sbmlsim.fit.result

Result of optimization.

Module Contents

Classes

OptimizationResult

Result of optimization problem.

Attributes

logger

sbmlsim.fit.result.logger[source]
class sbmlsim.fit.result.OptimizationResult(parameters, fits, trajectories, sid=None)[source]

Bases: sbmlsim.serialization.ObjectJSONEncoder

Result of optimization problem.

Parameters
to_tsv(path)[source]

Store fit results as TSV.

Parameters

path (pathlib.Path) –

to_dict()[source]

Convert to dictionary.

to_json(path=None)[source]

Store OptimizationResult as json.

Uses the to_dict method.

Parameters

path (Optional[pathlib.Path]) –

Return type

Union[str, pathlib.Path]

static from_json(json_info)[source]

Load OptimizationResult from Path or str.

Parameters

json_info (Union[str, pathlib.Path]) –

Returns

Return type

OptimizationResult

__str__()[source]

Get string representation.

Return type

str

static combine(opt_results)[source]

Combine results from multiple parameter fitting experiments.

Parameters

opt_results (List[OptimizationResult]) –

Return type

OptimizationResult

property size[source]

Get number of optimization runs in result.

Return type

int

property xopt[source]

Numerical values of optimal parameters.

Return type

numpy.ndarray

property xopt_fit_parameters[source]

Optimal parameters as Fit parameters.

Return type

List[sbmlsim.fit.objects.FitParameter]

_x_as_fit_parameters(x)[source]

Convert numerical parameter vector to fit parameters.

Return type

List[sbmlsim.fit.objects.FitParameter]

static process_traces(parameters, trajectories)[source]

Process the optimization results.

Parameters

parameters (List[sbmlsim.fit.objects.FitParameter]) –

static process_fits(parameters, fits)[source]

Process the optimization results.

Parameters
report(path=None, print_output=True)[source]

Report of optimization.

Parameters
  • path (Optional[pathlib.Path]) –

  • print_output (bool) –

Return type

str