def main():
usage="""prog --path <path to previous spt or subtlt refinement> [options]
This program will run subtilt refinement based on previous subtomogram or subtilt refinement."""
parser = EMArgumentParser(usage=usage,version=EMANVERSION)
#parser.add_header(name="orblock0", help='Just a visual separation', title="Inputs", row=0, col=1, rowspan=1, colspan=3, mode="model")
parser.add_argument("--path", type=str,help="Path to the previous spt/subtlt refinement", default=None, guitype='filebox', browser="EMBrowserWidget(withmodal=True,multiselect=False)",row=1, col=0,rowspan=1, colspan=2)
parser.add_argument("--iter", type=int,help="Start from iteration X of previous refinement", default=-1, guitype='intbox',row=1, col=2,rowspan=1, colspan=1)
#####################
parser.add_header(name="orblock1", help='Just a visual separation', title="Options", row=2, col=1, rowspan=1, colspan=3, mode="model")
parser.add_argument("--niters", type=int,help="Run this many iterations. Default is 4.", default=4, guitype='intbox',row=3, col=0,rowspan=1, colspan=1)
parser.add_argument("--keep", type=float,help="propotion of tilts to keep. default is 0.5", default=0.5, guitype='floatbox',row=3, col=1,rowspan=1, colspan=1)
parser.add_argument("--maxalt", type=float,help="max altitude to insert to volume", default=90.0, guitype='floatbox',row=3, col=2,rowspan=1, colspan=1)
parser.add_argument("--mask", type=str, default="Auto" ,help="Refinement and reprojection masking.",guitype='strbox',row=4, col=0,rowspan=1, colspan=2)
parser.add_argument("--nogs", action="store_true", default=False ,help="Skip gold standard. This is not a great idea...", guitype='boolbox',row=4, col=2,rowspan=1, colspan=1)
parser.add_argument("--threads", type=int,help="Number of CPU threads to use. Default is 12.", default=12, guitype='intbox',row=5, col=2,rowspan=1, colspan=1)
parser.add_argument("--parallel", type=str,help="Thread/mpi parallelism to use. Default is thread:12", default="thread:12", guitype='strbox',row=5, col=0,rowspan=1, colspan=2)
parser.add_argument("--buildsetonly", action="store_true", default=False ,help="will only prepare particle set for the refinement but skip the actual refinement process.",guitype='boolbox',row=6, col=0,rowspan=1, colspan=1)
parser.add_argument("--resume", action="store_true", default=False ,help="continue from previous run",guitype='boolbox',row=6, col=1,rowspan=1, colspan=1)
parser.add_argument("--reproject", action="store_true", default=False ,help="Reproject 3D particles into 2D particles.",guitype='boolbox',row=7, col=0,rowspan=1, colspan=1)
parser.add_argument("--reprj_offset", type=str, default="" ,help="Offset translation before reprojection")
parser.add_argument("--reprj_clip", type=int, default=-1 ,help="clip after reprojection")
parser.add_argument("--tophat", type=str, default="auto" ,help="Filter option for refine_postprocess. auto: same as spt refinement; local; global;")
parser.add_argument("--refineastep", type=float,help="Mean angular variation for refine alignment", default=2.)
parser.add_argument("--refinentry", type=int,help="number of starting points for refine alignment", default=16)
parser.add_argument("--maxshift", type=int,help="maximum shift allowed", default=8)
parser.add_argument("--padby", type=float,default=1.5, help="pad by factor. default is 1.5")
parser.add_argument("--output", type=str,help="Write results to this directory. We do not recommend changing this.", default="subtlt")#, guitype='intbox',row=2, col=1,rowspan=1, colspan=1)
parser.add_argument("--debug", action="store_true", default=False ,help="Turn on debug mode. This will only process a small subset of the data (threads * 8 particles)")
parser.add_argument("--localnorm",action="store_true",help="local normalization. do not use yet....",default=False)
parser.add_argument("--sym", type=str,help="symmetry. will use symmetry from spt refinement by default", default="c1")
parser.add_argument("--ppid", type=int,help="ppid...", default=-1)
(options, args) = parser.parse_args()
logid=E2init(sys.argv)
itr = options.iter
oldpath = options.path
if not oldpath:
print("No input path. Exit.")
return
if options.iter != -1:
itr = options.iter
elif "spt" in oldpath:
for f in sorted(os.listdir(oldpath)):
if "particle_parms" in f:
itrstr = f[15:].split(".")[0]
if os.path.isfile(os.path.join(oldpath,"threed_{}.hdf".format(itrstr))):
itr = int(itrstr)
else:
for f in sorted(os.listdir(oldpath)):
if re.match("threed_[0-9][0-9].hdf",f):
itr = int(f[7:].split(".")[0])
if not os.path.isfile(os.path.join(oldpath,"threed_{:02d}.hdf".format(itr))):
print("Could not locate {}/threed_{:02d}.hdf".format(oldpath,itr))
print("Please specify the iteration number (--iter) of a completed subtomogram refinement.")
