Source code for xmipp3.viewers.viewer_deep_consensus

# **************************************************************************
# *
# * Authors:     David Maluenda (
# *
# * Unidad de  Bioinformatica of Centro Nacional de Biotecnologia , CSIC
# *
# * 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.
# *
# * This program is distributed in the hope that it will be useful,
# * but WITHOUT ANY WARRANTY; without even the implied warranty of
# * 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
# *
# *  All comments concerning this program package may be sent to the
# *  e-mail address ''
# *
# **************************************************************************

import os

from pwem.viewers import ObjectView
from pwem.viewers.showj import MODE, MODE_MD, ORDER, VISIBLE, RENDER, SORT_BY
from pyworkflow.protocol.params import IntParam, LabelParam
from pyworkflow.viewer import DESKTOP_TKINTER, WEB_DJANGO, ProtocolViewer

from pwem import emlib
from xmipp3.protocols.protocol_screen_deepConsensus import XmippProtScreenDeepConsensus
from xmipp3.protocols.protocol_screen_deeplearning import XmippProtScreenDeepLearning
from .plotter import XmippPlotter

[docs]class XmippDeepConsensusViewer(ProtocolViewer): """ Viewer for the 'Xmipp - deep consensus picker' and 'Xmipp - screen deep learning' protocols.\n Select those particles/cooridantes with high (close to 1.0) 'zScoreDeepLearning1' value and save them. The Histogram may help you to decide a threshold. """ _label = 'viewer deepConsensus' _environments = [DESKTOP_TKINTER, WEB_DJANGO] _targets = [XmippProtScreenDeepConsensus, XmippProtScreenDeepLearning] def _defineParams(self, form): form.addSection(label='Visualization') form.addParam('noteViz', LabelParam, label="\n") form.addParam('visualizeParticles', LabelParam, important=True, label="Select particles/coordinates with high " "'zScoreDeepLearning1' values", help="A viewer with all particles/coordinates " "with a 'zScoreDeepLearning1' attached " "will be launched. Select all those " "particles/coordinates with high scores and " "save them.\n" "Particles can be sorted by any column.") form.addParam('visualizeHistogram', IntParam, default=100, label="Visualize Deep Scores Histogram (Bin size)", help="Plot a histogram of the 'zScoreDeepLearning1' " "to visual setting of a threshold.") def _getVisualizeDict(self): return {'visualizeParticles': self._visualizeParticles, 'visualizeHistogram': self._visualizeHistogram} def _visualizeParticles(self, e=None): views = [] labels = 'id enabled _index _filename _xmipp_zScoreDeepLearning1 ' labels += '_xmipp_zScore _xmipp_cumulativeSSNR ' labels += '_xmipp_scoreEmptiness' otherParam = {} objId = 0 if (isinstance(self.protocol, XmippProtScreenDeepConsensus) and self.protocol.hasAttribute('outputCoordinates')): fnParts = self.protocol._getPath("particles.sqlite") objId = self.protocol.outputCoordinates.strId() otherParam = {'other': 'deepCons'} elif (isinstance(self.protocol, XmippProtScreenDeepLearning) and self.protocol.hasAttribute('outputParticles')): parts = self.protocol.outputParticles fnParts = parts.getFileName() objId = parts.strId() if objId: views.append(ObjectView( self._project, objId, fnParts, viewParams={ORDER: labels, VISIBLE: labels, SORT_BY: '_xmipp_zScoreDeepLearning1 asc', RENDER: '_filename', MODE: MODE_MD}, **otherParam)) else: print(" > Not output found, yet.") return views def _visualizeHistogram(self, e=None): views = [] numberOfBins = self.visualizeHistogram.get() fnXml = self.protocol._getPath('particles.xmd') if os.path.isfile(fnXml): md = emlib.MetaData(fnXml) if md.containsLabel(emlib.MDL_ZSCORE_DEEPLEARNING1): xplotter = XmippPlotter(windowTitle="Deep consensus score") xplotter.createSubPlot("Deep consensus score", "Deep consensus score", "Number of Particles") xplotter.plotMd(md, False, mdLabelY=emlib.MDL_ZSCORE_DEEPLEARNING1, nbins=numberOfBins) views.append(xplotter) else: print(" > '%s' don't have 'xmipp_zScoreDeepLearning1' label." % fnXml) else: print(" > Metadata file is not found in '%s'" % fnXml) return views