Python/C API指南：3.7.0版

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"Python C API指南，用于在C语言中与Python交互的接口，涵盖了从对象、异常处理到系统功能的多个方面。" Python C API是Python编程语言提供的一种低级接口，允许开发者使用C语言编写扩展模块或嵌入Python解释器。这个API提供了丰富的功能，包括对象操作、异常处理、系统调用以及对Python内部机制的访问。以下是对标题和描述中所提及的Python C API的一些关键知识点的详细说明： 1. **编码标准**：遵循一定的编码规范对于保持代码的一致性和可读性至关重要。在Python C API中，开发者应遵循Python的PEP 8编码风格，并确保C代码的清晰和整洁。 2. **包含文件**：在C代码中，通常需要包含头文件来获取Python API的定义。例如，`#include <Python.h>`是基本的头文件，它包含了大部分你需要的宏和类型定义。 3. **有用的宏**：Python C API提供了一些预定义的宏，比如`Py_INCREF()`和`Py_DECREF()`，用来增加或减少对象的引用计数，确保内存管理的正确性。 4. **对象、类型和引用计数**：Python中的所有都是对象，每个对象都有一个引用计数。通过C API，你可以创建、操作并检查对象的引用计数，以确保正确地分配和释放内存。 5. **异常处理**：C API提供了处理Python异常的机制。`PyErr_Occurred()`可以检测是否有错误发生，`PyErr_Fetch()`和`PyErr_Restore()`用于保存和恢复异常状态，而`PyErr_SetString()`则可以设置新的异常。 6. **嵌入Python**：如果你想在C程序中运行Python脚本或调用Python函数，Python C API提供了必要的工具。这包括初始化和关闭Python解释器，以及调用Python函数和执行脚本。 7. **调试构建**：对于开发和调试目的，Python提供了带有调试信息的编译选项，帮助开发者查找和修复内存泄漏和其他问题。 8. **稳定的应用程序二进制接口（ABI）**：Python C API保证了一定版本的ABI稳定性，使得编译的C扩展模块可以在兼容的Python版本间通用，无需重新编译。 9. **引用计数**：Python的垃圾回收机制基于引用计数，理解如何正确地增加和减少对象的引用计数是避免内存泄漏的关键。 10. **异常类和对象**：C API提供了创建自定义异常类和实例化异常对象的方法，这些异常可以被Python代码捕获和处理。 11. **标准异常和警告类别**：Python有一系列预定义的异常和警告类别，如`PyExc_RuntimeError`或`PyExc_UserWarning`，开发者可以通过C API来抛出或处理这些异常。 12. **实用工具**：这部分包括了操作系统交互、系统函数、进程控制、模块导入、数据序列化支持、参数解析、字符串转换和格式化、反射等多个实用功能，它们极大地丰富了C扩展的功能。通过Python C API，开发者可以深入到Python的底层，实现高性能的扩展模块，或者高效地集成Python解释器到其他C/C++应用程序中。不过，使用C API需要对C语言和Python的内存管理有深入理解，否则可能会遇到难以调试的问题。

The Python/C API, Release 3.7.0

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if (item == NULL) {

/* Handle KeyError only: */

if (!PyErr_ExceptionMatches(PyExc_KeyError))

goto error;

/* Clear the error and use zero: */

PyErr_Clear();

item = PyLong_FromLong(0L);

if (item == NULL)

goto error;

}

const_one = PyLong_FromLong(1L);

if (const_one == NULL)

goto error;

incremented_item = PyNumber_Add(item, const_one);

if (incremented_item == NULL)

goto error;

if (PyObject_SetItem(dict, key, incremented_item) < 0)

goto error;

rv = 0; /* Success */

/* Continue with cleanup code */

error:

/* Cleanup code, shared by success and failure path */

/* Use Py_XDECREF() to ignore NULL references */

Py_XDECREF(item);

Py_XDECREF(const_one);

Py_XDECREF(incremented_item);

return rv; /* -1 for error, 0 for success */

}

This example represents an endorsed use of the goto statement in C! It illustrates the use of

PyErr_ExceptionMatches() and PyErr_Clear() to handle speciﬁc exceptions, and the use of Py_XDECREF()

to dispose of owned references that may be NULL (note the 'X' in the name; Py_DECREF() would crash

when confronted with a NULL reference). It is important that the variables used to hold owned references

are initialized to NULL for this to work; likewise, the proposed return value is initialized to -1 (failure) and

only set to success after the ﬁnal call made is successful.

