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"""
Helper functions for managing the Matplotlib API.
This documentation is only relevant for Matplotlib developers, not for users.
.. warning::
This module and its submodules are for internal use only. Do not use them
in your own code. We may change the API at any time with no warning.
"""
import functools
import itertools
import re
import sys
import warnings
from .deprecation import ( # noqa: F401
deprecated, warn_deprecated,
rename_parameter, delete_parameter, make_keyword_only,
deprecate_method_override, deprecate_privatize_attribute,
suppress_matplotlib_deprecation_warning,
MatplotlibDeprecationWarning)
class classproperty:
"""
Like `property`, but also triggers on access via the class, and it is the
*class* that's passed as argument.
Examples
--------
::
class C:
@classproperty
def foo(cls):
return cls.__name__
assert C.foo == "C"
"""
def __init__(self, fget, fset=None, fdel=None, doc=None):
self._fget = fget
if fset is not None or fdel is not None:
raise ValueError('classproperty only implements fget.')
self.fset = fset
self.fdel = fdel
# docs are ignored for now
self._doc = doc
def __get__(self, instance, owner):
return self._fget(owner)
@property
def fget(self):
return self._fget
# In the following check_foo() functions, the first parameter is positional-only to make
# e.g. `_api.check_isinstance([...], types=foo)` work.
def check_isinstance(types, /, **kwargs):
"""
For each *key, value* pair in *kwargs*, check that *value* is an instance
of one of *types*; if not, raise an appropriate TypeError.
As a special case, a ``None`` entry in *types* is treated as NoneType.
Examples
--------
>>> _api.check_isinstance((SomeClass, None), arg=arg)
"""
none_type = type(None)
types = ((types,) if isinstance(types, type) else
(none_type,) if types is None else
tuple(none_type if tp is None else tp for tp in types))
def type_name(tp):
return ("None" if tp is none_type
else tp.__qualname__ if tp.__module__ == "builtins"
else f"{tp.__module__}.{tp.__qualname__}")
for k, v in kwargs.items():
if not isinstance(v, types):
names = [*map(type_name, types)]
if "None" in names: # Move it to the end for better wording.
names.remove("None")
names.append("None")
raise TypeError(
"{!r} must be an instance of {}, not a {}".format(
k,
", ".join(names[:-1]) + " or " + names[-1]
if len(names) > 1 else names[0],
type_name(type(v))))
def check_in_list(values, /, *, _print_supported_values=True, **kwargs):
"""
For each *key, value* pair in *kwargs*, check that *value* is in *values*;
if not, raise an appropriate ValueError.
Parameters
----------
values : iterable
Sequence of values to check on.
_print_supported_values : bool, default: True
Whether to print *values* when raising ValueError.
**kwargs : dict
*key, value* pairs as keyword arguments to find in *values*.
Raises
------
ValueError
If any *value* in *kwargs* is not found in *values*.
Examples
--------
>>> _api.check_in_list(["foo", "bar"], arg=arg, other_arg=other_arg)
"""
if not kwargs:
raise TypeError("No argument to check!")
for key, val in kwargs.items():
if val not in values:
msg = f"{val!r} is not a valid value for {key}"
if _print_supported_values:
msg += f"; supported values are {', '.join(map(repr, values))}"
raise ValueError(msg)
def check_shape(shape, /, **kwargs):
"""
For each *key, value* pair in *kwargs*, check that *value* has the shape *shape*;
if not, raise an appropriate ValueError.
*None* in the shape is treated as a "free" size that can have any length.
e.g. (None, 2) -> (N, 2)
The values checked must be numpy arrays.
Examples
--------
To check for (N, 2) shaped arrays
>>> _api.check_shape((None, 2), arg=arg, other_arg=other_arg)
"""
for k, v in kwargs.items():
data_shape = v.shape
if (len(data_shape) != len(shape)
or any(s != t and t is not None for s, t in zip(data_shape, shape))):
dim_labels = iter(itertools.chain(
'NMLKJIH',
(f"D{i}" for i in itertools.count())))
text_shape = ", ".join([str(n) if n is not None else next(dim_labels)
for n in shape[::-1]][::-1])
if len(shape) == 1:
text_shape += ","
raise ValueError(
f"{k!r} must be {len(shape)}D with shape ({text_shape}), "
f"but your input has shape {v.shape}"
)
def check_getitem(mapping, /, **kwargs):
"""
*kwargs* must consist of a single *key, value* pair. If *key* is in
*mapping*, return ``mapping[value]``; else, raise an appropriate
ValueError.
