Source code for xmipp3.protocols.protocol_denoise_particles

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# * Authors:     I. Foche (
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# * Unidad de  Bioinformatica of Centro Nacional de Biotecnologia , CSIC
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import pwem.emlib.metadata as md
from pyworkflow.object import String
from pyworkflow.protocol.params import IntParam, PointerParam, LEVEL_ADVANCED
from pwem.protocols import ProtProcessParticles
from pwem.objects import SetOfVolumes
from pwem.objects import SetOfAverages

from xmipp3.convert import (writeSetOfParticles, writeSetOfClasses2D,

[docs]class XmippProtDenoiseParticles(ProtProcessParticles): """ Remove particles noise by filtering them. This filtering process is based on a projection over a basis created from some averages (extracted from classes). This filtering is not intended for processing particles. The huge filtering they will be passed through is known to remove part of the signal with the noise. However this is a good method for clearly see which particle are we going to process before it's done. """ _label = 'denoise particles' #--------------------------- DEFINE param functions -------------------------------------------- def _defineProcessParams(self, form): # First we customize the inputParticles param to fit our needs in this protocol form.getParam('inputParticles').pointerCondition = String('hasAlignment') form.getParam('inputParticles').help = String('Input images you want to filter. It is important that the images have alignment information with ' 'respect to the chosen set of classes. This is the standard situation ' 'after CL2D or ML2D.') form.addParam('inputClasses', PointerParam, label='Input Classes', important=True, pointerClass='SetOfClasses, SetOfAverages', help='Select the input classes for the basis construction against images will be projected to.') form.addSection(label='Basis construction') form.addParam('maxClasses', IntParam, default=128, label='Max. number of classes', expertLevel=LEVEL_ADVANCED, help='Maximum number of classes.') form.addParam('maxPCABases', IntParam, default=200, label='Number of PCA bases', expertLevel=LEVEL_ADVANCED, help='Number of PCA bases.') form.addSection(label='Denoising') form.addParam('PCABases2Project', IntParam, default=200, label='Number of PCA bases on which to project', expertLevel=LEVEL_ADVANCED, help='Number of PCA bases on which to project.') def _getDefaultParallel(self): """ Return the default value for thread and MPI for the parallel section definition. """ return (2, 4) #--------------------------- INSERT steps functions -------------------------------------------- def _insertAllSteps(self): """ Insert every step of the protocol""" # Convert input images if necessary self._insertFunctionStep('denoiseImages', self.inputParticles.getObjId(), self.inputClasses.getObjId()) self._insertFunctionStep('createOutputStep') #--------------------------- STEPS functions --------------------------------------------
[docs] def denoiseImages(self, inputId, inputClassesId): # We start preparing writing those elements we're using as input to keep them untouched imagesMd = self._getPath('images.xmd') writeSetOfParticles(self.inputParticles.get(), imagesMd) classesMd = self._getPath('classes.xmd') if isinstance(self.inputClasses.get(), SetOfAverages): writeSetOfClasses2D(self.inputClasses.get(), classesMd) else: writeSetOfClasses2D(self.inputClasses.get(), classesMd) fnRoot = self._getExtraPath('pca') fnRootDenoised = self._getExtraPath('imagesDenoised') args = '-i Particles@%s --oroot %s --eigenvectors %d --maxImages %d' % (imagesMd, fnRoot, self.maxPCABases.get(), self.maxClasses.get()) self.runJob("xmipp_image_rotational_pca", args) N=min(self.maxPCABases.get(), self.PCABases2Project.get()) args='-i %s -o %s.stk --save_metadata_stack %s.xmd --basis %s.stk %d'\ % (imagesMd, fnRootDenoised, fnRootDenoised, fnRoot, N) self.runJob("xmipp_transform_filter", args) self.outputMd = String('%s.stk' % fnRootDenoised)
[docs] def createOutputStep(self): imgSet = self.inputParticles.get() partSet = self._createSetOfParticles() partSet.copyInfo(imgSet) partSet.copyItems(imgSet, updateItemCallback=self._updateLocation, itemDataIterator=md.iterRows(self.outputMd.get(), sortByLabel=md.MDL_ITEM_ID)) self._defineOutputs(outputParticles=partSet) self._defineSourceRelation(imgSet, partSet)
#--------------------------- INFO functions -------------------------------------------- def _summary(self): summary = [] if not hasattr(self, 'outputParticles'): summary.append("Output particles not ready yet.") else: summary.append('PCA basis created by using %d classes' % len(self.inputClasses.get())) summary.append('Max. number of classes defined for PCA basis creation: %d' % self.maxClasses.get()) summary.append('Max. number of PCA bases defined for PCA basis creation: %d' % self.maxPCABases.get()) summary.append('PCA basis on which to project for denoising: %d' % self.PCABases2Project.get()) return summary def _validate(self): pass def _citations(self): return ['zhao2013', 'ponce2011'] def _methods(self): methods = [] if not hasattr(self, 'outputParticles'): methods.append("Output particles not ready yet.") else: methods.append('An input dataset of %d particles was filtered creating a PCA basis (%d components) with ' 'xmipp_image_rotational_pca and projecting the dataset into that base with xmipp_transform_filter.'\ % (len(self.inputParticles.get()), len(self.inputClasses.get()))) return methods #--------------------------- UTILS functions -------------------------------------------- def _updateLocation(self, item, row): index, filename = xmippToLocation(row.getValue(md.MDL_IMAGE)) item.setLocation(index, filename)