DOCS: New ufunc creation docs

5 files changed, 901 insertions, 183 deletions

diff --git a/doc/source/reference/c-api.coremath.rst b/doc/source/reference/c-api.coremath.rst
index 6584f216d..dba092a20 100644
--- a/doc/source/reference/c-api.coremath.rst
+++ b/doc/source/reference/c-api.coremath.rst
@@ -175,9 +175,9 @@ Linking against the core math library in an extension
 To use the core math library in your own extension, you need to add the npymath
 compile and link options to your extension in your setup.py:
 
-        >>> from numpy.distutils.misc_utils import get_info
+        >>> from numpy.distutils.misc_util import get_info
         >>> info = get_info('npymath')
-        >>> config.add_extension('foo', sources=['foo.c'], extra_info=**info)
+        >>> config.add_extension('foo', sources=['foo.c'], extra_info=info)
 
 In other words, the usage of info is exactly the same as when using blas_info
 and co.
diff --git a/doc/source/user/c-info.beyond-basics.rst b/doc/source/user/c-info.beyond-basics.rst
index 5ff92a122..9ed2ab386 100644
--- a/doc/source/user/c-info.beyond-basics.rst
+++ b/doc/source/user/c-info.beyond-basics.rst
@@ -204,186 +204,6 @@ There are several examples of using the multi-iterator in the NumPy
 source code as it makes N-dimensional broadcasting-code very simple to
 write. Browse the source for more examples.
 
-.. _`sec:Creating-a-new`:
-
-Creating a new universal function
-=================================
-
-.. index::
-   pair: ufunc; adding new
-
-The umath module is a computer-generated C-module that creates many
-ufuncs. It provides a great many examples of how to create a universal
-function. Creating your own ufunc that will make use of the ufunc
-machinery is not difficult either. Suppose you have a function that
-you want to operate element-by-element over its inputs. By creating a
-new ufunc you will obtain a function that handles
-
-- broadcasting
-
-- N-dimensional looping
-
-- automatic type-conversions with minimal memory usage
-
-- optional output arrays
-
-It is not difficult to create your own ufunc. All that is required is
-a 1-d loop for each data-type you want to support. Each 1-d loop must
-have a specific signature, and only ufuncs for fixed-size data-types
-can be used. The function call used to create a new ufunc to work on
-built-in data-types is given below. A different mechanism is used to
-register ufuncs for user-defined data-types.
-
-.. cfunction:: PyObject *PyUFunc_FromFuncAndData( PyUFuncGenericFunction* func,
-   void** data, char* types, int ntypes, int nin, int nout, int identity,
-   char* name, char* doc, int check_return)
-
-    *func*
-
-        A pointer to an array of 1-d functions to use. This array must be at
-        least ntypes long. Each entry in the array must be a
-        ``PyUFuncGenericFunction`` function. This function has the following
-        signature. An example of a valid 1d loop function is also given.
-
-        .. cfunction:: void loop1d(char** args, npy_intp* dimensions,
-           npy_intp* steps, void* data)
-
-        *args*
-
-            An array of pointers to the actual data for the input and output
-            arrays. The input arguments are given first followed by the output
-            arguments.
-
-        *dimensions*
-
-            A pointer to the size of the dimension over which this function is
-            looping.
-
-        *steps*
-
-            A pointer to the number of bytes to jump to get to the
-            next element in this dimension for each of the input and
-            output arguments.
-
-        *data*
-
-            Arbitrary data (extra arguments, function names, *etc.* )
-            that can be stored with the ufunc and will be passed in
-            when it is called.
-
-        .. code-block:: c
-
-            static void
-            double_add(char *args, npy_intp *dimensions, npy_intp *steps,
-               void *extra)
-            {
-                npy_intp i;
-                npy_intp is1=steps[0], is2=steps[1];
-                npy_intp os=steps[2], n=dimensions[0];
-                char *i1=args[0], *i2=args[1], *op=args[2];
-                for (i=0; i<n; i++) {
-                    *((double *)op) = *((double *)i1) + \
-                                      *((double *)i2);
-                    i1 += is1; i2 += is2; op += os;
-                 }
-            }
-
-    *data*
-
-        An array of data. There should be ntypes entries (or NULL) --- one for
-        every loop function defined for this ufunc. This data will be passed
-        in to the 1-d loop. One common use of this data variable is to pass in
-        an actual function to call to compute the result when a generic 1-d
-        loop (e.g. :cfunc:`PyUFunc_d_d`) is being used.
