def test_right_type_bonds(self):
assert_equal(self.bgroup.bonds(),
calc_bonds(self.bgroup.atom1.positions,
self.bgroup.atom2.positions))
assert_equal(self.bgroup.bonds(pbc=True),
calc_bonds(self.bgroup.atom1.positions,
self.bgroup.atom2.positions,
box=self.u.dimensions))
def test_right_type_bonds(self):
assert_equal(
self.bgroup.bonds(),
calc_bonds(self.bgroup.atom1.positions,
self.bgroup.atom2.positions))
assert_equal(
self.bgroup.bonds(pbc=True),
calc_bonds(self.bgroup.atom1.positions,
self.bgroup.atom2.positions,
box=self.u.dimensions))
def main():
u = Universe(conf,traj)
nmol = len(list(u.residues))
Nlen = len(u.residues[0].atoms)
Nhalf = Nlen/2
R = np.zeros((2,Nhalf))
for ts in u.trajectory:
print " frame ", ts.frame
for n in xrange(0,Nhalf):
npair = Nlen - n -1
R[1,n] = npair - n
#get the pairs of atoms with indices n-npair for all residues
ag1 = [myres.atoms[n].pos for myres in u.residues]
ag1 = np.array(ag1)
ag2 = [myres.atoms[npair].pos for myres in u.residues]
ag2 = np.array(ag2)
#calculate distance
distance_array = calc_bonds(ag1,ag2)
#average it
r2_loc = np.mean(distance_array*distance_array)
#divide <R(n)**2> by the n
R[0,n] = r2_loc/R[1,n]
print "n = ", n , " n* " , npair , " the n ", R[1,n] ," r2 = ", R[0,n], " r2_loc = ", r2_loc
plot(R[1,:], R[0,:], 'r--', lw=2)
xlabel("n")
ylabel(r" <R^2>/n")
plt.savefig(name + '.pdf')
def _single_run(self, start, stop):
"""Perform a single pass of the trajectory"""
self.u.trajectory[start]
# Calculate partners at t=0
box = self.u.dimensions if self.pbc else None
# 2d array of all distances
d = distance_array(self.h.positions, self.a.positions, box=box)
if self.exclusions:
# set to above dist crit to exclude
d[self.exclusions] = self.d_crit + 1.0
# find which partners satisfy distance criteria
hidx, aidx = numpy.where(d < self.d_crit)
a = calc_angles(self.d.positions[hidx],
self.h.positions[hidx],
self.a.positions[aidx],
box=box)
# from amongst those, who also satisfiess angle crit
idx2 = numpy.where(a > self.a_crit)
hidx = hidx[idx2]
aidx = aidx[idx2]
nbonds = len(hidx) # number of hbonds at t=0
results = numpy.zeros_like(numpy.arange(start, stop, self._skip),
dtype=numpy.float32)
if self.time_cut:
# counter for time criteria
count = numpy.zeros(nbonds, dtype=numpy.float64)
for i, ts in enumerate(self.u.trajectory[start:stop:self._skip]):
box = self.u.dimensions if self.pbc else None
d = calc_bonds(self.h.positions[hidx],
self.a.positions[aidx],
box=box)
a = calc_angles(self.d.positions[hidx],
self.h.positions[hidx],
self.a.positions[aidx],
box=box)
winners = (d < self.d_crit) & (a > self.a_crit)
results[i] = winners.sum()
if self.bond_type is 'continuous':
# Remove losers for continuous definition
hidx = hidx[numpy.where(winners)]
aidx = aidx[numpy.where(winners)]
elif self.bond_type is 'intermittent':
if self.time_cut:
# Add to counter of where losers are
count[~winners] += self._skip * self.u.trajectory.dt
count[winners] = 0 # Reset timer for winners
# Remove if you've lost too many times
# New arrays contain everything but removals
hidx = hidx[count < self.time_cut]
aidx = aidx[count < self.time_cut]
count = count[count < self.time_cut]
else:
pass
if len(hidx) == 0: # Once everyone has lost, the fun stops
break
results /= nbonds
return results
def _single_run(self, start, stop):
"""Perform a single pass of the trajectory"""
self.u.trajectory[start]
# Calculate partners at t=0
box = self.u.dimensions if self.pbc else None
# 2d array of all distances
d = distance_array(self.h.positions, self.a.positions, box=box)
if self.exclusions:
# set to above dist crit to exclude
d[self.exclusions] = self.d_crit + 1.0
# find which partners satisfy distance criteria
hidx, aidx = numpy.where(d < self.d_crit)
a = calc_angles(self.d.positions[hidx], self.h.positions[hidx], self.a.positions[aidx], box=box)
# from amongst those, who also satisfiess angle crit
idx2 = numpy.where(a > self.a_crit)
hidx = hidx[idx2]
aidx = aidx[idx2]
nbonds = len(hidx) # number of hbonds at t=0
results = numpy.zeros_like(numpy.arange(start, stop, self._skip), dtype=numpy.float32)
if self.time_cut:
# counter for time criteria
count = numpy.zeros(nbonds, dtype=numpy.float64)
for i, ts in enumerate(self.u.trajectory[start : stop : self._skip]):
box = self.u.dimensions if self.pbc else None
d = calc_bonds(self.h.positions[hidx], self.a.positions[aidx], box=box)
a = calc_angles(self.d.positions[hidx], self.h.positions[hidx], self.a.positions[aidx], box=box)
winners = (d < self.d_crit) & (a > self.a_crit)
results[i] = winners.sum()
if self.bond_type is "continuous":
# Remove losers for continuous definition
hidx = hidx[numpy.where(winners)]
aidx = aidx[numpy.where(winners)]
elif self.bond_type is "intermittent":
if self.time_cut:
# Add to counter of where losers are
count[~winners] += self._skip * self.u.trajectory.dt
count[winners] = 0 # Reset timer for winners
# Remove if you've lost too many times
# New arrays contain everything but removals
hidx = hidx[count < self.time_cut]
aidx = aidx[count < self.time_cut]
count = count[count < self.time_cut]
else:
pass
if len(hidx) == 0: # Once everyone has lost, the fun stops
break
results /= nbonds
return results
