def main(): (options, input_imagefiles, output_lstfile) = parse_command_line() Particle.sym = options.sym Particle.max_ang_diff = options.max_ang_diff * math.pi/180.0 Particle.max_cen_diff = options.max_cen_diff image_lists = [] for f in input_imagefiles: # build the image sets tmp_image_list = EMAN.image2list(f) # first check and remove duplicate images seen = sets.Set() tmp_image_list2 = [] for i, img in enumerate(tmp_image_list): imgid = "%s-%d" % (os.path.abspath(img[0]), img[1]) if imgid in seen: print "%s: particle %d/%d (%s %d) is a duplicate image, ignored" % (f, i, len(tmp_image_list), img[0], img[1]) else: seen.add(imgid) tmp_image_list2.append(img) image_list = sets.Set() for i, img in enumerate(tmp_image_list2): p = Particle(img) image_list.add(p) print "%s: %d images" % (f, len(image_list)) image_lists.append( image_list ) all_images = image_lists[0] print "Begining with %d images in %s" % (len(all_images), input_imagefiles[0]) for i in range(1,len(image_lists)): image_list = image_lists[i] if options.mode == "common": # all_images & imageset all_images = intersection(all_images, image_list) print "%d common images after processing %d images in %s" % ( len(all_images), len(image_list), input_imagefiles[i] ) elif options.mode == "union": all_images = union(all_images, image_list) # all_images | imageset print "%d images after merging %d images in %s" % ( len(all_images), len(image_list), input_imagefiles[i] ) elif options.mode == "diff": all_images = difference(all_images,image_list) # all_images-imageset print "%d different images after processing %d images in %s" % ( len(all_images), len(image_list), input_imagefiles[i] ) elif options.mode == "symdiff": all_images = symmetric_difference(all_images,image_list) # all_images ^ imageset print "%d different images after processing %d images in %s" % ( len(all_images), len(image_list), input_imagefiles[i] ) all_images=list(all_images) all_images.sort() all_images_output_list = [i.image for i in all_images] if len(all_images_output_list): print "%d images saved to %s" % ( len(all_images_output_list), output_lstfile ) EMAN.imagelist2lstfile(all_images_output_list, output_lstfile) else: print "No image left after the image sets operation"
def main():
EMAN.appinit(sys.argv)
if sys.argv[-1].startswith("usefs="):
sys.argv = sys.argv[:-1] # remove the runpar fileserver info
(options, rawimage, refmap) = parse_command_line()
sffile = options.sffile
verbose = options.verbose
shrink = options.shrink
mask = options.mask
first = options.first
last = options.last
scorefunc = options.scorefunc
projfile = options.projection
output_ptcls = options.update_rawimage
cmplstfile = options.cmplstfile
ortlstfile = options.ortlstfile
startSym = options.startSym
endSym = options.endSym
if not options.nocmdlog:
pid = EMAN.LOGbegin(sys.argv)
EMAN.LOGInfile(pid, rawimage)
EMAN.LOGInfile(pid, refmap)
if projfile:
EMAN.LOGOutfile(pid, projfile)
if output_ptcls:
EMAN.LOGOutfile(pid, output_ptcls)
if cmplstfile:
EMAN.LOGOutfile(pid, cmplstfile)
if ortlstfile:
EMAN.LOGOutfile(pid, ortlstfile)
ptcls = []
if not (mpi or pypar) or ((mpi and mpi.rank == 0) or (pypar and pypar.rank == 0)):
ptcls = EMAN.image2list(rawimage)
