neuPrint

Neuprint is a service for presenting and analyzing connectomics data. It is used to host the Janelia EM reconstructions from a Drosophila hemibrain at https://neuprint.janelia.org/.

neuprint-python is a Python library that lets you query data directly from a neuprint server. You can install it from PyPI:

pip3 install neuprint-python

navis.interfaces.neuprint wraps neuprint-python and adds a few functions to fetch and convert data into navis objects.

# Import navis
import navis

# Import neuprint wrapper by navis
import navis.interfaces.neuprint as neu

First set up the connection: You can either pass your API token directly or store as NEUPRINT_APPLICATION_CREDENTIALS environment variable. We will use the latter option here:

client = neu.Client(
    "https://neuprint.janelia.org/",
    # token="MYLONGTOKEN"  # use this to instead pass your token directly
    dataset="hemibrain:v1.1",
)

You can use all of neuprint’s functions:

mbons, roi_info = neu.fetch_neurons(
    neu.SegmentCriteria(instance=".*MBON.*", regex=True)
)
mbons.head(3)
bodyId instance type pre post downstream upstream size status cropped statusLabel cellBodyFiber somaRadius somaLocation roiInfo notes inputRois outputRois
0 300972942 MBON14(a3)_R MBON14 543 13634 4340 13634 1563154937 Traced False Roughly traced None NaN None {'MB(R)': {'pre': 17, 'post': 13295, 'downstre... None [MB(+ACA)(R), MB(R), SIP(R), SLP(R), SMP(R), S... [MB(+ACA)(R), MB(R), SIP(R), SLP(R), SMP(R), S...
1 394225044 MBON14(a3)_L MBON14 187 5172 768 5172 355138411 Traced False Roughly traced None NaN None {'MB(L)': {'pre': 185, 'post': 5143, 'downstre... None [MB(L), SIP(L), SNP(L), aL(L)] [MB(L), SIP(L), SNP(L), aL(L)]
2 422725634 MBON06(B1>a)(AVM07)_L MBON06 1356 21000 9584 21000 3118470391 Traced False Roughly traced None NaN None {'MB(R)': {'pre': 777, 'post': 20390, 'downstr... None [CRE(-ROB,-RUB)(R), CRE(R), INP, MB(+ACA)(R), ... [CRE(-ROB,-RUB)(R), CRE(R), INP, MB(+ACA)(R), ...
NAVis has added three functions to neu:
mb = neu.fetch_roi("MB(R)")
mb

<navis.Volume(name=MB(R), color=(0.85, 0.85, 0.85, 0.2), vertices.shape=(57913, 3), faces.shape=(115856, 3))>

mbon_skeletons = neu.fetch_skeletons(
    neu.SegmentCriteria(instance=".*MBON.*_R", regex=True), with_synapses=True
)
mbon_skeletons.head()
type name id n_nodes n_connectors n_branches n_leafs cable_length soma units
0 navis.TreeNeuron MBON23(a2sp)_R 423382015 14190 5200 1195 1225 449662.18750 2364.0 8 nanometer
1 navis.TreeNeuron MBON19(a2p3p)_R 423774471 6944 1783 549 561 246499.53125 1781.0 8 nanometer
2 navis.TreeNeuron MBON15-like(a'1a'2)_R 457175171 9051 2299 548 561 325558.90625 8810.0 8 nanometer
3 navis.TreeNeuron MBON16-like(a'3a)_R 457196643 9769 4016 900 913 408057.59375 5974.0 8 nanometer
4 navis.TreeNeuron MBON14(a3)_R 300972942 22834 14177 2297 2369 758249.75000 NaN 8 nanometer 
# Generate a 3D plot
fig = navis.plot3d([mbon_skeletons[0], mb])





mbon_skeletons[0]
type navis.TreeNeuron
name MBON23(a2sp)_R
id 423382015
n_nodes 14190
n_connectors 5200
n_branches 1195
n_leafs 1225
cable_length 449662.1875
soma 2364
units 8 nanometer 
# Make a 2d plot
import matplotlib.pyplot as plt

fig, ax = navis.plot2d(
    [mbon_skeletons[0], mb], c=(0, 0, 0, 0.5), method="3d", connectors=True, lw=0.5
)

# Rotate to top view
ax.elev = -90
plt.show()
../../_images/neuprint_12_0.png

All navis functions for analysis & visualization should work on these neurons. If not, please open an issue on Github.