Announcing RedbirdPy - Redbird for Python (v0.2)

[Qianqian Fang]

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Dear colleagues,

Last summer, we announced Redbird - a versatile MATLAB/Octave toolbox for diffusion-based forward modeling and DOT reconstructions. In the past 6 months, this toolbox has received nearly 1000 downloads!

Today, we are excited to announce that Redbird gets a twin - RedbirdPy - redbird for Python! RedbirdPy is now available on PyPi (https://pypi.org/project/redbirdpy/) and can be conveniently installed using

pip install redbirdpy

RedbirdPy shares nearly identical interfaces as redbird: for example, redbird functions rbrun(), rbrunrecon() and rbfemsolve() can now be called by first "import redbirdpy as rb", and then call rb.run(). rb.runrecon() and rb.femsolve()

The installation command automatically installs numpy, scipy and iso2mesh (yes, the Python version of iso2mesh). You are also recommended to install numba for accelerating Jacobian computation and pypardiso Python module to solve FEM forward matrix (pypardiso also requires one to install separate intel-mkl packages on your system). 

We also want you to be aware of a recently released high-performance multi-RHS iterative solver specifically designed for solving real/complex/sparse symmetric FEM systems - BLIT  - you can install it using "pip install blocksolver" to solve multiple sources/RHSs simultaneously to gain speed - checkout its benchmark analysis at https://neurojson.org/Page/blocksolver#benchmarks.

To showcase the key functionalities of Redbird and RedbirdPy, we are also happy to announce the official home page for this project at

https://neurojson.org/Page/Redbird

You can find feature highlights, download links, and sample code snippets for both MATLAB-based redbird and Python-based redbirdpy, and show you the steps for building forward cfg data structure, running forward simulations for CW and FD sources, simulating complex widefield/pattern sources in FEM, and various options for image reconstructions

https://neurojson.org/Page/Redbird#examples

full examples can be found at its Github repo

https://github.com/fangq/redbirdpy/tree/main/example

Feel free to post your questions regarding redbird and redbirdpy to our mcx mailing list. We will be continue growing these toolboxes and interface them with MCX/MMC to provide versatile forward modeling methods and comprehensive image reconstruction pipelines supporting both MC and DE.

Happy modeling!

Qianqian