akkudoktoreos.config.config.SettingsEOS

class akkudoktoreos.config.config.SettingsEOS(_case_sensitive: bool | None = None, _nested_model_default_partial_update: bool | None = None, _env_prefix: str | None = None, _env_file: DotenvType | None = Path('.'), _env_file_encoding: str | None = None, _env_ignore_empty: bool | None = None, _env_nested_delimiter: str | None = None, _env_nested_max_split: int | None = None, _env_parse_none_str: str | None = None, _env_parse_enums: bool | None = None, _cli_prog_name: str | None = None, _cli_parse_args: bool | list[str] | tuple[str, ...] | None = None, _cli_settings_source: CliSettingsSource[Any] | None = None, _cli_parse_none_str: str | None = None, _cli_hide_none_type: bool | None = None, _cli_avoid_json: bool | None = None, _cli_enforce_required: bool | None = None, _cli_use_class_docs_for_groups: bool | None = None, _cli_exit_on_error: bool | None = None, _cli_prefix: str | None = None, _cli_flag_prefix_char: str | None = None, _cli_implicit_flags: bool | None = None, _cli_ignore_unknown_args: bool | None = None, _cli_kebab_case: bool | None = None, _secrets_dir: PathType | None = None, *, general: GeneralSettings | None = None, cache: CacheCommonSettings | None = None, ems: EnergyManagementCommonSettings | None = None, logging: LoggingCommonSettings | None = None, devices: DevicesCommonSettings | None = None, measurement: MeasurementCommonSettings | None = None, optimization: OptimizationCommonSettings | None = None, prediction: PredictionCommonSettings | None = None, elecprice: ElecPriceCommonSettings | None = None, load: LoadCommonSettings | None = None, pvforecast: PVForecastCommonSettings | None = None, weather: WeatherCommonSettings | None = None, server: ServerCommonSettings | None = None, utils: UtilsCommonSettings | None = None)

Bases: BaseSettings, PydanticModelNestedValueMixin

Settings for all EOS.

Used by updating the configuration with specific settings only.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Methods

`__init__`([_case_sensitive, ...])	Create a new model by parsing and validating input data from keyword arguments.
`construct`([_fields_set])
`copy`(*[, include, exclude, update, deep])	Returns a copy of the model.
`dict`(*[, include, exclude, by_alias, ...])
`from_orm`(obj)
`get_nested_value`(path)	Retrieve a nested value from the model using a '/'-separated path.
`json`(*[, include, exclude, by_alias, ...])
`model_construct`([_fields_set])	Creates a new instance of the Model class with validated data.
`model_copy`(*[, update, deep])	Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy
`model_dump`(*[, mode, include, exclude, ...])	Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump
`model_dump_json`(*[, indent, include, ...])	Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump_json
`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`(_BaseModel__context)	Override this method to perform additional initialization after __init__ and model_construct.
`model_rebuild`(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
`model_validate`(obj, *[, strict, ...])	Validate a pydantic model instance.
`model_validate_json`(json_data, *[, strict, ...])	Usage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-parsing
`model_validate_strings`(obj, *[, strict, context])	Validate the given object with string data against the Pydantic model.
`parse_file`(path, *[, content_type, ...])
`parse_obj`(obj)
`parse_raw`(b, *[, content_type, encoding, ...])
`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.
`settings_customise_sources`(settings_cls, ...)	Define the sources and their order for loading the settings values.
`update_forward_refs`(**localns)
`validate`(value)

Attributes

`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.
`general`
`cache`
`ems`
`logging`
`devices`
`measurement`
`optimization`
`prediction`
`elecprice`
`load`
`pvforecast`
`weather`
`server`
`utils`

general: GeneralSettings | None

cache: CacheCommonSettings | None

ems: EnergyManagementCommonSettings | None

logging: LoggingCommonSettings | None

devices: DevicesCommonSettings | None

measurement: MeasurementCommonSettings | None

optimization: OptimizationCommonSettings | None

prediction: PredictionCommonSettings | None

elecprice: ElecPriceCommonSettings | None

load: LoadCommonSettings | None

pvforecast: PVForecastCommonSettings | None

weather: WeatherCommonSettings | None

server: ServerCommonSettings | None

utils: UtilsCommonSettings | None

model_config: ClassVar[SettingsConfigDict] = {'arbitrary_types_allowed': True, 'case_sensitive': False, 'cli_avoid_json': False, 'cli_enforce_required': False, 'cli_exit_on_error': True, 'cli_flag_prefix_char': '-', 'cli_hide_none_type': False, 'cli_ignore_unknown_args': False, 'cli_implicit_flags': False, 'cli_kebab_case': False, 'cli_parse_args': None, 'cli_parse_none_str': None, 'cli_prefix': '', 'cli_prog_name': None, 'cli_use_class_docs_for_groups': False, 'enable_decoding': True, 'env_file': None, 'env_file_encoding': None, 'env_ignore_empty': False, 'env_nested_delimiter': '__', 'env_nested_max_split': None, 'env_parse_enums': None, 'env_parse_none_str': None, 'env_prefix': 'EOS_', 'extra': 'forbid', 'ignored_types': (<class 'akkudoktoreos.core.decorators.classproperty'>,), 'json_file': None, 'json_file_encoding': None, 'nested_model_default_partial_update': True, 'protected_namespaces': ('model_validate', 'model_dump', 'settings_customise_sources'), 'secrets_dir': None, 'toml_file': None, 'validate_default': True, 'yaml_file': None, 'yaml_file_encoding': None}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

__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_core_schema__(source: type[BaseModel], handler: GetCoreSchemaHandler, /) → CoreSchema

Hook into generating the model’s CoreSchema.

