mirror of
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3d2daa694bcd03d808bcf9d456d28e50ccba30ea
182
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
  NormannandGitHub
|
3d2daa694b |
fix(dash): use reachable host in footer API docs link (#1354)
link to EOS API using reachable host |
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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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 AndreasandGitHub
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35f77c9b99 |
fix(optimization): let the AC setpoint cap the total charge where the inverter does (#1334)
* fix(optimization): let the AC setpoint cap the total charge where the inverter does Some hybrid inverters limit the battery's whole charge current to the grid charge setpoint while grid charging is on. A Deye 12K in time-of-use grid charging with max_grid_charge_current = 75 A charges at ~80 A even with 8 kW of PV, and exports the rest - including the surplus of micro inverters on the grid side. GENETIC modelled an AC slot as "PV surplus first, grid adds ac_charge x the remaining charge power", so under PV surplus an AC slot looked at least as good as a DC slot and the optimizer picked a low AC factor, while the real system exported what the plan meant to store. New inverter config option devices.inverters[].ac_charge_limits_total_charge (GENETIC scope, default False, so the existing model is unchanged). When True, an AC slot caps the battery's raw charge at ac_charge x max_charge_power_w from all sources: PV surplus above the cap is exported, the grid only fills what PV leaves of it. The option reaches the optimizer through InverterCommonSettings.to_genetic_param, i.e. POST /v1/optimize and the EMS run. GENETIC0 (POST /optimize) is left as it is. The AC slot logic moves into Inverter (ac_charge_factor, begin_ac_charge_slot, charge_battery_from_grid) so the simulation and the tail value curve share it; Battery gets a per-slot charge cap that reset() lifts again. * test(optimization): narrow optional simulation arrays for mypy The annotated fixture indexes GeneticSimulation arrays typed as Optional; assert them first so the locked mypy hook passes. |
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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 |
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3c862543a1 |
feat(devices): add bounded slot physics for GENETIC (#1327)
* 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
* feat(devices): port slot-aware battery export and direct-use physics
Port scoped device changes from
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1a18935667 |
feat(measurement): add typed energy, quality and capacity APIs (#1326)
* 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 * test(measurement): assert restored timestamps before timezone conversion * docs(measurement): align API version with refreshed prerequisites * fix(config): satisfy typed device conversion and migration contracts * docs(config): refresh validated configuration prerequisite schemas * fix(measurement): enforce typed capacity and sample validation * style(measurement): normalize imports for CI * docs(measurement): refresh typed measurement API 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> |
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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> |
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AndreasandAndreas
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5a3a3bf07e | style(pvforecast): apply CI import formatting | ||
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AndreasandAndreas
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6529c39df4 |
feat(pvforecast): add calibrated local Akkudoktor backend
Port local PV modeling and outage calibration from feature commits |
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AndreasandAndreas
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75a65f4ff4 | style: wrap imported tariff test parameter import | ||
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4b332d4c8a |
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> |
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AndreasandAndreas
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0a3ced689c | test: make optimization dispatch timezones explicit | ||
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AndreasandAndreas
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e5578e330c | test: type dynamic Optimize regression arguments | ||
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AndreasandAndreas
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cfa245b41a | fix: return only completed optimization results per run | ||
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AndreasandAndreas
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1e0579f6b6 | test(measurement): assert restored timestamps before timezone conversion | ||
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AndreasandAndreas
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1f29663eb5 | fix(measurement): restore JSON records into the existing singleton | ||
 dr-dimitriandGitHub
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7ebe6d714b |
fix: detect recent Energy-Charts source cadence (#1315)
