TypingStyle - gws_core

ModelDTO class.

Attributes

background_color: Optionalicon_color: Optionalicon_technical_name: stricon_type: TypingIconType

Functions

__init__

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

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

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

data : Any

clone

Return type : TypingStyle

clone_with_overrides

Method to clone the style and override some values

icon_technical_name : str

technical name of the icon if provided, the icon_type must also be provided

icon_type : TypingIconType

type of the icon if provided, the icon_technical_name must also be provided

background_color : str

background color

icon_color : TypingIconColor

icon color

Return type : TypingStyle

copy

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.

include : AbstractSetIntStr | MappingIntStrAny | None

exclude : AbstractSetIntStr | MappingIntStrAny | None

update : Dict[str, Any] | None

deep : bool - False

Return type : Model

copy_from_style

style : TypingStyle

dict

include : IncEx

exclude : IncEx

by_alias : bool - False

exclude_unset : bool - False

exclude_defaults : bool - False

exclude_none : bool - False

Return type : Dict[str, Any]

fill_empty_values

Method to fill the background color and icon color if they are not set.

json

include : IncEx

exclude : IncEx

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

Return type : str

model_copy

Usage docs: https://docs.pydantic.dev/2.7/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.

update : dict[str, Any] | None

deep : bool - False

Return type : Model

model_dump

Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump

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

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.

mode : Literal['json', 'python'] | str - python

include : IncEx

exclude : IncEx

context : dict[str, Any] | 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

Return type : dict[str, Any]

model_dump_json

Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump_json

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

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.

indent : int | None

include : IncEx

exclude : IncEx

context : dict[str, Any] | 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

Return type : str

model_post_init

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__context : Any

to_json_dict

Convert a ModelDTO to a json dictionary. This dict can be serialized.

Return type : dict

to_json_str

Convert a ModelDTO to a json string.

Return type : str

construct @classmethod

_fields_set : set[str] | None

values : Any

Return type : Model

from_json @classmethod

Create a ModelDTO from a json.

json_ : dict

Return type : BaseModelDTOType

from_json_list @classmethod

Create a list of ModelDTO from a list of json.

json_list : list

Return type : List

from_json_str @classmethod

Create a ModelDTO from a string json.

str_json : str

Return type : BaseModelDTOType

from_orm @classmethod

obj : Any

Return type : Model

from_record @classmethod

Create a dict of ModelDTO from a dict of json.

json_dict : dict

Return type : Dict

model_construct @classmethod

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.

_fields_set : set[str] | None

values : Any

Return type : Model

model_json_schema @classmethod

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.

by_alias : bool - True

ref_template : str - #/$defs/{model}

schema_generator : type[GenerateJsonSchema] - <class 'pydantic.json_schema.GenerateJsonSchema'>

mode : JsonSchemaMode - validation

Return type : dict[str, Any]

model_parametrized_name @classmethod

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.

params : tuple[type[Any], ...]

Return type : str

model_rebuild @classmethod

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.

force : bool - False

raise_errors : bool - True

_parent_namespace_depth : int - 2

_types_namespace : dict[str, Any] | None

Return type : bool | None

model_validate @classmethod

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.

obj : Any

strict : bool | None

from_attributes : bool | None

context : dict[str, Any] | None

Return type : Model

model_validate_json @classmethod

Usage docs: https://docs.pydantic.dev/2.7/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.

json_data : str | bytes | bytearray

strict : bool | None

context : dict[str, Any] | None

Return type : Model

model_validate_strings @classmethod

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

Args: obj: The object contains string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator.

Returns: The validated Pydantic model.

obj : Any

strict : bool | None

context : dict[str, Any] | None

Return type : Model

parse_file @classmethod

path : str | Path

content_type : str | None

encoding : str - utf8

proto : DeprecatedParseProtocol | None

allow_pickle : bool - False

Return type : Model

parse_obj @classmethod

obj : Any

Return type : Model

parse_raw @classmethod

b : str | bytes

content_type : str | None

encoding : str - utf8

proto : DeprecatedParseProtocol | None

allow_pickle : bool - False

Return type : Model

schema @classmethod

by_alias : bool - True

ref_template : str - #/$defs/{model}

Return type : Dict[str, Any]

schema_json @classmethod

by_alias : bool - True

ref_template : str - #/$defs/{model}

dumps_kwargs : Any

Return type : str

update_forward_refs @classmethod

localns : Any

validate @classmethod

value : Any

Return type : Model

check_background_color @staticmethod

background_color : str

Return type : str

check_icon_color @staticmethod

icon_color : TypingIconColor

Return type : TypingIconColor

community_icon @staticmethod

Use an icon from the community icon library. List of available icons are here : https://constellab.community/icons

icon_technical_name : str

technical name of the icon

background_color : str

background color of the typing as hex color code

icon_color : Optional

icon color (black or white) when displayed over the background color. If not defined the color is calculated based on background color to be visible. defaults to None

Return type : TypingStyle

community_image @staticmethod

Use an image from the community image library. List of available images are here : https://constellab.community/icons

icon_technical_name : str

technical name of the image

background_color : str

background color of the typing

Return type : TypingStyle

default_protocol @staticmethod

Return type : TypingStyle

default_resource @staticmethod

Return type : TypingStyle

default_task @staticmethod

Return type : TypingStyle

default_view @staticmethod

Return type : TypingStyle

get_contrast_color @staticmethod

color : str

Return type : TypingIconColor

material_icon @staticmethod

Use an icon from the material icon library. List of available icons are here : https://fonts.google.com/icons?icon.set=Material+Icons

material_icon_name : str

background_color : str

background color of the typing as hex color code

icon_color : Optional

icon color (black or white) when displayed over the background color If not defined the color is calculated based on background color to be visible. defaults to None

Return type : TypingStyle