import inspect
|
import sys
|
import os
|
import tempfile
|
from io import StringIO
|
from unittest import mock
|
|
import numpy as np
|
from numpy.testing import (
|
assert_, assert_equal, assert_raises, assert_raises_regex)
|
from numpy.core.overrides import (
|
_get_implementing_args, array_function_dispatch,
|
verify_matching_signatures, ARRAY_FUNCTION_ENABLED)
|
from numpy.compat import pickle
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import pytest
|
|
|
requires_array_function = pytest.mark.skipif(
|
not ARRAY_FUNCTION_ENABLED,
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reason="__array_function__ dispatch not enabled.")
|
|
|
def _return_not_implemented(self, *args, **kwargs):
|
return NotImplemented
|
|
|
# need to define this at the top level to test pickling
|
@array_function_dispatch(lambda array: (array,))
|
def dispatched_one_arg(array):
|
"""Docstring."""
|
return 'original'
|
|
|
@array_function_dispatch(lambda array1, array2: (array1, array2))
|
def dispatched_two_arg(array1, array2):
|
"""Docstring."""
|
return 'original'
|
|
|
class TestGetImplementingArgs:
|
|
def test_ndarray(self):
|
array = np.array(1)
|
|
args = _get_implementing_args([array])
|
assert_equal(list(args), [array])
|
|
args = _get_implementing_args([array, array])
|
assert_equal(list(args), [array])
|
|
args = _get_implementing_args([array, 1])
|
assert_equal(list(args), [array])
|
|
args = _get_implementing_args([1, array])
|
assert_equal(list(args), [array])
|
|
def test_ndarray_subclasses(self):
|
|
class OverrideSub(np.ndarray):
|
__array_function__ = _return_not_implemented
|
|
class NoOverrideSub(np.ndarray):
|
pass
|
|
array = np.array(1).view(np.ndarray)
|
override_sub = np.array(1).view(OverrideSub)
|
no_override_sub = np.array(1).view(NoOverrideSub)
|
|
args = _get_implementing_args([array, override_sub])
|
assert_equal(list(args), [override_sub, array])
|
|
args = _get_implementing_args([array, no_override_sub])
|
assert_equal(list(args), [no_override_sub, array])
|
|
args = _get_implementing_args(
|
[override_sub, no_override_sub])
|
assert_equal(list(args), [override_sub, no_override_sub])
|
|
def test_ndarray_and_duck_array(self):
|
|
class Other:
|
__array_function__ = _return_not_implemented
|
|
array = np.array(1)
|
other = Other()
|
|
args = _get_implementing_args([other, array])
|
assert_equal(list(args), [other, array])
|
|
args = _get_implementing_args([array, other])
|
assert_equal(list(args), [array, other])
|
|
def test_ndarray_subclass_and_duck_array(self):
|
|
class OverrideSub(np.ndarray):
|
__array_function__ = _return_not_implemented
|
|
class Other:
|
__array_function__ = _return_not_implemented
|
|
array = np.array(1)
|
subarray = np.array(1).view(OverrideSub)
|
other = Other()
|
|
assert_equal(_get_implementing_args([array, subarray, other]),
|
[subarray, array, other])
|
assert_equal(_get_implementing_args([array, other, subarray]),
|
[subarray, array, other])
|
|
def test_many_duck_arrays(self):
|
|
class A:
|
__array_function__ = _return_not_implemented
|
|
class B(A):
|
__array_function__ = _return_not_implemented
|
|
class C(A):
|
__array_function__ = _return_not_implemented
|
|
class D:
|
__array_function__ = _return_not_implemented
|
|
a = A()
|
b = B()
|
c = C()
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d = D()
|
|
assert_equal(_get_implementing_args([1]), [])
|
assert_equal(_get_implementing_args([a]), [a])
|
assert_equal(_get_implementing_args([a, 1]), [a])
|
assert_equal(_get_implementing_args([a, a, a]), [a])
|
assert_equal(_get_implementing_args([a, d, a]), [a, d])
|
assert_equal(_get_implementing_args([a, b]), [b, a])
|
assert_equal(_get_implementing_args([b, a]), [b, a])
|
assert_equal(_get_implementing_args([a, b, c]), [b, c, a])
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assert_equal(_get_implementing_args([a, c, b]), [c, b, a])
|
|
def test_too_many_duck_arrays(self):
|
namespace = dict(__array_function__=_return_not_implemented)
|
types = [type('A' + str(i), (object,), namespace) for i in range(33)]
|
relevant_args = [t() for t in types]
|
|
actual = _get_implementing_args(relevant_args[:32])
|
assert_equal(actual, relevant_args[:32])
