def doRefit(self): parms=self.parms[self.curset] apix=self.sapix.getValue() ds=1.0/(apix*parms[0]*parms[5]) try: parms[1]=e2ctf.ctf_fit(self.fft1d,parms[1].background,parms[1].background,self.fft,self.fftbg,parms[1].voltage,parms[1].cs,parms[1].ampcont,apix,bgadj=False,autohp=True,verbose=1) if self.cbgadj.getValue() : self.bgAdj() parms[1]=e2ctf.ctf_fit(self.fft1d,parms[1].background,parms[1].background,self.fft,self.fftbg,parms[1].voltage,parms[1].cs,parms[1].ampcont,apix,bgadj=False,autohp=True,verbose=1) if self.fitastig : e2ctf.ctf_fit_stig(self.fft,self.fftbg,parms[1],verbose=1) self.bgAdj() else: if self.fitastig : e2ctf.ctf_fit_stig(self.fft,self.fftbg,parms[1],verbose=1) except: print "CTF Autofit Failed" traceback.print_exc() parms[1].defocus=1.0 if self.constbfactor>0 : parms[1].bfactor=self.constbfactor self.sdefocus.setValue(parms[1].defocus,True) self.sbfactor.setValue(parms[1].bfactor,True) self.sampcont.setValue(parms[1].ampcont,True) self.update_plot()
def doRefit(self): parms=self.parms[self.curset] apix=self.sapix.getValue() ds=1.0/(apix*parms[0]*parms[5]) try: parms[1]=e2ctf.ctf_fit(self.fft1d,parms[1].background,parms[1].background,self.fft,self.fftbg,parms[1].voltage,parms[1].cs,parms[1].ampcont,apix,bgadj=False,autohp=True,verbose=1) if self.cbgadj.getValue() : self.bgAdj() parms[1]=e2ctf.ctf_fit(self.fft1d,parms[1].background,parms[1].background,self.fft,self.fftbg,parms[1].voltage,parms[1].cs,parms[1].ampcont,apix,bgadj=False,autohp=True,verbose=1) if self.fitastig : e2ctf.ctf_fit_stig(self.fft,self.fftbg,parms[1],verbose=1) self.bgAdj() else: if self.fitastig : e2ctf.ctf_fit_stig(self.fft,self.fftbg,parms[1],verbose=1) except: print "CTF Autofit Failed" traceback.print_exc() parms[1].defocus=1.0 if self.constbfactor>0 : parms[1].bfactor=self.constbfactor self.sdefocus.setValue(parms[1].defocus,True) self.sbfactor.setValue(parms[1].bfactor,True) self.sampcont.setValue(parms[1].ampcont,True) self.update_plot()
def importfn(i,arg,options):
base = base_name(arg,nodir=not options.usefoldername)
output = os.path.join(os.path.join(".","micrographs"),base+".hdf")
cmd = "e2proc2d.py %s %s --inplace"%(arg,output)
cmdext=[]
if options.invert: cmdext.append(" --mult=-1")
if options.edgenorm: cmdext.append(" --process=normalize.edgemean")
if options.xraypixel: cmdext.append(" --process=threshold.clampminmax.nsigma:nsigma=4")
if len(cmdext)>0 or arg!=output:
cmd+="".join(cmdext)
launch_childprocess(cmd)
if options.moverawdata:
os.rename(arg,os.path.join(originalsdir,os.path.basename(arg)))
# We estimate the defocus and B-factor (no astigmatism) from the micrograph and store it in info and the header
if options.ctfest :
d=EMData(output,0)
if d["nx"]<1000 or d["ny"]<1000 :
print("CTF estimation will only work with images at least 1000x1000 in size")
sys.exit(1)
if d["nx"]<2000 : box=256
elif d["nx"]<4000 : box=512
elif d["nx"]<6000 : box=768
else : box=1024
import e2ctf
ds=1.0/(options.apix*box)
ffta=None
nbx=0
for x in range(100,d["nx"]-box,box):
for y in range(100,d["ny"]-box,box):
clip=d.get_clip(Region(x,y,box,box))
clip.process_inplace("normalize.edgemean")
fft=clip.do_fft()
fft.ri2inten()
if ffta==None: ffta=fft
else: ffta+=fft
nbx+=1
ffta.mult(1.0/(nbx*box**2))
ffta.process_inplace("math.sqrt")
ffta["is_intensity"]=0 # These 2 steps are done so the 2-D display of the FFT looks better. Things would still work properly in 1-D without it
fftbg=ffta.process("math.nonconvex")
fft1d=ffta.calc_radial_dist(ffta.get_ysize()/2,0.0,1.0,1) # note that this handles the ri2inten averages properly
# Compute 1-D curve and background
bg_1d=e2ctf.low_bg_curve(fft1d,ds)
#initial fit, background adjustment, refine fit, final background adjustment
ctf=e2ctf.ctf_fit(fft1d,bg_1d,bg_1d,ffta,fftbg,options.voltage,options.cs,options.ac,options.phaseplate,options.apix,1,dfhint=(options.defocusmin,options.defocusmax))
bgAdj(ctf,fft1d)
ctf=e2ctf.ctf_fit(fft1d,ctf.background,ctf.background,ffta,fftbg,options.voltage,options.cs,options.ac,options.phaseplate,options.apix,1,dfhint=(options.defocusmin,options.defocusmax))
bgAdj(ctf,fft1d)
if options.astigmatism : e2ctf.ctf_fit_stig(ffta,fftbg,ctf)
#ctf.background=bg_1d
#ctf.dsbg=ds
db=js_open_dict(info_name(arg,nodir=not options.usefoldername))
db["ctf_frame"]=[box,ctf,(box/2,box/2),set(),5,1]
db["quality"]=5
db.close()
print(info_name(arg,nodir=not options.usefoldername),ctf)
def main():
progname = os.path.basename(sys.argv[0])
usage = """prog [options] <micrograph1, micrograph2....>
Use this program to import and filter raw micrographs. If you choose to filter and/or convert format, this program will process each micrograph
and dump them into the directory './micrographs.', otherwise the micrographs will simply be moved into './micrographs'. If you select the option
--moverawdata AND you filter or change format, your original micrographs will be moved into the directory './raw_micrographs' and your
filtered micrographs will be in './micrographs as usual. BDB files are not moved, but they can be processed."""
parser = EMArgumentParser(usage=usage, version=EMANVERSION)
parser.add_pos_argument(
name="micrographs",
help="List the micrographs to filter here.",
default="",
guitype='filebox',
browser="EMRawDataTable(withmodal=True,multiselect=True)",
row=0,
col=0,
rowspan=1,
colspan=2,
mode='filter')
parser.add_pos_argument(
name="import_files",
help="List the files to import/filter here.",
default="",
guitype='filebox',
browser="EMBrowserWidget(withmodal=True,multiselect=True)",
row=0,
col=0,
rowspan=1,
colspan=2,
mode='import')
parser.add_header(
name="filterheader",
help='Options below this label are specific to filtering',
title="### filtering options ###",
row=1,
col=0,
rowspan=1,
colspan=2,
mode='import,filter')
parser.add_argument("--invert",
action="store_true",
help="Invert contrast",
default=False,
guitype='boolbox',
row=2,
col=0,
rowspan=1,
colspan=1,
mode='filter[True]')
parser.add_argument("--edgenorm",
action="store_true",
help="Edge normalize",
default=False,
guitype='boolbox',
row=2,
col=1,
rowspan=1,
colspan=1,
mode='filter[True]')
parser.add_argument(
"--usefoldername",
action="store_true",
help=
"If you have the same image filename in multiple folders, and need to import into the same project, this will prepend the folder name on each image name",
default=False,
guitype='boolbox',
row=2,
col=2,
rowspan=1,
colspan=1,
mode="import[False]")
parser.add_argument("--xraypixel",
action="store_true",
help="Filter X-ray pixels",
default=False,
guitype='boolbox',
row=3,
col=0,
rowspan=1,
colspan=1,
mode='filter[True]')
parser.add_argument("--ctfest",
action="store_true",
help="Estimate defocus from whole micrograph",
default=False,
guitype='boolbox',
row=3,
col=1,
rowspan=1,
colspan=1,
mode='filter[True]')
parser.add_argument("--astigmatism",
action="store_true",
help="Includes astigmatism in automatic fitting",
default=False,
guitype='boolbox',
row=3,
col=2,
rowspan=1,
colspan=1,
mode='filter[False]')
