def calcPCA(self, ensemble, logger):
        '''
        calcPCA:
        #ensemble: prody ensmeble with structure information
        
        calculate PCA for a set of structures    
        
        return: prody.pca object
        '''
        logger.info("Calculate PCA")
             
        PCAname = ensemble.getTitle()
        pca = prody.PCA(PCAname)
        pca.buildCovariance(ensemble)

        logger.info("PCA")
        pca.calcModes()
        logger.info(repr(pca))
        
        outputname = PCAname + "_pca_modes.nmd"
                    
        prody.writeNMD(outputname, pca[:10], self.selection_ref_structure)
        
        if self.vmd == True:
            prody.viewNMDinVMD(outputname)
        logger.info(f"PCA is saved in: {outputname}")
        return pca, outputname
Exemple #2
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def createPCAMOdes(base_path, protein_list):
    for protein in protein_list:
        receptor = os.path.join(base_path,
                                protein) + "/{}A-unbound.pdb".format(protein)
        ligand = os.path.join(base_path,
                              protein) + "/{}B-unbound.pdb".format(protein)
        pca_rec_folder = "{}/{}/input/pca/concoord/receptor".format(
            base_path, protein)
        pca_lig_folder = "{}/{}/input/pca/concoord/ligand".format(
            base_path, protein)

        dist_rec = "{}/{}A-dist".format(pca_rec_folder, protein)
        dist_lig = "{}/{}B-dist".format(pca_lig_folder, protein)

        disco_rec = "{}/{}A-disco.pdb".format(pca_rec_folder, protein)
        disco_lig = "{}/{}B-disco.pdb".format(pca_lig_folder, protein)

        nmdfile_rec = "{}/{}A-nmd".format(pca_rec_folder, protein)
        nmdfile_lig = "{}/{}B-nmd".format(pca_lig_folder, protein)

        os.system("mkdir -p {}".format(pca_rec_folder))
        os.system("mkdir -p {}".format(pca_lig_folder))
        #pwd = os.getcwd()

        os.chdir(pca_rec_folder)
        p = Popen([
            "/home/glenn/Documents/Masterarbeit/concoord/bin/dist", "-p",
            receptor
        ],
                  stdin=PIPE)  #, shell=True #,"-op",dist_rec
        p.communicate(input=b'1\n1\n')
        os.system(
            "/home/glenn/Documents/Masterarbeit/concoord/bin/disco -on {} -n 200 -i 1000 -viol 1. -bump "
            .format(disco_rec))

        os.chdir(pca_lig_folder)
        p = Popen([
            "/home/glenn/Documents/Masterarbeit/concoord/bin/dist", "-p",
            ligand
        ],
                  stdin=PIPE)  #, shell=True
        p.communicate(input=b'1\n1\n')
        os.system(
            "/home/glenn/Documents/Masterarbeit/concoord/bin/disco -on {} -n 200 -i 1000 -viol 1. -bump  "
            .format(disco_lig))

        try:
            pca_rec = calcPCA(disco_rec)
            atoms_rec = dy.parsePDB(receptor, subset='ca')
            dy.writeNMD(nmdfile_rec, pca_rec, atoms_rec)
        except:
            pass

        try:
            pca_lig = calcPCA(disco_lig)
            atoms_lig = dy.parsePDB(ligand, subset='ca')
            dy.writeNMD(nmdfile_lig, pca_lig, atoms_lig)
        except:
            pass
Exemple #3
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    def __ready__(self):
        """
        Second stage of initialization

        It saves the parent coordinates, calculates the normal modes and initializes the allele
        """
        cached = self._CACHE.get('normal_modes')
        if not cached:
            normal_modes, normal_modes_samples, chimera2prody, prody_molecule = self.normal_modes_function(
            )
            self._CACHE['normal_modes'] = normal_modes
            self._CACHE['normal_modes_samples'] = normal_modes_samples
            self._CACHE['chimera2prody'] = chimera2prody
            self._CACHE['original_coords'] = chimeracoords2numpy(self.molecule)
            if self.write_modes:
                title = os.path.join(self.parent.cfg.output.path,
                                     '{}_modes.nmd'.format(self.molecule.name))
                prody.writeNMD(title, normal_modes, prody_molecule)
        self.allele = random.choice(self.NORMAL_MODES_SAMPLES)
 def calcANM(self, structure, logger):
     '''
     calcANM:
     #structure: prody PDB-structure
     
     calculate ANM for one specific structure    
     
     return: prody.anm object
     '''
     logger.info("Calculate ANM")
     ANMname = structure.getLabel()
     anm = prody.ANM(ANMname)
     anm.buildHessian(structure, cutoff=15.0)
     anm.calcModes()
     # write out the three lowest modes as NMD file (visualize with NMWizard in VMD
     outputname = ANMname + "_anm_modes.nmd"
     prody.writeNMD(outputname, anm[:10], self.selection_ref_structure)
     if self.vmd == True:
         prody.viewNMDinVMD(outputname)
     logger.info(f"ANM is saved in: {outputname}")
     return anm
Exemple #5
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def prody_pca(opt):
    """Perform PCA calculations based on command line arguments."""
    
    outdir = opt.outdir
    if not os.path.isdir(outdir):
        opt.subparser.error('{0:s} is not a valid path'.format(outdir))
        
    import prody
    LOGGER = prody.LOGGER
        
    coords = opt.coords
    prefix = opt.prefix
    nmodes, selstr = opt.nmodes, opt.select
    
    if os.path.splitext(coords)[1].lower() == '.dcd':     
        ag = opt.psf or opt.pdb
        if ag:
            if os.path.splitext(ag)[1].lower() == '.psf':
                ag = prody.parsePSF(ag)
            else:
                ag = prody.parsePDB(ag)
        dcd = prody.DCDFile(opt.coords)
        if len(dcd) < 2:
            opt.subparser("DCD file must contain multiple frames.")
        if ag:
            dcd.setAtomGroup(ag)
            select = dcd.select(selstr)
            LOGGER.info('{0:d} atoms are selected for calculations.'
                        .format(len(select)))
        else:
            select = prody.AtomGroup()
            select.setCoords(dcd.getCoords())
        pca = prody.PCA(dcd.getTitle())
        if len(dcd) > 1000:
            pca.buildCovariance(dcd)
            pca.calcModes(dcd)
        else:
            pca.performSVD(dcd[:])
    else:
        pdb = prody.parsePDB(opt.coords)
        if pdb.numCoordsets() < 2:
            opt.subparser("PDB file must contain multiple models.")
        if prefix == '_pca':
            prefix = pdb.getTitle() + '_pca'
        select = pdb.select(selstr)
        LOGGER.info('{0:d} atoms are selected for calculations.'
                    .format(len(select)))
        if select is None:
            opt.subparser('Selection "{0:s}" do not match any atoms.'
                          .format(selstr))
        LOGGER.info('{0:d} atoms will be used for PCA calculations.'
                    .format(len(select)))
        ensemble = prody.Ensemble(select)
        pca = prody.PCA(pdb.getTitle())
        ensemble.iterpose()
        pca.performSVD(ensemble)

    LOGGER.info('Writing numerical output.')
    if opt.npz:
        prody.saveModel(pca)
    prody.writeNMD(os.path.join(outdir, prefix + '.nmd'), pca[:nmodes], select)

    outall = opt.all
    delim, ext, format = opt.delim, opt.ext, opt.numformat
    if outall or opt.eigen:
        prody.writeArray(os.path.join(outdir, prefix + '_evectors'+ext), 
                         pca.getArray(), delimiter=delim, format=format)
        prody.writeArray(os.path.join(outdir, prefix + '_evalues'+ext), 
                         pca.getEigenvalues(), delimiter=delim, format=format)
    if outall or opt.covar:
        prody.writeArray(os.path.join(outdir, prefix + '_covariance'+ext), 
                         pca.getCovariance(), delimiter=delim, format=format)
    if outall or opt.ccorr:
        prody.writeArray(os.path.join(outdir, prefix + '_cross-correlations' + 
                                              ext), prody.calcCrossCorr(pca), 
                         delimiter=delim, format=format)
    if outall or opt.sqflucts:
        prody.writeArray(os.path.join(outdir, prefix + '_sqfluct'+ext), 
                         prody.calcSqFlucts(pca), delimiter=delim, 
                         format=format)
    if outall or opt.proj:
        prody.writeArray(os.path.join(outdir, prefix + '_proj'+ext), 
                         prody.calcProjection(ensemble, pca), delimiter=delim, 
                         format=format)
          
    figall, cc, sf, sp = opt.figures, opt.cc, opt.sf, opt.sp

    if figall or cc or sf or sp: 
        try:
            import matplotlib.pyplot as plt
        except ImportError:
            LOGGER.warning('Matplotlib could not be imported. '
                           'Figures are not saved.')
        else:
            LOGGER.info('Saving graphical output.')
            format, width, height, dpi = \
                opt.figformat, opt.width, opt.height, opt.dpi
            format = format.lower()
            if figall or cc:
                plt.figure(figsize=(width, height))
                prody.showCrossCorr(pca)
                plt.savefig(os.path.join(outdir, prefix + '_cc.'+format), 
                    dpi=dpi, format=format)
                plt.close('all')
            if figall or sf:
                plt.figure(figsize=(width, height))
                prody.showSqFlucts(pca)
                plt.savefig(os.path.join(outdir, prefix + '_sf.'+format), 
                    dpi=dpi, format=format)
                plt.close('all')                    
            if figall or sp:
                indices = []
                for item in sp.split():
                    try:
                        if '-' in item:
                            item = item.split('-')
                            if len(item) == 2:
                                indices.append(range(int(item[0])-1, 
                                               int(item[1])))
                        elif ',' in item:
                            indices.append([int(i)-1 for i in item.split(',')])
                        else:
                            indices.append(int(item)-1)
                    except:
                        pass
                for index in indices:
                        plt.figure(figsize=(width, height))
                        prody.showProjection(ensemble, pca[index])
                        if isinstance(index, int):
                            index = [index]
                        index = [str(i+1) for i in index]
                        plt.savefig(os.path.join(outdir, prefix + '_proj_' + 
                            '_'.join(index) + '.' + format),
                            dpi=dpi, format=format)
                        plt.close('all')                  
Exemple #6
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def prody_anm(opt):
    """Perform ANM calculations based on command line arguments."""
    
