def test_fitting():
mod_dar = _check_fitting(StarModel(Dartmouth_Isochrone, **props))
mod_mist = _check_fitting(StarModel(MIST_Isochrone, **props))
_check_saving(mod_dar)
_check_saving(mod_mist)
def _check_spec(ic):
mod = StarModel(ic, Teff=(5700, 100), logg=(4.5, 0.1), feh=(0.0, 0.2))
eep = ic.get_eep(1., 9.6, 0.1, accurate=True)
assert np.isfinite(mod.lnlike([eep, 9.6, 0.1, 200, 0.2]))
def iso_age(teff, logg, feh):
dar = Dartmouth_Isochrone()
mod = StarModel(dar, Teff=(teff, 80), Logg=(logg, 0.10), Feh=(feh, 0.1))
logage, _, _ = mod.maxlike()
age = np.exp(logage)
return age
def test_fitting():
mod_mist = _check_fitting(StarModel(MIST_Isochrone, **props))
_check_saving(mod_mist)
#("W3", "w3mpro", "w3sigmpro"),
#("G", "G", "ERR_G"),
#("R", "R", "ERR_R"),
#("I", "I", "ERR_I")
]
for magnitude, column, error_column in columns:
if np.isfinite(star[column]) and np.isfinite(star[error_column]):
kwds[magnitude] = (star[column], star[error_column])
# Update kwds with parallax if available.
if np.isfinite(star["parallax"]):
kwds["parallax"] = (star["parallax"], star["parallax_error"])
print(kwds)
model = StarModel(mist, **kwds)
# Update uniform priors on age and distance.
model._bounds["age"] = (np.log10(5.0e9), np.log10(13.721e9))
model._bounds["distance"] = distance_bounds.get(star["Name"], (0, 50000.0))
model.fit(refit=True, n_live_points=1000, evidence_tolerance=0.5)
model.samples.to_csv(output_path)
fig_output_path = os.path.join(DATA_FOLDER,
"{}_mist_samples.pdf".format(star["Name"]))
fig = model.corner_physical()
try:
fig.savefig(fig_output_path)
except:
def test_likelihood_rotation_giant():
"""
Make sure that the lhf can cope with zeros, NaNs and None values for the
rotation period.
Also, check that there is a drop in likelihood at the eep = 454 boundary.
"""
iso_params = pd.DataFrame(dict({"teff": (5777, 10),
"logg": (4.44, .05),
"feh": (0., .001),
"parallax": (1., .01)})) # mas
# Set up the StarModel isochrones object.
mod = StarModel(mist, **iso_params)
lnparams = [355, np.log10(4.56*1e9), 0., np.log(1000), 0.]
args = [mod, None, None, .65, 1., False, False] # the lnprob arguments]
none_lnprob = lnprob(lnparams, *args)
args = [mod, np.nan, np.nan, .65, 1., False, False] # the lnprob arguments]
nan_lnprob = lnprob(lnparams, *args)
args = [mod, 0., 0., .65, 1., False, False] # the lnprob arguments]
zero_lnprob = lnprob(lnparams, *args)
args = [mod, 26., 1., .65, 1., True, False] # the lnprob arguments]
iso_lnprob = lnprob(lnparams, *args)
args = [mod, 26., 1., .65, 1., False, True] # the lnprob arguments]
gyro_lnprob = lnprob(lnparams, *args)
# check that gyro is switched off for all of these.
none_lnprob == nan_lnprob
nan_lnprob == zero_lnprob
# check that gyro on gives different lnprob
assert gyro_lnprob != iso_lnprob
# Likelihood should be greater for dwarfs because gyro lnlike is a broad
# Gaussian for giants.
giant_params = [455, np.log10(4.56*1e9), 0., np.log(1000), 0.]
dwarf_params = [453, np.log10(4.56*1e9), 0., np.log(1000), 0.]
args = [mod, 26., 1., None, None, False, False]
giant_lnprob = lnprob(giant_params, *args)
dwarf_lnprob = lnprob(dwarf_params, *args)
assert giant_lnprob[0] < dwarf_lnprob[0]
# Likelihood should be greater for cool stars because gyro lnlike is a
# broad Gaussian for giants.
heep, hage, hfeh = 405, np.log10(2.295*1e9), 0.
ceep, cage, cfeh = 355, np.log10(4.56*1e9), 0.
hot_params = [heep, hage, hfeh, np.log(1000), 0.]
cool_params = [ceep, cage, cfeh, np.log(1000), 0.]
cool_prot = gyro_model_rossby(
cage, calc_bv(cool_params),
mist.interp_value([ceep, cage, cfeh], ["mass"]))
hot_prot = gyro_model_rossby(
hage, calc_bv(hot_params),
mist.interp_value([heep, hage, hfeh], ["mass"]))
cool_args = [mod, cool_prot, 1., None, None, False, False]
hot_args = [mod, hot_prot, 1., None, None, False, False]
hot_lnprob = lnprob(hot_params, *args)
cool_lnprob = lnprob(cool_params, *args)
assert hot_lnprob[0] < cool_lnprob[0], "cool star likelihood should be" \
" higher than hot star likelihood"