1. 以 C 或 C++ 擴充 Python

如果你會撰寫 C 程式語言，那要向 Python 新增內建模組就不困難。這種擴充模組 (extension modules) 可以做兩件在 Python 中無法直接完成的事：它們可以實作新的內建物件型別，並且可以呼叫 C 的函式庫函式和系統呼叫。

為了支援擴充，Python API (Application Programmers Interface) 定義了一組函式、巨集和變數，提供對 Python run-time 系統大部分面向的存取。Python API 是透過引入標頭檔 "Python.h" 來被納入到一個 C 原始碼檔案中。

擴充模組的編譯取決於其預期用途以及你的系統設定；詳細資訊將在後面的章節中提供。

備註

C 擴充介面是 CPython 所特有的，擴充模組在其他 Python 實作上無法運作。在許多情況下，可以避免撰寫 C 擴充並保留對其他實作的可移植性。例如，如果你的用例是呼叫 C 函式庫函式或系統呼叫，你應該考慮使用 ctypes 模組或 cffi 函式庫，而不是編寫自定義的 C 程式碼。這些模組讓你可以撰寫 Python 程式碼來與 C 程式碼介接，而且比起撰寫和編譯 C 擴充模組，這些模組在 Python 實作之間更容易移植。

1.1. 一個簡單範例

讓我們來建立一個叫做 spam（Monty Python 粉絲最愛的食物...）的擴充模組。假設我們要建立一個 Python 介面給 C 函式庫的函式 system() [1] 使用，這個函式接受一個以 null 終止的 (null-terminated) 字元字串做為引數，並回傳一個整數。我們希望這個函式可以在 Python 中被呼叫，如下所示：

>>> import spam
>>> status = spam.system("ls -l")

首先建立一個檔案 spammodule.c。（從過去歷史來看，如果一個模組叫做 spam，包含其實作的 C 檔案就會叫做 spammodule.c；如果模組名稱很長，像是 spammify，模組名稱也可以只是 spammify.c）。

我們檔案的前兩列可以為：

#define PY_SSIZE_T_CLEAN
#include <Python.h>

這會將 Python API 拉進來（你可以加入註解來說明模組的目的，也可以加入版權聲明)。

備註

由於 Python 可能定義一些影響系統上某些標準標頭檔的預處理器定義，你必須在引入任何標準標頭檔之前引入 Python.h。

#define PY_SSIZE_T_CLEAN 被用來表示在某些 API 中應該使用 Py_ssize_t 而不是 int。自 Python 3.13 起，它就不再是必要的了，但我們在此保留它以便向後相容。關於這個巨集的描述請參閱 Strings and buffers。

除了那些在標準標頭檔中定義的符號以外，所有由 Python.h 定義的使用者可見符號 (user-visible symbols) 的前綴都是 Py 或 PY。為了方便，也因為 Python 直譯器的大量使用，"Python.h" 也引入了一些標準的標頭檔：<stdio.h>、<string.h>、<errno.h> 和 <stdlib.h>。如果 <stdlib.h> 在你的系統上不存在，它會直接宣告 malloc()、free() 和 realloc() 函式。

接下來我們要加入到模組檔案的是 C 函式，當 Python 運算式 spam.system(string) 要被求值 (evaluated) 時就會被呼叫（我們很快就會看到它最後是如何被呼叫的）：

static PyObject *
spam_system(PyObject *self, PyObject *args)
{
    const char *command;
    int sts;

    if (!PyArg_ParseTuple(args, "s", &command))
        return NULL;
    sts = system(command);
    return PyLong_FromLong(sts);
}

可以很直觀地從 Python 的引數串列（例如單一的運算式 "ls -l"）直接轉換成傳給 C 函式的引數。C 函式總是有兩個引數，習慣上會命名為 self 和 args。

對於模組層級的函式，self 引數會指向模組物件；而對於方法來說則是指向物件的實例。

args 引數會是一個指向包含引數的 Python 元組物件的指標。元組中的每一項都對應於呼叫的引數串列中的一個引數。引數是 Python 物件 --- 為了在我們的 C 函式中對它們做任何事情，我們必須先將它們轉換成 C 值。Python API 中的 PyArg_ParseTuple() 函式能夠檢查引數型別並將他們轉換為 C 值。它使用模板字串來決定所需的引數型別以及儲存轉換值的 C 變數型別。稍後會再詳細說明。

如果所有的引數都有正確的型別，且其元件已儲存在傳入位址的變數中，則 PyArg_ParseTuple() 會回傳 true（非零）。如果傳入的是無效引數串列則回傳 false（零）。在後者情況下，它也會產生適當的例外，因此呼叫函式可以立即回傳 NULL（就像我們在範例中所看到的）。

1.2. 插曲：錯誤與例外

在整個 Python 直譯器中的一個重要慣例為：當一個函式失敗時，它就應該設定一個例外條件，並回傳一個錯誤值（通常是 -1 或一個 NULL 指標）。例外資訊會儲存在直譯器執行緒狀態的三個成員中。如果沒有例外，它們就會是 NULL。否則，它們是由 sys.exc_info() 所回傳的 Python 元組中的 C 等效元組。它們是例外型別、例外實例和回溯物件。了解它們對於理解錯誤是如何傳遞是很重要的。

Python API 定義了許多能夠設定各種類型例外的函式。

最常見的是 PyErr_SetString()。它的引數是一個例外物件和一個 C 字串。例外物件通常是預先定義的物件，例如 PyExc_ZeroDivisionError。C 字串則指出錯誤的原因，並被轉換為 Python 字串物件且被儲存為例外的「關聯值 (associated value)」。

