mirror of
https://github.com/Akkudoktor-EOS/EOS.git
synced 2026-07-21 01:08:12 +00:00
fix: move data management to async (#1015)
FAstAPI is an async framework. Data may be imported and exported, load and save, set and get asynchronously. Prevent interleaving data operations to corrupt the data. In the previous design sync and async data access was intermixed leading to data corruption. The basic data classes DataSequence and DataContainer and the derived classes like Provider and Measurement now are async. Data access is protected by several async locks. To support the async design of the data classes the database interface became async. The energy management is also adapted to the new async design. Optimization is still off-loaded to another thread, but the prepration for the optimization and the post optimization actions now follow the async design. Adapter operations are now also protected by async locks. Tests were adapted to the async design and new tests were created. Besides this major fix several other improvements and fixes are included in this PR. * fix: key_to_dict/list/array only regard data records with key value set. Before the exclusion of no value data records was only done if the dropna flag was set. * fix: test for visual result pdf generation Due to updates in the library the generated charts text was a little bit different. Adapt the test to create the comaprison pdf in the test data durectory and update the reference pdf. * chore: Remove MutableMapping from DataSequence and DataContainer. Mutable Mapping does not fit to the now async design. * chore: Add NoDB database backend This backend implements the full database backend interface but performs no actual persistence. It is intended for configurations where database persistence is disabled (`provider=None`). * chore: Improve measurement data import testing with real world scenarios. Added two new endpoints to support testing. * chore: Add mermaid to supported documentation tools * chore: Add documentation about async design * chore: Add documentation about generic data handling Covers the basics of measurement and prediction time series data handling. * chore: Add empty lines around markdown lists. * chore: sync pre-commit config to updated package versions Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
This commit is contained in:
Bobby Noelte
GitHub
@@ -2,8 +2,7 @@ import traceback
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from asyncio import Lock, get_running_loop
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from concurrent.futures import ThreadPoolExecutor
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from enum import StrEnum
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from functools import partial
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from typing import ClassVar, Optional
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from typing import ClassVar, Optional, cast
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from loguru import logger
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from pydantic import computed_field
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@@ -147,11 +146,11 @@ class EnergyManagement(
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"""
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return cls._genetic_solution
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@classmethod
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def _run(
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cls,
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start_datetime: DateTime,
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mode: EnergyManagementMode,
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async def run(
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self,
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start_datetime: Optional[DateTime] = None,
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mode: Optional[EnergyManagementMode] = None,
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algorithm: Optional[str] = None,
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genetic_parameters: Optional[GeneticOptimizationParameters] = None,
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genetic_individuals: Optional[int] = None,
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genetic_seed: Optional[int] = None,
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@@ -161,7 +160,6 @@ class EnergyManagement(
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"""Run the energy management.
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This method initializes the energy management run by setting its
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start datetime, updating predictions, and optionally starting
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optimization depending on the selected mode or configuration.
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@@ -171,161 +169,20 @@ class EnergyManagement(
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- "OPTIMIZATION": Runs the optimization process.
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- "PREDICTION": Updates the forecast without optimization.
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- "DISABLED": Does not run.
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genetic_parameters (GeneticOptimizationParameters, optional): The
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parameter set for the genetic algorithm. If not provided, it will
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be constructed based on the current configuration and predictions.
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genetic_individuals (int, optional): The number of individuals for the
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genetic algorithm. Defaults to the algorithm's internal default (400)
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if not specified.
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genetic_seed (int, optional): The seed for the genetic algorithm. Defaults
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to the algorithm's internal random seed if not specified.
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force_enable (bool, optional): If True, bypasses any disabled state
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to force the update process. This is mostly applicable to
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prediction providers.
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force_update (bool, optional): If True, forces data to be refreshed
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even if a cached version is still valid.
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Returns:
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None
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"""
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# Ensure there is only one optimization/ energy management run at a time
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if not mode in EnergyManagementMode._value2member_map_:
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raise ValueError(f"Unknown energy management mode {mode}.")
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if mode == EnergyManagementMode.DISABLED:
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return
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logger.info("Starting energy management run.")
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cls._stage = EnergyManagementStage.DATA_ACQUISITION
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# Remember/ set the start datetime of this energy management run.
