def test14_mult(self):
""" overloaded * operator """
myobj = LumpedMatrixSolverS(self.V)
myobj.set_operator(self.M)
self.uMdiag.vector().axpy(1.0, myobj*myobj.one)
diff = (myobj.Mdiag - self.uMdiag.vector()).array()
self.assertTrue(np.linalg.norm(diff)/np.linalg.norm(myobj.Mdiag.array()) < 1e-14)
def test13_basic(self):
""" solve """
myobj = LumpedMatrixSolverS(self.V)
myobj.set_operator(self.M)
myobj.solve(self.uMdiag.vector(), myobj.Mdiag)
diff = myobj.one.array() - self.uMdiag.vector().array()
self.assertTrue(np.linalg.norm(diff)/np.linalg.norm(myobj.one.array()) < 1e-14)
def test12(self):
""" Invert lumped matrix """
myobj = LumpedMatrixSolverS(self.V)
myobj.set_operator(self.M)
err = 0.0
for ii in range(len(myobj.Mdiag.array())):
err += abs(1./myobj.Mdiag[ii] - myobj.invMdiag[ii])
self.assertTrue(err < ii*1e-16)
def test11_entries(self):
""" Lump matrix """
myobj = LumpedMatrixSolverS(self.V)
myobj.set_operator(self.M)
Msum = np.dot(self.ones, self.M.array().dot(self.ones))
err = abs(myobj.Mdiag.array().dot(self.ones) - \
Msum) / Msum
self.assertTrue(err < 1e-14, err)
def test12(self):
""" Invert lumped matrix """
myobj = LumpedMatrixSolverS(self.V)
myobj.set_operator(self.M)
err = 0.0
for ii in range(len(myobj.Mdiag.array())):
err += abs(1. / myobj.Mdiag[ii] - myobj.invMdiag[ii])
self.assertTrue(err < ii * 1e-16)
def test14_mult(self):
""" overloaded * operator """
myobj = LumpedMatrixSolverS(self.V)
myobj.set_operator(self.M)
self.uMdiag.vector().axpy(1.0, myobj * myobj.one)
diff = (myobj.Mdiag - self.uMdiag.vector()).array()
self.assertTrue(
np.linalg.norm(diff) / np.linalg.norm(myobj.Mdiag.array()) < 1e-14)
def test11_entries(self):
""" Lump matrix """
myobj = LumpedMatrixSolverS(self.V)
myobj.set_operator(self.M)
Msum = np.dot(self.ones, self.M.array().dot(self.ones))
err = abs(myobj.Mdiag.array().dot(self.ones) - \
Msum) / Msum
self.assertTrue(err < 1e-14, err)
def test13_basic(self):
""" solve """
myobj = LumpedMatrixSolverS(self.V)
myobj.set_operator(self.M)
myobj.solve(self.uMdiag.vector(), myobj.Mdiag)
diff = myobj.one.array() - self.uMdiag.vector().array()
self.assertTrue(
np.linalg.norm(diff) / np.linalg.norm(myobj.one.array()) < 1e-14)
def test11_set(self):
""" Set operator """
myobj = LumpedMatrixSolverS(self.V)
myobj.set_operator(self.M)
def test11_set(self):
""" Set operator """
myobj = LumpedMatrixSolverS(self.V)
myobj.set_operator(self.M)
def test10(self):
""" Create a lumped solver """
myobj = LumpedMatrixSolverS(self.V)
class AcousticWave():
"""
Solution of forward and adjoint equations for acoustic inverse problem
"""
def __init__(self, functionspaces_V):
"""
Input:
functionspaces_V = dict containing functionspaces for state/adj
('V') and med param ('Vl' for lambda and 'Vr' for rho)
"""
self.readV(functionspaces_V)
self.verbose = False # print info
self.lump = False # Lump the mass matrix
self.lumpD = False # Lump the ABC matrix
self.timestepper = None # 'backward', 'centered'
self.exact = None # exact solution at final time
self.u0init = None # provides u(t=t0)
self.utinit = None # provides u_t(t=t0)
self.u1init = None # provides u1 = u(t=t0+/-Dt)
self.bc = None
self.abc = False
self.ftime = lambda t: 0.0 # ftime(tt) = src term @ t=tt (in np.array())
self.set_fwd() # default is forward problem
def copy(self):
"""(hard) copy constructor"""
newobj = self.__class__({'V':self.V, 'Vl':self.Vl, 'Vr':self.Vr})
newobj.lump = self.lump
