import numpy

from gtfspy.routing.label import LabelTimeWithBoardingsCount, merge_pareto_frontiers, compute_pareto_front, \
    LabelVehLegCount, LabelTime, LabelTimeBoardingsAndRoute, LabelTimeAndRoute
from gtfspy.routing.connection import Connection


class NodeProfileMultiObjective:
    """
    In the multi-objective connection scan algorithm,
    each stop has a profile entry containing all Pareto-optimal entries.
    """

    def __init__(self,
                 dep_times=None,
                 walk_to_target_duration=float('inf'),
                 label_class=LabelTimeWithBoardingsCount,
                 transit_connection_dep_times=None,
                 closest_target=None,
                 node_id=None):
        """
        Parameters
        ----------
        dep_times
        walk_to_target_duration
        label_class: label class to be used
        transit_connection_dep_times:
            if not given, all connections are assumed to be real connections
        closest_target: int, optional
            stop_I of the closest target if within walking distance (and Routes are recorded)
        """

        if dep_times is None:
            dep_times = []
        n_dep_times = len(dep_times)
        assert n_dep_times == len(set(dep_times)), "There should be no duplicate departure times"
        self._departure_times = list(reversed(sorted(dep_times)))
        self.dep_times_to_index = dict(zip(self._departure_times, range(len(self._departure_times))))
        self._label_bags = [[]] * len(self._departure_times)
        self._walk_to_target_duration = walk_to_target_duration
        self._min_dep_time = float('inf')
        self.label_class = label_class
        self.closest_target = closest_target
        if self.label_class == LabelTimeBoardingsAndRoute and self._walk_to_target_duration < float('inf'):
            assert (self.closest_target is not None)

        if transit_connection_dep_times is not None:
            self._connection_dep_times = transit_connection_dep_times
        else:
            self._connection_dep_times = dep_times
        assert (isinstance(self._connection_dep_times, (list, numpy.ndarray)))
        self._closed = False
        self._finalized = False
        self._final_pareto_optimal_labels = None
        self._real_connection_labels = None
        self.node_id = node_id

    def _check_dep_time_is_valid(self, dep_time):
        """
        A simple checker, that connections are coming in descending order of departure time
        and that no departure time has been "skipped".

        Parameters
        ----------
        dep_time

        Returns
        -------
        None
        """
        assert dep_time <= self._min_dep_time, "Labels should be entered in decreasing order of departure time."
        dep_time_index = self.dep_times_to_index[dep_time]
        if self._min_dep_time < float('inf'):
            min_dep_index = self.dep_times_to_index[self._min_dep_time]
            assert min_dep_index == dep_time_index or (min_dep_index == dep_time_index - 1), \
                "dep times should be ordered sequentially"
        else:
            assert dep_time_index is 0, "first dep_time index should be zero (ensuring that all connections are properly handled)"
        self._min_dep_time = dep_time

    def get_walk_to_target_duration(self):
        """
        Get walking distance to target node.

        Returns
        -------
        walk_to_target_duration: float
        """
        return self._walk_to_target_duration

    def update(self, new_labels, departure_time_backup=None):
        """
        Update the profile with the new labels.
        Each new label should have the same departure_time.

        Parameters
        ----------
        new_labels: list[LabelTime]

        Returns
        -------
        added: bool
            whether new_pareto_tuple was added to the set of pareto-optimal tuples
        """
        if self._closed:
            raise RuntimeError("Profile is closed, no updates can be made")
        try:
            departure_time = next(iter(new_labels)).departure_time
        except StopIteration:
            departure_time = departure_time_backup
        self._check_dep_time_is_valid(departure_time)

        for new_label in new_labels:
            assert (new_label.departure_time == departure_time)
        dep_time_index = self.dep_times_to_index[departure_time]

        if dep_time_index > 0:
            # Departure time is modified in order to not pass on labels which are not Pareto-optimal when departure time is ignored.
            mod_prev_labels = [label.get_copy_with_specified_departure_time(departure_time) for label
                               in self._label_bags[dep_time_index - 1]]
        else:
            mod_prev_labels = list()
        mod_prev_labels += self._label_bags[dep_time_index]

        walk_label = self._get_label_to_target(departure_time)
        if walk_label:
            new_labels = new_labels + [walk_label]
        new_frontier = merge_pareto_frontiers(new_labels, mod_prev_labels)

        self._label_bags[dep_time_index] = new_frontier
        return True

    def evaluate(self, dep_time, first_leg_can_be_walk=True, connection_arrival_time=None):

        """
        Get the pareto_optimal set of Labels, given a departure time.

