mirror of
https://github.com/Akkudoktor-EOS/EOS.git
synced 2025-10-29 13:56:21 +00:00
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>
230 lines
9.4 KiB
Python
230 lines
9.4 KiB
Python
import json
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import shutil
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import tempfile
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from pathlib import Path
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from typing import Any
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import pytest
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from akkudoktoreos.config import configmigrate
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from akkudoktoreos.config.config import ConfigEOS, SettingsEOSDefaults
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from akkudoktoreos.core.version import __version__
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# Test data directory and known migration pairs
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DIR_TESTDATA = Path(__file__).absolute().parent.joinpath("testdata")
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MIGRATION_PAIRS = [
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(
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DIR_TESTDATA / "eos_config_minimal_0_1_0.json",
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DIR_TESTDATA / "eos_config_minimal_now.json",
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),
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(
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DIR_TESTDATA / "eos_config_andreas_0_1_0.json",
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DIR_TESTDATA / "eos_config_andreas_now.json",
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),
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# Add more pairs here:
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# (DIR_TESTDATA / "old_config_X.json", DIR_TESTDATA / "expected_config_X.json"),
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]
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def _dict_contains(superset: Any, subset: Any, path="") -> list[str]:
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"""Recursively verify that all key-value pairs from a subset dictionary or list exist in a superset.
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Supports nested dictionaries and lists. Extra keys in superset are allowed.
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Numeric values (int/float) are compared with tolerance.
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Args:
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superset (Any): The dictionary or list that should contain all items from `subset`.
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subset (Any): The expected dictionary or list.
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path (str, optional): Current nested path used for error reporting. Defaults to "".
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Returns:
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list[str]: A list of strings describing mismatches or missing keys. Empty list if all subset items are present.
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"""
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errors = []
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if isinstance(subset, dict) and isinstance(superset, dict):
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for key, sub_value in subset.items():
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full_path = f"{path}/{key}" if path else key
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if key not in superset:
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errors.append(f"Missing key: {full_path}")
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continue
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errors.extend(_dict_contains(superset[key], sub_value, full_path))
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elif isinstance(subset, list) and isinstance(superset, list):
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for i, elem in enumerate(subset):
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if i >= len(superset):
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full_path = f"{path}[{i}]" if path else f"[{i}]"
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errors.append(f"List too short at {full_path}: expected element {elem}")
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continue
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errors.extend(_dict_contains(superset[i], elem, f"{path}[{i}]" if path else f"[{i}]"))
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else:
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# Compare values (with numeric tolerance)
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if isinstance(subset, (int, float)) and isinstance(superset, (int, float)):
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if abs(float(subset) - float(superset)) > 1e-6:
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errors.append(f"Value mismatch at {path}: expected {subset}, got {superset}")
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elif subset != superset:
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errors.append(f"Value mismatch at {path}: expected {subset}, got {superset}")
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return errors
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class TestConfigMigration:
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"""Tests for migrate_config_file()"""
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@pytest.fixture
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def tmp_config_file(self, config_default_dirs) -> Path:
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"""Create a temporary valid config file with an invalid version."""
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config_default_dir_user, _, _, _ = config_default_dirs
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config_file_user = config_default_dir_user.joinpath(ConfigEOS.CONFIG_FILE_NAME)
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# Create a default config object (simulates the latest schema)
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default_config = SettingsEOSDefaults()
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# Dump to JSON
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config_json = json.loads(default_config.model_dump_json())
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# Corrupt the version (simulate outdated config)
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config_json["general"]["version"] = "0.0.0-old"
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# Write file
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with config_file_user.open("w", encoding="utf-8") as f:
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json.dump(config_json, f, indent=4)
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return config_file_user
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def test_migrate_config_file_from_invalid_version(self, tmp_config_file: Path):
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"""Test that migration updates an outdated config version successfully."""
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backup_file = tmp_config_file.with_suffix(".bak")
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# Run migration
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result = configmigrate.migrate_config_file(tmp_config_file, backup_file)
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# Verify success
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assert result is True, "Migration should succeed even from invalid version."
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# Verify backup exists
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assert backup_file.exists(), "Backup file should be created before migration."
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# Verify version updated
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with tmp_config_file.open("r", encoding="utf-8") as f:
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migrated_data = json.load(f)
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assert migrated_data["general"]["version"] == __version__, \
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"Migrated config should have updated version."