newobj.timestepper = self.timestepper
newobj.exact = self.exact
newobj.utinit = self.utinit
newobj.u1init = self.u1init
newobj.bc = self.bc
if self.abc == True:
newobj.set_abc(self.V.mesh(), self.class_bc_abc, self.lumpD)
newobj.ftime = self.ftime
newobj.update({'lambda':self.lam, 'rho':self.rho, \
't0':self.t0, 'tf':self.tf, 'Dt':self.Dt, \
'u0init':self.u0init, 'utinit':self.utinit, 'u1init':self.u1init})
return newobj
def readV(self, functionspaces_V):
# Solutions:
self.V = functionspaces_V['V']
self.test = TestFunction(self.V)
self.trial = TrialFunction(self.V)
self.u0 = Function(self.V) # u(t-Dt)
self.u1 = Function(self.V) # u(t)
self.u2 = Function(self.V) # u(t+Dt)
self.rhs = Function(self.V)
self.sol = Function(self.V)
# Parameters:
self.Vl = functionspaces_V['Vl']
self.lam = Function(self.Vl)
self.Vr = functionspaces_V['Vr']
self.rho = Function(self.Vr)
if functionspaces_V.has_key('Vm'):
self.Vm = functionspaces_V['Vm']
self.mu = Function(self.Vm)
self.elastic = True
assert(False)
else:
self.elastic = False
self.weak_k = inner(self.lam*nabla_grad(self.trial), \
nabla_grad(self.test))*dx
self.weak_m = inner(self.rho*self.trial,self.test)*dx
def set_abc(self, mesh, class_bc_abc, lumpD=False):
self.abc = True # False means zero-Neumann all-around
if lumpD: self.lumpD = True
abc_boundaryparts = FacetFunction("size_t", mesh)
class_bc_abc.mark(abc_boundaryparts, 1)
self.ds = Measure("ds")[abc_boundaryparts]
self.weak_d = inner(sqrt(self.lam*self.rho)*self.trial, self.test)*self.ds(1)
self.class_bc_abc = class_bc_abc # to make copies
def set_fwd(self):
self.fwdadj = 1.0
self.ftime = None
def set_adj(self):
self.fwdadj = -1.0
self.ftime = None
def get_tt(self, nn):
if self.fwdadj > 0.0: return self.times[nn]
else: return self.times[-nn-1]
def update(self, parameters_m):
assert not self.timestepper == None, "You need to set a time stepping method"
# Time options:
if parameters_m.has_key('t0'): self.t0 = parameters_m['t0']
if parameters_m.has_key('tf'): self.tf = parameters_m['tf']
if parameters_m.has_key('Dt'): self.Dt = parameters_m['Dt']
if parameters_m.has_key('t0') or parameters_m.has_key('tf') or parameters_m.has_key('Dt'):
self.Nt = int(round((self.tf-self.t0)/self.Dt))
self.Tf = self.t0 + self.Dt*self.Nt
self.times = np.linspace(self.t0, self.Tf, self.Nt+1)
assert isequal(self.times[1]-self.times[0], self.Dt, 1e-16), "Dt modified"
self.Dt = self.times[1] - self.times[0]
assert isequal(self.Tf, self.tf, 1e-2), "Final time differs by more than 1%"
if not isequal(self.Tf, self.tf, 1e-12):
print 'Final time modified from {} to {} ({}%)'.\
format(self.tf, self.Tf, abs(self.Tf-self.tf)/self.tf)
# Initial conditions:
if parameters_m.has_key('u0init'): self.u0init = parameters_m['u0init']
if parameters_m.has_key('utinit'): self.utinit = parameters_m['utinit']
if parameters_m.has_key('u1init'): self.u1init = parameters_m['u1init']
if parameters_m.has_key('um1init'): self.um1init = parameters_m['um1init']
# Medium parameters:
setfct(self.lam, parameters_m['lambda'])
if self.verbose: print 'lambda updated '
if self.elastic == True:
setfct(self.mu, parameters_m['mu'])
if self.verbose: print 'mu updated'
if self.verbose: print 'assemble K',
self.K = assemble(self.weak_k)
if self.verbose: print ' -- K assembled'
if parameters_m.has_key('rho'):
setfct(self.rho, parameters_m['rho'])
# Mass matrix:
if self.verbose: print 'rho updated\nassemble M',
Mfull = assemble(self.weak_m)
if self.lump:
