def shift_vis(model):
N = model.shape[0]
rad = 0.8
kernel = 'lanczos'
kernsize = 4
xy, trunc_xy, truncmask = geometry.gencoords(N, 2, rad, True)
N_T = trunc_xy.shape[0]
premult = cryoops.compute_premultiplier(N,
kernel=kernel,
kernsize=kernsize)
TtoF = sincint.gentrunctofull(N=N, rad=rad)
fM = density.real_to_fspace(model)
prefM = density.real_to_fspace(
premult.reshape((1, 1, -1)) * premult.reshape(
(1, -1, 1)) * premult.reshape((-1, 1, 1)) * model)
pt = np.random.randn(3)
pt /= np.linalg.norm(pt)
psi = 2 * np.pi * np.random.rand()
ea = geometry.genEA(pt)[0]
ea[2] = psi
print('project model for Euler angel: ({:.2f}, {:.2f}, {:.2f}) degree'.
format(*np.rad2deg(ea)))
rot_matrix = geometry.rotmat3D_EA(*ea)[:, 0:2]
slop = cryoops.compute_projection_matrix([rot_matrix], N, kernel, kernsize,
rad, 'rots')
# trunc_slice = slop.dot(prefM.reshape((-1,)))
trunc_slice = cryoem.getslices(prefM, slop)
fourier_slice = TtoF.dot(trunc_slice).reshape(N, N)
real_proj = density.fspace_to_real(fourier_slice)
fig, axes = plt.subplots(4, 4, figsize=(12.8, 8))
im_real = axes[0, 0].imshow(real_proj)
im_fourier = axes[1, 0].imshow(np.log(np.abs(fourier_slice)))
for i, ax in enumerate(axes[:, 1:].T):
shift = np.random.randn(2) * (N / 4.0)
S = cryoops.compute_shift_phases(shift.reshape(1, 2), N, rad)[0]
shift_trunc_slice = S * trunc_slice
shift_fourier_slice = TtoF.dot(shift_trunc_slice).reshape(N, N)
shift_real_proj = density.fspace_to_real(shift_fourier_slice)
ax[0].imshow(shift_real_proj)
ax[1].imshow(np.log(np.abs(shift_fourier_slice)))
ax[2].imshow(np.log(shift_fourier_slice.real))
ax[3].imshow(np.log(shift_fourier_slice.imag))
fig.tight_layout()
plt.show()
def genphantomdata(N_D, phantompath, ctfparfile):
mscope_params = {
'akv': 200,
'wgh': 0.07,
'cs': 2.0,
'psize': 2.8,
'bfactor': 500.0
}
N = 128
rad = 0.95
shift_sigma = 3.0
sigma_noise = 25.0
M_totalmass = 80000
kernel = 'lanczos'
ksize = 6
premult = cryoops.compute_premultiplier(N, kernel, ksize)
tic = time.time()
N_D = int(N_D)
N = int(N)
rad = float(rad)
psize = mscope_params['psize']
bfactor = mscope_params['bfactor']
shift_sigma = float(shift_sigma)
sigma_noise = float(sigma_noise)
M_totalmass = float(M_totalmass)
srcctf_stack = CTFStack(ctfparfile, mscope_params)
genctf_stack = GeneratedCTFStack(
mscope_params, parfields=['PHI', 'THETA', 'PSI', 'SHX', 'SHY'])
TtoF = sincint.gentrunctofull(N=N, rad=rad)
Cmap = n.sort(
n.random.random_integers(0,
srcctf_stack.get_num_ctfs() - 1, N_D))
M = mrc.readMRC(phantompath)
cryoem.window(M, 'circle')
M[M < 0] = 0
if M_totalmass is not None:
M *= M_totalmass / M.sum()
V = density.real_to_fspace(
premult.reshape((1, 1, -1)) * premult.reshape(
(1, -1, 1)) * premult.reshape((-1, 1, 1)) * M)
print "Generating data..."
