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Commits
| Author | SHA1 | Message | Date | |
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  NormannandGitHub
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5d7198d090 |
test: consolidate suites and remove redundant cases (#1459)
Regression tests were scattered across feature- and bug-specific files, and some suites repeated the same checks. Group tests by responsibility, isolate feature fixtures in test classes, and remove duplicate or obsolete cases. The review covered all 127 collected test modules on main. This reduces them and removes or combines definitions:. No application code changes. |
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efab8cd0a0 |
fix(genetic): keep fitness-cache memory within pymalloc and release arena after each run (#1353)
* fix(genetic): keep fitness-cache memory in pymalloc and release arena after each run At fine time resolution (interval_sec=900, ~192 control slots over a multi-day horizon) the fitness-cache keys are ~1.6 KB int tuples, above CPython's 512-byte pymalloc threshold, so they are served by glibc malloc in the optimization worker thread's arena and are not returned to the OS on `self._fitness_cache.clear()`. With re-optimization every 15 min, RSS stair-steps up to the memory limit within about a day (OOM / forced restart). At hourly resolution the tuples stay < 512 B, so pymalloc reclaims them and the effect is negligible. See #1352. - Store the cache key/genome compactly as bytes (1 byte per gene, 8-byte fallback for larger state spaces) so entries stay within pymalloc regardless of resolution. - After each optimization run, gc.collect() + malloc_trim(0) (guarded, glibc-only) to return freed arena pages to the OS. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> * fix(genetic): pack fitness-cache genome as bounded chunks Storing the genome as a single bytes object still exceeds pymalloc's 512-byte threshold once the genome grows: at 15-min resolution over a 60 h horizon with EV genes the key is ~480 genes, so even the one-byte encoding is 481 bytes (514 with the object header) and any value >255 switches the whole genome to 8 bytes per gene. Such keys land in glibc malloc, which does not reliably return the pages (malloc_trim is glibc-only, absent on musl) — the platform-independent guarantee did not actually hold for supported settings. Encode the genome (key and FitnessCacheEntry.genome) as a tuple of bounded byte chunks instead — 256 one-byte genes or 32 signed-64-bit genes per chunk, 256 bytes each — built per chunk so the encoder never materialises an oversized temporary. Every object then stays inside pymalloc regardless of horizon, on every platform. malloc_trim after a run is kept as a secondary release for the rest of the run's heap. Tests: round-trips incl. 480-gene narrow/wide and negative genes, and sys.getsizeof for the key AND every chunk at 480 genes in both encodings. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com> |
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04f28997ea |
feat: deliver complete GENETIC optimization to main (#1330)
* 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(measurement): restore JSON records into the existing singleton * fix(devices): preserve charge-rate typing and public import compatibility * feat(measurement): integrate typed energy quality and capacity APIs Port the locally backed-up measurement extensions to main async storage and PR #1256 device maps. Preserve runtime capacity estimates across #1305 bulk updates. Confirm JSON singleton restore defect on unchanged main and add regression. No production configuration or measurements included. Co-authored-by: Andreas <drbacke@gmx.de> * docs(measurement): describe household settings and consolidate regression coverage * docs(measurement): regenerate configuration and API contracts * test(measurement): isolate capacity database state between tests * ruff format fix * fix(measurement): restore JSON records into the existing singleton * test(measurement): assert restored timestamps before timezone conversion * test(measurement): assert restored timestamps before timezone conversion * fix: preserve imported feed-in revenue during parameter preparation Cancel GENETIC preparation when imported revenue cannot be read or contains invalid values, preserving the chosen provider instead of replacing it with demo tariffs. Keep valid positive, zero and negative amount/Wh series unchanged. Adapt the revenue-preservation regressions from PRs #1224 and #1304 to the async main API, including real timestamped imports and simulation repricing. The feature-only direct-marketing override remains outside this main fix. Co-authored-by: Christin <info@bikinibottom.capital> Co-authored-by: Normann <github@koldrack.com> * feat(devices): port slot-aware battery export and direct-use physics Port scoped device changes from |