Fix 23 mutable default arguments across 9 files

README.md

@@ -484,6 +484,8 @@ NIST-MGI (https://www.nist.gov/mgi)
 
 NIST-CHIPS (https://www.nist.gov/chips)
 
+Note: This project was originally developed under the github.com/usnistgov organization. New updates and developments will be carried out here.
+
 Code of conduct
 --------------------
 

alignn/dataset.py

@@ -17,7 +17,7 @@
 # NOTE: Use lmd_dataset,
 # need to fix adding lattice in dataloader
 def load_graphs(
-    dataset=[],
+    dataset=None,
     name: str = "dft_3d",
     neighbor_strategy: str = "k-nearest",
     cutoff: float = 8,
@@ -42,6 +42,8 @@ def load_graphs(
           edata_schemes={'r': Scheme(shape=(3,)})
     ```
     """
+    if dataset is None:
+        dataset = []
 
     def atoms_to_graph(atoms):
         """Convert structure dict to DGLGraph."""
@@ -111,7 +113,7 @@ def atoms_to_graph(atoms):
 
 
 def get_torch_dataset(
-    dataset=[],
+    dataset=None,
     id_tag="jid",
     target="",
     target_atomwise="",
@@ -132,6 +134,8 @@ def get_torch_dataset(
     dtype="float32",
 ):
     """Get Torch Dataset."""
+    if dataset is None:
+        dataset = []
     df = pd.DataFrame(dataset)
     # df['natoms']=df['atoms'].apply(lambda x: len(x['elements']))
     # print(" data df", df)

alignn/ff/calculators.py

@@ -398,32 +398,34 @@ def __init__(
         ff_config_filename="config.json",
         ff_config=None,
         stress_wt=0.05,
-        props=[
-            "cbm",
-            "vbm",
-            "gap",
-            "efermi",
-            "optb88vdw_bandgap",
-            "mbj_bandgap",
-            "spillage",
-            "slme",
-            "bulk_modulus_kv",
-            "shear_modulus_gv",
-            "n-Seebeck",
-            "n-powerfact",
-            "avg_elec_mass",
-            "avg_hole_mass",
-            "epsx",
-            "mepsx",
-            "max_efg",
-            "dfpt_piezo_max_dielectric",
-            "dfpt_piezo_max_dij",
-            "exfoliation_energy",
-            "Tc_supercon",
-            "magmom_oszicar",
-        ],
+        props=None
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
     ):
         """Initialize class."""
+        if props is None:
+            props = []
         super().__init__()
         # super().__init__(**kwargs)
         self.device = device

alignn/ff/ff.py

@@ -949,13 +949,15 @@ def surface_energy(
     on_conventional_cell=True,
     dataset="dft_3d",
     max_index=None,
-    miller_index=[1, 1, 1],
+    miller_index=None,
     on_relaxed_struct=True,
     model_path=".",
     thickness=25,
     model_filename="best_model.pt",
 ):
     """Get surface energy."""
+    if miller_index is None:
+        miller_index = []
     if atoms is None:
         from jarvis.db.figshare import data
 
@@ -1030,8 +1032,8 @@ def surface_energy(
 def get_interface_energy(
     film_atoms=None,
     subs_atoms=None,
-    film_index=[1, 1, 1],
-    subs_index=[0, 0, 1],
+    film_index=None,
+    subs_index=None,
     film_thickness=25,
     subs_thickness=25,
     model_path="",
@@ -1046,6 +1048,10 @@ def get_interface_energy(
     gpaw_verify=False,
 ):
     """Get work of adhesion."""
+    if film_index is None:
+        film_index = []
+    if subs_index is None:
+        subs_index = []
     film_surf = Surface(
         film_atoms,
         indices=film_index,
@@ -1173,7 +1179,7 @@ def phonons(
     force_mult_natoms=False,
     stress_wt=0.1,
     force_multiplier=1,
-    dim=[2, 2, 2],
+    dim=None,
     freq_conversion_factor=33.3566830,  # ThztoCm-1
     phonopy_bands_figname="phonopy_bands.png",
     # phonopy_dos_figname="phonopy_dos.png",
@@ -1182,6 +1188,8 @@ def phonons(
     distance=0.2,
 ):
     """Make Phonon calculation setup."""
+    if dim is None:
+        dim = []
     if calc is None:
         calc = AlignnAtomwiseCalculator(
             path=model_path,
@@ -1322,12 +1330,14 @@ def phonons3(
     model_path=".",
     model_filename="best_model.pt",
     on_relaxed_struct=False,
-    dim=[2, 2, 2],
+    dim=None,
     distance=0.2,
     stress_wt=0.1,
     force_multiplier=1,
 ):
     """Make Phonon3 calculation setup."""
+    if dim is None:
+        dim = []
     from phono3py import Phono3py
 
     if calc is None:
@@ -1381,9 +1391,9 @@ def phonons3(
 
 
 def ase_phonon(
-    atoms=[],
+    atoms=None,
     N=2,
-    path=[],
+    path=None,
     jid=None,
     calc=None,
     npoints=100,
@@ -1397,6 +1407,10 @@ def ase_phonon(
     force_multiplier=1,
 ):
     """Get phonon bandstructure and DOS using ASE."""
+    if atoms is None:
+        atoms = []
+    if path is None:
+        path = []
     if calc is None:
         calc = AlignnAtomwiseCalculator(
             path=model_path, force_multiplier=force_multiplier

alignn/graphs.py

@@ -229,13 +229,15 @@ def nearest_neighbor_edges(
 
 def build_undirected_edgedata(
     atoms=None,
-    edges={},
+    edges=None,
 ):
     """Build undirected graph data from edge set.
 
