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* Fix logging configuration issues that made logging stop operation. Switch to Loguru logging (from Python logging). Enable console and file logging with different log levels. Add logging documentation. * Fix logging configuration and EOS configuration out of sync. Added tracking support for nested value updates of Pydantic models. This used to update the logging configuration when the EOS configurationm for logging is changed. Should keep logging config and EOS config in sync as long as all changes to the EOS logging configuration are done by set_nested_value(), which is the case for the REST API. * Fix energy management task looping endlessly after the second update when trying to update the last_update datetime. * Fix get_nested_value() to correctly take values from the dicts in a Pydantic model instance. * Fix usage of model classes instead of model instances in nested value access when evaluation the value type that is associated to each key. * Fix illegal json format in prediction documentation for PVForecastAkkudoktor provider. * Fix documentation qirks and add EOS Connect to integrations. * Support deprecated fields in configuration in documentation generation and EOSdash. * Enhance EOSdash demo to show BrightSky humidity data (that is often missing) * Update documentation reference to German EOS installation videos. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
59 lines
2.3 KiB
Python
59 lines
2.3 KiB
Python
"""Abstract and base classes for load predictions.
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Notes:
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- Ensure appropriate API keys or configurations are set up if required by external data sources.
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"""
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from abc import abstractmethod
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from typing import List, Optional
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from pydantic import Field
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from akkudoktoreos.prediction.predictionabc import PredictionProvider, PredictionRecord
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class LoadDataRecord(PredictionRecord):
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"""Represents a load data record containing various load attributes at a specific datetime."""
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load_mean: Optional[float] = Field(default=None, description="Predicted load mean value (W).")
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load_std: Optional[float] = Field(
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default=None, description="Predicted load standard deviation (W)."
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)
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load_mean_adjusted: Optional[float] = Field(
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default=None, description="Predicted load mean value adjusted by load measurement (W)."
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)
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class LoadProvider(PredictionProvider):
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"""Abstract base class for load providers.
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LoadProvider is a thread-safe singleton, ensuring only one instance of this class is created.
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Configuration variables:
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provider (str): Prediction provider for load.
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Attributes:
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hours (int, optional): The number of hours into the future for which predictions are generated.
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historic_hours (int, optional): The number of past hours for which historical data is retained.
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latitude (float, optional): The latitude in degrees, must be within -90 to 90.
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longitude (float, optional): The longitude in degrees, must be within -180 to 180.
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start_datetime (datetime, optional): The starting datetime for predictions, defaults to the current datetime if unspecified.
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end_datetime (datetime, computed): The datetime representing the end of the prediction range,
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calculated based on `start_datetime` and `hours`.
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keep_datetime (datetime, computed): The earliest datetime for retaining historical data, calculated
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based on `start_datetime` and `historic_hours`.
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"""
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# overload
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records: List[LoadDataRecord] = Field(
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default_factory=list, description="List of LoadDataRecord records"
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)
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@classmethod
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@abstractmethod
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def provider_id(cls) -> str:
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return "LoadProvider"
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def enabled(self) -> bool:
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return self.provider_id() == self.config.load.provider
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