rows, cols = IM.shape # new image size
c2 = cols // 2 # half-image
print('image size {:d}x{:d}'.format(rows, cols))
# Step 2: perform the Hansen & Law transform!
print('Performing Hansen and Law inverse Abel transform:')
AIM = iabel_hansenlaw(IM,
dr=1,
use_quadrants=(True, True, True, True),
vertical_symmetry=False,
horizontal_symmetry=False,
verbose=True)
speeds, r = calculate_speeds(AIM)
# Set up some axes
fig = plt.figure(figsize=(15, 4))
ax1 = plt.subplot(131)
ax2 = plt.subplot(132)
ax3 = plt.subplot(133)
# Plot the raw data
im1 = ax1.imshow(IM, origin='lower', aspect='auto')
fig.colorbar(im1, ax=ax1, fraction=.1, shrink=0.9, pad=0.03)
ax1.set_xlabel('x (pixels)')
ax1.set_ylabel('y (pixels)')
ax1.set_title('velocity map image')
# Plot the 2D transform
for q, method in enumerate(transforms.keys()):
Q0 = Q0fresh.copy() # top-right quadrant of O2- image
print ("\n------- {:s} inverse ...".format(method))
t0 = time()
IAQ0 = transforms[method](Q0) # inverse Abel transform using 'method'
print (" {:.1f} sec".format(time()-t0))
iabelQ.append(IAQ0) # store for plot
# polar projection and speed profile
speed, radial = calculate_speeds(IAQ0, origin=(0,0))
# normalize image intensity and speed distribution
IAQ0 /= IAQ0[mask].max()
speed /= speed[radial>50].max()
# plots #121 whole image, #122 speed distributions
plt.subplot(121)
# method label for each quadrant
annot_angle = -(45+q*90)*np.pi/180 # -ve because numpy coords from top
if q > 3:
annot_angle += 50*np.pi/180 # shared quadrant - move the label
annot_coord = ( h/2+(h*0.9)*np.cos(annot_angle)/2,
w/2+(w*0.9)*np.sin(annot_angle)/2 )
plt.annotate(method, annot_coord, color="yellow")
# covering the radial range [300:400] pixels from the center
IM = find_image_center_by_slice (IM, radial_range=(300,400))[0]
rows,cols = IM.shape # new image size
c2 = cols//2 # half-image
print ('image size {:d}x{:d}'.format(rows,cols))
# Step 2: perform the Hansen & Law transform!
print('Performing Hansen and Law inverse Abel transform:')
AIM = iabel_hansenlaw(IM, dr=1, use_quadrants=(True,True,True,True),
vertical_symmetry=False,
horizontal_symmetry=False,
verbose=True)
speeds, r = calculate_speeds (AIM)
# Set up some axes
fig = plt.figure(figsize=(15,4))
ax1 = plt.subplot(131)
ax2 = plt.subplot(132)
ax3 = plt.subplot(133)
# Plot the raw data
im1 = ax1.imshow(IM,origin='lower',aspect='auto')
fig.colorbar(im1,ax=ax1,fraction=.1,shrink=0.9,pad=0.03)
ax1.set_xlabel('x (pixels)')
ax1.set_ylabel('y (pixels)')
ax1.set_title('velocity map image')
# Plot the 2D transform
for q, method in enumerate(transforms.keys()):
Q0 = Q0fresh.copy() # top-right quadrant of O2- image
print("\n------- {:s} inverse ...".format(method))
t0 = time()
IAQ0 = transforms[method](Q0) # inverse Abel transform using 'method'
print(" {:.1f} sec".format(time() - t0))
iabelQ.append(IAQ0) # store for plot
# polar projection and speed profile
speed, radial = calculate_speeds(IAQ0, origin=(0, 0))
# normalize image intensity and speed distribution
IAQ0 /= IAQ0[mask].max()
speed /= speed[radial > 50].max()
# plots #121 whole image, #122 speed distributions
plt.subplot(121)
# method label for each quadrant
annot_angle = -(45 +
q * 90) * np.pi / 180 # -ve because numpy coords from top
if q > 3:
annot_angle += 50 * np.pi / 180 # shared quadrant - move the label
annot_coord = (h / 2 + (h * 0.9) * np.cos(annot_angle) / 2,
w / 2 + (w * 0.9) * np.sin(annot_angle) / 2)
