feat: add Home Assistant and NodeRED adapters (#764)

Adapters for Home Assistant and NodeRED integration are added.
Akkudoktor-EOS can now be run as Home Assistant add-on and standalone.

As Home Assistant add-on EOS uses ingress to fully integrate the EOSdash dashboard
in Home Assistant.

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

* fix: development version scheme

  The development versioning scheme is adaptet to fit to docker and
  home assistant expectations. The new scheme is x.y.z and x.y.z.dev<hash>.
  Hash is only digits as expected by home assistant. Development version
  is appended by .dev as expected by docker.

* fix: use mean value in interval on resampling for array

  When downsampling data use the mean value of all values within the new
  sampling interval.

* fix: default battery ev soc and appliance wh

  Make the genetic simulation return default values for the
  battery SoC, electric vehicle SoC and appliance load if these
  assets are not used.

* fix: import json string

  Strip outer quotes from JSON strings on import to be compliant to json.loads()
  expectation.

* fix: default interval definition for import data

  Default interval must be defined in lowercase human definition to
  be accepted by pendulum.

* fix: clearoutside schema change

* feat: add adapters for integrations

  Adapters for Home Assistant and NodeRED integration are added.
  Akkudoktor-EOS can now be run as Home Assistant add-on and standalone.

  As Home Assistant add-on EOS uses ingress to fully integrate the EOSdash dashboard
  in Home Assistant.

* feat: allow eos to be started with root permissions and drop priviledges

  Home assistant starts all add-ons with root permissions. Eos now drops
  root permissions if an applicable user is defined by paramter --run_as_user.
  The docker image defines the user eos to be used.

* feat: make eos supervise and monitor EOSdash

  Eos now not only starts EOSdash but also monitors EOSdash during runtime
  and restarts EOSdash on fault. EOSdash logging is captured by EOS
  and forwarded to the EOS log to provide better visibility.

* feat: add duration to string conversion

  Make to_duration to also return the duration as string on request.

* chore: Use info logging to report missing optimization parameters

  In parameter preparation for automatic optimization an error was logged for missing paramters.
  Log is now down using the info level.

* chore: make EOSdash use the EOS data directory for file import/ export

  EOSdash use the EOS data directory for file import/ export by default.
  This allows to use the configuration import/ export function also
  within docker images.

* chore: improve EOSdash config tab display

  Improve display of JSON code and add more forms for config value update.

* chore: make docker image file system layout similar to home assistant

  Only use /data directory for persistent data. This is handled as a
  docker volume. The /data volume is mapped to ~/.local/share/net.akkudoktor.eos
  if using docker compose.

* chore: add home assistant add-on development environment

  Add VSCode devcontainer and task definition for home assistant add-on
  development.

* chore: improve documentation

src/akkudoktoreos/core/cachesettings.py

@@ -27,7 +27,10 @@ class CacheCommonSettings(SettingsBaseModel):
     # Do not make this a pydantic computed field. The pydantic model must be fully initialized
     # to have access to config.general, which may not be the case if it is a computed field.
     def path(self) -> Optional[Path]:
-        """Compute cache path based on general.data_folder_path."""
+        """Computed cache path based on general.data_folder_path."""
+        if self.config.general.home_assistant_addon:
+            # Only /data is persistent for home assistant add-on
+            return Path("/data/cache")
         data_cache_path = self.config.general.data_folder_path
         if data_cache_path is None or self.subpath is None:
             return None

src/akkudoktoreos/core/coreabc.py

@@ -18,12 +18,53 @@ from loguru import logger
 from akkudoktoreos.core.decorators import classproperty
 from akkudoktoreos.utils.datetimeutil import DateTime
 
+adapter_eos: Any = None
 config_eos: Any = None
 measurement_eos: Any = None
 prediction_eos: Any = None
 ems_eos: Any = None
 
