Source code for

import logging
import itertools

import pandas as pd

import openpathsampling as paths
from openpathsampling.netcdfplus import StorableNamedObject

# from functools import reduce  # not built-in for py3

logger = logging.getLogger(__name__)

def _default_state_name(state):
    return if state.is_named else str(state)

def _name_unnamed_states(unnamed_states, all_names):
    name_index = 0
    for state in unnamed_states:
        while index_to_string(name_index) in all_names:
            name_index += 1
        state = state.named(index_to_string(name_index))
        name_index += 1

def _or_bar_namer(volumes):
    return "|".join([ for v in volumes])

# TODO: this should be moved into a general tools module
def listify(obj):
        _ = iter(obj)
    except TypeError:
        obj = [obj]
    return obj

def index_to_string(index):
    n_underscore = index // 26
    letter_value = index % 26
    mystr = "_"*n_underscore + chr(65 + letter_value)
    return mystr

# TODO: this will be removed when we start using the analysis.tis methods
# for the network.rate_matrix
def _set_hist_args(transition, hist_args):
    for histname in hist_args.keys():
        trans_hist = transition.ensemble_histogram_info[histname]
        if trans_hist.hist_args == {}:
            trans_hist.hist_args = hist_args[histname]

[docs]class TransitionNetwork(StorableNamedObject): """ Subclasses of TransitionNetwork are the main way to set up calculations Attributes ---------- sampling_ensembles all_ensembles sampling_transitions """
[docs] def __init__(self): super(TransitionNetwork, self).__init__()
# self.transitions = {} # self.special_ensembles = {} @property def sampling_ensembles(self): """ Ensembles from the sampling transitions, excluding special ensembles. """ return sum([t.ensembles for t in self.sampling_transitions], []) @property def analysis_ensembles(self): """ Ensembles from the analysis transitions, excluding special ensembles. """ return sum([t.ensembles for t in self.transitions.values()], []) @property def all_ensembles(self): """ All ensembles in the sampling transitions, including special ensembles. """ all_ens = self.sampling_ensembles for special_dict in self.special_ensembles.values(): all_ens.extend(list(special_dict.keys())) return all_ens @property def sampling_transitions(self): """The transitions used in sampling""" try: return self._sampling_transitions except AttributeError: return None
class GeneralizedTPSNetwork(TransitionNetwork): """General class for TPS-based method. The main differences between fixed-length and flexible-length TPS is a small change in the ensemble. In implementation, this means that they use different transition classes, and that they have slightly different function signatures (fixed-length requires a length argument). To simplify this, and to make the docstrings readable, we make each class into a simple subclass of this GeneralizedTPSNetwork, which acts as an abstract class that manages most of the relevant code. Parameters ---------- initial_states : list of :class:`.Volume` acceptable initial states final_states : list of :class:`.Volume` acceptable final states allow_self_transitions : bool whether self-transitions (A->A) are allowed; default is False Attributes ---------- TransitionType : :class:`paths.Transition` Type of transition used here. Sets, for example, fixed or flexible pathlengths. """ TransitionType = NotImplemented def __init__(self, initial_states, final_states, allow_self_transitions=False, **kwargs): # **kwargs gets passed to the transition super(GeneralizedTPSNetwork, self).__init__() self.initial_states = listify(initial_states) self.final_states = listify(final_states) self.special_ensembles = {None: {}} all_initial = paths.join_volumes(self.initial_states, _or_bar_namer) if set(self.initial_states) == set(self.final_states): all_final = all_initial # so we don't create 2 objs for it else: all_final = paths.join_volumes(self.final_states, _or_bar_namer) self._sampling_transitions, self.transitions = \ self._build_transitions(self.initial_states, self.final_states, allow_self_transitions, **kwargs) def _build_transitions(self, initial_states, final_states, allow_self_transitions, **kwargs): sampling_transitions = self._build_sampling_transitions( initial_states, final_states, allow_self_transitions, **kwargs ) transitions = self._build_analysis_transitions( initial_states, final_states, allow_self_transitions, **kwargs ) return sampling_transitions, transitions def _sampling_transitions_from_pairs(self, state_pairs, **kwargs): initial, final = state_pairs[0] sampling_transition = self.TransitionType(initial, final, **kwargs) for initial, final in state_pairs[1:]: sampling_transition.add_transition(initial, final) return [sampling_transition] def _build_sampling_transitions(self, initial_states, final_states, allow_self_transitions, **kwargs): if allow_self_transitions: initial_to_joined_final = { initial: paths.join_volumes(final_states, _or_bar_namer) for initial in initial_states } else: initial_to_joined_final = { initial: paths.join_volumes([final for final in final_states if initial != final], _or_bar_namer) for initial in initial_states } sampling_transitions = self._sampling_transitions_from_pairs( state_pairs=list(initial_to_joined_final.items()), **kwargs ) return sampling_transitions def _build_analysis_transitions(self, initial_states, final_states, allow_self_transitions, **kwargs): transitions = { (initial, final): self.TransitionType(initial, final, **kwargs) for (initial, final) in itertools.product(initial_states, final_states) if initial != final } return transitions def to_dict(self): ret_dict = { 'transitions': self.transitions, 'x_sampling_transitions': self._sampling_transitions, 'special_ensembles': self.special_ensembles } try: ret_dict['initial_states'] = self.initial_states ret_dict['final_states'] = self.final_states except AttributeError: # pragma: no cover # DEPRECATED: remove for 2.0 from openpathsampling.deprecations import \ SAVE_RELOAD_OLD_TPS_NETWORK SAVE_RELOAD_OLD_TPS_NETWORK.warn() pass # backward compatibility return ret_dict @property def all_states(self): """list of all initial and final states""" return list(set(self.initial_states + self.final_states)) @classmethod def from_dict(cls, dct): network = cls.__new__(cls) super(GeneralizedTPSNetwork, network).__init__() network._sampling_transitions = dct['x_sampling_transitions'] network.transitions = dct['transitions'] try: network.initial_states = dct['initial_states'] network.final_states = dct['final_states'] except KeyError: # pragma: no cover # DEPRECATED: remove for 2.0 pass # backward compatibility try: network.special_ensembles = dct['special_ensembles'] except KeyError: # pragma: no cover # DEPRECATED: remove for 2.0 network.special_ensembles = {None: {}} # default behavior for backward compatibility return network @classmethod def from_state_pairs(cls, state_pairs, **kwargs): # TODO: redo this to use the new _sampling_transitions_from_pairs # method sampling = [] transitions = {} initial_states = [] final_states = [] for (initial, final) in state_pairs: initial_states += [initial] final_states += [final] if len(sampling) == 1: sampling[0].add_transition(initial, final) elif len(sampling) == 0: sampling = [cls.TransitionType(initial, final, **kwargs)] else: raise RuntimeError("More than one sampling transition for TPS?") transitions[(initial, final)] = cls.TransitionType(initial, final, **kwargs) dict_result = { 'x_sampling_transitions': sampling, 'transitions': transitions } dict_result.update(kwargs) network = cls.from_dict(dict_result) network.initial_states = initial_states network.final_states = final_states return network @classmethod def from_states_all_to_all(cls, states, allow_self_transitions=False, **kwargs): return cls(states, states, allow_self_transitions=allow_self_transitions, **kwargs)
[docs]class TPSNetwork(GeneralizedTPSNetwork): """ Class for flexible pathlength TPS networks (2-state or multiple state). """ TransitionType = paths.TPSTransition # we implement these functions entirely to fix the signature (super's # version allow arbitrary kwargs) so the documentation can read them.
[docs] def __init__(self, initial_states, final_states, allow_self_transitions=False): super(TPSNetwork, self).__init__(initial_states, final_states, allow_self_transitions)
@classmethod def from_state_pairs(cls, state_pairs, allow_self_transitions=False): return super(TPSNetwork, cls).from_state_pairs(state_pairs) @classmethod def from_states_all_to_all(cls, states, allow_self_transitions=False): return super(TPSNetwork, cls).from_states_all_to_all( states, allow_self_transitions )
[docs]class FixedLengthTPSNetwork(GeneralizedTPSNetwork): """ Class for fixed pathlength TPS networks (2-states or multiple states). """ TransitionType = paths.FixedLengthTPSTransition # as with TPSNetwork, we don't really need to add these functions. # However, without them, we need to explicitly name `length` as # length=value in these functions. This frees us of that, and gives us # clearer documentation.
[docs] def __init__(self, initial_states, final_states, length, allow_self_transitions=False): super(FixedLengthTPSNetwork, self).__init__( initial_states, final_states, allow_self_transitions=allow_self_transitions, length=length )
@classmethod def from_state_pairs(cls, state_pairs, length): return super(FixedLengthTPSNetwork, cls).from_state_pairs( state_pairs, length=length ) @classmethod def from_states_all_to_all(cls, states, length, allow_self_transitions=False): return super(FixedLengthTPSNetwork, cls).from_states_all_to_all( states=states, allow_self_transitions=allow_self_transitions, length=length )
class TISNetwork(TransitionNetwork): # NOTE: this is an abstract class with several properties used by many # TIS-based networks # TODO: most of the analysis stuff should end up in here; the bigger # differences are in setup, not analysis def __init__(self, trans_info, ms_outers): self.trans_info = trans_info try: ms_outers = list(ms_outers) except TypeError: if ms_outers is not None: ms_outers = [ms_outers] self.ms_outer_objects = ms_outers self._sampling_to_analysis = None self._analysis_to_sampling = None self._sampling_ensemble_for = None super(TISNetwork, self).__init__() @property def sampling_to_analysis(self): """dict mapping sampling transitions to analysis transitions""" if self._sampling_to_analysis is None: self._sampling_to_analysis = { sampling_t: [t for t in self.transitions.values() if sampling_t.interfaces == t.interfaces] for sampling_t in self.sampling_transitions } return self._sampling_to_analysis @property def analysis_to_sampling(self): """dict mapping analysis transitions to sampling transitions""" # in current examples, the result list here is always length 1, but # perhaps future methods will use multiple sampling transitions # (different order parameters?) to describe one physical transition if self._analysis_to_sampling is None: self._analysis_to_sampling = { t: [sampling_t for sampling_t in self.sampling_to_analysis if t in self.sampling_to_analysis[sampling_t]] for t in self.transitions.values() } return self._analysis_to_sampling @property def sampling_ensemble_for(self): """dict mapping ensembles (incl. sampling) to sampling ensemble""" if self._sampling_ensemble_for is None: self._sampling_ensemble_for = {ens: ens for ens in self.sampling_ensembles} for ens in self.analysis_ensembles: analysis_transitions = [t for t in self.transitions.values() if ens in t.ensembles] analysis_trans = analysis_transitions[0] # could use any ens_idx = analysis_trans.ensembles.index(ens) sampling_trans = self.analysis_to_sampling[analysis_trans] assert len(sampling_trans) == 1 # this only works in this case sampling_ens = sampling_trans[0].ensembles[ens_idx] self._sampling_ensemble_for[ens] = sampling_ens return self._sampling_ensemble_for def set_fluxes(self, flux_dictionary): """ Parameters ---------- flux_dictionary : dict of 2-tuple to float keys are in the form (state, interface), and values are the associated flux Raises ------ KeyError If the flux for one of the transitions isn't in the dictionary. """ # for now, if you don't have all the fluxes needed, it raises a # KeyError for trans in self.transitions.values(): trans._flux = flux_dictionary[(trans.stateA, trans.interfaces[0])] @property def minus_ensembles(self): return list(self.special_ensembles['minus'].keys()) @property def ms_outers(self): return list(self.special_ensembles['ms_outer'].keys()) def add_ms_outer_interface(self, ms_outer, transitions, forbidden=None): relevant = ms_outer.relevant_transitions(transitions) ensemble = ms_outer.make_ensemble(relevant, forbidden) # TODO: this should use defaultdict, I think dct = {ensemble: relevant} try: self.special_ensembles['ms_outer'].update(dct) except KeyError: self.special_ensembles['ms_outer'] = dct @property def all_states(self): return list(set(self.initial_states + self.final_states)) def get_state(self, snapshot): """ Find which core state a snapshot is in, if any Parameters ---------- snapshot : `openpathsampling.engines.BaseSnapshot` the snapshot to be tested Returns ------- `openpathsampling.Volume` the volume object defining the state """ for state in self.all_states: if state(snapshot): return state return None
