def __init__(self,particleList=None,angleObject=None,maskFile=None,scoreObject=None,startClassNumber=0,destinationDirectory='.',preprocessing = None):
from pytom.tools.files import checkDirExists
from pytom.angles.angleList import AngleList
from pytom.basic.structures import ParticleList
self._particleList = particleList or ParticleList('/')
self._angleObject = angleObject or AngleList()
self._startClassNumber = startClassNumber
self._maskFile = maskFile or None
if preprocessing:
self._preprocessing = preprocessing
else:
from pytom.alignment.preprocessing import Preprocessing
self._preprocessing = Preprocessing()
if self._maskFile.__class__ == str:
from pytom.basic.structures import Mask
self._maskFile = Mask(self.maskFile)
self._score = scoreObject
if not checkDirExists(destinationDirectory):
raise Exception('Destination directory ' + destinationDirectory + ' does not exist.')
if not destinationDirectory[len(destinationDirectory)-1] == '/':
destinationDirectory = destinationDirectory + '/'
self._destinationDirectory = destinationDirectory
def fromXML(self, xmlObj):
"""
fromXML:
@param xmlObj: A xml object
@type xmlObj: L{lxml.etree._Element}
@author: Thomas Hrabe
"""
from lxml.etree import _Element
if xmlObj.__class__ != _Element:
from pytom.basic.exceptions import ParameterError
raise ParameterError(
'Is not a lxml.etree._Element! You must provide a valid XMLobject.'
)
if xmlObj.tag == 'GrowingAverageJob':
job_element = xmlObj
else:
from pytom.basic.exceptions import ParameterError
raise ParameterError(
'Is not a GrowingAverageJobXML! You must provide a valid GrowingAverageJobXML object.'
)
from pytom.angles.angle import AngleObject
from pytom.score.score import fromXML as scoreFromXML
from pytom.basic.structures import ParticleList
from pytom.alignment.preprocessing import Preprocessing
self.startParticleNumber = int(job_element.get('StartParticleNumber'))
mask = job_element.xpath('Mask')[0]
from pytom.basic.structures import Mask
self.maskFile = Mask('')
self.maskFile.fromXML(mask)
self.destinationDirectory = job_element.get('DestinationDirectory')
particleXML = job_element.xpath('ParticleList')[0]
self.particleList = ParticleList('/', [])
self.particleList.fromXML(particleXML)
angleXML = job_element.xpath('Angles')[0]
ang = AngleObject()
self.angleObject = ang.fromXML(angleXML)
scoreXML = job_element.xpath('Score')[0]
self.score = scoreFromXML(scoreXML)
self.preprocessing = Preprocessing()
preprocessingXML = job_element.xpath('Preprocessing')[0]
self.preprocessing.fromXML(preprocessingXML)
if not checkDirExists(destination):
raise RuntimeError('Destination directory ' + destination +
' does not exist!')
from pytom.cluster.mcoEXMXStructures import MCOEXMXJob
from pytom.basic.structures import ParticleList, Reference, Mask, Wedge, SampleInformation, PointSymmetry
from pytom.score.score import FLCFScore
from pytom.frontend.serverpages.createMCOEXMXJob import createRunscripts
from pytom.alignment.preprocessing import Preprocessing
p = ParticleList()
p.fromXMLFile(particleList)
m = Mask(mask)
w = Wedge([float(wedge1), float(wedge2)])
pre = Preprocessing(lowestFrequency=float(lowestFrequency),
highestFrequency=float(highestFrequency))
sample = SampleInformation(pixelSize=float(pixelSize),
particleDiameter=float(diameter))
if symmetryN is None or symmetryAxisZ is None or symmetryAxisX is None:
sym = None
else:
sym = PointSymmetry(nfold=int(symmetryN),
z2=float(symmetryAxisZ),
x=float(symmetryAxisX))
job = MCOEXMXJob(particleList=p,numberIterations=numberIterations,\
destinationDirectory=destination,mask=mask,score=FLCFScore(),preprocessing=pre,\
wedgeInfo=w,binning=int(binning),sampleInformation=sample,numberClasses=int(numberClasses),\
endThreshold=float(endThreshold),symmetry = sym,doAlignment = False,frmBandwidth = None)
class GrowingAverageJob(PyTomClass):
"""
GrowingAverageJob:
@ivar particleList: List of particles to be aligned
@ivar angleObject: Angle object L{pytom.angles.AngleObject}
@ivar startParticleNumber: Number of start particle (default 0)
@ivar maskFile: Mask used for appedizing
"""
def __init__(self,particleList=None,angleObject=None,maskFile=None,scoreObject=None,startClassNumber=0,destinationDirectory='.',preprocessing = None):
from pytom.tools.files import checkDirExists
from pytom.angles.angleList import AngleList
from pytom.basic.structures import ParticleList
self._particleList = particleList or ParticleList('/')
self._angleObject = angleObject or AngleList()
self._startClassNumber = startClassNumber
self._maskFile = maskFile or None
if preprocessing:
self._preprocessing = preprocessing
else:
from pytom.alignment.preprocessing import Preprocessing
self._preprocessing = Preprocessing()
if self._maskFile.__class__ == str:
from pytom.basic.structures import Mask
self._maskFile = Mask(self.maskFile)
self._score = scoreObject
if not checkDirExists(destinationDirectory):
raise Exception('Destination directory ' + destinationDirectory + ' does not exist.')