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# None leads
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cls.set_start_datetime(start_datetime)
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# Throw away any memory cached results of the last energy management run.
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CacheEnergyManagementStore().clear()
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# Do data aquisition by adapters
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try:
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cls.adapter.update_data(force_enable)
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except Exception as e:
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trace = "".join(traceback.TracebackException.from_exception(e).format())
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error_msg = f"Adapter update failed - phase {cls._stage}:\n{e}\n{trace}"
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logger.error(error_msg)
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cls._stage = EnergyManagementStage.FORECAST_RETRIEVAL
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if mode == EnergyManagementMode.PREDICTION:
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# Update the predictions
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cls.prediction.update_data(force_enable=force_enable, force_update=force_update)
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logger.info("Energy management run done (predictions updated)")
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cls._stage = EnergyManagementStage.IDLE
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return
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# Prepare optimization parameters
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# This also creates default configurations for missing values and updates the predictions
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logger.info(
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"Starting energy management prediction update and optimzation parameter preparation."
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)
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if genetic_parameters is None:
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genetic_parameters = GeneticOptimizationParameters.prepare()
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if not genetic_parameters:
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logger.error(
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"Energy management run canceled. Could not prepare optimisation parameters."
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)
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cls._stage = EnergyManagementStage.IDLE
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return
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cls._stage = EnergyManagementStage.OPTIMIZATION
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logger.info("Starting energy management optimization.")
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# Take values from config if not given
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if genetic_individuals is None:
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genetic_individuals = cls.config.optimization.genetic.individuals
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if genetic_seed is None:
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genetic_seed = cls.config.optimization.genetic.seed
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if cls._start_datetime is None: # Make mypy happy - already set by us
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raise RuntimeError("Start datetime not set.")
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try:
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optimization = GeneticOptimization(
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verbose=bool(cls.config.server.verbose),
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fixed_seed=genetic_seed,
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)
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solution = optimization.optimierung_ems(
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start_hour=cls._start_datetime.hour,
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parameters=genetic_parameters,
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ngen=genetic_individuals,
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)
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except:
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logger.exception("Energy management optimization failed.")
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cls._stage = EnergyManagementStage.IDLE
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return
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cls._stage = EnergyManagementStage.CONTROL_DISPATCH
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# Make genetic solution public
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cls._genetic_solution = solution
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# Make optimization solution public
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cls._optimization_solution = solution.optimization_solution()
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# Make plan public
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cls._plan = solution.energy_management_plan()
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logger.debug("Energy management genetic solution:\n{}", cls._genetic_solution)
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logger.debug("Energy management optimization solution:\n{}", cls._optimization_solution)
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logger.debug("Energy management plan:\n{}", cls._plan)
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logger.info("Energy management run done (optimization updated)")
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# Do control dispatch by adapters
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try:
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cls.adapter.update_data(force_enable)
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except Exception as e:
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trace = "".join(traceback.TracebackException.from_exception(e).format())
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error_msg = f"Adapter update failed - phase {cls._stage}:\n{e}\n{trace}"
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logger.error(error_msg)
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# Remember energy run datetime.
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EnergyManagement._last_run_datetime = to_datetime()
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# energy management run finished
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cls._stage = EnergyManagementStage.IDLE
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async def run(
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self,
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start_datetime: Optional[DateTime] = None,
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mode: Optional[EnergyManagementMode] = None,
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genetic_parameters: Optional[GeneticOptimizationParameters] = None,
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genetic_individuals: Optional[int] = None,
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genetic_seed: Optional[int] = None,
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force_enable: Optional[bool] = False,
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force_update: Optional[bool] = False,
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) -> None:
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"""Run the energy management.
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This method initializes the energy management run by setting its
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start datetime, updating predictions, and optionally starting
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optimization depending on the selected mode or configuration.
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Args:
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start_datetime (DateTime, optional): The starting timestamp
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of the energy management run. Defaults to the current datetime
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if not provided.
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mode (EnergyManagementMode, optional): The management mode to use. Must be one of:
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- "OPTIMIZATION": Runs the optimization process.
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- "PREDICTION": Updates the forecast without optimization.
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Defaults to the mode defined in the current configuration.
