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DSP and Comm package.

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scikit-dsp-comm

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Background

The origin of this package comes from the writing the book Signals and Systems for Dummies, published by Wiley in 2013. The original module for this book is named ssd.py. In scikit-dsp-comm this module is renamed to sigsys.py to better reflect the fact that signal processing and communications theory is founded in signals and systems, a traditional subject in electrical engineering curricula.

Package High Level Overview

This package is a collection of functions and classes to support signal processing and communications theory teaching and research. The foundation for this package is scipy.signal. The code in particular currently requires Python >=3.5x.

There are presently ten modules that make up scikit-dsp-comm:

  1. sigsys.py for basic signals and systems functions both continuous-time and discrete-time, including graphical display tools such as pole-zero plots, up-sampling and down-sampling.

  2. digitalcomm.py for digital modulation theory components, including asynchronous resampling and variable time delay functions, both useful in advanced modem testing.

  3. synchronization.py which contains phase-locked loop simulation functions and functions for carrier and phase synchronization of digital communications waveforms.

  4. fec_conv.py for the generation rate one-half and one-third convolutional codes and soft decision Viterbi algorithm decoding, including soft and hard decisions, trellis and trellis-traceback display functions, and puncturing.

  5. fir_design_helper.py which for easy design of lowpass, highpass, bandpass, and bandstop filters using the Kaiser window and equal-ripple designs, also includes a list plotting function for easily comparing magnitude, phase, and group delay frequency responses.

  6. iir_design_helper.py which for easy design of lowpass, highpass, bandpass, and bandstop filters using scipy.signal Butterworth, Chebyshev I and II, and elliptical designs, including the use of the cascade of second-order sections (SOS) topology from scipy.signal, also includes a list plotting function for easily comparing of magnitude, phase, and group delay frequency responses.

  7. multirate.py that encapsulate digital filters into objects for filtering, interpolation by an integer factor, and decimation by an integer factor.

  8. coeff2header.py write C/C++ header files for FIR and IIR filters implemented in C/C++, using the cascade of second-order section representation for the IIR case. This last module find use in real-time signal processing on embedded systems, but can be used for simulation models in C/C++.

Presently the collection of modules contains about 125 functions and classes. The authors/maintainers are working to get more detailed documentation in place.

Documentation

Documentation is now housed on readthedocs which you can get to by clicking the docs badge near the top of this README. Example notebooks can be viewed on GitHub pages. In time more notebook postings will be extracted from Dr. Wickert's Info Center.

Getting Set-up on Your System

The best way to use this package is to clone this repository and then install it.

git clone https://github.com/mwickert/scikit-dsp-comm.git

There are package dependencies for some modules that you may want to avoid. Specifically these are whenever hardware interfacing is involved. Specific hardware and software configuration details are discussed in wiki pages.

For Windows users pip install takes care of almost everything. I assume below you have Python on your path, so for example with Anaconda, I suggest letting the installer set these paths up for you.

Editable Install with Dependencies

With the terminal in the root directory of the cloned repo perform an editable pip install using

pip install -e .

Why an Editable Install?

The advantage of the editable pip install is that it is very easy to keep scikit-dsp-comm up to date. If you know that updates have been pushed to the master branch, you simply go to your local repo folder and

git pull origin master

This will update you local repo and automatically update the Python install without the need to run pip again. Note: If you have any Python kernels running, such as a Jupyter Notebook, you will need to restart the kernel to insure any module changes get reloaded.

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