def _array_vals():
for d in _integer_dtypes:
yield asarray(1, dtype=d)
for d in _boolean_dtypes:
yield asarray(False, dtype=d)
for d in _floating_dtypes:
yield asarray(1.0, dtype=d)
def test_bitwise_shift_error():
# bitwise shift functions should raise when the second argument is negative
assert_raises(
ValueError,
lambda: bitwise_left_shift(asarray([1, 1]), asarray([1, -1])))
assert_raises(
ValueError,
lambda: bitwise_right_shift(asarray([1, 1]), asarray([1, -1])))
def test_stable_desc_argsort(obj, axis, expected):
"""
Indices respect relative order of a descending stable-sort
See https://github.com/numpy/numpy/issues/20778
"""
x = xp.asarray(obj)
out = xp.argsort(x, axis=axis, stable=True, descending=True)
assert xp.all(out == xp.asarray(expected))
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_python_scalar_construtors():
b = asarray(False)
i = asarray(0)
f = asarray(0.0)
assert bool(b) == False
assert int(i) == 0
assert float(f) == 0.0
assert operator.index(i) == 0
# bool/int/float should only be allowed on 0-D arrays.
assert_raises(TypeError, lambda: bool(asarray([False])))
assert_raises(TypeError, lambda: int(asarray([0])))
assert_raises(TypeError, lambda: float(asarray([0.0])))
assert_raises(TypeError, lambda: operator.index(asarray([0])))
# bool/int/float should only be allowed on arrays of the corresponding
# dtype
assert_raises(ValueError, lambda: bool(i))
assert_raises(ValueError, lambda: bool(f))
assert_raises(ValueError, lambda: int(b))
assert_raises(ValueError, lambda: int(f))
assert_raises(ValueError, lambda: float(b))
assert_raises(ValueError, lambda: float(i))
assert_raises(TypeError, lambda: operator.index(b))
assert_raises(TypeError, lambda: operator.index(f))
import pytest
from cupy import array_api as xp
@pytest.mark.parametrize(
"from_, to, expected",
[
(xp.int8, xp.int16, True),
(xp.int16, xp.int8, False),
(xp.bool, xp.int8, False),
(xp.asarray(0, dtype=xp.uint8), xp.int8, False),
],
)
def test_can_cast(from_, to, expected):
"""
can_cast() returns correct result
"""
assert xp.can_cast(from_, to) == expected
def test_mask_0d_array_without_errors():
a = ones(())
a[asarray(True)]
def test_disallow_mask_with_newaxis():
a = ones((3, 3, 3))
with pytest.raises(IndexError):
a[None, asarray(True)]
with pytest.raises(IndexError):
a[None, asarray(True)]
@pytest.mark.parametrize("shape", [(), (5,), (3, 3, 3)])
@pytest.mark.parametrize("index", ["string", False, True])
def test_error_on_invalid_index(shape, index):
a = ones(shape)
with pytest.raises(IndexError):
a[index]
def test_mask_0d_array_without_errors():
a = ones(())
a[asarray(True)]
@pytest.mark.parametrize(
"i", [slice(5), slice(5, 0), asarray(True), asarray([0, 1])]
)
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_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!
