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

.. seealso::

    * anesthetic / :doc:`reading_writing`
    * anesthetic / :doc:`Samples and statistics <samples>`
    * anesthetic / :ref:`plotting:Plotting`


Plotting marginalised posteriors
================================

Plot Example 1: Marginalised 1D posteriors
------------------------------------------

.. plot::

    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
------------------------------------------

.. plot::

    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)

.. seealso::

    :meth:`anesthetic.plot.make_1d_axes`,
    :meth:`anesthetic.plot.make_2d_axes`,
    :meth:`anesthetic.samples.Samples.plot_1d`,
    :meth:`anesthetic.samples.Samples.plot_2d`


Nested sampling statistics
==========================

Providing Bayesian statistics from nested sampling data is where anesthetic
shines. With :meth:`anesthetic.samples.NestedSamples.stats` you can compute the
Bayesian evidence :math:`\ln\mathcal{Z}`, the Kullback--Leibler divergence
:math:`\mathcal{D}_\mathrm{KL}`, and the posterior average of the
log-likelihood :math:`\langle\ln\mathcal{L}\rangle_\mathcal{P}`, which together
allow you to jointly assess model quality, Occam penalty, and fit,
respectively. The Gaussian model dimensionality :math:`d_\mathrm{G}` (which is
directly related to the posterior variance of the log-likelihood) is a measure
of the model complexity (or dimensionality).

.. plot::

    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')

.. seealso::

    * anesthetic / :doc:`Samples and statistics <samples>` / :ref:`samples:Bayesian statistics`
    * :meth:`anesthetic.samples.NestedSamples.stats`