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ksteube |
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# $Id$ |
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####################################################### |
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# |
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# Copyright 2003-2007 by ACceSS MNRF |
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# Copyright 2007 by University of Queensland |
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# |
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# http://esscc.uq.edu.au |
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# Primary Business: Queensland, Australia |
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# Licensed under the Open Software License version 3.0 |
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# http://www.opensource.org/licenses/osl-3.0.php |
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# |
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####################################################### |
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# |
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""" |
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Test suite for the linearPDE and pdetools test on finley |
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@remark: |
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@var __author__: name of author |
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@var __licence__: licence agreement |
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@var __url__: url entry point on documentation |
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@var __version__: version |
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@var __date__: date of the version |
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""" |
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__copyright__=""" Copyright (c) 2006 by ACcESS MNRF |
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http://www.access.edu.au |
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Primary Business: Queensland, Australia""" |
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__license__="""Licensed under the Open Software License version 3.0 |
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http://www.opensource.org/licenses/osl-3.0.php""" |
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__author__="Lutz Gross, l.gross@uq.edu.au" |
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__url__="http://www.iservo.edu.au/esys/escript" |
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__version__="$Revision: 859 $" |
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__date__="$Date: 2006-09-26 12:19:18 +1000 (Tue, 26 Sep 2006) $" |
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import unittest |
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from esys.escript import * |
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from esys.finley import Rectangle,Brick |
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from esys.escript.linearPDEs import LinearPDE |
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OPTIMIZE=False |
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SOLVER_VERBOSE=False |
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try: |
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FINLEY_TEST_DATA=os.environ['FINLEY_TEST_DATA'] |
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except KeyError: |
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FINLEY_TEST_DATA='.' |
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FINLEY_TEST_MESH_PATH=FINLEY_TEST_DATA+"/data_meshes/" |
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# number of elements in the spatial directions |
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NE0=8 |
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NE1=10 |
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NE2=12 |
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NE0=12 |
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NE1=12 |
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NE2=8 |
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SOLVER_TOL=1.e-8 |
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REL_TOL=1.e-6 |
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FAC_DIAG=1. |
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FAC_OFFDIAG=-0.4 |
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class SimpleSolve_Rectangle_Order1_SinglePDE_Paso_PCG_Jacobi(unittest.TestCase): |
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def test_solve(self): |
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domain=Rectangle(NE0,NE1,1, optimize=OPTIMIZE) |
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x=Solution(domain).getX() |
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# --- set exact solution ---- |
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u_ex=Scalar(0,Solution(domain)) |
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u_ex=1.+2.*x[0]+3.*x[1] |
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# --- set exact gradient ----------- |
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g_ex=Data(0.,(2,),Solution(domain)) |
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g_ex[0]=2. |
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g_ex[1]=3. |
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# -------- test gradient -------------------------------- |
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g=grad(u_ex) |
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self.failUnless(Lsup(g_ex-g)<REL_TOL*Lsup(g_ex)) |
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# -------- set-up PDE ----------------------------------- |
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pde=LinearPDE(domain,numEquations=1) |
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mask=whereZero(x[0]) |
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pde.setValue(r=u_ex,q=mask) |
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pde.setValue(A=kronecker(2),y=inner(g_ex,domain.getNormal())) |
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# -------- get the solution --------------------------- |
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pde.setTolerance(SOLVER_TOL) |
