Argument Clinic 的用法¶

Symbolic default values¶

提供给参数的默认值不能是表达式。目前明确支持以下形式：

• 数值型常数（整数和浮点数）。

• 字符串常量

• True 、 False 和 None 。

• 以模块名开头的简单符号常量，如 sys.maxsize。

（未来可能需要加以细化，以便可以采用 CONSTANT - 1 之类的完整表达式。）

对 Argument Clinic 生成的 C 函数和变量进行重命名¶

Argument Clinic 会自动为其生成的函数命名。如果生成的名称与现有的 C 函数冲突，这偶尔可能会造成问题，有一个简单的解决方案：覆盖 C 函数的名称。只要在函数声明中加入关键字 "as" ，然后再加上要使用的函数名。Argument Clinic 将以该函数名为基础作为（生成的）函数名，然后在后面加上 "_impl"，并用作实现函数的名称。

例如，若对 pickle.Pickler.dump 生成的 C 函数进行重命名，应如下所示：

/*[clinic input]
pickle.Pickler.dump as pickler_dumper

...

原函数会被命名为 pickler_dumper()，而实现函数现在被命名为``pickler_dumper_impl()``。

同样的问题依然会出现：想给某个参数取个 Python 用名，但在 C 语言中可能用不了。Argument Clinic 允许在 Python 和 C 中为同一个参数取不同的名字，依然是利用 "as" 语法：

/*[clinic input]
pickle.Pickler.dump

    obj: object
    file as file_obj: object
    protocol: object = NULL
    *
    fix_imports: bool = True

这里 Python（签名和 keywords 数组中）中用的名称是 file，而 C 语言中的变量命名为 file_obj。

self 参数也可以进行重命名。

函数转换会用到 PyArg_UnpackTuple¶

若要将函数转换为采用 PyArg_UnpackTuple() 解析其参数，只需写出所有参数，并将每个参数定义为 object。可以指定 type 参数，以便能转换为合适的类型。所有参数都应标记为只认位置（在最后一个参数后面加上 /）。

目前，所生成的代码将会用到 PyArg_ParseTuple() ，但很快会做出改动。

可选参数组¶

有些过时的函数用到了一种让人头疼的函数解析方式：计算位置参数的数量，据此用 switch 语句进行各个不同的 PyArg_ParseTuple() 调用。（这些函数不能接受只认关键字的参数。）在没有 PyArg_ParseTupleAndKeywords() 之前，这种方式曾被用于模拟可选参数。

虽然这种函数通常可以转换为采用 PyArg_ParseTupleAndKeywords() 、可选参数和默认值的方式，但并不是全都可以做到。这些过时函数中， PyArg_ParseTupleAndKeywords() 并不能直接支持某些功能。最明显的例子是内置函数 range()，它的必需参数的 左 边存在一个可选参数！另一个例子是 curses.window.addch()，它的两个参数是一组，必须同时指定。（参数名为 x 和 y；如果调用函数时传入了 x，则必须同时传入``y``；如果未传入 x ，则也不能传入 y）。

不管怎么说，Argument Clinic 的目标就是在不改变语义的情况下支持所有现有 CPython 内置参数的解析。因此，Argument Clinic 采用所谓的 可选组 方案来支持这种解析方式。可选组是必须一起传入的参数组。他们可以在必需参数的左边或右边，只能 用于只认位置的参数。

若要定义可选组，可在要分组的参数前面加上 [，在这些参数后加上``]`` ，要在同一行上。举个例子，下面是 curses.window.addch 采用可选组的用法，前两个参数和最后一个参数可选：

/*[clinic input]

curses.window.addch

    [
    x: int
      X-coordinate.
    y: int
      Y-coordinate.
    ]

    ch: object
      Character to add.

    [
    attr: long
      Attributes for the character.
    ]
    /

...

