issue_comments: 328690191

@darothen and @shoyer -

Here's a little wrapper function that does the dask and bottleneck piece...

Python def dask_rolling_wrapper(moving_func, a, window, min_count=None, axis=-1): '''wrapper to apply bottleneck moving window funcs on dask arrays''' # inputs for ghost if axis < 0: axis = a.ndim + axis depth = {d: 0 for d in range(a.ndim)} depth[axis] = window - 1 boundary = {d: np.nan for d in range(a.ndim)} # create ghosted arrays ag = da.ghost.ghost(a, depth=depth, boundary=boundary) # apply rolling func out = ag.map_blocks(moving_func, window, min_count=min_count, axis=axis, dtype=a.dtype) # trim array result = da.ghost.trim_internal(out, depth) return result

I don't think this would be all that difficult to drop into our current Rolling class.