Add rest of the examples for pyrex and swig

16 files changed, 1880 insertions, 0 deletions

diff --git a/numpy/doc/pyrex/MANIFEST b/numpy/doc/pyrex/MANIFEST
new file mode 100644
index 000000000..feb3ec22a
--- /dev/null
+++ b/numpy/doc/pyrex/MANIFEST
@@ -0,0 +1,2 @@
+numpyx.pyx
+setup.py
diff --git a/numpy/doc/pyrex/Makefile b/numpy/doc/pyrex/Makefile
new file mode 100644
index 000000000..b5905e7be
--- /dev/null
+++ b/numpy/doc/pyrex/Makefile
@@ -0,0 +1,9 @@
+all:
+	python setup.py build_ext --inplace
+
+test:   all
+	python run_test.py
+
+.PHONY: clean
+clean:
+	rm -rf *~ *.so *.c *.o build
diff --git a/numpy/doc/pyrex/c_python.pxd b/numpy/doc/pyrex/c_python.pxd
new file mode 100644
index 000000000..53f6d9b19
--- /dev/null
+++ b/numpy/doc/pyrex/c_python.pxd
@@ -0,0 +1,20 @@
+# -*- Mode: Python -*-  Not really, but close enough
+
+# Expose as much of the Python C API as we need here
+
+cdef extern from "stdlib.h":
+    ctypedef int size_t
+
+cdef extern from "Python.h":
+    ctypedef int Py_intptr_t
+    void*  PyMem_Malloc(size_t)
+    void*  PyMem_Realloc(void *p, size_t n)
+    void   PyMem_Free(void *p)
+    char*  PyString_AsString(object string)
+    object PyString_FromString(char *v)
+    object PyString_InternFromString(char *v)
+    int    PyErr_CheckSignals()
+    object PyFloat_FromDouble(double v)
+    void   Py_XINCREF(object o)
+    void   Py_XDECREF(object o)
+    void   Py_CLEAR(object o) # use instead of decref
diff --git a/numpy/doc/pyrex/notes b/numpy/doc/pyrex/notes
new file mode 100644
index 000000000..301581cee
--- /dev/null
+++ b/numpy/doc/pyrex/notes
@@ -0,0 +1,3 @@
+- cimport with a .pxd file vs 'include foo.pxi'?
+
+- the need to repeat: pyrex does NOT parse C headers.
\ No newline at end of file
diff --git a/numpy/doc/pyrex/numpyx.c b/numpy/doc/pyrex/numpyx.c
new file mode 100644
index 000000000..0e2658242
--- /dev/null
+++ b/numpy/doc/pyrex/numpyx.c
@@ -0,0 +1,1038 @@
+/* Generated by Pyrex 0.9.2.1 on Sun Mar 12 19:19:16 2006 */
+
+#include "Python.h"
+#include "structmember.h"
+#ifndef PY_LONG_LONG
+  #define PY_LONG_LONG LONG_LONG
+#endif
+#include "stdlib.h"
+#include "numpy/arrayobject.h"
+
+
+typedef struct {PyObject **p; char *s;} __Pyx_InternTabEntry; /*proto*/
+typedef struct {PyObject **p; char *s; long n;} __Pyx_StringTabEntry; /*proto*/
+static PyObject *__Pyx_UnpackItem(PyObject *, int); /*proto*/
+static int __Pyx_EndUnpack(PyObject *, int); /*proto*/
+static int __Pyx_PrintItem(PyObject *); /*proto*/
+static int __Pyx_PrintNewline(void); /*proto*/
+static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb); /*proto*/
+static void __Pyx_ReRaise(void); /*proto*/
+static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list); /*proto*/
+static PyObject *__Pyx_GetExcValue(void); /*proto*/
+static int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, char *name); /*proto*/
+static int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/
+static int __Pyx_GetStarArgs(PyObject **args, PyObject **kwds, char *kwd_list[], int nargs, PyObject **args2, PyObject **kwds2); /*proto*/
+static void __Pyx_WriteUnraisable(char *name); /*proto*/
+static void __Pyx_AddTraceback(char *funcname); /*proto*/
+static PyTypeObject *__Pyx_ImportType(char *module_name, char *class_name, long size);  /*proto*/
+static int __Pyx_SetVtable(PyObject *dict, void *vtable); /*proto*/
+static int __Pyx_GetVtable(PyObject *dict, void **vtabptr); /*proto*/
+static PyObject *__Pyx_CreateClass(PyObject *bases, PyObject *dict, PyObject *name, char *modname); /*proto*/
+static int __Pyx_InternStrings(__Pyx_InternTabEntry *t); /*proto*/
+static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/
+static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name); /*proto*/
