results_file = objFolder / f'{obj}_{ext}_measurements.txt'
lineLog_file = objFolder / f'{obj}_{ext}_linesLog_{cycle}.txt'
outputDb = objFolder / f'{obj}_{ext}_fitting_{cycle}.db'
outputTxt = objFolder / f'{obj}_{ext}_fitting_{cycle}.txt'
outputPiDb = objFolder / f'{obj}_{ext}_pI_fitting_{cycle}.db'
outputPiTxt = objFolder / f'{obj}_{ext}_pI_fitting_{cycle}.txt'
outputDirectHIIchemDb = objFolder / f'{obj}_{ext}_Direct-Teff-logU_{cycle}.db'
outputDirectHIIchemTxt = objFolder / f'{obj}_{ext}_Direct-Teff-logU_{cycle}.txt'
obj1_model = sr.SpectraSynthesizer()
measurements_dict = sr.loadConfData(results_file,
group_variables=False)
objLinesDF = sr.import_emission_line_data(lineLog_file)
fit_pickle = sr.load_MC_fitting(outputDb)
pI_pickle = sr.load_MC_fitting(outputPiDb)
bayes_plus_PI_pickle = sr.load_MC_fitting(outputDirectHIIchemDb)
fit_inputs_dict = sr.loadConfData(outputTxt)
pI_inputs_dict = sr.loadConfData(outputPiTxt)
bayes_inputs_dict = sr.loadConfData(outputDirectHIIchemTxt)
# Recover Direct method Bayesian fitting
sr_lineLabels = fit_inputs_dict['Input_data']['lineLabels_list']
inFlux = fit_inputs_dict['Input_data']['inputFlux_array']
inErr = fit_inputs_dict['Input_data']['inputErr_array']
flux_matrix = fit_pickle['trace']['calcFluxes_Op']
mean_line_values = flux_matrix.mean(axis=0)
std_line_values = flux_matrix.std(axis=0)
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'
figure_file = objFolder/f'{obj}_DirectMethod_posteriors.png'
figure_file_flux = objFolder/f'{obj}_DirectMethod_ObsVsFitFluxes.png'
table_file_fluxes = objFolder/f'{obj}_DirectMethod_ObsVsFitFluxes'
print(outputDb)
print(figure_file)
# Load the results
fit_pickle = sr.load_MC_fitting(outputDb)
fit_inputs_dict = sr.loadConfData(outputTxt)
obj1_model = sr.SpectraSynthesizer()
# Format outputs from old storing container
inLines = fit_inputs_dict['Input_data']['lineLabels_list']
inFluxes, inErr = fit_inputs_dict['Input_data']['inputFlux_array'], fit_inputs_dict['Input_data']['inputErr_array']
inParameters = np.array(['T_high', 'n_e', 'Ar3', 'Ar4', 'Fe3', 'He1', 'He2', 'N2', 'Ne3', 'O2', 'O3', 'S2', 'S3', 'cHbeta'])
traces_dict = {param: fit_pickle['trace'][param] for param in inParameters}
for i, lineLabel in enumerate(inLines):
traces_dict[lineLabel] = fit_pickle['trace']['calcFluxes_Op'][:, i]
# Printint results
# obj1_model.tracesPosteriorPlot(figure_file, inParameters, traces_dict, dark_mode=False)
# obj1_model.fluxes_distribution(figure_file_flux, inLines, inFluxes, inErr, traces_dict)
obj1_model.table_line_fluxes(table_file_fluxes, inLines, inFluxes, inErr, traces_dict)
# 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)
#
# # Declare simulation inference model
# obj1_model.inference_model(fit_T_low=False)
#
# # Run the simulation
# obj1_model.run_sampler(objFolder/outputDb, 5000, 2000, njobs=1)
# Plot the results
fit_results = sr.load_MC_fitting(outputDb)
# print('-- Model parameters table')
# figure_file = objFolder/f'{obj}_MeanOutputs'
# obj1_model.table_mean_outputs(figure_file, fit_results, true_values=true_values)
#
# print('-- Flux values table')
# figure_file = objFolder/f'{obj}_FluxComparison'
# obj1_model.table_line_fluxes(figure_file, fit_results, combined_dict=combined_line_dict)
#
# print('-- Model parameters posterior diagram')
# figure_file = objFolder/f'{obj}_ParamsPosteriors.png'
# fig_conf = {'figure.figsize': (5, 10), 'axes.titlesize': 8, 'axes.labelsize': 8, 'legend.fontsize': 8}
# obj1_model.tracesPosteriorPlot(None, fit_results, true_values=None)
print('-- Line flux posteriors')
# 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)
# Plot the results
fit_results = sr.load_MC_fitting(simFolder / outputDb)
print('-- Model parameters table')
figure_file = simFolder / f'{objName}_MeanOutputs'
obj1_model.table_mean_outputs(figure_file, fit_results)
print('-- Flux values table')
figure_file = simFolder / f'{objName}_FluxComparison'
obj1_model.table_line_fluxes(figure_file, fit_results)
print('-- Model parameters posterior diagram')
figure_file = simFolder / f'{objName}_ParamsPosteriors.png'
obj1_model.tracesPosteriorPlot(figure_file, fit_results)
print('-- Line flux posteriors')
figure_file = simFolder / f'{objName}_lineFluxPosteriors.png'
row_data[2] = param_entry
# Planar fitting HII-CHI-mistry
grid_file = 'C17_bb_Teff_30-90_pp.dat'
section_label = f'HII_Tef_fit_{grid_file}_{cycle}_sed'
param_value = results_dict[section_label][param]
if param == 'Teff':
param_entry = r'${:.0f}\pm{:.0f}$'.format(param_value[0],
param_value[1])
else:
