How to use the pyglm.plotting.plot_results.plot_results function in PyGLM

# DEBUG Evaluate the firing rate and the simulated firing rate
    state = popn.eval_state(x_true)
    for n in np.arange(options.N):
        lam_true = state['glms'][n]['lam'].ravel()
        lam_sim =  popn.glms[n].nlin_model.f_nlin(data['X'][:,n])
        assert np.allclose(lam_true, lam_sim)

    # Pickle the data so we can open it more easily
    fname_pkl = os.path.join(options.resultsDir, 'data.pkl')
    print "Saving data to %s" % fname_pkl
    with open(fname_pkl,'w') as f:
        cPickle.dump(data, f, protocol=-1)

    # Plot firing rates, stimulus responses, etc
    do_plot_imp_resonses = int(options.N) <= 16
    plot_results(popn, data['vars'],
                 resdir=options.resultsDir,
                 do_plot_stim_resp=True,
                 do_plot_imp_responses=do_plot_imp_resonses)

import pdb; pdb.set_trace()

        # Perform inference
        # raw_input('Press any key to begin inference...\n')
        x_smpls = gibbs_sample(popn, x0=x0, N_samples=N_samples,
                               init_from_mle=False,
                               callback=None)

        # Save results
        print "Saving results to %s" % results_file
        with open(results_file, 'w') as f:
            cPickle.dump(x_smpls, f, protocol=-1)

    # Plot average of last 20% of samples
    smpl_frac = 0.2
    plot_results(popn, 
                 x_smpls[-1*int(smpl_frac*N_samples):],
                 popn_true=popn_true,
                 x_true=x_true,
                 resdir=options.resultsDir)

""" Run a test with synthetic data and MAP inference via
        parallel coordinate descent.
    """
    options, popn, data, client, popn_true, x_true = initialize_parallel_test_harness()

    print "Performing parallel inference"
    x_inf = parallel_coord_descent(client, data['N'], maxiter=1)
    ll_inf = popn.compute_log_p(x_inf)
    print "LL_inf: %f" % ll_inf

    # Save results
    with open(os.path.join(options.resultsDir, 'results.pkl'),'w') as f:
        cPickle.dump(x_inf,f, protocol=-1)
    
    # Plot results
    plot_results(popn, x_inf, 
                 popn_true, x_true, 
                 do_plot_imp_responses=False,
                 resdir=options.resultsDir)

# DEBUG Evaluate the firing rate and the simulated firing rate
    state = popn.eval_state(x_true)
    for n in np.arange(options.N):
        lam_true = state['glms'][n]['lam']
        lam_sim =  popn.glm.nlin_model.f_nlin(data['X'][:,n])
        assert np.allclose(lam_true, lam_sim)

    # Pickle the data so we can open it more easily
    fname_pkl = os.path.join(options.resultsDir, 'data.pkl')
    print "Saving data to %s" % fname_pkl
    with open(fname_pkl,'w') as f:
        cPickle.dump(data, f, protocol=-1)

    # Plot firing rates, stimulus responses, etc
    do_plot_imp_resonses = int(options.N) <= 16
    plot_results(popn, data['vars'],
                 resdir=options.resultsDir,
                 do_plot_stim_resp=True,
                 do_plot_imp_responses=do_plot_imp_resonses)

# Print results summary
    for i in np.arange(len(models)):
        print "Model %d:\tTrain LL: %.1f\tXV LL: %.1f\tTotal LL: %.1f" % (i, train_lls[i], xv_lls[i], total_lls[i])
    print "Best model: %d" % best_ind
    print "Best Total LL: %f" % popn.compute_ll(best_x)
    print "True LL: %f" % popn_true.compute_ll(x_true)

    # Save results
    results_file = os.path.join(options.resultsDir, 'results.pkl')
    print "Saving results to %s" % results_file
    with open(results_file, 'w') as f:
        cPickle.dump(best_x, f)

    # Plot results
    plot_results(popn, best_x, popn_true, x_true, resdir=options.resultsDir)

ll0 = popn.compute_log_p(x0)
    print "LL0: %f" % ll0

    # Perform inference
    x_inf = coord_descent(popn, x0=x0, maxiter=1)
    ll_inf = popn.compute_log_p(x_inf)
    print "LL_inf: %f" % ll_inf

    # Save results
    results_file = os.path.join(options.resultsDir, 'results.pkl')
    print "Saving results to %s" % results_file
    with open(results_file, 'w') as f:
        cPickle.dump(x_inf, f, protocol=-1)

    # Plot results
    plot_results(popn, x_inf, popn_true, x_true, resdir=options.resultsDir)

cbk = lambda x: plot_sample_callback(x, plotters)

        # Perform inference
        raw_input('Press any key to begin inference...\n')
        x_smpls = gibbs_sample(popn, data, x0=x0, N_samples=N_samples,
                               init_from_mle=False,
                               callback=cbk)

        # Save results
        print "Saving results to %s" % results_file
        with open(results_file, 'w') as f:
            cPickle.dump(x_smpls, f, protocol=-1)

    # Plot average of last 20% of samples
    smpl_frac = 0.2
    plot_results(popn, 
                 x_smpls[-1*int(smpl_frac*N_samples):],
                 popn_true=popn_true,
                 x_true=x_true,
                 resdir=options.resultsDir)