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Use standard time series convention for markov() input data (#508) · python-control/python-control@feb9fc9

@@ -44,13 +44,9 @@ def testMarkovSignature(self, matarrayout, matarrayin):

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

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

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# Default (in v0.8.4 and below) should be transpose=True (w/ warning)

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with pytest.warns(UserWarning, match="assumed to be in rows.*"

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"change in a future release"):

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# Generate Markov parameters without any arguments

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H = markov(np.transpose(Y), np.transpose(U), m)

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

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# Generate Markov parameters without any arguments

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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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# Test example from docstring

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T = np.linspace(0, 10, 100)

@@ -65,9 +61,8 @@ def testMarkovSignature(self, matarrayout, matarrayin):

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# Make sure MIMO generates an error

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U = np.ones((2, 100)) # 2 inputs (Y unchanged, with 1 output)

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with pytest.warns(UserWarning):

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with pytest.raises(ControlMIMONotImplemented):

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markov(Y, U, m)

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with pytest.raises(ControlMIMONotImplemented):

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markov(Y, U, m)

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# Make sure markov() returns the right answer

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

@@ -108,7 +103,7 @@ def testMarkovResults(self, k, m, n):

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T = np.array(range(n)) * Ts

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U = np.cos(T) + np.sin(T/np.pi)

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

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

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Mcomp = markov(Y, U, m)

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# Compare to results from markov()

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np.testing.assert_array_almost_equal(Mtrue, Mcomp)