    outdir = opt.outdir
    if not os.path.isdir(outdir):
        opt.subparser.error('{0:s} is not a valid path'.format(outdir))
        
    import numpy as np
    import prody
    LOGGER = prody.LOGGER


    pdb = opt.pdb
    prefix = opt.prefix
    cutoff, gamma = opt.cutoff, opt.gamma, 
    nmodes, selstr, model = opt.nmodes, opt.select, opt.model
    
    pdb = prody.parsePDB(pdb, model=model)
    if prefix == '_anm':
        prefix = pdb.getTitle() + '_anm'

    select = pdb.select(selstr)
    if select is None:
        opt.subparser('Selection "{0:s}" do not match any atoms.'
                       .format(selstr))
    LOGGER.info('{0:d} atoms will be used for ANM calculations.'
                .format(len(select)))

    anm = prody.ANM(pdb.getTitle())
    anm.buildHessian(select, cutoff, gamma)
    anm.calcModes(nmodes)
    LOGGER.info('Writing numerical output.')
    if opt.npz:
        prody.saveModel(anm)
    prody.writeNMD(os.path.join(outdir, prefix + '.nmd'), anm, select)

    outall = opt.all
    delim, ext, format = opt.delim, opt.ext, opt.numformat

    if outall or opt.eigen:
        prody.writeArray(os.path.join(outdir, prefix + '_evectors'+ext), 
                         anm.getArray(), delimiter=delim, format=format)
        prody.writeArray(os.path.join(outdir, prefix + '_evalues'+ext), 
                         anm.getEigenvalues(), delimiter=delim, format=format)
    if outall or opt.beta:
        fout = prody.openFile(prefix + '_beta.txt', 'w', folder=outdir)
        fout.write('{0[0]:1s} {0[1]:4s} {0[2]:4s} {0[3]:5s} {0[4]:5s}\n'
                       .format(['C', 'RES', '####', 'Exp.', 'The.']))
        for data in zip(select.getChids(), select.getResnames(), 
                        select.getResnums(), select.getBetas(), 
                        prody.calcTempFactors(anm, select)):
            fout.write('{0[0]:1s} {0[1]:4s} {0[2]:4d} {0[3]:5.2f} {0[4]:5.2f}\n'
                       .format(data))
        fout.close()
    if outall or opt.covar:
        prody.writeArray(os.path.join(outdir, prefix + '_covariance'+ext), 
                         anm.getCovariance(), delimiter=delim, format=format)
    if outall or opt.ccorr:
        prody.writeArray(os.path.join(outdir, prefix + '_cross-correlations' 
                                                     + ext), 
                         prody.calcCrossCorr(anm), delimiter=delim, 
                         format=format)
    if outall or opt.hessian:
        prody.writeArray(os.path.join(outdir, prefix + '_hessian'+ext), 
                         anm.getHessian(), delimiter=delim, format=format)
    if outall or opt.kirchhoff:
        prody.writeArray(os.path.join(outdir, prefix + '_kirchhoff'+ext), 
                         anm.getKirchhoff(), delimiter=delim, format=format)
    if outall or opt.sqflucts:
        prody.writeArray(os.path.join(outdir, prefix + '_sqflucts'+ext), 
                         prody.calcSqFlucts(anm), delimiter=delim, 
                         format=format)
          
    figall, cc, sf, bf, cm = opt.figures, opt.cc, opt.sf, opt.bf, opt.cm

    if figall or cc or sf or bf or cm: 
        try:
            import matplotlib.pyplot as plt
        except ImportError:
            LOGGER.warning('Matplotlib could not be imported. '
                           'Figures are not saved.')
        else:
            LOGGER.info('Saving graphical output.')
            format, width, height, dpi = \
                opt.figformat, opt.width, opt.height, opt.dpi
            format = format.lower()
            if figall or cc:
                plt.figure(figsize=(width, height))
                prody.showCrossCorr(anm)
                plt.savefig(os.path.join(outdir, prefix + '_cc.'+format), 
                    dpi=dpi, format=format)
                plt.close('all')
            if figall or cm:
                plt.figure(figsize=(width, height))
                prody.showContactMap(anm)
                plt.savefig(os.path.join(outdir, prefix + '_cm.'+format), 
                    dpi=dpi, format=format)
                plt.close('all')
            if figall or sf:
                plt.figure(figsize=(width, height))
                prody.showSqFlucts(anm)
                plt.savefig(os.path.join(outdir, prefix + '_sf.'+format), 
                    dpi=dpi, format=format)
                plt.close('all')
            if figall or bf:
                plt.figure(figsize=(width, height))
                bexp = select.getBetas()
                bcal = prody.calcTempFactors(anm, select)
                plt.plot(bexp, label='Experimental')
                plt.plot(bcal, label=('Theoretical (R={0:.2f})'
                                        .format(np.corrcoef(bcal, bexp)[0,1])))
                plt.legend(prop={'size': 10})
                plt.xlabel('Node index')
                plt.ylabel('Experimental B-factors')
                plt.title(pdb.getTitle() + ' B-factors')
                plt.savefig(os.path.join(outdir, prefix + '_bf.'+format), 
                    dpi=dpi, format=format)
                plt.close('all')
Exemple #7
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def prody_pca(coords, **kwargs):
    """Perform PCA calculations for PDB or DCD format *coords* file.

    """

    for key in DEFAULTS:
        if not key in kwargs:
            kwargs[key] = DEFAULTS[key]

    from os.path import isdir, splitext, join
    outdir = kwargs.get('outdir')
    if not isdir(outdir):
        raise IOError('{0} is not a valid path'.format(repr(outdir)))

    import prody
    LOGGER = prody.LOGGER

    prefix = kwargs.get('prefix')
    nmodes = kwargs.get('nmodes')
    selstr = kwargs.get('select')

    ext = splitext(coords)[1].lower()
    if ext == '.gz':
        ext = splitext(coords[:-3])[1].lower()

    if ext == '.dcd':
        pdb = kwargs.get('psf') or kwargs.get('pdb')
        if pdb:
            if splitext(pdb)[1].lower() == '.psf':
                pdb = prody.parsePSF(pdb)
            else:
                pdb = prody.parsePDB(pdb)
        dcd = prody.DCDFile(coords)
        if prefix == '_pca' or prefix == '_eda':
            prefix = dcd.getTitle() + prefix

        if len(dcd) < 2:
            raise ValueError('DCD file must have multiple frames')
        if pdb:
            if pdb.numAtoms() == dcd.numAtoms():
                select = pdb.select(selstr)
                dcd.setAtoms(select)
                LOGGER.info('{0} atoms are selected for calculations.'
                            .format(len(select)))
            else:
                select = pdb.select(selstr)
                if select.numAtoms() != dcd.numAtoms():
                    raise ValueError('number of selected atoms ({0}) does '
                                     'not match number of atoms in the DCD '
                                     'file ({1})'.format(select.numAtoms(),
                                                           dcd.numAtoms()))
                if pdb.numCoordsets():
                    dcd.setCoords(select.getCoords())

        else:
            select = prody.AtomGroup()
            select.setCoords(dcd.getCoords())
        pca = prody.PCA(dcd.getTitle())
        if len(dcd) > 1000:
            pca.buildCovariance(dcd, aligned=kwargs.get('aligned'))
            pca.calcModes(nmodes)
            ensemble = dcd
        else:
            ensemble = dcd[:]
            if not kwargs.get('aligned'):
                ensemble.iterpose()
            pca.performSVD(ensemble)

    else:
        pdb = prody.parsePDB(coords)
        if pdb.numCoordsets() < 2:
            raise ValueError('PDB file must contain multiple models')

        if prefix == '_pca' or prefix == '_eda':
            prefix = pdb.getTitle() + prefix

        select = pdb.select(selstr)
        LOGGER.info('{0} atoms are selected for calculations.'
                    .format(len(select)))
        if select is None:
            raise ValueError('selection {0} do not match any atoms'
                                .format(repr(selstr)))
        LOGGER.info('{0} atoms will be used for PCA calculations.'
                    .format(len(select)))
        ensemble = prody.Ensemble(select)
        pca = prody.PCA(pdb.getTitle())
        if not kwargs.get('aligned'):
            ensemble.iterpose()
        pca.performSVD(ensemble)


    LOGGER.info('Writing numerical output.')
    if kwargs.get('outnpz'):
        prody.saveModel(pca, join(outdir, prefix))

    prody.writeNMD(join(outdir, prefix + '.nmd'), pca[:nmodes], select)

    extend = kwargs.get('extend')
    if extend:
        if pdb:
            if extend == 'all':
                extended = prody.extendModel(pca[:nmodes], select, pdb)
            else:
                extended = prody.extendModel(pca[:nmodes], select,
                                             select | pdb.bb)
            prody.writeNMD(join(outdir, prefix + '_extended_' +
                           extend + '.nmd'), *extended)
        else:
            prody.LOGGER.warn('Model could not be extended, provide a PDB or '
                              'PSF file.')
    outall = kwargs.get('outall')
    delim = kwargs.get('numdelim')
    ext = kwargs.get('numext')
    format = kwargs.get('numformat')

    if outall or kwargs.get('outeig'):
        prody.writeArray(join(outdir, prefix + '_evectors'+ext),
                         pca.getArray(), delimiter=delim, format=format)
        prody.writeArray(join(outdir, prefix + '_evalues'+ext),
                         pca.getEigvals(), delimiter=delim, format=format)
    if outall or kwargs.get('outcov'):
        prody.writeArray(join(outdir, prefix + '_covariance'+ext),
                         pca.getCovariance(), delimiter=delim, format=format)
    if outall or kwargs.get('outcc') or kwargs.get('outhm'):
        cc = prody.calcCrossCorr(pca)
        if outall or kwargs.get('outcc'):
            prody.writeArray(join(outdir, prefix + '_cross-correlations' +
                             ext), cc, delimiter=delim, format=format)
        if outall or kwargs.get('outhm'):
            resnums = select.getResnums()
            hmargs = {} if resnums is None else {'resnums': resnums}
            prody.writeHeatmap(join(outdir, prefix + '_cross-correlations.hm'),
                               cc, xlabel='Residue', ylabel='Residue',
                               title=pca.getTitle() + ' cross-correlations',
                               **hmargs)

    if outall or kwargs.get('outsf'):
        prody.writeArray(join(outdir, prefix + '_sqfluct'+ext),
                         prody.calcSqFlucts(pca), delimiter=delim,
                         format=format)
    if outall or kwargs.get('outproj'):
        prody.writeArray(join(outdir, prefix + '_proj'+ext),
                         prody.calcProjection(ensemble, pca), delimiter=delim,
                         format=format)

    figall = kwargs.get('figall')
    cc = kwargs.get('figcc')
    sf = kwargs.get('figsf')
    sp = kwargs.get('figproj')

    if figall or cc or sf or sp:
        try:
            import matplotlib.pyplot as plt
        except ImportError:
            LOGGER.warning('Matplotlib could not be imported. '
                           'Figures are not saved.')
        else:
            prody.SETTINGS['auto_show'] = False
            LOGGER.info('Saving graphical output.')
            format = kwargs.get('figformat')
            width = kwargs.get('figwidth')
            height = kwargs.get('figheight')
            dpi = kwargs.get('figdpi')

            format = format.lower()
            if figall or cc:
                plt.figure(figsize=(width, height))
                prody.showCrossCorr(pca)
                plt.savefig(join(outdir, prefix + '_cc.'+format),
                    dpi=dpi, format=format)
                plt.close('all')
            if figall or sf:
                plt.figure(figsize=(width, height))
                prody.showSqFlucts(pca)
                plt.savefig(join(outdir, prefix + '_sf.'+format),
                    dpi=dpi, format=format)
                plt.close('all')
            if figall or sp:
                indices = []
                for item in sp.split():
                    try:
                        if '-' in item:
                            item = item.split('-')
                            if len(item) == 2:
                                indices.append(list(range(int(item[0])-1,
                                                          int(item[1]))))
                        elif ',' in item:
                            indices.append([int(i)-1 for i in item.split(',')])
                        else:
                            indices.append(int(item)-1)
                    except:
                        pass
                for index in indices:
                        plt.figure(figsize=(width, height))
                        prody.showProjection(ensemble, pca[index])
                        if isinstance(index, int):
                            index = [index]
                        index = [str(i+1) for i in index]
                        plt.savefig(join(outdir, prefix + '_proj_' +
                            '_'.join(index) + '.' + format),
                            dpi=dpi, format=format)
                        plt.close('all')
Exemple #8
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def corepagecalculation(pdbfilename, selatom, noma1, nummodes, gamcut, cut1, gam2, cut2, showresults, smodes, snmd, smodel, scollec, massnomass, sample1, modeens, confens, rmsdens, traverse1, modetra, steptra, rmsdtra, modelnumber, caanm, cagnm, nohanm, nohgnm, allanm, allgnm, bbanm, bbgnm, scanm, scgnm, nmdfolder, modesfolder, collectivityfolder, modelnewname, nmdnewname, modesnewname, modesendname, collectivitynewname, collectivityendname, samplenewname, traversenewname, crosscorr=0, corrfolder='', corrname='', corrend='', compmode01='7', compmode02='15', sqflucts=0, sqfluctsfolder='', sqfluctsname='', sqfluctsend='', separatevar1='0', temfac=0, temfacfolder='', temfacname='', temfacend='', fracovar=0, fraconame='', fracoend='', ovlap=0, ovlapfold='', ovlapname='', ovlapend='', ovlaptab=0, ovlaptabname='', ovlaptabend='', comppdbfilename=''):
# modelnumber
	import prody
	import time
	import os
	import Tkinter
	root=Tkinter.Tk()
	root.title('Info')
	onlypage=Tkinter.Frame(root)
	onlypage.pack(side='top')
	Tkinter.Label(onlypage,text='File: '+pdbfilename).grid(row=0,column=0,sticky='w')
	Tkinter.Label(onlypage,text='Atoms: '+selatom).grid(row=1,column=0,sticky='w')
	Tkinter.Label(onlypage,text='Analysis: '+noma1).grid(row=2,column=0,sticky='w')
	path=os.path.join(os.path.expanduser('~'),'.noma/')
	fin = open(path+'savefile.txt','r')
	global savedfile
	savedfile=fin.readlines()
	fin.close()
	i=0
	a=len(savedfile)
	while i<a:
		savedfile[i]=savedfile[i][:-1]
		i+=1
	if gamcut=='0':
		Tkinter.Label(onlypage,text='Gamma: r^'+savedfile[91]).grid(row=3,column=0,sticky='w')
		Tkinter.Label(onlypage,text='Cutoff: '+cut1).grid(row=4,column=0,sticky='w')
	elif gamcut=='1':
		Tkinter.Label(onlypage,text='Gamma: '+gam2).grid(row=3,column=0,sticky='w')
		Tkinter.Label(onlypage,text='Cutoff: '+cut2).grid(row=4,column=0,sticky='w')



