Deterministic Compartment Model

radcomp.solve_dcm_from_toml(filepath: str, t_eval: ndarray, prelayer: Prelayer | None = None, voiding_rules: list[radcomp.common.voiding.VoidingRule] | None = None) → DetCompModelSol

Solve a deterministic compartment model from a TOML configuration file (except for a possible prelayer).

For more information about the model or how to format the TOML file, see https://github.com/jakeforster/radcomp/blob/main/README.md.

Parameters:

filepath (str) – Filepath to TOML configuration file.
t_eval (np.ndarray) – Times (h) at which to solve the model. Must be sorted (ascending). Note the first element is the beginning of the integration period.
prelayer (Optional[Prelayer]) – Input time-activity curves for a nuclide that is able to transition to one or more layers in the model.
voiding_rules (Optional[list[VoidingRule]]) – Rules for voiding nuclei from compartments.

Returns:

Solution for the model.

Return type:

DetCompModelSol

radcomp.solve_dcm(trans_rates: ndarray, branching_fracs: ndarray, xfer_coeffs: ndarray, initial_nuclei: ndarray, t_eval: ndarray, prelayer: Prelayer | None = None, layer_names: list[str] | None = None, compartment_names: list[str] | None = None, voiding_rules: list[radcomp.common.voiding.VoidingRule] | None = None) → DetCompModelSol

Solve a deterministic compartment model.

For more information about the model, see https://github.com/jakeforster/radcomp/blob/main/README.md.

Parameters:

trans_rates (numpy.ndarray) – Transition rates (h-1) of nuclides in layers. Shape (num_layers,).
branching_fracs (numpy.ndarray) – Branching fractions (0 to 1). Shape (num_layers, num_layers). Element [i, j] is for layer j to layer i.
xfer_coeffs (numpy.ndarray) – Transfer coefficients (h-1) between compartments. Shape (num_layers, num_compartments, num_compartments). Element [i, j, k] is for compartment k to compartment j in layer i.
initial_nuclei (numpy.ndarray) – Number of nuclei in each compartment in each layer at first element of t_eval. Shape (num_layers, num_compartments). Element [i, j] is for layer i, compartment j.
t_eval (numpy.ndarray) – Times (h) at which to solve the model. Must be sorted (ascending). Note the first element is the beginning of the integration period.
prelayer (Optional[Prelayer]) – Input time-activity curves for a nuclide that is able to transition to one or more layers in the model.
layer_names (Optional[list[str]]) – Names of layers in model.
compartment_names (Optional[list[str]]) – Names of compartments in model.
voiding_rules (Optional[list[VoidingRule]]) – Rules for voiding nuclei from compartments.

Returns:

Solution for the model.

Return type:

DetCompModelSol

class radcomp.DetCompModelSol(trans_rates: ndarray, branching_fracs: ndarray, xfer_coeffs: ndarray, t_eval: ndarray, prelayer: Prelayer | None, layer_names: list[str] | None, compartment_names: list[str] | None, num_layers: int, num_compartments: int, nuclei: ndarray, voided_nuclei: list[numpy.ndarray])

Bases: object

Holds the solution for a deterministic compartment model.

Provides some convenient functions to inspect the model and its solution.

NB. Users should not create an instance of this class directly; use the functions solve_dcm() or solve_dcm_from_toml() to get an instance.

Parameters:

trans_rates (numpy.ndarray) – Transition rates (h-1) of nuclides in layers. Shape (num_layers,).
branching_fracs (numpy.ndarray) – Branching fractions (0 to 1). Shape (num_layers, num_layers). Element [i, j] is for layer j to layer i.
xfer_coeffs (numpy.ndarray) – Transfer coefficients (h-1) between compartments. Shape (num_layers, num_compartments, num_compartments). Element [i, j, k] is for compartment k to compartment j in layer i.
t_eval (numpy.ndarray) – Times (h) at which to solve the model. Must be sorted (ascending).
prelayer (Prelayer | None) – Input time-activity curves for a nuclide that is able to transition to one or more layers in the model.
layer_names (list[str] | None) – Names of layers in model. Length num_layers.
compartment_names (list[str] | None) – Names of compartments in model. Length num_compartments.
num_layers (int) – Number of layers in model (excluding any prelayer).
num_compartments (int) – Number of compartments in model.
nuclei (numpy.ndarray) – Shape (num_layers, num_compartments, len(t_eval)). The solution. Element [i, j, k] is the number of nuclei in layer i, compartment j at element k of t_eval.
voided_nuclei (list[numpy.ndarray]) – Element i is a 3D array containing the number of nuclei voided due to the ith voiding rule. This 3D array has shape (number of voiding times in ith voiding rule, num_layers, num_compartments). See also voided_activity().

activity() → ndarray

Activities (MBq) at times in t_eval.

Returns:: Shape (num_layers, num_compartments, len(t_eval)). Element at index [i, j, k] is the activtiy (MBq) in layer i, compartment j at element k of t_eval.
Return type:: numpy.ndarray

branching_fracs: ndarray

compartment_names: list[str] | None

cumulated_activity(t_start: float | None = None, t_end: float | None = None) → ndarray

Cumulated activity (MBq h) during t_eval.

Note result is sensitive to choice of t_eval.

Parameters:

t_start (Optional[float]) – Start time (h) of integration. Default is start of t_eval.
t_end (Optional[float]) – End time (h) of integration. Default is end of t_eval.

Returns:

Shape (num_layers, num_compartments). Element at index [i, j] is the cumulated activity (MBq h) in layer i, compartment j from t_start to t_end.

Return type:

numpy.ndarray

halflife() → ndarray

Half-lives (h) of nuclides in layers.

Stable nuclides are assigned a half-life of numpy.inf.

Returns:: Shape (num_layers,). Element at index i is the half-life (h) of nuclide in layer i.
Return type:: numpy.ndarray

info_growth() → str

Get information about the growth of nuclides in layers.

Returns:: Information.
Return type:: str

info_xfer() → str

Get information about transfer coefficients between compartments.

Returns:: Information.
Return type:: str

layer_names: list[str] | None

nuclei: ndarray

num_compartments: int

num_layers: int

plot() → None: Produce plots of time-activity curves or time-nuclei curves.

prelayer: Prelayer | None

save_arrays(filepath: str) → None

Save the solution to npz file.

The saved arrays t_eval, nuclei, and the return of activity(). The function read_arrays() can be used to read an npz file created using this method.

Parameters:: filepath (str) – Filepath of npz file to be created. Must end with “.npz”.

t_eval: ndarray

trans_rates: ndarray

voided_activity()

Activities (MBq) voided due to voiding rules.