def prody_fetch(opt):
"""Fetch PDB files from PDB FTP server."""
import prody
pdblist = opt.pdb
listfn = opt.listfn
if listfn:
if os.path.isfile(listfn):
inp = prody.openFile(listfn)
for line in inp:
line = line.strip()
for s in line.split(','):
for pdb in s.split():
if len(pdb) == 4:
pdblist.append(pdb)
inp.close()
else:
opt.subparser.error("No such file: '{0:s}'".format(listfn))
prody.fetchPDB(pdblist, opt.folder, compressed=opt.gzip, copy=True)
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')
