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  • leifdenby · 1 ✖

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  • bottleneck : Wrong mean for float32 array · 1 ✖

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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
456149964 https://github.com/pydata/xarray/issues/1346#issuecomment-456149964 https://api.github.com/repos/pydata/xarray/issues/1346 MDEyOklzc3VlQ29tbWVudDQ1NjE0OTk2NA== leifdenby 2405019 2019-01-21T17:33:31Z 2019-01-21T17:33:31Z CONTRIBUTOR

Sorry to unearth this issue again, but I just got bitten by this quite badly. I'm looking at absolute temperature perturbations and bottleneck's implementation together with my data being loaded as float32 (correctly, as it's stored like that) causes an error on the size of the perturbations I'm looking for.

Example:

``` In [1]: import numpy as np ...: import bottleneck

In [2]: a = 300np.ones((800*2,), dtype=np.float32)

In [3]: np.mean(a) Out[3]: 300.0

In [4]: bottleneck.nanmean(a) Out[4]: 302.6018981933594 ```

Would it be worth adding a warning (until the right solution is found) if someone is doing .mean() on a DataArray which is float32?

Based a little experimentation (https://gist.github.com/leifdenby/8e874d3440a1ac96f96465a418f158ab) bottleneck's mean function builds up significant errors even with moderately sized arrays if they are float32, so I'm going to stop using .mean() as-is from now and always pass in dtype=np.float64.

 
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   bottleneck : Wrong mean for float32 array 218459353

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