def create(self):
"""Loop through trajectory and build all densities.
.. SeeAlso::
:class:`DensityCollector`
"""
u = self.universe
pm = CustomProgressMeter(
u.trajectory.numframes,
interval=10,
format="Histogramming %(other)s atoms in frame %(step)5d/%(numsteps)d [%(percentage)5.1f%%]\r",
)
for ts in u.trajectory:
status = ""
for c in self.collectors:
natoms = c.collect()
status += "%s=%d " % (c.name, natoms)
pm.echo(ts.frame, status)
self.densities = {}
for c in self.collectors:
c.finish()
self.densities[c.name] = c.Density()
# should save precious files!!!
return self.densities
def _smear_rmsf(self, coordinates, grid, edges, rmsf):
# smear out each water with its individual Gaussian
# (slower than smear_sigma)
g = numpy.zeros(grid.shape) # holds the smeared out density
N, D = coordinates.shape
pm = CustomProgressMeter(
N,
interval=1,
offset=1,
format="Smearing out water position %(step)4d/%(numsteps)5d with RMSF %(other)4.2f A [%(percentage)5.2f%%\r",
)
for iwat, coord in enumerate(coordinates):
g += numpy.fromfunction(self._gaussian_cartesian, grid.shape, dtype=numpy.int, c=coord, sigma=rmsf[iwat])
pm.echo(iwat, rmsf[iwat])
return g
def _smear_sigma(self, grid, sigma):
# smear out points
# (not optimized -- just to test the principle; faster approach could use
# convolution of the whole density with a single Gaussian via FFTs:
# rho_smeared = F^-1[ F[g]*F[rho] ]
g = numpy.zeros(grid.shape) # holds the smeared out density
pos = numpy.where(grid <> 0) # position in histogram (as bin numbers)
pm = CustomProgressMeter(
len(pos[0]),
interval=1,
offset=1,
format="Smearing out water position %(step)4d/%(numsteps)5d with RMSF %(other)4.2f A [%(percentage)5.2f%%\r",
)
for iwat in xrange(len(pos[0])): # super-ugly loop
p = tuple([wp[iwat] for wp in pos])
g += grid[p] * numpy.fromfunction(self._gaussian, grid.shape, dtype=numpy.int, p=p, sigma=sigma)
pm.echo(iwat, sigma)
return g
def density_from_Universe(
universe,
delta=1.0,
atomselection="name OH2",
metadata=None,
padding=2.0,
cutoff=0,
soluteselection=None,
verbosity=3,
use_kdtree=True,
**kwargs
):
"""Create a density grid from a MDAnalysis.Universe object.
density_from_dcd(universe, delta=1.0, atomselection='name OH2', ...) --> density
:Arguments:
universe
:class:`MDAnalysis.Universe` object with a trajectory
:Keywords:
atomselection
selection string (MDAnalysis syntax) for the species to be analyzed
["name OH2"]
delta
approximate bin size for the density grid in Angstroem (same in x,y,z)
(It is slightly adjusted when the box length is not an integer multiple
of delta.) [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)
in Angstroem [2.0]
soluteselection
MDAnalysis selection for the solute, e.g. "protein" [``None``]
cutoff
With *cutoff*, select '<atomsel> NOT WITHIN <cutoff> OF <soluteselection>'
(Special routines that are faster than the standard AROUND selection) [0]
verbosity: int
level of chattiness; 0 is silent, 3 is verbose [3]
parameters
dict with some special parameters for :class:`~hop.sitemap.Density` (see doc)
kwargs
metadata, parameters are modified and passed on to :class:`~hop.sitemap.Density`
:Returns: :class:`hop.sitemap.Density`
"""
try:
universe.selectAtoms("all")
universe.trajectory.ts
except AttributeError:
errmsg = "density_from_Universe(): The universe must be a proper MDAnalysis.Universe instance."
logger.fatal(errmsg)
raise TypeError(errmsg)
u = universe
if cutoff > 0 and soluteselection is not None:
# special fast selection for '<atomsel> not within <cutoff> of <solutesel>'
notwithin_coordinates = notwithin_coordinates_factory(
u, atomselection, soluteselection, cutoff, use_kdtree=use_kdtree
)
def current_coordinates():
return notwithin_coordinates()
else:
group = u.selectAtoms(atomselection)
def current_coordinates():
return group.coordinates()
coord = current_coordinates()
logger.info(
"Selected %d atoms out of %d atoms (%s) from %d total.",
coord.shape[0],
len(u.selectAtoms(atomselection)),
atomselection,
len(u.atoms),
)
# mild warning; typically this is run on RMS-fitted trajectories and
# so the box information is rather meaningless
box, angles = u.trajectory.ts.dimensions[:3], u.trajectory.ts.dimensions[3:]
if tuple(angles) <> (90.0, 90.0, 90.0):
logger.warn("Non-orthorhombic unit-cell --- make sure that it has been remapped properly!")
# Make the box bigger to avoid as much as possible 'outlier'. This
# is important if the sites are defined at a high density: in this
# case the bulk regions don't have to be close to 1 * n0 but can
# be less. It's much more difficult to deal with outliers. The
# ideal solution would use images: implement 'looking across the
# periodic boundaries' but that gets complicate when the box
# rotates due to RMS fitting.
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"]
# create empty grid with the right dimensions (and get the edges)
grid, edges = numpy.histogramdd(numpy.zeros((1, 3)), bins=bins, range=arange, normed=False)
grid *= 0.0
h = grid.copy()
pm = CustomProgressMeter(
u.trajectory.numframes,
interval=10,
format="Histograming %(other)d atoms in frame %(step)5d/%(numsteps)d [%(percentage)5.1f%%]\r",
)
for ts in u.trajectory:
coord = current_coordinates()
if len(coord) == 0:
continue
h[:], edges[:] = numpy.histogramdd(coord, bins=bins, range=arange, normed=False)
grid += h # accumulate average histogram
pm.echo(ts.frame, len(coord))
numframes = u.trajectory.numframes / u.trajectory.skip
grid /= float(numframes)
# pick from kwargs
metadata = kwargs.pop("metadata", {})
metadata["psf"] = u.filename # named psf for historical reasons
metadata["dcd"] = u.trajectory.filename # named dcd for historical reasons
metadata["atomselection"] = atomselection
metadata["numframes"] = numframes
metadata["totaltime"] = round(
u.trajectory.numframes
* u.trajectory.delta
* u.trajectory.skip_timestep
* hop.constants.get_conversion_factor("time", "AKMA", "ps"),
3,
)
metadata["dt"] = (
u.trajectory.delta * u.trajectory.skip_timestep * hop.constants.get_conversion_factor("time", "AKMA", "ps")
)
metadata["time_unit"] = "ps"
metadata["dcd_skip"] = u.trajectory.skip_timestep # frames
metadata["dcd_delta"] = u.trajectory.delta # in AKMA
if cutoff > 0 and soluteselection is not None:
metadata["soluteselection"] = soluteselection
metadata["cutoff"] = cutoff # in Angstrom
parameters = kwargs.pop("parameters", {})
parameters["isDensity"] = False # must override
# all other kwargs are discarded
# Density automatically converts histogram to density for isDensity=False
g = Density(grid=grid, edges=edges, unit=dict(length="Angstrom"), parameters=parameters, metadata=metadata)
logger.info("Histogram completed (initial density in Angstrom**-3)")
return g