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

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Merged
merged 6 commits into from
Jan 14, 2021
Merged

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

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d0f29fc
Create python-package.yml
murrayrm Jan 10, 2021
2d44387
Merge branch 'master' of https://github.com/murrayrm/python-control
murrayrm Jan 10, 2021
97c71d6
Merge branch 'master' of https://github.com/python-control/python-con…
murrayrm Jan 13, 2021
a64f7f1
Revert "Create python-package.yml"
murrayrm Jan 13, 2021
81922dc
Merge branch 'master' of https://github.com/python-control/python-con…
murrayrm Jan 14, 2021
bc79086
use standard time series convention for markov() input data
murrayrm Jan 14, 2021
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14 changes: 2 additions & 12 deletions control/modelsimp.py
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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 Down Expand 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 Down Expand 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
17 changes: 6 additions & 11 deletions control/tests/modelsimp_test.py
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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 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