def setup(self): cv = paths.CoordinateFunctionCV('x', lambda x: x.xyz[0][0]) vol_A = paths.CVDefinedVolume(cv, float("-inf"), 0.0) vol_B = paths.CVDefinedVolume(cv, 1.0, float("inf")) ensembles = [ paths.LengthEnsemble(1).named("len1"), paths.LengthEnsemble(3).named("len3"), paths.SequentialEnsemble([ paths.LengthEnsemble(1) & paths.AllInXEnsemble(vol_A), paths.AllOutXEnsemble(vol_A | vol_B), paths.LengthEnsemble(1) & paths.AllInXEnsemble(vol_A) ]).named('return'), paths.SequentialEnsemble([ paths.LengthEnsemble(1) & paths.AllInXEnsemble(vol_A), paths.AllOutXEnsemble(vol_A | vol_B), paths.LengthEnsemble(1) & paths.AllInXEnsemble(vol_B) ]).named('transition'), ] self.ensembles = {ens.name: ens for ens in ensembles} self.traj_vals = [-0.1, 1.1, 0.5, -0.2, 0.1, -0.3, 0.4, 1.4, -1.0] self.trajectory = make_1d_traj(self.traj_vals) self.engine = CalvinistDynamics(self.traj_vals) self.satisfied_when_traj_len = { "len1": 1, "len3": 3, "return": 6, "transition": 8, } self.conditions = EnsembleSatisfiedContinueConditions(ensembles)
def __init__(self, storage, engine=None, states=None, randomizer=None, initial_snapshots=None, direction=None): all_state_volume = paths.join_volumes(states) no_state_volume = ~all_state_volume # shoot forward until we hit a state forward_ensemble = paths.SequentialEnsemble([ paths.AllOutXEnsemble(all_state_volume), paths.AllInXEnsemble(all_state_volume) & paths.LengthEnsemble(1) ]) # or shoot backward until we hit a state backward_ensemble = paths.SequentialEnsemble([ paths.AllInXEnsemble(all_state_volume) & paths.LengthEnsemble(1), paths.AllOutXEnsemble(all_state_volume) ]) super(CommittorSimulation, self).__init__( storage=storage, engine=engine, starting_volume=no_state_volume, forward_ensemble=forward_ensemble, backward_ensemble=backward_ensemble, randomizer=randomizer, initial_snapshots=initial_snapshots ) self.states = states self.direction = direction # override the default self.mover given by the superclass if self.direction is None: self.mover = paths.RandomChoiceMover([self.forward_mover, self.backward_mover]) elif self.direction > 0: self.mover = self.forward_mover elif self.direction < 0: self.mover = self.backward_mover
def get_lifetime_segments(trajectory, from_vol, to_vol, forbidden=None, padding=[0, -1]): """General script to get lifetimes. Lifetimes for a transition between volumes are used in several other calculations: obviously, the state lifetime, but also the flux through an interface. This is a generic function to calculate that. Parameters ---------- trajectory : :class:`.Trajectory` trajectory to analyze from_vol : :class:`.Volume` the volume for which this represents the lifetime: the trajectory segments returned are associated with the lifetime of `from_vol` to_vol : :class:`.Volume` the volume which indicates the end of the lifetime: a frame in this volume means the trajectory is no longer associated with `from_vol` forbidden : :class:`.Volume` if a frame is in `forbidden`, it cannot be part of the lifetime of `from_vol`. This isn't needed in 2-state lifetime calculations; however, it is useful to exclude other states from a flux calculation padding : list adjusts which frames are returned as list indices. That is, the returned segments are `full_segment[padding[0]:padding[1]]`. The `full_segment`s are the segments from (and including) each first frame in `from_vol` (after a visit to `to_vol`) until (and