akkudoktoreos.prediction.weatherabc.WeatherDataRecord

Bases: PredictionRecord

Represents a weather data record containing various weather attributes at a specific datetime.

date_time

The datetime of the record.

Type:: Optional[AwareDatetime]

total_clouds

Total cloud cover as a percentage of the sky obscured.

Type:: Optional[float]

low_clouds

Cloud cover in the lower atmosphere (% sky obscured).

Type:: Optional[float]

medium_clouds

Cloud cover in the middle atmosphere (% sky obscured).

Type:: Optional[float]

high_clouds

Cloud cover in the upper atmosphere (% sky obscured).

Type:: Optional[float]

visibility

Horizontal visibility in meters.

Type:: Optional[float]

fog

Fog cover percentage.

Type:: Optional[float]

precip_type

Type of precipitation (e.g., “rain”, “snow”).

Type:: Optional[str]

precip_prob

Probability of precipitation as a percentage.

Type:: Optional[float]

precip_amt

Precipitation amount in millimeters.

Type:: Optional[float]

preciptable_water

Precipitable water in centimeters.

Type:: Optional[float]

wind_speed

Wind speed in kilometers per hour.

Type:: Optional[float]

wind_direction

Wind direction in degrees (0-360°).

Type:: Optional[float]

frost_chance

Probability of frost.

Type:: Optional[str]

temp_air

Air temperature in degrees Celsius.

Type:: Optional[float]

feels_like

Feels-like temperature in degrees Celsius.

Type:: Optional[float]

dew_point

Dew point in degrees Celsius.

Type:: Optional[float]

relative_humidity

Relative humidity in percentage.

Type:: Optional[float]

pressure

Atmospheric pressure in millibars.

Type:: Optional[float]

ozone

Ozone concentration in Dobson units.

Type:: Optional[float]

ghi

Global Horizontal Irradiance in watts per square meter (W/m²).

Type:: Optional[float]

dni

Direct Normal Irradiance in watts per square meter (W/m²).

Type:: Optional[float]

dhi

Diffuse Horizontal Irradiance in watts per square meter (W/m²).

Type:: Optional[float]

__init__(**data: 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__`(**data)	Create a new model by parsing and validating input data from keyword arguments.
`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_from_description`(description[, threshold])	Returns the attribute key that best matches the provided description.
`keys`()
`keys_from_descriptions`(descriptions[, threshold])	Returns a list of attribute keys that best matches the provided list of descriptions.
`model_construct`([_fields_set])	Custom constructor to handle deserialization for DateTime fields.
`model_copy`(*[, update, deep])	Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy
`model_dump`(*args[, include_computed_fields])	Custom dump method to handle serialization for DateTime fields.
`model_dump_json`(*args[, indent])	Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump_json
`model_json_schema`([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
`model_parametrized_name`(params)	Compute the class name for parametrizations of generic classes.
`model_post_init`(_BaseModel__context)	Override this method to perform additional initialization after __init__ and model_construct.
`model_rebuild`(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
`model_validate`(obj, *[, strict, ...])	Validate a pydantic model instance.
`model_validate_json`(json_data, *[, strict, ...])	Usage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-parsing
`model_validate_strings`(obj, *[, strict, context])	Validate the given object with string data against the Pydantic model.
`parse_file`(path, *[, content_type, ...])
`parse_obj`(obj)
`parse_raw`(b, *[, content_type, encoding, ...])
`pop`(k[,d])	If key is not found, d is returned if given, otherwise KeyError is raised.
`popitem`()	as a 2-tuple; but raise KeyError if D is empty.
`record_keys`()	Returns the keys of all fields in the data record.
`record_keys_writable`()	Returns the keys of all fields in the data record that are writable.
`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.
`transform_to_datetime`(value)	Converts various datetime formats into DateTime.
`update`([E, ]**F)	If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v
`update_forward_refs`(**localns)
`validate`(value)
`validate_and_convert_pendulum`(value, info)	Validator to convert fields of type pendulum.DateTime.
`values`()

Attributes

`config`	Convenience method/ attribute to retrieve the EOS configuration data.
`ems`	Convenience method/ attribute to retrieve the EOS energy management system.
`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.
`start_datetime`	Returns the start datetime of the current or latest energy management.
`weather_total_clouds`
`weather_low_clouds`
`weather_medium_clouds`
`weather_high_clouds`
`weather_visibility`
`weather_fog`
`weather_precip_type`
`weather_precip_prob`
`weather_precip_amt`
`weather_preciptable_water`
`weather_wind_speed`
`weather_wind_direction`
`weather_frost_chance`
`weather_temp_air`
`weather_feels_like`
`weather_dew_point`
`weather_relative_humidity`
`weather_pressure`
`weather_ozone`
`weather_ghi`
`weather_dni`
`weather_dhi`
`date_time`

weather_total_clouds: float | None

weather_low_clouds: float | None

weather_medium_clouds: float | None

weather_high_clouds: float | None

weather_visibility: float | None

weather_fog: float | None

weather_precip_type: str | None

weather_precip_prob: float | None

weather_precip_amt: float | None

weather_preciptable_water: float | None

weather_wind_speed: float | None

weather_wind_direction: float | None

weather_frost_chance: str | None

weather_temp_air: float | None

weather_feels_like: float | None

weather_dew_point: float | None

weather_relative_humidity: float | None

weather_pressure: float | None

weather_ozone: float | None

weather_ghi: float | None

weather_dni: float | None

weather_dhi: float | None

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

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

__delattr__(key: str) → None

Delete an attribute by setting it to None if it exists as a field.

