diff --git a/notebooks/features_bib_neurokit2.ipynb b/notebooks/features_bib_neurokit2.ipynb
index 50948dd..3c10b0b 100644
--- a/notebooks/features_bib_neurokit2.ipynb
+++ b/notebooks/features_bib_neurokit2.ipynb
@@ -697,21 +697,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 44,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2 lead R Axis: 56.83 degrees\n",
-      "2 lead T Axis: 30.78 degrees\n",
-      "\n",
-      "6 lead R Axis: 56.83 degrees\n",
-      "6 lead T Axis: 30.78 degrees\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def calculate_axis(data, leads, sampling_rate=500, aVF=5, I=0):\n",
     "    # warnining if leads not include 0 and 5 or one lead is over 5\n",
@@ -743,6 +731,7 @@
     "        t_peaks = [ecg_signal[int(i)] for i in info['ECG_T_Peaks'] if not math.isnan(i)]\n",
     "        net_t[lead] = np.mean(t_peaks) if t_peaks else 0\n",
     "\n",
+    "    print(net_qrs.get(aVF, 0), net_qrs.get(I, 0))\n",
     "    # Calculate the R axis (Convert to degrees)\n",
     "    r_axis = np.arctan2(net_qrs.get(aVF, 0), net_qrs.get(I, 0)) * (180 / np.pi)\n",
     "\n",
@@ -752,25 +741,47 @@
     "    return r_axis, t_axis\n",
     "\n",
     "\n",
-    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Calculate R and T Axis for one dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8047692058411333 0.5260930603345693\n",
+      "2 lead R Axis: 56.83 degrees\n",
+      "2 lead T Axis: 30.78 degrees\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
     "# print(data_org['SB'][0].__dict__) 'sig_name': ['I', 'II', 'III', 'aVR', 'aVL', 'aVF', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6']\n",
     "\n",
     "i_sig = 0\n",
     "avf = 5\n",
     "sampling_rate = 500\n",
     "\n",
-    "ecg_signal_i = filtered_data.p_signal[:,i_sig]\n",
+    "example_data = data_org['SB'][0]\n",
+    "filtered_data = data_helper.filter_data(example_data, filter_params)\n",
     "\n",
-    "ecg_cleaned_i = nk.ecg_clean(ecg_signal_i, sampling_rate=sampling_rate)\n",
     "\n",
     "r_ax, t_ax = calculate_axis(filtered_data, [0, 5], sampling_rate=500)\n",
     "print(f\"2 lead R Axis: {r_ax:.2f} degrees\")\n",
     "print(f\"2 lead T Axis: {t_ax:.2f} degrees\")\n",
-    "print(\"\")\n",
-    "\n",
-    "r_ax, t_ax = calculate_axis(filtered_data, [0, 1, 2, 3, 4, 5], sampling_rate=500)\n",
-    "print(f\"6 lead R Axis: {r_ax:.2f} degrees\")\n",
-    "print(f\"6 lead T Axis: {t_ax:.2f} degrees\")"
+    "print(\"\")\n"
    ]
   }
  ],