diff --git a/src/gui_qt/main/management.py b/src/gui_qt/main/management.py
index 69cb626..4b8b25c 100644
--- a/src/gui_qt/main/management.py
+++ b/src/gui_qt/main/management.py
@@ -742,8 +742,10 @@ class UpperManagement(QtCore.QObject):
                 new_datasets[data_i.group] = [], []
             new_x_axis, _temp = new_datasets[data_i.group]
 
-            new_x_axis.append(data_i.value)
-            _temp.append(data_i.points(params))
+            pts = data_i.points(params)
+            if pts:
+                new_x_axis.append(data_i.value)
+                _temp.append(pts)
 
         key_list = []
         for label, (new_x_axis, _temp) in new_datasets.items():
diff --git a/src/nmreval/data/points.py b/src/nmreval/data/points.py
index a9d8cba..e4895f4 100644
--- a/src/nmreval/data/points.py
+++ b/src/nmreval/data/points.py
@@ -319,39 +319,46 @@ class Points:
         if pts is None:
             pts = []
 
+        _tmp_x = self._x[self.mask]
+        _tmp_y = self._y[self.mask]
         if idx is not None:
-            for x in idx:
-                if isinstance(x, tuple):
-                    x_idx = np.argmin(np.abs(self._x[self.mask] - (x[0]+x[1])/2))
-                    left_b = np.argmin(np.abs(self._x[self.mask] - x[0]))
-                    right_b = np.argmin(np.abs(self._x[self.mask] - x[1]))
+            for idx_i in idx:
+                if isinstance(idx_i, tuple):
+                    in_region = np.where((_tmp_x - idx_i[0] > 0) & (idx_i[1] - _tmp_x > 0))[0]
+                    if len(in_region) > 0:
+                        x_idx = in_region[in_region.size//2]
+                        left_b = in_region[0]
+                        right_b = in_region[-1] + 1
+                    else:
+                        continue
+
                 else:
-                    x_idx = np.argmin(np.abs(self._x[self.mask]-x))
+                    x_idx = np.argmin(np.abs(_tmp_x-idx_i))
                     left_b = int(max(0, x_idx - avg_range[0]))
                     right_b = int(min(len(self), x_idx + avg_range[1] + 1))
 
                 if left_b < right_b:
-                    pts.append([self._x[x_idx], *self._average(avg_mode, x_idx, left_b, right_b)])
+                    pts.append([_tmp_x[x_idx], *self._average(avg_mode, x_idx, left_b, right_b)])
                 else:
-                    pts.append([self._x[x_idx], self._y[x_idx], self._y_err[x_idx]])
+                    pts.append([_tmp_x[x_idx], _tmp_y[x_idx], self._y_err[x_idx]])
 
         if special is not None:
             if special not in ['max', 'min', 'absmax', 'absmin']:
                 raise ValueError('Parameter "special" must be "max", "min", "absmax", "absmin".')
 
             if special == 'max':
-                x_idx = np.argmax(self._y.real[self.mask])
+                x_idx = np.argmax(_tmp_y.real)
             elif special == 'min':
-                x_idx = np.argmax(self._y.real[self.mask])
+                x_idx = np.argmax(_tmp_y.real)
             elif special == 'absmax':
-                x_idx = np.argmax(np.abs(self._y[self.mask]))
+                x_idx = np.argmax(np.abs(_tmp_y))
             else:
-                x_idx = np.argmin(np.abs(self._y[self.mask]))
+                x_idx = np.argmin(np.abs(_tmp_y))
 
             left_b = int(max(0, x_idx - avg_range[0]))
             right_b = int(min(len(self), x_idx + avg_range[1] + 1))
 
-            pts.append([self._x[self.mask][x_idx], *self._average(avg_mode, x_idx, left_b, right_b)])
+            pts.append([_tmp_x[x_idx], *self._average(avg_mode, x_idx, left_b, right_b)])
 
         return pts