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Edit File: test_casting_floatingpoint_errors.py
import pytest from pytest import param from numpy.testing import IS_WASM import numpy as np def values_and_dtypes(): """ Generate value+dtype pairs that generate floating point errors during casts. The invalid casts to integers will generate "invalid" value warnings, the float casts all generate "overflow". (The Python int/float paths don't need to get tested in all the same situations, but it does not hurt.) """ # Casting to float16: yield param(70000, "float16", id="int-to-f2") yield param("70000", "float16", id="str-to-f2") yield param(70000.0, "float16", id="float-to-f2") yield param(np.longdouble(70000.), "float16", id="longdouble-to-f2") yield param(np.float64(70000.), "float16", id="double-to-f2") yield param(np.float32(70000.), "float16", id="float-to-f2") # Casting to float32: yield param(10**100, "float32", id="int-to-f4") yield param(1e100, "float32", id="float-to-f2") yield param(np.longdouble(1e300), "float32", id="longdouble-to-f2") yield param(np.float64(1e300), "float32", id="double-to-f2") # Casting to float64: # If longdouble is double-double, its max can be rounded down to the double # max. So we correct the double spacing (a bit weird, admittedly): max_ld = np.finfo(np.longdouble).max spacing = np.spacing(np.nextafter(np.finfo("f8").max, 0)) if max_ld - spacing > np.finfo("f8").max: yield param(np.finfo(np.longdouble).max, "float64", id="longdouble-to-f8") # Cast to complex32: yield param(2e300, "complex64", id="float-to-c8") yield param(2e300+0j, "complex64", id="complex-to-c8") yield param(2e300j, "complex64", id="complex-to-c8") yield param(np.longdouble(2e300), "complex64", id="longdouble-to-c8") # Invalid float to integer casts: with np.errstate(over="ignore"): for to_dt in np.typecodes["AllInteger"]: for value in [np.inf, np.nan]: for from_dt in np.typecodes["AllFloat"]: from_dt = np.dtype(from_dt) from_val = from_dt.type(value) yield param(from_val, to_dt, id=f"{from_val}-to-{to_dt}") def check_operations(dtype, value): """ There are many dedicated paths in NumPy which cast and should check for floating point errors which occurred during those casts. """ if dtype.kind != 'i': # These assignments use the stricter setitem logic: def assignment(): arr = np.empty(3, dtype=dtype) arr[0] = value yield assignment def fill(): arr = np.empty(3, dtype=dtype) arr.fill(value) yield fill def copyto_scalar(): arr = np.empty(3, dtype=dtype) np.copyto(arr, value, casting="unsafe") yield copyto_scalar def copyto(): arr = np.empty(3, dtype=dtype) np.copyto(arr, np.array([value, value, value]), casting="unsafe") yield copyto def copyto_scalar_masked(): arr = np.empty(3, dtype=dtype) np.copyto(arr, value, casting="unsafe", where=[True, False, True]) yield copyto_scalar_masked def copyto_masked(): arr = np.empty(3, dtype=dtype) np.copyto(arr, np.array([value, value, value]), casting="unsafe", where=[True, False, True]) yield copyto_masked def direct_cast(): np.array([value, value, value]).astype(dtype) yield direct_cast def direct_cast_nd_strided(): arr = np.full((5, 5, 5), fill_value=value)[:, ::2, :] arr.astype(dtype) yield direct_cast_nd_strided def boolean_array_assignment(): arr = np.empty(3, dtype=dtype) arr[[True, False, True]] = np.array([value, value]) yield boolean_array_assignment def integer_array_assignment(): arr = np.empty(3, dtype=dtype) values = np.array([value, value]) arr[[0, 1]] = values yield integer_array_assignment def integer_array_assignment_with_subspace(): arr = np.empty((5, 3), dtype=dtype) values = np.array([value, value, value]) arr[[0, 2]] = values yield integer_array_assignment_with_subspace def flat_assignment(): arr = np.empty((3,), dtype=dtype) values = np.array([value, value, value]) arr.flat[:] = values yield flat_assignment @pytest.mark.skipif(IS_WASM, reason="no wasm fp exception support") @pytest.mark.parametrize(["value", "dtype"], values_and_dtypes()) @pytest.mark.filterwarnings("ignore::numpy.ComplexWarning") def test_floatingpoint_errors_casting(dtype, value): dtype = np.dtype(dtype) for operation in check_operations(dtype, value): dtype = np.dtype(dtype) match = "invalid" if dtype.kind in 'iu' else "overflow" with pytest.warns(RuntimeWarning, match=match): operation() with np.errstate(all="raise"): with pytest.raises(FloatingPointError, match=match): operation()