def alignParticlesInLST(lstfile,outstack):
import EMAN
### create a stack of particles aligned according to an LST file
images=EMAN.readImages(lstfile,-1,-1,0)
for i in images:
i.edgeNormalize()
i.rotateAndTranslate()
if i.isFlipped():
i.hFlip()
i.writeImage(outstack,-1)
def alignParticlesInLST(lstfile,outstack):
import EMAN
### create a stack of particles aligned according to an LST file
images=EMAN.readImages(lstfile,-1,-1,0)
for i in images:
i.edgeNormalize()
i.rotateAndTranslate()
if i.isFlipped():
i.hFlip()
i.writeImage(outstack,-1)
def alignParticlesInLST(lstfile,outstack):
import EMAN
### create a stack of particles aligned according to an LST file
apDisplay.printMsg("aligning particles in '%s', saving to stack: %s"%(lstfile,outstack))
images=EMAN.readImages(lstfile,-1,-1,0)
for i in images:
i.edgeNormalize()
i.rotateAndTranslate()
if i.isFlipped():
i.hFlip()
i.writeImage(outstack,-1)
def alignParticlesInLST(lstfile, outstack):
import EMAN
### create a stack of particles aligned according to an LST file
apDisplay.printMsg("aligning particles in '%s', saving to stack: %s" %
(lstfile, outstack))
images = EMAN.readImages(lstfile, -1, -1, 0)
for i in images:
i.edgeNormalize()
i.rotateAndTranslate()
if i.isFlipped():
i.hFlip()
i.writeImage(outstack, -1)
def main(): if len(sys.argv) != 3: print "Usage:: %s <input particles file name> <output particles file name>"%(sys.argv[0]) sys.exit(-1) input = sys.argv[1] output = sys.argv[2] stack=EMAN.readImages(input,-1,-1) i = 0 for image in stack: print "Working on image %s"%(i) new_image = speckle_filter(image) new_image.writeImage(output,i) i=i+1
def makeClassAverages(lst, outputstack, e, mask):
import EMAN
#align images in class
print "creating class average from", lst, "to", outputstack
images = EMAN.readImages(lst, -1, -1, 0)
for image in images:
image.rotateAndTranslate()
if image.isFlipped():
image.hFlip()
#make class average
avg = EMAN.EMData()
avg.makeMedian(images)
#write class average
avg.setRAlign(e)
avg.setNImg(len(images))
avg.applyMask(mask, 0)
avg.writeImage(outputstack, -1)
def makeClassAverages(lst, outputstack,e,mask):
import EMAN
#align images in class
print "creating class average from",lst,"to",outputstack
images=EMAN.readImages(lst,-1,-1,0)
for image in images:
image.rotateAndTranslate()
if image.isFlipped():
image.hFlip()
#make class average
avg=EMAN.EMData()
avg.makeMedian(images)
#write class average
avg.setRAlign(e)
avg.setNImg(len(images))
avg.applyMask(mask,0)
avg.writeImage(outputstack,-1)
def makeClassAverages(lst, outputstack, classdata, params):
#align images in class
images = EMAN.readImages(lst, -1, -1, 0)
for image in images:
image.rotateAndTranslate()
if image.isFlipped():
image.hFlip()
#make class average
avg = EMAN.EMData()
avg.makeMedian(images)
#write class average
e = EMAN.Euler()
alt = classdata['euler1'] * math.pi / 180
az = classdata['euler2'] * math.pi / 180
phi = 0.0
e.setAngle(alt, az, phi)
avg.setRAlign(e)
avg.setNImg(len(images))
avg.applyMask(params['mask'], 0)
avg.writeImage(outputstack, -1)
def makeClassAverages(lst, outputstack, classdata, params):
# align images in class
images = EMAN.readImages(lst, -1, -1, 0)
for image in images:
image.rotateAndTranslate()
if image.isFlipped():
image.hFlip()
# make class average
avg = EMAN.EMData()
avg.makeMedian(images)
# write class average
e = EMAN.Euler()
