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change default value of statesp.remove_useless_states to False #509

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Merged
merged 2 commits into from
Jan 15, 2021
Merged

change default value of statesp.remove_useless_states to False #509

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114bce6
change default value of statesp.remove_useless_states to False
murrayrm Jan 15, 2021
b7945f3
added missing unit test
murrayrm Jan 15, 2021
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3 changes: 3 additions & 0 deletions control/config.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)
20 changes: 13 additions & 7 deletions control/statesp.py
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Expand Up @@ -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',
}
Expand Down Expand Up @@ -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.

Expand All @@ -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:
Expand All @@ -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
Expand Down Expand Up @@ -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):
Expand Down Expand Up @@ -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
Expand Down
11 changes: 4 additions & 7 deletions control/tests/statesp_test.py
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Expand Up @@ -395,9 +395,7 @@ 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())
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assert not StateSpace(A1, B0, C1, D1).is_static_gain() ?

assert not 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()
Expand Down Expand Up @@ -586,10 +584,9 @@ 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
Expand Down