-
-    *types*
-
-        An array of type-number signatures (type ``char`` ). This
-        array should be of size (nin+nout)*ntypes and contain the
-        data-types for the corresponding 1-d loop. The inputs should
-        be first followed by the outputs. For example, suppose I have
-        a ufunc that supports 1 integer and 1 double 1-d loop
-        (length-2 func and data arrays) that takes 2 inputs and
-        returns 1 output that is always a complex double, then the
-        types array would be
-
-
-        The bit-width names can also be used (e.g. :cdata:`NPY_INT32`,
-        :cdata:`NPY_COMPLEX128` ) if desired.
-
-    *ntypes*
-
-        The number of data-types supported. This is equal to the number of 1-d
-        loops provided.
-
-    *nin*
-
-        The number of input arguments.
-
-    *nout*
-
-        The number of output arguments.
-
-    *identity*
-
-        Either :cdata:`PyUFunc_One`, :cdata:`PyUFunc_Zero`,
-        :cdata:`PyUFunc_None`. This specifies what should be returned when
-        an empty array is passed to the reduce method of the ufunc.
-
-    *name*
-
-        A ``NULL`` -terminated string providing the name of this ufunc
-        (should be the Python name it will be called).
-
-    *doc*
-
-        A documentation string for this ufunc (will be used in generating the
-        response to ``{ufunc_name}.__doc__``). Do not include the function
-        signature or the name as this is generated automatically.
-
-    *check_return*
-
-        Not presently used, but this integer value does get set in the
-        structure-member of similar name.
-
-    .. index::
-       pair: ufunc; adding new
-
-    The returned ufunc object is a callable Python object. It should be
-    placed in a (module) dictionary under the same name as was used in the
-    name argument to the ufunc-creation routine. The following example is
-    adapted from the umath module
-
-    .. code-block:: c
-
-        static PyUFuncGenericFunction atan2_functions[]=\
-            {PyUFunc_ff_f, PyUFunc_dd_d,
-             PyUFunc_gg_g, PyUFunc_OO_O_method};
-        static void* atan2_data[]=\
-            {(void *)atan2f,(void *) atan2,
-             (void *)atan2l,(void *)"arctan2"};
-        static char atan2_signatures[]=\
-            {NPY_FLOAT, NPY_FLOAT, NPY_FLOAT,
-             NPY_DOUBLE, NPY_DOUBLE,
-             NPY_DOUBLE, NPY_LONGDOUBLE,
-             NPY_LONGDOUBLE, NPY_LONGDOUBLE
-             NPY_OBJECT, NPY_OBJECT,
-             NPY_OBJECT};
-        ...
-        /* in the module initialization code */
-        PyObject *f, *dict, *module;
-        ...
-        dict = PyModule_GetDict(module);
-        ...
-        f = PyUFunc_FromFuncAndData(atan2_functions,
-            atan2_data, atan2_signatures, 4, 2, 1,
-            PyUFunc_None, "arctan2",
-            "a safe and correct arctan(x1/x2)", 0);
-        PyDict_SetItemString(dict, "arctan2", f);
-        Py_DECREF(f);
-        ...
-
-
 .. _user.user-defined-data-types:
 
 User-defined data-types
diff --git a/doc/source/user/c-info.rst b/doc/source/user/c-info.rst
index 086f97c8d..2fb50c5ef 100644
--- a/doc/source/user/c-info.rst
+++ b/doc/source/user/c-info.rst
@@ -6,4 +6,5 @@ Using Numpy C-API
 
    c-info.how-to-extend
    c-info.python-as-glue
+   c-info.ufunc-tutorial
    c-info.beyond-basics
diff --git a/doc/source/user/c-info.ufunc-tutorial.rst b/doc/source/user/c-info.ufunc-tutorial.rst
new file mode 100644
index 000000000..d1d344099
--- /dev/null
+++ b/doc/source/user/c-info.ufunc-tutorial.rst
@@ -0,0 +1,897 @@
+**********************
+Writing your own ufunc
+**********************
+
+| I have the Power!
