diff --git a/notebooks/t3ss_geometry.ipynb b/notebooks/t3ss_geometry.ipynb
index 934252c00f8aac4568acffc496643a2ab1ef0f1a..9af9b0bafc9275b83a2c07cbeaf26dad1cc494b8 100644
--- a/notebooks/t3ss_geometry.ipynb
+++ b/notebooks/t3ss_geometry.ipynb
@@ -15,9 +15,10 @@
     "## Description\n",
     "\n",
     "Measurements in nm and degrees\n",
-    "1) distance 1<->2\n",
-    "2) distance 2<->3 (only if 3 exists within the same object)\n",
-    "3) distances between all 2\n",
+    "- distance 1<->2\n",
+    "- distance 2<->3 (only if 3 exists within the same object)\n",
+    "- angle between 21 and 23\n",
+    "- distances between all 2\n",
     "\n",
     "Perform measurements for\n",
     "- each dataset\n",
@@ -43,11 +44,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
+    "import os, glob\n",
     "import pandas as pd\n",
     "import numpy as np\n",
     "import imodmodel\n",
@@ -58,14 +59,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['/Volumes/Eirene/Points/20240502_Points/060B37G4', '/Volumes/Eirene/Points/20240502_Points/060B36G3', '/Volumes/Eirene/Points/20240502_Points/053B41G2', '/Volumes/Eirene/Points/Points_corrected/057B30G2', '/Volumes/Eirene/Points/Points_corrected/054B36G1', '/Volumes/Eirene/Points/Points_corrected/053B40G2']\n"
+     ]
+    }
+   ],
    "source": [
-    "base_dir = '/Volumes/Eirene/Points/Points_corrected'\n",
-    "output_dir = os.path.join(base_dir, 'Marvin_test', 'points_measurements')\n",
+    "base_dir = '/Volumes/Eirene/Points'\n",
+    "\n",
+    "output_dir = os.path.join(base_dir, 'point_measurements')\n",
     "\n",
-    "ds_dirs = [d for d in os.listdir(base_dir) if d.startswith('0')]\n",
+    "ds_paths = [p for p in glob.glob(base_dir + '/*Points*/0*') if os.path.isdir(p)]\n",
+    "print(ds_paths)\n",
     "\n",
     "if not os.path.exists(output_dir):\n",
     "    os.makedirs(output_dir)"
@@ -73,9 +84,250 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No T3SS model found for 057B30G2_TS_26_bin2_tiltcor_rec_corrected.mrc\n",
+      "No T3SS model found for 057B30G2_TS_05_bin2_tiltcor_rec_corrected.mrc\n",
+      "No T3SS model found for 057B30G2_TS_29_bin2_tiltcor_rec_corrected.mrc\n",
+      "No T3SS model found for 054B36G1_TS_05_bin2_tiltcor_rec_corrected.mrc\n",
+      "No T3SS model found for 054B36G1_TS_12_bin2_tiltcor_rec_corrected.mrc\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>object_id</th>\n",
+       "      <th>contour_id</th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "      <th>z</th>\n",
+       "      <th>source_fn</th>\n",
+       "      <th>type</th>\n",
+       "      <th>tomo_id</th>\n",
+       "      <th>tomo_fn</th>\n",
+       "      <th>ds</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1081.923292</td>\n",
+       "      <td>428.691392</td>\n",
+       "      <td>249.464728</td>\n",
+       "      <td>060B37G4_TS_09_bin2_tiltcor_rec_corrected_T3SS...</td>\n",
+       "      <td>T3SS</td>\n",
+       "      <td>9</td>\n",
+       "      <td>060B37G4_TS_09_bin2_tiltcor_rec_corrected.mrc</td>\n",
+       "      <td>060B37G4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1072.576086</td>\n",
+       "      <td>400.035547</td>\n",
+       "      <td>248.783097</td>\n",
+       "      <td>060B37G4_TS_09_bin2_tiltcor_rec_corrected_T3SS...</td>\n",
+       "      <td>T3SS</td>\n",
+       "      <td>9</td>\n",
+       "      <td>060B37G4_TS_09_bin2_tiltcor_rec_corrected.mrc</td>\n",
+       "      <td>060B37G4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1060.745905</td>\n",
+       "      <td>367.158035</td>\n",
+       "      <td>247.829845</td>\n",
