Source code for emantomo.protocols.protocol_tomo_reconstruction

# **************************************************************************
# *
# * Authors:     Adrian Quintana ( [1]
# *              Arnau Sanchez  ( [1]
# *
# * [1] EyeSeeTea Ltd, London, UK
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import os
import glob
# import numpy as np

import emantomo

from pyworkflow import BETA
from pyworkflow.protocol import params
import pyworkflow.utils as pwutils

# from pwem.emlib.image import ImageHandler
from pwem.protocols import EMProtocol

from tomo.protocols import ProtTomoBase
from tomo.objects import Tomogram, SetOfTomograms

[docs]class EmanProtTomoReconstruction(EMProtocol, ProtTomoBase): """ This protocol wraps ** EMAN2 program. Alignment of the tilt-series is performed iteratively in conjunction with tomogram reconstruction. Tomograms are not normally reconstructed at full resolution, generally limited to 1k x 1k or 2k x 2k, but the tilt-series are aligned at full resolution. For high resolution subtomogram averaging, the raw tilt-series data is used, based on coordinates from particle picking in the downsampled tomograms. On a typical workstation reconstruction takes about 4-5 minutes per tomogram. """ _label = 'tomo reconstruction' _devStatus = BETA choices = ['1k', '2k', '4k'] def __init__(self, **kwargs): EMProtocol.__init__(self, **kwargs) # --------------------------- DEFINE param functions ---------------------- def _defineParams(self, form): form.addSection(label='Input') form.addParam('tiltSeries', params.PointerParam, pointerClass='SetOfTiltSeries', label="Tilt Series", important=True, help='Select the set of tilt series to reconstruct a tomogram') form.addParam('rawtlt', params.BooleanParam, default=True, label="Use Tilt Angles stored in metadata?") form.addParam('tiltStep', params.FloatParam, default=2.0, condition="rawtlt==False", label='Tilt step', help='Step between tilts.') form.addParam('zeroid', params.IntParam, default=-1, condition="rawtlt==False", label='Zero ID', help='Index of the center tilt.') form.addParam('npk', params.IntParam, default=20, label='Number of landmarks (npk)', help='Number of landmarks to use (such as gold fiducials)') form.addParam('bxsz', params.IntParam, default=32, label='Box size for tracking (bxsz)', help='Box size of the particles for tracking. May be helpful to use a larger one for ' 'fiducial-less cases') form.addSection(label='Optimization') form.addParam('tltkeep', params.FloatParam, default=0.9, label='Fraction to keep', help='Fraction (0.0 -> 1.0) of tilts to keep in the reconstruction') form.addParam('tltax', params.FloatParam, allowsNull=True, default=None, label='Title axis angle', help='Angle of the tilt axis. The program will calculate one if this option is not provided') form.addParam('outsize', params.EnumParam, display=params.EnumParam.DISPLAY_COMBO, default=0, choices=['1k', '2k', '4k'], label='Size of output tomograms', help='Size of output tomograms:\n\t*1k*: Binning 4\n\t*2k*: Binning 2' '\n\t*4k*: Binning 1') form.addParam('niter', params.StringParam, default='2,1,1,1', label='Number of iterations', help='Number of iterations for bin8, bin4, bin2 images') form.addParam('clipz', params.IntParam, default=-1, label='Z thickness', help='Z thickness of the final tomogram output. default is -1, (5/16 of tomogram length)') form.addParam('bytile', params.BooleanParam, default=True, label='By tiles', help='Make final tomogram by tiles') form.addParam('autoclipxy', params.BooleanParam, default=True, condition="bytile==True", label='Non-Square Tomograms?', help='Optimize the x-y shape of the tomogram to fit in the tilt images. ' 'Only works in bytile reconstruction. ' 'Useful for non square cameras.') # form.addParam('fit', params.BooleanParam, # default=False, # label="Fit to tilt series size?", # help="By default, Eman increases the dimensions of the output tomograms by a small " # "factor compared to the tilt series. Use this parameter to rescale the Tomograms so " # "they completely fit in the tilt series size.") form.addParam('load', params.BooleanParam, default=False, label='Load existing parameters', help='Load existing tilt parameters') form.addParam('pkMindist', params.FloatParam, default=0.125, label='Min landmarks distance', help='Minimum distance between landmarks, as fraction of micrograph length') form.addParam('pkkeep', params.FloatParam, default=0.9, label='Landmarks to keep', help='Fraction of landmarks to keep in the tracking') form.addParam('filterto', params.FloatParam, default=0.45, label='ABS Filter', help='Filter to abs') form.addParam('rmbeadthr', params.FloatParam, default=-1.0, label='Density value threshold for removing beads', help='"Density value threshold (of sigma) for removing beads. High contrast objects beyond this ' 'value will be removed. Default is -1 for not removing. try 10 for removing fiducials') form.addParam('correctrot', params.BooleanParam, default=False, label='Correct