diff --git a/notebooks/process_surface_morphometrics_output.ipynb b/notebooks/process_surface_morphometrics_output.ipynb
index bae62dd759a401f1378822ac998f78e353c7520e..6ff1b764d3cdcdba524f3f3d00476ebab02eba2c 100644
--- a/notebooks/process_surface_morphometrics_output.ipynb
+++ b/notebooks/process_surface_morphometrics_output.ipynb
@@ -72,14 +72,6 @@
     "    \"\"\"\n",
     "    Clip meshes to avoid border artifacts\n",
     "    \"\"\"\n",
-    "    # bounds = [float(func(m.points[:, dim], 0))\n",
-    "    # for dim in range(3) for func in [np.min, np.max]]\n",
-    "    # bounds[0] = cut_dist\n",
-    "    # bounds[1] = 300 - cut_dist\n",
-    "    # bounds[2] = cut_dist\n",
-    "    # bounds[3] = 300 - cut_dist\n",
-    "    # bounds[4] = cut_dist\n",
-    "    # bounds[5] = 300 - cut_dist\n",
     "    bounds = [cut_dist, 300 - cut_dist] * 3\n",
     "    mc = m.clip_box(bounds, progress_bar=True, invert=False)\n",
     "    return mc\n",
@@ -185,7 +177,7 @@
     "## Read in coordinates\n",
     "\n",
     "import pickle\n",
-    "df = pickle.load(open(os.path.join(\"/Volumes/Eirene/Points/extracted_images\", 'T3SS_coordinates.pc'), 'rb'))"
+    "df = pickle.load(open('T3SS_coordinates.pc', 'rb'))"
    ]
   },
   {
@@ -215,17 +207,6 @@
    "outputs": [],
    "source": [
     "# alphabetical order\n",
-    "ruptured = [\n",
-    "    0,\n",
-    "    0,\n",
-    "    0,\n",
-    "    1,\n",
-    "    0,\n",
-    "    0,\n",
-    "    0,\n",
-    "    1,\n",
-    "    1,\n",
-    "]\n",
     "\n",
     "curvdfs = []\n",
     "poss = []\n",
@@ -256,14 +237,15 @@
     "    curvdf['distance'] = dist\n",
     "    curvdf['index'] = np.arange(len(c))\n",
     "    curvdf['file'] = p.name\n",
-    "    curvdf['touching'] = 'touching' if np.min(dist) < 10 else 'not touching'\n",
-    "    curvdf['ruptured'] = ruptured[ind]\n",
-    "    curvdf['condition'] = 'ruptured' if ruptured[ind] else ('touching' if np.min(dist) < 10 else 'not touching')\n",
+    "    curvdf['touching'] = 'touching' if np.min(dist) < 5 else 'not touching'\n",
     "    curvdf = pd.DataFrame(curvdf)\n",
     "    curvdfs.append(curvdf)\n",
     "\n",
     "curvdf = pd.concat(curvdfs)\n",
     "\n",
+    "# save curvdf to file\n",
+    "curvdf.to_csv('output/measurements_curvedness.csv', index=False)\n",
+    "\n",
     "# count number of files per condition\n",
     "curvdf.groupby(['touching', 'file']).count()"
    ]
@@ -301,156 +283,7 @@
     "plt.title(\"Curvedness vs distance\")\n",
     "plt.xlabel('Distance to T3SS tip [nm]')\n",
     "plt.ylabel('Curvedness')\n",
-    "plt.savefig(os.path.join(out_dir, 'curvedness_vs_distance.pdf'))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import seaborn as sns\n",
-    "from matplotlib import pyplot as plt\n",
-    "\n",
-    "# bin distances into bins of width 20\n",
-    "curvdf['distance_bin'] = pd.cut(curvdf['distance'], bins=np.arange(0, 100, 10))\n",
-    "\n",
-    "plt.figure()\n",
-    "tmp = curvdf.dropna().groupby([\n",
-    "    'distance_bin',\n",
-    "    'file',\n",
-    "    ]).agg({'curvedness': 'mean', 'touching': 'first'})\n",
-    "\n",
-    "# keep only touching\n",
-    "tmp = tmp[tmp['touching'] == 'touching']\n",
-    "\n",
-    "palette ={\"touching\": \"gray\", \"not touching\": \"white\"}\n",
-    "\n",
-    "g = sns.barplot(data=tmp, x='distance_bin', y='curvedness',# errorbar=('pi'),\n",
-    "    # palette=palette,\n",
-    "    color=\"lightgray\",\n",
-    "    )\n",
-    "\n",
-    "for patch in g.patches:\n",
-    "    clr = patch.get_facecolor()\n",
-    "    patch.set_edgecolor('black')\n",
-    "\n",
-    "plt.title(\"Curvedness vs distance - only touching\")\n",
-    "plt.xlabel('Distance to T3SS tip [nm]')\n",
