Advanced Topics
Now that you’ve had some experience working with Argument Clinic, it’s time
for some advanced topics.
Symbolic default values
The default value you provide for a parameter can’t be any arbitrary
expression. Currently the following are explicitly supported:
- Numeric constants (integer and float)
- String constants
True, False, and None
- Simple symbolic constants like
sys.maxsize, which must
start with the name of the module
In case you’re curious, this is implemented in from_builtin()
in Lib/inspect.py.
(In the future, this may need to get even more elaborate,
to allow full expressions like CONSTANT - 1.)
Renaming the C functions and variables generated by Argument Clinic
Argument Clinic automatically names the functions it generates for you.
Occasionally this may cause a problem, if the generated name collides with
the name of an existing C function. There’s an easy solution: override the names
used for the C functions. Just add the keyword "as"
to your function declaration line, followed by the function name you wish to use.
Argument Clinic will use that function name for the base (generated) function,
then add "_impl" to the end and use that for the name of the impl function.
For example, if we wanted to rename the C function names generated for
pickle.Pickler.dump, it’d look like this:
/*[clinic input]
pickle.Pickler.dump as pickler_dumper
...
The base function would now be named pickler_dumper(),
and the impl function would now be named pickler_dumper_impl().
Similarly, you may have a problem where you want to give a parameter
a specific Python name, but that name may be inconvenient in C. Argument
Clinic allows you to give a parameter different names in Python and in C,
using the same "as" syntax:
/*[clinic input]
pickle.Pickler.dump
obj: object
file as file_obj: object
protocol: object = NULL
*
fix_imports: bool = True
Here, the name used in Python (in the signature and the keywords
array) would be file, but the C variable would be named file_obj.
You can use this to rename the self parameter too!
Converting functions using PyArg_UnpackTuple
To convert a function parsing its arguments with PyArg_UnpackTuple(),
simply write out all the arguments, specifying each as an object. You
may specify the type argument to cast the type as appropriate. All
arguments should be marked positional-only (add a / on a line by itself
after the last argument).
Currently the generated code will use PyArg_ParseTuple(), but this
will change soon.
Optional Groups
Some legacy functions have a tricky approach to parsing their arguments:
they count the number of positional arguments, then use a switch statement
to call one of several different PyArg_ParseTuple() calls depending on
how many positional arguments there are. (These functions cannot accept
keyword-only arguments.) This approach was used to simulate optional
arguments back before PyArg_ParseTupleAndKeywords() was created.
While functions using this approach can often be converted to
use PyArg_ParseTupleAndKeywords(), optional arguments, and default values,
it’s not always possible. Some of these legacy functions have
behaviors PyArg_ParseTupleAndKeywords() doesn’t directly support.
The most obvious example is the builtin function range(), which has
an optional argument on the left side of its required argument!
Another example is curses.window.addch(), which has a group of two
arguments that must always be specified together. (The arguments are
called x and y; if you call the function passing in x,
you must also pass in y—and if you don’t pass in x you may not
pass in y either.)
In any case, the goal of Argument Clinic is to support argument parsing
for all existing CPython builtins without changing their semantics.
Therefore Argument Clinic supports
this alternate approach to parsing, using what are called optional groups.
Optional groups are groups of arguments that must all be passed in together.
They can be to the left or the right of the required arguments. They
can only be used with positional-only parameters.
Note
Optional groups are only intended for use when converting
functions that make multiple calls to PyArg_ParseTuple()!
Functions that use any other approach for parsing arguments
should almost never be converted to Argument Clinic using
optional groups. Functions using optional groups currently
cannot have accurate signatures in Python, because Python just
doesn’t understand the concept. Please avoid using optional
groups wherever possible.
To specify an optional group, add a [ on a line by itself before
the parameters you wish to group together, and a ] on a line by itself
after these parameters. As an example, here’s how curses.window.addch
uses optional groups to make the first two parameters and the last
parameter optional:
/*[clinic input]
curses.window.addch
[
x: int
X-coordinate.
y: int
Y-coordinate.
]
ch: object
Character to add.
[
attr: long
Attributes for the character.
]
/
...
Notes:
- For every optional group, one additional parameter will be passed into the
impl function representing the group. The parameter will be an int named
group_{direction}_{number},
where {direction} is either right or left depending on whether the group
is before or after the required parameters, and {number} is a monotonically
increasing number (starting at 1) indicating how far away the group is from
the required parameters. When the impl is called, this parameter will be set
to zero if this group was unused, and set to non-zero if this group was used.
(By used or unused, I mean whether or not the parameters received arguments
in this invocation.)
- If there are no required arguments, the optional groups will behave
as if they’re to the right of the required arguments.
- In the case of ambiguity, the argument parsing code
favors parameters on the left (before the required parameters).
- Optional groups can only contain positional-only parameters.
- Optional groups are only intended for legacy code. Please do not
use optional groups for new code.
Using real Argument Clinic converters, instead of “legacy converters”
To save time, and to minimize how much you need to learn
to achieve your first port to Argument Clinic, the walkthrough above tells
you to use “legacy converters”. “Legacy converters” are a convenience,
designed explicitly to make porting existing code to Argument Clinic
easier. And to be clear, their use is acceptable when porting code for
Python 3.4.
However, in the long term we probably want all our blocks to
use Argument Clinic’s real syntax for converters. Why? A couple
reasons:
- The proper converters are far easier to read and clearer in their intent.
- There are some format units that are unsupported as “legacy converters”,
because they require arguments, and the legacy converter syntax doesn’t
support specifying arguments.
- In the future we may have a new argument parsing library that isn’t
restricted to what
PyArg_ParseTuple() supports; this flexibility
won’t be available to parameters using legacy converters.
Therefore, if you don’t mind a little extra effort, please use the normal
converters instead of legacy converters.
In a nutshell, the syntax for Argument Clinic (non-legacy) converters
looks like a Python function call. However, if there are no explicit
arguments to the function (all functions take their default values),
you may omit the parentheses. Thus bool and bool() are exactly
the same converters.
All arguments to Argument Clinic converters are keyword-only.
All Argument Clinic converters accept the following arguments:
c_default
- The default value for this parameter when defined in C.
Specifically, this will be the initializer for the variable declared
in the “parse function”. See the section on default values
for how to use this.
Specified as a string.
annotation
- The annotation value for this parameter. Not currently supported,
because PEP 8 mandates that the Python library may not use
annotations.
In addition, some converters accept additional arguments. Here is a list
of these arguments, along with their meanings:
accept
A set of Python types (and possibly pseudo-types);
this restricts the allowable Python argument to values of these types.
(This is not a general-purpose facility; as a rule it only supports
specific lists of types as shown in the legacy converter table.)
To accept None, add NoneType to this set.
bitwise
- Only supported for unsigned integers. The native integer value of this
Python argument will be written to the parameter without any range checking,
even for negative values.
converter
- Only supported by the
object converter. Specifies the name of a
C “converter function”
to use to convert this object to a native type.
encoding
- Only supported for strings. Specifies the encoding to use when converting
this string from a Python str (Unicode) value into a C
char * value.
subclass_of
- Only supported for the
object converter. Requires that the Python
value be a subclass of a Python type, as expressed in C.
type
- Only supported for the
object and self converters. Specifies
the C type that will be used to declare the variable. Default value is
"PyObject *".
zeroes
- Only supported for strings. If true, embedded NUL bytes (
'\\0') are
permitted inside the value. The length of the string will be passed in
to the impl function, just after the string parameter, as a parameter named
<parameter_name>_length.
Please note, not every possible combination of arguments will work.
Usually these arguments are implemented by specific PyArg_ParseTuple
format units, with specific behavior. For example, currently you cannot
call unsigned_short without also specifying bitwise=True.
Although it’s perfectly reasonable to think this would work, these semantics don’t
map to any existing format unit. So Argument Clinic doesn’t support it. (Or, at
least, not yet.)
Below is a table showing the mapping of legacy converters into real
Argument Clinic converters. On the left is the legacy converter,
on the right is the text you’d replace it with.
'B' |
unsigned_char(bitwise=True) |
'b' |
unsigned_char |
'c' |
char |
'C' |
int(accept={str}) |
'd' |
double |
'D' |
Py_complex |
'es' |
str(encoding='name_of_encoding') |
'es#' |
str(encoding='name_of_encoding', zeroes=True) |
'et' |
str(encoding='name_of_encoding', accept={bytes, bytearray, str}) |
'et#' |
str(encoding='name_of_encoding', accept={bytes, bytearray, str}, zeroes=True) |
'f' |
float |
'h' |
short |
'H' |
unsigned_short(bitwise=True) |
'i' |
int |
'I' |
unsigned_int(bitwise=True) |
'k' |
unsigned_long(bitwise=True) |
'K' |
unsigned_long_long(bitwise=True) |
'l' |
long |
'L' |
long long |
'n' |
Py_ssize_t |
'O' |
object |
'O!' |
object(subclass_of='&PySomething_Type') |
'O&' |
object(converter='name_of_c_function') |
'p' |
bool |
'S' |
PyBytesObject |
's' |
str |
's#' |
str(zeroes=True) |
's*' |
Py_buffer(accept={buffer, str}) |
'U' |
unicode |
'u' |
Py_UNICODE |
'u#' |
Py_UNICODE(zeroes=True) |
'w*' |
Py_buffer(accept={rwbuffer}) |
'Y' |
PyByteArrayObject |
'y' |
str(accept={bytes}) |
'y#' |
str(accept={robuffer}, zeroes=True) |
'y*' |
Py_buffer |
'Z' |
Py_UNICODE(accept={str, NoneType}) |
'Z#' |
Py_UNICODE(accept={str, NoneType}, zeroes=True) |
'z' |
str(accept={str, NoneType}) |
'z#' |
str(accept={str, NoneType}, zeroes=True) |
'z*' |
Py_buffer(accept={buffer, str, NoneType}) |
As an example, here’s our sample pickle.Pickler.dump using the proper
converter:
/*[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]*/
Argument Clinic will show you all the converters it has
available. For each converter it’ll show you all the parameters
it accepts, along with the default value for each parameter.
