Parallelism
- class Para(**data)
Bases:
PredicateInterfacepara A B C D - Represent that the line AB is parallel to the line CD.
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.
- predicate_type: Literal[PredicateType.PARALLEL]
- line1: Line
- line2: Line
- static preparse(args)
Preparse the predicate arguments.
- Return type:
tuple[NewType(PredicateArgument,str),...] |None
- check_numerical()
Check numerically the predicate.
- Return type:
bool
- symbols(symbols)
Make symbols for the predicate in the symbols graph.
- Return type:
tuple[LineSymbol|CircleSymbol,...]
- to_constructive(point)
- Return type:
str
- to_tokens()
Convert the predicate to a tuple of strings.
- Return type:
tuple[NewType(PredicateArgument,str),...]
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- class NPara(**data)
Bases:
NumericalPredicatenpara A B C D - Represent that lines AB and CD are NOT parallel.
It can only be numerically checked (angular coefficient of the equations of the lines are different).
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.
- predicate_type: Literal[PredicateType.N_PARALLEL]
- line1: Line
- line2: Line
- static preparse(args)
Preparse the predicate arguments.
- Return type:
tuple[NewType(PredicateArgument,str),...] |None
- check_numerical()
Check numerically the predicate.
- Return type:
bool
- to_tokens()
Convert the predicate to a tuple of strings.
- Return type:
tuple[NewType(PredicateArgument,str),...]
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].