Module src.validation.models

Page contains validation models for application

Variables

ALLOW_BIND_ZIP_FILTER

ENABLE_HDX_EXPORTS

ENABLE_POLYGON_STATISTICS_ENDPOINTS

ENABLE_TILES

EXPORT_TYPE_MAPPING

Functions

to_camel

def to_camel(
    string: str
) -> str

Classes

AttributeFilter

class AttributeFilter(
    /,
    **data: 'Any'
)

Usage docs: https://docs.pydantic.dev/2.8/concepts/models/

A base class for creating Pydantic models.

Attributes: class_vars: The names of classvars defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The signature for instantiating the model.

__pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
__pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.
__pydantic_custom_init__: Whether the model has a custom `__init__` function.
__pydantic_decorators__: Metadata containing the decorators defined on the model.
    This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.
__pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to
    __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
__pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
__pydantic_post_init__: The name of the post-init method for the model, if defined.
__pydantic_root_model__: Whether the model is a `RootModel`.
__pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
__pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_extra__: An instance attribute with the values of extra fields from validation when
    `model_config['extra'] == 'allow'`.
__pydantic_fields_set__: An instance attribute with the names of fields explicitly set.
__pydantic_private__: Instance attribute with the values of private attributes set on the model instance.

Ancestors (in MRO)

• src.validation.models.BaseModel
• pydantic.main.BaseModel

Class variables

Config

model_computed_fields

model_config

model_fields

Static methods

construct

def construct(
    _fields_set: 'set[str] | None' = None,
    **values: 'Any'
) -> 'Self'

from_orm

def from_orm(
    obj: 'Any'
) -> 'Self'

model_construct

def 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.

Args: _fields_set: The set of field names accepted for the Model instance. values: Trusted or pre-validated data dictionary.

Returns: A new instance of the Model class with validated data.

model_json_schema

def model_json_schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
    mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'

Generates a JSON schema for a model class.

Args: 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.

model_parametrized_name

def 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.

Args: 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_rebuild

def model_rebuild(
    *,
    force: 'bool' = False,
    raise_errors: 'bool' = True,
    _parent_namespace_depth: 'int' = 2,
    _types_namespace: 'dict[str, Any] | 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.

Args: 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.

model_validate

def model_validate(
    obj: 'Any',
    *,
    strict: 'bool | None' = None,
    from_attributes: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Validate a pydantic model instance.

Args: 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.

model_validate_json

def model_validate_json(
    json_data: 'str | bytes | bytearray',
    *,
    strict: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Args: 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: ValueError: If json_data is not a JSON string.

model_validate_strings

def 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.

Args: 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.

parse_file

def parse_file(
    path: 'str | Path',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

parse_obj

def parse_obj(
    obj: 'Any'
) -> 'Self'

parse_raw

def parse_raw(
    b: 'str | bytes',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

schema

def schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}'
) -> 'Dict[str, Any]'

schema_json

def schema_json(
    *,
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    **dumps_kwargs: 'Any'
) -> 'str'

update_forward_refs

def update_forward_refs(
    **localns: 'Any'
) -> 'None'

validate

def validate(
    value: 'Any'
) -> 'Self'

Instance variables

model_extra

Get extra fields set during validation.

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

model_fields_set

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.

Methods

copy

def copy(
    self,
    *,
    include: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    update: 'Dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Returns a copy of the model.

!!! warning "Deprecated" This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)

Args: 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

def dict(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False
) -> 'Dict[str, Any]'

json

def json(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = 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'

model_copy

def model_copy(
    self,
    *,
    update: 'dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#model_copy

Returns a copy of the model.

Args: 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

def model_dump(
    self,
    *,
    mode: "Literal['json', 'python'] | str" = 'python',
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Args: mode: 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: A set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. by_alias: Whether to use the field's alias in the dictionary key if defined. 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 dictionary representation of the model.

model_dump_json

def model_dump_json(
    self,
    *,
    indent: 'int | None' = None,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'str'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump_json

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

Args: 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.

model_post_init

def model_post_init(
    self,
    _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.

BaseModel

class BaseModel(
    /,
    **data: 'Any'
)

Usage docs: https://docs.pydantic.dev/2.8/concepts/models/

A base class for creating Pydantic models.

Attributes: class_vars: The names of classvars defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The signature for instantiating the model.

__pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
__pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.
__pydantic_custom_init__: Whether the model has a custom `__init__` function.
__pydantic_decorators__: Metadata containing the decorators defined on the model.
    This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.
__pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to
    __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
__pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
__pydantic_post_init__: The name of the post-init method for the model, if defined.
__pydantic_root_model__: Whether the model is a `RootModel`.
__pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
__pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_extra__: An instance attribute with the values of extra fields from validation when
    `model_config['extra'] == 'allow'`.
__pydantic_fields_set__: An instance attribute with the names of fields explicitly set.
__pydantic_private__: Instance attribute with the values of private attributes set on the model instance.

Ancestors (in MRO)

• pydantic.main.BaseModel

Descendants

• src.validation.models.SQLFilter
• src.validation.models.TagsFilter
• src.validation.models.AttributeFilter
• src.validation.models.Filters
• src.validation.models.RawDataCurrentParamsBase
• src.validation.models.SnapshotResponse
• src.validation.models.SnapshotTaskResult
• src.validation.models.SnapshotTaskResponse
• src.validation.models.StatusResponse
• src.validation.models.StatsRequestParams
• src.validation.models.HDXModel
• src.validation.models.CategoryModel
• src.validation.models.DatasetConfig
• src.validation.models.CategoriesBase

Class variables

Config

model_computed_fields

model_config

model_fields

Static methods

construct

def construct(
    _fields_set: 'set[str] | None' = None,
    **values: 'Any'
) -> 'Self'

from_orm

def from_orm(
    obj: 'Any'
) -> 'Self'

model_construct

def 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.

Args: _fields_set: The set of field names accepted for the Model instance. values: Trusted or pre-validated data dictionary.

Returns: A new instance of the Model class with validated data.

model_json_schema

def model_json_schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
    mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'

Generates a JSON schema for a model class.

Args: 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.

model_parametrized_name

def 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.

Args: 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_rebuild

def model_rebuild(
    *,
    force: 'bool' = False,
    raise_errors: 'bool' = True,
    _parent_namespace_depth: 'int' = 2,
    _types_namespace: 'dict[str, Any] | 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.

Args: 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.

model_validate

def model_validate(
    obj: 'Any',
    *,
    strict: 'bool | None' = None,
    from_attributes: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Validate a pydantic model instance.

Args: 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.

model_validate_json

def model_validate_json(
    json_data: 'str | bytes | bytearray',
    *,
    strict: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Args: 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: ValueError: If json_data is not a JSON string.

model_validate_strings

def 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.

Args: 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.

parse_file

def parse_file(
    path: 'str | Path',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

parse_obj

def parse_obj(
    obj: 'Any'
) -> 'Self'

parse_raw

def parse_raw(
    b: 'str | bytes',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

schema

def schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}'
) -> 'Dict[str, Any]'

schema_json

def schema_json(
    *,
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    **dumps_kwargs: 'Any'
) -> 'str'

update_forward_refs

def update_forward_refs(
    **localns: 'Any'
) -> 'None'

validate

def validate(
    value: 'Any'
) -> 'Self'

Instance variables

model_extra

Get extra fields set during validation.

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

model_fields_set

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.

Methods

copy

def copy(
    self,
    *,
    include: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    update: 'Dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Returns a copy of the model.

!!! warning "Deprecated" This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)

Args: 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

def dict(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False
) -> 'Dict[str, Any]'

json

def json(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = 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'

model_copy

def model_copy(
    self,
    *,
    update: 'dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#model_copy

Returns a copy of the model.

Args: 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

def model_dump(
    self,
    *,
    mode: "Literal['json', 'python'] | str" = 'python',
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Args: mode: 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: A set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. by_alias: Whether to use the field's alias in the dictionary key if defined. 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 dictionary representation of the model.

model_dump_json

def model_dump_json(
    self,
    *,
    indent: 'int | None' = None,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'str'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump_json

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

Args: 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.

model_post_init

def model_post_init(
    self,
    _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.

CategoriesBase

class CategoriesBase(
    /,
    **data: 'Any'
)

Usage docs: https://docs.pydantic.dev/2.8/concepts/models/

A base class for creating Pydantic models.

Attributes: class_vars: The names of classvars defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The signature for instantiating the model.

__pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
__pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.
__pydantic_custom_init__: Whether the model has a custom `__init__` function.
__pydantic_decorators__: Metadata containing the decorators defined on the model.
    This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.
__pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to
    __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
__pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
__pydantic_post_init__: The name of the post-init method for the model, if defined.
__pydantic_root_model__: Whether the model is a `RootModel`.
__pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
__pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_extra__: An instance attribute with the values of extra fields from validation when
    `model_config['extra'] == 'allow'`.
__pydantic_fields_set__: An instance attribute with the names of fields explicitly set.
__pydantic_private__: Instance attribute with the values of private attributes set on the model instance.

