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  • Illviljan · 3 ✖

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  • Faster interp · 3 ✖

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778853034 https://github.com/pydata/xarray/pull/4740#issuecomment-778853034 https://api.github.com/repos/pydata/xarray/issues/4740 MDEyOklzc3VlQ29tbWVudDc3ODg1MzAzNA== Illviljan 14371165 2021-02-14T22:33:39Z 2021-02-14T22:33:39Z MEMBER

@max-sixty It's the profiler Spyder has. It's been quite useful for hunting down various bottlenecks but it has its quirks as well. 

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   Faster interp 776042664
767770962 https://github.com/pydata/xarray/pull/4740#issuecomment-767770962 https://api.github.com/repos/pydata/xarray/issues/4740 MDEyOklzc3VlQ29tbWVudDc2Nzc3MDk2Mg== Illviljan 14371165 2021-01-26T19:22:33Z 2021-01-26T19:22:33Z MEMBER

Well, I think this is good enough. It's quite an improvement already and I think the error will disappear if the CI is rerun. 

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   Faster interp 776042664
752286931 https://github.com/pydata/xarray/pull/4740#issuecomment-752286931 https://api.github.com/repos/pydata/xarray/issues/4740 MDEyOklzc3VlQ29tbWVudDc1MjI4NjkzMQ== Illviljan 14371165 2020-12-30T00:36:24Z 2020-12-30T00:36:24Z MEMBER

Looking good!

One thing I notice now with these changes is that ndim is now creeping up as the 2nd worst time consumer:

An easy improvement to this is adding ndim = var.ndim and replacing all the var.ndim around interp_func. That reduces the number of calls from 68000 to 58000. Another more difficult idea is to improve the dask arrays shape-calculation. Perhaps saving the value and only do the cached_cumsum call when the chunks actually has changed?

 
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   Faster interp 776042664

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