pymaid.adjacency_from_connectors

pymaid.adjacency_from_connectors(source, target=None, remote_instance=None)[source]

Regenerate adjacency matrices from neurons’ connectors.

Notes

This function creates an adjacency matrix from scratch using just the neurons’ connectors. This function is able to deal with non-unique skeleton IDs (most other functions are not). Use it e.g. when you split neurons into multiple fragments.

Parameters:

  • source (skeleton IDs | CatmaidNeuron | CatmaidNeuronList) – Neuron(s) for which to generate adjacency matrix. If target==None, will use target=source.

  • target (skeleton IDs | CatmaidNeuron | CatmaidNeuronList) – Neuron(s) for which to generate adjacency matrix. If target==None, will use target=source.

  • remote_instance (CatmaidInstance, optional) – If not passed, will try using globally defined.

Returns:

Matrix holding possible synaptic contacts. Sources are rows, targets are columns. Labels are skeleton IDs. Order is preserved:

        target1  target2  target3  ...
source1    5        1        0
source2    10       20       5
source3    4        3        15
...

Return type:

pandas.DataFrame

See also

adjacency_matrix()

If you are working with “intact” neurons. Much faster!

filter_connectivity()

Use this function if you have only a single fragment per neuron (e.g. just the axon). Also way faster.

Examples

>>> # Fetch some neurons
>>> x = pymaid.get_neuron('annotation:PD2a1/b1')
>>> # Split into axon / dendrites
>>> x.reroot(x.soma)
>>> split = pymaid.split_axon_dendrite(x)
>>> # Regenerate all-by-all adjacency matrix
>>> adj = pymaid.adjacency_from_connectors(split)
>>> # Skeleton IDs are non-unique but column/row order = input order:
>>> # in this example, the first occurrence is axon, the second dendrites
>>> adj.head()