fix mypy errors around numpy functions not being strictly type hinted (…

…#252)
zarr-developers · Oct 11, 2024 · 2d66e88 · 2d66e88
1 parent fadeeba
commit 2d66e88
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Showing 2 changed files with 20 additions and 10 deletions.
diff --git a/virtualizarr/manifests/array_api.py b/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,

diff --git a/virtualizarr/manifests/manifest.py b/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":