def test_connectors_between(self):
self.assertIsInstance(pymaid.get_connectors_between(config_test.test_skids,
config_test.test_skids),
pd.DataFrame)
Neuron_2 = pymaid.get_neuron(Neuron_2_skeleton_id_number)
#This function downsamples the neuron - it removes large stretches of skeleton that do not have any branch points.
#When the argument preserve_cn_treenodes = True, this preserves the treenodes where connectors (pre/postsynapses)
#have been placed. Downsampling is used to reduce the computational time, as some 3D reconstructed neurons can become
#very large.
Neuron.downsample(1000000, preserve_cn_treenodes = True)
Neuron_2.downsample(1000000, preserve_cn_treenodes = True)
#Get the connectors between the two neurons of interest
#When True, the directional argument will return the connectors (pre and post synapses)
#from neuron A to neuron B (A-->B; in this case Neuron_1 to Neuron_2). When False, it will return all
#connectors between neuron A to neuron B (A<-->B; in this case, all connectors between Neuron_1 to Neuron_2)
Neuron_1_to_Neuron_2 = pymaid.get_connectors_between(Neuron_1,Neuron_2, directional = True)
def plot_nx(x, plot_connectors=True, highlight_connectors=None, prog='dot'):
""" This lets you plot neurons as dendrograms using networkx and its bindings
to graphviz.
Parameters
----------
x : CatmaidNeuron
Neuron to plot. Strongly recommend to downsample the neuron!
plot_connectors : bool, optional
If True, connectors will be plotted
highlight_connectors : list of int
These connectors (or more precisely, the treenodes they
connect to) will be highlighted in green