+| --- *He-Man*
+
+
+.. _`sec:Creating-a-new`:
+
+Creating a new universal function
+=================================
+
+.. index::
+   pair: ufunc; adding new
+
+Before reading this, it may help to familiarize yourself with the basics
+of C extensions for Python by reading/skimming the tutorials in Section 1
+of `Extending and Embedding the Python Interpreter
+<http://docs.python.org/extending/index.html>`_ and in `How to extend
+Numpy <http://docs.scipy.org/doc/numpy/user/c-info.how-to-extend.html>`_
+
+The umath module is a computer-generated C-module that creates many
+ufuncs. It provides a great many examples of how to create a universal
+function. Creating your own ufunc that will make use of the ufunc
+machinery is not difficult either. Suppose you have a function that
+you want to operate element-by-element over its inputs. By creating a
+new ufunc you will obtain a function that handles
+
+- broadcasting
+
+- N-dimensional looping
+
+- automatic type-conversions with minimal memory usage
+
+- optional output arrays
+
+It is not difficult to create your own ufunc. All that is required is
+a 1-d loop for each data-type you want to support. Each 1-d loop must
+have a specific signature, and only ufuncs for fixed-size data-types
+can be used. The function call used to create a new ufunc to work on
+built-in data-types is given below. A different mechanism is used to
+register ufuncs for user-defined data-types.
+
+In the next several sections we give example code that can be
+easily modified to create your own ufuncs. The examples are
+successively more complete or complicated versions of the logit
+function, a common function in statistical modeling. Logit is also
+interesting because, due to the magic of IEEE standards (specifically
+IEEE 754), all of the logit functions created below
+automatically have the following behavior.
+
+>>> logit(0)
+-inf
+>>> logit(1)
+inf
+>>> logit(2)
+nan
+>>> logit(-2)
+nan
+
+This is wonderful because the function writer doesn't have to
+manually propagate infs or nans.
+
+.. _`sec:Non-numpy-example`:
+
+Example Non-ufunc extension
+===========================
+
+.. index::
+   pair: ufunc; adding new
+
+For comparison and general edificaiton of the reader we provide
+a simple implementation of a C extension of logit that uses no
+numpy.
+
+To do this we need two files. The first is the C file which contains
+the actual code, and the second is the setup.py file used to create
+the module.
+
+    .. code-block:: c
+
+        #include <Python.h>
+        #include <math.h>
+
+        /*
+            spammodule.c
+            This is the C code for a non-numpy Python extension to
+            define the logit function, where logit(p) = log(p/(1-p)).
+            This function will not work on numpy arrays automatically.
+            numpy.vectorize must be called in python to generate
+            a numpy-friendly function.
+
+            Details explaining the Python-C API can be found under
+            'Extending and Embedding' and 'Python/C API' at
+            docs.python.org .
+        */
+
+
+        /* This declares the logit function */
+        static PyObject* spam_logit(PyObject *self, PyObject *args);
+
+
+        /* This tells Python what methods this module has */
+        static PyMethodDef SpamMethods[] = {
+            {"logit", spam_logit, METH_VARARGS,
+             "compute logit"},
+            {NULL, NULL, 0, NULL}
+        };
+
+
+        /*
+            This actually defines the logit function for
+            input args from Python.
+        */
+
+        static PyObject* spam_logit(PyObject *self, PyObject *args){
+
+            double p;
+
+            /* This parses the Python argument into a double */
+            if(!PyArg_ParseTuple(args, "d", &p)){
+                return NULL;
+            }
+
+            /* THE ACTUAL LOGIT FUNCTION */
+            p = p/(1-p);
+            p = log(p);
+
+            /*This builds the answer back into a python object */
+            return Py_BuildValue("d", p);
+        }
+
+
+        /* This initiates the module using the above definitions. */
+        PyMODINIT_FUNC initspam(void){
+            PyObject *m;
+
+            m = Py_InitModule("spam", SpamMethods);
+            if( m==NULL){
+                return;
+            }
+
+        }
+
+To use the setup.py file, place setup.py and spammodule.c in the same
+folder. Then python setup.py build will build the module to import,
+or setup.py install will install the module to your site-packages
+directory.
+
+    .. code-block:: python
+
+        '''
+            setup.py file for spammodule.c
+
+            Calling
+            $python setup.py build
+            will build the extension library in a file
+            that looks like ./build/lib*, where lib* is
+            a file that begins with lib.
+
+            Calling
+            $python setup.py install
+            will install the module in your site-packages file.
+
+            See the distutils section of
+            'Extending and Embedding the Python Interpreter'
+            at docs.python.org for more information.
+        '''
+
+
+        from distutils.core import setup, Extension
+
+        module1 = Extension('spam', sources=['spammodule.c'],
+                                include_dirs=['/usr/local/lib'])
+
+        setup(name = 'spam',
+                version='1.0',
+                description='This is my spam package',
+                ext_modules = [module1])
+
+
+Once the spam module is imported into python, you can call logit
+via spam.logit. Note that the function used above cannot be applied
+as-is to numpy arrays. To do so we must call numpy.vectorize on it.
