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bladeRF_power

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3cbf7cf5ac97e45971d0b7085057eb466420d24b

Add README, make `install.sh` slightly smarter.

sstaticfloat committed 9 years ago
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944b50af34867b525f037c5c521c71412a9a3852

Modifications and fixes!

sstaticfloat committed 9 years ago
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ee10b3e5fe762a2d5a4834fe287c840fcfdde431

First commit

sstaticfloat committed 9 years ago

README

The README file for this repository.

bladerf_power.py

Similar in spirit to rtl_power, use this doohickey to do spectrum surveys using your fancy bladeRF!

To install easily, run ./install.sh build. I tried to get this to work inside of a virtualenv but failed, as pybladeRF uses cffi which searches for libbladerf at runtime, and the virtualenv isolates itself from the rest of the system at runtime. I am using my own, patched fork of pybladeRF and a whole lot of spaghetti code. This was an afternoon hack, so if there are features you want (most notably, there is no "integration time" option, so all output looks really noisy) feel free to open issues and pull requests.

Example usage (after installation):

$ ./bladerf_power.py 300M:3.7G:10k -e 4h -f output.csv
$ ./heatmap.py output.csv output.png

Note that wide, dense sweeps of the spectrum such as the one described above can take a lot of diskspace; use -z to gzip-on-the-fly to save those precious, precious bits. As an anecdotal example, the above command generates a 5.6GB .csv file which compresses down to a 1.6GB .csv.gz file. Either way, heatmap.py takes over two hours to crank through that beauty on my machine.

Note that if you see a bunch of scary, strong lines every 28MHz, this probably means that you need to calibrate your bladeRF. Also try adding the --demean option to bladerf_power.py.