        Parameters
        ----------
        dep_time : float, int
            time in unix seconds
        first_leg_can_be_walk : bool, optional
            whether to allow walking to target to be included into the profile
            (I.e. whether this function is called when scanning a pseudo-connection:
            "double" walks are not allowed.)
        connection_arrival_time: float, int, optional
            used for computing the walking label if dep_time, i.e., connection.arrival_stop_next_departure_time, is infinity)
        connection: connection object

        Returns
        -------
        pareto_optimal_labels : set
            Set of Labels
        """
        walk_labels = list()
        # walk label towards target
        if first_leg_can_be_walk and self._walk_to_target_duration != float('inf'):
            # add walk_label
            if connection_arrival_time is not None:
                walk_labels.append(self._get_label_to_target(connection_arrival_time))
            else:
                walk_labels.append(self._get_label_to_target(dep_time))

        # if dep time is larger than the largest dep time -> only walk labels are possible
        if dep_time in self.dep_times_to_index:
            assert (dep_time != float('inf'))
            index = self.dep_times_to_index[dep_time]
            labels = self._label_bags[index]
            pareto_optimal_labels = merge_pareto_frontiers(labels, walk_labels)
        else:
            pareto_optimal_labels = walk_labels

        if not first_leg_can_be_walk:
            pareto_optimal_labels = [label for label in pareto_optimal_labels if not label.first_leg_is_walk]
        return pareto_optimal_labels

    def _get_label_to_target(self, departure_time):
        if departure_time != float('inf') and self._walk_to_target_duration != float('inf'):
            if self._walk_to_target_duration == 0:
                first_leg_is_walk = False
            else:
                first_leg_is_walk = True
            if self.label_class == LabelTimeBoardingsAndRoute or self.label_class == LabelTimeAndRoute:
                if self._walk_to_target_duration > 0:
                    walk_connection = Connection(self.node_id,
                                                 self.closest_target,
                                                 departure_time,
                                                 departure_time + self._walk_to_target_duration,
                                                 Connection.WALK_TRIP_ID,
                                                 Connection.WALK_SEQ,
                                                 is_walk=True
                                                 )
                else:
                    walk_connection = None
                if self.label_class == LabelTimeAndRoute:
                    label = self.label_class(departure_time=float(departure_time),
                                             arrival_time_target=float(departure_time + self._walk_to_target_duration),
                                             movement_duration=self._walk_to_target_duration,
                                             first_leg_is_walk=first_leg_is_walk,
                                             connection=walk_connection)
                else:
                    label = self.label_class(departure_time=float(departure_time),
                                             arrival_time_target=float(departure_time + self._walk_to_target_duration),
                                             movement_duration=self._walk_to_target_duration,
                                             n_boardings=0,
                                             first_leg_is_walk=first_leg_is_walk,
                                             connection=walk_connection)
            else:
                label = self.label_class(departure_time=float(departure_time),
                                         arrival_time_target=float(departure_time + self._walk_to_target_duration),
                                         n_boardings=0,
                                         first_leg_is_walk=first_leg_is_walk)

            return label
        else:
            return None

    def get_labels_for_real_connections(self):
        self._closed = True
        if self._real_connection_labels is None:
            self._compute_real_connection_labels()
        return self._real_connection_labels

    def get_final_optimal_labels(self):
        """
        Get pareto-optimal labels.

