pull_requests: 1560652584

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id node_id number state locked title user body created_at updated_at closed_at merged_at merge_commit_sha assignee milestone draft head base author_association auto_merge repo url merged_by
1560652584 PR_kwDOAMm_X85dBaso 8322 open 0 Implementation of rust based cftime 57914115 As discussed in #8302, here is a first attempt to implement `cftime_rs`. There are a lot of tests and I struggle to understand all the processing in `coding/times.py`. However, with this first attempt I've been able to make the `test_cf_datetime` work (ignoring one test) https://github.com/pydata/xarray/blob/8423f2c47306cc3a4a52990818964f278179491f/xarray/tests/test_coding_times.py#L127-L131 Also there are some key differences betwwen `cftime` and `cftime-rs` : - A long int is used to represent the timestamp internally, so `cftime-rs` will not overflow as soon as `numpy`, `python `or `cftime`. It can go from -291,672,107,014 BC to 291,672,107,014 AD approximately and this depends on calendar. - There is no `only_use_python_datetimes` argument. Instead there are 4 distinct functions : - [date2num()](https://cftime-rs.readthedocs.io/en/latest/api_documentation/index.html#cftime_rs.date2num) - [num2date()](https://cftime-rs.readthedocs.io/en/latest/api_documentation/index.html#cftime_rs.num2date) - [num2pydate()](https://cftime-rs.readthedocs.io/en/latest/api_documentation/index.html#cftime_rs.num2pydate) - [pydate2num()](https://cftime-rs.readthedocs.io/en/latest/api_documentation/index.html#cftime_rs.pydate2num) - These functions only take a python list of one dimension and return a list of one dimension. A conversion should be done before. - There is no multiple datetime type (there are hidden) but instead a single object `PyCFDatetime` - There is no conda repository at the moment Finally, and regardless of this PR, I guess there could be a speed improvement by vectorizing operations by replacing this : https://github.com/pydata/xarray/blob/df0ddaf2e68a6b033b4e39990d7006dc346fcc8c/xarray/coding/times.py#L622-L649 by something like this : https://github.com/pydata/xarray/blob/8423f2c47306cc3a4a52990818964f278179491f/xarray/coding/times.py#L631-L670 We can use numpy instead of list comprehensions. It takes a bit more of memory though. 2023-10-17T14:00:45Z 2023-10-17T22:20:31Z     7ff8e3707b88a3a55e14c1595ee0d14a199f8af8     0 8423f2c47306cc3a4a52990818964f278179491f df0ddaf2e68a6b033b4e39990d7006dc346fcc8c CONTRIBUTOR   13221727 https://github.com/pydata/xarray/pull/8322  

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