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Hi Robert,
I'm also running a fully remote & automated system using a Raspberry Pi 500. I wrote a script, later converted to a systemd service, which runs continuous observation loops, each loop is 24 hours of 5-minute integrations. It uses VIRGO software and an Airspy SDR. The system automatically records both raw and calibrated data on the Pi.
On my main PC, a second script handles everything else, reprocessing with VIRGO, baseline correction, and plotting. It supports multiple plot types, including 3D VLSR-corrected stacked plots, etc.
The whole pipeline is largely automatic once running. It's not perfect and it has bugs for sure, which is why I haven't published it on GitHub. But if you're interested, I'm happy to share both scripts on GitHub public repo.
The attached plots are from this same pipeline.
Thanks














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Hi Robert,
I uploaded both scripts to GitHub https://github.com/ayushman-t/h-line-observer I'd suggest trying h_quick first to make sure everything works before going automated.
I cleaned up the paths and settings that were specific to my Pi and made them a bit more flexible, there may still be some bugs.
h_quick.py — a simple manual testing tool. Just run it, pick a calibration from the menu (or record a new one), then choose single observation or continuous loop. Good for checking everything works before going fully automated. Both scripts will also generate 5-minute plots using the default VIRGO output which should be fine for a quick test.
h_observer.py — the main automated script. Run it once with --install to set up, then --config to pick your SDR and output folder, record a calibration pointing at cold sky, and then start the systemd service. From that point it runs 24/7 on its own, creates a new folder each day and resumes automatically after a power cut.
I'll upload the pipeline script that handles the combined plotting and analysis of the full 24-hour loops soon.
If you face any issues, I'll be happy to help.
Thanks
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On 5 Mar 2026, at 20:04, Robert Hamers <rjha...@gmail.com> wrote:
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Hi Alex,
Thanks! Your H-Line 3D plots are really good. This is just a Python script I put together to automate the observations on a Raspberry Pi and handle the plotting/analysis, trying to make it easier to run 24/7 unattended.
Hi Robert,
Hope the wind calms down soon! I pushed the pipeline script to GitHub, this is the one that handles the plotting and analysis of the collected data:
Setup and command reference: https://github.com/ayushman-t/h-line-observer/blob/main/PIPELINE.md
Overview: https://github.com/ayushman-t/h-line-observer#hline_pipelinepy--analysis--plotting
One thing to note: the pipeline currently needs VIRGO installed on whatever machine you're doing the processing/plotting on, with a small patch applied (included in the repo as virgo_patch.py).
If you're running everything on the same Pi that collects data, you're all set, just patch that VIRGO and it'll work, though plotting will be slow on the Pi. If you're using a separate machine for analysis (laptop/PC), you'll need to install VIRGO + apply the patch there too and copy the loop folders and calibration file over.
I'll update this soon, so only the Pi's VIRGO needs to be patched, and the pipeline on PC will just do the plotting without needing VIRGO at all, I didn't set it up that way originally since I was experimenting on my local PC rather than the Pi as my Pi is fully remote. Fairly easy fix, I'll try to get to it this week. But it works fine for now as-is just it's not perfect :)
Let me know if you run into any issues.
Thanks
Hi Robert,
If you're experiencing any issues with this processing pipeline, I can update the Pi side observer script to output the processed CSVs directly during collection. That way you'd just copy the CSVs over and run the plotting, no VIRGO needed on the other machine at all. You can let me know if that would be easier, and I'll make the change.
Thanks.
Hi Robert,
I hope the weather clears up soon!
Yes, I'll add CSV output to the observation script soon.
Thanks.
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On 28 Mar 2026, at 06:34, 'b alex pettit jr' via Society of Amateur Radio Astronomers <sara...@googlegroups.com> wrote:
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Alex and Ayushman,
Thanks again for all your help and advice.
