Use standard time series convention for markov() input data by murrayrm · Pull Request #508 · python-control/python-control
Expand Up
@@ -44,13 +44,9 @@ def testMarkovSignature(self, matarrayout, matarrayin):
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) Expand All @@ -65,9 +61,8 @@ def testMarkovSignature(self, matarrayout, matarrayin):
# 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", Expand Down Expand Up @@ -108,7 +103,7 @@ def testMarkovResults(self, 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) Expand Down
# 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) Expand All @@ -65,9 +61,8 @@ def testMarkovSignature(self, matarrayout, matarrayin):
# 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", Expand Down Expand Up @@ -108,7 +103,7 @@ def testMarkovResults(self, 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) Expand Down