8 Commits

Include renames

Author	SHA1	Message	Date
5 people	70b72bfbce	feat(ems): notify a URL after every completed optimization (#1383) ... * feat(devices): add load-dependent DC-to-AC efficiency curve for GENETIC inverters Real inverters are much less efficient at low load than at rated power (e.g. 0.80 at 2 % vs. 0.93 at 7 % load). A constant dc_to_ac_efficiency overrates night-time battery discharge into a small base load and, with it, arbitrage and grid charging. Add two optional inverter settings for the GENETIC optimizer: - dc_to_ac_efficiency_curve: list of (load_fraction, efficiency) points, load fraction = AC power / max_power_w. Points must be strictly increasing in [0, 1] with efficiencies in (0, 1]; at least two points. Linear interpolation, clamped at both ends. When set, the curve replaces dc_to_ac_efficiency. - dc_to_ac_efficiency_reference_load_fraction (default 0.06): load at which the curve values stored energy without a specific conversion. Battery discharges in Inverter.process_energy() use the efficiency at the AC energy of each conversion. The end-of-horizon value, the terminal and tail value curves and the AC charge break-even penalty use Inverter.reference_dc_to_ac_efficiency. Without a curve, dc_to_ac_efficiency_at() and the reference efficiency return dc_to_ac_efficiency itself, so results are bit-identical to the previous model. GENETIC0 is unchanged. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * feat(genetic): make the fitness cache size configurable The per-run fitness cache of the GENETIC optimizer is unbounded. It grows with individuals x generations (~110k keys at 300 x 400, about 1.1 kB each, 1.6 kB with an EV) and adds 120-170 MB to the peak memory of a run, while only 6-15 % of the lookups hit. On 512 MB boards this is the largest part of the optimizer's memory. Add optimization.genetic.fitness_cache_max_entries: - None (default): unbounded, the previous behaviour. - 0: no cache; every genome is evaluated. - N > 0: at most N keys. The cache is then an OrderedDict and evicts the oldest key first (FIFO, O(1)); LRU would cost bookkeeping on every hit for a cache that rarely hits. A cache hit returns exactly what the deterministic evaluation would return, so the plan for a fixed seed is identical with any setting. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * perf: import heavy optional dependencies on first use A GENETIC server with external (import) providers and EOSdash disabled loads several large packages at start that it never uses, which costs a noticeable part of the memory on 512 MB boards: - matplotlib via the GENETIC/GENETIC0 PDF report modules (server/eos.py) - statsmodels via the Holt-Winters fallback in prediction/priceabc.py - pvlib via the weather base class and the pvlib-based providers - bs4/lxml via the ClearOutside provider - scipy via the self-consumption interpolator singletons, which were loaded at import time - the GENETIC0 optimizer, imported by core/ems.py - tzfpy, whose polygon data the computed general.timezone loads Import these where they are used. The interpolator tables are loaded on first use (thread-safe). Nothing changes in what is computed. general.timezone is computed from latitude/longitude and cannot be set. Add general.timezone_override (IANA name, validated with pendulum): when set, general.timezone returns it and the lookup is skipped. When unset (default) the lookup is unchanged. Tests that patched the moved names now patch them at their source. A new test starts the server module in a subprocess and checks that none of these modules is loaded. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * perf(prediction): evaluate self-consumption tables without SciPy The GENETIC inverter (and the GENETIC0 one) evaluates a pickled scipy.interpolate.RegularGridInterpolator for the self-consumption probabilities. SciPy is used nowhere else in EOS, yet loading it costs about 35 MB RSS (21 MB anonymous) on Linux arm64, which every optimization run pays. Add utils/gridinterpolator.py: - LinearGridInterpolator: 2-D linear interpolation with the same steps as SciPy's linear 2-D path (interval search, normalised distances, corner weights summed in the same order, fill value outside the grid, NaN for NaN input). - load_grid_interpolator(): reads the shipped pickles with a restricted unpickler that maps the SciPy class to a stand-in and accepts only the NumPy globals needed for the arrays. SciPy is not imported, and the data files no longer depend on SciPy's private module paths. Results are bit-identical to SciPy on Linux (aarch64, checked on 3.6M points per table including the query pattern of the inverter). The macOS arm64 SciPy wheel fuses multiply-add, so there SciPy itself differs by <= 1.1e-16; GENETIC plans for fixed seeds are unchanged on both platforms. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * feat(prediction): make the SciPy-free table evaluation opt-in Add optimization.self_consumption_interpolator (scipy | numpy, default scipy). The default keeps evaluating the self-consumption tables with SciPy's RegularGridInterpolator, built from the same grid, values and fill value the pickle holds, so results are unchanged. With numpy the tables are evaluated by LinearGridInterpolator and SciPy is never imported. The backend is read on every evaluation, so a config change applies to the next run. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * feat(genetic): evaluate candidates in parallel worker processes A GENETIC run evaluated every candidate in the server process, so a run used one CPU core however many the machine had. On small ARM boards (4x Cortex-A55, thermally limited) a 300x200 run took 12-15 minutes. Candidates are now evaluated in up to two worker processes, forked once per run after the run is fully prepared. Each worker owns a copy of the prepared optimizer; only genomes and results cross the process boundary. The fitness cache stays in the server process: hits are answered there, and identical genomes in a batch are evaluated once, as a serial run would hit the cache for the repeats. Results are applied only after the whole batch returned. If a batch fails or times out, the run continues serially. For a fixed seed the result is identical to a serial run. optimization.genetic.workers sets the number of processes. Unset (default) is automatic: one CPU core always stays free and at most two are used (1-2 cores -> 1, 3 or more -> 2), capped by a cgroup v2 CPU limit of the process or any parent (container limit, systemd CPUQuota). Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01EYWR1HVM8a8src23xuyL3T * feat(ems): notify a URL after every completed optimization A client that consumes EOS plans (here DVhub) had to poll /v1/energy-management/optimization/solution to notice a new plan. That delays the plan by up to the poll interval and makes EOS serialize the complete solution on every poll, also while it is computing. ems.notify_url (default None = off) receives an HTTP POST with a small JSON event ({"event": "optimization_completed", "algorithm", "completed_at", "duration_s"}) when a run has published its solution. The POST runs in a daemon thread with a 5 s timeout, so a slow or unreachable receiver never delays energy management; a failure is logged once per failure streak. Only http(s) URLs are accepted. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01EYWR1HVM8a8src23xuyL3T * fix(genetic): freeze the optimization configuration for the duration of a run The genetic optimization reads horizon_hours, tail_horizon_hours, interval_sec and prediction.hours from the live configuration on every access (control_slots, tail_slots, ... are properties). A client that updates the configuration while a run is in progress - DVhub shrinks or grows the control horizon as the known price horizon moves during the night - changed these values mid-run. Genomes and device arrays sized at the start then no longer matched the simulation loop and the final evaluation failed: IndexError: index 84 is out of bounds for axis 0 with size 84 Long runs on slow hardware hit this almost every night. optimize_ems() now takes a deep copy of the genetic configuration and the prediction hours at the start of a run, all reads in genetic.py go through it, and it is released when the run ends (also on errors). New values apply to the next run. Tests: tests/test_genetic_config_frozen_per_run.py reproduces the error without the fix (horizon 20 -> 24 mid-run: index 20 out of bounds) and passes with it; genetic/optimization tests 439 passed. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * feat(genetic): pin evaluation workers, keep the server responsive Workers are pinned to the last `workers` CPU cores (new setting optimization.genetic.pin_workers, default on); the server process stays unpinned. The run never spreads beyond `workers` cores without a CPU quota, and the server and the rest of the system keep the other cores. No pinning without a spare core. With a CPU limit (container cpu.max, systemd CPUQuota) the automatic worker count now keeps a quarter core of it for the server process (limit 2 -> 1 worker, 2.5 -> 2): two workers used a 2-core limit fully. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01EYWR1HVM8a8src23xuyL3T * fix(ems): tolerate settings without notify_url Test doubles and older configuration objects have no ems.notify_url. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01EYWR1HVM8a8src23xuyL3T * fix(devices): evaluate the efficiency curve at the delivered AC load When the battery limits a discharge (low state of charge or its power limit), the conversion ran with the efficiency of the requested AC energy. With a 1000 Wh battery at 10 % SoC, a 1000 W inverter and the curve [(0, .8), (.5, .95), (1, .95)], a request of 500 Wh AC returned 95 Wh AC from the available 100 Wh DC, although the curve gives 82.5 % at that load. The delivered AC energy now solves a = dc * efficiency(a / rated) exactly on the piecewise-linear curve (solve_ac_for_dc_energy), bounded by the request. Unlimited discharges and inverters without a curve are unchanged. Regression tests use a real Battery: low SoC and a power-limited discharge, each with 60- and 15-minute slots, battery discharge losses, the load path through process_energy, and constant efficiency. * docs: regenerate OpenAPI for the efficiency-curve fix * docs: regenerate OpenAPI after merging the efficiency-curve fix * docs: regenerate OpenAPI after merging the efficiency-curve fix * docs: regenerate OpenAPI after merging the efficiency-curve fix * test(genetic): cover serial fallback on platforms without fork --------- Co-authored-by: Christin <christin@BigBrother.bikini.bottom.zone> Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com> Co-authored-by: Andreas <drbacke@gmx.de>	2026-10-11 10:48:04 +02:00
