""" Test printing of scalar types.
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"""
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import platform
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import pytest
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import numpy as np
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from numpy.testing import IS_MUSL, assert_, assert_equal, assert_raises
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class TestRealScalars:
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def test_str(self):
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svals = [0.0, -0.0, 1, -1, np.inf, -np.inf, np.nan]
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styps = [np.float16, np.float32, np.float64, np.longdouble]
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wanted = [
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['0.0', '0.0', '0.0', '0.0' ], # noqa: E202
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['-0.0', '-0.0', '-0.0', '-0.0'],
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['1.0', '1.0', '1.0', '1.0' ], # noqa: E202
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['-1.0', '-1.0', '-1.0', '-1.0'],
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['inf', 'inf', 'inf', 'inf' ], # noqa: E202
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['-inf', '-inf', '-inf', '-inf'],
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['nan', 'nan', 'nan', 'nan' ]] # noqa: E202
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for wants, val in zip(wanted, svals):
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for want, styp in zip(wants, styps):
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msg = f'for str({np.dtype(styp).name}({val!r}))'
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assert_equal(str(styp(val)), want, err_msg=msg)
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def test_scalar_cutoffs(self):
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# test that both the str and repr of np.float64 behaves
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# like python floats in python3.
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def check(v):
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assert_equal(str(np.float64(v)), str(v))
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assert_equal(str(np.float64(v)), repr(v))
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assert_equal(repr(np.float64(v)), f"np.float64({v!r})")
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assert_equal(repr(np.float64(v)), f"np.float64({v})")
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# check we use the same number of significant digits
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check(1.12345678901234567890)
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check(0.0112345678901234567890)
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# check switch from scientific output to positional and back
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check(1e-5)
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check(1e-4)
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check(1e15)
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check(1e16)
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test_cases_gh_28679 = [
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(np.half, -0.000099, "-9.9e-05"),
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(np.half, 0.0001, "0.0001"),
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(np.half, 999, "999.0"),
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(np.half, -1000, "-1e+03"),
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(np.single, 0.000099, "9.9e-05"),
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(np.single, -0.000100001, "-0.000100001"),
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(np.single, 999999, "999999.0"),
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(np.single, -1000000, "-1e+06")
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]
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@pytest.mark.parametrize("dtype, input_val, expected_str", test_cases_gh_28679)
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def test_gh_28679(self, dtype, input_val, expected_str):
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# test cutoff to exponent notation for half and single
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assert_equal(str(dtype(input_val)), expected_str)
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test_cases_legacy_2_2 = [
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(np.half(65504), "65500.0"),
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(np.single(1.e15), "1000000000000000.0"),
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(np.single(1.e16), "1e+16"),
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]
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@pytest.mark.parametrize("input_val, expected_str", test_cases_legacy_2_2)
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def test_legacy_2_2_mode(self, input_val, expected_str):
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# test legacy cutoff to exponent notation for half and single
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with np.printoptions(legacy='2.2'):
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assert_equal(str(input_val), expected_str)
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def test_dragon4(self):
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# these tests are adapted from Ryan Juckett's dragon4 implementation,
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# see dragon4.c for details.
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fpos32 = lambda x, **k: np.format_float_positional(np.float32(x), **k)
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fsci32 = lambda x, **k: np.format_float_scientific(np.float32(x), **k)
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fpos64 = lambda x, **k: np.format_float_positional(np.float64(x), **k)
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fsci64 = lambda x, **k: np.format_float_scientific(np.float64(x), **k)
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preckwd = lambda prec: {'unique': False, 'precision': prec}
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assert_equal(fpos32('1.0'), "1.")
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assert_equal(fsci32('1.0'), "1.e+00")
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assert_equal(fpos32('10.234'), "10.234")
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assert_equal(fpos32('-10.234'), "-10.234")
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assert_equal(fsci32('10.234'), "1.0234e+01")
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assert_equal(fsci32('-10.234'), "-1.0234e+01")
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assert_equal(fpos32('1000.0'), "1000.")
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assert_equal(fpos32('1.0', precision=0), "1.")
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assert_equal(fsci32('1.0', precision=0), "1.e+00")
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assert_equal(fpos32('10.234', precision=0), "10.")
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assert_equal(fpos32('-10.234', precision=0), "-10.")