+For example:
+
+>>> import numpy as np
+>>> import spam
+>>> f = np.vectorize(spam.logit)
+
+THE RESULTING LOGIT FUNCTION IS NOT FAST! numpy.vectorize simply
+loops over spam.logit. The loop is done at the C level, but the numpy
+array is constantly being parsed and build back up. This is expensive.
+When the author compared numpy.vectorize(spam.logit) against the
+logit ufuncs constructed below, the logit ufuncs were almost exactly
+4 times faster. Larger or smaller speedups are, of course, possible
+depending on the nature of the function.
+
+
+.. _`sec:Numpy-one-loop`:
+
+Example Numpy ufunc for one dtype
+=================================
+
+.. index::
+   pair: ufunc; adding new
+
+For simplicity we give a ufunc for a single dtype, the 'f8' double.
+As in the previous section, we first give the .c file and then the
+setup.py file used to create the module containing the ufunc.
+
+The place in the code corresponding to the actual computations for
+the ufunc are marked with /\*BEGIN main ufunc computation\*/ and
+/\*END main ufunc computation\*/. The code in between those lines is
+the primary thing that must be changed to create your own ufunc.
+
+    .. code-block:: c
+
+        #include "Python.h"
+        #include "math.h"
+        #include "numpy/ndarraytypes.h"
+        #include "numpy/ufuncobject.h"
+        #include "numpy/halffloat.h"
+
+        /*
+            single_type_logit.c
+            This is the C code for creating your own
+            Numpy ufunc for a logit function.
+
+            In this code we only define the ufunc for
+            a single dtype. The computations that must
+            be replaced to create a ufunc for
+            a different funciton are marked with BEGIN
+            and END.
+
+            Details explaining the Python-C API can be found under
+            'Extending and Embedding' and 'Python/C API' at
+            docs.python.org .
+
+        */
+
+
+        static PyMethodDef LogitMethods[] = {
+                {NULL, NULL, 0, NULL}
+        };
+
+
+        static void double_logit(char **args, npy_intp *dimensions,
+                                    npy_intp* steps, void* data){
+
+            npy_intp i;
+            npy_intp n=dimensions[0];
+            char *in=args[0], *out=args[1];
+            npy_intp in_step=steps[0], out_step=steps[1];
+
+            double tmp;
+
+            for(i=0; i<n; i++){
+                /*BEGIN main ufunc computation*/
+                tmp = *(double *)in;
+                tmp /= 1-tmp;
+                *((double *)out) = log(tmp);
+                /*END main ufunc computation*/
+
+                in += in_step;
+                out += out_step;
+            }
+        }
+
+
+        /*
+            These definitions must be outside PyMODINIT_FUNC.
+            They must be global, but will be local if declared
+            inside PyMODINIT_FUNC.
+        */
+
+
+        /*This a pointer to the above function*/
+        PyUFuncGenericFunction funcs[1] = {&double_logit};
+
+        /* These are the input and return dtypes of logit.*/
+
+        char types[2] = {NPY_DOUBLE,NPY_DOUBLE};
+
+
+        void *data[1] = {NULL};
+
+        PyMODINIT_FUNC initnpspam(void){
+            PyObject *m, *logit, *d;
+
+
+            m  =Py_InitModule("npspam", LogitMethods);
+            if( m==NULL ){
+                return;
+            }
+
+            import_array();
+            import_umath();
+
+            logit = PyUFunc_FromFuncAndData(funcs,data, types, 1, 1, 1,
+                                        PyUFunc_None, "logit",
+                                        "logit_docstring", 0);
+
+            d = PyModule_GetDict(m);
+
+            PyDict_SetItemString(d , "logit", logit);
+            Py_DECREF(logit);
+        }
+
+
+This is a setup.py file for the above code. As before, the module
+can be build via calling python setup.py build at the command prompt,
+or installed to site-packages via python setup.py install.
+
+    .. code-block:: python
+
+        '''
+            setup.py file for logit.c
+            Note that since this is a numpy extension
+            we use numpy.distutils instead of
+            distutils from the python standard library.
+
+            Calling
+            $python setup.py build
+            will build the extension library in a file
+            that looks like ./build/lib*, where lib* is
+            a file that begins with lib.
+
+            Calling
+            $python setup.py install
+            will install the module in your site-packages file.
+
+            See the distutils section of
+            'Extending and Embedding the Python Interpreter'
+            at docs.python.org  and the documentation
+            on numpy.distutils for more information.
