akkudoktoreos.core.ems.EnergyManagement

class akkudoktoreos.core.ems.EnergyManagement(*args: ~typing.Any, load_energy_array: ~numpydantic.vendor.nptyping.base_meta_classes.NDArray[~numpydantic.vendor.nptyping.base_meta_classes.Shape[*], (<class 'numpy.float16'>, <class 'numpy.float32'>, <class 'numpy.float64'>, <class 'numpy.float32'>, <class 'numpy.float64'>)] | None = None, pv_prediction_wh: ~numpydantic.vendor.nptyping.base_meta_classes.NDArray[~numpydantic.vendor.nptyping.base_meta_classes.Shape[*], (<class 'numpy.float16'>, <class 'numpy.float32'>, <class 'numpy.float64'>, <class 'numpy.float32'>, <class 'numpy.float64'>)] | None = None, elect_price_hourly: ~numpydantic.vendor.nptyping.base_meta_classes.NDArray[~numpydantic.vendor.nptyping.base_meta_classes.Shape[*], (<class 'numpy.float16'>, <class 'numpy.float32'>, <class 'numpy.float64'>, <class 'numpy.float32'>, <class 'numpy.float64'>)] | None = None, elect_revenue_per_hour_arr: ~numpydantic.vendor.nptyping.base_meta_classes.NDArray[~numpydantic.vendor.nptyping.base_meta_classes.Shape[*], (<class 'numpy.float16'>, <class 'numpy.float32'>, <class 'numpy.float64'>, <class 'numpy.float32'>, <class 'numpy.float64'>)] | None = None, battery: ~akkudoktoreos.devices.battery.Battery | None = None, ev: ~akkudoktoreos.devices.battery.Battery | None = None, home_appliance: ~akkudoktoreos.devices.generic.HomeAppliance | None = None, inverter: ~akkudoktoreos.devices.inverter.Inverter | None = None, ac_charge_hours: ~numpydantic.vendor.nptyping.base_meta_classes.NDArray[~numpydantic.vendor.nptyping.base_meta_classes.Shape[*], (<class 'numpy.float16'>, <class 'numpy.float32'>, <class 'numpy.float64'>, <class 'numpy.float32'>, <class 'numpy.float64'>)] | None = None, dc_charge_hours: ~numpydantic.vendor.nptyping.base_meta_classes.NDArray[~numpydantic.vendor.nptyping.base_meta_classes.Shape[*], (<class 'numpy.float16'>, <class 'numpy.float32'>, <class 'numpy.float64'>, <class 'numpy.float32'>, <class 'numpy.float64'>)] | None = None, ev_charge_hours: ~numpydantic.vendor.nptyping.base_meta_classes.NDArray[~numpydantic.vendor.nptyping.base_meta_classes.Shape[*], (<class 'numpy.float16'>, <class 'numpy.float32'>, <class 'numpy.float64'>, <class 'numpy.float32'>, <class 'numpy.float64'>)] | None = None)

Bases: SingletonMixin, ConfigMixin, PredictionMixin, PydanticBaseModel

__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)
`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])	Custom constructor to handle deserialization for DateTime fields.
`model_copy`(*[, update, deep])	Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy
`model_dump`(*args[, include_computed_fields])	Custom dump method to handle serialization for DateTime fields.
`model_dump_json`(*args[, indent])	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, ...])
`reset`()
`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_hour, force_enable, force_update])	Run energy management.
`schema`([by_alias, ref_template])
`schema_json`(*[, by_alias, ref_template])
`set_akku_ac_charge_hours`(ds)
`set_akku_dc_charge_hours`(ds)
`set_akku_discharge_hours`(ds)
`set_ev_charge_hours`(ds)
`set_home_appliance_start`(ds[, global_start_hour])
`set_nested_value`(path, value)	Set a nested value in the model using a '/'-separated path.
`set_parameters`(parameters[, ev, ...])
`set_start_datetime`([start_datetime])	Set the start datetime for the next energy management cycle.
`set_start_hour`([start_hour])	Sets start datetime to given hour.
`simulate`(start_hour)	Simulate energy usage and costs for the given start hour.
`simulate_start_now`()
`to_dict`()	Convert this PredictionRecord instance to a dictionary representation.
`to_json`()	Convert the PydanticBaseModel instance to a JSON string.
`update_forward_refs`(**localns)
`validate`(value)
`validate_and_convert_pendulum`(value, info)	Validator to convert fields of type pendulum.DateTime.

Attributes

`config`	Convenience method/ attribute to retrieve the EOS configuration data.
`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`	Convenience method/ attribute to retrieve the EOS prediction data.
`start_datetime`	The starting datetime of the current or latest energy management.
`load_energy_array`
`pv_prediction_wh`
`elect_price_hourly`
`elect_revenue_per_hour_arr`
`battery`
`ev`
`home_appliance`
`inverter`
`ac_charge_hours`
`dc_charge_hours`
`ev_charge_hours`

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

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

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

Set the start datetime for the next energy management cycle.

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

load_energy_array: float64'>)] | None

pv_prediction_wh: float64'>)] | None

elect_price_hourly: float64'>)] | None

elect_revenue_per_hour_arr: float64'>)] | None

battery: Battery | None

ev: Battery | None

home_appliance: HomeAppliance | None

inverter: Inverter | None

ac_charge_hours: float64'>)] | None

dc_charge_hours: float64'>)] | None

ev_charge_hours: float64'>)] | None

__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.

set_parameters(parameters: EnergyManagementParameters, ev: Battery | None = None, home_appliance: HomeAppliance | None = None, inverter: Inverter | None = None) → None

set_akku_discharge_hours(ds: ndarray) → None

set_akku_ac_charge_hours(ds: ndarray) → None

set_akku_dc_charge_hours(ds: ndarray) → None

set_ev_charge_hours(ds: ndarray) → None

set_home_appliance_start(ds: int, global_start_hour: int = 0) → None

reset() → None

run(start_hour: int | None = None, force_enable: bool | None = False, force_update: bool | None = False) → None

Run energy management.

Sets start_datetime to current hour, updates the configuration and the prediction, and starts simulation at current hour.

Parameters:

start_hour (int, optional) – Hour to take as start time for the energy management. Defaults
now. (to)
force_enable (bool, optional) – If True, forces to update even if disabled. This
providers. (is mostly relevant to prediction)
force_update (bool, optional) – If True, forces to update the data even if still cached.

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.

set_start_hour(start_hour: int | None = None) → None

Sets start datetime to given hour.

Parameters:

start_hour (int, optional) – Hour to take as start time for the energy management. Defaults
now. (to)

simulate_start_now() → dict[str, Any]

simulate(start_hour: int) → dict[str, Any]

Simulate energy usage and costs for the given start hour.

akku_soc_pro_stunde begin of the hour, initial hour state! last_wh_pro_stunde integral of last hour (end state)

__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.

__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, 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.

property config: Any

Convenience method/ attribute to retrieve the EOS configuration data.

Returns:: The configuration.
Return type:: ConfigEOS

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: ClassVar[dict[str, ComputedFieldInfo]] = {'start_datetime': ComputedFieldInfo(wrapped_property=<property object>, return_type=<class 'pendulum.datetime.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)}

classmethod model_construct(_fields_set: set[str] | None = None, **values: Any) → PydanticBaseModel: Custom constructor to handle deserialization for DateTime fields.

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.

model_dump(*args: Any, include_computed_fields: bool = True, **kwargs: Any) → dict[str, Any]: Custom dump method to handle serialization for DateTime fields.

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

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]] = {'ac_charge_hours': FieldInfo(annotation=Union[NDArray, NoneType], required=False, default=None, description='TBD'), 'battery': FieldInfo(annotation=Union[Battery, NoneType], required=False, default=None, description='TBD.'), 'dc_charge_hours': FieldInfo(annotation=Union[NDArray, NoneType], required=False, default=None, description='TBD'), 'elect_price_hourly': FieldInfo(annotation=Union[NDArray, NoneType], required=False, default=None, description='An array of floats representing the electricity price in euros per watt-hour for different time intervals.'), 'elect_revenue_per_hour_arr': FieldInfo(annotation=Union[NDArray, NoneType], required=False, default=None, description='An array of floats representing the feed-in compensation in euros per watt-hour.'), 'ev': FieldInfo(annotation=Union[Battery, NoneType], required=False, default=None, description='TBD.'), 'ev_charge_hours': FieldInfo(annotation=Union[NDArray, NoneType], required=False, default=None, description='TBD'), 'home_appliance': FieldInfo(annotation=Union[HomeAppliance, NoneType], required=False, default=None, description='TBD.'), 'inverter': FieldInfo(annotation=Union[Inverter, NoneType], required=False, default=None, description='TBD.'), 'load_energy_array': FieldInfo(annotation=Union[NDArray, NoneType], required=False, default=None, description='An array of floats representing the total load (consumption) in watts for different time intervals.'), 'pv_prediction_wh': FieldInfo(annotation=Union[NDArray, NoneType], required=False, default=None, description='An array of floats representing the forecasted photovoltaic output in watts for different time intervals.')}

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

property prediction: Any

Convenience method/ attribute to retrieve the EOS prediction data.

Returns:: The prediction.
Return type:: Prediction

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.

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

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

classmethod validate(value: Any) → Self

classmethod validate_and_convert_pendulum(value: Any, info: ValidationInfo) → Any

Validator to convert fields of type pendulum.DateTime.

Converts fields to proper pendulum.DateTime objects, ensuring correct input types.

This method is invoked for every field before the field value is set. If the field’s type is pendulum.DateTime, it tries to convert string or timestamp values to pendulum.DateTime objects. If the value cannot be converted, a validation error is raised.

Parameters:

value – The value to be assigned to the field.
info – Validation information for the field.

Returns:

The converted value, if successful.

Raises:

ValidationError – If the value cannot be converted to pendulum.DateTime.