 
+class AdapterMixin:
+    """Mixin class for managing EOS adapter.
+
+    This class serves as a foundational component for EOS-related classes requiring access
+    to the global EOS adapters. It provides a `adapter` property that dynamically retrieves
+    the adapter instance.
+
+    Usage:
+        Subclass this base class to gain access to the `adapter` attribute, which retrieves the
+        global adapter instance lazily to avoid import-time circular dependencies.
+
+    Attributes:
+        adapter (Adapter): Property to access the global EOS adapter.
+
+    Example:
+        .. code-block:: python
+
+            class MyEOSClass(AdapterMixin):
+                def my_method(self):
+                    self.adapter.update_date()
+
+    """
+
+    @classproperty
+    def adapter(cls) -> Any:
+        """Convenience class method/ attribute to retrieve the EOS adapters.
+
+        Returns:
+            Adapter: The adapters.
+        """
+        # avoid circular dependency at import time
+        global adapter_eos
+        if adapter_eos is None:
+            from akkudoktoreos.adapter.adapter import get_adapter
+
+            adapter_eos = get_adapter()
+
+        return adapter_eos
+
+
 class ConfigMixin:
     """Mixin class for managing EOS configuration data.
 

src/akkudoktoreos/core/dataabc.py

@@ -1018,7 +1018,7 @@ class DataSequence(DataBase, MutableSequence):
         end_datetime: Optional[DateTime] = None,
         interval: Optional[Duration] = None,
         fill_method: Optional[str] = None,
-        dropna: Optional[bool] = None,
+        dropna: Optional[bool] = True,
     ) -> NDArray[Shape["*"], Any]:
         """Extract an array indexed by fixed time intervals from data records within an optional date range.
 
@@ -1032,17 +1032,19 @@ class DataSequence(DataBase, MutableSequence):
                 - 'ffill': Forward fill missing values.
                 - 'bfill': Backward fill missing values.
                 - 'none': Defaults to 'linear' for numeric values, otherwise 'ffill'.
-            dropna: (bool, optional): Whether to drop NAN/ None values before processing. Defaults to True.
+            dropna: (bool, optional): Whether to drop NAN/ None values before processing.
+                Defaults to True.
 
         Returns:
-            np.ndarray: A NumPy Array of the values extracted from the specified key.
+            np.ndarray: A NumPy Array of the values at the chosen frequency extracted from the
+                specified key.
 
         Raises:
             KeyError: If the specified key is not found in any of the DataRecords.
         """
         self._validate_key(key)
 
-        # General check on fill_method
+        # Validate fill method
         if fill_method not in ("ffill", "bfill", "linear", "none", None):
             raise ValueError(f"Unsupported fill method: {fill_method}")
 
@@ -1050,13 +1052,17 @@ class DataSequence(DataBase, MutableSequence):
         start_datetime = to_datetime(start_datetime, to_maxtime=False) if start_datetime else None
         end_datetime = to_datetime(end_datetime, to_maxtime=False) if end_datetime else None
 
-        resampled = None
         if interval is None:
             interval = to_duration("1 hour")
+            resample_freq = "1h"
+        else:
+            resample_freq = to_duration(interval, as_string="pandas")
 
+        # Load raw lists (already sorted & filtered)
         dates, values = self.key_to_lists(key=key, dropna=dropna)
         values_len = len(values)
 
+        # Bring lists into shape
         if values_len < 1:
             # No values, assume at least one value set to None
             if start_datetime is not None:
@@ -1092,40 +1098,40 @@ class DataSequence(DataBase, MutableSequence):
                 dates.append(end_datetime)
                 values.append(values[-1])
 
-        series = pd.Series(data=values, index=pd.DatetimeIndex(dates), name=key)
-        if not series.index.inferred_type == "datetime64":
+        # Construct series
+        series = pd.Series(values, index=pd.DatetimeIndex(dates), name=key)
+        if series.index.inferred_type != "datetime64":
             raise TypeError(
                 f"Expected DatetimeIndex, but got {type(series.index)} "
                 f"infered to {series.index.inferred_type}: {series}"
             )
 