+        '''
+
+
+        def configuration(parent_package='', top_path=None):
+            import numpy
+            from numpy.distutils.misc_util import Configuration
+
+            config = Configuration('npspam_directory', parent_package, top_path)
+            config.add_extension('npspam', ['single_type_logit.c'])
+
+            return config
+
+        if __name__ == "__main__":
+            from numpy.distutils.core import setup
+            setup(configuration=configuration)
+
+After the above has been installed, it can be imported and used as follows.
+
+>>> import numpy as np
+>>> import npspam
+>>> npspam.logit(0.5)
+0.0
+>>> a = np.linspace(0,1,5)
+>>> npspam.logit(a)
+array([       -inf, -1.09861229,  0.        ,  1.09861229,         inf])
+
+
+
+.. _`sec:Numpy-many-loop`:
+
+Example Numpy ufunc with multiple dtypes
+========================================
+
+.. index::
+   pair: ufunc; adding new
+
+We finally give an example of a full ufunc, with inner loops for
+half-floats, floats, doubles, and long doubles. As in the previous
+sections we first give the .c file and then the corresponding
+setup.py file.
+
+The places in the code corresponding to the actual computations for
+the ufunc are marked with /\*BEGIN main ufunc computation\*/ and
+/\*END main ufunc computation\*/. The code in between those lines is
+the primary thing that must be changed to create your own ufunc.
+
+
+    .. code-block:: c
+
+        #include "Python.h"
+        #include "math.h"
+        #include "numpy/ndarraytypes.h"
+        #include "numpy/ufuncobject.h"
+        #include "numpy/halffloat.h"
+
+        /*
+            multi_type_logit.c
+            This is the C code for creating your own
+            Numpy ufunc for a logit function.
+
+            Each function of the form type_logit defines the
+            logit function for a different numpy dtype. Each
+            of these functions must be modified when you
+            create your own ufunc. The computations that must
+            be replaced to create a ufunc for
+            a different funciton are marked with BEGIN
+            and END.
+
+            Details explaining the Python-C API can be found under
+            'Extending and Embedding' and 'Python/C API' at
+            docs.python.org .
+
+        */
+
+
+        static PyMethodDef LogitMethods[] = {
+                {NULL, NULL, 0, NULL}
+        };
+
+
+        static void long_double_logit(char **args, npy_intp *dimensions,
+                                    npy_intp* steps, void* data){
+
+            npy_intp i;
+            npy_intp n=dimensions[0];
+            char *in=args[0], *out=args[1];
+            npy_intp in_step=steps[0], out_step=steps[1];
+
+            long double tmp;
+
+            for(i=0; i<n; i++){
+                /*BEGIN main ufunc computation*/
+                tmp = *(long double *)in;
+                tmp /= 1-tmp;
+                *((long double *)out) = logl(tmp);
+                /*END main ufunc computation*/
+
+                in += in_step;
+                out += out_step;
+            }
+        }
+
+        static void double_logit(char **args, npy_intp *dimensions,
+                                    npy_intp* steps, void* data){
+
+            npy_intp i;
+            npy_intp n=dimensions[0];
+            char *in=args[0], *out=args[1];
+            npy_intp in_step=steps[0], out_step=steps[1];
+
+            double tmp;
+
+            for(i=0; i<n; i++){
+                /*BEGIN main ufunc computation*/
+                tmp = *(double *)in;
+                tmp /= 1-tmp;
+                *((double *)out) = log(tmp);
+                /*END main ufunc computation*/
+
+                in += in_step;
+                out += out_step;
+            }
+        }
+
+        static void float_logit(char **args, npy_intp *dimensions,
+                                    npy_intp* steps, void* data){
+
+            npy_intp i;
+            npy_intp n=dimensions[0];
+            char *in=args[0], *out=args[1];
+            npy_intp in_step=steps[0], out_step=steps[1];
+
+            float tmp;
+
+            for(i=0; i<n; i++){
+                /*BEGIN main ufunc computation*/
+                tmp = *(float *)in;
+                tmp /= 1-tmp;
+                *((float *)out) = logf(tmp);
+                /*END main ufunc computation*/
+
+                in += in_step;
+                out += out_step;
+            }
+        }
+
+
+        static void half_float_logit(char **args, npy_intp *dimensions,
+                                    npy_intp* steps, void* data){
+
+            npy_intp i;
+            npy_intp n=dimensions[0];
+            char *in=args[0], *out=args[1];
+            npy_intp in_step=steps[0], out_step=steps[1];
+
+            float tmp;
+
+            for(i=0; i<n; i++){
+
+                /*BEGIN main ufunc computation*/
+                tmp = *(npy_half *)in;
+                tmp = npy_half_to_float(tmp);
+                tmp /= 1-tmp;
+                tmp = logf(tmp);
+                *((npy_half *)out) = npy_float_to_half(tmp);
+                /*END main ufunc computation*/
+
+                in += in_step;
+                out += out_step;
+            }
+        }
+
+        /*
+            These definitions must be outside PyMODINIT_FUNC.
