Source code for cryosparc2.protocols.protocol_cryosparc_naive_local_refine

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# * Authors: Yunior C. Fonseca Reyna    (
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# * Unidad de  Bioinformatica of Centro Nacional de Biotecnologia , CSIC
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import os

import emtable

from pwem import ALIGN_PROJ
from pwem.protocols import ProtOperateParticles

import pyworkflow.utils as pwutils
from pyworkflow.object import String
from pyworkflow.protocol.params import (PointerParam, FloatParam,
                                        LEVEL_ADVANCED, IntParam, Positive,
                                        BooleanParam, EnumParam)
from pwem.objects import Volume

from .protocol_base import ProtCryosparcBase
from ..convert import (defineArgs, convertCs2Star, createItemMatrix,
from ..utils import (addComputeSectionParams, calculateNewSamplingRate,
                     cryosparcValidate, gpusValidate, enqueueJob,
                     waitForCryosparc, clearIntermediateResults, fixVolume,
from ..constants import *

[docs]class ProtCryoSparcNaiveLocalRefine(ProtCryosparcBase, ProtOperateParticles): """ Signal subtraction protocol of cryoSPARC. Subtract projections of a masked volume from particles. """ _label = 'naive local refinement(Legacy)' _className = "naive_local_refine" _fscColumns = 6 def _initialize(self): self._defineFileNames() def _defineFileNames(self): """ Centralize how files are called. """ myDict = { 'input_particles': self._getTmpPath(''), 'out_particles': self._getExtraPath(''), 'stream_log': self._getPath() + '/stream.log' } self._updateFilenamesDict(myDict) def _defineParams(self, form): form.addSection(label='Input') form.addParam('inputParticles', PointerParam, pointerClass='SetOfParticles', pointerCondition='hasAlignmentProj', label="Input particles", important=True, help='Select the experimental particles.') form.addParam('refVolume', PointerParam, pointerClass='Volume', label="Input map to be projected", important=True, 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('refMask', PointerParam, pointerClass='VolumeMask', label='Mask to be applied to this map', important=True, allowsNull=False, help="Provide a soft mask where the protein density " "you wish to subtract from the experimental " "particles is white (1) and the rest of the " "protein and the solvent is black (0). " "That is: *the mask should INCLUDE the part of the " "volume that you wish to SUBTRACT.*") # -----------[Local Refinement]------------------------ form.addSection(label="Naive local refinement") form.addParam('local_align_extent_pix', IntParam, default=3, validators=[Positive], label='Local shift search extent (pix)', help='The maximum extent of local shifts that will be ' 'searched over, in pixels') form.addParam('local_align_extent_deg', IntParam, default=10, label='Local rotation search extent (degrees)', help='The maximum magnitude of the change in rotations ' 'to search over, in degrees') form.addParam('local_align_max_align', FloatParam, default=0.5, validators=[Positive], label='Alignment resolution (degrees)', help='Smallest search distance between angles, in degrees') form.addParam('local_align_grid_r', IntParam, default=9, validators=[Positive], label='Local shift search grid size', help='The number of points on the search grid for local ' 'shifts') form.addParam('local_align_grid_t', IntParam, default=9, validators=[Positive], label='Local rotation search grid size', help='The number of points on the search grid for local ' 'rotations') form.addParam('override_final_radwn', BooleanParam, default=False, label='Override final radwn') form.addParam('n_iterations', IntParam, default=1, validators=[Positive], label='Override number of iterations') # -----[Non Uniform Refinement]---------------------------------------- form.addSection(label='Non-uniform