use pure pandas instead of graph to classify nodes

navis/graph/graph_utils.py

@@ -340,9 +340,9 @@ def _edge_count_to_root_old(x: 'core.TreeNeuron') -> dict:
 
 @utils.map_neuronlist(desc='Classifying', allow_parallel=True)
 @utils.lock_neuron
-def classify_nodes(x: 'core.NeuronObject',
-                   inplace: bool = True
-                   ) -> Optional['core.NeuronObject']:
+def _classify_nodes_old(x: 'core.NeuronObject',
+                        inplace: bool = True
+                        ) -> Optional['core.NeuronObject']:
     """Classify neuron's nodes into end nodes, branches, slabs or root.
 
     Adds ``'type'`` column to ``x.nodes``.
@@ -414,6 +414,70 @@ def classify_nodes(x: 'core.NeuronObject',
     return x
 
 
+
+@utils.map_neuronlist(desc='Classifying', allow_parallel=True)
+@utils.lock_neuron
+def classify_nodes(x: 'core.NeuronObject',
+                   categorical=True,
+                   inplace: bool = True
+                    ) -> Optional['core.NeuronObject']:
+    """Classify neuron's nodes into end nodes, branches, slabs or root.
+
+    Adds ``'type'`` column to ``x.nodes`` table.
+
+    Parameters
+    ----------
+    x :         TreeNeuron | NeuronList
+                Neuron(s) whose nodes to classify.
+    categorical : bool
+                If True (default), will use categorical data type which takes
+                up much less memory at a small run-time overhead.
+    inplace :   bool, optional
+                If ``False``, nodes will be classified on a copy which is then
+                returned leaving the original neuron unchanged.
+
+    Returns
+    -------
+    TreeNeuron/List
+
+    Examples
+    --------
+    >>> import navis
+    >>> nl = navis.example_neurons(2)
+    >>> _ = navis.graph.classify_nodes(nl, inplace=True)
+
+    """
+    if not inplace:
+        x = x.copy()
+
+    if not isinstance(x, core.TreeNeuron):
+        raise TypeError(f'Expected TreeNeuron(s), got "{type(x)}"')
+
+    # At this point x is TreeNeuron
+    x: core.TreeNeuron
+
+    # Make sure there are nodes to classify
+    if not x.nodes.empty:
+        x.nodes['type'] = 'slab'
+        x.nodes.loc[~x.nodes.node_id.isin(x.nodes.parent_id), 'type'] = 'end'
+        bp = x.nodes.parent_id.value_counts()
+        bp = bp[bp > 1].index.values
+        x.nodes.loc[x.nodes.node_id.isin(bp), 'type'] = 'branch'
+        x.nodes.loc[x.nodes.parent_id < 0, 'type'] = 'root'
+    else:
+        x.nodes['type'] = None
+
+    # Turn into categorical data - saves tons of memory
+    # Note that we have to make sure all categories are set even if they
+    # don't exist (e.g. if a neuron has no branch points)
+    if categorical:
+        cat_types = CategoricalDtype(categories=["end", "branch", "root", "slab"],
+                                    ordered=False)
+        x.nodes['type'] = x.nodes['type'].astype(cat_types)
+
+    return x
+
+
 #  only this combination will return a single bool
 @overload
 def distal_to(x: 'core.TreeNeuron',