navis.prune_by_strahler¶

navis.prune_by_strahler(x, to_prune, inplace=False, reroot_soma=True, force_strahler_update=False, relocate_connectors=False)[source]¶

Prune neuron based on Strahler order.

Parameters:

x (TreeNeuron | MeshNeuron | NeuronList) – Neuron(s) to prune.
to_prune (int | list | range | slice) –
Strahler indices (SI) to prune. For example:
1. to_prune=1 removes all leaf branches
2. to_prune=[1, 2] removes SI 1 and 2
3. to_prune=range(1, 4) removes SI 1, 2 and 3
4. to_prune=slice(0, -1) removes everything but the highest SI
5. to_prune=slice(-1, None) removes only the highest SI
reroot_soma (bool, optional) – If True, neuron will be rerooted to its soma.
inplace (bool, optional) – If False, pruning is performed on copy of original neuron which is then returned.
force_strahler_update (bool, optional) – If True, will force update of Strahler order even if already exists in node table.
relocate_connectors (bool, optional) – If True, connectors on removed nodes will be reconnected to the closest still existing node. Works only in child->parent direction.
parallel (bool) – If True and input is NeuronList, use parallel processing. Requires pathos.
n_cores (int, optional) – Numbers of cores to use if parallel=True. Defaults to half the available cores.
progress (bool) – Whether to show a progress bar. Overruled by navis.set_pbars.
omit_failures (bool) – If True will omit failures instead of raising an exception. Ignored if input is single neuron.

Returns:

Pruned neuron(s).

Return type:

TreeNeuron/List

Examples

>>> import navis
>>> n = navis.example_neurons(1)
>>> n_pr = navis.prune_by_strahler(n, to_prune=1, inplace=False)
>>> n.n_nodes > n_pr.n_nodes
True