xk_recon = result_loop['xk_recon']
yk_recon = result_loop['yk_recon']
alpha_k_recon = result_loop['alpha_k_recon']
K_est = result_loop['K_est']
fitting_error_all.append(result_loop['fitting_error'])
planar_plot_diracs_J2000(
x_plt_grid=x_plt,
y_plt_grid=y_plt,
RA_focus_rad=sky_ra,
DEC_focus_rad=sky_dec,
x_ref=x_ks,
y_ref=y_ks,
amplitude_ref=alpha_ks,
x_recon=xk_recon,
y_recon=yk_recon,
amplitude_recon=alpha_k_recon,
cmap=parameter_set['cmap'],
background_img=img_clean,
marker_scale=parameter_set['marker_scale'],
save_fig=save_fig,
file_name=file_name + '_Kest_{}'.format(K_est),
label_ref_sol='ground truth',
label_recon='reconstruction',
file_format=fig_format,
dpi=parameter_set['dpi'],
close_fig=True,
title_str=r'$K_{{\rm est}}={0}$'.format(K_est))
fitting_error_all = np.array(fitting_error_all)
# plot the objective function values against different K_est
fig = plt.figure(figsize=(4, 2.5), dpi=90)
ax = plt.axes([0.19, 0.185, 0.72, 0.72])
img_dirty = clean_data['img_dirty']
src_model_clean = clean_data['src_model']
x_plt_CLEAN = clean_data['x_plt_CLEAN_rad']
y_plt_CLEAN = clean_data['y_plt_CLEAN_rad']
file_name = fig_dir + \
'visual_comparison_2src_dynamic_range{dynamic_range}'.format(
dynamic_range=parameter_set['dynamic_range']
)
# back ground image: dirty image
planar_plot_diracs_J2000(
x_plt_grid=x_plt_CLEAN, y_plt_grid=y_plt_CLEAN,
RA_focus_rad=sky_ra, DEC_focus_rad=sky_dec,
background_img=img_dirty,
cmap=parameter_set['cmap'],
marker_scale=parameter_set['marker_scale'],
marker_alpha=0.7, save_fig=save_fig,
file_name=file_name + '_bg_img_dirty',
label_ref_sol='ground truth', label_recon='reconstruction',
file_format=fig_format, dpi=parameter_set['dpi'], close_fig=False,
title_str='dirty image'
)
# back ground image: CLEAN image
planar_plot_diracs_J2000(
x_plt_grid=x_plt_CLEAN, y_plt_grid=y_plt_CLEAN,
RA_focus_rad=sky_ra, DEC_focus_rad=sky_dec,
background_img=img_clean,
cmap=parameter_set['cmap'],
marker_scale=parameter_set['marker_scale'],
marker_alpha=0.7, save_fig=save_fig,
file_name=file_name + '_bg_img_clean',
label_ref_sol='ground truth', label_recon='reconstruction',
x_plt_CLEAN = clean_data['x_plt_CLEAN_rad']
y_plt_CLEAN = clean_data['y_plt_CLEAN_rad']
file_name = fig_dir + 'visual_comparison_2src_sep'
# back ground image: dirty image
planar_plot_diracs_J2000(x_plt_grid=x_plt_CLEAN,
y_plt_grid=y_plt_CLEAN,
RA_focus_rad=sky_ra,
DEC_focus_rad=sky_dec,
x_ref=x_ks,
y_ref=y_ks,
amplitude_ref=alpha_ks,
x_recon=xk_recon,
y_recon=yk_recon,
amplitude_recon=alpha_k_recon,
cmap=parameter_set['cmap'],
background_img=img_dirty,
marker_scale=parameter_set['marker_scale'],
save_fig=save_fig,
file_name=file_name + '_bg_img_dirty_updateG',
label_ref_sol='ground truth',
label_recon='reconstruction',
file_format=fig_format,
dpi=parameter_set['dpi'],
close_fig=False,
has_title=False)
# back ground image: CLEAN image
planar_plot_diracs_J2000(x_plt_grid=x_plt_CLEAN,
y_plt_grid=y_plt_CLEAN,
RA_focus_rad=sky_ra,
DEC_focus_rad=sky_dec,
yk_recon = intermidiate_result['yk_recon']
if script_purpose != 'plotting':
for stages in range(stage0, max_stage):
# estimated number of Diracs for the PARTIAL reconstruction
K_est_stage = K_est_stage_lst[stages]
if stages == 0:
file_name = (fig_dir + 'planar_K_{0}_numSta_{1}_locations_stage{2}'
).format(repr(K_est), repr(num_station), stages)
for bg_img in bg_img_lst:
planar_plot_diracs_J2000(
x_plt, y_plt,
RA_focus_rad=sky_ra, DEC_focus_rad=sky_dec,
background_img=background_img[bg_img],
cmap=parameter_set['cmap'],
marker_scale=parameter_set['marker_scale'],
save_fig=save_fig,
file_name=file_name + '_bg_img_' + bg_img.lower(),
file_format='png', dpi=parameter_set['dpi'], close_fig=True,
title_str=bg_img + ' image')
xk_recon, yk_recon, alpha_k_recon = \
planar_recon_2d_dirac_joint_beamforming(
visi_noisy, r_antenna_x, r_antenna_y,
2 * np.pi * freq_subbands_hz, light_speed, K=K_est_stage,
tau_x=tau_x, tau_y=tau_y, M=M, N=N, tau_inter_x=tau_inter_x,
tau_inter_y=tau_inter_y, max_ini=max_ini, num_rotation=1,
G_iter=parameter_set['G_iter'],
plane_norm_vec=plane_norm_vec, verbose=True,
backend=backend, theano_build_G_func=theano_build_G_func,
theano_build_amp_func=theano_build_amp_func