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+static PyObject *__pyx_m;
+static PyObject *__pyx_b;
+static int __pyx_lineno;
+static char *__pyx_filename;
+staticforward char **__pyx_f;
+
+/* Declarations from c_python */
+
+
+/* Declarations from c_numpy */
+
+static PyTypeObject *__pyx_ptype_7c_numpy_dtype = 0;
+static PyTypeObject *__pyx_ptype_7c_numpy_ndarray = 0;
+
+/* Declarations from numpyx */
+
+static PyObject *(__pyx_f_6numpyx_print_elements(char (*),int (*),int (*),int ,int ,PyObject *)); /*proto*/
+
+/* Implementation of numpyx */
+
+
+static PyObject *__pyx_n_c_python;
+static PyObject *__pyx_n_c_numpy;
+static PyObject *__pyx_n_numpy;
+static PyObject *__pyx_n_print_array_info;
+static PyObject *__pyx_n_test_methods;
+static PyObject *__pyx_n_test;
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+char (__pyx_k6[]) = "strides:";
+char (__pyx_k7[]) = "  stride %d:";
+char (__pyx_k8[]) = "memory dump:";
+char (__pyx_k9[]) = "-=";
+
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+
+static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list) {
+    PyObject *__import__ = 0;
+    PyObject *empty_list = 0;
+    PyObject *module = 0;
+    PyObject *global_dict = 0;
+    PyObject *empty_dict = 0;
+    PyObject *list;
+    __import__ = PyObject_GetAttrString(__pyx_b, "__import__");
+    if (!__import__)
+        goto bad;
+    if (from_list)
+        list = from_list;
+    else {
+        empty_list = PyList_New(0);
+        if (!empty_list)
+            goto bad;
+        list = empty_list;
+    }
+    global_dict = PyModule_GetDict(__pyx_m);
+    if (!global_dict)
+        goto bad;
+    empty_dict = PyDict_New();
+    if (!empty_dict)
+        goto bad;
+    module = PyObject_CallFunction(__import__, "OOOO",
+        name, global_dict, empty_dict, list);
+bad:
+    Py_XDECREF(empty_list);
+    Py_XDECREF(__import__);
+    Py_XDECREF(empty_dict);
+    return module;
+}
+
+static PyObject *__Pyx_GetStdout(void) {
+    PyObject *f = PySys_GetObject("stdout");
+    if (!f) {
+        PyErr_SetString(PyExc_RuntimeError, "lost sys.stdout");
+    }
+    return f;
+}
+
+static int __Pyx_PrintItem(PyObject *v) {
+    PyObject *f;
+    
+    if (!(f = __Pyx_GetStdout()))
+        return -1;
+    if (PyFile_SoftSpace(f, 1)) {
+        if (PyFile_WriteString(" ", f) < 0)
+            return -1;
+    }
+    if (PyFile_WriteObject(v, f, Py_PRINT_RAW) < 0)
+        return -1;
+    if (PyString_Check(v)) {
+        char *s = PyString_AsString(v);
+        int len = PyString_Size(v);
+        if (len > 0 &&
+            isspace(Py_CHARMASK(s[len-1])) &&
+            s[len-1] != ' ')
+                PyFile_SoftSpace(f, 0);
+    }
+    return 0;
+}
+
+static int __Pyx_PrintNewline(void) {
+    PyObject *f;
+    
+    if (!(f = __Pyx_GetStdout()))
+        return -1;
+    if (PyFile_WriteString("\n", f) < 0)
+        return -1;
+    PyFile_SoftSpace(f, 0);
+    return 0;
+}
+
+static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name) {
+    PyObject *result;
+    result = PyObject_GetAttr(dict, name);
+    if (!result)
+        PyErr_SetObject(PyExc_NameError, name);
+    return result;
+}
+
+static int __Pyx_InternStrings(__Pyx_InternTabEntry *t) {
+    while (t->p) {
+        *t->p = PyString_InternFromString(t->s);
+        if (!*t->p)
+            return -1;
+        ++t;
+    }
+    return 0;
+}
+
+static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
+    while (t->p) {
+        *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
+        if (!*t->p)
+            return -1;
+        ++t;
+    }
+    return 0;
+}
+
+static PyTypeObject *__Pyx_ImportType(char *module_name, char *class_name, 
+    long size) 
+{
+    PyObject *py_module_name = 0;
+    PyObject *py_class_name = 0;
+    PyObject *py_name_list = 0;