refinement') form.addParam('NU-refine', BooleanParam, default=False, label='Use Non-Uniform Refinement') # -----[Refinement]---------------------------------------- form.addSection(label='Refinement') form.addParam('refine_num_final_iterations', IntParam, default=1, label="Number of extra final passes", help='Number of extra passes through the data to do ' 'after the GS-FSC resolution has stopped improving') form.addParam('refine_res_init', IntParam, default=20, validators=[Positive], label="Initial lowpass resolution (A)", help='Applied to input structure') form.addParam('refine_res_gsfsc_split', IntParam, default=20, validators=[Positive], label="GSFSC split resolution (A)", help='Resolution beyond which two GS-FSC halves are ' 'independent') form.addParam('refine_FSC_inflate_factor', IntParam, default=1, validators=[Positive], expertLevel=LEVEL_ADVANCED, label="FSC Inflate Factor") form.addParam('refine_clip', BooleanParam, default=True, label="Enforce non-negativity", help='Clip negative density. Probably should be false') form.addParam('refine_window', BooleanParam, default=True, label="Skip interpolant premult", help='Softly window the structure in real space with a ' 'spherical window. Should be true') form.addParam('refine_skip_premult', BooleanParam, default=True, label="Window structure in real space", help='Leave this as true') form.addParam('refine_ignore_dc', BooleanParam, default=True, label="Ignore DC component", help='Ignore the DC component of images. Should be true') form.addParam('refine_batchsize_init', IntParam, default=0, expertLevel=LEVEL_ADVANCED, label="Initial batchsize", help='Number of images used in the initial iteration. ' 'Set to zero to autotune') form.addParam('refine_batchsize_epsilon', FloatParam, default=0.001, expertLevel=LEVEL_ADVANCED, validators=[Positive], label="Batchsize epsilon", help='Controls batch size when autotuning batchsizes. ' 'Set closer to zero for larger batches') form.addParam('refine_batchsize_snrfactor', FloatParam, default=40.0, expertLevel=LEVEL_ADVANCED, validators=[Positive], label="Batchsize snrfactor", help='Specifies the desired improvement in SNR from the ' 'images when autotuning batchsizes. Directly ' 'multiplies the number of images in the batch') form.addParam('refine_scale_min', BooleanParam, default=False, expertLevel=LEVEL_ADVANCED, label="Minimize over per-particle scale") form.addParam('refine_scale_align_use_prev', BooleanParam, default=True, expertLevel=LEVEL_ADVANCED, label="Use scales from previous iteration during " "alignment") form.addParam('refine_scale_ctf_use_current', BooleanParam, expertLevel=LEVEL_ADVANCED, default=True, label="Use scales from current alignment in reconstruction", help='Use scales from current alignment in reconstruction') form.addParam('refine_scale_start_iter', IntParam, default=0, label="Scale min/use start iter", help='Iteration to start minimizing over per-particle scale') form.addParam('refine_noise_model', EnumParam, choices=['symmetric', 'white', 'coloured'], default=0, label="Noise model:", help='Noise model to be used. Valid options are white, ' 'coloured or symmetric. Symmetric is the default, ' 'meaning coloured with radial symmetry') form.addParam('refine_noise_priorw', IntParam, default=50, validators=[Positive], expertLevel=LEVEL_ADVANCED, label="Noise priorw", help='Weight of the prior for estimating noise (units of ' '# of images)') form.addParam('refine_noise_initw', IntParam, default=200, validators=[Positive], expertLevel=LEVEL_ADVANCED, label="Noise initw", help='Weight of the initial noise estimate (units of # ' 'of images)') form.addParam('refine_noise_init_sigmascale', IntParam, default=3, validators=[Positive], expertLevel=LEVEL_ADVANCED, label="Noise initial sigma-scale", help='Scale factor initially applied to the base noise ' 'estimate') form.addParam('refine_mask', EnumParam, choices=['dynamic', 'static', 