"""Implementation of __array_function__ overrides from NEP-18."""
import collections
import functools
import os
from .._utils import set_module
from .._utils._inspect import getargspec
from numpy.core._multiarray_umath import (
add_docstring, _get_implementing_args, _ArrayFunctionDispatcher)
ARRAY_FUNCTIONS = set()
array_function_like_doc = (
"""like : array_like, optional
Reference object to allow the creation of arrays which are not
NumPy arrays. If an array-like passed in as ``like`` supports
the ``__array_function__`` protocol, the result will be defined
by it. In this case, it ensures the creation of an array object
compatible with that passed in via this argument."""
)
def set_array_function_like_doc(public_api):
if public_api.__doc__ is not None:
public_api.__doc__ = public_api.__doc__.replace(
"${ARRAY_FUNCTION_LIKE}",
array_function_like_doc,
)
return public_api
add_docstring(
_ArrayFunctionDispatcher,
"""
Class to wrap functions with checks for __array_function__ overrides.
All arguments are required, and can only be passed by position.
Parameters
----------
dispatcher : function or None
The dispatcher function that returns a single sequence-like object
of all arguments relevant. It must have the same signature (except
the default values) as the actual implementation.
If ``None``, this is a ``like=`` dispatcher and the
``_ArrayFunctionDispatcher`` must be called with ``like`` as the
first (additional and positional) argument.
implementation : function
Function that implements the operation on NumPy arrays without
overrides. Arguments passed calling the ``_ArrayFunctionDispatcher``
will be forwarded to this (and the ``dispatcher``) as if using
``*args, **kwargs``.
Attributes
----------
_implementation : function
The original implementation passed in.
""")
# exposed for testing purposes; used internally by _ArrayFunctionDispatcher
add_docstring(
_get_implementing_args,
"""
Collect arguments on which to call __array_function__.
Parameters
----------
relevant_args : iterable of array-like
Iterable of possibly array-like arguments to check for
__array_function__ methods.
Returns
-------
Sequence of arguments with __array_function__ methods, in the order in
which they should be called.
""")
ArgSpec = collections.namedtuple('ArgSpec', 'args varargs keywords defaults')
def verify_matching_signatures(implementation, dispatcher):
"""Verify that a dispatcher function has the right signature."""
implementation_spec = ArgSpec(*getargspec(implementation))
dispatcher_spec = ArgSpec(*getargspec(dispatcher))
if (implementation_spec.args != dispatcher_spec.args or
implementation_spec.varargs != dispatcher_spec.varargs or
implementation_spec.keywords != dispatcher_spec.keywords or
(bool(implementation_spec.defaults) !=
bool(dispatcher_spec.defaults)) or
(implementation_spec.defaults is not None and
len(implementation_spec.defaults) !=
len(dispatcher_spec.defaults))):
raise RuntimeError('implementation and dispatcher for %s have '
'different function signatures' % implementation)
if implementation_spec.defaults is not None:
if dispatcher_spec.defaults != (None,) * len(dispatcher_spec.defaults):
raise RuntimeError('dispatcher functions can only use None for '
'default argument values')
def array_function_dispatch(dispatcher=None, module=None, verify=True,
docs_from_dispatcher=False):
"""Decorator for adding dispatch with the __array_function__ protocol.
See NEP-18 for example usage.
Parameters
----------
dispatcher : callable or None
Function that when called like ``dispatcher(*args, **kwargs)`` with
arguments from the NumPy function call returns an iterable of
array-like arguments to check for ``__array_function__``.
If `None`, the first argument is used as the single `like=` argument
and not passed on. A function implementing `like=` must call its
dispatcher with `like` as the first non-keyword argument.
module : str, optional
__module__ attribute to set on new function, e.g., ``module='numpy'``.
By default, module is copied from the decorated function.
verify : bool, optional
If True, verify the that the signature of the dispatcher and decorated
function signatures match exactly: all required and optional arguments
should appear in order with the same names, but the default values for
all optional arguments should be ``None``. Only disable verification
if the dispatcher's signature needs to deviate for some particular
reason, e.g., because the function has a signature like
``func(*args, **kwargs)``.
docs_from_dispatcher : bool, optional
If True, copy docs from the dispatcher function onto the dispatched
function, rather than from the implementation. This is useful for
functions defined in C, which otherwise don't have docstrings.
Returns
-------
Function suitable for decorating the implementation of a NumPy function.
"""
def decorator(implementation):
if verify:
if dispatcher is not None:
verify_matching_signatures(implementation, dispatcher)
else:
# Using __code__ directly similar to verify_matching_signature
co = implementation.__code__
last_arg = co.co_argcount + co.co_kwonlyargcount - 1
last_arg = co.co_varnames[last_arg]
if last_arg != "like" or co.co_kwonlyargcount == 0:
raise RuntimeError(
"__array_function__ expects `like=` to be the last "
"argument and a keyword-only argument. "
f"{implementation} does not seem to comply.")
if docs_from_dispatcher:
add_docstring(implementation, dispatcher.__doc__)
public_api = _ArrayFunctionDispatcher(dispatcher, implementation)
public_api = functools.wraps(implementation)(public_api)
if module is not None:
public_api.__module__ = module
ARRAY_FUNCTIONS.add(public_api)
return public_api
return decorator
def array_function_from_dispatcher(
implementation, module=None, verify=True, docs_from_dispatcher=True):
"""Like array_function_dispatcher, but with function arguments flipped."""
def decorator(dispatcher):
return array_function_dispatch(
dispatcher, module, verify=verify,
docs_from_dispatcher=docs_from_dispatcher)(implementation)
return decorator
|