def __call__(self, x):
self.n += 1
if self.n == 1 or self.n % 10 == 0:
resnorm1 = norm(self.pde.matvec(x,0)-self.rhs)
resnorm = norm(self.pde.matvec(x, 1))
# Compute Gradient
grad = channel.uavgGrad(self.pde.n,self.pde.nb,self.T,self.Jbar,self.pde.zeta,profile=False)
print('iter ', self.n, resnorm, resnorm1, grad)
self.hist.append([self.n, resnorm, grad])
sys.stdout.flush()
def __call__(self, x):
self.n += 1
if self.n == 1 or self.n % 10 == 0:
mpi_rank = mpi_comm.Get_rank()
mpi_size = mpi_comm.Get_size()
resnorm = par_norm(self.pde.matvec(x, 1))
nb = self.pde.nb.copy()
if mpi_rank < (mpi_size - 1):
nb[-1] = nb[-1] - 1
# Compute Gradient
grad = channel.uavgGrad(self.pde.n,nb,self.T,self.Jbar,self.pde.zeta,profile=False)
grad = mpi_comm.allreduce(grad)
if mpi_rank == 0: print 'iter ', self.n, resnorm, grad
self.hist.append([self.n, resnorm, grad])
sys.stdout.flush()
# v and w plots
vxhist = []
for i in range(0,n_steps):
vx = (channel.Ivel(i,project=True).mean(2)).mean(0)
vxhist.append(vx)
t = channel.dt * arange(n_steps)
y,w = channel.quad()
figure()
contourf(y, t, vxhist, 100); colorbar()
axis([-1.0, 1.0, t[0], t[-1]])
# gradient plots
uxavg = channel.velAvg(0,n_steps,T,profile=True)
grad = channel.uavgGrad(n_chunk,chunk_bounds,T,uxavg,pde.zeta,profile=True)
figure()
mag = 1
#plot(uz2bar_avg,y,uz2bar_avg + mag*grad,y,uz2bar_avg - mag*grad,y)
plot(grad,y)
show()
'''
import sensitivities as sens
dirc = 0
y,w = channel.quad()
# gradient plots
ux2bar_avg = sens.vel2Avg(dirc,0,n_steps,T)
gradu2 = sens.u2BarGrad(dirc,n_chunk,chunk_bounds,T,ux2bar_avg,pde.zeta)
uxbar_avg = sens.velAvg(dirc,0,n_steps,T)
