Elep

""" 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)