Wave_StellarExtension = linspace(3000.0, 3399.0, 200)
Int_StellarExtension = zeros(len(Wave_StellarExtension))
#Increase the range of Wave_S so it is greater than the observational range
Int_S = hstack((Int_StellarExtension, Int_S))
Wave_S = hstack((Wave_StellarExtension, Wave_S))
#Resampling stellar spectra
Interpolation = interp1d(Wave_S, Int_S, kind='slinear')
Int_Stellar_Resampled = Interpolation(Wave_O)
#Perform the reddening correction
cHbeta = catalogue_df.iloc[i][cHbeta_type]
IntObs_dered = dz.derreddening_spectrum(Wave_O,
Int_O,
reddening_curve=red_curve,
cHbeta=cHbeta.nominal_value,
R_v=R_v)
IntEmi_dered = dz.derreddening_spectrum(Wave_E,
Int_E,
reddening_curve=red_curve,
cHbeta=cHbeta.nominal_value,
R_v=R_v)
Int_Sum = IntEmi_dered + Int_Stellar_Resampled + Int_N
dz.data_plot(Wave_O, IntObs_dered, 'Observed spectrum')
dz.data_plot(Wave_N, Int_N, 'Nebular continuum', linestyle=':')
dz.data_plot(Wave_S, Int_S, 'Stellar continuum', linestyle='--')
dz.insert_image(
'/home/vital/Dropbox/Astrophysics/Papers/Yp_AlternativeMethods/images/SHOC579_invert.png',
lineslog_extension=AbundancesFileExtension)
fits_name = basename(fits_file)
#Load object data
lineslog_frame = dz.load_lineslog_frame(lineslog_address)
wave, flux, header_0 = dz.get_spectra_data(fits_file)
#Declare starlight files
Grid_FileName = fits_name.replace('.fits', '.slGrid')
Sl_OutputFolder = dz.RootFolder + 'Starlight/Output/'
Sl_OutputFile = fits_name.replace('.fits', '.slOutput')
cHbeta = catalogue_df.loc[objName, cHbeta_type]
flux_dered = dz.derreddening_spectrum(wave,
flux,
reddening_curve=red_curve,
cHbeta=cHbeta.nominal_value,
R_v=R_v)
#Define the maximum sigma for the fitting from the Hbeta line
Sigma = ImportDispersionVelocity(lineslog_frame)
UpperDispersionVelocity_Limit = str(round(
Sigma, 1)) + ' [vd_upp (km/s)] = upper allowed vd\n'
dz.replace_line(GridFileAddress, 21, UpperDispersionVelocity_Limit)
#Launch starlight
print '--Initiating starlight for ', fits_name, Sigma
dz.Starlight_Launcher(Grid_FileName, dz.RootFolder)
print '--Starlight finished succesfully ended:', Sl_OutputFile
#Get stellar spectrum from starlight file
#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]
Obs_spectrum_dered = dz.derreddening_spectrum(Wave_T,
Int_T,
reddening_curve=red_curve,
cHbeta=cHbeta.nominal_value,
R_v=R_v)
spectrum_dered = Obs_spectrum_dered - Int_N
#Generating the starlight files
FileName = basename(fits_file) + stellar_files_exten
Grid_FileName, Sl_OutputFile, Sl_OutputFolder, X_1Angs, Y_1Angs = dz.GenerateStarlightFiles(
ouput_folder,
FileName,
objName,
catalogue_df.iloc[i],
Wave_T,
spectrum_dered,
ext_loop=stellar_files_exten)