'null'], default=0, label="Mask:", help='Type of masking to use. Either "dynamic", ' '"static", or "null"') form.addParam('refine_dynamic_mask_thresh_factor', FloatParam, expertLevel=LEVEL_ADVANCED, default=0.2, validators=[Positive], label="Dynamic mask threshold (0-1)", help='Level set threshold for selecting regions that are ' 'included in the dynamic mask. Probably don\'t need ' 'to change this') form.addParam('refine_dynamic_mask_near_ang', FloatParam, expertLevel=LEVEL_ADVANCED, default=3.0, validators=[Positive], label="Dynamic mask near (A)", help='Controls extent to which mask is expanded. At the ' 'near distance, the mask value is 1.0 (in A)') form.addParam('refine_dynamic_mask_far_ang', FloatParam, expertLevel=LEVEL_ADVANCED, default=6, validators=[Positive], label="Dynamic mask far (A)", help='Controls extent to which mask is expanded. At the ' 'far distance the mask value becomes 0.0 (in A)') # --------------[Compute settings]--------------------------- form.addSection(label="Compute settings") addComputeSectionParams(form, allowMultipleGPUs=False) # --------------------------- INSERT steps functions ----------------------- def _insertAllSteps(self): self._defineFileNames() self._defineParamsName() self._initializeCryosparcProject() self._insertFunctionStep(self.convertInputStep) self._insertFunctionStep(self.processStep) self._insertFunctionStep(self.createOutputStep) # --------------------------- STEPS functions ------------------------------
[docs] def processStep(self): print(pwutils.yellowStr("Local Refinement started..."), flush=True) self.doLocalRefine()
[docs] def createOutputStep(self): """ Create the protocol output. Convert cryosparc file to Relion file """ self._initializeUtilsVariables() idd, itera = self.findLastIteration(self.runLocalRefinement.get()) csOutputFolder = os.path.join(self.projectPath, self.projectName.get(), self.runLocalRefinement.get()) csOutputPattern = "cryosparc_%s_%s_%s" % (self.projectName.get(), self.runLocalRefinement.get(), itera) csParticlesName = csOutputPattern + "_particles.cs" fnVolName = csOutputPattern + "_volume_map.mrc" half1Name = csOutputPattern + "_volume_map_half_A.mrc" half2Name = csOutputPattern + "_volume_map_half_B.mrc" # Copy the CS output volume and half to extra folder copyFiles(csOutputFolder, self._getExtraPath(), files=[csParticlesName, fnVolName, half1Name, half2Name]) csFile = os.path.join(self._getExtraPath(), csParticlesName) outputStarFn = self._getFileName('out_particles') argsList = [csFile, outputStarFn] parser = defineArgs() args = parser.parse_args(argsList) convertCs2Star(args) fnVol = os.path.join(self._getExtraPath(), fnVolName) half1 = os.path.join(self._getExtraPath(), half1Name) half2 = os.path.join(self._getExtraPath(), half2Name) imgSet = self._getInputParticles() vol = Volume() fixVolume([fnVol, half1, half2]) vol.setFileName(fnVol) vol.setSamplingRate(calculateNewSamplingRate(vol.getDim(), imgSet.getSamplingRate(), imgSet.getDim())) vol.setHalfMaps([half1, half2]) outImgSet = self._createSetOfParticles() outImgSet.copyInfo(imgSet) self._fillDataFromIter(outImgSet) self._defineOutputs(outputVolume=vol) self._defineSourceRelation(self.inputParticles.get(), vol) self._defineOutputs(outputParticles=outImgSet) self._defineTransformRelation(self.inputParticles.get(), outImgSet) self.createFSC(idd, imgSet, vol)