return
if "0_subtlt_params.json" in os.listdir(oldpath):
print("Continuing from a subtilt refinement...")
fromspt=False
else:
print("Start from a spt refinement...")
fromspt=True
if options.resume:
if fromspt:
print("Cannot resume from a spt refinement...")
return
path=oldpath
e=EMData(os.path.join(path,"threed_{:02d}.hdf".format(itr)))
else:
path = make_path(options.output)
print("Writing in {}...".format(path))
oldmap = os.path.join(oldpath,"threed_{:02d}.hdf".format(itr))
oem = os.path.join(oldpath,"threed_{:02d}_even.hdf".format(itr))
oom = os.path.join(oldpath,"threed_{:02d}_odd.hdf".format(itr))
oldfsc = os.path.join(oldpath, "fsc_masked_{:02d}.txt".format(itr))
copy2(oldmap,os.path.join(path,"threed_00.hdf"))
copy2(oldfsc, os.path.join(path, "fsc_masked_00.txt"))
copy2(oem,os.path.join(path,"threed_00_even.hdf"))
copy2(oom,os.path.join(path,"threed_00_odd.hdf"))
if fromspt:
oldparm = os.path.join(oldpath,"particle_parms_{:02d}.json".format(itr))
copy2(oldparm,os.path.join(path,"particle_parms_00.json"))
else:
for eo in ["even", "odd"]:
oali = os.path.join(oldpath,"ali_ptcls_{:02d}_{}.lst".format(itr, eo))
copy2(oali,os.path.join(path,"ali_ptcls_00_{}.lst".format(eo)))
e=EMData(os.path.join(path,"threed_00.hdf"))
bxsz=e["nx"]
apix=e["apix_x"]
jd = js_open_dict(os.path.join(path, "0_subtlt_params.json"))
jd.update(vars(options))
jd["cmd"] = " ".join(sys.argv)
jd["path"] = oldpath
jd["iter"] = itr
jd["output"] = path
options.ptclkeep=1.0
if fromspt:
sptparms = os.path.join(oldpath,"0_spt_params.json")
else:
sptparms = os.path.join(oldpath,"0_subtlt_params.json")
if os.path.isfile(sptparms):
oldjd = js_open_dict(sptparms)
#print(oldjd.keys())
jd["mass"] = oldjd["mass"]
jd["setsf"] = oldjd["setsf"]
jd["sym"] = oldjd["sym"]
jd["localfilter"]=oldjd["localfilter"]
jd["mask"]=oldjd["mask"]
if oldjd.has_key("radref"):
jd["radref"]=oldjd["radref"]
if fromspt:
options.ptclkeep=oldjd["pkeep"]
oldjd.close()
else:
print("Cannot find {}. exit.".format(sptparms))
return
if options.mask.lower()!="none":
#print("Overwritting masking")
if options.mask.lower()=="auto":
jd["mask"]=""
else:
jd["mask"]=options.mask
#if options.localfilter==0:
#jd["localfilter"]=False
#elif options.localfilter==1:
#jd["localfilter"]=True
if len(options.sym)>0:
jd["sym"]=options.sym
jsparams=jd.data
jd.close()
jd = jsparams
if fromspt and not options.reproject:
js=js_open_dict(os.path.join(path,"particle_parms_00.json"))
k=list(js.keys())[0]
src=eval(k)[0]
print("loading 3D particles from {}".format(base_name(src)))
print("box size {}, apix {:.2f}".format(bxsz, apix))
lname=[os.path.join(path, "ali_ptcls_00_{}.lst".format(eo)) for eo in ["even", "odd"]]
for l in lname:
try: os.remove(l)
except:pass
lst=[LSXFile(m, False) for m in lname]
n3d=len(list(js.keys()))
if options.ptclkeep<1.0:
score=[]
for k in list(js.keys()):
score.append(float(js[k]["score"]))
simthr=np.sort(score)[int(len(score)*options.ptclkeep)]
print("removing bad particles...")
else:
simthr=10000
for ky in js.keys():
src,ii=eval(ky)
e=EMData(src, ii, True)
fname=e["class_ptcl_src"]
ids=e["class_ptcl_idxs"]
#ky="('{}', {})".format(src, ii)
dic=js[ky]
xali=dic["xform.align3d"]
scr=float(dic["score"])
if scr>simthr:
continue
if "__even" in src:
eo=0
elif "__odd" in src:
eo=1
else:
eo=ii%2
#print(src, eo)
for i in ids:
try:
m=EMData(fname, i, True)
except:
continue
xf=m["xform.projection"]
dc=xf.get_params("xyz")
if abs(dc["ytilt"])>options.maxalt:
continue
rot=xf*xali.inverse()
lst[eo].write(-1, i, fname, str(rot.get_params("eman")))
for l in lst:
l.close()
js=None
if fromspt and options.reproject:
print("Reprojecting 3D particles...")
