Mirrors/EOS
1
0
Fork 0
mirror of https://github.com/Akkudoktor-EOS/EOS.git synced 2026-10-08 23:46:38 +00:00
Code Issues Actions 1 Packages Projects Releases Wiki Activity

fix: unify mypy environments for local checks and CI (#1291)

Browse Source 
The isolated pre-commit mypy hook previously omitted runtime type information that
make mypy used, hiding errors involving dependencies such as Pydantic and Pendulum.
Makefile, pre-commit and CI now run the same full-project typing policy in the
development environment defined by uv.lock.

- Use uv run --locked --exact --extra dev and the same mypy arguments for Makefile
  and the local hook. Check all of src and tests, including on configuration-only
  changes.
- Pin Python 3.13 for local development and the pre-commit CI job, and install the
  locked pre-commit version in CI.
- Disable incremental analysis because existing Pendulum cache state changes mypy 2.3.1
  diagnostics. Document the policy, the performance tradeoff and the existing typing debt.
- Add a regression test that exercises Makefile, the hook and the CI command in a
  temporary project, accepting valid dependency types and detecting deliberate
  Pydantic/Pendulum assignment errors.

Resolve the newly detected mypy diagnostics.

- Enable the numpydantic and Pydantic mypy plugins, retaining strict Pydantic
  constructor typing with init_typed = true. Validate raw/coercible payloads through model_validate.
- Propagate concrete record, provider and time-window types through generic collections,
  factories and lookup methods. Preserve runtime field inspection and generated time-window
  documentation.
- Align Pendulum annotations with actual factory/arithmetic results while retaining Pydantic
  validation adapters at runtime. Correct optional values, array boundaries, REST handlers
  and plotting interfaces.
- Add pinned scipy-stubs and types-psutil, update uv.lock, and supply the plugins' dependencies.
- Add runtime regression coverage for validated path defaults, normalized time-series metadata,
  generic field inspection, invalid timestamps and unsupported provider imports.

Runtime and compatibility details:

- Validate path defaults as Path objects while retaining raw string defaults needed by
  migration serialization with exclude_defaults.
- Normalize feed-in tariff lists and default charge rates to NumPy arrays; reject missing
  timestamps/uninitialized values explicitly. Importing into a provider without import support
  returns HTTP 400.
- Public JSON schemas and OpenAPI structure match main (excluding the generated version).

