pymaid.cn_table_from_connectors¶

pymaid.cn_table_from_connectors(x, remote_instance=None)[source]¶

Generate connectivity table from neurons’ connectors.

This function creates the connectivity table from scratch using just the neurons’ connectors. This function is able to deal with non-unique skeleton IDs (most other functions won’t). Use it e.g. when you split neurons into multiple fragments. The order of the input CatmaidNeuronList is preserved!

Parameters

x (CatmaidNeuron | CatmaidNeuronList) – Neuron(s) for which to generate connectivity table.
remote_instance (CatmaidInstance, optional) – If not passed, will try using globally defined.

Returns

DataFrame in which each row represents a neuron and the number of synapses with the query neurons:

  neuron_name  skeleton_id   relation    total  skid1  skid2 ...
0   name1         skid1      upstream    n_syn  n_syn  ...
1   name2         skid2     downstream   n_syn  n_syn  ..
2   name3         skid3      usptream    n_syn  n_syn  .
... ...

relation can be 'upstream' (incoming), 'downstream' (outgoing), 'attachment' or 'gapjunction' (gap junction).

Return type

pandas.DataFrame

See also

get_partners(): 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 cn_table
>>> cn_table = pymaid.cn_table_from_connectors(split)
>>> # Skeleton IDs are non-unique but column order = input order:
>>> # in this example the first occurrence is axon, the second dendrites
>>> cn_table.head()