log.info('Making '+str(N)+' frames')
for i in tqdm(range(N)):
# xp, yp, zp = phantoms.sphere_spiral(i/(N-1))
# print(xp,yp,zp)
if i == 0:
x, y, z = (0.5, 0, 0)
xp, yp, zp = (1,0,1)
else:
x, y, z = (0, 0.5, 0)
xp, yp, zp = (0,1,1)
obj = R3S2toR.xyzj_list([[x,y,z]], [[xp,yp,zp]], shape=[10,10,4],
title='Single dipole radiator')
obj.build_actors()
d1 = det.FourFLF(ulens_aperture='square', irrad_title='Lightfield detector irradiance')
im1 = d1.xyzj_single_to_xy_det(obj)
im1.data /= 100
d2 = det.FourF()
im0 = d2.xyzj_single_to_xye_det(obj)
im2 = d2.xyzj_single_to_xy_det(obj)
im2.data /= 100
istr = '{:03d}'.format(i)
im1.to_tiff('./out2/'+istr+'.tif')
utilmpl.plot([[obj, im0, im2, im1]], './out/'+istr+'.png')
log = logging.getLogger('log')
# Load object
with open('guv-2um.pickle', 'rb') as handle:
xyzJ_list = pickle.load(handle)
# Load microscope
with open('det.pickle', 'rb') as handle:
det1 = pickle.load(handle)
xyzJ_list.data_xyz += np.array([0, 0, -2.5])
N = 50
for i in range(N):
# Shift up
xyzJ_list.data_xyz += np.array([0, 0, 0.1])
# Put object on a grid
input_shape = (101, 101, 50, 6)
input_vox_dims = (6.5 / 60, 6.5 / 60, 0.2)
xyzJ = xyzJ_list.to_xyzJ(input_shape, input_vox_dims)
# Simulate microscope
xy = det1.xyzJ_to_xy_det(xyzJ)
# Plot
xyzJ.skip_n = 2
xyzJ.rad_scale = 1.5
xyzJ.build_actors()
obj_list = [xyzJ, xy]
utilmpl.plot([obj_list], './out/' + str(i) + '.png', ss=2)
log = logging.getLogger('log')
log.info('Generating phantom.')
vox_dims = np.array([.1, .1, .1])
data = np.zeros((3, 3, 3, 6))
data[0, 0, 0, 0] = 1
data[1, 1, 1, 0] = 1
data[2, 2, 2, 0] = 1
data[2, 0, 0, 0] = 1
grid_obj = R3S2toR.xyzJ(data, vox_dims=vox_dims)
grid_obj.to_tiff('./guv.tiff')
grid_obj.rad_scale = 0.5
log.info('Calculating MIP')
mip = grid_obj.to_R3toR_xyz()
obj_list = [grid_obj, mip]
for obj in obj_list:
obj.build_actors()
# Flyaround
N = 80
log.info('Making ' + str(N) + ' frames')
for i in tqdm(range(N)):
for obj in obj_list:
obj.increment_camera(360 / N)
istr = '{:03d}'.format(i)
utilmpl.plot([obj_list], './test/' + istr + '.png', ss=2)
for i in tqdm(range(N)):
pos = np.array([0, 0, 0]) #phantoms.defocus_path(i/N)
ss = phantoms.sphere_spiral(i / (N - 1))
jj = phantoms.uniaxial_ellipsoid(1, 0.0001, ss, N=2**14)
obj = R3S2toR.xyzj_list([pos], [jj],
shape=[10, 10, 4],
title='Uniaxial distribution $a/b = 0.1$')
obj2 = obj.to_xyzJ_list(Jmax=6)
obj2.title = '$\ell = 2$ projection'
print(obj2.data_J)
obj.build_actors()
obj2.build_actors()
d1 = det.FourF()
im1 = d1.xyzJ_list_to_xy_det(obj2)
im1.data /= 100
d2 = det.FourFLF(irrad_title='Lightfield detector irradiance')
im2 = d2.xyzJ_list_to_xy_det(obj2)
im2.data /= 100
istr = '{:03d}'.format(i)
utilmpl.plot([[obj, im1, im2]], './test/' + istr + '.png')
# im1.to_tiff('./test1/'+istr+'.tif')
# im2.to_tiff('./test2/'+istr+'.tif')
from tqdm import tqdm
from polaris2.geomvis import R3S2toR, R2toR, S2toR, utilmpl
