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[python-control-discuss] SF.net SVN: python-control:[38] branches/control-0.4a/src
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From: <kk...@us...> - 2011-02-08 22:12:59
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Revision: 38
http://python-control.svn.sourceforge.net/python-control/?rev=38&view=rev
Author: kkchen
Date: 2011-02-08 22:12:53 +0000 (Tue, 08 Feb 2011)
Log Message:
-----------
Added modelsimp.py with interfaces for hsvd, era, and markov functions. Also, modified TestStatefbk.py so that we import numpy as np, as in other files.
Steven Brunton <sbr...@pr...>
Modified Paths:
--------------
branches/control-0.4a/src/TestStatefbk.py
Added Paths:
-----------
branches/control-0.4a/src/modelsimp.py
Modified: branches/control-0.4a/src/TestStatefbk.py
===================================================================
--- branches/control-0.4a/src/TestStatefbk.py 2011-02-08 22:11:21 UTC (rev 37)
+++ branches/control-0.4a/src/TestStatefbk.py 2011-02-08 22:12:53 UTC (rev 38)
@@ -2,68 +2,68 @@
from statefbk import ctrb, obsv, place, lqr, gram
from matlab import *
-import numpy as N
+import numpy as np
import unittest
class TestStatefbk(unittest.TestCase):
def testCtrbSISO(self):
- A = N.matrix("1. 2.; 3. 4.")
- B = N.matrix("5.; 7.")
- Wctrue = N.matrix("5. 19.; 7. 43.")
+ A = np.matrix("1. 2.; 3. 4.")
+ B = np.matrix("5.; 7.")
+ Wctrue = np.matrix("5. 19.; 7. 43.")
Wc = ctrb(A,B)
- N.testing.assert_array_almost_equal(Wc, Wctrue)
+ np.testing.assert_array_almost_equal(Wc, Wctrue)
def testCtrbMIMO(self):
- A = N.matrix("1. 2.; 3. 4.")
- B = N.matrix("5. 6.; 7. 8.")
- Wctrue = N.matrix("5. 6. 19. 22.; 7. 8. 43. 50.")
+ A = np.matrix("1. 2.; 3. 4.")
+ B = np.matrix("5. 6.; 7. 8.")
+ Wctrue = np.matrix("5. 6. 19. 22.; 7. 8. 43. 50.")
Wc = ctrb(A,B)
- N.testing.assert_array_almost_equal(Wc, Wctrue)
+ np.testing.assert_array_almost_equal(Wc, Wctrue)
def testObsvSISO(self):
- A = N.matrix("1. 2.; 3. 4.")
- C = N.matrix("5. 7.")
- Wotrue = N.matrix("5. 7.; 26. 38.")
+ A = np.matrix("1. 2.; 3. 4.")
+ C = np.matrix("5. 7.")
+ Wotrue = np.matrix("5. 7.; 26. 38.")
Wo = obsv(A,C)
- N.testing.assert_array_almost_equal(Wo, Wotrue)
+ np.testing.assert_array_almost_equal(Wo, Wotrue)
def testObsvMIMO(self):
- A = N.matrix("1. 2.; 3. 4.")
- C = N.matrix("5. 6.; 7. 8.")
- Wotrue = N.matrix("5. 6.; 7. 8.; 23. 34.; 31. 46.")
+ A = np.matrix("1. 2.; 3. 4.")
+ C = np.matrix("5. 6.; 7. 8.")
+ Wotrue = np.matrix("5. 6.; 7. 8.; 23. 34.; 31. 46.")
Wo = obsv(A,C)
- N.testing.assert_array_almost_equal(Wo, Wotrue)
+ np.testing.assert_array_almost_equal(Wo, Wotrue)
def testCtrbObsvDuality(self):
- A = N.matrix("1.2 -2.3; 3.4 -4.5")
- B = N.matrix("5.8 6.9; 8. 9.1")
+ A = np.matrix("1.2 -2.3; 3.4 -4.5")
+ B = np.matrix("5.8 6.9; 8. 9.1")
Wc = ctrb(A,B);
- A = N.transpose(A)
- C = N.transpose(B)
- Wo = N.transpose(obsv(A,C));
- N.testing.assert_array_almost_equal(Wc,Wo)
+ A = np.transpose(A)
+ C = np.transpose(B)
+ Wo = np.transpose(obsv(A,C));
+ np.testing.assert_array_almost_equal(Wc,Wo)
def testGramWc(self):
- A = N.matrix("1. -2.; 3. -4.")
