kittycad.models.output_format3d
Classes
|
Autodesk Filmbox (FBX) format. |
|
glTF 2.0. |
|
Wavefront OBJ format. |
|
The PLY Polygon File Format. |
|
ISO 10303-21 (STEP) format. |
|
*ST**ereo**L**ithography format. |
- class kittycad.models.output_format3d.OptionFbx(**data)[source][source]
Autodesk Filmbox (FBX) format.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError
][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.self
is explicitly positional-only to allowself
as a field name.- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copy
instead.
If you need
include
orexclude
, use:`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `
- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- classmethod from_dict(data)[source]
Create model instance from dictionary.
- Parameters:
- Return type:
- Returns:
Model instance.
Example
user_data = {“id”: “123”, “name”: “John”} user = User.from_dict(user_data)
- classmethod from_json(json_str)[source]
Create model instance from JSON string.
- Parameters:
json_str (
str
) – JSON string containing model data.- Return type:
- Returns:
Model instance.
Example
user_json = ‘{“id”: “123”, “name”: “John”}’ user = User.from_json(user_json)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'populate_by_name': True, 'protected_namespaces': (), 'use_enum_values': True, 'validate_by_alias': True, 'validate_by_name': True}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict
][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
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 themodel_config.extra
setting on the provided model. That is, ifmodel_config.extra == 'allow'
, then all extra passed values are added to the model instance’s__dict__
and__pydantic_extra__
fields. Ifmodel_config.extra == 'ignore'
(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct()
, havingmodel_config.extra == 'forbid'
does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set
[str
] |None
) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set
][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevalues
argument will be used.values (
Any
) – Trusted or pre-validated data dictionary.
- Return type:
Self
- Returns:
A new instance of the
Model
class with validated data.
- model_copy(*, update=None, deep=False)[source]
- !!! abstract “Usage Documentation”
[
model_copy
](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [
__dict__
][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)[source]
- !!! abstract “Usage Documentation”
[
model_dump
](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union
[Literal
['json'
,'python'
],str
]) – The mode in whichto_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 (
Union
[set
[int
],set
[str
],Mapping
[int
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],Mapping
[str
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],None
]) – A set of fields to include in the output.exclude (
Union
[set
[int
],set
[str
],Mapping
[int
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],Mapping
[str
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],None
]) – A set of fields to exclude from the output.context (
Any
|None
) – Additional context to pass to the serializer.by_alias (
bool
|None
) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool
) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool
) – Whether to exclude fields that are set to their default value.exclude_none (
bool
) – Whether to exclude fields that have a value ofNone
.round_trip (
bool
) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union
[bool
,Literal
['none'
,'warn'
,'error'
]]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError
][pydantic_core.PydanticSerializationError].fallback (
Optional
[Callable
[[Any
],Any
]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError
][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool
) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)[source]
- !!! abstract “Usage Documentation”
[
model_dump_json
](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s
to_json
method.- Parameters:
indent (
int
|None
) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union
[set
[int
],set
[str
],Mapping
[int
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],Mapping
[str
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],None
]) – Field(s) to include in the JSON output.exclude (
Union
[set
[int
],set
[str
],Mapping
[int
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],Mapping
[str
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],None
]) – Field(s) to exclude from the JSON output.context (
Any
|None
) – Additional context to pass to the serializer.by_alias (
bool
|None
) – Whether to serialize using field aliases.exclude_unset (
bool
) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool
) – Whether to exclude fields that are set to their default value.exclude_none (
bool
) – Whether to exclude fields that have a value ofNone
.round_trip (
bool
) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union
[bool
,Literal
['none'
,'warn'
,'error'
]]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError
][pydantic_core.PydanticSerializationError].fallback (
Optional
[Callable
[[Any
],Any
]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError
][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool
) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
None
ifconfig.extra
is not set to"allow"
.
- model_fields = {'created': FieldInfo(annotation=Union[datetime, NoneType], required=False, default=None), 'storage': FieldInfo(annotation=FbxStorage, required=True), 'type': FieldInfo(annotation=Literal['fbx'], required=False, default='fbx')}[source]
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool
) – Whether to use attribute aliases or not.ref_template (
str
) – The reference template.schema_generator (
type
[GenerateJsonSchema
]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchema
with your desired modificationsmode (
Literal
['validation'
,'serialization'
]) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple
[type
[Any
],...
]) – Tuple of types of the class. Given a generic classModel
with 2 type variables and a concrete modelModel[str, int]
, the value(str, int)
would be passed toparams
.- Return type:
- Returns:
String representing the new class where
params
are passed tocls
as type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)[source]
Override this method to perform additional initialization after
__init__
andmodel_construct
. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool
) – Whether to force the rebuilding of the model schema, defaults toFalse
.raise_errors (
bool
) – Whether to raise errors, defaults toTrue
._parent_namespace_depth (
int
) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping
[str
,Any
] |None
) – The types namespace, defaults toNone
.
