编写您自己的ufunc
I have the Power!
— He-Man
创建一个新的ufunc
在阅读本文之前，通过阅读/略读扩展和嵌入Python解释器的第1部分中的教程以及如何扩展NumPy，可以帮助您熟悉Python的C扩展基础知识。
umath模块是一个计算机生成的C模块，可以创建许多ufunc。它提供了许多如何创建通用函数的示例。使用ufunc机制创建自己的ufunc也不困难。假设您有一个函数，您想要在其输入上逐个元素地操作。通过创建一个新的ufunc，您将获得一个处理的函数
- 广播
- N维循环
- 自动类型转换，内存使用量最少
- 可选的输出数组
创建自己的ufunc并不困难。所需要的只是您想要支持的每种数据类型的1-d循环。每个1-d循环必须具有特定签名，并且只能使用固定大小数据类型的ufunc。下面给出了用于创建新的ufunc以处理内置数据类型的函数调用。使用不同的机制为用户定义的数据类型注册ufunc。
在接下来的几节中，我们提供了可以轻松修改的示例代码，以创建自己的ufunc。这些示例是logit函数的连续更完整或复杂版本，这是统计建模中的常见功能。Logit也很有趣，因为由于IEEE标准（特别是IEEE 754）的神奇之处，下面创建的所有logit函数都自动具有以下行为。
>>> logit(0)
-inf
>>> logit(1)
inf
>>> logit(2)
nan
>>> logit(-2)
nan
这很好，因为函数编写器不必手动传播infs或nans。
示例非ufunc扩展名
为了比较和阅读器的一般启发，我们提供了一个简单的logit C扩展实现，它没有使用numpy。
为此，我们需要两个文件。第一个是包含实际代码的C文件，第二个是用于创建模块的setup.py文件。
#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.
* See the Python-C API for more information.
*/
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. */
#if PY_VERSION_HEX >= 0x03000000
static struct PyModuleDef moduledef = {
PyModuleDef_HEAD_INIT,
"spam",
NULL,
-1,
SpamMethods,
NULL,
NULL,
NULL,
NULL
};
PyMODINIT_FUNC PyInit_spam(void)
{
PyObject *m;
m = PyModule_Create(&moduledef);
if (!m) {
return NULL;
}
return m;
}
#else
PyMODINIT_FUNC initspam(void)
{
PyObject *m;
m = Py_InitModule("spam", SpamMethods);
if (m == NULL) {
return;
}
}
#endif
要使用setup.py文件，请将setup.py和spammodule.c放在同一文件夹中。然后python setup.py build将构建要导入的模块，或者setup.py install将模块安装到您的site-packages目录。
'''
setup.py file for spammodule.c
Calling
$python setup.py build_ext --inplace
will build the extension library in the current file.
Calling
$python setup.py build
will build a file that looks like ./build/lib*, where
lib* is a file that begins with lib. The library will
be in this file and end with a C library extension,
such as .so
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])
将垃圾邮件模块导入python后，您可以通过spam.logit调用logit。请注意，上面使用的函数不能按原样应用于numpy数组。为此，我们必须在其上调用numpy.vectorize。例如，如果在包含垃圾邮件库或垃圾邮件的文件中打开了python解释器，则可以执行以下命令：
>>> import numpy as np
>>> import spam
>>> spam.logit(0)
-inf
>>> spam.logit(1)
inf
>>> spam.logit(0.5)
0.0
>>> x = np.linspace(0,1,10)
>>> spam.logit(x)
TypeError: only length-1 arrays can be converted to Python scalars
>>> f = np.vectorize(spam.logit)
>>> f(x)
array([ -inf, -2.07944154, -1.25276297, -0.69314718, -0.22314355,
0.22314355, 0.69314718, 1.25276297, 2.07944154, inf])
结果编辑功能并不快！numpy.vectorize只是循环遍历spam.logit。循环在C级完成，但numpy数组不断被解析并重新构建。这很贵。当作者将numpy.vectorize（spam.logit）与下面构造的logit ufuncs进行比较时，logit ufuncs几乎快4倍。当然，取决于功能的性质，可以实现更大或更小的加速。
一种dtype的NumPy ufunc示例
为简单起见，我们为单个dtype提供了一个ufunc，即'f8'双精度型。与前一节一样，我们首先给出.c文件，然后是用于创建包含ufunc的模块的setup.py文件。
代码中与ufunc的实际计算相对应的位置标有/ * BEGIN main ufunc computation * /和/ * END main ufunc computation * /。这些行之间的代码是必须更改以创建自己的ufunc的主要事物。
#include "Python.h"
#include "math.h"
#include "numpy/ndarraytypes.h"
#include "numpy/ufuncobject.h"
#include "numpy/npy_3kcompat.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 function 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}
};
/* The loop definition must precede the PyMODINIT_FUNC. */
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;
}
}
