Python与C/C++混合编程手册：扩展模块开发

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"该资源是一本关于Python、C和C++混合编程的手册，主要针对Python 3.6.3版本。手册详细介绍了如何利用C和C++扩展Python的功能，包括创建内置模块、实现新的内置对象类型以及调用C库函数和系统调用。Python API（应用程序编程接口）为C程序员提供了访问Python运行时系统大部分功能的函数、宏和变量。扩展模块的编译依赖于其预期用途和系统配置，手册后续章节将提供具体指导。需要注意的是，C扩展接口是CPython特有的，不适用于其他Python实现。在许多情况下，可以使用ctypes模块或cffi库来避免编写C扩展，以保持代码的跨平台性，这些模块允许在Python中直接与C库进行交互。" 在Python开发中，混合使用C和C++可以显著提升性能，尤其是在处理底层系统调用、硬件交互或者需要高效计算的情况下。Python的C API允许开发者定义新的数据类型，如自定义类，这些类可以像内置类型一样在Python环境中无缝使用。C扩展模块通常通过以下步骤创建： 1. **编写C/C++源码**：首先，你需要创建一个C或C++源文件，包含必要的函数实现和类型定义。在这个阶段，你会使用`Python.h`头文件，它包含了Python API的所有声明。 2. **定义Python类型对象**：为了使C函数能在Python中调用，你需要定义Python的类型对象，这通常包括创建`PyTypeObject`结构体实例，定义方法表等。 3. **初始化和注册模块**：在C代码中，你需要定义一个`init<module_name>`函数，这是Python加载模块时会调用的初始化函数。在这个函数中，你需要注册你的类型对象，并将C函数暴露给Python。 4. **编译和构建**：根据你的系统设置，你需要使用合适的编译器（如gcc或clang）将C源码编译为动态链接库（.dll或.so）。然后，你可以使用Python的`distutils`或`setuptools`工具将库打包成Python可安装的扩展模块。 5. **测试和使用**：在Python环境中，你可以通过`import`语句来加载并使用这个扩展模块，就像导入任何其他Python模块一样。在某些场景下，比如需要调用C库，使用Python的`ctypes`模块可以直接在Python代码中动态加载和调用C动态链接库，而无需编写C扩展。`ctypes`提供了一种灵活的方式来定义C数据类型和函数原型，并能直接调用库函数。另外，`cffi`(C Foreign Function Interface)是另一个选择，它提供了更接近C的接口，同时也支持类型检查和更复杂的类型定义。 Python、C和C++混合编程是提高Python程序性能的有效手段，特别是对于需要底层交互和高性能计算的项目。同时，`ctypes`和`cffi`提供了更简便的方案，可以在不编写C扩展的情况下实现C库的调用，保持代码的可移植性。

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(result == NULL)

return

NULL;

/* Pass error back */

/* Here maybe use the result */

Py_DECREF(result);

1.7. Extracting Parameters in Extension Functions

The PyArg_ParseTuple() function is declared as follows:

int PyArg_ParseTuple(PyObject *arg,

const

char *format, ...);

The arg argument must be a tuple object containing an argument list passed from Python to a

C function. The format argument must be a format string, whose syntax is explained in

Parsing arguments and building values in the Python/C API Reference Manual. The

remaining arguments must be addresses of variables whose type is determined by the format

string.

Note that while PyArg_ParseTuple() checks that the Python arguments have the required

types, it cannot check the validity of the addresses of C variables passed to the call: if you

make mistakes there, your code will probably crash or at least overwrite random bits in

memory. So be careful!

Note that any Python object references which are provided to the caller are borrowed

references; do not decrement their reference count!

Some example calls:

#define PY_SSIZE_T_CLEAN

/* Make "s#" use Py_ssize_t rather than int. */

#include

<Python.h>

int ok;

int i, j;

long k, l;

const

char *s;

Py_ssize_t size;

ok = PyArg_ParseTuple(args, "");

/* No arguments */

/* Python call: f() */

ok = PyArg_ParseTuple(args, "s", &s);

/* A string */

/* Possible Python call: f('whoops!') */

ok = PyArg_ParseTuple(args, "lls", &k, &l, &s);

/* Two longs and a string */

/* Possible Python call: f(1, 2, 'three') */

ok = PyArg_ParseTuple(args, "(ii)s#", &i, &j, &s, &size);

/* A pair of ints and a string, whose size is also returned */

/* Possible Python call: f((1, 2), 'three') */

One difference with PyArg_ParseTuple(): while the latter requires its first argument to be a

tuple (since Python argument lists are always represented as tuples internally),

Py_BuildValue() does not always build a tuple. It builds a tuple only if its format string

contains two or more format units. If the format string is empty, it returns None; if it contains

exactly one format unit, it returns whatever object is described by that format unit. To force it

to return a tuple of size 0 or one, parenthesize the format string.

Examples (to the left the call, to the right the resulting Python value):

Py_BuildValue("") None

Py_BuildValue("i", 123) 123

Py_BuildValue("iii", 123, 456, 789) (123, 456, 789)

Py_BuildValue("s", "hello") 'hello'

Py_BuildValue("y", "hello") b'hello'

Py_BuildValue("ss", "hello", "world") ('hello', 'world')

Py_BuildValue("s#", "hello", 4) 'hell'

Py_BuildValue("y#", "hello", 4) b'hell'

Py_BuildValue("()") ()

Py_BuildValue("(i)", 123) (123,)

Py_BuildValue("(ii)", 123, 456) (123, 456)

Py_BuildValue("(i,i)", 123, 456) (123, 456)

Py_BuildValue("[i,i]", 123, 456) [123, 456]

Py_BuildValue("{s:i,s:i}",

"abc", 123, "def", 456) {'abc': 123, 'def': 456}

Py_BuildValue("((ii)(ii)) (ii)",

1, 2, 3, 4, 5, 6) (((1, 2), (3, 4)), (5, 6))

1.10. Reference Counts

In languages like C or C++, the programmer is responsible for dynamic allocation and

deallocation of memory on the heap. In C, this is done using the functions malloc() and

free(). In C++, the operators new and delete are used with essentially the same meaning

and we’ll restrict the following discussion to the C case.

Every block of memory allocated with malloc() should eventually be returned to the pool of

available memory by exactly one call to free(). It is important to call free() at the right time.

If a block’s address is forgotten but free() is not called for it, the memory it occupies cannot

be reused until the program terminates. This is called a memory leak. On the other hand, if a

program calls free() for a block and then continues to use the block, it creates a conflict with

re-use of the block through another malloc() call. This is called using freed memory. It has

the same bad consequences as referencing uninitialized data — core dumps, wrong results,

mysterious crashes.

Common causes of memory leaks are unusual paths through the code. For instance, a

function may allocate a block of memory, do some calculation, and then free the block again.

Now a change in the requirements for the function may add a test to the calculation that

detects an error condition and can return prematurely from the function. It’s easy to forget to

free the allocated memory block when taking this premature exit, especially when it is added

later to the code. Such leaks, once introduced, often go undetected for a long time: the error

exit is taken only in a small fraction of all calls, and most modern machines have plenty of

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