Examples
--------
>>> _api.check_getitem({"foo": "bar"}, arg=arg)
"""
if len(kwargs) != 1:
raise ValueError("check_getitem takes a single keyword argument")
(k, v), = kwargs.items()
try:
return mapping[v]
except KeyError:
raise ValueError(
f"{v!r} is not a valid value for {k}; supported values are "
f"{', '.join(map(repr, mapping))}") from None
def caching_module_getattr(cls):
"""
Helper decorator for implementing module-level ``__getattr__`` as a class.
This decorator must be used at the module toplevel as follows::
@caching_module_getattr
class __getattr__: # The class *must* be named ``__getattr__``.
@property # Only properties are taken into account.
def name(self): ...
The ``__getattr__`` class will be replaced by a ``__getattr__``
function such that trying to access ``name`` on the module will
resolve the corresponding property (which may be decorated e.g. with
``_api.deprecated`` for deprecating module globals). The properties are
all implicitly cached. Moreover, a suitable AttributeError is generated
and raised if no property with the given name exists.
"""
assert cls.__name__ == "__getattr__"
# Don't accidentally export cls dunders.
props = {name: prop for name, prop in vars(cls).items()
if isinstance(prop, property)}
instance = cls()
@functools.cache
def __getattr__(name):
if name in props:
return props[name].__get__(instance)
raise AttributeError(
f"module {cls.__module__!r} has no attribute {name!r}")
return __getattr__
def define_aliases(alias_d, cls=None):
"""
Class decorator for defining property aliases.
Use as ::
@_api.define_aliases({"property": ["alias", ...], ...})
class C: ...
For each property, if the corresponding ``get_property`` is defined in the
class so far, an alias named ``get_alias`` will be defined; the same will
be done for setters. If neither the getter nor the setter exists, an
exception will be raised.
The alias map is stored as the ``_alias_map`` attribute on the class and
can be used by `.normalize_kwargs` (which assumes that higher priority
aliases come last).
"""
if cls is None: # Return the actual class decorator.
return functools.partial(define_aliases, alias_d)
def make_alias(name): # Enforce a closure over *name*.
@functools.wraps(getattr(cls, name))
def method(self, *args, **kwargs):
return getattr(self, name)(*args, **kwargs)
return method
for prop, aliases in alias_d.items():
exists = False
for prefix in ["get_", "set_"]:
if prefix + prop in vars(cls):
exists = True
for alias in aliases:
method = make_alias(prefix + prop)
method.__name__ = prefix + alias
method.__doc__ = f"Alias for `{prefix + prop}`."
setattr(cls, prefix + alias, method)
if not exists:
raise ValueError(
f"Neither getter nor setter exists for {prop!r}")
def get_aliased_and_aliases(d):
return {*d, *(alias for aliases in d.values() for alias in aliases)}
preexisting_aliases = getattr(cls, "_alias_map", {})
conflicting = (get_aliased_and_aliases(preexisting_aliases)
& get_aliased_and_aliases(alias_d))
if conflicting:
# Need to decide on conflict resolution policy.
raise NotImplementedError(
f"Parent class already defines conflicting aliases: {conflicting}")
cls._alias_map = {**preexisting_aliases, **alias_d}
return cls
def select_matching_signature(funcs, *args, **kwargs):
"""
Select and call the function that accepts ``*args, **kwargs``.
*funcs* is a list of functions which should not raise any exception (other
than `TypeError` if the arguments passed do not match their signature).
