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```bashconda env create -f environment.yml -n bayesiancme# ORmicromamba env create -f environment.yml -n bayesiancme # Then conda activate bayesiancme# ORmicromamba 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.