-        # Handle missing values
-        if series.dtype in [np.float64, np.float32, np.int64, np.int32]:
-            # Numeric types
-            if fill_method is None:
+        # Determine default fill method depending on dtype
+        if fill_method is None:
+            if pd.api.types.is_numeric_dtype(series):
                 fill_method = "linear"
-            # Resample the series to the specified interval
-            resampled = series.resample(interval, origin=resample_origin).first()
-            if fill_method == "linear":
-                resampled = resampled.interpolate(method="linear")
-            elif fill_method == "ffill":
-                resampled = resampled.ffill()
-            elif fill_method == "bfill":
-                resampled = resampled.bfill()
-            elif fill_method != "none":
-                raise ValueError(f"Unsupported fill method: {fill_method}")
-        else:
-            # Non-numeric types
-            if fill_method is None:
+            else:
                 fill_method = "ffill"
-            # Resample the series to the specified interval
+
+        # Perform the resampling
+        if pd.api.types.is_numeric_dtype(series):
+            # numeric → use mean
+            resampled = series.resample(interval, origin=resample_origin).mean()
+        else:
+            # non-numeric → fallback (first, last, mode, or ffill)
             resampled = series.resample(interval, origin=resample_origin).first()
-            if fill_method == "ffill":
-                resampled = resampled.ffill()
-            elif fill_method == "bfill":
-                resampled = resampled.bfill()
-            elif fill_method != "none":
-                raise ValueError(f"Unsupported fill method for non-numeric data: {fill_method}")
+
+        # Handle missing values after resampling
+        if fill_method == "linear" and pd.api.types.is_numeric_dtype(series):
+            resampled = resampled.interpolate("linear")
+        elif fill_method == "ffill":
+            resampled = resampled.ffill()
+        elif fill_method == "bfill":
+            resampled = resampled.bfill()
+        elif fill_method == "none":
+            pass
+        else:
+            raise ValueError(f"Unsupported fill method: {fill_method}")
 
         logger.debug(
             "Resampled for '{}' with length {}: {}...{}",
@@ -1141,6 +1147,16 @@ class DataSequence(DataBase, MutableSequence):
         if end_datetime is not None and len(resampled) > 0:
             resampled = resampled.truncate(after=end_datetime.subtract(seconds=1))
         array = resampled.values
+
+        # Convert NaN to None if there are actually NaNs
+        if (
+            isinstance(array, np.ndarray)
+            and np.issubdtype(array.dtype.type, np.floating)
+            and pd.isna(array).any()
+        ):
+            array = array.astype(object)
+            array[pd.isna(array)] = None
+
         logger.debug(
             "Array for '{}' with length {}: {}...{}", key, len(array), array[:10], array[-10:]
         )
@@ -1691,6 +1707,14 @@ class DataImportMixin:
                 }
 
         """
+        # Strip quotes if provided - does not effect unquoted string
+        json_str = json_str.strip()  # strip white space at start and end
+        if (json_str.startswith("'") and json_str.endswith("'")) or (
+            json_str.startswith('"') and json_str.endswith('"')
+        ):
+            json_str = json_str[1:-1]  # strip outer quotes
+        json_str = json_str.strip()  # strip remaining white space at start and end
+
         # Try pandas dataframe with orient="split"
         try:
             import_data = PydanticDateTimeDataFrame.model_validate_json(json_str)
@@ -1720,10 +1744,15 @@ class DataImportMixin:
             logger.debug(f"PydanticDateTimeData import: {error_msg}")
 
         # Use simple dict format
-        import_data = json.loads(json_str)
-        self.import_from_dict(
-            import_data, key_prefix=key_prefix, start_datetime=start_datetime, interval=interval
-        )
+        try:
+            import_data = json.loads(json_str)
+            self.import_from_dict(
+                import_data, key_prefix=key_prefix, start_datetime=start_datetime, interval=interval
+            )
+        except Exception as e:
+            error_msg = f"Invalid JSON string '{json_str}': {e}"
+            logger.debug(error_msg)
+            raise ValueError(error_msg) from e
 
     def import_from_file(
         self,

src/akkudoktoreos/core/decorators.py

@@ -25,11 +25,11 @@ class classproperty:
 
     Methods:
         __get__: Retrieves the value of the class property by calling the
-                 decorated method on the class.
+            decorated method on the class.
 
     Parameters:
         fget (Callable[[Any], Any]): A method that takes the class as an
-                                      argument and returns a value.
+            argument and returns a value.
 
