Source code for xmipp3.protocols.protocol_deep_hand

# **************************************************************************
# *
# * Authors:     Jorge Garcia Condado (jorgegarciacondado@gmail.com)
# *
# * Unidad de  Bioinformatica of Centro Nacional de Biotecnologia , CSIC
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# * This program is free software; you can redistribute it and/or modify
# * it under the terms of the GNU General Public License as published by
# * the Free Software Foundation; either version 2 of the License, or
# * (at your option) any later version.
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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.
# *
# * You should have received a copy of the GNU General Public License
# * along with this program; if not, write to the Free Software
# * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA
# * 02111-1307  USA
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# *  All comments concerning this program package may be sent to the
# *  e-mail address 'scipion@cnb.csic.es'
# *
# **************************************************************************

from pwem.protocols import EMProtocol
from pwem.objects import Volume

from pyworkflow.protocol.params import PointerParam, FloatParam
from pyworkflow.object import Float
from pyworkflow.utils.path import cleanPath

from xmipp3.base import XmippProtocol
from xmipp3.convert import getImageLocation

[docs]class XmippProtDeepHand(EMProtocol, XmippProtocol): """Protocol to returns handedness of structure from trained deep learning model """ _label ="deep hand" _conda_env = "xmipp_deepHand" def __init__(self, *args, **kwargs): EMProtocol.__init__(self, *args, **kwargs) XmippProtocol.__init__(self) self.vResizedVolFile = 'resizedVol.mrc' self.vMaskFile = 'mask.mrc' self.vFilteredVolFile = 'filteredVol.mrc' def _defineParams(self, form): form.addSection('Input') form.addParam('inputVolume', PointerParam, pointerClass="Volume", label='Input Volume', allowsNull=False, important=True, help="Volume to process") form.addParam('threshold', FloatParam, label='Mask Threshold', allowsNull=False, important=True, help="Threshold for mask creation") form.addParam('thresholdAlpha', FloatParam, label='Alpha Threshold', default=0.7, help="Threshold for alpha helix determination") form.addParam('thresholdHand', FloatParam, label='Hand Threshold', default=0.6, help="Hand threshold to flip volume") # --------------------------- INSERT steps functions -------------------------------------------- def _insertAllSteps(self): self._insertFunctionStep('preprocessStep') self._insertFunctionStep('predictStep') self._insertFunctionStep('flipStep') self._insertFunctionStep('createOutputStep')
[docs] def preprocessStep(self): # Get volume information volume = self.inputVolume.get() fn_vol = getImageLocation(volume) T_s = volume.getSamplingRate() # Paths to new files created self.resizedVolFile = self._getPath(self.vResizedVolFile) self.maskFile = self._getPath(self.vMaskFile) self.filteredVolFile = self._getPath(self.vFilteredVolFile) # Resize to 1A/px self.runJob("xmipp_image_resize", "-i %s -o %s --factor %f" % (fn_vol, self.resizedVolFile, T_s)) # Threshold to obtain mask self.runJob("xmipp_transform_threshold", "-i %s -o %s --select below %f --substitute binarize" % (self.resizedVolFile, self.maskFile, self.threshold.get())) # Filter to 5A self.runJob("xmipp_transform_filter", "-i %s -o %s "\ "--fourier low_pass %f --sampling 1" % (self.resizedVolFile, self.filteredVolFile, 5.0))
[docs] def predictStep(self): # Get saved models DTlK alpha_model= self.getModel('deepHand', '5A_SSE_experimental.pth') hand_model = self.getModel('deepHand', '5A_TL_hand_alpha.pth') # Predict hand args = "--alphaModel %s --handModel %s -o %s " \ "--alphaThr %f --pathVf %s --pathVmask %s" % ( alpha_model, hand_model, self._getExtraPath(), self.thresholdAlpha.get(), self._getPath(self.vFilteredVolFile), self._getPath(self.vMaskFile)) self.runJob("xmipp_deep_hand", args, env=self.getCondaEnv()) # Store hand value hand_file = self._getExtraPath('hand.txt') f = open(hand_file) self.hand = Float(float(f.read())) f.close()
[docs] def flipStep(self): if self.hand.get() > self.thresholdHand.get(): volume = self.inputVolume.get() fn_vol = getImageLocation(volume) pathFlipVol = self._getPath('flipVol.mrc') self.runJob("xmipp_transform_mirror", "-i %s -o %s --flipX" \ % (fn_vol, pathFlipVol))
[docs] def createOutputStep(self): self._defineOutputs(outputHand=self.hand) if self.hand.get() > self.thresholdHand.get(): vol = Volume() volFile = self._getPath('flipVol.mrc') vol.setFileName(volFile) Ts = self.inputVolume.get().getSamplingRate() vol.setSamplingRate(Ts) self.runJob("xmipp_image_header","-i %s --sampling_rate %f"%(volFile,Ts)) self._defineOutputs(outputVol=vol) else: self._defineOutputs(outputVol=self.inputVolume.get()) self._defineSourceRelation(self.inputVolume, self.outputVol) cleanPath(self._getPath(self.vResizedVolFile)) cleanPath(self._getPath(self.vMaskFile)) cleanPath(self._getPath(self.vFilteredVolFile))
# --------------------------- INFO functions ------------------------------- def _summary(self): summary = [] if hasattr(self, 'outputHand'): summary.append('Hand value is: %f' %self.outputHand.get()) summary.append('Hand values close to 1 mean the structure is predicted to be left handed') summary.append('Hand values close to 0 mean the structure is predicted to be right handed') if self.outputHand.get() > self.thresholdHand.get(): summary.append('Volume was flipped as it was deemed to be left handed') else: summary.append('Volume was not flipped as it was deemed to be right handed') else: summary.append("Output volume and handedness not ready yet.") return summary def _methods(self): methods = [] return methods def _validate(self): errors = [] if self.thresholdAlpha.get() > 1.0 or self.thresholdAlpha.get() < 0.0: errors.append("Alpha threshold must be between 0.0 and 1.0") if self.thresholdHand.get() > 1.0 or self.thresholdHand.get() < 0.0: errors.append("Hand threshold must be between 0.0 and 1.0") return errors