注：

• 每一个可选组，都会额外传入一个代表分组的参数。 参数为 int 型，名为 group_{direction}_{number}，其中 {direction} 取决于此参数组位于必需参数 right 还是 left，而 {number} 是一个递增数字（从 1 开始），表示此参数组与必需参数之间的距离。 在调用函数时，若未用到此参数组则此参数将设为零，若用到了参数组则该参数为非零。 所谓的用到或未用到，是指在本次调用中形参是否收到了实参。

• 如果不存在必需参数，可选组的行为等同于出现在必需参数的右侧。

• 在模棱两可的情况下，参数解析代码更倾向于参数左侧（在必需参数之前）。

• 可选组只能包含只认位置的参数。

• 可选组 仅限 用于过时代码。请勿在新的代码中使用可选组。

采用真正的 Argument Clinic 转换器，而不是 “传统转换器”¶

为了节省时间，尽量减少要学习的内容，实现第一次适用 Argument Clinic 的移植，上述练习简述的是“传统转换器”的用法。“传统转换器”只是一种简便用法，目的就是更容易地让现有代码移植为适用于 Argument Clinic 。说白了，在移植 Python 3.4 的代码时，可以考虑采用。

不过从长远来看，可能希望所有代码块都采用真正的 Argument Clinic 转换器语法。原因如下：

• 合适的转换器可读性更好，意图也更清晰。

• 有些格式单元是“传统转换器”无法支持的，因为这些格式需要带上参数，而传统转换器的语法不支持指定参数。

• 后续可能会有新版的参数解析库，提供超过 PyArg_ParseTuple() 支持的功能；而这种灵活性将无法适用于传统转换器转换的参数。

因此，若是不介意多花一点精力，请使用正常的转换器，而不是传统转换器。

简而言之，Argument Clinic（非传统）转换器的语法看起来像是 Python 函数调用。但如果函数没有明确的参数（所有函数都取默认值），则可以省略括号。因此 bool 和 bool() 是完全相同的转换器。

Argument Clinic 转换器的所有参数都只认关键字。所有 Argument Clinic 转换器均可接受以下参数：

c_default

该参数在 C 语言中的默认值。具体来说，将是在“解析函数”中声明的变量的初始化器。用法参见 the section on default values 。定义为字符串。

annotation

参数的注解值。目前尚不支持，因为 PEP 8 规定 Python 库不得使用注解。

此外，某些转换器还可接受额外的参数。下面列出了这些额外参数及其含义：

accept

一些 Python 类型的集合（可能还有伪类型）；用于限制只接受这些类型的 Python 参数。（并非通用特性；只支持传统转换器列表中给出的类型）。

若要能接受 None，请在集合中添加 NoneType。

bitwise

仅用于无符号整数。写入形参的将是 Python 实参的原生整数值，不做任何越界检查，即便是负值也一样。

converter

仅用于 object 转换器。为某个 C 转换函数 指定名称，用于将对象转换为原生类型。

encoding

仅用于字符串。指定将 Python str(Unicode) 转换为 C 语言的 char * 时应该采用的编码。

subclass_of

仅用于 object 转换器。要求 Python 值是 Python 类型的子类，用 C 语言表示。

type

仅用于 object 和 self 转换器。指定用于声明变量的 C 类型。 默认值是 "PyObject *"。

zeroes

仅用于字符串。如果为 True，则允许在值中嵌入 NUL 字节（'\\0'）。字符串的长度将通过名为 <parameter_name>_length 的参数传入，跟在字符串参数的后面。

请注意，并不是所有参数的组合都能正常生效。通常这些参数是由相应的 PyArg_ParseTuple 格式单元 实现的，行为是固定的。比如目前不能不指定 bitwise=True 就去调用 unsigned_short。虽然完全有理由认为这样可行，但这些语义并没有映射到任何现有的格式单元。所以 Argument Clinic 并不支持。（或者说，至少目前还不支持。）

下表列出了传统转换器与真正的 Argument Clinic 转换器之间的映射关系。左边是传统的转换器，右边是应该换成的文本。

举个例子，下面是采用合适的转换器的例子 pickle.Pickler.dump：

/*[clinic input]
pickle.Pickler.dump

    obj: object
        The object to be pickled.
    /

Write a pickled representation of obj to the open file.
[clinic start generated code]*/

真正的转换器有一个优点，就是比传统的转换器更加灵活。例如，unsigned_int 转换器（以及所有 unsigned_ 转换器）可以不设置 bitwise=True 。 他们默认会对数值进行范围检查，而且不会接受负数。 用传统转换器就做不到这一点。