        Returns
        -------
        """
        assert self._finalized, "finalize() first!"
        return self._final_pareto_optimal_labels

    def finalize(self, neighbor_label_bags=None, walk_durations=None, departure_arrival_stop_pairs=None):
        """
        Parameters
        ----------
        neighbor_label_bags: list
            each list element is a list of labels corresponding to a neighboring node
             (note: only labels with first connection being a departure should be included)
        walk_durations: list
        departure_arrival_stop_pairs: list of tuples
        Returns
        -------
        None
        """
        assert (not self._finalized)
        if self._final_pareto_optimal_labels is None:
            self._compute_real_connection_labels()
        if neighbor_label_bags is not None:
            assert (len(walk_durations) == len(neighbor_label_bags))
            self._compute_final_pareto_optimal_labels(neighbor_label_bags,
                                                      walk_durations,
                                                      departure_arrival_stop_pairs)
        else:
            self._final_pareto_optimal_labels = self._real_connection_labels
        self._finalized = True
        self._closed = True

    def _compute_real_connection_labels(self):
        pareto_optimal_labels = []
        # do not take those bags with first event is a pseudo-connection
        for dep_time in self._connection_dep_times:
            index = self.dep_times_to_index[dep_time]
            pareto_optimal_labels.extend([label for label in self._label_bags[index] if not label.first_leg_is_walk])
        if self.label_class == LabelTimeWithBoardingsCount or self.label_class == LabelTime \
                or self.label_class == LabelTimeBoardingsAndRoute:
            pareto_optimal_labels = [label for label in pareto_optimal_labels
                                     if label.duration() < self._walk_to_target_duration]

        if self.label_class == LabelVehLegCount and self._walk_to_target_duration < float('inf'):
            pareto_optimal_labels.append(LabelVehLegCount(0))
        self._real_connection_labels = [label.get_copy() for label in compute_pareto_front(pareto_optimal_labels,
                                                                                           finalization=True)]

    def _compute_final_pareto_optimal_labels(self, neighbor_label_bags, walk_durations, departure_arrival_stops):
        labels_from_neighbors = []
        for i, (label_bag, walk_duration)in enumerate(zip(neighbor_label_bags, walk_durations)):
            for label in label_bag:
                if self.label_class == LabelTimeBoardingsAndRoute or self.label_class == LabelTimeAndRoute:
                    departure_arrival_tuple = departure_arrival_stops[i]
                    departure_time = label.departure_time - walk_duration
                    arrival_time = label.departure_time
                    connection = Connection(departure_arrival_tuple[0],
                                            departure_arrival_tuple[1],
                                            departure_time,
                                            arrival_time,
                                            Connection.WALK_TRIP_ID,
                                            Connection.WALK_SEQ,
                                            is_walk=True)
                    labels_from_neighbors.append(label.get_copy_with_walk_added(walk_duration, connection))
                else:
                    labels_from_neighbors.append(label.get_copy_with_walk_added(walk_duration))

        pareto_front = compute_pareto_front(self._real_connection_labels +
                                            labels_from_neighbors,
                                            finalization=True)

        if pareto_front and hasattr(pareto_front[0], "duration"):
            self._final_pareto_optimal_labels = list(filter(lambda label: label.duration() < self._walk_to_target_duration, pareto_front))
        else:
            self._final_pareto_optimal_labels = pareto_front



import numpy

from gtfspy.routing.label import LabelTimeWithBoardingsCount, merge_pareto_frontiers, compute_pareto_front, \
    LabelVehLegCount, LabelTime, LabelTimeBoardingsAndRoute, LabelTimeAndRoute
from gtfspy.routing.connection import Connection


class NodeProfileMultiObjective:
    """
    In the multi-objective connection scan algorithm,
    each stop has a profile entry containing all Pareto-optimal entries.
    """

    def __init__(self,
                 dep_times=None,
                 walk_to_target_duration=float('inf'),
                 label_class=LabelTimeWithBoardingsCount,
                 transit_connection_dep_times=None,
                 closest_target=None,
                 node_id=None):
        """
        Parameters
        ----------
        dep_times
        walk_to_target_duration
        label_class: label class to be used
        transit_connection_dep_times:
            if not given, all connections are assumed to be real connections
        closest_target: int, optional
            stop_I of the closest target if within walking distance (and Routes are recorded)
        """

