Adapt Roman code to new required data files - #162
Open
ojustino wants to merge 3 commits into
Open
Conversation
Co-authored by: Marshall Perrin <mperrin@stsci.edu>
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Cleans up my test branch with code changes to handle the new structure of the required Roman data from GSFC – OPDs, pupils, and expanded Zernike coefficients.
Let's see if changing
DATA_VERSION_MINinstpsf/__init__.pyis enough to enforce the use of the new files as we test this branch. If we merge this branch before the release is ready, we may need to revert this change so others who want to use thedevelopbranch but don't have the files can still run STPSF.I included @mperrin's quick fix to prevent large slowdowns from the number of Zernike coefficients exceeding the
lru_cache's default maximum size, but we could still benefit from either aligning the calculation more with the approach taken in STPSF's JWST models (future task) or by making theR()function from poppy'szernike.pymore efficient.