Adventures in Signal Processing and Open Science

Tag: programming

Magni 1.7.0 Released

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.GWSdocumentation.

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.

Download

  • 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.
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Magni 1.6.0 released

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.amp and 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.

Download

  • 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.

Teaching with the IPython Notebook

I have been teaching introductory Python for modelling and simulation and for scientific computing for a couple of years now. I am still somewhat new to Python myself, having “converted” from Matlab a couple of years ago. I find the open approach of using free and open source software instead of expensive proprietary software very motivating and I was easily talked into using it by my colleagues and quickly decided to base my teaching on it as well.
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