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# Verify it still matches the structure of SettingsEOSDefaults
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new_model = SettingsEOSDefaults(**migrated_data)
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assert isinstance(new_model, SettingsEOSDefaults)
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def test_migrate_config_file_already_current(self, tmp_path: Path):
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"""Test that a current config file returns True immediately."""
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config_path = tmp_path / "EOS_current.json"
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default = SettingsEOSDefaults()
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with config_path.open("w", encoding="utf-8") as f:
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f.write(default.model_dump_json(indent=4))
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backup_file = config_path.with_suffix(".bak")
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result = configmigrate.migrate_config_file(config_path, backup_file)
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assert result is True
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assert not backup_file.exists(), "No backup should be made if config is already current."
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@pytest.mark.parametrize("old_file, expected_file", MIGRATION_PAIRS)
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def test_migrate_old_version_config(self, tmp_path: Path, old_file: Path, expected_file: Path):
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"""Ensure migration from old → new schema produces the expected output."""
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# --- Prepare temporary working file based on expected file name ---
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working_file = expected_file.with_suffix(".new")
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shutil.copy(old_file, working_file)
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# Backup file path (inside tmp_path to avoid touching repo files)
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backup_file = tmp_path / f"{old_file.stem}.bak"
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failed = False
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try:
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assert working_file.exists(), f"Working config file is missing: {working_file}"
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# --- Perform migration ---
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result = configmigrate.migrate_config_file(working_file, backup_file)
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# --- Assertions ---
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assert result is True, f"Migration failed for {old_file.name}"
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assert configmigrate.mapped_count >= 1, f"No mapped migrations for {old_file.name}"
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assert configmigrate.auto_count >= 1, f"No automatic migrations for {old_file.name}"
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assert len(configmigrate.skipped_paths) <= 7, (
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f"Too many skipped paths in {old_file.name}: {configmigrate.skipped_paths}"
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)
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assert backup_file.exists(), f"Backup file not created for {old_file.name}"
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# --- Compare migrated result with expected output ---
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new_data = json.loads(working_file.read_text(encoding="utf-8"))
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expected_data = json.loads(expected_file.read_text(encoding="utf-8"))
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# Check version
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assert new_data["general"]["version"] == __version__, (
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f"Expected version {__version__}, got {new_data['general']['version']}"
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)
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# Recursive subset comparison
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errors = _dict_contains(new_data, expected_data)
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assert not errors, (
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f"Migrated config for {old_file.name} is missing or mismatched fields:\n" +
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"\n".join(errors)
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)
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# --- Compare migrated result with migration map ---
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# Ensure all expected mapped fields are actually migrated and correct
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missing_migrations = []
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mismatched_values = []
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for old_path, mapping in configmigrate.MIGRATION_MAP.items():
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if mapping is None:
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continue # skip intentionally dropped fields
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# Determine new path (string or tuple)
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new_path = mapping[0] if isinstance(mapping, tuple) else mapping
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# Get value from expected data (if present)
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expected_value = configmigrate._get_json_nested_value(expected_data, new_path)
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if expected_value is None:
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continue # new field not present in expected config
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# Check that migration recorded this old path
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if old_path.strip("/") not in configmigrate.migrated_source_paths:
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missing_migrations.append(f"{old_path} → {new_path}")
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continue
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# Verify the migrated value matches the expected one
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new_value = configmigrate._get_json_nested_value(new_data, new_path)
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if new_value != expected_value:
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mismatched_values.append(
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f"{old_path} → {new_path}: expected {expected_value!r}, got {new_value!r}"
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)
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assert not missing_migrations, (
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"Some expected migration map entries were not migrated:\n"
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+ "\n".join(missing_migrations)
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)
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assert not mismatched_values, (
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"Migrated values differ from expected results:\n"
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+ "\n".join(mismatched_values)
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)
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# Validate migrated config with schema
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new_model = SettingsEOSDefaults(**new_data)
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assert isinstance(new_model, SettingsEOSDefaults)
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except Exception:
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# mark failure and re-raise so pytest records the error and the working_file is kept
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failed = True
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raise
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finally:
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# Remove the .new working file only if the test passed (failed == False)
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if not failed and working_file.exists():
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working_file.unlink(missing_ok=True)
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