#
# counter += 1
#
# #Plot WMAP prediction
# dz.data_plot(WMAP_coordinates[0].nominal_value, WMAP_coordinates[1].nominal_value, color = dz.colorVector['pink'], label='Planck prediction', markerstyle='o', x_error=WMAP_coordinates[0].std_dev, y_error=WMAP_coordinates[1].std_dev)
#
# #plotTitle = r'{title}: $Y_{{P}} = {n}_{{-{lowerlimit}}}^{{+{upperlimit}}}$'.format(title = Regresions_dict['title'][i], n = round_sig(n_Median,4, scien_notation=False), lowerlimit = round_sig(n_Median-n_16th,2, scien_notation=False), upperlimit = round_sig(n_84th-n_Median,2, scien_notation=False))
# dz.Axis.set_ylim(0.1,0.4)
# dz.FigWording(Regresions_dict['x label'][i], Regresions_dict['y label'][i], '', loc='lower center', ncols_leg=2)
#
# output_pickle = '{objFolder}{element}_regression_2nd'.format(objFolder=output_folder, element = element)
# dz.save_manager(output_pickle, save_pickle = False)
for key in regr_dict:
magnitude_entry = r'${}\pm{}$'.format(
round_sig(regr_dict[key][0], 3, scien_notation=False),
round_sig(regr_dict[key][1], 1, scien_notation=False))
print magnitude_entry
# # Combined regressions
# for i in range(len(Regresions_list)):
#
# regr_group = Regresions_list[i]
# dim_group = len(regr_group)
# ext_method = str(dim_group)
# p0 = array([0.005] * dim_group + [0.25])
#
# # Define lmfit model
# params = Parameters()
# for idx in range(dim_group):
# params.add('m' + str(idx), value=0.005)
rounddig=2,
scientific_notation=True)
row_F += [
'',
dz.format_for_table(group_dict[str(objName) + '_Hbeta_F'],
rounddig=2,
scientific_notation=True),
dz.format_for_table(group_dict[str(objName) + '_Hbeta_I'],
rounddig=2,
scientific_notation=True)
]
cHbeta_reduc, cHbeta_emis = catalogue_df.loc[
objName, 'cHbeta_reduc'], catalogue_df.loc[objName, 'cHbeta_emis']
cHbeta_reduc_entry = '{}$\pm${}'.format(
round_sig(cHbeta_reduc.nominal_value, 2, scien_notation=False),
round_sig(cHbeta_reduc.std_dev, 1, scien_notation=False))
cHbeta_emis_entry = '{}$\pm${}'.format(
round_sig(cHbeta_emis.nominal_value, 2, scien_notation=False),
round_sig(cHbeta_emis.std_dev, 1, scien_notation=False))
row_cHbeta += ['', cHbeta_emis_entry, '']
dz.addTableRow(row_F, last_row=False)
dz.addTableRow(row_cHbeta, last_row=True)
dz.table.add_hline()
#dz.generate_pdf()
dz.generate_pdf(output_address=pdf_address)
# table_row = [''] * 7
# # table_row[0] = '$(erg\,cm^{-2} s^{-1} \AA^{-1})$'
n_Median_cf, n_std_df, n_16th_cf, n_84th_cf = np.median(curvefit_matrix[3, :]), np.std(curvefit_matrix[3, :]), np.percentile(curvefit_matrix[3, :],16), np.percentile(curvefit_matrix[3, :], 84)
entry_key = r'$Y_{{P,\,{elemA}-{elemB}-{elemC}}}$'.format(elemA='O', elemB='N', elemC='S')
# Save the results
results_dict['ONS'] = np.array([entry_key, n_Median_cf, n_std_df, n_objects])
# Make the table
pdf_address = tables_folder + 'yp_determinations'
# dz.create_pdfDoc(pdf_address, pdf_type='table')
headers = ['Element regression', 'Value', 'Number of objects']
dz.pdf_insert_table(headers)
last_key = results_dict.keys()[-1]
for key in results_dict:
magnitude_entry = r'${}\pm{}$'.format(round_sig(results_dict[key][1], 3, scien_notation=False), round_sig(results_dict[key][2], 1, scien_notation=False))
row = [results_dict[key][0], magnitude_entry, str(int(results_dict[key][3]))]
dz.addTableRow(row, last_row = False if last_key != last_key else True)
dz.table.add_hline()
for key in inter_regr_dict:
magnitude_entry = r'${}\pm{}$'.format(inter_regr_dict[key][0], inter_regr_dict[key][1])
row = [key, magnitude_entry, inter_regr_dict[key][2]]
dz.addTableRow(row, last_row = False if last_key != last_key else True)
dz.table.add_hline()
for key in exter_regr_dict:
magnitude_entry = r'${}\pm{}$'.format(exter_regr_dict[key][0], exter_regr_dict[key][1])
row = [key, magnitude_entry, exter_regr_dict[key][2]]
dz.addTableRow(row, last_row = False if last_key != last_key else True)
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'$c(H\beta)$']
row_clean = ['$(erg\,cm^{-2} s^{-1} \AA^{-1})$'] + [''] * len(obj_group) * 3
for obj in obj_group:
#row_F += ['', dz.format_for_table(group_dict[str(obj) + '_Hbeta_F'], rounddig = 2, scientific_notation=True), dz.format_for_table(group_dict[str(obj) + '_Hbeta_I'], rounddig = 2, scientific_notation=True)]
row_F += ['', dz.format_for_table(group_dict[str(obj) + '_Hbeta_F'], rounddig = 2, scientific_notation=True), dz.format_for_table(group_dict[str(obj) + '_Hbeta_I'], rounddig = 2, scientific_notation=True)]
cHbeta_reduc, cHbeta_emis = catalogue_df.loc[obj, 'cHbeta_reduc'], catalogue_df.loc[obj, 'cHbeta_emis']
cHbeta_reduc_entry = '{}$\pm${}'.format(round_sig(cHbeta_reduc.nominal_value, 2, scien_notation=False), round_sig(cHbeta_reduc.std_dev, 1, scien_notation=False))
cHbeta_emis_entry = '{}$\pm${}'.format(round_sig(cHbeta_emis.nominal_value, 2, scien_notation=False), round_sig(cHbeta_emis.std_dev, 1, scien_notation=False))
#row_cHbeta += ['', cHbeta_reduc_entry, cHbeta_emis_entry]
row_cHbeta += ['', cHbeta_emis_entry, '']
print cHbeta_emis_entry
dz.addTableRow(row_F, last_row = False)
dz.addTableRow(row_clean, last_row = False)
dz.addTableRow(row_cHbeta, last_row = True)
dz.table.add_hline()
dz.generate_pdf(output_address=pdf_address)
# dz.generate_pdf()
# from numpy import nanmean, nanstd, mean, concatenate, unique, sum, round, nan