diff --git a/notebooks/AGIPD/Characterize_AGIPD_Gain_Darks_NBC.ipynb b/notebooks/AGIPD/Characterize_AGIPD_Gain_Darks_NBC.ipynb
index 71a6f1fbf80dda58437db0f38d3737085dac1730..27e79b8d200ccd6314ae0ede7e379b633d2253fa 100644
--- a/notebooks/AGIPD/Characterize_AGIPD_Gain_Darks_NBC.ipynb
+++ b/notebooks/AGIPD/Characterize_AGIPD_Gain_Darks_NBC.ipynb
@@ -8,7 +8,7 @@
     "\n",
     "Author: S. Hauf, Version: 0.1\n",
     "\n",
-    "The following code analyzes a set of dark images taken with the AGIPD detector to deduce detector offsets , noise and bad-pixel maps. All three types of constants are evaluated per-pixel and per-memory cell. Data for the detector's three gain stages needs to be present, separated into separate runs.\n",
+    "The following code analyzes a set of dark images taken with the AGIPD detector to deduce detector offsets , noise, bad-pixel maps and thresholding. All four types of constants are evaluated per-pixel and per-memory cell. Data for the detector's three gain stages needs to be present, separated into separate runs.\n",
     "\n",
     "The evaluated calibration constants are stored locally and injected in the calibration data base.\n",
     "\n",
@@ -60,7 +60,7 @@
    "source": [
     "cluster_profile = \"noDB\" # The ipcluster profile to use\n",
     "in_folder = \"/gpfs/exfel/d/raw/SPB/202030/p900138/\" # path to input data, required\n",
-    "out_folder = \"/gpfs/exfel/data/scratch/ahmedk/test/AGIPD\" # path to output to, required\n",
+    "out_folder = \"/gpfs/exfel/data/scratch/ahmedk/test/AGIPD3\" # path to output to, required\n",
     "sequences = [0] # sequence files to evaluate.\n",
     "\n",
     "run_high = 199 # run number in which high gain data was recorded, required\n",
@@ -599,8 +599,8 @@
     "fig, ax = plt.subplots(1, figsize=(10, 10))\n",
     "ax.set_axis_off()\n",
     "\n",
-    "ax.set_xlim(0, 97)\n",
-    "ax.set_ylim(0, 100)\n",
+    "ax.set_xlim(0, 125)\n",
+    "ax.set_ylim(0, 105)\n",
     "\n",
     "q_poses = np.array([[51, 47], [47, 1], [1, 5], [5, 51]])\n",
     "m_poses = np.array([[22.5, 20.5], [22.5, 0.5], [0.5, 0.5], [0.5, 20.5]])\n",