另一個有用的函式是 PyErr_SetFromErrno()，它只接受一個例外引數，並透過檢查全域變數 errno 來建立關聯值。最一般的函式是 PyErr_SetObject()，它接受兩個物件引數，即例外和它的關聯值。你不需要對傳給任何這些函式的物件呼叫 Py_INCREF()。

你可以使用 PyErr_Occurred() 來不具破壞性地測試例外是否已被設定。這會回傳當前的例外物件，如果沒有例外發生則回傳 NULL。你通常不需要呼叫 PyErr_Occurred() 來查看函式呼叫是否發生錯誤，因為你應可從回傳值就得知。

當函式 f 呼叫另一個函式 g 時檢測到後者失敗，f 本身應該回傳一個錯誤值（通常是 NULL 或 -1）。它不應該呼叫 PyErr_* 函式的其中一個，這會已被 g 呼叫過。f 的呼叫者然後也應該回傳一個錯誤指示給它的呼叫者，同樣不會呼叫 PyErr_*，依此類推 --- 最詳細的錯誤原因已經被首先檢測到它的函式回報了。一旦錯誤到達 Python 直譯器的主要迴圈，這會中止目前執行的 Python 程式碼，並嘗試尋找 Python 程式設計者指定的例外處理程式。

（在某些情況下，模組可以透過呼叫另一個 PyErr_* 函式來提供更詳細的錯誤訊息，在這種情況下這樣做是沒問題的。然而這一般來說並非必要，而且可能會導致錯誤原因資訊的遺失：大多數的操作都可能因為各種原因而失敗。）

要忽略由函式呼叫失敗所設定的例外，必須明確地呼叫 PyErr_Clear() 來清除例外條件。C 程式碼唯一要呼叫 PyErr_Clear() 的情況為當它不想將錯誤傳遞給直譯器而想要完全是自己來處理它時（可能是要再嘗試其他東西，或者假裝什麼都沒出錯）。

每個失敗的 malloc() 呼叫都必須被轉換成一個例外 --- malloc()（或 realloc()）的直接呼叫者必須呼叫 PyErr_NoMemory() 並回傳一個失敗指示器。所有建立物件的函式（例如 PyLong_FromLong()）都已經這麼做了，所以這個注意事項只和那些直接呼叫 malloc() 的函式有關。

還要注意的是，有 PyArg_ParseTuple() 及同系列函式的這些重要例外，回傳整數狀態的函式通常會回傳一個正值或 0 表示成功、回傳 -1 表示失敗，就像 Unix 系統呼叫一樣。

最後，在回傳錯誤指示器時要注意垃圾清理（透過對你已經建立的物件呼叫 Py_XDECREF() 或 Py_DECREF()）！

你完全可以自行選擇要產生的例外。有一些預先宣告的 C 物件會對應到所有內建的 Python 例外，例如 PyExc_ZeroDivisionError，你可以直接使用它們。當然，你應該明智地選擇例外，像是不要使用 PyExc_TypeError 來表示檔案無法打開（應該是 PyExc_OSError）。如果引數串列有問題，PyArg_ParseTuple() 函式通常會引發 PyExc_TypeError。如果你有一個引數的值必須在一個特定的範圍內或必須滿足其他條件，則可以使用 PyExc_ValueError。

你也可以定義一個你的模組特有的新例外。為此你通常會在檔案開頭宣告一個靜態物件變數：

static PyObject *SpamError;

並在你的模組初始化函式中使用一個例外物件來初始化它 (PyInit_spam())：

PyMODINIT_FUNC
PyInit_spam(void)
{
    PyObject *m;

    m = PyModule_Create(&spammodule);
    if (m == NULL)
        return NULL;

    SpamError = PyErr_NewException("spam.error", NULL, NULL);
    if (PyModule_AddObjectRef(m, "error", SpamError) < 0) {
        Py_CLEAR(SpamError);
        Py_DECREF(m);
        return NULL;
    }

    return m;
}

請注意，例外物件的 Python 名稱是 spam.error。如同內建的例外所述，PyErr_NewException() 函式可能會建立一個基底類別為 Exception 的類別（除非傳入另一個類別來代替 NULL）。

請注意，SpamError 變數保留了對新建立的例外類別的參照；這是故意的！因為外部程式碼可能會從模組中移除這個例外，所以需要一個對這個類別的參照來確保它不會被丟棄而導致 SpamError 變成一個迷途指標 (dangling pointer)。如果它變成迷途指標，那產生例外的 C 程式碼可能會導致核心轉儲 (core dump) 或其他不預期的 side effect。

我們稍後會討論 PyMODINIT_FUNC 作為函式回傳型別的用法。

可以在你的擴充模組中呼叫 PyErr_SetString() 來引發 spam.error 例外，如下所示：

static PyObject *
spam_system(PyObject *self, PyObject *args)
{
    const char *command;
    int sts;

    if (!PyArg_ParseTuple(args, "s", &command))
        return NULL;
    sts = system(command);
    if (sts < 0) {
        PyErr_SetString(SpamError, "System command failed");
        return NULL;
    }
    return PyLong_FromLong(sts);
}

1.3. 回到範例

回到我們的範例函式，現在你應該可以理解這個陳述式了：

if (!PyArg_ParseTuple(args, "s", &command))
    return NULL;

