navis.cable_overlap

navis.cable_overlap(a, b, dist=2, method='min')

Calculate the amount of cable of neuron A within distance of neuron B.

Parameters:

• a (TreeNeuron | NeuronList) – Neuron(s) for which to compute cable within distance. It is highly recommended to resample neurons to guarantee an even sampling rate.

• b (TreeNeuron | NeuronList) – Neuron(s) for which to compute cable within distance. It is highly recommended to resample neurons to guarantee an even sampling rate.

• dist (int | float, optional) – Maximum distance. If the neurons have their .units set, you can also provides this as a string such as “2 microns”.

• method ('min' | 'max' | 'mean' | 'forward' | 'reverse') –

  Method by which to calculate the overlapping cable between two cables:

  Assuming that neurons A and B have 300 and 150 um of cable
  within given distances, respectively:
  
    1. 'min' returns 150
    2. 'max' returns 300
    3. 'mean' returns 225
    4. 'forward' returns 300 (i.e. A->B)
    5. 'reverse' returns 150 (i.e. B->A)
  

Returns:

Matrix in which neurons A are rows, neurons B are columns. Cable within distance is given in the neuron’s native units:

          neuronD  neuronE   neuronF  ...
neuronA         5        1         0
neuronB        10       20         5
neuronC         4        3        15
...

Return type:

pandas.DataFrame

See also

navis.resample_skeleton()

Use to resample neurons before calculating overlap.

Examples

>>> import navis
>>> nl = navis.example_neurons(4)
>>> # Cable overlap is given in the neurons' units
>>> # Converting the example neurons from 8x8x8 voxel space into microns
>>> # make the results easier to interpret
>>> nl = nl.convert_units('um')
>>> # Resample to half a micron
>>> nl_res = nl.resample('.5 micron', inplace=False)
>>> # Get overlapping cable within 2 microns
>>> ol = navis.cable_overlap(nl_res[:2], nl_res[2:], dist='2 microns')