- Return type:
- Returns:
Returns
None
if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrue
if rebuilding was successful, otherwiseFalse
.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)[source]
Validate a pydantic model instance.
- Parameters:
obj (
Any
) – The object to validate.from_attributes (
bool
|None
) – Whether to extract data from object attributes.context (
Any
|None
) – Additional context to pass to the validator.by_alias (
bool
|None
) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool
|None
) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self
- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)[source]
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str
|bytes
|bytearray
) – The JSON data to validate.context (
Any
|None
) – Extra variables to pass to the validator.by_alias (
bool
|None
) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool
|None
) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self
- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_data
is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)[source]
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any
) – The object containing string data to validate.context (
Any
|None
) – Extra variables to pass to the validator.by_alias (
bool
|None
) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool
|None
) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self
- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]
- Return type:
-
storage:
FbxStorage
[source]
- class kittycad.models.output_format3d.OptionGltf(**data)[source][source]
glTF 2.0. We refer to this as glTF since that is how our customers refer to it, although by default it will be in binary format and thus technically (glb). If you prefer ASCII output, you can set that option for the export.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError
][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.self
is explicitly positional-only to allowself
as a field name.- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copy
instead.
If you need
include
orexclude
, use:`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `
- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- classmethod from_dict(data)[source]
Create model instance from dictionary.
- Parameters:
- Return type:
- Returns:
Model instance.
Example
user_data = {“id”: “123”, “name”: “John”} user = User.from_dict(user_data)
- classmethod from_json(json_str)[source]
Create model instance from JSON string.
- Parameters:
json_str (
str
) – JSON string containing model data.- Return type:
- Returns:
Model instance.
Example
user_json = ‘{“id”: “123”, “name”: “John”}’ user = User.from_json(user_json)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'populate_by_name': True, 'protected_namespaces': (), 'use_enum_values': True, 'validate_by_alias': True, 'validate_by_name': True}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict
][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
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 themodel_config.extra
setting on the provided model. That is, ifmodel_config.extra == 'allow'
, then all extra passed values are added to the model instance’s__dict__
and__pydantic_extra__
fields. Ifmodel_config.extra == 'ignore'
(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct()
, havingmodel_config.extra == 'forbid'
does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set
[str
] |None
) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set
][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevalues
argument will be used.values (
Any
) – Trusted or pre-validated data dictionary.
- Return type:
Self
- Returns:
A new instance of the
Model
class with validated data.
- model_copy(*, update=None, deep=False)[source]
- !!! abstract “Usage Documentation”
[
model_copy
](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [
__dict__
][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)[source]
- !!! abstract “Usage Documentation”
[
model_dump
](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union
[Literal
['json'
,'python'
],str
]) – The mode in whichto_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 (
Union
[set
[int
],set
[str
],Mapping
[int
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],Mapping
[str
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],None
]) – A set of fields to include in the output.exclude (
Union
[set
[int
],set
[str
],Mapping
[int
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],Mapping
[str
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],None
]) – A set of fields to exclude from the output.context (
Any
|None
) – Additional context to pass to the serializer.by_alias (
bool
|None
) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool
) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool
) – Whether to exclude fields that are set to their default value.exclude_none (
bool
) – Whether to exclude fields that have a value ofNone
.round_trip (
bool
) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union
[bool
,Literal
['none'
,'warn'
,'error'
]]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError
][pydantic_core.PydanticSerializationError].fallback (
Optional
[Callable
[[Any
],Any
]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError
][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool
) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)[source]
- !!! abstract “Usage Documentation”
[
model_dump_json
](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s
to_json
method.- Parameters:
indent (
int
|None
) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union
[set
[int
],set
[str
],Mapping
[int
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],Mapping
[str
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],None
]) – Field(s) to include in the JSON output.exclude (
Union
[set
[int
],set
[str
],Mapping
[int
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],Mapping
[str
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],None
]) – Field(s) to exclude from the JSON output.context (
Any
|None
) – Additional context to pass to the serializer.by_alias (
bool
|None
) – Whether to serialize using field aliases.exclude_unset (
bool
) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool
) – Whether to exclude fields that are set to their default value.exclude_none (
bool
) – Whether to exclude fields that have a value ofNone
.round_trip (
bool
) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union
[bool
,Literal
['none'
,'warn'
,'error'
]]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError
][pydantic_core.PydanticSerializationError].fallback (
Optional
[Callable
[[Any
],Any
]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError
][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool
) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
None
ifconfig.extra
is not set to"allow"
.