Argument Clinic 会列明其全部转换器。每个转换器都会给出可接受的全部参数，以及每个参数的默认值。只要运行 Tools/clinic/clinic.py --converters 就能得到完整的列表。

Py_buffer¶

在使用 Py_buffer 转换器（或者 's*'、'w*'、'*y' 或 'z*' 传统转换器）时，不可 在所提供的缓冲区上调用 PyBuffer_Release()。 Argument Clinic 生成的代码会自动完成此操作（在解析函数中）。

高级转换器¶

还记得编写第一个函数时跳过的那些格式单元吗，因为他们是高级内容？下面就来介绍这些内容。

其实诀窍在于，这些格式单元都需要给出参数——要么是转换函数，要么是类型，要么是指定编码的字符串。（但 “传统转换器”不支持参数。这就是为什么第一个函数要跳过这些内容）。为格式单元指定的参数于是就成了转换器的参数；参数可以是 converter``（对于 ``O&）、subclass_of``（对于 ``O!），或者是 encoding （对于 e 开头的格式单元）。

在使用 subclass_of 时，可能还需要用到 object() 的另一个自定义参数：type，用于设置参数的实际类型。例如，为了确保对象是 PyUnicode_Type 的子类，可能想采用转换器 object(type='PyUnicodeObject *', subclass_of='&PyUnicode_Type')。

Argument Clinic 用起来可能存在一个问题：丧失了 e 开头的格式单位的一些灵活性。在手工编写 PyArg_Parse 调用时，理论上可以在运行时决定传给 PyArg_ParseTuple() 的编码字符串。但现在这个字符串必须在 Argument-Clinic 预处理时进行硬编码。这个限制是故意设置的；以便简化对这种格式单元的支持，并允许以后进行优化。这个限制似乎并不合理；CPython 本身总是为 e 开头的格式单位参数传入静态的硬编码字符串。

参数的默认值¶

参数的默认值可以是多个值中的一个。最简单的可以是字符串、int 或 float 字面量。

foo: str = "abc"
bar: int = 123
bat: float = 45.6

还可以使用 Python 的任何内置常量。

yep:  bool = True
nope: bool = False
nada: object = None

对默认值 NULL 和简单表达式还提供特别的支持，下面将一一介绍。

默认值 NULL¶

对于字符串和对象参数而言，可以设为 None，表示没有默认值。但这意味着会将 C 变量初始化为 Py_None。为了方便起见，提供了一个特殊值``NULL``，目的就是为了让 Python 认为默认值就是 None，而 C 变量则会初始化为 NULL。

设为默认值的表达式¶

参数的默认值不仅可以是字面量。还可以是一个完整的表达式，可采用数学运算符及对象的属性。但这种支持并没有那么简单，因为存在一些不明显的语义。

请考虑以下例子：

foo: Py_ssize_t = sys.maxsize - 1

sys.maxsize 在不同的系统平台可能有不同的值。因此，Argument Clinic 不能简单地在本底环境对表达式求值并用 C 语言硬编码。所以默认值将用表达式的方式存储下来，运行的时候在请求函数签名时会被求值。

在对表达式进行求值时，可以使用什么命名空间呢？求值过程运行于内置模块的上下文中。 因此，如果模块带有名为 max_widgets 的属性，直接引用即可。

foo: Py_ssize_t = max_widgets

如果表达式不在当前模块中，就会去 sys.modules 查找。比如 sys.maxsize 就是如此找到的。（因为事先不知道用户会加载哪些模块到解释器中，所以最好只用到 Python 会预加载的模块。）

仅当运行时才对缺省值求值，意味着 Argument Clinic 无法计算出正确的 C 缺省值。所以需显式给出。在使用表达式时，必须同时用转换器的``c_default`` 参数指定 C 语言中的等价表达式。

foo: Py_ssize_t(c_default="PY_SSIZE_T_MAX - 1") = sys.maxsize - 1

还有一个问题也比较复杂。Argument Clinic 无法事先知道表达式是否有效。 解析只能保证看起来是有效值，但无法 实际 知晓。在用表达式时须十分小心，确保在运行时能得到有效值。