     edges: dictionary mapping (src_id, dst_id) to set of dst_image
     r: cartesian displacement vector from src -> dst
     """
+    if edges is None:
+        edges = {}
     # second pass: construct *undirected* graph
     # import pprint
     u, v, r, all_images = [], [], [], []
@@ -440,10 +442,10 @@ class Graph(object):
 
     def __init__(
         self,
-        nodes=[],
-        node_attributes=[],
-        edges=[],
-        edge_attributes=[],
+        nodes=None,
+        node_attributes=None,
+        edges=None,
+        edge_attributes=None,
         color_map=None,
         labels=None,
     ):
@@ -461,6 +463,14 @@ def __init__(
             edge_attributes: attributes for each connectivity.
                              as simple as euclidean distances.
         """
+        if nodes is None:
+            nodes = []
+        if node_attributes is None:
+            node_attributes = []
+        if edges is None:
+            edges = []
+        if edge_attributes is None:
+            edge_attributes = []
         self.nodes = nodes
         self.node_attributes = node_attributes
         self.edges = edges
@@ -753,8 +763,10 @@ def num_edges(self):
         return len(self.edges)
 
     @classmethod
-    def from_dict(self, d={}):
+    def from_dict(self, d=None):
         """Constuct class from a dictionary."""
+        if d is None:
+            d = {}
         return Graph(
             nodes=d["nodes"],
             edges=d["edges"],

alignn/lmdb_dataset.py

@@ -38,8 +38,10 @@ def prepare_line_graph_batch(
 class TorchLMDBDataset(Dataset):
     """Dataset of crystal DGLGraphs using LMDB."""
 
-    def __init__(self, lmdb_path="", line_graph=True, ids=[]):
+    def __init__(self, lmdb_path="", line_graph=True, ids=None):
         """Intitialize with path and ids array."""
+        if ids is None:
+            ids = []
         super(TorchLMDBDataset, self).__init__()
         self.lmdb_path = lmdb_path
         self.ids = ids
@@ -109,7 +111,7 @@ def collate_line_graph(
 
 
 def get_torch_dataset(
-    dataset=[],
+    dataset=None,
     id_tag="jid",
     target="",
     target_atomwise="",
@@ -133,6 +135,8 @@ def get_torch_dataset(
     dtype="float32",
 ):
     """Get Torch Dataset with LMDB."""
+    if dataset is None:
+        dataset = []
     vals = np.array([ii[target] for ii in dataset])  # df[target].values
     print("data range", np.max(vals), np.min(vals))
     print("line_graph", line_graph)

alignn/pretrained.py

@@ -369,8 +369,8 @@ def get_prediction(
 
 
 def get_multiple_predictions(
-    atoms_array=[],
-    jids=[],
+    atoms_array=None,
+    jids=None,
     cutoff=8,
     neighbor_strategy="k-nearest",
     max_neighbors=12,
@@ -390,6 +390,10 @@ def get_multiple_predictions(
     # use_lmdb=True,
 ):
     """Use pretrained model on a number of structures."""
+    if atoms_array is None:
+        atoms_array = []
+    if jids is None:
+        jids = []
     # if use_lmdb:
     #    print("Using LMDB dataset.")
     #    from alignn.lmdb_dataset import get_torch_dataset

alignn/scripts/cubic_mat_relax.py

@@ -26,7 +26,9 @@
 # model_path = "/wrk/knc6/ALINN_FC/ALIGNNFF_DB/temp_new"
 
 
-def relax(model_path=[]):
+def relax(model_path=None):
+    if model_path is None:
+        model_path = []
     exp_a = []
     aff_a = []
     for i in tqdm(d):

alignn/scripts/make_test_split_cross_pred.py

@@ -35,9 +35,11 @@
 
 
 def predict_for_db(
-    name="dft_3d", prop="formation_energy_peratom", id_tag="jid", ids=[]
+    name="dft_3d", prop="formation_energy_peratom", id_tag="jid", ids=None
 ):
     """Predict using ALIGNN for a DB."""
+    if ids is None:
+        ids = []
     db = data(name)
     df = pd.DataFrame(db)
     x = []

alignn/train.py

@@ -50,7 +50,7 @@ def train_dgl(
     config: Union[TrainingConfig, Dict[str, Any]],
     model: nn.Module = None,
     # checkpoint_dir: Path = Path("./"),
-    train_val_test_loaders=[],
+    train_val_test_loaders=None,
     rank=0,
     world_size=0,
     # log_tensorboard: bool = False,
@@ -60,6 +60,8 @@ def train_dgl(
     `config` should conform to alignn.conf.TrainingConfig, and
     if passed as a dict with matching keys, pydantic validation is used
     """
+    if train_val_test_loaders is None:
+        train_val_test_loaders = []
     # print("rank", rank)
     # setup(rank, world_size)
     if rank == 0: