diff --git a/notebooks/membrain_seg.ipynb b/notebooks/membrain_seg.ipynb
index aa64acab56c7b47749548a56e3d5f970cd1d5e0f..0c1c19d5aad0615de9351d9a3ae93c22bea28f60 100644
--- a/notebooks/membrain_seg.ipynb
+++ b/notebooks/membrain_seg.ipynb
@@ -61,34 +61,6 @@
     "# find and loop over all mrc files\n",
     "for file in Path(input_dir).glob(\"*.mrc\"):\n",
     "\n",
-    "    ####\n",
-    "    # with size matching\n",
-    "    ####\n",
-    "    # seg_file = os.path.join(output_dir, file.stem + \"_10A_{ckpt_stem}.ckpt_segmented.mrc\")\n",
-    "    # if os.path.exists(seg_file):\n",
-    "    #     print(f\"Skipping {file.stem}\")\n",
-    "    #     continue\n",
-    "\n",
-    "    # # match pixel size\n",
-    "    # input_file = os.path.join(input_dir, file)\n",
-    "    # output_file = os.path.join(output_dir, file.stem + \"_10A.mrc\")\n",
-    "    # input_pixel_size = float(mrcfile.mmap(input_file).voxel_size.x)\n",
-    "    # print(input_pixel_size)\n",
-    "\n",
-    "    # cmd = f\"tomo_preprocessing match_pixel_size --input-tomogram {input_file} --output-path {output_file} --pixel-size-out 10.0 --pixel-size-in {input_pixel_size} --disable-smooth\" # crashed on dp with smoothing\n",
-    "    # os.system(cmd)\n",
-    "    # print(cmd)\n",
-    "\n",
-    "    # # segment\n",
-    "    # input_file = output_file\n",
-    "    # cmd = f\"membrain segment --tomogram-path {output_file} --out-folder {output_dir} --ckpt-path {ckpt_path}\"\n",
-    "    # os.system(cmd)\n",
-    "    # print(cmd)\n",
-    "\n",
-    "    ####\n",
-    "    # without size matching\n",
-    "    ####\n",
-    "\n",
     "    seg_file = os.path.join(output_dir, file.stem + f\"_{ckpt_stem}.ckpt_segmented.mrc\")\n",
     "    if os.path.exists(seg_file):\n",
     "        print(f\"Skipping {file.stem}\")\n",
diff --git a/notebooks/membrane_processing.ipynb b/notebooks/membrane_processing.ipynb
index 0b79b11433043ddd405efd516b77ad3bfbcb9595..fd18205b22febf9ecfc865b31fafe0f9fadbcc73 100644
--- a/notebooks/membrane_processing.ipynb
+++ b/notebooks/membrane_processing.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Full tomogram processing\n",
+    "# Membrane surface reconstruction from full tomograms\n",
     "\n",
     "This notebook is used to\n",
     "- reconstruct the different surfaces segmented earlier\n",
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -55,7 +55,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# indicate where data is stored\n",
+    "data_dir = Path(\"/Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation\")\n",
+    "out_dir = Path(\"/Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology\")\n",
+    "\n",
+    "# use pathlib\n",
+    "files = sorted(list(data_dir.glob(\"*_cor.tif\")))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,7 +107,6 @@
     "    distances[np.where(np.isin(indices, border_indices_r))] = np.nan\n",
     "\n",
     "    # where distances are nan, assign distances of neighboring points\n",
-    "    # nan_indices = np.where(np.isnan(distances))[0]\n",
     "    tree = cKDTree(pts_q[~np.isnan(distances)])\n",
     "    _, indices = tree.query(pts_q[np.isnan(distances)])\n",
     "\n",
@@ -102,19 +115,6 @@
     "    return distances"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_dir = Path(\"/Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation\")\n",
-    "out_dir = Path(\"/Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology\")\n",
-    "\n",
-    "# use pathlib\n",
-    "files = sorted(list(data_dir.glob(\"*_cor.tif\")))"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -124,29 +124,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "skipping /Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology/053B40G2_TS_18_bin3_tiltcor_rec_corrected_flatcrop_MemBrain_seg_v9b.ckpt_segmented_cor_surf_3.obj\n",
-      "skipping /Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology/053B41G2_TS_13_bin2_tiltcor_rec_corrected_flatcrop_MemBrain_seg_v9b.ckpt_segmented_cor_surf_3.obj\n",
-      "skipping /Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology/054B36G1_TS_13_bin2_tiltcor_rec_corrected_flatcrop_MemBrain_seg_v9b.ckpt_segmented_cor_surf_3.obj\n",
-      "skipping /Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology/054B36G1_TS_14_bin2_tiltcor_rec_corrected_flatcrop_MemBrain_seg_v9b.ckpt_segmented_cor_surf_3.obj\n",
-      "skipping /Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology/057B30G2_TS_16_bin2_tiltcor_rec_corrected_flatcrop_MemBrain_seg_v9b.ckpt_segmented_cor_surf_3.obj\n",
-      "skipping /Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology/057B30G2_TS_20_bin2_tiltcor_rec_corrected_flatcrop_MemBrain_seg_v9b.ckpt_segmented_cor_surf_3.obj\n",
-      "skipping /Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology/057B30G2_TS_23_bin2_tiltcor_rec_flatcrop_MemBrain_seg_v9b.ckpt_segmented_cor_surf_3.obj\n",
-      "skipping /Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology/057B30G2_TS_29_bin2_tiltcor_rec_corrected_flatcrop_MemBrain_seg_v9b.ckpt_segmented_cor_surf_3.obj\n",
-      "skipping /Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology/057B30G2_TS_30_bin2_tiltcor_rec_corrected_flatcrop_MemBrain_seg_v9b.ckpt_segmented_cor_surf_3.obj\n",
-      "skipping /Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology/060B36G3_TS_01_bin2_tiltcor_rec_corrected_flatcrop_MemBrain_seg_v9b.ckpt_segmented_cor_surf_3.obj\n",
-      "skipping /Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology/060B36G3_TS_10_bin2_tiltcor_rec_corrected_flatcrop_MemBrain_seg_v9b.ckpt_segmented_cor_surf_3.obj\n",
-      "skipping /Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology/060B36G3_TS_12_bin2_tiltcor_rec_corrected_flatcrop_MemBrain_seg_v9b.ckpt_segmented_cor_surf_3.obj\n",
-      "skipping /Volumes/Eirene/Segmentation/membrain_seg_original_pixel_spacing/Auto_Seg_curation/surface_morphology/060B37G4_TS_09_bin2_tiltcor_rec_corrected_flatcrop_MemBrain_seg_v9b.ckpt_segmented_cor_surf_3.obj\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import tifffile\n",
     "from scipy import ndimage\n",
@@ -155,10 +135,6 @@
     "\n",
     "for i, file in enumerate(files):\n",
     "\n",
-    "    # 23, 01, 10\n",
-    "    # if i not in [23]: continue\n",
-    "    \n",
-    "\n",
     "    tmpout = out_dir / (file.stem + f\"_surf_{3}.obj\")\n",
     "    if os.path.exists(tmpout):\n",
     "        print('skipping', tmpout)\n",
@@ -173,10 +149,7 @@
     "    seg = ndimage.binary_erosion(seg, iterations=2)\n",
     "    im = seg * im\n",
     "\n",
-    "    # ds = distances[i]\n",
-    "    # spacing = spacings[i]\n",
     "    spacing = 1\n",
-    "\n",
     "    k_dec = 50\n",
     "\n",
     "    surfs = []\n",
@@ -209,15 +182,7 @@
     "        for surf_label in surf_labels[1:]:\n",
     "            surf += surf_label\n",
     "\n",
-    "        # pts = np.array(np.where(im == object_id)).T * spacing\n",
-    "        # pts_sub = pts[::k_dec]\n",
-    "        # cloud = pv.PolyData(pts_sub)\n",
-    "        # surf = cloud.reconstruct_surface(sample_spacing=spacing * 5, nbr_sz=100, progress_bar=True)\n",
-    "        # surfrc = copy.deepcopy(surf)\n",
-    "\n",
     "        # remove points that are too far from original points\n",
-    "        # points_to_remove = np.where((distances > 5) & np.isin(np.arange(len(pts_mesh)), border_indices))[0]\n",
-    "        # cloud, ridx = cloud.remove_points(points_to_remove)\n",
     "        pts_mesh = surf.points\n",
     "        tree = cKDTree(pts)\n",
     "        distances, indices = tree.query(pts_mesh)\n",
@@ -226,18 +191,9 @@
     "        # smooth\n",
     "        surf = surf.smooth_taubin(n_iter=50)\n",
