navis.voxelize¶

navis.voxelize(x, pitch, bounds=None, counts=False, vectors=False, alphas=False, smooth=0)[source]¶

Turn neuron into voxels.

Parameters:

x (TreeNeuron | MeshNeuron | Dotprops) – Neuron(s) to voxelize. Uses the neurons’ nodes, vertices and points, respectively.
pitch (float | iterable thereof) – Side length(s) voxels. Can be isometric (float) or an iterable of dimensions in (x, y, z).
bounds ((3, 2) or (2, 3) array, optional) – Boundaries [in units of x] for the voxel grid. If not provided, will use x.bbox.
counts (bool) – If True, voxel grid will have point counts for values instead of just True/False.
vectors (bool) – If True, will also attach a vector field as .vectors property.
alphas (bool) – If True, will also return a grid with alpha values as .alpha property.
smooth (int) – If non-zero, will apply a Gaussian filter with smooth as sigma.
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:

Has the voxel grid as .grid and (optionally) .vectors and .alphas properties. .grid data type depends on settings:

default = bool (i.e. True/False)

if counts=True = integer

if smooth=True = float

Empty voxels will have vector (0, 0, 0) and alpha 0. Also note that data tables (e.g. connectors) are not carried over from the input neuron.

Return type:

VoxelNeuron

Examples

>>> import navis
>>> # Get a skeleton
>>> n = navis.example_neurons(1)
>>> # Convert to voxel neuron
>>> vx = navis.voxelize(n, pitch='5 microns')