parser.add_argument(
"--moverawdata",
action="store_true",
help="Move raw data to directory ./raw_micrographs after filtration",
default=False)
parser.add_argument("--apix",
type=float,
help="Angstroms per pixel for all images",
default=None,
guitype='floatbox',
row=5,
col=0,
rowspan=1,
colspan=1,
mode="filter['self.pm().getAPIX()']")
parser.add_argument("--voltage",
type=float,
help="Microscope voltage in KV",
default=None,
guitype='floatbox',
row=5,
col=1,
rowspan=1,
colspan=1,
mode="filter['self.pm().getVoltage()']")
parser.add_argument("--cs",
type=float,
help="Microscope Cs (spherical aberation)",
default=None,
guitype='floatbox',
row=6,
col=0,
rowspan=1,
colspan=1,
mode="filter['self.pm().getCS()']")
parser.add_argument("--ac",
type=float,
help="Amplitude contrast (percentage, default=10)",
default=10,
guitype='floatbox',
row=6,
col=1,
rowspan=1,
colspan=1,
mode="filter")
parser.add_argument("--defocusmin",
type=float,
help="Minimum autofit defocus",
default=0.6,
guitype='floatbox',
row=8,
col=0,
rowspan=1,
colspan=1,
mode="filter[0.6]")
parser.add_argument("--defocusmax",
type=float,
help="Maximum autofit defocus",
default=4,
guitype='floatbox',
row=8,
col=1,
rowspan=1,
colspan=1,
mode='filter[4.0]')
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()
microdir = os.path.join(".", "micrographs")
if not os.access(microdir, os.R_OK):
os.mkdir("micrographs")
logid = E2init(sys.argv, options.ppid)
# After filtration we move micrographs to a directory 'raw_micrographs', if desired
if options.moverawdata:
originalsdir = os.path.join(".", "raw_micrographs")
if not os.access(originalsdir, os.R_OK):
os.mkdir("raw_micrographs")
for i, arg in enumerate(args):
base = base_name(arg, nodir=not options.usefoldername)
output = os.path.join(os.path.join(".", "micrographs"), base + ".hdf")
cmd = "e2proc2d.py %s %s --inplace" % (arg, output)
if options.invert: cmd += " --mult=-1"
if options.edgenorm: cmd += " --process=normalize.edgemean"
if options.xraypixel:
cmd += " --process=threshold.clampminmax.nsigma:nsigma=4"
launch_childprocess(cmd)
if options.moverawdata:
os.rename(arg, os.path.join(originalsdir, os.path.basename(arg)))
# We estimate the defocus and B-factor (no astigmatism) from the micrograph and store it in info and the header
if options.ctfest:
d = EMData(output, 0)
if d["nx"] < 1200 or d["ny"] < 1200:
print "CTF estimation will only work with images at least 1200x1200 in size"
sys.exit(1)
import e2ctf
ds = 1.0 / (options.apix * 512)
ffta = None
nbx = 0
for x in range(100, d["nx"] - 512, 512):
for y in range(100, d["ny"] - 512, 512):
clip = d.get_clip(Region(x, y, 512, 512))
clip.process_inplace("normalize.edgemean")
fft = clip.do_fft()
fft.ri2inten()
if ffta == None: ffta = fft
else: ffta += fft
nbx += 1
ffta.mult(1.0 / (nbx * 512**2))
ffta.process_inplace("math.sqrt")
ffta[
"is_intensity"] = 0 # These 2 steps are done so the 2-D display of the FFT looks better. Things would still work properly in 1-D without it
fftbg = ffta.process("math.nonconvex")
fft1d = ffta.calc_radial_dist(
ffta.get_ysize() / 2, 0.0, 1.0,
1) # note that this handles the ri2inten averages properly
# Compute 1-D curve and background
bg_1d = e2ctf.low_bg_curve(fft1d, ds)
#initial fit, background adjustment, refine fit, final background adjustment
ctf = e2ctf.ctf_fit(fft1d,
bg_1d,
bg_1d,
ffta,
fftbg,
options.voltage,
options.cs,
options.ac,
options.apix,
1,
dfhint=(options.defocusmin,
options.defocusmax))
bgAdj(ctf, fft1d)
ctf = e2ctf.ctf_fit(fft1d,