self.solverM = LumpedMatrixSolverS(self.V)
self.solverM.set_operator(Mfull, self.bc)
self.M = self.solverM
else:
if mpisize == 1:
self.solverM = LUSolver()
self.solverM.parameters['reuse_factorization'] = True
self.solverM.parameters['symmetric'] = True
else:
self.solverM = KrylovSolver('cg', 'amg')
self.solverM.parameters['report'] = False
self.M = Mfull
if not self.bc == None: self.bc.apply(Mfull)
self.solverM.set_operator(Mfull)
if self.verbose: print ' -- M assembled'
# Matrix D for abs BC
if self.abc == True:
if self.verbose: print 'assemble D',
Mfull = assemble(self.weak_m)
Dfull = assemble(self.weak_d)
if self.lumpD:
self.D = LumpedMatrixSolverS(self.V)
self.D.set_operator(Dfull, None, False)
if self.lump:
self.solverMplD = LumpedMatrixSolverS(self.V)
self.solverMplD.set_operators(Mfull, Dfull, .5*self.Dt, self.bc)
self.MminD = LumpedMatrixSolverS(self.V)
self.MminD.set_operators(Mfull, Dfull, -.5*self.Dt, self.bc)
else:
self.D = Dfull
if self.verbose: print ' -- D assembled'
else: self.D = 0.0
#@profile
def solve(self):
""" General solver method """
if self.timestepper == 'backward':
def iterate(tt): self.iteration_backward(tt)
elif self.timestepper == 'centered':
def iterate(tt): self.iteration_centered(tt)
else:
print "Time stepper not implemented"
sys.exit(1)
if self.verbose: print 'Compute solution'
solout = [] # Store computed solution
# u0:
tt = self.get_tt(0)
if self.verbose: print 'Compute solution -- time {}'.format(tt)
setfct(self.u0, self.u0init)
solout.append([self.u0.vector().array(), tt])
# Compute u1:
if not self.u1init == None: self.u1 = self.u1init
else:
assert(not self.utinit == None)
setfct(self.rhs, self.ftime(tt))
self.rhs.vector().axpy(-self.fwdadj, self.D*self.utinit.vector())
self.rhs.vector().axpy(-1.0, self.K*self.u0.vector())
if not self.bc == None: self.bc.apply(self.rhs.vector())
self.solverM.solve(self.sol.vector(), self.rhs.vector())
setfct(self.u1, self.u0)
self.u1.vector().axpy(self.fwdadj*self.Dt, self.utinit.vector())
self.u1.vector().axpy(0.5*self.Dt**2, self.sol.vector())
tt = self.get_tt(1)
if self.verbose: print 'Compute solution -- time {}'.format(tt)
solout.append([self.u1.vector().array(), tt])
# Iteration
for nn in xrange(2, self.Nt+1):
iterate(tt)
# Advance to next time step
setfct(self.u0, self.u1)
setfct(self.u1, self.u2)
tt = self.get_tt(nn)
if self.verbose:
print 'Compute solution -- time {}, rhs {}'.\
format(tt, np.max(np.abs(self.ftime(tt))))
solout.append([self.u1.vector().array(),tt])
if self.fwdadj > 0.0:
assert isequal(tt, self.Tf, 1e-16), \
'tt={}, Tf={}, reldiff={}'.format(tt, self.Tf, abs(tt-self.Tf)/self.Tf)
else:
assert isequal(tt, self.t0, 1e-16), \
'tt={}, t0={}, reldiff={}'.format(tt, self.t0, abs(tt-self.t0))
return solout, self.computeerror()
def iteration_centered(self, tt):
setfct(self.rhs, (self.Dt**2)*self.ftime(tt))
self.rhs.vector().axpy(-1.0, self.MminD*self.u0.vector())
self.rhs.vector().axpy(2.0, self.M*self.u1.vector())
self.rhs.vector().axpy(-self.Dt**2, self.K*self.u1.vector())
if not self.bc == None: self.bc.apply(self.rhs.vector())
self.solverMplD.solve(self.u2.vector(), self.rhs.vector())
def iteration_backward(self, tt):
setfct(self.rhs, self.Dt*self.ftime(tt))
self.rhs.vector().axpy(-self.Dt, self.K*self.u1.vector())
self.rhs.vector().axpy(-1.0, self.D*(self.u1.vector()-self.u0.vector()))
if not self.bc == None: self.bc.apply(self.rhs.vector())
self.solverM.solve(self.sol.vector(), self.rhs.vector())