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pde.setSolverMethod(pde.PCG,pde.JACOBI) |
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pde.setSolverPackage(pde.PASO) |
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u=pde.getSolution(verbose=SOLVER_VERBOSE) |
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# -------- test the solution --------------------------- |
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error=Lsup(u-u_ex)/Lsup(u_ex) |
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self.failUnless(error<REL_TOL*Lsup(u_ex), "solution error %s is too big."%error) |
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class SimpleSolve_Rectangle_Order1_SystemPDE_Paso_PCG_Jacobi(unittest.TestCase): |
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def test_solve(self): |
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domain=Rectangle(NE0,NE1,1,optimize=OPTIMIZE) |
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x=Solution(domain).getX() |
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# --- set exact solution ---- |
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u_ex=Vector(0,Solution(domain)) |
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u_ex[0]=1.+2.*x[0]+3.*x[1] |
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u_ex[1]=-1.+3.*x[0]+2.*x[1] |
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# --- set exact gradient ----------- |
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g_ex=Data(0.,(2,2),Solution(domain)) |
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g_ex[0,0]=2. |
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g_ex[0,1]=3. |
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g_ex[1,0]=3. |
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g_ex[1,1]=2. |
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# -------- test gradient -------------------------------- |
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self.failUnless(Lsup(g_ex-grad(u_ex))<REL_TOL*Lsup(g_ex)) |
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# -------- set-up PDE ----------------------------------- |
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pde=LinearPDE(domain,numEquations=2) |
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mask=whereZero(x[0]) |
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pde.setValue(r=u_ex,q=mask*numarray.ones(2,)) |
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A=Tensor4(0,Function(domain)) |
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A[0,:,0,:]=kronecker(2) |
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A[1,:,1,:]=kronecker(2) |
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Y=Vector(0.,Function(domain)) |
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Y[0]=u_ex[0]*FAC_DIAG+u_ex[1]*FAC_OFFDIAG |
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Y[1]=u_ex[1]*FAC_DIAG+u_ex[0]*FAC_OFFDIAG |
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pde.setValue(A=A, |
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D=kronecker(2)*(FAC_DIAG-FAC_OFFDIAG)+numarray.ones((2,2))*FAC_OFFDIAG, |
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Y=Y, |
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y=matrixmult(g_ex,domain.getNormal())) |
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# -------- get the solution --------------------------- |
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pde.setTolerance(SOLVER_TOL) |
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pde.setSolverMethod(pde.PCG,pde.JACOBI) |
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pde.setSolverPackage(pde.PASO) |
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u=pde.getSolution(verbose=SOLVER_VERBOSE) |
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# -------- test the solution --------------------------- |
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error=Lsup(u-u_ex)/Lsup(u_ex) |
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self.failUnless(error<REL_TOL*Lsup(u_ex), "solution error %s is too big."%error) |
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class SimpleSolve_Rectangle_Order2_SinglePDE_Paso_PCG_Jacobi(unittest.TestCase): |
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def test_solve(self): |
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domain=Rectangle(NE0,NE1,2,l0=1.,l1=1,optimize=OPTIMIZE) |
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x=Solution(domain).getX() |
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# --- set exact solution ---- |
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u_ex=1.+2.*x[0]+3.*x[1]+4.*x[0]**2+5.*x[1]*x[0]+6.*x[1]**2 |
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# --- set exact gradient ----------- |
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g_ex=Data(0.,(2,),Solution(domain)) |
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g_ex[0]=2.+8.*x[0]+5.*x[1] |
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g_ex[1]=3.+5.*x[0]+12.*x[1] |
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# -------- test gradient -------------------------------- |
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self.failUnless(Lsup(g_ex-grad(u_ex))<REL_TOL*Lsup(g_ex)) |
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# -------- set-up PDE ----------------------------------- |
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pde=LinearPDE(domain,numEquations=1) |
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mask=whereZero(x[0]) |
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pde.setValue(r=u_ex,q=mask) |
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pde.setValue(A=kronecker(2),y=inner(g_ex,domain.getNormal()),Y=-20.) |
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# -------- get the solution --------------------------- |
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pde.setTolerance(SOLVER_TOL) |
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pde.setSolverMethod(pde.PCG,pde.JACOBI) |
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pde.setSolverPackage(pde.PASO) |
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u=pde.getSolution(verbose=SOLVER_VERBOSE) |
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# -------- test the solution --------------------------- |