[docs]class MSTISNetwork(TISNetwork): """ Multiple state transition interface sampling network. The way this works is that it sees two effective sets of transitions. First, there are sampling transitions. These are based on ensembles which go to any final state. Second, there are analysis transitions. These are based on ensembles which go to a specific final state. Sampling is done using the sampling transitions. Sampling transitions are stored in the `from_state[state]` dictionary. For MSTIS, the flux and total crossing probabilities are independent of the final state, and so the analysis calculates them in the sampling transitions, and copies the results into the analysis transitions. This way flux and total crossing probably are only calculated once per interface set. The conditional transition probability depends on the final state, so it (and the rate) are calculated using the analysis transitions. The analysis transitions are obtained using `.transition[(stateA, stateB)]`. """ def to_dict(self): ret_dict = { 'from_state': self.from_state, 'states': self.states, 'special_ensembles': self.special_ensembles, 'trans_info': self.trans_info, 'ms_outer_objects': self.ms_outer_objects } return ret_dict @classmethod def from_dict(cls, dct): network = cls.__new__(cls) # replace automatically created attributes with stored ones network.from_state = dct['from_state'] network.special_ensembles = dct['special_ensembles'] network.states = dct['states'] network.__init__( trans_info=dct['trans_info'], ms_outers=dct['ms_outer_objects'] ) return network
[docs] def __init__(self, trans_info, ms_outers=None): """ Creates MSTISNetwork, including interfaces. Parameters ---------- trans_info : list of tuple Details of each state-based ensemble set. 2-tuple in the order (state, interface_set) where state is a Volume, and interface_set is an InterfaceSet (with associated CollectiveVariable) ms_outers : MSOuterTISInterface or list of MSOuterTISInterface mutliple state outer interfaces for this network """ super(MSTISNetwork, self).__init__(trans_info, ms_outers) # build sampling transitions states, interfaces = zip(*trans_info) self.states = states if not hasattr(self, "from_state"): self.special_ensembles = {} self.from_state = {} self._build_fromstate_transitions(trans_info) if self.ms_outer_objects is not None: for ms_outer in self.ms_outer_objects: all_transitions = list(self.from_state.values()) self.add_ms_outer_interface(ms_outer, all_transitions) self._sampling_transitions = list(self.from_state.values()) # by default, we set assign these values to all ensembles self.hist_args = {} self.transitions = self._build_analysis_transitions()
@property def all_states(self): return self.states def _build_transitions(self, trans_info, ms_outers, special_ensembles): sampling_ensembles = self._build_sampling_ensembles(trans_info) return sampling_transitions, transitions, special_ensembles @staticmethod def _build_analysis_transition_for_sampling(sampling_transition, all_states): local_transitions = {} state_A = sampling_transition.stateA other_states = set(all_states) - set([state_A]) str_A = _default_state_name(state_A) for state_B in other_states: str_B = _default_state_name(state_B) trans = paths.TISTransition( stateA=state_A, stateB=state_B, interfaces=sampling_transition.interfaces, name=str_A + "->" + str_B, orderparameter=sampling_transition.orderparameter ) # override created stuff trans.ensembles = sampling_transition.ensembles for i in range(len(trans.ensembles)): trans.ensembles[i].named( + "[" + str(i) + "]") trans.minus_ensemble = sampling_transition.minus_ensemble local_transitions[(state_A, state_B)] = trans return local_transitions def _build_analysis_transitions(self): # set up analysis transitions (not to be saved) transitions = {} for from_A in self.from_state.values(): local_transitions = self._build_analysis_transition_for_sampling( sampling_transition=from_A, all_states=self.all_states ) transitions.update(local_transitions) return transitions @staticmethod def build_one_state_sampling_transition(state, interfaces, all_states): other_states = list(set(all_states) - set([state])) union_others = paths.join_volumes( volume_list=other_states, name="all states except " + str( ) this_trans = paths.TISTransition( stateA=state, stateB=union_others, interfaces=interfaces, name="Out " +, ) return this_trans def _build_fromstate_transitions(self, trans_info): """ Builds the sampling transitions (the self.from_state dictionary). This also sets self.states (list of states volumes), self.outers (list of interface volumes making the MS-outer interface), and self.outer_ensembles (list of TISEnsembles associated with the self.outers interfaces). Additionally, it gives default names volumes, interfaces, and transitions. Parameters ---------- trans_info : list of 2-tuples See description in __init__. """ states, interfaces = zip(*trans_info) orderparams = [ for iface_set in interfaces] # NAMING STATES (give default names) all_states = paths.join_volumes(states).named("all states") all_names = list(set([ for s in states])) unnamed_states = [s for s in states if not s.is_named] _name_unnamed_states(unnamed_states, all_names) # BUILDING ENSEMBLES self.states = states for (state, ifaces) in trans_info: this_trans = self.build_one_state_sampling_transition( state=state, interfaces=ifaces, all_states=states ) # op = # state_index = states.index(state) # other_states = states[:state_index]+states[state_index+1:] # other_states = list(set(states) - set([state])) # union_others = paths.join_volumes( # volume_list=other_states, # name="all states except " + str( # ) # union_others = paths.volume.join_volumes(other_states) # union_others.named("all states except " + str( # out_others = paths.AllOutXEnsemble(union_others) # this_trans = paths.TISTransition( # stateA=state, # stateB=union_others, # interfaces=ifaces, # name="Out " +, # orderparameter=op # ) self.from_state[state] = this_trans this_minus = self.from_state[state].minus_ensemble #& out_others this_inner = self.from_state[state].ensembles[0] # TODO: this should use defaultdict, I think try: self.special_ensembles['minus'][this_minus] = [this_trans] except KeyError: self.special_ensembles['minus'] = {this_minus : [this_trans]} def __str__(self): mystr = "Multiple State TIS Network:\n" for state in self.from_state.keys(): mystr += str(self.from_state[state]) return mystr def rate_matrix(self, steps, force=False): """ Calculate the matrix of all rates. Parameters ---------- steps : iterable of :class:`.MCStep` steps to be analyzed force : bool (False) if True, cached results are overwritten Returns ------- pandas.DataFrame Rates from row_label to column_label. Diagonal is NaN. """ # for each transition in from_state: # 1. Calculate the flux and the TCP names = [ for s in self.states] self._rate_matrix = pd.DataFrame(columns=names, index=names) for stateA in self.from_state.keys(): transition = self.from_state[stateA] # set up the hist_args if necessary _set_hist_args(transition, self.hist_args) # for histname in self.hist_args.keys(): # trans_hist = transition.ensemble_histogram_info[histname] # if trans_hist.hist_args == {}: # trans_hist.hist_args = self.hist_args[histname] transition.total_crossing_probability(steps=steps, force=force) transition._minus_move_flux(steps=steps, force=force) for stateB in self.from_state.keys(): if stateA != stateB: analysis_trans = self.transitions[(stateA, stateB)] analysis_trans.copy_analysis_from(transition) for trans in self.transitions.values(): rate = trans.rate(steps) # self._rate_matrix.set_value(, #, # rate) name_A = name_B =[name_A, name_B] = rate #print,, #print rate return self._rate_matrix
[docs]class MISTISNetwork(TISNetwork): """ Multiple interface set TIS network. Input is given as a list of 4-tuples. Each 4-tuple represents a transition, and is in the order:: (initial_state, interfaces, order_parameter, final_states) This will create the `input_transitions` objects. Attributes ---------- input_transitions : list of TISTransition the transitions given as input sampling_transitions : list of TISTransition the transitions used in sampling transitions : list of TISTransition the transitions used in analysis Note ---- The distinction between the three types of transitions in the object are a bit subtle, but important. The `input_transitions` are, of