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