KineticSpectrumModel
- class glotaran.builtin.models.kinetic_spectrum.kinetic_spectrum_model.KineticSpectrumModel[source]
Bases:
glotaran.builtin.models.kinetic_image.kinetic_image_model.KineticImageModel
Attributes Summary
The type of the model as human readable string.
Methods Summary
Creates a model from a dictionary.
Calculates the matrix.
Formats the model as Markdown string.
Returns a list with all problems in the model and missing parameters if specified.
Simulates the model.
Returns True if the number problems in the model is 0, else False
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: Dict[str, glotaran.builtin.models.kinetic_spectrum.kinetic_spectrum_dataset_descriptor.KineticSpectrumDatasetDescriptor]
- property equal_area_penalties: Dict[str, glotaran.builtin.models.kinetic_spectrum.spectral_penalties.EqualAreaPenalty]
- 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_spectrum.kinetic_spectrum_dataset_descriptor.KineticSpectrumDatasetDescriptor
- get_initial_concentration(label) glotaran.builtin.models.kinetic_image.initial_concentration.InitialConcentration
- get_irf(label) glotaran.builtin.models.kinetic_image.irf.Irf
- get_k_matrix(label) glotaran.builtin.models.kinetic_image.k_matrix.KMatrix
- get_megacomplex(label) glotaran.model.decorator._set_megacomplexes.<locals>.MetaMegacomplex
- get_shape(label) glotaran.builtin.models.kinetic_spectrum.spectral_shape.SpectralShape
- global_dimension = 'spectral'
- static global_matrix(dataset, axis)
Calculates the matrix.
- grouped()
- property initial_concentration: Dict[str, glotaran.builtin.models.kinetic_image.initial_concentration.InitialConcentration]
- property irf: Dict[str, glotaran.builtin.models.kinetic_image.irf.Irf]
- property k_matrix: Dict[str, glotaran.builtin.models.kinetic_image.k_matrix.KMatrix]
- 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 = 'time'
- 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, item: glotaran.builtin.models.kinetic_spectrum.kinetic_spectrum_dataset_descriptor.KineticSpectrumDatasetDescriptor)
- set_initial_concentration(label, item: glotaran.builtin.models.kinetic_image.initial_concentration.InitialConcentration)
- set_irf(label, item: glotaran.builtin.models.kinetic_image.irf.Irf)
- set_k_matrix(label, item: glotaran.builtin.models.kinetic_image.k_matrix.KMatrix)
- set_megacomplex(label, item: glotaran.model.decorator._set_megacomplexes.<locals>.MetaMegacomplex)
- set_shape(label, item: glotaran.builtin.models.kinetic_spectrum.spectral_shape.SpectralShape)
- property shape: Dict[str, glotaran.builtin.models.kinetic_spectrum.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.
- property spectral_constraints: Dict[str, glotaran.builtin.models.kinetic_spectrum.spectral_constraints.SpectralConstraint]
- property spectral_relations: Dict[str, glotaran.builtin.models.kinetic_spectrum.spectral_relations.SpectralRelation]
- 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]