def getStiffnessMatrix(self):
"""
Reads in PETSC binary matrix for the stiffness matrix
"""
if not os.path.isfile(self.stiff):
print ' Error: the file %s does not exist.' % self.stiff
sys.exit(-1)
binaryfile = self.stiff
return petsc_binary_read(binaryfile,0)
def getMassMatrix (self):
"""
Reads in PETSc binary matrix for the mass matrix and returns it as a scipy.sparse COO matrix
"""
if not os.path.isfile(self.mass):
print ' Error: the file %s does not exist.' % self.mass
sys.exit(-1)
binaryfile = self.mass
return petsc_binary_read(binaryfile,0)
def getLumpedMassMatrixE (self):
"""
Reads in PETSc binary vector for the lumped mass matrix and returns it as
a scipy.sparse COO matrix
"""
if not os.path.isfile(self.masse):
print ' Error: the file %s does not exist.' % self.masse
sys.exit(-1)
binaryfile = self.masse
data = petsc_binary_read (binaryfile,0)
size = np.size(data)
rows = np.arange(0,size,1)
cols = np.arange(0,size,1)
mass_e = sparse.coo_matrix( (data,(rows,cols)) ,(size, size) )
return mass_e
def igb_mapped_norm(igbfile1, igbfile2, mapfile):
"""
Compute the L2 Norm of two IGB files based on a map file that
match the node numbering of the two different IGB files.
"""
[Vm1, hd1] = rigb.read_igb_slice(igbfile1)
[Vm2, hd2] = rigb.read_igb_slice(igbfile2)
vm1 = Vm1.squeeze(); vm2 = Vm2.squeeze()
shp1 = shape(vm1)
shp2 = shape(vm2)
xdim1 = shp1[1]; time1 = shp1[0];
xdim2 = shp2[1]; time2 = shp2[0]
print ""
if mapfile is not None:
# match nodes in hybrid and tetrahedra mesh
maparray = read_array_pts(mapfile)
size = len(maparray[:,0])
temp = zeros((time1,size))
for t in xrange(time1):
#count = 0
for i in xrange(len(maparray[:,0])):
ind1 = int(maparray[i][0])
ind2 = int(maparray[i][1])
temp[t][i] = vm1[t][ind1] - vm2[t][ind2]
#count = count + 1
else:
# compute the difference
temp = zeros((time1,xdim1))
for t in xrange(time1):
count = 0
temp[t][:] = vm1[t][:] - vm2[t][:]
erro = zeros(time1)
print '============ P O I N T W I S E C O M P A R I S O N ==========\n'
print 'At time t=%d ms' % (20)
print ' Maximum norm at time=20ms : ' , max( temp[20,:] )
print ' L2 norm at time=20ms : ' , linalg.norm( temp[20,:] )
print '\nAt time t=%d ms' % (100)
print ' Maximum norm at time=100ms: ' , max( temp[100,:] )
print ' L2 norm at time=100ms : ' , linalg.norm( temp[100,:] )
print '\nAt time t=%d ms' % (120)
print ' Maximum norm at time=120ms: ' , max( temp[120,:] )
print ' L2 norm at time=120ms : ' , linalg.norm( temp[120,:] )
n2chk = 450
print '\nDifference in arrival time (n=450): ' , chk_dif_in_max_atime(vm1[:,n2chk],vm2[:,n2chk])
for t in xrange(time1):
#erro[t] = linalg.norm(temp[t,:])
erro[t] = max(temp[t,:])
#ax = subplot(111)
#mytime = arange(0,151)
#ax.plot(mytime, vm1[:,n2chk], mytime, vm2[:,n2chk])
#ax.legend(('hyb','tet'), 'upper right')
#show()
### new stuff ###
print '\n============= E R R O R C O M P A R I S O N =============\n'
# interpolate solution
# compute error as a column vector
t = 10
e = temp[t][:]
# read and store CARP lumped mass matrix
massfile1 = '/data/sim/simulacao_1/hyb_75um/output/MatLabDump_Mi.bin'
M = petsc_binary_read (massfile1,0)
size = np.size(M)
data = M
rows = np.arange(0,size,1)
cols = np.arange(0,size,1)
A = sparse.coo_matrix( (data,(rows,cols)) ,(size, size) )
#aux = dot(e,A*e)
print '\nAt time t=%d ms' % (t)
print ' Mean-square-root L2 Norm (normal) : ', compute_L2_error(e,A)
print ' Mean-square-root L2 Norm (linalg.norm):' , linalg.norm(e)
print '\n'
pdb.set_trace()
