Accessing connectivity streamline data (the sequel!)

Technical
5

I have repackaged this data from the server into a new format…a “precomputed” skeleton format that is compatible with neuroglancer and for which there is a python library that is available for reading.

here is a neuroglancer link: Neuroglancer

[click login at bottom of screen to unshorten link]

the precomputed source location from the link is: precomputed://gs://allen_neuroglancer_ccf/allen_mesoscale

I would use the python library cloud-volume to access this bucket and pull streamlines skeletons for each of the experiments. GitHub - seung-lab/cloud-volume: Read and write Neuroglancer datasets programmatically.

here is an example snippet…

import cloudvolume
cv = cloudvolume.CloudVolume(‘precomputed://gs://allen_neuroglancer_ccf/allen_mesoscale’, use_https=True)
streamline = cv.skeleton.get(479983421)

Streamline has vertices in nanometers and edges as indices into the vertices, and if you perform connected components analysis of each graph you will find each streamline is a different component.

if you don’t want to use python you can get the steamline at this https address pattern.
https://storage.googleapis.com/allen_neuroglancer_ccf/allen_mesoscale/skeleton/[EXPERIMENT_ID]

The resulting binary needs to be interpreted according to the precomputed format

the reference info file for this dataset can be found here
https://storage.googleapis.com/allen_neuroglancer_ccf/allen_mesoscale/skeleton/info

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