ctf.background,
ctf.background,
ffta,
fftbg,
options.voltage,
options.cs,
options.ac,
options.apix,
1,
dfhint=(options.defocusmin,
options.defocusmax))
bgAdj(ctf, fft1d)
if options.astigmatism: e2ctf.ctf_fit_stig(ffta, fftbg, ctf)
#ctf.background=bg_1d
#ctf.dsbg=ds
db = js_open_dict(info_name(arg, nodir=not options.usefoldername))
db["ctf_frame"] = [512, ctf, (256, 256), set(), 5, 1]
print info_name(arg, nodir=not options.usefoldername), ctf
E2progress(logid, (float(i) / float(len(args))))
E2end(logid)
def fit_defocus(options,img_file,fft_avg,voltage,cs,ampcont,phaseplate,apix,target_defocus,defocusmin=0.5,defocusmax=9.0,defocusstep=0.1): """Fit defocus from an incoherent average of tiles Author: Jesus Montoya, [email protected], 09/2019 """ print("\n(fit_defocus) entering function") tilesize = fft_avg['ny'] fft_bg = fft_avg.process("math.nonconvex") fft_avg_1d = fft_avg.calc_radial_dist(old_div(fft_avg.get_ysize(),2),0.0,1.0,1) # note that this handles the ri2inten averages properly #print("\n(e2tomo_ctfraw)(fit_defocus) fft_avg_1d={}".format(fft_avg_1d)) ctf = EMAN2Ctf() #print("\n(e2tomo_ctfraw)(fit_defocus) apix={}".format(apix)) #print("\n(e2tomo_ctfraw)(fit_defocus) tilesize={}".format(tilesize)) # Compute 1-D curve and background ds = old_div(1.0,( apix * tilesize )) #print("\n(e2tomo_ctfraw)(fit_defocus) ds={}".format(ds)) bg_1d = e2ctf.low_bg_curve(fft_avg_1d,ds) #print("\n(e2tomo_ctfraw)(fit_defocus) bg_1d={}".format(bg_1d)) dfhint=( defocusmin, defocusmax, defocusstep ) ctf = e2ctf.ctf_fit( fft_avg_1d, bg_1d, bg_1d, fft_avg, fft_bg, float(voltage), float(cs), float(ampcont), phaseplate, float(apix), bgadj=1, autohp=True, dfhint=dfhint) #print("\n(e2tomo_ctfraw)(fit_defocus) ctf={}".format(ctf)) #bg_sub = numpy.array(fft_avg_1d) - numpy.array(bg_1d) bg_sub,bg_1d = e2ctf.calc_1dfrom2d(ctf, fft_avg, fft_bg) fit = ctf_get_fit_curve(ctf, ds, fft_avg_1d, bg_1d, bg_sub) fit/=max(fit) #ctf = adjust_ctf_bg(ctf,fft_avg_1d) #maxres,bfactor = ctf_fit_bfactor_and_maxres( list(bg_sub),ds,ctf) #print("\ndefocus={}, FIRST trial".format(ctf.defocus)) ctf2 = EMAN2Ctf() dfhint2=(max(dfhint[0],ctf.defocus-0.1),min(dfhint[1],ctf.defocus+0.1),min(old_div(dfhint[2],2.0),0.01)) ctf2 = e2ctf.ctf_fit( fft_avg_1d, bg_1d, bg_1d, fft_avg, fft_bg, float(voltage), float(cs), float(ampcont), phaseplate, float(apix), bgadj=1, autohp=True, dfhint=dfhint2 ) bg_sub2,bg_1d2 = e2ctf.calc_1dfrom2d(ctf2, fft_avg, fft_bg) #bg_sub2,bg_1d2 = e2ctf.calc_1dfrom2d(ctf2, fft_avg, fft_bg) fit2 = ctf_get_fit_curve(ctf2, ds, fft_avg_1d, bg_1d2, bg_sub2) fit2/=max(fit2) #maxres,bfactor = ctf_fit_bfactor_and_maxres( list(bg_sub2),ds,ctf) #print("\nmaxres calc={}, bfactor={}, defocus={}, SECOND trial".format(maxres,ctf2.bfactor,ctf2.defocus)) bg_sub_final = numpy.array(bg_sub2) - numpy.array(bg_1d2) bg_sub_final/=max(bg_sub_final) import matplotlib.pyplot as plt if options.plots or options.debug: r = len(ctf.background) s = numpy.arange(0,ds*r,ds) if options.debug: plt.plot(s, fit2, 'k') plt.plot(s, bg_sub_final, 'b') plt.show() if options.plots: extension = os.path.splitext(os.path.basename( img_file ))[-1] print("\n(fit_defocus) should save plots") ps_file = img_file.replace(extension,'_1d_ps.txt') if ps_file == img_file: print("\nERROR: risk of overwritting input image") sys.exit(1) write_txt(s,bg_sub_final,ps_file) #with open(options.path + '/' + pfile,'w') as f: # lines = [ str(s[i])+'\t'+str(bg_sub_final[i])+'\n' for i in