fig1, ax11, cc11 = graph.color_plot(xgrid,
ygrid,
ugrid,
fignum=1,
subplot=121,
figsize=(16, 8))
fig1, ax12, cc12 = graph.color_plot(xgrid,
ygrid,
vgrid,
fignum=1,
subplot=122,
figsize=(16, 8))
fig2, ax2, cc2 = graph.color_plot(xgrid, ygrid, omega, fignum=2)
# print np.nansum(omega), gamma / np.nansum(omega)
graph.add_colorbar(cc11, ax=ax11, label=r'$U_x$ (px/frame)')
graph.add_colorbar(cc12, ax=ax12, label=r'$U_y$ (px/frame)')
graph.add_colorbar(cc2, ax=ax2, label=r'$\omega_z$ (1/frame)')
graph.labelaxes(ax11, r'$X$', r'$Y$')
graph.labelaxes(ax12, r'$X$', r'$Y$')
graph.labelaxes(ax2, r'$X$', r'$Y$')
fig1.tight_layout()
fig2.tight_layout()
fig3, ax31 = graph.pdf(ugrid,
nbins=200,
fignum=3,
subplot=121,
figsize=(16, 8))
fig3, ax32 = graph.pdf(vgrid,
nrows_sub, ncolumns_sub = 16, 16
xx_coarse = fa.coarse_grain_2darr(xx, nrows_sub, ncolumns_sub)
yy_coarse = fa.coarse_grain_2darr(yy, nrows_sub, ncolumns_sub)
ux0_coarse = fa.coarse_grain_2darr(ux0, nrows_sub, ncolumns_sub)
uy0_coarse = fa.coarse_grain_2darr(uy0, nrows_sub, ncolumns_sub)
uz0_coarse = fa.coarse_grain_2darr(uz0, nrows_sub, ncolumns_sub)
#energy_coarse = (ux0_coarse ** 2 + uy0_coarse ** 2 + uz0_coarse ** 2) / 2
energy_coarse = np.sqrt((ux0_coarse**2 + uy0_coarse**2 + uz0_coarse**2))
#graph.color_plot(xx_coarse, yy_coarse, ux0_coarse, cmap='RdBu', vmin=-2, vmax=2, fignum=1)
print xx_coarse.shape
# # graph.color_plot(xx, yy, ux0, cmap='RdBu', vmin=-2, vmax=2, fignum=1)
fig = plt.figure(num=1, figsize=(18, 18))
ax = fig.add_subplot(111)
cc = ax.pcolormesh(xx_coarse,
yy_coarse,
uy0_coarse,
cmap='RdBu',
vmin=-2,
vmax=2)
ax.quiver(xx_coarse, yy_coarse, ux0_coarse, uy0_coarse)
# ax.invert_yaxis()
ax.set_aspect('equal')
# set edge color to face color
cc.set_edgecolor('face')
graph.add_colorbar(cc, ax=ax)
plt.show()
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()
cmap=cmap,
fignum=1,
subplot=236)
# Plotting stuff
axes = [ax11, ax12, ax13, ax14, ax15, ax16]
ccs = [cc11, cc12, cc13, cc14, cc15, cc16]
cblabels = [
r'$U_x$', r'$U_x$', r'$U_x$', r'$U_y$', r'$U_y$',
r'$U_y$'
]
xlabel, ylabel = r'$X$ (px)', r'$Y$ (px)'
title = r'W=%dpx, $v$=%.1f px/frame (PIVLab, Original, Diff.)' % (
iw, mag)
for ax, cc, cblabel in zip(axes, ccs, cblabels):
graph.add_colorbar(cc, ax=ax, label=cblabel)
graph.labelaxes(ax, xlabel, ylabel)
graph.setaxes(ax, 0, xx0.shape[1], 0, xx0.shape[0])
graph.suptitle(title, fignum=1)
fig1.tight_layout()
pivdata.close()
fdata.close()
figname = '/pivlab_fakedata_comp_W%d_v%spxframe_multiple_passes' % (
iw, mag_str_2)
graph.save(resultdir + figname, ext='png')
