'_treatedlinesLog.txt')
tableLogAddress = str(file_address).replace('.fits', '_treatedlinesLog')
figureLogAddress = str(file_address).replace('.fits',
'_treatedlinesLog.png')
# Set and crop the wavelength
wave_rest, flux, header = sr.import_fits_data(file_address,
instrument='SDSS')
idx_wave = (wave_rest >= obsData['sample_data']['wmin_array']) & (
wave_rest <= obsData['sample_data']['wmax_array'])
# Load line measurer object
lm = sr.LineMesurer(wave_rest[idx_wave],
flux[idx_wave] / fluxNorm,
linesDF_address=linesLogAddress)
pdf = PdfPrinter()
# Measure line fluxes
idcs_lines = ~lm.linesDF.index.str.contains('_b')
obsLines = lm.linesDF.loc[idcs_lines].index.values
pdf.create_pdfDoc(tableLogAddress, pdf_type='table')
pdf.pdf_insert_table(tableHeaders)
flux_Hbeta = lm.linesDF.loc['H1_4861A', 'intg_flux']
for lineLabel in obsLines:
label_entry = lm.linesDF.loc[lineLabel, 'latexLabel']
eqw, eqwErr = lm.linesDF.loc[lineLabel,
'eqw'], lm.linesDF.loc[lineLabel,
'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}'
traces_param.append(param)
traces_param = np.array(traces_param)
obj_Results = {}
traces = fit_results['trace']
for trace_code in traces_param:
trace_array = traces[trace_code]
mean_value = np.mean(trace_array)
std_dev = np.std(trace_array)
obj_Results[trace_code] = (mean_value, std_dev)
sample_Results[obj] = obj_Results
# Generate the table
pdf = PdfPrinter()
pdf.create_pdfDoc(pdf_type=None)
pdf.pdf_insert_table(['Parameter', 'SHOC148', 'GP101157', 'GP121903'])
for i, param in enumerate(rows):
row_data = ['-'] * 4
row_data[0] = latex_labels[param]
for j, obj in enumerate(objList[:3]):
param_mean, param_std = sample_Results[obj][param]
if param == 'logU':
param_formated = r'${:.2f}\pm{:.2f}$'.format(param_mean, param_std)
elif param != 'He2':
round_n = 0 if param_mean > 10 else 2
param_formated = r'${}\pm{}$'.format(
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'))
# Import the observation data
obsData = sr.loadConfData('D:/Pycharm Projects/vital_tests/astro/data/SDSS/flux_comparison.ini', group_variables=False)
linesFile = Path('D:/Pycharm Projects/spectra-synthesizer/src/specsiser/literature_data/lines_data.xlsx')
linesDb = pd.read_excel(linesFile, sheet_name=0, header=0, index_col=0)
data_folder = Path(obsData['file_information']['data_folder'])
fileList = list_files(data_folder, '.fits')
addressList = list(data_folder / file for file in fileList)
flux_norm = obsData['sample_data']['norm_flux']
tableHeaders = [r'Object name', r'$EW(H\beta)$ $(\AA)$', r'$c(H\beta)$', r'$F(H\alpha)/F(H\beta)$']
scaleTable = 1000.0
fitsFolder, fitsFile = addressList[0].parent, addressList[0].name
sampleFolder, tableName = fitsFolder/'sample_data', 'sample_properties'
pdf = PdfPrinter()
pdf.create_pdfDoc(sampleFolder / tableName, pdf_type='table')
pdf.pdf_insert_table(tableHeaders)
# Analyse the spectrum
for i, file_address in enumerate(addressList):
# Open lineslog
fitsFolder, fitsFile = file_address.parent, file_address.name
lineLogFolder, lineLogFile = fitsFolder/'flux_analysis', fitsFile.replace('.fits', '_linesLog.txt')
pdfLogFolder, pdfLogFile = fitsFolder / 'flux_analysis', fitsFile.replace('.fits', '_linesLog')
