fix(chunked): annotate ndarray with generic args; add py3.10 mypy gate

.github/workflows/ci.yml

@@ -64,6 +64,21 @@ jobs:
     - name: Test plotting too
       run: .venv/bin/python -m pytest --mpl
 
+  mypy:
+    name: Type check
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v6
+
+    - name: Setup uv
+      uses: astral-sh/setup-uv@v8.1.0
+
+    - name: Install nox
+      run: uv tool install nox
+
+    - name: Run mypy
+      run: nox -s mypy
+
   minimums:
     name: Check minimums
     runs-on: ubuntu-latest
@@ -87,7 +102,7 @@ jobs:
 
   pass:
     if: always()
-    needs: [pylint, checks, minimums]
+    needs: [pylint, checks, mypy, minimums]
     runs-on: ubuntu-slim
     timeout-minutes: 2
     steps:

noxfile.py

@@ -48,6 +48,19 @@ def tests(session):
     session.run("pytest", *args, *session.posargs)
 
 
+@nox.session(python="3.10", venv_backend="uv")
+def mypy(session):
+    """
+    Type check. Pinned to Python 3.10, where NumPy resolves to <2.3 and so
+    ``ndarray`` has no generic defaults -- this catches bare ``np.ndarray``
+    annotations that newer NumPy stubs silently accept. Mirrors the type check
+    in boost-histogram's downstream "hist" job.
+    """
+
+    session.install("-e.", "--group=test", "--group=plot", "mypy", "pandas-stubs")
+    session.run("mypy", *session.posargs)
+
+
 @nox.session(venv_backend="uv", default=False)
 def minimums(session):
     """

src/hist/chunked.py

@@ -57,7 +57,7 @@ def _validate_dense_view(
     *,
     shape: tuple[int, ...],
     dtype: np.dtype[tp.Any],
-) -> np.ndarray:
+) -> np.typing.NDArray[Any]:
     array = np.asarray(view)
     if array.shape != shape:
         msg = f"dense view shape mismatch: expected {shape}, got {array.shape}"
@@ -68,23 +68,25 @@ def _validate_dense_view(
     return array
 
 
-def _accumulate_dense_view(target: np.ndarray, source: np.ndarray) -> None:
+def _accumulate_dense_view(
+    target: np.typing.NDArray[Any], source: np.typing.NDArray[Any]
+) -> None:
     if target.dtype.fields is None:
         target[...] += source
         return
     for field_name in target.dtype.names or ():
         _accumulate_dense_view(target[field_name], source[field_name])
 
 
-def _zero_dense_view(view: np.ndarray) -> None:
+def _zero_dense_view(view: np.typing.NDArray[Any]) -> None:
     if view.dtype.fields is None:
         view.fill(0)
         return
     for field_name in view.dtype.names or ():
         _zero_dense_view(view[field_name])
 
 
-def _view_any_nonzero(view: np.ndarray) -> bool:
+def _view_any_nonzero(view: np.typing.NDArray[Any]) -> bool:
     if view.dtype.fields is None:
         return bool(np.any(view))
     return any(
@@ -168,7 +170,7 @@ class ChunkedHist:
     _dense_view_nbytes: int = field(init=False)
     _chunk_axis_names: tuple[str, ...] = field(init=False)
     _scratch_dense_hist: hist.Hist[Any] = field(init=False)
-    _chunks: dict[ChunkKey, np.ndarray] = field(default_factory=dict)
+    _chunks: dict[ChunkKey, np.typing.NDArray[Any]] = field(default_factory=dict)
 
     def __init__(
         self,
@@ -327,7 +329,9 @@ def dense_view_dtype(self) -> np.dtype[tp.Any]:
     def dense_axes(self) -> tuple[tp.Any, ...]:
         return tuple(self._scratch_dense_hist.axes)
 
-    def _save_chunk_view(self, key: ChunkKey, chunk_view: np.ndarray) -> None:
+    def _save_chunk_view(
+        self, key: ChunkKey, chunk_view: np.typing.NDArray[Any]
+    ) -> None:
         array = _validate_dense_view(
             chunk_view,
             shape=self.dense_view_shape,
@@ -373,7 +377,7 @@ def split_fill_kwargs(
             if name not in self.chunk_axis_names
         }
 
-    def add_dense_view(self, key: ChunkKey, dense_view: np.ndarray) -> None:
+    def add_dense_view(self, key: ChunkKey, dense_view: np.typing.NDArray[Any]) -> None:
         self._check_chunk_key(key)
         dense_view = _validate_dense_view(
             dense_view,
@@ -493,7 +497,7 @@ def empty_like(self) -> Self:
             label=self.label,
         )
 
-    def items(self) -> Iterable[tuple[ChunkKey, np.ndarray]]:
+    def items(self) -> Iterable[tuple[ChunkKey, np.typing.NDArray[Any]]]:
         """Iterate over ``(chunk key, chunk array)`` pairs.
 
         Like :meth:`chunk_view`, the yielded arrays are live views of the
@@ -559,7 +563,7 @@ def selection_dict(self, key: ChunkKey) -> dict[str, ChunkScalar]:
     def chunk_view(
         self,
         selection: Mapping[str, ChunkScalar | tp.Iterable[ChunkScalar]],
-    ) -> np.ndarray:
+    ) -> np.typing.NDArray[Any]:
         """Return the live array for one chunk.
 
         The returned array is a view of the internal storage; mutating it