plt.close()
break
elif args.mode == 'gradient':
max = fs.get_float_from_str(pivdatum_loc, 'max',
uy = filters.gaussian_filter(uy, [0.5, 0.5, 0])
uz = filters.gaussian_filter(uz, [0.5, 0.5, 0])
x, y, z = x / data_spacing, y / data_spacing, z / data_spacing
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),
# for ax in axes:
# graph.labelaxes(ax, r'$\Delta t$ (a.u.)', r'$U_x^{true}$ (px/unit time)')
# ax.set_facecolor('k')
# fig8.tight_layout()
fig8, ax81, cc81 = graph.color_plot(grid_deltat,
grid_ux,
chi_data,
cmap=cmap2,
aspect=None,
fignum=8,
subplot=121,
vmin=-100,
vmax=100)
# graph.add_colorbar(cc81, label=r'$\chi / |U_x^{true}|$', aspect=None)
graph.add_colorbar(cc81, label=r'$\chi$ (px/unit time)', aspect=None)
fig8, ax82, cc82 = graph.color_plot(grid_deltat,
grid_ux,
gamma_data,
cmap='plasma',
aspect=None,
fignum=8,
subplot=122,
vmin=0.0,
vmax=150,
figsize=(20, 10))
# graph.add_colorbar(cc82, label=r'$\gamma / |U_x^{true}|$', aspect=None)
graph.add_colorbar(cc82, label=r'$\gamma$ (px/unit time)', aspect=None)
axes = [ax81, ax82]
# Coarse-grain data
nrows_sub, ncolumns_sub = args.iw, args.iw # number of pixels to average over
xx_coarse = fa.coarse_grain_2darr_overwrap(xx, nrows_sub, ncolumns_sub, overwrap=0.5)
yy_coarse = fa.coarse_grain_2darr_overwrap(yy, nrows_sub, ncolumns_sub, overwrap=0.5)
ux0_coarse = fa.coarse_grain_2darr_overwrap(ux0, nrows_sub, ncolumns_sub, overwrap=0.5)
uy0_coarse = fa.coarse_grain_2darr_overwrap(uy0, nrows_sub, ncolumns_sub, overwrap=0.5)
fig1, ax11, cc11 = graph.color_plot(xx, yy, ux0, cmap=cmap, vmin=-2, vmax=2, fignum=1, subplot=221)
fig1, ax12, cc12 = graph.color_plot(xx_coarse, yy_coarse, ux0_coarse, cmap=cmap, vmin=-2, vmax=2, fignum=1, subplot=222)
fig1, ax13, cc13 = graph.color_plot(xx, yy, uy0, cmap=cmap, vmin=-2, vmax=2, fignum=1, subplot=223)
fig1, ax14, cc14 = graph.color_plot(xx_coarse, yy_coarse, uy0_coarse, cmap=cmap, vmin=-2, vmax=2, fignum=1, subplot=224, figsize=(18, 14))
axes1 = [ax11, ax12, ax13, ax14]
ccs1 = [cc11, cc12, cc13, cc14]
titles1 = ['Original $U_x$', 'Coarse-grained $U_x$', 'Original $U_y$', 'Coarse-grained $U_y$']
for ax, cc, title in zip(axes1, ccs1, titles1):
graph.add_colorbar(cc, ax=ax, ticklabelsize=10)
graph.title(ax, title)
# graph.setaxes(ax, 0, 2*np.pi, 0, 2*np.pi)
graph.labelaxes(ax, '$X$ (a.u.)', '$Y$ (a.u.)')
if cmap == 'RdBu':
ax.set_facecolor('k')
graph.suptitle('Fake data')
filename = 'fake_data_vel_fields_%s' %cmap
graph.save(resultdir + filename)
plt.close()
################
# PIV-processed data
################
# data architecture