"""This package contains the decay megacomplex item."""
from __future__ import annotations
from typing import List
import numpy as np
import xarray as xr
from glotaran.builtin.megacomplexes.decay.initial_concentration import InitialConcentration
from glotaran.builtin.megacomplexes.decay.irf import Irf
from glotaran.builtin.megacomplexes.decay.k_matrix import KMatrix
from glotaran.builtin.megacomplexes.decay.util import calculate_matrix
from glotaran.builtin.megacomplexes.decay.util import finalize_data
from glotaran.builtin.megacomplexes.decay.util import index_dependent
from glotaran.model import DatasetModel
from glotaran.model import Megacomplex
from glotaran.model import ModelError
from glotaran.model import megacomplex
[docs]@megacomplex(
dimension="time",
model_items={
"k_matrix": List[KMatrix],
},
properties={},
dataset_model_items={
"initial_concentration": {"type": InitialConcentration, "allow_none": True},
"irf": {"type": Irf, "allow_none": True},
},
register_as="decay",
)
class DecayMegacomplex(Megacomplex):
"""A Megacomplex with one or more K-Matrices."""
[docs] def get_compartments(self, dataset_model: DatasetModel) -> list[str]:
if dataset_model.initial_concentration is None:
raise ModelError(
f'No initial concentration specified in dataset "{dataset_model.label}"'
)
return [
compartment
for compartment in dataset_model.initial_concentration.compartments
if compartment in self.get_k_matrix().involved_compartments()
]
[docs] def get_initial_concentration(
self, dataset_model: DatasetModel, normalized: bool = True
) -> np.ndarray:
compartments = self.get_compartments(dataset_model)
idx = [
compartment in compartments
for compartment in dataset_model.initial_concentration.compartments
]
initial_concentration = (
dataset_model.initial_concentration.normalized()
if normalized
else np.asarray(dataset_model.initial_concentration.parameters)
)
return initial_concentration[idx]
[docs] def get_k_matrix(self) -> KMatrix:
full_k_matrix = None
for k_matrix in self.k_matrix:
if full_k_matrix is None:
full_k_matrix = k_matrix
# If multiple k matrices are present, we combine them
else:
full_k_matrix = full_k_matrix.combine(k_matrix)
return full_k_matrix
[docs] def get_a_matrix(self, dataset_model: DatasetModel) -> np.ndarray:
return self.get_k_matrix().a_matrix(
self.get_compartments(dataset_model), self.get_initial_concentration(dataset_model)
)
[docs] def index_dependent(self, dataset_model: DatasetModel) -> bool:
return index_dependent(dataset_model)
[docs] def calculate_matrix(
self,
dataset_model: DatasetModel,
indices: dict[str, int],
**kwargs,
):
return calculate_matrix(self, dataset_model, indices, **kwargs)
[docs] def finalize_data(
self,
dataset_model: DatasetModel,
dataset: xr.Dataset,
is_full_model: bool = False,
as_global: bool = False,
):
finalize_data(dataset_model, dataset, is_full_model, as_global)