sys.stdout.flush()
imgdata = n.empty((N_D, N, N), dtype=density.real_t)
pardata = {'R': [], 't': []}
prevctfI = None
for i, srcctfI in enumerate(Cmap):
ellapse_time = time.time() - tic
remain_time = float(N_D - i) * ellapse_time / max(i, 1)
print "\r%.2f Percent.. (Elapsed: %s, Remaining: %s) " % (
i / float(N_D) * 100.0, format_timedelta(ellapse_time),
format_timedelta(remain_time)),
sys.stdout.flush()
# Get the CTF for this image
cCTF = srcctf_stack.get_ctf(srcctfI)
if prevctfI != srcctfI:
genctfI = genctf_stack.add_ctf(cCTF)
C = cCTF.dense_ctf(N, psize, bfactor).reshape((N**2, ))
prevctfI = srcctfI
# Randomly generate the viewing direction/shift
pt = n.random.randn(3)
pt /= n.linalg.norm(pt)
psi = 2 * n.pi * n.random.rand()
EA = geom.genEA(pt)[0]
EA[2] = psi
shift = n.random.randn(2) * shift_sigma
R = geom.rotmat3D_EA(*EA)[:, 0:2]
slop = cryoops.compute_projection_matrix([R], N, kernel, ksize, rad,
'rots')
S = cryoops.compute_shift_phases(shift.reshape((1, 2)), N, rad)[0]
D = slop.dot(V.reshape((-1, )))
D *= S
imgdata[i] = density.fspace_to_real((C * TtoF.dot(D)).reshape(
(N, N))) + n.require(n.random.randn(N, N) * sigma_noise,
dtype=density.real_t)
genctf_stack.add_img(genctfI,
PHI=EA[0] * 180.0 / n.pi,
THETA=EA[1] * 180.0 / n.pi,
PSI=EA[2] * 180.0 / n.pi,
SHX=shift[0],
SHY=shift[1])
pardata['R'].append(R)
pardata['t'].append(shift)
print "\rDone in ", time.time() - tic, " seconds."
return imgdata, genctf_stack, pardata, mscope_params
def compute_operator(self,interp_params,inds=None):
pts = self.get_pts(inds)
return cops.compute_shift_phases(pts,interp_params['N'],interp_params['rad'])
def compute_operator(self, interp_params, inds=None):
pts = self.get_pts(inds)
return cops.compute_shift_phases(pts, interp_params['N'],
interp_params['rad'])
EAs_grid = healpix.gen_EAs_grid(nside=2, psi_step=360)
Rs = [geometry.rotmat3D_EA(*EA)[:, 0:2] for EA in EAs_grid]
slice_ops = cryoops.compute_projection_matrix(Rs,
N,
kern='lanczos',
kernsize=ksize,
rad=rad,
projdirtype='rots')
slices_sampled = cryoem.getslices(fM, slice_ops).reshape(
(EAs_grid.shape[0], trunc_xy.shape[0]))
premult_slices_sampled = cryoem.getslices(prefM, slice_ops).reshape(
(EAs_grid.shape[0], trunc_xy.shape[0]))
S = cryoops.compute_shift_phases(
np.asarray([100, -20]).reshape((1, 2)), N, rad)[0]
trunc_slice = slices_sampled[0]
premult_trunc_slice = premult_slices_sampled[0]
premult_trunc_slice_shift = S * premult_slices_sampled[0]
fourier_slice = TtoF.dot(trunc_slice).reshape((N, N))
real_proj = density.fspace_to_real(fourier_slice)
premult_fourier_slice = TtoF.dot(premult_trunc_slice).reshape((N, N))
premult_fourier_slice_shift = TtoF.dot(premult_trunc_slice_shift).reshape(
(N, N))
premult_real_proj = density.fspace_to_real(premult_fourier_slice)
premult_real_proj_shift = density.fspace_to_real(premult_fourier_slice_shift)
fig, axes = plt.subplots(2, 2)
ax = axes.flatten()
def genphantomdata(N_D, phantompath, ctfparfile):
# mscope_params = {'akv': 200, 'wgh': 0.07,
# 'cs': 2.0, 'psize': 2.8, 'bfactor': 500.0}
mscope_params = {'akv': 200, 'wgh': 0.07,
'cs': 2.0, 'psize': 3.0, 'bfactor': 500.0}
M = mrc.readMRC(phantompath)
N = M.shape[0]
rad = 0.95
shift_sigma = 3.0
sigma_noise = 25.0
M_totalmass = 80000
kernel = 'lanczos'
ksize = 6
premult = cryoops.compute_premultiplier(N, kernel, ksize)
tic = time.time()
N_D = int(N_D)
N = int(N)
rad = float(rad)
psize = mscope_params['psize']
bfactor = mscope_params['bfactor']
shift_sigma = float(shift_sigma)
sigma_noise = float(sigma_noise)
M_totalmass = float(M_totalmass)
srcctf_stack = CTFStack(ctfparfile, mscope_params)
genctf_stack = GeneratedCTFStack(mscope_params, parfields=[
'PHI', 'THETA', 'PSI', 'SHX', 'SHY'])
TtoF = sincint.gentrunctofull(N=N, rad=rad)
Cmap = np.sort(np.random.random_integers(
0, srcctf_stack.get_num_ctfs() - 1, N_D))
cryoem.window(M, 'circle')
M[M < 0] = 0
if M_totalmass is not None:
M *= M_totalmass / M.sum()
V = density.real_to_fspace(
premult.reshape((1, 1, -1)) * premult.reshape((1, -1, 1)) * premult.reshape((-1, 1, 1)) * M)
print("Generating data...")
