Apr 16, 2019 · Apr 16, 2019 · Apr 16, 2019 · Apr 16, 2019 · Apr 16, 2019 · Apr 16, 2019
diff --git a/control/bdalg.py b/control/bdalg.py
                K[inp,outp-1] = 1.
            elif outp < 0 and -outp >= -sys.outputs:
                K[inp,-outp-1] = -1.
    sys = sys.feedback(sp.matrix(K), sign=1)
    sys = sys.feedback(sp.array(K), sign=1)

    # now trim
    Ytrim = sp.zeros( (len(outputv), sys.outputs) )
        Utrim[u-1,i] = 1.
    for i,y in enumerate(outputv):
        Ytrim[i,y-1] = 1.
    return sp.matrix(Ytrim)*sys*sp.matrix(Utrim)
    return sp.array(Ytrim) *sys *sp.array(Utrim)
diff --git a/control/config.py b/control/config.py
 # files.  For now, you can just choose between MATLAB and FBS default
 # values.

 import warnings
 import numpy as np
 from .statesp import StateSpaceMatrix

 # Bode plot defaults
 bode_dB = False                 # Bode plot magnitude units
 bode_deg = True                 # Bode Plot phase units
 bode_Hz = False                 # Bode plot frequency units
 bode_number_of_samples = None   # Bode plot number of samples
 bode_feature_periphery_decade = 1.0  # Bode plot feature periphery in decades

 # State space return type (change to StateSpaceMatrix at next major revision)
 ss_return_type = np.matrix


 def reset_defaults():
    """Reset configuration values to their default values."""
    global bode_Hz; bode_Hz = False
    global bode_number_of_samples; bode_number_of_samples = None
    global bode_feature_periphery_decade; bode_feature_periphery_decade = 1.0
    global ss_return_type; ss_return_type = np.matrix   # TODO: update


 # Set defaults to match MATLAB
    global bode_deg; bode_deg = True
    global bode_Hz; bode_Hz = False

 #
 # State space function return type
 #
 # These functions are used to set the return type for state space functions
 # that return a matrix.  In the original version of python-control, these
 # functions returned a numpy.matrix object.  To handle the eventual
 # deprecation of the numpy.matrix type, the StateSpaceMatrix object type,
 # which implements matrix multiplications and related numpy.matrix operations
 # was created.  To avoid breaking existing code, the return type from
 # functions that used to return an np.matrix object now call the
 # get_ss_return_type function to obtain the return type.  The return type can
 # also be set using the `return_type` keyword, overriding the default state
 # space matrix return type.
 #


 # Set state space return type
 def set_ss_return_type(subtype, warn=True):
    global ss_return_type
    # If return_type is np.matrix, issue a pending deprecation warning
    if (subtype is np.matrix and warn):
        warnings.warn("Return type numpy.matrix is soon to be deprecated.",
                      stacklevel=2)
    ss_return_type = subtype


 # Get the state space return type
 def get_ss_return_type(subtype=None, warn=True):
    global ss_return_type
    return_type = ss_return_type if subtype is None else subtype
    # If return_type is np.matrix, issue a pending deprecation warning
    if (return_type is np.matrix and warn):
        warnings.warn("Returning numpy.matrix, soon to be deprecated; "
                      "make sure calling code can handle nparray.",
                      stacklevel=2)
    return return_type


 # Function to turn on/off use of np.matrix type
 def use_numpy_matrix(flag=True, warn=True):
    if flag and warn:
        warnings.warn("Return type numpy.matrix is soon to be deprecated.",
                      stacklevel=2)
        set_ss_return_type(np.matrix, warn=False)
    else:
        set_ss_return_type(StateSpaceMatrix)
diff --git a/control/frdata.py b/control/frdata.py
 from warnings import warn
 import numpy as np
 from numpy import angle, array, empty, ones, \
    real, imag,matrix,absolute, eye, linalg, where, dot
    real, imag, absolute, eye, linalg, where, dot
 from scipy.interpolate import splprep, splev
 from .lti import LTI


    """

