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  • shoyer · 2 ✖

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  • `decode_cf_datetime()` slow because `pd.to_timedelta()` is slow if floats are passed · 2 ✖

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  • MEMBER · 2 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
299916837 https://github.com/pydata/xarray/issues/1399#issuecomment-299916837 https://api.github.com/repos/pydata/xarray/issues/1399 MDEyOklzc3VlQ29tbWVudDI5OTkxNjgzNw== shoyer 1217238 2017-05-08T16:24:50Z 2017-05-08T16:24:50Z MEMBER

This does not make me as nervous as Fabien since my data is always quite recent, but I see that this is far from ideal for a tool for climate scientists.

@spencerkclark has been working on patch to natively support other datetime precisions in xarray (see https://github.com/pydata/xarray/pull/1252).

The only thing that bothers me is that I am not sure if the "number of nanoseconds" is always the same in every day or hour in the view of datetime64, due to leap seconds or other particularities.

For better or worse, NumPy's datetime64 ignores leap seconds.

Does this sound reasonable or did I forget to take into account any side effects?

This sounds pretty reasonable to me. The main challenge here will be guarding against integer overflow -- you might need to do the math twice, once with floats (to check for overflow) and then with integers.

You could also experiment with doing the conversion with NumPy instead of pandas, using .astype('timedelta64[{}]'.format(units)).

 
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   `decode_cf_datetime()` slow because `pd.to_timedelta()` is slow if floats are passed 226549366
299510444 https://github.com/pydata/xarray/issues/1399#issuecomment-299510444 https://api.github.com/repos/pydata/xarray/issues/1399 MDEyOklzc3VlQ29tbWVudDI5OTUxMDQ0NA== shoyer 1217238 2017-05-05T16:23:17Z 2017-05-05T16:23:17Z MEMBER

Good catch! We should definitely speed this up.

Hence, it would be great to automatically do the conversion from raw time value floats to integers in nanoseconds where possible (likely limited to resolutions bellow days or hours to avoid coping with different durations numbers of nanoseconds within e.g. different months).

Yes, very much agreed.

For units such as months or years, we already are giving the wrong result when we use pandas: In [18]: pd.to_timedelta(1, unit='M') Out[18]: Timedelta('30 days 10:29:06') In these cases, we should fall back to using netCDF4/netcdftime instead. We may also need to add more checks for numeric overflow.

As alternative, maybe avoid forcing the cast to floats and indicate in the docstring that the raw values should be integers to speed up the conversion.

Yes, this might also work. I no longer recall why we cast all inputs to floats (maybe just for consistency), but I suspect that that one of our time conversion libraries (probably netCDF4/netcdftime) expects a float array. Certainly we will still need to support floating point times saved in netCDF files, which are pretty common in my experience.

 
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   `decode_cf_datetime()` slow because `pd.to_timedelta()` is slow if floats are passed 226549366

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