def visualizeClasses(self, e=None):
prot = self.protocol
classDir = prot.getClassDir()
classAvg = 'classavg'
classVar = 'classvar'
classDoc = 'docclass'
params = {
'[class_dir]': classDir,
'[desired-classes]': self.numberOfClasses.get(),
'[particles]': prot._params['particles'] + '@******',
'[class_doc]': join(classDir, classDoc + '***'),
'[class_avg]': join(classDir, classAvg + '***'),
'[class_var]': join(classDir, classVar + '***'),
}
prot.runTemplate('mda/classavg.msa', prot.getExt(), params)
particles = prot.inputParticles.get()
particles.load()
sampling = particles.getSamplingRate()
setFn = prot._getTmpPath('classes2D.sqlite')
cleanPath(setFn)
classes2D = SetOfClasses2D(filename=setFn)
classes2D.setImages(particles)
# We need to first create a map between the particles index and
# the assigned class number
classDict = {}
for classId in range(1, self.numberOfClasses.get() + 1):
docClass = prot._getPath(classDir, classDoc + '%03d.stk' % classId)
doc = SpiderDocFile(docClass)
for values in doc.iterValues():
imgIndex = int(values[0])
classDict[imgIndex] = classId
doc.close()
updateItem = lambda p, i: p.setClassId(classDict[i])
def updateClass(cls):
rep = cls.getRepresentative()
rep.setSamplingRate(particles.getSamplingRate())
avgFn = prot._getPath(classDir,
classAvg + '%03d.stk' % cls.getObjId())
rep.setLocation(1, avgFn)
particlesRange = range(1, particles.getSize() + 1)
classes2D.classifyItems(updateItemCallback=updateItem,
updateClassCallback=updateClass,
itemDataIterator=iter(particlesRange))
classes2D.write()
classes2D.close()
return [
ClassesView(self.getProject(), prot.strId(),
classes2D.getFileName(), particles.strId())
]
def _iterAngles(self, it):
""" Iterate over the angular distribution for a given iteration. """
# Get the alignment files of each group for this iteration
files = glob(self._getFinalPath('align_%02d_???.stk' % it))
for anglesFile in files:
fscDoc = SpiderDocFile(anglesFile)
for values in fscDoc:
yield values[1], values[2]
fscDoc.close()
def visualizeClasses(self, e=None):
prot = self.protocol
classDir = prot.getClassDir()
classAvg = 'classavg'
classVar = 'classvar'
classDoc = 'docclass'
params = {'[class_dir]': classDir,
'[desired-classes]': self.numberOfClasses.get(),
'[particles]': prot._params['particles'] + '@******',
'[class_doc]': join(classDir, classDoc + '***'),
'[class_avg]': join(classDir, classAvg + '***'),
'[class_var]': join(classDir, classVar + '***'),
}
prot.runTemplate('mda/classavg.msa', prot.getExt(), params)
particles = prot.inputParticles.get()
particles.load()
sampling = particles.getSamplingRate()
setFn = prot._getTmpPath('classes2D.sqlite')
cleanPath(setFn)
classes2D = SetOfClasses2D(filename=setFn)
classes2D.setImages(particles)
# We need to first create a map between the particles index and
# the assigned class number
classDict = {}
for classId in range(1, self.numberOfClasses.get()+1):
docClass = prot._getPath(classDir, classDoc + '%03d.stk' % classId)
doc = SpiderDocFile(docClass)
for values in doc.iterValues():
imgIndex = int(values[0])
classDict[imgIndex] = classId
doc.close()
updateItem = lambda p, i: p.setClassId(classDict[i])
def updateClass(cls):
rep = cls.getRepresentative()
rep.setSamplingRate(particles.getSamplingRate())
avgFn = prot._getPath(classDir,
classAvg + '%03d.stk' % cls.getObjId())
rep.setLocation(1, avgFn)
particlesRange = range(1, particles.getSize()+1)
classes2D.classifyItems(updateItemCallback=updateItem,
updateClassCallback=updateClass,
itemDataIterator=iter(particlesRange))
classes2D.write()
classes2D.close()
return [ClassesView(self.getProject(), prot.strId(),
classes2D.getFileName(), particles.strId())]
def visualizeClasses(self, e=None):
prot = self.protocol
classDir = prot.getClassDir()
classAvg = 'classavg'
classVar = 'classvar'
classDoc = 'docclass'
ext = prot.getExt()
params = {'[class_dir]': classDir,
'[desired-classes]': self.numberOfClasses.get(),
'[particles]': prot._params['particles'] + '@******',
'[class_doc]': join(classDir, classDoc + '***'),
'[class_avg]': join(classDir, classAvg + '***'),
'[class_var]': join(classDir, classVar + '***'),
