def __init__(self, regimes=None, aliases=None, state_variables=None): """Dynamics object constructor :param aliases: A list of aliases, which must be either |Alias| objects or ``string``s. :param regimes: A list containing at least one |Regime| object. :param state_variables: An optional list of the state variables, which can either be |StateVariable| objects or `string` s. If provided, it must match the inferred state-variables from the regimes; if it is not provided it will be inferred automatically. """ aliases = normalise_parameter_as_list(aliases) regimes = normalise_parameter_as_list(regimes) state_variables = normalise_parameter_as_list(state_variables) # Load the aliases as objects or strings: alias_td = filter_discrete_types(aliases, (basestring, Alias)) aliases_from_strs = [StrToExpr.alias(o) for o in alias_td[basestring]] aliases = alias_td[Alias] + aliases_from_strs # Load the state variables as objects or strings: sv_types = (basestring, StateVariable) sv_td = filter_discrete_types(state_variables, sv_types) sv_from_strings = [StateVariable(o) for o in sv_td[basestring]] state_variables = sv_td[StateVariable] + sv_from_strings self._regimes = regimes self._aliases = aliases self._state_variables = state_variables
def __init__(self, *args, **kwargs): """Regime constructor :param name: The name of the constructor. If none, then a name will be automatically generated. :param time_derivatives: A list of time derivatives, as either ``string``s (e.g 'dg/dt = g/gtau') or as |TimeDerivative| objects. :param transitions: A list containing either |OnEvent| or |OnCondition| objects, which will automatically be sorted into the appropriate classes automatically. :param *args: Any non-keyword arguments will be treated as time_derivatives. """ valid_kwargs = ('name', 'transitions', 'time_derivatives') for arg in kwargs: if not arg in valid_kwargs: err = 'Unexpected Arg: %s' % arg raise NineMLRuntimeError(err) transitions = kwargs.get('transitions', None) name = kwargs.get('name', None) kw_tds = normalise_parameter_as_list(kwargs.get('time_derivatives', None)) time_derivatives = list(args) + kw_tds # Generate a name for unnamed regions: self._name = name.strip() if name else Regime.get_next_name() ensure_valid_c_variable_name(self._name) # Un-named arguments are time_derivatives: time_derivatives = normalise_parameter_as_list(time_derivatives) # time_derivatives.extend( args ) td_types = (basestring, TimeDerivative) td_type_dict = filter_discrete_types(time_derivatives, td_types) td_from_str = [StrToExpr.time_derivative(o) for o in td_type_dict[basestring]] self._time_derivatives = td_type_dict[TimeDerivative] + td_from_str # Check for double definitions: td_dep_vars = [td.dependent_variable for td in self._time_derivatives] assert_no_duplicates(td_dep_vars) # We support passing in 'transitions', which is a list of both OnEvents # and OnConditions. So, lets filter this by type and add them # appropriately: transitions = normalise_parameter_as_list(transitions) f_dict = filter_discrete_types(transitions, (OnEvent, OnCondition)) self._on_events = [] self._on_conditions = [] # Add all the OnEvents and OnConditions: for event in f_dict[OnEvent]: self.add_on_event(event) for condition in f_dict[OnCondition]: self.add_on_condition(condition)
def do_to_assignments_and_events(doList): if not doList: return [], [] # 'doList' is a list of strings, OutputEvents, and StateAssignments. do_type_list = (OutputEvent, basestring, StateAssignment) do_types = filter_discrete_types(doList, do_type_list) # Convert strings to StateAssignments: sa_from_strs = [StrToExpr.state_assignment(s) for s in do_types[basestring]] return do_types[StateAssignment] + sa_from_strs, do_types[OutputEvent]
def __init__(self, state_assignments=None, event_outputs=None, target_regime_name=None): """Abstract class representing a transition from one |Regime| to another. |Transition| objects are not created directly, but via the subclasses |OnEvent| and |OnCondition|. :param state_assignments: A list of the state-assignments performed when this transition occurs. Objects in this list are either `string` (e.g A = A+13) or |StateAssignment| objects. :param event_outputs: A list of |OutputEvent| objects emitted when this transition occurs. :param target_regime_name: The name of the regime to go into after this transition. ``None`` implies staying in the same regime. This has to be specified as a string, not the object, because in general the |Regime| object is not yet constructed. This is automatically resolved by the |ComponentClass| in ``_ResolveTransitionRegimeNames()`` during construction. .. todo:: For more information about what happens at a regime transition, see here: XXXXXXX """ if target_regime_name: assert isinstance(target_regime_name, basestring) # Load state-assignment objects as strings or StateAssignment objects state_assignments = state_assignments or [] sa_types = (basestring, StateAssignment) sa_type_dict = filter_discrete_types(state_assignments, sa_types) sa_from_str = [StrToExpr.state_assignment(o) for o in sa_type_dict[basestring]] self._state_assignments = sa_type_dict[StateAssignment] + sa_from_str self._event_outputs = event_outputs or [] self._target_regime_name = target_regime_name self._source_regime_name = None # Set later, once attached to a regime: self._target_regime = None self._source_regime = None