red_curve=objParams['simulation_properties']['reddenig_curve'],
data_folder=objParams['data_location']['external_data_folder'])
# Declare ion properties
objIons = sr.IonEmissivity(
tempGrid=objParams['simulation_properties']['temp_grid'],
denGrid=objParams['simulation_properties']['den_grid'])
# Generate interpolator from the emissivity grids
ionDict = objIons.get_ions_dict(np.unique(objLinesDF.ion.values))
objIons.computeEmissivityGrids(objLinesDF,
ionDict,
combined_dict=combined_line_dict)
# Declare chemical model
objChem = sr.DirectMethod(
linesDF=objLinesDF,
highTempIons=objParams['simulation_properties']
['high_temp_ions_list'])
# Declare region physical model
obj1_model = sr.SpectraSynthesizer()
# obj1_model.define_region(objLinesDF, objIons, objRed, objChem)
#
# # Replace the flamda values
# idcs_lines = objLinesDF.index.isin(obj1_model.lineLabels)
# flambda_new = objLinesDF.loc[idcs_lines, 'f_lambda']
# obj1_model.lineFlambda = flambda_new
#
# # Declare sampling properties
# obj1_model.simulation_configuration(objParams['inference_model_configuration']['parameter_list'],
# prior_conf_dict=objParams['priors_configuration'],
# photo_ionization_grid=False)
lineLabels = objLinesDF.loc[idcs_lines].index
lineIons = objLinesDF.loc[idcs_lines, 'ion'].values
lineFluxes = objLinesDF.loc[idcs_lines, 'obsFlux'].values
lineErr = objLinesDF.loc[idcs_lines, 'obsFluxErr'].values
# Declare simulation physical properties
objRed = sr.ExtinctionModel(
Rv=chem_conf['simulation_properties']['R_v'],
red_curve=chem_conf['simulation_properties']
['reddenig_curve'],
data_folder=chem_conf['data_location']
['external_data_folder'])
# Declare chemical model
objChem = sr.DirectMethod(
lineLabels,
highTempIons=chem_conf['simulation_properties']
['high_temp_ions_list'])
# Prepare simulation
obj1_model = sr.SpectraSynthesizer()
obj1_model.define_region(lineLabels, lineFluxes, lineErr,
objIons, objRed, objChem)
obj1_model.simulation_configuration(
chem_conf['inference_model_configuration']
['parameter_list'],
prior_conf_dict=chem_conf['priors_configuration'],
photo_ionization_grid=False)
obj1_model.simulation_configuration(
# 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)
red_curve=objParams['reddenig_curve'],
data_folder=objParams['external_data_folder'])
objIons = sr.IonEmissivity(tempGrid=objParams['temp_grid'],
denGrid=objParams['den_grid'])
# Generate interpolator from the emissivity grids
ionDict = objIons.get_ions_dict(np.unique(objLinesDF.ion.values))
objIons.computeEmissivityGrids(
objLinesDF,
ionDict,
linesDb=sr._linesDb,
combined_dict={'O2_3726A': 'O2_3726A-O2_3729A'})
# Declare chemical model
objChem = sr.DirectMethod(
linesDF=objLinesDF, highTempIons=objParams['high_temp_ions_list'])
# Declare region physical model
obj1_model.define_region(objLinesDF, objIons, objRed, objChem)
# Declare sampling properties
obj1_model.simulation_configuration(objParams['parameter_list'],
prior_conf_dict=objParams)
# Declare simulation inference model
obj1_model.inference_model(
include_Thigh_prior=objParams['T_high_check'])
# Run the simulation
obj1_model.run_sampler(simFolder / outputDb, 5000, 2000, njobs=1)
lineFlambdas = objRed.gasExtincParams(wave=objLinesDF.obsWave.values)
lineWaves = objLinesDF.pynebCode.values
# Establish atomic data references
ftau_file_path = os.path.join(ss._literatureDataFolder, objParams['ftau_file'])
objIons = ss.IonEmissivity(ftau_file_path=ftau_file_path, tempGrid=objParams['temp_grid'],
denGrid=objParams['den_grid'])
# Define the dictionary with the pyneb ion objects
ionDict = objIons.get_ions_dict(np.unique(objLinesDF.ion.values))
# Compute the emissivity surfaces for the observed emission lines # TODO this database is not necesary since we duplicate the logs
objIons.computeEmissivityGrid(objLinesDF, ionDict, linesDb=ss._linesDb)
# Declare the paradigm for the chemical composition measurement
objChem = ss.DirectMethod()
# Tag the emission features for the chemical model implementation
objChem.label_ion_features(linesDF=objLinesDF, highTempIons=objParams['high_temp_ions_list'])
# Declare a dictionary with the synthetic abundances
abundDict = {}
for ion in objChem.obsAtoms:
abundDict[ion] = objParams['true_values'][ion]
# We generate an object with the tensor emission functions
emtt = ss.EmissionTensors()
# Array with the equation labels
eqLabelArray = ss.assignFluxEq2Label(objLinesDF.index.values, objLinesDF.ion.values)