- model_fields = {'presentation': FieldInfo(annotation=GltfPresentation, required=True), 'storage': FieldInfo(annotation=GltfStorage, required=True), 'type': FieldInfo(annotation=Literal['gltf'], required=False, default='gltf')}[source]
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool
) – Whether to use attribute aliases or not.ref_template (
str
) – The reference template.schema_generator (
type
[GenerateJsonSchema
]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchema
with your desired modificationsmode (
Literal
['validation'
,'serialization'
]) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple
[type
[Any
],...
]) – Tuple of types of the class. Given a generic classModel
with 2 type variables and a concrete modelModel[str, int]
, the value(str, int)
would be passed toparams
.- Return type:
- Returns:
String representing the new class where
params
are passed tocls
as type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)[source]
Override this method to perform additional initialization after
__init__
andmodel_construct
. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool
) – Whether to force the rebuilding of the model schema, defaults toFalse
.raise_errors (
bool
) – Whether to raise errors, defaults toTrue
._parent_namespace_depth (
int
) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping
[str
,Any
] |None
) – The types namespace, defaults toNone
.
- Return type:
- Returns:
Returns
None
if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrue
if rebuilding was successful, otherwiseFalse
.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)[source]
Validate a pydantic model instance.
- Parameters:
obj (
Any
) – The object to validate.from_attributes (
bool
|None
) – Whether to extract data from object attributes.context (
Any
|None
) – Additional context to pass to the validator.by_alias (
bool
|None
) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool
|None
) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self
- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)[source]
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str
|bytes
|bytearray
) – The JSON data to validate.context (
Any
|None
) – Extra variables to pass to the validator.by_alias (
bool
|None
) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool
|None
) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self
- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_data
is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)[source]
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any
) – The object containing string data to validate.context (
Any
|None
) – Extra variables to pass to the validator.by_alias (
bool
|None
) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool
|None
) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self
- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
-
presentation:
GltfPresentation
[source]
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]
- Return type:
-
storage:
GltfStorage
[source]
- class kittycad.models.output_format3d.OptionObj(**data)[source][source]
Wavefront OBJ format.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError
][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.self
is explicitly positional-only to allowself
as a field name.- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copy
instead.
If you need
include
orexclude
, use:`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `
- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- classmethod from_dict(data)[source]
Create model instance from dictionary.
- Parameters:
- Return type:
- Returns:
Model instance.
Example
user_data = {“id”: “123”, “name”: “John”} user = User.from_dict(user_data)
- classmethod from_json(json_str)[source]
Create model instance from JSON string.
- Parameters:
json_str (
str
) – JSON string containing model data.- Return type:
- Returns:
Model instance.
Example
user_json = ‘{“id”: “123”, “name”: “John”}’ user = User.from_json(user_json)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'populate_by_name': True, 'protected_namespaces': (), 'use_enum_values': True, 'validate_by_alias': True, 'validate_by_name': True}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict
][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
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 themodel_config.extra
setting on the provided model. That is, ifmodel_config.extra == 'allow'
, then all extra passed values are added to the model instance’s__dict__
and__pydantic_extra__
fields. Ifmodel_config.extra == 'ignore'
(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct()
, havingmodel_config.extra == 'forbid'
does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set
[str
] |None
) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set
][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevalues
argument will be used.values (
Any
) – Trusted or pre-validated data dictionary.
- Return type:
Self
- Returns:
A new instance of the
Model
class with validated data.
- model_copy(*, update=None, deep=False)[source]
- !!! abstract “Usage Documentation”
[
model_copy
](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [
__dict__
][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)[source]
- !!! abstract “Usage Documentation”
[
model_dump
](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union
[Literal
['json'
,'python'
],str
]) – The mode in whichto_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 (
Union
[set
[int
],set
[str
],Mapping
[int
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],Mapping
[str
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],None
]) – A set of fields to include in the output.exclude (
Union
[set
[int
],set
[str
],Mapping
[int
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],Mapping
[str
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],None
]) – A set of fields to exclude from the output.context (
Any
|None
) – Additional context to pass to the serializer.by_alias (
bool
|None
) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool
) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool
) – Whether to exclude fields that are set to their default value.exclude_none (
bool
) – Whether to exclude fields that have a value ofNone
.round_trip (
bool
) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union
[bool
,Literal
['none'
,'warn'
,'error'
]]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError
][pydantic_core.PydanticSerializationError].fallback (
Optional
[Callable
[[Any
],Any
]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError
][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool
) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)[source]
- !!! abstract “Usage Documentation”
[
model_dump_json
](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s
to_json
method.- Parameters:
indent (
int
|None
) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union
[set
[int
],set
[str
],Mapping
[int
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],Mapping
[str
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],None
]) – Field(s) to include in the JSON output.exclude (
Union
[set
[int
],set
[str
],Mapping
[int
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],Mapping
[str
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],None
]) – Field(s) to exclude from the JSON output.context (
Any
|None
) – Additional context to pass to the serializer.by_alias (
bool
|None
) – Whether to serialize using field aliases.exclude_unset (
bool
) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool
) – Whether to exclude fields that are set to their default value.exclude_none (
bool
) – Whether to exclude fields that have a value ofNone
.round_trip (
bool
) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union
[bool
,Literal
['none'
,'warn'
,'error'
]]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError
][pydantic_core.PydanticSerializationError].fallback (
Optional
[Callable
[[Any
],Any
]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError
][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool
) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
None
ifconfig.extra
is not set to"allow"
.