}
prot.runTemplate('mda/classavg.msa', prot.getExt(), params)
particles = prot.inputParticles.get()
particles.load()
sampling = particles.getSamplingRate()
setFn = self._getPath('classes2D.sqlite')
cleanPath(setFn)
classes2D = SetOfClasses2D(filename=setFn)
classes2D.setImages(particles)
for classId in range(1, self.numberOfClasses.get()+1):
class2D = Class2D()
class2D.setObjId(classId)
avgImg = Particle()
avgImg.setSamplingRate(sampling)
avgFn = prot._getPath(classDir, classAvg + '%03d.stk' % classId)
avgImg.setLocation(1, avgFn)
#avgImg.setLocation(classId, 'classavg.stk')
class2D.setRepresentative(avgImg)
classes2D.append(class2D)
docClass = prot._getPath(classDir, classDoc + '%03d.stk' % classId)
doc = SpiderDocFile(docClass)
for values in doc.iterValues():
imgId = int(values[0])
img = particles[imgId]
class2D.append(img)
classes2D.update(class2D)
classes2D.write()
classes2D.close()
return [ClassesView(self.getProject(),
prot.strId(), classes2D.getFileName(),
prot.inputParticles.get().strId())]
def _iterAngles(self, it):
""" Iterate over the angular distribution for a given iteration. """
# Get the alignment files of each group for this iteration
files = glob(self._getFinalPath('align_%02d_???.stk' % it))
for anglesFile in files:
fscDoc = SpiderDocFile(anglesFile)
for values in fscDoc:
theta = values[1]
phi = values[2]
if theta > 90:
theta = abs(180. - theta)
phi += 180
yield phi, theta
fscDoc.close()
def _iterAngles(self, it):
""" Iterate over the angular distribution for a given iteration. """
# Get the alignment files of each group for this iteration
files = glob(self._getFinalPath('align_%02d_???.stk' % it))
for anglesFile in files:
fscDoc = SpiderDocFile(anglesFile)
for values in fscDoc:
theta = values[1]
phi = values[2]
if theta > 90:
theta = abs(180. - theta)
phi += 180
yield phi, theta
fscDoc.close()
def writeSetOfImages(imgSet, stackFn, selFn):
""" This function will write a SetOfMicrographs as a Spider stack and selfile.
Params:
imgSet: the SetOfMicrograph instance.
stackFn: the filename where to write the stack.
selFn: the filename of the Spider selection file.
"""
doc = SpiderDocFile(selFn, 'w+')
for i in range(imgSet.getSize()):
doc.writeValues(i+1)
imgSet.writeStack(stackFn, applyTransform=True)
doc.close()
convertEndian(stackFn, imgSet.getSize())
def writeSetOfImages(imgSet, stackFn, selFn):
""" This function will write a SetOfMicrographs as a Spider stack and selfile.
Params:
imgSet: the SetOfMicrograph instance.
stackFn: the filename where to write the stack.
selFn: the filename of the Spider selection file.
"""
ih = ImageHandler()
doc = SpiderDocFile(selFn, 'w+')
for i, img in enumerate(imgSet):
ih.convert(img, (i+1, stackFn))
doc.writeValues(i+1)
doc.close()
convertEndian(stackFn, imgSet.getSize())
def _plotFSC(self, a, fscFile):
resolution = []
fsc = []
fscDoc = SpiderDocFile(fscFile)
for values in fscDoc:
resolution.append(1/values[1])
fsc.append(values[2])
self.maxfsc = max(fsc)
self.minInv = min(resolution)
self.maxInv = max(resolution)
a.plot(resolution, fsc)
from matplotlib.ticker import FuncFormatter
a.xaxis.set_major_formatter(FuncFormatter(self._formatFreq))
a.set_ylim([-0.1, 1.1])
fscDoc.close()
def _plotFSC(self, a, fscFile):
resolution = []
fsc = []
fscDoc = SpiderDocFile(fscFile)
for values in fscDoc:
resolution.append(1 / values[1])
fsc.append(values[2])
self.maxfsc = max(fsc)
self.minInv = min(resolution)
self.maxInv = max(resolution)
a.plot(resolution, fsc)
from matplotlib.ticker import FuncFormatter
a.xaxis.set_major_formatter(FuncFormatter(self._formatFreq))
a.set_ylim([-0.1, 1.1])
fscDoc.close()
def writeSetOfImages(imgSet, stackFn, selFn):
""" This function will write a SetOfMicrographs as a Spider stack and selfile.
Params:
imgSet: the SetOfMicrograph instance.
stackFn: the filename where to write the stack.
selFn: the filename of the Spider selection file.
"""
doc = SpiderDocFile(selFn, 'w+')
for i in range(imgSet.getSize()):
doc.writeValues(i + 1)
imgSet.writeStack(stackFn, applyTransform=True)
doc.close()
convertEndian(stackFn, imgSet.getSize())