stellar_Wave, obj_input_flux, stellar_flux, fit_output = sw.load_starlight_output(starlight2012_folder/outputFile)
z_gp = obsData['sample_data']['z_array'][i]
Mcor, Mint = fit_output['Mcor_tot'], fit_output['Mini_tot']
mass_galaxy = computeSSP_galaxy_mass(Mcor, 1, z_gp)
massProcess_galaxy = computeSSP_galaxy_mass(Mint, 1, z_gp)
idcs_below_20Myr = fit_output['DF'].age_j < 2*10**7
mass_galaxy_20Myr_percent = np.sum(fit_output['DF'].loc[idcs_below_20Myr, 'Mcor_j'].values)
# Store starlight configuration values for linux runy
rc = pn.RedCorr(R_V=RV, E_BV=fit_output['Av_min'] / RV, law=red_law)
cHbeta_star = rc.cHbetaFromEbv(fit_output['Av_min']/RV)
starlight_cfg = {'gridFileName': outputFile,
'outputFile': outputFile,
'saveFolder': starlight2012_folder.as_posix(),
'Galaxy_mass_Current': mass_galaxy,
'Galaxy_mass_Prosessed': massProcess_galaxy,
'Galaxy_mass_Percentbelow20Myr': mass_galaxy_20Myr_percent,
'Chi2': fit_output['Chi2'],
'A_V_stellarr': fit_output['Av_min'],
'cHbeta_stellar': cHbeta_star,
'PixelMeanDevPer': fit_output['SumXdev'],
'SN': fit_output['SignalToNoise_magnitudeWave']}
sr.parseConfDict(results_file, starlight_cfg, f'Starlight_run_{cycle}', clear_section=True)
# Plot the results
plot_label = f'{obj} spectrum' if ext == '_BR' else f'{obj} blue arm spectrum'
sw.population_fraction_plots(fit_output, plot_label, 'Mass_fraction', massFracPlotFile, mass_galaxy=mass_galaxy)
sw.population_fraction_plots(fit_output, plot_label, 'Light_fraction', LightFracPlotFile)
sw.mask_plot(fit_output, obj, lm.wave, lm.flux, stellar_Wave, stellar_flux, obj_input_flux)#, outputAddress=maskPlotFile)
reddening_results = compute_cHbeta(extCorrDict,
linesDF,
red_law,
RV,
Te_low,
ne,
plot_address=plot_address)
# Save dictionary with the measurements
for cHbeta_label, inputLines in extCorrDict.items():
cHbeta, cHbeta_err = reddening_results[cHbeta_label][
'cHbeta'], reddening_results[cHbeta_label]['cHbeta_err']
extinction_data[cHbeta_label] = (f'{cHbeta:0.3f}',
f'{cHbeta_err:0.3f}')
sr.parseConfDict(results_file,
extinction_data,
f'Extinction_{cycle}',
clear_section=True)
# -------------------------------------- Apply extinction correction -------------------------------------
# Get favoured physical conditions for extinction calculation
results_dict = sr.loadConfData(results_file, group_variables=False)
cHbeta_label = obsData[obj]['cHbeta_label']
cHbeta = np.array(results_dict[f'Extinction_{cycle}'][cHbeta_label],
dtype=float)
print(f'-- Using {cHbeta_label}: {cHbeta}')
print(f'-- Wdiv: {w_div_array[i]}\n')
# Add new columns to dataframe
for column in [
# Declare input files
print(f'- Treating object ({i}): {obj}')
objFolder = resultsFolder / f'{obj}'
fits_file = dataFolder / f'{obj}_{ext}.fits'
objMask = dataFolder / f'{obj}_{ext}_mask.txt'
results_file = objFolder / f'{obj}_{ext}_measurements.txt'
lineLog_file = objFolder / f'{obj}_{ext}_linesLog_{cycle}.txt'
# Declare output files
outputDb = objFolder / f'{obj}_{ext}_fitting_{cycle}.db'
outputTxt = objFolder / f'{obj}_{ext}_fitting_{cycle}.txt'
simConf = dataFolder / f'{obj}_config.txt'
# Load data
objParams = sr.loadConfData(simConf, group_variables=False)
objLinesDF = sr.import_emission_line_data(
lineLog_file, include_lines=objParams[obj]['input_lines'])
# Plot the results
objChem = sr.DirectMethod()
table_file = objFolder / f'{obj}_elementalabundances'
objChem.abundances_from_db(outputDb, save_results_address=table_file)
mean_abund_dict = {}
for element, trace in objChem.element_traces.items():
mean_abund_dict[element] = np.array([trace.mean(), trace.std()])
sr.parseConfDict(results_file,
mean_abund_dict,
f'Elemental_abundances_{cycle}',
clear_section=True)
fits_address = papaderos_fittings_folder / ssp_lib
data, header = sr.import_fits_data(fits_address,
instrument=None,
frame_idx=0)
for param in list_params:
# print(f'{param} = {header[param]}')
fado_measurements[param] = header[param]
# for param in header:
# print(f'{param} = {header[param]}')
# Store the results
section_label = f'Large_FADO_fit'
sr.parseConfDict(results_file,
fado_measurements,
section_label,
clear_section=True)
# papaderos_fittings_folder = Path('D:/Dropbox/Astrophysics/Papers/gtc_greenpeas/data/Papaderos_Full/6March2021/BCall_z03')
# ssp_lib_list = ['ChabP94Z5N295', 'midChab94', 'Z3SalpP00', 'Z4P00']
#
# # Reading spectrum
# for i, obj in enumerate(objList):
#
# # Declare input files
# objFolder = resultsFolder / f'{obj}'
# results_file = objFolder / f'{obj}_{ext}_measurements.txt'
# fits_file = dataFolder / f'{obj}_{ext}.fits'
# ext_fit = '1D'
# fado_file = f'{obj}_FD.cxt.FADO_{ext_fit}.fits'
# previousCycle = cycle.replace('3', '2')