sum_frames=True,
sum_axis=1,
plot_colorbar=True,
reciprocal_space=False,
vmin=0,
vmax=np.nan,
is_orthogonal=True,
)
gu.savefig(
savedir=savedir,
figure=fig,
axes=axs,
tick_width=tick_width,
tick_length=tick_length,
tick_labelsize=14,
xlabels=axs.get_xlabel(),
ylabels=axs.get_ylabel(),
titles=axs.get_title(),
label_size=16,
filename=f"roi{roi}" + comment,
)
comment = comment + f"_{angular_step}deg"
result = {}
#########################################################
# 3D case (BCDI): loop over thresholds first #
# (the threshold needs to be applied before projecting) #
#########################################################
if ndim == 3:
plot_nb += 1
if cuts_limits is not None:
ax.set_xlim(left=cuts_limits[0], right=cuts_limits[1])
ax.set_ylim(bottom=cuts_limits[2], top=cuts_limits[3])
legend = False
if plot_nb < 15:
legend = True
gu.savefig(savedir=savedir,
figure=fig,
axes=ax,
tick_width=tick_width,
tick_length=tick_length,
tick_labelsize=16,
xlabels='width (nm)',
ylabels='modulus',
label_size=20,
legend=legend,
legend_labelsize=14,
filename='cuts' + comment,
only_labels=False)
#################################################################################
# calculate the evolution of the width of the object depending on the threshold #
#################################################################################
idx_point = 0
for key, value in result.items(): # loop over the linecuts
fit = interp1d(value['distance'], value['cut'])
dist_interp = np.linspace(value['distance'].min(),
value['distance'].max(),
gu.savefig(
savedir=savedir,
figure=fig,
axes=ax,
tick_width=2,
tick_length=10,
tick_labelsize=14,
label_size=16,
xlabels="q (1/nm)",
ylabels="PRTF",
filename=f"S{scan}_prtf" + comment,
text={
0: {
"x": 0.15,
"y": 0.30,
"s": "Scan " + str(scan) + comment,
"fontsize": 16
},
1: {
"x": 0.15,
"y": 0.25,
"s": f"q at PRTF=1/e: {q_resolution:.5f} (1/nm)",
"fontsize": 16,
},
2: {
"x": 0.15,
"y": 0.20,
"s": f"resolution d = {2*np.pi / q_resolution:.3f} nm",
"fontsize": 16,
},
},
)
extent=[q_range[4], q_range[5], q_range[3], q_range[2]],
)
ax0.invert_yaxis() # qz is pointing up
ax0.xaxis.set_major_locator(ticker.MultipleLocator(tick_spacing[2]))
ax0.yaxis.set_major_locator(ticker.MultipleLocator(tick_spacing[1]))
gu.colorbar(plt0, numticks=numticks_colorbar, pad=cbar_pad)
gu.savefig(
savedir=savedir,
figure=fig,
axes=ax0,
tick_width=tick_width,
tick_length=tick_length,
tick_direction=tick_direction,
label_size=16,
xlabels=labels[2],
ylabels=labels[1],
filename=sample_name + str(scan) + comment + "_fromrec_qyqz",
labelbottom=draw_ticks,
labelleft=draw_ticks,
labelright=False,
labeltop=False,
left=draw_ticks,
right=draw_ticks,
bottom=draw_ticks,
top=draw_ticks,
)
############################
# plot views in QyQx plane #
############################
if save_qyqx:
fig, ax0 = plt.subplots(1, 1, figsize=(9, 6))
scale=plot_scale,
vmin=vmin,
vmax=vmax,
reciprocal_space=False,
is_orthogonal=is_orthogonal,
plot_colorbar=True,
)
gu.savefig(
savedir=savedir,
figure=fig,
axes=ax,
tick_width=tick_width,
tick_length=tick_length,
tick_labelsize=16,
xlabels=labels[0][0],
ylabels=labels[0][1],
label_size=20,
titles=fname + " central slice in " + labels[0][2],
title_size=20,
legend_labelsize=14,
filename=fname + "_centralslice_z" + comment,
)
fig, ax, _ = gu.imshow_plot(
dataset[:, cen_y, :],
sum_frames=False,
width_v=2 * width[0],
width_h=2 * width[2],
scale=plot_scale,
vmin=vmin,
iterator=idx,
x_axis=fit_x_axis,
distribution='gaussian')
line, = axes_dict[idx][0].plot(fit_x_axis, y_fit, 'b-')
sig_name = f'sig_{idx+1}'
line.set_label(
f'fit, FWHM={2*np.sqrt(2*np.log(2))*minimization.params[sig_name].value:.2f}nm'
)
gu.savefig(savedir=savedir,
figure=fig,
axes=(ax0, ax1, ax2),
tick_width=tick_width,
tick_length=tick_length,
tick_labelsize=16,
xlabels='width (nm)',
ylabels='psf (a.u.)',
label_size=20,
legend=True,
legend_labelsize=14,
filename=f'cuts_{rl_iterations}' + comment,
only_labels=False)
################
# save the psf #
################
np.savez_compressed(savedir + f'psf_{rl_iterations}' + comment + '.npz',
psf=psf_partial_coh,
