def calc_full_ac(image, rad, outside=True, **ac_kwargs):
import pyximport
pyximport.install(setup_args={"include_dirs": np.get_include()},
reload_support=True)
import sincint
assert image.ndim == 2, "wrong dimension"
assert image.shape[0] == image.shape[1]
N = image.shape[0]
FtoT = sincint.genfulltotrunc(N, rad)
TtoF = FtoT.T
trunc = FtoT.dot(image.flatten())
corr_trunc = calc_angular_correlation(trunc, N, rad, **ac_kwargs)
full_angular_correlation = TtoF.dot(corr_trunc)
if outside:
_, _, outside_mask = gencoords_outside(N, 2, rad, True)
corr_trunc_outside = calc_angular_correlation(
image[outside_mask.reshape(N, N)].flatten(),
N,
rad,
outside=True,
**ac_kwargs)
full_angular_correlation[outside_mask] = corr_trunc_outside
return full_angular_correlation.reshape(N, N)
def correlation_trunc_example(M):
N = M.shape[0]
rad = 1
proj = M.sum(axis=0)
FtoT = sincint.genfulltotrunc(N=N, rad=rad)
TtoF = sincint.gentrunctofull(N=N, rad=rad)
trunc = FtoT.dot(proj.flatten())
corr_trunc = correlation.calc_angular_correlation(trunc, N, 1)
corr_proj = TtoF.dot(corr_trunc).reshape(N, N)
fig, ax = plt.subplots(1,2)
ax[0].imshow(proj)
ax[1].imshow(corr_proj)
plt.show()
def full_to_trunc(full_samples, rad, beamstop_rad=None):
"""convert samples in full shape to samples in truncation"""
dtype = full_samples.dtype
if full_samples.ndim == 3:
num_samples = full_samples.shape[0]
N = full_samples.shape[1]
elif full_samples.ndim == 2:
N = full_samples.shape[0]
num_samples = 1
full_samples = (full_samples,)
FtoT = sincint.genfulltotrunc(N=N, rad=rad, beamstop_rad=None)
N_T = FtoT.shape[0]
trunc_samples = np.zeros((num_samples, N_T), dtype=dtype)
for i, sample in enumerate(full_samples):
trunc_samples[i, :] = FtoT.dot(sample.flatten())
return trunc_samples
def ctf_correlation_benchmark(N=128):
akv = 200
wgh = 0.07
cs = 2.0
df1, df2, angast = 44722, 49349, 45.0 * (np.pi / 180.0)
dscale = 1.0
psz = 5.6
rad = 0.99
rots = np.arange(0, 2 * np.pi, 2*np.pi/360)
ctf_map = ctf.compute_full_ctf(
None, N, psz, akv, cs, wgh, df1, df2, angast, dscale, None)
ctf_rots = ctf.compute_full_ctf(
rots, N, psz, akv, cs, wgh, df1, df2, angast, dscale, None)
ctf_map = ctf_map.reshape(N, N)
ctf_rots = ctf_rots.reshape(N, N, -1).transpose((2, 0, 1))
FtoT = sincint.genfulltotrunc(N=N, rad=rad)
TtoF = FtoT.T
corr_ctf_map = TtoF.dot(calc_angular_correlation(
FtoT.dot(ctf_map.flatten()), N=N, rad=rad
)).reshape(N, N)
corr_ctf_rots = np.zeros_like(ctf_rots, dtype=ctf_rots.dtype)
for i, curr_ctf in enumerate(ctf_rots):
corr_ctf_rots[i] = TtoF.dot(calc_angular_correlation(
FtoT.dot(curr_ctf.flatten()), N=N, rad=rad
)).reshape(N, N)
difference = calc_difference(ctf_map, ctf_rots, only_real=True)
corr_difference = calc_difference(corr_ctf_map, corr_ctf_rots, only_real=True)
vis_real_space_comparison(ctf_rots, corr_ctf_rots, difference, corr_difference,
original_img=ctf_map, original_corr_img=corr_ctf_map,
save_animation=True, animation_name='ctf_correlation_benchmark')
with open(os.path.join(data_dir, 'ctf_gt.par')) as par:
par.readline()
# 'C PHI THETA PSI SHX SHY FILM DF1 DF2 ANGAST'
while True:
try:
line = par.readline().split()
euler_angles.append([float(line[1]), float(line[2]), float(line[3])])
except Exception:
break
euler_angles = np.deg2rad(np.asarray(euler_angles))
imgdata = mrc.readMRC(img_file)
num_data = imgdata.shape[2]
rad = 0.8
FtoT = sincint.genfulltotrunc(N=N, rad=rad)
N_T = FtoT.shape[0]
slices = np.zeros((num_data, N_T), dtype=np.float32)
for i in range(num_data):
curr_img = imgdata[:, :, i]
slices[i] = FtoT.dot(curr_img.reshape(-1, 1)).reshape(-1)
Rs = np.vstack([geometry.rotmat3D_EA(*ea)[:, 0:2].reshape((1, 3, 2)) for ea in euler_angles])
# cx, cy = int(N/2), int(N/2)
# nx, ny = N, N
# for i, ea in enumerate(euler_angles):