including) the first frame in `to_vol`. To get the full segment as output, use `padding=[None, None]`. The default is to remove the final frame (`padding=[0, -1]`) so that it doesn't include the frame in `to_vol`. Returns ------- list of :class:`.Trajectory` the frames from (and including) each first entry from `to_vol` into `from_vol` until (and including) the next entry into `to_vol`, with no frames in `forbidden`, and with frames removed from the ends according to `padding` """ if forbidden is None: forbidden = paths.EmptyVolume() ensemble_BAB = paths.SequentialEnsemble([ paths.LengthEnsemble(1) & paths.AllInXEnsemble(to_vol), paths.PartInXEnsemble(from_vol) & paths.AllOutXEnsemble(to_vol), paths.LengthEnsemble(1) & paths.AllInXEnsemble(to_vol) ]) & paths.AllOutXEnsemble(forbidden) ensemble_AB = paths.SequentialEnsemble([ paths.LengthEnsemble(1) & paths.AllInXEnsemble(from_vol), paths.OptionalEnsemble(paths.AllOutXEnsemble(to_vol)), paths.LengthEnsemble(1) & paths.AllInXEnsemble(to_vol) ]) BAB_split = ensemble_BAB.split(trajectory) AB_split = [ensemble_AB.split(part)[0] for part in BAB_split] return [subtraj[padding[0]:padding[1]] for subtraj in AB_split]
def __init__(self, storage, engine=None, state_S=None, randomizer=None, initial_snapshots=None, trajectory_length=None): # Defintion of state S (A and B are only required for analysis). self.state_S = state_S # Set forward/backward shot length. self.trajectory_length = trajectory_length l = self.trajectory_length # Define backward ensemble: # trajectory starts in S and has fixed length l. backward_ensemble = paths.SequentialEnsemble([ paths.LengthEnsemble(l), paths.AllInXEnsemble(state_S) & paths.LengthEnsemble(1) ]) # Define forward ensemble: # CAUTION: first trajectory is in backward ensemble, # then continues with fixed length l. forward_ensemble = paths.SequentialEnsemble([ paths.LengthEnsemble(l), paths.AllInXEnsemble(state_S) & paths.LengthEnsemble(1), paths.LengthEnsemble(l) ]) super(SShootingSimulation, self).__init__(storage=storage, engine=engine, starting_volume=state_S, forward_ensemble=forward_ensemble, backward_ensemble=backward_ensemble, randomizer=randomizer, initial_snapshots=initial_snapshots) # Create backward mover (starting from single point). self.backward_mover = paths.BackwardExtendMover( ensemble=self.starting_ensemble, target_ensemble=self.backward_ensemble) # Create forward mover (starting from the backward ensemble). self.forward_mover = paths.ForwardExtendMover( ensemble=self.backward_ensemble, target_ensemble=self.forward_ensemble) # Create mover combining forward and backward shooting. No condition # here, shots in both directions are executed in any case. self.mover = paths.NonCanonicalConditionalSequentialMover( [self.backward_mover, self.forward_mover])
def __init__(self, storage, engine=None, states=None, randomizer=None, initial_snapshots=None, direction=None): super(CommittorSimulation, self).