Ancestors (in MRO)

• src.validation.models.BaseModel
• pydantic.main.BaseModel

Descendants

• src.validation.models.DynamicCategoriesModel
• src.validation.models.CustomRequestsYaml

Class variables

Config

model_computed_fields

model_config

model_fields

Static methods

construct

def construct(
    _fields_set: 'set[str] | None' = None,
    **values: 'Any'
) -> 'Self'

from_orm

def from_orm(
    obj: 'Any'
) -> 'Self'

model_construct

def 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.

Args: _fields_set: The set of field names accepted for the Model instance. values: Trusted or pre-validated data dictionary.

Returns: A new instance of the Model class with validated data.

model_json_schema

def model_json_schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
    mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'

Generates a JSON schema for a model class.

Args: 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.

model_parametrized_name

def 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.

Args: 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_rebuild

def model_rebuild(
    *,
    force: 'bool' = False,
    raise_errors: 'bool' = True,
    _parent_namespace_depth: 'int' = 2,
    _types_namespace: 'dict[str, Any] | 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.

Args: 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.

model_validate

def model_validate(
    obj: 'Any',
    *,
    strict: 'bool | None' = None,
    from_attributes: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Validate a pydantic model instance.

Args: 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.

model_validate_json

def model_validate_json(
    json_data: 'str | bytes | bytearray',
    *,
    strict: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Args: 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: ValueError: If json_data is not a JSON string.

model_validate_strings

def 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.

Args: 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.

parse_file

def parse_file(
    path: 'str | Path',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

parse_obj

def parse_obj(
    obj: 'Any'
) -> 'Self'

parse_raw

def parse_raw(
    b: 'str | bytes',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

schema

def schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}'
) -> 'Dict[str, Any]'

schema_json

def schema_json(
    *,
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    **dumps_kwargs: 'Any'
) -> 'str'

update_forward_refs

def update_forward_refs(
    **localns: 'Any'
) -> 'None'

validate

def validate(
    value: 'Any'
) -> 'Self'

Instance variables

model_extra

Get extra fields set during validation.

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

model_fields_set

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.

Methods

copy

def copy(
    self,
    *,
    include: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    update: 'Dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Returns a copy of the model.

!!! warning "Deprecated" This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)

Args: 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

def dict(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False
) -> 'Dict[str, Any]'

json

def json(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = 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'

model_copy

def model_copy(
    self,
    *,
    update: 'dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#model_copy

Returns a copy of the model.

Args: 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

def model_dump(
    self,
    *,
    mode: "Literal['json', 'python'] | str" = 'python',
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Args: mode: 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: A set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. by_alias: Whether to use the field's alias in the dictionary key if defined. 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 dictionary representation of the model.

model_dump_json

def model_dump_json(
    self,
    *,
    indent: 'int | None' = None,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'str'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump_json

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

Args: 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.

model_post_init

def model_post_init(
    self,
    _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.

CategoryModel

class CategoryModel(
    /,
    **data: 'Any'
)

Model for category configuration settings.

Fields: - hdx (HDXModel): HDX configuration model. - types (List[str]): List of feature types (points, lines, polygons). - select (List[str]): List of selected fields. - where (str): SQL-like condition to filter features. - formats (List[str]): List of Export Formats (suffixes).

Ancestors (in MRO)

• src.validation.models.BaseModel
• pydantic.main.BaseModel

Class variables

Config

model_computed_fields

model_config

model_fields

Static methods

construct

def construct(
    _fields_set: 'set[str] | None' = None,
    **values: 'Any'
) -> 'Self'

from_orm

def from_orm(
    obj: 'Any'
) -> 'Self'

model_construct

def 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.

Args: _fields_set: The set of field names accepted for the Model instance. values: Trusted or pre-validated data dictionary.

Returns: A new instance of the Model class with validated data.

model_json_schema

def model_json_schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
    mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'

Generates a JSON schema for a model class.

Args: 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.

model_parametrized_name

def 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.

Args: 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_rebuild

def model_rebuild(
    *,
    force: 'bool' = False,
    raise_errors: 'bool' = True,
    _parent_namespace_depth: 'int' = 2,
    _types_namespace: 'dict[str, Any] | 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.

Args: 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.

model_validate

def model_validate(
    obj: 'Any',
    *,
    strict: 'bool | None' = None,
    from_attributes: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Validate a pydantic model instance.

Args: 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.

model_validate_json

def model_validate_json(
    json_data: 'str | bytes | bytearray',
    *,
    strict: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Args: 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: ValueError: If json_data is not a JSON string.

model_validate_strings

def 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.

Args: 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.

parse_file

def parse_file(
    path: 'str | Path',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

parse_obj

def parse_obj(
    obj: 'Any'
) -> 'Self'

parse_raw

def parse_raw(
    b: 'str | bytes',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

schema

def schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}'
) -> 'Dict[str, Any]'

schema_json

def schema_json(
    *,
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    **dumps_kwargs: 'Any'
) -> 'str'

update_forward_refs

def update_forward_refs(
    **localns: 'Any'
) -> 'None'

validate

def validate(
    value: 'Any'
) -> 'Self'

validate_export_types

def validate_export_types(
    value
)

Validates export types if they are supported

Args: value (type): description

Raises: ValueError: description

Returns: type: description

validate_types

def validate_types(
    value
)

validates geom types

Args: value (type): description

Raises: ValueError: description

Returns: type: description

Instance variables

model_extra

Get extra fields set during validation.

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

model_fields_set

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.

Methods

copy

def copy(
    self,
    *,
    include: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    update: 'Dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Returns a copy of the model.

!!! warning "Deprecated" This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)

Args: 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

def dict(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False
) -> 'Dict[str, Any]'

json

def json(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = 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'

model_copy

def model_copy(
    self,
    *,
    update: 'dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#model_copy

Returns a copy of the model.

Args: 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

def model_dump(
    self,
    *,
    mode: "Literal['json', 'python'] | str" = 'python',
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Args: mode: 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: A set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. by_alias: Whether to use the field's alias in the dictionary key if defined. 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 dictionary representation of the model.

model_dump_json

def model_dump_json(
    self,
    *,
    indent: 'int | None' = None,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'str'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump_json

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

Args: 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.

model_post_init

def model_post_init(
    self,
    _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.

CustomRequestsYaml

class CustomRequestsYaml(
    /,
    **data: 'Any'
)

Usage docs: https://docs.pydantic.dev/2.8/concepts/models/

A base class for creating Pydantic models.

Attributes: class_vars: The names of classvars defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The signature for instantiating the model.

__pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
__pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.
__pydantic_custom_init__: Whether the model has a custom `__init__` function.
__pydantic_decorators__: Metadata containing the decorators defined on the model.
    This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.
__pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to
    __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
__pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
__pydantic_post_init__: The name of the post-init method for the model, if defined.
__pydantic_root_model__: Whether the model is a `RootModel`.
__pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
__pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_extra__: An instance attribute with the values of extra fields from validation when
    `model_config['extra'] == 'allow'`.
__pydantic_fields_set__: An instance attribute with the names of fields explicitly set.
__pydantic_private__: Instance attribute with the values of private attributes set on the model instance.

Ancestors (in MRO)

• src.validation.models.CategoriesBase
• src.validation.models.BaseModel
• pydantic.main.BaseModel

Class variables

Config

model_computed_fields

model_config

model_fields

Static methods

construct

def construct(
    _fields_set: 'set[str] | None' = None,
    **values: 'Any'
) -> 'Self'

from_orm

def from_orm(
    obj: 'Any'
) -> 'Self'

model_construct

def 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.

Args: _fields_set: The set of field names accepted for the Model instance. values: Trusted or pre-validated data dictionary.

Returns: A new instance of the Model class with validated data.

model_json_schema

def model_json_schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
    mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'

Generates a JSON schema for a model class.

Args: 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.

model_parametrized_name

def 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.

Args: 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_rebuild

def model_rebuild(
    *,
    force: 'bool' = False,
    raise_errors: 'bool' = True,
    _parent_namespace_depth: 'int' = 2,
    _types_namespace: 'dict[str, Any] | 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.

Args: 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.

model_validate

def model_validate(
    obj: 'Any',
    *,
    strict: 'bool | None' = None,
    from_attributes: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Validate a pydantic model instance.

Args: 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.

model_validate_json

def model_validate_json(
    json_data: 'str | bytes | bytearray',
    *,
    strict: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Args: 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: ValueError: If json_data is not a JSON string.

model_validate_strings

def 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.