if options.reprj_offset!="":
options.reprj_offset=[int(i) for i in options.reprj_offset.split(',')]
print("Offset by {} before reprojection".format(options.reprj_offset))
else:
options.reprj_offset=None
if options.mask.lower()=="auto":
mask=EMData(os.path.join(oldpath,"mask.hdf"))
mask.process_inplace("filter.lowpass.gauss", {"cutoff_abs":.1})
mask.add(0.5)
mask.div(1.5)
mask.write_image(os.path.join(path,"mask_reproj.hdf"))
elif options.mask.lower()=="none":
mask=None
else:
mask=EMData(options.mask)
ptclfile=[os.path.join(path,"particles_reproj_{}.hdf".format(eo)) for eo in ["even","odd"]]
for p in ptclfile:
try: os.remove(p)
except: pass
lstfile=[os.path.join(path, "ali_ptcls_00_{}.lst".format(eo)) for eo in ["even", "odd"]]
for p in lstfile:
try: os.remove(p)
except: pass
js=js_open_dict(os.path.join(path,"particle_parms_00.json"))
jsdata=dict(js.data)
js.close()
if options.ptclkeep<1.0:
score=[]
for k in list(jsdata.keys()):
score.append(float(jsdata[k]["score"]))
simthr=np.sort(score)[int(len(score)*options.ptclkeep)]
print("removing bad particles with score > {:.2f}...".format(simthr))
else:
simthr=10000
keys=list(jsdata.keys())
nt=options.threads
if options.debug:
keys=keys[:nt*32]
thrds=[threading.Thread(target=do_reproject,
args=(jsdata, keys[i::nt], ptclfile, options, mask, simthr)) for i in range(nt)]
print(len(thrds)," threads")
thrtolaunch=0
while thrtolaunch<len(thrds) or threading.active_count()>1:
# If we haven't launched all threads yet, then we wait for an empty slot, and launch another
# note that it's ok that we wait here forever, since there can't be new results if an existing
# thread hasn't finished.
if thrtolaunch<len(thrds) :
while (threading.active_count()==options.threads ) : time.sleep(.1)
#if options.verbose : print("Starting thread {}/{}".format(thrtolaunch,len(thrds)))
thrds[thrtolaunch].start()
thrtolaunch+=1
else: time.sleep(1)
for t in thrds:
t.join()
print("Now writing list files...")
#### write a list file
for k in [0,1]:
n=EMUtil.get_image_count(ptclfile[k])
lst=LSXFile(lstfile[k], False)
for i in range(n):
e=EMData(ptclfile[k], i, True)
rot=e["xform.align3d"]
lst.write(-1, i, ptclfile[k], str(rot.get_params("eman")))
lst=None
if options.buildsetonly: return
if options.resume:
starti=itr
else:
starti=0
for itr in range(starti,options.niters+starti):
for eo in ["even", "odd"]:
if options.nogs:
threedname=os.path.join(path, "threed_{:02d}.hdf".format(itr))
else:
threedname=os.path.join(path, "threed_{:02d}_{}.hdf".format(itr, eo))
lstname=os.path.join(path, "ali_ptcls_{:02d}_{}.lst".format(itr, eo))
lname=os.path.join(path, "ali_ptcls_{:02d}_{}.lst".format(itr+1, eo))
threedout=os.path.join(path, "threed_{:02d}_{}.hdf".format(itr+1, eo))
cmd="e2spt_tiltrefine_oneiter.py --ptclin {} --ptclout {} --ref {} --threedout {} --keep {} --threads {} --parallel {} --refineastep {} --refinentry {} --maxshift {} --padby {} --sym {}".format(lstname, lname, threedname, threedout, options.keep, options.threads, options.parallel, options.refineastep, options.refinentry, options.maxshift, options.padby, jd["sym"])
if options.debug:
cmd+=" --debug"
run(cmd)
run("e2proc3d.py {} {}".format(threedout, os.path.join(path, "threed_raw_{}.hdf".format(eo))))
s = ""
if jd.has_key("goldstandard"):
if jd["goldstandard"] > 0:
s += " --align"
if jd.has_key("setsf"):
s += " --setsf {}".format(jd['setsf']) #options.setsf)
if options.tophat=="auto" and jd.has_key("localfilter") and jd["localfilter"]==True:
s += " --tophat local"
elif options.tophat=="local":
s += " --tophat local"
elif options.tophat=="global":
s += " --tophat global"
msk = jd["mask"] #{}/mask_tight.hdf".format(path)
if len(msk)>0:
if os.path.isfile(msk):
msk=" --automask3d mask.fromfile:filename={}".format(msk)
else:
msk=" --automask3d {}".format(msk)
s+=msk
# get target resolution from last iteration map
ref=os.path.join(path, "threed_{:02d}.hdf".format(itr))
fsc=np.loadtxt(os.path.join(path, "fsc_masked_{:02d}.txt".format(itr)))
try:
rs=1./fsc[fsc[:,1]<0.3, 0][0]
except:
rs=10
curres=rs*.5
even=os.path.join(path, "threed_{:02d}_even.hdf".format(itr+1))
odd=os.path.join(path, "threed_{:02d}_odd.hdf".format(itr+1))
#os.system("rm {}/mask*.hdf {}/*unmasked.hdf".format(path, path))
ppcmd="e2refine_postprocess.py --even {} --odd {} --output {} --iter {:d} --restarget {} --threads {} --sym {} --mass {} {}".format(even, odd,
os.path.join(path, "threed_{:02d}.hdf".format(itr+1)), itr+1, curres, options.threads, jd["sym"], jd["mass"], s)
