akkudoktoreos.prediction.predictionabc.PredictionImportProvider

class akkudoktoreos.prediction.predictionabc.PredictionImportProvider(*args: Any, records: list[DataRecord] = <factory>, update_datetime: AwareDatetime | None = None)

Bases: PredictionProvider, DataImportProvider

Abstract base class for prediction providers that import prediction data.

This class is designed to handle prediction data provided in the form of a key-value dictionary.

Keys: Represent identifiers from the record keys of a specific prediction.
Values: Are lists of prediction values starting at a specified start_datetime, where
each value corresponds to a subsequent time interval (e.g., hourly).

Subclasses must implement the logic for managing prediction data based on the imported records.

__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.
`construct`([_fields_set])
`copy`(*[, include, exclude, update, deep])	Returns a copy of the model.
`db_autosave`()
`db_compact`([compact_tiers])	Apply tiered compaction policy to all records in this namespace.
`db_compact_tiers`()	Compaction tiers as (age_threshold, target_interval) pairs.
`db_count_records`()	Return total logical number of records.
`db_delete_records`([start_timestamp, ...])
`db_generate_timestamps`(start_timestamp, ...)	Generate database timestamps using fixed absolute time stepping.
`db_get_record`(target_timestamp, *[, time_window])	Get the record at or nearest to the specified timestamp.
`db_get_stats`()	Get comprehensive statistics about database storage.
`db_initial_time_window`()	Return the initial time window used for database loading.
`db_insert_record`(record, *[, mark_dirty])
`db_iterate_records`([start_timestamp, ...])	Iterate records in requested range.
`db_keep_datetime`()	Earliest datetime from which database records should be retained.
`db_keep_duration`()	Duration for which database records should be retained.
`db_load_records`([start_timestamp, end_timestamp])	Load records from database into memory.
`db_mark_dirty_record`(record)
`db_namespace`()	Namespace of database.
`db_next_timestamp`(timestamp)	Find the smallest timestamp > given timestamp.
`db_previous_timestamp`(timestamp)	Find the largest timestamp < given timestamp.
`db_save_records`()
`db_timestamp_range`()	Get the timestamp range of records in database.
`db_vacuum`([keep_hours, keep_timestamp])	Remove old records from database to free space.
`delete_by_datetime`([start_datetime, ...])	Delete records in the given datetime range.
`dict`(*[, include, exclude, by_alias, ...])
`enabled`()	Return True if the provider is enabled according to configuration.
`field_deprecated`(field_name)	Return the deprecated metadata of a model field, if available.
`field_description`(field_name)	Return a human-readable description for a model field.
`field_examples`(field_name)	Return the examples metadata of a model field, if available.
`from_dict`(data)	Reconstruct a sequence from its serialized dictionary form.
`from_json`(json_str)	Create an instance of the PydanticBaseModel class or its subclass from a JSON string.
`from_orm`(obj)
`get_by_datetime`(target_datetime, *[, ...])	Get the record at the specified datetime, with an optional fallback search window.
`get_nearest_by_datetime`(target_datetime[, ...])	Get the record nearest to the specified datetime within an optional time window.
`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.
`import_from_dataframe`(df[, key_prefix, ...])	Updates generic data by importing it from a pandas DataFrame.
`import_from_dict`(import_data[, key_prefix, ...])	Updates generic data by importing it from a dictionary.
`import_from_file`(import_file_path[, ...])	Updates generic data by importing it from a file.
`import_from_json`(json_str[, key_prefix, ...])	Updates generic data by importing it from a JSON string.
`insert_by_datetime`(record)	Insert or merge a DataRecord into the sequence based on its date.
`json`(*[, include, exclude, by_alias, ...])
`key_delete_by_datetime`(key[, ...])	Delete an attribute specified by key from records in the sequence within a given datetime range.
`key_from_lists`(key, dates, values)	Update the DataSequence from lists of datetime and value elements.
`key_from_series`(key, series)	Update the DataSequence from a Pandas Series.
`key_to_array`(key, start_datetime, ...)	Extract an array indexed by fixed time intervals from data records within an optional date range.
`key_to_dict`(key[, start_datetime, ...])	Extract a dictionary indexed by the date_time field of the DataRecords.
`key_to_lists`(key[, start_datetime, ...])	Extracts two lists from data records within an optional date range.
`key_to_series`(key[, start_datetime, ...])	Extract a series indexed by the date_time field from data records within an optional date range.
`key_to_value`(key, target_datetime[, time_window])	Returns the value corresponding to the specified key that is nearest to the given datetime.
`load`()	Load data records from from persistent storage.
`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, ...])
`provider_id`()	Return the unique identifier for the data provider.
`record_class`()	Get the class of the data record handled by this data sequence.
`reset_instance`()	Resets the singleton instance, forcing it to be recreated on next access.
`reset_to_defaults`()	Resets the fields to their default values.
`save`()	Save data records to persistent storage.
`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.
`to_dataframe`([start_datetime, end_datetime])	Converts the sequence of DataRecord instances into a Pandas DataFrame.
`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_data`([force_enable, force_update])	Update prediction parameters and call the custom update function.
`update_forward_refs`(**localns)
`update_value`(date, args, *kwargs)	Updates specific values in the data record for a given date.
`validate`(value)