range(len(s-1))] # lines.append( str(s[-1]) + '\t' + str(bg_sub_final[-1]) ) #the last element does not need a 'return' or 'line break' fit_file = img_file.replace(extension,'_1d_fit.txt') if fit_file == img_file: print("\nERROR: risk of overwritting input image") sys.exit(1) #with open(options.path + '/' + pfile,'w') as f: # lines = [ str(s[i])+'\t'+str(bg_sub_final[i])+'\n' for i in range(len(s-1))] # lines.append( str(s[-1]) + '\t' + str(bg_sub_final[-1]) ) #the last element does not need a 'return' or 'line break' write_txt(s,fit2,fit_file) print("\n(fit_defocus) leaving function") return ctf
def main():
progname = os.path.basename(sys.argv[0])
usage = """prog [options] <micrograph1, micrograph2....>
Use this program to import and filter raw micrographs. If you choose to filter and/or convert format, this program will process each micrograph
and dump them into the directory './micrographs.', otherwise the micrographs will simply be moved into './micrographs'. If you select the option
--moverawdata AND you filter or change format, your original micrographs will be moved into the directory './raw_micrographs' and your
filtered micrographs will be in './micrographs as usual. BDB files are not moved, but they can be processed."""
parser = EMArgumentParser(usage=usage,version=EMANVERSION)
parser.add_pos_argument(name="micrographs",help="List the micrographs to filter here.", default="", guitype='filebox', browser="EMRawDataTable(withmodal=True,multiselect=True)", row=0, col=0,rowspan=1, colspan=2, mode='filter')
parser.add_pos_argument(name="import_files",help="List the files to import/filter here.", default="", guitype='filebox', browser="EMBrowserWidget(withmodal=True,multiselect=True)", row=0, col=0,rowspan=1, colspan=2, mode='import')
parser.add_header(name="filterheader", help='Options below this label are specific to filtering', title="### filtering options ###", row=1, col=0, rowspan=1, colspan=2, mode='import,filter')
parser.add_argument("--invert",action="store_true",help="Invert contrast",default=False, guitype='boolbox', row=2, col=0, rowspan=1, colspan=1, mode='filter[True]')
parser.add_argument("--edgenorm",action="store_true",help="Edge normalize",default=False, guitype='boolbox', row=2, col=1, rowspan=1, colspan=1, mode='filter[True]')
parser.add_argument("--usefoldername",action="store_true",help="If you have the same image filename in multiple folders, and need to import into the same project, this will prepend the folder name on each image name",default=False,guitype='boolbox',row=2, col=2, rowspan=1, colspan=1, mode="import[False]")
parser.add_argument("--xraypixel",action="store_true",help="Filter X-ray pixels",default=False, guitype='boolbox', row=3, col=0, rowspan=1, colspan=1, mode='filter[True]')
parser.add_argument("--ctfest",action="store_true",help="Estimate defocus from whole micrograph",default=False, guitype='boolbox', row=3, col=1, rowspan=1, colspan=1, mode='filter[True]')
parser.add_argument("--astigmatism",action="store_true",help="Includes astigmatism in automatic fitting",default=False, guitype='boolbox', row=3, col=2, rowspan=1, colspan=1, mode='filter[False]')
parser.add_argument("--moverawdata",action="store_true",help="Move raw data to directory ./raw_micrographs after filtration",default=False)
parser.add_argument("--apix",type=float,help="Angstroms per pixel for all images",default=None, guitype='floatbox', row=5, col=0, rowspan=1, colspan=1, mode="filter['self.pm().getAPIX()']")
