def calcPCA(self, ensemble, logger):
'''
calcPCA:
#ensemble: prody ensmeble with structure information
calculate PCA for a set of structures
return: prody.pca object
'''
logger.info("Calculate PCA")
PCAname = ensemble.getTitle()
pca = prody.PCA(PCAname)
pca.buildCovariance(ensemble)
logger.info("PCA")
pca.calcModes()
logger.info(repr(pca))
outputname = PCAname + "_pca_modes.nmd"
prody.writeNMD(outputname, pca[:10], self.selection_ref_structure)
if self.vmd == True:
prody.viewNMDinVMD(outputname)
logger.info(f"PCA is saved in: {outputname}")
return pca, outputname
def calcPCA(filenameEnsemble):
ubi = dy.parsePDB(filenameEnsemble, subset='ca')
ensemble = dy.Ensemble('ensemble')
ensemble.setCoords(ubi.getCoords())
ensemble.addCoordset(ubi.getCoordsets())
ensemble.iterpose()
pca = dy.PCA('Ubiquitin')
pca.buildCovariance(ensemble)
pca.calcModes()
return pca
def test_eigenvalues(self):
# do it with PCA metric
my_cov_matrix = PCAMetric.create_covariance_matrix(
testPCAMetric.coordsets)
biggest_eigenvalue = PCAMetric.calculate_biggest_eigenvalue(
my_cov_matrix)
# Do it with prody
pca = prody.PCA('pcametric_pca')
pca.buildCovariance(testPCAMetric.ensemble)
pca.calcModes(n_modes=1)
self.assertAlmostEqual(pca.getEigvals()[0], biggest_eigenvalue, 10)
def test_covariance_matrix_vs_prody(self):
# do it with PCA metric
my_cov_matrix = PCAMetric.create_covariance_matrix(
testPCAMetric.coordsets)
# Do it with prody
pca = prody.PCA('pcametric_pca')
pca.buildCovariance(testPCAMetric.ensemble)
prody_cov_matrix = pca._cov
# Compare
numpy.testing.assert_almost_equal(my_cov_matrix, prody_cov_matrix, 10)
def get_pca_fluctuations(ensemble, limit=3):
"""
Get squared fluctuations of each residue according to a PCA on the ensemble
Parameters
----------
ensemble
pd.PDBEnsemble object
limit
number of PCA modes to consider
Returns
-------
array of squared fluctuations per aligned residue
"""
pca = pd.PCA()
pca.buildCovariance(ensemble)
pca.calcModes()
return pd.calcSqFlucts(pca[:limit])
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')
os.mkdir(output_dir)
for pdb_FN, conf in zip(new_pdb_FNs,ensemble):
trans = conf.getTransformation()
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)');