最后一点，由于表达式必须能表示为静态的 C 语言值，所以存在许多限制。 以下列出了不得使用的 Python 特性：

• 功能

• 行内 if 语句（3 if foo else 5 ）

• 序列类自动解包（*[1, 2, 3]）

• 列表、集合、字典的解析和生成器表达式。

• 元组、列表、集合、字典的字面量

返回值转换器¶

Argument Clinic 生成的植入函数默认会返回 PyObject *。但是通常 C 函数的任务是要对某些 C 类型进行计算，然后将其转换为 PyObject * 作为结果。Argument Clinic 可以将输入参数由 Python 类型转换为本地 C 类型——为什么不让它将返回值由本地 C 类型转换为 Python 类型呢？

这就是“返回值转换器”的用途。它将植入函数修改成返回某种 C 语言类型，然后在生成的（非植入）函数中添加代码，以便将 C 语言值转换为合适的 PyObject *。

返回值转换器的语法与参数转换器的类似。返回值转换器的定义方式，类似于函数返回值的注解。返回值转换器的行为与参数转换器基本相同，接受参数，参数只认关键字，如果不修改默认参数则可省略括号。

（如果函数同时用到了 "as" 和返回值转换器， "as" 应位于返回值转换器之前。）

返回值转换器还存在一个复杂的问题：出错信息如何表示？通常函数在执行成功时会返回一个有效（非 NULL）指针，失败则返回 NULL。但如果使用了整数的返回值转换器，所有整数都是有效值。Argument Clinic 怎么检测错误呢？解决方案是：返回值转换器会隐含寻找一个代表错误的特殊值。如果返回该特殊值，且设置了出错标记（ PyErr_Occurred() 返回 True），那么生成的代码会传递该错误。否则，会对返回值进行正常编码。

目前 Argument Clinic 只支持少数几种返回值转换器。

bool
int
unsigned int
long
unsigned int
size_t
Py_ssize_t
float
double
DecodeFSDefault

这些转换器都不需要参数。前3个转换器如果返回 -1 则表示出错。DecodeFSDefault 的返回值类型是 const char *；若返回 NULL 指针则表示出错。

（还有一个 NoneType 转换器是实验性质的，成功时返回 Py_None ，失败则返回 NULL，且不会增加 Py_None 的引用计数。此转换器是否值得适用，尚不明确）。

只要运行 Tools/clinic/clinic.py --converters ，即可查看 Argument Clinic 支持的所有返回值转换器，包括其参数。

克隆已有的函数¶

如果已有一些函数比较相似，或许可以采用 Clinic 的“克隆”功能。 克隆之后能够复用以下内容：

• 参数，包括：

  • 名称

  • 转换器（带有全部参数）

  • 默认值

  • 参数前的文档字符串

  • 类别 （只认位置、位置或关键字、只认关键字）

• 返回值转换器

唯一不从原函数中复制的是文档字符串；这样就能指定一个新的文档串。

下面是函数的克隆方法：

/*[clinic input]
module.class.new_function [as c_basename] = module.class.existing_function

Docstring for new_function goes here.
[clinic start generated code]*/

（原函数可以位于不同的模块或类中。示例中的 module.class 只是为了说明，两个 函数都必须使用全路径）。

对不起，没有什么语法可对函数进行部分克隆或克隆后进行修改。克隆要么全有要么全无。

另外，要克隆的函数必须在当前文件中已有定义。

调用 Python 代码¶

下面的高级内容需要编写 Python 代码，存于 C 文件中，并修改 Argument Clinic 的运行状态。其实很简单：只需定义一个 Python 块。

Python 块的分隔线与 Argument Clinic 函数块不同。如下所示：

/*[python input]
# python code goes here
[python start generated code]*/

Python 块内的所有代码都会在解析时执行。块内写入 stdout 的所有文本都被重定向到块后的“输出”部分。

以下例子包含了 Python 块，用于在 C 代码中添加一个静态整数变量：

/*[python input]
print('static int __ignored_unused_variable__ = 0;')
[python start generated code]*/
static int __ignored_unused_variable__ = 0;
/*[python checksum:...]*/

self 转换器的用法¶

Argument Clinic 用一个默认的转换器自动添加一个“self”参数。自动将 self 参数的 type 设为声明类型时指定的“指向实例的指针”。不过 Argument Clinic 的转换器可被覆盖，也即自己指定一个转换器。只要将自己的 self 参数作为块的第一个参数即可，并确保其转换器是 self_converter 的实例或其子类。

