code: def interpolate_to_slice(data, points, interp_type='linear'):
      r
      try:
          x, y = data.metpy.coordinates('x', 'y')
      except AttributeError:
          raise ValueError('Required coordinate information not available. Verify that '
                           'your data has been parsed by MetPy with proper x and y '
                           'dimension coordinates.')
  
      data_sliced = data.interp({
          x.name: xr.DataArray(points[:, 0], dims='index', attrs=x.attrs),
          y.name: xr.DataArray(points[:, 1], dims='index', attrs=y.attrs)
      }, method=interp_type)
      data_sliced.coords['index'] = range(len(points))
  
      return data_sliced
docs: r"""Obtain an interpolated slice through data using xarray.
  
      Utilizing the interpolation functionality in `xarray`, this function takes a slice the
      given data (currently only regular grids are supported), which is given as an
      `xarray.DataArray` so that we can utilize its coordinate metadata.
  
      Parameters
      ----------
      data: `xarray.DataArray` or `xarray.Dataset`
          Three- (or higher) dimensional field(s) to interpolate. The DataArray (or each
          DataArray in the Dataset) must have been parsed by MetPy and include both an x and
          y coordinate dimension.
      points: (N, 2) array_like
          A list of x, y points in the data projection at which to interpolate the data
      interp_type: str, optional
          The interpolation method, either 'linear' or 'nearest' (see
          `xarray.DataArray.interp()` for details). Defaults to 'linear'.
  
      Returns
      -------
      `xarray.DataArray` or `xarray.Dataset`
          The interpolated slice of data, with new index dimension of size N.
  
      See Also
      --------
      cross_section