`select_matching_signature` tries to call each of the functions in *funcs*
with ``*args, **kwargs`` (in the order in which they are given). Calls
that fail with a `TypeError` are silently skipped. As soon as a call
succeeds, `select_matching_signature` returns its return value. If no
function accepts ``*args, **kwargs``, then the `TypeError` raised by the
last failing call is re-raised.
Callers should normally make sure that any ``*args, **kwargs`` can only
bind a single *func* (to avoid any ambiguity), although this is not checked
by `select_matching_signature`.
Notes
-----
`select_matching_signature` is intended to help implementing
signature-overloaded functions. In general, such functions should be
avoided, except for back-compatibility concerns. A typical use pattern is
::
def my_func(*args, **kwargs):
params = select_matching_signature(
[lambda old1, old2: locals(), lambda new: locals()],
*args, **kwargs)
if "old1" in params:
warn_deprecated(...)
old1, old2 = params.values() # note that locals() is ordered.
else:
new, = params.values()
# do things with params
which allows *my_func* to be called either with two parameters (*old1* and
*old2*) or a single one (*new*). Note that the new signature is given
last, so that callers get a `TypeError` corresponding to the new signature
if the arguments they passed in do not match any signature.
"""
# Rather than relying on locals() ordering, one could have just used func's
# signature (``bound = inspect.signature(func).bind(*args, **kwargs);
# bound.apply_defaults(); return bound``) but that is significantly slower.
for i, func in enumerate(funcs):
try:
return func(*args, **kwargs)
except TypeError:
if i == len(funcs) - 1:
raise
def nargs_error(name, takes, given):
"""Generate a TypeError to be raised by function calls with wrong arity."""
return TypeError(f"{name}() takes {takes} positional arguments but "
f"{given} were given")
def kwarg_error(name, kw):
"""
Generate a TypeError to be raised by function calls with wrong kwarg.
Parameters
----------
name : str
The name of the calling function.
kw : str or Iterable[str]
Either the invalid keyword argument name, or an iterable yielding
invalid keyword arguments (e.g., a ``kwargs`` dict).
"""
if not isinstance(kw, str):
kw = next(iter(kw))
return TypeError(f"{name}() got an unexpected keyword argument '{kw}'")
def recursive_subclasses(cls):
"""Yield *cls* and direct and indirect subclasses of *cls*."""
yield cls
for subcls in cls.__subclasses__():
yield from recursive_subclasses(subcls)
def warn_external(message, category=None):
"""
`warnings.warn` wrapper that sets *stacklevel* to "outside Matplotlib".
The original emitter of the warning can be obtained by patching this
function back to `warnings.warn`, i.e. ``_api.warn_external =
warnings.warn`` (or ``functools.partial(warnings.warn, stacklevel=2)``,
etc.).
"""
frame = sys._getframe()
for stacklevel in itertools.count(1):
if frame is None:
# when called in embedded context may hit frame is None
break
if not re.match(r"\A(matplotlib|mpl_toolkits)(\Z|\.(?!tests\.))",
# Work around sphinx-gallery not setting __name__.
frame.f_globals.get("__name__", "")):
break
frame = frame.f_back
# preemptively break reference cycle between locals and the frame
del frame
warnings.warn(message, category, stacklevel)

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from collections.abc import Callable, Generator, Mapping, Sequence
from typing import Any, Iterable, TypeVar, overload
from numpy.typing import NDArray
from .deprecation import ( # noqa: re-exported API
deprecated as deprecated,
warn_deprecated as warn_deprecated,
rename_parameter as rename_parameter,
delete_parameter as delete_parameter,
make_keyword_only as make_keyword_only,
deprecate_method_override as deprecate_method_override,
deprecate_privatize_attribute as deprecate_privatize_attribute,
suppress_matplotlib_deprecation_warning as suppress_matplotlib_deprecation_warning,
MatplotlibDeprecationWarning as MatplotlibDeprecationWarning,
)
_T = TypeVar("_T")
class classproperty(Any):
def __init__(
self,
fget: Callable[[_T], Any],
fset: None = ...,
fdel: None = ...,
doc: str | None = None,
): ...
# Replace return with Self when py3.9 is dropped
@overload
def __get__(self, instance: None, owner: None) -> classproperty: ...
