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/
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