import numpy as np
import requests
from sklearn.cluster import KMeans
import time
import datetime

from datastructs import *


host = "http://acetyl.net:5000"  # queries acetyl.net:5000, the OSRM engine

endtime = datetime.datetime(2023, 11, 18, 18, 35) # 11/18/2023 6:35pm

# external facing functions

# gets single leg route between bike and clue
# should be HD and GeoJSON
def getRouteFullJSON(bike, clue):
    bike = bike.toJSON(); clue = clue.toJSON()
    url = f"{host}/route/v1/bike/{bike['longitude']},{bike['latitude']};{clue['longitude']},{clue['latitude']}?steps=true&overview=full&geometries=geojson"
    r = requests.get(url)
    return r.json()


def getRouteHDPolyline(bike, clue):
    bike = bike.toJSON(); clue = clue.toJSON()
    url = f"{host}/route/v1/bike/{bike['longitude']},{bike['latitude']};{clue['longitude']},{clue['latitude']}?overview=full&geometries=geojson"
    r = requests.get(url)

    p = r.json()['routes'][0]['geometry']['coordinates']
    return p


def getRouteFastPolyline(bike, clue):
    bike = bike.toJSON(); clue = clue.toJSON()
    url = f"{host}/route/v1/bike/{bike['longitude']},{bike['latitude']};{clue['longitude']},{clue['latitude']}?geometries=geojson"
    r = requests.get(url)
    p = r.json()['routes'][0]['geometry']['coordinates']
    return p


def getZSP(bike, home, clue_cluster):
    pass


# determines clusters based on current bikes and clues
def getClusters(bikes, clues, endpoint):
    clusters = [[] for bike in bikes ]
    route_geos = [[] for bike in bikes ]
    times = {}
    active_indices = [i for i in range(len(bikes)) if bikes[i].status == "ACTIVE"]
    active_bikes = [bike for bike in bikes if bike.status == "ACTIVE"]
    if len(active_bikes) == 0:
        return clusters, route_geos, times
    active_clues = [clue for clue in clues if clue.status == "UNVISITED"]
    # select only active bikes
    # select only unvisited clues
    clusters_t, route_geos_t, times_t = cluster_and_optimize(active_clues, active_bikes, endpoint)
    for i in range(len(active_indices)):
        route_geos[active_indices[i]] = route_geos_t[i]
        clusters[active_indices[i]] = clusters_t[i] 
        bikes[active_indices[i]].setCluster(clusters_t[i])
        times[bikes[active_indices[i]].name] = times_t[i]

    # return list of clue clusters corresponding to bikes
    return clusters, route_geos, times


# utility functions (internal)
def cluster_and_optimize(clues: [Clue], bikes: [Bike], end: Point, time_diff=0.25, max_time=24, n=2):
    """
    Takes a dataframe of gps coordinates, a list of centroids, and an end point and returns a dataframe with a cluster
    :param clues: a list of clues
    :param bikes: a list of centroids
    :param end: the end point of the trip
    :param time_diff: the maximum time difference between the longest trip and the average trip
    :param max_time: the maximum time of the trip
    :param n: the number of routes to create
    :return: a list of lists of clues (ordered by position on route), and a list of json route geojsons
    """
    # OVERRIDE MAX TIME
    #max_time = datetime.datetime.now() - endtime
    #max_time = max_time.seconds//3600
    routes = [clues] # one bike = one set of routes. only need to remove the faraway waypoints
    if len(bikes) > 1:
        # Create a new column with normalized gps coordinates and centroids
        normalized_points, norm_centroids = __normalize_points(clues, bikes)
        # Cluster the coordinates
        kmeans = KMeans(n_clusters=len(norm_centroids), init=norm_centroids)
        kmeans.fit(normalized_points)

        # Split the clues into clusters based on the cluster labels
        routes = [[] for i in range(len(norm_centroids))]
        for i, label in enumerate(kmeans.labels_):
            routes[label].append(clues[i])

        routes = __minimize_route_time_diff(routes, bikes, end, time_diff, n)
    
    # Remove waypoints from the longest route until the trip time is less than the max time
    for i in range(len(routes)):
        routes[i] = __remove_longest_waypoints(routes[i], bikes[i], end, max_time)
    

