def __init__(self,ptclfiles,ns,ptcls,nc,euler,basisn,verbose):
"""ptclfiles is a list of 2 (even/odd) particle stacks. ns is the number of particles in each of ptcfiles. ptcls is a list of lists containing [eo,ptcl#,Transform]"""
# sys.stderr=file("task.err","a")
data={"particles1":["cache",ptclfiles[0],(0,ns[0])],"particles2":["cache",ptclfiles[1],(0,ns[1])]}
JSTask.__init__(self,"ClassSplit",data,{},"")
self.options={"particles":ptcls,"classnum":nc,"euler":euler,"basisn":basisn,"verbose":verbose}
def __init__(self, volume, sym, comp, xform):
data = {"volume": volume}
JSTask.__init__(self, "CmpTilt", data, {}, "")
self.sym = sym
self.cmp = comp
self.xform = xform
def __init__(self, image, options, angle, outimage):
data={"image":image}
JSTask.__init__(self,"TomoPreproc2d",data,{},"")
self.classoptions={"options":options, "angle":angle,"outimage":outimage }
def __init__(self,image,options,ptclnum,outname):
#def __init__(self,options,ptclnum,outname):
data={"image":image}
JSTask.__init__(self,"Preproc3d",data,{},"")
self.classoptions={"options":options,"ptclnum":ptclnum,"outname":outname}
def __init__(self, volume, sym, comp, xform):
data = {"volume": volume}
JSTask.__init__(self, "CmpTilt", data, {}, "")
self.sym = sym
self.cmp = comp
self.xform = xform
def __init__(self, image, options, angle, outimage):
data={"image":image}
JSTask.__init__(self,"TomoPreproc2d",data,{},"")
self.classoptions={"options":options, "angle":angle,"outimage":outimage }
def __init__(self,image,options,ptclnum,outname):
#def __init__(self,options,ptclnum,outname):
data={"image":image}
JSTask.__init__(self,"Preproc3d",data,{},"")
self.classoptions={"options":options,"ptclnum":ptclnum,"outname":outname}
def __init__(self,
ptclfile=None,
ptcln=0,
orts=[],
tryid=0,
strucfac=None,
niter=5,
sym="c1",
mask2=None,
randorient=False,
automaskexpand=-1,
verbose=0):
data = {
"images": ["cache", ptclfile, (0, ptcln)],
"strucfac": strucfac,
"orts": orts,
"mask2": mask2
}
JSTask.__init__(
self, "InitMdl", data, {
"tryid": tryid,
"iter": niter,
"sym": sym,
"randorient": randorient,
"automaskexpand": automaskexpand,
"verbose": verbose
}, "")
def __init__(self,ptclfiles,ns,ptcls,nc,euler,mask,usebasis,nbasis,novarimax,verbose):
"""ptclfiles is a list of 2 (even/odd) particle stacks. ns is the number of particles in each of ptcfiles. ptcls is a list of lists containing [eo,ptcl#,Transform]"""
# sys.stderr=file("task.err","a")
data={"particles1":["cache",ptclfiles[0],(0,ns[0])],"particles2":["cache",ptclfiles[1],(0,ns[1])]}
JSTask.__init__(self,"ClassSplit",data,{},"")
self.options={"particles":ptcls,"classnum":nc,"euler":euler,"usebasis":usebasis,"novarimax":novarimax,"nbasis":nbasis,"mask":mask,"verbose":verbose}
def __init__(self, options, imgfile, imgindx, outf):
JSTask.__init__(self, "TomoBoxer3d", {}, "")
self.classoptions = {
"options": options,
"imgfile": imgfile,
"imgindx": imgindx,
"outf": outf
}
def __init__(self,imagefile,imagenums,usefilt=None,ref=None,niter=1,normproc=("normalize.edgemean",{}),prefilt=0,align=("rotate_translate_flip",{}),
aligncmp=("ccc",{}),ralign=None,raligncmp=None,averager=("mean",{}),scmp=("ccc",{}),keep=1.5,keepsig=1,automask=0,saveali=0,setsfref=0,verbose=0,n=0,center="xform.center"):
if usefilt==None : usefilt=imagefile
self.center=center
data={"images":["cache",imagefile,imagenums],"usefilt":["cache",usefilt,imagenums]}
if ref!=None : data["ref"]=["cache",ref,n]
JSTask.__init__(self,"ClassAv",data,{},"")
self.options={"niter":niter, "normproc":normproc, "prefilt":prefilt, "align":align, "aligncmp":aligncmp,
"ralign":ralign,"raligncmp":raligncmp,"averager":averager,"scmp":scmp,"keep":keep,"keepsig":keepsig,
"automask":automask,"saveali":saveali,"setsfref":setsfref,"verbose":verbose,"n":n}
def __init__(self, options, xsize, ysize, y, projection_operator,
tiltangles, nimgs):
JSTask.__init__(self, "TVRecon", '', {}, "")
self.classoptions = {
"options": options,
"xsize": xsize,
"ysize": ysize,
"y": y,
"projection_operator": projection_operator,
"tiltangles": tiltangles,
"nimgs": nimgs
}
def __init__(self, command="e2project3d.py", data=None, options=None):
JSTask.__init__(self, command, data, options)
# data has these keys:
# input - which is the name of the threed model - a Task-style cache
# eulers - a list of Transforms with which to generate projections
# indices - indices that correspond to the ordering, has to be the same length as eulers. Returned to calling routine - use to write output in order. Not sure about this.
