def RemoveZeros(A,name ):
from PyTrilinos import EpetraExt
from numpy import array,loadtxt
import scipy.sparse as sps
import scipy.io
test ="".join([name,".txt"])
EpetraExt.RowMatrixToMatlabFile(test,A)
data = loadtxt(test)
col,row,values = data[:,0]-1,data[:,1]-1,data[:,2]
Asparse = sps.csr_matrix((values, (row, col)))
Asparse.eliminate_zeros()
comm = Epetra.PyComm()
Ap = scipy_csr_matrix2CrsMatrix(Asparse, comm)
return Ap
def SaveEpertaMatrix(A, name, xdim, ydim):
from PyTrilinos import EpetraExt
from numpy import array, loadtxt
import scipy.sparse as sps
import scipy.io
test = "".join([name, ".txt"])
EpetraExt.RowMatrixToMatlabFile(test, A)
data = loadtxt(test)
col, row, values = data[:, 0] - 1, data[:, 1] - 1, data[:, 2]
Asparse = sps.csr_matrix((values, (row, col)))
As = Asparse[0:xdim, 0:ydim]
comm = Epetra.PyComm()
Ap = scipy_csr_matrix2CrsMatrix(Aublas1, comm)
return Ap
def SaveEpertaMatrix(A,name,xdim,ydim):
from PyTrilinos import EpetraExt
from numpy import array,loadtxt
import scipy.sparse as sps
import scipy.io
test ="".join([name,".txt"])
EpetraExt.RowMatrixToMatlabFile(test,A)
data = loadtxt(test)
col,row,values = data[:,0]-1,data[:,1]-1,data[:,2]
Asparse = sps.csr_matrix((values, (row, col)))
As = Asparse[0:xdim,0:ydim]
comm = Epetra.PyComm()
Ap = scipy_csr_matrix2CrsMatrix(Aublas1, comm)
return Ap
def NullSpace(A,name):
from PyTrilinos import EpetraExt, Epetra
from numpy import array,loadtxt
import scipy.sparse as sps
import scipy.io
import matplotlib.pylab as plt
test ="".join([name,".txt"])
EpetraExt.RowMatrixToMatlabFile(test,A)
data = loadtxt(test)
col,row,values = data[:,0]-1,data[:,1]-1,data[:,2]
Asparse = sps.csr_matrix((values, (row, col)))
(Nb,Mb) = Asparse.shape
Aublas1 = Asparse[0:Nb-1,0:Mb-1]
comm = Epetra.PyComm()
Ap = scipy_csr_matrix2CrsMatrix(Aublas1, comm)
return Ap
def NullSpace(A, name):
from PyTrilinos import EpetraExt, Epetra
from numpy import array, loadtxt
import scipy.sparse as sps
import scipy.io
import matplotlib.pylab as plt
test = "".join([name, ".txt"])
EpetraExt.RowMatrixToMatlabFile(test, A)
data = loadtxt(test)
col, row, values = data[:, 0] - 1, data[:, 1] - 1, data[:, 2]
Asparse = sps.csr_matrix((values, (row, col)))
(Nb, Mb) = Asparse.shape
Aublas1 = Asparse[0:Nb - 1, 0:Mb - 1]
comm = Epetra.PyComm()
Ap = scipy_csr_matrix2CrsMatrix(Aublas1, comm)
return Ap
def NullSpace(Arank):
from PyTrilinos import Epetra, EpetraExt
from scipy2Trilinos import scipy_csr_matrix2CrsMatrix
from scipy.sparse import csr_matrix
Aublas = Arank.sparray()
# Aublas = sps.csr_matrix((values, (row, col)))
scipy.io.savemat("before.mat", {"Aublas": Aublas}, oned_as='row')
(Nb, Mb) = Aublas.shape
Aublas1 = Aublas[0:Nb - 1, 0:Mb - 1]
scipy.io.savemat("after.mat", {"Aublas1": Aublas1}, oned_as='row')
