# Path: temporal_network/chance_constraint.py
# class ChanceConstraint(object):
# 
#     def __init__(self, id, name, risk_bound):
#         self.id = id
#         self.name = name
#         self.risk_bound = risk_bound
#         self.relaxed_bound = None
#         self.relaxable_bound = False
#         self.relax_cost = 0.01
# 
# 
#     def get_bound(self):
#         if self.relaxed_bound is None:
#             return self.risk_bound
#         else:
#             return self.relaxed_bound
# 
# 
#     def pretty_print(self):
# 
#         expression_str = []
# 
#         expression_str.append('(%s:)[%.4f]' % (self.name, self.risk_bound))
# 
#         print(''.join(expression_str))
# 
# Path: temporal_network/temporal_constraint.py
# class TemporalConstraint(object):
# 
#     def __init__(self, id, name, fro, to, lower_bound, upper_bound):
#         self.id = id
#         self.name = name
#         self.fro = fro
#         self.to = to
#         self.lower_bound = lower_bound
#         self.upper_bound = upper_bound
#         self.guards = set()
#         self.controllable = True
#         self.probabilistic = False
# 
#         self.mean = 0
#         self.std = 0
# 
#         self.activated = False
#         self.relaxed_lb = None
#         self.relaxed_ub = None
# 
#         self.relaxable_lb = False
#         self.relaxable_ub = False
# 
#         self.relax_cost_lb = 0.01
#         self.relax_cost_ub = 0.01
# 
#     def add_guard(self,guard):
#         self.guards.add(guard)
# 
#     def get_lower_bound(self):
#         if self.relaxed_lb is None:
#             return self.lower_bound
#         else:
#             return self.relaxed_lb
# 
#     def get_upper_bound(self):
#         if self.relaxed_ub is None:
#             return self.upper_bound
#         else:
#             return self.relaxed_ub
# 
#     def pretty_print(self):
# 
#         expression_str = []
# 
#         expression_str.append('(%s:%s->%s)[%.4f,%.4f]' % (self.name, self.fro, self.to, self.lower_bound, self.upper_bound))
# 
#         print(''.join(expression_str))
# 
# Path: temporal_network/decision_variable.py
# class DecisionVariable(object):
# 
#     def __init__(self, id, name):
#         self.id = id
#         self.name = name
#         self.domain = set()
#         self.guards = set()
# 
#         self.optimal_utility = 0
# 
#     def __lt__(self, other):
#         return self.optimal_utility > other.optimal_utility
# 
#     def add_domain_value(self,value):
#         self.domain.add(value)
# 
#     def add_guard(self,guard):
#         self.guards.add(guard)
# 
# Path: temporal_network/assignment.py
# class Assignment(object):
# 
#     def __init__(self, decision_variable, value, utility):
#         self.decision_variable = decision_variable
#         self.value = value
#         self.utility = utility
# 
#     def __lt__(self, other):
#         return self.utility > other.utility
# 
#     def pretty_print(self):
#         print(self.decision_variable.name + " <- "+ self.value +" ("+str(self.utility)+")")


from temporal_network.chance_constraint import ChanceConstraint
from temporal_network.temporal_constraint import TemporalConstraint
from temporal_network.decision_variable import DecisionVariable
from temporal_network.assignment import Assignment
import xml.etree.ElementTree
import numpy as np
            if guard_list is not None:
                for guard in guard_list.get_guard():
                    guard_variable_id = guard.get_decision_variable_equals().get_variable()
                    guard_value = guard.get_decision_variable_equals().get_value()

                    # retrieve the assignment
                    guard_assignment = assignment_map[(guard_variable_id,guard_value)]
                    # and add to the guards of this decision variable
                    decision_variable.add_guard(guard_assignment)


        # parse the temporal constraints and episodes

        # if line below confuses you, that's expected... We need better automated code generation for parsing...
        for temporal_constraint in tpn.get_temporal_constraints().get_temporal_constraint() + tpn.get_episodes().get_episode():
            # duration can be one of three types - controllable, uncertain and probabilistic
            # here we are only handling the first two cases, which are sorted into two lists
            # in our resulting stnu class.

            controllable_duration = temporal_constraint.get_duration().get_bounded_duration()
            uncertain_duration = temporal_constraint.get_duration().get_set_bounded_uncertain_duration()


            duration = controllable_duration or uncertain_duration
            lower_bound = duration.get_lower_bound()
            upper_bound = duration.get_upper_bound()
            from_event = temporal_constraint.get_from_event()
            to_event = temporal_constraint.get_to_event()
            constraint_id = temporal_constraint.get_id()
            constraint_name = temporal_constraint.get_name()

