html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/pull/1517#issuecomment-330679808,https://api.github.com/repos/pydata/xarray/issues/1517,330679808,MDEyOklzc3VlQ29tbWVudDMzMDY3OTgwOA==,6628425,2017-09-19T21:32:00Z,2017-09-19T21:32:00Z,MEMBER,"I was not aware of dask's atop function before reading this PR (it looks pretty cool), so I defer to @nbren12 there.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,252358450
https://github.com/pydata/xarray/pull/1517#issuecomment-328341717,https://api.github.com/repos/pydata/xarray/issues/1517,328341717,MDEyOklzc3VlQ29tbWVudDMyODM0MTcxNw==,6628425,2017-09-10T13:09:40Z,2017-09-10T13:09:40Z,MEMBER,"@nbren12 for similar use cases I've had success writing a single function that does the ghosting, applies a function with `map_blocks`, and trims the edges.  Then I apply that single function on a DataArray with `apply_ufunc` (so a single call to `apply_ufunc` rather than three).  As an example, a simple centered difference on an array with periodic boundaries might be accomplished with:
```python
def centered_diff_numpy(arr, axis=-1, spacing=1.):
    return (np.roll(arr, -1, axis=axis) - np.roll(arr, 1, axis=axis)) / (2. * spacing)

def centered_diff(da, dim, spacing=1.):
    def apply_centered_diff(arr, spacing=1.):
        if isinstance(arr, np.ndarray):
            return centered_diff_numpy(arr, spacing=spacing)
        else:
            axis = len(arr.shape) - 1
            g = darray.ghost.ghost(arr, depth={axis: 1}, boundary={axis: 'periodic'})
            result = darray.map_blocks(centered_diff_numpy, g, spacing=spacing)
            return darray.ghost.trim_internal(result, {axis: 1})
    
    return computation.apply_ufunc(
        apply_centered_diff, da, input_core_dims=[[dim]],
        output_core_dims=[[dim]], dask_array='allowed', kwargs={'spacing': spacing})
```
Depending on your use case, you might also consider `dask.ghost.map_overlap` to do all of those three steps in one line, i.e. replace `apply_centered_diff` with the following:
```python
def apply_centered_diff(arr, spacing=1.):
    if isinstance(arr, np.ndarray):
        return centered_diff_numpy(arr, spacing=spacing)
    else:
        axis = len(arr.shape) - 1
        return darray.ghost.map_overlap(
            arr, centered_diff_numpy, depth={axis: 1}, boundary={axis: 'periodic'},
            spacing=spacing)
```
(Not sure if this is what @shoyer had in mind, but just offering an example)","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,252358450