nb_iter=rl_iterations,
isosurface_threshold=isosurface_threshold,
upsampling_factor=upsampling_factor)
gu.savefig(savedir=detector.savedir,
figure=fig,
axes=ax,
tick_width=2,
tick_length=10,
tick_labelsize=14,
label_size=16,
xlabels='q (1/nm)',
ylabels='PRTF',
filename=f'S{scan}_prtf' + comment,
text={
0: {
'x': 0.15,
'y': 0.30,
's': "Scan " + str(scan) + comment,
'fontsize': 16
},
1: {
'x': 0.15,
'y': 0.25,
's': f"q at PRTF=1/e: {q_resolution:.5f} (1/nm)",
'fontsize': 16
},
2: {
'x': 0.15,
'y': 0.20,
's': f"resolution d = {2*np.pi / q_resolution:.3f} nm",
'fontsize': 16
}
})
if plot_sem == 'fill':
title = f"fill between SEM_thres {sem_dict['threshold'][0]} and {sem_dict['threshold'][-1]}"
else:
title = ''
legend = False
if len(thres_bcdi) < 10:
legend = True
gu.savefig(savedir=savedir,
figure=fig,
axes=ax0,
tick_width=tick_width,
tick_length=tick_length,
label_size=16,
xlabels='angle (deg)',
ylabels='width (nm)',
filename='compa_width_vs_ang' + comment,
titles=title,
legend=legend,
legend_labelsize=12)
##############################################################################################################
# Plot the evolution of the Pearson correlation coefficient and squared residuals depending on the threshold #
##############################################################################################################
min_thres_idx = np.unravel_index(residuals.argmin(), shape=residuals.shape)[0]
fig, ax0 = plt.subplots(nrows=1, ncols=1, figsize=(12, 9))
ax0.plot(thres_bcdi,
correlation,
color='b',
marker='.',
obj[np.isnan(obj)] = 0 # remove nans
fig, axs, _ = gu.imshow_plot(array=obj,
sum_frames=True,
sum_axis=1,
plot_colorbar=True,
reciprocal_space=False,
vmin=0,
vmax=np.nan,
is_orthogonal=True)
gu.savefig(savedir=savedir,
figure=fig,
axes=axs,
tick_width=tick_width,
tick_length=tick_length,
tick_labelsize=14,
xlabels=axs.get_xlabel(),
ylabels=axs.get_ylabel(),
titles=axs.get_title(),
label_size=16,
filename=f'roi{roi}' + comment)
comment = comment + f'_{angular_step}deg'
result = dict()
####################################################################################################
# 3D case (BCDI): loop over thresholds first (the threshold needs to be applied before projecting) #
####################################################################################################
if ndim == 3:
# remove the voxel size along the projection axis
voxel_size = list(voxel_size)
if cuts_limits is not None:
ax.set_xlim(left=cuts_limits[0], right=cuts_limits[1])
ax.set_ylim(bottom=cuts_limits[2], top=cuts_limits[3])
legend = False
if plot_nb < 15:
legend = True
gu.savefig(
savedir=savedir,
figure=fig,
axes=ax,
tick_width=tick_width,
tick_length=tick_length,
tick_labelsize=16,
xlabels="width (nm)",
ylabels="modulus",
label_size=20,
legend=legend,
legend_labelsize=14,
filename="cuts" + comment,
only_labels=False,
)
#################################################################################
# calculate the evolution of the width of the object depending on the threshold #
#################################################################################
idx_point = 0
for key, value in result.items(): # loop over the linecuts
fit = interp1d(value["distance"], value["cut"])
dist_interp = np.linspace(value["distance"].min(),
title = (f"fill between SEM_thres {sem_dict['threshold'][0]} "
f"and {sem_dict['threshold'][-1]}")
else:
title = ""
legend = False
if len(thres_bcdi) < 10:
legend = True
gu.savefig(
savedir=savedir,
figure=fig,
axes=ax0,
tick_width=tick_width,
tick_length=tick_length,
label_size=16,
xlabels="angle (deg)",
ylabels="width (nm)",
filename="compa_width_vs_ang" + comment,
titles=title,
legend=legend,
legend_labelsize=12,
)
#############################################################
# Plot the evolution of the Pearson correlation coefficient #
# and squared residuals depending on the threshold #
#############################################################
min_thres_idx = np.unravel_index(residuals.argmin(), shape=residuals.shape)[0]
fig, ax0 = plt.subplots(nrows=1, ncols=1, figsize=(12, 9))
ax0.plot(
thres_bcdi,
