.. _other_libs:

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.. image:: ../_static/navis_ecosystem_dark.png

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NAVis & friends
===============
``navis`` comes with batteries included but is also highly extensible. Here are
some libraries that are built directly on top of ``navis``.

flybrains
---------
`flybrains <https://github.com/navis-org/navis-flybrains>`_ is a package that
bundles fly template brains and transforms that navis can use to map spatial
data (e.g. neurons) from one brain space to another. If you installed ``navis``
via ``pip`` with the ``[all]`` option, ``flybrains`` will already be on your
system.

pymaid
------
`pymaid <https://pymaid.readthedocs.io/en/latest/>`_ provides an interface with
`CATMAID <https://catmaid.readthedocs.io/en/stable/>`_ servers. It allows
you to pull data (neurons, connectivity) that can be directly plugged into
``navis``. Conversely, you can also take navis neurons and push them to a
CATMAID server. ``pymaid`` is a great example of how to extend ``navis``.

fafbseg
-------
`fafbseg <https://fafbseg-py.readthedocs.io/en/latest/index.html>`_ contains
tools to work with autosegmented data for the
`FAFB <https://www.temca2data.org>`_ (full adult fly brain)
EM dataset. It brings together data from `FlyWire <https://flywire.ai/>`_,
`Google's <http://fafb-ffn1.storage.googleapis.com/landing.html>`_ segmentation
of FAFB and `synapse predictions <https://github.com/funkelab/synful>`_ by
Buhmann et al. (2019).

natverse
--------
The `natverse <http://natverse.org/>`_ is ``navis'`` equivalent in R. While we
are aiming for feature parity, it can be useful to access ``natverse`` functions
from Python. For this, ``navis`` offers some convenience functions using the
R-Python interface ``rpy2``. Check out the :ref:`tutorial <rmaid_link>`.