model

import tensorwaves.model

Evaluatable physics models for amplitude analysis.

The model module takes care of lambdifying mathematical expressions to computational backends. Currently, mathematical expressions are implemented as sympy expressions only.

class LambdifiedFunction(model: Model, backend: Union[str, tuple, dict] = 'numpy')

Bases: tensorwaves.interfaces.Function

Implements Function based on a Model using {meth}`~.Model.lambdify`.

__call__(dataset: Mapping[str, ndarray]) → ndarray

Evaluate the function.

Parameters

dataset – a dict with domain variable names as keys.

Returns

Result of the function evaluation. Type depends on the input type.

property parameters: Dict[str, Union[complex, float]]

Get dict of parameters.

update_parameters(new_parameters: Mapping[str, Union[complex, float]]) → None

Update the collection of parameters.

class SympyModel(expression: Expr, parameters: Dict[Symbol, Union[float, complex]], max_complexity: Optional[int] = None)

Bases: tensorwaves.interfaces.Model

Full definition of an arbitrary model based on sympy.

Note that input for particle physics amplitude models are based on four momenta. However, for reasons of convenience, some models may define and use a distinct set of kinematic variables (e.g. in the helicity formalism: angles \(\theta\) and \(\phi\)). In this case, a DataTransformer instance (adapter) is needed to transform four momentum information into the custom set of kinematic variables.

Args: expressionA sympy expression that contains the complete

information of the model based on some inputs. The inputs are defined via the free_symbols attribute of the sympy.Expr. parameters: Defines which inputs of the model are parameters. The keys represent the parameter set, while the values represent their default values. Consequently the variables of the model are defined as the intersection of the total input set with the parameter set.

property argument_order: Tuple[str, ...]

Order of arguments of lambdified function signature.

lambdify(backend: Union[str, tuple, dict]) → Callable

Lambdify the model using lambdify.

property parameters: Dict[str, Union[complex, float]]

Get mapping of parameters to suggested initial values.

performance_optimize(fix_inputs: Mapping[str, ndarray]) → Model

Create a performance optimized model, based on fixed inputs.

property variables: FrozenSet[str]

Expected input variable names.

get_backend_modules(backend: Union[str, tuple, dict]) → Union[str, tuple, dict]

Preprocess the backend argument passed to lambdify.

Note in lambdify the backend is specified via the modules argument.

optimized_lambdify(args: Sequence[Symbol], expression: Expr, modules: Optional[Union[str, tuple, dict]] = None, *, min_complexity: int = 0, max_complexity: int, **kwargs: Any) → Callable

Speed up lambdify with split_expression.

See also

Speed up lambdifying

split_expression(expression: Expr, max_complexity: int, min_complexity: int = 0) → Tuple[Expr, Dict[Symbol, Expr]]

Split an expression into a ‘top expression’ and several sub-expressions.

Replace nodes in the expression tree of a sympy.Expr that lie within a certain complexity range (see count_ops()) with symbols and keep a mapping of each to these symbols to the sub-expressions that they replaced.

See also

Speed up lambdifying