Source code for relion.protocols.protocol_subtract

# **************************************************************************
# *
# * Authors:     Grigory Sharov (gsharov@mrc-lmb.cam.ac.uk)
# *
# * MRC Laboratory of Molecular Biology, MRC-LMB
# *
# * This program is free software; you can redistribute it and/or modify
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# * This program is distributed in the hope that it will be useful,
# * but WITHOUT ANY WARRANTY; without even the implied warranty of
# * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# * GNU General Public License for more details.
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# * You should have received a copy of the GNU General Public License
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# * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA
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# *  All comments concerning this program package may be sent to the
# *  e-mail address 'scipion@cnb.csic.es'
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from pyworkflow.object import String, Integer
from pyworkflow.protocol.params import (PointerParam, BooleanParam,
                                        IntParam, LabelParam)
from pwem.constants import ALIGN_PROJ
from pwem.protocols import ProtOperateParticles

import relion.convert as convert


[docs]class ProtRelionSubtract(ProtOperateParticles): """ Signal subtraction protocol of Relion. Subtract volume projections from the experimental particles. The particles must have projection alignment in order to properly generate volume projections. """ _label = 'subtract projection' def _initialize(self): self._createFilenameTemplates() def _createFilenameTemplates(self): """ Centralize how files are called. """ myDict = { 'input_star': self._getExtraPath('input_particles.star'), 'output_star': self._getExtraPath('particles_subtracted.star') } self._updateFilenamesDict(myDict) # -------------------------- DEFINE param functions ----------------------- def _defineParams(self, form): form.addSection(label='Input') form.addParam('relionInput', BooleanParam, default=True, important=True, label="Start from Relion protocol?", help="Set to Yes if you wish to use as input " "a Relion protocol. Otherwise set it to No") form.addParam('inputProtocol', PointerParam, important=True, pointerClass='ProtRelionRefine3D, ProtRelionClassify3D', label="Input Relion protocol", condition="relionInput", help="Select the 3D refinement/classification run which " "you want to use for subtraction. It will use the " "maps from this run for the subtraction.") form.addParam('inputVolume', PointerParam, pointerClass='Volume', label="Input map to be projected", important=True, condition="not relionInput", help='Provide the input volume that will be used to ' 'calculate projections, which will be subtracted ' 'from the experimental particles. Make sure this ' 'map was calculated by RELION from the same ' 'particles as above, and preferably with those ' 'orientations, as it is crucial that the absolute ' 'greyscale is the same as in the experimental ' 'particles.') form.addParam('useAll', BooleanParam, default=True, label="Use all particles from input protocol?", condition="relionInput", help="If No, then you need to provide a subset of " "particles below.") form.addParam('inputParticles', PointerParam, pointerClass='SetOfParticles', condition='not useAll', pointerCondition='hasAlignmentProj', label="Input particles subset", help='Select the particles which are a SUBSET of the ' 'input protocol provided above.') form.addParam('inputParticlesAll', PointerParam, pointerClass='SetOfParticles', condition="not relionInput", pointerCondition='hasAlignmentProj', label="Input particles", important=True, help='Select the input particles.') form.addParam('refMask', PointerParam, pointerClass='VolumeMask', label='Mask of the signal to keep', help="Provide a soft mask where the protein density " "you wish to subtract from the experimental " "particles is black (0) and the density you " "wish to keep is white (1).\n" "That is: *the mask should INCLUDE the part of the " "volume that you wish to KEEP.*") form.addSection('Centering') form.addParam('help1', LabelParam, condition="not relionInput", label="This section is only used if " "starting from Relion input.") form.addParam('centerOnMask', BooleanParam, default=True, label="Do center subtracted images on mask?", condition="relionInput", help="If set to Yes, the subtracted particles will " "be centered on projections of the " "center-of-mass of the input mask.") form.addParam('centerOnCoord', BooleanParam, default=False, condition='(not centerOnMask) and relionInput', label="Do center on my coordinates?", help="If set to Yes, the subtracted particles will " "be centered on projections of the x,y,z " "coordinates below. The unit is pixel, not " "angstrom. The origin is at the center of the box, " "not at the corner.") line = form.addLine('Center coordinate (px)', condition='centerOnCoord and relionInput', help='Coordinate of the 3D center (in pixels).') line.addParam('cX', IntParam, default=0, condition='centerOnCoord and relionInput', label='X') line.addParam('cY', IntParam, default=0, condition='centerOnCoord and relionInput', label='Y') line.addParam('cZ', IntParam, default=0, condition='centerOnCoord