Attributes

`config`
`database`
`db_enabled`
`ems`
`ems_start_datetime`
`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.
`max_datetime`	Maximum (latest) datetime in the time series sequence of data records.
`measurement`
`min_datetime`	Minimum (earliest) datetime in the time series sequence of data records.
`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.
`record_keys_writable`	Get the keys of all writable fields in the data records.
`total_hours`	Compute the hours from start_datetime to end_datetime.
`update_datetime`
`records`

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

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

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

Hook into generating the model’s JSON schema.

Parameters:

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

Returns:

A JSON schema, as a Python object.

__hash__() → int

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

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

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

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[DataRecord]

Create an iterator for accessing DataRecords sequentially.

Returns:: An iterator for the records.
Return type:: Iterator[DataRecord]

__len__() → int: Get total number of DataRecords in sequence (DB + memory-only).

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__() → str

Provide a string representation of the DataSequence.

Returns:: A string representation of the DataSequence.
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.

config = ConfigEOS(general=GeneralSettings(home_assistant_addon=False, version='0.2.0.dev58204789', data_folder_path=Path('/home/docs/.local/share/net.akkudoktor.eos'), data_output_subpath='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/checkouts/readthedocs.org/user_builds/akkudoktor-eos/checkouts/latest/docs'), config_file_path=Path('/home/docs/checkouts/readthedocs.org/user_builds/akkudoktor-eos/checkouts/latest/docs/EOS.config.json')), cache=CacheCommonSettings(subpath='cache', cleanup_interval=300.0), database=DatabaseCommonSettings(provider=None, compression_level=9, initial_load_window_h=None, keep_duration_h=None, autosave_interval_sec=10, compaction_interval_sec=604800, batch_size=100, providers=['LMDB', 'SQLite']), ems=EnergyManagementCommonSettings(startup_delay=5, interval=300.0, 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(historic_hours=17520, 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), keys=[]), prediction=PredictionCommonSettings(hours=48, historic_hours=48), elecprice=ElecPriceCommonSettings(provider=None, charges_kwh=None, vat_rate=1.19, elecpriceimport=ElecPriceImportCommonSettings(import_file_path=None, import_json=None), energycharts=ElecPriceEnergyChartsCommonSettings(bidding_zone=<EnergyChartsBiddingZones.DE_LU: 'DE-LU'>), providers=['ElecPriceAkkudoktor', 'ElecPriceEnergyCharts', 'ElecPriceImport']), feedintariff=FeedInTariffCommonSettings(provider=None, provider_settings=FeedInTariffCommonProviderSettings(FeedInTariffFixed=None, FeedInTariffImport=None), providers=['FeedInTariffFixed', 'FeedInTariffImport']), load=LoadCommonSettings(provider=None, provider_settings=LoadCommonProviderSettings(LoadAkkudoktor=None, LoadVrm=None, LoadImport=None), providers=['LoadAkkudoktor', 'LoadAkkudoktorAdjusted', 'LoadVrm', 'LoadImport']), pvforecast=PVForecastCommonSettings(provider=None, provider_settings=PVForecastCommonProviderSettings(PVForecastImport=None, PVForecastVrm=None), planes=None, max_planes=0, providers=['PVForecastAkkudoktor', 'PVForecastVrm', 'PVForecastImport'], planes_peakpower=[], planes_azimuth=[], planes_tilt=[], planes_userhorizon=[], planes_inverter_paco=[]), weather=WeatherCommonSettings(provider=None, provider_settings=WeatherCommonProviderSettings(WeatherImport=None), providers=['BrightSky', 'ClearOutside', 'WeatherImport']), server=ServerCommonSettings(host='127.0.0.1', port=8503, verbose=False, startup_eosdash=True, eosdash_host=None, eosdash_port=None), utils=UtilsCommonSettings(), adapter=AdapterCommonSettings(provider=None, homeassistant=HomeAssistantAdapterCommonSettings(config_entity_ids=None, load_emr_entity_ids=None, grid_export_emr_entity_ids=None, grid_import_emr_entity_ids=None, pv_production_emr_entity_ids=None, device_measurement_entity_ids=None, device_instruction_entity_ids=None, solution_entity_ids=None, homeassistant_entity_ids=[], eos_solution_entity_ids=[], eos_device_instruction_entity_ids=[]), nodered=NodeREDAdapterCommonSettings(host='127.0.0.1', port=1880), providers=['HomeAssistant', 'NodeRED']))

