def generate_synthetic(spectrum):
### Generates the best synth spectrum, given the KDE MCMC params
### grab those params
### NEED TO FINISH!!!!!!!!!!!!!!!!!
SYNTH_FLUX = INTERPOLATOR[spectrum.get_gravity_class()](spectrum.MCMC_COARSE['TEFF'][0],
spectrum.MCMC_REFINE['FEH'][0],
spectrum.MCMC_REFINE['CFE'][0])
spectrum.set_synth_spectrum(pd.DataFrame({'wave' : SYNTH_WAVE, 'norm' : SYNTH_FLUX.T}))
return
def mcmc_determination(spectrum, mode='COARSE', pool=4):
### Precondition: must have run archetype_classification
### spectrum: spectrum.Spectrum() object
# mode here means either "coarse" or "fine" , 09/02/2020, J. Yoon
print("\t MCMC run mode = ", mode)
# spectrum and its info
print('\t ' + spectrum.get_name().ljust(20) + ": " + spectrum.get_gravity_class() + " : " + spectrum.get_carbon_mode() + " : " + spectrum.print_KP_bounds())
## FOR initial FEH and CFE values, for temp use photometric temp.
PARAMS = ARCHETYPE_PARAMS[spectrum.get_environ_mode()][spectrum.get_arch_group()]
#### MAIN MODE BRANCH
if mode=='COARSE':
## if it's coarse, then you need the photometric teff and the Sigma/Xi
print('\t initializing with archetype parameters: ', PARAMS)
photo_teff = spectrum.get_photo_temp()
# inserted 01/04/2022 for comparison J.Yoon
print('Teff : %.0F [Fe/H] : %.2F [C/Fe] : %.2F A(C): %.2F'% (photo_teff[0], PARAMS['FEH'], PARAMS['CFE'], PARAMS['AC']))
initial = [photo_teff[0], PARAMS['FEH'], PARAMS['CFE']]
ARGS = (spectrum.regions, SYNTH_WAVE, photo_teff[0], photo_teff[1],
spectrum.get_SN_dict(), spectrum.get_gravity_class())
initial = np.concatenate([initial,
[spectrum.SN_DICT['CA']['XI_AVG'],
spectrum.SN_DICT['CH']['XI_AVG']]])
if spectrum.get_carbon_mode() == "CH+C2":
print("\t running with carbon mode: CH+C2")
### add the beta params
initial = np.concatenate([initial,
[spectrum.SN_DICT['C2']['XI_AVG']]])
else: print("\t running with carbon mode: CH only")
elif mode == 'REFINE':
### In this case we want to use the params determined from the COARSE run
PARAMS_0 = spectrum.get_mcmc_dict(mode = 'COARSE')
print('\t initializing with COARSE run result parameters:')
print(' Teff : %.0F [Fe/H] : %.2F [C/Fe] : %.2F A(C) : %.2F' % (PARAMS_0['TEFF'][0],PARAMS_0['FEH'][0], PARAMS_0['CFE'][0],PARAMS_0['AC'][0]) )
ARGS = (spectrum.regions, SYNTH_WAVE,
PARAMS_0, spectrum.get_gravity_class())
initial = [spectrum.MCMC_COARSE['FEH'][0], spectrum.MCMC_COARSE['CFE'][0]]
else:
print("Invalid mode")
############################################################################
### PREPARE SPECTRA SLICES
############################################################################
### Select the correct likelihood function
LL_FUNCTION = LL_FUNCTION_DICT[mode][spectrum.get_carbon_mode()]
#if pool == 'MAX':
#cpu_cores = cpu_count()
#else:
# cpu_cores = pool
#print("\t running on ", cpu_cores, " cores")
#pool_init = Pool(cpu_cores)
pos = initial + initial * (2e-2*np.random.rand(25, len(initial)))
nwalkers, ndim = pos.shape
bounds = 'default'
print("\t running for ", spectrum.get_MCMC_iterations(), " iterations...")
sampler = emcee.EnsembleSampler(nwalkers, ndim,
LL_FUNCTION,
args=(ARGS))
####
_ = sampler.run_mcmc(pos, spectrum.get_MCMC_iterations())
# want to print out the latest result from mcmc, 12/13/2021
#print('\t the latest result from sampler() after MCMC runs: ', _ )
spectrum.set_sampler(sampler, mode=mode)
print("\t\t mcmc mode = ", mode)
return
def mcmc_determination(spectrum, mode='COARSE'):
### Precondition: must have run archetype_classification
### spectrum: spectrum.Spectrum() object
print('\t ' + spectrum.get_name().ljust(20) + ": " +
spectrum.get_gravity_class() + " : " + spectrum.get_carbon_mode() +
" : " + spectrum.print_KP_bounds())
try:
interp = INTERPOLATOR[spectrum.get_gravity_class()]
except:
print("Error in mcmc_determination with gravity class: ",
spectrum.get_gravity_class())
## FOR initial FEH and CFE values, for temp use photo
PARAMS = ARCHETYPE_PARAMS[spectrum.get_environ_mode()][
spectrum.get_arch_group()]
print('\t initializing with archetype parameters: ', PARAMS)
#### MAIN MODE BRANCH
if mode == 'COARSE':
## if it's coarse, then you need the photo teff and the Sigma/Xi
photo_teff = spectrum.get_photo_temp()
initial = [photo_teff[0], PARAMS['FEH'], PARAMS['CFE']]
ARGS = (spectrum.regions, interp, SYNTH_WAVE, photo_teff[0],
photo_teff[1], spectrum.get_SN_dict())
initial = np.concatenate([
initial,
[
spectrum.SN_DICT['CA']['XI_AVG'],
spectrum.SN_DICT['CH']['XI_AVG']
]
])
if spectrum.get_carbon_mode() == "CH+C2":
print("\t running with carbon mode: C2")
### add the beta params
initial = np.concatenate(
[initial, [spectrum.SN_DICT['C2']['XI_AVG']]])
elif mode == 'REFINE':
### In this case we want to use the params determined from the COARSE run
PARAMS_0 = spectrum.get_mcmc_dict(mode='COARSE')
ARGS = (spectrum.regions, interp, SYNTH_WAVE, PARAMS_0)
initial = [
spectrum.MCMC_COARSE['FEH'][0], spectrum.MCMC_COARSE['CFE'][0]
]
else:
print("Invalid mode")
############################################################################
### PREPARE SPECTRA SLICES
############################################################################
### Select the correct likelihood function
LL_FUNCTION = LL_FUNCTION_DICT[mode][spectrum.get_carbon_mode()]
cpu_cores = cpu_count()
pool = Pool(cpu_cores)
print("\t running on ", cpu_cores, " cores")
pos = initial + initial * (2e-2 * np.random.rand(25, len(initial)))
nwalkers, ndim = pos.shape
bounds = 'default'
print("\t running for ", spectrum.get_MCMC_iterations(), " iterations...")
sampler = emcee.EnsembleSampler(nwalkers,
ndim,
LL_FUNCTION,
args=(ARGS),
pool=pool)
####
_ = sampler.run_mcmc(pos, spectrum.get_MCMC_iterations())
spectrum.set_sampler(sampler, mode=mode)
print()
return