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

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

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

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

"change in a future release"):

# Generate Markov parameters without any arguments

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

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

# Generate Markov parameters without any arguments

H = markov(Y, U, m)

np.testing.assert_array_almost_equal(H, Htrue)

# Test example from docstring

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

# Make sure MIMO generates an error

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

with pytest.warns(UserWarning):

with pytest.raises(ControlMIMONotImplemented):

markov(Y, U, m)

with pytest.raises(ControlMIMONotImplemented):

markov(Y, U, m)

# Make sure markov() returns the right answer

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

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

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

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

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

Mcomp = markov(Y, U, m)

# Compare to results from markov()

np.testing.assert_array_almost_equal(Mtrue, Mcomp)