Files
EOS/tests/test_configmigrate.py
Bobby Noelte 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

230 lines
9.4 KiB
Python

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