|
|
with assert_raises_regex(TypeError, 'distinct argument types'):
|
_get_implementing_args(relevant_args)
|
|
|
class TestNDArrayArrayFunction:
|
|
@requires_array_function
|
def test_method(self):
|
|
class Other:
|
__array_function__ = _return_not_implemented
|
|
class NoOverrideSub(np.ndarray):
|
pass
|
|
class OverrideSub(np.ndarray):
|
__array_function__ = _return_not_implemented
|
|
array = np.array([1])
|
other = Other()
|
no_override_sub = array.view(NoOverrideSub)
|
override_sub = array.view(OverrideSub)
|
|
result = array.__array_function__(func=dispatched_two_arg,
|
types=(np.ndarray,),
|
args=(array, 1.), kwargs={})
|
assert_equal(result, 'original')
|
|
result = array.__array_function__(func=dispatched_two_arg,
|
types=(np.ndarray, Other),
|
args=(array, other), kwargs={})
|
assert_(result is NotImplemented)
|
|
result = array.__array_function__(func=dispatched_two_arg,
|
types=(np.ndarray, NoOverrideSub),
|
args=(array, no_override_sub),
|
kwargs={})
|
assert_equal(result, 'original')
|
|
result = array.__array_function__(func=dispatched_two_arg,
|
types=(np.ndarray, OverrideSub),
|
args=(array, override_sub),
|
kwargs={})
|
assert_equal(result, 'original')
|
|
with assert_raises_regex(TypeError, 'no implementation found'):
|
np.concatenate((array, other))
|
|
expected = np.concatenate((array, array))
|
result = np.concatenate((array, no_override_sub))
|
assert_equal(result, expected.view(NoOverrideSub))
|
result = np.concatenate((array, override_sub))
|
assert_equal(result, expected.view(OverrideSub))
|
|
def test_no_wrapper(self):
|
# This shouldn't happen unless a user intentionally calls
|
# __array_function__ with invalid arguments, but check that we raise
|
# an appropriate error all the same.
|
array = np.array(1)
|
func = lambda x: x
|
with assert_raises_regex(AttributeError, '_implementation'):
|
array.__array_function__(func=func, types=(np.ndarray,),
|
args=(array,), kwargs={})
|
|
|
@requires_array_function
|
class TestArrayFunctionDispatch:
|
|
def test_pickle(self):
|
for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
|
roundtripped = pickle.loads(
|
pickle.dumps(dispatched_one_arg, protocol=proto))
|
assert_(roundtripped is dispatched_one_arg)
|
|
def test_name_and_docstring(self):
|
assert_equal(dispatched_one_arg.__name__, 'dispatched_one_arg')
|
if sys.flags.optimize < 2:
|
assert_equal(dispatched_one_arg.__doc__, 'Docstring.')
|
|
def test_interface(self):
|
|
class MyArray:
|
def __array_function__(self, func, types, args, kwargs):
|
return (self, func, types, args, kwargs)
|
|
original = MyArray()
|
(obj, func, types, args, kwargs) = dispatched_one_arg(original)
|
assert_(obj is original)
|
assert_(func is dispatched_one_arg)
|
assert_equal(set(types), {MyArray})
|
# assert_equal uses the overloaded np.iscomplexobj() internally
|
assert_(args == (original,))
|
assert_equal(kwargs, {})
|
|
def test_not_implemented(self):
|
|
class MyArray:
|
def __array_function__(self, func, types, args, kwargs):
|
return NotImplemented
|
|
array = MyArray()
|
with assert_raises_regex(TypeError, 'no implementation found'):
|
dispatched_one_arg(array)
|
|
|
@requires_array_function
|
class TestVerifyMatchingSignatures:
|
|
def test_verify_matching_signatures(self):
|
|
verify_matching_signatures(lambda x: 0, lambda x: 0)
|
verify_matching_signatures(lambda x=None: 0, lambda x=None: 0)
|
verify_matching_signatures(lambda x=1: 0, lambda x=None: 0)
|
|
with assert_raises(RuntimeError):
|
verify_matching_signatures(lambda a: 0, lambda b: 0)
|
with assert_raises(RuntimeError):
|
verify_matching_signatures(lambda x: 0, lambda x=None: 0)
|
with assert_raises(RuntimeError):
|
verify_matching_signatures(lambda x=None: 0, lambda y=None: 0)
|
with assert_raises(RuntimeError):
|
verify_matching_signatures(lambda x=1: 0, lambda y=1: 0)
|
|
def test_array_function_dispatch(self):
|
|
with assert_raises(RuntimeError):
|
@array_function_dispatch(lambda x: (x,))
|
def f(y):
|
pass
|
|
# should not raise
|
@array_function_dispatch(lambda x: (x,), verify=False)
|
def f(y):
|
pass
|
|
|
def _new_duck_type_and_implements():
|
"""Create a duck array type and implements functions."""