     "\n",
-    "        # surf = surf.smooth(n_iter=10000, boundary_smoothing=True, relaxation_factor=1)\n",
-    "\n",
     "        # creates wedges, so try to be more conservative\n",
     "        surf = surf.smooth(n_iter=1000, boundary_smoothing=True, relaxation_factor=.1)\n",
     "\n",
-    "        # # for visualization, smooth borders\n",
-    "        # s = surf.subdivide(2, subfilter='linear')\n",
-    "        # pts_mesh = s.points\n",
-    "        # tree = cKDTree(pts)\n",
-    "        # distances, indices = tree.query(pts_mesh)\n",
-    "        # s, ridx = s.remove_points(distances > 15)\n",
-    "\n",
     "        surfs.append(surf)\n",
     "\n",
     "        # save cloud points\n",
@@ -256,17 +212,7 @@
     "        if not out_file.parent.exists():\n",
     "            out_file.parent.mkdir(parents=True)\n",
     "        \n",
-    "        pv.save_meshio(out_file, surf)\n",
-    "\n",
-    "        # s = surf.subdivide(2, subfilter='linear')\n",
-    "        # pts_mesh = s.points\n",
-    "        # tree = cKDTree(pts)\n",
-    "        # distances, indices = tree.query(pts_mesh)\n",
-    "        # s, ridx = s.remove_points(distances > 15)\n",
-    "\n",
-    "        # only eliminate from border\n",
-    "\n",
-    "        # for qualities, use nearest neighbor for vis\n"
+    "        pv.save_meshio(out_file, surf)\n"
    ]
   },
   {
@@ -278,150 +224,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1300x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1300x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1300x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1300x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1300x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1300x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1300x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1300x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1300x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1300x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1300x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1300x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1300x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1300x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# get measurements for all files and combine into one dataframe\n",
     "\n",
@@ -486,693 +291,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Processing file 1 of 13\n",
-      "it 0 5706\n",
-      "it 1 3783\n",
-      "it 2 2784\n",
-      "it 3 1704\n",
-      "it 4 756\n",
-      "it 5 220\n",
-      "it 6 37\n",
-      "it 7 6\n",
-      "it 8 1\n",
-      "it 9 0\n",
-      "it 0 5700\n",
-      "it 1 3196\n",
-      "it 2 2290\n",
-      "it 3 1338\n",
-      "it 4 580\n",
-      "it 5 181\n",
-      "it 6 48\n",
-      "it 7 15\n",
-      "it 8 7\n",
-      "it 9 4\n",
-      "it 10 0\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "802483d3f9814a09be7d683871b7ef9f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:51729/index.html?ui=P_0x28e0884f0_1&reconnect=auto\" class=\"pyvista…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Processing file 2 of 13\n",
-      "it 0 6891\n",
-      "it 1 4965\n",
-      "it 2 3244\n",
-      "it 3 1869\n",
-      "it 4 783\n",
-      "it 5 260\n",
-      "it 6 74\n",
-      "it 7 20\n",
-      "it 8 6\n",
-      "it 9 0\n",
-      "it 0 5754\n",
-      "it 1 2683\n",
-      "it 2 1634\n",
-      "it 3 913\n",
-      "it 4 424\n",
-      "it 5 167\n",
-      "it 6 73\n",
-      "it 7 40\n",
-      "it 8 17\n",
-      "it 9 9\n",
-      "it 10 4\n",
-      "it 11 1\n",
-      "it 12 0\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "59a9ec3d217948e38cb65811eb2ba3c1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:51729/index.html?ui=P_0x2a0807e50_2&reconnect=auto\" class=\"pyvista…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Processing file 3 of 13\n",
-      "it 0 6646\n",
-      "it 1 4491\n",
-      "it 2 3124\n",
-      "it 3 1960\n",
-      "it 4 889\n",
-      "it 5 306\n",
-      "it 6 70\n",
-      "it 7 15\n",
-      "it 8 5\n",
-      "it 9 0\n",
-      "it 0 7064\n",
-      "it 1 5019\n",
-      "it 2 3721\n",
-      "it 3 2352\n",
-      "it 4 1077\n",
-      "it 5 437\n",
-      "it 6 156\n",
-      "it 7 63\n",
-      "it 8 34\n",
-      "it 9 24\n",
-      "it 10 18\n",
-      "it 11 17\n",
-      "it 12 13\n",
-      "it 13 11\n",
-      "it 14 8\n",
-      "it 15 8\n",
-      "it 16 7\n",
-      "it 17 4\n",
-      "it 18 3\n",
-      "it 19 3\n",
-      "it 20 0\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "75f6f56e055e42e390da272db4fe2691",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:51729/index.html?ui=P_0x28044bd60_3&reconnect=auto\" class=\"pyvista…"
-      ]
-     },
-     "metadata": {},
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Processing file 13 of 13\n",
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-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "93a21dcabede40f295b4233c60069997",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:51729/index.html?ui=P_0x177bc03d0_13&reconnect=auto\" class=\"pyvist…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Load meshes from disk and visualize\n",
     "\n",
@@ -1181,7 +302,6 @@
     "import copy\n",
     "from scipy.spatial import cKDTree\n",
     "\n",
-    "# df_vis = df[[int(r.tomogram[-2:]) in [1, 10, 23] for i, r in df.iterrows()]]\n",
     "\n",
     "distance_clim = [np.percentile(df.distance, p) for p in [5, 95]]\n",
     "distance_clim = None\n",
@@ -1192,17 +312,6 @@
     "\n",
     "for i, file in enumerate(files[:]):\n",
     "\n",
-    "    # if not 'TS_23' in file.stem:\n",
-    "    #     if (not 'TS_01' in file.stem):\n",
-    "    #         if (not 'TS_10' in file.stem):\n",
-    "    #             continue\n",
-    "\n",
-    "    # if not 'TS_10' in file.stem:\n",
-    "    #     continue\n",
-    "\n",
-    "    # if i not in [0]: continue\n",
-    "    # if not 'TS_23' in file.stem: continue\n",
-    "\n",
     "    print(f\"Processing file {i+1} of {len(files)}\")\n",
     "\n",
     "    orig_surfs = []\n",
@@ -1234,8 +343,6 @@
     "\n",
     "                print('it', it, len(points_to_remove))\n",
     "                \n",
-    "                # break # testing\n",
-    "\n",
     "                if len(points_to_remove) == 0:\n",
     "                    break\n",
     "                it += 1\n",
@@ -1254,7 +361,6 @@
     "\n",
     "    pl.add_mesh(surfs[-2], point_size=5,\n",
     "        color='Grey',\n",
-    "        # show_edges=True,\n",
     "        opacity=0.5,\n",
     "        )\n",
     "\n",
@@ -1262,7 +368,6 @@
     "        color='Grey',\n",
     "        scalars=d_smooth,\n",
     "        clim=distance_clim,\n",
-    "        # show_edges=True,\n",
     "        )\n",
     "\n",
     "    pl.show()\n",
@@ -1275,18 +380,8 @@
     "        os.remove(screenshot_path)\n",
     "    \n",
     "    # save in 4k\n",
-    "    pl.screenshot(screenshot_path, window_size=[3840, 2160])\n",
-    "\n",
-    "    # save in vector format (?)\n",
-    "    pl.save_graphic(graphic_path)"
+    "    pl.screenshot(screenshot_path, window_size=[3840, 2160])"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/notebooks/substack_extraction.ipynb b/notebooks/substack_extraction.ipynb
index 354029f3a566649bd13847de7fe3ec62b2cc67eb..70c739f0354260dc6361322e7914f28fcce2cbf8 100644
--- a/notebooks/substack_extraction.ipynb
+++ b/notebooks/substack_extraction.ipynb
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 215,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 227,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -54,348 +54,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 242,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/Volumes/Eirene/Points/Points_corrected/057B30G2\n",
