Source code for continuousflex.protocols.protocol_nma_dimred

# **************************************************************************
# *
# * Authors:
# * J.M. De la Rosa Trevin (jmdelarosa@cnb.csic.es), Nov 2014
# * Slavica Jonic (slavica.jonic@upmc.fr)
# *
# * 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
# * 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
# *
# *  All comments concerning this program package may be sent to the
# *  e-mail address 'scipion@cnb.csic.es'
# *
# **************************************************************************


from pyworkflow.object import String
from pyworkflow.protocol.params import (PointerParam, StringParam, EnumParam,
                                        IntParam, LEVEL_ADVANCED)
from pwem.protocols import ProtAnalysis3D
from pwem.utils import runProgram


DIMRED_PCA = 0
DIMRED_LTSA = 1
DIMRED_DM = 2
DIMRED_LLTSA = 3
DIMRED_LPP = 4
DIMRED_KPCA = 5
DIMRED_PPCA = 6
DIMRED_LE = 7
DIMRED_HLLE = 8
DIMRED_SPE = 9
DIMRED_NPE = 10

USE_PDBS = 0
USE_NMA_AMP = 1

# Values to be passed to the program
DIMRED_VALUES = ['PCA', 'LTSA', 'DM', 'LLTSA', 'LPP', 'kPCA', 'pPCA', 'LE', 'HLLE', 'SPE', 'NPE']

# Methods that allows mapping
DIMRED_MAPPINGS = [DIMRED_PCA, DIMRED_LLTSA, DIMRED_LPP, DIMRED_PPCA, DIMRED_NPE]

       
[docs]class FlexProtDimredNMA(ProtAnalysis3D): """ This protocol will take the images with NMA deformations as points in a N-dimensional space (where N is the number of computed normal modes) and will project them onto a reduced space """ _label = 'nma dimred' def __init__(self, **kwargs): ProtAnalysis3D.__init__(self, **kwargs) self.mappingFile = String() #--------------------------- DEFINE param functions -------------------------------------------- def _defineParams(self, form): form.addSection(label='Input') form.addParam('inputNMA', PointerParam, pointerClass='FlexProtAlignmentNMA', label="Conformational distribution", help='Select a previous run of the NMA alignment.') form.addParam('analyzeChoice', EnumParam, default=USE_NMA_AMP, choices=['Use deformed (pseudo)atomic models', 'Use normal mode amplitudes'], label='Data to analyze', help='Choosing to analyze the fitted PDBs is slower but more accurate.' ' You can choose to use normal mode amplitudes for preliminary results.') form.addParam('dimredMethod', EnumParam, default=DIMRED_PCA, choices=['Principal Component Analysis (PCA)', 'Local Tangent Space Alignment', 'Diffusion map', 'Linear Local Tangent Space Alignment', 'Linearity Preserving Projection', 'Kernel PCA', 'Probabilistic PCA', 'Laplacian Eigenmap', 'Hessian Locally Linear Embedding', 'Stochastic Proximity Embedding', 'Neighborhood Preserving Embedding'], label='Dimensionality reduction method', help=""" Choose among the following dimensionality reduction methods: PCA Principal Component Analysis LTSA <k=12> Local Tangent Space Alignment, k=number of nearest neighbours DM <s=1> <t=1> Diffusion map, t=Markov random walk, s=kernel sigma LLTSA <k=12> Linear Local Tangent Space Alignment, k=number of nearest neighbours LPP <k=12> <s=1> Linearity Preserving Projection, k=number of nearest neighbours, s=kernel sigma kPCA <s=1> Kernel PCA, s=kernel sigma pPCA <n=200> Probabilistic PCA, n=number of iterations LE <k=7> <s=1> Laplacian Eigenmap, k=number of nearest neighbours, s=kernel sigma HLLE <k=12> Hessian Locally Linear Embedding, k=number of nearest neighbours SPE <k=12> <global=1> Stochastic Proximity Embedding, k=number of nearest neighbours, global embedding or not NPE <k=12> Neighborhood Preserving Embedding, k=number of nearest neighbours """) form.addParam('extraParams', StringParam, level=LEVEL_ADVANCED, label="Extra params", help='This parameters will be passed to the program.') form.addParam('reducedDim', IntParam, default=2, label='Reduced dimension') form.addParallelSection(threads=0, mpi=0) #--------------------------- INSERT steps functions -------------------------------------------- def _insertAllSteps(self): # Take deforamtions text file and the number of images and modes inputSet = self.getInputParticles() rows = inputSet.getSize() reducedDim = self.reducedDim.get() method = self.dimredMethod.get() extraParams = self.extraParams.get('') deformationsFile = self.getDeformationFile() self._insertFunctionStep('convertInputStep', deformationsFile, inputSet.getObjId()) self._insertFunctionStep('performDimredStep', deformationsFile, method, extraParams, rows, reducedDim) self._insertFunctionStep('createOutputStep') #--------------------------- STEPS functions --------------------------------------------
[docs] def convertInputStep(self, deformationFile, inputId): """ Iterate through the images and write the plain deformation.txt file that will serve as input for dimensionality reduction. """ inputSet = self.getInputParticles() f = open(deformationFile, 'w') for particle in inputSet: f.write(' '.join(particle._xmipp_nmaDisplacements)) f.write('\n') f.close()
[docs] def performDimredStep(self, deformationsFile, method, extraParams, rows, reducedDim): outputMatrix = self.getOutputMatrixFile() methodName = DIMRED_VALUES[method] # Get number of columes in deformation files # it can be a subset of inputModes f = open(deformationsFile) columns = len(f.readline().split()) # count number of values in first line f.close() args = "-i %(deformationsFile)s -o %(outputMatrix)s -m %(methodName)s %(extraParams)s" args += "--din %(columns)d --samples %(rows)d --dout %(reducedDim)d" if method in DIMRED_MAPPINGS: mappingFile = self._getExtraPath('projector.txt') args += " --saveMapping %(mappingFile)s" self.mappingFile.set(mappingFile) runProgram("xmipp_matrix_dimred", args % locals())
[docs] def createOutputStep(self): pass
#--------------------------- INFO functions -------------------------------------------- def _summary(self): summary = [] return summary def _validate(self): errors = [] return errors def _citations(self): return [] def _methods(self): return [] #--------------------------- UTILS functions --------------------------------------------
[docs] def getInputParticles(self): """ Get the output particles of the input NMA protocol. """ return self.inputNMA.get().outputParticles
[docs] def getInputPdb(self): return self.inputNMA.get().getInputPdb()
[docs] def getOutputMatrixFile(self): return self._getExtraPath('output_matrix.txt')
[docs] def getDeformationFile(self): return self._getExtraPath('deformations.txt')
[docs] def getProjectorFile(self): return self.mappingFile.get()
[docs] def getMethodName(self): return DIMRED_VALUES[self.dimredMethod.get()]