# Path: temporal_network/chance_constraint.py
# class ChanceConstraint(object):
# 
#     def __init__(self, id, name, risk_bound):
#         self.id = id
#         self.name = name
#         self.risk_bound = risk_bound
#         self.relaxed_bound = None
#         self.relaxable_bound = False
#         self.relax_cost = 0.01
# 
# 
#     def get_bound(self):
#         if self.relaxed_bound is None:
#             return self.risk_bound
#         else:
#             return self.relaxed_bound
# 
# 
#     def pretty_print(self):
# 
#         expression_str = []
# 
#         expression_str.append('(%s:)[%.4f]' % (self.name, self.risk_bound))
# 
#         print(''.join(expression_str))
# 
# Path: temporal_network/temporal_constraint.py
# class TemporalConstraint(object):
# 
#     def __init__(self, id, name, fro, to, lower_bound, upper_bound):
#         self.id = id
#         self.name = name
#         self.fro = fro
#         self.to = to
#         self.lower_bound = lower_bound
#         self.upper_bound = upper_bound
#         self.guards = set()
#         self.controllable = True
#         self.probabilistic = False
# 
#         self.mean = 0
#         self.std = 0
# 
#         self.activated = False
#         self.relaxed_lb = None
#         self.relaxed_ub = None
# 
#         self.relaxable_lb = False
#         self.relaxable_ub = False
# 
#         self.relax_cost_lb = 0.01
#         self.relax_cost_ub = 0.01
# 
#     def add_guard(self,guard):
#         self.guards.add(guard)
# 
#     def get_lower_bound(self):
#         if self.relaxed_lb is None:
#             return self.lower_bound
#         else:
#             return self.relaxed_lb
# 
#     def get_upper_bound(self):
#         if self.relaxed_ub is None:
#             return self.upper_bound
#         else:
#             return self.relaxed_ub
# 
#     def pretty_print(self):
# 
#         expression_str = []
# 
#         expression_str.append('(%s:%s->%s)[%.4f,%.4f]' % (self.name, self.fro, self.to, self.lower_bound, self.upper_bound))
# 
#         print(''.join(expression_str))
# 
# Path: temporal_network/decision_variable.py
# class DecisionVariable(object):
# 
#     def __init__(self, id, name):
#         self.id = id
#         self.name = name
#         self.domain = set()
#         self.guards = set()
# 
#         self.optimal_utility = 0
# 
#     def __lt__(self, other):
#         return self.optimal_utility > other.optimal_utility
# 
#     def add_domain_value(self,value):
#         self.domain.add(value)
# 
#     def add_guard(self,guard):
#         self.guards.add(guard)
# 
# Path: temporal_network/assignment.py
# class Assignment(object):
# 
#     def __init__(self, decision_variable, value, utility):
#         self.decision_variable = decision_variable
#         self.value = value
#         self.utility = utility
# 
#     def __lt__(self, other):
#         return self.utility > other.utility
# 
#     def pretty_print(self):
#         print(self.decision_variable.name + " <- "+ self.value +" ("+str(self.utility)+")")


# Path: temporal_network/tpnu.py
from temporal_network.chance_constraint import ChanceConstraint
from temporal_network.temporal_constraint import TemporalConstraint
from temporal_network.decision_variable import DecisionVariable
from temporal_network.assignment import Assignment
import xml.etree.ElementTree
import numpy as np

            if guard_list is not None:
                for guard in guard_list.get_guard():
                    guard_variable_id = guard.get_decision_variable_equals().get_variable()
                    guard_value = guard.get_decision_variable_equals().get_value()

                    # retrieve the assignment
                    guard_assignment = assignment_map[(guard_variable_id,guard_value)]
                    # and add to the guards of this decision variable
                    decision_variable.add_guard(guard_assignment)


        # parse the temporal constraints and episodes

        # if line below confuses you, that's expected... We need better automated code generation for parsing...
        for temporal_constraint in tpn.get_temporal_constraints().get_temporal_constraint() + tpn.get_episodes().get_episode():
            # duration can be one of three types - controllable, uncertain and probabilistic
            # here we are only handling the first two cases, which are sorted into two lists
            # in our resulting stnu class.

            controllable_duration = temporal_constraint.get_duration().get_bounded_duration()
            uncertain_duration = temporal_constraint.get_duration().get_set_bounded_uncertain_duration()


            duration = controllable_duration or uncertain_duration
            lower_bound = duration.get_lower_bound()
            upper_bound = duration.get_upper_bound()
            from_event = temporal_constraint.get_from_event()
            to_event = temporal_constraint.get_to_event()
            constraint_id = temporal_constraint.get_id()
            constraint_name = temporal_constraint.get_name()

            constraint = TemporalConstraint(constraint_id, constraint_name, tpnu.node_id_to_number[from_event], tpnu.node_id_to_number[to_event], lower_bound, upper_bound)