objName = fitsFile.replace('.fits', '')
print(f'- {i}: {objName}')
# Set and crop the wavelength
r'$\chi^2$': 'CHI2_RED',
r'$T_{e} (K)$': 'TELECTRO',
r'$n_{e} (cm^{-3})$': 'DELECTRO',
r'$A_{V, neb}$': 'GNEBULAR',
r'$A_{V, \star}$': 'GEXTINCT',
'Mass ever formed': 'LOGMEAVE',
'Mass presently availabled': 'LOGMEAVE',
'Mass > 1 Myr': 'LOGPEAVE'
}
row_headers = list(dict_ref.keys())
tables_folder = Path(obsData['file_information']['tables_folder'])
for i, obj in enumerate(objList):
pdf = PdfPrinter()
row_object = [''] * len(row_headers)
row_object[5] = obj
pdf.create_pdfDoc(pdf_type='table')
pdf.pdf_insert_table(row_object, addfinalLine=False)
pdf.table.add_hline()
pdf.addTableRow(row_headers, last_row=False, rounddig=2)
pdf.table.add_hline()
# Declare input files
objFolder = resultsFolder / f'{obj}'
results_file = objFolder / f'{obj}_{ext}_measurements.txt'
# Declare output file
table_file = tables_folder / f'{obj}_FadoFitting'
# Establish files location
objName = obsData['file_information']['object_list'][i]
fitsFolder, fitsFile = file_address.parent, file_address.name
lineLogFolder, lineLogFile = fitsFolder / 'flux_analysis', fitsFile.replace(
'.fits', '_linesLog.txt')
pdfTableFolder, pdfTableFile = fitsFolder / 'flux_analysis', fitsFile.replace(
'.fits', '_linesTable')
txtTableFolder, txtTableFile = fitsFolder / 'flux_analysis', fitsFile.replace(
'.fits', '_linesTable.txt')
print(f'-{objName}')
# Load line measuerer object
lm = sr.LineMesurer(linesDF_address=lineLogFolder / lineLogFile)
pdf = PdfPrinter()
tableDF = pd.DataFrame(columns=tableHeaders[1:])
# Measure line fluxes
idcs_lines = ~lm.linesDF.index.str.contains('_b')
obsLines = lm.linesDF.loc[idcs_lines].index.values
# Measure line fluxes
pdf.create_pdfDoc(pdfTableFolder / pdfTableFile, pdf_type='table')
pdf.pdf_insert_table(tableHeaders)
# Normalizing flux
flux_Halpha = lm.linesDF.loc['H1_6563A', 'gauss_flux']
flux_Hbeta = lm.linesDF.loc['H1_4861A', 'intg_flux']
halpha_norm = flux_Halpha / flux_Hbeta
refernList = obsData['file_information']['refName_list']
resultsFolder = Path(obsData['file_information']['results_folder'])
tables_folder = Path('/home/vital/Dropbox/Astrophysics/Seminars/UniVapo 2021/')
ext = 'BR'
cycle = 'it3'
table_headers = [
'Galaxy', 'Parameter',
r'\makecell{\textsc{HII-CHI-mistry: } \\ Spherical}',
r'\makecell{\textsc{HII-CHI-mistry: } \\ Plane-Parallel}',
r'\makecell{Bayesian \textsc{HII-CHI-mistry: } \\ Plane-Parallel}',
r'\makecell{Bayesian \\ Direct Method + \textsc{HII-CHI-mistry} \\ Plane-Parallel}'
]
pdf = PdfPrinter()
pdf.create_pdfDoc(pdf_type='table')
pdf.pdfDoc.append(
pylatex.NoEscape('\definecolor{background}{rgb}{0.169, 0.169, 0.169}'))
pdf.pdfDoc.append(
pylatex.NoEscape('\definecolor{foreground}{rgb}{0.702, 0.780, 0.847}'))
pdf.pdf_insert_table(table_headers,
addfinalLine=True,
color_background='background',
color_font='foreground')
for i, obj in enumerate(objList[:3]):
objFolder = resultsFolder / f'{obj}'
results_file = objFolder / f'{obj}_{ext}_measurements.txt'
lineLog_file = objFolder / f'{obj}_{ext}_linesLog_{cycle}.txt'
