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MIRI LRS Example Notebooks and Scripts

This repository contains some demo and helper notebooks for MIRI LRS data processing. We focus especially on ways the JWST calibration pipeline can be improved from the automated default options, or requires a workaround for optimal results.

General JWST calibration pipeline help

For general help on how to install and run the JWST calibration pipeline, you can check out STScI's JWebbinar materials, which includes notebooks and instructional videos. Of particular interest for MIRI LRS are:

  • JWebbinar 1: Pipeline Information and Data Products
  • JWebbinar 4: Pipeline: Spectroscopic Mode
  • JWebbinar 11: Time-series observations
  • JWebbinar 16: JWST Time Series Observations: Performance and Caveats (*)
  • JWebbinar 18: JWST Pipeline refresher (*)
  • JWebbinar 22: NIRSpec and MIRI: Lessons Learned from Commissioning (*)

Those labelled (*) were presented post-commissioning so include more up-to-date information than the pre-launch tutorials.

End to End pipeline demos

Notebook miri_lrs_slit_end2end.ipynb contains a demo of how to run LRS slit data through the Spec2 and Spec3 pipelines, with default settings. We provide some suggestions for enhancements and modifications, but refer to step-specific notebooks for demonstrations of some of those. Test data can be retrieved from this Box folder, or the user can substitute their files.

Spectral Extraction

Spectral extraction is the step that most often requires some manual reprocessing for LRS data. We include here some notebooks to help understand the capabilities of the pipeline and how to use them.

  • miri_lrs_pipeline_extraction.ipynb: this notebook illustrates the spectral extraction capabilities of the JWST calibration pipeline for LRS data, beyond the simple fixed-width aperture method that is used for the automated processing. Test data can be retrieved from this Box folder.

Prior to May 2023 (CRDS context < 1089.pmap), the calibration pipeline used a sub-optimal method to locate the target in the aperture for spectral extraction, which often misplaced the extraction aperture. As a result, the extracted products (the x1d files) in MAST were often of poor quality. We produced a notebook and script to demonstrate how to work around this problem. As of CRDS context 1089.pmap, implemented in May 2023, this workaround has become the default method for automated processing, effectively resolving the issue. The following products are still available:

  • LRS_PointSource_Slit_Reduction.ipynb goes through stage 2 and stage 3 pipeline processing, including extraction, for a demo LRS slit observation, to show how to get science-ready extracted products. Test data can be retrieved from this Box folder.
  • Script miri_lrs_extract_L3.py is an abbreviated version of the notebook, providing a simple script that takes a Level 3 s2d file as input, and returns an extracted spectrum with appropriate extraction limits set.

Script usage:

python miri_lrs_extract_L3.py --s2dfile myfile_s2d.fits --parfile miri_lrs_demo_extract1d.json --save_plot

Questions or feedback?

If you have questions or would like to see additional notebooks here for MIRI LRS, please contact us via the Helpdesk!

-- the MIRI LRS team, Last updated: Nov 2023