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