KineticImageModel¶

class glotaran.builtin.models.kinetic_image.kinetic_image_model.KineticImageModel[source]¶

Bases: glotaran.model.base_model.Model

Attributes Summary

`additional_penalty_function`
`constrain_matrix_function`
`dataset`
`global_dimension`
`global_matrix`
`has_additional_penalty_function`
`has_matrix_constraints_function`
`index_dependent_matrix`
`initial_concentration`
`irf`
`k_matrix`
`megacomplex`
`model_dimension`
`model_type`	The type of the model as human readable string.
`retrieve_clp_function`
`weights`

Methods Summary

`add_weights`
`finalize_data`
`from_dict`	Creates a model from a dictionary.
`get_dataset`
`get_initial_concentration`
`get_irf`
`get_k_matrix`
`get_megacomplex`
`grouped`
`index_dependent`
`markdown`	Formats the model as Markdown string.
`matrix`
`problem_list`	Returns a list with all problems in the model and missing parameters if specified.
`set_dataset`
`set_initial_concentration`
`set_irf`
`set_k_matrix`
`set_megacomplex`
`simulate`	Simulates the model.
`valid`	Returns True if the number problems in the model is 0, else False
`validate`	Returns a string listing all problems in the model and missing parameters if specified.

Methods Documentation

add_weights(item: glotaran.model.weight.Weight)¶

additional_penalty_function = None¶

constrain_matrix_function = None¶

property dataset¶

finalize_data(problem: Problem, data: dict[str, xr.Dataset])¶

classmethod from_dict(model_dict_ref: dict) → glotaran.model.base_model.Model ¶

Creates a model from a dictionary.

Parameters: model_dict – Dictionary containing the model.

get_dataset(label: str) → glotaran.builtin.models.kinetic_image.kinetic_image_dataset_descriptor.KineticImageDatasetDescriptor ¶

get_initial_concentration(label: str) → glotaran.builtin.models.kinetic_image.initial_concentration.InitialConcentration ¶

get_irf(label: str) → glotaran.builtin.models.kinetic_image.irf.Irf ¶

get_k_matrix(label: str) → glotaran.builtin.models.kinetic_image.k_matrix.KMatrix ¶

get_megacomplex(label: str) → glotaran.builtin.models.kinetic_image.kinetic_image_megacomplex.KineticImageMegacomplex ¶

global_dimension = 'pixel'¶

global_matrix = None¶

grouped()¶

has_additional_penalty_function = None¶

has_matrix_constraints_function = None¶

index_dependent()¶

property index_dependent_matrix¶

property initial_concentration¶

property irf¶

property k_matrix¶

markdown(parameters: Optional[glotaran.parameter.parameter_group.ParameterGroup] = None, initial_parameters: Optional[glotaran.parameter.parameter_group.ParameterGroup] = None) → str ¶

Formats the model as Markdown string.

Parameters will be included if specified.

Parameters

parameter – Parameter to include.
initial – Initial values for the parameters.

static matrix(dataset_descriptor=None, axis=None, index=None, irf=None)¶

property megacomplex¶

model_dimension = 'time'¶

property model_type¶: The type of the model as human readable string.

problem_list(parameters: ParameterGroup = None) → list[str]¶

Returns a list with all problems in the model and missing parameters if specified.

Parameters: parameter – The parameter to validate.

retrieve_clp_function = None¶

set_dataset(label: str, item: glotaran.builtin.models.kinetic_image.kinetic_image_dataset_descriptor.KineticImageDatasetDescriptor)¶

set_initial_concentration(label: str, item: glotaran.builtin.models.kinetic_image.initial_concentration.InitialConcentration)¶

set_irf(label: str, item: glotaran.builtin.models.kinetic_image.irf.Irf)¶

set_k_matrix(label: str, item: glotaran.builtin.models.kinetic_image.k_matrix.KMatrix)¶

set_megacomplex(label: str, item: glotaran.builtin.models.kinetic_image.kinetic_image_megacomplex.KineticImageMegacomplex)¶

simulate(dataset: str, parameters: ParameterGroup, axes: dict[str, np.ndarray] = None, clp: np.ndarray | xr.DataArray = None, noise: bool = False, noise_std_dev: float = 1.0, noise_seed: int = None) → xr.Dataset¶

Simulates the model.

Parameters

dataset – Label of the dataset to simulate.
parameter – The parameters for the simulation.
axes – A dictionary with axes for simulation.
clp – Conditionally linear parameters. Used instead of model.global_matrix if provided.
noise – If True noise is added to the simulated data.
noise_std_dev – The standard deviation of the noise.
noise_seed – Seed for the noise.

valid(parameters: Optional[glotaran.parameter.parameter_group.ParameterGroup] = None) → bool ¶

Returns True if the number problems in the model is 0, else False

Parameters: parameter – The parameter to validate.

validate(parameters: Optional[glotaran.parameter.parameter_group.ParameterGroup] = None) → str ¶

Returns a string listing all problems in the model and missing parameters if specified.

Parameters: parameter – The parameter to validate.

property weights¶