akkudoktoreos.prediction.prediction.Prediction

class akkudoktoreos.prediction.prediction.Prediction(*args: ~typing.Any, providers: ~typing.List[~akkudoktoreos.prediction.elecpriceakkudoktor.ElecPriceAkkudoktor | ~akkudoktoreos.prediction.elecpriceimport.ElecPriceImport | ~akkudoktoreos.prediction.loadakkudoktor.LoadAkkudoktor | ~akkudoktoreos.prediction.loadimport.LoadImport | ~akkudoktoreos.prediction.pvforecastakkudoktor.PVForecastAkkudoktor | ~akkudoktoreos.prediction.pvforecastimport.PVForecastImport | ~akkudoktoreos.prediction.weatherbrightsky.WeatherBrightSky | ~akkudoktoreos.prediction.weatherclearoutside.WeatherClearOutside | ~akkudoktoreos.prediction.weatherimport.WeatherImport] = <factory>)

Bases: PredictionContainer

Prediction container to manage multiple prediction providers.

providers

List of forecast provider instances, in the order they should be updated. Providers may depend on updates from others.

Type:: List[Union[PVForecastAkkudoktor, WeatherBrightSky, WeatherClearOutside]]

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

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__`(args, *kwargs)	Create a new model by parsing and validating input data from keyword arguments.
`check_providers`(value)
`clear`()
`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`(k[,d])
`get_nested_value`(path)	Retrieve a nested value from the model using a '/'-separated path.
`historic_hours_min`()	Return the minimum historic prediction hours for specific data.
`items`()
`json`(*[, include, exclude, by_alias, ...])
`key_to_array`(key[, start_datetime, ...])	Retrieve an array indexed by fixed time intervals for a specified key from the data in each DataProvider.
`key_to_series`(key[, start_datetime, ...])	Extract a series indexed by the date_time field from data records within an optional date range.
`keys`()
`keys_to_dataframe`(keys[, start_datetime, ...])	Retrieve a dataframe indexed by fixed time intervals for specified keys from the data in each DataProvider.
`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, ...])
`pop`(k[,d])	If key is not found, d is returned if given, otherwise KeyError is raised.
`popitem`()	as a 2-tuple; but raise KeyError if D is empty.
`provider_by_id`(provider_id)	Retrieves a data provider by its unique identifier.
`reset_instance`()	Resets the singleton instance, forcing it to be recreated on next access.
`reset_to_defaults`()	Resets the fields to their default values.
`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.
`setdefault`(k[,d])
`to_dict`()	Convert this PredictionRecord instance to a dictionary representation.
`to_json`()	Convert the PydanticBaseModel instance to a JSON string.
`update`([E, ]**F)	If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v
`update_data`([force_enable, force_update])	Update data.
`update_forward_refs`(**localns)
`validate`(value)
`validate_and_convert_pendulum`(value, info)	Validator to convert fields of type pendulum.DateTime.
`values`()

Attributes

`config`	Convenience method/ attribute to retrieve the EOS configuration data.
`ems`	Convenience method/ attribute to retrieve the EOS energy management system.
`enabled_providers`	List of providers that are currently enabled.
`end_datetime`	Compute the end datetime based on the start_datetime and hours.
`keep_datetime`	Compute the keep datetime for historical data retention.
`keep_hours`	Compute the hours from keep_datetime to start_datetime.
`measurement`	Convenience method/ attribute to retrieve the EOS measurement 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.
`record_keys`	Returns the keys of all fields in the data records of all enabled providers.
`record_keys_writable`	Returns the keys of all fields in the data records that are writable of all enabled providers.
`start_datetime`	Returns the start datetime of the current or latest energy management.
`total_hours`	Compute the hours from start_datetime to end_datetime.
`providers`

providers: List[ElecPriceAkkudoktor | ElecPriceImport | LoadAkkudoktor | LoadImport | PVForecastAkkudoktor | PVForecastImport | WeatherBrightSky | WeatherClearOutside | WeatherImport]

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

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

__delitem__(key: str) → None

Set the value of the specified key in the data records of each provider to None.

Parameters:: key (str) – The field name in DataRecords to clear.
Raises:: KeyError – If the key is not found in any provider.

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.

__getitem__(key: str) → Series

Retrieve a Pandas Series for a specified key from the data in each DataProvider.

Iterates through providers to find and return the first available Series for the specified key.

Parameters:: key (str) – The field name to retrieve, representing a data attribute in DataRecords.
Returns:: A Pandas Series containing aggregated data for the specified key.
Return type:: pd.Series
Raises:: KeyError – If no provider contains data for the specified key.

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

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__() → Iterator[str]

Return an iterator over all unique keys available across providers.

