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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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99
src/akkudoktoreos/core/dataabc.py
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@@ -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,