+    PyObject *py_module = 0;
+    PyObject *result = 0;
+    
+    py_module_name = PyString_FromString(module_name);
+    if (!py_module_name)
+        goto bad;
+    py_class_name = PyString_FromString(class_name);
+    if (!py_class_name)
+        goto bad;
+    py_name_list = PyList_New(1);
+    if (!py_name_list)
+        goto bad;
+    Py_INCREF(py_class_name);
+    if (PyList_SetItem(py_name_list, 0, py_class_name) < 0)
+        goto bad;
+    py_module = __Pyx_Import(py_module_name, py_name_list);
+    if (!py_module)
+        goto bad;
+    result = PyObject_GetAttr(py_module, py_class_name);
+    if (!result)
+        goto bad;
+    if (!PyType_Check(result)) {
+        PyErr_Format(PyExc_TypeError, 
+            "%s.%s is not a type object",
+            module_name, class_name);
+        goto bad;
+    }
+    if (((PyTypeObject *)result)->tp_basicsize != size) {
+        PyErr_Format(PyExc_ValueError, 
+            "%s.%s does not appear to be the correct type object",
+            module_name, class_name);
+        goto bad;
+    }
+    goto done;
+bad:
+    Py_XDECREF(result);
+    result = 0;
+done:
+    Py_XDECREF(py_module_name);
+    Py_XDECREF(py_class_name);
+    Py_XDECREF(py_name_list);
+    return (PyTypeObject *)result;
+}
+
+#include "compile.h"
+#include "frameobject.h"
+#include "traceback.h"
+
+static void __Pyx_AddTraceback(char *funcname) {
+    PyObject *py_srcfile = 0;
+    PyObject *py_funcname = 0;
+    PyObject *py_globals = 0;
+    PyObject *empty_tuple = 0;
+    PyObject *empty_string = 0;
+    PyCodeObject *py_code = 0;
+    PyFrameObject *py_frame = 0;
+    
+    py_srcfile = PyString_FromString(__pyx_filename);
+    if (!py_srcfile) goto bad;
+    py_funcname = PyString_FromString(funcname);
+    if (!py_funcname) goto bad;
+    py_globals = PyModule_GetDict(__pyx_m);
+    if (!py_globals) goto bad;
+    empty_tuple = PyTuple_New(0);
+    if (!empty_tuple) goto bad;
+    empty_string = PyString_FromString("");
+    if (!empty_string) goto bad;
+    py_code = PyCode_New(
+        0,            /*int argcount,*/
+        0,            /*int nlocals,*/
+        0,            /*int stacksize,*/
+        0,            /*int flags,*/
+        empty_string, /*PyObject *code,*/
+        empty_tuple,  /*PyObject *consts,*/
+        empty_tuple,  /*PyObject *names,*/
+        empty_tuple,  /*PyObject *varnames,*/
+        empty_tuple,  /*PyObject *freevars,*/
+        empty_tuple,  /*PyObject *cellvars,*/
+        py_srcfile,   /*PyObject *filename,*/
+        py_funcname,  /*PyObject *name,*/
+        __pyx_lineno,   /*int firstlineno,*/
+        empty_string  /*PyObject *lnotab*/
+    );
+    if (!py_code) goto bad;
+    py_frame = PyFrame_New(
+        PyThreadState_Get(), /*PyThreadState *tstate,*/
+        py_code,             /*PyCodeObject *code,*/
+        py_globals,          /*PyObject *globals,*/
+        0                    /*PyObject *locals*/
+    );
+    if (!py_frame) goto bad;
+    py_frame->f_lineno = __pyx_lineno;
+    PyTraceBack_Here(py_frame);
+bad:
+    Py_XDECREF(py_srcfile);
+    Py_XDECREF(py_funcname);
+    Py_XDECREF(empty_tuple);
+    Py_XDECREF(empty_string);
+    Py_XDECREF(py_code);
+    Py_XDECREF(py_frame);
+}
diff --git a/numpy/doc/pyrex/numpyx.pyx b/numpy/doc/pyrex/numpyx.pyx
new file mode 100644
index 000000000..4cd6dbd29
--- /dev/null
+++ b/numpy/doc/pyrex/numpyx.pyx
@@ -0,0 +1,97 @@
+# -*- Mode: Python -*-  Not really, but close enough
+
+cimport c_python
+cimport c_numpy
+import numpy
+
+# Numpy must be initialized
+c_numpy.import_array()
+
+def print_array_info(c_numpy.ndarray arr):
+    cdef int i
+
+    print '-='*10
+    print 'printing array info for ndarray at 0x%0lx'%(<c_python.Py_intptr_t>arr,)
+    print 'print number of dimensions:',arr.nd
