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

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-23

lines changed

control
- modelsimp.py
- tests
  - modelsimp_test.py

2 files changed

-23

lines changed

`‎control/modelsimp.py‎`

Lines changed: 2 additions & 12 deletions

Original file line number	Diff line number	Diff line change
`@@ -395,7 +395,7 @@ def era(YY, m, n, nin, nout, r):`
`395`	`395`	`raiseNotImplementedError('This function is not implemented yet.')`
`396`	`396`
`397`	`397`
`398`		`-defmarkov(Y,U,m=None,transpose=None):`
	`398`	`+defmarkov(Y,U,m=None,transpose=False):`
`399`	`399`	"""Calculate the first `m` Markov parameters [D CB CAB ...]
`400`	`400`	from input `U`, output `Y`.
`401`	`401`
`@@ -424,8 +424,7 @@ def markov(Y, U, m=None, transpose=None):`
`424`	`424`	`Number of Markov parameters to output. Defaults to len(U).`
`425`	`425`	`transpose : bool, optional`
`426`	`426`	`Assume that input data is transposed relative to the standard`
`427`		- :ref:`time-series-convention`. The default value is true for
`428`		`- backward compatibility with legacy code.`
	`427`	+ :ref:`time-series-convention`. Default value is False.
`429`	`428`
`430`	`429`	`Returns`
`431`	`430`	`-------`
`@@ -456,15 +455,6 @@ def markov(Y, U, m=None, transpose=None):`
`456`	`455`	`>>> H = markov(Y, U, 3, transpose=False)`
`457`	`456`
`458`	`457`	`"""`
`459`		`-# Check on the specified format of the input`
`460`		`-iftransposeisNone:`
`461`		`-# For backwards compatibility, assume time series in rows but warn user`
`462`		`-warnings.warn(`
`463`		`-"Time-series data assumed to be in rows. This will change in a "`
`464`		-"future release. Use `transpose=True` to preserve current "
`465`		`-"behavior.")`
`466`		`-transpose=True`
`467`		`-`
`468`	`458`	`# Convert input parameters to 2D arrays (if they aren't already)`
`469`	`459`	`Umat=np.array(U,ndmin=2)`
`470`	`460`	`Ymat=np.array(Y,ndmin=2)`

`‎control/tests/modelsimp_test.py‎`

Lines changed: 6 additions & 11 deletions

Original file line number	Diff line number	Diff line change
`@@ -44,13 +44,9 @@ def testMarkovSignature(self, matarrayout, matarrayin):`
`44`	`44`	`H=markov(np.transpose(Y),np.transpose(U),m,transpose=True)`
`45`	`45`	`np.testing.assert_array_almost_equal(H,np.transpose(Htrue))`
`46`	`46`
`47`		`-# Default (in v0.8.4 and below) should be transpose=True (w/ warning)`
`48`		`-withpytest.warns(UserWarning,match="assumed to be in rows.*"`
`49`		`-"change in a future release"):`
`50`		`-# Generate Markov parameters without any arguments`
`51`		`-H=markov(np.transpose(Y),np.transpose(U),m)`
`52`		`-np.testing.assert_array_almost_equal(H,np.transpose(Htrue))`
`53`		`-`
	`47`	`+# Generate Markov parameters without any arguments`
	`48`	`+H=markov(Y,U,m)`
	`49`	`+np.testing.assert_array_almost_equal(H,Htrue)`
`54`	`50`
`55`	`51`	`# Test example from docstring`
`56`	`52`	`T=np.linspace(0,10,100)`
`@@ -65,9 +61,8 @@ def testMarkovSignature(self, matarrayout, matarrayin):`
`65`	`61`
`66`	`62`	`# Make sure MIMO generates an error`
`67`	`63`	`U=np.ones((2,100))# 2 inputs (Y unchanged, with 1 output)`
`68`		`-withpytest.warns(UserWarning):`
`69`		`-withpytest.raises(ControlMIMONotImplemented):`
`70`		`-markov(Y,U,m)`
	`64`	`+withpytest.raises(ControlMIMONotImplemented):`
	`65`	`+markov(Y,U,m)`
`71`	`66`
`72`	`67`	`# Make sure markov() returns the right answer`
`73`	`68`	`@pytest.mark.parametrize("k, m, n",`
`@@ -108,7 +103,7 @@ def testMarkovResults(self, k, m, n):`
`108`	`103`	`T=np.array(range(n))*Ts`
`109`	`104`	`U=np.cos(T)+np.sin(T/np.pi)`
`110`	`105`	`_,Y,_=forced_response(Hd,T,U,squeeze=True)`
`111`		`-Mcomp=markov(Y,U,m,transpose=False)`
	`106`	`+Mcomp=markov(Y,U,m)`
`112`	`107`
`113`	`108`	`# Compare to results from markov()`
`114`	`109`	`np.testing.assert_array_almost_equal(Mtrue,Mcomp)`

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`‎control/modelsimp.py‎`

`‎control/tests/modelsimp_test.py‎`

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