Source code for emantomo.protocols.protocol_tomo_initialmodel

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# * Authors:     Adrian Quintana ( [1]
# *              Arnau Sanchez  ( [1]
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# * [1] EyeSeeTea Ltd, London, UK
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from pyworkflow import BETA
from pyworkflow.protocol import params
from pyworkflow.utils.path import makePath

from pwem.protocols import EMProtocol

import emantomo
from emantomo.convert import writeSetOfSubTomograms, getLastParticlesParams, updateSetOfSubTomograms

from tomo.protocols import ProtTomoBase
from tomo.objects import AverageSubTomogram, SetOfSubTomograms, SetOfAverageSubTomograms

[docs]class EmanProtTomoInitialModel(EMProtocol, ProtTomoBase): """ This protocol wraps ** EMAN2 program. It will take a set of subtomograms (particles) and a subtomogram(reference) and build a subtomogram suitable for use as initial models in tomography. It also builds a set of subtomograms that contains the original particles plus the score, coverage and align matrix per subtomogram . """ _label = 'tomo initial model' _devStatus = BETA OUTPUT_DIR = 'sptsgd_00' def __init__(self, **kwargs): EMProtocol.__init__(self, **kwargs) # --------------------------- DEFINE param functions ---------------------- def _defineParams(self, form): form.addSection(label='Input') form.addParam('particles', params.PointerParam, pointerClass='SetOfSubTomograms', label="Particles", important=True, help='Select the set of subtomograms to build an initial model') form.addParam('reference', params.PointerParam, pointerClass='Volume', allowsNull=True, label="Reference volume", help='Specify a 3D volume') form.addParam('mask', params.PointerParam, label='Mask', allowsNull=True, pointerClass='VolumeMask', help='Select a 3D Mask to be applied to the initial model') form.addSection(label='Optimization') form.addParam('symmetry', params.TextParam, default='c1', expertLevel=params.LEVEL_ADVANCED, label='Symmetry', help='Specify the symmetry.\nChoices are: c(n), d(n), ' 'h(n), tet, oct, icos.\n' 'See\n' 'for a detailed description of symmetry in Eman.') form.addParam('filterto', params.FloatParam, default=0.02, expertLevel=params.LEVEL_ADVANCED, label='Filterto', help='Filter map to frequency after each iteration. Default is 0.02') form.addParam('fourier', params.BooleanParam, default=True, expertLevel=params.LEVEL_ADVANCED, label='Fourier', help='Gradient descent in fourier space') form.addParam('batchSize', params.IntParam, default=12, label='Batch Size', help='SGD batch size') form.addParam('learningRate', params.FloatParam, default=0.1, expertLevel=params.LEVEL_ADVANCED, label='Learn Rate', help='Learning Rate. Default is 0.1') form.addParam('numberOfIterations', params.IntParam, default=5, label='Number of iterations to perform', help='The total number of refinement to perform.') form.addParam('numberOfBatches', params.IntParam, default=10, label='Number of batches', help='Number of batches per iteration') form.addParam('shrink', params.IntParam, default=1, expertLevel=params.LEVEL_ADVANCED, label='Shrink factor', help='Using a box-size >64 is not optimal for making ' 'initial models. Suggest using this option to ' 'shrink the input particles by an integer amount ' 'prior to reconstruction. Default = 1, no shrinking') form.addParam('applySim', params.BooleanParam, default=False, expertLevel=params.LEVEL_ADVANCED, label='Apply Symmetry', help='Apply Symmetry') # --------------------------- INSERT steps functions ---------------------- def _insertAllSteps(self): self._insertFunctionStep('convertImagesStep') self._insertFunctionStep('createInitialModelStep') self._insertFunctionStep('createOutputStep') # --------------------------- STEPS functions ----------------------------- # Get Scipion references to subtomograms and write hdf files for emantomo to process.
[docs] def convertImagesStep(self): partSet = self.particles.get() partAlign = partSet.getAlignment() storePath = self._getExtraPath("particles") makePath(storePath) writeSetOfSubTomograms(partSet, storePath, alignType=partAlign)
[docs] def createInitialModelStep(self): command_params = { 'symmetry': self.symmetry.get(), 'filterto': self.filterto.get(), 'batchSize': self.batchSize.get(), 'learningRate': self.learningRate.get(), 'numberOfIterations': self.numberOfIterations.get(), 'numberOfBatches': self.numberOfBatches.get(), 'mask': self.mask.get(), 'shrink': self.shrink.get(), 'reference': self.reference.get().getFileName() if self.reference.get() else None, 'outputPath': self.getOutputPath(), } args = '%s/*.hdf' % self._getExtraPath("particles") if command_params['reference']: args += ' --reference=%(reference)s' args += (' --sym=%(symmetry)s --filterto=%(filterto)f' ' --batchsize=%(batchSize)d --learnrate=%(learningRate)f --niter=%(numberOfIterations)d' ' --nbatch=%(numberOfBatches)d') if command_params['shrink'] > 1: args += ' --shrink=%(shrink)d' if self.fourier.get(): args += ' --fourier' if self.applySim.get(): args += ' --applysim' if command_params['mask']: args += ' --mask=%(mask)s' args += ' --path=%(outputPath)s' program = emantomo.Plugin.getProgram("")'Launching: ' + program + ' ' + args % command_params) self.runJob(program, args % command_params)
[docs] def createOutputStep(self): particles = self.particles.get() # Output 1: Subtomogram averageSubTomogram = AverageSubTomogram() averageSubTomogram.setFileName(self.getOutputPath('output.hdf')) averageSubTomogram.setSamplingRate(particles.getSamplingRate() * self.shrink.get()) setOfAverageSubTomograms = self._createSet(SetOfAverageSubTomograms, 'subtomograms%s.sqlite', "") setOfAverageSubTomograms.copyInfo(particles) setOfAverageSubTomograms.setSamplingRate(particles.getSamplingRate() * self.shrink.get()) setOfAverageSubTomograms.append(averageSubTomogram) # Output 2: setOfSubTomograms particleParams = getLastParticlesParams(self.getOutputPath()) outputSetOfSubTomograms = self._createSet(SetOfSubTomograms, 'subtomograms%s.sqlite', "particles") outputSetOfSubTomograms.setCoordinates3D(particles.getCoordinates3D()) outputSetOfSubTomograms.copyInfo(particles) outputSetOfSubTomograms.setSamplingRate(particles.getSamplingRate() * self.shrink.get()) updateSetOfSubTomograms(particles, outputSetOfSubTomograms, particleParams) self._defineOutputs(averageSubTomogram=setOfAverageSubTomograms, outputParticles=outputSetOfSubTomograms) self._defineSourceRelation(self.particles, setOfAverageSubTomograms) self._defineSourceRelation(self.particles, outputSetOfSubTomograms)
[docs] def getOutputPath(self, *args): return self._getExtraPath(self.OUTPUT_DIR, *args)
def _methods(self): particles = self.particles.get() return [ "Created an initial model using (stochastic gradient descent)", "A total of %d particles of dimensions %s were used (shrink %d)" % (particles.getSize(), particles.getDimensions(), self.shrink.get()), ] def _summary(self): particles = self.particles.get() reference = self.reference.get() lines = [ "Particles: %d" % particles.getSize(), "Reference file used: %s" % reference.getFileName() if reference else None, ] return list(filter(bool, lines))