Scheme¶

class glotaran.analysis.scheme.Scheme(model: Model = None, parameters: ParameterGroup = None, data: dict[str, xr.DataArray | xr.Dataset] = None, 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')[source]¶

Bases: object

Attributes Summary

`data`
`ftol`
`group_tolerance`
`gtol`
`maximum_number_function_evaluations`
`model`
`non_negative_least_squares`
`optimization_method`
`parameters`
`xtol`

Methods Summary

`from_yaml_file`
`markdown`
`problem_list`	Returns a list with all problems in the model and missing parameters.
`valid`	Returns True if there are no problems with the model or the parameters, else False.
`validate`	Returns a string listing all problems in the model and missing parameters.

Methods Documentation

property data¶

classmethod from_yaml_file(filename: str) → glotaran.analysis.scheme.Scheme [source]¶

property ftol¶

property group_tolerance¶

property gtol¶

markdown()[source]¶

property maximum_number_function_evaluations¶

property model¶

property non_negative_least_squares¶

property optimization_method¶

property parameters¶

problem_list() → list[str][source]¶: Returns a list with all problems in the model and missing parameters.

valid(parameters: Optional[glotaran.parameter.parameter_group.ParameterGroup] = None) → bool [source]¶: Returns True if there are no problems with the model or the parameters, else False.

validate() → str [source]¶: Returns a string listing all problems in the model and missing parameters.

property xtol¶