#--------------------------------
# Prepare the clustering for this guy
#--------------------------------
## Load template and modify its contents for this case
CLUSTERING_PATH = os.path.join(RESULTS_PATH,"%s_%s_clustering"%(options.drug, options.protein))
MAX_CLUSTERS = 10
SCRIPT_PATH = os.path.join(RESULTS_PATH,"clustering.json")
OUT_FILE = os.path.join(RESULTS_PATH, "clustering.out")
script = load_dic_in_json(options.template)
script["global"]["workspace"]["base"] = CLUSTERING_PATH
script["data"]["files"].append(FILTERED_PDB_FILE)
script["clustering"]["evaluation"]["maximum_clusters"] = MAX_CLUSTERS
save_dic_in_json(script, SCRIPT_PATH)
os.system("python -m pyproct.main %s > %s"%(SCRIPT_PATH, OUT_FILE))
best_clustering = Clustering.from_dic(get_best_clustering(CLUSTERING_PATH)["clustering"])
#--------------------------------
# Now calculate the values
#--------------------------------
results = {}
for cluster in best_clustering.clusters:
energies = metrics[1][cluster.all_elements]
distances = metrics[0][cluster.all_elements]
results[cluster.id] = {}
results[cluster.id]["max_energy"] = numpy.max(energies)
results[cluster.id]["min_energy"] = numpy.min(energies)
results[cluster.id]["mean_energy"] = numpy.mean(energies)
results[cluster.id]["mean_distance"] = numpy.mean(distances)
results[cluster.id]["population"] = len(cluster.all_elements)
params['global']['workspace']['base'] = working_directory
params['data']['files'] = [os.path.join(os.getcwd(), traj_pdb)]
params['data']['matrix']['parameters']['path'] = matrix_file
save_dic_in_json(params, be_rmsd_clustering_script_path)
use_pyproct(working_directory, be_rmsd_clustering_script_path)
#######################################################################################################################
# Get 5 representatives. 2 strategies.
#######################################################################################################################
#####################################################################
#####################################################################
# Work only with best clustering/ find best cluster
#####################################################################
#####################################################################
best_clustering = get_best_clustering(working_directory)
best_clustering = Clustering.from_dic(best_clustering["clustering"])
scores = []
for a_cluster in best_clustering.clusters:
scores.append((score_cluster(a_cluster, m_handler.distance_matrix, all_metrics), a_cluster))
# Remember: first metric is spawning point, second metric must be energy
most_negative = find_most_negative_cluster(scores)
most_negative_cluster = most_negative[1]
#######################################################################################################################
# Plot clusters
#######################################################################################################################
plot_clusters(os.path.join(base_dir, "clusters.svg"), all_metrics, scores, scores.index(most_negative))
#######################################################################################################################
