akkudoktoreos.core.ems.EnergyManagement

class akkudoktoreos.core.ems.EnergyManagement(*args: Any)

Bases: SingletonMixin, ConfigMixin, PredictionMixin, PydanticBaseModel

Energy management.

__init__(*args: Any, **kwargs: Any) → None

Initializes the singleton instance if it has not been initialized previously.

Further calls to __init__ are ignored for the singleton instance.

Parameters:

*args – Positional arguments for initialization.
**kwargs – Keyword arguments for initialization.

Methods

`__init__`(args, *kwargs)	Initializes the singleton instance if it has not been initialized previously.
`construct`([_fields_set])
`copy`(*[, include, exclude, update, deep])	Returns a copy of the model.
`dict`(*[, include, exclude, by_alias, ...])
`from_dict`(data)	Create a PydanticBaseModel instance from a dictionary.
`from_json`(json_str)	Create an instance of the PydanticBaseModel class or its subclass from a JSON string.
`from_orm`(obj)
`genetic_solution`()	Get the latest solution of the genetic algorithm.
`get_nested_value`(path)	Retrieve a nested value from the model using a '/'-separated path.
`json`(*[, include, exclude, by_alias, ...])
`manage_energy`()	Repeating task for managing energy.
`model_construct`([_fields_set])	Creates a new instance of the Model class with validated data.
`model_copy`(*[, update, deep])	!!! abstract "Usage Documentation"
`model_dump`(*args[, include_computed_fields])	Custom dump method to serialize computed fields by default.
`model_dump_json`(*args[, indent])	!!! abstract "Usage Documentation"
`model_json_schema`([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
`model_parametrized_name`(params)	Compute the class name for parametrizations of generic classes.
`model_post_init`(context, /)	This function is meant to behave like a BaseModel method to initialise private attributes.
`model_rebuild`(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
`model_validate`(obj, *[, strict, extra, ...])	Validate a pydantic model instance.
`model_validate_json`(json_data, *[, strict, ...])	!!! abstract "Usage Documentation"
`model_validate_strings`(obj, *[, strict, ...])	Validate the given object with string data against the Pydantic model.
`optimization_solution`()	Get the latest optimization solution.
`parse_file`(path, *[, content_type, ...])
`parse_obj`(obj)
`parse_raw`(b, *[, content_type, encoding, ...])
`plan`()	Get the latest energy management plan.
`reset_instance`()	Resets the singleton instance, forcing it to be recreated on next access.
`reset_to_defaults`()	Resets the fields to their default values.
`run`([start_datetime, mode, ...])	Run the energy management.
`schema`([by_alias, ref_template])
`schema_json`(*[, by_alias, ref_template])
`set_nested_value`(path, value)	Set a nested value in the model using a '/'-separated path.
`set_start_datetime`([start_datetime])	Set the start datetime for the next energy management run.
`to_dict`()	Convert this PredictionRecord instance to a dictionary representation.
`to_json`()	Convert the PydanticBaseModel instance to a JSON string.
`track_nested_value`(path, callback)	Register a callback for a specific path (or subtree).
`update_forward_refs`(**localns)
`validate`(value)

Attributes

`config`
`last_run_datetime`	The datetime the last energy management was run.
`model_computed_fields`
`model_config`	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
`model_extra`	Get extra fields set during validation.
`model_fields`
`model_fields_set`	Returns the set of fields that have been explicitly set on this model instance.
`prediction`
`start_datetime`	The starting datetime of the current or latest energy management.

property start_datetime: DateTime: The starting datetime of the current or latest energy management.

property last_run_datetime: DateTime | None: The datetime the last energy management was run.

classmethod set_start_datetime(start_datetime: DateTime | None = None) → DateTime

Set the start datetime for the next energy management run.

If no datetime is provided, the current datetime is used.

The start datetime is always rounded down to the nearest hour (i.e., setting minutes, seconds, and microseconds to zero).

Parameters:: start_datetime (Optional[DateTime]) – The datetime to set as the start. If None, the current datetime is used.
Returns:: The adjusted start datetime.
Return type:: DateTime

classmethod plan() → EnergyManagementPlan | None

Get the latest energy management plan.

