add example of how to use pair finding without model fitting (#40)

Co-authored-by: Nicholas Lubbers <hippynn@lanl.gov>

examples/periodic_pairfinding.py

@@ -0,0 +1,42 @@
+import torch
+from hippynn.graphs import GraphModule
+from hippynn.graphs.nodes.inputs import SpeciesNode, PositionsNode, CellNode
+from hippynn.graphs.nodes.indexers import acquire_encoding_padding
+from hippynn.graphs.nodes.pairs import PeriodicPairIndexer
+
+
+n_atom = 30
+n_system = 30
+n_dim = 3
+distance_cutoff = 0.3
+
+if torch.cuda.is_available():
+    device = "cuda"
+else:
+    device = "cpu"
+
+floatX = torch.float32
+
+# Set up input nodes
+sp = SpeciesNode("Z")
+pos = PositionsNode("R")
+cell = CellNode("C")
+
+# Set up and compile calculation
+enc, pidxer = acquire_encoding_padding(sp, species_set=[0, 1])
+pairfinder = PeriodicPairIndexer("pair finder", (pos, enc, pidxer, cell), dist_hard_max=distance_cutoff)
+computer = GraphModule([sp, pos, cell], [*pairfinder.children])
+computer.to(device)
+
+# Get some random inputs
+species_tensor = torch.ones(n_system, n_atom, device=device, dtype=torch.int64)
+pos_tensor = torch.rand(n_system, n_atom, 3, device=device, dtype=floatX)
+cell_tensor = torch.eye(3, 3, device=device, dtype=floatX).unsqueeze(0).expand(n_system, n_dim, n_dim).clone()
+
+# Run calculation
+outputs = computer(species_tensor, pos_tensor, cell_tensor)
+
+# Print outputs
+output_as_dict = {c.name: o for c, o in zip(pairfinder.children, outputs)}
+for k, v in output_as_dict.items():
+    print(k, v.shape, v.dtype, v.min(), v.max())