        if dep_times is None:
            dep_times = []
        n_dep_times = len(dep_times)
        assert n_dep_times == len(set(dep_times)), "There should be no duplicate departure times"
        self._departure_times = list(reversed(sorted(dep_times)))
        self.dep_times_to_index = dict(zip(self._departure_times, range(len(self._departure_times))))
        self._label_bags = [[]] * len(self._departure_times)
        self._walk_to_target_duration = walk_to_target_duration
        self._min_dep_time = float('inf')
        self.label_class = label_class
        self.closest_target = closest_target
        if self.label_class == LabelTimeBoardingsAndRoute and self._walk_to_target_duration < float('inf'):
            assert (self.closest_target is not None)

        if transit_connection_dep_times is not None:
            self._connection_dep_times = transit_connection_dep_times
        else:
            self._connection_dep_times = dep_times
        assert (isinstance(self._connection_dep_times, (list, numpy.ndarray)))
        self._closed = False
        self._finalized = False
        self._final_pareto_optimal_labels = None
        self._real_connection_labels = None
        self.node_id = node_id

    def _check_dep_time_is_valid(self, dep_time):
        """
        A simple checker, that connections are coming in descending order of departure time
        and that no departure time has been "skipped".

        Parameters
        ----------
        dep_time

        Returns
        -------
        None
        """
        assert dep_time <= self._min_dep_time, "Labels should be entered in decreasing order of departure time."
        dep_time_index = self.dep_times_to_index[dep_time]
        if self._min_dep_time < float('inf'):
            min_dep_index = self.dep_times_to_index[self._min_dep_time]
            assert min_dep_index == dep_time_index or (min_dep_index == dep_time_index - 1), \
                "dep times should be ordered sequentially"
        else:
            assert dep_time_index is 0, "first dep_time index should be zero (ensuring that all connections are properly handled)"
        self._min_dep_time = dep_time

    def get_walk_to_target_duration(self):
        """
        Get walking distance to target node.

        Returns
        -------
        walk_to_target_duration: float
        """
        return self._walk_to_target_duration

    def update(self, new_labels, departure_time_backup=None):
        """
        Update the profile with the new labels.
        Each new label should have the same departure_time.

        Parameters
        ----------
        new_labels: list[LabelTime]

        Returns
        -------
        added: bool
            whether new_pareto_tuple was added to the set of pareto-optimal tuples
        """
        if self._closed:
            raise RuntimeError("Profile is closed, no updates can be made")
        try:
            departure_time = next(iter(new_labels)).departure_time
        except StopIteration:
            departure_time = departure_time_backup
        self._check_dep_time_is_valid(departure_time)

        for new_label in new_labels:
            assert (new_label.departure_time == departure_time)
        dep_time_index = self.dep_times_to_index[departure_time]

        if dep_time_index > 0:
            # Departure time is modified in order to not pass on labels which are not Pareto-optimal when departure time is ignored.
            mod_prev_labels = [label.get_copy_with_specified_departure_time(departure_time) for label
                               in self._label_bags[dep_time_index - 1]]
        else:
            mod_prev_labels = list()
        mod_prev_labels += self._label_bags[dep_time_index]

        walk_label = self._get_label_to_target(departure_time)
        if walk_label:
            new_labels = new_labels + [walk_label]
        new_frontier = merge_pareto_frontiers(new_labels, mod_prev_labels)

        self._label_bags[dep_time_index] = new_frontier
        return True

    def evaluate(self, dep_time, first_leg_can_be_walk=True, connection_arrival_time=None):

        """
        Get the pareto_optimal set of Labels, given a departure time.