if not destinationDirectory[len(destinationDirectory)-1] == '/':
destinationDirectory = destinationDirectory + '/'
self._destinationDirectory = destinationDirectory
def toXML(self):
"""
toXML : Compiles a XML file from result object
rtype : L{lxml.etree._Element}
@author: Thomas Hrabe
"""
from lxml import etree
job_element = etree.Element('GrowingAverageJob',StartClassNumber = str(self._startClassNumber), DestinationDirectory = str(self._destinationDirectory))
job_element.append(self._maskFile.toXML())
job_element.append(self._particleList.toXML())
job_element.append(self._angleObject.toXML())
job_element.append(self._score.toXML())
job_element.append(self._preprocessing.toXML())
return job_element
def fromXML(self,xmlObj):
"""
fromXML:
@param xmlObj: A xml object
@type xmlObj: L{lxml.etree._Element}
@author: Thomas Hrabe
"""
from lxml.etree import _Element
if xmlObj.__class__ != _Element :
from pytom.basic.exceptions import ParameterError
raise ParameterError('Is not a lxml.etree._Element! You must provide a valid XMLobject.')
if xmlObj.tag == 'GrowingAverageJob':
job_element = xmlObj
else:
from pytom.basic.exceptions import ParameterError
raise ParameterError('Is not a GrowingAverageJobXML! You must provide a valid GrowingAverageJobXML object.')
from pytom.angles.angle import AngleObject
from pytom.score.score import fromXML as scoreFromXML
from pytom.basic.structures import ParticleList
from pytom.alignment.preprocessing import Preprocessing
self._startClassNumber = int(job_element.get('StartClassNumber'))
mask = job_element.xpath('Mask')[0]
from pytom.basic.structures import Mask
self._maskFile = Mask('')
self._maskFile.fromXML(mask)
self._destinationDirectory = job_element.get('DestinationDirectory')
particleXML = job_element.xpath('ParticleList')[0]
self._particleList = ParticleList('/',[])
self._particleList.fromXML(particleXML)
angleXML = job_element.xpath('Angles')[0]
ang = AngleObject()
self._angleObject = ang.fromXML(angleXML)
scoreXML = job_element.xpath('Score')[0]
self._score = scoreFromXML(scoreXML)
self._preprocessing = Preprocessing()
preprocessingXML = job_element.xpath('Preprocessing')[0]
self._preprocessing.fromXML(preprocessingXML)
def growingAverageNew(particleList=None,angleObject=None,maskFile=None,scoreObject=None,startClassNumber=0,destinationDirectory='.',preprocessing = None,binning=1,verbose=False):
"""
"""
from pytom.alignment.alignmentFunctions import bestAlignment
from pytom.basic.structures import Reference,Particle,Rotation,ParticleList
from pytom.alignment.preprocessing import Preprocessing
if not preprocessing:
preprocessing = Preprocessing()
numberOfClasses = len(particleList.splitByClass())
if verbose:
print('Processing ' + str(numberOfClasses) + ' classes.')
print('Generating start average')
startAverageList = particleList.particlesFromClass(float(startClassNumber))
startAverageList.average(destinationDirectory + '/GA_it0.em',progressBar=verbose)
currentReference = Reference(destinationDirectory + '/GA_it0.em')
growingAverageParticleList = ParticleList(particleList.getDirectory())
for p in startAverageList:
p.setRotation(Rotation(0,0,0))
growingAverageParticleList.append(p)
for i in range(2,numberOfClasses):
currentParticleList = particleList.particlesFromClass(float(i))
if verbose:
print('Generating ' + str(i) + '. class average')
currentParticleList.average(destinationDirectory + '/CA_it'+str(i)+'.em',progressBar=verbose)
currentParticle = Particle(destinationDirectory + '/CA_it'+str(i)+'.em',wedgeInfo=currentParticleList[0].getWedgeInfo())
if verbose:
print('Running alignment iteration ' + str(i))
print(currentParticle)
print(currentReference)
currentPeak = bestAlignment(currentParticle.getVolume(),currentReference.getVolume(),currentReference.getWeighting(),currentParticle.getWedgeInfo(),angleObject,scoreObject,maskFile,preprocessing=preprocessing,binning=binning)
if verbose:
print('Parameters determined:')
print(currentPeak)
for p in currentParticleList:
p.setRotation(currentPeak.getRotation())
p.setShift(currentPeak.getShift())
growingAverageParticleList.append(p)
if verbose:
print('Generating growing average ' + str(i))
growingAverageParticleList.average(destinationDirectory + '/GA_it'+ str(i) +'.em',progressBar=verbose)
currentReference = Reference(destinationDirectory + '/GA_it'+ str(i) +'.em')
angleObject.reset()
def bestAlignment(particle, reference, referenceWeighting, wedgeInfo, rotations,
scoreObject=0, mask=None, preprocessing=None, progressBar=False, binning=1,
bestPeak=None, verbose=False):
"""
bestAlignment: Determines best alignment of particle relative to the reference
@param particle: A particle
@type particle: L{pytom_volume.vol}
@param reference: A reference
@type reference: L{pytom_volume.vol}
@param referenceWeighting: Fourier weighting of the reference (sum of wedges for instance)
@type referenceWeighting: L{pytom.basic.structures.vol}
@param wedgeInfo: What does the wedge look alike?