这有什么用呢？可用于覆盖 self 的类型，或为其给个不同的默认名称。

如何指定 self 对应的自定义类型呢？如果只有 self 类型相同的一两个函数，可以直接使用 Argument Clinic 现有的 self 转换器，把要用的类型作为 type 参数传入：

/*[clinic input]

_pickle.Pickler.dump

  self: self(type="PicklerObject *")
  obj: object
  /

Write a pickled representation of the given object to the open file.
[clinic start generated code]*/

如果有很多函数将使用同一类型的 self，则最好创建自己的转换器，继承自 self_converter 类但要覆盖其 type 成员：

/*[python input]
class PicklerObject_converter(self_converter):
    type = "PicklerObject *"
[python start generated code]*/

/*[clinic input]

_pickle.Pickler.dump

  self: PicklerObject
  obj: object
  /

Write a pickled representation of the given object to the open file.
[clinic start generated code]*/

“定义类”转换器¶

Argument Clinic 为访问方法的定义类提供了便利。这对 heap type 方法十分有用，因为它需要获取模块级的运行状态。用 PyType_FromModuleAndSpec() 将新的堆类型与模块联系起来。现在可以在定义类上用 PyType_GetModuleState() 获取模块状态了，例如从模块方法中获取。

示例来自 Modules/zlibmodule.c。首先，在 clinic 的输入块添加 defining_class ：

/*[clinic input]
zlib.Compress.compress

  cls: defining_class
  data: Py_buffer
    Binary data to be compressed.
  /

运行 Argument Clinic 工具后，会生成以下函数签名：

/*[clinic start generated code]*/
static PyObject *
zlib_Compress_compress_impl(compobject *self, PyTypeObject *cls,
                            Py_buffer *data)
/*[clinic end generated code: output=6731b3f0ff357ca6 input=04d00f65ab01d260]*/

现在，以下代码可以用 PyType_GetModuleState(cls) 获取模块状态了：

zlibstate *state = PyType_GetModuleState(cls);

Each method may only have one argument using this converter, and it must appear after self, or, if self is not used, as the first argument. The argument will be of type PyTypeObject *. The argument will not appear in the __text_signature__.

The defining_class converter is not compatible with __init__ and __new__ methods, which cannot use the METH_METHOD convention.

It is not possible to use defining_class with slot methods. In order to fetch the module state from such methods, use PyType_GetModuleByDef() to look up the module and then PyModule_GetState() to fetch the module state. Example from the setattro slot method in Modules/_threadmodule.c:

static int
local_setattro(localobject *self, PyObject *name, PyObject *v)
{
    PyObject *module = PyType_GetModuleByDef(Py_TYPE(self), &thread_module);
    thread_module_state *state = get_thread_state(module);
    ...
}

See also PEP 573.

Writing a custom converter¶

As we hinted at in the previous section... you can write your own converters! A converter is simply a Python class that inherits from CConverter. The main purpose of a custom converter is if you have a parameter using the O& format unit—parsing this parameter means calling a PyArg_ParseTuple() "converter function".

Your converter class should be named *something*_converter. If the name follows this convention, then your converter class will be automatically registered with Argument Clinic; its name will be the name of your class with the _converter suffix stripped off. (This is accomplished with a metaclass.)

You shouldn't subclass CConverter.__init__. Instead, you should write a converter_init() function. converter_init() always accepts a self parameter; after that, all additional parameters must be keyword-only. Any arguments passed in to the converter in Argument Clinic will be passed along to your converter_init().