Bobby NoelteandGitHub	894790f577	feat: add pvlib pv forecast provider (#1214) ... Add a PV forecast provider that calculates the forecast using a PVLib system model and weather forecast from the EOS weather forecast provider. Additional module and inverter models can be easily added as the database is build from PVLib and SAM databases and a bundled csv file. The module model and inververt model names are provided by new endpoints to be used in configuration. The provider is based on the fantastic work of EMHASS. See https://github.com/davidusb-geek/emhass/blob/master/src/emhass/forecast.py A short description of the provider is added to the documentation. Besides the new features there are the fixes and improvements: * feat: improve EOSdash config page * fix: kex_to_series for start_datetime Make key_to_series always start the series at start_datetime. * fix: default provider for GENETIC and GENETIC0 optimization To make the default less dependent on internet servers (with API changes and availability issues) the default for PVForecast is set to PVForecastPVLib and for ElecPrice to ElecPriceFixed. The default weather provider is changed to OpenMeteo. * fix: EOSdash display resampled prediction values Make EOSdash display resampled prediction values where resampling fits to the prediction value type. Use bar width that fits to 15 minutes value samples. * chore: add a UI hints system to EOSdash The UI hints system eases the definition of forms for configuration items. There are also forms for items in maps and lists. These forms allow to add and delete items to/ from maps and lists. The forms ensure that all required fields of newly added items are filled. * chore: Create an enum for valid optimization algorithms * chore. Make config also provide the available energy management modes. Used for configuration hints. * chore: Randomize default device id in configuration Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>	2026-08-07 13:13:17 +02:00
Bobby NoelteandGitHub	71e5abce88	fix: energy charts bidding zone in request (#948) ... Ensure that the bidding zone in the request is correctly set to a string value (not an enum). This seems to be also an issue with python version < 3.11. Add safeguards to only use python >= 3.11. Still keep a regression test for the enum conversion to string. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>	2026-03-15 13:32:05 +01:00
Bobby NoelteandGitHub	b67148c47f	feat: make home assistant add-on run optimization by default (#940) ... When running as Home Assistant add-on the only viable usage is running with cyclic optimization. Make this the default to als propvide a better experience for first time users. The optimization will start with demo data, which also helps to configure Akkudoktor-EOS to the personal usage. The disabling of the automatic energy management is now an explicit mode "DISABLED". Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>	2026-03-13 15:48:43 +01:00
Bobby NoelteandGitHub	6498c7dc32	Add database support for measurements and historic prediction data. (#848) ... The database supports backend selection, compression, incremental data load, automatic data saving to storage, automatic vaccum and compaction. Make SQLite3 and LMDB database backends available. Update tests for new interface conventions regarding data sequences, data containers, data providers. This includes the measurements provider and the prediction providers. Add database documentation. The fix includes several bug fixes that are not directly related to the database implementation but are necessary to keep EOS running properly and to test and document the changes. * fix: config eos test setup Make the config_eos fixture generate a new instance of the config_eos singleton. Use correct env names to setup data folder path. * fix: startup with no config Make cache and measurements complain about missing data path configuration but do not bail out. * fix: soc data preparation and usage for genetic optimization. Search for soc measurments 48 hours around the optimization start time. Only clamp soc to maximum in battery device simulation. * fix: dashboard bailout on zero value solution display Do not use zero values to calculate the chart values adjustment for display. * fix: openapi generation script Make the script also replace data_folder_path and data_output_path to hide real (test) environment pathes. * feat: add make repeated task function make_repeated_task allows to wrap a function to be repeated cyclically. * chore: removed index based data sequence access Index based data sequence access does not make sense as the sequence can be backed by the database. The sequence is now purely time series data. * chore: refactor eos startup to avoid module import startup Avoid module import initialisation expecially of the EOS configuration. Config mutation, singleton initialization, logging setup, argparse parsing, background task definitions depending on config and environment-dependent behavior is now done at function startup. * chore: introduce retention manager A single long-running background task that owns the scheduling of all periodic server-maintenance jobs (cache cleanup, DB autosave, …) * chore: canonicalize timezone name for UTC Timezone