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algorithm (str, optional):
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The algorithm to use. Must be one of:
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- "GENETIC": Optimization uses the `GENETIC` optimization algorithm.
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Defaults to the algorithm defined in the current configuration.
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genetic_parameters (GeneticOptimizationParameters, optional): The
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parameter set for the genetic algorithm. If not provided, it will
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parameter set for the `GENETIC` algorithm. If not provided, it will
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be constructed based on the current configuration and predictions.
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genetic_individuals (int, optional): The number of individuals for the
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genetic algorithm. Defaults to the algorithm's internal default (400)
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`GENETIC` algorithm. Defaults to the algorithm's internal default (400)
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if not specified.
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genetic_seed (int, optional): The seed for the genetic algorithm. Defaults
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genetic_seed (int, optional): The seed for the `GENETIC` algorithm. Defaults
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to the algorithm's internal random seed if not specified.
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force_enable (bool, optional): If True, bypasses any disabled state
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to force the update process. This is mostly applicable to
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@@ -336,22 +193,205 @@ class EnergyManagement(
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Returns:
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None
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"""
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async with self._run_lock:
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loop = get_running_loop()
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# Create a partial function with parameters "baked in"
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if start_datetime is None:
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start_datetime = to_datetime()
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async with EnergyManagement._run_lock:
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if mode is None:
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mode = self.config.ems.mode
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func = partial(
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EnergyManagement._run,
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start_datetime=start_datetime,
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mode=mode,
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genetic_parameters=genetic_parameters,
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genetic_individuals=genetic_individuals,
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genetic_seed=genetic_seed,
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force_enable=force_enable,
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force_update=force_update,
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if mode not in EnergyManagementMode._value2member_map_:
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raise ValueError(f"Unknown energy management mode {mode}.")
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if mode == EnergyManagementMode.DISABLED:
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logger.info("Energy management run disabled.")
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return
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logger.info("Starting energy management run.")
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# --- Data Aquisition ---
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EnergyManagement._stage = EnergyManagementStage.DATA_ACQUISITION
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# Remember/ set the start datetime of this energy management run.
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# None leads to current time as start datetime
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self.set_start_datetime(start_datetime)
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# Throw away any memory cached results of the last energy management run.
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CacheEnergyManagementStore().clear()
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# --- Adapter update ---
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try:
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await self.adapter.update_data(force_enable)
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except Exception as e:
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trace = "".join(traceback.TracebackException.from_exception(e).format())
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error_msg = (
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f"Adapter update failed - phase {EnergyManagement._stage}:\n{e}\n{trace}"
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)
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logger.error(error_msg)
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# --- Prediction ---
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EnergyManagement._stage = EnergyManagementStage.FORECAST_RETRIEVAL
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# Update the predictions
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logger.info("Starting energy management prediction update.")
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await self.prediction.update_data(force_enable=force_enable, force_update=force_update)
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if mode == EnergyManagementMode.PREDICTION:
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logger.info("Energy management run done (predictions updated)")
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EnergyManagement._stage = EnergyManagementStage.IDLE
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return
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# --- Optimization ---
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EnergyManagement._stage = EnergyManagementStage.OPTIMIZATION
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optimization_start = to_datetime()
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logger.info("Starting energy management optimization.")
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if algorithm is None:
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algorithm = self.config.optimization.algorithm
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if algorithm == "GENETIC":
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# Prepare optimization parameters
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# This also creates default configurations for missing values and updates the predictions
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logger.info("Starting optimzation parameter preparation.")
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if genetic_parameters is None:
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genetic_parameters = await GeneticOptimizationParameters.prepare()
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if genetic_parameters is None:
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logger.error(
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"Energy management run canceled. Could not prepare optimisation parameters."
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)
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EnergyManagement._stage = EnergyManagementStage.IDLE
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return
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# Take values from config if not given
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if genetic_individuals is None:
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genetic_individuals = self.config.optimization.genetic.individuals
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if genetic_seed is None:
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genetic_seed = self.config.optimization.genetic.seed
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if EnergyManagement._start_datetime is None: # Make mypy happy - already set by us
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raise RuntimeError("Start datetime not set.")
