nucleus.pydantic_base¶

NOTE: We started using pydantic during v1 and are kind of stuck with it now unless we write a compatibility layers. As a library we want to support v1 and v2 such that we’re not causing downstream problems for our users. This means we have to do some import shenanigans to support both v1 and v2.

`DictCompatibleImmutableModel`	Backwards compatible wrapper where we transform dictionaries into Pydantic Models
`DictCompatibleModel`	Backwards compatible wrapper where we transform dictionaries into Pydantic Models
`ImmutableModel`	Mixin to provide __str__, __repr__, and __pretty__ methods. See #884 for more details.

class nucleus.pydantic_base.DictCompatibleImmutableModel(**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:

Model

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:

Model

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_computed_fields()¶

A mapping of computed field names to their respective [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] instances.

!!! warning: Accessing this attribute from a model instance is deprecated, and will not work in Pydantic V3. Instead, you should access this attribute from the model class.

Return type:: dict[str, pydantic.fields.ComputedFieldInfo]

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) – 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 (Any) – Trusted or pre-validated data dictionary.

Returns:

A new instance of the Model class with validated data.

Return type:

typing_extensions.Self

model_copy(*, update=None, deep=False)¶

!!! 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 (collections.abc.Mapping[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:

typing_extensions.Self

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, exclude_computed_fields=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)¶

!!! abstract “Usage Documentation”: [model_dump](../concepts/serialization.md#python-mode)

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 | None) – A set of fields to include in the output.
exclude (IncEx | None) – A set of fields to exclude from the output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool | None) – 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.
exclude_computed_fields (bool) – 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 (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].
fallback (Callable[[Any], Any] | None) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.
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, ensure_ascii=False, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, exclude_computed_fields=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)¶

!!! 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 (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.
ensure_ascii (bool) – 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 (IncEx | None) – Field(s) to include in the JSON output.
exclude (IncEx | None) – Field(s) to exclude from the JSON output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool | None) – 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.
exclude_computed_fields (bool) – 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 (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].
fallback (Callable[[Any], Any] | None) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.
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_fields()¶

A mapping of field names to their respective [FieldInfo][pydantic.fields.FieldInfo] instances.

!!! warning: Accessing this attribute from a model instance is deprecated, and will not work in Pydantic V3. Instead, you should access this attribute from the model class.

Return type:: dict[str, pydantic.fields.FieldInfo]

classmethod model_json_schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, schema_generator=GenerateJsonSchema, mode='validation', *, union_format='any_of')¶

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.
union_format (Literal['any_of', 'primitive_type_array']) –
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 (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 (pydantic._internal._namespace_utils.MappingNamespace | 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, extra=None, from_attributes=None, context=None, by_alias=None, by_name=None)¶

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (bool | None) – Whether to enforce types strictly.
extra (pydantic.config.ExtraValues | None) – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.
from_attributes (bool | None) – Whether to extract data from object attributes.
context (Any | None) – Additional context to pass to the validator.
by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.
by_name (bool | None) – 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.

Return type:

typing_extensions.Self

classmethod model_validate_json(json_data, *, strict=None, extra=None, context=None, by_alias=None, by_name=None)¶

!!! abstract “Usage Documentation”: [JSON Parsing](../concepts/json.md#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.
extra (pydantic.config.ExtraValues | None) – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.
context (Any | None) – Extra variables to pass to the validator.
by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.
by_name (bool | None) – 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.

Return type:

typing_extensions.Self

classmethod model_validate_strings(obj, *, strict=None, extra=None, context=None, by_alias=None, by_name=None)¶

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

Parameters:

obj (Any) – The object containing string data to validate.
strict (bool | None) – Whether to enforce types strictly.
extra (pydantic.config.ExtraValues | None) – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.
context (Any | None) – Extra variables to pass to the validator.
by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.
by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Return type:

typing_extensions.Self

classmethod update_forward_refs(**localns)¶

Try to update ForwardRefs on fields based on this Model, globalns and localns.

Parameters:: localns (Any)
Return type:: None

class nucleus.pydantic_base.DictCompatibleModel(**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)¶

Parameters:

_fields_set (Optional[pydantic.v1.typing.SetStr])
values (Any)

Return type:

Model

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:

Model

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

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_computed_fields()¶

A mapping of computed field names to their respective [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] instances.

!!! warning: Accessing this attribute from a model instance is deprecated, and will not work in Pydantic V3. Instead, you should access this attribute from the model class.

Return type:: dict[str, pydantic.fields.ComputedFieldInfo]

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) – 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 (Any) – Trusted or pre-validated data dictionary.

Returns:

A new instance of the Model class with validated data.

Return type:

typing_extensions.Self

model_copy(*, update=None, deep=False)¶

!!! 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 (collections.abc.Mapping[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:

typing_extensions.Self

!!! abstract “Usage Documentation”: [model_dump](../concepts/serialization.md#python-mode)

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 | None) – A set of fields to include in the output.
exclude (IncEx | None) – A set of fields to exclude from the output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool | None) – 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.
exclude_computed_fields (bool) – 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 (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].
fallback (Callable[[Any], Any] | None) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.
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]

!!! 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 (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.
ensure_ascii (bool) – 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 (IncEx | None) – Field(s) to include in the JSON output.
exclude (IncEx | None) – Field(s) to exclude from the JSON output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool | None) – 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.
exclude_computed_fields (bool) – 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 (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].
fallback (Callable[[Any], Any] | None) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.
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_fields()¶

A mapping of field names to their respective [FieldInfo][pydantic.fields.FieldInfo] instances.

!!! warning: Accessing this attribute from a model instance is deprecated, and will not work in Pydantic V3. Instead, you should access this attribute from the model class.

Return type:: dict[str, pydantic.fields.FieldInfo]

classmethod model_json_schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, schema_generator=GenerateJsonSchema, mode='validation', *, union_format='any_of')¶

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.
union_format (Literal['any_of', 'primitive_type_array']) –
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 (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 (pydantic._internal._namespace_utils.MappingNamespace | 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, extra=None, from_attributes=None, context=None, by_alias=None, by_name=None)¶

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (bool | None) – Whether to enforce types strictly.
extra (pydantic.config.ExtraValues | None) – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.
from_attributes (bool | None) – Whether to extract data from object attributes.
context (Any | None) – Additional context to pass to the validator.
by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.
by_name (bool | None) – 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.

Return type:

typing_extensions.Self

classmethod model_validate_json(json_data, *, strict=None, extra=None, context=None, by_alias=None, by_name=None)¶

!!! abstract “Usage Documentation”: [JSON Parsing](../concepts/json.md#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.
extra (pydantic.config.ExtraValues | None) – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.
context (Any | None) – Extra variables to pass to the validator.
by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.
by_name (bool | None) – 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.

Return type:

typing_extensions.Self

classmethod model_validate_strings(obj, *, strict=None, extra=None, context=None, by_alias=None, by_name=None)¶

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

Parameters:

obj (Any) – The object containing string data to validate.
strict (bool | None) – Whether to enforce types strictly.
extra (pydantic.config.ExtraValues | None) – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.
context (Any | None) – Extra variables to pass to the validator.
by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.
by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Return type:

typing_extensions.Self

classmethod update_forward_refs(**localns)¶

Try to update ForwardRefs on fields based on this Model, globalns and localns.

Parameters:: localns (Any)
Return type:: None

class nucleus.pydantic_base.ImmutableModel(**data)¶

Mixin to provide __str__, __repr__, and __pretty__ methods. See #884 for more details.

__pretty__ is used by [devtools](https://python-devtools.helpmanual.io/) to provide human readable representations of objects.

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)¶

Parameters:

_fields_set (Optional[pydantic.v1.typing.SetStr])
values (Any)

Return type:

Model

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:

Model

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

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_computed_fields()¶

A mapping of computed field names to their respective [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] instances.

!!! warning: Accessing this attribute from a model instance is deprecated, and will not work in Pydantic V3. Instead, you should access this attribute from the model class.

Return type:: dict[str, pydantic.fields.ComputedFieldInfo]

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) – 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 (Any) – Trusted or pre-validated data dictionary.

Returns:

A new instance of the Model class with validated data.

Return type:

typing_extensions.Self

model_copy(*, update=None, deep=False)¶

!!! 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 (collections.abc.Mapping[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:

typing_extensions.Self

!!! abstract “Usage Documentation”: [model_dump](../concepts/serialization.md#python-mode)

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 | None) – A set of fields to include in the output.
exclude (IncEx | None) – A set of fields to exclude from the output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool | None) – 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.
exclude_computed_fields (bool) – 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 (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].
fallback (Callable[[Any], Any] | None) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.
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]

!!! 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 (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.
ensure_ascii (bool) – 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 (IncEx | None) – Field(s) to include in the JSON output.
exclude (IncEx | None) – Field(s) to exclude from the JSON output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool | None) – 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.
exclude_computed_fields (bool) – 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 (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].
fallback (Callable[[Any], Any] | None) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.
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_fields()¶

A mapping of field names to their respective [FieldInfo][pydantic.fields.FieldInfo] instances.

!!! warning: Accessing this attribute from a model instance is deprecated, and will not work in Pydantic V3. Instead, you should access this attribute from the model class.

Return type:: dict[str, pydantic.fields.FieldInfo]

classmethod model_json_schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, schema_generator=GenerateJsonSchema, mode='validation', *, union_format='any_of')¶

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.
union_format (Literal['any_of', 'primitive_type_array']) –
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 (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 (pydantic._internal._namespace_utils.MappingNamespace | 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, extra=None, from_attributes=None, context=None, by_alias=None, by_name=None)¶

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (bool | None) – Whether to enforce types strictly.
extra (pydantic.config.ExtraValues | None) – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.
from_attributes (bool | None) – Whether to extract data from object attributes.
context (Any | None) – Additional context to pass to the validator.
by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.
by_name (bool | None) – 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.

Return type:

typing_extensions.Self

classmethod model_validate_json(json_data, *, strict=None, extra=None, context=None, by_alias=None, by_name=None)¶

!!! abstract “Usage Documentation”: [JSON Parsing](../concepts/json.md#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.
extra (pydantic.config.ExtraValues | None) – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.
context (Any | None) – Extra variables to pass to the validator.
by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.
by_name (bool | None) – 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.

Return type:

typing_extensions.Self

classmethod model_validate_strings(obj, *, strict=None, extra=None, context=None, by_alias=None, by_name=None)¶

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

Parameters:

obj (Any) – The object containing string data to validate.
strict (bool | None) – Whether to enforce types strictly.
extra (pydantic.config.ExtraValues | None) – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.
context (Any | None) – Extra variables to pass to the validator.
by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.
by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Return type:

typing_extensions.Self

classmethod update_forward_refs(**localns)¶

Try to update ForwardRefs on fields based on this Model, globalns and localns.

Parameters:: localns (Any)
Return type:: None