Spectrum analyzer for multiple SDR platforms (PyQtGraph based GUI for soapy_power, rx_power, rtl_power, hackrf_sweep and other backends)
- Python >= 3.3
- PyQt >= 4.5
- PyQtGraph (http://www.pyqtgraph.org)
- soapy_power / rx_tools / rtl-sdr / rtl_power_fftw / hackrf
Universal SDR backends
- soapy_power (https://github.com/xmikos/soapy_power)
soapy_power is default recommended universal backend in QSpectrumAnalyzer.
It is based on SoapySDR and supports
nearly all SDR platforms (RTL-SDR, HackRF, Airspy, SDRplay, LimeSDR, bladeRF,
USRP and some other SDR devices).
- rx_power (https://github.com/rxseger/rx_tools)
rx_power (part of
rx_tools) is also based on SoapySDR and therefore
supports nearly all SDR platforms, but it is much slower than soapy_power, doesn't support
near real-time continuous measurement (minimum interval is 1 second - same as
and is little buggy.
- rtl_power (https://github.com/keenerd/rtl-sdr)
You should use Keenerds fork of rtl-sdr
(latest Git revision), because
rtl_power in original rtl-sdr package (from osmocom.org)
is broken (especially when used with cropping).
- rtl_power_fftw (https://github.com/AD-Vega/rtl-power-fftw)
Another alternative for RTL-SDR is
rtl_power_fftw which has various
rtl_power. E.g. better FFT performance (thanks to
fftw library) and possibility to use much shorter acquisition time
for more real-time continuous measurement (minimum interval in original
rtl_power is 1 second, but in
rtl_power_fftw you are only limited
by number of frequency hops).
- hackrf_sweep (https://github.com/mossmann/hackrf)
hackrf_sweep backend enables wideband spectrum monitoring by rapidly retuning the radio
without requiring individual tuning requests from the host computer. This allows unprecedented
sweep rate of 8 GHz per second.
Start QSpectrumAnalyzer by running
You can choose which backend you want to use in File -> Settings
soapy_power). Sample rate and path to backend executable
can be also manually specified there. You can also set waterfall plot
history size. Default is 100 lines, be aware that really large sweeps
(with a lot of bins) would require a lot of system memory,
so don't make this number too big.
Controls should be intuitive, but if you want consistent results, you should turn off automatic gain control (set it to some fixed number) and also set crop to 20% or more. For finding out ppm correction factor for your rtl-sdr stick, use kalibrate-rtl.
You can move and zoom plot with mouse, change plot settings or export plots from right-click menu. Waterfall plot black/white levels and color lookup table can be changed in mini-histogram widget (on Levels tab).
git clone https://aur.archlinux.org/qspectrumanalyzer.git cd qspectrumanalyzer makepkg -sri
Git master branch:
git clone https://aur.archlinux.org/qspectrumanalyzer-git.git cd qspectrumanalyzer-git makepkg -sri
Or simply use pacaur (or any other AUR helper):
pacaur -S qspectrumanalyzer pacaur -S qspectrumanalyzer-git
Debian / Ubuntu:
sudo apt-get install python3-pip python3-pyqt4 python3-numpy sudo pip3 install qspectrumanalyzer
pip will install packages system-wide by default, but you
should always use your distribution package manager for this.
You can install it locally only for your current user by running this (without
pip3 install --user qspectrumanalyzer
Executables will be then placed in
~/.local/bin directory, you can add it to your
If you want to install QSpectrumAnalyzer directly from Git master branch, you can use this procedure:
git clone https://github.com/xmikos/qspectrumanalyzer.git cd qspectrumanalyzer pip3 install --user .
- finish soapy_power backend (new universal default backend)
- show scan progress
- allow setting LNB LO frequency
- save & load FFT history (allow big waterfall plot saved to file)
- automatic peak detection / highlighting
- display average noise level
- frequency markers / bookmarks with notes (even importing / exporting .csv file with predefined channels, etc.)