Source code for imod.protocols.protocol_xCorrPrealignment

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# * Authors:     Federico P. de Isidro Gomez ( [1]
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# * [1] Centro Nacional de Biotecnologia, CSIC, Spain
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import os
import imod.utils as utils
import numpy as np
import pwem.objects as data
from pwem.objects import Transform
from pyworkflow import BETA
from pyworkflow.object import Set
import pyworkflow.protocol.params as params
import tomo.objects as tomoObj
from imod import Plugin
from pwem.emlib.image import ImageHandler
from imod.protocols.protocol_base import ProtImodBase

[docs]class ProtImodXcorrPrealignment(ProtImodBase): """ Tilt-series' cross correlation alignment based on the IMOD procedure. More info: """ _label = 'Xcorr prealignment' _devStatus = BETA # -------------------------- DEFINE param functions ----------------------- def _defineParams(self, form): form.addSection('Input') form.addParam('inputSetOfTiltSeries', params.PointerParam, pointerClass='SetOfTiltSeries', important=True, label='Input set of tilt-series.') form.addParam('cumulativeCorr', params.EnumParam, choices=['Yes', 'No'], default=1, label='Use cumulative correlation', display=params.EnumParam.DISPLAY_HLIST, help='Use this option to add up previously aligned pictures to get the reference for the next ' 'alignment. Alignments will start at low tilt and work up to high tilt.') form.addParam('computeAlignment', params.EnumParam, choices=['Yes', 'No'], default=1, label='Generate interpolated tilt-series', important=True, display=params.EnumParam.DISPLAY_HLIST, help='Generate and save the interpolated tilt-series applying the' 'obtained transformation matrices.') group = form.addGroup('Interpolated tilt-series', condition='computeAlignment==0') group.addParam('binning', params.FloatParam, default=1.0, label='Binning', help='Binning to be applied to the interpolated tilt-series in IMOD convention. Images will be ' 'binned by the given factor. Must be an integer bigger than 1') form.addParam('filterRadius1', params.FloatParam, label='Filter radius 1', default='0.0', expertLevel=params.LEVEL_ADVANCED, help="Low spatial frequencies in the cross-correlation will be attenuated by a Gaussian curve " "that is 1 at this cutoff radius and falls off below this radius with a standard deviation " "specified by FilterSigma2. Spatial frequency units range from 0 to 0.5. Use FilterSigma1 " "instead of this entry for more predictable attenuation of low frequencies.") form.addParam('filterRadius2', params.FloatParam, label='Filter radius 2', default='0.25', expertLevel=params.LEVEL_ADVANCED, help="High spatial frequencies in the cross-correlation will be attenuated by a Gaussian curve " "that is 1 at this cutoff radius and falls off above this radius with a standard deviation " "specified by FilterSigma2.") form.addParam('filterSigma1', params.FloatParam, label='Filter sigma 1', default='0.03', expertLevel=params.LEVEL_ADVANCED, help="Sigma value to filter low frequencies in the correlations with a curve that is an inverted " "Gaussian. This filter is 0 at 0 frequency and decays up to 1 with the given sigma value. " "However, if a negative value of radius1 is entered, this filter will be zero from 0 to " "|radius1| then decay up to 1.") form.addParam('filterSigma2', params.FloatParam, label='Filter sigma 