如果在引數串列中檢測到錯誤則會回傳 NULL（回傳物件指標之函式的錯誤指示器），其依賴於 PyArg_ParseTuple() 設定的例外，否則引數的字串值會已被複製到區域變數 command 中。這是一個指標賦值，你不應該修改它所指向的字串（所以在標準 C 中，command 變數應該正確地被宣告為 const char *command）。

接下來的陳述式會呼叫 Unix 函式 system()，並將剛才從 PyArg_ParseTuple() 得到的字串傳給它：

sts = system(command);

我們的 spam.system() 函式必須以 Python 物件的形式來回傳 sts 的值。這是透過 PyLong_FromLong() 函式來達成。

return PyLong_FromLong(sts);

在這種情況下它會回傳一個整數物件。(是的，在 Python 中連整數也是堆積 (heap) 上的物件！)

如果你有一個不回傳任何有用引數的 C 函式（一個回傳 void 的函式），對應的 Python 函式必須回傳 None。你需要以下這個慣例來達成（由 Py_RETURN_NONE 巨集實作）：

Py_INCREF(Py_None);
return Py_None;

Py_None 是特殊 Python 物件 None 的 C 名稱。它是一個真正的 Python 物件而不是一個 NULL 指標，在大多數的情況下它的意思是「錯誤」，如我們所見過的那樣。

1.4. 模組的方法表和初始化函式

我承諾過要展示 spam_system() 是如何從 Python 程式中呼叫的。首先，我們需要在「方法表」中列出它的名稱和位址：

static PyMethodDef SpamMethods[] = {
    ...
    {"system",  spam_system, METH_VARARGS,
     "Execute a shell command."},
    ...
    {NULL, NULL, 0, NULL}        /* Sentinel */
};

請注意第三個項目 (METH_VARARGS)。這是一個告訴直譯器 C 函式之呼叫方式的旗標。通常應該是 METH_VARARGS 或 METH_VARARGS | METH_KEYWORDS；0 表示是使用 PyArg_ParseTuple() 的一個過時變體。

當只使用 METH_VARARGS 時，函式應預期 Python 層級的參數是以元組形式傳入且能夠接受以 PyArg_ParseTuple() 進行剖析；有關此函式的更多資訊將在下面提供。

如果要將關鍵字引數傳給函式，可以在第三個欄位設定 METH_KEYWORDS 位元。在這種情況下，C 函式應該要能接受第三個 PyObject * 參數，這個參數將會是關鍵字的字典。可使用 PyArg_ParseTupleAndKeywords() 來剖析這種函式的引數。

方法表必須在模組定義結構中被參照：

static struct PyModuleDef spammodule = {
    PyModuleDef_HEAD_INIT,
    "spam",   /* 模組名稱 */
    spam_doc, /* 模組文件，可能為 NULL */
    -1,       /* 模組的個別直譯器狀態的大小，
                 如果模組將狀態保存在全域變數中則為 -1 */
    SpamMethods
};

反過來說，這個結構必須在模組的初始化函式中被傳給直譯器。初始化函式必須被命名為 PyInit_name()，其中 name 是模組的名稱，且應該是模組檔案中唯一定義的非「靜態 (static)」項目：

PyMODINIT_FUNC
PyInit_spam(void)
{
    return PyModule_Create(&spammodule);
}

請注意，PyMODINIT_FUNC 宣告函式的回傳型別為 PyObject *`、宣告平台所需的任何特殊連結宣告、並針對 C++ 宣告函式為 extern "C"。

當 Python 程式第一次引入模組 spam 時，PyInit_spam() 會被呼叫。（有關嵌入 Python 的註解請參見下文。）它會呼叫回傳一個模組物件的 PyModule_Create()，並根據在模組定義中所找到的表（一個 PyMethodDef 結構的陣列）將內建的函式物件插入到新建立的模組中。PyModule_Create() 會回傳一個指向它建立之模組物件的指標。對於某些錯誤情況，它可能會以嚴重錯誤的形式來中止；如果模組無法令人滿意地被初始化，它也會回傳 NULL。初始化函式必須把模組物件回傳給它的呼叫者，這樣它才會被插入到 sys.modules 中。

嵌入 Python 時，除非在 PyImport_Inittab 表中有相關條目，否則不會自動呼叫 PyInit_spam() 函式。要將模組加入初始化表，請使用 PyImport_AppendInittab() 並在隨後選擇性地將該模組引入：

#define PY_SSIZE_T_CLEAN
#include <Python.h>

int
main(int argc, char *argv[])
{
    PyStatus status;
    PyConfig config;
    PyConfig_InitPythonConfig(&config);

    /* 在 Py_Initialize 之前加入內建模組 */
    if (PyImport_AppendInittab("spam", PyInit_spam) == -1) {
        fprintf(stderr, "Error: could not extend in-built modules table\n");
        exit(1);
    }

    /* 將 argv[0] 傳給 Python 直譯器 */
    status = PyConfig_SetBytesString(&config, &config.program_name, argv[0]);
    if (PyStatus_Exception(status)) {
        goto exception;
    }

    /* 初始化 Python 直譯器。這會是必要的。
       如果此步驟失敗就會導致嚴重錯誤。*/
    status = Py_InitializeFromConfig(&config);
    if (PyStatus_Exception(status)) {
        goto exception;
    }
    PyConfig_Clear(&config);

    /* 可選擇引入模組；或者
       可以延遲引入，直至嵌入式腳本
       將其引入。*/
    PyObject *pmodule = PyImport_ImportModule("spam");
    if (!pmodule) {
        PyErr_Print();
        fprintf(stderr, "Error: could not import module 'spam'\n");
    }