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assert_equal(fsci32('10.234', precision=0), "1.e+01")
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assert_equal(fsci32('-10.234', precision=0), "-1.e+01")
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assert_equal(fpos32('10.234', precision=2), "10.23")
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assert_equal(fsci32('-10.234', precision=2), "-1.02e+01")
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assert_equal(fsci64('9.9999999999999995e-08', **preckwd(16)),
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'9.9999999999999995e-08')
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assert_equal(fsci64('9.8813129168249309e-324', **preckwd(16)),
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'9.8813129168249309e-324')
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assert_equal(fsci64('9.9999999999999694e-311', **preckwd(16)),
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'9.9999999999999694e-311')
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# test rounding
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# 3.1415927410 is closest float32 to np.pi
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assert_equal(fpos32('3.14159265358979323846', **preckwd(10)),
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"3.1415927410")
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assert_equal(fsci32('3.14159265358979323846', **preckwd(10)),
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"3.1415927410e+00")
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assert_equal(fpos64('3.14159265358979323846', **preckwd(10)),
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"3.1415926536")
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assert_equal(fsci64('3.14159265358979323846', **preckwd(10)),
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"3.1415926536e+00")
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# 299792448 is closest float32 to 299792458
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assert_equal(fpos32('299792458.0', **preckwd(5)), "299792448.00000")
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assert_equal(fsci32('299792458.0', **preckwd(5)), "2.99792e+08")
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assert_equal(fpos64('299792458.0', **preckwd(5)), "299792458.00000")
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assert_equal(fsci64('299792458.0', **preckwd(5)), "2.99792e+08")
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assert_equal(fpos32('3.14159265358979323846', **preckwd(25)),
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"3.1415927410125732421875000")
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assert_equal(fpos64('3.14159265358979323846', **preckwd(50)),
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"3.14159265358979311599796346854418516159057617187500")
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assert_equal(fpos64('3.14159265358979323846'), "3.141592653589793")
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# smallest numbers
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assert_equal(fpos32(0.5**(126 + 23), unique=False, precision=149),
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"0.00000000000000000000000000000000000000000000140129846432"
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"4817070923729583289916131280261941876515771757068283889791"
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"08268586060148663818836212158203125")
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assert_equal(fpos64(5e-324, unique=False, precision=1074),
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"0.00000000000000000000000000000000000000000000000000000000"
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"0000000000000000000000000000000000000000000000000000000000"
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"0000000000000000000000000000000000000000000000000000000000"
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"0000000000000000000000000000000000000000000000000000000000"
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"0000000000000000000000000000000000000000000000000000000000"
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"0000000000000000000000000000000000049406564584124654417656"
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"8792868221372365059802614324764425585682500675507270208751"
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"8652998363616359923797965646954457177309266567103559397963"
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"9877479601078187812630071319031140452784581716784898210368"
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"8718636056998730723050006387409153564984387312473397273169"
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"6151400317153853980741262385655911710266585566867681870395"
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"6031062493194527159149245532930545654440112748012970999954"
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"1931989409080416563324524757147869014726780159355238611550"
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"1348035264934720193790268107107491703332226844753335720832"
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"4319360923828934583680601060115061698097530783422773183292"
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"4790498252473077637592724787465608477820373446969953364701"
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"7972677717585125660551199131504891101451037862738167250955"
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"8373897335989936648099411642057026370902792427675445652290"
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"87538682506419718265533447265625")
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# largest numbers
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f32x = np.finfo(np.float32).max
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assert_equal(fpos32(f32x, **preckwd(0)),
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"340282346638528859811704183484516925440.")
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assert_equal(fpos64(np.finfo(np.float64).max, **preckwd(0)),
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"1797693134862315708145274237317043567980705675258449965989"
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"1747680315726078002853876058955863276687817154045895351438"
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"2464234321326889464182768467546703537516986049910576551282"
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"0762454900903893289440758685084551339423045832369032229481"
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"6580855933212334827479782620414472316873817718091929988125"
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"0404026184124858368.")
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# Warning: In unique mode only the integer digits necessary for
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# uniqueness are computed, the rest are 0.
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assert_equal(fpos32(f32x),
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"340282350000000000000000000000000000000.")
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# Further tests of zero-padding vs rounding in different combinations
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# of unique, fractional, precision, min_digits
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# precision can only reduce digits, not add them.
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# min_digits can only extend digits, not reduce them.
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assert_equal(fpos32(f32x, unique=True, fractional=True, precision=0),
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"340282350000000000000000000000000000000.")
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assert_equal(fpos32(f32x, unique=True, fractional=True, precision=4),
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"340282350000000000000000000000000000000.")
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assert_equal(fpos32(f32x, unique=True, fractional=True, min_digits=0),
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"340282346638528859811704183484516925440.")
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assert_equal(fpos32(f32x, unique=True, fractional=True, min_digits=4),
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"340282346638528859811704183484516925440.0000")
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assert_equal(fpos32(f32x, unique=True, fractional=True,
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min_digits=4, precision=4),
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"340282346638528859811704183484516925440.0000")
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assert_raises(ValueError, fpos32, f32x, unique=True, fractional=False,
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precision=0)
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assert_equal(fpos32(f32x, unique=True, fractional=False, precision=4),
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"340300000000000000000000000000000000000.")