+    print 'address of strides: 0x%0lx'%(<c_python.Py_intptr_t>arr.strides,)
+    print 'strides:'
+    for i from 0<=i<arr.nd:
+        # print each stride
+        print '  stride %d:'%i,<c_python.Py_intptr_t>arr.strides[i]
+    print 'memory dump:'
+    print_elements( arr.data, arr.strides, arr.dimensions,
+                    arr.nd, sizeof(double), arr.dtype )
+    print '-='*10
+    print
+
+cdef print_elements(char *data,
+                    c_python.Py_intptr_t* strides,
+                    c_python.Py_intptr_t* dimensions,
+                    int nd,
+                    int elsize,
+                    object dtype):
+    cdef c_python.Py_intptr_t i,j
+    cdef void* elptr
+
+    if dtype not in [numpy.dtype(numpy.object_),
+                     numpy.dtype(numpy.float64)]:
+        print '   print_elements() not (yet) implemented for dtype %s'%dtype.name
+        return
+
+    if nd ==0:
+        if dtype==numpy.dtype(numpy.object_):
+            elptr = (<void**>data)[0] #[0] dereferences pointer in Pyrex
+            print '  ',<object>elptr
+        elif dtype==numpy.dtype(numpy.float64):
+            print '  ',(<double*>data)[0]
+    elif nd == 1:
+        for i from 0<=i<dimensions[0]:
+            if dtype==numpy.dtype(numpy.object_):
+                elptr = (<void**>data)[0]
+                print '  ',<object>elptr
+            elif dtype==numpy.dtype(numpy.float64):
+                print '  ',(<double*>data)[0]
+            data = data + strides[0]
+    else:
+        for i from 0<=i<dimensions[0]:
+            print_elements(data, strides+1, dimensions+1, nd-1, elsize, dtype)
+            data = data + strides[0]
+
+def test_methods(c_numpy.ndarray arr):
+    """Test a few attribute accesses for an array.
+    
+    This illustrates how the pyrex-visible object is in practice a strange
+    hybrid of the C PyArrayObject struct and the python object.  Some
+    properties (like .nd) are visible here but not in python, while others
+    like flags behave very differently: in python flags appears as a dict,
+    while here we see the raw int holding the bit pattern.
+
+    This makes sense when we think of how pyrex resolves arr.foo: if foo is
+    listed as a field in the c_numpy.ndarray struct description, it will be
+    directly accessed as a C variable without going through Python at all.
+    This is why for arr.flags, we see the actual int which holds all the flags
+    as bit fields.  However, for any other attribute not listed in the struct,
+    it simply forwards the attribute lookup to python at runtime, just like
+    python would (which means that AttributeError can  be raised for
+    non-existent attributes, for example)."""
+    
+    print 'arr.any() :',arr.any()
+    print 'arr.nd    :',arr.nd
+    print 'arr.flags :',arr.flags
+
+def test():
+    """this function is pure Python"""
+    arr1 = numpy.array(-1e-30,dtype=numpy.Float64)
+    arr2 = numpy.array([1.0,2.0,3.0],dtype=numpy.Float64)
+
+    arr3 = numpy.arange(9,dtype=numpy.Float64)
+    arr3.shape = 3,3
+
+    four = 4
+    arr4 = numpy.array(['one','two',3,four],dtype=numpy.object_)
+
+    arr5 = numpy.array([1,2,3]) # int types not (yet) supported by print_elements
+
+    for arr in [arr1,arr2,arr3,arr4,arr5]:
+        print_array_info(arr)
+
diff --git a/numpy/doc/pyrex/run_test.py b/numpy/doc/pyrex/run_test.py
new file mode 100755
index 000000000..96388011e
--- /dev/null
+++ b/numpy/doc/pyrex/run_test.py
@@ -0,0 +1,3 @@
+#!/usr/bin/env python
+from numpyx import test
+test()
diff --git a/numpy/doc/pyrex/setup.py b/numpy/doc/pyrex/setup.py
new file mode 100644
index 000000000..d081080d3
--- /dev/null
+++ b/numpy/doc/pyrex/setup.py
@@ -0,0 +1,42 @@
+#!/usr/bin/env python
+"""Install file for example on how to use Pyrex with Numpy.
