def plotRepresentativeTrajectory(cluster_label, data, fname="", path="", show=False): """ cluster_label: length n data: length n, needs to be in X, Y coordinate plots the cluster centroids of the current clustering """ centroids = getClusterCentroids(cluster_label, data) centroids_arr = [] for class_label, centroid in centroids.iteritems(): centroids_arr.append(centroid) plotter.plotListOfTrajectories( centroids_arr, show=show, clean=True, save=(fname != "" and path != ""), fname=fname, path=path )
def plotRepresentativeTrajectory(cluster_label, data, fname="", path="", show=False): """ cluster_label: length n data: length n, needs to be in X, Y coordinate plots the cluster centroids of the current clustering """ centroids = getClusterCentroids(cluster_label, data) centroids_arr = [] for class_label, centroid in centroids.iteritems(): centroids_arr.append(centroid) plotter.plotListOfTrajectories(centroids_arr, show=show, clean=True, save=(fname != "" and path != ""), fname=fname, path=path)
def main(): root_folder = raw_input("Input the root_folder name:") """ Firstly, extract all .csv input file names from {root_folder}/input/*.csv """ # filenames = ["8514019.csv", "9116943.csv", "9267118.csv", "9443140.csv", "9383986.csv", "9343340.csv", "9417464.csv", "9664225.csv", "9538440.csv", "9327138.csv"] # filenames = ["9664225.csv"] # filenames = ["8514019.csv"] filenames = [] for input_filename in os.listdir("{root_folder}/input/".format(root_folder = root_folder)): if (input_filename.find(".csv") != -1): filenames.append(input_filename) """ Get min distance between vessels """ need_compute_mindistance = raw_input("Need to compute min_distance_matrix for vessel interaction? (y/n) :") == 'y' if (need_compute_mindistance): """sort the aggregateData with MMSI based on TS""" data_with_mmsi = writeToCSV.readDataFromCSVWithMMSI(path = root_folder + "/cleanedData", filename = "aggregateData_with_mmsi.csv") data_with_mmsi_sorted = compute_mindistance.sortDataBasedOnTS(data_with_mmsi) writeToCSV.writeDataToCSVWithMMSI(data_with_mmsi_sorted, root_folder + "/cleanedData", "aggregateData_with_mmsi_sorted") """Apply the computing of min distance using a timed window""" data_with_mmsi_sorted = writeToCSV.readDataFromCSVWithMMSI(path = root_folder + "/cleanedData", filename = "aggregateData_with_mmsi_sorted.csv") mmsi_set = compute_mindistance.getSetOfMMSI(data_with_mmsi_sorted) print mmsi_set print list(mmsi_set) start_time = time.time() mmsi_list_dict, min_distance_matrix, vessel_distance_speed_dict = \ compute_mindistance.computeVesselMinDistanceMatrix(data_with_mmsi_sorted, TIME_WINDOW = 1800) writeToCSV.saveData([{ \ 'mmsi_list_dict': mmsi_list_dict, \ 'min_distance_matrix': min_distance_matrix, \ 'vessel_distance_speed_dict': vessel_distance_speed_dict }], filename = root_folder + "/cleanedData" + "/min_distance_matrix_with_mmsi_time_window_1800_sec") print "time spent:", time.time() - start_time """From already computed""" # min_distance_matrix_result = writeToCSV.loadData(\ # root_folder + "/cleanedData" + "/min_distance_matrix_with_mmsi_time_window_1800_sec.npz") # print "min_distance_matrix_result type:\n", type(min_distance_matrix_result) # mmsi_list_dict = min_distance_matrix_result[0]["mmsi_list_dict"] # min_distance_matrix = min_distance_matrix_result[0]["min_distance_matrix"] # vessel_distance_speed_dict = min_distance_matrix_result[0]["vessel_distance_speed_dict"] # print "min_distance_matrix loaded:\n", min_distance_matrix # min_of_min_distance = sys.maxint # for i in range(0, min_distance_matrix.shape[0]): # for j in range(i + 1, min_distance_matrix.shape[1]): # if (min_distance_matrix[i][j] < min_of_min_distance): # min_of_min_distance = min_distance_matrix[i][j] # print "min_distance_matrix min of 10 tankers:", min_of_min_distance """write min distance records for Agent Based Simulator""" writeToCSV.writeVesselSpeedToDistance(\ path = utils.queryPath(root_folder+"LearningResult"),\ file_name = "vessel_speed_to_distance", \ vessel_distance_speed_dict = vessel_distance_speed_dict) writeToCSV.writeVesselMinDistanceMatrix(\ path = utils.queryPath(root_folder+"LearningResult"), \ file_name = "vessel_min_distance_matrix", \ mmsi_list_dict = mmsi_list_dict, \ min_distance_matrix = min_distance_matrix) writeToCSV.writeMMSIs(\ path = utils.queryPath(root_folder+"LearningResult"), \ file_name = "mmsi_list", \ mmsi_list = [key for key, index in mmsi_list_dict.iteritems()]) """ Test Clustering """ # trajectories_to_cluster = writeToCSV.loadData(root_folder + "/" + "all_OD_trajectories_with_1D_data_refined.npz") # # trajectories_to_cluster = writeToCSV.loadData(root_folder + "/" + "all_OD_trajectories_cleaned.npz") # # trajectories_to_cluster = writeToCSV.loadData(root_folder + "/" + "all_OD_trajectories_9664225.npz") # print "trajectories_to_cluster.shape: ", trajectories_to_cluster.shape # print "type(trajectories_to_cluster): ", type(trajectories_to_cluster) # print "len(trajectories_to_cluster): ", len(trajectories_to_cluster) # # convert Lat, Lon to XY for clustering # all_OD_trajectories_XY = convertListOfTrajectoriesToXY(utils.CENTER_LAT_SG, utils.CENTER_LON_SG, trajectories_to_cluster) # executeClustering(root_folder = root_folder, \ # all_OD_trajectories_XY = all_OD_trajectories_XY, \ # reference_lat = utils.CENTER_LAT_SG, \ # reference_lon = utils.CENTER_LON_SG, \ # filenames = filenames) # raise ValueError("purpose stop for testing clustering") """ plot out the value space of the features, speed, accelerations, etc, for the aggregateData """ # filename = "aggregateData.npz" # path = "tankers/cleanedData" # data = writeToCSV.loadArray("{p}/{f}".format(p = path, f=filename)) # for trajectory in trajectories_to_cluster: # plotter.plotFeatureSpace(trajectory) # raise ValueError("For plotting feature space only") """ Read the cleaned .csv input files form {root_folder}/cleanedData/ Extract endpoints """ endpoints = None all_OD_trajectories = [] utils.queryPath("{root_folder}/endpoints".format(root_folder = root_folder)) utils.queryPath("{root_folder}/trajectories".format(root_folder = root_folder)) for i in range(0, len(filenames)): this_vessel_trajectory_points = writeToCSV.readDataFromCSV(root_folder + "/cleanedData", filenames[i]) # Extract end points, along with MMSI this_vessel_endpoints = np.asarray(extractEndPoints(writeToCSV.readDataFromCSVWithMMSI(root_folder + "/cleanedData", filenames[i]))) # Save end points, along with MMSI writeToCSV.writeDataToCSVWithMMSI( \ this_vessel_endpoints, \ root_folder + "/endpoints", \ "{filename}_endpoints".format(filename = filenames[i][:filenames[i].find(".")])) print "this_vessel_endpoints.shape:", this_vessel_endpoints.shape # Append to the total end points if(endpoints is None): endpoints = this_vessel_endpoints else: endpoints = np.concatenate((endpoints, this_vessel_endpoints), axis=0) for s in range (0, len(this_vessel_endpoints) - 1): originLatitude = this_vessel_endpoints[s][utils.dataDict["latitude"]] originLongtitude = this_vessel_endpoints[s][utils.dataDict["longitude"]] origin_ts = this_vessel_endpoints[s][utils.dataDict["ts"]] endLatitude = this_vessel_endpoints[s + 1][utils.dataDict["latitude"]] endLongtitude = this_vessel_endpoints[s + 1][utils.dataDict["longitude"]] end_ts = this_vessel_endpoints[s + 1][utils.dataDict["ts"]] """Extracting trajectory between a pair of OD""" print "\n\nextracting endpoints between ", s, " and ", s + 1 OD_trajectories, OD_trajectories_lat_lon = extractTrajectoriesUntilOD(\ this_vessel_trajectory_points, \ origin_ts, \ originLatitude, \ originLongtitude, \ end_ts, \ endLatitude, \ endLongtitude, \ show = False, save = True, clean = False, \ fname = filenames[i][:filenames[i].find(".")] + "_trajectory_between_endpoint{s}_and{e}".format(s = s, e = s + 1)) # there will be one trajectory between each OD assert (len(OD_trajectories) > 0), "OD_trajectories