anesthetic
The facts below are what the maker publishes about anesthetic, checked and dated. Our editors have not written about it yet.
The facts
Each one read from anesthetic's own pages, with the date it was checked.
What it does
- Bayesian analysis
- Yeschecked 23 Sep 2026
- Bayesian statistics
- It computes Bayesian evidence, Kullback–Leibler divergence, posterior average log-likelihood, and Gaussian model dimensionality from nested samples.checked 11 Oct 2026
- Citation
- The documentation asks users who generate publication plots with anesthetic to cite a 2019 Journal of Open Source Software paper.checked 12 Oct 2026
- Customization
- Users can create derived parameters from existing sample columns and plot them.checked 11 Oct 2026
- Data import formats
- PolyChord, MultiNest, UltraNest, Nested_fit, CosmoMC, Cobaya, GetDist-compatible chains, anesthetic CSV fileschecked 23 Sep 2026
- Data model
- Samples and plotting axes are stored as pandas DataFrames with parameter names as indices.checked 12 Oct 2026
- Deployment
- desktopchecked 23 Sep 2026
- GUI file formats
- The GUI works out of the box with files following the structures used by PolyChord, MultiNest, or UltraNest.checked 12 Oct 2026
- Input compatibility
- The GUI works out of the box with file structures from PolyChord, MultiNest, or UltraNest; data can also be supplied manually.checked 11 Oct 2026
- Install and requirement
- Installation is documented through pip, conda-forge, or the GitHub repository, and the basic requirements include Python 3.9 or later.checked 11 Oct 2026
- Installation
- The documentation gives pip, conda-forge, and GitHub source installation methods.checked 12 Oct 2026
- Integrations
- The package uses NumPy, SciPy, Matplotlib, and pandas, and lists fastKDE as a basic dependency.checked 11 Oct 2026
- Intended data
- It was designed primarily for nested sampling outputs and can also be used with normal MCMC chains.checked 12 Oct 2026
- Interactive GUI
- The command-line script can open an interactive view of a nested sampling run.checked 12 Oct 2026
- Libraries
- The documentation lists NumPy, SciPy, Matplotlib, and pandas as standard libraries used for extension and modification.checked 12 Oct 2026
- License
- The GitHub repository identifies the project license as MIT.checked 11 Oct 2026
- MCMC support
- Although designed primarily for nested sampling outputs, the package can also process ordinary MCMC chains.checked 11 Oct 2026
- MCMC tools
- For MCMC samples, it tracks parallel chains and provides burn-in removal and a modified Gelman–Rubin convergence statistic.checked 11 Oct 2026
- Plots
- It supports marginalised one-dimensional and two-dimensional plots.checked 12 Oct 2026
- Plotting
- It supports marginalised one-dimensional and two-dimensional posterior plots, including triangle plots.checked 11 Oct 2026
- Plot types
- Available plotting types include KDE, histograms, and scatter plots.checked 11 Oct 2026
- Purpose
- anesthetic provides tools for processing nested sampling chains using standard scientific Python libraries.checked 12 Oct 2026
The company
- Founded
- 2019checked 19 Oct 2026
Money
- Free plan
- Yeschecked 23 Sep 2026
