# Scatter plot matrix¶

In this example, we illustrate the use of the ScatterPlotMatrix plot on the Sobieski’s SSBJ problem.

from __future__ import annotations

from gemseo.api import configure_logger
from gemseo.api import create_discipline
from gemseo.api import create_scenario
from gemseo.problems.sobieski.core.problem import SobieskiProblem


## Import¶

The first step is to import some functions from the API and a method to get the design space.

configure_logger()

<RootLogger root (INFO)>


## Description¶

The ScatterPlotMatrix post-processing builds 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.

## Create disciplines¶

At this point, we instantiate the disciplines of Sobieski’s SSBJ problem: Propulsion, Aerodynamics, Structure and Mission

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


## Create design space¶

We also read the design space from the SobieskiProblem.

design_space = SobieskiProblem().design_space


## Create and execute scenario¶

The next step is to build a DOE scenario in order to maximize the range, encoded ‘y_4’, with respect to the design parameters, while satisfying the inequality constraints ‘g_1’, ‘g_2’ and ‘g_3’. We can use the MDF formulation, the Monte Carlo DOE algorithm and 30 samples.

scenario = create_scenario(
disciplines,
formulation="MDF",
objective_name="y_4",
maximize_objective=True,
design_space=design_space,
scenario_type="DOE",
)
scenario.set_differentiation_method()
for constraint in ["g_1", "g_2", "g_3"]:
scenario.execute({"algo": "OT_MONTE_CARLO", "n_samples": 30})

