def __init__(self, universe, selectionstring, cutoff=15.0, pbc=False, sparse=None):
"""Initialize from a *universe* or pdb file.
:Arguments:
*universe*
:class:`MDAnalysis.Universe` or a PDB file name.
*selection*
:class:`MDAnalysis.core.AtomGroup.AtomGroup` or a
:meth:`MDAnalysis.Universe.selectAtoms` selection string
for atoms that define the lipid head groups, e.g.
universe.atoms.PO4 or "name PO4" or "name P*"
:Keywords:
*cutoff*
head group-defining atoms within a distance of *cutoff*
Angstroms are deemed to be in the same leaflet [15.0]
*pbc*
take periodic boundary conditions into account (only works
for orthorhombic boxes) [``False``]
*sparse*
``None``: use fastest possible routine; ``True``: use slow
sparse matrix implementation (for large systems); ``False``:
use fast :func:`~MDAnalysis.analysis.distances.distance_array`
implementation [``None``].
"""
universe = MDAnalysis.asUniverse(universe)
self.universe = universe
self.selectionstring = selectionstring
if type(self.selectionstring) == MDAnalysis.core.AtomGroup.AtomGroup:
self.selection = self.selectionstring
else:
self.selection = universe.selectAtoms(self.selectionstring)
self.pbc = pbc
self.sparse = sparse
self._init_graph(cutoff)
def __init__(self, *args, **kwargs):
"""Create a density grid from a trajectory.
density_from_trajectory(PSF, DCD, delta=1.0, atomselection='name OH2', ...) --> density
or
density_from_trajectory(PDB, XTC, delta=1.0, atomselection='name OH2', ...) --> density
:Arguments:
psf/pdb/gro
topology file
dcd/xtc/trr/pdb
trajectory; if reading a single PDB file it is sufficient to just provide it
once as a single argument
:Keywords:
mode
'solvent', 'bulk' or 'all' ('all' does both 'solvent' and \bulk' at the
same time and thus :meth:`DensityCreator.Density`` returns a list of
densities; this saves time!) ['all']
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]
:Returns: :class:`hop.sitemap.Density`
:TODO:
* Should be able to also set skip and start/stop for data collection.
.. Note::
* In order to calculate the bulk density, use
atomselection='name OH2',soluteselection='protein and not name H*',cutoff=3.5
This will select water oxygens not within 3.5 A of the protein heavy atoms.
Alternatively, use the VMD-based :func:`density_from_volmap` function.
* The histogramming grid is determined by the initial frames min and max.
* metadata will be populated with psf, dcd, and a few other items.
This allows more compact downstream processing.
"""
_kwargs = self.defaults.copy()
_kwargs.update(kwargs)
kwargs = _kwargs
# workaround for python 2.5 *args,**kwargs only allowed:
universe_kwargs = {'permissive': kwargs.pop('permissive', False)}
self.universe = MDAnalysis.asUniverse(*args, **universe_kwargs)
self.mode = kwargs.pop("mode", "all") # 'all' runs modes[1:]
if not self.mode in self.modes:
raise ValueError("mode must be one of %r, not %r" %
(self.modes, self.mode))
if self.mode == "all":
modes = self.modes[1:]
else:
modes = [self.mode]
self.collectors = []
min_coords = []
max_coords = []
for mode in modes:
modeargs = kwargs.copy()
if mode == "solvent":
modeargs['soluteselection'] = None
modeargs['cutoff'] = 0
c = DensityCollector(mode, self.universe, **modeargs)
self.collectors.append(c)
min_coords.append(
c.min_coordinates()) # with default padding from modeargs
max_coords.append(c.max_coordinates())
# determine maximum bounding box from initial positions of solvent
# (add generous padding... probably more than my default 2 A)
smin = numpy.sort(min_coords, axis=0)[0] # the three smallest values
smax = numpy.sort(max_coords, axis=0)[-1] # the three largest values
for c in self.collectors:
c.init_histogram(smin=smin,
smax=smax) # also guarantees compatible grid
self.densities = {} # densities will be stored with mode as key
def __init__(self, *args, **kwargs):
"""Calculate native contacts within a group or between two groups.
:Arguments:
*topology*
psf or pdb file
*trajectory*
dcd or xtc/trr file
*universe*
instead of a topology/trajectory combination, one can also supply
a :class:`MDAnalysis.Universe`
:Keywords:
*selection*
selection string that determines which distances are calculated; if this
is a tuple or list with two entries then distances are calculated between
these two different groups ["name CA or name B*"]
*refgroup*
reference group, either a single :class:`~MDAnalysis.core.AtomGroup.AtomGroup`
(if there is only a single *selection*) or a list of two such groups.
