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