Note
Go to the end to download the full example code.
Gantt Chart#
In this example, we illustrate the use of the Gantt chart plot on the Sobieski's SSBJ problem.
Import#
The first step is to import some high-level functions and a method to get the design space.
from __future__ import annotations
from gemseo import configure_logger
from gemseo import create_discipline
from gemseo import create_scenario
from gemseo.core.execution_statistics import ExecutionStatistics
from gemseo.post.core.gantt_chart import create_gantt_chart
from gemseo.problems.mdo.sobieski.core.design_space import SobieskiDesignSpace
configure_logger()
<RootLogger root (INFO)>
Create disciplines#
Then, we instantiate the disciplines of the Sobieski's SSBJ problem: Propulsion, Aerodynamics, Structure and Mission
disciplines = create_discipline([
"SobieskiPropulsion",
"SobieskiAerodynamics",
"SobieskiStructure",
"SobieskiMission",
])
Create design space#
We also create the SobieskiDesignSpace.
design_space = SobieskiDesignSpace()
Create and execute scenario#
The next step is to build an MDO 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 SLSQP optimization algorithm and a maximum number of iterations equal to 100.
scenario = create_scenario(
disciplines,
"y_4",
design_space,
formulation_name="MDF",
maximize_objective=True,
)
WARNING - 08:35:55: Unsupported feature 'minItems' in JSONGrammar 'SobieskiMission_discipline_output' for property 'y_4' in conversion to SimpleGrammar.
WARNING - 08:35:55: Unsupported feature 'maxItems' in JSONGrammar 'SobieskiMission_discipline_output' for property 'y_4' in conversion to SimpleGrammar.
Note that the formulation settings passed to create_scenario() can be provided
via a Pydantic model. For more information, see Formulation Settings.
for constraint in ["g_1", "g_2", "g_3"]:
scenario.add_constraint(constraint, constraint_type="ineq")
Enable time stamps#
Recording all time stamps is done by default; we have to enable it:
ExecutionStatistics.is_time_stamps_enabled = True
scenario.execute(algo_name="SLSQP", max_iter=10)
INFO - 08:35:55:
INFO - 08:35:55: *** Start MDOScenario execution ***
INFO - 08:35:55: MDOScenario
INFO - 08:35:55: Disciplines: SobieskiAerodynamics SobieskiMission SobieskiPropulsion SobieskiStructure
INFO - 08:35:55: MDO formulation: MDF
INFO - 08:35:55: Optimization problem:
INFO - 08:35:55: minimize -y_4(x_shared, x_1, x_2, x_3)
INFO - 08:35:55: with respect to x_1, x_2, x_3, x_shared
INFO - 08:35:55: subject to constraints:
INFO - 08:35:55: g_1(x_shared, x_1, x_2, x_3) <= 0
INFO - 08:35:55: g_2(x_shared, x_1, x_2, x_3) <= 0
INFO - 08:35:55: g_3(x_shared, x_1, x_2, x_3) <= 0
INFO - 08:35:55: over the design space:
INFO - 08:35:55: +-------------+-------------+-------+-------------+-------+
INFO - 08:35:55: | Name | Lower bound | Value | Upper bound | Type |
INFO - 08:35:55: +-------------+-------------+-------+-------------+-------+
INFO - 08:35:55: | x_shared[0] | 0.01 | 0.05 | 0.09 | float |
INFO - 08:35:55: | x_shared[1] | 30000 | 45000 | 60000 | float |
INFO - 08:35:55: | x_shared[2] | 1.4 | 1.6 | 1.8 | float |
INFO - 08:35:55: | x_shared[3] | 2.5 | 5.5 | 8.5 | float |
INFO - 08:35:55: | x_shared[4] | 40 | 55 | 70 | float |
INFO - 08:35:55: | x_shared[5] | 500 | 1000 | 1500 | float |
INFO - 08:35:55: | x_1[0] | 0.1 | 0.25 | 0.4 | float |
INFO - 08:35:55: | x_1[1] | 0.75 | 1 | 1.25 | float |
INFO - 08:35:55: | x_2 | 0.75 | 1 | 1.25 | float |
INFO - 08:35:55: | x_3 | 0.1 | 0.5 | 1 | float |
INFO - 08:35:55: +-------------+-------------+-------+-------------+-------+
INFO - 08:35:55: Solving optimization problem with algorithm SLSQP:
INFO - 08:35:55: 10%|█ | 1/10 [00:00<00:01, 6.69 it/sec, obj=-536]
INFO - 08:35:56: 20%|██ | 2/10 [00:00<00:01, 5.24 it/sec, obj=-2.12e+3]
WARNING - 08:35:56: MDAJacobi has reached its maximum number of iterations but the normed residual 5.741449586530469e-06 is still above the tolerance 1e-06.
