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