@type wedgeInfo: L{pytom.basic.structures.Wedge}
@param rotations: All rotations to be scanned
@type rotations: L{pytom.angles.AngleObject}
@param scoreObject:
@type scoreObject: L{pytom.score.score.Score}
@param mask: real-space mask for correlation function
@type mask: L{pytom.basic.structures.Particle}
@param preprocessing: Class storing preprocessing of particle and reference such as bandpass
@type preprocessing: L{pytom.alignment.preprocessing.Preprocessing}
@param progressBar: Display progress bar of alignment. False by default.
@param binning: binning factor - e.g. binning=2 reduces size by FACTOR of 2
@type binning: int or float
@param bestPeak: Initialise best peak with old values.
@param verbose: Print out infos. Writes CC volume to disk!!! Default is False
@return: Returns the best rotation for particle and the corresponding scoring result.
@author: Thomas Hrabe
"""
from pytom.basic.correlation import subPixelPeak, subPixelPeakParabolic
from pytom.alignment.structures import Peak
from pytom_volume import peak, vol, vol_comp
from pytom.basic.filter import filter,rotateWeighting
from pytom.basic.structures import Rotation, Shift, Particle, Mask
from pytom.angles.angle import AngleObject
from pytom.alignment.preprocessing import Preprocessing
from pytom.basic.transformations import resize, resizeFourier
binningType = 'Fourier' # or 'Fourier'
assert isinstance(rotations, AngleObject), "bestAlignment: rotations must be " \
"AngleObject!"
currentRotation = rotations.nextRotation()
if currentRotation == [None,None,None]:
raise Exception('bestAlignment: No rotations are sampled! Something is wrong with input rotations')
assert particle.__class__ == vol, "particle not of type vol"
assert reference.__class__ == vol, "reference not of type vol"
assert (referenceWeighting.__class__ == vol or referenceWeighting.__class__ == str), \
"referenceWeighting not volume or str"
if mask:
assert mask.__class__ == Mask, "Mask not of type Mask"
m = mask.getVolume()
if scoreObject == 0 or not scoreObject:
from pytom.score.score import xcfScore
scoreObject = xcfScore()
# fix binning
if binning == 0:
binning = 1
if binning != 1:
particleUnbinned = vol(particle.sizeX(), particle.sizeY(), particle.sizeZ())
particleUnbinned.copyVolume(particle)
particle = resize(volume=particle, factor=1./binning, interpolation=binningType)
if type(particle) == tuple:
particle = particle[0]
referenceUnbinned = vol(reference.sizeX(), reference.sizeY(), reference.sizeZ())
referenceUnbinned.copyVolume(reference)
reference = resize(volume=reference, factor=1./binning, interpolation=binningType)
if type(reference) == tuple:
reference = reference[0]
if mask:
m = resize(volume=m, factor=1./binning, interpolation='Spline')
if not referenceWeighting.__class__ == str:
referenceWeightingUnbinned = vol_comp(referenceWeighting.sizeX(), referenceWeighting.sizeY(),
referenceWeighting.sizeZ())
referenceWeightingUnbinned.copyVolume(referenceWeighting)
if binning != 1:
referenceWeighting = resizeFourier(fvol=referenceWeighting, factor=1./binning)
centerX, centerY, centerZ = int(particle.sizeX()/2), int(particle.sizeY()/2), int(particle.sizeZ()/2)
# create buffer volume for transformed particle
particleCopy = vol(particle.sizeX(),particle.sizeY(),particle.sizeZ())
particle = wedgeInfo.apply(particle) #apply wedge to itself
if preprocessing is None:
preprocessing = Preprocessing()
preprocessing.setTaper( taper=particle.sizeX()/10.)