    INFO - 20:57:40:
INFO - 20:57:40: *** Start DOEScenario execution ***
INFO - 20:57:40: DOEScenario
INFO - 20:57:40:    Disciplines: SobieskiAerodynamics SobieskiMission SobieskiPropulsion SobieskiStructure
INFO - 20:57:40:    MDO formulation: MDF
INFO - 20:57:40: Optimization problem:
INFO - 20:57:40:    minimize -y_4(x_shared, x_1, x_2, x_3)
INFO - 20:57:40:    with respect to x_1, x_2, x_3, x_shared
INFO - 20:57:40:    subject to constraints:
INFO - 20:57:40:       g_1(x_shared, x_1, x_2, x_3) <= 0.0
INFO - 20:57:40:       g_2(x_shared, x_1, x_2, x_3) <= 0.0
INFO - 20:57:40:       g_3(x_shared, x_1, x_2, x_3) <= 0.0
INFO - 20:57:40:    over the design space:
INFO - 20:57:40:    +-------------+-------------+-------+-------------+-------+
INFO - 20:57:40:    | name        | lower_bound | value | upper_bound | type  |
INFO - 20:57:40:    +-------------+-------------+-------+-------------+-------+
INFO - 20:57:40:    | x_shared[0] |     0.01    |  0.05 |     0.09    | float |
INFO - 20:57:40:    | x_shared[1] |    30000    | 45000 |    60000    | float |
INFO - 20:57:40:    | x_shared[2] |     1.4     |  1.6  |     1.8     | float |
INFO - 20:57:40:    | x_shared[3] |     2.5     |  5.5  |     8.5     | float |
INFO - 20:57:40:    | x_shared[4] |      40     |   55  |      70     | float |
INFO - 20:57:40:    | x_shared[5] |     500     |  1000 |     1500    | float |
INFO - 20:57:40:    | x_1[0]      |     0.1     |  0.25 |     0.4     | float |
INFO - 20:57:40:    | x_1[1]      |     0.75    |   1   |     1.25    | float |
INFO - 20:57:40:    | x_2         |     0.75    |   1   |     1.25    | float |
INFO - 20:57:40:    | x_3         |     0.1     |  0.5  |      1      | float |
INFO - 20:57:40:    +-------------+-------------+-------+-------------+-------+
INFO - 20:57:40: Solving optimization problem with algorithm OT_MONTE_CARLO:
INFO - 20:57:40: ...   0%|          | 0/30 [00:00<?, ?it]
INFO - 20:57:40: ...   3%|▎         | 1/30 [00:00<00:00, 269.81 it/sec, obj=-166]
INFO - 20:57:40: ...  13%|█▎        | 4/30 [00:00<00:00, 127.45 it/sec, obj=-384]
INFO - 20:57:40: ...  23%|██▎       | 7/30 [00:00<00:00, 84.12 it/sec, obj=-630]
INFO - 20:57:40: ...  33%|███▎      | 10/30 [00:00<00:00, 60.55 it/sec, obj=-621]
INFO - 20:57:41: ...  43%|████▎     | 13/30 [00:00<00:00, 44.60 it/sec, obj=-257]
INFO - 20:57:41: ...  50%|█████     | 15/30 [00:00<00:00, 37.94 it/sec, obj=-1.08e+3]
INFO - 20:57:41: ...  57%|█████▋    | 17/30 [00:00<00:00, 33.52 it/sec, obj=-368]
INFO - 20:57:41: ...  63%|██████▎   | 19/30 [00:01<00:00, 29.73 it/sec, obj=-129]
INFO - 20:57:41: ...  73%|███████▎  | 22/30 [00:01<00:00, 26.19 it/sec, obj=-1e+3]
INFO - 20:57:41: ...  80%|████████  | 24/30 [00:01<00:00, 23.91 it/sec, obj=-483]
INFO - 20:57:41: ...  90%|█████████ | 27/30 [00:01<00:00, 21.41 it/sec, obj=-207]
INFO - 20:57:41: ... 100%|██████████| 30/30 [00:01<00:00, 19.71 it/sec, obj=-664]
INFO - 20:57:41: ... 100%|██████████| 30/30 [00:01<00:00, 19.68 it/sec, obj=-664]
INFO - 20:57:41: Optimization result:
INFO - 20:57:41:    Optimizer info:
INFO - 20:57:41:       Status: None
INFO - 20:57:41:       Message: None
INFO - 20:57:41:       Number of calls to the objective function by the optimizer: 30
INFO - 20:57:41:    Solution:
INFO - 20:57:41:       The solution is feasible.
INFO - 20:57:41:       Objective: -367.45739115001027
INFO - 20:57:41:       Standardized constraints:
INFO - 20:57:41:          g_1 = [-0.02478574 -0.00310924 -0.00855146 -0.01702654 -0.02484732 -0.04764585
INFO - 20:57:41:  -0.19235415]
INFO - 20:57:41:          g_2 = -0.09000000000000008
INFO - 20:57:41:          g_3 = [-0.98722984 -0.01277016 -0.60760341 -0.0557087 ]
INFO - 20:57:41:       Design space:
INFO - 20:57:41:       +-------------+-------------+---------------------+-------------+-------+
INFO - 20:57:41:       | name        | lower_bound |        value        | upper_bound | type  |
INFO - 20:57:41:       +-------------+-------------+---------------------+-------------+-------+
INFO - 20:57:41:       | x_shared[0] |     0.01    | 0.01230934749207792 |     0.09    | float |
INFO - 20:57:41:       | x_shared[1] |    30000    |  43456.87364611478  |    60000    | float |
INFO - 20:57:41:       | x_shared[2] |     1.4     |  1.731884935123487  |     1.8     | float |
INFO - 20:57:41:       | x_shared[3] |     2.5     |  3.894765253193514  |     8.5     | float |
INFO - 20:57:41:       | x_shared[4] |      40     |  57.92631048228255  |      70     | float |
INFO - 20:57:41:       | x_shared[5] |     500     |  520.4048463450415  |     1500    | float |
INFO - 20:57:41:       | x_1[0]      |     0.1     |  0.3994784918586811 |     0.4     | float |
INFO - 20:57:41:       | x_1[1]      |     0.75    |  0.9500312867674923 |     1.25    | float |
INFO - 20:57:41:       | x_2         |     0.75    |  1.205851870260564  |     1.25    | float |
INFO - 20:57:41:       | x_3         |     0.1     |  0.2108042391973412 |      1      | float |
INFO - 20:57:41:       +-------------+-------------+---------------------+-------------+-------+
INFO - 20:57:41: *** End DOEScenario execution (time: 0:00:01.540849) ***

{'eval_jac': False, 'algo': 'OT_MONTE_CARLO', 'n_samples': 30}


## Post-process scenario¶

Lastly, we post-process the scenario by means of the ScatterPlotMatrix plot which builds scatter plot matrix among design variables, objective function and constraints.

Tip

Each post-processing method requires different inputs and offers a variety of customization options. Use the API function get_post_processing_options_schema() to print a table with the options for any post-processing algorithm. Or refer to our dedicated page: Post-processing algorithms.

design_variables = ["x_shared", "x_1", "x_2", "x_3"]
scenario.post_process(
"ScatterPlotMatrix",
variable_names=design_variables + ["-y_4"],
save=False,
show=True,
)

<gemseo.post.scatter_mat.ScatterPlotMatrix object at 0x7f31a6ff0790>


Total running time of the script: ( 0 minutes 6.339 seconds)

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