parameters: (self, timestamp, state_recs, params)
return_statement: return state_recs
docstring: This method will cluster all nearby moving points. The following is
          done:
          1. The clustering of moving points is done by calling
          :func:`~reid.MulticamTracker.prune_nearby_points_in_list`
          2. All the parked records are clustered as "Parked" and logged
  
          Arguments:
              timestamp {[datetime]} -- The timestamp time-step at which the
              clustering is being done
              state_recs {[dict]} -- A list of day2 schema dictionary of
              detections. This dictionary will have moving points under the
              state_recs['moving']
              params {dict} -- Dictionary parameters for clustering. It can have
              the following keys:
              a. "dist_thresh" key: indicates the distance threshold for
                 clustering
  
              If "dist_thresh" key is not provided or if params is None, then
              "dist_thresh" for clustering defaults to CLUSTER_DIST_THRESH_IN_M
              in the config file or consants.DEFAULT_CLUSTER_DIST_THRESH_IN_M
  
          Keyword Arguments:
              match_id {int} -- The id of the timestep at which this clustering
              is done (default: {0})
  
          Returns:
              [dict] -- the state_recs dictionary itself. Note that moving
              records in state_recs (state_recs['moving']) will be substituted
              with a single point for each cluster
docstring_summary: This method will cluster all nearby moving points. The following is
          done:
          1. The clustering of moving points is done by calling
          :func:`~reid.MulticamTracker.prune_nearby_points_in_list`
          2. All the parked records are clustered as "Parked" and logged
docstring_tokens: This method will cluster all nearby moving points . The following is done : 1 . The clustering of moving points is done by calling : func : ~reid . MulticamTracker . prune_nearby_points_in_list 2 . All the parked records are clustered as Parked and logged
function: def prune_nearby_points(self, timestamp, state_recs, params):
          """
          This method will cluster all nearby moving points. The following is
          done:
          1. The clustering of moving points is done by calling
          :func:`~reid.MulticamTracker.prune_nearby_points_in_list`
          2. All the parked records are clustered as "Parked" and logged
  
          Arguments:
              timestamp {[datetime]} -- The timestamp time-step at which the
              clustering is being done
              state_recs {[dict]} -- A list of day2 schema dictionary of
              detections. This dictionary will have moving points under the
              state_recs['moving']
              params {dict} -- Dictionary parameters for clustering. It can have
              the following keys:
              a. "dist_thresh" key: indicates the distance threshold for
                 clustering
  
              If "dist_thresh" key is not provided or if params is None, then
              "dist_thresh" for clustering defaults to CLUSTER_DIST_THRESH_IN_M
              in the config file or consants.DEFAULT_CLUSTER_DIST_THRESH_IN_M
  
          Keyword Arguments:
              match_id {int} -- The id of the timestep at which this clustering
              is done (default: {0})
  
          Returns:
              [dict] -- the state_recs dictionary itself. Note that moving
              records in state_recs (state_recs['moving']) will be substituted
              with a single point for each cluster
          """
          # Merge points which were moving + newly parked
          moving_recs = state_recs['moving']
          state_recs['moving'] = self.prune_nearby_points_in_list(
              timestamp, moving_recs, params)
          self.mclogger.log_cluster_points(
              timestamp, state_recs['parked'], "Parked")
          return state_recs
function_tokens: def prune_nearby_points ( self , timestamp , state_recs , params ) : # Merge points which were moving + newly parked moving_recs = state_recs [ 'moving' ] state_recs [ 'moving' ] = self . prune_nearby_points_in_list ( timestamp , moving_recs , params ) self . mclogger . log_cluster_points ( timestamp , state_recs [ 'parked' ] , "Parked" ) return state_recs