There are some additional members of CConverter you may wish to specify in your subclass. Here's the current list:

type

The C type to use for this variable. type should be a Python string specifying the type, e.g. int. If this is a pointer type, the type string should end with ' *'.

default

The Python default value for this parameter, as a Python value. Or the magic value unspecified if there is no default.

py_default

default as it should appear in Python code, as a string. Or None if there is no default.

c_default

default as it should appear in C code, as a string. Or None if there is no default.

c_ignored_default

The default value used to initialize the C variable when there is no default, but not specifying a default may result in an "uninitialized variable" warning. This can easily happen when using option groups—although properly-written code will never actually use this value, the variable does get passed in to the impl, and the C compiler will complain about the "use" of the uninitialized value. This value should always be a non-empty string.

converter

The name of the C converter function, as a string.

impl_by_reference

A boolean value. If true, Argument Clinic will add a & in front of the name of the variable when passing it into the impl function.

parse_by_reference

A boolean value. If true, Argument Clinic will add a & in front of the name of the variable when passing it into PyArg_ParseTuple().

Here's the simplest example of a custom converter, from Modules/zlibmodule.c:

/*[python input]

class ssize_t_converter(CConverter):
    type = 'Py_ssize_t'
    converter = 'ssize_t_converter'

[python start generated code]*/
/*[python end generated code: output=da39a3ee5e6b4b0d input=35521e4e733823c7]*/

This block adds a converter to Argument Clinic named ssize_t. Parameters declared as ssize_t will be declared as type Py_ssize_t, and will be parsed by the 'O&' format unit, which will call the ssize_t_converter converter function. ssize_t variables automatically support default values.

More sophisticated custom converters can insert custom C code to handle initialization and cleanup. You can see more examples of custom converters in the CPython source tree; grep the C files for the string CConverter.

Writing a custom return converter¶

Writing a custom return converter is much like writing a custom converter. Except it's somewhat simpler, because return converters are themselves much simpler.

Return converters must subclass CReturnConverter. There are no examples yet of custom return converters, because they are not widely used yet. If you wish to write your own return converter, please read Tools/clinic/clinic.py, specifically the implementation of CReturnConverter and all its subclasses.

METH_O and METH_NOARGS¶

To convert a function using METH_O, make sure the function's single argument is using the object converter, and mark the arguments as positional-only:

/*[clinic input]
meth_o_sample

     argument: object
     /
[clinic start generated code]*/

To convert a function using METH_NOARGS, just don't specify any arguments.

You can still use a self converter, a return converter, and specify a type argument to the object converter for METH_O.

tp_new and tp_init functions¶

You can convert tp_new and tp_init functions. Just name them __new__ or __init__ as appropriate. Notes:

• The function name generated for __new__ doesn't end in __new__ like it would by default. It's just the name of the class, converted into a valid C identifier.

• No PyMethodDef #define is generated for these functions.

• __init__ functions return int, not PyObject *.

• Use the docstring as the class docstring.

• Although __new__ and __init__ functions must always accept both the args and kwargs objects, when converting you may specify any signature for these functions that you like. (If your function doesn't support keywords, the parsing function generated will throw an exception if it receives any.)

Changing and redirecting Clinic's output¶

It can be inconvenient to have Clinic's output interspersed with your conventional hand-edited C code. Luckily, Clinic is configurable: you can buffer up its output for printing later (or earlier!), or write its output to a separate file. You can also add a prefix or suffix to every line of Clinic's generated output.

While changing Clinic's output in this manner can be a boon to readability, it may result in Clinic code using types before they are defined, or your code attempting to use Clinic-generated code before it is defined. These problems can be easily solved by rearranging the declarations in your file, or moving where Clinic's generated code goes. (This is why the default behavior of Clinic is to output everything into the current block; while many people consider this hampers readability, it will never require rearranging your code to fix definition-before-use problems.)

Let's start with defining some terminology:

field

A field, in this context, is a subsection of Clinic's output. For example, the #define for the PyMethodDef structure is a field, called methoddef_define. Clinic has seven different fields it can output per function definition:

docstring_prototype
docstring_definition
methoddef_define
impl_prototype
parser_prototype
parser_definition
impl_definition

All the names are of the form "<a>_<b>", where "<a>" is the semantic object represented (the parsing function, the impl function, the docstring, or the methoddef structure) and "<b>" represents what kind of statement the field is. Field names that end in "_prototype" represent forward declarations of that thing, without the actual body/data of the thing; field names that end in "_definition" represent the actual definition of the thing, with the body/data of the thing. ("methoddef" is special, it's the only one that ends with "_define", representing that it's a preprocessor #define.)

destination

A destination is a place Clinic can write output to. There are five built-in destinations:

block

The default destination: printed in the output section of the current Clinic block.

buffer

A text buffer where you can save text for later. Text sent here is appended to the end of any existing text. It's an error to have any text left in the buffer when Clinic finishes processing a file.

file

A separate "clinic file" that will be created automatically by Clinic. The filename chosen for the file is {basename}.clinic{extension}, where basename and extension were assigned the output from os.path.splitext() run on the current file. (Example: the file destination for _pickle.c would be written to _pickle.clinic.c.)

