A Python package for software-defined radio
Project description
sdr
The sdr
library is a Python 3 package for software-defined radio (SDR).
The goal of sdr
is to provide tools to design, analyze, build, and test digital communication systems
in Python. The library relies on and is designed to be interoperable with NumPy, SciPy, and Matplotlib.
Performance is also very important. So, where possible, Numba is used to accelerate computationally-intensive
functions.
Additionally, the library aims to replicate relevant functionality from MATLAB's Communications and DSP Toolboxes.
We are progressively adding functionality to the library. If there is something you'd like to see included
in sdr
, please open an issue on GitHub.
Enjoying the library? Give us a :star: on GitHub!
Documentation
The documentation for sdr
is located at https://mhostetter.github.io/sdr/latest/.
Installation
The latest version of sdr
can be installed from PyPI using pip
.
python3 -m pip install sdr
Features
View all available classes and functions in the API Reference.
- Digital signal processing: Finite impulse response (FIR) filters, FIR filter design, infinite impulse response (IIR) filters, polyphase interpolator, polyphase decimator, polyphase resampler, Farrow arbitrary resampler, fractional delay filter design, FIR differentiator, IIR integrator, complex mixing, real/complex conversion.
- Sequences: Barker, Zadoff-Chu.
- Modulation: Phase-shift keying (PSK), $\pi/M$ PSK, offset QPSK, rectangular pulse shape, half-sine pulse shape, raised cosine pulse shape, root raised cosine pulse shape, Gaussian pulse shape, binary and Gray symbol mapping, differential encoding.
- Synchronization: Numerically-controlled oscillators (NCO), loop filters, closed-loop PLL analysis.
- Measurement: Energy, power, voltage, Euclidean distance, Hamming distance, bit/symbol error rate, error vector magnitude (EVM).
- Conversions: Between linear units and decibels. Between $E_b/N_0$, $E_s/N_0$, and $S/N$.
- Simulation: Binary symmetric channel (BSC), binary erasure channel (BEC), discrete memoryless channel (DMC), additive white Gaussian noise (AWGN), frequency offset, sample rate offset, IQ imbalance.
- Link budgets: Channel capacities, free-space path loss, antenna gains.
- Data manipulation: Packing and unpacking binary data, hexdump of binary data.
- Plotting: Time-domain, raster, periodogram, spectrogram, constellation, symbol map, eye diagram, bit error rate (BER), symbol error rate (SER), impulse response, step response, magnitude response, phase response, phase delay, group delay, and zeros/poles.
Examples
There are detailed examples published at https://mhostetter.github.io/sdr/latest/examples/pulse-shapes/.
The Jupyter notebooks behind the examples are available for experimentation in docs/examples/
.
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