flux_star = flux_star[0][0]
star_dict[objects[i] + "_wave"], star_dict[objects[i] + "_flux"] = wave_star, flux_star
star_dict[objects[i] + "_idx_join"] = idx_join_region
dz.data_plot(wave_star, flux_star, label=objects[i], graph_axis=dz.ax2)
obj_red_region = array(range(idx_obj_join, idx_obj_join + len_red_region))
mean_flux = mean(flux_obs)
# Loop through the diagnostic lines
obj_dict = {}
for line in lines_interest:
if line in lick_idcs_df.index:
dz.Current_Label = lick_idcs_df.loc[line].name
dz.Current_Ion = lick_idcs_df.loc[line].Ion
dz.Current_TheoLoc = redshift_factor * lick_idcs_df.loc[line].TheoWavelength
selections = redshift_factor * lick_idcs_df.loc[line][3:9].values
# Measure the line intensity
line_fit_orig = dz.measure_line(wave_obs, flux_obs, selections, None, "lmfit", store_data=False)
# Area to plot
subwave, subflux, lineHeight, LineExpLoc = Emission_Threshold(
dz.Current_TheoLoc, wave_obs, flux_obs
)
obj_dict[line + "_x_reduc"] = line_fit_orig["x_resample"]
obj_dict[line + "_y_reduc"] = line_fit_orig["y_resample"]
obj_dict[line + "_flux_reduc"] = line_fit_orig["flux_intg"]
obj_dict[line + "_fluxEr_reduc"] = line_fit_orig["flux_intg_er"]
flux_star = flux_star[0][0]
star_dict[objects[i]+'_wave'], star_dict[objects[i]+'_flux'] = wave_star, flux_star
star_dict[objects[i]+'_idx_join'] = idx_join_region
dz.data_plot(wave_star, flux_star, label='Standard star Feige 34', color=colorVector['green'], graph_axis=dz.ax2)
obj_red_region = array(range(idx_obj_join,idx_obj_join + len_red_region))
mean_flux = mean(flux_obs)
#Loop through the diagnostic lines
obj_dict = {}
for line in lines_interest:
if line in lick_idcs_df.index:
dz.Current_Label = lick_idcs_df.loc[line].name
dz.Current_Ion = lick_idcs_df.loc[line].Ion
dz.Current_TheoLoc = redshift_factor * lick_idcs_df.loc[line].lambda_theo
selections = redshift_factor * lick_idcs_df.loc[line][3:9].values
#Measure the line intensity
line_fit_orig = dz.measure_line(wave_obs, flux_obs, selections, None, 'lmfit', store_data = False)
#Area to plot
subwave, subflux, lineHeight, LineExpLoc = Emission_Threshold(dz.Current_TheoLoc, wave_obs, flux_obs)
obj_dict[line + '_x_reduc'] = line_fit_orig['x_resample']
obj_dict[line + '_y_reduc'] = line_fit_orig['y_resample']
obj_dict[line + '_flux_reduc'] = line_fit_orig['flux_intg']
obj_dict[line + '_fluxEr_reduc'] = line_fit_orig['flux_intg_er']
obj_dict[line + '_Peak'] = line_fit_orig['A0']
obj_dict[line + '_continuum'] = line_fit_orig['zerolev_mean']
lick_idcs_df = pd.read_csv(
ouput_folder + objName + lickIndcs_extension,
delim_whitespace=True,
header=0,
index_col=0,
comment='L') #Dirty trick to avoid the Line_label row
#Start to loop through recorded line
for i in range(len(lick_idcs_df.index)):
try:
print '-- Treating line:', lick_idcs_df.iloc[i].name
#Establish current line
dz.Current_Label = lick_idcs_df.iloc[i].name
dz.Current_Ion = lick_idcs_df.iloc[i].Ion
dz.Current_TheoLoc = lick_idcs_df.iloc[i].lambda_theo
selections = lick_idcs_df.iloc[i][3:9].values
#Proceed to measure
line_data = dz.measure_line(wave,
flux,
selections,
lineslog_df,
Measuring_Method='MC_lmfit',
store_data=True)
except:
dz.log_emLine_error(objName, dz.Current_Label)
for i in range(len(catalogue_df.index)):
#Object
objName = catalogue_df.iloc[i].name
fits_file = catalogue_df.iloc[i].reduction_fits
ouput_folder = '{}{}/'.format(catalogue_dict['Obj_Folder'], objName)
print '-- Treating {} @ {}'.format(objName, fits_file)
#Spectrum data
wave, flux, header_0 = dz.get_spectra_data(fits_file)
subwave, subflux, lineHeight, LineExpLoc = Emission_Threshold(
6548.0, wave, flux)
#Establish current line
dz.Current_Label = 'N2_6548A'
dz.Current_Ion = 'N2'
dz.Current_TheoLoc = 6548.050
selections = array(
[6497.701, 6522.331, 6537.998, 6591.29, 6592.324, 6610.83])
#Proceed to measure
line_data = dz.measure_line(wave,
flux,
selections,
lines_dataframe=None,
store_data=False)
#Plot Global component
dz.data_plot(subwave,
subflux,
print '\n-- Treating {} @ {}'.format(objName, fits_file), i, '/', n_objects
#Spectrum data
wave, flux, header_0 = dz.get_spectra_data(fits_file)
lick_idcs_df = pd.read_csv(ouput_folder + objName + lickIndcs_extension, delim_whitespace = True, header = 0, index_col = 0, comment='L') #Dirty trick to avoid the Line_label row
#Start to loop through recorded line
for i in range(len(lick_idcs_df.index)):
try:
print '-- Treating line:', lick_idcs_df.iloc[i].name
#Establish current line
dz.Current_Label = lick_idcs_df.iloc[i].name
dz.Current_Ion = lick_idcs_df.iloc[i].Ion
dz.Current_TheoLoc = lick_idcs_df.iloc[i].lambda_theo
selections = lick_idcs_df.iloc[i][3:9].values
#Proceed to measure
line_data = dz.measure_line(wave, flux, selections, lineslog_df, Measuring_Method = 'MC_lmfit', store_data = True)
except:
dz.log_emLine_error(objName, dz.Current_Label)
dz.save_lineslog_dataframe(lineslog_df, lines_log_address)
print '-', lines_log_address, 'saving'
print "\nAll data treated", dz.display_errors('emLine measurement', extra_verbose=True)
for i in range(len(catalogue_df.index)):
#Object
objName = catalogue_df.iloc[i].name
fits_file = catalogue_df.iloc[i].reduction_fits
ouput_folder = '{}{}/'.format(catalogue_dict['Obj_Folder'], objName)
print '-- Treating {} @ {}'.format(objName, fits_file)
#Spectrum data
wave, flux, header_0 = dz.get_spectra_data(fits_file)
subwave, subflux, lineHeight, LineExpLoc = Emission_Threshold(
4363.21, wave, flux)
#Establish current line
dz.Current_Label = 'O2_7319A'
dz.Current_Ion = 'O2'
dz.Current_TheoLoc = 4363.21
selections = array([4349.10, 4356.77, 4360.31, 4367.7, 4394.3, 4413.6])
#Proceed to measure
line_data = dz.measure_line(wave,
flux,
selections,
lines_dataframe=None,
store_data=False)
#Plot Global component
dz.data_plot(subwave, subflux, label='Spectrum', linestyle='step')
dz.data_plot(line_data['x_resample'],
line_data['y_resample'],
#Loop through the objects
for i in range(len(catalogue_df.index)):
#Object
objName = catalogue_df.iloc[i].name
fits_file = catalogue_df.iloc[i].reduction_fits
ouput_folder = '{}{}/'.format(catalogue_dict['Obj_Folder'], objName)
print '-- Treating {} @ {}'.format(objName, fits_file)
#Spectrum data
wave, flux, header_0 = dz.get_spectra_data(fits_file)
subwave, subflux, lineHeight, LineExpLoc = Emission_Threshold(6548.0, wave, flux)
#Establish current line
dz.Current_Label = 'N2_6548A'
dz.Current_Ion = 'N2'
dz.Current_TheoLoc = 6548.050
selections = array([6497.701, 6522.331, 6537.998, 6591.29, 6592.324, 6610.83])
#Proceed to measure
line_data = dz.measure_line(wave, flux, selections, lines_dataframe=None, store_data = False)
#Plot Global component
dz.data_plot(subwave, subflux, label='Spectrum', linestyle='step', color = dz.colorVector['silver'])
dz.data_plot(line_data['x_resample'], line_data['y_resample'], 'Gaussian mixture', linewidth=2.0, color = dz.colorVector['yellow'])
#Plot individual components
for i in range(line_data['line_number']):
dz.data_plot(line_data['x_resample'], line_data['y_comps'][i], 'Components', linestyle = '--', color=dz.colorVector['orangish'], linewidth=1.0)