-      "No T3SS model found for 057B30G2_TS_26_bin2_tiltcor_rec_corrected.mrc, /Volumes/Eirene/Points/Points_corrected/057B30G2/057B30G2_TS_20_bin2_tiltcor_T3SS.mod\n",
-      "No T3SS model found for 057B30G2_TS_05_bin2_tiltcor_rec_corrected.mrc, /Volumes/Eirene/Points/Points_corrected/057B30G2/057B30G2_TS_20_bin2_tiltcor_T3SS.mod\n",
-      "No breaks model found for 057B30G2_TS_10_bin2_tiltcor_rec_corrected.mrc\n",
-      "No T3SS model found for 057B30G2_TS_29_bin2_tiltcor_rec_corrected.mrc, /Volumes/Eirene/Points/Points_corrected/057B30G2/057B30G2_TS_10_bin2_tiltcor_T3SS.mod\n",
-      "No breaks model found for 057B30G2_TS_12_bin2_tiltcor_rec_corrected.mrc\n",
-      "/Volumes/Eirene/Points/Points_corrected/054B36G1\n",
-      "No T3SS model found for 054B36G1_TS_05_bin2_tiltcor_rec_corrected.mrc, /Volumes/Eirene/Points/Points_corrected/057B30G2/057B30G2_TS_12_bin2_tiltcor_T3SS.mod\n",
-      "No breaks model found for 054B36G1_TS_03_bin2_tiltcor_rec_corrected.mrc\n",
-      "No breaks model found for 054B36G1_TS_13_bin2_tiltcor_rec_corrected.mrc\n",
-      "No T3SS model found for 054B36G1_TS_12_bin2_tiltcor_rec_corrected.mrc, /Volumes/Eirene/Points/Points_corrected/054B36G1/054B36G1_TS_13_bin2_tiltcor_T3SS.mod\n",
-      "/Volumes/Eirene/Points/Points_corrected/053B40G2\n",
-      "No breaks model found for 053B40G2_TS_22_bin3_tiltcor_rec_corrected.mrc\n",
-      "No breaks model found for 053B40G2_TS_08_bin3_tiltcor_rec_corrected.mrc\n",
-      "No breaks model found for 053B40G2_TS_04_bin3_tiltcor_rec_corrected.mrc\n",
-      "/Volumes/Eirene/Points/20240502_Points/060B37G4\n",
-      "No breaks model found for 060B37G4_TS_09_bin2_tiltcor_rec_corrected.mrc\n",
-      "No breaks model found for 060B37G4_TS_02_bin2_tiltcor_rec_corrected.mrc\n",
-      "No breaks model found for 060B37G4_TS_10_bin2_tiltcor_rec_corrected.mrc\n",
-      "/Volumes/Eirene/Points/20240502_Points/060B36G3\n",
-      "No breaks model found for 060B36G3_TS_07_bin2_tiltcor_rec_corrected.mrc\n",
-      "No breaks model found for 060B36G3_TS_12_bin2_tiltcor_rec_corrected.mrc\n",
-      "No breaks model found for 060B36G3_TS_01_bin2_tiltcor_rec_corrected.mrc\n",
-      "No breaks model found for 060B36G3_TS_09_bin2_tiltcor_rec_corrected.mrc\n",
-      "No breaks model found for 060B36G3_TS_05_bin2_tiltcor_rec_corrected.mrc\n",
-      "No breaks model found for 060B36G3_TS_10_bin2_tiltcor_rec_corrected.mrc\n",
-      "No breaks model found for 060B36G3_TS_06_bin2_tiltcor_rec_corrected.mrc\n",
-      "/Volumes/Eirene/Points/20240502_Points/053B41G2\n",
-      "No breaks model found for 053B41G2_TS_13_bin2_tiltcor_rec_corrected.mrc\n",
-      "No breaks model found for 053B41G2_TS_21_bin2_tiltcor_rec_corrected.mrc\n",
-      "No T3SS model found for 053B41G2_TS_03_bin2_tiltcor_rec.mrc, /Volumes/Eirene/Points/20240502_Points/053B41G2/053B41G2_TS_21_bin2_tiltcor_rec_corrected_T3SS.mod\n",
-      "No T3SS model found for 053B41G2_TS_07_bin2_tiltcor_rec.mrc, /Volumes/Eirene/Points/20240502_Points/053B41G2/053B41G2_TS_21_bin2_tiltcor_rec_corrected_T3SS.mod\n",
-      "No breaks model found for 053B41G2_TS_20_bin2_tiltcor_rec_corrected.mrc\n",
-      "No breaks model found for 053B41G2_TS_15_bin2_tiltcor_rec_corrected.mrc\n",
-      "No T3SS model found for 053B41G2_TS_01_bin2_tiltcor_rec.mrc, /Volumes/Eirene/Points/20240502_Points/053B41G2/053B41G2_TS_15_bin2_tiltcor_rec_corrected_T3SS.mod\n",
-      "No breaks model found for 053B41G2_TS_17_bin2_tiltcor_rec_corrected.mrc\n",
-      "No breaks model found for 053B41G2_TS_16_bin2_tiltcor_rec_corrected.mrc\n",
-      "No breaks model found for 053B41G2_TS_19_bin2_tiltcor_rec_corrected.mrc\n",
-      "No breaks model found for 053B41G2_TS_25_bin2_tiltcor_rec_corrected.mrc\n",
-      "No breaks model found for 053B41G2_TS_12_bin2_tiltcor_rec_corrected.mrc\n",
-      "No breaks model found for 053B41G2_TS_23_bin2_tiltcor_rec_corrected.mrc\n",
-      "No breaks model found for 053B41G2_TS_14_bin2_tiltcor_rec_corrected.mrc\n"
-     ]
-    },
-    {
-     "data": {
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-       "      <th></th>\n",
-       "      <th>object_id</th>\n",
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-       "      <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",
-       "      <th>x_nm</th>\n",
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-       "      <th>z_nm</th>\n",
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-       "      <td>113.297363</td>\n",
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-       "      <td>T3SS</td>\n",
-       "      <td>16</td>\n",
-       "      <td>/Volumes/Eirene/Points/Points_corrected/057B30...</td>\n",
-       "      <td>057B30G2</td>\n",
-       "      <td>430.220528</td>\n",
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-       "      <td>108.992062</td>\n",
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-       "      <th>0</th>\n",
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-       "      <td>1387.116699</td>\n",
-       "      <td>121.973129</td>\n",
-       "      <td>057B30G2_TS_16_bin2_tiltcor_T3SS.mod</td>\n",
-       "      <td>T3SS</td>\n",
-       "      <td>16</td>\n",
-       "      <td>/Volumes/Eirene/Points/Points_corrected/057B30...</td>\n",
-       "      <td>057B30G2</td>\n",
-       "      <td>410.232665</td>\n",
-       "      <td>1334.406249</td>\n",
-       "      <td>117.338149</td>\n",
-       "      <td>9.62</td>\n",
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-       "      <td>103.879143</td>\n",
-       "      <td>057B30G2_TS_16_bin2_tiltcor_T3SS.mod</td>\n",
-       "      <td>T3SS</td>\n",
-       "      <td>16</td>\n",
-       "      <td>/Volumes/Eirene/Points/Points_corrected/057B30...</td>\n",
-       "      <td>057B30G2</td>\n",
-       "      <td>560.002364</td>\n",
-       "      <td>1406.025222</td>\n",
-       "      <td>99.931734</td>\n",
-       "      <td>9.62</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>573.605469</td>\n",
-       "      <td>1485.683716</td>\n",
-       "      <td>117.515335</td>\n",
-       "      <td>057B30G2_TS_16_bin2_tiltcor_T3SS.mod</td>\n",
-       "      <td>T3SS</td>\n",
-       "      <td>16</td>\n",
-       "      <td>/Volumes/Eirene/Points/Points_corrected/057B30...</td>\n",
-       "      <td>057B30G2</td>\n",
-       "      <td>551.808454</td>\n",
-       "      <td>1429.227718</td>\n",
-       "      <td>113.049751</td>\n",
-       "      <td>9.62</td>\n",
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-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>425.504181</td>\n",
-       "      <td>790.534546</td>\n",
-       "      <td>133.468887</td>\n",
-       "      <td>057B30G2_TS_16_bin2_tiltcor_T3SS.mod</td>\n",
-       "      <td>T3SS</td>\n",
-       "      <td>16</td>\n",
-       "      <td>/Volumes/Eirene/Points/Points_corrected/057B30...</td>\n",
-       "      <td>057B30G2</td>\n",
-       "      <td>409.335017</td>\n",
-       "      <td>760.494224</td>\n",
-       "      <td>128.397068</td>\n",
-       "      <td>9.62</td>\n",
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-       "    <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",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
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-       "    <tr>\n",
-       "      <th>0</th>\n",
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-       "      <td>1</td>\n",
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-       "      <td>561.571533</td>\n",