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assert_equal(fpos32(f32x, unique=True, fractional=False, precision=20),
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"340282350000000000000000000000000000000.")
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assert_equal(fpos32(f32x, unique=True, fractional=False, min_digits=4),
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"340282350000000000000000000000000000000.")
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assert_equal(fpos32(f32x, unique=True, fractional=False,
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min_digits=20),
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"340282346638528859810000000000000000000.")
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assert_equal(fpos32(f32x, unique=True, fractional=False,
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min_digits=15),
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"340282346638529000000000000000000000000.")
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assert_equal(fpos32(f32x, unique=False, fractional=False, precision=4),
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"340300000000000000000000000000000000000.")
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# test that unique rounding is preserved when precision is supplied
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# but no extra digits need to be printed (gh-18609)
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a = np.float64.fromhex('-1p-97')
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assert_equal(fsci64(a, unique=True), '-6.310887241768095e-30')
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assert_equal(fsci64(a, unique=False, precision=15),
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'-6.310887241768094e-30')
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assert_equal(fsci64(a, unique=True, precision=15),
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'-6.310887241768095e-30')
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assert_equal(fsci64(a, unique=True, min_digits=15),
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'-6.310887241768095e-30')
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assert_equal(fsci64(a, unique=True, precision=15, min_digits=15),
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'-6.310887241768095e-30')
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# adds/remove digits in unique mode with unbiased rnding
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assert_equal(fsci64(a, unique=True, precision=14),
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'-6.31088724176809e-30')
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assert_equal(fsci64(a, unique=True, min_digits=16),
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'-6.3108872417680944e-30')
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assert_equal(fsci64(a, unique=True, precision=16),
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'-6.310887241768095e-30')
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assert_equal(fsci64(a, unique=True, min_digits=14),
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'-6.310887241768095e-30')
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# test min_digits in unique mode with different rounding cases
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assert_equal(fsci64('1e120', min_digits=3), '1.000e+120')
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assert_equal(fsci64('1e100', min_digits=3), '1.000e+100')
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# test trailing zeros
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assert_equal(fpos32('1.0', unique=False, precision=3), "1.000")
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assert_equal(fpos64('1.0', unique=False, precision=3), "1.000")
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assert_equal(fsci32('1.0', unique=False, precision=3), "1.000e+00")
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assert_equal(fsci64('1.0', unique=False, precision=3), "1.000e+00")
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assert_equal(fpos32('1.5', unique=False, precision=3), "1.500")
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assert_equal(fpos64('1.5', unique=False, precision=3), "1.500")
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assert_equal(fsci32('1.5', unique=False, precision=3), "1.500e+00")
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assert_equal(fsci64('1.5', unique=False, precision=3), "1.500e+00")
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# gh-10713
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assert_equal(fpos64('324', unique=False, precision=5,
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fractional=False), "324.00")
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available_float_dtypes = [np.float16, np.float32, np.float64, np.float128]\
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if hasattr(np, 'float128') else [np.float16, np.float32, np.float64]
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@pytest.mark.parametrize("tp", available_float_dtypes)
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def test_dragon4_positional_interface(self, tp):
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# test is flaky for musllinux on np.float128
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if IS_MUSL and tp == np.float128:
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pytest.skip("Skipping flaky test of float128 on musllinux")
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fpos = np.format_float_positional
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# test padding
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assert_equal(fpos(tp('1.0'), pad_left=4, pad_right=4), " 1. ")
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assert_equal(fpos(tp('-1.0'), pad_left=4, pad_right=4), " -1. ")
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assert_equal(fpos(tp('-10.2'),
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pad_left=4, pad_right=4), " -10.2 ")
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# test fixed (non-unique) mode
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assert_equal(fpos(tp('1.0'), unique=False, precision=4), "1.0000")
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@pytest.mark.parametrize("tp", available_float_dtypes)
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def test_dragon4_positional_interface_trim(self, tp):
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# test is flaky for musllinux on np.float128
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if IS_MUSL and tp == np.float128:
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pytest.skip("Skipping flaky test of float128 on musllinux")
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fpos = np.format_float_positional
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# test trimming
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# trim of 'k' or '.' only affects non-unique mode, since unique
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# mode will not output trailing 0s.
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assert_equal(fpos(tp('1.'), unique=False, precision=4, trim='k'),
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"1.0000")
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assert_equal(fpos(tp('1.'), unique=False, precision=4, trim='.'),
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"1.")