# options has these keys
# projector - [string,dict] convention
self.projections = {} # key will be index, value will be projection
def __init__(self, command="e2project3d.py", data=None, options=None):
JSTask.__init__(self, command, data, options)
# data has these keys:
# input - which is the name of the threed model - a Task-style cache
# eulers - a list of Transforms with which to generate projections
# indices - indices that correspond to the ordering, has to be the same length as eulers. Returned to calling routine - use to write output in order. Not sure about this.
# options has these keys
# projector - [string,dict] convention
self.projections = {} # key will be index, value will be projection
def __init__(self,imagefile,imagenums,usefilt=None,ref=None,focused=None,niter=1,normproc=("normalize.edgemean",{}),prefilt=0,align=("rotate_translate_flip",{}),
aligncmp=("ccc",{}),ralign=None,raligncmp=None,averager=("mean",{}),scmp=("ccc",{}),keep=1.5,keepsig=1,automask=0,saveali=0,setsfref=0,verbose=0,n=0,center="xform.center"):
if usefilt==None : usefilt=imagefile
self.center=center
data={"images":["cache",imagefile,imagenums],"usefilt":["cache",usefilt,imagenums]}
if ref!=None : data["ref"]=["cache",ref,n]
if focused!=None : data["focused"]=["cache",focused,n]
JSTask.__init__(self,"ClassAv",data,{},"")
self.options={"niter":niter, "normproc":normproc, "prefilt":prefilt, "align":align, "aligncmp":aligncmp,
"ralign":ralign,"raligncmp":raligncmp,"averager":averager,"scmp":scmp,"keep":keep,"keepsig":keepsig,
"automask":automask,"saveali":saveali,"setsfref":setsfref,"verbose":verbose,"n":n}
def __init__(self, ptclfiles, ns, ptcls, nc, euler, verbose):
"""ptclfiles is a list of 2 (even/odd) particle stacks. ns is the number of particles in each of ptcfiles. ptcls is a list of lists containing [eo,ptcl#,Transform]"""
data = {
"particles1": ["cache", ptclfiles[0], (0, ns[0])],
"particles2": ["cache", ptclfiles[1], (0, ns[1])]
}
JSTask.__init__(self, "ClassSplit", data, {}, "")
self.options = {
"particles": ptcls,
"classnum": nc,
"euler": euler,
"verbose": verbose
}
def __init__(self,ptcl,ptid,nodes,align=None,alicmp=("dot",{}),cmp=("dot",{}), ralign=None, alircmp=("dot",{}),cmptmp=None,masktmp=None):
rt=EMUtil.get_image_count(nodes)
if cmptmp==None or masktmp==None:
### Compare to the two children seperately
data={"images":["cache",ptcl,ptid], "nodes":["cache",nodes,0,rt]}
cmpdiff=False
else:
### Mask out the difference between the two children
cn=EMUtil.get_image_count(cmptmp)
mn=EMUtil.get_image_count(masktmp)
data={"images":["cache",ptcl,ptid], "nodes":["cache",nodes,0,rt], "cmptmp":["cache",cmptmp,0,cn], "masktmp":["cache",masktmp,0,mn] }
cmpdiff=True
JSTask.__init__(self,"TreeClassify",data,{},"")
self.options={"align":align, "alicmp":alicmp, "cmp":cmp, "ralign":ralign, "alircmp":alircmp,"cmpdiff":cmpdiff, "id":ptid}
def __init__(self, volume, tilted, imgnum, eulerxform, zrot, distplot, tiltrange, tiltstep, options):
if options.shrink:
shrunkvol = volume.process("math.meanshrink",{"n":options.shrink})
shrunktilted = tilted.process("math.meanshrink",{"n":options.shrink})
data = {"volume":shrunkvol,"tilted":shrunktilted}
else:
data = {"volume":volume,"tilted":tilted}
JSTask.__init__(self,"CmpTilt",data,options,"")
self.imgnum = imgnum
self.eulerxform=eulerxform
self.zrot=zrot
self.distplot=distplot
self.tiltrange=tiltrange
self.tiltstep=tiltstep
def __init__(self,ptcl,ptid,nodes,align=None,alicmp=("dot",{}),cmp=("dot",{}), ralign=None, alircmp=("dot",{}),cmptmp=None,masktmp=None):
rt=EMUtil.get_image_count(nodes)
if cmptmp==None or masktmp==None:
### Compare to the two children seperately
data={"images":["cache",ptcl,ptid], "nodes":["cache",nodes,0,rt]}
cmpdiff=False
else:
### Mask out the difference between the two children
cn=EMUtil.get_image_count(cmptmp)
mn=EMUtil.get_image_count(masktmp)
data={"images":["cache",ptcl,ptid], "nodes":["cache",nodes,0,rt], "cmptmp":["cache",cmptmp,0,cn], "masktmp":["cache",masktmp,0,mn] }
cmpdiff=True
JSTask.__init__(self,"TreeClassify",data,{},"")
self.options={"align":align, "alicmp":alicmp, "cmp":cmp, "ralign":ralign, "alircmp":alircmp,"cmpdiff":cmpdiff, "id":ptid}
