code: def create_from_fits(cls, fitsfile, norm_type='eflux',
                           hdu_scan="SCANDATA",
                           hdu_energies="EBOUNDS",
                           irow=None):
          
          if irow is not None:
              tab_s = Table.read(fitsfile, hdu=hdu_scan)[irow]
          else:
              tab_s = Table.read(fitsfile, hdu=hdu_scan)
          tab_e = Table.read(fitsfile, hdu=hdu_energies)
  
          tab_s = convert_sed_cols(tab_s)
          tab_e = convert_sed_cols(tab_e)
  
          return cls.create_from_tables(norm_type, tab_s, tab_e)
docs: Create a CastroData object from a tscube FITS file.
  
          Parameters
          ----------
          fitsfile  : str
              Name of the fits file
  
          norm_type : str
              Type of normalization to use.  Valid options are:
  
              * norm : Normalization w.r.t. to test source
              * flux : Flux of the test source ( ph cm^-2 s^-1 )
              * eflux: Energy Flux of the test source ( MeV cm^-2 s^-1 )
              * npred: Number of predicted photons (Not implemented)
              * dnde : Differential flux of the test source ( ph cm^-2 s^-1
                MeV^-1 )
  
          hdu_scan : str
              Name of the FITS HDU with the scan data
  
          hdu_energies : str        
              Name of the FITS HDU with the energy binning and
              normalization data
  
          irow : int or None
              If none, then this assumes that there is a single row in
              the scan data table Otherwise, this specifies which row of
              the table to use
  
          Returns
          -------
          castro : `~fermipy.castro.CastroData`