element_abundance_check = isnull(catalogue_df.loc[objName, element_abundance_key])
print objName, element, element_abundance_check
if element_abundance_check is False:
if (validity_entry not in ['ignored', 'NO_excess', 'Wide Component']):
regressions_employed.append(element)
else:
print 'Fallo', objName, element
name_superscript = r'\textsuperscript{{{regrens}}}'.format(regrens = ', '.join(regressions_employed))
entry_name = r'{text}{expo}'.format(text=catalogue_df.loc[objName].quick_index, expo=name_superscript)
objData = catalogue_df.loc[objName]
abundValues = objData[metals_list].values
objData[metals_list] = 12.0 + unumpy.log10(abundValues)
HeI_HI_entry = dz.format_for_table(catalogue_df.loc[objName, 'HeII_HII_from_O' + ext_data], rounddig=3, rounddig_er=2)
Ymass_O_entry = dz.format_for_table(catalogue_df.loc[objName, 'Ymass_O' + ext_data], rounddig=3, rounddig_er=2)
Ymass_S_entry = dz.format_for_table(catalogue_df.loc[objName, 'Ymass_S' + ext_data], rounddig=3, rounddig_er=2)
print objName, objData[['OI_HI' + ext_data]].values, objData[['OI_HI' + ext_data]].isnull().values.any(), regressions_employed
row = [entry_name] + [HeI_HI_entry, Ymass_O_entry, Ymass_S_entry]
row += list(objData[['OI_HI' + ext_data, 'NI_HI' + ext_data, 'SI_HI' + ext_data]].values)
dz.addTableRow(row, last_row = False if catalogue_df.index[-1] != objName else True, rounddig=3, rounddig_er=1)
# dz.generate_pdf()
dz.generate_pdf(output_address=pdf_address)
#Establish table format
dz.latex_header(table_address = FileFolder + CodeName + Table_Name, TitleColumn = ['', '', 'HII Galaxy', CodeName, ''])
#Loop through all the emission recordings
objlines = obj_lines_frame.index.values
for j in range(len(objlines)):
if '_w' not in objlines[j]:
#Loop through the columns and prepare the data to store
for k in range(len(Object_row)):
thekey = data_keys[k]
if thekey == 'Emission':
format_index = where(dz.LinesLogFormat_dict['0'] == objlines[j])[0][0]
wavelength = dz.format_for_table(obj_lines_frame.iloc[j]['TheoWavelength'], rounddig = 4)
ion = dz.LinesLogFormat_dict['3'][format_index]
Object_row[k] = r'${wavelength}$ ${ion}$'.format(wavelength = wavelength, ion = ion)
elif thekey == 'f_lambda':
Object_row[k] = dz.format_for_table(obj_lines_frame.iloc[j]['line_f'], rounddig = 4)
elif thekey == 'Eqw':
Eqw = obj_lines_frame.iloc[j]['line_Int'] / ufloat(obj_lines_frame.iloc[j]['Continuum_Median'], obj_lines_frame.iloc[j]['Continuum_sigma'])
# print objlines[j], obj_lines_frame.iloc[j]['line_Int'].nominal_value, ufloat(obj_lines_frame.iloc[j]['Continuum_Median'], obj_lines_frame.iloc[j]['Continuum_sigma']).nominal_value, Eqw.nominal_value
Object_row[k] = dz.format_for_table(Eqw, rounddig = 3)
elif thekey == 'Flux_undim':
value = obj_lines_frame.iloc[j]['line_Flux'] * 1000.0 / Hbeta_Flux
Object_row[k] = dz.format_for_table(value, rounddig = 3)
TitleColumn=['', '', 'HII Galaxy', CodeName, ''])
#Loop through all the emission recordings
objlines = obj_lines_frame.index.values
for j in range(len(objlines)):
if '_w' not in objlines[j]:
#Loop through the columns and prepare the data to store
for k in range(len(Object_row)):
thekey = data_keys[k]
if thekey == 'Emission':
format_index = where(
dz.LinesLogFormat_dict['0'] == objlines[j])[0][0]
wavelength = dz.format_for_table(
obj_lines_frame.iloc[j]['TheoWavelength'], rounddig=4)
ion = dz.LinesLogFormat_dict['3'][format_index]
Object_row[k] = r'${wavelength}$ ${ion}$'.format(
wavelength=wavelength, ion=ion)
elif thekey == 'f_lambda':
Object_row[k] = dz.format_for_table(
