navis-org / navis

Python library for analysis of neuroanatomical data.
https://navis-org.github.io/navis/
GNU General Public License v3.0
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3d anatomy blender connectome flywire hemibrain meshes morphometric nblast neuprint neuroanatomy neurons neuroscience python segmentation skeletons swc transform visualization volumes

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NAVis is a Python 3 library for Neuron Analysis and Visualization.

Documentation

Visit our documentation here!

Features

Getting started

See the documentation for detailed installation instructions, tutorials and examples. For the impatient:

pip3 install "navis[all]"

which includes all optional extras providing features and/or performance improvements. Currently, this is igraph, navis-fastcore, pathos, shapely, kdtree, hash, flybrains, cloudvolume, meshes, and vispy-default.

3D plotting from a python REPL is provided by vispy, which has a choice of backends. Different backends work best on different combinations of hardware, OS, python distribution, and REPL, so there may be some trial and error involved. vispy's backends are listed here, and each can be installed as a navis extra, e.g. pip3 install 'navis[vispy-pyqt6]'.

movie

Questions?

Questions on how to use navis are best placed in discussions. Same goes for cool projects or analyses you made using navis - we'd love to hear from you!

Changelog

A summary of changes can be found here.

NAVis & friends

NAVis comes with batteries included but is also highly extensible. Some libraries built on top of NAVis:

Who uses NAVis?

NAVis has been used in a range of neurobiological publications. See here for a list.

We have implemented various published algorithms and methods:

  1. NBLAST: Comparison of neurons based on morphology (Costa et al., 2016)
  2. Vertex Similarity: Comparison of neurons based on connectivity (Jarrell et al., 2012)
  3. Comparison of neurons based on synapse distribution (Schlegel et al., 2016)
  4. Synapse flow centrality for axon-dendrite splits (Schneider-Mizell et al., 2016)

Working on your own cool new method? Consider adding it to NAVis!

Citing NAVis

We'd love to know if you found NAVis useful for your research! You can help us spread the word by citing the DOI provided by Zenodo DOI

License

This code is under GNU GPL V3.

Acknowledgments

NAVis is inspired by and inherits much of its design from the excellent natverse R packages by Greg Jefferis, Alex Bates, James Manton and others.

Contributing

Want to contribute? Great, here is how!

Report bugs or request features

Open an issue. For bug reports please make sure to include some code/data with a minimum example for us to reproduce the bug.

Contribute code

We're always happy for people to contribute code - be it a small bug fix, a new feature or improved documentation.

Here's how you'd do it in a nutshell:

  1. Fork this repository
  2. git clone it to your local machine
  3. Install the full development dependencies with pip install -r requirements.txt
  4. Install the package in editable mode with pip install -e ".[all]"
  5. Create, git add, git commit, git push, and pull request your changes.

Run the tests locally with pytest -v.

Docstrings should use the numpydoc format, and make sure you include any relevant links and citations. Unit tests should be doctests and/or use pytest in the ./tests directory.

Doctests have access to the tmp_dir: pathlib.Path variable, which should be used if any files need to be written.

Feel free to get in touch either through an issue or discussion if you need pointers or input on how to implement an idea.