to test doe , added the "doe_test_example.py" script, which returns a doe object that uses the "reactor_experiment" example in doe directory. Also added the png files to gitignore that are stored when "reactor_example.py" is run.

Author: smondal13

.gitignore

@@ -34,3 +34,9 @@ cplex.log
 
 # Mac tracking files
 *.DS_Store*
+
+# DOE example .png files
+example_reactor_compute_FIM_E_opt.png
+example_reactor_compute_FIM_A_opt.png
+example_reactor_compute_FIM_D_opt.png
+example_reactor_compute_FIM_ME_opt.png

pyomo/contrib/doe/tests/doe_test_example.py

@@ -0,0 +1,150 @@
+#  ___________________________________________________________________________
+#
+#  Pyomo: Python Optimization Modeling Objects
+#  Copyright (c) 2008-2025
+#  National Technology and Engineering Solutions of Sandia, LLC
+#  Under the terms of Contract DE-NA0003525 with National Technology and
+#  Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
+#  rights in this software.
+#  This software is distributed under the 3-clause BSD License.
+#  ___________________________________________________________________________
+from pyomo.common.dependencies import numpy as np
+
+from pyomo.contrib.doe.examples.reactor_experiment import ReactorExperiment
+from pyomo.contrib.doe import DesignOfExperiments
+
+import pyomo.environ as pyo
+
+import json
+from pathlib import Path
+import idaes.core.solvers.get_solver  # import the idaes solvers
+
+
+# Example for sensitivity analysis on the reactor experiment
+# After sensitivity analysis is done, we perform optimal DoE
+def run_reactor_doe(
+    run_doe=False,
+    compute_FIM_full_factorial=False,
+    draw_factorial_figure=False,
+    save_draw_factorial_figure=False,
+):
+    # Read in file
+    DATA_DIR = Path(__file__).parent.parent / "examples"
+    file_path = DATA_DIR / "result.json"
+
+    with open(file_path) as f:
+        data_ex = json.load(f)
+
+    # Put temperature control time points into correct format for reactor experiment
+    data_ex["control_points"] = {
+        float(k): v for k, v in data_ex["control_points"].items()
+    }
+
+    # Create a ReactorExperiment object; data and discretization information are part
+    # of the constructor of this object
+    experiment = ReactorExperiment(data=data_ex, nfe=10, ncp=3)
+
+    # Use a central difference, with step size 1e-3
+    fd_formula = "central"
+    step_size = 1e-3
+
+    # Use the determinant objective with scaled sensitivity matrix
+    objective_option = "determinant"
+    scale_nominal_param_value = True
+
+    # Create the DesignOfExperiments object
+    # We will not be passing any prior information in this example
+    # and allow the experiment object and the DesignOfExperiments
+    # call of ``run_doe`` perform model initialization.
+    doe_obj = DesignOfExperiments(
+        experiment,
+        fd_formula=fd_formula,
+        step=step_size,
+        objective_option=objective_option,
+        scale_constant_value=1,
+        scale_nominal_param_value=scale_nominal_param_value,
+        prior_FIM=None,
+        jac_initial=None,
+        fim_initial=None,
+        L_diagonal_lower_bound=1e-7,
+        solver=None,
+        tee=False,
+        get_labeled_model_args=None,
+        _Cholesky_option=True,
+        _only_compute_fim_lower=True,
+    )
+
+    # Make design ranges to compute the full factorial design
+    design_ranges = {"CA[0]": [1, 5, 9], "T[0]": [300, 700, 9]}
+
+    # Compute the full factorial design with the sequential FIM calculation
+    if compute_FIM_full_factorial:
+        doe_obj.compute_FIM_full_factorial(
+            design_ranges=design_ranges, method="sequential"
+        )
+
+    # Plot the results
+    if draw_factorial_figure:
+        if save_draw_factorial_figure:
+            figure_file_name = "example_reactor_compute_FIM"
+        else:
+            figure_file_name = None
+
+        doe_obj.draw_factorial_figure(
+            sensitivity_design_variables=["CA[0]", "T[0]"],
+            fixed_design_variables={
+                "T[0.125]": 300,
+                "T[0.25]": 300,
+                "T[0.375]": 300,
+                "T[0.5]": 300,
+                "T[0.625]": 300,
+                "T[0.75]": 300,
+                "T[0.875]": 300,
+                "T[1]": 300,
+            },
+            title_text="Reactor Example",
+            xlabel_text="Concentration of A (M)",
+            ylabel_text="Initial Temperature (K)",
+            figure_file_name=figure_file_name,
+            log_scale=False,
+        )
+
+    ###########################
+    # End sensitivity analysis
+
+    # Begin optimal DoE
+    ####################
+    if run_doe:
+        doe_obj.run_doe()
+
+        # Print out a results summary
+        print("Optimal experiment values: ")
+        print(
+            "\tInitial concentration: {:.2f}".format(
+                doe_obj.results["Experiment Design"][0]
+            )
+        )
+        print(
+            ("\tTemperature values: [" + "{:.2f}, " * 8 + "{:.2f}]").format(
+                *doe_obj.results["Experiment Design"][1:]
+            )
+        )
+        print("FIM at optimal design:\n {}".format(np.array(doe_obj.results["FIM"])))
+        print(
+            "Objective value at optimal design: {:.2f}".format(
+                pyo.value(doe_obj.model.objective)
+            )
+        )
+
+        print(doe_obj.results["Experiment Design Names"])
+
+        ###################
+        # End optimal DoE
+
+    # retrun the doe object for further use
+    return doe_obj
+
+
+# uncomment the following line to run the example
+# if __name__ == "__main__":
+#     run_reactor_doe(run_doe=True)

pyomo/contrib/doe/tests/test_doe_FIM_metrics.py

@@ -7,12 +7,21 @@
 
 import pyomo.common.unittest as unittest
 from pyomo.contrib.doe import DesignOfExperiments
-from pyomo.contrib.doe.doe import compute_FIM_full_factorial, _SMALL_TOLERANCE_IMG
+from pyomo.contrib.doe.tests import doe_test_example
+from pyomo.contrib.doe.doe import _SMALL_TOLERANCE_IMG
 from pyomo.opt import SolverFactory
+import pyomo.environ as pyo
+
+import json
+from pathlib import Path
+import idaes
+
+ipopt_available = SolverFactory("ipopt").available()
+k_aug_available = SolverFactory("k_aug", solver_io="nl", validate=False)
 
 
 @unittest.skipIf(not numpy_available, "Numpy is not available")
-class TestFullFactrialMetrics(unittest.TestCase):
+class TestFullFactorialMetrics(unittest.TestCase):
     def test_compute_FIM_full_factorial_metrics(self):
         # Create a sample Fisher Information Matrix (FIM)
         FIM = np.array([[4, 2], [2, 3]])