issue_comments: 290851733

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https://github.com/pydata/xarray/issues/1346#issuecomment-290851733 https://api.github.com/repos/pydata/xarray/issues/1346 290851733 MDEyOklzc3VlQ29tbWVudDI5MDg1MTczMw== 1217238 2017-03-31T22:55:18Z 2017-03-31T22:55:18Z MEMBER

@matteodefelice you didn't decide on float32, but your data is stored that way. It's really hard to make choices about numerical precision for computations automatically: if we converted automatically to float64, somebody else would be complaining about unexpected memory usage :).

Looking at our options, we could:

  1. Stop using bottleneck on float32 arrays, or provide a flag or option to disable using bottleneck. This is not ideal, because bottleneck is much faster.
  2. Automatically convert float32 arrays to float64 before doing aggregations. This is not ideal, because it could significant increase memory requirements.
  3. Add a dtype option for aggregations (like NumPy) and consider defaulting to dype=np.float64 when doing aggregations on float32 arrays. I would generally be happy with this, but bottleneck currently doesn't provide the option currently.
  4. Write a higher precision algorithm for bottleneck's mean.
 
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