https://github.com/navis-org/navis
Python library for analysis of neuroanatomical data.
https://github.com/navis-org/navis
3d anatomy blender connectome flywire hemibrain meshes morphometric nblast neuprint neuroanatomy neurons neuroscience python segmentation skeletons swc transform visualization volumes
Last synced: about 2 months ago
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Python library for analysis of neuroanatomical data.
- Host: GitHub
- URL: https://github.com/navis-org/navis
- Owner: navis-org
- License: gpl-3.0
- Created: 2019-01-29T11:19:00.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2025-11-28T14:46:38.000Z (2 months ago)
- Last Synced: 2025-11-30T20:55:35.419Z (2 months ago)
- Topics: 3d, anatomy, blender, connectome, flywire, hemibrain, meshes, morphometric, nblast, neuprint, neuroanatomy, neurons, neuroscience, python, segmentation, skeletons, swc, transform, visualization, volumes
- Language: Python
- Homepage: https://navis-org.github.io/navis/
- Size: 295 MB
- Stars: 108
- Watchers: 6
- Forks: 34
- Open Issues: 46
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
[](https://github.com/navis-org/navis/actions/workflows/build-docs.yml) [](https://github.com/navis-org/navis/actions/workflows/test-package.yml) [](https://github.com/navis-org/navis/actions/workflows/test-tutorials.yml) [](https://colab.research.google.com/github/navis-org/navis/blob/master/examples/colab.ipynb) [](https://zenodo.org/doi/10.5281/zenodo.4699382) [](https://pepy.tech/project/navis)
NAVis is a Python 3 library for **N**euron **A**nalysis and **Vis**ualization.
## Documentation
Visit our documentation [here](https://navis-org.github.io/navis/ "NAVis Documentation")!
## Features
* **polyglot**: work with and convert between neuron skeletons, meshes, dotprops and images
* **visualize**: 2D (matplotlib) and 3D (octarine, vispy, plotly or k3d) plots
* **process**: skeletonization, meshing, smoothing, repair, downsampling, etc.
* **morphometrics**: Strahler analysis, cable length, volume, tortuosity and more
* **similarity**: compare & cluster by morphology (e.g. NBLAST, persistence or form factor) or connectivity metrics
* **transform**: move data between template brains (built-in support for HDF5, CMTK, Elastix and landmark-based transforms)
* **interface**: load neurons directly from [neuPrint](https://neuprint.janelia.org), [neuromorpho.org](http://neuromorpho.org) and other remote data repositories
* **model** neurons and networks using the *NEURON* simulator
* **render**: use Blender 3D for high quality [visualizations](https://youtu.be/wl3sFG7WQJc)
* **R** neuron libraries: interfaces with [nat](https://github.com/jefferis/nat), [rcatmaid](https://github.com/jefferis/rcatmaid), [elmr](https://github.com/jefferis/elmr) and more
* **import-export**: read/write SWCs, neuroglancer's ["*precomputed*"](https://github.com/google/neuroglancer/tree/master/src/datasource/precomputed) format, NMX/NML, NRRD, mesh-files and more
* **fast**: uses functions compiled in Rust under-the-hood (see [fastcore](https://github.com/schlegelp/fastcore-rs))
* **scalable**: out-of-the-box support for multiprocessing
* **extensible**: build your own package on top of navis - see [pymaid](https://pymaid.readthedocs.io/en/latest/) for example
## Getting started
See the [documentation](http://navis.readthedocs.io/ "NAVis ReadTheDocs") for detailed installation instructions, tutorials and examples. For the impatient:
```sh
pip3 install "navis[all]"
```
which includes all optional extras providing features and/or performance improvements. If you encounter issues, you may want
to try a minimal installation and install optional dependencies as needed. Please see the
[Install Instructions](https://navis-org.github.io/navis/installation/) for a detailed explanation.
## Questions?
Questions on how to use `navis` are best placed in [discussions](https://github.com/navis-org/navis/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](https://navis.readthedocs.io/en/latest/source/whats_new.html).
## NAVis & friends
NAVis comes with batteries included but is also highly extensible. Some
libraries built on top of NAVis:
* [flybrains](https://github.com/navis-org/navis-flybrains) provides templates and transforms for *Drosophila* brains to use with navis
* [pymaid](https://pymaid.readthedocs.io/en/latest/) pulls and pushes data from/to CATMAID servers
* [fafbseg](https://fafbseg-py.readthedocs.io/en/latest/index.html) contains tools to work with auto-segmented data for the FAFB EM dataset including FlyWire
## Who uses NAVis?
NAVis has been used in a range of neurobiological publications. See [here](publications.md) for a list.
We have implemented various published algorithms and methods:
1. NBLAST: Comparison of neurons based on morphology [(Costa et al., 2016)](https://www.cell.com/neuron/fulltext/S0896-6273(16)30265-3?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0896627316302653%3Fshowall%3Dtrue)
2. Vertex Similarity: Comparison of neurons based on connectivity [(Jarrell et al., 2012)](http://science.sciencemag.org/content/337/6093/437.long)
3. Comparison of neurons based on synapse distribution
[(Schlegel et al., 2016)](https://elifesciences.org/content/5/e16799)
4. Synapse flow centrality for axon-dendrite splits [(Schneider-Mizell et al., 2016)](https://elifesciences.org/articles/12059)
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 [](https://zenodo.org/doi/10.5281/zenodo.4699382)
## License
This code is under [GNU GPL V3](LICENSE).
## Acknowledgments
NAVis is inspired by and inherits much of its design from the excellent
[natverse](http://natverse.org) R packages by
[Greg Jefferis](https://github.com/jefferis), [Alex Bates](https://github.com/alexanderbates),
[James Manton](https://github.com/ajdm) and others.
## Contributing
Want to contribute? Great, here is how!
#### Report bugs or request features
Open an [issue](https://github.com/navis-org/navis/issues). 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](https://numpydoc.readthedocs.io/en/latest/format.html) format,
and make sure you include any relevant links and citations.
Unit tests should be [doctests](https://docs.python.org/3/library/doctest.html)
and/or use [pytest](https://docs.pytest.org/en/stable/) 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](https://github.com/navis-org/navis/issues)
or [discussion](https://github.com/navis-org/navis/discussions) if you need
pointers or input on how to implement an idea.