     Raises:
         RuntimeError: If `fget` is not defined when `__get__` is called.

src/akkudoktoreos/core/emplan.py

@@ -10,9 +10,11 @@ Demand Driven Based Control.
 
 import uuid
 from abc import ABC, abstractmethod
+from collections import defaultdict
 from enum import Enum
 from typing import Annotated, Literal, Optional, Union
 
+from loguru import logger
 from pydantic import Field, computed_field, model_validator
 
 from akkudoktoreos.core.pydantic import PydanticBaseModel
@@ -2257,20 +2259,60 @@ class EnergyManagementPlan(PydanticBaseModel):
         self.valid_from = to_datetime()
         self.valid_until = None
 
+    def get_resources(self) -> list[str]:
+        """Retrieves the resource_ids for the resources the plan currently holds instructions for.
+
+        Returns a list of resource ids.
+        """
+        resource_ids = []
+        for instr in self.instructions:
+            resource_id = instr.resource_id
+            if resource_id not in resource_ids:
+                resource_ids.append(resource_id)
+        return resource_ids
+
     def get_active_instructions(
         self, now: Optional[DateTime] = None
-    ) -> list[EnergyManagementInstruction]:
-        """Retrieves all currently active instructions at the specified time."""
+    ) -> list["EnergyManagementInstruction"]:
+        """Retrieves the currently active instruction for each resource at the specified time.
+
+        Semantics:
+        - For each resource, consider only instructions with execution_time <= now.
+        - Choose the instruction with the latest execution_time (the most recent).
+        - If that instruction has a duration (timedelta), it's active only if now < execution_time + duration.
+        - If that instruction has no duration (None), treat it as open-ended (active until superseded).
+
+        Returns a list with at most one instruction per resource (the active one).
+        """
         now = now or to_datetime()
-        active = []
+        # Group instructions by resource_id
+        by_resource: dict[str, list["EnergyManagementInstruction"]] = defaultdict(list)
         for instr in self.instructions:
-            instr_duration = instr.duration()
+            # skip instructions scheduled in the future
+            if instr.execution_time <= now:
+                by_resource[instr.resource_id].append(instr)
+
+        active: list["EnergyManagementInstruction"] = []
+
+        for resource_id, instrs in by_resource.items():
+            # pick latest instruction by execution_time
+            latest = max(instrs, key=lambda i: i.execution_time)
+
+            if len(instrs) == 0:
+                # No instructions, ther shall be at least one
+                error_msg = f"No instructions for {resource_id}"
+                logger.error(error_msg)
+                raise ValueError(error_msg)
+
+            instr_duration = latest.duration()  # expected: Duration| None
             if instr_duration is None:
-                if instr.execution_time <= now:
-                    active.append(instr)
+                # open-ended (active until replaced) -> active because we selected latest <= now
+                active.append(latest)
             else:
-                if instr.execution_time <= now < instr.execution_time + instr_duration:
-                    active.append(instr)
+                # active only if now is strictly before execution_time + duration
+                if latest.execution_time + instr_duration > now:
+                    active.append(latest)
+
         return active
 
     def get_next_instruction(

src/akkudoktoreos/core/ems.py

@@ -1,6 +1,7 @@
 import traceback
 from asyncio import Lock, get_running_loop
 from concurrent.futures import ThreadPoolExecutor
+from enum import Enum
 from functools import partial
 from typing import ClassVar, Optional
 
@@ -8,7 +9,12 @@ from loguru import logger
 from pydantic import computed_field
 
 from akkudoktoreos.core.cache import CacheEnergyManagementStore
-from akkudoktoreos.core.coreabc import ConfigMixin, PredictionMixin, SingletonMixin
+from akkudoktoreos.core.coreabc import (
+    AdapterMixin,
+    ConfigMixin,
+    PredictionMixin,
+    SingletonMixin,
+)
 from akkudoktoreos.core.emplan import EnergyManagementPlan
 from akkudoktoreos.core.emsettings import EnergyManagementMode
 from akkudoktoreos.core.pydantic import PydanticBaseModel
@@ -24,7 +30,23 @@ from akkudoktoreos.utils.datetimeutil import DateTime, compare_datetimes, to_dat
 executor = ThreadPoolExecutor(max_workers=1)
 