Returns:: The latest energy management plan or None.
Return type:: Optional[EnergyManagementPlan]

classmethod optimization_solution() → OptimizationSolution | None

Get the latest optimization solution.

Returns:: The latest optimization solution.
Return type:: Optional[OptimizationSolution]

classmethod genetic_solution() → GeneticSolution | None

Get the latest solution of the genetic algorithm.

Returns:: The latest solution of the genetic algorithm.
Return type:: Optional[GeneticSolution]

Run the energy management.

This method initializes the energy management run by setting its start datetime, updating predictions, and optionally starting optimization depending on the selected mode or configuration.

Parameters:

start_datetime (DateTime, optional) – The starting timestamp of the energy management run. Defaults to the current datetime if not provided.
mode (EnergyManagementMode, optional) –
The management mode to use. Must be one of: - “OPTIMIZATION”: Runs the optimization process. - “PREDICTION”: Updates the forecast without optimization.

Defaults to the mode defined in the current configuration.
genetic_parameters (GeneticOptimizationParameters, optional) – The parameter set for the genetic algorithm. If not provided, it will be constructed based on the current configuration and predictions.
genetic_individuals (int, optional) – The number of individuals for the genetic algorithm. Defaults to the algorithm’s internal default (400) if not specified.
genetic_seed (int, optional) – The seed for the genetic algorithm. Defaults to the algorithm’s internal random seed if not specified.
force_enable (bool, optional) – If True, bypasses any disabled state to force the update process. This is mostly applicable to prediction providers.
force_update (bool, optional) – If True, forces data to be refreshed even if a cached version is still valid.

Returns:

None

async manage_energy() → None

Repeating task for managing energy.

This task should be executed by the server regularly (e.g., every 10 seconds) to ensure proper energy management. Configuration changes to the energy management interval will only take effect if this task is executed.

Initializes and runs the energy management for the first time if it has never been run before.
If the energy management interval is not configured or invalid (NaN), the task will not trigger any repeated energy management runs.
Compares the current time with the last run time and runs the energy management if the interval has elapsed.
Logs any exceptions that occur during the initialization or execution of the energy management.

Note: The task maintains the interval even if some intervals are missed.

__copy__() → Self: Returns a shallow copy of the model.

__deepcopy__(memo: dict[int, Any] | None = None) → Self: Returns a deep copy of the model.

classmethod __get_pydantic_json_schema__(core_schema: CoreSchema, handler: GetJsonSchemaHandler, /) → JsonSchemaValue

Hook into generating the model’s JSON schema.

Parameters:

core_schema – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.
handler – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Returns:

A JSON schema, as a Python object.

__hash__() → int

Returns a stable hash based on the instance’s UUID.

Returns:: Hash value derived from the model’s UUID.
Return type:: int

__init__(*args: Any, **kwargs: Any) → None

Initializes the singleton instance if it has not been initialized previously.

Further calls to __init__ are ignored for the singleton instance.

Parameters:

*args – Positional arguments for initialization.
**kwargs – Keyword arguments for initialization.

__iter__() → Generator[tuple[str, Any], None, None]: So dict(model) works.

static __new__(cls: Type[SingletonMixin], *args: Any, **kwargs: Any) → SingletonMixin

Creates or returns the singleton instance of the class.

Ensures thread-safe instance creation by locking during the first instantiation.

Parameters:

*args – Positional arguments for instance creation (ignored if instance exists).
**kwargs – Keyword arguments for instance creation (ignored if instance exists).

Returns:

The singleton instance of the derived class.

Return type:

SingletonMixin

__pretty__(fmt: Callable[[Any], Any], **kwargs: Any) → Generator[Any]: Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

classmethod __pydantic_init_subclass__(**kwargs: Any) → None

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after basic class initialization is complete. In particular, attributes like model_fields will be present when this is called, but forward annotations are not guaranteed to be resolved yet, meaning that creating an instance of the class may fail.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by Pydantic.

Parameters:: **kwargs – Any keyword arguments passed to the class definition that aren’t used internally by Pydantic.

Note

You may want to override [__pydantic_on_complete__()][pydantic.main.BaseModel.__pydantic_on_complete__] instead, which is called once the class and its fields are fully initialized and ready for validation.

classmethod __pydantic_on_complete__() → None

This is called once the class and its fields are fully initialized and ready to be used.