- model_fields = {'coords': FieldInfo(annotation=System, required=True), 'type': FieldInfo(annotation=Literal['obj'], required=False, default='obj'), 'units': FieldInfo(annotation=UnitLength, required=True)}[source]
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool
) – Whether to use attribute aliases or not.ref_template (
str
) – The reference template.schema_generator (
type
[GenerateJsonSchema
]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchema
with your desired modificationsmode (
Literal
['validation'
,'serialization'
]) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple
[type
[Any
],...
]) – Tuple of types of the class. Given a generic classModel
with 2 type variables and a concrete modelModel[str, int]
, the value(str, int)
would be passed toparams
.- Return type:
- Returns:
String representing the new class where
params
are passed tocls
as type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)[source]
Override this method to perform additional initialization after
__init__
andmodel_construct
. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool
) – Whether to force the rebuilding of the model schema, defaults toFalse
.raise_errors (
bool
) – Whether to raise errors, defaults toTrue
._parent_namespace_depth (
int
) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping
[str
,Any
] |None
) – The types namespace, defaults toNone
.
- Return type:
- Returns:
Returns
None
if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrue
if rebuilding was successful, otherwiseFalse
.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)[source]
Validate a pydantic model instance.
- Parameters:
obj (
Any
) – The object to validate.from_attributes (
bool
|None
) – Whether to extract data from object attributes.context (
Any
|None
) – Additional context to pass to the validator.by_alias (
bool
|None
) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool
|None
) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self
- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)[source]
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str
|bytes
|bytearray
) – The JSON data to validate.context (
Any
|None
) – Extra variables to pass to the validator.by_alias (
bool
|None
) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool
|None
) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self
- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_data
is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)[source]
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any
) – The object containing string data to validate.context (
Any
|None
) – Extra variables to pass to the validator.by_alias (
bool
|None
) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool
|None
) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self
- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]
- Return type:
- to_json()[source]
Convert model to JSON string with alias support.
- Return type:
- Returns:
JSON string representation of the model.
-
units:
UnitLength
[source]
- class kittycad.models.output_format3d.OptionPly(**data)[source][source]
The PLY Polygon File Format.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError
][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.self
is explicitly positional-only to allowself
as a field name.- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copy
instead.
If you need
include
orexclude
, use:`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `
- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- classmethod from_dict(data)[source]
Create model instance from dictionary.
- Parameters:
- Return type:
- Returns:
Model instance.
Example
user_data = {“id”: “123”, “name”: “John”} user = User.from_dict(user_data)
- classmethod from_json(json_str)[source]
Create model instance from JSON string.
- Parameters:
json_str (
str
) – JSON string containing model data.- Return type:
- Returns:
Model instance.
Example
user_json = ‘{“id”: “123”, “name”: “John”}’ user = User.from_json(user_json)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'populate_by_name': True, 'protected_namespaces': (), 'use_enum_values': True, 'validate_by_alias': True, 'validate_by_name': True}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict
][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
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 themodel_config.extra
setting on the provided model. That is, ifmodel_config.extra == 'allow'
, then all extra passed values are added to the model instance’s__dict__
and__pydantic_extra__
fields. Ifmodel_config.extra == 'ignore'
(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct()
, havingmodel_config.extra == 'forbid'
does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set
[str
] |None
) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set
][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevalues
argument will be used.values (
Any
) – Trusted or pre-validated data dictionary.
- Return type:
Self
- Returns:
A new instance of the
Model
class with validated data.