sys.stdout.flush()
imgdata = np.empty((N_D, N, N), dtype=density.real_t)
pardata = {'R': [], 't': []}
prevctfI = None
for i, srcctfI in enumerate(Cmap):
ellapse_time = time.time() - tic
remain_time = float(N_D - i) * ellapse_time / max(i, 1)
print("\r%.2f Percent.. (Elapsed: %s, Remaining: %s)" % (i / float(N_D)
* 100.0, format_timedelta(ellapse_time), format_timedelta(remain_time)))
sys.stdout.flush()
# Get the CTF for this image
cCTF = srcctf_stack.get_ctf(srcctfI)
if prevctfI != srcctfI:
genctfI = genctf_stack.add_ctf(cCTF)
C = cCTF.dense_ctf(N, psize, bfactor).reshape((N**2,))
prevctfI = srcctfI
# Randomly generate the viewing direction/shift
pt = np.random.randn(3)
pt /= np.linalg.norm(pt)
psi = 2 * np.pi * np.random.rand()
EA = geometry.genEA(pt)[0]
EA[2] = psi
shift = np.random.randn(2) * shift_sigma
R = geometry.rotmat3D_EA(*EA)[:, 0:2]
slop = cryoops.compute_projection_matrix(
[R], N, kernel, ksize, rad, 'rots')
S = cryoops.compute_shift_phases(shift.reshape((1, 2)), N, rad)[0]
D = slop.dot(V.reshape((-1,)))
D *= S
imgdata[i] = density.fspace_to_real((C * TtoF.dot(D)).reshape((N, N))) + np.require(
np.random.randn(N, N) * sigma_noise, dtype=density.real_t)
genctf_stack.add_img(genctfI,
PHI=EA[0] * 180.0 / np.pi, THETA=EA[1] * 180.0 / np.pi, PSI=EA[2] * 180.0 / np.pi,
SHX=shift[0], SHY=shift[1])
pardata['R'].append(R)
pardata['t'].append(shift)
print("\rDone in ", time.time() - tic, " seconds.")
return imgdata, genctf_stack, pardata, mscope_params
premult = cryoops.compute_premultiplier(N, kernel, ksize)
TtoF = sincint.gentrunctofull(N=N, rad=rad)
fM = density.real_to_fspace(M)
prefM = density.real_to_fspace(premult.reshape(
(1, 1, -1)) * premult.reshape((1, -1, 1)) * premult.reshape((-1, 1, 1)) * M)
EAs_grid = healpix.gen_EAs_grid(nside=2, psi_step=360)
Rs = [geometry.rotmat3D_EA(*EA)[:, 0:2] for EA in EAs_grid]
slice_ops = cryoops.compute_projection_matrix(Rs, N, kern='lanczos', kernsize=ksize, rad=rad, projdirtype='rots')
slices_sampled = cryoem.getslices(fM, slice_ops).reshape((EAs_grid.shape[0], trunc_xy.shape[0]))
premult_slices_sampled = cryoem.getslices(prefM, slice_ops).reshape((EAs_grid.shape[0], trunc_xy.shape[0]))
S = cryoops.compute_shift_phases(np.asarray([100, -20]).reshape((1,2)), N, rad)[0]
trunc_slice = slices_sampled[0]
premult_trunc_slice = premult_slices_sampled[0]
premult_trunc_slice_shift = S * premult_slices_sampled[0]
fourier_slice = TtoF.dot(trunc_slice).reshape((N, N))
real_proj = density.fspace_to_real(fourier_slice)
premult_fourier_slice = TtoF.dot(premult_trunc_slice).reshape((N, N))
premult_fourier_slice_shift = TtoF.dot(premult_trunc_slice_shift).reshape((N, N))
premult_real_proj = density.fspace_to_real(premult_fourier_slice)
premult_real_proj_shift = density.fspace_to_real(premult_fourier_slice_shift)
fig, axes = plt.subplots(2, 2)
ax = axes.flatten()
ax[0].imshow(real_proj)