Infer interval coverage from consistent recent original price spacings while preserving history and forecasting resolution policies. Reuse source data until a successful refresh and cover cadence transitions, fallback, and history repair. Fixes #1279 |
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dr-dimitriandGitHub
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446ea3da9d |
fix: preserve local time when parsing naive datetime strings (#1294)
to_datetime() previously interpreted naive strings differently depending on their precision. For example, 2026-01-15 23:45 in Europe/Berlin became 2026-01-16 00:45+01:00, while the equivalent string with seconds kept the intended date and time. Minutes, seconds, and fractional seconds now consistently represent 2026-01-15 23:45+01:00. Pass the resolved target/local timezone to Pendulum's fallback parser. Explicit Z and numeric offsets continue to determine the input instant, and Unix timestamps retain their UTC semantics. Update the affected docstring examples to distinguish local wall time from UTC conversion. Regression coverage includes space and T separators, explicit and default Europe/Berlin timezones, winter/summer dates near midnight, fractional-second precision, explicit offsets, and Unix timestamps. The Open-Meteo integration test now independently checks the timestamps and all three irradiance series for all 72 fixture hours, with both UTC and Europe/Berlin as the host timezone. This replaces an expectation that incorrectly associated the 08:00 values with 09:00. The previous parser misplaces all 72 source hours; the corrected parser maps all 72 correctly. |
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dr-dimitriandGitHub
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3b9eecc52b |
fix: handle denied inspection in test server cleanup (#1301)
Track owned test processes and restrict fallback cleanup to verified EOS servers using the test configuration. Add regression coverage for denied inspection and cleanup failures. Fixes #1297 |
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dr-dimitriandGitHub
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5469ba836c |
fix: check port availability without system-wide process inspection (#1300)
* fix: check port availability without process inspection Probe local TCP addresses and use process inspection only for optional diagnostics. Preserve occupied-port detection, bounded waits, and port reuse after closed connections. Fixes #1298 * fix: preserve IPv6 scope IDs in port availability probes |
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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> |
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e9bd55caac |
fix: normalize expected configuration paths on macOS (#1282)
Co-authored-by: Normann <github@koldrack.com> |
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NormannandGitHub
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25430bd42d | fix: infer Energy-Charts coverage from recent source intervals (#1280) | ||
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dr-dimitriandGitHub
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9d816acbd9 |
fix: honor Energy-Charts electricity price interval coverage (#1278)
* fix: honor Energy-Charts electricity price interval coverage * test: make Energy-Charts coverage checks timezone-independent |
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dr-dimitriandGitHub
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940aa1021f | fix: honor Energy-Charts feed-in interval coverage (#1275) | ||
  
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7bafc8d02d |
feat: add PVForecastHomeAssistant provider (#1258)
* feat: add PVForecastHomeAssistant provider
Reads a PV forecast time series directly from a Home Assistant entity
attribute (matching the {"forecast": [{"datetime", "watts"}]} shape
already exposed by common HA PV forecast integrations, e.g. Helios
Forecast and Solcast) and feeds it into pvforecast_ac_power, following
the same self-polling provider pattern as PVForecastVrm.
Closes #1232.
* fix: use MagicMock instead of monkeypatched Response for mypy
requests.Response().json is a typed bound method; reassigning it to a
lambda fails mypy's method-assign check. Use MagicMock(spec=...) instead,
which mocks the response without fighting its static type.
* fix: address PR review on PVForecastHomeAssistant provider
Fixes two issues raised in review on PR #1258:
- _update_data() left stale pvforecast_ac_power values in place when a
refreshed forecast was empty or shorter than a previous one; it now
clears the active forecast window before writing and raises instead
of silently no-op'ing when the response has no usable data.
- pvforecast.homeassistant.entity_id used a "select" widget with no
manual-entry fallback in EOSdash, leaving it unusable in standalone
mode where the entity list can't be resolved without SUPERVISOR_TOKEN;
switched to a plain text field.
Also fills in the config docs and openapi.json for the new
pvforecast.homeassistant.* fields, which were missing from the
original commit.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
* fix: preserve retained forecast history when clearing stale entries
The previous fix cleared pvforecast_ac_power from start-of-day, but
PredictionProvider deliberately retains historical records back to
keep_datetime (prediction.historic_hours). Since Home Assistant
forecasts are future-only, that clear wiped out retained history
between midnight and the EMS start on every refresh.
