Our newest version of the Magni software package was just released on the 2nd of November. This particular release has some interesting features we (the team behind the Magni package) hope some of you find particularly interesting.
The major new features in this release are approximate message passing (AMP) and generalised approximate message passing (GAMP) estimation algorithms for signal reconstruction. These new algorithms can be found in the
magni.cs.reconstruction.gamp modules, respectively. Note that the
magni.cs sub-package contains algorithms applicable to compressed sensing (CS) and CS-like reconstruction problems in general – and not just atomic force microscopy (AFM).
If you are not familiar with the Magni package and are interested in compressed sensing and/or atomic force microscopy, we invite you to explore the functionality the package offers. It also contains various iterative thresholding reconstruction algorithms, dictionary and measurement matrices for 1D and 2D compressed sensing, various features for combining this with AFM imaging, and mechanisms for validating function input and storing meta-data to aid reproducibility.
The Magni package was designed and developed with a strong focus on well-tested, -validated and -documented code.
The Magni package is a product of the FastAFM research project.
- The package can be found on GitHub where we continually release new versions: GitHub – release 1.6.0 here.
- The package documentation can be read here: Magni documentation
- The package can be installed from PyPI or from Anaconda.