@overload
def __get__(self, instance: object, owner: type[object]) -> Any: ...
@property
def fget(self) -> Callable[[_T], Any]: ...
def check_isinstance(
types: type | tuple[type | None, ...], /, **kwargs: Any
) -> None: ...
def check_in_list(
values: Sequence[Any], /, *, _print_supported_values: bool = ..., **kwargs: Any
) -> None: ...
def check_shape(shape: tuple[int | None, ...], /, **kwargs: NDArray) -> None: ...
def check_getitem(mapping: Mapping[Any, Any], /, **kwargs: Any) -> Any: ...
def caching_module_getattr(cls: type) -> Callable[[str], Any]: ...
@overload
def define_aliases(
alias_d: dict[str, list[str]], cls: None = ...
) -> Callable[[type[_T]], type[_T]]: ...
@overload
def define_aliases(alias_d: dict[str, list[str]], cls: type[_T]) -> type[_T]: ...
def select_matching_signature(
funcs: list[Callable], *args: Any, **kwargs: Any
) -> Any: ...
def nargs_error(name: str, takes: int | str, given: int) -> TypeError: ...
def kwarg_error(name: str, kw: str | Iterable[str]) -> TypeError: ...
def recursive_subclasses(cls: type) -> Generator[type, None, None]: ...
def warn_external(
message: str | Warning, category: type[Warning] | None = ...
) -> None: ...

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"""
Helper functions for deprecating parts of the Matplotlib API.
This documentation is only relevant for Matplotlib developers, not for users.
.. warning::
This module is for internal use only. Do not use it in your own code.
We may change the API at any time with no warning.
"""
import contextlib
import functools
import inspect
import math
import warnings
class MatplotlibDeprecationWarning(DeprecationWarning):
"""A class for issuing deprecation warnings for Matplotlib users."""
def _generate_deprecation_warning(
since, message='', name='', alternative='', pending=False, obj_type='',
addendum='', *, removal=''):
if pending:
if removal:
raise ValueError(
"A pending deprecation cannot have a scheduled removal")
else:
if not removal:
macro, meso, *_ = since.split('.')
removal = f'{macro}.{int(meso) + 2}'
removal = f"in {removal}"
if not message:
message = (
("The %(name)s %(obj_type)s" if obj_type else "%(name)s")
+ (" will be deprecated in a future version"
if pending else
" was deprecated in Matplotlib %(since)s and will be removed %(removal)s"
)
+ "."
+ (" Use %(alternative)s instead." if alternative else "")
+ (" %(addendum)s" if addendum else ""))
warning_cls = (PendingDeprecationWarning if pending
else MatplotlibDeprecationWarning)
return warning_cls(message % dict(
func=name, name=name, obj_type=obj_type, since=since, removal=removal,
alternative=alternative, addendum=addendum))
def warn_deprecated(
since, *, message='', name='', alternative='', pending=False,
obj_type='', addendum='', removal=''):
"""
Display a standardized deprecation.
Parameters
----------
since : str
The release at which this API became deprecated.
message : str, optional
Override the default deprecation message. The ``%(since)s``,
``%(name)s``, ``%(alternative)s``, ``%(obj_type)s``, ``%(addendum)s``,
and ``%(removal)s`` format specifiers will be replaced by the values
of the respective arguments passed to this function.
name : str, optional
The name of the deprecated object.
alternative : str, optional
An alternative API that the user may use in place of the deprecated
API. The deprecation warning will tell the user about this alternative
if provided.
pending : bool, optional
If True, uses a PendingDeprecationWarning instead of a
DeprecationWarning. Cannot be used together with *removal*.
obj_type : str, optional
The object type being deprecated.
addendum : str, optional
Additional text appended directly to the final message.
removal : str, optional
The expected removal version. With the default (an empty string), a
removal version is automatically computed from *since*. Set to other
Falsy values to not schedule a removal date. Cannot be used together
with *pending*.