This typically happens when the class is created (just before [__pydantic_init_subclass__()][pydantic.main.BaseModel.__pydantic_init_subclass__] is called on the superclass), except when forward annotations are used that could not immediately be resolved. In that case, it will be called later, when the model is rebuilt automatically or explicitly using [model_rebuild()][pydantic.main.BaseModel.model_rebuild].

__repr_name__() → str: Name of the instance’s class, used in __repr__.

__repr_recursion__(object: Any) → str: Returns the string representation of a recursive object.

__rich_repr__() → RichReprResult: Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

config = ConfigEOS(general=GeneralSettings(version='0.2.0', data_folder_path=Path('/home/docs/.local/share/net.akkudoktor.eos'), data_output_subpath=Path('output'), latitude=52.52, longitude=13.405, timezone='Europe/Berlin', data_output_path=Path('/home/docs/.local/share/net.akkudoktor.eos/output'), config_folder_path=Path('/home/docs/.config/net.akkudoktor.eos'), config_file_path=Path('/home/docs/.config/net.akkudoktor.eos/EOS.config.json')), cache=CacheCommonSettings(subpath=Path('cache'), cleanup_interval=300.0), ems=EnergyManagementCommonSettings(startup_delay=5.0, interval=None, mode=None), logging=LoggingCommonSettings(console_level='INFO', file_level=None, file_path=Path('/home/docs/.local/share/net.akkudoktor.eos/output/eos.log')), devices=DevicesCommonSettings(batteries=None, max_batteries=None, electric_vehicles=None, max_electric_vehicles=None, inverters=None, max_inverters=None, home_appliances=None, max_home_appliances=None, measurement_keys=[]), measurement=MeasurementCommonSettings(load_emr_keys=None, grid_export_emr_keys=None, grid_import_emr_keys=None, pv_production_emr_keys=None, keys=[]), optimization=OptimizationCommonSettings(horizon_hours=24, interval=3600, algorithm='GENETIC', genetic=GeneticCommonSettings(individuals=300, generations=400, seed=None, penalties=None)), prediction=PredictionCommonSettings(hours=48, historic_hours=48), elecprice=ElecPriceCommonSettings(provider=None, charges_kwh=None, vat_rate=1.19, provider_settings=ElecPriceCommonProviderSettings(ElecPriceImport=None)), feedintariff=FeedInTariffCommonSettings(provider=None, provider_settings=FeedInTariffCommonProviderSettings(FeedInTariffFixed=None, FeedInTariffImport=None)), load=LoadCommonSettings(provider=None, provider_settings=LoadCommonProviderSettings(LoadAkkudoktor=None, LoadVrm=None, LoadImport=None)), pvforecast=PVForecastCommonSettings(provider=None, provider_settings=PVForecastCommonProviderSettings(PVForecastImport=None, PVForecastVrm=None), planes=None, max_planes=0, planes_peakpower=[], planes_azimuth=[], planes_tilt=[], planes_userhorizon=[], planes_inverter_paco=[]), weather=WeatherCommonSettings(provider=None, provider_settings=WeatherCommonProviderSettings(WeatherImport=None)), server=ServerCommonSettings(host='127.0.0.1', port=8503, verbose=False, startup_eosdash=True, eosdash_host=None, eosdash_port=None), utils=UtilsCommonSettings())

classmethod construct(_fields_set: set[str] | None = None, **values: Any) → Self

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → Dict[str, Any]

classmethod from_dict(data: dict) → PydanticBaseModel

Create a PydanticBaseModel instance from a dictionary.

Parameters:: data (dict) – A dictionary containing data to initialize the PydanticBaseModel. Keys should match the field names defined in the model.
Returns:: An instance of the PydanticBaseModel populated with the data.
Return type:: PydanticBaseModel

Notes

Works with derived classes by ensuring the cls argument is used to instantiate the object.

classmethod from_json(json_str: str) → PydanticBaseModel

Create an instance of the PydanticBaseModel class or its subclass from a JSON string.

