'poisson_multiplier': 2e6,
'free_prop_method': 'TF',
'kwargs': {'probe_mag_sigma': 100,
'probe_phase_sigma': 100,
'probe_phase_max': 0.5},
}
params = params_2d_cell
# n_ls = ['nonoise', 'n1e9', 'n1e8', 'n1e7', 'n1e6', 'n1e5', 'n1e4']
n_ls = ['nonoise']
# n_ls = ['n4e8', 'n4e7', 'n4e6', 'n4e5', 'n4e4', 'n1.75e8', 'n1.75e7', 'n1.75e6']
# n_ls = [x + '_ref' for x in n_ls]
for n_ph in n_ls:
if 'nonoise' in n_ph:
params['fname'] = 'data_cell_phase.h5'
params['poisson_multiplier'] = 2e6
else:
params['fname'] = 'data_cell_phase_{}.h5'.format(n_ph)
if '_ref' in n_ph:
n_ph_1 = float(n_ph[1:n_ph.find('_ref')])
else:
n_ph_1 = float(n_ph[1:])
params['poisson_multiplier'] = n_ph_1 / 5e4
params['output_folder'] = os.path.join(params['cost_function'], n_ph)
reconstruct_fullfield(**params)
reconstruct_fullfield(fname=params['fname'],
theta_st=0,
theta_end=params['theta_end'],
n_epochs=params['n_epochs'],
n_epochs_mask_release=params['n_epochs_mask_release'],
shrink_cycle=params['shrink_cycle'],
crit_conv_rate=0.03,
max_nepochs=200,
alpha_d=params['alpha_d'],
alpha_b=params['alpha_b'],
gamma=params['gamma'],
free_prop_cm=params['free_prop_cm'],
learning_rate=params['learning_rate'],
output_folder=params['output_folder'],
minibatch_size=params['batch_size'],
theta_downsample=params['theta_downsample'],
save_intermediate=params['save_intermediate'],
full_intermediate=params['full_intermediate'],
energy_ev=params['energy_ev'],
psize_cm=params['psize_cm'],
cpu_only=params['cpu_only'],
save_path=params['save_folder'],
phantom_path=params['phantom_path'],
multiscale_level=params['multiscale_level'],
n_epoch_final_pass=params['n_epoch_final_pass'],
initial_guess=params['initial_guess'],
n_batch_per_update=params['n_batch_per_update'],
dynamic_rate=True,
probe_type=params['probe_type'],
probe_initial=None,
probe_learning_rate=1e-3,
pupil_function=None,
forward_algorithm=params['forward_algorithm'],
random_theta=False,
**params['kwargs'])