# Treatment for the line ratios
if thekey in ["O3_ratio", "N2_ratio", "S3_ratio"]:
value = format_for_table(Ratios_dict[thekey])
color = RatiosColor_dict[thekey]
Object_row[j] = r"\textcolor{" + color + "}{" + value + "}"
# Treatment for the physical conditions
if thekey in ["TOIII", "TOII", "TSIII", "TSII", "nSII", "cHBeta"]:
value = pv.GetParameter_ObjLog(CodeName, FileFolder, thekey, "string", sigfig=5, strformat="{:.2f}")
Object_row[j] = value
# Treatment for metallic abundances
elif thekey in ["OI_HI", "NI_HI", "SI_HI", "SI_HI_ArCorr"]:
value = pv.GetParameter_ObjLog(CodeName, FileFolder, thekey, "float")
if value != None:
abund_log = 12 + umath_log10(value)
formated_abundance = format_for_table(abund_log)
else:
formated_abundance = None
Object_row[j] = formated_abundance
# Treatment for Helium abundances
elif thekey in [
"HeI_HI",
"HeII_HII",
"HeIII_HII",
"Y_Mass_O",
"Y_Inference_O",
"Y_Mass_S",
"Y_Inference_S",
entry_name = '{}'.format(bayes_catalogue_df.loc[objName].quick_index)
if entry_name not in [
'SHOC588', 'SHOC592', 'SHOC036', 'SHOC575', 'SHOC579', 'SHOC220'
]:
objData = bayes_catalogue_df.loc[objName]
row = [entry_name]
for param in properties_list:
param_value = objData[param]
param_err = objData[param + '_err']
param_un = un.ufloat(param_value, param_err)
if param not in ['HeI_HI', 'Ymass_O', 'Ymass_S']:
param_un = 12 + umath_log10(param_un)
if np.isnan(param_un.nominal_value):
param_un = np.nan
row.append(param_un)
dz.addTableRow(
row,
last_row=False if bayes_catalogue_df.index[-1] != objName else True,
rounddig=3,
rounddig_er=1)
# regressions_employed = []
# for element in ['O', 'N', 'S']:
# validity_entry = catalogue_df.loc[objName, element + '_valid']
# Treatment for the line ratios
if thekey in ['O3_ratio', 'N2_ratio', 'S3_ratio']:
value = format_for_table(Ratios_dict[thekey])
color = RatiosColor_dict[thekey]
Object_row[j] = r'\textcolor{' + color + '}{' + value + '}'
#Treatment for the physical conditions
if thekey in ['TOIII', 'TOII', 'TSIII', 'TSII', 'nSII', 'cHBeta']:
value = pv.GetParameter_ObjLog(CodeName, FileFolder, thekey, 'string', sigfig=5, strformat= '{:.2f}')
Object_row[j] = value
#Treatment for metallic abundances
elif thekey in ['OI_HI', 'NI_HI', 'SI_HI', 'SI_HI_ArCorr']:
value = pv.GetParameter_ObjLog(CodeName, FileFolder, thekey, 'float')
if value != None:
abund_log = 12 + umath_log10(value)
formated_abundance = format_for_table(abund_log)
else:
formated_abundance = None
Object_row[j] = formated_abundance
#Treatment for Helium abundances
elif thekey in ['HeI_HI', 'HeII_HII', 'HeIII_HII', 'Y_Mass_O', 'Y_Inference_O', 'Y_Mass_S', 'Y_Inference_S']:
value = pv.GetParameter_ObjLog(CodeName, FileFolder, thekey, 'float')
if value != None:
formated_abundance = format_for_table(value, 3)
else:
formated_abundance = None
Object_row[j] = formated_abundance
SI_HI_un = SII_HII_un + SIII_HII_un
save_unVariables(catalogueDf, objName, 'SI_HI', SI_HI_un.nominal_value,
SI_HI_un.std_dev)
# Add S+3 contribution from Argon lines
if np.isnan(catalogueDf.loc[
objName,
['SI_HI', 'ArIII_HII', 'ArIV_HII']].values).any() == False:
SI_HI_un, SII_HII_un, SIII_HII_un = load_unVariables(
catalogueDf, objName, ['SI_HI', 'SII_HII', 'SIII_HII'])
ArIII_HII_un, ArIV_HII_un = load_unVariables(catalogueDf, objName,
['ArIII_HII', 'ArIV_HII'])
logAr2Ar3 = umath_log10(ArIII_HII_un) / umath_log10(ArIV_HII_un)
logSIV = umath_log10(
SIII_HII_un) - (logAr2Ar3 - n_SIV_correction) / m_SIV_correction
SIV_HII_un = umath_pow(10, logSIV)
SI_HI_un += SIV_HII_un
ICF_SIV = SI_HI_un / (SII_HII_un + SIII_HII_un)
save_unVariables(catalogueDf, objName, 'SI_HI', SI_HI_un.nominal_value,
SI_HI_un.std_dev)
save_unVariables(catalogueDf, objName, 'SIV_HII',
SIV_HII_un.nominal_value, SIV_HII_un.std_dev)
save_unVariables(catalogueDf, objName, 'ICF_SIV',
ICF_SIV.nominal_value, ICF_SIV.std_dev)
# Compute nitrogen abundance