Parameters:: json_str (str) – JSON string to parse and convert into a PydanticBaseModel instance.
Returns:: A new instance of the class, populated with data from the JSON string.
Return type:: PydanticBaseModel

Notes

Works with derived classes by ensuring the cls argument is used to instantiate the object.

classmethod from_orm(obj: Any) → Self

get_nested_value(path: str) → Any

Retrieve a nested value from the model using a ‘/’-separated path.

Supports accessing nested attributes and list indices.

Parameters:

path (str) – A ‘/’-separated path to the nested attribute (e.g., “key1/key2/0”).

Returns:

The retrieved value.

Return type:

Any

Raises:

KeyError – If a key is not found in the model.
IndexError – If a list index is out of bounds or invalid.

Example

```python class Address(PydanticBaseModel):

city: str

class User(PydanticBaseModel):: name: str address: Address

user = User(name=”Alice”, address=Address(city=”New York”)) city = user.get_nested_value(“address/city”) print(city) # Output: “New York” ```

json(*, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = PydanticUndefined, models_as_dict: bool = PydanticUndefined, **dumps_kwargs: Any) → str

model_computed_fields = {'last_run_datetime': ComputedFieldInfo(wrapped_property=<property object>, return_type=typing.Optional[pydantic_extra_types.pendulum_dt.DateTime], alias=None, alias_priority=None, title=None, field_title_generator=None, description='The datetime the last energy management was run.', deprecated=None, examples=None, json_schema_extra=None, repr=True), 'start_datetime': ComputedFieldInfo(wrapped_property=<property object>, return_type=<class 'pydantic_extra_types.pendulum_dt.DateTime'>, alias=None, alias_priority=None, title=None, field_title_generator=None, description='The starting datetime of the current or latest energy management.', deprecated=None, examples=None, json_schema_extra=None, repr=True)}

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'use_enum_values': True, 'validate_assignment': True}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set: set[str] | None = None, **values: Any) → Self

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values – Trusted or pre-validated data dictionary.

Returns:

A new instance of the Model class with validated data.

model_copy(*, update: Mapping[str, Any] | None = None, deep: bool = False) → Self

!!! abstract “Usage Documentation”: [model_copy](../concepts/models.md#model-copy)

Returns a copy of the model.

!!! note: The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

update – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep – Set to True to make a deep copy of the model.

Returns:

New model instance.

model_dump(*args: Any, include_computed_fields: bool = True, **kwargs: Any) → dict[str, Any]: Custom dump method to serialize computed fields by default.

model_dump_json(*args: Any, indent: int | None = None, **kwargs: Any) → str

!!! abstract “Usage Documentation”: [model_dump_json](../concepts/serialization.md#json-mode)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent – Indentation to use in the JSON output. If None is passed, the output will be compact.
ensure_ascii – If True, the output is guaranteed to have all incoming non-ASCII characters escaped. If False (the default), these characters will be output as-is.
include – Field(s) to include in the JSON output.
exclude – Field(s) to exclude from the JSON output.
context – Additional context to pass to the serializer.
by_alias – Whether to serialize using field aliases.
exclude_unset – Whether to exclude fields that have not been explicitly set.
exclude_defaults – Whether to exclude fields that are set to their default value.
exclude_none – Whether to exclude fields that have a value of None.
exclude_computed_fields – Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.
round_trip – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
fallback – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.
serialize_as_any – Whether to serialize fields with duck-typing serialization behavior.

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}', schema_generator: type[~pydantic.json_schema.GenerateJsonSchema] = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: ~typing.Literal['validation', 'serialization'] = 'validation', *, union_format: ~typing.Literal['any_of', 'primitive_type_array'] = 'any_of') → dict[str, Any]

Generates a JSON schema for a model class.

Parameters:

by_alias – Whether to use attribute aliases or not.
ref_template – The reference template.
union_format –
The format to use when combining schemas from unions together. Can be one of:
- ’any_of’: Use the [anyOf](https://json-schema.org/understanding-json-schema/reference/combining#anyOf)
keyword to combine schemas (the default). - ‘primitive_type_array’: Use the [type](https://json-schema.org/understanding-json-schema/reference/type) keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive type (string, boolean, null, integer or number) or contains constraints/metadata, falls back to any_of.
