diff --git a/cal_tools/cal_tools/plotting.py b/cal_tools/cal_tools/plotting.py
index 440b4f688372b3f9f9748b19dab4b854256181a6..60124779bafab18b85773dbce7dbd0201169c6dc 100644
--- a/cal_tools/cal_tools/plotting.py
+++ b/cal_tools/cal_tools/plotting.py
@@ -5,7 +5,7 @@ import numpy as np
 
 
 def show_overview(d, cell_to_preview, gain_to_preview, out_folder=None, infix=None):
-    
+
     for module, data in d.items():
         fig = plt.figure(figsize=(20,20))
         grid = AxesGrid(fig, 111,
@@ -120,9 +120,15 @@ def create_constant_overview(constant, name, cells, vmin, vmax, entries=3,
         fig = plt.figure(figsize=(10, 5))
         ax = fig.add_subplot(111)
         table = []
-        
+
         for qm in constant.keys():
-            d = constant[qm][...,g]
+            if len(constant[qm].shape) == 4:
+                d = constant[qm][..., g]
+            else:
+                # This case was introduced for DSSC dark
+                # it uses this function but have a constant
+                # of shape (x, y, z), unlike AGIPD.
+                d = constant[qm]
             #print("{} {}, gain {:0.2f}: mean: {:0.2f}, median: {:0.2f}, std: {:0.2f}".format(name, qm, g,
             #                                                          np.nanmean(d),
             #                                                          np.nanmedian(d),
diff --git a/notebooks/DSSC/Characterize_DSSC_Darks_NBC.ipynb b/notebooks/DSSC/Characterize_DSSC_Darks_NBC.ipynb
index eb7b7721c105dc12548ac3a1fb181961ecde740c..fc2f5b66b25838d79a6cd15a851b1502044192fd 100644
--- a/notebooks/DSSC/Characterize_DSSC_Darks_NBC.ipynb
+++ b/notebooks/DSSC/Characterize_DSSC_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 and noise. 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 DSSC detector to deduce detector offsets and noise. Data for the detector is presented in one run and don't acquire multiple gain stages.\n",
     "\n",
     "The notebook explicitely does what pyDetLib provides in its offset calculation method for streaming data."
    ]
@@ -26,10 +26,10 @@
    "source": [
     "cluster_profile = \"noDB\" # The ipcluster profile to use\n",
     "in_folder = \"/gpfs/exfel/exp/SCS/201931/p900095/raw\" # path to input data, required\n",
-    "out_folder = \"/gpfs/exfel/data/scratch/ahmedk/test/\" # path to output to, required\n",
+    "out_folder = \"/gpfs/exfel/data/scratch/ahmedk/test/DSSC\" # path to output to, required\n",
     "sequences = [0] # sequence files to evaluate.\n",
     "\n",
-    "run = 1417 # run number in which data was recorded, required\n",
+    "run = 1497 # run number in which data was recorded, required\n",
     "\n",
     "mem_cells = 0 # number of memory cells used, set to 0 to automatically infer\n",
     "local_output = False # output constants locally\n",
@@ -144,7 +144,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The following lines will create a queue of files which will the be executed module-parallel. Distiguishing between different gains."
+    "The following lines will create a queue of files which will the be executed module-parallel. Distinguishing between different gains."
    ]
   },
   {
@@ -164,7 +164,6 @@
     "\n",
     "gmf = gain_map_files(in_folder, offset_runs, sequences, DET_FILE_INSET, QUADRANTS, MODULES_PER_QUAD)\n",
     "gain_mapped_files, total_sequences, total_file_size = gmf\n",
-    "\n",
     "print(\"Will process at total of {} sequences: {:0.2f} GB of data.\".format(total_sequences, total_file_size))"
    ]
   },
@@ -504,7 +503,7 @@
     "\n",
     "gain = 0\n",
     "for mod, data in badpix_g.items():\n",
-    "    plot_badpix_3d(data[...,gain], cols, title=mod, rebin_fac=rebin)"
+    "    plot_badpix_3d(data, cols, title=mod, rebin_fac=rebin)"
    ]
   },
   {
@@ -525,7 +524,7 @@
    "outputs": [],
    "source": [
     "create_constant_overview(offset_g, \"Offset (ADU)\", max_cells, 4000, 8000,\n",
-    "                         out_folder=out_folder, infix=\"_\".join(offset_runs.values()))"
+    "                         out_folder=out_folder, infix=\"_\".join(offset_runs.values()), entries=1)"
    ]
   },
   {
@@ -537,7 +536,7 @@
    "outputs": [],
    "source": [
     "create_constant_overview(noise_g, \"Noise (ADU)\", max_cells, 0, 100,\n",
-    "                         out_folder=out_folder, infix=\"_\".join(offset_runs.values()))"
+    "                         out_folder=out_folder, infix=\"_\".join(offset_runs.values()), entries=1)"
    ]
   },
   {
@@ -549,7 +548,7 @@
     "bad_pixel_aggregate_g = OrderedDict()\n",
     "for m, d in badpix_g.items():\n",
     "    bad_pixel_aggregate_g[m] = d.astype(np.bool).astype(np.float)\n",
-    "create_constant_overview(bad_pixel_aggregate_g, \"Bad pixel fraction\", max_cells, 0, 0.10, 3,\n",
+    "create_constant_overview(bad_pixel_aggregate_g, \"Bad pixel fraction\", max_cells, 0, 0.10, entries=1,\n",
     "                         out_folder=out_folder, infix=\"_\".join(offset_runs.values()))"
    ]
   },