webknossos 2.4.0

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

WEBKNOSSOS Python Library

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/