alt = classdata["euler1"] * math.pi / 180
az = classdata["euler2"] * math.pi / 180
phi = 0.0
e.setAngle(alt, az, phi)
avg.setRAlign(e)
avg.setNImg(len(images))
avg.applyMask(params["mask"], 0)
avg.writeImage(outputstack, -1)
def makeClassAverages(self, classlist, outputstack, classdata, maskrad):
#align images in class
#print classlist
images = EMAN.readImages(classlist, -1, -1, 0)
for image in images:
image.rotateAndTranslate()
if image.isFlipped():
image.hFlip()
#make class average
avg = EMAN.EMData()
avg.makeMedian(images)
#write class average
e = EMAN.Euler()
alt = classdata['euler1']*math.pi/180
az = classdata['euler2']*math.pi/180
phi = 0.0 #classdata['euler3']*math.pi/180
e.setAngle(alt, az, phi)
avg.setRAlign(e)
avg.setNImg(len(images))
avg.applyMask(maskrad, 0)
avg.writeImage(outputstack,-1)
#!/usr/bin/env python
import EMAN
import sys
from sys import argv
import os
import math
RD = 180.0 / math.pi
if len(argv) < 3:
print "fsctest.py <refs> <ref #> <ptcl file> <ptcl #>"
sys.exit(1)
refs = EMAN.readImages(argv[1], -1, -1)
ptcl = EMAN.EMData()
ptcl.readImage(argv[3], int(argv[4]))
ptcl.edgeNormalize()
ptclf = ptcl.copy(0, 0)
ptclf.vFlip()
for i in refs:
i.normalize()
ali = ptcl.RTFAlign(refs[int(argv[2])], ptclf, 1)
ali.refineAlign(i)
fsc = (1.0 + ali.fscmp(refs[int(argv[2])], None)) * 500.0
print fsc
if __name__ == '__main__':
# write command & time to emanlog if exists:
if os.path.exists('refine.log'):
cmd = string.join(sys.argv, ' ')
apEMAN.writeEMANTime('refine.log', cmd)
#Parse inputs
args = sys.argv[1:]
params = createDefaults()
parseInput(args, params)
if params['coranmask'] is None:
params['coranmask'] = params['mask']
#Determine box size
tmpimg = EMAN.readImages('start.hed', 1, 1)
params['boxsize'] = tmpimg[0].xSize()
#Set up for coran
params['corandir'] = params['corandir'] + str(params['iter'])
if os.path.exists(params['corandir']):
print "WARNING!!! %s exists and is being overwritten" % params[
'corandir']
shutil.rmtree(params['corandir'])
os.mkdir(params['corandir'])
else:
os.mkdir(params['corandir'])
classfile = 'cls.' + str(params['iter']) + '.tar'
shutil.copy(classfile, os.path.join(params['corandir'], classfile))
shutil.copy('proj.hed', os.path.join(params['corandir'], 'proj.hed'))
shutil.copy('proj.img', os.path.join(params['corandir'], 'proj.img'))
def createClassAverages(stack,
projs,
apmq,
numprojs,
boxsz,
outclass="classes",
rotated=False,
shifted=False,
dataext=".spi"):
"""
creates EMAN-style class average file "classes.hed" & "classes.img"
and variance files "variances.hed" & "variances.img"
from spider classification
"""
apFile.removeFile(outclass)
outf = spyder.fileFilter(outclass)
outvf = "tmpvar"
apmqlist = readDocFile(apmq)
mySpi = spyder.SpiderSession(dataext=dataext, logo=False, log=False)
# create file containing the number of particles matched to each projection
mySpi.toSpiderQuiet(
"VO MQ",
"0.0",
spyder.fileFilter(apmq),
str(numprojs),
"cls*****",
"numinclass",
)
mySpi.close()
os.remove("numinclass%s" % dataext)
# create class average file
mySpi = spyder.SpiderSession(dataext=dataext, logo=False, log=False)
for i in range(0, numprojs):
clsfile = readDocFile("cls%05d%s" % (i + 1, dataext))
if clsfile == []:
apDisplay.printMsg("class %d has no particles" % (i + 1))
mySpi.toSpiderQuiet(
"BL",
"%s@%d" % (outf, (i + 1)),
"(%d,%d)" % (boxsz, boxsz),
"N",
"(0.0)",
)
