Add database support for measurements and historic prediction data. (#848)

The database supports backend selection, compression, incremental data load,
automatic data saving to storage, automatic vaccum and compaction.

Make SQLite3 and LMDB database backends available.

Update tests for new interface conventions regarding data sequences,
data containers, data providers. This includes the measurements provider and
the prediction providers.

Add database documentation.

The fix includes several bug fixes that are not directly related to the database
implementation but are necessary to keep EOS running properly and to test and
document the changes.

* fix: config eos test setup

  Make the config_eos fixture generate a new instance of the config_eos singleton.
  Use correct env names to setup data folder path.

* fix: startup with no config

  Make cache and measurements complain about missing data path configuration but
  do not bail out.

* fix: soc data preparation and usage for genetic optimization.

  Search for soc measurments 48 hours around the optimization start time.
  Only clamp soc to maximum in battery device simulation.

* fix: dashboard bailout on zero value solution display

  Do not use zero values to calculate the chart values adjustment for display.

* fix: openapi generation script

  Make the script also replace data_folder_path and data_output_path to hide
  real (test) environment pathes.

* feat: add make repeated task function

  make_repeated_task allows to wrap a function to be repeated cyclically.

* chore: removed index based data sequence access

  Index based data sequence access does not make sense as the sequence can be backed
  by the database. The sequence is now purely time series data.

* chore: refactor eos startup to avoid module import startup

  Avoid module import initialisation expecially of the EOS configuration.
  Config mutation, singleton initialization, logging setup, argparse parsing,
  background task definitions depending on config and environment-dependent behavior
  is now done at function startup.

* chore: introduce retention manager

  A single long-running background task that owns the scheduling of all periodic
  server-maintenance jobs (cache cleanup, DB autosave, …)

* chore: canonicalize timezone name for UTC

  Timezone names that are semantically identical to UTC are canonicalized to UTC.

* chore: extend config file migration for default value handling

  Extend the config file migration handling values None or nonexisting values
  that will invoke a default value generation in the new config file. Also
  adapt test to handle this situation.

* chore: extend datetime util test cases

* chore: make version test check for untracked files

  Check for files that are not tracked by git. Version calculation will be
  wrong if these files will not be commited.

* chore: bump pandas to 3.0.0

  Pandas 3.0 now performs inference on the appropriate resolution (a.k.a. unit)
  for the output dtype which may become datetime64[us] (before it was ns). Also
  numeric dtype detection is now more strict which needs a different detection for
  numerics.

* chore: bump pydantic-settings to 2.12.0

  pydantic-settings 2.12.0 under pytest creates a different behaviour. The tests
  were adapted and a workaround was introduced. Also ConfigEOS was adapted
  to allow for fine grain initialization control to be able to switch
  off certain settings such as file settings during test.

* chore: remove sci learn kit from dependencies

  The sci learn kit is not strictly necessary as long as we have scipy.

* chore: add documentation mode guarding for sphinx autosummary

  Sphinx autosummary excecutes functions. Prevent exceptions in case of pure doc
  mode.

* chore: adapt docker-build CI workflow to stricter GitHub handling

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>

src/akkudoktoreos/core/pydantic.py

@@ -47,7 +47,12 @@ from pydantic import (
 )
 from pydantic.fields import ComputedFieldInfo, FieldInfo
 
-from akkudoktoreos.utils.datetimeutil import DateTime, to_datetime, to_duration
+from akkudoktoreos.utils.datetimeutil import (
+    DateTime,
+    to_datetime,
+    to_duration,
+    to_timezone,
+)
 
 # Global weakref dictionary to hold external state per model instance
 # Used as a workaround for PrivateAttr not working in e.g. Mixin Classes
@@ -683,13 +688,8 @@ class PydanticBaseModel(PydanticModelNestedValueMixin, BaseModel):
         self, *args: Any, include_computed_fields: bool = True, **kwargs: Any
     ) -> dict[str, Any]:
         """Custom dump method to serialize computed fields by default."""
-        result = super().model_dump(*args, **kwargs)
-
-        if not include_computed_fields:
-            for computed_field_name in self.__class__.model_computed_fields:
-                result.pop(computed_field_name, None)
-
-        return result
+        kwargs.setdefault("exclude_computed_fields", not include_computed_fields)
+        return super().model_dump(*args, **kwargs)
 
     def to_dict(self) -> dict:
         """Convert this PredictionRecord instance to a dictionary representation.
@@ -1061,8 +1061,8 @@ class PydanticDateTimeDataFrame(PydanticBaseModel):
         valid_base_dtypes = {"int64", "float64", "bool", "object", "string"}
 
         def is_valid_dtype(dtype: str) -> bool:
-            # Allow timezone-aware or naive datetime64
-            if dtype.startswith("datetime64[ns"):
+            # Allow timezone-aware or naive datetime64 - pandas 3.0 also has us
+            if dtype.startswith("datetime64[ns") or dtype.startswith("datetime64[us"):
                 return True
             return dtype in valid_base_dtypes
 
@@ -1102,7 +1102,7 @@ class PydanticDateTimeDataFrame(PydanticBaseModel):
 
         # Apply dtypes
         for col, dtype in self.dtypes.items():
-            if dtype.startswith("datetime64[ns"):
+            if dtype.startswith("datetime64[ns") or dtype.startswith("datetime64[us"):
                 df[col] = pd.to_datetime(df[col], utc=True)
             elif dtype in dtype_mapping.keys():
                 df[col] = df[col].astype(dtype_mapping[dtype])
@@ -1111,20 +1111,59 @@ class PydanticDateTimeDataFrame(PydanticBaseModel):
 
         return df
 
+    @classmethod
+    def _detect_data_tz(cls, df: pd.DataFrame) -> Optional[str]:
+        """Detect timezone of pandas data."""
+        # Index first (strongest signal)
+        if isinstance(df.index, pd.DatetimeIndex) and df.index.tz is not None:
+            return str(df.index.tz)
+
+        # Then datetime columns
+        for col in df.columns:
+            if is_datetime64_any_dtype(df[col]):
+                tz = getattr(df[col].dt, "tz", None)
+                if tz is not None:
+                    return str(tz)
+
+        return None
+
     @classmethod
     def from_dataframe(
         cls, df: pd.DataFrame, tz: Optional[str] = None
     ) -> "PydanticDateTimeDataFrame":
         """Create a PydanticDateTimeDataFrame instance from a pandas DataFrame."""
-        index = pd.Index([to_datetime(dt, as_string=True, in_timezone=tz) for dt in df.index])
+        # resolve timezone
+        data_tz = cls._detect_data_tz(df)
+
+        if tz is not None:
+            if data_tz and data_tz != tz:
+                raise ValueError(f"Timezone mismatch: tz='{tz}' but data uses '{data_tz}'")
+            resolved_tz = tz
+        else:
+            if data_tz:
+                resolved_tz = data_tz
+            else:
+                # Use local timezone
+                resolved_tz = to_timezone(as_string=True)
+
+        # normalize index
+        index = pd.Index(
+            [to_datetime(dt, as_string=True, in_timezone=resolved_tz) for dt in df.index]
+        )
         df.index = index
 
+        # normalize datetime columns
         datetime_columns = [col for col in df.columns if is_datetime64_any_dtype(df[col])]
+        for col in datetime_columns:
+            if df[col].dt.tz is None:
+                df[col] = df[col].dt.tz_localize(resolved_tz)
+            else:
+                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()},
-            tz=tz,
+            tz=resolved_tz,
             datetime_columns=datetime_columns,
         )