-       "      <td>209.283905</td>\n",
-       "      <td>053B41G2_TS_14_bin2_tiltcor_rec_corrected_T3SS...</td>\n",
-       "      <td>T3SS</td>\n",
-       "      <td>14</td>\n",
-       "      <td>/Volumes/Eirene/Points/20240502_Points/053B41G...</td>\n",
-       "      <td>053B41G2</td>\n",
-       "      <td>1154.695562</td>\n",
-       "      <td>540.231809</td>\n",
-       "      <td>201.331114</td>\n",
-       "      <td>9.62</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>8</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1242.644043</td>\n",
-       "      <td>543.317078</td>\n",
-       "      <td>225.512543</td>\n",
-       "      <td>053B41G2_TS_14_bin2_tiltcor_rec_corrected_T3SS...</td>\n",
-       "      <td>T3SS</td>\n",
-       "      <td>14</td>\n",
-       "      <td>/Volumes/Eirene/Points/20240502_Points/053B41G...</td>\n",
-       "      <td>053B41G2</td>\n",
-       "      <td>1195.423555</td>\n",
-       "      <td>522.671022</td>\n",
-       "      <td>216.943064</td>\n",
-       "      <td>9.62</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>9</td>\n",
-       "      <td>0</td>\n",
-       "      <td>886.989563</td>\n",
-       "      <td>459.770081</td>\n",
-       "      <td>170.415802</td>\n",
-       "      <td>053B41G2_TS_14_bin2_tiltcor_rec_corrected_T3SS...</td>\n",
-       "      <td>T3SS</td>\n",
-       "      <td>14</td>\n",
-       "      <td>/Volumes/Eirene/Points/20240502_Points/053B41G...</td>\n",
-       "      <td>053B41G2</td>\n",
-       "      <td>853.283949</td>\n",
-       "      <td>442.298812</td>\n",
-       "      <td>163.940000</td>\n",
-       "      <td>9.62</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>9</td>\n",
-       "      <td>1</td>\n",
-       "      <td>910.624268</td>\n",
-       "      <td>429.405426</td>\n",
-       "      <td>160.421310</td>\n",
-       "      <td>053B41G2_TS_14_bin2_tiltcor_rec_corrected_T3SS...</td>\n",
-       "      <td>T3SS</td>\n",
-       "      <td>14</td>\n",
-       "      <td>/Volumes/Eirene/Points/20240502_Points/053B41G...</td>\n",
-       "      <td>053B41G2</td>\n",
-       "      <td>876.020535</td>\n",
-       "      <td>413.088015</td>\n",
-       "      <td>154.325299</td>\n",
-       "      <td>9.62</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>9</td>\n",
-       "      <td>2</td>\n",
-       "      <td>945.903870</td>\n",
-       "      <td>404.319824</td>\n",
-       "      <td>147.044846</td>\n",
-       "      <td>053B41G2_TS_14_bin2_tiltcor_rec_corrected_T3SS...</td>\n",
-       "      <td>T3SS</td>\n",
-       "      <td>14</td>\n",
-       "      <td>/Volumes/Eirene/Points/20240502_Points/053B41G...</td>\n",
-       "      <td>053B41G2</td>\n",
-       "      <td>909.959512</td>\n",
-       "      <td>388.955666</td>\n",
-       "      <td>141.457140</td>\n",
-       "      <td>9.62</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>548 rows × 14 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    object_id  contour_id            x            y           z  \\\n",
-       "0           0           0   447.214691  1366.812134  113.297363   \n",
-       "0           0           1   426.437286  1387.116699  121.973129   \n",
-       "0           1           0   582.123047  1461.564697  103.879143   \n",
-       "0           1           1   573.605469  1485.683716  117.515335   \n",
-       "0           2           0   425.504181   790.534546  133.468887   \n",
-       "..        ...         ...          ...          ...         ...   \n",
-       "0           8           1  1200.307251   561.571533  209.283905   \n",
-       "0           8           2  1242.644043   543.317078  225.512543   \n",
-       "0           9           0   886.989563   459.770081  170.415802   \n",
-       "0           9           1   910.624268   429.405426  160.421310   \n",
-       "0           9           2   945.903870   404.319824  147.044846   \n",
-       "\n",
-       "                                            source_fn  type  tomo_id  \\\n",
-       "0                057B30G2_TS_16_bin2_tiltcor_T3SS.mod  T3SS       16   \n",
-       "0                057B30G2_TS_16_bin2_tiltcor_T3SS.mod  T3SS       16   \n",
-       "0                057B30G2_TS_16_bin2_tiltcor_T3SS.mod  T3SS       16   \n",
-       "0                057B30G2_TS_16_bin2_tiltcor_T3SS.mod  T3SS       16   \n",
-       "0                057B30G2_TS_16_bin2_tiltcor_T3SS.mod  T3SS       16   \n",
-       "..                                                ...   ...      ...   \n",
-       "0   053B41G2_TS_14_bin2_tiltcor_rec_corrected_T3SS...  T3SS       14   \n",
-       "0   053B41G2_TS_14_bin2_tiltcor_rec_corrected_T3SS...  T3SS       14   \n",
-       "0   053B41G2_TS_14_bin2_tiltcor_rec_corrected_T3SS...  T3SS       14   \n",
-       "0   053B41G2_TS_14_bin2_tiltcor_rec_corrected_T3SS...  T3SS       14   \n",
-       "0   053B41G2_TS_14_bin2_tiltcor_rec_corrected_T3SS...  T3SS       14   \n",
-       "\n",
-       "                                              tomo_fn        ds         x_nm  \\\n",
-       "0   /Volumes/Eirene/Points/Points_corrected/057B30...  057B30G2   430.220528   \n",
-       "0   /Volumes/Eirene/Points/Points_corrected/057B30...  057B30G2   410.232665   \n",
-       "0   /Volumes/Eirene/Points/Points_corrected/057B30...  057B30G2   560.002364   \n",
-       "0   /Volumes/Eirene/Points/Points_corrected/057B30...  057B30G2   551.808454   \n",
-       "0   /Volumes/Eirene/Points/Points_corrected/057B30...  057B30G2   409.335017   \n",
-       "..                                                ...       ...          ...   \n",
-       "0   /Volumes/Eirene/Points/20240502_Points/053B41G...  053B41G2  1154.695562   \n",
-       "0   /Volumes/Eirene/Points/20240502_Points/053B41G...  053B41G2  1195.423555   \n",
-       "0   /Volumes/Eirene/Points/20240502_Points/053B41G...  053B41G2   853.283949   \n",
-       "0   /Volumes/Eirene/Points/20240502_Points/053B41G...  053B41G2   876.020535   \n",
-       "0   /Volumes/Eirene/Points/20240502_Points/053B41G...  053B41G2   909.959512   \n",
-       "\n",
-       "           y_nm        z_nm  voxel_size  \n",
-       "0   1314.873257  108.992062        9.62  \n",
-       "0   1334.406249  117.338149        9.62  \n",
-       "0   1406.025222   99.931734        9.62  \n",
-       "0   1429.227718  113.049751        9.62  \n",
-       "0    760.494224  128.397068        9.62  \n",
-       "..          ...         ...         ...  \n",
-       "0    540.231809  201.331114        9.62  \n",
-       "0    522.671022  216.943064        9.62  \n",
-       "0    442.298812  163.940000        9.62  \n",
-       "0    413.088015  154.325299        9.62  \n",
-       "0    388.955666  141.457140        9.62  \n",
-       "\n",
-       "[548 rows x 14 columns]"
-      ]
-     },
-     "execution_count": 242,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Extract info from files\n",
     "\n",
@@ -416,11 +77,7 @@
     "\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",
-    "\n",
     "            t3ss_paths = [os.path.join(ds_path, f) for f in os.listdir(ds_path) if f.startswith(root_name) and f.endswith('T3SS.mod')]\n",
-    "            # if not os.path.exists(t3ss_path):\n",
     "            if not len(t3ss_paths):\n",
     "                msg = 'No T3SS model found for {}, {}'.format(fn, t3ss_path)\n",
     "                log_msgs.append(msg)\n",
@@ -429,23 +86,11 @@
     "            t3ss_path = t3ss_paths[0]\n",
     "            t3ss_name = os.path.basename(t3ss_path)\n",
     "\n",
-    "            breaks_name = root_name + 'break.mod'\n",
-    "            breaks_path = os.path.join(base_dir, ds_dir, breaks_name)\n",
-    "\n",
