len = uz2bar_avg.shape[0]
for i in range(len):
uxbar_avg[i] = mpi_comm.allreduce(uxbar_avg[i])
uybar_avg[i] = mpi_comm.allreduce(uybar_avg[i])
uzbar_avg[i] = mpi_comm.allreduce(uzbar_avg[i])
ux2bar_avg[i] = mpi_comm.allreduce(ux2bar_avg[i])
uy2bar_avg[i] = mpi_comm.allreduce(uy2bar_avg[i])
uz2bar_avg[i] = mpi_comm.allreduce(uz2bar_avg[i])
nb = chunk_bounds
if mpi_rank < (mpi_size - 1):
nb[-1] = nb[-1] - 1
gradux = sens.uBarGrad(0,n_chunk,nb,T,uxbar_avg,pde.zeta)
graduy = sens.uBarGrad(1,n_chunk,nb,T,uybar_avg,pde.zeta)
graduz = sens.uBarGrad(2,n_chunk,nb,T,uzbar_avg,pde.zeta)
gradux2 = sens.u2BarGrad(0,n_chunk,nb,T,ux2bar_avg,pde.zeta)
graduy2 = sens.u2BarGrad(1,n_chunk,nb,T,uy2bar_avg,pde.zeta)
graduz2 = sens.u2BarGrad(2,n_chunk,nb,T,uz2bar_avg,pde.zeta)
for i in range(len):
gradux[i] = mpi_comm.allreduce(gradux[i])
graduy[i] = mpi_comm.allreduce(graduy[i])
graduz[i] = mpi_comm.allreduce(graduz[i])
gradux2[i] = mpi_comm.allreduce(gradux2[i])
graduy2[i] = mpi_comm.allreduce(graduy2[i])
graduz2[i] = mpi_comm.allreduce(graduz2[i])
if mpi_rank == 0:
y,w = channel.quad()
import sensitivities as sens
dirc = 0
if mpi_rank == (mpi_size-1):
uxbar_avg = sens.velAvg(dirc,0,n_steps,T)
else:
uxbar_avg = sens.velAvg(dirc,0,n_steps-1,T)
len = uxbar_avg.shape[0]
for i in range(len):
uxbar_avg[i] = mpi_comm.allreduce(uxbar_avg[i])
nb = chunk_bounds
grad = sens.uBarGrad(dirc,n_chunk,nb,T,uxbar_avg,pde.zeta)
for i in range(len):
grad[i] = mpi_comm.allreduce(grad[i])
if mpi_rank == 0:
y,w = channel.quad()
figure()
plot(uxbar_avg,y)
figure()
plot(grad,y)
# save data
filename = file_address + results_dir + 'grad_profile.npy'
save(filename,grad)
# v and w plots
