Accessing connectivity streamline data (the sequel!)

Hi all,

Making a new post following on from a previous post (as people’s roles may have changed in the meantime and I don’t want to cause them unnecessary noise)

I am working on maintaining the Brainrender tool. From what I understand in the post, Brainrender relied on a cache of some AIBS streamline data from experiment 174957972 hosted at

However, this URL now returns 403 Forbidden (see this Brainrender issue for more details)

I am wondering

  • is there now a way to easily download the streamline data from Python (ideally again as a compressed JSON file)?
  • if not, would it be OK to manually download the data and host it on the BrainGlobe GIN server instead of the out-dated cache?

Any advice is appreciated!

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As a new user I couldn’t post more than two links in one post, so linking the previous post this follows on from in this comment.

Same question here, but I wonder whether one can use a post request to access this link:

Just bumping this, does anybody know the best way to retrieve the streamlines for a specific experiment?

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.[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


Thanks @fcollman, much appreciated.