# 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
lineslog_address = "{objfolder}{codeName}{lineslog_extension}".format(
objfolder=ouput_folder, codeName=objName, lineslog_extension=AbundancesFileExtension
)
# Load object data
lineslog_frame = dz.load_lineslog_frame(lineslog_address)
wave, flux, header_0 = dz.get_spectra_data(fits_file)
# Perform the reddening correction
cHbeta = catalogue_df.iloc[i][cHbeta_type]
dz.deredden_lines(cHbeta, lineslog_frame)
spectrum_dered = dz.derreddening_continuum(wave, flux, cHbeta.nominal_value)
# Import cHbeta coefficient
# Te = catalogue_df.iloc[i].TeSIII if notnull(catalogue_df.iloc[i].TeSIII) else 10000.0
# nHeII_HII = catalogue_df.iloc[i].HeII_HII_from_S if notnull(catalogue_df.iloc[i].HeII_HII_from_S) else 0.1
# nHeIII_HII = catalogue_df.iloc[i].HeIII_HII_from_S if notnull(catalogue_df.iloc[i].HeIII_HII_from_S) else 0.0
# Hbeta_Flux = lineslog_frame.loc['H1_4861A', 'line_Int']
# Halpha_Flux = lineslog_frame.loc['H1_6563A', 'line_Int']
Te = 10000.0
nHeII_HII = 0.1
nHeIII_HII = 0.0
Hbeta_Flux = lineslog_frame.loc["H1_4861A", "line_Int"]
Halpha_Flux = lineslog_frame.loc["H1_6563A", "line_Int"]
for objName in catalogue_df.loc[dz.idx_include].index:
ouput_folder = '{}{}/'.format(catalogue_dict['Obj_Folder'], objName)
lineslog_address = '{objfolder}{codeName}{lineslog_extension}'.format(
objfolder=ouput_folder,
codeName=objName,
lineslog_extension=AbundancesFileExtension)
#Load lines frame
lineslog_frame = dz.load_lineslog_frame(lineslog_address)
#Perform the reddening correction
cHbeta = catalogue_df.loc[objName, cHbeta_type]
dz.deredden_lines(lineslog_frame,
reddening_curve=red_curve,
cHbeta=cHbeta,
R_v=R_v)
#Sulfur ratios
if set(lineslog_frame.index) >= set(['S3_9069A', 'S3_9531A']):
s3_ratio = lineslog_frame.loc[
'S3_9531A'].line_Int / lineslog_frame.loc['S3_9069A'].line_Int
s3_color = colorChooser(s3_ratio.nominal_value, dz.S3_ratio)
s3_entry = r'\textcolor{' + s3_color + '}{' + dz.format_for_table(
s3_ratio, rounddig=3) + '}'
else:
s3_entry = '-'
#Oxygen ratios
if set(lineslog_frame.index) >= set(['O3_4959A', 'O3_5007A']):
O3_ratio = lineslog_frame.loc[
dz.pdf_insert_table(table_format='l' + 'c' * (3 * obj_per_page))
group_dict = OrderedDict()
# Make dict with all the objects lines dataframes
ouput_folder = '{}{}/'.format(catalogue_dict['Obj_Folder'], objName)
linelog_reducAddress = '{objfolder}{codeName}_WHT_linesLog_reduc.txt'.format(
objfolder=ouput_folder, codeName=objName)
linelog_emisAddress = '{objfolder}{codeName}_WHT_linesLog_emission_2nd.txt'.format(
objfolder=ouput_folder, codeName=objName)
reduc_linedf = dz.load_lineslog_frame(linelog_reducAddress)
emission_linedf = dz.load_lineslog_frame(linelog_emisAddress)
cHbeta = catalogue_df.loc[objName, cHbeta_type]
dz.deredden_lines(reduc_linedf,
reddening_curve=red_curve,
cHbeta=cHbeta,
R_v=R_v)
dz.deredden_lines(emission_linedf,
reddening_curve=red_curve,
cHbeta=cHbeta,
R_v=R_v)
group_dict[str(objName) + '_df'] = reduc_linedf
group_dict[str(objName) + '_dfemis'] = emission_linedf
group_dict[str(objName) +
'_Hbeta_F'] = emission_linedf.loc['H1_4861A'].line_Flux
group_dict[str(objName) +
'_Hbeta_I'] = emission_linedf.loc['H1_4861A'].line_Int
lambdas_array = emission_linedf['lambda_theo'].values
fits_emission = catalogue_df.iloc[i].stellar_fits
fits_stellar = ouput_folder + objName + '_StellarContinuum.fits'
lineslog_address = '{objfolder}{codeName}{lineslog_extension}'.format(objfolder = ouput_folder, codeName=objName, lineslog_extension=AbundancesFileExtension)
if isfile(fits_stellar):
# if i < 3:
#Extract observational data
wave_reduc, flux_reduc, header_0_reduc = dz.get_spectra_data(fits_reduc)
wave_emis, flux_emis, header_emis = dz.get_spectra_data(fits_emission)
wave_stellar, flux_stellar, header_stellar = dz.get_spectra_data(fits_stellar)
reduc_lineslog_df = dz.load_lineslog_frame(lineslog_address)
#Perform the reddening correction
cHbeta = catalogue_df.iloc[i][cHbeta_type]
dz.deredden_lines(cHbeta, reduc_lineslog_df)
#Measure Hbeta flux
Hbeta_wavelengths = reduc_lineslog_df.loc['H1_4861A', ['Wave1', 'Wave2', 'Wave3', 'Wave4', 'Wave5', 'Wave6']].values
Hbeta_dist = random.normal(reduc_lineslog_df.loc['H1_4861A'].line_Int.nominal_value, reduc_lineslog_df.loc['H1_4861A'].line_Int.std_dev, MC_length)
#Insert new section in pdf
with dz.pdfDoc.create(Section('HII Galaxy: {}'.format(objName))):
dz.add_page()
#------Plot Oxygen lines
element_lines = reduc_lineslog_df.loc[(reduc_lineslog_df.index.isin(oxygen_emision))].index.values
if len(element_lines) > 0:
dz.FigConf(plotStyle='seaborn-colorblind', Figtype = 'grid', plotSize = sizing_dict,
n_columns = int(len(element_lines)), n_rows = int(np_ceil(len(element_lines)/n_columns)))
lineslog_extension=AbundancesFileExtension)
if isfile(fits_stellar):
# if i < 3:
#Extract observational data
wave_reduc, flux_reduc, header_0_reduc = dz.get_spectra_data(
fits_reduc)
wave_emis, flux_emis, header_emis = dz.get_spectra_data(fits_emission)
wave_stellar, flux_stellar, header_stellar = dz.get_spectra_data(
fits_stellar)
reduc_lineslog_df = dz.load_lineslog_frame(lineslog_address)
#Perform the reddening correction
cHbeta = catalogue_df.iloc[i][cHbeta_type]
dz.deredden_lines(cHbeta, reduc_lineslog_df)
#Measure Hbeta flux
Hbeta_wavelengths = reduc_lineslog_df.loc[
'H1_4861A',
['Wave1', 'Wave2', 'Wave3', 'Wave4', 'Wave5', 'Wave6']].values
Hbeta_dist = random.normal(
reduc_lineslog_df.loc['H1_4861A'].line_Int.nominal_value,
reduc_lineslog_df.loc['H1_4861A'].line_Int.std_dev, MC_length)
#Insert new section in pdf
with dz.pdfDoc.create(Section('HII Galaxy: {}'.format(objName))):
dz.add_page()
#------Plot Oxygen lines