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.

database = <akkudoktoreos.core.database.Database object>

db_autosave() → int

db_compact(compact_tiers: list[tuple[Duration, Duration]] | None = None) → int

Apply tiered compaction policy to all records in this namespace.

Tiers are processed coarsest-first (longest age threshold first) to avoid compacting fine-grained data that an inner tier would immediately re-compact anyway.

Parameters:: compact_tiers – Override tiers for this call. If None, uses db_compact_tiers(). Each entry is (age_threshold, target_interval), ordered shortest to longest age threshold.
Returns:: Total number of original records deleted across all tiers.

db_compact_tiers() → list[tuple[Duration, Duration]]

Compaction tiers as (age_threshold, target_interval) pairs.

Records older than age_threshold are downsampled to target_interval. Tiers must be ordered from shortest to longest age threshold.

Default policy:

older than 2 hours → 15 min resolution

older than 14 days → 1 hour resolution

Return empty list to disable compaction entirely. Override in derived classes for domain-specific behaviour.

Example override to disable:

Example override for price data (already at 15 min, skip first tier):

db_count_records() → int

Return total logical number of records.

Memory is authoritative. If DB is enabled but not fully loaded, we conservatively include storage-only records.

db_delete_records(start_timestamp: DatabaseTimestamp | _DatabaseTimestampUnbound | None = None, end_timestamp: DatabaseTimestamp | _DatabaseTimestampUnbound | None = None) → int

property db_enabled: bool

db_generate_timestamps(start_timestamp: DatabaseTimestamp, values_count: int, interval: Duration | None = None) → Iterator[DatabaseTimestamp]

Generate database timestamps using fixed absolute time stepping.

The iterator advances strictly in UTC, guaranteeing constant spacing in seconds across daylight saving transitions.

Returned database timestamps are in UTC. This avoids ambiguity during fall-back transitions and prevents accidental overwriting when inserting into UTC-normalized storage backends.

Parameters:

start_timestamp (DatabaseTimestamp) – Starting database timestamp.
values_count (int) – Number of timestamps to generate.
interval (Optional[Duration]) – Fixed duration between timestamps. Defaults to 1 hour if not provided.

Yields:

DatabaseTimestamp – UTC-based database timestamps.

Raises:

ValueError – If values_count is negative.

db_get_record(target_timestamp: DatabaseTimestamp, *, time_window: Duration | None | _DatabaseTimeWindowUnbound = None) → T_Record | None

Get the record at or nearest to the specified timestamp.

The search strategies are:

None - exact match only.
UNBOUND_WINDOW - nearest record across all stored records.
Duration - nearest record within a symmetric window of this total width around target_timestamp.

Parameters:

target_timestamp – The timestamp to search for.
time_window – Controls the search strategy (None, UNBOUND_WINDOW, Duration).

Returns:

Exact match, nearest record within the window, or None.

db_get_stats() → dict

Get comprehensive statistics about database storage.

Returns:: Dictionary with statistics

db_initial_time_window() → Duration | None

Return the initial time window used for database loading.

This window defines the initial symmetric time span around a target datetime that should be loaded from the database when no explicit search time window is specified. It serves as a loading hint and may be expanded by the caller if no records are found within the initial range.

Subclasses may override this method to provide a domain-specific default.

