OptimizationGroup

class glotaran.optimization.optimization_group.OptimizationGroup(scheme: glotaran.project.scheme.Scheme, dataset_group: glotaran.model.dataset_group.DatasetGroup)[source]

Bases: object

Create OptimizationGroup instance from a scheme (Scheme)

Args:

scheme (Scheme): An instance of Scheme: which defines your model, parameters, and data

Attributes Summary

`additional_penalty`
`clps`
`cost`
`data`
`dataset_models`
`full_penalty`
`matrices`
`model`	Property providing access to the used model
`parameters`
`reduced_clps`
`reduced_matrices`
`residuals`
`weighted_residuals`

Methods Summary

`create_result_data`
`create_result_dataset`
`reset`	Resets all results and DatasetModels.

Methods Documentation

property additional_penalty: dict[str, list[float]]

property clps: dict[str, list[np.ndarray]]

property cost: float

create_result_data(parameter_history: ParameterHistory = None, copy: bool = True, success: bool = True, add_svd: bool = True) → dict[str, xr.Dataset][source]

create_result_dataset(label: str, copy: bool = True, add_svd: bool = True) → xarray.core.dataset.Dataset[source]

property data: dict[str, xr.Dataset]

property dataset_models: dict[str, DatasetModel]

property full_penalty: numpy.ndarray

property matrices: dict[str, np.ndarray | list[np.ndarray]]

property model: glotaran.model.model.Model

Property providing access to the used model

The model is a subclass of glotaran.model.Model decorated with the @model decorator glotaran.model.model_decorator.model For an example implementation see e.g. glotaran.builtin.models.kinetic_spectrum

Returns:

Model: A subclass of glotaran.model.Model: The model must be decorated with the @model decorator glotaran.model.model_decorator.model

property parameters: glotaran.parameter.parameter_group.ParameterGroup

property reduced_clps: dict[str, list[np.ndarray]]

property reduced_matrices: dict[str, np.ndarray] | dict[str, list[np.ndarray]] | list[np.ndarray]

reset()[source]: Resets all results and DatasetModels. Use after updating parameters.

property residuals: dict[str, list[np.ndarray]]

property weighted_residuals: dict[str, list[np.ndarray]]