Examples
--------
::
# To warn of the deprecation of "matplotlib.name_of_module"
warn_deprecated('1.4.0', name='matplotlib.name_of_module',
obj_type='module')
"""
warning = _generate_deprecation_warning(
since, message, name, alternative, pending, obj_type, addendum,
removal=removal)
from . import warn_external
warn_external(warning, category=MatplotlibDeprecationWarning)
def deprecated(since, *, message='', name='', alternative='', pending=False,
obj_type=None, addendum='', removal=''):
"""
Decorator to mark a function, a class, or a property as deprecated.
When deprecating a classmethod, a staticmethod, or a property, the
``@deprecated`` decorator should go *under* ``@classmethod`` and
``@staticmethod`` (i.e., `deprecated` should directly decorate the
underlying callable), but *over* ``@property``.
When deprecating a class ``C`` intended to be used as a base class in a
multiple inheritance hierarchy, ``C`` *must* define an ``__init__`` method
(if ``C`` instead inherited its ``__init__`` from its own base class, then
``@deprecated`` would mess up ``__init__`` inheritance when installing its
own (deprecation-emitting) ``C.__init__``).
Parameters are the same as for `warn_deprecated`, except that *obj_type*
defaults to 'class' if decorating a class, 'attribute' if decorating a
property, and 'function' otherwise.
Examples
--------
::
@deprecated('1.4.0')
def the_function_to_deprecate():
pass
"""
def deprecate(obj, message=message, name=name, alternative=alternative,
pending=pending, obj_type=obj_type, addendum=addendum):
from matplotlib._api import classproperty
if isinstance(obj, type):
if obj_type is None:
obj_type = "class"
func = obj.__init__
name = name or obj.__name__
old_doc = obj.__doc__
def finalize(wrapper, new_doc):
try:
obj.__doc__ = new_doc
except AttributeError: # Can't set on some extension objects.
pass
obj.__init__ = functools.wraps(obj.__init__)(wrapper)
return obj
elif isinstance(obj, (property, classproperty)):
if obj_type is None:
obj_type = "attribute"
func = None
name = name or obj.fget.__name__
old_doc = obj.__doc__
class _deprecated_property(type(obj)):
def __get__(self, instance, owner=None):
if instance is not None or owner is not None \
and isinstance(self, classproperty):
emit_warning()
return super().__get__(instance, owner)
def __set__(self, instance, value):
if instance is not None:
emit_warning()
return super().__set__(instance, value)
def __delete__(self, instance):
if instance is not None:
emit_warning()
return super().__delete__(instance)
def __set_name__(self, owner, set_name):
nonlocal name
if name == "<lambda>":
name = set_name
def finalize(_, new_doc):
return _deprecated_property(
fget=obj.fget, fset=obj.fset, fdel=obj.fdel, doc=new_doc)
else:
if obj_type is None:
obj_type = "function"
func = obj
name = name or obj.__name__
old_doc = func.__doc__
def finalize(wrapper, new_doc):
wrapper = functools.wraps(func)(wrapper)
wrapper.__doc__ = new_doc
return wrapper
def emit_warning():
warn_deprecated(
since, message=message, name=name, alternative=alternative,
pending=pending, obj_type=obj_type, addendum=addendum,
removal=removal)
def wrapper(*args, **kwargs):
emit_warning()
return func(*args, **kwargs)
old_doc = inspect.cleandoc(old_doc or '').strip('\n')
notes_header = '\nNotes\n-----'
second_arg = ' '.join([t.strip() for t in
(message, f"Use {alternative} instead."
if alternative else "", addendum) if t])
new_doc = (f"[*Deprecated*] {old_doc}\n"
f"{notes_header if notes_header not in old_doc else ''}\n"
f".. deprecated:: {since}\n"
f" {second_arg}")
if not old_doc:
# This is to prevent a spurious 'unexpected unindent' warning from
# docutils when the original docstring was blank.
new_doc += r'\ '
return finalize(wrapper, new_doc)
return deprecate
class deprecate_privatize_attribute:
"""
Helper to deprecate public access to an attribute (or method).
