Merge branch 'main' into update-plotly-kaleido

Author: janosg

.pre-commit-config.yaml

@@ -67,7 +67,7 @@ repos:
       - id: yamllint
         exclude: tests/optimagic/optimizers/_pounders/fixtures
   - repo: https://github.com/PyCQA/docformatter
-    rev: eb1df34
+    rev: v1.7.7
     hooks:
       - id: docformatter
         args:
@@ -79,7 +79,7 @@ repos:
           - --blank
         exclude: src/optimagic/optimization/algo_options.py
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.11.8
+    rev: v0.12.2
     hooks:
       # Run the linter.
       - id: ruff
@@ -131,7 +131,7 @@ repos:
         args:
           - --drop-empty-cells
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.15.0
+    rev: v1.14.1
     hooks:
       - id: mypy
         files: src|tests
@@ -143,8 +143,6 @@ repos:
           - types-cffi
           - types-openpyxl
           - types-jinja2
-        args:
-          - --config=pyproject.toml
 ci:
   autoupdate_schedule: monthly
   skip:

src/optimagic/deprecations.py

@@ -371,11 +371,11 @@ def throw_dict_constraints_future_warning_if_required(
     if not isinstance(constraints, list):
         constraints = [constraints]
 
-    types = [
+    types_or_none = [
         constraint.get("type", None) if isinstance(constraint, dict) else None
         for constraint in constraints
     ]
-    types = list(set(types) - {None})
+    types = [t for t in types_or_none if t is not None]
 
     if types:
         msg = (

src/optimagic/examples/criterion_functions.py

@@ -105,16 +105,11 @@ def rosenbrock_scalar(params: PyTree) -> float:
 def rosenbrock_gradient(params: PyTree) -> PyTree:
     """Calculate gradient of rosenbrock function."""
     x = _get_x(params)
-    l1 = np.delete(x, [-1])
-    l1 = np.append(l1, 0)
-    l2 = np.insert(x, 0, 0)
-    l2 = np.delete(l2, [1])
-    l3 = np.insert(x, 0, 0)
-    l3 = np.delete(l3, [-1])
-    l4 = np.delete(x, [0])
-    l4 = np.append(l4, 0)
-    l5 = np.full((len(x) - 1), 2)
-    l5 = np.append(l5, 0)  # type: ignore[assignment]
+    l1 = np.append(np.delete(x, [-1]), 0)
+    l2 = np.delete(np.insert(x, 0, 0), [1])
+    l3 = np.delete(np.insert(x, 0, 0), [-1])
+    l4 = np.append(np.delete(x, [0]), 0)
+    l5 = np.append(np.full((len(x) - 1), 2), 0)
     flat = 100 * (4 * (l1**3) + 2 * l2 - 2 * (l3**2) - 4 * (l4 * x)) + 2 * l1 - l5
     return _unflatten_gradient(flat, params)
 

src/optimagic/logging/logger.py

@@ -242,7 +242,7 @@ def _extract_best_history(
             best_idx, level="step"
         )
 
-        def _to_dict(pandas_obj: pd.DataFrame | pd.Series) -> dict[str, Any]:  # type:ignore
+        def _to_dict(pandas_obj: pd.DataFrame | pd.Series) -> dict[str, Any]:
             if isinstance(pandas_obj, pd.DataFrame):
                 result = pandas_obj.to_dict(orient="list")
             else: