cluster_tools.py 文件源码

def cluster_analysis(dpsi, psivec, sig_threshold, dpsi_threshold, eps, minpts, metric, indexes, clustering,
                     separation, output):

    path = os.path.dirname(os.path.realpath(dpsi))
    os.chdir(path)

    psi_matrix, eventid_lst = process_cluster_input(dpsi, psivec, sig_threshold, dpsi_threshold, indexes)

    if(clustering=="DBSCAN"):
        eventid_labels_dict, labels = DBSCAN_cluster(psi_matrix, eventid_lst, eps, minpts, metric)
        #eventid_labels_dict are the labels of the clustering for eacg event

        write_averaged_cluster_output(psi_matrix, eventid_lst, eventid_labels_dict, output)
        calculate_cluster_scores(psi_matrix, labels, output)

    else:
        #OPTICS
        points_list = create_points_list(psi_matrix, eventid_lst) #Transform the points on psi_matrix to Points from optics.py
        optics = Optics(points_list, eps, minpts)  # Maximum radius to be considered, cluster size >= 2 points
        optics.run()  # run the algorithm
        clusters = optics.cluster(separation)  # minimum threshold for clustering (upper limit to separate the clusters)
        eventid_labels_dict, labels = generate_labels(clusters, eventid_lst)
        write_averaged_cluster_output(psi_matrix, eventid_lst, eventid_labels_dict, output)
        calculate_cluster_scores(psi_matrix, labels, output)