row_i = [row] * 3
line_F = reduc_linedf.iloc[i].line_Flux
line_emisF = emission_linedf.iloc[i].line_Flux
row_i[0] = line_F / Hbeta_F * norm_factor
row_i[1] = line_emisF / Hbeta_F * norm_factor
diff = (1.0 - line_F / line_emisF) * 100
row_i[2] = round(diff.nominal_value,
1) #/Hbeta_emisF * norm_factor
row = row + row_i
dz.addTableRow(row, last_row=False)
dz.generate_pdf()
# for wave in lambdas_array:
#
# if wave in linformat_df['lambda_theo'].values:
# idx_label = (linformat_df['lambda_theo'] == wave)
# ion = linformat_df.loc[idx_label, 'latex_format'].values[0]
# label = linformat_df.loc[idx_label, 'line_label'].values[0]
# wave = 4861.0 if label == 'H1_4861A' else wave
# wave_lam_ref = int(round(wave, 0))
# line_label = r'{} ${}$'.format(wave_lam_ref, ion)
# row = [line_label]
# table_row = [None] * 7
#
# if ion in ['H1', 'He1', 'He2']:
#
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)
# from dazer_methods import Dazer
# from uncertainties import unumpy
# from collections import OrderedDict
# from pylatex import Package, NoEscape
#
# #Import library object
# dz = Dazer()
#
# #Load observational data
# catalogue_df = dz.load_excel_DF('/home/vital/Dropbox/Astrophysics/Data/WHT_observations/WHT_Galaxies_properties.xlsx')
# dz.quick_indexing(catalogue_df)
#
objectFolder = '{}{}/'.format(
root_folder, objName) # '{}{}\\'.format(root_folder, objName)
dataFileAddress = '{}{}_objParams.txt'.format(objectFolder, objName)
obsData = specS.load_obsData(dataFileAddress, objName)
# Adapt the temperature
Tlow_key, Thigh_key = catalogue_df.loc[objName].T_low, catalogue_df.loc[
objName].T_high
# # Case with a sulfur temperature
# if (Tlow_key == 'TeSIII') and (Tlow_key == 'TeOIII_from_TeSIII'):
# Case with an oxygen temperature
if (Tlow_key == 'TeSIII_from_TeOIII') and (Thigh_key == 'TeOIII'):
obsData['T_high'] = obsData.pop('T_low', '-')
# Load observational data
row_i = [quick_reference] + ['-'] * varsNum
keys_list = headers_dic.keys()
for j in range(len(keys_list)):
item = keys_list[j]
row_i[j + 1] = checkDictValue(obsData, item)
dz.addTableRow(row_i,
last_row=False if sampleObjects[-1] != objName else True,
rounddig=3,
rounddig_er=1)
# dz.generate_pdf()
dz.generate_pdf(output_address=tableAddress)
O3_entry = '-'
#Fill the table
if (catalogue_df.loc[objName].T_low
== 'TeSIII') and (catalogue_df.loc[objName].T_high == 'TeOIII'):
exponent = ''
elif (catalogue_df.loc[objName].T_low != 'TeSIII'):
exponent = 'O'
else:
exponent = 'S'
entry_name = '{codename}$^{{{elements}}}$'.format(
codename=catalogue_df.loc[objName].quick_index, elements=exponent)
print entry_name
T_low_entry = r'$T_{e}[SIII]$' if catalogue_df.loc[
objName].T_low == 'TeSIII' else r'$T_{e}[SIII] eq.16$'
T_high_entry = r'$T_{e}[OIII]$' if catalogue_df.loc[
objName].T_high == 'TeOIII' else r'$T_{e}[OIII] eq.16$'
row = [entry_name] + [O3_entry] + [s3_entry] + list(
catalogue_df.loc[
objName, properties_list].values) # + [T_low_entry, T_high_entry]
dz.addTableRow(
row,
last_row=False if catalogue_df.index[-1] != objName else True,
rounddig=3)
dz.generate_pdf(clean_tex=False)
#dz.generate_pdf(output_address=pdf_address)
print 'Table generated'
atom = dz.He2_atom
atom_label = 'HeIII'
wave_label = int(round(reduc_lineslog_df.loc[lines_labels[j]].lambda_theo))
Helium_i_dist = random.normal(reduc_lineslog_df.loc[lines_labels[j]].line_Int.nominal_value, reduc_lineslog_df.loc[lines_labels[j]].line_Int.std_dev, MC_length)
abund_dist = atom.getIonAbundance(int_ratio=Helium_i_dist, tem=Te, den=ne, wave=wave_label, Hbeta = Hbeta_dist)
abund_log = ufloat(nanmean(abund_dist), nanstd(abund_dist))
abund_label = '{:L}'.format(abund_log)
#Plot the data
title_line_plot = """{label}:
$\\frac{{{at_label}}}{{HII}}={abund}$""".format(label=lines_labels[j], at_label=atom_label, abund=abund_label)
dz.data_plot(subWwave, subFlux, label='', linestyle='step', graph_axis=dz.Axis[j])
dz.data_plot(subWwave_emis, subFlux_emis, label='', linestyle='step', graph_axis=dz.Axis[j])
dz.Axis[j].fill_between(wave_reduc[idcs_obj[2]:idcs_obj[3]], flux_reduc[idcs_obj[2]:idcs_obj[3]], interFlux_solar, step='mid', alpha=0.5)
if lines_labels[j] == 'He1_5016A':
dz.Axis[j].set_ylim(0, reduc_lineslog_df.loc[lines_labels[j]].A * 4)
dz.data_plot(subwave_solar, subFlux_solar, label='', linestyle='step', graph_axis=dz.Axis[j])
dz.FigWording(xlabel='', ylabel='', title=title_line_plot, graph_axis=dz.Axis[j])
plt.tight_layout()
dz.fig_to_pdf(label='{} Helium lines:'.format(objName.replace('_','-')), add_page=True)
dz.reset_fig()
else:
print '--There is not stellar spectrum'
dz.generate_pdf(True)
print 'Data treated'
wave_reduc[idcs_obj[2]:idcs_obj[3]],
flux_reduc[idcs_obj[2]:idcs_obj[3]],
interFlux_solar,
step='mid',
alpha=0.5)
if lines_labels[j] == 'He1_5016A':
dz.Axis[j].set_ylim(
0, reduc_lineslog_df.loc[lines_labels[j]].A * 4)
dz.data_plot(subwave_solar,
subFlux_solar,
label='',
linestyle='step',
graph_axis=dz.Axis[j])
dz.FigWording(xlabel='',
ylabel='',
title=title_line_plot,
graph_axis=dz.Axis[j])
plt.tight_layout()
dz.fig_to_pdf(label='{} Helium lines:'.format(
objName.replace('_', '-')),
add_page=True)
dz.reset_fig()
else:
print '--There is not stellar spectrum'
dz.generate_pdf(True)
print 'Data treated'
