pymaid.get_paths

pymaid.get_paths(sources, targets, n_hops=2, min_synapses=1, return_graph=False, remove_isolated=False, remote_instance=None)

Fetch paths between two sets of neurons.

Parameters:

• sources – Source neurons.

• targets –

  Target neurons. sources and targets can be:

  1. list of skeleton ID(s) (int or str)

  2. list of neuron name(s) (str, exact match)

  3. an annotation as e.g. ‘annotation:PN right’

  4. CatmaidNeuron or CatmaidNeuronList object

• n_hops (int | list | range, optional) –

  Number of hops allowed between sources and targets. Direct connection would be 1 hop.

  1. int, e.g. n_hops=3 will return paths with EXACTLY 3 hops 2. list, e.g. n_hops=[2,4] will return all paths with 2 and 4 hops 3. range, e.g. n_hops=range(2,4) will be converted to a list and return paths with 2 and 3 hops.

• min_synapses (int, optional) – Minimum number of synpases between source and target.

• return_graph (bool, optional) – If True, will return NetworkX Graph (see below).

• remove_isolated (bool, optional) – Remove isolated nodes from NetworkX Graph. Only relevant if return_graph=True.

• remote_instance (CatmaidInstance, optional) – If not passed directly, will try using global.

Returns:

• paths (list) – List of skeleton IDs that constitute paths from sources to targets:

  [[source1, ..., target1], [source2, ..., target2], ...]
  

• networkx.DiGraph – Only if return_graph=True. Graph contains all neurons that connect sources and targets. Important: Does only contain edges that connect sources and targets via max n_hops! Other edges have been removed.

Examples

>>> # This assumes that you have already set up a CatmaidInstance
>>> import networkx as nx
>>> import matplotlib.pyplot as plt
>>> g, paths = pymaid.get_paths(['annotation:glomerulus DA1'],
...                             ['2333007'])
>>> g
<networkx.classes.digraph.DiGraph at 0x127d12390>
>>> paths
[['57381', '4376732', '2333007'], ['57323', '630823', '2333007'], ...
>>> nx.draw(g)
>>> plt.show()