	find = 0					#
	while find < len(pdbfilename):			#
		if pdbfilename[-(find+1):-find] == '/':	#
			bgn = len(pdbfilename)-find		#
			break				#
		else:					# helps in the
			find +=1			# saving of files
	try:						#
		float(bgn)				#
	except (NameError):				#
		bgn = 0					#
	find = 0					#
	while bgn+find<len(pdbfilename):			#
		if pdbfilename[bgn+find:bgn+find+1] == '.':	#
			end = len(pdbfilename)-(bgn+find)	#
			break				#
		else:					#
			find +=1			#
	try:						#
		name = pdbfilename[bgn:-end]			#
	except (NameError):				#
		name = pdbfilename[bgn:len(pdbfilename)]		# name of the file
	bgn = pdbfilename[:bgn]				# path for file
	mytimeis = time.asctime(time.localtime(time.time()))
	start = time.time()
	try:
		p38 = prody.parsePDB(pdbfilename,model=int(modelnumber))
	except:
		import tkMessageBox
		tkMessageBox.askokcancel("File Error","""This is not the correct path or name. Try entering /some/path/nameoffile.pdb
If you need help finding the path, open a new terminal and enter:
find -name 'filename.pdb'        use the output as the pdb input
If this doesn't work, make sure the file is in PDB format.""")
		p38 = prody.parsePDB(pdbfilename)
	print 'Submitted: '+pdbfilename+' at '+mytimeis
	Tkinter.Label(onlypage,text='Submitted at: '+mytimeis).grid(row=5,column=0,sticky='w')
	root.update()
	if selatom == "C-alpha" and noma1 == "Gaussian Normal Mode":
		folder = cagnm+'/'
		pro = p38.select('protein and name CA')	# selects only carbon alpahs
	elif selatom == "C-alpha" and noma1 == "Anisotropic Normal Mode":
		folder = caanm+'/'
		pro = p38.select('protein and name CA')
	elif selatom == "Heavy" and noma1 == "Gaussian Normal Mode":
		folder = nohgnm+'/'
		pro = p38.select('protein and not name "[1-9]?H.*"') # gets rid of all Hydrogens
	elif selatom == "Heavy" and noma1 == "Anisotropic Normal Mode":
		folder = nohanm+'/'
		pro = p38.select('protein and not name "[1-9]?H.*"')
	elif selatom == "All" and noma1 == "Gaussian Normal Mode":
		folder = allgnm+'/'
		pro = p38.select('protein')
	elif selatom == "All" and noma1 == "Anisotropic Normal Mode":
		folder = allanm+'/'
		pro = p38.select('protein')
	elif selatom == "Backbone" and noma1 == "Gaussian Normal Mode":
		folder = bbgnm+'/'
		pro = p38.select('protein and name CA C O N H')	# selects backbone
	elif selatom == "Backbone" and noma1 == "Anisotropic Normal Mode":
		folder = bbanm+'/'
		pro = p38.select('protein and name CA C O N H')	# selects backbone
	elif selatom == "Sidechain" and noma1 == "Gaussian Normal Mode":
		folder = scgnm+'/'
		pro = p38.select('protein and not name CA C O N H')	# selects sidechain
	elif selatom == "Sidechain" and noma1 == "Anisotropic Normal Mode":
		folder = scanm+'/'
		pro = p38.select('protein and not name CA C O N H')	# selects sidechain
	try:							#
		open(bgn+folder)				# creates the folders
	except (IOError):					# where the files will
		try:						# be saved only if they
			os.makedirs(bgn+folder)			# are not there
		except (OSError):				#
			mer = 0					#
	if noma1 == "Gaussian Normal Mode":
		print 'Building the Kirchhoff matrix'
		Tkinter.Label(onlypage,text='Building Kirchhoff').grid(row=6,column=0,sticky='w')
		root.update()
		anm = prody.GNM(name)###
		if gamcut=='0':
			anm.buildKirchhoff(pro,cutoff=float(cut1),gamma=gammaDistanceDependent)###
			anm.setKirchhoff(anm.getKirchhoff())
		elif gamcut=='1':
			anm.buildKirchhoff(pro,cutoff=float(cut2),gamma=float(gam2))###
		brat = 2
	elif noma1 == "Anisotropic Normal Mode":
		print 'Building the Hessian matrix'
		Tkinter.Label(onlypage,text='Building Hessian').grid(row=6,column=0,sticky='w')
		root.update()
		anm = prody.ANM(name)###
		if gamcut=='0':
			anm.buildHessian(pro,cutoff=float(cut1),gamma=gammaDistanceDependent)###
			anm.setHessian(anm.getHessian())###
		elif gamcut=='1':
			anm.buildHessian(pro,cutoff=float(cut2),gamma=float(gam2))###
		brat = 7
	print 'Calculating modes'
	Tkinter.Label(onlypage,text='Calculating modes').grid(row=7,column=0,sticky='w')
	root.update()
	anm.calcModes(int(nummodes),zeros = True)###
	numatom=anm.numAtoms()###
	eigval=anm.getEigvals()###
	atomname=pro.getNames()###
	if smodel==1:
		if brat==2:
			modelfilename=bgn+folder+name+modelnewname+'.gnm.npz'
		elif brat==7:
			modelfilename=bgn+folder+name+modelnewname+'.anm.npz'
		print 'Saving Model'
		Tkinter.Label(onlypage,text='Saving Model').grid(row=8,column=0,sticky='w')
		root.update()
		try:
			prody.saveModel(anm,bgn+folder+name+modelnewname,True)###
		except:
			print 'Matrix not saved due to size'
			Tkinter.Label(onlypage,text='Matrix not saved').grid(row=8,column=0,sticky='w')
			root.update()
			prody.saveModel(anm,bgn+folder+name+modelnewname)###
	if snmd==1:
		print 'Saving NMD'
		Tkinter.Label(onlypage,text='Saving NMD').grid(row=9,column=0,sticky='w')
		root.update()
		try:						#
			os.makedirs(bgn+folder+nmdfolder+'/')		#
		except (OSError):				#
			mer = 0					#
		prody.writeNMD(bgn+folder+nmdfolder+'/'+name+nmdnewname+'.nmd',anm[:len(eigval)],pro)###	# this can be viewed in VMD
	if smodes==1:
		print 'Saving Modes'
		Tkinter.Label(onlypage,text='Saving Modes').grid(row=10,column=0,sticky='w')
		root.update()
		try:						#
			os.makedirs(bgn+folder+modesfolder+'/')	#
		except (OSError):				#
			mer = 0					#
		modefile = bgn+folder+modesfolder+'/'+name+modesnewname+'.'+modesendname
		fout = open(modefile,'w')
		mer = 0
		while mer< len(eigval):
			slowest_mode = anm[mer]###
			r = slowest_mode.getEigvec()###
			p = slowest_mode.getEigval()###
			tq = 0
			tt = 0
			ttt = 1
			tttt = 2
			fout.write('MODE {0:3d}		{1:15e}'.format(mer+1,p))
			fout.write("""
-------------------------------------------------
""")
			if noma1 == "Gaussian Normal Mode":
				while tq < numatom:
					fout.write("""{0:4s}{1:15e}
""".format(atomname[tq],r[tq]))
					tq +=1
			elif noma1 == "Anisotropic Normal Mode":
				while tt < numatom*3:
					fout.write("""{0:4s}{1:15e}{2:15e}{3:15e}
""".format(atomname[tq],r[tt],r[ttt],r[tttt]))
					tq+=1
					tt +=3
					ttt+=3
					tttt+=3
			mer +=1
		fout.close()
		if showresults=='1':
			os.system('/usr/bin/gnome-open '+modefile)
	if scollec==1:
		print 'Saving collectivity'
		Tkinter.Label(onlypage,text='Saving collectivity').grid(row=11,column=0,sticky='w')
		root.update()
		try:						#
			os.makedirs(bgn+folder+collectivityfolder+'/')	#
		except (OSError):				#
			mer = 0					#
		mer = 0
		xx = [0]*(numatom) # sets the array to zero and other initial conditions
		i = 0
		aa = 0
		no = 0
		var3 = 0
		sss = [0]*(len(eigval))
		while mer< len(eigval):
			slowest_mode = anm[mer]###
			r = slowest_mode.getEigvec()###
			p = slowest_mode.getEigval()###
			a = 0
			tt = 0
			ttt = 1
			tttt = 2
			while a < numatom:
				atom = atomname[a]
				mass = 0
				while mass < 2:
					if atom[mass] == "N": # all nitrogen
						m = 14.0067
						break
					elif atom[mass] == 'H': # all hydrogen
						m = 1.00794
						break
					elif atom[mass] == "C" : # all carbon
						m = 12.0107
						break
					elif atom[mass] == "O" : # all oxygen
						m = 15.9994
						break
					elif atom[mass] == 'S': # all sulfur
						m = 32.065
						break
					elif atom[mass] == 'P' : # all phosphorus
						m = 30.973762
						break
					else:
						if mass == 0:
							mass +=1
							try:
								atom[mass]
							except (IndexError):
								m = 1
								if no == 0:
									print 'Enter atom '+atom+' in to the system. Its mass was set to 1 in this simulation.'
									no +=1
								break
						else:
							m = 1
							if no == 0:
								print 'Enter atom '+atom+' in to the system. Its mass was set to 1 in this simulation'
								no +=1
							break
				if len(r)/numatom == 3:
					xx[i] = (r[tt]**2 + r[ttt]**2 + r[tttt]**2)/m
					i +=1
					tt +=3
					ttt+=3
					tttt+=3
				else:
					xx[i] = (r[tt]**2)/m
					i +=1
					tt +=1
				a +=1
			var3 = 0
			j = 0
			loop = 1
			while loop == 1:
				if sum(xx) == 0: # need this because you can't divide by 0
					loop = 0
				elif j <(numatom):
					var1 = xx[j]/sum(xx)
					if var1 == 0:
						var2 = 0
					elif var1 != 0:
						from math import log # this means natural log
						var2 = var1* log(var1)
					var3 += var2
					j +=1
				else:
					from math import exp
					k = exp(-var3)/numatom
					sss[aa] = k, aa+1
					aa +=1
					mer +=1
					loop = 0
					i = 0
					xx = [0]*(numatom)  # goes through all this until the big loop is done
		a = 0
		k=[0]*(len(eigval))
		while a < len(eigval):
			k[a]=prody.calcCollectivity(anm[a]),a+1
			a +=1


		collectivefile = bgn+folder+collectivityfolder+'/'+name+collectivitynewname+'.'+collectivityendname
		fout = open(collectivefile,'w')
		if massnomass=='0':
			fout.write('MODE      COLLECTIVITY(mass)')
			fout.write("""
---------------------------
""")
			for h in sorted(sss,reverse=True):
				fout.write(str(h)[-3:-1]+'        '+str(h)[1:19]+"""
""")
			fout.write("""