Returns:: The initial loading time window as a Duration, or None to indicate that no initial window constraint should be applied.

db_insert_record(record: T_Record, *, mark_dirty: bool = True) → None

db_iterate_records(start_timestamp: DatabaseTimestamp | _DatabaseTimestampUnbound | None = None, end_timestamp: DatabaseTimestamp | _DatabaseTimestampUnbound | None = None) → Iterator[T_Record]

Iterate records in requested range.

Ensures storage is loaded into memory first, then iterates over in-memory records only.

db_keep_datetime() → DateTime | None

Earliest datetime from which database records should be retained.

Used when removing old records from database to free space.

Subclasses may override this method to provide a domain-specific default.

Returns:: Datetime or None.

db_keep_duration() → Duration | None

Duration for which database records should be retained.

Used when removing old records from database to free space.

Defaults to general database configuration.

May be provided by derived class.

Returns:: Duration or None (forever).

db_load_records(start_timestamp: DatabaseTimestamp | _DatabaseTimestampUnbound | None = None, end_timestamp: DatabaseTimestamp | _DatabaseTimestampUnbound | None = None) → int

Load records from database into memory.

Merges database records into in-memory records while preserving: - Memory-only records - Sorted order - No duplicates (DB overwrites memory)

This requested load range is extended to include the first record < start_timestamp and the first record >= end_timestamp, so nearest-neighbor searches do not require additional DB lookups.

The _db_loaded_range is updated to reflect the total timestamp span currently present in memory after this method completes.

Parameters:

start_timestamp – Load records from this timestamp (inclusive)
end_timestamp – Load records until this timestamp (exclusive)

Returns:

Number of records loaded from database

Note

record.date_time shall be DateTime or None

db_mark_dirty_record(record: T_Record) → None

db_namespace() → str: Namespace of database.

db_next_timestamp(timestamp: DatabaseTimestamp) → DatabaseTimestamp | None

Find the smallest timestamp > given timestamp.

Search memory-first, then fallback to database if necessary.

db_previous_timestamp(timestamp: DatabaseTimestamp) → DatabaseTimestamp | None

Find the largest timestamp < given timestamp.

Search memory-first, then fallback to database if necessary.

db_save_records() → int

db_timestamp_range() → tuple[DatabaseTimestamp | None, DatabaseTimestamp | None]

Get the timestamp range of records in database.

Regards records in storage plus extra records in memory.

db_vacuum(keep_hours: int | None = None, keep_timestamp: DatabaseTimestamp | _DatabaseTimestampUnbound | None = None) → int

Remove old records from database to free space.

Semantics:

keep_hours is relative to the DB’s max timestamp: cutoff = db_max - keep_hours, and records
with timestamp < cutoff are deleted.
keep_timestamp is an absolute cutoff; records with timestamp < cutoff are deleted (exclusive).

Uses self.keep_duration() if both of keep_hours and keep_timestamp are None.

Parameters:

keep_hours – Keep only records from the last N hours (relative to the data’s max timestamp)
keep_timestamp – Keep only records from this timestamp on (absolute cutoff)

Returns:

Number of records deleted

delete_by_datetime(start_datetime: DateTime | None = None, end_datetime: DateTime | None = None) → int

Delete records in the given datetime range.

Deletes records from memory and, if database storage is enabled, from the database. Returns the maximum of in-memory and database deletions.

Parameters:

start_datetime – Start datetime (inclusive)
end_datetime – End datetime (exclusive)

Returns:

Number of records deleted (max of memory and database deletions)

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(2026, 2, 22, 14, 0, 0, tzinfo=Timezone('Etc/UTC')), last_run_datetime=None)

ems_start_datetime = DateTime(2026, 2, 22, 14, 0, 0, tzinfo=Timezone('Etc/UTC'))

abstractmethod enabled() → bool

Return True if the provider is enabled according to configuration.

To be implemented by derived classes.

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 field_deprecated(field_name: str) → str | None

Return the deprecated metadata of a model field, if available.

This method retrieves the Field specification from the model’s model_fields registry and extracts its description from the field’s json_schema_extra / extra metadata (as provided by _field_extra_dict). If the field does not exist or no description is present, None is returned.

Parameters:: field_name (str) – Name of the field whose deprecated info should be returned.
Returns:: The textual deprecated info if present, otherwise None.
Return type:: Optional[str]

classmethod field_description(field_name: str) → str | None

Return a human-readable description for a model field.