run(ppcmd)
if options.localnorm:
for f in [even, odd]:
run("e2proc3d.py {} {} --process normalize --process normalize.local:threshold=1:radius=16".format(f,f))
run("e2proc3d.py {} {} --addfile {} --process normalize".format(
even, os.path.join(path, "threed_{:02d}.hdf".format(itr+1)), odd))
fsc=np.loadtxt(os.path.join(path, "fsc_masked_{:02d}.txt".format(itr+1)))
print("Resolution (FSC<0.3) is ~{:.1f} A".format(rs))
E2end(logid)
def main():
usage=" "
parser = EMArgumentParser(usage=usage,version=EMANVERSION)
parser.add_argument("--path", type=str,help="path", default=None, guitype='strbox',row=0, col=0,rowspan=1, colspan=1)
parser.add_argument("--iter", type=int,help="start from iteration X", default=-1, guitype='intbox',row=0, col=1,rowspan=1, colspan=1)
parser.add_argument("--niters", type=int,help="run this many iterations. Default is 4.", default=4, guitype='intbox',row=0, col=2,rowspan=1, colspan=1)
parser.add_argument("--sym", type=str,help="symmetry. will use symmetry from spt refinement by default", default="", guitype='strbox',row=2, col=0,rowspan=1, colspan=1)
parser.add_argument("--padby", type=float,help="pad by factor. default is 2", default=2., guitype='floatbox',row=1, col=1,rowspan=1, colspan=1)
parser.add_argument("--keep", type=float,help="propotion of tilts to keep. default is 0.5", default=0.5, guitype='floatbox',row=1, col=2,rowspan=1, colspan=1)
parser.add_argument("--maxalt", type=float,help="max altitude to insert to volume", default=90.0, guitype='floatbox',row=1, col=0,rowspan=1, colspan=1)
parser.add_argument("--nogs", action="store_true", default=False ,help="skip gold standard...", guitype='boolbox',row=2, col=1,rowspan=1, colspan=1)
parser.add_argument("--localfilter", type=int, default=-1 ,help="use tophat local. specify 0 or 1 to overwrite the setting in the spt refinement")
parser.add_argument("--mask", type=str, default="None" ,help="Refinement masking. default is the same as the spt refinement. Leave this empty for automasking",guitype='strbox',row=3, col=0,rowspan=1, colspan=2)
parser.add_argument("--threads", type=int,help="Number of CPU threads to use. Default is 12.", default=12, guitype='intbox',row=2, col=2,rowspan=1, colspan=1)
parser.add_argument("--parallel", type=str,help="Thread/mpi parallelism to use. Default is thread:12", default="thread:12", guitype='strbox',row=4, col=0,rowspan=1, colspan=3)
parser.add_argument("--refineastep", type=float,help="angular variation for refine alignment (gauss std)", default=8.)
parser.add_argument("--refinentry", type=int,help="number of starting points for refine alignment", default=32)
parser.add_argument("--maxshift", type=int,help="maximum shift allowed", default=10)
parser.add_argument("--buildsetonly", action="store_true", default=False ,help="build sets only")
parser.add_argument("--output", type=str,help="Write results to this directory. We do not recommend changing this.", default="subtlt")#, guitype='intbox',row=2, col=1,rowspan=1, colspan=1)
parser.add_argument("--debug", action="store_true", default=False ,help="Turn on debug mode. This will only process a small subset of the data (threads * 8 particles)")
parser.add_argument("--ppid", type=int,help="ppid...", default=-1)
(options, args) = parser.parse_args()
logid=E2init(sys.argv)
itr = options.iter
oldpath = options.path
if not oldpath:
print("No input path. Exit.")
return
if options.iter != -1:
itr = options.iter
elif "spt" in oldpath:
for f in sorted(os.listdir(oldpath)):
if "particle_parms" in f:
itrstr = f[15:].split(".")[0]
if os.path.isfile("{}/threed_{}.hdf".format(oldpath,itrstr)):
itr = int(itrstr)
else:
for f in sorted(os.listdir(oldpath)):
if re.match("threed_[0-9][0-9].hdf",f):
itr = int(f[7:].split(".")[0])
# print(oldpath)
fromspt=True
if "0_subtlt_params.json" in os.listdir(oldpath):
print("Continuing from a subtilt refinement...")
fromspt=False
path = make_path(options.output)
if not os.path.isfile("{}/threed_{:02d}.hdf".format(oldpath,itr)):
print("Could not locate {}/threed_{:02d}.hdf".format(oldpath,itr))
print("Please specify the iteration number (--iter) of a completed subtomogram refinement.")
sys.exit(1)
#elif not os.path.isfile("{}/particle_parms_{:02d}.json".format(oldpath,itr)):
#print("Could not locate {}/particle_parms_{:02d}.json".format(oldpath,itr))
#print("Please specify the iteration number (--iter) of a completed subtomogram refinement.")
#sys.exit(1)
else:
#copy2("{}/0_spt_params.json".format(oldpath),"{}/0_subtlt_params.json".format(path))
oldmap = os.path.join(oldpath,"threed_{:02d}.hdf".format(itr))
oem = os.path.join(oldpath,"threed_{:02d}_even.hdf".format(itr))
oom = os.path.join(oldpath,"threed_{:02d}_odd.hdf".format(itr))
oldfsc = os.path.join(oldpath, "fsc_masked_{:02d}.txt".format(itr))
copy2(oldmap,os.path.join(path,"threed_00.hdf"))
copy2(oldfsc, os.path.join(path, "fsc_masked_00.txt"))
copy2(oem,os.path.join(path,"threed_00_even.hdf"))
copy2(oom,os.path.join(path,"threed_00_odd.hdf"))
if fromspt:
oldparm = os.path.join(oldpath,"particle_parms_{:02d}.json".format(itr))
copy2(oldparm,os.path.join(path,"particle_parms_00.json"))
else:
for eo in ["even", "odd"]:
oali = os.path.join(oldpath,"ali_ptcls_{:02d}_{}.lst".format(itr, eo))
copy2(oali,os.path.join(path,"ali_ptcls_00_{}.lst".format(eo)))
e=EMData(os.path.join(path,"threed_00.hdf"))
bxsz=e["nx"]
apix=e["apix_x"]
jd = js_open_dict("{}/0_subtlt_params.json".format(path))
jd.update(vars(options))
jd["cmd"] = " ".join(sys.argv)
jd["path"] = oldpath
jd["iter"] = itr
jd["output"] = path
if fromspt:
sptparms = os.path.join(oldpath,"0_spt_params.json")
else:
sptparms = os.path.join(oldpath,"0_subtlt_params.json")
if os.path.isfile(sptparms):
oldjd = js_open_dict(sptparms)
#print(oldjd.keys())
jd["mass"] = oldjd["mass"]
jd["setsf"] = oldjd["setsf"]
jd["sym"] = oldjd["sym"]
jd["localfilter"]=oldjd["localfilter"]
jd["mask"]=oldjd["mask"]
oldjd.close()
else:
print("Cannot find {}. exit.".format(sptparms))
if options.mask.lower()!="none":
print("Overwritting masking")
jd["mask"]=options.mask
if options.localfilter==0:
jd["localfilter"]=False
elif options.localfilter==1:
jd["localfilter"]=True
if len(options.sym)>0:
jd["sym"]=options.sym
jsparams=jd.data
jd.close()
jd = jsparams
if fromspt:
js=js_open_dict(os.path.join(path,"particle_parms_00.json"))
k=list(js.keys())[0]
src=eval(k)[0]
print("loading 3D particles from {}".format(src))
print("box size {}, apix {:.2f}".format(bxsz, apix))
lname=[os.path.join(path, "ali_ptcls_00_{}.lst".format(eo)) for eo in ["even", "odd"]]
for l in lname:
try: os.remove(l)
except:pass
lst=[LSXFile(m, False) for m in lname]
n3d=len(list(js.keys()))
for ii in range(n3d):
e=EMData(src, ii, True)
fname=e["class_ptcl_src"]
ids=e["class_ptcl_idxs"]
ky="('{}', {})".format(src, ii)
dic=js[ky]
xali=dic["xform.align3d"]
for i in ids:
try:
m=EMData(fname, i, True)
except:
continue
xf=m["xform.projection"]
dc=xf.get_params("xyz")
if abs(dc["ytilt"])>options.maxalt:
continue
rot=xf*xali.inverse()
lst[ii%2].write(-1, i, fname, str(rot.get_params("eman")))
for l in lst:
l.close()
js=None
if options.buildsetonly: return
for itr in range(0,options.niters):
from EMAN2PAR import EMTaskCustomer
for eo in ["even", "odd"]:
if options.nogs:
threedname="{}/threed_{:02d}.hdf".format(path, itr)
else:
threedname="{}/threed_{:02d}_{}.hdf".format(path, itr, eo)
lstname="{}/ali_ptcls_{:02d}_{}.lst".format(path, itr, eo)
lst=LSXFile(lstname, True)
m=EMData(threedname)
m.process_inplace('normalize.edgemean')
pinfo=[]
if options.debug: nptcl=options.threads*8
else: nptcl=lst.n
for i in range(nptcl):
pinfo.append(lst.read(i))
lst=None
etc=EMTaskCustomer(options.parallel)
num_cpus = etc.cpu_est()
print("{} total CPUs available".format(num_cpus))
print("{} jobs".format(nptcl))
infos=[[] for i in range(num_cpus)]
for i,info in enumerate(pinfo):
infos[i%num_cpus].append([i, info])
tids=[]
for info in infos:
task = SptTltRefineTask(info, m, options)
tid=etc.send_task(task)
tids.append(tid)
while 1:
st_vals = etc.check_task(tids)
#print("{:.1f}/{} finished".format(np.mean(st_vals), 100))
#print(tids)
if np.min(st_vals) == 100: break
time.sleep(5)
dics=[0]*nptcl
for i in tids:
ret=etc.get_results(i)[1]
for r in ret:
#print(r)
ii=r.pop("idx")
dics[ii]=r
del etc
allscr=np.array([d["score"] for d in dics])
print(np.min(allscr), np.mean(allscr), np.max(allscr), np.std(allscr))
allscr*=-1
s=allscr.copy()
s-=np.mean(s)
s/=np.std(s)
clp=2
ol=abs(s)>clp
print("Removing {} outliers from {} particles..".format(np.sum(ol), len(s)))
s=old_div(old_div((s+clp),clp),2)
s[ol]=0
allscr=s
#allscr-=np.min(allscr)-1e-5
#allscr/=np.max(allscr)
lname="{}/ali_ptcls_{:02d}_{}.lst".format(path, itr+1, eo)
try: os.remove(lname)
except: pass
lout=LSXFile(lname, False)
for i, dc in enumerate(dics):
d=dc["xform.align3d"].get_params("eman")
d["score"]=float(allscr[i])
l=pinfo[i]
lout.write(-1, l[0], l[1], str(d))
lout=None
pb=options.padby
threedout="{}/threed_{:02d}_{}.hdf".format(path, itr+1, eo)
cmd="e2make3dpar.py --input {inp} --output {out} --pad {pd} --padvol {pdv} --threads {trd} --outsize {bx} --apix {apx} --mode gauss_var --keep {kp} --sym {sm}".format(
inp=lname,
out=threedout,
bx=bxsz, pd=int(bxsz*pb), pdv=int(bxsz*pb), apx=apix, kp=options.keep, sm=jd["sym"], trd=options.threads)
run(cmd)
run("e2proc3d.py {} {}".format(threedout, "{}/threed_raw_{}.hdf".format(path, eo)))
s = ""
if jd.has_key("goldstandard"):
if jd["goldstandard"] > 0:
s += " --align"
if jd.has_key("setsf"):
s += " --setsf {}".format(jd['setsf']) #options.setsf)
if jd.has_key("localfilter"):
s += " --tophat local"
msk = jd["mask"] #{}/mask_tight.hdf".format(path)
if len(msk)>0:
if os.path.isfile(msk):
msk=" --automask3d mask.fromfile:filename={}".format(msk)
else:
msk=" --automask3d {}".format(msk)
# get target resolution from last iteration map
ref=os.path.join(path, "threed_{:02d}.hdf".format(itr))
fsc=np.loadtxt(os.path.join(path, "fsc_masked_{:02d}.txt".format(itr)))
rs=1./fsc[fsc[:,1]<0.3, 0][0]
curres=rs*.5
#os.system("rm {}/mask*.hdf {}/*unmasked.hdf".format(path, path))
ppcmd="e2refine_postprocess.py --even {} --odd {} --output {} --iter {:d} --restarget {} --threads {} --sym {} --mass {} {}".format(
os.path.join(path, "threed_{:02d}_even.hdf".format(itr+1)),
os.path.join(path, "threed_{:02d}_odd.hdf".format(itr+1)),
os.path.join(path, "threed_{:02d}.hdf".format(itr+1)), itr+1, curres, options.threads, jd["sym"], jd["mass"], s)
run(ppcmd)
fsc=np.loadtxt(os.path.join(path, "fsc_masked_{:02d}.txt".format(itr+1)))
rs=1./fsc[fsc[:,1]<0.3, 0][0]
print("Resolution (FSC<0.3) is ~{:.1f} A".format(rs))
E2end(logid)
def main():
progname = os.path.basename(sys.argv[0])
usage = """Usage: e2spt_pcasplit.py --path <spt_XX> [options]"""
parser = EMArgumentParser(usage=usage,version=EMANVERSION)
parser.add_argument("--path",type=str,required=True,default=None,help="Path to a folder where results should be stored, following standard naming conventions (default = spt_XX)",guitype='filebox',row=0, col=0, rowspan=1, colspan=2)
parser.add_argument("--iter",type=int,required=True,help="Iteration number within path. Default is the second to last iteration (-2).",default=-2,guitype='intbox',row=1, col=0, rowspan=1, colspan=1)
parser.add_argument("--nclass",type=int,required=True,help="Number of classes. Default is 2.",default=2,guitype="intbox",row=1, col=1, rowspan=1, colspan=1)
parser.add_header(name="orblock1", help='', title="Optional", row=2, col=0, rowspan=1, colspan=2)
parser.add_argument("--maxres",type=float,help="Filter particles to this resolution (in Angstroms) before classification",default=30.0,guitype="floatbox",row=3, col=0, rowspan=1, colspan=1)
parser.add_argument("--sym",type=str,help="Apply this symmetry.",default="c1",guitype="strbox",row=3, col=1, rowspan=1, colspan=1)
parser.add_argument("--mask",type=str,help="Apply this mask. Default is 'mask_tight.hdf' from <--path>_<--iter>. Specify 'none' for no masking",default="",guitype="filebox",row=4, col=0, rowspan=1, colspan=2)
parser.add_argument("--nbasis",type=int,required=True,help="Number of PCA basis vectors. Default is 3.",default=3,guitype="intbox",row=5, col=0, rowspan=1, colspan=1)
# parser.add_argument("--keepthresh",type=float,help="Center PCA outliers beyond this value before performing K-means clustering. Default is no threshold (-1).",default=,guitype="floatbox",row=5, col=0, rowspan=1, colspan=1)
parser.add_argument("--nowedgefill",action='store_true',help="Do not fill the missing wedge before classification.",default=False,guitype="boolbox",row=5, col=1, rowspan=1, colspan=1)
parser.add_argument("--clean",action='store_true',help="remove outliers before PCA.",default=False,guitype="boolbox",row=6, col=1, rowspan=1, colspan=1)
parser.add_argument("--verbose", "-v", dest="verbose", action="store", metavar="n", type=int, default=0, help="verbose level [0-9], higner number means higher level of verboseness")
parser.add_argument("--shrink",type=int,help="Shrink particles before classification",default=1)
parser.add_argument("--dotest",type=int,help="test using N random particles",default=-1)
parser.add_argument("--ppid", type=int, help="Set the PID of the parent process, used for cross platform PPID",default=-1)
(options, args) = parser.parse_args()
if options.path == None:
print("You must specify the path to an existing spt_XX refinement directory.")
