def calc_2d_projection( particle_volume, projection_angle_and_shift, interpolation=1, return_real=True ): """ :param particle_volume: input volume :param projection_angle_and_shift: input parameters given as a list [phi, theta, psi, s2x, s2y], projection in calculated using the three Eulerian angles and then shifted by sx,sy :param interpolation: interpolation_method,1 = triliniear :param return_real: True - return real; False - return FT of a projection. :return: 2D Projection """ projection = prgl( particle_volume, projection_angle_and_shift, interpolation, return_real ) return projection
def mode_meridien(reconfile,
classavgstack,
classdocs,
partangles,
selectdoc,
maxshift,
outerrad,
outanglesdoc,
outaligndoc,
interpolation_method=1,
outliers=None,
goodclassparttemplate=None,
alignopt='apsh',
ringstep=1,
log=None,
verbose=False):
# Resample reference
recondata = EMAN2.EMData(reconfile)
idim = recondata['nx']
reconprep = prep_vol(recondata,
npad=2,
interpolation_method=interpolation_method)
# Initialize output angles
outangleslist = []
outalignlist = []
# Read class lists
classdoclist = glob.glob(classdocs)
partangleslist = read_text_row(partangles)
# Loop through class lists
for classdoc in classdoclist: # [classdoclist[32]]: #
# Strip out three-digit filenumber
classexample = os.path.splitext(classdoc)
classnum = int(classexample[0][-3:])
# Initial average
[avg_phi_init, avg_theta_init] = average_angles(partangleslist,
classdoc,
selectdoc=selectdoc)
# Look for outliers
if outliers:
[avg_phi_final, avg_theta_final] = average_angles(
partangleslist,
classdoc,
selectdoc=selectdoc,
init_angles=[avg_phi_init, avg_theta_init],
threshold=outliers,
goodpartdoc=goodclassparttemplate.format(classnum),
log=log,
verbose=verbose)
else:
[avg_phi_final, avg_theta_final] = [avg_phi_init, avg_theta_init]
# Compute re-projection
refprjreal = prgl(reconprep, [avg_phi_final, avg_theta_final, 0, 0, 0],
interpolation_method=1,
return_real=True)
# Align to class average
classavg = get_im(classavgstack, classnum)
# Alignment using self-correlation function
if alignopt == 'scf':
ang_align2d, sxs, sys, mirrorflag, peak = align2d_scf(classavg,
refprjreal,
maxshift,
maxshift,
ou=outerrad)
# Weird results
elif alignopt == 'align2d':
# Set search range
currshift = 0
txrng = tyrng = search_range(idim, outerrad, currshift, maxshift)
# Perform alignment
ang_align2d, sxs, sys, mirrorflag, peak = align2d(
classavg, refprjreal, txrng, tyrng, last_ring=outerrad)
# Direct3 (angles seemed to be quantized)
elif alignopt == 'direct3':
[[ang_align2d, sxs, sys, mirrorflag,
peak]] = align2d_direct3([classavg],
refprjreal,
maxshift,
maxshift,
ou=outerrad)
# APSH-like alignment (default)
else:
[[ang_align2d, sxs, sys, mirrorflag,
scale]] = apsh(refprjreal,
classavg,
outerradius=outerrad,
maxshift=maxshift,
ringstep=ringstep)
outalignlist.append([ang_align2d, sxs, sys, mirrorflag, 1])
msg = "Particle list %s: ang_align2d=%s sx=%s sy=%s mirror=%s\n" % (
classdoc, ang_align2d, sxs, sys, mirrorflag)
print_log_msg(msg, log, verbose)
# Check for mirroring
if mirrorflag == 1:
tempeulers = list(
compose_transform3(avg_phi_final, avg_theta_final, 0, 0, 0, 0,
1, 0, 180, 0, 0, 0, 0, 1))
combinedparams = list(
compose_transform3(tempeulers[0], tempeulers[1], tempeulers[2],
tempeulers[3], tempeulers[4], 0, 1, 0, 0,
-ang_align2d, 0, 0, 0, 1))
else:
combinedparams = list(
compose_transform3(avg_phi_final, avg_theta_final, 0, 0, 0, 0,
1, 0, 0, -ang_align2d, 0, 0, 0, 1))
# compose_transform3: returns phi,theta,psi, tx,ty,tz, scale
outangleslist.append(combinedparams)
# End class-loop
write_text_row(outangleslist, outanglesdoc)
write_text_row(outalignlist, outaligndoc)
print_log_msg(
'Wrote alignment parameters to %s and %s\n' %
(outanglesdoc, outaligndoc), log, verbose)
del recondata # Clean up
def compare_projs(reconfile,
classavgstack,
inputanglesdoc,
outdir,
interpolation_method=1,
log=None,
verbose=False):
"""
Make comparison stack between class averages (even-numbered (starts from 0)) and re-projections (odd-numbered).
