Scheme

class glotaran.project.scheme.Scheme(model: Model, parameters: ParameterGroup, data: Mapping[str, xr.Dataset], 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

`add_svd`
`clp_link_tolerance`
`ftol`
`global_dimensions`	Return the dataset model's global dimension.
`group`
`group_tolerance`
`gtol`
`maximum_number_function_evaluations`
`model_dimensions`	Return the dataset model's model dimension.
`non_negative_least_squares`
`optimization_method`
`result_path`
`source_path`
`xtol`
`model`
`parameters`
`data`

Methods Summary

`from_yaml_file`	Create `Scheme` from specs in yaml file.
`loader`	Create a `Scheme` instance from the specs defined in a file.
`markdown`	Format the `Scheme` as markdown string.
`problem_list`	Return a list with all problems in the model and missing parameters.
`valid`	Check if there are no problems with the model or the parameters.
`validate`	Return a string listing all problems in the model and missing parameters.

Methods Documentation

add_svd: bool = True

clp_link_tolerance: float = 0.0

data: Mapping[str, xr.Dataset]

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

property global_dimensions: dict[str, str]

Return the dataset model’s global dimension.

Returns: A dictionary with the dataset labels as key and the global dimension of the dataset as value.
Return type: dict[str, str]

group: bool | None = None

group_tolerance: float | None = None

gtol: float = 1e-08

loader(format_name: str = None, **kwargs: Any) → Scheme

Create a Scheme instance from the specs defined in a file.

Parameters

file_name (StrOrPath) – File containing the parameter specs.
format_name (str) – Format the file is in, if not provided it will be inferred from the file extension.
**kwargs (Any) – Additional keyword arguments passes to the load_scheme implementation of the project io plugin.

Returns

Scheme instance created from the file.

Return type

Scheme

markdown()[source]

Format the Scheme as markdown string.

Returns: The scheme as markdown string.
Return type: MarkdownStr

maximum_number_function_evaluations: int | None = None

model: Model

property model_dimensions: dict[str, str]

Return the dataset model’s model dimension.

Returns: A dictionary with the dataset labels as key and the model dimension of the dataset as value.
Return type: dict[str, str]

non_negative_least_squares: bool | None = None

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.

Returns: A list of all problems found in the scheme’s model.
Return type: list[str]

result_path: str | None = None

source_path: StrOrPath = 'scheme.yml'

valid() → bool[source]

Check if there are no problems with the model or the parameters.

Returns: Whether the scheme is valid.
Return type: bool

validate() → str[source]

Return a string listing all problems in the model and missing parameters.

Returns: A user-friendly string containing all the problems of a model if any. Defaults to ‘Your model is valid.’ if no problems are found.
Return type: str

xtol: float = 1e-08