6 Commits

Include renames

Author	SHA1	Message	Date
4 people	8224c64654	feat: integrate device and runtime configuration foundation on main (#1328) ... * feat: adapt configuration for multi optimization algorithms Decouple configuration from optimization algorithm parameters. Add to_[algorithm]_param() methods to the configuration that derive optimization algorithm specific parameters from the configuration. Add x-scope tags to the configuration options that describe for which specific algorithms the configuration option is for. The whole device settings are restructured. There are now general settings for the device classes with the afore mentioned to_[algorithm]_param() methods. The general device settings got their own directory `devices/settings`. By this the parameter class also does not have to be a pydantic model which can be used for future optimization/ simulations speed up. Also the parameter class for a device is now part of the device module. This better decouples and also is the natural place for parameters of a device. Besides this feature there are also fixes and improvements: * feat: extend home appliance time window settings and simulation Home appliance can now be configured for multiple runs with per-cycle allowed time windows. The number of remaining cycles to plan is determined at runtime by reading the ``cycles_completed_measurement_key`` from the measurement store. * feat: specialiced CycleTimeWindowSequence for time window sequences Sequence of time windows associated to cycles. This model specializes ``ValueTimeWindowSequence`` so that the ``value`` field of each ``ValueTimeWindow`` encodes the **cycle index** (0-based integer) the window belongs to. Typical use: an appliance that must run ``n`` times per day, each run constrained to a distinct time window. Assign ``value=0`` to windows for the first cycle, ``value=1`` for the second, and so on. Multiple windows may share the same cycle index (their allowed regions are unioned). Windows with ``value=None`` are silently ignored by all cycle-aware methods. * fix: Make test_configmigrate also regard the _ANY_SENTENIEL in key values * chore: Make devices configurations a map instead of a list This makes config paths stable regardless of declaration order and lets each device settings class build its own config path from ``self.device_id`` without needing an external index. Tests are adapted likewise. Devices configurations are automatically migrated from lists to maps. * chore: rename levelized_cost_of_storage_kwh to levelized_cost_of_storage_amt kwh This better fits in the naming scheme and also makes clear the costs are money. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> * fix: runtime config update ignored by config file Runtime settings were handed back to pydantic-settings as init settings, which rank below the config file and the environment. Any key already present in EOS.config.json or in the environment silently discarded the update, so a bulk PUT /v1/config returned 200 without applying anything, while the granular PUT /v1/config/{path} endpoint kept working. Add a dedicated runtime settings source ranked directly below the command line arguments and record granular updates there as well, so both endpoints share one store that survives re-evaluation of the settings sources. Environment variables keep precedence over the config file for all keys that were not set at runtime. Also repairs revert_settings() and update(), which passed their data through the same init settings. Closes #1303 * fix: env vars ignored on first config build ConfigEOS.__init__ passed self as first positional argument to _setup, which forwards it to pydantic_settings.BaseSettings.__init__. Its first positional parameter is _case_sensitive, so the environment source matched the upper case variable names against the lower case field names and returned nothing. Environment settings only took effect after the next configuration setup. * docs: changelog for config priority fixes * fix(config): preserve device identities and storage costs during migration * fix(devices): preserve charge-rate typing and public import compatibility * ruff format fix * fix(config): satisfy typed device conversion and migration contracts * docs(config): refresh validated configuration prerequisite schemas --------- Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: r0b2g1t <r0b2g1t@users.noreply.github.com>	2026-09-17 17:51:40 +02:00