Important: When using a file destination, you must check in the generated file!

two-pass

A buffer like buffer. However, a two-pass buffer can only be dumped once, and it prints out all text sent to it during all processing, even from Clinic blocks after the dumping point.

suppress

The text is suppressed—thrown away.

Clinic defines five new directives that let you reconfigure its output.

The first new directive is dump:

dump <destination>

This dumps the current contents of the named destination into the output of the current block, and empties it. This only works with buffer and two-pass destinations.

The second new directive is output. The most basic form of output is like this:

output <field> <destination>

This tells Clinic to output field to destination. output also supports a special meta-destination, called everything, which tells Clinic to output all fields to that destination.

output has a number of other functions:

output push
output pop
output preset <preset>

output push and output pop allow you to push and pop configurations on an internal configuration stack, so that you can temporarily modify the output configuration, then easily restore the previous configuration. Simply push before your change to save the current configuration, then pop when you wish to restore the previous configuration.

output preset sets Clinic's output to one of several built-in preset configurations, as follows:

block

Clinic's original starting configuration. Writes everything immediately after the input block.

Suppress the parser_prototype and docstring_prototype, write everything else to block.

file

Designed to write everything to the "clinic file" that it can. You then #include this file near the top of your file. You may need to rearrange your file to make this work, though usually this just means creating forward declarations for various typedef and PyTypeObject definitions.

Suppress the parser_prototype and docstring_prototype, write the impl_definition to block, and write everything else to file.

The default filename is "{dirname}/clinic/{basename}.h".

buffer

Save up most of the output from Clinic, to be written into your file near the end. For Python files implementing modules or builtin types, it's recommended that you dump the buffer just above the static structures for your module or builtin type; these are normally very near the end. Using buffer may require even more editing than file, if your file has static PyMethodDef arrays defined in the middle of the file.

Suppress the parser_prototype, impl_prototype, and docstring_prototype, write the impl_definition to block, and write everything else to file.

two-pass

Similar to the buffer preset, but writes forward declarations to the two-pass buffer, and definitions to the buffer. This is similar to the buffer preset, but may require less editing than buffer. Dump the two-pass buffer near the top of your file, and dump the buffer near the end just like you would when using the buffer preset.

Suppresses the impl_prototype, write the impl_definition to block, write docstring_prototype, methoddef_define, and parser_prototype to two-pass, write everything else to buffer.

partial-buffer

Similar to the buffer preset, but writes more things to block, only writing the really big chunks of generated code to buffer. This avoids the definition-before-use problem of buffer completely, at the small cost of having slightly more stuff in the block's output. Dump the buffer near the end, just like you would when using the buffer preset.

Suppresses the impl_prototype, write the docstring_definition and parser_definition to buffer, write everything else to block.

The third new directive is destination:

destination <name> <command> [...]

This performs an operation on the destination named name.

There are two defined subcommands: new and clear.

The new subcommand works like this:

destination <name> new <type>

This creates a new destination with name <name> and type <type>.

There are five destination types:

suppress

Throws the text away.

block

Writes the text to the current block. This is what Clinic originally did.

buffer

A simple text buffer, like the "buffer" builtin destination above.

file

A text file. The file destination takes an extra argument, a template to use for building the filename, like so:

destination <name> new <type> <file_template>

The template can use three strings internally that will be replaced by bits of the filename:

{path}

The full path to the file, including directory and full filename.

{dirname}

The name of the directory the file is in.

{basename}

Just the name of the file, not including the directory.

{basename_root}

Basename with the extension clipped off (everything up to but not including the last '.').