and relionInput', label='Z') form.addParam('newBoxSize', IntParam, default=-1, condition="relionInput", label="New box size", help="Provide a non-negative value to re-window the " "subtracted particles in a smaller box size.") form.addSection(label='CTF') form.addParam('help', LabelParam, condition="relionInput", label="This section is only used if " "NOT starting from Relion protocol.") form.addParam('doCTF', BooleanParam, default=True, label='Do CTF-correction?', condition="not relionInput", help='If set to Yes, CTFs will be corrected inside the ' 'MAP refinement. The resulting algorithm ' 'intrinsically implements the optimal linear, or ' 'Wiener filter. Note that input particles should ' 'contains CTF parameters.') form.addParam('ignoreCTFUntilFirstPeak', BooleanParam, default=False, label='Ignore CTFs until first peak?', condition="not relionInput", help='If set to Yes, then CTF-amplitude correction will ' 'only be performed from the first peak ' 'of each CTF onward. This can be useful if the CTF ' 'model is inadequate at the lowest resolution. ' 'Still, in general using higher amplitude contrast ' 'on the CTFs (e.g. 10-20%) often yields better ' 'results. Therefore, this option is not generally ' 'recommended.') form.addParallelSection(threads=0, mpi=1) # -------------------------- INSERT steps functions ----------------------- def _insertAllSteps(self): self.isRelionInput = self.relionInput.get() self._initialize() if not self.useAll or not self.isRelionInput: self._insertFunctionStep('convertInputStep') self._insertFunctionStep('subtractStep') self._insertFunctionStep('createOutputStep') # -------------------------- STEPS functions ------------------------------
[docs] def convertInputStep(self): """ Write the input images as a Relion star file. """ imgSet = self.inputParticles.get() if self.isRelionInput else self.inputParticlesAll.get() convert.writeSetOfParticles( imgSet, self._getFileName('input_star'), outputDir=self._getExtraPath(), alignType=ALIGN_PROJ)
[docs] def subtractStep(self): if self.isRelionInput: self.subtractStepRelion() else: self.subtractStepNoRelion()
[docs] def subtractStepNoRelion(self): volume = self.inputVolume.get() volFn = convert.convertBinaryVol(volume, self._getExtraPath()) params = ' --i %s --subtract_exp' % volFn params += ' --angpix %0.3f' % volume.getSamplingRate() params += self._convertMask() if self._getInputParticles().isPhaseFlipped(): params += ' --ctf_phase_flip' if self.doCTF: params += ' --ctf' if self.ignoreCTFUntilFirstPeak: params += ' --ctf_intact_first_peak' params += ' --ang %s --o %s' % ( self._getFileName('input_star'), self._getFileName('output_star').replace(".star", "")) self.runJob('relion_project', params)
[docs] def subtractStepRelion(self): inputProt = self.inputProtocol.get() inputProt._initialize() fnOptimiser = inputProt._getFileName('optimiser', iter=inputProt._lastIter()) params = " --i %s --o %s --new_box %s" % (fnOptimiser, self._getExtraPath(), self.newBoxSize.get()) if not self.useAll: params += " --data %s" % self._getFileName('input_star') if self.centerOnMask: params += " --recenter_on_mask" elif self.centerOnCoord: params += " --center_x %d --center_y %d --center_z %d" % ( self.cX, self.cY, self.cZ) params += self._convertMask() prog = "relion_particle_subtract" + ("_mpi" if self.numberOfMpi > 1 else "") self.runJob(prog, params)
[docs] def createOutputStep(self): imgSet = self._getInputParticles() outImgSet = self._createSetOfParticles() outImgsFn = self._getFileName('output_star') outImgSet.copyInfo(imgSet) outImgSet.setAlignmentProj() self.reader = convert.createReader(alignType=ALIGN_PROJ) mdIter = convert.Table.iterRows('particles@' + outImgsFn) outImgSet.copyItems(imgSet, doClone=False, updateItemCallback=self._updateItem, itemDataIterator=mdIter) self._defineOutputs(outputParticles=outImgSet) self._defineTransformRelation(imgSet, outImgSet)
# -------------------------- INFO functions ------------------------------- def _validate(self): errors = [] if not self.useAll: self._validateDim(self.inputParticles(), self._getInputParticles().getXDim(), errors, 'Input particles subset', 'Input particles from 3D protocol') return errors def _summary(self): summary = [] if not hasattr(self, 'outputParticles'): summary.append("Output is not ready yet.") else: summary.append('Projections of the masked input volume were ' 'subtracted from original particles.') return summary # -------------------------- UTILS functions ------------------------------ def _updateItem(self, particle, row): if self.isRelionInput: # FIXME: check if other attrs need saving particle._rlnRandomSubset = Integer(row.rlnRandomSubset) self.reader.setParticleTransform(particle, row) particle._rlnImageOriginalName = String(row.rlnImageOriginalName) newFn = row.rlnImageName newLoc = convert.relionToLocation(newFn) particle.setLocation(newLoc) def _getInputParticles(self): if self.isRelionInput: inputProt = self.inputProtocol.get() return inputProt.outputParticles else: return self.inputParticlesAll.get() def _convertMask(self): tmp = self._getTmpPath() newDim = self._getInputParticles().getXDim() newPix = self._getInputParticles().getSamplingRate() maskFn = convert.convertMask(self.refMask.get(), tmp, newPix, newDim) return ' --mask %s' % maskFn