particle = preprocessing.apply(volume=particle, bypassFlag=True) # filter particle to some resolution
particleCopy.copyVolume(particle)
# compute standard veviation volume really only if needed
if mask and (scoreObject._type=='FLCFScore'):
from pytom_volume import sum
from pytom.basic.correlation import meanUnderMask, stdUnderMask
p = sum(m)
meanV = meanUnderMask(particle, m, p)
stdV = stdUnderMask(particle, m, p, meanV)
else:
meanV = None
stdV = None
while currentRotation != [None,None,None]:
if mask:
m = mask.getVolume(currentRotation)
if binning != 1:
m = resize(volume=m, factor=1./binning, interpolation='Spline')
#update stdV if mask is not a sphere
# compute standard deviation volume really only if needed
if not mask.isSphere() and (scoreObject._type=='FLCFScore'):
meanV = meanUnderMask(particle, m, p)
stdV = stdUnderMask(particle, m, p, meanV)
else:
m = None
simulatedVol = _rotateWedgeReference(reference, currentRotation, wedgeInfo, m, [centerX, centerY, centerZ])
simulatedVol = preprocessing.apply(volume=simulatedVol, bypassFlag=True)
#weight particle
if not referenceWeighting.__class__ == str:
from pytom_freqweight import weight
weightingRotated = rotateWeighting(weighting=referenceWeighting, z1=currentRotation[0],
z2=currentRotation[1], x=currentRotation[2], isReducedComplex=True,
returnReducedComplex=True, binarize=False)
particleCopy.copyVolume(particle)
r = list(filter(particleCopy, weight(weightingRotated)))
particleCopy = r[0]
scoringResult = scoreObject.score(particleCopy, simulatedVol, m, stdV)
pk = peak(scoringResult)
#with subPixelPeak
[peakValue,peakPosition] = subPixelPeak(scoreVolume=scoringResult, coordinates=pk,
interpolation='Quadratic', verbose=False)
#[peakValue,peakPosition] = subPixelPeakParabolic(scoreVolume=scoringResult, coordinates=pk, verbose=False)
# determine shift relative to center
shiftX = (peakPosition[0] - centerX) * binning
shiftY = (peakPosition[1] - centerY) * binning
shiftZ = (peakPosition[2] - centerZ) * binning
#NANs would fail this test.
assert peakValue == peakValue, "peakValue seems to be NaN"
newPeak = Peak(peakValue, Rotation(currentRotation), Shift(shiftX, shiftY, shiftZ))
if verbose:
print('Rotation: z1=%3.1f, z2=%3.1f, x=%3.1f; Dx=%2.2f, Dy=%2.2f, Dz=%2.2f, CC=%2.3f' % \
(currentRotation[0], currentRotation[1], currentRotation[2], shiftX, shiftY, shiftZ, peakValue))
if bestPeak is None:
bestPeak = newPeak
if verbose:
scoringResult.write('BestScore.em')
if bestPeak < newPeak:
bestPeak = newPeak
if verbose:
scoringResult.write('BestScore.em')
currentRotation = rotations.nextRotation()
# repeat ccf for binned sampling to get translation accurately
if binning != 1:
m = mask.getVolume(bestPeak.getRotation())
centerX, centerY, centerZ = int(particleUnbinned.sizeX()/2), int(particleUnbinned.sizeY()/2), \
int(particleUnbinned.sizeZ()/2)
simulatedVol = _rotateWedgeReference(referenceUnbinned, bestPeak.getRotation(), wedgeInfo, m,
[centerX, centerY, centerZ])
simulatedVol = preprocessing.apply(volume=simulatedVol, bypassFlag=True)
if mask and scoreObject._type=='FLCFScore':
p = sum(m)
meanV = meanUnderMask(volume=particleUnbinned, mask=m, p=p)
stdV = stdUnderMask(volume=particleUnbinned, mask=m, p=p, meanV=meanV)
scoreObject._peakPrior.reset_weight()
scoringResult = scoreObject.score(particle=particleUnbinned, reference=simulatedVol, mask=m, stdV=stdV)
pk = peak(scoringResult)
[peakValue,peakPosition] = subPixelPeak(scoreVolume=scoringResult, coordinates=pk, interpolation='Quadratic',
verbose=False)
shiftX = (peakPosition[0] - centerX)
shiftY = (peakPosition[1] - centerY)
shiftZ = (peakPosition[2] - centerZ)
bestPeak = Peak(peakValue, bestPeak.getRotation(), Shift(shiftX, shiftY, shiftZ))
if verbose:
print('BestAlignment: z1=%3.1f, z2=%3.1f, x=%3.1f; Dx=%2.2f, Dy=%2.2f, Dz=%2.2f, CC=%2.3f' % \
(bestPeak.getRotation()[0], bestPeak.getRotation()[1], bestPeak.getRotation()[2],
bestPeak.getShift()[0], bestPeak.getShift()[1], bestPeak.getShift()[2], bestPeak.getScoreValue()))
rotations.reset()
scoreObject._peakPrior.reset_weight()
return bestPeak
weighting = False
if compound:
compound = True
else:
compound = False
################# fixed parameters #################
if (scoreObject == None or scoreObject.lower() == 'flcf'):
score = FLCFScore()
elif scoreObject.lower() == 'nxcf':
score = nxcfScore()
else:
score = FLCFScore()
score.setRemoveAutocorrelation(flag=False)
#pre = Preprocessing(lowestFrequency = float(lowestFrequency),highestFrequency = float(highestFrequency))
pre = Preprocessing()
adaptive_res = 0.1
fsc_criterion = 0.143
locJob = GLocalSamplingJob(pl=pl, ref=ref, mask=m,
sample_info=sampleI, rotations=rot,
preprocessing=pre,
dest=destination, max_iter=int(numberIterations), score=score, binning=int(binning),
weighting=weighting, compoundWedge=compound,
symmetries=None, adaptive_res=adaptive_res, fsc_criterion=fsc_criterion)
locJob.toXMLFile(jobName)
# run script
mainAlignmentLoop( alignmentJob=locJob, verbose=False)
def interpretRequestParameters(parameters):
"""
interpretRequestParameters
"""
from pytom.basic.structures import ParticleList, SampleInformation, Reference, Mask, Wedge
from pytom.alignment.preprocessing import Preprocessing
from pytom.frontend.serverpages.serverMessages import FileMessage
particleList = ParticleList()
if 'plXML' in parameters:
particleList.fromXMLFile(parameters['plXML'])
elif 'plDIR' in parameters:
particleList = ParticleList(parameters['plDIR'])
particleList.loadDirectory()
else:
raise RuntimeError('ParticleList parameter missing in request!')
