diff --git a/control/modelsimp.py b/control/modelsimp.py
index 8f6124481..ec015c16b 100644
--- 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
index 1e06cb4b7..4def0b4d7 100644
--- 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)
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