--- ase/neighborlist.py.orig	2020-04-03 23:41:21.000000000 +0000

+++ ase/neighborlist.py	2020-05-07 19:35:07.257677128 +0000

@@ -2,6 +2,7 @@

 import itertools

 from scipy import sparse as sp

 from scipy.spatial import cKDTree

+import scipy.sparse.csgraph as csgraph

 from ase.data import atomic_numbers, covalent_radii

 from ase.geometry import complete_cell, find_mic, wrap_positions

@@ -74,7 +75,7 @@

     Why not dok_matrix like the connectivity-matrix? Because row-picking

     is most likely and this is super fast with csr.

     """

-    mat = sp.csgraph.dijkstra(graph, directed=False, limit=limit)

+    mat = csgraph.dijkstra(graph, directed=False, limit=limit)

     mat[mat == np.inf] = 0

     return sp.csr_matrix(mat, dtype=np.int8)

@@ -1050,6 +1051,10 @@

         See :meth:`ase.neighborlist.PrimitiveNeighborList.get_neighbors` or

         :meth:`ase.neighborlist.PrimitiveNeighborList.get_neighbors`.

         """

+        if self.nl.nupdates <= 0:

+            raise RuntimeError('Must call update(atoms) on your neighborlist '

+                               'first!')

+

         return self.nl.get_neighbors(a)

     def get_connectivity_matrix(self, sparse=True):