mySpi.toSpiderQuiet(
"BL",
"%s@%d" % (outvf, (i + 1)),
"(%d,%d)" % (boxsz, boxsz),
"N",
"(0.0)",
)
else:
mySpi.toSpiderQuiet("DE", "_2@")
mySpi.toSpiderQuiet("MS", "_2@", "%d,%d,1" % (boxsz, boxsz),
str(len(clsfile)))
for p in range(0, len(clsfile)):
mySpi.toSpiderQuiet("DE", "_1")
# get the particle
part = int(float(clsfile[p][2])) - 1
pimg = spyder.fileFilter(stack) + "@%d" % (part + 1)
rot = float(apmqlist[part][4])
shx = float(apmqlist[part][5])
shy = float(apmqlist[part][6])
if shifted is True:
shx = 0
shy = 0
if rotated is True:
rot = 0
p_out = "_2@%d" % (p + 1)
rotAndShiftImg(pimg, "_1", rot, shx, shy, inMySpi=mySpi)
# mirror
if int(float(apmqlist[part][2])) < 0:
mirrorImg("_1", p_out, inMySpi=mySpi)
else:
mySpi.toSpiderQuiet("CP", "_1", p_out)
if len(clsfile) == 1:
mySpi.toSpiderQuiet("CP", "_2@1", "%s@%d" % (outf, (i + 1)))
mySpi.toSpiderQuiet(
"BL",
"%s@%d" % (outvf, (i + 1)),
"(%d,%d)" % (boxsz, boxsz),
"N",
"(0.0)",
)
else:
mySpi.toSpiderQuiet("AS R", "_2@*****", "1-%d" % len(clsfile),
"A", "%s@%d" % (outf, (i + 1)),
"%s@%d" % (outvf, i + 1))
mySpi.close()
# convert the class averages to EMAN class averages
# read classes & projections for EMAN classes output
if os.path.exists('variances.hed'):
os.remove('variances.hed')
if os.path.exists('variances.img'):
os.remove('variances.img')
if os.path.exists('classes.hed'):
os.remove('classes.hed')
if os.path.exists('classes.img'):
os.remove('classes.img')
### I would prefer to use apImagicFile.readImagic, writeImagic
variances = EMAN.readImages(outvf + dataext, -1, -1, 0)
averages = EMAN.readImages(outf + dataext, -1, -1, 0)
projections = EMAN.readImages(projs, -1, -1, 0)
for i in range(0, numprojs):
# copy projection to class average file
projections[i].writeImage('classes.hed')
projections[i].writeImage('variances.hed')
# get num of particles in class
clsfile = readDocFile("cls%05d%s" % (i + 1, dataext))
averages[i].setNImg(len(clsfile))
averages[i].writeImage('classes.hed', -1)
variances[i].setNImg(len(clsfile))
variances[i].writeImage('variances.hed', -1)
os.remove("cls%05d%s" % (i + 1, dataext))
os.remove(outf + dataext)
return
if __name__== '__main__':
# write command & time to emanlog if exists:
if os.path.exists('refine.log'):
cmd = string.join(sys.argv,' ')
apEMAN.writeEMANTime('refine.log',cmd)
#Parse inputs
args=sys.argv[1:]
params=createDefaults()
parseInput(args,params)
if params['coranmask'] is None:
params['coranmask'] = params['mask']
#Determine box size
tmpimg=EMAN.readImages('start.hed',1,1)
params['boxsize']=tmpimg[0].xSize()
#Set up for coran
params['corandir']=params['corandir']+str(params['iter'])
if os.path.exists(params['corandir']):
print "WARNING!!! %s exists and is being overwritten" % params['corandir']
shutil.rmtree(params['corandir'])
os.mkdir(params['corandir'])
else:
os.mkdir(params['corandir'])
classfile='cls.'+str(params['iter'])+'.tar'
shutil.copy(classfile,os.path.join(params['corandir'],classfile))
shutil.copy('proj.hed',os.path.join(params['corandir'],'proj.hed'))
shutil.copy('proj.img',os.path.join(params['corandir'],'proj.img'))
os.chdir(params['corandir'])
def createClassAverages(stack,projs,apmq,numprojs,boxsz,outclass="classes",rotated=False,shifted=False,dataext=".spi"):
"""
creates EMAN-style class average file "classes.hed" & "classes.img"
and variance files "variances.hed" & "variances.img"
from spider classification