2', default='0.05', expertLevel=params.LEVEL_ADVANCED, help="Sigma value for the Gaussian rolloff below and above the cutoff frequencies specified by " "FilterRadius1 and FilterRadius2") # -------------------------- INSERT steps functions --------------------- def _insertAllSteps(self): for ts in self.inputSetOfTiltSeries.get(): self._insertFunctionStep(self.convertInputStep, ts.getObjId()) self._insertFunctionStep(self.computeXcorrStep, ts.getObjId()) self._insertFunctionStep(self.generateOutputStackStep, ts.getObjId()) if self.computeAlignment.get() == 0: self._insertFunctionStep(self.computeInterpolatedStackStep, ts.getObjId()) self._insertFunctionStep(self.closeOutputSetsStep) # --------------------------- STEPS functions ----------------------------
[docs] def computeXcorrStep(self, tsObjId): """Compute transformation matrix for each tilt series""" ts = self.inputSetOfTiltSeries.get()[tsObjId] tsId = ts.getTsId() extraPrefix = self._getExtraPath(tsId) tmpPrefix = self._getTmpPath(tsId) paramsXcorr = { 'input': os.path.join(tmpPrefix, ts.getFirstItem().parseFileName()), 'output': os.path.join(extraPrefix, ts.getFirstItem().parseFileName(extension=".prexf")), 'tiltfile': os.path.join(tmpPrefix, ts.getFirstItem().parseFileName(extension=".tlt")), 'rotationAngle': ts.getAcquisition().getTiltAxisAngle(), 'filterSigma1': self.filterSigma1.get(), 'filterSigma2': self.filterSigma2.get(), 'filterRadius1': self.filterRadius1.get(), 'filterRadius2': self.filterRadius2.get() } argsXcorr = "-input %(input)s " \ "-output %(output)s " \ "-tiltfile %(tiltfile)s " \ "-RotationAngle %(rotationAngle).2f " \ "-FilterSigma1 %(filterSigma1)f " \ "-FilterSigma2 %(filterSigma2)f " \ "-FilterRadius1 %(filterRadius1)f " \ "-FilterRadius2 %(filterRadius2)f " if self.cumulativeCorr == 0: argsXcorr += " -CumulativeCorrelation " Plugin.runImod(self, 'tiltxcorr', argsXcorr % paramsXcorr) paramsXftoxg = { 'input': os.path.join(extraPrefix, ts.getFirstItem().parseFileName(extension=".prexf")), 'goutput': os.path.join(extraPrefix, ts.getFirstItem().parseFileName(extension=".prexg")), } argsXftoxg = "-input %(input)s " \ "-goutput %(goutput)s" Plugin.runImod(self, 'xftoxg', argsXftoxg % paramsXftoxg)
[docs] def generateOutputStackStep(self, tsObjId): """ Generate tilt-serie with the associated transform matrix """ ts = self.inputSetOfTiltSeries.get()[tsObjId] tsId = ts.getTsId() extraPrefix = self._getExtraPath(tsId) self.getOutputSetOfTiltSeries(self.inputSetOfTiltSeries.get()) alignmentMatrix = utils.formatTransformationMatrix( os.path.join(extraPrefix, ts.getFirstItem().parseFileName(extension=".prexg"))) newTs = tomoObj.TiltSeries(tsId=tsId) newTs.copyInfo(ts) self.outputSetOfTiltSeries.append(newTs) for index, tiltImage in enumerate(ts): newTi = tomoObj.TiltImage() newTi.copyInfo(tiltImage, copyId=True, copyTM=False) if tiltImage.hasTransform(): transform = Transform() previousTransform = tiltImage.getTransform().getMatrix() newTransform = alignmentMatrix[:, :, index] previousTransformArray = np.array(previousTransform) newTransformArray = np.array(newTransform) outputTransformMatrix = np.matmul(newTransformArray, previousTransformArray) transform.setMatrix(outputTransformMatrix) newTi.setTransform(transform) else: transform = Transform() newTransform = alignmentMatrix[:, :, index] newTransformArray = np.array(newTransform) transform.setMatrix(newTransformArray) newTi.setTransform(transform) newTi.setAcquisition(tiltImage.getAcquisition()) newTi.setLocation(tiltImage.getLocation()) newTs.append(newTi) newTs.write(properties=False) self.outputSetOfTiltSeries.update(newTs) self.outputSetOfTiltSeries.write() self._store()
