diff options
| -rw-r--r-- | numpy/core/tests/test_scalarmath.py | 54 |
1 files changed, 42 insertions, 12 deletions
diff --git a/numpy/core/tests/test_scalarmath.py b/numpy/core/tests/test_scalarmath.py index 13a23ab8a..16194d023 100644 --- a/numpy/core/tests/test_scalarmath.py +++ b/numpy/core/tests/test_scalarmath.py @@ -75,17 +75,7 @@ class TestTypes: np.add(1, 1) -@pytest.mark.slow -@settings(max_examples=10000, deadline=2000) -@given(sampled_from(reasonable_operators_for_scalars), - hynp.arrays(dtype=hynp.scalar_dtypes(), shape=()), - hynp.arrays(dtype=hynp.scalar_dtypes(), shape=())) -def test_array_scalar_ufunc_equivalence(op, arr1, arr2): - """ - This is a thorough test attempting to cover important promotion paths - and ensuring that arrays and scalars stay as aligned as possible. - However, if it creates troubles, it should maybe just be removed. - """ +def check_ufunc_scalar_equivalence(op, arr1, arr2): scalar1 = arr1[()] scalar2 = arr2[()] assert isinstance(scalar1, np.generic) @@ -107,7 +97,47 @@ def test_array_scalar_ufunc_equivalence(op, arr1, arr2): op(scalar1, scalar2) else: scalar_res = op(scalar1, scalar2) - assert_array_equal(scalar_res, res) + assert_array_equal(scalar_res, res, strict=True) + + +@pytest.mark.slow +@settings(max_examples=10000, deadline=2000) +@given(sampled_from(reasonable_operators_for_scalars), + hynp.arrays(dtype=hynp.scalar_dtypes(), shape=()), + hynp.arrays(dtype=hynp.scalar_dtypes(), shape=())) +def test_array_scalar_ufunc_equivalence(op, arr1, arr2): + """ + This is a thorough test attempting to cover important promotion paths + and ensuring that arrays and scalars stay as aligned as possible. + However, if it creates troubles, it should maybe just be removed. + """ + check_ufunc_scalar_equivalence(op, arr1, arr2) + + +@pytest.mark.slow +@given(sampled_from(reasonable_operators_for_scalars), + hynp.scalar_dtypes(), hynp.scalar_dtypes()) +def test_array_scalar_ufunc_dtypes(op, dt1, dt2): + # Same as above, but don't worry about sampling weird values so that we + # do not have to sample as much + arr1 = np.array(2, dtype=dt1) + arr2 = np.array(1, dtype=dt2) # power of 2 does weird things for arrays + + check_ufunc_scalar_equivalence(op, arr1, arr2) + + +@pytest.mark.parametrize("fscalar", [np.float16, np.float32]) +def test_int_float_promotion_truediv(fscalar): + # Promotion for mixed int and float32/float16 must not go to float64 + i = np.int8(1) + f = fscalar(1) + expected = np.result_type(i, f) + assert (i / f).dtype == expected + assert (f / i).dtype == expected + # But normal int / int true division goes to float64: + assert (i / i).dtype == np.dtype("float64") + # For int16, result has to be ast least float32 (takes ufunc path): + assert (np.int16(1) / f).dtype == np.dtype("float32") class TestBaseMath: |