MODE      COLLECTIVITY(without mass)""")
			fout.write("""
---------------------------
""")
			for hh in sorted(k,reverse=True):
				fout.write(str(hh)[-3:-1]+'        '+str(hh)[1:19]+"""
""")
		elif massnomass=='1':
			fout.write('MODE      COLLECTIVITY(without mass)')
			fout.write("""
---------------------------
""")
			for hh in sorted(k,reverse=True):
				fout.write(str(hh)[-3:-1]+'        '+str(hh)[1:19]+"""
""")
			fout.write("""

MODE      COLLECTIVITY(mass)""")
			fout.write("""
---------------------------
""")
			for h in sorted(sss,reverse=True):
				fout.write(str(h)[-3:-1]+'        '+str(h)[1:19]+"""
""")
		fout.close()
		if showresults=='1':
			os.system('/usr/bin/gnome-open '+collectivefile)

		fin = open(collectivefile,'r')
		lst = fin.readlines()
		hi0 = 2
		looop = 1
		prut=0
		secoll=0
		thicoll=0
		while looop == 1:
			fine = lst[hi0]
			if int(fine[0:2]) >= brat:
				if prut==0:
					prut=fine[0:2]
				elif secoll==0:
					secoll=fine[0:2]
				elif thicoll==0:
					thicoll=fine[0:2]
				else:
					foucoll=fine[0:2]
					looop = 0
			else:
				hi0 +=1
		mostcollective= "Mode "+prut+" is the most collective."
		Tkinter.Label(onlypage,text='Mode '+prut+' is the most collective').grid(row=12,column=0,sticky='w')
		root.update()
		print mostcollective
		fin.close()

	if sample1 == 1:
		print 'Saving sample file'
		Tkinter.Label(onlypage,text='Saving sample file').grid(row=13,column=0,sticky='w')
		root.update()
		a = modeens+' '
		b = [0]*(len(a)+1)
		i = 0
		j = 0
		b1 = 0
		while i < len(a):
			if a[i:i+1] ==' ' or a[i:i+1]==',':
				try:
					b[b1]=int(a[j:i])-1
				except:
					if '1c' in a[j:i]:
						b[b1]=int(prut)-1
					elif '2c' in a[j:i]:
						b[b1]=int(prut)-1
						b1 +=1
						b[b1]=int(secoll)-1
					elif '3c' in a[j:i]:
						b[b1]=int(prut)-1
						b1 +=1
						b[b1]=int(secoll)-1
						b1 +=1
						b[b1]=int(thicoll)-1
					elif '4c' in a[j:i]:
						b[b1]=int(prut)-1
						b1 +=1
						b[b1]=int(secoll)-1
						b1 +=1
						b[b1]=int(thicoll)-1
						b1+=1
						b[b1]=int(foucoll)-1
				j = i+1
				i +=1
				b1 +=1
			else:
				i +=1
		del b[b1:]
		ensemble = prody.sampleModes(anm[b],pro, n_confs=int(confens), rmsd =float(rmsdens))
		p38ens=pro.copy()
		p38ens.delCoordset(0)
		p38ens.addCoordset(ensemble.getCoordsets())
		prody.writePDB(bgn+folder+name+samplenewname+'.pdb',p38ens)


	if traverse1 ==1:
		print 'Saving traverse file'
		Tkinter.Label(onlypage,text='Saving traverse file').grid(row=14,column=0,sticky='w')
		root.update()
		if modetra=='c':
			modefortra=int(prut)-1
		else:
			modefortra=int(modetra)-1
		trajectory=prody.traverseMode(anm[modefortra],pro,n_steps=int(steptra),rmsd=float(rmsdtra))
		prody.calcRMSD(trajectory).round(2)
		p38traj=pro.copy()
		p38traj.delCoordset(0)
		p38traj.addCoordset(trajectory.getCoordsets())
		prody.writePDB(bgn+folder+name+'_mode'+str(modefortra+1)+traversenewname+'.pdb',p38traj)
	if crosscorr==1:
		print 'Saving cross correlation'
		Tkinter.Label(onlypage,text='Saving cross-correlation').grid(row=15,column=0,sticky='w')
		root.update()
		try:						#
			os.makedirs(bgn+folder+corrfolder+'/')	#
		except (OSError):				#
			mer = 0
		i=int(compmode01)
		while i <= int(compmode02):
			x=i-1
			correlationdataname=bgn+folder+corrfolder+'/'+name+corrname+'_mode'+str(x+1)+'.'+corrend
			prody.writeArray(correlationdataname,prody.calcCrossCorr(anm[x]),'%.18e')
			print correlationdataname
			i+=1

##
	if sqflucts==1:
		print 'Saving square fluctuation'
		Tkinter.Label(onlypage,text='Saving square fluctuation').grid(row=16,column=0,sticky='w')
		root.update()
		try:						#
			os.makedirs(bgn+folder+sqfluctsfolder+'/')	#
		except (OSError):				#
			mer = 0
		i=int(compmode01)
		while i < int(compmode02):
			yelp = i-1
			sqfluctdataname = bgn+folder+sqfluctsfolder+'/'+name+sqfluctsname+'_mode'+str(yelp+1)+'.'+sqfluctsend
			fout = open(sqfluctdataname,'w')
			if separatevar1=='0':
				a = 0
				while a < numatom:
					fout.write(str(a))
					fout.write("""	""")
					fout.write(str(prody.calcSqFlucts(anm[yelp])[a]))
					fout.write("""
""")
					a +=1
			elif separatevar1=='1':
				a=0
				while a <numatom:
					firstresnum=int(p38.getResnums()[0:1][0])
					origiresnum=int(p38.getResnums()[0:1][0])
					while firstresnum<(int(numatom*1.0/p38.numChains())+origiresnum):
						fout.write(str(firstresnum))
						fout.write('\t')
						fout.write(str(prody.calcSqFlucts(anm[yelp])[a]))
						fout.write('\n')
						a+=1
						firstresnum+=1
					fout.write('&\n')
			fout.close()
			print sqfluctdataname
			i+=1
	if temfac==1:
		print 'Saving temperature factors'
		Tkinter.Label(onlypage,text='Saving temperature factors').grid(row=17,column=0,sticky='w')
		root.update()
		try:						#
			os.makedirs(bgn+folder+temfacfolder+'/')	#
		except (OSError):				#
			mer = 0

		fin=open(pdbfilename,'r')
		d = [None]*len(atomname)
		e = 0
		for line in fin:
			pair = line.split()
			if 'ATOM  ' in line and e < len(atomname):
				if str(pair[2]) == str(atomname[e]):
					d[e]=str(pair[1])
					e+=1
				else:
					e+=0
			else:
				continue
		fin.close()
		sqf = prody.calcSqFlucts(anm)
		x = sqf/((sqf**2).sum()**.5)
		y = prody.calcTempFactors(anm,pro)
		a = 0
		tempfactorsdataname =bgn+folder+temfacfolder+'/'+name+temfacname+'.'+temfacend
		fout=open(tempfactorsdataname,'w')
		fout.write("""Atom	Residue	      TempFactor   TempFactor with exp beta
""")
		while a < numatom:
			fout.write("""{0:4s}	{1:4d}	{2:15f}	{3:15f}
""".format(d[a],a+1,x[a],y[a]))
			a +=1
		fout.close()
		print tempfactorsdataname
	if fracovar==1:
		try:
			import matplotlib.pyplot as plt
			print 'Saving Fraction of Variance'
			Tkinter.Label(onlypage,text='Saving Fraction of Variance').grid(row=18,column=0,sticky='w')
			root.update()
			try:						#
				os.makedirs(bgn+folder+modesfolder+'/')	#
			except (OSError):				#
				mer = 0					#
			plt.figure(figsize = (5,4))
			prody.showFractVars(anm)
			prody.showCumulFractVars(anm)
			fracvardataname =bgn+folder+modesfolder+'/'+name+fraconame+'.'+fracoend
			plt.savefig(fracvardataname)
			print fracvardataname
			if showresults=='1':
				os.system('/usr/bin/gnome-open '+fracvardataname)
		except:
			print 'Error: Fraction of Variance'
			Tkinter.Label(onlypage,text='Error: Fraction of Variance').grid(row=18,column=0,sticky='w')
			root.update()
			mer=0

	if ovlap==1 or ovlaptab==1:
		try:
			import matplotlib.pyplot as plt
			print 'Saving Overlap'
			Tkinter.Label(onlypage,text='Saving Overlap').grid(row=19,column=0,sticky='w')
			root.update()


			Tkinter.Label(onlypage,text='Comparison: '+comppdbfilename).grid(row=20,column=0,sticky='w')


##
			find = 0
			while find < len(comppdbfilename):
				if comppdbfilename[-(find+1):-find] == '/':
					bgn1 = len(comppdbfilename)-find
					break
				else:
					find +=1
			try:
				float(bgn1)
			except (NameError):
				bgn1 = 0
			find = 0
			while bgn1+find<len(comppdbfilename):
				if comppdbfilename[bgn1+find:bgn1+find+1] == '.':
					end1 = len(comppdbfilename)-(bgn1+find)
					break
				else:
					find +=1
			try:
				name1 = comppdbfilename[bgn1:-end1]
			except (NameError):
				name1 = comppdbfilename[bgn1:len(comppdbfilename)]
			bgn1 = comppdbfilename[:bgn1]
			p381 = prody.parsePDB(comppdbfilename,model=int(modelnumber))
			if selatom == "C-alpha" and noma1 == "Gaussian Normal Mode":
				pro1 = p381.select('protein and name CA')
			elif selatom == "C-alpha" and noma1 == "Anisotropic Normal Mode":
				pro1 = p381.select('protein and name CA')
			elif selatom == "Heavy" and noma1 == "Gaussian Normal Mode":
				pro1 = p381.select('protein and not name "[1-9]?H.*"')
			elif selatom == "Heavy" and noma1 == "Anisotropic Normal Mode":
				pro1 = p381.select('protein and not name "[1-9]?H.*"')
			elif selatom == "All" and noma1 == "Gaussian Normal Mode":
				pro1 = p381.select('protein')
			elif selatom == "All" and noma1 == "Anisotropic Normal Mode":
				pro1 = p381.select('protein')
			elif selatom == "Backbone" and noma1 == "Gaussian Normal Mode":
				pro1 = p381.select('protein and name CA C O N H')
			elif selatom == "Backbone" and noma1 == "Anisotropic Normal Mode":
				pro1 = p381.select('protein and name CA C O N H')
			elif selatom == "Sidechain" and noma1 == "Gaussian Normal Mode":
				pro1 = p381.select('protein and not name CA C O N H')
			elif selatom == "Sidechain" and noma1 == "Anisotropic Normal Mode":
				pro1 = p381.select('protein and not name CA C O N H')
			if noma1 == "Gaussian Normal Mode":
				print 'Building the Kirchhoff matrix'
				Tkinter.Label(onlypage,text='Building Kirchhoff').grid(row=21,column=0,sticky='w')
				root.update()
				anm1 = prody.GNM(name1)
				if gamcut=='0':
					anm1.buildKirchhoff(pro1,cutoff=float(cut1),gamma=gammaDistanceDependent)
					anm1.setKirchhoff(anm1.getKirchhoff())
				elif gamcut=='1':
					anm1.buildKirchhoff(pro1,cutoff=float(cut2),gamma=float(gam2))
				brat = 2
			elif noma1 == "Anisotropic Normal Mode":
				print 'Building the Hessian matrix'
				Tkinter.Label(onlypage,text='Building Hessian').grid(row=21,column=0,sticky='w')
				root.update()
				anm1 = prody.ANM(name1)
				if gamcut=='0':
					anm1.buildHessian(pro1,cutoff=float(cut1),gamma=gammaDistanceDependent)
					anm1.setHessian(anm1.getHessian())
				elif gamcut=='1':
					anm1.buildHessian(pro1,cutoff=float(cut2),gamma=float(gam2))
				brat = 7
			print 'Calculating modes'
			Tkinter.Label(onlypage,text='Calculating modes').grid(row=22,column=0,sticky='w')
			root.update()
			anm1.calcModes(int(nummodes),zeros = True)
##
			try:
				os.makedirs(bgn+folder+ovlapfold+'/')
			except (OSError):
				mer = 0
			if ovlap==1:
				i=int(compmode01)
				while i < int(compmode02):
					a = i-1
					plt.figure(figsize=(5,4))
					prody.showCumulOverlap(anm[a],anm1)
					prody.showOverlap(anm[a],anm1)
					plt.title('Overlap with Mode '+str(a+1)+' from '+name)
					plt.xlabel(name1+' mode index')
					overlapname = bgn+folder+ovlapfold+'/'+name+'_'+name1+ovlapname+'_mode'+str(a+1)+'.'+ovlapend
					plt.savefig(overlapname)
					print overlapname
					i+=1
			if ovlaptab==1:
				plt.figure(figsize=(5,4))
				prody.showOverlapTable(anm1,anm)
				plt.xlim(int(compmode01)-1,int(compmode02))
				plt.ylim(int(compmode01)-1,int(compmode02))
				plt.title(name1+' vs '+name+' Overlap')
				plt.ylabel(name1)
				plt.xlabel(name)
				overlapname = bgn+folder+ovlapfold+'/'+name+'_'+name1+ovlaptabname+'.'+ovlaptabend
				plt.savefig(overlapname)
				print overlapname
		except:
			mer=0


