feat (typegen): typegen.generate_typing tool for auto-generating types,

has Justfile target
chore(dependency): Add Pyright, static analyzer, dependency
test(pyright): Pyright testing can be run from Justfile, and github workflows
chore(version): Bump PATCH version (4.1.1)

.github/workflows/test.yaml

@@ -67,6 +67,30 @@ jobs:
           --strict-optional \
           -p pydantic_numpy
 
+  pyright:
+    runs-on: ubuntu-latest
+
+    strategy:
+      matrix:
+        python-version: [ "3.9", "3.10", "3.11" ]
+
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Setup the Python Environment ${{ matrix.python-version }}
+        uses: Qwerty-133/python-setup@v1
+        with:
+          python-version: ${{ matrix.python-version }}
+          skip-pre-commit: true
+
+      - name: Install dependencies
+        run: |
+          poetry install --all-extras --with dev,typecheck
+
+      - name: Validate type-hints with pyright
+        run: |
+          poetry run pyright pydantic_numpy
+
   format:
     runs-on: ubuntu-latest
 

Justfile

@@ -18,3 +18,12 @@ mypy:
 
 mypy_test:
     poetry run mypy tests/
+
+pyright:
+    poetry run pyright pydantic_numpy
+
+pyright_test:
+    poetry run pyright tests/
+
+typegen:
+    poetry run python typegen/generate_typing.py

pydantic_numpy/helper/annotation.py

@@ -116,8 +116,8 @@ def np_array_pydantic_annotated_typing(
             FilePath,
             MultiArrayNumpyFile,
             np.ndarray[  # type: ignore[misc]
-                _int_to_dim_type[dimensions] if dimensions else Any,
-                np.dtype[data_type] if _data_type_resolver(data_type) else data_type,
+                _int_to_dim_type[dimensions] if dimensions else Any,  # pyright: ignore
+                np.dtype[data_type] if _data_type_resolver(data_type) else data_type,  # type: ignore[misc]
             ],
         ],
         NpArrayPydanticAnnotation.factory(
@@ -126,15 +126,15 @@ def np_array_pydantic_annotated_typing(
     ]
 
 
-def _data_type_resolver(data_type: DTypeLike):
-    return data_type is not None and issubclass(data_type, np.generic)
+def _data_type_resolver(data_type: DTypeLike) -> bool:
+    return data_type is not None and issubclass(data_type, np.generic)  # pyright: ignore
 
 
 def _serialize_numpy_array_to_data_dict(array: np.ndarray) -> NumpyDataDict:
     if issubclass(array.dtype.type, np.timedelta64) or issubclass(array.dtype.type, np.datetime64):
-        return dict(data_type=str(array.dtype), data=array.astype(int).tolist())
+        return NumpyDataDict(data_type=str(array.dtype), data=array.astype(int).tolist())
 
-    return dict(data_type=str(array.dtype), data=array.astype(float).tolist())
+    return NumpyDataDict(data_type=str(array.dtype), data=array.astype(float).tolist())
 
 
 @validate_call

pydantic_numpy/helper/validation.py

@@ -1,4 +1,4 @@
-from typing import Callable, Optional
+from typing import Callable, Optional, cast
 
 import numpy as np
 import numpy.typing as npt
@@ -118,6 +118,7 @@ def _resolve_type_of_array_dtype(array_dtype: npt.DTypeLike) -> type:
     type
     """
     if hasattr(array_dtype, "type"):
-        return array_dtype.type
+        assert array_dtype is not None
+        return array_dtype.type  # pyright: ignore
     else:
-        return array_dtype
+        return cast(type, array_dtype)

pydantic_numpy/model.py

@@ -99,12 +99,12 @@ def load(
         npz_file = np.load(object_directory_path / cls._dump_numpy_savez_file_name)
 
         other_path: FilePath
-        if (other_path := object_directory_path / cls._dump_compressed_pickle_file_name).exists():
+        if (other_path := object_directory_path / cls._dump_compressed_pickle_file_name).exists():  # pyright: ignore
             other_field_to_value = compress_pickle.load(other_path)
-        elif (other_path := object_directory_path / cls._dump_pickle_file_name).exists():
+        elif (other_path := object_directory_path / cls._dump_pickle_file_name).exists():  # pyright: ignore
             with open(other_path, "rb") as in_pickle:
                 other_field_to_value = pickle_pkg.load(in_pickle)
-        elif (other_path := object_directory_path / cls._dump_non_array_yaml_name).exists():
+        elif (other_path := object_directory_path / cls._dump_non_array_yaml_name).exists():  # pyright: ignore
             with open(other_path, "r") as in_yaml:
                 other_field_to_value = yaml.load(in_yaml)
         else:
@@ -139,15 +139,15 @@ def dump(
                 if compress:
                     compress_pickle.dump(
                         other_field_to_value,
-                        dump_directory_path / self._dump_compressed_pickle_file_name,
+                        dump_directory_path / self._dump_compressed_pickle_file_name,  # pyright: ignore
                         compression=self._dump_compression,
                     )
                 else:
-                    with open(dump_directory_path / self._dump_pickle_file_name, "wb") as out_pickle:
+                    with open(dump_directory_path / self._dump_pickle_file_name, "wb") as out_pickle:  # pyright: ignore
                         pickle_pkg.dump(other_field_to_value, out_pickle)
 
             else:
-                with open(dump_directory_path / self._dump_non_array_yaml_name, "w") as out_yaml:
+                with open(dump_directory_path / self._dump_non_array_yaml_name, "w") as out_yaml:  # pyright: ignore
                     yaml.dump(other_field_to_value, out_yaml)
 
         return dump_directory_path