416139C08E@new.scipy.org>

Author: wfspotz at sandia.gov
Date: 2007-03-14 10:47:12 -0500 (Wed, 14 Mar 2007)
New Revision: 3574

Added:
   trunk/numpy/doc/swig/numpy_swig.html
   trunk/numpy/doc/swig/numpy_swig.txt
Removed:
   trunk/numpy/doc/swig/HelperFunctions.txt
Modified:
   trunk/numpy/doc/swig/Makefile
   trunk/numpy/doc/swig/README
   trunk/numpy/doc/swig/Series.i
   trunk/numpy/doc/swig/numpy.i
   trunk/numpy/doc/swig/series.cxx
   trunk/numpy/doc/swig/series.h
   trunk/numpy/doc/swig/setup.py
   trunk/numpy/doc/swig/testSeries.py
Log:
In numpy.i:

  * I consolidated several macros

      TYPEMAP_IN1
      TYPEMAP_IN2
      TYPEMAP_INPLACE1
      TYPEMAP_INPLACE2
      TYPEMAP_ARGOUT1
      TYPEMAP_ARGOUT2

    into a single macros, %numpy_typemaps().  This makes expanding the
    typemaps for a given data type much easier.

  * The %numpy_typemaps() macro is now expanded for::

      signed char
      unsigned char
      short
      unsigned short
      int
      unsigned int
      long
      unsigned long
      long long
      unsigned long long
      float
      double
      PyObject
      char

  * I changed the ARGOUT typemaps to check for multiple output
    arguments, and form a tuple if necessary.

  * Updated the code to work with released numpy versions 0.9.6,
    0.9.8, 1.0 and 1.0.1.

  * Upgraded the dimension arrays to use type npy_intp.

I also added documentation: numpy_swig.txt is a numpy.i users guide in
restructured text format; numpy_swig.html is the same content in
HTML.  The Makefile can produce this with 'make html', assuming
docutils is installed.

The series.h and series.cxx files have been refactored to use macros
to generate repetitive code for different types.  All the supported
types have tests for all of the non-ARGOUT typemaps.  ARGOUT typemap
tests still need to be written.


Deleted: trunk/numpy/doc/swig/HelperFunctions.txt
===================================================================
--- trunk/numpy/doc/swig/HelperFunctions.txt	2007-03-12 17:07:19 UTC (rev 3573)
+++ trunk/numpy/doc/swig/HelperFunctions.txt	2007-03-14 15:47:12 UTC (rev 3574)
@@ -1,104 +0,0 @@
-/***********************************************************************
-    Helper functions from numpy.i, translated by John Hunter
-************************************************************************/
-
-#define is_array(a)            ((a) && PyArray_Check((PyArrayObject *)a))
-#define array_type(a)          (int)(((PyArrayObject *)a)->descr->type_num)
-#define array_dimensions(a)    (((PyArrayObject *)a)->nd)
-#define array_size(a,i)        (((PyArrayObject *)a)->dimensions[i])
-#define array_is_contiguous(a) (PyArray_ISCONTIGUOUS(a))
-
-/* Given a PyObject, return a string describing its type.
- */
-char* pytype_string(PyObject* py_obj)
-
-/* Given a Numeric typecode, return a string describing the type.
- */
-char* typecode_string(int typecode)
-
-/* Make sure input has correct numeric type.  Allow character and byte
- * to match.  Also allow int and long to match.
- */
-int type_match(int actual_type, int desired_type)
-
-/* Given a PyObject pointer, cast it to a PyArrayObject pointer if
- * legal.  If not, set the python error string appropriately and
- * return NULL.
- */
-PyArrayObject* obj_to_array_no_conversion(PyObject* input, int typecode)
-
-/* Convert the given PyObject to a Numeric array with the given
- * typecode.  On Success, return a valid PyArrayObject* with the
- * correct type.  On failure, the python error string will be set and
- * the routine returns NULL.
- */
-PyArrayObject* obj_to_array_allow_conversion(PyObject* input, int typecode,
-                                             int* is_new_object)
-
-/* Given a PyArrayObject, check to see if it is contiguous.  If so,
- * return the input pointer and flag it as not a new object.  If it is
- * not contiguous, create a new PyArrayObject using the original data,
- * flag it as a new object and return the pointer.
- */
-PyArrayObject* make_contiguous(PyArrayObject* ary, int* is_new_object,
-                               int min_dims, int max_dims)
-
-/* Convert a given PyObject to a contiguous PyArrayObject of the
- * specified type.  If the input object is not a contiguous
- * PyArrayObject, a new one will be created and the new object flag
- * will be set.
- */
-PyArrayObject* obj_to_array_contiguous_allow_conversion(PyObject* input,
-                                                        int typecode,
-                                                        int* is_new_object)
-
-/* Test whether a python object is contiguous.  If array is
- * contiguous, return 1.  Otherwise, set the python error string and
- * return 0.
- */
-int require_contiguous(PyArrayObject* ary)
-
-/* Require the given PyArrayObject to have a specified number of
- * dimensions.  If the array has the specified number of dimensions,
- * return 1.  Otherwise, set the python error string and return 0.
- */
-int require_dimensions(PyArrayObject* ary, int exact_dimensions)
-
-/* Require the given PyArrayObject to have one of a list of specified
- * number of dimensions.  If the array has one of the specified number
- * of dimensions, return 1.  Otherwise, set the python error string
- * and return 0.
- */
-int require_dimensions_n(PyArrayObject* ary, int* exact_dimensions, int n)
-
-/* Require the given PyArrayObject to have a specified shape.  If the
- * array has the specified shape, return 1.  Otherwise, set the python
- * error string and return 0.
- */
-int require_size(PyArrayObject* ary, int* size, int n)
-
-/***********************************************************************
-    Helper functions from Anna Omelchenko and Michel Sanner
-************************************************************************/
-
-/********************************************************************
-  The following function tries to create a contiguous numeric array of type 
- typecode from a Python object. Works for list, tuples and numeric arrays.
-
-  obj:        Numeric array , Python object
-  typecode:   data type PyArray_{ CHAR, UBYTE, SBYTE, SHORT, INT, LONG, FLOAT,
-                                DOUBLE, CFLOAT, CDOUBLE }
-  expectnd:   required number of dimensions. Used for checking. Ignored if <=0.
-  expectdims: array of expected extends. Used for checking. Ignored if <=0.
-
-  Raises ValueError exceptions if:
-  - the PyArray_ContiguousFromObject fails
-  - the array has a bad shape
-  - the extent of a given dimension doesn't match the specified extent.
-If obj  is a contiguous PyArrayObject  then a reference is returned;
-If op  is PyObject sequence object(list, tuple) then a new PyArrayObject  is created
- and returned. 
-********************************************************************/
-
-static PyArrayObject *contiguous_typed_array(PyObject *obj, int typecode,
-                                      int expectnd, int *expectdims)

Modified: trunk/numpy/doc/swig/Makefile
===================================================================
--- trunk/numpy/doc/swig/Makefile	2007-03-12 17:07:19 UTC (rev 3573)
+++ trunk/numpy/doc/swig/Makefile	2007-03-14 15:47:12 UTC (rev 3574)
@@ -1,3 +1,4 @@
+# SWIG
 INTERFACES = Series.i
 WRAPPERS   = $(INTERFACES:.i=_wrap.cxx)
 PROXIES    = $(INTERFACES:.i=.py      )
@@ -2,2 +3,12 @@
 
+# ReStructured Text
+RST2HTML = rst2html.py
+RFLAGS = --generator --time --no-xml-declaration
+
+# Web pages that need to be made
+WEB_PAGES = numpy_swig.html
+
+# List all of the subdirectories here for recursive make
+SUBDIRS = 
+
 all: $(WRAPPERS) series.cxx series.h
@@ -8,7 +19,14 @@
 %_wrap.cxx: %.i numpy.i series.h
 	swig -c++ -python $<
 
+html: $(WEB_PAGES)
+
+%.html: %.txt
+	$(RST2HTML) $(RFLAGS) $< $@
+
 clean:
 	$(RM) -r build
 	$(RM) $(WRAPPERS)
 	$(RM) $(PROXIES)
+
+.PHONY : html clean

Modified: trunk/numpy/doc/swig/README
===================================================================
--- trunk/numpy/doc/swig/README	2007-03-12 17:07:19 UTC (rev 3573)
+++ trunk/numpy/doc/swig/README	2007-03-14 15:47:12 UTC (rev 3574)
@@ -1,16 +1,15 @@
 Notes for the swig_numpy/new directory
 ======================================
 
-This set of files is for developing and testing file numpy.i, which
-is intended to be a set of typemaps for helping SWIG interface between
-C and C++ code that uses C arrays and the python module Numeric (also
-known informally as NumPy, also to be replaced in the future with
-scipy_core).  It is ultimately hoped that numpy.i will be included
-as part of the SWIG and/or SciPy distribution(s).
+This set of files is for developing and testing file numpy.i, which is
+intended to be a set of typemaps for helping SWIG interface between C
+and C++ code that uses C arrays and the python module NumPy.  It is
+ultimately hoped that numpy.i will be included as part of the SWIG
+distribution.
 
 In the spirit of "writing your tests first", I will begin by
 describing the tests, as they are a good example of what we are trying
-to do with numpy.i.  The files related to testing are
+to do with numpy.i.  The files related to testing are::
 
     series.h
     series.cxx
@@ -18,34 +17,34 @@
 
 The series.h header file contains prototypes for functions that
 illustrate the wrapping issues we wish to address.  Right now, this
-consists of functions with argument signatures of the form
+consists of functions with argument signatures of the form::
 
     (type* IN_ARRAY1, int DIM1)
     (type* IN_ARRAY2, int DIM1, int DIM2)
     (type* INPLACE_ARRAY1, int DIM1)
+    (type* INPLACE_ARRAY2, int DIM1, int DIM2)
 
 which take a pointer to an array of type "type", whose length is
 specified by the integer(s) DIM1 (and DIM2).
 
 The objective for the IN_ARRAY signatures is for SWIG to generate
 python wrappers that take a container that constitutes a valid
-argument to the Numeric.array constructor, and can be used to build an
+argument to the numpy array constructor, and can be used to build an
 array of type "type".  Currently, types "char", "unsigned char",
 "signed char", "short", "int", "long", "float", "double" and
 "PyObject" are supported, although only the types "short", "int",
 "long", "float" and "double" are tested.
 
 The objective for the INPLACE_ARRAY signatures is for SWIG to generate
-python wrappers that accept a Numeric array of any of the above-listed
+python wrappers that accept a numpy array of any of the above-listed
 types.
 
 The source file series.cxx contains the actual implementations of the
 functions described in series.h.  The python script testSeries.py
 tests the resulting python wrappers using the unittest module.
 
-The SWIG interface file Series.i is used to generate the wrapper
-code.  It is pretty short, but everything in it is important.  The
-SWIG_FILE_WITH_INIT macro allows numpy.i to be used with multiple
+The SWIG interface file Series.i is used to generate the wrapper code.
+The SWIG_FILE_WITH_INIT macro allows numpy.i to be used with multiple
 python modules.  If it is specified, then the %init block found in
 Series.i is required.  The other things done in Series.i are the
 inclusion of the series.h and numpy.i files, and the "%apply"
@@ -55,10 +54,10 @@
 a _Series extension module and a Series python module.  The Makefile
 automates everything, setting up the dependencies, calling swig to
 generate the wrappers, and calling setup.py to compile the wrapper
-code and generate the shared object.  Targets "all" (default) and
-"clean" ar supported.
+code and generate the shared object.  Targets "all" (default),
+"clean" and "html" (generate HTML documentation) are supported.
 
-To build and test the code, simply execute from the shell,
+To build and test the code, simply execute from the shell::
 
     $ make
     $ testSeries.py
@@ -66,57 +65,15 @@
 ================================================================================
 
 ToDo
-====
+----
 
-  * Tests for the (INPLACE_ARRAY2, DIM1, DIM2) nedd to be added to
-    series.h, series.cxx and testSeries.py.
+  * Support for complex data types should be added.  Currently swig
+    dies with a syntax error when the %numpy_typemaps macro isused
+    with complex types.
 
-  * Support for complex data types should be added.  Currently the
-    macro substitution doesn't seem to like the two words you need to
-    specify "complex double", etc.
-
-  * ARGOUT typemaps need to be implemented and tested.  I stalled on
-    this because SWIG implements a new method for aggregating output
-    arguments; it has changed from a tuple to a list and requires
-    importing a different SWIG library file.  I didn't particularly
-    want to spend time trying to support both approaches, and I ended
-    up not finishing a typemap for either.
-
-
-Notes Bill:
-
-The numpy.i file currently defines typemaps of the form
-
-      (type* IN_ARRAY1, int DIM1)
-      (type* IN_ARRAY2, int DIM1, int DIM2)
-      (type* INPLACE_ARRAY1, int DIM1)
-      (type* INPLACE_ARRAY2, int DIM1, int DIM2)
-
-and has the beginnings of typemaps of the form
-
-      (type* ARGOUT_ARRAY[ANY])
-      (type* ARGOUT_ARRAY[ANY][ANY])
-
-where "type" is almost any type supported by Numeric (I have had 
-trouble with complex), the "1" suffix can be used with any dimension 
-array as long as DIM1 reflects the total size, and the "2" suffix is 
-for explicitly 2-dimensional arrays.  IN_ARRAYs can be any reasonable 
-container that can be used to construct a Numeric array.  
-INPLACE_ARRAYs must be Numeric arrays.
-
-I hit a snag with the ARGOUT typemaps, because the swig libraries have 
-changed and I did not know the best way to proceed.  Basically, the old 
-way produced a tuple on the LHS if needed and the new way produces a 
-list.  I forget why swig made the change, but the actual swig library 
-file you import changed because of it and I didn't feel like spending 
-time trying to support both approaches.
-
-So the ARGOUT typemaps need to be finished, functions that would use 
-them need to be added to series.{h,cxx} and tests for the resulting 
-python interface need to be added to testSeries.py.
-
-Most of the functions I deal with cannot use the ARGOUT typemaps 
-anyway, because they usually involve input arrays that share the same 
-dimension arguments as the output arrays, which will require a special 
-approach.  So I don't have a lot of motivation there . . . but it 
-definitely needs to be done before any kind of release.
+  * Better ARGOUT typemaps need to be implemented and tested.  I
+    stalled on this because SWIG implements a new method for
+    aggregating output arguments; it has changed from a tuple to a
+    list and requires importing a different SWIG library file.  I
+    didn't particularly want to spend time trying to support both
+    approaches, and I ended up not finishing a typemap for either.

Modified: trunk/numpy/doc/swig/Series.i
===================================================================
--- trunk/numpy/doc/swig/Series.i	2007-03-12 17:07:19 UTC (rev 3573)
+++ trunk/numpy/doc/swig/Series.i	2007-03-14 15:47:12 UTC (rev 3574)
@@ -1,3 +1,4 @@
+// -*- c++ -*-
 %module Series
 
 %{
@@ -5,27 +6,124 @@
 #include "series.h"
 %}
 
-/* Get the Numeric typemaps */
+// Get the Numeric typemaps
 %include "numpy.i"
 
 %init %{
   import_array();
 %}
 
-/* Apply the Numeric typemaps for 1D input arrays */
-%apply (short*  IN_ARRAY1, int DIM1) {(short*  series, int size)};
-%apply (int*    IN_ARRAY1, int DIM1) {(int*    series, int size)};
-%apply (long*   IN_ARRAY1, int DIM1) {(long*   series, int size)};
-%apply (float*  IN_ARRAY1, int DIM1) {(float*  series, int size)};
-%apply (double* IN_ARRAY1, int DIM1) {(double* series, int size)};
+// Apply the Numeric typemaps for 1D input arrays
+%apply (signed char*        IN_ARRAY1, int DIM1)
+      {(signed char*        series,    int size)};
+%apply (unsigned char*      IN_ARRAY1, int DIM1)
+      {(unsigned char*      series,    int size)};
+%apply (short*              IN_ARRAY1, int DIM1)
+      {(short*              series,    int size)};
+%apply (unsigned short*     IN_ARRAY1, int DIM1)
+      {(unsigned short*     series,    int size)};
+%apply (int*                IN_ARRAY1, int DIM1)
+      {(int*                series,    int size)};
+%apply (unsigned int*       IN_ARRAY1, int DIM1)
+      {(unsigned int*       series,    int size)};
+%apply (long*               IN_ARRAY1, int DIM1)
+      {(long*               series,    int size)};
+%apply (unsigned long*      IN_ARRAY1, int DIM1)
+      {(unsigned long*      series,    int size)};
+%apply (long long*          IN_ARRAY1, int DIM1)
+      {(long long*          series,    int size)};
+%apply (unsigned long long* IN_ARRAY1, int DIM1)
+      {(unsigned long long* series,    int size)};
+%apply (float*              IN_ARRAY1, int DIM1)
+      {(float*              series,    int size)};
+%apply (double*             IN_ARRAY1, int DIM1)
+      {(double*             series,    int size)};
+%apply (long double*        IN_ARRAY1, int DIM1)
+      {(long double*        series,    int size)};
 
-/* Apply the Numeric typemaps for 2D input arrays */
-%apply (int*    IN_ARRAY2, int DIM1, int DIM2) {(int*    matrix, int rows, int cols)};
-%apply (double* IN_ARRAY2, int DIM1, int DIM2) {(double* matrix, int rows, int cols)};
+// Apply the Numeric typemaps for 1D input/output arrays
+%apply (signed char*        INPLACE_ARRAY1, int DIM1)
+      {(signed char*        array,          int size)};
+%apply (unsigned char*      INPLACE_ARRAY1, int DIM1)
+      {(unsigned char*      array,          int size)};
+%apply (short*              INPLACE_ARRAY1, int DIM1)
+      {(short*              array,          int size)};
+%apply (unsigned short*     INPLACE_ARRAY1, int DIM1)
+      {(unsigned short*     array,          int size)};
+%apply (int*                INPLACE_ARRAY1, int DIM1)
+      {(int*                array,          int size)};
+%apply (unsigned int*       INPLACE_ARRAY1, int DIM1)
+      {(unsigned int*       array,          int size)};
+%apply (long*               INPLACE_ARRAY1, int DIM1)
+      {(long*               array,          int size)};
+%apply (unsigned long*      INPLACE_ARRAY1, int DIM1)
+      {(unsigned long*      array,          int size)};
+%apply (long long*          INPLACE_ARRAY1, int DIM1)
+      {(long long*          array,          int size)};
+%apply (unsigned long long* INPLACE_ARRAY1, int DIM1)
+      {(unsigned long long* array,          int size)};
+%apply (float*              INPLACE_ARRAY1, int DIM1)
+      {(float*              array,          int size)};
+%apply (double*             INPLACE_ARRAY1, int DIM1)
+      {(double*             array,          int size)};
+%apply (long double*        INPLACE_ARRAY1, int DIM1)
+      {(long double*        array,          int size)};
 
-/* Apply the Numeric typemaps for 1D input/output arrays */
-%apply (int*    INPLACE_ARRAY1, int DIM1) {(int*    array,   int size)};
-%apply (double* INPLACE_ARRAY1, int DIM1) {(double* array,   int size)};
+// Apply the Numeric typemaps for 2D input arrays
+%apply (signed char*        IN_ARRAY2, int DIM1, int DIM2)
+      {(signed char*        matrix,    int rows, int cols)};
+%apply (unsigned char*      IN_ARRAY2, int DIM1, int DIM2)
+      {(unsigned char*      matrix,    int rows, int cols)};
+%apply (short*              IN_ARRAY2, int DIM1, int DIM2)
+      {(short*              matrix,    int rows, int cols)};
+%apply (unsigned short*     IN_ARRAY2, int DIM1, int DIM2)
+      {(unsigned short*     matrix,    int rows, int cols)};
+%apply (int*                IN_ARRAY2, int DIM1, int DIM2)
+      {(int*                matrix,    int rows, int cols)};
+%apply (unsigned int*       IN_ARRAY2, int DIM1, int DIM2)
+      {(unsigned int*       matrix,    int rows, int cols)};
+%apply (long*               IN_ARRAY2, int DIM1, int DIM2)
+      {(long*               matrix,    int rows, int cols)};
+%apply (unsigned long*      IN_ARRAY2, int DIM1, int DIM2)
+      {(unsigned long*      matrix,    int rows, int cols)};
+%apply (long long*          IN_ARRAY2, int DIM1, int DIM2)
+      {(long long*          matrix,    int rows, int cols)};
+%apply (unsigned long long* IN_ARRAY2, int DIM1, int DIM2)
+      {(unsigned long long* matrix,    int rows, int cols)};
+%apply (float*              IN_ARRAY2, int DIM1, int DIM2)
+      {(float*              matrix,    int rows, int cols)};
+%apply (double*             IN_ARRAY2, int DIM1, int DIM2)
+      {(double*             matrix,    int rows, int cols)};
+%apply (long double*        IN_ARRAY2, int DIM1, int DIM2)
+      {(long double*        matrix,    int rows, int cols)};
 
-/* Include the header file to be wrapped */
+// Apply the Numeric typemaps for 2D input/output arrays
+%apply (signed char*        INPLACE_ARRAY2, int DIM1, int DIM2)
+      {(signed char*        array,          int rows, int cols)};
+%apply (unsigned char*      INPLACE_ARRAY2, int DIM1, int DIM2)
+      {(unsigned char*      array,          int rows, int cols)};
+%apply (short*              INPLACE_ARRAY2, int DIM1, int DIM2)
+      {(short*              array,          int rows, int cols)};
+%apply (unsigned short*     INPLACE_ARRAY2, int DIM1, int DIM2)
+      {(unsigned short*     array,          int rows, int cols)};
+%apply (int*                INPLACE_ARRAY2, int DIM1, int DIM2)
+      {(int*                array,          int rows, int cols)};
+%apply (unsigned int*       INPLACE_ARRAY2, int DIM1, int DIM2)
+      {(unsigned int*       array,          int rows, int cols)};
+%apply (long*               INPLACE_ARRAY2, int DIM1, int DIM2)
+      {(long*               array,          int rows, int cols)};
+%apply (unsigned long*      INPLACE_ARRAY2, int DIM1, int DIM2)
+      {(unsigned long*      array,          int rows, int cols)};
+%apply (long long*          INPLACE_ARRAY2, int DIM1, int DIM2)
+      {(long long*          array,          int rows, int cols)};
+%apply (unsigned long long* INPLACE_ARRAY2, int DIM1, int DIM2)
+      {(unsigned long long* array,          int rows, int cols)};
+%apply (float*              INPLACE_ARRAY2, int DIM1, int DIM2)
+      {(float*              array,          int rows, int cols)};
+%apply (double*             INPLACE_ARRAY2, int DIM1, int DIM2)
+      {(double*             array,          int rows, int cols)};
+%apply (long double*        INPLACE_ARRAY2, int DIM1, int DIM2)
+      {(long double*        array,          int rows, int cols)};
+
+// Include the header file to be wrapped
 %include "series.h"

Modified: trunk/numpy/doc/swig/numpy.i
===================================================================
--- trunk/numpy/doc/swig/numpy.i	2007-03-12 17:07:19 UTC (rev 3573)
+++ trunk/numpy/doc/swig/numpy.i	2007-03-14 15:47:12 UTC (rev 3574)
@@ -6,10 +6,11 @@
 #include "stdio.h"
 #include <numpy/arrayobject.h>
 
-/* The following code originally appeared in enthought/kiva/agg/src/numeric.i,
- * author unknown.  It was translated from C++ to C by John Hunter.  Bill
- * Spotz has modified it slightly to fix some minor bugs, add some comments
- * and some functionality.
+/* The following code originally appeared in
+ * enthought/kiva/agg/src/numeric.i, author unknown.  It was
+ * translated from C++ to C by John Hunter.  Bill Spotz has modified
+ * it slightly to fix some minor bugs, upgrade to numpy (all
+ * versions), add some comments and some functionality.
  */
 
 /* Macros to extract array attributes.
@@ -20,6 +21,37 @@
 #define array_size(a,i)        (((PyArrayObject *)a)->dimensions[i])
 #define array_is_contiguous(a) (PyArray_ISCONTIGUOUS(a))
 
+/* Support older NumPy data type names
+*/
+#if NDARRAY_VERSION < 0x01000000
+#define NPY_BOOL        PyArray_BOOL
+#define NPY_BYTE        PyArray_BYTE
+#define NPY_UBYTE       PyArray_UBYTE
+#define NPY_SHORT       PyArray_SHORT
+#define NPY_USHORT      PyArray_USHORT
+#define NPY_INT         PyArray_INT
+#define NPY_UINT        PyArray_UINT
+#define NPY_LONG        PyArray_LONG
+#define NPY_ULONG       PyArray_ULONG
+#define NPY_LONGLONG    PyArray_LONGLONG
+#define NPY_ULONGLONG   PyArray_ULONGLONG
+#define NPY_FLOAT       PyArray_FLOAT
+#define NPY_DOUBLE      PyArray_DOUBLE
+#define NPY_LONGDOUBLE  PyArray_LONGDOUBLE
+#define NPY_CFLOAT      PyArray_CFLOAT
+#define NPY_CDOUBLE     PyArray_CDOUBLE
+#define NPY_CLONGDOUBLE PyArray_CLONGDOUBLE
+#define NPY_OBJECT      PyArray_OBJECT
+#define NPY_STRING      PyArray_STRING
+#define NPY_UNICODE     PyArray_UNICODE
+#define NPY_VOID        PyArray_VOID
+#define NPY_NTYPES      PyArray_NTYPES
+#define NPY_NOTYPE      PyArray_NOTYPE
+#define NPY_CHAR        PyArray_CHAR
+#define NPY_USERDEF     PyArray_USERDEF
+#define npy_intp        intp
+#endif
+
 /* Given a PyObject, return a string describing its type.
  */
 char* pytype_string(PyObject* py_obj) {
@@ -38,13 +70,18 @@
   return "unkown type";
 }
 
-/* Given a Numeric typecode, return a string describing the type.
+/* Given a NumPy typecode, return a string describing the type.
  */
 char* typecode_string(int typecode) {
-  char* type_names[20] = {"char","unsigned byte","byte","short",
-			  "unsigned short","int","unsigned int","long",
-			  "float","double","complex float","complex double",
-			  "object","ntype","unkown"};
+  static char* type_names[24] = {"bool", "byte", "unsigned byte",
+				 "short", "unsigned short", "int",
+				 "unsigned int", "long", "unsigned long",
+				 "long long", "unsigned long long",
+				 "float", "double", "long double",
+				 "complex float", "complex double",
+				 "complex long double", "object",
+				 "string", "unicode", "void", "ntypes",
+				 "notype", "char"};
   return type_names[typecode];
 }
 
@@ -57,45 +94,43 @@
 
 /* Given a PyObject pointer, cast it to a PyArrayObject pointer if
  * legal.  If not, set the python error string appropriately and
- * return NULL./
+ * return NULL.
  */
 PyArrayObject* obj_to_array_no_conversion(PyObject* input, int typecode) {
   PyArrayObject* ary = NULL;
-  if (is_array(input) && (typecode == PyArray_NOTYPE || 
-			  PyArray_EquivTypenums(array_type(input), 
-						typecode))) {
-        ary = (PyArrayObject*) input;
-    }
-    else if is_array(input) {
-      char* desired_type = typecode_string(typecode);
-      char* actual_type = typecode_string(array_type(input));
-      PyErr_Format(PyExc_TypeError, 
-		   "Array of type '%s' required.  Array of type '%s' given", 
-		   desired_type, actual_type);
-      ary = NULL;
-    }
-    else {
-      char * desired_type = typecode_string(typecode);
-      char * actual_type = pytype_string(input);
-      PyErr_Format(PyExc_TypeError, 
-		   "Array of type '%s' required.  A %s was given", 
-		   desired_type, actual_type);
-      ary = NULL;
-    }
+  if (is_array(input) && (typecode == NPY_NOTYPE ||
+			  type_match(array_type(input), typecode))) {
+    ary = (PyArrayObject*) input;
+  }
+  else if is_array(input) {
+    char* desired_type = typecode_string(typecode);
+    char* actual_type = typecode_string(array_type(input));
+    PyErr_Format(PyExc_TypeError, 
+		 "Array of type '%s' required.  Array of type '%s' given", 
+		 desired_type, actual_type);
+    ary = NULL;
+  }
+  else {
+    char * desired_type = typecode_string(typecode);
+    char * actual_type = pytype_string(input);
+    PyErr_Format(PyExc_TypeError, 
+		 "Array of type '%s' required.  A %s was given", 
+		 desired_type, actual_type);
+    ary = NULL;
+  }
   return ary;
 }
 
-/* Convert the given PyObject to a Numeric array with the given
+/* Convert the given PyObject to a NumPy array with the given
  * typecode.  On Success, return a valid PyArrayObject* with the
  * correct type.  On failure, the python error string will be set and
  * the routine returns NULL.
  */
 PyArrayObject* obj_to_array_allow_conversion(PyObject* input, int typecode,
-                                             int* is_new_object)
-{
+                                             int* is_new_object) {
   PyArrayObject* ary = NULL;
   PyObject* py_obj;
-  if (is_array(input) && (typecode == PyArray_NOTYPE || type_match(array_type(input),typecode))) {
+  if (is_array(input) && (typecode == NPY_NOTYPE || type_match(array_type(input),typecode))) {
     ary = (PyArrayObject*) input;
     *is_new_object = 0;
   }
@@ -114,8 +149,7 @@
  * flag it as a new object and return the pointer.
  */
 PyArrayObject* make_contiguous(PyArrayObject* ary, int* is_new_object,
-                               int min_dims, int max_dims)
-{
+                               int min_dims, int max_dims) {
   PyArrayObject* result;
   if (array_is_contiguous(ary)) {
     result = ary;
@@ -216,7 +250,7 @@
  * array has the specified shape, return 1.  Otherwise, set the python
  * error string and return 0.
  */
-int require_size(PyArrayObject* ary, int* size, int n) {
+int require_size(PyArrayObject* ary, npy_intp* size, int n) {
   int i;
   int success = 1;
   int len;
@@ -253,236 +287,202 @@
   }
   return success;
 }
-/* End John Hunter translation (with modifications by Bill Spotz) */
+/* End John Hunter translation (with modifications by Bill Spotz)
+ */
 
 %}
 
-/* TYPEMAP_IN macros
+/* %numpy_typemaps() macro
  *
- * This family of typemaps allows pure input C arguments of the form
+ * This macro defines a family of typemaps that allow pure input C
+ * arguments of the form
  *
- *     (type* IN_ARRAY1, int DIM1)
- *     (type* IN_ARRAY2, int DIM1, int DIM2)
+ *     (TYPE* IN_ARRAY1, int DIM1)
+ *     (TYPE* IN_ARRAY2, int DIM1, int DIM2)
+ *     (TYPE* INPLACE_ARRAY1, int DIM1)
+ *     (TYPE* INPLACE_ARRAY2, int DIM1, int DIM2)
+ *     (TYPE* ARGOUT_ARRAY1[ANY])
+ *     (TYPE* ARGOUT_ARRAY2[ANY][ANY])
  *
- * where "type" is any type supported by the Numeric module, to be
- * called in python with an argument list of a single array (or any
- * python object that can be passed to the Numeric.array constructor
- * to produce an arrayof te specified shape).  This can be applied to
- * a existing functions using the %apply directive:
+ * where "TYPE" is any type supported by the NumPy module.  In python,
+ * the dimensions will not need to be specified.  The IN_ARRAYs can be
+ * a numpy array or any sequence that can be converted to a numpy
+ * array of the specified type.  The INPLACE_ARRAYs must be numpy
+ * arrays of the appropriate type.  The ARGOUT_ARRAYs will be returned
+ * as numpy arrays of the appropriate type.
+
+ * These typemaps can be applied to existing functions using the
+ * %apply directive:
  *
- *     %apply (double* IN_ARRAY1, int DIM1) {double* series, int length}
- *     %apply (double* IN_ARRAY2, int DIM1, int DIM2) {double* mx, int rows, int cols}
+ *     %apply (double* IN_ARRAY1, int DIM1) {double* series, int length};
  *     double sum(double* series, int length);
+ *
+ *     %apply (double* IN_ARRAY2, int DIM1, int DIM2) {double* mx, int rows, int cols};
  *     double max(double* mx, int rows, int cols);
  *
- * or with
+ *     %apply (double* INPLACE_ARRAY1, int DIM1) {double* series, int length};
+ *     void negate(double* series, int length);
  *
+ *     %apply (double* INPLACE_ARRAY2, int DIM1, int DIM2) {double* mx, int rows, int cols};
+ *     void normalize(double* mx, int rows, int cols);
+ *
+ *     %apply (double* ARGOUT_ARRAY1[ANY] {double series, int length};
+ *     void negate(double* series, int length);
+ *
+ *     %apply (double* ARGOUT_ARRAY2[ANY][ANY]) {double* mx, int rows, int cols};
+ *     void normalize(double* mx, int rows, int cols);
+ *
+ * or directly with
+ *
  *     double sum(double* IN_ARRAY1, int DIM1);
  *     double max(double* IN_ARRAY2, int DIM1, int DIM2);
+ *     void sum(double* INPLACE_ARRAY1, int DIM1);
+ *     void sum(double* INPLACE_ARRAY2, int DIM1, int DIM2);
+ *     void sum(double* ARGOUT_ARRAY1[ANY]);
+ *     void sum(double* ARGOUT_ARRAY2[ANY][ANY]);
  */
 
-/* One dimensional input arrays */
-%define TYPEMAP_IN1(type,typecode)
-%typemap(in) (type* IN_ARRAY1, int DIM1)
-             (PyArrayObject* array=NULL, int is_new_object) {
-  int size[1] = {-1};
-  array = obj_to_array_contiguous_allow_conversion($input, typecode, &is_new_object);
-  if (!array || !require_dimensions(array,1) || !require_size(array,size,1)) SWIG_fail;
-  $1 = (type*) array->data;
-  $2 = array->dimensions[0];
+%define %numpy_typemaps(TYPE, TYPECODE)
+
+/* Typemap suite for (TYPE* IN_ARRAY1, int DIM1)
+ */
+%typemap(in) (TYPE* IN_ARRAY1, int DIM1)
+             (PyArrayObject* array=NULL, int is_new_object=0) {
+  array = obj_to_array_contiguous_allow_conversion($input, TYPECODE, &is_new_object);
+  npy_intp size[1] = {-1};
+  if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail;
+  $1 = (TYPE*) array->data;
+  $2 = (int) array->dimensions[0];
 }
-%typemap(freearg) (type* IN_ARRAY1, int DIM1) {
+%typemap(freearg) (TYPE* IN_ARRAY1, int DIM1) {
   if (is_new_object$argnum && array$argnum) Py_DECREF(array$argnum);
 }
-%enddef
 
-/* Define concrete examples of the TYPEMAP_IN1 macros */
-TYPEMAP_IN1(char,          PyArray_CHAR  )
-TYPEMAP_IN1(unsigned char, PyArray_UBYTE )
-TYPEMAP_IN1(signed char,   PyArray_SBYTE )
-TYPEMAP_IN1(short,         PyArray_SHORT )
-TYPEMAP_IN1(int,           PyArray_INT   )
-TYPEMAP_IN1(long,          PyArray_LONG  )
-TYPEMAP_IN1(float,         PyArray_FLOAT )
-TYPEMAP_IN1(double,        PyArray_DOUBLE)
-TYPEMAP_IN1(PyObject,      PyArray_OBJECT)
-
-#undef TYPEMAP_IN1
-
- /* Two dimensional input arrays */
-%define TYPEMAP_IN2(type,typecode)
-  %typemap(in) (type* IN_ARRAY2, int DIM1, int DIM2)
-               (PyArrayObject* array=NULL, int is_new_object) {
-  int size[2] = {-1,-1};
-  array = obj_to_array_contiguous_allow_conversion($input, typecode, &is_new_object);
-  if (!array || !require_dimensions(array,2) || !require_size(array,size,1)) SWIG_fail;
-  $1 = (type*) array->data;
-  $2 = array->dimensions[0];
-  $3 = array->dimensions[1];
+/* Typemap suite for (TYPE* IN_ARRAY2, int DIM1, int DIM2)
+ */
+%typemap(in) (TYPE* IN_ARRAY2, int DIM1, int DIM2)
+             (PyArrayObject* array=NULL, int is_new_object=0) {
+  array = obj_to_array_contiguous_allow_conversion($input, TYPECODE, &is_new_object);
+  npy_intp size[2] = {-1,-1};
+  if (!array || !require_dimensions(array, 2) || !require_size(array, size, 1)) SWIG_fail;
+  $1 = (TYPE*) array->data;
+  $2 = (int) array->dimensions[0];
+  $3 = (int) array->dimensions[1];
 }
-%typemap(freearg) (type* IN_ARRAY2, int DIM1, int DIM2) {
+%typemap(freearg) (TYPE* IN_ARRAY2, int DIM1, int DIM2) {
   if (is_new_object$argnum && array$argnum) Py_DECREF(array$argnum);
 }
-%enddef
 
-/* Define concrete examples of the TYPEMAP_IN2 macros */
-TYPEMAP_IN2(char,          PyArray_CHAR  )
-TYPEMAP_IN2(unsigned char, PyArray_UBYTE )
-TYPEMAP_IN2(signed char,   PyArray_SBYTE )
-TYPEMAP_IN2(short,         PyArray_SHORT )
-TYPEMAP_IN2(int,           PyArray_INT   )
-TYPEMAP_IN2(long,          PyArray_LONG  )
-TYPEMAP_IN2(float,         PyArray_FLOAT )
-TYPEMAP_IN2(double,        PyArray_DOUBLE)
-TYPEMAP_IN2(PyObject,      PyArray_OBJECT)
-
-#undef TYPEMAP_IN2
-
-/* TYPEMAP_INPLACE macros
- *
- * This family of typemaps allows input/output C arguments of the form
- *
- *     (type* INPLACE_ARRAY1, int DIM1)
- *     (type* INPLACE_ARRAY2, int DIM1, int DIM2)
- *
- * where "type" is any type supported by the Numeric module, to be
- * called in python with an argument list of a single contiguous
- * Numeric array.  This can be applied to an existing function using
- * the %apply directive:
- *
- *     %apply (double* INPLACE_ARRAY1, int DIM1) {double* series, int length}
- *     %apply (double* INPLACE_ARRAY2, int DIM1, int DIM2) {double* mx, int rows, int cols}
- *     void negate(double* series, int length);
- *     void normalize(double* mx, int rows, int cols);
- *     
- *
- * or with
- *
- *     void sum(double* INPLACE_ARRAY1, int DIM1);
- *     void sum(double* INPLACE_ARRAY2, int DIM1, int DIM2);
+/* Typemap suite for (TYPE* INPLACE_ARRAY1, int DIM1)
  */
-
- /* One dimensional input/output arrays */
-%define TYPEMAP_INPLACE1(type,typecode)
-%typemap(in) (type* INPLACE_ARRAY1, int DIM1) (PyArrayObject* temp=NULL) {
-  int i;
-  temp = obj_to_array_no_conversion($input,typecode);
+%typemap(in) (TYPE* INPLACE_ARRAY1, int DIM1) (PyArrayObject* temp=NULL) {
+  temp = obj_to_array_no_conversion($input, TYPECODE);
   if (!temp  || !require_contiguous(temp)) SWIG_fail;
-  $1 = (type*) temp->data;
+  $1 = (TYPE*) temp->data;
   $2 = 1;
-  for (i=0; i<temp->nd; ++i) $2 *= temp->dimensions[i];
+  for (int i=0; i<temp->nd; ++i) $2 *= temp->dimensions[i];
 }
-%enddef
 
-/* Define concrete examples of the TYPEMAP_INPLACE1 macro */
-TYPEMAP_INPLACE1(char,          PyArray_CHAR  )
-TYPEMAP_INPLACE1(unsigned char, PyArray_UBYTE )
-TYPEMAP_INPLACE1(signed char,   PyArray_SBYTE )
-TYPEMAP_INPLACE1(short,         PyArray_SHORT )
-TYPEMAP_INPLACE1(int,           PyArray_INT   )
-TYPEMAP_INPLACE1(long,          PyArray_LONG  )
-TYPEMAP_INPLACE1(float,         PyArray_FLOAT )
-TYPEMAP_INPLACE1(double,        PyArray_DOUBLE)
-TYPEMAP_INPLACE1(PyObject,      PyArray_OBJECT)
-
-#undef TYPEMAP_INPLACE1
-
- /* Two dimensional input/output arrays */
-%define TYPEMAP_INPLACE2(type,typecode)
-  %typemap(in) (type* INPLACE_ARRAY2, int DIM1, int DIM2) (PyArrayObject* temp=NULL) {
-  temp = obj_to_array_no_conversion($input,typecode);
+/* Typemap suite for (TYPE* INPLACE_ARRAY2, int DIM1, int DIM2)
+ */
+%typemap(in) (TYPE* INPLACE_ARRAY2, int DIM1, int DIM2) (PyArrayObject* temp=NULL) {
+  temp = obj_to_array_no_conversion($input, TYPECODE);
   if (!temp || !require_contiguous(temp)) SWIG_fail;
-  $1 = (type*) temp->data;
-  $2 = temp->dimensions[0];
-  $3 = temp->dimensions[1];
+  $1 = (TYPE*) temp->data;
+  $2 = (int) temp->dimensions[0];
+  $3 = (int) temp->dimensions[1];
 }
-%enddef
 
-/* Define concrete examples of the TYPEMAP_INPLACE2 macro */
-TYPEMAP_INPLACE2(char,          PyArray_CHAR  )
-TYPEMAP_INPLACE2(unsigned char, PyArray_UBYTE )
-TYPEMAP_INPLACE2(signed char,   PyArray_SBYTE )
-TYPEMAP_INPLACE2(short,         PyArray_SHORT )
-TYPEMAP_INPLACE2(int,           PyArray_INT   )
-TYPEMAP_INPLACE2(long,          PyArray_LONG  )
-TYPEMAP_INPLACE2(float,         PyArray_FLOAT )
-TYPEMAP_INPLACE2(double,        PyArray_DOUBLE)
-TYPEMAP_INPLACE2(PyObject,      PyArray_OBJECT)
+/* Typemap suite for (TYPE ARGOUT_ARRAY1[ANY])
+ */
+%typemap(in,numinputs=0) (TYPE ARGOUT_ARRAY1[ANY]) {
+  $1 = (TYPE*) malloc($1_dim0*sizeof(TYPE));
+  if (!$1) {
+    PyErr_SetString(PyExc_RuntimeError, "Failed to allocate memory");
+    SWIG_fail;
+  }
+}
+%typemap(argout) (TYPE ARGOUT_ARRAY1[ANY]) {
+  PyObject * obj = NULL;
+  npy_intp dimensions[1] = { $1_dim0 };
+  PyObject* outArray = PyArray_FromDimsAndData(1, dimensions, TYPECODE, (char*)$1);
+  if ($result == Py_None) {
+    Py_DECREF($result);
+    $result = outArray;
+  }
+  else {
+    if (!PyTuple_Check($result)) $result = Py_BuildValue("(O)", $result);
+    obj = Py_Build_Value("(O)", outArray);
+    $result = PySequence_Concat($result, obj);
+  }
+}
 
-#undef TYPEMAP_INPLACE2
-
-/* TYPEMAP_ARGOUT macros
- *
- * This family of typemaps allows output C arguments of the form
- *
- *     (type* ARGOUT_ARRAY[ANY])
- *     (type* ARGOUT_ARRAY[ANY][ANY])
- *
- * where "type" is any type supported by the Numeric module, to be
- * called in python with an argument list of a single contiguous
- * Numeric array.  This can be applied to an existing function using
- * the %apply directive:
- *
- *     %apply (double* ARGOUT_ARRAY[ANY] {double series, int length}
- *     %apply (double* ARGOUT_ARRAY[ANY][ANY]) {double* mx, int rows, int cols}
- *     void negate(double* series, int length);
- *     void normalize(double* mx, int rows, int cols);
- *     
- *
- * or with
- *
- *     void sum(double* ARGOUT_ARRAY[ANY]);
- *     void sum(double* ARGOUT_ARRAY[ANY][ANY]);
+/* Typemap suite for (TYPE ARGOUT_ARRAY2[ANY][ANY])
  */
-
- /* One dimensional input/output arrays */
-%define TYPEMAP_ARGOUT1(type,typecode)
-%typemap(in,numinputs=0) type ARGOUT_ARRAY[ANY] {
-  $1 = (type*) malloc($1_dim0*sizeof(type));
+%typemap(in,numinputs=0) (TYPE ARGOUT_ARRAY2[ANY][ANY]) {
+  $1 = (TYPE*) malloc($1_dim0 * $1_dim1 * sizeof(TYPE));
   if (!$1) {
     PyErr_SetString(PyExc_RuntimeError, "Failed to allocate memory");
     SWIG_fail;
   }
 }
-%typemap(argout) ARGOUT_ARRAY[ANY] {
-  int dimensions[1] = {$1_dim0};
-  PyObject* outArray = PyArray_FromDimsAndData(1, dimensions, typecode, (char*)$1);
+%typemap(argout) (TYPE ARGOUT_ARRAY1[ANY][ANY]) {
+  PyObject * obj = NULL;
+  npy_intp dimensions[2] = { $1_dim0, $1_dim1 };
+  PyObject* outArray = PyArray_FromDimsAndData(1, dimensions, TYPECODE, (char*)$1);
+  if ($result == Py_None) {
+    Py_DECREF($result);
+    $result = outArray;
+  }
+  else {
+    if (!PyTuple_Check($result)) $result = Py_BuildValue("(O)", $result);
+    obj = Py_Build_Value("(O)", outArray);
+    $result = PySequence_Concat($result, obj);
+  }
 }
-%enddef
 
-/* Define concrete examples of the TYPEMAP_ARGOUT1 macro */
-TYPEMAP_ARGOUT1(char,          PyArray_CHAR  )
-TYPEMAP_ARGOUT1(unsigned char, PyArray_UBYTE )
-TYPEMAP_ARGOUT1(signed char,   PyArray_SBYTE )
-TYPEMAP_ARGOUT1(short,         PyArray_SHORT )
-TYPEMAP_ARGOUT1(int,           PyArray_INT   )
-TYPEMAP_ARGOUT1(long,          PyArray_LONG  )
-TYPEMAP_ARGOUT1(float,         PyArray_FLOAT )
-TYPEMAP_ARGOUT1(double,        PyArray_DOUBLE)
-TYPEMAP_ARGOUT1(PyObject,      PyArray_OBJECT)
+%enddef    /* %numpy_typemaps() macro */
 
-#undef TYPEMAP_ARGOUT1
 
- /* Two dimensional input/output arrays */
-%define TYPEMAP_ARGOUT2(type,typecode)
-  %typemap(in) (type* ARGOUT_ARRAY2, int DIM1, int DIM2) (PyArrayObject* temp=NULL) {
-  temp = obj_to_array_no_conversion($input,typecode);
-  if (!temp || !require_contiguous(temp)) SWIG_fail;
-  $1 = (type*) temp->data;
-  $2 = temp->dimensions[0];
-  $3 = temp->dimensions[1];
-}
-%enddef
+/* Concrete instances of the %numpy_typemaps() macro: Each invocation
+ * below applies all of the typemaps above to the specified data type.
+ */
+%numpy_typemaps(signed char,         NPY_BYTE     ) /**/
+%numpy_typemaps(unsigned char,       NPY_UBYTE    ) /**/
+%numpy_typemaps(short,               NPY_SHORT    ) /**/
+%numpy_typemaps(unsigned short,      NPY_USHORT   ) /**/
+%numpy_typemaps(int,                 NPY_INT      ) /**/
+%numpy_typemaps(unsigned int,        NPY_UINT     ) /**/
+%numpy_typemaps(long,                NPY_LONG     ) /**/
+%numpy_typemaps(unsigned long,       NPY_ULONG    ) /**/
+%numpy_typemaps(long long,           NPY_LONGLONG ) /**/
+%numpy_typemaps(unsigned long long,  NPY_ULONGLONG) /**/
+%numpy_typemaps(float,               NPY_FLOAT    ) /**/
+%numpy_typemaps(double,              NPY_DOUBLE   ) /**/
+%numpy_typemaps(PyObject,            NPY_OBJECT   )
+%numpy_typemaps(char,                NPY_CHAR     )
 
-/* Define concrete examples of the TYPEMAP_ARGOUT2 macro */
-TYPEMAP_ARGOUT2(char,          PyArray_CHAR  )
-TYPEMAP_ARGOUT2(unsigned char, PyArray_UBYTE )
-TYPEMAP_ARGOUT2(signed char,   PyArray_SBYTE )
-TYPEMAP_ARGOUT2(short,         PyArray_SHORT )
-TYPEMAP_ARGOUT2(int,           PyArray_INT   )
-TYPEMAP_ARGOUT2(long,          PyArray_LONG  )
-TYPEMAP_ARGOUT2(float,         PyArray_FLOAT )
-TYPEMAP_ARGOUT2(double,        PyArray_DOUBLE)
-TYPEMAP_ARGOUT2(PyObject,      PyArray_OBJECT)
+/* ***************************************************************
+ * The follow macro expansion does not work, because C++ bool is 4
+ * bytes and NPY_BOOL is 1 byte
+ */
+/*%numpy_typemaps(bool, NPY_BOOL)
+ */
 
-#undef TYPEMAP_ARGOUT2
+/* ***************************************************************
+ * On my Mac, I get the following warning for this macro expansion:
+ * 'swig/python detected a memory leak of type 'long double *', no destructor found.'
+ */
+/*%numpy_typemaps(long double, NPY_LONGDOUBLE)
+ */
+
+/* ***************************************************************
+ * Swig complains about a syntax error for the following macros
+ * expansions:
+ */
+/*%numpy_typemaps(complex float,       NPY_CFLOAT     )
+ */
+/*%numpy_typemaps(complex double,      NPY_CDOUBLE    )
+ */
+/*%numpy_typemaps(complex long double, NPY_CLONGDOUBLE)
+ */

Added: trunk/numpy/doc/swig/numpy_swig.html
===================================================================
--- trunk/numpy/doc/swig/numpy_swig.html	2007-03-12 17:07:19 UTC (rev 3573)
+++ trunk/numpy/doc/swig/numpy_swig.html	2007-03-14 15:47:12 UTC (rev 3574)
@@ -0,0 +1,604 @@
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<head>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
+<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="10 March, 2007" />
+<style type="text/css">
+
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+:Contact: goodger at users.sourceforge.net
+:Date: $Date: 2005-12-18 01:56:14 +0100 (Sun, 18 Dec 2005) $
+:Revision: $Revision: 4224 $
+:Copyright: This stylesheet has been placed in the public domain.
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+Default cascading style sheet for the HTML output of Docutils.
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+See http://docutils.sf.net/docs/howto/html-stylesheets.html for how to
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+*/
+
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+  border: medium outset ;
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+
+div.compound .compound-last, div.compound .compound-middle {
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+  margin: 2em 5em ;
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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>10 March, 2007</td></tr>
+</tbody>
+</table>
+<div class="section">
+<h1><a 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>Making an educated guess, humans can conclude that this is probably 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 arrays of data.  Therefore, the most logical
+python interface for the <tt class="docutils literal"><span class="pre">rms</span></tt> function would be:</p>
+<pre class="literal-block">
+def rms(seq):
+</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 python 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">
+%{
+#include &quot;rms.h&quot;
+%}
+%include &quot;numpy.i&quot;
+%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>Hopefully, no actual prototypes to be wrapped will have the 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>
+</div>
+<div class="section">
+<h1><a id="using-numpy-i" name="using-numpy-i">Using numpy.i</a></h1>
+<p>The <tt class="docutils literal"><span class="pre">numpy.i</span></tt> file is currently located in the <tt class="docutils literal"><span class="pre">numpy/docs/swig</span></tt>
+sub-directory under the <tt class="docutils literal"><span class="pre">numpy</span></tt> installation directory.  Typically,
+you will want to copy it to the directory where you are developing
+your wrappers.</p>
+<p>A simple module that only uses a single <a class="reference" href="http://www.swig.org">SWIG</a> interface file should
+include the following:</p>
+<pre class="literal-block">
+%{
+#define SWIG_FILE_WITH_INIT
+%}
+%include &quot;numpy.i&quot;
+%init %{
+import_array();
+%}
+</pre>
+<p>Only one interface file should call <tt class="docutils literal"><span class="pre">import_array()</span></tt>.  If you have
+more than one <a class="reference" href="http://www.swig.org">SWIG</a> interface file, then subsequent files should not
+<tt class="docutils literal"><span class="pre">#define</span> <span class="pre">SWIG_FILE_WITH_INIT</span></tt> and should not call
+<tt class="docutils literal"><span class="pre">import_array()</span></tt>.</p>
+</div>
+<div class="section">
+<h1><a id="available-typemaps" name="available-typemaps">Available Typemaps</a></h1>
+<p>The typemap directives provided by <tt class="docutils literal"><span class="pre">numpy.i</span></tt> for arrays of different
+data types, say <tt class="docutils literal"><span class="pre">double</span></tt> and <tt class="docutils literal"><span class="pre">int</span></tt>, are identical to one another
+except for the C and <a class="reference" href="http://numpy.scipy.org">NumPy</a> type specifications.  The typemaps are
+therefore implemented via a macro:</p>
+<pre class="literal-block">
+%numpy_typemaps(TYPE, TYPECODE)
+</pre>
+<p>that can be invoked for appropriate <tt class="docutils literal"><span class="pre">(TYPE,</span> <span class="pre">TYPECODE)</span></tt> pairs.  For
+example:</p>
+<pre class="literal-block">
+%numpy_typemaps(double, NPY_DOUBLE)
+%numpy_typemaps(int,    NPY_INT   )
+</pre>
+<p>The <tt class="docutils literal"><span class="pre">numpy.i</span></tt> interface file uses the <tt class="docutils literal"><span class="pre">%numpy_typemaps</span></tt> macro to
+implement typemaps for the following C-types:</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>
+<li><tt class="docutils literal"><span class="pre">PyObject</span></tt></li>
+<li><tt class="docutils literal"><span class="pre">char</span></tt></li>
+</ul>
+</blockquote>
+<p>Note that C++ type <tt class="docutils literal"><span class="pre">bool</span></tt> is not supported in the list above.  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)
+</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)
+</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> will fail type-checking.</p>
+<p>In the following descriptions, we reference a generic <tt class="docutils literal"><span class="pre">TYPE</span></tt>, which
+could be any of the C-types listed above.</p>
+<div class="section">
+<h2><a id="input-arrays" name="input-arrays">Input Arrays</a></h2>
+<p>Input arrays are defined as arrays of data that are passed into a
+routine but are not altered in-place or returned to the user.  The
+python input array is therefore allowed to be almost any python
+sequence (such as a list) that can be converted to the requested type
+of array.  The input array signatures are</p>
+<blockquote>
+<ul class="simple">
+<li><tt class="docutils literal"><span class="pre">(TYPE*</span> <span class="pre">IN_ARRAY1,</span> <span class="pre">int</span> <span class="pre">DIM1)</span></tt></li>
+<li><tt class="docutils literal"><span class="pre">(TYPE*</span> <span class="pre">IN_ARRAY2,</span> <span class="pre">int</span> <span class="pre">DIM1,</span> <span class="pre">int</span> <span class="pre">DIM2)</span></tt></li>
+</ul>
+</blockquote>
+</div>
+<div class="section">
+<h2><a 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
+input values may or may not be used, but the values at the time the
+function returns are significant.  The provided python argument must
+therefore be a <a class="reference" href="http://numpy.scipy.org">NumPy</a> array of the required type.  The in-place
+signatures are</p>
+<blockquote>
+<ul class="simple">
+<li><tt class="docutils literal"><span class="pre">(TYPE*</span> <span class="pre">INPLACE_ARRAY1,</span> <span class="pre">int</span> <span class="pre">DIM1)</span></tt></li>
+<li><tt class="docutils literal"><span class="pre">(TYPE*</span> <span class="pre">INPLACE_ARRAY2,</span> <span class="pre">int</span> <span class="pre">DIM1,</span> <span class="pre">int</span> <span class="pre">DIM2)</span></tt></li>
+</ul>
+</blockquote>
+</div>
+<div class="section">
+<h2><a id="argout-arrays" name="argout-arrays">Argout Arrays</a></h2>
+<p>Argout arrays are arrays that appear in the input arguments in C, but
+are in fact output arrays.  This pattern occurs often when there is
+more than one output variable and the single return argument is
+therefore not sufficient.  In python, the convential way to return
+multiple arguments is to pack them into a tuple and return the tuple.
+This is what the argout typemaps do.  If a wrapped function that uses
+argout these argout typemaps has more than one return argument, they
+are so packed.  The python user does not pass these arrays in, they
+simply get returned.  The argout signatures are</p>
+<blockquote>
+<ul class="simple">
+<li><tt class="docutils literal"><span class="pre">(TYPE</span> <span class="pre">ARGOUT_ARRAY1[ANY])</span></tt></li>
+<li><tt class="docutils literal"><span class="pre">(TYPE</span> <span class="pre">ARGOUT_ARRAY2[ANY][ANY])</span></tt></li>
+</ul>
+</blockquote>
+</div>
+</div>
+<div class="section">
+<h1><a id="helper-functions" name="helper-functions">Helper Functions</a></h1>
+<p>The <tt class="docutils literal"><span class="pre">numpy.i</span></tt> file containes several macros and routines that it
+uses internally to build its typemaps.  However, these functions may
+be useful elsewhere in your interface file.</p>
+<div class="section">
+<h2><a 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_dimensions(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_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_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>
+</dl>
+</blockquote>
+</div>
+<div class="section">
+<h2><a 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.</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
+<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_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>
+<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>
+</div>
+<div class="section">
+<h1><a 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> (and its
+predecessors Numeric and numarray) together.  The effort to
+standardize this work into <tt class="docutils literal"><span class="pre">numpy.i</span></tt> began at the 2005 SciPy
+Conference with a conversation between Fernando Perez and myself.
+Fernando collected helper functions and typemaps from Michael Hunter,
+Anna Omelchenko and Michael Sanner.  Their work has made this end
+result possible.</p>
+</div>
+</div>
+<div class="footer">
+<hr class="footer" />
+Generated on: 2007-03-14 15:24 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>

Added: trunk/numpy/doc/swig/numpy_swig.txt
===================================================================
--- trunk/numpy/doc/swig/numpy_swig.txt	2007-03-12 17:07:19 UTC (rev 3573)
+++ trunk/numpy/doc/swig/numpy_swig.txt	2007-03-14 15:47:12 UTC (rev 3574)
@@ -0,0 +1,319 @@
+========================================
+numpy.i: a SWIG Interface File for NumPy
+========================================
+
+:Author:      Bill Spotz
+:Institution: Sandia National Laboratories
+:Date:        10 March, 2007
+
+Introduction
+============
+
+The Simple Wrapper and Interface Generator (or `SWIG
+<http://www.swig.org>`_) is a powerful tool for generating wrapper
+code for interfacing to a wide variety of scripting languages.
+`SWIG`_ can parse header files, and using only the code prototypes,
+create an interface to the target language.  But `SWIG`_ is not
+omnipotent.  For example, it cannot know from the prototype::
+
+    double rms(double* seq, int n);
+
+what exactly ``seq`` 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?  `SWIG`_ cannot determine these details,
+and does not attempt to do so.
+
+Making an educated guess, humans can conclude that this is probably a
+routine that takes an input-only array of length ``n`` of ``double``
+values called ``seq`` and returns the root mean square.  The default
+behavior of `SWIG`_, 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.
+
+For `python <http://www.python.org>`_, the preferred way of handling
+contiguous (or technically, *strided*) blocks of homogeneous data is
+with the module `NumPy <http://numpy.scipy.org>`_, which provides full
+object-oriented access to arrays of data.  Therefore, the most logical
+python interface for the ``rms`` function would be::
+
+    def rms(seq):
+
+where ``seq`` would be a `NumPy`_ array of ``double`` values, and its
+length ``n`` would be extracted from ``seq`` internally before being
+passed to the C routine.  Even better, since `NumPy`_ supports
+construction of arrays from arbitrary `python`_ sequences, ``seq``
+itself could be a nearly arbitrary sequence (so long as each element
+can be converted to a ``double``) and the wrapper code would
+internally convert it to a `NumPy`_ array before extracting its data
+and length.
+
+`SWIG`_ allows these types of conversions to be defined via a
+mechanism called typemaps.  This document provides information on how
+to use ``numpy.i``, a `SWIG`_ 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 ``rms`` function prototype defined above was in a header file
+named ``rms.h``.  To obtain the python interface discussed above, your
+`SWIG`_ interface file would need the following::
+
+    %{
+    #include "rms.h"
+    %}
+    %include "numpy.i"
+    %apply (double* IN_ARRAY1, int DIM1) {(double* seq, int n)};
+    %include "rms.h"
+
+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
+*signatures*.  One of the many typemaps defined by ``numpy.i`` is used
+above and has the signature ``(double* IN_ARRAY1, int DIM1)``.  The
+argument names are intended to suggest that the ``double*`` argument
+is an input array of one dimension and that the ``int`` represents
+that dimension.  This is precisely the pattern in the ``rms``
+prototype.
+
+Hopefully, no actual prototypes to be wrapped will have the names
+``IN_ARRAY1`` and ``DIM1``.  We use the ``%apply`` directive to apply
+the typemap for one-dimensional input arrays of type ``double`` to the
+actual prototype used by ``rms``.  Using ``numpy.i`` effectively,
+therefore, requires knowing what typemaps are available and what they
+do.
+
+Using numpy.i
+=============
+
+The ``numpy.i`` file is currently located in the ``numpy/docs/swig``
+sub-directory under the ``numpy`` installation directory.  Typically,
+you will want to copy it to the directory where you are developing
+your wrappers.
+
+A simple module that only uses a single `SWIG`_ interface file should
+include the following::
+
+    %{
+    #define SWIG_FILE_WITH_INIT
+    %}
+    %include "numpy.i"
+    %init %{
+    import_array();
+    %}
+
+Only one interface file should call ``import_array()``.  If you have
+more than one `SWIG`_ interface file, then subsequent files should not
+``#define SWIG_FILE_WITH_INIT`` and should not call
+``import_array()``.
+
+Available Typemaps
+==================
+
+The typemap directives provided by ``numpy.i`` for arrays of different
+data types, say ``double`` and ``int``, are identical to one another
+except for the C and `NumPy`_ type specifications.  The typemaps are
+therefore implemented via a macro::
+
+    %numpy_typemaps(TYPE, TYPECODE)
+
+that can be invoked for appropriate ``(TYPE, TYPECODE)`` pairs.  For
+example::
+
+    %numpy_typemaps(double, NPY_DOUBLE)
+    %numpy_typemaps(int,    NPY_INT   )
+
+The ``numpy.i`` interface file uses the ``%numpy_typemaps`` macro to
+implement typemaps for the following C-types:
+
+  * ``signed char``
+  * ``unsigned char``
+  * ``short``
+  * ``unsigned short``
+  * ``int``
+  * ``unsigned int``
+  * ``long``
+  * ``unsigned long``
+  * ``long long``
+  * ``unsigned long long``
+  * ``float``
+  * ``double``
+  * ``PyObject``
+  * ``char``
+
+Note that C++ type ``bool`` is not supported in the list above.  NumPy
+bools are a single byte, while the C++ ``bool`` is four bytes (at
+least on my system).  Therefore::
+
+    %numpy_typemaps(bool, NPY_BOOL)
+
+will result in typemaps that will produce code that reference
+improper data lengths.  You can implement the following macro
+expansion::
+
+    %numpy_typemaps(bool, NPY_UINT)
+
+to fix the data length problem, and `Input Arrays`_ will work fine,
+but `In-place Arrays`_ will fail type-checking.
+
+In the following descriptions, we reference a generic ``TYPE``, which
+could be any of the C-types listed above.
+
+Input Arrays
+------------
+
+Input arrays are defined as arrays of data that are passed into a
+routine but are not altered in-place or returned to the user.  The
+python input array is therefore allowed to be almost any python
+sequence (such as a list) that can be converted to the requested type
+of array.  The input array signatures are
+
+  * ``(TYPE* IN_ARRAY1, int DIM1)``
+  * ``(TYPE* IN_ARRAY2, int DIM1, int DIM2)``
+
+In-place Arrays
+---------------
+
+In-place arrays are defined as arrays that are modified in-place.  The
+input values may or may not be used, but the values at the time the
+function returns are significant.  The provided python argument must
+therefore be a `NumPy`_ array of the required type.  The in-place
+signatures are
+
+  * ``(TYPE* INPLACE_ARRAY1, int DIM1)``
+  * ``(TYPE* INPLACE_ARRAY2, int DIM1, int DIM2)``
+
+Argout Arrays
+-------------
+
+Argout arrays are arrays that appear in the input arguments in C, but
+are in fact output arrays.  This pattern occurs often when there is
+more than one output variable and the single return argument is
+therefore not sufficient.  In python, the convential way to return
+multiple arguments is to pack them into a tuple and return the tuple.
+This is what the argout typemaps do.  If a wrapped function that uses
+argout these argout typemaps has more than one return argument, they
+are so packed.  The python user does not pass these arrays in, they
+simply get returned.  The argout signatures are
+
+  * ``(TYPE ARGOUT_ARRAY1[ANY])``
+  * ``(TYPE ARGOUT_ARRAY2[ANY][ANY])``
+
+Helper Functions
+================
+
+The ``numpy.i`` 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.
+
+Macros
+------
+
+  **is_array(a)**
+    Evaluates as true if ``a`` is non-``NULL`` and can be cast to a
+    ``PyArrayObject*``.
+
+  **array_type(a)**
+    Evaluates to the integer data type code of ``a``, assuming ``a`` can
+    be cast to a ``PyArrayObject*``.
+
+  **array_dimensions(a)**
+    Evaluates to the integer number of dimensions of ``a``, assuming
+    ``a`` can be cast to a ``PyArrayObject*``.
+
+  **array_size(a,i)**
+    Evaluates to the ``i``-th dimension size of ``a``, assuming ``a``
+    can be cast to a ``PyArrayObject*``.
+
+  **array_is_contiguous(a)**
+    Evaluates as true if ``a`` is a contiguous array.  Equivalent to
+    ``(PyArray_ISCONTIGUOUS(a))``.
+
+Routines
+--------
+
+  **char* pytype_string(PyObject* py_obj)**
+    Given a ``PyObject*``, return a string describing its type.
+
+
+  **char* typecode_string(int typecode)**
+    Given a `NumPy`_ integer typecode, return a string describing the type.
+
+
+  **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.
+
+
+  **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``.
+
+
+  **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``.
+
+
+  **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.
+
+
+  **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
+    ``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
+    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.
+
+
+  **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.
+
+
+  **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.
+
+
+  **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.
+
+Acknowledgements
+================
+
+Many people have worked to glue `SWIG`_ and `NumPy`_ (and its
+predecessors Numeric and numarray) together.  The effort to
+standardize this work into ``numpy.i`` began at the 2005 SciPy
+Conference with a conversation between Fernando Perez and myself.
+Fernando collected helper functions and typemaps from Michael Hunter,
+Anna Omelchenko and Michael Sanner.  Their work has made this end
+result possible.

Modified: trunk/numpy/doc/swig/series.cxx
===================================================================
--- trunk/numpy/doc/swig/series.cxx	2007-03-12 17:07:19 UTC (rev 3573)
+++ trunk/numpy/doc/swig/series.cxx	2007-03-14 15:47:12 UTC (rev 3574)
@@ -3,143 +3,68 @@
 #include <iostream>
 #include "series.h"
 
-// *** One Dimensional Arrays ***
-
-// Functions that take 1D arrays of type SHORT as input
-short shortSum(short* series, int size) {
-  short result = 0;
-  for (int i=0; i<size; ++i) result += series[i];
-  return result;
+// The following macro defines a family of functions with the forms
+//
+//     TYPE SNAMEProd( TYPE * series, int size);
+//     void SNAMEOnes( TYPE * array,  int size);
+//     TYPE SNAMEMax(  TYPE * matrix, int rows, int cols);
+//     void SNAMEFloor(TYPE * array,  int rows, int cols, TYPE floor);
+//
+// for any specified type TYPE (for example: short, unsigned int, long
+// long, etc.) with given short name SNAME (for example: short, uint,
+// longLong, etc.).  The macro is then expanded for the given
+// TYPE/SNAME pairs.  The resulting functions are for testing numpy
+// interfaces, respectively, for:
+//
+//  * 1D input arrays
+//  * 1D in-place arrays
+//  * 2D input arrays
+//  * 2D in-place arrays
+//
+#define TEST_FUNCS(TYPE, SNAME) \
+\
+TYPE SNAME ## Prod(TYPE * series, int size) {     \
+  TYPE result = 1;                                \
+  for (int i=0; i<size; ++i) result *= series[i]; \
+  return result;                                  \
+}                                                 \
+\
+void SNAME ## Ones(TYPE * array, int size) { \
+  for (int i=0; i<size; ++i) array[i] = 1;   \
+}                                            \
+\
+TYPE SNAME ## Max(TYPE * matrix, int rows, int cols) {	  \
+  int i, j, index;                                        \
+  TYPE result = matrix[0];                                \
+  for (j=0; j<cols; ++j) {                                \
+    for (i=0; i<rows; ++i) {                              \
+      index = j*rows + i;                                 \
+      if (matrix[index] > result) result = matrix[index]; \
+    }                                                     \
+  }                                                       \
+  return result;                                          \
+}                                                         \
+\
+void SNAME ## Floor(TYPE * array, int rows, int cols, TYPE floor) { \
+  int i, j, index;                                                    \
+  for (j=0; j<cols; ++j) {                                            \
+    for (i=0; i<rows; ++i) {                                          \
+      index = j*rows + i;                                             \
+      if (array[index] < floor) array[index] = 0;                     \
+    }                                                                 \
+  }                                                                   \
 }
 
-short shortProd(short* series, int size) {
-  short result = 1;
-  for (int i=0; i<size; ++i) result *= series[i];
-  return result;
-}
-
-// Functions that take 1D arrays of type INT as input
-int intSum(int* series, int size) {
-  int result = 0;
-  for (int i=0; i<size; ++i) result += series[i];
-  return result;
-}
-
-int intProd(int* series, int size) {
-  int result = 1;
-  for (int i=0; i<size; ++i) result *= series[i];
-  return result;
-}
-
-// Functions that take 1D arrays of type LONG as input
-long longSum(long* series, int size) {
-  long result = 0;
-  for (int i=0; i<size; ++i) result += series[i];
-  return result;
-}
-
-long longProd(long* series, int size) {
-  long result = 1;
-  for (int i=0; i<size; ++i) result *= series[i];
-  return result;
-}
-
-// Functions that take 1D arrays of type FLOAT as input
-float floatSum(float* series, int size) {
-  float result = 0.0;
-  for (int i=0; i<size; ++i) result += series[i];
-  return result;
-}
-
-float floatProd(float* series, int size) {
-  float result = 1.0;
-  for (int i=0; i<size; ++i) result *= series[i];
-  return result;
-}
-
-// Functions that take 1D arrays of type DOUBLE as input
-double doubleSum(double* series, int size) {
-  double result = 0.0;
-  for (int i=0; i<size; ++i) result += series[i];
-  return result;
-}
-
-double doubleProd(double* series, int size) {
-  double result = 1.0;
-  for (int i=0; i<size; ++i) result *= series[i];
-  return result;
-}
-
-// Functions that manipulate 1D arrays of type INT in-place
-void intZeros(int* array, int size) {
-  for (int i=0; i<size; ++i) array[i] = 0;
-}
-
-void intOnes(int* array, int size) {
-  for (int i=0; i<size; ++i) array[i] = 1;
-}
-
-void intNegate(int* array, int size) {
-  for (int i=0; i<size; ++i) array[i] *= -1;
-}
-
-// Functions that manipulate 1D arrays of type DOUBLE in-place
-void doubleZeros(double* array, int size) {
-  for (int i=0; i<size; ++i) array[i] = 0.0;
-}
-
-void doubleOnes(double* array, int size) {
-  for (int i=0; i<size; ++i) array[i] = 1.0;
-}
-
-void doubleNegate(double* array, int size) {
-  for (int i=0; i<size; ++i) array[i] *= -1.0;
-}
-
-// *** Two Dimensional Arrays ***
-
-// Functions that take 2D arrays of type INT as input
-int intMax(int* matrix, int rows, int cols) {
-  int i, j, index;
-  int result = matrix[0];
-  for (j=0; j<cols; ++j) {
-    for (i=0; i<rows; ++i) {
-      index = j*rows + i;
-      if (matrix[index] > result) result = matrix[index];
-    }
-  }
-  return result;
-}
-
-void intFloor(int* matrix, int rows, int cols, int floor) {
-  int i, j, index;
-  for (j=0; j<cols; ++j) {
-    for (i=0; i<rows; ++i) {
-      index = j*rows + i;
-      if (matrix[index] < floor) matrix[index] = 0;
-    }
-  }
-}
-
-// Functions that take 2D arrays of type DOUBLE as input
-double doubleMax(double* matrix, int rows, int cols) {
-  int i, j, index;
-  double result = matrix[0];
-  for (j=0; j<cols; ++j) {
-    for (i=0; i<rows; ++i) {
-      index = j*rows + i;
-      if (matrix[index] > result) result = matrix[index];
-    }
-  }
-  return result;
-}
-
-void doubleFloor(double* matrix, int rows, int cols, double floor) {
-  int i, j, index;
-  for (j=0; j<cols; ++j) {
-    for (i=0; i<rows; ++i) {
-      index = j*rows + i;
-      if (matrix[index] < floor) matrix[index] = 0.0;
-    }
-  }
-}
+TEST_FUNCS(signed char       , schar     )
+TEST_FUNCS(unsigned char     , uchar     )
+TEST_FUNCS(short             , short     )
+TEST_FUNCS(unsigned short    , ushort    )
+TEST_FUNCS(int               , int       )
+TEST_FUNCS(unsigned int      , uint      )
+TEST_FUNCS(long              , long      )
+TEST_FUNCS(unsigned long     , ulong     )
+TEST_FUNCS(long long         , longLong  )
+TEST_FUNCS(unsigned long long, ulongLong )
+TEST_FUNCS(float             , float     )
+TEST_FUNCS(double            , double    )
+TEST_FUNCS(long double       , longDouble)

Modified: trunk/numpy/doc/swig/series.h
===================================================================
--- trunk/numpy/doc/swig/series.h	2007-03-12 17:07:19 UTC (rev 3573)
+++ trunk/numpy/doc/swig/series.h	2007-03-14 15:47:12 UTC (rev 3574)
@@ -1,40 +1,44 @@
 #ifndef SERIES_H
 #define SERIES_H
 
-/*** One Dimensional Arrays ***/
+// The following macro defines the prototypes for a family of
+// functions with the forms
+//
+//     TYPE SNAMEProd( TYPE * series, int size);
+//     void SNAMEOnes( TYPE * array,  int size);
+//     TYPE SNAMEMax(  TYPE * matrix, int rows, int cols);
+//     void SNAMEFloor(TYPE * array,  int rows, int cols, TYPE floor);
+//
+// for any specified type TYPE (for example: short, unsigned int, long
+// long, etc.) with given short name SNAME (for example: short, uint,
+// longLong, etc.).  The macro is then expanded for the given
+// TYPE/SNAME pairs.  The resulting functions are for testing numpy
+// interfaces, respectively, for:
+//
+//  * 1D input arrays
+//  * 1D in-place arrays
+//  * 2D input arrays
+//  * 2D in-place arrays
+//
+#define TEST_FUNC_PROTOS(TYPE, SNAME) \
+\
+TYPE SNAME ## Prod( TYPE * series, int size); \
+void SNAME ## Ones( TYPE * array,  int size); \
+TYPE SNAME ## Max(  TYPE * matrix, int rows, int cols); \
+void SNAME ## Floor(TYPE * array,  int rows, int cols, TYPE floor);
 
-/* Examples of functions that take 1D C arrays as input */
-short shortSum( short* series, int size);
-short shortProd(short* series, int size);
+TEST_FUNC_PROTOS(signed char       , schar     )
+TEST_FUNC_PROTOS(unsigned char     , uchar     )
+TEST_FUNC_PROTOS(short             , short     )
+TEST_FUNC_PROTOS(unsigned short    , ushort    )
+TEST_FUNC_PROTOS(int               , int       )
+TEST_FUNC_PROTOS(unsigned int      , uint      )
+TEST_FUNC_PROTOS(long              , long      )
+TEST_FUNC_PROTOS(unsigned long     , ulong     )
+TEST_FUNC_PROTOS(long long         , longLong  )
+TEST_FUNC_PROTOS(unsigned long long, ulongLong )
+TEST_FUNC_PROTOS(float             , float     )
+TEST_FUNC_PROTOS(double            , double    )
+TEST_FUNC_PROTOS(long double       , longDouble)
 
-int intSum( int* series, int size);
-int intProd(int* series, int size);
-
-long longSum( long* series, int size);
-long longProd(long* series, int size);
-
-float floatSum( float* series, int size);
-float floatProd(float* series, int size);
-
-double doubleSum( double* series, int size);
-double doubleProd(double* series, int size);
-
-/* Examples of functions that manipulate 1D C arrays as in-place */
-void intZeros( int* array, int size);
-void intOnes(  int* array, int size);
-void intNegate(int* array, int size);
-
-void doubleZeros( double* array, int size);
-void doubleOnes(  double* array, int size);
-void doubleNegate(double* array, int size);
-
-/*** Two Dimensional Arrays ***/
-
-/* Examples of functions that take 2D arrays as input */
-int intMax(int* matrix, int rows, int cols);
-void intFloor(int* matrix, int rows, int cols, int floor);
-
-double doubleMax(double* matrix, int rows, int cols);
-void doubleFloor(double* matrix, int rows, int cols, double floor);
-
 #endif

Modified: trunk/numpy/doc/swig/setup.py
===================================================================
--- trunk/numpy/doc/swig/setup.py	2007-03-12 17:07:19 UTC (rev 3573)
+++ trunk/numpy/doc/swig/setup.py	2007-03-14 15:47:12 UTC (rev 3574)
@@ -7,12 +7,17 @@
 # Third-party modules - we depend on numpy for everything
 import numpy
 
+# Obtain the numpy include directory.  This logic works across numpy versions.
+try:
+    numpy_include = numpy.get_include()
+except AttributeError:
+    numpy_include = numpy.get_numpy_include()
+
 # _Series extension module
 _Series = Extension("_Series",
                     ["Series_wrap.cxx",
                      "series.cxx"],
-                    include_dirs = [numpy.get_numpy_include()],
-#                    libraries = ["m"]
+                    include_dirs = [numpy_include],
                     )
 
 # Series setup

Modified: trunk/numpy/doc/swig/testSeries.py
===================================================================
--- trunk/numpy/doc/swig/testSeries.py	2007-03-12 17:07:19 UTC (rev 3573)
+++ trunk/numpy/doc/swig/testSeries.py	2007-03-14 15:47:12 UTC (rev 3574)
@@ -6,7 +6,11 @@
 import sys
 import unittest
 
+# Import NumPy
 import numpy as N
+major, minor = [ int(d) for d in N.__version__.split(".")[:2] ]
+if major == 0: BadListError = TypeError
+else:          BadListError = ValueError
 
 # Add the distutils-generated build directory to the python search path and then
 # import the extension module
@@ -18,26 +22,148 @@
 
 class SeriesTestCase(unittest.TestCase):
 
-    ########################################################
-    ### Test functions that take 1D arrays of type SHORT ###
-    def testShortSum(self):
-        "Test the shortSum function"
-        self.assertEquals(Series.shortSum([1,2,3,4]), 10)
+    ####################################################
+    ### Test functions that take arrays of type BYTE ###
+    def testScharProd(self):
+        "Test the scharProd function"
+        self.assertEquals(Series.scharProd([1,2,3,4]), 24)
 
+    def testScharProdNonContainer(self):
+        "Test the scharProd function with None"
+        self.assertRaises(TypeError, Series.scharProd, None)
+
+    def testScharOnes(self):
+        "Test the scharOnes function"
+        myArray = N.zeros(5,'b')
+        Series.scharOnes(myArray)
+        N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
+
+    def testScharMax(self):
+        "Test the scharMax function"
+        matrix = [[-6,5,-4],[3,-2,1]]
+        self.assertEquals(Series.scharMax(matrix), 5)
+
+    def testScharMaxNonContainer(self):
+        "Test the scharMax function with None"
+        self.assertRaises(TypeError, Series.scharMax, None)
+
+    def testScharMaxWrongDim(self):
+        "Test the scharMax function with a 1D array"
+        self.assertRaises(TypeError, Series.scharMax, [0, -1, 2, -3])
+
+    def testScharFloor(self):
+        "Test the scharFloor function"
+        matrix = N.array([[10,-2],[-6,7]],'b')
+        Series.scharFloor(matrix,0)
+        N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
+
+    #####################################################
+    ### Test functions that take arrays of type UBYTE ###
+    def testUcharProd(self):
+        "Test the ucharProd function"
+        self.assertEquals(Series.ucharProd([1,2,3,4]), 24)
+
+    def testUcharProdNonContainer(self):
+        "Test the ucharProd function with None"
+        self.assertRaises(TypeError, Series.ucharProd, None)
+
+    def testUcharOnes(self):
+        "Test the ucharOnes function"
+        myArray = N.zeros(5,'B')
+        Series.ucharOnes(myArray)
+        N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
+
+    def testUcharMax(self):
+        "Test the ucharMax function"
+        matrix = [[6,5,4],[3,2,1]]
+        self.assertEquals(Series.ucharMax(matrix), 6)
+
+    def testUcharMaxNonContainer(self):
+        "Test the ucharMax function with None"
+        self.assertRaises(TypeError, Series.ucharMax, None)
+
+    def testUcharMaxWrongDim(self):
+        "Test the ucharMax function with a 1D array"
+        self.assertRaises(TypeError, Series.ucharMax, [0, 1, 2, 3])
+
+    def testUcharFloor(self):
+        "Test the ucharFloor function"
+        matrix = N.array([[10,2],[6,7]],'B')
+        Series.ucharFloor(matrix,7)
+        N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
+
+    #####################################################
+    ### Test functions that take arrays of type SHORT ###
     def testShortProd(self):
         "Test the shortProd function"
         self.assertEquals(Series.shortProd([1,2,3,4]), 24)
 
+    def testShortProdNonContainer(self):
+        "Test the shortProd function with None"
+        self.assertRaises(TypeError, Series.shortProd, None)
+
+    def testShortOnes(self):
+        "Test the shortOnes function"
+        myArray = N.zeros(5,'h')
+        Series.shortOnes(myArray)
+        N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
+
+    def testShortMax(self):
+        "Test the shortMax function"
+        matrix = [[-6,5,-4],[3,-2,1]]
+        self.assertEquals(Series.shortMax(matrix), 5)
+
+    def testShortMaxNonContainer(self):
+        "Test the shortMax function with None"
+        self.assertRaises(TypeError, Series.shortMax, None)
+
+    def testShortMaxWrongDim(self):
+        "Test the shortMax function with a 1D array"
+        self.assertRaises(TypeError, Series.shortMax, [0, -1, 2, -3])
+
+    def testShortFloor(self):
+        "Test the shortFloor function"
+        matrix = N.array([[10,-2],[-6,7]],'h')
+        Series.shortFloor(matrix,0)
+        N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
+
     ######################################################
-    ### Test functions that take 1D arrays of type INT ###
-    def testIntSum(self):
-        "Test the intSum function"
-        self.assertEquals(Series.intSum([1,2,3,4]), 10)
+    ### Test functions that take arrays of type USHORT ###
+    def testUshortProd(self):
+        "Test the ushortProd function"
+        self.assertEquals(Series.ushortProd([1,2,3,4]), 24)
 
-    def testIntSumBadContainer(self):
-        "Test the intSum function with an invalid list"
-        self.assertRaises(TypeError, Series.intSum, [1,2,"junk"])
+    def testUshortProdNonContainer(self):
+        "Test the ushortProd function with None"
+        self.assertRaises(TypeError, Series.ushortProd, None)
 
+    def testUshortOnes(self):
+        "Test the ushortOnes function"
+        myArray = N.zeros(5,'H')
+        Series.ushortOnes(myArray)
+        N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
+
+    def testUshortMax(self):
+        "Test the ushortMax function"
+        matrix = [[6,5,4],[3,2,1]]
+        self.assertEquals(Series.ushortMax(matrix), 6)
+
+    def testUshortMaxNonContainer(self):
+        "Test the ushortMax function with None"
+        self.assertRaises(TypeError, Series.ushortMax, None)
+
+    def testUshortMaxWrongDim(self):
+        "Test the ushortMax function with a 1D array"
+        self.assertRaises(TypeError, Series.ushortMax, [0, 1, 2, 3])
+
+    def testUshortFloor(self):
+        "Test the ushortFloor function"
+        matrix = N.array([[10,2],[6,7]],'H')
+        Series.ushortFloor(matrix,7)
+        N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
+
+    ###################################################
+    ### Test functions that take arrays of type INT ###
     def testIntProd(self):
         "Test the intProd function"
         self.assertEquals(Series.intProd([1,2,3,4]), 24)
@@ -46,84 +172,255 @@
         "Test the intProd function with None"
         self.assertRaises(TypeError, Series.intProd, None)
 
-    def testIntZeros(self):
-        "Test the intZeros function"
-        myArray = N.ones(5,'i')
-        Series.intZeros(myArray)
-        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
-
-    def testIntZerosNonArray(self):
-        "Test the intZeros function with an integer"
-        self.assertRaises(TypeError, Series.intZeros, 5)
-
     def testIntOnes(self):
         "Test the intOnes function"
         myArray = N.zeros(5,'i')
         Series.intOnes(myArray)
         N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
 
-    def testIntNegate(self):
-        "Test the intNegate function"
-        myArray = N.arange(5,dtype='i')
-        Series.intNegate(myArray)
-        N.testing.assert_array_equal(myArray, N.array([0,-1,-2,-3,-4]))
+    def testIntMax(self):
+        "Test the intMax function"
+        matrix = [[-6,5,-4],[3,-2,1]]
+        self.assertEquals(Series.intMax(matrix), 5)
 
-    #######################################################
-    ### Test functions that take 1D arrays of type LONG ###
-    def testLongSum(self):
-        "Test the longSum function"
-        self.assertEquals(Series.longSum([1,2,3,4]), 10)
+    def testIntMaxNonContainer(self):
+        "Test the intMax function with None"
+        self.assertRaises(TypeError, Series.intMax, None)
 
-    def testLongSumMultiDimensional(self):
-        "Test the longSum function with multi-dimensional array"
-        self.assertRaises(TypeError, Series.longSum, [[1,2,3],[9,8,7]])
+    def testIntMaxWrongDim(self):
+        "Test the intMax function with a 1D array"
+        self.assertRaises(TypeError, Series.intMax, [0, -1, 2, -3])
 
+    def testIntFloor(self):
+        "Test the intFloor function"
+        matrix = N.array([[10,-2],[-6,7]],'i')
+        Series.intFloor(matrix,0)
+        N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
+
+    ####################################################
+    ### Test functions that take arrays of type UINT ###
+    def testUintProd(self):
+        "Test the uintProd function"
+        self.assertEquals(Series.uintProd([1,2,3,4]), 24)
+
+    def testUintProdNonContainer(self):
+        "Test the uintProd function with None"
+        self.assertRaises(TypeError, Series.uintProd, None)
+
+    def testUintOnes(self):
+        "Test the uintOnes function"
+        myArray = N.zeros(5,'I')
+        Series.uintOnes(myArray)
+        N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
+
+    def testUintMax(self):
+        "Test the uintMax function"
+        matrix = [[6,5,4],[3,2,1]]
+        self.assertEquals(Series.uintMax(matrix), 6)
+
+    def testUintMaxNonContainer(self):
+        "Test the uintMax function with None"
+        self.assertRaises(TypeError, Series.uintMax, None)
+
+    def testUintMaxWrongDim(self):
+        "Test the uintMax function with a 1D array"
+        self.assertRaises(TypeError, Series.uintMax, [0, 1, 2, 3])
+
+    def testUintFloor(self):
+        "Test the uintFloor function"
+        matrix = N.array([[10,2],[6,7]],'I')
+        Series.uintFloor(matrix,7)
+        N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
+
+    ####################################################
+    ### Test functions that take arrays of type LONG ###
     def testLongProd(self):
         "Test the longProd function"
         self.assertEquals(Series.longProd([1,2,3,4]), 24)
 
+    def testLongProdNonContainer(self):
+        "Test the longProd function with None"
+        self.assertRaises(TypeError, Series.longProd, None)
+
+    def testLongOnes(self):
+        "Test the longOnes function"
+        myArray = N.zeros(5,'l')
+        Series.longOnes(myArray)
+        N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
+
+    def testLongMax(self):
+        "Test the longMax function"
+        matrix = [[-6,5,-4],[3,-2,1]]
+        self.assertEquals(Series.longMax(matrix), 5)
+
+    def testLongMaxNonContainer(self):
+        "Test the longMax function with None"
+        self.assertRaises(TypeError, Series.longMax, None)
+
+    def testLongMaxWrongDim(self):
+        "Test the longMax function with a 1D array"
+        self.assertRaises(TypeError, Series.longMax, [0, -1, 2, -3])
+
+    def testLongFloor(self):
+        "Test the longFloor function"
+        matrix = N.array([[10,-2],[-6,7]],'l')
+        Series.longFloor(matrix,0)
+        N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
+
+    #####################################################
+    ### Test functions that take arrays of type ULONG ###
+    def testUlongProd(self):
+        "Test the ulongProd function"
+        self.assertEquals(Series.ulongProd([1,2,3,4]), 24)
+
+    def testUlongProdNonContainer(self):
+        "Test the ulongProd function with None"
+        self.assertRaises(TypeError, Series.ulongProd, None)
+
+    def testUlongOnes(self):
+        "Test the ulongOnes function"
+        myArray = N.zeros(5,'L')
+        Series.ulongOnes(myArray)
+        N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
+
+    def testUlongMax(self):
+        "Test the ulongMax function"
+        matrix = [[6,5,4],[3,2,1]]
+        self.assertEquals(Series.ulongMax(matrix), 6)
+
+    def testUlongMaxNonContainer(self):
+        "Test the ulongMax function with None"
+        self.assertRaises(TypeError, Series.ulongMax, None)
+
+    def testUlongMaxWrongDim(self):
+        "Test the ulongMax function with a 1D array"
+        self.assertRaises(TypeError, Series.ulongMax, [0, 1, 2, 3])
+
+    def testUlongFloor(self):
+        "Test the ulongFloor function"
+        matrix = N.array([[10,2],[6,7]],'L')
+        Series.ulongFloor(matrix,7)
+        N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
+
     ########################################################
-    ### Test functions that take 1D arrays of type FLOAT ###
-    def testFloatSum(self):
-        "Test the floatSum function (to 5 decimal places)"
-        self.assertAlmostEquals(Series.floatSum([1,2,3.14,4]), 10.14, 5)
+    ### Test functions that take arrays of type LONGLONG ###
+    def testLongLongProd(self):
+        "Test the longLongProd function"
+        self.assertEquals(Series.longLongProd([1,2,3,4]), 24)
 
-    def testFloatSumBadContainer(self):
-        "Test the floatSum function with a dictionary"
-        self.assertRaises(TypeError, Series.floatSum, {"key":"value"})
+    def testLongLongProdNonContainer(self):
+        "Test the longLongProd function with None"
+        self.assertRaises(TypeError, Series.longLongProd, None)
 
+    def testLongLongOnes(self):
+        "Test the longLongOnes function"
+        myArray = N.zeros(5,'q')
+        Series.longLongOnes(myArray)
+        N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
+
+    def testLongLongMax(self):
+        "Test the longLongMax function"
+        matrix = [[-6,5,-4],[3,-2,1]]
+        self.assertEquals(Series.longLongMax(matrix), 5)
+
+    def testLongLongMaxNonContainer(self):
+        "Test the longLongMax function with None"
+        self.assertRaises(TypeError, Series.longLongMax, None)
+
+    def testLongLongMaxWrongDim(self):
+        "Test the longLongMax function with a 1D array"
+        self.assertRaises(TypeError, Series.longLongMax, [0, -1, 2, -3])
+
+    def testLongLongFloor(self):
+        "Test the longLongFloor function"
+        matrix = N.array([[10,-2],[-6,7]],'q')
+        Series.longLongFloor(matrix,0)
+        N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
+
+    #########################################################
+    ### Test functions that take arrays of type ULONGLONG ###
+    def testUlonglongProd(self):
+        "Test the ulongLongProd function"
+        self.assertEquals(Series.ulongLongProd([1,2,3,4]), 24)
+
+    def testUlongLongProdNonContainer(self):
+        "Test the ulongLongProd function with None"
+        self.assertRaises(TypeError, Series.ulongLongProd, None)
+
+    def testUlongLongOnes(self):
+        "Test the ulongLongOnes function"
+        myArray = N.zeros(5,'Q')
+        Series.ulongLongOnes(myArray)
+        N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
+
+    def testUlongLongMax(self):
+        "Test the ulongLongMax function"
+        matrix = [[6,5,4],[3,2,1]]
+        self.assertEquals(Series.ulongLongMax(matrix), 6)
+
+    def testUlongLongMaxNonContainer(self):
+        "Test the ulongLongMax function with None"
+        self.assertRaises(TypeError, Series.ulongLongMax, None)
+
+    def testUlongLongMaxWrongDim(self):
+        "Test the ulongLongMax function with a 1D array"
+        self.assertRaises(TypeError, Series.ulongLongMax, [0, 1, 2, 3])
+
+    def testUlongLongFloor(self):
+        "Test the ulongLongFloor function"
+        matrix = N.array([[10,2],[6,7]],'Q')
+        Series.ulongLongFloor(matrix,7)
+        N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
+
+    #####################################################
+    ### Test functions that take arrays of type FLOAT ###
     def testFloatProd(self):
         "Test the floatProd function (to 5 decimal places)"
         self.assertAlmostEquals(Series.floatProd((1,2.718,3,4)), 32.616, 5)
 
     def testFloatProdBadContainer(self):
         "Test the floatProd function with an invalid list"
-        self.assertRaises(TypeError, Series.floatProd, [3.14, "pi"])
+        self.assertRaises(BadListError, Series.floatProd, [3.14, "pi"])
 
-    #########################################################
-    ### Test functions that take 1D arrays of type DOUBLE ###
-    def testDoubleSum(self):
-        "Test the doubleSum function"
-        self.assertEquals(Series.doubleSum([1,2,3.14,4]), 10.14)
+    def testFloatOnes(self):
+        "Test the floatOnes function"
+        myArray = N.zeros(5,'f')
+        Series.floatOnes(myArray)
+        N.testing.assert_array_equal(myArray, N.array([1.,1.,1.,1.,1.]))
 
-    def testDoubleSumNonContainer(self):
-        "Test the doubleSum function with None"
-        self.assertRaises(TypeError, Series.doubleSum, None)
+    def testFloatOnesNonArray(self):
+        "Test the floatOnes function with a list"
+        self.assertRaises(TypeError, Series.floatOnes, [True, 0, 2.718, "pi"])
 
+    def testFloatMax(self):
+        "Test the floatMax function"
+        matrix = [[-6,5,-4],[3.14,-2.718,1]]
+        self.assertEquals(Series.floatMax(matrix), 5.0)
+
+    def testFloatMaxNonContainer(self):
+        "Test the floatMax function with None"
+        self.assertRaises(TypeError, Series.floatMax, None)
+
+    def testFloatMaxWrongDim(self):
+        "Test the floatMax function with a 1D array"
+        self.assertRaises(TypeError, Series.floatMax, [0.0, -1, 2.718, -3.14])
+
+    def testFloatFloor(self):
+        "Test the floatFloor function"
+        matrix = N.array([[10,-2],[-6,7]],'f')
+        Series.floatFloor(matrix,0)
+        N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
+
+    ######################################################
+    ### Test functions that take arrays of type DOUBLE ###
     def testDoubleProd(self):
         "Test the doubleProd function"
         self.assertEquals(Series.doubleProd((1,2.718,3,4)), 32.616)
 
     def testDoubleProdBadContainer(self):
         "Test the doubleProd function with an invalid list"
-        self.assertRaises(TypeError, Series.doubleProd, [3.14, "pi"])
+        self.assertRaises(BadListError, Series.doubleProd, [3.14, "pi"])
 
-    def testDoubleZeros(self):
-        "Test the doubleZeros function"
-        myArray = N.ones(5,'d')
-        Series.doubleZeros(myArray)
-        N.testing.assert_array_equal(myArray, N.array([0.,0.,0.,0.,0.]))
-
     def testDoubleOnes(self):
         "Test the doubleOnes function"
         myArray = N.zeros(5,'d')
@@ -134,44 +431,45 @@
         "Test the doubleOnes function with a list"
         self.assertRaises(TypeError, Series.doubleOnes, [True, 0, 2.718, "pi"])
 
-    def testDoubleNegate(self):
-        "Test the doubleNegate function"
-        myArray = N.arange(5) * 1.0
-        Series.doubleNegate(myArray)
-        N.testing.assert_array_equal(myArray, N.array([0.,-1.,-2.,-3.,-4.]))
-
-    #########################################################
-    ### Test functions that take 2D arrays of type DOUBLE ###
-    def testIntMax(self):
-        "Test the intMax function"
-        matrix = [[6,-5,4],[-3,2,-1]]
-        self.assertEquals(Series.intMax(matrix), 6)
-
-    def testIntMaxNonContainer(self):
-        "Test the intMax function with None"
-        self.assertRaises(TypeError, Series.intMax, None)
-
-    def testIntFloor(self):
-        "Test the intFloor function"
-        matrix = N.array([[10,-2],[-6,7]])
-        Series.intFloor(matrix,0)
-        N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
-
     def testDoubleMax(self):
         "Test the doubleMax function"
         matrix = [[-6,5,-4],[3.14,-2.718,1]]
         self.assertEquals(Series.doubleMax(matrix), 5.0)
 
+    def testDoubleMaxNonContainer(self):
+        "Test the doubleMax function with None"
+        self.assertRaises(TypeError, Series.doubleMax, None)
+
     def testDoubleMaxWrongDim(self):
         "Test the doubleMax function with a 1D array"
         self.assertRaises(TypeError, Series.doubleMax, [0.0, -1, 2.718, -3.14])
 
     def testDoubleFloor(self):
         "Test the doubleFloor function"
-        matrix = N.array([[10,-2.718],[-6,3.14]])
-        Series.doubleFloor(matrix,5.0)
-        N.testing.assert_array_equal(matrix, N.array([[10.0,0],[0,0]]))
+        matrix = N.array([[10,-2],[-6,7]],'d')
+        Series.doubleFloor(matrix,0)
+        N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
 
+    # ##########################################################
+    # ### Test functions that take arrays of type LONGDOUBLE ###
+    # def testLongDoubleProd(self):
+    #     "Test the longDoubleProd function"
+    #     self.assertEquals(Series.longDoubleProd((1,2.718,3,4)), 32.616)
+
+    # def testLongDoubleProdBadContainer(self):
+    #     "Test the longDoubleProd function with an invalid list"
+    #     self.assertRaises(BadListError, Series.longDoubleProd, [3.14, "pi"])
+
+    # def testLongDoubleOnes(self):
+    #     "Test the longDoubleOnes function"
+    #     myArray = N.zeros(5,'g')
+    #     Series.longDoubleOnes(myArray)
+    #     N.testing.assert_array_equal(myArray, N.array([1.,1.,1.,1.,1.]))
+
+    # def testLongDoubleOnesNonArray(self):
+    #     "Test the longDoubleOnes function with a list"
+    #     self.assertRaises(TypeError, Series.longDoubleOnes, [True, 0, 2.718, "pi"])
+
 ######################################################################
 
 if __name__ == "__main__":



From numpy-svn at scipy.org  Wed Mar 14 15:57:30 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Wed, 14 Mar 2007 14:57:30 -0500 (CDT)
Subject: [Numpy-svn] r3575 - trunk/numpy/doc/swig
Message-ID: <20070314195730.306DD39C0B4@new.scipy.org>

Author: wfspotz at sandia.gov
Date: 2007-03-14 14:57:23 -0500 (Wed, 14 Mar 2007)
New Revision: 3575

Modified:
   trunk/numpy/doc/swig/README
   trunk/numpy/doc/swig/Series.i
   trunk/numpy/doc/swig/numpy.i
   trunk/numpy/doc/swig/numpy_swig.html
   trunk/numpy/doc/swig/numpy_swig.txt
   trunk/numpy/doc/swig/series.cxx
   trunk/numpy/doc/swig/series.h
   trunk/numpy/doc/swig/testSeries.py
Log:
Added typemap signatures where the dimensions come before the data pointer

Modified: trunk/numpy/doc/swig/README
===================================================================
--- trunk/numpy/doc/swig/README	2007-03-14 15:47:12 UTC (rev 3574)
+++ trunk/numpy/doc/swig/README	2007-03-14 19:57:23 UTC (rev 3575)
@@ -1,4 +1,4 @@
-Notes for the swig_numpy/new directory
+Notes for the numpy/doc/swig directory
 ======================================
 
 This set of files is for developing and testing file numpy.i, which is
@@ -21,8 +21,12 @@
 
     (type* IN_ARRAY1, int DIM1)
     (type* IN_ARRAY2, int DIM1, int DIM2)
+    (int DIM1, type* IN_ARRAY1)``
+    (int DIM1, int DIM2, type* IN_ARRAY2)``
     (type* INPLACE_ARRAY1, int DIM1)
     (type* INPLACE_ARRAY2, int DIM1, int DIM2)
+    (int DIM1, type* INPLACE_ARRAY1)
+    (int DIM1, int DIM2, type* INPLACE_ARRAY2)
 
 which take a pointer to an array of type "type", whose length is
 specified by the integer(s) DIM1 (and DIM2).
@@ -30,10 +34,10 @@
 The objective for the IN_ARRAY signatures is for SWIG to generate
 python wrappers that take a container that constitutes a valid
 argument to the numpy array constructor, and can be used to build an
-array of type "type".  Currently, types "char", "unsigned char",
-"signed char", "short", "int", "long", "float", "double" and
-"PyObject" are supported, although only the types "short", "int",
-"long", "float" and "double" are tested.
+array of type "type".  Currently, types "signed char", "unsigned
+char", "short", "unsigned short", "int", "unsigned int", "long",
+"unsigned long", "long long", "unsigned long long", "float", "double",
+"PyObject" and "char" are supported and tested.
 
