Note
Click here to download the full example code
Dotprops#
This tutorial will show you have 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
From image data#
For this example I downloaded one of Janelia's Fly Light confocal stacks (link) and converted it to NRRD format using ImageJ.
Load NRRD file into Dotprops instead of VoxelNeuron:
dp = navis.read_nrrd(
"~/Downloads/JRC_SS86025_JRC_SS86025-20211112_49_B6.nrrd",
output="dotprops",
threshold=3000,
)
Note
Note the threshold parameter? It determines which voxels (by brightness) are used and which are ignored!
From other neurons#
Let's say you have a bunch of skeletons and you need to convert them to dotprops for NBLAST. For that you navis.make_dotprops:
sk = navis.example_neurons(3, kind="skeleton")
dp = navis.make_dotprops(sk, k=5)
# Plot one of the dotprops
fig, ax = navis.plot2d(dp[0], view=("x", "-z"), method="2d", color="red")
# Add a zoom-in
axins = ax.inset_axes([0.03, 0.03, 0.47, 0.47], xticklabels=[], yticklabels=[])
_ = navis.plot2d(dp[0], view=("x", "-z"), method="2d", color="red", ax=axins)
axins.set_xlim(17e3, 19e3)
axins.set_ylim(15e3, 13e3)
ax.indicate_inset_zoom(axins, edgecolor="black")
Out:
(<matplotlib.patches.Rectangle object at 0x7fd82aac3610>, (<matplotlib.patches.ConnectionPatch object at 0x7fd881b25120>, <matplotlib.patches.ConnectionPatch object at 0x7fd881b27c70>, <matplotlib.patches.ConnectionPatch object at 0x7fd8818a4bb0>, <matplotlib.patches.ConnectionPatch object at 0x7fd8818a4ee0>))
Note
The k parameter in make_dotprops determines how many neighbours are considered to generated the tangent vector for a given point. Higher k = smoother. Lower k = more detailed but also more noisy. If you have clean data such as these connectome-derived skeletons, you can go with a low k. For confocal data, you might want to go with a higher k (e.g. 20) to smooth out the noise. You can pass k to navis.read_nrrd as well.
Manual construction#
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], [1, 1, 1], [2, 2, 2]])
# Create vectors for each point
# You can skip this point and just provide the `k` parameter
vect = np.array([[1, 0, 0], [0, 1, 0], [0, 1, 0]])
dp = navis.Dotprops(points, k=None, vect=vect)
dp
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.
Total running time of the script: ( 0 minutes 0.304 seconds)
Download Python source code: plot_02_io_dotprops.py