def save_matrix(self, matrix_path):
"""
Writes matrix contents to disk.
@param matrix_save_file: Complete path (with filename) where to save the matrix.
"""
pyRMSD_MatrixHandler.save_matrix(matrix_path, self.distance_matrix)
def save_statistics(self, matrix_base_path):
"""
Writes matrix statistics to disk in JSON format.
@param matrix_base_path: The folder where to save the 'statistics.json' file.
"""
return pyRMSD_MatrixHandler.save_statistics(matrix_base_path, self.distance_matrix)
def calculate(cls, data_handler, matrix_params):
"""
:param matrix_params: The parameters to build the matrix. In this base case the only
option is "load".
Base parameters :
{
"method": STRING,
"parameters":{
...
}
}
Options:
- "load": Load an already created matrix from disk
"parameters":{
"path": STRING
}
"path": The path from where the matrix is going to be loaded.
:return: A CondensedMatrix.
"""
return pyRMSD_MatrixHandler.load_matrix(matrix_params["path"])
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 calcDistMatrix(coordsets): from pyRMSD.matrixHandler import MatrixHandler matrix = MatrixHandler().createMatrix(coordsets,'NOSUP_SERIAL_CALCULATOR') return matrix.get_data()
"chain":ligand_file_description[1],
"atoms":ligand_file_description[2:]
}
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)