__init__(storage) self.engine = engine paths.EngineMover.default_engine = engine self.states = states self.randomizer = randomizer try: initial_snapshots = list(initial_snapshots) except TypeError: initial_snapshots = [initial_snapshots] self.initial_snapshots = initial_snapshots self.direction = direction all_state_volume = paths.join_volumes(states) # we should always start from a single frame not in any state self.starting_ensemble = (paths.AllOutXEnsemble(all_state_volume) & paths.LengthEnsemble(1)) # shoot forward until we hit a state self.forward_ensemble = paths.SequentialEnsemble([ paths.AllOutXEnsemble(all_state_volume), paths.AllInXEnsemble(all_state_volume) & paths.LengthEnsemble(1) ]) # or shoot backward until we hit a state self.backward_ensemble = paths.SequentialEnsemble([ paths.AllInXEnsemble(all_state_volume) & paths.LengthEnsemble(1), paths.AllOutXEnsemble(all_state_volume) ]) self.forward_mover = paths.ForwardExtendMover( ensemble=self.starting_ensemble, target_ensemble=self.forward_ensemble) self.backward_mover = paths.BackwardExtendMover( ensemble=self.starting_ensemble, target_ensemble=self.backward_ensemble) if self.direction is None: self.mover = paths.RandomChoiceMover( [self.forward_mover, self.backward_mover]) elif self.direction > 0: self.mover = self.forward_mover elif self.direction < 0: self.mover = self.backward_mover
def analyze_transition_duration(self, trajectory, stateA, stateB): """Analysis to obtain transition durations for given state. Parameters ---------- trajectory : :class:`.Trajectory` trajectory to analyze stateA : :class:`.Volume` initial state volume for the transition stateB : :class:`.Volume` final state volume for the transition Returns ------- :class:`.TrajectorySegmentContainer` transitions from `stateA` to `stateB` within `trajectory` """ # we define the transitions ensemble just in case the transition is, # e.g., fixed path length TPS. We want flexible path length ensemble transition_ensemble = paths.SequentialEnsemble([ paths.AllInXEnsemble(stateA) & paths.LengthEnsemble(1), paths.OptionalEnsemble( # optional to allow instantaneous hops paths.AllOutXEnsemble(stateA) & paths.AllOutXEnsemble(stateB) ), paths.AllInXEnsemble(stateB) & paths.LengthEnsemble(1) ]) segments = [seg[1:-1] for seg in transition_ensemble.split(trajectory)] return TrajectorySegmentContainer(segments, self.dt)
def A2BEnsemble(volume_a, volume_b, trusted=True): # this is a little replacement for the same name that used to be in # EnsembleFactory. It was only used in tests. return paths.SequentialEnsemble([ paths.AllInXEnsemble(volume_a) & paths.LengthEnsemble(1), paths.AllOutXEnsemble(volume_a | volume_b), paths.AllInXEnsemble(volume_b) & paths.LengthEnsemble(1) ])
def add_transition(self, stateA, stateB): new_ens = paths.SequentialEnsemble([ paths.AllInXEnsemble(stateA) & paths.LengthEnsemble(1), paths.AllOutXEnsemble(stateA | stateB), paths.AllInXEnsemble(stateB) & paths.LengthEnsemble(1) ]) try: self.ensembles[0] = self.ensembles[0] | new_ens except AttributeError: self.ensembles = [new_ens]
def __init__(self, stateA, stateB, name=None): super(TPSTransition, self).__init__(stateA, stateB) if name is not None: self.name = name if not hasattr(self, "ensembles"): self.ensembles = [ paths.SequentialEnsemble([ paths.AllInXEnsemble(stateA) & paths.LengthEnsemble(1), paths.AllOutXEnsemble(stateA | stateB), paths.AllInXEnsemble(stateB) & paths.LengthEnsemble(1) ]) ]
