content: # Copyright (c) 2019, MD2K Center of Excellence
  # - Nasir Ali <nasir.ali08@gmail.com>
  # All rights reserved.
  #
  # Redistribution and use in source and binary forms, with or without
  # modification, are permitted provided that the following conditions are met:
  #
  # * Redistributions of source code must retain the above copyright notice, this
  # list of conditions and the following disclaimer.
  #
  # * Redistributions in binary form must reproduce the above copyright notice,
  # this list of conditions and the following disclaimer in the documentation
  # and/or other materials provided with the distribution.
  #
  # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
  # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
  # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
  # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
  # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
  # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
  # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
  # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
  # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
  # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  
  import numpy as np
  import pandas as pd
  from geopy.distance import great_circle
  from pyspark.sql.functions import pandas_udf, PandasUDFType
  from shapely.geometry.multipoint import MultiPoint
  from sklearn.cluster import DBSCAN
  from pyspark.sql.types import StructField, StructType, StringType, FloatType
  
  
  EPSILON_CONSTANT = 1000
  LATITUDE = 0
  LONGITUDE = 1
  ACCURACY = -1
  GPS_ACCURACY_THRESHOLD = 41.0
  KM_PER_RADIAN = 6371.0088
  GEO_FENCE_DISTANCE = 2
  MINIMUM_POINTS_IN_CLUSTER = 500
  
  def get_centermost_point(cluster: object) -> object:
      """
  
      :param cluster:
      :return:
      :rtype: object
      """
      centroid = (
          MultiPoint(cluster).centroid.x, MultiPoint(cluster).centroid.y)
      centermost_point = min(cluster, key=lambda point: great_circle(point,
                                                                     centroid).m)
      return tuple(centermost_point)
  
  
  schema = StructType([
      StructField("user", StringType()),
      StructField("latitude", FloatType()),
      StructField("longitude", FloatType())
  ])
  
  @pandas_udf(schema, PandasUDFType.GROUPED_MAP)
  def gps_clusters(data: object) -> object:
      """
      Computes the clusters
  
      :rtype: object
      :param list data: list of interpolated gps data
      :param float geo_fence_distance: Maximum distance between points in a
      cluster
      :param int min_points_in_cluster: Minimum number of points in a cluster
      :return: list of cluster-centroids coordinates
      """
      geo_fence_distance = GEO_FENCE_DISTANCE
      min_points_in_cluster = MINIMUM_POINTS_IN_CLUSTER
  
      data = data[data.accuracy < GPS_ACCURACY_THRESHOLD]
  
      id = data.user.iloc[0]
      dataframe = pd.DataFrame(
          {'latitude': data.latitude, 'longitude': data.longitude})
      coords = dataframe.as_matrix(columns=['latitude', 'longitude'])
  
      epsilon = geo_fence_distance / (
              EPSILON_CONSTANT * KM_PER_RADIAN)
      db = DBSCAN(eps=epsilon, min_samples=min_points_in_cluster,
                  algorithm='ball_tree', metric='haversine').fit(
          np.radians(coords))
      cluster_labels = db.labels_
      num_clusters = len(set(cluster_labels))
      clusters = pd.Series(
          [coords[cluster_labels == n] for n in range(-1, num_clusters)])
      clusters = clusters.apply(lambda y: np.nan if len(y) == 0 else y)
      clusters.dropna(how='any', inplace=True)
      centermost_points = clusters.map(get_centermost_point)
      centermost_points = np.array(centermost_points)
      all_centroid = []
      for cols in centermost_points:
          cols = np.array(cols)
          cols.flatten()
          cs = ([id, cols[LATITUDE], cols[LONGITUDE]])
          all_centroid.append(cs)
      df = pd.DataFrame(all_centroid, columns=['user', 'latitude', 'longitude'])
      return df
  