KineticSpectrumModel¶

class glotaran.builtin.models.kinetic_spectrum.kinetic_spectrum_model.KineticSpectrumModel[source]¶

Bases: glotaran.builtin.models.kinetic_image.kinetic_image_model.KineticImageModel

Attributes Summary

`dataset`
`equal_area_penalties`
`global_dimension`
`index_dependent_matrix`
`initial_concentration`
`irf`
`k_matrix`
`megacomplex`
`model_dimension`
`model_type`	The type of the model as human readable string.
`shape`
`spectral_constraints`
`spectral_relations`
`weights`

Methods Summary

`add_equal_area_penalties`
`add_spectral_constraints`
`add_spectral_relations`
`add_weights`
`additional_penalty_function`
`constrain_matrix_function`
`finalize_data`
`from_dict`	Creates a model from a dictionary.
`get_dataset`
`get_initial_concentration`
`get_irf`
`get_k_matrix`
`get_megacomplex`
`get_shape`
`global_matrix`	Calculates the matrix.
`grouped`
`has_additional_penalty_function`
`has_matrix_constraints_function`
`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.
`retrieve_clp_function`
`set_dataset`
`set_initial_concentration`
`set_irf`
`set_k_matrix`
`set_megacomplex`
`set_shape`
`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_equal_area_penalties(item: glotaran.builtin.models.kinetic_spectrum.spectral_penalties.EqualAreaPenalty)¶

add_spectral_constraints(item: glotaran.builtin.models.kinetic_spectrum.spectral_constraints.SpectralConstraint)¶

add_spectral_relations(item: glotaran.builtin.models.kinetic_spectrum.spectral_relations.SpectralRelation)¶

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

additional_penalty_function(parameters: ParameterGroup, clp_labels: dict[str, list[str] | list[list[str]]], clps: dict[str, list[np.ndarray]], matrices: dict[str, np.ndarray | list[np.ndarray]], data: dict[str, xr.Dataset], group_tolerance: float) → np.ndarray¶

constrain_matrix_function(dataset: str, parameters: ParameterGroup, clp_labels: list[str], matrix: np.ndarray, index: float) → tuple[list[str], np.ndarray]¶

property dataset¶

property equal_area_penalties¶

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_spectrum.kinetic_spectrum_dataset_descriptor.KineticSpectrumDatasetDescriptor ¶

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 ¶

get_shape(label: str) → glotaran.builtin.models.kinetic_spectrum.spectral_shape.SpectralShape ¶

global_dimension = 'spectral'¶

static global_matrix(dataset, axis)¶

Calculates the matrix.

Parameters

matrix (np.array) – The preallocated matrix.
compartment_order (list(str)) – A list of compartment labels to map compartments to indices in the matrix.
parameter (glotaran.model.ParameterGroup) –

grouped()¶

has_additional_penalty_function() → bool ¶

has_matrix_constraints_function() → bool ¶

index_dependent() → bool ¶

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(parameters: ParameterGroup, clp_labels: dict[str, list[str] | list[list[str]]], reduced_clp_labels: dict[str, list[str] | list[list[str]]], reduced_clps: dict[str, list[np.ndarray]], data: dict[str, xr.Dataset]) → dict[str, list[np.ndarray]]¶

set_dataset(label: str, item: glotaran.builtin.models.kinetic_spectrum.kinetic_spectrum_dataset_descriptor.KineticSpectrumDatasetDescriptor)¶

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)¶

set_shape(label: str, item: glotaran.builtin.models.kinetic_spectrum.spectral_shape.SpectralShape)¶

property shape¶

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.

property spectral_constraints¶

property spectral_relations¶

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¶