# 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
