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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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 792946756 | PR_kwDOAMm_X84vQ2hE | 6037 | closed | 0 | Fix wrong typing for tolerance in reindex | antscloud 57914115 | In the `xarray.core.dataset.py `module, more particulary in the reindex method the tolerance argument is set to be a `Number` https://github.com/pydata/xarray/blob/f08672847abec18f46df75e2f620646d27fa41a2/xarray/core/dataset.py#L2743 But the `_reindex` function call `reindex_variable` function. In the `reindex_variable` the type for tolerance is `Any`. This function ends to call `get_indexer_nd` which call the pandas function `get_indexer` : https://github.com/pydata/xarray/blob/f08672847abec18f46df75e2f620646d27fa41a2/xarray/core/indexes.py#L137 In pandas the type of tolerance according to the docs can be a scalar or a list-like object - [x] Tests added - [x] Passes `pre-commit run --all-files` - [x] User visible changes (including notable bug fixes) are documented in `whats-new.rst` | 2021-12-01T17:19:08Z | 2022-01-15T17:28:08Z | 2022-01-15T17:27:56Z | 2022-01-15T17:27:56Z | 3666a6fb7226601002f408d56e81837ab69b8d4e | 0 | c659cc4f8738900e9c11a5407b8f4cdc801613d5 | 4c865d607e6e03605b7050d9fb6991e86346bf08 | CONTRIBUTOR | xarray 13221727 | https://github.com/pydata/xarray/pull/6037 | ||||
| 1560652584 | PR_kwDOAMm_X85dBaso | 8322 | open | 0 | Implementation of rust based cftime | antscloud 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 | xarray 13221727 | https://github.com/pydata/xarray/pull/8322 | ||||||
| 1560972943 | PR_kwDOAMm_X85dCo6P | 8324 | open | 0 | Implement cftime vectorization as discussed in PR #8322 | antscloud 57914115 | As discussed in #8322, here is the test for implementing the vectorization Only this test seems to fail in `test_coding_times.py` : https://github.com/pydata/xarray/blob/f895dc1a748b41d727c5e330e8d664a8b8780800/xarray/tests/test_coding_times.py#L1061-L1071 I don't really understand why though if you have an idea | 2023-10-17T17:01:25Z | 2023-10-23T05:11:11Z | f7e19c4c9224059e2362ab88b17a9101e2d58beb | 0 | cff06d4c59acb9cc6fb8ed18c593d9008e4a0883 | f895dc1a748b41d727c5e330e8d664a8b8780800 | CONTRIBUTOR | xarray 13221727 | https://github.com/pydata/xarray/pull/8324 |
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