Messages - geometry_3d#

This package contains messages that are used in the 3D geometry space.

Position#

This message represents a position in the 3D space.

pydantic model duckietown_messages.geometry_3d.position.Position#

Show JSON schema
{
   "title": "Position",
   "type": "object",
   "properties": {
      "header": {
         "title": "Header",
         "description": "Auto-generated header",
         "allOf": [
            {
               "$ref": "#/definitions/Header"
            }
         ]
      },
      "x": {
         "title": "X",
         "description": "X coordinate of the point",
         "type": "number"
      },
      "y": {
         "title": "Y",
         "description": "Y coordinate of the point",
         "type": "number"
      },
      "z": {
         "title": "Z",
         "description": "Z coordinate of the point",
         "type": "number"
      }
   },
   "required": [
      "x",
      "y",
      "z"
   ],
   "definitions": {
      "Header": {
         "title": "Header",
         "type": "object",
         "properties": {
            "version": {
               "title": "Version",
               "description": "Version of the message this header is attached to",
               "default": "1.0",
               "pattern": "^[0-9]+\\.[0-9]+(\\.[0-9]+)?$",
               "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"
            }
         }
      }
   }
}

Fields
field header: duckietown_messages.standard.header.Header [Optional]#

Auto-generated header

field x: float [Required]#

X coordinate of the point

field y: float [Required]#

Y coordinate of the point

field z: float [Required]#

Z coordinate of the point

classmethod from_p(p: numpy.ndarray, header: duckietown_messages.standard.header.Header = None) → duckietown_messages.geometry_3d.position.Position#
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#

Quaternion#

This message represents a quaternion in the 3D space.

pydantic model duckietown_messages.geometry_3d.quaternion.Quaternion#

Show JSON schema
{
   "title": "Quaternion",
   "type": "object",
   "properties": {
      "header": {
         "title": "Header",
         "description": "Auto-generated header",
         "allOf": [
            {
               "$ref": "#/definitions/Header"
            }
         ]
      },
      "w": {
         "title": "W",
         "description": "W component of the quaternion",
         "type": "number"
      },
      "x": {
         "title": "X",
         "description": "X component of the quaternion",
         "type": "number"
      },
      "y": {
         "title": "Y",
         "description": "Y component of the quaternion",
         "type": "number"
      },
      "z": {
         "title": "Z",
         "description": "Z component of the quaternion",
         "type": "number"
      }
   },
   "required": [
      "w",
      "x",
      "y",
      "z"
   ],
   "definitions": {
      "Header": {
         "title": "Header",
         "type": "object",
         "properties": {
            "version": {
               "title": "Version",
               "description": "Version of the message this header is attached to",
               "default": "1.0",
               "pattern": "^[0-9]+\\.[0-9]+(\\.[0-9]+)?$",
               "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"
            }
         }
      }
   }
}

Fields
field header: duckietown_messages.standard.header.Header [Optional]#

Auto-generated header

field w: float [Required]#

W component of the quaternion

field x: float [Required]#

X component of the quaternion

field y: float [Required]#

Y component of the quaternion

field z: float [Required]#

Z component of the quaternion

classmethod from_q(q: numpy.ndarray, header: duckietown_messages.standard.header.Header = None) → duckietown_messages.geometry_3d.quaternion.Quaternion#
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#

Transformation#

This message represents a transformation in the 3D space.

pydantic model duckietown_messages.geometry_3d.transformation.Transformation#

Show JSON schema
{
   "title": "Transformation",
   "type": "object",
   "properties": {
      "header": {
         "title": "Header",
         "description": "Auto-generated header",
         "allOf": [
            {
               "$ref": "#/definitions/Header"
            }
         ]
      },
      "source": {
         "title": "Source",
         "description": "The frame id of the source frame",
         "type": "string"
      },
      "target": {
         "title": "Target",
         "description": "The frame id of the target frame",
         "type": "string"
      },
      "position": {
         "title": "Position",
         "description": "The position of the target frame in the source frame",
         "allOf": [
            {
               "$ref": "#/definitions/Position"
            }
         ]
      },
      "rotation": {
         "title": "Rotation",
         "description": "The rotation of the target frame in the source frame",
         "allOf": [
            {
               "$ref": "#/definitions/Quaternion"
            }
         ]
      }
   },
   "required": [
      "position",
      "rotation"
   ],
   "definitions": {
      "Header": {
         "title": "Header",
         "type": "object",
         "properties": {
            "version": {
               "title": "Version",
               "description": "Version of the message this header is attached to",
               "default": "1.0",
               "pattern": "^[0-9]+\\.[0-9]+(\\.[0-9]+)?$",
               "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"
            }
         }
      },
      "Position": {
         "title": "Position",
         "type": "object",
         "properties": {
            "header": {
               "title": "Header",
               "description": "Auto-generated header",
               "allOf": [
                  {
                     "$ref": "#/definitions/Header"
                  }
               ]
            },
            "x": {
               "title": "X",
               "description": "X coordinate of the point",
               "type": "number"
            },
            "y": {
               "title": "Y",
               "description": "Y coordinate of the point",
               "type": "number"
            },
            "z": {
               "title": "Z",
               "description": "Z coordinate of the point",
               "type": "number"
            }
         },
         "required": [
            "x",
            "y",
            "z"
         ]
      },
      "Quaternion": {
         "title": "Quaternion",
         "type": "object",
         "properties": {
            "header": {
               "title": "Header",
               "description": "Auto-generated header",
               "allOf": [
                  {
                     "$ref": "#/definitions/Header"
                  }
               ]
            },
            "w": {
               "title": "W",
               "description": "W component of the quaternion",
               "type": "number"
            },
            "x": {
               "title": "X",
               "description": "X component of the quaternion",
               "type": "number"
            },
            "y": {
               "title": "Y",
               "description": "Y component of the quaternion",
               "type": "number"
            },
            "z": {
               "title": "Z",
               "description": "Z component of the quaternion",
               "type": "number"
            }
         },
         "required": [
            "w",
            "x",
            "y",
            "z"
         ]
      }
   }
}

Fields
field header: duckietown_messages.standard.header.Header [Optional]#

Auto-generated header

field source: Optional[str] = None#

The frame id of the source frame

field target: Optional[str] = None#

The frame id of the target frame

field position: duckietown_messages.geometry_3d.position.Position [Required]#

The position of the target frame in the source frame

field rotation: duckietown_messages.geometry_3d.quaternion.Quaternion [Required]#

The rotation of the target frame in the source frame

classmethod from_pq(pq: numpy.ndarray, source: Optional[str] = None, target: Optional[str] = None, header: duckietown_messages.standard.header.Header = None) → duckietown_messages.geometry_3d.transformation.Transformation#
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#