 The objective for the INPLACE_ARRAY signatures is for SWIG to generate
 python wrappers that accept a numpy array of any of the above-listed

Modified: trunk/numpy/doc/swig/Series.i
===================================================================
--- trunk/numpy/doc/swig/Series.i	2007-03-14 15:47:12 UTC (rev 3574)
+++ trunk/numpy/doc/swig/Series.i	2007-03-14 19:57:23 UTC (rev 3575)
@@ -6,124 +6,38 @@
 #include "series.h"
 %}
 
-// Get the Numeric typemaps
+// Get the NumPy typemaps
 %include "numpy.i"
 
 %init %{
   import_array();
 %}
 
-// Apply the Numeric typemaps for 1D input arrays
-%apply (signed char*        IN_ARRAY1, int DIM1)
-      {(signed char*        series,    int size)};
-%apply (unsigned char*      IN_ARRAY1, int DIM1)
-      {(unsigned char*      series,    int size)};
-%apply (short*              IN_ARRAY1, int DIM1)
-      {(short*              series,    int size)};
-%apply (unsigned short*     IN_ARRAY1, int DIM1)
-      {(unsigned short*     series,    int size)};
-%apply (int*                IN_ARRAY1, int DIM1)
-      {(int*                series,    int size)};
-%apply (unsigned int*       IN_ARRAY1, int DIM1)
-      {(unsigned int*       series,    int size)};
-%apply (long*               IN_ARRAY1, int DIM1)
-      {(long*               series,    int size)};
-%apply (unsigned long*      IN_ARRAY1, int DIM1)
-      {(unsigned long*      series,    int size)};
-%apply (long long*          IN_ARRAY1, int DIM1)
-      {(long long*          series,    int size)};
-%apply (unsigned long long* IN_ARRAY1, int DIM1)
-      {(unsigned long long* series,    int size)};
-%apply (float*              IN_ARRAY1, int DIM1)
-      {(float*              series,    int size)};
-%apply (double*             IN_ARRAY1, int DIM1)
-      {(double*             series,    int size)};
-%apply (long double*        IN_ARRAY1, int DIM1)
-      {(long double*        series,    int size)};
+%define %apply_numpy_typemaps(TYPE)
 
-// Apply the Numeric typemaps for 1D input/output arrays
-%apply (signed char*        INPLACE_ARRAY1, int DIM1)
-      {(signed char*        array,          int size)};
-%apply (unsigned char*      INPLACE_ARRAY1, int DIM1)
-      {(unsigned char*      array,          int size)};
-%apply (short*              INPLACE_ARRAY1, int DIM1)
-      {(short*              array,          int size)};
-%apply (unsigned short*     INPLACE_ARRAY1, int DIM1)
-      {(unsigned short*     array,          int size)};
-%apply (int*                INPLACE_ARRAY1, int DIM1)
-      {(int*                array,          int size)};
-%apply (unsigned int*       INPLACE_ARRAY1, int DIM1)
-      {(unsigned int*       array,          int size)};
-%apply (long*               INPLACE_ARRAY1, int DIM1)
-      {(long*               array,          int size)};
-%apply (unsigned long*      INPLACE_ARRAY1, int DIM1)
-      {(unsigned long*      array,          int size)};
-%apply (long long*          INPLACE_ARRAY1, int DIM1)
-      {(long long*          array,          int size)};
-%apply (unsigned long long* INPLACE_ARRAY1, int DIM1)
-      {(unsigned long long* array,          int size)};
-%apply (float*              INPLACE_ARRAY1, int DIM1)
-      {(float*              array,          int size)};
-%apply (double*             INPLACE_ARRAY1, int DIM1)
-      {(double*             array,          int size)};
-%apply (long double*        INPLACE_ARRAY1, int DIM1)
-      {(long double*        array,          int size)};
+%apply (TYPE* IN_ARRAY1, int DIM1) {(TYPE* series, int size)};
+%apply (TYPE* IN_ARRAY2, int DIM1, int DIM2) {(TYPE* matrix, int rows, int cols)};
+%apply (TYPE* INPLACE_ARRAY1, int DIM1) {(TYPE* array, int size)};
+%apply (TYPE* INPLACE_ARRAY2, int DIM1, int DIM2) {(TYPE* array, int rows, int cols)};
+%apply (int DIM1, TYPE* IN_ARRAY1) {(int size, TYPE* series)};
+%apply (int DIM1, int DIM2, TYPE* IN_ARRAY2) {(int rows, int cols, TYPE* matrix)};
+%apply (int DIM1, TYPE* INPLACE_ARRAY1) {(int size, TYPE* array)};
+%apply (int DIM1, int DIM2, TYPE* INPLACE_ARRAY2) {(int rows, int cols, TYPE* array)};
 
-// Apply the Numeric typemaps for 2D input arrays
-%apply (signed char*        IN_ARRAY2, int DIM1, int DIM2)
-      {(signed char*        matrix,    int rows, int cols)};
-%apply (unsigned char*      IN_ARRAY2, int DIM1, int DIM2)
-      {(unsigned char*      matrix,    int rows, int cols)};
-%apply (short*              IN_ARRAY2, int DIM1, int DIM2)
-      {(short*              matrix,    int rows, int cols)};
-%apply (unsigned short*     IN_ARRAY2, int DIM1, int DIM2)
-      {(unsigned short*     matrix,    int rows, int cols)};
-%apply (int*                IN_ARRAY2, int DIM1, int DIM2)
-      {(int*                matrix,    int rows, int cols)};
-%apply (unsigned int*       IN_ARRAY2, int DIM1, int DIM2)
-      {(unsigned int*       matrix,    int rows, int cols)};
-%apply (long*               IN_ARRAY2, int DIM1, int DIM2)
-      {(long*               matrix,    int rows, int cols)};
-%apply (unsigned long*      IN_ARRAY2, int DIM1, int DIM2)
-      {(unsigned long*      matrix,    int rows, int cols)};
-%apply (long long*          IN_ARRAY2, int DIM1, int DIM2)
-      {(long long*          matrix,    int rows, int cols)};
-%apply (unsigned long long* IN_ARRAY2, int DIM1, int DIM2)
-      {(unsigned long long* matrix,    int rows, int cols)};
-%apply (float*              IN_ARRAY2, int DIM1, int DIM2)
-      {(float*              matrix,    int rows, int cols)};
-%apply (double*             IN_ARRAY2, int DIM1, int DIM2)
-      {(double*             matrix,    int rows, int cols)};
-%apply (long double*        IN_ARRAY2, int DIM1, int DIM2)
-      {(long double*        matrix,    int rows, int cols)};
+%enddef    /* %apply_numpy_typemaps() macro */
 
-// Apply the Numeric typemaps for 2D input/output arrays
-%apply (signed char*        INPLACE_ARRAY2, int DIM1, int DIM2)
-      {(signed char*        array,          int rows, int cols)};
-%apply (unsigned char*      INPLACE_ARRAY2, int DIM1, int DIM2)
-      {(unsigned char*      array,          int rows, int cols)};
-%apply (short*              INPLACE_ARRAY2, int DIM1, int DIM2)
-      {(short*              array,          int rows, int cols)};
-%apply (unsigned short*     INPLACE_ARRAY2, int DIM1, int DIM2)
-      {(unsigned short*     array,          int rows, int cols)};
-%apply (int*                INPLACE_ARRAY2, int DIM1, int DIM2)
-      {(int*                array,          int rows, int cols)};
-%apply (unsigned int*       INPLACE_ARRAY2, int DIM1, int DIM2)
-      {(unsigned int*       array,          int rows, int cols)};
-%apply (long*               INPLACE_ARRAY2, int DIM1, int DIM2)
-      {(long*               array,          int rows, int cols)};
-%apply (unsigned long*      INPLACE_ARRAY2, int DIM1, int DIM2)
-      {(unsigned long*      array,          int rows, int cols)};
-%apply (long long*          INPLACE_ARRAY2, int DIM1, int DIM2)
-      {(long long*          array,          int rows, int cols)};
-%apply (unsigned long long* INPLACE_ARRAY2, int DIM1, int DIM2)
-      {(unsigned long long* array,          int rows, int cols)};
-%apply (float*              INPLACE_ARRAY2, int DIM1, int DIM2)
-      {(float*              array,          int rows, int cols)};
-%apply (double*             INPLACE_ARRAY2, int DIM1, int DIM2)
-      {(double*             array,          int rows, int cols)};
-%apply (long double*        INPLACE_ARRAY2, int DIM1, int DIM2)
-      {(long double*        array,          int rows, int cols)};
+%apply_numpy_typemaps(signed char       )
+%apply_numpy_typemaps(unsigned char     )
+%apply_numpy_typemaps(short             )
+%apply_numpy_typemaps(unsigned short    )
+%apply_numpy_typemaps(int               )
+%apply_numpy_typemaps(unsigned int      )
+%apply_numpy_typemaps(long              )
+%apply_numpy_typemaps(unsigned long     )
+%apply_numpy_typemaps(long long         )
+%apply_numpy_typemaps(unsigned long long)
+%apply_numpy_typemaps(float             )
+%apply_numpy_typemaps(double            )
 
 // Include the header file to be wrapped
 %include "series.h"

Modified: trunk/numpy/doc/swig/numpy.i
===================================================================
--- trunk/numpy/doc/swig/numpy.i	2007-03-14 15:47:12 UTC (rev 3574)
+++ trunk/numpy/doc/swig/numpy.i	2007-03-14 19:57:23 UTC (rev 3575)
@@ -304,6 +304,10 @@
  *     (TYPE* ARGOUT_ARRAY1[ANY])
  *     (TYPE* ARGOUT_ARRAY2[ANY][ANY])
  *
+ *     (int DIM1, TYPE* IN_ARRAY1)
+ *     (int DIM1, int DIM2, TYPE* IN_ARRAY2)
+ *     (int DIM1, TYPE* INPLACE_ARRAY1)
+ *
  * where "TYPE" is any type supported by the NumPy module.  In python,
  * the dimensions will not need to be specified.  The IN_ARRAYs can be
  * a numpy array or any sequence that can be converted to a numpy
@@ -314,32 +318,45 @@
  * These typemaps can be applied to existing functions using the
  * %apply directive:
  *
- *     %apply (double* IN_ARRAY1, int DIM1) {double* series, int length};
- *     double sum(double* series, int length);
+ *     %apply (double* IN_ARRAY1, int DIM1) {(double* series, int length)};
+ *     double prod(double* series, int length);
  *
- *     %apply (double* IN_ARRAY2, int DIM1, int DIM2) {double* mx, int rows, int cols};
+ *     %apply (double* IN_ARRAY2, int DIM1, int DIM2) {(double* mx, int rows, int cols)};
  *     double max(double* mx, int rows, int cols);
  *
- *     %apply (double* INPLACE_ARRAY1, int DIM1) {double* series, int length};
- *     void negate(double* series, int length);
+ *     %apply (double* INPLACE_ARRAY1, int DIM1) {(double* series, int length)};
+ *     void ones(double* series, int length);
  *
- *     %apply (double* INPLACE_ARRAY2, int DIM1, int DIM2) {double* mx, int rows, int cols};
- *     void normalize(double* mx, int rows, int cols);
+ *     %apply (double* INPLACE_ARRAY2, int DIM1, int DIM2) {(double* mx, int rows, int cols)};
+ *     void floor(double* mx, int rows, int cols);
  *
- *     %apply (double* ARGOUT_ARRAY1[ANY] {double series, int length};
+ *     %apply (double* ARGOUT_ARRAY1[ANY] {(double series, int length)};
  *     void negate(double* series, int length);
  *
- *     %apply (double* ARGOUT_ARRAY2[ANY][ANY]) {double* mx, int rows, int cols};
+ *     %apply (double* ARGOUT_ARRAY2[ANY][ANY]) {(double* mx, int rows, int cols)};
  *     void normalize(double* mx, int rows, int cols);
  *
+ *     %apply (int DIM1, double* IN_ARRAY1) {(int length, double* series)}
+ *     double sum(int length, double* series)
+ *
+ *     %apply (int DIM1, int DIM2, double* IN_ARRAY2) {(int rows, int cols, double* matrix)}
+ *     double min(int length, double* series)
+ *
+ *     %apply (int DIM1, double* INPLACE_ARRAY1) {(int length, double* series)}
+ *     double zeros(int length, double* series)
+ *
  * or directly with
  *
- *     double sum(double* IN_ARRAY1, int DIM1);
+ *     double prod(double* IN_ARRAY1, int DIM1);
  *     double max(double* IN_ARRAY2, int DIM1, int DIM2);
- *     void sum(double* INPLACE_ARRAY1, int DIM1);
- *     void sum(double* INPLACE_ARRAY2, int DIM1, int DIM2);
- *     void sum(double* ARGOUT_ARRAY1[ANY]);
- *     void sum(double* ARGOUT_ARRAY2[ANY][ANY]);
+ *     void ones(double* INPLACE_ARRAY1, int DIM1);
+ *     void floor(double* INPLACE_ARRAY2, int DIM1, int DIM2);
+ *     void negate(double* ARGOUT_ARRAY1[ANY]);
+ *     void normalize(double* ARGOUT_ARRAY2[ANY][ANY]);
+ *
+ *     double sum(int DIM1, double* IN_ARRAY1)
+ *     double min(int DIM1, int DIM2, double* IN_ARRAY2)
+ *     void zeros(int DIM1, double* INPLACE_ARRAY1)
  */
 
 %define %numpy_typemaps(TYPE, TYPECODE)
@@ -441,6 +458,55 @@
   }
 }
 
+/* Typemap suite for (int DIM1, TYPE* IN_ARRAY1)
+ */
+%typemap(in) (int DIM1, TYPE* IN_ARRAY1)
+             (PyArrayObject* array=NULL, int is_new_object=0) {
+  array = obj_to_array_contiguous_allow_conversion($input, TYPECODE, &is_new_object);
+  npy_intp size[1] = {-1};
+  if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail;
+  $1 = (int) array->dimensions[0];
+  $2 = (TYPE*) array->data;
+}
+%typemap(freearg) (int DIM1, TYPE* IN_ARRAY1) {
+  if (is_new_object$argnum && array$argnum) Py_DECREF(array$argnum);
+}
+
+/* Typemap suite for (int DIM1, int DIM2, TYPE* IN_ARRAY2)
+ */
+%typemap(in) (int DIM1, int DIM2, TYPE* IN_ARRAY2)
+             (PyArrayObject* array=NULL, int is_new_object=0) {
+  array = obj_to_array_contiguous_allow_conversion($input, TYPECODE, &is_new_object);
+  npy_intp size[2] = {-1,-1};
+  if (!array || !require_dimensions(array, 2) || !require_size(array, size, 1)) SWIG_fail;
+  $1 = (int) array->dimensions[0];
+  $2 = (int) array->dimensions[1];
+  $3 = (TYPE*) array->data;
+}
+%typemap(freearg) (int DIM1, int DIM2, TYPE* IN_ARRAY2) {
+  if (is_new_object$argnum && array$argnum) Py_DECREF(array$argnum);
+}
+
+/* Typemap suite for (int DIM1, TYPE* INPLACE_ARRAY1)
+ */
+%typemap(in) (int DIM1, TYPE* INPLACE_ARRAY1) (PyArrayObject* temp=NULL) {
+  temp = obj_to_array_no_conversion($input, TYPECODE);
+  if (!temp  || !require_contiguous(temp)) SWIG_fail;
+  $1 = 1;
+  for (int i=0; i<temp->nd; ++i) $1 *= temp->dimensions[i];
+  $2 = (TYPE*) temp->data;
+}
+
+/* Typemap suite for (int DIM1, int DIM2, TYPE* INPLACE_ARRAY2)
+ */
+%typemap(in) (int DIM1, int DIM2, TYPE* INPLACE_ARRAY2) (PyArrayObject* temp=NULL) {
+  temp = obj_to_array_no_conversion($input, TYPECODE);
+  if (!temp || !require_contiguous(temp)) SWIG_fail;
+  $1 = (int) temp->dimensions[0];
+  $2 = (int) temp->dimensions[1];
+  $3 = (TYPE*) temp->data;
+}
+
 %enddef    /* %numpy_typemaps() macro */
 
 

Modified: trunk/numpy/doc/swig/numpy_swig.html
===================================================================
--- trunk/numpy/doc/swig/numpy_swig.html	2007-03-14 15:47:12 UTC (rev 3574)
+++ trunk/numpy/doc/swig/numpy_swig.html	2007-03-14 19:57:23 UTC (rev 3575)
@@ -351,9 +351,16 @@
 <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>
@@ -365,19 +372,30 @@
 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>Hopefully, no actual prototypes to be wrapped will have the names
+<p>Most likely, no actual prototypes to be wrapped will have the 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>Note that if the C function signature was in a different order:</p>
+<pre class="literal-block">
+double rms(int n, double* seq);
+</pre>
+<p>that <a class="reference" href="http://www.swig.org">SWIG</a> would not match the typemap signature given above with
+the argument list for <tt class="docutils literal"><span class="pre">rms</span></tt>.  Fortunately, <tt class="docutils literal"><span class="pre">numpy.i</span></tt> has a set of
+typemaps with the data pointer given last:</p>
+<pre class="literal-block">
+%apply (int DIM1, double* IN_ARRAY1) {(int n, double* seq)};
+</pre>
 </div>
 <div class="section">
 <h1><a id="using-numpy-i" name="using-numpy-i">Using numpy.i</a></h1>
 <p>The <tt class="docutils literal"><span class="pre">numpy.i</span></tt> file is currently located in the <tt class="docutils literal"><span class="pre">numpy/docs/swig</span></tt>
 sub-directory under the <tt class="docutils literal"><span class="pre">numpy</span></tt> installation directory.  Typically,
 you will want to copy it to the directory where you are developing
-your wrappers.</p>
+your wrappers.  If it is ever adopted by <a class="reference" href="http://www.swig.org">SWIG</a> developers, then it
+will be installed in a standard place where <a class="reference" href="http://www.swig.org">SWIG</a> can find it.</p>
 <p>A simple module that only uses a single <a class="reference" href="http://www.swig.org">SWIG</a> interface file should
 include the following:</p>
 <pre class="literal-block">
@@ -389,17 +407,22 @@
 import_array();
 %}
 </pre>
-<p>Only one interface file should call <tt class="docutils literal"><span class="pre">import_array()</span></tt>.  If you have
-more than one <a class="reference" href="http://www.swig.org">SWIG</a> interface file, then subsequent files should not
-<tt class="docutils literal"><span class="pre">#define</span> <span class="pre">SWIG_FILE_WITH_INIT</span></tt> and should not call
-<tt class="docutils literal"><span class="pre">import_array()</span></tt>.</p>
+<p>Within a compiled python module, <tt class="docutils literal"><span class="pre">import_array()</span></tt> should only get
+called once.  This could be in a C/C++ file that you have written and
+is linked to the module.  If this is the case, then none of your
+interface files should <tt class="docutils literal"><span class="pre">#define</span> <span class="pre">SWIG_FILE_WITH_INIT</span></tt> or call
+<tt class="docutils literal"><span class="pre">import_array()</span></tt>.  Or, this initialization call could be in a
+wrapper file generated by <a class="reference" href="http://www.swig.org">SWIG</a> from an interface file that has the
+<tt class="docutils literal"><span class="pre">%init</span></tt> block as above.  If this is the case, and you have more than
+one <a class="reference" href="http://www.swig.org">SWIG</a> interface file, then only one interface file should
+<tt class="docutils literal"><span class="pre">#define</span> <span class="pre">SWIG_FILE_WITH_INIT</span></tt> and call <tt class="docutils literal"><span class="pre">import_array()</span></tt>.</p>
 </div>
 <div class="section">
 <h1><a id="available-typemaps" name="available-typemaps">Available Typemaps</a></h1>
 <p>The typemap directives provided by <tt class="docutils literal"><span class="pre">numpy.i</span></tt> for arrays of different
 data types, say <tt class="docutils literal"><span class="pre">double</span></tt> and <tt class="docutils literal"><span class="pre">int</span></tt>, are identical to one another
 except for the C and <a class="reference" href="http://numpy.scipy.org">NumPy</a> type specifications.  The typemaps are
-therefore implemented via a macro:</p>
+therefore implemented (typically behind the scenes) via a macro:</p>
 <pre class="literal-block">
 %numpy_typemaps(TYPE, TYPECODE)
 </pre>
@@ -442,7 +465,7 @@
 %numpy_typemaps(bool, NPY_UINT)
 </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> will fail type-checking.</p>
+but <a class="reference" href="#in-place-arrays">In-place Arrays</a> might fail type-checking.</p>
 <p>In the following descriptions, we reference a generic <tt class="docutils literal"><span class="pre">TYPE</span></tt>, which
 could be any of the C-types listed above.</p>
 <div class="section">
@@ -456,6 +479,8 @@
 <ul class="simple">
 <li><tt class="docutils literal"><span class="pre">(TYPE*</span> <span class="pre">IN_ARRAY1,</span> <span class="pre">int</span> <span class="pre">DIM1)</span></tt></li>
 <li><tt class="docutils literal"><span class="pre">(TYPE*</span> <span class="pre">IN_ARRAY2,</span> <span class="pre">int</span> <span class="pre">DIM1,</span> <span class="pre">int</span> <span class="pre">DIM2)</span></tt></li>
+<li><tt class="docutils literal"><span class="pre">(int</span> <span class="pre">DIM1,</span> <span class="pre">TYPE*</span> <span class="pre">IN_ARRAY1)</span></tt></li>
+<li><tt class="docutils literal"><span class="pre">(int</span> <span class="pre">DIM1,</span> <span class="pre">int</span> <span class="pre">DIM2,</span> <span class="pre">TYPE*</span> <span class="pre">IN_ARRAY2)</span></tt></li>
 </ul>
 </blockquote>
 </div>
@@ -470,6 +495,8 @@
 <ul class="simple">
 <li><tt class="docutils literal"><span class="pre">(TYPE*</span> <span class="pre">INPLACE_ARRAY1,</span> <span class="pre">int</span> <span class="pre">DIM1)</span></tt></li>
 <li><tt class="docutils literal"><span class="pre">(TYPE*</span> <span class="pre">INPLACE_ARRAY2,</span> <span class="pre">int</span> <span class="pre">DIM1,</span> <span class="pre">int</span> <span class="pre">DIM2)</span></tt></li>
+<li><tt class="docutils literal"><span class="pre">(int</span> <span class="pre">DIM1,</span> <span class="pre">TYPE*</span> <span class="pre">INPLACE_ARRAY1)</span></tt></li>
+<li><tt class="docutils literal"><span class="pre">(int</span> <span class="pre">DIM1,</span> <span class="pre">int</span> <span class="pre">DIM2,</span> <span class="pre">TYPE*</span> <span class="pre">INPLACE_ARRAY2)</span></tt></li>
 </ul>
 </blockquote>
 </div>
@@ -481,9 +508,9 @@
 therefore not sufficient.  In python, the convential way to return
 multiple arguments is to pack them into a tuple and return the tuple.
 This is what the argout typemaps do.  If a wrapped function that uses
-argout these argout typemaps has more than one return argument, they
-are so packed.  The python user does not pass these arrays in, they
-simply get returned.  The argout signatures are</p>
+these argout typemaps has more than one return argument, they are so
+packed.  The python user does not pass these arrays in, they simply
+get returned.  The argout signatures are</p>
 <blockquote>
 <ul class="simple">
 <li><tt class="docutils literal"><span class="pre">(TYPE</span> <span class="pre">ARGOUT_ARRAY1[ANY])</span></tt></li>
@@ -596,7 +623,7 @@
 </div>
 <div class="footer">
 <hr class="footer" />
-Generated on: 2007-03-14 15:24 UTC.
+Generated on: 2007-03-14 19:50 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: trunk/numpy/doc/swig/numpy_swig.txt
===================================================================
--- trunk/numpy/doc/swig/numpy_swig.txt	2007-03-14 15:47:12 UTC (rev 3574)
+++ trunk/numpy/doc/swig/numpy_swig.txt	2007-03-14 19:57:23 UTC (rev 3575)
@@ -57,9 +57,16 @@
 `SWIG`_ interface file would need the following::
 
     %{
+    #define SWIG_FILE_WITH_INIT
     #include "rms.h"
     %}
+
     %include "numpy.i"
+
+    %init %{
+    import_array();
+    %}
+
     %apply (double* IN_ARRAY1, int DIM1) {(double* seq, int n)};
     %include "rms.h"
 
@@ -72,20 +79,31 @@
 that dimension.  This is precisely the pattern in the ``rms``
 prototype.
 
-Hopefully, no actual prototypes to be wrapped will have the names
+Most likely, no actual prototypes to be wrapped will have the names
 ``IN_ARRAY1`` and ``DIM1``.  We use the ``%apply`` directive to apply
 the typemap for one-dimensional input arrays of type ``double`` to the
 actual prototype used by ``rms``.  Using ``numpy.i`` effectively,
 therefore, requires knowing what typemaps are available and what they
 do.
 
+Note that if the C function signature was in a different order::
+
+    double rms(int n, double* seq);
+
+that `SWIG`_ would not match the typemap signature given above with
+the argument list for ``rms``.  Fortunately, ``numpy.i`` has a set of
+typemaps with the data pointer given last::
+
+    %apply (int DIM1, double* IN_ARRAY1) {(int n, double* seq)};
+
 Using numpy.i
 =============
 
 The ``numpy.i`` file is currently located in the ``numpy/docs/swig``
 sub-directory under the ``numpy`` installation directory.  Typically,
 you will want to copy it to the directory where you are developing
-your wrappers.
+your wrappers.  If it is ever adopted by `SWIG`_ developers, then it
+will be installed in a standard place where `SWIG`_ can find it.
 
 A simple module that only uses a single `SWIG`_ interface file should
 include the following::
@@ -98,10 +116,15 @@
     import_array();
     %}
 
-Only one interface file should call ``import_array()``.  If you have
-more than one `SWIG`_ interface file, then subsequent files should not
-``#define SWIG_FILE_WITH_INIT`` and should not call
-``import_array()``.
+Within a compiled python module, ``import_array()`` should only get
+called once.  This could be in a C/C++ file that you have written and
+is linked to the module.  If this is the case, then none of your
+interface files should ``#define SWIG_FILE_WITH_INIT`` or call
+``import_array()``.  Or, this initialization call could be in a
+wrapper file generated by `SWIG`_ from an interface file that has the
+``%init`` block as above.  If this is the case, and you have more than
+one `SWIG`_ interface file, then only one interface file should
+``#define SWIG_FILE_WITH_INIT`` and call ``import_array()``.
 
 Available Typemaps
 ==================
@@ -109,7 +132,7 @@
 The typemap directives provided by ``numpy.i`` for arrays of different
 data types, say ``double`` and ``int``, are identical to one another
 except for the C and `NumPy`_ type specifications.  The typemaps are
-therefore implemented via a macro::
+therefore implemented (typically behind the scenes) via a macro::
 
     %numpy_typemaps(TYPE, TYPECODE)
 
@@ -150,7 +173,7 @@
     %numpy_typemaps(bool, NPY_UINT)
 
 to fix the data length problem, and `Input Arrays`_ will work fine,
-but `In-place Arrays`_ will fail type-checking.
+but `In-place Arrays`_ might fail type-checking.
 
 In the following descriptions, we reference a generic ``TYPE``, which
 could be any of the C-types listed above.
@@ -166,6 +189,8 @@
 
   * ``(TYPE* IN_ARRAY1, int DIM1)``
   * ``(TYPE* IN_ARRAY2, int DIM1, int DIM2)``
+  * ``(int DIM1, TYPE* IN_ARRAY1)``
+  * ``(int DIM1, int DIM2, TYPE* IN_ARRAY2)``
 
 In-place Arrays
 ---------------
@@ -178,6 +203,8 @@
 
   * ``(TYPE* INPLACE_ARRAY1, int DIM1)``
   * ``(TYPE* INPLACE_ARRAY2, int DIM1, int DIM2)``
+  * ``(int DIM1, TYPE* INPLACE_ARRAY1)``
+  * ``(int DIM1, int DIM2, TYPE* INPLACE_ARRAY2)``
 
 Argout Arrays
 -------------
@@ -188,9 +215,9 @@
 therefore not sufficient.  In python, the convential way to return
 multiple arguments is to pack them into a tuple and return the tuple.
 This is what the argout typemaps do.  If a wrapped function that uses
-argout these argout typemaps has more than one return argument, they
-are so packed.  The python user does not pass these arrays in, they
-simply get returned.  The argout signatures are
+these argout typemaps has more than one return argument, they are so
+packed.  The python user does not pass these arrays in, they simply
+get returned.  The argout signatures are
 
   * ``(TYPE ARGOUT_ARRAY1[ANY])``
   * ``(TYPE ARGOUT_ARRAY2[ANY][ANY])``

Modified: trunk/numpy/doc/swig/series.cxx
===================================================================
--- trunk/numpy/doc/swig/series.cxx	2007-03-14 15:47:12 UTC (rev 3574)
+++ trunk/numpy/doc/swig/series.cxx	2007-03-14 19:57:23 UTC (rev 3575)
@@ -9,6 +9,10 @@
 //     void SNAMEOnes( TYPE * array,  int size);
 //     TYPE SNAMEMax(  TYPE * matrix, int rows, int cols);
 //     void SNAMEFloor(TYPE * array,  int rows, int cols, TYPE floor);
+//     TYPE SNAMESum(  int size, TYPE * series);
+//     void SNAMEZeros(int size, TYPE * array);
+//     TYPE SNAMEMin(  int rows, int cols, TYPE * matrix);
+//     void SNAMECeil( int rows, int cols, TYPE * array, TYPE ceil);
 //
 // for any specified type TYPE (for example: short, unsigned int, long
 // long, etc.) with given short name SNAME (for example: short, uint,
@@ -20,6 +24,10 @@
 //  * 1D in-place arrays
 //  * 2D input arrays
 //  * 2D in-place arrays
+//  * 1D input arrays, data last
+//  * 1D in-place arrays, data last
+//  * 2D input arrays, data last
+//  * 2D in-place arrays, data last
 //
 #define TEST_FUNCS(TYPE, SNAME) \
 \
@@ -46,13 +54,45 @@
 }                                                         \
 \
 void SNAME ## Floor(TYPE * array, int rows, int cols, TYPE floor) { \
-  int i, j, index;                                                    \
-  for (j=0; j<cols; ++j) {                                            \
-    for (i=0; i<rows; ++i) {                                          \
-      index = j*rows + i;                                             \
-      if (array[index] < floor) array[index] = 0;                     \
-    }                                                                 \
-  }                                                                   \
+  int i, j, index;                                                  \
+  for (j=0; j<cols; ++j) {                                          \
+    for (i=0; i<rows; ++i) {                                        \
+      index = j*rows + i;                                           \
+      if (array[index] < floor) array[index] = floor;               \
+    }                                                               \
+  }                                                                 \
+}                                                                   \
+\
+TYPE SNAME ## Sum(int size, TYPE * series) {      \
+  TYPE result = 0;                                \
+  for (int i=0; i<size; ++i) result += series[i]; \
+  return result;                                  \
+}                                                 \
+\
+void SNAME ## Zeros(int size, TYPE * array) { \
+  for (int i=0; i<size; ++i) array[i] = 0;    \
+}                                             \
+\
+TYPE SNAME ## Min(int rows, int cols, TYPE * matrix) {    \
+  int i, j, index;                                        \
+  TYPE result = matrix[0];                                \
+  for (j=0; j<cols; ++j) {                                \
+    for (i=0; i<rows; ++i) {                              \
+      index = j*rows + i;                                 \
+      if (matrix[index] < result) result = matrix[index]; \
+    }                                                     \
+  }                                                       \
+  return result;                                          \
+}                                                         \
+\
+void SNAME ## Ceil(int rows, int cols, TYPE * array, TYPE ceil) { \
+  int i, j, index;                                                \
+  for (j=0; j<cols; ++j) {                                        \
+    for (i=0; i<rows; ++i) {                                      \
+      index = j*rows + i;                                         \
+      if (array[index] > ceil) array[index] = ceil;               \
+    }                                                             \
+  }                                                               \
 }
 
 TEST_FUNCS(signed char       , schar     )
@@ -67,4 +107,3 @@
 TEST_FUNCS(unsigned long long, ulongLong )
 TEST_FUNCS(float             , float     )
 TEST_FUNCS(double            , double    )
-TEST_FUNCS(long double       , longDouble)

Modified: trunk/numpy/doc/swig/series.h
===================================================================
--- trunk/numpy/doc/swig/series.h	2007-03-14 15:47:12 UTC (rev 3574)
+++ trunk/numpy/doc/swig/series.h	2007-03-14 19:57:23 UTC (rev 3575)
@@ -8,6 +8,10 @@
 //     void SNAMEOnes( TYPE * array,  int size);
 //     TYPE SNAMEMax(  TYPE * matrix, int rows, int cols);
 //     void SNAMEFloor(TYPE * array,  int rows, int cols, TYPE floor);
+//     TYPE SNAMESum(  int size, TYPE * series);
+//     void SNAMEZeros(int size, TYPE * array);
+//     TYPE SNAMEMin(  int rows, int cols, TYPE * matrix);
+//     void SNAMECeil( int rows, int cols, TYPE * array,  TYPE ceil);
 //
 // for any specified type TYPE (for example: short, unsigned int, long
 // long, etc.) with given short name SNAME (for example: short, uint,
@@ -19,13 +23,21 @@
 //  * 1D in-place arrays
 //  * 2D input arrays
 //  * 2D in-place arrays
+//  * 1D input arrays, data last
+//  * 1D in-place arrays, data last
+//  * 2D input arrays, data last
+//  * 2D in-place arrays, data last
 //
 #define TEST_FUNC_PROTOS(TYPE, SNAME) \
 \
 TYPE SNAME ## Prod( TYPE * series, int size); \
 void SNAME ## Ones( TYPE * array,  int size); \
 TYPE SNAME ## Max(  TYPE * matrix, int rows, int cols); \
-void SNAME ## Floor(TYPE * array,  int rows, int cols, TYPE floor);
+void SNAME ## Floor(TYPE * array,  int rows, int cols, TYPE floor); \
+TYPE SNAME ## Sum(  int size, TYPE * series); \
+void SNAME ## Zeros(int size, TYPE * array); \
+TYPE SNAME ## Min(  int rows, int cols, TYPE * matrix); \
+void SNAME ## Ceil( int rows, int cols, TYPE * array, TYPE ceil);
 
 TEST_FUNC_PROTOS(signed char       , schar     )
 TEST_FUNC_PROTOS(unsigned char     , uchar     )
@@ -39,6 +51,5 @@
 TEST_FUNC_PROTOS(unsigned long long, ulongLong )
 TEST_FUNC_PROTOS(float             , float     )
 TEST_FUNC_PROTOS(double            , double    )
-TEST_FUNC_PROTOS(long double       , longDouble)
 
 #endif

Modified: trunk/numpy/doc/swig/testSeries.py
===================================================================
--- trunk/numpy/doc/swig/testSeries.py	2007-03-14 15:47:12 UTC (rev 3574)
+++ trunk/numpy/doc/swig/testSeries.py	2007-03-14 19:57:23 UTC (rev 3575)
@@ -25,450 +25,682 @@
     ####################################################
     ### Test functions that take arrays of type BYTE ###
     def testScharProd(self):
-        "Test the scharProd function"
+        "Test scharProd function"
         self.assertEquals(Series.scharProd([1,2,3,4]), 24)
 
     def testScharProdNonContainer(self):
-        "Test the scharProd function with None"
+        "Test scharProd function with None"
         self.assertRaises(TypeError, Series.scharProd, None)
 
     def testScharOnes(self):
-        "Test the scharOnes function"
+        "Test scharOnes function"
         myArray = N.zeros(5,'b')
         Series.scharOnes(myArray)
         N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
 
     def testScharMax(self):
-        "Test the scharMax function"
+        "Test scharMax function"
         matrix = [[-6,5,-4],[3,-2,1]]
         self.assertEquals(Series.scharMax(matrix), 5)
 
     def testScharMaxNonContainer(self):
-        "Test the scharMax function with None"
+        "Test scharMax function with None"
         self.assertRaises(TypeError, Series.scharMax, None)
 
     def testScharMaxWrongDim(self):
-        "Test the scharMax function with a 1D array"
+        "Test scharMax function with a 1D array"
         self.assertRaises(TypeError, Series.scharMax, [0, -1, 2, -3])
 
     def testScharFloor(self):
-        "Test the scharFloor function"
+        "Test scharFloor function"
         matrix = N.array([[10,-2],[-6,7]],'b')
         Series.scharFloor(matrix,0)
         N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
 
+    def testScharSum(self):
+        "Test scharSum function"
+        self.assertEquals(Series.scharSum([-5,6,-7,8]), 2)
+
+    def testScharZeros(self):
+        "Test scharZeros function"
+        myArray = N.ones(5,'b')
+        Series.scharZeros(myArray)
+        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
+
+    def testScharMin(self):
+        "Test scharMin function"
+        matrix = [[9,8],[7,6],[5,4]]
+        self.assertEquals(Series.scharMin(matrix), 4)
+
+    def testScharCeil(self):
+        "Test scharCeil function"
+        matrix = N.array([[10,-2],[-6,7]],'b')
+        Series.scharCeil(matrix,5)
+        N.testing.assert_array_equal(matrix, N.array([[5,-2],[-6,5]]))
+
     #####################################################
     ### Test functions that take arrays of type UBYTE ###
     def testUcharProd(self):
-        "Test the ucharProd function"
+        "Test ucharProd function"
         self.assertEquals(Series.ucharProd([1,2,3,4]), 24)
 
     def testUcharProdNonContainer(self):
-        "Test the ucharProd function with None"
+        "Test ucharProd function with None"
         self.assertRaises(TypeError, Series.ucharProd, None)
 
     def testUcharOnes(self):
-        "Test the ucharOnes function"
+        "Test ucharOnes function"
         myArray = N.zeros(5,'B')
         Series.ucharOnes(myArray)
         N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
 
     def testUcharMax(self):
-        "Test the ucharMax function"
+        "Test ucharMax function"
         matrix = [[6,5,4],[3,2,1]]
         self.assertEquals(Series.ucharMax(matrix), 6)
 
     def testUcharMaxNonContainer(self):
-        "Test the ucharMax function with None"
+        "Test ucharMax function with None"
         self.assertRaises(TypeError, Series.ucharMax, None)
 
     def testUcharMaxWrongDim(self):
-        "Test the ucharMax function with a 1D array"
+        "Test ucharMax function with a 1D array"
         self.assertRaises(TypeError, Series.ucharMax, [0, 1, 2, 3])
 
     def testUcharFloor(self):
-        "Test the ucharFloor function"
+        "Test ucharFloor function"
         matrix = N.array([[10,2],[6,7]],'B')
         Series.ucharFloor(matrix,7)
-        N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
+        N.testing.assert_array_equal(matrix, N.array([[10,7],[7,7]]))
 
+    def testUcharSum(self):
+        "Test ucharSum function"
+        self.assertEquals(Series.ucharSum([5,6,7,8]), 26)
+
+    def testUcharZeros(self):
+        "Test ucharZeros function"
+        myArray = N.ones(5,'B')
+        Series.ucharZeros(myArray)
+        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
+
+    def testUcharMin(self):
+        "Test ucharMin function"
+        matrix = [[9,8],[7,6],[5,4]]
+        self.assertEquals(Series.ucharMin(matrix), 4)
+
+    def testUcharCeil(self):
+        "Test ucharCeil function"
+        matrix = N.array([[10,2],[6,7]],'B')
+        Series.ucharCeil(matrix,5)
+        N.testing.assert_array_equal(matrix, N.array([[5,2],[5,5]]))
+
     #####################################################
     ### Test functions that take arrays of type SHORT ###
     def testShortProd(self):
-        "Test the shortProd function"
+        "Test shortProd function"
         self.assertEquals(Series.shortProd([1,2,3,4]), 24)
 
     def testShortProdNonContainer(self):
-        "Test the shortProd function with None"
+        "Test shortProd function with None"
         self.assertRaises(TypeError, Series.shortProd, None)
 
     def testShortOnes(self):
-        "Test the shortOnes function"
+        "Test shortOnes function"
         myArray = N.zeros(5,'h')
         Series.shortOnes(myArray)
         N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
 
     def testShortMax(self):
-        "Test the shortMax function"
+        "Test shortMax function"
         matrix = [[-6,5,-4],[3,-2,1]]
         self.assertEquals(Series.shortMax(matrix), 5)
 
     def testShortMaxNonContainer(self):
-        "Test the shortMax function with None"
+        "Test shortMax function with None"
         self.assertRaises(TypeError, Series.shortMax, None)
 
     def testShortMaxWrongDim(self):
-        "Test the shortMax function with a 1D array"
+        "Test shortMax function with a 1D array"
         self.assertRaises(TypeError, Series.shortMax, [0, -1, 2, -3])
 
     def testShortFloor(self):
-        "Test the shortFloor function"
+        "Test shortFloor function"
         matrix = N.array([[10,-2],[-6,7]],'h')
         Series.shortFloor(matrix,0)
         N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
 
+    def testShortSum(self):
+        "Test shortSum function"
+        self.assertEquals(Series.shortSum([-5,6,-7,8]), 2)
+
+    def testShortZeros(self):
+        "Test shortZeros function"
+        myArray = N.ones(5,'h')
+        Series.shortZeros(myArray)
+        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
+
+    def testShortMin(self):
+        "Test shortMin function"
+        matrix = [[9,8],[7,6],[5,4]]
+        self.assertEquals(Series.shortMin(matrix), 4)
+
+    def testShortCeil(self):
+        "Test shortCeil function"
+        matrix = N.array([[10,-2],[-6,7]],'h')
+        Series.shortCeil(matrix,5)
+        N.testing.assert_array_equal(matrix, N.array([[5,-2],[-6,5]]))
+
     ######################################################
     ### Test functions that take arrays of type USHORT ###
     def testUshortProd(self):
-        "Test the ushortProd function"
+        "Test ushortProd function"
         self.assertEquals(Series.ushortProd([1,2,3,4]), 24)
 
     def testUshortProdNonContainer(self):
-        "Test the ushortProd function with None"
+        "Test ushortProd function with None"
         self.assertRaises(TypeError, Series.ushortProd, None)
 
     def testUshortOnes(self):
-        "Test the ushortOnes function"
+        "Test ushortOnes function"
         myArray = N.zeros(5,'H')
         Series.ushortOnes(myArray)
         N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
 
     def testUshortMax(self):
-        "Test the ushortMax function"
+        "Test ushortMax function"
         matrix = [[6,5,4],[3,2,1]]
         self.assertEquals(Series.ushortMax(matrix), 6)
 
     def testUshortMaxNonContainer(self):
-        "Test the ushortMax function with None"
+        "Test ushortMax function with None"
         self.assertRaises(TypeError, Series.ushortMax, None)
 
     def testUshortMaxWrongDim(self):
-        "Test the ushortMax function with a 1D array"
+        "Test ushortMax function with a 1D array"
         self.assertRaises(TypeError, Series.ushortMax, [0, 1, 2, 3])
 
     def testUshortFloor(self):
-        "Test the ushortFloor function"
+        "Test ushortFloor function"
         matrix = N.array([[10,2],[6,7]],'H')
         Series.ushortFloor(matrix,7)
-        N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
+        N.testing.assert_array_equal(matrix, N.array([[10,7],[7,7]]))
 
+    def testUshortSum(self):
+        "Test ushortSum function"
+        self.assertEquals(Series.ushortSum([5,6,7,8]), 26)
+
+    def testUshortZeros(self):
+        "Test ushortZeros function"
+        myArray = N.ones(5,'H')
+        Series.ushortZeros(myArray)
+        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
+
+    def testUshortMin(self):
+        "Test ushortMin function"
+        matrix = [[9,8],[7,6],[5,4]]
+        self.assertEquals(Series.ushortMin(matrix), 4)
+
+    def testUshortCeil(self):
+        "Test ushortCeil function"
+        matrix = N.array([[10,2],[6,7]],'H')
+        Series.ushortCeil(matrix,5)
+        N.testing.assert_array_equal(matrix, N.array([[5,2],[5,5]]))
+
     ###################################################
     ### Test functions that take arrays of type INT ###
     def testIntProd(self):
-        "Test the intProd function"
+        "Test intProd function"
         self.assertEquals(Series.intProd([1,2,3,4]), 24)
 
     def testIntProdNonContainer(self):
-        "Test the intProd function with None"
+        "Test intProd function with None"
         self.assertRaises(TypeError, Series.intProd, None)
 
     def testIntOnes(self):
-        "Test the intOnes function"
+        "Test intOnes function"
         myArray = N.zeros(5,'i')
         Series.intOnes(myArray)
         N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
 
     def testIntMax(self):
-        "Test the intMax function"
+        "Test intMax function"
         matrix = [[-6,5,-4],[3,-2,1]]
         self.assertEquals(Series.intMax(matrix), 5)
 
     def testIntMaxNonContainer(self):
-        "Test the intMax function with None"
+        "Test intMax function with None"
         self.assertRaises(TypeError, Series.intMax, None)
 
     def testIntMaxWrongDim(self):
-        "Test the intMax function with a 1D array"
+        "Test intMax function with a 1D array"
         self.assertRaises(TypeError, Series.intMax, [0, -1, 2, -3])
 
     def testIntFloor(self):
-        "Test the intFloor function"
+        "Test intFloor function"
         matrix = N.array([[10,-2],[-6,7]],'i')
         Series.intFloor(matrix,0)
         N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
 
+    def testIntSum(self):
+        "Test intSum function"
+        self.assertEquals(Series.intSum([-5,6,-7,8]), 2)
+
+    def testIntZeros(self):
+        "Test intZeros function"
+        myArray = N.ones(5,'i')
+        Series.intZeros(myArray)
+        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
+
+    def testIntMin(self):
+        "Test intMin function"
+        matrix = [[9,8],[7,6],[5,4]]
+        self.assertEquals(Series.intMin(matrix), 4)
+
+    def testIntCeil(self):
+        "Test intCeil function"
+        matrix = N.array([[10,-2],[-6,7]],'i')
+        Series.intCeil(matrix,5)
+        N.testing.assert_array_equal(matrix, N.array([[5,-2],[-6,5]]))
+
     ####################################################
     ### Test functions that take arrays of type UINT ###
     def testUintProd(self):
-        "Test the uintProd function"
+        "Test uintProd function"
         self.assertEquals(Series.uintProd([1,2,3,4]), 24)
 
     def testUintProdNonContainer(self):
-        "Test the uintProd function with None"
+        "Test uintProd function with None"
         self.assertRaises(TypeError, Series.uintProd, None)
 
     def testUintOnes(self):
-        "Test the uintOnes function"
+        "Test uintOnes function"
         myArray = N.zeros(5,'I')
         Series.uintOnes(myArray)
         N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
 
     def testUintMax(self):
-        "Test the uintMax function"
+        "Test uintMax function"
         matrix = [[6,5,4],[3,2,1]]
         self.assertEquals(Series.uintMax(matrix), 6)
 
     def testUintMaxNonContainer(self):
-        "Test the uintMax function with None"
+        "Test uintMax function with None"
         self.assertRaises(TypeError, Series.uintMax, None)
 
     def testUintMaxWrongDim(self):
-        "Test the uintMax function with a 1D array"
+        "Test uintMax function with a 1D array"
         self.assertRaises(TypeError, Series.uintMax, [0, 1, 2, 3])
 
     def testUintFloor(self):
-        "Test the uintFloor function"
+        "Test uintFloor function"
         matrix = N.array([[10,2],[6,7]],'I')
         Series.uintFloor(matrix,7)
-        N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
+        N.testing.assert_array_equal(matrix, N.array([[10,7],[7,7]]))
 
+    def testUintSum(self):
+        "Test uintSum function"
+        self.assertEquals(Series.uintSum([5,6,7,8]), 26)
+
+    def testUintZeros(self):
+        "Test uintZeros function"
+        myArray = N.ones(5,'I')
+        Series.uintZeros(myArray)
+        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
+
+    def testUintMin(self):
+        "Test uintMin function"
+        matrix = [[9,8],[7,6],[5,4]]
+        self.assertEquals(Series.uintMin(matrix), 4)
+
+    def testUintCeil(self):
+        "Test uintCeil function"
+        matrix = N.array([[10,2],[6,7]],'I')
+        Series.uintCeil(matrix,5)
+        N.testing.assert_array_equal(matrix, N.array([[5,2],[5,5]]))
+
     ####################################################
     ### Test functions that take arrays of type LONG ###
     def testLongProd(self):
-        "Test the longProd function"
+        "Test longProd function"
         self.assertEquals(Series.longProd([1,2,3,4]), 24)
 
     def testLongProdNonContainer(self):
-        "Test the longProd function with None"
+        "Test longProd function with None"
         self.assertRaises(TypeError, Series.longProd, None)
 
     def testLongOnes(self):
-        "Test the longOnes function"
+        "Test longOnes function"
         myArray = N.zeros(5,'l')
         Series.longOnes(myArray)
         N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
 
     def testLongMax(self):
-        "Test the longMax function"
+        "Test longMax function"
         matrix = [[-6,5,-4],[3,-2,1]]
         self.assertEquals(Series.longMax(matrix), 5)
 
     def testLongMaxNonContainer(self):
-        "Test the longMax function with None"
+        "Test longMax function with None"
         self.assertRaises(TypeError, Series.longMax, None)
 
     def testLongMaxWrongDim(self):
-        "Test the longMax function with a 1D array"
+        "Test longMax function with a 1D array"
         self.assertRaises(TypeError, Series.longMax, [0, -1, 2, -3])
 
     def testLongFloor(self):
-        "Test the longFloor function"
+        "Test longFloor function"
         matrix = N.array([[10,-2],[-6,7]],'l')
         Series.longFloor(matrix,0)
         N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
 
+    def testLongSum(self):
+        "Test longSum function"
+        self.assertEquals(Series.longSum([-5,6,-7,8]), 2)
+
+    def testLongZeros(self):
+        "Test longZeros function"
+        myArray = N.ones(5,'l')
+        Series.longZeros(myArray)
+        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
+
+    def testLongMin(self):
+        "Test longMin function"
+        matrix = [[9,8],[7,6],[5,4]]
+        self.assertEquals(Series.longMin(matrix), 4)
+
+    def testLongCeil(self):
+        "Test longCeil function"
+        matrix = N.array([[10,-2],[-6,7]],'l')
+        Series.longCeil(matrix,5)
+        N.testing.assert_array_equal(matrix, N.array([[5,-2],[-6,5]]))
+
     #####################################################
     ### Test functions that take arrays of type ULONG ###
     def testUlongProd(self):
-        "Test the ulongProd function"
+        "Test ulongProd function"
         self.assertEquals(Series.ulongProd([1,2,3,4]), 24)
 
     def testUlongProdNonContainer(self):
-        "Test the ulongProd function with None"
+        "Test ulongProd function with None"
         self.assertRaises(TypeError, Series.ulongProd, None)
 
     def testUlongOnes(self):
-        "Test the ulongOnes function"
+        "Test ulongOnes function"
         myArray = N.zeros(5,'L')
         Series.ulongOnes(myArray)
         N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
 
     def testUlongMax(self):
-        "Test the ulongMax function"
+        "Test ulongMax function"
         matrix = [[6,5,4],[3,2,1]]
         self.assertEquals(Series.ulongMax(matrix), 6)
 
     def testUlongMaxNonContainer(self):
-        "Test the ulongMax function with None"
+        "Test ulongMax function with None"
         self.assertRaises(TypeError, Series.ulongMax, None)
 
     def testUlongMaxWrongDim(self):
-        "Test the ulongMax function with a 1D array"
+        "Test ulongMax function with a 1D array"
         self.assertRaises(TypeError, Series.ulongMax, [0, 1, 2, 3])
 
     def testUlongFloor(self):
-        "Test the ulongFloor function"
+        "Test ulongFloor function"
         matrix = N.array([[10,2],[6,7]],'L')
         Series.ulongFloor(matrix,7)
-        N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
+        N.testing.assert_array_equal(matrix, N.array([[10,7],[7,7]]))
 
+    def testUlongSum(self):
+        "Test ulongSum function"
+        self.assertEquals(Series.ulongSum([5,6,7,8]), 26)
+
+    def testUlongZeros(self):
+        "Test ulongZeros function"
+        myArray = N.ones(5,'L')
+        Series.ulongZeros(myArray)
+        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
+
+    def testUlongMin(self):
+        "Test ulongMin function"
+        matrix = [[9,8],[7,6],[5,4]]
+        self.assertEquals(Series.ulongMin(matrix), 4)
+
+    def testUlongCeil(self):
+        "Test ulongCeil function"
+        matrix = N.array([[10,2],[6,7]],'L')
+        Series.ulongCeil(matrix,5)
+        N.testing.assert_array_equal(matrix, N.array([[5,2],[5,5]]))
+
     ########################################################
     ### Test functions that take arrays of type LONGLONG ###
     def testLongLongProd(self):
-        "Test the longLongProd function"
+        "Test longLongProd function"
         self.assertEquals(Series.longLongProd([1,2,3,4]), 24)
 
     def testLongLongProdNonContainer(self):
-        "Test the longLongProd function with None"
+        "Test longLongProd function with None"
         self.assertRaises(TypeError, Series.longLongProd, None)
 
     def testLongLongOnes(self):
-        "Test the longLongOnes function"
+        "Test longLongOnes function"
         myArray = N.zeros(5,'q')
         Series.longLongOnes(myArray)
         N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
 
     def testLongLongMax(self):
-        "Test the longLongMax function"
+        "Test longLongMax function"
         matrix = [[-6,5,-4],[3,-2,1]]
         self.assertEquals(Series.longLongMax(matrix), 5)
 
     def testLongLongMaxNonContainer(self):
-        "Test the longLongMax function with None"
+        "Test longLongMax function with None"
         self.assertRaises(TypeError, Series.longLongMax, None)
 
     def testLongLongMaxWrongDim(self):
-        "Test the longLongMax function with a 1D array"
+        "Test longLongMax function with a 1D array"
         self.assertRaises(TypeError, Series.longLongMax, [0, -1, 2, -3])
 
     def testLongLongFloor(self):
-        "Test the longLongFloor function"
+        "Test longLongFloor function"
         matrix = N.array([[10,-2],[-6,7]],'q')
         Series.longLongFloor(matrix,0)
         N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
 
+    def testLongLongSum(self):
+        "Test longLongSum function"
+        self.assertEquals(Series.longLongSum([-5,6,-7,8]), 2)
+
+    def testLongLongZeros(self):
+        "Test longLongZeros function"
+        myArray = N.ones(5,'q')
+        Series.longLongZeros(myArray)
+        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
+
+    def testLongLongMin(self):
+        "Test longLongMin function"
+        matrix = [[9,8],[7,6],[5,4]]
+        self.assertEquals(Series.longLongMin(matrix), 4)
+
+    def testLongLongCeil(self):
+        "Test longLongCeil function"
+        matrix = N.array([[10,-2],[-6,7]],'q')
+        Series.longLongCeil(matrix,5)
+        N.testing.assert_array_equal(matrix, N.array([[5,-2],[-6,5]]))
+
     #########################################################
     ### Test functions that take arrays of type ULONGLONG ###
     def testUlonglongProd(self):
-        "Test the ulongLongProd function"
+        "Test ulongLongProd function"
         self.assertEquals(Series.ulongLongProd([1,2,3,4]), 24)
 
     def testUlongLongProdNonContainer(self):
-        "Test the ulongLongProd function with None"
+        "Test ulongLongProd function with None"
         self.assertRaises(TypeError, Series.ulongLongProd, None)
 
     def testUlongLongOnes(self):
-        "Test the ulongLongOnes function"
+        "Test ulongLongOnes function"
         myArray = N.zeros(5,'Q')
         Series.ulongLongOnes(myArray)
         N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
 
     def testUlongLongMax(self):
-        "Test the ulongLongMax function"
+        "Test ulongLongMax function"
         matrix = [[6,5,4],[3,2,1]]
         self.assertEquals(Series.ulongLongMax(matrix), 6)
 
     def testUlongLongMaxNonContainer(self):
-        "Test the ulongLongMax function with None"
+        "Test ulongLongMax function with None"
         self.assertRaises(TypeError, Series.ulongLongMax, None)
 
     def testUlongLongMaxWrongDim(self):
-        "Test the ulongLongMax function with a 1D array"
+        "Test ulongLongMax function with a 1D array"
         self.assertRaises(TypeError, Series.ulongLongMax, [0, 1, 2, 3])
 
     def testUlongLongFloor(self):
-        "Test the ulongLongFloor function"
+        "Test ulongLongFloor function"
         matrix = N.array([[10,2],[6,7]],'Q')
         Series.ulongLongFloor(matrix,7)
-        N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
+        N.testing.assert_array_equal(matrix, N.array([[10,7],[7,7]]))
 
+    def testUlongLongSum(self):
+        "Test ulongLongSum function"
+        self.assertEquals(Series.ulongLongSum([5,6,7,8]), 26)
+
+    def testUlongLongZeros(self):
+        "Test ulongLongZeros function"
+        myArray = N.ones(5,'Q')
+        Series.ulongLongZeros(myArray)
+        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
+
+    def testUlongLongMin(self):
+        "Test ulongLongMin function"
+        matrix = [[9,8],[7,6],[5,4]]
+        self.assertEquals(Series.ulongLongMin(matrix), 4)
+
+    def testUlongLongCeil(self):
+        "Test ulongLongCeil function"
+        matrix = N.array([[10,2],[6,7]],'Q')
+        Series.ulongLongCeil(matrix,5)
+        N.testing.assert_array_equal(matrix, N.array([[5,2],[5,5]]))
+
     #####################################################
     ### Test functions that take arrays of type FLOAT ###
     def testFloatProd(self):
-        "Test the floatProd function (to 5 decimal places)"
+        "Test floatProd function (to 5 decimal places)"
         self.assertAlmostEquals(Series.floatProd((1,2.718,3,4)), 32.616, 5)
 
     def testFloatProdBadContainer(self):
-        "Test the floatProd function with an invalid list"
+        "Test floatProd function with an invalid list"
         self.assertRaises(BadListError, Series.floatProd, [3.14, "pi"])
 
     def testFloatOnes(self):
-        "Test the floatOnes function"
+        "Test floatOnes function"
         myArray = N.zeros(5,'f')
         Series.floatOnes(myArray)
         N.testing.assert_array_equal(myArray, N.array([1.,1.,1.,1.,1.]))
 
     def testFloatOnesNonArray(self):
-        "Test the floatOnes function with a list"
+        "Test floatOnes function with a list"
         self.assertRaises(TypeError, Series.floatOnes, [True, 0, 2.718, "pi"])
 
     def testFloatMax(self):
-        "Test the floatMax function"
+        "Test floatMax function"
         matrix = [[-6,5,-4],[3.14,-2.718,1]]
         self.assertEquals(Series.floatMax(matrix), 5.0)
 
     def testFloatMaxNonContainer(self):
-        "Test the floatMax function with None"
+        "Test floatMax function with None"
         self.assertRaises(TypeError, Series.floatMax, None)
 
     def testFloatMaxWrongDim(self):
-        "Test the floatMax function with a 1D array"
+        "Test floatMax function with a 1D array"
         self.assertRaises(TypeError, Series.floatMax, [0.0, -1, 2.718, -3.14])
 
     def testFloatFloor(self):
-        "Test the floatFloor function"
+        "Test floatFloor function"
         matrix = N.array([[10,-2],[-6,7]],'f')
         Series.floatFloor(matrix,0)
         N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
 
+    def testFloatSum(self):
+        "Test floatSum function"
+        self.assertEquals(Series.floatSum([-5,6,-7,8]), 2)
+
+    def testFloatZeros(self):
+        "Test floatZeros function"
+        myArray = N.ones(5,'f')
+        Series.floatZeros(myArray)
+        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
+
+    def testFloatMin(self):
+        "Test floatMin function"
+        matrix = [[9,8],[7,6],[5,4]]
+        self.assertEquals(Series.floatMin(matrix), 4)
+
+    def testFloatCeil(self):
+        "Test floatCeil function"
+        matrix = N.array([[10,-2],[-6,7]],'f')
+        Series.floatCeil(matrix,5)
+        N.testing.assert_array_equal(matrix, N.array([[5,-2],[-6,5]]))
+
     ######################################################
     ### Test functions that take arrays of type DOUBLE ###
     def testDoubleProd(self):
-        "Test the doubleProd function"
+        "Test doubleProd function"
         self.assertEquals(Series.doubleProd((1,2.718,3,4)), 32.616)
 
     def testDoubleProdBadContainer(self):
-        "Test the doubleProd function with an invalid list"
+        "Test doubleProd function with an invalid list"
         self.assertRaises(BadListError, Series.doubleProd, [3.14, "pi"])
 
     def testDoubleOnes(self):
-        "Test the doubleOnes function"
+        "Test doubleOnes function"
         myArray = N.zeros(5,'d')
         Series.doubleOnes(myArray)
         N.testing.assert_array_equal(myArray, N.array([1.,1.,1.,1.,1.]))
 
     def testDoubleOnesNonArray(self):
-        "Test the doubleOnes function with a list"
+        "Test doubleOnes function with a list"
         self.assertRaises(TypeError, Series.doubleOnes, [True, 0, 2.718, "pi"])
 
     def testDoubleMax(self):
-        "Test the doubleMax function"
+        "Test doubleMax function"
         matrix = [[-6,5,-4],[3.14,-2.718,1]]
         self.assertEquals(Series.doubleMax(matrix), 5.0)
 
     def testDoubleMaxNonContainer(self):
-        "Test the doubleMax function with None"
+        "Test doubleMax function with None"
         self.assertRaises(TypeError, Series.doubleMax, None)
 
     def testDoubleMaxWrongDim(self):
-        "Test the doubleMax function with a 1D array"
+        "Test doubleMax function with a 1D array"
         self.assertRaises(TypeError, Series.doubleMax, [0.0, -1, 2.718, -3.14])
 
     def testDoubleFloor(self):
-        "Test the doubleFloor function"
+        "Test doubleFloor function"
         matrix = N.array([[10,-2],[-6,7]],'d')
         Series.doubleFloor(matrix,0)
         N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
 
-    # ##########################################################
-    # ### Test functions that take arrays of type LONGDOUBLE ###
-    # def testLongDoubleProd(self):
-    #     "Test the longDoubleProd function"
-    #     self.assertEquals(Series.longDoubleProd((1,2.718,3,4)), 32.616)
+    def testDoubleSum(self):
+        "Test doubleSum function"
+        self.assertEquals(Series.doubleSum([-5,6,-7,8]), 2)
 
-    # def testLongDoubleProdBadContainer(self):
-    #     "Test the longDoubleProd function with an invalid list"
-    #     self.assertRaises(BadListError, Series.longDoubleProd, [3.14, "pi"])
+    def testDoubleZeros(self):
+        "Test doubleZeros function"
+        myArray = N.ones(5,'d')
+        Series.doubleZeros(myArray)
+        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
 
-    # def testLongDoubleOnes(self):
-    #     "Test the longDoubleOnes function"
-    #     myArray = N.zeros(5,'g')
-    #     Series.longDoubleOnes(myArray)
-    #     N.testing.assert_array_equal(myArray, N.array([1.,1.,1.,1.,1.]))
+    def testDoubleMin(self):
+        "Test doubleMin function"
+        matrix = [[9,8],[7,6],[5,4]]
+        self.assertEquals(Series.doubleMin(matrix), 4)
 
-    # def testLongDoubleOnesNonArray(self):
-    #     "Test the longDoubleOnes function with a list"
-    #     self.assertRaises(TypeError, Series.longDoubleOnes, [True, 0, 2.718, "pi"])
+    def testDoubleCeil(self):
+        "Test doubleCeil function"
+        matrix = N.array([[10,-2],[-6,7]],'d')
+        Series.doubleCeil(matrix,5)
+        N.testing.assert_array_equal(matrix, N.array([[5,-2],[-6,5]]))
 
 ######################################################################
 



From numpy-svn at scipy.org  Wed Mar 14 16:15:35 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Wed, 14 Mar 2007 15:15:35 -0500 (CDT)
Subject: [Numpy-svn] r3576 - trunk/numpy/doc/swig
Message-ID: <20070314201535.10D7539C0A5@new.scipy.org>

Author: wfspotz at sandia.gov
Date: 2007-03-14 15:15:31 -0500 (Wed, 14 Mar 2007)
New Revision: 3576

Modified:
   trunk/numpy/doc/swig/Makefile
   trunk/numpy/doc/swig/README
   trunk/numpy/doc/swig/testSeries.py
Log:
testSeries.py now prints numpy version; documentation updated

Modified: trunk/numpy/doc/swig/Makefile
===================================================================
--- trunk/numpy/doc/swig/Makefile	2007-03-14 19:57:23 UTC (rev 3575)
+++ trunk/numpy/doc/swig/Makefile	2007-03-14 20:15:31 UTC (rev 3576)
@@ -13,7 +13,7 @@
 # List all of the subdirectories here for recursive make
 SUBDIRS = 
 
-all: $(WRAPPERS) series.cxx series.h
+all: $(WRAPPERS) series.cxx series.h html
 	./setup.py build
 
 %_wrap.cxx: %.i numpy.i series.h

Modified: trunk/numpy/doc/swig/README
===================================================================
--- trunk/numpy/doc/swig/README	2007-03-14 19:57:23 UTC (rev 3575)
+++ trunk/numpy/doc/swig/README	2007-03-14 20:15:31 UTC (rev 3576)
@@ -72,7 +72,7 @@
 ----
 
   * Support for complex data types should be added.  Currently swig
-    dies with a syntax error when the %numpy_typemaps macro isused
+    dies with a syntax error when the %numpy_typemaps macro is used
     with complex types.
 
   * Better ARGOUT typemaps need to be implemented and tested.  I

Modified: trunk/numpy/doc/swig/testSeries.py
===================================================================
--- trunk/numpy/doc/swig/testSeries.py	2007-03-14 19:57:23 UTC (rev 3575)
+++ trunk/numpy/doc/swig/testSeries.py	2007-03-14 20:15:31 UTC (rev 3576)
@@ -711,6 +711,8 @@
     suite.addTest(unittest.makeSuite(SeriesTestCase))
 
     # Execute the test suite
-    print "Testing Module Series\n"
+    print "Testing Module Series"
+    print "NumPy version", N.__version__
+    print
     result = unittest.TextTestRunner(verbosity=2).run(suite)
     sys.exit(len(result.errors) + len(result.failures))



From numpy-svn at scipy.org  Wed Mar 14 17:07:11 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Wed, 14 Mar 2007 16:07:11 -0500 (CDT)
Subject: [Numpy-svn] r3577 - trunk/numpy/doc/swig
Message-ID: <20070314210711.6E67D39C0B7@new.scipy.org>

Author: wfspotz at sandia.gov
Date: 2007-03-14 16:07:05 -0500 (Wed, 14 Mar 2007)
New Revision: 3577

Added:
   trunk/numpy/doc/swig/numpy_swig.pdf
Modified:
   trunk/numpy/doc/swig/Makefile
   trunk/numpy/doc/swig/numpy_swig.html
Log:
Added LaTeX/PDF documentation

Modified: trunk/numpy/doc/swig/Makefile
===================================================================
--- trunk/numpy/doc/swig/Makefile	2007-03-14 20:15:31 UTC (rev 3576)
+++ trunk/numpy/doc/swig/Makefile	2007-03-14 21:07:05 UTC (rev 3577)
@@ -5,28 +5,53 @@
 
 # ReStructured Text
 RST2HTML = rst2html.py
-RFLAGS = --generator --time --no-xml-declaration
+RST2LATEX = rst2latex.py
+RFLAGS = --generator --time
+HTML_FLAGS = --no-xml-declaration
+LATEX_FLAGS =
+LATEX = pdflatex
 
 # Web pages that need to be made
 WEB_PAGES = numpy_swig.html
 
+# LaTeX files that need to be made
+LATEX_FILES = numpy_swig.tex
+
+# PDF files that need to be made
+PDF_FILES = numpy_swig.pdf
+
 # List all of the subdirectories here for recursive make
 SUBDIRS = 
 
-all: $(WRAPPERS) series.cxx series.h html
+all: $(WRAPPERS) series.cxx series.h
 	./setup.py build
 
+doc: html pdf
+
 %_wrap.cxx: %.i numpy.i series.h
 	swig -c++ -python $<
 
 html: $(WEB_PAGES)
 
 %.html: %.txt
-	$(RST2HTML) $(RFLAGS) $< $@
+	$(RST2HTML) $(RFLAGS) $(HTML_FLAGS) $< $@
 
+tex: $(LATEX_FILES)
+
+%.tex: %.txt
+	$(RST2LATEX) $(RFLAGS) $(LATEX_FLAGS) $< $@
+
+pdf: $(PDF_FILES)
+
+%.pdf: %.tex
+	$(LATEX) $<
+	$(LATEX) $<
+
 clean:
 	$(RM) -r build
 	$(RM) $(WRAPPERS)
 	$(RM) $(PROXIES)
+	$(RM) $(LATEX_FILES)
+	$(RM) *.aux *.dvi *.log *.out *~
 
-.PHONY : html clean
+.PHONY : alll doc html tex pdf clean

Modified: trunk/numpy/doc/swig/numpy_swig.html
===================================================================
--- trunk/numpy/doc/swig/numpy_swig.html	2007-03-14 20:15:31 UTC (rev 3576)
+++ trunk/numpy/doc/swig/numpy_swig.html	2007-03-14 21:07:05 UTC (rev 3577)
@@ -623,7 +623,7 @@
 </div>
 <div class="footer">
 <hr class="footer" />
-Generated on: 2007-03-14 19:50 UTC.
+Generated on: 2007-03-14 20:27 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>

Added: trunk/numpy/doc/swig/numpy_swig.pdf
===================================================================
--- trunk/numpy/doc/swig/numpy_swig.pdf	2007-03-14 20:15:31 UTC (rev 3576)
+++ trunk/numpy/doc/swig/numpy_swig.pdf	2007-03-14 21:07:05 UTC (rev 3577)
@@ -0,0 +1,1420 @@
+%PDF-1.4
+5 0 obj
+<< /S /GoTo /D (introduction.0) >>
+endobj
+8 0 obj
+(Introduction)
+endobj
+9 0 obj
+<< /S /GoTo /D (using-numpy-i.0) >>
+endobj
+12 0 obj
+(Using numpy.i)
+endobj
+13 0 obj
+<< /S /GoTo /D (available-typemaps.0) >>
+endobj
+16 0 obj
+(Available Typemaps)
+endobj
+17 0 obj
+<< /S /GoTo /D (input-arrays.1) >>
+endobj
+20 0 obj
+(Input Arrays)
+endobj
+21 0 obj
+<< /S /GoTo /D (in-place-arrays.1) >>
+endobj
+24 0 obj
+(In-place Arrays)
+endobj
+25 0 obj
+<< /S /GoTo /D (argout-arrays.1) >>
+endobj
+28 0 obj
+(Argout Arrays)
+endobj
+29 0 obj
+<< /S /GoTo /D (helper-functions.0) >>
+endobj
+32 0 obj
+(Helper Functions)
+endobj
+33 0 obj
+<< /S /GoTo /D (macros.1) >>
+endobj
+36 0 obj
+(Macros)
+endobj
+37 0 obj
+<< /S /GoTo /D (routines.1) >>
+endobj
+40 0 obj
+(Routines)
+endobj
+41 0 obj
+<< /S /GoTo /D (acknowledgements.0) >>
+endobj
+44 0 obj
+(Acknowledgements)
+endobj
+45 0 obj
+<< /S /GoTo /D [46 0 R  /Fit ] >>
+endobj
+48 0 obj <<
+/Length 3630      
+/Filter /FlateDecode
+>>
+stream
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+<<
+/Size 171
+/Root 169 0 R
+/Info 170 0 R
+/ID [<8D2853DC6A1BBFAC7BA2723500BA9EB7> <8D2853DC6A1BBFAC7BA2723500BA9EB7>]
+>>
+startxref
+97211
+%%EOF



From numpy-svn at scipy.org  Wed Mar 14 17:08:28 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Wed, 14 Mar 2007 16:08:28 -0500 (CDT)
Subject: [Numpy-svn] r3578 - trunk/numpy/doc/swig
Message-ID: <20070314210828.9F13939C0B7@new.scipy.org>

Author: wfspotz at sandia.gov
Date: 2007-03-14 16:08:25 -0500 (Wed, 14 Mar 2007)
New Revision: 3578

Modified:
   trunk/numpy/doc/swig/Makefile
Log:
Fixed typo in Makefile

Modified: trunk/numpy/doc/swig/Makefile
===================================================================
--- trunk/numpy/doc/swig/Makefile	2007-03-14 21:07:05 UTC (rev 3577)
+++ trunk/numpy/doc/swig/Makefile	2007-03-14 21:08:25 UTC (rev 3578)
@@ -54,4 +54,4 @@
 	$(RM) $(LATEX_FILES)
 	$(RM) *.aux *.dvi *.log *.out *~
 
-.PHONY : alll doc html tex pdf clean
+.PHONY : all doc html tex pdf clean



From numpy-svn at scipy.org  Wed Mar 14 17:12:04 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Wed, 14 Mar 2007 16:12:04 -0500 (CDT)
Subject: [Numpy-svn] r3579 - trunk/numpy/doc/swig
Message-ID: <20070314211204.4CDD639C0C8@new.scipy.org>

Author: wfspotz at sandia.gov
Date: 2007-03-14 16:11:59 -0500 (Wed, 14 Mar 2007)
New Revision: 3579

Modified:
   trunk/numpy/doc/swig/README
Log:
Updated README file

Modified: trunk/numpy/doc/swig/README
===================================================================
--- trunk/numpy/doc/swig/README	2007-03-14 21:08:25 UTC (rev 3578)
+++ trunk/numpy/doc/swig/README	2007-03-14 21:11:59 UTC (rev 3579)
@@ -58,8 +58,10 @@
 a _Series extension module and a Series python module.  The Makefile
 automates everything, setting up the dependencies, calling swig to
 generate the wrappers, and calling setup.py to compile the wrapper
-code and generate the shared object.  Targets "all" (default),
-"clean" and "html" (generate HTML documentation) are supported.
+code and generate the shared object.  Targets "all" (default), "doc"
+and "clean" are supported.  The "doc" target creates HTML
+documentation (with make target "html"), and PDF documentation (with
+make targets "tex" and "pdf").
 
 To build and test the code, simply execute from the shell::
 



From numpy-svn at scipy.org  Thu Mar 15 10:45:39 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Thu, 15 Mar 2007 09:45:39 -0500 (CDT)
Subject: [Numpy-svn] r3580 - trunk/numpy/core/src
Message-ID: <20070315144539.0BAE039C096@new.scipy.org>

Author: oliphant
Date: 2007-03-15 09:45:35 -0500 (Thu, 15 Mar 2007)
New Revision: 3580

Modified:
   trunk/numpy/core/src/arrayobject.c
Log:
Fix missing addition bug.

Modified: trunk/numpy/core/src/arrayobject.c
===================================================================
--- trunk/numpy/core/src/arrayobject.c	2007-03-14 21:11:59 UTC (rev 3579)
+++ trunk/numpy/core/src/arrayobject.c	2007-03-15 14:45:35 UTC (rev 3580)
@@ -8942,7 +8942,7 @@
         for (i=0; i<multi->nd; i++) {
                 sumstrides[i] = 0;
                 for (j=0; j<multi->numiter; j++) {
-                        sumstrides[i] = multi->iters[j]->strides[i];
+                        sumstrides[i] += multi->iters[j]->strides[i];
                 }
         }
         axis=0;



From numpy-svn at scipy.org  Fri Mar 16 08:53:15 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Fri, 16 Mar 2007 07:53:15 -0500 (CDT)
Subject: [Numpy-svn] r3581 - trunk/numpy/lib
Message-ID: <20070316125315.2259539C015@new.scipy.org>

Author: stefan
Date: 2007-03-16 07:53:05 -0500 (Fri, 16 Mar 2007)
New Revision: 3581

Modified:
   trunk/numpy/lib/index_tricks.py
Log:
Remove invalid part of r_ docstring.


Modified: trunk/numpy/lib/index_tricks.py
===================================================================
--- trunk/numpy/lib/index_tricks.py	2007-03-15 14:45:35 UTC (rev 3580)
+++ trunk/numpy/lib/index_tricks.py	2007-03-16 12:53:05 UTC (rev 3581)
@@ -327,8 +327,6 @@
 
 class c_class(concatenator):
     """Translates slice objects to concatenation along the second axis.
-
-       This is equivalent to r_['-1,2,0',...]
     """
     def __init__(self):
         concatenator.__init__(self, -1, ndmin=2, trans1d=0)



From numpy-svn at scipy.org  Mon Mar 19 13:19:50 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Mon, 19 Mar 2007 12:19:50 -0500 (CDT)
Subject: [Numpy-svn] r3582 - trunk/numpy/random/mtrand
Message-ID: <20070319171950.1C84039C097@new.scipy.org>

Author: oliphant
Date: 2007-03-19 12:19:39 -0500 (Mon, 19 Mar 2007)
New Revision: 3582

Modified:
   trunk/numpy/random/mtrand/Python.pxi
   trunk/numpy/random/mtrand/mtrand.c
   trunk/numpy/random/mtrand/mtrand.pyx
Log:
Make random number generators faster for scalar parameters.

Modified: trunk/numpy/random/mtrand/Python.pxi
===================================================================
--- trunk/numpy/random/mtrand/Python.pxi	2007-03-16 12:53:05 UTC (rev 3581)
+++ trunk/numpy/random/mtrand/Python.pxi	2007-03-19 17:19:39 UTC (rev 3582)
@@ -10,9 +10,13 @@
 
     # String API
     char* PyString_AsString(object string)
+    char* PyString_AS_STRING(object string)
     object PyString_FromString(char* c_string)
     object PyString_FromStringAndSize(char* c_string, int length)
 
+    # Float API
+    double PyFloat_AsDouble(object ob)
+
     # Memory API
     void* PyMem_Malloc(size_t n)
     void* PyMem_Realloc(void* buf, size_t n)
@@ -32,8 +36,16 @@
         destructor2 destr)
     void* PyCObject_AsVoidPtr(object self)
     void* PyCObject_GetDesc(object self)
-    int PyCObject_SetVoidPtr(object self, void* cobj)
-   
+    int PyCObject_SetVoidPtr(object self, void* cobj)  
+
+    # TypeCheck API
+    int PyFloat_Check(object obj)
+    int PyInt_Check(object obj)
+
+    # Error API
+    int PyErr_Occurred()
+    void PyErr_Clear()
+ 
 cdef extern from "string.h":
     void *memcpy(void *s1, void *s2, int n)
 

Modified: trunk/numpy/random/mtrand/mtrand.c
===================================================================
--- trunk/numpy/random/mtrand/mtrand.c	2007-03-16 12:53:05 UTC (rev 3581)
+++ trunk/numpy/random/mtrand/mtrand.c	2007-03-19 17:19:39 UTC (rev 3582)
@@ -1,4 +1,4 @@
-/* Generated by Pyrex 0.9.5.1a on Thu Feb  1 14:12:28 2007 */
+/* Generated by Pyrex 0.9.5.1 on Sat Mar 17 23:42:27 2007 */
 
 #include "Python.h"
 #include "structmember.h"
@@ -124,12 +124,18 @@
 static PyObject *__pyx_k59;
 static PyObject *__pyx_k60;
 static PyObject *(__pyx_f_6mtrand_cont0_array(rk_state (*),double ((*)(rk_state (*))),PyObject *)); /*proto*/
+static PyObject *(__pyx_f_6mtrand_cont1_array_sc(rk_state (*),double ((*)(rk_state (*),double )),PyObject *,double )); /*proto*/
 static PyObject *(__pyx_f_6mtrand_cont1_array(rk_state (*),double ((*)(rk_state (*),double )),PyObject *,PyArrayObject *)); /*proto*/
+static PyObject *(__pyx_f_6mtrand_cont2_array_sc(rk_state (*),double ((*)(rk_state (*),double ,double )),PyObject *,double ,double )); /*proto*/
 static PyObject *(__pyx_f_6mtrand_cont2_array(rk_state (*),double ((*)(rk_state (*),double ,double )),PyObject *,PyArrayObject *,PyArrayObject *)); /*proto*/
+static PyObject *(__pyx_f_6mtrand_cont3_array_sc(rk_state (*),double ((*)(rk_state (*),double ,double ,double )),PyObject *,double ,double ,double )); /*proto*/
 static PyObject *(__pyx_f_6mtrand_cont3_array(rk_state (*),double ((*)(rk_state (*),double ,double ,double )),PyObject *,PyArrayObject *,PyArrayObject *,PyArrayObject *)); /*proto*/
 static PyObject *(__pyx_f_6mtrand_disc0_array(rk_state (*),long ((*)(rk_state (*))),PyObject *)); /*proto*/
+static PyObject *(__pyx_f_6mtrand_discnp_array_sc(rk_state (*),long ((*)(rk_state (*),long ,double )),PyObject *,long ,double )); /*proto*/
 static PyObject *(__pyx_f_6mtrand_discnp_array(rk_state (*),long ((*)(rk_state (*),long ,double )),PyObject *,PyArrayObject *,PyArrayObject *)); /*proto*/
+static PyObject *(__pyx_f_6mtrand_discnmN_array_sc(rk_state (*),long ((*)(rk_state (*),long ,long ,long )),PyObject *,long ,long ,long )); /*proto*/
 static PyObject *(__pyx_f_6mtrand_discnmN_array(rk_state (*),long ((*)(rk_state (*),long ,long ,long )),PyObject *,PyArrayObject *,PyArrayObject *,PyArrayObject *)); /*proto*/
+static PyObject *(__pyx_f_6mtrand_discd_array_sc(rk_state (*),long ((*)(rk_state (*),double )),PyObject *,double )); /*proto*/
 static PyObject *(__pyx_f_6mtrand_discd_array(rk_state (*),long ((*)(rk_state (*),double )),PyObject *,PyArrayObject *)); /*proto*/
 static double (__pyx_f_6mtrand_kahan_sum(double (*),long )); /*proto*/
 
@@ -270,6 +276,88 @@
   return __pyx_r;
 }
 
+static PyObject *__pyx_n_Float64;
+
+static PyObject *__pyx_f_6mtrand_cont1_array_sc(rk_state (*__pyx_v_state),double ((*__pyx_v_func)(rk_state (*),double )),PyObject *__pyx_v_size,double __pyx_v_a) {
+  double (*__pyx_v_array_data);
+  PyArrayObject *arrayObject;
+  long __pyx_v_length;
+  long __pyx_v_i;
+  PyObject *__pyx_r;
+  int __pyx_1;
+  PyObject *__pyx_2 = 0;
+  PyObject *__pyx_3 = 0;
+  PyObject *__pyx_4 = 0;
+  Py_INCREF(__pyx_v_size);
+  arrayObject = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
+
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":146 */
+  __pyx_1 = __pyx_v_size == Py_None;
+  if (__pyx_1) {
+
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":147 */
+    __pyx_2 = PyFloat_FromDouble(__pyx_v_func(__pyx_v_state,__pyx_v_a)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 147; goto __pyx_L1;}
+    __pyx_r = __pyx_2;
+    __pyx_2 = 0;
+    goto __pyx_L0;
+    goto __pyx_L2;
+  }
+  /*else*/ {
+
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":149 */
+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 149; goto __pyx_L1;}
+    __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 149; goto __pyx_L1;}
+    Py_DECREF(__pyx_2); __pyx_2 = 0;
+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 149; goto __pyx_L1;}
+    __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_n_Float64); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 149; goto __pyx_L1;}
+    Py_DECREF(__pyx_2); __pyx_2 = 0;
+    __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 149; goto __pyx_L1;}
+    Py_INCREF(__pyx_v_size);
+    PyTuple_SET_ITEM(__pyx_2, 0, __pyx_v_size);
+    PyTuple_SET_ITEM(__pyx_2, 1, __pyx_4);
+    __pyx_4 = 0;
+    __pyx_4 = PyObject_CallObject(__pyx_3, __pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 149; goto __pyx_L1;}
+    Py_DECREF(__pyx_3); __pyx_3 = 0;
+    Py_DECREF(__pyx_2); __pyx_2 = 0;
+    Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_4)));
+    Py_DECREF(((PyObject *)arrayObject));
+    arrayObject = ((PyArrayObject *)__pyx_4);
+    Py_DECREF(__pyx_4); __pyx_4 = 0;
+
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":150 */
+    __pyx_v_length = PyArray_SIZE(arrayObject);
+
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":151 */
+    __pyx_v_array_data = ((double (*))arrayObject->data);
+
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":152 */
+    for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; ++__pyx_v_i) {
+
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":153 */
+      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,__pyx_v_a);
+    }
+
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":154 */
+    Py_INCREF(((PyObject *)arrayObject));
+    __pyx_r = ((PyObject *)arrayObject);
+    goto __pyx_L0;
+  }
+  __pyx_L2:;
+
+  __pyx_r = Py_None; Py_INCREF(Py_None);
+  goto __pyx_L0;
+  __pyx_L1:;
+  Py_XDECREF(__pyx_2);
+  Py_XDECREF(__pyx_3);
+  Py_XDECREF(__pyx_4);
+  __Pyx_AddTraceback("mtrand.cont1_array_sc");
+  __pyx_r = 0;
+  __pyx_L0:;
+  Py_DECREF(arrayObject);
+  Py_DECREF(__pyx_v_size);
+  return __pyx_r;
+}
+
 static PyObject *__pyx_n_ValueError;
 
 static PyObject *__pyx_k61p;
@@ -284,7 +372,6 @@
   npy_intp __pyx_v_i;
   PyArrayIterObject *__pyx_v_itera;
   PyArrayMultiIterObject *__pyx_v_multi;
-  int __pyx_v_scalar;
   PyObject *__pyx_r;
   int __pyx_1;
   PyObject *__pyx_2 = 0;
@@ -297,87 +384,56 @@
   __pyx_v_itera = ((PyArrayIterObject *)Py_None); Py_INCREF(Py_None);
   __pyx_v_multi = ((PyArrayMultiIterObject *)Py_None); Py_INCREF(Py_None);
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":149 */
-  __pyx_v_scalar = 0;
-
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":150 */
-  __pyx_1 = (__pyx_v_oa->nd == 0);
-  if (__pyx_1) {
-
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":151 */
-    __pyx_v_oa_data = ((double (*))__pyx_v_oa->data);
-
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":152 */
-    __pyx_v_scalar = 1;
-    goto __pyx_L2;
-  }
-  __pyx_L2:;
-
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":154 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":165 */
   __pyx_1 = __pyx_v_size == Py_None;
   if (__pyx_1) {
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":155 */
-    __pyx_1 = __pyx_v_scalar;
-    if (__pyx_1) {
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":166 */
+    __pyx_2 = PyArray_SimpleNew(__pyx_v_oa->nd,__pyx_v_oa->dimensions,NPY_DOUBLE); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 166; goto __pyx_L1;}
+    Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_2)));
+    Py_DECREF(((PyObject *)arrayObject));
+    arrayObject = ((PyArrayObject *)__pyx_2);
+    Py_DECREF(__pyx_2); __pyx_2 = 0;
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":156 */
-      __pyx_2 = PyFloat_FromDouble(__pyx_v_func(__pyx_v_state,(__pyx_v_oa_data[0]))); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 156; goto __pyx_L1;}
-      __pyx_r = __pyx_2;
-      __pyx_2 = 0;
-      goto __pyx_L0;
-      goto __pyx_L4;
-    }
-    /*else*/ {
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":167 */
+    __pyx_v_length = PyArray_SIZE(arrayObject);
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":158 */
-      __pyx_2 = PyArray_SimpleNew(__pyx_v_oa->nd,__pyx_v_oa->dimensions,NPY_DOUBLE); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 158; goto __pyx_L1;}
-      Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_2)));
-      Py_DECREF(((PyObject *)arrayObject));
-      arrayObject = ((PyArrayObject *)__pyx_2);
-      Py_DECREF(__pyx_2); __pyx_2 = 0;
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":168 */
+    __pyx_v_array_data = ((double (*))arrayObject->data);
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":159 */
-      __pyx_v_length = PyArray_SIZE(arrayObject);
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":169 */
+    __pyx_2 = PyArray_IterNew(((PyObject *)__pyx_v_oa)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 169; goto __pyx_L1;}
+    Py_INCREF(((PyObject *)((PyArrayIterObject *)__pyx_2)));
+    Py_DECREF(((PyObject *)__pyx_v_itera));
+    __pyx_v_itera = ((PyArrayIterObject *)__pyx_2);
+    Py_DECREF(__pyx_2); __pyx_2 = 0;
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":160 */
-      __pyx_v_array_data = ((double (*))arrayObject->data);
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":170 */
+    for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; ++__pyx_v_i) {
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":161 */
-      __pyx_2 = PyArray_IterNew(((PyObject *)__pyx_v_oa)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 161; goto __pyx_L1;}
-      Py_INCREF(((PyObject *)((PyArrayIterObject *)__pyx_2)));
-      Py_DECREF(((PyObject *)__pyx_v_itera));
-      __pyx_v_itera = ((PyArrayIterObject *)__pyx_2);
-      Py_DECREF(__pyx_2); __pyx_2 = 0;
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":171 */
+      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,(((double (*))__pyx_v_itera->dataptr)[0]));
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":162 */
-      for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; ++__pyx_v_i) {
-
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":163 */
-        (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,(((double (*))__pyx_v_itera->dataptr)[0]));
-
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":164 */
-        PyArray_ITER_NEXT(__pyx_v_itera);
-      }
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":172 */
+      PyArray_ITER_NEXT(__pyx_v_itera);
     }
-    __pyx_L4:;
-    goto __pyx_L3;
+    goto __pyx_L2;
   }
   /*else*/ {
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":166 */
-    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 166; goto __pyx_L1;}
-    __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 166; goto __pyx_L1;}
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":174 */
+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 174; goto __pyx_L1;}
+    __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 174; goto __pyx_L1;}
     Py_DECREF(__pyx_2); __pyx_2 = 0;
-    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 166; goto __pyx_L1;}
-    __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_n_float64); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 166; goto __pyx_L1;}
+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 174; goto __pyx_L1;}
+    __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_n_float64); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 174; goto __pyx_L1;}
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-    __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 166; goto __pyx_L1;}
+    __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 174; goto __pyx_L1;}
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-    __pyx_4 = PyObject_CallObject(__pyx_3, __pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 166; goto __pyx_L1;}
+    __pyx_4 = PyObject_CallObject(__pyx_3, __pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 174; goto __pyx_L1;}
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     Py_DECREF(__pyx_4); __pyx_4 = 0;
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":167 */
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":168 */
-    __pyx_1 = __pyx_v_scalar;
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+    __pyx_3 = PyArray_MultiIterNew(2,((void (*))arrayObject),((void (*))__pyx_v_oa)); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 176; goto __pyx_L1;}
+    Py_INCREF(((PyObject *)((PyArrayMultiIterObject *)__pyx_3)));
+    Py_DECREF(((PyObject *)__pyx_v_multi));
+    __pyx_v_multi = ((PyArrayMultiIterObject *)__pyx_3);
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+    __pyx_1 = (__pyx_v_multi->size != PyArray_SIZE(arrayObject));
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-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":169 */
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-      goto __pyx_L7;
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+      __pyx_2 = __Pyx_GetName(__pyx_b, __pyx_n_ValueError); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 179; goto __pyx_L1;}
+      __pyx_4 = PyTuple_New(1); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 179; goto __pyx_L1;}
+      Py_INCREF(__pyx_k61p);
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+      Py_DECREF(__pyx_2); __pyx_2 = 0;
+      Py_DECREF(__pyx_4); __pyx_4 = 0;
+      __Pyx_Raise(__pyx_3, 0, 0);
+      Py_DECREF(__pyx_3); __pyx_3 = 0;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 179; goto __pyx_L1;}
+      goto __pyx_L5;
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-    /*else*/ {
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-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":173 */
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-      Py_DECREF(((PyObject *)__pyx_v_multi));
-      __pyx_v_multi = ((PyArrayMultiIterObject *)__pyx_3);
-      Py_DECREF(__pyx_3); __pyx_3 = 0;
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":180 */
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+    for (__pyx_v_i = 0; __pyx_v_i < __pyx_5; ++__pyx_v_i) {
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":175 */
-      __pyx_1 = (__pyx_v_multi->size != PyArray_SIZE(arrayObject));
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+      __pyx_v_oa_data = ((double (*))PyArray_MultiIter_DATA(__pyx_v_multi,1));
 
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":176 */
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-        Py_INCREF(__pyx_k61p);
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-        Py_DECREF(__pyx_2); __pyx_2 = 0;
-        Py_DECREF(__pyx_4); __pyx_4 = 0;
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-        Py_DECREF(__pyx_3); __pyx_3 = 0;
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-        goto __pyx_L10;
-      }
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-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":177 */
-      __pyx_5 = __pyx_v_multi->size;
-      for (__pyx_v_i = 0; __pyx_v_i < __pyx_5; ++__pyx_v_i) {
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-        __pyx_v_oa_data = ((double (*))PyArray_MultiIter_DATA(__pyx_v_multi,1));
-
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":179 */
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-
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":180 */
-        PyArray_MultiIter_NEXTi(__pyx_v_multi,1);
-      }
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":183 */
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-    __pyx_L7:;
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-  __pyx_L3:;
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":181 */
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   Py_INCREF(((PyObject *)arrayObject));
   __pyx_r = ((PyObject *)arrayObject);
   goto __pyx_L0;
@@ -471,6 +508,86 @@
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 }
 
