def test_sparseToSBM1():
from siconos.numerics import sparseToSBM,getValueSBM, newFromFileSBM, printSBM, SBMtoSparse
from scipy.sparse import csr_matrix, lil_matrix
A = lil_matrix((100, 100))
A.setdiag(range(100))
A[0, :10] = range(10)
A[1, 10:20] = A[0, :10]
M = csr_matrix(A)
v,SBM=sparseToSBM(2,M)
for i in range(M.shape[0]):
for j in range(M.shape[1]):
assert abs(getValueSBM(SBM,i,j) - M[i,j]) < eps
def test_SBMtoSparseToSBM():
from siconos.numerics import getValueSBM, newFromFileSBM, printSBM, SBMtoSparse, sparseToSBM
from scipy.sparse.csr import csr_matrix
SBM1=newFromFileSBM(os.path.join(working_dir, 'data/SBM1.dat'))
r,SPARSE = SBMtoSparse(SBM1)
v,SBM2 = sparseToSBM(3,SPARSE)
assert SBM1.nbblocks == SBM2.nbblocks
assert SBM1.blocknumber0 == SBM2.blocknumber0
assert SBM1.blocknumber1 == SBM2.blocknumber1
for i in range(SPARSE.shape[0]):
for j in range(SPARSE.shape[1]):
assert (getValueSBM(SBM1,i,j) - getValueSBM(SBM2,i,j)) < eps
def condensed_from_global(fcp):
# spsolve expect the indices to be cint aka 32 bits int
# Hence, we do some magic to cirumvent those issues.
Mcoo = scipy.sparse.coo_matrix(fcp.M)
Mcsc = scipy.sparse.csc_matrix(Mcoo)
Hcoo = scipy.sparse.coo_matrix(fcp.H)
Hcsc = scipy.sparse.csc_matrix(Hcoo)
WW = Hcsc.T.dot(scipy.sparse.linalg.spsolve(Mcsc, Hcsc))
qprime = fcp.H.T.dot(scipy.sparse.linalg.spsolve(Mcsc, fcp.q))
fcp_reduced = sn.FrictionContactProblem()
fcp_reduced.dimension = fcp.dimension
fcp_reduced.numberOfContacts = fcp.numberOfContacts
# this is a hack to deal with the inability of fc3d solvers to work with
# sparse matrices
_, Wsbm = sn.sparseToSBM(3, WW)
fcp_reduced.M = Wsbm
fcp_reduced.mu = fcp.mu
fcp_reduced.q = fcp.b + qprime
return fcp_reduced
def condensed_from_global(fcp):
# spsolve expect the indices to be cint aka 32 bits int
# Hence, we do some magic to cirumvent those issues.
Mcoo = scipy.sparse.coo_matrix(fcp.M)
Mcsc = scipy.sparse.csc_matrix(Mcoo)
Hcoo = scipy.sparse.coo_matrix(fcp.H)
Hcsc = scipy.sparse.csc_matrix(Hcoo)
WW = Hcsc.T.dot(scipy.sparse.linalg.spsolve(Mcsc, Hcsc))
qprime = fcp.H.T.dot(scipy.sparse.linalg.spsolve(Mcsc, fcp.q))
fcp_reduced = sn.FrictionContactProblem()
fcp_reduced.dimension = fcp.dimension
fcp_reduced.numberOfContacts = fcp.numberOfContacts
# this is a hack to deal with the inability of fc3d solvers to work with
# sparse matrices
_, Wsbm = sn.sparseToSBM(3, WW)
fcp_reduced.M = Wsbm
fcp_reduced.mu = fcp.mu
fcp_reduced.q = fcp.b + qprime
return fcp_reduced
def test_from_csr1():
from siconos.numerics import sparseToSBM, getValueSBM
from scipy.sparse.csr import csr_matrix
M = csr_matrix([[1,2,3],
[4,5,6],
[7,8,9]])
print(M.indices)
print(M.indptr)
print(M.data)
r,SBM = sparseToSBM(3,M)
assert getValueSBM(SBM,0,0) == 1
assert getValueSBM(SBM,0,1) == 2
assert getValueSBM(SBM,0,2) == 3
assert getValueSBM(SBM,1,0) == 4
assert getValueSBM(SBM,1,1) == 5
assert getValueSBM(SBM,1,2) == 6
assert getValueSBM(SBM,2,0) == 7
assert getValueSBM(SBM,2,1) == 8
assert getValueSBM(SBM,2,2) == 9
def test_from_csr1():
from siconos.numerics import sparseToSBM, getValueSBM
from scipy.sparse.csr import csr_matrix
M = csr_matrix([[1,2,3],
[4,5,6],
[7,8,9]])
print(M.indices)
print(M.indptr)
print(M.data)
r,SBM = sparseToSBM(3,M)
assert getValueSBM(SBM,0,0) == 1
assert getValueSBM(SBM,0,1) == 2
assert getValueSBM(SBM,0,2) == 3
assert getValueSBM(SBM,1,0) == 4
assert getValueSBM(SBM,1,1) == 5
assert getValueSBM(SBM,1,2) == 6
assert getValueSBM(SBM,2,0) == 7
assert getValueSBM(SBM,2,1) == 8
assert getValueSBM(SBM,2,2) == 9
