def ppm_em_experiment(seed, image_idx, SNR, Ncopy, Ndir, use_sPCA,
use_signal_prior, gamma, BW, path_learning, P):
Coeff, Freqs, rad_freqs, Mean, Phi_ns, sigma, Coeff_raw, rotations = make_data(
Ncopy=Ncopy, SNR=SNR, seed=seed, image_idx=image_idx, sPCA=use_sPCA)
image0 = recover_image(Coeff_raw, Phi_ns, Freqs, r_max, Mean)
TT = time()
Coeff_s, est_rotations = synchronize_2d(Coeff, Freqs, Ndir)
t_synch = time() - TT
x_s = np.expand_dims(np.mean(Coeff_s, axis=-1), axis=-1)
x = align_image(x_s, Coeff_raw, Freqs)
imager = recover_image(x, Phi_ns, Freqs, r_max, Mean)
err_synch = np.sqrt(
np.sum(np.sum((image0 - imager)**2, axis=-1), axis=0) /
np.sum(np.sum(image0**2, axis=-1), axis=0))
x, num_iter_synch_em, t_synch_em = EM_General_Prior(Coeff,
Freqs,
sigma,
Ndir,
1 / Ndir * np.ones(
(Ndir, 1)),
Ndir,
0,
Coeff_raw,
use_signal_prior,
uniform=False)
x = align_image(x, Coeff_raw, Freqs)
imager = recover_image(x, Phi_ns, Freqs, r_max, Mean)
err_synch_em = np.sqrt(
np.sum(np.sum((image0 - imager)**2, axis=-1), axis=0) /
np.sum(np.sum(image0**2, axis=-1), axis=0))
results = pd.DataFrame()
results = results.append(
{
'use_signal_prior': use_signal_prior,
'use_sPCA': use_sPCA,
'seed': seed,
'SNR': SNR,
'sigma': sigma,
'N': Ncopy,
'L': Coeff.shape[0],
'image_idx': image_idx,
'err': err_synch_em,
'num_iter': num_iter_synch_em,
'BW': BW,
'gamma': gamma,
't': t_synch + t_synch_em,
't_synch': t_synch,
't_em': t_synch_em,
'err_synch': err_synch
},
ignore_index=True)
return results
def synchronize_and_match_experiment(seed, image_idx, SNR, Ncopy, Ndir,
use_sPCA, P):
Coeff, Freqs, rad_freqs, Mean, Phi_ns, sigma, Coeff_raw, rotations = make_data(
Ncopy=Ncopy, SNR=SNR, seed=seed, image_idx=image_idx, sPCA=use_sPCA)
image0 = recover_image(Coeff_raw, Phi_ns, Freqs, r_max, Mean)
TT = time()
Coeff_s, est_rotations = synchronize_and_match_2d(Coeff,
Freqs,
P=P,
L=Ndir)
t_synch = time() - TT
h, bin_edges = np.histogram((est_rotations - rotations) % 360,
bins=np.arange(-0.5, 360 + 0.5))
measured_rho = np.expand_dims(h / np.sum(h), axis=-1)
x_s = np.expand_dims(np.mean(Coeff_s, axis=-1), axis=-1)
x = align_image(x_s, Coeff_raw, Freqs)
imager = recover_image(x, Phi_ns, Freqs, r_max, Mean)
err_synch = np.sqrt(
np.sum(np.sum((image0 - imager)**2, axis=-1), axis=0) /
np.sum(np.sum(image0**2, axis=-1), axis=0))
print('#### synchronize and match ####')
print('e = %f' % err_synch)
results = pd.DataFrame()
results = results.append(
{
'use_sPCA': use_sPCA,
'seed': seed,
'SNR': SNR,
'sigma': sigma,
'N': Ncopy,
'L': Coeff.shape[0],
'image_idx': image_idx,
'rho': measured_rho,
'err': err_synch,
'num_iter': 0,
't': t_synch
},
ignore_index=True)
return results
def known_rotations_experiment(seed, image_idx, SNR, Ncopy, Ndir, use_sPCA):
Coeff, Freqs, rad_freqs, Mean, Phi_ns, sigma, Coeff_raw, rotations = make_data(
Ncopy=Ncopy, SNR=SNR, seed=seed, image_idx=image_idx, sPCA=use_sPCA)
image0 = recover_image(Coeff_raw, Phi_ns, Freqs, r_max, Mean)
est_rotations = rotations
Coeff_s = np.zeros_like(Coeff)
for i in range(Ncopy):
Coeff_s[:, i] = Coeff[:, i] * np.exp(
-1j * 2 * np.pi * est_rotations[i] / 360 * Freqs)
t_synch = 0
h, bin_edges = np.histogram((est_rotations - rotations) % 360,
bins=np.arange(-0.5, 360 + 0.5))
measured_rho = np.expand_dims(h / np.sum(h), axis=-1)
x_s = np.expand_dims(np.mean(Coeff_s, axis=-1), axis=-1)
x = align_image(x_s, Coeff_raw, Freqs)
imager = recover_image(x, Phi_ns, Freqs, r_max, Mean)
err_synch = np.sqrt(
np.sum(np.sum((image0 - imager)**2, axis=-1), axis=0) /
np.sum(np.sum(image0**2, axis=-1), axis=0))
print('#### oracle ####')
print('e = %f' % err_synch)
results = pd.DataFrame()
results = results.append(
{
'use_sPCA': use_sPCA,
'seed': seed,
'SNR': SNR,
'sigma': sigma,
'N': Ncopy,
'L': Coeff.shape[0],
'image_idx': image_idx,
'rho': measured_rho,
'err': err_synch,
'num_iter': 0,
't': t_synch
},
ignore_index=True)
return results
def standard_em_experiment(seed, image_idx, SNR, Ncopy, Ndir, use_sPCA,
use_signal_prior):
Coeff, Freqs, rad_freqs, Mean, Phi_ns, sigma, Coeff_raw, rotations = make_data(
Ncopy=Ncopy, SNR=SNR, seed=seed, image_idx=image_idx, sPCA=use_sPCA)
image0 = recover_image(Coeff_raw, Phi_ns, Freqs, r_max, Mean)
x, num_iter_em_uniform, t_em_uniform = EM_General_Prior(Coeff,
Freqs,
sigma,
Ndir,
1 / Ndir * np.ones(
(Ndir, 1)),
Ndir,
0,
Coeff_raw,
use_signal_prior,
uniform=True)
x = align_image(x, Coeff_raw, Freqs)
imager = recover_image(x, Phi_ns, Freqs, r_max, Mean)
err_em_uniform = np.sqrt(
np.sum(np.sum((image0 - imager)**2, axis=-1), axis=0) /
np.sum(np.sum(image0**2, axis=-1), axis=0))
results = pd.DataFrame()
results = results.append(
{
'use_signal_prior': use_signal_prior,
'use_sPCA': use_sPCA,
'seed': seed,
'SNR': SNR,
'sigma': sigma,
'N': Ncopy,
'L': Coeff.shape[0],
'image_idx': image_idx,
'err': err_em_uniform,
'num_iter': num_iter_em_uniform,
't': t_em_uniform
},
ignore_index=True)
return results