Function interp_na

xarray/core/missing.py:331–406

Interpolate values according to different methods.

(
    self,
    dim: Hashable | None = None,
    use_coordinate: bool | str = True,
    method: InterpOptions = "linear",
    limit: int | None = None,
    max_gap: (
        int | float | str | pd.Timedelta | np.timedelta64 | dt.timedelta | None
    ) = None,
    keep_attrs: bool | None = None,
    **kwargs,
)

def interp_na(
    self,
    dim: Hashable | None = None,
    use_coordinate: bool | str = True,
    method: InterpOptions = "linear",
    limit: int | None = None,
    max_gap: (
        int | float | str | pd.Timedelta | np.timedelta64 | dt.timedelta | None
    ) = None,
    keep_attrs: bool | None = None,
    **kwargs,
):
    """Interpolate values according to different methods."""
    from xarray.coding.cftimeindex import CFTimeIndex

    if dim is None:
        raise NotImplementedError("dim is a required argument")

    if limit is not None:
        valids = _get_valid_fill_mask(self, dim, limit)

    if max_gap is not None:
        max_type = type(max_gap).__name__
        if not is_scalar(max_gap):
            raise ValueError("max_gap must be a scalar.")

        if (
            dim in self._indexes
            and isinstance(
                self._indexes[dim].to_pandas_index(), pd.DatetimeIndex | CFTimeIndex
            )
            and use_coordinate
        ):
            # Convert to float
            max_gap = timedelta_to_numeric(max_gap)

        if not use_coordinate and not isinstance(max_gap, Number | np.number):
            raise TypeError(
                f"Expected integer or floating point max_gap since use_coordinate=False. Received {max_type}."
            )

    # method
    index = get_clean_interp_index(self, dim, use_coordinate=use_coordinate)
    interp_class, kwargs = _get_interpolator(method, **kwargs)
    interpolator = partial(func_interpolate_na, interp_class, **kwargs)

    if keep_attrs is None:
        keep_attrs = _get_keep_attrs(default=True)

    with warnings.catch_warnings():
        warnings.filterwarnings("ignore", "overflow", RuntimeWarning)
        warnings.filterwarnings("ignore", "invalid value", RuntimeWarning)
        arr = apply_ufunc(
            interpolator,
            self,
            index,
            input_core_dims=[[dim], [dim]],
            output_core_dims=[[dim]],