	root.destroy()
	mynewtimeis = float(time.time()-start)
	if mynewtimeis <= 60.00:
		timeittook= "The calculations took %.2f s."%(mynewtimeis)
	elif mynewtimeis > 60.00 and mynewtimeis <= 3600.00:
		timeittook= "The calculations took %.2f min."%((mynewtimeis/60.00))
	else:
		timeittook= "The calculations took %.2f hrs."%((mynewtimeis/3600.00))
	print timeittook
	if smodel==1 and scollec==1:
		return (timeittook,modelfilename,str(int(prut)))
	elif scollec==1:
		return (timeittook,'nofile',str(int(prut)))
	elif smodel==1:
		return (timeittook,modelfilename,'nocoll')
	else:
		return (timeittook,'nofile','nocoll')
Exemple #9
0
def prody_anm(pdb, **kwargs):
    """Perform ANM calculations for *pdb*.

    """

    for key in DEFAULTS:
        if not key in kwargs:
            kwargs[key] = DEFAULTS[key]

    from os.path import isdir, join
    outdir = kwargs.get('outdir')
    if not isdir(outdir):
        raise IOError('{0} is not a valid path'.format(repr(outdir)))

    import numpy as np
    import prody
    LOGGER = prody.LOGGER

    selstr = kwargs.get('select')
    prefix = kwargs.get('prefix')
    cutoff = kwargs.get('cutoff')
    gamma = kwargs.get('gamma')
    nmodes = kwargs.get('nmodes')
    selstr = kwargs.get('select')
    model = kwargs.get('model')

    pdb = prody.parsePDB(pdb, model=model)
    if prefix == '_anm':
        prefix = pdb.getTitle() + '_anm'

    select = pdb.select(selstr)
    if select is None:
        LOGGER.warn('Selection {0} did not match any atoms.'
                    .format(repr(selstr)))
        return
    LOGGER.info('{0} atoms will be used for ANM calculations.'
                .format(len(select)))

    anm = prody.ANM(pdb.getTitle())
    anm.buildHessian(select, cutoff, gamma)
    anm.calcModes(nmodes)
    LOGGER.info('Writing numerical output.')
    if kwargs.get('outnpz'):
        prody.saveModel(anm, join(outdir, prefix))
    prody.writeNMD(join(outdir, prefix + '.nmd'), anm, select)

    extend = kwargs.get('extend')
    if extend:
        if extend == 'all':
            extended = prody.extendModel(anm, select, pdb)
        else:
            extended = prody.extendModel(anm, select, select | pdb.bb)
        prody.writeNMD(join(outdir, prefix + '_extended_' +
                       extend + '.nmd'), *extended)

    outall = kwargs.get('outall')
    delim = kwargs.get('numdelim')
    ext = kwargs.get('numext')
    format = kwargs.get('numformat')


    if outall or kwargs.get('outeig'):
        prody.writeArray(join(outdir, prefix + '_evectors'+ext),
                         anm.getArray(), delimiter=delim, format=format)
        prody.writeArray(join(outdir, prefix + '_evalues'+ext),
                         anm.getEigvals(), delimiter=delim, format=format)

    if outall or kwargs.get('outbeta'):
        from prody.utilities import openFile
        fout = openFile(prefix + '_beta.txt', 'w', folder=outdir)
        fout.write('{0[0]:1s} {0[1]:4s} {0[2]:4s} {0[3]:5s} {0[4]:5s}\n'
                       .format(['C', 'RES', '####', 'Exp.', 'The.']))
        for data in zip(select.getChids(), select.getResnames(),
                        select.getResnums(), select.getBetas(),
                        prody.calcTempFactors(anm, select)):
            fout.write('{0[0]:1s} {0[1]:4s} {0[2]:4d} {0[3]:5.2f} {0[4]:5.2f}\n'
                       .format(data))
        fout.close()

    if outall or kwargs.get('outcov'):
        prody.writeArray(join(outdir, prefix + '_covariance' + ext),
                         anm.getCovariance(), delimiter=delim, format=format)

    if outall or kwargs.get('outcc') or kwargs.get('outhm'):
        cc = prody.calcCrossCorr(anm)
        if outall or kwargs.get('outcc'):
            prody.writeArray(join(outdir, prefix +
                             '_cross-correlations' + ext),
                             cc, delimiter=delim,  format=format)
        if outall or kwargs.get('outhm'):
            prody.writeHeatmap(join(outdir, prefix + '_cross-correlations.hm'),
                               cc, resnum=select.getResnums(),
                               xlabel='Residue', ylabel='Residue',
                               title=anm.getTitle() + ' cross-correlations')

    if outall or kwargs.get('hessian'):
        prody.writeArray(join(outdir, prefix + '_hessian'+ext),
                         anm.getHessian(), delimiter=delim, format=format)

    if outall or kwargs.get('kirchhoff'):
        prody.writeArray(join(outdir, prefix + '_kirchhoff'+ext),
                         anm.getKirchhoff(), delimiter=delim, format=format)

    if outall or kwargs.get('outsf'):
        prody.writeArray(join(outdir, prefix + '_sqflucts'+ext),
                         prody.calcSqFlucts(anm), delimiter=delim,
                         format=format)

    figall = kwargs.get('figall')
    cc = kwargs.get('figcc')
    sf = kwargs.get('figsf')
    bf = kwargs.get('figbeta')
    cm = kwargs.get('figcmap')


    if figall or cc or sf or bf or cm:
        try:
            import matplotlib.pyplot as plt
        except ImportError:
            LOGGER.warning('Matplotlib could not be imported. '
                           'Figures are not saved.')
        else:
            prody.SETTINGS['auto_show'] = False
            LOGGER.info('Saving graphical output.')
            format = kwargs.get('figformat')
            width = kwargs.get('figwidth')
            height = kwargs.get('figheight')
            dpi = kwargs.get('figdpi')
            format = format.lower()

            if figall or cc:
                plt.figure(figsize=(width, height))
                prody.showCrossCorr(anm)
                plt.savefig(join(outdir, prefix + '_cc.'+format),
                    dpi=dpi, format=format)
                plt.close('all')

            if figall or cm:
                plt.figure(figsize=(width, height))
                prody.showContactMap(anm)
                plt.savefig(join(outdir, prefix + '_cm.'+format),
                    dpi=dpi, format=format)
                plt.close('all')

            if figall or sf:
                plt.figure(figsize=(width, height))
                prody.showSqFlucts(anm)
                plt.savefig(join(outdir, prefix + '_sf.'+format),
                    dpi=dpi, format=format)
                plt.close('all')

            if figall or bf:
                plt.figure(figsize=(width, height))
                bexp = select.getBetas()
                bcal = prody.calcTempFactors(anm, select)
                plt.plot(bexp, label='Experimental')
                plt.plot(bcal, label=('Theoretical (R={0:.2f})'
                                        .format(np.corrcoef(bcal, bexp)[0,1])))
                plt.legend(prop={'size': 10})
                plt.xlabel('Node index')
                plt.ylabel('Experimental B-factors')
                plt.title(pdb.getTitle() + ' B-factors')
                plt.savefig(join(outdir, prefix + '_bf.'+format),
                    dpi=dpi, format=format)
                plt.close('all')
Exemple #10
0
def prody_pca(coords, **kwargs):
    """Perform PCA calculations for PDB or DCD format *coords* file.

    """

    for key in DEFAULTS:
        if not key in kwargs:
            kwargs[key] = DEFAULTS[key]

    from os.path import isdir, splitext, join
    outdir = kwargs.get('outdir')
    if not isdir(outdir):
        raise IOError('{0} is not a valid path'.format(repr(outdir)))

    import prody
    LOGGER = prody.LOGGER

    prefix = kwargs.get('prefix')
    nmodes = kwargs.get('nmodes')
    selstr = kwargs.get('select')
    quiet = kwargs.pop('quiet', False)
    altloc = kwargs.get('altloc')

    ext = splitext(coords)[1].lower()
    if ext == '.gz':
        ext = splitext(coords[:-3])[1].lower()

    if ext == '.dcd':
        pdb = kwargs.get('psf') or kwargs.get('pdb')
        if pdb:
            if splitext(pdb)[1].lower() == '.psf':
                pdb = prody.parsePSF(pdb)
            else:
                pdb = prody.parsePDB(pdb, altlocs=altlocs)
        dcd = prody.DCDFile(coords)
        if prefix == '_pca' or prefix == '_eda':
            prefix = dcd.getTitle() + prefix

        if len(dcd) < 2:
            raise ValueError('DCD file must have multiple frames')
        if pdb:
            if pdb.numAtoms() == dcd.numAtoms():
                select = pdb.select(selstr)
                dcd.setAtoms(select)
                LOGGER.info('{0} atoms are selected for calculations.'.format(
                    len(select)))
            else:
                select = pdb.select(selstr)
                if select.numAtoms() != dcd.numAtoms():
                    raise ValueError('number of selected atoms ({0}) does '
                                     'not match number of atoms in the DCD '
                                     'file ({1})'.format(
                                         select.numAtoms(), dcd.numAtoms()))
                if pdb.numCoordsets():
                    dcd.setCoords(select.getCoords())

        else:
            select = prody.AtomGroup()
            select.setCoords(dcd.getCoords())
        pca = prody.PCA(dcd.getTitle())

        nproc = kwargs.get('nproc')
        if nproc:
            try:
                from threadpoolctl import threadpool_limits
            except ImportError:
                raise ImportError(
                    'Please install threadpoolctl to control threads')

            with threadpool_limits(limits=nproc, user_api="blas"):
                if len(dcd) > 1000:
                    pca.buildCovariance(dcd,
                                        aligned=kwargs.get('aligned'),
                                        quiet=quiet)
                    pca.calcModes(nmodes)
                    ensemble = dcd
                else:
                    ensemble = dcd[:]
                    if not kwargs.get('aligned'):
                        ensemble.iterpose(quiet=quiet)
                    pca.performSVD(ensemble)
                nmodes = pca.numModes()
        else:
            if len(dcd) > 1000:
                pca.buildCovariance(dcd,
                                    aligned=kwargs.get('aligned'),
                                    quiet=quiet)
                pca.calcModes(nmodes)
                ensemble = dcd
            else:
                ensemble = dcd[:]
                if not kwargs.get('aligned'):
                    ensemble.iterpose(quiet=quiet)
                pca.performSVD(ensemble)
            nmodes = pca.numModes()

    else:
        pdb = prody.parsePDB(coords)
        if pdb.numCoordsets() < 2:
            raise ValueError('PDB file must contain multiple models')

        if prefix == '_pca' or prefix == '_eda':
            prefix = pdb.getTitle() + prefix

        select = pdb.select(selstr)
        LOGGER.info('{0} atoms are selected for calculations.'.format(
            len(select)))
        if select is None:
            raise ValueError('selection {0} do not match any atoms'.format(
                repr(selstr)))
        LOGGER.info('{0} atoms will be used for PCA calculations.'.format(
            len(select)))
        ensemble = prody.Ensemble(select)
        pca = prody.PCA(pdb.getTitle())
        if not kwargs.get('aligned'):
            ensemble.iterpose()

        nproc = kwargs.get('nproc')
        if nproc:
            try:
                from threadpoolctl import threadpool_limits
            except ImportError:
                raise ImportError(
                    'Please install threadpoolctl to control threads')

            with threadpool_limits(limits=nproc, user_api="blas"):
                pca.performSVD(ensemble)
        else:
            pca.performSVD(ensemble)

