Freedom251 › Statistical Analysis Software
anesthetic
by anesthetic
What anesthetic is
anesthetic is a free Linux desktop package for processing nested-sampling chains with scientific Python tools. It calculates Bayesian evidence, Kullback–Leibler divergence, posterior average log-likelihood, and Gaussian model dimensionality from nested samples. Marginalised one- and two-dimensional posterior plots include triangle views, with KDE, histogram, and scatter styles. A command-line script launches an interactive view that can replay a nested-sampling run. The view accepts PolyChord, MultiNest, or UltraNest file structures, and data can also be supplied manually. Although focused on nested sampling, anesthetic processes ordinary MCMC chains, tracks parallel chains, removes burn-in, and provides a modified Gelman–Rubin statistic. Samples use weighted pandas data frames whose weights remain when sliced. Users can create derived parameters and plot them. Installation is documented through pip, conda-forge, or GitHub. Requirements include Python 3.9 or later and listed scientific libraries.
Who it suits
It suits Linux users analyzing nested-sampling or MCMC results with Python. Researchers needing Bayesian statistics, chain diagnostics, interactive replay, and posterior plots may find its tools relevant.
What it does well
- Computes several Bayesian statistics
- Supports nested-sampling and MCMC chains
- Provides one- and two-dimensional plots
- Interactive replay handles listed file structures
- Free and MIT-licensed
What to know first
- Requires Python 3.9 or later
- Basic requirements include several dependencies
- Primarily designed for nested-sampling outputs
- Linux platform listed
Verdict
anesthetic provides statistical calculations, diagnostics, plotting, and replay for scientific sampling chains. Its Python requirements and Linux focus suit users comfortable installing and scripting scientific software.
Facts from anesthetic's own pages
- Bayesian analysis
- Yes · anesthetic.readthedocs.io, 23 Sept 2026
- Bayesian statistics
- It computes Bayesian evidence, Kullback–Leibler divergence, posterior average log-likelihood, and Gaussian model dimensionality from nested samples. · anesthetic.readthedocs.io, 11 Oct 2026
- Citation
- The documentation asks users who generate publication plots with anesthetic to cite a 2019 Journal of Open Source Software paper. · anesthetic.readthedocs.io, 12 Oct 2026
- Customization
- Users can create derived parameters from existing sample columns and plot them. · anesthetic.readthedocs.io, 11 Oct 2026
- Data import formats
- PolyChord, MultiNest, UltraNest, Nested_fit, CosmoMC, Cobaya, GetDist-compatible chains, anesthetic CSV files · anesthetic.readthedocs.io, 23 Sept 2026
- Data model
- Samples and plotting axes are stored as pandas DataFrames with parameter names as indices. · anesthetic.readthedocs.io, 12 Oct 2026
- Deployment
- desktop · anesthetic.readthedocs.io, 23 Sept 2026
- Free plan
- Yes · anesthetic.readthedocs.io, 23 Sept 2026
- GUI file formats
- The GUI works out of the box with files following the structures used by PolyChord, MultiNest, or UltraNest. · anesthetic.readthedocs.io, 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. · anesthetic.readthedocs.io, 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. · anesthetic.readthedocs.io, 11 Oct 2026
- Installation
- The documentation gives pip, conda-forge, and GitHub source installation methods. · anesthetic.readthedocs.io, 12 Oct 2026
- Integrations
- The package uses NumPy, SciPy, Matplotlib, and pandas, and lists fastKDE as a basic dependency. · anesthetic.readthedocs.io, 11 Oct 2026
- Intended data
- It was designed primarily for nested sampling outputs and can also be used with normal MCMC chains. · anesthetic.readthedocs.io, 12 Oct 2026
- Interactive GUI
- The command-line script can open an interactive view of a nested sampling run. · anesthetic.readthedocs.io, 12 Oct 2026
- Libraries
- The documentation lists NumPy, SciPy, Matplotlib, and pandas as standard libraries used for extension and modification. · anesthetic.readthedocs.io, 12 Oct 2026
- License
- The GitHub repository identifies the project license as MIT. · github.com, 11 Oct 2026
- MCMC support
- Although designed primarily for nested sampling outputs, the package can also process ordinary MCMC chains. · anesthetic.readthedocs.io, 11 Oct 2026
- MCMC tools
- For MCMC samples, it tracks parallel chains and provides burn-in removal and a modified Gelman–Rubin convergence statistic. · anesthetic.readthedocs.io, 11 Oct 2026
- Plots
- It supports marginalised one-dimensional and two-dimensional plots. · anesthetic.readthedocs.io, 12 Oct 2026
- Plotting
- It supports marginalised one-dimensional and two-dimensional posterior plots, including triangle plots. · anesthetic.readthedocs.io, 11 Oct 2026
- Plot types
- Available plotting types include KDE, histograms, and scatter plots. · anesthetic.readthedocs.io, 11 Oct 2026
- Purpose
- anesthetic provides tools for processing nested sampling chains using standard scientific Python libraries. · anesthetic.readthedocs.io, 12 Oct 2026
- Replay
- It supports dynamic replaying of nested sampling. · anesthetic.readthedocs.io, 12 Oct 2026
Where anesthetic stands on Freedom251
- Statistical Analysis Software#54 of 107