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name: anesthetic description: Use this skill when the user asks to "create corner plots", "visualize posteriors", "plot chains", "analyze nested sampling output", "plot marginal distributions", "compare prior and posterior", "make triangle plots", or work with posterior samples from PolyChord, MultiNest, UltraNest, Cobaya, or other Bayesian inference tools. Provides complete guidance for creating publication-quality posterior visualizations from nested sampling or MCMC chains.

Anesthetic

Overview

Anesthetic is a Python package for visualizing and analyzing posterior samples from Bayesian inference. It creates publication-quality corner plots, 1D/2D marginal distributions, and computes Bayesian statistics (evidence, KL divergence) from nested sampling or MCMC chains.

Key capabilities:

• Load chains from PolyChord, MultiNest, UltraNest, Cobaya, GetDist formats
• Create corner/triangle plots with KDE, histogram, or scatter representations
• Compare prior vs posterior distributions
• Compute log-evidence, KL divergence, model dimensionality
• Built on pandas DataFrames for easy parameter manipulation

Workflow position: This skill provides component (4) Visualization in the physics workflow.

Installation

First, ensure anesthetic is installed in your environment:

# Using uv (recommended)
uv pip install anesthetic

# Or using pip
pip install anesthetic

# Or using conda
conda install -c conda-forge anesthetic

Optional for faster KDE: uv pip install fastkde

Quick Start

from anesthetic import read_chains, make_2d_axes
import matplotlib.pyplot as plt

# Load posterior chains (auto-detects format)
samples = read_chains('path/to/chains_root')

# Create corner plot (two-step process)
fig, axes = make_2d_axes(['x0', 'x1', 'x2'])  # Create axes first
samples.plot_2d(axes)                          # Then plot onto them
fig.savefig('corner.png')
plt.close(fig)

Important: Always create axes first with make_2d_axes() or make_1d_axes(), then pass them to plot_2d() or plot_1d(). The plot methods return axes, not (fig, axes) tuples.

Core Workflow

1. Load chains - read_chains() auto-detects format, or use format-specific readers
2. Select parameters - Choose which parameters to plot
3. Create axes - make_1d_axes() or make_2d_axes()
4. Plot - samples.plot_1d() or samples.plot_2d()
5. Customize - Labels, colors, reference lines

Reference Documentation

Detailed documentation is split by topic:

• Loading data: See references/loading-chains.md for all supported formats and creating samples from arrays
• Plotting: See references/plotting-guide.md for 1D/2D plots, plot kinds, and multi-chain comparisons
• Customization: See references/customization.md for labels, colors, axes, legends, and reference values
• Statistics: See references/statistics.md for Bayesian evidence, KL divergence, and MCMC diagnostics
• Full API: See references/api-reference.md for complete function signatures and options

Common Patterns

Basic Corner Plot

from anesthetic import read_chains, make_2d_axes

samples = read_chains('chains/run')
fig, axes = make_2d_axes(['param1', 'param2', 'param3'])
samples.plot_2d(axes, kind='kde')
fig.savefig('corner.png')

Prior vs Posterior Comparison

samples = read_chains('chains/run')  # NestedSamples
prior = samples.prior()

fig, axes = make_2d_axes(['x0', 'x1', 'x2'])
prior.plot_2d(axes, kind='kde', label='Prior', alpha=0.5)
samples.plot_2d(axes, kind='kde', label='Posterior')
axes.iloc[-1, 0].legend(bbox_to_anchor=(len(axes)/2, len(axes)), loc='lower center')

Multiple Chain Comparison

samples1 = read_chains('chains/model1')
samples2 = read_chains('chains/model2')

fig, axes = make_2d_axes(['x0', 'x1'])
samples1.plot_2d(axes, kind='kde', label='Model 1')
samples2.plot_2d(axes, kind='kde', label='Model 2')
axes.iloc[-1, 0].legend()

Compute Bayesian Evidence

samples = read_chains('chains/run')  # Must be NestedSamples
print(f"log(Z) = {samples.logZ():.2f}")
print(f"D_KL = {samples.D_KL():.2f}")

# With uncertainties
stats = samples.stats(nsamples=1000)
print(f"log(Z) = {stats['logZ'].mean():.2f} +/- {stats['logZ'].std():.2f}")

Plot Bayesian Statistics

# stats is a Samples object, so use anesthetic's plot_2d
stats = samples.stats(nsamples=1000)
fig, axes = make_2d_axes(['logZ', 'D_KL', 'd_G'])
stats.plot_2d(axes)
fig.savefig('stats_corner.png')

Add Truth Values

fig, axes = make_2d_axes(['x0', 'x1', 'x2'])
samples.plot_2d(axes)
truth = {'x0': 0.3, 'x1': 0.8, 'x2': 0.7}
axes.axlines(truth, color='red', linestyle='--')
axes.scatter(truth, marker='*', s=100, c='red')

Runnable Example

A complete example script is available at scripts/example_plots.py. Run it with:

python scripts/example_plots.py path/to/chains_root

This creates corner_plot.png, 1d_marginals.png, and prior_posterior.png.