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error=Lsup(u-u_ex)/Lsup(u_ex) |
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self.failUnless(error<REL_TOL*Lsup(u_ex), "solution error %s is too big."%error) |
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class SimpleSolve_Rectangle_Order2_SystemPDE_Paso_PCG_Jacobi(unittest.TestCase): |
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def test_solve(self): |
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domain=Rectangle(NE0,NE1,2,optimize=OPTIMIZE) |
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x=Solution(domain).getX() |
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# --- set exact solution ---- |
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u_ex=Vector(0,Solution(domain)) |
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u_ex[0]=1.+2.*x[0]+3.*x[1]+4.*x[0]**2+5.*x[1]*x[0]+6.*x[1]**2 |
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u_ex[1]=-1.+4.*x[0]+2.*x[1]+1.*x[0]**2+6.*x[1]*x[0]+4.*x[1]**2 |
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# --- set exact gradient ----------- |
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g_ex=Data(0.,(2,2),Solution(domain)) |
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g_ex[0,0]=2.+8.*x[0]+5.*x[1] |
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g_ex[0,1]=3.+5.*x[0]+12.*x[1] |
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g_ex[1,0]=4.+2.*x[0]+6.*x[1] |
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g_ex[1,1]=2.+6.*x[0]+8.*x[1] |
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# -------- test gradient -------------------------------- |
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self.failUnless(Lsup(g_ex-grad(u_ex))<REL_TOL*Lsup(g_ex)) |
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# -------- set-up PDE ----------------------------------- |
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pde=LinearPDE(domain,numEquations=2) |
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mask=whereZero(x[0]) |
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pde.setValue(r=u_ex,q=mask*numarray.ones(2,)) |
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A=Tensor4(0,Function(domain)) |
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A[0,:,0,:]=kronecker(2) |
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A[1,:,1,:]=kronecker(2) |
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Y=Vector(0.,Function(domain)) |
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Y[0]=u_ex[0]*FAC_DIAG+u_ex[1]*FAC_OFFDIAG |
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Y[1]=u_ex[1]*FAC_DIAG+u_ex[0]*FAC_OFFDIAG |
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pde.setValue(A=A, |
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D=kronecker(2)*(FAC_DIAG-FAC_OFFDIAG)+numarray.ones((2,2))*FAC_OFFDIAG, |
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Y=Y-[20.,10.], |
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y=matrixmult(g_ex,domain.getNormal())) |
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# -------- get the solution --------------------------- |
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pde.setTolerance(SOLVER_TOL) |
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pde.setSolverMethod(pde.PCG,pde.JACOBI) |
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pde.setSolverPackage(pde.PASO) |
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u=pde.getSolution(verbose=SOLVER_VERBOSE) |
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# -------- test the solution --------------------------- |
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error=Lsup(u-u_ex)/Lsup(u_ex) |
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self.failUnless(error<REL_TOL*Lsup(u_ex), "solution error %s is too big."%error) |
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class SimpleSolve_Brick_Order1_SinglePDE_Paso_PCG_Jacobi(unittest.TestCase): |
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def test_solve(self): |
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domain=Brick(NE0,NE1,NE2,1,optimize=OPTIMIZE) |
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x=Solution(domain).getX() |
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u_ex=1.+2.*x[0]+3.*x[1]+4.*x[2] |
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# --- set exact gradient ----------- |
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g_ex=Data(0.,(3,),Solution(domain)) |
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g_ex[0]=2. |
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g_ex[1]=3. |
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g_ex[2]=4. |
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# -------- test gradient -------------------------------- |
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self.failUnless(Lsup(g_ex-grad(u_ex))<REL_TOL*Lsup(g_ex)) |
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# -------- set-up PDE ----------------------------------- |
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pde=LinearPDE(domain,numEquations=1) |
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mask=whereZero(x[0]) |
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pde.setValue(r=u_ex,q=mask) |
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pde.setValue(A=kronecker(3),y=inner(g_ex,domain.getNormal())) |
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# -------- get the solution --------------------------- |
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pde.setTolerance(SOLVER_TOL) |
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pde.setSolverMethod(pde.PCG,pde.JACOBI) |
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pde.setSolverPackage(pde.PASO) |
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u=pde.getSolution(verbose=SOLVER_VERBOSE) |
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# -------- test the solution --------------------------- |
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error=Lsup(u-u_ex)/Lsup(u_ex) |
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self.failUnless(error<REL_TOL*Lsup(u_ex), "solution error %s is too big."%error) |
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class SimpleSolve_Brick_Order1_SystemPDE_Paso_PCG_Jacobi(unittest.TestCase): |
