SOFIA Data Analysis Cookbooks
Aim: These documents provide simple recipes (i.e., descriptions and guided examples) for common data analysis objectives using SOFIA processed data.
Skill level: These are generally written for a graduate student audience. Some of the recipes are Jupyter notebooks designed for a researcher with a working knowledge of the numpy, scipy, matplotlib, and astropy. For more general information on these tools see Jupyter notebooks usage and installation and the Python Data Science Handbook.
Contact us: For questions not addressed in these notebooks or the SOFIA handbooks, please contact us at the sofia_help@sofia.usra.edu.
Suggestions: We also encourage you to submit any comments or suggestions on these notebooks through a new Github “Issue”.
Contribute: If you have some code that you think other SOFIA users might be interested in, please contact us!!!
Additional Resources: For additional examples of data analysis of infrared data see the Data Analysis Talks for JWST data.
Recipes
Python |
Description |
Aim: Aperture photometry using FORCAST imaging data. Tools: astropy, photutils |
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Aim: Inspection of FORCAST grism data. Tools: astropy, DS9 |
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Aim: Basic line fluxes and line fitting of grism data. Tools: astropy |
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Aim: Extract grism data with a user-defined aperture. Tools: astropy |
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Aim: Basic inspection and analysis. Tools: astropy |
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Aim: Basic inspection and plotting spectra. Tools: astropy |
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Aim: Model and remove atmosphere using PSG model. |
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Aim: Estimate velocity shift of spectral lines. Tools: astropy |
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Aim: How to view GREAT spectra. Tools: astropy |
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Aim: Reproject other datasets to GREAT pixel map. Tools: astropy, reproject |
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Aim: Visualize GREAT datacubes in 2D and 3D. Tools: astropy, jdaviz/Cubeviz, Glue |
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Other |
Description |
Aim: Download SOFIA data through the IRSA. |
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Aim: Detailed description of aperture photometry. |
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Aim: Basic cube analysis. Tools: SOSPEX |
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Aim: How to view GREAT spectra. Tools: CLASS |