Looks up descriptions for both regular and computed fields. Resolution order:

Normal fields:

json_schema_extra[“description”]
field.description

Computed fields:

ComputedFieldInfo.description
function docstring (func.__doc__)
json_schema_extra[“description”]

If a field exists but no description is found, returns “-“. If the field does not exist, returns None.

Parameters:: field_name – Field name.
Returns:: Description string, “-” if missing, or None if not a field.

classmethod field_examples(field_name: str) → list[Any] | None

Return the examples metadata of a model field, if available.

This method retrieves the Field specification from the model’s model_fields registry and extracts its description from the field’s json_schema_extra / extra metadata (as provided by _field_extra_dict). If the field does not exist or no description is present, None is returned.

Parameters:: field_name (str) – Name of the field whose examples should be returned.
Returns:: The examples if present, otherwise None.
Return type:: Optional[list[Any]]

classmethod from_dict(data: dict) → DataSequence

Reconstruct a sequence from its serialized dictionary form.

Fully subclass-safe and invariant-safe.

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_by_datetime(target_datetime: DateTime, *, time_window: Duration | None = None) → DataRecord | None

Get the record at the specified datetime, with an optional fallback search window.

Parameters:

target_datetime – The datetime to search for.
time_window – Optional total width of the symmetric search window centered on target_datetime. If provided and no exact match exists, the nearest record within this window is returned.

Returns:

The matching DataRecord, the nearest DataRecord within the specified time window if no exact match exists, or None if no suitable record is found.

get_nearest_by_datetime(target_datetime: DateTime, time_window: Duration | None = None) → DataRecord | None

Get the record nearest to the specified datetime within an optional time window.

Parameters:

target_datetime – The datetime to search near.
time_window – Total width of the symmetric search window centered on target_datetime. If None, searches all records.

Returns:

The nearest DataRecord within the specified time window, or None if no records exist or no records fall within the window.

Raises:

ValueError – If time_window is negative.

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

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.

import_from_dataframe(df: DataFrame, key_prefix: str = '', start_datetime: DateTime | None = None, interval: Duration | None = None) → None

Updates generic data by importing it from a pandas DataFrame.

This method reads generic data from a DataFrame, matches columns based on the record keys and the provided key_prefix, and updates the data values using the DataFrame’s index as timestamps.

Parameters:

df (pd.DataFrame) – DataFrame containing the generic data with datetime index or sequential values.
key_prefix (str, optional) – A prefix to filter relevant columns from the DataFrame. Only columns starting with this prefix will be considered. Defaults to an empty string.
start_datetime (DateTime, optional) – Start datetime if DataFrame doesn’t have datetime index.
interval (Duration, optional) – The fixed time interval if DataFrame doesn’t have datetime index.

Raises:

ValueError – If DataFrame structure is invalid or datetime conversion fails.

import_from_dict(import_data: dict, key_prefix: str = '', start_datetime: DateTime | None = None, interval: Duration | None = None) → None

Updates generic data by importing it from a dictionary.

This method reads generic data from a dictionary, matches keys based on the record keys and the provided key_prefix, and updates the data values sequentially. All value lists must have the same length.

Parameters:

import_data (dict) – Dictionary containing the generic data with optional ‘start_datetime’ and ‘interval’ keys.
key_prefix (str, optional) – A prefix to filter relevant keys from the generic data. Only keys starting with this prefix will be considered. Defaults to an empty string.
start_datetime (DateTime, optional) – Start datetime of values if not in dict.
interval (Duration, optional) – The fixed time interval if not in dict.

Raises:

ValueError – If value lists have different lengths or if datetime conversion fails.

import_from_file(import_file_path: Path, key_prefix: str = '', start_datetime: DateTime | None = None, interval: Duration | None = None) → None

Updates generic data by importing it from a file.

This method reads generic data from a JSON file, matches keys based on the record keys and the provided key_prefix, and updates the data values sequentially, starting from the start_datetime. Each data value is associated with an hourly interval.

If start_datetime and or interval is given in the JSON dict it will be used. Otherwise the given parameters are used. If None is given start_datetime defaults to ‘self.ems_start_datetime’ and interval defaults to 1 hour.