sys.exit(1)
if not os.path.isdir(options.path):
print("Could not locate --path {}".format(options.path))
sys.exit(1)
if options.iter==-2:
fls=[int(i[15:17]) for i in os.listdir(options.path) if i[:15]=="particle_parms_" and str.isdigit(i[15:17])]
if len(fls)==0 :
print("No subtomogram alignment was complered for this refinement. Please try another")
sys.exit(1)
options.iter=max(fls)-1
logid=E2init(sys.argv, options.ppid)
threed = EMData("{}/threed_{:02d}.hdf".format(options.path,options.iter))
if options.mask=="none":
msk=threed.copy()
msk.to_one()
elif options.mask=="":
msk = EMData("{}/mask_tight.hdf".format(options.path))
else:
msk = EMData(options.mask)
#refparms=js_open_dict("{}/0_spt_params.json".format(options.path))
#try:
#inptcls = refparms["input_ptcls"]
#except:
#print("Could not locate input particles. Path and iteration must reference a standard 3D refinement (e2spt_refine.py) and not a subtilt refinement (e2spt_subtilt.py).")
#sys.exit(1)
parmsfile = "{}/particle_parms_{:02d}.json".format(options.path,options.iter)
js=js_open_dict(parmsfile)
nptcl=len(js.keys())
irange=np.arange(nptcl, dtype=int)
if options.dotest>0:
np.random.shuffle(irange)
nptcl=min(nptcl, options.dotest)
irange=irange[:nptcl]
print("Test with {} particles".format(nptcl))
inptcls=pname=eval(js.keys()[0])[0]
print("Preprocessing {} particles...".format(nptcl))
#### doing everything in fourier space with numpy
data=[]
wgs=[]
keys=[]
### to clip fourier space based on resolution
sz=threed["nx"]//options.shrink
apix=threed["apix_x"]*options.shrink
freq=np.fft.fftfreq(sz, apix)[:sz//2]
clip=sz//2-np.argmax(freq>1./options.maxres)
if clip==sz//2: clip=0
#n=EMUtil.get_image_count(pname)
for i in irange.tolist():
k="('{}', {})".format(pname, i)
if js.has_key(k)==False: continue
xf=js[k]['xform.align3d']
e=EMData(pname, i)
e.transform(xf)
e.mult(msk)
if options.shrink>1:
e.process_inplace("math.meanshrink",{"n":options.shrink})
en=e.numpy().copy()
#print(en.shape, clip)
#### numpy fft is actually significantly slower than EMAN fft. so probably should change to EMAN if I can get the coordinates right..
