def schmidt_results_plots(sampler, model, x, y, yerr, burnin=200, akmap=None, bins=None, scale=None, triangle_plot=True):
    '\n    model: should pass schmidt_law()\n    '
    try:
        mpl.style.use('john')
    except:
        None
    if hasattr(sampler, '__getitem__'):
        chain = sampler
        dim = chain.shape[(- 1)]
    else:
        chain = sampler.chain
        dim = sampler.dim
    samples = chain[:, burnin:, :].reshape(((- 1), dim))
    theta_mcmc = np.percentile(samples, [16, 50, 84], axis=0).T
    n_params = len(theta_mcmc[:, 1])
    for (i, item) in enumerate(theta_mcmc):
        j = ['beta', 'kappa', 'A_{K,0}', 'A_{K,f}']
        inserts = (j[i], item[1], (item[2] - item[1]), (item[1] - item[0]))
        print(('%s = %0.2f (+%0.2f,-%0.2f)' % inserts))
    if triangle_plot:
        if (n_params == 3):
            labels = ['beta', 'kappa', 'A_{K,0}']
        elif (n_params == 4):
            labels = ['beta', 'kappa', 'A_{K,0}', 'A_{K,f}']
        else:
            labels = ['beta', 'kappa']
        _ = triangle.corner(samples, labels=labels, truths=theta_mcmc[:, 1], quantiles=[0.16, 0.84], verbose=False)
    xln = np.logspace(np.log10((x.min() * 0.5)), np.log10((x.max() * 2.0)), 100)
    smlaw_samps = np.asarray([schmidt_law(xln, samp) for samp in samples])
    percent = (lambda x: np.nanpercentile(smlaw_samps, x, interpolation='linear', axis=0))
    fig = plt.figure()
    plt.plot(xln, percent(50), 'k')
    plt.errorbar(x, y, yerr, fmt='rs', alpha=0.7, mec='none')
    plt.legend(['Median', 'Data'], loc='upper left', fontsize=12)
    plt.fill_between(xln, percent(1), percent(99), color='0.9')
    plt.fill_between(xln, percent(2), percent(98), color='0.75')
    plt.fill_between(xln, percent(16), percent(84), color='0.5')
    plt.loglog(nonposy='clip')
    return plt.gca()
model: should pass schmidt_law()
def schmidt_results_plots(sampler, model, x, y, yerr, burnin=200, akmap=None, bins=None, scale=None, triangle_plot=True):
    '\n    \n    '
    try:
        mpl.style.use('john')
    except:
        None
    if hasattr(sampler, '__getitem__'):
        chain = sampler
        dim = chain.shape[(- 1)]
    else:
        chain = sampler.chain
        dim = sampler.dim
    samples = chain[:, burnin:, :].reshape(((- 1), dim))
    theta_mcmc = np.percentile(samples, [16, 50, 84], axis=0).T
    n_params = len(theta_mcmc[:, 1])
    for (i, item) in enumerate(theta_mcmc):
        j = ['beta', 'kappa', 'A_{K,0}', 'A_{K,f}']
        inserts = (j[i], item[1], (item[2] - item[1]), (item[1] - item[0]))
        print(('%s = %0.2f (+%0.2f,-%0.2f)' % inserts))
    if triangle_plot:
        if (n_params == 3):
            labels = ['beta', 'kappa', 'A_{K,0}']
        elif (n_params == 4):
            labels = ['beta', 'kappa', 'A_{K,0}', 'A_{K,f}']
        else:
            labels = ['beta', 'kappa']
        _ = triangle.corner(samples, labels=labels, truths=theta_mcmc[:, 1], quantiles=[0.16, 0.84], verbose=False)
    xln = np.logspace(np.log10((x.min() * 0.5)), np.log10((x.max() * 2.0)), 100)
    smlaw_samps = np.asarray([schmidt_law(xln, samp) for samp in samples])
    percent = (lambda x: np.nanpercentile(smlaw_samps, x, interpolation='linear', axis=0))
    fig = plt.figure()
    plt.plot(xln, percent(50), 'k')
    plt.errorbar(x, y, yerr, fmt='rs', alpha=0.7, mec='none')
    plt.legend(['Median', 'Data'], loc='upper left', fontsize=12)
    plt.fill_between(xln, percent(1), percent(99), color='0.9')
    plt.fill_between(xln, percent(2), percent(98), color='0.75')
    plt.fill_between(xln, percent(16), percent(84), color='0.5')
    plt.loglog(nonposy='clip')
    return plt.gca()