* Add EOS_CONFIG_DIR to set config dir (relative path to EOS_DIR or
absolute path).
- config_folder_path read-only
- config_file_path read-only
* Default values to support app start with empty config:
- latitude/longitude (Berlin)
- optimization_ev_available_charge_rates_percent (null, so model
default value is used)
- Enable Akkudoktor electricity price forecast (docker-compose).
* Fix some endpoints (empty data, remove unused params, fix types).
* cacheutil: Use cache dir. Closes#240
* Support EOS_LOGGING_LEVEL environment variable to set log level.
* tests: All tests use separate temporary config
- Add pytest switch --check-config-side-effect to check user
config file existence after each test. Will also fail if user config
existed before test execution (but will only check after the test has
run).
Enable flag in github workflow.
- Globally mock platformdirs in config module. Now no longer required
to patch individually.
Function calls to config instance (e.g. merge_settings_from_dict)
were unaffected previously.
* Set Berlin as default location (default config/docker-compose).
* Discharge Mask Bug, Tests updated, simple Price Forecast with linear weighting
* Price Forecast with linear weighting, last value = highest weighting Discharge enforce when soc = 0 -> mask bug
* optimization states for AC, DC and IDLE now similar probab. Also AC states taken from config. Maybe a single config option for AC and E-Auto States is sensefull.
* test_class_optimize: Update testdata
* Write pdf and json to test/testdata/new.... so it can be analyzed
manually or just copied as new expected result.
* workflow: Upload pytest optimization result artifacts (pdf, json)
* Update utilities in utils submodule.
* Add base configuration modules.
* Add server base configuration modules.
* Add devices base configuration modules.
* Add optimization base configuration modules.
* Add utils base configuration modules.
* Add prediction abstract and base classes plus tests.
* Add PV forecast to prediction submodule.
The PV forecast modules are adapted from the class_pvforecast module and
replace it.
* Add weather forecast to prediction submodule.
The modules provide classes and methods to retrieve, manage, and process weather forecast data
from various sources. Includes are structured representations of weather data and utilities
for fetching forecasts for specific locations and time ranges.
BrightSky and ClearOutside are currently supported.
* Add electricity price forecast to prediction submodule.
* Adapt fastapi server to base config and add fasthtml server.
* Add ems to core submodule.
* Adapt genetic to config.
* Adapt visualize to config.
* Adapt common test fixtures to config.
* Add load forecast to prediction submodule.
* Add core abstract and base classes.
* Adapt single test optimization to config.
* Adapt devices to config.
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
* Mypy: Initial support
* Add to pre-commit (currently installs own deps, could maybe changed
to poetry venv in the future to reuse environment and don't need
duplicated types deps).
* Add type hints.
* Mypy: Add missing annotations
* Migrate from Flask to FastAPI
* FastAPI migration:
- Use pydantic model classes as input parameters to the
data/calculation classes.
- Interface field names changed to constructor parameter names (for
simplicity only during transition, should be updated in a followup
PR).
- Add basic interface requirements (e.g. some values > 0, etc.).
* Update tests for new data format.
* Python requirement down to 3.9 (TypeGuard no longer needed)
* Makefile: Add helpful targets (e.g. development server with reload)
* Move API doc from README to pydantic model classes (swagger)
* Link to swagger.io with own openapi.yml.
* Commit openapi.json and check with pytest for changes so the
documentation is always up-to-date.
* Streamline docker
* FastAPI: Run startup action on dev server
* Fix config for /strompreis, endpoint still broken however.
* test_openapi: Compare against docs/.../openapi.json
* Move fastapi to server/ submodule
* See #187 for new repository structure.
* Integrated single_test_optimization into pytest to run a basic optimization test with tolerance set to 1e-6, ensuring quick detection of deviations.
* Added a long-run test (400 generations, like single_test_optimization), which can be triggered using --full-run in pytest.
* Mocked PDF creation in optimization tests and added a new PDF generation test with image comparison validation.
Note: Current tolerance is set to 1e-6; feedback on whether this tolerance is tight enough is welcome.
---------
Co-authored-by: Normann <github@koldrack.com>
Co-authored-by: Michael Osthege <michael.osthege@outlook.com>
* Dockerfile: Use non-root user, buildx cache, setup for readonly
container, remove unused apt deps.
For now don't install pip package and keep development flask server
as this will be replaced in the future (fastapi). Then a proper
webserver (e.g. nginx) should be used and the pip package can be
created and deployed just to the run-stage (with the webserver).
* docker-compose: Set to readonly (anonymous volumes declared in
Dockerfile should maintain all writable data).
Mount config.py for easier development. Should be replaced by
environment support for all config file variables.
* Remove unused runtime dependencies: mariadb, joblib, pytest,
pytest-cov.
* Move pytest-cov to dev dependencies.
* Add output_dir to config.py.
* Fix visualization_results.pdf endpoint.
* Update docs.
