change default value of statesp.remove_useless_states to False

murrayrm · murrayrm · commit 114bce6499a0 · 2021-01-14T22:49:56.000-08:00
diff --git a/control/config.py b/control/config.py
@@ -226,4 +226,7 @@ def use_legacy_defaults(version):
                      linearized_system_name_prefix='',
                      linearized_system_name_suffix='_linearized')
 
+        # turned off _remove_useless_states
+        set_defaults('statesp', remove_useless_states=True)
+
     return (major, minor, patch)
diff --git a/control/statesp.py b/control/statesp.py
@@ -72,7 +72,7 @@
 # Define module default parameter values
 _statesp_defaults = {
     'statesp.use_numpy_matrix': False,  # False is default in 0.9.0 and above
-    'statesp.remove_useless_states': True,
+    'statesp.remove_useless_states': False,
     'statesp.latex_num_format': '.3g',
     'statesp.latex_repr_type': 'partitioned',
     }
@@ -217,8 +217,7 @@ class StateSpace(LTI):
     __array_priority__ = 11     # override ndarray and matrix types
 
     def __init__(self, *args, **kwargs):
-        """
-        StateSpace(A, B, C, D[, dt])
+        """StateSpace(A, B, C, D[, dt])
 
         Construct a state space object.
 
@@ -228,6 +227,13 @@ def __init__(self, *args, **kwargs):
         True for unspecified sampling time).  To call the copy constructor,
         call StateSpace(sys), where sys is a StateSpace object.
 
+        The `remove_useless_states` keyword can be used to scan the A, B, and
+        C matrices for rows or columns of zeros.  If the zeros are such that a
+        particular state has no effect on the input-output dynamics, then that
+        state is removed from the A, B, and C matrices.  If not specified, the
+        value is read from `config.defaults['statesp.remove_useless_states']
+        (default = False).
+
         """
         # first get A, B, C, D matrices
         if len(args) == 4:
@@ -251,8 +257,8 @@ def __init__(self, *args, **kwargs):
                 "Expected 1, 4, or 5 arguments; received %i." % len(args))
 
         # Process keyword arguments
-        remove_useless = kwargs.get(
-            'remove_useless',
+        remove_useless_states = kwargs.get(
+            'remove_useless_states',
             config.defaults['statesp.remove_useless_states'])
 
         # Convert all matrices to standard form
@@ -321,7 +327,7 @@ def __init__(self, *args, **kwargs):
             raise ValueError("C and D must have the same number of rows.")
 
         # Check for states that don't do anything, and remove them.
-        if remove_useless:
+        if remove_useless_states:
             self._remove_useless_states()
 
     def _remove_useless_states(self):
@@ -1274,7 +1280,7 @@ def _convert_to_statespace(sys, **kw):
         # Generate a simple state space system of the desired dimension
         # The following Doesn't work due to inconsistencies in ltisys:
         #   return StateSpace([[]], [[]], [[]], eye(outputs, inputs))
-        return StateSpace(0., zeros((1, inputs)), zeros((outputs, 1)),
+        return StateSpace([], zeros((0, inputs)), zeros((outputs, 0)),
                           sys * ones((outputs, inputs)))
 
     # If this is a matrix, try to create a constant feedthrough
diff --git a/control/tests/statesp_test.py b/control/tests/statesp_test.py
@@ -395,9 +395,6 @@ def test_is_static_gain(self):
         D0 = 0
         D1 = np.ones((2,1))
         assert StateSpace(A0, B0, C1, D1).is_static_gain()
-        # TODO: fix this once remove_useless_states is false by default
-        # should be False when remove_useless is false
-        # print(StateSpace(A1, B0, C1, D1).is_static_gain())
         assert not StateSpace(A0, B1, C1, D1).is_static_gain()
         assert not StateSpace(A1, B1, C1, D1).is_static_gain()
         assert StateSpace(A0, B0, C0, D0).is_static_gain()
@@ -586,10 +583,9 @@ def test_matrix_static_gain(self):
 
     def test_remove_useless_states(self):
         """Regression: _remove_useless_states gives correct ABC sizes."""
-        g1 = StateSpace(np.zeros((3, 3)),
-                        np.zeros((3, 4)),
-                        np.zeros((5, 3)),
-                        np.zeros((5, 4)))
+        g1 = StateSpace(np.zeros((3, 3)), np.zeros((3, 4)),
+                        np.zeros((5, 3)), np.zeros((5, 4)),
+                        remove_useless_states=True)
         assert (0, 0) == g1.A.shape
         assert (0, 4) == g1.B.shape
         assert (5, 0) == g1.C.shape
