Source_PolState = np.zeros((len(Source), 2), complex)
for i in range(len(Source)):
Source_PolState[i, 0] = E_in[0]
Source_PolState[i, 1] = E_in[1]
# Reconstruct parameters
setup = wo.waveorder_microscopy((Ns, Ms),
lambda_illu,
ps,
NA_obj,
NA_illu,
z_defocus,
n_media=n_media,
cali=cali,
bg_option=bg_option,
A_matrix=A_matrix,
phase_deconv='3D',
inc_recon='3D',
illu_mode='Arbitrary',
Source=Source,
Source_PolState=Source_PolState,
use_gpu=use_gpu,
gpu_id=gpu_id)
# ### Writer setup
uPTI_file_name = 'uPTI_subFOVs.zarr'
writer = WaveorderWriter(output_path, hcs=False, hcs_meta=None, verbose=True)
writer.create_zarr_root(uPTI_file_name)
data_shape = (1, 9, N_defocus, int(Ns), int(Ms))
# ### Load data
# Load simulations
file_name = '/data_sm/home/lihao/project/Polscope/Simulation/3D_Pol_Phase/uPTI_repo_demo/3D_PODT_simulation.npz'
array_loaded = np.load(file_name)
list_of_array_names = sorted(array_loaded)
for array_name in list_of_array_names:
globals()[array_name] = array_loaded[array_name]
print(list_of_array_names)
N, M, L = I_meas.shape
# ### Refractive index reconstruction
z_defocus = (np.r_[:L]-L//2)*psz
chi = 0.1*2*np.pi
setup = wo.waveorder_microscopy((N,M), lambda_illu, ps, NA_obj, NA_illu, z_defocus, chi,
n_media = n_media, phase_deconv='3D', pad_z=10)
H_re_vis = fftshift(setup.H_re)
wo.plot_multicolumn([np.real(H_re_vis)[:,:,L//2], np.transpose(np.real(H_re_vis)[N//2,:,:]),
np.imag(H_re_vis)[:,:,L//2], np.transpose(np.imag(H_re_vis)[N//2,:,:])],
num_col=2, size=8, set_title=True,
titles=['$xy$-slice of Re{$H_{re}$} at $u_z=0$', '$xz$-slice of Re{$H_{re}$} at $u_y=0$',
'$xy$-slice of Im{$H_{re}$} at $u_z=0$', '$xz$-slice of Im{$H_{re}$} at $u_y=0$'], colormap='jet')
plt.show()
H_im_vis = fftshift(setup.H_im)
wo.plot_multicolumn([np.real(H_im_vis)[:,:,L//2], np.transpose(np.real(H_im_vis)[N//2,:,:]),
np.imag(H_im_vis)[:,:,L//2], np.transpose(np.imag(H_im_vis)[N//2,:,:])],
num_col=2, size=8, set_title=True,
titles=['$xy$-slice of Re{$H_{im}$} at $u_z=0$', '$xz$-slice of Re{$H_{im}$} at $u_y=0$',
'$xy$-slice of Im{$H_{im}$} at $u_z=0$', '$xz$-slice of Im{$H_{im}$} at $u_y=0$'], colormap='jet')
lambda_illu * fxx,
lambda_illu * fyy,
subsampled_NA=0.1)
plt.figure(figsize=(10, 10))
plt.imshow(fftshift(Source_discrete), cmap='gray')
plt.show()
print(np.sum(Source_discrete))
z_defocus = (np.r_[:L] - L // 2) * psz
chi = 0.1 * 2 * np.pi
setup = wo.waveorder_microscopy((N, M),
lambda_illu,
ps,
NA_obj,
NA_illu,
z_defocus,
chi,
n_media=n_media,
phase_deconv='3D',
illu_mode='Arbitrary',
Source=Source_cont)
simulator = wo.waveorder_microscopy_simulator((N, M),
lambda_illu,
ps,
NA_obj,
NA_illu,
z_defocus,
chi,
n_media=n_media,
illu_mode='Arbitrary',
for array_name in list_of_array_names:
globals()[array_name] = array_loaded[array_name]
print(list_of_array_names)
# ### Reconstruction of Stokes parameters and anisotropy
_, N, M, L = I_meas.shape
cali = False
bg_option = 'global'
setup = wo.waveorder_microscopy((N, M),
lambda_illu,
ps,
NA_obj,
NA_illu,
z_defocus,
chi,
n_media=n_media,
phase_deconv='2D',
bire_in_plane_deconv='2D',
illu_mode='BF')
S_image_recon = setup.Stokes_recon(I_meas)
S_image_tm = setup.Stokes_transform(S_image_recon)
Recon_para = setup.Polarization_recon(
S_image_tm) # Without accounting for diffraction
wo.plot_multicolumn(
np.array([
Recon_para[0, :, :, L // 2], Recon_para[1, :, :, L // 2],
Recon_para[2, :, :, L // 2], Recon_para[3, :, :, L // 2]
]),
_, _, N, M, L = I_meas.shape
cali = False
bg_option = 'global'
use_gpu = True
gpu_id = 0
A_matrix = 0.5*np.array([[1, 1, 0], \
[1, 0, 1], \
[1, -1, 0], \
[1, 0, -1]])
setup = wo.waveorder_microscopy((N,M), lambda_illu, ps, NA_obj, NA_illu, z_defocus, chi, \
n_media=n_media, cali=cali, \
A_matrix=A_matrix, inc_recon='3D', \
illu_mode='Arbitrary', Source = Source_cont, Source_PolState=Source_PolState,\
pad_z=5, use_gpu=use_gpu, gpu_id=gpu_id)
### Illumination patterns used
wo.plot_multicolumn(fftshift(Source_cont, axes=(1, 2)),
origin='lower',
num_col=5,
size=5)
plt.show()
## Reconstruct Stokes images and visualize them as a function of illumination pattern
S_image_recon = setup.Stokes_recon(I_meas)
S_bg_mean_0 = np.mean(S_image_recon[0, :, :, :, :],
axis=(1, 2, 3))[:, np.newaxis, np.newaxis, np.newaxis]