navis.geodesic_matrix¶

navis.geodesic_matrix(x, from_=None, directed=False, weight='weight', limit=inf)[source]¶

Generate geodesic (“along-the-arbor”) distance matrix between nodes/vertices.

Parameters:

x (TreeNeuron | MeshNeuron | NeuronList) – If list, must contain a SINGLE neuron.
from (list | numpy.ndarray, optional) – Node IDs (for TreeNeurons) or vertex indices (for MeshNeurons). If provided, will compute distances only FROM this subset to all other nodes/vertices.
directed (bool, optional) – If True, pairs without a child->parent path will be returned with distance = "inf". Only relevant for TreeNeurons.
weight ('weight' | None, optional) – If weight distances are given as physical length. If None distances is number of nodes.
limit (int | float, optional) – Use to limit distance calculations. Nodes that are not within limit will have distance np.inf. If neuron has its .units set, you can also pass a string such as “10 microns”.

Returns:

Geodesic distance matrix. Distances in nanometres.

Return type:

pd.DataFrame

See also

navis.distal_to(): Check if a node A is distal to node B.
navis.dist_between(): Get point-to-point geodesic distances.
navis.dist_to_root(): Distances from all skeleton node to their root(s).

Examples

Find average geodesic distance between all leaf nodes

>>> import navis
>>> n = navis.example_neurons(1)
>>> # Generate distance matrix
>>> m = navis.geodesic_matrix(n)
>>> # Subset matrix to leaf nodes
>>> leafs = n.nodes[n.nodes.type=='end'].node_id.values
>>> l_dist = m.loc[leafs, leafs]
>>> # Get mean
>>> round(l_dist.mean().mean())
12983