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  • andrew-c-ross · 2 ✖

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  • bottleneck : Wrong mean for float32 array · 2 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
290755867 https://github.com/pydata/xarray/issues/1346#issuecomment-290755867 https://api.github.com/repos/pydata/xarray/issues/1346 MDEyOklzc3VlQ29tbWVudDI5MDc1NTg2Nw== andrew-c-ross 5852283 2017-03-31T16:07:56Z 2017-03-31T16:07:56Z CONTRIBUTOR

I think this might be a problem with bottleneck? My interpretation of _create_nan_agg_method in xarray/core/ops.py is that it may use bottleneck to get the mean unless you pass skipna=False or specify multiple axes. And,

```python In [2]: import bottleneck In [3]: bottleneck.version Out[3]: '1.2.0'

In [6]: bottleneck.nanmean(ds.var167.data) Out[6]: 261.6441345214844 ```

Forgive me if I'm wrong, I'm still a bit new.

 
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   bottleneck : Wrong mean for float32 array 218459353
290747253 https://github.com/pydata/xarray/issues/1346#issuecomment-290747253 https://api.github.com/repos/pydata/xarray/issues/1346 MDEyOklzc3VlQ29tbWVudDI5MDc0NzI1Mw== andrew-c-ross 5852283 2017-03-31T15:38:12Z 2017-03-31T15:53:07Z CONTRIBUTOR

Also on macOS, and I can reproduce.

Using python 2.7.11, xarray 0.9.1, dask 0.14.1 installed through Anaconda. I get the same results with xarray 0.9.1-38-gc0178b7 from GitHub.

```python In [3]: ds = xarray.open_dataset('ERAIN-t2m-1983-2012.seasmean.nc')

In [4]: ds.var167.mean() Out[4]: <xarray.DataArray 'var167' ()> array(261.6441345214844, dtype=float32) ```

Curiously, I get the right results with skipna=False...

python In [10]: ds.var167.mean(skipna=False) Out[10]: <xarray.DataArray 'var167' ()> array(278.6246643066406, dtype=float32)

... or by specifying coordinates to average over:

python In [5]: ds.var167.mean(('time', 'lat', 'lon')) Out[5]: <xarray.DataArray 'var167' ()> array(278.6246643066406, dtype=float32)

 
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   bottleneck : Wrong mean for float32 array 218459353

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