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04f28997ea8ccf38682bb942a3a114e10c151df3
14
Commits
| Author | SHA1 | Message | Date | |
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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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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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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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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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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> |
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Bobby NoelteandGitHub
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cf477d91a3 |
feat: add fixed electricity prediction with time window support (#930)
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Add a fixed electricity prediction that supports prices per time window.
The time windows may flexible be defined by day or date.
The prediction documentation is updated to also cover the ElecPriceFixed
provider.
The feature includes several changes that are not directly related to the
electricity price prediction implementation but are necessary to keep
EOS running properly and to test and document the changes.
* feat: add value time windows
Add time windows with an associated float value.
* feat: harden eos measurements endpoints error detection and reporting
Cover more errors that may be raised during endpoint access. Report the
errors including trace information to ease debugging.
* feat: extend server configuration to cover all arguments
Make the argument controlled options also available in server configuration.
* fix: eos config configuration by cli arguments
Move the command line argument handling to config eos so that it is
excuted whenever eos config is rebuild or reset.
* chore: extend measurement endpoint system test
* chore: refactor time windows
Move time windows to configabc as they are only used in configurations.
Also move all tests to test_configabc.
* chore: provide config update errors in eosdash with summarized error text
If there is an update error provide the error text as a summary. On click
provide the full error text.
* chore: force eosdash ip address and port in makefile dev run
Ensure eosdash ip address and port are correctly set for development runs.
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
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Bobby NoelteandGitHub
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237af5289f |
fix: eosdash startup (#915)
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Adapt uvicorn log level to allowed levels. Ensure that EOSdash is started after EOS configuration is available. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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 Christopher NadlerandGitHub
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3ccc25d731 |
Adds inverter AC/DC efficiency and break-even penalty (#888)
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* 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 |
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Bobby NoelteandGitHub
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6498c7dc32 |
Add database support for measurements and historic prediction data. (#848)
The database supports backend selection, compression, incremental data load, automatic data saving to storage, automatic vaccum and compaction. Make SQLite3 and LMDB database backends available. Update tests for new interface conventions regarding data sequences, data containers, data providers. This includes the measurements provider and the prediction providers. Add database documentation. The fix includes several bug fixes that are not directly related to the database implementation but are necessary to keep EOS running properly and to test and document the changes. * fix: config eos test setup Make the config_eos fixture generate a new instance of the config_eos singleton. Use correct env names to setup data folder path. * fix: startup with no config Make cache and measurements complain about missing data path configuration but do not bail out. * fix: soc data preparation and usage for genetic optimization. Search for soc measurments 48 hours around the optimization start time. Only clamp soc to maximum in battery device simulation. * fix: dashboard bailout on zero value solution display Do not use zero values to calculate the chart values adjustment for display. * fix: openapi generation script Make the script also replace data_folder_path and data_output_path to hide real (test) environment pathes. * feat: add make repeated task function make_repeated_task allows to wrap a function to be repeated cyclically. * chore: removed index based data sequence access Index based data sequence access does not make sense as the sequence can be backed by the database. The sequence is now purely time series data. * chore: refactor eos startup to avoid module import startup Avoid module import initialisation expecially of the EOS configuration. Config mutation, singleton initialization, logging setup, argparse parsing, background task definitions depending on config and environment-dependent behavior is now done at function startup. * chore: introduce retention manager A single long-running background task that owns the scheduling of all periodic server-maintenance jobs (cache cleanup, DB autosave, …) * chore: canonicalize timezone name for UTC Timezone names that are semantically identical to UTC are canonicalized to