obj_lines_frame.iloc[j]['line_f'], rounddig=4)
elif thekey == 'Eqw':
Eqw = obj_lines_frame.iloc[j]['line_Int'] / ufloat(
obj_lines_frame.iloc[j]['Continuum_Median'],
obj_lines_frame.iloc[j]['Continuum_sigma'])
# print objlines[j], obj_lines_frame.iloc[j]['line_Int'].nominal_value, ufloat(obj_lines_frame.iloc[j]['Continuum_Median'], obj_lines_frame.iloc[j]['Continuum_sigma']).nominal_value, Eqw.nominal_value
Object_row[k] = dz.format_for_table(Eqw, rounddig=3)
#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[
'O3_5007A'].line_Int / lineslog_frame.loc['O3_4959A'].line_Int
O3_color = colorChooser(O3_ratio.nominal_value, dz.O3_5000_ratio)
O3_entry = r'\textcolor{' + O3_color + '}{' + dz.format_for_table(
O3_ratio, rounddig=3) + '}'
else:
O3_entry = '-'
#Fill the table
if (catalogue_df.loc[objName].T_low
column_code].values)
# Special treatment for the equivalent length: Using flux
continua_reduc = group_dict[str(objName) +
'_df'].loc[label, 'con_dered']
Eqw_special = row_i[2] / continua_reduc
if Eqw_special >= 5.0:
rounddig = 3
rounddig_er = 3
else:
rounddig = 1
rounddig_er = 1
row_i[0] = dz.format_for_table(Eqw_special,
rounddig=rounddig,
rounddig_er=rounddig_er)
row_i[1] = row_i[1] / group_dict[str(objName) +
'_Hbeta_F'] * norm_factor
row_i[2] = row_i[2] / group_dict[str(objName) +
'_Hbeta_I'] * norm_factor
row = row + row_i
dz.addTableRow(row, last_row=False)
# Ascii row
table_row[0] = ion
table_row[1] = row_i[0].split('$\pm$')[0]
table_row[2] = row_i[0].split('$\pm$')[1]
flux_entry = dz.format_for_table(row_i[1])
row_i = ['-'] * 3 #By default empty cells
if label in group_dict[str(obj) + '_dfemis'].index:
row_i = list(group_dict[str(obj) + '_dfemis'].loc[label, column_code].values)
#Special treatment for the equivalent length: Using flux
continua_reduc = group_dict[str(obj) + '_df'].loc[label, 'con_dered']
Eqw_special = row_i[2] / continua_reduc
if Eqw_special >= 5.0:
rounddig = 3
rounddig_er = 3
else:
rounddig = 1
rounddig_er = 1
row_i[0] = dz.format_for_table(Eqw_special, rounddig = rounddig, rounddig_er=rounddig_er)
row_i[1] = row_i[1] / group_dict[str(obj) + '_Hbeta_F'] * norm_factor
row_i[2] = row_i[2] / group_dict[str(obj) + '_Hbeta_I'] * norm_factor
row = row + row_i
dz.addTableRow(row, last_row = False)
else:
print 'ESTA FALLA', wave
dz.table.add_hline()
#Add bottom rows with the Hbeta flux and reddening
row_F, row_cHbeta = [r'$I(H\beta)$'], [r'$c(H\beta)$']
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)
#Get Hbeta flux for normalization
Hbeta_flux = reduc_linedf.loc['H1_4861A'].line_Flux
Hbeta_Int = reduc_linedf.loc['H1_4861A'].line_Int
#Generate pdf
table_address = '{objfolder}{codeName}_WHT_linesLog_reduc'.format(objfolder = ouput_folder, codeName=objName)
dz.create_pdfDoc(table_address, pdf_type='table')
#Set the pdf format
dz.pdf_insert_table(Headers)
for line in reduc_linedf.index.values:
ion = r'${}$'.format(lines_df.loc[line].notation) if '_w' not in line else r'$H\alpha$'
row = list(reduc_linedf.loc[line, column_code].values)
row[2] = row[2] / Hbeta_flux * norm_factor
row[4] = row[4] / Hbeta_Int * norm_factor
row = [r'{} {}'.format(row[0], ion)] + row[1:]
dz.addTableRow(row, last_row = False if reduc_linedf.index[-1] != line else True)
row = [r'$F(H\beta)$ $(erg\,cm^{-2} s^{-1} \AA^{-1})$', '', dz.format_for_table(Hbeta_flux, rounddig = 3, scientific_notation=True), '', dz.format_for_table(Hbeta_Int, rounddig = 3, scientific_notation=True)]
dz.addTableRow(row, last_row = False)
row = [r'$c(H\beta)$', '', cHbeta, '', '']
dz.addTableRow(row, last_row = True)
dz.generate_pdf()
