issue_comments: 299916837

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https://github.com/pydata/xarray/issues/1399#issuecomment-299916837 https://api.github.com/repos/pydata/xarray/issues/1399 299916837 MDEyOklzc3VlQ29tbWVudDI5OTkxNjgzNw== 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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