コード例 #1
0
ファイル: routines.py プロジェクト: crosvera/ProDy
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')
コード例 #2
0
ファイル: routines.py プロジェクト: crosvera/ProDy
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')                  
コード例 #3
0
ファイル: maincalculation.py プロジェクト: marmy28/NOMA
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')
コード例 #4
0
ファイル: prody_pca.py プロジェクト: barettog1/ProDy
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')
コード例 #5
0
ファイル: prody_pca.py プロジェクト: SHZ66/ProDy
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')
コード例 #6
0
ファイル: prody_anm.py プロジェクト: njekin/ProDy
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')
コード例 #7
0
ファイル: prody_anm.py プロジェクト: emalacs/FF_creator
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')
コード例 #8
0
            random_rotation = Transformation(random_rotation_matrix)
            applyTransformation(random_rotation, protein)

        ca_atoms = protein.select('protein and name CA')
        protein_anm = ANM('%s ca' % args.structure_name)
        protein_anm.buildHessian(ca_atoms)
        protein_anm.calcModes(n_modes=args.normal_modes)

        print 'Normal modes calculated'
        
        protein_anm_ext, protein_all = extendModel(protein_anm, ca_atoms, protein, norm=True)

        print 'Normal modes extended'
        if args.save_models:
            saveAtoms(protein, args.structure_name)
            saveModel(protein_anm, args.structure_name)
            saveModel(protein_anm_ext, args.structure_name)

        ens = sampleModes(protein_anm_ext, atoms=protein.protein, n_confs=args.num_decoys, rmsd=args.rmsd)

        print 'Normal modes sampled'

        print ens.getCoordsets().shape
        protein.addCoordset(ens.getCoordsets())
        protein.all.setBetas(0)
        protein.ca.setBetas(1)

        print 'Writing files'

        if not os.path.exists(args.output_folder_path):
            os.mkdir(args.output_folder_path)
コード例 #9
0
ファイル: prody_gnm.py プロジェクト: njekin/ProDy
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")
コード例 #10
0
    chain_coords = prody.parsePDB(pdb_FN)
    trans.apply(chain_coords)
    outFN = os.path.join(output_dir,os.path.basename(pdb_FN))
    prody.writePDB(outFN, chain_coords)

print 'There are %d structures in the ensemble\n'%len(ensemble)

print "\n*** Principal Components Analysis ***"
pca_FN = os.path.join('prody.pca.npz')
if os.path.exists(pca_FN):
  pca = prody.loadModel(pca_FN)
else:
  pca = prody.PCA()
  pca.buildCovariance(ensemble) # Build covariance matrix
  pca.calcModes() # Calculate modes
  prody.saveModel(pca, filename=pca_FN[:-8])

if not os.path.isdir('figures'):
  os.makedirs('figures')

import matplotlib.pyplot as plt

if not os.path.isfile('rmsd.png'):
  rmsd = prody.calcRMSD(ensemble)
  plt.clf()
  plt.plot(rmsd);
  plt.xlabel('Conformation index');
  plt.ylabel('RMSD (A)');
  plt.title('RMSD %f (%f)'%(rmsd.mean(), rmsd.std()))
  plt.savefig('figures/rmsd.png')
コード例 #11
0
ファイル: prody_gnm.py プロジェクト: SHZ66/ProDy
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')