anesthetic
OfficialPublish publication-quality posterior plots.
Authorfundamental-physics
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Visualizes posterior samples from Bayesian inference to produce publication-quality corner plots, 1D/2D marginal distributions, and Bayesian statistics from nested sampling or MCMC chains.
Core Features & Use Cases
- Load chains from PolyChord, MultiNest, UltraNest, Cobaya, GetDist, and other supported formats.
- Create 1D marginals and 2D corner plots with KDE, histograms, or scatter representations.
- Compare prior vs posterior and compute Bayesian statistics (logZ, D_KL, logL_P, d_G) for model evaluation.
- Transform and label parameters, derive new quantities, and manage multiple chains for comparative analyses.
Quick Start
Load posterior chains with read_chains and generate a publication-quality corner plot for your model parameters.
Dependency Matrix
Required Modules
matplotlibanesthetic
Components
scriptsreferences
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: anesthetic Download link: https://github.com/fundamental-physics/marketplace/archive/main.zip#anesthetic Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
Agent Skills Search Helper
Install a tiny helper to your Agent, search and equip skill from 223,000+ vetted skills library on demand.