    # Allow NDarray * StateSpace to give StateSpace._rmul_() priority
    # https://docs.scipy.org/doc/numpy/reference/arrays.classes.html#numpy.class.__array_priority__
    __array_priority__ = 11     # override ndarray and matrix types

    epsw = 1e-8

    def __init__(self, *args, **kwargs):
        # TODO: vectorize this
        # TODO: handle omega re-mapping
        for k, w in enumerate(other.omega):
            fresp[:, :, k] = self.fresp[:, :, k].view(type=matrix)* \
            fresp[:, :, k] = np.dot(
                self.fresp[:, :, k],
                linalg.solve(
                eye(self.inputs) +
 other.fresp[:, :, k].view(type=matrix) *
 self.fresp[:, :, k].view(type=matrix),
    eye(self.inputs))
 eye(self.inputs) + np.dot(other.fresp[:, :, k],
                              self.fresp[:, :, k]),
    eye(self.inputs))
            )

        return FRD(fresp, other.omega, smooth=(self.ifunc is not None))

diff --git a/control/mateqn.py b/control/mateqn.py
 Author: Bjorn Olofsson
 """

 from scipy import shape, size,asarray, asmatrix, copy, zeros, eye, dot
 from scipy import shape, size,array, asarray, copy, zeros, eye, dot
 from scipy.linalg import eigvals, solve_discrete_are, solve
 from .exception import ControlSlycot, ControlArgument

    if S is not None or E is not None or not stabilizing:
        return dare_old(A, B, Q, R, S, E, stabilizing)
    else:
        Rmat =asmatrix(R)
        Qmat =asmatrix(Q)
        Rmat =array(R, ndmin=2)
        Qmat =array(Q, ndmin=2)
        X = solve_discrete_are(A, B, Qmat, Rmat)
        G = solve(B.T.dot(X).dot(B) + Rmat, B.T.dot(X).dot(A))
        L = eigvals(A - B.dot(G))
diff --git a/control/modelsimp.py b/control/modelsimp.py
 from .lti import isdtime, isctime
 from .statesp import StateSpace
 from .statefbk import gram
 from .config import get_ss_return_type

 __all__ = ['hsvd', 'balred', 'modred', 'era', 'markov', 'minreal']

 # Hankel Singular Value Decomposition
 #   The following returns the Hankel singular values, which are singular values
 #of the matrix formed by multiplying the controllability and observability
 #grammians
 def hsvd(sys):
 def hsvd(sys, return_type=None):
    """Calculate the Hankel singular values.

    Parameters

    Returns
    -------
    H :Matrix
    H :matrix
        A list of Hankel singular values

    return_type: nparray subtype, optional (default = numpy.matrix)
        Set the ndarray subtype for the return value

    See Also
    --------
    gram
    w, v = np.linalg.eig(WoWc)

    hsv = np.sqrt(w)
    hsv = np.matrix(hsv)
    hsv = np.array(hsv, ndmin=2)        # was np.matrix(hsv)
    hsv = np.sort(hsv)
    hsv = np.fliplr(hsv)
    # Return the Hankel singular values
    return hsv

    # Return the Hankel singular values (casting type, if needed)
    return hsv.view(type=get_ss_return_type(return_type))

 def modred(sys, ELIM, method='matchdc'):
    """
    """

    # Convert input parameters to matrices (if they aren't already)
    Ymat = np.mat(Y)
    Umat = np.mat(U)
    Ymat = np.array(Y)
    Umat = np.array(U)
    n = np.size(U)

    # Construct a matrix of control inputs to invert
    UU = Umat
    for i in range(1, M-1):
        newCol = np.vstack((0, UU[0:n-1,i-2]))
        #! TODO: second index on UU doesn't seem right; could be neg or pos??
        newCol = np.vstack((0, np.reshape(UU[0:n-1,i-2], (-1,1))))
        UU = np.hstack((UU, newCol))
    Ulast = np.vstack((0, UU[0:n-1,M-2]))
    Ulast = np.vstack((0,np.reshape(UU[0:n-1,M-2], (-1,1))))
    for i in range(n-1,0,-1):
        Ulast[i] = np.sum(Ulast[0:i-1])
    UU = np.hstack((UU, Ulast))
Original file line number	Diff line number	Diff line change
Expand Up		@@ -334,7 +334,7 @@ def connect(sys, Q, inputv, outputv):
		K[inp,outp-1] = 1.
		elif outp < 0 and -outp >= -sys.outputs:
		K[inp,-outp-1] = -1.
		sys = sys.feedback(sp.matrix(K), sign=1)
		sys = sys.feedback(sp.array(K), sign=1)

