Source code for eman2.protocols.protocol_refine2d_bispec

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# *  Authors:     Grigory Sharov (
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# * MRC Laboratory of Molecular Biology (MRC-LMB)
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import os
import re
from glob import glob

from pwem.objects import SetOfClasses2D
from pwem.protocols import ProtClassify2D
from pyworkflow.protocol.constants import LEVEL_ADVANCED
from pyworkflow.protocol.params import (PointerParam, FloatParam, IntParam,
                                        EnumParam, StringParam, BooleanParam,
from pyworkflow.utils import createLink, cleanPath

from .. import Plugin
from ..constants import *

[docs]class EmanProtRefine2DBispec(ProtClassify2D): """ This protocol wraps ** EMAN2 program. This program is used to produce reference-free class averages from a population of mixed, unaligned particle images. These averages can be used to generate initial models or assess the structural variability of the data. They are not normally themselves used as part of the single particle reconstruction refinement process, which uses the raw particles in a reference-based classification approach. However, with a good structure, projections of the final 3-D model should be consistent with the results of this reference-free analysis. This variant of the program uses rotational/translational invariants derived from the bispectrum of each particle. """ _label = 'refine 2D bispec' def _createFilenameTemplates(self): """ Centralize the names of the files. """ myDict = { 'partSetFlipFullRes': self._getExtraPath('sets/all__ctf_flip_fullres.lst'), 'partSetFlipLp5': self._getExtraPath('sets/all__ctf_flip_lp5.lst'), 'partSetFlipLp7': self._getExtraPath('sets/all__ctf_flip_lp7.lst'), 'partSetFlipLp12': self._getExtraPath('sets/all__ctf_flip_lp12.lst'), 'partSetFlipLp20': self._getExtraPath('sets/all__ctf_flip_lp20.lst'), 'partBispecSet': self._getExtraPath('sets/all__ctf_flip_bispec.lst'), 'partInvarSet': self._getExtraPath('sets/all__ctf_flip_invar.lst'), 'classes_scipion': self._getExtraPath('classes_scipion_it%(iter)02d.sqlite'), 'classes': 'r2db_%(run)02d/classes_%(iter)02d.hdf', 'cls': 'r2db_%(run)02d/classmx_%(iter)02d.hdf', 'results': self._getExtraPath('results_it%(iter)02d.txt'), 'basis': self._getExtraPath('r2db_%(run)02d/basis_%(iter)02d.hdf') } self._updateFilenamesDict(myDict) def _createIterTemplates(self, currRun): """ Setup the regex on how to find iterations. """ clsFn = self._getExtraPath(self._getFileName('classes', run=currRun, iter=1)) self._iterTemplate = clsFn.replace('classes_01', 'classes_??') # Iterations will be identify by classes_XX_ where XX is the iteration # number and is restricted to only 2 digits. self._iterRegex = re.compile('classes_(\d{2})') # --------------------------- DEFINE param functions ---------------------- def _defineParams(self, form): form.addSection(label='Input') form.addParam('inputBispec', PointerParam, label='Choose e2ctf auto protocol', pointerClass='EmanProtCTFAuto', help='Select EMAN CTF auto protocol that has ' 'generated bispectra.') form.addParam('numberOfClassAvg', IntParam, default=32, label='Number of class-averages', help='Number of class-averages to generate. Normally you ' 'would want a minimum of ~10-20 particles per class on ' 'average, but it is fine to have 100-200 for a large data ' 'set. If you plan on making a large number (>100) of ' 'classes, you should use the *Fast seed* option. Note ' 'that these averages are not used for final 3-D ' 'refinement, so generating a very large number is not ' 'useful in most situations.') form.addParam('numberOfIterations', IntParam, default=3, label='Number of iterations', help='Number of iterations of the overall 2-D refinement ' 'process to run. For high contrast data, 4-5 iterations ' 'may be more than enough, but for low contrast data ' 'it could take 10-12 iterations to converge