issue_comments: 287840030

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https://github.com/pydata/xarray/issues/1288#issuecomment-287840030 https://api.github.com/repos/pydata/xarray/issues/1288 287840030 MDEyOklzc3VlQ29tbWVudDI4Nzg0MDAzMA== 1217238 2017-03-20T17:43:12Z 2017-03-20T17:43:12Z MEMBER

By the way, the cumtrapz implementation I pasted above matches the scipy version when initial=0, which I also think would be a more sane default for integration.

Yes, I agree with both of you that we should fix initial=0. (I don't know if I would even bother with adding the option.)

As far as implementation is concerned. Is there any performance downside to using xarrays shift operators versus delving deeper into dask with map_blocks, etc? I looked into using dasks cumreduction function, but am not sure it is possible to implement the trapezoid method in that way without changing dask.

From a performance perspective, it would be totally fine to implement this either in terms of high level xarray operations like shift/sum/cumsum (manipulating full xarray objects) or in terms of high level dask.array operations like dask.array.cumsum (manipulating dask arrays). I would whatever is easiest. I'm pretty sure there is no reason why you need to get into dask's low-level API like map_blocks and cumreduction.

 
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