""" This file adds basic tests to test the NEP 50 style promotion compatibility mode. Most of these test are likely to be simply deleted again once NEP 50 is adopted in the main test suite. A few may be moved elsewhere. """ import operator import numpy as np import pytest from numpy.testing import IS_WASM @pytest.fixture(scope="module", autouse=True) def _weak_promotion_enabled(): state = np._get_promotion_state() np._set_promotion_state("weak_and_warn") yield np._set_promotion_state(state) @pytest.mark.skipif(IS_WASM, reason="wasm doesn't have support for fp errors") def test_nep50_examples(): with pytest.warns(UserWarning, match="result dtype changed"): res = np.uint8(1) + 2 assert res.dtype == np.uint8 with pytest.warns(UserWarning, match="result dtype changed"): res = np.array([1], np.uint8) + np.int64(1) assert res.dtype == np.int64 with pytest.warns(UserWarning, match="result dtype changed"): res = np.array([1], np.uint8) + np.array(1, dtype=np.int64) assert res.dtype == np.int64 with pytest.warns(UserWarning, match="result dtype changed"): # Note: For "weak_and_warn" promotion state the overflow warning is # unfortunately not given (because we use the full array path). with np.errstate(over="raise"): res = np.uint8(100) + 200 assert res.dtype == np.uint8 with pytest.warns(Warning) as recwarn: res = np.float32(1) + 3e100 # Check that both warnings were given in the one call: warning = str(recwarn.pop(UserWarning).message) assert warning.startswith("result dtype changed") warning = str(recwarn.pop(RuntimeWarning).message) assert warning.startswith("overflow") assert len(recwarn) == 0 # no further warnings assert np.isinf(res) assert res.dtype == np.float32 # Changes, but we don't warn for it (too noisy) res = np.array([0.1], np.float32) == np.float64(0.1) assert res[0] == False # Additional test, since the above silences the warning: with pytest.warns(UserWarning, match="result dtype changed"): res = np.array([0.1], np.float32) + np.float64(0.1) assert res.dtype == np.float64 with pytest.warns(UserWarning, match="result dtype changed"): res = np.array([1.], np.float32) + np.int64(3) assert res.dtype == np.float64 @pytest.mark.parametrize("dtype", np.typecodes["AllInteger"]) def test_nep50_weak_integers(dtype): # Avoids warning (different code path for scalars) np._set_promotion_state("weak") scalar_type = np.dtype(dtype).type maxint = int(np.iinfo(dtype).max) with np.errstate(over="warn"): with pytest.warns(RuntimeWarning): res = scalar_type(100) + maxint assert res.dtype == dtype # Array operations are not expected to warn, but should give the same # result dtype. res = np.array(100, dtype=dtype) + maxint assert res.dtype == dtype @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) def test_nep50_weak_integers_with_inexact(dtype): # Avoids warning (different code path for scalars) np._set_promotion_state("weak") scalar_type = np.dtype(dtype).type too_big_int = int(np.finfo(dtype).max) * 2 if dtype in "dDG": # These dtypes currently convert to Python float internally, which # raises an OverflowError, while the other dtypes overflow to inf. # NOTE: It may make sense to normalize the behavior! with pytest.raises(OverflowError): scalar_type(1) + too_big_int with pytest.raises(OverflowError): np.array(1, dtype=dtype) + too_big_int else: # NumPy uses (or used) `int -> string -> longdouble` for the # conversion. But Python may refuse `str(int)` for huge ints. # In that case, RuntimeWarning would be correct, but conversion # fails earlier (seems to happen on 32bit linux, possibly only debug). if dtype in "gG": try: str(too_big_int) except ValueError: pytest.skip("`huge_int -> string -> longdouble` failed") # Otherwise, we overflow to infinity: with pytest.warns(RuntimeWarning): res = scalar_type(1) + too_big_int assert res.dtype == dtype assert res == np.inf with pytest.warns(RuntimeWarning): # We force the