Mypy ty migration

.github/workflows/main.yml

@@ -131,8 +131,8 @@ 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@34e114876b0b11c390a56381ad16ebd13914f8d5  # v4.3.1
@@ -145,6 +145,6 @@ 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

@@ -104,62 +104,62 @@ def _get_all_algorithms(modules: list[ModuleType]) -> dict[str, Type[Algorithm]]
 def _get_algorithms_in_module(module: ModuleType) -> dict[str, Type[Algorithm]]:
     """Collect all algorithms in a single module."""
     candidate_dict = dict(inspect.getmembers(module, inspect.isclass))
-    candidate_dict = {
-        k: v for k, v in candidate_dict.items() if hasattr(v, "__algo_info__")
-    }
     algos = {}
     for candidate in candidate_dict.values():
-        name = candidate.algo_info.name
-        if issubclass(candidate, Algorithm) and candidate is not Algorithm:
-            algos[name] = candidate
+        if (
+            issubclass(candidate, Algorithm)
+            and candidate is not Algorithm
+            and candidate.__algo_info__ is not None
+        ):
+            algos[candidate.__algo_info__.name] = candidate
     return algos
 
 
 # ======================================================================================
 # 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]]:
@@ -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

.tools/update_algo_selection_hook.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-import importlib.util
 import subprocess
 import sys
 from pathlib import Path
@@ -15,13 +14,14 @@ def run(cmd: list[str], **kwargs: Any) -> None:
     subprocess.check_call(cmd, cwd=ROOT, **kwargs)
 
 
-def ensure_optimagic_is_locally_installed() -> None:
-    if importlib.util.find_spec("optimagic") is None:
-        run(["uv", "pip", "install", "--python", sys.executable, "-e", "."])
+def install_optimagic_locally() -> None:
+    # Always (re)install so that dependencies added after the hook environment was
+    # created are picked up. This is fast if nothing changed.
+    run(["uv", "pip", "install", "--quiet", "--python", sys.executable, "-e", "."])
 
 
 def main() -> int:
-    ensure_optimagic_is_locally_installed()
+    install_optimagic_locally()
     run(PYTHON + [".tools/create_algo_selection_code.py"])
 
     ruff_args = [

docs/source/development/ep-02-typing.md

@@ -1653,6 +1653,13 @@ access to currently internal objects such as the MSM objective function.
 
 We choose mypy as static type checker and run it as part of our continuous integration.
 
+```{note}
+We have since replaced mypy by [ty](https://docs.astral.sh/ty/). ty is much faster and
+also checks the bodies of unannotated functions and our tests. Its configuration lives
+in the `[tool.ty]` section of `pyproject.toml`. The mypy settings below are kept for
+reference.
+```
+
 Once this enhancement proposal is fully implemented, we want to use the following
 settings:
 

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: