diff --git a/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb b/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb
index a7d9edcae615d51f22b018d72a134311782bf693..139fdd748d934e5b3fac9f3e685217457c291126 100644
--- a/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb
+++ b/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb
@@ -1044,29 +1044,26 @@
     "display(Markdown(f'## Preview and statistics for {gains.shape[0]} images of the train {tid} ##\\n'))"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Signal vs. Analogue Gain ###"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "raw_float = raw.astype(np.float32)\n",
-    "signal = raw[:, 0, ...]\n",
+    "# As part of data reduction efforts, the DAQ now has an option to discard AGIPD gain data\n",
+    "# when it is known that all data is in the same gain stage. In such cases, the gain data\n",
+    "# will be set to zeros. Consequently, the signal vs. analog gain 2D histogram can be skipped.\n",
     "gain = raw[:, 1, ...]\n",
-    "hist, bins_x, bins_y = calgs.histogram2d(\n",
+    "if gain.max() > 0:\n",
+    "    signal = raw[:, 0, ...]\n",
+    "    display(Markdown(\"### Signal vs. Analogue Gain\"))\n",
+    "    hist, bins_x, bins_y = calgs.histogram2d(\n",
     "    signal.flatten().astype(np.float32),\n",
     "    gain.flatten().astype(np.float32),\n",
     "    bins=(100, 100),\n",
     "    range=[\n",
     "        np.percentile(signal, [0.02, 99.8]),\n",
-    "        [0, 3],\n",
+    "        np.percentile(signal, [0.02, 99.8]),\n",
     "        ],\n",
     "    )\n",
     "do_2d_plot(hist, (bins_x, bins_y), \"Signal (ADU)\", \"Analogue gain (ADU)\")"