akkudoktoreos.core.dataabc.DataContainer

class akkudoktoreos.core.dataabc.DataContainer(*args: ~typing.Any, providers: ~typing.List[~akkudoktoreos.core.dataabc.DataProvider] = <factory>)

Bases: SingletonMixin, DataBase, MutableMapping

A container for managing multiple DataProvider instances.

This class enables access to data from multiple data providers, supporting retrieval and aggregation of their data as Pandas Series objects. It acts as a dictionary-like structure where each key represents a specific data field, and the value is a Pandas Series containing combined data from all DataProvider instances for that key.

Note

Derived classes have to provide their own providers field with correct provider type set.

__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.

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.

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])

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.

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.

track_nested_value(path, callback)

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

update([E, ]**F)

If E present and has a .keys() method, does: for k in E.keys(): 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)

values()

Attributes

config

ems

ems_start_datetime

enabled_providers

List of providers that are currently enabled.

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.

providers

providers: List[DataProvider]
classmethod check_providers(value: List[DataProvider]) → List[DataProvider]
property enabled_providers: List[Any]

List of providers that are currently enabled.

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.

__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.

__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.

__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.

__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.

__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

__repr__() → str

Provide a string representation of the DataContainer instance.

Returns:

A string representing the container and its contained providers.

Return type:

str

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.

__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

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.

clear() → None.  Remove all items from D.
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
copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False) → 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]
ems = EnergyManagement(start_datetime=DateTime(2025, 11, 9, 8, 0, 0, tzinfo=Timezone('Etc/UTC')), last_run_datetime=None)
ems_start_datetime = DateTime(2025, 11, 9, 8, 0, 0, tzinfo=Timezone('Etc/UTC'))
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” ```

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
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
model_computed_fields = {}
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 = {'providers': FieldInfo(annotation=List[DataProvider], required=False, default_factory=list, description='List of data 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', *, 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:

    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.

classmethod model_validate(obj: Any, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, from_attributes: bool | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

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.

classmethod model_validate_json(json_data: str | bytes | bytearray, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self
!!! 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.

classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | 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.

  • 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
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.

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’} ```

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

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.keys(): 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

classmethod update_forward_refs(**localns: Any) → None
classmethod validate(value: Any) → Self
values() → an object providing a view on D's values
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.

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.

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”)