Args: 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.

parse_file

def parse_file(
    path: 'str | Path',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

parse_obj

def parse_obj(
    obj: 'Any'
) -> 'Self'

parse_raw

def parse_raw(
    b: 'str | bytes',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

schema

def schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}'
) -> 'Dict[str, Any]'

schema_json

def schema_json(
    *,
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    **dumps_kwargs: 'Any'
) -> 'str'

update_forward_refs

def update_forward_refs(
    **localns: 'Any'
) -> 'None'

validate

def validate(
    value: 'Any'
) -> 'Self'

Instance variables

model_extra

Get extra fields set during validation.

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

model_fields_set

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.

Methods

copy

def copy(
    self,
    *,
    include: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    update: 'Dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Returns a copy of the model.

!!! warning "Deprecated" This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)

Args: 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

def dict(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False
) -> 'Dict[str, Any]'

json

def json(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = 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'

model_copy

def model_copy(
    self,
    *,
    update: 'dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#model_copy

Returns a copy of the model.

Args: 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

def model_dump(
    self,
    *,
    mode: "Literal['json', 'python'] | str" = 'python',
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Args: mode: 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: A set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. by_alias: Whether to use the field's alias in the dictionary key if defined. 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 dictionary representation of the model.

model_dump_json

def model_dump_json(
    self,
    *,
    indent: 'int | None' = None,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'str'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump_json

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

Args: 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.

model_post_init

def model_post_init(
    self,
    _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.

DatasetConfig

class DatasetConfig(
    /,
    **data: 'Any'
)

Model for dataset configuration settings.

Fields: - private (bool): Make dataset private. By default False, public is recommended. - subnational (bool): Make it true if the dataset doesn't cover the nation/country. - update_frequency (str): Update frequency to be added on uploads. - dataset_title (str): Dataset title that appears at the top of the page. - dataset_prefix (str): Dataset prefix to be appended before the category name. Ignored if iso3 is supplied. - dataset_locations (List[str]): Valid dataset locations iso3.

Ancestors (in MRO)

• src.validation.models.BaseModel
• pydantic.main.BaseModel

Class variables

Config

model_computed_fields

model_config

model_fields

Static methods

construct

def construct(
    _fields_set: 'set[str] | None' = None,
    **values: 'Any'
) -> 'Self'

from_orm

def from_orm(
    obj: 'Any'
) -> 'Self'

model_construct

def 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.

Args: _fields_set: The set of field names accepted for the Model instance. values: Trusted or pre-validated data dictionary.

Returns: A new instance of the Model class with validated data.

model_json_schema

def model_json_schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
    mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'

Generates a JSON schema for a model class.

Args: 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.

model_parametrized_name

def 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.

Args: 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_rebuild

def model_rebuild(
    *,
    force: 'bool' = False,
    raise_errors: 'bool' = True,
    _parent_namespace_depth: 'int' = 2,
    _types_namespace: 'dict[str, Any] | 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.

Args: 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.

model_validate

def model_validate(
    obj: 'Any',
    *,
    strict: 'bool | None' = None,
    from_attributes: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Validate a pydantic model instance.

Args: 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.

model_validate_json

def model_validate_json(
    json_data: 'str | bytes | bytearray',
    *,
    strict: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Args: 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: ValueError: If json_data is not a JSON string.

model_validate_strings

def 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.

Args: 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.

parse_file

def parse_file(
    path: 'str | Path',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

parse_obj

def parse_obj(
    obj: 'Any'
) -> 'Self'

parse_raw

def parse_raw(
    b: 'str | bytes',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

schema

def schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}'
) -> 'Dict[str, Any]'

schema_json

def schema_json(
    *,
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    **dumps_kwargs: 'Any'
) -> 'str'

update_forward_refs

def update_forward_refs(
    **localns: 'Any'
) -> 'None'

validate

def validate(
    value: 'Any'
) -> 'Self'

validate_frequency

def validate_frequency(
    value
)

Validates frequency

Args: value (type): description

Raises: ValueError: description

Returns: type: description

Instance variables

model_extra

Get extra fields set during validation.

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

model_fields_set

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.

Methods

copy

def copy(
    self,
    *,
    include: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    update: 'Dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Returns a copy of the model.

!!! warning "Deprecated" This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)

Args: 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

def dict(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False
) -> 'Dict[str, Any]'

json

def json(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = 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'

model_copy

def model_copy(
    self,
    *,
    update: 'dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#model_copy

Returns a copy of the model.

Args: 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

def model_dump(
    self,
    *,
    mode: "Literal['json', 'python'] | str" = 'python',
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Args: mode: 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: A set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. by_alias: Whether to use the field's alias in the dictionary key if defined. 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 dictionary representation of the model.

model_dump_json

def model_dump_json(
    self,
    *,
    indent: 'int | None' = None,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'str'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump_json

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

Args: 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.

model_post_init

def model_post_init(
    self,
    _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.

DynamicCategoriesModel

class DynamicCategoriesModel(
    /,
    **data: 'Any'
)

Model for dynamic categories.

Fields: - iso3 (Optional[str]): ISO3 Country Code. - dataset (Optional[DatasetConfig]): Dataset Configurations for HDX Upload. - meta (bool): Dumps Meta db in parquet format & HDX config JSON to S3. - hdx_upload (bool): Enable/Disable uploading the dataset to HDX. - categories (List[Dict[str, CategoryModel]]): List of dynamic categories. - geometry (Optional[Union[Polygon, MultiPolygon]]): Custom polygon geometry.

Ancestors (in MRO)

• src.validation.models.CategoriesBase
• src.validation.models.BaseModel
• pydantic.main.BaseModel
• src.validation.models.GeometryValidatorMixin

Class variables

Config

model_computed_fields

model_config

model_fields

Static methods

construct

def construct(
    _fields_set: 'set[str] | None' = None,
    **values: 'Any'
) -> 'Self'

from_orm

def from_orm(
    obj: 'Any'
) -> 'Self'

model_construct

def 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.

Args: _fields_set: The set of field names accepted for the Model instance. values: Trusted or pre-validated data dictionary.

Returns: A new instance of the Model class with validated data.

model_json_schema

def model_json_schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
    mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'

Generates a JSON schema for a model class.

Args: 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.

model_parametrized_name

def 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.

Args: 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_rebuild

def model_rebuild(
    *,
    force: 'bool' = False,
    raise_errors: 'bool' = True,
    _parent_namespace_depth: 'int' = 2,
    _types_namespace: 'dict[str, Any] | 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.

Args: 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.

model_validate

def model_validate(
    obj: 'Any',
    *,
    strict: 'bool | None' = None,
    from_attributes: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Validate a pydantic model instance.

Args: 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.

model_validate_json

def model_validate_json(
    json_data: 'str | bytes | bytearray',
    *,
    strict: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Args: 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: ValueError: If json_data is not a JSON string.

model_validate_strings

def 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.

Args: 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.

parse_file

def parse_file(
    path: 'str | Path',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

parse_obj

def parse_obj(
    obj: 'Any'
) -> 'Self'

parse_raw

def parse_raw(
    b: 'str | bytes',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

schema

def schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}'
) -> 'Dict[str, Any]'

schema_json

def schema_json(
    *,
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    **dumps_kwargs: 'Any'
) -> 'str'

set_geometry_or_iso3

def set_geometry_or_iso3(
    value,
    values
)

Either geometry or iso3 should be supplied.

update_forward_refs

def update_forward_refs(
    **localns: 'Any'
) -> 'None'

validate

def validate(
    value: 'Any'
) -> 'Self'

validate_geometry

def validate_geometry(
    value
)

Validates geometry

Instance variables

model_extra

Get extra fields set during validation.

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

model_fields_set

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.

Methods

copy

def copy(
    self,
    *,
    include: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    update: 'Dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Returns a copy of the model.

!!! warning "Deprecated" This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)

Args: 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

def dict(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False
) -> 'Dict[str, Any]'

json

def json(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = 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'

model_copy

def model_copy(
    self,
    *,
    update: 'dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#model_copy

Returns a copy of the model.

Args: 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

def model_dump(
    self,
    *,
    mode: "Literal['json', 'python'] | str" = 'python',
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Args: mode: 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: A set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. by_alias: Whether to use the field's alias in the dictionary key if defined. 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 dictionary representation of the model.

model_dump_json

def model_dump_json(
    self,
    *,
    indent: 'int | None' = None,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'str'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump_json

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

Args: 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.

model_post_init

def model_post_init(
    self,
    _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.

ExportTypeInfo

class ExportTypeInfo(
    suffix,
    driver_name,
    layer_creation_options,
    format_option
)

Class representing export type information.

Fields: - suffix (str): File suffix for the export type. - driver_name (str): GDAL driver name. - layer_creation_options (List[str]): Layer creation options. - format_option (str): Format option for GDAL.

Filters

class Filters(
    /,
    **data: 'Any'
)

Usage docs: https://docs.pydantic.dev/2.8/concepts/models/

A base class for creating Pydantic models.