schema_generator – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode – The mode in which to generate the schema.

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params: tuple[type[Any], ...]) → str

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

self – The BaseModel instance.
context – The context.

classmethod model_rebuild(*, force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, _types_namespace: MappingNamespace | None = None) → bool | None

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors – Whether to raise errors, defaults to True.
_parent_namespace_depth – The depth level of the parent namespace, defaults to 2.
_types_namespace – The types namespace, defaults to None.

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

Validate a pydantic model instance.

Parameters:

obj – The object to validate.
strict – Whether to enforce types strictly.
extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.
from_attributes – Whether to extract data from object attributes.
context – Additional context to pass to the validator.
by_alias – Whether to use the field’s alias when validating against the provided input data.
by_name – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Returns:

The validated model instance.

!!! abstract “Usage Documentation”: [JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

json_data – The JSON data to validate.
strict – Whether to enforce types strictly.
extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.
context – Extra variables to pass to the validator.
by_alias – Whether to use the field’s alias when validating against the provided input data.
by_name – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

Validate the given object with string data against the Pydantic model.

Parameters:

obj – The object containing string data to validate.
strict – Whether to enforce types strictly.
extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.
context – Extra variables to pass to the validator.
by_alias – Whether to use the field’s alias when validating against the provided input data.
by_name – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

classmethod parse_file(path: str | Path, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self

classmethod parse_obj(obj: Any) → Self

classmethod parse_raw(b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self

prediction = Prediction([ElecPriceAkkudoktor([]), ElecPriceEnergyCharts([]), ElecPriceImport([]), FeedInTariffFixed([]), FeedInTariffImport([]), LoadAkkudoktor([]), LoadAkkudoktorAdjusted([]), LoadVrm([]), LoadImport([]), PVForecastAkkudoktor([]), PVForecastVrm([]), PVForecastImport([]), WeatherBrightSky([]), WeatherClearOutside([]), WeatherImport([])])

classmethod reset_instance() → None: Resets the singleton instance, forcing it to be recreated on next access.

reset_to_defaults() → PydanticBaseModel: Resets the fields to their default values.

classmethod schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}') → Dict[str, Any]

classmethod schema_json(*, by_alias: bool = True, ref_template: str = '#/$defs/{model}', **dumps_kwargs: Any) → str

set_nested_value(path: str, value: Any) → None

Set a nested value in the model using a ‘/’-separated path.

Supports modifying nested attributes and list indices while preserving Pydantic validation. Automatically initializes missing Optional, Union, dict, and list fields if necessary. If a missing field cannot be initialized, raises an exception.

Triggers the callbacks registered by track_nested_value().

Parameters:

path (str) – A ‘/’-separated path to the nested attribute (e.g., “key1/key2/0”).
value (Any) – The new value to set.

Raises:

KeyError – If a key is not found in the model.
IndexError – If a list index is out of bounds or invalid.
ValueError – If a validation error occurs.
TypeError – If a missing field cannot be initialized.

Example

```python class Address(PydanticBaseModel):

city: Optional[str]

class User(PydanticBaseModel):: name: str address: Optional[Address] settings: Optional[Dict[str, Any]]

user = User(name=”Alice”, address=None, settings=None) user.set_nested_value(“address/city”, “Los Angeles”) user.set_nested_value(“settings/theme”, “dark”)

print(user.address.city) # Output: “Los Angeles” print(user.settings) # Output: {‘theme’: ‘dark’} ```

to_dict() → dict

Convert this PredictionRecord instance to a dictionary representation.

Returns:

A dictionary where the keys are the field names of the PydanticBaseModel,: and the values are the corresponding field values.

Return type:

dict

to_json() → str

Convert the PydanticBaseModel instance to a JSON string.

Returns:: The JSON representation of the instance.
Return type:: str

track_nested_value(path: str, callback: Callable[[Any, str, Any, Any], None]) → None

Register a callback for a specific path (or subtree).

Callback triggers if set path is equal or deeper.

Parameters:

path (str) – ‘/’-separated path to track.
callback (callable) – Function called as callback(model_instance, set_path, old_value, new_value).

classmethod update_forward_refs(**localns: Any) → None

classmethod validate(value: Any) → Self