from polaris2.micro.micro import det
l2m_2 = S2toR.Jeven([0, 0, 1, 0], title='$Y_{2,-1}$')
l2m_1 = S2toR.Jeven([0, 0, 0, 0, 0, 1], title='$Y_{2,2}$')
l2m_2.precompute_tripling()
l3 = l2m_2 * l2m_1
ell = [l2m_2, l2m_1, l3]
# l2m_2.interact()
for el in ell:
el.build_actors()
N = 100
for i in tqdm(range(N)):
istr = '{:03d}'.format(i)
utilmpl.plot([ell], './out/' + istr + '.png')
for el in ell:
el.increment_camera(360 / N)
# Threshold
xyzJ_recon.threshold = 0.3*np.max(xyzJ_recon.data)
# Convert to other visuals
grid_peak_recon = xyzJ_recon.to_R3toR3_xyz()
grid_int_recon = xyzJ_recon.to_R3toR_xyz()
xyzJ_recon.build_actors()
grid_peak_recon.rad_scale = 1
grid_peak_recon.build_actors()
grid_int_recon.build_actors()
# Calculate im3
# im3 = d2.xyzJ_to_xy_det(xyzJ_recon)
im3 = d2.fwd(xyzJ_recon)
# Titles
xyzJ.title = '$\mathbf{f}$'
grid_peak.title = 'Peaks$(\mathbf{f})$'
grid_int.title = 'MIP$(\mathbf{f})$'
im2.title = '$\mathcal{H}\mathbf{f}$'
xyzJ_recon.title = '$\mathcal{H^+H}\mathbf{f}$'
grid_peak_recon.title = 'Peaks$(\mathcal{H^+H}\mathbf{f})$'
grid_int_recon.title = 'MIP$(\mathcal{H^+H}\mathbf{f})$'
im3.title = '$\mathcal{HH^+H}\mathbf{f}$'
# Plot
obj_list = [xyzJ, grid_peak, grid_int, im2]
res_list = [xyzJ_recon, grid_peak_recon, grid_int_recon, im3]
utilmpl.plot([obj_list, res_list], './test-guv.png', ss=2)
# g = Hf
g = lfdet.fwd(f)
g.title = '$\mathbf{g} = \mathcal{H}\mathbf{f}$'
# f_hat = H^+Hf
f_hat = lfdet.pinv(g, out_vox_dims=input_vox_dims)
f_hat.threshold = 0.3 * np.max(f_hat.data)
f_hat.build_actors()
f_hat.title = '$\hat{\mathbf{f}} = \mathcal{H^+H}\mathbf{f}$'
# Peak(f_hat)
peakf_hat = f_hat.to_R3toR3_xyz()
peakf_hat.rad_scale = 1
peakf_hat.build_actors()
peakf_hat.title = 'Peaks$(\hat{\mathbf{f}})$'
# MIP(f)
MIPf_hat = f_hat.to_R3toR_xyz()
MIPf_hat.build_actors()
MIPf_hat.title = 'MIP$(\hat{\mathbf{f}})$'
# g_hat = Hf_hat
g_hat = lfdet.fwd(f_hat)
g_hat.title = '$\hat{\mathbf{g}} = \mathcal{H}\hat{\mathbf{f}}$'
obj_list = [f_list, peakf, MIPf, g]
res_list = [f_hat, peakf_hat, MIPf_hat, g_hat]
nstr = '{:03d}'.format(n)
utilmpl.plot([obj_list, res_list], './out/' + nstr + '.png', ss=2)
xyz_list, j_list = phantoms.guv(radius=2.0,
ellip_ratio=0.2,
M=2**13,
dist_type='ellipsoid')
ss_obj = R3S2toR.xyzj_list(xyz_list,
j_list,
title='Dense object',
rad_scale=0.125)
log.info('Converting phantom to grid.')
vox_dims = np.array([6.5 / 60, 6.5 / 60, .2])
npx = np.array([45, 45, 25])
grid_obj = ss_obj.to_xyzJ(npx=npx, vox_dims=vox_dims, lmax=4)
grid_obj.to_tiff('./guv-2um.tiff')
grid_obj = R3S2toR.xyzJ(np.zeros((1, )), title='Dense object')
grid_obj.from_tiff('./guv-2um.tiff')
log.info('Calculating peaks')
peaks = grid_obj.to_R3toR3_xyz()
log.info('Calculating MIP')
mip = grid_obj.to_R3toR_xyz()
# Visualize
obj_list = [grid_obj, peaks, mip]
for obj in obj_list:
obj.build_actors()
utilmpl.plot([obj_list], './visuals.png', ss=2)