This helper should only be used at class scope, as follows::
class Foo:
attr = _deprecate_privatize_attribute(*args, **kwargs)
where *all* parameters are forwarded to `deprecated`. This form makes
``attr`` a property which forwards read and write access to ``self._attr``
(same name but with a leading underscore), with a deprecation warning.
Note that the attribute name is derived from *the name this helper is
assigned to*. This helper also works for deprecating methods.
"""
def __init__(self, *args, **kwargs):
self.deprecator = deprecated(*args, **kwargs)
def __set_name__(self, owner, name):
setattr(owner, name, self.deprecator(
property(lambda self: getattr(self, f"_{name}"),
lambda self, value: setattr(self, f"_{name}", value)),
name=name))
# Used by _copy_docstring_and_deprecators to redecorate pyplot wrappers and
# boilerplate.py to retrieve original signatures. It may seem natural to store
# this information as an attribute on the wrapper, but if the wrapper gets
# itself functools.wraps()ed, then such attributes are silently propagated to
# the outer wrapper, which is not desired.
DECORATORS = {}
def rename_parameter(since, old, new, func=None):
"""
Decorator indicating that parameter *old* of *func* is renamed to *new*.
The actual implementation of *func* should use *new*, not *old*. If *old*
is passed to *func*, a DeprecationWarning is emitted, and its value is
used, even if *new* is also passed by keyword (this is to simplify pyplot
wrapper functions, which always pass *new* explicitly to the Axes method).
If *new* is also passed but positionally, a TypeError will be raised by the
underlying function during argument binding.
Examples
--------
::
@_api.rename_parameter("3.1", "bad_name", "good_name")
def func(good_name): ...
"""
decorator = functools.partial(rename_parameter, since, old, new)
if func is None:
return decorator
signature = inspect.signature(func)
assert old not in signature.parameters, (
f"Matplotlib internal error: {old!r} cannot be a parameter for "
f"{func.__name__}()")
assert new in signature.parameters, (
f"Matplotlib internal error: {new!r} must be a parameter for "
f"{func.__name__}()")
@functools.wraps(func)
def wrapper(*args, **kwargs):
if old in kwargs:
warn_deprecated(
since, message=f"The {old!r} parameter of {func.__name__}() "
f"has been renamed {new!r} since Matplotlib {since}; support "
f"for the old name will be dropped %(removal)s.")
kwargs[new] = kwargs.pop(old)
return func(*args, **kwargs)
# wrapper() must keep the same documented signature as func(): if we
# instead made both *old* and *new* appear in wrapper()'s signature, they
# would both show up in the pyplot function for an Axes method as well and
# pyplot would explicitly pass both arguments to the Axes method.
DECORATORS[wrapper] = decorator
return wrapper
class _deprecated_parameter_class:
def __repr__(self):
return "<deprecated parameter>"
_deprecated_parameter = _deprecated_parameter_class()
def delete_parameter(since, name, func=None, **kwargs):
"""
Decorator indicating that parameter *name* of *func* is being deprecated.
The actual implementation of *func* should keep the *name* parameter in its
signature, or accept a ``**kwargs`` argument (through which *name* would be
passed).
Parameters that come after the deprecated parameter effectively become
keyword-only (as they cannot be passed positionally without triggering the
DeprecationWarning on the deprecated parameter), and should be marked as
such after the deprecation period has passed and the deprecated parameter
is removed.
Parameters other than *since*, *name*, and *func* are keyword-only and
forwarded to `.warn_deprecated`.
Examples
--------
::
@_api.delete_parameter("3.1", "unused")
def func(used_arg, other_arg, unused, more_args): ...