    // ... 在此使用 Python C API ...

    return 0;

  exception:
     PyConfig_Clear(&config);
     Py_ExitStatusException(status);
}

備註

從 sys.modules 中移除項目，或在一個行程中將已編譯模組引入到多個直譯器中（或在沒有 exec() 介入的情況下使用 fork()）可能會對某些擴充模組造成問題。擴充模組作者在初始化內部資料結構時應特別小心。

Python 原始碼發行版本中包含了一個更實質的範例模組 Modules/xxmodule.c。這個檔案可以當作模板使用，也可以簡單地當作範例來閱讀。

備註

不像我們的 spam 範例，xxmodule 使用了多階段初始化 (multi-phase initialization)（Python 3.5 新增），其中的 PyModuleDef 結構會從 PyInit_spam 回傳，而模組的建立則交由引入機制來完成。關於多階段初始化的詳細資訊請參閱 PEP 489。

1.5. Compilation and Linkage

There are two more things to do before you can use your new extension: compiling and linking it with the Python system. If you use dynamic loading, the details may depend on the style of dynamic loading your system uses; see the chapters about building extension modules (chapter 建立 C 與 C++ 擴充套件) and additional information that pertains only to building on Windows (chapter 建置 Windows 上的 C 和 C++ 擴充) for more information about this.

If you can't use dynamic loading, or if you want to make your module a permanent part of the Python interpreter, you will have to change the configuration setup and rebuild the interpreter. Luckily, this is very simple on Unix: just place your file (spammodule.c for example) in the Modules/ directory of an unpacked source distribution, add a line to the file Modules/Setup.local describing your file:

spam spammodule.o

and rebuild the interpreter by running make in the toplevel directory. You can also run make in the Modules/ subdirectory, but then you must first rebuild Makefile there by running 'make Makefile'. (This is necessary each time you change the Setup file.)

If your module requires additional libraries to link with, these can be listed on the line in the configuration file as well, for instance:

spam spammodule.o -lX11

1.6. Calling Python Functions from C

So far we have concentrated on making C functions callable from Python. The reverse is also useful: calling Python functions from C. This is especially the case for libraries that support so-called "callback" functions. If a C interface makes use of callbacks, the equivalent Python often needs to provide a callback mechanism to the Python programmer; the implementation will require calling the Python callback functions from a C callback. Other uses are also imaginable.

Fortunately, the Python interpreter is easily called recursively, and there is a standard interface to call a Python function. (I won't dwell on how to call the Python parser with a particular string as input --- if you're interested, have a look at the implementation of the -c command line option in Modules/main.c from the Python source code.)

Calling a Python function is easy. First, the Python program must somehow pass you the Python function object. You should provide a function (or some other interface) to do this. When this function is called, save a pointer to the Python function object (be careful to Py_INCREF() it!) in a global variable --- or wherever you see fit. For example, the following function might be part of a module definition:

static PyObject *my_callback = NULL;

static PyObject *
my_set_callback(PyObject *dummy, PyObject *args)
{
    PyObject *result = NULL;
    PyObject *temp;

    if (PyArg_ParseTuple(args, "O:set_callback", &temp)) {
        if (!PyCallable_Check(temp)) {
            PyErr_SetString(PyExc_TypeError, "parameter must be callable");
            return NULL;
        }
        Py_XINCREF(temp);         /* Add a reference to new callback */
        Py_XDECREF(my_callback);  /* Dispose of previous callback */
        my_callback = temp;       /* Remember new callback */
        /* Boilerplate to return "None" */
        Py_INCREF(Py_None);
        result = Py_None;
    }
    return result;
}

This function must be registered with the interpreter using the METH_VARARGS flag; this is described in section 模組的方法表和初始化函式. The PyArg_ParseTuple() function and its arguments are documented in section Extracting Parameters in Extension Functions.

The macros Py_XINCREF() and Py_XDECREF() increment/decrement the reference count of an object and are safe in the presence of NULL pointers (but note that temp will not be NULL in this context). More info on them in section Reference Counts.

Later, when it is time to call the function, you call the C function PyObject_CallObject(). This function has two arguments, both pointers to arbitrary Python objects: the Python function, and the argument list. The argument list must always be a tuple object, whose length is the number of arguments. To call the Python function with no arguments, pass in NULL, or an empty tuple; to call it with one argument, pass a singleton tuple. Py_BuildValue() returns a tuple when its format string consists of zero or more format codes between parentheses. For example:

int arg;
PyObject *arglist;
PyObject *result;
...
arg = 123;
...
/* Time to call the callback */
arglist = Py_BuildValue("(i)", arg);
result = PyObject_CallObject(my_callback, arglist);
Py_DECREF(arglist);

PyObject_CallObject() returns a Python object pointer: this is the return value of the Python function. PyObject_CallObject() is "reference-count-neutral" with respect to its arguments. In the example a new tuple was created to serve as the argument list, which is Py_DECREF()-ed immediately after the PyObject_CallObject() call.

The return value of PyObject_CallObject() is "new": either it is a brand new object, or it is an existing object whose reference count has been incremented. So, unless you want to save it in a global variable, you should somehow Py_DECREF() the result, even (especially!) if you are not interested in its value.