The reference contacts are directly computed from *refgroup* and hence
the atoms in the reference group(s) must be equivalent to the ones produced
by the *selection* on the input trajectory.
*radius*
contacts are deemed any atoms within radius [8.0 A]
*outfile*
name of the output file; with the gz or bz2 suffix, a compressed
file is written. The average <q> is written to a second, gzipped
file that has the same name with 'array' included. E.g. for the
default name "q1.dat.gz" the <q> file will be "q1.array.gz". The
format is the matrix in column-row format, i.e. selection 1
residues are the columns and selection 2 residues are rows. The
file can be read with :func:`numpy.loadtxt`. ["q1.dat.gz"]
The function calculates the percentage of native contacts q1
along a trajectory. "Contacts" are defined as the number of atoms
within *radius* of a given other atom. *q1* is the fraction of contacts
relative to the reference state 1 (typically the starting conformation
of the trajectory).
The timeseries is written to a file *outfile* and is also accessible as
the attribute :attr:`ContactAnalysis1.timeseries`.
"""
# XX or should I use as input
# sel = (group1, group2), ref = (refgroup1, refgroup2)
# and get the universe from sel?
# Currently it's a odd hybrid.
#
# Enhancements:
# - select contact pairs to write out as a timecourse
# - make this selection based on qavg
from os.path import splitext
self.selection_strings = self._return_tuple2(
kwargs.pop('selection', "name CA or name B*"), "selection")
self.references = self._return_tuple2(kwargs.pop('refgroup', None),
"refgroup")
self.radius = kwargs.pop('radius', 8.0)
self.targetdir = kwargs.pop('targetdir', os.path.curdir)
self.output = kwargs.pop('outfile', "q1.dat.gz")
self.outarray = splitext(splitext(self.output)[0])[0] + ".array.gz"
self.force = kwargs.pop('force', False)
self.timeseries = None # final result
self.filenames = args
self.universe = MDAnalysis.asUniverse(*args, **kwargs)
self.selections = [
self.universe.selectAtoms(s) for s in self.selection_strings
]
# sanity checkes
for x in self.references:
if x is None:
raise ValueError("a reference AtomGroup must be supplied")
for ref, sel, s in izip(self.references, self.selections,
self.selection_strings):
if ref.atoms.numberOfAtoms() != sel.atoms.numberOfAtoms():
raise ValueError(
"selection=%r: Number of atoms differ between "
"reference (%d) and trajectory (%d)" %
(s, ref.atoms.numberOfAtoms(), sel.atoms.numberOfAtoms()))
# compute reference contacts
dref = MDAnalysis.core.distances.distance_array(
self.references[0].coordinates(), self.references[1].coordinates())
self.qref = self.qarray(dref)
self.nref = self.qref.sum()
# setup arrays for the trajectory
self.d = numpy.zeros_like(dref)
self.q = self.qarray(self.d)
self._qtmp = numpy.zeros_like(self.q) # pre-allocated array
self.qavg = numpy.zeros(shape=self.q.shape, dtype=numpy.float64)
def __init__(self, *args, **kwargs):
"""Create a density grid from a trajectory.
density_from_trajectory(PSF, DCD, delta=1.0, atomselection='name OH2', ...) --> density
or
density_from_trajectory(PDB, XTC, delta=1.0, atomselection='name OH2', ...) --> density
:Arguments:
psf/pdb/gro
topology file
dcd/xtc/trr/pdb
trajectory; if reading a single PDB file it is sufficient to just provide it
once as a single argument
:Keywords:
mode
'solvent', 'bulk' or 'all' ('all' does both 'solvent' and \bulk' at the
same time and thus :meth:`DensityCreator.Density`` returns a list of
densities; this saves time!) ['all']
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]
:Returns: :class:`hop.sitemap.Density`
:TODO:
* Should be able to also set skip and start/stop for data collection.
.. Note::
* In order to calculate the bulk density, use
atomselection='name OH2',soluteselection='protein and not name H*',cutoff=3.5
This will select water oxygens not within 3.5 A of the protein heavy atoms.
Alternatively, use the VMD-based :func:`density_from_volmap` function.
* The histogramming grid is determined by the initial frames min and max.
* metadata will be populated with psf, dcd, and a few other items.
This allows more compact downstream processing.