    LOGGER.info('Writing numerical output.')
    if kwargs.get('outnpz'):
        prody.saveModel(pca, join(outdir, prefix))

    if kwargs.get('outscipion'):
        prody.writeScipionModes(outdir, pca)

    prody.writeNMD(join(outdir, prefix + '.nmd'), pca[:nmodes], select)

    extend = kwargs.get('extend')
    if extend:
        if pdb:
            if extend == 'all':
                extended = prody.extendModel(pca[:nmodes], select, pdb)
            else:
                extended = prody.extendModel(pca[:nmodes], select,
                                             select | pdb.bb)
            prody.writeNMD(
                join(outdir, prefix + '_extended_' + extend + '.nmd'),
                *extended)
        else:
            prody.LOGGER.warn('Model could not be extended, provide a PDB or '
                              'PSF file.')
    outall = kwargs.get('outall')
    delim = kwargs.get('numdelim')
    ext = kwargs.get('numext')
    format = kwargs.get('numformat')

    if outall or kwargs.get('outeig'):
        prody.writeArray(join(outdir, prefix + '_evectors' + ext),
                         pca.getArray(),
                         delimiter=delim,
                         format=format)
        prody.writeArray(join(outdir, prefix + '_evalues' + ext),
                         pca.getEigvals(),
                         delimiter=delim,
                         format=format)
    if outall or kwargs.get('outcov'):
        prody.writeArray(join(outdir, prefix + '_covariance' + ext),
                         pca.getCovariance(),
                         delimiter=delim,
                         format=format)
    if outall or kwargs.get('outcc') or kwargs.get('outhm'):
        cc = prody.calcCrossCorr(pca)
        if outall or kwargs.get('outcc'):
            prody.writeArray(join(outdir,
                                  prefix + '_cross-correlations' + ext),
                             cc,
                             delimiter=delim,
                             format=format)
        if outall or kwargs.get('outhm'):
            resnums = select.getResnums()
            hmargs = {} if resnums is None else {'resnums': resnums}
            prody.writeHeatmap(join(outdir, prefix + '_cross-correlations.hm'),
                               cc,
                               xlabel='Residue',
                               ylabel='Residue',
                               title=pca.getTitle() + ' cross-correlations',
                               **hmargs)

    if outall or kwargs.get('outsf'):
        prody.writeArray(join(outdir, prefix + '_sqfluct' + ext),
                         prody.calcSqFlucts(pca),
                         delimiter=delim,
                         format=format)
    if outall or kwargs.get('outproj'):
        prody.writeArray(join(outdir, prefix + '_proj' + ext),
                         prody.calcProjection(ensemble, pca),
                         delimiter=delim,
                         format=format)

    figall = kwargs.get('figall')
    cc = kwargs.get('figcc')
    sf = kwargs.get('figsf')
    sp = kwargs.get('figproj')

    if figall or cc or sf or sp:
        try:
            import matplotlib.pyplot as plt
        except ImportError:
            LOGGER.warning('Matplotlib could not be imported. '
                           'Figures are not saved.')
        else:
            prody.SETTINGS['auto_show'] = False
            LOGGER.info('Saving graphical output.')
            format = kwargs.get('figformat')
            width = kwargs.get('figwidth')
            height = kwargs.get('figheight')
            dpi = kwargs.get('figdpi')

            format = format.lower()
            if figall or cc:
                plt.figure(figsize=(width, height))
                prody.showCrossCorr(pca)
                plt.savefig(join(outdir, prefix + '_cc.' + format),
                            dpi=dpi,
                            format=format)
                plt.close('all')
            if figall or sf:
                plt.figure(figsize=(width, height))
                prody.showSqFlucts(pca)
                plt.savefig(join(outdir, prefix + '_sf.' + format),
                            dpi=dpi,
                            format=format)
                plt.close('all')
            if figall or sp:
                indices = []
                for item in sp.split():
                    try:
                        if '-' in item:
                            item = item.split('-')
                            if len(item) == 2:
                                indices.append(
                                    list(range(int(item[0]) - 1,
                                               int(item[1]))))
                        elif ',' in item:
                            indices.append(
                                [int(i) - 1 for i in item.split(',')])
                        else:
                            indices.append(int(item) - 1)
                    except:
                        pass
                for index in indices:
                    plt.figure(figsize=(width, height))
                    prody.showProjection(ensemble, pca[index])
                    if isinstance(index, Integral):
                        index = [index]
                    index = [str(i + 1) for i in index]
                    plt.savefig(join(
                        outdir,
                        prefix + '_proj_' + '_'.join(index) + '.' + format),
                                dpi=dpi,
                                format=format)
                    plt.close('all')
Exemple #11
0
def prody_anm(pdb, **kwargs):
    """Perform ANM calculations for *pdb*.

    """

    for key in DEFAULTS:
        if not key in kwargs:
            kwargs[key] = DEFAULTS[key]

    from os.path import isdir, join
    outdir = kwargs.get('outdir')
    if not isdir(outdir):
        raise IOError('{0} is not a valid path'.format(repr(outdir)))

    import numpy as np
    import prody
    LOGGER = prody.LOGGER

    selstr = kwargs.get('select')
    prefix = kwargs.get('prefix')
    cutoff = kwargs.get('cutoff')
    gamma = kwargs.get('gamma')
    nmodes = kwargs.get('nmodes')
    selstr = kwargs.get('select')
    model = kwargs.get('model')

    pdb = prody.parsePDB(pdb, model=model)
    if prefix == '_anm':
        prefix = pdb.getTitle() + '_anm'

    select = pdb.select(selstr)
    if select is None:
        LOGGER.warn('Selection {0} did not match any atoms.'.format(
            repr(selstr)))
        return
    LOGGER.info('{0} atoms will be used for ANM calculations.'.format(
        len(select)))

    anm = prody.ANM(pdb.getTitle())
    anm.buildHessian(select, cutoff, gamma)
    anm.calcModes(nmodes)
    LOGGER.info('Writing numerical output.')
    if kwargs.get('outnpz'):
        prody.saveModel(anm, join(outdir, prefix))
    prody.writeNMD(join(outdir, prefix + '.nmd'), anm, select)

    extend = kwargs.get('extend')
    if extend:
        if extend == 'all':
            extended = prody.extendModel(anm, select, pdb)
        else:
            extended = prody.extendModel(anm, select, select | pdb.bb)
        prody.writeNMD(join(outdir, prefix + '_extended_' + extend + '.nmd'),
                       *extended)

    outall = kwargs.get('outall')
    delim = kwargs.get('numdelim')
    ext = kwargs.get('numext')
    format = kwargs.get('numformat')

    if outall or kwargs.get('outeig'):
        prody.writeArray(join(outdir, prefix + '_evectors' + ext),
                         anm.getArray(),
                         delimiter=delim,
                         format=format)
        prody.writeArray(join(outdir, prefix + '_evalues' + ext),
                         anm.getEigvals(),
                         delimiter=delim,
                         format=format)

    if outall or kwargs.get('outbeta'):
        from prody.utilities import openFile
        fout = openFile(prefix + '_beta.txt', 'w', folder=outdir)
        fout.write(
            '{0[0]:1s} {0[1]:4s} {0[2]:4s} {0[3]:5s} {0[4]:5s}\n'.format(
                ['C', 'RES', '####', 'Exp.', 'The.']))
        for data in zip(select.getChids(), select.getResnames(),
                        select.getResnums(), select.getBetas(),
                        prody.calcTempFactors(anm, select)):
            fout.write(
                '{0[0]:1s} {0[1]:4s} {0[2]:4d} {0[3]:5.2f} {0[4]:5.2f}\n'.
                format(data))
        fout.close()

    if outall or kwargs.get('outcov'):
        prody.writeArray(join(outdir, prefix + '_covariance' + ext),
                         anm.getCovariance(),
                         delimiter=delim,
                         format=format)

    if outall or kwargs.get('outcc') or kwargs.get('outhm'):
        cc = prody.calcCrossCorr(anm)
        if outall or kwargs.get('outcc'):
            prody.writeArray(join(outdir,
                                  prefix + '_cross-correlations' + ext),
                             cc,
                             delimiter=delim,
                             format=format)
        if outall or kwargs.get('outhm'):
            prody.writeHeatmap(join(outdir, prefix + '_cross-correlations.hm'),
                               cc,
                               resnum=select.getResnums(),
                               xlabel='Residue',
                               ylabel='Residue',
                               title=anm.getTitle() + ' cross-correlations')

    if outall or kwargs.get('hessian'):
        prody.writeArray(join(outdir, prefix + '_hessian' + ext),
                         anm.getHessian(),
                         delimiter=delim,
                         format=format)

    if outall or kwargs.get('kirchhoff'):
        prody.writeArray(join(outdir, prefix + '_kirchhoff' + ext),
                         anm.getKirchhoff(),
                         delimiter=delim,
                         format=format)

    if outall or kwargs.get('outsf'):
        prody.writeArray(join(outdir, prefix + '_sqflucts' + ext),
                         prody.calcSqFlucts(anm),
                         delimiter=delim,
                         format=format)

    figall = kwargs.get('figall')
    cc = kwargs.get('figcc')
    sf = kwargs.get('figsf')
    bf = kwargs.get('figbeta')
    cm = kwargs.get('figcmap')

    if figall or cc or sf or bf or cm:
        try:
            import matplotlib.pyplot as plt
        except ImportError:
            LOGGER.warning('Matplotlib could not be imported. '
                           'Figures are not saved.')
        else:
            prody.SETTINGS['auto_show'] = False
            LOGGER.info('Saving graphical output.')
            format = kwargs.get('figformat')
            width = kwargs.get('figwidth')
            height = kwargs.get('figheight')
            dpi = kwargs.get('figdpi')
            format = format.lower()

            if figall or cc:
                plt.figure(figsize=(width, height))
                prody.showCrossCorr(anm)
                plt.savefig(join(outdir, prefix + '_cc.' + format),
                            dpi=dpi,
                            format=format)
                plt.close('all')

            if figall or cm:
                plt.figure(figsize=(width, height))
                prody.showContactMap(anm)
                plt.savefig(join(outdir, prefix + '_cm.' + format),
                            dpi=dpi,
                            format=format)
                plt.close('all')

            if figall or sf:
                plt.figure(figsize=(width, height))
                prody.showSqFlucts(anm)
                plt.savefig(join(outdir, prefix + '_sf.' + format),
                            dpi=dpi,
                            format=format)
                plt.close('all')

            if figall or bf:
                plt.figure(figsize=(width, height))
                bexp = select.getBetas()
                bcal = prody.calcTempFactors(anm, select)
                plt.plot(bexp, label='Experimental')
                plt.plot(bcal,
                         label=('Theoretical (R={0:.2f})'.format(
                             np.corrcoef(bcal, bexp)[0, 1])))
                plt.legend(prop={'size': 10})
                plt.xlabel('Node index')
                plt.ylabel('Experimental B-factors')
                plt.title(pdb.getTitle() + ' B-factors')
                plt.savefig(join(outdir, prefix + '_bf.' + format),
                            dpi=dpi,
                            format=format)
                plt.close('all')
Exemple #12
0
def prody_gnm(pdb, **kwargs):
    """Perform GNM calculations for *pdb*.
    
