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 base currently runs under Python 2.7x and recently has made great strides to run under Python 3.6. As of Scipy 2017, the Python 3.6 compatibility is very good.

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 convolutional codes and soft decision Viterbi algorithm decoding, including trellis and trellis-traceback display functions.

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.

This package contains the helper modules rtlsdr_helper, and pyaudio_helper which require the packages pyrtlsdr and PyAudio. To use the full functionality of these helpers, install these package with the extras as follows:

pip install scikit-dsp-comm[helpers]

1. pyaudio_help.py wraps a class around the code required in PyAudio (wraps the C++ library PortAudio) to set up a non-blocking audio input/output stream. The user only has to write the callback function to implement real-time DSP processing using any of the input/output devices available on the platform. This resulting object also contains a capture buffer for use in post processing and a timing markers for assessing the processing time utilized by the callback function.

2. rtlsdr_helper.py interfaces with pyrtldsr to provide a simple captures means for complex baseband software defined radio (SDR) samples from the low-cost (~$20) RTL-SDR USB hardware dongle. The remaining functions in this module support the implementation of demodulators for FM modulation and examples of complete receivers for FM mono, FM stereo, and tools for FSK demodulation, including bit synchronization.

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.

Until new documentation is ready to post, documentation from the original ssd.py module can be found at http://www.eas.uccs.edu/~mwickert/ssd/. Jupyter notebooks and PDF documents related to much of the remaining scikit-dsp-comm modules can be found under the various course home pages of http://www.eas.uccs.edu/~mwickert/.