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  • leifdenby · 5 ✖

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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
964310406 https://github.com/pydata/xarray/issues/5937#issuecomment-964310406 https://api.github.com/repos/pydata/xarray/issues/5937 IC_kwDOAMm_X845ejWG leifdenby 2405019 2021-11-09T16:21:03Z 2021-11-09T16:21:03Z CONTRIBUTOR

Your proposal sounds good to me.

Would you mind raising an issue on the pandas bug tracker asking if this is expected behaviour?

Great! Yes, I'm happy to raise it with the pandas developers.

 
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   DataArray.dt.seconds returns incorrect value for negative `timedelta64[ns]` 1044693438
668501905 https://github.com/pydata/xarray/issues/4306#issuecomment-668501905 https://api.github.com/repos/pydata/xarray/issues/4306 MDEyOklzc3VlQ29tbWVudDY2ODUwMTkwNQ== leifdenby 2405019 2020-08-04T09:54:46Z 2020-08-04T09:54:46Z CONTRIBUTOR

Ah, sorry. I tried to look for an existing issue. I'll close this in that case.

 
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   Indexing datetime broken with pandas 1.1.0 672662079
620456062 https://github.com/pydata/xarray/issues/4010#issuecomment-620456062 https://api.github.com/repos/pydata/xarray/issues/4010 MDEyOklzc3VlQ29tbWVudDYyMDQ1NjA2Mg== leifdenby 2405019 2020-04-28T08:20:16Z 2020-04-28T08:20:32Z CONTRIBUTOR

Ah! It's an issue with how I am using np.timedelta64 then. I (stupidly) assumed that np.timedelta64 has the same call signature as datetime.timedelta. It appears that np.timedelta64 silently ignores any kwargs 😕

Anyway, I won't be making that mistake again. Thank you @keewis !

 
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   Issue indexing by xarray's own time values + offset 607678694
606715109 https://github.com/pydata/xarray/issues/3320#issuecomment-606715109 https://api.github.com/repos/pydata/xarray/issues/3320 MDEyOklzc3VlQ29tbWVudDYwNjcxNTEwOQ== leifdenby 2405019 2020-03-31T15:52:36Z 2020-03-31T15:52:36Z CONTRIBUTOR

I just had this issue too @keewis, I think you're right. Once I remove timezone information from my datetime object it works just fine. Looking at https://github.com/pydata/xarray/issues/2512 it looks like the change which is required might be in xarray/core/variable.py (https://github.com/pydata/xarray/blob/master/xarray/core/variable.py#L165). I'll have a look and see if I can make it work

 
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   Error saving xr.Dataset with timezone aware time index to netcdf format. 495382528
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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