Skip to content

Fix tascCODA always returning insignificant results #1016

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

Merged
Zethson merged 2 commits into from
Jun 10, 2026
Merged

Fix tascCODA always returning insignificant results #1016

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
6fa34b6
Fix tascCODA always returning insignificant results
Zethson Jun 10, 2026
70c4689
ci: fix codecov upload — token auth + bump to v7
Zethson Jun 10, 2026
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
4 changes: 2 additions & 2 deletions .github/workflows/test.yml
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -89,10 +89,10 @@ jobs:
uvx hatch run ${{ matrix.env.name }}:cov-report # report visibly
uvx hatch run ${{ matrix.env.name }}:coverage xml # create report for upload
- name: Upload coverage
uses: codecov/codecov-action@v6
uses: codecov/codecov-action@v7
with:
token: ${{ secrets.CODECOV_TOKEN }}
fail_ci_if_error: true
use_oidc: true

check:
name: Tests pass in all hatch environments
Expand Down
5 changes: 4 additions & 1 deletion pertpy/tools/_coda/_base_coda.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -556,8 +556,11 @@ def summary_prepare(
)
summ = summ.convert_dtypes(dtype_backend="numpy_nullable").infer_objects()

# az.summary orders rows by var_names then C-order of each variable's dims, matching values.flatten(), so align the medians positionally.
posterior = arviz_data["posterior"].to_dataset()
summ["median"] = posterior[var_names].median(dim=["chain", "draw"]).to_dataframe().stack().reindex(summ.index)
summ["median"] = np.concatenate(
[posterior[var].median(dim=["chain", "draw"]).values.flatten() for var in var_names]
)

effect_df = summ.loc[summ.index.str.match("|".join([r"beta\["]))].copy()
intercept_df = summ.loc[summ.index.str.match("|".join([r"alpha\["]))].copy()
Expand Down
Loading