ouput_folder = '{}{}/'.format(catalogue_dict['Obj_Folder'], objName) lineslog_address = '{objfolder}{codeName}{lineslog_extension}'.format( objfolder=ouput_folder, codeName=objName, lineslog_extension=lineslog_extension) print '-- Treating {} @ {}'.format(objName, fits_file) #Load object data object_data = catalogue_df.iloc[i] lineslog_frame = dz.load_lineslog_frame(lineslog_address) Wave, Flux, ExtraData = dz.get_spectra_data(fits_file) #Load reddening curve for the lines lines_wavelengths = lineslog_frame.lambda_theo.values lines_Xx = dz.reddening_Xx(lines_wavelengths, red_curve, R_v) lines_f = dz.flambda_from_Xx(lines_Xx, red_curve, R_v) lineslog_frame['line_f'] = lines_f #Determine recombination coefficients for several combinations ratios_dict = dz.compare_RecombCoeffs(object_data, lineslog_frame) cHbeta_all_MagEr, n_all_MagEr = LinfitLinearRegression( ratios_dict['all_x'], ratios_dict['all_y']) trendline_all = cHbeta_all_MagEr * ratios_dict['all_x'] + n_all_MagEr cHbeta_in_MagEr, n_in_MagEr = LinfitLinearRegression( ratios_dict['in_x'], ratios_dict['in_y']) trendline_in = cHbeta_in_MagEr * ratios_dict['in_x'] + n_in_MagEr #--Blue points if len(ratios_dict['blue_x']) > 0:
fits_file = catalogue_df.iloc[i].reduction_fits ouput_folder = '{}{}/'.format(catalogue_dict['Obj_Folder'], objName) lineslog_address = '{objfolder}{codeName}{lineslog_extension}'.format( objfolder=ouput_folder, codeName=objName, lineslog_extension=lineslog_extension) print '-- Treating {} @ {}'.format(objName, lineslog_address) #Load object data lineslog_frame = dz.load_lineslog_frame(lineslog_address) #Load reddening curve for the lines lines_wavelengths = lineslog_frame.lambda_theo.values lines_Xx = dz.reddening_Xx(lines_wavelengths, red_curve, R_v) lines_f = dz.flambda_from_Xx(lines_Xx, red_curve, R_v) lineslog_frame['line_f'] = lines_f #Determine recombination coefficients for several combinations object_data = catalogue_df.iloc[i] ratios_dict = dz.compare_RecombCoeffs(object_data, lineslog_frame) cHbeta_all_MagEr, n_all_MagEr = LinfitLinearRegression( ratios_dict['all_x'], ratios_dict['all_y']) trendline_all = cHbeta_all_MagEr * ratios_dict[ 'all_x'] + n_all_MagEr cHbeta_in_MagEr, n_in_MagEr = LinfitLinearRegression( ratios_dict['in_x'], ratios_dict['in_y']) trendline_in = cHbeta_in_MagEr * ratios_dict['in_x'] + n_in_MagEr