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')
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')
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')