Narrow the clear to [ems_start_datetime, end_datetime) - the actual
active forecast window, DST-adjusted - instead of the day boundary.
Addresses review feedback from @NormannK on PR #1258.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
---------
Co-authored-by: Mathias <mathias@Mathiass-MacBook-Air.local>
Co-authored-by: Mathias <mathias@Mathiass-Air.localdomain>
Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com>
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Bobby NoelteandGitHub
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ba76087db9 |
feat: electricity fee provider framework and generic providers (#1235)
Add new provider class for electricity fees providers. Add the generic providers: - ElecFeeFixed - ElecFeeImport The providers provide predictions for: - elecfee_consumption_amt_wh: Total fixed fee for consumed energy per Wh [amount/Wh]. This is the accumulation of all fixed per-Wh fees payable on "consumed energy - such as network charge, concession fee, and electricity charge - into a single amount. - elecfee_consumption_percent_amt: Total fixed surcharge on consumed energy, given as a percentage of the monetary amount already charged for that energy [%]. This is the accumulation of all percentage-based surcharges payable on top of the consumed-energy fee - such as VAT - into a single percentage. This is a percentage of the fee amount, not a per-Wh rate. - elecfee_feedin_amt_wh: Total fixed deduction from feed-in energy per Wh [amount/Wh]. This is the accumulation of all fixed per-Wh charges deducted from feed-in energy - such as metering fees or grid-operator handling "charges - into a single amount. Applied after the percentage-based deduction, i.e. it reduces the price by a flat amount per Wh rather than by a share of the raw price. - elecfee_feedin_percent_amt: Total percentage deducted from the raw feed-in price (spot price) [%]. This is the accumulation of all percentage-based deductions payable on the feed-in tariff - such as a marketing or balancing fee retained by the aggregator - into a single percentage. It is applied as `raw_price * (100 - percent) / 100`, i.e. it scales down the raw price rather than adding a surcharge to it. A new _apply_fee() method is added to the base class for ElecPrice and FeedInTariff to be used to add the fees in a consistent way. Fees are taken from the active ElecFee provider and applied to the raw prices given to the _apply_fee() method. The optional application of fees is added to: - ElecPriceAkkudoktor - ElecPriceFixed - ElecPriceEnergyCharts - ElecPriceSMARD - FeedInTariffEnergyCharts - FeedInTariffFixed - FeedInTariffSMARD The import providers ElecPriceImport and FeedInTariffImport do not apply fees by intentention. The following providers currently do not handle fees defined by ElecFee: - ElecPriceTibber - FeedInTariffAkkudoktor - FeedInTariffDvhubOnline - FeedInTariffTibber The tests for this feature are either added or existing tests are extended. The documentation was extended for the electricity fee provider settings. Besides this feature further improvements are added: * feat: add SMARD quarter-hour electricty price and feed-in tariff provider * feat: to_series method for TimeWindows and ValueTimeWindows Additional to to_array the time window sequence can now also produce a pandas series. Test have been extended to cover the series generation. * feat: use time windows in fixed feedin tariff provider Feedin tariff can now be configured by time windows - not a single value. * feat: EOSdash select for PVLib inverters and modules Provide PVLib inverter and module names in config selection. * feat: EOSdash lazy select for big option sets Add a new form for lazy selection of big option sets. Filtering and generation of the option set is done server-side. * fix: use raw data for ETS/ median prediction Use to raw time series data for ETS/ median prediction to avoid interference by e.g. dynamic grid charges. * fix: EOSdash config drops by type only on details resolve Drop configuration by type and path. Prevents dropping of configuration items with same type and level but different path. * fix: EOSdash configuration section closes on update Open section if searching or if last update touched this category — including updates on deeply nested sub-fields. * chore: make elecfeefixed, elecpricefixed and feedintarifffixed warn about no windows and default to 0 Missining configuration creates default 0 value and a warning instead of an exception. * fix: test setup for providers Reset db state on each test run. * chore: improve config option naming for elecpricefixed. * chore: adapt elecpricefixed test to changed time_windows naming * chore: factorized common price provider helpers to priceabc.py Factorized common price provider helpers to priceabc.py. Add tests for these helpers. Reduce/ change testing of elecpriceabc.py and feedintariffabc.py to cover only specifics. Rest of testing is already covered by test_priceabc.py. * chore: update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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NormannandGitHub
