diff --git a/src/visuals/results.py b/src/visuals/results.py
index 5f5adc6..a584c2a 100644
--- a/src/visuals/results.py
+++ b/src/visuals/results.py
@@ -26,19 +26,6 @@ def print_results(dic_stats, show=False, save=False):
     yy_gender = target_error(xx, mm_gender)
     yy_gps = np.linspace(5., 5., xx.shape[0])
 
-    # # Recall results
-    # plt.figure(1)
-    # plt.xlabel("Distance [meters]")
-    # plt.ylabel("Detected instances")
-    # plt.xlim(x_min, x_max)
-    # for method in ['our', 'm3d', '3dop']:
-    #     xxs = get_distances(clusters)
-    #     dic_cnt = dic_stats[phase][method]['cnt']
-    #     cnts = get_values(dic_cnt, clusters)
-    #     plt.plot(xxs, cnts, marker='s', label=method + '_method')
-    # plt.legend()
-    # plt.show()
-    #
     # Precision on same instances
     fig_name = 'results.png'
     plt.xlabel("Distance [meters]")
@@ -158,103 +145,4 @@ def get_confidence_points(confidences, distances, errors):
         distance_points.append(dd)
         distance_points.append(dd)
 
-    return distance_points, confidence_points
-
-
-
-# def histogram(self):
-#     """
-#     Visualize histograms to compare error performances of net and baseline
-#     """
-#     # for mode in ['mean', 'std']:
-#     for mode in ['mean']:
-#
-#         # for phase in ['train', 'val', 'test']:
-#         for phase in ['test']:
-#
-#             err_geom = []
-#             err_net = []
-#             counts = []
-#             clusters = ('10', '20', '30', '40', '50', '>50', 'all')
-#             for clst in clusters:
-#
-#                 err_geom.append(dic_geom[phase]['error'][clst][mode])
-#                 counts.append(dic_geom[phase]['error'][clst]['count'])
-#                 err_net.append(dic_net[phase][clst][mode])
-#
-#             nn = len(clusters)
-#
-#             # TODO Shortcut to print 2 models in a single histogram
-#             err_net_l1 = [1.1, 1.32, 2.19, 3.29, 4.38, 6.58, 2.21]
-#
-#             ind = np.arange(nn)  # the x locations for the groups
-#             width = 0.30  # the width of the bars
-#
-#             fig, ax = plt.subplots()
-#             rects1 = ax.bar(ind, err_geom, width, color='b')
-#             rects2 = ax.bar(ind + width, err_net_l1, width, color='r')
-#             rects3 = ax.bar(ind + 2 * width, err_net, width, color='g')
-#
-#             # add some text for labels, title and axes ticks
-#             ax.set_ylabel('Distance error [m]')
-#             ax.set_title(mode + ' of errors in ' + phase)
-#             ax.set_xticks(ind + width / 2)
-#             ax.set_xticklabels(clusters)
-#
-#             ax.legend((rects1[0], rects2[0], rects3[0]), ('Geometrical', 'L1 Loss', 'Laplacian Loss'))
-#
-#             # Attach a text label above each bar displaying number of annotations
-#             for idx, rect in enumerate(rects1):
-#                 height = rect.get_height()
-#                 count = counts[idx]
-#                 ax.text(rect.get_x() + rect.get_width() / 2., 1.05 * height,
-#                         '%d' % int(count), ha='center', va='bottom')
-#
-#         plt.show()
-
-    # def error(self):
-#     """
-#     Visualize error as function of the distance on the test set and compare it with target errors based on human
-#     height analyses. We consider:
-#     Position error in meters due to a height variation of 7 cm (Standard deviation already knowing the sex)
-#     Position error not knowing the gender (13cm as average difference --> 7.5cm of error to add)
-#     """
-#     phase = 'test'
-#     xx = np.linspace(0, 60, 100)
-#     mm_std = 0.04
-#     mm_gender = 0.0556
-#     clusters = tuple([clst for clst in dic_net[phase] if clst not in ['all', '>50']])
-#     # errors_geom = [dic_geom[phase]['error'][clst][mode] for clst in clusters]
-#     errors_net = [dic_net[phase][clst]['mean'] for clst in clusters]
-#     confidences = [dic_net[phase][clst]['std'][0] for clst in clusters]
-#     distances = get_distances(clusters)
-#     dds, zzs = get_confidence_points(confidences, distances, errors_net)
-#
-#     # Set the plot
-#     fig = plt.figure()
-#     ax = plt.subplot(111)
-#     plt.xlim(0, 50)
-#     plt.xlabel("Distance [meters]")
-#     plt.ylabel("Error [meters]")
-#     plt.title("Error on Z Position Estimate due to height variation")
-#
-#     # Plot the target errors
-#     yy_std = target_error(xx, mm_std)
-#     yy_gender = target_error(xx, mm_gender)
-#     # yy_child = target_error(xx, mm_child)
-#     # yy_test = target_error(xx, mm_test)
-#     yy_gps = np.linspace(5., 5., xx.shape[0])
-#     plt.plot(xx, yy_std, '--', label="Knowing the gender", color='g')
-#     plt.plot(xx, yy_gender, '--', label="NOT knowing the gender", color='b')
-#     plt.plot(xx, yy_gps, '-', label="GPS Error", color='y')
-#
-#     for idx in range(0, len(dds), 2):
-#
-#         plt.plot(dds[idx: idx+2], zzs[idx: idx+2], color='k')
-#
-#     # Plot the geometric and net errors as dots
-#     _ = ax.plot(distances, errors_net, 'ro', marker='s', label="Network Error (test)", markersize='8')
-#     # _ = ax.plot(distances, errors_geom, 'ko', marker='o', label="Baseline Error", markersize='5')
-#
-#     ax.legend(loc='upper left')
-#     plt.show()
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+    return distance_points, confidence_points
\ No newline at end of file