def _minus_move_flux(self, steps, force=False):
"""
Calculate the flux based on the minus ensemble trajectories.
"""
if not force and self._flux != None:
return self._flux
self.minus_count_sides = {"in": [], "out": []}
# NOTE: this assumes that minus mover is the only thing with the
# minus mover's signature. TODO: switch this back to being
# mover-based when we move all analysis out of the network objects
minus_steps = (
step for step in steps
if (self.minus_ensemble in [s.ensemble for s in step.change.trials]
and step.change.accepted and step.change.mover is not None)
)
#for move in minus_moves:
#minus_samp = [s for s in move.results
#if s.ensemble is self.minus_ensemble][0]
minus_movers_used = {}
for step in minus_steps:
minus_samp = step.active[self.minus_ensemble]
minus_trajectory = minus_samp.trajectory
minus_summ = minus_sides_summary(minus_trajectory,
self.minus_ensemble)
for key in self.minus_count_sides.keys():
self.minus_count_sides[key].extend(minus_summ[key])
try:
minus_movers_used[step.change.canonical.mover] += 1
except KeyError:
minus_movers_used[step.change.canonical.mover] = 1
for key in self.minus_count_sides.keys():
if len(self.minus_count_sides[key]) == 0:
logger.warn("No instances of "+str(key)+" for minus move.")
# print minus_movers_used
t_in_avg = np.array(self.minus_count_sides['in']).mean()
t_out_avg = np.array(self.minus_count_sides['out']).mean()
if len(set(minus_movers_used)) != 1:
# TODO: someday, this may not need to be forbidden, although I
# don't think it will be useful. For now, this is important for
# testing. Minimum, important that all have the same timestep
raise RuntimeError(str(len(minus_movers_used)) +
" minus movers for the same ensemble?")
engine_dt = list(minus_movers_used.keys())[0].engine.snapshot_timestep
flux = 1.0 / (t_in_avg + t_out_avg) / engine_dt
self._flux = flux
return self._flux
def minus_move_flux(self, steps, force=False):
"""
Calculate the flux based on the minus ensemble trajectories.
"""
if not force and self._flux != None:
return self._flux
self.minus_count_sides = {"in": [], "out": []}
# NOTE: this assumes that minus mover is the only thing with the
# minus mover's signature. TODO: switch this back to being
# mover-based when we move all analysis out of the network objects
minus_steps = (
step for step in steps
if (self.minus_ensemble in [s.ensemble for s in step.change.trials]
and step.change.accepted and step.change.mover is not None)
)
#for move in minus_moves:
#minus_samp = [s for s in move.results
#if s.ensemble is self.minus_ensemble][0]
minus_movers_used = {}
for step in minus_steps:
minus_samp = step.active[self.minus_ensemble]
minus_trajectory = minus_samp.trajectory
minus_summ = minus_sides_summary(minus_trajectory,
self.minus_ensemble)
for key in self.minus_count_sides.keys():
self.minus_count_sides[key].extend(minus_summ[key])
try:
minus_movers_used[step.change.canonical.mover] += 1
except KeyError:
minus_movers_used[step.change.canonical.mover] = 1
for key in self.minus_count_sides.keys():
if len(self.minus_count_sides[key]) == 0:
logger.warn("No instances of "+str(key)+" for minus move.")
# print minus_movers_used
t_in_avg = np.array(self.minus_count_sides['in']).mean()
t_out_avg = np.array(self.minus_count_sides['out']).mean()
if len(set(minus_movers_used)) != 1:
# TODO: someday, this may not need to be forbidden, although I
# don't think it will be useful. For now, this is important for
# testing. Minimum, important that all have the same timestep
raise RuntimeError(str(len(minus_movers_used)) +
" minus movers for the same ensemble?")
engine_dt = minus_movers_used.keys()[0].engine.snapshot_timestep
flux = 1.0 / (t_in_avg + t_out_avg) / engine_dt
self._flux = flux
return self._flux