"""
decorator = functools.partial(delete_parameter, since, name, **kwargs)
if func is None:
return decorator
signature = inspect.signature(func)
# Name of `**kwargs` parameter of the decorated function, typically
# "kwargs" if such a parameter exists, or None if the decorated function
# doesn't accept `**kwargs`.
kwargs_name = next((param.name for param in signature.parameters.values()
if param.kind == inspect.Parameter.VAR_KEYWORD), None)
if name in signature.parameters:
kind = signature.parameters[name].kind
is_varargs = kind is inspect.Parameter.VAR_POSITIONAL
is_varkwargs = kind is inspect.Parameter.VAR_KEYWORD
if not is_varargs and not is_varkwargs:
name_idx = (
# Deprecated parameter can't be passed positionally.
math.inf if kind is inspect.Parameter.KEYWORD_ONLY
# If call site has no more than this number of parameters, the
# deprecated parameter can't have been passed positionally.
else [*signature.parameters].index(name))
func.__signature__ = signature = signature.replace(parameters=[
param.replace(default=_deprecated_parameter)
if param.name == name else param
for param in signature.parameters.values()])
else:
name_idx = -1 # Deprecated parameter can always have been passed.
else:
is_varargs = is_varkwargs = False
# Deprecated parameter can't be passed positionally.
name_idx = math.inf
assert kwargs_name, (
f"Matplotlib internal error: {name!r} must be a parameter for "
f"{func.__name__}()")
addendum = kwargs.pop('addendum', None)
@functools.wraps(func)
def wrapper(*inner_args, **inner_kwargs):
if len(inner_args) <= name_idx and name not in inner_kwargs:
# Early return in the simple, non-deprecated case (much faster than
# calling bind()).
return func(*inner_args, **inner_kwargs)
arguments = signature.bind(*inner_args, **inner_kwargs).arguments
if is_varargs and arguments.get(name):
warn_deprecated(
since, message=f"Additional positional arguments to "
f"{func.__name__}() are deprecated since %(since)s and "
f"support for them will be removed %(removal)s.")
elif is_varkwargs and arguments.get(name):
warn_deprecated(
since, message=f"Additional keyword arguments to "
f"{func.__name__}() are deprecated since %(since)s and "
f"support for them will be removed %(removal)s.")
# We cannot just check `name not in arguments` because the pyplot
# wrappers always pass all arguments explicitly.
elif any(name in d and d[name] != _deprecated_parameter
for d in [arguments, arguments.get(kwargs_name, {})]):
deprecation_addendum = (
f"If any parameter follows {name!r}, they should be passed as "
f"keyword, not positionally.")
warn_deprecated(
since,
name=repr(name),
obj_type=f"parameter of {func.__name__}()",
addendum=(addendum + " " + deprecation_addendum) if addendum
else deprecation_addendum,
**kwargs)
return func(*inner_args, **inner_kwargs)
DECORATORS[wrapper] = decorator
return wrapper
def make_keyword_only(since, name, func=None):
"""
Decorator indicating that passing parameter *name* (or any of the following
ones) positionally to *func* is being deprecated.
When used on a method that has a pyplot wrapper, this should be the
outermost decorator, so that :file:`boilerplate.py` can access the original
signature.
"""
decorator = functools.partial(make_keyword_only, since, name)
if func is None:
return decorator
signature = inspect.signature(func)
POK = inspect.Parameter.POSITIONAL_OR_KEYWORD
KWO = inspect.Parameter.KEYWORD_ONLY
assert (name in signature.parameters
and signature.parameters[name].kind == POK), (
f"Matplotlib internal error: {name!r} must be a positional-or-keyword "
f"parameter for {func.__name__}()")
names = [*signature.parameters]
name_idx = names.index(name)
kwonly = [name for name in names[name_idx:]
if signature.parameters[name].kind == POK]
@functools.wraps(func)
def wrapper(*args, **kwargs):
# Don't use signature.bind here, as it would fail when stacked with
# rename_parameter and an "old" argument name is passed in
# (signature.bind would fail, but the actual call would succeed).
if len(args) > name_idx:
warn_deprecated(
since, message="Passing the %(name)s %(obj_type)s "
"positionally is deprecated since Matplotlib %(since)s; the "
"parameter will become keyword-only %(removal)s.",
name=name, obj_type=f"parameter of {func.__name__}()")
return func(*args, **kwargs)
# Don't modify *func*'s signature, as boilerplate.py needs it.
wrapper.__signature__ = signature.replace(parameters=[
param.replace(kind=KWO) if param.name in kwonly else param
for param in signature.parameters.values()])
DECORATORS[wrapper] = decorator
return wrapper
def deprecate_method_override(method, obj, *, allow_empty=False, **kwargs):
"""
Return ``obj.method`` with a deprecation if it was overridden, else None.