ef=np.fft.fftshift(np.fft.fftn(en))
if clip>0:
ef=ef[clip:-clip,clip:-clip,clip:-clip]
if len(data)==0:
sz=len(ef)
idx=np.indices((sz,sz,sz))-sz//2
r=np.sqrt(np.sum(idx**2, 0))
r = r.astype(np.int)
nr = np.bincount(r.ravel())
efa=abs(ef)
tbin = np.bincount(r.ravel(), efa.ravel())
sf = tbin / nr
sf[sf==0]=1e-5
div=np.divide(efa,sf[r])
wdg=np.logical_and(div<1., r>1)
ef[wdg]=0
data.append(ef.flatten())
wgs.append(wdg.flatten())
keys.append(k)
sys.stdout.write("\r {}/{} particles".format(len(data),nptcl))
sys.stdout.flush()
print()
js.close()
data=np.array(data)
wgs=np.array(wgs)
print(data.shape)
#avg=np.mean(data, axis=0)
avg=np.sum(data, axis=0)
w=np.sum(1-np.array(wgs), axis=0)+1
avg=avg/w
dv=data-avg
std=np.std(abs(dv))
dv/=std
#for i,w in enumerate(wgs):
#data[i][w]=avg[w]
#dv=data
imgsnp=np.hstack([dv.real, dv.imag])
print(dv.real.shape, imgsnp.shape)
options.outpath = make_path("sptcls")
print("Output will be written to {}".format(options.outpath))
#### real space version:
#refft = threed.do_fft()
#imgs00 = []
#for i in range(nptcl):
#sys.stdout.write("\r{}/{} particles".format(i+1,nptcl))
#sys.stdout.flush()
#k="('{}', {})".format(pname, i)
#xf=js[k]['xform.align3d']
#e=EMData(pname, i)
#e.transform(xf)
#e["score"]=js[k]['score']
#if options.nowedgefill:
#enew = e
#else:
#eft=e.do_fft()
#eft.process_inplace("mask.wedgefill",{"fillsource":refft, "thresh_sigma":0.0})
#enew=eft.do_ift()
#enew.mult(msk)
#enew.process_inplace("math.meanshrink",{"n":options.shrink})
#enew.process_inplace("filter.lowpass.gauss",{"cutoff_freq":1/options.maxres})
#if options.sym != None:
#enew.process_inplace("xform.applysym",{"averager":"mean.tomo", "sym":options.sym})
#enew["xform.align3d"]=xf
#enew.write_image("{}/aliptcls.hdf".format(options.outpath), i)
#imgs00.append(enew)
#js.close()
#nptcl = len(imgs00)
#imgsnp=np.array([m.numpy().copy() for m in imgs00])
print("Performing PCA...")
#print(imgsnp)
ptclids=np.arange(nptcl,dtype=int)[:, None]
nptcl=len(imgsnp)
if options.clean:
#### do pca twice to remove outliers
pca=PCA(options.nbasis)
pout=pca.fit_transform(imgsnp)
dst=np.linalg.norm(pout-np.mean(pout, 0), axis=1)
outlr=dst>np.mean(dst)+np.std(dst)*2
np.savetxt("{}/pca_rmoutlier.txt".format(options.outpath),
np.hstack([ptclids, pout]))
print("Removing {} outliers...".format(np.sum(outlr)))
imgsnp=imgsnp[outlr==False]
ptclids=ptclids[outlr==False]
pca=PCA(options.nbasis)
pout=pca.fit_transform(imgsnp)
np.savetxt("{}/pca_ptcls.txt".format(options.outpath),
np.hstack([ptclids, pout]))
basisfile = "{}/pca_basis.hdf".format(options.outpath)
#threed.process("math.meanshrink",{"n":options.shrink}).write_image(basisfile, 0)
l=len(data[0])
for i,c in enumerate(pca.components_):
eg=c[:l]+c[l:]*1j
egmap=eg.reshape((sz,sz,sz))
o=np.real(np.fft.ifftn(np.fft.ifftshift(egmap)))
m=from_numpy(o.copy())
m.write_image(basisfile,i)
print("Classifying particles...")
kmeans = KMeans(n_clusters=options.nclass).fit(pout)
lb=kmeans.labels_
if options.clean:
lbfull=np.zeros(nptcl, dtype=int)
lbfull[outlr==False]=lb
lbfull[outlr]=-1
lb=lbfull
print("Class: Particle count")
for i in range(kmeans.n_clusters):
print("{}: {}".format(lb,np.sum(lb==i)))
## subtomogram average particles from each class
#avgr=[Averagers.get("mean.tomo") for i in range(kmeans.n_clusters)]
#for i in range(nptcl):
#avgr[lb[i]].add_image(imgs00[i])
#classfile = "{}/classes3d.hdf".format(options.outpath)
#for i in range(kmeans.n_clusters):
#e=avgr[i].finish()
#e.process_inplace("normalize")
#e.write_image(classfile,i)
inlst=LSXFile(inptcls, True)
outlsts=[]
for lbl in sorted(np.unique(lb)):
#outlst = LSXFile(inptcls.replace(".lst","_{}.lst".format(lbl)))
outlst = LSXFile("{}/ptcls_cls{:02d}.lst".format(options.outpath, lbl+1))
for i in range(nptcl):
if lb[i]==lbl:
l=inlst.read(i)
outlst.write(-1,l[0], l[1],l[2])
outlst.close()
inlst.close()
js0=js_open_dict(parmsfile)
#pname=eval(js.keys()[0])[0]
dics=[{} for i in range(kmeans.n_clusters)]
for i in range(nptcl):
if lb[i]>=0:
k=keys[i]
dics[lb[i]][k]=js0[k]
js0.close()
for i,d in enumerate(dics):
js=js_open_dict("{}/particle_parms_{:02d}.json".format(options.outpath, i+1))
js.update(d)
js.close()
os.system("e2spt_average.py --path {} --iter {} --threads 10 --sym {} --skippostp --simthr 1".format(options.outpath, i+1, options.sym))
E2end(logid)
