try:
#Locate the objects
objName = catalogue_df.iloc[i].name
ouput_folder = '{}{}/'.format(catalogue_dict['Obj_Folder'], objName)
fits_file = catalogue_df.iloc[i].reduction_fits
lineslog_address = '{objfolder}{codeName}{lineslog_extension}'.format(
objfolder=ouput_folder,
codeName=objName,
lineslog_extension=AbundancesFileExtension)
fits_name = basename(fits_file)
#Load object data
lineslog_frame = dz.load_lineslog_frame(lineslog_address)
wave, flux, header_0 = dz.get_spectra_data(fits_file)
#Declare starlight files
Grid_FileName = fits_name.replace('.fits', '.slGrid')
Sl_OutputFolder = dz.RootFolder + 'Starlight/Output/'
Sl_OutputFile = fits_name.replace('.fits', '.slOutput')
cHbeta = catalogue_df.loc[objName, cHbeta_type]
flux_dered = dz.derreddening_spectrum(wave,
flux,
reddening_curve=red_curve,
cHbeta=cHbeta.nominal_value,
R_v=R_v)
#Define the maximum sigma for the fitting from the Hbeta line
pdf_address = '/home/vital/Dropbox/Astrophysics/Thesis/images/{}_absEffect.png'.format(
quick_reference)
dz.create_pdfDoc(pdf_address, pdf_type='table')
dz.pdf_insert_table(table_format='l' + 'ccc')
group_dict = OrderedDict()
# Make dict with all the objects lines dataframes
ouput_folder = '{}{}/'.format(catalogue_dict['Obj_Folder'], objName)
linelog_reducAddress = '{objfolder}{codeName}_WHT_linesLog_reduc.txt'.format(
objfolder=ouput_folder, codeName=objName)
linelog_emisAddress = '{objfolder}{codeName}_WHT_linesLog_emission_2nd.txt'.format(
objfolder=ouput_folder, codeName=objName)
reduc_linedf = dz.load_lineslog_frame(linelog_reducAddress)
emission_linedf = dz.load_lineslog_frame(linelog_emisAddress)
Hbeta_F = reduc_linedf.loc['H1_4861A'].line_Flux
Hbeta_emisF = emission_linedf.loc['H1_4861A'].line_Flux
lambdas_array = emission_linedf['lambda_theo'].values
# Short array and remove repeated entries
lambdas_array = unique(lambdas_array)
# Add the parameters row
Headers = [
r'$\lambda(\AA)$', 'Observed flux', 'Emission flux',
'\% Difference'
]
LickIndexesHeader = ['Ion', 'lambda_theo', 'group_label','Wave1', 'Wave2', 'Wave3', 'Wave4', 'Wave5', 'Wave6', 'add_wide_component']
columns_format = ['%11.6f', '%11.6f', '%11.6f','%11.6f', '%11.6f', '%11.6f', '%11.6f', '%11.6f', '%11.6f', '%11.6f']
#Loop through the objects
for i in range(len(catalogue_df.index)):
#Object
objName = catalogue_df.iloc[i].name
fits_file = catalogue_df.iloc[i].reduction_fits
ouput_folder = '{}{}/'.format(catalogue_dict['Obj_Folder'], objName)
lineslog_address = '{objfolder}{codeName}_WHT_linesLog_reduc.txt'.format(objfolder = ouput_folder, codeName=objName)
print '-- Treating {} @ {}'.format(objName, fits_file)
#Load the lines log
lineslog_frame = dz.load_lineslog_frame(lineslog_address)
#Extract the information corresponding to the lines selection
lick_icds_frame = lineslog_frame[LickIndexesHeader]
#Convert to string format and save to text file
string_frame = lick_icds_frame.to_string()
lick_txt = '{}{}_lick_indeces.txt'.format(ouput_folder, objName)
with open(lick_txt, 'wb') as f:
f.write(string_frame.encode('UTF-8'))
print 'Data treated'
# #Save the data frame
# lick_txt = '{}{}_lick_indeces_text.txt'.format(ouput_folder, objName)
# lick_icds_frame.to_csv(lick_txt, sep=' ', float_format='%.6f', columns=None, header=True, index=True)
quick_reference = catalogue_df.loc[objName].quick_index
print objName, (quick_reference)
# Object folder
oldFolder = '{}{}/'.format(catalogue_dict['Obj_Folder'], objName)
