bayes_hfs

bayes_hfs implements two models for molecular hyperfine spectroscopy. The first is HFSModel, which is a general purpose model. The second is HFSRatioModel, which predicts observations of two species in order to infer the column density ratio. bayes_hfs is written in the bayes_spec Bayesian modeling framework, which provides methods to fit these models to data using Monte Carlo Markov Chain techniques.

Useful information can be found in the bayes_hfs Github repository, the bayes_spec Github repository, and in the tutorials below.

Installation

conda create --name bayes_hfs -c conda-forge pymc pip
conda activate bayes_hfs
pip install bayes_hfs

Tutorials:

• HFSModel Tutorial
  • Preparing Molecule Data
  • Simulating Data
  • Model Definition
  • Variational Inference
  • Posterior Sampling: MCMC
• HFSModel Hyperfine Anomalies
  • Preparing Molecule Data
  • Simulate data
  • Model Definition
  • Variational Inference
  • MCMC
• HFSRatioModel Tutorial
  • Preparing Molecule Data
  • Simulate Data
  • Model Definition
  • Variational Inference
  • Posterior Sampling: MCMC
• Optimization Tutorial
  • Preparing Molecule Data
  • Model Definition and Simulated Data
  • Optimize

API:

• bayes_hfs
  • bayes_hfs