sampleInfo = SampleInformation()
if 'pixSize' in parameters:
sampleInfo.setPixelSize(parameters['pixSize'])
else:
raise RuntimeError('Pixelsize parameter missing in request!')
if 'partDia' in parameters:
sampleInfo.setParticleDiameter(parameters['partDia'])
else:
raise RuntimeError('Particle diameter missing in request!')
if 'wa1' in parameters:
wedgeAngle1 = float(parameters['wa1'])
else:
raise RuntimeError('Wedge angle 1 parameter missing in request!')
if 'wa2' in parameters:
wedgeAngle2 = float(parameters['wa2'])
else:
raise RuntimeError('Wedge angle 2 parameter missing in request!')
wedgeInfo = Wedge([wedgeAngle1, wedgeAngle2])
if 'mask' in parameters:
mask = Mask(parameters['mask'])
else:
raise RuntimeError('Mask parameter missing in request!')
if not 'lowestF' in parameters:
raise RuntimeError('Lowest frequency parameter missing in request!')
if not 'highestF' in parameters:
raise RuntimeError('Highest frequency parameter missing in request!')
if not 'filtSm' in parameters:
raise RuntimeError('Filter smooth parameter missing in request!')
preprocessing = Preprocessing(float(parameters['lowestF']),
float(parameters['highestF']),
float(parameters['filtSm']))
score = None
if 'score' in parameters:
if parameters['score'] == 'xcf':
from pytom.score.score import xcfScore as scoreClass
elif parameters['score'] == 'nxcf':
from pytom.score.score import nxcfScore as scoreClass
elif parameters['score'] == 'flcf':
from pytom.score.score import FLCFScore as scoreClass
score = scoreClass()
else:
raise RuntimeError('Score parameter missing in request!')
if 'iter' in parameters:
iterations = int(parameters['iter'])
else:
raise RuntimeError('Number of iterations missing in request!')
if 'binning' in parameters:
binning = int(parameters['binning'])
else:
raise RuntimeError('Scaling parameter missing in request!')
if 'classes' in parameters:
numberClasses = float(parameters['classes'])
else:
raise RuntimeError('Number classes parameter missing in request!')
if 'conv' in parameters:
convergence = float(parameters['conv'])
else:
raise RuntimeError('Convergence parameter missing in request!')
if 'dest' in parameters:
destination = parameters['dest']
else:
raise RuntimeError('Destination parameter missing in request!')
sampleInfo = SampleInformation()
if 'pixSize' in parameters:
sampleInfo.setPixelSize(float(parameters['pixSize']))
else:
raise RuntimeError('Pixelsize parameter missing in request!')
if 'partDia' in parameters:
sampleInfo.setParticleDiameter(float(parameters['partDia']))
else:
raise RuntimeError('Particle diameter missing in request!')
from pytom.cluster.mcoEXMXStructures import MCOEXMXJob
job = MCOEXMXJob(particleList,
iterations,
destination,
mask,
score,
preprocessing,
wedgeInfo,
binning,
sampleInfo,
numberClasses,
convergence,
symmetry=None)
jobXMLFile = ''
if 'jobFile' in parameters:
jobXMLFile = parameters['jobFile']
job.toXMLFile(jobXMLFile)
jobRunFile = jobXMLFile[0:-3]
createRunscripts(jobRunFile + 'sh', jobXMLFile)
return FileMessage('MCOEXMXJob', jobXMLFile, 'created')
def interpretRequestParameters(parameters):
"""
interpretRequestParameters
"""
from pytom.basic.structures import ParticleList, SampleInformation, Reference, Mask, Wedge
from pytom.alignment.preprocessing import Preprocessing
from pytom.frontend.serverpages.serverMessages import FileMessage
particleList = ParticleList('.')
if 'plXML' in parameters:
particleList.fromXMLFile(parameters['plXML'])
elif 'plDIR' in parameters:
particleList = ParticleList(parameters['plDIR'])
particleList.loadDirectory()
else:
raise RuntimeError('ParticleList parameter missing in request!')
sampleInfo = SampleInformation()
if 'pixSize' in parameters:
sampleInfo.setPixelSize(parameters['pixSize'])
else:
raise RuntimeError('Pixelsize parameter missing in request!')
if 'partDia' in parameters:
sampleInfo.setParticleDiameter(parameters['partDia'])
else:
raise RuntimeError('Particle diameter missing in request!')
if 'wa1' in parameters:
wedgeAngle1 = float(parameters['wa1'])
else:
raise RuntimeError('Wedge angle 1 parameter missing in request!')
if 'wa2' in parameters:
wedgeAngle2 = float(parameters['wa2'])
else:
raise RuntimeError('Wedge angle 2 parameter missing in request!')
wedgeInfo = Wedge([wedgeAngle1, wedgeAngle2])
if 'mask' in parameters:
mask = Mask(parameters['mask'])
else:
raise RuntimeError('Mask parameter missing in request!')
if not 'lowestF' in parameters:
raise RuntimeError('Lowest frequency parameter missing in request!')
if not 'highestF' in parameters:
raise RuntimeError('Highest frequency parameter missing in request!')
if not 'filtSm' in parameters:
raise RuntimeError('Filter smooth parameter missing in request!')
preprocessing = Preprocessing(float(parameters['lowestF']),
float(parameters['highestF']),
float(parameters['filtSm']))
score = None
if 'score' in parameters:
if parameters['score'] == 'xcf':
from pytom.score.score import xcfScore as scoreClass
elif parameters['score'] == 'nxcf':
from pytom.score.score import nxcfScore as scoreClass
elif parameters['score'] == 'flcf':
from pytom.score.score import FLCFScore as scoreClass
score = scoreClass()
else:
raise RuntimeError('Score parameter missing in request!')
if 'binning' in parameters:
binning = int(parameters['binning'])
else:
raise RuntimeError('Scaling parameter missing in request!')
if 'classes' in parameters:
numberClasses = float(parameters['classes'])
else:
raise RuntimeError('Number classes parameter missing in request!')
if 'conv' in parameters:
convergence = float(parameters['conv'])
else:
raise RuntimeError('Convergence parameter missing in request!')
if 'dest' in parameters:
destination = parameters['dest']
else:
raise RuntimeError('Destination parameter missing in request!')
sampleInfo = SampleInformation()
if 'pixSize' in parameters:
sampleInfo.setPixelSize(float(parameters['pixSize']))
else:
raise RuntimeError('Pixelsize parameter missing in request!')
if 'partDia' in parameters:
sampleInfo.setParticleDiameter(float(parameters['partDia']))
else:
raise RuntimeError('Particle diameter missing in request!')
temperature = None
if 'temp' in parameters:
temperature = parameters['temp']
if 'stemp' in parameters:
startTemperature = float(parameters['stemp'])
else:
raise RuntimeError(
'Start temperature parameter missing in request!')
if 'astep' in parameters:
annealingStep = float(parameters['astep'])
else:
raise RuntimeError('Annealing step parameter missing in request!')
from pytom.cluster.mcoACStructures import SigmaTemperature
if temperature == 'sigma':
temperature = SigmaTemperature(startTemperature, annealingStep)
else:
raise RuntimeError('Temperature missing in request!')
criterion = None
if 'crit' in parameters:
from pytom.cluster.mcoACStructures import MetropolisCriterion, ThresholdAcceptance
criterion = parameters['crit']
if criterion == 'metropolis':
criterion = MetropolisCriterion()
elif criterion == 'threshold':
criterion = ThresholdAcceptance()
else:
raise RuntimeError('Criterion missing in request!')
if 'refin' in parameters:
localSearchIncrement = float(parameters['refin'])
else:
raise RuntimeError('Number of refinement rounds missing in request!')
from pytom.cluster.mcoACStructures import MCOACJob
job = MCOACJob(particleList,
destination,
mask,
score,
preprocessing,
wedgeInfo,
binning,
sampleInfo,
numberClasses,
temperature,
criterion,
convergence,
localSearchIncrement,
symmetry=None)
jobXMLFile = ''
if 'jobFile' in parameters:
jobXMLFile = parameters['jobFile']
job.toXMLFile(jobXMLFile)
jobRunFile = jobXMLFile[0:-3]
createRunscripts(jobRunFile + 'sh', jobXMLFile)
return FileMessage('MCOACJob', jobXMLFile, 'created')
def fromXML(self, xmlObj):
"""
fromXML : Assigns values to job attributes from XML object
@param xmlObj: A xml object
@author: Thomas Hrabe
"""
from lxml.etree import _Element
if xmlObj.__class__ != _Element:
from pytom.basic.exceptions import ParameterError
raise ParameterError(
'You must provide a valid XML-MaximisationJob object.')
if xmlObj.tag == "JobDescription":
jobDescription = xmlObj
else:
jobDescription = xmlObj.xpath('JobDescription')
if len(jobDescription) == 0:
from pytom.basic.structures import PyTomClassError
raise PyTomClassError("This XML is not an JobDescription.")