+
+For more details, see:
+http://www.scipy.org/Cookbook/Pyrex_and_NumPy
+http://www.scipy.org/Cookbook/ArrayStruct_and_Pyrex
+"""
+
+from distutils.core import setup
+from distutils.extension import Extension
+
+# Make this usable by people who don't have pyrex installed (I've committed
+# the generated C sources to SVN).
+try:
+    from Pyrex.Distutils import build_ext
+    has_pyrex = True
+except ImportError:
+    has_pyrex = False
+
+import numpy
+
+# Define a pyrex-based extension module, using the generated sources if pyrex
+# is not available.
+if has_pyrex:
+    pyx_sources = ['numpyx.pyx']
+    cmdclass    = {'build_ext': build_ext}
+else:
+    pyx_sources = ['numpyx.c']
+    cmdclass    = {}
+
+
+pyx_ext = Extension('numpyx',
+                 pyx_sources,
+                 include_dirs = [numpy.get_numpy_include()])
+
+# Call the routine which does the real work
+setup(name        = 'numpyx',
+      description = 'Small example on using Pyrex to write a Numpy extension',
+      url         = 'http://www.scipy.org/Cookbook/Pyrex_and_NumPy',
+      ext_modules = [pyx_ext],
+      cmdclass    = cmdclass,
+      )
diff --git a/numpy/doc/swig/HelperFunctions.txt b/numpy/doc/swig/HelperFunctions.txt
new file mode 100644
index 000000000..f195d29df
--- /dev/null
+++ b/numpy/doc/swig/HelperFunctions.txt
@@ -0,0 +1,104 @@
+/***********************************************************************
+    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)
diff --git a/numpy/doc/swig/Makefile b/numpy/doc/swig/Makefile
new file mode 100644
index 000000000..4469dc608
--- /dev/null
+++ b/numpy/doc/swig/Makefile
@@ -0,0 +1,14 @@
+INTERFACES = Series.i
+WRAPPERS   = $(INTERFACES:.i=_wrap.cxx)
+PROXIES    = $(INTERFACES:.i=.py      )
+
+all: $(WRAPPERS) series.cxx series.h
+	./setup.py build
+
+%_wrap.cxx: %.i numpy.i series.h
+	swig -c++ -python $<
+
+clean:
+	$(RM) -r build
+	$(RM) $(WRAPPERS)
+	$(RM) $(PROXIES)
diff --git a/numpy/doc/swig/README b/numpy/doc/swig/README
new file mode 100644
index 000000000..494da98ba
--- /dev/null
+++ b/numpy/doc/swig/README
@@ -0,0 +1,122 @@
+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).
+
+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
+
+    series.h
+    series.cxx
+    testSeries.py
+
+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
+
+    (type* IN_ARRAY1, int DIM1)
+    (type* IN_ARRAY2, int DIM1, int DIM2)
+    (type* INPLACE_ARRAY1, int DIM1)
+
+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
+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
+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
+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"
+directives to force the functions to use the typemaps.
+
+The setup.py script is a standard python distutils script.  It defines
+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.
+
+To build and test the code, simply execute from the shell,
+
+    $ make
+    $ testSeries.py
+
+================================================================================
+
+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 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.
