Open Nat1405 opened 4 years ago
One algorithm idea:
For each telluric or science observation directory:
- Make two clusters of images based on P and Q offsets
- Take the cluster with the higher median counts to be the science frames.
47 programs at time of writing have a skyFrameError. I've been working to update how sky frames are identified. I implemented the clustering idea, and it seems to work pretty well for bright objects like telluric observations. However, for very faint targets like in GN-2013A-Q-62
both the skyFrame and scienceFrame clusters have very similar median counts. In that case, it's better to fall back to the "outside 1.5 arcsecond offset" strategy....
Sky frames are currently identified by their P and Q offsets. If the "radius" of the offsets (sqrt(POFF^2 + QOFF^2) ) is larger than a threshold value the frame is deemed to be a sky frame. However, this can fail if the science frames were not centred as well and every frame has a non-zero offset radius.
I think moving the sky frame identification from makePythonLists to sortScienceAndTellurics() is a good idea. Then, a new way to identify sky frames vs science frames automatically is needed.