pierrePalud/beetroots
Beetroots (BayEsian invErsion with spaTial Regularization of nOisy multi-line ObservaTion mapS) is a Python package that performs Bayesian inference with the sampling algorithm described in (Palud et al., 2023).
This tool helps scientists analyze noisy, multi-line observation maps, such as those from astronomical surveys, to understand physical conditions. It takes your raw multispectral data and a physical model as input, then provides estimated maps of physical parameters along with their credibility intervals. It also evaluates how well your physical model fits the observations. Researchers in fields like astrophysics would use this for robust data interpretation.
No commits in the last 6 months. Available on PyPI.
Use this if you need to infer physical parameters from noisy, spatially structured multispectral data and want to quantify the uncertainty of your estimates, while also validating your underlying physical models.
Not ideal if your data is not spatially structured, you don't have a forward physical model, or you are not interested in Bayesian uncertainty quantification.
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Language
Python
License
MIT
Category
Last pushed
Feb 17, 2025
Commits (30d)
0
Dependencies
15
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