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Quickstart

Here are some quickstart examples making use of the example data that comes with anesthetic and can be found in anesthetic's test folder.

  • anesthetic / reading_writing
  • anesthetic / Samples and statistics <samples>
  • anesthetic / plotting:Plotting

Plotting marginalised posteriors

Plot Example 1: Marginalised 1D posteriors

from anesthetic import read_chains, make_1d_axes samples = read_chains("../../tests/example_data/pc") params = ['x0', 'x1', 'x2', 'x3', 'x4'] fig, axes = make_1d_axes(params, figsize=(6, 1.8), facecolor='w', ncol=5) samples.plot_1d(axes, label="default: kind='kde_1d'") samples.plot_1d(axes, kind='hist_1d', color='C0', alpha=0.5, zorder=0, label="kind='hist_1d'") axes['x0'].legend(bbox_to_anchor=(2.5, 1), loc='lower center', ncol=2)

Plot Example 2: Marginalised 2D posteriors

from anesthetic import read_chains, make_2d_axes samples = read_chains("../../tests/example_data/pc_250") prior = samples.prior() params = ['x0', 'x1', 'x2', 'x3', 'x4'] fig, axes = make_2d_axes(params, figsize=(6, 6), facecolor='w') prior.plot_2d(axes, alpha=0.9, label="prior") samples.plot_2d(axes, alpha=0.9, label="posterior") axes.iloc[-1, 0].legend(bbox_to_anchor=(len(axes)/2, len(axes)), loc='lower center', ncols=2)

anesthetic.plot.make_1d_axes, anesthetic.plot.make_2d_axes, anesthetic.samples.Samples.plot_1d, anesthetic.samples.Samples.plot_2d

Nested sampling statistics

Providing Bayesian statistics from nested sampling data is where anesthetic shines. With anesthetic.samples.NestedSamples.stats you can compute the Bayesian evidence ln 𝒵, the Kullback--Leibler divergence 𝒟KL, and the posterior average of the log-likelihood ⟨ln ℒ⟩𝒫, which together allow you to jointly assess model quality, Occam penalty, and fit, respectively. The Gaussian model dimensionality dG (which is directly related to the posterior variance of the log-likelihood) is a measure of the model complexity (or dimensionality).

from anesthetic import read_chains, make_2d_axes samples1 = read_chains("../../tests/example_data/pc") samples2 = read_chains("../../tests/example_data/pc_250") stats1 = samples1.stats(nsamples=2000) stats2 = samples2.stats(nsamples=2000) params = ['logZ', 'D_KL', 'logL_P', 'd_G'] fig, axes = make_2d_axes(params, figsize=(6, 6), facecolor='w', upper=False) stats1.plot_2d(axes, label="model 1") stats2.plot_2d(axes, label="model 2") axes.iloc[-1, 0].legend(bbox_to_anchor=(len(axes), len(axes)), loc='upper right')

  • anesthetic / Samples and statistics <samples> / samples:Bayesian statistics
  • anesthetic.samples.NestedSamples.stats