Mypy ty migration

.github/workflows/main.yml

@@ -119,8 +119,8 @@ jobs:
         run: >-
           pixi run -e tests-linux-py314
           python -m doctest -v docs/source/how_to/how_to_constraints.md
-  run-mypy:
-    name: Run mypy
+  run-ty:
+    name: Run ty
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
@@ -131,6 +131,6 @@ jobs:
           cache-write: ${{ github.event_name == 'push' && github.ref_name == 'main' }}
           frozen: true
           environments: type-checking
-      - name: Run mypy
+      - name: Run ty
         shell: bash -el {0}
-        run: pixi run -e type-checking mypy
+        run: pixi run -e type-checking ty --output-format github

.tools/create_algo_selection_code.py

@@ -109,7 +109,7 @@ def _get_algorithms_in_module(module: ModuleType) -> dict[str, Type[Algorithm]]:
     }
     algos = {}
     for candidate in candidate_dict.values():
-        name = candidate.algo_info.name
+        name = candidate.algo_info.name  # ty:ignore[unresolved-attribute]
         if issubclass(candidate, Algorithm) and candidate is not Algorithm:
             algos[name] = candidate
     return algos
@@ -119,47 +119,47 @@ def _get_algorithms_in_module(module: ModuleType) -> dict[str, Type[Algorithm]]:
 # Functions to filter algorithms by selectors
 # ======================================================================================
 def _is_gradient_based(algo: Type[Algorithm]) -> bool:
-    return algo.algo_info.needs_jac  # type: ignore
+    return algo.algo_info.needs_jac
 
 
 def _is_gradient_free(algo: Type[Algorithm]) -> bool:
     return not _is_gradient_based(algo)
 
 
 def _is_global(algo: Type[Algorithm]) -> bool:
-    return algo.algo_info.is_global  # type: ignore
+    return algo.algo_info.is_global
 
 
 def _is_local(algo: Type[Algorithm]) -> bool:
     return not _is_global(algo)
 
 
 def _is_bounded(algo: Type[Algorithm]) -> bool:
-    return algo.algo_info.supports_bounds  # type: ignore
+    return algo.algo_info.supports_bounds
 
 
 def _is_linear_constrained(algo: Type[Algorithm]) -> bool:
-    return algo.algo_info.supports_linear_constraints  # type: ignore
+    return algo.algo_info.supports_linear_constraints
 
 
 def _is_nonlinear_constrained(algo: Type[Algorithm]) -> bool:
-    return algo.algo_info.supports_nonlinear_constraints  # type: ignore
+    return algo.algo_info.supports_nonlinear_constraints
 
 
 def _is_scalar(algo: Type[Algorithm]) -> bool:
-    return algo.algo_info.solver_type == AggregationLevel.SCALAR  # type: ignore
+    return algo.algo_info.solver_type == AggregationLevel.SCALAR
 
 
 def _is_least_squares(algo: Type[Algorithm]) -> bool:
-    return algo.algo_info.solver_type == AggregationLevel.LEAST_SQUARES  # type: ignore
+    return algo.algo_info.solver_type == AggregationLevel.LEAST_SQUARES
 
 
 def _is_likelihood(algo: Type[Algorithm]) -> bool:
-    return algo.algo_info.solver_type == AggregationLevel.LIKELIHOOD  # type: ignore
+    return algo.algo_info.solver_type == AggregationLevel.LIKELIHOOD
 
 
 def _is_parallel(algo: Type[Algorithm]) -> bool:
-    return algo.algo_info.supports_parallelism  # type: ignore
+    return algo.algo_info.supports_parallelism
 
 
 def _get_filters() -> dict[str, Callable[[Type[Algorithm]], bool]]:
@@ -223,7 +223,7 @@ def _generate_category_combinations(categories: list[str]) -> list[tuple[str, ..
     result: list[tuple[str, ...]] = []
     for r in range(len(categories) + 1):
         result.extend(map(tuple, map(sorted, combinations(categories, r))))
-    return sorted(result, key=len, reverse=True)
+    return sorted(result, key=len, reverse=True)  # ty:ignore[invalid-return-type]
 
 
 def _apply_filters(
@@ -385,7 +385,7 @@ def _all(self) -> list[Type[Algorithm]]:
             def _available(self) -> list[Type[Algorithm]]:
                 _all = self._all()
                 return [
-                    a for a in _all if a.algo_info.is_available # type: ignore
+                    a for a in _all if a.algo_info.is_available
                 ]
 
             @property

docs/source/development/how_to_contribute.md

@@ -64,7 +64,7 @@ For regular contributors: **Clone** the [repository](https://github.com/optimagi
    use:
 
    ```console
-   $ pixi run mypy
+   $ pixi run ty
    ```
 
    To see all available pixi tasks, run: