Added in sysnorm.py:

Henrik Sandberg · Henrik Sandberg · commit 99e49d966083 · 2024-02-18T17:28:11.000+01:00
* type check when calling ct.norm
* metod argument in ct.norm (slycot or scipy) using function _slycot_or_scipy from ct.mateqn

Change in sysnorm_test.py
* set print_warning=False
diff --git a/control/sysnorm.py b/control/sysnorm.py
@@ -27,19 +27,19 @@ def _h2norm_slycot(sys, print_warning=True):
 
     See also
     --------
-    slycot.ab13bd : the Slycot routine that does the calculation
-    https://github.com/python-control/Slycot/issues/199 : Post on issue with ab13bf
+    ``slycot.ab13bd`` : the Slycot routine that does the calculation
+    https://github.com/python-control/Slycot/issues/199 : Post on issue with ``ab13bf``
     """
     
     try:
         from slycot import ab13bd
     except ImportError:
-        ct.ControlSlycot("Can't find slycot module 'ab13bd'!")
+        ct.ControlSlycot("Can't find slycot module ``ab13bd``!")
 
     try:
         from slycot.exceptions import SlycotArithmeticError
     except ImportError: 
-        raise ct.ControlSlycot("Can't find slycot class 'SlycotArithmeticError'!")
+        raise ct.ControlSlycot("Can't find slycot class ``SlycotArithmeticError``!")
 
     A, B, C, D = ct.ssdata(ct.ss(sys))
 
@@ -83,7 +83,7 @@ def _h2norm_slycot(sys, print_warning=True):
 
 #------------------------------------------------------------------------------
 
-def norm(system, p=2, tol=1e-10, print_warning=True, use_slycot=True):
+def norm(system, p=2, tol=1e-10, print_warning=True, method=None):
     """Computes norm of system.
     
     Parameters
@@ -97,13 +97,15 @@ def norm(system, p=2, tol=1e-10, print_warning=True, use_slycot=True):
         unless p='inf'.
     print_warning : bool
         Print warning message in case norm value may be uncertain.
-    use_slycot : bool
-        Use Slycot routines if available.
+    method : str, optional
+        Set the method used for computing the result.  Current methods are
+        'slycot' and 'scipy'. If set to None (default), try 'slycot' first
+        and then 'scipy'.
     
     Returns
     -------
-    norm_value : float or NoneType
-        Norm value of system (float) or None if computation could not be completed.
+    norm_value : float
+        Norm value of system.
    
     Notes
     -----
@@ -112,17 +114,24 @@ def norm(system, p=2, tol=1e-10, print_warning=True, use_slycot=True):
     Examples
     --------
     >>> Gc = ct.tf([1], [1, 2, 1])
-    >>> ct.norm(Gc,2)
+    >>> ct.norm(Gc, 2)
     0.5000000000000001
-    >>> ct.norm(Gc,'inf',tol=1e-10)
-    1.0000000000582077
+    >>> ct.norm(Gc, 'inf', tol=1e-11, method='scipy')
+    1.000000000007276
     """
+           
+    if not isinstance(system, (ct.StateSpace, ct.TransferFunction)):
+        raise TypeError('Parameter ``system``: must be a ``StateSpace`` or ``TransferFunction``')
+    
     G = ct.ss(system)
     A = G.A
     B = G.B
     C = G.C
     D = G.D
     
+    # Decide what method to use
+    method = ct.mateqn._slycot_or_scipy(method)
+    
     # -------------------
     # H2 norm computation
     # -------------------
@@ -149,12 +158,12 @@ def norm(system, p=2, tol=1e-10, print_warning=True, use_slycot=True):
             
             else:      
                 # Use slycot, if available, to compute (finite) norm
-                if ct.slycot_check() and use_slycot:
+                if method == 'slycot':
                     return _h2norm_slycot(G, print_warning)  
                 
                 # Else use scipy 
                 else:
-                    P = ct.lyap(A, B@B.T)  # Solve for controllability Gramian
+                    P = ct.lyap(A, B@B.T, method=method)  # Solve for controllability Gramian
                                         
                     # System is stable to reach this point, and P should be positive semi-definite. 
                     # Test next is a precaution in case the Lyapunov equation is ill conditioned.
@@ -187,12 +196,12 @@ def norm(system, p=2, tol=1e-10, print_warning=True, use_slycot=True):
             
             else:
                 # Use slycot, if available, to compute (finite) norm
-                if ct.slycot_check() and use_slycot:
+                if method == 'slycot':
                     return _h2norm_slycot(G, print_warning)  
                 
                 # Else use scipy 
                 else:
-                    P = ct.dlyap(A, B@B.T)
+                    P = ct.dlyap(A, B@B.T, method=method)
                 
                 # System is stable to reach this point, and P should be positive semi-definite. 
                 # Test next is a precaution in case the Lyapunov equation is ill conditioned.
@@ -226,7 +235,7 @@ def norm(system, p=2, tol=1e-10, print_warning=True, use_slycot=True):
                 return float('inf')
     
         # Use slycot, if available, to compute (finite) norm
-        if ct.slycot_check() and use_slycot:
+        if method == 'slycot':
             return ct.linfnorm(G, tol)[0]
         
