Apply Black formatting to DoE files

Author: Shilpa Narasimhan

pyomo/contrib/doe/doe.py

@@ -2971,7 +2971,9 @@ def initialize_fim_inv(m, j, d):
                                 model.L_inv[c, d].setlb(self.L_diagonal_lower_bound)
 
         if self._gradient_method == GradientMethod.pynumero:
-            experiment_grad.construct_sensitivity_constraints(model.fd_scenario_blocks[0])
+            experiment_grad.construct_sensitivity_constraints(
+                model.fd_scenario_blocks[0]
+            )
 
         def jacobian_rule(m, n, p):
             """
@@ -2987,7 +2989,9 @@ def jacobian_rule(m, n, p):
                 output_var = output_cuid.find_component_on(m.fd_scenario_blocks[0])
 
                 parameter_cuid = pyo.ComponentUID(p)
-                parameter_var = parameter_cuid.find_component_on(m.fd_scenario_blocks[0])
+                parameter_var = parameter_cuid.find_component_on(
+                    m.fd_scenario_blocks[0]
+                )
 
                 i = experiment_grad.measurement_mapping[output_var]
                 j = experiment_grad.parameter_mapping[parameter_var]

pyomo/contrib/doe/tests/test_doe_solve.py

@@ -767,12 +767,7 @@ def test_rescale_FIM(self):
 
         # Get rescaled FIM from the scaled version
         param_vals = np.array(
-            [
-                [
-                    v
-                    for k, v in doe_obj.compute_FIM_model.unknown_parameters.items()
-                ]
-            ]
+            [[v for k, v in doe_obj.compute_FIM_model.unknown_parameters.items()]]
         )
 
         resc_FIM = rescale_FIM(FIM, param_vals)

pyomo/contrib/doe/tests/test_greybox.py

@@ -47,12 +47,7 @@
 if numpy_available:
     # Randomly generated P.S.D. matrix
     # Matrix is 2x2 to match Rooney-Biegler parameters.
-    testing_matrix = np.array(
-        [
-            [5.13730123, 1.08084953],
-            [1.08084953, 1.57183404],
-        ]
-    )
+    testing_matrix = np.array([[5.13730123, 1.08084953], [1.08084953, 1.57183404]])
 
     masking_matrix = np.triu(np.ones_like(testing_matrix))
 

pyomo/contrib/doe/tests/test_utils.py

@@ -25,6 +25,7 @@
     _SMALL_TOLERANCE_SYMMETRY,
     _SMALL_TOLERANCE_IMG,
 )
+
 if not (numpy_available and scipy_available):
     raise unittest.SkipTest("Pyomo.DoE needs scipy and numpy to run tests")
 
@@ -172,6 +173,7 @@ def test_get_FIM_metrics(self):
             fim_metrics["log10(Modified E-Optimality)"], expected['ME_opt']
         )
 
+
 @unittest.skipIf(not ipopt_available, "The 'ipopt' command is not available")
 @unittest.skipIf(not numpy_available, "Numpy is not available")
 class TestExperimentGradients(unittest.TestCase):
@@ -278,16 +280,15 @@ def test_polynomial_automatic_only_still_sets_both_jacobians(self):
             model, symbolic=False, automatic=True
         )
 
-        jacobian =(
+        jacobian = (
             experiment_gradients.compute_gradient_outputs_wrt_unknown_parameters()
         )
         expected = self._get_expected_polynomial_gradient()
 
         self.assertIsNotNone(experiment_gradients.jac_dict_sd)
         self.assertIsNotNone(experiment_gradients.jac_dict_ad)
         self.assertEqual(jacobian.shape, expected.shape)
-        self.assertTrue(np.allclose(jacobian,expected))
-
+        self.assertTrue(np.allclose(jacobian, expected))
 
     def test_polynomial_symbolic_only_still_sets_both_jacobians(self):
         """Check that symbolic-only requests still initialize both Jacobian maps."""
@@ -298,7 +299,7 @@ def test_polynomial_symbolic_only_still_sets_both_jacobians(self):
             model, symbolic=True, automatic=False
         )
 
-        jacobian =(
+        jacobian = (
             experiment_gradients.compute_gradient_outputs_wrt_unknown_parameters()
         )
         expected = self._get_expected_polynomial_gradient()
@@ -343,19 +344,20 @@ def test_rooney_biegler_gradients_match_closed_form(self):
         self.assertEqual(jacobian.shape, expected.shape)
         self.assertTrue(np.allclose(jacobian, expected, atol=1e-7, rtol=1e-7))
 
-    
-
     @unittest.skipIf(not scipy_available, "scipy is not available")
     @unittest.skipIf(not ipopt_available, "The 'ipopt' command is not available")
-    def test_rooney_biegler_symbolic_and_automatic_jacobians_agree_at_perturbed_point(self):
+    def test_rooney_biegler_symbolic_and_automatic_jacobians_agree_at_perturbed_point(
+        self,
+    ):
         """Check Rooney-Biegler Jacobian agreement at a perturbed operating point."""
-        experiment = self._get_rooney_biegler_experiment(hour=7.0, y=19.8, asymptote=14.0, rate_constant=0.4)
+        experiment = self._get_rooney_biegler_experiment(
+            hour=7.0, y=19.8, asymptote=14.0, rate_constant=0.4
+        )
         model = experiment.get_labeled_model()
 
         experiment_gradients = self._assert_symbolic_and_automatic_jacobians_agree(
             model, atol=1e-6, rtol=1e-6
         )
-        
 
         self.assertGreater(len(experiment_gradients.jac_dict_sd), 0)
         self.assertEqual(

pyomo/contrib/doe/utils.py

@@ -210,7 +210,6 @@ def compute_FIM_metrics(FIM):
     # Modified E-optimality is based on the FIM condition number and penalizes
     # confidence ellipsoids that are highly elongated in one direction.
 
-
     ME_opt = np.log10(np.linalg.cond(FIM))
 
     return (
@@ -294,7 +293,7 @@ class ExperimentGradients:
 
     This helper implements the symbolic sensitivity path used in Pyomo.DoE.
     Instead of approximating sensitivities by finite-difference perturbations
-    of the unknown parameters theta, it differentiates the 
+    of the unknown parameters theta, it differentiates the
     model F(x, u, theta) = 0 with respect to theta, with the design variables
     u fixed, and solves the resulting auxiliary sensitivity system