Parameters:

import_file_path (Path) – The path to the JSON file containing the generic data.
key_prefix (str, optional) – A prefix to filter relevant keys from the generic data. Only keys starting with this prefix will be considered. Defaults to an empty string.
start_datetime (DateTime, optional) – Start datetime of values.
interval (duration, optional) – The fixed time interval. Defaults to 1 hour.

Raises:

FileNotFoundError – If the specified file does not exist.
JSONDecodeError – If the file content is not valid JSON.

Example

Given a JSON file with the following content and key_prefix = “load”, only the “loadforecast_power_w” key will be processed even though both keys are in the record.

{
    "loadforecast_power_w": [20.5, 21.0, 22.1],
    "other_xyz: [10.5, 11.0, 12.1],
}

import_from_json(json_str: str, key_prefix: str = '', start_datetime: DateTime | None = None, interval: Duration | None = None) → None

Updates generic data by importing it from a JSON string.

This method reads generic data from a JSON string, matches keys based on the record keys and the provided key_prefix, and updates the data values sequentially, starting from the start_datetime.

If start_datetime and or interval is given in the JSON dict it will be used. Otherwise the given parameters are used. If None is given start_datetime defaults to ‘self.ems_start_datetime’ and interval defaults to 1 hour.

Parameters:

json_str (str) – The JSON string containing the generic data.
key_prefix (str, optional) – A prefix to filter relevant keys from the generic data. Only keys starting with this prefix will be considered. Defaults to an empty string.
start_datetime (DateTime, optional) – Start datetime of values.
interval (duration, optional) – The fixed time interval. Defaults to 1 hour.

Raises:

JSONDecodeError – If the file content is not valid JSON.

Example

Given a JSON string with the following content and key_prefix = “load”, only the “loadforecast_power_w” key will be processed even though both keys are in the record.

{
    "start_datetime": "2024-11-10 00:00:00",
    "interval": "30 minutes",
    "loadforecast_power_w": [20.5, 21.0, 22.1],
    "other_xyz: [10.5, 11.0, 12.1]
}

insert_by_datetime(record: DataRecord) → None

Insert or merge a DataRecord into the sequence based on its date.

If a record with the same date exists, merges new data fields with the existing record. Otherwise, appends the record and maintains chronological order.

Parameters:: record (DataRecord) – The record to add or merge.

Note

record.date_time shall be a DateTime or None

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_delete_by_datetime(key: str, start_datetime: DateTime | None = None, end_datetime: DateTime | None = None) → None

Delete an attribute specified by key from records in the sequence within a given datetime range.

This method removes the attribute identified by key from records that have a date_time value falling within the specified start_datetime (inclusive) and end_datetime (exclusive) range.

If only start_datetime is specified, attributes will be removed from records from that date onward.
If only end_datetime is specified, attributes will be removed from records up to that date.
If neither start_datetime nor end_datetime is given, the attribute will be removed from all records.

Parameters:

key (str) – The attribute name to delete from each record.
start_datetime (datetime, optional) – The start datetime to begin attribute deletion (inclusive).
end_datetime (datetime, optional) – The end datetime to stop attribute deletion (exclusive).

Raises:

KeyError – If key is not a valid attribute of the records.

key_from_lists(key: str, dates: list[DateTime], values: list[float]) → None

Update the DataSequence from lists of datetime and value elements.

The dates list should represent the date_time of each DataRecord, and the values list should represent the corresponding data values for the specified key.

The list must be ordered starting with the oldest date.

Parameters:

key (str) – The field name in the DataRecord that corresponds to the values in the Series.
dates – List of datetime elements.
values – List of values corresponding to the specified key in the data records.

key_from_series(key: str, series: Series) → None

Update the DataSequence from a Pandas Series.

The series index should represent the date_time of each DataRecord, and the series values should represent the corresponding data values for the specified key.

Parameters:

series (pd.Series) – A Pandas Series containing data to update the DataSequence.
key (str) – The field name in the DataRecord that corresponds to the values in the Series.

key_to_array(key: str, start_datetime: DateTime | None = None, end_datetime: DateTime | None = None, interval: Duration | None = None, fill_method: str | None = None, dropna: bool | None = True, boundary: Literal['strict', 'context'] = 'context', align_to_interval: bool = False) → Any]

Extract an array indexed by fixed time intervals from data records within an optional date range.