extracted must have length > 0" print "number of trajectory points extracted : ", len(OD_trajectories[0]) if(len(OD_trajectories[0]) > 2): # more than just the origin and destination endpoints along the trajectory writeToCSV.writeDataToCSV( \ data = OD_trajectories_lat_lon[0], path = root_folder + "/trajectories", \ file_name = "{filename}_trajectory_endpoint_{s}_to_{e}".format(filename = filenames[i][:filenames[i].find(".")], \ s = s, \ e = s + 1)) """ Interpolation based on pure geographical trajectory, ignore temporal information """ interpolated_OD_trajectories = interpolator.geographicalTrajetoryInterpolation(OD_trajectories) plotter.plotListOfTrajectories( \ interpolated_OD_trajectories, \ show = False, \ clean = True, \ save = True, \ fname = filenames[i][:filenames[i].find(".")] + "_interpolated_algo_3_between_endpoint{s}_and{e}".format(\ s = s, \ e = s + 1)) """ Interpolation of 1D data: speed, rate_of_turn, etc; interpolated_OD_trajectories / OD_trajectories are both in X, Y coordinates """ if(len(interpolated_OD_trajectories) > 0): interpolated_OD_trajectories[0] = interpolator.interpolate1DFeatures( \ interpolated_OD_trajectories[0], \ OD_trajectories[0]) # change X, Y coordinate to Lat, Lon interpolated_OD_trajectories_lat_lon = convertListOfTrajectoriesToLatLon( \ originLatitude, originLongtitude, interpolated_OD_trajectories) if(len(interpolated_OD_trajectories_lat_lon) > 0): # since there should be only one trajectory between each pair of OD all_OD_trajectories.append(interpolated_OD_trajectories_lat_lon[0]) else: print "no trajectories extracted between endpoints ", s , " and ", s + 1 plt.clf() assert (not endpoints is None), "Error!: No endpoints extracted from the historial data of vessels" + "_".join(filenames) print "Final endpoints.shape:", endpoints.shape print "number of interpolated all_OD_trajectories:", len(all_OD_trajectories) """ save the augmented trajectories between endpoints as npz data file and the plot """ # remove error trajectories that are too far from Singapore all_OD_trajectories = utils.removeErrorTrajectoryFromList(all_OD_trajectories) writeToCSV.saveData(all_OD_trajectories, root_folder + "/all_OD_trajectories_with_1D_data") # convert Lat, Lon to XY for displaying all_OD_trajectories_XY = convertListOfTrajectoriesToXY(utils.CENTER_LAT_SG, utils.CENTER_LON_SG, all_OD_trajectories) plotter.plotListOfTrajectories(all_OD_trajectories_XY, show = False, clean = True, save = True, \ fname = "{root_folder}_all_OD_trajectories".format(root_folder = root_folder)) """ Execute Clustering """ executeClustering(root_folder = root_folder, \ all_OD_trajectories_XY = all_OD_trajectories_XY, \ reference_lat = utils.CENTER_LAT_SG, \ reference_lon = utils.CENTER_LON_SG, \ filenames = filenames)
def main(): metric_to_use = int( raw_input("use metric?\n" + "1. l2\n" + "2. center of mass\n")) root_folder = "tankers/out_sample_test" """read centroids""" centroids = None if (metric_to_use == 1): centroids = writeToCSV.loadData( "tankers/cleanedData/centroids_arr_l2.npz") elif (metric_to_use == 2): centroids = writeToCSV.loadData( "tankers/cleanedData/centroids_arr_center_mass.npz") """Extract endpoints, trajectories, augmentation""" filenames = [ "9050462.csv", "9259769.csv", "9327138.csv", "9408475.csv", "9417464.csv", "9548440.csv" ] # for out sample test # filenames = ["9408475.csv"] endpoints = None all_OD_trajectories = [] """Do the augmentation if not yet done""" if (not os.path.exists(root_folder + "/all_OD_trajectories_with_1D_data.npz")): for i in range(0, len(filenames)): this_vessel_trajectory_points = writeToCSV.readDataFromCSV( root_folder + "/cleanedData", filenames[i]) # Extract end points, along with MMSI this_vessel_endpoints = np.asarray( trajectory_modeller.extractEndPoints( writeToCSV.readDataFromCSVWithMMSI( root_folder + "/cleanedData", filenames[i]))) # Save end points, along with MMSI writeToCSV.writeDataToCSVWithMMSI( \ this_vessel_endpoints, \ utils.queryPath(root_folder + "/endpoints"), \ "{filename}_endpoints".format(filename = filenames[i][:filenames[i].find(".")])) print "this_vessel_endpoints.shape:", this_vessel_endpoints.shape # Append to the total end points if (endpoints is None): endpoints = this_vessel_endpoints else: endpoints = np.concatenate((endpoints, this_vessel_endpoints), axis=0) for s in range(0, len(this_vessel_endpoints) - 1): originLatitude = this_vessel_endpoints[s][ utils.dataDict["latitude"]] originLongtitude = this_vessel_endpoints[s][ utils.dataDict["longitude"]] origin_ts = this_vessel_endpoints[s][utils.dataDict["ts"]] endLatitude = this_vessel_endpoints[s + 1][ utils.dataDict["latitude"]] endLongtitude = this_vessel_endpoints[s + 1][ utils.dataDict["longitude"]] end_ts = this_vessel_endpoints[s + 1][utils.dataDict["ts"]] """Extracting trajectory between a pair of OD""" print "\n\nextracting endpoints between ", s, " and ", s + 1 OD_trajectories, OD_trajectories_lat_lon = trajectory_modeller.extractTrajectoriesUntilOD(\ this_vessel_trajectory_points, \ origin_ts, \ originLatitude, \ originLongtitude, \ end_ts, \ endLatitude, \ endLongtitude, \ show = False, save = True, clean = False, \ fname = filenames[i][:filenames[i].find(".")] + "_trajectory_between_endpoint{s}_and{e}".format(s = s, e = s + 1), \ path = utils.queryPath(root_folder + "/plots")) # there will be one trajectory between each OD assert (len(OD_trajectories) > 0), "OD_trajectories extracted must have length > 0" print "number of trajectory points extracted : ", len( OD_trajectories[0]) if ( len(OD_trajectories[0]) > 2 ): # more than just the origin and destination endpoints along the trajectory writeToCSV.writeDataToCSV( \ data = OD_trajectories_lat_lon[0], path = utils.queryPath(root_folder + "/trajectories"), \ file_name = "{filename}_trajectory_endpoint_{s}_to_{e}".format(filename = filenames[i][:filenames[i].find(".")], \ s = s, \ e = s + 1)) """ Interpolation based on pure geographical trajectory, ignore temporal information """ interpolated_OD_trajectories = interpolator.geographicalTrajetoryInterpolation( OD_trajectories) plotter.plotListOfTrajectories( \ interpolated_OD_trajectories, \ show = False, \ clean = True, \ save = True, \ fname = filenames[i][:filenames[i].find(".")] + "_interpolated_algo_3_between_endpoint{s}_and{e}".format(\ s = s, \ e = s + 1), \ path = utils.queryPath(root_folder + "/plots")) """ Interpolation of 1D data: speed, rate_of_turn, etc; interpolated_OD_trajectories / OD_trajectories are both in X, Y coordinates """ if (len(interpolated_OD_trajectories) > 0): interpolated_OD_trajectories[0] = interpolator.interpolate1DFeatures( \ interpolated_OD_trajectories[0], \ OD_trajectories[0]) # change X, Y coordinate to Lat, Lon interpolated_OD_trajectories_lat_lon = trajectory_modeller.convertListOfTrajectoriesToLatLon( \ originLatitude, originLongtitude, interpolated_OD_trajectories) if (len(interpolated_OD_trajectories_lat_lon) > 0): # since there should be only one trajectory between each pair of OD all_OD_trajectories.append( interpolated_OD_trajectories_lat_lon[0]) else: print "no trajectories extracted between endpoints ", s, " and ", s + 1 plt.clf() assert ( not endpoints is None ), "Error!: No endpoints extracted from the historial data of vessels" + "_".join( filenames) print "Final endpoints.shape:", endpoints.shape print "number of interpolated all_OD_trajectories:", len( all_OD_trajectories) all_OD_trajectories = utils.removeErrorTrajectoryFromList( all_OD_trajectories) writeToCSV.saveData(all_OD_trajectories, root_folder + "/all_OD_trajectories_with_1D_data") else: all_OD_trajectories = writeToCSV.loadData( root_folder + "/all_OD_trajectories_with_1D_data.npz") """convert Lat, Lon to XY for displaying""" all_OD_trajectories_XY = trajectory_modeller.convertListOfTrajectoriesToXY( utils.CENTER_LAT_SG, utils.CENTER_LON_SG, all_OD_trajectories) plotter.plotListOfTrajectories(\ all_OD_trajectories_XY, \ show = True, \ clean = True, \ save = False, \ fname = "out_sample_tanker_all_OD_trajectories", path = utils.queryPath(root_folder + "/plots")) """Test distance to cluster centroids""" centroids_XY = trajectory_modeller.convertListOfTrajectoriesToXY(\ utils.CENTER_LAT_SG, utils.CENTER_LON_SG, centroids) for i in range(0, len(all_OD_trajectories_XY)): this_tr_XY = all_OD_trajectories_XY[i] if (metric_to_use == 1): this_tr_centroids_dist, according_pattern_index = minDistanceAgainstCentroids(\ this_tr_XY, centroids_XY, clustering_worker.trajectoryDissimilarityL2) print "augmented trajectories[{i}]".format(i = i), \ "'s best l2 distance is against cluster centroids[{i}], = ".format(i = according_pattern_index), \ this_tr_centroids_dist, ", max allowed distance = ", 1000 elif (metric_to_use == 2): this_tr_centroids_dist, according_pattern_index = minDistanceAgainstCentroids(\ this_tr_XY, centroids_XY, clustering_worker.trajectoryDissimilarityCenterMass) print "augmented trajectories[{i}]".format(i = i), \ "'s best center of mass distance is against cluster centroids[{i}], = ".format(i = according_pattern_index), \ this_tr_centroids_dist, ", max allowed distance = ", 1.5 # plotter.plotFeatureSpace(centroids[according_pattern_index]) # plotter.plotFeatureSpace(\ # trajectory_modeller.convertListOfTrajectoriesToLatLon(utils.CENTER_LAT_SG, utils.CENTER_LON_SG, [this_tr_XY])[0]) return
def main(): metric_to_use = int(raw_input("use metric?\n" + "1. l2\n" + "2. center of mass\n")) root_folder = "tankers/out_sample_test" """read centroids""" centroids = None if metric_to_use == 1: centroids = writeToCSV.loadData("tankers/cleanedData/centroids_arr_l2.npz") elif metric_to_use == 2: centroids = writeToCSV.loadData("tankers/cleanedData/centroids_arr_center_mass.npz") """Extract endpoints, trajectories, augmentation""" filenames = [ "9050462.csv", "9259769.csv", "9327138.csv", "9408475.csv", "9417464.csv", "9548440.csv", ] # for out sample test # filenames = ["9408475.csv"] endpoints = None all_OD_trajectories = [] """Do the augmentation if not yet done""" if not os.path.exists(root_folder + "/all_OD_trajectories_with_1D_data.npz"): for i in range(0, len(filenames)): this_vessel_trajectory_points = writeToCSV.readDataFromCSV(root_folder + "/cleanedData", filenames[i]) # Extract end points, along with MMSI this_vessel_endpoints = np.asarray( trajectory_modeller.extractEndPoints( writeToCSV.readDataFromCSVWithMMSI(root_folder + "/cleanedData", filenames[i]) ) ) # Save end points, along with MMSI writeToCSV.writeDataToCSVWithMMSI( this_vessel_endpoints, utils.queryPath(root_folder + "/endpoints"), "{filename}_endpoints".format(filename=filenames[i][: filenames[i].find(".")]), ) print "this_vessel_endpoints.shape:", this_vessel_endpoints.shape # Append to the total end points if endpoints is None: endpoints = this_vessel_endpoints else: endpoints = np.concatenate((endpoints, this_vessel_endpoints), axis=0) for s in range(0, len(this_vessel_endpoints) - 1): originLatitude = this_vessel_endpoints[s][utils.dataDict["latitude"]] originLongtitude = this_vessel_endpoints[s][utils.dataDict["longitude"]] origin_ts = this_vessel_endpoints[s][utils.dataDict["ts"]] endLatitude = this_vessel_endpoints[s + 1][utils.dataDict["latitude"]] endLongtitude = this_vessel_endpoints[s + 1][utils.dataDict["longitude"]] end_ts = this_vessel_endpoints[s + 1][utils.dataDict["ts"]] """Extracting trajectory between a pair of OD""" print "\n\nextracting endpoints between ", s, " and ", s + 1 OD_trajectories, OD_trajectories_lat_lon = trajectory_modeller.extractTrajectoriesUntilOD( this_vessel_trajectory_points, origin_ts, originLatitude, originLongtitude, end_ts, endLatitude, endLongtitude, show=False, save=True, clean=False, fname=filenames[i][: filenames[i].find(".")] + "_trajectory_between_endpoint{s}_and{e}".format(s=s, e=s + 1), path=utils.queryPath(root_folder + "/plots"), ) # there will be one trajectory between each OD assert len(OD_trajectories) > 0, "OD_trajectories extracted must have length > 0" print "number of trajectory points extracted : ", len(OD_trajectories[0]) if ( len(OD_trajectories[0]) > 2 ): # more than just the origin and destination endpoints along the trajectory writeToCSV.writeDataToCSV( data=OD_trajectories_lat_lon[0], path=utils.queryPath(root_folder + "/trajectories"), file_name="{filename}_trajectory_endpoint_{s}_to_{e}".format( filename=filenames[i][: filenames[i].find(".")], s=s, e=s + 1 ), ) """ Interpolation based on pure geographical trajectory, ignore temporal information """ interpolated_OD_trajectories = interpolator.geographicalTrajetoryInterpolation(OD_trajectories) plotter.plotListOfTrajectories( interpolated_OD_trajectories, show=False, clean=True, save=True, fname=filenames[i][: filenames[i].find(".")] + "_interpolated_algo_3_between_endpoint{s}_and{e}".format(s=s, e=s + 1), path=utils.queryPath(root_folder + "/plots"), ) """ Interpolation of 1D data: speed, rate_of_turn, etc; interpolated_OD_trajectories / OD_trajectories are both in X, Y coordinates """ if len(interpolated_OD_trajectories) > 0: interpolated_OD_trajectories[0] = interpolator.interpolate1DFeatures( interpolated_OD_trajectories[0], OD_trajectories[0] ) # change X, Y coordinate to Lat, Lon interpolated_OD_trajectories_lat_lon = trajectory_modeller.convertListOfTrajectoriesToLatLon( originLatitude, originLongtitude, interpolated_OD_trajectories ) if len(interpolated_OD_trajectories_lat_lon) > 0: # since there should be only one trajectory between each pair of OD all_OD_trajectories.append(interpolated_OD_trajectories_lat_lon[0]) else: print "no trajectories extracted between endpoints ", s, " and ", s + 1 plt.clf() assert not endpoints is None, "Error!: No endpoints extracted from the historial data of vessels" + "_".join( filenames ) print "Final endpoints.shape:", endpoints.shape print "number of interpolated all_OD_trajectories:", len(all_OD_trajectories) all_OD_trajectories = utils.removeErrorTrajectoryFromList(all_OD_trajectories) writeToCSV.saveData(all_OD_trajectories, root_folder + "/all_OD_trajectories_with_1D_data") else: all_OD_trajectories = writeToCSV.loadData(root_folder + "/all_OD_trajectories_with_1D_data.npz") """convert Lat, Lon to XY for displaying""" all_OD_trajectories_XY = trajectory_modeller.convertListOfTrajectoriesToXY( utils.CENTER_LAT_SG, utils.CENTER_LON_SG, all_OD_trajectories ) plotter.plotListOfTrajectories( all_OD_trajectories_XY, show=True, clean=True, save=False, fname="out_sample_tanker_all_OD_trajectories", path=utils.queryPath(root_folder + "/plots"), ) """Test distance to cluster centroids""" centroids_XY = trajectory_modeller.convertListOfTrajectoriesToXY( utils.CENTER_LAT_SG, utils.CENTER_LON_SG, centroids ) for i in range(0, len(all_OD_trajectories_XY)): this_tr_XY = all_OD_trajectories_XY[i] if metric_to_use == 1: this_tr_centroids_dist, according_pattern_index = minDistanceAgainstCentroids( this_tr_XY, centroids_XY, clustering_worker.trajectoryDissimilarityL2 ) print "augmented trajectories[{i}]".format( i=i ), "'s best l2 distance is against cluster centroids[{i}], = ".format( i=according_pattern_index ), this_tr_centroids_dist, ", max allowed distance = ", 1000 elif metric_to_use == 2: this_tr_centroids_dist, according_pattern_index = minDistanceAgainstCentroids( this_tr_XY, centroids_XY, clustering_worker.trajectoryDissimilarityCenterMass ) print "augmented trajectories[{i}]".format( i=i ), "'s best center of mass distance is against cluster centroids[{i}], = ".format( i=according_pattern_index ), this_tr_centroids_dist, ", max allowed distance = ", 1.5 # plotter.plotFeatureSpace(centroids[according_pattern_index]) # plotter.plotFeatureSpace(\ # trajectory_modeller.convertListOfTrajectoriesToLatLon(utils.CENTER_LAT_SG, utils.CENTER_LON_SG, [this_tr_XY])[0]) return