add fits keywords to NRMModel schema

[braingram]

Closes #359

The NRM reference files contain FITS keywords useful to CAL.
https://jwst-crds.stsci.edu/browse/jwst_niriss_nrm_0001.fits

This PR adds those keywords to the schema so they are accessible via the datamodel interface:

• model.flat_to_flat
• model.x_a1 etc...

Regression tests running at https://github.com/spacetelescope/RegressionTests/actions/runs/11802888443

Tasks

• update or add relevant tests
• update relevant docstrings and / or docs/ page
• Does this PR change any API used downstream? (if not, label with no-changelog-entry-needed)
  • write news fragment(s) in changes/: echo "changed something" > changes/<PR#>.<changetype>.rst (see below for change types)
  • run jwst regression tests with this branch installed ("git+https://github.com/<fork>/stdatamodels@<branch>")

news fragment change types...

• changes/<PR#>.feature.rst: new feature
• changes/<PR#>.bugfix.rst: fixes an issue
• changes/<PR#>.doc.rst: documentation change
• changes/<PR#>.removal.rst: deprecation or removal of public API
• changes/<PR#>.misc.rst: infrastructure or miscellaneous change

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 67.51%. Comparing base (a6994fa) to head (c6133f4).
Report is 1 commits behind head on main.

Additional details and impacted files

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##             main     #361   +/-   ##
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  Coverage   67.51%   67.51%           
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  Files         114      114           
  Lines        5913     5913           
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  Hits         3992     3992           
  Misses       1921     1921           

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[rcooper295]

I think this looks good! To make sure I understand, these will not be accessed via the meta tree but rather from the "top level" of the datamodel? I.e. model.x_a1 rather than model.meta.x_a1?

[braingram]

I think this looks good! To make sure I understand, these will not be accessed via the meta tree but rather from the "top level" of the datamodel? I.e. model.x_a1 rather than model.meta.x_a1?

Exactly! Feel free to give it a try with the current NRM reference file (and this branch) if you're curious.

[rcooper295]

Thanks! I was able to try it with your branch and it looks good! I realized that there may be two additional keywords whose values are currently hardcoded but that we could read in from this model instead, but I need to confirm if they are actually the same as values we already have in the FITS header, and if so, what we should call the attributes. I should have an answer on that tomorrow.
(For more details, this is the message I sent the AMI team):

mask_definitions.py contains:

self.activeD = 6.559 * m  # webbpsf kwd DIAM  - not a 'circle including all holes'
self.OD = 6.610645669291339 * m  # Full pupil file size, incl padding, webbpsf kwd PUPLDIAM

while the NRM reference file currently contains:

DIAM    =             6.559348 / Flat-to-flat distance across pupil in V3 axis  
PUPLDIAM=             6.603464 / Full pupil file size, incl padding.            
PUPL_CRC=             6.603464 / Circumscribing diameter for JWST primary

I'm not sure if activeD should be the same as DIAM (it is, at least up to the fourth decimal place) and if OD should be the same as PUPLDIAM (it isn't quite) but if these are referring to the same quantity we should probably make them consistent.

[braingram]

I'll mark this as draft until we hear back about the extra keywords.

[rcooper295]

I spoke to my AMI team members and chased down the source of these numbers (or what we should be using) and we'd like to add these two:

 diameter:
      fits_keyword: DIAM
      title: "[m] Flat-to-flat distance across pupil in V3 axis"
pupil_circumscribed:
      fits_keyword: PUPL_CRC
      title: "[m] Circumscribing diameter for JWST primary"

Thanks for your patience!!