SpectralModel

class glotaran.builtin.models.spectral.spectral_model.SpectralModel[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`
`megacomplex`
`model_dimension`
`model_type`	The type of the model as human readable string.
`retrieve_clp_function`
`shape`
`weights`

Methods Summary

`add_weights`
`finalize_data`
`from_dict`	Creates a model from a dictionary.
`get_dataset`
`get_megacomplex`
`get_shape`
`grouped`
`index_dependent`
`markdown`	Formats the model as Markdown string.
`problem_list`	Returns a list with all problems in the model and missing parameters if specified.
`set_dataset`
`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_weights(item: glotaran.model.weight.Weight)

additional_penalty_function = None

constrain_matrix_function = None

property dataset: Dict[str, glotaran.builtin.models.kinetic_image.kinetic_image_dataset_descriptor.KineticImageDatasetDescriptor]

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) → glotaran.builtin.models.kinetic_image.kinetic_image_dataset_descriptor.KineticImageDatasetDescriptor

get_megacomplex(label) → glotaran.model.decorator._set_megacomplexes.<locals>.MetaMegacomplex

get_shape(label) → glotaran.builtin.models.spectral.shape.SpectralShape

global_dimension = 'time'

global_matrix = None

grouped()

has_additional_penalty_function = None

has_matrix_constraints_function = None

index_dependent()

markdown(parameters: Optional[glotaran.parameter.parameter_group.ParameterGroup] = None, initial_parameters: Optional[glotaran.parameter.parameter_group.ParameterGroup] = None, base_heading_level: int = 1) → glotaran.utils.ipython.MarkdownStr

Formats the model as Markdown string.

Parameters will be included if specified.

Parameters

parameter (ParameterGroup) – Parameter to include.
initial_parameters (ParameterGroup) – Initial values for the parameters.
base_heading_level (int) –
Base heading level of the markdown sections.

E.g.:
- If it is 1 the string will start with ‘# Model’.
- If it is 3 the string will start with ‘### Model’.

property megacomplex: Dict[str, glotaran.model.decorator._set_megacomplexes.<locals>.MetaMegacomplex]

model_dimension = 'spectral'

property model_type: str: 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, item: glotaran.builtin.models.kinetic_image.kinetic_image_dataset_descriptor.KineticImageDatasetDescriptor)

set_megacomplex(label, item: glotaran.model.decorator._set_megacomplexes.<locals>.MetaMegacomplex)

set_shape(label, item: glotaran.builtin.models.spectral.shape.SpectralShape)

property shape: Dict[str, glotaran.builtin.models.spectral.shape.SpectralShape]

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: Dict[str, glotaran.model.weight.Weight]