test_array_helpers.py

fromhypothesisimportgiven
fromhypothesisimportstrategiesasst

fromarray_api_testsimport_array_moduleasxp
fromarray_api_tests.hypothesis_helpersimport (int_dtypes, arrays,
two_mutually_broadcastable_shapes)
fromarray_api_tests.shape_helpersimportiter_indices, broadcast_shapes
fromarray_api_tests .array_helpersimportexactly_equal, notequal, less

# TODO: These meta-tests currently only work with NumPy

deftest_exactly_equal():
a=xp.asarray([0, 0., -0., -0., xp.nan, xp.nan, 1, 1])
b=xp.asarray([0, -1, -0., 0., xp.nan, 1, 1, 2])

res=xp.asarray([True, False, True, False, True, False, True, False])
assertxp.all(xp.equal(exactly_equal(a, b), res))

deftest_notequal():
a=xp.asarray([0, 0., -0., -0., xp.nan, xp.nan, 1, 1])
b=xp.asarray([0, -1, -0., 0., xp.nan, 1, 1, 2])

res=xp.asarray([False, True, False, False, False, True, False, True])
assertxp.all(xp.equal(notequal(a, b), res))


@given(two_mutually_broadcastable_shapes, int_dtypes, int_dtypes, st.data())
deftest_less(shapes, dtype1, dtype2, data):
x=data.draw(arrays(shape=shapes[0], dtype=dtype1))
y=data.draw(arrays(shape=shapes[1], dtype=dtype2))

res=less(x, y)

fori, j, kiniter_indices(x.shape, y.shape, broadcast_shapes(x.shape, y.shape)):
assertres[k] == (int(x[i]) <int(y[j]))