        Parameters
        ----------
        dep_time : float, int
            time in unix seconds
        first_leg_can_be_walk : bool, optional
            whether to allow walking to target to be included into the profile
            (I.e. whether this function is called when scanning a pseudo-connection:
            "double" walks are not allowed.)
        connection_arrival_time: float, int, optional
            used for computing the walking label if dep_time, i.e., connection.arrival_stop_next_departure_time, is infinity)
        connection: connection object

        Returns
        -------
        pareto_optimal_labels : set
            Set of Labels
        """
        walk_labels = list()
        # walk label towards target
        if first_leg_can_be_walk and self._walk_to_target_duration != float('inf'):
            # add walk_label
            if connection_arrival_time is not None:
                walk_labels.append(self._get_label_to_target(connection_arrival_time))
            else:
                walk_labels.append(self._get_label_to_target(dep_time))

        # if dep time is larger than the largest dep time -> only walk labels are possible
        if dep_time in self.dep_times_to_index:
            assert (dep_time != float('inf'))
            index = self.dep_times_to_index[dep_time]
            labels = self._label_bags[index]
            pareto_optimal_labels = merge_pareto_frontiers(labels, walk_labels)
        else:
            pareto_optimal_labels = walk_labels

        if not first_leg_can_be_walk:
            pareto_optimal_labels = [label for label in pareto_optimal_labels if not label.first_leg_is_walk]
        return pareto_optimal_labels

    def _get_label_to_target(self, departure_time):
        if departure_time != float('inf') and self._walk_to_target_duration != float('inf'):
            if self._walk_to_target_duration == 0:
                first_leg_is_walk = False
            else:
                first_leg_is_walk = True
            if self.label_class == LabelTimeBoardingsAndRoute or self.label_class == LabelTimeAndRoute:
                if self._walk_to_target_duration > 0:
                    walk_connection = Connection(self.node_id,
                                                 self.closest_target,
                                                 departure_time,
                                                 departure_time + self._walk_to_target_duration,
                                                 Connection.WALK_TRIP_ID,
                                                 Connection.WALK_SEQ,
                                                 is_walk=True
                                                 )
                else:
                    walk_connection = None
                if self.label_class == LabelTimeAndRoute:
                    label = self.label_class(departure_time=float(departure_time),
                                             arrival_time_target=float(departure_time + self._walk_to_target_duration),
                                             movement_duration=self._walk_to_target_duration,
                                             first_leg_is_walk=first_leg_is_walk,
                                             connection=walk_connection)
                else:
                    label = self.label_class(departure_time=float(departure_time),
                                             arrival_time_target=float(departure_time + self._walk_to_target_duration),
                                             movement_duration=self._walk_to_target_duration,
                                             n_boardings=0,
                                             first_leg_is_walk=first_leg_is_walk,
                                             connection=walk_connection)
            else:
                label = self.label_class(departure_time=float(departure_time),
                                         arrival_time_target=float(departure_time + self._walk_to_target_duration),
                                         n_boardings=0,
                                         first_leg_is_walk=first_leg_is_walk)

            return label
        else:
            return None

    def get_labels_for_real_connections(self):
        self._closed = True
        if self._real_connection_labels is None:
            self._compute_real_connection_labels()
        return self._real_connection_labels

    def get_final_optimal_labels(self):
        """
        Get pareto-optimal labels.

        Returns
        -------
        """
        assert self._finalized, "finalize() first!"
        return self._final_pareto_optimal_labels

    def finalize(self, neighbor_label_bags=None, walk_durations=None, departure_arrival_stop_pairs=None):
        """
        Parameters
        ----------
        neighbor_label_bags: list
            each list element is a list of labels corresponding to a neighboring node
             (note: only labels with first connection being a departure should be included)
        walk_durations: list
        departure_arrival_stop_pairs: list of tuples
        Returns
        -------
        None
        """
        assert (not self._finalized)
        if self._final_pareto_optimal_labels is None:
            self._compute_real_connection_labels()
        if neighbor_label_bags is not None:
            assert (len(walk_durations) == len(neighbor_label_bags))
            self._compute_final_pareto_optimal_labels(neighbor_label_bags,
                                                      walk_durations,
                                                      departure_arrival_stop_pairs)
        else:
            self._final_pareto_optimal_labels = self._real_connection_labels
        self._finalized = True
        self._closed = True

