def setup(self):
self.cv = paths.FunctionCV("Id", lambda snap: snap.xyz[0][0])
cv_neg = paths.FunctionCV("Neg", lambda snap: -snap.xyz[0][0])
self.stateA = paths.CVDefinedVolume(self.cv, -1.0, 0.0)
self.stateB = paths.CVDefinedVolume(self.cv, 1.0, 2.0)
self.stateC = paths.CVDefinedVolume(self.cv, 3.0, 4.0)
interfacesAB = paths.VolumeInterfaceSet(
self.cv, -1.0, [0.0, 0.2, 0.4]
)
interfacesBC = paths.VolumeInterfaceSet(
self.cv, 1.0, [2.0, 2.2, 2.4]
)
interfacesBA = paths.VolumeInterfaceSet(
cv_neg, -1.0, [-1.0, -0.8, -0.6]
)
network = paths.MISTISNetwork([
(self.stateA, interfacesAB, self.stateB),
(self.stateB, interfacesBC, self.stateC),
(self.stateB, interfacesBA, self.stateA)
])
self.tisAB = network.input_transitions[(self.stateA, self.stateB)]
self.tisBC = network.input_transitions[(self.stateB, self.stateC)]
self.tisBA = network.input_transitions[(self.stateB, self.stateA)]
self.network = network
self.snapA = make_1d_traj([-0.5])[0]
self.noforbid_noextra_AB = paths.FullBootstrapping(
transition=self.tisAB,
snapshot=self.snapA
)
def test_initial_max_length(self):
engine = CalvinistDynamics([-0.5, -0.4, -0.3, -0.2, -0.1, 0.1, -0.1])
bootstrap_AB_maxlength = paths.FullBootstrapping(transition=self.tisAB,
snapshot=self.snapA,
initial_max_length=3,
engine=engine)
bootstrap_AB_maxlength.output_stream = open(os.devnull, "w")
gs = bootstrap_AB_maxlength.run(build_attempts=1)
def ratcheter(self, interfaces, direction="out_in"):
if direction == "in_out":
stateA = self.DFG_in
stateB = self.DFG_out
elif direction == "out_in":
stateA = self.DFG_out
stateB = self.DFG_in
else:
raise RuntimeError("direction is " + str(direction) +
": must be 'in_out' or 'out_in'.")
transition = paths.TISTransition(stateA, stateB, interfaces, self.cv)
ratcheter = paths.FullBootstrapping(
transition=transition,
snapshot=self.engine.current_snapshot,
storage=self.storage,
engine=self.engine)
return ratcheter
def bootstrap(sampling_transition, init_frame, forbidden_states, engine,
ms_outers):
my_state = sampling_transition.stateA
print("Ratcheting initial conditions for state " + my_state.name)
my_interfaces = sampling_transition.interfaces
try:
outer_interface = ms_outers.volume_for_interface_set(my_interfaces)
except KeyError:
extra_interfaces = None
else:
extra_interfaces = [outer_interface]
ratchet = paths.FullBootstrapping(
transition=sampling_transition,
snapshot=init_frame,
engine=engine,
forbidden_states=forbidden_states,
extra_interfaces=extra_interfaces
)
init_conds = ratchet.run()
init_outer = init_conds[max([s.replica for s in init_conds.samples])]
init_inner = init_conds[0]
return init_inner.trajectory, init_outer.trajectory
print(distances)
attempt += 1
print('')
# Create a network to study unbinding paths
# note the list/tuple structure: that's because this is normally a list of tuples,
# each tuple representing a transition to study
scheme = paths.DefaultScheme(mistis, engine=engine)
sset = scheme.initial_conditions_from_trajectories(
initial_trajectories + minus_trajectories)
print scheme.initial_conditions_report(sset)
elif initial_trajectory_method == 'bootstrap':
print('Bootstrapping initial trajectory...')
bootstrap = paths.FullBootstrapping(
transition=mistis.transitions[(bound, unbound)],
snapshot=initial_snapshot,
engine=engine_hot)
#storage.save(bootstrap) # Causes Dataframe error
initial_sample_set = bootstrap.run()
initial_trajectories = [s.trajectory for s in initial_sample_set]
print(initial_trajectories)
# Set of initial move scheme and total sample set for normal temperature engine
scheme = paths.DefaultScheme(mistis, engine=engine)
sset = scheme.initial_conditions_from_trajectories(
trajectories=initial_trajectories,
# this strategy is useful for MSTIS Networks and similar ones when generating
# from a single long trajectory that could contain MinusInterface samples
strategies=[
# 1. split and pick shortest and exclude for MinusInterfaceEnsembles
('split', {
