Scatter plot matrix

In this example, we illustrate the use of the ScatterPlotMatrix post-processing on the Sobieski's SSBJ problem.

The ScatterPlotMatrix post-processing provide the scatter plot matrix among design variables and outputs functions. Each non-diagonal block represents the samples according to the x- and y- coordinates names while the diagonal ones approximate the probability distributions of the variables, using a kernel-density estimator.

    INFO - 16:25:52: Importing the optimization problem from the file sobieski_mdf_scenario.h5

<gemseo.post.scatter_plot_matrix.ScatterPlotMatrix object at 0x72a4d5d87410>

from __future__ import annotations

from gemseo import execute_post
from gemseo.settings.post import ScatterPlotMatrix_Settings

execute_post(
    "sobieski_mdf_scenario.h5",
    settings_model=ScatterPlotMatrix_Settings(
        variable_names=["x_shared", "x_1", "x_2", "x_3", "-y_4"],
        save=False,
        show=True,
    ),
)