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9b3f37e172 |
test: clean up git and artifact warnings (#1244)
* initialize the temporary Git repository in test_workflow_git with --initial-branch=main * avoid Git's default-branch advisory in pytest logs without changing global Git configuration or suppressing stderr * upload generated optimization result artifacts only when the test job fails * use the actual nested tests/testdata/**/new_optimize_result* paths * ignore the no-files case so unrelated test failures do not produce an artifact warning |
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9eb3c7e483 |
build(deps): refresh dependencies and clean up test warnings (#1243)
Consolidates the currently applicable dependency updates into one PR, including the closed Dependabot backlog such as #1241, plus dependency surfaces that were not covered by the repository's previous pip-only Dependabot configuration. Cleanup of test warnings. Most importent: * GitPython + pypdf security hardening. * Uvicorn WebSocket close/backpressure/header fixes for server/dashboard reliability. * FastAPI dependency-memory/OpenAPI improvements for the API process. * Bokeh WebSocket/resource-leak/prefix fixes for EOSdash and proxied deployments. * cachebox cancellation/lock cleanup fixes for long-running/concurrent work. * pandas 3.0.5 avoiding the yanked 3.0.4 datetime/segfault build. * Ruff security-lint and pydocstyle correctness fixes, plus faster release builds via PGO. * platformdirs malformed-XDG and duplicate-directory fixes for deployment portability. * CI action modernization, regenerated uv.lock, and expanded Dependabot coverage. Runtime dependencies cachebox: 6.1.2 → 6.2.2 fastapi: 0.139.2 → 0.141.1 python-fasthtml: 0.14.9 → 0.14.11 MonsterUI: 1.0.46 → 1.0.47 bokeh: 3.9.1 → 3.9.2 uvicorn: 0.51.0 → 0.52.4 (build(deps): bump uvicorn from 0.51.0 to 0.52.3 #1241, refreshed to latest patch) pandas: 3.0.3 → 3.0.5 platformdirs: 4.11.0 → 4.11.3 Development/test dependencies pandas-stubs: 3.0.3.260530 → 3.0.5.260730 types-PyYAML: 6.0.12.20260518 → 6.0.12.20260724 GitPython: 3.1.53 → 3.1.58 (security/fix releases) coverage: 7.15.2 → 7.15.4 pypdf: 6.14.2 → 6.16.1 (includes security fixes) Pre-commit/tooling ruff-pre-commit: v0.15.21 → v0.16.3 synchronize pandas-stubs, types-docutils, and types-PyYAML pins with pyproject.toml CI / repository dependencies Python 3.13.9 → 3.13.15 in CI, Docker, .env, and local Docker Make targets actions/checkout → v7 in pytest, pre-commit, CodeQL, and release workflows actions/setup-python → v7 in pytest, pre-commit, and release workflows actions/upload-artifact → v7 in pytest workflow actions/stale: v9.1.0 → v11.0.0 (SHA-pinned) regenerate uv.lock from the final dependency pins so locked/frozen installs match pyproject.toml Future update coverage Expand Dependabot from pip-only to also monitor: GitHub Actions Docker The existing open docutils 0.23 update (#1085) is intentionally excluded because it has separate compatibility/ignore handling and should remain isolated. docker-build.yml was audited and is already using the newer action generations, so no changes were needed there. --------- Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> |
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Bobby NoelteandGitHub
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886c93c92b |
fix: default server settings prevent env var config (#1234)
Change configuration source priorities to: - cli - environment vars - dotenv settings - config file settings - init settings By this the environment vars supersede any configuration var provided by the configuration file or by the initialisation with pydantic. The test_config.py::test_computed_path was fixed to to not use the defaul env var overwrite defined by conftest.py. This seemed to indicate non working env vars, but in fact was a test setupt fault. Besides this fix there are other fixes and changes added: * fix: exclude computed fields when merging settings Pydantic may overwrite settings by values given for computed fields and use these values instead of re-computing the field. Avoid computed fields in merging settings. * chore: improve Windows compatability of development setup Improve scripts to better run also on Windows. When doing path checks keep compatibility also to Windows pathes. A lot of changes to avoid the famous Windows CRLF handling and keep line endings to LF. * chore: add development hint for Windows Windows developers should set core.autocrlf to false. * chore: update version Signed-off-by: b0661 <b0661n0e17e@gmail.com> |