Parameters
----------
method
An unbound method, i.e. an expression of the form
``Class.method_name``. Remember that within the body of a method, one
can always use ``__class__`` to refer to the class that is currently
being defined.
obj
Either an object of the class where *method* is defined, or a subclass
of that class.
allow_empty : bool, default: False
Whether to allow overrides by "empty" methods without emitting a
warning.
**kwargs
Additional parameters passed to `warn_deprecated` to generate the
deprecation warning; must at least include the "since" key.
"""
def empty(): pass
def empty_with_docstring(): """doc"""
name = method.__name__
bound_child = getattr(obj, name)
bound_base = (
method # If obj is a class, then we need to use unbound methods.
if isinstance(bound_child, type(empty)) and isinstance(obj, type)
else method.__get__(obj))
if (bound_child != bound_base
and (not allow_empty
or (getattr(getattr(bound_child, "__code__", None),
"co_code", None)
not in [empty.__code__.co_code,
empty_with_docstring.__code__.co_code]))):
warn_deprecated(**{"name": name, "obj_type": "method", **kwargs})
return bound_child
return None
@contextlib.contextmanager
def suppress_matplotlib_deprecation_warning():
with warnings.catch_warnings():
warnings.simplefilter("ignore", MatplotlibDeprecationWarning)
yield

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@ -0,0 +1,76 @@
from collections.abc import Callable
import contextlib
from typing import Any, TypedDict, TypeVar, overload
from typing_extensions import (
ParamSpec, # < Py 3.10
Unpack, # < Py 3.11
)
_P = ParamSpec("_P")
_R = TypeVar("_R")
_T = TypeVar("_T")
class MatplotlibDeprecationWarning(DeprecationWarning): ...
class DeprecationKwargs(TypedDict, total=False):
message: str
alternative: str
pending: bool
obj_type: str
addendum: str
removal: str
class NamedDeprecationKwargs(DeprecationKwargs, total=False):
name: str
def warn_deprecated(since: str, **kwargs: Unpack[NamedDeprecationKwargs]) -> None: ...
def deprecated(
since: str, **kwargs: Unpack[NamedDeprecationKwargs]
) -> Callable[[_T], _T]: ...
class deprecate_privatize_attribute(Any):
def __init__(self, since: str, **kwargs: Unpack[NamedDeprecationKwargs]): ...
def __set_name__(self, owner: type[object], name: str) -> None: ...
DECORATORS: dict[Callable, Callable] = ...
@overload
def rename_parameter(
since: str, old: str, new: str, func: None = ...
) -> Callable[[Callable[_P, _R]], Callable[_P, _R]]: ...
@overload
def rename_parameter(
since: str, old: str, new: str, func: Callable[_P, _R]
) -> Callable[_P, _R]: ...
class _deprecated_parameter_class: ...
_deprecated_parameter: _deprecated_parameter_class
@overload
def delete_parameter(
since: str, name: str, func: None = ..., **kwargs: Unpack[DeprecationKwargs]
) -> Callable[[Callable[_P, _R]], Callable[_P, _R]]: ...
@overload
def delete_parameter(
since: str, name: str, func: Callable[_P, _R], **kwargs: Unpack[DeprecationKwargs]
) -> Callable[_P, _R]: ...
@overload
def make_keyword_only(
since: str, name: str, func: None = ...
) -> Callable[[Callable[_P, _R]], Callable[_P, _R]]: ...
@overload
def make_keyword_only(
since: str, name: str, func: Callable[_P, _R]
) -> Callable[_P, _R]: ...
def deprecate_method_override(
method: Callable[_P, _R],
obj: object | type,
*,
allow_empty: bool = ...,
since: str,
**kwargs: Unpack[NamedDeprecationKwargs]
) -> Callable[_P, _R]: ...
def suppress_matplotlib_deprecation_warning() -> (
contextlib.AbstractContextManager[None]
): ...