kernel3 = np.ones_like(x) kernel3[xmin:xmax, ymin:ymax] = 0.7 xc, yc = 750., 520., sigma = 150. kernel4 = 1 - A * np.exp(- (0.1*(x-xc)**2+(y-yc)**2) / (2. * sigma**2)) e_mod = np.empty_like(e) for i in range(e.shape[2]): print data['x'].shape fig, ax, cc = graph.color_plot(data['y'][..., i], data['x'][..., i], e[..., i] / kernel[i], vmin=0, vmax=1*10**4) ax.invert_yaxis() graph.add_colorbar(cc, option='scientific') graph.title(ax, 'z=%.3f' % data['z'][0, 0, i]) graph.save(dir + '/mod_deltadx_%s_-30/zm%03d' % (str(deltafx).replace('.', 'p'), i), ext='png') plt.close('all') if i < 100: fig, ax, cc = graph.color_plot(data['y'][..., i], data['x'][..., i], e[..., i] / kernel[i] * kernel4, vmin=0, vmax=10 * 10 ** 3) e_mod[..., i] = e[..., i] / kernel[i] * kernel4 else: fig, ax, cc = graph.color_plot(data['y'][..., i], data['x'][..., i], e[..., i] / kernel[i], vmin=0, vmax=10 * 10 ** 3) e_mod[..., i] = e[..., i] / kernel[i] ax.scatter(yc, xc) ax.invert_yaxis() graph.add_colorbar(cc, option='scientific')
fig2, ax2, cc2 = graph.color_plot(iw_grid, disp_grid, fitdata, vmin=vmins[i], vmax=vmaxs[i], fignum=2, subplot=241+i, cmap=cmaps[i]) plt.scatter(iws, disps, color='m', s=4) axes.append(ax2) ccs.append(cc2) def vel_upper_limit_func(iw, npass): iw_ini = iw * 2**(npass-1) return iw_ini/2 for ax, title, cc in zip(axes, titles, ccs): ax.plot(np.linspace(0, 80), vel_upper_limit_func(np.linspace(0, 80), 4), linestyle='--', color='r') ax.set_facecolor('k') graph.setaxes(ax, 0, 80, 0, 80) graph.add_colorbar(cc, ax=ax) graph.labelaxes(ax, xlabel, ylabel) graph.title(ax, title) graph.save(datafilename+'4') #sort arrays first import library.basics.formatarray as fa iws_s, disps_s = fa.sort_two_arrays_using_order_of_first_array(iws, disps) # Accuracy iws_s, gauss_peaks_ux_s = fa.sort_two_arrays_using_order_of_first_array(iws, gauss_peaks_ux) iws_s, gauss_peaks_ux_err_s = fa.sort_two_arrays_using_order_of_first_array(iws, gauss_peaks_ux_err) iws_s, lorentz_peaks_ux_s = fa.sort_two_arrays_using_order_of_first_array(iws, lorentz_peaks_ux) iws_s, lorentz_peaks_ux_err_s = fa.sort_two_arrays_using_order_of_first_array(iws, lorentz_peaks_ux_err) # Precision iws_s, sigmas_ux_s = fa.sort_two_arrays_using_order_of_first_array(iws, sigmas_ux) iws_s, sigmas_ux_err_s = fa.sort_two_arrays_using_order_of_first_array(iws, sigmas_ux_err) iws_s, gammas_ux_s = fa.sort_two_arrays_using_order_of_first_array(iws, gammas_ux)
e = (ux**2 + uy**2) / 2. for i in range(x.shape[2]): print i, np.min(z[..., i]), np.max(z[..., i]), np.mean(z[..., i]) fig, ax, cc = graph.color_plot(x[..., i], y[..., i], e[..., i], cmap='plasma', vmin=vmin, vmax=vmax) graph.add_colorbar( cc, label=r'$\bar{E}_{2D}=\frac{1}{2}(\bar{U_x}^2)$', option='scientific') graph.labelaxes(ax, 'X (px)', 'Y (px)') graph.title(ax, '<z>=%.2f px' % np.mean(z[..., i])) fig.tight_layout() filename = '/time_avg_energy_raw_%s/zm%03d' % (args.mode, i) graph.save(args.dir + filename, ext='png', close=True, verbose=True) print x.shape, ux.shape xmin, xmax, ymin, ymax, zmin, zmax = np.min(x), np.max(x), np.min( y), np.max(y), np.min(z), np.max(z) points = zip(np.ravel(x), np.ravel(y), np.ravel(z)) # px after piv processing # values = np.ravel(ux)*scale*frame_rate #mm/s