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