Parameters:: key (str) – The name of the attribute/field to delete.
Raises:: AttributeError – If the attribute/field does not exist.

__delitem__(key: str) → None

Delete the value of a field by key name by setting it to None.

Parameters:: key (str) – The name of the field to delete.
Raises:: KeyError – If the specified key does not exist in the fields.

classmethod __get_pydantic_core_schema__(source: type[BaseModel], handler: GetCoreSchemaHandler, /) → CoreSchema

Hook into generating the model’s CoreSchema.

Parameters:

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

Returns:

A pydantic-core CoreSchema.

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

Hook into generating the model’s JSON schema.

Parameters:

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

Returns:

A JSON schema, as a Python object.

__getattr__(key: str) → Any

Dynamic attribute access for fields.

Parameters:: key (str) – The name of the field to access.
Returns:: The value of the requested field.
Return type:: Any
Raises:: AttributeError – If the field does not exist.

__getitem__(key: str) → Any

Retrieve the value of a field by key name.

Parameters:: key (str) – The name of the field to retrieve.
Returns:: The value of the requested field.
Return type:: Any
Raises:: KeyError – If the specified key does not exist.

__init__(**data: Any) → None

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

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

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

__iter__() → Iterator[str]

Iterate over the field names in the data record.

Returns:: An iterator over field names.
Return type:: Iterator[str]

__len__() → int

Return the number of fields in the data record.

Returns:: The number of defined fields.
Return type:: int

__pretty__(fmt: Callable[[Any], Any], **kwargs: Any) → Generator[Any, None, None]: Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

classmethod __pydantic_init_subclass__(**kwargs: Any) → None

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

__repr__() → str

Provide a string representation of the data record.

Returns:: A string representation showing field names and their values.
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.

__setattr__(key: str, value: Any) → None

Set attribute values directly if they are recognized fields.

Parameters:

key (str) – The name of the attribute/field to set.
value (Any) – The value to assign to the attribute/field.

Raises:

AttributeError – If the attribute/field does not exist.

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

Set the value of a field by key name.

Parameters:

key (str) – The name of the field to set.
value (Any) – The value to assign to the field.

Raises:

KeyError – If the specified key does not exist in the fields.

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

property config: Any

Convenience method/ attribute to retrieve the EOS configuration data.

Returns:: The configuration.
Return type:: ConfigEOS

classmethod construct(_fields_set: set[str] | None = None, **values: Any) → Self

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include: Set[int] | Set[str] | Mapping[int, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: Set[int] | Set[str] | Mapping[int, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, Set[int] | Set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → Dict[str, Any]

property ems: Any

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

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

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

classmethod key_from_description(description: str, threshold: float = 0.8) → str | None

Returns the attribute key that best matches the provided description.

Fuzzy matching is used.

Parameters:

description (str) – The description text to search for.
threshold (float) – The minimum ratio for a match (0-1). Default is 0.8.

Returns:

The attribute key if a match is found above the threshold, else None.

Return type:

Optional[str]

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

classmethod keys_from_descriptions(descriptions: List[str], threshold: float = 0.8) → List[str | None]

Returns a list of attribute keys that best matches the provided list of descriptions.

Fuzzy matching is used.

Parameters:

descriptions (List[str]) – A list of description texts to search for.
threshold (float) – The minimum ratio for a match (0-1). Default is 0.8.

Returns:

A list of attribute keys matching the descriptions, with None for unmatched descriptions.

Return type:

List[Optional[str]]

model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {'start_datetime': ComputedFieldInfo(wrapped_property=<property object>, return_type=typing.Optional[pendulum.datetime.DateTime], alias=None, alias_priority=None, title=None, field_title_generator=None, description='Returns the start datetime of the current or latest energy management.\n\nReturns:\n DateTime: The starting datetime of the current or latest energy management, or None.', deprecated=None, examples=None, json_schema_extra=None, repr=True)}

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

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

model_copy(*, update: Mapping[str, Any] | None = None, deep: bool = False) → Self

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

Returns a copy of the model.

Parameters:

update – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep – Set to True to make a deep copy of the model.

Returns:

New model instance.

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

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

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

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent – Indentation to use in the JSON output. If None is passed, the output will be compact.
include – Field(s) to include in the JSON output.
exclude – Field(s) to exclude from the JSON output.
context – Additional context to pass to the serializer.
by_alias – Whether to serialize using field aliases.
exclude_unset – Whether to exclude fields that have not been explicitly set.
exclude_defaults – Whether to exclude fields that are set to their default value.
exclude_none – Whether to exclude fields that have a value of None.
round_trip – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any – Whether to serialize fields with duck-typing serialization behavior.

