Merge branch 'main' into read_existing_refs

.github/workflows/main.yml

@@ -46,10 +46,6 @@ jobs:
           conda env list
           conda list
 
-      - name: Type check
-        run: |
-          mypy virtualizarr
-
       - name: Running Tests
         run: |
           python -m pytest ./virtualizarr --run-network-tests --cov=./ --cov-report=xml --verbose

.github/workflows/typing.yml

@@ -0,0 +1,38 @@
+name: Typing
+
+on:
+  push:
+    branches: [ "main" ]
+    paths-ignore:
+    - 'docs/**'
+  pull_request:
+    branches: [ "main" ]
+    paths-ignore:
+    - 'docs/**'
+  schedule:
+    - cron: "0 0 * * *"
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  mypy:
+    name: mypy
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.12'
+
+      - name: Install deps
+        run: |
+          # We need to test optional dep to add all the library stubs
+          pip install -e '.[test]'
+
+      - name: Type check
+        run: |
+          mypy virtualizarr

virtualizarr/manifests/array_api.py

@@ -1,4 +1,4 @@
-from typing import TYPE_CHECKING, Callable, Iterable
+from typing import TYPE_CHECKING, Any, Callable, Iterable, cast
 
 import numpy as np
 
@@ -217,9 +217,12 @@ def stack(
     new_shape.insert(axis, length_along_new_stacked_axis)
 
     # do stacking of entries in manifest
-    stacked_paths = np.stack(
-        [arr.manifest._paths for arr in arrays],
-        axis=axis,
+    stacked_paths = cast(  # `np.stack` apparently is type hinted as if the output could have Any dtype
+        np.ndarray[Any, np.dtypes.StringDType],
+        np.stack(
+            [arr.manifest._paths for arr in arrays],
+            axis=axis,
+        ),
     )
     stacked_offsets = np.stack(
         [arr.manifest._offsets for arr in arrays],
@@ -296,10 +299,14 @@ def broadcast_to(x: "ManifestArray", /, shape: tuple[int, ...]) -> "ManifestArra
     )
 
     # do broadcasting of entries in manifest
-    broadcasted_paths = np.broadcast_to(
-        x.manifest._paths,
-        shape=new_chunk_grid_shape,
+    broadcasted_paths = cast(  # `np.broadcast_to` apparently is type hinted as if the output could have Any dtype
+        np.ndarray[Any, np.dtypes.StringDType],
+        np.broadcast_to(
+            x.manifest._paths,
+            shape=new_chunk_grid_shape,
+        ),
     )
+
     broadcasted_offsets = np.broadcast_to(
         x.manifest._offsets,
         shape=new_chunk_grid_shape,

virtualizarr/manifests/manifest.py

@@ -84,7 +84,7 @@ class ChunkManifest:
     so it's not possible to have a ChunkManifest object that does not represent a valid grid of chunks.
     """
 
-    _paths: np.ndarray[Any, np.dtypes.StringDType]  # type: ignore[name-defined]
+    _paths: np.ndarray[Any, np.dtypes.StringDType]
     _offsets: np.ndarray[Any, np.dtype[np.uint64]]
     _lengths: np.ndarray[Any, np.dtype[np.uint64]]
 
@@ -113,7 +113,10 @@ def __init__(self, entries: dict) -> None:
         shape = get_chunk_grid_shape(entries.keys())
 
         # Initializing to empty implies that entries with path='' are treated as missing chunks
-        paths = np.empty(shape=shape, dtype=np.dtypes.StringDType())  # type: ignore[attr-defined]
+        paths = cast(  # `np.empty` apparently is type hinted as if the output could have Any dtype
+            np.ndarray[Any, np.dtypes.StringDType],
+            np.empty(shape=shape, dtype=np.dtypes.StringDType()),
+        )
         offsets = np.empty(shape=shape, dtype=np.dtype("uint64"))
         lengths = np.empty(shape=shape, dtype=np.dtype("uint64"))
 
@@ -141,7 +144,7 @@ def __init__(self, entries: dict) -> None:
     @classmethod
     def from_arrays(
         cls,
-        paths: np.ndarray[Any, np.dtype[np.dtypes.StringDType]],  # type: ignore[name-defined]
+        paths: np.ndarray[Any, np.dtypes.StringDType],
         offsets: np.ndarray[Any, np.dtype[np.uint64]],
         lengths: np.ndarray[Any, np.dtype[np.uint64]],
     ) -> "ChunkManifest":