navis lets you import neurons from a variety of local and remote sources. In this tutorial you will learn how to work with Dotprops.

navis.Dotprops are point clouds with associated principal vectors which are mostly used for NBLASTing. They are typically derivatives of skeletons or meshes but you can load them straight from confocal data using navis.read_nrrd():

import navis

For this example I downloaded one of Janelia’s Fly Light confocal stacks (link) and converted it to nrrd using ImageJ.

# Load NRRD file into Dotprops instead of VoxelNeuron
# Note the threshold parameter that determines which voxels
# (by brightness) are used and which are ignored!
dp = navis.read_nrrd('~/Downloads/JRC_SS86025_JRC_SS86025-20211112_49_B6.nrrd', output='dotprops', threshold=3000)
type navis.Dotprops
name JRC_SS86025_JRC_SS86025-20211112_49_B6
k 20
units 1 micrometer
n_points 92416

If not loaded from file, you would typically create Dotprops via navis.make_dotprops() but just like all other neuron types, Dotprops can be constructed manually:

import numpy as np

# Create some x/y/z coordinates
points = np.array([[0,0,0],

# Create vectors for each point
# You can skip this point and just provide the `k` parameter
vect = np.array([[1,0,0],

dp = navis.Dotprops(points, k=None, vect=vect)
type navis.Dotprops
name None
k None
units 1 dimensionless
n_points 3

There is no established format to store dotprops. But like all other neuron types in navis, you can pickle data for later (re)use - see the pickling tutorial. See also the I/O API reference api_io>.