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+  double (*__pyx_v_array_data);
+  PyArrayObject *arrayObject;
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+  PyObject *__pyx_r;
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+  PyObject *__pyx_3 = 0;
+  PyObject *__pyx_4 = 0;
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+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":193 */
+  __pyx_1 = __pyx_v_size == Py_None;
+  if (__pyx_1) {
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+    __pyx_2 = PyFloat_FromDouble(__pyx_v_func(__pyx_v_state,__pyx_v_a,__pyx_v_b)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 194; goto __pyx_L1;}
+    __pyx_r = __pyx_2;
+    __pyx_2 = 0;
+    goto __pyx_L0;
+    goto __pyx_L2;
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+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 196; goto __pyx_L1;}
+    __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_n_float64); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 196; goto __pyx_L1;}
+    Py_DECREF(__pyx_2); __pyx_2 = 0;
+    __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 196; goto __pyx_L1;}
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+    PyTuple_SET_ITEM(__pyx_2, 0, __pyx_v_size);
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+    Py_DECREF(__pyx_3); __pyx_3 = 0;
+    Py_DECREF(__pyx_2); __pyx_2 = 0;
+    Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_4)));
+    Py_DECREF(((PyObject *)arrayObject));
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+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":199 */
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+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":200 */
+      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,__pyx_v_a,__pyx_v_b);
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+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":201 */
+    Py_INCREF(((PyObject *)arrayObject));
+    __pyx_r = ((PyObject *)arrayObject);
+    goto __pyx_L0;
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+  __pyx_L2:;
+
+  __pyx_r = Py_None; Py_INCREF(Py_None);
+  goto __pyx_L0;
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+  Py_XDECREF(__pyx_3);
+  Py_XDECREF(__pyx_4);
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+}
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 static char (__pyx_k62[]) = "size is not compatible with inputs";
@@ -480,10 +597,8 @@
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@@ -496,97 +611,60 @@
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":196 */
-    __pyx_v_oa_data = ((double (*))__pyx_v_oa->data);
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":198 */
-    __pyx_v_scalar = 1;
-    goto __pyx_L2;
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-  __pyx_L2:;
-
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":200 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":214 */
   __pyx_1 = __pyx_v_size == Py_None;
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":201 */
-    __pyx_1 = __pyx_v_scalar;
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-      __pyx_2 = 0;
-      goto __pyx_L0;
-      goto __pyx_L4;
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-      Py_INCREF(((PyObject *)((PyArrayMultiIterObject *)__pyx_2)));
-      Py_DECREF(((PyObject *)__pyx_v_multi));
-      __pyx_v_multi = ((PyArrayMultiIterObject *)__pyx_2);
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-      Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_2)));
-      Py_DECREF(((PyObject *)arrayObject));
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-      __pyx_v_array_data = ((double (*))arrayObject->data);
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-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":207 */
-      __pyx_3 = __pyx_v_multi->size;
-      for (__pyx_v_i = 0; __pyx_v_i < __pyx_3; ++__pyx_v_i) {
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-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":208 */
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-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":211 */
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+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 224; goto __pyx_L1;}
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-    __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 213; goto __pyx_L1;}
+    __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 224; goto __pyx_L1;}
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-    __pyx_5 = PyObject_CallObject(__pyx_4, __pyx_2); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 213; goto __pyx_L1;}
+    __pyx_5 = PyObject_CallObject(__pyx_4, __pyx_2); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 224; goto __pyx_L1;}
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@@ -594,77 +672,58 @@
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     Py_DECREF(__pyx_5); __pyx_5 = 0;
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":214 */
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     __pyx_v_array_data = ((double (*))arrayObject->data);
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":215 */
-    __pyx_1 = __pyx_v_scalar;
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+    __pyx_4 = PyArray_MultiIterNew(3,((void (*))arrayObject),((void (*))__pyx_v_oa),((void (*))__pyx_v_ob)); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 226; goto __pyx_L1;}
+    Py_INCREF(((PyObject *)((PyArrayMultiIterObject *)__pyx_4)));
+    Py_DECREF(((PyObject *)__pyx_v_multi));
+    __pyx_v_multi = ((PyArrayMultiIterObject *)__pyx_4);
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+    __pyx_1 = (__pyx_v_multi->size != PyArray_SIZE(arrayObject));
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-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":216 */
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+      Py_DECREF(__pyx_5); __pyx_5 = 0;
+      __Pyx_Raise(__pyx_4, 0, 0);
+      Py_DECREF(__pyx_4); __pyx_4 = 0;
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-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":220 */
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-      __pyx_v_multi = ((PyArrayMultiIterObject *)__pyx_4);
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-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":221 */
-      __pyx_1 = (__pyx_v_multi->size != PyArray_SIZE(arrayObject));
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-        goto __pyx_L10;
-      }
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+      __pyx_v_ob_data = ((double (*))PyArray_MultiIter_DATA(__pyx_v_multi,2));
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":223 */
-      __pyx_3 = __pyx_v_multi->size;
-      for (__pyx_v_i = 0; __pyx_v_i < __pyx_3; ++__pyx_v_i) {
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-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":224 */
-        __pyx_v_oa_data = ((double (*))PyArray_MultiIter_DATA(__pyx_v_multi,1));
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-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":225 */
-        __pyx_v_ob_data = ((double (*))PyArray_MultiIter_DATA(__pyx_v_multi,2));
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-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":227 */
-        PyArray_MultiIter_NEXTi(__pyx_v_multi,1);
-
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":228 */
-        PyArray_MultiIter_NEXTi(__pyx_v_multi,2);
-      }
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":234 */
+      PyArray_MultiIter_NEXTi(__pyx_v_multi,2);
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-    __pyx_L7:;
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-  __pyx_L3:;
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":229 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":235 */
   Py_INCREF(((PyObject *)arrayObject));
   __pyx_r = ((PyObject *)arrayObject);
   goto __pyx_L0;
@@ -686,6 +745,86 @@
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 }
 
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+  double (*__pyx_v_array_data);
+  PyArrayObject *arrayObject;
+  long __pyx_v_length;
+  long __pyx_v_i;
+  PyObject *__pyx_r;
+  int __pyx_1;
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+  PyObject *__pyx_3 = 0;
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+  __pyx_1 = __pyx_v_size == Py_None;
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+    __pyx_r = __pyx_2;
+    __pyx_2 = 0;
+    goto __pyx_L0;
+    goto __pyx_L2;
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+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":251 */
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+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":253 */
+    Py_INCREF(((PyObject *)arrayObject));
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+    goto __pyx_L0;
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+  __pyx_L2:;
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+  __pyx_r = Py_None; Py_INCREF(Py_None);
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+  Py_XDECREF(__pyx_3);
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+}
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 static char (__pyx_k63[]) = "size is not compatible with inputs";
@@ -696,10 +835,8 @@
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@@ -713,106 +850,63 @@
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":251 */
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   __pyx_1 = __pyx_v_size == Py_None;
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":252 */
-    __pyx_1 = __pyx_v_scalar;
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-
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":262 */
-        (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,(__pyx_v_oa_data[0]),(__pyx_v_ob_data[0]),(__pyx_v_oc_data[0]));
-
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":263 */
-        PyArray_MultiIter_NEXT(__pyx_v_multi);
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+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":276 */
+      PyArray_MultiIter_NEXT(__pyx_v_multi);
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-    __pyx_L4:;
-    goto __pyx_L3;
+    goto __pyx_L2;
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":265 */
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+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":278 */
+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 278; goto __pyx_L1;}
+    __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 278; goto __pyx_L1;}
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-    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 265; goto __pyx_L1;}
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+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 278; goto __pyx_L1;}
+    __pyx_5 = PyObject_GetAttr(__pyx_2, __pyx_n_float64); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 278; goto __pyx_L1;}
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-    __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 265; goto __pyx_L1;}
+    __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 278; goto __pyx_L1;}
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-    __pyx_5 = PyObject_CallObject(__pyx_4, __pyx_2); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 265; goto __pyx_L1;}
+    __pyx_5 = PyObject_CallObject(__pyx_4, __pyx_2); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 278; goto __pyx_L1;}
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@@ -820,77 +914,58 @@
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":266 */
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":279 */
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":267 */
-    __pyx_1 = __pyx_v_scalar;
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":280 */
+    __pyx_4 = PyArray_MultiIterNew(4,((void (*))arrayObject),((void (*))__pyx_v_oa),((void (*))__pyx_v_ob),((void (*))__pyx_v_oc)); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 280; goto __pyx_L1;}
+    Py_INCREF(((PyObject *)((PyArrayMultiIterObject *)__pyx_4)));
+    Py_DECREF(((PyObject *)__pyx_v_multi));
+    __pyx_v_multi = ((PyArrayMultiIterObject *)__pyx_4);
+    Py_DECREF(__pyx_4); __pyx_4 = 0;
+
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":282 */
+    __pyx_1 = (__pyx_v_multi->size != PyArray_SIZE(arrayObject));
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-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":268 */
-      __pyx_v_length = PyArray_SIZE(arrayObject);
-
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":269 */
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-      goto __pyx_L7;
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":283 */
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+      __pyx_5 = PyTuple_New(1); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 283; goto __pyx_L1;}
+      Py_INCREF(__pyx_k63p);
+      PyTuple_SET_ITEM(__pyx_5, 0, __pyx_k63p);
+      __pyx_4 = PyObject_CallObject(__pyx_2, __pyx_5); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 283; goto __pyx_L1;}
+      Py_DECREF(__pyx_2); __pyx_2 = 0;
+      Py_DECREF(__pyx_5); __pyx_5 = 0;
+      __Pyx_Raise(__pyx_4, 0, 0);
+      Py_DECREF(__pyx_4); __pyx_4 = 0;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 283; goto __pyx_L1;}
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-      __pyx_v_multi = ((PyArrayMultiIterObject *)__pyx_4);
-      Py_DECREF(__pyx_4); __pyx_4 = 0;
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":284 */
+    __pyx_3 = __pyx_v_multi->size;
+    for (__pyx_v_i = 0; __pyx_v_i < __pyx_3; ++__pyx_v_i) {
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":274 */
-      __pyx_1 = (__pyx_v_multi->size != PyArray_SIZE(arrayObject));
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+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":285 */
+      __pyx_v_oa_data = ((double (*))PyArray_MultiIter_DATA(__pyx_v_multi,1));
 
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":275 */
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-        Py_DECREF(__pyx_4); __pyx_4 = 0;
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-        goto __pyx_L10;
-      }
-      __pyx_L10:;
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+      __pyx_v_ob_data = ((double (*))PyArray_MultiIter_DATA(__pyx_v_multi,2));
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":276 */
-      __pyx_3 = __pyx_v_multi->size;
-      for (__pyx_v_i = 0; __pyx_v_i < __pyx_3; ++__pyx_v_i) {
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":287 */
+      __pyx_v_oc_data = ((double (*))PyArray_MultiIter_DATA(__pyx_v_multi,3));
 
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":277 */
-        __pyx_v_oa_data = ((double (*))PyArray_MultiIter_DATA(__pyx_v_multi,1));
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-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":278 */
-        __pyx_v_ob_data = ((double (*))PyArray_MultiIter_DATA(__pyx_v_multi,2));
-
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":279 */
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-
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":280 */
-        (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,(__pyx_v_oa_data[0]),(__pyx_v_ob_data[0]),(__pyx_v_oc_data[0]));
-
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":281 */
-        PyArray_MultiIter_NEXT(__pyx_v_multi);
-      }
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":289 */
+      PyArray_MultiIter_NEXT(__pyx_v_multi);
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-    __pyx_L7:;
   }
-  __pyx_L3:;
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":282 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":290 */
   Py_INCREF(((PyObject *)arrayObject));
   __pyx_r = ((PyObject *)arrayObject);
   goto __pyx_L0;
@@ -928,12 +1003,12 @@
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   arrayObject = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":290 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":298 */
   __pyx_1 = __pyx_v_size == Py_None;
   if (__pyx_1) {
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":291 */
-    __pyx_2 = PyInt_FromLong(__pyx_v_func(__pyx_v_state)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 291; goto __pyx_L1;}
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":299 */
+    __pyx_2 = PyInt_FromLong(__pyx_v_func(__pyx_v_state)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 299; goto __pyx_L1;}
     __pyx_r = __pyx_2;
     __pyx_2 = 0;
     goto __pyx_L0;
@@ -941,17 +1016,17 @@
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":293 */
-    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 293; goto __pyx_L1;}
-    __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 293; goto __pyx_L1;}
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":301 */
+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 301; goto __pyx_L1;}
+    __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 301; goto __pyx_L1;}
     Py_DECREF(__pyx_2); __pyx_2 = 0;
-    __pyx_2 = __Pyx_GetName(__pyx_b, __pyx_n_int); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 293; goto __pyx_L1;}
-    __pyx_4 = PyTuple_New(2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 293; goto __pyx_L1;}
+    __pyx_2 = __Pyx_GetName(__pyx_b, __pyx_n_int); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 301; goto __pyx_L1;}
+    __pyx_4 = PyTuple_New(2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 301; goto __pyx_L1;}
     Py_INCREF(__pyx_v_size);
     PyTuple_SET_ITEM(__pyx_4, 0, __pyx_v_size);
     PyTuple_SET_ITEM(__pyx_4, 1, __pyx_2);
     __pyx_2 = 0;
-    __pyx_2 = PyObject_CallObject(__pyx_3, __pyx_4); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 293; goto __pyx_L1;}
+    __pyx_2 = PyObject_CallObject(__pyx_3, __pyx_4); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 301; goto __pyx_L1;}
     Py_DECREF(__pyx_3); __pyx_3 = 0;
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     Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_2)));
@@ -959,20 +1034,20 @@
     arrayObject = ((PyArrayObject *)__pyx_2);
     Py_DECREF(__pyx_2); __pyx_2 = 0;
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":294 */
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":302 */
     __pyx_v_length = PyArray_SIZE(arrayObject);
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":295 */
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":303 */
     __pyx_v_array_data = ((long (*))arrayObject->data);
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":296 */
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":304 */
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-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":297 */
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       (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state);
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":298 */
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":306 */
     Py_INCREF(((PyObject *)arrayObject));
     __pyx_r = ((PyObject *)arrayObject);
     goto __pyx_L0;
@@ -993,6 +1068,88 @@
   return __pyx_r;
 }
 
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+static PyObject *__pyx_f_6mtrand_discnp_array_sc(rk_state (*__pyx_v_state),long ((*__pyx_v_func)(rk_state (*),long ,double )),PyObject *__pyx_v_size,long __pyx_v_n,double __pyx_v_p) {
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+  __pyx_1 = __pyx_v_size == Py_None;
+  if (__pyx_1) {
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+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":315 */
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+    __pyx_r = __pyx_2;
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+    goto __pyx_L2;
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+}
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 static PyObject *__pyx_k64p;
 
 static char (__pyx_k64[]) = "size is not compatible with inputs";
@@ -1000,11 +1157,9 @@
 static PyObject *__pyx_f_6mtrand_discnp_array(rk_state (*__pyx_v_state),long ((*__pyx_v_func)(rk_state (*),long ,double )),PyObject *__pyx_v_size,PyArrayObject *__pyx_v_on,PyArrayObject *__pyx_v_op) {
   long (*__pyx_v_array_data);
   PyArrayObject *arrayObject;
-  npy_intp __pyx_v_length;
   npy_intp __pyx_v_i;
   double (*__pyx_v_op_data);
   long (*__pyx_v_on_data);
-  int __pyx_v_scalar;
   PyArrayMultiIterObject *__pyx_v_multi;
   PyObject *__pyx_r;
   int __pyx_1;
@@ -1018,95 +1173,58 @@
   arrayObject = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
   __pyx_v_multi = ((PyArrayMultiIterObject *)Py_None); Py_INCREF(Py_None);
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":310 */
-  __pyx_v_scalar = 0;
-
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":311 */
-  __pyx_1 = (__pyx_v_on->nd == 0);
-  if (__pyx_1) {
-    __pyx_1 = (__pyx_v_op->nd == 0);
-  }
-  if (__pyx_1) {
-
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":312 */
-    __pyx_v_on_data = ((long (*))__pyx_v_on->data);
-
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":313 */
-    __pyx_v_op_data = ((double (*))__pyx_v_op->data);
-
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":314 */
-    __pyx_v_scalar = 1;
-    goto __pyx_L2;
-  }
-  __pyx_L2:;
-
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":316 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":333 */
   __pyx_1 = __pyx_v_size == Py_None;
   if (__pyx_1) {
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":317 */
-    __pyx_1 = __pyx_v_scalar;
-    if (__pyx_1) {
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":334 */
+    __pyx_2 = PyArray_MultiIterNew(2,((void (*))__pyx_v_on),((void (*))__pyx_v_op)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 334; goto __pyx_L1;}
+    Py_INCREF(((PyObject *)((PyArrayMultiIterObject *)__pyx_2)));
+    Py_DECREF(((PyObject *)__pyx_v_multi));
+    __pyx_v_multi = ((PyArrayMultiIterObject *)__pyx_2);
+    Py_DECREF(__pyx_2); __pyx_2 = 0;
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":318 */
-      __pyx_2 = PyInt_FromLong(__pyx_v_func(__pyx_v_state,(__pyx_v_on_data[0]),(__pyx_v_op_data[0]))); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 318; goto __pyx_L1;}
-      __pyx_r = __pyx_2;
-      __pyx_2 = 0;
-      goto __pyx_L0;
-      goto __pyx_L4;
-    }
-    /*else*/ {
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":335 */
+    __pyx_2 = PyArray_SimpleNew(__pyx_v_multi->nd,__pyx_v_multi->dimensions,NPY_LONG); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 335; goto __pyx_L1;}
+    Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_2)));
+    Py_DECREF(((PyObject *)arrayObject));
+    arrayObject = ((PyArrayObject *)__pyx_2);
+    Py_DECREF(__pyx_2); __pyx_2 = 0;
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":320 */
-      __pyx_2 = PyArray_MultiIterNew(2,((void (*))__pyx_v_on),((void (*))__pyx_v_op)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 320; goto __pyx_L1;}
-      Py_INCREF(((PyObject *)((PyArrayMultiIterObject *)__pyx_2)));
-      Py_DECREF(((PyObject *)__pyx_v_multi));
-      __pyx_v_multi = ((PyArrayMultiIterObject *)__pyx_2);
-      Py_DECREF(__pyx_2); __pyx_2 = 0;
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":336 */
+    __pyx_v_array_data = ((long (*))arrayObject->data);
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":321 */
-      __pyx_2 = PyArray_SimpleNew(__pyx_v_multi->nd,__pyx_v_multi->dimensions,NPY_LONG); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 321; goto __pyx_L1;}
-      Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_2)));
-      Py_DECREF(((PyObject *)arrayObject));
-      arrayObject = ((PyArrayObject *)__pyx_2);
-      Py_DECREF(__pyx_2); __pyx_2 = 0;
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":337 */
+    __pyx_3 = __pyx_v_multi->size;
+    for (__pyx_v_i = 0; __pyx_v_i < __pyx_3; ++__pyx_v_i) {
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":322 */
-      __pyx_v_array_data = ((long (*))arrayObject->data);
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":338 */
+      __pyx_v_on_data = ((long (*))PyArray_MultiIter_DATA(__pyx_v_multi,0));
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":323 */
-      __pyx_3 = __pyx_v_multi->size;
-      for (__pyx_v_i = 0; __pyx_v_i < __pyx_3; ++__pyx_v_i) {
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":339 */
+      __pyx_v_op_data = ((double (*))PyArray_MultiIter_DATA(__pyx_v_multi,1));
 
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":324 */
-        __pyx_v_on_data = ((long (*))PyArray_MultiIter_DATA(__pyx_v_multi,0));
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":340 */
+      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,(__pyx_v_on_data[0]),(__pyx_v_op_data[0]));
 
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":325 */
-        __pyx_v_op_data = ((double (*))PyArray_MultiIter_DATA(__pyx_v_multi,1));
-
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":326 */
-        (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,(__pyx_v_on_data[0]),(__pyx_v_op_data[0]));
-
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":327 */
-        PyArray_MultiIter_NEXT(__pyx_v_multi);
-      }
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":341 */
+      PyArray_MultiIter_NEXT(__pyx_v_multi);
     }
-    __pyx_L4:;
-    goto __pyx_L3;
+    goto __pyx_L2;
   }
   /*else*/ {
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":329 */
-    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 329; goto __pyx_L1;}
-    __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 329; goto __pyx_L1;}
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":343 */
+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 343; goto __pyx_L1;}
+    __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 343; goto __pyx_L1;}
     Py_DECREF(__pyx_2); __pyx_2 = 0;
-    __pyx_2 = __Pyx_GetName(__pyx_b, __pyx_n_int); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 329; goto __pyx_L1;}
-    __pyx_5 = PyTuple_New(2); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 329; goto __pyx_L1;}
+    __pyx_2 = __Pyx_GetName(__pyx_b, __pyx_n_int); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 343; goto __pyx_L1;}
+    __pyx_5 = PyTuple_New(2); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 343; goto __pyx_L1;}
     Py_INCREF(__pyx_v_size);
     PyTuple_SET_ITEM(__pyx_5, 0, __pyx_v_size);
     PyTuple_SET_ITEM(__pyx_5, 1, __pyx_2);
     __pyx_2 = 0;
-    __pyx_2 = PyObject_CallObject(__pyx_4, __pyx_5); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 329; goto __pyx_L1;}
+    __pyx_2 = PyObject_CallObject(__pyx_4, __pyx_5); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 343; goto __pyx_L1;}
     Py_DECREF(__pyx_4); __pyx_4 = 0;
     Py_DECREF(__pyx_5); __pyx_5 = 0;
     Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_2)));
@@ -1114,77 +1232,58 @@
     arrayObject = ((PyArrayObject *)__pyx_2);
     Py_DECREF(__pyx_2); __pyx_2 = 0;
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":330 */
-    __pyx_1 = __pyx_v_scalar;
-    if (__pyx_1) {
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":344 */
+    __pyx_v_array_data = ((long (*))arrayObject->data);
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":331 */
-      __pyx_v_length = PyArray_SIZE(arrayObject);
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":345 */
+    __pyx_4 = PyArray_MultiIterNew(3,((void (*))arrayObject),((void (*))__pyx_v_on),((void (*))__pyx_v_op)); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 345; goto __pyx_L1;}
+    Py_INCREF(((PyObject *)((PyArrayMultiIterObject *)__pyx_4)));
+    Py_DECREF(((PyObject *)__pyx_v_multi));
+    __pyx_v_multi = ((PyArrayMultiIterObject *)__pyx_4);
+    Py_DECREF(__pyx_4); __pyx_4 = 0;
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":332 */
-      __pyx_v_array_data = ((long (*))arrayObject->data);
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":346 */
+    __pyx_1 = (__pyx_v_multi->size != PyArray_SIZE(arrayObject));
+    if (__pyx_1) {
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":333 */
-      for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; ++__pyx_v_i) {
-
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":334 */
-        (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,(__pyx_v_on_data[0]),(__pyx_v_op_data[0]));
-      }
-      goto __pyx_L7;
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":347 */
+      __pyx_5 = __Pyx_GetName(__pyx_b, __pyx_n_ValueError); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 347; goto __pyx_L1;}
+      __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 347; goto __pyx_L1;}
+      Py_INCREF(__pyx_k64p);
+      PyTuple_SET_ITEM(__pyx_2, 0, __pyx_k64p);
+      __pyx_4 = PyObject_CallObject(__pyx_5, __pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 347; goto __pyx_L1;}
+      Py_DECREF(__pyx_5); __pyx_5 = 0;
+      Py_DECREF(__pyx_2); __pyx_2 = 0;
+      __Pyx_Raise(__pyx_4, 0, 0);
+      Py_DECREF(__pyx_4); __pyx_4 = 0;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 347; goto __pyx_L1;}
+      goto __pyx_L5;
     }
-    /*else*/ {
+    __pyx_L5:;
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":336 */
-      __pyx_4 = PyArray_MultiIterNew(3,((void (*))arrayObject),((void (*))__pyx_v_on),((void (*))__pyx_v_op)); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 336; goto __pyx_L1;}
-      Py_INCREF(((PyObject *)((PyArrayMultiIterObject *)__pyx_4)));
-      Py_DECREF(((PyObject *)__pyx_v_multi));
-      __pyx_v_multi = ((PyArrayMultiIterObject *)__pyx_4);
-      Py_DECREF(__pyx_4); __pyx_4 = 0;
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":348 */
+    __pyx_3 = __pyx_v_multi->size;
+    for (__pyx_v_i = 0; __pyx_v_i < __pyx_3; ++__pyx_v_i) {
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":337 */
-      __pyx_1 = (__pyx_v_multi->size != PyArray_SIZE(arrayObject));
-      if (__pyx_1) {
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":349 */
+      __pyx_v_on_data = ((long (*))PyArray_MultiIter_DATA(__pyx_v_multi,1));
 
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":338 */
-        __pyx_5 = __Pyx_GetName(__pyx_b, __pyx_n_ValueError); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 338; goto __pyx_L1;}
-        __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 338; goto __pyx_L1;}
-        Py_INCREF(__pyx_k64p);
-        PyTuple_SET_ITEM(__pyx_2, 0, __pyx_k64p);
-        __pyx_4 = PyObject_CallObject(__pyx_5, __pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 338; goto __pyx_L1;}
-        Py_DECREF(__pyx_5); __pyx_5 = 0;
-        Py_DECREF(__pyx_2); __pyx_2 = 0;
-        __Pyx_Raise(__pyx_4, 0, 0);
-        Py_DECREF(__pyx_4); __pyx_4 = 0;
-        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 338; goto __pyx_L1;}
-        goto __pyx_L10;
-      }
-      __pyx_L10:;
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":350 */
+      __pyx_v_op_data = ((double (*))PyArray_MultiIter_DATA(__pyx_v_multi,2));
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":339 */
-      __pyx_3 = __pyx_v_multi->size;
-      for (__pyx_v_i = 0; __pyx_v_i < __pyx_3; ++__pyx_v_i) {
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":351 */
+      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,(__pyx_v_on_data[0]),(__pyx_v_op_data[0]));
 
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":340 */
-        __pyx_v_on_data = ((long (*))PyArray_MultiIter_DATA(__pyx_v_multi,1));
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":352 */
+      PyArray_MultiIter_NEXTi(__pyx_v_multi,1);
 
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":341 */
-        __pyx_v_op_data = ((double (*))PyArray_MultiIter_DATA(__pyx_v_multi,2));
-
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":342 */
-        (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,(__pyx_v_on_data[0]),(__pyx_v_op_data[0]));
-
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":343 */
-        PyArray_MultiIter_NEXTi(__pyx_v_multi,1);
-
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":344 */
-        PyArray_MultiIter_NEXTi(__pyx_v_multi,2);
-      }
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":353 */
+      PyArray_MultiIter_NEXTi(__pyx_v_multi,2);
     }
-    __pyx_L7:;
   }
-  __pyx_L3:;
+  __pyx_L2:;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":346 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":355 */
   Py_INCREF(((PyObject *)arrayObject));
   __pyx_r = ((PyObject *)arrayObject);
   goto __pyx_L0;
@@ -1206,6 +1305,86 @@
   return __pyx_r;
 }
 
+static PyObject *__pyx_f_6mtrand_discnmN_array_sc(rk_state (*__pyx_v_state),long ((*__pyx_v_func)(rk_state (*),long ,long ,long )),PyObject *__pyx_v_size,long __pyx_v_n,long __pyx_v_m,long __pyx_v_N) {
+  long (*__pyx_v_array_data);
+  PyArrayObject *arrayObject;
+  long __pyx_v_length;
+  long __pyx_v_i;
+  PyObject *__pyx_r;
+  int __pyx_1;
+  PyObject *__pyx_2 = 0;
+  PyObject *__pyx_3 = 0;
+  PyObject *__pyx_4 = 0;
+  Py_INCREF(__pyx_v_size);
+  arrayObject = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
+
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":364 */
+  __pyx_1 = __pyx_v_size == Py_None;
+  if (__pyx_1) {
+
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":365 */
+    __pyx_2 = PyInt_FromLong(__pyx_v_func(__pyx_v_state,__pyx_v_n,__pyx_v_m,__pyx_v_N)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 365; goto __pyx_L1;}
+    __pyx_r = __pyx_2;
+    __pyx_2 = 0;
+    goto __pyx_L0;
+    goto __pyx_L2;
+  }
+  /*else*/ {
+
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":367 */
+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 367; goto __pyx_L1;}
+    __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 367; goto __pyx_L1;}
+    Py_DECREF(__pyx_2); __pyx_2 = 0;
+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 367; goto __pyx_L1;}
+    __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_n_Int); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 367; goto __pyx_L1;}
+    Py_DECREF(__pyx_2); __pyx_2 = 0;
+    __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 367; goto __pyx_L1;}
+    Py_INCREF(__pyx_v_size);
+    PyTuple_SET_ITEM(__pyx_2, 0, __pyx_v_size);
+    PyTuple_SET_ITEM(__pyx_2, 1, __pyx_4);
+    __pyx_4 = 0;
+    __pyx_4 = PyObject_CallObject(__pyx_3, __pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 367; goto __pyx_L1;}
+    Py_DECREF(__pyx_3); __pyx_3 = 0;
+    Py_DECREF(__pyx_2); __pyx_2 = 0;
+    Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_4)));
+    Py_DECREF(((PyObject *)arrayObject));
+    arrayObject = ((PyArrayObject *)__pyx_4);
+    Py_DECREF(__pyx_4); __pyx_4 = 0;
+
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":368 */
+    __pyx_v_length = PyArray_SIZE(arrayObject);
+
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":369 */
+    __pyx_v_array_data = ((long (*))arrayObject->data);
+
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":370 */
+    for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; ++__pyx_v_i) {
+
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":371 */
+      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,__pyx_v_n,__pyx_v_m,__pyx_v_N);
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+
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":372 */
+    Py_INCREF(((PyObject *)arrayObject));
+    __pyx_r = ((PyObject *)arrayObject);
+    goto __pyx_L0;
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+  __pyx_r = Py_None; Py_INCREF(Py_None);
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+  Py_XDECREF(__pyx_3);
+  Py_XDECREF(__pyx_4);
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 static char (__pyx_k65[]) = "size is not compatible with inputs";
@@ -1216,10 +1395,8 @@
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@@ -1233,104 +1410,61 @@
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":360 */
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-
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-  __pyx_L2:;
-
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":367 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":385 */
   __pyx_1 = __pyx_v_size == Py_None;
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":368 */
-    __pyx_1 = __pyx_v_scalar;
-    if (__pyx_1) {
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-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":369 */
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-      goto __pyx_L4;
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-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":371 */
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-      Py_INCREF(((PyObject *)((PyArrayMultiIterObject *)__pyx_2)));
-      Py_DECREF(((PyObject *)__pyx_v_multi));
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-      Py_DECREF(__pyx_2); __pyx_2 = 0;
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-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":372 */
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-      Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_2)));
-      Py_DECREF(((PyObject *)arrayObject));
-      arrayObject = ((PyArrayObject *)__pyx_2);
-      Py_DECREF(__pyx_2); __pyx_2 = 0;
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-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":373 */
-      __pyx_v_array_data = ((long (*))arrayObject->data);
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-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":374 */
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-      for (__pyx_v_i = 0; __pyx_v_i < __pyx_3; ++__pyx_v_i) {
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-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":375 */
-        __pyx_v_on_data = ((long (*))PyArray_MultiIter_DATA(__pyx_v_multi,0));
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-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":376 */
-        __pyx_v_om_data = ((long (*))PyArray_MultiIter_DATA(__pyx_v_multi,1));
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-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":379 */
-        PyArray_MultiIter_NEXT(__pyx_v_multi);
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-    __pyx_5 = PyTuple_New(2); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 381; goto __pyx_L1;}
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@@ -1338,77 +1472,58 @@
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":382 */
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":383 */
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+    Py_DECREF(((PyObject *)__pyx_v_multi));
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-    /*else*/ {
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-      __pyx_1 = (__pyx_v_multi->size != PyArray_SIZE(arrayObject));
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-      __pyx_3 = __pyx_v_multi->size;
-      for (__pyx_v_i = 0; __pyx_v_i < __pyx_3; ++__pyx_v_i) {
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-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":393 */
-        __pyx_v_on_data = ((long (*))PyArray_MultiIter_DATA(__pyx_v_multi,1));
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-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":394 */
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-
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":397 */
-        PyArray_MultiIter_NEXT(__pyx_v_multi);
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+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":407 */
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-    __pyx_L7:;
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-  __pyx_L3:;
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":399 */
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   goto __pyx_L0;
@@ -1431,6 +1546,86 @@
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 }
 
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+  PyObject *__pyx_3 = 0;
+  PyObject *__pyx_4 = 0;
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+  arrayObject = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
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+    __pyx_r = __pyx_2;
+    __pyx_2 = 0;
+    goto __pyx_L0;
+    goto __pyx_L2;
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+    Py_DECREF(__pyx_2); __pyx_2 = 0;
+    __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 420; goto __pyx_L1;}
+    Py_INCREF(__pyx_v_size);
+    PyTuple_SET_ITEM(__pyx_2, 0, __pyx_v_size);
+    PyTuple_SET_ITEM(__pyx_2, 1, __pyx_4);
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+    __pyx_4 = PyObject_CallObject(__pyx_3, __pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 420; goto __pyx_L1;}
+    Py_DECREF(__pyx_3); __pyx_3 = 0;
+    Py_DECREF(__pyx_2); __pyx_2 = 0;
+    Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_4)));
+    Py_DECREF(((PyObject *)arrayObject));
+    arrayObject = ((PyArrayObject *)__pyx_4);
+    Py_DECREF(__pyx_4); __pyx_4 = 0;
+
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":421 */
+    __pyx_v_length = PyArray_SIZE(arrayObject);
+
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":422 */
+    __pyx_v_array_data = ((long (*))arrayObject->data);
+
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":423 */
+    for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; ++__pyx_v_i) {
+
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":424 */
+      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,__pyx_v_a);
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+
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":425 */
+    Py_INCREF(((PyObject *)arrayObject));
+    __pyx_r = ((PyObject *)arrayObject);
+    goto __pyx_L0;
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+  __pyx_L2:;
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+  __pyx_r = Py_None; Py_INCREF(Py_None);
+  goto __pyx_L0;
+  __pyx_L1:;
+  Py_XDECREF(__pyx_2);
+  Py_XDECREF(__pyx_3);
+  Py_XDECREF(__pyx_4);
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+}
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 static char (__pyx_k66[]) = "size is not compatible with inputs";
@@ -1443,7 +1638,6 @@
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@@ -1456,85 +1650,54 @@
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   __pyx_v_itera = ((PyArrayIterObject *)Py_None); Py_INCREF(Py_None);
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":411 */
-  __pyx_v_scalar = 0;
-
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":412 */
-  __pyx_1 = (__pyx_v_oa->nd == 0);
-  if (__pyx_1) {
-
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":413 */
-    __pyx_v_oa_data = ((double (*))__pyx_v_oa->data);
-
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":414 */
-    __pyx_v_scalar = 1;
-    goto __pyx_L2;
-  }
-  __pyx_L2:;
-
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":416 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":436 */
   __pyx_1 = __pyx_v_size == Py_None;
   if (__pyx_1) {
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":417 */
-    __pyx_1 = __pyx_v_scalar;
-    if (__pyx_1) {
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":437 */
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+    Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_2)));
+    Py_DECREF(((PyObject *)arrayObject));
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-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":418 */
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-      __pyx_r = __pyx_2;
-      __pyx_2 = 0;
-      goto __pyx_L0;
-      goto __pyx_L4;
-    }
-    /*else*/ {
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+    __pyx_v_length = PyArray_SIZE(arrayObject);
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":420 */
-      __pyx_2 = PyArray_SimpleNew(__pyx_v_oa->nd,__pyx_v_oa->dimensions,NPY_LONG); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 420; goto __pyx_L1;}
-      Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_2)));
-      Py_DECREF(((PyObject *)arrayObject));
-      arrayObject = ((PyArrayObject *)__pyx_2);
-      Py_DECREF(__pyx_2); __pyx_2 = 0;
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":439 */
+    __pyx_v_array_data = ((long (*))arrayObject->data);
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":421 */
-      __pyx_v_length = PyArray_SIZE(arrayObject);
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":440 */
+    __pyx_2 = PyArray_IterNew(((PyObject *)__pyx_v_oa)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 440; goto __pyx_L1;}
+    Py_INCREF(((PyObject *)((PyArrayIterObject *)__pyx_2)));
+    Py_DECREF(((PyObject *)__pyx_v_itera));
+    __pyx_v_itera = ((PyArrayIterObject *)__pyx_2);
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-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":422 */
-      __pyx_v_array_data = ((long (*))arrayObject->data);
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":441 */
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-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":423 */
-      __pyx_2 = PyArray_IterNew(((PyObject *)__pyx_v_oa)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 423; goto __pyx_L1;}
-      Py_INCREF(((PyObject *)((PyArrayIterObject *)__pyx_2)));
-      Py_DECREF(((PyObject *)__pyx_v_itera));
-      __pyx_v_itera = ((PyArrayIterObject *)__pyx_2);
-      Py_DECREF(__pyx_2); __pyx_2 = 0;
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-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":424 */
-      for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; ++__pyx_v_i) {
-
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":425 */
-        (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,(((double (*))__pyx_v_itera->dataptr)[0]));
-
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":426 */
-        PyArray_ITER_NEXT(__pyx_v_itera);
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+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":443 */
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-    __pyx_L4:;
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":428 */
-    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 428; goto __pyx_L1;}
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+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":445 */
+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 445; goto __pyx_L1;}
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-    __pyx_2 = __Pyx_GetName(__pyx_b, __pyx_n_int); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 428; goto __pyx_L1;}
-    __pyx_4 = PyTuple_New(2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 428; goto __pyx_L1;}
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+    __pyx_4 = PyTuple_New(2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 445; goto __pyx_L1;}
     Py_INCREF(__pyx_v_size);
     PyTuple_SET_ITEM(__pyx_4, 0, __pyx_v_size);
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     __pyx_2 = 0;
-    __pyx_2 = PyObject_CallObject(__pyx_3, __pyx_4); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 428; goto __pyx_L1;}
+    __pyx_2 = PyObject_CallObject(__pyx_3, __pyx_4); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 445; goto __pyx_L1;}
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     Py_DECREF(__pyx_4); __pyx_4 = 0;
     Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_2)));
@@ -1542,71 +1705,52 @@
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     Py_DECREF(__pyx_2); __pyx_2 = 0;
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":429 */
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":446 */
     __pyx_v_array_data = ((long (*))arrayObject->data);
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":430 */
-    __pyx_1 = __pyx_v_scalar;
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":447 */
+    __pyx_3 = PyArray_MultiIterNew(2,((void (*))arrayObject),((void (*))__pyx_v_oa)); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 447; goto __pyx_L1;}
+    Py_INCREF(((PyObject *)((PyArrayMultiIterObject *)__pyx_3)));
+    Py_DECREF(((PyObject *)__pyx_v_multi));
+    __pyx_v_multi = ((PyArrayMultiIterObject *)__pyx_3);
+    Py_DECREF(__pyx_3); __pyx_3 = 0;
+
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":448 */
+    __pyx_1 = (__pyx_v_multi->size != PyArray_SIZE(arrayObject));
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-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":431 */
-      __pyx_v_length = PyArray_SIZE(arrayObject);
-
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":432 */
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-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":433 */
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-      }
-      goto __pyx_L7;
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+      Py_DECREF(__pyx_4); __pyx_4 = 0;
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-    /*else*/ {
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-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":435 */
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-      Py_INCREF(((PyObject *)((PyArrayMultiIterObject *)__pyx_3)));
-      Py_DECREF(((PyObject *)__pyx_v_multi));
-      __pyx_v_multi = ((PyArrayMultiIterObject *)__pyx_3);
-      Py_DECREF(__pyx_3); __pyx_3 = 0;
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":450 */
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-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":436 */
-      __pyx_1 = (__pyx_v_multi->size != PyArray_SIZE(arrayObject));
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-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":437 */
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-        goto __pyx_L10;
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-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":438 */
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-
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":441 */
-        PyArray_MultiIter_NEXTi(__pyx_v_multi,1);
-      }
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":453 */
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-  __pyx_L3:;
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":442 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":454 */
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@@ -1636,29 +1780,29 @@
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":447 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":459 */
   __pyx_v_sum = (__pyx_v_darr[0]);
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":448 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":460 */
   __pyx_v_c = 0.0;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":449 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":461 */
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":450 */
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     __pyx_v_y = ((__pyx_v_darr[__pyx_v_i]) - __pyx_v_c);
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":451 */
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":463 */
     __pyx_v_t = (__pyx_v_sum + __pyx_v_y);
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":452 */
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":464 */
     __pyx_v_c = ((__pyx_v_t - __pyx_v_sum) - __pyx_v_y);
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":453 */
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":465 */
     __pyx_v_sum = __pyx_v_t;
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":454 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":466 */
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@@ -1680,15 +1824,15 @@
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   Py_INCREF(__pyx_v_seed);
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":477 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":489 */
   ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state = ((rk_state (*))PyMem_Malloc((sizeof(rk_state ))));
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":479 */
-  __pyx_1 = PyObject_GetAttr(__pyx_v_self, __pyx_n_seed); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 479; goto __pyx_L1;}
-  __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 479; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":491 */
+  __pyx_1 = PyObject_GetAttr(__pyx_v_self, __pyx_n_seed); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 491; goto __pyx_L1;}
+  __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 491; goto __pyx_L1;}
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-  __pyx_3 = PyObject_CallObject(__pyx_1, __pyx_2); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 479; goto __pyx_L1;}
+  __pyx_3 = PyObject_CallObject(__pyx_1, __pyx_2); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 491; goto __pyx_L1;}
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":482 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":494 */
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":483 */
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":495 */
     PyMem_Free(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state);
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":484 */
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":496 */
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":498 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":510 */
   __pyx_1 = __pyx_v_seed == Py_None;
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":499 */
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":511 */
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-  __pyx_3 = PyTuple_New(1); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 500; goto __pyx_L1;}
+  __pyx_2 = __Pyx_GetName(__pyx_b, __pyx_n_type); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 512; goto __pyx_L1;}
+  __pyx_3 = PyTuple_New(1); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 512; goto __pyx_L1;}
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+  __pyx_4 = PyObject_CallObject(__pyx_2, __pyx_3); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 512; goto __pyx_L1;}
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+  __pyx_2 = __Pyx_GetName(__pyx_b, __pyx_n_int); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 512; goto __pyx_L1;}
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+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":513 */
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":503 */
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+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":515 */
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":504 */
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+  __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 525; goto __pyx_L1;}
+  __pyx_2 = PyObject_GetAttr(__pyx_1, __pyx_n_empty); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 525; goto __pyx_L1;}
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-  __pyx_4 = PyTuple_New(2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 513; goto __pyx_L1;}
+  __pyx_1 = PyInt_FromLong(624); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 525; goto __pyx_L1;}
+  __pyx_3 = __Pyx_GetName(__pyx_b, __pyx_n_int); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 525; goto __pyx_L1;}
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+  __pyx_1 = PyObject_CallObject(__pyx_2, __pyx_4); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 525; goto __pyx_L1;}
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":514 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":526 */
   memcpy(((void (*))arrayObject_state->data),((void (*))((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state->key),(624 * (sizeof(long ))));
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":515 */
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-  __pyx_2 = PyTuple_New(3); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 515; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":527 */
+  __pyx_3 = PyInt_FromLong(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state->pos); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 527; goto __pyx_L1;}
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":526 */
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+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":538 */
+  __pyx_1 = PyInt_FromLong(0); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 538; goto __pyx_L1;}
+  __pyx_2 = PyObject_GetItem(__pyx_v_state, __pyx_1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 538; goto __pyx_L1;}
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":527 */
-  if (PyObject_Cmp(__pyx_v_algorithm_name, __pyx_n_MT19937, &__pyx_3) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 527; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":539 */
+  if (PyObject_Cmp(__pyx_v_algorithm_name, __pyx_n_MT19937, &__pyx_3) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 539; goto __pyx_L1;}
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":528 */
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-    __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 528; goto __pyx_L1;}
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":540 */
+    __pyx_1 = __Pyx_GetName(__pyx_b, __pyx_n_ValueError); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 540; goto __pyx_L1;}
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+    __pyx_4 = PyObject_CallObject(__pyx_1, __pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 540; goto __pyx_L1;}
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-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 528; goto __pyx_L1;}
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":529 */
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-  __pyx_2 = PyObject_GetIter(__pyx_1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 529; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":541 */
+  __pyx_1 = PySequence_GetSlice(__pyx_v_state, 1, 0x7fffffff); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 541; goto __pyx_L1;}
+  __pyx_2 = PyObject_GetIter(__pyx_1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 541; goto __pyx_L1;}
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-  __pyx_4 = __Pyx_UnpackItem(__pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 529; goto __pyx_L1;}
+  __pyx_4 = __Pyx_UnpackItem(__pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 541; goto __pyx_L1;}
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-  __pyx_3 = PyInt_AsLong(__pyx_1); if (PyErr_Occurred()) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 529; goto __pyx_L1;}
+  __pyx_1 = __Pyx_UnpackItem(__pyx_2); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 541; goto __pyx_L1;}
+  __pyx_3 = PyInt_AsLong(__pyx_1); if (PyErr_Occurred()) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 541; goto __pyx_L1;}
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-  if (__Pyx_EndUnpack(__pyx_2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 529; goto __pyx_L1;}
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":530 */
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+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":542 */
+  __pyx_4 = PyArray_ContiguousFromObject(__pyx_v_key,NPY_LONG,1,1); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 542; goto __pyx_L1;}
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":531 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":543 */
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":532 */
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-    __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 532; goto __pyx_L1;}
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":544 */
+    __pyx_1 = __Pyx_GetName(__pyx_b, __pyx_n_ValueError); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 544; goto __pyx_L1;}
+    __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 544; goto __pyx_L1;}
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+    __pyx_4 = PyObject_CallObject(__pyx_1, __pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 544; goto __pyx_L1;}
     Py_DECREF(__pyx_1); __pyx_1 = 0;
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     Py_DECREF(__pyx_4); __pyx_4 = 0;
-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 532; goto __pyx_L1;}
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 544; goto __pyx_L1;}
     goto __pyx_L3;
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":533 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":545 */
   memcpy(((void (*))((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state->key),((void (*))arrayObject_obj->data),(624 * (sizeof(long ))));
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":534 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":546 */
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@@ -1999,9 +2143,9 @@
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-  __pyx_2 = PyObject_CallObject(__pyx_1, 0); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 538; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":550 */
+  __pyx_1 = PyObject_GetAttr(__pyx_v_self, __pyx_n_get_state); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 550; goto __pyx_L1;}
+  __pyx_2 = PyObject_CallObject(__pyx_1, 0); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 550; goto __pyx_L1;}
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@@ -2031,12 +2175,12 @@
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":541 */
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-  __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 541; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":553 */
+  __pyx_1 = PyObject_GetAttr(__pyx_v_self, __pyx_n_set_state); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 553; goto __pyx_L1;}
+  __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 553; goto __pyx_L1;}
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-  __pyx_3 = PyObject_CallObject(__pyx_1, __pyx_2); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 541; goto __pyx_L1;}
+  __pyx_3 = PyObject_CallObject(__pyx_1, __pyx_2); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 553; goto __pyx_L1;}
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@@ -2069,17 +2213,17 @@
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":544 */
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-  __pyx_2 = PyObject_GetAttr(__pyx_1, __pyx_n_random); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 544; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":556 */
+  __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; goto __pyx_L1;}
+  __pyx_2 = PyObject_GetAttr(__pyx_1, __pyx_n_random); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; goto __pyx_L1;}
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-  __pyx_1 = PyObject_GetAttr(__pyx_2, __pyx_n___RandomState_ctor); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 544; goto __pyx_L1;}
+  __pyx_1 = PyObject_GetAttr(__pyx_2, __pyx_n___RandomState_ctor); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; goto __pyx_L1;}
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-  __pyx_2 = PyTuple_New(0); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 544; goto __pyx_L1;}
-  __pyx_3 = PyObject_GetAttr(__pyx_v_self, __pyx_n_get_state); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 544; goto __pyx_L1;}
-  __pyx_4 = PyObject_CallObject(__pyx_3, 0); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 544; goto __pyx_L1;}
+  __pyx_2 = PyTuple_New(0); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; goto __pyx_L1;}
+  __pyx_3 = PyObject_GetAttr(__pyx_v_self, __pyx_n_get_state); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; goto __pyx_L1;}
+  __pyx_4 = PyObject_CallObject(__pyx_3, 0); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; goto __pyx_L1;}
   Py_DECREF(__pyx_3); __pyx_3 = 0;
-  __pyx_3 = PyTuple_New(3); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 544; goto __pyx_L1;}
+  __pyx_3 = PyTuple_New(3); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; goto __pyx_L1;}
   PyTuple_SET_ITEM(__pyx_3, 0, __pyx_1);
   PyTuple_SET_ITEM(__pyx_3, 1, __pyx_2);
   PyTuple_SET_ITEM(__pyx_3, 2, __pyx_4);
@@ -2116,8 +2260,8 @@
   Py_INCREF(__pyx_v_self);
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":552 */
-  __pyx_1 = __pyx_f_6mtrand_cont0_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,rk_double,__pyx_v_size); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 552; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":564 */
+  __pyx_1 = __pyx_f_6mtrand_cont0_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,rk_double,__pyx_v_size); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 564; goto __pyx_L1;}
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   __pyx_1 = 0;
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@@ -2146,8 +2290,8 @@
   Py_INCREF(__pyx_v_self);
   Py_INCREF(__pyx_v_size);
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":559 */
-  __pyx_1 = __pyx_f_6mtrand_disc0_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,rk_long,__pyx_v_size); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 559; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":571 */
+  __pyx_1 = __pyx_f_6mtrand_disc0_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,rk_long,__pyx_v_size); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 571; goto __pyx_L1;}
   __pyx_r = __pyx_1;
   __pyx_1 = 0;
   goto __pyx_L0;
@@ -2197,58 +2341,58 @@
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   arrayObject = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":574 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":586 */
   __pyx_1 = __pyx_v_high == Py_None;
   if (__pyx_1) {
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":575 */
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":587 */
     __pyx_v_lo = 0;
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":576 */
-    __pyx_2 = PyInt_AsLong(__pyx_v_low); if (PyErr_Occurred()) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 576; goto __pyx_L1;}
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":588 */
+    __pyx_2 = PyInt_AsLong(__pyx_v_low); if (PyErr_Occurred()) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 588; goto __pyx_L1;}
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     goto __pyx_L2;
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":578 */
-    __pyx_2 = PyInt_AsLong(__pyx_v_low); if (PyErr_Occurred()) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 578; goto __pyx_L1;}
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":590 */
+    __pyx_2 = PyInt_AsLong(__pyx_v_low); if (PyErr_Occurred()) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 590; goto __pyx_L1;}
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":579 */
-    __pyx_2 = PyInt_AsLong(__pyx_v_high); if (PyErr_Occurred()) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 579; goto __pyx_L1;}
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":591 */
+    __pyx_2 = PyInt_AsLong(__pyx_v_high); if (PyErr_Occurred()) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 591; goto __pyx_L1;}
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":581 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":593 */
   __pyx_v_diff = ((__pyx_v_hi - __pyx_v_lo) - 1);
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":582 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":594 */
   __pyx_1 = (__pyx_v_diff < 0);
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":583 */
-    __pyx_3 = __Pyx_GetName(__pyx_b, __pyx_n_ValueError); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 583; goto __pyx_L1;}
-    __pyx_4 = PyTuple_New(1); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 583; goto __pyx_L1;}
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":595 */
+    __pyx_3 = __Pyx_GetName(__pyx_b, __pyx_n_ValueError); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 595; goto __pyx_L1;}
+    __pyx_4 = PyTuple_New(1); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 595; goto __pyx_L1;}
     Py_INCREF(__pyx_k71p);
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-    __pyx_5 = PyObject_CallObject(__pyx_3, __pyx_4); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 583; goto __pyx_L1;}
+    __pyx_5 = PyObject_CallObject(__pyx_3, __pyx_4); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 595; goto __pyx_L1;}
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     Py_DECREF(__pyx_5); __pyx_5 = 0;
-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 583; goto __pyx_L1;}
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 595; goto __pyx_L1;}
     goto __pyx_L3;
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":585 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":597 */
   __pyx_1 = __pyx_v_size == Py_None;
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":586 */
-    __pyx_3 = PyLong_FromUnsignedLong((rk_interval(__pyx_v_diff,((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state) + __pyx_v_lo)); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 586; goto __pyx_L1;}
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":598 */
+    __pyx_3 = PyLong_FromUnsignedLong((rk_interval(__pyx_v_diff,((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state) + __pyx_v_lo)); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 598; goto __pyx_L1;}
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@@ -2256,17 +2400,17 @@
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":588 */
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-    __pyx_5 = PyObject_GetAttr(__pyx_4, __pyx_n_empty); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 588; goto __pyx_L1;}
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":600 */
+    __pyx_4 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 600; goto __pyx_L1;}
+    __pyx_5 = PyObject_GetAttr(__pyx_4, __pyx_n_empty); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 600; goto __pyx_L1;}
     Py_DECREF(__pyx_4); __pyx_4 = 0;
-    __pyx_3 = __Pyx_GetName(__pyx_b, __pyx_n_int); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 588; goto __pyx_L1;}
-    __pyx_4 = PyTuple_New(2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 588; goto __pyx_L1;}
+    __pyx_3 = __Pyx_GetName(__pyx_b, __pyx_n_int); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 600; goto __pyx_L1;}
+    __pyx_4 = PyTuple_New(2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 600; goto __pyx_L1;}
     Py_INCREF(__pyx_v_size);
     PyTuple_SET_ITEM(__pyx_4, 0, __pyx_v_size);
     PyTuple_SET_ITEM(__pyx_4, 1, __pyx_3);
     __pyx_3 = 0;
-    __pyx_3 = PyObject_CallObject(__pyx_5, __pyx_4); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 588; goto __pyx_L1;}
+    __pyx_3 = PyObject_CallObject(__pyx_5, __pyx_4); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 600; goto __pyx_L1;}
     Py_DECREF(__pyx_5); __pyx_5 = 0;
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     Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_3)));
@@ -2274,20 +2418,20 @@
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     Py_DECREF(__pyx_3); __pyx_3 = 0;
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":589 */
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":601 */
     __pyx_v_length = PyArray_SIZE(arrayObject);
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":590 */
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":602 */
     __pyx_v_array_data = ((long (*))arrayObject->data);
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":591 */
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":603 */
     for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; ++__pyx_v_i) {
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":592 */
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":604 */
       (__pyx_v_array_data[__pyx_v_i]) = (__pyx_v_lo + ((long )rk_interval(__pyx_v_diff,((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state)));
     }
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":593 */
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":605 */
     Py_INCREF(((PyObject *)arrayObject));
     __pyx_r = ((PyObject *)arrayObject);
     goto __pyx_L0;
@@ -2324,22 +2468,19 @@
   Py_INCREF(__pyx_v_self);
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":601 */
-  __pyx_v_bytes = PyMem_Malloc(__pyx_v_length);
-
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":602 */
-  rk_fill(__pyx_v_bytes,__pyx_v_length,((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state);
-
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":603 */
-  __pyx_1 = PyString_FromStringAndSize(((char (*))__pyx_v_bytes),__pyx_v_length); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 603; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":613 */
+  __pyx_1 = PyString_FromStringAndSize(NULL,__pyx_v_length); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 613; goto __pyx_L1;}
   Py_DECREF(__pyx_v_bytestring);
   __pyx_v_bytestring = __pyx_1;
   __pyx_1 = 0;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":604 */
-  PyMem_Free(__pyx_v_bytes);
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":614 */
+  __pyx_v_bytes = PyString_AS_STRING(__pyx_v_bytestring);
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":605 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":615 */
+  rk_fill(__pyx_v_bytes,__pyx_v_length,((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state);
+
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":616 */
   Py_INCREF(__pyx_v_bytestring);
   __pyx_r = __pyx_v_bytestring;
   goto __pyx_L0;
@@ -2367,11 +2508,14 @@
   PyArrayObject *__pyx_v_olow;
   PyArrayObject *__pyx_v_ohigh;
   PyArrayObject *__pyx_v_odiff;
+  double __pyx_v_flow;
+  double __pyx_v_fhigh;
   PyObject *__pyx_v_temp;
   PyObject *__pyx_r;
-  PyObject *__pyx_1 = 0;
+  int __pyx_1;
   PyObject *__pyx_2 = 0;
   PyObject *__pyx_3 = 0;
+  PyObject *__pyx_4 = 0;
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@@ -2386,58 +2530,80 @@
   __pyx_v_odiff = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
   __pyx_v_temp = Py_None; Py_INCREF(Py_None);
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":616 */
-  __pyx_1 = PyArray_FROM_OTF(__pyx_v_low,NPY_DOUBLE,NPY_ALIGNED); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 616; goto __pyx_L1;}
-  Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_1)));
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":627 */
+  __pyx_v_flow = PyFloat_AsDouble(__pyx_v_low);
+
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":628 */
+  __pyx_v_fhigh = PyFloat_AsDouble(__pyx_v_high);
+
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":629 */
+  __pyx_1 = (!PyErr_Occurred());
+  if (__pyx_1) {
+
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":630 */
+    __pyx_2 = __pyx_f_6mtrand_cont2_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,rk_uniform,__pyx_v_size,__pyx_v_flow,(__pyx_v_fhigh - __pyx_v_flow)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 630; goto __pyx_L1;}
+    __pyx_r = __pyx_2;
+    __pyx_2 = 0;
+    goto __pyx_L0;
+    goto __pyx_L2;
+  }
+  __pyx_L2:;
+
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":631 */
+  PyErr_Clear();
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+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":632 */
+  __pyx_2 = PyArray_FROM_OTF(__pyx_v_low,NPY_DOUBLE,NPY_ALIGNED); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 632; goto __pyx_L1;}
+  Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_2)));
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-  __pyx_v_olow = ((PyArrayObject *)__pyx_1);
-  Py_DECREF(__pyx_1); __pyx_1 = 0;
+  __pyx_v_olow = ((PyArrayObject *)__pyx_2);
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":663 */
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 static char (__pyx_k73[]) = "scale <= 0";
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-  Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_1)));
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 static PyObject *__pyx_k75p;
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 static PyObject *__pyx_f_6mtrand_11RandomState_beta(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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-  Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_1)));
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-  __pyx_v_oscale = ((PyArrayObject *)__pyx_1);
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-    goto __pyx_L2;
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-  __pyx_r = __pyx_4;
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-  Py_XDECREF(__pyx_1);
   Py_XDECREF(__pyx_2);
   Py_XDECREF(__pyx_3);
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-static PyObject *__pyx_k94p;
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 static PyObject *__pyx_f_6mtrand_11RandomState_gumbel(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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   PyObject *__pyx_3 = 0;
   PyObject *__pyx_4 = 0;
-  int __pyx_5;
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+  __pyx_v_fscale = PyFloat_AsDouble(__pyx_v_scale);
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+  __pyx_1 = (!PyErr_Occurred());
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+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1075 */
+    __pyx_1 = (__pyx_v_fscale <= 0);
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+    __pyx_4 = __Pyx_GetName(__pyx_b, __pyx_n_ValueError); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1105; goto __pyx_L1;}
+    __pyx_3 = PyTuple_New(1); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1105; goto __pyx_L1;}
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-    goto __pyx_L2;
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1105; goto __pyx_L1;}
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-  __pyx_L2:;
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-  __pyx_r = __pyx_4;
-  __pyx_4 = 0;
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+  __pyx_5 = __pyx_f_6mtrand_cont2_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,rk_logistic,__pyx_v_size,__pyx_v_oloc,__pyx_v_oscale); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1106; goto __pyx_L1;}
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-  Py_XDECREF(__pyx_1);
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   Py_XDECREF(__pyx_4);
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@@ -4803,9 +5846,11 @@
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 }
 
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 static PyObject *__pyx_f_6mtrand_11RandomState_lognormal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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@@ -4815,12 +5860,14 @@
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+  double __pyx_v_fsigma;
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-  int __pyx_5;
+  PyObject *__pyx_5 = 0;
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+  __pyx_1 = (!PyErr_Occurred());
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+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1125 */
+    __pyx_1 = (__pyx_v_fsigma <= 0);
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+      PyTuple_SET_ITEM(__pyx_3, 0, __pyx_k119p);
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+      Py_DECREF(__pyx_2); __pyx_2 = 0;
+      Py_DECREF(__pyx_3); __pyx_3 = 0;
+      __Pyx_Raise(__pyx_4, 0, 0);
+      Py_DECREF(__pyx_4); __pyx_4 = 0;
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+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1127 */
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+    __pyx_r = __pyx_2;
+    __pyx_2 = 0;
+    goto __pyx_L0;
+    goto __pyx_L2;
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+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1129 */
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+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1131 */
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-  __pyx_v_omean = ((PyArrayObject *)__pyx_1);
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-  if (!__Pyx_TypeTest(__pyx_1, __pyx_ptype_6mtrand_ndarray)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 928; goto __pyx_L1;}
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-  __pyx_v_osigma = ((PyArrayObject *)__pyx_1);
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-    Py_INCREF(__pyx_k96p);
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-    Py_DECREF(__pyx_1); __pyx_1 = 0;
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+    __pyx_3 = PyTuple_New(1); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1134; goto __pyx_L1;}
+    Py_INCREF(__pyx_k120p);
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+    Py_DECREF(__pyx_4); __pyx_4 = 0;
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     Py_DECREF(__pyx_2); __pyx_2 = 0;
-    __Pyx_Raise(__pyx_3, 0, 0);
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-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 930; goto __pyx_L1;}
-    goto __pyx_L2;
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1134; goto __pyx_L1;}
+    goto __pyx_L4;
   }
-  __pyx_L2:;
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":931 */
-  __pyx_4 = __pyx_f_6mtrand_cont2_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,rk_lognormal,__pyx_v_size,__pyx_v_omean,__pyx_v_osigma); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 931; goto __pyx_L1;}
-  __pyx_r = __pyx_4;
-  __pyx_4 = 0;
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+  __pyx_5 = __pyx_f_6mtrand_cont2_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,rk_lognormal,__pyx_v_size,__pyx_v_omean,__pyx_v_osigma); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1135; goto __pyx_L1;}
+  __pyx_r = __pyx_5;
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   goto __pyx_L0;
 
   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
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-  Py_XDECREF(__pyx_1);
   Py_XDECREF(__pyx_2);
   Py_XDECREF(__pyx_3);
   Py_XDECREF(__pyx_4);
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   __pyx_L0:;
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-static PyObject *__pyx_k97p;
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   PyObject *__pyx_v_size = 0;
   PyArrayObject *__pyx_v_oscale;
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   PyObject *__pyx_r;
-  PyObject *__pyx_1 = 0;
+  int __pyx_1;
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   PyObject *__pyx_3 = 0;
   PyObject *__pyx_4 = 0;
-  int __pyx_5;
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-  __pyx_1 = PyArray_FROM_OTF(__pyx_v_scale,NPY_DOUBLE,NPY_ALIGNED); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 939; goto __pyx_L1;}
-  Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_1)));
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1145 */
+  __pyx_v_fscale = PyFloat_AsDouble(__pyx_v_scale);
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+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1147 */
+  __pyx_1 = (!PyErr_Occurred());
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-  Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_1)));
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-    goto __pyx_L2;
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-  __pyx_r = __pyx_4;
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 static PyObject *__pyx_f_6mtrand_11RandomState_zipf(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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-  PyObject *__pyx_1 = 0;
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-  __pyx_1 = PyTuple_New(2); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1183; goto __pyx_L1;}
+  __pyx_3 = __Pyx_GetName(__pyx_b, __pyx_n_int); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1518; goto __pyx_L1;}
+  __pyx_1 = PyTuple_New(2); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1518; goto __pyx_L1;}
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-  __pyx_3 = PyObject_CallObject(__pyx_2, __pyx_1); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1183; goto __pyx_L1;}
+  __pyx_3 = PyObject_CallObject(__pyx_2, __pyx_1); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1518; goto __pyx_L1;}
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1184 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1519 */
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1185 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1520 */
   __pyx_v_mnix = ((long (*))arrayObject_mnarr->data);
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1186 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1521 */
   __pyx_v_i = 0;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1187 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1522 */
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     __pyx_5 = (__pyx_v_i < PyArray_SIZE(arrayObject_mnarr));
     if (!__pyx_5) break;
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1188 */
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1523 */
     __pyx_v_Sum = 1.0;
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1189 */
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1524 */
     __pyx_v_dn = __pyx_v_n;
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1190 */
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1525 */
     __pyx_4 = (__pyx_v_d - 1);
     for (__pyx_v_j = 0; __pyx_v_j < __pyx_4; ++__pyx_v_j) {
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1191 */
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1526 */
       (__pyx_v_mnix[(__pyx_v_i + __pyx_v_j)]) = rk_binomial(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,__pyx_v_dn,((__pyx_v_pix[__pyx_v_j]) / __pyx_v_Sum));
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1192 */
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1527 */
       __pyx_v_dn = (__pyx_v_dn - (__pyx_v_mnix[(__pyx_v_i + __pyx_v_j)]));
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1193 */
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1528 */
       __pyx_5 = (__pyx_v_dn <= 0);
       if (__pyx_5) {
 
-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1194 */
+        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1529 */
         goto __pyx_L7;
         goto __pyx_L8;
       }
       __pyx_L8:;
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1195 */
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1530 */
       __pyx_v_Sum = (__pyx_v_Sum - (__pyx_v_pix[__pyx_v_j]));
     }
     __pyx_L7:;
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1196 */
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1531 */
     __pyx_5 = (__pyx_v_dn > 0);
     if (__pyx_5) {
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1197 */
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1532 */
       (__pyx_v_mnix[((__pyx_v_i + __pyx_v_d) - 1)]) = __pyx_v_dn;
       goto __pyx_L9;
     }
     __pyx_L9:;
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1199 */
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1534 */
     __pyx_v_i = (__pyx_v_i + __pyx_v_d);
   }
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1201 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1536 */
   Py_INCREF(__pyx_v_multin);
   __pyx_r = __pyx_v_multin;
   goto __pyx_L0;
@@ -7202,35 +9039,35 @@
   __pyx_v_shape = Py_None; Py_INCREF(Py_None);
   __pyx_v_diric = Py_None; Py_INCREF(Py_None);
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1258 */
-  __pyx_1 = __Pyx_GetName(__pyx_b, __pyx_n_len); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1258; goto __pyx_L1;}
-  __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1258; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1593 */
+  __pyx_1 = __Pyx_GetName(__pyx_b, __pyx_n_len); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1593; goto __pyx_L1;}
+  __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1593; goto __pyx_L1;}
   Py_INCREF(__pyx_v_alpha);
   PyTuple_SET_ITEM(__pyx_2, 0, __pyx_v_alpha);
-  __pyx_3 = PyObject_CallObject(__pyx_1, __pyx_2); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1258; goto __pyx_L1;}
+  __pyx_3 = PyObject_CallObject(__pyx_1, __pyx_2); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1593; goto __pyx_L1;}
   Py_DECREF(__pyx_1); __pyx_1 = 0;
   Py_DECREF(__pyx_2); __pyx_2 = 0;
-  __pyx_4 = PyInt_AsLong(__pyx_3); if (PyErr_Occurred()) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1258; goto __pyx_L1;}
+  __pyx_4 = PyInt_AsLong(__pyx_3); if (PyErr_Occurred()) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1593; goto __pyx_L1;}
   Py_DECREF(__pyx_3); __pyx_3 = 0;
   __pyx_v_k = __pyx_4;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1259 */
-  __pyx_1 = PyArray_ContiguousFromObject(__pyx_v_alpha,NPY_DOUBLE,1,1); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1259; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1594 */
+  __pyx_1 = PyArray_ContiguousFromObject(__pyx_v_alpha,NPY_DOUBLE,1,1); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1594; goto __pyx_L1;}
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   Py_DECREF(((PyObject *)__pyx_v_alpha_arr));
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   Py_DECREF(__pyx_1); __pyx_1 = 0;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1260 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1595 */
   __pyx_v_alpha_data = ((double (*))__pyx_v_alpha_arr->data);
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1262 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1597 */
   __pyx_5 = __pyx_v_size == Py_None;
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1263 */
-    __pyx_2 = PyInt_FromLong(__pyx_v_k); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1263; goto __pyx_L1;}
-    __pyx_3 = PyTuple_New(1); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1263; goto __pyx_L1;}
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1598 */
+    __pyx_2 = PyInt_FromLong(__pyx_v_k); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1598; goto __pyx_L1;}
+    __pyx_3 = PyTuple_New(1); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1598; goto __pyx_L1;}
     PyTuple_SET_ITEM(__pyx_3, 0, __pyx_2);
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@@ -7238,22 +9075,22 @@
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-  __pyx_1 = __Pyx_GetName(__pyx_b, __pyx_n_type); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1264; goto __pyx_L1;}
-  __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1264; goto __pyx_L1;}
+  __pyx_1 = __Pyx_GetName(__pyx_b, __pyx_n_type); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1599; goto __pyx_L1;}
+  __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1599; goto __pyx_L1;}
   Py_INCREF(__pyx_v_size);
   PyTuple_SET_ITEM(__pyx_2, 0, __pyx_v_size);
-  __pyx_3 = PyObject_CallObject(__pyx_1, __pyx_2); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1264; goto __pyx_L1;}
+  __pyx_3 = PyObject_CallObject(__pyx_1, __pyx_2); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1599; goto __pyx_L1;}
   Py_DECREF(__pyx_1); __pyx_1 = 0;
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-  __pyx_1 = __Pyx_GetName(__pyx_b, __pyx_n_int); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1264; goto __pyx_L1;}
+  __pyx_1 = __Pyx_GetName(__pyx_b, __pyx_n_int); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1599; goto __pyx_L1;}
   __pyx_5 = __pyx_3 == __pyx_1;
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1265 */
-    __pyx_2 = PyInt_FromLong(__pyx_v_k); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1265; goto __pyx_L1;}
-    __pyx_3 = PyTuple_New(2); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1265; goto __pyx_L1;}
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1600 */
+    __pyx_2 = PyInt_FromLong(__pyx_v_k); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1600; goto __pyx_L1;}
+    __pyx_3 = PyTuple_New(2); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1600; goto __pyx_L1;}
     Py_INCREF(__pyx_v_size);
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@@ -7265,12 +9102,12 @@
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1267 */
-    __pyx_1 = PyInt_FromLong(__pyx_v_k); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1267; goto __pyx_L1;}
-    __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1267; goto __pyx_L1;}
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1602 */
+    __pyx_1 = PyInt_FromLong(__pyx_v_k); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1602; goto __pyx_L1;}
+    __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1602; goto __pyx_L1;}
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     __pyx_1 = 0;
-    __pyx_3 = PyNumber_Add(__pyx_v_size, __pyx_2); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1267; goto __pyx_L1;}
+    __pyx_3 = PyNumber_Add(__pyx_v_size, __pyx_2); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1602; goto __pyx_L1;}
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@@ -7278,72 +9115,72 @@
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1269 */
-  __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1269; goto __pyx_L1;}
-  __pyx_2 = PyObject_GetAttr(__pyx_1, __pyx_n_zeros); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1269; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1604 */
+  __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1604; goto __pyx_L1;}
+  __pyx_2 = PyObject_GetAttr(__pyx_1, __pyx_n_zeros); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1604; goto __pyx_L1;}
   Py_DECREF(__pyx_1); __pyx_1 = 0;
-  __pyx_3 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1269; goto __pyx_L1;}
-  __pyx_1 = PyObject_GetAttr(__pyx_3, __pyx_n_float64); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1269; goto __pyx_L1;}
+  __pyx_3 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1604; goto __pyx_L1;}
+  __pyx_1 = PyObject_GetAttr(__pyx_3, __pyx_n_float64); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1604; goto __pyx_L1;}
   Py_DECREF(__pyx_3); __pyx_3 = 0;
-  __pyx_3 = PyTuple_New(2); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1269; goto __pyx_L1;}
+  __pyx_3 = PyTuple_New(2); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1604; goto __pyx_L1;}
   Py_INCREF(__pyx_v_shape);
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   __pyx_1 = 0;
-  __pyx_1 = PyObject_CallObject(__pyx_2, __pyx_3); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1269; goto __pyx_L1;}
+  __pyx_1 = PyObject_CallObject(__pyx_2, __pyx_3); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1604; goto __pyx_L1;}
   Py_DECREF(__pyx_2); __pyx_2 = 0;
   Py_DECREF(__pyx_3); __pyx_3 = 0;
   Py_DECREF(__pyx_v_diric);
   __pyx_v_diric = __pyx_1;
   __pyx_1 = 0;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1270 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1605 */
   Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_v_diric)));
   Py_DECREF(((PyObject *)__pyx_v_val_arr));
   __pyx_v_val_arr = ((PyArrayObject *)__pyx_v_diric);
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1271 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1606 */
   __pyx_v_val_data = ((double (*))__pyx_v_val_arr->data);
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1273 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1608 */
   __pyx_v_i = 0;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1274 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1609 */
   __pyx_v_totsize = PyArray_SIZE(__pyx_v_val_arr);
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1275 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1610 */
   while (1) {
     __pyx_5 = (__pyx_v_i < __pyx_v_totsize);
     if (!__pyx_5) break;
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1276 */
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1611 */
     __pyx_v_acc = 0.0;
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1277 */
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1612 */
     for (__pyx_v_j = 0; __pyx_v_j < __pyx_v_k; ++__pyx_v_j) {
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1278 */
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1613 */
       (__pyx_v_val_data[(__pyx_v_i + __pyx_v_j)]) = rk_standard_gamma(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,(__pyx_v_alpha_data[__pyx_v_j]));
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1279 */
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1614 */
       __pyx_v_acc = (__pyx_v_acc + (__pyx_v_val_data[(__pyx_v_i + __pyx_v_j)]));
     }
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1280 */
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1615 */
     __pyx_v_invacc = (1 / __pyx_v_acc);
 
-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1281 */
+    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1616 */
     for (__pyx_v_j = 0; __pyx_v_j < __pyx_v_k; ++__pyx_v_j) {
 
-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1282 */
+      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1617 */
       (__pyx_v_val_data[(__pyx_v_i + __pyx_v_j)]) = ((__pyx_v_val_data[(__pyx_v_i + __pyx_v_j)]) * __pyx_v_invacc);
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-    /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1283 */
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-    __pyx_1 = PyTuple_New(2); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1310; goto __pyx_L1;}
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-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1312 */
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-      /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1317 */
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-        /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1318 */
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+  if (PyType_Ready(&__pyx_type_6mtrand_RandomState) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 468; goto __pyx_L1;}
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-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":561 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":573 */
   Py_INCREF(Py_None);
   __pyx_k6 = Py_None;
   Py_INCREF(Py_None);
   __pyx_k7 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":607 */
-  __pyx_1 = PyFloat_FromDouble(0.0); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 607; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":618 */
+  __pyx_1 = PyFloat_FromDouble(0.0); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 618; goto __pyx_L1;}
   __pyx_k8 = __pyx_1;
   __pyx_1 = 0;
-  __pyx_2 = PyFloat_FromDouble(1.0); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 607; goto __pyx_L1;}
+  __pyx_2 = PyFloat_FromDouble(1.0); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 618; goto __pyx_L1;}
   __pyx_k9 = __pyx_2;
   __pyx_2 = 0;
   Py_INCREF(Py_None);
   __pyx_k10 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":655 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":671 */
   Py_INCREF(Py_None);
   __pyx_k11 = Py_None;
   Py_INCREF(Py_None);
   __pyx_k12 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":668 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":684 */
   Py_INCREF(Py_None);
   __pyx_k13 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":675 */
-  __pyx_3 = PyFloat_FromDouble(0.0); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 675; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":691 */
+  __pyx_3 = PyFloat_FromDouble(0.0); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 691; goto __pyx_L1;}
   __pyx_k14 = __pyx_3;
   __pyx_3 = 0;
-  __pyx_4 = PyFloat_FromDouble(1.0); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 675; goto __pyx_L1;}
+  __pyx_4 = PyFloat_FromDouble(1.0); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 691; goto __pyx_L1;}
   __pyx_k15 = __pyx_4;
   __pyx_4 = 0;
   Py_INCREF(Py_None);
   __pyx_k16 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":688 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":714 */
   Py_INCREF(Py_None);
   __pyx_k17 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":704 */
-  __pyx_5 = PyFloat_FromDouble(1.0); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 704; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":741 */
+  __pyx_5 = PyFloat_FromDouble(1.0); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 741; goto __pyx_L1;}
   __pyx_k18 = __pyx_5;
   __pyx_5 = 0;
   Py_INCREF(Py_None);
   __pyx_k19 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":715 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":762 */
   Py_INCREF(Py_None);
   __pyx_k20 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":722 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":769 */
   Py_INCREF(Py_None);
   __pyx_k21 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":733 */
-  __pyx_6 = PyFloat_FromDouble(1.0); if (!__pyx_6) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 733; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":789 */
+  __pyx_6 = PyFloat_FromDouble(1.0); if (!__pyx_6) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 789; goto __pyx_L1;}
   __pyx_k22 = __pyx_6;
   __pyx_6 = 0;
   Py_INCREF(Py_None);
   __pyx_k23 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":748 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":815 */
   Py_INCREF(Py_None);
   __pyx_k24 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":763 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":842 */
   Py_INCREF(Py_None);
   __pyx_k25 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":784 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":878 */
   Py_INCREF(Py_None);
   __pyx_k26 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":795 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":899 */
   Py_INCREF(Py_None);
   __pyx_k27 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":811 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":927 */
   Py_INCREF(Py_None);
   __pyx_k28 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":818 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":934 */
   Py_INCREF(Py_None);
   __pyx_k29 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":829 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":955 */
   Py_INCREF(Py_None);
   __pyx_k30 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":843 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":979 */
   Py_INCREF(Py_None);
   __pyx_k31 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":854 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1000 */
   Py_INCREF(Py_None);
   __pyx_k32 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":865 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1021 */
   Py_INCREF(Py_None);
   __pyx_k33 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":876 */
-  __pyx_7 = PyFloat_FromDouble(0.0); if (!__pyx_7) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 876; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1042 */
+  __pyx_7 = PyFloat_FromDouble(0.0); if (!__pyx_7) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1042; goto __pyx_L1;}
   __pyx_k34 = __pyx_7;
   __pyx_7 = 0;
-  __pyx_8 = PyFloat_FromDouble(1.0); if (!__pyx_8) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 876; goto __pyx_L1;}
+  __pyx_8 = PyFloat_FromDouble(1.0); if (!__pyx_8) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1042; goto __pyx_L1;}
   __pyx_k35 = __pyx_8;
   __pyx_8 = 0;
   Py_INCREF(Py_None);
   __pyx_k36 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":889 */
-  __pyx_9 = PyFloat_FromDouble(0.0); if (!__pyx_9) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 889; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1064 */
+  __pyx_9 = PyFloat_FromDouble(0.0); if (!__pyx_9) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1064; goto __pyx_L1;}
   __pyx_k37 = __pyx_9;
   __pyx_9 = 0;
-  __pyx_10 = PyFloat_FromDouble(1.0); if (!__pyx_10) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 889; goto __pyx_L1;}
+  __pyx_10 = PyFloat_FromDouble(1.0); if (!__pyx_10) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1064; goto __pyx_L1;}
   __pyx_k38 = __pyx_10;
   __pyx_10 = 0;
   Py_INCREF(Py_None);
   __pyx_k39 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":902 */
-  __pyx_11 = PyFloat_FromDouble(0.0); if (!__pyx_11) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 902; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1086 */
+  __pyx_11 = PyFloat_FromDouble(0.0); if (!__pyx_11) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1086; goto __pyx_L1;}
   __pyx_k40 = __pyx_11;
   __pyx_11 = 0;
-  __pyx_12 = PyFloat_FromDouble(1.0); if (!__pyx_12) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 902; goto __pyx_L1;}
+  __pyx_12 = PyFloat_FromDouble(1.0); if (!__pyx_12) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1086; goto __pyx_L1;}
   __pyx_k41 = __pyx_12;
   __pyx_12 = 0;
   Py_INCREF(Py_None);
   __pyx_k42 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":915 */
-  __pyx_13 = PyFloat_FromDouble(0.0); if (!__pyx_13) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 915; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1108 */
+  __pyx_13 = PyFloat_FromDouble(0.0); if (!__pyx_13) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1108; goto __pyx_L1;}
   __pyx_k43 = __pyx_13;
   __pyx_13 = 0;
-  __pyx_14 = PyFloat_FromDouble(1.0); if (!__pyx_14) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 915; goto __pyx_L1;}
+  __pyx_14 = PyFloat_FromDouble(1.0); if (!__pyx_14) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1108; goto __pyx_L1;}
   __pyx_k44 = __pyx_14;
   __pyx_14 = 0;
   Py_INCREF(Py_None);
   __pyx_k45 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":933 */
-  __pyx_15 = PyFloat_FromDouble(1.0); if (!__pyx_15) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 933; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1137 */
+  __pyx_15 = PyFloat_FromDouble(1.0); if (!__pyx_15) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1137; goto __pyx_L1;}
   __pyx_k46 = __pyx_15;
   __pyx_15 = 0;
   Py_INCREF(Py_None);
   __pyx_k47 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":944 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1159 */
   Py_INCREF(Py_None);
   __pyx_k48 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":959 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1187 */
   Py_INCREF(Py_None);
   __pyx_k49 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":982 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1224 */
   Py_INCREF(Py_None);
   __pyx_k50 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":999 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1256 */
   Py_INCREF(Py_None);
   __pyx_k51 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1017 */
-  __pyx_16 = PyFloat_FromDouble(1.0); if (!__pyx_16) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1017; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1291 */
+  __pyx_16 = PyFloat_FromDouble(1.0); if (!__pyx_16) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1291; goto __pyx_L1;}
   __pyx_k52 = __pyx_16;
   __pyx_16 = 0;
   Py_INCREF(Py_None);
   __pyx_k53 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1028 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1311 */
   Py_INCREF(Py_None);
   __pyx_k54 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1039 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1332 */
   Py_INCREF(Py_None);
   __pyx_k55 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1053 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1359 */
   Py_INCREF(Py_None);
   __pyx_k56 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1081 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1404 */
   Py_INCREF(Py_None);
   __pyx_k57 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1095 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1430 */
   Py_INCREF(Py_None);
   __pyx_k58 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1153 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1488 */
   Py_INCREF(Py_None);
   __pyx_k59 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1203 */
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1538 */
   Py_INCREF(Py_None);
   __pyx_k60 = Py_None;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1335 */
-  __pyx_17 = PyObject_CallObject(((PyObject*)__pyx_ptype_6mtrand_RandomState), 0); if (!__pyx_17) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1335; goto __pyx_L1;}
-  if (PyObject_SetAttr(__pyx_m, __pyx_n__rand, __pyx_17) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1335; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1670 */
+  __pyx_17 = PyObject_CallObject(((PyObject*)__pyx_ptype_6mtrand_RandomState), 0); if (!__pyx_17) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1670; goto __pyx_L1;}
+  if (PyObject_SetAttr(__pyx_m, __pyx_n__rand, __pyx_17) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1670; goto __pyx_L1;}
   Py_DECREF(__pyx_17); __pyx_17 = 0;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1336 */
-  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_n__rand); if (!__pyx_17) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1336; goto __pyx_L1;}
-  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_n_seed); if (!__pyx_18) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1336; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1671 */
+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_n__rand); if (!__pyx_17) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1671; goto __pyx_L1;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_n_seed); if (!__pyx_18) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1671; goto __pyx_L1;}
   Py_DECREF(__pyx_17); __pyx_17 = 0;
-  if (PyObject_SetAttr(__pyx_m, __pyx_n_seed, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1336; goto __pyx_L1;}
+  if (PyObject_SetAttr(__pyx_m, __pyx_n_seed, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1671; goto __pyx_L1;}
   Py_DECREF(__pyx_18); __pyx_18 = 0;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1337 */
-  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_n__rand); if (!__pyx_17) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1337; goto __pyx_L1;}
-  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_n_get_state); if (!__pyx_18) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1337; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1672 */
+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_n__rand); if (!__pyx_17) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1672; goto __pyx_L1;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_n_get_state); if (!__pyx_18) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1672; goto __pyx_L1;}
   Py_DECREF(__pyx_17); __pyx_17 = 0;
-  if (PyObject_SetAttr(__pyx_m, __pyx_n_get_state, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1337; goto __pyx_L1;}
+  if (PyObject_SetAttr(__pyx_m, __pyx_n_get_state, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1672; goto __pyx_L1;}
   Py_DECREF(__pyx_18); __pyx_18 = 0;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1338 */
-  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_n__rand); if (!__pyx_17) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1338; goto __pyx_L1;}
-  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_n_set_state); if (!__pyx_18) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1338; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1673 */
+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_n__rand); if (!__pyx_17) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1673; goto __pyx_L1;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_n_set_state); if (!__pyx_18) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1673; goto __pyx_L1;}
   Py_DECREF(__pyx_17); __pyx_17 = 0;
-  if (PyObject_SetAttr(__pyx_m, __pyx_n_set_state, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1338; goto __pyx_L1;}
+  if (PyObject_SetAttr(__pyx_m, __pyx_n_set_state, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1673; goto __pyx_L1;}
   Py_DECREF(__pyx_18); __pyx_18 = 0;
 
-  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1339 */
-  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_n__rand); if (!__pyx_17) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1339; goto __pyx_L1;}
-  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_n_random_sample); if (!__pyx_18) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1339; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1674 */
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+  if (PyObject_SetAttr(__pyx_m, __pyx_n_binomial, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1706; goto __pyx_L1;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_n_negative_binomial, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1707; goto __pyx_L1;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_n_poisson, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1708; goto __pyx_L1;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_n_zipf, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1709; goto __pyx_L1;}
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+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_n__rand); if (!__pyx_17) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1710; goto __pyx_L1;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_n_geometric); if (!__pyx_18) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1710; goto __pyx_L1;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_n_geometric, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1710; goto __pyx_L1;}
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+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_n__rand); if (!__pyx_17) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1711; goto __pyx_L1;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_n_hypergeometric, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1711; goto __pyx_L1;}
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+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_n__rand); if (!__pyx_17) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1712; goto __pyx_L1;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_n_logseries); if (!__pyx_18) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1712; goto __pyx_L1;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_n_logseries, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1712; goto __pyx_L1;}
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+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1714 */
+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_n__rand); if (!__pyx_17) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1714; goto __pyx_L1;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_n_multivariate_normal); if (!__pyx_18) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1714; goto __pyx_L1;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_n_multivariate_normal, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1714; goto __pyx_L1;}
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-  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_n_multinomial); if (!__pyx_18) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1380; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1715 */
+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_n__rand); if (!__pyx_17) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1715; goto __pyx_L1;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_n_multinomial); if (!__pyx_18) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1715; goto __pyx_L1;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_n_multinomial, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1715; goto __pyx_L1;}
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-  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_n_dirichlet); if (!__pyx_18) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1381; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1716 */
+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_n__rand); if (!__pyx_17) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1716; goto __pyx_L1;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_n_dirichlet); if (!__pyx_18) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1716; goto __pyx_L1;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_n_dirichlet, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1716; goto __pyx_L1;}
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-  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_n_shuffle); if (!__pyx_18) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1383; goto __pyx_L1;}
+  /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":1718 */
+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_n__rand); if (!__pyx_17) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1718; goto __pyx_L1;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_n_shuffle); if (!__pyx_18) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1718; goto __pyx_L1;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_n_shuffle, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1718; goto __pyx_L1;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_n_permutation, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1719; goto __pyx_L1;}
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   __pyx_L1:;

Modified: trunk/numpy/random/mtrand/mtrand.pyx
===================================================================
--- trunk/numpy/random/mtrand/mtrand.pyx	2007-03-16 12:53:05 UTC (rev 3581)
+++ trunk/numpy/random/mtrand/mtrand.pyx	2007-03-19 17:19:39 UTC (rev 3582)
@@ -136,6 +136,23 @@
             array_data[i] = func(state)
         return array
 
+
+cdef object cont1_array_sc(rk_state *state, rk_cont1 func, object size, double a):
+    cdef double *array_data
+    cdef ndarray array "arrayObject"
+    cdef long length
+    cdef long i
+    
+    if size is None:
+        return func(state, a)
+    else:
+        array = <ndarray>_sp.empty(size, _sp.Float64)
+        length = PyArray_SIZE(array)
+        array_data = <double *>array.data
+        for i from 0 <= i < length:
+            array_data[i] = func(state, a)
+        return array
+
 cdef object cont1_array(rk_state *state, rk_cont1 func, object size, ndarray oa):
     cdef double *array_data
     cdef double *oa_data
@@ -144,42 +161,46 @@
     cdef npy_intp i
     cdef flatiter itera
     cdef broadcast multi
-    cdef int scalar
 
-    scalar = 0
-    if oa.nd == 0:
-        oa_data = <double *>oa.data
-        scalar = 1
-
     if size is None:
-        if scalar:
-            return func(state, oa_data[0])
-        else:
-            array = <ndarray>PyArray_SimpleNew(oa.nd, oa.dimensions, NPY_DOUBLE)
-            length = PyArray_SIZE(array)
-            array_data = <double *>array.data
-            itera = <flatiter>PyArray_IterNew(<object>oa)
-            for i from 0 <= i < length:
-                array_data[i] = func(state, (<double *>(itera.dataptr))[0])
-                PyArray_ITER_NEXT(itera)
+        array = <ndarray>PyArray_SimpleNew(oa.nd, oa.dimensions, NPY_DOUBLE)
+        length = PyArray_SIZE(array)
+        array_data = <double *>array.data
+        itera = <flatiter>PyArray_IterNew(<object>oa)
+        for i from 0 <= i < length:
+            array_data[i] = func(state, (<double *>(itera.dataptr))[0])
+            PyArray_ITER_NEXT(itera)
     else:
         array = <ndarray>_sp.empty(size, _sp.float64)
         array_data = <double *>array.data
-        if scalar:
-            length = PyArray_SIZE(array)
-            for i from 0 <= i < length:
-                array_data[i] = func(state, oa_data[0])
-        else:
-            multi = <broadcast>PyArray_MultiIterNew(2, <void *>array,
-                                                    <void *>oa)
-            if (multi.size != PyArray_SIZE(array)): 
-                raise ValueError("size is not compatible with inputs")
-            for i from 0 <= i < multi.size:
-                oa_data = <double *>PyArray_MultiIter_DATA(multi, 1)
-                array_data[i] = func(state, oa_data[0])
-                PyArray_MultiIter_NEXTi(multi, 1)
+        multi = <broadcast>PyArray_MultiIterNew(2, <void *>array,
+                                                <void *>oa)
+        if (multi.size != PyArray_SIZE(array)): 
+            raise ValueError("size is not compatible with inputs")
+        for i from 0 <= i < multi.size:
+            oa_data = <double *>PyArray_MultiIter_DATA(multi, 1)
+            array_data[i] = func(state, oa_data[0])
+            PyArray_MultiIter_NEXTi(multi, 1)
     return array
 
+cdef object cont2_array_sc(rk_state *state, rk_cont2 func, object size, double a,
+                           double b):
+    cdef double *array_data
+    cdef ndarray array "arrayObject"
+    cdef long length
+    cdef long i
+
+    if size is None:
+        return func(state, a, b)
+    else:
+        array = <ndarray>_sp.empty(size, _sp.float64)
+        length = PyArray_SIZE(array)
+        array_data = <double *>array.data
+        for i from 0 <= i < length:
+            array_data[i] = func(state, a, b)
+        return array
+                               
+
 cdef object cont2_array(rk_state *state, rk_cont2 func, object size,
                         ndarray oa, ndarray ob):
     cdef double *array_data
@@ -189,45 +210,48 @@
     cdef npy_intp length
     cdef npy_intp i
     cdef broadcast multi
-    cdef int scalar
-
-    scalar = 0
-    if oa.nd == 0 and ob.nd == 0:
-        oa_data = <double *>oa.data
-        ob_data = <double *>ob.data
-        scalar = 1
         
     if size is None:
-        if scalar:
-            return func(state, oa_data[0], ob_data[0])
-        else:
-            multi = <broadcast> PyArray_MultiIterNew(2, <void *>oa, <void *>ob)
-            array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_DOUBLE)
-            array_data = <double *>array.data
-            for i from 0 <= i < multi.size:
-                oa_data = <double *>PyArray_MultiIter_DATA(multi, 0)
-                ob_data = <double *>PyArray_MultiIter_DATA(multi, 1)
-                array_data[i] = func(state, oa_data[0], ob_data[0])
-                PyArray_MultiIter_NEXT(multi)
+        multi = <broadcast> PyArray_MultiIterNew(2, <void *>oa, <void *>ob)
+        array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_DOUBLE)
+        array_data = <double *>array.data
+        for i from 0 <= i < multi.size:
+            oa_data = <double *>PyArray_MultiIter_DATA(multi, 0)
+            ob_data = <double *>PyArray_MultiIter_DATA(multi, 1)
+            array_data[i] = func(state, oa_data[0], ob_data[0])
+            PyArray_MultiIter_NEXT(multi)
     else:
         array = <ndarray>_sp.empty(size, _sp.float64)
         array_data = <double *>array.data
-        if scalar:
-            length = PyArray_SIZE(array)
-            for i from 0 <= i < length:
-                array_data[i] = func(state, oa_data[0], ob_data[0])
-        else:
-            multi = <broadcast>PyArray_MultiIterNew(3, <void*>array, <void *>oa, <void *>ob)
-            if (multi.size != PyArray_SIZE(array)):
-                raise ValueError("size is not compatible with inputs")                 
-            for i from 0 <= i < multi.size:
-                oa_data = <double *>PyArray_MultiIter_DATA(multi, 1)
-                ob_data = <double *>PyArray_MultiIter_DATA(multi, 2)
-                array_data[i] = func(state, oa_data[0], ob_data[0])
-                PyArray_MultiIter_NEXTi(multi, 1)
-                PyArray_MultiIter_NEXTi(multi, 2)
+        multi = <broadcast>PyArray_MultiIterNew(3, <void*>array, <void *>oa, <void *>ob)
+        if (multi.size != PyArray_SIZE(array)):
+            raise ValueError("size is not compatible with inputs")                 
+        for i from 0 <= i < multi.size:
+            oa_data = <double *>PyArray_MultiIter_DATA(multi, 1)
+            ob_data = <double *>PyArray_MultiIter_DATA(multi, 2)
+            array_data[i] = func(state, oa_data[0], ob_data[0])
+            PyArray_MultiIter_NEXTi(multi, 1)
+            PyArray_MultiIter_NEXTi(multi, 2)
     return array
 
+cdef object cont3_array_sc(rk_state *state, rk_cont3 func, object size, double a, 
+                           double b, double c):
+
+    cdef double *array_data
+    cdef ndarray array "arrayObject"
+    cdef long length
+    cdef long i
+    
+    if size is None:
+        return func(state, a, b, c)
+    else:
+        array = <ndarray>_sp.empty(size, _sp.Float64)
+        length = PyArray_SIZE(array)
+        array_data = <double *>array.data
+        for i from 0 <= i < length:
+            array_data[i] = func(state, a, b, c)
+        return array
+
 cdef object cont3_array(rk_state *state, rk_cont3 func, object size, ndarray oa, 
     ndarray ob, ndarray oc):
 
@@ -239,46 +263,30 @@
     cdef npy_intp length
     cdef npy_intp i
     cdef broadcast multi
-    cdef int scalar
-
-    scalar = 0
-    if (oa.nd ==0 and ob.nd==0 and oc.nd == 0):
-        oa_data = <double *>oa.data
-        ob_data = <double *>ob.data
-        oc_data = <double *>oc.data
-        scalar = 1
         
     if size is None:
-        if scalar:
-            return func(state, oa_data[0], ob_data[0], oc_data[0])
-        else:
-            multi = <broadcast> PyArray_MultiIterNew(3, <void *>oa, <void *>ob, <void *>oc)
-            array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_DOUBLE)
-            array_data = <double *>array.data
-            for i from 0 <= i < multi.size:
-                oa_data = <double *>PyArray_MultiIter_DATA(multi, 0)
-                ob_data = <double *>PyArray_MultiIter_DATA(multi, 1)
-                oc_data = <double *>PyArray_MultiIter_DATA(multi, 2)
-                array_data[i] = func(state, oa_data[0], ob_data[0], oc_data[0])
-                PyArray_MultiIter_NEXT(multi)
+        multi = <broadcast> PyArray_MultiIterNew(3, <void *>oa, <void *>ob, <void *>oc)
+        array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_DOUBLE)
+        array_data = <double *>array.data
+        for i from 0 <= i < multi.size:
+            oa_data = <double *>PyArray_MultiIter_DATA(multi, 0)
+            ob_data = <double *>PyArray_MultiIter_DATA(multi, 1)
+            oc_data = <double *>PyArray_MultiIter_DATA(multi, 2)
+            array_data[i] = func(state, oa_data[0], ob_data[0], oc_data[0])
+            PyArray_MultiIter_NEXT(multi)
     else:
         array = <ndarray>_sp.empty(size, _sp.float64)
         array_data = <double *>array.data        
-        if scalar:
-            length = PyArray_SIZE(array)
-            for i from 0 <= i < length:
-                array_data[i] = func(state, oa_data[0], ob_data[0], oc_data[0])
-        else:
-            multi = <broadcast>PyArray_MultiIterNew(4, <void*>array, <void *>oa,
-                                                    <void *>ob, <void *>oc)
-            if (multi.size != PyArray_SIZE(array)):
-                raise ValueError("size is not compatible with inputs")                 
-            for i from 0 <= i < multi.size:
-                oa_data = <double *>PyArray_MultiIter_DATA(multi, 1)
-                ob_data = <double *>PyArray_MultiIter_DATA(multi, 2)
-                oc_data = <double *>PyArray_MultiIter_DATA(multi, 3)
-                array_data[i] = func(state, oa_data[0], ob_data[0], oc_data[0])
-                PyArray_MultiIter_NEXT(multi)                
+        multi = <broadcast>PyArray_MultiIterNew(4, <void*>array, <void *>oa,
+                                                <void *>ob, <void *>oc)
+        if (multi.size != PyArray_SIZE(array)):
+            raise ValueError("size is not compatible with inputs")                 
+        for i from 0 <= i < multi.size:
+            oa_data = <double *>PyArray_MultiIter_DATA(multi, 1)
+            ob_data = <double *>PyArray_MultiIter_DATA(multi, 2)
+            oc_data = <double *>PyArray_MultiIter_DATA(multi, 3)
+            array_data[i] = func(state, oa_data[0], ob_data[0], oc_data[0])
+            PyArray_MultiIter_NEXT(multi)                
     return array
 
 cdef object disc0_array(rk_state *state, rk_disc0 func, object size):
@@ -297,6 +305,22 @@
             array_data[i] = func(state)
         return array
 