names that are semantically identical to UTC are canonicalized to UTC. * chore: extend config file migration for default value handling Extend the config file migration handling values None or nonexisting values that will invoke a default value generation in the new config file. Also adapt test to handle this situation. * chore: extend datetime util test cases * chore: make version test check for untracked files Check for files that are not tracked by git. Version calculation will be wrong if these files will not be commited. * chore: bump pandas to 3.0.0 Pandas 3.0 now performs inference on the appropriate resolution (a.k.a. unit) for the output dtype which may become datetime64[us] (before it was ns). Also numeric dtype detection is now more strict which needs a different detection for numerics. * chore: bump pydantic-settings to 2.12.0 pydantic-settings 2.12.0 under pytest creates a different behaviour. The tests were adapted and a workaround was introduced. Also ConfigEOS was adapted to allow for fine grain initialization control to be able to switch off certain settings such as file settings during test. * chore: remove sci learn kit from dependencies The sci learn kit is not strictly necessary as long as we have scipy. * chore: add documentation mode guarding for sphinx autosummary Sphinx autosummary excecutes functions. Prevent exceptions in case of pure doc mode. * chore: adapt docker-build CI workflow to stricter GitHub handling Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>	2026-02-22 14:12:42 +01:00
Bobby NoelteandGitHub	e7b43782a4	fix: pydantic extra keywords deprecated (#753) ... Pydantic deprecates using extra keyword arguments on Field. Used json_schema_extra instead. Deprecated in Pydantic V2.0 to be removed in V3.0. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>	2025-11-10 16:57:44 +01:00
Bobby NoelteandGitHub	b397b5d43e	fix: automatic optimization (#596) ... This fix implements the long term goal to have the EOS server run optimization (or energy management) on regular intervals automatically. Thus clients can request the current energy management plan at any time and it is updated on regular intervals without interaction by the client. This fix started out to "only" make automatic optimization (or energy management) runs working. It turned out there are several endpoints that in some way update predictions or run the optimization. To lock against such concurrent attempts the code had to be refactored to allow control of execution. During refactoring it became clear that some classes and files are named without a proper reference to their usage. Thus not only refactoring but also renaming became necessary. The names are still not the best, but I hope they are more intuitive. The fix includes several bug fixes that are not directly related to the automatic optimization but are necessary to keep EOS running properly to do the automatic optimization and to test and document the changes. This is a breaking change as the configuration structure changed once again and the server API was also enhanced and streamlined. The server API that is used by Andreas and Jörg in their videos has not changed. * fix: automatic optimization Allow optimization to automatically run on configured intervals gathering all optimization parameters from configuration and predictions. The automatic run can be configured to only run prediction updates skipping the optimization. Extend documentaion to also cover automatic optimization. Lock automatic runs against runs initiated by the /optimize or other endpoints. Provide new endpoints to retrieve the energy management plan and the genetic solution of the latest automatic optimization run. Offload energy management to thread pool executor to keep the app more responsive during the CPU heavy optimization run. * fix: EOS servers recognize environment variables on startup Force initialisation of EOS configuration on server startup to assure all sources of EOS configuration are properly set up and read. Adapt server tests and configuration tests to also test for environment variable configuration. * fix: Remove 0.0.0.0 to localhost translation under Windows EOS imposed a 0.0.0.0 to localhost translation under Windows for convenience. This caused some trouble in user configurations. Now, as the default IP address configuration is 127.0.0.1, the user is responsible for to set up the correct Windows compliant IP address. * fix: allow names for hosts additional to IP addresses * fix: access pydantic model fields by class Access by instance is deprecated. * fix: down sampling key_to_array * fix: make cache clear endpoint clear all cache files Make /v1/admin/cache/clear clear all cache files. Before it only cleared expired cache files by default. Add new endpoint /v1/admin/clear-expired to only clear expired cache files. * fix: timezonefinder returns Europe/Paris instead of Europe/Berlin timezonefinder 8.10 got more inaccurate for timezones in europe as there is a common timezone. Use new package tzfpy instead which is still returning Europe/Berlin if you are in Germany. tzfpy also claims to be faster than timezonefinder. * fix: provider settings configuration Provider configuration used to be a union holding the settings for several providers. Pydantic union handling does not always find the correct type for a provider setting. This