navis.downsample_neuron¶

navis.downsample_neuron(x, downsampling_factor, inplace=False, preserve_nodes=None)[source]¶

Downsample neuron(s) by a given factor.

For skeletons: preserves root, leafs, branchpoints by default. Preservation of nodes with synapses can be toggled - see preserve_nodes parameter. Use downsampling_factor=float('inf') to get a skeleton consisting only of root, branch and end points.

Parameters:

x (single neuron | NeuronList) – Neuron(s) to downsample. Note that for MeshNeurons we use the first available backend.
downsampling_factor (int | float('inf')) – Factor by which downsample. For TreeNeuron, Dotprops and MeshNeurons this reduces the node, point and face count, respectively. For VoxelNeurons it reduces the dimensions by given factor.
preserve_nodes (str | list, optional) – Can be either list of node IDs to exclude from downsampling or a string to a DataFrame attached to the neuron (e.g. “connectors”). DataFrame must have node_id` column. Only relevant for TreeNeurons.
inplace (bool, optional) – If True, will modify original neuron. If False, we will operate and return o a copy.
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:

Same datatype as input.

Return type:

TreeNeuron/Dotprops/VoxelNeurons/NeuronList

Examples

>>> import navis
>>> n = navis.example_neurons(1)
>>> n_ds = navis.downsample_neuron(n,
...                                downsampling_factor=5,
...                                inplace=False)
>>> n.n_nodes > n_ds.n_nodes
True