"""Evaluateable physics models for amplitude analysis.
The `.model` module takes care of lambdifying mathematical expressions to
computational backends.
"""
# pyright: reportUnusedImport=false
from typing import Dict, Mapping, Union
import numpy as np
from tensorwaves.interface import DataSample, Function, Model, ParameterValue
from .sympy import SympyModel # noqa: F401
[docs]class LambdifiedFunction(Function):
"""Implements `.Function` based on a `.Model` using {meth}`~.Model.lambdify`."""
def __init__(
self,
model: Model,
backend: Union[str, tuple, dict] = "numpy",
) -> None:
self.__lambdified_model = model.lambdify(backend=backend)
self.__parameters = model.parameters
self.__ordered_args = model.argument_order
[docs] def __call__(self, dataset: DataSample) -> np.ndarray:
return self.__lambdified_model(
*[
dataset[var_name]
if var_name in dataset
else self.__parameters[var_name]
for var_name in self.__ordered_args
],
)
@property
def parameters(self) -> Dict[str, ParameterValue]:
return self.__parameters
[docs] def update_parameters(
self, new_parameters: Mapping[str, ParameterValue]
) -> None:
over_defined = set(new_parameters) - set(self.__parameters)
if over_defined:
sep = "\n "
parameter_listing = f"{sep}".join(sorted(self.__parameters))
raise ValueError(
f"Parameters {over_defined} do not exist in function"
f" arguments. Expecting one of:{sep}{parameter_listing}"
)
self.__parameters.update(new_parameters)
