val[0] = b'drop_tol=<1.0e-4>'
desc[0] = b'Drop tolerance (0 <= tol <= 1) for an incomplete LU decomposition.'
hpddm.optionParseDoubles(opt, args, 1, ctypes.cast(val, ctypes.POINTER(ctypes.c_char_p)), ctypes.cast(desc, ctypes.POINTER(ctypes.c_char_p)))
val[0] = b'matrix_filename=<input_file>'
desc[0] = b'Name of the file in which the matrix is stored.'
hpddm.optionParseArgs(opt, args, 1, ctypes.cast(val, ctypes.POINTER(ctypes.c_char_p)), ctypes.cast(desc, ctypes.POINTER(ctypes.c_char_p)))
val = None
desc = None
appArgs()
filename = hpddm.optionPrefix(opt, b'matrix_filename')
if len(filename) == 0:
print('Please specity a -matrix_filename=<input_file>')
sys.exit(1)
n, m, nnz, a, ia, ja, sym = hpddm.parse_file(filename)
Mat = hpddm.matrixCSRCreate(n, m, nnz, a, ia, ja, sym)
if hpddm.numbering.value == b'F':
ia[:] -= 1
ja[:] -= 1
csr = scipy.sparse.csr_matrix((a, ja, ia), shape = (n, m), dtype = hpddm.scalar)
mu = int(hpddm.optionApp(opt, b'generate_random_rhs'))
shape = n if mu == 1 else (n, mu)
sol = numpy.zeros(shape, order = 'F', dtype = hpddm.scalar)
f = numpy.empty_like(sol)
f[:] = numpy.random.random_sample(shape)
if hpddm.scalar == numpy.complex64 or hpddm.scalar == numpy.complex128:
f[:] += numpy.random.random_sample(shape) * 1j
HPDDM is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License
along with HPDDM. If not, see <http://www.gnu.org/licenses/>.
"""
import sys
sys.path.append('interface')
import ctypes
import numpy
import hpddm
n, m, nnz, a, ia, ja, sym = hpddm.parse_file(sys.argv[1])
Mat = hpddm.matrixCSRCreate(n, m, nnz, a, ia, ja, sym)
S = ctypes.POINTER(hpddm.Subdomain)()
hpddm.subdomainNumfact(ctypes.byref(S), Mat)
f = numpy.empty(n, hpddm.scalar)
f[:] = numpy.random.random_sample(n)
if hpddm.scalar == numpy.complex64 or hpddm.scalar == numpy.complex128:
f[:] += numpy.random.random_sample(n) * 1j
sol = numpy.empty_like(f)
hpddm.subdomainSolve(S, f, sol)
nrmb = numpy.linalg.norm(f)
tmp = numpy.empty_like(f)
hpddm.csrmv(Mat, sol, tmp)
tmp -= f
nrmAx = numpy.linalg.norm(tmp)