from src.specsiser.data_printing import PdfPrinter, latex_labels
from pylatex import NoEscape
atomic_data_dict = {'T_low': r'$Normal(\mu=15,000K,\sigma=5000K)$',
'T_high': r'$Normal(\mu=15,000K,\sigma=5000K)$',
'n_e': r'$HalfCauchy(\mu=2.0,\sigma=0)$',
'cHbeta': r'$HalfCauchy(\mu=2.0,\sigma=0)$',
'log(X_i+)': r'$Normal(\mu=5,\sigma=5)$',
'log(He1r)': r'$Normal(\mu=0,\sigma=3)$',
'log(He2r)': r'$Normal(\mu=0,\sigma=3)$',
'Teff': r'$Uniform(min=30,000K, max=90,000K)$',
'logU': r'$Uniform(min=-4.0, max=-1.0K)$'}
tables_folder = Path('/home/vital/Dropbox/Astrophysics/Seminars/UniVapo 2021/')
pdf = PdfPrinter()
pdf.create_pdfDoc(pdf_type='table')
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(['Parameter', 'Prior distribution'],
addfinalLine=True, color_font='foreground', color_background='background')
for ion, params in atomic_data_dict.items():
ion_label = latex_labels[ion]
row = [ion_label, params]
pdf.addTableRow(row, last_row=False, color_font='foreground', color_background='background')
pdf.table.add_hline()
scaleTable = 1000
pdfTableFile, txtTableFile = tables_folder / f'sample_emission_lines', tables_folder / f'sample_emission_lines.txt'
table_header_format = 'lccccccc'
row_headers = [
'', '',
pylatex.MultiColumn(2, align='c', data=objSubList[0].upper()),
pylatex.MultiColumn(2, align='c', data=objSubList[1].upper()),
pylatex.MultiColumn(2, align='c', data=objSubList[2].upper())
]
row_subHeaders = [
'Line label', r'$f_{\lambda}$', r'$F(\lambda)$', r'$I(\lambda)$',
r'$F(\lambda)$', r'$I(\lambda)$', r'$F(\lambda)$', r'$I(\lambda)$'
]
# Table heading
pdf = PdfPrinter()
pdf.create_pdfDoc(pdf_type='table')
pdf.pdf_insert_table(row_headers,
table_format=table_header_format,
addfinalLine=False)
# pdf.table.add_hline(3, 4, cmidruleoption='l{10pt}r{2pt}')
# pdf.table.add_hline(5, 6)
# pdf.table.add_hline(7, 8)
pdf.addTableRow(row_subHeaders, last_row=True)
# Table body
for i, linelabel in enumerate(tableDF.index):
# Line reference
if (tableDF.loc[linelabel, 'blended_label'] == 'None') or ('_m'
in linelabel):
label_ref = tableDF.loc[linelabel, 'latexLabel']
conf_file_address = '../../../papers/gtc_greenpeas/gtc_greenpeas_data.ini'
obsData = sr.loadConfData(conf_file_address,
objList_check=True,
group_variables=False)
objList = obsData['file_information']['object_list']
objRef = obsData['file_information']['refName_list']
tables_folder = Path(obsData['file_information']['tables_folder'])
table_headers = {}
table_headers = [
'SDSS ID', 'Label', 'R.A', 'DEC', 'z', '$Grisms^{1}$', '$T_{exp}$',
'Seeing', r'Standard stars$^{2}$'
]
pdf = PdfPrinter()
pdf.create_pdfDoc(pdf_type=None)
pdf.pdf_insert_table(table_headers, addfinalLine=True)
for i, obj in enumerate(objList):
if i < 3:
row = ['-'] * len(table_headers)
objData = obsData[obj]
# row[0] = objData['SDSS_label']
row[0] = r'\href{{{}}}{{{}}}'.format(
objData['SDSS_website'].replace('&', '\&'), objData['SDSS_label'])
print(row[0])
row[1] = objRef[i]
resultsFolder = Path(obsData['file_information']['results_folder'])
tables_folder = Path(obsData['file_information']['tables_folder'])
ext = 'BR'
cycle = 'it3'
table_headers = ['Galaxy',