		# now trim
		Ytrim = sp.zeros( (len(outputv), sys.outputs) )
Expand All		@@ -343,4 +343,4 @@ def connect(sys, Q, inputv, outputv):
		Utrim[u-1,i] = 1.
		for i,y in enumerate(outputv):
		Ytrim[i,y-1] = 1.
		return sp.matrix(Ytrim)syssp.matrix(Utrim)
		return sp.array(Ytrim) sys sp.array(Utrim)
Original file line number	Diff line number	Diff line change
Expand Up		@@ -7,13 +7,20 @@
		# files. For now, you can just choose between MATLAB and FBS default
		# values.

		import warnings
		import numpy as np
		from .statesp import StateSpaceMatrix

		# Bode plot defaults
		bode_dB = False # Bode plot magnitude units
		bode_deg = True # Bode Plot phase units
		bode_Hz = False # Bode plot frequency units
		bode_number_of_samples = None # Bode plot number of samples
		bode_feature_periphery_decade = 1.0 # Bode plot feature periphery in decades

		# State space return type (change to StateSpaceMatrix at next major revision)
		ss_return_type = np.matrix


		def reset_defaults():
		"""Reset configuration values to their default values."""
Expand All		@@ -22,6 +29,7 @@ def reset_defaults():
		global bode_Hz; bode_Hz = False
		global bode_number_of_samples; bode_number_of_samples = None
		global bode_feature_periphery_decade; bode_feature_periphery_decade = 1.0
		global ss_return_type; ss_return_type = np.matrix # TODO: update


		# Set defaults to match MATLAB
Expand DownExpand Up		@@ -52,3 +60,49 @@ def use_fbs_defaults():
		global bode_deg; bode_deg = True
		global bode_Hz; bode_Hz = False

		#
		# State space function return type
		#
		# These functions are used to set the return type for state space functions
		# that return a matrix. In the original version of python-control, these
		# functions returned a numpy.matrix object. To handle the eventual
		# deprecation of the numpy.matrix type, the StateSpaceMatrix object type,
		# which implements matrix multiplications and related numpy.matrix operations
		# was created. To avoid breaking existing code, the return type from
		# functions that used to return an np.matrix object now call the
		# get_ss_return_type function to obtain the return type. The return type can
		# also be set using the `return_type` keyword, overriding the default state
		# space matrix return type.
		#


		# Set state space return type
		def set_ss_return_type(subtype, warn=True):
		global ss_return_type
		# If return_type is np.matrix, issue a pending deprecation warning
		if (subtype is np.matrix and warn):
		warnings.warn("Return type numpy.matrix is soon to be deprecated.",
		stacklevel=2)
		ss_return_type = subtype


		# Get the state space return type
		def get_ss_return_type(subtype=None, warn=True):
		global ss_return_type
		return_type = ss_return_type if subtype is None else subtype
		# If return_type is np.matrix, issue a pending deprecation warning
		if (return_type is np.matrix and warn):
		warnings.warn("Returning numpy.matrix, soon to be deprecated; "
		"make sure calling code can handle nparray.",
		stacklevel=2)
		return return_type


		# Function to turn on/off use of np.matrix type
		def use_numpy_matrix(flag=True, warn=True):
		if flag and warn:
		warnings.warn("Return type numpy.matrix is soon to be deprecated.",
		stacklevel=2)
		set_ss_return_type(np.matrix, warn=False)
		else:
		set_ss_return_type(StateSpaceMatrix)
Original file line number	Diff line number	Diff line change
Expand Up		@@ -52,7 +52,7 @@
		from warnings import warn
		import numpy as np
		from numpy import angle, array, empty, ones, \
		real, imag,matrix,absolute, eye, linalg, where, dot
		real, imag, absolute, eye, linalg, where, dot
		from scipy.interpolate import splprep, splev
		from .lti import LTI

Expand DownExpand Up		@@ -81,6 +81,10 @@ class FRD(LTI):

		"""

