tools.py 文件源码

def interpolate(coords, var, interp_coords, missing_value=None, fill=True, kind="linear"):
    """Interpolate globally defined data to a different (regular) grid.

    Arguments:
       coords: Tuple of coordinate arrays for each dimension.
       var (:obj:`ndarray` of dim (nx1, ..., nxd)): Variable data to interpolate.
       interp_coords: Tuple of coordinate arrays to interpolate to.
       missing_value (optional): Value denoting cells of missing data in ``var``.
          Is replaced by `NaN` before interpolating. Defaults to `None`, which means
          no replacement is taking place.
       fill (bool, optional): Whether `NaN` values should be replaced by the nearest
          finite value after interpolating. Defaults to ``True``.
       kind (str, optional): Order of interpolation. Supported are `nearest` and
          `linear` (default).

    Returns:
       :obj:`ndarray` containing the interpolated values on the grid spanned by
       ``interp_coords``.

    """
    if len(coords) != len(interp_coords) or len(coords) != var.ndim:
        raise ValueError("Dimensions of coordinates and values do not match")
    var = np.array(var)
    if missing_value is not None:
        invalid_mask = np.isclose(var, missing_value)
        var[invalid_mask] = np.nan
    if var.ndim > 1 and coords[0].ndim == 1:
        interp_grid = np.rollaxis(np.array(np.meshgrid(
            *interp_coords, indexing="ij", copy=False)), 0, len(interp_coords) + 1)
    else:
        interp_grid = coords
    var = scipy.interpolate.interpn(coords, var, interp_grid,
                                    bounds_error=False, fill_value=np.nan, method=kind)

    if fill:
        var = fill_holes(var)
    return var