- model_copy(*, update=None, deep=False)[source]
- !!! abstract “Usage Documentation”
[
model_copy
](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [
__dict__
][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)[source]
- !!! abstract “Usage Documentation”
[
model_dump
](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union
[Literal
['json'
,'python'
],str
]) – The mode in whichto_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 (
Union
[set
[int
],set
[str
],Mapping
[int
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],Mapping
[str
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],None
]) – A set of fields to include in the output.exclude (
Union
[set
[int
],set
[str
],Mapping
[int
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],Mapping
[str
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],None
]) – A set of fields to exclude from the output.context (
Any
|None
) – Additional context to pass to the serializer.by_alias (
bool
|None
) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool
) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool
) – Whether to exclude fields that are set to their default value.exclude_none (
bool
) – Whether to exclude fields that have a value ofNone
.round_trip (
bool
) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union
[bool
,Literal
['none'
,'warn'
,'error'
]]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError
][pydantic_core.PydanticSerializationError].fallback (
Optional
[Callable
[[Any
],Any
]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError
][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool
) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)[source]
- !!! abstract “Usage Documentation”
[
model_dump_json
](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s
to_json
method.- Parameters:
indent (
int
|None
) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union
[set
[int
],set
[str
],Mapping
[int
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],Mapping
[str
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],None
]) – Field(s) to include in the JSON output.exclude (
Union
[set
[int
],set
[str
],Mapping
[int
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],Mapping
[str
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],None
]) – Field(s) to exclude from the JSON output.context (
Any
|None
) – Additional context to pass to the serializer.by_alias (
bool
|None
) – Whether to serialize using field aliases.exclude_unset (
bool
) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool
) – Whether to exclude fields that are set to their default value.exclude_none (
bool
) – Whether to exclude fields that have a value ofNone
.round_trip (
bool
) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union
[bool
,Literal
['none'
,'warn'
,'error'
]]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError
][pydantic_core.PydanticSerializationError].fallback (
Optional
[Callable
[[Any
],Any
]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError
][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool
) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
None
ifconfig.extra
is not set to"allow"
.
- model_fields = {'coords': FieldInfo(annotation=System, required=True), 'selection': FieldInfo(annotation=RootModel[Annotated[Union[OptionDefaultScene, OptionSceneByIndex, OptionSceneByName, OptionMeshByIndex, OptionMeshByName], FieldInfo(annotation=NoneType, required=True, discriminator='type')]], required=True), 'storage': FieldInfo(annotation=PlyStorage, required=True), 'type': FieldInfo(annotation=Literal['ply'], required=False, default='ply'), 'units': FieldInfo(annotation=UnitLength, required=True)}[source]
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool
) – Whether to use attribute aliases or not.ref_template (
str
) – The reference template.schema_generator (
type
[GenerateJsonSchema
]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchema
with your desired modificationsmode (
Literal
['validation'
,'serialization'
]) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple
[type
[Any
],...
]) – Tuple of types of the class. Given a generic classModel
with 2 type variables and a concrete modelModel[str, int]
, the value(str, int)
would be passed toparams
.- Return type:
- Returns:
String representing the new class where
params
are passed tocls
as type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)[source]
Override this method to perform additional initialization after
__init__
andmodel_construct
. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool
) – Whether to force the rebuilding of the model schema, defaults toFalse
.raise_errors (
bool
) – Whether to raise errors, defaults toTrue
._parent_namespace_depth (
int
) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping
[str
,Any
] |None
) – The types namespace, defaults toNone
.
- Return type:
- Returns:
Returns
None
if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrue
if rebuilding was successful, otherwiseFalse
.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)[source]
Validate a pydantic model instance.
- Parameters:
obj (
Any
) – The object to validate.from_attributes (
bool
|None
) – Whether to extract data from object attributes.context (
Any
|None
) – Additional context to pass to the validator.by_alias (
bool
|None
) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool
|None
) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self
- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)[source]
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str
|bytes
|bytearray
) – The JSON data to validate.context (
Any
|None
) – Extra variables to pass to the validator.by_alias (
bool
|None
) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool
|None
) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self
- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_data
is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)[source]
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any
) – The object containing string data to validate.context (
Any
|None
) – Extra variables to pass to the validator.by_alias (
bool
|None
) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool
|None
) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self
- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]
- Return type:
-
selection:
RootModel[Annotated[Union[OptionDefaultScene, OptionSceneByIndex, OptionSceneByName, OptionMeshByIndex, OptionMeshByName], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]
[source]
-
storage:
PlyStorage
[source]
- to_json()[source]
Convert model to JSON string with alias support.
- Return type:
- Returns:
JSON string representation of the model.
-
units:
UnitLength
[source]
- class kittycad.models.output_format3d.OptionStep(**data)[source][source]
ISO 10303-21 (STEP) format.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError
][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.self
is explicitly positional-only to allowself
as a field name.- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copy
instead.
