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Commitfd87d92

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Add unit tests to increase code coverage
1 parent8d62775 commitfd87d92

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2 files changed

+79
-23
lines changed

2 files changed

+79
-23
lines changed

‎control/modelsimp.py

Lines changed: 3 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -527,6 +527,7 @@ def markov(*args, m=None, transpose=False, dt=None, truncate=False):
527527
t=0
528528
iftruncate:
529529
t=m
530+
print(f"{t=}")
530531

531532
q=Ymat.shape[0]# number of outputs
532533
p=Umat.shape[0]# number of inputs
@@ -579,6 +580,8 @@ def markov(*args, m=None, transpose=False, dt=None, truncate=False):
579580
# Truncate first t=0 or t=m time steps, transpose the problem for lsq
580581
YY=Ymat[:,t:].T
581582
UU=UUT[:,t:].T
583+
print(f"{YY=}")
584+
print(f"{UU=}")
582585

583586
# Solve for the Markov parameters from YY = UU @ H.T
584587
HT,_,_,_=np.linalg.lstsq(UU,YY,rcond=None)

‎control/tests/modelsimp_test.py

Lines changed: 76 additions & 23 deletions
Original file line numberDiff line numberDiff line change
@@ -8,6 +8,7 @@
88

99

1010
fromcontrolimportStateSpace,forced_response,impulse_response,tf,rss,c2d,TimeResponseData
11+
fromcontrol.exceptionimportControlArgument,ControlDimension
1112
fromcontrol.tests.conftestimportslycotonly
1213
fromcontrol.modelsimpimportbalred,hsvd,markov,modred
1314

@@ -32,7 +33,7 @@ def testHSVD(self):
3233
assertnotisinstance(hsv,np.matrix)
3334

3435
deftestMarkovSignature(self):
35-
U=np.array([[1.,1.,1.,1.,1.]])
36+
U=np.array([[1.,1.,1.,1.,1.,1.,1.]])
3637
Y=U
3738
response=TimeResponseData(time=np.arange(U.shape[-1]),
3839
outputs=Y,
@@ -41,27 +42,40 @@ def testMarkovSignature(self):
4142
input_labels='u',
4243
)
4344

44-
#Basic usage
45+
#setup
4546
m=3
4647
Htrue=np.array([1.,0.,0.])
48+
Htrue_l=np.array([1.,0.,0.,0.,0.,0.,0.])
4749

48-
H=markov(Y,U,m=m,transpose=False)
49-
np.testing.assert_array_almost_equal(H,Htrue)
50+
# test not enough input arguments
51+
withpytest.raises(ControlArgument):
52+
H=markov(Y)
53+
withpytest.raises(ControlArgument):
54+
H=markov()
5055

51-
response.transpose=False
52-
H=markov(response,m=m)
53-
np.testing.assert_array_almost_equal(H,Htrue)
56+
# to many positional arguments
57+
withpytest.raises(ControlArgument):
58+
H=markov(Y,U,m,1)
59+
withpytest.raises(ControlArgument):
60+
H=markov(response,m,1)
5461

55-
# Make sure that transposed data also works
56-
H=markov(Y.T,U.T,m,transpose=True)
57-
np.testing.assert_array_almost_equal(H,np.transpose(Htrue))
62+
# to many positional arguments
63+
withpytest.raises(ControlDimension):
64+
U2=np.hstack([U,U])
65+
H=markov(Y,U2,m)
5866

59-
response.transpose=True
60-
H=markov(response,m)
61-
np.testing.assert_array_almost_equal(H,np.transpose(Htrue))
62-
response.transpose=False
67+
# not enough data
68+
withpytest.warns(Warning):
69+
H=markov(Y,U,8)
70+
71+
# Basic Usage, m=l
72+
H=markov(Y,U)
73+
np.testing.assert_array_almost_equal(H,Htrue_l)
6374

64-
# Generate Markov parameters without any arguments
75+
H=markov(response)
76+
np.testing.assert_array_almost_equal(H,Htrue_l)
77+
78+
# Basic Usage, m
6579
H=markov(Y,U,m)
6680
np.testing.assert_array_almost_equal(H,Htrue)
6781