Before you do this, however, it is important to check that the return value isn't NULL. If it is, the Python function terminated by raising an exception. If the C code that called PyObject_CallObject() is called from Python, it should now return an error indication to its Python caller, so the interpreter can print a stack trace, or the calling Python code can handle the exception. If this is not possible or desirable, the exception should be cleared by calling PyErr_Clear(). For example:

if (result == NULL)
    return NULL; /* Pass error back */
...use result...
Py_DECREF(result);

Depending on the desired interface to the Python callback function, you may also have to provide an argument list to PyObject_CallObject(). In some cases the argument list is also provided by the Python program, through the same interface that specified the callback function. It can then be saved and used in the same manner as the function object. In other cases, you may have to construct a new tuple to pass as the argument list. The simplest way to do this is to call Py_BuildValue(). For example, if you want to pass an integral event code, you might use the following code:

PyObject *arglist;
...
arglist = Py_BuildValue("(l)", eventcode);
result = PyObject_CallObject(my_callback, arglist);
Py_DECREF(arglist);
if (result == NULL)
    return NULL; /* Pass error back */
/* Here maybe use the result */
Py_DECREF(result);

Note the placement of Py_DECREF(arglist) immediately after the call, before the error check! Also note that strictly speaking this code is not complete: Py_BuildValue() may run out of memory, and this should be checked.

You may also call a function with keyword arguments by using PyObject_Call(), which supports arguments and keyword arguments. As in the above example, we use Py_BuildValue() to construct the dictionary.

PyObject *dict;
...
dict = Py_BuildValue("{s:i}", "name", val);
result = PyObject_Call(my_callback, NULL, dict);
Py_DECREF(dict);
if (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 剖析引數與建置數值 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!

一些呼叫範例：

#define PY_SSIZE_T_CLEAN
#include <Python.h>

int ok;
int i, j;
long k, l;
const char *s;
Py_ssize_t size;

ok = PyArg_ParseTuple(args, ""); /* 沒有引數 */
    /* Python 呼叫：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') */

{
    const char *file;
    const char *mode = "r";
    int bufsize = 0;
    ok = PyArg_ParseTuple(args, "s|si", &file, &mode, &bufsize);
    /* A string, and optionally another string and an integer */
    /* Possible Python calls:
       f('spam')
       f('spam', 'w')
       f('spam', 'wb', 100000) */
}

{
    int left, top, right, bottom, h, v;
    ok = PyArg_ParseTuple(args, "((ii)(ii))(ii)",
             &left, &top, &right, &bottom, &h, &v);
    /* A rectangle and a point */
    /* Possible Python call:
       f(((0, 0), (400, 300)), (10, 10)) */
}

{
    Py_complex c;
    ok = PyArg_ParseTuple(args, "D:myfunction", &c);
    /* a complex, also providing a function name for errors */
    /* Possible Python call: myfunction(1+2j) */
}

1.8. Keyword Parameters for Extension Functions

The PyArg_ParseTupleAndKeywords() function is declared as follows:

int PyArg_ParseTupleAndKeywords(PyObject *arg, PyObject *kwdict,
                                const char *format, char * const *kwlist, ...);

The arg and format parameters are identical to those of the PyArg_ParseTuple() function. The kwdict parameter is the dictionary of keywords received as the third parameter from the Python runtime. The kwlist parameter is a NULL-terminated list of strings which identify the parameters; the names are matched with the type information from format from left to right. On success, PyArg_ParseTupleAndKeywords() returns true, otherwise it returns false and raises an appropriate exception.

備註

Nested tuples cannot be parsed when using keyword arguments! Keyword parameters passed in which are not present in the kwlist will cause TypeError to be raised.

Here is an example module which uses keywords, based on an example by Geoff Philbrick (philbrick@hks.com):

#define PY_SSIZE_T_CLEAN
#include <Python.h>

static PyObject *
keywdarg_parrot(PyObject *self, PyObject *args, PyObject *keywds)
{
    int voltage;
    const char *state = "a stiff";
    const char *action = "voom";
    const char *type = "Norwegian Blue";

    static char *kwlist[] = {"voltage", "state", "action", "type", NULL};

    if (!PyArg_ParseTupleAndKeywords(args, keywds, "i|sss", kwlist,
                                     &voltage, &state, &action, &type))
        return NULL;

    printf("-- This parrot wouldn't %s if you put %i Volts through it.\n",
           action, voltage);
    printf("-- Lovely plumage, the %s -- It's %s!\n", type, state);

    Py_RETURN_NONE;
}

static PyMethodDef keywdarg_methods[] = {
    /* The cast of the function is necessary since PyCFunction values
     * only take two PyObject* parameters, and keywdarg_parrot() takes
     * three.
     */
    {"parrot", (PyCFunction)(void(*)(void))keywdarg_parrot, METH_VARARGS | METH_KEYWORDS,
     "Print a lovely skit to standard output."},
    {NULL, NULL, 0, NULL}   /* sentinel */
};

static struct PyModuleDef keywdargmodule = {
    PyModuleDef_HEAD_INIT,
    "keywdarg",
    NULL,
    -1,
    keywdarg_methods
};

PyMODINIT_FUNC
PyInit_keywdarg(void)
{
    return PyModule_Create(&keywdargmodule);
}

1.9. Building Arbitrary Values

This function is the counterpart to PyArg_ParseTuple(). It is declared as follows:

PyObject *Py_BuildValue(const char *format, ...);

It recognizes a set of format units similar to the ones recognized by PyArg_ParseTuple(), but the arguments (which are input to the function, not output) must not be pointers, just values. It returns a new Python object, suitable for returning from a C function called from Python.