/*This a pointer to the above function*/
PyUFuncGenericFunction funcs[1] = {&double_logit};
/* These are the input and return dtypes of logit.*/
static char types[2] = {NPY_DOUBLE, NPY_DOUBLE};
static void *data[1] = {NULL};
#if PY_VERSION_HEX >= 0x03000000
static struct PyModuleDef moduledef = {
PyModuleDef_HEAD_INIT,
"npufunc",
NULL,
-1,
LogitMethods,
NULL,
NULL,
NULL,
NULL
};
PyMODINIT_FUNC PyInit_npufunc(void)
{
PyObject *m, *logit, *d;
m = PyModule_Create(&moduledef);
if (!m) {
return NULL;
}
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);
return m;
}
#else
PyMODINIT_FUNC initnpufunc(void)
{
PyObject *m, *logit, *d;
m = Py_InitModule("npufunc", 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);
}
#endif
这是上面代码的setup.py文件。和以前一样，可以通过在命令提示符下调用python setup.py build来构建模块，也可以通过python setup.py install将其安装到site-packages。
'''
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_ext --inplace
will build the extension library in the current file.
Calling
$python setup.py build
will build a file that looks like ./build/lib*, where
lib* is a file that begins with lib. The library will
be in this file and end with a C library extension,
such as .so
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('npufunc_directory',
parent_package,
top_path)
config.add_extension('npufunc', ['single_type_logit.c'])
return config
if __name__ == "__main__":
from numpy.distutils.core import setup
setup(configuration=configuration)
安装完上述内容后，可以按如下方式导入和使用。
>>> import numpy as np
>>> import npufunc
>>> npufunc.logit(0.5)
0.0
>>> a = np.linspace(0,1,5)
>>> npufunc.logit(a)
array([ -inf, -1.09861229, 0. , 1.09861229, inf])
示例具有多个dtypes的NumPy ufunc
我们最后给出了一个完整的ufunc示例，内部循环用于半浮点数，浮点数，双精度数和长双精度数。与前面的部分一样，我们首先给出.c文件，然后是相应的setup.py文件。
代码中与ufunc的实际计算相对应的位置标有/ * BEGIN main ufunc computation * /和/ * END main ufunc computation * /。这些行之间的代码是必须更改以创建自己的ufunc的主要事物。
#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 function 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}
};
/* The loop definitions must precede the PyMODINIT_FUNC. */
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;
}
}
/*This gives pointers to the above functions*/
PyUFuncGenericFunction funcs[4] = {&half_float_logit,
&float_logit,
&double_logit,
&long_double_logit};
static char types[8] = {NPY_HALF, NPY_HALF,
NPY_FLOAT, NPY_FLOAT,
NPY_DOUBLE,NPY_DOUBLE,
NPY_LONGDOUBLE, NPY_LONGDOUBLE};
static void *data[4] = {NULL, NULL, NULL, NULL};
#if PY_VERSION_HEX >= 0x03000000
static struct PyModuleDef moduledef = {
PyModuleDef_HEAD_INIT,
"npufunc",
NULL,
-1,
LogitMethods,
NULL,
NULL,
NULL,
NULL
};
PyMODINIT_FUNC PyInit_npufunc(void)
{
PyObject *m, *logit, *d;
m = PyModule_Create(&moduledef);
if (!m) {
return NULL;
}
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);
return m;
}
#else
PyMODINIT_FUNC initnpufunc(void)
{
PyObject *m, *logit, *d;
m = Py_InitModule("npufunc", 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);
}
#endif
这是上面代码的setup.py文件。和以前一样，可以通过在命令提示符下调用python setup.py build来构建模块，也可以通过python setup.py install将其安装到site-packages。
'''
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_ext --inplace
will build the extension library in the current file.