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Bobby NoelteandGitHub
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894790f577 |
feat: add pvlib pv forecast provider (#1214)
Add a PV forecast provider that calculates the forecast using a PVLib system model and weather forecast from the EOS weather forecast provider. Additional module and inverter models can be easily added as the database is build from PVLib and SAM databases and a bundled csv file. The module model and inververt model names are provided by new endpoints to be used in configuration. The provider is based on the fantastic work of EMHASS. See https://github.com/davidusb-geek/emhass/blob/master/src/emhass/forecast.py A short description of the provider is added to the documentation. Besides the new features there are the fixes and improvements: * feat: improve EOSdash config page * fix: kex_to_series for start_datetime Make key_to_series always start the series at start_datetime. * fix: default provider for GENETIC and GENETIC0 optimization To make the default less dependent on internet servers (with API changes and availability issues) the default for PVForecast is set to PVForecastPVLib and for ElecPrice to ElecPriceFixed. The default weather provider is changed to OpenMeteo. * fix: EOSdash display resampled prediction values Make EOSdash display resampled prediction values where resampling fits to the prediction value type. Use bar width that fits to 15 minutes value samples. * chore: add a UI hints system to EOSdash The UI hints system eases the definition of forms for configuration items. There are also forms for items in maps and lists. These forms allow to add and delete items to/ from maps and lists. The forms ensure that all required fields of newly added items are filled. * chore: Create an enum for valid optimization algorithms * chore. Make config also provide the available energy management modes. Used for configuration hints. * chore: Randomize default device id in configuration Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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9189fc890e |
fix(dataabc): drop NaN values in key_to_dict/key_to_lists (#1211)
The dropna filter compared values against float("nan") using ==, which is
always False (NaN != NaN). As a result NaN values were never dropped when
dropna=True, letting them leak into key_to_series/key_to_array and downstream
resampling.
Use pd.isna() to detect NaN, matching the rest of the module. Add regression
tests that fail before and pass after the fix.
Co-authored-by: Cornelius Mund <cornim@users.noreply.github.com>
Co-authored-by: Normann <github@koldrack.com>
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b59012c1f7 |
fix(database): open backend so records actually persist and reload (#1209)
* fix(database): open backend so records actually persist and reload Measurement and prediction records were never persisted to the configured database backend (e.g. LMDB). They only survived while the process was running; every restart lost the accumulated history. For LoadAkkudoktorAdjusted this silently zeroed the measurement-based adjustment (the adjusted load forecast collapsed onto the raw mean). Root cause: `db_enabled` is defined as `database.is_open`, and every database code path (initialization, load, save, insert) is guarded behind `db_enabled`. Nothing ever opened the backend first -- the only lazy open (via `_run_db`) was unreachable because those calls sit behind the same guard. The backend therefore stayed closed, `db_enabled` stayed False, and all writes fell through to the JSON file fallback, which only holds the current in-memory snapshot and is not reloaded into the record store on startup. Fix: explicitly open the configured backend in `_db_ensure_initialized` before the `db_enabled` gate, wrapped in try/except so a failure degrades gracefully to file storage. Verified via an A/B test (identical persisted config, push -> save -> restart): without the fix the backend reports enabled=false and data is lost on restart; with the fix the backend is enabled, records persist to LMDB, and reload correctly after restart. * fix(database): skip disabled providers and avoid open retry loop Address review feedback: - Skip opening the backend for disabled providers (None/"NoDB"), whose is_open is always False and would otherwise be reopened on every call. - Add a one-shot _db_open_attempted flag so a closed/failed backend is not retried (and re-logged) on every record operation. - Add tests covering that NoDB never calls open() and that an unavailable backend does not raise per operation and is opened at most once. * fix(database): drop file-storage-fallback wording from open failure log --------- Co-authored-by: Cornelius Mund <cornim@users.noreply.github.com> |
