"""This package contains the decay megacomplex item."""
from __future__ import annotations
from typing import TYPE_CHECKING
import numpy as np
import xarray as xr
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.model import DatasetModel
from glotaran.model import Megacomplex
from glotaran.model import ModelItemType
from glotaran.model import ParameterType
from glotaran.model import item
from glotaran.model import megacomplex
if TYPE_CHECKING:
from glotaran.typing.types import ArrayLike
[docs]
@item
class DecayDatasetModel(DatasetModel):
irf: ModelItemType[Irf] | None = None
[docs]
@megacomplex(dataset_model_type=DecayDatasetModel)
class DecayParallelMegacomplex(Megacomplex):
dimension: str = "time"
type: str = "decay-parallel"
compartments: list[str]
rates: list[ParameterType]
[docs]
def get_compartments(self, dataset_model: DatasetModel) -> list[str]:
return self.compartments
[docs]
def get_initial_concentration(
self, dataset_model: DatasetModel, normalized: bool = True
) -> np.ndarray:
initial_concentration = np.ones((len(self.compartments)), dtype=np.float64)
if normalized:
initial_concentration /= initial_concentration.size
return initial_concentration
[docs]
def get_k_matrix(self) -> KMatrix:
return KMatrix(
label="",
matrix={
(self.compartments[i], self.compartments[i]): self.rates[i]
for i in range(len(self.compartments))
},
)
[docs]
def get_a_matrix(self, dataset_model: DatasetModel) -> np.ndarray:
return self.get_k_matrix().a_matrix_general(
self.get_compartments(dataset_model), self.get_initial_concentration(dataset_model)
)
[docs]
def calculate_matrix(
self,
dataset_model: DecayDatasetModel,
global_axis: ArrayLike,
model_axis: ArrayLike,
**kwargs,
):
return calculate_matrix(self, dataset_model, global_axis, model_axis, **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)