comm = Epetra.PyComm()
tic()
Ap = scipy_csr_matrix2CrsMatrix(Aublas1, comm)
print toc()
return Ap
def NullSpace(Arank):
from PyTrilinos import Epetra, EpetraExt
from scipy2Trilinos import scipy_csr_matrix2CrsMatrix
from scipy.sparse import csr_matrix
Aublas = Arank.sparray()
# Aublas = sps.csr_matrix((values, (row, col)))
scipy.io.savemat( "before.mat", {"Aublas": Aublas},oned_as='row')
(Nb,Mb) = Aublas.shape
Aublas1 = Aublas[0:Nb-1,0:Mb-1]
scipy.io.savemat( "after.mat", {"Aublas1": Aublas1},oned_as='row')
comm = Epetra.PyComm()
tic()
Ap = scipy_csr_matrix2CrsMatrix(Aublas1, comm)
print toc()
return Ap
def GradientCorrectSize(A,name,xdim,ydim):
from PyTrilinos import EpetraExt
from numpy import array,loadtxt
import scipy.sparse as sps
import scipy.io
test ="".join([name,".txt"])
EpetraExt.RowMatrixToMatlabFile(test,A)
data = loadtxt(test)
col,row,values = data[:,0]-1,data[:,1]-1,data[:,2]
Asparse = sps.csr_matrix((values, (row, col)))
print xdim,ydim
As = Asparse[xdim:,0:xdim]
As.eliminate_zeros()
As = As.T
comm = Epetra.PyComm()
Ap = scipy_csr_matrix2CrsMatrix(As, comm)
return Ap
"smoother: type (level 4)": "Hiptmair",
"smoother: type (level 5)": "Hiptmair",
"smoother: type (level 6)": "Hiptmair",
"smoother: Hiptmair efficient symmetric": True,
"subsmoother: type": "Chebyshev",
"subsmoother: Chebyshev alpha": 27.0,
"subsmoother: node sweeps": 4,
"subsmoother: edge sweeps": 4,
"coarse: type": "Amesos-KLU",
"coarse: max size": 128,
"coarse: pre or post": "post",
"coarse: sweeps": 1
}
comm = Epetra.PyComm()
C = scipy_csr_matrix2CrsMatrix(System["C"].tocsr(), comm)
CurlCurl = scipy_csr_matrix2CrsMatrix(System["CurlCurl"].tocsr(), comm)
node = scipy_csr_matrix2CrsMatrix(System["node"].tocsr(), comm)
ML_Hiptmair = ML.MultiLevelPreconditioner(CurlCurl, C, node, MLList)
ML_Hiptmair.ComputePreconditioner()
x = System["rhs"][0]
b_epetra = TrilinosIO._numpyToTrilinosVector(x)
x_epetra = TrilinosIO._numpyToTrilinosVector(System["rhs"] * 0)
solver = AztecOO.AztecOO(CurlCurl, x_epetra, b_epetra)
solver.SetPrecOperator(ML_Hiptmair)
solver.SetAztecOption(AztecOO.AZ_solver, AztecOO.AZ_cg)
solver.SetAztecOption(AztecOO.AZ_output, 50)
err = solver.Iterate(155000, 1e-10)
"smoother: Hiptmair efficient symmetric":True, "subsmoother: type": "Chebyshev", "subsmoother: Chebyshev alpha": 27.0, "subsmoother: node sweeps":4, "subsmoother: edge sweeps":4, "coarse: type":"Amesos-KLU", "coarse: max size":128, "coarse: pre or post":"post", "coarse: sweeps":1 } comm = Epetra.PyComm() C = scipy_csr_matrix2CrsMatrix(System["C"].tocsr(), comm) CurlCurl = scipy_csr_matrix2CrsMatrix(System["CurlCurl"].tocsr(), comm) node = scipy_csr_matrix2CrsMatrix(System["node"].tocsr(), comm) ML_Hiptmair = ML.MultiLevelPreconditioner(CurlCurl,C,node,MLList) ML_Hiptmair.ComputePreconditioner() x = System["rhs"][0] b_epetra = TrilinosIO._numpyToTrilinosVector(x) x_epetra = TrilinosIO._numpyToTrilinosVector(System["rhs"]*0) solver = AztecOO.AztecOO(CurlCurl, x_epetra, b_epetra) solver.SetPrecOperator(ML_Hiptmair) solver.SetAztecOption(AztecOO.AZ_solver, AztecOO.AZ_cg); solver.SetAztecOption(AztecOO.AZ_output, 50); err = solver.Iterate(155000, 1e-10)
def copy_vec(Vp, Vn):
vmap = Vp.Map();
for ii in range(Vp.MyLength()):
i = vmap.GID(ii)
Vp[ii] =Vn[i]
mycomm = Epetra.PyComm()
verbose = (mycomm.MyPID() == 0)
tps = Epetra.Time(mycomm)
tps.ResetStartTime()
# load H and convert it
#Hs=load_mat("H.mat.npz")
Hs=load_mat_matlab("H.mat")
H=scipy_csr_matrix2CrsMatrix(Hs, mycomm)
#load B and convert it
#Bs=load_mat("B.mat.npz")
Bs=load_mat_matlab("B.mat")
B=scipy_csr_matrix2CrsMatrix(Bs, mycomm)
# build Qh diagonal precond
Qh=Epetra.Vector(H.DomainMap())
copy_vec(Qh, 2./(Hs.diagonal()))
# buld Qs diagonal precond
#mpc=load_vec("mpc.mat.npz")
m_dic_mat=scipy.io.loadmat('mpc.mat')
mpc=m_dic_mat['mpc'][:,0]
Qs=Epetra.Vector(B.DomainMap())
from PyTrilinos import EpetraExt, Epetra
from numpy import array, loadtxt
import scipy.sparse as sps
import scipy.io
from scipy2Trilinos import scipy_csr_matrix2CrsMatrix
import ipdb
from dolfin import *
tic()
data = loadtxt("A_epetra.txt")
print toc()
tic()
col, row, values = data[:, 0] - 1, data[:, 1] - 1, data[:, 2]
print toc()
tic()
A = sps.csr_matrix((values, (row, col)))
print toc()
tic()
comm = Epetra.PyComm()
print toc()
tic()
A = scipy_csr_matrix2CrsMatrix(A, comm)
print toc()
zz
from PyTrilinos import EpetraExt, Epetra
from numpy import array,loadtxt
import scipy.sparse as sps
import scipy.io
from scipy2Trilinos import scipy_csr_matrix2CrsMatrix
import ipdb
from dolfin import *
tic()
data = loadtxt("A_epetra.txt")
print toc()
tic()
col,row,values = data[:,0]-1,data[:,1]-1,data[:,2]
print toc()
tic()
A = sps.csr_matrix((values, (row, col)))
print toc()
tic()
comm = Epetra.PyComm()
print toc()
tic()
A = scipy_csr_matrix2CrsMatrix(A, comm)
print toc()
zz
}
comm = Epetra.PyComm()
Acurl,b = assemble_system(a,L1,bc)
Anode = assemble(l)
scipy.io.savemat( "System.mat", {"CurlCurl":Acurl.sparray(),"node":Anode.sparray(),"C":C,"rhs":b.array()},oned_as='row')
scipy.io.savemat( "node.mat", {"node":Anode.sparray()},oned_as='row')
scipy.io.savemat( "rhs.mat", {"rhs":b.array()},oned_as='row')
C = scipy_csr_matrix2CrsMatrix(C, comm)
Acurl = scipy_csr_matrix2CrsMatrix(Acurl.sparray(), comm)
Anode = scipy_csr_matrix2CrsMatrix(Anode.sparray(), comm)
# Acurl = as_backend_type(Acurl).mat()
# Anode = as_backend_type(Anode).mat()
ML_Hiptmair = ML.MultiLevelPreconditioner(Acurl,C,Anode,MLList,True)
