def createOutputStep(self):
particles = self.inputParticles.get()
# Generate the SetOfAlignmet
alignedSet = self._createSetOfParticles()
alignedSet.copyInfo(particles)
inputMd = self._getPath('aligned_particles.xmd')
alignedSet.copyItems(particles,
updateItemCallback=self._updateItem,
itemDataIterator=iterMdRows(inputMd))
# Remove alignment 2D
alignedSet.setAlignment(ALIGN_NONE)
# Define the output average
avgFile = self._getExtraPath("average.xmp")
imgh = ImageHandler()
avgImage = imgh.computeAverage(alignedSet)
avgImage.write(avgFile)
avg = Particle()
avg.setLocation(1, avgFile)
avg.copyInfo(alignedSet)
self._defineOutputs(outputAverage=avg)
self._defineSourceRelation(self.inputParticles, avg)
self._defineOutputs(outputParticles=alignedSet)
self._defineSourceRelation(self.inputParticles, alignedSet)
def createClassesFromImages2(inputImages, inputMd,
classesFn, ClassType, classLabel,
alignType, getClassFn=None, preprocessImageRow=None):
""" From an intermediate X.xmd file produced by xmipp, create
the set of classes in which those images are classified.
Params:
inputImages: the SetOfImages that were initially classified by relion.
inputMd: the filename metadata.
classesFn: filename where to write the classes.
ClassType: the output type of the classes set ( usually SetOfClass2D or 3D )
classLabel: label that is the class reference.
classFnTemplate: the template to get the classes averages filenames
iter: the iteration number, just used in Class template
"""
mdIter = iterMdRows(inputMd)
clsDict = {} # Dictionary to store the (classId, classSet) pairs
clsSet = ClassType(filename=classesFn)
clsSet.setImages(inputImages)
hasCtf = inputImages.hasCTF()
sampling = inputImages.getSamplingRate()
for img, row in izip(inputImages, mdIter):
ref = row.getValue(xmipp.MDL_REF)
if ref is None:
raise Exception('MDL_REF not found in metadata: %s' % inputMd)
if not ref in clsDict: # Register a new class set if the ref was not found.
classItem = clsSet.ITEM_TYPE(objId=ref)
if getClassFn is None:
refFn = ''
else:
refFn = getClassFn(ref)
refLocation = xmippToLocation(refFn)
rep = clsSet.REP_TYPE()
rep.setLocation(refLocation)
rep.setSamplingRate(sampling)
classItem.setRepresentative(rep)
clsDict[ref] = classItem
classItem.copyInfo(inputImages)
classItem.setAlignment(alignType)
classItem.setAcquisition(inputImages.getAcquisition())
clsSet.append(classItem)
else:
classItem = clsDict[ref] # Try to get the class set given its ref number
img.setTransform(rowToAlignment(row, alignType))
classItem.append(img)
for classItem in clsDict.values():
clsSet.update(classItem)
clsSet.write()
return clsSet
def createClassesFromImages2(inputImages, inputMd,
classesFn, ClassType, classLabel,
alignType, getClassFn=None, preprocessImageRow=None):
""" From an intermediate X.xmd file produced by xmipp, create
the set of classes in which those images are classified.
Params:
inputImages: the SetOfImages that were initially classified by relion.
inputMd: the filename metadata.
classesFn: filename where to write the classes.
ClassType: the output type of the classes set ( usually SetOfClass2D or 3D )
classLabel: label that is the class reference.
classFnTemplate: the template to get the classes averages filenames
iter: the iteration number, just used in Class template
"""
mdIter = iterMdRows(inputMd)
clsDict = {} # Dictionary to store the (classId, classSet) pairs
clsSet = ClassType(filename=classesFn)
clsSet.setImages(inputImages)
hasCtf = inputImages.hasCTF()
sampling = inputImages.getSamplingRate()
for img, row in izip(inputImages, mdIter):
ref = row.getValue(xmipp.MDL_REF)
if ref is None:
raise Exception('MDL_REF not found in metadata: %s' % inputMd)
if not ref in clsDict: # Register a new class set if the ref was not found.
classItem = clsSet.ITEM_TYPE(objId=ref)
if getClassFn is None:
refFn = ''
else:
refFn = getClassFn(ref)
refLocation = xmippToLocation(refFn)
rep = clsSet.REP_TYPE()
rep.setLocation(refLocation)
rep.setSamplingRate(sampling)
classItem.setRepresentative(rep)
clsDict[ref] = classItem
classItem.copyInfo(inputImages)
classItem.setAlignment(alignType)
classItem.setAcquisition(inputImages.getAcquisition())
clsSet.append(classItem)
else:
classItem = clsDict[ref] # Try to get the class set given its ref number
img.setTransform(rowToAlignment(row, alignType))
classItem.append(img)
for classItem in clsDict.values():
clsSet.update(classItem)
clsSet.write()
return clsSet
def createOutputStep(self):
inputSet = self.inputParticles.get()
# outputSet could be SetOfParticles, SetOfAverages or any future sub-class of SetOfParticles
className = inputSet.getClassName()
outputSet = self._createSetFromName(className)
outputSet.copyInfo(inputSet)
self._preprocessOutput(outputSet)
outputSet.copyItems(inputSet,
updateItemCallback=self._updateItem,
itemDataIterator=iterMdRows(self.outputMd))
#readSetOfParticles(self.outputMd, outputSet)
self._postprocessOutput(outputSet)
outputKey = className.replace('SetOf', 'output')
self._defineOutputs(**{outputKey: outputSet})
self._defineTransformRelation(inputSet, outputSet)
def createOutputStep(self, outImgsFn):
inputSet = self.inputSet.get()
if isinstance(inputSet, SetOfClasses2D):
outputSet = self._createSetOfClasses2D(inputSet.getImages())
outputName = 'outputClasses'
else: # SetOfAverages
outputSet = self._createSetOfAverages()
outputSet.setAlignment3D()
outputName = 'outputAverages'
md = xmipp.MetaData(outImgsFn)
outputSet.copyInfo(inputSet)
outputSet.copyItems(inputSet,
updateItemCallback=self.updateItem,
itemDataIterator=iterMdRows(md))
self._defineOutputs(**{outputName: outputSet})
self._defineTransformRelation(inputSet, outputSet)