     "            tdf = imodmodel.read(t3ss_path)\n",
     "            tdf['source_fn'] = t3ss_name\n",
     "            tdf['type'] = 'T3SS'\n",
     "\n",
-    "            if os.path.exists(breaks_path):\n",
-    "                bdf = imodmodel.read(breaks_path)\n",
-    "                bdf['source_fn'] = breaks_name\n",
-    "                bdf['type'] = 'break'\n",
-    "                cdf = pd.concat([tdf, bdf])\n",
-    "            else:\n",
-    "                cdf = tdf\n",
-    "                msg = 'No breaks model found for {}'.format(fn)\n",
-    "                log_msgs.append(msg)\n",
-    "                print(msg)\n",
+    "            cdf = tdf\n",
     "\n",
     "            cdf['tomo_id'] = extract_ts_id_from_fn(fn)\n",
     "            cdf['tomo_fn'] = os.path.join(ds_path, fn)\n",
@@ -470,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 244,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -487,431 +132,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 246,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
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-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th>ds</th>\n",
-       "      <th>tomo_id</th>\n",
-       "      <th>object_id</th>\n",
-       "      <th>voxel_size</th>\n",
-       "      <th>source_fn</th>\n",
-       "      <th>tomo_fn</th>\n",
-       "      <th colspan=\"3\" halign=\"left\">x</th>\n",
-       "      <th colspan=\"4\" halign=\"left\">y</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">z</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">x</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">y</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">z</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>contour_id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>0</th>\n",
-       "      <th>...</th>\n",
-       "      <th>12</th>\n",
-       "      <th>10</th>\n",
-       "      <th>12</th>\n",
-       "      <th>10</th>\n",
-       "      <th>12_t</th>\n",
-       "      <th>10_t</th>\n",
-       "      <th>12_t</th>\n",
-       "      <th>10_t</th>\n",
-       "      <th>12_t</th>\n",
-       "      <th>10_t</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>053B40G2</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9.311999</td>\n",
-       "      <td>053B40G2_TS_04_bin3_tiltcor_T3SS.mod</td>\n",
-       "      <td>/Volumes/Eirene/Points/Points_corrected/053B40...</td>\n",
-       "      <td>728.589233</td>\n",
-       "      <td>714.438110</td>\n",
-       "      <td>659.801331</td>\n",
-       "      <td>368.882446</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-34.701508</td>\n",
-       "      <td>9.379944</td>\n",
-       "      <td>0.744720</td>\n",
-       "      <td>14.999207</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>-0.104016</td>\n",
-       "      <td>-64.729645</td>\n",
-       "      <td>16.800645</td>\n",
-       "      <td>-2.220446e-16</td>\n",
-       "      <td>28.340858</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>053B40G2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>9.311999</td>\n",
-       "      <td>053B40G2_TS_05_bin3_tiltcor_T3SS.mod</td>\n",
-       "      <td>/Volumes/Eirene/Points/Points_corrected/053B40...</td>\n",
-       "      <td>693.882324</td>\n",
-       "      <td>684.748718</td>\n",
-       "      <td>658.552246</td>\n",
-       "      <td>1216.467163</td>\n",
-       "      <td>...</td>\n",
-       "      <td>32.204224</td>\n",
-       "      <td>-18.961182</td>\n",
-       "      <td>-20.806656</td>\n",
-       "      <td>16.376190</td>\n",
-       "      <td>-3.552714e-15</td>\n",
-       "      <td>-7.820059</td>\n",
-       "      <td>-46.435806</td>\n",
-       "      <td>25.640389</td>\n",
-       "      <td>7.105427e-15</td>\n",
-       "      <td>-4.196947</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>053B40G2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>9.311999</td>\n",
-       "      <td>053B40G2_TS_05_bin3_tiltcor_T3SS.mod</td>\n",
-       "      <td>/Volumes/Eirene/Points/Points_corrected/053B40...</td>\n",
-       "      <td>1136.781128</td>\n",
-       "      <td>1166.509277</td>\n",
-       "      <td>1194.055298</td>\n",
-       "      <td>546.148804</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-8.783081</td>\n",
-       "      <td>13.524414</td>\n",
-       "      <td>1.146484</td>\n",
-       "      <td>6.916412</td>\n",
-       "      <td>-7.105427e-15</td>\n",
-       "      <td>9.193258</td>\n",
-       "      <td>-28.935103</td>\n",
-       "      <td>32.132206</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>28.122263</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>053B40G2</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9.311999</td>\n",
-       "      <td>053B40G2_TS_08_bin3_tiltcor_T3SS.mod</td>\n",
-       "      <td>/Volumes/Eirene/Points/Points_corrected/053B40...</td>\n",
-       "      <td>659.922302</td>\n",
-       "      <td>632.182800</td>\n",
-       "      <td>621.798279</td>\n",
-       "      <td>811.559875</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-2.920349</td>\n",
-       "      <td>5.416809</td>\n",
-       "      <td>-1.805672</td>\n",
-       "      <td>0.898895</td>\n",
-       "      <td>-8.881784e-16</td>\n",
-       "      <td>12.375231</td>\n",
-       "      <td>-10.937421</td>\n",
-       "      <td>27.931954</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>-6.639047</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>053B40G2</td>\n",
-       "      <td>18</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9.311999</td>\n",
-       "      <td>053B40G2_TS_18_bin3_tiltcor_T3SS.mod</td>\n",
-       "      <td>/Volumes/Eirene/Points/Points_corrected/053B40...</td>\n",
-       "      <td>666.500000</td>\n",
-       "      <td>660.333313</td>\n",
-       "      <td>651.250000</td>\n",
-       "      <td>1190.425171</td>\n",
-       "      <td>...</td>\n",
-       "      <td>61.833374</td>\n",
-       "      <td>-31.241455</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2.957275</td>\n",
-       "      <td>1.776357e-15</td>\n",
-       "      <td>1.577322</td>\n",
-       "      <td>-62.496982</td>\n",
-       "      <td>31.805992</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>-2.989013</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",
-       "      <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",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>109</th>\n",
-       "      <td>060B37G4</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2</td>\n",
-       "      <td>9.620000</td>\n",
-       "      <td>060B37G4_TS_09_bin2_tiltcor_rec_corrected_T3SS...</td>\n",
-       "      <td>/Volumes/Eirene/Points/20240502_Points/060B37G...</td>\n",
-       "      <td>1815.878540</td>\n",
-       "      <td>1825.607788</td>\n",
-       "      <td>1856.567627</td>\n",
-       "      <td>1285.116211</td>\n",
-       "      <td>...</td>\n",
-       "      <td>43.918091</td>\n",
-       "      <td>-27.732544</td>\n",
-       "      <td>-33.255699</td>\n",
-       "      <td>18.096863</td>\n",
-       "      <td>-3.552714e-15</td>\n",
-       "      <td>8.835842</td>\n",
-       "      <td>-63.192182</td>\n",
-       "      <td>33.564281</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>2.654621</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>110</th>\n",
-       "      <td>060B37G4</td>\n",
-       "      <td>10</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9.620000</td>\n",
-       "      <td>060B37G4_TS_10_bin2_tiltcor_rec_corrected_T3SS...</td>\n",
-       "      <td>/Volumes/Eirene/Points/20240502_Points/060B37G...</td>\n",
-       "      <td>1130.231934</td>\n",
-       "      <td>1110.025513</td>\n",
-       "      <td>1072.568115</td>\n",
-       "      <td>1364.009277</td>\n",
-       "      <td>...</td>\n",
-       "      <td>40.578247</td>\n",
-       "      <td>-23.065186</td>\n",
-       "      <td>7.318817</td>\n",
-       "      <td>-0.768005</td>\n",
-       "      <td>-7.105427e-15</td>\n",
-       "      <td>-0.537872</td>\n",
-       "      <td>-55.706515</td>\n",
-       "      <td>30.489174</td>\n",
-       "      <td>-1.776357e-15</td>\n",
-       "      <td>-4.510022</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>111</th>\n",
-       "      <td>060B37G4</td>\n",
-       "      <td>10</td>\n",
-       "      <td>1</td>\n",
-       "      <td>9.620000</td>\n",
-       "      <td>060B37G4_TS_10_bin2_tiltcor_rec_corrected_T3SS...</td>\n",
-       "      <td>/Volumes/Eirene/Points/20240502_Points/060B37G...</td>\n",
-       "      <td>1418.198730</td>\n",
-       "      <td>1404.029663</td>\n",
-       "      <td>1387.274658</td>\n",
-       "      <td>1547.150391</td>\n",
-       "      <td>...</td>\n",
-       "      <td>44.911133</td>\n",
-       "      <td>-24.726318</td>\n",
-       "      <td>7.034332</td>\n",
-       "      <td>-4.091049</td>\n",
-       "      <td>3.552714e-15</td>\n",
-       "      <td>4.931969</td>\n",
-       "      <td>-48.448136</td>\n",
-       "      <td>28.415284</td>\n",
-       "      <td>1.776357e-15</td>\n",
-       "      <td>0.229769</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>112</th>\n",
-       "      <td>060B37G4</td>\n",
-       "      <td>10</td>\n",
-       "      <td>2</td>\n",
-       "      <td>9.620000</td>\n",
-       "      <td>060B37G4_TS_10_bin2_tiltcor_rec_corrected_T3SS...</td>\n",
-       "      <td>/Volumes/Eirene/Points/20240502_Points/060B37G...</td>\n",
-       "      <td>676.623718</td>\n",
-       "      <td>647.250366</td>\n",
-       "      <td>597.244324</td>\n",
-       "      <td>589.625671</td>\n",
-       "      <td>...</td>\n",
-       "      <td>15.999146</td>\n",
-       "      <td>-9.414612</td>\n",
-       "      <td>10.191650</td>\n",
-       "      <td>-5.982864</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>-0.032102</td>\n",
-       "      <td>-53.483144</td>\n",
-       "      <td>31.420107</td>\n",
-       "      <td>-3.552714e-15</td>\n",
-       "      <td>-0.034142</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>113</th>\n",
-       "      <td>060B37G4</td>\n",
-       "      <td>10</td>\n",
-       "      <td>3</td>\n",
-       "      <td>9.620000</td>\n",
-       "      <td>060B37G4_TS_10_bin2_tiltcor_rec_corrected_T3SS...</td>\n",
-       "      <td>/Volumes/Eirene/Points/20240502_Points/060B37G...</td>\n",
-       "      <td>1515.624390</td>\n",
-       "      <td>1546.256470</td>\n",
-       "      <td>1601.316040</td>\n",
-       "      <td>477.224579</td>\n",
-       "      <td>...</td>\n",
-       "      <td>12.226410</td>\n",
-       "      <td>-2.622223</td>\n",
-       "      <td>21.913696</td>\n",
-       "      <td>-10.990875</td>\n",
-       "      <td>1.776357e-15</td>\n",
-       "      <td>-6.769159</td>\n",
-       "      <td>-60.508276</td>\n",
-       "      <td>32.384004</td>\n",
-       "      <td>-3.552714e-15</td>\n",
-       "      <td>7.390998</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>114 rows × 27 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  ds tomo_id object_id voxel_size  \\\n",
-       "contour_id                                          \n",
-       "0           053B40G2       4         0   9.311999   \n",
-       "1           053B40G2       5         1   9.311999   \n",
-       "2           053B40G2       5         3   9.311999   \n",
-       "3           053B40G2       8         0   9.311999   \n",
-       "4           053B40G2      18         0   9.311999   \n",
-       "..               ...     ...       ...        ...   \n",
-       "109         060B37G4       9         2   9.620000   \n",
-       "110         060B37G4      10         0   9.620000   \n",
-       "111         060B37G4      10         1   9.620000   \n",
-       "112         060B37G4      10         2   9.620000   \n",
-       "113         060B37G4      10         3   9.620000   \n",
-       "\n",
-       "                                                    source_fn  \\\n",
-       "contour_id                                                      \n",
-       "0                        053B40G2_TS_04_bin3_tiltcor_T3SS.mod   \n",
-       "1                        053B40G2_TS_05_bin3_tiltcor_T3SS.mod   \n",
-       "2                        053B40G2_TS_05_bin3_tiltcor_T3SS.mod   \n",
-       "3                        053B40G2_TS_08_bin3_tiltcor_T3SS.mod   \n",
-       "4                        053B40G2_TS_18_bin3_tiltcor_T3SS.mod   \n",
-       "..                                                        ...   \n",
-       "109         060B37G4_TS_09_bin2_tiltcor_rec_corrected_T3SS...   \n",
-       "110         060B37G4_TS_10_bin2_tiltcor_rec_corrected_T3SS...   \n",
-       "111         060B37G4_TS_10_bin2_tiltcor_rec_corrected_T3SS...   \n",
-       "112         060B37G4_TS_10_bin2_tiltcor_rec_corrected_T3SS...   \n",
-       "113         060B37G4_TS_10_bin2_tiltcor_rec_corrected_T3SS...   \n",
-       "\n",
-       "                                                      tomo_fn            x  \\\n",
-       "contour_id                                                               0   \n",
-       "0           /Volumes/Eirene/Points/Points_corrected/053B40...   728.589233   \n",
-       "1           /Volumes/Eirene/Points/Points_corrected/053B40...   693.882324   \n",
-       "2           /Volumes/Eirene/Points/Points_corrected/053B40...  1136.781128   \n",
-       "3           /Volumes/Eirene/Points/Points_corrected/053B40...   659.922302   \n",
-       "4           /Volumes/Eirene/Points/Points_corrected/053B40...   666.500000   \n",
-       "..                                                        ...          ...   \n",
-       "109         /Volumes/Eirene/Points/20240502_Points/060B37G...  1815.878540   \n",
-       "110         /Volumes/Eirene/Points/20240502_Points/060B37G...  1130.231934   \n",
-       "111         /Volumes/Eirene/Points/20240502_Points/060B37G...  1418.198730   \n",
-       "112         /Volumes/Eirene/Points/20240502_Points/060B37G...   676.623718   \n",
-       "113         /Volumes/Eirene/Points/20240502_Points/060B37G...  1515.624390   \n",
-       "\n",
-       "                                                y  ...                        \\\n",
-       "contour_id            1            2            0  ...         12         10   \n",
-       "0            714.438110   659.801331   368.882446  ... -34.701508   9.379944   \n",
-       "1            684.748718   658.552246  1216.467163  ...  32.204224 -18.961182   \n",
-       "2           1166.509277  1194.055298   546.148804  ...  -8.783081  13.524414   \n",
-       "3            632.182800   621.798279   811.559875  ...  -2.920349   5.416809   \n",
-       "4            660.333313   651.250000  1190.425171  ...  61.833374 -31.241455   \n",
-       "..                  ...          ...          ...  ...        ...        ...   \n",
-       "109         1825.607788  1856.567627  1285.116211  ...  43.918091 -27.732544   \n",
-       "110         1110.025513  1072.568115  1364.009277  ...  40.578247 -23.065186   \n",
-       "111         1404.029663  1387.274658  1547.150391  ...  44.911133 -24.726318   \n",
-       "112          647.250366   597.244324   589.625671  ...  15.999146  -9.414612   \n",
-       "113         1546.256470  1601.316040   477.224579  ...  12.226410  -2.622223   \n",