Calling
$python setup.py build
will build a file that looks like ./build/lib*, where
lib* is a file that begins with lib. The library will
be in this file and end with a C library extension,
such as .so
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('npufunc_directory',
parent_package,
top_path)
config.add_extension('npufunc',
['multi_type_logit.c'],
extra_info=info)
return config
if __name__ == "__main__":
from numpy.distutils.core import setup
setup(configuration=configuration)
安装完上述内容后，可以按如下方式导入和使用。
>>> import numpy as np
>>> import npufunc
>>> npufunc.logit(0.5)
0.0
>>> a = np.linspace(0,1,5)
>>> npufunc.logit(a)
array([ -inf, -1.09861229, 0. , 1.09861229, inf])
示例具有多个参数/返回值的NumPy ufunc
我们的最后一个例子是一个带有多个参数的ufunc。它是对具有单个dtype的数据的logit ufunc的代码的修改。我们计算 (A*B, logit(A*B))。
我们只给出 C 代码，因为setup.py文件与一种dtype的NumPy ufunc示例中的setup.py文件完全相同，只是一行
config.add_extension('npufunc', ['single_type_logit.c'])
被替换为
config.add_extension('npufunc', ['multi_arg_logit.c'])
C文件如下。生成的ufunc接受两个参数A和B.它返回一个元组，其第一个元素是A * B，第二个元素是logit（A * B）。请注意，它会自动支持广播以及ufunc的所有其他属性。
#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}
};
/* The loop definition must precede the PyMODINIT_FUNC. */
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;
}
}
/*This a pointer to the above function*/
PyUFuncGenericFunction funcs[1] = {&double_logitprod};
/* These are the input and return dtypes of logit.*/
static char types[4] = {NPY_DOUBLE, NPY_DOUBLE,
NPY_DOUBLE, NPY_DOUBLE};
static void *data[1] = {NULL};
#if PY_VERSION_HEX >= 0x03000000
static struct PyModuleDef moduledef = {
PyModuleDef_HEAD_INIT,
"npufunc",
NULL,
-1,
LogitMethods,
NULL,
NULL,
NULL,
NULL
};
PyMODINIT_FUNC PyInit_npufunc(void)
{
PyObject *m, *logit, *d;
m = PyModule_Create(&moduledef);
if (!m) {
return NULL;
}
import_array();
import_umath();
logit = PyUFunc_FromFuncAndData(funcs, data, types, 1, 2, 2,
PyUFunc_None, "logit",
"logit_docstring", 0);
d = PyModule_GetDict(m);
PyDict_SetItemString(d, "logit", logit);
Py_DECREF(logit);
return m;
}
#else
PyMODINIT_FUNC initnpufunc(void)
{
PyObject *m, *logit, *d;
m = Py_InitModule("npufunc", LogitMethods);
if (m == NULL) {
return;
}
import_array();
import_umath();
logit = PyUFunc_FromFuncAndData(funcs, data, types, 1, 2, 2,
PyUFunc_None, "logit",
"logit_docstring", 0);
d = PyModule_GetDict(m);
PyDict_SetItemString(d, "logit", logit);
Py_DECREF(logit);
}
#endif
示例带有结构化数组dtype参数的NumPy ufunc
此示例显示如何为结构化数组dtype创建ufunc。在这个例子中，我们展示了一个简单的ufunc，用于添加两个带有dtype'u8，u8，u8'的数组。该过程与其他示例略有不同，因为调用PyUFunc_FromFuncAndData不会为自定义dtypes和结构化数组dtypes完全注册ufunc。我们还需要调用
PyUFunc_RegisterLoopForDescr完成设置ufunc。
我们只提供C代码，因为setup.py文件与一种dtype的NumPy ufunc示例的setup.py文件完全相同，只有一行。
config.add_extension('npufunc', ['single_type_logit.c'])
被替换为
config.add_extension('npufunc', ['add_triplet.c'])
C文件如下。
#include "Python.h"
#include "math.h"
#include "numpy/ndarraytypes.h"
#include "numpy/ufuncobject.h"
#include "numpy/npy_3kcompat.h"
/*
* add_triplet.c
* This is the C code for creating your own
* NumPy ufunc for a structured array dtype.
