Observation format

This section will help you convert your observationnal data into the ForMoSA format

Imports

from astropy.io import fits
from astropy.table import Table

Data .fits

Your observed data (spectroscopy and/or photometry) should be formated in a .fits file with the following extensions :

• ‘WAV’ The wavelengths sampling

• ‘WAVE_UNIT’ The wavelengths unit

• ‘FLX’ The corresponding flux

• ‘ERR’ or ‘COV’ The corresponding flux error (or covariance matrix). Note : The covariance matrix should have diag(COV)=ERR².

• ‘RES’ The corresponding spectral resolution given for each wavelength. Note : You must put res = 0.0 where you have photometric points.

• ‘FAC’ The observatory where the data come from. Note :need to be consistent with VOSA for photometric points.

• ‘INS’ The instrument used to obtain the data. Note : need to be consistent with VOSA for photometric points.

• ‘FILT’ The photometric filter used in case of photometric point. Note : need to be consistent with VOSA.

Note : All of these extensions need to be numpy arrays of the same length.

We can use the example given in the demo XXXXXXX:

# CHECKUP FORMAT
hdul = fits.open('/Users/spetrus/Desktop/ForMoSA_pkg/ForMoSA/docs/demos/sinfoni/abpicb/data/ABPicb_SINFONI_K.fits')
print(hdul[1].columns)
wav = hdul[1].data['WAV']
wav_u = hdul[1].data['WAVE_UNIT']
flx = hdul[1].data['FLX']
err = hdul[1].data['ERR']
res = hdul[1].data['RES']
fac = hdul[1].data['FAC']
ins = hdul[1].data['INS']
filt = hdul[1].data['FILT']
print('WAV :         ', wav[:3])
print('WAVE_UNIT :   ', wav_u[:3])
print('FLX :         ', flx[:3])
print('ERR :         ', err[:3])
print('RES :         ', res[:3])
print('FAC :         ', fac[:3])
print('INS :         ', ins[:3])
print('FILT :        ', filt[:3])

ColDefs(
    name = 'WAV'; format = 'D'
    name = 'WAVE_UNIT'; format = '2A'
    name = 'FLX'; format = 'D'
    name = 'ERR'; format = 'D'
    name = 'RES'; format = 'D'
    name = 'FAC'; format = '7A'
    name = 'INS'; format = '7A'
    name = 'FILT'; format = '2A'
)
WAV :          [1.95304004 1.95328504 1.95353004]
WAVE_UNIT :    ['um' 'um' 'um']
FLX :          [1.15922346e-15 1.01987308e-15 9.42198880e-16]
ERR :          [9.69466216e-17 1.14293769e-16 1.09448441e-16]
RES :          [5090. 5090. 5090.]
FAC :          ['Paranal' 'Paranal' 'Paranal']
INS :          ['SINFONI' 'SINFONI' 'SINFONI']
FILT :         ['NA' 'NA' 'NA']

optional extensions can also be used when dealing with stellar-contaminated high-resolution spectroscopy:

• ‘TRANSM’ : (array) transmission (atmospheric + instrumental)

• ‘STAR_FLX’ or ‘STAR_FLXi’ : (array or i arrays) star flux or shifted star flux (to account for LSF changes)

• ‘SYSTEM’ or ‘SYSTEMj’ : (array or j arrays) systematic model(s) (usually computed from PCA)

See XXXXXX for more details

Format your data

To format your data, you can use the simple Python routine below :

# FITS converter :
table = Table([wav, wav_u, flx, err, res, fac, ins, filt], names=('WAV', 'WAVE_UNIT', 'FLX', 'ERR', 'RES', 'FAC', 'INS', 'FILT'))
hdul = fits.HDUList()
hdu = fits.BinTableHDU(table)
hdul.append(hdu)
hdul.writeto('/Users/spetrus/Desktop/ForMoSA_pkg/ForMoSA/docs/demos/sinfoni/abpicb/data/ABPicb_SINFONI_K.fits', overwrite=True)
print('correction successful')

correction successful

If you have multiple observations, you can put them in a same .fits file by giving their properties accordingly in the different Tables. You can also put them into separated .fits files (e.g data_1.fits, data_2.fits, …) to treat them differently during the inversion. For instance with different likelihood function, different normalizatoin factor, etc.

Note : We encourage you to put every photometric points into a same .fits file.

Note : If you need to calculate the dilution facto Ck analytically, we encourage you to put all of your datasets into a same .fits file.