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

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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
This commit is contained in:
Bobby Noelte
2025-12-30 22:08:21 +01:00
committed by GitHub
parent 02c794460f
commit 58d70e417b
111 changed files with 6815 additions and 1199 deletions
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80
src/akkudoktoreos/core/pydantic.py
View File

@@ -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]
}