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# --- Optimization (CPU-bound → MUST offload) ---
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try:
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optimization = GeneticOptimization(
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verbose=bool(self.config.server.verbose),
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fixed_seed=genetic_seed,
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)
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loop = get_running_loop()
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start_hour = EnergyManagement._start_datetime.hour
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solution = await loop.run_in_executor(
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None,
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lambda: optimization.optimierung_ems(
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start_hour=start_hour,
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parameters=cast(
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GeneticOptimizationParameters, genetic_parameters
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), # cast for mypy
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ngen=genetic_individuals,
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),
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)
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except Exception:
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logger.exception("Energy management optimization failed.")
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EnergyManagement._stage = EnergyManagementStage.IDLE
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return
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else:
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logger.error(f"Unknown optimization algorithm: '{algorithm}'. Skipping.")
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EnergyManagement._stage = EnergyManagementStage.IDLE
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return
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optimization_duration = to_datetime() - optimization_start
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logger.info(
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"Energy management optimization ({}) completed in {:.1f} seconds.",
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algorithm,
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optimization_duration.total_seconds(),
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)
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# Run optimization in background thread to avoid blocking event loop
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await loop.run_in_executor(executor, func)
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logger.debug(
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"Energy management optimization solution:\n{}",
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EnergyManagement._optimization_solution,
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)
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logger.debug("Energy management plan:\n{}", EnergyManagement._plan)
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# --- Control dispatch by adapters ---
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EnergyManagement._stage = EnergyManagementStage.CONTROL_DISPATCH
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# Make genetic solution public
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EnergyManagement._genetic_solution = solution
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# Make optimization solution public
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EnergyManagement._optimization_solution = await solution.optimization_solution()
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# Make plan public
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EnergyManagement._plan = solution.energy_management_plan()
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logger.debug(
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"Energy management genetic solution:\n{}", EnergyManagement._genetic_solution
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)
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if genetic_parameters is None:
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genetic_parameters = await GeneticOptimizationParameters.prepare()
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if not genetic_parameters:
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logger.error("Energy management run canceled. Could not prepare parameters.")
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EnergyManagement._stage = EnergyManagementStage.IDLE
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return
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EnergyManagement._stage = EnergyManagementStage.OPTIMIZATION
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if genetic_individuals is None:
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genetic_individuals = self.config.optimization.genetic.individuals
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if genetic_seed is None:
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genetic_seed = self.config.optimization.genetic.seed
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if EnergyManagement._start_datetime is None:
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raise RuntimeError("Start datetime not set.")
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# --- Optimization (CPU-bound → MUST offload) ---
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try:
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optimization = GeneticOptimization(
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verbose=bool(self.config.server.verbose),
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fixed_seed=genetic_seed,
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)
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loop = get_running_loop()
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start_hour = EnergyManagement._start_datetime.hour
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solution = await loop.run_in_executor(
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None,
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lambda: optimization.optimierung_ems(
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start_hour=start_hour,
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parameters=genetic_parameters,
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ngen=genetic_individuals,
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),
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)
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except Exception:
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logger.exception("Energy management optimization failed.")
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EnergyManagement._stage = EnergyManagementStage.IDLE
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return
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EnergyManagement._genetic_solution = solution
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EnergyManagement._optimization_solution = await solution.optimization_solution()
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EnergyManagement._plan = solution.energy_management_plan()
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logger.debug("Genetic solution:\n{}", EnergyManagement._genetic_solution)
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logger.debug("Optimization solution:\n{}", EnergyManagement._optimization_solution)
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logger.debug("Plan:\n{}", EnergyManagement._plan)
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logger.info("Energy management run done (optimization updated)")
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# --- Dispatch control by adapters ---
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EnergyManagement._stage = EnergyManagementStage.CONTROL_DISPATCH
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# Dispatch (sync → optionally offload)
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try:
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await self.adapter.update_data(force_enable)
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except Exception as e:
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trace = "".join(traceback.TracebackException.from_exception(e).format())
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error_msg = (
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f"Adapter update failed - phase {EnergyManagement._stage}:\n{e}\n{trace}"
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)
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logger.error(error_msg)
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# --- Idle ---
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# Remember energy run datetime.
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EnergyManagement._last_run_datetime = to_datetime()
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# energy management run finished
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EnergyManagement._stage = EnergyManagementStage.IDLE
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