If you need
include
orexclude
, use:`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `
- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- classmethod from_dict(data)[source]
Create model instance from dictionary.
- Parameters:
- Return type:
- Returns:
Model instance.
Example
user_data = {“id”: “123”, “name”: “John”} user = User.from_dict(user_data)
- classmethod from_json(json_str)[source]
Create model instance from JSON string.
- Parameters:
json_str (
str
) – JSON string containing model data.- Return type:
- Returns:
Model instance.
Example
user_json = ‘{“id”: “123”, “name”: “John”}’ user = User.from_json(user_json)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'populate_by_name': True, 'protected_namespaces': (), 'use_enum_values': True, 'validate_by_alias': True, 'validate_by_name': True}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict
][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
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 themodel_config.extra
setting on the provided model. That is, ifmodel_config.extra == 'allow'
, then all extra passed values are added to the model instance’s__dict__
and__pydantic_extra__
fields. Ifmodel_config.extra == 'ignore'
(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct()
, havingmodel_config.extra == 'forbid'
does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set
[str
] |None
) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set
][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevalues
argument will be used.values (
Any
) – Trusted or pre-validated data dictionary.
- Return type:
Self
- Returns:
A new instance of the
Model
class with validated data.
- model_copy(*, update=None, deep=False)[source]
- !!! abstract “Usage Documentation”
[
model_copy
](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [
__dict__
][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)[source]
- !!! abstract “Usage Documentation”
[
model_dump
](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union
[Literal
['json'
,'python'
],str
]) – The mode in whichto_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 (
Union
[set
[int
],set
[str
],Mapping
[int
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],Mapping
[str
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],None
]) – A set of fields to include in the output.exclude (
Union
[set
[int
],set
[str
],Mapping
[int
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],Mapping
[str
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],None
]) – A set of fields to exclude from the output.context (
Any
|None
) – Additional context to pass to the serializer.by_alias (
bool
|None
) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool
) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool
) – Whether to exclude fields that are set to their default value.exclude_none (
bool
) – Whether to exclude fields that have a value ofNone
.round_trip (
bool
) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union
[bool
,Literal
['none'
,'warn'
,'error'
]]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError
][pydantic_core.PydanticSerializationError].fallback (
Optional
[Callable
[[Any
],Any
]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError
][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool
) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)[source]
- !!! abstract “Usage Documentation”
[
model_dump_json
](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s
to_json
method.- Parameters:
indent (
int
|None
) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union
[set
[int
],set
[str
],Mapping
[int
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],Mapping
[str
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],None
]) – Field(s) to include in the JSON output.exclude (
Union
[set
[int
],set
[str
],Mapping
[int
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],Mapping
[str
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],None
]) – Field(s) to exclude from the JSON output.context (
Any
|None
) – Additional context to pass to the serializer.by_alias (
bool
|None
) – Whether to serialize using field aliases.exclude_unset (
bool
) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool
) – Whether to exclude fields that are set to their default value.exclude_none (
bool
) – Whether to exclude fields that have a value ofNone
.round_trip (
bool
) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union
[bool
,Literal
['none'
,'warn'
,'error'
]]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError
][pydantic_core.PydanticSerializationError].fallback (
Optional
[Callable
[[Any
],Any
]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError
][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool
) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
None
ifconfig.extra
is not set to"allow"
.
- model_fields = {'coords': FieldInfo(annotation=System, required=True), 'created': FieldInfo(annotation=Union[datetime, NoneType], required=False, default=None), 'type': FieldInfo(annotation=Literal['step'], required=False, default='step')}[source]
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool
) – Whether to use attribute aliases or not.ref_template (
str
) – The reference template.schema_generator (
type
[GenerateJsonSchema
]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchema
with your desired modificationsmode (
Literal
['validation'
,'serialization'
]) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple
[type
[Any
],...
]) – Tuple of types of the class. Given a generic classModel
with 2 type variables and a concrete modelModel[str, int]
, the value(str, int)
would be passed toparams
.- Return type:
- Returns:
String representing the new class where
params
are passed tocls
as type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)[source]
Override this method to perform additional initialization after
__init__
andmodel_construct
. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool
) – Whether to force the rebuilding of the model schema, defaults toFalse
.raise_errors (
bool
) – Whether to raise errors, defaults toTrue
._parent_namespace_depth (
int
) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping
[str
,Any
] |None
) – The types namespace, defaults toNone
.
- Return type:
- Returns:
Returns
None
if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrue
if rebuilding was successful, otherwiseFalse
.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)[source]
Validate a pydantic model instance.
