code: def make_wcs_from_hpx(self, sum_ebins=False, proj='CAR', oversample=2,
                            normalize=True):
          
          self._wcs_proj = proj
          self._wcs_oversample = oversample
          self._wcs_2d = self.hpx.make_wcs(2, proj=proj, oversample=oversample)
          self._hpx2wcs = HpxToWcsMapping(self.hpx, self._wcs_2d)
          wcs, wcs_data = self.convert_to_cached_wcs(self.counts, sum_ebins,
                                                     normalize)
          return wcs, wcs_data
docs: Make a WCS object and convert HEALPix data into WCS projection
  
          NOTE: this re-calculates the mapping, if you have already
          calculated the mapping it is much faster to use
          convert_to_cached_wcs() instead
  
          Parameters
          ----------
          sum_ebins  : bool
             sum energy bins over energy bins before reprojecting
  
          proj       : str
             WCS-projection
  
          oversample : int
             Oversampling factor for WCS map
  
          normalize  : bool
             True -> perserve integral by splitting HEALPix values between bins
  
          returns (WCS object, np.ndarray() with reprojected data)