xarray.computation.rolling.DataArrayRolling.construct#

DataArrayRolling.construct(window_dim=None, *, stride=1, fill_value=<NA>, keep_attrs=None, sliding_window_view_kwargs=None, **window_dim_kwargs)[source] #

Convert this rolling object to xr.DataArray, where the window dimension is stacked as a new dimension

Parameters:

• window_dim (Hashable or dict-like to Hashable, optional) – A mapping from dimension name to the new window dimension names.

• stride (int or mapping of int, default: 1) – Size of stride for the rolling window.

• fill_value (default: dtypes.NA) – Filling value to match the dimension size.

• keep_attrs (bool, default: None) – If True, the attributes (attrs) will be copied from the original object to the new one. If False, the new object will be returned without attributes. If None uses the global default.

• sliding_window_view_kwargs (Mapping) – Keyword arguments that should be passed to the underlying array type’s sliding_window_view function.

• **window_dim_kwargs (Hashable, optional) – The keyword arguments form of window_dim {dim: new_name, ...}.

Returns:

DataArray – a view of the original array. By default, the returned array is not writeable. For numpy arrays, one can pass writeable=True in sliding_window_view_kwargs.

See also

numpy.lib.stride_tricks.sliding_window_view, dask.array.lib.stride_tricks.sliding_window_view

Notes

With dask arrays, it’s possible to pass the automatic_rechunk kwarg as sliding_window_view_kwargs={"automatic_rechunk": True}. This controls whether dask should automatically rechunk the output to avoid exploding chunk sizes. Automatically rechunking is the default behaviour. Importantly, each chunk will be a view of the data so large chunk sizes are only safe if no copies are made later.

Examples

>>> da = xr.DataArray(np.arange(8).reshape(2, 4), dims=("a", "b"))

>>> rolling = da.rolling(b=3)
>>> rolling.construct("window_dim")
<xarray.DataArray (a: 2, b: 4, window_dim: 3)> Size: 192B
array([[[nan, nan, 0.],
 [nan, 0., 1.],
 [ 0., 1., 2.],
 [ 1., 2., 3.]],
 [[nan, nan, 4.],
 [nan, 4., 5.],
 [ 4., 5., 6.],
 [ 5., 6., 7.]]])
Dimensions without coordinates: a, b, window_dim

>>> rolling = da.rolling(b=3, center=True)
>>> rolling.construct("window_dim")
<xarray.DataArray (a: 2, b: 4, window_dim: 3)> Size: 192B
array([[[nan, 0., 1.],
 [ 0., 1., 2.],
 [ 1., 2., 3.],
 [ 2., 3., nan]],
 [[nan, 4., 5.],
 [ 4., 5., 6.],
 [ 5., 6., 7.],
 [ 6., 7., nan]]])
Dimensions without coordinates: a, b, window_dim

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