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Python/Scipy tools for the design and simulation of ΔΣ modulators

This is a Python/Scipy toolbox for the design and simulation of Digital ΔΣ Modulators.

Currently, it focuses on methods for the design of the modulator NTF. It also includes a fast simulator for digital modulators. Furthermore, it includes a Python/Scipy port of some functions from the DELSIG toolbox by R. Schreier.

As a highlight, the code includes an original NTF design technique fully described in the papers:

• Sergio Callegari, Federico Bizzarri “Output Filter Aware Optimization of the Noise Shaping Properties of ΔΣ Modulators via Semi-Definite Programming”, IEEE Transactions on Circuits and Systems - Part I: Regular Papers, Vol. 60, N. 9, pp. 2352-2365. Sept. 2013. DOI: 10.1109/TCSI.2013.2239091. Pre-print available on arXiv.
• Sergio Callegari, Federico Bizzarri “Noise Weighting in the Design of ΔΣ Modulators (with a Psychoacoustic Coder as an Example),” IEEE Transactions on Circuits and Systems - Part II: Express Briefs, Vol. 60, N. 11, pp. 756-760. Nov. 2013. DOI: 10.1109/TCSII.2013.2281892. Pre-print available on arXiv.

If you find the code useful, please, cite the papers in your work

The code base also includes sample code to replicate the results in the above papers and in:

• Sergio Callegari, Federico Bizzarri “Should ΔΣ modulators used in AC motor drives be adapted to the mechanical load of the motor?”, Proceedings of the 19th IEEE International Conference on Electronics, Circuits and Systems (ICECS), 2012, pp. 849-852. DOI: 10.1109/ICECS.2012.6463619. Pre-print available on arXiv.
• Sergio Callegari, “Should ΔΣ modulators used in AC motor drives be adapted to the mechanical load of the motor?”, Proceedings of the 20th IEEE International Conference on Electronics, Circuits and Systems (ICECS), 2013, pp. 589-592. DOI: 10.1109/ICECS.2013.6815483.

While downloading the software, note that some documentation is available. Why not taking a look at it? With this, you'll be immediately ready to use the code.

Licensing notice

Code License is GPL V3. Up to version 0.8.1 code licensing was indicated as BSD, but this forbade distribution with the optimization toolboxes that are used from inside PyDSM. Consequently, the license has been fixed.

If you intend to redistribute PyDSM or parts of it, you should consider that versions prior to 0.8.1 are not redistributable unless the licence change is interpreted retroactively.