A new version of the Magni software package was just released on the 1st of March. The previous release (1.6.0) introduced approximate message passing (AMP) and generalised approximate message passing (GAMP) reconstruction algorithms. This time we are extending the functionality of the GAMP algorithm to include weighted sparse priors. This effectively means that you can model sparse signals with non-identically distributed entries.
As far as I know, this way of modelling sparse signals in GAMP reconstruction are not part of any existing algorithms and will be described in further detail in an upcoming paper.
This new feature in GAMP can be found in the
magni.cs.reconstruction.gamp module, more specifically
magni.cs.reconstruction.gamp.input_channel.GWS – documentation.
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 1.7.0 here.
- The package documentation can be read here: Magni documentation
- The package can be installed from PyPI or from Anaconda.