def sparse_tests(spm):
print('in sparse_tests')
# sp_formats = ('bsr', 'coo', 'csc', 'csr', 'dia', 'dok', 'lil')
sp_formats = ('bsr', 'coo', 'csc', 'csr', 'dia', 'lil')
spm.check_format(True)
spm_shape = spm.shape
nm = sn.NumericsMatrix(spm)
assert nm is not None
check_size((nm.size0, nm.size1), spm_shape)
for fmt in sp_formats:
func = getattr(scipy.sparse, fmt + '_matrix')
mm = func(spm)
if fmt in ('csc', 'csr'):
mm.check_format(True)
elif fmt == 'coo':
mm._check()
nm = sn.NumericsMatrix(mm)
check_size((nm.size0, nm.size1), spm_shape)
assert nm is not None
t1 = sn.NM_triplet(nm)
t1._check()
check_size(t1.shape, spm_shape)
assert ((spm - t1).nnz == 0)
t2 = sn.NM_csc(nm)
t2.check_format(True)
check_size(t2.shape, spm_shape)
assert ((spm - t2).nnz == 0)
t2bis = sn.NM_csc_trans(nm)
t2bis.check_format(True)
check_size(t2bis.T.shape, spm_shape)
assert ((spm.T - t2bis).nnz == 0)
t3 = sn.NM_triplet(mm)
t3._check()
check_size(t3.shape, spm_shape)
assert ((spm - t3).nnz == 0)
t4 = sn.NM_csc(mm)
t4.check_format(True)
check_size(t4.shape, spm_shape)
assert ((spm - t4).nnz == 0)
t4bis = sn.NM_csc_trans(mm)
t4bis.check_format(True)
check_size(t4bis.T.shape, spm_shape)
assert ((spm.T - t4bis).nnz == 0)
def test_nm_mumps():
# this may be run with mpirun -np <nb processes>
from siconos import numerics
from mpi4py import MPI
import scipy.sparse
import numpy
comm = MPI.COMM_WORLD
# build an empty sparse matrix
M = numerics.NumericsMatrix(scipy.sparse.eye(0))
numerics.NM_MPI_set_comm(M, comm)
numerics.NM_MUMPS_set_control_params(M)
# start parallel MUMPS, processes with rank > 0 execute NM_MUMPS(M, -1)
# then all next NM_MUMPS(M, x) with x!=0 then return when process with
# rank 0 executes NM_MUMPS(M, 0)
numerics.NM_MUMPS(M, -1)
if (comm.Get_rank() > 0):
# when rank 0 executes NM_MUMPS(M, 0)
exit(0)
# only on rank 0:
# fill the matrix
# 2*x - y = 1
# x + y = 1
# solution x: 2/3, y: 1/3 */
numerics.NM_entry(M, 0, 0, 2.)
numerics.NM_entry(M, 0, 1, -1.)
numerics.NM_entry(M, 1, 0, 1.)
numerics.NM_entry(M, 1, 1, 1.)
b = numpy.array([1.0, 1.0])
numerics.NM_MUMPS_set_matrix(M)
numerics.NM_MUMPS_set_dense_rhs(M, 1, b)
# analysis, factorization, solve.
numerics.NM_MUMPS(M, 6)
# end of the work with this matrix
numerics.NM_MUMPS(M, -2)
# send end of listening for ranks > 0
numerics.NM_MUMPS(M, 0)
print('solution:', b)
assert (abs(b[0] - 2. / 3.) < 1e-7)
assert (abs(b[1] - 1. / 3.) < 1e-7)
def SBM_tests(sbm):
print('in SBM_tests')
# SBM_to_dense
# SBM_to_sparse
# SBM_from_csparse
nm = sn.NumericsMatrix(sbm)
assert nm is not None
mcoo = sn.NM_triplet(nm)
mcoo._check()
check_size((nm.size0, nm.size1), mcoo.shape)
compare_with_SBM(sbm, mcoo)
mcsc = sn.NM_csc(nm)
mcsc.check_format(True)
check_size((nm.size0, nm.size1), mcsc.shape)
compare_with_SBM(sbm, mcsc)
mcsc_trans = sn.NM_csc_trans(nm)
mcsc_trans.check_format(True)
check_size((nm.size0, nm.size1), mcsc_trans.T.shape)
compare_with_SBM(sbm, mcsc_trans.T)
return copy.deepcopy(mcsc)
def dense_tests(mdense):
print('in dense_tests')
nm = sn.NumericsMatrix(mdense)
check_size((nm.size0, nm.size1), mdense.shape)
assert nm is not None