course, the transitions given in the input. These are A->B transitions, but would allow any other state. The `sampling_transitions` are what are used in sampling. These are A->any transitions if strict sampling is off, or "A->B & not_others" if strict sampling is on. Finally, the regular `transitions` are the transitions that are used for analysis (use the sampling ensembles for the interfaces, but also A->B). Parameters ---------- trans_info : list of tuple Details of each interface set. 3-tuple in the order (initial_state, interfaces, final_state) where initial_state and final_state are Volumes, and interfaces is an InterfaceSet ms_outers : MSOuterTISInterface or list of MSOuterTISInterface mutliple state outer interfaces for this network strict_sampling : bool whether the final state from the tuple is the *only* allowed final state in the sampling; default False """ # NOTE: input_transitions are in addition to the sampling_transitions # and the transitions (analysis transitions)
[docs] def __init__(self, trans_info, ms_outers=None, strict_sampling=False): super(MISTISNetwork, self).__init__(trans_info, ms_outers) self.strict_sampling = strict_sampling states_A, interfaces, states_B = zip(*trans_info) orderparams = [ for iface_set in interfaces] self.initial_states = list(set(states_A)) self.final_states = list(set(states_B)) list_all_states = list(set(self.initial_states + self.final_states)) # name states all_state_names = list(set([ for s in list_all_states])) unnamed_states = [s for s in list_all_states if not s.is_named] name_index = 0 for state in unnamed_states: while index_to_string(name_index) in all_state_names: name_index += 1 state.named(index_to_string(name_index)) name_index += 1 if not hasattr(self, "input_transitions"): self.input_transitions = { (stateA, stateB): paths.TISTransition(stateA, stateB, interface,, + "->" +, name_suffix=" (input)") for (stateA, interface, stateB) in self.trans_info } if not hasattr(self, 'x_sampling_transitions'): self.special_ensembles = {} self._build_sampling_transitions(self.input_transitions.values()) if self.ms_outer_objects is not None: for ms_outer in self.ms_outer_objects: all_transitions = self.x_sampling_transitions if not self.strict_sampling: self.add_ms_outer_interface(ms_outer, all_transitions) else: relevant = ms_outer.relevant_transitions(all_transitions) allowed = set(sum([[t.stateA, t.stateB] for t in relevant], [])) forbidden = set(list_all_states) - allowed self.add_ms_outer_interface(ms_outer, all_transitions, forbidden) self._sampling_transitions = self.x_sampling_transitions # by default, we set assign these values to all ensembles self.hist_args = {} self._build_analysis_transitions()
def to_dict(self): ret_dict = { 'special_ensembles': self.special_ensembles, 'transition_pairs': self.transition_pairs, 'x_sampling_transitions': self.x_sampling_transitions, 'transition_to_sampling': self.transition_to_sampling, 'input_transitions': self.input_transitions, 'trans_info': self.trans_info, 'strict_sampling': self.strict_sampling, 'ms_outer_objects': self.ms_outer_objects } return ret_dict @staticmethod def from_dict(dct): network = MISTISNetwork.__new__(MISTISNetwork) network.special_ensembles = dct['special_ensembles'] network.transition_pairs = dct['transition_pairs'] network.transition_to_sampling = dct['transition_to_sampling'] network.input_transitions = dct['input_transitions'] network.x_sampling_transitions = dct['x_sampling_transitions'] network.__init__(trans_info=dct['trans_info'], ms_outers=dct['ms_outer_objects'], strict_sampling=dct['strict_sampling']) return network def _build_transition_pairs(self, transitions): # identify transition pairs transition_pair_set_dict = {} for initial in self.initial_states: for t1 in [t for t in transitions if t.stateA == initial]: t_reverse = [ t for t in transitions if t.stateA == t1.stateB and t.stateB == t1.stateA ] if len(t_reverse) == 1: key = frozenset([t1.stateA, t1.stateB]) new_v = [t1, t_reverse[0]] if key not in transition_pair_set_dict.keys(): transition_pair_set_dict[key] = new_v elif len(t_reverse) > 1: # pragma: no cover raise RuntimeError("More than one reverse transition") # if len(t_reverse) is 