led to exceptions in specific configurations. Now provider settings are explicit comfiguration items for each possible provider. This is a breaking change as the configuration structure was changed. * fix: ClearOutside weather prediction irradiance calculation Pvlib needs a pandas time index. Convert time index. * fix: test config file priority Do not use config_eos fixture as this fixture already creates a config file. * fix: optimization sample request documentation Provide all data in documentation of optimization sample request. * fix: gitlint blocking pip dependency resolution Replace gitlint by commitizen. Gitlint is not actively maintained anymore. Gitlint dependencies blocked pip from dependency resolution. * fix: sync pre-commit config to actual dependency requirements .pre-commit-config.yaml was out of sync, also requirements-dev.txt. * fix: missing babel in requirements.txt Add babel to requirements.txt * feat: setup default device configuration for automatic optimization In case the parameters for automatic optimization are not fully defined a default configuration is setup to allow the automatic energy management run. The default configuration may help the user to correctly define the device configuration. * feat: allow configuration of genetic algorithm parameters The genetic algorithm parameters for number of individuals, number of generations, the seed and penalty function parameters are now avaliable as configuration options. * feat: allow configuration of home appliance time windows The time windows a home appliance is allowed to run are now configurable by the configuration (for /v1 API) and also by the home appliance parameters (for the classic /optimize API). If there is no such configuration the time window defaults to optimization hours, which was the standard before the change. Documentation on how to configure time windows is added. * feat: standardize mesaurement keys for battery/ ev SoC measurements The standardized measurement keys to report battery SoC to the device simulations can now be retrieved from the device configuration as a read-only config option. * feat: feed in tariff prediction Add feed in tarif predictions needed for automatic optimization. The feed in tariff can be retrieved as fixed feed in tarif or can be imported. Also add tests for the different feed in tariff providers. Extend documentation to cover the feed in tariff providers. * feat: add energy management plan based on S2 standard instructions EOS can generate an energy management plan as a list of simple instructions. May be retrieved by the /v1/energy-management/plan endpoint. The instructions loosely follow the S2 energy management standard. * feat: make measurement keys configurable by EOS configuration. The fixed measurement keys are replaced by configurable measurement keys. * feat: make pendulum DateTime, Date, Duration types usable for pydantic models Use pydantic_extra_types.pendulum_dt to get pydantic pendulum types. Types are added to the datetimeutil utility. Remove custom made pendulum adaptations from EOS pydantic module. Make EOS modules use the pydantic pendulum types managed by the datetimeutil module instead of the core pendulum types. * feat: Add Time, TimeWindow, TimeWindowSequence and to_time to datetimeutil. The time windows are are added to support home appliance time window configuration. All time classes are also pydantic models. Time is the base class for time definition derived from pendulum.Time. * feat: Extend DataRecord by configurable field like data. Configurable field like data was added to support the configuration of measurement records. * feat: Add additional information to health information Version information is added to the health endpoints of eos and eosDash. The start time of the last optimization and the latest run time of the energy management is added to the EOS health information. * feat: add pydantic merge model tests * feat: add plan tab to EOSdash The plan tab displays the current energy management instructions. * feat: add predictions tab to EOSdash The predictions tab displays the current predictions. * feat: add cache management to EOSdash admin tab The admin tab is extended by a section for cache management. It allows to clear the cache. * feat: add about tab to EOSdash The about tab resembles the former hello tab and provides extra information. * feat: Adapt changelog and prepare for release management Release management using commitizen is added. The changelog file is adapted and teh changelog and a description for release management is added in the documentation. * feat(doc): Improve install and devlopment documentation Provide a more concise installation description in Readme.md and add extra installation page and development page to documentation. * chore: Use memory cache for interpolation instead of dict in inverter Decorate calculate_self_consumption() with @cachemethod_until_update to cache results in memory during an energy management/ optimization run. Replacement of dict type caching in inverter is now possible because all optimization runs are properly locked and the memory cache CacheUntilUpdateStore is properly cleared at the start of any energy management/ optimization operation. * chore: refactor genetic Refactor the genetic algorithm modules for enhanced module structure and better readability. Removed