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields: ClassVar[dict[str, FieldInfo]] = {'date_time': FieldInfo(annotation=Union[DateTime, NoneType], required=False, default=None, description='DateTime'), 'weather_dew_point': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='Dew Point (°C)'), 'weather_dhi': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='Diffuse Horizontal Irradiance (W/m2)'), 'weather_dni': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='Direct Normal Irradiance (W/m2)'), 'weather_feels_like': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='Feels Like (°C)'), 'weather_fog': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='Fog (%)'), 'weather_frost_chance': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Chance of Frost'), 'weather_ghi': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='Global Horizontal Irradiance (W/m2)'), 'weather_high_clouds': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='High Clouds (% Sky Obscured)'), 'weather_low_clouds': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='Low Clouds (% Sky Obscured)'), 'weather_medium_clouds': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='Medium Clouds (% Sky Obscured)'), 'weather_ozone': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='Ozone (du)'), 'weather_precip_amt': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='Precipitation Amount (mm)'), 'weather_precip_prob': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='Precipitation Probability (%)'), 'weather_precip_type': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Precipitation Type'), 'weather_preciptable_water': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='Precipitable Water (cm)'), 'weather_pressure': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='Pressure (mb)'), 'weather_relative_humidity': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='Relative Humidity (%)'), 'weather_temp_air': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='Temperature (°C)'), 'weather_total_clouds': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='Total Clouds (% Sky Obscured)'), 'weather_visibility': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='Visibility (m)'), 'weather_wind_direction': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='Wind Direction (°)'), 'weather_wind_speed': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='Wind Speed (kmph)')}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}', schema_generator: type[~pydantic.json_schema.GenerateJsonSchema] = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: ~typing.Literal['validation', 'serialization'] = 'validation') → dict[str, Any]

Generates a JSON schema for a model class.

Parameters:

by_alias – Whether to use attribute aliases or not.
ref_template – The reference template.
schema_generator – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode – The mode in which to generate the schema.

Returns:

The JSON schema for the given model class.

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

Compute the class name for parametrizations of generic classes.

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

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

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

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

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

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

Parameters:

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

Returns:

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

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

Validate a pydantic model instance.

Parameters:

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

Raises:

ValidationError – If the object could not be validated.

Returns:

The validated model instance.

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

Usage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data – The JSON data to validate.
strict – Whether to enforce types strictly.
context – Extra variables to pass to the validator.

Returns:

The validated Pydantic model.

Raises:

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

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

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

Parameters:

obj – The object containing string data to validate.
strict – Whether to enforce types strictly.
context – Extra variables to pass to the validator.

Returns:

The validated Pydantic model.

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

classmethod parse_obj(obj: Any) → Self

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

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

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

classmethod record_keys() → List[str]: Returns the keys of all fields in the data record.

classmethod record_keys_writable() → List[str]: Returns the keys of all fields in the data record that are writable.

reset_to_defaults() → PydanticBaseModel: Resets the fields to their default values.

classmethod schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}') → Dict[str, Any]

classmethod schema_json(*, by_alias: bool = True, ref_template: str = '#/$defs/{model}', **dumps_kwargs: Any) → str

set_nested_value(path: str, value: Any) → None

Set a nested value in the model using a ‘/’-separated path.

Supports modifying nested attributes and list indices while preserving Pydantic validation. Automatically initializes missing Optional, Union, dict, and list fields if necessary. If a missing field cannot be initialized, raises an exception.

Parameters:

path (str) – A ‘/’-separated path to the nested attribute (e.g., “key1/key2/0”).
value (Any) – The new value to set.

Raises:

KeyError – If a key is not found in the model.
IndexError – If a list index is out of bounds or invalid.
ValueError – If a validation error occurs.
TypeError – If a missing field cannot be initialized.

Example

```python class Address(PydanticBaseModel):

city: Optional[str]

class User(PydanticBaseModel):: name: str address: Optional[Address] settings: Optional[Dict[str, Any]]

user = User(name=”Alice”, address=None, settings=None) user.set_nested_value(“address/city”, “Los Angeles”) user.set_nested_value(“settings/theme”, “dark”)

print(user.address.city) # Output: “Los Angeles” print(user.settings) # Output: {‘theme’: ‘dark’} ```

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

property start_datetime: DateTime | None

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

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

to_dict() → dict

Convert this PredictionRecord instance to a dictionary representation.

Returns:

A dictionary where the keys are the field names of the PydanticBaseModel,: and the values are the corresponding field values.

Return type:

dict

to_json() → str

Convert the PydanticBaseModel instance to a JSON string.

Returns:: The JSON representation of the instance.
Return type:: str

classmethod transform_to_datetime(value: Any) → DateTime | None: Converts various datetime formats into DateTime.

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

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

classmethod validate(value: Any) → Self

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

Validator to convert fields of type pendulum.DateTime.

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

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

Parameters:

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

Returns:

The converted value, if successful.

Raises:

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

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

date_time: DateTime | None