Apr 2, 2021 · Mar 24, 2021 · Mar 24, 2021 · Mar 25, 2021 · Mar 25, 2021 · Mar 25, 2021
diff --git a/control/tests/modelsimp_test.py b/control/tests/modelsimp_test.py
            markov(Y, U, m)

    # Make sure markov() returns the right answer
    # forced response can return wrong shape until gh-488 is merged
    @pytest.mark.xfail
    @pytest.mark.parametrize("k, m, n",
                             [(2, 2, 2),
                              (2, 5, 5),
        # m = number of Markov parameters
        # n = size of the data vector
        #
        # Values should match exactly for n = m, otherewise you get a
        # Values*should* match exactly for n = m, otherewise you get a
        # close match but errors due to the assumption that C A^k B =
        # 0 for k > m-2 (see modelsimp.py).
        #
        Mcomp = markov(Y, U, m)

        # Compare to results from markov()
        np.testing.assert_array_almost_equal(Mtrue, Mcomp)
        # experimentally determined probability to get non matching results
        # with rtot=1e-6 and atol=1e-8 due to numerical errors
        # for k=5, m=n=10: 0.015 %
        np.testing.assert_allclose(Mtrue, Mcomp, rtol=1e-6, atol=1e-8)

    def testModredMatchDC(self, matarrayin):
        #balanced realization computed in matlab for the transfer function:
        np.testing.assert_array_almost_equal(rsys.B, Brtrue, decimal=4)
        np.testing.assert_array_almost_equal(rsys.C, Crtrue, decimal=4)
        np.testing.assert_array_almost_equal(rsys.D, Drtrue, decimal=4)
Original file line number	Diff line number	Diff line change
Expand Up		@@ -65,8 +65,6 @@ def testMarkovSignature(self, matarrayout, matarrayin):
		markov(Y, U, m)

		# Make sure markov() returns the right answer
		# forced response can return wrong shape until gh-488 is merged
		@pytest.mark.xfail
		@pytest.mark.parametrize("k, m, n",
		[(2, 2, 2),
		(2, 5, 5),
Expand All		@@ -81,7 +79,7 @@ def testMarkovResults(self, k, m, n):
		# m = number of Markov parameters
		# n = size of the data vector
		#
		# Values should match exactly for n = m, otherewise you get a
		# Valuesshould match exactly for n = m, otherewise you get a
		# close match but errors due to the assumption that C A^k B =
		# 0 for k > m-2 (see modelsimp.py).
		#
Expand All		@@ -108,7 +106,10 @@ def testMarkovResults(self, k, m, n):
		Mcomp = markov(Y, U, m)

		# Compare to results from markov()
		np.testing.assert_array_almost_equal(Mtrue, Mcomp)
		# experimentally determined probability to get non matching results
		# with rtot=1e-6 and atol=1e-8 due to numerical errors
		# for k=5, m=n=10: 0.015 %
		np.testing.assert_allclose(Mtrue, Mcomp, rtol=1e-6, atol=1e-8)

		def testModredMatchDC(self, matarrayin):
		#balanced realization computed in matlab for the transfer function:
Expand DownExpand Up		@@ -219,4 +220,3 @@ def testBalredMatchDC(self, matarrayin):
		np.testing.assert_array_almost_equal(rsys.B, Brtrue, decimal=4)
		np.testing.assert_array_almost_equal(rsys.C, Crtrue, decimal=4)
		np.testing.assert_array_almost_equal(rsys.D, Drtrue, decimal=4)