diff --git a/numpy/doc/swig/Series.i b/numpy/doc/swig/Series.i
new file mode 100644
index 000000000..8265dd35d
--- /dev/null
+++ b/numpy/doc/swig/Series.i
@@ -0,0 +1,31 @@
+%module Series
+
+%{
+#define SWIG_FILE_WITH_INIT
+#include "series.h"
+%}
+
+// 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 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 (int*    INPLACE_ARRAY1, int DIM1) {(int*    array,   int size)};
+%apply (double* INPLACE_ARRAY1, int DIM1) {(double* array,   int size)};
+
+// Include the header file to be wrapped
+%include "series.h"
diff --git a/numpy/doc/swig/series.cxx b/numpy/doc/swig/series.cxx
new file mode 100644
index 000000000..7c1e5f049
--- /dev/null
+++ b/numpy/doc/swig/series.cxx
@@ -0,0 +1,145 @@
+#include <stdlib.h>
+#include <math.h>
+#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;
+}
+
+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;
+    }
+  }
+}
diff --git a/numpy/doc/swig/series.h b/numpy/doc/swig/series.h
new file mode 100644
index 000000000..f1b788c1b
--- /dev/null
+++ b/numpy/doc/swig/series.h
@@ -0,0 +1,40 @@
+#ifndef SERIES_H
+#define SERIES_H
+
+/*** One Dimensional Arrays ***/
+
+/* Examples of functions that take 1D C arrays as input */
+short shortSum( short* series, int size);
+short shortProd(short* series, int size);
+
+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
diff --git a/numpy/doc/swig/setup.py b/numpy/doc/swig/setup.py
new file mode 100755
index 000000000..4bf842c1f
--- /dev/null
+++ b/numpy/doc/swig/setup.py
@@ -0,0 +1,24 @@
+#! /usr/bin/env python
+
+# System imports
+from distutils.core import *
+from distutils      import sysconfig
+
+# Third-party modules - we depend on numpy for everything
+import numpy
+
+# _Series extension module
+_Series = Extension("_Series",
+                    ["Series_wrap.cxx",
+                     "series.cxx"],
+                    include_dirs = [numpy.get_numpy_include()],
+#                    libraries = ["m"]
+                    )
+
+# Series setup
+setup(name        = "Series",
+      description = "Functions that work on series",
+      author      = "Bill Spotz",
+      py_modules  = ["Series"],
+      ext_modules = [_Series]
+      )
diff --git a/numpy/doc/swig/testSeries.py b/numpy/doc/swig/testSeries.py
new file mode 100755
index 000000000..845f8ce2e
--- /dev/null
+++ b/numpy/doc/swig/testSeries.py
@@ -0,0 +1,186 @@
+#! /usr/bin/env python
+
+# System imports
+from   distutils.util import get_platform
+import os
+import sys
+import unittest
+
+import numpy as N
+
+# Add the distutils-generated build directory to the python search path and then
+# import the extension module
+libDir = "lib.%s-%s" % (get_platform(), sys.version[:3])
+sys.path.insert(0,os.path.join("build", libDir))
+import Series
+
+######################################################################
+
+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)
+
+    def testShortProd(self):
+        "Test the shortProd function"
+        self.assertEquals(Series.shortProd([1,2,3,4]), 24)
+
+    ######################################################
+    ### 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)
+
+    def testIntSumBadContainer(self):
+        "Test the intSum function with an invalid list"
+        self.assertRaises(TypeError, Series.intSum, [1,2,"junk"])
+
+    def testIntProd(self):
+        "Test the intProd function"
+        self.assertEquals(Series.intProd([1,2,3,4]), 24)
+
+    def testIntProdNonContainer(self):
+        "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]))
+
+    #######################################################
+    ### 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 testLongSumMultiDimensional(self):
+        "Test the longSum function with multi-dimensional array"
+        self.assertRaises(TypeError, Series.longSum, [[1,2,3],[9,8,7]])
+
+    def testLongProd(self):
+        "Test the longProd function"
+        self.assertEquals(Series.longProd([1,2,3,4]), 24)
+
+    ########################################################
+    ### 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)
+
+    def testFloatSumBadContainer(self):
+        "Test the floatSum function with a dictionary"
+        self.assertRaises(TypeError, Series.floatSum, {"key":"value"})
+
+    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"])
+
+    #########################################################
+    ### 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 testDoubleSumNonContainer(self):
+        "Test the doubleSum function with None"
+        self.assertRaises(TypeError, Series.doubleSum, None)
+
+    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"])
+
+    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')
+        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"
+        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 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]]))
+
+######################################################################
+
+if __name__ == "__main__":
+
+    # Build the test suite
+    suite = unittest.TestSuite()
+    suite.addTest(unittest.makeSuite(SeriesTestCase))
+
+    # Execute the test suite
+    print "Testing Module Series\n"
+    result = unittest.TextTestRunner(verbosity=2).run(suite)
+    sys.exit(len(result.errors) + len(result.failures))