Merge pull request #1015 from PowerGridModel/fix/mypy-version

Fix nightly: mypy version bump + github actions report failing

Author: mgovers

.github/workflows/ci.yml

@@ -58,8 +58,12 @@ jobs:
   ci-passed:
     runs-on: ubuntu-latest
     needs: [ci-started, build-test-release, check-code-quality, reuse-compliance, clang-tidy]
+    if: always()
 
     steps:
+      # this explicit check is needed cfr. https://github.com/orgs/community/discussions/75568
+      - name: "Check whether all jobs passed"
+        run: echo '${{ toJSON(needs) }}' | jq -e 'to_entries | all(.value.result == "success")'
       - run: echo "ci passed"
 
   publish:

.pre-commit-config.yaml

@@ -16,7 +16,7 @@ repos:
       # Run the formatter.
       - id: ruff-format
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.13.0
+    rev: v1.16.0
     hooks:
       - id: mypy
         additional_dependencies:

src/power_grid_model/_core/utils.py

@@ -364,7 +364,7 @@ def convert_single_dataset_to_python_single_dataset(
     # For example: {"node": [{"id": 0, ...}, {"id": 1, ...}], "line": [{"id": 2, ...}]}
     def _convert_component(objects: SingleComponentData):
         # This should be a single data set
-        if not isinstance(objects, np.ndarray) or objects.ndim != 1:
+        if not isinstance(objects, np.ndarray) or objects.ndim != 1 or objects.dtype.names is None:
             raise ValueError("Invalid data format")
 
         return [
@@ -441,6 +441,8 @@ def _convert_data_to_row_or_columnar(
         return {}
     if isinstance(attrs, ComponentAttributeFilterOptions):
         names = cast(SingleArray, data).dtype.names if not is_columnar(data) else cast(SingleColumnarData, data).keys()
+        if names is None:
+            raise ValueError("No attributes available in meta")
         return {attr: deepcopy(data[attr]) for attr in names}
     return {attr: deepcopy(data[attr]) for attr in attrs}
 

src/power_grid_model/validation/utils.py

@@ -69,12 +69,14 @@ def _eval_field_expression(data: np.ndarray, expression: str) -> np.ndarray:
         'foo/bar' -> data['foo'] / data['bar']
 
     """
-
     # Validate the expression
     match = re.fullmatch(r"[a-z][a-z0-9_]*(\s*/\s*[a-z][a-z0-9_]*)?", expression)
     if not match:
         raise ValueError(f"Invalid field expression '{expression}'")
 
+    if data.dtype.names is None:
+        raise ValueError("No attributes available in meta")
+
     # Find all field names and check if they exist in the dataset
     fields = [f.strip() for f in expression.split("/")]
     for field in fields:
@@ -124,6 +126,9 @@ def _update_component_array_data(
     Update the data in a numpy array, with another numpy array,
     indexed on the "id" field and only non-NaN values are overwritten.
     """
+    if update_data.dtype.names is None:
+        raise ValueError("Invalid data format")
+
     optional_ids_active = (
         "id" in update_data.dtype.names
         and np.all(update_data["id"] == np.iinfo(update_data["id"].dtype).min)

tests/unit/test_serialization.py

@@ -455,6 +455,7 @@ def assert_almost_equal(value: np.ndarray, reference: Any):
             assert_almost_equal(v, r)
     elif isinstance(reference, dict):
         assert len(value) == len(reference)
+        assert value.dtype.names is not None
         for attribute in value.dtype.names:
             if attribute in reference:
                 assert_almost_equal(value[attribute], reference[attribute])