import folium
import numpy as np
import pandas as pd
import requests
from sklearn.cluster import KMeans


def cluster_and_optimize(df, centroids, end, time_diff=0.25, max_time=24, n=2):
    # Create a new column with normalized gps coordinates and centroids
    df['normalized_gps'], norm_centroids = __normalize_gps(df['gps'].values.tolist(), centroids)

    # Cluster the coordinates
    kmeans = KMeans(n_clusters=len(norm_centroids), init=norm_centroids)
    kmeans.fit(df['normalized_gps'].values.tolist())

    df['cluster'] = kmeans.labels_

    routes = []
    starts = []
    for i in range(len(centroids)):
        routes.append(df[df['cluster'] == i]['gps'].values.tolist())
        starts.append(list_to_string([centroids[i]]))

    routes = __minimize_route_time_diff(routes, starts, 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], starts[i], end, max_time)
        df.loc[df['gps'].astype(str).isin(map(str, routes[i])), 'cluster'] = i

    return df, routes


def list_to_string(list_of_lists):
    """
    Takes a list of lists of coordinates and returns a string of the coordinates
    """
    string = ''
    for i in list_of_lists:
        string += str(i[1]) + ',' + str(i[0]) + ';'

    return string


def create_json_df(coordinate_string, start, end):
    coordinates = requests.get(
        'http://acetyl.net:5000/trip/v1/bike/' + start + coordinate_string + end + '?roundtrip=false&source=first&destination=last')
    coordinates = coordinates.json()

    # Create a dataframe from the JSON
    df = pd.DataFrame(coordinates['waypoints'])

    # Separate the location column into lon and lat columns
    df['lat'] = df['location'].apply(lambda x: x[0])
    df['lon'] = df['location'].apply(lambda x: x[1])

    df['waypoint_index'] = df['waypoint_index'].astype(int)

    # Map out the waypoints in order of the waypoint index
    df = df.sort_values(by=['waypoint_index'])

    return df


def get_trip_time(coordinate_string, num_waypoints, start, end, time_per_waypoint=90):
    """
    Takes a list of lists of coordinates and returns the time of the trip 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

    total_time_hours = (travel_time_seconds + waypoint_time_seconds) / 3600

    return total_time_hours


def __minimize_route_time_diff(routes, starts, end,
                               time_diff, n):
    """
    Takes two routes and a time difference and returns routes that have time differences less than the time difference
    """
    times = []

    for i, route in enumerate(routes):
        times.append(get_trip_time(list_to_string(route), len(route), starts[i], end))

    # 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, start, end, max_time):
    """
    Takes a list of lists of coordinates and returns a list of lists of coordinates that is the same length as the
    original list but has a trip time less than the max time
    """
    # Find the trip time for the route
    route_time = get_trip_time(list_to_string(route_coordinates), len(route_coordinates), start, end)

    # 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_gps(coordinates, centroids):
    """
    Takes a list of lists of coordinates and centroids and returns a list of lists of normalized coordinates and centroids
    """

    # Create a list of latitudes and longitudes
    latitudes = [i[0] for i in coordinates]
    longitudes = [i[1] for i in coordinates]

    # 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 coordinates:
        normalized_coordinates.append(
            [__min_max_normalize(i[0], min_lat, max_lat), __min_max_normalize(i[1], min_lon, max_lon)])
    for i in centroids:
        normalized_centroids.append(
            [__min_max_normalize(i[0], min_lat, max_lat), __min_max_normalize(i[1], min_lon, max_lon)])

    return normalized_coordinates, normalized_centroids


def __min_max_normalize(value, min_value, max_value):
    """
    Takes a value, min value, and max value and returns the normalized value
    """
    return (value - min_value) / (max_value - min_value)


def __find_closest_coordinate(coordinates, centroid):
    """
    Takes a list of coordinates and a centroid and returns the coordinate in the list that is closest to the centroid
    """
    closest_coordinate = coordinates[0]
    closest_coordinate_distance = __distance(closest_coordinate, centroid)

    for coordinate in coordinates:
        if __distance(coordinate, centroid) < closest_coordinate_distance:
            closest_coordinate = coordinate
            closest_coordinate_distance = __distance(coordinate, centroid)

    return closest_coordinate


def __find_farthest_coordinate(coordinates, centroid):
    """
    Takes a list of coordinates and a centroid and returns the coordinate in the list that is furthest from the centroid
    """
    farthest_coordinate = coordinates[0]
    farthest_coordinate_distance = __distance(farthest_coordinate, centroid)

    for coordinate in coordinates:
        if __distance(coordinate, centroid) > farthest_coordinate_distance:
            farthest_coordinate = coordinate
            farthest_coordinate_distance = __distance(coordinate, centroid)

    return farthest_coordinate


def __mean_center(coordinates):
    """
    Takes a list of coordinates and returns the mean center of the coordinates
    """
    return [sum([i[0] for i in coordinates]) / len(coordinates), sum([i[1] for i in coordinates]) / len(coordinates)]


def __distance(coordinate1, coordinate2):
    """
    Takes two coordinates and returns the distance between them
    """
    return ((coordinate1[0] - coordinate2[0]) ** 2 + (coordinate1[1] - coordinate2[1]) ** 2) ** 0.5