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).
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). - ‘time’: 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’.
dropna – (bool, optional): Whether to drop NAN/ None values before processing. Defaults to True.
boundary (Literal["strict", "context"]) – “strict” → only values inside [start, end) “context” → include one value before and after for proper resampling
align_to_interval (bool) –
When True, snap the resample origin to the nearest UTC epoch-aligned boundary of interval before resampling. This ensures that bucket timestamps always fall on wall-clock-round times regardless of when start_datetime falls:
- 15-minute interval → buckets on :00, :15, :30, :45
- 1-hour interval → buckets on the hour
When False (default), the origin is query_start (or "start_day" when no start is given), preserving the existing behaviour where buckets are aligned to the query window rather than the clock.

Set to True when storing compacted records back to the database so that the resulting timestamps are predictable and human-readable. Leave False for forecast or reporting queries where alignment to the exact query window is more important than clock-round boundaries.

Returns:

A NumPy Array of the values at the chosen frequency extracted from the: specified key.

Return type:

np.ndarray

Raises:

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

key_to_dict(key: str, start_datetime: DateTime | None = None, end_datetime: DateTime | None = None, dropna: bool | None = None) → Dict[DateTime, Any]

Extract a dictionary indexed by the date_time field of the DataRecords.

The dictionary will contain values extracted from the specified key attribute of each DataRecord, using the date_time field as the key.

Parameters:

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

Returns:

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

Return type:

Dict[datetime, Any]

Raises:

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

key_to_lists(key: str, start_datetime: DateTime | None = None, end_datetime: DateTime | None = None, dropna: bool | None = None) → Tuple[list[DateTime], list[float | None]]

Extracts two lists from data records within an optional date range.

The lists are:: Dates: List of datetime elements. Values: List of values corresponding to the specified key in the data records.

Parameters:

key (str) – The key of the attribute in DataRecord to extract.
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 tuple containing a list of datetime values and a list of extracted values.

Return type:

tuple

Raises:

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

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.

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.

key_to_value(key: str, target_datetime: DateTime, time_window: Duration | None = None) → float | None

Returns the value corresponding to the specified key that is nearest to the given datetime.

Parameters:

key (str) – The key of the attribute in DataRecord to extract.
target_datetime (datetime) – The datetime to search for.
time_window – Optional total width of the symmetric search window centered on target_datetime. If provided and no exact match exists, the nearest record within this window is returned.

Returns:

The value nearest to the given datetime, or None if no valid records are found.

Return type:

Optional[float]

Raises:

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

load() → bool

Load data records from from persistent storage.

Returns:: True in case the data records were loaded, False otherwise.

property max_datetime: DateTime | None

Maximum (latest) datetime in the time series sequence of data records.

This property computes the latest datetime from the sequence of data records. If no records are present, it returns None.

Returns:

The latest datetime in the sequence, or None if no: data records exist.

Return type:

Optional[DateTime]

measurement = Measurement([])

property min_datetime: DateTime | None

Minimum (earliest) datetime in the time series sequence of data records.

This property computes the earliest datetime from the sequence of data records. If no records are present, it returns None.

Returns:

The earliest datetime in the sequence, or None if no: data records exist.

Return type:

Optional[DateTime]

model_computed_fields = {'end_datetime': ComputedFieldInfo(wrapped_property=<property object>, return_type=typing.Optional[pydantic_extra_types.pendulum_dt.DateTime], alias=None, alias_priority=None, title=None, field_title_generator=None, description='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[pydantic_extra_types.pendulum_dt.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), 'max_datetime': ComputedFieldInfo(wrapped_property=<property object>, return_type=typing.Optional[pydantic_extra_types.pendulum_dt.DateTime], alias=None, alias_priority=None, title=None, field_title_generator=None, description='Maximum (latest) datetime in the time series sequence of data records.\n\nThis property computes the latest datetime from the sequence of data records.\nIf no records are present, it returns `None`.\n\nReturns:\n Optional[DateTime]: The latest datetime in the sequence, or `None` if no\n data records exist.', deprecated=None, examples=None, json_schema_extra=None, repr=True), 'min_datetime': ComputedFieldInfo(wrapped_property=<property object>, return_type=typing.Optional[pydantic_extra_types.pendulum_dt.DateTime], alias=None, alias_priority=None, title=None, field_title_generator=None, description='Minimum (earliest) datetime in the time series sequence of data records.\n\nThis property computes the earliest datetime from the sequence of data records.\nIf no records are present, it returns `None`.\n\nReturns:\n Optional[DateTime]: The earliest datetime in the sequence, or `None` if no\n data records exist.', deprecated=None, examples=None, json_schema_extra=None, repr=True), 'record_keys': ComputedFieldInfo(wrapped_property=<property object>, return_type=list[str], alias=None, alias_priority=None, title=None, field_title_generator=None, description='Returns the keys of all fields in the data records.', deprecated=None, examples=None, json_schema_extra=None, repr=True), 'record_keys_writable': ComputedFieldInfo(wrapped_property=<property object>, return_type=list[str], alias=None, alias_priority=None, title=None, field_title_generator=None, description="Get the keys of all writable fields in the data records.\n\nThis property retrieves the keys of all fields in the data records that\ncan be written to. It uses the `record_class` to determine the model's\nfield structure.\n\nReturns:\n list[str]: A list of field keys that are writable in the data records.", 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 = {'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 = {'records': FieldInfo(annotation=list[DataRecord], required=False, default_factory=list, json_schema_extra={'description': 'List of data records'}), 'update_datetime': FieldInfo(annotation=Union[AwareDatetime, NoneType], required=False, default=None, json_schema_extra={'description': 'Latest update datetime for generic data'})}

