Updated tests

src/sisl/geom/__init__.py

@@ -69,7 +69,7 @@
 # TODO add category and neighbors in the above discussion
 from ._category import *
 from ._composite import *
-from ._neighbors import *
+from ._neighbors import NeighborFinder
 
 # isort: split
 from .basic import *

src/sisl/geom/_neighbors/__init__.py

@@ -3,4 +3,4 @@
 # file, You can obtain one at https://mozilla.org/MPL/2.0/.
 from __future__ import annotations
 
-from ._finder import NeighborFinder
+from ._finder import *

src/sisl/geom/_neighbors/_finder.py

@@ -14,6 +14,18 @@
 
 from . import _operations
 
+__all__ = [
+    "NeighborFinder",
+    "Neighbors",
+    "AtomsNeighborList",
+    "UniqueNeighborList",
+    "FullNeighborList",
+    "PartialNeighborList",
+    "AtomNeighborList",
+    "PointsNeighborList",
+    "PointNeighborList",
+]
+
 
 class Neighbors:
 
@@ -36,7 +48,10 @@ def isc(self):
 
     @cached_property
     def nsc(self):
-        return np.max(np.abs(self.isc), axis=0) * 2 + 1
+        if len(self.isc) == 0:
+            return np.ones(3, dtype=int)
+        else:
+            return np.max(np.abs(self.isc), axis=0) * 2 + 1
 
     @property
     def j_sc(self):
@@ -98,7 +113,7 @@ def n_neighbors(self):
         else:
             return np.diff(self._split_indices, prepend=0)
 
-    def to_full(self):
+    def to_full(self) -> FullNeighborList:
         """Converts the unique neighbors list to a full neighbors list."""
         upper_tri = self._finder_results
         lower_tri = np.column_stack(
@@ -108,10 +123,11 @@ def to_full(self):
         self_interactions = (self.i == self.j) & np.all(self.isc == 0, axis=1)
         lower_tri = lower_tri[~self_interactions]
 
-        # Sort
+        # Concatenate the lower triangular with the upper triangular part
         all_finder_results = np.concatenate([upper_tri, lower_tri], axis=0)
 
-        sorted_indices = np.lexsort(all_finder_results[:, [1, 0]])
+        # Sort by i and then by j
+        sorted_indices = np.lexsort(all_finder_results[:, [1, 0]].T)
         all_finder_results = all_finder_results[sorted_indices]
 
         return FullNeighborList(
@@ -140,6 +156,17 @@ def __getitem__(self, item):
         else:
             raise ValueError("Only integer indexing is supported.")
 
+    def to_unique(self) -> UniqueNeighborList:
+        """Converts the full neighbors list to a unique neighbors list."""
+
+        full_finder_results = self._finder_results
+        unique_finder_results = full_finder_results[self.i <= self.j]
+
+        # Concatenate the uc connections with the rest of the connections.
+        return UniqueNeighborList(
+            geometry=self.geometry, finder_results=unique_finder_results
+        )
+
 
 class PartialNeighborList(AtomsNeighborList):
 
@@ -168,7 +195,7 @@ def __getitem__(self, item):
             return AtomNeighborList(
                 self.geometry,
                 self._finder_results[start:end],
-                atom=self.atoms[at],
+                atom=self.atoms[item],
                 nsc=self.nsc,
             )
         else:
@@ -187,6 +214,11 @@ def __init__(self, geometry, finder_results, atom: int, nsc: np.ndarray):
     def nsc(self):
         return self._nsc
 
+    @cached_property
+    def n_neighbors(self):
+        """Number of neighbors of the atom."""
+        return len(self._finder_results)
+
 
 class PointsNeighborList(Neighbors):
     """List of atoms that are close to a set of points in space."""
@@ -238,6 +270,11 @@ def __init__(self, geometry, point: np.ndarray, finder_results, nsc):
     def nsc(self):
         return self._nsc
 
+    @cached_property
+    def n_neighbors(self):
+        """Number of neighbors of the point."""
+        return len(self._finder_results)
+
 
 class NeighborFinder:
     """Efficient linear scaling finding of neighbors.
@@ -774,20 +811,9 @@ def find_unique_pairs(
         # just find all neighbors and then drop duplicate connections. Otherwise it is a bit of a mess.
         if self._R_too_big:
             # Find all neighbors
-            all_neighbors = self.find_neighbors(
-                as_pairs=True, self_interaction=self_interaction
-            )
-
-            # Find out which of the pairs are uc connections
-            is_uc_neigh = ~np.any(all_neighbors[:, 2:], axis=1)
-
-            # Create an array with unit cell connections where duplicates are removed
-            unique_uc = np.unique(np.sort(all_neighbors[is_uc_neigh][:, :2]), axis=0)
-            uc_neighbors = np.zeros((len(unique_uc), 5), dtype=int)
-            uc_neighbors[:, :2] = unique_uc
+            all_neighbors = self.find_neighbors(self_interaction=self_interaction)
 
-            # Concatenate the uc connections with the rest of the connections.
-            return np.concatenate((uc_neighbors, all_neighbors[~is_uc_neigh]))
+            return all_neighbors.to_unique()
 
         # Cast R into array of appropiate shape and type.
         thresholds = np.full(self.geometry.na, self.R, dtype=np.float64)