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Viewing File: test_getlimits.py
""" Test functions for limits module.
"""
import warnings
import numpy as np
import pytest
from numpy.core import finfo, iinfo
from numpy import half, single, double, longdouble
from numpy.testing import assert_equal, assert_, assert_raises
from numpy.core.getlimits import _discovered_machar, _float_ma
##################################################
class TestPythonFloat:
def test_singleton(self):
ftype = finfo(float)
ftype2 = finfo(float)
assert_equal(id(ftype), id(ftype2))
class TestHalf:
def test_singleton(self):
ftype = finfo(half)
ftype2 = finfo(half)
assert_equal(id(ftype), id(ftype2))
class TestSingle:
def test_singleton(self):
ftype = finfo(single)
ftype2 = finfo(single)
assert_equal(id(ftype), id(ftype2))
class TestDouble:
def test_singleton(self):
ftype = finfo(double)
ftype2 = finfo(double)
assert_equal(id(ftype), id(ftype2))
class TestLongdouble:
def test_singleton(self):
ftype = finfo(longdouble)
ftype2 = finfo(longdouble)
assert_equal(id(ftype), id(ftype2))
def assert_finfo_equal(f1, f2):
# assert two finfo instances have the same attributes
for attr in ('bits', 'eps', 'epsneg', 'iexp', 'machep',
'max', 'maxexp', 'min', 'minexp', 'negep', 'nexp',
'nmant', 'precision', 'resolution', 'tiny',
'smallest_normal', 'smallest_subnormal'):
assert_equal(getattr(f1, attr), getattr(f2, attr),
f'finfo instances {f1} and {f2} differ on {attr}')
def assert_iinfo_equal(i1, i2):
# assert two iinfo instances have the same attributes
for attr in ('bits', 'min', 'max'):
assert_equal(getattr(i1, attr), getattr(i2, attr),
f'iinfo instances {i1} and {i2} differ on {attr}')
class TestFinfo:
def test_basic(self):
dts = list(zip(['f2', 'f4', 'f8', 'c8', 'c16'],
[np.float16, np.float32, np.float64, np.complex64,
np.complex128]))
for dt1, dt2 in dts:
assert_finfo_equal(finfo(dt1), finfo(dt2))
assert_raises(ValueError, finfo, 'i4')
def test_regression_gh23108(self):
# np.float32(1.0) and np.float64(1.0) have the same hash and are
# equal under the == operator
f1 = np.finfo(np.float32(1.0))
f2 = np.finfo(np.float64(1.0))
assert f1 != f2
def test_regression_gh23867(self):
class NonHashableWithDtype:
__hash__ = None
dtype = np.dtype('float32')
x = NonHashableWithDtype()
assert np.finfo(x) == np.finfo(x.dtype)
class TestIinfo:
def test_basic(self):
dts = list(zip(['i1', 'i2', 'i4', 'i8',
'u1', 'u2', 'u4', 'u8'],
[np.int8, np.int16, np.int32, np.int64,
np.uint8, np.uint16, np.uint32, np.uint64]))
for dt1, dt2 in dts:
assert_iinfo_equal(iinfo(dt1), iinfo(dt2))
assert_raises(ValueError, iinfo, 'f4')
def test_unsigned_max(self):
types = np.sctypes['uint']
for T in types:
with np.errstate(over="ignore"):
max_calculated = T(0) - T(1)
assert_equal(iinfo(T).max, max_calculated)
class TestRepr:
def test_iinfo_repr(self):
expected = "iinfo(min=-32768, max=32767, dtype=int16)"
assert_equal(repr(np.iinfo(np.int16)), expected)
def test_finfo_repr(self):
expected = "finfo(resolution=1e-06, min=-3.4028235e+38," + \
" max=3.4028235e+38, dtype=float32)"
assert_equal(repr(np.finfo(np.float32)), expected)
def test_instances():
# Test the finfo and iinfo results on numeric instances agree with
# the results on the corresponding types
for c in [int, np.int16, np.int32, np.int64]:
class_iinfo = iinfo(c)
instance_iinfo = iinfo(c(12))
assert_iinfo_equal(class_iinfo, instance_iinfo)
for c in [float, np.float16, np.float32, np.float64]:
class_finfo = finfo(c)
instance_finfo = finfo(c(1.2))
assert_finfo_equal(class_finfo, instance_finfo)
with pytest.raises(ValueError):
iinfo(10.)
with pytest.raises(ValueError):
iinfo('hi')
with pytest.raises(ValueError):
finfo(np.int64(1))
def assert_ma_equal(discovered, ma_like):
# Check MachAr-like objects same as calculated MachAr instances
for key, value in discovered.__dict__.items():
assert_equal(value, getattr(ma_like, key))
if hasattr(value, 'shape'):
assert_equal(value.shape, getattr(ma_like, key).shape)
assert_equal(value.dtype, getattr(ma_like, key).dtype)
def test_known_types():
# Test we are correctly compiling parameters for known types
for ftype, ma_like in ((np.float16, _float_ma[16]),
(np.float32, _float_ma[32]),
(np.float64, _float_ma[64])):
assert_ma_equal(_discovered_machar(ftype), ma_like)
# Suppress warning for broken discovery of double double on PPC
with np.errstate(all='ignore'):
ld_ma = _discovered_machar(np.longdouble)
bytes = np.dtype(np.longdouble).itemsize
if (ld_ma.it, ld_ma.maxexp) == (63, 16384) and bytes in (12, 16):
# 80-bit extended precision
assert_ma_equal(ld_ma, _float_ma[80])
elif (ld_ma.it, ld_ma.maxexp) == (112, 16384) and bytes == 16:
# IEE 754 128-bit
assert_ma_equal(ld_ma, _float_ma[128])
def test_subnormal_warning():
"""Test that the subnormal is zero warning is not being raised."""
with np.errstate(all='ignore'):
ld_ma = _discovered_machar(np.longdouble)
bytes = np.dtype(np.longdouble).itemsize
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
if (ld_ma.it, ld_ma.maxexp) == (63, 16384) and bytes in (12, 16):
# 80-bit extended precision
ld_ma.smallest_subnormal
assert len(w) == 0
elif (ld_ma.it, ld_ma.maxexp) == (112, 16384) and bytes == 16:
# IEE 754 128-bit
ld_ma.smallest_subnormal
assert len(w) == 0
else:
# Double double
ld_ma.smallest_subnormal
# This test may fail on some platforms
assert len(w) == 0
def test_plausible_finfo():
# Assert that finfo returns reasonable results for all types
for ftype in np.sctypes['float'] + np.sctypes['complex']:
info = np.finfo(ftype)
assert_(info.nmant > 1)
assert_(info.minexp < -1)
assert_(info.maxexp > 1)
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