Attributes: class_vars: The names of classvars defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The signature for instantiating the model.

__pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
__pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.
__pydantic_custom_init__: Whether the model has a custom `__init__` function.
__pydantic_decorators__: Metadata containing the decorators defined on the model.
    This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.
__pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to
    __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
__pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
__pydantic_post_init__: The name of the post-init method for the model, if defined.
__pydantic_root_model__: Whether the model is a `RootModel`.
__pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
__pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_extra__: An instance attribute with the values of extra fields from validation when
    `model_config['extra'] == 'allow'`.
__pydantic_fields_set__: An instance attribute with the names of fields explicitly set.
__pydantic_private__: Instance attribute with the values of private attributes set on the model instance.

Ancestors (in MRO)

• src.validation.models.BaseModel
• pydantic.main.BaseModel

Class variables

Config

model_computed_fields

model_config

model_fields

Static methods

construct

def construct(
    _fields_set: 'set[str] | None' = None,
    **values: 'Any'
) -> 'Self'

from_orm

def from_orm(
    obj: 'Any'
) -> 'Self'

model_construct

def 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.

Args: _fields_set: The set of field names accepted for the Model instance. values: Trusted or pre-validated data dictionary.

Returns: A new instance of the Model class with validated data.

model_json_schema

def model_json_schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
    mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'

Generates a JSON schema for a model class.

Args: 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.

model_parametrized_name

def 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.

Args: 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_rebuild

def model_rebuild(
    *,
    force: 'bool' = False,
    raise_errors: 'bool' = True,
    _parent_namespace_depth: 'int' = 2,
    _types_namespace: 'dict[str, Any] | 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.

Args: 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.

model_validate

def model_validate(
    obj: 'Any',
    *,
    strict: 'bool | None' = None,
    from_attributes: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Validate a pydantic model instance.

Args: 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.

model_validate_json

def model_validate_json(
    json_data: 'str | bytes | bytearray',
    *,
    strict: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Args: 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: ValueError: If json_data is not a JSON string.

model_validate_strings

def 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.

Args: 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.

parse_file

def parse_file(
    path: 'str | Path',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

parse_obj

def parse_obj(
    obj: 'Any'
) -> 'Self'

parse_raw

def parse_raw(
    b: 'str | bytes',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

schema

def schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}'
) -> 'Dict[str, Any]'

schema_json

def schema_json(
    *,
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    **dumps_kwargs: 'Any'
) -> 'str'

update_forward_refs

def update_forward_refs(
    **localns: 'Any'
) -> 'None'

validate

def validate(
    value: 'Any'
) -> 'Self'

Instance variables

model_extra

Get extra fields set during validation.

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

model_fields_set

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.

Methods

copy

def copy(
    self,
    *,
    include: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    update: 'Dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Returns a copy of the model.

!!! warning "Deprecated" This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)

Args: 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

def dict(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False
) -> 'Dict[str, Any]'

json

def json(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = 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'

model_copy

def model_copy(
    self,
    *,
    update: 'dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#model_copy

Returns a copy of the model.

Args: 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

def model_dump(
    self,
    *,
    mode: "Literal['json', 'python'] | str" = 'python',
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Args: mode: 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: A set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. by_alias: Whether to use the field's alias in the dictionary key if defined. 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 dictionary representation of the model.

model_dump_json

def model_dump_json(
    self,
    *,
    indent: 'int | None' = None,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'str'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump_json

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

Args: 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.

model_post_init

def model_post_init(
    self,
    _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.

GeometryValidatorMixin

class GeometryValidatorMixin(
    /,
    *args,
    **kwargs
)

Descendants

• src.validation.models.RawDataCurrentParamsBase
• src.validation.models.StatsRequestParams
• src.validation.models.DynamicCategoriesModel

Static methods

validate_geometry

def validate_geometry(
    value
)

Validates geometry

HDXModel

class HDXModel(
    /,
    **data: 'Any'
)

Model for HDX configuration settings.

Fields: - tags (List[str]): List of tags for the HDX model. - caveats (str): Caveats/Warning for the Datasets. - notes (str): Extra notes to append in the notes section of HDX datasets.

Ancestors (in MRO)

• src.validation.models.BaseModel
• pydantic.main.BaseModel

Class variables

Config

model_computed_fields

model_config

model_fields

Static methods

construct

def construct(
    _fields_set: 'set[str] | None' = None,
    **values: 'Any'
) -> 'Self'

from_orm

def from_orm(
    obj: 'Any'
) -> 'Self'

model_construct

def 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.

Args: _fields_set: The set of field names accepted for the Model instance. values: Trusted or pre-validated data dictionary.

Returns: A new instance of the Model class with validated data.

model_json_schema

def model_json_schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
    mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'

Generates a JSON schema for a model class.

Args: 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.

model_parametrized_name

def 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.

Args: 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_rebuild

def model_rebuild(
    *,
    force: 'bool' = False,
    raise_errors: 'bool' = True,
    _parent_namespace_depth: 'int' = 2,
    _types_namespace: 'dict[str, Any] | 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.

Args: 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.

model_validate

def model_validate(
    obj: 'Any',
    *,
    strict: 'bool | None' = None,
    from_attributes: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Validate a pydantic model instance.

Args: 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.

model_validate_json

def model_validate_json(
    json_data: 'str | bytes | bytearray',
    *,
    strict: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Args: 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: ValueError: If json_data is not a JSON string.

model_validate_strings

def 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.

Args: 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.

parse_file

def parse_file(
    path: 'str | Path',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

parse_obj

def parse_obj(
    obj: 'Any'
) -> 'Self'

parse_raw

def parse_raw(
    b: 'str | bytes',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

schema

def schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}'
) -> 'Dict[str, Any]'

schema_json

def schema_json(
    *,
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    **dumps_kwargs: 'Any'
) -> 'str'

update_forward_refs

def update_forward_refs(
    **localns: 'Any'
) -> 'None'

validate

def validate(
    value: 'Any'
) -> 'Self'

validate_tags

def validate_tags(
    value
)

Validates tags if they are allowed from hdx allowed approved tags

Args: value (type): description

Raises: ValueError: description

Returns: type: description

Instance variables

model_extra

Get extra fields set during validation.

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

model_fields_set

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.

Methods

copy

def copy(
    self,
    *,
    include: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    update: 'Dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Returns a copy of the model.

!!! warning "Deprecated" This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)

Args: 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

def dict(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False
) -> 'Dict[str, Any]'

json

def json(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = 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'

model_copy

def model_copy(
    self,
    *,
    update: 'dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#model_copy

Returns a copy of the model.

Args: 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

def model_dump(
    self,
    *,
    mode: "Literal['json', 'python'] | str" = 'python',
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Args: mode: 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: A set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. by_alias: Whether to use the field's alias in the dictionary key if defined. 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 dictionary representation of the model.

model_dump_json

def model_dump_json(
    self,
    *,
    indent: 'int | None' = None,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'str'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump_json

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

Args: 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.

model_post_init

def model_post_init(
    self,
    _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.

JoinFilterType

class JoinFilterType(
    *args,
    **kwds
)

Create a collection of name/value pairs.

Example enumeration:

class Color(Enum): ... RED = 1 ... BLUE = 2 ... GREEN = 3

Access them by:

• attribute access::

Color.RED

• value lookup:

Color(1)

• name lookup:

Color['RED']

Enumerations can be iterated over, and know how many members they have:

len(Color) 3

list(Color) [, , ]

Methods can be added to enumerations, and members can have their own attributes -- see the documentation for details.

Ancestors (in MRO)

• enum.Enum

Class variables

AND

OR

name

value

RawDataCurrentParams

class RawDataCurrentParams(
    /,
    **data: 'Any'
)

Usage docs: https://docs.pydantic.dev/2.8/concepts/models/

A base class for creating Pydantic models.

Attributes: class_vars: The names of classvars defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The signature for instantiating the model.

__pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
__pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.
__pydantic_custom_init__: Whether the model has a custom `__init__` function.
__pydantic_decorators__: Metadata containing the decorators defined on the model.
    This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.
__pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to
    __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
__pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
__pydantic_post_init__: The name of the post-init method for the model, if defined.
__pydantic_root_model__: Whether the model is a `RootModel`.
__pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
__pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_extra__: An instance attribute with the values of extra fields from validation when
    `model_config['extra'] == 'allow'`.
__pydantic_fields_set__: An instance attribute with the names of fields explicitly set.
__pydantic_private__: Instance attribute with the values of private attributes set on the model instance.

Ancestors (in MRO)

• src.validation.models.RawDataCurrentParamsBase
• src.validation.models.BaseModel
• pydantic.main.BaseModel
• src.validation.models.GeometryValidatorMixin

Class variables

Config

model_computed_fields

model_config

model_fields

Static methods

check_bind_option

def check_bind_option(
    value,
    values
)

Checks if cloud optimized output format or geoJSON is selected along with bind to zip file

construct

def construct(
    _fields_set: 'set[str] | None' = None,
    **values: 'Any'
) -> 'Self'

from_orm

def from_orm(
    obj: 'Any'
) -> 'Self'

model_construct

def 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.

