Scheme
- class glotaran.project.scheme.Scheme(model: Model, parameters: ParameterGroup, data: dict[str, xr.DataArray | xr.Dataset], model_file: str | None = None, parameters_file: str | None = None, data_files: dict[str, str] | None = None, clp_link_tolerance: float = 0.0, maximum_number_function_evaluations: int | None = None, non_negative_least_squares: bool | None = None, group_tolerance: float | None = None, group: bool | None = None, add_svd: bool = True, ftol: float = 1e-08, gtol: float = 1e-08, xtol: float = 1e-08, optimization_method: Literal['TrustRegionReflection', 'Dogbox', 'Levenberg-Marquardt'] = 'TrustRegionReflection', result_path: str | None = None)[source]
Bases:
object
A scheme is a collection of a model, parameters and a dataset.
A scheme also holds options for optimization.
Attributes Summary
Return the dataset model's global dimension.
Return the dataset model's model dimension.
Methods Summary
Create
Scheme
from specs in yaml file.Format the
Scheme
as markdown string.Return a list with all problems in the model and missing parameters.
Check if there are no problems with the model or the parameters.
Return 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.
- markdown()[source]
Format the
Scheme
as markdown string.- Returns
The scheme as markdown string.
- Return type
- model: Model
- optimization_method: Literal['TrustRegionReflection', 'Dogbox', 'Levenberg-Marquardt'] = 'TrustRegionReflection'
- parameters: ParameterGroup
- problem_list() list[str] [source]
Return a list with all problems in the model and missing parameters.
- valid() bool [source]
Check if there are no problems with the model or the parameters.
- Returns
Whether the scheme is valid.
- Return type