def setup(self): cv = paths.FunctionCV("Id", lambda snap: snap.xyz[0][0]) self.state_A = paths.CVDefinedVolume(cv, -0.1, 0.1) self.state_B = ~paths.CVDefinedVolume(cv, -1.0, 1.0) nml_increasing = paths.CVDefinedVolume(cv, 0.1, 1.0) nml_decreasing = paths.CVDefinedVolume(cv, -1.0, -0.1) increasing = paths.AllInXEnsemble(nml_increasing) decreasing = paths.AllInXEnsemble(nml_decreasing) self.ensemble = paths.SequentialEnsemble([ paths.LengthEnsemble(1) & paths.AllInXEnsemble(self.state_A), paths.AllOutXEnsemble(self.state_A | self.state_B), paths.LengthEnsemble(1) & paths.AllInXEnsemble(self.state_B) ]) self.incr_1 = self._make_active([0.0, 0.5, 1.1]) self.incr_2 = self._make_active([0.05, 0.6, 1.2]) self.decr_1 = self._make_active([0.0, -0.5, -1.1]) self.both_1 = self._make_active([0.0, 0.5, -0.5, 1.1]) self.both_2 = self._make_active([0.0, -0.4, 0.4, -1.1]) self.none_1 = self._make_active([0.0, 1.1]) self.none_2 = self._make_active([0.0, -1.1]) self.channels = {'incr': increasing, 'decr': decreasing} # used in simplest tests of relabeling self.toy_results = { 'a': [(0, 5), (8, 10)], 'b': [(3, 9)], 'c': [(7, 9)] } self.results_with_none = { 'a': [(0, 2), (6, 9)], 'b': [(5, 7), (9, 10)], None: [(2, 5)] } self.set_a = frozenset(['a']) self.set_b = frozenset(['b']) self.set_c = frozenset(['c']) self.toy_expanded_results = [(0, 5, self.set_a), (3, 9, self.set_b), (7, 9, self.set_c), (8, 10, self.set_a)] self.expanded_results_simultaneous_ending = [(0, 5, self.set_a), (3, 9, self.set_b), (7, 10, self.set_c), (8, 10, self.set_a)] self.expanded_oldest_skips_internal = [(0, 5, self.set_a), (3, 9, self.set_b), (7, 8, self.set_c), (8, 10, self.set_a), (10, 11, self.set_b)]
def __init__(self, states, progress='default', timestep=None): self.states = states self.all_states = paths.join_volumes(states) all_states_ens = paths.join_ensembles([paths.AllOutXEnsemble(s) for s in states]) ensemble = paths.SequentialEnsemble([ all_states_ens, paths.AllInXEnsemble(self.all_states) & paths.LengthEnsemble(1) ]) super(VisitAllStatesEnsemble, self).__init__(ensemble) self.timestep = timestep self.report_frequency = 10 self.progress_formatter, self.progress_emitter = \ self._progress_indicator(progress) self.cache = EnsembleCache(direction=+1) self._reset_cache_contents()
def __init__(self, transition, snapshot, storage=None, engine=None, extra_interfaces=None, forbidden_states=None): super(FullBootstrapping, self).__init__(storage, engine) if extra_interfaces is None: extra_interfaces = list() if forbidden_states is None: forbidden_states = list() interface0 = transition.interfaces[0] ensemble0 = transition.ensembles[0] state = transition.stateA self.state = state self.first_traj_ensemble = paths.SequentialEnsemble([ paths.OptionalEnsemble(paths.AllOutXEnsemble(state)), paths.AllInXEnsemble(state), paths.OptionalEnsemble( paths.AllOutXEnsemble(state) & paths.AllInXEnsemble(interface0) ), paths.OptionalEnsemble(paths.AllInXEnsemble(interface0)), paths.AllOutXEnsemble(interface0), paths.OptionalEnsemble(paths.AllOutXEnsemble(state)), paths.SingleFrameEnsemble(paths.AllInXEnsemble(state)) ]) & paths.AllOutXEnsemble(paths.join_volumes(forbidden_states)) self.extra_ensembles = [paths.TISEnsemble(transition.stateA, transition.stateB, iface, transition.orderparameter) for iface in extra_interfaces ] self.transition_shooters = [ paths.OneWayShootingMover(selector=paths.UniformSelector(), ensemble=ens) for ens in transition.ensembles ] self.extra_shooters = [ paths.OneWayShootingMover(selector=paths.UniformSelector(), ensemble=ens) for ens in self.extra_ensembles ] self.snapshot = snapshot.copy() self.ensemble0 = ensemble0 self.all_ensembles = transition.ensembles + self.extra_ensembles self.n_ensembles = len(self.all_ensembles) self.error_max_rounds = True
