xarray.Dataset.coarsen¶

Dataset.coarsen(dim: Optional[Mapping[Hashable, int]] = None, boundary: str = 'exact', side: Union[str, Mapping[Hashable, str]] = 'left', coord_func: str = 'mean', **window_kwargs)¶

Coarsen object.

Parameters

dim: dict, optional

Mapping from the dimension name to the window size. dim : str

Name of the dimension to create the rolling iterator along (e.g., time).

windowint: Size of the moving window.

boundary‘exact’ | ‘trim’ | ‘pad’

If ‘exact’, a ValueError will be raised if dimension size is not a multiple of the window size. If ‘trim’, the excess entries are dropped. If ‘pad’, NA will be padded.

side‘left’ or ‘right’ or mapping from dimension to ‘left’ or ‘right’

coord_func: function (name) that is applied to the coordintes,

or a mapping from coordinate name to function (name).

Returns

Coarsen object (core.rolling.DataArrayCoarsen for DataArray,
core.rolling.DatasetCoarsen for Dataset.)

See also

core.rolling.DataArrayCoarsen, core.rolling.DatasetCoarsen

Examples

Coarsen the long time series by averaging over every four days.

>>> da = xr.DataArray(np.linspace(0, 364, num=364),
...                   dims='time',
...                   coords={'time': pd.date_range(
...                       '15/12/1999', periods=364)})
>>> da
<xarray.DataArray (time: 364)>
array([  0.      ,   1.002755,   2.00551 , ..., 361.99449 , 362.997245,
       364.      ])
Coordinates:
  * time     (time) datetime64[ns] 1999-12-15 1999-12-16 ... 2000-12-12
>>>
>>> da.coarsen(time=3, boundary='trim').mean()
<xarray.DataArray (time: 121)>
array([  1.002755,   4.011019,   7.019284,  ...,  358.986226,
       361.99449 ])
Coordinates:
  * time     (time) datetime64[ns] 1999-12-16 1999-12-19 ... 2000-12-10
>>>