def points_from_selection(*args, **kwargs):
"""Create a list of points from selected atoms in a format suitable for ``qhull``.
points_from_selection(topology, structure, selection="name CA", filename="points.dat", scale=None)
:Arguments:
- psf: Charmm topology file
- pdb: coordinates
- selection: MDAnalysis selectAtoms() selection string [C-alpha atoms]
- filename: name of the output file; used as input for :class:`ConvexHull`
- scale: scale points around the centre of geometry; values of 0.5 - 0.7 typically ensure that
the convex hull is inside the protein; default is to not to scale, i.e. scale = 1.
"""
from MDAnalysis import asUniverse
u = asUniverse(*args, permissive=kwargs.pop('permissive', None))
coordinates = u.selectAtoms(kwargs.pop('selection', "name CA")).coordinates()
write_coordinates(kwargs.pop('filename', "points.dat"), coordinates, scale=kwargs.pop('scale',None))
def points_from_selection(*args, **kwargs):
"""Create a list of points from selected atoms in a format suitable for ``qhull``.
points_from_selection(topology, structure, selection="name CA", filename="points.dat", scale=None)
:Arguments:
- psf: Charmm topology file
- pdb: coordinates
- selection: MDAnalysis selectAtoms() selection string [C-alpha atoms]
- filename: name of the output file; used as input for :class:`ConvexHull`
- scale: scale points around the centre of geometry; values of 0.5 - 0.7 typically ensure that
the convex hull is inside the protein; default is to not to scale, i.e. scale = 1.
"""
from MDAnalysis import asUniverse
u = asUniverse(*args, permissive=kwargs.pop('permissive', None))
coordinates = u.selectAtoms(kwargs.pop('selection',
"name CA")).coordinates()
write_coordinates(kwargs.pop('filename', "points.dat"),
coordinates,
scale=kwargs.pop('scale', None))
def __init__(self, pdb, delta=1.0, atomselection='resname HOH and name O',
metadata=None, padding=1.0, sigma=None):
"""Construct the density from psf and pdb and the atomselection.
DC = BfactorDensityCreator(pdb, delta=<delta>, atomselection=<MDAnalysis selection>,
metadata=<dict>, padding=2, sigma=None)
density = DC.Density()
:Arguments:
pdb
PDB file or :class:`MDAnalysis.Universe`; a PDB is read with the
simpl PDB reader. If the Bio.PDB reader is required, either set
the *permissive_pdb_reader* flag to ``False`` in
:data:`MDAnalysis.core.flags` or supply a Universe
that was created with the `permissive` = ``False`` keyword.
atomselection
selection string (MDAnalysis syntax) for the species to be analyzed
delta
bin size for the density grid in Angstroem (same in x,y,z) [1.0]
metadata
dictionary of additional data to be saved with the object
padding
increase histogram dimensions by padding (on top of initial box size)
sigma
width (in Angstrom) of the gaussians that are used to build up the
density; if None then uses B-factors from pdb
For assigning X-ray waters to MD densities one might have to use a sigma
of about 0.5 A to obtain a well-defined and resolved x-ray water density
that can be easily matched to a broader density distribution.
"""
from MDAnalysis import asUniverse
u = asUniverse(pdb)
group = u.selectAtoms(atomselection)
coord = group.coordinates()
logger.info("Selected %d atoms (%s) out of %d total." %
(coord.shape[0], atomselection, len(u.atoms)))
smin = numpy.min(coord, axis=0) - padding
smax = numpy.max(coord, axis=0) + padding
BINS = fixedwidth_bins(delta, smin, smax)
arange = zip(BINS['min'], BINS['max'])
bins = BINS['Nbins']
# get edges by doing a fake run
grid, self.edges = numpy.histogramdd(numpy.zeros((1, 3)),
bins=bins, range=arange, normed=False)
self.delta = numpy.diag(map(lambda e: (e[-1] - e[0]) / (len(e) - 1), self.edges))
self.midpoints = map(lambda e: 0.5 * (e[:-1] + e[1:]), self.edges)
self.origin = map(lambda m: m[0], self.midpoints)
numframes = 1
if sigma is None:
# histogram individually, and smear out at the same time
# with the appropriate B-factor
if numpy.any(group.bfactors == 0.0):
wmsg = "Some B-factors are Zero (will be skipped)."
logger.warn(wmsg)
warnings.warn(wmsg, category=MissingDataWarning)
rmsf = Bfactor2RMSF(group.bfactors)
grid *= 0.0 # reset grid
self.g = self._smear_rmsf(coord, grid, self.edges, rmsf)
else:
# histogram 'delta functions'
grid, self.edges = numpy.histogramdd(coord, bins=bins, range=arange, normed=False)
logger.info("Histogrammed %6d atoms from pdb." % len(group.atoms))
# just a convolution of the density with a Gaussian
self.g = self._smear_sigma(grid, sigma)
try:
metadata['pdb'] = pdb
except TypeError:
metadata = {'pdb': pdb}
metadata['atomselection'] = atomselection
metadata['numframes'] = numframes
metadata['sigma'] = sigma
self.metadata = metadata
logger.info("Histogram completed (initial density in Angstrom**-3)")