-    "plt.ylabel('Curvedness')\n",
-    "plt.savefig(os.path.join(out_dir, 'curvedness_vs_distance_only_touching.pdf'))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import seaborn as sns\n",
-    "from matplotlib import pyplot as plt\n",
-    "\n",
-    "# bin distances into bins of width 20\n",
-    "curvdf['distance_bin'] = pd.cut(curvdf['distance'], bins=np.arange(0, 100, 10))\n",
-    "\n",
-    "plt.figure()\n",
-    "tmp = curvdf.dropna().groupby([\n",
-    "    'distance_bin',\n",
-    "    'file',\n",
-    "    ]).agg({'curvedness': 'mean', 'touching': 'first'}).reset_index()\n",
-    "\n",
-    "# keep only touching\n",
-    "tmp = tmp[tmp['touching'] == 'touching']\n",
-    "tmp['distance_bin_center'] = tmp['distance_bin'].apply(lambda x: x.mid)\n",
-    "\n",
-    "palette ={\"touching\": \"gray\", \"not touching\": \"white\"}\n",
-    "\n",
-    "g = sns.lineplot(data=tmp, x='distance_bin_center', y='curvedness', errorbar=('sd'),\n",
-    "    # palette=palette,\n",
-    "    color=\"gray\",\n",
-    "    )\n",
-    "\n",
-    "for patch in g.patches:\n",
-    "    clr = patch.get_facecolor()\n",
-    "    patch.set_edgecolor('black')\n",
-    "\n",
-    "plt.title(\"Curvedness vs distance - only touching\")\n",
-    "plt.xlabel('Distance to T3SS tip [nm]')\n",
-    "plt.ylabel('Curvedness')\n",
-    "plt.savefig(os.path.join(out_dir, 'curvedness_vs_distance_only_touching_line.pdf'))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import seaborn as sns\n",
-    "from matplotlib import pyplot as plt\n",
-    "\n",
-    "# bin distances into bins of width 20\n",
-    "curvdf['distance_bin'] = pd.cut(curvdf['distance'], bins=np.arange(0, 100, 10))\n",
-    "\n",
-    "plt.figure()\n",
-    "tmp = curvdf.dropna().groupby([\n",
-    "    'distance_bin',\n",
-    "    'file',\n",
-    "    ]).agg({'curvedness': 'mean', 'touching': 'first'})\n",
-    "\n",
-    "palette ={\"touching\": \"gray\", \"not touching\": \"white\"}\n",
-    "\n",
-    "g = sns.barplot(data=tmp, x='distance_bin', y='curvedness',#, errorbar=('pi'),\n",
-    "    # palette=palette,\n",
-    "    color=\"lightgray\",\n",
-    "    )\n",
-    "\n",
-    "for patch in g.patches:\n",
-    "    clr = patch.get_facecolor()\n",
-    "    patch.set_edgecolor('black')\n",
-    "\n",
-    "plt.title(\"Curvedness vs distance - all substacks\")\n",
-    "plt.xlabel('Distance to T3SS tip [nm]')\n",
-    "plt.ylabel('Curvedness')\n",
-    "plt.savefig(os.path.join(out_dir, 'curvedness_vs_distance_all.pdf'))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import seaborn as sns\n",
-    "from matplotlib import pyplot as plt\n",
-    "\n",
-    "# bin distances into bins of width 20\n",
-    "curvdf['distance_bin'] = pd.cut(curvdf['distance'], bins=np.arange(0, 100, 10))\n",
-    "\n",
-    "# tmp = curvdf.dropna().groupby([\n",
-    "#     'distance_bin',\n",
-    "#     'file',\n",
-    "#     ]).agg({'curvedness': 'mean', 'touching': 'first'})\n",
-    "tmp = curvdf\n",
-    "\n",
-    "tmp = tmp.reset_index().dropna()\n",
-    "tmp['distance_centers'] = [i.mid for i in tmp['distance_bin']]\n",
-    "tmp['substack'] = [i.split('_Mem')[0] for i in tmp['file']]\n",
-    "\n",
-    "# palette ={\"touching\": \"gray\", \"not touching\": \"white\"}\n",
-    "\n",
-    "plt.figure(figsize=(15, 8))\n",
-    "# plt.figure()\n",
-    "# plt.figure(figsize=(10, 5))\n",
-    "g = sns.barplot(data=tmp,\n",
-    "    # x='distance_centers',\n",
-    "    x='distance_bin',\n",
-    "    y='curvedness', errorbar=('pi', 50),\n",
-    "    # palette=palette,\n",
-    "    # color=\"lightgray\",\n",
-    "    hue='substack',\n",
-    "    )"
+    "# plt.savefig(os.path.join(out_dir, 'curvedness_vs_distance.pdf'))\n"
    ]
   },
   {