def modalSolver(self, nev = 3, ncv = -1, tol = -1, mxiter = -1):
if SOLVER == 'default':
if ncv < 0:
ncv = 4*nev
ncv = ncv + nev
if tol < 0.:
tol = 1e-2
if mxiter < 0:
mxiter = 1000.
return solver.arpack(self.GK, self.GM, nev, ncv, tol, mxiter)
elif SOLVER == 'pysparse':
# copy matrixes
GK = self.GK
A = spmatrix.ll_mat_sym(self.dofcount, len(GK))
# TODO change to scipy
for row, col in GK:
A[row,col] = GK[row,col]
GM = self.GM
M = spmatrix.ll_mat_sym(self.dofcount, len(GM))
# TODO change to scipy
for row, col in GM:
M[row,col] = GM[row,col]
# Atau = A + tau*M
tau = -1.0
Atau = A.copy()
Atau.shift(tau, M)
K = precon.jacobi(Atau)
# convert to skyline
A = A.to_sss()
M = M.to_sss()
# solve
k_conv, lmbd, Q, it, it_innser = \
jdsym.jdsym(A, M, K, nev, tau,
1e-6, 1000, itsolvers.qmrs,
jmin=5, jmax=10, clvl=0, strategy=1)
# eigen values
return lmbd
else:
raise FEError('unknown solver')
def poisson2d_sym_blk(n):
n2 = n*n
L = spmatrix.ll_mat_sym(n2, 3*n2-2*n)
I = spmatrix.ll_mat_sym(n, n)
for i in range(n):
I[i,i] = -1
P = spmatrix.ll_mat_sym(n, 2*n-1)
for i in range(n):
P[i,i] = 4
if i > 0:
P[i,i-1] = -1
for i in range(0, n*n, n):
L[i:i+n,i:i+n] = P
if i > 0: L[i:i+n,i-n:i] = I
return L
def poisson1d_sym(n):
L = spmatrix.ll_mat_sym(n, 2*n-1)
for i in range(n):
L[i,i] = 2
if i > 0:
L[i,i-1] = -1
return L
def modalSolver(self, nev=3, ncv=-1, tol=-1, mxiter=-1):
if SOLVER == 'default':
if ncv < 0:
ncv = 4 * nev
ncv = ncv + nev
if tol < 0.:
tol = 1e-2
if mxiter < 0:
mxiter = 1000.
return solver.arpack(self.GK, self.GM, nev, ncv, tol, mxiter)
elif SOLVER == 'pysparse':
# copy matrixes
GK = self.GK
A = spmatrix.ll_mat_sym(self.dofcount, len(GK))
for row, col in GK:
A[row, col] = GK[row, col]
GM = self.GM
M = spmatrix.ll_mat_sym(self.dofcount, len(GM))
for row, col in GM:
M[row, col] = GM[row, col]
# Atau = A + tau*M
tau = -1.0
Atau = A.copy()
Atau.shift(tau, M)
K = precon.jacobi(Atau)
# convert to skyline
A = A.to_sss()
M = M.to_sss()
# solve
k_conv, lmbd, Q, it, it_innser = \
jdsym.jdsym(A, M, K, nev, tau,
1e-6, 1000, itsolvers.qmrs,
jmin=5, jmax=10, clvl=0, strategy=1)
# eigen values
return lmbd
else:
raise FEError('unknown solver')
def convert_to_py_sparse_format(a):
# check symmetric
import spmatrix
assert (a - a.T).nnz == 0
l_mat = spmatrix.ll_mat_sym(a.shape[0], a.nnz)
a_coo = scipy.sparse.triu(a).tocoo()
l_mat.put(a_coo.data, a_coo.row.astype(int), a_coo.col.astype(int))
return l_mat
def poisson2d_sym(n):
n2 = n*n
L = spmatrix.ll_mat_sym(n2, 3*n2-2*n)
for i in range(n):
for j in range(n):
k = i + n*j
L[k,k] = 4
if i > 0:
L[k,k-1] = -1
if j > 0:
L[k,k-n] = -1
return L
def assembleElementM(self):
'''
Assembly: Evaluate element mass matrix and fill GM.
'''
# Evaluate ESM's and assemble them to GSM
if SOLVER == 'default':
self.GM = spmatrix.LLd((self.dofcount, self.dofcount), isSym=True)
elif SOLVER == 'pysparse':
self.GM = spmatrix.ll_mat_sym(self.dofcount, self.dofcount)
else:
raise FEError('unknown solver')
for element in self.elements:
element.assembleElementM(self.GM)
def assembleElementM(self):
'''
Assembly: Evaluate element mass matrix and fill GM.
'''
# Evaluate ESM's and assemble them to GSM
if SOLVER == 'default':
self.GM = spmatrix.LLd((self.dofcount,self.dofcount), isSym=True)
elif SOLVER == 'pysparse':
self.GM = spmatrix.ll_mat_sym(self.dofcount, self.dofcount)
else:
raise FEError('unknown solver')
for element in self.elements:
element.assembleElementM(self.GM)
def assembleElementM(self, solver_arg):
'''
Assembly: Evaluate element mass matrix and fill GM.
'''
# Evaluate ESM's and assemble them to GSM
if solver_arg == 'default':
self.GM = spmatrix.LLd((self.dofcount,self.dofcount), isSym=True)
elif solver_arg == 'pysparse':
self.GM = spmatrix.ll_mat_sym(self.dofcount, self.dofcount)
elif solver_arg == 'sparse':
self.GM = sparse.lil_matrix((self.dofcount, self.dofcount))
else:
raise FEError('unknown solver')
for i, element in enumerate(self.elements):
element.assembleElementM(self.GM)
if solver_arg == 'sparse':
pass
self.GM = sparse.csc_matrix(symmetrise(self.GM))