 
-class EnergyManagement(SingletonMixin, ConfigMixin, PredictionMixin, PydanticBaseModel):
+class EnergyManagementStage(Enum):
+    """Enumeration of the main stages in the energy management lifecycle."""
+
+    IDLE = "IDLE"
+    DATA_ACQUISITION = "DATA_AQUISITION"
+    FORECAST_RETRIEVAL = "FORECAST_RETRIEVAL"
+    OPTIMIZATION = "OPTIMIZATION"
+    CONTROL_DISPATCH = "CONTROL_DISPATCH"
+
+    def __str__(self) -> str:
+        """Return the string representation of the stage."""
+        return self.value
+
+
+class EnergyManagement(
+    SingletonMixin, ConfigMixin, PredictionMixin, AdapterMixin, PydanticBaseModel
+):
     """Energy management."""
 
     # Start datetime.
@@ -33,6 +55,9 @@ class EnergyManagement(SingletonMixin, ConfigMixin, PredictionMixin, PydanticBas
     # last run datetime. Used by energy management task
     _last_run_datetime: ClassVar[Optional[DateTime]] = None
 
+    # Current energy management stage
+    _stage: ClassVar[EnergyManagementStage] = EnergyManagementStage.IDLE
+
     # energy management plan of latest energy management run with optimization
     _plan: ClassVar[Optional[EnergyManagementPlan]] = None
 
@@ -81,6 +106,15 @@ class EnergyManagement(SingletonMixin, ConfigMixin, PredictionMixin, PydanticBas
         cls._start_datetime = start_datetime.set(minute=0, second=0, microsecond=0)
         return cls._start_datetime
 
+    @classmethod
+    def stage(cls) -> EnergyManagementStage:
+        """Get the the stage of the energy management.
+
+        Returns:
+            EnergyManagementStage: The current stage of energy management.
+        """
+        return cls._stage
+
     @classmethod
     def plan(cls) -> Optional[EnergyManagementPlan]:
         """Get the latest energy management plan.
@@ -122,6 +156,7 @@ class EnergyManagement(SingletonMixin, ConfigMixin, PredictionMixin, PydanticBas
         """Run the energy management.
 
         This method initializes the energy management run by setting its
+
         start datetime, updating predictions, and optionally starting
         optimization depending on the selected mode or configuration.
 
@@ -157,6 +192,8 @@ class EnergyManagement(SingletonMixin, ConfigMixin, PredictionMixin, PydanticBas
 
         logger.info("Starting energy management run.")
 
+        cls._stage = EnergyManagementStage.DATA_ACQUISITION
+
         # Remember/ set the start datetime of this energy management run.
         # None leads
         cls.set_start_datetime(start_datetime)
@@ -164,12 +201,23 @@ class EnergyManagement(SingletonMixin, ConfigMixin, PredictionMixin, PydanticBas
         # Throw away any memory cached results of the last energy management run.
         CacheEnergyManagementStore().clear()
 
+        # Do data aquisition by adapters
+        try:
+            cls.adapter.update_data(force_enable)
+        except Exception as e:
+            trace = "".join(traceback.TracebackException.from_exception(e).format())
+            error_msg = f"Adapter update failed - phase {cls._stage}: {e}\n{trace}"
+            logger.error(error_msg)
+
+        cls._stage = EnergyManagementStage.FORECAST_RETRIEVAL
+
         if mode is None:
             mode = cls.config.ems.mode
         if mode is None or mode == "PREDICTION":
             # Update the predictions
             cls.prediction.update_data(force_enable=force_enable, force_update=force_update)
             logger.info("Energy management run done (predictions updated)")
+            cls._stage = EnergyManagementStage.IDLE
             return
 
         # Prepare optimization parameters
@@ -184,8 +232,12 @@ class EnergyManagement(SingletonMixin, ConfigMixin, PredictionMixin, PydanticBas
             logger.error(
                 "Energy management run canceled. Could not prepare optimisation parameters."
             )
+            cls._stage = EnergyManagementStage.IDLE
             return
 
+        cls._stage = EnergyManagementStage.OPTIMIZATION
+        logger.info("Starting energy management optimization.")
+
         # Take values from config if not given
         if genetic_individuals is None:
             genetic_individuals = cls.config.optimization.genetic.individuals
@@ -195,7 +247,6 @@ class EnergyManagement(SingletonMixin, ConfigMixin, PredictionMixin, PydanticBas
         if cls._start_datetime is None:  # Make mypy happy - already set by us
             raise RuntimeError("Start datetime not set.")
 