1.6 Embedding Python

The one important task that only embedders (as opposed to extension writers) of the Python interpreter

have to worry about is the initialization, and possibly the ﬁnalization, of the Python interpreter. Most

functionality of the interpreter can only be used after the interpreter has been initialized.

The basic initialization function is Py_Initialize(). This initializes the table of loaded modules, and

creates the fundamental modules builtins, __main__, and sys. It also initializes the module search path

(sys.path).

Py_Initialize() does not set the “script argument list” (sys.argv). If this variable is needed by Python

code that will be executed later, it must be set explicitly with a call to PySys_SetArgvEx(argc, argv,

updatepath) after the call to Py_Initialize().

10 Chapter 1. Introduction

The Python/C API, Release 3.7.0

On most systems (in particular, on Unix and Windows, although the details are slightly diﬀerent),

Py_Initialize() calculates the module search path based upon its best guess for the location of the stan-

dard Python interpreter executable, assuming that the Python library is found in a ﬁxed location relative

to the Python interpreter executable. In particular, it looks for a directory named lib/pythonX.Y relative

to the parent directory where the executable named python is found on the shell command search path (the

environment variable PATH).

For instance, if the Python executable is found in /usr/local/bin/python, it will assume that the libraries

are in /usr/local/lib/pythonX.Y . (In fact, this particular path is also the “fallback” location, used when

no executable ﬁle named python is found along PATH.) The user can override this behavior by setting the

environment variable PYTHONHOME, or insert additional directories in front of the standard path by setting

PYTHONPATH.

The embedding application can steer the search by calling Py_SetProgramName(file) before calling

Py_Initialize(). Note that PYTHONHOME still overrides this and PYTHONPATH is still inserted in front

of the standard path. An application that requires total control has to provide its own implementation

of Py_GetPath(), Py_GetPrefix(), Py_GetExecPrefix(), and Py_GetProgramFullPath() (all deﬁned in

Modules/getpath.c).

Sometimes, it is desirable to “uninitialize” Python. For instance, the application may want to start over

(make another call to Py_Initialize()) or the application is simply done with its use of Python and wants

to free memory allocated by Python. This can be accomplished by calling Py_FinalizeEx(). The function

Py_IsInitialized() returns true if Python is currently in the initialized state. More information about

these functions is given in a later chapter. Notice that Py_FinalizeEx() does not free all memory allocated

by the Python interpreter, e.g. memory allocated by extension modules currently cannot be released.

1.7 Debugging Builds

Python can be built with several macros to enable extra checks of the interpreter and extension modules.

These checks tend to add a large amount of overhead to the runtime so they are not enabled by default.

A full list of the various types of debugging builds is in the ﬁle Misc/SpecialBuilds.txt in the Python

source distribution. Builds are available that support tracing of reference counts, debugging the memory

allocator, or low-level proﬁling of the main interpreter loop. Only the most frequently-used builds will be

described in the remainder of this section.

Compiling the interpreter with the Py_DEBUG macro deﬁned produces what is generally meant by “a debug

build” of Python. Py_DEBUG is enabled in the Unix build by adding --with-pydebug to the ./configure

command. It is also implied by the presence of the not-Python-speciﬁc _DEBUG macro. When Py_DEBUG is

enabled in the Unix build, compiler optimization is disabled.

In addition to the reference count debugging described below, the following extra checks are performed:

• Extra checks are added to the object allocator.

• Extra checks are added to the parser and compiler.

• Downcasts from wide types to narrow types are checked for loss of information.

• A number of assertions are added to the dictionary and set implementations. In addition, the set

object acquires a test_c_api() method.

• Sanity checks of the input arguments are added to frame creation.

• The storage for ints is initialized with a known invalid pattern to catch reference to uninitialized digits.

• Low-level tracing and extra exception checking are added to the runtime virtual machine.

• Extra checks are added to the memory arena implementation.

• Extra debugging is added to the thread module.

1.7. Debugging Builds 11

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