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Bobby NoelteandGitHub
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1905682113 |
chore: adapt pdf visualization (#1205)
Change PDF visualization to be created on demand and per optimization algorithm. The PDF for the GENETIC0 optimization is provided by the /visualization_results.pdf endpoint. There is no change in the interface. By this the optimization algorithm is offloaded from the PDF generation which spares some time. To cope with several users may call the /visualization_results.pdf endpoint at the same time the PDF is generated on the fly without any intermediate file taking the stored GENETIC0 solution as an input. SVG picture generation is removed as this would again create intermediate files. Chart pictures can easily be taken from the PDF. To allow on demand creation of the optimization results visualization the optimisation solution stored is extended by several new attributes. To keep the deprecated /optimize endpoint compatible the optimization solution is stripped to the legacy content before returned. Due to the extension of the solution the optimization tests were adapted to cover the extended content. The optimization tests are adapted to test the generated visualization report by the pypdf reader. Pypdf is added to the development dependencies. Besides the adaptation several fixes and improvements are added: * feat: extend /v1/prediction/series endpoint by resampling and filling Add parameters for resampling and filling. Add the processing parameter to control wether raw data or resampled data shall be returned. * feat: extend /v1/measurement/series endpoint by resampling and filling Add parameters for resampling and filling: Add the processing parameter to control wether raw data or resampled data shall be returned. * feat: standardize and improve API error response Use FASTApi exception handlers to provide a standardized API exception handling. All exceptions are logged. Exception traces are only returned if the new logging configuration parameter logging.api_logging_level is set to "DEBUG" or "TRACE". Avoids unwanted leackage of server internals on exceptions. * fix: align to intervall when resampling Ensure resampling is aligned to interval also when the buckets are shifted due to the align_to_intervall parameter is set. * chore: make dropna mandatory and default to True * chore: refactor key_to_xxx data management methods Make key_to_series the central method for data resampling and fill. Add a new key_to_raw_series to retrieve the data as it is stored (without resampling and filling). Users of key_to_series were mostly moved to key_to_raw_series as this resembles the former interface. Especially in predictions and tests this was done. * chore: create test data sub-directory for each optimization algorithm To prevent cluttering the test data directory and ease test data management for optimization algorithms each algorithm got it's own sub-directory. The current test data was moved to these sub-directories. * chore: update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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Bobby NoelteandGitHub
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8344974c16 |
chore: adapt deprecated endpoints to 15-minutes predictions (#1195)
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Ensure the deprecated endpoints get /strompreis, post /gesamtlast, get /gesamtlast_simple,
get /pvforecast to work on 1-hour intervalls even if the prediction provides 15-minutes
intervall data. This keeps the interface compliant to the legacy functionality.
Besides this adaptations several other improvements and fixes are included in this PR.
* feat: extend data management method key_to array by resample_method
One can now define the resample method on how to aggregate the values in an interval
for resampling. Three methods are provided:
- "first": Use the first value in each interval.
- "mean": Compute the arithmetic mean of all samples in each interval.
- "interval_mean": Compute the time-weighted mean assuming each value remains valid
until the next timestamp (piecewise-constant signal).
* feat: extend the get /v1/prediction/dataframe endpoint with resampling parameters
Make all parameters for resampling available at the endpoint.
* feat: extend the get /v1/prediction/list endpoint with resampling parameters
Make all parameters for resampling available at the endpoint.
* feat: add new delete /v1/prediction/range endpoint
The endpoint allows to delete prediction values for a given time span.