-        logger.info("Starting energy management optimization.")
         try:
             optimization = GeneticOptimization(
                 verbose=bool(cls.config.server.verbose),
@@ -208,8 +259,11 @@ class EnergyManagement(SingletonMixin, ConfigMixin, PredictionMixin, PydanticBas
             )
         except:
             logger.exception("Energy management optimization failed.")
+            cls._stage = EnergyManagementStage.IDLE
             return
 
+        cls._stage = EnergyManagementStage.CONTROL_DISPATCH
+
         # Make genetic solution public
         cls._genetic_solution = solution
 
@@ -224,6 +278,17 @@ class EnergyManagement(SingletonMixin, ConfigMixin, PredictionMixin, PydanticBas
         logger.debug("Energy management plan:\n{}", cls._plan)
         logger.info("Energy management run done (optimization updated)")
 
+        # Do control dispatch by adapters
+        try:
+            cls.adapter.update_data(force_enable)
+        except Exception as e:
+            trace = "".join(traceback.TracebackException.from_exception(e).format())
+            error_msg = f"Adapter update failed - phase {cls._stage}: {e}\n{trace}"
+            logger.error(error_msg)
+
+        # energy management run finished
+        cls._stage = EnergyManagementStage.IDLE
+
     async def run(
         self,
         start_datetime: Optional[DateTime] = None,

src/akkudoktoreos/core/logging.py

@@ -65,7 +65,7 @@ console_handler_id = None
 file_handler_id = None
 
 
-def track_logging_config(config_eos: Any, path: str, old_value: Any, value: Any) -> None:
+def logging_track_config(config_eos: Any, path: str, old_value: Any, value: Any) -> None:
     """Track logging config changes."""
     global console_handler_id, file_handler_id
 

src/akkudoktoreos/core/pydantic.py

@@ -400,7 +400,21 @@ class PydanticModelNestedValueMixin:
 
             # Get next value
             next_value = None
-            if isinstance(model, BaseModel):
+            if isinstance(model, RootModel):
+                # If this is the final key, set the value
+                if is_final_key:
+                    try:
+                        model.validate_and_set(key, value)
+                    except Exception as e:
+                        raise ValueError(f"Error updating model: {e}") from e
+                    return
+
+                next_value = model.root
+
+            elif isinstance(model, BaseModel):
+                logger.debug(
+                    f"Detected base model {model.__class__.__name__} of type {type(model)}"
+                )
                 # Track parent and key for possible assignment later
                 parent = model
                 parent_key = [
@@ -432,6 +446,7 @@ class PydanticModelNestedValueMixin:
                     next_value = getattr(model, key, None)
 
             elif isinstance(model, list):
+                logger.debug(f"Detected list of type {type(model)}")
                 # Handle lists (ensure index exists and modify safely)
                 try:
                     idx = int(key)
@@ -468,6 +483,7 @@ class PydanticModelNestedValueMixin:
                     return
 
             elif isinstance(model, dict):
+                logger.debug(f"Detected dict of type {type(model)}")
                 # Handle dictionaries (auto-create missing keys)
 
                 # Get next type from parent key type information
@@ -795,29 +811,61 @@ class PydanticBaseModel(PydanticModelNestedValueMixin, BaseModel):
 
     @classmethod
     def field_description(cls, field_name: str) -> Optional[str]:
-        """Return the description metadata of a model field, if available.
+        """Return a human-readable description for a model field.
 
-        This method retrieves the `Field` specification from the model's
-        `model_fields` registry and extracts its description from the field's
-        `json_schema_extra` / `extra` metadata (as provided by
-        `_field_extra_dict`). If the field does not exist or no description is
-        present, ``None`` is returned.
+        Looks up descriptions for both regular and computed fields.
+        Resolution order:
+
+        Normal fields:
+            1) json_schema_extra["description"]
+            2) field.description
+
+        Computed fields:
+            1) ComputedFieldInfo.description
+            2) function docstring (func.__doc__)
+            3) json_schema_extra["description"]
+
+        If a field exists but no description is found, returns "-".
+        If the field does not exist, returns None.
 
