nucleus.data_transfer_object.dataset_size¶
Backwards compatible wrapper where we transform dictionaries into Pydantic Models |
- class nucleus.data_transfer_object.dataset_size.DatasetSize(**data)¶
Backwards compatible wrapper where we transform dictionaries into Pydantic Models
Allows us to access model.key with model[“key”].
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
- Parameters:
data (Any)
- classmethod construct(_fields_set=None, **values)¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
- Parameters:
_fields_set (Optional[pydantic.v1.typing.SetStr])
values (Any)
- Return type:
- copy(*, include=None, exclude=None, update=None, deep=False)¶
Duplicate a model, optionally choose which fields to include, exclude and change.
- Parameters:
include (Optional[Union[pydantic.v1.typing.AbstractSetIntStr, pydantic.v1.typing.MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[pydantic.v1.typing.AbstractSetIntStr, pydantic.v1.typing.MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[pydantic.v1.typing.DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model
- Returns:
new model instance
- Return type:
- dict(*, include=None, exclude=None, by_alias=False, skip_defaults=None, exclude_unset=False, exclude_defaults=False, exclude_none=False)¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
include (Optional[Union[pydantic.v1.typing.AbstractSetIntStr, pydantic.v1.typing.MappingIntStrAny]])
exclude (Optional[Union[pydantic.v1.typing.AbstractSetIntStr, pydantic.v1.typing.MappingIntStrAny]])
by_alias (bool)
skip_defaults (Optional[bool])
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
- Return type:
pydantic.v1.typing.DictStrAny
- json(*, include=None, exclude=None, by_alias=False, skip_defaults=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=None, models_as_dict=True, **dumps_kwargs)¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
- Parameters:
include (Optional[Union[pydantic.v1.typing.AbstractSetIntStr, pydantic.v1.typing.MappingIntStrAny]])
exclude (Optional[Union[pydantic.v1.typing.AbstractSetIntStr, pydantic.v1.typing.MappingIntStrAny]])
by_alias (bool)
skip_defaults (Optional[bool])
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
encoder (Optional[Callable[[Any], Any]])
models_as_dict (bool)
dumps_kwargs (Any)
- Return type:
str
- classmethod model_construct(_fields_set=None, **values)¶
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 (set[str] | None) – The set of field names accepted for the Model instance.
values (Any) – Trusted or pre-validated data dictionary.
- Returns:
A new instance of the Model class with validated data.
- Return type:
- model_copy(*, update=None, deep=False)¶
Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#model_copy
Returns a copy of the model.
- Parameters:
update (dict[str, Any] | None) – 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 (bool) – Set to True to make a deep copy of the model.
- Returns:
New model instance.
- Return type:
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)¶
Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (Literal[json, python] | str) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (IncEx) – A set of fields to include in the output.
exclude (IncEx) – A set of fields to exclude from the output.
context (dict[str, Any] | None) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (bool | Literal[none, warn, error]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.
- Returns:
A dictionary representation of the model.
- Return type:
dict[str, Any]
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)¶
Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s to_json method.
- Parameters:
indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (IncEx) – Field(s) to include in the JSON output.
exclude (IncEx) – Field(s) to exclude from the JSON output.
context (dict[str, Any] | None) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (bool | Literal[none, warn, error]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.
- Returns:
A JSON string representation of the model.
- Return type:
str
- classmethod model_json_schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, schema_generator=GenerateJsonSchema, mode='validation')¶
Generates a JSON schema for a model class.
- Parameters:
by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[pydantic.json_schema.GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (pydantic.json_schema.JsonSchemaMode) – The mode in which to generate the schema.
- Returns:
The JSON schema for the given model class.
- Return type:
dict[str, Any]
- classmethod model_parametrized_name(params)¶
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[type[Any], Ellipsis]) – 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.
- Return type:
str
- model_post_init(__context)¶
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.
- Parameters:
__context (Any)
- Return type:
None
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=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 (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (dict[str, Any] | None) – 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.
- Return type:
bool | None
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)¶
Validate a pydantic model instance.
- Parameters:
obj (Any) – The object to validate.
strict (bool | None) – Whether to enforce types strictly.
from_attributes (bool | None) – Whether to extract data from object attributes.
context (dict[str, Any] | None) – Additional context to pass to the validator.
- Raises:
ValidationError – If the object could not be validated.
- Returns:
The validated model instance.
- Return type:
- classmethod model_validate_json(json_data, *, strict=None, context=None)¶
Usage docs: https://docs.pydantic.dev/2.7/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (str | bytes | bytearray) – The JSON data to validate.
strict (bool | None) – Whether to enforce types strictly.
context (dict[str, Any] | None) – Extra variables to pass to the validator.
- Returns:
The validated Pydantic model.
- Raises:
ValueError – If json_data is not a JSON string.
- Return type:
- classmethod model_validate_strings(obj, *, strict=None, context=None)¶
Validate the given object contains string data against the Pydantic model.
- Parameters:
obj (Any) – The object contains string data to validate.
strict (bool | None) – Whether to enforce types strictly.
context (dict[str, Any] | None) – Extra variables to pass to the validator.
- Returns:
The validated Pydantic model.
- Return type:
- classmethod update_forward_refs(**localns)¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
- Parameters:
localns (Any)
- Return type:
None