issues

Table actions

3 rows where type = "pull" and user = 57914115 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

Suggested facets: created_at (date), updated_at (date), closed_at (date)

state 2

type 1

  • pull · 3 ✖

repo 1

id node_id number title user state locked assignee milestone comments created_at updated_at ▲ closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
1947869312 PR_kwDOAMm_X85dCo6P 8324 Implement cftime vectorization as discussed in PR #8322 antscloud 57914115 open 0     0 2023-10-17T17:01:25Z 2023-10-23T05:11:11Z   CONTRIBUTOR   0 pydata/xarray/pulls/8324

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

 
{
    "url": "https://api.github.com/repos/pydata/xarray/issues/8324/reactions",
    "total_count": 2,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 2,
    "eyes": 0
}
     xarray 13221727 pull
1947508727 PR_kwDOAMm_X85dBaso 8322 Implementation of rust based cftime antscloud 57914115 open 0     1 2023-10-17T14:00:45Z 2023-10-17T22:20:31Z   CONTRIBUTOR   0 pydata/xarray/pulls/8322

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, pythonor 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() - num2date() - num2pydate() - 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. 

{
    "url": "https://api.github.com/repos/pydata/xarray/issues/8322/reactions",
    "total_count": 1,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 1,
    "eyes": 0
}
     xarray 13221727 pull
1068680815 PR_kwDOAMm_X84vQ2hE 6037 Fix wrong typing for tolerance in reindex antscloud 57914115 closed 0     6 2021-12-01T17:19:08Z 2022-01-15T17:28:08Z 2022-01-15T17:27:56Z CONTRIBUTOR   0 pydata/xarray/pulls/6037

In the xarray.core.dataset.pymodule, 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
 
{
    "url": "https://api.github.com/repos/pydata/xarray/issues/6037/reactions",
    "total_count": 2,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 2,
    "rocket": 0,
    "eyes": 0
}
     xarray 13221727 pull

Advanced export

JSON shape: default, array, newline-delimited, object

CSV options: 

CREATE TABLE [issues] (
   [id] INTEGER PRIMARY KEY,
   [node_id] TEXT,
   [number] INTEGER,
   [title] TEXT,
   [user] INTEGER REFERENCES [users]([id]),
   [state] TEXT,
   [locked] INTEGER,
   [assignee] INTEGER REFERENCES [users]([id]),
   [milestone] INTEGER REFERENCES [milestones]([id]),
   [comments] INTEGER,
   [created_at] TEXT,
   [updated_at] TEXT,
   [closed_at] TEXT,
   [author_association] TEXT,
   [active_lock_reason] TEXT,
   [draft] INTEGER,
   [pull_request] TEXT,
   [body] TEXT,
   [reactions] TEXT,
   [performed_via_github_app] TEXT,
   [state_reason] TEXT,
   [repo] INTEGER REFERENCES [repos]([id]),
   [type] TEXT
);
CREATE INDEX [idx_issues_repo]
    ON [issues] ([repo]);
CREATE INDEX [idx_issues_milestone]
    ON [issues] ([milestone]);
CREATE INDEX [idx_issues_assignee]
    ON [issues] ([assignee]);
CREATE INDEX [idx_issues_user]
    ON [issues] ([user]);