|
HANDLED_FUNCTIONS = {}
|
|
class MyArray:
|
def __array_function__(self, func, types, args, kwargs):
|
if func not in HANDLED_FUNCTIONS:
|
return NotImplemented
|
if not all(issubclass(t, MyArray) for t in types):
|
return NotImplemented
|
return HANDLED_FUNCTIONS[func](*args, **kwargs)
|
|
def implements(numpy_function):
|
"""Register an __array_function__ implementations."""
|
def decorator(func):
|
HANDLED_FUNCTIONS[numpy_function] = func
|
return func
|
return decorator
|
|
return (MyArray, implements)
|
|
|
@requires_array_function
|
class TestArrayFunctionImplementation:
|
|
def test_one_arg(self):
|
MyArray, implements = _new_duck_type_and_implements()
|
|
@implements(dispatched_one_arg)
|
def _(array):
|
return 'myarray'
|
|
assert_equal(dispatched_one_arg(1), 'original')
|
assert_equal(dispatched_one_arg(MyArray()), 'myarray')
|
|
def test_optional_args(self):
|
MyArray, implements = _new_duck_type_and_implements()
|
|
@array_function_dispatch(lambda array, option=None: (array,))
|
def func_with_option(array, option='default'):
|
return option
|
|
@implements(func_with_option)
|
def my_array_func_with_option(array, new_option='myarray'):
|
return new_option
|
|
# we don't need to implement every option on __array_function__
|
# implementations
|
assert_equal(func_with_option(1), 'default')
|
assert_equal(func_with_option(1, option='extra'), 'extra')
|
assert_equal(func_with_option(MyArray()), 'myarray')
|
with assert_raises(TypeError):
|
func_with_option(MyArray(), option='extra')
|
|
# but new options on implementations can't be used
|
result = my_array_func_with_option(MyArray(), new_option='yes')
|
assert_equal(result, 'yes')
|
with assert_raises(TypeError):
|
func_with_option(MyArray(), new_option='no')
|
|
def test_not_implemented(self):
|
MyArray, implements = _new_duck_type_and_implements()
|
|
@array_function_dispatch(lambda array: (array,), module='my')
|
def func(array):
|
return array
|
|
array = np.array(1)
|
assert_(func(array) is array)
|
assert_equal(func.__module__, 'my')
|
|
with assert_raises_regex(
|
TypeError, "no implementation found for 'my.func'"):
|
func(MyArray())
|
|
def test_signature_error_message(self):
|
# The lambda function will be named "<lambda>", but the TypeError
|
# should show the name as "func"
|
def _dispatcher():
|
return ()
|
|
@array_function_dispatch(_dispatcher)
|
def func():
|
pass
|
|
try:
|
func(bad_arg=3)
|
except TypeError as e:
|
expected_exception = e
|
|
try:
|
func(bad_arg=3)
|
raise AssertionError("must fail")
|
except TypeError as exc:
|
assert exc.args == expected_exception.args
|
|
@pytest.mark.parametrize("value", [234, "this func is not replaced"])
|
def test_dispatcher_error(self, value):
|
# If the dispatcher raises an error, we must not attempt to mutate it
|
error = TypeError(value)
|
|
def dispatcher():
|
raise error
|
|
@array_function_dispatch(dispatcher)
|
def func():
|
return 3
|
|
try:
|
func()
|
raise AssertionError("must fail")
|
except TypeError as exc:
|
assert exc is error # unmodified exception
|
|
|
class TestNDArrayMethods:
|
|
def test_repr(self):
|
# gh-12162: should still be defined even if __array_function__ doesn't
|
# implement np.array_repr()
|
|
class MyArray(np.ndarray):
|
def __array_function__(*args, **kwargs):
|
return NotImplemented
|
|
array = np.array(1).view(MyArray)
|
assert_equal(repr(array), 'MyArray(1)')
|
assert_equal(str(array), '1')
|
|
|
class TestNumPyFunctions:
|
|
def test_set_module(self):
|
assert_equal(np.sum.__module__, 'numpy')
|
assert_equal(np.char.equal.__module__, 'numpy.char')
|
assert_equal(np.fft.fft.__module__, 'numpy.fft')
|
assert_equal(np.linalg.solve.__module__, 'numpy.linalg')
