问题:记录** kwargs参数的正确方法是什么?
我正在使用sphinx和autodoc插件为我的Python模块生成API文档。虽然可以看到如何很好地记录特定参数,但是找不到如何记录**kwargs
参数的示例。
有没有人有一个很好的例子来记录这些问题?
回答 0
我认为**kwargs
。
回答 1
找到这个问题后,我决定使用以下内容,它是有效的Sphinx,效果很好:
def some_function(first, second="two", **kwargs):
r"""Fetches and returns this thing
:param first:
The first parameter
:type first: ``int``
:param second:
The second parameter
:type second: ``str``
:param \**kwargs:
See below
:Keyword Arguments:
* *extra* (``list``) --
Extra stuff
* *supplement* (``dict``) --
Additional content
"""
的r"""..."""
要求,使这个“原始”文档字符串,从而保持\*
完好(为狮身人面像拿起作为文字*
“强调”,而不是开始)。
选定的格式(带括号的类型的项目符号列表和用短划线分隔的描述)仅与Sphinx提供的自动格式匹配。
一旦完成了使“关键字参数”部分看起来像默认的“参数”部分的工作,从一开始就似乎可以更轻松地滚动自己的参数部分(根据其他一些答案) ,但作为概念证明,**kwargs
如果您已经在使用Sphinx,则这是一种使辅助外观更好看的方法。
回答 2
Sphinx解析的Google Style文档字符串
免责声明:未经测试。
从sphinx docstring示例的此切口中,*args
和**kwargs
保持未展开状态:
def module_level_function(param1, param2=None, *args, **kwargs):
"""
...
Args:
param1 (int): The first parameter.
param2 (Optional[str]): The second parameter. Defaults to None.
Second line of description should be indented.
*args: Variable length argument list.
**kwargs: Arbitrary keyword arguments.
我建议使用以下紧凑性解决方案:
"""
Args:
param1 (int): The first parameter.
param2 (Optional[str]): The second parameter. Defaults to None.
Second line of description should be indented.
*param3 (int): description
*param4 (str):
...
**key1 (int): description
**key2 (int): description
...
注意,参数Optional
不需要**key
。
否则,您可以尝试在下方Other Parameters
和**kwargs
下方显式列出* args Keyword Args
(请参阅解析的部分):
"""
Args:
param1 (int): The first parameter.
param2 (Optional[str]): The second parameter. Defaults to None.
Second line of description should be indented.
Other Parameters:
param3 (int): description
param4 (str):
...
Keyword Args:
key1 (int): description
key2 (int): description
...
回答 3
Sphinx的文档中有一个doctstring示例。具体来说,它们显示以下内容:
def public_fn_with_googley_docstring(name, state=None):
"""This function does something.
Args:
name (str): The name to use.
Kwargs:
state (bool): Current state to be in.
Returns:
int. The return code::
0 -- Success!
1 -- No good.
2 -- Try again.
Raises:
AttributeError, KeyError
A really great idea. A way you might use me is
>>> print public_fn_with_googley_docstring(name='foo', state=None)
0
BTW, this always returns 0. **NEVER** use with :class:`MyPublicClass`.
"""
return 0
虽然你问过 狮身人面像明确地,我还将指向Google Python样式指南。他们的文档字符串示例似乎暗示着他们没有特别指出kwarg。(other_silly_variable =无)
def fetch_bigtable_rows(big_table, keys, other_silly_variable=None):
"""Fetches rows from a Bigtable.
Retrieves rows pertaining to the given keys from the Table instance
represented by big_table. Silly things may happen if
other_silly_variable is not None.
Args:
big_table: An open Bigtable Table instance.
keys: A sequence of strings representing the key of each table row
to fetch.
other_silly_variable: Another optional variable, that has a much
longer name than the other args, and which does nothing.