Args: _fields_set: The set of field names accepted for the Model instance. values: Trusted or pre-validated data dictionary.

Returns: A new instance of the Model class with validated data.

model_json_schema

def model_json_schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
    mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'

Generates a JSON schema for a model class.

Args: 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.

model_parametrized_name

def 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.

Args: 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_rebuild

def model_rebuild(
    *,
    force: 'bool' = False,
    raise_errors: 'bool' = True,
    _parent_namespace_depth: 'int' = 2,
    _types_namespace: 'dict[str, Any] | 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.

Args: 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.

model_validate

def model_validate(
    obj: 'Any',
    *,
    strict: 'bool | None' = None,
    from_attributes: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Validate a pydantic model instance.

Args: 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.

model_validate_json

def model_validate_json(
    json_data: 'str | bytes | bytearray',
    *,
    strict: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Args: 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: ValueError: If json_data is not a JSON string.

model_validate_strings

def 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.

Args: 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.

parse_file

def parse_file(
    path: 'str | Path',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

parse_obj

def parse_obj(
    obj: 'Any'
) -> 'Self'

parse_raw

def parse_raw(
    b: 'str | bytes',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

return_unique_value

def return_unique_value(
    value
)

return unique list

schema

def schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}'
) -> 'Dict[str, Any]'

schema_json

def schema_json(
    *,
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    **dumps_kwargs: 'Any'
) -> 'str'

update_forward_refs

def update_forward_refs(
    **localns: 'Any'
) -> 'None'

validate

def validate(
    value: 'Any'
) -> 'Self'

validate_geometry

def validate_geometry(
    value
)

Validates geometry

Instance variables

model_extra

Get extra fields set during validation.

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

model_fields_set

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.

Methods

copy

def copy(
    self,
    *,
    include: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    update: 'Dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Returns a copy of the model.

!!! warning "Deprecated" This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)

Args: 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

def dict(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False
) -> 'Dict[str, Any]'

json

def json(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = 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'

model_copy

def model_copy(
    self,
    *,
    update: 'dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#model_copy

Returns a copy of the model.

Args: 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

def model_dump(
    self,
    *,
    mode: "Literal['json', 'python'] | str" = 'python',
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Args: mode: 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: A set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. by_alias: Whether to use the field's alias in the dictionary key if defined. 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 dictionary representation of the model.

model_dump_json

def model_dump_json(
    self,
    *,
    indent: 'int | None' = None,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'str'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump_json

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

Args: 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.

model_post_init

def model_post_init(
    self,
    _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.

RawDataCurrentParamsBase

class RawDataCurrentParamsBase(
    /,
    **data: 'Any'
)

Usage docs: https://docs.pydantic.dev/2.8/concepts/models/

A base class for creating Pydantic models.

Attributes: class_vars: The names of classvars defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The signature for instantiating the model.

__pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
__pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.
__pydantic_custom_init__: Whether the model has a custom `__init__` function.
__pydantic_decorators__: Metadata containing the decorators defined on the model.
    This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.
__pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to
    __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
__pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
__pydantic_post_init__: The name of the post-init method for the model, if defined.
__pydantic_root_model__: Whether the model is a `RootModel`.
__pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
__pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_extra__: An instance attribute with the values of extra fields from validation when
    `model_config['extra'] == 'allow'`.
__pydantic_fields_set__: An instance attribute with the names of fields explicitly set.
__pydantic_private__: Instance attribute with the values of private attributes set on the model instance.

Ancestors (in MRO)

• src.validation.models.BaseModel
• pydantic.main.BaseModel
• src.validation.models.GeometryValidatorMixin

Descendants

• src.validation.models.RawDataCurrentParams

Class variables

Config

model_computed_fields

model_config

model_fields

Static methods

construct

def construct(
    _fields_set: 'set[str] | None' = None,
    **values: 'Any'
) -> 'Self'

from_orm

def from_orm(
    obj: 'Any'
) -> 'Self'

model_construct

def 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.

Args: _fields_set: The set of field names accepted for the Model instance. values: Trusted or pre-validated data dictionary.

Returns: A new instance of the Model class with validated data.

model_json_schema

def model_json_schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
    mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'

Generates a JSON schema for a model class.

Args: 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.

model_parametrized_name

def 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.

Args: 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_rebuild

def model_rebuild(
    *,
    force: 'bool' = False,
    raise_errors: 'bool' = True,
    _parent_namespace_depth: 'int' = 2,
    _types_namespace: 'dict[str, Any] | 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.

Args: 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.

model_validate

def model_validate(
    obj: 'Any',
    *,
    strict: 'bool | None' = None,
    from_attributes: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Validate a pydantic model instance.

Args: 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.

model_validate_json

def model_validate_json(
    json_data: 'str | bytes | bytearray',
    *,
    strict: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Args: 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: ValueError: If json_data is not a JSON string.

model_validate_strings

def 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.

Args: 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.

parse_file

def parse_file(
    path: 'str | Path',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

parse_obj

def parse_obj(
    obj: 'Any'
) -> 'Self'

parse_raw

def parse_raw(
    b: 'str | bytes',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

return_unique_value

def return_unique_value(
    value
)

return unique list

schema

def schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}'
) -> 'Dict[str, Any]'

schema_json

def schema_json(
    *,
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    **dumps_kwargs: 'Any'
) -> 'str'

update_forward_refs

def update_forward_refs(
    **localns: 'Any'
) -> 'None'

validate

def validate(
    value: 'Any'
) -> 'Self'

validate_geometry

def validate_geometry(
    value
)

Validates geometry

Instance variables

model_extra

Get extra fields set during validation.

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

model_fields_set

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.

Methods

copy

def copy(
    self,
    *,
    include: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    update: 'Dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Returns a copy of the model.

!!! warning "Deprecated" This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)

Args: 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

def dict(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False
) -> 'Dict[str, Any]'

json

def json(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = 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'

model_copy

def model_copy(
    self,
    *,
    update: 'dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#model_copy

Returns a copy of the model.

Args: 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

def model_dump(
    self,
    *,
    mode: "Literal['json', 'python'] | str" = 'python',
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Args: mode: 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: A set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. by_alias: Whether to use the field's alias in the dictionary key if defined. 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 dictionary representation of the model.

model_dump_json

def model_dump_json(
    self,
    *,
    indent: 'int | None' = None,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'str'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump_json

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

Args: 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.

model_post_init

def model_post_init(
    self,
    _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.

RawDataOutputType

class RawDataOutputType(
    *args,
    **kwds
)

Create a collection of name/value pairs.

Example enumeration:

class Color(Enum): ... RED = 1 ... BLUE = 2 ... GREEN = 3

Access them by:

• attribute access::

Color.RED

• value lookup:

Color(1)

• name lookup:

Color['RED']

Enumerations can be iterated over, and know how many members they have:

len(Color) 3

list(Color) [, , ]

Methods can be added to enumerations, and members can have their own attributes -- see the documentation for details.

Ancestors (in MRO)

• enum.Enum

Class variables

CSV

FLATGEOBUF

GEOJSON

GEOPACKAGE

GEOPARQUET

KML

PGDUMP

SHAPEFILE

name

value

SQLFilter

class SQLFilter(
    /,
    **data: 'Any'
)

Usage docs: https://docs.pydantic.dev/2.8/concepts/models/

A base class for creating Pydantic models.

Attributes: class_vars: The names of classvars defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The signature for instantiating the model.

__pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
__pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.
__pydantic_custom_init__: Whether the model has a custom `__init__` function.
__pydantic_decorators__: Metadata containing the decorators defined on the model.
    This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.
__pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to
    __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
__pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
__pydantic_post_init__: The name of the post-init method for the model, if defined.
__pydantic_root_model__: Whether the model is a `RootModel`.
__pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
__pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_extra__: An instance attribute with the values of extra fields from validation when
    `model_config['extra'] == 'allow'`.
__pydantic_fields_set__: An instance attribute with the names of fields explicitly set.
__pydantic_private__: Instance attribute with the values of private attributes set on the model instance.

Ancestors (in MRO)

• src.validation.models.BaseModel
• pydantic.main.BaseModel

Class variables

Config

model_computed_fields

model_config

model_fields

Static methods

construct

def construct(
    _fields_set: 'set[str] | None' = None,
    **values: 'Any'
) -> 'Self'

from_orm

def from_orm(
    obj: 'Any'
) -> 'Self'

model_construct

def 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.

Args: _fields_set: The set of field names accepted for the Model instance. values: Trusted or pre-validated data dictionary.

