# Locate the objects objName = catalogue_df.iloc[i].name ouput_folder = "{}{}/".format(catalogue_dict["Obj_Folder"], objName) fits_file = catalogue_df.iloc[i].reduction_fits lineslog_address = "{objfolder}{codeName}{lineslog_extension}".format( objfolder=ouput_folder, codeName=objName, lineslog_extension=AbundancesFileExtension ) # Load object data lineslog_frame = dz.load_lineslog_frame(lineslog_address) wave, flux, header_0 = dz.get_spectra_data(fits_file) # Perform the reddening correction cHbeta = catalogue_df.iloc[i][cHbeta_type] dz.deredden_lines(cHbeta, lineslog_frame) spectrum_dered = dz.derreddening_continuum(wave, flux, cHbeta.nominal_value) # Import cHbeta coefficient # Te = catalogue_df.iloc[i].TeSIII if notnull(catalogue_df.iloc[i].TeSIII) else 10000.0 # nHeII_HII = catalogue_df.iloc[i].HeII_HII_from_S if notnull(catalogue_df.iloc[i].HeII_HII_from_S) else 0.1 # nHeIII_HII = catalogue_df.iloc[i].HeIII_HII_from_S if notnull(catalogue_df.iloc[i].HeIII_HII_from_S) else 0.0 # Hbeta_Flux = lineslog_frame.loc['H1_4861A', 'line_Int'] # Halpha_Flux = lineslog_frame.loc['H1_6563A', 'line_Int'] Te = 10000.0 nHeII_HII = 0.1 nHeIII_HII = 0.0 Hbeta_Flux = lineslog_frame.loc["H1_4861A", "line_Int"] Halpha_Flux = lineslog_frame.loc["H1_6563A", "line_Int"] print "--Using physical parameters", Te, nHeII_HII, nHeIII_HII, Hbeta_Flux, Halpha_Flux
#Loop through files for i in range(len(catalogue_df.index)): #Locate the objects objName = catalogue_df.iloc[i].name ouput_folder = '{}{}/'.format(catalogue_dict['Obj_Folder'], objName) fits_file = catalogue_df.iloc[i].reduction_fits nebular_fits = ouput_folder + objName + nebular_fits_exten Wave_T, Int_T, header_T = dz.get_spectra_data(fits_file) Wave_N, Int_N, header_T = dz.get_spectra_data(ouput_folder + objName + nebular_fits_exten) #Perform the reddening correction cHbeta = catalogue_df.iloc[i][cHbeta_type] spectrum_dered = dz.derreddening_continuum(Wave_T, Int_T - Int_N, cHbeta.nominal_value) #Generating the starlight files FileName = basename(fits_file) Grid_FileName, Sl_OutputFile, Sl_OutputFolder, X_1Angs, Y_1Angs = dz.GenerateStarlightFiles(ouput_folder, FileName, objName, catalogue_df.iloc[i], Wave_T, spectrum_dered) print '--Output file ', Sl_OutputFile #Plot the data dz.data_plot(Wave_T, spectrum_dered, "Reduced spectrum") dz.data_plot(X_1Angs, Y_1Angs, "Resampled spectrum", linestyle='--') # Set titles and legend PlotTitle = 'Object {} Resampled spectrum for starlight'.format(objName) dz.FigWording(r'Wavelength $(\AA)$', 'Flux' + r'$(erg\,cm^{-2} s^{-1} \AA^{-1})$', PlotTitle)
print '-- Treating {} @ {}'.format(catalogue_df.iloc[i].name, AbundancesFileExtension) #Locate the objects objName = catalogue_df.iloc[i].name ouput_folder = '{}{}/'.format(catalogue_dict['Obj_Folder'], objName) fits_file = catalogue_df.iloc[i].reduction_fits lineslog_address = '{objfolder}{codeName}{lineslog_extension}'.format(objfolder = ouput_folder, codeName=objName, lineslog_extension=AbundancesFileExtension) #Load object data lineslog_frame = dz.load_lineslog_frame(lineslog_address) wave, flux, header_0 = dz.get_spectra_data(fits_file) #Perform the reddening correction cHbeta = catalogue_df.iloc[i][cHbeta_type] dz.deredden_lines(cHbeta, lineslog_frame) spectrum_dered = dz.derreddening_continuum(wave, flux, cHbeta.nominal_value) #Import cHbeta coefficient # Te = catalogue_df.iloc[i].TeSIII if notnull(catalogue_df.iloc[i].TeSIII) else 10000.0 # nHeII_HII = catalogue_df.iloc[i].HeII_HII_from_S if notnull(catalogue_df.iloc[i].HeII_HII_from_S) else 0.1 # nHeIII_HII = catalogue_df.iloc[i].HeIII_HII_from_S if notnull(catalogue_df.iloc[i].HeIII_HII_from_S) else 0.0 # Hbeta_Flux = lineslog_frame.loc['H1_4861A', 'line_Int'] # Halpha_Flux = lineslog_frame.loc['H1_6563A', 'line_Int'] Te = 10000.0 nHeII_HII = 0.1 nHeIII_HII = 0.0 Hbeta_Flux = lineslog_frame.loc['H1_4861A', 'line_Int'] Halpha_Flux = lineslog_frame.loc['H1_6563A', 'line_Int'] print '--Using physical parameters', Te, nHeII_HII, nHeIII_HII, Hbeta_Flux, Halpha_Flux