def __init__(self, volume, tilted, imgnum, eulerxform, zrot, distplot, tiltrange, tiltstep, options):
if options.shrink:
shrunkvol = volume.process("math.meanshrink",{"n":options.shrink})
shrunktilted = tilted.process("math.meanshrink",{"n":options.shrink})
data = {"volume":shrunkvol,"tilted":shrunktilted}
else:
data = {"volume":volume,"tilted":tilted}
JSTask.__init__(self,"CmpTilt",data,options,"")
self.imgnum = imgnum
self.eulerxform=eulerxform
self.zrot=zrot
self.distplot=distplot
self.tiltrange=tiltrange
self.tiltstep=tiltstep
def __init__(self, command="e2simmx.py", data=None, options=None):
JSTask.__init__(self, command, data, options)
# options should have these keys:
# align - the main aligner, a list of two strings
# alligncmp - the main align cmp - a list of two strings
# ralign - the refine aligner, a list of two string. May be None which turns it off
# raligncmp - the refinealigncmp - a list of two strings. Needs to specified if ralign is not None
# cmp - the final cmp - a list of two strings
# shrink - a shrink value (float), may be None or unspecified - shrink the data prior to computing similarity scores, will adjust to produce a good box size
# data should have
# particles - a Task-style cached list of input images
# references - a Task-style cached list of reference images
self.sim_data = {} # this will store the eventual results
def __init__(self,command="e2simmx.py",data=None,options=None):
JSTask.__init__(self,command,data,options)
# options should have these keys:
# align - the main aligner, a list of two strings
# alligncmp - the main align cmp - a list of two strings
# ralign - the refine aligner, a list of two string. May be None which turns it off
# raligncmp - the refinealigncmp - a list of two strings. Needs to specified if ralign is not None
# cmp - the final cmp - a list of two strings
# shrink - a shrink value (int), may be None or unspecified - shrink the data by an integer amount prior to computing similarity scores
# data should have
# particles - a Task-style cached list of input images
# references - a Task-style cached list of reference images
self.sim_data = {} # this will store the eventual results
def __init__(self, info, ref, options):
data = {"info": info, "ref": ref}
JSTask.__init__(self, "SpaClassifyTask", data, {}, "")
self.options = options
def __init__(self, info, ref, options):
data={"info":info, "ref": ref}
JSTask.__init__(self,"SptTltRefine",data,{},"")
self.options=options
def __init__(self, inp, options):
data = {"data": inp}
JSTask.__init__(self, "Sptavg", data, {}, "")
self.options = options
def __init__(self, options, imgfile, imgindx, outf,nfs):
JSTask.__init__(self,"TomoBoxer3d",{},"")
self.classoptions={"options":options,"imgfile":imgfile,"imgindx":imgindx,"outf":outf,"nfs":nfs}
def __init__(self, inp, seed, options):
data={"data":inp,"seed":seed}
JSTask.__init__(self,"Make3d",data,{},"")
self.options=options
def __init__(self, inp, refs, options):
data = {"data": inp, "refnames": refs}
JSTask.__init__(self, "Sptavg", data, {}, "")
self.options = options
def __init__(self,ptclfile=None,ptcln=0,orts=[],tryid=0,strucfac=None,niter=5,sym="c1",mask2=None,randorient=False,verbose=0) :
data={"images":["cache",ptclfile,(0,ptcln)],"strucfac":strucfac,"orts":orts,"mask2":mask2}
JSTask.__init__(self,"InitMdl",data,{"tryid":tryid,"iter":niter,"sym":sym,"randorient":randorient,"verbose":verbose},"")
def __init__(self, fsp, i, ref, options):
data = {"fsp": fsp, "i": i, "ref": ref}
JSTask.__init__(self, "SptAlign", data, {}, "")
self.options = options
def __init__(self, data, options):
JSTask.__init__(self, "SptAlign", data, {}, "")
self.options = options
def __init__(self, data, options, allxfs, allinfo):
JSTask.__init__(self, "SptExtract", data, {}, "")
self.options = options
self.allxfs = allxfs
self.allinfo = allinfo
def __init__(self, data, options):
JSTask.__init__(self,"SptAlign",data,{},"")
self.options=options
if options.mask!=None: raise Exception("--mask not supported in scipy mode")
def __init__(self, info, options):
data = {"info": info}
JSTask.__init__(self, "SpaAlignTask", data, {}, "")
self.options = options
def __init__(self, inp, ref, options):
data={"data":inp, "ref":ref}
JSTask.__init__(self,"Weightptcl",data,{},"")
self.options=options