+cdef object discnp_array_sc(rk_state *state, rk_discnp func, object size, long n, double p):
+    cdef long *array_data
+    cdef ndarray array "arrayObject"
+    cdef long length
+    cdef long i
+
+    if size is None:
+        return func(state, n, p)
+    else:
+        array = <ndarray>_sp.empty(size, _sp.Int)
+        length = PyArray_SIZE(array)
+        array_data = <long *>array.data
+        for i from 0 <= i < length:
+            array_data[i] = func(state, n, p)
+        return array
+
 cdef object discnp_array(rk_state *state, rk_discnp func, object size, ndarray on, ndarray op):
     cdef long *array_data
     cdef ndarray array "arrayObject"
@@ -304,47 +328,49 @@
     cdef npy_intp i
     cdef double *op_data
     cdef long *on_data
-    cdef int scalar
     cdef broadcast multi
-
-    scalar = 0
-    if (on.nd == 0 and op.nd == 0):
-        on_data = <long *>on.data
-        op_data = <double *>op.data
-        scalar = 1
         
     if size is None:
-        if (scalar):
-            return func(state, on_data[0], op_data[0])
-        else:
-            multi = <broadcast> PyArray_MultiIterNew(2, <void *>on, <void *>op)
-            array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_LONG)
-            array_data = <long *>array.data
-            for i from 0 <= i < multi.size:
-                on_data = <long *>PyArray_MultiIter_DATA(multi, 0)
-                op_data = <double *>PyArray_MultiIter_DATA(multi, 1)
-                array_data[i] = func(state, on_data[0], op_data[0])
-                PyArray_MultiIter_NEXT(multi)
+        multi = <broadcast> PyArray_MultiIterNew(2, <void *>on, <void *>op)
+        array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_LONG)
+        array_data = <long *>array.data
+        for i from 0 <= i < multi.size:
+            on_data = <long *>PyArray_MultiIter_DATA(multi, 0)
+            op_data = <double *>PyArray_MultiIter_DATA(multi, 1)
+            array_data[i] = func(state, on_data[0], op_data[0])
+            PyArray_MultiIter_NEXT(multi)
     else:
-        array = <ndarray>_sp.empty(size, int)        
-        if (scalar):
-            length = PyArray_SIZE(array)
-            array_data = <long *>array.data
-            for i from 0 <= i < length:
-                array_data[i] = func(state, on_data[0], op_data[0])
-        else:
-            multi = <broadcast>PyArray_MultiIterNew(3, <void*>array, <void *>on, <void *>op)
-            if (multi.size != PyArray_SIZE(array)):
-                raise ValueError("size is not compatible with inputs")                 
-            for i from 0 <= i < multi.size:
-                on_data = <long *>PyArray_MultiIter_DATA(multi, 1)
-                op_data = <double *>PyArray_MultiIter_DATA(multi, 2)
-                array_data[i] = func(state, on_data[0], op_data[0])
-                PyArray_MultiIter_NEXTi(multi, 1)
-                PyArray_MultiIter_NEXTi(multi, 2)
+        array = <ndarray>_sp.empty(size, int)
+        array_data = <long *>array.data
+        multi = <broadcast>PyArray_MultiIterNew(3, <void*>array, <void *>on, <void *>op)
+        if (multi.size != PyArray_SIZE(array)):
+            raise ValueError("size is not compatible with inputs")                 
+        for i from 0 <= i < multi.size:
+            on_data = <long *>PyArray_MultiIter_DATA(multi, 1)
+            op_data = <double *>PyArray_MultiIter_DATA(multi, 2)
+            array_data[i] = func(state, on_data[0], op_data[0])
+            PyArray_MultiIter_NEXTi(multi, 1)
+            PyArray_MultiIter_NEXTi(multi, 2)
             
     return array
 
+cdef object discnmN_array_sc(rk_state *state, rk_discnmN func, object size, 
+    long n, long m, long N):
+    cdef long *array_data
+    cdef ndarray array "arrayObject"
+    cdef long length
+    cdef long i
+
+    if size is None:
+        return func(state, n, m, N)
+    else:
+        array = <ndarray>_sp.empty(size, _sp.Int)
+        length = PyArray_SIZE(array)
+        array_data = <long *>array.data
+        for i from 0 <= i < length:
+            array_data[i] = func(state, n, m, N)
+        return array
+
 cdef object discnmN_array(rk_state *state, rk_discnmN func, object size, 
     ndarray on, ndarray om, ndarray oN):
     cdef long *array_data
@@ -355,49 +381,49 @@
     cdef npy_intp length
     cdef npy_intp i
     cdef broadcast multi
-    cdef int scalar
 
-    scalar = 0
-    if (on.nd == 0 and om.nd == 0 and oN.nd==0):
-        scalar = 1
-        on_data = <long *>on.data
-        om_data = <long *>om.data
-        oN_data = <long *>oN.data        
-
     if size is None:
-        if (scalar):
-            return func(state, on_data[0], om_data[0], oN_data[0])
-        else:
-            multi = <broadcast> PyArray_MultiIterNew(3, <void *>on, <void *>om, <void *>oN)
-            array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_LONG)
-            array_data = <long *>array.data
-            for i from 0 <= i < multi.size:
-                on_data = <long *>PyArray_MultiIter_DATA(multi, 0)
-                om_data = <long *>PyArray_MultiIter_DATA(multi, 1)
-                oN_data = <long *>PyArray_MultiIter_DATA(multi, 2)                
-                array_data[i] = func(state, on_data[0], om_data[0], oN_data[0])
-                PyArray_MultiIter_NEXT(multi)
+        multi = <broadcast> PyArray_MultiIterNew(3, <void *>on, <void *>om, <void *>oN)
+        array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_LONG)
+        array_data = <long *>array.data
+        for i from 0 <= i < multi.size:
+            on_data = <long *>PyArray_MultiIter_DATA(multi, 0)
+            om_data = <long *>PyArray_MultiIter_DATA(multi, 1)
+            oN_data = <long *>PyArray_MultiIter_DATA(multi, 2)                
+            array_data[i] = func(state, on_data[0], om_data[0], oN_data[0])
+            PyArray_MultiIter_NEXT(multi)
     else:
         array = <ndarray>_sp.empty(size, int)
         array_data = <long *>array.data
-        if (scalar):
-            length = PyArray_SIZE(array)
-            for i from 0 <= i < length:
-                array_data[i] = func(state, on_data[0], om_data[0], oN_data[0])
-        else:
-            multi = <broadcast>PyArray_MultiIterNew(4, <void*>array, <void *>on, <void *>om,
-                                                    <void *>oN)
-            if (multi.size != PyArray_SIZE(array)):
-                raise ValueError("size is not compatible with inputs") 
-            for i from 0 <= i < multi.size:
-                on_data = <long *>PyArray_MultiIter_DATA(multi, 1)
-                om_data = <long *>PyArray_MultiIter_DATA(multi, 2)
-                oN_data = <long *>PyArray_MultiIter_DATA(multi, 3)
-                array_data[i] = func(state, on_data[0], om_data[0], oN_data[0])
-                PyArray_MultiIter_NEXT(multi)
+        multi = <broadcast>PyArray_MultiIterNew(4, <void*>array, <void *>on, <void *>om,
+                                                <void *>oN)
+        if (multi.size != PyArray_SIZE(array)):
+            raise ValueError("size is not compatible with inputs") 
+        for i from 0 <= i < multi.size:
+            on_data = <long *>PyArray_MultiIter_DATA(multi, 1)
+            om_data = <long *>PyArray_MultiIter_DATA(multi, 2)
+            oN_data = <long *>PyArray_MultiIter_DATA(multi, 3)
+            array_data[i] = func(state, on_data[0], om_data[0], oN_data[0])
+            PyArray_MultiIter_NEXT(multi)
 
     return array
 
+cdef object discd_array_sc(rk_state *state, rk_discd func, object size, double a):
+    cdef long *array_data
+    cdef ndarray array "arrayObject"
+    cdef long length
+    cdef long i
+
+    if size is None:
+        return func(state, a)
+    else:
+        array = <ndarray>_sp.empty(size, _sp.Int)
+        length = PyArray_SIZE(array)
+        array_data = <long *>array.data
+        for i from 0 <= i < length:
+            array_data[i] = func(state, a)
+        return array
+
 cdef object discd_array(rk_state *state, rk_discd func, object size, ndarray oa):
     cdef long *array_data
     cdef double *oa_data
@@ -406,39 +432,25 @@
     cdef npy_intp i
     cdef broadcast multi
     cdef flatiter itera
-    cdef int scalar 
 
-    scalar = 0
-    if (oa.nd == 0):
-        oa_data = <double *>oa.data
-        scalar =1
-
     if size is None:
-        if (scalar):
-            return func(state, oa_data[0])
-        else:
-            array = <ndarray>PyArray_SimpleNew(oa.nd, oa.dimensions, NPY_LONG)
-            length = PyArray_SIZE(array)
-            array_data = <long *>array.data
-            itera = <flatiter>PyArray_IterNew(<object>oa)
-            for i from 0 <= i < length:
-                array_data[i] = func(state, (<double *>(itera.dataptr))[0])
-                PyArray_ITER_NEXT(itera)
+        array = <ndarray>PyArray_SimpleNew(oa.nd, oa.dimensions, NPY_LONG)
+        length = PyArray_SIZE(array)
+        array_data = <long *>array.data
+        itera = <flatiter>PyArray_IterNew(<object>oa)
+        for i from 0 <= i < length:
+            array_data[i] = func(state, (<double *>(itera.dataptr))[0])
+            PyArray_ITER_NEXT(itera)
     else:
         array = <ndarray>_sp.empty(size, int)
         array_data = <long *>array.data
-        if (scalar):
-            length = PyArray_SIZE(array)
-            for i from 0 <= i < length:
-                array_data[i] = func(state, oa_data[0])
-        else:
-            multi = <broadcast>PyArray_MultiIterNew(2, <void *>array, <void *>oa)
-            if (multi.size != PyArray_SIZE(array)): 
-                raise ValueError("size is not compatible with inputs")                
-            for i from 0 <= i < multi.size:
-                oa_data = <double *>PyArray_MultiIter_DATA(multi, 1)
-                array_data[i] = func(state, oa_data[0])
-                PyArray_MultiIter_NEXTi(multi, 1)
+        multi = <broadcast>PyArray_MultiIterNew(2, <void *>array, <void *>oa)
+        if (multi.size != PyArray_SIZE(array)): 
+            raise ValueError("size is not compatible with inputs")                
+        for i from 0 <= i < multi.size:
+            oa_data = <double *>PyArray_MultiIter_DATA(multi, 1)
+            array_data[i] = func(state, oa_data[0])
+            PyArray_MultiIter_NEXTi(multi, 1)
     return array
 
 cdef double kahan_sum(double *darr, long n):
@@ -598,10 +610,9 @@
         bytes(length) -> str
         """
         cdef void *bytes
-        bytes = PyMem_Malloc(length)
+        bytestring = PyString_FromStringAndSize(NULL, length) 
+        bytes = PyString_AS_STRING(bytestring)
         rk_fill(bytes, length, self.internal_state)
-        bytestring = PyString_FromStringAndSize(<char*>bytes, length)
-        PyMem_Free(bytes)
         return bytestring
 
     def uniform(self, low=0.0, high=1.0, size=None):
@@ -609,10 +620,15 @@
 
         uniform(low=0.0, high=1.0, size=None) -> random values
         """
-        cdef ndarray olow
-        cdef ndarray ohigh
-        cdef ndarray odiff
+        cdef ndarray olow, ohigh, odiff
+        cdef double flow, fhigh
         cdef object temp
+
+        flow = PyFloat_AsDouble(low)
+        fhigh = PyFloat_AsDouble(high)
+        if not PyErr_Occurred():
+            return cont2_array_sc(self.internal_state, rk_uniform, size, flow, fhigh-flow)
+        PyErr_Clear()
         olow = <ndarray>PyArray_FROM_OTF(low, NPY_DOUBLE, NPY_ALIGNED)
         ohigh = <ndarray>PyArray_FROM_OTF(high, NPY_DOUBLE, NPY_ALIGNED)
         temp = _sp.subtract(ohigh, olow)
@@ -677,8 +693,18 @@
 
         normal(loc=0.0, scale=1.0, size=None) -> random values
         """
-        cdef ndarray oloc
-        cdef ndarray oscale
+        cdef ndarray oloc, oscale
+        cdef double floc, fscale
+
+        floc = PyFloat_AsDouble(loc)
+        fscale = PyFloat_AsDouble(scale)
+        if not PyErr_Occurred():
+            if fscale <= 0:
+                raise ValueError("scale <= 0")
+            return cont2_array_sc(self.internal_state, rk_normal, size, floc, fscale)
+
+        PyErr_Clear()
+        
         oloc = <ndarray>PyArray_FROM_OTF(loc, NPY_DOUBLE, NPY_ALIGNED)
         oscale = <ndarray>PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED)
         if _sp.any(_sp.less_equal(oscale, 0)):
@@ -690,11 +716,22 @@
 
         beta(a, b, size=None) -> random values
         """
-        cdef ndarray oa
-        cdef ndarray ob
+        cdef ndarray oa, ob
+        cdef double fa, fb
+
+        fa = PyFloat_AsDouble(a)
+        fb = PyFloat_AsDouble(b)
+        if not PyErr_Occurred():
+            if fa <= 0:
+                raise ValueError("a <= 0")
+            if fb <= 0:
+                raise ValueError("b <= 0")
+            return cont2_array_sc(self.internal_state, rk_beta, size, fa, fb)
+
+        PyErr_Clear()
+        
         oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ALIGNED)
         ob = <ndarray>PyArray_FROM_OTF(b, NPY_DOUBLE, NPY_ALIGNED)
-
         if _sp.any(_sp.less_equal(oa, 0)):
             raise ValueError("a <= 0")
         if _sp.any(_sp.less_equal(ob, 0)):        
@@ -707,6 +744,16 @@
         exponential(scale=1.0, size=None) -> random values
         """
         cdef ndarray oscale
+        cdef double fscale
+
+        fscale = PyFloat_AsDouble(scale)
+        if not PyErr_Occurred():
+            if fscale <= 0:
+                raise ValueError("scale <= 0")
+            return cont1_array_sc(self.internal_state, rk_exponential, size, fscale)
+
+        PyErr_Clear()
+        
         oscale = <ndarray> PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED)
         if _sp.any(_sp.less_equal(oscale, 0.0)):
             raise ValueError("scale <= 0")
@@ -725,6 +772,15 @@
         standard_gamma(shape, size=None) -> random values
         """
         cdef ndarray oshape
+        cdef double fshape
+
+        fshape = PyFloat_AsDouble(shape)
+        if not PyErr_Occurred():
+            if fscale <= 0:
+                raise ValueError("shape <= 0")
+            return cont1_array_sc(self.internal_state, rk_standard_gamma, size, fshape)
+
+        PyErr_Clear()
         oshape = <ndarray> PyArray_FROM_OTF(shape, NPY_DOUBLE, NPY_ALIGNED)
         if _sp.any(_sp.less_equal(oshape, 0.0)):
             raise ValueError("shape <= 0")
@@ -735,8 +791,19 @@
 
         gamma(shape, scale=1.0, size=None) -> random values
         """
-        cdef ndarray oshape
-        cdef ndarray oscale
+        cdef ndarray oshape, oscale
+        cdef double fshape, fscale
+
+        fshape = PyFloat_AsDouble(shape)
+        fscale = PyFloat_AsDouble(scale)
+        if not PyErr_Occurred():
+            if fshape <= 0:
+                raise ValueError("shape <= 0")
+            if fscale <= 0:
+                raise ValueError("scale <= 0")
+            return cont2_array_sc(self.internal_state, rk_gamma, size, fshape, fscale)
+
+        PyErr_Clear()
         oshape = <ndarray>PyArray_FROM_OTF(shape, NPY_DOUBLE, NPY_ALIGNED)
         oscale = <ndarray>PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED)
         if _sp.any(_sp.less_equal(oshape, 0.0)):
@@ -750,8 +817,20 @@
 
         f(dfnum, dfden, size=None) -> random values
         """
-        cdef ndarray odfnum
-        cdef ndarray odfden
+        cdef ndarray odfnum, odfden
+        cdef double fdfnum, fdfden
+
+        fdfnum = PyFloat_AsDouble(dfnum)
+        fdfden = PyFloat_AsDouble(dfden)
+        if not PyErr_Occurred():
+            if fdfnum <= 0:
+                raise ValueError("shape <= 0")
+            if fdfden <= 0:
+                raise ValueError("scale <= 0")
+            return cont2_array_sc(self.internal_state, rk_f, size, fdfnum, fdfden)
+
+        PyErr_Clear()
+
         odfnum = <ndarray>PyArray_FROM_OTF(dfnum, NPY_DOUBLE, NPY_ALIGNED)
         odfden = <ndarray>PyArray_FROM_OTF(dfden, NPY_DOUBLE, NPY_ALIGNED)
         if _sp.any(_sp.less_equal(odfnum, 0.0)):
@@ -764,10 +843,25 @@
         """Noncentral F distribution.
 
         noncentral_f(dfnum, dfden, nonc, size=None) -> random values
-        """
-        cdef ndarray odfnum
-        cdef ndarray odfden
-        cdef ndarray ononc
+        """        
+        cdef ndarray odfnum, odfden, ononc
+        cdef double fdfnum, fdfden, fnonc
+
+        fdfnum = PyFloat_AsDouble(dfnum)
+        fdfden = PyFloat_AsDouble(dfden)
+        fnonc = PyFloat_AsDouble(nonc)
+        if not PyErr_Occurred():
+            if fdfnum <= 1:
+                raise ValueError("dfnum <= 1")
+            if fdfden <= 0:
+                raise ValueError("dfden <= 0")
+            if fnonc < 0:
+                raise ValueError("nonc < 0")
+            return cont3_array_sc(self.internal_state, rk_noncentral_f, size,
+                                  fdfnum, fdfden, fnonc)
+
+        PyErr_Clear()
+        
         odfnum = <ndarray>PyArray_FROM_OTF(dfnum, NPY_DOUBLE, NPY_ALIGNED)
         odfden = <ndarray>PyArray_FROM_OTF(dfden, NPY_DOUBLE, NPY_ALIGNED)
         ononc = <ndarray>PyArray_FROM_OTF(nonc, NPY_DOUBLE, NPY_ALIGNED)        
@@ -787,6 +881,16 @@
         chisquare(df, size=None) -> random values
         """
         cdef ndarray odf
+        cdef double fdf
+
+        fdf = PyFloat_AsDouble(df)
+        if not PyErr_Occurred():
+            if fdf <= 0:
+                raise ValueError("df <= 0")
+            return cont1_array(self.internal_state, rk_chisquare, size, fdf)
+
+        PyErr_Clear()
+
         odf = <ndarray>PyArray_FROM_OTF(df, NPY_DOUBLE, NPY_ALIGNED)
         if _sp.any(_sp.less_equal(odf, 0.0)):        
             raise ValueError("df <= 0")
@@ -797,8 +901,20 @@
 
         noncentral_chisquare(df, nonc, size=None) -> random values
         """
-        cdef ndarray odf
-        cdef ndarray ononc
+        cdef ndarray odf, ononc
+        cdef double fdf, fnonc
+        fdf = PyFloat_AsDouble(df)
+        fnonc = PyFloat_AsDouble(nonc)
+        if not PyErr_Occurred():
+            if fdf <= 1:
+                raise ValueError("df <= 0")
+            if fnonc <= 0:
+                raise ValueError("nonc <= 0")
+            return cont2_array_sc(self.internal_state, rk_noncentral_chisquare,
+                                  size, fdf, fnonc)
+        
+        PyErr_Clear()
+
         odf = <ndarray>PyArray_FROM_OTF(df, NPY_DOUBLE, NPY_ALIGNED)
         ononc = <ndarray>PyArray_FROM_OTF(nonc, NPY_DOUBLE, NPY_ALIGNED)
         if _sp.any(_sp.less_equal(odf, 0.0)):
@@ -821,6 +937,16 @@
         standard_t(df, size=None)
         """
         cdef ndarray odf
+        cdef double fdf
+
+        fdf = PyFloat_AsDouble(df)
+        if not PyErr_Occurred():
+            if fdf <= 0:
+                raise ValueError("df <= 0")
+            return cont1_array_sc(self.internal_state, rk_standard_t, size, fdf)
+
+        PyErr_Clear()
+        
         odf = <ndarray> PyArray_FROM_OTF(df, NPY_DOUBLE, NPY_ALIGNED)
         if _sp.any(_sp.less_equal(odf, 0.0)):        
             raise ValueError("df <= 0")
@@ -832,11 +958,21 @@
 
         vonmises(mu, kappa, size=None)
         """
-        cdef ndarray omu
-        cdef ndarray okappa
+        cdef ndarray omu, okappa
+        cdef double fmu, fkappa
+
+        fmu = PyFloat_AsDouble(mu)
+        fkappa = PyFloat_AsDouble(kappa)
+        if not PyErr_Occurred():
+            if fkappa < 0:
+                raise ValueError("kappa < 0")
+            return cont2_array_sc(self.internal_state, rk_vonmises, size, fmu, fkappa)
+
+        PyErr_Clear()
+        
         omu = <ndarray> PyArray_FROM_OTF(mu, NPY_DOUBLE, NPY_ALIGNED)
         okappa = <ndarray> PyArray_FROM_OTF(kappa, NPY_DOUBLE, NPY_ALIGNED)
-        if _sp.any(_sp.less_equal(okappa, 0.0)):        
+        if _sp.any(_sp.less(okappa, 0.0)):
             raise ValueError("kappa < 0")
         return cont2_array(self.internal_state, rk_vonmises, size, omu, okappa)
 
@@ -846,28 +982,58 @@
         pareto(a, size=None)
         """
         cdef ndarray oa
+        cdef double fa
+
+        fa = PyFloat_AsDouble(a)
+        if not PyErr_Occurred():
+            if fa <= 0:
+                raise ValueError("a <= 0")
+            return cont1_array_sc(self.internal_state, rk_pareto, size, fa)
+
+        PyErr_Clear()
+        
         oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ALIGNED)
         if _sp.any(_sp.less_equal(oa, 0.0)):        
             raise ValueError("a <= 0")
         return cont1_array(self.internal_state, rk_pareto, size, oa)
 
-    def weibull(self, double a, size=None):
+    def weibull(self, a, size=None):
         """Weibull distribution.
 
         weibull(a, size=None)
         """
         cdef ndarray oa
+        cdef double fa
+
+        fa = PyFloat_AsDouble(a)
+        if not PyErr_Occurred():
+            if fa <= 0:
+                raise ValueError("a <= 0")
+            return cont1_array_sc(self.internal_state, rk_weibull, size, fa)
+
+        PyErr_Clear()
+        
         oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ALIGNED)
         if _sp.any(_sp.less_equal(oa, 0.0)):                
             raise ValueError("a <= 0")
         return cont1_array(self.internal_state, rk_weibull, size, oa)
 
-    def power(self, double a, size=None):
+    def power(self, a, size=None):
         """Power distribution.
 
         power(a, size=None)
         """
         cdef ndarray oa
+        cdef double fa
+
+        fa = PyFloat_AsDouble(a)
+        if not PyErr_Occurred():
+            if fa <= 0:
+                raise ValueError("a <= 0")
+            return cont1_array_sc(self.internal_state, rk_power, size, fa)
+
+        PyErr_Clear()
+
         oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ALIGNED)
         if _sp.any(_sp.less_equal(oa, 0.0)):       
             raise ValueError("a <= 0")
@@ -878,8 +1044,17 @@
         
         laplace(loc=0.0, scale=1.0, size=None)
         """
-        cdef ndarray oloc
-        cdef ndarray oscale
+        cdef ndarray oloc, oscale
+        cdef double floc, fscale
+
+        floc = PyFloat_AsDouble(loc)
+        fscale = PyFloat_AsDouble(scale)
+        if not PyErr_Occurred():
+            if fscale <= 0:
+                raise ValueError("scale <= 0")
+            return cont2_array_sc(self.internal_state, rk_laplace, size, floc, fscale)
+
+        PyErr_Clear()
         oloc = PyArray_FROM_OTF(loc, NPY_DOUBLE, NPY_ALIGNED)
         oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED)
         if _sp.any(_sp.less_equal(oscale, 0.0)):        
@@ -891,8 +1066,17 @@
         
         gumbel(loc=0.0, scale=1.0, size=None)
         """
-        cdef ndarray oloc
-        cdef ndarray oscale
+        cdef ndarray oloc, oscale
+        cdef double floc, fscale
+
+        floc = PyFloat_AsDouble(loc)
+        fscale = PyFloat_AsDouble(scale)
+        if not PyErr_Occurred():
+            if fscale <= 0:
+                raise ValueError("scale <= 0")
+            return cont2_array_sc(self.internal_state, rk_gumbel, size, floc, fscale)
+
+        PyErr_Clear()
         oloc = PyArray_FROM_OTF(loc, NPY_DOUBLE, NPY_ALIGNED)
         oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED)
         if _sp.any(_sp.less_equal(oscale, 0.0)):        
@@ -904,8 +1088,17 @@
         
         logistic(loc=0.0, scale=1.0, size=None)
         """
-        cdef ndarray oloc
-        cdef ndarray oscale
+        cdef ndarray oloc, oscale
+        cdef double floc, fscale
+
+        floc = PyFloat_AsDouble(loc)
+        fscale = PyFloat_AsDouble(scale)
+        if not PyErr_Occurred():
+            if fscale <= 0:
+                raise ValueError("scale <= 0")
+            return cont2_array_sc(self.internal_state, rk_logistic, size, floc, fscale)
+
+        PyErr_Clear()
         oloc = PyArray_FROM_OTF(loc, NPY_DOUBLE, NPY_ALIGNED)
         oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED)
         if _sp.any(_sp.less_equal(oscale, 0.0)):        
@@ -922,8 +1115,19 @@
         
         lognormal(mean=0.0, sigma=1.0, size=None)
         """
-        cdef ndarray omean
-        cdef ndarray osigma
+        cdef ndarray omean, osigma
+        cdef double fmean, fsigma        
+
+        fmean = PyFloat_AsDouble(mean)
+        fsigma = PyFloat_AsDouble(sigma)
+
+        if not PyErr_Occurred():
+            if fsigma <= 0:
+                raise ValueError("sigma <= 0")
+            return cont2_array_sc(self.internal_state, rk_lognormal, size, fmean, fsigma)
+
+        PyErr_Clear()
+
         omean = PyArray_FROM_OTF(mean, NPY_DOUBLE, NPY_ALIGNED)
         osigma = PyArray_FROM_OTF(sigma, NPY_DOUBLE, NPY_ALIGNED)
         if _sp.any(_sp.less_equal(osigma, 0.0)):
@@ -936,6 +1140,17 @@
         rayleigh(scale=1.0, size=None)
         """
         cdef ndarray oscale
+        cdef double fscale
+
+        fscale = PyFloat_AsDouble(scale)
+
+        if not PyErr_Occurred():
+            if fscale <= 0:
+                raise ValueError("scale <= 0")
+            return cont1_array_sc(self.internal_state, rk_rayleigh, size, fscale)
+
+        PyErr_Clear()
+
         oscale = <ndarray>PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED)
         if _sp.any(_sp.less_equal(oscale, 0.0)):        
             raise ValueError("scale <= 0.0")
@@ -946,8 +1161,19 @@
         
         wald(mean, scale, size=None)
         """
-        cdef ndarray omean
-        cdef ndarray oscale
+        cdef ndarray omean, oscale
+        cdef double fmean, fscale
+
+        fmean = PyFloat_AsDouble(mean)
+        fscale = PyFloat_AsDouble(scale)
+        if not PyErr_Occurred():
+            if fmean <= 0:
+                raise ValueError("mean <= 0")
+            if fscale <= 0:
+                raise ValueError("scale <= 0")
+            return cont2_array_sc(self.internal_state, rk_wald, size, fmean, fscale)
+
+        PyErr_Clear()
         omean = PyArray_FROM_OTF(mean, NPY_DOUBLE, NPY_ALIGNED)
         oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED)
         if _sp.any(_sp.less_equal(omean,0.0)):
@@ -956,15 +1182,31 @@
             raise ValueError("scale <= 0.0")
         return cont2_array(self.internal_state, rk_wald, size, omean, oscale)
 
+
+
     def triangular(self, left, mode, right, size=None):
         """Triangular distribution starting at left, peaking at mode, and 
         ending at right (left <= mode <= right).
         
         triangular(left, mode, right, size=None)
         """
-        cdef ndarray oleft
-        cdef ndarray omode
-        cdef ndarray oright
+        cdef ndarray oleft, omode, oright
+        cdef double fleft, fmode, fright
+
+        fleft = PyFloat_AsDouble(left)
+        fright = PyFloat_AsDouble(right)
+        fmode = PyFloat_AsDouble(mode)
+        if not PyErr_Occurred():
+            if fleft > fmode:
+                raise ValueError("left > mode")
+            if fmode > fright:
+                raise ValueError("mode > right")
+            if fleft == fright:
+                raise ValueError("left == right")
+            return cont3_array_sc(self.internal_state, rk_triangular, size, fleft,
+                                  fmode, fright)
+        
+        PyErr_Clear()            
         oleft = <ndarray>PyArray_FROM_OTF(left, NPY_DOUBLE, NPY_ALIGNED)
         omode = <ndarray>PyArray_FROM_OTF(mode, NPY_DOUBLE, NPY_ALIGNED)
         oright = <ndarray>PyArray_FROM_OTF(right, NPY_DOUBLE, NPY_ALIGNED)        
@@ -984,8 +1226,23 @@
 
         binomial(n, p, size=None) -> random values
         """
-        cdef ndarray on
-        cdef ndarray op
+        cdef ndarray on, op
+        cdef long ln
+        cdef double fp
+
+        fp = PyFloat_AsDouble(p)
+        ln = PyInt_AsLong(n)
+        if not PyErr_Occurred():
+            if ln <= 0:
+                raise ValueError("n <= 0")
+            if fp < 0:
+                raise ValueError("p < 0")
+            elif fp > 1:
+                raise ValueError("p > 1")
+            return discnp_array_sc(self.internal_state, rk_binomial, size, ln, fp)
+
+        PyErr_Clear()
+        
         on = <ndarray>PyArray_FROM_OTF(n, NPY_LONG, NPY_ALIGNED)
         op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ALIGNED)        
         if _sp.any(_sp.less_equal(n, 0)):
@@ -1003,6 +1260,23 @@
         """
         cdef ndarray on
         cdef ndarray op
+        cdef long ln
+        cdef double fp
+
+        fp = PyFloat_AsDouble(p)
+        ln = PyInt_AsLong(n)
+        if not PyErr_Occurred():
+            if ln <= 0:
+                raise ValueError("n <= 0")
+            if fp < 0:
+                raise ValueError("p < 0")
+            elif fp > 1:
+                raise ValueError("p > 1")
+            return discnp_array_sc(self.internal_state, rk_negative_binomial,
+                                   size, ln, fp)
+
+        PyErr_Clear()            
+        
         on = <ndarray>PyArray_FROM_OTF(n, NPY_LONG, NPY_ALIGNED)
         op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ALIGNED)        
         if _sp.any(_sp.less_equal(n, 0)):
@@ -1020,6 +1294,15 @@
         poisson(lam=1.0, size=None) -> random values
         """
         cdef ndarray olam
+        cdef double flam
+        flam = PyFloat_AsDouble(lam)
+        if not PyErr_Occurred():
+            if lam < 0:
+                raise ValueError("lam < 0")
+            return discd_array_sc(self.internal_state, rk_poisson, size, flam)
+
+        PyErr_Clear()
+        
         olam = <ndarray>PyArray_FROM_OTF(lam, NPY_DOUBLE, NPY_ALIGNED)
         if _sp.any(_sp.less(olam, 0)):
             raise ValueError("lam < 0")
@@ -1031,6 +1314,16 @@
         zipf(a, size=None)
         """
         cdef ndarray oa
+        cdef double fa
+
+        fa = PyFloat_AsDouble(a)
+        if not PyErr_Occurred():
+            if fa <= 1.0:
+                raise ValueError("a <= 1.0")
+            return discd_array_sc(self.internal_state, rk_zipf, size, fa)
+
+        PyErr_Clear()
+        
         oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ALIGNED)
         if _sp.any(_sp.less_equal(oa, 1.0)):
             raise ValueError("a <= 1.0")
@@ -1043,6 +1336,19 @@
         geometric(p, size=None)
         """
         cdef ndarray op
+        cdef double fp
+
+        fp = PyFloat_AsDouble(p)
+        if not PyErr_Occurred():
+            if fp < 0.0:
+                raise ValueError("p < 0.0")
+            if fp > 1.0:
+                raise ValueError("p > 1.0")
+            return discd_array_sc(self.internal_state, rk_geometric, size, fp)
+
+        PyErr_Clear()
+
+        
         op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ALIGNED)
         if _sp.any(_sp.less(op, 0.0)):        
             raise ValueError("p < 0.0")
@@ -1060,10 +1366,27 @@
         
         hypergeometric(ngood, nbad, nsample, size=None)        
         """
-        cdef ndarray ongood
-        cdef ndarray onbad
-        cdef ndarray onsample
+        cdef ndarray ongood, onbad, onsample
+        cdef long lngood, lnbad, lnsample
 
+        lngood = PyInt_AsLong(ngood)
+        lnbad = PyInt_AsLong(nbad)
+        lnsample = PyInt_AsLong(nsample)
+        if not PyErr_Occurred():
+            if ngood < 1:
+                raise ValueError("ngood < 1")
+            if nbad < 1:
+                raise ValueError("nbad < 1")
+            if nsample < 1:
+                raise ValueError("nsample < 1")
+            if ngood + nbad < nsample:
+                raise ValueError("ngood + nbad < nsample")
+            return discnmN_array_sc(self.internal_state, rk_hypergeometric, size,
+                                    lngood, lnbad, lnsample)
+
+
+        PyErr_Clear()
+
         ongood = <ndarray>PyArray_FROM_OTF(ngood, NPY_LONG, NPY_ALIGNED)
         onbad = <ndarray>PyArray_FROM_OTF(nbad, NPY_LONG, NPY_ALIGNED)
         onsample = <ndarray>PyArray_FROM_OTF(nsample, NPY_LONG, NPY_ALIGNED)
@@ -1071,10 +1394,10 @@
             raise ValueError("ngood < 1")
         if _sp.any(_sp.less(onbad, 1)):
             raise ValueError("nbad < 1")
+        if _sp.any(_sp.less(onsample, 1)):
+            raise ValueError("nsample < 1")        
         if _sp.any(_sp.less(_sp.add(ongood, onbad),onsample)):
             raise ValueError("ngood + nbad < nsample")
-        if _sp.any(_sp.less(onsample, 1)):
-            raise ValueError("nsample < 1")
         return discnmN_array(self.internal_state, rk_hypergeometric, size,
             ongood, onbad, onsample)
 
@@ -1084,6 +1407,18 @@
         logseries(p, size=None)
         """
         cdef ndarray op
+        cdef double fp
+
+        fp = PyFloat_AsDouble(p)
+        if not PyErr_Occurred():
+            if fp < 0.0:
+                raise ValueError("p < 0.0")
+            if fp > 1.0:
+                raise ValueError("p > 1.0")
+            return discd_array_sc(self.internal_state, rk_logseries, size, fp)
+
+        PyErr_Clear()
+        
         op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ALIGNED)
         if _sp.any(_sp.less(op, 0.0)):        
             raise ValueError("p < 0.0")



From numpy-svn at scipy.org  Mon Mar 19 15:15:30 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Mon, 19 Mar 2007 14:15:30 -0500 (CDT)
Subject: [Numpy-svn] r3583 - trunk/numpy/doc/swig
Message-ID: <20070319191530.EF87839C08E@new.scipy.org>

Author: wfspotz at sandia.gov
Date: 2007-03-19 14:15:23 -0500 (Mon, 19 Mar 2007)
New Revision: 3583

Modified:
   trunk/numpy/doc/swig/README
   trunk/numpy/doc/swig/numpy.i
   trunk/numpy/doc/swig/numpy_swig.html
   trunk/numpy/doc/swig/numpy_swig.pdf
   trunk/numpy/doc/swig/numpy_swig.txt
   trunk/numpy/doc/swig/series.cxx
   trunk/numpy/doc/swig/series.h
Log:
Removed stubs for CHAR and OBJECT; added macro variable for dimension type; updated documentation

Modified: trunk/numpy/doc/swig/README
===================================================================
--- trunk/numpy/doc/swig/README	2007-03-19 17:19:39 UTC (rev 3582)
+++ trunk/numpy/doc/swig/README	2007-03-19 19:15:23 UTC (rev 3583)
@@ -13,6 +13,7 @@
 
     series.h
     series.cxx
+    Series.i
     testSeries.py
 
 The series.h header file contains prototypes for functions that

Modified: trunk/numpy/doc/swig/numpy.i
===================================================================
--- trunk/numpy/doc/swig/numpy.i	2007-03-19 17:19:39 UTC (rev 3582)
+++ trunk/numpy/doc/swig/numpy.i	2007-03-19 19:15:23 UTC (rev 3583)
@@ -210,7 +210,7 @@
   int success = 1;
   if (array_dimensions(ary) != exact_dimensions) {
     PyErr_Format(PyExc_TypeError, 
-		 "Array must be have %d dimensions.  Given array has %d dimensions", 
+		 "Array must have %d dimensions.  Given array has %d dimensions", 
 		 exact_dimensions, array_dimensions(ary));
     success = 0;
   }
@@ -297,18 +297,19 @@
  * This macro defines a family of typemaps that allow pure input C
  * arguments of the form
  *
- *     (TYPE* IN_ARRAY1, int DIM1)
- *     (TYPE* IN_ARRAY2, int DIM1, int DIM2)
- *     (TYPE* INPLACE_ARRAY1, int DIM1)
- *     (TYPE* INPLACE_ARRAY2, int DIM1, int DIM2)
- *     (TYPE* ARGOUT_ARRAY1[ANY])
- *     (TYPE* ARGOUT_ARRAY2[ANY][ANY])
+ *     (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1)
+ *     (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
+ *     (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1)
+ *     (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
+ *     (DATA_TYPE* ARGOUT_ARRAY1[ANY])
+ *     (DATA_TYPE* ARGOUT_ARRAY2[ANY][ANY])
  *
- *     (int DIM1, TYPE* IN_ARRAY1)
- *     (int DIM1, int DIM2, TYPE* IN_ARRAY2)
- *     (int DIM1, TYPE* INPLACE_ARRAY1)
+ *     (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1)
+ *     (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2)
+ *     (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1)
+ *     (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY1)
  *
- * where "TYPE" is any type supported by the NumPy module.  In python,
+ * where "DATA_TYPE" is any type supported by the NumPy module.  In python,
  * the dimensions will not need to be specified.  The IN_ARRAYs can be
  * a numpy array or any sequence that can be converted to a numpy
  * array of the specified type.  The INPLACE_ARRAYs must be numpy
@@ -359,70 +360,70 @@
  *     void zeros(int DIM1, double* INPLACE_ARRAY1)
  */
 
-%define %numpy_typemaps(TYPE, TYPECODE)
+%define %numpy_typemaps(DATA_TYPE, DATA_TYPECODE, DIM_TYPE)
 
-/* Typemap suite for (TYPE* IN_ARRAY1, int DIM1)
+/* Typemap suite for (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1)
  */
-%typemap(in) (TYPE* IN_ARRAY1, int DIM1)
+%typemap(in) (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1)
              (PyArrayObject* array=NULL, int is_new_object=0) {
-  array = obj_to_array_contiguous_allow_conversion($input, TYPECODE, &is_new_object);
+  array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object);
   npy_intp size[1] = {-1};
   if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail;
-  $1 = (TYPE*) array->data;
-  $2 = (int) array->dimensions[0];
+  $1 = (DATA_TYPE*) array->data;
+  $2 = (DIM_TYPE) array->dimensions[0];
 }
-%typemap(freearg) (TYPE* IN_ARRAY1, int DIM1) {
+%typemap(freearg) (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) {
   if (is_new_object$argnum && array$argnum) Py_DECREF(array$argnum);
 }
 
-/* Typemap suite for (TYPE* IN_ARRAY2, int DIM1, int DIM2)
+/* Typemap suite for (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
  */
-%typemap(in) (TYPE* IN_ARRAY2, int DIM1, int DIM2)
+%typemap(in) (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
              (PyArrayObject* array=NULL, int is_new_object=0) {
-  array = obj_to_array_contiguous_allow_conversion($input, TYPECODE, &is_new_object);
+  array = obj_to_array_contiguous_allow_conversion($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;
-  $1 = (TYPE*) array->data;
-  $2 = (int) array->dimensions[0];
-  $3 = (int) array->dimensions[1];
+  $1 = (DATA_TYPE*) array->data;
+  $2 = (DIM_TYPE) array->dimensions[0];
+  $3 = (DIM_TYPE) array->dimensions[1];
 }
-%typemap(freearg) (TYPE* IN_ARRAY2, int DIM1, int DIM2) {
+%typemap(freearg) (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) {
   if (is_new_object$argnum && array$argnum) Py_DECREF(array$argnum);
 }
 
-/* Typemap suite for (TYPE* INPLACE_ARRAY1, int DIM1)
+/* Typemap suite for (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1)
  */
-%typemap(in) (TYPE* INPLACE_ARRAY1, int DIM1) (PyArrayObject* temp=NULL) {
-  temp = obj_to_array_no_conversion($input, TYPECODE);
+%typemap(in) (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1) (PyArrayObject* temp=NULL) {
+  temp = obj_to_array_no_conversion($input, DATA_TYPECODE);
   if (!temp  || !require_contiguous(temp)) SWIG_fail;
-  $1 = (TYPE*) temp->data;
+  $1 = (DATA_TYPE*) temp->data;
   $2 = 1;
   for (int i=0; i<temp->nd; ++i) $2 *= temp->dimensions[i];
 }
 
-/* Typemap suite for (TYPE* INPLACE_ARRAY2, int DIM1, int DIM2)
+/* Typemap suite for (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
  */
-%typemap(in) (TYPE* INPLACE_ARRAY2, int DIM1, int DIM2) (PyArrayObject* temp=NULL) {
-  temp = obj_to_array_no_conversion($input, TYPECODE);
+%typemap(in) (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) (PyArrayObject* temp=NULL) {
+  temp = obj_to_array_no_conversion($input, DATA_TYPECODE);
   if (!temp || !require_contiguous(temp)) SWIG_fail;
-  $1 = (TYPE*) temp->data;
-  $2 = (int) temp->dimensions[0];
-  $3 = (int) temp->dimensions[1];
+  $1 = (DATA_TYPE*) temp->data;
+  $2 = (DIM_TYPE) temp->dimensions[0];
+  $3 = (DIM_TYPE) temp->dimensions[1];
 }
 
-/* Typemap suite for (TYPE ARGOUT_ARRAY1[ANY])
+/* Typemap suite for (DATA_TYPE ARGOUT_ARRAY1[ANY])
  */
-%typemap(in,numinputs=0) (TYPE ARGOUT_ARRAY1[ANY]) {
-  $1 = (TYPE*) malloc($1_dim0*sizeof(TYPE));
+%typemap(in,numinputs=0) (DATA_TYPE ARGOUT_ARRAY1[ANY]) {
+  $1 = (DATA_TYPE*) malloc($1_dim0*sizeof(DATA_TYPE));
   if (!$1) {
     PyErr_SetString(PyExc_RuntimeError, "Failed to allocate memory");
     SWIG_fail;
   }
 }
-%typemap(argout) (TYPE ARGOUT_ARRAY1[ANY]) {
+%typemap(argout) (DATA_TYPE ARGOUT_ARRAY1[ANY]) {
   PyObject * obj = NULL;
   npy_intp dimensions[1] = { $1_dim0 };
-  PyObject* outArray = PyArray_FromDimsAndData(1, dimensions, TYPECODE, (char*)$1);
+  PyObject* outArray = PyArray_FromDimsAndData(1, dimensions, DATA_TYPECODE, (char*)$1);
   if ($result == Py_None) {
     Py_DECREF($result);
     $result = outArray;
@@ -434,19 +435,19 @@
   }
 }
 
-/* Typemap suite for (TYPE ARGOUT_ARRAY2[ANY][ANY])
+/* Typemap suite for (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY])
  */
-%typemap(in,numinputs=0) (TYPE ARGOUT_ARRAY2[ANY][ANY]) {
-  $1 = (TYPE*) malloc($1_dim0 * $1_dim1 * sizeof(TYPE));
+%typemap(in,numinputs=0) (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY]) {
+  $1 = (DATA_TYPE*) malloc($1_dim0 * $1_dim1 * sizeof(DATA_TYPE));
   if (!$1) {
     PyErr_SetString(PyExc_RuntimeError, "Failed to allocate memory");
     SWIG_fail;
   }
 }
-%typemap(argout) (TYPE ARGOUT_ARRAY1[ANY][ANY]) {
+%typemap(argout) (DATA_TYPE ARGOUT_ARRAY1[ANY][ANY]) {
   PyObject * obj = NULL;
   npy_intp dimensions[2] = { $1_dim0, $1_dim1 };
-  PyObject* outArray = PyArray_FromDimsAndData(1, dimensions, TYPECODE, (char*)$1);
+  PyObject* outArray = PyArray_FromDimsAndData(1, dimensions, DATA_TYPECODE, (char*)$1);
   if ($result == Py_None) {
     Py_DECREF($result);
     $result = outArray;
@@ -458,53 +459,53 @@
   }
 }
 
-/* Typemap suite for (int DIM1, TYPE* IN_ARRAY1)
+/* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1)
  */
-%typemap(in) (int DIM1, TYPE* IN_ARRAY1)
+%typemap(in) (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1)
              (PyArrayObject* array=NULL, int is_new_object=0) {
-  array = obj_to_array_contiguous_allow_conversion($input, TYPECODE, &is_new_object);
+  array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object);
   npy_intp size[1] = {-1};
   if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail;
-  $1 = (int) array->dimensions[0];
-  $2 = (TYPE*) array->data;
+  $1 = (DIM_TYPE) array->dimensions[0];
+  $2 = (DATA_TYPE*) array->data;
 }
-%typemap(freearg) (int DIM1, TYPE* IN_ARRAY1) {
+%typemap(freearg) (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) {
   if (is_new_object$argnum && array$argnum) Py_DECREF(array$argnum);
 }
 
-/* Typemap suite for (int DIM1, int DIM2, TYPE* IN_ARRAY2)
+/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2)
  */
-%typemap(in) (int DIM1, int DIM2, TYPE* IN_ARRAY2)
+%typemap(in) (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2)
              (PyArrayObject* array=NULL, int is_new_object=0) {
-  array = obj_to_array_contiguous_allow_conversion($input, TYPECODE, &is_new_object);
+  array = obj_to_array_contiguous_allow_conversion($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;
-  $1 = (int) array->dimensions[0];
-  $2 = (int) array->dimensions[1];
-  $3 = (TYPE*) array->data;
+  $1 = (DIM_TYPE) array->dimensions[0];
+  $2 = (DIM_TYPE) array->dimensions[1];
+  $3 = (DATA_TYPE*) array->data;
 }
-%typemap(freearg) (int DIM1, int DIM2, TYPE* IN_ARRAY2) {
+%typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) {
   if (is_new_object$argnum && array$argnum) Py_DECREF(array$argnum);
 }
 
-/* Typemap suite for (int DIM1, TYPE* INPLACE_ARRAY1)
+/* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1)
  */
-%typemap(in) (int DIM1, TYPE* INPLACE_ARRAY1) (PyArrayObject* temp=NULL) {
-  temp = obj_to_array_no_conversion($input, TYPECODE);
+%typemap(in) (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1) (PyArrayObject* temp=NULL) {
+  temp = obj_to_array_no_conversion($input, DATA_TYPECODE);
   if (!temp  || !require_contiguous(temp)) SWIG_fail;
   $1 = 1;
   for (int i=0; i<temp->nd; ++i) $1 *= temp->dimensions[i];
-  $2 = (TYPE*) temp->data;
+  $2 = (DATA_TYPE*) temp->data;
 }
 
-/* Typemap suite for (int DIM1, int DIM2, TYPE* INPLACE_ARRAY2)
+/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2)
  */
-%typemap(in) (int DIM1, int DIM2, TYPE* INPLACE_ARRAY2) (PyArrayObject* temp=NULL) {
-  temp = obj_to_array_no_conversion($input, TYPECODE);
+%typemap(in) (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2) (PyArrayObject* temp=NULL) {
+  temp = obj_to_array_no_conversion($input, DATA_TYPECODE);
   if (!temp || !require_contiguous(temp)) SWIG_fail;
-  $1 = (int) temp->dimensions[0];
-  $2 = (int) temp->dimensions[1];
-  $3 = (TYPE*) temp->data;
+  $1 = (DIM_TYPE) temp->dimensions[0];
+  $2 = (DIM_TYPE) temp->dimensions[1];
+  $3 = (DATA_TYPE*) temp->data;
 }
 
 %enddef    /* %numpy_typemaps() macro */
@@ -513,20 +514,18 @@
 /* Concrete instances of the %numpy_typemaps() macro: Each invocation
  * below applies all of the typemaps above to the specified data type.
  */
-%numpy_typemaps(signed char,         NPY_BYTE     ) /**/
-%numpy_typemaps(unsigned char,       NPY_UBYTE    ) /**/
-%numpy_typemaps(short,               NPY_SHORT    ) /**/
-%numpy_typemaps(unsigned short,      NPY_USHORT   ) /**/
-%numpy_typemaps(int,                 NPY_INT      ) /**/
-%numpy_typemaps(unsigned int,        NPY_UINT     ) /**/
-%numpy_typemaps(long,                NPY_LONG     ) /**/
-%numpy_typemaps(unsigned long,       NPY_ULONG    ) /**/
-%numpy_typemaps(long long,           NPY_LONGLONG ) /**/
-%numpy_typemaps(unsigned long long,  NPY_ULONGLONG) /**/
-%numpy_typemaps(float,               NPY_FLOAT    ) /**/
-%numpy_typemaps(double,              NPY_DOUBLE   ) /**/
-%numpy_typemaps(PyObject,            NPY_OBJECT   )
-%numpy_typemaps(char,                NPY_CHAR     )
+%numpy_typemaps(signed char       , NPY_BYTE     , int)
+%numpy_typemaps(unsigned char     , NPY_UBYTE    , int)
+%numpy_typemaps(short             , NPY_SHORT    , int)
+%numpy_typemaps(unsigned short    , NPY_USHORT   , int)
+%numpy_typemaps(int               , NPY_INT      , int)
+%numpy_typemaps(unsigned int      , NPY_UINT     , int)
+%numpy_typemaps(long              , NPY_LONG     , int)
+%numpy_typemaps(unsigned long     , NPY_ULONG    , int)
+%numpy_typemaps(long long         , NPY_LONGLONG , int)
+%numpy_typemaps(unsigned long long, NPY_ULONGLONG, int)
+%numpy_typemaps(float             , NPY_FLOAT    , int)
+%numpy_typemaps(double            , NPY_DOUBLE   , int)
 
 /* ***************************************************************
  * The follow macro expansion does not work, because C++ bool is 4

Modified: trunk/numpy/doc/swig/numpy_swig.html
===================================================================
--- trunk/numpy/doc/swig/numpy_swig.html	2007-03-19 17:19:39 UTC (rev 3582)
+++ trunk/numpy/doc/swig/numpy_swig.html	2007-03-19 19:15:23 UTC (rev 3583)
@@ -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="10 March, 2007" />
+<meta name="date" content="18 March, 2007" />
 <style type="text/css">
 
 /*
@@ -302,11 +302,33 @@
 <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>10 March, 2007</td></tr>
+<td>18 March, 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="#other-common-types-bool" id="id7" name="id7">Other Common Types: bool</a></li>
+<li><a class="reference" href="#other-common-types-complex" id="id8" name="id8">Other Common Types: complex</a></li>
+</ul>
+</li>
+<li><a class="reference" href="#helper-functions" id="id9" name="id9">Helper Functions</a><ul>
+<li><a class="reference" href="#macros" id="id10" name="id10">Macros</a></li>
+<li><a class="reference" href="#routines" id="id11" name="id11">Routines</a></li>
+</ul>
+</li>
+<li><a class="reference" href="#beyond-the-provided-typemaps" id="id12" name="id12">Beyond the Provided Typemaps</a></li>
+<li><a class="reference" href="#acknowledgements" id="id13" name="id13">Acknowledgements</a></li>
+</ul>
+</div>
 <div class="section">
-<h1><a id="introduction" name="introduction">Introduction</a></h1>
+<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,
@@ -329,7 +351,7 @@
 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 arrays of data.  Therefore, the most logical
-python interface for the <tt class="docutils literal"><span class="pre">rms</span></tt> function would be:</p>
+<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:</p>
 <pre class="literal-block">
 def rms(seq):
 </pre>
@@ -347,8 +369,8 @@
 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 python interface discussed above, your
-<a class="reference" href="http://www.swig.org">SWIG</a> interface file would need the following:</p>
+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
@@ -372,12 +394,12 @@
 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 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>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>Note that if the C function signature was in a different order:</p>
 <pre class="literal-block">
 double rms(int n, double* seq);
@@ -390,7 +412,7 @@
 </pre>
 </div>
 <div class="section">
-<h1><a id="using-numpy-i" name="using-numpy-i">Using numpy.i</a></h1>
+<h1><a class="toc-backref" href="#id2" id="using-numpy-i" name="using-numpy-i">Using numpy.i</a></h1>
 <p>The <tt class="docutils literal"><span class="pre">numpy.i</span></tt> file is currently located in the <tt class="docutils literal"><span class="pre">numpy/docs/swig</span></tt>
 sub-directory under the <tt class="docutils literal"><span class="pre">numpy</span></tt> installation directory.  Typically,
 you will want to copy it to the directory where you are developing
@@ -407,7 +429,7 @@
 import_array();
 %}
 </pre>
-<p>Within a compiled python module, <tt class="docutils literal"><span class="pre">import_array()</span></tt> should only get
+<p>Within a compiled <a class="reference" href="http://www.python.org">python</a> module, <tt class="docutils literal"><span class="pre">import_array()</span></tt> should only get
 called once.  This could be in a C/C++ file that you have written and
 is linked to the module.  If this is the case, then none of your
 interface files should <tt class="docutils literal"><span class="pre">#define</span> <span class="pre">SWIG_FILE_WITH_INIT</span></tt> or call
@@ -418,22 +440,24 @@
 <tt class="docutils literal"><span class="pre">#define</span> <span class="pre">SWIG_FILE_WITH_INIT</span></tt> and call <tt class="docutils literal"><span class="pre">import_array()</span></tt>.</p>
 </div>
 <div class="section">
-<h1><a id="available-typemaps" name="available-typemaps">Available Typemaps</a></h1>
+<h1><a class="toc-backref" href="#id3" id="available-typemaps" name="available-typemaps">Available Typemaps</a></h1>
 <p>The typemap directives provided by <tt class="docutils literal"><span class="pre">numpy.i</span></tt> for arrays of different
-data types, say <tt class="docutils literal"><span class="pre">double</span></tt> and <tt class="docutils literal"><span class="pre">int</span></tt>, are identical to one another
-except for the C and <a class="reference" href="http://numpy.scipy.org">NumPy</a> type specifications.  The typemaps are
+data types, say <tt class="docutils literal"><span class="pre">double</span></tt> and <tt class="docutils literal"><span class="pre">int</span></tt>, and dimensions of different
+types, say <tt class="docutils literal"><span class="pre">int</span></tt> or <tt class="docutils literal"><span class="pre">long</span></tt>, are identical to one another except
+for the C and <a class="reference" href="http://numpy.scipy.org">NumPy</a> type specifications.  The typemaps are
 therefore implemented (typically behind the scenes) via a macro:</p>
 <pre class="literal-block">
-%numpy_typemaps(TYPE, TYPECODE)
+%numpy_typemaps(DATA_TYPE, DATA_TYPECODE, DIM_TYPE)
 </pre>
-<p>that can be invoked for appropriate <tt class="docutils literal"><span class="pre">(TYPE,</span> <span class="pre">TYPECODE)</span></tt> pairs.  For
-example:</p>
+<p>that can be invoked for appropriate <tt class="docutils literal"><span class="pre">(DATA_TYPE,</span> <span class="pre">DATA_TYPECODE,</span>
+<span class="pre">DIM_TYPE)</span></tt> triplets.  For example:</p>
 <pre class="literal-block">
-%numpy_typemaps(double, NPY_DOUBLE)
-%numpy_typemaps(int,    NPY_INT   )
+%numpy_typemaps(double, NPY_DOUBLE, int)
+%numpy_typemaps(int,    NPY_INT   , int)
 </pre>
 <p>The <tt class="docutils literal"><span class="pre">numpy.i</span></tt> interface file uses the <tt class="docutils literal"><span class="pre">%numpy_typemaps</span></tt> macro to
-implement typemaps for the following C-types:</p>
+implement typemaps for the following C data types and <tt class="docutils literal"><span class="pre">int</span></tt>
+dimension types:</p>
 <blockquote>
 <ul class="simple">
 <li><tt class="docutils literal"><span class="pre">signed</span> <span class="pre">char</span></tt></li>
@@ -448,84 +472,118 @@
 <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>
-<li><tt class="docutils literal"><span class="pre">PyObject</span></tt></li>
-<li><tt class="docutils literal"><span class="pre">char</span></tt></li>
 </ul>
 </blockquote>
-<p>Note that C++ type <tt class="docutils literal"><span class="pre">bool</span></tt> is not supported in the list above.  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)
-</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)
-</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>
-<p>In the following descriptions, we reference a generic <tt class="docutils literal"><span class="pre">TYPE</span></tt>, which
-could be any of the C-types listed above.</p>
+<p>In the following descriptions, we reference a generic <tt class="docutils literal"><span class="pre">DATA_TYPE</span></tt>, which
+could be any of the C data types listed above.</p>
 <div class="section">
-<h2><a id="input-arrays" name="input-arrays">Input Arrays</a></h2>
+<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
 routine but are not altered in-place or returned to the user.  The
-python input array is therefore allowed to be almost any python
+<a class="reference" href="http://www.python.org">python</a> input array is therefore allowed to be almost any <a class="reference" href="http://www.python.org">python</a>
 sequence (such as a list) that can be converted to the requested type
 of array.  The input array signatures are</p>
 <blockquote>
 <ul class="simple">
-<li><tt class="docutils literal"><span class="pre">(TYPE*</span> <span class="pre">IN_ARRAY1,</span> <span class="pre">int</span> <span class="pre">DIM1)</span></tt></li>
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+<li><tt class="docutils literal"><span class="pre">(DATA_TYPE*</span> <span class="pre">IN_ARRAY1,</span> <span class="pre">int</span> <span class="pre">DIM1)</span></tt></li>
+<li><tt class="docutils literal"><span class="pre">(DATA_TYPE*</span> <span class="pre">IN_ARRAY2,</span> <span class="pre">int</span> <span class="pre">DIM1,</span> <span class="pre">int</span> <span class="pre">DIM2)</span></tt></li>
+<li><tt class="docutils literal"><span class="pre">(int</span> <span class="pre">DIM1,</span> <span class="pre">DATA_TYPE*</span> <span class="pre">IN_ARRAY1)</span></tt></li>
+<li><tt class="docutils literal"><span class="pre">(int</span> <span class="pre">DIM1,</span> <span class="pre">int</span> <span class="pre">DIM2,</span> <span class="pre">DATA_TYPE*</span> <span class="pre">IN_ARRAY2)</span></tt></li>
 </ul>
 </blockquote>
 </div>
 <div class="section">
-<h2><a id="in-place-arrays" name="in-place-arrays">In-place Arrays</a></h2>
+<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
 input values may or may not be used, but the values at the time the
-function returns are significant.  The provided python argument must
-therefore be a <a class="reference" href="http://numpy.scipy.org">NumPy</a> array of the required type.  The in-place
+function returns are significant.  The provided <a class="reference" href="http://www.python.org">python</a> argument
+must therefore be a <a class="reference" href="http://numpy.scipy.org">NumPy</a> array of the required type.  The in-place
 signatures are</p>
 <blockquote>
 <ul class="simple">
-<li><tt class="docutils literal"><span class="pre">(TYPE*</span> <span class="pre">INPLACE_ARRAY1,</span> <span class="pre">int</span> <span class="pre">DIM1)</span></tt></li>
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+<li><tt class="docutils literal"><span class="pre">(DATA_TYPE*</span> <span class="pre">INPLACE_ARRAY1,</span> <span class="pre">int</span> <span class="pre">DIM1)</span></tt></li>
+<li><tt class="docutils literal"><span class="pre">(DATA_TYPE*</span> <span class="pre">INPLACE_ARRAY2,</span> <span class="pre">int</span> <span class="pre">DIM1,</span> <span class="pre">int</span> <span class="pre">DIM2)</span></tt></li>
+<li><tt class="docutils literal"><span class="pre">(int</span> <span class="pre">DIM1,</span> <span class="pre">DATA_TYPE*</span> <span class="pre">INPLACE_ARRAY1)</span></tt></li>
+<li><tt class="docutils literal"><span class="pre">(int</span> <span class="pre">DIM1,</span> <span class="pre">int</span> <span class="pre">DIM2,</span> <span class="pre">DATA_TYPE*</span> <span class="pre">INPLACE_ARRAY2)</span></tt></li>
 </ul>
 </blockquote>
 </div>
 <div class="section">
-<h2><a id="argout-arrays" name="argout-arrays">Argout Arrays</a></h2>
+<h2><a class="toc-backref" href="#id6" id="argout-arrays" name="argout-arrays">Argout Arrays</a></h2>
 <p>Argout arrays are arrays that appear in the input arguments in C, but
 are in fact output arrays.  This pattern occurs often when there is
 more than one output variable and the single return argument is
-therefore not sufficient.  In python, the convential way to return
+therefore not sufficient.  In <a class="reference" href="http://www.python.org">python</a>, the convential way to return
 multiple arguments is to pack them into a tuple and return the tuple.
 This is what the argout typemaps do.  If a wrapped function that uses
 these argout typemaps has more than one return argument, they are so
-packed.  The python user does not pass these arrays in, they simply
+packed.  The <a class="reference" href="http://www.python.org">python</a> user does not pass these arrays in, they simply
 get returned.  The argout signatures are</p>
 <blockquote>
 <ul class="simple">
-<li><tt class="docutils literal"><span class="pre">(TYPE</span> <span class="pre">ARGOUT_ARRAY1[ANY])</span></tt></li>
-<li><tt class="docutils literal"><span class="pre">(TYPE</span> <span class="pre">ARGOUT_ARRAY2[ANY][ANY])</span></tt></li>
+<li><tt class="docutils literal"><span class="pre">(DATA_TYPE</span> <span class="pre">ARGOUT_ARRAY1[ANY])</span></tt></li>
+<li><tt class="docutils literal"><span class="pre">(DATA_TYPE</span> <span class="pre">ARGOUT_ARRAY2[ANY][ANY])</span></tt></li>
 </ul>
 </blockquote>
 </div>
+<div class="section">
+<h2><a class="toc-backref" href="#id7" 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">
-<h1><a id="helper-functions" name="helper-functions">Helper Functions</a></h1>
+<h2><a class="toc-backref" href="#id8" 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_DOUBLE , 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 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>.
+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="#id9" id="helper-functions" name="helper-functions">Helper Functions</a></h1>
 <p>The <tt class="docutils literal"><span class="pre">numpy.i</span></tt> file containes several macros and routines that it
 uses internally to build its typemaps.  However, these functions may
 be useful elsewhere in your interface file.</p>
 <div class="section">
-<h2><a id="macros" name="macros">Macros</a></h2>
+<h2><a class="toc-backref" href="#id10" id="macros" name="macros">Macros</a></h2>
 <blockquote>
 <dl class="docutils">
 <dt><strong>is_array(a)</strong></dt>
@@ -547,7 +605,7 @@
 </blockquote>
 </div>
 <div class="section">
-<h2><a id="routines" name="routines">Routines</a></h2>
+<h2><a class="toc-backref" href="#id11" id="routines" name="routines">Routines</a></h2>
 <blockquote>
 <dl class="docutils">
 <dt><strong>char* pytype_string(PyObject* py_obj)</strong></dt>
@@ -611,7 +669,45 @@
 </div>
 </div>
 <div class="section">
-<h1><a id="acknowledgements" name="acknowledgements">Acknowledgements</a></h1>
+<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>
+<blockquote>
+<ul>
+<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
+your dimensions are of a type other than <tt class="docutils literal"><span class="pre">int</span></tt> (say <tt class="docutils literal"><span class="pre">long</span></tt> for
+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 three-dimensional array as a function
+argument, you could cut-and-paste the appropriate two-dimensional
+typemap into your interface file.  The modification for the third
+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">
+<h1><a class="toc-backref" href="#id13" 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> (and its
 predecessors Numeric and numarray) together.  The effort to
 standardize this work into <tt class="docutils literal"><span class="pre">numpy.i</span></tt> began at the 2005 SciPy
@@ -623,7 +719,7 @@
 </div>
 <div class="footer">
 <hr class="footer" />
-Generated on: 2007-03-14 20:27 UTC.
+Generated on: 2007-03-19 04:09 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>

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===================================================================
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Modified: trunk/numpy/doc/swig/numpy_swig.txt
===================================================================
--- trunk/numpy/doc/swig/numpy_swig.txt	2007-03-19 17:19:39 UTC (rev 3582)
+++ trunk/numpy/doc/swig/numpy_swig.txt	2007-03-19 19:15:23 UTC (rev 3583)
@@ -4,8 +4,10 @@
 
 :Author:      Bill Spotz
 :Institution: Sandia National Laboratories
-:Date:        10 March, 2007
+:Date:        18 March, 2007
 
+.. contents::
+
 Introduction
 ============
 
@@ -34,7 +36,7 @@
 contiguous (or technically, *strided*) blocks of homogeneous data is
 with the module `NumPy <http://numpy.scipy.org>`_, which provides full
 object-oriented access to arrays of data.  Therefore, the most logical
-python interface for the ``rms`` function would be::
+`python`_ interface for the ``rms`` function would be::
 
     def rms(seq):
 
@@ -53,8 +55,8 @@
 typemaps intended to make the type of array-related conversions
 described above relatively simple to implement.  For example, suppose
 that the ``rms`` function prototype defined above was in a header file
-named ``rms.h``.  To obtain the python interface discussed above, your
-`SWIG`_ interface file would need the following::
+named ``rms.h``.  To obtain the `python`_ interface discussed above,
+your `SWIG`_ interface file would need the following::
 
     %{
     #define SWIG_FILE_WITH_INIT
@@ -79,12 +81,12 @@
 that dimension.  This is precisely the pattern in the ``rms``
 prototype.
 
-Most likely, no actual prototypes to be wrapped will have the names
-``IN_ARRAY1`` and ``DIM1``.  We use the ``%apply`` directive to apply
-the typemap for one-dimensional input arrays of type ``double`` to the
-actual prototype used by ``rms``.  Using ``numpy.i`` effectively,
-therefore, requires knowing what typemaps are available and what they
-do.
+Most likely, no actual prototypes to be wrapped will have the argument
+names ``IN_ARRAY1`` and ``DIM1``.  We use the ``%apply`` directive to
+apply the typemap for one-dimensional input arrays of type ``double``
+to the actual prototype used by ``rms``.  Using ``numpy.i``
+effectively, therefore, requires knowing what typemaps are available
+and what they do.
 
 Note that if the C function signature was in a different order::
 
@@ -116,7 +118,7 @@
     import_array();
     %}
 
-Within a compiled python module, ``import_array()`` should only get
+Within a compiled `python`_ module, ``import_array()`` should only get
 called once.  This could be in a C/C++ file that you have written and
 is linked to the module.  If this is the case, then none of your
 interface files should ``#define SWIG_FILE_WITH_INIT`` or call
@@ -130,20 +132,22 @@
 ==================
 
 The typemap directives provided by ``numpy.i`` for arrays of different
-data types, say ``double`` and ``int``, are identical to one another
-except for the C and `NumPy`_ type specifications.  The typemaps are
+data types, say ``double`` and ``int``, and dimensions of different
+types, say ``int`` or ``long``, are identical to one another except
+for the C and `NumPy`_ type specifications.  The typemaps are
 therefore implemented (typically behind the scenes) via a macro::
 
-    %numpy_typemaps(TYPE, TYPECODE)
+    %numpy_typemaps(DATA_TYPE, DATA_TYPECODE, DIM_TYPE)
 
-that can be invoked for appropriate ``(TYPE, TYPECODE)`` pairs.  For
-example::
+that can be invoked for appropriate ``(DATA_TYPE, DATA_TYPECODE,
+DIM_TYPE)`` triplets.  For example::
 
-    %numpy_typemaps(double, NPY_DOUBLE)
-    %numpy_typemaps(int,    NPY_INT   )
+    %numpy_typemaps(double, NPY_DOUBLE, int)
+    %numpy_typemaps(int,    NPY_INT   , int)
 
 The ``numpy.i`` interface file uses the ``%numpy_typemaps`` macro to
-implement typemaps for the following C-types:
+implement typemaps for the following C data types and ``int``
+dimension types:
 
   * ``signed char``
   * ``unsigned char``
@@ -157,54 +161,37 @@
   * ``unsigned long long``
   * ``float``
   * ``double``
-  * ``PyObject``
-  * ``char``
 
-Note that C++ type ``bool`` is not supported in the list above.  NumPy
-bools are a single byte, while the C++ ``bool`` is four bytes (at
-least on my system).  Therefore::
+In the following descriptions, we reference a generic ``DATA_TYPE``, which
+could be any of the C data types listed above.
 
-    %numpy_typemaps(bool, NPY_BOOL)
-
-will result in typemaps that will produce code that reference
-improper data lengths.  You can implement the following macro
-expansion::
-
-    %numpy_typemaps(bool, NPY_UINT)
-
-to fix the data length problem, and `Input Arrays`_ will work fine,
-but `In-place Arrays`_ might fail type-checking.
-
-In the following descriptions, we reference a generic ``TYPE``, which
-could be any of the C-types listed above.
-
 Input Arrays
 ------------
 
 Input arrays are defined as arrays of data that are passed into a
 routine but are not altered in-place or returned to the user.  The
-python input array is therefore allowed to be almost any python
+`python`_ input array is therefore allowed to be almost any `python`_
 sequence (such as a list) that can be converted to the requested type
 of array.  The input array signatures are
 
-  * ``(TYPE* IN_ARRAY1, int DIM1)``
-  * ``(TYPE* IN_ARRAY2, int DIM1, int DIM2)``
-  * ``(int DIM1, TYPE* IN_ARRAY1)``
-  * ``(int DIM1, int DIM2, TYPE* IN_ARRAY2)``
+  * ``(DATA_TYPE* IN_ARRAY1, int DIM1)``
+  * ``(DATA_TYPE* IN_ARRAY2, int DIM1, int DIM2)``
+  * ``(int DIM1, DATA_TYPE* IN_ARRAY1)``
+  * ``(int DIM1, int DIM2, DATA_TYPE* IN_ARRAY2)``
 
-In-place Arrays
+In-Place Arrays
 ---------------
 
 In-place arrays are defined as arrays that are modified in-place.  The
 input values may or may not be used, but the values at the time the
-function returns are significant.  The provided python argument must
-therefore be a `NumPy`_ array of the required type.  The in-place
+function returns are significant.  The provided `python`_ argument
+must therefore be a `NumPy`_ array of the required type.  The in-place
 signatures are
 
-  * ``(TYPE* INPLACE_ARRAY1, int DIM1)``
-  * ``(TYPE* INPLACE_ARRAY2, int DIM1, int DIM2)``
-  * ``(int DIM1, TYPE* INPLACE_ARRAY1)``
-  * ``(int DIM1, int DIM2, TYPE* INPLACE_ARRAY2)``
+  * ``(DATA_TYPE* INPLACE_ARRAY1, int DIM1)``
+  * ``(DATA_TYPE* INPLACE_ARRAY2, int DIM1, int DIM2)``
+  * ``(int DIM1, DATA_TYPE* INPLACE_ARRAY1)``
+  * ``(int DIM1, int DIM2, DATA_TYPE* INPLACE_ARRAY2)``
 
 Argout Arrays
 -------------
@@ -212,16 +199,68 @@
 Argout arrays are arrays that appear in the input arguments in C, but
 are in fact output arrays.  This pattern occurs often when there is
 more than one output variable and the single return argument is
-therefore not sufficient.  In python, the convential way to return
+therefore not sufficient.  In `python`_, the convential way to return
 multiple arguments is to pack them into a tuple and return the tuple.
 This is what the argout typemaps do.  If a wrapped function that uses
 these argout typemaps has more than one return argument, they are so
-packed.  The python user does not pass these arrays in, they simply
+packed.  The `python`_ user does not pass these arrays in, they simply
 get returned.  The argout signatures are
 
-  * ``(TYPE ARGOUT_ARRAY1[ANY])``
-  * ``(TYPE ARGOUT_ARRAY2[ANY][ANY])``
+  * ``(DATA_TYPE ARGOUT_ARRAY1[ANY])``
+  * ``(DATA_TYPE ARGOUT_ARRAY2[ANY][ANY])``
 
+Other Common Types: bool
+------------------------
+
+Note that C++ type ``bool`` is not supported in the list in the
+`Available Typemaps`_ section.  NumPy bools are a single byte, while
+the C++ ``bool`` is four bytes (at least on my system).  Therefore::
+
+    %numpy_typemaps(bool, NPY_BOOL, int)
+
+will result in typemaps that will produce code that reference
+improper data lengths.  You can implement the following macro
+expansion::
+
+    %numpy_typemaps(bool, NPY_UINT, int)
+
+to fix the data length problem, and `Input Arrays`_ will work fine,
+but `In-Place Arrays`_ might fail type-checking.
+
+Other Common Types: complex
+---------------------------
+
+Typemap conversions for complex floating-point types is also not
+supported automatically.  This is because `python`_ and `NumPy`_ are
+written in C, which does not have native complex types.  Both
+`python`_ and `NumPy`_ implement their own (essentially equivalent)
+``struct`` definitions for complex variables::
+
+    /* Python */
+    typedef struct {double real; double imag;} Py_complex;
+
+    /* NumPy */
+    typedef struct {float  real, imag;} npy_cfloat;
+    typedef struct {double real, imag;} npy_cdouble;
+
+We could have implemented::
+
+    %numpy_typemaps(Py_complex , NPY_DOUBLE , int)
+    %numpy_typemaps(npy_cfloat , NPY_CFLOAT , int)
+    %numpy_typemaps(npy_cdouble, NPY_CDOUBLE, int)
+
+which would have provided automatic type conversions for arrays of
+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``.
+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
+work.
+
 Helper Functions
 ================
 
@@ -334,6 +373,40 @@
     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.
+
+  * In some situations, it is possible that you could use the
+    ``%numpy_templates`` macro to implement typemaps for your own
+    types.  See the `Other Common Types: bool`_ or `Other Common
+    Types: complex`_ sections for examples.  Another situation is if
+    your dimensions are of a type other than ``int`` (say ``long`` for
+    example)::
+
+        %numpy_typemaps(double, NPY_DOUBLE, long)
+
+  * You can use the code in ``numpy.i`` to write your own typemaps.
+    For example, if you had a three-dimensional array as a function
+    argument, you could cut-and-paste the appropriate two-dimensional
+    typemap into your interface file.  The modification for the third
+    dimension would be trivial.
+
+  * Sometimes, the best approach is to use the ``%extend`` directive
+    to define new methods for your classes (or overload existing ones)
+    that take a ``PyObject*`` (that either is or can be converted to a
+    ``PyArrayObject*``) instead of a pointer to a buffer.  In this
+    case, the helper routines in ``numpy.i`` can be very useful.
+
+  * Writing typemaps can be a bit nonintuitive.  If you have specific
+    questions about writing `SWIG`_ typemaps for `NumPy`_, the
+    developers of ``numpy.i`` do monitor the
+    `Numpy-discussion <mailto:Numpy-discussion at scipy.org>`_ and
+    `Swig-user <mailto:Swig-user at lists.sourceforge.net>`_ mail lists.
+
 Acknowledgements
 ================
 

Modified: trunk/numpy/doc/swig/series.cxx
===================================================================
--- trunk/numpy/doc/swig/series.cxx	2007-03-19 17:19:39 UTC (rev 3582)
+++ trunk/numpy/doc/swig/series.cxx	2007-03-19 19:15:23 UTC (rev 3583)
@@ -95,15 +95,15 @@
   }                                                               \
 }
 
-TEST_FUNCS(signed char       , schar     )
-TEST_FUNCS(unsigned char     , uchar     )
-TEST_FUNCS(short             , short     )
-TEST_FUNCS(unsigned short    , ushort    )
-TEST_FUNCS(int               , int       )
-TEST_FUNCS(unsigned int      , uint      )
-TEST_FUNCS(long              , long      )
-TEST_FUNCS(unsigned long     , ulong     )
-TEST_FUNCS(long long         , longLong  )
-TEST_FUNCS(unsigned long long, ulongLong )
-TEST_FUNCS(float             , float     )
-TEST_FUNCS(double            , double    )
+TEST_FUNCS(signed char       , schar    )
+TEST_FUNCS(unsigned char     , uchar    )
+TEST_FUNCS(short             , short    )
+TEST_FUNCS(unsigned short    , ushort   )
+TEST_FUNCS(int               , int      )
+TEST_FUNCS(unsigned int      , uint     )
+TEST_FUNCS(long              , long     )
+TEST_FUNCS(unsigned long     , ulong    )
+TEST_FUNCS(long long         , longLong )
+TEST_FUNCS(unsigned long long, ulongLong)
+TEST_FUNCS(float             , float    )
+TEST_FUNCS(double            , double   )

Modified: trunk/numpy/doc/swig/series.h
===================================================================
--- trunk/numpy/doc/swig/series.h	2007-03-19 17:19:39 UTC (rev 3582)
+++ trunk/numpy/doc/swig/series.h	2007-03-19 19:15:23 UTC (rev 3583)
@@ -39,17 +39,17 @@
 TYPE SNAME ## Min(  int rows, int cols, TYPE * matrix); \
 void SNAME ## Ceil( int rows, int cols, TYPE * array, TYPE ceil);
 
-TEST_FUNC_PROTOS(signed char       , schar     )
-TEST_FUNC_PROTOS(unsigned char     , uchar     )
-TEST_FUNC_PROTOS(short             , short     )
-TEST_FUNC_PROTOS(unsigned short    , ushort    )
-TEST_FUNC_PROTOS(int               , int       )
-TEST_FUNC_PROTOS(unsigned int      , uint      )
-TEST_FUNC_PROTOS(long              , long      )
-TEST_FUNC_PROTOS(unsigned long     , ulong     )
-TEST_FUNC_PROTOS(long long         , longLong  )
-TEST_FUNC_PROTOS(unsigned long long, ulongLong )
-TEST_FUNC_PROTOS(float             , float     )
-TEST_FUNC_PROTOS(double            , double    )
+TEST_FUNC_PROTOS(signed char       , schar    )
+TEST_FUNC_PROTOS(unsigned char     , uchar    )
+TEST_FUNC_PROTOS(short             , short    )
+TEST_FUNC_PROTOS(unsigned short    , ushort   )
+TEST_FUNC_PROTOS(int               , int      )
+TEST_FUNC_PROTOS(unsigned int      , uint     )
+TEST_FUNC_PROTOS(long              , long     )
+TEST_FUNC_PROTOS(unsigned long     , ulong    )
+TEST_FUNC_PROTOS(long long         , longLong )
+TEST_FUNC_PROTOS(unsigned long long, ulongLong)
+TEST_FUNC_PROTOS(float             , float    )
+TEST_FUNC_PROTOS(double            , double   )
 
 #endif



From numpy-svn at scipy.org  Tue Mar 20 06:05:45 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Tue, 20 Mar 2007 05:05:45 -0500 (CDT)
Subject: [Numpy-svn] r3584 - trunk/numpy/doc
Message-ID: <20070320100545.9751839C0EB@new.scipy.org>

Author: oliphant
Date: 2007-03-20 05:05:43 -0500 (Tue, 20 Mar 2007)
New Revision: 3584

Modified:
   trunk/numpy/doc/pep_buffer.txt
Log:
Update buffer interface PEP.

Modified: trunk/numpy/doc/pep_buffer.txt
===================================================================
--- trunk/numpy/doc/pep_buffer.txt	2007-03-19 19:15:23 UTC (rev 3583)
+++ trunk/numpy/doc/pep_buffer.txt	2007-03-20 10:05:43 UTC (rev 3584)
@@ -24,7 +24,7 @@
 
    The buffer protocol allows different Python types to exchange a
    pointer to a sequence of internal buffers.  This functionality is
-   '''extremely''' useful for sharing large segments of memory between
+   *extremely* useful for sharing large segments of memory between
    different high-level objects, but it's too limited and has issues.
 
     1. There is the little (never?) used "sequence-of-segments" option
@@ -36,36 +36,37 @@
     3. There is no way for a consumer to tell the buffer-API-exporting
        object it is "finished" with its view of the memory and
        therefore no way for the exporting object to be sure that it is
-       safe to reallocate the pointer to the memory that it owns (the
-       array object reallocating its memory after sharing it with the
-       buffer object which held the original pointer led to the
-       infamous buffer-object problem).
+       safe to reallocate the pointer to the memory that it owns (for
+       example, the array object reallocating its memory after sharing
+       it with the buffer object which held the original pointer led
+       to the infamous buffer-object problem).
 
     4. Memory is just a pointer with a length. There is no way to
-       describe what's "in" the memory (float, int, C-structure, etc.)
+       describe what is "in" the memory (float, int, C-structure, etc.)
 
     5. There is no shape information provided for the memory.  But,
        several array-like Python types could make use of a standard
        way to describe the shape-interpretation of the memory
-       (!wxPython, GTK, pyQT, CVXOPT, !PyVox, Audio and Video
-       Libraries, ctypes, !NumPy, data-base interfaces, etc.)
+       (wxPython, GTK, pyQT, CVXOPT, PyVox, Audio and Video
+       Libraries, ctypes, NumPy, data-base interfaces, etc.)
 
     There are two widely used libraries that use the concept of
     discontiguous memory: PIL and NumPy.  Their view of discontiguous
     arrays is a bit different, though.  NumPy uses the notion of
-    constant striding in each dimension as it's basic concept of an
+    constant striding in each dimension as its basic concept of an
     array. In this way a simple sub-region of a larger array can be
-    described without copying the data.  Strided memory is a common
-    way to describe data to many computing libraries (such as the BLAS
+    described without copying the data.  Strided memory is also a common
+    way to describe data in many computing libraries (such as the BLAS
     and LAPACK).
 
     The PIL uses a more opaque memory representation. Sometimes an
     image is contained in a contiguous segment of memory, but
     sometimes it is contained in an array of pointers to the
     contiguous segments (usually lines) of the image.  This allows the
-    image to not be loaded entirely into memory.  The PIL is where the
-    idea of multiple buffer segments in the original buffer interface
-    came from, I believe.
+    image to not be loaded entirely into memory but still managed
+    abstractly as if it were. I believe, the PIL is where the idea of
+    multiple buffer segments in the original buffer interface came
+    from, I believe.
 
     The buffer interface should allow discontiguous memory areas to
     share standard striding information.  However, consumers that do
@@ -80,18 +81,21 @@
 
    * Unify the read/write versions of getting the buffer.
 
-   * Add a new function to the protocol that should be called when
+   * Add a new function to the interface that should be called when
      the consumer object is "done" with the view.
 
-   * Add a new function to allow the protocol to describe what is in
+   * Add a new memory_view object that is returned from the 
+     buffer interface getbuffer call.  This memory_view object
+     contains
+   * Add a new function to allow the interface to describe what is in
      memory (unifying what is currently done now in struct and
      array)
 
-   * Add a new function to allow the protocol to share shape
-     information
+   * Add a new function to allow the protocol to share shape and 
+     stride information
 
-   * Fix all objects in core and standard library to conform to the
-     new interface
+   * Fix all objects in the core and the standard library to conform
+     to the new interface
 
    * Extend the struct module to handle more format specifiers
 
@@ -102,89 +106,74 @@
     typedef struct {
          getbufferproc bf_getbuffer
          releasebufferproc bf_releasebuffer
-         formatbufferproc bf_getbufferformat
-         shapebufferproc bf_getbuffershape 
     }
 
     typedef PyObject *(*getbufferproc)(PyObject *obj, void **buf,
-                                       Py_ssize_t *len, int requires)
+                                       Py_ssize_t *len, int *writeable,
+                                       char **format, int *ndims,
+                                       Py_ssize_t **shape,
+                                       Py_ssize_t **strides)
  
       Return a pointer to memory in *buf and the length of that memory
-      buffer in *len.  Requirements for the memory are provided in
-      requires (PYBUFFER_WRITE, PYBUFFER_ONESEGMENT).  NULL is
-      returned and an error raised if the object cannot return a view
-      with those requirements.  Otherwise, an object-specific "view"
-      object is returned (which can just be as simple as a borrowed 
-      reference to obj).
+      buffer (in bytes) in *len.  The next arguments are optional.  
+      NULL is returned on failure.   On success an oject-specific 
+      view is returned (which may just be a borrowed reference to obj).
+      This view should be passed to bf_releasebuffer when the consumer
+      is done with the view. 
 
-      This view object should be used in the other API calls and 
+      writeable -- address of an integer variable to hold 
+                     whether or not the memory is writeable.
+                     If this is NULL, then you must assume the memory 
+                     is read-only.
+      format    -- address of a format-string (following extended struct 
+                     syntax) indicating what is in each element of
+                     of memory.  The number of elements is len / itemsize,
+                     where itemsize is the number of bytes implied by the format.
+                     NULL if not needed in which case format == "B" for 
+                     unsigned bytes.  The memory for this string must not
+                     be freed by the consumer --- it is managed by the exporter.
+      ndims     -- address of a variable storing the number of dimensions 
+                     or NULL if not needed.  If shape and/or strides are given
+                     then this must be non NULL.  If this variable is 
+                     not provided then it is assumed that *ndims == 1
+      shape     -- 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 shape of the memory as an N-D array.  
+                     Ignored if this is NULL.  Note that
+                     ((*shape)[0] * ... * (*shape)[ndims-1])*itemsize = len
+                     If this variable is not provided then it is assumed that
+                     (*shape[0]) == len / itemsize. 
+      stride    -- 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 NULL, then
+                     the memory is assumed to be C-style contigous with
+                     the last dimension varying the fastest.  An  
+                     error should be raised if this is not accurate and
+                     strides are not requested.  This variable may be
+                     set to NULL when called if memory is C-style
+                     contiguous. 
+                
+      This view object should be used in the other API call and 
       does not need to be decref'd.  It should be "released" if the
       interface exporter provides the bf_releasebuffer function.
 
     typedef int (*releasebufferproc)(PyObject *view)
 
-      This function is called when a view of memory previously
-      acquired from the object is no longer needed.  It is up to the
-      exporter of the API to make sure all views have been released
-      before re-allocating the previously returned pointer.
-      It is up to consumers of the API to call this function on the
-      object whose view is obtained when it is no longer needed.  A -1
-      is returned on error and 0 on success.
+      This function is called (if defined by the exporting object)
+      when a view of memory previously acquired from the object is no
+      longer needed.  It is up to the exporter of the API to make sure
+      all views have been released before re-allocating any previously
+      shared memory.  It is up to consumers of the API to call this
+      function on the object whose view is obtained when it is no
+      longer needed.   Any format string, shape array or strides array
+      returned through the interface should also not be referenced after 
+      the releasebuffer call is made. 
+      A -1 is returned on error and 0 on success.
 
-    typedef PyObject *(*formatbufferproc)(PyObject *view, int *itemsize)
+    Both of these routines are optional for a type object 
 
-      Get the format-string of the memory using the struct-module
-      string syntax (see below for proposed additions to that syntax).
-      Also, there is never an alignment assumption in this
-      string---the full byte-layout is always required.  If the
-      implied size of this string is smaller than the length of the
-      buffer then it is assumed that the string is repeated.
 
-      If itemsize is not NULL, then return the size implied by the
-      format string.  This could be the entire length of the buffer or
-      just the length of each element.  It is equivalent to *itemsize
-      = PyObject_SizeFromFormat(ret) if ret is the returned string.
-      However, very often objects already know the itemsize without
-      having to compute it separately.
-
-      The returned object is a Python CObject surrounding a char *
-      pointer which will manage the memory for the char * when the
-      reference disappears.
-
-      If this is routine is not provided, then it is the same as if
-      "B" were the returned string (i.e. it's just a block of bytes)
-      and itemsize==1.
-
-
-    typedef PyObject *(*shapebufferproc)(PyObject *view)
-
-      Return a Python CObject surrounding a pointer to the structure
-
-      struct {
-         int ndim
-         Py_ssize_t *shape;
-         Py_ssize_t *strides; 
-      }
-
-      The strides pointer can be NULL if the memory is C-style contiguous
-      otherwise it provides the striding in each dimension (how many bytes
-      to skip to get to the next element along a particular dimension).
-
-      When the returned object is collected, the memory for the shape
-      and strides is freed by the deallocator stored in the CObject.
-
-      If this routine is not provided, then it's equivalent to
-      ndim == 1 and shape == [len]
-
-      Notice that the buffer length, len, should be
-          (shape[0]*...*shape[ndim-1])*itemsize regardless of the strides.
-
-
-    All of these routines are optional for a type object (but the last
-    three make no sense unless the first one is implemented).
-
-
-
 New C-API calls are proposed
 
    int 
@@ -194,65 +183,29 @@
 
    PyObject * 
    PyObject_GetBuffer(PyObject *obj, void **buf, Py_ssize_t *len,
-                      int requires)
+                      int *writeable, char **format, int *ndims,
+                      Py_ssize_t **shape, Py_ssize_t **strides)
 
-      return a borrowed reference to a "view" object of memory for the
-      object.  Requirements for the memory should be given in requires
-      (PYBUFFER_WRITE, PYBUFFER_ONESEGMENT).  The memory pointer is in
-      *buf and its length in *len. 
-
-      Note, the memory is not considered a single segment of memory 
-      unless PYBUFFER_ONESEGMENT is used in requires. Get possible
-      striding using PyObject_GetBufferShape on the view object. 
+      Get the buffer and optional information variables about the buffer.
+      Return an object-specific view object (which may be simply a 
+      borrowed reference to the object itself). 
       
    int
    PyObject_ReleaseBuffer(PyObject *view)
       
       call this function to tell obj that you are done with your "view"
-      This is a no-op if the object doesn't implement a release function.
-      Only call this after a previous PyObject_GetBuffer has succeeded. 
-      Return -1 on error. 
+      This doesn't do anything if the object doesn't implement a release function.
+      Only call this after a previous PyObject_GetBuffer has succeeded and when
+      you will not be needing or referring to the memory (or the format, shape, 
+      and strides memory used in the view -- if you will use these for a longer
+      period of time make copies). 
+      Returns -1 on error. 
       
-   char *
-   PyObject_GetBufferFormat(PyObject *view, int *itemsize)
-
-      Return a NULL-terminated string indicating the data-format of
-      the memory buffer.  The string is in struct-module syntax with
-      the exception that there is never an alignment assumption (all
-      bytes must be accounted for). If the length of the buffer
-      indicated by this string is smaller than the total length of the
-      buffer, then a repeat of the string is implied to fill the
-      length of the buffer.
-
-      If itemsize is not NULL, then return the implied size
-      of each item (this could be calculated from the format string
-      but it is often known by the view object anyway). 
-
-   PyObject *
-   PyObject_GetBufferShape(PyObject *view)
-
-      Return a 2-tuple of lists (shape, stride) providing the
-      multi-dimensional shape of the memory area.  The stride
-      shows how many bytes to skip in each dimension to move
-      in that dimension from the start of the array. 
-
-      Memory that is not a single contiguous-buffer can be represented
-      with the pointer returned from GetBuffer and the shape and
-      strides returned from GetBufferShape.
-
    int PyObject_SizeFromFormat(char *)
-      Return the implied size of the data-format area from a struct-style
-      description.
+      Return the implied itemsize of the data-format area from a struct-style
+      description. 
 
-   PyObject *PyObject_BufferFormat(char *format, int copy)
-      Construct a CObject to return as the format in the buffer interface
-      from a string being sure to copy if specified.
 
-   PyObject *PyObject_BufferShape(int ndim, Py_ssize_t *shape, Py_ssize_t *strides)
-      Construct a CObject to return as the shape object in the buffer interface.
-      The values are copied from the arrays pointed to by shape and strides.
-      Strides can be NULL if the memory is C-style contiguous. 
-
 Additions to the struct string-syntax
 
    The struct string-syntax is missing some characters to fully
@@ -275,7 +228,7 @@
    '&'               specific pointer (prefix before another charater) 
    'X{}'             pointer to a function (optional function 
                                              signature inside {})
-   ' '               ignored (allow readability)
+   ' '               ignored (allow better readability)
 
    The struct module will be changed to understand these as well and
    return appropriate Python objects on unpacking.  Un-packing a
@@ -343,36 +296,36 @@
 
    anything else using the buffer API
 
+
+
 Issues and Details
 
+
    The proposed locking mechanism relies entirely on the objects
    implementing the buffer interface to do their own thing.  Ideally
    an object that implements the buffer interface should keep at least
    a number indicating how many releases are extant.  If there are views
-   to a memory location, then reallocation should fail and raise
+   to a memory location, then any subsequent reallocation should fail and raise
    an error. 
 
-   The handling of discontiguous memory is new and can be seen as a
+   The sharing of strided memory is new and can be seen as a
    modification of the multiple-segment interface.  It is motivated by
-   NumPy (used to be Numeric).  NumPy objects should be able to share
-   their strided memory with code that understands how to manage
-   strided memory.
+   NumPy.  NumPy objects should be able to share their strided memory
+   with code that understands how to manage strided memory because
+   strided memory is very common when interfacing with compute libraries.
 
-   Code should also be able to request contiguous memory if needed and
-   objects exporting the buffer interface should be able to handle
-   that either by raising an error (or constructing a read-only
-   contiguous object and returning that as the view).
-
    Currently the struct module does not allow specification of nested
    structures.  It seems like specifying a nested structure should be
-   specified as several ways of viewing memory areas (ctypes and
+   specified as several ways of viewing memory areas (e.g. ctypes and
    NumPy) already allow this.
 
-   Python Objects are returned for Format and Shape descriptions so 
-   that memory-management is simply handled using reference-counting. 
+   Memory management of the format string and the shape and strides
+   array is always the responsibility of the exporting object and can
+   be shared between different views. If the consuming object needs to
+   keep these memory areas longer than the view is held, then it must
+   copy them to its own memory. 
 
