Method cumulative

xarray/core/dataarray.py:7282–7355

Accumulating object for DataArrays. Parameters ---------- dims : iterable of hashable The name(s) of the dimensions to create the cumulative window along min_periods : int, default: 1 Minimum number of observations in window required

(
        self,
        dim: str | Iterable[Hashable],
        min_periods: int = 1,
    )

        return DataArrayRolling(self, dim, min_periods=min_periods, center=center)

    def cumulative(
        self,
        dim: str | Iterable[Hashable],
        min_periods: int = 1,
    ) -> DataArrayRolling:
        """
        Accumulating object for DataArrays.

        Parameters
        ----------
        dims : iterable of hashable
            The name(s) of the dimensions to create the cumulative window along
        min_periods : int, default: 1
            Minimum number of observations in window required to have a value
            (otherwise result is NA). The default is 1 (note this is different
            from ``Rolling``, whose default is the size of the window).

        Returns
        -------
        computation.rolling.DataArrayRolling

        Examples
        --------
        Create rolling seasonal average of monthly data e.g. DJF, JFM, ..., SON:

        >>> da = xr.DataArray(
        ...     np.linspace(0, 11, num=12),
        ...     coords=[
        ...         pd.date_range(
        ...             "1999-12-15",
        ...             periods=12,
        ...             freq=pd.DateOffset(months=1),
        ...         )
        ...     ],
        ...     dims="time",
        ... )

        >>> da
        <xarray.DataArray (time: 12)> Size: 96B
        array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.])
        Coordinates:
          * time     (time) datetime64[us] 96B 1999-12-15 2000-01-15 ... 2000-11-15

        >>> da.cumulative("time").sum()
        <xarray.DataArray (time: 12)> Size: 96B
        array([ 0.,  1.,  3.,  6., 10., 15., 21., 28., 36., 45., 55., 66.])
        Coordinates:
          * time     (time) datetime64[us] 96B 1999-12-15 2000-01-15 ... 2000-11-15

        See Also
        --------
        DataArray.rolling
        Dataset.cumulative
        computation.rolling.DataArrayRolling
        """
        from xarray.computation.rolling import DataArrayRolling

        # Could we abstract this "normalize and check 'dim'" logic? It's currently shared