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def test_solve(self): |
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domain=Brick(NE0,NE1,NE2,1,optimize=OPTIMIZE) |
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x=Solution(domain).getX() |
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# --- set exact solution ---- |
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u_ex=Vector(0,Solution(domain)) |
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u_ex[0]=1.+2.*x[0]+3.*x[1]+4.*x[2] |
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u_ex[1]=-1.+4.*x[0]+1.*x[1]-2.*x[2] |
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u_ex[2]=5.+8.*x[0]+4.*x[1]+5.*x[2] |
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# --- set exact gradient ----------- |
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g_ex=Data(0.,(3,3),Solution(domain)) |
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g_ex[0,0]=2. |
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g_ex[0,1]=3. |
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g_ex[0,2]=4. |
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g_ex[1,0]=4. |
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g_ex[1,1]=1. |
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g_ex[1,2]=-2. |
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g_ex[2,0]=8. |
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g_ex[2,1]=4. |
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g_ex[2,2]=5. |
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# -------- test gradient -------------------------------- |
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self.failUnless(Lsup(g_ex-grad(u_ex))<REL_TOL*Lsup(g_ex)) |
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# -------- set-up PDE ----------------------------------- |
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pde=LinearPDE(domain,numEquations=3) |
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mask=whereZero(x[0]) |
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pde.setValue(r=u_ex,q=mask*numarray.ones(3,)) |
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A=Tensor4(0,Function(domain)) |
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A[0,:,0,:]=kronecker(3) |
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A[1,:,1,:]=kronecker(3) |
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A[2,:,2,:]=kronecker(3) |
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Y=Vector(0.,Function(domain)) |
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Y[0]=u_ex[0]*FAC_DIAG+u_ex[2]*FAC_OFFDIAG+u_ex[1]*FAC_OFFDIAG |
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Y[1]=u_ex[1]*FAC_DIAG+u_ex[0]*FAC_OFFDIAG+u_ex[2]*FAC_OFFDIAG |
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Y[2]=u_ex[2]*FAC_DIAG+u_ex[1]*FAC_OFFDIAG+u_ex[0]*FAC_OFFDIAG |
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pde.setValue(A=A, |
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D=kronecker(3)*(FAC_DIAG-FAC_OFFDIAG)+numarray.ones((3,3))*FAC_OFFDIAG, |
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Y=Y, |
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y=matrixmult(g_ex,domain.getNormal())) |
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# -------- get the solution --------------------------- |
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pde.setTolerance(SOLVER_TOL) |
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pde.setSolverMethod(pde.PCG,pde.JACOBI) |
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pde.setSolverPackage(pde.PASO) |
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u=pde.getSolution(verbose=SOLVER_VERBOSE) |
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# -------- test the solution --------------------------- |
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error=Lsup(u-u_ex)/Lsup(u_ex) |
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self.failUnless(error<REL_TOL*Lsup(u_ex), "solution error %s is too big."%error) |
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class SimpleSolve_Brick_Order2_SinglePDE_Paso_PCG_Jacobi(unittest.TestCase): |
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def test_solve(self): |
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domain=Brick(NE0,NE1,NE2,2,optimize=OPTIMIZE) |
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x=Solution(domain).getX() |
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# --- set exact solution ---- |
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u_ex=1.+2.*x[0]+3.*x[1]+4.*x[2]+6.*x[0]*x[1]+7.*x[1]*x[2]+8.*x[2]*x[0]+9.*x[0]**2+10.*x[1]**2+11.*x[2]**2 |
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# --- set exact gradient ----------- |
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g_ex=Data(0.,(3,),Solution(domain)) |
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g_ex[0]=2.+6.*x[1]+8.*x[2]+18.*x[0] |
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g_ex[1]=3.+6.*x[0]+7.*x[2]+20.*x[1] |
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g_ex[2]=4.+7.*x[1]+8.*x[0]+22.*x[2] |
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# -------- test gradient -------------------------------- |
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self.failUnless(Lsup(g_ex-grad(u_ex))<REL_TOL*Lsup(g_ex)) |
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# -------- set-up PDE ----------------------------------- |
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pde=LinearPDE(domain,numEquations=1) |
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mask=whereZero(x[0]) |
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pde.setValue(r=u_ex,q=mask) |
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pde.setValue(A=kronecker(3),y=inner(g_ex,domain.getNormal()),Y=-60.) |
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# -------- get the solution --------------------------- |
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pde.setTolerance(SOLVER_TOL) |
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pde.setSolverMethod(pde.PCG,pde.JACOBI) |
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pde.setSolverPackage(pde.PASO) |
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u=pde.getSolution(verbose=SOLVER_VERBOSE) |
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# -------- test the solution --------------------------- |