setfct(self.u2, 2.0*self.u1.vector())
self.u2.vector().axpy(-1.0, self.u0.vector())
self.u2.vector().axpy(self.Dt, self.sol.vector())
def computeerror(self):
return self.computerelativeerror()
def computerelativeerror(self):
if not self.exact == None:
#MM = assemble(inner(self.trial, self.test)*dx)
MM = self.M
norm_ex = np.sqrt(\
(MM*self.exact.vector()).inner(self.exact.vector()))
diff = self.exact.vector() - self.u1.vector()
if norm_ex > 1e-16: return np.sqrt((MM*diff).inner(diff))/norm_ex
else: return np.sqrt((MM*diff).inner(diff))
else: return []
def computeabserror(self):
if not self.exact == None:
MM = self.M
diff = self.exact.vector() - self.u1.vector()
return np.sqrt((MM*diff).inner(diff))
else: return []
def update(self, parameters_m):
assert not self.timestepper == None, "You need to set a time stepping method"
# Time options:
if parameters_m.has_key('t0'): self.t0 = parameters_m['t0']
if parameters_m.has_key('tf'): self.tf = parameters_m['tf']
if parameters_m.has_key('Dt'): self.Dt = parameters_m['Dt']
if parameters_m.has_key('t0') or parameters_m.has_key('tf') or parameters_m.has_key('Dt'):
self.Nt = int(round((self.tf-self.t0)/self.Dt))
self.Tf = self.t0 + self.Dt*self.Nt
self.times = np.linspace(self.t0, self.Tf, self.Nt+1)
assert isequal(self.times[1]-self.times[0], self.Dt, 1e-16), "Dt modified"
self.Dt = self.times[1] - self.times[0]
assert isequal(self.Tf, self.tf, 1e-2), "Final time differs by more than 1%"
if not isequal(self.Tf, self.tf, 1e-12):
print 'Final time modified from {} to {} ({}%)'.\
format(self.tf, self.Tf, abs(self.Tf-self.tf)/self.tf)
# Initial conditions:
if parameters_m.has_key('u0init'): self.u0init = parameters_m['u0init']
if parameters_m.has_key('utinit'): self.utinit = parameters_m['utinit']
if parameters_m.has_key('u1init'): self.u1init = parameters_m['u1init']
if parameters_m.has_key('um1init'): self.um1init = parameters_m['um1init']
# Medium parameters:
setfct(self.lam, parameters_m['lambda'])
if self.verbose: print 'lambda updated '
if self.elastic == True:
setfct(self.mu, parameters_m['mu'])
if self.verbose: print 'mu updated'
if self.verbose: print 'assemble K',
self.K = assemble(self.weak_k)
if self.verbose: print ' -- K assembled'
if parameters_m.has_key('rho'):
setfct(self.rho, parameters_m['rho'])
# Mass matrix:
if self.verbose: print 'rho updated\nassemble M',
Mfull = assemble(self.weak_m)
if self.lump:
self.solverM = LumpedMatrixSolverS(self.V)
self.solverM.set_operator(Mfull, self.bc)
self.M = self.solverM
else:
if mpisize == 1:
self.solverM = LUSolver()
self.solverM.parameters['reuse_factorization'] = True
self.solverM.parameters['symmetric'] = True
else:
self.solverM = KrylovSolver('cg', 'amg')
self.solverM.parameters['report'] = False
self.M = Mfull
if not self.bc == None: self.bc.apply(Mfull)
self.solverM.set_operator(Mfull)
if self.verbose: print ' -- M assembled'
# Matrix D for abs BC
if self.abc == True:
if self.verbose: print 'assemble D',
Mfull = assemble(self.weak_m)
Dfull = assemble(self.weak_d)
if self.lumpD:
self.D = LumpedMatrixSolverS(self.V)
self.D.set_operator(Dfull, None, False)
if self.lump:
self.solverMplD = LumpedMatrixSolverS(self.V)
self.solverMplD.set_operators(Mfull, Dfull, .5*self.Dt, self.bc)
self.MminD = LumpedMatrixSolverS(self.V)
self.MminD.set_operators(Mfull, Dfull, -.5*self.Dt, self.bc)
else:
self.D = Dfull
if self.verbose: print ' -- D assembled'
else: self.D = 0.0
