def fit_visit_spectra_single_star_model(norm_spectra,
spec_errs,
NN_coeffs_norm,
NN_coeffs_flux,
v_helios,
p0,
num_p0=1):
'''
fit a set of visit spectra for a single object simultaneously with a single-star
model, with the restriction that the velocity be the same at each epoch.
v_helios is a guess of the heliocentric velocity at each visit, taken from
the allStar catalog.
p0 is a starting guess for the optimizer, typically obtained from fitting
the combined spectrum
'''
tol = 5e-4 # tolerance for when the optimizer should stop optimizing.
stitched_spec, stitched_errs = np.concatenate(
norm_spectra), np.concatenate(spec_errs)
def fit_func(dummy_variable, *labels):
stitched_model = spectral_model.single_star_model_visit_spectra(
labels=labels,
spec_errs=spec_errs,
NN_coeffs_norm=NN_coeffs_norm,
NN_coeffs_flux=NN_coeffs_flux)
return stitched_model
lower = np.array([4200, 4.0, -1, -0.3, 0, -100 + np.median(v_helios)])
upper = np.array([7000, 5.0, 0.5, 0.5, 45, 100 + np.median(v_helios)])
bounds = [lower, upper]
# coming from the combined fit, the velocity label in p0 will be relative the
# rest frame assumed in making the combined spectrum.
p00 = np.copy(p0)
p00[5] += np.median(v_helios)
# if we want to initialize many walkers in different parts of parameter space, do so now.
all_x0 = generate_starting_guesses_to_initialze_optimizers(
p0=p00,
bounds=bounds,
num_p0=num_p0,
vrange=(np.max(v_helios) - np.min(v_helios)) / 2,
model='single_star')
# run the optimizer
popt, pcov, model_spec = fit_all_p0s(fit_func=fit_func,
norm_spec=stitched_spec,
spec_err=stitched_errs,
all_x0=all_x0,
bounds=bounds,
tol=tol)
model_specs = utils.unstitch_model_spectra(model_spec=model_spec,
wavelength=wavelength)
return popt, pcov, model_specs
def fit_visit_spectra_sb1_model(norm_spectra,
spec_errs,
NN_coeffs_norm,
NN_coeffs_flux,
v_helios,
p0,
num_p0=5):
'''
fit a set of visit spectra for a single object simultaneously with an sb1
model. I.e., the same single-star spectral model makes the spectrum at
each visit, but allow the heliocentric velocity to vary between visits.
v_helios is a guess of the heliocentric velocity at each visit, taken from
the allStar catalog.
p0 is a starting guess for the optimizer, typically obtained from fitting
the combined spectrum
'''
tol = 5e-4 # tolerance for when the optimizer should stop optimizing.
stitched_spec, stitched_errs = np.concatenate(
norm_spectra), np.concatenate(spec_errs)
def fit_func(dummy_variable, *labels):
stitched_model = spectral_model.sb1_model_visit_spectra(
labels=labels,
spec_errs=spec_errs,
NN_coeffs_norm=NN_coeffs_norm,
NN_coeffs_flux=NN_coeffs_flux)
return stitched_model
v_min, v_max = np.min(v_helios), np.max(v_helios)
lower = np.concatenate([[4200, 4.0, -1, -0.3, 0],
len(v_helios) * [-100 + v_min]])
upper = np.concatenate([[7000, 5.0, 0.5, 0.5, 45],
len(v_helios) * [100 + v_max]])
bounds = [lower, upper]
p00 = np.concatenate([p0[:5], v_helios])
all_x0 = generate_starting_guesses_to_initialze_optimizers(
p0=p00,
bounds=bounds,
num_p0=num_p0,
vrange=(v_max - v_min) / 2,
model='sb1')
popt, pcov, model_spec = fit_all_p0s(fit_func=fit_func,
norm_spec=stitched_spec,
spec_err=stitched_errs,
all_x0=all_x0,
bounds=bounds,
tol=tol)
model_specs = utils.unstitch_model_spectra(model_spec=model_spec,
wavelength=wavelength)
return popt, pcov, model_specs
def fit_visit_spectra_sb2_model(norm_spectra,
spec_errs,
NN_coeffs_norm,
NN_coeffs_flux,
NN_coeffs_R,
NN_coeffs_Teff2_logg2,
v_helios,
p0_combined,
num_p0=5):
'''
fit a set of visit spectra for a single target simultaneously with an sb2
model. This requires the velocities of the two stars to follow conservation
of momentum; i.e., to fall on a line with negative slope when plotted
against each other.
v_helios is a guess of the heliocentric velocity at each visit, taken from
the allStar catalog.
p0_combined is a starting guess for the optimizer. It's expected to be in the format
returned from fitting a binary model to a *combined* spectrum, i.e.,
p0_combined = [Teff1, logg1, [Fe/H], [Mg/Fe], q, vmacro1, vmacro2, dv1, dv2]
'''
tol = 5e-4 # tolerance for when the optimizer should stop optimizing.
