Messages - colors
Contents
Messages - colors
#
This package contains messages that are used to represent colors.
RGB#
This message represents a color in the RGB color space.
- pydantic model duckietown_messages.colors.rgb.RGB#
Show JSON schema
{ "title": "RGB", "type": "object", "properties": { "header": { "title": "Header", "description": "Auto-generated header", "allOf": [ { "$ref": "#/definitions/Header" } ] }, "r": { "title": "R", "description": "Intensity of the red component of the color in the range [0, 1]", "minimum": 0, "maximum": 1, "type": "number" }, "g": { "title": "G", "description": "Intensity of the green component of the color in the range [0, 1]", "minimum": 0, "maximum": 1, "type": "number" }, "b": { "title": "B", "description": "Intensity of the blue component of the color in the range [0, 1]", "minimum": 0, "maximum": 1, "type": "number" } }, "required": [ "r", "g", "b" ], "definitions": { "Header": { "title": "Header", "type": "object", "properties": { "version": { "title": "Version", "description": "Version of the message this header is attached to", "default": "1.0", "pattern": "^[0-9]+\\.[0-9]+(\\.[0-9]+)?$", "example": "0.1.3", "type": "string" }, "frame": { "title": "Frame", "description": "Reference frame this data is captured in", "type": "string" }, "txt": { "title": "Txt", "description": "Auxiliary data attached to the message", "type": "object" } } } } }
- field header: duckietown_messages.standard.header.Header [Optional]#
Auto-generated header
- field r: float [Required]#
Intensity of the red component of the color in the range [0, 1]
- Constraints
minimum = 0
maximum = 1
- field g: float [Required]#
Intensity of the green component of the color in the range [0, 1]
- Constraints
minimum = 0
maximum = 1
- field b: float [Required]#
Intensity of the blue component of the color in the range [0, 1]
- Constraints
minimum = 0
maximum = 1
- classmethod zero(header: duckietown_messages.standard.header.Header = None) duckietown_messages.colors.rgb.RGB #
- classmethod from_list(lst: List[float], header: duckietown_messages.standard.header.Header = None) duckietown_messages.colors.rgb.RGB #
- classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) Model #
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
- copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) Model #
Duplicate a model, optionally choose which fields to include, exclude and change.
- Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
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
- dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) DictStrAny #
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- classmethod from_orm(obj: Any) Model #
- classmethod from_rawdata(rd: dtps_http.structures.RawData) duckietown_messages.base.BaseMessage #
- json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) unicode #
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
- classmethod parse_file(path: Union[str, pathlib.Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) Model #
- classmethod parse_obj(obj: Any) Model #
- classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) Model #
- classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') DictStrAny #
- classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) unicode #
- to_rawdata() dtps_http.structures.RawData #
- classmethod update_forward_refs(**localns: Any) None #
Try to update ForwardRefs on fields based on this Model, globalns and localns.
- classmethod validate(value: Any) Model #
RGBA#
This message represents a color in the RGBA color space.
- pydantic model duckietown_messages.colors.rgba.RGBA#
Show JSON schema
{ "title": "RGBA", "type": "object", "properties": { "header": { "title": "Header", "description": "Auto-generated header", "allOf": [ { "$ref": "#/definitions/Header" } ] }, "r": { "title": "R", "description": "Intensity of the red component of the color in the range [0, 1]", "minimum": 0, "maximum": 1, "type": "number" }, "g": { "title": "G", "description": "Intensity of the green component of the color in the range [0, 1]", "minimum": 0, "maximum": 1, "type": "number" }, "b": { "title": "B", "description": "Intensity of the blue component of the color in the range [0, 1]", "minimum": 0, "maximum": 1, "type": "number" }, "a": { "title": "A", "type": "number" } }, "required": [ "r", "g", "b", "a" ], "definitions": { "Header": { "title": "Header", "type": "object", "properties": { "version": { "title": "Version", "description": "Version of the message this header is attached to", "default": "1.0", "pattern": "^[0-9]+\\.[0-9]+(\\.[0-9]+)?$", "example": "0.1.3", "type": "string" }, "frame": { "title": "Frame", "description": "Reference frame this data is captured in", "type": "string" }, "txt": { "title": "Txt", "description": "Auxiliary data attached to the message", "type": "object" } } } } }
- field header: duckietown_messages.standard.header.Header [Optional]#
Auto-generated header
- field r: float [Required]#
Intensity of the red component of the color in the range [0, 1]
- Constraints
minimum = 0
maximum = 1
- field g: float [Required]#
Intensity of the green component of the color in the range [0, 1]
- Constraints
minimum = 0
maximum = 1
- field b: float [Required]#
Intensity of the blue component of the color in the range [0, 1]
- Constraints
minimum = 0
maximum = 1
- field a: float [Required]#
- classmethod zero(header: duckietown_messages.standard.header.Header = None) duckietown_messages.colors.rgba.RGBA #
- classmethod from_list(lst: List[float], header: duckietown_messages.standard.header.Header = None) duckietown_messages.colors.rgba.RGBA #
- classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) Model #
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
- copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) Model #
Duplicate a model, optionally choose which fields to include, exclude and change.
- Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
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
- dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) DictStrAny #
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- classmethod from_orm(obj: Any) Model #
- classmethod from_rawdata(rd: dtps_http.structures.RawData) duckietown_messages.base.BaseMessage #
- json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) unicode #
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
- classmethod parse_file(path: Union[str, pathlib.Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) Model #
- classmethod parse_obj(obj: Any) Model #
- classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) Model #
- classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') DictStrAny #
- classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) unicode #
- to_rawdata() dtps_http.structures.RawData #
- classmethod update_forward_refs(**localns: Any) None #
Try to update ForwardRefs on fields based on this Model, globalns and localns.
- classmethod validate(value: Any) Model #