WEBKNOSSOS Python Library

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Python API for working with WEBKNOSSOS datasets, annotations, and for WEBKNOSSOS server interaction.

For the WEBKNOSSOS server, please refer to https://github.com/scalableminds/webknossos.

Features

  • easy-to-use dataset API for reading/writing/editing raw 2D/3D image data and volume annotations/segmentation in WEBKNOSSOS-compatiböe format
    • convert from other formats, e.g. tiff stacks
    • add/remove layers
    • update metadata (datasource-properties.json)
    • up/downsample layers
    • compress layers
    • add/remove magnifications
  • Command line tool (CLI) for manipulating and creating WEBKNOSSOS datasets
  • manipulation of WEBKNOSSOS skeleton annotations (*.nml) as Python objects
    • access to nodes, comments, trees, bounding boxes, metadata, etc.
    • create new skeleton annotation from Graph structures or Python objects
  • interaction, connection & scripting with your WEBKNOSSOS instance over the REST API
    • list datastets, annotations, and tasks
    • up- & downloading annotations and datasets

Please refer to the documentation for further instructions.

Installation

The webknossos package requires at least Python 3.10.

You can install it from pypi, e.g. via pip:

pip install webknossos

To install webknossos with the dependencies for all examples, support for more file types, and BioFormats conversions, run: pip install webknossos[all].

For working with Zeiss CZI microscopy data use pip install --extra-index-url https://pypi.scm.io/simple/ webknossos[czi].

By default webknossos can only distribute any computations through multiprocessing or Slurm. For Kubernetes or Dask install these additional dependencies:

pip install cluster_tools[kubernetes]
pip install cluster_tools[dask]

Examples

See the examples folder or the the documentation. The dependencies for the examples are not installed by default. Use pip install webknossos[examples] to install them.

Contributions & Development

Please see the respective documentation page.

License

AGPLv3 Copyright scalable minds

Test Data Credits

Excerpts for testing purposes have been sampled from:

  • Dow Jacobo Hossain Siletti Hudspeth (2018). Connectomics of the zebrafish's lateral-line neuromast reveals wiring and miswiring in a simple microcircuit. eLife. DOI:10.7554/eLife.33988
  • Zheng Lauritzen Perlman Robinson Nichols Milkie Torrens Price Fisher Sharifi Calle-Schuler Kmecova Ali Karsh Trautman Bogovic Hanslovsky Jefferis Kazhdan Khairy Saalfeld Fetter Bock (2018). A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster. Cell. DOI:10.1016/j.cell.2018.06.019. License: CC BY-NC 4.0
  • Bosch Ackels Pacureanu et al (2022). Functional and multiscale 3D structural investigation of brain tissue through correlative in vivo physiology, synchrotron microtomography and volume electron microscopy. Nature Communications. DOI:10.1038/s41467-022-30199-6
  • Hanke, M., Baumgartner, F. J., Ibe, P., Kaule, F. R., Pollmann, S., Speck, O., Zinke, W. & Stadler, J. (2014). A high-resolution 7-Tesla fMRI dataset from complex natural stimulation with an audio movie. Scientific Data, 1:140003. DOI:10.1038/sdata.2014.3
  • Sample OME-TIFF files (c) by the OME Consortium https://downloads.openmicroscopy.org/images/OME-TIFF/2016-06/bioformats-artificial/

Project links

Key dates

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Credits

Author: scalable minds

License

AGPL-3.0

Requires

Python <3.15, >=3.10

Provides Extra

fastremap tifffile imagecodecs bioformats czi mrcfile s3 examples all

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