From 09e3da933bdab73090c471a32fb455c40601e28e Mon Sep 17 00:00:00 2001
From: Bernard Knueven <Bernard.Knueven@nrel.gov>
Date: Thu, 20 Jun 2024 11:59:36 -0600
Subject: [PATCH] fix tests

---
 .../tests/test_external_grey_box_model.py     | 37 +++++++++++++++--
 .../tests/test_pyomo_grey_box_nlp.py          | 41 +++++++++++++++++--
 2 files changed, 70 insertions(+), 8 deletions(-)

diff --git a/pyomo/contrib/pynumero/interfaces/tests/test_external_grey_box_model.py b/pyomo/contrib/pynumero/interfaces/tests/test_external_grey_box_model.py
index 0fc342c4e40..c1006068055 100644
--- a/pyomo/contrib/pynumero/interfaces/tests/test_external_grey_box_model.py
+++ b/pyomo/contrib/pynumero/interfaces/tests/test_external_grey_box_model.py
@@ -1827,18 +1827,47 @@ def create_model_two_equalities_two_outputs(self, external_model):
         m.egb.outputs['Pout'].setub(70)
         return m
 
-    def test_scaling_all_missing(self):
+    def test_scaling_all_one(self):
         m = self.create_model_two_equalities_two_outputs(
             ex_models.PressureDropTwoEqualitiesTwoOutputs()
         )
         m.obj = pyo.Objective(expr=(m.egb.outputs['Pout'] - 20) ** 2)
         pyomo_nlp = PyomoGreyBoxNLP(m)
         fs = pyomo_nlp.get_obj_scaling()
+        self.assertEqual(fs, 1.0)
+
+        comparison_x_order = [
+            'egb.inputs[Pin]',
+            'egb.inputs[c]',
+            'egb.inputs[F]',
+            'egb.inputs[P1]',
+            'egb.inputs[P3]',
+            'egb.outputs[P2]',
+            'egb.outputs[Pout]',
+            'hin',
+            'hout',
+        ]
+        x_order = pyomo_nlp.variable_names()
         xs = pyomo_nlp.get_primals_scaling()
+        comparison_xs = np.asarray([1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=np.float64)
+        check_vectors_specific_order(
+            self, xs, x_order, comparison_xs, comparison_x_order
+        )
+
+        comparison_c_order = [
+            'egb.pdrop1',
+            'egb.pdrop3',
+            'egb.P2_con',
+            'egb.Pout_con',
+            'incon',
+            'outcon',
+        ]
+        c_order = pyomo_nlp.constraint_names()
         cs = pyomo_nlp.get_constraints_scaling()
-        self.assertIsNone(fs)
-        self.assertIsNone(xs)
-        self.assertIsNone(cs)
+        comparison_cs = np.asarray([1, 1, 1, 1, 1, 1], dtype=np.float64)
+        check_vectors_specific_order(
+            self, cs, c_order, comparison_cs, comparison_c_order
+        )
 
     def test_scaling_pyomo_model_only(self):
         m = self.create_model_two_equalities_two_outputs(
diff --git a/pyomo/contrib/pynumero/interfaces/tests/test_pyomo_grey_box_nlp.py b/pyomo/contrib/pynumero/interfaces/tests/test_pyomo_grey_box_nlp.py
index ecadf40e5cf..0263508bb51 100644
--- a/pyomo/contrib/pynumero/interfaces/tests/test_pyomo_grey_box_nlp.py
+++ b/pyomo/contrib/pynumero/interfaces/tests/test_pyomo_grey_box_nlp.py
@@ -2248,18 +2248,51 @@ def create_model_two_equalities_two_outputs(self, external_model):
         m.egb.outputs['Pout'].setub(70)
         return m
 
-    def test_scaling_all_missing(self):
+    def test_scaling_all_one(self):
         m = self.create_model_two_equalities_two_outputs(
             ex_models.PressureDropTwoEqualitiesTwoOutputs()
         )
         m.obj = pyo.Objective(expr=(m.egb.outputs['Pout'] - 20) ** 2)
         pyomo_nlp = PyomoNLPWithGreyBoxBlocks(m)
+
+        comparison_x_order = [
+            'egb.inputs[Pin]',
+            'egb.inputs[c]',
+            'egb.inputs[F]',
+            'egb.inputs[P1]',
+            'egb.inputs[P3]',
+            'egb.outputs[P2]',
+            'egb.outputs[Pout]',
+            'hin',
+            'hout',
+        ]
+        x_order = pyomo_nlp.primals_names()
+        comparison_c_order = [
+            'egb.pdrop1',
+            'egb.pdrop3',
+            'egb.output_constraints[P2]',
+            'egb.output_constraints[Pout]',
+            'incon',
+            'outcon',
+        ]
+        c_order = pyomo_nlp.constraint_names()
+
         fs = pyomo_nlp.get_obj_scaling()
+        self.assertEqual(fs, 1.0)
+
         xs = pyomo_nlp.get_primals_scaling()
+        comparison_xs = np.asarray(
+            [1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=np.float64
+        )
+        check_vectors_specific_order(
+            self, xs, x_order, comparison_xs, comparison_x_order
+        )
+
         cs = pyomo_nlp.get_constraints_scaling()
-        self.assertIsNone(fs)
-        self.assertIsNone(xs)
-        self.assertIsNone(cs)
+        comparison_cs = np.asarray([1, 1, 1, 1, 1, 1], dtype=np.float64)
+        check_vectors_specific_order(
+            self, cs, c_order, comparison_cs, comparison_c_order
+        )
 
     def test_scaling_pyomo_model_only(self):
         m = self.create_model_two_equalities_two_outputs(