(initial merge) Add tess_individ_lc.py for individual TESS time series extraction and fitting without global fit. #1257
Conversation
This file contains 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
|
Switching to working directly in the original repository. |
jpl-jengelke
changed the title
|
Tracking this the actual merge occurred in PR #1262. No need to track further. |
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Outdated suggestions cannot be applied.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.
This pull request adds the tess_individ_lc.py script, which allows for the extraction and fitting of individual exoplanet transits from TESS data without performing a global fit in the process.
This script improves efficiency for users who need quicker access to individual light curve data to perform a joint fit or individual light curve analysis with other Python tools.
Works with confirmed exoplanets.
This was created from tess.py. In tess.py the global fit is performed first and the derived parameters are used as priors in the individual fits. In tess_individ_lc.py since we do not perform the global fit, the NEA priors are used. This may give slightly different results on the individual fits.
Outputs go to "tess/tess_individ_lc_output" folder.