|
|
def test_inspect_sum(self):
|
signature = inspect.signature(np.sum)
|
assert_('axis' in signature.parameters)
|
|
@requires_array_function
|
def test_override_sum(self):
|
MyArray, implements = _new_duck_type_and_implements()
|
|
@implements(np.sum)
|
def _(array):
|
return 'yes'
|
|
assert_equal(np.sum(MyArray()), 'yes')
|
|
@requires_array_function
|
def test_sum_on_mock_array(self):
|
|
# We need a proxy for mocks because __array_function__ is only looked
|
# up in the class dict
|
class ArrayProxy:
|
def __init__(self, value):
|
self.value = value
|
def __array_function__(self, *args, **kwargs):
|
return self.value.__array_function__(*args, **kwargs)
|
def __array__(self, *args, **kwargs):
|
return self.value.__array__(*args, **kwargs)
|
|
proxy = ArrayProxy(mock.Mock(spec=ArrayProxy))
|
proxy.value.__array_function__.return_value = 1
|
result = np.sum(proxy)
|
assert_equal(result, 1)
|
proxy.value.__array_function__.assert_called_once_with(
|
np.sum, (ArrayProxy,), (proxy,), {})
|
proxy.value.__array__.assert_not_called()
|
|
@requires_array_function
|
def test_sum_forwarding_implementation(self):
|
|
class MyArray(np.ndarray):
|
|
def sum(self, axis, out):
|
return 'summed'
|
|
def __array_function__(self, func, types, args, kwargs):
|
return super().__array_function__(func, types, args, kwargs)
|
|
# note: the internal implementation of np.sum() calls the .sum() method
|
array = np.array(1).view(MyArray)
|
assert_equal(np.sum(array), 'summed')
|
|
|
class TestArrayLike:
|
def setup_method(self):
|
class MyArray():
|
def __init__(self, function=None):
|
self.function = function
|
|
def __array_function__(self, func, types, args, kwargs):
|
assert func is getattr(np, func.__name__)
|
try:
|
my_func = getattr(self, func.__name__)
|
except AttributeError:
|
return NotImplemented
|
return my_func(*args, **kwargs)
|
|
self.MyArray = MyArray
|
|
class MyNoArrayFunctionArray():
|
def __init__(self, function=None):
|
self.function = function
|
|
self.MyNoArrayFunctionArray = MyNoArrayFunctionArray
|
|
def add_method(self, name, arr_class, enable_value_error=False):
|
def _definition(*args, **kwargs):
|
# Check that `like=` isn't propagated downstream
|
assert 'like' not in kwargs
|
|
if enable_value_error and 'value_error' in kwargs:
|
raise ValueError
|
|
return arr_class(getattr(arr_class, name))
|
setattr(arr_class, name, _definition)
|
|
def func_args(*args, **kwargs):
|
return args, kwargs
|
|
@requires_array_function
|
def test_array_like_not_implemented(self):
|
self.add_method('array', self.MyArray)
|
|
ref = self.MyArray.array()
|
|
with assert_raises_regex(TypeError, 'no implementation found'):
|
array_like = np.asarray(1, like=ref)
|
|
_array_tests = [
|
('array', *func_args((1,))),
|
('asarray', *func_args((1,))),
|
('asanyarray', *func_args((1,))),
|
('ascontiguousarray', *func_args((2, 3))),
|
('asfortranarray', *func_args((2, 3))),
|
('require', *func_args((np.arange(6).reshape(2, 3),),
|
requirements=['A', 'F'])),
|
('empty', *func_args((1,))),
|
('full', *func_args((1,), 2)),
|
('ones', *func_args((1,))),
|
('zeros', *func_args((1,))),
|
('arange', *func_args(3)),
|
('frombuffer', *func_args(b'\x00' * 8, dtype=int)),
|
('fromiter', *func_args(range(3), dtype=int)),
|
('fromstring', *func_args('1,2', dtype=int, sep=',')),
|
('loadtxt', *func_args(lambda: StringIO('0 1\n2 3'))),
|
('genfromtxt', *func_args(lambda: StringIO('1,2.1'),
|
dtype=[('int', 'i8'), ('float', 'f8')],
|
delimiter=',')),
|
]
|
|
@pytest.mark.parametrize('function, args, kwargs', _array_tests)
|
@pytest.mark.parametrize('numpy_ref', [True, False])
|
@requires_array_function
|
def test_array_like(self, function, args, kwargs, numpy_ref):
