"""
Object-Document Map for bike model
"""
from mongoengine import Document, DateTimeField, GeoPointField, \
    FloatField, connection
import bson
import datetime
from LatLon23 import LatLon, Latitude, Longitude
from geopy import distance


class Flock:
    """
    Flock object is created from a list of agents
    and has a useful centroid
    """
    def __init__(self, bikes):
        lats = list()
        lons = list()
        speeds = list()
        bearings = list()
        n = 0
        for b in bikes:

            # next point is determined by the distance travelled since the last update
            dt = datetime.datetime.now() - b['last_update']
            progress = (b['speed'] * dt.seconds) / 1000  # in km
            point = LatLon(b['point'][1], b['point'][0])
            next_point = point.offset(b['bearing'], progress)

            # collect all speeds, bearings and next points, to find centroid and average speed
            speeds.append(b['speed'])
            bearings.append(b.get('bearing', 0))
            lats.append(float(next_point.lat))
            lons.append(float(next_point.lon))
            n += 1


        self.mean_speed = 0
        self.size = 0
        self.centroid = None

        if n > 0:
            self.size = n
            self.mean_speed = sum(speeds) / float(len(speeds))

            self.centroid = LatLon(Latitude(sum(lats) / len(lats)),
                                   Longitude(sum(lons) / len(lons)))


class Bike(Document):
    point = GeoPointField()
    destination = GeoPointField(required=True)
    speed = FloatField(default=0)
    bearing = FloatField(default=0)
    last_update = DateTimeField(default=datetime.datetime.utcnow)

    def __init__(self, *args, **kwargs):
        super(Document, self).__init__(*args, **kwargs)
        self.db = connection.get_db()

    def update(self, lat, lon, speed, bearing):
        self.point = (lon, lat)
        self.speed = speed
        self.bearing = bearing
        self.last_update = datetime.datetime.now()
        self.save()

    def heading_to(self, target):
        s = LatLon(Latitude(self.point[1]),
                   Longitude(self.point[0]))
        if type(target) is list:
            t = LatLon(Latitude(target[0]),
                       Longitude(target[1]))
        else:
            t = target

        return s.heading_initial(t)

    def distance_to(self, point):
        return distance.geodesic(self.get_point(),
                                 (point[1],
                                  point[0])).meters

    def get_point(self):
        return (self.point[1],
                self.point[0])

    def get_dest(self):
        return (self.destination[1],
                self.destination[0])

    def find_flock(self,
                   point_altruism=0.1,
                   dest_altruism=0.2,
                   min_age=None):
        """
        min_age of agents in database, in seconds
        :return: list of Bike objects
        """

        if min_age is None:
            min_step = 100   # update at least every 100 meters
            if self.speed > 0:
                min_age = min_step / self.speed
            else:
                min_age = 60
        
        trip_len = distance.geodesic(self.get_point(), self.get_dest()).meters

        local_radius = trip_len * point_altruism
        destination_radius = trip_len * dest_altruism

        # these are bikes recently known to be around me
        local = [bike for bike in
                 self.db.bike.find(
                     {'point': {
                         '$near': {
                             '$geometry': {
                                 'type': 'Point',
                                 'coordinates': self.point},
                             '$maxDistance': local_radius}},
                      'last_update': {
                          '$gte': datetime.datetime.now() - datetime.timedelta(seconds=min_age)},
                      '_id': {'$ne': bson.objectid.ObjectId(self.id)}})]

        local_ids = set([bike['_id'] for bike in local])

        # these are going near my destination
        remote = [bike for bike in
                  self.db.bike.find(
                      {'destination':
                          {'$near':
                            {'$geometry': {'type': 'Point',
                                           'coordinates': self.destination},
                             '$maxDistance': destination_radius}},
                       'last_update': {
                           '$gte': datetime.datetime.now() - datetime.timedelta(seconds=min_age)}
                      })]
        remote_ids = set([bike['_id'] for bike in remote])

        # intersect them!
        flock_ids = local_ids.intersection(remote_ids)

        return (bike for bike in local if bike['_id'] in flock_ids)

    def flock_data(self):
        """
        :return: heading from point to flock centroid, in degrees
        """
        flock = Flock(self.find_flock())
        if flock.size > 0:
            flock_lat = float(flock.centroid.lat)
            flock_lon = float(flock.centroid.lon)

            return {'flock_lat': flock_lat,
                    'flock_lon': flock_lon,
                    'flock_avg_speed': flock.mean_speed,
                    'flock_size': flock.size}
        else:
            return {}