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 reuse 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 virtual memory, so the leak only becomes apparent in a long-running process that uses the leaking function frequently. Therefore, it's important to prevent leaks from happening by having a coding convention or strategy that minimizes this kind of errors.

Since Python makes heavy use of malloc() and free(), it needs a strategy to avoid memory leaks as well as the use of freed memory. The chosen method is called reference counting. The principle is simple: every object contains a counter, which is incremented when a reference to the object is stored somewhere, and which is decremented when a reference to it is deleted. When the counter reaches zero, the last reference to the object has been deleted and the object is freed.

An alternative strategy is called automatic garbage collection. (Sometimes, reference counting is also referred to as a garbage collection strategy, hence my use of "automatic" to distinguish the two.) The big advantage of automatic garbage collection is that the user doesn't need to call free() explicitly. (Another claimed advantage is an improvement in speed or memory usage --- this is no hard fact however.) The disadvantage is that for C, there is no truly portable automatic garbage collector, while reference counting can be implemented portably (as long as the functions malloc() and free() are available --- which the C Standard guarantees). Maybe some day a sufficiently portable automatic garbage collector will be available for C. Until then, we'll have to live with reference counts.

While Python uses the traditional reference counting implementation, it also offers a cycle detector that works to detect reference cycles. This allows applications to not worry about creating direct or indirect circular references; these are the weakness of garbage collection implemented using only reference counting. Reference cycles consist of objects which contain (possibly indirect) references to themselves, so that each object in the cycle has a reference count which is non-zero. Typical reference counting implementations are not able to reclaim the memory belonging to any objects in a reference cycle, or referenced from the objects in the cycle, even though there are no further references to the cycle itself.

The cycle detector is able to detect garbage cycles and can reclaim them. The gc module exposes a way to run the detector (the collect() function), as well as configuration interfaces and the ability to disable the detector at runtime.

1.10.1. Reference Counting in Python

There are two macros, Py_INCREF(x) and Py_DECREF(x), which handle the incrementing and decrementing of the reference count. Py_DECREF() also frees the object when the count reaches zero. For flexibility, it doesn't call free() directly --- rather, it makes a call through a function pointer in the object's type object. For this purpose (and others), every object also contains a pointer to its type object.

The big question now remains: when to use Py_INCREF(x) and Py_DECREF(x)? Let's first introduce some terms. Nobody "owns" an object; however, you can own a reference to an object. An object's reference count is now defined as the number of owned references to it. The owner of a reference is responsible for calling Py_DECREF() when the reference is no longer needed. Ownership of a reference can be transferred. There are three ways to dispose of an owned reference: pass it on, store it, or call Py_DECREF(). Forgetting to dispose of an owned reference creates a memory leak.

It is also possible to borrow [2] a reference to an object. The borrower of a reference should not call Py_DECREF(). The borrower must not hold on to the object longer than the owner from which it was borrowed. Using a borrowed reference after the owner has disposed of it risks using freed memory and should be avoided completely [3].

The advantage of borrowing over owning a reference is that you don't need to take care of disposing of the reference on all possible paths through the code --- in other words, with a borrowed reference you don't run the risk of leaking when a premature exit is taken. The disadvantage of borrowing over owning is that there are some subtle situations where in seemingly correct code a borrowed reference can be used after the owner from which it was borrowed has in fact disposed of it.

A borrowed reference can be changed into an owned reference by calling Py_INCREF(). This does not affect the status of the owner from which the reference was borrowed --- it creates a new owned reference, and gives full owner responsibilities (the new owner must dispose of the reference properly, as well as the previous owner).

1.10.2. Ownership Rules

Whenever an object reference is passed into or out of a function, it is part of the function's interface specification whether ownership is transferred with the reference or not.

Most functions that return a reference to an object pass on ownership with the reference. In particular, all functions whose function it is to create a new object, such as PyLong_FromLong() and Py_BuildValue(), pass ownership to the receiver. Even if the object is not actually new, you still receive ownership of a new reference to that object. For instance, PyLong_FromLong() maintains a cache of popular values and can return a reference to a cached item.

Many functions that extract objects from other objects also transfer ownership with the reference, for instance PyObject_GetAttrString(). The picture is less clear, here, however, since a few common routines are exceptions: PyTuple_GetItem(), PyList_GetItem(), PyDict_GetItem(), and PyDict_GetItemString() all return references that you borrow from the tuple, list or dictionary.

The function PyImport_AddModule() also returns a borrowed reference, even though it may actually create the object it returns: this is possible because an owned reference to the object is stored in sys.modules.

When you pass an object reference into another function, in general, the function borrows the reference from you --- if it needs to store it, it will use Py_INCREF() to become an independent owner. There are exactly two important exceptions to this rule: PyTuple_SetItem() and PyList_SetItem(). These functions take over ownership of the item passed to them --- even if they fail! (Note that PyDict_SetItem() and friends don't take over ownership --- they are "normal.")

When a C function is called from Python, it borrows references to its arguments from the caller. The caller owns a reference to the object, so the borrowed reference's lifetime is guaranteed until the function returns. Only when such a borrowed reference must be stored or passed on, it must be turned into an owned reference by calling Py_INCREF().

The object reference returned from a C function that is called from Python must be an owned reference --- ownership is transferred from the function to its caller.

1.10.3. Thin Ice

There are a few situations where seemingly harmless use of a borrowed reference can lead to problems. These all have to do with implicit invocations of the interpreter, which can cause the owner of a reference to dispose of it.

