How to deal with post-processing

In this section we describe the post processing features of GEMSEO, used to analyze OptimizationResult, called the optimization history.

What data to post-process?

Post-processing features are applicable to any OptimizationProblem that has been solved, which may have been loaded from the disk.

In practice,

Illustration on the Sobieski use case

The post-processing features are illustrated on MDO results obtained on the SSBJ use case, using different types of formulation (MDF formulation, IDF formulation, …)

To setup the case, please see Application: Sobieski’s Super-Sonic Business Jet (MDO).

The code lines follow, modulo the formulation value:

from gemseo.api import create_discipline, create_scenario

formulation = 'MDF'

disciplines = create_discipline(["SobieskiPropulsion", "SobieskiAerodynamics",
                                 "SobieskiMission", "SobieskiStructure"])

scenario = create_scenario(disciplines,


algo_options = {'max_iter': 10, 'algo': "SLSQP"}
for constraint in ["g_1","g_2","g_3"]:
    scenario.add_constraint(constraint, 'ineq')


How to apply a post-process feature?

From this scenario, we can apply any kind of post-processing dedicated to Scenario instances,

  • either by means of its post_process() method:

    Scenario.post_process(post_name, **options)[source]

    Finds the appropriate library and executes the post processing on the problem.

    • post_name – the post processing name

    • options – options for the post method, see its package

  • or by means of the execute_post() API method:

    api.execute_post(post_name, **options)

    Execute a post-processing method.

    • to_post_proc (MDOScenario, DOEScenario, OptimizationProblem, or str) – MDO or DOE scenario, or an optimization problem, or a path to a HDF file containing a saved OptimizationProblem.

    • post_name (str) – Post processing name.

    • options – Post-processing options.


    >>> from gemseo.api import create_discipline, create_scenario, execute_post
    >>> from gemseo.problems.sellar.sellar_design_space import SellarDesignSpace
    >>> disciplines = create_discipline(['Sellar1', 'Sellar2', 'SellarSystem'])
    >>> design_space = SellarDesignSpace()
    >>> scenario = create_scenario(disciplines, 'MDF', 'obj', design_space,
    >>>                            'SellarMDFScenario', 'MDO')
    >>> scenario.execute({'algo': 'NLOPT_SLSQP', 'max_iter': 100})
    >>> execute_post(scenario, 'OptHistoryView', show=False, save=True)

What are the post-processing features?

Basic history

Constraints history

Objective and constraints history

Gradient sensitivity

Optimization history view

Parallel coordinates

Quadratic approximation

Radar chart



Self-Organizing Maps

Scatter plot matrix

Variable influence