rotation', help='Correct for global rotation and position sample flat in tomogram') form.addParam('normslice', params.BooleanParam, default=False, label='Normalize slices', help='Normalize each 2D slice') form.addParam('extrapad', params.BooleanParam, default=False, label='Extra pad', help='Pad extra for tilted reconstruction. Slower and costs more memory, but reduces boundary ' 'artifacts when the sample is thick') form.addParallelSection(threads=12, mpi=0) # --------------------------- INSERT steps functions ---------------------- def _insertAllSteps(self): self._insertFunctionStep('createCommandStep') self._insertFunctionStep('createOutputStep') # --------------------------- STEPS functions -----------------------------
[docs] def createCommandStep(self): command_params = { 'rawtlt': self.rawtlt.get(), 'tiltStep': self.tiltStep.get(), 'zeroid': self.zeroid.get(), 'npk': self.npk.get(), 'bxsz': self.bxsz.get(), 'tltkeep': self.tltkeep.get(), 'tltax': self.tltax.get(), 'outsize': self.choices[self.outsize.get()], 'niter': self.niter.get(), 'clipz': self.clipz.get(), 'pk_mindist': self.pkMindist.get(), 'pkkeep': self.pkkeep.get(), 'filterto': self.filterto.get(), 'rmbeadthr': self.rmbeadthr.get(), 'threads': self.numberOfThreads.get() } args = (' --notmp --npk=%(npk)d --tltkeep=%(tltkeep)f --outsize=%(outsize)s' ' --bxsz=%(bxsz)d --niter=%(niter)s --clipz=%(clipz)d' ' --pk_mindist=%(pk_mindist)f --pkkeep=%(pkkeep)f' ' --filterto=%(filterto)f --rmbeadthr=%(rmbeadthr)f' ) if command_params["rawtlt"]: self._getRawTiltAngles() if command_params["tltax"] is not None: args += ' --tltax=%(tltax)f' if self.bytile.get(): args += ' --bytile' if self.autoclipxy.get() and self.bytile.get(): args += ' --autoclipxy' if self.load.get(): args += ' --load' if self.correctrot.get(): args += ' --correctrot' if self.normslice.get(): args += ' --normslice' if self.extrapad.get(): args += ' --extrapad' args += ' --threads=%(threads)d' for path in self._getInputPaths(): tlt_file = os.path.abspath(self._getExtraPath(pwutils.removeBaseExt(path) + ".txt")) if os.path.isfile(tlt_file): args_file = '--rawtlt=%s ' % tlt_file + args else: args_file = '--tltstep=%(tiltStep)f --zeroid=%(zeroid)d ' + args args_file = path + " " + args_file program = emantomo.Plugin.getProgram("")'Launching: ' + program + ' ' + args_file % command_params) self.runJob(program, args_file % command_params, cwd=self._getExtraPath(), numberOfMpi=1, numberOfThreads=1)
# if # program = emantomo.Plugin.getProgram('') # ih = ImageHandler() # tomograms_paths = self._getOutputTomograms() # size_tomos =[0]).getDimensions() # size_ts = np.asarray(self.tiltSeries.get().getDimensions()) # factor = size_ts[:-1] / size_tomos[:-2] # size_ts = size_ts[:-1] / np.round(factor).astype(int) # args = " ".join(tomograms_paths) + " " + " ".join(tomograms_paths) # args += " --fftclip=%d,%d,%d" % (size_ts[0], size_ts[1], size_tomos[2]) # self.runJob(program, args, cwd=self._getExtraPath(), # numberOfMpi=1, numberOfThreads=1)
[docs] def createOutputStep(self): tilt_series = self.tiltSeries.get() sampling_rate = tilt_series.getSamplingRate() if self.outsize.get() == 0: sampling_rate *= 4 elif self.outsize.get() == 1: sampling_rate *= 2 # Output 1: Main tomograms tomograms_paths = self._getOutputTomograms() tomograms = self._createSet(SetOfTomograms, 'tomograms%s.sqlite', "") tomograms.copyInfo(tilt_series) tomograms.setSamplingRate(sampling_rate) for tomogram_path in tomograms_paths:'Main tomogram: ' + tomogram_path) tomogram = Tomogram() tomogram.setFileName(tomogram_path) tomogram.copyInfo(tilt_series) tomogram.setSamplingRate(sampling_rate) tomograms.append(tomogram) self._defineOutputs(tomograms=tomograms) self._defineSourceRelation(self.tiltSeries, tomograms)
def _getInputPaths(self): tilt_series = self.tiltSeries.get() return [os.path.abspath(path) for item in tilt_series for path in item.getFiles()] def _getRawTiltAngles(self): tilt_series = self.tiltSeries.get() for tilt in tilt_series.iterItems(): tilt_angles_dict = tilt.aggregate(["MAX"], "_tiltAngle", ["_tiltAngle"]) tilt_angles = ["%f" % d['_tiltAngle'] for d in tilt_angles_dict] file_tilt_angles = self._getExtraPath(pwutils.removeBaseExt(tilt.getTsId()) + ".txt") with open(file_tilt_angles, 'w') as file: file.write('\n'.join(tilt_angles)) def _getOutputTomograms(self): pattern = os.path.join(self._getExtraPath("tomograms"), '*.hdf') files = glob.glob(pattern) assert files, "Output tomogram file not found" return [os.path.abspath(path) for path in sorted(files)] def _methods(self): return [ "From an unaligned tilt series: aligned, and generated a tomogram using", "Note: Tiltseries must have the correct Apix values in their headers" ] def _summary(self): tilt_series = self.tiltSeries.get() return [ "Input tilt series: {} (size: {})".format(tilt_series.getName(), tilt_series.getSize()), "Tilt step: {}".format(self.tiltStep.get()) ]