Returns:
A dict mapping keys to the corresponding table row data
fetched. Each row is represented as a tuple of strings. For
example:
{'Serak': ('Rigel VII', 'Preparer'),
'Zim': ('Irk', 'Invader'),
'Lrrr': ('Omicron Persei 8', 'Emperor')}
If a key from the keys argument is missing from the dictionary,
then that row was not found in the table.
Raises:
IOError: An error occurred accessing the bigtable.Table object.
"""
pass
ABB有一个关于接受子流程管理文档的可接受答案的问题。如果导入模块,则可以通过inspect.getsource快速查看模块文档字符串。
python解释器中使用Silent Ghost推荐的示例:
>>> import subprocess
>>> import inspect
>>> import print inspect.getsource(subprocess)
当然,您也可以通过帮助功能查看模块文档。例如帮助(子过程)
我个人不喜欢kwargs的子进程docstring,但是像Google的例子一样,它不会像Sphinx文档示例中那样单独列出kwargs。
def call(*popenargs, **kwargs):
"""Run command with arguments. Wait for command to complete, then
return the returncode attribute.
The arguments are the same as for the Popen constructor. Example:
retcode = call(["ls", "-l"])
"""
return Popen(*popenargs, **kwargs).wait()
我之所以要回答ABB的问题,是因为值得注意的是,您可以以这种方式查看任何模块的源代码或文档,以获取洞察力和注释代码的灵感。
回答 4
如果还有其他人正在寻找一些有效的语法。.这是一个示例文档字符串。这就是我所做的,我希望它对您有用,但是我不能断言它特别符合任何要求。
def bar(x=True, y=False):
"""
Just some silly bar function.
:Parameters:
- `x` (`bool`) - dummy description for x
- `y` (`string`) - dummy description for y
:return: (`string`) concatenation of x and y.
"""
return str(x) + y
def foo (a, b, **kwargs):
"""
Do foo on a, b and some other objects.
:Parameters:
- `a` (`int`) - A number.
- `b` (`int`, `string`) - Another number, or maybe a string.
- `\**kwargs` - remaining keyword arguments are passed to `bar`
:return: Success
:rtype: `bool`
"""
return len(str(a) + str(b) + bar(**kwargs)) > 20
回答 5
这取决于你使用的文档的风格,但如果您使用的是numpydoc风格则建议记录**kwargs
使用Other Parameters
。
例如,遵循quornian的示例:
def some_function(first, second="two", **kwargs):
"""Fetches and returns this thing
Parameters
----------
first : `int`
The first parameter
second : `str`, optional
The second parameter
Other Parameters
----------------
extra : `list`, optional
Extra stuff. Default ``[]``.
suplement : `dict`, optional
Additional content. Default ``{'key' : 42}``.
"""
请特别注意,建议提供kwargs的默认值,因为这些默认值在函数签名中并不明显。
回答 6
如果您正在寻找如何以numpydoc样式执行此操作,则可以仅在Parameters部分中提及**kwargs
而无需指定类型 -如pandas文档sprint 2018 的sphinx扩展napolean和docstring指南中的numpydoc示例所示。
下面是我从发现一个例子的LSST开发人员指南这很好解释了什么是应该是描述的**kwargs
参数:
def demoFunction(namedArg, *args, flag=False, **kwargs):
"""Demonstrate documentation for additional keyword and
positional arguments.
Parameters
----------
namedArg : `str`
A named argument that is documented like always.
*args : `str`
Additional names.
Notice how the type is singular since the user is expected to pass individual
`str` arguments, even though the function itself sees ``args`` as an iterable
of `str` objects).
flag : `bool`
A regular keyword argument.
**kwargs
Additional keyword arguments passed to `otherApi`.
Usually kwargs are used to pass parameters to other functions and
methods. If that is the case, be sure to mention (and link) the
API or APIs that receive the keyword arguments.
If kwargs are being used to generate a `dict`, use the description to
document the use of the keys and the types of the values.
"""
另外,在@Jonas Adler的建议的基础上,我发现最好将**kwargs
其及其描述放在本Other Parameters
节中 -即使是matplotlib文档指南中的示例也表明了这一点。