)
ang_width = np.empty((len(threshold), nb_dir))
for idx, thres in enumerate(threshold):
# apply the threshold
tmp_obj = np.copy(obj)
tmp_obj[tmp_obj < thres] = 0
if ndim == 3: # project the object
tmp_obj = tmp_obj.sum(axis=sum_axis)
tmp_obj = abs(tmp_obj) / abs(
tmp_obj).max() # normalize the modulus to 1
for idy, direction in enumerate(directions):
# get the distances and the modulus values along the linecut
distance, cut = util.linecut(array=tmp_obj,
point=origin,
direction=direction,
voxel_size=voxel_size)
# get the indices where the linecut is non_zero
indices = np.nonzero(cut)
# get the width along the cut for that threshold
ang_width[idx, idy] = distance[max(indices[0])] - distance[min(
indices[0])]
# store the result in the dictionary
result["ang_width_threshold"] = ang_width
###################################################
# 2D case (SEM): one can create the linecut for #
# each direction first and apply thresholds later #
###################################################
else:
else:
gu.multislices_plot(array=obj,
sum_frames=False,
plot_colorbar=True,
reciprocal_space=False,
is_orthogonal=True,
slice_position=(25, 37, 25))
#####################################
# create the linecut for each point #
#####################################
result = dict()
for point in points:
# get the distances and the modulus values along the linecut
distance, cut = util.linecut(array=obj,
point=point,
direction=direction,
voxel_size=voxel_size)
# store the result in a dictionary (cuts can have different lengths depending on the direction)
result[f'voxel {point}'] = {'distance': distance, 'cut': cut}
######################
# plot the linecuts #
######################
fig = plt.figure(figsize=fig_size)
ax = plt.subplot(111)
plot_nb = 0
for key, value in result.items():
# value is a dictionary {'distance': 1D array, 'cut': 1D array}
line, = ax.plot(value['distance'],
value['cut'],
color=colors[plot_nb % len(colors)],
slice_position=psf_cen)
fig.savefig(savedir + f'psf_slices_{rl_iterations}' + comment + '.png')
fig, ax = plt.subplots(figsize=(12, 9))
ax.plot(error, 'r.')
ax.set_yscale('log')
ax.set_xlabel('iteration number')
ax.set_ylabel('difference between consecutive iterates')
fig.savefig(savedir + f'error_metric_{rl_iterations}' + comment + '.png')
#######################################################################################
# calculate linecuts of the blurring function through its center of mass and fit them #
#######################################################################################
# create linecuts in the three orthogonal directions
width_z, cut_z = util.linecut(array=psf_partial_coh,
point=psf_cen,
direction=(1, 0, 0),
voxel_size=voxel_size)
width_y, cut_y = util.linecut(array=psf_partial_coh,
point=psf_cen,
direction=(0, 1, 0),
voxel_size=voxel_size)
width_x, cut_x = util.linecut(array=psf_partial_coh,
point=psf_cen,
direction=(0, 0, 1),
voxel_size=voxel_size)
linecuts_dict = OrderedDict([('width_z', width_z), ('width_y', width_y),
('width_x', width_x), ('cut_z', cut_z),
('cut_y', cut_y), ('cut_x', cut_x)])
# define the maximum identical length that can share the linecuts (we need to concatenate them)
width_length = min(width_z.size, width_y.size, width_x.size)