navis.distal_to¶

navis.distal_to(x, a=None, b=None)[source]¶

Check if nodes A are distal to nodes B.

Important

Please note that if node A is not distal to node B, this does not automatically mean it is proximal instead: if nodes are on different branches, they are neither distal nor proximal to one another! To test for this case run a->b and b->a - if both return False, nodes are on different branches.

Also: if a and b are the same node, this function will return True!

Parameters:

x (TreeNeuron) –
a (single node ID | list of node IDs | None, optional) – If no node IDs are provided, will consider all node. Note that for large sets of nodes it might be more efficient to use navis.geodesic_matrix() (see examples).
b (single node ID | list of node IDs | None, optional) – If no node IDs are provided, will consider all node. Note that for large sets of nodes it might be more efficient to use navis.geodesic_matrix() (see examples).

Return type:

Union[bool, DataFrame]

Returns:

bool – If a and b are single node IDs respectively.
pd.DataFrame – If a and/or b are lists of node IDs. Columns and rows (index) represent node IDs. Neurons a are rows, neurons b are columns.

Examples

>>> import navis
>>> # Get a neuron
>>> x = navis.example_neurons(1)
>>> # Get a random node
>>> n = x.nodes.iloc[100].node_id
>>> # Check all nodes if they are distal or proximal to that node
>>> df = navis.distal_to(x, n)
>>> # Get the IDs of the nodes that are distal
>>> dist = df.loc[n, df.loc[n]].index.values
>>> len(dist)
101

For large neurons and/or large sets of a/b it can be much faster to use geodesic_matrix instead:

>>> import navis
>>> import numpy as np
>>> x = navis.example_neurons(1)
>>> # Get an all-by-all distal_to
>>> df = navis.geodesic_matrix(x, weight=None, directed=True) < np.inf
>>> # Get distal_to for specific nodes
>>> df = navis.geodesic_matrix(x, weight=None, directed=True) < np.inf
>>> # Get distal_to for specific nodes
>>> a, b = x.nodes.node_id.values[:100], x.nodes.node_id.values[-100:]
>>> dist = navis.geodesic_matrix(x, weight=None, directed=True, from_=a)
>>> distal_to = dist[b] < np.inf