Class _Normalize

xarray/plot/utils.py:1367–1679

Normalize numerical or categorical values to numerical values. The class includes helper methods that simplifies transforming to and from normalized values. Parameters ---------- data : DataArray DataArray to normalize. width : Sequence of three numbers, option

class _Normalize(Sequence):
    """
    Normalize numerical or categorical values to numerical values.

    The class includes helper methods that simplifies transforming to
    and from normalized values.

    Parameters
    ----------
    data : DataArray
        DataArray to normalize.
    width : Sequence of three numbers, optional
        Normalize the data to these (min, default, max) values.
        The default is None.
    """

    _data: DataArray | None
    _data_unique: np.ndarray
    _data_unique_index: np.ndarray
    _data_unique_inverse: np.ndarray
    _data_is_numeric: bool
    _width: tuple[float, float, float] | None

    __slots__ = (
        "_data",
        "_data_is_numeric",
        "_data_unique",
        "_data_unique_index",
        "_data_unique_inverse",
        "_width",
    )

    def __init__(
        self,
        data: DataArray | None,
        width: tuple[float, float, float] | None = None,
        _is_facetgrid: bool = False,
    ) -> None:
        self._data = data
        self._width = width if not _is_facetgrid else None

        pint_array_type = DuckArrayModule("pint").type
        to_unique = (
            data.to_numpy()  # type: ignore[union-attr]
            if isinstance(data if data is None else data.data, pint_array_type)
            else data
        )
        data_unique, data_unique_inverse = np.unique(to_unique, return_inverse=True)  # type: ignore[call-overload]
        self._data_unique = data_unique
        self._data_unique_index = np.arange(0, data_unique.size)
        self._data_unique_inverse = data_unique_inverse
        self._data_is_numeric = False if data is None else _is_numeric(data)

    def __repr__(self) -> str:
        with np.printoptions(precision=4, suppress=True, threshold=5):
            return (
                f"<_Normalize(data, width={self._width})>\n"
                f"{self._data_unique} -> {self._values_unique}"