"""
apFile.removeFile(outclass)
outf=spyder.fileFilter(outclass)
outvf="tmpvar"
apmqlist = readDocFile(apmq)
mySpi = spyder.SpiderSession(dataext=dataext, logo=False, log=False)
# create file containing the number of particles matched to each projection
mySpi.toSpiderQuiet(
"VO MQ",
"0.0",
spyder.fileFilter(apmq),
str(numprojs),
"cls*****",
"numinclass",
)
mySpi.close()
os.remove("numinclass%s" % dataext)
# create class average file
mySpi = spyder.SpiderSession(dataext=dataext, logo=False, log=False)
for i in range(0,numprojs):
clsfile = readDocFile("cls%05d%s" % (i+1,dataext))
if clsfile == []:
apDisplay.printMsg("class %d has no particles" %(i+1))
mySpi.toSpiderQuiet("BL","%s@%d" % (outf,(i+1)),"(%d,%d)" % (boxsz,boxsz),"N","(0.0)",)
mySpi.toSpiderQuiet("BL","%s@%d" % (outvf,(i+1)),"(%d,%d)" % (boxsz,boxsz),"N","(0.0)",)
else:
mySpi.toSpiderQuiet("DE","_2@")
mySpi.toSpiderQuiet("MS","_2@","%d,%d,1" % (boxsz,boxsz), str(len(clsfile)))
for p in range(0,len(clsfile)):
mySpi.toSpiderQuiet("DE","_1")
# get the particle
part = int(float(clsfile[p][2]))-1
pimg = spyder.fileFilter(stack)+"@%d"%(part+1)
rot=float(apmqlist[part][4])
shx=float(apmqlist[part][5])
shy=float(apmqlist[part][6])
if shifted is True:
shx=0
shy=0
if rotated is True:
rot=0
p_out="_2@%d" % (p+1)
rotAndShiftImg(
pimg,
"_1",
rot,
shx,
shy,
inMySpi=mySpi
)
# mirror
if int(float(apmqlist[part][2])) < 0:
mirrorImg("_1",p_out,inMySpi=mySpi)
else:
mySpi.toSpiderQuiet("CP","_1",p_out)
if len(clsfile) == 1:
mySpi.toSpiderQuiet("CP","_2@1","%s@%d" % (outf,(i+1)))
mySpi.toSpiderQuiet("BL","%s@%d" % (outvf,(i+1)),"(%d,%d)" % (boxsz,boxsz),"N","(0.0)",)
else:
mySpi.toSpiderQuiet("AS R","_2@*****","1-%d" % len(clsfile), "A","%s@%d" % (outf,(i+1)),"%s@%d" % (outvf,i+1))
mySpi.close()
# convert the class averages to EMAN class averages
# read classes & projections for EMAN classes output
if os.path.exists('variances.hed'):
os.remove('variances.hed')
if os.path.exists('variances.img'):
os.remove('variances.img')
if os.path.exists('classes.hed'):
os.remove('classes.hed')
if os.path.exists('classes.img'):
os.remove('classes.img')
### I would prefer to use apImagicFile.readImagic, writeImagic
variances = EMAN.readImages(outvf+dataext,-1,-1,0)
averages = EMAN.readImages(outf+dataext,-1,-1,0)
projections=EMAN.readImages(projs,-1,-1,0)
for i in range (0,numprojs):
# copy projection to class average file
projections[i].writeImage('classes.hed')
projections[i].writeImage('variances.hed')
# get num of particles in class
clsfile=readDocFile("cls%05d%s" % (i+1,dataext))
averages[i].setNImg(len(clsfile))
averages[i].writeImage('classes.hed',-1)
variances[i].setNImg(len(clsfile))
variances[i].writeImage('variances.hed',-1)
os.remove("cls%05d%s" % (i+1,dataext))
os.remove(outf+dataext)
return
for i in argv[1:]:
s = i.split("=")
if s[0] == "classes":
Ncls = int(s[1])
elif s[0] == "average":
averageout = s[1]
elif s[0] == "clsfiles":
clsfiles = 1
elif s[0] == "listout":
listout = s[1]
elif s[0] == "nosingle":
nosingle = 1
elif s[0] == "stack":
data = EMAN.readImages(s[1], -1, -1)
stackname = s[1]
elif s[0] == "list":
fin = open(s[1], "r")
fsp = fin.readlines()
fin.close()
for i in fsp:
x = EMAN.EMData()
x.readImage(i.strip())
data.append(x)
else:
print "Unknown argument ", i
exit(1)
if len(data) == 0:
print "No data to classify. Please specify stack= or list=."
