Quickstart

The first step in each SME project is to create an SME structure

>>> from pysme.sme import SME_Structure as SME_Struct

This can be done in done in a few different ways:

• load an existing SME save file (from Python or IDL)

  >>> sme = SME_Struct.load("sme.inp")
  

• load an .ech file spectrum

  >>> sme = SME_Struct.load("obs.ech")
  

• assign values manually

  >>> sme = SME_Struct()
  

Either way one has to make sure that a few essential properties are set in the object, those are:

• Stellar parameters (teff, logg, monh, abund)

  >>> from pysme.abund import Abund
  >>> sme.teff, sme.logg, sme.monh = 5700, 4.4, -0.1
  >>> sme.abund = Abund.solar()
  

• LineList (linelist), e.g. from VALD

  >>> from pysme.linelist.vald import ValdFile
  >>> vald = ValdFile("linelist.lin")
  >>> sme.linelist = vald
  

• Atmosphere (atmo), the file has to be in PySME/src/sme/atmospheres

  >>> # SME comes with a few model atmospheres see Atmosphere section
  >>> sme.atmo.source = "marcs2012p_t1.0.sav"
  >>> sme.atmo.method = "grid"
  >>> sme.atmo.geom = "PP"
  

If no wavelength grid (sme.wave) is set, one has to set the wavelength range:

• Wavelength range(s) in Ångstrom

  >>> sme.wran = [[4500, 4600], [5200, 5400]]
  

Furthermore for fitting to an observation an observation is required:

• Wavelength wave

  >>> # The observation may be split into segments (orders etc)
  >>> # Then Wavelength is a list of arrays [segment1, segment2, ...]
  >>> # The same applies to spec, uncs, and mask
  >>> # The wavelength is always given in Angstrom
  >>> sme.wave = Wavelength
  

• Spectrum spec

  >>> sme.spec = Spectrum
  

• Uncertainties uncs

  >>> sme.uncs = Uncertainties
  

• Mask mask

  >>> # The mask values are: 0: bad pixel, 1: line pixel, 2: continuum pixel
  >>> sme.mask = np.ones(len(Spectrum))
  

• radial velocity and continuum flags

  >>> # possible values are: "each", "whole", "fix", "none"
  >>> # "each": Each segment is fitted individually
  >>> # "whole": All segments are fit with the same radial velocity
  >>> # "fix": use the set radial velocity
  >>> # "none": Apply no radial velocity offset
  >>> sme.vrad_flag = "whole"
  >>> # possible values are: "constant", "linear", "fix", "none"
  >>> # "constant": Scale the synthetic spectrum by a constant
  >>> # "linear": Scale the synthetic spectrum by a linear polynomial
  >>> # "fix": Use the set continnuum scale
  >>> # "none": apply no continuum correction
  >>> sme.cscale_flag = "linear"
  >>> # possible values are: "whole", "mask"
  >>> # "whole": use MCMC to match the synthetic to the observed spectrum
  >>> # "mask": use the continuum mask points to fit the continuum
  >>> sme.cscale_type = "mask"
  

Optionally the following can be set:

• NLTE nlte for non local thermal equilibrium calculations

  >>> # SME also comes with a few NLTE grids, see NLTE section
  >>> # The NLTE grid is atmosphere model dependant!
  >>> sme.nlte.set_nlte("Ca", "marcs2012p_t1.0_Ca.grd")
  

Once the SME structure is prepared, SME can be run in one of its two modes:

1. Synthesize a spectrum

   >>> from sme.solve import synthesize_spectrum
   >>> sme = synthesize_spectrum(sme)
   

2. Finding the best fit (least squares) solution

   >>> from sme.solve import solve
   >>> # for more details on the fitparameter option, see fitparameters
   >>> fitparameters = ["teff", "logg", "monh", "abund Mg"]
   >>> sme = solve(sme, fitparameters)
   

The results will be contained in the output sme structure. These can for example be plotted using the gui module.

>>> from gui import plot_plotly
>>> fig = plot_plotly.FinalPlot(sme)
>>> fig.save(filename="sme.html")

or saved with

>>> sme.save("out.npy")