-       "\n",
-       "                    z                        x                     y  \\\n",
-       "contour_id         12         10          12_t       10_t       12_t   \n",
-       "0            0.744720  14.999207  0.000000e+00  -0.104016 -64.729645   \n",
-       "1          -20.806656  16.376190 -3.552714e-15  -7.820059 -46.435806   \n",
-       "2            1.146484   6.916412 -7.105427e-15   9.193258 -28.935103   \n",
-       "3           -1.805672   0.898895 -8.881784e-16  12.375231 -10.937421   \n",
-       "4            0.000000   2.957275  1.776357e-15   1.577322 -62.496982   \n",
-       "..                ...        ...           ...        ...        ...   \n",
-       "109        -33.255699  18.096863 -3.552714e-15   8.835842 -63.192182   \n",
-       "110          7.318817  -0.768005 -7.105427e-15  -0.537872 -55.706515   \n",
-       "111          7.034332  -4.091049  3.552714e-15   4.931969 -48.448136   \n",
-       "112         10.191650  -5.982864  0.000000e+00  -0.032102 -53.483144   \n",
-       "113         21.913696 -10.990875  1.776357e-15  -6.769159 -60.508276   \n",
-       "\n",
-       "                                  z             \n",
-       "contour_id       10_t          12_t       10_t  \n",
-       "0           16.800645 -2.220446e-16  28.340858  \n",
-       "1           25.640389  7.105427e-15  -4.196947  \n",
-       "2           32.132206  0.000000e+00  28.122263  \n",
-       "3           27.931954  0.000000e+00  -6.639047  \n",
-       "4           31.805992  0.000000e+00  -2.989013  \n",
-       "..                ...           ...        ...  \n",
-       "109         33.564281  0.000000e+00   2.654621  \n",
-       "110         30.489174 -1.776357e-15  -4.510022  \n",
-       "111         28.415284  1.776357e-15   0.229769  \n",
-       "112         31.420107 -3.552714e-15  -0.034142  \n",
-       "113         32.384004 -3.552714e-15   7.390998  \n",
-       "\n",
-       "[114 rows x 27 columns]"
-      ]
-     },
-     "execution_count": 246,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "coord_cols = ['x', 'y', 'z']\n",
     "\n",
@@ -951,7 +174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 247,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -964,18 +187,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(1, 322, 322)\n",
-      "(322, 322, 322)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# produce substacks\n",
     "\n",
diff --git a/notebooks/t3ss_geometry.ipynb b/notebooks/t3ss_geometry.ipynb
index 4dd71e50c9c310a94b4ad27364f323718c72cc23..7092963b9684640774d6d88c2f8cfb2f0fb3379a 100644
--- a/notebooks/t3ss_geometry.ipynb
+++ b/notebooks/t3ss_geometry.ipynb
@@ -17,10 +17,7 @@
     "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) angle between 21 and 23\n",
-    "4) for every 3 the closest membrane break point\n",
-    "\n",
-    "5) distances between all 2\n",
+    "3) distances between all 2\n",
     "\n",
     "Perform measurements for\n",
     "- each dataset\n",
@@ -28,7 +25,7 @@
     "\n",
     "Output is a table containing\n",
     "- dataset\n",
-    "- needle number\n",
+    "- T3SS id\n",
     "- measurements\n"
    ]
   },
@@ -46,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -61,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -76,257 +73,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "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 breaks model found for 057B30G2_TS_10_bin2_tiltcor_rec_corrected.mrc\n",
-      "No T3SS model found for 057B30G2_TS_29_bin2_tiltcor_rec_corrected.mrc\n",
-      "No breaks model found for 057B30G2_TS_12_bin2_tiltcor_rec_corrected.mrc\n",
-      "No T3SS model found for 054B36G1_TS_05_bin2_tiltcor_rec_corrected.mrc\n",
-      "No breaks model found for 054B36G1_TS_03_bin2_tiltcor_rec_corrected.mrc\n",
-      "No breaks model found for 054B36G1_TS_13_bin2_tiltcor_rec_corrected.mrc\n",
-      "No T3SS model found for 054B36G1_TS_12_bin2_tiltcor_rec_corrected.mrc\n",
-      "No breaks model found for 053B40G2_TS_22_bin3_tiltcor_rec_corrected.mrc\n",
-      "No breaks model found for 053B40G2_TS_08_bin3_tiltcor_rec_corrected.mrc\n",
-      "No breaks model found for 053B40G2_TS_04_bin3_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>430.220528</td>\n",
-       "      <td>1314.873257</td>\n",
-       "      <td>108.992062</td>\n",
-       "      <td>057B30G2_TS_16_bin2_tiltcor_T3SS.mod</td>\n",
-       "      <td>T3SS</td>\n",
-       "      <td>16</td>\n",
-       "      <td>057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc</td>\n",
-       "      <td>057B30G2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>410.232665</td>\n",
-       "      <td>1334.406249</td>\n",
-       "      <td>117.338149</td>\n",
-       "      <td>057B30G2_TS_16_bin2_tiltcor_T3SS.mod</td>\n",
-       "      <td>T3SS</td>\n",
-       "      <td>16</td>\n",
-       "      <td>057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc</td>\n",
-       "      <td>057B30G2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>560.002364</td>\n",
-       "      <td>1406.025222</td>\n",
-       "      <td>99.931734</td>\n",
-       "      <td>057B30G2_TS_16_bin2_tiltcor_T3SS.mod</td>\n",
-       "      <td>T3SS</td>\n",
-       "      <td>16</td>\n",
-       "      <td>057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc</td>\n",
-       "      <td>057B30G2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>551.808454</td>\n",
-       "      <td>1429.227718</td>\n",
-       "      <td>113.049751</td>\n",
-       "      <td>057B30G2_TS_16_bin2_tiltcor_T3SS.mod</td>\n",
-       "      <td>T3SS</td>\n",
-       "      <td>16</td>\n",
-       "      <td>057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc</td>\n",
-       "      <td>057B30G2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>409.335017</td>\n",
-       "      <td>760.494224</td>\n",
-       "      <td>128.397068</td>\n",
-       "      <td>057B30G2_TS_16_bin2_tiltcor_T3SS.mod</td>\n",
-       "      <td>T3SS</td>\n",
-       "      <td>16</td>\n",
-       "      <td>057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc</td>\n",
-       "      <td>057B30G2</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>0</td>\n",
-       "      <td>9</td>\n",
-       "      <td>863.843156</td>\n",
-       "      <td>1125.199956</td>\n",
-       "      <td>27.004798</td>\n",
-       "      <td>053B40G2_TS_18_bin3_tiltcor_break.mod</td>\n",
-       "      <td>break</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>10</td>\n",
-       "      <td>856.827783</td>\n",
-       "      <td>1128.298310</td>\n",
-       "      <td>28.606712</td>\n",
-       "      <td>053B40G2_TS_18_bin3_tiltcor_break.mod</td>\n",
-       "      <td>break</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>299 rows × 10 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    object_id  contour_id           x            y           z  \\\n",
-       "0           0           0  430.220528  1314.873257  108.992062   \n",
-       "0           0           1  410.232665  1334.406249  117.338149   \n",
-       "0           1           0  560.002364  1406.025222   99.931734   \n",
-       "0           1           1  551.808454  1429.227718  113.049751   \n",
-       "0           2           0  409.335017   760.494224  128.397068   \n",
-       "..        ...         ...         ...          ...         ...   \n",
-       "0           0           9  863.843156  1125.199956   27.004798   \n",
-       "0           0          10  856.827783  1128.298310   28.606712   \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    057B30G2_TS_16_bin2_tiltcor_T3SS.mod   T3SS       16   \n",