Parameters:

source – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Returns:

A pydantic-core CoreSchema.

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.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

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

__pretty__(fmt: Callable[[Any], Any], **kwargs: Any) → Generator[Any, None, None]: 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 the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

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.

__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.

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_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: ClassVar[dict[str, ComputedFieldInfo]] = {}

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

Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy

Returns a copy of the model.

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.

Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include – A set of fields to include in the output.
exclude – A set of fields to exclude from the output.
context – Additional context to pass to the serializer.
by_alias – Whether to use the field’s alias in the dictionary key if defined.
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.
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].
serialize_as_any – Whether to serialize fields with duck-typing serialization behavior.

Returns:

A dictionary representation of the model.

Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump_json

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.
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.
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].
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: ClassVar[dict[str, FieldInfo]] = {'cache': FieldInfo(annotation=Union[CacheCommonSettings, NoneType], required=False, default=None, description='Cache Settings'), 'devices': FieldInfo(annotation=Union[DevicesCommonSettings, NoneType], required=False, default=None, description='Devices Settings'), 'elecprice': FieldInfo(annotation=Union[ElecPriceCommonSettings, NoneType], required=False, default=None, description='Electricity Price Settings'), 'ems': FieldInfo(annotation=Union[EnergyManagementCommonSettings, NoneType], required=False, default=None, description='Energy Management Settings'), 'general': FieldInfo(annotation=Union[GeneralSettings, NoneType], required=False, default=None, description='General Settings'), 'load': FieldInfo(annotation=Union[LoadCommonSettings, NoneType], required=False, default=None, description='Load Settings'), 'logging': FieldInfo(annotation=Union[LoggingCommonSettings, NoneType], required=False, default=None, description='Logging Settings'), 'measurement': FieldInfo(annotation=Union[MeasurementCommonSettings, NoneType], required=False, default=None, description='Measurement Settings'), 'optimization': FieldInfo(annotation=Union[OptimizationCommonSettings, NoneType], required=False, default=None, description='Optimization Settings'), 'prediction': FieldInfo(annotation=Union[PredictionCommonSettings, NoneType], required=False, default=None, description='Prediction Settings'), 'pvforecast': FieldInfo(annotation=Union[PVForecastCommonSettings, NoneType], required=False, default=None, description='PV Forecast Settings'), 'server': FieldInfo(annotation=Union[ServerCommonSettings, NoneType], required=False, default=None, description='Server Settings'), 'utils': FieldInfo(annotation=Union[UtilsCommonSettings, NoneType], required=False, default=None, description='Utilities Settings'), 'weather': FieldInfo(annotation=Union[WeatherCommonSettings, NoneType], required=False, default=None, description='Weather Settings')}

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') → 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.
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(_BaseModel__context: Any) → None: Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

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.

classmethod model_validate(obj: Any, *, strict: bool | None = None, from_attributes: bool | None = None, context: Any | None = None) → Self

Validate a pydantic model instance.

Parameters:

obj – The object to validate.
strict – Whether to enforce types strictly.
from_attributes – Whether to extract data from object attributes.
context – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Returns:

The validated model instance.

classmethod model_validate_json(json_data: str | bytes | bytearray, *, strict: bool | None = None, context: Any | None = None) → Self

Usage docs: https://docs.pydantic.dev/2.10/concepts/json/#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.
context – Extra variables to pass to the validator.

Returns:

The validated Pydantic model.

Raises:

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

classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, context: Any | None = None) → Self

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.
context – Extra variables to pass to the validator.

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

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.

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’} ```

classmethod settings_customise_sources(settings_cls: type[BaseSettings], init_settings: PydanticBaseSettingsSource, env_settings: PydanticBaseSettingsSource, dotenv_settings: PydanticBaseSettingsSource, file_secret_settings: PydanticBaseSettingsSource) → tuple[PydanticBaseSettingsSource, ...]

Define the sources and their order for loading the settings values.

Parameters:

settings_cls – The Settings class.
init_settings – The InitSettingsSource instance.
env_settings – The EnvSettingsSource instance.
dotenv_settings – The DotEnvSettingsSource instance.
file_secret_settings – The SecretsSettingsSource instance.

Returns:

A tuple containing the sources and their order for loading the settings values.

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

classmethod validate(value: Any) → Self