Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Handle exceptionally rare linear algebra error #2015

Merged
merged 2 commits into from
Dec 10, 2024
Merged

Handle exceptionally rare linear algebra error #2015

Show file tree
Hide file tree
Changes from 1 commit
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
136a4cc
handle exceptionally rare error
Zeitsperre Dec 10, 2024
3060439
update CHANGELOG.rst
Zeitsperre Dec 10, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 1 addition & 0 deletions CHANGELOG.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -33,6 +33,7 @@ Internal changes
* The `numpydoc` linting tool has been added to the development dependencies, linting checks, and the `pre-commit` configuration. (:pull:`1988`).
* `xclim` now uses a `src` layout for the codebase. Structure-dependent functions, documentation, and build commands have been adapted to reflect these changes. (:pull:`1971`).
* Added a more robust `yamllint` configuration to ensure that all YAML files are linted consistently. (:pull:`1971`).
* Addressed a very rare singular matrix error that can happen in ``test_loess_smoothing_nan``.

CI changes
^^^^^^^^^^
Expand Down
11 changes: 10 additions & 1 deletion tests/test_sdba/test_loess.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,5 +1,7 @@
from __future__ import annotations

import logging

import numpy as np
import pandas as pd
import pytest
Expand Down Expand Up @@ -84,4 +86,11 @@ def test_loess_smoothing_nan(use_dask):

assert out.dims == da.dims
# check that the output is all nan on the axis with nan in the input
assert np.isnan(out.values[0, 0]).all()
try:
assert np.isnan(out.values[0, 0]).all()
except np.linalg.LinAlgError:
msg = (
"This has roughly a 1/50,000,000 chance of occurring. Buy a lottery ticket!"
)
logging.error(msg)
pass
Loading