# As = cpa_space.Avees2As(sample_Avees_all_levels[level])
# As[:,0,:]=0
# sample_Avees_all_levels[level] = cpa_space.As2Avees(As)
# sample_cpa_all_levels[level] = cpa_space.project(sample_Avees_all_levels[level])
# ipshell('hi')
# pat= PAT(pa_space=cpa_space,Avees=sample_Avees_all_levels[level])
cpa_space.update_pat(Avees=sample_Avees_all_levels[level])
pts = CpuGpuArray(cpa_space.x_dense_img)
v_dense = CpuGpuArray.zeros_like(pts)
cpa_space.calc_v(pts=pts, out=v_dense)
v_dense.gpu2cpu() # for display
plt.figure(level)
of.plt.maximize_figure()
scale = [.4, 0.25][cpa_space.vol_preserve]
scale = 1 * Nx * 30
scale = np.sqrt((v_dense.cpu**2).sum(axis=1)).mean() / 10
for h in [233, 236][:1]:
plt.subplot(h)
cpa_space.quiver(cpa_space.x_dense_grid_img,
v_dense,
scale=scale,
ds=16)
if cpa_space.nC > 1:
cpa_space.plot_cells()
cpa_space.theta2Avees(theta=theta)
cpa_space.update_pat()
# 1/0
# params_flow_int.nTimeSteps *= 10
cell_idx = CpuGpuArray.zeros(len(pts), dtype=np.int32)
cpa_space.calc_cell_idx(pts, cell_idx)
cell_idx.gpu2cpu()
print cell_idx
img = cell_idx.cpu.reshape(cpa_space.x_dense_grid.shape[1:])
# img = pts.cpu[:,0].reshape(cpa_space.x_dense_grid.shape[1:])
plt.figure(1)
of.plt.set_figure_size_and_location(0, 0, 800, 800)
plt.clf()
plt.subplot(131)
plt.imshow(img[:, :, 0, 0], interpolation="None")
plt.colorbar()
v = CpuGpuArray.zeros_like(pts)
cpa_space.calc_v(pts=pts, out=v)
v.gpu2cpu()
img = v.cpu[:, 1].reshape(cpa_space.x_dense_grid.shape[1:])
plt.subplot(132)
plt.imshow(img[:, :, 0, 0], interpolation="None")
plt.colorbar()
cpa_space.theta2Avees(theta=theta)
cpa_space.update_pat()
# 1/0
# params_flow_int.nTimeSteps *= 10
cell_idx = CpuGpuArray.zeros(len(pts),dtype=np.int32)
cpa_space.calc_cell_idx(pts,cell_idx)
cell_idx.gpu2cpu()
print cell_idx
img=cell_idx.cpu.reshape(cpa_space.x_dense_grid.shape[1:])
# img = pts.cpu[:,0].reshape(cpa_space.x_dense_grid.shape[1:])
plt.figure(1)
of.plt.set_figure_size_and_location(0,0,800,800)
plt.clf()
plt.subplot(131)
plt.imshow(img[:,:,0,0],interpolation="None");plt.colorbar()
v = CpuGpuArray.zeros_like(pts)
cpa_space.calc_v(pts=pts,out=v)
v.gpu2cpu()
img=v.cpu[:,1].reshape(cpa_space.x_dense_grid.shape[1:])
plt.subplot(132)
plt.imshow(img[:,:,0,0],interpolation="None");plt.colorbar()
# As[:,0,:]=0
# sample_Avees_all_levels[level] = cpa_space.As2Avees(As)
# sample_cpa_all_levels[level] = cpa_space.project(sample_Avees_all_levels[level])
# ipshell('hi')
# pat= PAT(pa_space=cpa_space,Avees=sample_Avees_all_levels[level])
cpa_space.update_pat(Avees=sample_Avees_all_levels[level])
pts = CpuGpuArray(cpa_space.x_dense_img)
v_dense = CpuGpuArray.zeros_like(pts)
cpa_space.calc_v(pts=pts,out=v_dense )
v_dense.gpu2cpu() # for display
plt.figure(level);
of.plt.maximize_figure()
scale=[.4,0.25][cpa_space.vol_preserve]
scale = 1 * Nx * 30
scale = np.sqrt((v_dense.cpu**2).sum(axis=1)).mean() / 10
for h in [233,236][:1]:
plt.subplot(h)
cpa_space.quiver(cpa_space.x_dense_grid_img,v_dense,scale=scale,ds=16)
if cpa_space.nC>1:
cpa_space.plot_cells()
config_plt()
