issue_comments: 42789906
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| html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| https://github.com/pydata/xarray/issues/121#issuecomment-42789906 | https://api.github.com/repos/pydata/xarray/issues/121 | 42789906 | MDEyOklzc3VlQ29tbWVudDQyNzg5OTA2 | 1217238 | 2014-05-12T01:31:11Z | 2014-05-12T01:31:11Z | MEMBER | Yes, this is certainly related to #118. Virtual variables work by using pandas.DatetimeIndex methods, but if you're not using a standard calendar, you end up with an object array of netCDF4.datetime objects instead of an array of numpy.datetime64 objects (which can be turned into a DatetimeIndex). Unfortunately, we do need to be able to make a DatetimeIndex to be able to use its (very quick) calculations for properties like year. The alternative is to write our own implementation, which would likely mean far slower pure-python code. We could also write a function to cast an array into a DatetimeIndex from datetime objects, which I'm guessing would be your preferred solution, even though there are issues like the difference between dates, as DatetimeIndex objects and numpy's datetime64 always assume a standard gregorian calendar. |
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