4 people	1abdd345c4	fix: unify mypy environments for local checks and CI (#1291) ... The isolated pre-commit mypy hook previously omitted runtime type information that make mypy used, hiding errors involving dependencies such as Pydantic and Pendulum. Makefile, pre-commit and CI now run the same full-project typing policy in the development environment defined by uv.lock. - Use uv run --locked --exact --extra dev and the same mypy arguments for Makefile and the local hook. Check all of src and tests, including on configuration-only changes. - Pin Python 3.13 for local development and the pre-commit CI job, and install the locked pre-commit version in CI. - Disable incremental analysis because existing Pendulum cache state changes mypy 2.3.1 diagnostics. Document the policy, the performance tradeoff and the existing typing debt. - Add a regression test that exercises Makefile, the hook and the CI command in a temporary project, accepting valid dependency types and detecting deliberate Pydantic/Pendulum assignment errors. Resolve the newly detected mypy diagnostics. - Enable the numpydantic and Pydantic mypy plugins, retaining strict Pydantic constructor typing with init_typed = true. Validate raw/coercible payloads through model_validate. - Propagate concrete record, provider and time-window types through generic collections, factories and lookup methods. Preserve runtime field inspection and generated time-window documentation. - Align Pendulum annotations with actual factory/arithmetic results while retaining Pydantic validation adapters at runtime. Correct optional values, array boundaries, REST handlers and plotting interfaces. - Add pinned scipy-stubs and types-psutil, update uv.lock, and supply the plugins' dependencies. - Add runtime regression coverage for validated path defaults, normalized time-series metadata, generic field inspection, invalid timestamps and unsupported provider imports. Runtime and compatibility details: - Validate path defaults as Path objects while retaining raw string defaults needed by migration serialization with exclude_defaults. - Normalize feed-in tariff lists and default charge rates to NumPy arrays; reject missing timestamps/uninitialized values explicitly. Importing into a provider without import support returns HTTP 400. - Public JSON schemas and OpenAPI structure match main (excluding the generated version). Signed-off-by: dr-dimitry Signed-off-by: dr-dimitry Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: dr-dimitri <87113560+dr-dimitri@users.noreply.github.com> Co-authored-by: Normann <github@koldrack.com>	2026-09-10 23:20:35 +02:00
Bobby NoelteandGitHub	e23bb7b497	chore: prepare for update of genetic algorithm (#1190) ... Andreas will update the genetic algorithm for 15-minutes optimization intervals. Copy the current GENETIC optimization algorithm to GENETIC0 to enable to keep the algorithm with the current functionality. Also copy resources like the load interpolator to the GENETIC0 algorithm to keep them despite possible later changes to the interpolator. Make the deprecated legacy /optimize endpoint use the GENETIC0 optimization algorithm to in-fact behave the same way even if there will later be changes to the GENETIC algorithm by Andreas. Add a new REST endpoint to provide the unprocessed optimisation results of the GENETIC and GENETIC0 algorithm in case one wants to use them as done with the deprecated /optimize endpoint. Adapt the optimization configuration to have distinct configurations for the GENETIC and the GENETIC0 algorithm. Create a copy of the current tests for the GENETIC algorithm to be used for the GENETIC0 algorithm. This avoids the tests for the GENETIC0 algorithm to be influenced by later changes by Andreas. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>	2026-07-29 12:56:08 +02:00
3 people	6933b33542	feat: rename genetic optimization API fields to English (#675) ... Rename the German field names of the genetic optimization API to English with full backward compatibility: - English names are canonical and documented in the OpenAPI schema; the German names are still accepted on input via validation aliases and re-emitted in responses as deprecated computed fields - fix visualization receiving the raw German-keyed simulation dict (KeyError: load_wh_per_hour) - rename internal German identifiers (optimize_ems, total_balance, battery_residual_value, self_consumption, extra_data keys) - use amount instead of currency/euro in chart labels and schema descriptions; document currency handling (whole currency units, never cents) in the API description - regenerate openapi.json and generated docs Co-authored-by: Tobias Welz <tobias.wizneteu@gmail.com>	2026-07-18 08:08:25 +02:00
Bobby NoelteandGitHub	eb9e966de9	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>	2026-07-15 16:38:53 +02:00
Christopher NadlerandGitHub	3ccc25d731	Adds inverter AC/DC efficiency and break-even penalty (#888) ... * feat: add inverter AC/DC efficiency and break-even penalty * test: update tests/test_geneticoptimize.py with new ac_charge_break_even parameter * docs: update documentation * chore: update version numbers in configuration files to v0.2.0.dev2602272006923535	2026-02-27 23:12:08 +01:00