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


# Given a dataframe of coordinates and centroids, cluster the coordinates, minimize the time difference, and return the routes
def cluster_and_minimize(df, centroids, norm_centroids, end, time_diff):
    # Cluster the coordinates
    kmeans = KMeans(n_clusters=len(norm_centroids), init=norm_centroids)

    # Fit the coordinates to the clusters
    kmeans.fit(df['normalized_gps'].values.tolist())

    # Add the cluster labels to the dataframe
    df['cluster'] = kmeans.labels_

    # Create centroid strings
    centroid_1 = list_to_string([centroids[0]])
    centroid_2 = list_to_string([centroids[1]])

    # Return the list of locations in each cluster
    route_1 = df[df['cluster'] == 0]
    route_1_stops = len(route_1['gps'].values.tolist())
    route_1_str = list_to_string(route_1['gps'].values.tolist())

    route_2 = df[df['cluster'] == 1]
    route_2_stops = len(route_2['gps'].values.tolist())
    route_2_str = list_to_string(route_2['gps'].values.tolist())

    # Get the trip time for each route
    trip_hrs_1 = get_trip_time(route_1_str, route_1_stops, centroid_1, end)
    trip_hrs_2 = get_trip_time(route_2_str, route_2_stops, centroid_2, end)

    # if the absolute value of the difference in trip times is greater than the time difference, minimize the time difference
    if abs(trip_hrs_1 - trip_hrs_2) > time_diff:
        route_1_coordinates, route_2_coordinates = minimize_route_time_diff(route_1['gps'].values.tolist(),
                                                                            route_2['gps'].values.tolist(),
                                                                            centroid_1, centroid_2, end, time_diff)
    else:
        route_1_coordinates = route_1['gps'].values.tolist()
        route_2_coordinates = route_2['gps'].values.tolist()

    # Edit the dataframe to reflect the new coordinate clusters
    df.loc[df['gps'].astype(str).isin(map(str, route_1_coordinates)), 'cluster'] = 0
    df.loc[df['gps'].astype(str).isin(map(str, route_2_coordinates)), 'cluster'] = 1

    return df, route_1_coordinates, route_2_coordinates


def minimize_route_time_diff(route_1_coordinates, route_2_coordinates, route_1_start, route_2_start, end,
                             time_diff):
    """
    Takes two routes and a time difference and returns a route that is the same length as the shorter route but has a time difference that is less than the time difference
    """
    # Find the difference in time between the two routes
    route_1_time = get_trip_time(list_to_string(route_1_coordinates),
                                 len(route_1_coordinates), route_1_start, end)
    route_2_time = get_trip_time(list_to_string(route_2_coordinates),
                                 len(route_2_coordinates), route_2_start, end)
    route_time_diff = abs(route_1_time - route_2_time)

    # If the difference in time is greater than the time difference, move the closest coordinate from the longer route to the shorter route
    if route_time_diff > time_diff:
        # Find which route is longer
        if len(route_1_coordinates) > len(route_2_coordinates):
            longer_route = route_1_coordinates
            shorter_route = route_2_coordinates

            # Move the closest coordinate from the longer route to the shorter route
            closest_coordinate = move_coordinate(longer_route, shorter_route)
            longer_route.remove(closest_coordinate)
            shorter_route.append(closest_coordinate)

            # Recursively call the function
            return minimize_route_time_diff(longer_route, shorter_route, route_1_start, route_2_start, end, time_diff)

        else:
            longer_route = route_2_coordinates
            shorter_route = route_1_coordinates

            # Move the closest coordinate from the longer route to the shorter route
            closest_coordinate = move_coordinate(longer_route, shorter_route)
            longer_route.remove(closest_coordinate)
            shorter_route.append(closest_coordinate)

            # Recursively call the function
            return minimize_route_time_diff(shorter_route, longer_route, route_1_start, route_2_start, end, time_diff)

    # If the difference in time is less than the time difference, return the routes
    return route_1_coordinates, route_2_coordinates


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):
    """
    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 * 60

    total_time_hours = (travel_time_seconds + waypoint_time_seconds) / 3600

    return total_time_hours


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)


# Given two clusters and their respective lists of coordinates, move one coordinate from the larger centroid to the smaller centroid
def move_coordinate(larger_centroid_coordinates, smaller_centroid_coordinates):
    # Calculate the centroid of the smaller cluster
    smaller_centroid = [sum([i[0] for i in smaller_centroid_coordinates]) / len(smaller_centroid_coordinates),
                        sum([i[1] for i in smaller_centroid_coordinates]) / len(smaller_centroid_coordinates)]

    # Find the coordinate in larger_centroid_coordinates that is closest to smaller_centroid
    closest_coordinate = larger_centroid_coordinates[0]
    closest_coordinate_distance = __distance__(closest_coordinate, smaller_centroid)

    for coordinate in larger_centroid_coordinates:
        if __distance__(coordinate, smaller_centroid) < closest_coordinate_distance:
            closest_coordinate = coordinate
            closest_coordinate_distance = __distance__(coordinate, smaller_centroid)

    return closest_coordinate


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