# --------------------------- INFO functions ------------------------------- def _validate(self): """ Should be overwritten in subclasses to return summary message for NORMAL EXECUTION. """ validateMsgs = cryosparcValidate() if not validateMsgs: validateMsgs = gpusValidate(self.getGpuList(), checkSingleGPU=True) if not validateMsgs: self._validateDim(self._getInputParticles(), self.refVolume.get(), validateMsgs, 'Input particles', 'Input volume') if not validateMsgs: particles = self._getInputParticles() if not particles.hasCTF(): validateMsgs.append( "The Particles has not associated a " "CTF model") return validateMsgs def _summary(self): summary = [] if (not hasattr(self, 'outputVolume') or not hasattr(self, 'outputParticles')): summary.append("Output objects not ready yet.") else: summary.append("Input Particles: %s" % self.getObjectTag('inputParticles')) summary.append("Input Volume: %s" % self.getObjectTag('refVolume')) summary.append("Input Mask: %s" % self.getObjectTag('refMask')) summary.append("------------------------------------------") summary.append("Output particles %s" % self.getObjectTag('outputParticles')) summary.append("Output volume %s" % self.getObjectTag('outputVolume')) if self.hasAttribute('mapResolution'): summary.append("\nMap Resolution: %s" % self.mapResolution.get()) if self.hasAttribute('estBFactor'): summary.append('\nEstimated Bfactor: %s' % self.estBFactor.get()) return summary # ---------------Utils Functions------------------------------------------- def _fillDataFromIter(self, imgSet): outImgsFn = 'particles@' + self._getFileName('out_particles') imgSet.setAlignmentProj() imgSet.copyItems(self._getInputParticles(), updateItemCallback=self._createItemMatrix, itemDataIterator=emtable.Table.iterRows(outImgsFn)) def _createItemMatrix(self, particle, row): createItemMatrix(particle, row, align=ALIGN_PROJ) setCryosparcAttributes(particle, row, RELIONCOLUMNS.rlnRandomSubset.value) def _defineParamsName(self): """ Define a list with all protocol parameters names""" self._paramsName = ['local_align_extent_pix', 'local_align_extent_deg', 'local_align_max_align', 'local_align_grid_r', 'local_align_grid_t', 'override_final_radwn', 'n_iterations', 'refine_num_final_iterations', 'refine_res_init', 'refine_res_gsfsc_split', 'refine_clip', 'refine_window', 'refine_skip_premult', 'refine_ignore_dc', 'refine_batchsize_init', 'refine_batchsize_snrfactor', 'refine_batchsize_epsilon', 'refine_scale_min', 'refine_scale_align_use_prev', 'refine_scale_ctf_use_current', 'refine_scale_start_iter', 'refine_noise_model', 'refine_noise_priorw', 'refine_noise_initw', 'refine_mask', 'refine_dynamic_mask_thresh_factor', 'refine_dynamic_mask_near_ang', 'refine_dynamic_mask_far_ang', 'compute_use_ssd'] self.lane = str(self.getAttributeValue('compute_lane'))
[docs] def doLocalRefine(self): """ :return: """ if self.mask.get() is not None: input_group_connect = {"particles": self.particles.get(), "volume": self.volume.get(), "mask": self.mask.get()} else: input_group_connect = {"particles": self.particles.get(), "volume": self.volume.get()} input_result_connect = None if self._getInputVolume().hasHalfMaps(): input_result_connect = {"volume.0.map_half_A": self.importVolumeHalfA.get(), "volume.0.map_half_B": self.importVolumeHalfB.get()} params = {} for paramName in self._paramsName: if (paramName != 'refine_noise_model' and paramName != 'refine_mask'): params[str(paramName)] = str(self.getAttributeValue(paramName)) elif paramName == 'refine_noise_model': params[str(paramName)] = str( NOISE_MODEL_CHOICES[self.refine_noise_model.get()]) elif paramName == 'refine_mask': params[str(paramName)] = str( REFINE_MASK_CHOICES[self.refine_mask.get()]) # Determinate the GPUs to use (in dependence of # the cryosparc version) try: gpusToUse = self.getGpuList() except Exception: gpusToUse = False runLocalRefinementJob = enqueueJob(self._className, self.projectName.get(), self.workSpaceName.get(), str(params).replace('\'', '"'), str(input_group_connect).replace('\'', '"'), self.lane, gpusToUse, result_connect=input_result_connect) self.runLocalRefinement = String(runLocalRefinementJob.get()) self.currenJob.set(runLocalRefinementJob.get()) self._store(self) waitForCryosparc(self.projectName.get(), self.runLocalRefinement.get(), "An error occurred in the local refinement process. " "Please, go to cryoSPARC software for more " "details.") clearIntermediateResults(self.projectName.get(), self.runLocalRefinement.get())