parser.add_argument("--voltage",type=float,help="Microscope voltage in KV",default=None, guitype='floatbox', row=5, col=1, rowspan=1, colspan=1, mode="filter['self.pm().getVoltage()']")
parser.add_argument("--cs",type=float,help="Microscope Cs (spherical aberation)",default=None, guitype='floatbox', row=6, col=0, rowspan=1, colspan=1, mode="filter['self.pm().getCS()']")
parser.add_argument("--ac",type=float,help="Amplitude contrast (percentage, default=10)",default=10, guitype='floatbox', row=6, col=1, rowspan=1, colspan=1, mode="filter")
parser.add_argument("--defocusmin",type=float,help="Minimum autofit defocus",default=0.6, guitype='floatbox', row=8, col=0, rowspan=1, colspan=1, mode="filter[0.6]")
parser.add_argument("--defocusmax",type=float,help="Maximum autofit defocus",default=4, guitype='floatbox', row=8, col=1, rowspan=1, colspan=1, mode='filter[4.0]')
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()
microdir = os.path.join(".","micrographs")
if not os.access(microdir, os.R_OK):
os.mkdir("micrographs")
logid=E2init(sys.argv,options.ppid)
# After filtration we move micrographs to a directory 'raw_micrographs', if desired
if options.moverawdata:
originalsdir = os.path.join(".","raw_micrographs")
if not os.access(originalsdir, os.R_OK):
os.mkdir("raw_micrographs")
for i,arg in enumerate(args):
base = base_name(arg,nodir=not options.usefoldername)
output = os.path.join(os.path.join(".","micrographs"),base+".hdf")
cmd = "e2proc2d.py %s %s --inplace"%(arg,output)
if options.invert: cmd += " --mult=-1"
if options.edgenorm: cmd += " --process=normalize.edgemean"
if options.xraypixel: cmd += " --process=threshold.clampminmax.nsigma:nsigma=4"
launch_childprocess(cmd)
if options.moverawdata:
os.rename(arg,os.path.join(originalsdir,os.path.basename(arg)))
# We estimate the defocus and B-factor (no astigmatism) from the micrograph and store it in info and the header
if options.ctfest :
d=EMData(output,0)
if d["nx"]<1200 or d["ny"]<1200 :
print "CTF estimation will only work with images at least 1200x1200 in size"
sys.exit(1)
import e2ctf
ds=1.0/(options.apix*512)
ffta=None
nbx=0
for x in range(100,d["nx"]-512,512):
for y in range(100,d["ny"]-512,512):
clip=d.get_clip(Region(x,y,512,512))
clip.process_inplace("normalize.edgemean")
fft=clip.do_fft()
fft.ri2inten()
if ffta==None: ffta=fft
else: ffta+=fft
nbx+=1
ffta.mult(1.0/(nbx*512**2))
ffta.process_inplace("math.sqrt")
ffta["is_intensity"]=0 # These 2 steps are done so the 2-D display of the FFT looks better. Things would still work properly in 1-D without it
fftbg=ffta.process("math.nonconvex")
fft1d=ffta.calc_radial_dist(ffta.get_ysize()/2,0.0,1.0,1) # note that this handles the ri2inten averages properly
# Compute 1-D curve and background
bg_1d=e2ctf.low_bg_curve(fft1d,ds)
#initial fit, background adjustment, refine fit, final background adjustment
ctf=e2ctf.ctf_fit(fft1d,bg_1d,bg_1d,ffta,fftbg,options.voltage,options.cs,options.ac,options.apix,1,dfhint=(options.defocusmin,options.defocusmax))
bgAdj(ctf,fft1d)
ctf=e2ctf.ctf_fit(fft1d,ctf.background,ctf.background,ffta,fftbg,options.voltage,options.cs,options.ac,options.apix,1,dfhint=(options.defocusmin,options.defocusmax))
bgAdj(ctf,fft1d)
if options.astigmatism : e2ctf.ctf_fit_stig(ffta,fftbg,ctf)
#ctf.background=bg_1d
#ctf.dsbg=ds
db=js_open_dict(info_name(arg,nodir=not options.usefoldername))
db["ctf_frame"]=[512,ctf,(256,256),set(),5,1]
print info_name(arg,nodir=not options.usefoldername),ctf
E2progress(logid,(float(i)/float(len(args))))
E2end(logid)