===================
Multiple-axes plots
===================

.. plot::

    # -------- EXAMPLE DATA GENERATION --------------------
    # Generate example data. Each condition will
    # be a differently-sized and placed bivariate normal
    # distribution.
    np_rand = np.random.RandomState(seed=2022)
    example_data = np.vstack((
        np_rand.multivariate_normal( # A
            [5, 15],[[1,0.2], [0,3]], size=(1000,)),
        np_rand.multivariate_normal( # B
            [15, 5], [[3,0], [0.2,1]], size=(1000,)),
        np_rand.multivariate_normal( # C
            [5, 5], [[1,0], [0,1]], size=(1000,)),
        np_rand.multivariate_normal( # D
            [15, 15], [[1,1], [0.2,-0.2]], size=(1000,)),
    ))
    df = pd.DataFrame({
       'condition': (
            (['A'] * 1000) + (['B'] * 1000) +
            (['C'] * 1000) + (['D'] * 1000)
        ),
       'x': example_data[:,0],
       'y': example_data[:,1]
    })
    # -------- END EXAMPLE DATA GENERATION ---------------

    # Specify a color palette
    beach_towel = {
    'A': '#225A9B',
    'B': '#19D2BF',
    'C': '#FFB133',
    'D': '#FE484E',
    }

    # Create a multi-plot subplot layout, where we specify
    # a one-row, two-column set of plots, where the first
    # axis takes five times the space as the right axis.
    _, axes = plt.subplots(
                    ncols=2,
                    sharey=False,
                    gridspec_kw={'width_ratios': [5,1]})
    # Use Seaborn to plot the joint distributions.
    sns.kdeplot(data=df, x='x', y='y',
                hue='condition', palette=beach_towel,
                levels=8,
                ax=axes[0], legend=None)
    # For each condition, calculate intrinsic and extrinsic noise
    for condition in df.condition.unique():
        # subset the dataframe by condition
        df_subset = df[df.condition == condition]
        extrinsic_noise = np.var((df_subset.x + df_subset.y) / 2.0)
        intrinsic_noise = np.var((df_subset.x - df_subset.y) / 2.0)
        # use matplotlib to plot circles for each condition.
        # Jitter the x-axis locations so the dots don't overlap.
        axes[1].plot(
            np.random.uniform(-0.1,0.1),
            intrinsic_noise / extrinsic_noise,
            'o',
            color=beach_towel[condition])
    # Modify axis titles
    axes[0].set_title('Joint distribution')
    axes[1].set_title('Noise\nratio', loc='left')
    # Set uniform x and ytick labels
    axes[0].set_xticks([5,10,15,20])
    axes[0].set_yticks([5,10,15,20])
    # Despine both axes, also removing the bottom spine
    # on the right axis.
    sns.despine(ax=axes[0])
    sns.despine(ax=axes[1], bottom=True)
    # Hide the right-most x axis.
    axes[1].xaxis.set_visible(False)
    # Scale the axis so our jittered points are visible.
    axes[1].set_xlim([-0.3, 0.3])