Note
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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.discipline import MDODiscipline
from gemseo.post.core.gantt_chart import create_gantt_chart
from gemseo.problems.sobieski.core.problem import SobieskiProblem
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 read the design space from the SobieskiProblem.
design_space = SobieskiProblem().design_space
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,
formulation="MDF",
objective_name="y_4",
maximize_objective=True,
design_space=design_space,
)
for constraint in ["g_1", "g_2", "g_3"]:
scenario.add_constraint(constraint, "ineq")
Activate time stamps¶
In order to record all time stamps recording, we have to call this method before the execution of the scenarios
MDODiscipline.activate_time_stamps()
scenario.execute({"algo": "SLSQP", "max_iter": 10})
INFO - 08:37:12:
INFO - 08:37:12: *** Start MDOScenario execution ***
INFO - 08:37:12: MDOScenario
INFO - 08:37:12: Disciplines: SobieskiAerodynamics SobieskiMission SobieskiPropulsion SobieskiStructure
INFO - 08:37:12: MDO formulation: MDF
INFO - 08:37:12: Optimization problem:
INFO - 08:37:12: minimize -y_4(x_shared, x_1, x_2, x_3)
INFO - 08:37:12: with respect to x_1, x_2, x_3, x_shared
INFO - 08:37:12: subject to constraints:
INFO - 08:37:12: g_1(x_shared, x_1, x_2, x_3) <= 0.0
INFO - 08:37:12: g_2(x_shared, x_1, x_2, x_3) <= 0.0
INFO - 08:37:12: g_3(x_shared, x_1, x_2, x_3) <= 0.0
INFO - 08:37:12: over the design space:
INFO - 08:37:12: +-------------+-------------+-------+-------------+-------+
INFO - 08:37:12: | name | lower_bound | value | upper_bound | type |
INFO - 08:37:12: +-------------+-------------+-------+-------------+-------+
INFO - 08:37:12: | x_shared[0] | 0.01 | 0.05 | 0.09 | float |
INFO - 08:37:12: | x_shared[1] | 30000 | 45000 | 60000 | float |
INFO - 08:37:12: | x_shared[2] | 1.4 | 1.6 | 1.8 | float |
INFO - 08:37:12: | x_shared[3] | 2.5 | 5.5 | 8.5 | float |
INFO - 08:37:12: | x_shared[4] | 40 | 55 | 70 | float |
INFO - 08:37:12: | x_shared[5] | 500 | 1000 | 1500 | float |
INFO - 08:37:12: | x_1[0] | 0.1 | 0.25 | 0.4 | float |
INFO - 08:37:12: | x_1[1] | 0.75 | 1 | 1.25 | float |
INFO - 08:37:12: | x_2 | 0.75 | 1 | 1.25 | float |
INFO - 08:37:12: | x_3 | 0.1 | 0.5 | 1 | float |
INFO - 08:37:12: +-------------+-------------+-------+-------------+-------+
INFO - 08:37:12: Solving optimization problem with algorithm SLSQP:
INFO - 08:37:12: ... 0%| | 0/10 [00:00<?, ?it]
INFO - 08:37:13: ... 10%|█ | 1/10 [00:00<00:01, 5.08 it/sec, obj=-536]
INFO - 08:37:13: ... 20%|██ | 2/10 [00:00<00:02, 3.87 it/sec, obj=-2.12e+3]
WARNING - 08:37:13: MDAJacobi has reached its maximum number of iterations but the normed residual 1.100785719705894e-05 is still above the tolerance 1e-06.
INFO - 08:37:13: ... 30%|███ | 3/10 [00:00<00:02, 3.18 it/sec, obj=-3.75e+3]
INFO - 08:37:14: ... 40%|████ | 4/10 [00:01<00:01, 3.06 it/sec, obj=-3.96e+3]
INFO - 08:37:14: ... 50%|█████ | 5/10 [00:01<00:01, 2.99 it/sec, obj=-3.96e+3]
INFO - 08:37:14: ... 60%|██████ | 6/10 [00:02<00:01, 2.97 it/sec, obj=-4e+3]
INFO - 08:37:15: ... 70%|███████ | 7/10 [00:02<00:00, 3.11 it/sec, obj=-3.98e+3]
INFO - 08:37:15: ... 80%|████████ | 8/10 [00:02<00:00, 3.21 it/sec, obj=-3.97e+3]
INFO - 08:37:15: ... 90%|█████████ | 9/10 [00:02<00:00, 3.29 it/sec, obj=-3.97e+3]
INFO - 08:37:15: ... 100%|██████████| 10/10 [00:02<00:00, 3.36 it/sec, obj=-3.96e+3]
INFO - 08:37:15: Optimization result:
INFO - 08:37:15: Optimizer info:
INFO - 08:37:15: Status: None
INFO - 08:37:15: Message: Maximum number of iterations reached. GEMSEO Stopped the driver
INFO - 08:37:15: Number of calls to the objective function by the optimizer: 12
INFO - 08:37:15: Solution:
INFO - 08:37:15: The solution is feasible.
INFO - 08:37:15: Objective: -3963.408265187933
INFO - 08:37:15: Standardized constraints:
INFO - 08:37:15: g_1 = [-0.01806104 -0.03334642 -0.04424946 -0.0518346 -0.05732607 -0.13720865
INFO - 08:37:15: -0.10279135]
INFO - 08:37:15: g_2 = 3.333278582928756e-06
INFO - 08:37:15: g_3 = [-7.67181773e-01 -2.32818227e-01 8.30379541e-07 -1.83255000e-01]
INFO - 08:37:15: Design space:
INFO - 08:37:15: +-------------+-------------+---------------------+-------------+-------+
INFO - 08:37:15: | name | lower_bound | value | upper_bound | type |
INFO - 08:37:15: +-------------+-------------+---------------------+-------------+-------+
INFO - 08:37:15: | x_shared[0] | 0.01 | 0.06000083331964572 | 0.09 | float |
INFO - 08:37:15: | x_shared[1] | 30000 | 60000 | 60000 | float |
INFO - 08:37:15: | x_shared[2] | 1.4 | 1.4 | 1.8 | float |
INFO - 08:37:15: | x_shared[3] | 2.5 | 2.5 | 8.5 | float |
INFO - 08:37:15: | x_shared[4] | 40 | 70 | 70 | float |
INFO - 08:37:15: | x_shared[5] | 500 | 1500 | 1500 | float |
INFO - 08:37:15: | x_1[0] | 0.1 | 0.4 | 0.4 | float |
INFO - 08:37:15: | x_1[1] | 0.75 | 0.75 | 1.25 | float |
INFO - 08:37:15: | x_2 | 0.75 | 0.75 | 1.25 | float |
INFO - 08:37:15: | x_3 | 0.1 | 0.1562448753887276 | 1 | float |
INFO - 08:37:15: +-------------+-------------+---------------------+-------------+-------+
INFO - 08:37:15: *** End MDOScenario execution (time: 0:00:02.991458) ***
{'max_iter': 10, 'algo': 'SLSQP'}
Post-process scenario¶
Lastly, we plot the Gantt chart.
create_gantt_chart(save=False, show=True)
# Finally, we deactivate the time stamps for other executions
MDODiscipline.deactivate_time_stamps()
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.0.1/lib/python3.9/site-packages/gemseo/post/core/gantt_chart.py:87: UserWarning: FixedFormatter should only be used together with FixedLocator
ax.set_yticklabels(disc_names)
Total running time of the script: ( 0 minutes 3.306 seconds)