model

import tensorwaves.model

Evaluateable 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: tensorwaves.interfaces.Model, backend: Union[str, tuple, dict] = 'numpy')[source]

Bases: tensorwaves.interfaces.Function

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

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

Get dict of parameters.

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

Update the collection of parameters.

class SympyModel(expression: sympy.core.expr.Expr, parameters: Dict[sympy.core.symbol.Symbol, Union[float, complex]])[source]

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

Order of arguments of lambdified function signature.

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

Lambdify the model using lambdify.

property parameters

Get mapping of parameters to suggested initial values.

performance_optimize(fix_inputs: Mapping[str, numpy.ndarray]) → tensorwaves.interfaces.Model[source]

Create a performance optimized model, based on fixed inputs.

property variables

Expected input variable names.

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

Preprocess the backend argument passed to lambdify.

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

interfaces physics