jobDescription = jobDescription[0]
from pytom.angles.angle import AngleObject
from pytom.score.score import fromXML as fromXMLScore
from pytom.alignment.preprocessing import Preprocessing
from pytom.basic.structures import Mask, Particle, Reference, ReferenceList
self.binning = int(jobDescription.get('Binning'))
particle_element = jobDescription.xpath('Particle')[0]
p = Particle('')
p.fromXML(particle_element)
self.particle = p
r = jobDescription.xpath('Reference')
if len(r) > 0:
ref = Reference('')
ref.fromXML(r[0])
self.reference = ref
else:
r = jobDescription.xpath('ReferenceList')
ref = ReferenceList()
ref.fromXML(r[0])
self.reference = ref
mask = jobDescription.xpath('Mask')[0]
self.mask = Mask('')
self.mask.fromXML(mask)
self.numberRefinementRounds = jobDescription.get(
'NumberRefinementRounds')
self.numberRefinementRounds = int(self.numberRefinementRounds)
score = jobDescription.xpath('Score')
self.score = fromXMLScore(score[0])
angles = jobDescription.xpath('Angles')
ang = AngleObject()
self.rotations = ang.fromXML(angles[0])
preObj = xmlObj.xpath('Preprocessing')
if len(preObj) == 0:
self.preprocessing = Preprocessing()
else:
p = Preprocessing()
p.fromXML(preObj[0])
self.preprocessing = p
class MaximisationJob(PyTomClass):
"""
MaximisationJob : Stores all infos needed for a maximisation job
"""
def __init__(self,
particle='',
reference='',
score='',
rotations='',
mask='',
numberRefinementRounds=1,
preprocessing='',
binning=1):
"""
@param particle: particle to be aligned
@type particle: L{pytom.basic.structures.Particle} or str
@param reference: reference for particle alignment
@param score: type of score used for alignment
@param rotations: rotations scanned in search
@param mask: mask on reference
@param numberRefinementRounds: iteration number
@param preprocessing: preprocessing parameters
@param binning: Binning Factor
@type binning: int
"""
from lxml.etree import _Element
if particle.__class__ == _Element:
self.fromXML(particle)
else:
if particle.__class__ == str:
from pytom.basic.structures import Particle
self.particle = Particle(particle)
else:
self.particle = particle
self.reference = reference
self.score = score
self.rotations = rotations
self.mask = mask
self.numberRefinementRounds = numberRefinementRounds
self.preprocessing = preprocessing
self.binning = binning
def fromXML(self, xmlObj):
"""
fromXML : Assigns values to job attributes from XML object
@param xmlObj: A xml object
@author: Thomas Hrabe
"""
from lxml.etree import _Element
if xmlObj.__class__ != _Element:
from pytom.basic.exceptions import ParameterError
raise ParameterError(
'You must provide a valid XML-MaximisationJob object.')
if xmlObj.tag == "JobDescription":
jobDescription = xmlObj
else:
jobDescription = xmlObj.xpath('JobDescription')
if len(jobDescription) == 0:
from pytom.basic.structures import PyTomClassError
raise PyTomClassError("This XML is not an JobDescription.")
jobDescription = jobDescription[0]
from pytom.angles.angle import AngleObject
from pytom.score.score import fromXML as fromXMLScore
from pytom.alignment.preprocessing import Preprocessing
from pytom.basic.structures import Mask, Particle, Reference, ReferenceList
self.binning = int(jobDescription.get('Binning'))
particle_element = jobDescription.xpath('Particle')[0]
p = Particle('')
p.fromXML(particle_element)
self.particle = p
r = jobDescription.xpath('Reference')
if len(r) > 0:
ref = Reference('')
ref.fromXML(r[0])
self.reference = ref
else:
r = jobDescription.xpath('ReferenceList')
ref = ReferenceList()
ref.fromXML(r[0])
self.reference = ref
mask = jobDescription.xpath('Mask')[0]
self.mask = Mask('')
self.mask.fromXML(mask)
self.numberRefinementRounds = jobDescription.get(
'NumberRefinementRounds')
self.numberRefinementRounds = int(self.numberRefinementRounds)
score = jobDescription.xpath('Score')
self.score = fromXMLScore(score[0])
angles = jobDescription.xpath('Angles')
ang = AngleObject()
self.rotations = ang.fromXML(angles[0])
preObj = xmlObj.xpath('Preprocessing')
if len(preObj) == 0:
self.preprocessing = Preprocessing()
else:
p = Preprocessing()
p.fromXML(preObj[0])
self.preprocessing = p
def toXML(self):
"""
toXML : Compiles a XML file from job object
@author: Thomas Hrabe
"""
from lxml import etree
jobElement = etree.Element("JobDescription")
jobElement.set("NumberRefinementRounds",
str(self.numberRefinementRounds))
jobElement.set("Binning", str(self.binning))
jobElement.append(self.particle.toXML())
jobElement.append(self.reference.toXML())
jobElement.append(self.mask.toXML())
jobElement.append(self.rotations.toXML())
if self.score.__class__ == str:
jobElement.append(self.score)
else:
jobElement.append(self.score.toXML())
preObj = self.preprocessing.toXML()
jobElement.append(preObj)
return jobElement
def check(self):
"""
check: Performs check on self whether all settings were sane. Paths and Files exists
"""
from pytom.tools.files import checkFileExists, checkDirExists
returnValue = checkFileExists(self.particle.getFilename())
if not returnValue:
raise IOError(str(self.particle.getFilename()) + ' not found!')
returnValue = returnValue and checkFileExists(
self.reference.getFilename())
if not returnValue:
raise IOError(str(self.reference) + ' not found!')
