Use standard time series convention for markov() input data (#508)

murrayrm · web-flow · commit feb9fc9bb741 · 2021-01-14T11:29:03.000+01:00
diff --git a/control/modelsimp.py b/control/modelsimp.py
@@ -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`.
 
@@ -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
     -------
@@ -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)
diff --git a/control/tests/modelsimp_test.py b/control/tests/modelsimp_test.py
@@ -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)
@@ -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",
@@ -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)
