Scheme¶
- class glotaran.project.scheme.Scheme(model: Model | str, parameters: ParameterGroup | str, data: dict[str, xr.DataArray | xr.Dataset | str], group_tolerance: float = 0.0, non_negative_least_squares: bool = False, maximum_number_function_evaluations: int = None, ftol: float = 1e-08, gtol: float = 1e-08, xtol: float = 1e-08, optimization_method: Literal[(‘TrustRegionReflection’, ‘Dogbox’, ‘Levenberg-Marquardt’)] = 'TrustRegionReflection', saving: SavingOptions = SavingOptions(level='full', data_filter=None, data_format='nc', parameter_format='csv', report=True), result_path: str | None = None)[source]¶
Bases:
object
Attributes Summary
Methods Summary
Create
Scheme
from specs in yaml file.Formats the
Scheme
as markdown string.Returns a list with all problems in the model and missing parameters.
Returns True if there are no problems with the model or the parameters, else False.
Returns a string listing all problems in the model and missing parameters.
Methods Documentation
- static from_yaml_file(filename: str) → glotaran.project.scheme.Scheme[source]¶
Create
Scheme
from specs in yaml file.Warning
Deprecated use
glotaran.io.load_scheme(filename)
instead.
- optimization_method: Literal[TrustRegionReflection, Dogbox, Levenberg-Marquardt] = 'TrustRegionReflection'¶
- problem_list() → list[str][source]¶
Returns a list with all problems in the model and missing parameters.
- saving: SavingOptions = SavingOptions(level='full', data_filter=None, data_format='nc', parameter_format='csv', report=True)¶
- valid(parameters: ParameterGroup = None) → bool[source]¶
Returns True if there are no problems with the model or the parameters, else False.