- Parameters:
obj (
Any
) – The object to validate.from_attributes (
bool
|None
) – Whether to extract data from object attributes.context (
Any
|None
) – Additional context to pass to the validator.by_alias (
bool
|None
) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool
|None
) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self
- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)[source]
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str
|bytes
|bytearray
) – The JSON data to validate.context (
Any
|None
) – Extra variables to pass to the validator.by_alias (
bool
|None
) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool
|None
) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self
- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_data
is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)[source]
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any
) – The object containing string data to validate.context (
Any
|None
) – Extra variables to pass to the validator.by_alias (
bool
|None
) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool
|None
) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self
- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]
- Return type:
- class kittycad.models.output_format3d.OptionStl(**data)[source][source]
*ST**ereo**L**ithography format.
Create a new model by parsing and validating input data from keyword arguments.
Raises [
ValidationError
][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.self
is explicitly positional-only to allowself
as a field name.- copy(*, include=None, exclude=None, update=None, deep=False)[source]
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copy
instead.
If you need
include
orexclude
, use:`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `
- Parameters:
include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]
- classmethod from_dict(data)[source]
Create model instance from dictionary.
- Parameters:
- Return type:
- Returns:
Model instance.
Example
user_data = {“id”: “123”, “name”: “John”} user = User.from_dict(user_data)
- classmethod from_json(json_str)[source]
Create model instance from JSON string.
- Parameters:
json_str (
str
) – JSON string containing model data.- Return type:
- Returns:
Model instance.
Example
user_json = ‘{“id”: “123”, “name”: “John”}’ user = User.from_json(user_json)
- json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]
- Return type:
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'populate_by_name': True, 'protected_namespaces': (), 'use_enum_values': True, 'validate_by_alias': True, 'validate_by_name': True}[source]
Configuration for the model, should be a dictionary conforming to [
ConfigDict
][pydantic.config.ConfigDict].
- classmethod model_construct(_fields_set=None, **values)[source]
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 themodel_config.extra
setting on the provided model. That is, ifmodel_config.extra == 'allow'
, then all extra passed values are added to the model instance’s__dict__
and__pydantic_extra__
fields. Ifmodel_config.extra == 'ignore'
(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct()
, havingmodel_config.extra == 'forbid'
does not result in an error if extra values are passed, but they will be ignored.
- Parameters:
_fields_set (
set
[str
] |None
) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set
][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevalues
argument will be used.values (
Any
) – Trusted or pre-validated data dictionary.
- Return type:
Self
- Returns:
A new instance of the
Model
class with validated data.
- model_copy(*, update=None, deep=False)[source]
- !!! abstract “Usage Documentation”
[
model_copy
](../concepts/serialization.md#model_copy)
Returns a copy of the model.
- !!! note
The underlying instance’s [
__dict__
][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).
- model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)[source]
- !!! abstract “Usage Documentation”
[
model_dump
](../concepts/serialization.md#modelmodel_dump)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Parameters:
mode (
Union
[Literal
['json'
,'python'
],str
]) – The mode in whichto_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 (
Union
[set
[int
],set
[str
],Mapping
[int
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],Mapping
[str
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],None
]) – A set of fields to include in the output.exclude (
Union
[set
[int
],set
[str
],Mapping
[int
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],Mapping
[str
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],None
]) – A set of fields to exclude from the output.context (
Any
|None
) – Additional context to pass to the serializer.by_alias (
bool
|None
) – Whether to use the field’s alias in the dictionary key if defined.exclude_unset (
bool
) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool
) – Whether to exclude fields that are set to their default value.exclude_none (
bool
) – Whether to exclude fields that have a value ofNone
.round_trip (
bool
) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union
[bool
,Literal
['none'
,'warn'
,'error'
]]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError
][pydantic_core.PydanticSerializationError].fallback (
Optional
[Callable
[[Any
],Any
]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError
][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool
) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)[source]
- !!! abstract “Usage Documentation”
[
model_dump_json
](../concepts/serialization.md#modelmodel_dump_json)
Generates a JSON representation of the model using Pydantic’s
to_json
method.- Parameters:
indent (
int
|None
) – Indentation to use in the JSON output. If None is passed, the output will be compact.include (
Union
[set
[int
],set
[str
],Mapping
[int
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],Mapping
[str
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],None
]) – Field(s) to include in the JSON output.exclude (
Union
[set
[int
],set
[str
],Mapping
[int
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],Mapping
[str
,Union
[set
[int
],set
[str
],Mapping
[int
,Union
[IncEx,bool
]],Mapping
[str
,Union
[IncEx,bool
]],bool
]],None
]) – Field(s) to exclude from the JSON output.context (
Any
|None
) – Additional context to pass to the serializer.by_alias (
bool
|None
) – Whether to serialize using field aliases.exclude_unset (
bool
) – Whether to exclude fields that have not been explicitly set.exclude_defaults (
bool
) – Whether to exclude fields that are set to their default value.exclude_none (
bool
) – Whether to exclude fields that have a value ofNone
.round_trip (
bool
) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].warnings (
Union
[bool
,Literal
['none'
,'warn'
,'error'
]]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError
][pydantic_core.PydanticSerializationError].fallback (
Optional
[Callable
[[Any
],Any
]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError
][pydantic_core.PydanticSerializationError] error is raised.serialize_as_any (
bool
) – Whether to serialize fields with duck-typing serialization behavior.
