Change DICO parameter to boolean dtime. Update tests as well.

rabraker · rabraker · commit 33c59a86a497 · 2018-11-22T15:44:07.000-07:00
diff --git a/control/statefbk.py b/control/statefbk.py
@@ -113,9 +113,9 @@ def place(A, B, p):
     return K
 
 
-def place_varga(A, B, p, DICO='C', alpha=None):
+def place_varga(A, B, p, dtime=False, alpha=None):
     """Place closed loop eigenvalues
-    K = place_varga(A, B, p, DICO='C', alpha=None)
+    K = place_varga(A, B, p, dtime=False, alpha=None)
 
     Required Parameters
     ----------
@@ -128,8 +128,8 @@ def place_varga(A, B, p, DICO='C', alpha=None):
 
     Optional Parameters
     ---------------
-    DICO : 'C' for continuous time pole placement or 'D' for discrete time.
-            The default is DICO='C'.
+    dtime: False for continuous time pole placement or True for discrete time.
+            The default is dtime=False.
     alpha: double scalar
            If DICO='C', then place_varga will leave the eigenvalues with real
            real part less than alpha untouched.
@@ -160,7 +160,7 @@ def place_varga(A, B, p, DICO='C', alpha=None):
     --------
     >>> A = [[-1, -1], [0, 1]]
     >>> B = [[0], [1]]
-    >>> K = place(A, B, [-2, -5])
+    >>> K = place_varga(A, B, [-2, -5])
 
     See Also:
     --------
@@ -184,25 +184,32 @@ def place_varga(A, B, p, DICO='C', alpha=None):
     system_eigs = np.linalg.eig(A_mat)[0]
     placed_eigs = np.array(p)
 
+    # Need a character parameter for SB01BD
+    if dtime:
+        DICO = 'D'
+    else:
+        DICO = 'C'
+
     if alpha is None:
         # SB01BD ignores eigenvalues with real part less than alpha
         # (if DICO='C') or with modulus less than alpha
         # (if DICO = 'D').
-        if DICO == 'C':
+        if dtime:
+            # For discrete time, slycot only cares about modulus, so just make
+            # alpha the smallest it can be.
+            alpha = 0.0
+        else:
             # Choosing alpha=min_eig is insufficient and can lead to an
             # error or not having all the eigenvalues placed that we wanted.
             # Evidently, what python thinks are the eigs is not precisely
             # the same as what slicot thinks are the eigs. So we need some
             # numerical breathing room. The following is pretty heuristic,
             # but does the trick
             alpha = -2*abs(min(system_eigs.real))
-        elif DICO == 'D':
-            # For discrete time, slycot only cares about modulus, so just make
-            # alpha the smallest it can be.
-            alpha = 0.0
-    elif DICO == 'D' and alpha < 0.0:
+    elif dtime and alpha < 0.0:
         raise ValueError("Need alpha > 0 when DICO='D'")
 
+
     # Call SLICOT routine to place the eigenvalues
     A_z,w,nfp,nap,nup,F,Z = \
         sb01bd(B_mat.shape[0], B_mat.shape[1], len(placed_eigs), alpha,
diff --git a/control/tests/statefbk_test.py b/control/tests/statefbk_test.py
@@ -188,7 +188,7 @@ def testPlace(self):
     @unittest.skipIf(not slycot_check(), "slycot not installed")
     def testPlace_varga_continuous(self):
         """
-        Check that we can place eigenvalues for DICO='C'
+        Check that we can place eigenvalues for dtime=False
         """
         A = np.array([[1., -2.], [3., -4.]])
         B = np.array([[5.], [7.]])
@@ -243,13 +243,13 @@ def testPlace_varga_continuous_partial_eigs(self):
     @unittest.skipIf(not slycot_check(), "slycot not installed")
     def testPlace_varga_discrete(self):
         """
-        Check that we can place poles using DICO='D' (discrete time)
+        Check that we can place poles using dtime=True (discrete time)
         """
         A = np.array([[1., 0], [0, 0.5]])
         B = np.array([[5.], [7.]])
 
         P = np.array([0.5, 0.5])
-        K = place_varga(A, B, P, DICO='D')
+        K = place_varga(A, B, P, dtime=True)
         P_placed = np.linalg.eigvals(A - B.dot(K))
         # No guarantee of the ordering, so sort them
         P.sort()
@@ -269,7 +269,7 @@ def testPlace_varga_discrete_partial_eigs(self):
         P = np.array([0.2, 0.6])
         P_expected = np.array([0.5, 0.6])
         alpha = 0.51
-        K = place_varga(A, B, P, DICO='D', alpha=alpha)
+        K = place_varga(A, B, P, dtime=True, alpha=alpha)
         P_placed = np.linalg.eigvals(A - B.dot(K))
         P_expected.sort()
         P_placed.sort()
