classify_nodes: account for bug (?) in numpy.isin

navis/graph/graph_utils.py

@@ -85,7 +85,7 @@ def _generate_segments(x: 'core.NeuronObject',
     endNodeIDs = sorted(endNodeIDs, key=lambda x: d.get(x, 0), reverse=True)
 
     if config.use_igraph and x.igraph:
-        g: igraph.Graph = x.igraph
+        g: igraph.Graph = x.igraph  # noqa
         # Convert endNodeIDs to indices
         id2ix = dict(zip(x.igraph.vs['node_id'], range(len(x.igraph.vs))))
         endNodeIDs = [id2ix[n] for n in endNodeIDs]
@@ -151,7 +151,7 @@ def _connected_components(x: Union['core.TreeNeuron', 'core.MeshNeuron']) -> Lis
     assert isinstance(x, (core.TreeNeuron, core.MeshNeuron))
 
     if config.use_igraph and x.igraph:
-        G: igraph.Graph = x.igraph
+        G: igraph.Graph = x.igraph  # noqa
         # Get the vertex clustering
         vc = G.components(mode='WEAK')
         # Membership maps indices to connected components
@@ -207,7 +207,7 @@ def _break_segments(x: 'core.NeuronObject') -> list:
     x: core.TreeNeuron
 
     if x.igraph and config.use_igraph:
-        g: Union['igraph.Graph', 'nx.DiGraph'] = x.igraph
+        g: Union['igraph.Graph', 'nx.DiGraph'] = x.igraph # noqa
         end = g.vs.select(_indegree=0).indices
         branch = g.vs.select(_indegree_gt=1, _outdegree=1).indices
         root = g.vs.select(_outdegree=0).indices
@@ -308,7 +308,7 @@ def _edge_count_to_root_old(x: 'core.TreeNeuron') -> dict:
 
     """
     current_level: List[int]
-    g: Union['igraph.Graph', 'nx.DiGraph']
+    g: Union['igraph.Graph', 'nx.DiGraph']  # noqa
     if x.igraph and config.use_igraph:
         g = x.igraph
         current_level = g.vs(_outdegree=0).indices
@@ -458,12 +458,23 @@ def classify_nodes(x: "core.NeuronObject", categorical=True, inplace: bool = Tru
     # Note: I have tried to optimized the s**t out of this, i.e. every
     # single line of code here has been tested for speed. Do not
     # change anything unless you know what you're doing!
+
+    # Turns out that numpy.isin() recently started to complain if the
+    # node_ids are uint64 and the parent_ids are int64 (but strangely
+    # not with 32bit integers). If that's the case we have to convert
+    # the node_ids to int64.
+    node_ids = x.nodes.node_id.values
+    parent_ids = x.nodes.parent_id.values
+
+    if node_ids.dtype == np.uint64:
+        node_ids = node_ids.astype(np.int64)
+
     cl = np.full(len(x.nodes), "slab", dtype="<U6")
-    cl[~np.isin(x.nodes.node_id, x.nodes.parent_id)] = "end"
+    cl[~np.isin(node_ids, parent_ids)] = "end"
     bp = x.nodes.parent_id.value_counts()
     bp = bp.index.values[bp.values > 1]
-    cl[np.isin(x.nodes.node_id.values, bp)] = "branch"
-    cl[x.nodes.parent_id.values < 0] = "root"
+    cl[np.isin(node_ids, bp)] = "branch"
+    cl[parent_ids < 0] = "root"
     if categorical:
         cl = pd.Categorical(cl, categories=["end", "branch", "root", "slab"], ordered=False)
     x.nodes["type"] = cl
@@ -838,7 +849,7 @@ def dist_between(x: 'core.NeuronObject',
             raise ValueError(f'Need a single TreeNeuron, got {len(x)}')
 
     if isinstance(x, (core.TreeNeuron, core.MeshNeuron)):
-        G: Union['igraph.Graph',
+        G: Union['igraph.Graph',  # noqa
                  'nx.DiGraph'] = x.igraph if (x.igraph and config.use_igraph) else x.graph
     elif isinstance(x, nx.DiGraph):
         G = x