def test_dihedral_feat():
print(base_dir)
pool = Pool(6)
yaml_file = load_yaml_file(os.path.join(base_dir,"mdl_dir","project.yaml"))
for prt in ["kinase_1", "kinase_2"]:
print(prt)
prj = yaml_file["project_dict"][prt][0]
featurize_project_wrapper(yaml_file, prt, feat=None, stride=1, view=pool)
feat = DihedralFeaturizer(types=['phi', 'psi','chi1'])
flist = glob.glob(os.path.join(base_dir, prt , yaml_file["protein_dir"],"*.hdf5"))
for i in np.random.choice(flist, 3):
trj = mdt.load(i)
my_feat = feat.partial_transform(trj)
expected_fname = os.path.join(base_dir, prt,
yaml_file["feature_dir"],
os.path.splitext(os.path.basename(i))[0]+".jl")
calc_feat = verboseload(expected_fname)
assert np.allclose(my_feat, calc_feat)
return True
def featurize_series(yaml_file, ip_view, protein_list = None):
"""
:param yaml_file: The yaml file to work with
:param ip_view: ipython view(required)
:param protein_list: list of proteins, if None then all
the proteins in yaml_file["protein_list"] are processed
:return: converted and concatenated trajectories in
yaml_file["base_dir"]+protein_name+trajectories
and the stripped files in
yaml_file["base_dir"]+protein_name+protein_traj
"""
yaml_file = load_yaml_file(yaml_file)
if protein_list is None:
protein_list = yaml_file["protein_list"]
for protein in protein_list:
featurize_project_wrapper(yaml_file, protein, None, 1, ip_view)
return
def featurize_series(yaml_file, ip_view, protein_list=None):
"""
:param yaml_file: The yaml file to work with
:param ip_view: ipython view(required)
:param protein_list: list of proteins, if None then all
the proteins in yaml_file["protein_list"] are processed
:return: converted and concatenated trajectories in
yaml_file["base_dir"]+protein_name+trajectories
and the stripped files in
yaml_file["base_dir"]+protein_name+protein_traj
"""
yaml_file = load_yaml_file(yaml_file)
if protein_list is None:
protein_list = yaml_file["protein_list"]
for protein in protein_list:
featurize_project_wrapper(yaml_file, protein, None, 1, ip_view)
return