def test_write_and_load(self):
mh = MatrixHandler(".")
data = range(1000)
matrix = CondensedMatrix(data)
mh.distance_matrix = matrix
mh.saveMatrix("matrix")
mh2 = MatrixHandler(None)
mh2.loadMatrix("matrix")
recovered_data = mh2.distance_matrix.get_data()
numpy.testing.assert_array_equal(mh.distance_matrix.get_data(), data)
numpy.testing.assert_array_equal(recovered_data, data)
# Clean it!
os.system("rm matrix.npy")
def test_write_and_load(self):
mh = MatrixHandler(".")
data = range(1000)
matrix = CondensedMatrix(data)
mh.distance_matrix = matrix
mh.saveMatrix("matrix")
mh2 = MatrixHandler(None)
mh2.loadMatrix("matrix")
recovered_data = mh2.distance_matrix.get_data()
numpy.testing.assert_array_equal(mh.distance_matrix.get_data(), data)
numpy.testing.assert_array_equal(recovered_data, data)
# Clean it!
os.system("rm matrix.npy")
ligand_description = "resname %s and name %s"%(ligand["resname"],"".join( a+" " for a in ligand["atoms"]))
print "* Ligand parsed: ",ligand_description
#######################################################################################################################
# Generate matrix with metrics (so now we are going to cluster based on Energy and spawning
#######################################################################################################################
print "* Creating Spawning - totalE matrix"
records = []
processFile(traj_pdb, records, True)
all_metrics = genMetrics(plots["totale_spawning"], records)
matrix_data = scipy.spatial.distance.pdist(normalize_metrics(all_metrics), 'euclidean')
m_handler = MatrixHandler()
m_handler.distance_matrix = CondensedMatrix(matrix_data)
matrix_file = os.path.join(base_dir, TENERGY_SPAWN_MATRIX)
m_handler.saveMatrix(matrix_file)
#######################################################################################################################
# Cluster by metrics
#######################################################################################################################
print "* Spawning - totalE clustering"
be_rmsd_clustering_script_path = os.path.join(base_dir, 'scripts', CLUSTERING_SPAWN_TOTE_SCRIPT)
working_directory = os.path.join(base_dir, TOTALE_SPAWN_WORKSPACE)
params = load_dic_in_json(be_rmsd_clustering_script_path)
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)
#######################################################################################################################