+       "      <td>060B37G4_TS_09_bin2_tiltcor_rec_corrected_T3SS...</td>\n",
+       "      <td>T3SS</td>\n",
+       "      <td>9</td>\n",
+       "      <td>060B37G4_TS_09_bin2_tiltcor_rec_corrected.mrc</td>\n",
+       "      <td>060B37G4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1126.942120</td>\n",
+       "      <td>416.072804</td>\n",
+       "      <td>257.729156</td>\n",
+       "      <td>060B37G4_TS_09_bin2_tiltcor_rec_corrected_T3SS...</td>\n",
+       "      <td>T3SS</td>\n",
+       "      <td>9</td>\n",
+       "      <td>060B37G4_TS_09_bin2_tiltcor_rec_corrected.mrc</td>\n",
+       "      <td>060B37G4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1117.331632</td>\n",
+       "      <td>387.183329</td>\n",
+       "      <td>256.087051</td>\n",
+       "      <td>060B37G4_TS_09_bin2_tiltcor_rec_corrected_T3SS...</td>\n",
+       "      <td>T3SS</td>\n",
+       "      <td>9</td>\n",
+       "      <td>060B37G4_TS_09_bin2_tiltcor_rec_corrected.mrc</td>\n",
+       "      <td>060B37G4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>370.818516</td>\n",
+       "      <td>206.210713</td>\n",
+       "      <td>61.402381</td>\n",
+       "      <td>053B40G2_TS_18_bin3_tiltcor_T3SS.mod</td>\n",
+       "      <td>T3SS</td>\n",
+       "      <td>18</td>\n",
+       "      <td>053B40G2_TS_18_bin3_tiltcor_rec_corrected.mrc</td>\n",
+       "      <td>053B40G2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>324.699282</td>\n",
+       "      <td>181.196337</td>\n",
+       "      <td>68.270705</td>\n",
+       "      <td>053B40G2_TS_18_bin3_tiltcor_T3SS.mod</td>\n",
+       "      <td>T3SS</td>\n",
+       "      <td>18</td>\n",
+       "      <td>053B40G2_TS_18_bin3_tiltcor_rec_corrected.mrc</td>\n",
+       "      <td>053B40G2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>678.462245</td>\n",
+       "      <td>343.503309</td>\n",
+       "      <td>100.492182</td>\n",
+       "      <td>053B40G2_TS_04_bin3_tiltcor_T3SS.mod</td>\n",
+       "      <td>T3SS</td>\n",
+       "      <td>4</td>\n",
+       "      <td>053B40G2_TS_04_bin3_tiltcor_rec_corrected.mrc</td>\n",
+       "      <td>053B40G2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>665.284720</td>\n",
+       "      <td>334.768706</td>\n",
+       "      <td>86.524922</td>\n",
+       "      <td>053B40G2_TS_04_bin3_tiltcor_T3SS.mod</td>\n",
+       "      <td>T3SS</td>\n",
+       "      <td>4</td>\n",
+       "      <td>053B40G2_TS_04_bin3_tiltcor_rec_corrected.mrc</td>\n",
+       "      <td>053B40G2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>614.406954</td>\n",
+       "      <td>302.454664</td>\n",
+       "      <td>87.218406</td>\n",
+       "      <td>053B40G2_TS_04_bin3_tiltcor_T3SS.mod</td>\n",
+       "      <td>T3SS</td>\n",
+       "      <td>4</td>\n",
+       "      <td>053B40G2_TS_04_bin3_tiltcor_rec_corrected.mrc</td>\n",
+       "      <td>053B40G2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>415 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    object_id  contour_id            x           y           z  \\\n",
+       "0           0           0  1081.923292  428.691392  249.464728   \n",
+       "0           0           1  1072.576086  400.035547  248.783097   \n",
+       "0           0           2  1060.745905  367.158035  247.829845   \n",
+       "0           1           0  1126.942120  416.072804  257.729156   \n",
+       "0           1           1  1117.331632  387.183329  256.087051   \n",
+       "..        ...         ...          ...         ...         ...   \n",
+       "0           3           1   370.818516  206.210713   61.402381   \n",
+       "0           3           2   324.699282  181.196337   68.270705   \n",
+       "0           0           0   678.462245  343.503309  100.492182   \n",
+       "0           0           1   665.284720  334.768706   86.524922   \n",
+       "0           0           2   614.406954  302.454664   87.218406   \n",
+       "\n",
+       "                                            source_fn  type  tomo_id  \\\n",
+       "0   060B37G4_TS_09_bin2_tiltcor_rec_corrected_T3SS...  T3SS        9   \n",
+       "0   060B37G4_TS_09_bin2_tiltcor_rec_corrected_T3SS...  T3SS        9   \n",
+       "0   060B37G4_TS_09_bin2_tiltcor_rec_corrected_T3SS...  T3SS        9   \n",
+       "0   060B37G4_TS_09_bin2_tiltcor_rec_corrected_T3SS...  T3SS        9   \n",
+       "0   060B37G4_TS_09_bin2_tiltcor_rec_corrected_T3SS...  T3SS        9   \n",
+       "..                                                ...   ...      ...   \n",
+       "0                053B40G2_TS_18_bin3_tiltcor_T3SS.mod  T3SS       18   \n",
+       "0                053B40G2_TS_18_bin3_tiltcor_T3SS.mod  T3SS       18   \n",
+       "0                053B40G2_TS_04_bin3_tiltcor_T3SS.mod  T3SS        4   \n",
+       "0                053B40G2_TS_04_bin3_tiltcor_T3SS.mod  T3SS        4   \n",
+       "0                053B40G2_TS_04_bin3_tiltcor_T3SS.mod  T3SS        4   \n",
+       "\n",
+       "                                          tomo_fn        ds  \n",
+       "0   060B37G4_TS_09_bin2_tiltcor_rec_corrected.mrc  060B37G4  \n",
+       "0   060B37G4_TS_09_bin2_tiltcor_rec_corrected.mrc  060B37G4  \n",
+       "0   060B37G4_TS_09_bin2_tiltcor_rec_corrected.mrc  060B37G4  \n",
+       "0   060B37G4_TS_09_bin2_tiltcor_rec_corrected.mrc  060B37G4  \n",
+       "0   060B37G4_TS_09_bin2_tiltcor_rec_corrected.mrc  060B37G4  \n",
+       "..                                            ...       ...  \n",
+       "0   053B40G2_TS_18_bin3_tiltcor_rec_corrected.mrc  053B40G2  \n",
+       "0   053B40G2_TS_18_bin3_tiltcor_rec_corrected.mrc  053B40G2  \n",
+       "0   053B40G2_TS_04_bin3_tiltcor_rec_corrected.mrc  053B40G2  \n",
+       "0   053B40G2_TS_04_bin3_tiltcor_rec_corrected.mrc  053B40G2  \n",
+       "0   053B40G2_TS_04_bin3_tiltcor_rec_corrected.mrc  053B40G2  \n",
+       "\n",
+       "[415 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Extract info from files\n",
     "\n",
@@ -86,24 +338,31 @@
     "\n",
     "log_msgs = []\n",
     "\n",
-    "for ds_dir in ds_dirs[:]:\n",
-    "    ds_path = os.path.join(base_dir, ds_dir)\n",
+    "for ds_path in ds_paths:\n",
+    "    ds_dir = os.path.basename(ds_path)\n",
     "    fns = [fn for fn in os.listdir(ds_path) if fn.startswith(ds_dir) and fn.endswith('.mrc')]\n",
     "    for fn in fns:\n",
     "\n",
-    "        root_name = fn.split('rec_corrected.mrc')[0]\n",
-    "\n",
-    "        t3ss_name = root_name + 'T3SS.mod'\n",
-    "        t3ss_path = os.path.join(base_dir, ds_dir, t3ss_name)\n",
+    "        if 'rec_corrected.mrc' not in fn:\n",
+    "            root_name = fn.split('.mrc')[0]\n",
+    "        else:\n",
+    "            root_name = fn.split('rec_corrected.mrc')[0]\n",
     "\n",
-    "        breaks_name = root_name + 'break.mod'\n",
-    "        breaks_path = os.path.join(base_dir, ds_dir, breaks_name)\n",
-    "\n",
-    "        if not os.path.exists(t3ss_path):\n",
+    "        # find file that starts with root_name and ends with T3SS.mod\n",
+    "        t3ss_name = [fn for fn in os.listdir(ds_path)\n",
+    "        if fn.startswith(root_name) and fn.endswith('T3SS.mod')]\n",
+    "        if not len(t3ss_name): \n",
     "            msg = 'No T3SS model found for {}'.format(fn)\n",
