'DirectMethod_fitSigma', 'DirectMethod_difPercentage',
'HIICHImistry_Spherical_fitFlux',
'HIICHImistry_Spherical_fitSigma',
'HIICHImistry_Spherical_difPercentage',
'HIICHImistry_PlaneParallel_fitFlux',
'HIICHImistry_PlaneParallel_fitSigma',
'HIICHImistry_PlaneParallel_difPercentage',
'BayesHIICHImistry_fitFlux', 'BayesHIICHImistry_fitSigma',
'BayesHIICHImistry_difPercentage',
'DirectMethod-BayesHIICHImistry_fitFlux',
'DirectMethod-BayesHIICHImistry_fitSigma',
'DirectMethod-BayesHIICHImistry_difPercentage'
]
tableDF = pd.DataFrame(columns=txt_headers)
pdf = PdfPrinter()
pdf.create_pdfDoc(pdf_type=None)
pdf.pdf_insert_table(table_headers, addfinalLine=True)
for i, lineLabel in enumerate(tableLines):
row_data = ['-'] * len(table_headers)
row_txt = ['-'] * (len(txt_headers))
row_data[0] = latexLabel_array[i]
obsFlux, obsErr = objLinesDF.loc[
lineLabel, 'obsFlux'], objLinesDF.loc[lineLabel, 'obsFluxErr']
row_data[1] = r'${:.3f}\pm{:.3f}$'.format(obsFlux, obsErr)
row_txt[0], row_txt[1] = f'{obsFlux:.3f}', f'{obsErr:.3f}'
def table_line_fluxes(table_address,
db_dict,
combined_dict={},
file_type='table'):
# Table headers
headers = [
'Line', 'Observed flux', 'Mean', 'Standard deviation', 'Median',
r'$16^{th}$ $percentil$', r'$84^{th}$ $percentil$', r'$Difference\,\%$'
]
# Create containers
tableDF = pd.DataFrame(columns=headers[1:])
pdf = PdfPrinter()
pdf.create_pdfDoc(pdf_type=file_type)
pdf.pdfDoc.append(
NoEscape('\definecolor{background}{rgb}{0.169, 0.169, 0.169}'))
pdf.pdfDoc.append(
NoEscape('\definecolor{foreground}{rgb}{0.702, 0.780, 0.847}'))
pdf.pdfDoc.append(NoEscape(r'\arrayrulecolor{foreground}'))
pdf.pdf_insert_table(headers,
color_font='foreground',
color_background='background')
# Input data
inputLabels = db_dict['Input_data']['lineLabels_list']
inFlux, inErr = db_dict['Input_data']['inputFlux_array'], db_dict[
'Input_data']['inputErr_array']
# Output data
flux_matrix = db_dict['trace']['calcFluxes_Op']
mean_line_values = flux_matrix.mean(axis=0)
std_line_values = flux_matrix.std(axis=0)
median_line_values = np.median(flux_matrix, axis=0)
p16th_line_values = np.percentile(flux_matrix, 16, axis=0)
p84th_line_values = np.percentile(flux_matrix, 84, axis=0)
# Array wih true error values for flux
diff_Percentage = np.round((1 - (median_line_values / inFlux)) * 100, 2)
diff_Percentage = list(map(str, diff_Percentage))
ion_array, wave_array, latexLabel_array = label_decomposition(
inputLabels, combined_dict=combined_dict)
for i in range(inFlux.size):
# label = label_formatting(inputLabels[i])
row_i = [
latexLabel_array[i], inFlux[i], mean_line_values[i],
std_line_values[i], median_line_values[i], p16th_line_values[i],
p84th_line_values[i], diff_Percentage[i]
]
pdf.addTableRow(
row_i,
last_row=False if inputLabels[-1] != inputLabels[i] else True,
color_font='foreground',
color_background='background')
tableDF.loc[inputLabels[i]] = row_i[1:]
pdf.generate_pdf(table_address)
# Save the table as a dataframe.
with open(f'{table_address}.txt', 'wb') as output_file:
string_DF = tableDF.to_string()
output_file.write(string_DF.encode('UTF-8'))