- Return type:
- Returns:
A JSON string representation of the model.
- property model_extra: dict[str, Any] | None[source]
Get extra fields set during validation.
- Returns:
A dictionary of extra fields, or
None
ifconfig.extra
is not set to"allow"
.
- model_fields = {'coords': FieldInfo(annotation=System, required=True), 'selection': FieldInfo(annotation=RootModel[Annotated[Union[OptionDefaultScene, OptionSceneByIndex, OptionSceneByName, OptionMeshByIndex, OptionMeshByName], FieldInfo(annotation=NoneType, required=True, discriminator='type')]], required=True), 'storage': FieldInfo(annotation=StlStorage, required=True), 'type': FieldInfo(annotation=Literal['stl'], required=False, default='stl'), 'units': FieldInfo(annotation=UnitLength, required=True)}[source]
- property model_fields_set: set[str][source]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]
Generates a JSON schema for a model class.
- Parameters:
by_alias (
bool
) – Whether to use attribute aliases or not.ref_template (
str
) – The reference template.schema_generator (
type
[GenerateJsonSchema
]) – To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchema
with your desired modificationsmode (
Literal
['validation'
,'serialization'
]) – The mode in which to generate the schema.
- Return type:
- Returns:
The JSON schema for the given model class.
- classmethod model_parametrized_name(params)[source]
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
- Parameters:
params (
tuple
[type
[Any
],...
]) – Tuple of types of the class. Given a generic classModel
with 2 type variables and a concrete modelModel[str, int]
, the value(str, int)
would be passed toparams
.- Return type:
- Returns:
String representing the new class where
params
are passed tocls
as type variables.- Raises:
TypeError – Raised when trying to generate concrete names for non-generic models.
- model_post_init(context, /)[source]
Override this method to perform additional initialization after
__init__
andmodel_construct
. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
- classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
- Parameters:
force (
bool
) – Whether to force the rebuilding of the model schema, defaults toFalse
.raise_errors (
bool
) – Whether to raise errors, defaults toTrue
._parent_namespace_depth (
int
) – The depth level of the parent namespace, defaults to 2._types_namespace (
Mapping
[str
,Any
] |None
) – The types namespace, defaults toNone
.
- Return type:
- Returns:
Returns
None
if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrue
if rebuilding was successful, otherwiseFalse
.
- classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)[source]
Validate a pydantic model instance.
- Parameters:
obj (
Any
) – The object to validate.from_attributes (
bool
|None
) – Whether to extract data from object attributes.context (
Any
|None
) – Additional context to pass to the validator.by_alias (
bool
|None
) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool
|None
) – Whether to use the field’s name when validating against the provided input data.
- Raises:
ValidationError – If the object could not be validated.
- Return type:
Self
- Returns:
The validated model instance.
- classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)[source]
- !!! abstract “Usage Documentation”
[JSON Parsing](../concepts/json.md#json-parsing)
Validate the given JSON data against the Pydantic model.
- Parameters:
json_data (
str
|bytes
|bytearray
) – The JSON data to validate.context (
Any
|None
) – Extra variables to pass to the validator.by_alias (
bool
|None
) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool
|None
) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self
- Returns:
The validated Pydantic model.
- Raises:
ValidationError – If
json_data
is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)[source]
Validate the given object with string data against the Pydantic model.
- Parameters:
obj (
Any
) – The object containing string data to validate.context (
Any
|None
) – Extra variables to pass to the validator.by_alias (
bool
|None
) – Whether to use the field’s alias when validating against the provided input data.by_name (
bool
|None
) – Whether to use the field’s name when validating against the provided input data.
- Return type:
Self
- Returns:
The validated Pydantic model.
- classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]
- Return type:
Self
- classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]
- Return type:
-
selection:
RootModel[Annotated[Union[OptionDefaultScene, OptionSceneByIndex, OptionSceneByName, OptionMeshByIndex, OptionMeshByName], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]
[source]
-
storage:
StlStorage
[source]
- to_json()[source]
Convert model to JSON string with alias support.
- Return type:
- Returns:
JSON string representation of the model.
-
units:
UnitLength
[source]