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error=Lsup(u-u_ex)/Lsup(u_ex) |
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self.failUnless(error<REL_TOL*Lsup(u_ex), "solution error %s is too big."%error) |
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class SimpleSolve_Brick_Order2_SystemPDE_Paso_PCG_Jacobi(unittest.TestCase): |
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def test_solve(self): |
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domain=Brick(NE0,NE1,NE2,2,optimize=OPTIMIZE) |
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x=Solution(domain).getX() |
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# --- set exact solution ---- |
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u_ex=Vector(0,Solution(domain)) |
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u_ex[0]=1.+2.*x[0]+3.*x[1]+4.*x[2]+6.*x[0]*x[1]+7.*x[1]*x[2]+8.*x[2]*x[0]+9.*x[0]**2+10.*x[1]**2+11.*x[2]**2 |
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u_ex[1]=2.+4.*x[0]+1.*x[1]-6.*x[2]+3.*x[0]*x[1]+2.*x[1]*x[2]-8.*x[2]*x[0]-2.*x[0]**2+7.*x[1]**2+5.*x[2]**2 |
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u_ex[2]=-2.+7.*x[0]+9.*x[1]+2*x[2]-6.*x[0]*x[1]+8.*x[1]*x[2]+2.*x[2]*x[0]+2.*x[0]**2+8.*x[1]**2+1.*x[2]**2 |
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# --- set exact gradient ----------- |
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g_ex=Data(0.,(3,3),Solution(domain)) |
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g_ex[0,0]=2.+6.*x[1]+8.*x[2]+18.*x[0] |
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g_ex[0,1]=3.+6.*x[0]+7.*x[2]+20.*x[1] |
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g_ex[0,2]=4.+7.*x[1]+8.*x[0]+22.*x[2] |
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g_ex[1,0]=4.+3.*x[1]-8.*x[2]-4.*x[0] |
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g_ex[1,1]=1+3.*x[0]+2.*x[2]+14.*x[1] |
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g_ex[1,2]=-6.+2.*x[1]-8.*x[0]+10.*x[2] |
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g_ex[2,0]=7.-6.*x[1]+2.*x[2]+4.*x[0] |
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g_ex[2,1]=9.-6.*x[0]+8.*x[2]+16.*x[1] |
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g_ex[2,2]=2+8.*x[1]+2.*x[0]+2.*x[2] |
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# -------- test gradient -------------------------------- |
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self.failUnless(Lsup(g_ex-grad(u_ex))<REL_TOL*Lsup(g_ex)) |
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# -------- set-up PDE ----------------------------------- |
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pde=LinearPDE(domain,numEquations=3) |
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mask=whereZero(x[0]) |
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pde.setValue(r=u_ex,q=mask*numarray.ones(3,)) |
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Y=Vector(0.,Function(domain)) |
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Y[0]=u_ex[0]*FAC_DIAG+u_ex[2]*FAC_OFFDIAG+u_ex[1]*FAC_OFFDIAG |
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Y[1]=u_ex[1]*FAC_DIAG+u_ex[0]*FAC_OFFDIAG+u_ex[2]*FAC_OFFDIAG |
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Y[2]=u_ex[2]*FAC_DIAG+u_ex[1]*FAC_OFFDIAG+u_ex[0]*FAC_OFFDIAG |
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A=Tensor4(0,Function(domain)) |
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A[0,:,0,:]=kronecker(3) |
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A[1,:,1,:]=kronecker(3) |
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A[2,:,2,:]=kronecker(3) |
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pde.setValue(A=A, |
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D=kronecker(3)*(FAC_DIAG-FAC_OFFDIAG)+numarray.ones((3,3))*FAC_OFFDIAG, |
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Y=Y-numarray.array([60.,20.,22.]), |
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y=matrixmult(g_ex,domain.getNormal())) |
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# -------- get the solution --------------------------- |
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pde.setTolerance(SOLVER_TOL) |
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pde.setSolverMethod(pde.PCG,pde.JACOBI) |
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pde.setSolverPackage(pde.PASO) |
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u=pde.getSolution(verbose=SOLVER_VERBOSE) |
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# -------- test the solution --------------------------- |
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error=Lsup(u-u_ex)/Lsup(u_ex) |
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self.failUnless(error<REL_TOL*Lsup(u_ex), "solution error %s is too big."%error) |
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|
| 342 |
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if __name__ == '__main__': |
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suite = unittest.TestSuite() |
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suite.addTest(unittest.makeSuite(SimpleSolve_Rectangle_Order1_SinglePDE_Paso_PCG_Jacobi)) |
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suite.addTest(unittest.makeSuite(SimpleSolve_Rectangle_Order1_SystemPDE_Paso_PCG_Jacobi)) |
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suite.addTest(unittest.makeSuite(SimpleSolve_Rectangle_Order2_SinglePDE_Paso_PCG_Jacobi)) |
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suite.addTest(unittest.makeSuite(SimpleSolve_Rectangle_Order2_SystemPDE_Paso_PCG_Jacobi)) |
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suite.addTest(unittest.makeSuite(SimpleSolve_Brick_Order1_SinglePDE_Paso_PCG_Jacobi)) |
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suite.addTest(unittest.makeSuite(SimpleSolve_Brick_Order1_SystemPDE_Paso_PCG_Jacobi)) |
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suite.addTest(unittest.makeSuite(SimpleSolve_Brick_Order2_SinglePDE_Paso_PCG_Jacobi)) |
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suite.addTest(unittest.makeSuite(SimpleSolve_Brick_Order2_SystemPDE_Paso_PCG_Jacobi)) |
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s=unittest.TextTestRunner(verbosity=2).run(suite) |
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