return returnValue
def interpretRequestParameters(parameters):
"""
interpretRequestParameters
"""
from pytom.basic.structures import ParticleList, SampleInformation, Reference, Mask, Wedge, PointSymmetry
from pytom.alignment.preprocessing import Preprocessing
from pytom.frontend.serverpages.serverMessages import FileMessage
particleList = ParticleList('.')
if 'plXML' in parameters:
particleList.fromXMLFile(parameters['plXML'])
elif 'plDIR' in parameters:
particleList = ParticleList(parameters['plDIR'])
particleList.loadDirectory()
else:
raise RuntimeError('ParticleList parameter missing in request!')
sampleInfo = SampleInformation()
if 'pixSize' in parameters:
sampleInfo.setPixelSize(parameters['pixSize'])
else:
raise RuntimeError('Pixelsize parameter missing in request!')
if 'partDia' in parameters:
sampleInfo.setParticleDiameter(parameters['partDia'])
else:
raise RuntimeError('Particle diameter missing in request!')
reference = ''
if 'ref' in parameters:
reference = Reference(parameters['ref'])
else:
raise RuntimeError('Reference parameter missing in request!')
if 'mask' in parameters:
mask = Mask(parameters['mask'])
else:
raise RuntimeError('Mask parameter missing in request!')
angles = None
if 'sampling' in parameters:
if parameters['sampling'] == 'GLOBAL':
from pytom.angles.globalSampling import GlobalSampling
if 'angFile' in parameters:
angles = GlobalSampling(parameters['angFile'])
else:
raise RuntimeError('Angle file missing in request!')
else:
from pytom.angles.localSampling import LocalSampling
if 'angStart' in parameters:
startIncrement = int(parameters['angStart'])
else:
raise RuntimeError('Angle start missing in request!')
if 'angShells' in parameters:
shells = int(parameters['angShells'])
else:
raise RuntimeError('Angle shells missing in request!')
if 'angInc' in parameters:
shellIncrement = int(parameters['angInc'])
else:
raise RuntimeError('Angle increment missing in request!')
angles = LocalSampling(shells=shells, increment=startIncrement)
else:
raise RuntimeError('Sampling completely missing in request!')
if not 'lowestF' in parameters:
raise RuntimeError('Lowest frequency parameter missing in request!')
if not 'highestF' in parameters:
raise RuntimeError('Highest frequency parameter missing in request!')
if not 'filtSm' in parameters:
raise RuntimeError('Filter smooth parameter missing in request!')
preprocessing = Preprocessing(float(parameters['lowestF']),
float(parameters['highestF']),
float(parameters['filtSm']))
adaptive = True
if 'adapt' in parameters:
fscCriterion = 0.5
resOffset = 0.1
angleFactor = 0.5
adaptive = parameters['adapt'] == 'ON'
if parameters['adapt'] == 'ON':
if 'adResC' in parameters:
fscCriterion = float(parameters['adResC'])
else:
raise RuntimeError('Resolution criterion missing in request!')
if 'adResOf' in parameters:
resOffset = parameters['adResOf']
else:
raise RuntimeError(
'Resolution offset parameter missing in request!')
if 'angFac' in parameters:
angleFactor = float(parameters['angFac'])
else:
raise RuntimeError(
'Angular factor parameter missing in request!')
else:
raise RuntimeError('Adaptive parameter missing in request!')
score = None
if 'score' in parameters:
if parameters['score'] == 'xcf':
from pytom.score.score import xcfScore as scoreClass
elif parameters['score'] == 'nxcf':
from pytom.score.score import nxcfScore as scoreClass
elif parameters['score'] == 'flcf':
from pytom.score.score import FLCFScore as scoreClass
score = scoreClass()
else:
raise RuntimeError('Score parameter missing in request!')
if 'pkPriRad' in parameters:
radius = float(parameters['pkPriRad'])
else:
raise RuntimeError('Peak distance parameter missing in request!')
if 'pkSmooth' in parameters:
smooth = float(parameters['pkSmooth'])
else:
raise RuntimeError('Peak distance smooth missing in request!')
score.setPeakPrior(radius=radius, smooth=smooth)
if 'iter' in parameters:
iterations = int(parameters['iter'])
else:
raise RuntimeError('Number of iterations missing in request!')
if 'binning' in parameters:
binning = int(parameters['binning'])
else:
raise RuntimeError('Scaling parameter missing in request!')
if 'dest' in parameters:
destination = parameters['dest']
else:
raise RuntimeError('Destination parameter missing in request!')
from pytom.alignment.ExMaxAlignment import ExMaxJob
job = ExMaxJob(particleList, destination, reference, score, angles, mask,
PointSymmetry(1), 1, iterations, preprocessing, -1, binning,
sampleInfo, fscCriterion, adaptive, resOffset, angleFactor)
#print job
jobXMLFile = ''
if 'jobFile' in parameters:
jobXMLFile = parameters['jobFile']
job.toXMLFile(jobXMLFile)
if jobXMLFile[-4:] == '.xml':
jobRunFile = jobXMLFile[0:-4]
else:
jobRunFile = jobXMLFile
createRunscripts(jobRunFile + '.sh', jobXMLFile)
return FileMessage('AlignmentJob', jobXMLFile, 'created')