[docs] def computeInterpolatedStackStep(self, tsObjId): self.getOutputInterpolatedSetOfTiltSeries(self.inputSetOfTiltSeries.get()) ts = self.inputSetOfTiltSeries.get()[tsObjId] tsId = ts.getTsId() extraPrefix = self._getExtraPath(tsId) tmpPrefix = self._getTmpPath(tsId) paramsAlignment = { 'input': os.path.join(tmpPrefix, ts.getFirstItem().parseFileName()), 'output': os.path.join(extraPrefix, ts.getFirstItem().parseFileName()), 'xform': os.path.join(extraPrefix, ts.getFirstItem().parseFileName(extension=".prexg")), 'bin': int(self.binning.get()), 'imagebinned': 1.0 } argsAlignment = "-input %(input)s " \ "-output %(output)s " \ "-xform %(xform)s " \ "-bin %(bin)d " \ "-imagebinned %(imagebinned)s" Plugin.runImod(self, 'newstack', argsAlignment % paramsAlignment) newTs = tomoObj.TiltSeries(tsId=tsId) newTs.copyInfo(ts) self.outputInterpolatedSetOfTiltSeries.append(newTs) if self.binning > 1: newTs.setSamplingRate(ts.getSamplingRate() * int(self.binning.get())) for index, tiltImage in enumerate(ts): newTi = tomoObj.TiltImage() newTi.copyInfo(tiltImage, copyId=True) newTi.setLocation(index + 1, (os.path.join(extraPrefix, tiltImage.parseFileName()))) if self.binning > 1: newTi.setSamplingRate(tiltImage.getSamplingRate() * int(self.binning.get())) newTs.append(newTi) ih = ImageHandler() x, y, z, _ = ih.getDimensions(newTs.getFirstItem().getFileName()) newTs.setDim((x, y, z)) newTs.write(properties=False) self.outputInterpolatedSetOfTiltSeries.update(newTs) self.outputInterpolatedSetOfTiltSeries.updateDim() self.outputInterpolatedSetOfTiltSeries.write() self._store()
[docs] def closeOutputSetsStep(self): self.outputSetOfTiltSeries.setStreamState(Set.STREAM_CLOSED) self.outputSetOfTiltSeries.write() if self.computeAlignment.get() == 0: self.outputInterpolatedSetOfTiltSeries.setStreamState(Set.STREAM_CLOSED) self.outputInterpolatedSetOfTiltSeries.write() self._store()
# --------------------------- INFO functions ---------------------------- def _summary(self): summary = [] if not hasattr(self, 'outputInterpolatedSetOfTiltSeries'): summary.append("Input Tilt-Series: %d.\nTransformation matrices calculated: %d.\n" % (self.inputSetOfTiltSeries.get().getSize(), self.outputSetOfTiltSeries.getSize())) elif hasattr(self, 'outputInterpolatedSetOfTiltSeries'): summary.append("Input Tilt-Series: %d.\nTransformation matrices calculated: %d.\n" "Interpolated Tilt-Series: %d.\n" % (self.outputSetOfTiltSeries.getSize(), self.outputSetOfTiltSeries.getSize(), self.outputInterpolatedSetOfTiltSeries.getSize())) else: summary.append("Output classes not ready yet.") return summary def _methods(self): methods = [] if not hasattr(self, 'outputInterpolatedSetOfTiltSeries'): methods.append("The transformation matrix has been calculated for %d " "Tilt-series using the IMOD procedure.\n" % (self.outputSetOfTiltSeries.getSize())) elif hasattr(self, 'outputInterpolatedSetOfTiltSeries'): methods.append("The transformation matrix has been calculated for %d " "Tilt-series using the IMOD procedure.\n" "Also, interpolation has been completed for %d Tilt-series.\n" % (self.outputSetOfTiltSeries.getSize(), self.outputInterpolatedSetOfTiltSeries.getSize())) else: methods.append("Output classes not ready yet.") return methods