"""
_kwargs = self.defaults.copy()
_kwargs.update(kwargs)
kwargs = _kwargs
# workaround for python 2.5 *args,**kwargs only allowed:
universe_kwargs = {'permissive':kwargs.pop('permissive',False)}
self.universe = MDAnalysis.asUniverse(*args, **universe_kwargs)
self.mode = kwargs.pop("mode", "all") # 'all' runs modes[1:]
if not self.mode in self.modes:
raise ValueError("mode must be one of %r, not %r" % (self.modes, self.mode))
if self.mode == "all":
modes = self.modes[1:]
else:
modes = [self.mode]
self.collectors = []
min_coords = []
max_coords = []
for mode in modes:
modeargs = kwargs.copy()
if mode == "solvent":
modeargs['soluteselection'] = None
modeargs['cutoff'] = 0
c = DensityCollector(mode, self.universe, **modeargs)
self.collectors.append(c)
min_coords.append(c.min_coordinates()) # with default padding from modeargs
max_coords.append(c.max_coordinates())
# determine maximum bounding box from initial positions of solvent
# (add generous padding... probably more than my default 2 A)
smin = numpy.sort(min_coords, axis=0)[0] # the three smallest values
smax = numpy.sort(max_coords, axis=0)[-1] # the three largest values
for c in self.collectors:
c.init_histogram(smin=smin, smax=smax) # also guarantees compatible grid
self.densities = {} # densities will be stored with mode as key
def __init__(self, *args, **kwargs):
"""Calculate native contacts within a group or between two groups.
:Arguments:
*topology*
psf or pdb file
*trajectory*
dcd or xtc/trr file
*universe*
instead of a topology/trajectory combination, one can also supply
a :class:`MDAnalysis.Universe`
:Keywords:
*selection*
selection string that determines which distances are calculated; if this
is a tuple or list with two entries then distances are calculated between
these two different groups ["name CA or name B*"]
*refgroup*
reference group, either a single :class:`~MDAnalysis.core.AtomGroup.AtomGroup`
(if there is only a single *selection*) or a list of two such groups.
The reference contacts are directly computed from *refgroup* and hence
the atoms in the reference group(s) must be equivalent to the ones produced
by the *selection* on the input trajectory.
*radius*
contacts are deemed any atoms within radius [8.0 A]
*outfile*
name of the output file; with the gz or bz2 suffix, a compressed
file is written. The average <q> is written to a second, gzipped
file that has the same name with 'array' included. E.g. for the
default name "q1.dat.gz" the <q> file will be "q1.array.gz". The
format is the matrix in column-row format, i.e. selection 1
residues are the columns and selection 2 residues are rows. The
file can be read with :func:`numpy.loadtxt`. ["q1.dat.gz"]
The function calculates the percentage of native contacts q1
along a trajectory. "Contacts" are defined as the number of atoms
within *radius* of a given other atom. *q1* is the fraction of contacts
relative to the reference state 1 (typically the starting conformation
of the trajectory).
The timeseries is written to a file *outfile* and is also accessible as
the attribute :attr:`ContactAnalysis1.timeseries`.
"""
# XX or should I use as input
# sel = (group1, group2), ref = (refgroup1, refgroup2)
# and get the universe from sel?
# Currently it's a odd hybrid.
#
# Enhancements:
# - select contact pairs to write out as a timecourse
# - make this selection based on qavg
from os.path import splitext
self.selection_strings = self._return_tuple2(kwargs.pop('selection', "name CA or name B*"), "selection")
self.references = self._return_tuple2(kwargs.pop('refgroup', None), "refgroup")
self.radius = kwargs.pop('radius', 8.0)
self.targetdir = kwargs.pop('targetdir', os.path.curdir)
self.output = kwargs.pop('outfile', "q1.dat.gz")
self.outarray = splitext(splitext(self.output)[0])[0] + ".array.gz"
self.force = kwargs.pop('force', False)
self.timeseries = None # final result
self.filenames = args
self.universe = MDAnalysis.asUniverse(*args, **kwargs)
self.selections = [self.universe.selectAtoms(s) for s in self.selection_strings]
# sanity checkes
for x in self.references:
if x is None:
raise ValueError("a reference AtomGroup must be supplied")
for ref, sel, s in izip(self.references, self.selections, self.selection_strings):
if ref.atoms.numberOfAtoms() != sel.atoms.numberOfAtoms():
raise ValueError("selection=%r: Number of atoms differ between "
"reference (%d) and trajectory (%d)" %
(s, ref.atoms.numberOfAtoms(), sel.atoms.numberOfAtoms()))
# compute reference contacts
dref = MDAnalysis.core.distances.distance_array(
self.references[0].coordinates(), self.references[1].coordinates())
self.qref = self.qarray(dref)
self.nref = self.qref.sum()
# setup arrays for the trajectory
self.d = numpy.zeros_like(dref)
self.q = self.qarray(self.d)
self._qtmp = numpy.zeros_like(self.q) # pre-allocated array
self.qavg = numpy.zeros(shape=self.q.shape, dtype=numpy.float64)