 
-
 Copyright
 
    This PEP is placed in the public domain



From numpy-svn at scipy.org  Tue Mar 20 06:14:19 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Tue, 20 Mar 2007 05:14:19 -0500 (CDT)
Subject: [Numpy-svn] r3585 - trunk/numpy/random/mtrand
Message-ID: <20070320101419.661A139C0EB@new.scipy.org>

Author: oliphant
Date: 2007-03-20 05:14:14 -0500 (Tue, 20 Mar 2007)
New Revision: 3585

Modified:
   trunk/numpy/random/mtrand/mtrand.c
   trunk/numpy/random/mtrand/mtrand.pyx
Log:
Fix ticket #471

Modified: trunk/numpy/random/mtrand/mtrand.c
===================================================================
--- trunk/numpy/random/mtrand/mtrand.c	2007-03-20 10:05:43 UTC (rev 3584)
+++ trunk/numpy/random/mtrand/mtrand.c	2007-03-20 10:14:14 UTC (rev 3585)
@@ -1,4 +1,4 @@
-/* Generated by Pyrex 0.9.5.1 on Sat Mar 17 23:42:27 2007 */
+/* Generated by Pyrex 0.9.5.1 on Tue Mar 20 04:22:44 2007 */
 
 #include "Python.h"
 #include "structmember.h"
@@ -276,8 +276,6 @@
   return __pyx_r;
 }
 
-static PyObject *__pyx_n_Float64;
-
 static PyObject *__pyx_f_6mtrand_cont1_array_sc(rk_state (*__pyx_v_state),double ((*__pyx_v_func)(rk_state (*),double )),PyObject *__pyx_v_size,double __pyx_v_a) {
   double (*__pyx_v_array_data);
   PyArrayObject *arrayObject;
@@ -309,7 +307,7 @@
     __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 149; goto __pyx_L1;}
     Py_DECREF(__pyx_2); __pyx_2 = 0;
     __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 149; goto __pyx_L1;}
-    __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_n_Float64); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 149; goto __pyx_L1;}
+    __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_n_float64); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 149; goto __pyx_L1;}
     Py_DECREF(__pyx_2); __pyx_2 = 0;
     __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 149; goto __pyx_L1;}
     Py_INCREF(__pyx_v_size);
@@ -776,7 +774,7 @@
     __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 248; goto __pyx_L1;}
     Py_DECREF(__pyx_2); __pyx_2 = 0;
     __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 248; goto __pyx_L1;}
-    __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_n_Float64); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 248; goto __pyx_L1;}
+    __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_n_float64); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 248; goto __pyx_L1;}
     Py_DECREF(__pyx_2); __pyx_2 = 0;
     __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 248; goto __pyx_L1;}
     Py_INCREF(__pyx_v_size);
@@ -1576,21 +1574,19 @@
     __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 420; goto __pyx_L1;}
     __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 420; goto __pyx_L1;}
     Py_DECREF(__pyx_2); __pyx_2 = 0;
-    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 420; goto __pyx_L1;}
-    __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_n_Int); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 420; goto __pyx_L1;}
-    Py_DECREF(__pyx_2); __pyx_2 = 0;
-    __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 420; goto __pyx_L1;}
+    __pyx_2 = __Pyx_GetName(__pyx_b, __pyx_n_int); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 420; goto __pyx_L1;}
+    __pyx_4 = PyTuple_New(2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 420; goto __pyx_L1;}
     Py_INCREF(__pyx_v_size);
-    PyTuple_SET_ITEM(__pyx_2, 0, __pyx_v_size);
-    PyTuple_SET_ITEM(__pyx_2, 1, __pyx_4);
-    __pyx_4 = 0;
-    __pyx_4 = PyObject_CallObject(__pyx_3, __pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 420; goto __pyx_L1;}
+    PyTuple_SET_ITEM(__pyx_4, 0, __pyx_v_size);
+    PyTuple_SET_ITEM(__pyx_4, 1, __pyx_2);
+    __pyx_2 = 0;
+    __pyx_2 = PyObject_CallObject(__pyx_3, __pyx_4); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 420; goto __pyx_L1;}
     Py_DECREF(__pyx_3); __pyx_3 = 0;
-    Py_DECREF(__pyx_2); __pyx_2 = 0;
-    Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_4)));
-    Py_DECREF(((PyObject *)arrayObject));
-    arrayObject = ((PyArrayObject *)__pyx_4);
     Py_DECREF(__pyx_4); __pyx_4 = 0;
+    Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_2)));
+    Py_DECREF(((PyObject *)arrayObject));
+    arrayObject = ((PyArrayObject *)__pyx_2);
+    Py_DECREF(__pyx_2); __pyx_2 = 0;
 
     /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":421 */
     __pyx_v_length = PyArray_SIZE(arrayObject);
@@ -9531,7 +9527,6 @@
 }
 
 static __Pyx_InternTabEntry __pyx_intern_tab[] = {
-  {&__pyx_n_Float64, "Float64"},
   {&__pyx_n_Int, "Int"},
   {&__pyx_n_MT19937, "MT19937"},
   {&__pyx_n_PyInt_AsLong, "PyInt_AsLong"},

Modified: trunk/numpy/random/mtrand/mtrand.pyx
===================================================================
--- trunk/numpy/random/mtrand/mtrand.pyx	2007-03-20 10:05:43 UTC (rev 3584)
+++ trunk/numpy/random/mtrand/mtrand.pyx	2007-03-20 10:14:14 UTC (rev 3585)
@@ -146,7 +146,7 @@
     if size is None:
         return func(state, a)
     else:
-        array = <ndarray>_sp.empty(size, _sp.Float64)
+        array = <ndarray>_sp.empty(size, _sp.float64)
         length = PyArray_SIZE(array)
         array_data = <double *>array.data
         for i from 0 <= i < length:
@@ -245,7 +245,7 @@
     if size is None:
         return func(state, a, b, c)
     else:
-        array = <ndarray>_sp.empty(size, _sp.Float64)
+        array = <ndarray>_sp.empty(size, _sp.float64)
         length = PyArray_SIZE(array)
         array_data = <double *>array.data
         for i from 0 <= i < length:
@@ -417,7 +417,7 @@
     if size is None:
         return func(state, a)
     else:
-        array = <ndarray>_sp.empty(size, _sp.Int)
+        array = <ndarray>_sp.empty(size, int)
         length = PyArray_SIZE(array)
         array_data = <long *>array.data
         for i from 0 <= i < length:



From numpy-svn at scipy.org  Tue Mar 20 06:16:09 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Tue, 20 Mar 2007 05:16:09 -0500 (CDT)
Subject: [Numpy-svn] r3586 - trunk/numpy/random/mtrand
Message-ID: <20070320101609.0283339C0EB@new.scipy.org>

Author: oliphant
Date: 2007-03-20 05:16:03 -0500 (Tue, 20 Mar 2007)
New Revision: 3586

Modified:
   trunk/numpy/random/mtrand/mtrand.c
   trunk/numpy/random/mtrand/mtrand.pyx
Log:
More fixes related to #471

Modified: trunk/numpy/random/mtrand/mtrand.c
===================================================================
--- trunk/numpy/random/mtrand/mtrand.c	2007-03-20 10:14:14 UTC (rev 3585)
+++ trunk/numpy/random/mtrand/mtrand.c	2007-03-20 10:16:03 UTC (rev 3586)
@@ -1,4 +1,4 @@
-/* Generated by Pyrex 0.9.5.1 on Tue Mar 20 04:22:44 2007 */
+/* Generated by Pyrex 0.9.5.1 on Tue Mar 20 04:24:28 2007 */
 
 #include "Python.h"
 #include "structmember.h"
@@ -1066,8 +1066,6 @@
   return __pyx_r;
 }
 
-static PyObject *__pyx_n_Int;
-
 static PyObject *__pyx_f_6mtrand_discnp_array_sc(rk_state (*__pyx_v_state),long ((*__pyx_v_func)(rk_state (*),long ,double )),PyObject *__pyx_v_size,long __pyx_v_n,double __pyx_v_p) {
   long (*__pyx_v_array_data);
   PyArrayObject *arrayObject;
@@ -1098,21 +1096,19 @@
     __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 317; goto __pyx_L1;}
     __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 317; goto __pyx_L1;}
     Py_DECREF(__pyx_2); __pyx_2 = 0;
-    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 317; goto __pyx_L1;}
-    __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_n_Int); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 317; goto __pyx_L1;}
-    Py_DECREF(__pyx_2); __pyx_2 = 0;
-    __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 317; goto __pyx_L1;}
+    __pyx_2 = __Pyx_GetName(__pyx_b, __pyx_n_int); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 317; goto __pyx_L1;}
+    __pyx_4 = PyTuple_New(2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 317; goto __pyx_L1;}
     Py_INCREF(__pyx_v_size);
-    PyTuple_SET_ITEM(__pyx_2, 0, __pyx_v_size);
-    PyTuple_SET_ITEM(__pyx_2, 1, __pyx_4);
-    __pyx_4 = 0;
-    __pyx_4 = PyObject_CallObject(__pyx_3, __pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 317; goto __pyx_L1;}
+    PyTuple_SET_ITEM(__pyx_4, 0, __pyx_v_size);
+    PyTuple_SET_ITEM(__pyx_4, 1, __pyx_2);
+    __pyx_2 = 0;
+    __pyx_2 = PyObject_CallObject(__pyx_3, __pyx_4); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 317; goto __pyx_L1;}
     Py_DECREF(__pyx_3); __pyx_3 = 0;
-    Py_DECREF(__pyx_2); __pyx_2 = 0;
-    Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_4)));
-    Py_DECREF(((PyObject *)arrayObject));
-    arrayObject = ((PyArrayObject *)__pyx_4);
     Py_DECREF(__pyx_4); __pyx_4 = 0;
+    Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_2)));
+    Py_DECREF(((PyObject *)arrayObject));
+    arrayObject = ((PyArrayObject *)__pyx_2);
+    Py_DECREF(__pyx_2); __pyx_2 = 0;
 
     /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":318 */
     __pyx_v_length = PyArray_SIZE(arrayObject);
@@ -1333,21 +1329,19 @@
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     Py_DECREF(__pyx_2); __pyx_2 = 0;
-    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n__sp); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 367; goto __pyx_L1;}
-    __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_n_Int); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 367; goto __pyx_L1;}
-    Py_DECREF(__pyx_2); __pyx_2 = 0;
-    __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 367; goto __pyx_L1;}
+    __pyx_2 = __Pyx_GetName(__pyx_b, __pyx_n_int); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 367; goto __pyx_L1;}
+    __pyx_4 = PyTuple_New(2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 367; goto __pyx_L1;}
     Py_INCREF(__pyx_v_size);
-    PyTuple_SET_ITEM(__pyx_2, 0, __pyx_v_size);
-    PyTuple_SET_ITEM(__pyx_2, 1, __pyx_4);
-    __pyx_4 = 0;
-    __pyx_4 = PyObject_CallObject(__pyx_3, __pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 367; goto __pyx_L1;}
+    PyTuple_SET_ITEM(__pyx_4, 0, __pyx_v_size);
+    PyTuple_SET_ITEM(__pyx_4, 1, __pyx_2);
+    __pyx_2 = 0;
+    __pyx_2 = PyObject_CallObject(__pyx_3, __pyx_4); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 367; goto __pyx_L1;}
     Py_DECREF(__pyx_3); __pyx_3 = 0;
-    Py_DECREF(__pyx_2); __pyx_2 = 0;
-    Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_4)));
-    Py_DECREF(((PyObject *)arrayObject));
-    arrayObject = ((PyArrayObject *)__pyx_4);
     Py_DECREF(__pyx_4); __pyx_4 = 0;
+    Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_2)));
+    Py_DECREF(((PyObject *)arrayObject));
+    arrayObject = ((PyArrayObject *)__pyx_2);
+    Py_DECREF(__pyx_2); __pyx_2 = 0;
 
     /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":368 */
     __pyx_v_length = PyArray_SIZE(arrayObject);
@@ -9527,7 +9521,6 @@
 }
 
 static __Pyx_InternTabEntry __pyx_intern_tab[] = {
-  {&__pyx_n_Int, "Int"},
   {&__pyx_n_MT19937, "MT19937"},
   {&__pyx_n_PyInt_AsLong, "PyInt_AsLong"},
   {&__pyx_n_ValueError, "ValueError"},

Modified: trunk/numpy/random/mtrand/mtrand.pyx
===================================================================
--- trunk/numpy/random/mtrand/mtrand.pyx	2007-03-20 10:14:14 UTC (rev 3585)
+++ trunk/numpy/random/mtrand/mtrand.pyx	2007-03-20 10:16:03 UTC (rev 3586)
@@ -314,7 +314,7 @@
     if size is None:
         return func(state, n, p)
     else:
-        array = <ndarray>_sp.empty(size, _sp.Int)
+        array = <ndarray>_sp.empty(size, int)
         length = PyArray_SIZE(array)
         array_data = <long *>array.data
         for i from 0 <= i < length:
@@ -364,7 +364,7 @@
     if size is None:
         return func(state, n, m, N)
     else:
-        array = <ndarray>_sp.empty(size, _sp.Int)
+        array = <ndarray>_sp.empty(size, int)
         length = PyArray_SIZE(array)
         array_data = <long *>array.data
         for i from 0 <= i < length:



From numpy-svn at scipy.org  Tue Mar 20 06:34:08 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Tue, 20 Mar 2007 05:34:08 -0500 (CDT)
Subject: [Numpy-svn] r3587 - trunk/numpy/random/mtrand
Message-ID: <20070320103408.9B55B39C0EB@new.scipy.org>

Author: oliphant
Date: 2007-03-20 05:34:04 -0500 (Tue, 20 Mar 2007)
New Revision: 3587

Modified:
   trunk/numpy/random/mtrand/Python.pxi
   trunk/numpy/random/mtrand/mtrand.c
   trunk/numpy/random/mtrand/mtrand.pyx
Log:
More fixes to mtrand from recent speed ups.

Modified: trunk/numpy/random/mtrand/Python.pxi
===================================================================
--- trunk/numpy/random/mtrand/Python.pxi	2007-03-20 10:16:03 UTC (rev 3586)
+++ trunk/numpy/random/mtrand/Python.pxi	2007-03-20 10:34:04 UTC (rev 3587)
@@ -16,6 +16,7 @@
 
     # Float API
     double PyFloat_AsDouble(object ob)
+    long PyInt_AsLong(object ob)
 
     # Memory API
     void* PyMem_Malloc(size_t n)

Modified: trunk/numpy/random/mtrand/mtrand.c
===================================================================
--- trunk/numpy/random/mtrand/mtrand.c	2007-03-20 10:16:03 UTC (rev 3586)
+++ trunk/numpy/random/mtrand/mtrand.c	2007-03-20 10:34:04 UTC (rev 3587)
@@ -1,4 +1,4 @@
-/* Generated by Pyrex 0.9.5.1 on Tue Mar 20 04:24:28 2007 */
+/* Generated by Pyrex 0.9.5.1 on Tue Mar 20 04:39:16 2007 */
 
 #include "Python.h"
 #include "structmember.h"
@@ -3410,8 +3410,6 @@
   return __pyx_r;
 }
 
-static PyObject *__pyx_n_fscale;
-
 static PyObject *__pyx_k81p;
 static PyObject *__pyx_k82p;
 
@@ -3447,12 +3445,7 @@
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     /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":779 */
-    __pyx_2 = __Pyx_GetName(__pyx_b, __pyx_n_fscale); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 779; goto __pyx_L1;}
-    __pyx_3 = PyInt_FromLong(0); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 779; goto __pyx_L1;}
-    if (PyObject_Cmp(__pyx_2, __pyx_3, &__pyx_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 779; goto __pyx_L1;}
-    __pyx_1 = __pyx_1 <= 0;
-    Py_DECREF(__pyx_2); __pyx_2 = 0;
-    Py_DECREF(__pyx_3); __pyx_3 = 0;
+    __pyx_1 = (__pyx_v_fshape <= 0);
     if (__pyx_1) {
 
       /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":780 */
@@ -4342,12 +4335,9 @@
     __pyx_L3:;
 
     /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":890 */
-    __pyx_2 = PyFloat_FromDouble(__pyx_v_fdf); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 890; goto __pyx_L1;}
-    if (!__Pyx_TypeTest(__pyx_2, __pyx_ptype_6mtrand_ndarray)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 890; goto __pyx_L1;}
-    __pyx_3 = __pyx_f_6mtrand_cont1_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,rk_chisquare,__pyx_v_size,((PyArrayObject *)__pyx_2)); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 890; goto __pyx_L1;}
-    Py_DECREF(__pyx_2); __pyx_2 = 0;
-    __pyx_r = __pyx_3;
-    __pyx_3 = 0;
+    __pyx_2 = __pyx_f_6mtrand_cont1_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,rk_chisquare,__pyx_v_size,__pyx_v_fdf); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 890; goto __pyx_L1;}
+    __pyx_r = __pyx_2;
+    __pyx_2 = 0;
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     goto __pyx_L2;
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@@ -4357,48 +4347,48 @@
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-  Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_4)));
+  __pyx_3 = PyArray_FROM_OTF(__pyx_v_df,NPY_DOUBLE,NPY_ALIGNED); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 894; goto __pyx_L1;}
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-  __pyx_v_odf = ((PyArrayObject *)__pyx_4);
-  Py_DECREF(__pyx_4); __pyx_4 = 0;
+  __pyx_v_odf = ((PyArrayObject *)__pyx_3);
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   /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":895 */
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-  Py_DECREF(__pyx_2); __pyx_2 = 0;
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-    __pyx_3 = PyTuple_New(1); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 896; goto __pyx_L1;}
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-    Py_DECREF(__pyx_4); __pyx_4 = 0;
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-    __Pyx_Raise(__pyx_2, 0, 0);
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   double __pyx_v_fp;
   PyObject *__pyx_r;
-  PyObject *__pyx_1 = 0;
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   PyObject *__pyx_3 = 0;
-  long __pyx_4;
-  int __pyx_5;
-  PyObject *__pyx_6 = 0;
+  PyObject *__pyx_4 = 0;
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-  int __pyx_5;
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+  __pyx_6 = 0;
   goto __pyx_L0;
 
   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
   __pyx_L1:;
-  Py_XDECREF(__pyx_1);
   Py_XDECREF(__pyx_2);
   Py_XDECREF(__pyx_3);
+  Py_XDECREF(__pyx_4);
+  Py_XDECREF(__pyx_5);
   Py_XDECREF(__pyx_6);
-  Py_XDECREF(__pyx_7);
   __Pyx_AddTraceback("mtrand.RandomState.hypergeometric");
   __pyx_r = 0;
   __pyx_L0:;
@@ -9522,7 +9462,6 @@
 
 static __Pyx_InternTabEntry __pyx_intern_tab[] = {
   {&__pyx_n_MT19937, "MT19937"},
-  {&__pyx_n_PyInt_AsLong, "PyInt_AsLong"},
   {&__pyx_n_ValueError, "ValueError"},
   {&__pyx_n___RandomState_ctor, "__RandomState_ctor"},
   {&__pyx_n__rand, "_rand"},
@@ -9544,7 +9483,6 @@
   {&__pyx_n_exponential, "exponential"},
   {&__pyx_n_f, "f"},
   {&__pyx_n_float64, "float64"},
-  {&__pyx_n_fscale, "fscale"},
   {&__pyx_n_gamma, "gamma"},
   {&__pyx_n_geometric, "geometric"},
   {&__pyx_n_get_state, "get_state"},

Modified: trunk/numpy/random/mtrand/mtrand.pyx
===================================================================
--- trunk/numpy/random/mtrand/mtrand.pyx	2007-03-20 10:16:03 UTC (rev 3586)
+++ trunk/numpy/random/mtrand/mtrand.pyx	2007-03-20 10:34:04 UTC (rev 3587)
@@ -776,7 +776,7 @@
 
         fshape = PyFloat_AsDouble(shape)
         if not PyErr_Occurred():
-            if fscale <= 0:
+            if fshape <= 0:
                 raise ValueError("shape <= 0")
             return cont1_array_sc(self.internal_state, rk_standard_gamma, size, fshape)
 
@@ -887,7 +887,7 @@
         if not PyErr_Occurred():
             if fdf <= 0:
                 raise ValueError("df <= 0")
-            return cont1_array(self.internal_state, rk_chisquare, size, fdf)
+            return cont1_array_sc(self.internal_state, rk_chisquare, size, fdf)
 
         PyErr_Clear()
 



From numpy-svn at scipy.org  Tue Mar 20 14:43:32 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Tue, 20 Mar 2007 13:43:32 -0500 (CDT)
Subject: [Numpy-svn] r3588 - trunk/numpy/doc
Message-ID: <20070320184332.EE1C139C00B@new.scipy.org>

Author: cookedm
Date: 2007-03-20 13:43:28 -0500 (Tue, 20 Mar 2007)
New Revision: 3588

Modified:
   trunk/numpy/doc/pep_buffer.txt
Log:
ReSTify pep_buffer.txt
(hope that's ok, Travis)


Modified: trunk/numpy/doc/pep_buffer.txt
===================================================================
--- trunk/numpy/doc/pep_buffer.txt	2007-03-20 10:34:04 UTC (rev 3587)
+++ trunk/numpy/doc/pep_buffer.txt	2007-03-20 18:43:28 UTC (rev 3588)
@@ -1,332 +1,365 @@
-PEP: <unassigned>
-Title: Revising the buffer protocol
-Version: $Revision: $
-Last-Modified: $Date:  $
-Author: Travis Oliphant <oliphant at ee.byu.edu>
-Status: Draft
-Type: Standards Track
-Created: 28-Aug-2006
-Python-Version: 3000
+:PEP: XXX
+:Title: Revising the buffer protocol
+:Version: $Revision: $
+:Last-Modified: $Date:  $
+:Author: Travis Oliphant <oliphant at ee.byu.edu>
+:Status: Draft
+:Type: Standards Track
+:Content-Type: text/x-rst
+:Created: 28-Aug-2006
+:Python-Version: 3000
 
 Abstract
+========
 
-   This PEP proposes re-designing the buffer API (PyBufferProcs
-   function pointers) to improve the way Python allows memory sharing
-   in Python 3.0
+This PEP proposes re-designing the buffer API (PyBufferProcs
+function pointers) to improve the way Python allows memory sharing
+in Python 3.0
 
-   In particular, it is proposed that the multiple-segment and
-   character buffer portions of the buffer API are eliminated and
-   additional function pointers are provided to allow sharing any
-   multi-dimensional nature of the memory and what data-format the
-   memory contains.
+In particular, it is proposed that the multiple-segment and
+character buffer portions of the buffer API are eliminated and
+additional function pointers are provided to allow sharing any
+multi-dimensional nature of the memory and what data-format the
+memory contains.
 
 Rationale
+=========
 
-   The buffer protocol allows different Python types to exchange a
-   pointer to a sequence of internal buffers.  This functionality is
-   *extremely* useful for sharing large segments of memory between
-   different high-level objects, but it's too limited and has issues.
+The buffer protocol allows different Python types to exchange a
+pointer to a sequence of internal buffers.  This functionality is
+*extremely* useful for sharing large segments of memory between
+different high-level objects, but it's too limited and has issues.
 
-    1. There is the little (never?) used "sequence-of-segments" option
-       (bf_getsegcount)
+1. There is the little (never?) used "sequence-of-segments" option
+   (bf_getsegcount)
 
-    2. There is the apparently redundant character-buffer option
-       (bf_getcharbuffer)
+2. There is the apparently redundant character-buffer option
+   (bf_getcharbuffer)
 
-    3. There is no way for a consumer to tell the buffer-API-exporting
-       object it is "finished" with its view of the memory and
-       therefore no way for the exporting object to be sure that it is
-       safe to reallocate the pointer to the memory that it owns (for
-       example, the array object reallocating its memory after sharing
-       it with the buffer object which held the original pointer led
-       to the infamous buffer-object problem).
+3. There is no way for a consumer to tell the buffer-API-exporting
+   object it is "finished" with its view of the memory and
+   therefore no way for the exporting object to be sure that it is
+   safe to reallocate the pointer to the memory that it owns (for
+   example, the array object reallocating its memory after sharing
+   it with the buffer object which held the original pointer led
+   to the infamous buffer-object problem).
 
-    4. Memory is just a pointer with a length. There is no way to
-       describe what is "in" the memory (float, int, C-structure, etc.)
+4. Memory is just a pointer with a length. There is no way to
+   describe what is "in" the memory (float, int, C-structure, etc.)
 
-    5. There is no shape information provided for the memory.  But,
-       several array-like Python types could make use of a standard
-       way to describe the shape-interpretation of the memory
-       (wxPython, GTK, pyQT, CVXOPT, PyVox, Audio and Video
-       Libraries, ctypes, NumPy, data-base interfaces, etc.)
+5. There is no shape information provided for the memory.  But,
+   several array-like Python types could make use of a standard
+   way to describe the shape-interpretation of the memory
+   (wxPython, GTK, pyQT, CVXOPT, PyVox, Audio and Video
+   Libraries, ctypes, NumPy, data-base interfaces, etc.)
 
-    There are two widely used libraries that use the concept of
-    discontiguous memory: PIL and NumPy.  Their view of discontiguous
-    arrays is a bit different, though.  NumPy uses the notion of
-    constant striding in each dimension as its basic concept of an
-    array. In this way a simple sub-region of a larger array can be
-    described without copying the data.  Strided memory is also a common
-    way to describe data in many computing libraries (such as the BLAS
-    and LAPACK).
+There are two widely used libraries that use the concept of
+discontiguous memory: PIL and NumPy.  Their view of discontiguous
+arrays is a bit different, though.  NumPy uses the notion of
+constant striding in each dimension as its basic concept of an
+array. In this way a simple sub-region of a larger array can be
+described without copying the data.  Strided memory is also a common
+way to describe data in many computing libraries (such as the BLAS
+and LAPACK).
 
-    The PIL uses a more opaque memory representation. Sometimes an
-    image is contained in a contiguous segment of memory, but
-    sometimes it is contained in an array of pointers to the
-    contiguous segments (usually lines) of the image.  This allows the
-    image to not be loaded entirely into memory but still managed
-    abstractly as if it were. I believe, the PIL is where the idea of
-    multiple buffer segments in the original buffer interface came
-    from, I believe.
+The PIL uses a more opaque memory representation. Sometimes an
+image is contained in a contiguous segment of memory, but
+sometimes it is contained in an array of pointers to the
+contiguous segments (usually lines) of the image.  This allows the
+image to not be loaded entirely into memory but still managed
+abstractly as if it were. I believe, the PIL is where the idea of
+multiple buffer segments in the original buffer interface came
+from, I believe.
 
-    The buffer interface should allow discontiguous memory areas to
-    share standard striding information.  However, consumers that do
-    not want to deal with strided memory should also be able to
-    request a contiguous segment easily.    
+The buffer interface should allow discontiguous memory areas to
+share standard striding information.  However, consumers that do
+not want to deal with strided memory should also be able to
+request a contiguous segment easily.
 
-
 Proposal Overview
+=================
 
-   * Eliminate the char-buffer and multiple-segment sections of the
-     buffer-protocol.
+* Eliminate the char-buffer and multiple-segment sections of the
+  buffer-protocol.
 
-   * Unify the read/write versions of getting the buffer.
+* Unify the read/write versions of getting the buffer.
 
-   * Add a new function to the interface that should be called when
-     the consumer object is "done" with the view.
+* Add a new function to the interface that should be called when
+  the consumer object is "done" with the view.
 
-   * Add a new memory_view object that is returned from the 
-     buffer interface getbuffer call.  This memory_view object
-     contains
-   * Add a new function to allow the interface to describe what is in
-     memory (unifying what is currently done now in struct and
-     array)
+* Add a new memory_view object that is returned from the 
+  buffer interface getbuffer call.  This memory_view object
+  contains
+* Add a new function to allow the interface to describe what is in
+  memory (unifying what is currently done now in struct and
+  array)
 
-   * Add a new function to allow the protocol to share shape and 
-     stride information
+* Add a new function to allow the protocol to share shape and 
+  stride information
 
-   * Fix all objects in the core and the standard library to conform
-     to the new interface
+* Fix all objects in the core and the standard library to conform
+  to the new interface
 
-   * Extend the struct module to handle more format specifiers
+* Extend the struct module to handle more format specifiers
 
 Specification
+=============
 
-    Change the PyBufferProcs structure to
+Change the PyBufferProcs structure to
 
+::
+
     typedef struct {
          getbufferproc bf_getbuffer
          releasebufferproc bf_releasebuffer
     }
 
+::
+
     typedef PyObject *(*getbufferproc)(PyObject *obj, void **buf,
                                        Py_ssize_t *len, int *writeable,
                                        char **format, int *ndims,
                                        Py_ssize_t **shape,
                                        Py_ssize_t **strides)
- 
-      Return a pointer to memory in *buf and the length of that memory
-      buffer (in bytes) in *len.  The next arguments are optional.  
-      NULL is returned on failure.   On success an oject-specific 
-      view is returned (which may just be a borrowed reference to obj).
-      This view should be passed to bf_releasebuffer when the consumer
-      is done with the view. 
 
-      writeable -- address of an integer variable to hold 
-                     whether or not the memory is writeable.
-                     If this is NULL, then you must assume the memory 
-                     is read-only.
-      format    -- address of a format-string (following extended struct 
-                     syntax) indicating what is in each element of
-                     of memory.  The number of elements is len / itemsize,
-                     where itemsize is the number of bytes implied by the format.
-                     NULL if not needed in which case format == "B" for 
-                     unsigned bytes.  The memory for this string must not
-                     be freed by the consumer --- it is managed by the exporter.
-      ndims     -- address of a variable storing the number of dimensions 
-                     or NULL if not needed.  If shape and/or strides are given
-                     then this must be non NULL.  If this variable is 
-                     not provided then it is assumed that *ndims == 1
-      shape     -- 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 shape of the memory as an N-D array.  
-                     Ignored if this is NULL.  Note that
-                     ((*shape)[0] * ... * (*shape)[ndims-1])*itemsize = len
-                     If this variable is not provided then it is assumed that
-                     (*shape[0]) == len / itemsize. 
-      stride    -- 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 NULL, then
-                     the memory is assumed to be C-style contigous with
-                     the last dimension varying the fastest.  An  
-                     error should be raised if this is not accurate and
-                     strides are not requested.  This variable may be
-                     set to NULL when called if memory is C-style
-                     contiguous. 
-                
-      This view object should be used in the other API call and 
-      does not need to be decref'd.  It should be "released" if the
-      interface exporter provides the bf_releasebuffer function.
+Return a pointer to memory in ``*buf`` and the length of that memory
+buffer (in bytes) in ``*len``.  The next arguments are optional.
+NULL is returned on failure.   On success an oject-specific
+view is returned (which may just be a borrowed reference to obj).
+This view should be passed to bf_releasebuffer when the consumer
+is done with the view.
 
-    typedef int (*releasebufferproc)(PyObject *view)
+writeable
+    address of an integer variable to hold whether or not the memory
+    is writeable. If this is NULL, then you must assume the memory
+    is read-only.
 
-      This function is called (if defined by the exporting object)
-      when a view of memory previously acquired from the object is no
-      longer needed.  It is up to the exporter of the API to make sure
-      all views have been released before re-allocating any previously
-      shared memory.  It is up to consumers of the API to call this
-      function on the object whose view is obtained when it is no
-      longer needed.   Any format string, shape array or strides array
-      returned through the interface should also not be referenced after 
-      the releasebuffer call is made. 
-      A -1 is returned on error and 0 on success.
+format
+    address of a format-string (following extended struct
+    syntax) indicating what is in each element of
+    of memory.  The number of elements is len / itemsize,
+    where itemsize is the number of bytes implied by the format.
+    NULL if not needed in which case format is "B" for
+    unsigned bytes.  The memory for this string must not
+    be freed by the consumer --- it is managed by the exporter.
 
-    Both of these routines are optional for a type object 
+ndims
+    address of a variable storing the number of dimensions
+    or NULL if not needed.  If shape and/or strides are given
+    then this must be non NULL.  If this variable is
+    not provided then it is assumed that ``*ndims == 1``.
 
+shape
+    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 shape of the memory as an N-D array.
+    Ignored if this is NULL.  Note that
+    ``((*shape)[0] * ... * (*shape)[ndims-1])*itemsize = len``.
+    If this variable is not provided then it is assumed that
+    ``(*shape[0]) == len / itemsize``.
 
+stride
+    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 NULL, then
+    the memory is assumed to be C-style contigous with
+    the last dimension varying the fastest.  An
+    error should be raised if this is not accurate and
+    strides are not requested.  This variable may be
+    set to NULL when called if memory is C-style
+    contiguous.
+
+    This view object should be used in the other API call and 
+    does not need to be decref'd.  It should be "released" if the
+    interface exporter provides the bf_releasebuffer function.
+
+``typedef int (*releasebufferproc)(PyObject *view)``
+    This function is called (if defined by the exporting object)
+    when a view of memory previously acquired from the object is no
+    longer needed.  It is up to the exporter of the API to make sure
+    all views have been released before re-allocating any previously
+    shared memory.  It is up to consumers of the API to call this
+    function on the object whose view is obtained when it is no
+    longer needed.   Any format string, shape array or strides array
+    returned through the interface should also not be referenced after
+    the releasebuffer call is made.
+    A -1 is returned on error and 0 on success.
+
+    Both of these routines are optional for a type object
+
+
 New C-API calls are proposed
+============================
 
-   int 
-   PyObject_CheckBuffer(PyObject *obj)
+::
 
-      return 1 if the getbuffer function is available otherwise 0
+    int PyObject_CheckBuffer(PyObject *obj)
 
-   PyObject * 
-   PyObject_GetBuffer(PyObject *obj, void **buf, Py_ssize_t *len,
-                      int *writeable, char **format, int *ndims,
-                      Py_ssize_t **shape, Py_ssize_t **strides)
+Return 1 if the getbuffer function is available otherwise 0.
 
-      Get the buffer and optional information variables about the buffer.
-      Return an object-specific view object (which may be simply a 
-      borrowed reference to the object itself). 
-      
-   int
-   PyObject_ReleaseBuffer(PyObject *view)
-      
-      call this function to tell obj that you are done with your "view"
-      This doesn't do anything if the object doesn't implement a release function.
-      Only call this after a previous PyObject_GetBuffer has succeeded and when
-      you will not be needing or referring to the memory (or the format, shape, 
-      and strides memory used in the view -- if you will use these for a longer
-      period of time make copies). 
-      Returns -1 on error. 
-      
-   int PyObject_SizeFromFormat(char *)
-      Return the implied itemsize of the data-format area from a struct-style
-      description. 
+::
 
+    PyObject * PyObject_GetBuffer(PyObject *obj, void **buf,
+                                  Py_ssize_t *len, int *writeable,
+                                  char **format, int *ndims,
+                                  Py_ssize_t **shape, Py_ssize_t **strides)
 
+Get the buffer and optional information variables about the buffer.
+Return an object-specific view object (which may be simply a
+borrowed reference to the object itself).
+
+::
+
+    int PyObject_ReleaseBuffer(PyObject *view)
+
+Call this function to tell obj that you are done with your "view"
+This doesn't do anything if the object doesn't implement a release function.
+Only call this after a previous PyObject_GetBuffer has succeeded and when
+you will not be needing or referring to the memory (or the format, shape, 
+and strides memory used in the view -- if you will use these for a longer
+period of time make copies).
+Returns -1 on error.
+
+::
+
+    int PyObject_SizeFromFormat(char *)
+
+Return the implied itemsize of the data-format area from a struct-style
+description.
+
+
 Additions to the struct string-syntax
+=====================================
 
-   The struct string-syntax is missing some characters to fully
-   implement data-format descriptions already available elsewhere (in
-   ctypes and NumPy for example).  Here are the proposed additions:
+The struct string-syntax is missing some characters to fully
+implement data-format descriptions already available elsewhere (in
+ctypes and NumPy for example).  Here are the proposed additions:
 
-   Character         Description
-   =============================================================
-   't'               bit (number before states how many bits)
-   '?'               platform _Bool type
-   'g'               long double  
-   'Z'               complex (whatever the next specifier is)
-   'c'               ucs-1 (latin-1) encoding 
-   'u'               ucs-2 
-   'w'               ucs-4 
-   'O'               pointer to Python Object 
-   'T{}'             structure (detailed layout inside {}) 
-   '(k1,k2,...,kn)'  multi-dimensional array of whatever follows 
-   ':name:'          optional name of the preceeding element 
-   '&'               specific pointer (prefix before another charater) 
-   'X{}'             pointer to a function (optional function 
-                                             signature inside {})
-   ' '               ignored (allow better readability)
 
-   The struct module will be changed to understand these as well and
-   return appropriate Python objects on unpacking.  Un-packing a
-   long-double will return a c-types long_double.  Unpacking 'u' or
-   'w' will return Python unicode.  Unpacking a multi-dimensional
-   array will return a list of lists.  Un-packing a pointer will
-   return a ctypes pointer object.  Un-packing a bit will return a
-   Python Bool.  Spaces in the struct-string syntax will be ignored.
+================  ===========
+Character         Description
+================  ===========
+'t'               bit (number before states how many bits)
+'?'               platform _Bool type
+'g'               long double  
+'Z'               complex (whatever the next specifier is)
+'c'               ucs-1 (latin-1) encoding 
+'u'               ucs-2 
+'w'               ucs-4 
+'O'               pointer to Python Object 
+'T{}'             structure (detailed layout inside {}) 
+'(k1,k2,...,kn)'  multi-dimensional array of whatever follows 
+':name:'          optional name of the preceeding element 
+'&'               specific pointer (prefix before another charater) 
+'X{}'             pointer to a function (optional function 
+                                         signature inside {})
+' '               ignored (allow better readability)
+================  ===========
 
-   Endian-specification ('=','>','<') is also allowed inside the
-   string so that it can change if needed.  The previously-specified
-   endian string is enforce until changed.  The default endian is '='.
+The struct module will be changed to understand these as well and
+return appropriate Python objects on unpacking.  Un-packing a
+long-double will return a c-types long_double.  Unpacking 'u' or
+'w' will return Python unicode.  Unpacking a multi-dimensional
+array will return a list of lists.  Un-packing a pointer will
+return a ctypes pointer object.  Un-packing a bit will return a
+Python Bool.  Spaces in the struct-string syntax will be ignored.
 
-   According to the struct-module, a number can preceed a character
-   code to specify how many of that type there are.  The
-   (k1,k2,...,kn) extension also allows specifying if the data is
-   supposed to be viewed as a (C-style contiguous, last-dimension
-   varies the fastest) multi-dimensional array of a particular format.
+Endian-specification ('=','>','<') is also allowed inside the
+string so that it can change if needed.  The previously-specified
+endian string is enforce until changed.  The default endian is '='.
 
-   Functions should be added to ctypes to create a ctypes object from
-   a struct description, and add long-double, and ucs-2 to ctypes.
+According to the struct-module, a number can preceed a character
+code to specify how many of that type there are.  The
+(k1,k2,...,kn) extension also allows specifying if the data is
+supposed to be viewed as a (C-style contiguous, last-dimension
+varies the fastest) multi-dimensional array of a particular format.
 
+Functions should be added to ctypes to create a ctypes object from
+a struct description, and add long-double, and ucs-2 to ctypes.
+
 Examples of Data-Format Descriptions
+====================================
 
-   Here are some examples of C-structures and how they would be
-   represented using the struct-style syntax:
+Here are some examples of C-structures and how they would be
+represented using the struct-style syntax:
 
-   float                     
-      'f'
-   complex double
-      'Zd'
-   RGB Pixel data
-      'BBB' or 'B:r: B:g: B:b:'
-   Mixed endian (weird but possible)
-      '>i:big: <i:little:'
-   Nested structure
-      struct {
+float
+    'f'
+complex double
+    'Zd'
+RGB Pixel data
+    'BBB' or 'B:r: B:g: B:b:'
+Mixed endian (weird but possible)
+    '>i:big: <i:little:'
+Nested structure
+    ::
+
+        struct {
              int ival;
              struct {
                  unsigned short sval;
                  unsigned char bval;
                  unsigned char cval;
              } sub;
-      }
-      'i:ival: T{H:sval: B:bval: B:cval:}:sub:'
-   Nested array
-      struct {
+        }
+        'i:ival: T{H:sval: B:bval: B:cval:}:sub:'
+Nested array
+    ::
+
+        struct {
              int ival;
              double data[16*4];
-      }
-      'i:ival: (16,4)d:data:'
+        }
+        'i:ival: (16,4)d:data:'
 
 Code to be affected
+===================
 
-   All objects and modules in Python that export or consume the old
-   buffer interface will be modified.  Here is a partial list.
-     
-   * buffer object
-   * bytes object
-   * string object
-   * array module
-   * struct module
-   * mmap module
-   * ctypes module
+All objects and modules in Python that export or consume the old
+buffer interface will be modified.  Here is a partial list.
 
-   anything else using the buffer API
+* buffer object
+* bytes object
+* string object
+* array module
+* struct module
+* mmap module
+* ctypes module
 
+Anything else using the buffer API
 
 
 Issues and Details
+==================
 
+The proposed locking mechanism relies entirely on the objects
+implementing the buffer interface to do their own thing.  Ideally
+an object that implements the buffer interface should keep at least
+a number indicating how many releases are extant.  If there are views
+to a memory location, then any subsequent reallocation should fail and raise
+an error.
 
-   The proposed locking mechanism relies entirely on the objects
-   implementing the buffer interface to do their own thing.  Ideally
-   an object that implements the buffer interface should keep at least
-   a number indicating how many releases are extant.  If there are views
-   to a memory location, then any subsequent reallocation should fail and raise
-   an error. 
+The sharing of strided memory is new and can be seen as a
+modification of the multiple-segment interface.  It is motivated by
+NumPy.  NumPy objects should be able to share their strided memory
+with code that understands how to manage strided memory because
+strided memory is very common when interfacing with compute libraries.
 
-   The sharing of strided memory is new and can be seen as a
-   modification of the multiple-segment interface.  It is motivated by
-   NumPy.  NumPy objects should be able to share their strided memory
-   with code that understands how to manage strided memory because
-   strided memory is very common when interfacing with compute libraries.
+Currently the struct module does not allow specification of nested
+structures.  It seems like specifying a nested structure should be
+specified as several ways of viewing memory areas (e.g. ctypes and
+NumPy) already allow this.
 
-   Currently the struct module does not allow specification of nested
-   structures.  It seems like specifying a nested structure should be
-   specified as several ways of viewing memory areas (e.g. ctypes and
-   NumPy) already allow this.
+Memory management of the format string and the shape and strides
+array is always the responsibility of the exporting object and can
+be shared between different views. If the consuming object needs to
+keep these memory areas longer than the view is held, then it must
+copy them to its own memory.
 
-   Memory management of the format string and the shape and strides
-   array is always the responsibility of the exporting object and can
-   be shared between different views. If the consuming object needs to
-   keep these memory areas longer than the view is held, then it must
-   copy them to its own memory. 
 
-
 Copyright
+=========
 
-   This PEP is placed in the public domain
+This PEP is placed in the public domain
 



From numpy-svn at scipy.org  Wed Mar 21 17:19:14 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Wed, 21 Mar 2007 16:19:14 -0500 (CDT)
Subject: [Numpy-svn] r3589 - trunk/numpy/doc
Message-ID: <20070321211914.C3CA239C00C@new.scipy.org>

Author: oliphant
Date: 2007-03-21 16:19:11 -0500 (Wed, 21 Mar 2007)
New Revision: 3589

Modified:
   trunk/numpy/doc/pep_buffer.txt
Log:
Buffer interface changes -- add array-of-array possibility, allow optional buf and len variables, and add 2 C-API calls.

Modified: trunk/numpy/doc/pep_buffer.txt
===================================================================
--- trunk/numpy/doc/pep_buffer.txt	2007-03-20 18:43:28 UTC (rev 3588)
+++ trunk/numpy/doc/pep_buffer.txt	2007-03-21 21:19:11 UTC (rev 3589)
@@ -17,8 +17,8 @@
 in Python 3.0
 
 In particular, it is proposed that the multiple-segment and
-character buffer portions of the buffer API are eliminated and
-additional function pointers are provided to allow sharing any
+character buffer portions of the buffer API be eliminated and
+additional function pointers be provided to allow sharing any
 multi-dimensional nature of the memory and what data-format the
 memory contains.
 
@@ -28,7 +28,7 @@
 The buffer protocol allows different Python types to exchange a
 pointer to a sequence of internal buffers.  This functionality is
 *extremely* useful for sharing large segments of memory between
-different high-level objects, but it's too limited and has issues.
+different high-level objects, but it is too limited and has issues.
 
 1. There is the little (never?) used "sequence-of-segments" option
    (bf_getsegcount)
@@ -53,29 +53,39 @@
    (wxPython, GTK, pyQT, CVXOPT, PyVox, Audio and Video
    Libraries, ctypes, NumPy, data-base interfaces, etc.)
 
-There are two widely used libraries that use the concept of
-discontiguous memory: PIL and NumPy.  Their view of discontiguous
-arrays is a bit different, though.  NumPy uses the notion of
-constant striding in each dimension as its basic concept of an
-array. In this way a simple sub-region of a larger array can be
-described without copying the data.  Strided memory is also a common
-way to describe data in many computing libraries (such as the BLAS
-and LAPACK).
+6. There is no way to share discontiguous memory (except through
+   the sequence of segments notion).  
 
-The PIL uses a more opaque memory representation. Sometimes an
-image is contained in a contiguous segment of memory, but
-sometimes it is contained in an array of pointers to the
-contiguous segments (usually lines) of the image.  This allows the
-image to not be loaded entirely into memory but still managed
-abstractly as if it were. I believe, the PIL is where the idea of
-multiple buffer segments in the original buffer interface came
-from, I believe.
+   There are two widely used libraries that use the concept of
+   discontiguous memory: PIL and NumPy.  Their view of discontiguous
+   arrays is different, though.  This buffer interface allows
+   sharing of either memory model.  Exporters will only use one         
+   approach and consumers may choose to support discontiguous 
+   arrays of each type however they choose. 
 
-The buffer interface should allow discontiguous memory areas to
-share standard striding information.  However, consumers that do
-not want to deal with strided memory should also be able to
-request a contiguous segment easily.
+   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
+   Thus, stride information is the additional information that must be
+   shared. 
 
+   The PIL uses a more opaque memory representation. Sometimes an
+   image is contained in a contiguous segment of memory, but sometimes
+   it is contained in an array of pointers to the contiguous segments
+   (usually lines) of the image.  The PIL is where the idea of multiple
+   buffer segments in the original buffer interface came from. 
+  
+
+   NumPy's strided memory model is used more often in computational
+   libraries and because it is so simple it makes sense to support
+   memory sharing using this model.  The PIL memory model is used often
+   in C-code where a 2-d array can be then accessed using double
+   pointer indirection:  e.g. image[i][j].  
+
+   The buffer interface should allow the object to export either of these
+   memory models.  Consumers are free to either require contiguous memory
+   or write code to handle either memory model.  
+
 Proposal Overview
 =================
 
@@ -87,16 +97,16 @@
 * Add a new function to the interface that should be called when
   the consumer object is "done" with the view.
 
-* Add a new memory_view object that is returned from the 
-  buffer interface getbuffer call.  This memory_view object
-  contains
-* Add a new function to allow the interface to describe what is in
+* Add a new variable to allow the interface to describe what is in
   memory (unifying what is currently done now in struct and
   array)
 
-* Add a new function to allow the protocol to share shape and 
-  stride information
+* Add a new variable to allow the protocol to share shape information
 
+* Add a new variable for sharing stride information
+
+* Add a new mechanism for sharing array of arrays. 
+
 * Fix all objects in the core and the standard library to conform
   to the new interface
 
@@ -120,15 +130,26 @@
                                        Py_ssize_t *len, int *writeable,
                                        char **format, int *ndims,
                                        Py_ssize_t **shape,
-                                       Py_ssize_t **strides)
+                                       Py_ssize_t **strides,
+                                       void **segments)
 
-Return a pointer to memory in ``*buf`` and the length of that memory
-buffer (in bytes) in ``*len``.  The next arguments are optional.
-NULL is returned on failure.   On success an oject-specific
-view is returned (which may just be a borrowed reference to obj).
-This view should be passed to bf_releasebuffer when the consumer
-is done with the view.
+All variables except the first are optional.  Use NULL for all
+un-needed variables.  Thus, this function can be called to get only
+the desired information from an object. NULL is returned on failure.
+On success an object-specific view is returned (which may just be a
+borrowed reference to obj).  This view should be passed to
+bf_releasebuffer when the consumer is done with the view.
 
+buf
+     a pointer to the start of the memory for the object is returned in
+    ``*buf``
+
+len
+     adress of an integer variable to hold the total bytes
+     of memory the object uses.  This should be the same
+     as the product of the shape array multiplied by the
+     number of bytes per item of memory. 
+
 writeable
     address of an integer variable to hold whether or not the memory
     is writeable. If this is NULL, then you must assume the memory
@@ -158,22 +179,40 @@
     If this variable is not provided then it is assumed that
     ``(*shape[0]) == len / itemsize``.
 
-stride
-    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 NULL, then
-    the memory is assumed to be C-style contigous with
-    the last dimension varying the fastest.  An
-    error should be raised if this is not accurate and
-    strides are not requested.  This variable may be
-    set to NULL when called if memory is C-style
-    contiguous.
 
-    This view object should be used in the other API call and 
-    does not need to be decref'd.  It should be "released" if the
-    interface exporter provides the bf_releasebuffer function.
+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 NULL, then the memory is assumed to
+    be C-style contigous with the last dimension varying the fastest.
+    An error should be raised if this is not accurate and strides are
+    not requested.  This variable may be set to NULL (with no error
+    set) if memory is actually C-style contiguous.
 
+
+segments
+    address to array-of-pointers-style array model.  Only one of
+    strides or segments can be used (the other one must be NULL).  
+    If the object does not support this kind of memory model and it
+    is requested, then an error should be raised and *segments set
+    to NULL.  The segments variable should be recast to a 
+    pointer-to-a-pointer-to-a-pointer-...-to-a-pointer depending on 
+    the output of ndims.   
+
+    Thus, if ndims is 2, segments should be cast to (<type> ***)
+    so that (*segments)[i][j] refers to the (i,j)th element
+    of the array.  If ndims is 3, segments should be cast to (<type> ****)
+    so that (*segments)[i][j][k] refers to the (i,j,k)th element
+    of the array. 
+
+
+The view object should be used in the other API call and does not need
+to be decref'd.  It should be "released" if the interface exporter
+provides the bf_releasebuffer function.  Otherwise, it may be
+discared.  The view object is exporter-specific.
+
+
 ``typedef int (*releasebufferproc)(PyObject *view)``
     This function is called (if defined by the exporting object)
     when a view of memory previously acquired from the object is no
@@ -203,7 +242,8 @@
     PyObject * PyObject_GetBuffer(PyObject *obj, void **buf,
                                   Py_ssize_t *len, int *writeable,
                                   char **format, int *ndims,
-                                  Py_ssize_t **shape, Py_ssize_t **strides)
+                                  Py_ssize_t **shape, Py_ssize_t **strides,
+                                  void **segments)
 
 Get the buffer and optional information variables about the buffer.
 Return an object-specific view object (which may be simply a
@@ -228,7 +268,34 @@
 Return the implied itemsize of the data-format area from a struct-style
 description.
 
+::
 
+    int PyObject_GetContiguous(PyObject *obj, void **buf, Py_ssize_t *len)
+
+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``.  The buffer is C-style contiguous
+meaning the last dimension varies the fastest. 
+
+:: 
+
+    int PyObject_CopyToObject(PyObject *obj, void *buf, Py_ssize_t len)
+
+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.  
+The data is copied into an array in C-style contiguous fashion meaning the
+last variable varies the fastest. 
+
+The last two C-API calls allow a standard way of getting data in and out
+of Python objects no matter how it is actually stored.  These calls use
+the buffer interface to perform their work. 
+
+
+
 Additions to the struct string-syntax
 =====================================
 
@@ -259,11 +326,13 @@
 
 The struct module will be changed to understand these as well and
 return appropriate Python objects on unpacking.  Un-packing a
-long-double will return a c-types long_double.  Unpacking 'u' or
+long-double will return a decimal object.  Unpacking 'u' or
 'w' will return Python unicode.  Unpacking a multi-dimensional
 array will return a list of lists.  Un-packing a pointer will
 return a ctypes pointer object.  Un-packing a bit will return a
 Python Bool.  Spaces in the struct-string syntax will be ignored.
+Unpacking a named-object will return a Python class with attributes 
+having those names. 
 
 Endian-specification ('=','>','<') is also allowed inside the
 string so that it can change if needed.  The previously-specified
@@ -334,11 +403,12 @@
 ==================
 
 The proposed locking mechanism relies entirely on the objects
-implementing the buffer interface to do their own thing.  Ideally
-an object that implements the buffer interface should keep at least
-a number indicating how many releases are extant.  If there are views
-to a memory location, then any subsequent reallocation should fail and raise
-an error.
+implementing the buffer interface to do their own thing.  Ideally an
+object that implements the buffer interface and can re-allocate
+memory, should store in its structure at least a number indicating how
+many views are extant.  If there are still un-released views to a
+memory location, then any subsequent reallocation should fail and
+raise an error.
 
 The sharing of strided memory is new and can be seen as a
 modification of the multiple-segment interface.  It is motivated by
@@ -347,9 +417,9 @@
 strided memory is very common when interfacing with compute libraries.
 
 Currently the struct module does not allow specification of nested
-structures.  It seems like specifying a nested structure should be
-specified as several ways of viewing memory areas (e.g. ctypes and
-NumPy) already allow this.
+structures.  The modifications to struct requested allow for
+specifying nested structures as several ways of viewing memory areas
+(e.g. ctypes and NumPy) already allow this.
 
 Memory management of the format string and the shape and strides
 array is always the responsibility of the exporting object and can



From numpy-svn at scipy.org  Thu Mar 22 18:49:30 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Thu, 22 Mar 2007 17:49:30 -0500 (CDT)
Subject: [Numpy-svn] r3590 - trunk/numpy/lib
Message-ID: <20070322224930.3DB5E39C1B4@new.scipy.org>

Author: stefan
Date: 2007-03-22 17:49:17 -0500 (Thu, 22 Mar 2007)
New Revision: 3590

Modified:
   trunk/numpy/lib/function_base.py
   trunk/numpy/lib/polynomial.py
Log:
Replace tabs with spaces (see ticket #470).


Modified: trunk/numpy/lib/function_base.py
===================================================================
--- trunk/numpy/lib/function_base.py	2007-03-21 21:19:11 UTC (rev 3589)
+++ trunk/numpy/lib/function_base.py	2007-03-22 22:49:17 UTC (rev 3590)
@@ -143,7 +143,7 @@
     H, edges = histogramdd(x, bins = (5, 6, 7))
 
     See also: histogram
-    
+
     """
 
     try:
@@ -264,7 +264,7 @@
 
     Raises ZeroDivisionError if appropriate.  (The version in MA does
     not -- it returns masked values).
-    
+
     """
     if axis is None:
         a = array(a).ravel()
@@ -403,7 +403,7 @@
 
     Note that one of the condition arrays must be large enough to handle
     the largest array in the choice list.
-    
+
     """
     n = len(condlist)
     n2 = len(choicelist)
@@ -456,7 +456,7 @@
 
       N arrays of the same shape as f giving the derivative of f with respect
       to each dimension.
-       
+
     """
     N = len(f.shape)  # number of dimensions
     n = len(varargs)
@@ -641,7 +641,7 @@
         >>> a = array((0, 0, 0, 1, 2, 3, 2, 1, 0))
         >>> numpy.trim_zeros(a)
         array([1, 2, 3, 2, 1])
-        
+
     """
     first = 0
     trim = trim.upper()
@@ -666,7 +666,7 @@
     Example:
     >>> unique([5,2,4,0,4,4,2,2,1])
     array([0, 1, 2, 4, 5])
-    
+
     """
     try:
         tmp = x.flatten()
@@ -692,7 +692,7 @@
     """Similar to putmask arr[mask] = vals but the 1D array vals has the
     same number of elements as the non-zero values of mask. Inverse of
     extract.
-    
+
     """
     return _insert(arr, mask, vals)
 

Modified: trunk/numpy/lib/polynomial.py
===================================================================
--- trunk/numpy/lib/polynomial.py	2007-03-21 21:19:11 UTC (rev 3589)
+++ trunk/numpy/lib/polynomial.py	2007-03-22 22:49:17 UTC (rev 3590)
@@ -53,11 +53,11 @@
     If the input is a matrix, return the characteristic polynomial.
 
     Example:
-        
+
         >>> b = roots([1,3,1,5,6])
         >>> poly(b)
         array([ 1.,  3.,  1.,  5.,  6.])
-        
+
     """
     seq_of_zeros = atleast_1d(seq_of_zeros)
     sh = seq_of_zeros.shape
@@ -655,5 +655,3 @@
 # Stuff to do on module import
 
 warnings.simplefilter('always',RankWarning)
-
-    



From numpy-svn at scipy.org  Fri Mar 23 08:08:23 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Fri, 23 Mar 2007 07:08:23 -0500 (CDT)
Subject: [Numpy-svn] r3591 - in trunk/numpy: core lib/tests
Message-ID: <20070323120823.6695939C0CD@new.scipy.org>

Author: stefan
Date: 2007-03-23 07:08:06 -0500 (Fri, 23 Mar 2007)
New Revision: 3591

Modified:
   trunk/numpy/core/memmap.py
   trunk/numpy/lib/tests/test_function_base.py
Log:
Replace tabs with spaces.


Modified: trunk/numpy/core/memmap.py
===================================================================
--- trunk/numpy/core/memmap.py	2007-03-22 22:49:17 UTC (rev 3590)
+++ trunk/numpy/core/memmap.py	2007-03-23 12:08:06 UTC (rev 3591)
@@ -85,7 +85,7 @@
         self._mmap.flush()
 
     def close(self):
-	self._mmap.close()
+        self._mmap.close()
 
     def __del__(self):
         if self._mmap is not None:

Modified: trunk/numpy/lib/tests/test_function_base.py
===================================================================
--- trunk/numpy/lib/tests/test_function_base.py	2007-03-22 22:49:17 UTC (rev 3590)
+++ trunk/numpy/lib/tests/test_function_base.py	2007-03-23 12:08:06 UTC (rev 3591)
@@ -283,19 +283,19 @@
 
 class test_digitize(NumpyTestCase):
     def check_forward(self):
-	x = arange(-6,5)
-	bins = arange(-5,5)
-	assert_array_equal(digitize(x,bins),arange(11))
+        x = arange(-6,5)
+        bins = arange(-5,5)
+        assert_array_equal(digitize(x,bins),arange(11))
 
     def check_reverse(self):
-	x = arange(5,-6,-1)
-	bins = arange(5,-5,-1)
-	assert_array_equal(digitize(x,bins),arange(11))
+        x = arange(5,-6,-1)
+        bins = arange(5,-5,-1)
+        assert_array_equal(digitize(x,bins),arange(11))
 
     def check_random(self):
-	x = rand(10)
-	bin = linspace(x.min(), x.max(), 10)
-	assert all(digitize(x,bin) != 0)
+        x = rand(10)
+        bin = linspace(x.min(), x.max(), 10)
+        assert all(digitize(x,bin) != 0)
 
 class test_unwrap(NumpyTestCase):
     def check_simple(self):



From numpy-svn at scipy.org  Fri Mar 23 13:36:23 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Fri, 23 Mar 2007 12:36:23 -0500 (CDT)
Subject: [Numpy-svn] r3592 - trunk/numpy/doc/swig
Message-ID: <20070323173623.BD8C539C247@new.scipy.org>

Author: wfspotz at sandia.gov
Date: 2007-03-23 12:36:16 -0500 (Fri, 23 Mar 2007)
New Revision: 3592

Modified:
   trunk/numpy/doc/swig/numpy.i
   trunk/numpy/doc/swig/numpy_swig.html
   trunk/numpy/doc/swig/numpy_swig.pdf
   trunk/numpy/doc/swig/numpy_swig.txt
Log:
Added '#ifdef SWIGPYTHON' protection

Modified: trunk/numpy/doc/swig/numpy.i
===================================================================
--- trunk/numpy/doc/swig/numpy.i	2007-03-23 12:08:06 UTC (rev 3591)
+++ trunk/numpy/doc/swig/numpy.i	2007-03-23 17:36:16 UTC (rev 3592)
@@ -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 @@
 /* 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 @@
 				 "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 @@
 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 @@
   }
   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 @@
 }
 
 /* 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 @@
                                              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 @@
 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 @@
 /* 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($input, DATA_TYPECODE, &is_new_object);
   npy_intp size[1] = {-1};
   if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail;
@@ -379,7 +384,7 @@
 /* Typemap suite for (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
  */
 %typemap(in) (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
-             (PyArrayObject* array=NULL, int is_new_object=0) {
+  (PyArrayObject* array=NULL, int is_new_object=0) {
   array = obj_to_array_contiguous_allow_conversion($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 @@
 
 /* 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;
   $1 = (DATA_TYPE*) temp->data;
@@ -462,7 +468,7 @@
 /* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1)
  */
 %typemap(in) (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1)
-             (PyArrayObject* array=NULL, int is_new_object=0) {
+  (PyArrayObject* array=NULL, int is_new_object=0) {
   array = obj_to_array_contiguous_allow_conversion($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 @@
 /* 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 @@
 
 /* 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 @@
  */
 /*%numpy_typemaps(complex long double, NPY_CLONGDOUBLE)
  */
+
+#endif /* SWIGPYTHON */

Modified: trunk/numpy/doc/swig/numpy_swig.html
===================================================================
--- trunk/numpy/doc/swig/numpy_swig.html	2007-03-23 12:08:06 UTC (rev 3591)
+++ trunk/numpy/doc/swig/numpy_swig.html	2007-03-23 17:36:16 UTC (rev 3592)
@@ -614,7 +614,8 @@
 <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 @@
 <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 @@
 <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 @@
 </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>

Modified: trunk/numpy/doc/swig/numpy_swig.pdf
===================================================================
--- trunk/numpy/doc/swig/numpy_swig.pdf	2007-03-23 12:08:06 UTC (rev 3591)
+++ trunk/numpy/doc/swig/numpy_swig.pdf	2007-03-23 17:36:16 UTC (rev 3592)
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 %%EOF

Modified: trunk/numpy/doc/swig/numpy_swig.txt
===================================================================
--- trunk/numpy/doc/swig/numpy_swig.txt	2007-03-23 12:08:06 UTC (rev 3591)
+++ trunk/numpy/doc/swig/numpy_swig.txt	2007-03-23 17:36:16 UTC (rev 3592)
@@ -304,7 +304,8 @@
 
   **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 @@
     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



From numpy-svn at scipy.org  Fri Mar 23 14:04:40 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Fri, 23 Mar 2007 13:04:40 -0500 (CDT)
Subject: [Numpy-svn] r3593 - trunk/numpy/doc/swig
Message-ID: <20070323180440.4759139C1DA@new.scipy.org>

Author: wfspotz at sandia.gov
Date: 2007-03-23 13:04:37 -0500 (Fri, 23 Mar 2007)
New Revision: 3593

Modified:
   trunk/numpy/doc/swig/numpy.i
Log:
Added typecheck typemaps to INPLACE_ARRAY typemap suites

Modified: trunk/numpy/doc/swig/numpy.i
===================================================================
--- trunk/numpy/doc/swig/numpy.i	2007-03-23 17:36:16 UTC (rev 3592)
+++ trunk/numpy/doc/swig/numpy.i	2007-03-23 18:04:37 UTC (rev 3593)
@@ -369,22 +369,28 @@
 
 /* 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) {
+%typemap(in)
+  (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1)
+  (PyArrayObject* array=NULL, int is_new_object=0)
+{
   array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object);
   npy_intp size[1] = {-1};
   if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail;
   $1 = (DATA_TYPE*) array->data;
   $2 = (DIM_TYPE) array->dimensions[0];
 }
-%typemap(freearg) (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1) {
+%typemap(freearg)
+  (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1)
+{
   if (is_new_object$argnum && array$argnum) Py_DECREF(array$argnum);
 }
 
 /* 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) {
+%typemap(in)
+  (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
+  (PyArrayObject* array=NULL, int is_new_object=0)
+{
   array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object);
   npy_intp size[2] = {-1,-1};
   if (!array || !require_dimensions(array, 2) || !require_size(array, size, 1)) SWIG_fail;
@@ -392,13 +398,23 @@
   $2 = (DIM_TYPE) array->dimensions[0];
   $3 = (DIM_TYPE) array->dimensions[1];
 }
-%typemap(freearg) (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2) {
+%typemap(freearg)
+  (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
+{
   if (is_new_object$argnum && array$argnum) Py_DECREF(array$argnum);
 }
 
 /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1)
  */
-%typemap(in) (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1) (PyArrayObject* temp=NULL) {
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
+  (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1)
+{
+  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),DATA_TYPECODE);
+}
+%typemap(in)
+  (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1)
+  (PyArrayObject* temp=NULL)
+{
   temp = obj_to_array_no_conversion($input, DATA_TYPECODE);
   if (!temp  || !require_contiguous(temp)) SWIG_fail;
   $1 = (DATA_TYPE*) temp->data;
@@ -408,8 +424,15 @@
 
 /* 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) {
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
+  (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
+{
+  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),DATA_TYPECODE);
+}
+%typemap(in)
+  (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
+  (PyArrayObject* temp=NULL)
+{
   temp = obj_to_array_no_conversion($input, DATA_TYPECODE);
   if (!temp || !require_contiguous(temp)) SWIG_fail;
   $1 = (DATA_TYPE*) temp->data;
@@ -419,14 +442,18 @@
 
 /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY1[ANY])
  */
-%typemap(in,numinputs=0) (DATA_TYPE ARGOUT_ARRAY1[ANY]) {
+%typemap(in,numinputs=0)
+  (DATA_TYPE ARGOUT_ARRAY1[ANY])
+{
   $1 = (DATA_TYPE*) malloc($1_dim0*sizeof(DATA_TYPE));
   if (!$1) {
     PyErr_SetString(PyExc_RuntimeError, "Failed to allocate memory");
     SWIG_fail;
   }
 }
-%typemap(argout) (DATA_TYPE ARGOUT_ARRAY1[ANY]) {
+%typemap(argout)
+  (DATA_TYPE ARGOUT_ARRAY1[ANY])
+{
   PyObject * obj = NULL;
   npy_intp dimensions[1] = { $1_dim0 };
   PyObject* outArray = PyArray_FromDimsAndData(1, dimensions, DATA_TYPECODE, (char*)$1);
@@ -443,14 +470,18 @@
 
 /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY])
  */
-%typemap(in,numinputs=0) (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY]) {
+%typemap(in,numinputs=0)
+  (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY])
+{
   $1 = (DATA_TYPE*) malloc($1_dim0 * $1_dim1 * sizeof(DATA_TYPE));
   if (!$1) {
     PyErr_SetString(PyExc_RuntimeError, "Failed to allocate memory");
     SWIG_fail;
   }
 }
-%typemap(argout) (DATA_TYPE ARGOUT_ARRAY1[ANY][ANY]) {
+%typemap(argout)
+  (DATA_TYPE ARGOUT_ARRAY1[ANY][ANY])
+{
   PyObject * obj = NULL;
   npy_intp dimensions[2] = { $1_dim0, $1_dim1 };
   PyObject* outArray = PyArray_FromDimsAndData(1, dimensions, DATA_TYPECODE, (char*)$1);
@@ -467,22 +498,28 @@
 
 /* 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) {
+%typemap(in)
+  (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1)
+  (PyArrayObject* array=NULL, int is_new_object=0)
+{
   array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object);
   npy_intp size[1] = {-1};
   if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail;
   $1 = (DIM_TYPE) array->dimensions[0];
   $2 = (DATA_TYPE*) array->data;
 }
-%typemap(freearg) (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1) {
+%typemap(freearg)
+  (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1)
+{
   if (is_new_object$argnum && array$argnum) Py_DECREF(array$argnum);
 }
 
 /* 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) {
+%typemap(in)
+  (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2)
+  (PyArrayObject* array=NULL, int is_new_object=0)
+{
   array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object);
   npy_intp size[2] = {-1,-1};
   if (!array || !require_dimensions(array, 2) || !require_size(array, size, 1)) SWIG_fail;
@@ -490,13 +527,23 @@
   $2 = (DIM_TYPE) array->dimensions[1];
   $3 = (DATA_TYPE*) array->data;
 }
-%typemap(freearg) (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2) {
+%typemap(freearg)
+  (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2)
+{
   if (is_new_object$argnum && array$argnum) Py_DECREF(array$argnum);
 }
 
 /* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1)
  */
-%typemap(in) (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1) (PyArrayObject* temp=NULL) {
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
+  (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1)
+{
+  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),DATA_TYPECODE);
+}
+%typemap(in)
+  (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1)
+  (PyArrayObject* temp=NULL)
+{
   temp = obj_to_array_no_conversion($input, DATA_TYPECODE);
   if (!temp  || !require_contiguous(temp)) SWIG_fail;
   $1 = 1;
@@ -506,8 +553,15 @@
 
 /* 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) {
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
+  (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2)
+{
+  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),DATA_TYPECODE);
+}
+%typemap(in)
+  (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2)
+  (PyArrayObject* temp=NULL)
+{
   temp = obj_to_array_no_conversion($input, DATA_TYPECODE);
   if (!temp || !require_contiguous(temp)) SWIG_fail;
   $1 = (DIM_TYPE) temp->dimensions[0];



From numpy-svn at scipy.org  Sat Mar 24 16:34:12 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Sat, 24 Mar 2007 15:34:12 -0500 (CDT)
Subject: [Numpy-svn] r3594 - trunk/numpy/doc/swig
Message-ID: <20070324203412.408F239C0AD@new.scipy.org>

Author: wfspotz at sandia.gov
Date: 2007-03-24 15:34:05 -0500 (Sat, 24 Mar 2007)
New Revision: 3594

Modified:
   trunk/numpy/doc/swig/Series.i
   trunk/numpy/doc/swig/numpy.i
   trunk/numpy/doc/swig/numpy_swig.html
   trunk/numpy/doc/swig/numpy_swig.pdf
   trunk/numpy/doc/swig/numpy_swig.txt
   trunk/numpy/doc/swig/series.cxx
   trunk/numpy/doc/swig/series.h
   trunk/numpy/doc/swig/testSeries.py
Log:
Added typemaps for signatures (TYPE IN_ARRAY1[ANY]) and (TYPE IN_ARRAY2[ANY][ANY])

Modified: trunk/numpy/doc/swig/Series.i
===================================================================
--- trunk/numpy/doc/swig/Series.i	2007-03-23 18:04:37 UTC (rev 3593)
+++ trunk/numpy/doc/swig/Series.i	2007-03-24 20:34:05 UTC (rev 3594)
@@ -15,13 +15,15 @@
 
 %define %apply_numpy_typemaps(TYPE)
 
+%apply (TYPE IN_ARRAY1[ANY]) {(TYPE vector[ANY])};
 %apply (TYPE* IN_ARRAY1, int DIM1) {(TYPE* series, int size)};
+%apply (int DIM1, TYPE* IN_ARRAY1) {(int size, TYPE* series)};
+%apply (TYPE IN_ARRAY2[ANY][ANY]) {(TYPE matrix[ANY][ANY])};
 %apply (TYPE* IN_ARRAY2, int DIM1, int DIM2) {(TYPE* matrix, int rows, int cols)};
+%apply (int DIM1, int DIM2, TYPE* IN_ARRAY2) {(int rows, int cols, TYPE* matrix)};
 %apply (TYPE* INPLACE_ARRAY1, int DIM1) {(TYPE* array, int size)};
+%apply (int DIM1, TYPE* INPLACE_ARRAY1) {(int size, TYPE* array)};
 %apply (TYPE* INPLACE_ARRAY2, int DIM1, int DIM2) {(TYPE* array, int rows, int cols)};
-%apply (int DIM1, TYPE* IN_ARRAY1) {(int size, TYPE* series)};
-%apply (int DIM1, int DIM2, TYPE* IN_ARRAY2) {(int rows, int cols, TYPE* matrix)};
-%apply (int DIM1, TYPE* INPLACE_ARRAY1) {(int size, TYPE* array)};
 %apply (int DIM1, int DIM2, TYPE* INPLACE_ARRAY2) {(int rows, int cols, TYPE* array)};
 
 %enddef    /* %apply_numpy_typemaps() macro */

Modified: trunk/numpy/doc/swig/numpy.i
===================================================================
--- trunk/numpy/doc/swig/numpy.i	2007-03-23 18:04:37 UTC (rev 3593)
+++ trunk/numpy/doc/swig/numpy.i	2007-03-24 20:34:05 UTC (rev 3594)
@@ -302,18 +302,26 @@
  * This macro defines a family of typemaps that allow pure input C
  * arguments of the form
  *
+ *     (DATA_TYPE IN_ARRAY1[ANY])
+ *
  *     (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1)
+ *     (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1)
+ *
+ *     (DATA_TYPE IN_ARRAY2[ANY][ANY])
+ *
  *     (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
+ *     (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2)
+ *
  *     (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1)
+ *     (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1)
+ *
  *     (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
- *     (DATA_TYPE* ARGOUT_ARRAY1[ANY])
- *     (DATA_TYPE* ARGOUT_ARRAY2[ANY][ANY])
+ *     (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2)
  *
- *     (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1)
- *     (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2)
- *     (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1)
- *     (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY1)
+ *     (DATA_TYPE ARGOUT_ARRAY1[ANY])
  *
+ *     (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY])
+ *
  * where "DATA_TYPE" is any type supported by the NumPy module.  In python,
  * the dimensions will not need to be specified.  The IN_ARRAYs can be
  * a numpy array or any sequence that can be converted to a numpy
@@ -324,49 +332,84 @@
  * These typemaps can be applied to existing functions using the
  * %apply directive:
  *
+ *     %apply (double IN_ARRAY1[ANY]) {(double vector[ANY])};
+ *     double length(double vector[3]);
+ *
  *     %apply (double* IN_ARRAY1, int DIM1) {(double* series, int length)};
  *     double prod(double* series, int length);
  *
- *     %apply (double* IN_ARRAY2, int DIM1, int DIM2) {(double* mx, int rows, int cols)};
- *     double max(double* mx, int rows, int cols);
+ *     %apply (int DIM1, double* IN_ARRAY1) {(int length, double* series)}
+ *     double sum(int length, double* series)
  *
+ *     %apply (double IN_ARRAY2[ANY][ANY]) {(double matrix[2][2])};
+ *     double det(double matrix[2][2]);
+ *
+ *     %apply (double* IN_ARRAY2, int DIM1, int DIM2) {(double* matrix, int rows, int cols)};
+ *     double max(double* matrix, int rows, int cols);
+ *
+ *     %apply (int DIM1, int DIM2, double* IN_ARRAY2) {(int rows, int cols, double* matrix)}
+ *     double min(int length, double* series)
+ *
  *     %apply (double* INPLACE_ARRAY1, int DIM1) {(double* series, int length)};
  *     void ones(double* series, int length);
  *
- *     %apply (double* INPLACE_ARRAY2, int DIM1, int DIM2) {(double* mx, int rows, int cols)};
- *     void floor(double* mx, int rows, int cols);
+ *     %apply (int DIM1, double* INPLACE_ARRAY1) {(int length, double* series)}
+ *     double zeros(int length, double* series)
  *
- *     %apply (double* ARGOUT_ARRAY1[ANY] {(double series, int length)};
+ *     %apply (double* INPLACE_ARRAY2, int DIM1, int DIM2) {(double* matrix, int rows, int cols)};
+ *     void floor(double* matrix, int rows, int cols);
+ *
+ *     %apply (int DIM1, int DIM2, double* INPLACE_ARRAY2) {(int rows, int cols, double* matrix)};
+ *     void ceil(int rows, int cols, double* matrix);
+ *
+ *     %apply (double ARGOUT_ARRAY1[ANY] {(double series[int length])};
  *     void negate(double* series, int length);
  *
- *     %apply (double* ARGOUT_ARRAY2[ANY][ANY]) {(double* mx, int rows, int cols)};
- *     void normalize(double* mx, int rows, int cols);
+ *     %apply (double ARGOUT_ARRAY2[ANY][ANY]) {(double matrix[int rows][int cols])};
+ *     void normalize(double* matrix, int rows, int cols);
  *
- *     %apply (int DIM1, double* IN_ARRAY1) {(int length, double* series)}
- *     double sum(int length, double* series)
+ * or directly with
  *
- *     %apply (int DIM1, int DIM2, double* IN_ARRAY2) {(int rows, int cols, double* matrix)}
- *     double min(int length, double* series)
+ *     double length(double IN_ARRAY[ANY]);
  *
- *     %apply (int DIM1, double* INPLACE_ARRAY1) {(int length, double* series)}
- *     double zeros(int length, double* series)
+ *     double prod(double* IN_ARRAY1, int DIM1);
+ *     double sum( int DIM1, double* IN_ARRAY1)
  *
- * or directly with
+ *     double det(double IN_ARRAY2[ANY][ANY]);
  *
- *     double prod(double* IN_ARRAY1, int DIM1);
  *     double max(double* IN_ARRAY2, int DIM1, int DIM2);
- *     void ones(double* INPLACE_ARRAY1, int DIM1);
- *     void floor(double* INPLACE_ARRAY2, int DIM1, int DIM2);
- *     void negate(double* ARGOUT_ARRAY1[ANY]);
- *     void normalize(double* ARGOUT_ARRAY2[ANY][ANY]);
+ *     double min(int DIM1, int DIM2, double* IN_ARRAY2)
  *
- *     double sum(int DIM1, double* IN_ARRAY1)
- *     double min(int DIM1, int DIM2, double* IN_ARRAY2)
+ *     void ones( double* INPLACE_ARRAY1, int DIM1);
  *     void zeros(int DIM1, double* INPLACE_ARRAY1)
+ *
+ *     void floor(double* INPLACE_ARRAY2, int DIM1, int DIM2, double floor);
+ *     void ceil( int DIM1, int DIM2, double* INPLACE_ARRAY2, double ceil );
+ *
+ *     void negate(double ARGOUT_ARRAY1[ANY]);
+ *
+ *     void normalize(double ARGOUT_ARRAY2[ANY][ANY]);
  */
 
 %define %numpy_typemaps(DATA_TYPE, DATA_TYPECODE, DIM_TYPE)
 
+/* Typemap suite for (DATA_TYPE IN_ARRAY1[ANY])
+ */
+%typemap(in)
+  (DATA_TYPE IN_ARRAY1[ANY])
+  (PyArrayObject* array=NULL, int is_new_object=0)
+{
+  array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object);
+  npy_intp size[1] = { $1_dim0 };
+  if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail;
+  $1 = ($1_ltype) array->data;
+}
+%typemap(freearg)
+  (DATA_TYPE IN_ARRAY1[ANY])
+{
+  if (is_new_object$argnum && array$argnum) Py_DECREF(array$argnum);
+}
+
 /* Typemap suite for (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1)
  */
 %typemap(in)
@@ -374,7 +417,7 @@
   (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};
+  npy_intp size[1] = { -1 };
   if (!array || !require_dimensions(array, 1) || !require_size(array, size, 1)) SWIG_fail;
   $1 = (DATA_TYPE*) array->data;
   $2 = (DIM_TYPE) array->dimensions[0];
@@ -385,6 +428,41 @@
   if (is_new_object$argnum && array$argnum) Py_DECREF(array$argnum);
 }
 
+/* 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)
+{
+  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;
+  $1 = (DIM_TYPE) array->dimensions[0];
+  $2 = (DATA_TYPE*) array->data;
+}
+%typemap(freearg)
+  (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1)
+{
+  if (is_new_object$argnum && array$argnum) Py_DECREF(array$argnum);
+}
+
+/* Typemap suite for (DATA_TYPE IN_ARRAY2[ANY][ANY])
+ */
+%typemap(in)
+  (DATA_TYPE IN_ARRAY2[ANY][ANY])
+  (PyArrayObject* array=NULL, int is_new_object=0)
+{
+  array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, &is_new_object);
+  npy_intp size[2] = { $1_dim0, $1_dim1 };
+  if (!array || !require_dimensions(array, 2) || !require_size(array, size, 2)) SWIG_fail;
+  $1 = ($1_ltype) array->data;
+}
+%typemap(freearg)
+  (DATA_TYPE IN_ARRAY2[ANY][ANY])
+{
+  if (is_new_object$argnum && array$argnum) Py_DECREF(array$argnum);
+}
+
 /* Typemap suite for (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
  */
 %typemap(in)
@@ -404,6 +482,25 @@
   if (is_new_object$argnum && array$argnum) Py_DECREF(array$argnum);
 }
 
+/* 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)
+{
+  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;
+  $1 = (DIM_TYPE) array->dimensions[0];
+  $2 = (DIM_TYPE) array->dimensions[1];
+  $3 = (DATA_TYPE*) array->data;
+}
+%typemap(freearg)
+  (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2)
+{
+  if (is_new_object$argnum && array$argnum) Py_DECREF(array$argnum);
+}
+
 /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1)
  */
 %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
@@ -422,6 +519,24 @@
   for (int i=0; i<temp->nd; ++i) $2 *= temp->dimensions[i];
 }
 
+/* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
+  (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1)
+{
+  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),DATA_TYPECODE);
+}
+%typemap(in)
+  (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1)
+  (PyArrayObject* temp=NULL)
+{
+  temp = obj_to_array_no_conversion($input, DATA_TYPECODE);
+  if (!temp  || !require_contiguous(temp)) SWIG_fail;
+  $1 = 1;
+  for (int i=0; i<temp->nd; ++i) $1 *= temp->dimensions[i];
+  $2 = (DATA_TYPE*) temp->data;
+}
+
 /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
  */
 %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
@@ -440,6 +555,24 @@
   $3 = (DIM_TYPE) temp->dimensions[1];
 }
 
+/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2)
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
+  (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2)
+{
+  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),DATA_TYPECODE);
+}
+%typemap(in)
+  (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2)
+  (PyArrayObject* temp=NULL)
+{
+  temp = obj_to_array_no_conversion($input, DATA_TYPECODE);
+  if (!temp || !require_contiguous(temp)) SWIG_fail;
+  $1 = (DIM_TYPE) temp->dimensions[0];
+  $2 = (DIM_TYPE) temp->dimensions[1];
+  $3 = (DATA_TYPE*) temp->data;
+}
+
 /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY1[ANY])
  */
 %typemap(in,numinputs=0)
@@ -496,79 +629,6 @@
   }
 }
 
-/* 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)
-{
-  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;
-  $1 = (DIM_TYPE) array->dimensions[0];
-  $2 = (DATA_TYPE*) array->data;
-}
-%typemap(freearg)
-  (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1)
-{
-  if (is_new_object$argnum && array$argnum) Py_DECREF(array$argnum);
-}
-
-/* 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)
-{
-  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;
-  $1 = (DIM_TYPE) array->dimensions[0];
-  $2 = (DIM_TYPE) array->dimensions[1];
-  $3 = (DATA_TYPE*) array->data;
-}
-%typemap(freearg)
-  (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2)
-{
-  if (is_new_object$argnum && array$argnum) Py_DECREF(array$argnum);
-}
-
-/* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
-  (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1)
-{
-  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),DATA_TYPECODE);
-}
-%typemap(in)
-  (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1)
-  (PyArrayObject* temp=NULL)
-{
-  temp = obj_to_array_no_conversion($input, DATA_TYPECODE);
-  if (!temp  || !require_contiguous(temp)) SWIG_fail;
-  $1 = 1;
-  for (int i=0; i<temp->nd; ++i) $1 *= temp->dimensions[i];
-  $2 = (DATA_TYPE*) temp->data;
-}
-
-/* Typemap suite for (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2)
- */
-%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
-  (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2)
-{
-  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),DATA_TYPECODE);
-}
-%typemap(in)
-  (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2)
-  (PyArrayObject* temp=NULL)
-{
-  temp = obj_to_array_no_conversion($input, DATA_TYPECODE);
-  if (!temp || !require_contiguous(temp)) SWIG_fail;
-  $1 = (DIM_TYPE) temp->dimensions[0];
-  $2 = (DIM_TYPE) temp->dimensions[1];
-  $3 = (DATA_TYPE*) temp->data;
-}
-
 %enddef    /* %numpy_typemaps() macro */
 
 

Modified: trunk/numpy/doc/swig/numpy_swig.html
===================================================================
--- trunk/numpy/doc/swig/numpy_swig.html	2007-03-23 18:04:37 UTC (rev 3593)
+++ trunk/numpy/doc/swig/numpy_swig.html	2007-03-24 20:34:05 UTC (rev 3594)
@@ -485,9 +485,11 @@
 of array.  The input array signatures are</p>
 <blockquote>
 <ul class="simple">
+<li><tt class="docutils literal"><span class="pre">(DATA_TYPE</span> <span class="pre">IN_ARRAY[ANY])</span></tt></li>
 <li><tt class="docutils literal"><span class="pre">(DATA_TYPE*</span> <span class="pre">IN_ARRAY1,</span> <span class="pre">int</span> <span class="pre">DIM1)</span></tt></li>
+<li><tt class="docutils literal"><span class="pre">(int</span> <span class="pre">DIM1,</span> <span class="pre">DATA_TYPE*</span> <span class="pre">IN_ARRAY1)</span></tt></li>
+<li><tt class="docutils literal"><span class="pre">(DATA_TYPE</span> <span class="pre">IN_ARRAY[ANY])</span></tt></li>
 <li><tt class="docutils literal"><span class="pre">(DATA_TYPE*</span> <span class="pre">IN_ARRAY2,</span> <span class="pre">int</span> <span class="pre">DIM1,</span> <span class="pre">int</span> <span class="pre">DIM2)</span></tt></li>
-<li><tt class="docutils literal"><span class="pre">(int</span> <span class="pre">DIM1,</span> <span class="pre">DATA_TYPE*</span> <span class="pre">IN_ARRAY1)</span></tt></li>
 <li><tt class="docutils literal"><span class="pre">(int</span> <span class="pre">DIM1,</span> <span class="pre">int</span> <span class="pre">DIM2,</span> <span class="pre">DATA_TYPE*</span> <span class="pre">IN_ARRAY2)</span></tt></li>
 </ul>
 </blockquote>
@@ -502,8 +504,8 @@
 <blockquote>
 <ul class="simple">
 <li><tt class="docutils literal"><span class="pre">(DATA_TYPE*</span> <span class="pre">INPLACE_ARRAY1,</span> <span class="pre">int</span> <span class="pre">DIM1)</span></tt></li>
+<li><tt class="docutils literal"><span class="pre">(int</span> <span class="pre">DIM1,</span> <span class="pre">DATA_TYPE*</span> <span class="pre">INPLACE_ARRAY1)</span></tt></li>
 <li><tt class="docutils literal"><span class="pre">(DATA_TYPE*</span> <span class="pre">INPLACE_ARRAY2,</span> <span class="pre">int</span> <span class="pre">DIM1,</span> <span class="pre">int</span> <span class="pre">DIM2)</span></tt></li>
-<li><tt class="docutils literal"><span class="pre">(int</span> <span class="pre">DIM1,</span> <span class="pre">DATA_TYPE*</span> <span class="pre">INPLACE_ARRAY1)</span></tt></li>
 <li><tt class="docutils literal"><span class="pre">(int</span> <span class="pre">DIM1,</span> <span class="pre">int</span> <span class="pre">DIM2,</span> <span class="pre">DATA_TYPE*</span> <span class="pre">INPLACE_ARRAY2)</span></tt></li>
 </ul>
 </blockquote>
@@ -708,7 +710,7 @@
 </div>
 <div class="footer">
 <hr class="footer" />
-Generated on: 2007-03-23 12:04 UTC.
+Generated on: 2007-03-24 20:29 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: trunk/numpy/doc/swig/numpy_swig.pdf
===================================================================
--- trunk/numpy/doc/swig/numpy_swig.pdf	2007-03-23 18:04:37 UTC (rev 3593)
+++ trunk/numpy/doc/swig/numpy_swig.pdf	2007-03-24 20:34:05 UTC (rev 3594)
@@ -479,17 +479,26 @@
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 trailer
 <<
 /Size 238
 /Root 236 0 R
 /Info 237 0 R
-/ID [<D956596DBB17048F6AF4523CB9CC3418> <D956596DBB17048F6AF4523CB9CC3418>]
+/ID [<B05210FFE40F81D6FA81BC0E256235F3> <B05210FFE40F81D6FA81BC0E256235F3>]
 >>
 startxref
-110484
+110440
 %%EOF

Modified: trunk/numpy/doc/swig/numpy_swig.txt
===================================================================
--- trunk/numpy/doc/swig/numpy_swig.txt	2007-03-23 18:04:37 UTC (rev 3593)
+++ trunk/numpy/doc/swig/numpy_swig.txt	2007-03-24 20:34:05 UTC (rev 3594)
@@ -174,9 +174,11 @@
 sequence (such as a list) that can be converted to the requested type
 of array.  The input array signatures are
 
+  * ``(DATA_TYPE IN_ARRAY[ANY])``
   * ``(DATA_TYPE* IN_ARRAY1, int DIM1)``
+  * ``(int DIM1, DATA_TYPE* IN_ARRAY1)``
+  * ``(DATA_TYPE IN_ARRAY[ANY])``
   * ``(DATA_TYPE* IN_ARRAY2, int DIM1, int DIM2)``
-  * ``(int DIM1, DATA_TYPE* IN_ARRAY1)``
   * ``(int DIM1, int DIM2, DATA_TYPE* IN_ARRAY2)``
 
 In-Place Arrays
@@ -189,8 +191,8 @@
 signatures are
 
   * ``(DATA_TYPE* INPLACE_ARRAY1, int DIM1)``
+  * ``(int DIM1, DATA_TYPE* INPLACE_ARRAY1)``
   * ``(DATA_TYPE* INPLACE_ARRAY2, int DIM1, int DIM2)``
-  * ``(int DIM1, DATA_TYPE* INPLACE_ARRAY1)``
   * ``(int DIM1, int DIM2, DATA_TYPE* INPLACE_ARRAY2)``
 
 Argout Arrays

Modified: trunk/numpy/doc/swig/series.cxx
===================================================================
--- trunk/numpy/doc/swig/series.cxx	2007-03-23 18:04:37 UTC (rev 3593)
+++ trunk/numpy/doc/swig/series.cxx	2007-03-24 20:34:05 UTC (rev 3594)
@@ -5,6 +5,7 @@
 
 // The following macro defines a family of functions with the forms
 //
+//     TYPE SNAMELength(TYPE vector[3]);
 //     TYPE SNAMEProd( TYPE * series, int size);
 //     void SNAMEOnes( TYPE * array,  int size);
 //     TYPE SNAMEMax(  TYPE * matrix, int rows, int cols);
@@ -20,27 +21,49 @@
 // TYPE/SNAME pairs.  The resulting functions are for testing numpy
 // interfaces, respectively, for:
 //
+//  * 1D input arrays, hard-coded length
 //  * 1D input arrays
+//  * 1D input arrays, data last
 //  * 1D in-place arrays
+//  * 1D in-place arrays, data last
+//  * 2D input arrays, hard-coded length
 //  * 2D input arrays
+//  * 2D input arrays, data last
 //  * 2D in-place arrays
-//  * 1D input arrays, data last
-//  * 1D in-place arrays, data last
-//  * 2D input arrays, data last
 //  * 2D in-place arrays, data last
 //
 #define TEST_FUNCS(TYPE, SNAME) \
 \
+TYPE SNAME ## Length(TYPE vector[3]) {                   \
+  double result = 0;                                     \
+  for (int i=0; i<3; ++i) result += vector[i]*vector[i]; \
+  return (TYPE)sqrt(result);   			         \
+}                                                        \
+\
 TYPE SNAME ## Prod(TYPE * series, int size) {     \
   TYPE result = 1;                                \
   for (int i=0; i<size; ++i) result *= series[i]; \
   return result;                                  \
 }                                                 \
 \
+TYPE SNAME ## Sum(int size, TYPE * series) {      \
+  TYPE result = 0;                                \
+  for (int i=0; i<size; ++i) result += series[i]; \
+  return result;                                  \
+}                                                 \
+\
 void SNAME ## Ones(TYPE * array, int size) { \
   for (int i=0; i<size; ++i) array[i] = 1;   \
 }                                            \
 \
+void SNAME ## Zeros(int size, TYPE * array) { \
+  for (int i=0; i<size; ++i) array[i] = 0;    \
+}                                             \
+\
+TYPE SNAME ## Det(TYPE matrix[2][2]) {                          \
+  return matrix[0][0]*matrix[1][1] - matrix[0][1]*matrix[1][0]; \
+}                                                               \
+\
 TYPE SNAME ## Max(TYPE * matrix, int rows, int cols) {	  \
   int i, j, index;                                        \
   TYPE result = matrix[0];                                \
@@ -53,26 +76,6 @@
   return result;                                          \
 }                                                         \
 \
-void SNAME ## Floor(TYPE * array, int rows, int cols, TYPE floor) { \
-  int i, j, index;                                                  \
-  for (j=0; j<cols; ++j) {                                          \
-    for (i=0; i<rows; ++i) {                                        \
-      index = j*rows + i;                                           \
-      if (array[index] < floor) array[index] = floor;               \
-    }                                                               \
-  }                                                                 \
-}                                                                   \
-\
-TYPE SNAME ## Sum(int size, TYPE * series) {      \
-  TYPE result = 0;                                \
-  for (int i=0; i<size; ++i) result += series[i]; \
-  return result;                                  \
-}                                                 \
-\
-void SNAME ## Zeros(int size, TYPE * array) { \
-  for (int i=0; i<size; ++i) array[i] = 0;    \
-}                                             \
-\
 TYPE SNAME ## Min(int rows, int cols, TYPE * matrix) {    \
   int i, j, index;                                        \
   TYPE result = matrix[0];                                \
@@ -85,6 +88,16 @@
   return result;                                          \
 }                                                         \
 \
+void SNAME ## Floor(TYPE * array, int rows, int cols, TYPE floor) { \
+  int i, j, index;                                                  \
+  for (j=0; j<cols; ++j) {                                          \
+    for (i=0; i<rows; ++i) {                                        \
+      index = j*rows + i;                                           \
+      if (array[index] < floor) array[index] = floor;               \
+    }                                                               \
+  }                                                                 \
+}                                                                   \
+\
 void SNAME ## Ceil(int rows, int cols, TYPE * array, TYPE ceil) { \
   int i, j, index;                                                \
   for (j=0; j<cols; ++j) {                                        \

Modified: trunk/numpy/doc/swig/series.h
===================================================================
--- trunk/numpy/doc/swig/series.h	2007-03-23 18:04:37 UTC (rev 3593)
+++ trunk/numpy/doc/swig/series.h	2007-03-24 20:34:05 UTC (rev 3594)
@@ -4,14 +4,16 @@
 // The following macro defines the prototypes for a family of
 // functions with the forms
 //
-//     TYPE SNAMEProd( TYPE * series, int size);
-//     void SNAMEOnes( TYPE * array,  int size);
-//     TYPE SNAMEMax(  TYPE * matrix, int rows, int cols);
-//     void SNAMEFloor(TYPE * array,  int rows, int cols, TYPE floor);
-//     TYPE SNAMESum(  int size, TYPE * series);
-//     void SNAMEZeros(int size, TYPE * array);
-//     TYPE SNAMEMin(  int rows, int cols, TYPE * matrix);
-//     void SNAMECeil( int rows, int cols, TYPE * array,  TYPE ceil);
+//     TYPE SNAMELength(TYPE vector[3]);
+//     TYPE SNAMEProd(  TYPE * series, int size);
+//     TYPE SNAMESum(   int size, TYPE * series);
+//     void SNAMEOnes(  TYPE * array,  int size);
+//     void SNAMEZeros( int size, TYPE * array);
+//     TYPE SNAMEDet(   TYPE matrix[2][2]);
+//     TYPE SNAMEMax(   TYPE * matrix, int rows, int cols);
+//     TYPE SNAMEMin(   int rows, int cols, TYPE * matrix);
+//     void SNAMEFloor( TYPE * array,  int rows, int cols, TYPE floor);
+//     void SNAMECeil(  int rows, int cols, TYPE * array,  TYPE ceil );
 //
 // for any specified type TYPE (for example: short, unsigned int, long
 // long, etc.) with given short name SNAME (for example: short, uint,
@@ -19,25 +21,29 @@
 // TYPE/SNAME pairs.  The resulting functions are for testing numpy
 // interfaces, respectively, for:
 //
+//  * 1D input arrays, hard-coded length
 //  * 1D input arrays
+//  * 1D input arrays, data last
 //  * 1D in-place arrays
+//  * 1D in-place arrays, data last
+//  * 2D input arrays, hard-coded lengths
 //  * 2D input arrays
+//  * 2D input arrays, data last
 //  * 2D in-place arrays
-//  * 1D input arrays, data last
-//  * 1D in-place arrays, data last
-//  * 2D input arrays, data last
 //  * 2D in-place arrays, data last
 //
 #define TEST_FUNC_PROTOS(TYPE, SNAME) \
 \
+TYPE SNAME ## Length(TYPE vector[3]); \
 TYPE SNAME ## Prod( TYPE * series, int size); \
+TYPE SNAME ## Sum(  int size, TYPE * series); \
 void SNAME ## Ones( TYPE * array,  int size); \
+void SNAME ## Zeros(int size, TYPE * array); \
+TYPE SNAME ## Det(  TYPE matrix[2][2]); \
 TYPE SNAME ## Max(  TYPE * matrix, int rows, int cols); \
-void SNAME ## Floor(TYPE * array,  int rows, int cols, TYPE floor); \
-TYPE SNAME ## Sum(  int size, TYPE * series); \
-void SNAME ## Zeros(int size, TYPE * array); \
 TYPE SNAME ## Min(  int rows, int cols, TYPE * matrix); \
-void SNAME ## Ceil( int rows, int cols, TYPE * array, TYPE ceil);
+void SNAME ## Floor(TYPE * array, int rows, int cols, TYPE floor); \
+void SNAME ## Ceil( int rows, int cols, TYPE * array, TYPE ceil );
 
 TEST_FUNC_PROTOS(signed char       , schar    )
 TEST_FUNC_PROTOS(unsigned char     , uchar    )

Modified: trunk/numpy/doc/swig/testSeries.py
===================================================================
--- trunk/numpy/doc/swig/testSeries.py	2007-03-23 18:04:37 UTC (rev 3593)
+++ trunk/numpy/doc/swig/testSeries.py	2007-03-24 20:34:05 UTC (rev 3594)
@@ -24,6 +24,14 @@
 
     ####################################################
     ### Test functions that take arrays of type BYTE ###
+    def testScharLength(self):
+        "Test scharLength function"
+        self.assertEquals(Series.scharLength([5, 12, 0]), 13)
+
+    def testScharLengthBad(self):
+        "Test scharLength function for wrong size"
+        self.assertRaises(TypeError, Series.scharLength, [5, 12])
+
     def testScharProd(self):
         "Test scharProd function"
         self.assertEquals(Series.scharProd([1,2,3,4]), 24)
@@ -32,12 +40,27 @@
         "Test scharProd function with None"
         self.assertRaises(TypeError, Series.scharProd, None)
 
+    def testScharSum(self):
+        "Test scharSum function"
+        self.assertEquals(Series.scharSum([-5,6,-7,8]), 2)
+
     def testScharOnes(self):
         "Test scharOnes function"
         myArray = N.zeros(5,'b')
         Series.scharOnes(myArray)
         N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
 
+    def testScharZeros(self):
+        "Test scharZeros function"
+        myArray = N.ones(5,'b')
+        Series.scharZeros(myArray)
+        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
+
+    def testScharDet(self):
+        "Test scharDet function"
+        matrix = [[6,7],[8,9]]
+        self.assertEquals(Series.scharDet(matrix), -2)
+
     def testScharMax(self):
         "Test scharMax function"
         matrix = [[-6,5,-4],[3,-2,1]]
@@ -51,27 +74,17 @@
         "Test scharMax function with a 1D array"
         self.assertRaises(TypeError, Series.scharMax, [0, -1, 2, -3])
 
+    def testScharMin(self):
+        "Test scharMin function"
+        matrix = [[9,8],[7,6],[5,4]]
+        self.assertEquals(Series.scharMin(matrix), 4)
+
     def testScharFloor(self):
         "Test scharFloor function"
         matrix = N.array([[10,-2],[-6,7]],'b')
         Series.scharFloor(matrix,0)
         N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
 
-    def testScharSum(self):
-        "Test scharSum function"
-        self.assertEquals(Series.scharSum([-5,6,-7,8]), 2)
-
-    def testScharZeros(self):
-        "Test scharZeros function"
-        myArray = N.ones(5,'b')
-        Series.scharZeros(myArray)
-        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
-
-    def testScharMin(self):
-        "Test scharMin function"
-        matrix = [[9,8],[7,6],[5,4]]
-        self.assertEquals(Series.scharMin(matrix), 4)
-
     def testScharCeil(self):
         "Test scharCeil function"
         matrix = N.array([[10,-2],[-6,7]],'b')
@@ -80,6 +93,14 @@
 
     #####################################################
     ### Test functions that take arrays of type UBYTE ###
+    def testUcharLength(self):
+        "Test ucharLength function"
+        self.assertEquals(Series.ucharLength([5, 12, 0]), 13)
+
+    def testUcharLengthBad(self):
+        "Test ucharLength function for wrong size"
+        self.assertRaises(TypeError, Series.ucharLength, [5, 12])
+
     def testUcharProd(self):
         "Test ucharProd function"
         self.assertEquals(Series.ucharProd([1,2,3,4]), 24)
@@ -88,12 +109,27 @@
         "Test ucharProd function with None"
         self.assertRaises(TypeError, Series.ucharProd, None)
 
+    def testUcharSum(self):
+        "Test ucharSum function"
+        self.assertEquals(Series.ucharSum([5,6,7,8]), 26)
+
     def testUcharOnes(self):
         "Test ucharOnes function"
         myArray = N.zeros(5,'B')
         Series.ucharOnes(myArray)
         N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
 
+    def testUcharZeros(self):
+        "Test ucharZeros function"
+        myArray = N.ones(5,'B')
+        Series.ucharZeros(myArray)
+        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
+
+    def testUcharDet(self):
+        "Test ucharDet function"
+        matrix = [[7,6],[9,8]]
+        self.assertEquals(Series.ucharDet(matrix), 2)
+
     def testUcharMax(self):
         "Test ucharMax function"
         matrix = [[6,5,4],[3,2,1]]
@@ -107,27 +143,17 @@
         "Test ucharMax function with a 1D array"
         self.assertRaises(TypeError, Series.ucharMax, [0, 1, 2, 3])
 
+    def testUcharMin(self):
+        "Test ucharMin function"
+        matrix = [[9,8],[7,6],[5,4]]
+        self.assertEquals(Series.ucharMin(matrix), 4)
+
     def testUcharFloor(self):
         "Test ucharFloor function"
         matrix = N.array([[10,2],[6,7]],'B')
         Series.ucharFloor(matrix,7)
         N.testing.assert_array_equal(matrix, N.array([[10,7],[7,7]]))
 
-    def testUcharSum(self):
-        "Test ucharSum function"
-        self.assertEquals(Series.ucharSum([5,6,7,8]), 26)
-
-    def testUcharZeros(self):
-        "Test ucharZeros function"
-        myArray = N.ones(5,'B')
-        Series.ucharZeros(myArray)
-        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
-
-    def testUcharMin(self):
-        "Test ucharMin function"
-        matrix = [[9,8],[7,6],[5,4]]
-        self.assertEquals(Series.ucharMin(matrix), 4)
-
     def testUcharCeil(self):
         "Test ucharCeil function"
         matrix = N.array([[10,2],[6,7]],'B')
@@ -136,6 +162,14 @@
 
