issue_comments: 438749995

Also, the following code seems to accomplish the same as the above:

``` def apply_func_rolling(func, args, kwargs): # determine rolling parameters, and remove them from kwargs apply_func_kwargs = {'input_core_dims', 'output_core_dims', 'vectorize', 'join', 'dataset_join', 'keep_attrs', 'exclude_dims', 'dataset_fill_value', 'kwargs', 'dask', 'output_dtypes', 'output_sizes'} min_periods = kwargs.pop('min_periods', None) center = kwargs.pop('center', False) dim = xr.core.utils.either_dict_or_kwargs(kwargs.pop('dim', None), {k: v for k, v in kwargs.items() if k not in apply_func_kwargs}, 'apply_func_rolling') if len(dim) != 1: raise ValueError("precisely one rolling dimension must be specified") rolling_dim = list(dim.keys())[0] kwargs.pop(rolling_dim) temp_rolling_dim = 'temp{}__'.format(rolling_dim) # change input_core_dims rolling_dim values to temp_rolling_dim input_core_dims = kwargs.get('input_core_dims', None) if input_core_dims: kwargs['input_core_dims'] = [[(temp_rolling_dim if (dim_ == rolling_dim) else dim_) for dim_ in dims_] for dims_ in input_core_dims] # change exclude_dims rolling_dim values to temp_rolling_dim exclude_dims = kwargs.get('exclude_dims', None) if exclude_dims: kwargs['exclude_dims'] = [[(temp_rolling_dim if (dim_ == rolling_dim) else dim_) for dim_ in dims_] for dims_ in exclude_dims] # call apply_func() with rolling-constructed objects return xr.apply_ufunc(func, [(arg.rolling(dim=dim, min_periods=min_periods, center=center). construct(temp_rolling_dim) if (rolling_dim in arg.dims) else arg) for arg in args], **kwargs)

apply_func_rolling(lambda a, b, w: ..., variables, observations, weights, date=N, input_core_dims=[['date', 'dim1', 'dim2', 'var'], ['date', 'dim1', 'dim2'], ['date', 'dim1', 'dim2']], output_core_dims=[['var']], vectorize=True) ```