# 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()