Problem

class glotaran.analysis.problem.Problem(scheme: glotaran.project.scheme.Scheme)[source]

Bases: object

A Problem class

Initializes the Problem class from a scheme (glotaran.analysis.scheme.Scheme)

Args:

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

Attributes Summary

`additional_penalty`
`bag`
`clp_labels`
`clps`
`cost`
`data`
`filled_dataset_descriptors`
`full_penalty`
`grouped`
`groups`
`index_dependent`
`matrices`
`model`	Property providing access to the used model
`parameter_history`
`parameters`
`reduced_clp_labels`
`reduced_clps`
`reduced_matrices`
`residuals`
`scheme`	Property providing access to the used scheme
`weighted_residuals`

Methods Summary

`calculate_additional_penalty`	Calculates additional penalties by calling the model.additional_penalty function.
`calculate_index_dependent_matrices`	Calculates the index dependent model matrices.
`calculate_index_dependent_residual`	Calculates the index dependent residuals.
`calculate_index_independent_matrices`	Calculates the index independent model matrices.
`calculate_index_independent_residual`	Calculates the index independent residuals.
`calculate_matrices`
`calculate_residual`
`create_index_dependent_result_dataset`	Creates a result datasets for index dependent matrices.
`create_index_independent_result_dataset`	Creates a result datasets for index independent matrices.
`create_result_data`
`create_result_dataset`
`init_bag`	Initializes a problem bag.
`reset`	Resets all results and DatasetDescriptors.
`save_parameters_for_history`

Methods Documentation

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

property bag: UngroupedBag | GroupedBag

calculate_additional_penalty() → np.ndarray | dict[str, np.ndarray][source]: Calculates additional penalties by calling the model.additional_penalty function.

calculate_index_dependent_matrices() → tuple[dict[str, list[list[str]]], dict[str, list[np.ndarray]], dict[str, list[str]], dict[str, list[np.ndarray]]][source]: Calculates the index dependent model matrices.

calculate_index_dependent_residual() → tuple[dict[str, list[np.ndarray]], dict[str, list[np.ndarray]], dict[str, list[np.ndarray]], dict[str, list[np.ndarray]]][source]: Calculates the index dependent residuals.

calculate_index_independent_matrices() → tuple[dict[str, list[str]], dict[str, np.ndarray], dict[str, list[str]], dict[str, np.ndarray]][source]: Calculates the index independent model matrices.

calculate_index_independent_residual() → tuple[dict[str, list[np.ndarray]], dict[str, list[np.ndarray]], dict[str, list[np.ndarray]], dict[str, list[np.ndarray]]][source]: Calculates the index independent residuals.

calculate_matrices()[source]

calculate_residual()[source]

property clp_labels: dict[str, list[str] | list[list[str]]]

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

property cost: float

create_index_dependent_result_dataset(label: str, dataset: xarray.core.dataset.Dataset) → xarray.core.dataset.Dataset[source]: Creates a result datasets for index dependent matrices.

create_index_independent_result_dataset(label: str, dataset: xarray.core.dataset.Dataset) → xarray.core.dataset.Dataset[source]: Creates a result datasets for index independent matrices.

create_result_data(copy: bool = True, history_index: int | None = None) → dict[str, xr.Dataset][source]

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

property data: dict[str, xr.Dataset]

property filled_dataset_descriptors: dict[str, DatasetDescriptor]

property full_penalty: numpy.ndarray

property grouped: bool

property groups: dict[str, list[str]]

property index_dependent: bool

init_bag()[source]: Initializes a problem bag.

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

property model: glotaran.model.base_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 parameter_history: list[ParameterGroup]

property parameters: glotaran.parameter.parameter_group.ParameterGroup

property reduced_clp_labels: dict[str, list[str] | list[list[str]]]

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 DatasetDescriptors. Use after updating parameters.

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

save_parameters_for_history()[source]

property scheme: glotaran.project.scheme.Scheme

Property providing access to the used scheme

Returns:

Scheme: An instance of glotaran.analysis.scheme.Scheme: Provides access to data, model, parameters and optimization arguments.

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