         Args:
-            field_name (str):
-                Name of the field whose description should be returned.
+            field_name: Field name.
 
         Returns:
-            Optional[str]:
-                The textual description if present, otherwise ``None``.
+            Description string, "-" if missing, or None if not a field.
         """
-        field = cls.model_fields.get(field_name)
-        if not field:
+        # 1) Regular declared fields
+        field: FieldInfo | None = cls.model_fields.get(field_name)
+        if field is not None:
+            extra = cls._field_extra_dict(field)
+            if "description" in extra:
+                return str(extra["description"])
+            # some FieldInfo may also have .description directly
+            if getattr(field, "description", None):
+                return str(field.description)
+
             return None
-        extra = cls._field_extra_dict(field)
+
+        # 2) Computed fields live in a separate mapping
+        cfield: ComputedFieldInfo | None = cls.model_computed_fields.get(field_name)
+        if cfield is None:
+            return None
+
+        # 2a) ComputedFieldInfo may have a description attribute
+        if getattr(cfield, "description", None):
+            return str(cfield.description)
+
+        # 2b) fallback to wrapped property's docstring
+        func = getattr(cfield, "func", None)
+        if func and func.__doc__:
+            return func.__doc__.strip()
+
+        # 2c) last resort: json_schema_extra if you use it for computed fields
+        extra = cls._field_extra_dict(cfield)
         if "description" in extra:
             return str(extra["description"])
-        return None
+
+        return "-"
 
     @classmethod
     def field_deprecated(cls, field_name: str) -> Optional[str]:
@@ -887,7 +935,7 @@ class PydanticDateTimeData(RootModel):
 
             {
                 "start_datetime": "2024-01-01 00:00:00",  # optional
-                "interval": "1 Hour",                     # optional
+                "interval": "1 hour",                     # optional
                 "loadforecast_power_w": [20.5, 21.0, 22.1],
                 "load_min": [18.5, 19.0, 20.1]
             }

src/akkudoktoreos/core/version.py

@@ -6,13 +6,15 @@ from fnmatch import fnmatch
 from pathlib import Path
 from typing import Optional
 
-# For development add `+dev` to previous release
-# For release omit `+dev`.
-VERSION_BASE = "0.2.0+dev"
+# For development add `.dev` to previous release
+# For release omit `.dev`.
+VERSION_BASE = "0.2.0.dev"
 
 # Project hash of relevant files
 HASH_EOS = ""
 
+# Number of digits to append to .dev to identify a development version
+VERSION_DEV_PRECISION = 8
 
 # ------------------------------
 # Helpers for version generation
@@ -91,8 +93,11 @@ def _version_calculate() -> str:
     """Compute version."""
     global HASH_EOS
     HASH_EOS = _version_hash()
-    if VERSION_BASE.endswith("+dev"):
-        return f"{VERSION_BASE}.{HASH_EOS[:6]}"
+    if VERSION_BASE.endswith("dev"):
+        # After dev only digits are allowed - convert hexdigest to digits
+        hash_value = int(HASH_EOS, 16)
+        hash_digits = str(hash_value % (10**VERSION_DEV_PRECISION)).zfill(VERSION_DEV_PRECISION)
+        return f"{VERSION_BASE}{hash_digits}"
     else:
         return VERSION_BASE
 
@@ -114,10 +119,10 @@ __version__ = _version_calculate()
 VERSION_RE = re.compile(
     r"""
     ^(?P<base>\d+\.\d+\.\d+)            # x.y.z
-    (?:\+                               # +dev.hash starts here
+    (?:[\.\+\-]                         # .dev<hash> starts here
         (?:
             (?P<dev>dev)                # literal 'dev'
-            (?:\.(?P<hash>[A-Za-z0-9]+))?  # optional .hash
+            (?:(?P<hash>[A-Za-z0-9]+))?  # optional <hash>
         )
     )?
     $
@@ -131,8 +136,8 @@ def version() -> dict[str, Optional[str]]:
 
     The version string shall be of the form:
         x.y.z
-        x.y.z+dev
-        x.y.z+dev.HASH
+        x.y.z.dev
+        x.y.z.dev<HASH>
 
     Returns:
         .. code-block:: python