ML_Hiptmair.ComputePreconditioner()
x = Function(V)
b_epetra = x_epetra = TrilinosIO._numpyToTrilinosVector(b.array())
x_epetra = TrilinosIO._numpyToTrilinosVector(x.vector().array())
tic()
#u = M.SolveSystem(A,b,V,"cg","amg",1e-6,1e-6,1)
print toc()
MLList.set("viz: output format","vtk");
MLList.set("viz: print starting solution", True);
comm = Epetra.PyComm()
parameters['linear_algebra_backend'] = 'Epetra'
Mass = assemble(inner(u,v)*dx)
bc.apply(Mass)
Acurl,b = assemble_system(a,L1,bc)
Anode = assemble(l)
# scipy.io.savemat( "CurlCurl.mat", {"CurlCurl":Acurl.sparray()},oned_as='row')
# scipy.io.savemat( "node.mat", {"node":Anode.sparray()},oned_as='row')
# scipy.io.savemat( "rhs.mat", {"rhs":b.array()},oned_as='row')
C = scipy_csr_matrix2CrsMatrix(C, comm)
Acurl = as_backend_type(Acurl).mat()
Mass = as_backend_type(Mass).mat()
Anode = as_backend_type(Anode).mat()
ML_Hiptmair = ML.MultiLevelPreconditioner(Acurl,C,Anode,MLList,False)
# ML_Hiptmair = ML.MultiLevelPreconditioner(Acurl,False)
ML_Hiptmair.ComputePreconditioner()
x = Function(Magnetic)
# print 'time to create preconditioner ', toc()
# A_epetra = as_backend_type(AAA).mat()
b_epetra = as_backend_type(b).vec()
x_epetra = as_backend_type(x.vector()).vec()
tic()
#u = M.SolveSystem(A,b,V,"cg","amg",1e-6,1e-6,1)
from PyTrilinos import Epetra, EpetraExt from scipy2Trilinos import scipy_csr_matrix2CrsMatrix from scipy.sparse import csr_matrix, rand import numpy as np comm = Epetra.PyComm() dense = np.array([[0,0,1,4],[1,0,0,0], [0,8,9,7], [0,1,0,0]], dtype='float') sp_csr=csr_matrix(dense) #sp_coo=rand(1000,1000,0.1) #sp_csr = csr_matrix(sp_coo) A = scipy_csr_matrix2CrsMatrix(sp_csr, comm) print A
MLList.set("print unused", 2)
MLList.set("viz: output format", "vtk")
MLList.set("viz: print starting solution", True)
comm = Epetra.PyComm()
parameters['linear_algebra_backend'] = 'Epetra'
Mass = assemble(inner(u, v) * dx)
bc.apply(Mass)
Acurl, b = assemble_system(a, L1, bc)
Anode = assemble(l)
# scipy.io.savemat( "CurlCurl.mat", {"CurlCurl":Acurl.sparray()},oned_as='row')
# scipy.io.savemat( "node.mat", {"node":Anode.sparray()},oned_as='row')
# scipy.io.savemat( "rhs.mat", {"rhs":b.array()},oned_as='row')
C = scipy_csr_matrix2CrsMatrix(C, comm)
Acurl = as_backend_type(Acurl).mat()
Mass = as_backend_type(Mass).mat()
Anode = as_backend_type(Anode).mat()
ML_Hiptmair = ML.MultiLevelPreconditioner(Acurl, C, Anode, MLList, False)
# ML_Hiptmair = ML.MultiLevelPreconditioner(Acurl,False)
ML_Hiptmair.ComputePreconditioner()
x = Function(Magnetic)
# print 'time to create preconditioner ', toc()
# A_epetra = as_backend_type(AAA).mat()
b_epetra = as_backend_type(b).vec()
x_epetra = as_backend_type(x.vector()).vec()
tic()
#u = M.SolveSystem(A,b,V,"cg","amg",1e-6,1e-6,1)
