example_hansenlaw_Xe_test.py

# -*- coding: utf-8 -*-

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import numpy as np
import matplotlib.pyplot as plt

import abel
import scipy.misc

# This example demonstrates Hansen and Law inverse Abel transform
# of an image obtained using a velocity map imaging (VMI) photoelecton 
# spectrometer to record the photoelectron angular distribution resulting 
# from photodetachement of O2- at 454 nm.
# This spectrum was recorded in 2010  
# ANU / The Australian National University
# J. Chem. Phys. 133, 174311 (2010) DOI: 10.1063/1.3493349

filename = 'data/Xenon_ATI_VMI_800_nm_649x519.tif'

# Name the output files
name = filename.split('.')[0].split('/')[1]
output_image = name + '_inverse_Abel_transform_HansenLaw.png'
output_text  = name + '_speeds_HansenLaw.dat'
output_plot  = 'plot_' + name + '_comparison_HansenLaw.png'

print('Loading ' + filename)
#im = np.loadtxt(filename)
im = plt.imread(filename) 
(rows,cols) = np.shape(im)
print ('image size {:d}x{:d}'.format(rows,cols))


# Step 2: perform the Hansen & Law transform!
print('Performing Hansen and Law inverse Abel transform:')

recon = abel.Transform(im, method="hansenlaw", direction="inverse", 
                       symmetry_axis=None, verbose=True, 
                       origin=(240,340)).transform
                       
r, speeds = abel.tools.vmi.angular_integration(recon)

# Set up some axes
fig = plt.figure(figsize=(15,4))
ax1 = plt.subplot(131)
ax2 = plt.subplot(132)
ax3 = plt.subplot(133)

# 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')

# 2D transform
im2 = ax2.imshow(recon, origin='lower', aspect='auto')
fig.colorbar(im2, ax=ax2, fraction=.1, shrink=0.9, pad=0.03)
ax2.set_xlabel('x (pixels)')
ax2.set_ylabel('y (pixels)')
ax2.set_title('Hansen Law inverse Abel')

# 1D speed distribution
ax3.plot(speeds)
ax3.set_xlabel('Speed (pixel)')
ax3.set_ylabel('Yield (log)')
ax3.set_title('Speed distribution')
#ax3.set_yscale('log')

# Prettify the plot a little bit:
plt.subplots_adjust(left=0.06, bottom=0.17, right=0.95, top=0.89, wspace=0.35,
                    hspace=0.37)

# Save a image of the plot
plt.savefig(output_plot, dpi=100)

# save the transformed data
np.savetxt('abel_transform.txt', recon)

# Show the plots
plt.show()