def __init__(self, cv, minvals, maxvals, period_min=None,
period_max=None, intersect_with=None):
volume_func = lambda minv, maxv: paths.PeriodicCVDefinedVolume(
cv, minv, maxv, period_min, period_max
)
self.period_min = period_min
self.period_max = period_max
super(PeriodicVolumeInterfaceSet, self).__init__(cv, minvals,
maxvals,
intersect_with,
volume_func)
def from_dict(cls, dct):
interface_set = cls.__new__(cls)
interface_set._load_from_dict(dct)
interface_set.period_min = dct['period_min']
interface_set.period_max = dct['period_max']
volume_func = lambda minv, maxv: paths.PeriodicCVDefinedVolume(
interface_set.cv, minv, maxv, interface_set.period_min,
interface_set.period_max)
super(InterfaceSet, interface_set).__init__()
interface_set._set_volume_func(volume_func)
return interface_set
def test_new_interface(self):
new_iface = self.increasing_set.new_interface(-140)
expected = paths.PeriodicCVDefinedVolume(self.cv, 0.0, -140, -180, 180)
assert_equal(new_iface, expected)
# In[6]:
# define the CVs
psi = paths.MDTrajFunctionCV("psi", md.compute_dihedrals, template.topology, indices=[[6,8,14,16]])
phi = paths.MDTrajFunctionCV("phi", md.compute_dihedrals, template.topology, indices=[[4,6,8,14]])
# In[7]:
# define the states
deg = 180.0/np.pi
C_7eq = (
paths.PeriodicCVDefinedVolume(phi, lambda_min=-180/deg, lambda_max=0/deg,
period_min=-np.pi, period_max=np.pi) &
paths.PeriodicCVDefinedVolume(psi, lambda_min=100/deg, lambda_max=200/deg,
period_min=-np.pi, period_max=np.pi)
).named("C_7eq")
# similarly, without bothering with the labels:
alpha_R = (paths.PeriodicCVDefinedVolume(phi, -180/deg, 0/deg, -np.pi, np.pi) &
paths.PeriodicCVDefinedVolume(psi, -100/deg, 0/deg, -np.pi, np.pi)).named("alpha_R")
# ## Getting a first trajectory
#
# The idea here is a little subtle, but it makes nice use of our generalized path ensemble idea.
#
# We want a path which contains at least one frame in each state. The question is, what ensemble can we use to create such a trajectory?
#
# The first obvious thought would be `goal_ensemble = PartInXEnsemble(stateA) & PartInXEnsemble(stateB)` (which can, of course, be further generalized to more states). However, while that *is* the ensemble we want to eventually satisfy, we can't use its `can_append` to create it, because its `can_append` always returns `True`: the trajectory will go on forever!