Address some review comments.

Mark · Mark · commit b73e6fe0eac0 · 2022-06-13T14:19:36.000-05:00
diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml
@@ -4,7 +4,7 @@ on: [push, pull_request]
 
 jobs:
   test-linux:
-    name: Python ${{ matrix.python-version }}${{ matrix.slycot && format(' with Slycot from {0}', matrix.slycot) || ' without Slycot' }}${{ matrix.pandas && ', with pandas' || '' }}${{ matrix.array-and-matrix == 1 && ', array and matrix' || '' }}
+    name: Python ${{ matrix.python-version }}${{ matrix.slycot && format(' with Slycot from {0}', matrix.slycot) || ' without Slycot' }}${{ matrix.pandas && ', with pandas' || '' }}${{ matrix.array-and-matrix == 1 && ', array and matrix' || '' }}${{ matrix.cvxopt && format(' with cvxopt from {0}', matrix.cvxopt) || ' without cvxopt' }}
     runs-on: ubuntu-latest
 
     strategy:
diff --git a/control/passivity.py b/control/passivity.py
@@ -10,6 +10,7 @@
 except ImportError as e:
     cvx = None
 
+
 def is_passive(sys):
     '''
     Indicates if a linear time invarient system is passive
@@ -18,11 +19,13 @@ def is_passive(sys):
     such that is a solution exists, the system is passive.
 
     The source for the algorithm is: 
-    McCourt, Michael J., and Panos J. Antsaklis. "Demonstrating passivity and dissipativity using computational methods." ISIS 8 (2013).
+    McCourt, Michael J., and Panos J. Antsaklis. 
+        "Demonstrating passivity and dissipativity using computational methods." ISIS 8 (2013).
     '''
     if cvx is None:
+        print("cvxopt required for passivity module")
         raise ModuleNotFoundError
-    
+
     A = sys.A
     B = sys.B
     C = sys.C
@@ -35,13 +38,12 @@ def make_LMI_matrix(P):
         )
         return V
 
-    P = np.zeros_like(A)
     matrix_list = []
     state_space_size = sys.nstates
     for i in range(0, state_space_size):
         for j in range(0, state_space_size):
             if j <= i:
-                P = P*0.0
+                P = np.zeros_like(A)
                 P[i, j] = 1.0
                 P[j, i] = 1.0
                 matrix_list.append(make_LMI_matrix(P).flatten())
