def genphantomdata(N_D, phantompath):
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
M_totalmass = 1000000
# M_totalmass = 1500000
kernel = 'lanczos'
ksize = 6
tic = time.time()
N_D = int(N_D)
N = int(N)
rad = float(rad)
psize = mscope_params['psize']
bfactor = mscope_params['bfactor']
M_totalmass = float(M_totalmass)
ctfparfile = 'particle/examplectfs.par'
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()
# oversampling
oversampling_factor = 3
psize = psize * oversampling_factor
V = density.real_to_fspace_with_oversampling(M, oversampling_factor)
fM = V.real**2 + V.imag**2
# mrc.writeMRC('particle/EMD6044_fM_totalmass_{}_oversampling_{}.mrc'.format(str(int(M_totalmass)).zfill(5), oversampling_factor), fM, psz=psize)
print("Generating data...")
sys.stdout.flush()
imgdata = np.empty((N_D, N, N), dtype=density.real_t)
pardata = {'R': []}
prevctfI = None
coords = geometry.gencoords(N, 2, rad)
slicing_func = RegularGridInterpolator((np.arange(N), ) * 3,
fM,
bounds_error=False,
fill_value=0.0)
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)),
end='')
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, N))
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
# Rotate coordinates and get slice image by interpolation
R = geometry.rotmat3D_EA(*EA)[:, 0:2]
rotated_coords = R.dot(coords.T).T + int(N / 2)
slice_data = slicing_func(rotated_coords)
intensity = TtoF.dot(slice_data)
np.maximum(intensity, 0.0, out=intensity)
# Add poisson noise
img = np.float_(np.random.poisson(intensity.reshape(N, N)))
np.maximum(1e-8, img, out=img)
imgdata[i] = np.require(img, 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=0.0,
SHY=0.0)
pardata['R'].append(R)
print("\n\rDone in ", time.time() - tic, " seconds.")
return imgdata, genctf_stack, pardata, mscope_params
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 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