Bayesian framework for the inference of CME ice cream cone model parameters.

# Bayesian CME Kinematics

Code for Bayesian inference of full ice cream cone models of Coronal Mass Ejections (CMEs) from white light coronagraph data.

## Paper

This code accompanies our paper on Bayesian CME kinematics (DOI to be added).

## Notebooks

- `run_case_study.ipynb` - Full workflow for fitting a CME (same as in the paper)
- `synthetic_tests.ipynb` - Tests using synthetic CMEs
- `paper_figures.ipynb` - Generate figures from the paper

## Installation

Download the repository, install conda or micromamba and run
```bash
conda env create -f environment.yml -n bayesiancme
# OR
micromamba env create -f environment.yml -n bayesiancme

# Then

conda activate bayesiancme
# OR
micromamba activate bayesiancme

# Finally

pip install -e . # This installs the code from the project as if it was a package

# Then use this environment when running the jupyter notebooks
```

**IMPORTANT**: You must also download the data file from Zenodo (https://zenodo.org/records/18232128) and put it in `data/external/huw/` for the case study notebook to run.

Then you can have a look at the notebooks in `notebooks/` to reproduce the figures in the paper and the analysis of the case study CME.