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"]
obj_dict[line + "_Peak"] = line_fit_orig["A0"]
obj_dict[line + "_continuum"] = line_fit_orig["zerolev_mean"]
obj_dict[line + "_Emis_reduc"] = ufloat(line_fit_orig["flux_intg"], line_fit_orig["flux_intg_er"])
# Measure the lines after the telluric correction for each case
for star in objects:
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']
obj_dict[line + '_Emis_reduc'] = ufloat(line_fit_orig['flux_intg'], line_fit_orig['flux_intg_er'])
#Measure the lines after the telluric correction for each case
for star in objects:
star_red_region = array(range(star_dict['{}_idx_join'.format(star)], star_dict['{}_idx_join'.format(star)] + len_red_region))
wave_tell, flux_tell = wave_obs, flux_obs / star_dict[star + '_flux']
#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)
#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'])
#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)
#Declare line to measure
line = 'N2_6548A'
#Establish current line
dz.Current_Label = lick_idcs_df.loc[line].name
dz.Current_Ion = lick_idcs_df.loc[line].Ion
dz.Current_TheoLoc = lick_idcs_df.loc[line].lambda_theo
selections = lick_idcs_df.loc[line][3:9].values
#Proceed to measure
# print '--Single fit'
# start = timer()
line_data = dz.measure_line(wave,
flux,
selections,
None,
Measuring_Method='lmfit',
store_data=False)
# end = timer()
# print 'lmfit', (end - start)
#Plot features
dz.Axis.plot(line_data.x_resample, line_data.y_resample, linestyle='--')
dz.Axis.step(wave[dz.fit_dict.idx0 - 5:dz.fit_dict.idx5 + 5],
flux[dz.fit_dict.idx0 - 5:dz.fit_dict.idx5 + 5],
label='Line: {}'.format(line),
where='mid')
dz.Axis.fill_between(wave[dz.fit_dict.idx0:dz.fit_dict.idx1],
dz.fit_dict.zerolev_mean,
flux[dz.fit_dict.idx0:dz.fit_dict.idx1],
facecolor=dz.colorVector['green'],
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)
#Plot peaks
#dz.data_plot(line_data['peak_waves'], line_data['peak_Maxima'], 'Input values', markerstyle='o', color = dz.colorVector['pink'])
dz.Axis.set_xlim([subwave[0],subwave[-1]])
dz.Axis.set_ylim(median(subflux)/10, lineHeight*2)
dz.FigWording(r'Wavelength $(\AA)$', 'Flux' + r'$(erg\,cm^{-2} s^{-1} \AA^{-1})$', r'Object {} $H\alpha$ wide component'.format(objName))