@@ -74,6 +88,20 @@ def testMarkovSignature(self):
7488
H=markov(response,m=m)
7589
np.testing.assert_array_almost_equal(H,Htrue)
7690

91+
response.transpose=False
92+
H=markov(response,m=m)
93+
np.testing.assert_array_almost_equal(H,Htrue)
94+
95+
# Make sure that transposed data also works, siso
96+
HT=markov(Y.T,U.T,m,transpose=True)
97+
np.testing.assert_array_almost_equal(HT,np.transpose(Htrue))
98+
99+
response.transpose=True
100+
HT=markov(response,m)
101+
np.testing.assert_array_almost_equal(HT,np.transpose(Htrue))
102+
response.transpose=False
103+
104+
77105
# Test example from docstring
78106
# TODO: There is a problem here, last markov parameter does not fit
79107
# the approximation error could be to big
@@ -114,16 +142,41 @@ def testMarkovSignature(self):
114142
dt=0.25
115143
sysd=sys.sample(dt,method='zoh')
116144

117-
t=np.arange(0,100,dt)
118-
u=np.random.randn(sysd.B.shape[-1],len(t))
119-
response=forced_response(sysd,U=u)
145+
T=np.arange(0,100,dt)
146+
U=np.random.randn(sysd.B.shape[-1],len(T))
147+
response=forced_response(sysd,U=U)
148+
Y=response.outputs
120149

121150
m=100
122-
H=markov(response,m,dt=dt)
123151
_,Htrue=impulse_response(sysd,T=dt*(m-1))
124152

153+
154+
# test array_like
155+
H=markov(Y,U,m,dt=dt)
125156
np.testing.assert_array_almost_equal(H,Htrue)
126157

158+
# test array_like, truncate
159+
H=markov(Y,U,m,dt=dt,truncate=True)
160+
np.testing.assert_array_almost_equal(H,Htrue)
161+
162+
# test array_like, transpose
163+
HT=markov(Y.T,U.T,m,dt=dt,transpose=True)
164+
np.testing.assert_array_almost_equal(HT,np.transpose(Htrue))
165+
166+
# test response data
167+
H=markov(response,m,dt=dt)
168+
np.testing.assert_array_almost_equal(H,Htrue)
169+
170+
# test response data
171+
H=markov(response,m,dt=dt,truncate=True)
172+
np.testing.assert_array_almost_equal(H,Htrue)
173+
174+
# test response data, transpose
175+
response.transpose=True
176+
HT=markov(response,m,dt=dt)
177+
np.testing.assert_array_almost_equal(HT,np.transpose(Htrue))
178+
179+
127180
# Make sure markov() returns the right answer
128181
@pytest.mark.parametrize("k, m, n",
129182
[(2,2,2),
@@ -168,14 +221,14 @@ def testMarkovResults(self, k, m, n):
168221
ir_true=impulse_response(Hd,T)
169222
Mtrue_scaled=ir_true[1][:m]
170223

171-
T,Y=forced_response(Hd,T,U,squeeze=True)
172-
Mcomp=markov(Y,U,m,dt=True)
173-
Mcomp_scaled=markov(Y,U,m,dt=Ts)
174-
175224
# Compare to results from markov()
176225
# experimentally determined probability to get non matching results
177226
# with rtot=1e-6 and atol=1e-8 due to numerical errors
178227
# for k=5, m=n=10: 0.015 %
228+
T,Y=forced_response(Hd,T,U,squeeze=True)
229+
Mcomp=markov(Y,U,m,dt=True)
230+
Mcomp_scaled=markov(Y,U,m,dt=Ts)
231+
179232
np.testing.assert_allclose(Mtrue,Mcomp,rtol=1e-6,atol=1e-8)
180233
np.testing.assert_allclose(Mtrue_scaled,Mcomp_scaled,rtol=1e-6,atol=1e-8)
181234

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