def calc_bfactors_from_alphaCAs(pdbid):
"""
Calculate b-factors from the alpha CA network
Input
-----
pdbid: fname or pdbID
PDB file or pdbID
Output
------
bfact_alphaCA: numpy
bfactors calculated from the alpha carbon network
"""
calphas = prdy.parsePDB(pdbid).select('calpha and chain A')
gnm1 = prdy.GNM()
gnm1.buildKirchhoff(calphas)
gnm1.calcModes()
return prdy.calcTempFactors(gnm1[:],calphas)
def calc_bfactors_from_evoD(pdbid,evod_fname,nres):
"""
Calculate b-factors from evoD
Input
-----
pdbid: fname or pdbID
PDB file or pdbID
Output
------
bfact_evfold: numpy
bfactors calculated from the alpha carbon network
"""
calphas = prdy.parsePDB(pdbid).select('calpha and chain A')
n = nres
_build_kirchhoff(evod_fname,n)
kirchhoff = prdy.parseSparseMatrix('evfold_kirchhoff.txt',
symmetric=True)
gnm3 = prdy.GNM('GNM for RASH_HUMAN (5p21)')
gnm3.setKirchhoff(kirchhoff)
gnm3.calcModes()
return prdy.calcTempFactors(gnm3[:],calphas)
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')
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')
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')
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')
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")
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')