stitched_spec, stitched_errs = np.concatenate(
norm_spectra), np.concatenate(spec_errs)
def fit_func(dummy_variable, *labels):
stitched_model = spectral_model.sb2_model_visit_spectra(
labels=labels,
spec_errs=spec_errs,
NN_coeffs_norm=NN_coeffs_norm,
NN_coeffs_flux=NN_coeffs_flux,
NN_coeffs_R=NN_coeffs_R,
NN_coeffs_Teff2_logg2=NN_coeffs_Teff2_logg2)
return stitched_model
v_med, v_min, v_max = np.median(v_helios), np.min(v_helios), np.max(
v_helios)
sb2_p0 = np.concatenate(
[p0_combined[:7], [p0_combined[4]], [v_med], v_helios])
teff1, logg1, feh, alphafe = p0_combined[:4]
min_q = get_minimum_q_for_this_teff(
Teff1=teff1,
logg1=logg1,
feh=feh,
NN_coeffs_Teff2_logg2=NN_coeffs_Teff2_logg2)
lower = np.concatenate(
[[4200, 4.0, -1, -0.3, min_q, 0, 0, 0.2, -100 + v_med],
len(v_helios) * [-100 + v_min]])
upper = np.concatenate([[7000, 5.0, 0.5, 0.5, 1, 45, 45, 1.5, 100 + v_med],
len(v_helios) * [100 + v_max]])
bounds = [lower, upper]
all_x0 = generate_starting_guesses_to_initialze_optimizers(
p0=sb2_p0,
bounds=bounds,
num_p0=num_p0,
vrange=(v_max - v_min) / 2,
model='sb2')
popt, pcov, model_spec = fit_all_p0s(fit_func=fit_func,
norm_spec=stitched_spec,
spec_err=stitched_errs,
all_x0=all_x0,
bounds=bounds,
tol=tol)
model_specs = utils.unstitch_model_spectra(model_spec=model_spec,
wavelength=wavelength)
return popt, pcov, model_specs
def fit_visit_spectra_N(norm_spectra,
spec_errs,
NN_coeffs_norm,
NN_coeffs_flux,
NN_coeffs_Teff2_logg2,
NN_coeffs_R,
v_helios,
p0_single,
num_p0=10,
N=3,
fix_vmac=0,
vmac=6):
'''
fit a set of visit spectra for a single object simultaneously, N components
v_helios is a guess of the heliocentric velocity at each visit, taken from
the allStar catalog.
p0 is a starting guess for the optimizer, typically obtained from fitting
the combined spectrum
labels = [Teff, logg, [Fe/H], [Mg/Fe], vmacro1, dv1]
+ [q2, vmacro2, dv2] + .. [qN, vmacroN, dvN] ## then below is new
+ [RV1_v2, RV2_v2.. RVN_v2] +
...
+ [RV1_vN, RV2_vN.. RVN_vN]
len(labels)=6+3(N-1)+N*(Nv-1)
'''
tol = 5e-4 # tolerance for when the optimizer should stop optimizing.
stitched_spec, stitched_errs = np.concatenate(
norm_spectra), np.concatenate(spec_errs)
Nv = len(spec_errs)
def fit_func(dummy_variable, *labels):
#print ('test')
#print (labels)
stitched_model = spectral_model.sbN_model_visit_spectra(
labels=labels,
spec_errs=spec_errs,
NN_coeffs_norm=NN_coeffs_norm,
NN_coeffs_flux=NN_coeffs_flux,
NN_coeffs_Teff2_logg2=NN_coeffs_Teff2_logg2,
NN_coeffs_R=NN_coeffs_R)
return stitched_model
teff1, logg1, feh, alphafe, vmacro1, dv1 = p0_single
# a cluster where all stars are identical (should have a spectrum identical to
# that of the primary only)
p0 = [teff1, logg1, feh, alphafe, vmacro1, dv1
] + [1, vmacro1, dv1] * (N - 1) + [
dv1,
] * N * (Nv - 1)
min_q = 0 #get_minimum_q_for_this_teff(Teff1 = teff1, logg1 = logg1, feh = feh,
#NN_coeffs_Teff2_logg2 = NN_coeffs_Teff2_logg2)
# print ('JL note: min_q=',min_q)
v_min, v_max = np.min(v_helios), np.max(v_helios)
lower = [3000, 0.0, -1, -0.3, 0, -100
] + [min_q, 0, -100 + v_min] * (N - 1) + [
-100 + v_min,
] * N * (Nv - 1)
upper = [7200, 5.0, 0.5, 0.5, 45, 100] + [1, 45, 100 + v_max] * (N - 1) + [
100 + v_max,
] * N * (Nv - 1)
#lower = np.concatenate([[3000, 0.0, -1, -0.3, 0], len(v_helios) * [-100 + v_min]])
#upper = np.concatenate([[7200, 5.0, 0.5, 0.5, 45], len(v_helios) * [100 + v_max]])
bounds = [lower, upper]
#p00 = np.concatenate([p0[:5], v_helios])
##### initial walkers
all_x0 = generate_starting_guesses_to_initialze_optimizers(
p0=p0, bounds=bounds, num_p0=num_p0, model='sbN', Nv=Nv,
N=N) ##vrange = (v_max - v_min)/2
popt, pcov, model_spec = fit_all_p0s(fit_func=fit_func,
norm_spec=stitched_spec,
spec_err=stitched_errs,
all_x0=all_x0,
bounds=bounds,
tol=tol)
model_specs = utils.unstitch_model_spectra(model_spec=model_spec,
wavelength=wavelength)
return popt, pcov, model_specs