cloud-volume 0.1.0

Read and write neuroglancer Precomputed formats to cloud storage

[](https://travis-ci.org/seung-lab/cloud-volume)

# cloud-volume

Python client for reading and writing to Neuroglancer Precomputed volumes on cloud services. (https://github.com/google/neuroglancer/tree/master/src/neuroglancer/datasource/precomputed)

When working with a particular dataset, say an EM scan of a mouse, fish, or fly brain, you’ll typically store that as a grayscale data layer accessible to neuroglanger. You may store additional labellings and processing results as other layers.

## Usage

Supports reading and writing to neuroglancer data layers on Amazon S3, Google Storage, and the local file system.

Supported URLs are of the forms:

$PROTOCOL://$BUCKET/$DATASET/$LAYER

### Supported Protocols * gs: Google Storage * s3: Amazon S3 * boss: The BOSS (https://docs.theboss.io/docs) * file: Local File System (absolute path)

### Examples

` vol = CloudVolume('gs://mybucket/retina/image') # Basic Example image = vol[:,:,:] # Download the entire image stack into a numpy array vol[64:128, 64:128, 64:128] = image # Write a 64^3 image to the volume vol.save_mesh(12345) # save 12345 as ./12345.obj vol.save_mesh([12345, 12346, 12347]) # merge three segments into one obj `

## Setup

You’ll need to set up your cloud credentials as well as the main install.

### Credentials

` mkdir -p ~/.neuroglancer/secrets/ echo $GOOGLE_STORAGE_PROJECT > ~/.neuroglancer/project_name # needed for Google mv aws-secret.json ~/.neuroglancer/secrets/ # needed for Google mv google-secret.json ~/.neuroglancer/secrets/ # needed for Amazon `

### pip

` pip install cloud-volume `

### Manual ` git clone [e-mail]:seung-lab/cloud-volume.git cd cloud-volume mkvirtualenv cloud-volume workon cloud-volume pip install -e . `

## Other Languages

Julia - https://github.com/seung-lab/CloudVolume.jl