Returns: A new instance of the Model class with validated data.

model_json_schema

def model_json_schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
    mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'

Generates a JSON schema for a model class.

Args: 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.

model_parametrized_name

def 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.

Args: 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_rebuild

def model_rebuild(
    *,
    force: 'bool' = False,
    raise_errors: 'bool' = True,
    _parent_namespace_depth: 'int' = 2,
    _types_namespace: 'dict[str, Any] | 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.

Args: 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.

model_validate

def model_validate(
    obj: 'Any',
    *,
    strict: 'bool | None' = None,
    from_attributes: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Validate a pydantic model instance.

Args: 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.

model_validate_json

def model_validate_json(
    json_data: 'str | bytes | bytearray',
    *,
    strict: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Args: 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: ValueError: If json_data is not a JSON string.

model_validate_strings

def 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.

Args: 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.

parse_file

def parse_file(
    path: 'str | Path',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

parse_obj

def parse_obj(
    obj: 'Any'
) -> 'Self'

parse_raw

def parse_raw(
    b: 'str | bytes',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

schema

def schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}'
) -> 'Dict[str, Any]'

schema_json

def schema_json(
    *,
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    **dumps_kwargs: 'Any'
) -> 'str'

update_forward_refs

def update_forward_refs(
    **localns: 'Any'
) -> 'None'

validate

def validate(
    value: 'Any'
) -> 'Self'

Instance variables

model_extra

Get extra fields set during validation.

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

model_fields_set

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.

Methods

copy

def copy(
    self,
    *,
    include: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    update: 'Dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Returns a copy of the model.

!!! warning "Deprecated" This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)

Args: 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

def dict(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False
) -> 'Dict[str, Any]'

json

def json(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = 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'

model_copy

def model_copy(
    self,
    *,
    update: 'dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#model_copy

Returns a copy of the model.

Args: 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

def model_dump(
    self,
    *,
    mode: "Literal['json', 'python'] | str" = 'python',
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Args: mode: 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: A set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. by_alias: Whether to use the field's alias in the dictionary key if defined. 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 dictionary representation of the model.

model_dump_json

def model_dump_json(
    self,
    *,
    indent: 'int | None' = None,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'str'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump_json

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

Args: 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.

model_post_init

def model_post_init(
    self,
    _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.

SnapshotResponse

class SnapshotResponse(
    /,
    **data: 'Any'
)

Usage docs: https://docs.pydantic.dev/2.8/concepts/models/

A base class for creating Pydantic models.

Attributes: class_vars: The names of classvars defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The signature for instantiating the model.

__pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
__pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.
__pydantic_custom_init__: Whether the model has a custom `__init__` function.
__pydantic_decorators__: Metadata containing the decorators defined on the model.
    This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.
__pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to
    __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
__pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
__pydantic_post_init__: The name of the post-init method for the model, if defined.
__pydantic_root_model__: Whether the model is a `RootModel`.
__pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
__pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_extra__: An instance attribute with the values of extra fields from validation when
    `model_config['extra'] == 'allow'`.
__pydantic_fields_set__: An instance attribute with the names of fields explicitly set.
__pydantic_private__: Instance attribute with the values of private attributes set on the model instance.

Ancestors (in MRO)

• src.validation.models.BaseModel
• pydantic.main.BaseModel

Class variables

Config

model_computed_fields

model_config

model_fields

Static methods

construct

def construct(
    _fields_set: 'set[str] | None' = None,
    **values: 'Any'
) -> 'Self'

from_orm

def from_orm(
    obj: 'Any'
) -> 'Self'

model_construct

def 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.

Args: _fields_set: The set of field names accepted for the Model instance. values: Trusted or pre-validated data dictionary.

Returns: A new instance of the Model class with validated data.

model_json_schema

def model_json_schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
    mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'

Generates a JSON schema for a model class.

Args: 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.

model_parametrized_name

def 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.

Args: 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_rebuild

def model_rebuild(
    *,
    force: 'bool' = False,
    raise_errors: 'bool' = True,
    _parent_namespace_depth: 'int' = 2,
    _types_namespace: 'dict[str, Any] | 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.

Args: 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.

model_validate

def model_validate(
    obj: 'Any',
    *,
    strict: 'bool | None' = None,
    from_attributes: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Validate a pydantic model instance.

Args: 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.

model_validate_json

def model_validate_json(
    json_data: 'str | bytes | bytearray',
    *,
    strict: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Args: 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: ValueError: If json_data is not a JSON string.

model_validate_strings

def 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.

Args: 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.

parse_file

def parse_file(
    path: 'str | Path',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

parse_obj

def parse_obj(
    obj: 'Any'
) -> 'Self'

parse_raw

def parse_raw(
    b: 'str | bytes',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

schema

def schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}'
) -> 'Dict[str, Any]'

schema_json

def schema_json(
    *,
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    **dumps_kwargs: 'Any'
) -> 'str'

update_forward_refs

def update_forward_refs(
    **localns: 'Any'
) -> 'None'

validate

def validate(
    value: 'Any'
) -> 'Self'

Instance variables

model_extra

Get extra fields set during validation.

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

model_fields_set

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.

Methods

copy

def copy(
    self,
    *,
    include: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    update: 'Dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Returns a copy of the model.

!!! warning "Deprecated" This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)

Args: 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

def dict(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False
) -> 'Dict[str, Any]'

json

def json(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = 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'

model_copy

def model_copy(
    self,
    *,
    update: 'dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#model_copy

Returns a copy of the model.

Args: 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

def model_dump(
    self,
    *,
    mode: "Literal['json', 'python'] | str" = 'python',
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Args: mode: 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: A set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. by_alias: Whether to use the field's alias in the dictionary key if defined. 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 dictionary representation of the model.

model_dump_json

def model_dump_json(
    self,
    *,
    indent: 'int | None' = None,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'str'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump_json

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

Args: 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.

model_post_init

def model_post_init(
    self,
    _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.

SnapshotTaskResponse

class SnapshotTaskResponse(
    /,
    **data: 'Any'
)

Usage docs: https://docs.pydantic.dev/2.8/concepts/models/

A base class for creating Pydantic models.

Attributes: class_vars: The names of classvars defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The signature for instantiating the model.

__pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
__pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.
__pydantic_custom_init__: Whether the model has a custom `__init__` function.
__pydantic_decorators__: Metadata containing the decorators defined on the model.
    This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.
__pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to
    __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
__pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
__pydantic_post_init__: The name of the post-init method for the model, if defined.
__pydantic_root_model__: Whether the model is a `RootModel`.
__pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
__pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_extra__: An instance attribute with the values of extra fields from validation when
    `model_config['extra'] == 'allow'`.
__pydantic_fields_set__: An instance attribute with the names of fields explicitly set.
__pydantic_private__: Instance attribute with the values of private attributes set on the model instance.

Ancestors (in MRO)

• src.validation.models.BaseModel
• pydantic.main.BaseModel

Class variables

Config

model_computed_fields

model_config

model_fields

Static methods

construct

def construct(
    _fields_set: 'set[str] | None' = None,
    **values: 'Any'
) -> 'Self'

from_orm

def from_orm(
    obj: 'Any'
) -> 'Self'

model_construct

def 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.

Args: _fields_set: The set of field names accepted for the Model instance. values: Trusted or pre-validated data dictionary.

Returns: A new instance of the Model class with validated data.

model_json_schema

def model_json_schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
    mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'

Generates a JSON schema for a model class.

Args: 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.

model_parametrized_name

def 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.

Args: 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_rebuild

def model_rebuild(
    *,
    force: 'bool' = False,
    raise_errors: 'bool' = True,
    _parent_namespace_depth: 'int' = 2,
    _types_namespace: 'dict[str, Any] | 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.

Args: 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.

model_validate

def model_validate(
    obj: 'Any',
    *,
    strict: 'bool | None' = None,
    from_attributes: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Validate a pydantic model instance.

Args: 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.

model_validate_json

def model_validate_json(
    json_data: 'str | bytes | bytearray',
    *,
    strict: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Args: 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: ValueError: If json_data is not a JSON string.

model_validate_strings

def 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.

Args: 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.

parse_file

def parse_file(
    path: 'str | Path',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

parse_obj

def parse_obj(
    obj: 'Any'
) -> 'Self'

parse_raw

def parse_raw(
    b: 'str | bytes',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

schema

def schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}'
) -> 'Dict[str, Any]'

schema_json

def schema_json(
    *,
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    **dumps_kwargs: 'Any'
) -> 'str'

update_forward_refs

def update_forward_refs(
    **localns: 'Any'
) -> 'None'

validate

def validate(
    value: 'Any'
) -> 'Self'

Instance variables

model_extra

Get extra fields set during validation.

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

model_fields_set

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.

Methods

copy

def copy(
    self,
    *,
    include: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    update: 'Dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Returns a copy of the model.