well.') form.addParam('nbasisfp', IntParam, default=8, label='Number of MSA vectors to use', help='Number of MSa basis vectors to use when ' 'classifying particles.') form.addParam('alignSort', BooleanParam, default=True, label='Align and sort?', help='This will align and sort the final class-averages ' 'based on mutual similarity.') line = form.addLine('Centering: ', help="If the default centering algorithm " "( doesn't work well, " "you can specify one of the others " "here ( processor center)") line.addParam('centerType', EnumParam, choices=['nocenter', '', 'xform.centeracf', 'xform.centerofmass', 'None'], label="", default=XFORM_CENTER, display=EnumParam.DISPLAY_COMBO) line.addParam('centerParams', StringParam, default='', label='params') form.addParam('extraParams', StringParam, default='', expertLevel=LEVEL_ADVANCED, label='Additional arguments:', help='In this box command-line arguments may be provided ' 'that are not generated by the GUI. This may be ' 'useful for testing developmental options and/or ' 'expert use of the program. \n' 'The command " -h" will print a list ' 'of possible options.') form.addSection(label='Class averaging') form.addParam('paramsMsg2', LabelParam, default=True, label='These parameters are for advanced users only!\n', help='For help please address to EMAN2 %s or run:\n' '*scipion run cmp -v 2* or\n' '*scipion run averagers -v 2*' % WIKI_URL) form.addParam('classIter', IntParam, default=4, label='Number of iterations for class-averages', help='Number of iterations to use when making ' 'class-averages (default=5)') form.addParam('classKeep', FloatParam, default=0.8, label='Fraction of particles to keep', help='The fraction of particles to keep in each class, ' 'based on the similarity score generated by cmp ' '(default=0.8)') form.addParam('classKeepSig', BooleanParam, default=False, label='Keep particles based on sigma?', help='Change the *keep* criterion from fraction-based ' 'to sigma-based') form.addParam('classAveragerType', EnumParam, choices=['absmaxmin', '', 'ctf.weight', 'ctf.weight.autofilt', '', 'iteration', 'localweight', 'mean', 'mean.tomo', 'minmax', 'sigma', 'weightedfourier'], label='Class averager: ', default=AVG_CTF_WEIGHT_AUTOFILT, display=EnumParam.DISPLAY_COMBO, help='The averager used to generated class-averages') line = form.addLine('classnormproc: ', help='Normalization applied during class-averaging') line.addParam('classnormprocType', EnumParam, choices=['normalize', 'normalize.bymass', 'normalize.circlemean', 'normalize.edgemean', 'normalize.local', 'normalize.lredge', 'normalize.mask', 'normalize.maxmin', 'normalize.ramp.normvar', 'normalize.rows', 'normalize.toimage', 'normalize.unitlen', 'normalize.unitsum', 'None'], label='', default=PROC_NORMALIZE_EDGEMEAN, display=EnumParam.DISPLAY_COMBO) line.addParam('classnormprocParams', StringParam, default='', label='params') line = form.addLine('classcmp: ') line.addParam('classcmpType', EnumParam, choices=['ccc', 'dot', 'frc', 'lod', 'optsub', 'optvariance', 'phase', 'quadmindot', 'sqeuclidean', 'vertical', 'None'], label='', default=CMP_CCC, display=EnumParam.DISPLAY_COMBO) line.addParam('classcmpParams', StringParam, default='', label='params', help='The name of a cmp to be used in class averaging ' '(default=ccc)') group = form.addGroup('First stage aligner (clsavg)') line = group.addLine('classalign: ') line.addParam('classalignType', EnumParam, choices=['frm2d', 'rotate_flip', 'rotate_flip_iterative', 'rotate_precenter', 'rotate_trans_flip_scale', 'rotate_trans_flip_scale_iter', 'rotate_trans_scale_iter', 'rotate_translate', 'rotate_translate_bispec', 'rotate_translate_flip', 'rotate_translate_flip_iterative', 'rotate_translate_flip_resample', 'rotate_translate_iterative', 'rotate_translate_resample', 'rotate_translate_scale', 'rotate_translate_tree', 'rotational', 'rotational_bispec', 'rotational_iterative', 'rtf_exhaustive', 'rtf_slow_exhaustive', 'scale', 'symalign', 'symalignquat', 