    # Get the json of the routes
    route_waypoints = []
    geometries = []
    times = []
    for i, route in enumerate(routes):
        route_json = __get_json(__clues_to_string(route), __clues_to_string([bikes[i]]), __clues_to_string([end])[:-1])
        geometries.append(route_json['trips'][0]['geometry']['coordinates'])
        route_waypoints.append(route_json['waypoints'])
        eta = time.time() + route_json['trips'][0]['duration'] + 90 * len(route)
        eta_str = datetime.datetime.fromtimestamp(eta).strftime("%I:%M:%S%p")
        times.append(eta_str)

    # Use the waypoint_index to reorder each route
    for i, route in enumerate(routes):
        route2 = ["" for x in route]
        for j,k in enumerate(route_waypoints[i][1:-1]):
            route2[ k['waypoint_index']-1 ] = route[j]
        routes[i] = route2

    return routes, geometries, times


def __clues_to_string(points: [Clue]):
    """
    Takes a list of points and returns a string of the list of points
    :param points: a list of points
    :return: a string of the list of points
    """
    string = ''
    for i in points:
        string += str(i.longitude) + ',' + str(i.latitude) + ';'

    return string


def __get_json(coordinate_string, start, end):
    """
    Takes a string of coordinates and returns the json of the route
    :param coordinate_string: a string of coordinates
    :param start: the start point of the trip
    :param end: the end point of the trip
    :return: the json of the route
    """
    coordinates = requests.get(
        'http://acetyl.net:5000/trip/v1/bike/' + start + coordinate_string + end + '?roundtrip=false&source=first&destination=last&geometries=geojson&overview=full')
    coordinates = coordinates.json()

    return coordinates


def __get_trip_time(coordinate_string, num_waypoints, start, end, time_per_waypoint=90, seconds=False):
    """
    Takes a string of coordinates and returns the trip time in hours
    :param coordinate_string: a string of coordinates
    :param num_waypoints: the number of waypoints
    :param start: the start point of the trip
    :param end: the end point of the trip
    :param time_per_waypoint: the time per waypoint in seconds
    :return: the trip time in hours
    """
    coordinates = requests.get(
        'http://acetyl.net:5000/trip/v1/bike/' + start + coordinate_string + end + '?roundtrip=false&source=first&destination=last')
    coordinates = coordinates.json()

    travel_time_seconds = int(coordinates['trips'][0]['duration'])
    waypoint_time_seconds = num_waypoints * time_per_waypoint
    if seconds:
        return (travel_time_seconds + waypoint_time_seconds)
    total_time_hours = (travel_time_seconds + waypoint_time_seconds) / 3600

    return total_time_hours


def __minimize_route_time_diff(routes: [Clue], starts: [Point], end: Point, time_diff, n):
    """
    Takes a list of lists of coordinates, a list of start points, an end point, a time difference, and a number of routes
    :param routes: the list of lists of coordinates
    :param starts: the list of start points
    :param end: the end point
    :param time_diff: the time difference
    :param n: the number of routes
    :return: a list of lists of coordinates
    """
    times = []

    for i, route in enumerate(routes):
        times.append(__get_trip_time(__clues_to_string(route), len(route), __clues_to_string([starts[i]]),
                                     __clues_to_string([end])[:-1]))

    # Find the average trip time
    average_time = np.mean(times)

    # Sort the trip times
    sorted_indices = np.argsort(times)

    # Find the difference between the longest trip time and the average trip time
    time_difference = times[sorted_indices[-1]] - average_time

    # If the difference is greater than the time difference, move a coordinate from the longest route to the shortest route
    if time_difference > time_diff:
        # Move a coordinate from the longest route to the shortest route
        closest_coordinate = __find_closest_coordinate(routes[sorted_indices[-1]],
                                                       __mean_center(routes[sorted_indices[0]]))
        routes[sorted_indices[0]].append(closest_coordinate)
        routes[sorted_indices[-1]].remove(closest_coordinate)

        # Recursively minimize the time difference between the routes
        return __minimize_route_time_diff(routes, starts, end, time_diff, n)

