def prody_gnm(opt):
"""Perform GNM calculations based on command line arguments."""
outdir = opt.outdir
if not os.path.isdir(outdir):
opt.subparser('{0:s} is not a valid path'.format(outdir))
import numpy as np
import prody
LOGGER = prody.LOGGER
pdb, prefix = opt.pdb, 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 == '_gnm':
prefix = pdb.getTitle() + '_gnm'
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 GNM calculations.'
.format(len(select)))
gnm = prody.GNM(pdb.getTitle())
gnm.buildKirchhoff(select, cutoff, gamma)
gnm.calcModes(nmodes)
LOGGER.info('Writing numerical output.')
if opt.npz:
prody.saveModel(gnm)
prody.writeNMD(os.path.join(outdir, prefix + '.nmd'), gnm, 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),
gnm.getArray(), delimiter=delim, format=format)
prody.writeArray(os.path.join(outdir, prefix + '_evalues'+ext),
gnm.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(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 opt.covar:
prody.writeArray(os.path.join(outdir, prefix + '_covariance'+ext),
gnm.getCovariance(), delimiter=delim, format=format)
if outall or opt.ccorr:
prody.writeArray(os.path.join(outdir, prefix + '_cross-correlations'
+ ext),
prody.calcCrossCorr(gnm), delimiter=delim,
format=format)
if outall or opt.kirchhoff:
prody.writeArray(os.path.join(outdir, prefix + '_kirchhoff'+ext),
gnm.getKirchhoff(), delimiter=delim, format=format)
if outall or opt.sqflucts:
prody.writeArray(os.path.join(outdir, prefix + '_sqfluct'+ext),
prody.calcSqFlucts(gnm), delimiter=delim,
format=format)
figall, cc, sf, bf, cm, modes = \
opt.figures, opt.cc, opt.sf, opt.bf, opt.cm, opt.modes
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:
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(gnm)
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(gnm)
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(gnm)
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(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(os.path.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(os.path.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')
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')
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')