fix: broadcast pandas/DataArray bounds in coords and preserve dim order

[FBumann]

Closes #706, #709. Partially addresses #723 (helper relocated to common.py; call-site audit deferred to follow-up — see issue comment for scope).

What changes

Principle. 0.7.0 made coords the source of truth for DataArray bounds in add_variables. This PR closes the two remaining gaps so the rule holds across every bound type and dimension order:

• Pandas Series / DataFrame bounds missing a dimension in coords are now broadcast to coords instead of being silently dropped (add_variables: pandas Series/DataFrame bounds with missing dimensions silently drop the dimension #709).
• The variable's dimension order always follows coords regardless of bound type. Previously the order of expanded array bounds depended on the type of the bound (scalar bounds kept coords order; two array bounds missing the same dimension produced a prepended order, add_variables: DataArray bounds with missing dimensions yield wrong dimension order #706).

Implementation. _validate_dataarray_bounds and the downstream as_dataarray(arr, coords) call in add_variables collapse into a single helper, as_dataarray_in_coords (in linopy/common.py, alongside as_dataarray). It converts any input (pandas with named axes via to_xarray, otherwise via as_dataarray), validates the result against coords, expands missing dims, transposes to coords order, and reconstructs the coord variables in that order. expand_dims({}) and identity transpose are metadata-only no-ops, so scalar bounds and full-dim DataArray bounds already in coords order pay no extra cost.

Pandas conversion. A new _named_pandas_to_dataarray helper handles pandas with fully named axes (including MultiIndex on either axis) by stacking all column levels into the row index in one call: arr.stack(level=list(range(arr.columns.nlevels)), future_stack=True), followed by to_xarray(). Pandas with any unnamed axis falls through to as_dataarray.

Piecewise. Same family of fix: linopy.piecewise._broadcast_points built its expand_dims map from a set, giving a hash-randomized dim order across processes. Iterates expressions and dims in declaration order now.

Supersedes

• fix: keep coords dimension order for DataArray bounds (#706) #710 — DataArray dim-order fix. Carried over.
• Feature/pandas coords #719 — pandas broadcast fix. Carried over, but the test in Feature/pandas coords #719 used set(var.dims) == {…} and didn't catch the dim-order regression; rewritten to assert tuple-equal dim order, with additional MultiIndex coverage and a one-liner for the same-values-different-order reindex branch.

Test plan

• test/test_variable.py::TestAddVariablesBoundsWithCoords — 57/57. Includes:
  • test_bound_broadcast_missing_dim parametrized over DataArray, Series, DataFrame, Series-multiindex, DataFrame-multicolumns, DataFrame-multiindex.
  • test_dataarray_broadcast_missing_dim_order parametrized over scalar/DA bound combinations.
  • test_dataarray_coord_reorder for the same-values-different-order reindex.
  • test_pandas_bound_with_unnamed_axis_falls_through, test_unnamed_coords_short_circuit, test_dataarray_bound_with_multiindex_coord covering edge branches in the new helpers.
• test/test_piecewise_constraints.py::test_broadcast_points_dim_order_follows_exprs.
• Broader sweep: test_model.py, test_linear_expression.py, test_constraint.py, test_constraints.py, test_piecewise_constraints.py, test_variable_assignment.py, test_variables.py — 784 pass, no regressions.
• Pre-commit (ruff/format/blackdoc/codespell) clean.

🤖 Generated with Claude Code

[RobbieKiwi]

Maybe it would make sense to give mask the same treatment as the bounds to avoid the same pandas issue. Then mask could have the same type hint as lower/upper bounds and the user could for example pass pd.DataFrame for all 3

[FBumann]

It would! But this is somewhat breaking (we had a FutureWarning in place, but still...). I will split this into a separate PR!

[FBumann]

Agreed?