    """

    for key in DEFAULTS:
        if not key in kwargs:
            kwargs[key] = DEFAULTS[key]

    from os.path import isdir, splitext, join

    outdir = kwargs.get("outdir")
    if not isdir(outdir):
        raise IOError("{0} is not a valid path".format(repr(outdir)))

    import numpy as np
    import prody

    LOGGER = prody.LOGGER

    selstr = kwargs.get("select")
    prefix = kwargs.get("prefix")
    cutoff = kwargs.get("cutoff")
    gamma = kwargs.get("gamma")
    nmodes = kwargs.get("nmodes")
    selstr = kwargs.get("select")
    model = kwargs.get("model")

    pdb = prody.parsePDB(pdb, model=model)
    if prefix == "_gnm":
        prefix = pdb.getTitle() + "_gnm"

    select = pdb.select(selstr)
    if select is None:
        raise ValueError("selection {0} do not match any atoms".format(repr(selstr)))
    LOGGER.info("{0} atoms will be used for GNM calculations.".format(len(select)))

    gnm = prody.GNM(pdb.getTitle())
    gnm.buildKirchhoff(select, cutoff, gamma)
    gnm.calcModes(nmodes)

    LOGGER.info("Writing numerical output.")

    if kwargs.get("outnpz"):
        prody.saveModel(gnm, join(outdir, prefix))

    prody.writeNMD(join(outdir, prefix + ".nmd"), gnm, select)

    extend = kwargs.get("extend")
    if extend:
        if extend == "all":
            extended = prody.extendModel(gnm, select, pdb)
        else:
            extended = prody.extendModel(gnm, select, select | pdb.bb)
        prody.writeNMD(join(outdir, prefix + "_extended_" + extend + ".nmd"), *extended)

    outall = kwargs.get("outall")
    delim = kwargs.get("numdelim")
    ext = kwargs.get("numext")
    format = kwargs.get("numformat")

    if outall or kwargs.get("outeig"):
        prody.writeArray(join(outdir, prefix + "_evectors" + ext), gnm.getArray(), delimiter=delim, format=format)
        prody.writeArray(join(outdir, prefix + "_evalues" + ext), gnm.getEigvals(), delimiter=delim, format=format)

    if outall or kwargs.get("outbeta"):
        from prody.utilities import openFile

        fout = openFile(prefix + "_beta.txt", "w", folder=outdir)
        fout.write("{0[0]:1s} {0[1]:4s} {0[2]:4s} {0[3]:5s} {0[4]:5s}\n".format(["C", "RES", "####", "Exp.", "The."]))
        for data in zip(
            select.getChids(),
            select.getResnames(),
            select.getResnums(),
            select.getBetas(),
            prody.calcTempFactors(gnm, select),
        ):
            fout.write("{0[0]:1s} {0[1]:4s} {0[2]:4d} {0[3]:5.2f} {0[4]:5.2f}\n".format(data))
        fout.close()

    if outall or kwargs.get("outcov"):
        prody.writeArray(
            join(outdir, prefix + "_covariance" + ext), gnm.getCovariance(), delimiter=delim, format=format
        )

    if outall or kwargs.get("outcc") or kwargs.get("outhm"):
        cc = prody.calcCrossCorr(gnm)
        if outall or kwargs.get("outcc"):
            prody.writeArray(join(outdir, prefix + "_cross-correlations" + ext), cc, delimiter=delim, format=format)
        if outall or kwargs.get("outhm"):
            prody.writeHeatmap(
                join(outdir, prefix + "_cross-correlations.hm"),
                cc,
                resnum=select.getResnums(),
                xlabel="Residue",
                ylabel="Residue",
                title=gnm.getTitle() + " cross-correlations",
            )

    if outall or kwargs.get("kirchhoff"):
        prody.writeArray(join(outdir, prefix + "_kirchhoff" + ext), gnm.getKirchhoff(), delimiter=delim, format=format)

    if outall or kwargs.get("outsf"):
        prody.writeArray(
            join(outdir, prefix + "_sqfluct" + ext), prody.calcSqFlucts(gnm), delimiter=delim, format=format
        )

    figall = kwargs.get("figall")
    cc = kwargs.get("figcc")
    sf = kwargs.get("figsf")
    bf = kwargs.get("figbeta")
    cm = kwargs.get("figcmap")
    modes = kwargs.get("figmode")

    if figall or cc or sf or bf or cm or modes:
        try:
            import matplotlib.pyplot as plt
        except ImportError:
            LOGGER.warning("Matplotlib could not be imported. " "Figures are not saved.")
        else:
            prody.SETTINGS["auto_show"] = False
            LOGGER.info("Saving graphical output.")
            format = kwargs.get("figformat")
            width = kwargs.get("figwidth")
            height = kwargs.get("figheight")
            dpi = kwargs.get("figdpi")
            format = format.lower()

            if figall or cc:
                plt.figure(figsize=(width, height))
                prody.showCrossCorr(gnm)
                plt.savefig(join(outdir, prefix + "_cc." + format), dpi=dpi, format=format)
                plt.close("all")

            if figall or cm:
                plt.figure(figsize=(width, height))
                prody.showContactMap(gnm)
                plt.savefig(join(outdir, prefix + "_cm." + format), dpi=dpi, format=format)
                plt.close("all")

            if figall or sf:
                plt.figure(figsize=(width, height))
                prody.showSqFlucts(gnm)
                plt.savefig(join(outdir, prefix + "_sf." + format), dpi=dpi, format=format)
                plt.close("all")

            if figall or bf:
                plt.figure(figsize=(width, height))
                bexp = select.getBetas()
                bcal = prody.calcTempFactors(gnm, select)
                plt.plot(bexp, label="Experimental")
                plt.plot(bcal, label=("Theoretical (corr coef = {0:.2f})".format(np.corrcoef(bcal, bexp)[0, 1])))
                plt.legend(prop={"size": 10})
                plt.xlabel("Node index")
                plt.ylabel("Experimental B-factors")
                plt.title(pdb.getTitle() + " B-factors")
                plt.savefig(join(outdir, prefix + "_bf." + format), dpi=dpi, format=format)
                plt.close("all")

            if modes:
                indices = []
                items = modes.split()
                items = sum([item.split(",") for item in items], [])
                for item in items:
                    try:
                        item = item.split("-")
                        if len(item) == 1:
                            indices.append(int(item[0]) - 1)
                        elif len(item) == 2:
                            indices.extend(range(int(item[0]) - 1, int(item[1])))
                    except:
                        pass
                for index in indices:
                    try:
                        mode = gnm[index]
                    except:
                        pass
                    else:
                        plt.figure(figsize=(width, height))
                        prody.showMode(mode)
                        plt.grid()
                        plt.savefig(
                            join(outdir, prefix + "_mode_" + str(mode.getIndex() + 1) + "." + format),
                            dpi=dpi,
                            format=format,
                        )
                        plt.close("all")
Exemple #13
0
    def prody_anm(self, variables, txtOutput):
        '''
        PRODY DRIVER is the function to read in variables from GUI input and
        used to run a prody normal mode calculation using the anisotropic network model
        (ANM) on a structure provide in a pdb file.

        INPUT:  variable descriptions:

        pdbfile:          input pdb file (reference)

        OUTPUT:
                        model_anm_extended_bb.nmd
                        model_traverse.dcd
                        model_samp.dcd
                        model_samp.pdb
                        model_anm_sqflucts.txt
                        model_anm_kirchhoff.txt
                        model_anm_hessian.txt
                        model_anm_cross-correlations.hm
                        model_anm_cross-correlations.txt
                        model_anm_covariance.txt
                        model_anm_beta.txt
                        model_anm_evalues.txt
                        model_anm_evectors.txt
                        model_anm_extended_all.nmd
                        model_anm.nmd

        txtOutput:        TK handler for output to GUI textbox

        files stored in ~/runname/prody directory:
        outfile:          output filename

        '''
        log = self.log
        pgui = self.run_utils.print_gui

        # start gui output
        pgui("\n%s \n" % ('=' * 60))
        pgui("DATA FROM RUN: %s \n\n" % time.asctime( time.gmtime( time.time() ) ))

        mvars = self.mvars
        #path = os.path.join(os.getcwd(),mvars.runname, 'prody')
        path = os.path.join(mvars.runname, 'prody')
        direxist = os.path.exists(path)
        if(direxist == 0):
            try:
                result = os.system('mkdir -p ' + path)
            except:
                message = 'can not create project directory: ' + path
                message += '\nstopping here\n'
                print_failure(message, txtOutput)
            if(result != 0):
                message = 'can not create project directory: ' + path
                message += '\nstopping here\n'
                print_failure(message, txtOutput)
        if mvars.advanced_usage == 1:
            run_cmd = prody_exe + mvars.advanced_usage_cmd
            os.system(run_cmd)
            run_cmd = 'mv *.nmd *.txt *.hm prody'
            os.system(run_cmd)
            exit()

        # display progress
        fraction_done = (0 + 1) * 1.0 / 10.0
        report_string = 'STATUS\t%f' % fraction_done
        pgui(report_string)

        prody.confProDy(verbosity='none')  #option to set silent verbosity
        model = mvars.pdbfile[0:len(mvars.pdbfile) - 4]
        run_cmd = prody_exe + ' anm ' + \
            mvars.pdbfile + ' -t all -n ' + str(mvars.number_modes) + ' -a'
        log.info('staring prody_exe %s' % run_cmd)
        prody_run = subprocess.Popen(run_cmd,shell=True,executable='/bin/bash')
        prody_run.wait() 
        #prody.confProDy(verbosity='none')  #option to set silent verbosity
        file_anm = model + '_anm_extended_all.nmd'

        # display progress
        fraction_done = (1 + 1) * 1.0 / 10.0
        report_string = 'STATUS\t%f' % fraction_done
        pgui(report_string)

        # parse nmd file with resuts extended to all atoms
        log.info('staring prody.parseNMD %s' % file_anm)
        mod, ag = prody.parseNMD(file_anm, type=None)
        allatoms = ag.copy()
        # set up to randomly sample number_conformations_samp modes
        log.info('staring prody.sampleModes')
        ensemble = prody.sampleModes(mod[:mvars.number_modes],
                                     ag,
                                     n_confs=mvars.number_conformations_samp,
                                     rmsd=mvars.rmsd_conformations_samp)
        ensemble
        log.info('staring prody ensemble and writing pdb/dcd files')
        allatoms.addCoordset(ensemble)
        prody.writePDB('model_samp.pdb', allatoms)
        prody.writeDCD('model_samp.dcd', allatoms)
        trajectory_names = []
        