+            They must be global, but will be local if declared
+            inside PyMODINIT_FUNC.
+        */
+
+
+        /*This gives pointers to the above functions*/
+        PyUFuncGenericFunction funcs[4] = {&half_float_logit, &float_logit,
+                                            &double_logit, &long_double_logit};
+
+        /*
+            These are the input and return dtypes of logit. They must
+            be in the same order as the funcs array immediately above.
+        */
+        char types[8] = {NPY_HALF, NPY_HALF, NPY_FLOAT, NPY_FLOAT,
+                        NPY_DOUBLE,NPY_DOUBLE, NPY_LONGDOUBLE, NPY_LONGDOUBLE};
+
+
+        void *data[4] = {NULL, NULL, NULL, NULL};
+
+        PyMODINIT_FUNC initnpspam(void){
+            PyObject *m, *logit, *d;
+
+
+            m  =Py_InitModule("npspam", LogitMethods);
+            if( m==NULL ){
+                return;
+            }
+
+            import_array();
+            import_umath();
+
+            logit = PyUFunc_FromFuncAndData(funcs,data, types, 4, 1, 1,
+                                        PyUFunc_None, "logit",
+                                        "logit_docstring", 0);
+
+            d = PyModule_GetDict(m);
+
+            PyDict_SetItemString(d , "logit", logit);
+            Py_DECREF(logit);
+        }
+
+
+This is a setup.py file for the above code. As before, the module
+can be build via calling python setup.py build at the command prompt,
+or installed to site-packages via python setup.py install.
+
+    .. code-block:: python
+
+        '''
+            setup.py file for logit.c
+            Note that since this is a numpy extension
+            we use numpy.distutils instead of
+            distutils from the python standard library.
+
+            Calling
+            $python setup.py build
+            will build the extension library in a file
+            that looks like ./build/lib*, where lib* is
+            a file that begins with lib.
+
+            Calling
+            $python setup.py install
+            will install the module in your site-packages file.
+
+            See the distutils section of
+            'Extending and Embedding the Python Interpreter'
+            at docs.python.org  and the documentation
+            on numpy.distutils for more information.
+        '''
+
+
+        def configuration(parent_package='', top_path=None):
+            import numpy
+            from numpy.distutils.misc_util import Configuration
+            from numpy.distutils.misc_util import get_info
+
+            #Necessary for the half-float d-type.
+            info = get_info('npymath')
+
+            config = Configuration('npspam_directory', parent_package, top_path)
+            config.add_extension('npspam', ['multi_type_logit.c'], extra_info=info)
+
+            return config
+
+        if __name__ == "__main__":
+            from numpy.distutils.core import setup
+            setup(configuration=configuration)
+
+After the above has been installed, it can be imported and used as follows.
+
+>>> import numpy as np
+>>> import npspam
+>>> npspam.logit(0.5)
+0.0
+>>> a = np.linspace(0,1,5)
+>>> npspam.logit(a)
+array([       -inf, -1.09861229,  0.        ,  1.09861229,         inf])
+
+
+
+.. _`sec:Numpy-many-arg`:
+
+Example Numpy ufunc with multiple arguments/return values
+=========================================================
+
+Our final example is a ufunc with multiple arguments. It is a modification
+of the code for a logit ufunc for data with a single dtype. We
+compute (A*B, logit(A*B)).
+
+We only give the C code as the setup.py file is exactly the same as
+the setup.py file in `Example Numpy ufunc for one dtype`_, except that
+the line
+
+    .. code-block:: python
+
+        config.add_extension('npspam', ['single_type_logit.c'])
+
+is replaced with
+
+    .. code-block:: python
+
+        config.add_extension('npspam', ['multi_arg_logit.c'])
+
+The C file is given below. The ufunc generated takes two arguments A
+and B. It returns a tuple whose first element is A*B and whose second
+element is logit(A*B). Note that it automatically supports broadcasting,
+as well as all other properties of a ufunc.