{basename_extension}

The last '.' and everything after it. If the basename does not contain a period, this will be the empty string.

If there are no periods in the filename, {basename} and {filename} are the same, and {extension} is empty. "{basename}{extension}" is always exactly the same as "{filename}"."

two-pass

A two-pass buffer, like the "two-pass" builtin destination above.

The clear subcommand works like this:

destination <name> clear

It removes all the accumulated text up to this point in the destination. (I don't know what you'd need this for, but I thought maybe it'd be useful while someone's experimenting.)

The fourth new directive is set:

set line_prefix "string"
set line_suffix "string"

set lets you set two internal variables in Clinic. line_prefix is a string that will be prepended to every line of Clinic's output; line_suffix is a string that will be appended to every line of Clinic's output.

Both of these support two format strings:

{block comment start}

Turns into the string /*, the start-comment text sequence for C files.

{block comment end}

Turns into the string */, the end-comment text sequence for C files.

The final new directive is one you shouldn't need to use directly, called preserve:

preserve

This tells Clinic that the current contents of the output should be kept, unmodified. This is used internally by Clinic when dumping output into file files; wrapping it in a Clinic block lets Clinic use its existing checksum functionality to ensure the file was not modified by hand before it gets overwritten.

The #ifdef trick¶

If you're converting a function that isn't available on all platforms, there's a trick you can use to make life a little easier. The existing code probably looks like this:

#ifdef HAVE_FUNCTIONNAME
static module_functionname(...)
{
...
}
#endif /* HAVE_FUNCTIONNAME */

And then in the PyMethodDef structure at the bottom the existing code will have:

#ifdef HAVE_FUNCTIONNAME
{'functionname', ... },
#endif /* HAVE_FUNCTIONNAME */

In this scenario, you should enclose the body of your impl function inside the #ifdef, like so:

#ifdef HAVE_FUNCTIONNAME
/*[clinic input]
module.functionname
...
[clinic start generated code]*/
static module_functionname(...)
{
...
}
#endif /* HAVE_FUNCTIONNAME */

Then, remove those three lines from the PyMethodDef structure, replacing them with the macro Argument Clinic generated:

MODULE_FUNCTIONNAME_METHODDEF

(You can find the real name for this macro inside the generated code. Or you can calculate it yourself: it's the name of your function as defined on the first line of your block, but with periods changed to underscores, uppercased, and "_METHODDEF" added to the end.)

Perhaps you're wondering: what if HAVE_FUNCTIONNAME isn't defined? The MODULE_FUNCTIONNAME_METHODDEF macro won't be defined either!

Here's where Argument Clinic gets very clever. It actually detects that the Argument Clinic block might be deactivated by the #ifdef. When that happens, it generates a little extra code that looks like this:

#ifndef MODULE_FUNCTIONNAME_METHODDEF
    #define MODULE_FUNCTIONNAME_METHODDEF
#endif /* !defined(MODULE_FUNCTIONNAME_METHODDEF) */

That means the macro always works. If the function is defined, this turns into the correct structure, including the trailing comma. If the function is undefined, this turns into nothing.

However, this causes one ticklish problem: where should Argument Clinic put this extra code when using the "block" output preset? It can't go in the output block, because that could be deactivated by the #ifdef. (That's the whole point!)

In this situation, Argument Clinic writes the extra code to the "buffer" destination. This may mean that you get a complaint from Argument Clinic:

Warning in file "Modules/posixmodule.c" on line 12357:
Destination buffer 'buffer' not empty at end of file, emptying.

When this happens, just open your file, find the dump buffer block that Argument Clinic added to your file (it'll be at the very bottom), then move it above the PyMethodDef structure where that macro is used.

Using Argument Clinic in Python files¶

It's actually possible to use Argument Clinic to preprocess Python files. There's no point to using Argument Clinic blocks, of course, as the output wouldn't make any sense to the Python interpreter. But using Argument Clinic to run Python blocks lets you use Python as a Python preprocessor!

Since Python comments are different from C comments, Argument Clinic blocks embedded in Python files look slightly different. They look like this:

#/*[python input]
#print("def foo(): pass")
#[python start generated code]*/
def foo(): pass
#/*[python checksum:...]*/