Result

class glotaran.project.result.Result(additional_penalty: np.ndarray | None, cost: ArrayLike, data: dict[str, xr.Dataset], free_parameter_labels: list[str], number_of_function_evaluations: int, initial_parameters: ParameterGroup, optimized_parameters: ParameterGroup, scheme: Scheme, success: bool, termination_reason: str, chi_square: float | None = None, covariance_matrix: ArrayLike | None = None, degrees_of_freedom: int | None = None, jacobian: ArrayLike | 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

`chi_square`	The chi-square of the optimization.
`covariance_matrix`	Covariance matrix.
`degrees_of_freedom`	Degrees of freedom in optimization $N - N_{vars}$ .
`jacobian`	Modified Jacobian matrix at the solution
`model`
`number_of_data_points`	Number of data points $N$ .
`number_of_jacobian_evaluations`	The number of jacobian evaluations.
`number_of_variables`	Number of variables in optimization $N_{vars}$
`optimality`
`reduced_chi_square`	The reduced chi-square of the optimization.
`root_mean_square_error`	The root mean square error the optimization.
`additional_penalty`	A vector with the value for each additional penalty, or None
`cost`
`data`	The resulting data as a dictionary of xarray.Dataset.
`free_parameter_labels`	List of labels of the free parameters used in optimization.
`number_of_function_evaluations`	The number of function evaluations.
`initial_parameters`
`optimized_parameters`	The optimized parameters, organized in a `ParameterGroup`
`scheme`
`success`	Indicates if the optimization was successful.
`termination_reason`	The reason (message when) the optimizer terminated

Methods Summary

`get_dataset`	Returns the result dataset for the given dataset label.
`get_scheme`	Return a new scheme from the Result object with optimized parameters.
`markdown`	Formats the model as a markdown text.
`save`	Saves the result to given folder.

Methods Documentation

additional_penalty: np.ndarray | None: A vector with the value for each additional penalty, or None

chi_square: float | None = None

The chi-square of the optimization.

$\chi^2 = \sum_i^N [{Residual}_i]^2$ .

cost: ArrayLike

covariance_matrix: ArrayLike | None = None

Covariance matrix.

The rows and columns are corresponding to free_parameter_labels.

data: dict[str, xr.Dataset]

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.

degrees_of_freedom: int | None = None: Degrees of freedom in optimization $N - N_{vars}$ .

free_parameter_labels: list[str]: List of labels of the free parameters used in optimization.

get_dataset(dataset_label: str) → xarray.core.dataset.Dataset[source]

Returns the result dataset for the given dataset label.

Warning

Deprecated use glotaran.project.result.Result.data[dataset_label] instead.

Parameters: dataset_label – The label of the 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: Scheme

initial_parameters: ParameterGroup

jacobian: ArrayLike | None = None

Modified Jacobian matrix at the solution

markdown(with_model: bool = True, base_heading_level: int = 1) → glotaran.utils.ipython.MarkdownStr[source]

Formats the model as a markdown text.

Parameters: with_model – If True, the model will be printed with initial and optimized parameters filled in.

property model: glotaran.model.base_model.Model

number_of_data_points: int | None = None: Number of data points $N$ .

number_of_function_evaluations: int: The number of function evaluations.

number_of_jacobian_evaluations: int | None = None: The number of jacobian evaluations.

number_of_variables: int | None = None: Number of variables in optimization $N_{vars}$

optimality: float | None = None

optimized_parameters: ParameterGroup: The optimized parameters, organized in a ParameterGroup

reduced_chi_square: float | None = None

The reduced chi-square of the optimization.

$\chi^2_{red}= {\chi^2} / {(N - N_{vars})}$ .

root_mean_square_error: float | None = None

The root mean square error the optimization.

$rms = \sqrt{\chi^2_{red}}$

save(path: str) → list[str][source]

Saves the result to given folder.

Warning

Deprecated use save_result(result_path=result_path, result=result, format_name="legacy", allow_overwrite=True) instead.

Returns a list with paths of all saved items. The following files are saved:

result.md: The result with the model formatted as markdown text.
optimized_parameters.csv: The optimized parameter as csv file.
{dataset_label}.nc: The result data for each dataset as NetCDF file.

Parameters: path – The path to the folder in which to save the result.

scheme: Scheme

success: bool: Indicates if the optimization was successful.

termination_reason: str: The reason (message when) the optimizer terminated