Just run Tools/clinic/clinic.py --converters to see the full list.
Py_buffer
When using the Py_buffer converter
(or the 's*', 'w*', '*y', or 'z*' legacy converters),
you must not call PyBuffer_Release() on the provided buffer.
Argument Clinic generates code that does it for you (in the parsing function).
Advanced converters
Remember those format units you skipped for your first
time because they were advanced? Here’s how to handle those too.
The trick is, all those format units take arguments—either
conversion functions, or types, or strings specifying an encoding.
(But “legacy converters” don’t support arguments. That’s why we
skipped them for your first function.) The argument you specified
to the format unit is now an argument to the converter; this
argument is either converter (for O&), subclass_of (for O!),
or encoding (for all the format units that start with e).
When using subclass_of, you may also want to use the other
custom argument for object(): type, which lets you set the type
actually used for the parameter. For example, if you want to ensure
that the object is a subclass of PyUnicode_Type, you probably want
to use the converter object(type='PyUnicodeObject *', subclass_of='&PyUnicode_Type').
One possible problem with using Argument Clinic: it takes away some possible
flexibility for the format units starting with e. When writing a
PyArg_Parse call by hand, you could theoretically decide at runtime what
encoding string to pass in to PyArg_ParseTuple(). But now this string must
be hard-coded at Argument-Clinic-preprocessing-time. This limitation is deliberate;
it made supporting this format unit much easier, and may allow for future optimizations.
This restriction doesn’t seem unreasonable; CPython itself always passes in static
hard-coded encoding strings for parameters whose format units start with e.
Parameter default values
Default values for parameters can be any of a number of values.
At their simplest, they can be string, int, or float literals:
foo: str = "abc"
bar: int = 123
bat: float = 45.6
They can also use any of Python’s built-in constants:
yep: bool = True
nope: bool = False
nada: object = None
There’s also special support for a default value of NULL, and
for simple expressions, documented in the following sections.
The NULL default value
For string and object parameters, you can set them to None to indicate
that there’s no default. However, that means the C variable will be
initialized to Py_None. For convenience’s sakes, there’s a special
value called NULL for just this reason: from Python’s perspective it
behaves like a default value of None, but the C variable is initialized
with NULL.
Expressions specified as default values
The default value for a parameter can be more than just a literal value.
It can be an entire expression, using math operators and looking up attributes
on objects. However, this support isn’t exactly simple, because of some
non-obvious semantics.
Consider the following example:
foo: Py_ssize_t = sys.maxsize - 1
sys.maxsize can have different values on different platforms. Therefore
Argument Clinic can’t simply evaluate that expression locally and hard-code it
in C. So it stores the default in such a way that it will get evaluated at
runtime, when the user asks for the function’s signature.
What namespace is available when the expression is evaluated? It’s evaluated
in the context of the module the builtin came from. So, if your module has an
attribute called “max_widgets”, you may simply use it:
foo: Py_ssize_t = max_widgets
If the symbol isn’t found in the current module, it fails over to looking in
sys.modules. That’s how it can find sys.maxsize for example. (Since you
don’t know in advance what modules the user will load into their interpreter,
it’s best to restrict yourself to modules that are preloaded by Python itself.)
Evaluating default values only at runtime means Argument Clinic can’t compute
the correct equivalent C default value. So you need to tell it explicitly.
When you use an expression, you must also specify the equivalent expression
in C, using the c_default parameter to the converter:
foo: Py_ssize_t(c_default="PY_SSIZE_T_MAX - 1") = sys.maxsize - 1
Another complication: Argument Clinic can’t know in advance whether or not the
expression you supply is valid. It parses it to make sure it looks legal, but
it can’t actually know. You must be very careful when using expressions to
specify values that are guaranteed to be valid at runtime!