+
+    .. code-block:: c
+
+        #include "Python.h"
+        #include "math.h"
+        #include "numpy/ndarraytypes.h"
+        #include "numpy/ufuncobject.h"
+        #include "numpy/halffloat.h"
+
+        /*
+            multi_arg_logit.c
+            This is the C code for creating your own
+            Numpy ufunc for a multiple argument, multiple
+            return value ufunc. The places where the
+            ufunc computation is carried out are marked
+            with comments.
+
+            Details explaining the Python-C API can be found under
+            'Extending and Embedding' and 'Python/C API' at
+            docs.python.org .
+
+        */
+
+
+        static PyMethodDef LogitMethods[] = {
+                {NULL, NULL, 0, NULL}
+        };
+
+
+        static void double_logitprod(char **args, npy_intp *dimensions,
+                                    npy_intp* steps, void* data){
+
+            npy_intp i;
+            npy_intp n=dimensions[0];
+            char *in1=args[0], *in2=args[1];
+            char *out1=args[2], *out2=args[3];
+            npy_intp in1_step=steps[0], in2_step=steps[1];
+            npy_intp out1_step=steps[2], out2_step=steps[3];
+
+            double tmp;
+
+            for(i=0; i<n; i++){
+                /*BEGIN main ufunc computation*/
+                tmp = *(double *)in1;
+                tmp *= *(double *)in2;
+                *((double *)out1) = tmp;
+                *((double *)out2) = log(tmp/(1-tmp));
+                /*END main ufunc computation*/
+
+                in1 += in1_step;
+                in2 += in2_step;
+                out1 += out1_step;
+                out2 += out2_step;
+            }
+        }
+
+
+        /*
+            These definitions must be outside PyMODINIT_FUNC.
+            They must be global, but will be local if declared
+            inside PyMODINIT_FUNC.
+        */
+
+
+        /*This a pointer to the above function*/
+        PyUFuncGenericFunction funcs[1] = {&double_logitprod};
+
+        /* These are the input and return dtypes of logit.*/
+
+        char types[4] = {NPY_DOUBLE,NPY_DOUBLE, NPY_DOUBLE, NPY_DOUBLE};
+
+
+        void *data[1] = {NULL};
+
+        PyMODINIT_FUNC initnpspam(void){
+            PyObject *m, *logitprod, *d;
+
+
+            m  =Py_InitModule("npspam", LogitMethods);
+            if( m==NULL ){
+                return;
+            }
+
+            import_array();
+            import_umath();
+
+            logitprod = PyUFunc_FromFuncAndData(funcs,data, types, 1, 2, 2,
+                                        PyUFunc_None, "logitprod",
+                                        "logitprod_docstring", 0);
+
+            d = PyModule_GetDict(m);
+
+            PyDict_SetItemString(d , "logitprod", logitprod);
+            Py_DECREF(logitprod);
+        }
+
+
+
+
+.. _`sec:PyUFunc-spec`:
+
+PyUFunc_FromFuncAndData Specification
+=====================================
+
+What follows is the full specification of PyUFunc_FromFuncAndData, which
+automatically generates a ufunc from a C function with the correct signature.
+
+
+.. cfunction:: PyObject *PyUFunc_FromFuncAndData( PyUFuncGenericFunction* func,
+   void** data, char* types, int ntypes, int nin, int nout, int identity,
+   char* name, char* doc, int check_return)
+
+    *func*
+
+        A pointer to an array of 1-d functions to use. This array must be at
+        least ntypes long. Each entry in the array must be a
+        ``PyUFuncGenericFunction`` function. This function has the following
+        signature. An example of a valid 1d loop function is also given.
+
+        .. cfunction:: void loop1d(char** args, npy_intp* dimensions,
+           npy_intp* steps, void* data)
+
+        *args*
+
+            An array of pointers to the actual data for the input and output
+            arrays. The input arguments are given first followed by the output
+            arguments.
+
+        *dimensions*
+
+            A pointer to the size of the dimension over which this function is
+            looping.
+
+        *steps*
+
+            A pointer to the number of bytes to jump to get to the
+            next element in this dimension for each of the input and
+            output arguments.
+
+        *data*
+
+            Arbitrary data (extra arguments, function names, *etc.* )
+            that can be stored with the ufunc and will be passed in
+            when it is called.