* fix: adapt for PVForecastAkkudoktor server side cache handling
/api.akkudoktor/forecast does it's own caching on requests. Call it with slightly
randomized request values to avoid getting cached values in the case we need fresh
data. The requests are anyway rate limited to one request per hour on our side.
* chore: add core.types
This module centralizes reusable type definitions shared across multiple
packages. Defining common types here avoids duplication of complex type
annotations (such as Literal aliases), ensures consistent typing across the
code base, and helps prevent circular import dependencies between modules.
* chore: extend cache testing
* chore: add system test for deprecated /strompreis endpoint
* chore: add unit test module for server endpoints
Add a new test module to do unit tests on server endpoints. First test added
for deprecated get /strompreis endpoint.
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
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Bobby NoelteandGitHub
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52fe489d4e |
feat: add dvhubonline feed-in tariff provider (#1193)
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Fetches GET /api/prices?start&end&zone (15-min slots, EUR/MWh) and stores the raw market price as feed_in_tariff_wh (EUR/Wh) — no import charges/VAT. Slots beyond the day-ahead horizon are left to the consumer's forward-fill (FeedInTariffImport behaviour). 5 unit tests + opt-in live smoke (EOS_DVHUB_ONLINE_LIVE=1, passing). Signed-off-by: Christin <info@bikinibottom.capital> Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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Bobby NoelteandGitHub
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e23bb7b497 |
chore: prepare for update of genetic algorithm (#1190)
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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> |
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Bobby NoelteandGitHub
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7e5aa2f218 |
feat: add Tibber feed-in tariff provider (#1189)
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The `FeedInTariffTibber` provider requests `priceInfo` and `priceInfoRange` with `resolution: QUARTER_HOURLY` and preserves the native 15-minute timestamps. It uses Tibber's `energy` spot-price component without the `tax` part or EOS electricity-price charges. The end-customer `total` component is deliberately ignored. The provider deliberately rejects hourly API responses instead of silently repeating them. It reuses `elecprice.tibber.access_token` and `elecprice.tibber.home_id`, so no duplicate credentials are needed. Signed-off-by: Andreas Schmitz <akkudoktor.net> Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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Bobby NoelteandGitHub
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4c8a8e40f5 |
feat: add Akkudoktor feed-in provider (#1187)
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The `FeedInTariffAkkudoktor` provider uses raw day-ahead market prices from `https://api.akkudoktor.net/prices` as `feed_in_tariff_wh`. It does not add electricity import charges or VAT. Published prices are extended to the configured prediction horizon with the same seasonal ETS or median fallback used by the Akkudoktor electricity-price provider. The Akkudoktor endpoint currently forwards hourly market prices from aWATTar. With a 15-minute optimization interval, EOS holds each hourly price constant for its four quarter-hour slots. This keeps the slot grid consistent but does not create genuine quarter-hour market prices. Signed-off-by: Andreas Schmitz <akkudoktor.net> Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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Bobby NoelteandGitHub
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ed61918fe0 |
feat: add EnergyCharts feed-in tariff provider (#1165)
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The `FeedInTariffEnergyCharts` provider uses the raw Energy-Charts day-ahead market price as the feed-in tariff. It stores prices in `feed_in_tariff_wh` without adding electricity import charges or VAT. The data is loaded from the Energy-Charts `/price` endpoint for the configured bidding zone. The native Energy-Charts resolution, including quarter-hour data, is retained. Energy-Charts usually supplies prices only for the published day-ahead period. If that data does not cover the complete configured prediction horizon, the provider extends it as follows: - With more than 800 hours of history, an ETS (Holt-Winters exponential smoothing) forecast with weekly seasonality is used. - With more than 168 hours of history, an ETS forecast with daily seasonality is used. - With less history, the median of the available values is used as a constant fallback. The seasonal periods are adjusted to the source resolution. For example, quarter-hour data uses four values per hour. Values already supplied by Energy-Charts are kept unchanged; only missing future slots after the last published price are forecast. Consequently, a 15-minute optimization uses four forecast values per hour without converting them to hourly averages. Signed-off-by: Andreas Schmitz <akkudoktor.net> Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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Bobby NoelteandGitHub