!!! warning "Deprecated" This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)

Args: 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

def dict(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False
) -> 'Dict[str, Any]'

json

def json(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = 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'

model_copy

def model_copy(
    self,
    *,
    update: 'dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#model_copy

Returns a copy of the model.

Args: 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

def model_dump(
    self,
    *,
    mode: "Literal['json', 'python'] | str" = 'python',
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Args: mode: 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: A set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. by_alias: Whether to use the field's alias in the dictionary key if defined. 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 dictionary representation of the model.

model_dump_json

def model_dump_json(
    self,
    *,
    indent: 'int | None' = None,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'str'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump_json

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

Args: 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.

model_post_init

def model_post_init(
    self,
    _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.

SnapshotTaskResult

class SnapshotTaskResult(
    /,
    **data: 'Any'
)

Usage docs: https://docs.pydantic.dev/2.8/concepts/models/

A base class for creating Pydantic models.

Attributes: class_vars: The names of classvars defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The signature for instantiating the model.

__pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
__pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.
__pydantic_custom_init__: Whether the model has a custom `__init__` function.
__pydantic_decorators__: Metadata containing the decorators defined on the model.
    This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.
__pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to
    __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
__pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
__pydantic_post_init__: The name of the post-init method for the model, if defined.
__pydantic_root_model__: Whether the model is a `RootModel`.
__pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
__pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_extra__: An instance attribute with the values of extra fields from validation when
    `model_config['extra'] == 'allow'`.
__pydantic_fields_set__: An instance attribute with the names of fields explicitly set.
__pydantic_private__: Instance attribute with the values of private attributes set on the model instance.

Ancestors (in MRO)

• src.validation.models.BaseModel
• pydantic.main.BaseModel

Class variables

Config

model_computed_fields

model_config

model_fields

Static methods

construct

def construct(
    _fields_set: 'set[str] | None' = None,
    **values: 'Any'
) -> 'Self'

from_orm

def from_orm(
    obj: 'Any'
) -> 'Self'

model_construct

def 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.

Args: _fields_set: The set of field names accepted for the Model instance. values: Trusted or pre-validated data dictionary.

Returns: A new instance of the Model class with validated data.

model_json_schema

def model_json_schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
    mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'

Generates a JSON schema for a model class.

Args: 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.

model_parametrized_name

def 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.

Args: 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_rebuild

def model_rebuild(
    *,
    force: 'bool' = False,
    raise_errors: 'bool' = True,
    _parent_namespace_depth: 'int' = 2,
    _types_namespace: 'dict[str, Any] | 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.

Args: 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.

model_validate

def model_validate(
    obj: 'Any',
    *,
    strict: 'bool | None' = None,
    from_attributes: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Validate a pydantic model instance.

Args: 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.

model_validate_json

def model_validate_json(
    json_data: 'str | bytes | bytearray',
    *,
    strict: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Args: 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: ValueError: If json_data is not a JSON string.

model_validate_strings

def 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.

Args: 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.

parse_file

def parse_file(
    path: 'str | Path',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

parse_obj

def parse_obj(
    obj: 'Any'
) -> 'Self'

parse_raw

def parse_raw(
    b: 'str | bytes',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

schema

def schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}'
) -> 'Dict[str, Any]'

schema_json

def schema_json(
    *,
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    **dumps_kwargs: 'Any'
) -> 'str'

update_forward_refs

def update_forward_refs(
    **localns: 'Any'
) -> 'None'

validate

def validate(
    value: 'Any'
) -> 'Self'

Instance variables

model_extra

Get extra fields set during validation.

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

model_fields_set

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.

Methods

copy

def copy(
    self,
    *,
    include: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    update: 'Dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Returns a copy of the model.

!!! warning "Deprecated" This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)

Args: 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

def dict(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False
) -> 'Dict[str, Any]'

json

def json(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = 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'

model_copy

def model_copy(
    self,
    *,
    update: 'dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#model_copy

Returns a copy of the model.

Args: 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

def model_dump(
    self,
    *,
    mode: "Literal['json', 'python'] | str" = 'python',
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Args: mode: 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: A set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. by_alias: Whether to use the field's alias in the dictionary key if defined. 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 dictionary representation of the model.

model_dump_json

def model_dump_json(
    self,
    *,
    indent: 'int | None' = None,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'str'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump_json

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

Args: 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.

model_post_init

def model_post_init(
    self,
    _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.

StatsRequestParams

class StatsRequestParams(
    /,
    **data: 'Any'
)

Usage docs: https://docs.pydantic.dev/2.8/concepts/models/

A base class for creating Pydantic models.

Attributes: class_vars: The names of classvars defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The signature for instantiating the model.

__pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
__pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.
__pydantic_custom_init__: Whether the model has a custom `__init__` function.
__pydantic_decorators__: Metadata containing the decorators defined on the model.
    This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.
__pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to
    __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
__pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
__pydantic_post_init__: The name of the post-init method for the model, if defined.
__pydantic_root_model__: Whether the model is a `RootModel`.
__pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
__pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_extra__: An instance attribute with the values of extra fields from validation when
    `model_config['extra'] == 'allow'`.
__pydantic_fields_set__: An instance attribute with the names of fields explicitly set.
__pydantic_private__: Instance attribute with the values of private attributes set on the model instance.

Ancestors (in MRO)

• src.validation.models.BaseModel
• pydantic.main.BaseModel
• src.validation.models.GeometryValidatorMixin

Class variables

Config

model_computed_fields

model_config

model_fields

Static methods

construct

def construct(
    _fields_set: 'set[str] | None' = None,
    **values: 'Any'
) -> 'Self'

from_orm

def from_orm(
    obj: 'Any'
) -> 'Self'

model_construct

def 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.

Args: _fields_set: The set of field names accepted for the Model instance. values: Trusted or pre-validated data dictionary.

Returns: A new instance of the Model class with validated data.

model_json_schema

def model_json_schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
    mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'

Generates a JSON schema for a model class.

Args: 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.

model_parametrized_name

def 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.

Args: 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_rebuild

def model_rebuild(
    *,
    force: 'bool' = False,
    raise_errors: 'bool' = True,
    _parent_namespace_depth: 'int' = 2,
    _types_namespace: 'dict[str, Any] | 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.

Args: 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.

model_validate

def model_validate(
    obj: 'Any',
    *,
    strict: 'bool | None' = None,
    from_attributes: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Validate a pydantic model instance.

Args: 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.

model_validate_json

def model_validate_json(
    json_data: 'str | bytes | bytearray',
    *,
    strict: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Args: 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: ValueError: If json_data is not a JSON string.

model_validate_strings

def 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.

Args: 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.

parse_file

def parse_file(
    path: 'str | Path',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

parse_obj

def parse_obj(
    obj: 'Any'
) -> 'Self'

parse_raw

def parse_raw(
    b: 'str | bytes',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

schema

def schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}'
) -> 'Dict[str, Any]'

schema_json

def schema_json(
    *,
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    **dumps_kwargs: 'Any'
) -> 'str'

set_geometry_or_iso3

def set_geometry_or_iso3(
    value,
    values
)

Either geometry or iso3 should be supplied.

update_forward_refs

def update_forward_refs(
    **localns: 'Any'
) -> 'None'

validate

def validate(
    value: 'Any'
) -> 'Self'

validate_geometry

def validate_geometry(
    value
)

Validates geometry

Instance variables

model_extra

Get extra fields set during validation.

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

model_fields_set

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.

Methods

copy

def copy(
    self,
    *,
    include: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    update: 'Dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Returns a copy of the model.

!!! warning "Deprecated" This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)

Args: 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

def dict(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False
) -> 'Dict[str, Any]'

json

def json(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = 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'

model_copy

def model_copy(
    self,
    *,
    update: 'dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#model_copy

Returns a copy of the model.

Args: 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

def model_dump(
    self,
    *,
    mode: "Literal['json', 'python'] | str" = 'python',
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Args: mode: 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: A set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. by_alias: Whether to use the field's alias in the dictionary key if defined. 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 dictionary representation of the model.

model_dump_json

def model_dump_json(
    self,
    *,
    indent: 'int | None' = None,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'str'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump_json

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

Args: 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.

model_post_init

def model_post_init(
    self,
    _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.

StatusResponse

class StatusResponse(
    /,
    **data: 'Any'
)

Usage docs: https://docs.pydantic.dev/2.8/concepts/models/

A base class for creating Pydantic models.

Attributes: class_vars: The names of classvars defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The signature for instantiating the model.

__pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
__pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.
__pydantic_custom_init__: Whether the model has a custom `__init__` function.
__pydantic_decorators__: Metadata containing the decorators defined on the model.
    This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.
__pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to
    __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
__pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
__pydantic_post_init__: The name of the post-init method for the model, if defined.
__pydantic_root_model__: Whether the model is a `RootModel`.
__pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
__pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_extra__: An instance attribute with the values of extra fields from validation when
    `model_config['extra'] == 'allow'`.
__pydantic_fields_set__: An instance attribute with the names of fields explicitly set.
__pydantic_private__: Instance attribute with the values of private attributes set on the model instance.