    # If the difference of the longest trip time from average is less than the time difference, return the routes
    return routes


def __remove_longest_waypoints(route_coordinates: [Clue], start: Bike, end: Point, max_time):
    """
    Takes a list of coordinates, a start point, an end point, and a maximum time and returns a list of coordinates
    :param route_coordinates: the list of coordinates
    :param start: the start point
    :param end: the end point
    :param max_time: the maximum time
    :return: a list of coordinates
    """
    # Find the trip time for the route
    route_time = __get_trip_time(__clues_to_string(route_coordinates), len(route_coordinates),
                                 __clues_to_string([start]), __clues_to_string([end])[:-1])

    # If the trip time is greater than the max time, remove the waypoint with the longest distance from the mean
    if route_time > max_time:
        route_mean = __mean_center(route_coordinates)
        furthest_coordinate = __find_farthest_coordinate(route_coordinates, route_mean)
        route_coordinates.remove(furthest_coordinate)

        return __remove_longest_waypoints(route_coordinates, start, end, max_time)

    return route_coordinates


def __normalize_points(clues: [Clue], bikes: [Bike]):
    """
    Takes a list of coordinates and a list of centroids and returns a list of normalized coordinates and a list of
    normalized centroids
    :param clues: the list of coordinates
    :param bikes: the list of centroids
    :return: the list of normalized coordinates and the list of normalized centroids
    """

    # Create a list of latitudes and longitudes
    latitudes = [i.latitude for i in clues]
    longitudes = [i.longitude for i in clues]

    # Find the minimum and maximum latitudes and longitudes
    min_lat = min(latitudes)
    max_lat = max(latitudes)
    min_lon = min(longitudes)
    max_lon = max(longitudes)

    # Normalize the coordinates and centroids using min-max normalization
    normalized_coordinates = []
    normalized_centroids = []

    for i in clues:
        normalized_coordinates.append(
            [__min_max_normalize(i.latitude, min_lat, max_lat), __min_max_normalize(i.longitude, min_lon, max_lon)])
    for i in bikes:
        normalized_centroids.append(
            [__min_max_normalize(i.latitude, min_lat, max_lat), __min_max_normalize(i.longitude, min_lon, max_lon)])

    return normalized_coordinates, normalized_centroids


def __min_max_normalize(value, min_value, max_value):
    """
    Takes a value, a minimum value, and a maximum value and returns the normalized value
    :param value: the value
    :param min_value: the minimum value
    :param max_value: the maximum value
    :return: the normalized value
    """
    return (value - min_value) / (max_value - min_value)


def __find_closest_coordinate(clues: [Clue], centroid: Point):
    """
    Takes a list of coordinates and a centroid and returns the clue in the list that is closest to the centroid
    :param clues: the list of coordinates
    :param centroid: the centroid
    :return: the clue in the list that is closest to the centroid
    """
    # Convert the clues to a list of points
    closest_coordinate = clues[0]
    closest_coordinate_distance = __distance(closest_coordinate, centroid)

    for clue in clues:
        if __distance(clue, centroid) < closest_coordinate_distance:
            closest_coordinate = clue
            closest_coordinate_distance = __distance(clue, centroid)

    return closest_coordinate


def __find_farthest_coordinate(clues: [Clue], centroid: Point):
    """
    Takes a list of coordinates and a centroid and returns the clue in the list that is farthest from the centroid
    :param clues: the list of coordinates
    :param centroid: the centroid
    :return: the clue in the list that is farthest from the centroid
    """
    farthest_coordinate = clues[0]
    farthest_coordinate_distance = __distance(farthest_coordinate, centroid)

    for clue in clues:
        if __distance(clue, centroid) > farthest_coordinate_distance:
            farthest_coordinate = clue
            farthest_coordinate_distance = __distance(clue, centroid)

    return farthest_coordinate


def __mean_center(clues: [Clue]):
    """
    Takes a list of coordinates and returns the mean center of the coordinates
    :param clues: the list of coordinates
    :return: the mean center of the coordinates
    """
    return Point(np.mean([coordinate.latitude for coordinate in clues]),
                    np.mean([coordinate.longitude for coordinate in clues]))


def __distance(coordinate1: Clue, coordinate2: Point):
    """
    Takes two coordinates and returns the distance between them
    :param coordinate1: the first coordinate
    :param coordinate2: the second coordinate
    :return: the distance between the two coordinates
    """
    return ((coordinate1.latitude - coordinate2.latitude) ** 2 + (
            coordinate1.longitude - coordinate2.longitude) ** 2) ** 0.5