unnecessary and overcomplex devices singleton. Also split devices configuration from genetic algorithm parameters to allow further development independently from genetic algorithm parameter format. Move charge rates configuration for electric vehicles from optimization to devices configuration to allow to have different charge rates for different cars in the future. * chore: Rename memory cache to CacheEnergyManagementStore The name better resembles the task of the cache to chache function and method results for an energy management run. Also the decorator functions are renamed accordingly: cachemethod_energy_management, cache_energy_management * chore: use class properties for config/ems/prediction mixin classes * chore: skip debug logs from mathplotlib Mathplotlib is very noisy in debug mode. * chore: automatically sync bokeh js to bokeh python package bokeh was updated to 3.8.0, make JS CDN automatically follow the package version. * chore: rename hello.py to about.py Make hello.py the adapted EOSdash about page. * chore: remove demo page from EOSdash As no the plan and prediction pages are working without configuration, the demo page is no longer necessary * chore: split test_server.py for system test Split test_server.py to create explicit test_system.py for system tests. * chore: move doc utils to generate_config_md.py The doc utils are only used in scripts/generate_config_md.py. Move it there to attribute for strong cohesion. * chore: improve pydantic merge model documentation * chore: remove pendulum warning from readme * chore: remove GitHub discussions from contributing documentation Github discussions is to be replaced by Akkudoktor.net. * chore(release): bump version to 0.1.0+dev for development * build(deps): bump fastapi[standard] from 0.115.14 to 0.117.1 bump fastapi and make coverage version (for pytest-cov) explicit to avoid pip break. * build(deps): bump uvicorn from 0.36.0 to 0.37.0 BREAKING CHANGE: EOS configuration changed. V1 API changed. - The available_charge_rates_percent configuration is removed from optimization. Use the new charge_rate configuration for the electric vehicle - Optimization configuration parameter hours renamed to horizon_hours - Device configuration now has to provide the number of devices and device properties per device. - Specific prediction provider configuration to be provided by explicit configuration item (no union for all providers). - Measurement keys to be provided as a list. - New feed in tariff providers have to be configured. - /v1/measurement/loadxxx endpoints are removed. Use generic mesaurement endpoints. - /v1/admin/cache/clear now clears all cache files. Use /v1/admin/cache/clear-expired to only clear all expired cache files. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>	2025-10-28 02:50:31 +01:00
Bobby NoelteandGitHub	80bfe4d0f0	Improve caching. (#431) ... * Move the caching module to core. Add an in memory cache that for caching function and method results during an energy management run (optimization run). Two decorators are provided for methods and functions. * Improve the file cache store by load and save functions. Make EOS load the cache file store on startup and save it on shutdown. Add a cyclic task that cleans the cache file store from outdated cache files. * Improve startup of EOSdash by EOS Make EOS starting EOSdash adhere to path configuration given in EOS. The whole environment from EOS is now passed to EOSdash. Should also prevent test errors due to unwanted/ wrong config file creation. Both servers now provide a health endpoint that can be used to detect whether the server is running. This is also used for testing now. * Improve startup of EOS EOS now has got an energy management task that runs shortly after startup. It tries to execute energy management runs with predictions newly fetched or initialized from cached data on first run. * Improve shutdown of EOS EOS has now a shutdown task that shuts EOS down gracefully with some time delay to allow REST API requests for shutdwon or restart to be fully serviced. * Improve EMS Add energy management task for repeated energy management controlled by startup delay and interval configuration parameters. Translate EnergieManagementSystem to english EnergyManagement. * Add administration endpoints - endpoints to control caching from REST API. - endpoints to control server restart (will not work on Windows) and shutdown from REST API * Improve doc generation Use "\n" linenend convention also on Windows when generating doc files. Replace Windows specific 127.0.0.1 address by standard 0.0.0.0. * Improve test support (to be able to test caching) - Add system test option to pytest for running tests with "real" resources - Add new test fixture to start server for test class and test function - Make kill signal adapt to Windows/ Linux - Use consistently "\n" for lineends when writing text files in doc test - Fix test_logging under Windows - Fix conftest config_default_dirs test fixture under Windows From @Lasall * Improve Windows support - Use 127.0.0.1 as default config host (model defaults) and addionally redirect 0.0.0.0 to localhost on Windows (because default config file still has 0.0.0.0). - Update install/startup instructions as package installation is required atm. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>	2025-02-12 21:35:51 +01:00