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assert_equal(fpos(tp('1.2'), unique=False, precision=4, trim='.'),
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"1.2" if tp != np.float16 else "1.2002")
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assert_equal(fpos(tp('1.'), unique=False, precision=4, trim='0'),
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"1.0")
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assert_equal(fpos(tp('1.2'), unique=False, precision=4, trim='0'),
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"1.2" if tp != np.float16 else "1.2002")
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assert_equal(fpos(tp('1.'), trim='0'), "1.0")
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assert_equal(fpos(tp('1.'), unique=False, precision=4, trim='-'),
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"1")
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assert_equal(fpos(tp('1.2'), unique=False, precision=4, trim='-'),
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"1.2" if tp != np.float16 else "1.2002")
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assert_equal(fpos(tp('1.'), trim='-'), "1")
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assert_equal(fpos(tp('1.001'), precision=1, trim='-'), "1")
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@pytest.mark.parametrize("tp", available_float_dtypes)
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@pytest.mark.parametrize("pad_val", [10**5, np.iinfo("int32").max])
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def test_dragon4_positional_interface_overflow(self, tp, pad_val):
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# test is flaky for musllinux on np.float128
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if IS_MUSL and tp == np.float128:
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pytest.skip("Skipping flaky test of float128 on musllinux")
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fpos = np.format_float_positional
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# gh-28068
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with pytest.raises(RuntimeError,
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match="Float formatting result too large"):
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fpos(tp('1.047'), unique=False, precision=pad_val)
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with pytest.raises(RuntimeError,
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match="Float formatting result too large"):
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fpos(tp('1.047'), precision=2, pad_left=pad_val)
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with pytest.raises(RuntimeError,
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match="Float formatting result too large"):
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fpos(tp('1.047'), precision=2, pad_right=pad_val)
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@pytest.mark.parametrize("tp", available_float_dtypes)
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def test_dragon4_scientific_interface(self, tp):
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# test is flaky for musllinux on np.float128
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if IS_MUSL and tp == np.float128:
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pytest.skip("Skipping flaky test of float128 on musllinux")
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fsci = np.format_float_scientific
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# test exp_digits
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assert_equal(fsci(tp('1.23e1'), exp_digits=5), "1.23e+00001")
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# test fixed (non-unique) mode
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assert_equal(fsci(tp('1.0'), unique=False, precision=4),
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"1.0000e+00")
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@pytest.mark.skipif(not platform.machine().startswith("ppc64"),
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reason="only applies to ppc float128 values")
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def test_ppc64_ibm_double_double128(self):
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# check that the precision decreases once we get into the subnormal
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# range. Unlike float64, this starts around 1e-292 instead of 1e-308,
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# which happens when the first double is normal and the second is
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# subnormal.
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x = np.float128('2.123123123123123123123123123123123e-286')
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got = [str(x / np.float128('2e' + str(i))) for i in range(40)]
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expected = [
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"1.06156156156156156156156156156157e-286",
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"1.06156156156156156156156156156158e-287",
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"1.06156156156156156156156156156159e-288",
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"1.0615615615615615615615615615616e-289",
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"1.06156156156156156156156156156157e-290",
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"1.06156156156156156156156156156156e-291",
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"1.0615615615615615615615615615616e-292",
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"1.0615615615615615615615615615615e-293",
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"1.061561561561561561561561561562e-294",
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"1.06156156156156156156156156155e-295",
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"1.0615615615615615615615615616e-296",
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"1.06156156156156156156156156e-297",
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"1.06156156156156156156156157e-298",
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"1.0615615615615615615615616e-299",
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"1.06156156156156156156156e-300",
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"1.06156156156156156156155e-301",
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"1.0615615615615615615616e-302",
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"1.061561561561561561562e-303",
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"1.06156156156156156156e-304",
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"1.0615615615615615618e-305",
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"1.06156156156156156e-306",
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"1.06156156156156157e-307",
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"1.0615615615615616e-308",
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"1.06156156156156e-309",
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"1.06156156156157e-310",
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"1.0615615615616e-311",
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"1.06156156156e-312",
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"1.06156156154e-313",
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"1.0615615616e-314",
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"1.06156156e-315",
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"1.06156155e-316",
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"1.061562e-317",
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"1.06156e-318",
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"1.06155e-319",
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"1.0617e-320",
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"1.06e-321",
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"1.04e-322",
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"1e-323",
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"0.0",
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"0.0"]
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assert_equal(got, expected)
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# Note: we follow glibc behavior, but it (or gcc) might not be right.
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# In particular we can get two values that print the same but are not
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# equal:
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a = np.float128('2') / np.float128('3')
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b = np.float128(str(a))
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assert_equal(str(a), str(b))
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assert_(a != b)
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def float32_roundtrip(self):
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# gh-9360
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x = np.float32(1024 - 2**-14)
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y = np.float32(1024 - 2**-13)
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assert_(repr(x) != repr(y))
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assert_equal(np.float32(repr(x)), x)
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assert_equal(np.float32(repr(y)), y)
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def float64_vs_python(self):
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# gh-2643, gh-6136, gh-6908
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assert_equal(repr(np.float64(0.1)), repr(0.1))
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assert_(repr(np.float64(0.20000000000000004)) != repr(0.2))
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