Jan 14, 2021 · Jan 10, 2021 · Jan 10, 2021 · Jan 13, 2021 · Jan 13, 2021 · Jan 14, 2021
diff --git a/control/modelsimp.py b/control/modelsimp.py
    raise NotImplementedError('This function is not implemented yet.')


 def markov(Y, U, m=None, transpose=None):
 def markov(Y, U, m=None, transpose=False):
    """Calculate the first `m` Markov parameters [D CB CAB ...]
    from input `U`, output `Y`.

        Number of Markov parameters to output.  Defaults to len(U).
    transpose : bool, optional
        Assume that input data is transposed relative to the standard
        :ref:`time-series-convention`. The default value is true for
        backward compatibility with legacy code.
        :ref:`time-series-convention`. Default value is False.

    Returns
    -------
    >>> H = markov(Y, U, 3, transpose=False)

    """
    # Check on the specified format of the input
    if transpose is None:
        # For backwards compatibility, assume time series in rows but warn user
        warnings.warn(
            "Time-series data assumed to be in rows. This will change in a "
            "future release.  Use `transpose=True` to preserve current "
            "behavior.")
        transpose = True

    # Convert input parameters to 2D arrays (if they aren't already)
    Umat = np.array(U, ndmin=2)
    Ymat = np.array(Y, ndmin=2)
diff --git a/control/tests/modelsimp_test.py b/control/tests/modelsimp_test.py
        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)
Original file line number	Diff line number	Diff line change
Expand Up		@@ -395,7 +395,7 @@ def era(YY, m, n, nin, nout, r):
		raise NotImplementedError('This function is not implemented yet.')


		def markov(Y, U, m=None, transpose=None):
		def markov(Y, U, m=None, transpose=False):
		"""Calculate the first `m` Markov parameters [D CB CAB ...]
		from input `U`, output `Y`.

Expand DownExpand Up		@@ -424,8 +424,7 @@ def markov(Y, U, m=None, transpose=None):
		Number of Markov parameters to output. Defaults to len(U).
		transpose : bool, optional
		Assume that input data is transposed relative to the standard
		:ref:`time-series-convention`. The default value is true for
		backward compatibility with legacy code.
		:ref:`time-series-convention`. Default value is False.

		Returns
		-------
Expand DownExpand Up		@@ -456,15 +455,6 @@ def markov(Y, U, m=None, transpose=None):
		>>> H = markov(Y, U, 3, transpose=False)

		"""
		# Check on the specified format of the input
		if transpose is None:
		# For backwards compatibility, assume time series in rows but warn user
		warnings.warn(
		"Time-series data assumed to be in rows. This will change in a "
		"future release. Use `transpose=True` to preserve current "
		"behavior.")
		transpose = True

		# Convert input parameters to 2D arrays (if they aren't already)
		Umat = np.array(U, ndmin=2)
		Ymat = np.array(Y, ndmin=2)
Expand Down
Original file line number	Diff line number	Diff line change
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 DownExpand 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