- B = N.matrix("5. 6.; 7. 8.")
- C = N.matrix("4. 5.; 6. 7.")
- D = N.matrix("13. 14.; 15. 16.")
+ A = np.matrix("1. -2.; 3. -4.")
+ B = np.matrix("5. 6.; 7. 8.")
+ C = np.matrix("4. 5.; 6. 7.")
+ D = np.matrix("13. 14.; 15. 16.")
# sys = ss(A, B, C, D)
sys = 1.
- Wctrue = N.matrix("18.5 24.5; 24.5 32.5")
+ Wctrue = np.matrix("18.5 24.5; 24.5 32.5")
Wc = gram(sys,'c')
- N.testing.assert_array_almost_equal(Wc, Wctrue)
+ np.testing.assert_array_almost_equal(Wc, Wctrue)
def testGramWo(self):
- A = N.matrix("1. -2.; 3. -4.")
- B = N.matrix("5. 6.; 7. 8.")
- C = N.matrix("4. 5.; 6. 7.")
- D = N.matrix("13. 14.; 15. 16.")
+ A = np.matrix("1. -2.; 3. -4.")
+ B = np.matrix("5. 6.; 7. 8.")
+ C = np.matrix("4. 5.; 6. 7.")
+ D = np.matrix("13. 14.; 15. 16.")
sys = ss(A, B, C, D)
sys = 1.
- Wotrue = N.matrix("257.5 -94.5; -94.5 56.5")
+ Wotrue = np.matrix("257.5 -94.5; -94.5 56.5")
Wo = gram(sys,'o')
- N.testing.assert_array_almost_equal(Wo, Wotrue)
+ np.testing.assert_array_almost_equal(Wo, Wotrue)
if __name__ == '__main__':
unittest.main()
Added: branches/control-0.4a/src/modelsimp.py
===================================================================
--- branches/control-0.4a/src/modelsimp.py (rev 0)
+++ branches/control-0.4a/src/modelsimp.py 2011-02-08 22:12:53 UTC (rev 38)
@@ -0,0 +1,115 @@
+# modelsimp.py - tools for model simplification
+#
+# Author: Steve Brunton, Kevin Chen, Lauren Padilla
+# Date: 30 Nov 2010
+#
+# This file contains routines for obtaining reduced order models
+#
+# Copyright (c) 2010 by California Institute of Technology
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#
+# 1. Redistributions of source code must retain the above copyright
+# notice, this list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above copyright
+# notice, this list of conditions and the following disclaimer in the
+# documentation and/or other materials provided with the distribution.
+#
+# 3. Neither the name of the California Institute of Technology nor
+# the names of its contributors may be used to endorse or promote
+# products derived from this software without specific prior
+# written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH
+# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF
+# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT
+# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
+# SUCH DAMAGE.
+#
+# $Id$
+
+# External packages and modules
+import numpy as np
+import ctrlutil
+from control.exception import *
+
+# 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):
+ """Calculate the Hankel singular values
+
+ Usage
+ =====
+ H = hsvd(sys)
+
+ Inputs
+ ------
+ sys : a state space system
+
+ Outputs
+ -------
+ H : a list of Hankel singular values
+
+ """
+
+ # Make sure that SLICOT is installed
+ try:
+ from slycot import sb01bd
+ except ImportError:
+ raise ControlSlycot("can't find slycot module 'sb01bd'")
+
+ H = 1.
+ # Return the Hankel singular values
+ return H
+
+def era(YY,m,n,nin,nout,r):
+ """Calculate an ERA model of order r based on the impulse-response data YY
+
+ Usage
+ =====
+ sys = era(YY,m,n,nin,nout,r)
+
+ Inputs
+ ------
+ YY : nout x nin dimensional impulse-response data
+ m : number of rows in Hankel matrix
+ n : number of columns in Hankel matrix
+ nin : number of input variables
+ nout : number of output variables
+ r : order of model
+
+ Outputs
+ -------
+ sys : a reduced order model sys=ss(Ar,Br,Cr,Dr)
+
+ """
+def markov(Y,U,M):
+ """Calculate the first M Markov parameters [D CB CAB ...] from input U, output Y
+
+ Usage
+ =====
+ H = markov(Y,U,M)
+
+ Inputs
+ ------
+ Y : output data
+ U : input data
+ M : number of Markov parameters to output
+
+ Outputs
+ -------
+ H : first M Markov parameters
+
+ """
+
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