*
* Details explaining the Python-C API can be found under
* 'Extending and Embedding' and 'Python/C API' at
* docs.python.org .
*/
static PyMethodDef StructUfuncTestMethods[] = {
{NULL, NULL, 0, NULL}
};
/* The loop definition must precede the PyMODINIT_FUNC. */
static void add_uint64_triplet(char **args, npy_intp *dimensions,
npy_intp* steps, void* data)
{
npy_intp i;
npy_intp is1=steps[0];
npy_intp is2=steps[1];
npy_intp os=steps[2];
npy_intp n=dimensions[0];
uint64_t *x, *y, *z;
char *i1=args[0];
char *i2=args[1];
char *op=args[2];
for (i = 0; i < n; i++) {
x = (uint64_t*)i1;
y = (uint64_t*)i2;
z = (uint64_t*)op;
z[0] = x[0] + y[0];
z[1] = x[1] + y[1];
z[2] = x[2] + y[2];
i1 += is1;
i2 += is2;
op += os;
}
}
/* This a pointer to the above function */
PyUFuncGenericFunction funcs[1] = {&add_uint64_triplet};
/* These are the input and return dtypes of add_uint64_triplet. */
static char types[3] = {NPY_UINT64, NPY_UINT64, NPY_UINT64};
static void *data[1] = {NULL};
#if defined(NPY_PY3K)
static struct PyModuleDef moduledef = {
PyModuleDef_HEAD_INIT,
"struct_ufunc_test",
NULL,
-1,
StructUfuncTestMethods,
NULL,
NULL,
NULL,
NULL
};
#endif
#if defined(NPY_PY3K)
PyMODINIT_FUNC PyInit_struct_ufunc_test(void)
#else
PyMODINIT_FUNC initstruct_ufunc_test(void)
#endif
{
PyObject *m, *add_triplet, *d;
PyObject *dtype_dict;
PyArray_Descr *dtype;
PyArray_Descr *dtypes[3];
#if defined(NPY_PY3K)
m = PyModule_Create(&moduledef);
#else
m = Py_InitModule("struct_ufunc_test", StructUfuncTestMethods);
#endif
if (m == NULL) {
#if defined(NPY_PY3K)
return NULL;
#else
return;
#endif
}
import_array();
import_umath();
/* Create a new ufunc object */
add_triplet = PyUFunc_FromFuncAndData(NULL, NULL, NULL, 0, 2, 1,
PyUFunc_None, "add_triplet",
"add_triplet_docstring", 0);
dtype_dict = Py_BuildValue("[(s, s), (s, s), (s, s)]",
"f0", "u8", "f1", "u8", "f2", "u8");
PyArray_DescrConverter(dtype_dict, &dtype);
Py_DECREF(dtype_dict);
dtypes[0] = dtype;
dtypes[1] = dtype;
dtypes[2] = dtype;
/* Register ufunc for structured dtype */
PyUFunc_RegisterLoopForDescr(add_triplet,
dtype,
&add_uint64_triplet,
dtypes,
NULL);
d = PyModule_GetDict(m);
PyDict_SetItemString(d, "add_triplet", add_triplet);
Py_DECREF(add_triplet);
#if defined(NPY_PY3K)
return m;
#endif
}
返回的ufunc对象是一个可调用的Python对象。它应该放在一个（模块）字典中，其名称与ufunc-creation例程的name参数中使用的字典相同。以下示例是从umath模块改编而来的
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);
...