     #####################################################
     ### Test functions that take arrays of type SHORT ###
+    def testShortLength(self):
+        "Test shortLength function"
+        self.assertEquals(Series.shortLength([5, 12, 0]), 13)
+
+    def testShortLengthBad(self):
+        "Test shortLength function for wrong size"
+        self.assertRaises(TypeError, Series.shortLength, [5, 12])
+
     def testShortProd(self):
         "Test shortProd function"
         self.assertEquals(Series.shortProd([1,2,3,4]), 24)
@@ -144,12 +178,27 @@
         "Test shortProd function with None"
         self.assertRaises(TypeError, Series.shortProd, None)
 
+    def testShortSum(self):
+        "Test shortSum function"
+        self.assertEquals(Series.shortSum([-5,6,-7,8]), 2)
+
     def testShortOnes(self):
         "Test shortOnes function"
         myArray = N.zeros(5,'h')
         Series.shortOnes(myArray)
         N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
 
+    def testShortZeros(self):
+        "Test shortZeros function"
+        myArray = N.ones(5,'h')
+        Series.shortZeros(myArray)
+        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
+
+    def testShortDet(self):
+        "Test shortDet function"
+        matrix = [[6,7],[8,9]]
+        self.assertEquals(Series.shortDet(matrix), -2)
+
     def testShortMax(self):
         "Test shortMax function"
         matrix = [[-6,5,-4],[3,-2,1]]
@@ -163,27 +212,17 @@
         "Test shortMax function with a 1D array"
         self.assertRaises(TypeError, Series.shortMax, [0, -1, 2, -3])
 
+    def testShortMin(self):
+        "Test shortMin function"
+        matrix = [[9,8],[7,6],[5,4]]
+        self.assertEquals(Series.shortMin(matrix), 4)
+
     def testShortFloor(self):
         "Test shortFloor function"
         matrix = N.array([[10,-2],[-6,7]],'h')
         Series.shortFloor(matrix,0)
         N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
 
-    def testShortSum(self):
-        "Test shortSum function"
-        self.assertEquals(Series.shortSum([-5,6,-7,8]), 2)
-
-    def testShortZeros(self):
-        "Test shortZeros function"
-        myArray = N.ones(5,'h')
-        Series.shortZeros(myArray)
-        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
-
-    def testShortMin(self):
-        "Test shortMin function"
-        matrix = [[9,8],[7,6],[5,4]]
-        self.assertEquals(Series.shortMin(matrix), 4)
-
     def testShortCeil(self):
         "Test shortCeil function"
         matrix = N.array([[10,-2],[-6,7]],'h')
@@ -192,6 +231,14 @@
 
     ######################################################
     ### Test functions that take arrays of type USHORT ###
+    def testUshortLength(self):
+        "Test ushortLength function"
+        self.assertEquals(Series.ushortLength([5, 12, 0]), 13)
+
+    def testUshortLengthBad(self):
+        "Test ushortLength function for wrong size"
+        self.assertRaises(TypeError, Series.ushortLength, [5, 12])
+
     def testUshortProd(self):
         "Test ushortProd function"
         self.assertEquals(Series.ushortProd([1,2,3,4]), 24)
@@ -200,12 +247,27 @@
         "Test ushortProd function with None"
         self.assertRaises(TypeError, Series.ushortProd, None)
 
+    def testUshortSum(self):
+        "Test ushortSum function"
+        self.assertEquals(Series.ushortSum([5,6,7,8]), 26)
+
     def testUshortOnes(self):
         "Test ushortOnes function"
         myArray = N.zeros(5,'H')
         Series.ushortOnes(myArray)
         N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
 
+    def testUshortZeros(self):
+        "Test ushortZeros function"
+        myArray = N.ones(5,'H')
+        Series.ushortZeros(myArray)
+        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
+
+    def testUshortDet(self):
+        "Test ushortDet function"
+        matrix = [[7,6],[9,8]]
+        self.assertEquals(Series.ushortDet(matrix), 2)
+
     def testUshortMax(self):
         "Test ushortMax function"
         matrix = [[6,5,4],[3,2,1]]
@@ -219,27 +281,17 @@
         "Test ushortMax function with a 1D array"
         self.assertRaises(TypeError, Series.ushortMax, [0, 1, 2, 3])
 
+    def testUshortMin(self):
+        "Test ushortMin function"
+        matrix = [[9,8],[7,6],[5,4]]
+        self.assertEquals(Series.ushortMin(matrix), 4)
+
     def testUshortFloor(self):
         "Test ushortFloor function"
         matrix = N.array([[10,2],[6,7]],'H')
         Series.ushortFloor(matrix,7)
         N.testing.assert_array_equal(matrix, N.array([[10,7],[7,7]]))
 
-    def testUshortSum(self):
-        "Test ushortSum function"
-        self.assertEquals(Series.ushortSum([5,6,7,8]), 26)
-
-    def testUshortZeros(self):
-        "Test ushortZeros function"
-        myArray = N.ones(5,'H')
-        Series.ushortZeros(myArray)
-        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
-
-    def testUshortMin(self):
-        "Test ushortMin function"
-        matrix = [[9,8],[7,6],[5,4]]
-        self.assertEquals(Series.ushortMin(matrix), 4)
-
     def testUshortCeil(self):
         "Test ushortCeil function"
         matrix = N.array([[10,2],[6,7]],'H')
@@ -248,6 +300,14 @@
 
     ###################################################
     ### Test functions that take arrays of type INT ###
+    def testIntLength(self):
+        "Test intLength function"
+        self.assertEquals(Series.intLength([5, 12, 0]), 13)
+
+    def testIntLengthBad(self):
+        "Test intLength function for wrong size"
+        self.assertRaises(TypeError, Series.intLength, [5, 12])
+
     def testIntProd(self):
         "Test intProd function"
         self.assertEquals(Series.intProd([1,2,3,4]), 24)
@@ -256,12 +316,27 @@
         "Test intProd function with None"
         self.assertRaises(TypeError, Series.intProd, None)
 
+    def testIntSum(self):
+        "Test intSum function"
+        self.assertEquals(Series.intSum([-5,6,-7,8]), 2)
+
     def testIntOnes(self):
         "Test intOnes function"
         myArray = N.zeros(5,'i')
         Series.intOnes(myArray)
         N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
 
+    def testIntZeros(self):
+        "Test intZeros function"
+        myArray = N.ones(5,'i')
+        Series.intZeros(myArray)
+        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
+
+    def testIntDet(self):
+        "Test intDet function"
+        matrix = [[6,7],[8,9]]
+        self.assertEquals(Series.intDet(matrix), -2)
+
     def testIntMax(self):
         "Test intMax function"
         matrix = [[-6,5,-4],[3,-2,1]]
@@ -275,27 +350,17 @@
         "Test intMax function with a 1D array"
         self.assertRaises(TypeError, Series.intMax, [0, -1, 2, -3])
 
+    def testIntMin(self):
+        "Test intMin function"
+        matrix = [[9,8],[7,6],[5,4]]
+        self.assertEquals(Series.intMin(matrix), 4)
+
     def testIntFloor(self):
         "Test intFloor function"
         matrix = N.array([[10,-2],[-6,7]],'i')
         Series.intFloor(matrix,0)
         N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
 
-    def testIntSum(self):
-        "Test intSum function"
-        self.assertEquals(Series.intSum([-5,6,-7,8]), 2)
-
-    def testIntZeros(self):
-        "Test intZeros function"
-        myArray = N.ones(5,'i')
-        Series.intZeros(myArray)
-        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
-
-    def testIntMin(self):
-        "Test intMin function"
-        matrix = [[9,8],[7,6],[5,4]]
-        self.assertEquals(Series.intMin(matrix), 4)
-
     def testIntCeil(self):
         "Test intCeil function"
         matrix = N.array([[10,-2],[-6,7]],'i')
@@ -304,6 +369,15 @@
 
     ####################################################
     ### Test functions that take arrays of type UINT ###
+    def testUintLength(self):
+        "Test uintLength function"
+        self.assertEquals(Series.uintLength([5, 12, 0]), 13)
+
+    def testUintLengthBad(self):
+        "Test uintLength function for wrong size"
+
+        self.assertRaises(TypeError, Series.uintLength, [5, 12])
+
     def testUintProd(self):
         "Test uintProd function"
         self.assertEquals(Series.uintProd([1,2,3,4]), 24)
@@ -312,12 +386,27 @@
         "Test uintProd function with None"
         self.assertRaises(TypeError, Series.uintProd, None)
 
+    def testUintSum(self):
+        "Test uintSum function"
+        self.assertEquals(Series.uintSum([5,6,7,8]), 26)
+
     def testUintOnes(self):
         "Test uintOnes function"
         myArray = N.zeros(5,'I')
         Series.uintOnes(myArray)
         N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
 
+    def testUintZeros(self):
+        "Test uintZeros function"
+        myArray = N.ones(5,'I')
+        Series.uintZeros(myArray)
+        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
+
+    def testUintDet(self):
+        "Test uintDet function"
+        matrix = [[7,6],[9,8]]
+        self.assertEquals(Series.uintDet(matrix), 2)
+
     def testUintMax(self):
         "Test uintMax function"
         matrix = [[6,5,4],[3,2,1]]
@@ -331,27 +420,17 @@
         "Test uintMax function with a 1D array"
         self.assertRaises(TypeError, Series.uintMax, [0, 1, 2, 3])
 
+    def testUintMin(self):
+        "Test uintMin function"
+        matrix = [[9,8],[7,6],[5,4]]
+        self.assertEquals(Series.uintMin(matrix), 4)
+
     def testUintFloor(self):
         "Test uintFloor function"
         matrix = N.array([[10,2],[6,7]],'I')
         Series.uintFloor(matrix,7)
         N.testing.assert_array_equal(matrix, N.array([[10,7],[7,7]]))
 
-    def testUintSum(self):
-        "Test uintSum function"
-        self.assertEquals(Series.uintSum([5,6,7,8]), 26)
-
-    def testUintZeros(self):
-        "Test uintZeros function"
-        myArray = N.ones(5,'I')
-        Series.uintZeros(myArray)
-        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
-
-    def testUintMin(self):
-        "Test uintMin function"
-        matrix = [[9,8],[7,6],[5,4]]
-        self.assertEquals(Series.uintMin(matrix), 4)
-
     def testUintCeil(self):
         "Test uintCeil function"
         matrix = N.array([[10,2],[6,7]],'I')
@@ -360,6 +439,14 @@
 
     ####################################################
     ### Test functions that take arrays of type LONG ###
+    def testLongLength(self):
+        "Test longLength function"
+        self.assertEquals(Series.longLength([5, 12, 0]), 13)
+
+    def testLongLengthBad(self):
+        "Test longLength function for wrong size"
+        self.assertRaises(TypeError, Series.longLength, [5, 12])
+
     def testLongProd(self):
         "Test longProd function"
         self.assertEquals(Series.longProd([1,2,3,4]), 24)
@@ -368,12 +455,27 @@
         "Test longProd function with None"
         self.assertRaises(TypeError, Series.longProd, None)
 
+    def testLongSum(self):
+        "Test longSum function"
+        self.assertEquals(Series.longSum([-5,6,-7,8]), 2)
+
     def testLongOnes(self):
         "Test longOnes function"
         myArray = N.zeros(5,'l')
         Series.longOnes(myArray)
         N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
 
+    def testLongZeros(self):
+        "Test longZeros function"
+        myArray = N.ones(5,'l')
+        Series.longZeros(myArray)
+        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
+
+    def testLongDet(self):
+        "Test longDet function"
+        matrix = [[6,7],[8,9]]
+        self.assertEquals(Series.longDet(matrix), -2)
+
     def testLongMax(self):
         "Test longMax function"
         matrix = [[-6,5,-4],[3,-2,1]]
@@ -387,27 +489,17 @@
         "Test longMax function with a 1D array"
         self.assertRaises(TypeError, Series.longMax, [0, -1, 2, -3])
 
+    def testLongMin(self):
+        "Test longMin function"
+        matrix = [[9,8],[7,6],[5,4]]
+        self.assertEquals(Series.longMin(matrix), 4)
+
     def testLongFloor(self):
         "Test longFloor function"
         matrix = N.array([[10,-2],[-6,7]],'l')
         Series.longFloor(matrix,0)
         N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
 
-    def testLongSum(self):
-        "Test longSum function"
-        self.assertEquals(Series.longSum([-5,6,-7,8]), 2)
-
-    def testLongZeros(self):
-        "Test longZeros function"
-        myArray = N.ones(5,'l')
-        Series.longZeros(myArray)
-        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
-
-    def testLongMin(self):
-        "Test longMin function"
-        matrix = [[9,8],[7,6],[5,4]]
-        self.assertEquals(Series.longMin(matrix), 4)
-
     def testLongCeil(self):
         "Test longCeil function"
         matrix = N.array([[10,-2],[-6,7]],'l')
@@ -416,6 +508,14 @@
 
     #####################################################
     ### Test functions that take arrays of type ULONG ###
+    def testUlongLength(self):
+        "Test ulongLength function"
+        self.assertEquals(Series.ulongLength([5, 12, 0]), 13)
+
+    def testUlongLengthBad(self):
+        "Test ulongLength function for wrong size"
+        self.assertRaises(TypeError, Series.ulongLength, [5, 12])
+
     def testUlongProd(self):
         "Test ulongProd function"
         self.assertEquals(Series.ulongProd([1,2,3,4]), 24)
@@ -424,12 +524,27 @@
         "Test ulongProd function with None"
         self.assertRaises(TypeError, Series.ulongProd, None)
 
+    def testUlongSum(self):
+        "Test ulongSum function"
+        self.assertEquals(Series.ulongSum([5,6,7,8]), 26)
+
     def testUlongOnes(self):
         "Test ulongOnes function"
         myArray = N.zeros(5,'L')
         Series.ulongOnes(myArray)
         N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
 
+    def testUlongZeros(self):
+        "Test ulongZeros function"
+        myArray = N.ones(5,'L')
+        Series.ulongZeros(myArray)
+        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
+
+    def testUlongDet(self):
+        "Test ulongDet function"
+        matrix = [[7,6],[9,8]]
+        self.assertEquals(Series.ulongDet(matrix), 2)
+
     def testUlongMax(self):
         "Test ulongMax function"
         matrix = [[6,5,4],[3,2,1]]
@@ -443,27 +558,17 @@
         "Test ulongMax function with a 1D array"
         self.assertRaises(TypeError, Series.ulongMax, [0, 1, 2, 3])
 
+    def testUlongMin(self):
+        "Test ulongMin function"
+        matrix = [[9,8],[7,6],[5,4]]
+        self.assertEquals(Series.ulongMin(matrix), 4)
+
     def testUlongFloor(self):
         "Test ulongFloor function"
         matrix = N.array([[10,2],[6,7]],'L')
         Series.ulongFloor(matrix,7)
         N.testing.assert_array_equal(matrix, N.array([[10,7],[7,7]]))
 
-    def testUlongSum(self):
-        "Test ulongSum function"
-        self.assertEquals(Series.ulongSum([5,6,7,8]), 26)
-
-    def testUlongZeros(self):
-        "Test ulongZeros function"
-        myArray = N.ones(5,'L')
-        Series.ulongZeros(myArray)
-        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
-
-    def testUlongMin(self):
-        "Test ulongMin function"
-        matrix = [[9,8],[7,6],[5,4]]
-        self.assertEquals(Series.ulongMin(matrix), 4)
-
     def testUlongCeil(self):
         "Test ulongCeil function"
         matrix = N.array([[10,2],[6,7]],'L')
@@ -472,6 +577,14 @@
 
     ########################################################
     ### Test functions that take arrays of type LONGLONG ###
+    def testLongLongLength(self):
+        "Test longLongLength function"
+        self.assertEquals(Series.longLongLength([5, 12, 0]), 13)
+
+    def testLongLongLengthBad(self):
+        "Test longLongLength function for wrong size"
+        self.assertRaises(TypeError, Series.longLongLength, [5, 12])
+
     def testLongLongProd(self):
         "Test longLongProd function"
         self.assertEquals(Series.longLongProd([1,2,3,4]), 24)
@@ -480,12 +593,27 @@
         "Test longLongProd function with None"
         self.assertRaises(TypeError, Series.longLongProd, None)
 
+    def testLongLongSum(self):
+        "Test longLongSum function"
+        self.assertEquals(Series.longLongSum([-5,6,-7,8]), 2)
+
     def testLongLongOnes(self):
         "Test longLongOnes function"
         myArray = N.zeros(5,'q')
         Series.longLongOnes(myArray)
         N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
 
+    def testLongLongZeros(self):
+        "Test longLongZeros function"
+        myArray = N.ones(5,'q')
+        Series.longLongZeros(myArray)
+        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
+
+    def testLongLongDet(self):
+        "Test longLongDet function"
+        matrix = [[6,7],[8,9]]
+        self.assertEquals(Series.longLongDet(matrix), -2)
+
     def testLongLongMax(self):
         "Test longLongMax function"
         matrix = [[-6,5,-4],[3,-2,1]]
@@ -499,27 +627,17 @@
         "Test longLongMax function with a 1D array"
         self.assertRaises(TypeError, Series.longLongMax, [0, -1, 2, -3])
 
+    def testLongLongMin(self):
+        "Test longLongMin function"
+        matrix = [[9,8],[7,6],[5,4]]
+        self.assertEquals(Series.longLongMin(matrix), 4)
+
     def testLongLongFloor(self):
         "Test longLongFloor function"
         matrix = N.array([[10,-2],[-6,7]],'q')
         Series.longLongFloor(matrix,0)
         N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
 
-    def testLongLongSum(self):
-        "Test longLongSum function"
-        self.assertEquals(Series.longLongSum([-5,6,-7,8]), 2)
-
-    def testLongLongZeros(self):
-        "Test longLongZeros function"
-        myArray = N.ones(5,'q')
-        Series.longLongZeros(myArray)
-        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
-
-    def testLongLongMin(self):
-        "Test longLongMin function"
-        matrix = [[9,8],[7,6],[5,4]]
-        self.assertEquals(Series.longLongMin(matrix), 4)
-
     def testLongLongCeil(self):
         "Test longLongCeil function"
         matrix = N.array([[10,-2],[-6,7]],'q')
@@ -528,6 +646,14 @@
 
     #########################################################
     ### Test functions that take arrays of type ULONGLONG ###
+    def testUlongLongLength(self):
+        "Test ulongLongLength function"
+        self.assertEquals(Series.ulongLongLength([5, 12, 0]), 13)
+
+    def testUlongLongLengthBad(self):
+        "Test ulongLongLength function for wrong size"
+        self.assertRaises(TypeError, Series.ulongLongLength, [5, 12])
+
     def testUlonglongProd(self):
         "Test ulongLongProd function"
         self.assertEquals(Series.ulongLongProd([1,2,3,4]), 24)
@@ -536,12 +662,27 @@
         "Test ulongLongProd function with None"
         self.assertRaises(TypeError, Series.ulongLongProd, None)
 
+    def testUlongLongSum(self):
+        "Test ulongLongSum function"
+        self.assertEquals(Series.ulongLongSum([5,6,7,8]), 26)
+
     def testUlongLongOnes(self):
         "Test ulongLongOnes function"
         myArray = N.zeros(5,'Q')
         Series.ulongLongOnes(myArray)
         N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
 
+    def testUlongLongZeros(self):
+        "Test ulongLongZeros function"
+        myArray = N.ones(5,'Q')
+        Series.ulongLongZeros(myArray)
+        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
+
+    def testUlongLongDet(self):
+        "Test ulongLongDet function"
+        matrix = [[7,6],[9,8]]
+        self.assertEquals(Series.ulongLongDet(matrix), 2)
+
     def testUlongLongMax(self):
         "Test ulongLongMax function"
         matrix = [[6,5,4],[3,2,1]]
@@ -555,27 +696,17 @@
         "Test ulongLongMax function with a 1D array"
         self.assertRaises(TypeError, Series.ulongLongMax, [0, 1, 2, 3])
 
+    def testUlongLongMin(self):
+        "Test ulongLongMin function"
+        matrix = [[9,8],[7,6],[5,4]]
+        self.assertEquals(Series.ulongLongMin(matrix), 4)
+
     def testUlongLongFloor(self):
         "Test ulongLongFloor function"
         matrix = N.array([[10,2],[6,7]],'Q')
         Series.ulongLongFloor(matrix,7)
         N.testing.assert_array_equal(matrix, N.array([[10,7],[7,7]]))
 
-    def testUlongLongSum(self):
-        "Test ulongLongSum function"
-        self.assertEquals(Series.ulongLongSum([5,6,7,8]), 26)
-
-    def testUlongLongZeros(self):
-        "Test ulongLongZeros function"
-        myArray = N.ones(5,'Q')
-        Series.ulongLongZeros(myArray)
-        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
-
-    def testUlongLongMin(self):
-        "Test ulongLongMin function"
-        matrix = [[9,8],[7,6],[5,4]]
-        self.assertEquals(Series.ulongLongMin(matrix), 4)
-
     def testUlongLongCeil(self):
         "Test ulongLongCeil function"
         matrix = N.array([[10,2],[6,7]],'Q')
@@ -584,6 +715,14 @@
 
     #####################################################
     ### Test functions that take arrays of type FLOAT ###
+    def testFloatLength(self):
+        "Test floatLength function"
+        self.assertEquals(Series.floatLength([5, 12, 0]), 13)
+
+    def testFloatLengthBad(self):
+        "Test floatLength function for wrong size"
+        self.assertRaises(TypeError, Series.floatLength, [5, 12])
+
     def testFloatProd(self):
         "Test floatProd function (to 5 decimal places)"
         self.assertAlmostEquals(Series.floatProd((1,2.718,3,4)), 32.616, 5)
@@ -592,6 +731,10 @@
         "Test floatProd function with an invalid list"
         self.assertRaises(BadListError, Series.floatProd, [3.14, "pi"])
 
+    def testFloatSum(self):
+        "Test floatSum function"
+        self.assertEquals(Series.floatSum([-5,6,-7,8]), 2)
+
     def testFloatOnes(self):
         "Test floatOnes function"
         myArray = N.zeros(5,'f')
@@ -602,6 +745,17 @@
         "Test floatOnes function with a list"
         self.assertRaises(TypeError, Series.floatOnes, [True, 0, 2.718, "pi"])
 
+    def testFloatDet(self):
+        "Test floatDet function"
+        matrix = [[6,7],[8,9]]
+        self.assertEquals(Series.floatDet(matrix), -2)
+
+    def testFloatZeros(self):
+        "Test floatZeros function"
+        myArray = N.ones(5,'f')
+        Series.floatZeros(myArray)
+        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
+
     def testFloatMax(self):
         "Test floatMax function"
         matrix = [[-6,5,-4],[3.14,-2.718,1]]
@@ -615,27 +769,17 @@
         "Test floatMax function with a 1D array"
         self.assertRaises(TypeError, Series.floatMax, [0.0, -1, 2.718, -3.14])
 
+    def testFloatMin(self):
+        "Test floatMin function"
+        matrix = [[9,8],[7,6],[5,4]]
+        self.assertEquals(Series.floatMin(matrix), 4)
+
     def testFloatFloor(self):
         "Test floatFloor function"
         matrix = N.array([[10,-2],[-6,7]],'f')
         Series.floatFloor(matrix,0)
         N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
 
-    def testFloatSum(self):
-        "Test floatSum function"
-        self.assertEquals(Series.floatSum([-5,6,-7,8]), 2)
-
-    def testFloatZeros(self):
-        "Test floatZeros function"
-        myArray = N.ones(5,'f')
-        Series.floatZeros(myArray)
-        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
-
-    def testFloatMin(self):
-        "Test floatMin function"
-        matrix = [[9,8],[7,6],[5,4]]
-        self.assertEquals(Series.floatMin(matrix), 4)
-
     def testFloatCeil(self):
         "Test floatCeil function"
         matrix = N.array([[10,-2],[-6,7]],'f')
@@ -644,6 +788,14 @@
 
     ######################################################
     ### Test functions that take arrays of type DOUBLE ###
+    def testDoubleLength(self):
+        "Test doubleLength function"
+        self.assertEquals(Series.doubleLength([5, 12, 0]), 13)
+
+    def testDoubleLengthBad(self):
+        "Test doubleLength function for wrong size"
+        self.assertRaises(TypeError, Series.doubleLength, [5, 12])
+
     def testDoubleProd(self):
         "Test doubleProd function"
         self.assertEquals(Series.doubleProd((1,2.718,3,4)), 32.616)
@@ -652,6 +804,10 @@
         "Test doubleProd function with an invalid list"
         self.assertRaises(BadListError, Series.doubleProd, [3.14, "pi"])
 
+    def testDoubleSum(self):
+        "Test doubleSum function"
+        self.assertEquals(Series.doubleSum([-5,6,-7,8]), 2)
+
     def testDoubleOnes(self):
         "Test doubleOnes function"
         myArray = N.zeros(5,'d')
@@ -662,6 +818,17 @@
         "Test doubleOnes function with a list"
         self.assertRaises(TypeError, Series.doubleOnes, [True, 0, 2.718, "pi"])
 
+    def testDoubleZeros(self):
+        "Test doubleZeros function"
+        myArray = N.ones(5,'d')
+        Series.doubleZeros(myArray)
+        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
+
+    def testDoubleDet(self):
+        "Test doubleDet function"
+        matrix = [[6,7],[8,9]]
+        self.assertEquals(Series.doubleDet(matrix), -2)
+
     def testDoubleMax(self):
         "Test doubleMax function"
         matrix = [[-6,5,-4],[3.14,-2.718,1]]
@@ -675,27 +842,17 @@
         "Test doubleMax function with a 1D array"
         self.assertRaises(TypeError, Series.doubleMax, [0.0, -1, 2.718, -3.14])
 
+    def testDoubleMin(self):
+        "Test doubleMin function"
+        matrix = [[9,8],[7,6],[5,4]]
+        self.assertEquals(Series.doubleMin(matrix), 4)
+
     def testDoubleFloor(self):
         "Test doubleFloor function"
         matrix = N.array([[10,-2],[-6,7]],'d')
         Series.doubleFloor(matrix,0)
         N.testing.assert_array_equal(matrix, N.array([[10,0],[0,7]]))
 
-    def testDoubleSum(self):
-        "Test doubleSum function"
-        self.assertEquals(Series.doubleSum([-5,6,-7,8]), 2)
-
-    def testDoubleZeros(self):
-        "Test doubleZeros function"
-        myArray = N.ones(5,'d')
-        Series.doubleZeros(myArray)
-        N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
-
-    def testDoubleMin(self):
-        "Test doubleMin function"
-        matrix = [[9,8],[7,6],[5,4]]
-        self.assertEquals(Series.doubleMin(matrix), 4)
-
     def testDoubleCeil(self):
         "Test doubleCeil function"
         matrix = N.array([[10,-2],[-6,7]],'d')



From numpy-svn at scipy.org  Sat Mar 24 19:14:10 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Sat, 24 Mar 2007 18:14:10 -0500 (CDT)
Subject: [Numpy-svn] r3595 - trunk/numpy/core
Message-ID: <20070324231410.BA85139C0AE@new.scipy.org>

Author: charris
Date: 2007-03-24 18:14:07 -0500 (Sat, 24 Mar 2007)
New Revision: 3595

Modified:
   trunk/numpy/core/defmatrix.py
Log:
Fix matrix multiply to return error when has is a 1-D vector. Fixes ticket #473

Modified: trunk/numpy/core/defmatrix.py
===================================================================
--- trunk/numpy/core/defmatrix.py	2007-03-24 20:34:05 UTC (rev 3594)
+++ trunk/numpy/core/defmatrix.py	2007-03-24 23:14:07 UTC (rev 3595)
@@ -154,11 +154,12 @@
 
 
     def __mul__(self, other):
-        if isinstance(other, N.ndarray) or N.isscalar(other) or \
-               not hasattr(other, '__rmul__'):
-            return N.dot(self, other)
-        else:
+        try :
+            rhs = asmatrix(other)
+        except :
             return NotImplemented
+        else :
+            return N.dot(self, rhs)
 
     def __rmul__(self, other):
         return N.dot(other, self)



From numpy-svn at scipy.org  Sat Mar 24 19:46:05 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Sat, 24 Mar 2007 18:46:05 -0500 (CDT)
Subject: [Numpy-svn] r3596 - in trunk/numpy/core: . tests
Message-ID: <20070324234605.4A2D439C26C@new.scipy.org>

Author: charris
Date: 2007-03-24 18:46:03 -0500 (Sat, 24 Mar 2007)
New Revision: 3596

Modified:
   trunk/numpy/core/defmatrix.py
   trunk/numpy/core/tests/test_regression.py
Log:
Fix matrix-scalar multiplication. Add test for case Matrix*1d-vector

Modified: trunk/numpy/core/defmatrix.py
===================================================================
--- trunk/numpy/core/defmatrix.py	2007-03-24 23:14:07 UTC (rev 3595)
+++ trunk/numpy/core/defmatrix.py	2007-03-24 23:46:03 UTC (rev 3596)
@@ -154,12 +154,12 @@
 
 
     def __mul__(self, other):
-        try :
-            rhs = asmatrix(other)
-        except :
-            return NotImplemented
-        else :
-            return N.dot(self, rhs)
+        if not isscalar(other) :
+            try :
+                other = asmatrix(other)
+            except :
+                return NotImplemented
+        return N.dot(self, other)
 
     def __rmul__(self, other):
         return N.dot(other, self)

Modified: trunk/numpy/core/tests/test_regression.py
===================================================================
--- trunk/numpy/core/tests/test_regression.py	2007-03-24 23:14:07 UTC (rev 3595)
+++ trunk/numpy/core/tests/test_regression.py	2007-03-24 23:46:03 UTC (rev 3596)
@@ -633,5 +633,12 @@
         assert_equal(N.array("a\x00\x0b\x0c\x00").item(),
                      'a\x00\x0b\x0c')
 
+    def check_matrix_multiply_by_1d_vector(self, level=rlevel) :
+        """Ticket #473"""
+        def mul() :
+            N.mat(N.eye(2))*N.ones(2)
+
+        self.failUnlessRaises(ValueError,mul)
+
 if __name__ == "__main__":
     NumpyTest().run()



From numpy-svn at scipy.org  Sat Mar 24 23:04:33 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Sat, 24 Mar 2007 22:04:33 -0500 (CDT)
Subject: [Numpy-svn] r3597 - trunk/numpy/doc/swig
Message-ID: <20070325030433.F2BE739C030@new.scipy.org>

Author: wfspotz at sandia.gov
Date: 2007-03-24 22:04:27 -0500 (Sat, 24 Mar 2007)
New Revision: 3597

Modified:
   trunk/numpy/doc/swig/Series.i
   trunk/numpy/doc/swig/numpy.i
   trunk/numpy/doc/swig/numpy_swig.html
   trunk/numpy/doc/swig/numpy_swig.pdf
   trunk/numpy/doc/swig/numpy_swig.txt
   trunk/numpy/doc/swig/series.cxx
   trunk/numpy/doc/swig/series.h
   trunk/numpy/doc/swig/testSeries.py
Log:
Added typemaps for signatures (TYPE INPLACE_ARRAY1[ANY]) and (TYPE INPLACE_ARRAY2[ANY][ANY])

Modified: trunk/numpy/doc/swig/Series.i
===================================================================
--- trunk/numpy/doc/swig/Series.i	2007-03-24 23:46:03 UTC (rev 3596)
+++ trunk/numpy/doc/swig/Series.i	2007-03-25 03:04:27 UTC (rev 3597)
@@ -15,14 +15,16 @@
 
 %define %apply_numpy_typemaps(TYPE)
 
-%apply (TYPE IN_ARRAY1[ANY]) {(TYPE vector[ANY])};
+%apply (TYPE IN_ARRAY1[ANY]) {(TYPE vector[3])};
 %apply (TYPE* IN_ARRAY1, int DIM1) {(TYPE* series, int size)};
 %apply (int DIM1, TYPE* IN_ARRAY1) {(int size, TYPE* series)};
-%apply (TYPE IN_ARRAY2[ANY][ANY]) {(TYPE matrix[ANY][ANY])};
+%apply (TYPE IN_ARRAY2[ANY][ANY]) {(TYPE matrix[2][2])};
 %apply (TYPE* IN_ARRAY2, int DIM1, int DIM2) {(TYPE* matrix, int rows, int cols)};
 %apply (int DIM1, int DIM2, TYPE* IN_ARRAY2) {(int rows, int cols, TYPE* matrix)};
+%apply (TYPE INPLACE_ARRAY1[ANY]) {(TYPE array[3])};
 %apply (TYPE* INPLACE_ARRAY1, int DIM1) {(TYPE* array, int size)};
 %apply (int DIM1, TYPE* INPLACE_ARRAY1) {(int size, TYPE* array)};
+%apply (TYPE INPLACE_ARRAY2[ANY][ANY]) {(TYPE array[3][3])};
 %apply (TYPE* INPLACE_ARRAY2, int DIM1, int DIM2) {(TYPE* array, int rows, int cols)};
 %apply (int DIM1, int DIM2, TYPE* INPLACE_ARRAY2) {(int rows, int cols, TYPE* array)};
 

Modified: trunk/numpy/doc/swig/numpy.i
===================================================================
--- trunk/numpy/doc/swig/numpy.i	2007-03-24 23:46:03 UTC (rev 3596)
+++ trunk/numpy/doc/swig/numpy.i	2007-03-25 03:04:27 UTC (rev 3597)
@@ -303,18 +303,18 @@
  * arguments of the form
  *
  *     (DATA_TYPE IN_ARRAY1[ANY])
- *
  *     (DATA_TYPE* IN_ARRAY1, DIM_TYPE DIM1)
  *     (DIM_TYPE DIM1, DATA_TYPE* IN_ARRAY1)
  *
  *     (DATA_TYPE IN_ARRAY2[ANY][ANY])
- *
  *     (DATA_TYPE* IN_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
  *     (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* IN_ARRAY2)
  *
+ *     (DATA_TYPE INPLACE_ARRAY1[ANY])
  *     (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1)
  *     (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1)
  *
+ *     (DATA_TYPE IN_ARRAY2[ANY][ANY])
  *     (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
  *     (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2)
  *
@@ -350,12 +350,18 @@
  *     %apply (int DIM1, int DIM2, double* IN_ARRAY2) {(int rows, int cols, double* matrix)}
  *     double min(int length, double* series)
  *
+ *     %apply (double INPLACE_ARRAY1[ANY]) {(double vector[3])};
+ *     void reverse(double vector[3]);
+ *
  *     %apply (double* INPLACE_ARRAY1, int DIM1) {(double* series, int length)};
  *     void ones(double* series, int length);
  *
  *     %apply (int DIM1, double* INPLACE_ARRAY1) {(int length, double* series)}
  *     double zeros(int length, double* series)
  *
+ *     %apply (double INPLACE_ARRAY2[ANY][ANY]) {(double matrix[3][3])};
+ *     void scale(double matrix[3][3]);
+ *
  *     %apply (double* INPLACE_ARRAY2, int DIM1, int DIM2) {(double* matrix, int rows, int cols)};
  *     void floor(double* matrix, int rows, int cols);
  *
@@ -370,19 +376,19 @@
  *
  * or directly with
  *
- *     double length(double IN_ARRAY[ANY]);
- *
+ *     double length(double IN_ARRAY1[ANY]);
  *     double prod(double* IN_ARRAY1, int DIM1);
  *     double sum( int DIM1, double* IN_ARRAY1)
  *
  *     double det(double IN_ARRAY2[ANY][ANY]);
- *
  *     double max(double* IN_ARRAY2, int DIM1, int DIM2);
  *     double min(int DIM1, int DIM2, double* IN_ARRAY2)
  *
+ *     void reverse(double INPLACE_ARRAY1[ANY]);
  *     void ones( double* INPLACE_ARRAY1, int DIM1);
  *     void zeros(int DIM1, double* INPLACE_ARRAY1)
  *
+ *     void scale(double INPLACE_ARRAY2[ANY][ANY]);
  *     void floor(double* INPLACE_ARRAY2, int DIM1, int DIM2, double floor);
  *     void ceil( int DIM1, int DIM2, double* INPLACE_ARRAY2, double ceil );
  *
@@ -501,6 +507,23 @@
   if (is_new_object$argnum && array$argnum) Py_DECREF(array$argnum);
 }
 
+/* Typemap suite for (DATA_TYPE INPLACE_ARRAY1[ANY])
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
+  (DATA_TYPE INPLACE_ARRAY1[ANY])
+{
+  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),DATA_TYPECODE);
+}
+%typemap(in)
+  (DATA_TYPE INPLACE_ARRAY1[ANY])
+  (PyArrayObject* temp=NULL)
+{
+  temp = obj_to_array_no_conversion($input, DATA_TYPECODE);
+  npy_intp size[1] = { $1_dim0 };
+  if (!temp  || !require_dimensions(temp,1) || !require_size(temp, size, 1)) SWIG_fail;
+  $1 = ($1_ltype) temp->data;
+}
+
 /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1)
  */
 %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
@@ -537,6 +560,23 @@
   $2 = (DATA_TYPE*) temp->data;
 }
 
+/* Typemap suite for (DATA_TYPE INPLACE_ARRAY2[ANY][ANY])
+ */
+%typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)
+  (DATA_TYPE INPLACE_ARRAY2[ANY][ANY])
+{
+  $1 = is_array($input) && PyArray_EquivTypenums(array_type($input),DATA_TYPECODE);
+}
+%typemap(in)
+  (DATA_TYPE INPLACE_ARRAY2[ANY][ANY])
+  (PyArrayObject* temp=NULL)
+{
+  temp = obj_to_array_no_conversion($input, DATA_TYPECODE);
+  npy_intp size[2] = { $1_dim0, $1_dim1 };
+  if (!temp  || !require_dimensions(temp,2) || !require_size(temp, size, 2)) SWIG_fail;
+  $1 = ($1_ltype) temp->data;
+}
+
 /* Typemap suite for (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
  */
 %typecheck(SWIG_TYPECHECK_DOUBLE_ARRAY)

Modified: trunk/numpy/doc/swig/numpy_swig.html
===================================================================
--- trunk/numpy/doc/swig/numpy_swig.html	2007-03-24 23:46:03 UTC (rev 3596)
+++ trunk/numpy/doc/swig/numpy_swig.html	2007-03-25 03:04:27 UTC (rev 3597)
@@ -485,14 +485,17 @@
 of array.  The input array signatures are</p>
 <blockquote>
 <ul class="simple">
-<li><tt class="docutils literal"><span class="pre">(DATA_TYPE</span> <span class="pre">IN_ARRAY[ANY])</span></tt></li>
+<li><tt class="docutils literal"><span class="pre">(DATA_TYPE</span> <span class="pre">IN_ARRAY1[ANY])</span></tt></li>
 <li><tt class="docutils literal"><span class="pre">(DATA_TYPE*</span> <span class="pre">IN_ARRAY1,</span> <span class="pre">int</span> <span class="pre">DIM1)</span></tt></li>
 <li><tt class="docutils literal"><span class="pre">(int</span> <span class="pre">DIM1,</span> <span class="pre">DATA_TYPE*</span> <span class="pre">IN_ARRAY1)</span></tt></li>
-<li><tt class="docutils literal"><span class="pre">(DATA_TYPE</span> <span class="pre">IN_ARRAY[ANY])</span></tt></li>
+<li><tt class="docutils literal"><span class="pre">(DATA_TYPE</span> <span class="pre">IN_ARRAY2[ANY][ANY])</span></tt></li>
 <li><tt class="docutils literal"><span class="pre">(DATA_TYPE*</span> <span class="pre">IN_ARRAY2,</span> <span class="pre">int</span> <span class="pre">DIM1,</span> <span class="pre">int</span> <span class="pre">DIM2)</span></tt></li>
 <li><tt class="docutils literal"><span class="pre">(int</span> <span class="pre">DIM1,</span> <span class="pre">int</span> <span class="pre">DIM2,</span> <span class="pre">DATA_TYPE*</span> <span class="pre">IN_ARRAY2)</span></tt></li>
 </ul>
 </blockquote>
+<p>The first signature listed, <tt class="docutils literal"><span class="pre">(DATA_TYPE</span> <span class="pre">IN_ARRAY[ANY])</span></tt> is for
+hard-coded one-dimensional arrays.  Likewise, <tt class="docutils literal"><span class="pre">(DATA_TYPE</span>
+<span class="pre">IN_ARRAY2[ANY][ANY])</span></tt> is for two-dimensional arrays.</p>
 </div>
 <div class="section">
 <h2><a class="toc-backref" href="#id5" id="in-place-arrays" name="in-place-arrays">In-Place Arrays</a></h2>
@@ -503,8 +506,10 @@
 signatures are</p>
 <blockquote>
 <ul class="simple">
+<li><tt class="docutils literal"><span class="pre">(DATA_TYPE</span> <span class="pre">INPLACE_ARRAY1[ANY])</span></tt></li>
 <li><tt class="docutils literal"><span class="pre">(DATA_TYPE*</span> <span class="pre">INPLACE_ARRAY1,</span> <span class="pre">int</span> <span class="pre">DIM1)</span></tt></li>
 <li><tt class="docutils literal"><span class="pre">(int</span> <span class="pre">DIM1,</span> <span class="pre">DATA_TYPE*</span> <span class="pre">INPLACE_ARRAY1)</span></tt></li>
+<li><tt class="docutils literal"><span class="pre">(DATA_TYPE</span> <span class="pre">INPLACE_ARRAY2[ANY][ANY])</span></tt></li>
 <li><tt class="docutils literal"><span class="pre">(DATA_TYPE*</span> <span class="pre">INPLACE_ARRAY2,</span> <span class="pre">int</span> <span class="pre">DIM1,</span> <span class="pre">int</span> <span class="pre">DIM2)</span></tt></li>
 <li><tt class="docutils literal"><span class="pre">(int</span> <span class="pre">DIM1,</span> <span class="pre">int</span> <span class="pre">DIM2,</span> <span class="pre">DATA_TYPE*</span> <span class="pre">INPLACE_ARRAY2)</span></tt></li>
 </ul>
@@ -710,7 +715,7 @@
 </div>
 <div class="footer">
 <hr class="footer" />
-Generated on: 2007-03-24 20:29 UTC.
+Generated on: 2007-03-25 03:02 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: trunk/numpy/doc/swig/numpy_swig.pdf
===================================================================
--- trunk/numpy/doc/swig/numpy_swig.pdf	2007-03-24 23:46:03 UTC (rev 3596)
+++ trunk/numpy/doc/swig/numpy_swig.pdf	2007-03-25 03:04:27 UTC (rev 3597)
@@ -479,26 +479,24 @@
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Modified: trunk/numpy/doc/swig/numpy_swig.txt
===================================================================
--- trunk/numpy/doc/swig/numpy_swig.txt	2007-03-24 23:46:03 UTC (rev 3596)
+++ trunk/numpy/doc/swig/numpy_swig.txt	2007-03-25 03:04:27 UTC (rev 3597)
@@ -4,7 +4,7 @@
 
 :Author:      Bill Spotz
 :Institution: Sandia National Laboratories
-:Date:        18 March, 2007
+:Date:        24 March, 2007
 
 .. contents::
 
@@ -174,13 +174,17 @@
 sequence (such as a list) that can be converted to the requested type
 of array.  The input array signatures are
 
-  * ``(DATA_TYPE IN_ARRAY[ANY])``
+  * ``(DATA_TYPE IN_ARRAY1[ANY])``
   * ``(DATA_TYPE* IN_ARRAY1, int DIM1)``
   * ``(int DIM1, DATA_TYPE* IN_ARRAY1)``
-  * ``(DATA_TYPE IN_ARRAY[ANY])``
+  * ``(DATA_TYPE IN_ARRAY2[ANY][ANY])``
   * ``(DATA_TYPE* IN_ARRAY2, int DIM1, int DIM2)``
   * ``(int DIM1, int DIM2, DATA_TYPE* IN_ARRAY2)``
 
+The first signature listed, ``(DATA_TYPE IN_ARRAY[ANY])`` is for
+hard-coded one-dimensional arrays.  Likewise, ``(DATA_TYPE
+IN_ARRAY2[ANY][ANY])`` is for two-dimensional arrays.
+
 In-Place Arrays
 ---------------
 
@@ -190,8 +194,10 @@
 must therefore be a `NumPy`_ array of the required type.  The in-place
 signatures are
 
+  * ``(DATA_TYPE INPLACE_ARRAY1[ANY])``
   * ``(DATA_TYPE* INPLACE_ARRAY1, int DIM1)``
   * ``(int DIM1, DATA_TYPE* INPLACE_ARRAY1)``
+  * ``(DATA_TYPE INPLACE_ARRAY2[ANY][ANY])``
   * ``(DATA_TYPE* INPLACE_ARRAY2, int DIM1, int DIM2)``
   * ``(int DIM1, int DIM2, DATA_TYPE* INPLACE_ARRAY2)``
 

Modified: trunk/numpy/doc/swig/series.cxx
===================================================================
--- trunk/numpy/doc/swig/series.cxx	2007-03-24 23:46:03 UTC (rev 3596)
+++ trunk/numpy/doc/swig/series.cxx	2007-03-25 03:04:27 UTC (rev 3597)
@@ -5,15 +5,18 @@
 
 // The following macro defines a family of functions with the forms
 //
-//     TYPE SNAMELength(TYPE vector[3]);
-//     TYPE SNAMEProd( TYPE * series, int size);
-//     void SNAMEOnes( TYPE * array,  int size);
-//     TYPE SNAMEMax(  TYPE * matrix, int rows, int cols);
-//     void SNAMEFloor(TYPE * array,  int rows, int cols, TYPE floor);
-//     TYPE SNAMESum(  int size, TYPE * series);
-//     void SNAMEZeros(int size, TYPE * array);
-//     TYPE SNAMEMin(  int rows, int cols, TYPE * matrix);
-//     void SNAMECeil( int rows, int cols, TYPE * array, TYPE ceil);
+//     TYPE SNAMELength( TYPE vector[3]);
+//     TYPE SNAMEProd(   TYPE * series, int size);
+//     TYPE SNAMESum(    int size, TYPE * series);
+//     void SNAMEReverse(TYPE array[3]);
+//     void SNAMEOnes(   TYPE * array,  int size);
+//     void SNAMEZeros(  int size, TYPE * array);
+//     TYPE SNAMEDet(    TYPE matrix[2][2]);
+//     TYPE SNAMEMax(    TYPE * matrix, int rows, int cols);
+//     TYPE SNAMEMin(    int rows, int cols, TYPE * matrix);
+//     void SNAMEScale(  TYPE matrix[3][3]);
+//     void SNAMEFloor(  TYPE * array,  int rows, int cols, TYPE floor);
+//     void SNAMECeil(   int rows, int cols, TYPE * array, TYPE ceil);
 //
 // for any specified type TYPE (for example: short, unsigned int, long
 // long, etc.) with given short name SNAME (for example: short, uint,
@@ -24,11 +27,13 @@
 //  * 1D input arrays, hard-coded length
 //  * 1D input arrays
 //  * 1D input arrays, data last
+//  * 1D in-place arrays, hard-coded length
 //  * 1D in-place arrays
 //  * 1D in-place arrays, data last
 //  * 2D input arrays, hard-coded length
 //  * 2D input arrays
 //  * 2D input arrays, data last
+//  * 2D in-place arrays, hard-coded lengths
 //  * 2D in-place arrays
 //  * 2D in-place arrays, data last
 //
@@ -52,6 +57,12 @@
   return result;                                  \
 }                                                 \
 \
+void SNAME ## Reverse(TYPE array[3]) { \
+  TYPE temp = array[0];		       \
+  array[0] = array[2];                 \
+  array[2] = temp;                     \
+}                                      \
+\
 void SNAME ## Ones(TYPE * array, int size) { \
   for (int i=0; i<size; ++i) array[i] = 1;   \
 }                                            \
@@ -88,6 +99,12 @@
   return result;                                          \
 }                                                         \
 \
+void SNAME ## Scale(TYPE array[3][3], TYPE val) { \
+  for (int i=0; i<3; ++i)                         \
+    for (int j=0; j<3; ++j)                       \
+      array[i][j] *= val;                         \
+}                                                 \
+\
 void SNAME ## Floor(TYPE * array, int rows, int cols, TYPE floor) { \
   int i, j, index;                                                  \
   for (j=0; j<cols; ++j) {                                          \

Modified: trunk/numpy/doc/swig/series.h
===================================================================
--- trunk/numpy/doc/swig/series.h	2007-03-24 23:46:03 UTC (rev 3596)
+++ trunk/numpy/doc/swig/series.h	2007-03-25 03:04:27 UTC (rev 3597)
@@ -4,16 +4,18 @@
 // The following macro defines the prototypes for a family of
 // functions with the forms
 //
-//     TYPE SNAMELength(TYPE vector[3]);
-//     TYPE SNAMEProd(  TYPE * series, int size);
-//     TYPE SNAMESum(   int size, TYPE * series);
-//     void SNAMEOnes(  TYPE * array,  int size);
-//     void SNAMEZeros( int size, TYPE * array);
-//     TYPE SNAMEDet(   TYPE matrix[2][2]);
-//     TYPE SNAMEMax(   TYPE * matrix, int rows, int cols);
-//     TYPE SNAMEMin(   int rows, int cols, TYPE * matrix);
-//     void SNAMEFloor( TYPE * array,  int rows, int cols, TYPE floor);
-//     void SNAMECeil(  int rows, int cols, TYPE * array,  TYPE ceil );
+//     TYPE SNAMELength(   TYPE vector[3]);
+//     TYPE SNAMEProd(     TYPE * series, int size);
+//     TYPE SNAMESum(      int size, TYPE * series);
+//     void SNAMEReverse(TYPE array[3]);
+//     void SNAMEOnes(     TYPE * array,  int size);
+//     void SNAMEZeros(    int size, TYPE * array);
+//     TYPE SNAMEDet(      TYPE matrix[2][2]);
+//     TYPE SNAMEMax(      TYPE * matrix, int rows, int cols);
+//     TYPE SNAMEMin(      int rows, int cols, TYPE * matrix);
+//     void SNAMEScale(    TYPE array[3][3]);
+//     void SNAMEFloor(    TYPE * array,  int rows, int cols, TYPE floor);
+//     void SNAMECeil(     int rows, int cols, TYPE * array,  TYPE ceil );
 //
 // for any specified type TYPE (for example: short, unsigned int, long
 // long, etc.) with given short name SNAME (for example: short, uint,
@@ -24,26 +26,30 @@
 //  * 1D input arrays, hard-coded length
 //  * 1D input arrays
 //  * 1D input arrays, data last
+//  * 1D in-place arrays, hard-coded length
 //  * 1D in-place arrays
 //  * 1D in-place arrays, data last
 //  * 2D input arrays, hard-coded lengths
 //  * 2D input arrays
 //  * 2D input arrays, data last
+//  * 2D in-place arrays, hard-coded lengths
 //  * 2D in-place arrays
 //  * 2D in-place arrays, data last
 //
 #define TEST_FUNC_PROTOS(TYPE, SNAME) \
 \
-TYPE SNAME ## Length(TYPE vector[3]); \
-TYPE SNAME ## Prod( TYPE * series, int size); \
-TYPE SNAME ## Sum(  int size, TYPE * series); \
-void SNAME ## Ones( TYPE * array,  int size); \
-void SNAME ## Zeros(int size, TYPE * array); \
-TYPE SNAME ## Det(  TYPE matrix[2][2]); \
-TYPE SNAME ## Max(  TYPE * matrix, int rows, int cols); \
-TYPE SNAME ## Min(  int rows, int cols, TYPE * matrix); \
-void SNAME ## Floor(TYPE * array, int rows, int cols, TYPE floor); \
-void SNAME ## Ceil( int rows, int cols, TYPE * array, TYPE ceil );
+TYPE SNAME ## Length( TYPE vector[3]); \
+TYPE SNAME ## Prod(   TYPE * series, int size); \
+TYPE SNAME ## Sum(    int size, TYPE * series); \
+void SNAME ## Reverse(TYPE array[3]); \
+void SNAME ## Ones(   TYPE * array,  int size); \
+void SNAME ## Zeros(  int size, TYPE * array); \
+TYPE SNAME ## Det(    TYPE matrix[2][2]); \
+TYPE SNAME ## Max(    TYPE * matrix, int rows, int cols); \
+TYPE SNAME ## Min(    int rows, int cols, TYPE * matrix); \
+void SNAME ## Scale(  TYPE array[3][3], TYPE val); \
+void SNAME ## Floor(  TYPE * array, int rows, int cols, TYPE floor); \
+void SNAME ## Ceil(   int rows, int cols, TYPE * array, TYPE ceil );
 
 TEST_FUNC_PROTOS(signed char       , schar    )
 TEST_FUNC_PROTOS(unsigned char     , uchar    )

Modified: trunk/numpy/doc/swig/testSeries.py
===================================================================
--- trunk/numpy/doc/swig/testSeries.py	2007-03-24 23:46:03 UTC (rev 3596)
+++ trunk/numpy/doc/swig/testSeries.py	2007-03-25 03:04:27 UTC (rev 3597)
@@ -44,6 +44,12 @@
         "Test scharSum function"
         self.assertEquals(Series.scharSum([-5,6,-7,8]), 2)
 
+    def testScharReverse(self):
+        "Test scharReverse function"
+        vector = N.array([1,2,4],'b')
+        Series.scharReverse(vector)
+        self.assertEquals((vector == [4,2,1]).all(), True)
+
     def testScharOnes(self):
         "Test scharOnes function"
         myArray = N.zeros(5,'b')
@@ -79,6 +85,12 @@
         matrix = [[9,8],[7,6],[5,4]]
         self.assertEquals(Series.scharMin(matrix), 4)
 
+    def testScharScale(self):
+        "Test scharScale function"
+        matrix = N.array([[1,2,3],[2,1,2],[3,2,1]],'b')
+        Series.scharScale(matrix,4)
+        self.assertEquals((matrix == [[4,8,12],[8,4,8],[12,8,4]]).all(), True)
+
     def testScharFloor(self):
         "Test scharFloor function"
         matrix = N.array([[10,-2],[-6,7]],'b')
@@ -113,6 +125,12 @@
         "Test ucharSum function"
         self.assertEquals(Series.ucharSum([5,6,7,8]), 26)
 
+    def testUcharReverse(self):
+        "Test ucharReverse function"
+        vector = N.array([1,2,4],'B')
+        Series.ucharReverse(vector)
+        self.assertEquals((vector == [4,2,1]).all(), True)
+
     def testUcharOnes(self):
         "Test ucharOnes function"
         myArray = N.zeros(5,'B')
@@ -148,6 +166,12 @@
         matrix = [[9,8],[7,6],[5,4]]
         self.assertEquals(Series.ucharMin(matrix), 4)
 
+    def testUcharScale(self):
+        "Test ucharScale function"
+        matrix = N.array([[1,2,3],[2,1,2],[3,2,1]],'B')
+        Series.ucharScale(matrix,4)
+        self.assertEquals((matrix == [[4,8,12],[8,4,8],[12,8,4]]).all(), True)
+
     def testUcharFloor(self):
         "Test ucharFloor function"
         matrix = N.array([[10,2],[6,7]],'B')
@@ -182,6 +206,12 @@
         "Test shortSum function"
         self.assertEquals(Series.shortSum([-5,6,-7,8]), 2)
 
+    def testShortReverse(self):
+        "Test shortReverse function"
+        vector = N.array([1,2,4],'h')
+        Series.shortReverse(vector)
+        self.assertEquals((vector == [4,2,1]).all(), True)
+
     def testShortOnes(self):
         "Test shortOnes function"
         myArray = N.zeros(5,'h')
@@ -217,6 +247,12 @@
         matrix = [[9,8],[7,6],[5,4]]
         self.assertEquals(Series.shortMin(matrix), 4)
 
+    def testShortScale(self):
+        "Test shortScale function"
+        matrix = N.array([[1,2,3],[2,1,2],[3,2,1]],'h')
+        Series.shortScale(matrix,4)
+        self.assertEquals((matrix == [[4,8,12],[8,4,8],[12,8,4]]).all(), True)
+
     def testShortFloor(self):
         "Test shortFloor function"
         matrix = N.array([[10,-2],[-6,7]],'h')
@@ -251,6 +287,12 @@
         "Test ushortSum function"
         self.assertEquals(Series.ushortSum([5,6,7,8]), 26)
 
+    def testUshortReverse(self):
+        "Test ushortReverse function"
+        vector = N.array([1,2,4],'H')
+        Series.ushortReverse(vector)
+        self.assertEquals((vector == [4,2,1]).all(), True)
+
     def testUshortOnes(self):
         "Test ushortOnes function"
         myArray = N.zeros(5,'H')
@@ -286,6 +328,12 @@
         matrix = [[9,8],[7,6],[5,4]]
         self.assertEquals(Series.ushortMin(matrix), 4)
 
+    def testUshortScale(self):
+        "Test ushortScale function"
+        matrix = N.array([[1,2,3],[2,1,2],[3,2,1]],'H')
+        Series.ushortScale(matrix,4)
+        self.assertEquals((matrix == [[4,8,12],[8,4,8],[12,8,4]]).all(), True)
+
     def testUshortFloor(self):
         "Test ushortFloor function"
         matrix = N.array([[10,2],[6,7]],'H')
@@ -326,6 +374,12 @@
         Series.intOnes(myArray)
         N.testing.assert_array_equal(myArray, N.array([1,1,1,1,1]))
 
+    def testIntReverse(self):
+        "Test intReverse function"
+        vector = N.array([1,2,4],'i')
+        Series.intReverse(vector)
+        self.assertEquals((vector == [4,2,1]).all(), True)
+
     def testIntZeros(self):
         "Test intZeros function"
         myArray = N.ones(5,'i')
@@ -355,6 +409,12 @@
         matrix = [[9,8],[7,6],[5,4]]
         self.assertEquals(Series.intMin(matrix), 4)
 
+    def testIntScale(self):
+        "Test intScale function"
+        matrix = N.array([[1,2,3],[2,1,2],[3,2,1]],'i')
+        Series.intScale(matrix,4)
+        self.assertEquals((matrix == [[4,8,12],[8,4,8],[12,8,4]]).all(), True)
+
     def testIntFloor(self):
         "Test intFloor function"
         matrix = N.array([[10,-2],[-6,7]],'i')
@@ -390,6 +450,12 @@
         "Test uintSum function"
         self.assertEquals(Series.uintSum([5,6,7,8]), 26)
 
+    def testUintReverse(self):
+        "Test uintReverse function"
+        vector = N.array([1,2,4],'I')
+        Series.uintReverse(vector)
+        self.assertEquals((vector == [4,2,1]).all(), True)
+
     def testUintOnes(self):
         "Test uintOnes function"
         myArray = N.zeros(5,'I')
@@ -425,6 +491,12 @@
         matrix = [[9,8],[7,6],[5,4]]
         self.assertEquals(Series.uintMin(matrix), 4)
 
+    def testUintScale(self):
+        "Test uintScale function"
+        matrix = N.array([[1,2,3],[2,1,2],[3,2,1]],'I')
+        Series.uintScale(matrix,4)
+        self.assertEquals((matrix == [[4,8,12],[8,4,8],[12,8,4]]).all(), True)
+
     def testUintFloor(self):
         "Test uintFloor function"
         matrix = N.array([[10,2],[6,7]],'I')
@@ -459,6 +531,12 @@
         "Test longSum function"
         self.assertEquals(Series.longSum([-5,6,-7,8]), 2)
 
+    def testLongReverse(self):
+        "Test longReverse function"
+        vector = N.array([1,2,4],'l')
+        Series.longReverse(vector)
+        self.assertEquals((vector == [4,2,1]).all(), True)
+
     def testLongOnes(self):
         "Test longOnes function"
         myArray = N.zeros(5,'l')
@@ -494,6 +572,12 @@
         matrix = [[9,8],[7,6],[5,4]]
         self.assertEquals(Series.longMin(matrix), 4)
 
+    def testLongScale(self):
+        "Test longScale function"
+        matrix = N.array([[1,2,3],[2,1,2],[3,2,1]],'l')
+        Series.longScale(matrix,4)
+        self.assertEquals((matrix == [[4,8,12],[8,4,8],[12,8,4]]).all(), True)
+
     def testLongFloor(self):
         "Test longFloor function"
         matrix = N.array([[10,-2],[-6,7]],'l')
@@ -528,6 +612,12 @@
         "Test ulongSum function"
         self.assertEquals(Series.ulongSum([5,6,7,8]), 26)
 
+    def testUlongReverse(self):
+        "Test ulongReverse function"
+        vector = N.array([1,2,4],'L')
+        Series.ulongReverse(vector)
+        self.assertEquals((vector == [4,2,1]).all(), True)
+
     def testUlongOnes(self):
         "Test ulongOnes function"
         myArray = N.zeros(5,'L')
@@ -563,6 +653,12 @@
         matrix = [[9,8],[7,6],[5,4]]
         self.assertEquals(Series.ulongMin(matrix), 4)
 
+    def testUlongScale(self):
+        "Test ulongScale function"
+        matrix = N.array([[1,2,3],[2,1,2],[3,2,1]],'L')
+        Series.ulongScale(matrix,4)
+        self.assertEquals((matrix == [[4,8,12],[8,4,8],[12,8,4]]).all(), True)
+
     def testUlongFloor(self):
         "Test ulongFloor function"
         matrix = N.array([[10,2],[6,7]],'L')
@@ -597,6 +693,12 @@
         "Test longLongSum function"
         self.assertEquals(Series.longLongSum([-5,6,-7,8]), 2)
 
+    def testLongLongReverse(self):
+        "Test longLongReverse function"
+        vector = N.array([1,2,4],'q')
+        Series.longLongReverse(vector)
+        self.assertEquals((vector == [4,2,1]).all(), True)
+
     def testLongLongOnes(self):
         "Test longLongOnes function"
         myArray = N.zeros(5,'q')
@@ -632,6 +734,12 @@
         matrix = [[9,8],[7,6],[5,4]]
         self.assertEquals(Series.longLongMin(matrix), 4)
 
+    def testLongLongScale(self):
+        "Test longLongScale function"
+        matrix = N.array([[1,2,3],[2,1,2],[3,2,1]],'q')
+        Series.longLongScale(matrix,4)
+        self.assertEquals((matrix == [[4,8,12],[8,4,8],[12,8,4]]).all(), True)
+
     def testLongLongFloor(self):
         "Test longLongFloor function"
         matrix = N.array([[10,-2],[-6,7]],'q')
@@ -666,6 +774,12 @@
         "Test ulongLongSum function"
         self.assertEquals(Series.ulongLongSum([5,6,7,8]), 26)
 
+    def testUlongLongReverse(self):
+        "Test ulongLongReverse function"
+        vector = N.array([1,2,4],'Q')
+        Series.ulongLongReverse(vector)
+        self.assertEquals((vector == [4,2,1]).all(), True)
+
     def testUlongLongOnes(self):
         "Test ulongLongOnes function"
         myArray = N.zeros(5,'Q')
@@ -701,6 +815,12 @@
         matrix = [[9,8],[7,6],[5,4]]
         self.assertEquals(Series.ulongLongMin(matrix), 4)
 
+    def testUlongLongScale(self):
+        "Test ulongLongScale function"
+        matrix = N.array([[1,2,3],[2,1,2],[3,2,1]],'Q')
+        Series.ulongLongScale(matrix,4)
+        self.assertEquals((matrix == [[4,8,12],[8,4,8],[12,8,4]]).all(), True)
+
     def testUlongLongFloor(self):
         "Test ulongLongFloor function"
         matrix = N.array([[10,2],[6,7]],'Q')
@@ -735,6 +855,12 @@
         "Test floatSum function"
         self.assertEquals(Series.floatSum([-5,6,-7,8]), 2)
 
+    def testFloatReverse(self):
+        "Test floatReverse function"
+        vector = N.array([1,2,4],'f')
+        Series.floatReverse(vector)
+        self.assertEquals((vector == [4,2,1]).all(), True)
+
     def testFloatOnes(self):
         "Test floatOnes function"
         myArray = N.zeros(5,'f')
@@ -774,6 +900,12 @@
         matrix = [[9,8],[7,6],[5,4]]
         self.assertEquals(Series.floatMin(matrix), 4)
 
+    def testFloatScale(self):
+        "Test floatScale function"
+        matrix = N.array([[1,2,3],[2,1,2],[3,2,1]],'f')
+        Series.floatScale(matrix,4)
+        self.assertEquals((matrix == [[4,8,12],[8,4,8],[12,8,4]]).all(), True)
+
     def testFloatFloor(self):
         "Test floatFloor function"
         matrix = N.array([[10,-2],[-6,7]],'f')
@@ -808,6 +940,12 @@
         "Test doubleSum function"
         self.assertEquals(Series.doubleSum([-5,6,-7,8]), 2)
 
+    def testDoubleReverse(self):
+        "Test doubleReverse function"
+        vector = N.array([1,2,4],'d')
+        Series.doubleReverse(vector)
+        self.assertEquals((vector == [4,2,1]).all(), True)
+
     def testDoubleOnes(self):
         "Test doubleOnes function"
         myArray = N.zeros(5,'d')
@@ -847,6 +985,12 @@
         matrix = [[9,8],[7,6],[5,4]]
         self.assertEquals(Series.doubleMin(matrix), 4)
 
+    def testDoubleScale(self):
+        "Test doubleScale function"
+        matrix = N.array([[1,2,3],[2,1,2],[3,2,1]],'d')
+        Series.doubleScale(matrix,4)
+        self.assertEquals((matrix == [[4,8,12],[8,4,8],[12,8,4]]).all(), True)
+
     def testDoubleFloor(self):
         "Test doubleFloor function"
         matrix = N.array([[10,-2],[-6,7]],'d')



From numpy-svn at scipy.org  Sun Mar 25 19:12:03 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Sun, 25 Mar 2007 18:12:03 -0500 (CDT)
Subject: [Numpy-svn] r3598 - trunk/numpy/distutils/fcompiler
Message-ID: <20070325231203.3407539C084@new.scipy.org>

Author: rkern
Date: 2007-03-25 18:12:00 -0500 (Sun, 25 Mar 2007)
New Revision: 3598

Modified:
   trunk/numpy/distutils/fcompiler/gnu.py
Log:
Make the gfortran version matching regex accept gfortran 4.3 on OS X

Modified: trunk/numpy/distutils/fcompiler/gnu.py
===================================================================
--- trunk/numpy/distutils/fcompiler/gnu.py	2007-03-25 03:04:27 UTC (rev 3597)
+++ trunk/numpy/distutils/fcompiler/gnu.py	2007-03-25 23:12:00 UTC (rev 3598)
@@ -250,12 +250,13 @@
 class Gnu95FCompiler(GnuFCompiler):
 
     compiler_type = 'gnu95'
-    version_match = simple_version_match(start='GNU Fortran 95')
+    version_match = simple_version_match(start='GNU Fortran (95|\(GCC\))')
 
     # 'gfortran --version' results:
     # Debian: GNU Fortran 95 (GCC 4.0.3 20051023 (prerelease) (Debian 4.0.2-3))
     # OS X: GNU Fortran 95 (GCC) 4.1.0
     #       GNU Fortran 95 (GCC) 4.2.0 20060218 (experimental)
+    #       GNU Fortran (GCC) 4.3.0 20070316 (experimental)
 
     for fc_exe in map(find_executable,['gfortran','f95']):
         if os.path.isfile(fc_exe):



From numpy-svn at scipy.org  Mon Mar 26 15:23:06 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Mon, 26 Mar 2007 14:23:06 -0500 (CDT)
Subject: [Numpy-svn] r3599 - trunk/numpy/core
Message-ID: <20070326192306.D0FF439C130@new.scipy.org>

Author: charris
Date: 2007-03-26 14:23:04 -0500 (Mon, 26 Mar 2007)
New Revision: 3599

Modified:
   trunk/numpy/core/defmatrix.py
Log:
Fix errors involving matrix*sparse

Modified: trunk/numpy/core/defmatrix.py
===================================================================
--- trunk/numpy/core/defmatrix.py	2007-03-25 23:12:00 UTC (rev 3598)
+++ trunk/numpy/core/defmatrix.py	2007-03-26 19:23:04 UTC (rev 3599)
@@ -154,12 +154,12 @@
 
 
     def __mul__(self, other):
-        if not isscalar(other) :
-            try :
-                other = asmatrix(other)
-            except :
-                return NotImplemented
-        return N.dot(self, other)
+        if isinstance(other,(N.ndarray, list, tuple)) :
+            # This promotes 1-D vectors to row vectors
+            return N.dot(self, asmatrix(other))
+        if N.isscalar(other) or not hasattr(other, '__rmul__') :
+            return N.dot(self, other)
+        return NotImplemented
 
     def __rmul__(self, other):
         return N.dot(other, self)



From numpy-svn at scipy.org  Mon Mar 26 16:40:04 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Mon, 26 Mar 2007 15:40:04 -0500 (CDT)
Subject: [Numpy-svn] r3600 - trunk/numpy/core/src
Message-ID: <20070326204004.BB49139C170@new.scipy.org>

Author: oliphant
Date: 2007-03-26 15:40:01 -0500 (Mon, 26 Mar 2007)
New Revision: 3600

Modified:
   trunk/numpy/core/src/multiarraymodule.c
   trunk/numpy/core/src/ufuncobject.c
Log:
Fix scalar coercion rules between different basic kinds.

Modified: trunk/numpy/core/src/multiarraymodule.c
===================================================================
--- trunk/numpy/core/src/multiarraymodule.c	2007-03-26 19:23:04 UTC (rev 3599)
+++ trunk/numpy/core/src/multiarraymodule.c	2007-03-26 20:40:01 UTC (rev 3600)
@@ -1926,7 +1926,7 @@
 	PyObject *otmp;
 	PyArray_Descr *intype=NULL, *stype=NULL;
 	PyArray_Descr *newtype=NULL;
-	NPY_SCALARKIND scalarkind;
+	NPY_SCALARKIND scalarkind, intypekind;
 
 	*retn = n = PySequence_Length(op);
 	if (PyErr_Occurred()) {*retn = 0; return NULL;}
@@ -1960,6 +1960,8 @@
 			Py_XDECREF(intype);
 			intype = newtype;
 			mps[i] = NULL;
+                        intypekind = PyArray_ScalarKind(intype->type_num,
+                                                        NULL);
 		}
 		else {
 			newtype = PyArray_DescrFromObject(otmp, stype);
@@ -1967,13 +1969,6 @@
 			stype = newtype;
 			scalarkind = PyArray_ScalarKind(newtype->type_num,
 							NULL);
-			if (intype && \
-			    !PyArray_CanCoerceScalar(newtype->type_num,
-						     intype->type_num,
-						     scalarkind)) {
-				Py_XDECREF(intype);
-				intype = stype;
-			}
 			mps[i] = (PyArrayObject *)Py_None;
 			Py_INCREF(Py_None);
 		}
@@ -1988,6 +1983,23 @@
 			mps[i] = NULL;
 		}
 	}
+        else if (intypekind != scalarkind) { \
+                /* we need to upconvert to type that
+                   handles both intype and stype
+                   and don't forcecast the scalars.
+                */
+                
+                if (!PyArray_CanCoerceScalar(stype->type_num,
+                                             intype->type_num,
+                                             scalarkind)) {
+                        Py_XDECREF(intype);
+                        intype = stype;
+                }
+                for (i=0; i<n; i++) {
+                        Py_XDECREF(mps[i]);
+                        mps[i] = NULL;
+                }
+        }
 	/* Make sure all arrays are actual array objects. */
 	for(i=0; i<n; i++) {
 		int flags = CARRAY;

Modified: trunk/numpy/core/src/ufuncobject.c
===================================================================
--- trunk/numpy/core/src/ufuncobject.c	2007-03-26 19:23:04 UTC (rev 3599)
+++ trunk/numpy/core/src/ufuncobject.c	2007-03-26 20:40:01 UTC (rev 3600)
@@ -1118,8 +1118,10 @@
         int nargs, i;
         int arg_types[NPY_MAXARGS];
 	PyArray_SCALARKIND scalars[NPY_MAXARGS];
+        PyArray_SCALARKIND kindof, new;
 	PyUFuncObject *self=loop->ufunc;
 	Bool allscalars=TRUE;
+        Bool samekind=TRUE;
 	PyTypeObject *subtype=&PyArray_Type;
         PyObject *context=NULL;
         PyObject *obj;
@@ -1160,11 +1162,18 @@
 		   at this point
 		*/
 		if (mps[i]->nd > 0) {
-			scalars[i] = PyArray_NOSCALAR;
+                        scalars[i] = PyArray_NOSCALAR;
 			allscalars=FALSE;
+                        new = PyArray_ScalarKind(arg_types[i], NULL);
 		}
-		else scalars[i] = PyArray_ScalarKind(arg_types[i], &(mps[i]));
-
+		else {
+                        scalars[i] = PyArray_ScalarKind(arg_types[i], &(mps[i]));
+                        new = scalars[i];
+                }
+                if (i==0) kindof=new;
+                else if (kindof != new) {
+                        samekind=FALSE;
+                }
         }
 
         if (flexible && !object) {
@@ -1172,13 +1181,15 @@
                 return nargs;
         }
 
-	/* If everything is a scalar, then use normal coercion rules */
-	if (allscalars) {
+	/* If everything is a scalar, or scalars mixed with arrays of different kinds
+           then use normal coercion rules */
+	if (allscalars || !samekind) {
 		for (i=0; i<self->nin; i++) {
 			scalars[i] = PyArray_NOSCALAR;
 		}
 	}
 
+
         /* Select an appropriate function for these argument types. */
         if (select_types(loop->ufunc, arg_types, &(loop->function),
                          &(loop->funcdata), scalars, typetup) == -1)



From numpy-svn at scipy.org  Mon Mar 26 16:47:13 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Mon, 26 Mar 2007 15:47:13 -0500 (CDT)
Subject: [Numpy-svn] r3601 - trunk/numpy/core/src
Message-ID: <20070326204713.AF23D39C0F9@new.scipy.org>

Author: oliphant
Date: 2007-03-26 15:47:11 -0500 (Mon, 26 Mar 2007)
New Revision: 3601

Modified:
   trunk/numpy/core/src/multiarraymodule.c
Log:
Fix some errors with recent scalar coercion changes.

Modified: trunk/numpy/core/src/multiarraymodule.c
===================================================================
--- trunk/numpy/core/src/multiarraymodule.c	2007-03-26 20:40:01 UTC (rev 3600)
+++ trunk/numpy/core/src/multiarraymodule.c	2007-03-26 20:47:11 UTC (rev 3601)
@@ -1983,7 +1983,7 @@
 			mps[i] = NULL;
 		}
 	}
-        else if (intypekind != scalarkind) { \
+        else if ((stype != NULL) && (intypekind != scalarkind)) {       \
                 /* we need to upconvert to type that
                    handles both intype and stype
                    and don't forcecast the scalars.
@@ -1994,6 +1994,7 @@
                                              scalarkind)) {
                         Py_XDECREF(intype);
                         intype = stype;
+                        Py_INCREF(stype);
                 }
                 for (i=0; i<n; i++) {
                         Py_XDECREF(mps[i]);



From numpy-svn at scipy.org  Mon Mar 26 17:28:32 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Mon, 26 Mar 2007 16:28:32 -0500 (CDT)
Subject: [Numpy-svn] r3602 - trunk/numpy/core/src
Message-ID: <20070326212832.ED10A39C092@new.scipy.org>

Author: oliphant
Date: 2007-03-26 16:28:30 -0500 (Mon, 26 Mar 2007)
New Revision: 3602

Modified:
   trunk/numpy/core/src/ufuncobject.c
Log:
Fix scalar coercion rules so that mixed scalar array coercion with arrays of different types uses normal rules when the kind of the scalar is larger than the kind of the array.

Modified: trunk/numpy/core/src/ufuncobject.c
===================================================================
--- trunk/numpy/core/src/ufuncobject.c	2007-03-26 20:47:11 UTC (rev 3601)
+++ trunk/numpy/core/src/ufuncobject.c	2007-03-26 21:28:30 UTC (rev 3602)
@@ -1118,10 +1118,9 @@
         int nargs, i;
         int arg_types[NPY_MAXARGS];
 	PyArray_SCALARKIND scalars[NPY_MAXARGS];
-        PyArray_SCALARKIND kindof, new;
+        PyArray_SCALARKIND maxarrkind, maxsckind, new;
 	PyUFuncObject *self=loop->ufunc;
 	Bool allscalars=TRUE;
-        Bool samekind=TRUE;
 	PyTypeObject *subtype=&PyArray_Type;
         PyObject *context=NULL;
         PyObject *obj;
@@ -1137,6 +1136,8 @@
         }
 
         /* Get each input argument */
+        maxarrkind = PyArray_NOSCALAR;
+        maxsckind = PyArray_NOSCALAR;
         for (i=0; i<self->nin; i++) {
                 obj = PyTuple_GET_ITEM(args,i);
                 if (!PyArray_Check(obj) && !PyArray_IsScalar(obj, Generic)) {
@@ -1161,19 +1162,21 @@
 		/* Scalars are 0-dimensional arrays
 		   at this point
 		*/
+
+                /* We need to keep track of whether or not scalars
+                   are mixed with arrays of different kinds.
+                */
+
 		if (mps[i]->nd > 0) {
                         scalars[i] = PyArray_NOSCALAR;
 			allscalars=FALSE;
                         new = PyArray_ScalarKind(arg_types[i], NULL);
+                        maxarrkind = NPY_MAX(new, maxarrkind);
 		}
 		else {
                         scalars[i] = PyArray_ScalarKind(arg_types[i], &(mps[i]));
-                        new = scalars[i];
+                        maxsckind = NPY_MAX(scalars[i], maxsckind);
                 }
-                if (i==0) kindof=new;
-                else if (kindof != new) {
-                        samekind=FALSE;
-                }
         }
 
         if (flexible && !object) {
@@ -1181,15 +1184,14 @@
                 return nargs;
         }
 
-	/* If everything is a scalar, or scalars mixed with arrays of different kinds
-           then use normal coercion rules */
-	if (allscalars || !samekind) {
+	/* If everything is a scalar, or scalars mixed with arrays of 
+           different kinds of lesser types then use normal coercion rules */
+	if (allscalars || (maxsckind > maxarrkind)) {
 		for (i=0; i<self->nin; i++) {
 			scalars[i] = PyArray_NOSCALAR;
 		}
 	}
 
-
         /* Select an appropriate function for these argument types. */
         if (select_types(loop->ufunc, arg_types, &(loop->function),
                          &(loop->funcdata), scalars, typetup) == -1)



From numpy-svn at scipy.org  Mon Mar 26 17:41:48 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Mon, 26 Mar 2007 16:41:48 -0500 (CDT)
Subject: [Numpy-svn] r3603 - trunk/numpy/numarray
Message-ID: <20070326214148.5D3F139C28D@new.scipy.org>

Author: oliphant
Date: 2007-03-26 16:41:45 -0500 (Mon, 26 Mar 2007)
New Revision: 3603

Modified:
   trunk/numpy/numarray/_capi.c
Log:
Added patch in ticket #458 for compilation on Cygwin.

Modified: trunk/numpy/numarray/_capi.c
===================================================================
--- trunk/numpy/numarray/_capi.c	2007-03-26 21:28:30 UTC (rev 3602)
+++ trunk/numpy/numarray/_capi.c	2007-03-26 21:41:45 UTC (rev 3603)
@@ -224,8 +224,12 @@
 }
 
 /* Likewise for Integer overflows */
-#if defined(linux)
+#if defined(linux) || defined(__APPLE__) || defined(__CYGWIN__) || defined(__MINGW32__)
+#if defined(__GLIBC__) || defined(__APPLE__) || defined(__MINGW32__)
 #include <fenv.h>
+#elif defined(__CYGWIN__)
+#include "numpy/fenv/fenv.c"
+#endif
 static int int_overflow_error(Float64 value) { /* For x86_64 */
 	feraiseexcept(FE_OVERFLOW);
 	return (int) value;



From numpy-svn at scipy.org  Mon Mar 26 18:13:30 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Mon, 26 Mar 2007 17:13:30 -0500 (CDT)
Subject: [Numpy-svn] r3604 - trunk/numpy/lib
Message-ID: <20070326221330.E72BF39C068@new.scipy.org>

Author: oliphant
Date: 2007-03-26 17:13:28 -0500 (Mon, 26 Mar 2007)
New Revision: 3604

Modified:
   trunk/numpy/lib/function_base.py
Log:
Fix ticket #459

Modified: trunk/numpy/lib/function_base.py
===================================================================
--- trunk/numpy/lib/function_base.py	2007-03-26 21:41:45 UTC (rev 3603)
+++ trunk/numpy/lib/function_base.py	2007-03-26 22:13:28 UTC (rev 3604)
@@ -422,7 +422,10 @@
         pfac = asarray(1)
         for k in range(n2+1):
             pfac = pfac + asarray(choicelist[k])
-        S = S*ones(asarray(pfac).shape)
+        if type(S) in ScalarType:
+            S = S*ones(asarray(pfac).shape, type(S))
+        else:
+            S = S*ones(asarray(pfac).shape, S.dtype)
     return choose(S, tuple(choicelist))
 
 def _asarray1d(arr, copy=False):



From numpy-svn at scipy.org  Mon Mar 26 18:28:10 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Mon, 26 Mar 2007 17:28:10 -0500 (CDT)
Subject: [Numpy-svn] r3605 - trunk/numpy/core/src
Message-ID: <20070326222810.6BB5E39C01F@new.scipy.org>

Author: oliphant
Date: 2007-03-26 17:28:04 -0500 (Mon, 26 Mar 2007)
New Revision: 3605

Modified:
   trunk/numpy/core/src/arrayobject.c
Log:
Fix ticket #476: string comparisons with leading NULL

Modified: trunk/numpy/core/src/arrayobject.c
===================================================================
--- trunk/numpy/core/src/arrayobject.c	2007-03-26 22:13:28 UTC (rev 3604)
+++ trunk/numpy/core/src/arrayobject.c	2007-03-26 22:28:04 UTC (rev 3605)
@@ -4297,7 +4297,7 @@
         int val;
         int diff;
 
-        val = strncmp(s1, s2, MIN(len1, len2));
+        val = memcmp(s1, s2, MIN(len1, len2));
         if ((val != 0) || (len1 == len2)) return val;
         if (len2 > len1) {sptr = s2+len1; val = -1; diff=len2-len1;}
         else {sptr = s1+len2; val = 1; diff=len1-len2;}



From numpy-svn at scipy.org  Mon Mar 26 20:10:51 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Mon, 26 Mar 2007 19:10:51 -0500 (CDT)
Subject: [Numpy-svn] r3606 - trunk/numpy/doc
Message-ID: <20070327001051.E456739C0F4@new.scipy.org>

Author: oliphant
Date: 2007-03-26 19:10:49 -0500 (Mon, 26 Mar 2007)
New Revision: 3606

Modified:
   trunk/numpy/doc/pep_buffer.txt
Log:
Revised buffer.

Modified: trunk/numpy/doc/pep_buffer.txt
===================================================================
--- trunk/numpy/doc/pep_buffer.txt	2007-03-26 22:28:04 UTC (rev 3605)
+++ trunk/numpy/doc/pep_buffer.txt	2007-03-27 00:10:49 UTC (rev 3606)
@@ -12,16 +12,22 @@
 Abstract
 ========
 
-This PEP proposes re-designing the buffer API (PyBufferProcs
+This PEP proposes re-designing the buffer interface (PyBufferProcs
 function pointers) to improve the way Python allows memory sharing
 in Python 3.0
 
-In particular, it is proposed that the multiple-segment and
-character buffer portions of the buffer API be eliminated and
-additional function pointers be provided to allow sharing any
-multi-dimensional nature of the memory and what data-format the
-memory contains.
+In particular, it is proposed that the character buffer portion 
+of the API be elminated and the multiple-segment portion be 
+re-designed in conjunction with allowing for strided memory
+to be shared.   In addition, the new buffer interface will 
+allow the sharing of any multi-dimensional nature of the
+memory and what data-format the memory contains. 
 
+This interface will allow any extension module to either 
+create objects that share memory or create algorithms that
+use memory from arbitrary objects. 
+
+
 Rationale
 =========
 
@@ -30,8 +36,8 @@
 *extremely* useful for sharing large segments of memory between
 different high-level objects, but it is too limited and has issues.
 
-1. There is the little (never?) used "sequence-of-segments" option
-   (bf_getsegcount)
+1. There is the little used "sequence-of-segments" option
+   (bf_getsegcount) that is not motivated very well. 
 
 2. There is the apparently redundant character-buffer option
    (bf_getcharbuffer)
@@ -58,8 +64,8 @@
 
    There are two widely used libraries that use the concept of
    discontiguous memory: PIL and NumPy.  Their view of discontiguous
-   arrays is different, though.  This buffer interface allows
-   sharing of either memory model.  Exporters will only use one         
+   arrays is different, though.  The proposed buffer interface allows
+   sharing of either memory model.  Exporters will use only one        
    approach and consumers may choose to support discontiguous 
    arrays of each type however they choose. 
 
@@ -73,8 +79,7 @@
    image is contained in a contiguous segment of memory, but sometimes
    it is contained in an array of pointers to the contiguous segments
    (usually lines) of the image.  The PIL is where the idea of multiple
-   buffer segments in the original buffer interface came from. 
-  
+   buffer segments in the original buffer interface came from.   
 
    NumPy's strided memory model is used more often in computational
    libraries and because it is so simple it makes sense to support
@@ -95,7 +100,7 @@
 * Unify the read/write versions of getting the buffer.
 
 * Add a new function to the interface that should be called when
-  the consumer object is "done" with the view.
+  the consumer object is "done" with the memory area. 
 
 * Add a new variable to allow the interface to describe what is in
   memory (unifying what is currently done now in struct and
@@ -105,7 +110,8 @@
 
 * Add a new variable for sharing stride information
 
-* Add a new mechanism for sharing array of arrays. 
+* Add a new mechanism for sharing arrays that must 
+  be accessed using pointer indirection. 
 
 * Fix all objects in the core and the standard library to conform
   to the new interface
@@ -131,14 +137,14 @@
                                        char **format, int *ndims,
                                        Py_ssize_t **shape,
                                        Py_ssize_t **strides,
-                                       void **segments)
+                                       int **isptr)
 
 All variables except the first are optional.  Use NULL for all
 un-needed variables.  Thus, this function can be called to get only
 the desired information from an object. NULL is returned on failure.
-On success an object-specific view is returned (which may just be a
-borrowed reference to obj).  This view should be passed to
-bf_releasebuffer when the consumer is done with the view.
+On success a "buffer-provider" object is returned (which may just be a
+borrowed reference to obj).  This provider should be passed to 
+bf_releasebuffer when the consumer is done with the memory. 
 
 buf
      a pointer to the start of the memory for the object is returned in
@@ -179,7 +185,6 @@
     If this variable is not provided then it is assumed that
     ``(*shape[0]) == len / itemsize``.
 
-
 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``
@@ -191,38 +196,57 @@
     set) if memory is actually C-style contiguous.
 
 
-segments
-    address to array-of-pointers-style array model.  Only one of
-    strides or segments can be used (the other one must be NULL).  
-    If the object does not support this kind of memory model and it
-    is requested, then an error should be raised and *segments set
-    to NULL.  The segments variable should be recast to a 
-    pointer-to-a-pointer-to-a-pointer-...-to-a-pointer depending on 
-    the output of ndims.   
+isptr
 
-    Thus, if ndims is 2, segments should be cast to (<type> ***)
-    so that (*segments)[i][j] refers to the (i,j)th element
-    of the array.  If ndims is 3, segments should be cast to (<type> ****)
-    so that (*segments)[i][j][k] refers to the (i,j,k)th element
-    of the array. 
+    address of a ``int *`` variable that will be filled with a pointer
+    to an array of ``int`` of length ``*ndims`` indicating whether or
+    not the value stored in the memory strided to along this dimension
+    must be de-referenced to continue the striding process. 
 
+    Here is a function that returns a pointer to the element in an 
+    N-D array pointed to by an N-dimesional index array:
 
-The view object should be used in the other API call and does not need
+    void* get_item_pointer(int ndim, void* buf, Py_ssize_t* strides,
+                           int* isptr, Py_ssize_t *indices) {
+        char* pointer = (char*)buf;
+        int i;
+        for (i = 0; i < ndim; i++) {
+            pointer += strides[i]*indices[i];
+            if (isptr[i]) {
+                pointer = *(char**)pointer;
+            }   
+        }
+        return (void*)pointer;
+    } 
+
+
+The provider object should be used in the other API call and does not need
 to be decref'd.  It should be "released" if the interface exporter
 provides the bf_releasebuffer function.  Otherwise, it may be
-discared.  The view object is exporter-specific.
+discarded.  The provider object is exporter-specific.
 
+The memory for the shape, strides, isptr arrays and the format string is
+managed by the provider object.  This memory must be guaranteed by the provider
+as long as the appropriate lock is held.
 
-``typedef int (*releasebufferproc)(PyObject *view)``
+``typedef int (*releasebufferproc)(PyObject *view, int which)``
     This function is called (if defined by the exporting object)
-    when a view of memory previously acquired from the object is no
+    when memory previously acquired from the object is no
     longer needed.  It is up to the exporter of the API to make sure
-    all views have been released before re-allocating any previously
+    all exported buffers have been released before re-allocating any previously
     shared memory.  It is up to consumers of the API to call this
-    function on the object whose view is obtained when it is no
-    longer needed.   Any format string, shape array or strides array
-    returned through the interface should also not be referenced after
-    the releasebuffer call is made.
+    function on the object whose buffer is obtained when it is no
+    longer needed.   
+
+    which is a flag which states what lock can be released.  It can 
+    be an 'or'ing of any of the following flags. 
+
+    PYBUF_MEMORY
+    PYBUF_SHAPE 
+    PYBUF_STRIDES (strides and isptr)
+    PYBUF_FORMAT
+    PYBUF_ALL (all of the above). 
+
     A -1 is returned on error and 0 on success.
 
     Both of these routines are optional for a type object



From numpy-svn at scipy.org  Tue Mar 27 15:43:08 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Tue, 27 Mar 2007 14:43:08 -0500 (CDT)
Subject: [Numpy-svn] r3607 - trunk/numpy/doc
Message-ID: <20070327194308.B10D939C128@new.scipy.org>

Author: oliphant
Date: 2007-03-27 14:43:06 -0500 (Tue, 27 Mar 2007)
New Revision: 3607

Modified:
   trunk/numpy/doc/pep_buffer.txt
Log:
Updates to Buffer PEP

Modified: trunk/numpy/doc/pep_buffer.txt
===================================================================
--- trunk/numpy/doc/pep_buffer.txt	2007-03-27 00:10:49 UTC (rev 3606)
+++ trunk/numpy/doc/pep_buffer.txt	2007-03-27 19:43:06 UTC (rev 3607)
@@ -25,7 +25,8 @@
 
 This interface will allow any extension module to either 
 create objects that share memory or create algorithms that
-use memory from arbitrary objects. 
+use and manipulate raw memory from arbitrary objects that 
+export the interface. 
 
 
 Rationale
@@ -137,14 +138,15 @@
                                        char **format, int *ndims,
                                        Py_ssize_t **shape,
                                        Py_ssize_t **strides,
-                                       int **isptr)
+                                       Py_ssize_t **suboffsets)
 
-All variables except the first are optional.  Use NULL for all
+All variables except the first are optional.  Pass NULL for all
 un-needed variables.  Thus, this function can be called to get only
 the desired information from an object. NULL is returned on failure.
 On success a "buffer-provider" object is returned (which may just be a
 borrowed reference to obj).  This provider should be passed to 
-bf_releasebuffer when the consumer is done with the memory. 
+bf_releasebuffer when the consumer is done with the memory.  The consumer
+is responsible for keeping a reference to obj until releasebuffer is called.
 
 buf
      a pointer to the start of the memory for the object is returned in
@@ -196,24 +198,29 @@
     set) if memory is actually C-style contiguous.
 
 
-isptr
+suboffsets
 
-    address of a ``int *`` variable that will be filled with a pointer
-    to an array of ``int`` of length ``*ndims`` indicating whether or
-    not the value stored in the memory strided to along this dimension
-    must be de-referenced to continue the striding process. 
+    address of a ``Py_ssize_t *`` variable that will be filled with a
+    pointer to an array of ``Py_ssize_t`` of length ``*ndims``.  If
+    these suboffset numbers are >=0, then the value stored along the
+    respective dimension is a pointer and the suboffset value dictates
+    how many bytes to add to the pointer before de-referencing.  A
+    suboffset value that it negative indicates that no de-referencing
+    should occur (striding in a contiguous memory block). If the value
+    returned in *suboffsets is NULL, then all suboffsets are assumed
+    to be negative.
 
-    Here is a function that returns a pointer to the element in an 
-    N-D array pointed to by an N-dimesional index array:
+    For clarity, here is a function that returns a pointer to the
+    element in an N-D array pointed to by an N-dimesional index:
 
     void* get_item_pointer(int ndim, void* buf, Py_ssize_t* strides,
-                           int* isptr, Py_ssize_t *indices) {
+                           Py_ssize_t* suboffsets, Py_ssize_t *indices) {
         char* pointer = (char*)buf;
         int i;
         for (i = 0; i < ndim; i++) {
             pointer += strides[i]*indices[i];
-            if (isptr[i]) {
-                pointer = *(char**)pointer;
+            if (suboffsets[i] >=0 ) {
+                pointer = *((char**)pointer) + suboffsets[i];
             }   
         }
         return (void*)pointer;



From numpy-svn at scipy.org  Wed Mar 28 01:20:54 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Wed, 28 Mar 2007 00:20:54 -0500 (CDT)
Subject: [Numpy-svn] r3608 - trunk/numpy/doc
Message-ID: <20070328052054.7DB8B39C019@new.scipy.org>

Author: oliphant
Date: 2007-03-28 00:20:23 -0500 (Wed, 28 Mar 2007)
New Revision: 3608

Modified:
   trunk/numpy/doc/pep_buffer.txt
Log:
Modified buffer PEP.

Modified: trunk/numpy/doc/pep_buffer.txt
===================================================================
--- trunk/numpy/doc/pep_buffer.txt	2007-03-27 19:43:06 UTC (rev 3607)
+++ trunk/numpy/doc/pep_buffer.txt	2007-03-28 05:20:23 UTC (rev 3608)
@@ -32,10 +32,11 @@
 Rationale
 =========
 
-The buffer protocol allows different Python types to exchange a
-pointer to a sequence of internal buffers.  This functionality is
-*extremely* useful for sharing large segments of memory between
-different high-level objects, but it is too limited and has issues.
+The Python 2.X buffer protocol allows different Python types to
+exchange a pointer to a sequence of internal buffers.  This
+functionality is *extremely* useful for sharing large segments of
+memory between different high-level objects, but it is too limited and
+has issues:
 
 1. There is the little used "sequence-of-segments" option
    (bf_getsegcount) that is not motivated very well. 
@@ -101,7 +102,7 @@
 * Unify the read/write versions of getting the buffer.
 
 * Add a new function to the interface that should be called when
-  the consumer object is "done" with the memory area. 
+  the consumer object is "done" with the memory area.  
 
 * Add a new variable to allow the interface to describe what is in
   memory (unifying what is currently done now in struct and
@@ -119,6 +120,10 @@
 
 * Extend the struct module to handle more format specifiers
 
+* Extend the buffer object into a new memory object which places
+  a Python veneer around the buffer interface. 
+
+
 Specification
 =============
 
@@ -133,59 +138,60 @@
 
 ::
 
-    typedef PyObject *(*getbufferproc)(PyObject *obj, void **buf,
-                                       Py_ssize_t *len, int *writeable,
-                                       char **format, int *ndims,
-                                       Py_ssize_t **shape,
-                                       Py_ssize_t **strides,
-                                       Py_ssize_t **suboffsets)
+    typedef int (*getbufferproc)(PyObject *obj, struct bufferinfo *view)
 
-All variables except the first are optional.  Pass NULL for all
-un-needed variables.  Thus, this function can be called to get only
-the desired information from an object. NULL is returned on failure.
-On success a "buffer-provider" object is returned (which may just be a
-borrowed reference to obj).  This provider should be passed to 
-bf_releasebuffer when the consumer is done with the memory.  The consumer
-is responsible for keeping a reference to obj until releasebuffer is called.
+This function returns 0 on success and -1 on failure (and raises an error). 
+The first variable is the "exporting" object.  The second argument is the
+address to a bufferinfo structure
 
+struct bufferinfo {
+       PyObject *releaseobj;
+       void *buf;
+       Py_ssize_t len;
+       int readonly;
+       char *format;
+       int ndims;
+       Py_ssize_t *shape;
+       Py_ssize_t *strides;
+       Py_ssize_t *suboffsets;
+}
+
+Upon return, the bufferinfo structure is filled in with relevant
+information about the buffer.  This same bufferinfo structure should
+be passed to bf_releasebuffer when the consumer is done with the
+memory. The caller is responsible for keeping a reference to obj until
+releasebuffer is called.
+
+The members of the bufferinfo structure are:
+
 buf
-     a pointer to the start of the memory for the object is returned in
-    ``*buf``
+    a pointer to the start of the memory for the object
 
-len
-     adress of an integer variable to hold the total bytes
-     of memory the object uses.  This should be the same
-     as the product of the shape array multiplied by the
-     number of bytes per item of memory. 
+len 
+    the total bytes of memory the object uses.  This should be the
+    same as the product of the shape array multiplied by the number of
+    bytes per item of memory.
 