param_entry = r'${:.2f}\pm{:.2f}$'.format(param_value[0],
param_value[1])
row_data[3] = param_entry
# Bayesian HII-CHI-mistry
model_fit = sr.load_MC_fitting(outputPiDb)
trace = model_fit['trace'][param]
param_value = (np.mean(trace), np.std(trace))
if param == 'Teff':
param_entry = r'${:.0f}\pm{:.0f}$'.format(param_value[0],
param_value[1])
else:
param_entry = r'${:.2f}\pm{:.2f}$'.format(param_value[0],
param_value[1])
row_data[4] = param_entry
# Bayesian Direct method + HII-CHI-mistry
if outputDirectHIIchemDb.is_file():
model_fit = sr.load_MC_fitting(outputDirectHIIchemDb)
trace = model_fit['trace'][param]
param_value = (np.mean(trace), np.std(trace))
for i, obj in enumerate(objList):
if i < 3:
objFolder = resultsFolder / f'{obj}'
results_file = objFolder / f'{obj}_{ext}_measurements.txt'
lineLog_file = objFolder / f'{obj}_{ext}_linesLog_{cycle}.txt'
outputDb = objFolder / f'{obj}_{ext}_fitting_{cycle}.db'
outputPiDb = objFolder / f'{obj}_{ext}_pI_fitting_{cycle}.db'
outputDirectHIIchemDb = objFolder / f'{obj}_{ext}_Direct-Teff-logU_{cycle}.db'
obj1_model = sr.SpectraSynthesizer()
measurements_dict = sr.loadConfData(results_file, group_variables=False)
objLinesDF = sr.import_emission_line_data(lineLog_file)
pI_results = sr.load_MC_fitting(outputPiDb)
fit_results = sr.load_MC_fitting(outputDb)
bayes_plus_PI_results = sr.load_MC_fitting(outputDirectHIIchemDb)
# Recover Direct method Bayesian fitting
sr_lineLabels = fit_results['Input_data']['lineLabels_list']
inFlux = fit_results['Input_data']['inputFlux_array']
inErr = fit_results['Input_data']['inputErr_array']
flux_matrix = fit_results['trace']['calcFluxes_Op']
mean_line_values = flux_matrix.mean(axis=0)
std_line_values = flux_matrix.std(axis=0)
# Recover Direct method + HIIchem fitting
srPlus_lineLabels = bayes_plus_PI_results['Input_data']['lineLabels_list']
inFluxPlus = bayes_plus_PI_results['Input_data']['inputFlux_array']
inErrPlus = bayes_plus_PI_results['Input_data']['inputErr_array']
displaySimulationData(inferenModel)
# Run the model
trace = pymc3.sample(5000, tune=2000, chains=2, cores=1)
# Save the database
with open(output_db, 'wb') as trace_pickle:
pickle.dump({'model': inferenModel, 'trace': trace}, trace_pickle)
# Load the .db file
with open(output_db, 'rb') as trace_restored:
db = pickle.load(trace_restored)
# Restore parameters data
inferenModel, trace = db['model'], db['trace']
traces_dict = ss.load_MC_fitting(output_db)
# Declare sampler
obj1_model = ss.SpectraSynthesizer()
modelParams = list(traces_dict.keys())
modelParams.remove('calcFluxes_Op')
# Traces and Posteriors
print('-- Model parameters posterior diagram')
figure_file = f'{user_folder}interPolationTesting_ParamsPosteriors.txt'
obj1_model.tracesPosteriorPlot(modelParams, traces_dict, true_values=objParams)
obj1_model.savefig(figure_file, resolution=200)
# # Traces and Posteriors
# print('-- Model parameters corner diagram')
obj1_model.define_region(lineLabels, lineFluxes, lineErr, extinction_model=objRed)
# Declare region physical model
obj1_model.simulation_configuration(model_parameters=conf_fit['inference_model_configuration']['parameter_list'],
prior_conf_dict=conf_fit['priors_configuration'],
grid_interpolator=grid_interpolators)
obj1_model.photoionization_sampling(conf_fit['inference_model_configuration']['parameter_list'])
obj1_model.run_sampler(1000, 3000, nchains=8, njobs=4, init='advi')
# obj1_model.save_fit(outputCfg, cord_label, output_format='cfg')
obj1_model.save_fit(outputDb, cord_label, output_format='pickle')
# obj1_model.save_fit(outputFits, cord_label, output_format='fits', user_header=header_params)
# Plot the results
fit_pickle = sr.load_MC_fitting(outputDb, output_format='pickle')
inLines, inParameters = fit_pickle['inputs']['line_list'], fit_pickle['inputs']['parameter_list']
inFlux, inErr = fit_pickle['inputs']['line_fluxes'].astype(float), fit_pickle['inputs']['line_err'].astype(float)
traces_dict = fit_pickle['outputs']
# Load the results
# fit_fits = sr.load_MC_fitting(outputFits, output_format='fits')
# in_header, out_header, trace_header = fit_fits['inputs'][1], fit_fits['outputs'][1], fit_fits['traces'][1]
# inLines, inParameters = fit_fits['inputs'][0]['line_list'], fit_fits['outputs'][0]['parameters_list']
# inFlux, inErr = fit_fits['inputs'][0]['line_fluxes'], fit_fits['inputs'][0]['line_err']
# traces_dict = fit_fits['traces'][0]
# print('-- Model parameters table')
figure_file = objFolder / f'{cord_label}_pI_fitting_MeanOutputs'
obj1_model.table_mean_outputs(figure_file, inParameters, traces_dict, true_values=header_params)