0, we just pass transition_pairs = list(transition_pair_set_dict.values()) return transition_pairs def _build_sampling_transitions(self, transitions): transitions = list(transitions) # input may be iterator # TODO: I don't think transition pairs are used (see comment below; # I think that was the previous use case -- as input to all_in_pairs # However, existing files store this, so we won't actually remove it # yet. self.transition_pairs = self._build_transition_pairs(transitions) # this seems to no longer be used; I think it was necessary when the # MSOuter interface was done implicitly, instead of explicitly. Then # we turn the outermost to MS if and only if it was paired with the # reverse transition # if len(self.transition_pairs) > 0: # all_in_pairs = reduce(list.__add__, map(lambda x: list(x), # self.transition_pairs)) # else: # all_in_pairs = [] # build sampling transitions all_states = paths.join_volumes(self.initial_states + self.final_states) all_states_set = set(self.initial_states + self.final_states) self.transition_to_sampling = {} for transition in transitions: stateA = transition.stateA stateB = transition.stateB if self.strict_sampling: final_state = stateB other_states = paths.join_volumes(all_states_set - set([stateA, stateB])) ensemble_to_intersect = paths.AllOutXEnsemble(other_states) else: final_state = paths.join_volumes(all_states_set) ensemble_to_intersect = paths.FullEnsemble() sample_trans = paths.TISTransition( stateA=stateA, stateB=final_state, interfaces=transition.interfaces, + "->" +, orderparameter=transition.orderparameter ) new_ensembles = [e & ensemble_to_intersect for e in sample_trans.ensembles] if self.strict_sampling: for (old, new) in zip(new_ensembles, sample_trans.ensembles): = + " strict" sample_trans.ensembles = new_ensembles sample_trans.named("Sampling " + str(stateA) + "->" + str(stateB)) self.transition_to_sampling[transition] = sample_trans self.x_sampling_transitions = \ list(self.transition_to_sampling.values()) self._build_sampling_minus_ensembles() def _build_sampling_minus_ensembles(self): # combining the minus interfaces all_states_set = set(self.initial_states + self.final_states) for initial in self.initial_states: innermosts = [] # trans_from_initial: list of transition from initial trans_from_initial = [ t for t in self.x_sampling_transitions if t.stateA == initial ] for t1 in trans_from_initial: innermosts.append(t1.interfaces[0]) forbidden = list(all_states_set - {initial}) minus = paths.MinusInterfaceEnsemble( state_vol=initial, innermost_vols=innermosts, forbidden=forbidden ).named( + " MIS minus") try: self.special_ensembles['minus'][minus] = trans_from_initial except KeyError: self.special_ensembles['minus'] = {minus: trans_from_initial} def _build_analysis_transitions(self): self.transitions = {} for trans in self.input_transitions.values(): sample_trans = self.transition_to_sampling[trans] stateA = trans.stateA stateB = trans.stateB analysis_trans = paths.TISTransition( stateA=stateA, stateB=stateB, interfaces=sample_trans.interfaces, orderparameter=sample_trans.orderparameter ) analysis_trans.ensembles = sample_trans.ensembles analysis_trans.named( #analysis_trans.special_ensembles = sample_trans.special_ensembles self.transitions[(stateA, stateB)] = analysis_trans def rate_matrix(self, steps, force=False): initial_names = [ for s in self.initial_states] final_names = [ for s in self.final_states] self._rate_matrix = pd.DataFrame(columns=final_names, index=initial_names) for trans in self.transitions.values(): # set up the hist_args if necessary _set_hist_args(trans, self.hist_args) # for histname in self.hist_args.keys(): # trans_hist = trans.ensemble_histogram_info[histname] # if trans_hist.hist_args == {}: # trans_hist.hist_args = self.hist_args[histname] tcp = trans.total_crossing_probability(steps=steps, force=force) if trans._flux is None: logger.warning("No flux for transition " + str( + ": Rate will be NaN") trans._flux = float("nan") # we give NaN so we can calculate the condition transition # probability automatically rate = trans.rate(steps) # self._rate_matrix.set_value(, #, # rate) name_A = name_B =[name_A, name_B] = rate return self._rate_matrix