'Parameter',
r'\makecell{\textsc{HII-CHI-mistry: } \\ Spherical}',
r'\makecell{\textsc{HII-CHI-mistry: } \\ Plane-Parallel}',
r'\makecell{Bayesian \textsc{HII-CHI-mistry: } \\ Plane-Parallel}',
r'\makecell{Bayesian \\ Direct Method + \textsc{HII-CHI-mistry} \\ Plane-Parallel}'
]
pdf = PdfPrinter()
pdf.create_pdfDoc(pdf_type=None)
pdf.pdf_insert_table(table_headers, addfinalLine=True)
for i, obj in enumerate(objList[:3]):
objFolder = resultsFolder / f'{obj}'
results_file = objFolder / f'{obj}_{ext}_measurements.txt'
lineLog_file = objFolder / f'{obj}_{ext}_linesLog_{cycle}.txt'
outputPiDb = objFolder / f'{obj}_{ext}_pI_fitting_{cycle}.db'
outputDirectHIIchemDb = objFolder / f'{obj}_{ext}_Direct-Teff-logU_{cycle}.db'
results_dict = sr.loadConfData(results_file, group_variables=False)
for j, param in enumerate(('Teff', 'logU')):
base_DF.loc[line] = lm.linesDF.loc[line]
#Sort the data
objectList = list(df_dict.keys())
objectList.sort()
base_DF.sort_values(by=['wavelength'], inplace=True)
idcsLines = ~base_DF.index.str.contains('_b')
lineList = base_DF.loc[idcsLines].index.values
# Print the data
for lineLabel in lineList:
sampleFolder, tableName = fitsFolder / 'sample_data' / 'properties_by_line', f'{lineLabel}_fluxes'
pdf = PdfPrinter()
pdf.create_pdfDoc(sampleFolder / tableName, pdf_type='table')
refarenceTable = base_DF.loc[lineLabel, 'latexLabel']
print(refarenceTable)
tableHeaders = [
refarenceTable, '$EW(\AA)$', '$F(\lambda)$', '$I(\lambda)$',
r'$c(H\beta)$'
]
pdf.pdf_insert_table(tableHeaders)
for obj in objectList:
objDF = df_dict[obj]
if lineLabel in objDF.index:
'Kaufman \& Sugar (1986); Galav\'is et al. (1995)'
],
'Ar4': ['Ramsbottom \& Bell (1997)', 'Mendoza \& Zeippen (1982)'],
'Cl3': ['Butler \& Zeippen (1989)', 'Mendoza (1983)'],
'Fe3': ['Zhang (1996)', 'Quinet (1996), Johansson et al. (2000)'],
'Ni3': ['Bautista (2001)', 'Bautista (2001)']
}
tables_folder = Path('/home/vital/Dropbox/Astrophysics/Seminars/UniVapo 2021/')
table_headers = [
'SDSS ID', 'Label', 'R.A', 'DEC', 'z', '$Grisms^{1}$', '$T_{exp}$',
'Seeing', r'Standard stars$^{2}$'
]
pdf = PdfPrinter()
pdf.create_pdfDoc(pdf_type='table')
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(
['Ion', 'Collision Strengths', 'Transition probabilities'],
addfinalLine=True,
color_font='foreground',
color_background='background')
for ion, params in atomic_data_dict.items():
conf_file_address = '../../../papers/gtc_greenpeas/gtc_greenpeas_data.ini'
obsData = sr.loadConfData(conf_file_address,
objList_check=True,
group_variables=False)
objList = obsData['file_information']['object_list']
objRef = obsData['file_information']['refName_list']
tables_folder = Path('/home/vital/Dropbox/Astrophysics/Seminars/UniVapo 2021/')
table_headers = {}
table_headers = [
'SDSS ID', 'Label', 'R.A', 'DEC', 'z', '$Grisms^{1}$', '$T_{exp}$',
'Seeing', r'Standard stars$^{2}$'
]
pdf = PdfPrinter()
pdf.create_pdfDoc(pdf_type='table')
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(table_headers, addfinalLine=True, color_font='foreground')
for i, obj in enumerate(objList):
if i < 3:
row = ['-'] * len(table_headers)
objData = obsData[obj]