diff --git a/TweetSentQuant/gen_plots.py b/TweetSentQuant/gen_plots.py
index 82818e8..6f19d56 100644
--- a/TweetSentQuant/gen_plots.py
+++ b/TweetSentQuant/gen_plots.py
@@ -12,8 +12,8 @@ from os.path import join
 qp.environ['SAMPLE_SIZE'] = settings.SAMPLE_SIZE
 plotext='png'
 
-resultdir = './results'
-plotdir = './plots'
+resultdir = './results_npp'
+plotdir = './plots_npp'
 os.makedirs(plotdir, exist_ok=True)
 
 def gather_results(methods, error_name):
@@ -50,6 +50,7 @@ def plot_error_by_drift(methods, error_name, logscale=False, path=None):
         logscale=logscale,
         title=f'Quantification error as a function of distribution shift',
         savepath=path,
+        vlines=[0.02, 0.1055],
         method_order=method_order
     )
 
diff --git a/quapy/plot.py b/quapy/plot.py
index b902257..a32ce15 100644
--- a/quapy/plot.py
+++ b/quapy/plot.py
@@ -176,10 +176,12 @@ def _set_colors(ax, n_methods):
     ax.set_prop_cycle(color=[cm(1. * i / NUM_COLORS) for i in range(NUM_COLORS)])
 
 
-def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, n_bins=20, error_name='ae', show_std=True,
+def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, n_bins=20, error_name='ae', show_std=False,
+                   show_density=True,
                    logscale=False,
                    title=f'Quantification error as a function of distribution shift',
                    savepath=None,
+                   vlines=None,
                    method_order=None):
 
     fig, ax = plt.subplots()
@@ -246,15 +248,20 @@ 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)
 
-    ax.bar([ind * binwidth-binwidth/2 for ind in range(len(bins))], max_y*npoints/np.max(npoints), alpha=0.15, color='g', width=binwidth, label='density')
+    if show_density:
+        ax.bar([ind * binwidth-binwidth/2 for ind in range(len(bins))],
+               max_y*npoints/np.max(npoints), alpha=0.15, color='g', width=binwidth, label='density')
 
     ax.set(xlabel=f'Distribution shift between training set and test sample',
            ylabel=f'{error_name.upper()} (true distribution, predicted distribution)',
            title=title)
     box = ax.get_position()
     ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
-    ax.axvline(0.02, 0, 1, linestyle='--', color='k')
-    ax.axvline(0.1055, 0, 1, linestyle='--', color='k')
+    if vlines:
+        for vline in vlines:
+            ax.axvline(vline, 0, 1, linestyle='--', color='k')
+        # ax.axvline(0.02, 0, 1, linestyle='--', color='k')
+        # ax.axvline(0.1055, 0, 1, linestyle='--', color='k')
     ax.set_xlim(0, max_x)
     ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))