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  • Inconsistent type conversion when doing numpy.sum gvies different results · 2 ✖

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373226888 https://github.com/pydata/xarray/issues/1989#issuecomment-373226888 https://api.github.com/repos/pydata/xarray/issues/1989 MDEyOklzc3VlQ29tbWVudDM3MzIyNjg4OA== fujiisoup 6815844 2018-03-15T01:10:37Z 2018-03-15T01:15:33Z MEMBER

I notice that bottleneck does the dtype conversion. I think in your environment bottleneck is installed.

```python In [9]: np.sum(a) # equivalent to a.sum(), using bottleneck Out[9]: <xarray.DataArray ()> array(499943.21875)

In [10]: np.sum(a.data) # numpy native Out[10]: 499941.53

In [15]: bn.nansum(a.data) Out[15]: 499943.21875

In [11]: a.sum(dim=('x', 'y')) # multiple dims calls native np.sum Out[11]: <xarray.DataArray ()> array(499941.53, dtype=float32) ```

It might be an upstream issue.

 
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   Inconsistent type conversion when doing numpy.sum gvies different results 305373563
373221066 https://github.com/pydata/xarray/issues/1989#issuecomment-373221066 https://api.github.com/repos/pydata/xarray/issues/1989 MDEyOklzc3VlQ29tbWVudDM3MzIyMTA2Ng== shoyer 1217238 2018-03-15T00:36:02Z 2018-03-15T00:36:02Z MEMBER

Strangely, it says: AttributeError: module 'xarray' has no attribute 'show_versions' Perhaps I am on a very old version?

Yes, you're using a version of xarray prior to 0.10.

What value are you using for rows/cols in your example?

Note that due to a quirk of NumPy, np.sum(a) actually corresponds to a.sum(). For xarray, a.sum() skips NaNs by default, so it's equivalent to np.nansum() or bottleneck.nansum() (if bottleneck is installed).

 
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   Inconsistent type conversion when doing numpy.sum gvies different results 305373563

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