I managed to get my hands on another AirSpy R2, and it is behaving significantly better than the one I was using. There is still a peak at 1420.0000 MHz, but it is more manageable.
I spent some time today comparing SDR# with IF averaging (windows 11 laptop), and gnuradio/DSPIRA flowgraph, and H-quick. (both on Raspberry Pi 5).
Some observations:
I’m attaching some graphs showing these results. The H_quick y values are almost exactly 32 times those of the DSPIRA values, so I think this is just some difference in bit-conversions, as multiplying the DSPIRA values by 32 makes them almost exactly overlay the H_quick values. In the attached graphs I displaced the graphs vertically a small amount so that you can see them more clearly. Also attaching an excel file with the original data. I generally do all my analysis and plotting in a different program (Igor Pro) that I use a lot for my day job, so I’m pasting the graphs from that.
AYUSHMAN- For some reason the obs and cal files produced have 2048 points but appear to be two identical 1024-point spectra in each file. I thought perhaps these are Real^2 and Imaginary^2 parts of the power spectrum but not sure, so I just took the first 1024 points for these graphs. I’m also still getting an error message about plotting, which is a problem with my local configuration. I have a separate environment on the RPI for the virgo/H_quick software and in that subfolder it has Matplotlib ver 7 (and no evidence of ver 10) but for some reason it’s still finding matplotlib 10 on the system. I’ll have to dig deeper in to how the RPI does paths to figure this part out…. For now I’m just taking the raw data from H_quick and converting to txt files so I can import into Igor.
Happy to hear any thoughts/suggestions…. I’m going to try another 24-hour drift scan and see how it goes. See graphs below!
Best wishes,
Bob


From:
'b alex pettit jr' via Society of Amateur Radio Astronomers <sara...@googlegroups.com>
Reply-To: "sara...@googlegroups.com" <sara...@googlegroups.com>
Date: Saturday, March 28, 2026 at 1:29 AM
To: "sara...@googlegroups.com" <sara...@googlegroups.com>
Subject: Re: [SARA] Re: First-time radio astronomer, looking for advice
Hello Robert,
I'd suggest : Purchase an AirSpy mini & try it.
IF it works, you probably can send all this info to AS & get a replacement on the R2,.
You can remove narrow spikes pretty well with this automated process:
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Alex
On Saturday, March 28, 2026 at 12:33:38 AM EDT, Robert Hamers <rjha...@gmail.com> wrote:
With all this said, I’ve made to get some decent drift spectra and made a 2-D that I’m pretty happy with, but only up to 1420 MHz, and losing the red-shifted parts of the spectrum. I have written some routines in python to selectively remove the 1420 spike, but they’re not perfect and it’s not just very palatable to have to do this.
Any thought appreciated !
Thanks much!
Bob
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On Mar 28, 2026, at 6:54 PM, 'Stephen Arbogast' via Society of Amateur Radio Astronomers <sara...@googlegroups.com> wrote:
The 1420 Mhz is a spur from the AirSpy SDR. I have been dealing with this for over a year now. I don't see the spur when using my RTL-SDR. You might try disconnecting your AirSpy from your antenna output and feed into a Tiny Spectrum Analyzer. I bet you won't see the spur. I use software to remove the spur before further processing..... no problems.
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Hi Robert,
For the matplotlib path issue: you can try running h_quick.py explicitly using the venv's Python instead of the system one:
/path/to/your-venv/bin/python3 h_quick.py
Or activate the venv first:
source /path/to/your-venv/bin/activate
python3 h_quick.py
That guarantees it picks up matplotlib 3.7.5 from the venv and not the system.
I'll add the CSV export in the next 1-2 days, which will contain raw, calibrated, and median-filtered calibrated data.
On the 1024/2048 question, you're right to take the first 1024. The two halves are the negative and positive frequency portions of the IQ FFT. They're nearly identical (correlation of 0.997) with tiny differences due to IQ imbalance in the SDR. The first 1024 is all you need.
Thanks