dtype here, since windows may otherwise pick the # double instead of the longdouble loop. That leads to slightly # different results (conversion of the int fails as above). res = np.add(np.array(1, dtype=dtype), too_big_int, dtype=dtype) assert res.dtype == dtype assert res == np.inf @pytest.mark.parametrize("op", [operator.add, operator.pow, operator.eq]) def test_weak_promotion_scalar_path(op): # Some additional paths exercising the weak scalars. np._set_promotion_state("weak") # Integer path: res = op(np.uint8(3), 5) assert res == op(3, 5) assert res.dtype == np.uint8 or res.dtype == bool with pytest.raises(OverflowError): op(np.uint8(3), 1000) # Float path: res = op(np.float32(3), 5.) assert res == op(3., 5.) assert res.dtype == np.float32 or res.dtype == bool def test_nep50_complex_promotion(): np._set_promotion_state("weak") with pytest.warns(RuntimeWarning, match=".*overflow"): res = np.complex64(3) + complex(2**300) assert type(res) == np.complex64 def test_nep50_integer_conversion_errors(): # Do not worry about warnings here (auto-fixture will reset). np._set_promotion_state("weak") # Implementation for error paths is mostly missing (as of writing) with pytest.raises(OverflowError, match=".*uint8"): np.array([1], np.uint8) + 300 with pytest.raises(OverflowError, match=".*uint8"): np.uint8(1) + 300 # Error message depends on platform (maybe unsigned int or unsigned long) with pytest.raises(OverflowError, match="Python integer -1 out of bounds for uint8"): np.uint8(1) + -1 def test_nep50_integer_regression(): # Test the old integer promotion rules. When the integer is too large, # we need to keep using the old-style promotion. np._set_promotion_state("legacy") arr = np.array(1) assert (arr + 2**63).dtype == np.float64 assert (arr[()] + 2**63).dtype == np.float64 def test_nep50_with_axisconcatenator(): # I promised that this will be an error in the future in the 1.25 # release notes; test this (NEP 50 opt-in makes the deprecation an error). np._set_promotion_state("weak") with pytest.raises(OverflowError): np.r_[np.arange(5, dtype=np.int8), 255] @pytest.mark.parametrize("ufunc", [np.add, np.power]) @pytest.mark.parametrize("state", ["weak", "weak_and_warn"]) def test_nep50_huge_integers(ufunc, state): # Very large integers are complicated, because they go to uint64 or # object dtype. This tests covers a few possible paths (some of which # cannot give the NEP 50 warnings). np._set_promotion_state(state) with pytest.raises(OverflowError): ufunc(np.int64(0), 2**63) # 2**63 too large for int64 if state == "weak_and_warn": with pytest.warns(UserWarning, match="result dtype changed.*float64.*uint64"): with pytest.raises(OverflowError): ufunc(np.uint64(0), 2**64) else: with pytest.raises(OverflowError): ufunc(np.uint64(0), 2**64) # 2**64 cannot be represented by uint64 # However, 2**63 can be represented by the uint64 (and that is used): if state == "weak_and_warn": with pytest.warns(UserWarning, match="result dtype changed.*float64.*uint64"): res = ufunc(np.uint64(1), 2**63) else: res = ufunc(np.uint64(1), 2**63) assert res.dtype == np.uint64 assert res == ufunc(1, 2**63, dtype=object) # The following paths fail to warn correctly about the change: with pytest.raises(OverflowError): ufunc(np.int64(1), 2**63) # np.array(2**63) would go to uint with pytest.raises(OverflowError): ufunc(np.int64(1), 2**100) # np.array(2**100) would go to object # This would go to object and thus a Python float, not a NumPy one: res = ufunc(1.0, 2**100) assert isinstance(res, np.float64) def test_nep50_in_concat_and_choose(): np._set_promotion_state("weak_and_warn") with pytest.warns(UserWarning, match="result dtype changed"): res = np.concatenate([np.float32(1), 1.], axis=None) assert res.dtype == "float32" with pytest.warns(UserWarning, match="result dtype changed"): res = np.choose(1, [np.float32(1), 1.]) assert res.dtype == "float32"