Finally, because expressions must be representable as static C values, there
are many restrictions on legal expressions. Here’s a list of Python features
you’re not permitted to use:
- Function calls.
- Inline if statements (
3 if foo else 5).
- Automatic sequence unpacking (
*[1, 2, 3]).
- List/set/dict comprehensions and generator expressions.
- Tuple/list/set/dict literals.
Using a return converter
By default the impl function Argument Clinic generates for you returns PyObject *.
But your C function often computes some C type, then converts it into the PyObject *
at the last moment. Argument Clinic handles converting your inputs from Python types
into native C types—why not have it convert your return value from a native C type
into a Python type too?
That’s what a “return converter” does. It changes your impl function to return
some C type, then adds code to the generated (non-impl) function to handle converting
that value into the appropriate PyObject *.
The syntax for return converters is similar to that of parameter converters.
You specify the return converter like it was a return annotation on the
function itself. Return converters behave much the same as parameter converters;
they take arguments, the arguments are all keyword-only, and if you’re not changing
any of the default arguments you can omit the parentheses.
(If you use both "as" and a return converter for your function,
the "as" should come before the return converter.)
There’s one additional complication when using return converters: how do you
indicate an error has occurred? Normally, a function returns a valid (non-NULL)
pointer for success, and NULL for failure. But if you use an integer return converter,
all integers are valid. How can Argument Clinic detect an error? Its solution: each return
converter implicitly looks for a special value that indicates an error. If you return
that value, and an error has been set (PyErr_Occurred() returns a true
value), then the generated code will propagate the error. Otherwise it will
encode the value you return like normal.
Currently Argument Clinic supports only a few return converters:
bool
int
unsigned int
long
unsigned int
size_t
Py_ssize_t
float
double
DecodeFSDefault
None of these take parameters. For the first three, return -1 to indicate
error. For DecodeFSDefault, the return type is char *; return a NULL
pointer to indicate an error.
(There’s also an experimental NoneType converter, which lets you
return Py_None on success or NULL on failure, without having
to increment the reference count on Py_None. I’m not sure it adds
enough clarity to be worth using.)
To see all the return converters Argument Clinic supports, along with
their parameters (if any),
just run Tools/clinic/clinic.py --converters for the full list.
Cloning existing functions
If you have a number of functions that look similar, you may be able to
use Clinic’s “clone” feature. When you clone an existing function,
you reuse:
- its parameters, including
- their names,
- their converters, with all parameters,
- their default values,
- their per-parameter docstrings,
- their kind (whether they’re positional only,
positional or keyword, or keyword only), and
- its return converter.
The only thing not copied from the original function is its docstring;
the syntax allows you to specify a new docstring.
Here’s the syntax for cloning a function:
/*[clinic input]
module.class.new_function [as c_basename] = module.class.existing_function
Docstring for new_function goes here.
[clinic start generated code]*/
(The functions can be in different modules or classes. I wrote
module.class in the sample just to illustrate that you must
use the full path to both functions.)
Sorry, there’s no syntax for partially-cloning a function, or cloning a function
then modifying it. Cloning is an all-or nothing proposition.
Also, the function you are cloning from must have been previously defined
in the current file.
Calling Python code
The rest of the advanced topics require you to write Python code
which lives inside your C file and modifies Argument Clinic’s
runtime state. This is simple: you simply define a Python block.
A Python block uses different delimiter lines than an Argument
Clinic function block. It looks like this:
/*[python input]
# python code goes here
[python start generated code]*/
All the code inside the Python block is executed at the
time it’s parsed. All text written to stdout inside the block
is redirected into the “output” after the block.
As an example, here’s a Python block that adds a static integer
variable to the C code:
/*[python input]
print('static int __ignored_unused_variable__ = 0;')
[python start generated code]*/
static int __ignored_unused_variable__ = 0;
/*[python checksum:...]*/
Using a “self converter”
Argument Clinic automatically adds a “self” parameter for you
using a default converter. It automatically sets the type
of this parameter to the “pointer to an instance” you specified
when you declared the type. However, you can override
Argument Clinic’s converter and specify one yourself.
Just add your own self parameter as the first parameter in a
block, and ensure that its converter is an instance of
self_converter or a subclass thereof.
What’s the point? This lets you override the type of self,
or give it a different default name.
How do you specify the custom type you want to cast self to?
If you only have one or two functions with the same type for self,
you can directly use Argument Clinic’s existing self converter,
passing in the type you want to use as the type parameter:
/*[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]*/
On the other hand, if you have a lot of functions that will use the same
type for self, it’s best to create your own converter, subclassing
self_converter but overwriting the type member:
/*[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]*/
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:
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:
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:
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:...]*/