Signed-off-by: dr-dimitry

Signed-off-by: dr-dimitry
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
Co-authored-by: dr-dimitri <87113560+dr-dimitri@users.noreply.github.com>
Co-authored-by: Normann <github@koldrack.com>
This commit is contained in:
Bobby Noelte
2026-09-10 23:20:35 +02:00
committed by GitHub
co-authored by dr-dimitri Normann
parent 5b584cbb57
commit 1abdd345c4
114 changed files with 2217 additions and 1322 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/akkudoktoreos/core/pydantic.py
+30 -15
View File
@@ -20,13 +20,17 @@ import uuid
import weakref
from copy import deepcopy
from typing import (
Annotated,
Any,
Callable,
Dict,
List,
Optional,
Self,
Type,
TypeVar,
Union,
cast,
get_args,
get_origin,
)
@@ -39,6 +43,7 @@ from pydantic import (
BaseModel,
ConfigDict,
Field,
GetPydanticSchema,
PrivateAttr,
RootModel,
ValidationError,
@@ -49,6 +54,7 @@ from pydantic.fields import ComputedFieldInfo, FieldInfo
from akkudoktoreos.utils.datetimeutil import (
DateTime,
Duration,
to_datetime,
to_duration,
to_timezone,
@@ -415,7 +421,7 @@ class PydanticModelNestedValueMixin:
# If this is the final key, set the value
if is_final_key:
try:
model.validate_and_set(key, value)
getattr(model, "validate_and_set")(key, value)
except Exception as e:
raise ValueError(f"Error updating model: {e}") from e
return
@@ -549,10 +555,10 @@ class PydanticModelNestedValueMixin:
if not inspect.isclass(model):
raise TypeError(f"Model '{model}' is not of class type.")
if key not in model.model_fields: # type: ignore[attr-defined]
if key not in model.model_fields:
raise TypeError(f"Field '{key}' does not exist in model '{model.__name__}'.")
field_annotation = model.model_fields[key].annotation # type: ignore[attr-defined]
field_annotation = model.model_fields[key].annotation
if not field_annotation:
raise TypeError(
f"Missing type annotation for field '{key}' in model '{model.__name__}'."
@@ -563,6 +569,8 @@ class PydanticModelNestedValueMixin:
while queue:
annotation = queue.pop(0)
if isinstance(annotation, TypeVar):
annotation = annotation.__bound__ or Any
origin = get_origin(annotation)
args = get_args(annotation)
@@ -679,7 +687,7 @@ class PydanticBaseModel(PydanticModelNestedValueMixin, BaseModel):
"""Resets the fields to their default values."""
for field_name, field_info in self.__class__.model_fields.items():
if field_info.default_factory is not None: # Handle fields with default_factory
default_value = field_info.default_factory()
default_value = field_info.get_default(call_default_factory=True)
else:
default_value = field_info.default
try:
@@ -707,7 +715,7 @@ class PydanticBaseModel(PydanticModelNestedValueMixin, BaseModel):
return self.model_dump()
@classmethod
def from_dict(cls: Type["PydanticBaseModel"], data: dict) -> "PydanticBaseModel":
def from_dict(cls, data: dict) -> Self:
"""Create a PydanticBaseModel instance from a dictionary.
Args:
@@ -735,7 +743,7 @@ class PydanticBaseModel(PydanticModelNestedValueMixin, BaseModel):
return self.model_dump_json()
@classmethod
def from_json(cls: Type["PydanticBaseModel"], json_str: str) -> "PydanticBaseModel":
def from_json(cls, json_str: str) -> Self:
"""Create an instance of the PydanticBaseModel class or its subclass from a JSON string.
Args:
@@ -926,6 +934,10 @@ class PydanticBaseModel(PydanticModelNestedValueMixin, BaseModel):
return None
DateTimeDataInput = dict[str, str | list[float | int | str | None]]
DateTimeDataValues = dict[str, str | DateTime | Duration | list[float | int | str | None]]
class PydanticDateTimeData(RootModel):
"""Pydantic model for time series data with consistent value lengths.
@@ -948,13 +960,16 @@ class PydanticDateTimeData(RootModel):
"""
root: Dict[str, Union[str, List[Union[float, int, str, None]]]]
# The wire format contains strings; validate_root normalizes the two
# indexing values to Pendulum objects. Keep the existing input schema.
root: Annotated[
DateTimeDataValues,
GetPydanticSchema(lambda source_type, handler: handler(DateTimeDataInput)),
]
@field_validator("root", mode="after")
@classmethod
def validate_root(
cls, value: Dict[str, Union[str, List[Union[float, int, str, None]]]]
) -> Dict[str, Union[str, List[Union[float, int, str, None]]]]:
def validate_root(cls, value: dict[str, Any]) -> DateTimeDataValues:
# Validate that all keys are strings
if not all(isinstance(k, str) for k in value.keys()):
raise ValueError("All keys in the dictionary must be strings.")
@@ -977,7 +992,7 @@ class PydanticDateTimeData(RootModel):
return value
def to_dict(self) -> Dict[str, Union[str, List[Union[float, int, str, None]]]]:
def to_dict(self) -> DateTimeDataValues:
"""Convert the model to a plain dictionary.
Returns:
@@ -1176,8 +1191,8 @@ class PydanticDateTimeDataFrame(PydanticBaseModel):
df[col] = df[col].dt.tz_convert(resolved_tz)
return cls(
data=df.to_dict(orient="index"),
dtypes={col: str(dtype) for col, dtype in df.dtypes.items()},
data=cast(dict[str, dict[str, Any]], df.to_dict(orient="index")),
dtypes=cast(dict[str, str], {col: str(dtype) for col, dtype in df.dtypes.items()}),
tz=resolved_tz,
datetime_columns=datetime_columns,
)
@@ -1401,10 +1416,10 @@ class PydanticDateTimeSeries(PydanticBaseModel):
series.index = index
if len(index) > 0:
tz = to_datetime(series.index[0]).timezone.name
tz = to_datetime(series.index[0]).timezone_name
return cls(
data=series.to_dict(),
data=cast(dict[str, Any], series.to_dict()),
dtype=str(series.dtype),
tz=tz,
)