		# Allow NDarray * StateSpace to give StateSpace._rmul_() priority
		# https://docs.scipy.org/doc/numpy/reference/arrays.classes.html#numpy.class.__array_priority__
		__array_priority__ = 11 # override ndarray and matrix types

		epsw = 1e-8

		def __init__(self, args, *kwargs):
Expand DownExpand Up		@@ -436,12 +440,13 @@ def feedback(self, other=1, sign=-1):
		# TODO: vectorize this
		# TODO: handle omega re-mapping
		for k, w in enumerate(other.omega):
		fresp[:, :, k] = self.fresp[:, :, k].view(type=matrix)* \
		fresp[:, :, k] = np.dot(
		self.fresp[:, :, k],
		linalg.solve(
		eye(self.inputs) +
		other.fresp[:, :, k].view(type=matrix) *
		self.fresp[:, :, k].view(type=matrix),
		eye(self.inputs))
		eye(self.inputs) + np.dot(other.fresp[:, :, k],
		self.fresp[:, :, k]),
		eye(self.inputs))
		)

		return FRD(fresp, other.omega, smooth=(self.ifunc is not None))

Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -41,7 +41,7 @@
		Author: Bjorn Olofsson
		"""

		from scipy import shape, size,asarray, asmatrix, copy, zeros, eye, dot
		from scipy import shape, size,array, asarray, copy, zeros, eye, dot
		from scipy.linalg import eigvals, solve_discrete_are, solve
		from .exception import ControlSlycot, ControlArgument

Expand DownExpand Up		@@ -703,8 +703,8 @@ def dare(A, B, Q, R, S=None, E=None, stabilizing=True):
		if S is not None or E is not None or not stabilizing:
		return dare_old(A, B, Q, R, S, E, stabilizing)
		else:
		Rmat =asmatrix(R)
		Qmat =asmatrix(Q)
		Rmat =array(R, ndmin=2)
		Qmat =array(Q, ndmin=2)
		X = solve_discrete_are(A, B, Qmat, Rmat)
		G = solve(B.T.dot(X).dot(B) + Rmat, B.T.dot(X).dot(A))
		L = eigvals(A - B.dot(G))
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -49,14 +49,15 @@
		from .lti import isdtime, isctime
		from .statesp import StateSpace
		from .statefbk import gram
		from .config import get_ss_return_type

		__all__ = ['hsvd', 'balred', 'modred', 'era', 'markov', 'minreal']

		# Hankel Singular Value Decomposition
		# The following returns the Hankel singular values, which are singular values
		#of the matrix formed by multiplying the controllability and observability
		#grammians
		def hsvd(sys):
		def hsvd(sys, return_type=None):
		"""Calculate the Hankel singular values.

		Parameters
Expand All		@@ -66,9 +67,12 @@ def hsvd(sys):

		Returns
		-------
		H :Matrix
		H :matrix
		A list of Hankel singular values

		return_type: nparray subtype, optional (default = numpy.matrix)
		Set the ndarray subtype for the return value

		See Also
		--------
		gram
Expand DownExpand Up		@@ -96,11 +100,12 @@ def hsvd(sys):
		w, v = np.linalg.eig(WoWc)

		hsv = np.sqrt(w)
		hsv = np.matrix(hsv)
		hsv = np.array(hsv, ndmin=2) # was np.matrix(hsv)
		hsv = np.sort(hsv)
		hsv = np.fliplr(hsv)
		# Return the Hankel singular values
		return hsv

		# Return the Hankel singular values (casting type, if needed)
		return hsv.view(type=get_ss_return_type(return_type))

		def modred(sys, ELIM, method='matchdc'):
		"""
Expand DownExpand Up		@@ -409,16 +414,17 @@ def markov(Y, U, M):
		"""

		# Convert input parameters to matrices (if they aren't already)
		Ymat = np.mat(Y)
		Umat = np.mat(U)
		Ymat = np.array(Y)
		Umat = np.array(U)
		n = np.size(U)

		# Construct a matrix of control inputs to invert
		UU = Umat
		for i in range(1, M-1):
		newCol = np.vstack((0, UU[0:n-1,i-2]))
		#! TODO: second index on UU doesn't seem right; could be neg or pos??
		newCol = np.vstack((0, np.reshape(UU[0:n-1,i-2], (-1,1))))
		UU = np.hstack((UU, newCol))
		Ulast = np.vstack((0, UU[0:n-1,M-2]))
		Ulast = np.vstack((0,np.reshape(UU[0:n-1,M-2], (-1,1))))
		for i in range(n-1,0,-1):
		Ulast[i] = np.sum(Ulast[0:i-1])
		UU = np.hstack((UU, Ulast))
Expand Down