Ensure memory efficiency when working with peak images

[alex-l-kong]

What is the purpose of this PR?

With 1000+ peak images, keeping them all stored in memory is infeasible. They should be saved and loaded in on an individual basis.

How did you implement your changes

This will change the peak image workflow. Currently, they're all housed in a single in-memory xarray. Instead:

1. In extraction.coordinate_integration, save the peak images directly instead of storing them in the xarray. This will replace the functionality in plotting.save_peak_images
2. Add a function plotting.plot_peak_hist to replace the notebook cell image_data.sel(peak=[desired_peak_hist], method="nearest").plot.hist(bins=bin_count)
3. Update extraction.library_matching to use io_utils.list_files to retrieve the peak values instead of using the xarray dimension
4. Change plotting.save_matched_peak_images to load the matched peak image from extraction_dir instead of indexing into the xarray directly

Remaining issues

Ongoing testing, full testing suite still needs to be implemented.

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Pull Request Test Coverage Report for Build 12037261796

Details

• 38 of 38 (100.0%) changed or added relevant lines in 2 files are covered.
• No unchanged relevant lines lost coverage.
• Overall coverage increased (+0.3%) to 97.705%

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Totals
Change from base Build 11787590598:	0.3%
Covered Lines:	239
Relevant Lines:	239

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