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6093d8d348 |
chore: improve error msg on home assistant add-on port config (#1186)
When running as a Home Assistant add-on the ports shall not be changed as this would break config.yaml that is used by Home Assistant. Prevent the change and return an error message. Extra fixes: * fix: EOS configuration initialisation by cli under EOSdash. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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Bobby NoelteandGitHub
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c914bae667 |
fix: data management conversion to async (#1185)
Fix test warnings left over from data management conversion to async design. Mostly tagging async tests. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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8d72177671 |
feat: rename remaining German API fields and deprecate legacy endpoints (#1164)
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Complete the English API translation started in #675: - rename input fields pv_akku to pv_battery and eauto to ev, and the solution fields eautocharge_hours_float to ev_charge_hours_float and eauto_obj to ev_obj; the German names are still accepted on input via validation aliases and re-emitted in responses as deprecated computed fields, same pattern as #675 - mark the legacy endpoints /strompreis, /gesamtlast and /gesamtlast_simple as deprecated in the OpenAPI schema - use amount instead of a euro reference in the battery LCOS log message Co-authored-by: Tobias Welz <tobias.wizneteu@gmail.com> |
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Bobby NoelteandGitHub
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641873d867 |
fix: akkudoktor api requests (#1181)
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The akkudoktor api for PV forecast seems to be changed and does not support to concatenate several planes into one request anymore. Make a request for every plane and add up the power results. Do not use the new 15 minutes slots as the returned data does not include the hourly values for windspeed and temperature as does the hourly slots. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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Bobby NoelteandGitHub
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3bb0e02aed |
fix: provider settings in top level field of configuration (#1161)
Configuration for some providers was given in the sub-field `provider_settings` combining the settings of several providers. Pydantic does not understand this very well and the configuration became cumbersome, especially in EOSdash. All provider settings from the `provider-settings` sub-field are now lifted to the top level field of the configuration. The changes are automatically migrated in the configuration. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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Bobby NoelteandGitHub
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cc8193216a |
feat: add tibber electricity price provider (#1160)
Signed-off-by: Andreas Schmitz <akkudoktor.net> Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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Bobby NoelteandGitHub
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2ed04d5573 |
fix: db compaction run (#1156)
These fixes were taken from https://github.com/arneman/EOS/tree/refactor/economic-objective. 1. Fix compaction job registration bug in eos.py: - compact_eos_database was registered with save_eos_database function - Now correctly calls compact_eos_database() - Root cause: compaction/vacuum never ran, allowing records to grow unbounded 2. Optimize db_iterate_records() from O(n) to O(log n): - Previous: linear scan from index 0 for every key_to_array() call - Now: uses bisect_left on _db_sorted_timestamps to skip to start position - Critical for large datasets: 9400 measurement records → 94s/call overhead → 5 calls/optimization 3. Reduce compaction_interval_sec default from 604800s (7 days) to 3600s (1 hour): - With 7-day interval: 65000 records accumulate before first cleanup - Bridge pushes ~1 record/9s (grid_export_emr) - Hourly compaction + 2h data window → stable ~950 records Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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Bobby NoelteandGitHub
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1ca716c30d |
fix(test): flaky test_datetimutil test case TC026 (#1158)
This fix was taken from https://github.com/arneman/EOS/tree/refactor/economic-objective. Avoid flaky TC026 by comparing timestamps at assertion time. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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8bf798daeb |
fix: bare except (#1159)
Bare `except:` catches SystemExit and KeyboardInterrupt, which can: - Mask Ctrl+C handling during long optimization runs - Hide critical system signals - Make debugging harder Replacement by `except Exception:` preserves the same error handling while respecting system-level exceptions. Co-authored-by: Milo @KeloYuan Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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6933b33542 |
feat: rename genetic optimization API fields to English (#675)
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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> |