     "            log_msgs.append(msg)\n",
     "            print(msg)\n",
     "            continue\n",
+    "        else:\n",
+    "            t3ss_name = t3ss_name[0]\n",
+    "\n",
+    "        t3ss_path = os.path.join(ds_path, t3ss_name)\n",
+    "\n",
+    "        breaks_name = root_name + 'break.mod'\n",
+    "        breaks_path = os.path.join(ds_path, breaks_name)\n",
     "\n",
     "        tdf = imodmodel.read(t3ss_path)\n",
     "        tdf['source_fn'] = t3ss_name\n",
@@ -119,7 +378,7 @@
     "        cdf['object_id'] = cdf['object_id'].astype(int)\n",
     "\n",
     "        # multiply with voxel size and convert to nm\n",
-    "        voxel_size = mrcfile.mmap(os.path.join(base_dir, ds_dir, fn), mode='r+').voxel_size.x\n",
+    "        voxel_size = mrcfile.mmap(os.path.join(ds_path, fn), mode='r+').voxel_size.x\n",
     "        for dim in ['x', 'y', 'z']:\n",
     "            cdf[dim] = cdf[dim] * voxel_size / 10\n",
     "\n",
@@ -132,9 +391,169 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/fb/ccf_crrx195fclngv32r4xcc0000gq/T/ipykernel_61378/1535911639.py:42: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "  mdf = df.groupby(['ds', 'tomo_id', 'object_id']).apply(measure)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "    .dataframe tbody tr th {\n",
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+       "\n",
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+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>distance_1_2</th>\n",
+       "      <th>distance_2_3</th>\n",
+       "      <th>angle_21_23</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ds</th>\n",
+       "      <th>tomo_id</th>\n",
+       "      <th>object_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">053B40G2</th>\n",
+       "      <th>4</th>\n",
+       "      <th>0</th>\n",
+       "      <th>0</th>\n",
+       "      <td>21.095611</td>\n",
+       "      <td>60.276241</td>\n",
+       "      <td>137.868919</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">5</th>\n",
+       "      <th>0</th>\n",
+       "      <th>0</th>\n",
+       "      <td>29.502676</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <th>0</th>\n",
+       "      <td>24.832307</td>\n",
+       "      <td>43.241020</td>\n",
+       "      <td>164.050163</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <th>0</th>\n",
+       "      <td>30.172572</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <th>0</th>\n",
+       "      <td>31.087429</td>\n",
+       "      <td>26.944366</td>\n",
+       "      <td>164.258483</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">060B37G4</th>\n",
+       "      <th>9</th>\n",
+       "      <th>2</th>\n",
+       "      <th>0</th>\n",
+       "      <td>33.202921</td>\n",
+       "      <td>60.790878</td>\n",
+       "      <td>166.524540</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">10</th>\n",
+       "      <th>0</th>\n",
+       "      <th>0</th>\n",
+       "      <td>29.508353</td>\n",
+       "      <td>53.589667</td>\n",
+       "      <td>173.707688</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <th>0</th>\n",
+       "      <td>27.696412</td>\n",
+       "      <td>46.607106</td>\n",
+       "      <td>170.740293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <th>0</th>\n",
+       "      <td>30.226147</td>\n",
+       "      <td>51.450784</td>\n",
+       "      <td>179.969690</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <th>0</th>\n",
+       "      <td>31.408964</td>\n",
+       "      <td>58.208961</td>\n",