def test_subtrajectory_indices(self): # simplify more complicated expressions stateA = self.stateA stateB = self.stateB pretraj = [ 0.20, 0.30, 0.60, 0.40, 0.65, 2.10, 2.20, 2.60, 2.10, 0.80, 0.55, 0.40, 0.20 ] # 00, 01, 02, 03, 04, 05, 06, 07, 08, 09, 10, 11, 12 # A, A, I, A, I, B, B, X, B, X, I, A, A trajectory = make_1d_traj(coordinates=pretraj, velocities=[1.0] * len(pretraj)) ensemble_A = paths.AllInXEnsemble(stateA) ensemble_B = paths.AllInXEnsemble(stateB) ensemble_ABA = paths.SequentialEnsemble([ paths.AllInXEnsemble(stateA) & paths.LengthEnsemble(1), paths.PartInXEnsemble(stateB) & paths.AllOutXEnsemble(stateA), paths.AllInXEnsemble(stateA) & paths.LengthEnsemble(1) ]) subtrajectoriesA = ensemble_A.split(trajectory, overlap=0) subtrajectoriesB = ensemble_B.split(trajectory, overlap=0) subtrajectoriesABA = ensemble_ABA.split(trajectory) # make sure we have the trajectories we expect assert_equal(len(subtrajectoriesA), 3) assert_equal(len(subtrajectoriesB), 2) assert_equal(len(subtrajectoriesABA), 1) # the following assertions check that the subtrajectories are the # ones that we expect; the numbers here are linked to the indices # we'll test next assert_equal(subtrajectoriesA[0], trajectory[0:2]) assert_equal(subtrajectoriesA[1], trajectory[3:4]) assert_equal(subtrajectoriesA[2], trajectory[11:13]) assert_equal(subtrajectoriesB[0], trajectory[5:7]) assert_equal(subtrajectoriesB[1], trajectory[8:9]) assert_equal(subtrajectoriesABA[0], trajectory[3:12]) # now we run the subtrajectory_indices function and test it indicesA = trajectory.subtrajectory_indices(subtrajectoriesA) indicesB = trajectory.subtrajectory_indices(subtrajectoriesB) indicesABA = trajectory.subtrajectory_indices(subtrajectoriesABA) assert_equal(indicesA, [[0, 1], [3], [11, 12]]) assert_equal(indicesB, [[5, 6], [8]]) assert_equal(indicesABA, [[3, 4, 5, 6, 7, 8, 9, 10, 11]])
def __init__(self, storage, initial_file, mover, network, options=None, options_rejected=None): # TODO: mke the initial file into an initial trajectory if options is None: options = TPSConverterOptions() if options_rejected is None: options_rejected = options self.options = options self.options_rejected = options_rejected self.initial_file = initial_file # needed for restore traj = self.load_trajectory(initial_file) # assume we're TPS here ensemble = network.sampling_ensembles[0] initial_trajectories = ensemble.split(traj) if len(initial_trajectories) == 0: # pragma: no cover raise RuntimeError("Initial trajectory in " + str(initial_file) + " has no subtrajectory satisfying the " + "TPS ensemble.") elif len(initial_trajectories) > 1: # pragma: no cover raise RuntimeWarning("More than one potential initial " + "subtrajectory. We use the first.") initial_trajectory = initial_trajectories[0] initial_conditions = paths.SampleSet([ paths.Sample(replica=0, trajectory=initial_trajectory, ensemble=ensemble) ]) self.extra_bw_frames = traj.index(initial_trajectory[0]) final_frame_index = traj.index(initial_trajectory[-1]) self.extra_fw_frames = len(traj) - final_frame_index - 1 # extra -1 bc frame index counts from 0; len counts from 1 self.summary_root_dir = None self.report_progress = None super(OneWayTPSConverter, self).