[Bug]: Setting meta.centroids fails when nreads is not constant in each WFC3 exposure.

[jbrande]

FAQ check

• Yes, I checked the FAQ and my question has not been addressed.

Instrument

Other (any stage)

What happened?

Some WFC3 datasets don't have consistent numbers of reads in each exposure. When this is the case, setting meta.centroids = np.array(meta.centroids) in the WFC conclusion_step() will fail, due to very so slightly ragged array with an error ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (85,) + inhomogeneous part.

I guess that step should have a dtype=object parameter to preserve all the read-level information for future steps rather than trying to trim or pad the number of reads.

Error traceback output

Traceback (most recent call last):
  File "/home/jbrande/miniconda3/envs/eureka/lib/python3.10/pdb.py", line 1723, in main
    pdb._runscript(mainpyfile)
  File "/home/jbrande/miniconda3/envs/eureka/lib/python3.10/pdb.py", line 1583, in _runscript
    self.run(statement)
  File "/home/jbrande/miniconda3/envs/eureka/lib/python3.10/bdb.py", line 598, in run
    exec(cmd, globals, locals)
  File "<string>", line 1, in <module>
  File "/mnt/c/Users/yonib/OneDrive - University of Kansas/ExoLab/hst/TOI1759/ecfs_laptop/run_eureka_wfc3.py", line 16, in <module>
    s3_spec, s3_meta = s3.reduce(eventlabel, ecf_path=ecf_path)
  File "/home/jbrande/Planetary/Eureka/src/eureka/S3_data_reduction/s3_reduce.py", line 755, in reduce
    spec, meta, log = inst.conclusion_step(spec, meta, log)
  File "/home/jbrande/Planetary/Eureka/src/eureka/S3_data_reduction/wfc3.py", line 162, in conclusion_step
    meta.centroids = np.array(meta.centroids)
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (85,) + inhomogeneous part.

What operating system are you using?

Ubuntu 22.04.03 on WSLg on Win10

What version of Python are you running?

3.10.14

What Python packages do you have installed?

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Code of Conduct

• I agree to follow this project's Code of Conduct

[jbrande]

Still not sure why two of the scans in this dataset I'm working on are missing a read, but maybe we should check for consistent numbers of reads and then come up with some more informative failure mode that informs the user about the problem. Attempting to continue with the analysis gives useless results due to the inability of Eureka! to correct for "spectral drift" when a significant chunk of the scan is just missing.

[jbrande]

As it happens, this is sometimes done for scheduling reasons (like in this case), so we're not actually guaranteed to have spatial scans of the same size. Those scans are still good data, so there should be some way to recover them, I guess. Can we anchor the drift calculation to a particular common start point?

e.g. measure the drift relative just to the green line (subpixel drift) as opposed to the red rectangle (tens of pixels)