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

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

Methods Summary

`from_yaml_file`	Create `Scheme` from specs in yaml file.
`markdown`	Formats the `Scheme` as markdown string.
`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

data: dict[str, xr.DataArray | xr.Dataset | str]¶

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.

Parameters: filename (str) – Path to the spec file.
Returns: Analysis schmeme
Return type: Scheme

ftol: float = 1e-08¶

group_tolerance: float = 0.0¶

gtol: float = 1e-08¶

markdown()[source]¶: Formats the Scheme as markdown string.

maximum_number_function_evaluations: int = None¶

model: Model | str¶

non_negative_least_squares: bool = False¶

optimization_method: Literal[TrustRegionReflection, Dogbox, Levenberg-Marquardt] = 'TrustRegionReflection'¶

parameters: ParameterGroup | str¶

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

result_path: str | None = None¶

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.

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

xtol: float = 1e-08¶