Ancestors (in MRO)

• src.validation.models.BaseModel
• pydantic.main.BaseModel

Class variables

Config

model_computed_fields

model_config

model_fields

Static methods

construct

def construct(
    _fields_set: 'set[str] | None' = None,
    **values: 'Any'
) -> 'Self'

from_orm

def from_orm(
    obj: 'Any'
) -> 'Self'

model_construct

def 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.

Args: _fields_set: The set of field names accepted for the Model instance. values: Trusted or pre-validated data dictionary.

Returns: A new instance of the Model class with validated data.

model_json_schema

def model_json_schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
    mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'

Generates a JSON schema for a model class.

Args: 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.

model_parametrized_name

def 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.

Args: 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_rebuild

def model_rebuild(
    *,
    force: 'bool' = False,
    raise_errors: 'bool' = True,
    _parent_namespace_depth: 'int' = 2,
    _types_namespace: 'dict[str, Any] | 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.

Args: 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.

model_validate

def model_validate(
    obj: 'Any',
    *,
    strict: 'bool | None' = None,
    from_attributes: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Validate a pydantic model instance.

Args: 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.

model_validate_json

def model_validate_json(
    json_data: 'str | bytes | bytearray',
    *,
    strict: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Args: 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: ValueError: If json_data is not a JSON string.

model_validate_strings

def 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.

Args: 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.

parse_file

def parse_file(
    path: 'str | Path',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

parse_obj

def parse_obj(
    obj: 'Any'
) -> 'Self'

parse_raw

def parse_raw(
    b: 'str | bytes',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

schema

def schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}'
) -> 'Dict[str, Any]'

schema_json

def schema_json(
    *,
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    **dumps_kwargs: 'Any'
) -> 'str'

update_forward_refs

def update_forward_refs(
    **localns: 'Any'
) -> 'None'

validate

def validate(
    value: 'Any'
) -> 'Self'

Instance variables

model_extra

Get extra fields set during validation.

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

model_fields_set

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.

Methods

copy

def copy(
    self,
    *,
    include: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    update: 'Dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Returns a copy of the model.

!!! warning "Deprecated" This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)

Args: 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

def dict(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False
) -> 'Dict[str, Any]'

json

def json(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = 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'

model_copy

def model_copy(
    self,
    *,
    update: 'dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#model_copy

Returns a copy of the model.

Args: 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

def model_dump(
    self,
    *,
    mode: "Literal['json', 'python'] | str" = 'python',
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Args: mode: 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: A set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. by_alias: Whether to use the field's alias in the dictionary key if defined. 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 dictionary representation of the model.

model_dump_json

def model_dump_json(
    self,
    *,
    indent: 'int | None' = None,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'str'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump_json

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

Args: 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.

model_post_init

def model_post_init(
    self,
    _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.

SupportedFilters

class SupportedFilters(
    *args,
    **kwds
)

Create a collection of name/value pairs.

Example enumeration:

class Color(Enum): ... RED = 1 ... BLUE = 2 ... GREEN = 3

Access them by:

• attribute access::

Color.RED

• value lookup:

Color(1)

• name lookup:

Color['RED']

Enumerations can be iterated over, and know how many members they have:

len(Color) 3

list(Color) [, , ]

Methods can be added to enumerations, and members can have their own attributes -- see the documentation for details.

Ancestors (in MRO)

• enum.Enum

Class variables

ATTRIBUTES

TAGS

name

value

SupportedGeometryFilters

class SupportedGeometryFilters(
    *args,
    **kwds
)

Create a collection of name/value pairs.

Example enumeration:

class Color(Enum): ... RED = 1 ... BLUE = 2 ... GREEN = 3

Access them by:

• attribute access::

Color.RED

• value lookup:

Color(1)

• name lookup:

Color['RED']

Enumerations can be iterated over, and know how many members they have:

len(Color) 3

list(Color) [, , ]

Methods can be added to enumerations, and members can have their own attributes -- see the documentation for details.

Ancestors (in MRO)

• enum.Enum

Class variables

ALLGEOM

LINE

POINT

POLYGON

name

value

TagsFilter

class TagsFilter(
    /,
    **data: 'Any'
)

Usage docs: https://docs.pydantic.dev/2.8/concepts/models/

A base class for creating Pydantic models.

Attributes: class_vars: The names of classvars defined on the model. private_attributes: Metadata about the private attributes of the model. signature: The signature for instantiating the model.

__pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
__pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.
__pydantic_custom_init__: Whether the model has a custom `__init__` function.
__pydantic_decorators__: Metadata containing the decorators defined on the model.
    This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.
__pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to
    __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
__pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
__pydantic_post_init__: The name of the post-init method for the model, if defined.
__pydantic_root_model__: Whether the model is a `RootModel`.
__pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
__pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_extra__: An instance attribute with the values of extra fields from validation when
    `model_config['extra'] == 'allow'`.
__pydantic_fields_set__: An instance attribute with the names of fields explicitly set.
__pydantic_private__: Instance attribute with the values of private attributes set on the model instance.

Ancestors (in MRO)

• src.validation.models.BaseModel
• pydantic.main.BaseModel

Class variables

Config

model_computed_fields

model_config

model_fields

Static methods

construct

def construct(
    _fields_set: 'set[str] | None' = None,
    **values: 'Any'
) -> 'Self'

from_orm

def from_orm(
    obj: 'Any'
) -> 'Self'

model_construct

def 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.

Args: _fields_set: The set of field names accepted for the Model instance. values: Trusted or pre-validated data dictionary.

Returns: A new instance of the Model class with validated data.

model_json_schema

def model_json_schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
    mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'

Generates a JSON schema for a model class.

Args: 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.

model_parametrized_name

def 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.

Args: 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_rebuild

def model_rebuild(
    *,
    force: 'bool' = False,
    raise_errors: 'bool' = True,
    _parent_namespace_depth: 'int' = 2,
    _types_namespace: 'dict[str, Any] | 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.

Args: 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.

model_validate

def model_validate(
    obj: 'Any',
    *,
    strict: 'bool | None' = None,
    from_attributes: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Validate a pydantic model instance.

Args: 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.

model_validate_json

def model_validate_json(
    json_data: 'str | bytes | bytearray',
    *,
    strict: 'bool | None' = None,
    context: 'Any | None' = None
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Args: 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: ValueError: If json_data is not a JSON string.

model_validate_strings

def 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.

Args: 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.

parse_file

def parse_file(
    path: 'str | Path',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

parse_obj

def parse_obj(
    obj: 'Any'
) -> 'Self'

parse_raw

def parse_raw(
    b: 'str | bytes',
    *,
    content_type: 'str | None' = None,
    encoding: 'str' = 'utf8',
    proto: 'DeprecatedParseProtocol | None' = None,
    allow_pickle: 'bool' = False
) -> 'Self'

schema

def schema(
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}'
) -> 'Dict[str, Any]'

schema_json

def schema_json(
    *,
    by_alias: 'bool' = True,
    ref_template: 'str' = '#/$defs/{model}',
    **dumps_kwargs: 'Any'
) -> 'str'

update_forward_refs

def update_forward_refs(
    **localns: 'Any'
) -> 'None'

validate

def validate(
    value: 'Any'
) -> 'Self'

Instance variables

model_extra

Get extra fields set during validation.

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

model_fields_set

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.

Methods

copy

def copy(
    self,
    *,
    include: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
    update: 'Dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Returns a copy of the model.

!!! warning "Deprecated" This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)

Args: 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

def dict(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False
) -> 'Dict[str, Any]'

json

def json(
    self,
    *,
    include: 'IncEx' = None,
    exclude: 'IncEx' = 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'

model_copy

def model_copy(
    self,
    *,
    update: 'dict[str, Any] | None' = None,
    deep: 'bool' = False
) -> 'Self'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#model_copy

Returns a copy of the model.

Args: 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

def model_dump(
    self,
    *,
    mode: "Literal['json', 'python'] | str" = 'python',
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Args: mode: 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: A set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. by_alias: Whether to use the field's alias in the dictionary key if defined. 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 dictionary representation of the model.

model_dump_json

def model_dump_json(
    self,
    *,
    indent: 'int | None' = None,
    include: 'IncEx' = None,
    exclude: 'IncEx' = None,
    context: 'Any | None' = None,
    by_alias: 'bool' = False,
    exclude_unset: 'bool' = False,
    exclude_defaults: 'bool' = False,
    exclude_none: 'bool' = False,
    round_trip: 'bool' = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: 'bool' = False
) -> 'str'

Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump_json

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

Args: 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.

model_post_init

def model_post_init(
    self,
    _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.