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Add unit tests to increase code coverage · python-control/python-control@e61ac53

@@ -8,6 +8,7 @@

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from control import StateSpace, forced_response, impulse_response, tf, rss, c2d, TimeResponseData

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from control.exception import ControlArgument, ControlDimension

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from control.tests.conftest import slycotonly

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from control.modelsimp import balred, hsvd, markov, modred

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@@ -32,7 +33,7 @@ def testHSVD(self):

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assert not isinstance(hsv, np.matrix)

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def testMarkovSignature(self):

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U = np.array([[1., 1., 1., 1., 1.]])

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U = np.array([[1., 1., 1., 1., 1., 1., 1.]])

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Y = U

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response = TimeResponseData(time=np.arange(U.shape[-1]),

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outputs=Y,

@@ -41,27 +42,40 @@ def testMarkovSignature(self):

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input_labels='u',

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)

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# Basic usage

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# setup

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m = 3

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Htrue = np.array([1., 0., 0.])

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Htrue_l = np.array([1., 0., 0., 0., 0., 0., 0.])

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H = markov(Y, U, m=m, transpose=False)

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np.testing.assert_array_almost_equal(H, Htrue)

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# test not enough input arguments

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with pytest.raises(ControlArgument):

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H = markov(Y)

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with pytest.raises(ControlArgument):

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H = markov()

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response.transpose=False

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H = markov(response, m=m)

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np.testing.assert_array_almost_equal(H, Htrue)

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# to many positional arguments

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with pytest.raises(ControlArgument):

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H = markov(Y,U,m,1)

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with pytest.raises(ControlArgument):

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H = markov(response,m,1)

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# Make sure that transposed data also works

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H = markov(Y.T, U.T, m, transpose=True)

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np.testing.assert_array_almost_equal(H, np.transpose(Htrue))

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# to many positional arguments

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with pytest.raises(ControlDimension):

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U2 = np.hstack([U,U])

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H = markov(Y,U2,m)

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response.transpose=True

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H = markov(response, m)

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np.testing.assert_array_almost_equal(H, np.transpose(Htrue))

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response.transpose=False

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# not enough data

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with pytest.warns(Warning):

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H = markov(Y,U,8)

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# Basic Usage, m=l

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H = markov(Y, U)

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np.testing.assert_array_almost_equal(H, Htrue_l)

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# Generate Markov parameters without any arguments

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H = markov(response)

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np.testing.assert_array_almost_equal(H, Htrue_l)

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# Basic Usage, m

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H = markov(Y, U, m)

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np.testing.assert_array_almost_equal(H, Htrue)

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@@ -74,6 +88,20 @@ def testMarkovSignature(self):

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H = markov(response, m=m)

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np.testing.assert_array_almost_equal(H, Htrue)

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response.transpose=False

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H = markov(response, m=m)

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np.testing.assert_array_almost_equal(H, Htrue)

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# Make sure that transposed data also works, siso

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HT = markov(Y.T, U.T, m, transpose=True)

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np.testing.assert_array_almost_equal(HT, np.transpose(Htrue))

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response.transpose = True

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HT = markov(response, m)

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np.testing.assert_array_almost_equal(HT, np.transpose(Htrue))

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response.transpose=False

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# Test example from docstring

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# TODO: There is a problem here, last markov parameter does not fit

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# the approximation error could be to big

@@ -114,16 +142,41 @@ def testMarkovSignature(self):

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dt = 0.25

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sysd = sys.sample(dt, method='zoh')

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t = np.arange(0,100,dt)

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u = np.random.randn(sysd.B.shape[-1], len(t))

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response = forced_response(sysd, U=u)

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T = np.arange(0,100,dt)

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U = np.random.randn(sysd.B.shape[-1], len(T))

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response = forced_response(sysd, U=U)

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Y = response.outputs

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m = 100

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H = markov(response, m, dt=dt)

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_, Htrue = impulse_response(sysd, T=dt*(m-1))

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# test array_like

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H = markov(Y, U, m, dt=dt)

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np.testing.assert_array_almost_equal(H, Htrue)

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# test array_like, truncate

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H = markov(Y, U, m, dt=dt, truncate=True)

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np.testing.assert_array_almost_equal(H, Htrue)

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# test array_like, transpose

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HT = markov(Y.T, U.T, m, dt=dt, transpose=True)

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np.testing.assert_array_almost_equal(HT, np.transpose(Htrue))

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# test response data

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H = markov(response, m, dt=dt)

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np.testing.assert_array_almost_equal(H, Htrue)

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# test response data

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H = markov(response, m, dt=dt, truncate=True)

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np.testing.assert_array_almost_equal(H, Htrue)

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# test response data, transpose

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response.transpose = True

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HT = markov(response, m, dt=dt)

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np.testing.assert_array_almost_equal(HT, np.transpose(Htrue))

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# Make sure markov() returns the right answer

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@pytest.mark.parametrize("k, m, n",

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[(2, 2, 2),

@@ -168,14 +221,14 @@ def testMarkovResults(self, k, m, n):

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ir_true = impulse_response(Hd,T)

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Mtrue_scaled = ir_true[1][:m]

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T, Y = forced_response(Hd, T, U, squeeze=True)

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Mcomp = markov(Y, U, m, dt=True)

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Mcomp_scaled = markov(Y, U, m, dt=Ts)

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# Compare to results from markov()

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# experimentally determined probability to get non matching results

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# with rtot=1e-6 and atol=1e-8 due to numerical errors

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# for k=5, m=n=10: 0.015 %

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T, Y = forced_response(Hd, T, U, squeeze=True)

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Mcomp = markov(Y, U, m, dt=True)

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Mcomp_scaled = markov(Y, U, m, dt=Ts)

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np.testing.assert_allclose(Mtrue, Mcomp, rtol=1e-6, atol=1e-8)

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np.testing.assert_allclose(Mtrue_scaled, Mcomp_scaled, rtol=1e-6, atol=1e-8)

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