    def _compute_real_connection_labels(self):
        pareto_optimal_labels = []
        # do not take those bags with first event is a pseudo-connection
        for dep_time in self._connection_dep_times:
            index = self.dep_times_to_index[dep_time]
            pareto_optimal_labels.extend([label for label in self._label_bags[index] if not label.first_leg_is_walk])
        if self.label_class == LabelTimeWithBoardingsCount or self.label_class == LabelTime \
                or self.label_class == LabelTimeBoardingsAndRoute:
            pareto_optimal_labels = [label for label in pareto_optimal_labels
                                     if label.duration() < self._walk_to_target_duration]

        if self.label_class == LabelVehLegCount and self._walk_to_target_duration < float('inf'):
            pareto_optimal_labels.append(LabelVehLegCount(0))
        self._real_connection_labels = [label.get_copy() for label in compute_pareto_front(pareto_optimal_labels,
                                                                                           finalization=True)]

    def _compute_final_pareto_optimal_labels(self, neighbor_label_bags, walk_durations, departure_arrival_stops):
        labels_from_neighbors = []
        for i, (label_bag, walk_duration)in enumerate(zip(neighbor_label_bags, walk_durations)):
            for label in label_bag:
                if self.label_class == LabelTimeBoardingsAndRoute or self.label_class == LabelTimeAndRoute:
                    departure_arrival_tuple = departure_arrival_stops[i]
                    departure_time = label.departure_time - walk_duration
                    arrival_time = label.departure_time
                    connection = Connection(departure_arrival_tuple[0],
                                            departure_arrival_tuple[1],
                                            departure_time,
                                            arrival_time,
                                            Connection.WALK_TRIP_ID,
                                            Connection.WALK_SEQ,
                                            is_walk=True)
                    labels_from_neighbors.append(label.get_copy_with_walk_added(walk_duration, connection))
                else:
                    labels_from_neighbors.append(label.get_copy_with_walk_added(walk_duration))

        pareto_front = compute_pareto_front(self._real_connection_labels +
                                            labels_from_neighbors,
                                            finalization=True)

        if pareto_front and hasattr(pareto_front[0], "duration"):
            self._final_pareto_optimal_labels = list(filter(lambda label: label.duration() < self._walk_to_target_duration, pareto_front))
        else:
            self._final_pareto_optimal_labels = pareto_front



In the multi-objective connection scan algorithm,     each stop has a profile entry containing all Pareto-optimal entries. Parameters         ----------         dep_times         walk_to_target_duration         label_class: label class to be used         transit_connection_dep_times:             if not given, all connections are assumed to be real connections         closest_target: int, optional             stop_I of the closest target if within walking distance (and Routes are recorded) A simple checker, that connections are coming in descending order of departure time         and that no departure time has been "skipped".          Parameters         ----------         dep_time          Returns         -------         None Get walking distance to target node.          Returns         -------         walk_to_target_duration: float Update the profile with the new labels.         Each new label should have the same departure_time.          Parameters         ----------         new_labels: list[LabelTime]          Returns         -------         added: bool             whether new_pareto_tuple was added to the set of pareto-optimal tuples # Departure time is modified in order to not pass on labels which are not Pareto-optimal when departure time is ignored. Get the pareto_optimal set of Labels, given a departure time.          Parameters         ----------         dep_time : float, int             time in unix seconds         first_leg_can_be_walk : bool, optional             whether to allow walking to target to be included into the profile             (I.e. whether this function is called when scanning a pseudo-connection:             "double" walks are not allowed.)         connection_arrival_time: float, int, optional             used for computing the walking label if dep_time, i.e., connection.arrival_stop_next_departure_time, is infinity)         connection: connection object          Returns         -------         pareto_optimal_labels : set             Set of Labels # walk label towards target # add walk_label # if dep time is larger than the largest dep time -> only walk labels are possible Get pareto-optimal labels.          Returns         ------- Parameters         ----------         neighbor_label_bags: list             each list element is a list of labels corresponding to a neighboring node              (note: only labels with first connection being a departure should be included)         walk_durations: list         departure_arrival_stop_pairs: list of tuples         Returns         -------         None # do not take those bags with first event is a pseudo-connection