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  • djhoese · 2 ✖

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  • Inconsistent type conversion when doing numpy.sum gvies different results · 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
373221321 https://github.com/pydata/xarray/issues/1989#issuecomment-373221321 https://api.github.com/repos/pydata/xarray/issues/1989 MDEyOklzc3VlQ29tbWVudDM3MzIyMTMyMQ== djhoese 1828519 2018-03-15T00:37:26Z 2018-03-15T00:38:04Z CONTRIBUTOR

@shoyer In my examples rows = cols = 1000 (xarray 0.10.1).

 
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   Inconsistent type conversion when doing numpy.sum gvies different results 305373563
373219624 https://github.com/pydata/xarray/issues/1989#issuecomment-373219624 https://api.github.com/repos/pydata/xarray/issues/1989 MDEyOklzc3VlQ29tbWVudDM3MzIxOTYyNA== djhoese 1828519 2018-03-15T00:27:35Z 2018-03-15T00:27:35Z CONTRIBUTOR

Example:

``` import numpy as np import xarray as xr a = xr.DataArray(np.random.random((rows, cols)).astype(np.float32), dims=('y', 'x')) In [65]: np.sum(a).data Out[65]: array(499858.0625)

In [66]: np.sum(a.data) Out[66]: 499855.19

In [67]: np.sum(a.data.astype(np.float64)) Out[67]: 499855.21635645436

In [68]: np.sum(a.data.astype(np.float32)) Out[68]: 499855.19 ```

I realized after making this example that nansum gives expected results: ``` a = xr.DataArray(np.random.random((rows, cols)).astype(np.float32), dims=('y', 'x')) In [83]: np.nansum(a.data) Out[83]: 500027.81

In [84]: np.nansum(a) Out[84]: 500027.81

In [85]: np.nansum(a.data.astype(np.float64)) Out[85]: 500027.77103802469

In [86]: np.nansum(a.astype(np.float64)) Out[86]: 500027.77103802469 ```

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

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