Returns:: An iterator over the unique keys from all providers.
Return type:: Iterator[str]

__len__() → int

Return the number of keys in the container.

Returns:: The total number of keys in this container.
Return type:: int

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__() → str

Provide a string representation of the DataContainer instance.

Returns:: A string representing the container and its contained providers.
Return type:: str

__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.

__setitem__(key: str, value: Series) → None

Add or merge a Pandas Series for a specified key into the records of an appropriate provider.

Attempts to update or insert the provided Series data in each provider. If no provider supports the specified key, an error is raised.

Parameters:

key (str) – The field name to update, representing a data attribute in DataRecords.
value (pd.Series) – A Pandas Series containing data for the specified key.

Raises:

ValueError – If value is not an instance of pd.Series.
KeyError – If no provider supports the specified key.

classmethod check_providers(value: List[DataProvider]) → List[DataProvider]

clear() → None. Remove all items from D.

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]

property ems: Any

Convenience method/ attribute to retrieve the EOS energy management system.

Returns:: The energy management system.
Return type:: EnergyManagementSystem

property enabled_providers: List[Any]: List of providers that are currently enabled.

property end_datetime: DateTime | None

Compute the end datetime based on the start_datetime and hours.

Ajusts the calculated end time if DST transitions occur within the prediction window.

Returns:: The calculated end datetime, or None if inputs are missing.
Return type:: Optional[DateTime]

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(k[, d]) → D[k] if k in D, else d. d defaults to None.

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” ```

historic_hours_min() → int

Return the minimum historic prediction hours for specific data.

To be implemented by derived classes if default 0 is not appropriate.

items() → a set-like object providing a view on D's items

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

property keep_datetime: DateTime | None

Compute the keep datetime for historical data retention.

Returns:: The calculated retention cutoff datetime, or None if inputs are missing.
Return type:: Optional[DateTime]

property keep_hours: int | None

Compute the hours from keep_datetime to start_datetime.

Returns:: The duration hours, or None if either datetime is unavailable.
Return type:: Optional[pendulum.period]

key_to_array(key: str, start_datetime: DateTime | None = None, end_datetime: DateTime | None = None, interval: Duration | None = None, fill_method: str | None = None) → Any]

Retrieve an array indexed by fixed time intervals for a specified key from the data in each DataProvider.

Iterates through providers to find and return the first available array for the specified key.

Parameters:

key (str) – The field name to retrieve, representing a data attribute in DataRecords.
start_datetime (datetime, optional) – The start date for filtering the records (inclusive).
end_datetime (datetime, optional) – The end date for filtering the records (exclusive).
interval (duration, optional) – The fixed time interval. Defaults to 1 hour.
fill_method (str) – Method to handle missing values during resampling. - ‘linear’: Linearly interpolate missing values (for numeric data only). - ‘ffill’: Forward fill missing values. - ‘bfill’: Backward fill missing values. - ‘none’: Defaults to ‘linear’ for numeric values, otherwise ‘ffill’.

Returns:

A NumPy array containing aggregated data for the specified key.

Return type:

np.ndarray

Raises:

KeyError – If no provider contains data for the specified key.

key_to_series(key: str, start_datetime: DateTime | None = None, end_datetime: DateTime | None = None, dropna: bool | None = None) → Series

Extract a series indexed by the date_time field from data records within an optional date range.

Iterates through providers to find and return the first available series for the specified key.

Parameters:

key (str) – The field name in the DataRecord from which to extract values.
start_datetime (datetime, optional) – The start date for filtering the records (inclusive).
end_datetime (datetime, optional) – The end date for filtering the records (exclusive).
dropna – (bool, optional): Whether to drop NAN/ None values before processing. Defaults to True.

Returns:

A Pandas Series with the index as the date_time of each record: and the values extracted from the specified key.

Return type:

pd.Series

Raises:

KeyError – If the specified key is not found in any of the DataRecords.

keys() → a set-like object providing a view on D's keys

keys_to_dataframe(keys: list[str], start_datetime: DateTime | None = None, end_datetime: DateTime | None = None, interval: Any | None = None, fill_method: str | None = None) → DataFrame

Retrieve a dataframe indexed by fixed time intervals for specified keys from the data in each DataProvider.

Generates a pandas DataFrame using the NumPy arrays for each specified key, ensuring a common time index..

Parameters:

keys (list[str]) – A list of field names to retrieve.
start_datetime (datetime, optional) – Start date for filtering records (inclusive).
end_datetime (datetime, optional) – End date for filtering records (exclusive).
interval (duration, optional) – The fixed time interval. Defaults to 1 hour.
fill_method (str, optional) – Method to handle missing values during resampling. - ‘linear’: Linearly interpolate missing values (for numeric data only). - ‘ffill’: Forward fill missing values. - ‘bfill’: Backward fill missing values. - ‘none’: Defaults to ‘linear’ for numeric values, otherwise ‘ffill’.