__init__(storage=storage, initial_conditions=initial_conditions, mover=mover, network=network) # initial_states = self.network.initial_states # final_states = self.network.final_states # TODO: prefer the above, but the below work until fix for network # storage initial_states = [self.network.sampling_transitions[0].stateA] final_states = [self.network.sampling_transitions[0].stateB] all_states = paths.join_volumes(initial_states + final_states) self.fw_ensemble = paths.SequentialEnsemble([ paths.AllOutXEnsemble(all_states), paths.AllInXEnsemble(all_states) & paths.LengthEnsemble(1) ]) self.bw_ensemble = paths.SequentialEnsemble([ paths.AllInXEnsemble(all_states) & paths.LengthEnsemble(1), paths.AllOutXEnsemble(all_states) ]) self.full_ensemble = paths.SequentialEnsemble([ paths.AllInXEnsemble(all_states) & paths.LengthEnsemble(1), paths.AllOutXEnsemble(all_states), paths.AllInXEnsemble(all_states) & paths.LengthEnsemble(1) ]) self.all_states = all_states
def _tps_ensemble(self, stateA, stateB): return paths.SequentialEnsemble([ paths.AllInXEnsemble(stateA) & paths.LengthEnsemble(1), paths.AllOutXEnsemble(stateA | stateB), paths.AllInXEnsemble(stateB) & paths.LengthEnsemble(1) ])
def _tps_ensemble(self, stateA, stateB): return paths.SequentialEnsemble([ paths.LengthEnsemble(1) & paths.AllInXEnsemble(stateA), paths.LengthEnsemble(self.length - 2), paths.LengthEnsemble(1) & paths.AllInXEnsemble(stateB) ])
def __init__(self, storage, engine=None, states=None, randomizer=None, initial_snapshots=None, rc=None): # state definition self.states = states state_A = states[0] state_B = states[1] # get min/max reaction coordinate of initial snapshots self.rc = rc rc_array = np.array(self.rc(initial_snapshots)) rc_min = np.nextafter(rc_array.min(), -np.inf) rc_max = np.nextafter(rc_array.max(), np.inf) # define reaction coordinate region of initial snapshots # = starting_volume self.dividing_surface = paths.CVDefinedVolume(self.rc, rc_min, rc_max) # define volume between state A and the dividing surface (including A) self.volume_towards_A = paths.CVDefinedVolume(self.rc, -np.inf, rc_max) # shoot backward until we hit A but never cross the dividing surface backward_ensemble = paths.SequentialEnsemble([ paths.AllInXEnsemble(state_A) & paths.LengthEnsemble(1), paths.AllInXEnsemble(self.volume_towards_A - state_A) ]) # shoot forward until we hit state B without hitting A first # caution: since the mover will consist of backward and forward # shoot in sequence, the starting ensemble for the forward # shoot is the output of the backward shoot, i.e. a # trajectory that runs from A to the dividing surface and # not just a point there. forward_ensemble = paths.SequentialEnsemble([ paths.AllInXEnsemble(state_A) & paths.LengthEnsemble(1), paths.AllOutXEnsemble(state_A | state_B), paths.AllInXEnsemble(state_B) & paths.LengthEnsemble(1), ]) super(ReactiveFluxSimulation, self).__init__( storage=storage, engine=engine, starting_volume=self.dividing_surface, forward_ensemble=forward_ensemble, backward_ensemble=backward_ensemble, randomizer=randomizer, initial_snapshots=initial_snapshots ) # create backward mover (starting from single point) self.backward_mover = paths.BackwardExtendMover( ensemble=self.starting_ensemble, target_ensemble=self.backward_ensemble ) # create forward mover (starting from the backward ensemble) self.forward_mover = paths.ForwardExtendMover( ensemble=self.backward_ensemble, target_ensemble=self.forward_ensemble ) # create mover combining forward and backward shooting, # abort if backward mover fails self.mover = paths.NonCanonicalConditionalSequentialMover([ self.backward_mover, self.forward_mover ])