Result
- class glotaran.project.result.Result(number_of_function_evaluations: int, success: bool, termination_reason: str, glotaran_version: str, free_parameter_labels: list[str], scheme: Scheme = None, scheme_file: str | None = None, initial_parameters: ParameterGroup = None, initial_parameters_file: str | None = None, optimized_parameters: ParameterGroup = None, optimized_parameters_file: str | None = None, parameter_history: ParameterHistory = None, parameter_history_file: str | None = None, data: dict[str, xr.Dataset] = None, data_files: dict[str, str] | None = None, additional_penalty: np.ndarray | None = None, cost: ArrayLike | None = None, chi_square: float | None = None, covariance_matrix: ArrayLike | None = None, degrees_of_freedom: int | None = None, jacobian: ArrayLike | list | None = None, number_of_data_points: int | None = None, number_of_jacobian_evaluations: int | None = None, number_of_variables: int | None = None, optimality: float | None = None, reduced_chi_square: float | None = None, root_mean_square_error: float | None = None)[source]
Bases:
object
The result of a global analysis.
Attributes Summary
A vector with the value for each additional penalty, or None
The chi-square of the optimization.
The final cost.
Covariance matrix.
The resulting data as a dictionary of xarray.Dataset.
Degrees of freedom in optimization .
Modified Jacobian matrix at the solution
Return the model used to fit result.
Number of data points .
The number of jacobian evaluations.
Number of variables in optimization
The optimized parameters, organized in a
ParameterGroup
The parameter history.
The reduced chi-square of the optimization.
The root mean square error the optimization.
The number of function evaluations.
Indicates if the optimization was successful.
The reason (message when) the optimizer terminated
The glotaran version used to create the result.
List of labels of the free parameters used in optimization.
Methods Summary
Return the result dataset for the given dataset label.
Return a new scheme from the Result object with optimized parameters.
Format the model as a markdown text.
Recrate a result from the initial parameters.
Save the result to given folder.
Verify a result.
Methods Documentation
- additional_penalty: np.ndarray | None = None
A vector with the value for each additional penalty, or None
- covariance_matrix: ArrayLike | None = None
Covariance matrix.
The rows and columns are corresponding to
free_parameter_labels
.
- data: dict[str, xr.Dataset] = None
The resulting data as a dictionary of xarray.Dataset.
Notes
The actual content of the data depends on the actual model and can be found in the documentation for the model.
- get_dataset(dataset_label: str) xarray.core.dataset.Dataset [source]
Return the result dataset for the given dataset label.
Warning
Deprecated use
glotaran.project.result.Result.data[dataset_label]
instead.- Parameters
dataset_label (str) – The label of the dataset.
- Returns
The dataset.
- Return type
xr.Dataset
- get_scheme() glotaran.project.scheme.Scheme [source]
Return a new scheme from the Result object with optimized parameters.
- Returns
A new scheme with the parameters set to the optimized values. For the dataset weights the (precomputed) weights from the original scheme are used.
- Return type
- initial_parameters: ParameterGroup = None
- jacobian: ArrayLike | list | None = None
Modified Jacobian matrix at the solution
See also:
scipy.optimize.least_squares()
- markdown(with_model: bool = True, base_heading_level: int = 1) glotaran.utils.ipython.MarkdownStr [source]
Format the model as a markdown text.
- property model: glotaran.model.model.Model
Return the model used to fit result.
- Returns
The model instance.
- Return type
- optimized_parameters: ParameterGroup = None
The optimized parameters, organized in a
ParameterGroup
- parameter_history: ParameterHistory = None
The parameter history.
- recreate() glotaran.project.result.Result [source]
Recrate a result from the initial parameters.
- Returns
The recreated result.
- Return type
- scheme: Scheme = None