Returns:

A DataFrame where each column represents a key’s array with a common time index.

Return type:

pd.DataFrame

Raises:

KeyError – If no valid data is found for any of the requested keys.
ValueError – If any retrieved array has a different time index than the first one.

property measurement: Any

Convenience method/ attribute to retrieve the EOS measurement data.

Returns:: The measurement.
Return type:: Measurement

model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {'end_datetime': ComputedFieldInfo(wrapped_property=<property object>, return_type=typing.Optional[pendulum.datetime.DateTime], alias=None, alias_priority=None, title=None, field_title_generator=None, description='Compute the end datetime based on the `start_datetime` and `hours`.\n\nAjusts the calculated end time if DST transitions occur within the prediction window.\n\nReturns:\n Optional[DateTime]: The calculated end datetime, or `None` if inputs are missing.', deprecated=None, examples=None, json_schema_extra=None, repr=True), 'keep_datetime': ComputedFieldInfo(wrapped_property=<property object>, return_type=typing.Optional[pendulum.datetime.DateTime], alias=None, alias_priority=None, title=None, field_title_generator=None, description='Compute the keep datetime for historical data retention.\n\nReturns:\n Optional[DateTime]: The calculated retention cutoff datetime, or `None` if inputs are missing.', deprecated=None, examples=None, json_schema_extra=None, repr=True), 'keep_hours': ComputedFieldInfo(wrapped_property=<property object>, return_type=typing.Optional[int], alias=None, alias_priority=None, title=None, field_title_generator=None, description='Compute the hours from `keep_datetime` to `start_datetime`.\n\nReturns:\n Optional[pendulum.period]: The duration hours, or `None` if either datetime is unavailable.', deprecated=None, examples=None, json_schema_extra=None, repr=True), 'start_datetime': ComputedFieldInfo(wrapped_property=<property object>, return_type=typing.Optional[pendulum.datetime.DateTime], alias=None, alias_priority=None, title=None, field_title_generator=None, description='Returns the start datetime of the current or latest energy management.\n\nReturns:\n DateTime: The starting datetime of the current or latest energy management, or None.', deprecated=None, examples=None, json_schema_extra=None, repr=True), 'total_hours': ComputedFieldInfo(wrapped_property=<property object>, return_type=typing.Optional[int], alias=None, alias_priority=None, title=None, field_title_generator=None, description='Compute the hours from `start_datetime` to `end_datetime`.\n\nReturns:\n Optional[pendulum.period]: The duration hours, or `None` if either datetime is unavailable.', 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) → 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]] = {'providers': FieldInfo(annotation=List[Union[ElecPriceAkkudoktor, ElecPriceImport, LoadAkkudoktor, LoadImport, PVForecastAkkudoktor, PVForecastImport, WeatherBrightSky, WeatherClearOutside, WeatherImport]], required=False, default_factory=list, description='List of prediction providers')}

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

pop(k[, d]) → v, remove specified key and return the corresponding value.: If key is not found, d is returned if given, otherwise KeyError is raised.

popitem() → (k, v), remove and return some (key, value) pair: as a 2-tuple; but raise KeyError if D is empty.

provider_by_id(provider_id: str) → DataProvider

Retrieves a data provider by its unique identifier.

This method searches through the list of all available providers and returns the first provider whose provider_id matches the given provider_id. If no matching provider is found, the method returns None.

Parameters:: provider_id (str) – The unique identifier of the desired data provider.
Returns:: The data provider matching the given provider_id.
Return type:: DataProvider
Raises:: ValueError if provider id is unknown. –

Example

provider = data.provider_by_id(“WeatherImport”)

property record_keys: list[str]: Returns the keys of all fields in the data records of all enabled providers.

property record_keys_writable: list[str]: Returns the keys of all fields in the data records that are writable of all enabled providers.

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’} ```

setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D

property start_datetime: DateTime | None

Returns the start datetime of the current or latest energy management.

Returns:: The starting datetime of the current or latest energy management, or None.
Return type:: DateTime

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

property total_hours: int | None

Compute the hours from start_datetime to end_datetime.

Returns:: The duration hours, or None if either datetime is unavailable.
Return type:: Optional[pendulum.period]

update([E, ]**F) → None. Update D from mapping/iterable E and F.: If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

update_data(force_enable: bool | None = False, force_update: bool | None = False) → None

Update data.

Parameters:

force_enable (bool, optional) – If True, forces the update even if a provider is disabled.
force_update (bool, optional) – If True, forces the providers to update the data even if still cached.

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.

values() → an object providing a view on D's values