        # display progress
        fraction_done = (1 + 2) * 1.0 / 10.0
        report_string = 'STATUS\t%f' % fraction_done
        pgui(report_string)

        log.info('starting prody traverse')
        for i in xrange(0, mvars.number_modes):
            #print i
            # setup to tranverse slowest mode
            traverse = prody.traverseMode(
                mod[i],
                allatoms,
                n_steps=mvars.number_steps_traverse,
                rmsd=mvars.rmsd_traverse)
            traverse
            prody.writeDCD('traverse.dcd', traverse)
            this_dcd = str(os.path.join(path, 'traverse_' + str(i) + '.dcd'))
            cmd = 'mv traverse.dcd ' + this_dcd
            os.system(cmd)
            trajectory_names.append(this_dcd)

        # display progress
        fraction_done = (1 + 7) * 1.0 / 10.0
        report_string = 'STATUS\t%f' % fraction_done
        pgui(report_string)

        m1 = sasmol.SasMol(0)
        m2 = sasmol.SasMol(0)
        m1.read_pdb(mvars.pdbfile)
        m2.read_pdb(mvars.pdbfile,fastread=True)

        mvars.dcdfile = mvars.runname + '.dcd'
        log.info('opening new dcd file to store trajectory: %s' %
                 os.path.join(self.runpath, mvars.dcdfile))

        outfile_name = str(os.path.join(path, mvars.dcdfile))
        dcdoutfile = m2.open_dcd_write(outfile_name)
        count = 0
        coor = numpy.zeros((1,m2.natoms(),3),numpy.float32)
        for this_trajectory_name in trajectory_names:

            dcdfile = m1.open_dcd_read(this_trajectory_name)
            number_of_frames = dcdfile[2]

            for j in xrange(number_of_frames):
                m1.read_dcd_step(dcdfile,j)
                coor[0,:,:] = m1.coor()[0]
                m2.setCoor(coor)
                m2.write_dcd_step(dcdoutfile,0, count + 1)
                count += 1

        m2.close_dcd_write(dcdoutfile)

        log.info('moving files to runname / prody')

        file_anm = model + '_anm.nmd'
        mod, ag = prody.parseNMD(file_anm, type=None)
        mod1 = prody.parsePDB(mvars.pdbfile)
        calphas = mod1.select('calpha')
        bb_anm, bb_atoms = prody.extendModel(mod, calphas, mod1.select(
            'backbone'))  # extend model to backbone atoms
        prody.writeNMD('model_anm_extended_bb.nmd', bb_anm, bb_atoms)

        cmd = 'mv model_samp.pdb ' + path + os.sep + os.path.basename(model) + '_samp.pdb'
        os.system(cmd)

        cmd = 'mv model_samp.dcd ' + path + os.sep + os.path.basename(model) + '_samp.dcd'
        os.system(cmd)

        cmd = 'mv model_anm_extended_bb.nmd ' + \
            model + '_anm_extended_bb.nmd'
        os.system(cmd)

        cmd = 'mv *.hm *.nmd *.txt ' + path + os.sep
        os.system(cmd)
        
        # display progress
        fraction_done = (1 + 9) * 1.0 / 10.0
        report_string = 'STATUS\t%f' % fraction_done
        pgui(report_string)

        return
Exemple #14
0
def prody_gnm(pdb, **kwargs):
    """Perform GNM calculations for *pdb*.

    """

    for key in DEFAULTS:
        if not key in kwargs:
            kwargs[key] = DEFAULTS[key]

    from os.path import isdir, splitext, join
    outdir = kwargs.get('outdir')
    if not isdir(outdir):
        raise IOError('{0} is not a valid path'.format(repr(outdir)))

    import numpy as np
    import prody
    LOGGER = prody.LOGGER

    selstr = kwargs.get('select')
    prefix = kwargs.get('prefix')
    cutoff = kwargs.get('cutoff')
    gamma = kwargs.get('gamma')
    nmodes = kwargs.get('nmodes')
    selstr = kwargs.get('select')
    model = kwargs.get('model')
    altloc = kwargs.get('altloc')
    zeros = kwargs.get('zeros')

    pdb = prody.parsePDB(pdb, model=model, altloc=altloc)
    if prefix == '_gnm':
        prefix = pdb.getTitle() + '_gnm'

    select = pdb.select(selstr)
    if select is None:
        raise ValueError('selection {0} do not match any atoms'.format(
            repr(selstr)))
    LOGGER.info('{0} atoms will be used for GNM calculations.'.format(
        len(select)))

    gnm = prody.GNM(pdb.getTitle())

    nproc = kwargs.get('nproc')
    if nproc:
        try:
            from threadpoolctl import threadpool_limits
        except ImportError:
            raise ImportError(
                'Please install threadpoolctl to control threads')

        with threadpool_limits(limits=nproc, user_api="blas"):
            gnm.buildKirchhoff(select, cutoff, gamma)
            gnm.calcModes(nmodes, zeros=zeros)
    else:
        gnm.buildKirchhoff(select, cutoff, gamma)
        gnm.calcModes(nmodes, zeros=zeros)

    LOGGER.info('Writing numerical output.')

    if kwargs.get('outnpz'):
        prody.saveModel(gnm, join(outdir, prefix))

    if kwargs.get('outscipion'):
        prody.writeScipionModes(outdir, gnm)

    prody.writeNMD(join(outdir, prefix + '.nmd'), gnm, select)

    extend = kwargs.get('extend')
    if extend:
        if extend == 'all':
            extended = prody.extendModel(gnm, select, pdb)
        else:
            extended = prody.extendModel(gnm, select, select | pdb.bb)
        prody.writeNMD(join(outdir, prefix + '_extended_' + extend + '.nmd'),
                       *extended)

    outall = kwargs.get('outall')
    delim = kwargs.get('numdelim')
    ext = kwargs.get('numext')
    format = kwargs.get('numformat')

    if outall or kwargs.get('outeig'):
        prody.writeArray(join(outdir, prefix + '_evectors' + ext),
                         gnm.getArray(),
                         delimiter=delim,
                         format=format)
        prody.writeArray(join(outdir, prefix + '_evalues' + ext),
                         gnm.getEigvals(),
                         delimiter=delim,
                         format=format)

    if outall or kwargs.get('outbeta'):
        from prody.utilities import openFile
        fout = openFile(prefix + '_beta' + ext, 'w', folder=outdir)
        fout.write(
            '{0[0]:1s} {0[1]:4s} {0[2]:4s} {0[3]:5s} {0[4]:5s}\n'.format(
                ['C', 'RES', '####', 'Exp.', 'The.']))
        for data in zip(select.getChids(), select.getResnames(),
                        select.getResnums(), select.getBetas(),
                        prody.calcTempFactors(gnm, select)):
            fout.write(
                '{0[0]:1s} {0[1]:4s} {0[2]:4d} {0[3]:5.2f} {0[4]:5.2f}\n'.
                format(data))
        fout.close()

    if outall or kwargs.get('outcov'):
        prody.writeArray(join(outdir, prefix + '_covariance' + ext),
                         gnm.getCovariance(),
                         delimiter=delim,
                         format=format)

    if outall or kwargs.get('outcc') or kwargs.get('outhm'):
        cc = prody.calcCrossCorr(gnm)
        if outall or kwargs.get('outcc'):
            prody.writeArray(join(outdir,
                                  prefix + '_cross-correlations' + ext),
                             cc,
                             delimiter=delim,
                             format=format)
        if outall or kwargs.get('outhm'):
            prody.writeHeatmap(join(outdir, prefix + '_cross-correlations.hm'),
                               cc,
                               resnum=select.getResnums(),
                               xlabel='Residue',
                               ylabel='Residue',
                               title=gnm.getTitle() + ' cross-correlations')

    if outall or kwargs.get('kirchhoff'):
        prody.writeArray(join(outdir, prefix + '_kirchhoff' + ext),
                         gnm.getKirchhoff(),
                         delimiter=delim,
                         format=format)

    if outall or kwargs.get('outsf'):
        prody.writeArray(join(outdir, prefix + '_sqfluct' + ext),
                         prody.calcSqFlucts(gnm),
                         delimiter=delim,
                         format=format)

    figall = kwargs.get('figall')
    cc = kwargs.get('figcc')
    sf = kwargs.get('figsf')
    bf = kwargs.get('figbeta')
    cm = kwargs.get('figcmap')
    modes = kwargs.get('figmode')

    if figall or cc or sf or bf or cm or modes:
        try:
            import matplotlib.pyplot as plt
        except ImportError:
            LOGGER.warning('Matplotlib could not be imported. '
                           'Figures are not saved.')
        else:
            prody.SETTINGS['auto_show'] = False
            LOGGER.info('Saving graphical output.')
            format = kwargs.get('figformat')
            width = kwargs.get('figwidth')
            height = kwargs.get('figheight')
            dpi = kwargs.get('figdpi')
            format = format.lower()

            if figall or cc:
                plt.figure(figsize=(width, height))
                prody.showCrossCorr(gnm)
                plt.savefig(join(outdir, prefix + '_cc.' + format),
                            dpi=dpi,
                            format=format)
                plt.close('all')

            if figall or cm:
                plt.figure(figsize=(width, height))
                prody.showContactMap(gnm)
                plt.savefig(join(outdir, prefix + '_cm.' + format),
                            dpi=dpi,
                            format=format)
                plt.close('all')

            if figall or sf:
                plt.figure(figsize=(width, height))
                prody.showSqFlucts(gnm)
                plt.savefig(join(outdir, prefix + '_sf.' + format),
                            dpi=dpi,
                            format=format)
                plt.close('all')

            if figall or bf:
                plt.figure(figsize=(width, height))
                bexp = select.getBetas()
                bcal = prody.calcTempFactors(gnm, select)
                plt.plot(bexp, label='Experimental')
                plt.plot(bcal,
                         label=('Theoretical (corr coef = {0:.2f})'.format(
                             np.corrcoef(bcal, bexp)[0, 1])))
                plt.legend(prop={'size': 10})
                plt.xlabel('Node index')
                plt.ylabel('Experimental B-factors')
                plt.title(pdb.getTitle() + ' B-factors')
                plt.savefig(join(outdir, prefix + '_bf.' + format),
                            dpi=dpi,
                            format=format)
                plt.close('all')

            if modes:
                indices = []
                items = modes.split()
                items = sum([item.split(',') for item in items], [])
                for item in items:
                    try:
                        item = item.split('-')
                        if len(item) == 1:
                            indices.append(int(item[0]) - 1)
                        elif len(item) == 2:
                            indices.extend(
                                list(range(int(item[0]) - 1, int(item[1]))))
                    except:
                        pass
                for index in indices:
                    try:
                        mode = gnm[index]
                    except:
                        pass
                    else:
                        plt.figure(figsize=(width, height))
                        prody.showMode(mode)
                        plt.grid()
                        plt.savefig(join(
                            outdir, prefix + '_mode_' +
                            str(mode.getIndex() + 1) + '.' + format),
                                    dpi=dpi,
                                    format=format)
                        plt.close('all')
Exemple #15
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        T = T + 1

#
# saving data and creating nmd file for visualization of modes
#

    data_path = "{}/data/".format(str(name))
    # creating and saving nmd file
    CG_modes = clu_vectors.flatten()
    anm1 = prody.NMA("CG_modes_search")
    anm1.addEigenpair(CG_modes)

    filename = data_path + "CG_NMD.nmd"
    clus = atoms(clusters_pos)
    prody.writeNMD(filename, anm1, clus)

    # saving data
    np.savetxt(data_path + "distances_and_Nclu.csv", np.array([R,
                                                               clu_lengths]))
    np.savetxt(data_path + "clu_pos.csv", np.array(clusters_pos))
    np.savetxt(data_path + "clu_masses.csv", np.array(clusters_masses))
    np.savetxt(data_path + "clu_vectors.csv", np.array(clu_vectors))
    # creo file csv con lista di atomi in cluster - un fil per ogni cluster
    for i in range(len(cluster_list)):
        np.savetxt(data_path + "clulist/" + "clu_list{}.csv".format(str(i)),
                   np.array(cluster_list[i]))

    #
    # code for figures and further analysis is not uploaded
    #