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[GenerateJsonSchema] = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: Literal['validation', 'serialization']='validation', *, union_format: Literal['any_of', 'primitive_type_array']='any_of') → dict[str, Any]

Generates a JSON schema for a model class.

Parameters:

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

Returns:

The JSON schema for the given model class.

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

Compute the class name for parametrizations of generic classes.

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

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

model_post_init(context: Any, /) → None

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

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

Parameters:

self – The BaseModel instance.
context – The context.

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

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

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

Parameters:

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

Returns:

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

Validate a pydantic model instance.

Parameters:

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

Raises:

ValidationError – If the object could not be validated.

Returns:

The validated model instance.

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

Validate the given JSON data against the Pydantic model.

Parameters:

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

Returns:

The validated Pydantic model.

Raises:

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

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

Parameters:

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

Returns:

The validated Pydantic model.

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

classmethod parse_obj(obj: Any) → Self

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

abstractmethod provider_id() → str

Return the unique identifier for the data provider.

To be implemented by derived classes.

classmethod record_class() → Type

Get the class of the data record handled by this data sequence.

This method determines the class of the data record type associated with the records field of the model. The field is expected to be a list, and the element type of the list should be a subclass of DataRecord.

Raises:: ValueError – If the record type is not a subclass of DataRecord.
Returns:: The class of the data record handled by the data sequence.
Return type:: Type

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

property record_keys_writable: list[str]

Get the keys of all writable fields in the data records.

This property retrieves the keys of all fields in the data records that can be written to. It uses the record_class to determine the model’s field structure.

Returns:: A list of field keys that are writable in the data records.
Return type:: list[str]

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.

save() → bool

Save data records to persistent storage.

Returns:: True in case the data records were saved, False otherwise.

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

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_dataframe(start_datetime: DateTime | None = None, end_datetime: DateTime | None = None) → DataFrame

Converts the sequence of DataRecord instances into a Pandas DataFrame.

Parameters:

start_datetime (Optional[datetime]) – The lower bound for filtering (inclusive). Defaults to the earliest possible datetime if None.
end_datetime (Optional[datetime]) – The upper bound for filtering (exclusive). Defaults to the latest possible datetime if None.

Returns:

A DataFrame containing the filtered data from all records.

Return type:

pd.DataFrame

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]

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

Update prediction parameters and call the custom update function.

Updates the configuration, deletes outdated records, and performs the custom update logic.

Parameters:

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

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

update_value(date: DateTime, *args: Any, **kwargs: Any) → None

Updates specific values in the data record for a given date.

If a record for the date exists, updates the specified attributes with the new values. Otherwise, appends a new record with the given values and maintains chronological order.

Parameters:

date (datetime) – The date for which the values are to be added or updated.
key (str), value (Any) – Single key-value pair to update OR
values (Dict[str, Any]) – Dictionary of key-value pairs to update OR
**kwargs – Key-value pairs as keyword arguments

Examples

update_value(date, 'temperature', 25.5)
update_value(date, {'temperature': 25.5, 'humidity': 80})
update_value(date, temperature=25.5, humidity=80)

classmethod validate(value: Any) → Self

update_datetime

records