# 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
        tpnu = Tpnu(tpn_id, tpn_name)

        # In TPN format every node (or event in TPN terminology) has a non-unique name
        # and an unique id. Both of those are strings. For efficiency DC checking algorithms
        # denote each node by a number, such that we can cheaply check their equality.

        # parse the event

        event_ids = set()
        tpnu.node_id_to_name = {}
        tpnu.node_number_to_id = {}
        tpnu.node_id_to_number = {}

        for event in tpn.get_events().get_event():
            eid, ename = event.get_id(), event.get_name()
            event_ids.add(eid)
            tpnu.node_id_to_name[eid] = ename

        for eid in event_ids:
            next_number = len(tpnu.node_number_to_id) + 1
            tpnu.node_number_to_id[next_number] = eid
            tpnu.node_id_to_number[eid] = next_number

        tpnu.num_nodes = len(tpnu.node_number_to_id)

        assignment_map = {}
        # parse the decision variables
        for tpn_dv in tpn.get_decision_variables().get_decision_variable():
            dv_id = tpn_dv.get_id()
            dv_name = tpn_dv.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

        tpnu = Tpnu(tpn_id, tpn_name)

        # In TPN format every node (or event in TPN terminology) has a non-unique name
        # and an unique id. Both of those are strings. For efficiency DC checking algorithms
        # denote each node by a number, such that we can cheaply check their equality.

        # parse the event

        event_ids = set()
        tpnu.node_id_to_name = {}
        tpnu.node_number_to_id = {}
        tpnu.node_id_to_number = {}

        for event in tpn.get_events().get_event():
            eid, ename = event.get_id(), event.get_name()
            event_ids.add(eid)
            tpnu.node_id_to_name[eid] = ename

        for eid in event_ids:
            next_number = len(tpnu.node_number_to_id) + 1
            tpnu.node_number_to_id[next_number] = eid
            tpnu.node_id_to_number[eid] = next_number

        tpnu.num_nodes = len(tpnu.node_number_to_id)

        assignment_map = {}
        # parse the decision variables
        for tpn_dv in tpn.get_decision_variables().get_decision_variable():
            dv_id = tpn_dv.get_id()
            dv_name = tpn_dv.get_name()

            decision_variable = DecisionVariable(dv_id,dv_name)