-writeable
-    address of an integer variable to hold whether or not the memory
-    is writeable. If this is NULL, then you must assume the memory
-    is read-only.
+readonly
+    an integer variable to hold whether or not the memory is
+    readonly.  Non-zero means the memory is readonly, zero means the
+    memory is writeable. 
 
 format
-    address of a format-string (following extended struct
-    syntax) indicating what is in each element of
-    of memory.  The number of elements is len / itemsize,
-    where itemsize is the number of bytes implied by the format.
-    NULL if not needed in which case format is "B" for
-    unsigned bytes.  The memory for this string must not
-    be freed by the consumer --- it is managed by the exporter.
+    a format-string (following extended struct syntax) indicating what
+    is in each element of of memory.  The number of elements is len /
+    itemsize, where itemsize is the number of bytes implied by the
+    format.  For standard unsigned bytes use a format string of "B".
 
 ndims
-    address of a variable storing the number of dimensions
-    or NULL if not needed.  If shape and/or strides are given
-    then this must be non NULL.  If this variable is
-    not provided then it is assumed that ``*ndims == 1``.
+    a variable storing the number of dimensions the memory represents.
+    Should be >=0. 
 
 shape
-    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 shape of the memory as an N-D array.
-    Ignored if this is NULL.  Note that
-    ``((*shape)[0] * ... * (*shape)[ndims-1])*itemsize = len``.
-    If this variable is not provided then it is assumed that
-    ``(*shape[0]) == len / itemsize``.
+    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``.  This can be NULL
+    to indicate 1-d arrays. 
 
 strides 
     address of a ``Py_ssize_t*`` variable that will be filled with a
@@ -193,25 +199,21 @@
     indicating the number of bytes to skip to get to the next element
     in each dimension.  If this is NULL, then the memory is assumed to
     be C-style contigous with the last dimension varying the fastest.
-    An error should be raised if this is not accurate and strides are
-    not requested.  This variable may be set to NULL (with no error
-    set) if memory is actually C-style contiguous.
 
-
 suboffsets
-
     address of a ``Py_ssize_t *`` variable that will be filled with a
     pointer to an array of ``Py_ssize_t`` of length ``*ndims``.  If
     these suboffset numbers are >=0, then the value stored along the
     respective dimension is a pointer and the suboffset value dictates
-    how many bytes to add to the pointer before de-referencing.  A
+    how many bytes to add to the pointer after de-referencing.  A
     suboffset value that it negative indicates that no de-referencing
     should occur (striding in a contiguous memory block). If the value
-    returned in *suboffsets is NULL, then all suboffsets are assumed
-    to be negative.
+    returned in suboffsets is NULL, then all suboffsets are assumed
+    to be negative (i.e no de-referencing is needed). 
 
     For clarity, here is a function that returns a pointer to the
-    element in an N-D array pointed to by an N-dimesional index:
+    element in an N-D array pointed to by an N-dimesional index when
+    there are both strides and suboffsets.  
 
     void* get_item_pointer(int ndim, void* buf, Py_ssize_t* strides,
                            Py_ssize_t* suboffsets, Py_ssize_t *indices) {
@@ -227,38 +229,42 @@
     } 
 
 
-The provider object should be used in the other API call and does not need
-to be decref'd.  It should be "released" if the interface exporter
-provides the bf_releasebuffer function.  Otherwise, it may be
-discarded.  The provider object is exporter-specific.
+The exporter is responsible for making sure the memory pointed to by
+buf, format, shape, strides, and suboffsets is valid until
+releasebuffer is called.  If the exporter wants to be able to change
+shape, strides, and/or suboffsets before releasebuffer is called then
+it should allocate those arrays when getbuffer is called and free them
+when releasebuffer is called.
 
-The memory for the shape, strides, isptr arrays and the format string is
-managed by the provider object.  This memory must be guaranteed by the provider
-as long as the appropriate lock is held.
 
-``typedef int (*releasebufferproc)(PyObject *view, int which)``
-    This function is called (if defined by the exporting object)
-    when memory previously acquired from the object is no
-    longer needed.  It is up to the exporter of the API to make sure
-    all exported buffers have been released before re-allocating any previously
-    shared memory.  It is up to consumers of the API to call this
-    function on the object whose buffer is obtained when it is no
-    longer needed.   
+The same bufferinfo struct should be used in the other buffer
+interface call. The caller is responsible for the memory of the
+bufferinfo object itself.
 
-    which is a flag which states what lock can be released.  It can 
-    be an 'or'ing of any of the following flags. 
+``typedef int (*releasebufferproc)(PyObject *obj, struct bufferinfo *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
+    any memory pointed to in the bufferinfo structure remains valid
+    until releasebuffer is called.
 
-    PYBUF_MEMORY
-    PYBUF_SHAPE 
-    PYBUF_STRIDES (strides and isptr)
-    PYBUF_FORMAT
-    PYBUF_ALL (all of the above). 
+    Both of these routines are optional for a type object
 
-    A -1 is returned on error and 0 on success.
+    If the releasebuffer function is not provided then it does not ever
+    need to be called. 
+    
+Exporters will need to define a releasebuffer function if they can
+re-allocate their memory, strides, shape, suboffsets, or format
+variables which they might share through the struct bufferinfo.
+Several mechanisms could be used to keep track of how many getbuffer
+calls have been made and shared.  Either a single variable could be
+used to keep track of how many "views" have been exported, or a
+linked-list of bufferinfo structures filled in could be maintained in
+each objet.  All that is needed is to ensure that any memory shared
+through the bufferinfo structure remains valid until releasebuffer is
+called on that memory.
 
-    Both of these routines are optional for a type object
 
-
 New C-API calls are proposed
 ============================
 
@@ -270,28 +276,50 @@
 
 ::
 
-    PyObject * PyObject_GetBuffer(PyObject *obj, void **buf,
-                                  Py_ssize_t *len, int *writeable,
-                                  char **format, int *ndims,
-                                  Py_ssize_t **shape, Py_ssize_t **strides,
-                                  void **segments)
+    PyObject *PyObject_GetBuffer(PyObject *obj)
 
-Get the buffer and optional information variables about the buffer.
-Return an object-specific view object (which may be simply a
-borrowed reference to the object itself).
+Return a memory-view object.
 
-::
+A memory-view object is an extended buffer object that can replace
+the buffer object.  It's C-structure is
 
-    int PyObject_ReleaseBuffer(PyObject *view)
+typedef struct {
+    PyObject_HEAD
+    PyObject *base;
+    void *ptr;
+    Py_ssize_t len;
+    int readonly;
+    char *format;
+    int ndims;
+    Py_ssize_t *shape;
+    Py_ssize_t *strides;
+    Py_ssize_t *suboffsets;
+    int itemsize;
+} PyBufferObject;
 
-Call this function to tell obj that you are done with your "view"
-This doesn't do anything if the object doesn't implement a release function.
-Only call this after a previous PyObject_GetBuffer has succeeded and when
-you will not be needing or referring to the memory (or the format, shape, 
-and strides memory used in the view -- if you will use these for a longer
-period of time make copies).
-Returns -1 on error.
+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.  Also the hash has been removed because
+using the buffer object has a hash even if it is read-only is rarely
+useful.  The id of the buffer object should be used instead.
 
+Also, the format, ndims, shape, strides, and suboffsets has 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 itemsize is a convenience and specifies the number of bytes
+indicated by the format string if positive.  If negative, then the
+number of bytes must be computed from the format string. 
+
+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.
+
+Thus, it does not define a releasebuffer function. 
+
 ::
 
     int PyObject_SizeFromFormat(char *)
@@ -325,8 +353,18 @@
 of Python objects no matter how it is actually stored.  These calls use
 the buffer interface to perform their work. 
 
+::
+    int PyObject_IsContiguous(int *ndims, Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t *suboffsets)
 
+Return 1 if the memory defined by shape, strides, and suboffsets is contiguous.  Return 0 otherwise. 
 
+::
+    void PyObject_FillContiguousStrides(int *ndims, Py_ssize_t *shape, Py_ssize_t *strides)
+
+Fill the strides array with byte-strides of a contiguous array of the given shape. 
+
+
+
 Additions to the struct string-syntax
 =====================================
 
@@ -367,7 +405,7 @@
 
 Endian-specification ('=','>','<') is also allowed inside the
 string so that it can change if needed.  The previously-specified
-endian string is enforce until changed.  The default endian is '='.
+endian string is in force until changed.  The default endian is '='.
 
 According to the struct-module, a number can preceed a character
 code to specify how many of that type there are.  The
@@ -427,38 +465,42 @@
 * mmap module
 * ctypes module
 
-Anything else using the buffer API
+Anything else using the buffer API.  
 
 
 Issues and Details
 ==================
 
-The proposed locking mechanism relies entirely on the objects
-implementing the buffer interface to do their own thing.  Ideally an
-object that implements the buffer interface and can re-allocate
-memory, should store in its structure at least a number indicating how
-many views are extant.  If there are still un-released views to a
-memory location, then any subsequent reallocation should fail and
-raise an error.
+The proposed locking mechanism relies entirely on the exporter 
+object to not alter the memory pointed to by the buffer structure 
+until a corresponding releasebuffer is called.  
 
-The sharing of strided memory is new and can be seen as a
+The sharing of strided memory and suboffsets is new and can be seen as a
 modification of the multiple-segment interface.  It is motivated by
-NumPy.  NumPy objects should be able to share their strided memory
+NumPy and the PIL.  NumPy objects should be able to share their strided memory
 with code that understands how to manage strided memory because
 strided memory is very common when interfacing with compute libraries.
 
+Also it should be able to write generic code that works with both
+kinds of memory.
+
 Currently the struct module does not allow specification of nested
-structures.  The modifications to struct requested allow for
-specifying nested structures as several ways of viewing memory areas
-(e.g. ctypes and NumPy) already allow this.
+structures.  The proposed modifications to struct allow for specifying
+nested structures as several ways of viewing memory areas (e.g. ctypes
+and NumPy) already allow this.
 
 Memory management of the format string and the shape and strides
 array is always the responsibility of the exporting object and can
 be shared between different views. If the consuming object needs to
 keep these memory areas longer than the view is held, then it must
-copy them to its own memory.
+copy them to its own memory.  
 
+Code
+========
 
+The author of the PEP promises to contribute and maintain the code for this proposal but will welcome any help. 
+
+
 Copyright
 =========
 



From numpy-svn at scipy.org  Wed Mar 28 01:21:36 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Wed, 28 Mar 2007 00:21:36 -0500 (CDT)
Subject: [Numpy-svn] r3609 - trunk/numpy/doc
Message-ID: <20070328052136.D3C1339C019@new.scipy.org>

Author: oliphant
Date: 2007-03-28 00:21:18 -0500 (Wed, 28 Mar 2007)
New Revision: 3609

Modified:
   trunk/numpy/doc/pep_buffer.txt
Log:
Modified buffer PEP.

Modified: trunk/numpy/doc/pep_buffer.txt
===================================================================
--- trunk/numpy/doc/pep_buffer.txt	2007-03-28 05:20:23 UTC (rev 3608)
+++ trunk/numpy/doc/pep_buffer.txt	2007-03-28 05:21:18 UTC (rev 3609)
@@ -344,24 +344,28 @@
 
 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.  
-The data is copied into an array in C-style contiguous fashion meaning the
-last variable varies the fastest. 
+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.  The
+data is copied into an array in C-style contiguous fashion meaning the
+last variable varies the fastest.
 
-The last two C-API calls allow a standard way of getting data in and out
-of Python objects no matter how it is actually stored.  These calls use
-the buffer interface to perform their work. 
+The last two C-API calls allow a standard way of getting data in and
+out of Python objects no matter how it is actually stored.  These
+calls use the buffer interface to perform their work.
 
 ::
-    int PyObject_IsContiguous(int *ndims, Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t *suboffsets)
+    int PyObject_IsContiguous(int *ndims, Py_ssize_t *shape, 
+                              Py_ssize_t *strides, Py_ssize_t *suboffsets)
 
-Return 1 if the memory defined by shape, strides, and suboffsets is contiguous.  Return 0 otherwise. 
+Return 1 if the memory defined by shape, strides, and suboffsets is
+contiguous.  Return 0 otherwise.
 
 ::
-    void PyObject_FillContiguousStrides(int *ndims, Py_ssize_t *shape, Py_ssize_t *strides)
+    void PyObject_FillContiguousStrides(int *ndims, Py_ssize_t *shape, 
+                                        Py_ssize_t *strides)
 
-Fill the strides array with byte-strides of a contiguous array of the given shape. 
+Fill the strides array with byte-strides of a contiguous array of the
+given shape.
 
 
 



From numpy-svn at scipy.org  Wed Mar 28 01:45:36 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Wed, 28 Mar 2007 00:45:36 -0500 (CDT)
Subject: [Numpy-svn] r3610 - trunk/numpy/doc
Message-ID: <20070328054536.1FE9239C1D2@new.scipy.org>

Author: oliphant
Date: 2007-03-28 00:45:16 -0500 (Wed, 28 Mar 2007)
New Revision: 3610

Modified:
   trunk/numpy/doc/pep_buffer.txt
Log:
More pep_buffer changes.

Modified: trunk/numpy/doc/pep_buffer.txt
===================================================================
--- trunk/numpy/doc/pep_buffer.txt	2007-03-28 05:21:18 UTC (rev 3609)
+++ trunk/numpy/doc/pep_buffer.txt	2007-03-28 05:45:16 UTC (rev 3610)
@@ -207,9 +207,9 @@
     respective dimension is a pointer and the suboffset value dictates
     how many bytes to add to the pointer after de-referencing.  A
     suboffset value that it negative indicates that no de-referencing
-    should occur (striding in a contiguous memory block). If the value
-    returned in suboffsets is NULL, then all suboffsets are assumed
-    to be negative (i.e no de-referencing is needed). 
+    should occur (striding in a contiguous memory block).  If all 
+    suboffsets are negative (i.e. no de-referencing is needed, then
+    this must be NULL.
 
     For clarity, here is a function that returns a pointer to the
     element in an N-D array pointed to by an N-dimesional index when
@@ -220,10 +220,10 @@
         char* pointer = (char*)buf;
         int i;
         for (i = 0; i < ndim; i++) {
-            pointer += strides[i]*indices[i];
             if (suboffsets[i] >=0 ) {
                 pointer = *((char**)pointer) + suboffsets[i];
-            }   
+            }
+            pointer += strides[i]*indices[i];
         }
         return (void*)pointer;
     } 
@@ -329,43 +329,53 @@
 
 ::
 
-    int PyObject_GetContiguous(PyObject *obj, void **buf, Py_ssize_t *len)
+    int PyObject_GetContiguous(PyObject *obj, void **buf, Py_ssize_t *len,
+                               int 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``.  The buffer is C-style contiguous
-meaning the last dimension varies the fastest. 
+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
+does not matter and you will get whatever the object decides is easiest.
 
 :: 
 
-    int PyObject_CopyToObject(PyObject *obj, void *buf, Py_ssize_t len)
+    int PyObject_CopyToObject(PyObject *obj, void *buf, Py_ssize_t len,
+                              int 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.  The
-data is copied into an array in C-style contiguous fashion meaning the
-last variable varies the fastest.
+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
+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
+it does not matter and the copy will be made in whatever way is
+easiest. 
 
 The last two C-API calls allow a standard way of getting data in and
-out of Python objects no matter how it is actually stored.  These
-calls use the buffer interface to perform their work.
+out of Python objects into contiguous memory areas no matter how it is
+actually stored.  These calls use the buffer interface to perform
+their work. 
 
 ::
-    int PyObject_IsContiguous(int *ndims, Py_ssize_t *shape, 
-                              Py_ssize_t *strides, Py_ssize_t *suboffsets)
+    int PyObject_IsContiguous(struct bufferinfo *view);
 
-Return 1 if the memory defined by shape, strides, and suboffsets is
+Return 1 if the memory defined by the view object is C-style
 contiguous.  Return 0 otherwise.
 
 ::
-    void PyObject_FillContiguousStrides(int *ndims, Py_ssize_t *shape, 
+    void PyObject_FillContiguousStrides(int *ndims, Py_ssize_t *shape,
+                                        int itemsize, 
                                         Py_ssize_t *strides)
 
 Fill the strides array with byte-strides of a contiguous array of the
-given shape.
+given shape with the given number of bytes per element. 
 
 
 
@@ -374,9 +384,12 @@
 
 The struct string-syntax is missing some characters to fully
 implement data-format descriptions already available elsewhere (in
-ctypes and NumPy for example).  Here are the proposed additions:
+ctypes and NumPy for example).  The Python 2.5 specification is 
+at http://docs.python.org/lib/module-struct.html
 
+Here are the proposed additions:
 
+
 ================  ===========
 Character         Description
 ================  ===========
@@ -475,15 +488,16 @@
 Issues and Details
 ==================
 
-The proposed locking mechanism relies entirely on the exporter 
-object to not alter the memory pointed to by the buffer structure 
-until a corresponding releasebuffer is called.  
+The proposed locking mechanism relies entirely on the exporter object
+to not alter the memory pointed to by the buffer structure until a
+corresponding releasebuffer is called.
 
-The sharing of strided memory and suboffsets is new and can be seen as a
-modification of the multiple-segment interface.  It is motivated by
-NumPy and the PIL.  NumPy objects should be able to share their strided memory
-with code that understands how to manage strided memory because
-strided memory is very common when interfacing with compute libraries.
+The sharing of strided memory and suboffsets is new and can be seen as
+a modification of the multiple-segment interface.  It is motivated by
+NumPy and the PIL.  NumPy objects should be able to share their
+strided memory with code that understands how to manage strided memory
+because strided memory is very common when interfacing with compute
+libraries.
 
 Also it should be able to write generic code that works with both
 kinds of memory.
@@ -493,18 +507,31 @@
 nested structures as several ways of viewing memory areas (e.g. ctypes
 and NumPy) already allow this.
 
-Memory management of the format string and the shape and strides
-array is always the responsibility of the exporting object and can
-be shared between different views. If the consuming object needs to
-keep these memory areas longer than the view is held, then it must
-copy them to its own memory.  
+Memory management of the format string, the shape array, the strides
+array, and the suboffsets array is always the responsibility of the
+exporting object and can be shared between different views. If the
+consuming object needs to keep these memory areas longer than the view
+is held, then it must copy them to its own memory.
 
 Code
 ========
 
-The author of the PEP promises to contribute and maintain the code for this proposal but will welcome any help. 
+The author of the PEP promises to contribute and maintain the code for
+this proposal but will welcome any help.
 
 
+Examples
+=========
+
+NumPy
+
+PIL
+
+Array Object
+
+Bytes Object
+
+
 Copyright
 =========
 



From numpy-svn at scipy.org  Wed Mar 28 19:59:22 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Wed, 28 Mar 2007 18:59:22 -0500 (CDT)
Subject: [Numpy-svn] r3611 - trunk/numpy/doc
Message-ID: <20070328235922.3D6D239C089@new.scipy.org>

Author: oliphant
Date: 2007-03-28 18:59:15 -0500 (Wed, 28 Mar 2007)
New Revision: 3611

Modified:
   trunk/numpy/doc/pep_buffer.txt
Log:
More Buffer protocol fixes.

Modified: trunk/numpy/doc/pep_buffer.txt
===================================================================
--- trunk/numpy/doc/pep_buffer.txt	2007-03-28 05:45:16 UTC (rev 3610)
+++ trunk/numpy/doc/pep_buffer.txt	2007-03-28 23:59:15 UTC (rev 3611)
@@ -2,7 +2,7 @@
 :Title: Revising the buffer protocol
 :Version: $Revision: $
 :Last-Modified: $Date:  $
-:Author: Travis Oliphant <oliphant at ee.byu.edu>
+:Authors: Travis Oliphant <oliphant at ee.byu.edu>, Carl Banks <pythondev at aerojockey.com>
 :Status: Draft
 :Type: Standards Track
 :Content-Type: text/x-rst
@@ -91,7 +91,7 @@
 
    The buffer interface should allow the object to export either of these
    memory models.  Consumers are free to either require contiguous memory
-   or write code to handle either memory model.  
+   or write code to handle one or both of these memory models. 
 
 Proposal Overview
 =================
@@ -123,6 +123,8 @@
 * Extend the buffer object into a new memory object which places
   a Python veneer around the buffer interface. 
 
+* Add a few functions to make it easy to copy contiguous data
+  in and out of object supporting the buffer interface. 
 
 Specification
 =============
@@ -132,10 +134,13 @@
 ::
 
     typedef struct {
-         getbufferproc bf_getbuffer
-         releasebufferproc bf_releasebuffer
+         getbufferproc bf_getbuffer;
+         releasebufferproc bf_releasebuffer;
+         lockbufferproc bf_lockbuffer;
+         robufferproc bf_robuffer;
     }
 
+
 ::
 
     typedef int (*getbufferproc)(PyObject *obj, struct bufferinfo *view)
@@ -145,7 +150,7 @@
 address to a bufferinfo structure
 
 struct bufferinfo {
-       PyObject *releaseobj;
+       PyObject *releaser;
        void *buf;
        Py_ssize_t len;
        int readonly;
@@ -154,7 +159,7 @@
        Py_ssize_t *shape;
        Py_ssize_t *strides;
        Py_ssize_t *suboffsets;
-}
+};
 
 Upon return, the bufferinfo structure is filled in with relevant
 information about the buffer.  This same bufferinfo structure should
@@ -164,6 +169,12 @@
 
 The members of the bufferinfo structure are:
 
+releaser
+    the Python object whose bf_releasebuffer function should be called
+    when the consumer is done with the memory. After return, a new
+    reference to this object is obtained.  Normally, this will
+    be the same object 
+   
 buf
     a pointer to the start of the memory for the object
 
@@ -174,8 +185,8 @@
 
 readonly
     an integer variable to hold whether or not the memory is
-    readonly.  Non-zero means the memory is readonly, zero means the
-    memory is writeable. 
+    readonly.  1 means the memory is readonly, zero means the
+    memory is writeable,
 
 format
     a format-string (following extended struct syntax) indicating what
@@ -204,7 +215,7 @@
     address of a ``Py_ssize_t *`` variable that will be filled with a
     pointer to an array of ``Py_ssize_t`` of length ``*ndims``.  If
     these suboffset numbers are >=0, then the value stored along the
-    respective dimension is a pointer and the suboffset value dictates
+    indicated dimension is a pointer and the suboffset value dictates
     how many bytes to add to the pointer after de-referencing.  A
     suboffset value that it negative indicates that no de-referencing
     should occur (striding in a contiguous memory block).  If all 
@@ -220,14 +231,20 @@
         char* pointer = (char*)buf;
         int i;
         for (i = 0; i < ndim; i++) {
+            pointer += strides[i]*indices[i];
             if (suboffsets[i] >=0 ) {
                 pointer = *((char**)pointer) + suboffsets[i];
             }
-            pointer += strides[i]*indices[i];
         }
         return (void*)pointer;
     } 
 
+    Notice the suboffset is added "after" the dereferencing occurs.
+    Thus slicing in the ith dimension would add to the suboffsets in
+    the i-1st dimension.  Slicing in the first dimension would change
+    the location of the starting pointer directly (i.e. buf would
+    be modified).  
+    
 
 The exporter is responsible for making sure the memory pointed to by
 buf, format, shape, strides, and suboffsets is valid until
@@ -265,6 +282,19 @@
 called on that memory.
 
 
+``typedef int (*lockbufferproc)(PyObject *obj)`` This
+
+    This function allows the caller to lock the buffer object of
+    ``obj`` without retrieving all the information about the memory
+    area.
+
+    A 0 is returned on success and a -1 is returned (with an error
+    message set) on error.  An error is returned if the memory cannot
+    be set readonly when it is attempted. 
+
+    This routine is optional. 
+
+
 New C-API calls are proposed
 ============================
 
@@ -278,24 +308,18 @@
 
     PyObject *PyObject_GetBuffer(PyObject *obj)
 
-Return a memory-view object.
+Return a memory-view object from an object that defines the buffer interface. 
+If make_ro is non-zero then request that the memory is made read-only until 
+release buffer is called. 
 
-A memory-view object is an extended buffer object that can replace
-the buffer object.  It's C-structure is
+A memory-view object is an extended buffer object that should replace
+the buffer object in Python 3K.  It's C-structure is
 
 typedef struct {
     PyObject_HEAD
-    PyObject *base;
-    void *ptr;
-    Py_ssize_t len;
-    int readonly;
-    char *format;
-    int ndims;
-    Py_ssize_t *shape;
-    Py_ssize_t *strides;
-    Py_ssize_t *suboffsets;
+    struct bufferinfo view;
     int itemsize;
-} PyBufferObject;
+} 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
@@ -318,10 +342,28 @@
 format and therefore does not need to keep track of how many views it
 has exported.
 
-Thus, it does not define a releasebuffer function. 
+Thus, it does not define a releasebuffer function, or have a numviews
+variable. 
 
 ::
+    int PyObject_ReleaseBuffer(PyObject *obj)
 
+Release the buffer lock for the object, ``obj``.  This function does nothing
+if the object does not define a release buffer function.  Always returns 0. 
+
+::
+    int PyObject_LockBuffer(PyObject *obj, int make_ro)
+
+Lock the buffer for the object, ``obj``.  This function does nothing
+if the object does not define a lock buffer function.  If make_ro is
+0, then just lock the buffer.  If make_ro is 1, then lock the buffer
+and make the memory read only.  If make_ro is -1, then just make the
+memory read only without locking the buffer.  if make_ro != 0 and the
+object cannot change the state of the memory to read only, then raise
+a MemoryError.
+
+::
+
     int PyObject_SizeFromFormat(char *)
 
 Return the implied itemsize of the data-format area from a struct-style
@@ -489,8 +531,9 @@
 ==================
 
 The proposed locking mechanism relies entirely on the exporter object
-to not alter the memory pointed to by the buffer structure until a
-corresponding releasebuffer is called.
+to not invalidate any of the memory pointed to by the buffer structure
+until a corresponding releasebuffer is called.  The data area can be
+modified (unless it is set read-only), but the 
 
 The sharing of strided memory and suboffsets is new and can be seen as
 a modification of the multiple-segment interface.  It is motivated by
@@ -516,22 +559,140 @@
 Code
 ========
 
-The author of the PEP promises to contribute and maintain the code for
+The authors of the PEP promise to contribute and maintain the code for
 this proposal but will welcome any help.
 
 
 Examples
 =========
 
-NumPy
+Ex 1.
+--------
+Here is skeleton implementation of a ByteBufferSlice array, sans boilerplate and error checking.
+This shows the use of releasebuffer and lockbuffer and why both are useful. 
 
-PIL
+::
 
-Array Object
+    typedef struct  {
+      PyObject_HEAD
+      PyObject* releaser;
+      unsigned char* buf;
+      Py_ssize_t length;
+    }
+    ByteBufferSliceObject;
+    
+    
+    PyObject* ByteBufferSlice_new(PyObject* bufobj, Py_ssize_t start, Py_ssize_t end) {
+      ByteBufferSliceObject* self;
+      BufferInfoObject* bufinfo;
+    
+      self = (ByteBufferSliceObject*)type->tp_alloc(type, 0);
+      bufinfo = PyObject_GetBuffer(bufobj);
+    
+      self->releaser = bufinfo->view.releaser;
+      self->buf = bufinfo->view.buf + start;
+      self->length = end-start;
+    
+      /* look how soon we're done with this information */
+      Py_DECREF(bufinfo);
+    
+      return self;
+    }
+    
+    
+    PyObject* ByteBufferSlice_dealloc(PyObject* self) {
+      PyObject_ReleaseBuffer(self->releaser);
+      self->ob_type->tp_free((PyObject*)self);
+    }
+    
+    
+    int ByteBufferSlice_getbuffer(PyObject* self, struct bufferinfo *view, int make_ro) {
+      BufferInfoObject* bufinfo;
+      static Py_ssize_t stridesarray[] = { 1 };
+      int ret;
+    
+      view->releaser = self->releaser;
+      view->buf = self->buf;
+      view->length = self->length;
+      view->itemsize = 1;
+      view->format = "B";
+      view->ndims = 1;
+      view->strides = stridesarray;
+      view->shape = &self->length;
+      view->suboffsets = NULL;
+    
+      /* Before we go, increase the original buffer's lock count */
+      ret = PyObject_LockBuffer(self->releaser, make_ro);
+      if (ret == -1) return ret; 
 
-Bytes Object
+      self->readonly = 
+      if (make_ro && ret != -1) {
+         self->readonly = -1;
+      }
+      return ret;
+    }
+    
+    /* don't define releasebuffer or lockbuffer */
+    /* only objects that could actually re-allocate memory would define these */
+    
+    /* Now look how easy this is */
+    /* Everything works out if ByteBufferSlice reexports the buffer */
+    
+    PyObject* ByteBufferSlice_getslice(PyObject* self, Py_ssize_t start, Py_ssize_t end) {
+      return ByteBufferSlice_new(self,start,end);
+    }
 
 
+Ex. 2
+----------
+
+This example shows how an image object that uses contiguous lines might expose its buffer. 
+
+struct rgba {
+    unsigned char r, g, b, a;
+};
+
+struct ImageObject {
+    PyObject_HEAD;
+    ...
+    struct rgba** lines;
+    Py_ssize_t height;
+    Py_ssize_t width;
+    Py_ssize_t shape_array[2];
+    Py_ssize_t stride_array[2];
+    Py_ssize_t view_count;
+};
+
+"lines" points to malloced 1-D array of (struct rgba*).  Each pointer
+in THAT block points to a seperately malloced array of (struct rgba).
+
+In order to access, say, the red value of the pixel at x=30, y=50, you'd use "lines[50][30].r".
+
+So what does ImageObject's getbuffer do?  Leaving error checking out:
+
+int Image_getbuffer(PyObject *self, struct bufferinfo *view) {
+
+    static Py_ssize_t = suboffsets[2] = { -1, 0 };
+
+    view->buf = self->lines;
+    view->len = self->height*self->width;
+    view->readonly = 0;
+    *ndims = 2;
+    self->shape_array[0] = height;
+    self->shape_array[1] = width;
+    *shape = &self->shape_array;
+    self->stride_array[0] = sizeof(struct rgba*);  /* yep */
+    self->stride_array[1] = sizeof(struct rgba);
+    *strides = &self->stride_array;
+    *isptr = _isptr;
+
+    self->view_count ++;
+    /* create and return view object here, but for what? */
+} 
+
+
+
+
 Copyright
 =========
 



From numpy-svn at scipy.org  Thu Mar 29 14:23:15 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Thu, 29 Mar 2007 13:23:15 -0500 (CDT)
Subject: [Numpy-svn] r3612 - trunk/numpy/lib
Message-ID: <20070329182315.7C9A239C04D@new.scipy.org>

Author: oliphant
Date: 2007-03-29 13:23:11 -0500 (Thu, 29 Mar 2007)
New Revision: 3612

Modified:
   trunk/numpy/lib/index_tricks.py
Log:
Fix mgrid inconsistency ticket #467

Modified: trunk/numpy/lib/index_tricks.py
===================================================================
--- trunk/numpy/lib/index_tricks.py	2007-03-28 23:59:15 UTC (rev 3611)
+++ trunk/numpy/lib/index_tricks.py	2007-03-29 18:23:11 UTC (rev 3612)
@@ -10,6 +10,7 @@
 import sys
 import numpy.core.numeric as _nx
 from numpy.core.numeric import asarray, ScalarType, array
+import math
 
 import function_base
 import numpy.core.defmatrix as matrix
@@ -141,7 +142,7 @@
                     size.append(int(abs(step)))
                     typ = float
                 else:
-                    size.append(int((key[k].stop - start)/(step*1.0)))
+                    size.append(math.ceil((key[k].stop - start)/(step*1.0)))
                 if isinstance(step, float) or \
                     isinstance(start, float) or \
                     isinstance(key[k].stop, float):



From numpy-svn at scipy.org  Thu Mar 29 15:22:00 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Thu, 29 Mar 2007 14:22:00 -0500 (CDT)
Subject: [Numpy-svn] r3613 - trunk/numpy/core/src
Message-ID: <20070329192200.5B1BB39C0D8@new.scipy.org>

Author: oliphant
Date: 2007-03-29 14:21:58 -0500 (Thu, 29 Mar 2007)
New Revision: 3613

Modified:
   trunk/numpy/core/src/arrayobject.c
Log:
Fix ticket #485 by improving how a dtype variable reports its length.

Modified: trunk/numpy/core/src/arrayobject.c
===================================================================
--- trunk/numpy/core/src/arrayobject.c	2007-03-29 18:23:11 UTC (rev 3612)
+++ trunk/numpy/core/src/arrayobject.c	2007-03-29 19:21:58 UTC (rev 3613)
@@ -11438,7 +11438,7 @@
         PyArray_Descr *self = (PyArray_Descr *)self0;
 
         if (self->names)
-                return PyDict_Size(self->fields);
+                return PyTuple_GET_SIZE(self->names);
         else return 0;
 }
 



From numpy-svn at scipy.org  Thu Mar 29 16:47:46 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Thu, 29 Mar 2007 15:47:46 -0500 (CDT)
Subject: [Numpy-svn] r3614 - trunk/numpy/doc/swig
Message-ID: <20070329204746.32D7139C060@new.scipy.org>

Author: wfspotz at sandia.gov
Date: 2007-03-29 15:47:40 -0500 (Thu, 29 Mar 2007)
New Revision: 3614

Modified:
   trunk/numpy/doc/swig/numpy_swig.html
   trunk/numpy/doc/swig/numpy_swig.pdf
   trunk/numpy/doc/swig/numpy_swig.txt
Log:
Updated examples in the documentation

Modified: trunk/numpy/doc/swig/numpy_swig.html
===================================================================
--- trunk/numpy/doc/swig/numpy_swig.html	2007-03-29 19:21:58 UTC (rev 3613)
+++ trunk/numpy/doc/swig/numpy_swig.html	2007-03-29 20:47:40 UTC (rev 3614)
@@ -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="18 March, 2007" />
+<meta name="date" content="29 March, 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>18 March, 2007</td></tr>
+<td>29 March, 2007</td></tr>
 </tbody>
 </table>
 <div class="contents topic">
@@ -314,18 +314,24 @@
 <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="#other-common-types-bool" id="id7" name="id7">Other Common Types: bool</a></li>
-<li><a class="reference" href="#other-common-types-complex" id="id8" name="id8">Other Common Types: complex</a></li>
+<li><a class="reference" href="#output-arrays" id="id7" name="id7">Output Arrays</a></li>
+<li><a class="reference" href="#other-common-types-bool" id="id8" name="id8">Other Common Types: bool</a></li>
+<li><a class="reference" href="#other-common-types-complex" id="id9" name="id9">Other Common Types: complex</a></li>
 </ul>
 </li>
-<li><a class="reference" href="#helper-functions" id="id9" name="id9">Helper Functions</a><ul>
-<li><a class="reference" href="#macros" id="id10" name="id10">Macros</a></li>
-<li><a class="reference" href="#routines" id="id11" name="id11">Routines</a></li>
+<li><a class="reference" href="#helper-functions" id="id10" name="id10">Helper Functions</a><ul>
+<li><a class="reference" href="#macros" id="id11" name="id11">Macros</a></li>
+<li><a class="reference" href="#routines" id="id12" name="id12">Routines</a></li>
 </ul>
 </li>
-<li><a class="reference" href="#beyond-the-provided-typemaps" id="id12" name="id12">Beyond the Provided Typemaps</a></li>
-<li><a class="reference" href="#acknowledgements" id="id13" name="id13">Acknowledgements</a></li>
+<li><a class="reference" href="#beyond-the-provided-typemaps" id="id13" name="id13">Beyond the Provided Typemaps</a><ul>
+<li><a class="reference" href="#a-common-example" id="id14" name="id14">A Common Example</a></li>
+<li><a class="reference" href="#other-situations" id="id15" name="id15">Other Situations</a></li>
+<li><a class="reference" href="#a-final-note" id="id16" name="id16">A Final Note</a></li>
 </ul>
+</li>
+<li><a class="reference" href="#acknowledgements" id="id17" name="id17">Acknowledgements</a></li>
+</ul>
 </div>
 <div class="section">
 <h1><a class="toc-backref" href="#id1" id="introduction" name="introduction">Introduction</a></h1>
@@ -494,8 +500,9 @@
 </ul>
 </blockquote>
 <p>The first signature listed, <tt class="docutils literal"><span class="pre">(DATA_TYPE</span> <span class="pre">IN_ARRAY[ANY])</span></tt> is for
-hard-coded one-dimensional arrays.  Likewise, <tt class="docutils literal"><span class="pre">(DATA_TYPE</span>
-<span class="pre">IN_ARRAY2[ANY][ANY])</span></tt> is for two-dimensional arrays.</p>
+one-dimensional arrays with hard-coded dimensions.  Likewise,
+<tt class="docutils literal"><span class="pre">(DATA_TYPE</span> <span class="pre">IN_ARRAY2[ANY][ANY])</span></tt> is for two-dimensional arrays with
+hard-coded dimensions.</p>
 </div>
 <div class="section">
 <h2><a class="toc-backref" href="#id5" id="in-place-arrays" name="in-place-arrays">In-Place Arrays</a></h2>
@@ -534,7 +541,31 @@
 </blockquote>
 </div>
 <div class="section">
-<h2><a class="toc-backref" href="#id7" id="other-common-types-bool" name="other-common-types-bool">Other Common Types: bool</a></h2>
+<h2><a class="toc-backref" href="#id7" 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++ function return arguments
+do not have names, so signatures for <tt class="docutils literal"><span class="pre">%typemap(out)</span></tt> do not include
+names.  This means that if <tt class="docutils literal"><span class="pre">numpy.i</span></tt> supported them, they would
+apply to all pointer return arguments for the supported numeric
+types.  This seems too dangerous.  Second, C/C++ return arguments are
+limited to a single value.  This prevents obtaining dimension
+information in a general way.  Third, 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="#id8" 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>
@@ -551,7 +582,7 @@
 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="#id8" id="other-common-types-complex" name="other-common-types-complex">Other Common Types: complex</a></h2>
+<h2><a class="toc-backref" href="#id9" 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
@@ -585,12 +616,12 @@
 </div>
 </div>
 <div class="section">
-<h1><a class="toc-backref" href="#id9" id="helper-functions" name="helper-functions">Helper Functions</a></h1>
+<h1><a class="toc-backref" href="#id10" id="helper-functions" name="helper-functions">Helper Functions</a></h1>
 <p>The <tt class="docutils literal"><span class="pre">numpy.i</span></tt> file containes several macros and routines that it
 uses internally to build its typemaps.  However, these functions may
 be useful elsewhere in your interface file.</p>
 <div class="section">
-<h2><a class="toc-backref" href="#id10" id="macros" name="macros">Macros</a></h2>
+<h2><a class="toc-backref" href="#id11" id="macros" name="macros">Macros</a></h2>
 <blockquote>
 <dl class="docutils">
 <dt><strong>is_array(a)</strong></dt>
@@ -612,7 +643,7 @@
 </blockquote>
 </div>
 <div class="section">
-<h2><a class="toc-backref" href="#id11" id="routines" name="routines">Routines</a></h2>
+<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>
@@ -664,11 +695,50 @@
 </div>
 </div>
 <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>
+<h1><a class="toc-backref" href="#id13" 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.
-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.</p>
+<div class="section">
+<h2><a class="toc-backref" href="#id14" 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 python interface that we want is:</p>
+<pre class="literal-block">
+def dot(vec1, vec2):
+</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 python 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;
+%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>
+<p>If the header file that contains the prototype for <tt class="docutils literal"><span class="pre">dot()</span></tt> also
+contains other prototypes that you want to wrap, so that you need to
+<tt class="docutils literal"><span class="pre">%include</span></tt> this header file, then you will also need a <tt class="docutils literal"><span class="pre">%ignore</span>
+<span class="pre">dot;</span></tt> directive, placed after the <tt class="docutils literal"><span class="pre">%rename</span></tt> and before the
+<tt class="docutils literal"><span class="pre">%include</span></tt> directives.</p>
+</div>
+<div class="section">
+<h2><a class="toc-backref" href="#id15" id="other-situations" name="other-situations">Other Situations</a></h2>
+<p>There are other wrapping situations in which <tt class="docutils literal"><span class="pre">numpy.i</span></tt> may be
+helpful when you encounter them.</p>
 <blockquote>
 <ul>
 <li><p class="first">In some situations, it is possible that you could use the
@@ -684,7 +754,7 @@
 <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 three-dimensional array as a function
 argument, you could cut-and-paste the appropriate two-dimensional
-typemap into your interface file.  The modification for the third
+typemap into your interface file.  The modifications for the third
 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
@@ -703,19 +773,37 @@
 </blockquote>
 </div>
 <div class="section">
-<h1><a class="toc-backref" href="#id13" 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> (and its
-predecessors Numeric and numarray) together.  The effort to
-standardize this work into <tt class="docutils literal"><span class="pre">numpy.i</span></tt> began at the 2005 SciPy
-Conference with a conversation between Fernando Perez and myself.
-Fernando collected helper functions and typemaps from Michael Hunter,
-Anna Omelchenko and Michael Sanner.  Their work has made this end
-result possible.</p>
+<h2><a class="toc-backref" href="#id16" id="a-final-note" name="a-final-note">A Final Note</a></h2>
+<p>When you use the <tt class="docutils literal"><span class="pre">%apply</span></tt> directive, as is usually necessary to use
+<tt class="docutils literal"><span class="pre">numpy.i</span></tt>, it will remain in effect until you tell <a class="reference" href="http://www.swig.org">SWIG</a> that it
+shouldn't be.  If the arguments to the functions or methods that you
+are wrapping have common names, such as <tt class="docutils literal"><span class="pre">length</span></tt> or <tt class="docutils literal"><span class="pre">vector</span></tt>,
+these typemaps may get applied in situations you do not expect or
+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="#id17" id="acknowledgements" name="acknowledgements">Acknowledgements</a></h1>
+<p>Many people have worked to glue <a class="reference" href="http://www.swig.org">SWIG</a> and <a class="reference" href="http://numpy.scipy.org">NumPy</a> together (as well
+as <a class="reference" href="http://www.swig.org">SWIG</a> and the predecessors of <a class="reference" href="http://numpy.scipy.org">NumPy</a>, Numeric and numarray).
+The effort to standardize this work into <tt class="docutils literal"><span class="pre">numpy.i</span></tt> began at the 2005
+<a class="reference" href="http://scipy.org">SciPy</a> Conference with a conversation between
+Fernando Perez and myself.  Fernando collected helper functions and
+typemaps from Michael Hunter, Anna Omelchenko and Michael Sanner.
+Their work has made this end result possible.</p>
+</div>
+</div>
 <div class="footer">
 <hr class="footer" />
-Generated on: 2007-03-25 03:02 UTC.
+Generated on: 2007-03-29 20:45 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: trunk/numpy/doc/swig/numpy_swig.pdf
===================================================================
--- trunk/numpy/doc/swig/numpy_swig.pdf	2007-03-29 19:21:58 UTC (rev 3613)
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 %%EOF

Modified: trunk/numpy/doc/swig/numpy_swig.txt
===================================================================
--- trunk/numpy/doc/swig/numpy_swig.txt	2007-03-29 19:21:58 UTC (rev 3613)
+++ trunk/numpy/doc/swig/numpy_swig.txt	2007-03-29 20:47:40 UTC (rev 3614)
@@ -4,7 +4,7 @@
 
 :Author:      Bill Spotz
 :Institution: Sandia National Laboratories
-:Date:        24 March, 2007
+:Date:        29 March, 2007
 
 .. contents::
 
@@ -182,8 +182,9 @@
   * ``(int DIM1, int DIM2, DATA_TYPE* IN_ARRAY2)``
 
 The first signature listed, ``(DATA_TYPE IN_ARRAY[ANY])`` is for
-hard-coded one-dimensional arrays.  Likewise, ``(DATA_TYPE
-IN_ARRAY2[ANY][ANY])`` is for two-dimensional arrays.
+one-dimensional arrays with hard-coded dimensions.  Likewise,
+``(DATA_TYPE IN_ARRAY2[ANY][ANY])`` is for two-dimensional arrays with
+hard-coded dimensions.
 
 In-Place Arrays
 ---------------
@@ -217,6 +218,31 @@
   * ``(DATA_TYPE ARGOUT_ARRAY1[ANY])``
   * ``(DATA_TYPE ARGOUT_ARRAY2[ANY][ANY])``
 
+Output Arrays
+-------------
+
+The ``numpy.i`` interface file does not support typemaps for output
+arrays, for several reasons.  First, C/C++ function return arguments
+do not have names, so signatures for ``%typemap(out)`` do not include
+names.  This means that if ``numpy.i`` supported them, they would
+apply to all pointer return arguments for the supported numeric
+types.  This seems too dangerous.  Second, C/C++ return arguments are
+limited to a single value.  This prevents obtaining dimension
+information in a general way.  Third, arrays with hard-coded lengths
+are not permitted as return arguments.  In other words::
+
+    double[3] newVector(double x, double y, double z);
+
+is not legal C/C++ syntax.  Therefore, we cannot provide typemaps of
+the form::
+
+    %typemap(out) (TYPE[ANY]);
+
+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 ``%extend`` for the case of class
+methods or ``%ignore`` and ``%rename`` for the case of functions.
+
 Other Common Types: bool
 ------------------------
 
@@ -373,9 +399,51 @@
 
 There are many C or C++ array/`NumPy`_ array situations not covered by
 a simple ``%include "numpy.i"`` and subsequent ``%apply`` directives.
-Nevertheless, ``numpy.i`` may still be helpful when you encounter
-them.
 
+A Common Example
+----------------
+
+Consider a reasonable prototype for a dot product function::
+
+    double dot(int len, double* vec1, double* vec2);
+
+The python interface that we want is::
+
+    def dot(vec1, vec2):
+
+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
+arguments).  The recommended solution is the following::
+
+    %apply (int DIM1, double* IN_ARRAY1) {(int len1, double* vec1),
+                                          (int len2, double* vec2)}
+    %rename (dot) my_dot;
+    %inline %{
+    double my_dot(int len1, double* vec1, int len2, double* vec2) {
+        if (len1 != len2) {
+	    PyErr_Format(PyExc_ValueError,
+                         "Arrays of lengths (%d,%d) given",
+                         len1, len2);
+	    return 0.0;
+        }
+        return dot(len1, vec1, vec2);
+    }
+    %}
+
+If the header file that contains the prototype for ``dot()`` also
+contains other prototypes that you want to wrap, so that you need to
+``%include`` this header file, then you will also need a ``%ignore
+dot;`` directive, placed after the ``%rename`` and before the
+``%include`` directives.
+
+Other Situations
+----------------
+
+There are other wrapping situations in which ``numpy.i`` may 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
     types.  See the `Other Common Types: bool`_ or `Other Common
@@ -388,7 +456,7 @@
   * You can use the code in ``numpy.i`` to write your own typemaps.
     For example, if you had a three-dimensional array as a function
     argument, you could cut-and-paste the appropriate two-dimensional
-    typemap into your interface file.  The modification for the third
+    typemap into your interface file.  The modifications for the third
     dimension would be trivial.
 
   * Sometimes, the best approach is to use the ``%extend`` directive
@@ -403,13 +471,31 @@
     `Numpy-discussion <mailto:Numpy-discussion at scipy.org>`_ and
     `Swig-user <mailto:Swig-user at lists.sourceforge.net>`_ mail lists.
 
+A Final Note
+------------
+
+When you use the ``%apply`` directive, as is usually necessary to use
+``numpy.i``, it will remain in effect until you tell `SWIG`_ that it
+shouldn't be.  If the arguments to the functions or methods that you
+are wrapping have common names, such as ``length`` or ``vector``,
+these typemaps may get applied in situations you do not expect or
+want.  Therefore, it is always a good idea to add a ``%clear``
+directive after you are done with a specific typemap::
+
+    %apply (double* IN_ARRAY1, int DIM1) {(double* vector, int length)}
+    %include "my_header.h"
+    %clear (double* vector, int length);
+
+In general, you should target these typemap signatures specifically
+where you want them, and then clear them after you are done.
+
 Acknowledgements
 ================
 
-Many people have worked to glue `SWIG`_ and `NumPy`_ (and its
-predecessors Numeric and numarray) together.  The effort to
-standardize this work into ``numpy.i`` began at the 2005 SciPy
-Conference with a conversation between Fernando Perez and myself.
-Fernando collected helper functions and typemaps from Michael Hunter,
-Anna Omelchenko and Michael Sanner.  Their work has made this end
-result possible.
+Many people have worked to glue `SWIG`_ and `NumPy`_ together (as well
+as `SWIG`_ and the predecessors of `NumPy`_, Numeric and numarray).
+The effort to standardize this work into ``numpy.i`` began at the 2005
+`SciPy <http://scipy.org>`_ Conference with a conversation between
+Fernando Perez and myself.  Fernando collected helper functions and
+typemaps from Michael Hunter, Anna Omelchenko and Michael Sanner.
+Their work has made this end result possible.



From numpy-svn at scipy.org  Thu Mar 29 17:38:01 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Thu, 29 Mar 2007 16:38:01 -0500 (CDT)
Subject: [Numpy-svn] r3615 - trunk/numpy/distutils
Message-ID: <20070329213801.3E9F039C03E@new.scipy.org>

Author: cookedm
Date: 2007-03-29 16:37:58 -0500 (Thu, 29 Mar 2007)
New Revision: 3615

Modified:
   trunk/numpy/distutils/cpuinfo.py
Log:
cpuinfo: Nocona is a PentiumIV, not 686. Fixes #438

Modified: trunk/numpy/distutils/cpuinfo.py
===================================================================
--- trunk/numpy/distutils/cpuinfo.py	2007-03-29 20:47:40 UTC (rev 3614)
+++ trunk/numpy/distutils/cpuinfo.py	2007-03-29 21:37:58 UTC (rev 3615)
@@ -185,13 +185,13 @@
         return self.is_PentiumIV() and self.has_sse3()
 
     def _is_Nocona(self):
-        return self.is_64bit() and self.is_i686()
-    
+        return self.is_64bit() and self.is_PentiumIV()
+
     def _is_Core2(self):
         return self.is_64bit() and self.is_Intel() and \
                re.match(r'.*?Core\(TM\)2\b', \
                         self.info[0]['model name']) is not None
-    
+
     def _is_Itanium(self):
         return re.match(r'.*?Itanium\b',
                         self.info[0]['family']) is not None



From numpy-svn at scipy.org  Fri Mar 30 01:10:17 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Fri, 30 Mar 2007 00:10:17 -0500 (CDT)
Subject: [Numpy-svn] r3616 - in trunk/numpy/core: include/numpy src
Message-ID: <20070330051017.68F0439C0B7@new.scipy.org>

Author: oliphant
Date: 2007-03-30 00:09:53 -0500 (Fri, 30 Mar 2007)
New Revision: 3616

Modified:
   trunk/numpy/core/include/numpy/ndarrayobject.h
   trunk/numpy/core/src/arrayobject.c
   trunk/numpy/core/src/arraytypes.inc.src
   trunk/numpy/core/src/multiarraymodule.c
Log:
Make a fastclip function (adapted from ticket #425 and fix conjugate method to use ufunc when complex matrix

Modified: trunk/numpy/core/include/numpy/ndarrayobject.h
===================================================================
--- trunk/numpy/core/include/numpy/ndarrayobject.h	2007-03-29 21:37:58 UTC (rev 3615)
+++ trunk/numpy/core/include/numpy/ndarrayobject.h	2007-03-30 05:09:53 UTC (rev 3616)
@@ -1040,6 +1040,9 @@
 
 typedef int (PyArray_ScalarKindFunc)(void *);
 
+typedef void (PyArray_FastClipFunc)(void *in, npy_intp n_in, void *min,
+                                    void *max, void *out);
+
 typedef struct {
         npy_intp *ptr;
         int len;
@@ -1115,8 +1118,7 @@
         int **cancastscalarkindto;
         int *cancastto;
 
-        int listpickle;           /* Unused */
-
+        PyArray_FastClipFunc *fastclip;
 } PyArray_ArrFuncs;
 
 #define NPY_ITEM_REFCOUNT   0x01  /* The item must be reference counted
@@ -1317,8 +1319,17 @@
 
 #define PyArray_MAX(a,b) (((a)>(b))?(a):(b))
 #define PyArray_MIN(a,b) (((a)<(b))?(a):(b))
+#define PyArray_CLT(p,q) ((((p).real==(q).real) ? ((p).imag < (q).imag) : \
+                               ((p).real < (q).real)))
+#define PyArray_CGT(p,q) ((((p).real==(q).real) ? ((p).imag > (q).imag) : \
+                               ((p).real > (q).real)))
+#define PyArray_CLE(p,q) ((((p).real==(q).real) ? ((p).imag <= (q).imag) : \
+                               ((p).real <= (q).real)))
+#define PyArray_CGE(p,q) ((((p).real==(q).real) ? ((p).imag >= (q).imag) : \
+                               ((p).real >= (q).real)))
+#define PyArray_CEQ(p,q) (((p).real==(q).real) && ((p).imag == (q).imag))
+#define PyArray_CNE(p,q) (((p).real!=(q).real) || ((p).imag != (q).imag))
 
-
 /*
  * C API:  consists of Macros and functions.  The MACROS are defined here.
  */

Modified: trunk/numpy/core/src/arrayobject.c
===================================================================
--- trunk/numpy/core/src/arrayobject.c	2007-03-29 21:37:58 UTC (rev 3615)
+++ trunk/numpy/core/src/arrayobject.c	2007-03-30 05:09:53 UTC (rev 3616)
@@ -3167,7 +3167,8 @@
                 *ceil,
                 *maximum,
                 *minimum,
-                *rint;
+                *rint,
+                *conjugate;
 } NumericOps;
 
 static NumericOps n_ops; /* NB: static objects inlitialized to zero */
@@ -3224,6 +3225,7 @@
         SET(maximum);
         SET(minimum);
         SET(rint);
+        SET(conjugate);
         return 0;
 }
 
@@ -3273,6 +3275,7 @@
         GET(maximum);
         GET(minimum);
         GET(rint);
+        GET(conjugate);
         return dict;
 
  fail:

Modified: trunk/numpy/core/src/arraytypes.inc.src
===================================================================
--- trunk/numpy/core/src/arraytypes.inc.src	2007-03-29 21:37:58 UTC (rev 3615)
+++ trunk/numpy/core/src/arraytypes.inc.src	2007-03-30 05:09:53 UTC (rev 3616)
@@ -2053,6 +2053,63 @@
 /**end repeat**/
 
 
+
+/************************ 
+ * Fast clip functions 
+ *************************/ 
+ 
+/**begin repeat 
+#name=BOOL,BYTE, UBYTE, SHORT, USHORT, INT, UINT, LONG, ULONG, LONGLONG, ULONGLONG, FLOAT, DOUBLE, LONGDOUBLE# 
+#type= Bool, byte, ubyte, short, ushort, int, uint, long, ulong, longlong, ulonglong, float, double, longdouble# 
+*/ 
+static void
+ at name@_fastclip(@type@ *in, intp ni, @type@ *min, @type@ *max, @type@ *out)
+{ 
+        register npy_intp i; 
+        @type@ max_val, min_val;
+        
+        max_val = *max;
+        min_val = *min;        
+  
+        for (i = 0; i < ni; i++) {   
+                if (in[i] < min_val) { 
+                        out[i]   = min_val; 
+                } else if (in[i] > max_val) { 
+                        out[i]   = max_val; 
+                } 
+        } 
+        
+        return;
+} 
+/**end repeat**/ 
+
+/**begin repeat 
+#name=CFLOAT, CDOUBLE, CLONGDOUBLE# 
+#type= cfloat, cdouble, clongdouble# 
+*/ 
+static void
+ at name@_fastclip(@type@ *in, intp ni, @type@ *min, @type@ *max, @type@ *out) 
+{ 
+        register npy_intp i; 
+        @type@ max_val, min_val;
+        
+        max_val = *max;
+        min_val = *min;
+ 
+        for (i = 0; i < ni; i++) {   
+                if (PyArray_CLT(in[i], max_val)) { 
+                        out[i]   = max_val; 
+                } else if (PyArray_CGT(in[i], max_val)) { 
+                        out[i]   = max_val; 
+                } 
+        }         
+        return;
+} 
+ 
+/**end repeat**/ 
+
+#define OBJECT_fastclip NULL
+
 #define _ALIGN(type) offsetof(struct {char c; type v;},v)
 
 /* Disable harmless compiler warning "4116: unnamed type definition in
@@ -2117,7 +2174,7 @@
         (PyArray_ScalarKindFunc*)NULL,
         NULL,
         NULL,
-        0
+        (PyArray_FastClipFunc *)NULL
 };
 
 static PyArray_Descr @from at _Descr = {
@@ -2194,7 +2251,7 @@
         (PyArray_ScalarKindFunc*)NULL,
         NULL,
         NULL,
-        0,
+        (PyArray_FastClipFunc*)@from at _fastclip
 };
 
 static PyArray_Descr @from at _Descr = {

Modified: trunk/numpy/core/src/multiarraymodule.c
===================================================================
--- trunk/numpy/core/src/multiarraymodule.c	2007-03-29 21:37:58 UTC (rev 3615)
+++ trunk/numpy/core/src/multiarraymodule.c	2007-03-30 05:09:53 UTC (rev 3616)
@@ -1100,11 +1100,8 @@
 
 }
 
-/*MULTIARRAY_API
- Clip
-*/
 static PyObject *
-PyArray_Clip(PyArrayObject *self, PyObject *min, PyObject *max, PyArrayObject *out)
+_slow_array_clip(PyArrayObject *self, PyObject *min, PyObject *max, PyArrayObject *out)
 {
 	PyObject *selector=NULL, *newtup=NULL, *ret=NULL;
 	PyObject *res1=NULL, *res2=NULL, *res3=NULL;
@@ -1137,10 +1134,141 @@
 	return ret;
 }
 
-/* Why doesn't this just call the ufunc?
-   All we need to do is add it to the list of needed ufuncs.
- */
+/*MULTIARRAY_API
+ Clip
+*/
+static PyObject *
+PyArray_Clip(PyArrayObject *self, PyObject *min, PyObject *max, PyArrayObject *out)
+{
+        PyArray_FastClipFunc *func;
+        int outgood=0, ingood=0;                
+        PyArrayObject *maxa=NULL;
+        PyArrayObject *mina=NULL;
+        PyArrayObject *newout=NULL, *newin=NULL;
+        PyArray_Descr *indescr;
 
+        func = self->descr->f->fastclip;
+        if (func == NULL || !PyArray_CheckAnyScalar(min) || 
+            !PyArray_CheckAnyScalar(max))
+                return _slow_array_clip(self, min, max, out);
+
+        /* Use the fast scalar clip function */
+
+        if (PyArray_ISNOTSWAPPED(self)) {
+                indescr = PyArray_DescrNewByteorder(self->descr, '=');
+                if (indescr == NULL) goto fail;
+        }
+        else { 
+                indescr = self->descr;
+                Py_INCREF(indescr);
+        }
+
+        maxa = (NPY_AO *)PyArray_FromAny(max, indescr, 0, 0, 
+                                         NPY_DEFAULT, NULL);
+        if (maxa == NULL) return NULL;
+        Py_INCREF(indescr);
+        mina = (NPY_AO *)PyArray_FromAny(min, indescr, 0, 0, 
+                                         NPY_DEFAULT, NULL);
+        if (mina == NULL) goto fail;
+        
+        if (PyArray_ISONESEGMENT(self) && PyArray_CHKFLAGS(self, ALIGNED) &&
+            PyArray_ISNOTSWAPPED(self)) 
+                ingood = 1;
+
+        if (!ingood) {
+                int flags;
+                if (PyArray_ISFORTRAN(self)) flags = NPY_FARRAY;
+                else flags = NPY_CARRAY;
+                Py_INCREF(indescr);
+                newin = (NPY_AO *)PyArray_FromArray(self, indescr, flags);
+                if (newin == NULL) goto fail;
+        }
+        else {
+                newin = self;
+                Py_INCREF(newin);
+        }
+
+        /* If we have already made a copy of the data, then use 
+           that as the output array
+        */
+        if (out == NULL && !ingood) {
+                out = newin;
+        }
+
+        /* Now, we know newin is a usable array for fastclip,
+           we need to make sure the output array is available
+           and usable */
+        if (out == NULL) {
+                Py_INCREF(indescr);
+                out = (NPY_AO*)PyArray_NewFromDescr(self->ob_type,
+						    indescr, self->nd, 
+                                                    self->dimensions,
+						    NULL, NULL,
+						    PyArray_ISFORTRAN(self),
+                                                    NULL);
+                if (out == NULL) goto fail;
+                outgood = 1;
+        }
+        /* Input is good at this point */
+        if (out == newin) {
+                outgood = 1;
+        }
+        if (!outgood && PyArray_ISONESEGMENT(out) && 
+            PyArray_CHKFLAGS(out, ALIGNED) && 
+            PyArray_ISNOTSWAPPED(out)) {
+                outgood = 1;
+        }
+
+        /* Do we still not have a suitable output array? */
+        /* Create one, now */
+        if (!outgood) {
+                int oflags;
+                PyArray_Descr *odescr;
+                if (PyArray_ISFORTRAN(out)) 
+                        oflags = NPY_FARRAY | NPY_UPDATEIFCOPY;
+                else 
+                        oflags = NPY_CARRAY | NPY_UPDATEIFCOPY;
+                if (PyArray_ISNOTSWAPPED(out)) {
+                        odescr = PyArray_DescrNewByteorder(out->descr, '=');
+                        if (odescr == NULL) goto fail;
+                }
+                else { 
+                        odescr = out->descr;
+                        Py_INCREF(odescr);
+                }
+                newout = (NPY_AO*)PyArray_FromArray(out, odescr, oflags);
+                if (newout == NULL) goto fail;
+        }
+        else {
+                newout = out;
+                Py_INCREF(newout);
+        }
+
+        if (newout->data != newin->data) {
+                memcpy(newout->data, newin->data, PyArray_NBYTES(newin));
+        }
+        
+        /* Now we can call the fast-clip function */
+
+        func(newin->data, PyArray_SIZE(newin), mina->data, maxa->data,
+             newout->data);
+        
+        /* Clean up temporary variables */
+        Py_DECREF(mina);
+        Py_DECREF(maxa);
+        Py_DECREF(newin);
+        /* Copy back into out if out was not already a nice array. */
+        Py_DECREF(newout);
+        return (PyObject *)out;
+        
+ fail:
+        Py_XDECREF(maxa);
+        Py_XDECREF(mina);
+        Py_XDECREF(newin);
+        return NULL;
+}
+
+
 /*MULTIARRAY_API
  Conjugate
 */
@@ -1148,41 +1276,15 @@
 PyArray_Conjugate(PyArrayObject *self, PyArrayObject *out)
 {
 	if (PyArray_ISCOMPLEX(self)) {
-		PyObject *new;
-		intp size, i;
-		/* Make a copy */
-                new = PyArray_NewCopy(self, -1);
-                if (new==NULL) return NULL;
-		size = PyArray_SIZE(new);
-		if (self->descr->type_num == PyArray_CFLOAT) {
-			cfloat *dptr = (cfloat *) PyArray_DATA(new);
-			for (i=0; i<size; i++) {
-				dptr->imag = -dptr->imag;
-				dptr++;
-			}
-		}
-		else if (self->descr->type_num == PyArray_CDOUBLE) {
-			cdouble *dptr = (cdouble *)PyArray_DATA(new);
-			for (i=0; i<size; i++) {
-				dptr->imag = -dptr->imag;
-				dptr++;
-			}
-		}
-		else if (self->descr->type_num == PyArray_CLONGDOUBLE) {
-			clongdouble *dptr = (clongdouble *)PyArray_DATA(new);
-			for (i=0; i<size; i++) {
-				dptr->imag = -dptr->imag;
-				dptr++;
-			}
-		}
-                if (out) {
-                        if (PyArray_CopyAnyInto(out, (PyArrayObject *)new)<0)
-                                return NULL;
-                        Py_INCREF(out);
-                        Py_DECREF(new);
-                        return (PyObject *)out;
+                if (out == NULL) {
+                        return PyArray_GenericUnaryFunction(self, 
+                                                            n_ops.conjugate);
                 }
-		return new;
+                else {
+                        return PyArray_GenericBinaryFunction(self, 
+                                                             (PyObject *)out,
+                                                             n_ops.conjugate);
+                }
 	}
 	else {
                 PyArrayObject *ret;
@@ -1926,7 +2028,7 @@
 	PyObject *otmp;
 	PyArray_Descr *intype=NULL, *stype=NULL;
 	PyArray_Descr *newtype=NULL;
-	NPY_SCALARKIND scalarkind, intypekind;
+	NPY_SCALARKIND scalarkind=NPY_NOSCALAR, intypekind=NPY_NOSCALAR;
 
 	*retn = n = PySequence_Length(op);
 	if (PyErr_Occurred()) {*retn = 0; return NULL;}



From numpy-svn at scipy.org  Fri Mar 30 09:17:56 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Fri, 30 Mar 2007 08:17:56 -0500 (CDT)
Subject: [Numpy-svn] r3617 - trunk/numpy/core/tests
Message-ID: <20070330131756.D7DEE39C0A6@new.scipy.org>

Author: stefan
Date: 2007-03-30 08:17:40 -0500 (Fri, 30 Mar 2007)
New Revision: 3617

Modified:
   trunk/numpy/core/tests/test_multiarray.py
Log:
Add basic tests for clip.


Modified: trunk/numpy/core/tests/test_multiarray.py
===================================================================
--- trunk/numpy/core/tests/test_multiarray.py	2007-03-30 05:09:53 UTC (rev 3616)
+++ trunk/numpy/core/tests/test_multiarray.py	2007-03-30 13:17:40 UTC (rev 3617)
@@ -1,6 +1,7 @@
 from numpy.testing import *
 from numpy.core import *
 from numpy import random
+import numpy as N
 
 class test_flags(NumpyTestCase):
     def setUp(self):
@@ -349,6 +350,35 @@
         res = 250*sk[:,newaxis]
         assert_almost_equal(res.ravel(),250*sk)
 
+class test_clip(NumpyTestCase):
+    def _check_range(self,x,cmin,cmax):
+        assert N.all(x >= cmin)
+        assert N.all(x <= cmax)
+
+    def _clip_type(self,type_group,amax,cmin,cmax,inplace=False):
+        for T in N.sctypes[type_group]:
+            x = (N.random.random(1000) * amax).astype(T)
+            if inplace:
+                x.clip(cmin,cmax,x)
+            else:
+                x = x.clip(cmin,cmax)
+
+            self._check_range(x,cmin,cmax)
+            return x
+
+    def check_basic(self):
+        for inplace in [False]: # XXX fixme -> ,True]:
+            self._clip_type('float',1024,-12.8,100.2)
+            self._clip_type('float',1024,0,0)
+
+            self._clip_type('int',1024,-120,100.5)
+            self._clip_type('int',1024,0,0)
+
+            # XXX fixme
+            #x = self._check_type('uint',1024,-120,100)
+            #assert N.all(x >= 0)
+            x = self._clip_type('uint',1024,0,0)
+
 # Import tests from unicode
 set_local_path()
 from test_unicode import *



From numpy-svn at scipy.org  Fri Mar 30 09:47:25 2007
From: numpy-svn at scipy.org (Canadian Pharmacy)
Date: Fri, 30 Mar 2007 08:47:25 -0500 (CDT)
Subject: [Numpy-svn] st.Doctor Ollie
Message-ID: <20070331134925.23360.qmail@RECEPTION>

An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/numpy-svn/attachments/20070330/ce66645b/attachment.html>

From numpy-svn at scipy.org  Fri Mar 30 11:03:33 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Fri, 30 Mar 2007 10:03:33 -0500 (CDT)
Subject: [Numpy-svn] r3618 - trunk/numpy/core/tests
Message-ID: <20070330150333.69FD239C0C6@new.scipy.org>

Author: stefan
Date: 2007-03-30 10:03:18 -0500 (Fri, 30 Mar 2007)
New Revision: 3618

Modified:
   trunk/numpy/core/tests/test_multiarray.py
Log:
Check clip for byteorder.


Modified: trunk/numpy/core/tests/test_multiarray.py
===================================================================
--- trunk/numpy/core/tests/test_multiarray.py	2007-03-30 13:17:40 UTC (rev 3617)
+++ trunk/numpy/core/tests/test_multiarray.py	2007-03-30 15:03:18 UTC (rev 3618)
@@ -355,17 +355,34 @@
         assert N.all(x >= cmin)
         assert N.all(x <= cmax)
 
-    def _clip_type(self,type_group,amax,cmin,cmax,inplace=False):
+    def _clip_type(self,type_group,array_max,
+                   clip_min,clip_max,inplace=False,
+                   expected_min=None,expected_max=None):
+        if expected_min is None:
+            expected_min = clip_min
+        if expected_max is None:
+            expected_max = clip_max
+
         for T in N.sctypes[type_group]:
-            x = (N.random.random(1000) * amax).astype(T)
-            if inplace:
-                x.clip(cmin,cmax,x)
+            if sys.byteorder == 'little':
+                byte_orders = ['=','>']
             else:
-                x = x.clip(cmin,cmax)
+                byte_orders = ['<','=']
 
-            self._check_range(x,cmin,cmax)
-            return x
+            for byteorder in byte_orders:
+                dtype = N.dtype(T).newbyteorder(byteorder)
+                if dtype.byteorder == '|': byteorder = '|'
 
+                x = (N.random.random(1000) * array_max).astype(dtype)
+                if inplace:
+                    x.clip(clip_min,clip_max,x)
+                else:
+                    x = x.clip(clip_min,clip_max)
+
+                assert_equal(byteorder,x.dtype.byteorder)
+                self._check_range(x,expected_min,expected_max)
+                return x
+
     def check_basic(self):
         for inplace in [False]: # XXX fixme -> ,True]:
             self._clip_type('float',1024,-12.8,100.2)
@@ -375,8 +392,7 @@
             self._clip_type('int',1024,0,0)
 
             # XXX fixme
-            #x = self._check_type('uint',1024,-120,100)
-            #assert N.all(x >= 0)
+            #x = self._check_type('uint',1024,-120,100,expected_min=0)
             x = self._clip_type('uint',1024,0,0)
 
 # Import tests from unicode



From numpy-svn at scipy.org  Fri Mar 30 11:39:38 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Fri, 30 Mar 2007 10:39:38 -0500 (CDT)
Subject: [Numpy-svn] r3619 - trunk/numpy/core/tests
Message-ID: <20070330153938.6E1A939C279@new.scipy.org>

Author: stefan
Date: 2007-03-30 10:39:22 -0500 (Fri, 30 Mar 2007)
New Revision: 3619

Modified:
   trunk/numpy/core/tests/test_multiarray.py
Log:
Add test for record array clipping.


Modified: trunk/numpy/core/tests/test_multiarray.py
===================================================================
--- trunk/numpy/core/tests/test_multiarray.py	2007-03-30 15:03:18 UTC (rev 3618)
+++ trunk/numpy/core/tests/test_multiarray.py	2007-03-30 15:39:22 UTC (rev 3619)
@@ -395,6 +395,13 @@
             #x = self._check_type('uint',1024,-120,100,expected_min=0)
             x = self._clip_type('uint',1024,0,0)
 
+    # XXX fixme
+    def check_record_array(self,level=2):
+        rec = N.array([(-5, 2.0, 3.0), (5.0, 4.0, 3.0)],
+                      dtype=[('x', '<f8'), ('y', '<f8'), ('z', '<f8')])
+        rec['x'].clip(-0.3,0.5)
+        self._check_range(rec['x'],-0.3,0.5)
+
 # Import tests from unicode
 set_local_path()
 from test_unicode import *



From numpy-svn at scipy.org  Fri Mar 30 14:19:14 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Fri, 30 Mar 2007 13:19:14 -0500 (CDT)
Subject: [Numpy-svn] r3620 - in trunk/numpy/core: . src tests
Message-ID: <20070330181914.7F30939C03E@new.scipy.org>

Author: oliphant
Date: 2007-03-30 13:19:07 -0500 (Fri, 30 Mar 2007)
New Revision: 3620

Modified:
   trunk/numpy/core/ma.py
   trunk/numpy/core/src/arrayobject.c
   trunk/numpy/core/tests/test_ma.py
Log:
Allow Boolean mask indexing for 0-d arrays.

Modified: trunk/numpy/core/ma.py
===================================================================
--- trunk/numpy/core/ma.py	2007-03-30 15:39:22 UTC (rev 3619)
+++ trunk/numpy/core/ma.py	2007-03-30 18:19:07 UTC (rev 3620)
@@ -1264,14 +1264,6 @@
                     value = numeric.array(value, dtype=object)
                     d = d.astype(object)
                     result = fromnumeric.choose(m, (d, value))
-                except IndexError:
-                    #ok, if scalar
-                    if d.shape:
-                        raise
-                    elif m:
-                        result = numeric.array(value, dtype=d.dtype)
-                    else:
-                        result = d
             return result
 
     def ids (self):

Modified: trunk/numpy/core/src/arrayobject.c
===================================================================
--- trunk/numpy/core/src/arrayobject.c	2007-03-30 15:39:22 UTC (rev 3619)
+++ trunk/numpy/core/src/arrayobject.c	2007-03-30 18:19:07 UTC (rev 3620)
@@ -2720,6 +2720,25 @@
                                 return NULL;
                         return add_new_axes_0d(self, nd);
                 }
+                /* Allow Boolean mask selection also */
+                if (PyBool_Check(op) || PyArray_IsScalar(op, Bool) || 
+                    (PyArray_Check(op) && (PyArray_DIMS(op)==0) &&
+                     PyArray_ISBOOL(op))) {
+                        if (PyObject_IsTrue(op)) {
+                                Py_INCREF(self);
+                                return (PyObject *)self;
+                        }
+                        else {
+                                oned = 0;
+                                Py_INCREF(self->descr);
+                                return PyArray_NewFromDescr(self->ob_type,
+                                                            self->descr,
+                                                            1, &oned,
+                                                            NULL, NULL,
+                                                            NPY_DEFAULT,
+                                                            NULL);
+                        }
+                }
                 PyErr_SetString(PyExc_IndexError,
                                 "0-d arrays can't be indexed.");
                 return NULL;
@@ -2886,10 +2905,27 @@
         }
 
         if (self->nd == 0) {
+                /* Several different exceptions to the 0-d no-indexing rule
+
+                   1) ellipses
+                   2) empty tuple
+                   3) Using newaxis (None)
+                   4) Boolean mask indexing
+                */
                 if (index == Py_Ellipsis || index == Py_None ||         \
                     (PyTuple_Check(index) && (0 == PyTuple_GET_SIZE(index) || \
                                               count_new_axes_0d(index) > 0)))
                         return self->descr->f->setitem(op, self->data, self);
+                if (PyBool_Check(index) || PyArray_IsScalar(index, Bool) ||
+                    (PyArray_Check(index) && (PyArray_DIMS(index)==0) &&
+                     PyArray_ISBOOL(index))) {
+                        if (PyObject_IsTrue(index)) {
+                                return self->descr->f->setitem(op, self->data, self);
+                        }
+                        else { /* don't do anything */
+                                return 0;
+                        }
+                }
                 PyErr_SetString(PyExc_IndexError,
                                 "0-d arrays can't be indexed.");
                 return -1;
@@ -3008,6 +3044,10 @@
                 Bool noellipses = TRUE;
                 if (op == Py_Ellipsis)
                         noellipses = FALSE;
+                else if (PyBool_Check(op) || PyArray_IsScalar(op, Bool) || 
+                         (PyArray_Check(op) && (PyArray_DIMS(op)==0) &&
+                          PyArray_ISBOOL(op))) 
+                        noellipses = FALSE;
                 else if (PySequence_Check(op)) {
                         int n, i;
                         PyObject *temp;

Modified: trunk/numpy/core/tests/test_ma.py
===================================================================
--- trunk/numpy/core/tests/test_ma.py	2007-03-30 15:39:22 UTC (rev 3619)
+++ trunk/numpy/core/tests/test_ma.py	2007-03-30 18:19:07 UTC (rev 3620)
@@ -611,7 +611,7 @@
         self.failUnless(minimum(xm, xm).mask)
         self.failUnless(xm.filled().dtype is xm.data.dtype)
         x = array(0, mask=0)
-        self.failUnless(x.filled() is x.data)
+        self.failUnless(x.filled() == x.data)
         self.failUnlessEqual(str(xm), str(masked_print_option))
 
     def check_testArrayMethods(self):



From numpy-svn at scipy.org  Fri Mar 30 14:25:17 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Fri, 30 Mar 2007 13:25:17 -0500 (CDT)
Subject: [Numpy-svn] r3621 - trunk/numpy/core/src
Message-ID: <20070330182517.6F1D939C018@new.scipy.org>

Author: oliphant
Date: 2007-03-30 13:25:12 -0500 (Fri, 30 Mar 2007)
New Revision: 3621

Modified:
   trunk/numpy/core/src/arraymethods.c
   trunk/numpy/core/src/arrayobject.c
Log:
Change 0-d record arrays so that field selection returns a 0-d array and not a scalar.

Modified: trunk/numpy/core/src/arraymethods.c
===================================================================
--- trunk/numpy/core/src/arraymethods.c	2007-03-30 18:19:07 UTC (rev 3620)
+++ trunk/numpy/core/src/arraymethods.c	2007-03-30 18:25:12 UTC (rev 3621)
@@ -264,7 +264,7 @@
 					 PyArray_DescrConverter,
 					 &dtype, &offset)) return NULL;
 
-	return _ARET(PyArray_GetField(self, dtype, offset));
+	return PyArray_GetField(self, dtype, offset);
 }
 
 

Modified: trunk/numpy/core/src/arrayobject.c
===================================================================
--- trunk/numpy/core/src/arrayobject.c	2007-03-30 18:19:07 UTC (rev 3620)
+++ trunk/numpy/core/src/arrayobject.c	2007-03-30 18:25:12 UTC (rev 3621)
@@ -3042,7 +3042,7 @@
 
         if (mp->nd == 0) {
                 Bool noellipses = TRUE;
-                if (op == Py_Ellipsis)
+                if ((op == Py_Ellipsis) || PyString_Check(op) || PyUnicode_Check(op))
                         noellipses = FALSE;
                 else if (PyBool_Check(op) || PyArray_IsScalar(op, Bool) || 
                          (PyArray_Check(op) && (PyArray_DIMS(op)==0) &&



From numpy-svn at scipy.org  Fri Mar 30 15:25:18 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Fri, 30 Mar 2007 14:25:18 -0500 (CDT)
Subject: [Numpy-svn] r3622 - trunk/numpy/core/src
Message-ID: <20070330192518.01B5339C018@new.scipy.org>

Author: oliphant
Date: 2007-03-30 14:25:13 -0500 (Fri, 30 Mar 2007)
New Revision: 3622

Modified:
   trunk/numpy/core/src/multiarraymodule.c
Log:
Add comments to PyArray_Clip.  Fix unsigned problem.  Fix reference counting problem with the output array to fix certain memory errors.

Modified: trunk/numpy/core/src/multiarraymodule.c
===================================================================
--- trunk/numpy/core/src/multiarraymodule.c	2007-03-30 18:25:12 UTC (rev 3621)
+++ trunk/numpy/core/src/multiarraymodule.c	2007-03-30 19:25:13 UTC (rev 3622)
@@ -1146,6 +1146,7 @@
         PyArrayObject *mina=NULL;
         PyArrayObject *newout=NULL, *newin=NULL;
         PyArray_Descr *indescr;
+        PyObject *zero;
 
         func = self->descr->f->fastclip;
         if (func == NULL || !PyArray_CheckAnyScalar(min) || 
@@ -1163,14 +1164,47 @@
                 Py_INCREF(indescr);
         }
 
+        /* Convert max to an array */
         maxa = (NPY_AO *)PyArray_FromAny(max, indescr, 0, 0, 
                                          NPY_DEFAULT, NULL);
         if (maxa == NULL) return NULL;
         Py_INCREF(indescr);
+
+
+        /* If we are unsigned, then make sure min is not <0 */
+        /* This is to match the behavior of 
+           _slow_array_clip
+
+           We allow min and max to go beyond the limits
+           for other data-types in which case they
+           are interpreted as their modular counterparts.
+        */           
+        if (PyArray_ISUNSIGNED(self)) {
+                int cmp;
+                zero = PyInt_FromLong(0);
+                cmp = PyObject_RichCompareBool(min, zero, Py_LT);
+                if (cmp == -1) { Py_DECREF(zero); goto fail;}
+                if (cmp == 1) {
+                        min = zero;
+                }
+                else {
+                        Py_DECREF(zero);
+                        Py_INCREF(min);
+                }
+        }
+        else {
+                Py_INCREF(min);
+        }
+
+        /* Convert min to an array */
         mina = (NPY_AO *)PyArray_FromAny(min, indescr, 0, 0, 
                                          NPY_DEFAULT, NULL);
+        Py_DECREF(min);
         if (mina == NULL) goto fail;
-        
+
+
+        /* Check to see if input is single-segment, aligned,
+           and in native byteorder */
         if (PyArray_ISONESEGMENT(self) && PyArray_CHKFLAGS(self, ALIGNED) &&
             PyArray_ISNOTSWAPPED(self)) 
                 ingood = 1;
@@ -1188,6 +1222,11 @@
                 Py_INCREF(newin);
         }
 
+        /* At this point, newin is a single-segment, aligned, and correct
+           byte-order array if ingood == 0, then it is a copy, otherwise, 
+           it is the input
+        */
+
         /* If we have already made a copy of the data, then use 
            that as the output array
         */
@@ -1198,7 +1237,7 @@
         /* Now, we know newin is a usable array for fastclip,
            we need to make sure the output array is available
            and usable */
-        if (out == NULL) {
+        if (out == NULL) { 
                 Py_INCREF(indescr);
                 out = (NPY_AO*)PyArray_NewFromDescr(self->ob_type,
 						    indescr, self->nd, 
@@ -1209,6 +1248,7 @@
                 if (out == NULL) goto fail;
                 outgood = 1;
         }
+        else Py_INCREF(out);
         /* Input is good at this point */
         if (out == newin) {
                 outgood = 1;



From numpy-svn at scipy.org  Fri Mar 30 19:47:46 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Fri, 30 Mar 2007 18:47:46 -0500 (CDT)
Subject: [Numpy-svn] r3623 - in trunk/numpy/core: include/numpy src tests
Message-ID: <20070330234746.BD1F539C0C3@new.scipy.org>

Author: oliphant
Date: 2007-03-30 18:47:39 -0500 (Fri, 30 Mar 2007)
New Revision: 3623

Modified:
   trunk/numpy/core/include/numpy/ndarrayobject.h
   trunk/numpy/core/src/arrayobject.c
   trunk/numpy/core/src/arraytypes.inc.src
   trunk/numpy/core/src/multiarraymodule.c
   trunk/numpy/core/tests/test_multiarray.py
   trunk/numpy/core/tests/test_numeric.py
Log:
Add tests for clipping.  Also some good tests on choose and convert_tocommontype.  Fix some mixed-type problems.  Fix problems with clipping. More to be done to close ticket #425

Modified: trunk/numpy/core/include/numpy/ndarrayobject.h
===================================================================
--- trunk/numpy/core/include/numpy/ndarrayobject.h	2007-03-30 19:25:13 UTC (rev 3622)
+++ trunk/numpy/core/include/numpy/ndarrayobject.h	2007-03-30 23:47:39 UTC (rev 3623)
@@ -1760,7 +1760,10 @@
 #define PyArray_ISBEHAVED_RO(m) PyArray_FLAGSWAP(m, NPY_ALIGNED)
 
 
+#define PyDataType_ISNOTSWAPPED(d) PyArray_ISNBO(((PyArray_Descr *)(d))->byteorder)
 
+
+
 /* This is the form of the struct that's returned pointed by the
    PyCObject attribute of an array __array_struct__. See
    http://numeric.scipy.org/array_interface.html for the full

Modified: trunk/numpy/core/src/arrayobject.c
===================================================================
--- trunk/numpy/core/src/arrayobject.c	2007-03-30 19:25:13 UTC (rev 3622)
+++ trunk/numpy/core/src/arrayobject.c	2007-03-30 23:47:39 UTC (rev 3623)
@@ -10389,6 +10389,7 @@
                 PyErr_Format(PyExc_ValueError,
                              "Need between 2 and (%d) "                 \
                              "array objects (inclusive).", NPY_MAXARGS);
+                return NULL;
         }
 
         /* fprintf(stderr, "multi new...");*/
@@ -11972,6 +11973,17 @@
 }
 
 
+static int
+arrayflags_compare(PyArrayFlagsObject *self, PyArrayFlagsObject *other)
+{
+        if (self->flags == other->flags)
+                return 0;
+        else if (self->flags < other->flags)
+                return -1;
+        else
+                return 1;
+}
+
 static PyMappingMethods arrayflags_as_mapping = {
 #if PY_VERSION_HEX >= 0x02050000
         (lenfunc)NULL,                       /*mp_length*/
@@ -12009,7 +12021,7 @@
         0,                                      /* tp_print */
         0,                                      /* tp_getattr */
         0,                                      /* tp_setattr */
-        0,                                      /* tp_compare */
+        (cmpfunc)arrayflags_compare,            /* tp_compare */
         (reprfunc)arrayflags_print,             /* tp_repr */
         0,                                      /* tp_as_number */
         0,                                      /* tp_as_sequence */

Modified: trunk/numpy/core/src/arraytypes.inc.src
===================================================================
--- trunk/numpy/core/src/arraytypes.inc.src	2007-03-30 19:25:13 UTC (rev 3622)
+++ trunk/numpy/core/src/arraytypes.inc.src	2007-03-30 23:47:39 UTC (rev 3623)
@@ -2069,7 +2069,7 @@
         @type@ max_val, min_val;
         
         max_val = *max;
-        min_val = *min;        
+        min_val = *min;
   
         for (i = 0; i < ni; i++) {   
                 if (in[i] < min_val) { 
@@ -2093,14 +2093,14 @@
         register npy_intp i; 
         @type@ max_val, min_val;
         
+        min_val = *min;
         max_val = *max;
-        min_val = *min;
- 
+
         for (i = 0; i < ni; i++) {   
-                if (PyArray_CLT(in[i], max_val)) { 
-                        out[i]   = max_val; 
+                if (PyArray_CLT(in[i], min_val)) { 
+                        out[i] = min_val;
                 } else if (PyArray_CGT(in[i], max_val)) { 
-                        out[i]   = max_val; 
+                        out[i] = max_val;
                 } 
         }         
         return;

Modified: trunk/numpy/core/src/multiarraymodule.c
===================================================================
--- trunk/numpy/core/src/multiarraymodule.c	2007-03-30 19:25:13 UTC (rev 3622)
+++ trunk/numpy/core/src/multiarraymodule.c	2007-03-30 23:47:39 UTC (rev 3623)
@@ -1145,7 +1145,7 @@
         PyArrayObject *maxa=NULL;
         PyArrayObject *mina=NULL;
         PyArrayObject *newout=NULL, *newin=NULL;
-        PyArray_Descr *indescr;
+        PyArray_Descr *indescr, *newdescr;
         PyObject *zero;
 
         func = self->descr->f->fastclip;
@@ -1155,20 +1155,40 @@
 
         /* Use the fast scalar clip function */
 
-        if (PyArray_ISNOTSWAPPED(self)) {
-                indescr = PyArray_DescrNewByteorder(self->descr, '=');
-                if (indescr == NULL) goto fail;
+        /* First we need to figure out the correct type */
+        indescr = PyArray_DescrFromObject(min, NULL);
+        if (indescr == NULL) return NULL;
+        newdescr = PyArray_DescrFromObject(max, indescr);
+        Py_DECREF(indescr);
+
+        if (newdescr == NULL) return NULL;
+        /* Use the scalar descriptor only if it is of a bigger
+           KIND than the input array (and then find the
+           type that matches both).
+        */
+        if (PyArray_ScalarKind(newdescr->type_num, NULL) > 
+            PyArray_ScalarKind(self->descr->type_num, NULL)) {
+                indescr = _array_small_type(newdescr, self->descr);
+                func = indescr->f->fastclip;
         }
-        else { 
+        else {
                 indescr = self->descr;
                 Py_INCREF(indescr);
         }
+        Py_DECREF(newdescr);
+        
+        if (!PyDataType_ISNOTSWAPPED(indescr)) {
+                PyArray_Descr *descr2;
+                descr2 = PyArray_DescrNewByteorder(indescr, '=');
+                Py_DECREF(indescr);
+                if (descr2 == NULL) goto fail;
+                indescr = descr2;
+        }
 
         /* Convert max to an array */
         maxa = (NPY_AO *)PyArray_FromAny(max, indescr, 0, 0, 
                                          NPY_DEFAULT, NULL);
         if (maxa == NULL) return NULL;
-        Py_INCREF(indescr);
 
 
         /* If we are unsigned, then make sure min is not <0 */
@@ -1197,6 +1217,7 @@
         }
 
         /* Convert min to an array */
+        Py_INCREF(indescr);
         mina = (NPY_AO *)PyArray_FromAny(min, indescr, 0, 0, 
                                          NPY_DEFAULT, NULL);
         Py_DECREF(min);
@@ -1206,7 +1227,7 @@
         /* Check to see if input is single-segment, aligned,
            and in native byteorder */
         if (PyArray_ISONESEGMENT(self) && PyArray_CHKFLAGS(self, ALIGNED) &&
-            PyArray_ISNOTSWAPPED(self)) 
+            PyArray_ISNOTSWAPPED(self) && (self->descr == indescr))
                 ingood = 1;
 
         if (!ingood) {
@@ -1223,8 +1244,10 @@
         }
 
         /* At this point, newin is a single-segment, aligned, and correct
-           byte-order array if ingood == 0, then it is a copy, otherwise, 
-           it is the input
+           byte-order array of the correct type
+
+           if ingood == 0, then it is a copy, otherwise, 
+           it is the original input.
         */
 
         /* If we have already made a copy of the data, then use 
@@ -1254,8 +1277,8 @@
                 outgood = 1;
         }
         if (!outgood && PyArray_ISONESEGMENT(out) && 
-            PyArray_CHKFLAGS(out, ALIGNED) && 
-            PyArray_ISNOTSWAPPED(out)) {
+            PyArray_CHKFLAGS(out, ALIGNED) && PyArray_ISNOTSWAPPED(out) &&  
+            PyArray_EquivTypes(out->descr, indescr)) {
                 outgood = 1;
         }
 
@@ -1263,20 +1286,12 @@
         /* Create one, now */
         if (!outgood) {
                 int oflags;
-                PyArray_Descr *odescr;
                 if (PyArray_ISFORTRAN(out)) 
-                        oflags = NPY_FARRAY | NPY_UPDATEIFCOPY;
+                        oflags = NPY_FARRAY;
                 else 
-                        oflags = NPY_CARRAY | NPY_UPDATEIFCOPY;
-                if (PyArray_ISNOTSWAPPED(out)) {
-                        odescr = PyArray_DescrNewByteorder(out->descr, '=');
-                        if (odescr == NULL) goto fail;
-                }
-                else { 
-                        odescr = out->descr;
-                        Py_INCREF(odescr);
-                }
-                newout = (NPY_AO*)PyArray_FromArray(out, odescr, oflags);
+                        oflags = NPY_CARRAY;
+                oflags |= NPY_UPDATEIFCOPY | NPY_FORCECAST;
+                newout = (NPY_AO*)PyArray_FromArray(out, indescr, oflags);
                 if (newout == NULL) goto fail;
         }
         else {
@@ -1284,6 +1299,13 @@
                 Py_INCREF(newout);
         }
 
+        /* make sure the shape of the output array is the same */
+        if (!PyArray_SAMESHAPE(newin, newout)) {
+                PyErr_SetString(PyExc_ValueError, "clip: Output array must have the"
+                                "same shape as the input.");
+                goto fail;
+        }
+
         if (newout->data != newin->data) {
                 memcpy(newout->data, newin->data, PyArray_NBYTES(newin));
         }
@@ -1305,6 +1327,7 @@
         Py_XDECREF(maxa);
         Py_XDECREF(mina);
         Py_XDECREF(newin);
+        PyArray_XDECREF_ERR(newout);
         return NULL;
 }
 
@@ -2130,19 +2153,21 @@
                    handles both intype and stype
                    and don't forcecast the scalars.
                 */
-                
+
                 if (!PyArray_CanCoerceScalar(stype->type_num,
                                              intype->type_num,
                                              scalarkind)) {
+                        newtype = _array_small_type(intype, stype);
                         Py_XDECREF(intype);
-                        intype = stype;
-                        Py_INCREF(stype);
+                        intype = newtype;
                 }
                 for (i=0; i<n; i++) {
                         Py_XDECREF(mps[i]);
                         mps[i] = NULL;
                 }
         }
+
+
 	/* Make sure all arrays are actual array objects. */
 	for(i=0; i<n; i++) {
 		int flags = CARRAY;
@@ -2217,25 +2242,23 @@
 		sizes[i] = PyArray_NBYTES(mps[i]);
 	}
 
-        Py_INCREF(mps[0]->descr);
         if (!ret) {
+                Py_INCREF(mps[0]->descr);
                 ret = (PyArrayObject *)PyArray_NewFromDescr(ap->ob_type,
                                                             mps[0]->descr,
                                                             ap->nd,
                                                             ap->dimensions,
                                                             NULL, NULL, 0,
                                                             (PyObject *)ap);
-                if (ret == NULL) goto fail;
         }
         else {
                 PyArrayObject *obj;
-                int flags = NPY_CARRAY | NPY_UPDATEIFCOPY;
+                int flags = NPY_CARRAY | NPY_UPDATEIFCOPY | NPY_FORCECAST;
 
                 if (PyArray_SIZE(ret) != PyArray_SIZE(ap)) {
                         PyErr_SetString(PyExc_TypeError,
                                         "invalid shape for output array.");
                         ret = NULL;
-                        Py_DECREF(mps[0]->descr);
                         goto fail;
                 }
                 if (clipmode == NPY_RAISE) {
@@ -2245,12 +2268,14 @@
                         */
                         flags |= NPY_ENSURECOPY;
                 }
+                Py_INCREF(mps[0]->descr);
                 obj = (PyArrayObject *)PyArray_FromArray(ret, mps[0]->descr,
                                                          flags);
                 if (obj != ret) copyret = 1;
                 ret = obj;
         }
 
+        if (ret == NULL) goto fail;
 	elsize = ret->descr->elsize;
 	m = PyArray_SIZE(ret);
 	self_data = (intp *)ap->data;

Modified: trunk/numpy/core/tests/test_multiarray.py
===================================================================
--- trunk/numpy/core/tests/test_multiarray.py	2007-03-30 19:25:13 UTC (rev 3622)
+++ trunk/numpy/core/tests/test_multiarray.py	2007-03-30 23:47:39 UTC (rev 3623)
@@ -371,7 +371,6 @@
 
             for byteorder in byte_orders:
                 dtype = N.dtype(T).newbyteorder(byteorder)
-                if dtype.byteorder == '|': byteorder = '|'
 
                 x = (N.random.random(1000) * array_max).astype(dtype)
                 if inplace:
@@ -379,28 +378,27 @@
                 else:
                     x = x.clip(clip_min,clip_max)
 
+                if x.dtype.byteorder == '|': byteorder = '|'
                 assert_equal(byteorder,x.dtype.byteorder)
                 self._check_range(x,expected_min,expected_max)
                 return x
 
     def check_basic(self):
-        for inplace in [False]: # XXX fixme -> ,True]:
-            self._clip_type('float',1024,-12.8,100.2)
-            self._clip_type('float',1024,0,0)
+        for inplace in [False, True]: 
+            self._clip_type('float',1024,-12.8,100.2, inplace=inplace)
+            self._clip_type('float',1024,0,0, inplace=inplace)
 
-            self._clip_type('int',1024,-120,100.5)
-            self._clip_type('int',1024,0,0)
+            self._clip_type('int',1024,-120,100.5, inplace=inplace)
+            self._clip_type('int',1024,0,0, inplace=inplace)
 
-            # XXX fixme
-            #x = self._check_type('uint',1024,-120,100,expected_min=0)
-            x = self._clip_type('uint',1024,0,0)
+            x = self._clip_type('uint',1024,-120,100,expected_min=0, inplace=inplace)
+            x = self._clip_type('uint',1024,0,0, inplace=inplace)
 
-    # XXX fixme
-    def check_record_array(self,level=2):
+    def check_record_array(self):
         rec = N.array([(-5, 2.0, 3.0), (5.0, 4.0, 3.0)],
                       dtype=[('x', '<f8'), ('y', '<f8'), ('z', '<f8')])
-        rec['x'].clip(-0.3,0.5)
-        self._check_range(rec['x'],-0.3,0.5)
+        y = rec['x'].clip(-0.3,0.5)
+        self._check_range(y,-0.3,0.5)
 
 # Import tests from unicode
 set_local_path()

Modified: trunk/numpy/core/tests/test_numeric.py
===================================================================
--- trunk/numpy/core/tests/test_numeric.py	2007-03-30 19:25:13 UTC (rev 3622)
+++ trunk/numpy/core/tests/test_numeric.py	2007-03-30 23:47:39 UTC (rev 3623)
@@ -1,5 +1,5 @@
 from numpy.core import *
-from numpy.random import rand, randint
+from numpy.random import rand, randint, randn
 from numpy.testing import *
 from numpy.core.multiarray import dot as dot_
 import sys
@@ -248,6 +248,419 @@
     def test_large(self):
         assert_equal(binary_repr(10736848),'101000111101010011010000')
 
+
+def assert_array_strict_equal(x, y):
+    assert_array_equal(x, y)
+    # Check flags
+    assert x.flags == y.flags
+    # check endianness
+    assert x.dtype.isnative == y.dtype.isnative
+
+
+class test_clip(NumpyTestCase):
+    def setUp(self):
+        self.nr = 5
+        self.nc = 3
+
+    def fastclip(self, a, m, M, out=None):
+        if out is None:
+            return a.clip(m,M)
+        else:
+            return a.clip(m,M,out)
+
+    def clip(self, a, m, M, out=None):
+        # use slow-clip
+        selector = less(a, m)+2*greater(a, M)
+        return selector.choose((a, m, M), out=out)
+    
+    # Handy functions
+    def _generate_data(self, n, m):
+        return randn(n, m)
+
+    def _generate_data_complex(self, n, m):
+        return randn(n, m) + 1.j *rand(n, m) 
+
+    def _generate_flt_data(self, n, m):
+        return (randn(n, m)).astype(float32)
+
+    def _neg_byteorder(self, a):
+        import sys
+        a   = asarray(a)
+        if sys.byteorder == 'little':
+            a   = a.astype(a.dtype.newbyteorder('>'))
+        else:
+            a   = a.astype(a.dtype.newbyteorder('<'))
+        return a
+
+    def _generate_non_native_data(self, n, m):
+        data    = randn(n, m)
+        data    = self._neg_byteorder(data)
+        assert not data.dtype.isnative
+        return data
+
+    def _generate_int_data(self, n, m):
+        return (10 * rand(n, m)).astype(int64)
+
+    def _generate_int32_data(self, n, m):
+        return (10 * rand(n, m)).astype(int32)
+
+    # Now the real test cases
+    def test_simple_double(self):
+        print "=== testing native double input with scalar min/max ==="
+        a   = self._generate_data(self.nr, self.nc)
+        m   = 0.1
+        M   = 0.6
+        ac  = self.fastclip(a, m, M)
+        act = self.clip(a, m, M)
+        assert_array_strict_equal(ac, act)
+
+    def test_simple_int(self):
+        print "=== testing native int input with scalar min/max ==="
+        a   = self._generate_int_data(self.nr, self.nc)
+        a   = a.astype(int) 
+        m   = -2
+        M   = 4
+        ac  = self.fastclip(a, m, M)
+        act = self.clip(a, m, M)
+        assert_array_strict_equal(ac, act)
+
+    def test_array_double(self):
+        print "=== testing native double input with array min/max ==="
+        a   = self._generate_data(self.nr, self.nc)
+        m   = zeros(a.shape)
+        M   = m + 0.5
+        ac  = self.fastclip(a, m, M)
+        act = self.clip(a, m, M)
+        assert_array_strict_equal(ac, act)
+
+    def test_simple_nonnative(self):
+        print "=== testing non native double input with scalar min/max ==="
+        a   = self._generate_non_native_data(self.nr, self.nc)
+        m   = -0.5
+        M   = 0.6
+        ac  = self.fastclip(a, m, M)
+        act = self.clip(a, m, M)
+        assert_array_equal(ac, act)
+
+        print "=== testing native double input with non native double scalar min/max ==="
+        a   = self._generate_data(self.nr, self.nc)
+        m   = -0.5
+        M   = self._neg_byteorder(0.6)
+        assert not M.dtype.isnative
+        ac  = self.fastclip(a, m, M)
+        act = self.clip(a, m, M)
+        assert_array_equal(ac, act)
+
+    def test_simple_complex(self):
+        print "=== testing native complex input with native double scalar min/max ==="
+        a   = 3 * self._generate_data_complex(self.nr, self.nc)
+        m   = -0.5
+        M   = 1.
+        ac  = self.fastclip(a, m, M)
+        act = self.clip(a, m, M)
+        assert_array_strict_equal(ac, act)
+
+        print "=== testing native input with complex double scalar min/max ==="
+        a   = 3 * self._generate_data(self.nr, self.nc)
+        m   = -0.5 + 1.j
+        M   = 1. + 2.j
+        ac  = self.fastclip(a, m, M)
+        act = self.clip(a, m, M)
+        assert_array_strict_equal(ac, act)
+
+    def test_clip_non_contig(self):
+        print "=== testing clip for non contiguous native input and native scalar min/max ==="
+        a   = self._generate_data(self.nr * 2, self.nc * 3)
+        a   = a[::2, ::3]
+        assert not a.flags['F_CONTIGUOUS']
+        assert not a.flags['C_CONTIGUOUS']
+        ac  = self.fastclip(a, -1.6, 1.7)
+        act = self.clip(a, -1.6, 1.7)
+        assert_array_strict_equal(ac, act)
+
+    def test_simple_out(self):
+        print "=== testing native double input with scalar min/max ==="
+        a   = self._generate_data(self.nr, self.nc)
+        m   = -0.5
+        M   = 0.6
+        ac  = zeros(a.shape)
+        act = zeros(a.shape)
+        self.fastclip(a, m, M, ac)
+        self.clip(a, m, M, act)
+        assert_array_strict_equal(ac, act)
+
+    def test_simple_int32_inout(self):
+        print "=== testing native int32 input with double min/max and int32 out ==="
+        a   = self._generate_int32_data(self.nr, self.nc)
+        m   = float64(0)
+        M   = float64(2)
+        ac  = zeros(a.shape, dtype = int32)
+        act = ac.copy()
+        self.fastclip(a, m, M, ac)
+        self.clip(a, m, M, act)
+        assert_array_strict_equal(ac, act)
+
+    def test_simple_int64_out(self):
+        print "=== testing native int32 input with int32 scalar min/max and int64 out ==="
+        a   = self._generate_int32_data(self.nr, self.nc)
+        m   = int32(-1)
+        M   = int32(1)
+        ac  = zeros(a.shape, dtype = int64)
+        act = ac.copy()
+        self.fastclip(a, m, M, ac)
+        self.clip(a, m, M, act)
+        assert_array_strict_equal(ac, act)
+
+    def test_simple_int64_inout(self):
+        print "=== testing native in32 input with double array min/max and int32 out ==="
+        a   = self._generate_int32_data(self.nr, self.nc)
+        m   = zeros(a.shape, float64)
+        M   = float64(1)
+        ac  = zeros(a.shape, dtype = int32)
+        act = ac.copy()
+        self.fastclip(a, m, M, ac)
+        self.clip(a, m, M, act)
+        assert_array_strict_equal(ac, act)
+
+    def test_simple_int32_out(self):
+        print "=== testing native double input with scalar min/max and int out ==="
+        a   = self._generate_data(self.nr, self.nc)
+        m   = -1.0
+        M   = 2.0
+        ac  = zeros(a.shape, dtype = int32)
+        act = ac.copy()
+        self.fastclip(a, m, M, ac)
+        self.clip(a, m, M, act)
+        assert_array_strict_equal(ac, act)
+
+    def test_simple_inplace(self):
+        print "=== INPLACE: testing native double input with array min/max ==="
+        a   = self._generate_data(self.nr, self.nc)
+        ac  = a.copy()
+        m   = zeros(a.shape)
+        M   = 1.0
+        self.fastclip(a, m, M, a)
+        self.clip(a, m, M, ac)
+        assert_array_strict_equal(a, ac)
+
+        print "=== INPLACE: testing native double input with scalar min/max ==="
+        a   = self._generate_data(self.nr, self.nc)
+        ac  = a.copy()
+        m   = -0.5
+        M   = 0.6
+        self.fastclip(a, m, M, a)
+        self.clip(a, m, M, ac)
+        assert_array_strict_equal(a, ac)
+
+    def test_noncontig_inplace(self):
+        print "=== INPLACE: testing non contiguous double input with double scalar min/max ==="
+        a   = self._generate_data(self.nr * 2, self.nc * 3)
+        a   = a[::2, ::3]
+        assert not a.flags['F_CONTIGUOUS']
+        assert not a.flags['C_CONTIGUOUS']
+        ac  = a.copy()
+        m   = -0.5
+        M   = 0.6
+        self.fastclip(a, m, M, a)
+        self.clip(a, m, M, ac)
+        assert_array_equal(a, ac)
+
+    def test_type_cast(self):
+        print "\n==================="
+        print "    Test cast      "
+        print "==================="
+
+        print "=== testing native double input with scalar min/max ==="
+        a   = self._generate_data(self.nr, self.nc)
+        m   = -0.5
+        M   = 0.6
+        ac  = self.fastclip(a, m, M)
+        act = self.clip(a, m, M)
+        assert_array_strict_equal(ac, act)
+
+        print "=== testing native int32 input with int32 scalar min/max ==="
+        a   = self._generate_int_data(self.nr, self.nc)
+        a   = a.astype(int32) 
+        m   = -2
+        M   = 4
+        ac  = self.fastclip(a, m, M)
+        act = self.clip(a, m, M)
+        assert_array_strict_equal(ac, act)
+
+        print "=== testing native int32 input with float64 scalar min/max ==="
+        a   = self._generate_int32_data(self.nr, self.nc)
+        m   = -2
+        M   = 4
+        ac  = self.fastclip(a, float64(m), float64(M))
+        act = self.clip(a, float64(m), float64(M))
+        assert_array_strict_equal(ac, act)
+
+        print "=== testing native int32 input with float32 scalar min/max ==="
+        a   = self._generate_int32_data(self.nr, self.nc)
+        m   = float32(-2)
+        M   = float32(4)
+        act = self.fastclip(a,m,M)
+        ac  = self.clip(a,m,M)
+        assert_array_strict_equal(ac, act)        
+
+        print "=== testing native int32 with double arrays min/max ==="
+        a   = self._generate_int_data(self.nr, self.nc)
+        m   = -0.5
+        M   = 1.
+        ac  = self.fastclip(a, m * zeros(a.shape), M)
+        act = self.clip(a, m * zeros(a.shape), M)
+        assert_array_strict_equal(ac, act)
+
+ 
+        print "=== testing native with NON native scalar min/max==="
+        a   = self._generate_data(self.nr, self.nc)
+        m   = 0.5
+        m_s = self._neg_byteorder(m)
+        M   = 1.
+        act = self.clip(a, m_s, M)
+        ac  = self.fastclip(a, m_s, M)
+        assert_array_strict_equal(ac, act)
+
+        print "=== testing NON native with native array min/max==="
+        a   = self._generate_data(self.nr, self.nc)
+        m   = -0.5 * ones(a.shape)
+        M   = 1.
+        a_s = self._neg_byteorder(a)
+        assert not a_s.dtype.isnative
+        act = a_s.clip(m, M)
+        ac  = self.fastclip(a_s, m, M)
+        assert_array_strict_equal(ac, act)
+
+        print "=== testing NON native with native scalar min/max==="
+        a   = self._generate_data(self.nr, self.nc)
+        m   = -0.5
+        M   = 1.
+        a_s = self._neg_byteorder(a)
+        assert not a_s.dtype.isnative
+        ac  = self.fastclip(a_s, m , M)
+        act = a_s.clip(m, M)
+        assert_array_strict_equal(ac, act)
+
+        print "=== testing native with NON native array min/max==="
+        a   = self._generate_data(self.nr, self.nc)
+        m   = -0.5 * ones(a.shape)
+        M   = 1.
+        m_s = self._neg_byteorder(m)
+        assert not m_s.dtype.isnative
+        ac  = self.fastclip(a, m_s , M)
+        act = self.clip(a, m_s, M)
+        assert_array_strict_equal(ac, act)
+
+        print "=== OUT arg: testing native int32 with float min/max and float out ==="
+        a   = self._generate_int_data(self.nr, self.nc)
+        b   = zeros(a.shape, dtype = float32)
+        m   = float32(-0.5)
+        M   = float32(1)
+        act = self.clip(a, m, M, out = b)
+        ac  = self.fastclip(a, m , M, out = b)
+        assert_array_strict_equal(ac, act)
+
+##        # This looks like a data-type descriptor is not reference
+##        #  counted right.
+##        print "=== OUT arg: testing non native with native scalar, min/max, " + \
+##              "out non native==="
+##        a   = self._generate_non_native_data(self.nr, self.nc)
+##        b   = a.copy()
+##        b   = b.astype(b.dtype.newbyteorder('>'))
+##        bt  = b.copy()
+##        m   = -0.5
+##        M   = 1.
+##        self.fastclip(a, m , M, out = b)
+##        self.clip(a, m, M, out = bt)
+##        assert_array_strict_equal(b, bt)
+
+##        print "=== OUT arg: testing native int32 input and min/max and float out ==="
+##        print "\t NOT TESTED (current clip is buggy in this case)"
+##        a   = self._generate_int_data(self.nr, self.nc)
+##        b   = zeros(a.shape, dtype = float32)
+##        m   = int32(0)
+##        M   = int32(1)
+##        act = self.clip(a, m, M, out = b)
+##        ac  = self.fastclip(a, m , M, out = b)
+##        assert_array_strict_equal(ac, act)
+
+##    def test_clip_with_out_simple(self):
+##        print "=== WITH OUT testing native double input with scalar min/max ==="
+##        a   = self._generate_data(self.nr, self.nc)
+##        m   = -0.5
+##        M   = 0.6
+##        ac  = zeros(a.shape)
+##        act = zeros(a.shape)
+##        self.fastclip(a, m, M, ac)
+##        self.clip(a, m, M, act)
+##        assert_array_strict_equal(ac, act)
+
+##    def test_clip_with_out_simple2(self):
+##        print "=== WITH OUT testing native int32 input "+\
+##                "with double min/max and int32 out ==="
+##        a   = self._generate_int32_data(self.nr, self.nc)
+##        m   = float64(0)
+##        M   = float64(2)
+##        ac  = zeros(a.shape, dtype = int32)
+##        act = ac.copy()
+##        self.fastclip(a, m, M, ac)
+##        self.clip(a, m, M, act)
+##        assert_array_strict_equal(ac, act)
+
+##    def test_clip_with_out_simple_int32(self):
+##        print "=== WITH OUT testing native int32 input with int32 scalar min/max and int64 out ==="
+##        a   = self._generate_int32_data(self.nr, self.nc)
+##        m   = int32(-1)
+##        M   = int32(1)
+##        ac  = zeros(a.shape, dtype = int64)
+##        act = ac.copy()
+##        self.fastclip(a, m, M, ac)
+##        self.clip(a, m, M, act)
+##        assert_array_strict_equal(ac, act)
+
+##    def test_clip_with_out_array_int32(self):
+##        print "=== WITH OUT testing native int32 input with double array min/max and int32 out ==="
+##        a   = self._generate_int32_data(self.nr, self.nc)
+##        m   = zeros(a.shape, float64)
+##        M   = float64(1)
+##        ac  = zeros(a.shape, dtype = int32)
+##        act = ac.copy()
+##        self.fastclip(a, m, M, ac)
+##        self.clip(a, m, M, act)
+##        assert_array_strict_equal(ac, act)
+
+##    def test_clip_with_out_array_outint32(self):
+##        print "=== WITH OUT testing native double input with scalar min/max and int out ==="
+##        a   = self._generate_data(self.nr, self.nc)
+##        m   = -1.0
+##        M   = 2.0
+##        ac  = zeros(a.shape, dtype = int32)
+##        act = ac.copy()
+##        self.fastclip(a, m, M, ac)
+##        self.clip(a, m, M, act)
+##        assert_array_strict_equal(ac, act)
+
+##    def test_clip_inplace_array(self):
+##        print "=== INPLACE: testing native double input with array min/max ==="
+##        a   = self._generate_data(self.nr, self.nc)
+##        ac  = a.copy()
+##        m   = zeros(a.shape)
+##        M   = 1.0
+##        self.fastclip(a, m, M, a)
+##        self.clip(a, m, M, ac)
+##        assert_array_strict_equal(a, ac)
+
+##    def test_clip_inplace_simple(self):
+##        print "=== INPLACE: testing native double input with scalar min/max ==="
+##        a   = self._generate_data(self.nr, self.nc)
+##        ac  = a.copy()
+##        m   = -0.5
+##        M   = 0.6
+##        self.fastclip(a, m, M, a)
+##        self.clip(a, m, M, ac)
+##        assert_array_strict_equal(a, ac)
+
+        
 import sys
 if sys.version_info[:2] >= (2, 5):
     set_local_path()



From numpy-svn at scipy.org  Fri Mar 30 20:15:52 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Fri, 30 Mar 2007 19:15:52 -0500 (CDT)
Subject: [Numpy-svn] r3624 - trunk/numpy/core/tests
Message-ID: <20070331001552.042F239C018@new.scipy.org>

Author: stefan
Date: 2007-03-30 19:15:41 -0500 (Fri, 30 Mar 2007)
New Revision: 3624

Modified:
   trunk/numpy/core/tests/test_numeric.py
Log:
Split tests. Make them run quietly.


