Run ty in pre-commit and CI

.github/workflows/python.yml

@@ -31,6 +31,28 @@ concurrency:
   cancel-in-progress: true
 
 jobs:
+  type-check:
+    timeout-minutes: 10
+    if: "! contains(github.event.head_commit.message, '[skip ci]')"
+    name: Type check
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v6
+
+      - name: Set up Python 3.10
+        uses: actions/setup-python@v6
+        with:
+          python-version: '3.10'
+          cache: 'pip'
+
+      - name: Install tox
+        run: python3 -m pip install tox
+
+      - name: Run ty
+        run: tox -e type
+        working-directory: python
+
   run-tests:
     timeout-minutes: 15
     if: "! contains(github.event.head_commit.message, '[skip ci]')"
@@ -78,7 +100,7 @@ jobs:
 
   build_sdist:
     timeout-minutes: 10
-    needs: run-tests
+    needs: [run-tests, type-check]
     if: "! contains(github.event.head_commit.message, '[skip ci]')"
     runs-on:  ${{ matrix.os }}
     strategy:

.pre-commit-config.yaml

@@ -1,5 +1,7 @@
 ci:
-  skip: [roxygenize] # Produces renv errors.
+  # roxygenize produces renv errors. ty needs network access while running,
+  # which is unavailable in pre-commit.ci; it runs in GitHub Actions instead.
+  skip: [roxygenize, ty]
   autofix_prs: true
   autofix_commit_msg: |
         [pre-commit.ci] auto fixes from pre-commit.com hooks
@@ -51,6 +53,12 @@ repos:
         types_or: [ python, pyi, jupyter ]
         require_serial: true
 
+  - repo: https://github.com/astral-sh/ty-pre-commit
+    rev: v0.0.89
+    hooks:
+      - id: ty
+        args: [--project=python, --isolated]
+
   - repo: https://github.com/sphinx-contrib/sphinx-lint
     rev: v1.0.2
     hooks:

python/pyproject.toml

@@ -188,6 +188,8 @@ collapse_tables = [ "project.authors" ]
 
 [tool.ty.rules]
 invalid-assignment = "ignore"
+[tool.ty.src]
+include = [ "src" ]
 
 [tool.pytest.ini_options]
 faulthandler_timeout = 10

python/src/moocore/_docsubstitute.py

@@ -1,9 +1,11 @@
-from typing import Any
+from typing import Any, TypeVar
 from collections.abc import Callable
 
 from string import Template
 import inspect
 
+_CallableT = TypeVar("_CallableT", bound=Callable[..., Any])
+
 COMMON_PARAMS = {
     "points": """2D array of numerical values, where each row gives the coordinates of a point in objective space.
     If the array is created by the :func:`read_datasets()` function, remove the last column.""",
@@ -32,7 +34,7 @@ class DocSubstitute:
     def __init__(self) -> None:
         self.params = COMMON_PARAMS
 
-    def __call__(self, func: Callable[..., Any]) -> Callable[..., Any]:
+    def __call__(self, func: _CallableT) -> _CallableT:
         if func.__doc__:
             try:
                 func.__doc__ = Template(

python/src/moocore/_generate.py

@@ -1,3 +1,5 @@
+from typing import cast
+
 import numpy as np
 
 # FIXME: Implement also WFG hard type described here:
@@ -172,15 +174,17 @@ def generate_ndset(
            [0.16824287, 0.4448603 , 0.35613488, 0.17723839, 0.5565858 ]])
     """
     if seed is None or is_integer_value(seed):
-        seed = np.random.default_rng(seed)
+        rng = np.random.default_rng(seed)
+    else:
+        rng = cast(np.random.Generator, seed)
 
     def _sample_simplex(n: int, d: int) -> np.ndarray:
-        x = seed.exponential(size=(n, d))
+        x = rng.exponential(size=(n, d))
         x /= x.sum(axis=1, keepdims=True)
         return x
 
     def _sample_sphere(n: int, d: int) -> np.ndarray:
-        x = np.abs(seed.normal(size=(n, d)))
+        x = np.abs(rng.normal(size=(n, d)))
         x /= np.linalg.norm(x, axis=1, keepdims=True)
         return x
 
@@ -190,7 +194,7 @@ def _sample_convex_sphere(n: int, d: int) -> np.ndarray:
     def _sample_cliff_concave(n: int, d: int) -> np.ndarray:
         x = np.empty((n, d))
         x[:, :2] = _sample_sphere(n, 2)
-        x[:, 2:] = seed.uniform(0, 1, size=(n, d - 2))
+        x[:, 2:] = rng.uniform(0, 1, size=(n, d - 2))
         return x
 
     def _sample_cliff_convex(n: int, d: int) -> np.ndarray:
@@ -563,23 +567,25 @@ def generate_sequence(
 
     """
     if seed is None or is_integer_value(seed):
-        seed = np.random.default_rng(seed)
+        rng = np.random.default_rng(seed)
+    else:
+        rng = cast(np.random.Generator, seed)
 
     match method:
         case "cube":
-            sample = _generate_within_hypercube(n, d, seed)
+            sample = _generate_within_hypercube(n, d, rng)
         case "sphere":
-            sample = _generate_within_hypersphere(n, d, seed, r_min=r_min)
+            sample = _generate_within_hypersphere(n, d, rng, r_min=r_min)
         case "each-dominates-previous":
             sample = _generate_total_dominance_chain(
-                n, d, rng=seed, dominates_previous=True
+                n, d, rng=rng, dominates_previous=True
             )
         case "each-dominates-next":
             sample = _generate_total_dominance_chain(
-                n, d, rng=seed, dominates_previous=False
+                n, d, rng=rng, dominates_previous=False
             )
         case "glas2017":
-            sample = _generate_glas2017_sequence(n, d, seed, c_value=c_value)
+            sample = _generate_glas2017_sequence(n, d, rng, c_value=c_value)
         case _:
             raise ValueError(f"unknown method={method}")