Refactor api, keep old api working

KybernetikJo · KybernetikJo · commit a3276b72f32a · 2024-07-11T15:11:18.000+02:00
diff --git a/control/modelsimp.py b/control/modelsimp.py
@@ -402,7 +402,7 @@ def era(YY, m, n, nin, nout, r):
     raise NotImplementedError('This function is not implemented yet.')
 
 
-def markov(*args, **kwargs):
+def markov(*args, m=None, transpose=False, dt=True, truncate=False):
     """markov(Y, U, [, m])
     
     Calculate the first `m` Markov parameters [D CB CAB ...]
@@ -420,12 +420,12 @@ def markov(*args, **kwargs):
     the input data is less than the desired number of Markov parameters (a
     warning message is generated in this case).
 
-    The function can be called with either 1, 2, or 3 arguments:
+    The function can be called with either 1, 2 or 3 arguments:
 
-    * ``K, S, E = lqr(response)``
-    * ``K, S, E = lqr(respnose, m)``
-    * ``K, S, E = lqr(Y, U)``
-    * ``K, S, E = lqr(Y, U, m)``
+    * ``H = markov(response)``
+    * ``H = markov(respnose, m)``
+    * ``H = markov(Y, U)``
+    * ``H = markov(Y, U, m)``
 
     where `response` is an `TimeResponseData` object, and `Y`, `U`, are 1D or 2D
     array and m is an integer.
@@ -446,26 +446,20 @@ def markov(*args, **kwargs):
         Number of Markov parameters to output.  Defaults to len(U).
     dt : True of float, optional
         True indicates discrete time with unspecified sampling time,
-        positive number is discrete time with specified sampling time.
-        It can be used to scale the markov parameters in order to match
-        the impulse response of this library.
-        Default values is True.
+        positive number is discrete time with specified sampling time.It
+        can be used to scale the markov parameters in order to match the
+        impulse response of this library. Default is True.
     truncate : bool, optional
-        Do not use first m equation for least least squares.
-        Default value is False.
+        Do not use first m equation for least least squares. Default is False.
+    transpose : bool, optional
+        Assume that input data is transposed relative to the standard
+        :ref:`time-series-convention`. For TimeResponseData this parameter
+        is ignored. Default is False.
 
     Returns
     -------
     H : ndarray
         First m Markov parameters, [D CB CAB ...]
-    
-
-    Notes
-    -----
-    It works for SISO and MIMO systems.
-
-    This function does comply with the Python Control Library
-    :ref:`time-series-convention` for representation of time series data.
 
     References
     ----------
@@ -494,25 +488,21 @@ def markov(*args, **kwargs):
         transpose = args[0].transpose
         if args[0].transpose and not args[0].issiso:
             Umat, Ymat = np.transpose(Umat), np.transpose(Ymat)
-        index = 1
+        if (len(args) == 2):
+            m = args[1]
+        elif (len(args) > 2):
+            raise ControlArgument("too many positional arguments")
     else:
         if (len(args) < 2):
             raise ControlArgument("not enough input arguments")
-        Umat = np.array(args[0], ndmin=2)
-        Ymat = np.array(args[1], ndmin=2)
-        transpose = kwargs.pop('transpose', False)
+        Umat = np.array(args[1], ndmin=2)
+        Ymat = np.array(args[0], ndmin=2)
         if transpose:
             Umat, Ymat = np.transpose(Umat), np.transpose(Ymat)
-        index = 2
-
-
-    if (len(args) > index):
-        m = args[index]
-    else:
-        m = None
-
-    dt = kwargs.pop('dt', True)
-    truncate = kwargs.pop('truncate', False)
+        if (len(args) == 3):
+            m = args[2]
+        elif (len(args) > 3):
+            raise ControlArgument("too many positional arguments")
 
     # Make sure the number of time points match
     if Umat.shape[1] != Ymat.shape[1]:
diff --git a/control/tests/modelsimp_test.py b/control/tests/modelsimp_test.py
@@ -45,11 +45,11 @@ def testMarkovSignature(self):
         m = 3
         Htrue = np.array([1., 0., 0.])
 
-        H = markov(Y, U, m, transpose=False)
+        H = markov(Y, U, m=m, transpose=False)
         np.testing.assert_array_almost_equal(H, Htrue)
 
         response.transpose=False
-        H = markov(response, m)
+        H = markov(response, m=m)
         np.testing.assert_array_almost_equal(H, Htrue)
 
         # Make sure that transposed data also works
@@ -68,20 +68,25 @@ def testMarkovSignature(self):
         H = markov(response, m)
         np.testing.assert_array_almost_equal(H, Htrue)
 
+        H = markov(Y, U, m=m)
+        np.testing.assert_array_almost_equal(H, Htrue)
+
+        H = markov(response, m=m)
+        np.testing.assert_array_almost_equal(H, Htrue)
+
         # Test example from docstring
-        # TODO: There is a problem here
-        # Htrue = np.array([1., 0.5, 0.])
+        # TODO: There is a problem here, last markov parameter does not fit
+        # the approximation error could be to big
+        Htrue = np.array([0, 1., -0.5])
         T = np.linspace(0, 10, 100)
         U = np.ones((1, 100))
         T, Y = forced_response(tf([1], [1, 0.5], True), T, U)
-        H = markov(Y, U, 3, transpose=False)
-        #np.testing.assert_array_almost_equal(H, Htrue)
+        H = markov(Y, U, 4, transpose=False)
+        np.testing.assert_array_almost_equal(H[:3], Htrue[:3])
 
-        T = np.linspace(0, 10, 100)
-        U = np.ones((1, 100))
         response = forced_response(tf([1], [1, 0.5], True), T, U)
-        H = markov(response, 3)
-        #np.testing.assert_array_almost_equal(H, Htrue)
+        H = markov(response, 4)
+        np.testing.assert_array_almost_equal(H[:3], Htrue[:3])
 
         # Test example from issue #395
         inp = np.array([1, 2])
