Biodivine/AEON.py

AEON.py provides Python bindings for the internal and experimental functionality of the tool AEON. You can use it to perform analysis of Boolean networks with symbolic methods. In particular, AEON.py supports:

Classical and partially specificed Boolean networks (i.e. with missing or partially unknown update functions).
Major network formats like .sbml and .bnet, including model validation.
Competitive attractor detection methods using binary decision diagrams (BDDs).
Competitive fixed-point enumeration methods.
Basic control/reprogramming approaches.
Arbitrary symbolic operations on sets of Boolean states/functions represented through BDDs.
Symbolic model checking of HCTL properties.

Installation

The package is available through PyPI for all major operating systems (Windows, Linux and macOS). To install it, you can simply run:

pip install biodivine-aeon

AEON.py is also available through conda and the CoLoMoTo Docker environment.

Citation

If you used AEON.py for some academic work, we'd be very happy if you could cite it using the following publication:

Beneš, N., Brim, L., Huvar, O., Pastva, S., Šafránek, D., & Šmijáková, E. (2022). 
AEON. py: Python library for attractor analysis in asynchronous Boolean networks. 
Bioinformatics, 38(21), 4978-4980.

Documentation

While AEON.py is relatively mature, as with many academic tools, there are still aspects of the documentation that are not completely finalized. If you find that something is missing, or just want us to give you a demo of what the tool is capable of, feel free to get in touch!

For new users that are already familiar with the concept of Boolean networks, we recommend the Jupyter notebooks available in the examples directory:

There are three non-trivial case studies using AEON.py for analysing attractor and phenotype bifurcations in real-world Boolean networks.
There are several "workflow" examples. Some are focused on a specific task (e.g. attractor or fixed-point detection) while others provide a general "overview" of a particular topic ( like BDDs and symbolic algorithms in general).

Additionally, the manual of the standalone AEON tool can be helpful to understand some of the high-level concepts related to Boolean networks (both classical and partially specified).

Finally, more informed users can inspect a detailed API documentation available here. Note that this is a documentation generated for the Rust codebase, which is then exported into Python using the PyO3 tool. As such, naming can be different in the exported Python library (observe the name attribute on most structs that is used for this reason). Nevertheless, the documentation should describe all available methods and data structures.

A proper Python documentation of the full library API is planned later for the 1.0.0 release. Until then, you may also inspect the internal documentation of the underlying Rust libraries to see what functionality is generally available: lib-bdd, lib-param-bn.

Development instructions

To build AEON.py from source, you generally need to follow the guides/instructions available for the maturin tool. However, since some of the functionality in AEON.py requires the Z3 solver, the process is slightly more error prone as it also involves C dependencies, not only pure Rust (this also complicates builds on Apple Silicon and more exotic CPUs).

Local builds

To build and test AEON.py locally, you can generally follow the official instructions for building packages using maturin. However, you have two options for integrating with Z3: either as a static or as a dynamic library.

Using static integration is more "stable" since the library will use a known version of Z3 tested by us. However, Z3 will need to be built during the first compilation, which can take ~30min (subsequent builds should be faster thanks to build cache). You can also encounter build errors if there are issues with your C/C++ toolchain. To use the static linking method, you'll need to add extra commandline arguments when building the library (see below).
Dynamic integration uses the version of Z3 installed on your system. As such, the compilation is faster since there's no need to build Z3. However, we do not guarantee that your installed version is compatible. Furthermore, you'll need to make sure your version is installed in such a way that it can be used as a dynamic library (the .h and .so/.dylib/.dll files are available in their respective include paths). Ideally, to use this method, you should only need to install Z3 on your system using the official method (e.g. apt install z3, brew install z3, or use the official windows installer).

In general, we recommend starting with dynamic linking, because if everything works, it is faster and easier. However, in case you run into trouble, static linking could be actually easier to debug, since it depends less on your actual configuration and is thus easier to reproduce. Similarly, it can be easier to use static linking on systems where Z3 is not available through an official installer or cannot be installed globally.

In any case, on linux, you'll need typical "essential" build tools like cmake and clang to even build the Z3 dependency, regardless of the linking process. On debian-ish distros, apt install build-essential clang should be sufficient.

On Apple Silicon, dynamic linking for Z3 is currently not working out of the box if you installed Z3 through brew. To fix this issue, you need to ensure that the compiler can find Z3 include files in /opt/homebrew/include. However, static linking should work fine. As such, we recommend this option for now.

To install a local version of AEON.py, you then simply need to follow the same steps outlined in the maturin tutorial:

Install maturin (see here).
Create a Python virtual environment for testing and activate it.
[Dynamic linking] Run maturin develop to install a local version of AOEN.py into this virtual environment.
[Static linking] Run maturin develop --features static-z3 to do the same, but with a static version of Z3.

If the build passes, you should be able to use the library on your local machine. Feel free to also install jupyter notebooks and test the library in the interactive environment (or on one of the examples).

Publishing

Finally, you may want to release an alpha/beta version of the library to test that everything is working correctly on all platforms (builds are notoriously finicky in these situations, since we essentially have to build for multiple platforms and multiple versions of Python). Fortunately, the CI is set up to automatically build and publish the library on all relevant platforms every time a new tag is pushed.

Before you publish a new version, make sure that the build works at least on your own machine. Then, make sure to update the library versions in all build files. Specifically, you should update the version in pyproject.toml (publishing on PyPI), cargo.toml (Rust crate version, not published at the moment), and conda/meta.yml (publishing on Anaconda).

Not everything is relevant for every publishing method, but it is generally a good idea to update all files to ensure consistency. For pyproject.toml and conda/meta.yml, you can use suffix aX to indicate that the version is an "alpha" version (e.g. 0.4.0a2). In Cargo.toml, you have to use -alphaX instead (e.g. 0.4.0-alpha2).

Finally, either create a new git tag and push it, or create a new github release with the new tag. Ideally, the tag should be equivalent to the Rust crate version (e.g. 0.4.0-alpha2).

If the build fails and you want to fix it, you can actually reuse the same tag: Once you've made the changes, delete the tag locally and push the change (this may need a force push, but since you are the only person using this tag, it should be ok). Then create the tag again and push it again. It should be also possible to overwrite the tag directly.

Once everything is working as expected, you can remove the alpha suffixes and properly release a new version (in which case, please include a detailed changelog in the release description on github).