# #Declare the MCMC dictionary
# MCMC_dict = nb.model_parameters(wave_clean, flux_clean_n, 0.05)
#
# #Run MCMC with MAP
# MAP_Model = pymc.MAP(MCMC_dict)
# MAP_Model.fit(method = 'fmin_powell')
# MAP_Model.revert_to_max()
#
#
# M = pymc.MCMC(MAP_Model.variables, db = 'pickle', dbname = Folder_database + Db_name)
# M.sample(iter=5000, burn=1000)
# M.write_csv(Folder_database + Db_global_file_name, variables=['He_abud', 'Te', 'Flux_Recomb'])
# M.db.close()
#Load inference data
dz.load_pymc_database(Folder_database + Db_name)
statistics = dz.extract_traces_statistics(traces_list = ['He_abud', 'Te', 'Flux_Recomb'])
HeII_HII_inf = statistics['He_abud']['mean']
Te_inf = statistics['Te']['mean']
Flux_Recomb_inf = statistics['Flux_Recomb']['mean']
nebular_flux_bayes = nb.Calculate_Nebular_gamma(Te_inf, Flux_Recomb_inf * Hbeta_Flux.nominal_value, HeII_HII_inf, nHeIII_HII.nominal_value, Wavelength_Range=wave_clean)
nebular_flux_theo = nb.Calculate_Nebular_gamma(8000.0, Flux_Recomb_inf * Hbeta_Flux.nominal_value, HeII_HII_inf, nHeIII_HII.nominal_value, Wavelength_Range=wave_clean)
print
print Te_inf
print Flux_Recomb_inf
print HeII_HII_inf
print
#nebular_flux = nb.Calculate_Nebular_gamma(Te.nominal_value, Hbeta_Flux.nominal_value, nHeII_HII.nominal_value, nHeIII_HII.nominal_value, Wavelength_Range=wave_clean)
#nebular_flux_n = nebular_flux / Hbeta_Flux.nominal_value
#dz.data_plot(wave_clean, nebular_flux, 'Inference prediction', dz.ColorVector[2][3])
