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test_casting_floatingpoint_errors.py
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test_casting_floatingpoint_errors.py
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import pytest
from pytest import param
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.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()