objectNewFolder = root_folder + objName + '/'
make_folder(objectNewFolder)
# Get fits data
fits_file = catalogue_df.loc[objName].reduction_fits
wave, flux, etraData = dz.get_spectra_data(fits_file)
# Get lines log data
lineslog_address = '{}{}{}'.format(oldFolder,objName,lineslog_ext)
linesLogDf = dz.load_lineslog_frame(lineslog_address)
linesLogDf.rename(columns=lineslogDfNewHeaders, inplace=True)
linesLogDf.rename(index=lineslogDfNewIndeces, inplace=True)
# Generate a grid plot and add continuum contribution in recombination lines
lineFluxes = linesLogDf['line_Flux'].values
linesLogDf['obs_flux'], linesLogDf['obs_fluxErr'] = nominal_values(lineFluxes), std_devs(lineFluxes)
plotFolder = '{}/input_data/'.format(objectNewFolder)
make_folder(plotFolder)
plotAddress = '{}{}_lineGrid'.format(plotFolder, objName)
bp.linesGrid(linesLogDf, wave, flux, plotAddress)
linesLogDf['line_Flux'] = nominal_values(lineFluxes)
# Add continuum masks
masksLogAddress = '{}{}{}'.format(oldFolder,objName,masklog_ext)
#Locate the files
ouput_folder = '{}{}/'.format(catalogue_dict['Obj_Folder'], objName)
fits_reduc = catalogue_df.iloc[i].reduction_fits
fits_emission = catalogue_df.iloc[i].stellar_fits
fits_stellar = ouput_folder + objName + '_StellarContinuum.fits'
lineslog_address = '{objfolder}{codeName}{lineslog_extension}'.format(objfolder = ouput_folder, codeName=objName, lineslog_extension=AbundancesFileExtension)
if isfile(fits_stellar):
# if i < 3:
#Extract observational data
wave_reduc, flux_reduc, header_0_reduc = dz.get_spectra_data(fits_reduc)
wave_emis, flux_emis, header_emis = dz.get_spectra_data(fits_emission)
wave_stellar, flux_stellar, header_stellar = dz.get_spectra_data(fits_stellar)
reduc_lineslog_df = dz.load_lineslog_frame(lineslog_address)
#Perform the reddening correction
cHbeta = catalogue_df.iloc[i][cHbeta_type]
dz.deredden_lines(cHbeta, reduc_lineslog_df)
#Measure Hbeta flux
Hbeta_wavelengths = reduc_lineslog_df.loc['H1_4861A', ['Wave1', 'Wave2', 'Wave3', 'Wave4', 'Wave5', 'Wave6']].values
Hbeta_dist = random.normal(reduc_lineslog_df.loc['H1_4861A'].line_Int.nominal_value, reduc_lineslog_df.loc['H1_4861A'].line_Int.std_dev, MC_length)
#Insert new section in pdf
with dz.pdfDoc.create(Section('HII Galaxy: {}'.format(objName))):
dz.add_page()
#------Plot Oxygen lines
fits_stellar = ouput_folder + objName + '_StellarContinuum.fits'
lineslog_address = '{objfolder}{codeName}{lineslog_extension}'.format(
objfolder=ouput_folder,
codeName=objName,
lineslog_extension=AbundancesFileExtension)
if isfile(fits_stellar):
# if i < 3:
#Extract observational data
wave_reduc, flux_reduc, header_0_reduc = dz.get_spectra_data(
fits_reduc)
wave_emis, flux_emis, header_emis = dz.get_spectra_data(fits_emission)
wave_stellar, flux_stellar, header_stellar = dz.get_spectra_data(
fits_stellar)
reduc_lineslog_df = dz.load_lineslog_frame(lineslog_address)
#Perform the reddening correction
cHbeta = catalogue_df.iloc[i][cHbeta_type]
dz.deredden_lines(cHbeta, reduc_lineslog_df)
#Measure Hbeta flux
Hbeta_wavelengths = reduc_lineslog_df.loc[
'H1_4861A',
['Wave1', 'Wave2', 'Wave3', 'Wave4', 'Wave5', 'Wave6']].values
Hbeta_dist = random.normal(
reduc_lineslog_df.loc['H1_4861A'].line_Int.nominal_value,
reduc_lineslog_df.loc['H1_4861A'].line_Int.std_dev, MC_length)
#Insert new section in pdf
with dz.pdfDoc.create(Section('HII Galaxy: {}'.format(objName))):
