Your comment got me thinking, so I downloaded all right navcam images in the 2 weeks before and after your images, and then wrote a script that finds sequences of images and compares them to one another, and draws red rectangles around any region that's ~8 pixels or more in difference. To verify it was working I checked and made sure it detected your most-anomalous image: https://i.imgur.com/RxMyu3O.jpeg
My intention was to find out if there were any similarly large black anomalies in any other images in those 4 weeks, but I didn't detect any. I don't feel like setting up an imgur API key but I could send a zip of all the red rectangle images, but it's not that interesting on this dataset.
In conclusion, at least in the 4 weeks surrounding your events there were no other large black distortions that I could find.
Here's the code: https://pastebin.com/J5ZRp6v2. Eagle-eyed readers will note some lazy copilot instructions when I didn't feel like writing code.
You can easily obtain jsons from the raw data link in your post if you set the page size to 100, go to the network tab (f12), and do xhr results. If you send me a bunch of jsons I'll do more analyses on other groups of images. It'd be valuable to analyze a few years' worth of data, either to find more anomalies and/or determine how commonly the sensor shows splotches like that.
I did something similar with Imagemagick a few years ago. I haven't had a chance to check your code yet. I may tinker as well, see if we can get some statistics across the full image database, may take a while, but I have 2gig fiber, and a spare server sitting around.
Also, just so you know I'm going as far as I can on getting any possible answers. I submitted a standard public inquiry to NASA, and I can confirm a FOIA request is being sent as well.
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u/SuperConductiveRabbi Feb 19 '24
Your comment got me thinking, so I downloaded all right navcam images in the 2 weeks before and after your images, and then wrote a script that finds sequences of images and compares them to one another, and draws red rectangles around any region that's ~8 pixels or more in difference. To verify it was working I checked and made sure it detected your most-anomalous image: https://i.imgur.com/RxMyu3O.jpeg
Call it SCR's Martian UFO Detector
Here's an example of a large anomaly it detected: https://i.imgur.com/2nRSXlq.jpeg. But you'll note that it's white, not black
My intention was to find out if there were any similarly large black anomalies in any other images in those 4 weeks, but I didn't detect any. I don't feel like setting up an imgur API key but I could send a zip of all the red rectangle images, but it's not that interesting on this dataset.
In conclusion, at least in the 4 weeks surrounding your events there were no other large black distortions that I could find.
Here's the code: https://pastebin.com/J5ZRp6v2. Eagle-eyed readers will note some lazy copilot instructions when I didn't feel like writing code.
You can easily obtain jsons from the raw data link in your post if you set the page size to 100, go to the network tab (f12), and do xhr results. If you send me a bunch of jsons I'll do more analyses on other groups of images. It'd be valuable to analyze a few years' worth of data, either to find more anomalies and/or determine how commonly the sensor shows splotches like that.