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 functions from the API and a method to get the design space.

from __future__ import annotations

from gemseo.api import configure_logger
from gemseo.api import create_discipline
from gemseo.api 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
from matplotlib import pyplot as plt

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 - 14:43:28:
    INFO - 14:43:28: *** Start MDOScenario execution ***
    INFO - 14:43:28: MDOScenario
    INFO - 14:43:28:    Disciplines: SobieskiAerodynamics SobieskiMission SobieskiPropulsion SobieskiStructure
    INFO - 14:43:28:    MDO formulation: MDF
    INFO - 14:43:28: Optimization problem:
    INFO - 14:43:28:    minimize -y_4(x_shared, x_1, x_2, x_3)
    INFO - 14:43:28:    with respect to x_1, x_2, x_3, x_shared
    INFO - 14:43:28:    subject to constraints:
    INFO - 14:43:28:       g_1(x_shared, x_1, x_2, x_3) <= 0.0
    INFO - 14:43:28:       g_2(x_shared, x_1, x_2, x_3) <= 0.0
    INFO - 14:43:28:       g_3(x_shared, x_1, x_2, x_3) <= 0.0
    INFO - 14:43:28:    over the design space:
    INFO - 14:43:28:    +-------------+-------------+-------+-------------+-------+
    INFO - 14:43:28:    | name        | lower_bound | value | upper_bound | type  |
    INFO - 14:43:28:    +-------------+-------------+-------+-------------+-------+
    INFO - 14:43:28:    | x_shared[0] |     0.01    |  0.05 |     0.09    | float |
    INFO - 14:43:28:    | x_shared[1] |    30000    | 45000 |    60000    | float |
    INFO - 14:43:28:    | x_shared[2] |     1.4     |  1.6  |     1.8     | float |
    INFO - 14:43:28:    | x_shared[3] |     2.5     |  5.5  |     8.5     | float |
    INFO - 14:43:28:    | x_shared[4] |      40     |   55  |      70     | float |
    INFO - 14:43:28:    | x_shared[5] |     500     |  1000 |     1500    | float |
    INFO - 14:43:28:    | x_1[0]      |     0.1     |  0.25 |     0.4     | float |
    INFO - 14:43:28:    | x_1[1]      |     0.75    |   1   |     1.25    | float |
    INFO - 14:43:28:    | x_2         |     0.75    |   1   |     1.25    | float |
    INFO - 14:43:28:    | x_3         |     0.1     |  0.5  |      1      | float |
    INFO - 14:43:28:    +-------------+-------------+-------+-------------+-------+
    INFO - 14:43:28: Solving optimization problem with algorithm SLSQP:
    INFO - 14:43:28: ...   0%|          | 0/10 [00:00<?, ?it]
    INFO - 14:43:28: ...  10%|█         | 1/10 [00:00<00:00, 44.42 it/sec, obj=-536]
    INFO - 14:43:29: ...  20%|██        | 2/10 [00:00<00:00, 18.25 it/sec, obj=-2.12e+3]
 WARNING - 14:43:29: MDAJacobi has reached its maximum number of iterations but the normed residual 1.4486313079508508e-06 is still above the tolerance 1e-06.
    INFO - 14:43:29: ...  30%|███       | 3/10 [00:00<00:00, 10.24 it/sec, obj=-3.75e+3]
    INFO - 14:43:30: ...  40%|████      | 4/10 [00:01<00:00,  7.55 it/sec, obj=-4.01e+3]
 WARNING - 14:43:30: MDAJacobi has reached its maximum number of iterations but the normed residual 2.928004141058104e-06 is still above the tolerance 1e-06.
    INFO - 14:43:30: ...  50%|█████     | 5/10 [00:01<00:00,  5.71 it/sec, obj=-4.49e+3]
    INFO - 14:43:30: ...  60%|██████    | 6/10 [00:02<00:00,  4.90 it/sec, obj=-3.4e+3]
    INFO - 14:43:30: ...  70%|███████   | 7/10 [00:02<00:00,  4.44 it/sec, obj=-4.93e+3]
    INFO - 14:43:31: ...  80%|████████  | 8/10 [00:02<00:00,  3.92 it/sec, obj=-4.76e+3]
    INFO - 14:43:31: ...  90%|█████████ | 9/10 [00:02<00:00,  3.55 it/sec, obj=-4.62e+3]
    INFO - 14:43:31: ... 100%|██████████| 10/10 [00:03<00:00,  3.27 it/sec, obj=-4.56e+3]
    INFO - 14:43:31: ... 100%|██████████| 10/10 [00:03<00:00,  3.27 it/sec, obj=-4.56e+3]
    INFO - 14:43:31: Optimization result:
    INFO - 14:43:31:    Optimizer info:
    INFO - 14:43:31:       Status: None
    INFO - 14:43:31:       Message: Maximum number of iterations reached. GEMSEO Stopped the driver
    INFO - 14:43:31:       Number of calls to the objective function by the optimizer: 12
    INFO - 14:43:31:    Solution:
    INFO - 14:43:31:       The solution is feasible.
    INFO - 14:43:31:       Objective: -3749.8868975554387
    INFO - 14:43:31:       Standardized constraints:
    INFO - 14:43:31:          g_1 = [-0.01671296 -0.03238836 -0.04350867 -0.05123129 -0.05681738 -0.13780658
    INFO - 14:43:31:  -0.10219342]
    INFO - 14:43:31:          g_2 = -0.0004062839430756249
    INFO - 14:43:31:          g_3 = [-0.66482546 -0.33517454 -0.11023156 -0.183255  ]
    INFO - 14:43:31:       Design space:
    INFO - 14:43:31:       +-------------+-------------+---------------------+-------------+-------+
    INFO - 14:43:31:       | name        | lower_bound |        value        | upper_bound | type  |
    INFO - 14:43:31:       +-------------+-------------+---------------------+-------------+-------+
    INFO - 14:43:31:       | x_shared[0] |     0.01    | 0.05989842901423112 |     0.09    | float |
    INFO - 14:43:31:       | x_shared[1] |    30000    |  59853.73840058666  |    60000    | float |
    INFO - 14:43:31:       | x_shared[2] |     1.4     |         1.4         |     1.8     | float |
    INFO - 14:43:31:       | x_shared[3] |     2.5     |  2.527371250092273  |     8.5     | float |
    INFO - 14:43:31:       | x_shared[4] |      40     |  69.86825198198687  |      70     | float |
    INFO - 14:43:31:       | x_shared[5] |     500     |  1495.734648986894  |     1500    | float |
    INFO - 14:43:31:       | x_1[0]      |     0.1     |         0.4         |     0.4     | float |
    INFO - 14:43:31:       | x_1[1]      |     0.75    |  0.7521124139939552 |     1.25    | float |
    INFO - 14:43:31:       | x_2         |     0.75    |  0.7520888531444992 |     1.25    | float |
    INFO - 14:43:31:       | x_3         |     0.1     |  0.1398000762238233 |      1      | float |
    INFO - 14:43:31:       +-------------+-------------+---------------------+-------------+-------+
    INFO - 14:43:31: *** End MDOScenario execution (time: 0:00:03.081333) ***

{'max_iter': 10, 'algo': 'SLSQP'}

Post-process scenario

Lastly, we plot the Gantt chart.

create_gantt_chart(show=False, save=False)
# Workaround for HTML rendering, instead of ``show=True``
plt.show()

# Finally, we deactivate the time stamps for other executions
MDODiscipline.deactivate_time_stamps()
plot gantt chart
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/stable/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 3.427 seconds)

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