'translational', 'None'], label='', default=ALN_ROTATE_TRANSLATE_TREE, display=EnumParam.DISPLAY_COMBO) line.addParam('classalignParams', StringParam, default='flip=1', label='params') line = group.addLine('classaligncmp: ') line.addParam('classaligncmpType', EnumParam, choices=['ccc', 'dot', 'frc', 'lod', 'optsub', 'optvariance', 'phase', 'quadmindot', 'sqeuclidean', 'vertical', 'None'], label='', default=CMP_CCC, display=EnumParam.DISPLAY_COMBO) line.addParam('classaligncmpParams', StringParam, default='', label='params') group = form.addGroup('Second stage aligner (clsavg)') line = group.addLine('classralign: ') line.addParam('classralignType', EnumParam, choices=['None', 'refine'], label='', default=RALN_NONE, display=EnumParam.DISPLAY_COMBO) line.addParam('classralignParams', StringParam, default='', label='params') line = group.addLine('classraligncmp: ') line.addParam('classraligncmpType', EnumParam, choices=['ccc', 'dot', 'frc', 'lod', 'optsub', 'optvariance', 'phase', 'quadmindot', 'sqeuclidean', 'vertical', 'None'], label='', default=CMP_CCC, display=EnumParam.DISPLAY_COMBO) line.addParam('classraligncmpParams', StringParam, default='', label='params') form.addParallelSection(threads=4, mpi=1) # --------------------------- INSERT steps functions ---------------------- def _insertAllSteps(self): self._createFilenameTemplates() self._createIterTemplates(currRun=self._getRun()) self._insertFunctionStep('createLinksStep') args = self._prepareParams() self._insertFunctionStep('refineStep', args) self._insertFunctionStep('createOutputStep') # --------------------------- STEPS functions -----------------------------
[docs] def createLinksStep(self): prot = self._inputProt() prevPartDir = prot._getExtraPath("particles") currPartDir = self._getExtraPath("particles") prevSetsDir = prot._getExtraPath("sets") currSetsDir = self._getExtraPath("sets") createLink(prevPartDir, currPartDir) createLink(prevSetsDir, currSetsDir)
[docs] def refineStep(self, args): """ Run the EMAN program to refine 2d. """ program = Plugin.getProgram('') # mpi and threads are handled by EMAN itself self.runJob(program, args, cwd=self._getExtraPath(), numberOfMpi=1, numberOfThreads=1)
[docs] def createOutputStep(self): partSet = self._getInputParticles() classes2D = self._createSetOfClasses2D(partSet) self._fillClassesFromIter(classes2D, self._lastIter()) self._defineOutputs(outputClasses=classes2D) self._defineSourceRelation(partSet, classes2D)
# --------------------------- INFO functions ------------------------------ def _validate(self): errors = [] return errors def _summary(self): summary = [] if not hasattr(self, 'outputClasses'): summary.append("Output classes not ready yet.") else: summary.append("Input CTF protocol: %s" % self.getObjectTag('inputBispec')) summary.append("Classified into *%d* classes." % self.numberOfClassAvg) summary.append("Output set: %s" % self.getObjectTag('outputClasses')) summary.append('\n\n*Note:* output particles are not ' 'aligned when using this classification method.') return summary def _methods(self): methods = "We classified input particles from %s" % ( self.getObjectTag('inputBispec')) methods += "into %d classes using " % \ self.numberOfClassAvg return [methods] # --------------------------- UTILS functions ----------------------------- def _prepareParams(self): args1 = " --input=%s" % self._getParticlesStack() args2 = self._commonParams() args = args1 + args2 return args def _commonParams(self): args = " --ncls=%(ncls)d --iter=%(numberOfIterations)d --nbasisfp=%(nbasisfp)d" args += " --classkeep=%(classKeep)f --classiter=%(classiter)d " args += " --classaverager=%s" % self.getEnumText('classAveragerType') if self.alignSort: args += " --alignsort" if self.classKeepSig: args += " --classkeepsig" for param in ['classnormproc', 'classcmp', 'classalign', 'center', 'classaligncmp', 'classralign', 'classraligncmp']: args += self._getOptsString(param) if self.numberOfMpi > 1: args += " --parallel=mpi:%(mpis)d:%(scratch)s --threads=%(threads)d" else: args += " --parallel=thread:%(threads)d --threads=%(threads)d" params = {'ncls': self.numberOfClassAvg.get(), 'numberOfIterations': self.numberOfIterations.get(), 'nbasisfp': self.nbasisfp.get(), 'classKeep': self.classKeep.get(), 'classiter': self.classIter.get(), 'threads': self.numberOfThreads.get(), 'mpis': self.numberOfMpi.get(), 'scratch': Plugin.getVar(EMAN2SCRATCHDIR) } args %= params if self.extraParams.hasValue(): args += " " + self.extraParams.get() return args def _getBaseName(self, key, **args): """ Remove the folders and return the file from the filename. """ return os.path.basename(self._getFileName(key, **args)) def _getParticlesStack(self): protType = self._inputProt().type.get() if protType == HIRES: return "sets/" + os.path.basename(self._getFileName("partSetFlipLp5")) elif protType == MIDRES: return "sets/" + os.path.basename(self._getFileName("partSetFlipLp7")) else: return "sets/" + os.path.basename(self._getFileName("partSetFlipLp12")) def _iterTextFile(self, iterN): with open(self._getFileName('results', iter=iterN)) as f: for line in f: if '#' not in line and line.strip(): yield [float(x) for x in line.split()] def _getRun(self): return 0 if Plugin.isVersion('2.91') else 1 def _getIterNumber(self, index): """ Return the list of iteration files, give the iterTemplate. """ result = None files = sorted(glob(self._iterTemplate)) if files: f = files[index] s = if s: result = int( # group 1 is 2 digits iteration number return result def _lastIter(self): return self._getIterNumber(-1) def _firstIter(self): return self._getIterNumber(0) or 1 def _getIterClasses(self, it, clean=False): """ Return a classes .sqlite file for this iteration. If the file doesn't exists, it will be created by converting from this iteration file. """ data_classes = self._getFileName('classes_scipion', iter=it) if clean: cleanPath(data_classes) if not os.path.exists(data_classes): clsSet = SetOfClasses2D(filename=data_classes) clsSet.setImages(self._getInputParticles()) self._fillClassesFromIter(clsSet, it) clsSet.write() clsSet.close() return data_classes def _getInputParticles(self): protType = self._inputProt().type.get() if protType == HIRES: return self._inputProt().outputParticles_flip_lp5 elif protType == MIDRES: return self._inputProt().outputParticles_flip_lp7 else: return self._inputProt().outputParticles_flip_lp12 def _fillClassesFromIter(self, clsSet, iterN): self._execEmanProcess(iterN) params = {'orderBy': ['_micId', 'id'], 'direction': 'ASC'} clsSet.classifyItems(updateItemCallback=self._updateParticle, updateClassCallback=self._updateClass, itemDataIterator=self._iterTextFile(iterN), iterParams=params) def _execEmanProcess(self, iterN): runN = self._getRun() clsFn = self._getFileName("cls", run=runN, iter=iterN) classesFn = self._getFileName("classes", run=runN, iter=iterN) proc = Plugin.createEmanProcess(args='read %s %s %s %s 2d' % (self._getParticlesStack(), clsFn, classesFn, self._getBaseName('results', iter=iterN)), direc=self._getExtraPath()) proc.wait() self._classesInfo = {} # store classes info, indexed by class id for classId in range(self.numberOfClassAvg.get()): self._classesInfo[classId + 1] = (classId + 1, self._getExtraPath(classesFn)) def _getOptsString(self, option): optionType = self.getEnumText(option + 'Type') optionParams = getattr(self, option + 'Params').get() if optionType == 'None': return '' if optionParams != '': argStr = ' --%s=%s:%s' % (option, optionType, optionParams) else: argStr = ' --%s=%s' % (option, optionType) return argStr def _updateParticle(self, item, row): if row[1] == 1: # enabled item.setClassId(row[2] + 1) else: setattr(item, "_appendItem", False) def _updateClass(self, item): classId = item.getObjId() if classId in self._classesInfo: index, fn = self._classesInfo[classId] item.getRepresentative().setLocation(classId, fn) def _inputProt(self): return self.inputBispec.get()