The first and most important case to know about is using Py_DECREF() on an unrelated object while borrowing a reference to a list item. For instance:

void
bug(PyObject *list)
{
    PyObject *item = PyList_GetItem(list, 0);

    PyList_SetItem(list, 1, PyLong_FromLong(0L));
    PyObject_Print(item, stdout, 0); /* BUG! */
}

This function first borrows a reference to list[0], then replaces list[1] with the value 0, and finally prints the borrowed reference. Looks harmless, right? But it's not!

Let's follow the control flow into PyList_SetItem(). The list owns references to all its items, so when item 1 is replaced, it has to dispose of the original item 1. Now let's suppose the original item 1 was an instance of a user-defined class, and let's further suppose that the class defined a __del__() method. If this class instance has a reference count of 1, disposing of it will call its __del__() method.

Since it is written in Python, the __del__() method can execute arbitrary Python code. Could it perhaps do something to invalidate the reference to item in bug()? You bet! Assuming that the list passed into bug() is accessible to the __del__() method, it could execute a statement to the effect of del list[0], and assuming this was the last reference to that object, it would free the memory associated with it, thereby invalidating item.

The solution, once you know the source of the problem, is easy: temporarily increment the reference count. The correct version of the function reads:

void
no_bug(PyObject *list)
{
    PyObject *item = PyList_GetItem(list, 0);

    Py_INCREF(item);
    PyList_SetItem(list, 1, PyLong_FromLong(0L));
    PyObject_Print(item, stdout, 0);
    Py_DECREF(item);
}

This is a true story. An older version of Python contained variants of this bug and someone spent a considerable amount of time in a C debugger to figure out why his __del__() methods would fail...

The second case of problems with a borrowed reference is a variant involving threads. Normally, multiple threads in the Python interpreter can't get in each other's way, because there is a global lock protecting Python's entire object space. However, it is possible to temporarily release this lock using the macro Py_BEGIN_ALLOW_THREADS, and to re-acquire it using Py_END_ALLOW_THREADS. This is common around blocking I/O calls, to let other threads use the processor while waiting for the I/O to complete. Obviously, the following function has the same problem as the previous one:

void
bug(PyObject *list)
{
    PyObject *item = PyList_GetItem(list, 0);
    Py_BEGIN_ALLOW_THREADS
    ...some blocking I/O call...
    Py_END_ALLOW_THREADS
    PyObject_Print(item, stdout, 0); /* BUG! */
}

1.10.4. NULL 指標

In general, functions that take object references as arguments do not expect you to pass them NULL pointers, and will dump core (or cause later core dumps) if you do so. Functions that return object references generally return NULL only to indicate that an exception occurred. The reason for not testing for NULL arguments is that functions often pass the objects they receive on to other function --- if each function were to test for NULL, there would be a lot of redundant tests and the code would run more slowly.

It is better to test for NULL only at the "source:" when a pointer that may be NULL is received, for example, from malloc() or from a function that may raise an exception.

The macros Py_INCREF() and Py_DECREF() do not check for NULL pointers --- however, their variants Py_XINCREF() and Py_XDECREF() do.

The macros for checking for a particular object type (Pytype_Check()) don't check for NULL pointers --- again, there is much code that calls several of these in a row to test an object against various different expected types, and this would generate redundant tests. There are no variants with NULL checking.

The C function calling mechanism guarantees that the argument list passed to C functions (args in the examples) is never NULL --- in fact it guarantees that it is always a tuple [4].

It is a severe error to ever let a NULL pointer "escape" to the Python user.

1.11. Writing Extensions in C++

It is possible to write extension modules in C++. Some restrictions apply. If the main program (the Python interpreter) is compiled and linked by the C compiler, global or static objects with constructors cannot be used. This is not a problem if the main program is linked by the C++ compiler. Functions that will be called by the Python interpreter (in particular, module initialization functions) have to be declared using extern "C". It is unnecessary to enclose the Python header files in extern "C" {...} --- they use this form already if the symbol __cplusplus is defined (all recent C++ compilers define this symbol).

1.12. Providing a C API for an Extension Module

Many extension modules just provide new functions and types to be used from Python, but sometimes the code in an extension module can be useful for other extension modules. For example, an extension module could implement a type "collection" which works like lists without order. Just like the standard Python list type has a C API which permits extension modules to create and manipulate lists, this new collection type should have a set of C functions for direct manipulation from other extension modules.

At first sight this seems easy: just write the functions (without declaring them static, of course), provide an appropriate header file, and document the C API. And in fact this would work if all extension modules were always linked statically with the Python interpreter. When modules are used as shared libraries, however, the symbols defined in one module may not be visible to another module. The details of visibility depend on the operating system; some systems use one global namespace for the Python interpreter and all extension modules (Windows, for example), whereas others require an explicit list of imported symbols at module link time (AIX is one example), or offer a choice of different strategies (most Unices). And even if symbols are globally visible, the module whose functions one wishes to call might not have been loaded yet!

Portability therefore requires not to make any assumptions about symbol visibility. This means that all symbols in extension modules should be declared static, except for the module's initialization function, in order to avoid name clashes with other extension modules (as discussed in section 模組的方法表和初始化函式). And it means that symbols that should be accessible from other extension modules must be exported in a different way.

Python provides a special mechanism to pass C-level information (pointers) from one extension module to another one: Capsules. A Capsule is a Python data type which stores a pointer (void*). Capsules can only be created and accessed via their C API, but they can be passed around like any other Python object. In particular, they can be assigned to a name in an extension module's namespace. Other extension modules can then import this module, retrieve the value of this name, and then retrieve the pointer from the Capsule.