INFO - 08:35:56: 30%|███ | 3/10 [00:00<00:01, 4.15 it/sec, obj=-3.46e+3]
INFO - 08:35:56: 40%|████ | 4/10 [00:01<00:01, 3.94 it/sec, obj=-3.96e+3]
INFO - 08:35:56: 50%|█████ | 5/10 [00:01<00:01, 4.06 it/sec, obj=-4.61e+3]
INFO - 08:35:57: 60%|██████ | 6/10 [00:01<00:00, 4.23 it/sec, obj=-4.5e+3]
INFO - 08:35:57: 70%|███████ | 7/10 [00:01<00:00, 4.29 it/sec, obj=-4.26e+3]
INFO - 08:35:57: 80%|████████ | 8/10 [00:01<00:00, 4.34 it/sec, obj=-4.11e+3]
INFO - 08:35:57: 90%|█████████ | 9/10 [00:02<00:00, 4.37 it/sec, obj=-4.02e+3]
INFO - 08:35:58: 100%|██████████| 10/10 [00:02<00:00, 4.40 it/sec, obj=-3.99e+3]
INFO - 08:35:58: Optimization result:
INFO - 08:35:58: Optimizer info:
INFO - 08:35:58: Status: None
INFO - 08:35:58: Message: Maximum number of iterations reached. GEMSEO stopped the driver.
INFO - 08:35:58: Number of calls to the objective function by the optimizer: 12
INFO - 08:35:58: Solution:
INFO - 08:35:58: The solution is feasible.
INFO - 08:35:58: Objective: -3463.120411437138
INFO - 08:35:58: Standardized constraints:
INFO - 08:35:58: g_1 = [-0.01112145 -0.02847064 -0.04049911 -0.04878943 -0.05476349 -0.14014207
INFO - 08:35:58: -0.09985793]
INFO - 08:35:58: g_2 = -0.0020925663903177405
INFO - 08:35:58: g_3 = [-0.71359843 -0.28640157 -0.05926796 -0.183255 ]
INFO - 08:35:58: Design space:
INFO - 08:35:58: +-------------+-------------+---------------------+-------------+-------+
INFO - 08:35:58: | Name | Lower bound | Value | Upper bound | Type |
INFO - 08:35:58: +-------------+-------------+---------------------+-------------+-------+
INFO - 08:35:58: | x_shared[0] | 0.01 | 0.05947685840242058 | 0.09 | float |
INFO - 08:35:58: | x_shared[1] | 30000 | 59246.692998739 | 60000 | float |
INFO - 08:35:58: | x_shared[2] | 1.4 | 1.4 | 1.8 | float |
INFO - 08:35:58: | x_shared[3] | 2.5 | 2.64097355362077 | 8.5 | float |
INFO - 08:35:58: | x_shared[4] | 40 | 69.32144380869019 | 70 | float |
INFO - 08:35:58: | x_shared[5] | 500 | 1478.031626737187 | 1500 | float |
INFO - 08:35:58: | x_1[0] | 0.1 | 0.4 | 0.4 | float |
INFO - 08:35:58: | x_1[1] | 0.75 | 0.7608797907508461 | 1.25 | float |
INFO - 08:35:58: | x_2 | 0.75 | 0.7607584987262048 | 1.25 | float |
INFO - 08:35:58: | x_3 | 0.1 | 0.1514057659459843 | 1 | float |
INFO - 08:35:58: +-------------+-------------+---------------------+-------------+-------+
INFO - 08:35:58: *** End MDOScenario execution (time: 0:00:02.287542) ***
Note that the algorithm settings passed to DriverLibrary.execute() can be provided
via a Pydantic model. For more information, see Algorithm Settings.
Post-process scenario#
Lastly, we plot the Gantt chart.
create_gantt_chart(save=False, show=True)
# Finally, we disable the recording of time stamps for other executions:
ExecutionStatistics.is_time_stamps_enabled = False
Total running time of the script: (0 minutes 2.568 seconds)