Triangles Similar

two_triangles(a, b, c, p, q, r)

Return type:

Optional[tuple[TypeVar(T, bound= str), TypeVar(T, bound= str), TypeVar(T, bound= str), TypeVar(T, bound= str), TypeVar(T, bound= str), TypeVar(T, bound= str)]]

class SimtriClock(**data)

Bases: PredicateInterface

simtri A B C P Q R -

Represent that triangles ABC and PQR are similar under orientation-preserving transformations taking A to P, B to Q and C to R.

It is equivalent to the three eqangle and eqratio predicates on the corresponding angles and sides.

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.SIMTRI_CLOCK]

triangle1: Triangle

triangle2: Triangle

static preparse(args)

Preparse the predicate arguments.

Return type:

Optional[tuple[NewType(PredicateArgument, str), ...]]

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].

class SimtriReflect(**data)

Bases: PredicateInterface

simtrir A B C P Q R -

Represent that triangles ABC and PQR are similar under orientation-preserving transformations taking A to P, B to Q and C to R.

It is equivalent to the three eqangle and eqratio predicates on the corresponding angles and sides.

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.SIMTRI_REFLECT]

triangle1: Triangle

triangle2: Triangle

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].