+       "      <td>172.686151</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>147 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              distance_1_2  distance_2_3  angle_21_23\n",
+       "ds       tomo_id object_id                                           \n",
+       "053B40G2 4       0         0     21.095611     60.276241   137.868919\n",
+       "         5       0         0     29.502676           NaN          NaN\n",
+       "                 1         0     24.832307     43.241020   164.050163\n",
+       "                 2         0     30.172572           NaN          NaN\n",
+       "                 3         0     31.087429     26.944366   164.258483\n",
+       "...                                    ...           ...          ...\n",
+       "060B37G4 9       2         0     33.202921     60.790878   166.524540\n",
+       "         10      0         0     29.508353     53.589667   173.707688\n",
+       "                 1         0     27.696412     46.607106   170.740293\n",
+       "                 2         0     30.226147     51.450784   179.969690\n",
+       "                 3         0     31.408964     58.208961   172.686151\n",
+       "\n",
+       "[147 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Perform measurements\n",
     "\n",
@@ -164,9 +583,14 @@
     "    # 2) distance 2<->3 (only if 3 exists within the same object)\n",
     "    d23 = np.linalg.norm(positions[1] - positions[2])\n",
     "\n",
+    "    # 3) angle between 21 and 23\n",
+    "    a = np.arccos(np.dot(positions[0] - positions[1], positions[2] - positions[1]) / (np.linalg.norm(positions[0] - positions[1]) * np.linalg.norm(positions[2] - positions[1])))\n",
+    "    a = np.degrees(a)\n",
+    "\n",
     "    ms = pd.DataFrame({\n",
     "        'distance_1_2': [d12],\n",
-    "        'distance_2_3': [d23],        \n",
+    "        'distance_2_3': [d23],\n",
+    "        'angle_21_23': [a],\n",
     "        })\n",
     "\n",
     "    return ms\n",
@@ -180,11 +604,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/malbert/miniconda3/envs/t3ss_geo/lib/python3.10/site-packages/xarray/namedarray/core.py:264: UserWarning: Duplicate dimension names present: dimensions {'object_id'} appear more than once in dims=('object_id', 'object_id'). We do not yet support duplicate dimension names, but we do allow initial construction of the object. We recommend you rename the dims immediately to become distinct, as most xarray functionality is likely to fail silently if you do not. To rename the dimensions you will need to set the ``.dims`` attribute of each variable, ``e.g. var.dims=('x0', 'x1')``.\n",
+      "  self._dims = self._parse_dimensions(dims)\n"
+     ]
+    }
+   ],
    "source": [
-    "# 5) distances between 2\n",
+    "# 4) distances between 2\n",
     "\n",
     "tdf = df[df.type=='T3SS']\n",
     "for ds in np.unique(tdf.ds):\n",
@@ -201,9 +634,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import seaborn as sns\n",
     "import matplotlib.pyplot as plt\n",
@@ -211,7 +675,7 @@
     "pmdf = mdf.reset_index()\n",
     "\n",
     "xcol = 'ds'\n",
-    "for ycol in ['distance_1_2', 'distance_2_3']:\n",
+    "for ycol in ['distance_1_2', 'distance_2_3', 'angle_21_23']:\n",
     "    plt.figure()\n",
     "    ax = sns.boxplot(data=pmdf, x='ds', y=ycol, fliersize=0)\n",
     "    ax2 = sns.stripplot(data=pmdf, x='ds', y=ycol, size=5)\n",
@@ -232,7 +696,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pyclem",
+   "display_name": "t3ss_geo",
    "language": "python",
    "name": "python3"
   },
@@ -246,7 +710,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.10.16"
   }
  },
  "nbformat": 4,