+
+        .. code-block:: c
+
+            static void
+            double_add(char *args, npy_intp *dimensions, npy_intp *steps,
+               void *extra)
+            {
+                npy_intp i;
+                npy_intp is1=steps[0], is2=steps[1];
+                npy_intp os=steps[2], n=dimensions[0];
+                char *i1=args[0], *i2=args[1], *op=args[2];
+                for (i=0; i<n; i++) {
+                    *((double *)op) = *((double *)i1) + \
+                                      *((double *)i2);
+                    i1 += is1; i2 += is2; op += os;
+                 }
+            }
+
+    *data*
+
+        An array of data. There should be ntypes entries (or NULL) --- one for
+        every loop function defined for this ufunc. This data will be passed
+        in to the 1-d loop. One common use of this data variable is to pass in
+        an actual function to call to compute the result when a generic 1-d
+        loop (e.g. :cfunc:`PyUFunc_d_d`) is being used.
+
+    *types*
+
+        An array of type-number signatures (type ``char`` ). This
+        array should be of size (nin+nout)*ntypes and contain the
+        data-types for the corresponding 1-d loop. The inputs should
+        be first followed by the outputs. For example, suppose I have
+        a ufunc that supports 1 integer and 1 double 1-d loop
+        (length-2 func and data arrays) that takes 2 inputs and
+        returns 1 output that is always a complex double, then the
+        types array would be
+
+        .. code-block:: c
+
+            char types[3] = {NPY_INT, NPY_DOUBLE, NPY_CDOUBLE}
+
+        The bit-width names can also be used (e.g. :cdata:`NPY_INT32`,
+        :cdata:`NPY_COMPLEX128` ) if desired.
+
+    *ntypes*
+
+        The number of data-types supported. This is equal to the number of 1-d
+        loops provided.
+
+    *nin*
+
+        The number of input arguments.
+
+    *nout*
+
+        The number of output arguments.
+
+    *identity*
+
+        Either :cdata:`PyUFunc_One`, :cdata:`PyUFunc_Zero`,
+        :cdata:`PyUFunc_None`. This specifies what should be returned when
+        an empty array is passed to the reduce method of the ufunc.
+
+    *name*
+
+        A ``NULL`` -terminated string providing the name of this ufunc
+        (should be the Python name it will be called).
+
+    *doc*
+
+        A documentation string for this ufunc (will be used in generating the
+        response to ``{ufunc_name}.__doc__``). Do not include the function
+        signature or the name as this is generated automatically.
+
+    *check_return*
+
+        Not presently used, but this integer value does get set in the
+        structure-member of similar name.
+
+.. index::
+   pair: ufunc; adding new
+
+The returned ufunc object is a callable Python object. It should be
+placed in a (module) dictionary under the same name as was used in the
+name argument to the ufunc-creation routine. The following example is
+adapted from the umath module
+
+    .. code-block:: c
+
+        static PyUFuncGenericFunction atan2_functions[]=\
+            {PyUFunc_ff_f, PyUFunc_dd_d,
+             PyUFunc_gg_g, PyUFunc_OO_O_method};
+        static void* atan2_data[]=\
+            {(void *)atan2f,(void *) atan2,
+             (void *)atan2l,(void *)"arctan2"};
+        static char atan2_signatures[]=\
+            {NPY_FLOAT, NPY_FLOAT, NPY_FLOAT,
+             NPY_DOUBLE, NPY_DOUBLE,
+             NPY_DOUBLE, NPY_LONGDOUBLE,
+             NPY_LONGDOUBLE, NPY_LONGDOUBLE
+             NPY_OBJECT, NPY_OBJECT,
+             NPY_OBJECT};
+        ...
+        /* in the module initialization code */
+        PyObject *f, *dict, *module;
+        ...
+        dict = PyModule_GetDict(module);
+        ...
+        f = PyUFunc_FromFuncAndData(atan2_functions,
+            atan2_data, atan2_signatures, 4, 2, 1,
+            PyUFunc_None, "arctan2",
+            "a safe and correct arctan(x1/x2)", 0);
+        PyDict_SetItemString(dict, "arctan2", f);
+        Py_DECREF(f);
+        ...
diff --git a/numpy/testing/utils.py b/numpy/testing/utils.py
index f0c4cae44..73f659faa 100644
--- a/numpy/testing/utils.py
+++ b/numpy/testing/utils.py
@@ -572,7 +572,7 @@ def assert_array_compare(comparison, x, y, err_msg='', verbose=True,
     y = array(y, copy=False, subok=True)
 
     def isnumber(x):
-        return x.dtype.char in '?bhilqpBHILQPfdgFDG'
+        return x.dtype.char in '?behilqpBHILQPfdgFDG'
 
     def chk_same_position(x_id, y_id, hasval='nan'):
         """Handling nan/inf: check that x and y have the nan/inf at the same