def get_angle_trajectory(self, atomSelectionArray, _stop =-1):
'''This function returns the alangle between three specified atoms over the whole trajectory\n
The preferred input is in dcd format. Otherwise the first trajectory frame is missing\n
ToDo: fix issues with xtc input trajectory that angles for all frames are computed.
Args:
atomSelectionArray(numpy array): list of 3 atom indices as in the structure file
_stop (int): Default =-1 (i.e. all frames)
Returns:
anglesTraj trajectory containing angles between the three atoms specified in the atomSelectionArray
Useage:
>>> dihedralTraj = get_dihedral_trajectory(_trajectoryFile='test.xtc/dcd', _topologyFile='test.gro',atoms)
'''
#if filename is a dcd file use fast method
if self.extension_DcD(self._trajectoryFile):
collection.clear()
selectionString =self.generate_selection_string(atomSelectionArray, "bynum ")
selection = self._u.selectAtoms(selectionString)
collection.addTimeseries(Timeseries.Angle(selection))
if _stop == -1:
_stop = self._u.trajectory.numframes-1
collection.compute(self._u.trajectory, stop=_stop)
return collection[0][0]
#else
else:
print "Using frame iteration and not collections module"
frames = self._u.trajectory.numframes-1
angles = []
selectionString =self.generate_selection_string(atomSelectionArray, "bynum ")
selection = self._u.selectAtoms(selectionString)
count =0
for ts in self._u.trajectory:
if count == _stop:
break
angle = selection.angle()
angles.append(angle)
count = count+1
angles = np.array(angles)
angles = np.radians(angles)
return angles
def run_dcd_timeseries(self, start, stop, skip=1):
collection.clear()
for path, analysis in self._dcd_timeseries.items():
print " Adding timeseries: %s" % path
collection.addTimeseries(analysis.func)
print " Computing..."
collection.compute(self._u, start=start, stop=stop, skip=skip)
print " Done computing."
print "Loading data..."
for i, path in enumerate(self._dcd_timeseries.keys()):
print " loading table %s with %d values..." % (path, len(collection[i][0]))
self.sim.data[path] = collection[i][0]
self.sim.data[path+"/analysis_stats"] = np.array([start, stop, skip, -1, -1])
def get_custom_distances_trajectory(self, _atomsArray,_stop=-1):
if self.extension_DcD(self._trajectoryFile):
collection.clear()
selections = []
for l in range(len(_atomsArray)):
selectionString =self.generate_selection_string(_atomsArray[l], "bynum ")
s = self._u.selectAtoms(selectionString)
selections.append(s)
for s in selections:
collection.addTimeseries(Timeseries.Distance('r',s))
if _stop == -1:
_stop = self._u.trajectory.numframes-1
collection.compute(self._u.trajectory, stop=_stop)
distances = np.array(collection)
distances = distances[:,0,:].transpose()
return distances
else:
selectionsList = []
for atoms in _atomsArray:
selection=[]
selection.append(self._u.selectAtoms("bynum "+str(atoms[0])))
selection.append(self._u.selectAtoms("bynum "+str(atoms[1])))
selectionsList.append(selection)
count = 0
allDist = []
for ts in self._u.trajectory:
distArray = []
for s in selectionsList:
var = dist(s[0], s[1])
distArray.append(var[2])
allDist.append(distArray)
if count == _stop:
break
count = count+1
allDist = np.array(allDist)
allDist = allDist[:,:,0]
return allDist
def run(self, trj, ref):
self._trj = trj
self._ref = ref
if len(self._timeseries) > 0:
print "Starting timeseries analysis..."
collection.clear()
for path, tpl in self._timeseries.items():
print " Adding timeseries: %s" % path
collection.addTimeseries(tpl[0])
print " Computing..."
collection.compute(self._trj.trajectory)
print " Done computing."
print "Loading data..."
for i, path in enumerate(self._timeseries.keys()):
print " loading table %s with %d values..." % (path, len(collection[i][0]))
self._timeseries[path][1].load(list(collection[i][0]))
print "Done timeseries analysis."
if len(self._sequential) > 0:
print "Running sequential analyses..."
for path, tpl in self._sequential.items():
print " Preparing %s" % path
tpl[0].prepare(ref=self._ref, trj=self._trj)
frames = self._trj.trajectory
print " Processing %d frames..." % frames.numframes
for i, f in enumerate(frames):
if i % len(frames) / 10 == 0:
print ".",
for path, tpl in self._sequential.items():
tpl[0].process(f)
print " done."
print " Loading result data..."
for path, tpl in self._sequential.items():
tpl[1].load(tpl[0].results())
print "Done sequential analysis."
collection.clear()
for res in range(2, numresidues-1):
print "Processing residue %d" % res
# selection of the atoms involved for the phi for resid '%d' %res
## selectAtoms("atom 4AKE %d C"%(res-1), "atom 4AKE %d N"%res, "atom %d 4AKE CA"%res, "atom 4AKE %d C" % res)
phi_sel = universe.residues[res].phi_selection()
print phi_sel;
# selection of the atoms involved for the psi for resid '%d' %res
psi_sel = universe.residues[res].psi_selection()
print psi_sel;
# collect the timeseries of a dihedral
collection.addTimeseries(Timeseries.Dihedral(phi_sel))
collection.addTimeseries(Timeseries.Dihedral(psi_sel))
# iterate through trajectory and compute (see docs for start/stop/skip options)
collection.compute(universe.trajectory)
# finding the avg and stdev for each residue
phi = []
psi = []
for data_phi in collection[0::2]:
dih = numpy.rad2deg(data_phi[0])
phi.append([dih.mean(), dih.std()])
for data_psi in collection[1::2]:
dih = numpy.rad2deg(data_psi[0])
psi.append([dih.mean(), dih.std()])
# making an array for phi and psi data
res = numpy.arange(2, numresidues-1)
phi = numpy.array(phi)
psi = numpy.array(psi)
collection.clear()
for res in range(2, numresidues - 1):
print "Processing residue {0:d}".format(res)
# selection of the atoms involved for the phi for resid '%d' %res
## select_atoms("atom 4AKE %d C"%(res-1), "atom 4AKE %d N"%res, "atom %d 4AKE CA"%res, "atom 4AKE %d C" % res)
phi_sel = universe.residues[res].phi_selection()
# selection of the atoms involved for the psi for resid '%d' %res
psi_sel = universe.residues[res].psi_selection()
# collect the timeseries of a dihedral
collection.addTimeseries(Timeseries.Dihedral(phi_sel))
collection.addTimeseries(Timeseries.Dihedral(psi_sel))
# iterate through trajectory and compute (see docs for start/stop/skip options)
collection.compute(universe.trajectory)
# finding the avg and stdev for each residue
phi = []
psi = []
for data_phi in collection[0::2]:
dih = np.rad2deg(data_phi[0])
phi.append([dih.mean(), dih.std()])
for data_psi in collection[1::2]:
dih = np.rad2deg(data_psi[0])
psi.append([dih.mean(), dih.std()])
# making an array for phi and psi data
res = np.arange(2, numresidues - 1)
phi = np.array(phi)
psi = np.array(psi)