         # Else use scipy
diff --git a/control/tests/sysnorm_test.py b/control/tests/sysnorm_test.py
@@ -13,35 +13,35 @@ def test_norm_1st_order_stable_system():
     """First-order stable continuous-time system"""
     s = ct.tf('s')
     G1 = 1/(s+1)
-    assert np.allclose(ct.norm(G1, p='inf', tol=1e-9), 1.0) # Comparison to norm computed in MATLAB
-    assert np.allclose(ct.norm(G1, p=2), 0.707106781186547) # Comparison to norm computed in MATLAB
+    assert np.allclose(ct.norm(G1, p='inf', tol=1e-9, print_warning=False), 1.0) # Comparison to norm computed in MATLAB
+    assert np.allclose(ct.norm(G1, p=2, print_warning=False), 0.707106781186547) # Comparison to norm computed in MATLAB
     
     Gd1 = ct.sample_system(G1, 0.1)
-    assert np.allclose(ct.norm(Gd1, p='inf', tol=1e-9), 1.0) # Comparison to norm computed in MATLAB
-    assert np.allclose(ct.norm(Gd1, p=2), 0.223513699524858) # Comparison to norm computed in MATLAB
+    assert np.allclose(ct.norm(Gd1, p='inf', tol=1e-9, print_warning=False), 1.0) # Comparison to norm computed in MATLAB
+    assert np.allclose(ct.norm(Gd1, p=2, print_warning=False), 0.223513699524858) # Comparison to norm computed in MATLAB
 
 
 def test_norm_1st_order_unstable_system():
     """First-order unstable continuous-time system"""
     s = ct.tf('s')
     G2 = 1/(1-s)
-    assert np.allclose(ct.norm(G2, p='inf', tol=1e-9), 1.0) # Comparison to norm computed in MATLAB
-    assert ct.norm(G2, p=2) == float('inf') # Comparison to norm computed in MATLAB
+    assert np.allclose(ct.norm(G2, p='inf', tol=1e-9, print_warning=False), 1.0) # Comparison to norm computed in MATLAB
+    assert ct.norm(G2, p=2, print_warning=False) == float('inf') # Comparison to norm computed in MATLAB
     
     Gd2 = ct.sample_system(G2, 0.1)
-    assert np.allclose(ct.norm(Gd2, p='inf', tol=1e-9), 1.0) # Comparison to norm computed in MATLAB
-    assert ct.norm(Gd2, p=2) == float('inf') # Comparison to norm computed in MATLAB
+    assert np.allclose(ct.norm(Gd2, p='inf', tol=1e-9, print_warning=False), 1.0) # Comparison to norm computed in MATLAB
+    assert ct.norm(Gd2, p=2, print_warning=False) == float('inf') # Comparison to norm computed in MATLAB
 
 def test_norm_2nd_order_system_imag_poles():
     """Second-order continuous-time system with poles on imaginary axis"""
     s = ct.tf('s')
     G3 = 1/(s**2+1)
-    assert ct.norm(G3, p='inf') == float('inf') # Comparison to norm computed in MATLAB
-    assert ct.norm(G3, p=2) == float('inf') # Comparison to norm computed in MATLAB
+    assert ct.norm(G3, p='inf', print_warning=False) == float('inf') # Comparison to norm computed in MATLAB
+    assert ct.norm(G3, p=2, print_warning=False) == float('inf') # Comparison to norm computed in MATLAB
     
     Gd3 = ct.sample_system(G3, 0.1)
-    assert ct.norm(Gd3, p='inf') == float('inf') # Comparison to norm computed in MATLAB
-    assert ct.norm(Gd3, p=2) == float('inf') # Comparison to norm computed in MATLAB
+    assert ct.norm(Gd3, p='inf', print_warning=False) == float('inf') # Comparison to norm computed in MATLAB
+    assert ct.norm(Gd3, p=2, print_warning=False) == float('inf') # Comparison to norm computed in MATLAB
 
 def test_norm_3rd_order_mimo_system():
     """Third-order stable MIMO continuous-time system"""
@@ -55,9 +55,9 @@ def test_norm_3rd_order_mimo_system():
                   [-0.863652821988714,  -1.214117043615409,  -0.006849328103348]])
     D = np.zeros((2,2))
     G4 = ct.ss(A,B,C,D) # Random system generated in MATLAB
-    assert np.allclose(ct.norm(G4, p='inf', tol=1e-9), 4.276759162964244) # Comparison to norm computed in MATLAB
-    assert np.allclose(ct.norm(G4, p=2), 2.237461821810309) # Comparison to norm computed in MATLAB
+    assert np.allclose(ct.norm(G4, p='inf', tol=1e-9, print_warning=False), 4.276759162964244) # Comparison to norm computed in MATLAB
+    assert np.allclose(ct.norm(G4, p=2, print_warning=False), 2.237461821810309) # Comparison to norm computed in MATLAB
     
     Gd4 = ct.sample_system(G4, 0.1)
-    assert np.allclose(ct.norm(Gd4, p='inf', tol=1e-9), 4.276759162964228) # Comparison to norm computed in MATLAB
-    assert np.allclose(ct.norm(Gd4, p=2), 0.707434962289554) # Comparison to norm computed in MATLAB
+    assert np.allclose(ct.norm(Gd4, p='inf', tol=1e-9, print_warning=False), 4.276759162964228) # Comparison to norm computed in MATLAB
+    assert np.allclose(ct.norm(Gd4, p=2, print_warning=False), 0.707434962289554) # Comparison to norm computed in MATLAB
