def calculate_RMSF(best_clustering, data_handler): ca_pdb_coordsets = data_handler.get_data().getSelectionCoordinates("name CA") global_cluster = Cluster(None, best_clustering.get_all_clustered_elements()) global_cluster.id = "global" clusters = best_clustering.clusters + [global_cluster] rmsf_per_cluster = {} for cluster in clusters: rmsf_per_cluster[cluster.id] = superpose_and_calc_rmsf(ca_pdb_coordsets, cluster) return rmsf_per_cluster
def calculate_RMSF(best_clustering, data_handler): ca_pdb_coordsets = data_handler.get_data().getSelectionCoordinates( "name CA") global_cluster = Cluster(None, best_clustering.get_all_clustered_elements()) global_cluster.id = "global" clusters = best_clustering.clusters + [global_cluster] rmsf_per_cluster = {} for cluster in clusters: rmsf_per_cluster[cluster.id] = superpose_and_calc_rmsf( ca_pdb_coordsets, cluster) return rmsf_per_cluster
def calculate_RMSF(best_clustering, trajectoryHandler, workspaceHandler, matrixHandler): ca_pdb_coordsets = numpy.copy( trajectoryHandler.getMergedStructure().select( "name CA").getCoordsets()) global_cluster = Cluster(None, best_clustering.get_all_clustered_elements()) global_cluster.id = "global" clusters = best_clustering.clusters + [global_cluster] rmsf_per_cluster = {} for cluster in clusters: rmsf_per_cluster[cluster.id] = superpose_and_calc_rmsf( ca_pdb_coordsets, cluster) return rmsf_per_cluster