reference_obj, _ = util.load_file(file_path[raw])
reference_obj = abs(reference_obj) / abs(reference_obj).max()
nbz, nby, nbx = reference_obj.shape
reference_obj = util.crop_pad(array=reference_obj,
output_shape=[nbz + 10, nby + 10, nbx + 10])
correlation[raw, raw] = 1
for col in range(raw + 1, nbfiles):
test_obj, _ = util.load_file(file_path[col]) # which index?
test_obj = abs(test_obj) / abs(test_obj).max()
test_obj = util.crop_pad(array=test_obj,
output_shape=[nbz + 10, nby + 10, nbx + 10])
# align reconstructions
shiftz, shifty, shiftx = reg.getimageregistration(abs(reference_obj),
abs(test_obj),
precision=100)
test_obj = reg.subpixel_shift(test_obj, shiftz, shifty, shiftx)
print("\nReference =", raw, " Test =", col)
print(
"z shift",
str("{:.2f}".format(shiftz)),
", y shift",
str("{:.2f}".format(shifty)),
", x shift",
str("{:.2f}".format(shiftx)),
)
correlation[raw, col] = pearsonr(
np.ndarray.flatten(
abs(reference_obj[reference_obj > threshold_correlation])),
np.ndarray.flatten(
abs(test_obj[reference_obj > threshold_correlation])),
plt.figure()
plt.imshow(np.angle(obj)[numz // 2, :, :],
cmap=my_cmap,
vmin=-phase_range,
vmax=phase_range)
plt.title('Phase before subpixel shift')
plt.pause(0.1)
##############################
# align datasets
##############################
# dft registration and subpixel shift (see Matlab code)
shiftz, shifty, shiftx = reg.getimageregistration(amp_simu,
abs(obj),
precision=1000)
obj = reg.subpixel_shift(obj, shiftz, shifty, shiftx)
print("Shift calculated from dft registration: (",
str('{:.2f}'.format(shiftz)), ',', str('{:.2f}'.format(shifty)), ',',
str('{:.2f}'.format(shiftx)), ') pixels')
new_amp = abs(obj)
new_phase = np.angle(obj)
del obj
_, new_strain, _ = np.gradient(planar_dist / (2 * np.pi) * new_phase,
voxel_size)
# q is along y after rotating the crystal
plt.figure()
plt.imshow(new_phase[numz // 2, :, :],
cmap=my_cmap,
vmin=-phase_range,
vmax=phase_range)
plt.title('Phase after subpixel shift')
rgi = RegularGridInterpolator((old_z, old_y, old_x),
data,
method='linear',
bounds_error=False,
fill_value=0)
data = rgi(
np.concatenate((new_z.reshape(
(1, new_z.size)), new_y.reshape(
(1, new_z.size)), new_x.reshape(
(1, new_z.size)))).transpose())
data = data.reshape((nbz, nby, nbx)).astype(refdata.dtype)
else:
shiftz, shifty, shiftx = reg.getimageregistration(abs(refdata),
abs(data),
precision=1000)
data = reg.subpixel_shift(data, shiftz, shifty, shiftx)
print('\n x shift', shiftx, 'y shift', shifty, 'z shift', shiftz)
correlation = pearsonr(np.ndarray.flatten(abs(refdata)),
np.ndarray.flatten(abs(data)))[0]
print('\n Rocking curve ', idx + 1, ', pearson correlation coefficient = ',
str('{:.3f}'.format(correlation)))
if correlation >= correlation_threshold:
sumdata = sumdata + data
savedir = datadir + sample_name + 'sum_S' + str(scan_list[0]) + '_to_S' + str(
scan_list[-1]) + '/'
pathlib.Path(savedir).mkdir(parents=True, exist_ok=True)
np.savez_compressed(savedir + 'pynx_S' + str(scan_list[0]) + '_to_S' +
str(scan_list[-1]) + '.npz',
obj=sumdata)
