def _matmul_array_vals():
for a in _array_vals():
yield a
for d in _all_dtypes:
yield ones((3, 4), dtype=d)
yield ones((4, 2), dtype=d)
yield ones((4, 4), dtype=d)
def test_array_properties():
a = ones((1, 2, 3))
b = ones((2, 3))
assert_raises(ValueError, lambda: a.T)
assert isinstance(b.T, Array)
assert b.T.shape == (3, 2)
assert isinstance(a.mT, Array)
assert a.mT.shape == (1, 3, 2)
assert isinstance(b.mT, Array)
assert b.mT.shape == (3, 2)
def test_validate_index():
# The indexing tests in the official array API test suite test that the
# array object correctly handles the subset of indices that are required
# by the spec. But the NumPy array API implementation specifically
# disallows any index not required by the spec, via Array._validate_index.
# This test focuses on testing that non-valid indices are correctly
# rejected. See
# https://data-apis.org/array-api/latest/API_specification/indexing.html
# and the docstring of Array._validate_index for the exact indexing
# behavior that should be allowed. This does not test indices that are
# already invalid in NumPy itself because Array will generally just pass
# such indices directly to the underlying cp.ndarray.
a = ones((3, 4))
# Out of bounds slices are not allowed
assert_raises(IndexError, lambda: a[:4])
assert_raises(IndexError, lambda: a[:-4])
assert_raises(IndexError, lambda: a[:3:-1])
assert_raises(IndexError, lambda: a[:-5:-1])
assert_raises(IndexError, lambda: a[4:])
assert_raises(IndexError, lambda: a[-4:])
assert_raises(IndexError, lambda: a[4::-1])
assert_raises(IndexError, lambda: a[-4::-1])
assert_raises(IndexError, lambda: a[..., :5])
assert_raises(IndexError, lambda: a[..., :-5])
assert_raises(IndexError, lambda: a[..., :5:-1])
assert_raises(IndexError, lambda: a[..., :-6:-1])
assert_raises(IndexError, lambda: a[..., 5:])
assert_raises(IndexError, lambda: a[..., -5:])
assert_raises(IndexError, lambda: a[..., 5::-1])
assert_raises(IndexError, lambda: a[..., -5::-1])
# Boolean indices cannot be part of a larger tuple index
assert_raises(IndexError, lambda: a[a[:, 0] == 1, 0])
assert_raises(IndexError, lambda: a[a[:, 0] == 1, ...])
assert_raises(IndexError, lambda: a[..., a[0] == 1])
assert_raises(IndexError, lambda: a[[True, True, True]])
assert_raises(IndexError, lambda: a[(True, True, True), ])
# Integer array indices are not allowed (except for 0-D)
idx = asarray([[0, 1]])
assert_raises(IndexError, lambda: a[idx])
assert_raises(IndexError, lambda: a[idx, ])
assert_raises(IndexError, lambda: a[[0, 1]])
assert_raises(IndexError, lambda: a[(0, 1), (0, 1)])
assert_raises(IndexError, lambda: a[[0, 1]])
assert_raises(IndexError, lambda: a[cp.array([[0, 1]])])
# cp.newaxis is not allowed
assert_raises(IndexError, lambda: a[None])
assert_raises(IndexError, lambda: a[None, ...])
assert_raises(IndexError, lambda: a[..., None])
# Multiaxis indices must contain exactly as many indices as dimensions
assert_raises(IndexError, lambda: a[()])
assert_raises(IndexError, lambda: a[0, ])
assert_raises(IndexError, lambda: a[0])
assert_raises(IndexError, lambda: a[:])
def test_array_keys_use_private_array():
"""
Indexing operations convert array keys before indexing the internal array
Fails when array_api array keys are not converted into NumPy-proper arrays
in __getitem__(). This is achieved by passing array_api arrays with 0-sized
dimensions, which NumPy-proper treats erroneously - not sure why!
TODO: Find and use appropiate __setitem__() case.
"""
a = ones((0, 0), dtype=bool_)
assert a[a].shape == (0,)
a = ones((0,), dtype=bool_)
key = ones((0, 0), dtype=bool_)
with pytest.raises(IndexError):
a[key]
def test___array__():
a = ones((2, 3), dtype=int16)
assert cp.asarray(a) is a._array
b = cp.asarray(a, dtype=cp.float64)
assert cp.all(cp.equal(b, cp.ones((2, 3), dtype=cp.float64)))
assert b.dtype == cp.float64
def test_error_on_invalid_index_with_ellipsis(i):
a = ones((3, 3, 3))
with pytest.raises(IndexError):
a[..., i]
with pytest.raises(IndexError):
a[i, ...]
def test_mask_0d_array_without_errors():
a = ones(())
a[asarray(True)]
def test_error_on_invalid_index(shape, index):
a = ones(shape)
with pytest.raises(IndexError):
a[index]
def test_disallow_mask_with_newaxis():
a = ones((3, 3, 3))
with pytest.raises(IndexError):
a[None, asarray(True)]
def test_disallow_flat_indexing_with_newaxis():
a = ones((3, 3, 3))
with pytest.raises(IndexError):
a[None, 0, 0]
def test_allow_newaxis():
a = ones(5)
indexed_a = a[None, :]
assert indexed_a.shape == (1, 5)