AtomsDataset works

.gitignore

@@ -1,6 +1,7 @@
 *POSCAR*
 *CONTCAR*
 *.csv
+data/*
 !data/dband_centers.csv
 __pycache__
 *.cif

src/data.py

@@ -4,12 +4,14 @@
 from pathlib import Path
 
 import torch
+import tqdm
 
 from ase.io import read
 from torch_geometric.data import Data, Dataset
 
 from utils import partition_structure, featurize_atoms
 from featurizers import OneHotEncoder
+from constants import REPO_PATH
 
 class AtomsDataset(Dataset):
     """Class to hold a dataset containing graphs of atomic_structures."""
@@ -22,7 +24,7 @@ def __init__(self, root, prop_csv):
         ---------
         root: str
             Path to the directory in which atomic structures are stored
-        pro_csv: str
+        prop_csv: str
             Path to the file mapping atomic structure filename and property.
             This filename will typically have two columns, the first with the
             names of the cif files and the second with the
@@ -31,6 +33,10 @@ def __init__(self, root, prop_csv):
         super().__init__(root)
         self.root_path = Path(self.root)
 
+        # Create processed path if it doesn't exist
+        self.processed_path = Path(self.processed_dir)
+        self.processed_path.mkdir(exist_ok=True)
+
         # Read csv
         self.prop_csv = prop_csv
         self.names = []
@@ -46,8 +52,8 @@ def __init__(self, root, prop_csv):
             name: prop for name, prop in zip(self.names, self.props)
         }
 
-    def process(self, z_cutoffs, node_features, edge_features, max_atoms=12,
-                encoder=OneHotEncoder()):
+    def process_data(self, z_cutoffs, node_features, edge_features, pad=False,
+                max_atoms=12, encoder=OneHotEncoder()):
         """Process raw data in the root directory into PyTorch Data and save.
 
         Each atomic structure in the root directory is partitioned based on the
@@ -71,14 +77,17 @@ def process(self, z_cutoffs, node_features, edge_features, max_atoms=12,
             partition. For e.g., specify [["bulk_bond_distance"], 
             ["surface_bond_distance"], ["adsorbate_bond_distance"]] for
             a typical bulk + surface + adsorbate partition.
+        pad: bool
+            Whether to pad the graph with empty nodes to make total nodes add
+            up to max_atoms
+        max_atoms: int
+            Maximum number of nodes in graph. Only used if pad is True.
         encoder: OneHotEncoder object
             Encoder to convert properties to vectors
         """
-        # Create processed path if it doesn't exist
-        self.processed_path = Path(self.processed_dir).mkdir(exist_ok=True)
-
         # Iterate over files and process them
-        for i, name in enumerate(self.names):
+        for i, name in tqdm.tqdm(enumerate(self.names), desc="Processing data",
+                                 total=len(self.names)):
             # Set file path
             file_path = self.root_path / name
 
@@ -95,7 +104,8 @@ def process(self, z_cutoffs, node_features, edge_features, max_atoms=12,
                     atoms,
                     part_idx,
                     node_features=node_features[j],
-                    edge_features=edge_features[j],\
+                    edge_features=edge_features[j],
+                    pad=pad,
                     max_atoms=max_atoms,
                     encoder=encoder
                 )
@@ -122,4 +132,24 @@ def len(self):
     def get(self, i):
         """Fetch the processed graph(s) at the i-th index."""
         data_objects = torch.load(self.processed_path / f"data_{i}.pt")
-        return data_objects
+        return data_objects
+
+if __name__ == "__main__":
+    # Get path to root directory
+    data_root_path = Path(REPO_PATH) / "data" / "S_calcs"
+    prop_csv_path = data_root_path / "name_prop.csv"
+
+    # Create dataset
+    dataset = AtomsDataset(data_root_path, prop_csv_path)
+    dataset.process_data(z_cutoffs=[13., 20.], 
+                         node_features=[
+                             ["atomic_number", "dband_center"],
+                             ["atomic_number", "reactivity"],
+                             ["atomic_number", "reactivity"],
+                         ],
+                         edge_features=[
+                             ["bulk_bond_distance"],
+                             ["surface_bond_distance"],
+                             ["adsorbate_bond_distance"],
+                         ])
+    print(dataset[0][-2].x)