def test_save_gyre(self):
header1, data1 = gyre.load_gyre('data/mesa.gyre')
gyre.save_gyre(tmpfile, header1, data1)
header2, data2 = gyre.load_gyre(tmpfile)
self.assertEqual(header1, header2)
for row1, row2 in zip(data1, data2):
self.assertEqual(row1, row2)
def test_save_gyre(self):
m1 = gyre.load_gyre('data/mesa.gyre')
m1.to_file(tmpfile)
m2 = gyre.load_gyre(tmpfile)
np.testing.assert_equal(m1.header, m2.header)
np.testing.assert_equal(m1.data, m2.data)
np.testing.assert_allclose(m1.r, m2.x * m2.R)
np.testing.assert_allclose(m1.cs2, m2.Gamma_1 * m2.P / m2.rho)
np.testing.assert_allclose(m1.AA[1:], m2.N2[1:] * m2.r[1:] / m2.g[1:])
def test_load_spb_mesa_versions(self):
filenames = [
'data/spb.mesa.78677cc', 'data/spb.mesa.813eed2',
'data/spb.mesa.adc6989'
]
for filename in filenames:
header1, data1 = gyre.load_gyre(filename)
gyre.save_gyre(tmpfile, header1, data1)
header2, data2 = gyre.load_gyre(tmpfile)
self.assertEqual(header1, header2)
for row1, row2 in zip(data1, data2):
self.assertEqual(row1, row2)
def test_load_gyre(self):
header, data = gyre.load_gyre('data/mesa.gyre')
self.assertEqual(header['n'], 601)
self.assertAlmostEqual(header['M'], 1.9882053999999999E+33)
self.assertAlmostEqual(header['R'], 6.2045507132959908E+10)
self.assertAlmostEqual(header['L'], 3.3408563666602257E+33)
self.assertEqual(header['version'], 101)
def test_load_spb_mesa_versions(self):
filenames = [
'data/spb.mesa.78677cc', 'data/spb.mesa.813eed2',
'data/spb.mesa.adc6989'
]
for filename in filenames:
m1 = gyre.load_gyre('data/mesa.gyre')
m1.to_file(tmpfile)
m2 = gyre.load_gyre(tmpfile)
np.testing.assert_equal(m1.header, m2.header)
np.testing.assert_equal(m1.data, m2.data)
np.testing.assert_allclose(m1.r, m2.x * m2.R)
np.testing.assert_allclose(m1.cs2, m2.Gamma_1 * m2.P / m2.rho)
np.testing.assert_allclose(m1.AA[1:],
m2.N2[1:] * m2.r[1:] / m2.g[1:])
def test_load_gyre(self):
m = gyre.load_gyre('data/mesa.gyre')
self.assertEqual(len(m.data), 601)
self.assertAlmostEqual(m.M, 1.9882053999999999E+33)
self.assertAlmostEqual(m.R, 6.2045507132959908E+10)
self.assertAlmostEqual(m.L, 3.3408563666602257E+33)
self.assertEqual(m.version, 101)
np.testing.assert_allclose(m.Omega, 0)
np.testing.assert_allclose(m.Gamma_1 / (m.Gamma_3 - 1),
m.Gamma_2 / (m.Gamma_2 - 1),
rtol=1e-14,
atol=1e-14)
s = '%r' % m
r = gyre.load_gyre(remote_url + 'data/mesa.gyre')
np.testing.assert_equal(m.header, r.header)
np.testing.assert_equal(m.data, r.data)
def convert(args):
"""Convert function for `tomso` command-line script."""
from_format = (guess_format(args.input_file)
if args.from_format == 'guess'
else args.from_format)
to_format = (guess_format(args.output_file)
if args.to_format == 'guess'
else args.to_format)
if from_format == to_format:
raise ValueError("input format and output format are both %s\n"
"did you mean to copy the file?" % from_format)
kwargs = {}
if args.G is not None:
kwargs['G'] = args.G
if from_format == 'fgong':
from tomso.fgong import load_fgong
m = load_fgong(args.input_file, **kwargs)
elif from_format == 'amdl':
from tomso.adipls import load_amdl
m = load_amdl(args.input_file, **kwargs)
elif from_format == 'gyre':
from tomso.gyre import load_gyre
m = load_gyre(args.input_file, **kwargs)
else:
raise ValueError("%s is not a valid input format" % from_format)
if to_format == 'fgong':
m.to_fgong(ivers=args.ivers).to_file(args.output_file)
elif to_format == 'amdl':
m.to_amdl().to_file(args.output_file)
elif to_format == 'gyre':
m.to_gyre().to_file(args.output_file)
else:
raise ValueError("%s is not a valid output format" % to_format)
def test_gyre_to_gyre(self):
g = load_gyre('data/mesa.gyre')
self.compare_models(g, g.to_fgong().to_gyre(), thermo=True)
'ELat': '[degrees] ecliptic latitude',
'tred': '',
'region': '',
'max_T': '',
'Pdet_fixedBeta': 'detection probability',
'SNR_fixedBeta': 'signal-to-noise ratio',
'Pdet_varyBeta': 'detection probability',
'SNR_varyBeta': 'signal-to-noise ratio',
'P_mix': 'average detection probability',
'Rank_Pmix': 'rank of average detection probability'
}
history_header, history_data = mesa.load_history(folder +
'/LOGS/history.data.gz')
profile_header, profile_data = mesa.load_profile(folder + '/final.profile.gz')
gyre_header, gyre_profile = gyre.load_gyre(folder + '/final.profile.GYRE.rot')
nml = AADG3.load_namelist(basename + '.in')
L = 10.**history_data['log_L'][-1]
R = 10.**history_data[-1]['log_R']
M = history_data[-1]['star_mass']
Teff = 10.**history_data[-1]['log_Teff']
numax = numax_sun * (M / R**2 / (Teff / Teff_sun)**0.5)
Dnu = Dnu_sun * np.sqrt(M / R**3)
Tred = Tred_sun * L**-0.093
beta = 1.0 - np.exp((Teff - Tred) / dT)
Amax = Amax_sun * beta * L / M * (Teff / Teff_sun)**-2
Henv = Amax**2 / Dnu
wenv = 0.66 * numax**0.88
'--output',
type=str,
default=None,
help="Filename for output GYRE model "
"(default=append .rot to source model filename)")
parser.add_argument(
'--no-core-noise',
action='store_true',
help="don't randomly sample uncertainty in core rotation rate")
parser.add_argument(
'--no-env-noise',
action='store_true',
help="don't randomly sample uncertainty in envelope rotation rate")
args = parser.parse_args()
gyre_header, profile = gyre.load_gyre(args.model)
mesa_header, history = mesa.load_history(args.history)
# P = P_mamabrand(10.**history['log_Teff'][-1], history['star_age'][-1]/1e6,
# noisy=not args.no_env_noise)*86400.0
P = P_angus(10.**history['log_Teff'][-1],
10.0,
history['star_age'][-1] / 1e6,
noisy=not args.no_env_noise) * 86400.0
Omega_env = 2. * np.pi / P
profile['Omega'] = Omega_env
if history['center_h1'][
-1] < 1e-4: # and np.log10(history['gravity'][-1]) < 3.8:
# decrease rotation rate by a factor (R/R_TAMS)^2
R = 10.**history['log_R'][-1]