ptcls = ptcls[first:last]
print "Read %d particle parameters" % (len(ptcls))
# ptcls = ptcls[0:10]
if mpi and mpi.size > 1:
ptcls = mpi.bcast(ptcls)
print "rank=%d\t%d particles" % (mpi.rank, len(ptcls))
elif pypar and pypar.size() > 1:
ptcls = pypar.broadcast(ptcls)
print "rank=%d\t%d particles" % (pypar.rank(), len(ptcls))
if sffile:
sf = EMAN.XYData()
sf.readFile(sffile)
sf.logy()
if not mpi or ((mpi and mpi.rank == 0) or (pypar and pypar.rank() == 0)):
if cmplstfile and projfile:
if output_ptcls:
raw_tmp = output_ptcls
else:
raw_tmp = rawimage
raw_tmp = rawimage
fp = open("tmp-" + cmplstfile, "w")
fp.write("#LST\n")
for i in range(len(ptcls)):
fp.write("%d\t%s\n" % (first + i, projfile))
fp.write("%d\t%s\n" % (first + i, raw_tmp))
fp.close()
if (mpi and mpi.size > 1 and mpi.rank == 0) or (pypar and pypar.size() > 1 and pypar.rank() == 0):
total_recv = 0
if output_ptcls:
total_recv += len(ptcls)
if projfile:
total_recv += len(ptcls)
for r in range(total_recv):
# print "before recv from %d" % (r)
if mpi:
msg, status = mpi.recv()
else:
msg = pypar.receive(r)
# print "after recv from %d" % (r)
# print msg, status
d = emdata_load(msg[0])
fname = msg[1]
index = msg[2]
d.writeImage(fname, index)
print "wrtie %s %d" % (fname, index)
if options.ortlstfile:
solutions = []
for r in range(1, mpi.size):
msg, status = mpi.recv(source=r, tag=r)
solutions += msg
def ptcl_cmp(x, y):
eq = cmp(x[0], y[0])
if not eq:
return cmp(x[1], y[1])
else:
return eq
solutions.sort(ptcl_cmp)
if (not mpi or (mpi and ((mpi.size > 1 and mpi.rank > 0) or mpi.size == 1))) or (
not pypar or (pypar and ((pypar.size() > 1 and pypar.rank() > 0) or pypar.size() == 1))
):
map3d = EMAN.EMData()
map3d.readImage(refmap, -1)
map3d.normalize()
if shrink > 1:
map3d.meanShrink(shrink)
map3d.realFilter(0, 0) # threshold, remove negative pixels
imgsize = map3d.ySize()
img = EMAN.EMData()
ctffilter = EMAN.EMData()
ctffilter.setSize(imgsize + 2, imgsize, 1)
ctffilter.setComplex(1)
ctffilter.setRI(1)
if (mpi and mpi.size > 1) or (pypar and pypar.size() > 1):
ptclset = range(mpi.rank - 1, len(ptcls), mpi.size - 1)
else:
ptclset = range(0, len(ptcls))
if mpi:
print "Process %d/%d: %d/%d particles" % (mpi.rank, mpi.size, len(ptclset), len(ptcls))
solutions = []
for i in ptclset:
ptcl = ptcls[i]
e = EMAN.Euler(ptcl[2], ptcl[3], ptcl[4])
dx = ptcl[5] - imgsize / 2
dy = ptcl[6] - imgsize / 2
print "%d\talt,az,phi=%8g,%8g,%8g\tx,y=%8g,%8g" % (
i + first,
e.alt() * 180 / pi,
e.az() * 180 / pi,
e.phi() * 180 / pi,
dx,
dy,
),
img.readImage(ptcl[0], ptcl[1])
img.setTAlign(-dx, -dy, 0)
img.setRAlign(0, 0, 0)
img.rotateAndTranslate() # now img is centered
img.applyMask(int(mask - max(abs(dx), abs(dy))), 6, 0, 0, 0)
if img.hasCTF():
fft = img.doFFT()
ctfparm = img.getCTF()
ctffilter.setCTF(ctfparm)
if options.phasecorrected:
if sffile:
ctffilter.ctfMap(64, sf) # Wiener filter with 1/CTF (no sign) correction
else:
if sffile:
ctffilter.ctfMap(32, sf) # Wiener filter with 1/CTF (including sign) correction
else:
ctffilter.ctfMap(2, EMAN.XYData()) # flip phase
fft.mult(ctffilter)
img2 = fft.doIFT() # now img2 is the CTF-corrected raw image
img.gimmeFFT()
del fft
else:
img2 = img
img2.normalize()
if shrink > 1:
img2.meanShrink(shrink)
# if sffile:
# snrcurve = img2.ctfCurve(9, sf) # absolute SNR
# else:
# snrcurve = img2.ctfCurve(3, EMAN.XYData()) # relative SNR
e.setSym(startSym)
maxscore = -1e30 # the larger the better
scores = []
for s in range(e.getMaxSymEl()):
ef = e.SymN(s)
# proj = map3d.project3d(ef.alt(), ef.az(), ef.phi(), -6) # Wen's direct 2D accumulation projection
proj = map3d.project3d(
ef.alt(), ef.az(), ef.phi(), -1
) # Pawel's fast projection, ~3 times faster than mode -6 with 216^3
# don't use mode -4, it modifies its own data
# proj2 = proj
proj2 = proj.matchFilter(img2)
proj2.applyMask(int(mask - max(abs(dx), abs(dy))), 6, 0, 0, 0)
if scorefunc == "ncccmp":
score = proj2.ncccmp(img2)
elif scorefunc == "lcmp":
score = -proj2.lcmp(img2)[0]
elif scorefunc == "pcmp":
score = -proj2.pcmp(img2)
elif scorefunc == "fsccmp":
score = proj2.fscmp(img2, [])
elif scorefunc == "wfsccmp":
score = proj2.fscmp(img2, snrcurve)
if score > maxscore:
maxscore = score
best_proj = proj2
best_ef = ef
best_s = s
scores.append(score)
# proj2.writeImage("proj-debug.img",s)
# print "\tsym %2d/%2d: euler=%8g,%8g,%8g\tscore=%12.7g\tbest=%2d euler=%8g,%8g,%8g score=%12.7g\n" % \
# (s,60,ef.alt()*180/pi,ef.az()*180/pi,ef.phi()*180/pi,score,best_s,best_ef.alt()*180/pi,best_ef.az()*180/pi,best_ef.phi()*180/pi,maxscore)
scores = Numeric.array(scores)
print "\tbest=%2d euler=%8g,%8g,%8g max score=%12.7g\tmean=%12.7g\tmedian=%12.7g\tmin=%12.7g\n" % (
best_s,
best_ef.alt() * 180 / pi,
best_ef.az() * 180 / pi,
best_ef.phi() * 180 / pi,
maxscore,
MLab.mean(scores),
MLab.median(scores),
MLab.min(scores),
)
if projfile:
best_proj.setTAlign(dx, dy, 0)
best_proj.setRAlign(0, 0, 0)
best_proj.rotateAndTranslate()
best_proj.set_center_x(ptcl[5])
best_proj.set_center_y(ptcl[6])
best_proj.setRAlign(best_ef)
# print "before proj send from %d" % (mpi.rank)
if mpi and mpi.size > 1:
mpi.send((emdata_dump(best_proj), projfile, i + first), 0)
elif pypar and pypar.size() > 1:
pypar.send((emdata_dump(best_proj), projfile, i + first), 0)
# print "after proj send from %d" % (mpi.rank)
else:
best_proj.writeImage(projfile, i + first)
img2.setTAlign(0, 0, 0)
img2.setRAlign(best_ef)
img2.setNImg(1)
# print "before raw send from %d" % (mpi.rank)
if output_ptcls:
if mpi and mpi.size > 1:
mpi.send((emdata_dump(img2), output_ptcls, i + first), 0)
elif pypar and pypar.size() > 1:
pypar.send((emdata_dump(img2), output_ptcls, i + first), 0)
# print "after raw send from %d" % (mpi.rank)
else:
img2.writeImage(output_ptcls, i + first)
solutions.append((ptcl[0], ptcl[1], best_ef.alt(), best_ef.az(), best_ef.phi(), ptcl[5], ptcl[6]))
if mpi and (mpi.size > 1 and mpi.rank > 0):
mpi.send(solutions, 0, tag=mpi.rank)
if mpi:
mpi.barrier()
elif pypar:
pypar.barrier()
if mpi:
mpi.finalize()
elif pypar:
pypar.finalize()
if options.cmplstfile:
os.rename("tmp-" + cmplstfile, cmplstfile)
if options.ortlstfile:
lFile = open(options.ortlstfile, "w")
lFile.write("#LST\n")
for i in solutions:
lFile.write(
"%d\t%s\t%g\t%g\t%g\t%g\t%g\n"
% (i[1], i[0], i[2] * 180.0 / pi, i[3] * 180.0 / pi, i[4] * 180.0 / pi, i[5], i[6])
)
lFile.close()
if not options.nocmdlog:
EMAN.LOGend()