There are many ways in which Capsules can be used to export the C API of an extension module. Each function could get its own Capsule, or all C API pointers could be stored in an array whose address is published in a Capsule. And the various tasks of storing and retrieving the pointers can be distributed in different ways between the module providing the code and the client modules.

Whichever method you choose, it's important to name your Capsules properly. The function PyCapsule_New() takes a name parameter (const char*); you're permitted to pass in a NULL name, but we strongly encourage you to specify a name. Properly named Capsules provide a degree of runtime type-safety; there is no feasible way to tell one unnamed Capsule from another.

In particular, Capsules used to expose C APIs should be given a name following this convention:

modulename.attributename

The convenience function PyCapsule_Import() makes it easy to load a C API provided via a Capsule, but only if the Capsule's name matches this convention. This behavior gives C API users a high degree of certainty that the Capsule they load contains the correct C API.

The following example demonstrates an approach that puts most of the burden on the writer of the exporting module, which is appropriate for commonly used library modules. It stores all C API pointers (just one in the example!) in an array of void pointers which becomes the value of a Capsule. The header file corresponding to the module provides a macro that takes care of importing the module and retrieving its C API pointers; client modules only have to call this macro before accessing the C API.

The exporting module is a modification of the spam module from section 一個簡單範例. The function spam.system() does not call the C library function system() directly, but a function PySpam_System(), which would of course do something more complicated in reality (such as adding "spam" to every command). This function PySpam_System() is also exported to other extension modules.

The function PySpam_System() is a plain C function, declared static like everything else:

static int
PySpam_System(const char *command)
{
    return system(command);
}

The function spam_system() is modified in a trivial way:

static PyObject *
spam_system(PyObject *self, PyObject *args)
{
    const char *command;
    int sts;

    if (!PyArg_ParseTuple(args, "s", &command))
        return NULL;
    sts = PySpam_System(command);
    return PyLong_FromLong(sts);
}

In the beginning of the module, right after the line

#include <Python.h>

two more lines must be added:

#define SPAM_MODULE
#include "spammodule.h"

The #define is used to tell the header file that it is being included in the exporting module, not a client module. Finally, the module's initialization function must take care of initializing the C API pointer array:

PyMODINIT_FUNC
PyInit_spam(void)
{
    PyObject *m;
    static void *PySpam_API[PySpam_API_pointers];
    PyObject *c_api_object;

    m = PyModule_Create(&spammodule);
    if (m == NULL)
        return NULL;

    /* Initialize the C API pointer array */
    PySpam_API[PySpam_System_NUM] = (void *)PySpam_System;

    /* Create a Capsule containing the API pointer array's address */
    c_api_object = PyCapsule_New((void *)PySpam_API, "spam._C_API", NULL);

    if (PyModule_Add(m, "_C_API", c_api_object) < 0) {
        Py_DECREF(m);
        return NULL;
    }

    return m;
}

Note that PySpam_API is declared static; otherwise the pointer array would disappear when PyInit_spam() terminates!

The bulk of the work is in the header file spammodule.h, which looks like this:

#ifndef Py_SPAMMODULE_H
#define Py_SPAMMODULE_H
#ifdef __cplusplus
extern "C" {
#endif

/* Header file for spammodule */

/* C API functions */
#define PySpam_System_NUM 0
#define PySpam_System_RETURN int
#define PySpam_System_PROTO (const char *command)

/* Total number of C API pointers */
#define PySpam_API_pointers 1


#ifdef SPAM_MODULE
/* This section is used when compiling spammodule.c */

static PySpam_System_RETURN PySpam_System PySpam_System_PROTO;

#else
/* This section is used in modules that use spammodule's API */

static void **PySpam_API;

#define PySpam_System \
 (*(PySpam_System_RETURN (*)PySpam_System_PROTO) PySpam_API[PySpam_System_NUM])

/* Return -1 on error, 0 on success.
 * PyCapsule_Import will set an exception if there's an error.
 */
static int
import_spam(void)
{
    PySpam_API = (void **)PyCapsule_Import("spam._C_API", 0);
    return (PySpam_API != NULL) ? 0 : -1;
}

#endif

#ifdef __cplusplus
}
#endif

#endif /* !defined(Py_SPAMMODULE_H) */

All that a client module must do in order to have access to the function PySpam_System() is to call the function (or rather macro) import_spam() in its initialization function:

PyMODINIT_FUNC
PyInit_client(void)
{
    PyObject *m;

    m = PyModule_Create(&clientmodule);
    if (m == NULL)
        return NULL;
    if (import_spam() < 0)
        return NULL;
    /* additional initialization can happen here */
    return m;
}

The main disadvantage of this approach is that the file spammodule.h is rather complicated. However, the basic structure is the same for each function that is exported, so it has to be learned only once.

Finally it should be mentioned that Capsules offer additional functionality, which is especially useful for memory allocation and deallocation of the pointer stored in a Capsule. The details are described in the Python/C API Reference Manual in the section Capsules and in the implementation of Capsules (files Include/pycapsule.h and Objects/pycapsule.c in the Python source code distribution).

註解

[1]

An interface for this function already exists in the standard module os --- it was chosen as a simple and straightforward example.

[2]

The metaphor of "borrowing" a reference is not completely correct: the owner still has a copy of the reference.

[3]

Checking that the reference count is at least 1 does not work --- the reference count itself could be in freed memory and may thus be reused for another object!

[4]

These guarantees don't hold when you use the "old" style calling convention --- this is still found in much existing code.