Arguments:
reconfile : Input volume from which to generate re-projections
classavgstack ; Input image stack
inputanglesdoc : Input Euler angles doc
outdir ; Output directory
interpolation_method : Interpolation method: nearest neighbor (nn, 0), trilinear (1, default), gridding (-1)
log : Logger object
verbose : (boolean) Whether to write additional information to screen
Returns:
compstack : Stack of comparisons between input image stack (even-numbered (starts from 0)) and input volume (odd-numbered)
"""
recondata = EMAN2.EMData(reconfile)
nx = recondata.get_xsize()
# Resample reference
reconprep = prep_vol(recondata,
npad=2,
interpolation_method=interpolation_method)
ccclist = []
# Here you need actual radius to compute proper ccc's, but if you do, you have to deal with translations, PAP
mask = model_circle(nx // 2 - 2, nx, nx)
mask.write_image(os.path.join(outdir, 'maskalign.hdf'))
compstack = os.path.join(outdir, 'comp-proj-reproj.hdf')
# Number of images may have changed
nimg1 = EMAN2.EMUtil.get_image_count(classavgstack)
angleslist = read_text_row(inputanglesdoc)
for imgnum in range(nimg1):
# Get class average
classimg = get_im(classavgstack, imgnum)
# Compute re-projection
prjimg = prgl(reconprep,
angleslist[imgnum],
interpolation_method=1,
return_real=False)
# Calculate 1D power spectra
rops_dst = rops_table(classimg * mask)
rops_src = rops_table(prjimg)
# Set power spectrum of reprojection to the data.
# Since data has an envelope, it would make more sense to set data to reconstruction,
# but to do it one would have to know the actual resolution of the data.
# you can check sxprocess.py --adjpw to see how this is done properly PAP
table = [0.0] * len(rops_dst) # initialize table
for j in range(len(rops_dst)):
table[j] = sqrt(rops_dst[j] / rops_src[j])
prjimg = fft(filt_table(
prjimg,
table)) # match FFT amplitudes of re-projection and class average
cccoeff = ccc(prjimg, classimg, mask)
#print imgnum, cccoeff
classimg.set_attr_dict({'cross-corr': cccoeff})
prjimg.set_attr_dict({'cross-corr': cccoeff})
montagestack = []
montagestack.append(prjimg)
montagestack.append(classimg)
comparison_pair = montage2(montagestack, ncol=2, marginwidth=1)
comparison_pair.write_image(compstack, imgnum)
ccclist.append(cccoeff)
del angleslist
meanccc = sum(ccclist) / nimg1
print_log_msg("Average CCC is %s\n" % meanccc, log, verbose)
nimg2 = EMAN2.EMUtil.get_image_count(compstack)
for imgnum in range(nimg2): # xrange will be deprecated in Python3
prjimg = get_im(compstack, imgnum)
meanccc1 = prjimg.get_attr_default('mean-cross-corr', -1.0)
prjimg.set_attr_dict({'mean-cross-corr': meanccc})
write_header(compstack, prjimg, imgnum)
return compstack