Fit an observed spectrum

Assuming that we have an observed spectrum, with its wavelength, normalized flux, and uncertainties, as an array of wave, flux and uncertainties.

They can be inserted into the SME structure with:

sme = SME_Structure()
sme.teff, sme.logg, sme.monh, sme.vmic, sme.vmac, sme.vsini = 5777, 4.4, 0, 1.09, 4.19, 1.60
sme.abund = Abund.solar()
sme.linelist = vald
sme.iptype = 'gauss'
sme.ipres = 42000
sme.wave = wave
sme.spec = flux
sme.uncs = uncertainties

The new inputs are the instrument resolution and observed spectra.

Then the solve function can be used to find the best fit solution:

from pysme.solve import solve
fitparameters = ["teff", "logg", "monh", "abund Mg"]
sme = solve(sme, fitparameters)

The fitresults are stored in sme.fitresults.