rndnoise = np.random.randn(nbobspt*dimsol).reshape((nbobspt, dimsol))
DD[ii] = dd + sigmas.reshape((len(sigmas),1))*rndnoise
waveobj.dd = DD
waveobj.solvefwd_cost()
if mpirank == 0:
print 'noise misfit={}, regul cost={}, ratio={}'.format(waveobj.cost_misfit, \
waveobj.cost_reg, waveobj.cost_misfit/waveobj.cost_reg)
#myplot.plot_timeseries(waveobj.solfwd[2], 'pd', 0, skip, dl.Function(V))
# Matvec for Hessian
if mpirank == 0: print 'Compute gradient'
waveobj.update_PDE({'b':b_target_fn})
waveobj.solvefwd_cost()
waveobj.solveadj_constructgrad()
x, y = dl.Function(Vm), dl.Function(Vm)
setfct(x, 1.0)
if mpirank == 0: print 'Time Hessian matvec'
for ii in range(10):
MPI.barrier(mpicomm)
t0 = Wtime()
waveobj.mult(x.vector(), y.vector())
t1 = Wtime()
dt = t1-t0
mindt = MPI.min(mpicomm, t1-t0)
maxdt = MPI.max(mpicomm, t1-t0)
avgdt = MPI.sum(mpicomm, t1-t0) / float(mpisize)
if mpirank == 0:
print 'min={}, max={}, avg={}'.format(mindt, maxdt, avgdt)
diffbnorm = diffb.norm('l2')
if mpirank == 0:
print '|MGaa|={} (df={:.2e}), |MGba|={} (df={:.2e})'.format(\
MGaanorm, diffanorm, MGbanorm, diffbnorm)
waveobjab.assemble_hessian()
regula.assemble_hessian(evaluationpoint['a'])
waveobja = restrictobjabtoa(waveobjabnoregul, regula)
print ' Check Hessian are the same'
yy = dl.Function(Vl*Vl)
xx = dl.Function(Vl*Vl)
xx.vector().zero()
xx.vector().axpy(-1.0, waveobjab.MGv)
waveobjab.mult(xx.vector(), yy.vector())
xx.vector().zero()
xx.vector().axpy(1.0, yy.vector())
ya, yb = yy.split(deepcopy=True)
yan = ya.vector().norm('l2')
ybn = yb.vector().norm('l2')
y = dl.Function(Vl)
x = dl.Function(Vl)
x.vector().zero()
x.vector().axpy(-1.0, MGaa.vector())
waveobja.mult(x.vector(), y.vector())
x.vector().zero()
x.vector().axpy(1.0, y.vector())
yn = y.vector().norm('l2')
diff = y.vector() - ya.vector()
+ '_' + str(a) + '-' + str(b) + '.dat'
except:
myrange = xrange(Vm.dim())
Hessfilename = outputdirectory + 'Hessian' + str(freq) + '.dat'
# Assemble data Hessian
if mpirank == 0: print 'Assemble data misfit part of the Hessian'
waveobj.alpha_reg = 0.0
Hei, ei = dl.Function(Vm), dl.Function(Vm)
Hessian, VrDM, VcDM = setupPETScmatrix(Vm, Vm, 'dense', mpicomm)
for ii in myrange:
if ii % 100 == 0 and mpirank == 0:
print 'ii={} out of {}'.format(ii, Vm.dim())
ei.vector().zero()
setglobalvalue(ei, ii, 1.0)
waveobj.mult(ei.vector(), Hei.vector())
normvec = Hei.vector().norm('l2')
Hei_arr = Hei.vector().array()
cols = np.where(np.abs(Hei_arr) > 1e-16 * normvec)[0]
for cc, val in zip(cols, Hei_arr[cols]):
global_cc = VcDM.dofs()[cc]
Hessian[ii, global_cc] = val
Hessian.assemblyBegin()
Hessian.assemblyEnd()
# Print Hessian to file
if mpirank == 0: print 'Print Hessian to file'
myviewer = PETSc.Viewer().createBinary(Hessfilename, \
mode='w', format=PETSc.Viewer.Format.NATIVE, comm=mpicomm)
myviewer(Hessian)
