The first step is too obtain an appropriate specgrid from the authors. The munari specgrid is publicly available here.
For all examples we will use that grid.
It is very easy to open the spectral grid with SpectralGrid
>>> from specgrid import SpectralGrid
>>> munari_grid = SpectralGrid('munari.h5')
Opening munari.h5 in read-only mode
>>> munari.parameters
['teff', 'logg', 'feh']
>>> munari.teff
3500.
The easiest way do an interpolation is to use the evaluate method:
>>> spec = munari.evaluate(5780, 4.4, 0.0)
>>> spec
Spectrum1D([ 587668.18 , 578633.564, 678022.4 , ..., 920261.52 ,
912980.964, 885117.936])
>>> spec.wavelength
<Quantity [ 2500.5, 2501.5, 2502.5,..., 10497.5, 10498.5, 10499.5] Angstrom>
>>> spec.flux
<Quantity [ 587668.18 , 578633.564, 678022.4 ,..., 920261.52 ,
912980.964, 885117.936] erg / (Angstrom cm2 s)>
>>> munari.teff
5780.0
As can be seen this also sets the parameters. The most generic way to interpolate is to just call the object, which will use the currently set attributes as the point of interpolation (currently set to solar values from the last example):
>>> munari()
Spectrum1D([ 587668.18 , 578633.564, 678022.4 , ..., 920261.52 ,
912980.964, 885117.936])
The final way to interpolate is to use the evaluate with keyword arguments. This will only update the selected keyword values and leave the rest the same:
>>> munari.evaluate(teff=7000)
Spectrum1D([ 4185839.8, 4061805.8, 4112774.6, ..., 1457224.2,
1444824. , 1400608.6])
>>> munari.teff
7000.0
>>> munari.logg
4.4
>>> munari.feh
0.0
| SpectralGrid(grid_hdf5_fname[, interpolator]) | A SpectralGrid interpolation class. |
| Spectrum1D(flux, wcs[, unit, uncertainty, ...]) |