diff --git a/NewMethods/common.py b/NewMethods/common.py
index e03052d..252a941 100644
--- a/NewMethods/common.py
+++ b/NewMethods/common.py
@@ -61,6 +61,8 @@ nice = {
     'Average': 'Average',
     'EMdiag':'EM$_{diag}$', 'EMfull':'EM$_{full}$', 'EMtied':'EM$_{tied}$', 'EMspherical':'EM$_{sph}$',
     'VEMdiag':'VEM$_{diag}$', 'VEMfull':'VEM$_{full}$', 'VEMtied':'VEM$_{tied}$', 'VEMspherical':'VEM$_{sph}$',
+    'epaccmaemae1k': 'E(PACC)$_\mathrm{AE}$',
+    'quanet': 'QuaNet'
 }
 
 
diff --git a/NewMethods/uci_plots.py b/NewMethods/uci_plots.py
index fca5e23..a7367c4 100644
--- a/NewMethods/uci_plots.py
+++ b/NewMethods/uci_plots.py
@@ -18,7 +18,7 @@ os.makedirs(plotdir, exist_ok=True)
 
 N_RUNS = N_FOLDS * N_REPEATS
 
-def gather_results(methods, error_name):
+def gather_results(methods, error_name, resultdir):
     method_names, true_prevs, estim_prevs, tr_prevs = [], [], [], []
     for method in methods:
         for run in range(N_RUNS):
@@ -35,7 +35,7 @@ def plot_error_by_drift(methods, error_name, logscale=False, path=None):
     print('plotting error by drift')
     if path is not None:
         path = join(path, f'error_by_drift_{error_name}.{plotext}')
-    method_names, true_prevs, estim_prevs, tr_prevs = gather_results(methods, error_name)
+    method_names, true_prevs, estim_prevs, tr_prevs = gather_results(methods, error_name, resultdir)
     qp.plot.error_by_drift(
         method_names,
         true_prevs,
@@ -54,7 +54,7 @@ def diagonal_plot(methods, error_name, path=None):
     print('plotting diagonal plots')
     if path is not None:
         path = join(path, f'diag_{error_name}')
-    method_names, true_prevs, estim_prevs, tr_prevs = gather_results(methods, error_name)
+    method_names, true_prevs, estim_prevs, tr_prevs = gather_results(methods, error_name, resultdir)
     qp.plot.binary_diagonal(method_names, true_prevs, estim_prevs, pos_class=1, title='Positive', legend=True, show_std=True, savepath=f'{path}_pos.{plotext}')
 
 
@@ -62,7 +62,7 @@ def binary_bias_global(methods, error_name, path=None):
     print('plotting bias global')
     if path is not None:
         path = join(path, f'globalbias_{error_name}')
-    method_names, true_prevs, estim_prevs, tr_prevs = gather_results(methods, error_name)
+    method_names, true_prevs, estim_prevs, tr_prevs = gather_results(methods, error_name, resultdir)
     qp.plot.binary_bias_global(method_names, true_prevs, estim_prevs, pos_class=1, title='Positive', savepath=f'{path}_pos.{plotext}')
 
 
@@ -70,15 +70,15 @@ def binary_bias_bins(methods, error_name, path=None):
     print('plotting bias local')
     if path is not None:
         path = join(path, f'localbias_{error_name}')
-    method_names, true_prevs, estim_prevs, tr_prevs = gather_results(methods, error_name)
+    method_names, true_prevs, estim_prevs, tr_prevs = gather_results(methods, error_name, resultdir)
     qp.plot.binary_bias_bins(method_names, true_prevs, estim_prevs, pos_class=1, title='Positive', legend=True, savepath=f'{path}_pos.{plotext}')
 
 
+if __name__ == '__main__':
+    plot_error_by_drift(METHODS, error_name='ae', path=plotdir)
 
-plot_error_by_drift(METHODS, error_name='ae', path=plotdir)
+    diagonal_plot(METHODS, error_name='ae', path=plotdir)
 
-diagonal_plot(METHODS, error_name='ae', path=plotdir)
+    binary_bias_global(METHODS, error_name='ae', path=plotdir)
 
-binary_bias_global(METHODS, error_name='ae', path=plotdir)
-
-binary_bias_bins(METHODS, error_name='ae', path=plotdir)
+    binary_bias_bins(METHODS, error_name='ae', path=plotdir)
diff --git a/quapy/plot.py b/quapy/plot.py
index 8d46ea5..dadcca8 100644
--- a/quapy/plot.py
+++ b/quapy/plot.py
@@ -228,10 +228,10 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, n_bins=20, e
         if show_std:
             ax.fill_between(xs, ys-ystds, ys+ystds, alpha=0.25)
 
-    # xs = bins[:-1]
-    # ys = inds_histogram_global
-    # print(xs.shape, ys.shape)
-    # ax.errorbar(xs, ys, label='density')
+    xs = bins[:-1]
+    ys = inds_histogram_global
+    print(xs.shape, ys.shape)
+    ax.errorbar(xs, ys, label='density')
 
     ax.set(xlabel=f'Distribution shift between training set and test sample',
            ylabel=f'{error_name.upper()} (true distribution, predicted distribution)',