|
self.add_method('array', self.MyArray)
|
self.add_method(function, self.MyArray)
|
np_func = getattr(np, function)
|
my_func = getattr(self.MyArray, function)
|
|
if numpy_ref is True:
|
ref = np.array(1)
|
else:
|
ref = self.MyArray.array()
|
|
like_args = tuple(a() if callable(a) else a for a in args)
|
array_like = np_func(*like_args, **kwargs, like=ref)
|
|
if numpy_ref is True:
|
assert type(array_like) is np.ndarray
|
|
np_args = tuple(a() if callable(a) else a for a in args)
|
np_arr = np_func(*np_args, **kwargs)
|
|
# Special-case np.empty to ensure values match
|
if function == "empty":
|
np_arr.fill(1)
|
array_like.fill(1)
|
|
assert_equal(array_like, np_arr)
|
else:
|
assert type(array_like) is self.MyArray
|
assert array_like.function is my_func
|
|
@pytest.mark.parametrize('function, args, kwargs', _array_tests)
|
@pytest.mark.parametrize('ref', [1, [1], "MyNoArrayFunctionArray"])
|
@requires_array_function
|
def test_no_array_function_like(self, function, args, kwargs, ref):
|
self.add_method('array', self.MyNoArrayFunctionArray)
|
self.add_method(function, self.MyNoArrayFunctionArray)
|
np_func = getattr(np, function)
|
|
# Instantiate ref if it's the MyNoArrayFunctionArray class
|
if ref == "MyNoArrayFunctionArray":
|
ref = self.MyNoArrayFunctionArray.array()
|
|
like_args = tuple(a() if callable(a) else a for a in args)
|
|
with assert_raises_regex(TypeError,
|
'The `like` argument must be an array-like that implements'):
|
np_func(*like_args, **kwargs, like=ref)
|
|
@pytest.mark.parametrize('numpy_ref', [True, False])
|
def test_array_like_fromfile(self, numpy_ref):
|
self.add_method('array', self.MyArray)
|
self.add_method("fromfile", self.MyArray)
|
|
if numpy_ref is True:
|
ref = np.array(1)
|
else:
|
ref = self.MyArray.array()
|
|
data = np.random.random(5)
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
fname = os.path.join(tmpdir, "testfile")
|
data.tofile(fname)
|
|
array_like = np.fromfile(fname, like=ref)
|
if numpy_ref is True:
|
assert type(array_like) is np.ndarray
|
np_res = np.fromfile(fname, like=ref)
|
assert_equal(np_res, data)
|
assert_equal(array_like, np_res)
|
else:
|
assert type(array_like) is self.MyArray
|
assert array_like.function is self.MyArray.fromfile
|
|
@requires_array_function
|
def test_exception_handling(self):
|
self.add_method('array', self.MyArray, enable_value_error=True)
|
|
ref = self.MyArray.array()
|
|
with assert_raises(TypeError):
|
# Raises the error about `value_error` being invalid first
|
np.array(1, value_error=True, like=ref)
|
|
@pytest.mark.parametrize('function, args, kwargs', _array_tests)
|
def test_like_as_none(self, function, args, kwargs):
|
self.add_method('array', self.MyArray)
|
self.add_method(function, self.MyArray)
|
np_func = getattr(np, function)
|
|
like_args = tuple(a() if callable(a) else a for a in args)
|
# required for loadtxt and genfromtxt to init w/o error.
|
like_args_exp = tuple(a() if callable(a) else a for a in args)
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array_like = np_func(*like_args, **kwargs, like=None)
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expected = np_func(*like_args_exp, **kwargs)
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# Special-case np.empty to ensure values match
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if function == "empty":
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array_like.fill(1)
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expected.fill(1)
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assert_equal(array_like, expected)
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