Modified: trunk/numpy/core/tests/test_numeric.py
===================================================================
--- trunk/numpy/core/tests/test_numeric.py	2007-03-30 23:47:39 UTC (rev 3623)
+++ trunk/numpy/core/tests/test_numeric.py	2007-03-31 00:15:41 UTC (rev 3624)
@@ -306,7 +306,7 @@
 
     # Now the real test cases
     def test_simple_double(self):
-        print "=== testing native double input with scalar min/max ==="
+        """Test native double input with scalar min/max."""
         a   = self._generate_data(self.nr, self.nc)
         m   = 0.1
         M   = 0.6
@@ -315,7 +315,7 @@
         assert_array_strict_equal(ac, act)
 
     def test_simple_int(self):
-        print "=== testing native int input with scalar min/max ==="
+        """Test native int input with scalar min/max."""
         a   = self._generate_int_data(self.nr, self.nc)
         a   = a.astype(int) 
         m   = -2
@@ -325,7 +325,7 @@
         assert_array_strict_equal(ac, act)
 
     def test_array_double(self):
-        print "=== testing native double input with array min/max ==="
+        """Test native double input with array min/max."""
         a   = self._generate_data(self.nr, self.nc)
         m   = zeros(a.shape)
         M   = m + 0.5
@@ -334,7 +334,8 @@
         assert_array_strict_equal(ac, act)
 
     def test_simple_nonnative(self):
-        print "=== testing non native double input with scalar min/max ==="
+        """Test non native double input with scalar min/max.
+        Test native double input with non native double scalar min/max."""
         a   = self._generate_non_native_data(self.nr, self.nc)
         m   = -0.5
         M   = 0.6
@@ -342,7 +343,7 @@
         act = self.clip(a, m, M)
         assert_array_equal(ac, act)
 
-        print "=== testing native double input with non native double scalar min/max ==="
+        "Test native double input with non native double scalar min/max."
         a   = self._generate_data(self.nr, self.nc)
         m   = -0.5
         M   = self._neg_byteorder(0.6)
@@ -352,7 +353,9 @@
         assert_array_equal(ac, act)
 
     def test_simple_complex(self):
-        print "=== testing native complex input with native double scalar min/max ==="
+        """Test native complex input with native double scalar min/max.
+        Test native input with complex double scalar min/max.
+        """
         a   = 3 * self._generate_data_complex(self.nr, self.nc)
         m   = -0.5
         M   = 1.
@@ -360,7 +363,7 @@
         act = self.clip(a, m, M)
         assert_array_strict_equal(ac, act)
 
-        print "=== testing native input with complex double scalar min/max ==="
+        "Test native input with complex double scalar min/max."
         a   = 3 * self._generate_data(self.nr, self.nc)
         m   = -0.5 + 1.j
         M   = 1. + 2.j
@@ -369,7 +372,7 @@
         assert_array_strict_equal(ac, act)
 
     def test_clip_non_contig(self):
-        print "=== testing clip for non contiguous native input and native scalar min/max ==="
+        """Test clip for non contiguous native input and native scalar min/max."""
         a   = self._generate_data(self.nr * 2, self.nc * 3)
         a   = a[::2, ::3]
         assert not a.flags['F_CONTIGUOUS']
@@ -379,7 +382,7 @@
         assert_array_strict_equal(ac, act)
 
     def test_simple_out(self):
-        print "=== testing native double input with scalar min/max ==="
+        """Test native double input with scalar min/max."""
         a   = self._generate_data(self.nr, self.nc)
         m   = -0.5
         M   = 0.6
@@ -390,7 +393,7 @@
         assert_array_strict_equal(ac, act)
 
     def test_simple_int32_inout(self):
-        print "=== testing native int32 input with double min/max and int32 out ==="
+        """Test native int32 input with double min/max and int32 out."""
         a   = self._generate_int32_data(self.nr, self.nc)
         m   = float64(0)
         M   = float64(2)
@@ -401,7 +404,7 @@
         assert_array_strict_equal(ac, act)
 
     def test_simple_int64_out(self):
-        print "=== testing native int32 input with int32 scalar min/max and int64 out ==="
+        """Test native int32 input with int32 scalar min/max and int64 out."""
         a   = self._generate_int32_data(self.nr, self.nc)
         m   = int32(-1)
         M   = int32(1)
@@ -412,7 +415,7 @@
         assert_array_strict_equal(ac, act)
 
     def test_simple_int64_inout(self):
-        print "=== testing native in32 input with double array min/max and int32 out ==="
+        """Test native in32 input with double array min/max and int32 out."""
         a   = self._generate_int32_data(self.nr, self.nc)
         m   = zeros(a.shape, float64)
         M   = float64(1)
@@ -423,7 +426,7 @@
         assert_array_strict_equal(ac, act)
 
     def test_simple_int32_out(self):
-        print "=== testing native double input with scalar min/max and int out ==="
+        """Test native double input with scalar min/max and int out."""
         a   = self._generate_data(self.nr, self.nc)
         m   = -1.0
         M   = 2.0
@@ -433,8 +436,8 @@
         self.clip(a, m, M, act)
         assert_array_strict_equal(ac, act)
 
-    def test_simple_inplace(self):
-        print "=== INPLACE: testing native double input with array min/max ==="
+    def test_simple_inplace_01(self):
+        """Test native double input with array min/max in-place."""
         a   = self._generate_data(self.nr, self.nc)
         ac  = a.copy()
         m   = zeros(a.shape)
@@ -443,7 +446,8 @@
         self.clip(a, m, M, ac)
         assert_array_strict_equal(a, ac)
 
-        print "=== INPLACE: testing native double input with scalar min/max ==="
+    def test_simple_inplace_02(self):
+        """Test native double input with scalar min/max in-place."""
         a   = self._generate_data(self.nr, self.nc)
         ac  = a.copy()
         m   = -0.5
@@ -453,7 +457,7 @@
         assert_array_strict_equal(a, ac)
 
     def test_noncontig_inplace(self):
-        print "=== INPLACE: testing non contiguous double input with double scalar min/max ==="
+        """Test non contiguous double input with double scalar min/max in-place."""
         a   = self._generate_data(self.nr * 2, self.nc * 3)
         a   = a[::2, ::3]
         assert not a.flags['F_CONTIGUOUS']
@@ -465,12 +469,8 @@
         self.clip(a, m, M, ac)
         assert_array_equal(a, ac)
 
-    def test_type_cast(self):
-        print "\n==================="
-        print "    Test cast      "
-        print "==================="
-
-        print "=== testing native double input with scalar min/max ==="
+    def test_type_cast_01(self):
+        "Test native double input with scalar min/max."
         a   = self._generate_data(self.nr, self.nc)
         m   = -0.5
         M   = 0.6
@@ -478,7 +478,8 @@
         act = self.clip(a, m, M)
         assert_array_strict_equal(ac, act)
 
-        print "=== testing native int32 input with int32 scalar min/max ==="
+    def test_type_cast_02(self):
+        "Test native int32 input with int32 scalar min/max."
         a   = self._generate_int_data(self.nr, self.nc)
         a   = a.astype(int32) 
         m   = -2
@@ -487,7 +488,8 @@
         act = self.clip(a, m, M)
         assert_array_strict_equal(ac, act)
 
-        print "=== testing native int32 input with float64 scalar min/max ==="
+    def test_type_cast_03(self):
+        "Test native int32 input with float64 scalar min/max."
         a   = self._generate_int32_data(self.nr, self.nc)
         m   = -2
         M   = 4
@@ -495,7 +497,8 @@
         act = self.clip(a, float64(m), float64(M))
         assert_array_strict_equal(ac, act)
 
-        print "=== testing native int32 input with float32 scalar min/max ==="
+    def test_type_cast_04(self):
+        "Test native int32 input with float32 scalar min/max."
         a   = self._generate_int32_data(self.nr, self.nc)
         m   = float32(-2)
         M   = float32(4)
@@ -503,7 +506,8 @@
         ac  = self.clip(a,m,M)
         assert_array_strict_equal(ac, act)        
 
-        print "=== testing native int32 with double arrays min/max ==="
+    def test_type_cast_04(self):
+        "Test native int32 with double arrays min/max."
         a   = self._generate_int_data(self.nr, self.nc)
         m   = -0.5
         M   = 1.
@@ -511,8 +515,8 @@
         act = self.clip(a, m * zeros(a.shape), M)
         assert_array_strict_equal(ac, act)
 
- 
-        print "=== testing native with NON native scalar min/max==="
+    def test_type_cast_05(self):
+        "Test native with NON native scalar min/max."
         a   = self._generate_data(self.nr, self.nc)
         m   = 0.5
         m_s = self._neg_byteorder(m)
@@ -521,7 +525,8 @@
         ac  = self.fastclip(a, m_s, M)
         assert_array_strict_equal(ac, act)
 
-        print "=== testing NON native with native array min/max==="
+    def test_type_cast_06(self):
+        "Test NON native with native array min/max."
         a   = self._generate_data(self.nr, self.nc)
         m   = -0.5 * ones(a.shape)
         M   = 1.
@@ -531,7 +536,8 @@
         ac  = self.fastclip(a_s, m, M)
         assert_array_strict_equal(ac, act)
 
-        print "=== testing NON native with native scalar min/max==="
+    def test_type_cast_07(self):
+        "Test NON native with native scalar min/max."
         a   = self._generate_data(self.nr, self.nc)
         m   = -0.5
         M   = 1.
@@ -541,7 +547,8 @@
         act = a_s.clip(m, M)
         assert_array_strict_equal(ac, act)
 
-        print "=== testing native with NON native array min/max==="
+    def test_type_cast_08(self):
+        "Test native with NON native array min/max."
         a   = self._generate_data(self.nr, self.nc)
         m   = -0.5 * ones(a.shape)
         M   = 1.
@@ -551,7 +558,8 @@
         act = self.clip(a, m_s, M)
         assert_array_strict_equal(ac, act)
 
-        print "=== OUT arg: testing native int32 with float min/max and float out ==="
+    def test_type_cast_09(self):
+        """Test native int32 with float min/max and float out for output argument."""
         a   = self._generate_int_data(self.nr, self.nc)
         b   = zeros(a.shape, dtype = float32)
         m   = float32(-0.5)



From numpy-svn at scipy.org  Sat Mar 31 03:40:52 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Sat, 31 Mar 2007 02:40:52 -0500 (CDT)
Subject: [Numpy-svn] r3625 - in trunk: . numpy/core/src numpy/core/tests
Message-ID: <20070331074052.B492139C093@new.scipy.org>

Author: oliphant
Date: 2007-03-31 02:40:31 -0500 (Sat, 31 Mar 2007)
New Revision: 3625

Modified:
   trunk/THANKS.txt
   trunk/numpy/core/src/multiarraymodule.c
   trunk/numpy/core/tests/test_numeric.py
Log:
Finish adding and checking remaining tests for clip (and choose).   All tests now pass for new clip method.

Modified: trunk/THANKS.txt
===================================================================
--- trunk/THANKS.txt	2007-03-31 00:15:41 UTC (rev 3624)
+++ trunk/THANKS.txt	2007-03-31 07:40:31 UTC (rev 3625)
@@ -34,4 +34,4 @@
     Valgrind expertise.
 Stefan van der Walt for documentation, bug-fixes and regression-tests.
 Andrew Straw for help with http://www.scipy.org, documentation, and testing. 
-David Cournapeau for documentation, bug-fixes, and code contributions.
+David Cournapeau for documentation, bug-fixes, and code contributions including fast_clipping.

Modified: trunk/numpy/core/src/multiarraymodule.c
===================================================================
--- trunk/numpy/core/src/multiarraymodule.c	2007-03-31 00:15:41 UTC (rev 3624)
+++ trunk/numpy/core/src/multiarraymodule.c	2007-03-31 07:40:31 UTC (rev 3625)
@@ -1291,6 +1291,7 @@
                 else 
                         oflags = NPY_CARRAY;
                 oflags |= NPY_UPDATEIFCOPY | NPY_FORCECAST;
+                Py_INCREF(indescr);
                 newout = (NPY_AO*)PyArray_FromArray(out, indescr, oflags);
                 if (newout == NULL) goto fail;
         }

Modified: trunk/numpy/core/tests/test_numeric.py
===================================================================
--- trunk/numpy/core/tests/test_numeric.py	2007-03-31 00:15:41 UTC (rev 3624)
+++ trunk/numpy/core/tests/test_numeric.py	2007-03-31 07:40:31 UTC (rev 3625)
@@ -568,105 +568,102 @@
         ac  = self.fastclip(a, m , M, out = b)
         assert_array_strict_equal(ac, act)
 
-##        # This looks like a data-type descriptor is not reference
-##        #  counted right.
-##        print "=== OUT arg: testing non native with native scalar, min/max, " + \
-##              "out non native==="
-##        a   = self._generate_non_native_data(self.nr, self.nc)
-##        b   = a.copy()
-##        b   = b.astype(b.dtype.newbyteorder('>'))
-##        bt  = b.copy()
-##        m   = -0.5
-##        M   = 1.
-##        self.fastclip(a, m , M, out = b)
-##        self.clip(a, m, M, out = bt)
-##        assert_array_strict_equal(b, bt)
+    def test_type_cast_10(self):
+        "Test non native with native scalar, min/max, out non native"
+        a   = self._generate_non_native_data(self.nr, self.nc)
+        b   = a.copy()
+        b   = b.astype(b.dtype.newbyteorder('>'))
+        bt  = b.copy()
+        m   = -0.5
+        M   = 1.
+        self.fastclip(a, m , M, out = b)
+        self.clip(a, m, M, out = bt)
+        assert_array_strict_equal(b, bt)
 
-##        print "=== OUT arg: testing native int32 input and min/max and float out ==="
-##        print "\t NOT TESTED (current clip is buggy in this case)"
-##        a   = self._generate_int_data(self.nr, self.nc)
-##        b   = zeros(a.shape, dtype = float32)
-##        m   = int32(0)
-##        M   = int32(1)
-##        act = self.clip(a, m, M, out = b)
-##        ac  = self.fastclip(a, m , M, out = b)
-##        assert_array_strict_equal(ac, act)
+    def test_type_cast_11(self):
+        "Test native int32 input and min/max and float out"
+        a   = self._generate_int_data(self.nr, self.nc)
+        b   = zeros(a.shape, dtype = float32)
+        m   = int32(0)
+        M   = int32(1)
+        act = self.clip(a, m, M, out = b)
+        ac  = self.fastclip(a, m , M, out = b)
+        assert_array_strict_equal(ac, act)
 
-##    def test_clip_with_out_simple(self):
-##        print "=== WITH OUT testing native double input with scalar min/max ==="
-##        a   = self._generate_data(self.nr, self.nc)
-##        m   = -0.5
-##        M   = 0.6
-##        ac  = zeros(a.shape)
-##        act = zeros(a.shape)
-##        self.fastclip(a, m, M, ac)
-##        self.clip(a, m, M, act)
-##        assert_array_strict_equal(ac, act)
+    def test_clip_with_out_simple(self):
+        "Test native double input with scalar min/max"
+        a   = self._generate_data(self.nr, self.nc)
+        m   = -0.5
+        M   = 0.6
+        ac  = zeros(a.shape)
+        act = zeros(a.shape)
+        self.fastclip(a, m, M, ac)
+        self.clip(a, m, M, act)
+        assert_array_strict_equal(ac, act)
 
-##    def test_clip_with_out_simple2(self):
-##        print "=== WITH OUT testing native int32 input "+\
-##                "with double min/max and int32 out ==="
-##        a   = self._generate_int32_data(self.nr, self.nc)
-##        m   = float64(0)
-##        M   = float64(2)
-##        ac  = zeros(a.shape, dtype = int32)
-##        act = ac.copy()
-##        self.fastclip(a, m, M, ac)
-##        self.clip(a, m, M, act)
-##        assert_array_strict_equal(ac, act)
+    def test_clip_with_out_simple2(self):
+        "Test native int32 input with double min/max and int32 out"
+        a   = self._generate_int32_data(self.nr, self.nc)
+        m   = float64(0)
+        M   = float64(2)
+        ac  = zeros(a.shape, dtype = int32)
+        act = ac.copy()
+        self.fastclip(a, m, M, ac)
+        self.clip(a, m, M, act)
+        assert_array_strict_equal(ac, act)
 
-##    def test_clip_with_out_simple_int32(self):
-##        print "=== WITH OUT testing native int32 input with int32 scalar min/max and int64 out ==="
-##        a   = self._generate_int32_data(self.nr, self.nc)
-##        m   = int32(-1)
-##        M   = int32(1)
-##        ac  = zeros(a.shape, dtype = int64)
-##        act = ac.copy()
-##        self.fastclip(a, m, M, ac)
-##        self.clip(a, m, M, act)
-##        assert_array_strict_equal(ac, act)
+    def test_clip_with_out_simple_int32(self):
+        "Test native int32 input with int32 scalar min/max and int64 out"
+        a   = self._generate_int32_data(self.nr, self.nc)
+        m   = int32(-1)
+        M   = int32(1)
+        ac  = zeros(a.shape, dtype = int64)
+        act = ac.copy()
+        self.fastclip(a, m, M, ac)
+        self.clip(a, m, M, act)
+        assert_array_strict_equal(ac, act)
 
-##    def test_clip_with_out_array_int32(self):
-##        print "=== WITH OUT testing native int32 input with double array min/max and int32 out ==="
-##        a   = self._generate_int32_data(self.nr, self.nc)
-##        m   = zeros(a.shape, float64)
-##        M   = float64(1)
-##        ac  = zeros(a.shape, dtype = int32)
-##        act = ac.copy()
-##        self.fastclip(a, m, M, ac)
-##        self.clip(a, m, M, act)
-##        assert_array_strict_equal(ac, act)
+    def test_clip_with_out_array_int32(self):
+        "Test native int32 input with double array min/max and int32 out"
+        a   = self._generate_int32_data(self.nr, self.nc)
+        m   = zeros(a.shape, float64)
+        M   = float64(1)
+        ac  = zeros(a.shape, dtype = int32)
+        act = ac.copy()
+        self.fastclip(a, m, M, ac)
+        self.clip(a, m, M, act)
+        assert_array_strict_equal(ac, act)
 
-##    def test_clip_with_out_array_outint32(self):
-##        print "=== WITH OUT testing native double input with scalar min/max and int out ==="
-##        a   = self._generate_data(self.nr, self.nc)
-##        m   = -1.0
-##        M   = 2.0
-##        ac  = zeros(a.shape, dtype = int32)
-##        act = ac.copy()
-##        self.fastclip(a, m, M, ac)
-##        self.clip(a, m, M, act)
-##        assert_array_strict_equal(ac, act)
+    def test_clip_with_out_array_outint32(self):
+        "Test native double input with scalar min/max and int out"
+        a   = self._generate_data(self.nr, self.nc)
+        m   = -1.0
+        M   = 2.0
+        ac  = zeros(a.shape, dtype = int32)
+        act = ac.copy()
+        self.fastclip(a, m, M, ac)
+        self.clip(a, m, M, act)
+        assert_array_strict_equal(ac, act)
 
-##    def test_clip_inplace_array(self):
-##        print "=== INPLACE: testing native double input with array min/max ==="
-##        a   = self._generate_data(self.nr, self.nc)
-##        ac  = a.copy()
-##        m   = zeros(a.shape)
-##        M   = 1.0
-##        self.fastclip(a, m, M, a)
-##        self.clip(a, m, M, ac)
-##        assert_array_strict_equal(a, ac)
+    def test_clip_inplace_array(self):
+        "Test native double input with array min/max"
+        a   = self._generate_data(self.nr, self.nc)
+        ac  = a.copy()
+        m   = zeros(a.shape)
+        M   = 1.0
+        self.fastclip(a, m, M, a)
+        self.clip(a, m, M, ac)
+        assert_array_strict_equal(a, ac)
 
-##    def test_clip_inplace_simple(self):
-##        print "=== INPLACE: testing native double input with scalar min/max ==="
-##        a   = self._generate_data(self.nr, self.nc)
-##        ac  = a.copy()
-##        m   = -0.5
-##        M   = 0.6
-##        self.fastclip(a, m, M, a)
-##        self.clip(a, m, M, ac)
-##        assert_array_strict_equal(a, ac)
+    def test_clip_inplace_simple(self):
+        "Test native double input with scalar min/max"
+        a   = self._generate_data(self.nr, self.nc)
+        ac  = a.copy()
+        m   = -0.5
+        M   = 0.6
+        self.fastclip(a, m, M, a)
+        self.clip(a, m, M, ac)
+        assert_array_strict_equal(a, ac)
 
         
 import sys



From numpy-svn at scipy.org  Sat Mar 31 18:56:24 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Sat, 31 Mar 2007 17:56:24 -0500 (CDT)
Subject: [Numpy-svn] r3626 - in trunk/numpy: . core/include/numpy
Message-ID: <20070331225624.505C039C171@new.scipy.org>

Author: oliphant
Date: 2007-03-31 17:56:19 -0500 (Sat, 31 Mar 2007)
New Revision: 3626

Modified:
   trunk/numpy/__init__.py
   trunk/numpy/_import_tools.py
   trunk/numpy/core/include/numpy/ndarrayobject.h
Log:
Add new MACRO.  Fix ticket #454 by changing pkgload to a function instead of an object.

Modified: trunk/numpy/__init__.py
===================================================================
--- trunk/numpy/__init__.py	2007-03-31 07:40:31 UTC (rev 3625)
+++ trunk/numpy/__init__.py	2007-03-31 22:56:19 UTC (rev 3626)
@@ -29,8 +29,11 @@
     from version import version as __version__
 
     from _import_tools import PackageLoader
-    pkgload = PackageLoader()
 
+    def pkgload(*packages, **options):
+        loader = PackageLoader(infunc=True)
+        return loader(*packages, **options)
+
     import testing
     from testing import ScipyTest, NumpyTest
     import core

Modified: trunk/numpy/_import_tools.py
===================================================================
--- trunk/numpy/_import_tools.py	2007-03-31 07:40:31 UTC (rev 3625)
+++ trunk/numpy/_import_tools.py	2007-03-31 22:56:19 UTC (rev 3626)
@@ -7,11 +7,15 @@
 __all__ = ['PackageLoader']
 
 class PackageLoader:
-    def __init__(self, verbose=False):
+    def __init__(self, verbose=False, infunc=False):
         """ Manages loading packages.
         """
 
-        self.parent_frame = frame = sys._getframe(1)
+        if infunc:
+            _level = 2
+        else:
+            _level = 1
+        self.parent_frame = frame = sys._getframe(_level)
         self.parent_name = eval('__name__',frame.f_globals,frame.f_locals)
         parent_path = eval('__path__',frame.f_globals,frame.f_locals)
         if isinstance(parent_path, str):

Modified: trunk/numpy/core/include/numpy/ndarrayobject.h
===================================================================
--- trunk/numpy/core/include/numpy/ndarrayobject.h	2007-03-31 07:40:31 UTC (rev 3625)
+++ trunk/numpy/core/include/numpy/ndarrayobject.h	2007-03-31 22:56:19 UTC (rev 3626)
@@ -1761,9 +1761,9 @@
 
 
 #define PyDataType_ISNOTSWAPPED(d) PyArray_ISNBO(((PyArray_Descr *)(d))->byteorder)
+#define PyDataType_ISBYTESWAPPED(d) (!PyDataType_ISNOTSWAPPED(d))
 
 
-
 /* This is the form of the struct that's returned pointed by the
    PyCObject attribute of an array __array_struct__. See
    http://numeric.scipy.org/array_interface.html for the full



From numpy-svn at scipy.org  Sat Mar 31 19:45:48 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Sat, 31 Mar 2007 18:45:48 -0500 (CDT)
Subject: [Numpy-svn] r3627 - trunk/numpy/core
Message-ID: <20070331234548.4AF0839C170@new.scipy.org>

Author: oliphant
Date: 2007-03-31 18:45:45 -0500 (Sat, 31 Mar 2007)
New Revision: 3627

Modified:
   trunk/numpy/core/numeric.py
Log:
Speed up indicies significantly (about 10x).

Modified: trunk/numpy/core/numeric.py
===================================================================
--- trunk/numpy/core/numeric.py	2007-03-31 22:56:19 UTC (rev 3626)
+++ trunk/numpy/core/numeric.py	2007-03-31 23:45:45 UTC (rev 3627)
@@ -478,26 +478,34 @@
 
 little_endian = (sys.byteorder == 'little')
 
+
 def indices(dimensions, dtype=int):
     """Returns an array representing a grid of indices with row-only, and
     column-only variation.
     """
-    tmp = ones(dimensions, dtype)
-    lst = []
-    for i in range(len(dimensions)):
-        lst.append( add.accumulate(tmp, i, dtype)-1 )
-    return array(lst)
+    dimensions = tuple(dimensions)
+    N = len(dimensions)
+    if N == 0:
+        return array([],dtype=dtype)
+    res = empty((N,)+dimensions, dtype=dtype)
+    for i, dim in enumerate(dimensions):
+        tmp = arange(dim,dtype=dtype)
+        tmp.shape = (1,)*i + (dim,)+(1,)*(N-i-1)
+        newdim = dimensions[:i] + (1,)+ dimensions[i+1:]
+        val = zeros(newdim, dtype)
+        add(tmp, val, res[i])
+    return res
 
 def fromfunction(function, shape, **kwargs):
     """Returns an array constructed by calling a function on a tuple of number
     grids.
 
-    The function should accept as many arguments as the length of shape and work
-    on array inputs.  The shape argument is a sequence of numbers indicating the
-    length of the desired output for each axis.
+    The function should accept as many arguments as the length of shape and
+    work on array inputs.  The shape argument is a sequence of numbers
+    indicating the length of the desired output for each axis.
 
-    The function can also accept keyword arguments (except dtype), which will be
-    passed through fromfunction to the function itself.  The dtype argument
+    The function can also accept keyword arguments (except dtype), which will
+    be passed through fromfunction to the function itself.  The dtype argument
     (default float) determines the data-type of the index grid passed to the
     function.
     """



From numpy-svn at scipy.org  Sat Mar 31 21:06:53 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Sat, 31 Mar 2007 20:06:53 -0500 (CDT)
Subject: [Numpy-svn] r3628 - trunk/numpy/doc/swig
Message-ID: <20070401010653.03554C7C025@new.scipy.org>

Author: wfspotz at sandia.gov
Date: 2007-03-31 20:06:47 -0500 (Sat, 31 Mar 2007)
New Revision: 3628

Modified:
   trunk/numpy/doc/swig/Series.i
   trunk/numpy/doc/swig/numpy.i
   trunk/numpy/doc/swig/series.cxx
   trunk/numpy/doc/swig/series.h
   trunk/numpy/doc/swig/testSeries.py
Log:
Added tests for ARGOUT_ARRAY1 and ARGOUT_ARRAY2 typemaps

Modified: trunk/numpy/doc/swig/Series.i
===================================================================
--- trunk/numpy/doc/swig/Series.i	2007-03-31 23:45:45 UTC (rev 3627)
+++ trunk/numpy/doc/swig/Series.i	2007-04-01 01:06:47 UTC (rev 3628)
@@ -18,16 +18,25 @@
 %apply (TYPE IN_ARRAY1[ANY]) {(TYPE vector[3])};
 %apply (TYPE* IN_ARRAY1, int DIM1) {(TYPE* series, int size)};
 %apply (int DIM1, TYPE* IN_ARRAY1) {(int size, TYPE* series)};
-%apply (TYPE IN_ARRAY2[ANY][ANY]) {(TYPE matrix[2][2])};
-%apply (TYPE* IN_ARRAY2, int DIM1, int DIM2) {(TYPE* matrix, int rows, int cols)};
-%apply (int DIM1, int DIM2, TYPE* IN_ARRAY2) {(int rows, int cols, TYPE* matrix)};
+
 %apply (TYPE INPLACE_ARRAY1[ANY]) {(TYPE array[3])};
 %apply (TYPE* INPLACE_ARRAY1, int DIM1) {(TYPE* array, int size)};
 %apply (int DIM1, TYPE* INPLACE_ARRAY1) {(int size, TYPE* array)};
+
+%apply (TYPE ARGOUT_ARRAY1[ANY]) {(TYPE even[3])};
+%apply (TYPE ARGOUT_ARRAY1[ANY]) {(TYPE odd[ 3])};
+
+%apply (TYPE IN_ARRAY2[ANY][ANY]) {(TYPE matrix[ANY][ANY])};
+%apply (TYPE* IN_ARRAY2, int DIM1, int DIM2) {(TYPE* matrix, int rows, int cols)};
+%apply (int DIM1, int DIM2, TYPE* IN_ARRAY2) {(int rows, int cols, TYPE* matrix)};
+
 %apply (TYPE INPLACE_ARRAY2[ANY][ANY]) {(TYPE array[3][3])};
 %apply (TYPE* INPLACE_ARRAY2, int DIM1, int DIM2) {(TYPE* array, int rows, int cols)};
 %apply (int DIM1, int DIM2, TYPE* INPLACE_ARRAY2) {(int rows, int cols, TYPE* array)};
 
+%apply (TYPE ARGOUT_ARRAY2[ANY][ANY]) {(TYPE lower[3][3])};
+%apply (TYPE ARGOUT_ARRAY2[ANY][ANY]) {(TYPE upper[3][3])};
+
 %enddef    /* %apply_numpy_typemaps() macro */
 
 %apply_numpy_typemaps(signed char       )

Modified: trunk/numpy/doc/swig/numpy.i
===================================================================
--- trunk/numpy/doc/swig/numpy.i	2007-03-31 23:45:45 UTC (rev 3627)
+++ trunk/numpy/doc/swig/numpy.i	2007-04-01 01:06:47 UTC (rev 3628)
@@ -292,6 +292,7 @@
   }
   return success;
 }
+
 /* End John Hunter translation (with modifications by Bill Spotz)
  */
 
@@ -617,56 +618,32 @@
  */
 %typemap(in,numinputs=0)
   (DATA_TYPE ARGOUT_ARRAY1[ANY])
+  (PyObject * array = NULL)
 {
-  $1 = (DATA_TYPE*) malloc($1_dim0*sizeof(DATA_TYPE));
-  if (!$1) {
-    PyErr_SetString(PyExc_RuntimeError, "Failed to allocate memory");
-    SWIG_fail;
-  }
+  npy_intp dims[1] = { $1_dim0 };
+  array = PyArray_SimpleNew(1, dims, DATA_TYPECODE);
+  $1 = ($1_ltype)((PyArrayObject*)array)->data;
 }
 %typemap(argout)
   (DATA_TYPE ARGOUT_ARRAY1[ANY])
 {
-  PyObject * obj = NULL;
-  npy_intp dimensions[1] = { $1_dim0 };
-  PyObject* outArray = PyArray_FromDimsAndData(1, dimensions, DATA_TYPECODE, (char*)$1);
-  if ($result == Py_None) {
-    Py_DECREF($result);
-    $result = outArray;
-  }
-  else {
-    if (!PyTuple_Check($result)) $result = Py_BuildValue("(O)", $result);
-    obj = Py_Build_Value("(O)", outArray);
-    $result = PySequence_Concat($result, obj);
-  }
+  $result = SWIG_Python_AppendOutput($result,array$argnum);
 }
 
 /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY])
  */
 %typemap(in,numinputs=0)
   (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY])
+  (PyObject * array = NULL)
 {
-  $1 = (DATA_TYPE*) malloc($1_dim0 * $1_dim1 * sizeof(DATA_TYPE));
-  if (!$1) {
-    PyErr_SetString(PyExc_RuntimeError, "Failed to allocate memory");
-    SWIG_fail;
-  }
+  npy_intp dims[2] = { $1_dim0, $1_dim1 };
+  array = PyArray_SimpleNew(2, dims, DATA_TYPECODE);
+  $1 = ($1_ltype)((PyArrayObject*)array)->data;
 }
 %typemap(argout)
-  (DATA_TYPE ARGOUT_ARRAY1[ANY][ANY])
+  (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY])
 {
-  PyObject * obj = NULL;
-  npy_intp dimensions[2] = { $1_dim0, $1_dim1 };
-  PyObject* outArray = PyArray_FromDimsAndData(1, dimensions, DATA_TYPECODE, (char*)$1);
-  if ($result == Py_None) {
-    Py_DECREF($result);
-    $result = outArray;
-  }
-  else {
-    if (!PyTuple_Check($result)) $result = Py_BuildValue("(O)", $result);
-    obj = Py_Build_Value("(O)", outArray);
-    $result = PySequence_Concat($result, obj);
-  }
+  $result = SWIG_Python_AppendOutput($result,array$argnum);
 }
 
 %enddef    /* %numpy_typemaps() macro */

Modified: trunk/numpy/doc/swig/series.cxx
===================================================================
--- trunk/numpy/doc/swig/series.cxx	2007-03-31 23:45:45 UTC (rev 3627)
+++ trunk/numpy/doc/swig/series.cxx	2007-04-01 01:06:47 UTC (rev 3628)
@@ -5,18 +5,20 @@
 
 // The following macro defines a family of functions with the forms
 //
-//     TYPE SNAMELength( TYPE vector[3]);
-//     TYPE SNAMEProd(   TYPE * series, int size);
-//     TYPE SNAMESum(    int size, TYPE * series);
-//     void SNAMEReverse(TYPE array[3]);
-//     void SNAMEOnes(   TYPE * array,  int size);
-//     void SNAMEZeros(  int size, TYPE * array);
-//     TYPE SNAMEDet(    TYPE matrix[2][2]);
-//     TYPE SNAMEMax(    TYPE * matrix, int rows, int cols);
-//     TYPE SNAMEMin(    int rows, int cols, TYPE * matrix);
-//     void SNAMEScale(  TYPE matrix[3][3]);
-//     void SNAMEFloor(  TYPE * array,  int rows, int cols, TYPE floor);
-//     void SNAMECeil(   int rows, int cols, TYPE * array, TYPE ceil);
+//     TYPE SNAMELength(  TYPE vector[3]);
+//     TYPE SNAMEProd(    TYPE * series, int size);
+//     TYPE SNAMESum(     int size, TYPE * series);
+//     void SNAMEReverse( TYPE array[3]);
+//     void SNAMEOnes(    TYPE * array,  int size);
+//     void SNAMEZeros(   int size, TYPE * array);
+//     void SNAMEEOSplit( TYPE vector[3], TYPE even[3], odd[3]);
+//     TYPE SNAMEDet(     TYPE matrix[2][2]);
+//     TYPE SNAMEMax(     TYPE * matrix, int rows, int cols);
+//     TYPE SNAMEMin(     int rows, int cols, TYPE * matrix);
+//     void SNAMEScale(   TYPE matrix[3][3]);
+//     void SNAMEFloor(   TYPE * array,  int rows, int cols, TYPE floor);
+//     void SNAMECeil(    int rows, int cols, TYPE * array, TYPE ceil);
+//     void SNAMELUSplit( TYPE in[3][3], TYPE lower[3][3], TYPE upper[3][3]);
 //
 // for any specified type TYPE (for example: short, unsigned int, long
 // long, etc.) with given short name SNAME (for example: short, uint,
@@ -30,12 +32,14 @@
 //  * 1D in-place arrays, hard-coded length
 //  * 1D in-place arrays
 //  * 1D in-place arrays, data last
+//  * 1D argout arrays, hard-coded length
 //  * 2D input arrays, hard-coded length
 //  * 2D input arrays
 //  * 2D input arrays, data last
 //  * 2D in-place arrays, hard-coded lengths
 //  * 2D in-place arrays
 //  * 2D in-place arrays, data last
+//  * 2D argout arrays, hard-coded length
 //
 #define TEST_FUNCS(TYPE, SNAME) \
 \
@@ -71,6 +75,18 @@
   for (int i=0; i<size; ++i) array[i] = 0;    \
 }                                             \
 \
+void SNAME ## EOSplit(TYPE vector[3], TYPE even[3], TYPE odd[3]) { \
+  for (int i=0; i<3; ++i) {					   \
+    if (i % 2 == 0) {						   \
+      even[i] = vector[i];					   \
+      odd[ i] = 0;						   \
+    } else {							   \
+      even[i] = 0;						   \
+      odd[ i] = vector[i];					   \
+    }								   \
+  }								   \
+}								   \
+\
 TYPE SNAME ## Det(TYPE matrix[2][2]) {                          \
   return matrix[0][0]*matrix[1][1] - matrix[0][1]*matrix[1][0]; \
 }                                                               \
@@ -123,6 +139,20 @@
       if (array[index] > ceil) array[index] = ceil;               \
     }                                                             \
   }                                                               \
+}								  \
+\
+void SNAME ## LUSplit(TYPE matrix[3][3], TYPE lower[3][3], TYPE upper[3][3]) { \
+  for (int i=0; i<3; ++i) {						       \
+    for (int j=0; j<3; ++j) {						       \
+      if (i >= j) {						 	       \
+	lower[i][j] = matrix[i][j];					       \
+	upper[i][j] = 0;					 	       \
+      } else {							 	       \
+	lower[i][j] = 0;					 	       \
+	upper[i][j] = matrix[i][j];					       \
+      }								 	       \
+    }								 	       \
+  }								 	       \
 }
 
 TEST_FUNCS(signed char       , schar    )

Modified: trunk/numpy/doc/swig/series.h
===================================================================
--- trunk/numpy/doc/swig/series.h	2007-03-31 23:45:45 UTC (rev 3627)
+++ trunk/numpy/doc/swig/series.h	2007-04-01 01:06:47 UTC (rev 3628)
@@ -4,18 +4,20 @@
 // The following macro defines the prototypes for a family of
 // functions with the forms
 //
-//     TYPE SNAMELength(   TYPE vector[3]);
-//     TYPE SNAMEProd(     TYPE * series, int size);
-//     TYPE SNAMESum(      int size, TYPE * series);
-//     void SNAMEReverse(TYPE array[3]);
-//     void SNAMEOnes(     TYPE * array,  int size);
-//     void SNAMEZeros(    int size, TYPE * array);
-//     TYPE SNAMEDet(      TYPE matrix[2][2]);
-//     TYPE SNAMEMax(      TYPE * matrix, int rows, int cols);
-//     TYPE SNAMEMin(      int rows, int cols, TYPE * matrix);
-//     void SNAMEScale(    TYPE array[3][3]);
-//     void SNAMEFloor(    TYPE * array,  int rows, int cols, TYPE floor);
-//     void SNAMECeil(     int rows, int cols, TYPE * array,  TYPE ceil );
+//     TYPE SNAMELength(  TYPE vector[3]);
+//     TYPE SNAMEProd(    TYPE * series, int size);
+//     TYPE SNAMESum(     int size, TYPE * series);
+//     void SNAMEReverse( TYPE array[3]);
+//     void SNAMEOnes(    TYPE * array,  int size);
+//     void SNAMEZeros(   int size, TYPE * array);
+//     void SNAMEEOSplit( TYPE vector[3], TYPE even[3], TYPE odd[3]);
+//     TYPE SNAMEDet(     TYPE matrix[2][2]);
+//     TYPE SNAMEMax(     TYPE * matrix, int rows, int cols);
+//     TYPE SNAMEMin(     int rows, int cols, TYPE * matrix);
+//     void SNAMEScale(   TYPE array[3][3]);
+//     void SNAMEFloor(   TYPE * array,  int rows, int cols, TYPE floor);
+//     void SNAMECeil(    int rows, int cols, TYPE * array,  TYPE ceil );
+//     void SNAMESetIdent(TYPE in[3][3], TYPE lower[3][3], TYPE upper[3][3]);
 //
 // for any specified type TYPE (for example: short, unsigned int, long
 // long, etc.) with given short name SNAME (for example: short, uint,
@@ -29,27 +31,31 @@
 //  * 1D in-place arrays, hard-coded length
 //  * 1D in-place arrays
 //  * 1D in-place arrays, data last
+//  * 1D argout arrays, hard-coded length
 //  * 2D input arrays, hard-coded lengths
 //  * 2D input arrays
 //  * 2D input arrays, data last
 //  * 2D in-place arrays, hard-coded lengths
 //  * 2D in-place arrays
 //  * 2D in-place arrays, data last
+//  * 2D argout arrays, hard-coded length
 //
 #define TEST_FUNC_PROTOS(TYPE, SNAME) \
 \
-TYPE SNAME ## Length( TYPE vector[3]); \
-TYPE SNAME ## Prod(   TYPE * series, int size); \
-TYPE SNAME ## Sum(    int size, TYPE * series); \
-void SNAME ## Reverse(TYPE array[3]); \
-void SNAME ## Ones(   TYPE * array,  int size); \
-void SNAME ## Zeros(  int size, TYPE * array); \
-TYPE SNAME ## Det(    TYPE matrix[2][2]); \
-TYPE SNAME ## Max(    TYPE * matrix, int rows, int cols); \
-TYPE SNAME ## Min(    int rows, int cols, TYPE * matrix); \
-void SNAME ## Scale(  TYPE array[3][3], TYPE val); \
-void SNAME ## Floor(  TYPE * array, int rows, int cols, TYPE floor); \
-void SNAME ## Ceil(   int rows, int cols, TYPE * array, TYPE ceil );
+TYPE SNAME ## Length(  TYPE vector[3]); \
+TYPE SNAME ## Prod(    TYPE * series, int size); \
+TYPE SNAME ## Sum(     int size, TYPE * series); \
+void SNAME ## Reverse( TYPE array[3]); \
+void SNAME ## Ones(    TYPE * array,  int size); \
+void SNAME ## Zeros(   int size, TYPE * array); \
+void SNAME ## EOSplit( TYPE vector[3], TYPE even[3], TYPE odd[3]); \
+TYPE SNAME ## Det(     TYPE matrix[2][2]); \
+TYPE SNAME ## Max(     TYPE * matrix, int rows, int cols); \
+TYPE SNAME ## Min(     int rows, int cols, TYPE * matrix); \
+void SNAME ## Scale(   TYPE array[3][3], TYPE val); \
+void SNAME ## Floor(   TYPE * array, int rows, int cols, TYPE floor); \
+void SNAME ## Ceil(    int rows, int cols, TYPE * array, TYPE ceil ); \
+void SNAME ## LUSplit( TYPE matrix[3][3], TYPE lower[3][3], TYPE upper[3][3]);
 
 TEST_FUNC_PROTOS(signed char       , schar    )
 TEST_FUNC_PROTOS(unsigned char     , uchar    )

Modified: trunk/numpy/doc/swig/testSeries.py
===================================================================
--- trunk/numpy/doc/swig/testSeries.py	2007-03-31 23:45:45 UTC (rev 3627)
+++ trunk/numpy/doc/swig/testSeries.py	2007-04-01 01:06:47 UTC (rev 3628)
@@ -62,6 +62,12 @@
         Series.scharZeros(myArray)
         N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
 
+    def testScharEOSplit(self):
+        "Test scharEOSplit function"
+        even, odd = Series.scharEOSplit([1,2,3])
+        self.assertEquals((even == [1,0,3]).all(), True)
+        self.assertEquals((odd  == [0,2,0]).all(), True)
+
     def testScharDet(self):
         "Test scharDet function"
         matrix = [[6,7],[8,9]]
@@ -103,6 +109,12 @@
         Series.scharCeil(matrix,5)
         N.testing.assert_array_equal(matrix, N.array([[5,-2],[-6,5]]))
 
+    def testScharLUSplit(self):
+        "Test scharLUSplit function"
+        lower, upper = Series.scharLUSplit([[1,2,3],[4,5,6],[7,8,9]])
+        self.assertEquals((lower == [[1,0,0],[4,5,0],[7,8,9]]).all(), True)
+        self.assertEquals((upper == [[0,2,3],[0,0,6],[0,0,0]]).all(), True)
+
     #####################################################
     ### Test functions that take arrays of type UBYTE ###
     def testUcharLength(self):
@@ -143,6 +155,12 @@
         Series.ucharZeros(myArray)
         N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
 
+    def testUcharEOSplit(self):
+        "Test ucharEOSplit function"
+        even, odd = Series.ucharEOSplit([1,2,3])
+        self.assertEquals((even == [1,0,3]).all(), True)
+        self.assertEquals((odd  == [0,2,0]).all(), True)
+
     def testUcharDet(self):
         "Test ucharDet function"
         matrix = [[7,6],[9,8]]
@@ -184,6 +202,12 @@
         Series.ucharCeil(matrix,5)
         N.testing.assert_array_equal(matrix, N.array([[5,2],[5,5]]))
 
+    def testUcharLUSplit(self):
+        "Test ucharLUSplit function"
+        lower, upper = Series.ucharLUSplit([[1,2,3],[4,5,6],[7,8,9]])
+        self.assertEquals((lower == [[1,0,0],[4,5,0],[7,8,9]]).all(), True)
+        self.assertEquals((upper == [[0,2,3],[0,0,6],[0,0,0]]).all(), True)
+
     #####################################################
     ### Test functions that take arrays of type SHORT ###
     def testShortLength(self):
@@ -224,6 +248,12 @@
         Series.shortZeros(myArray)
         N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
 
+    def testShortEOSplit(self):
+        "Test shortEOSplit function"
+        even, odd = Series.shortEOSplit([1,2,3])
+        self.assertEquals((even == [1,0,3]).all(), True)
+        self.assertEquals((odd  == [0,2,0]).all(), True)
+
     def testShortDet(self):
         "Test shortDet function"
         matrix = [[6,7],[8,9]]
@@ -265,6 +295,12 @@
         Series.shortCeil(matrix,5)
         N.testing.assert_array_equal(matrix, N.array([[5,-2],[-6,5]]))
 
+    def testShortLUSplit(self):
+        "Test shortLUSplit function"
+        lower, upper = Series.shortLUSplit([[1,2,3],[4,5,6],[7,8,9]])
+        self.assertEquals((lower == [[1,0,0],[4,5,0],[7,8,9]]).all(), True)
+        self.assertEquals((upper == [[0,2,3],[0,0,6],[0,0,0]]).all(), True)
+
     ######################################################
     ### Test functions that take arrays of type USHORT ###
     def testUshortLength(self):
@@ -305,6 +341,12 @@
         Series.ushortZeros(myArray)
         N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
 
+    def testUshortEOSplit(self):
+        "Test ushortEOSplit function"
+        even, odd = Series.ushortEOSplit([1,2,3])
+        self.assertEquals((even == [1,0,3]).all(), True)
+        self.assertEquals((odd  == [0,2,0]).all(), True)
+
     def testUshortDet(self):
         "Test ushortDet function"
         matrix = [[7,6],[9,8]]
@@ -346,6 +388,12 @@
         Series.ushortCeil(matrix,5)
         N.testing.assert_array_equal(matrix, N.array([[5,2],[5,5]]))
 
+    def testUshortLUSplit(self):
+        "Test ushortLUSplit function"
+        lower, upper = Series.ushortLUSplit([[1,2,3],[4,5,6],[7,8,9]])
+        self.assertEquals((lower == [[1,0,0],[4,5,0],[7,8,9]]).all(), True)
+        self.assertEquals((upper == [[0,2,3],[0,0,6],[0,0,0]]).all(), True)
+
     ###################################################
     ### Test functions that take arrays of type INT ###
     def testIntLength(self):
@@ -386,6 +434,12 @@
         Series.intZeros(myArray)
         N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
 
+    def testIntEOSplit(self):
+        "Test intEOSplit function"
+        even, odd = Series.intEOSplit([1,2,3])
+        self.assertEquals((even == [1,0,3]).all(), True)
+        self.assertEquals((odd  == [0,2,0]).all(), True)
+
     def testIntDet(self):
         "Test intDet function"
         matrix = [[6,7],[8,9]]
@@ -427,6 +481,12 @@
         Series.intCeil(matrix,5)
         N.testing.assert_array_equal(matrix, N.array([[5,-2],[-6,5]]))
 
+    def testIntLUSplit(self):
+        "Test intLUSplit function"
+        lower, upper = Series.intLUSplit([[1,2,3],[4,5,6],[7,8,9]])
+        self.assertEquals((lower == [[1,0,0],[4,5,0],[7,8,9]]).all(), True)
+        self.assertEquals((upper == [[0,2,3],[0,0,6],[0,0,0]]).all(), True)
+
     ####################################################
     ### Test functions that take arrays of type UINT ###
     def testUintLength(self):
@@ -468,6 +528,12 @@
         Series.uintZeros(myArray)
         N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
 
+    def testUintEOSplit(self):
+        "Test uintEOSplit function"
+        even, odd = Series.uintEOSplit([1,2,3])
+        self.assertEquals((even == [1,0,3]).all(), True)
+        self.assertEquals((odd  == [0,2,0]).all(), True)
+
     def testUintDet(self):
         "Test uintDet function"
         matrix = [[7,6],[9,8]]
@@ -509,6 +575,12 @@
         Series.uintCeil(matrix,5)
         N.testing.assert_array_equal(matrix, N.array([[5,2],[5,5]]))
 
+    def testUintLUSplit(self):
+        "Test uintLUSplit function"
+        lower, upper = Series.uintLUSplit([[1,2,3],[4,5,6],[7,8,9]])
+        self.assertEquals((lower == [[1,0,0],[4,5,0],[7,8,9]]).all(), True)
+        self.assertEquals((upper == [[0,2,3],[0,0,6],[0,0,0]]).all(), True)
+
     ####################################################
     ### Test functions that take arrays of type LONG ###
     def testLongLength(self):
@@ -549,6 +621,12 @@
         Series.longZeros(myArray)
         N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
 
+    def testLongEOSplit(self):
+        "Test longEOSplit function"
+        even, odd = Series.longEOSplit([1,2,3])
+        self.assertEquals((even == [1,0,3]).all(), True)
+        self.assertEquals((odd  == [0,2,0]).all(), True)
+
     def testLongDet(self):
         "Test longDet function"
         matrix = [[6,7],[8,9]]
@@ -590,6 +668,12 @@
         Series.longCeil(matrix,5)
         N.testing.assert_array_equal(matrix, N.array([[5,-2],[-6,5]]))
 
+    def testLongLUSplit(self):
+        "Test longLUSplit function"
+        lower, upper = Series.longLUSplit([[1,2,3],[4,5,6],[7,8,9]])
+        self.assertEquals((lower == [[1,0,0],[4,5,0],[7,8,9]]).all(), True)
+        self.assertEquals((upper == [[0,2,3],[0,0,6],[0,0,0]]).all(), True)
+
     #####################################################
     ### Test functions that take arrays of type ULONG ###
     def testUlongLength(self):
@@ -630,6 +714,12 @@
         Series.ulongZeros(myArray)
         N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
 
+    def testUlongEOSplit(self):
+        "Test ulongEOSplit function"
+        even, odd = Series.ulongEOSplit([1,2,3])
+        self.assertEquals((even == [1,0,3]).all(), True)
+        self.assertEquals((odd  == [0,2,0]).all(), True)
+
     def testUlongDet(self):
         "Test ulongDet function"
         matrix = [[7,6],[9,8]]
@@ -671,6 +761,12 @@
         Series.ulongCeil(matrix,5)
         N.testing.assert_array_equal(matrix, N.array([[5,2],[5,5]]))
 
+    def testUlongLUSplit(self):
+        "Test ulongLUSplit function"
+        lower, upper = Series.ulongLUSplit([[1,2,3],[4,5,6],[7,8,9]])
+        self.assertEquals((lower == [[1,0,0],[4,5,0],[7,8,9]]).all(), True)
+        self.assertEquals((upper == [[0,2,3],[0,0,6],[0,0,0]]).all(), True)
+
     ########################################################
     ### Test functions that take arrays of type LONGLONG ###
     def testLongLongLength(self):
@@ -711,6 +807,12 @@
         Series.longLongZeros(myArray)
         N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
 
+    def testLongLongEOSplit(self):
+        "Test longLongEOSplit function"
+        even, odd = Series.longLongEOSplit([1,2,3])
+        self.assertEquals((even == [1,0,3]).all(), True)
+        self.assertEquals((odd  == [0,2,0]).all(), True)
+
     def testLongLongDet(self):
         "Test longLongDet function"
         matrix = [[6,7],[8,9]]
@@ -752,6 +854,12 @@
         Series.longLongCeil(matrix,5)
         N.testing.assert_array_equal(matrix, N.array([[5,-2],[-6,5]]))
 
+    def testLongLongLUSplit(self):
+        "Test longLongLUSplit function"
+        lower, upper = Series.longLongLUSplit([[1,2,3],[4,5,6],[7,8,9]])
+        self.assertEquals((lower == [[1,0,0],[4,5,0],[7,8,9]]).all(), True)
+        self.assertEquals((upper == [[0,2,3],[0,0,6],[0,0,0]]).all(), True)
+
     #########################################################
     ### Test functions that take arrays of type ULONGLONG ###
     def testUlongLongLength(self):
@@ -792,6 +900,12 @@
         Series.ulongLongZeros(myArray)
         N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
 
+    def testUlongLongEOSplit(self):
+        "Test ulongLongEOSplit function"
+        even, odd = Series.ulongLongEOSplit([1,2,3])
+        self.assertEquals((even == [1,0,3]).all(), True)
+        self.assertEquals((odd  == [0,2,0]).all(), True)
+
     def testUlongLongDet(self):
         "Test ulongLongDet function"
         matrix = [[7,6],[9,8]]
@@ -833,6 +947,12 @@
         Series.ulongLongCeil(matrix,5)
         N.testing.assert_array_equal(matrix, N.array([[5,2],[5,5]]))
 
+    def testUlongLongLUSplit(self):
+        "Test ulongLongLUSplit function"
+        lower, upper = Series.ulongLongLUSplit([[1,2,3],[4,5,6],[7,8,9]])
+        self.assertEquals((lower == [[1,0,0],[4,5,0],[7,8,9]]).all(), True)
+        self.assertEquals((upper == [[0,2,3],[0,0,6],[0,0,0]]).all(), True)
+
     #####################################################
     ### Test functions that take arrays of type FLOAT ###
     def testFloatLength(self):
@@ -882,6 +1002,12 @@
         Series.floatZeros(myArray)
         N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
 
+    def testFloatEOSplit(self):
+        "Test floatEOSplit function"
+        even, odd = Series.floatEOSplit([1,2,3])
+        self.assertEquals((even == [1,0,3]).all(), True)
+        self.assertEquals((odd  == [0,2,0]).all(), True)
+
     def testFloatMax(self):
         "Test floatMax function"
         matrix = [[-6,5,-4],[3.14,-2.718,1]]
@@ -918,6 +1044,12 @@
         Series.floatCeil(matrix,5)
         N.testing.assert_array_equal(matrix, N.array([[5,-2],[-6,5]]))
 
+    def testFloatLUSplit(self):
+        "Test floatLUSplit function"
+        lower, upper = Series.floatLUSplit([[1,2,3],[4,5,6],[7,8,9]])
+        self.assertEquals((lower == [[1,0,0],[4,5,0],[7,8,9]]).all(), True)
+        self.assertEquals((upper == [[0,2,3],[0,0,6],[0,0,0]]).all(), True)
+
     ######################################################
     ### Test functions that take arrays of type DOUBLE ###
     def testDoubleLength(self):
@@ -962,6 +1094,12 @@
         Series.doubleZeros(myArray)
         N.testing.assert_array_equal(myArray, N.array([0,0,0,0,0]))
 
+    def testDoubleEOSplit(self):
+        "Test doubleEOSplit function"
+        even, odd = Series.doubleEOSplit([1,2,3])
+        self.assertEquals((even == [1,0,3]).all(), True)
+        self.assertEquals((odd  == [0,2,0]).all(), True)
+
     def testDoubleDet(self):
         "Test doubleDet function"
         matrix = [[6,7],[8,9]]
@@ -1003,6 +1141,12 @@
         Series.doubleCeil(matrix,5)
         N.testing.assert_array_equal(matrix, N.array([[5,-2],[-6,5]]))
 
+    def testDoubleLUSplit(self):
+        "Test doubleLUSplit function"
+        lower, upper = Series.doubleLUSplit([[1,2,3],[4,5,6],[7,8,9]])
+        self.assertEquals((lower == [[1,0,0],[4,5,0],[7,8,9]]).all(), True)
+        self.assertEquals((upper == [[0,2,3],[0,0,6],[0,0,0]]).all(), True)
+
 ######################################################################
 
 if __name__ == "__main__":



From numpy-svn at scipy.org  Sat Mar 31 22:31:51 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Sat, 31 Mar 2007 21:31:51 -0500 (CDT)
Subject: [Numpy-svn] r3629 - trunk/numpy/doc/swig
Message-ID: <20070401023151.94FFC39C034@new.scipy.org>

Author: wfspotz at sandia.gov
Date: 2007-03-31 21:31:40 -0500 (Sat, 31 Mar 2007)
New Revision: 3629

Modified:
   trunk/numpy/doc/swig/Series.i
   trunk/numpy/doc/swig/numpy.i
   trunk/numpy/doc/swig/numpy_swig.html
   trunk/numpy/doc/swig/numpy_swig.pdf
   trunk/numpy/doc/swig/numpy_swig.txt
   trunk/numpy/doc/swig/series.cxx
   trunk/numpy/doc/swig/series.h
   trunk/numpy/doc/swig/testSeries.py
Log:
Added (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1) and (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1) typemaps, and tests, and documentation

Modified: trunk/numpy/doc/swig/Series.i
===================================================================
--- trunk/numpy/doc/swig/Series.i	2007-04-01 01:06:47 UTC (rev 3628)
+++ trunk/numpy/doc/swig/Series.i	2007-04-01 02:31:40 UTC (rev 3629)
@@ -25,6 +25,8 @@
 
 %apply (TYPE ARGOUT_ARRAY1[ANY]) {(TYPE even[3])};
 %apply (TYPE ARGOUT_ARRAY1[ANY]) {(TYPE odd[ 3])};
+%apply (TYPE* ARGOUT_ARRAY1, int DIM1) {(TYPE* twoVec, int size)};
+%apply (int DIM1, TYPE* ARGOUT_ARRAY1) {(int size, TYPE* threeVec)};
 
 %apply (TYPE IN_ARRAY2[ANY][ANY]) {(TYPE matrix[ANY][ANY])};
 %apply (TYPE* IN_ARRAY2, int DIM1, int DIM2) {(TYPE* matrix, int rows, int cols)};

Modified: trunk/numpy/doc/swig/numpy.i
===================================================================
--- trunk/numpy/doc/swig/numpy.i	2007-04-01 01:06:47 UTC (rev 3628)
+++ trunk/numpy/doc/swig/numpy.i	2007-04-01 02:31:40 UTC (rev 3629)
@@ -315,21 +315,25 @@
  *     (DATA_TYPE* INPLACE_ARRAY1, DIM_TYPE DIM1)
  *     (DIM_TYPE DIM1, DATA_TYPE* INPLACE_ARRAY1)
  *
- *     (DATA_TYPE IN_ARRAY2[ANY][ANY])
+ *     (DATA_TYPE INPLACE_ARRAY2[ANY][ANY])
  *     (DATA_TYPE* INPLACE_ARRAY2, DIM_TYPE DIM1, DIM_TYPE DIM2)
  *     (DIM_TYPE DIM1, DIM_TYPE DIM2, DATA_TYPE* INPLACE_ARRAY2)
  *
  *     (DATA_TYPE ARGOUT_ARRAY1[ANY])
+ *     (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1)
+ *     (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1)
  *
  *     (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY])
  *
- * where "DATA_TYPE" is any type supported by the NumPy module.  In python,
- * the dimensions will not need to be specified.  The IN_ARRAYs can be
- * a numpy array or any sequence that can be converted to a numpy
- * array of the specified type.  The INPLACE_ARRAYs must be numpy
- * arrays of the appropriate type.  The ARGOUT_ARRAYs will be returned
- * as numpy arrays of the appropriate type.
-
+ * where "DATA_TYPE" is any type supported by the NumPy module, and
+ * "DIM_TYPE" is any int-like type suitable for specifying dimensions.
+ * In python, the dimensions will not need to be specified (except for
+ * the "DATA_TYPE* ARGOUT_ARRAY1" typemaps).  The IN_ARRAYs can be a
+ * numpy array or any sequence that can be converted to a numpy array
+ * of the specified type.  The INPLACE_ARRAYs must be numpy arrays of
+ * the appropriate type.  The ARGOUT_ARRAYs will be returned as numpy
+ * arrays of the appropriate type.
+ *
  * These typemaps can be applied to existing functions using the
  * %apply directive:
  *
@@ -369,12 +373,22 @@
  *     %apply (int DIM1, int DIM2, double* INPLACE_ARRAY2) {(int rows, int cols, double* matrix)};
  *     void ceil(int rows, int cols, double* matrix);
  *
- *     %apply (double ARGOUT_ARRAY1[ANY] {(double series[int length])};
- *     void negate(double* series, int length);
+ *     %apply (double IN_ARRAY1[ANY]    ) {(double vector[ANY])};
+ *     %apply (double ARGOUT_ARRAY1[ANY]) {(double even[    3])};
+ *     %apply (double ARGOUT_ARRAY1[ANY]) {(double odd[     3])};
+ *     void eoSplit(double vector[3], double even[3], double odd[3]);
  *
- *     %apply (double ARGOUT_ARRAY2[ANY][ANY]) {(double matrix[int rows][int cols])};
- *     void normalize(double* matrix, int rows, int cols);
+ *     %apply (double* ARGOUT_ARRAY1, int DIM1) {(double* twoVec, int size)};
+ *     void twos(double* twoVec, int size);
  *
+ *     %apply (int DIM1, double* ARGOUT_ARRAY1) {(int size, double* threeVec)};
+ *     void threes(int size, double* threeVec);
+ *
+ *     %apply (double IN_ARRAY2[ANY][ANY])     {(double matrix[2][2])};
+ *     %apply (double ARGOUT_ARRAY2[ANY][ANY]) {(double upper[ 3][3])};
+ *     %apply (double ARGOUT_ARRAY2[ANY][ANY]) {(double lower[ 3][3])};
+ *     void luSplit(double matrix[3][3], double upper[3][3], double lower[3][3]);
+ *
  * or directly with
  *
  *     double length(double IN_ARRAY1[ANY]);
@@ -393,9 +407,13 @@
  *     void floor(double* INPLACE_ARRAY2, int DIM1, int DIM2, double floor);
  *     void ceil( int DIM1, int DIM2, double* INPLACE_ARRAY2, double ceil );
  *
- *     void negate(double ARGOUT_ARRAY1[ANY]);
+ *     void eoSplit(double IN_ARRAY1[ANY], double ARGOUT_ARRAY1[ANY],
+ *                  double ARGOUT_ARRAY1[ANY]);
+ *     void twos(double* ARGOUT_ARRAY1, int DIM1)
+ *     void threes(int DIM1, double* ARGOUT_ARRAY1)
  *
- *     void normalize(double ARGOUT_ARRAY2[ANY][ANY]);
+ *     void luSplit(double IN_ARRAY2[ANY][ANY], double ARGOUT_ARRAY2[ANY][ANY],
+ *                  double ARGOUT_ARRAY2[ANY][ANY]);
  */
 
 %define %numpy_typemaps(DATA_TYPE, DATA_TYPECODE, DIM_TYPE)
@@ -630,6 +648,54 @@
   $result = SWIG_Python_AppendOutput($result,array$argnum);
 }
 
+/* Typemap suite for (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1)
+ */
+%typemap(in,numinputs=1)
+  (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1)
+  (PyObject * array = NULL)
+{
+  if (!PyInt_Check($input)) {
+    char* typestring = pytype_string($input);
+    PyErr_Format(PyExc_TypeError, 
+		 "Int dimension expected.  '%s' given.", 
+		 typestring);
+    SWIG_fail;
+  }
+  $2 = (DIM_TYPE) PyInt_AsLong($input);
+  npy_intp dims[1] = { (npy_intp) $2 };
+  array = PyArray_SimpleNew(1, dims, DATA_TYPECODE);
+  $1 = (DATA_TYPE*)((PyArrayObject*)array)->data;
+}
+%typemap(argout)
+  (DATA_TYPE* ARGOUT_ARRAY1, DIM_TYPE DIM1)
+{
+  $result = SWIG_Python_AppendOutput($result,array$argnum);
+}
+
+/* Typemap suite for (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1)
+ */
+%typemap(in,numinputs=1)
+  (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1)
+  (PyObject * array = NULL)
+{
+  if (!PyInt_Check($input)) {
+    char* typestring = pytype_string($input);
+    PyErr_Format(PyExc_TypeError, 
+		 "Int dimension expected.  '%s' given.", 
+		 typestring);
+    SWIG_fail;
+  }
+  $1 = (DIM_TYPE) PyInt_AsLong($input);
+  npy_intp dims[1] = { (npy_intp) $1 };
+  array = PyArray_SimpleNew(1, dims, DATA_TYPECODE);
+  $2 = (DATA_TYPE*)((PyArrayObject*)array)->data;
+}
+%typemap(argout)
+  (DIM_TYPE DIM1, DATA_TYPE* ARGOUT_ARRAY1)
+{
+  $result = SWIG_Python_AppendOutput($result,array$argnum);
+}
+
 /* Typemap suite for (DATA_TYPE ARGOUT_ARRAY2[ANY][ANY])
  */
 %typemap(in,numinputs=0)

Modified: trunk/numpy/doc/swig/numpy_swig.html
===================================================================
--- trunk/numpy/doc/swig/numpy_swig.html	2007-04-01 01:06:47 UTC (rev 3628)
+++ trunk/numpy/doc/swig/numpy_swig.html	2007-04-01 02:31:40 UTC (rev 3629)
@@ -528,17 +528,28 @@
 are in fact output arrays.  This pattern occurs often when there is
 more than one output variable and the single return argument is
 therefore not sufficient.  In <a class="reference" href="http://www.python.org">python</a>, the convential way to return
-multiple arguments is to pack them into a tuple and return the tuple.
-This is what the argout typemaps do.  If a wrapped function that uses
-these argout typemaps has more than one return argument, they are so
-packed.  The <a class="reference" href="http://www.python.org">python</a> user does not pass these arrays in, they simply
-get returned.  The argout signatures are</p>
+multiple arguments is to pack them into a sequence (tuple, list, etc.)
+and return the sequence.  This is what the argout typemaps do.  If a
+wrapped function that uses these argout typemaps has more than one
+return argument, they are packed into a tuple or list, depending on
+the version of <a class="reference" href="http://www.python.org">python</a>.  The <a class="reference" href="http://www.python.org">python</a> user does not pass these
+arrays in, they simply get returned.  The argout signatures are</p>
 <blockquote>
 <ul class="simple">
 <li><tt class="docutils literal"><span class="pre">(DATA_TYPE</span> <span class="pre">ARGOUT_ARRAY1[ANY])</span></tt></li>
+<li><tt class="docutils literal"><span class="pre">(DATA_TYPE*</span> <span class="pre">ARGOUT_ARRAY1,</span> <span class="pre">int</span> <span class="pre">DIM1)</span></tt></li>
+<li><tt class="docutils literal"><span class="pre">(int</span> <span class="pre">DIM1,</span> <span class="pre">DATA_TYPE*</span> <span class="pre">ARGOUT_ARRAY1)</span></tt></li>
 <li><tt class="docutils literal"><span class="pre">(DATA_TYPE</span> <span class="pre">ARGOUT_ARRAY2[ANY][ANY])</span></tt></li>
 </ul>
 </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
+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.  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>
 </div>
 <div class="section">
 <h2><a class="toc-backref" href="#id7" id="output-arrays" name="output-arrays">Output Arrays</a></h2>
@@ -803,7 +814,7 @@
 </div>
 <div class="footer">
 <hr class="footer" />
-Generated on: 2007-03-29 20:45 UTC.
+Generated on: 2007-04-01 02:30 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: trunk/numpy/doc/swig/numpy_swig.pdf
===================================================================
--- trunk/numpy/doc/swig/numpy_swig.pdf	2007-04-01 01:06:47 UTC (rev 3628)
+++ trunk/numpy/doc/swig/numpy_swig.pdf	2007-04-01 02:31:40 UTC (rev 3629)
@@ -519,25 +519,20 @@
 /ProcSet [ /PDF /Text ]
 >> endobj
 178 0 obj <<
-/Length 2988      
+/Length 3116      
 /Filter /FlateDecode
 >>
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 trailer
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-/Size 275
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+/Size 277
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 >>
 startxref
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+121004
 %%EOF

Modified: trunk/numpy/doc/swig/numpy_swig.txt
===================================================================
--- trunk/numpy/doc/swig/numpy_swig.txt	2007-04-01 01:06:47 UTC (rev 3628)
+++ trunk/numpy/doc/swig/numpy_swig.txt	2007-04-01 02:31:40 UTC (rev 3629)
@@ -209,15 +209,28 @@
 are in fact output arrays.  This pattern occurs often when there is
 more than one output variable and the single return argument is
 therefore not sufficient.  In `python`_, the convential way to return
-multiple arguments is to pack them into a tuple and return the tuple.
-This is what the argout typemaps do.  If a wrapped function that uses
-these argout typemaps has more than one return argument, they are so
-packed.  The `python`_ user does not pass these arrays in, they simply
-get returned.  The argout signatures are
+multiple arguments is to pack them into a sequence (tuple, list, etc.)
+and return the sequence.  This is what the argout typemaps do.  If a
+wrapped function that uses these argout typemaps has more than one
+return argument, they are packed into a tuple or list, depending on
+the version of `python`_.  The `python`_ user does not pass these
+arrays in, they simply get returned.  The argout signatures are
 
   * ``(DATA_TYPE ARGOUT_ARRAY1[ANY])``
+  * ``(DATA_TYPE* ARGOUT_ARRAY1, int DIM1)``
+  * ``(int DIM1, DATA_TYPE* ARGOUT_ARRAY1)``
   * ``(DATA_TYPE ARGOUT_ARRAY2[ANY][ANY])``
 
+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
+returned as new array objects.
+
+Note that we support ``DATA_TYPE*`` argout typemaps in 1D, but not
+2D.  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``.
+
 Output Arrays
 -------------
 

Modified: trunk/numpy/doc/swig/series.cxx
===================================================================
--- trunk/numpy/doc/swig/series.cxx	2007-04-01 01:06:47 UTC (rev 3628)
+++ trunk/numpy/doc/swig/series.cxx	2007-04-01 02:31:40 UTC (rev 3629)
@@ -87,6 +87,14 @@
   }								   \
 }								   \
 \
+void SNAME ## Twos(TYPE* twoVec, int size) { \
+  for (int i=0; i<size; ++i) twoVec[i] = 2;  \
+}					     \
+\
+void SNAME ## Threes(int size, TYPE* threeVec) { \
+  for (int i=0; i<size; ++i) threeVec[i] = 3;	 \
+}						 \
+\
 TYPE SNAME ## Det(TYPE matrix[2][2]) {                          \
   return matrix[0][0]*matrix[1][1] - matrix[0][1]*matrix[1][0]; \
 }                                                               \

Modified: trunk/numpy/doc/swig/series.h
===================================================================
--- trunk/numpy/doc/swig/series.h	2007-04-01 01:06:47 UTC (rev 3628)
+++ trunk/numpy/doc/swig/series.h	2007-04-01 02:31:40 UTC (rev 3629)
@@ -32,6 +32,8 @@
 //  * 1D in-place arrays
 //  * 1D in-place arrays, data last
 //  * 1D argout arrays, hard-coded length
+//  * 1D argout arrays
+//  * 1D argout arrays, data last
 //  * 2D input arrays, hard-coded lengths
 //  * 2D input arrays
 //  * 2D input arrays, data last
@@ -49,6 +51,8 @@
 void SNAME ## Ones(    TYPE * array,  int size); \
 void SNAME ## Zeros(   int size, TYPE * array); \
 void SNAME ## EOSplit( TYPE vector[3], TYPE even[3], TYPE odd[3]); \
+void SNAME ## Twos(    TYPE * twoVec, int size); \
+void SNAME ## Threes(  int size, TYPE * threeVec); \
 TYPE SNAME ## Det(     TYPE matrix[2][2]); \
 TYPE SNAME ## Max(     TYPE * matrix, int rows, int cols); \
 TYPE SNAME ## Min(     int rows, int cols, TYPE * matrix); \

Modified: trunk/numpy/doc/swig/testSeries.py
===================================================================
--- trunk/numpy/doc/swig/testSeries.py	2007-04-01 01:06:47 UTC (rev 3628)
+++ trunk/numpy/doc/swig/testSeries.py	2007-04-01 02:31:40 UTC (rev 3629)
@@ -68,6 +68,16 @@
         self.assertEquals((even == [1,0,3]).all(), True)
         self.assertEquals((odd  == [0,2,0]).all(), True)
 
+    def testScharTwos(self):
+        "Test scharTwos function"
+        twos = Series.scharTwos(5)
+        self.assertEquals((twos == [2,2,2,2,2]).all(), True)
+
+    def testScharThrees(self):
+        "Test scharThrees function"
+        threes = Series.scharThrees(6)
+        self.assertEquals((threes == [3,3,3,3,3,3]).all(), True)
+
     def testScharDet(self):
         "Test scharDet function"
         matrix = [[6,7],[8,9]]
@@ -161,6 +171,16 @@
         self.assertEquals((even == [1,0,3]).all(), True)
         self.assertEquals((odd  == [0,2,0]).all(), True)
 
+    def testUcharTwos(self):
+        "Test ucharTwos function"
+        twos = Series.ucharTwos(5)
+        self.assertEquals((twos == [2,2,2,2,2]).all(), True)
+
+    def testUcharThrees(self):
+        "Test ucharThrees function"
+        threes = Series.ucharThrees(6)
+        self.assertEquals((threes == [3,3,3,3,3,3]).all(), True)
+
     def testUcharDet(self):
         "Test ucharDet function"
         matrix = [[7,6],[9,8]]
@@ -254,6 +274,16 @@
         self.assertEquals((even == [1,0,3]).all(), True)
         self.assertEquals((odd  == [0,2,0]).all(), True)
 
+    def testShortTwos(self):
+        "Test shortTwos function"
+        twos = Series.shortTwos(5)
+        self.assertEquals((twos == [2,2,2,2,2]).all(), True)
+
+    def testShortThrees(self):
+        "Test shortThrees function"
+        threes = Series.shortThrees(6)
+        self.assertEquals((threes == [3,3,3,3,3,3]).all(), True)
+
     def testShortDet(self):
         "Test shortDet function"
         matrix = [[6,7],[8,9]]
@@ -347,6 +377,16 @@
         self.assertEquals((even == [1,0,3]).all(), True)
         self.assertEquals((odd  == [0,2,0]).all(), True)
 
+    def testUshortTwos(self):
+        "Test ushortTwos function"
+        twos = Series.ushortTwos(5)
+        self.assertEquals((twos == [2,2,2,2,2]).all(), True)
+
+    def testUshortThrees(self):
+        "Test ushortThrees function"
+        threes = Series.ushortThrees(6)
+        self.assertEquals((threes == [3,3,3,3,3,3]).all(), True)
+
     def testUshortDet(self):
         "Test ushortDet function"
         matrix = [[7,6],[9,8]]
@@ -440,6 +480,16 @@
         self.assertEquals((even == [1,0,3]).all(), True)
         self.assertEquals((odd  == [0,2,0]).all(), True)
 
+    def testIntTwos(self):
+        "Test intTwos function"
+        twos = Series.intTwos(5)
+        self.assertEquals((twos == [2,2,2,2,2]).all(), True)
+
+    def testIntThrees(self):
+        "Test intThrees function"
+        threes = Series.intThrees(6)
+        self.assertEquals((threes == [3,3,3,3,3,3]).all(), True)
+
     def testIntDet(self):
         "Test intDet function"
         matrix = [[6,7],[8,9]]
@@ -534,6 +584,16 @@
         self.assertEquals((even == [1,0,3]).all(), True)
         self.assertEquals((odd  == [0,2,0]).all(), True)
 
+    def testUintTwos(self):
+        "Test uintTwos function"
+        twos = Series.uintTwos(5)
+        self.assertEquals((twos == [2,2,2,2,2]).all(), True)
+
+    def testUintThrees(self):
+        "Test uintThrees function"
+        threes = Series.uintThrees(6)
+        self.assertEquals((threes == [3,3,3,3,3,3]).all(), True)
+
     def testUintDet(self):
         "Test uintDet function"
         matrix = [[7,6],[9,8]]
@@ -627,6 +687,16 @@
         self.assertEquals((even == [1,0,3]).all(), True)
         self.assertEquals((odd  == [0,2,0]).all(), True)
 
+    def testLongTwos(self):
+        "Test longTwos function"
+        twos = Series.longTwos(5)
+        self.assertEquals((twos == [2,2,2,2,2]).all(), True)
+
+    def testLongThrees(self):
+        "Test longThrees function"
+        threes = Series.longThrees(6)
+        self.assertEquals((threes == [3,3,3,3,3,3]).all(), True)
+
     def testLongDet(self):
         "Test longDet function"
         matrix = [[6,7],[8,9]]
@@ -720,6 +790,16 @@
         self.assertEquals((even == [1,0,3]).all(), True)
         self.assertEquals((odd  == [0,2,0]).all(), True)
 
+    def testUlongTwos(self):
+        "Test ulongTwos function"
+        twos = Series.ulongTwos(5)
+        self.assertEquals((twos == [2,2,2,2,2]).all(), True)
+
+    def testUlongThrees(self):
+        "Test ulongThrees function"
+        threes = Series.ulongThrees(6)
+        self.assertEquals((threes == [3,3,3,3,3,3]).all(), True)
+
     def testUlongDet(self):
         "Test ulongDet function"
         matrix = [[7,6],[9,8]]
@@ -813,6 +893,16 @@
         self.assertEquals((even == [1,0,3]).all(), True)
         self.assertEquals((odd  == [0,2,0]).all(), True)
 
+    def testLongLongTwos(self):
+        "Test longLongTwos function"
+        twos = Series.longLongTwos(5)
+        self.assertEquals((twos == [2,2,2,2,2]).all(), True)
+
+    def testLongLongThrees(self):
+        "Test longLongThrees function"
+        threes = Series.longLongThrees(6)
+        self.assertEquals((threes == [3,3,3,3,3,3]).all(), True)
+
     def testLongLongDet(self):
         "Test longLongDet function"
         matrix = [[6,7],[8,9]]
@@ -906,6 +996,16 @@
         self.assertEquals((even == [1,0,3]).all(), True)
         self.assertEquals((odd  == [0,2,0]).all(), True)
 
+    def testUlongLongTwos(self):
+        "Test ulongLongTwos function"
+        twos = Series.ulongLongTwos(5)
+        self.assertEquals((twos == [2,2,2,2,2]).all(), True)
+
+    def testUlongLongThrees(self):
+        "Test ulongLongThrees function"
+        threes = Series.ulongLongThrees(6)
+        self.assertEquals((threes == [3,3,3,3,3,3]).all(), True)
+
     def testUlongLongDet(self):
         "Test ulongLongDet function"
         matrix = [[7,6],[9,8]]
@@ -1008,6 +1108,16 @@
         self.assertEquals((even == [1,0,3]).all(), True)
         self.assertEquals((odd  == [0,2,0]).all(), True)
 
+    def testFloatTwos(self):
+        "Test floatTwos function"
+        twos = Series.floatTwos(5)
+        self.assertEquals((twos == [2,2,2,2,2]).all(), True)
+
+    def testFloatThrees(self):
+        "Test floatThrees function"
+        threes = Series.floatThrees(6)
+        self.assertEquals((threes == [3,3,3,3,3,3]).all(), True)
+
     def testFloatMax(self):
         "Test floatMax function"
         matrix = [[-6,5,-4],[3.14,-2.718,1]]
@@ -1100,6 +1210,16 @@
         self.assertEquals((even == [1,0,3]).all(), True)
         self.assertEquals((odd  == [0,2,0]).all(), True)
 
+    def testDoubleTwos(self):
+        "Test doubleTwos function"
+        twos = Series.doubleTwos(5)
+        self.assertEquals((twos == [2,2,2,2,2]).all(), True)
+
+    def testDoubleThrees(self):
+        "Test doubleThrees function"
+        threes = Series.doubleThrees(6)
+        self.assertEquals((threes == [3,3,3,3,3,3]).all(), True)
+
     def testDoubleDet(self):
         "Test doubleDet function"
         matrix = [[6,7],[8,9]]



From numpy-svn at scipy.org  Sat Mar 31 22:35:43 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Sat, 31 Mar 2007 21:35:43 -0500 (CDT)
Subject: [Numpy-svn] r3630 - trunk/numpy/doc/swig
Message-ID: <20070401023543.AA24739C0E6@new.scipy.org>

Author: wfspotz at sandia.gov
Date: 2007-03-31 21:35:36 -0500 (Sat, 31 Mar 2007)
New Revision: 3630

Modified:
   trunk/numpy/doc/swig/series.cxx
   trunk/numpy/doc/swig/series.h
Log:
Added documentation to series.h and series.cxx

Modified: trunk/numpy/doc/swig/series.cxx
===================================================================
--- trunk/numpy/doc/swig/series.cxx	2007-04-01 02:31:40 UTC (rev 3629)
+++ trunk/numpy/doc/swig/series.cxx	2007-04-01 02:35:36 UTC (rev 3630)
@@ -12,6 +12,8 @@
 //     void SNAMEOnes(    TYPE * array,  int size);
 //     void SNAMEZeros(   int size, TYPE * array);
 //     void SNAMEEOSplit( TYPE vector[3], TYPE even[3], odd[3]);
+//     void SNAMETwos(    TYPE * twoVec, int size);
+//     void SNAMEThrees(  int size, TYPE * threeVec);
 //     TYPE SNAMEDet(     TYPE matrix[2][2]);
 //     TYPE SNAMEMax(     TYPE * matrix, int rows, int cols);
 //     TYPE SNAMEMin(     int rows, int cols, TYPE * matrix);
@@ -33,6 +35,8 @@
 //  * 1D in-place arrays
 //  * 1D in-place arrays, data last
 //  * 1D argout arrays, hard-coded length
+//  * 1D argout arrays
+//  * 1D argout arrays, data last
 //  * 2D input arrays, hard-coded length
 //  * 2D input arrays
 //  * 2D input arrays, data last

Modified: trunk/numpy/doc/swig/series.h
===================================================================
--- trunk/numpy/doc/swig/series.h	2007-04-01 02:31:40 UTC (rev 3629)
+++ trunk/numpy/doc/swig/series.h	2007-04-01 02:35:36 UTC (rev 3630)
@@ -11,6 +11,8 @@
 //     void SNAMEOnes(    TYPE * array,  int size);
 //     void SNAMEZeros(   int size, TYPE * array);
 //     void SNAMEEOSplit( TYPE vector[3], TYPE even[3], TYPE odd[3]);
+//     void SNAMETwos(    TYPE * twoVec, int size);
+//     void SNAMEThrees(  int size, TYPE * threeVec);
 //     TYPE SNAMEDet(     TYPE matrix[2][2]);
 //     TYPE SNAMEMax(     TYPE * matrix, int rows, int cols);
 //     TYPE SNAMEMin(     int rows, int cols, TYPE * matrix);



From numpy-svn at scipy.org  Sat Mar 31 23:44:30 2007
From: numpy-svn at scipy.org (numpy-svn at scipy.org)
Date: Sat, 31 Mar 2007 22:44:30 -0500 (CDT)
Subject: [Numpy-svn] r3631 - trunk/numpy/core/src
Message-ID: <20070401034430.22930C7C039@new.scipy.org>

Author: oliphant
Date: 2007-03-31 22:44:27 -0500 (Sat, 31 Mar 2007)
New Revision: 3631

Modified:
   trunk/numpy/core/src/multiarraymodule.c
Log:
Make sure dot product is not too large for dimensions array in PyArray_MatrixProduct.  Fixes ticket #418

Modified: trunk/numpy/core/src/multiarraymodule.c
===================================================================
--- trunk/numpy/core/src/multiarraymodule.c	2007-04-01 02:35:36 UTC (rev 3630)
+++ trunk/numpy/core/src/multiarraymodule.c	2007-04-01 03:44:27 UTC (rev 3631)
@@ -3287,6 +3287,11 @@
 	}
 
 	nd = ap1->nd+ap2->nd-2;
+        if (nd > NPY_MAXDIMS) {
+                PyErr_SetString(PyExc_ValueError, 
+                                "dot: too many dimensions in result");
+                goto fail;
+        }
 	j = 0;
 	for(i=0; i<ap1->nd-1; i++) {
 		dimensions[j++] = ap1->dimensions[i];