UTC. * chore: extend config file migration for default value handling Extend the config file migration handling values None or nonexisting values that will invoke a default value generation in the new config file. Also adapt test to handle this situation. * chore: extend datetime util test cases * chore: make version test check for untracked files Check for files that are not tracked by git. Version calculation will be wrong if these files will not be commited. * chore: bump pandas to 3.0.0 Pandas 3.0 now performs inference on the appropriate resolution (a.k.a. unit) for the output dtype which may become datetime64[us] (before it was ns). Also numeric dtype detection is now more strict which needs a different detection for numerics. * chore: bump pydantic-settings to 2.12.0 pydantic-settings 2.12.0 under pytest creates a different behaviour. The tests were adapted and a workaround was introduced. Also ConfigEOS was adapted to allow for fine grain initialization control to be able to switch off certain settings such as file settings during test. * chore: remove sci learn kit from dependencies The sci learn kit is not strictly necessary as long as we have scipy. * chore: add documentation mode guarding for sphinx autosummary Sphinx autosummary excecutes functions. Prevent exceptions in case of pure doc mode. * chore: adapt docker-build CI workflow to stricter GitHub handling Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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Bobby NoelteandGitHub
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58d70e417b |
feat: add Home Assistant and NodeRED adapters (#764)
Adapters for Home Assistant and NodeRED integration are added. Akkudoktor-EOS can now be run as Home Assistant add-on and standalone. As Home Assistant add-on EOS uses ingress to fully integrate the EOSdash dashboard in Home Assistant. The fix includes several bug fixes that are not directly related to the adapter implementation but are necessary to keep EOS running properly and to test and document the changes. * fix: development version scheme The development versioning scheme is adaptet to fit to docker and home assistant expectations. The new scheme is x.y.z and x.y.z.dev<hash>. Hash is only digits as expected by home assistant. Development version is appended by .dev as expected by docker. * fix: use mean value in interval on resampling for array When downsampling data use the mean value of all values within the new sampling interval. * fix: default battery ev soc and appliance wh Make the genetic simulation return default values for the battery SoC, electric vehicle SoC and appliance load if these assets are not used. * fix: import json string Strip outer quotes from JSON strings on import to be compliant to json.loads() expectation. * fix: default interval definition for import data Default interval must be defined in lowercase human definition to be accepted by pendulum. * fix: clearoutside schema change * feat: add adapters for integrations Adapters for Home Assistant and NodeRED integration are added. Akkudoktor-EOS can now be run as Home Assistant add-on and standalone. As Home Assistant add-on EOS uses ingress to fully integrate the EOSdash dashboard in Home Assistant. * feat: allow eos to be started with root permissions and drop priviledges Home assistant starts all add-ons with root permissions. Eos now drops root permissions if an applicable user is defined by paramter --run_as_user. The docker image defines the user eos to be used. * feat: make eos supervise and monitor EOSdash Eos now not only starts EOSdash but also monitors EOSdash during runtime and restarts EOSdash on fault. EOSdash logging is captured by EOS and forwarded to the EOS log to provide better visibility. * feat: add duration to string conversion Make to_duration to also return the duration as string on request. * chore: Use info logging to report missing optimization parameters In parameter preparation for automatic optimization an error was logged for missing paramters. Log is now down using the info level. * chore: make EOSdash use the EOS data directory for file import/ export EOSdash use the EOS data directory for file import/ export by default. This allows to use the configuration import/ export function also within docker images. * chore: improve EOSdash config tab display Improve display of JSON code and add more forms for config value update. * chore: make docker image file system layout similar to home assistant Only use /data directory for persistent data. This is handled as a docker volume. The /data volume is mapped to ~/.local/share/net.akkudoktor.eos if using docker compose. * chore: add home assistant add-on development environment Add VSCode devcontainer and task definition for home assistant add-on development. * chore: improve documentation |
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Bobby NoelteandGitHub
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7bf9dd723e |
chore: improve doc generation and test (#762)
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Improve documentation generation and add tests for documentation. Extend sphinx by todo directive. The configuration table is now split into several tables. The test is adapted accordingly. There is a new test that checks the docstrings to be compliant to the RST format as used by sphinx to create the documentation. We can not use Markdown in docstrings. The docstrings are adapted accordingly. An additional test checks that the documentation can be build with sphinx. This test takes very long is only enabled in full run (aka. ci) mode. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |