###################
# define colormap #
###################
bad_color = '1.0' # white background
colormap = gu.Colormap(bad_color=bad_color)
my_cmap = colormap.cmap
plt.ion()
######################
# create the lattice #
######################
pivot, _, q_values, lattice, peaks = simu.lattice(
energy=energy,
sdd=sdd,
direct_beam=direct_beam,
detector=detector,
unitcell=unitcell,
unitcell_param=unitcell_param,
euler_angles=angles,
offset_indices=False)
# peaks in the format [[h, l, k], ...]: CXI convention downstream , vertical up, outboard
for idx in range(len(peaks)):
print('Miller indices:', peaks[idx], ' at pixels:', lattice[idx])
##############################################
# convolute the lattice with a 3D peak shape #
##############################################
# since we have a small list of peaks, do not use convolution (too slow) but for loop
peak_shape = pu.gaussian_kernel(ndim=3,
kernel_length=kernel_length,
sigma=3,
##########################
# apply photon threshold #
##########################
data[data < photon_threshold] = 0
print('Sparsity of the data after photon threshold:',
str('{:.2f}'.format((data == 0).sum() / (nz * ny * nx) * 100)), '%')
######################
# calculate q values #
######################
if unitcell == 'bct':
pivot, _, q_values, _, _ = simu.lattice(
energy=energy,
sdd=sdd,
direct_beam=direct_beam,
detector=detector,
unitcell=unitcell,
unitcell_param=[unitcell_ranges[0], unitcell_ranges[2]],
euler_angles=[0, 0, 0],
offset_indices=True)
else:
pivot, _, q_values, _, _ = simu.lattice(energy=energy,
sdd=sdd,
direct_beam=direct_beam,
detector=detector,
unitcell=unitcell,
unitcell_param=unitcell_ranges[0],
euler_angles=[0, 0, 0],
offset_indices=True)
nbz, nby, nbx = len(q_values[0]), len(q_values[1]), len(q_values[2])