-       "0    057B30G2_TS_16_bin2_tiltcor_T3SS.mod   T3SS       16   \n",
-       "0    057B30G2_TS_16_bin2_tiltcor_T3SS.mod   T3SS       16   \n",
-       "0    057B30G2_TS_16_bin2_tiltcor_T3SS.mod   T3SS       16   \n",
-       "0    057B30G2_TS_16_bin2_tiltcor_T3SS.mod   T3SS       16   \n",
-       "..                                    ...    ...      ...   \n",
-       "0   053B40G2_TS_18_bin3_tiltcor_break.mod  break       18   \n",
-       "0   053B40G2_TS_18_bin3_tiltcor_break.mod  break       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   057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc  057B30G2  \n",
-       "0   057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc  057B30G2  \n",
-       "0   057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc  057B30G2  \n",
-       "0   057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc  057B30G2  \n",
-       "0   057B30G2_TS_16_bin2_tiltcor_rec_corrected.mrc  057B30G2  \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",
-       "[299 rows x 10 columns]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Extract info from files\n",
     "\n",
@@ -360,16 +109,7 @@
     "        tdf['source_fn'] = t3ss_name\n",
     "        tdf['type'] = 'T3SS'\n",
     "\n",
-    "        if os.path.exists(breaks_path):\n",
-    "            bdf = imodmodel.read(breaks_path)\n",
-    "            bdf['source_fn'] = breaks_name\n",
-    "            bdf['type'] = 'break'\n",
-    "            cdf = pd.concat([tdf, bdf])\n",
-    "        else:\n",
-    "            cdf = tdf\n",
-    "            msg = 'No breaks model found for {}'.format(fn)\n",
-    "            log_msgs.append(msg)\n",
-    "            print(msg)\n",
+    "        cdf = tdf\n",
     "\n",
     "        cdf['tomo_id'] = extract_ts_id_from_fn(fn)\n",
     "        cdf['tomo_fn'] = fn\n",
@@ -392,719 +132,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "<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",
-       "      <th>distance_to_closest_break</th>\n",
-       "      <th>closest_break_contour_id</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",
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"14\" valign=\"top\">053B40G2</th>\n",
-       "      <th>4</th>\n",
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-       "      <td>21.095611</td>\n",
-       "      <td>60.276241</td>\n",
-       "      <td>137.868919</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>&lt;NA&gt;</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",
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-       "    <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",
-       "      <td>10.670890</td>\n",
-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <th>0</th>\n",
-       "      <td>30.172572</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
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-       "    <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",
-       "      <td>603.879757</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <th>0</th>\n",
-       "      <th>0</th>\n",
-       "      <td>26.332217</td>\n",
-       "      <td>10.184926</td>\n",
-       "      <td>171.030794</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
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-       "    <tr>\n",
-       "      <th rowspan=\"4\" valign=\"top\">18</th>\n",
-       "      <th>0</th>\n",
-       "      <th>0</th>\n",
-       "      <td>29.780962</td>\n",
-       "      <td>58.197185</td>\n",
-       "      <td>173.998510</td>\n",
-       "      <td>3.937672</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <th>0</th>\n",
-       "      <td>29.499533</td>\n",
-       "      <td>49.947940</td>\n",
-       "      <td>173.269796</td>\n",
-       "      <td>3.776994</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <th>0</th>\n",
-       "      <td>27.753017</td>\n",
-       "      <td>59.075236</td>\n",
-       "      <td>171.329462</td>\n",
-       "      <td>5.644370</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <th>0</th>\n",
-       "      <td>26.119723</td>\n",
-       "      <td>52.913860</td>\n",
-       "      <td>175.469543</td>\n",
-       "      <td>1040.640748</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">20</th>\n",
-       "      <th>0</th>\n",
-       "      <th>0</th>\n",
-       "      <td>27.564635</td>\n",
-       "      <td>51.528415</td>\n",
-       "      <td>171.741981</td>\n",
-       "      <td>5.901630</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <th>0</th>\n",
-       "      <td>29.986906</td>\n",
-       "      <td>54.243535</td>\n",
-       "      <td>173.332289</td>\n",
-       "      <td>1091.114905</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">22</th>\n",
-       "      <th>0</th>\n",
-       "      <th>0</th>\n",
-       "      <td>29.095342</td>\n",
-       "      <td>65.611610</td>\n",
-       "      <td>167.552579</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <th>0</th>\n",
-       "      <td>34.082424</td>\n",
-       "      <td>45.047355</td>\n",
-       "      <td>172.796835</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"21\" valign=\"top\">054B36G1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>0</th>\n",
-       "      <th>0</th>\n",
-       "      <td>28.860724</td>\n",
-       "      <td>50.738282</td>\n",
-       "      <td>169.974732</td>\n",
-       "      <td>13.976631</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <th>0</th>\n",
-       "      <th>0</th>\n",
-       "      <td>32.918810</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"7\" valign=\"top\">7</th>\n",
-       "      <th>0</th>\n",
-       "      <th>0</th>\n",
-       "      <td>28.186633</td>\n",
-       "      <td>28.223016</td>\n",
-       "      <td>154.695752</td>\n",
-       "      <td>129.037390</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <th>0</th>\n",
-       "      <td>28.329459</td>\n",
-       "      <td>48.747746</td>\n",
-       "      <td>162.660341</td>\n",
-       "      <td>9.551492</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <th>0</th>\n",
-       "      <td>30.429561</td>\n",
-       "      <td>47.480343</td>\n",
-       "      <td>160.759520</td>\n",
-       "      <td>23.269485</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <th>0</th>\n",
-       "      <td>31.647358</td>\n",
-       "      <td>45.451366</td>\n",
-       "      <td>166.210177</td>\n",
-       "      <td>7.781381</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <th>0</th>\n",
-       "      <td>26.638607</td>\n",
-       "      <td>48.631320</td>\n",
-       "      <td>164.303959</td>\n",
-       "      <td>16.315059</td>\n",
-       "      <td>46</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <th>0</th>\n",
-       "      <td>30.272416</td>\n",
-       "      <td>53.902126</td>\n",
-       "      <td>168.519110</td>\n",
-       "      <td>97.418625</td>\n",
-       "      <td>39</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <th>0</th>\n",
-       "      <td>65.285148</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">11</th>\n",
-       "      <th>0</th>\n",
-       "      <th>0</th>\n",
-       "      <td>48.298107</td>\n",
-       "      <td>55.133918</td>\n",
-       "      <td>102.926047</td>\n",
-       "      <td>15.566473</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <th>0</th>\n",
-       "      <td>27.684032</td>\n",
-       "      <td>72.729038</td>\n",
-       "      <td>152.148465</td>\n",
-       "      <td>10.977328</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <th>0</th>\n",
-       "      <td>30.399087</td>\n",
-       "      <td>46.203811</td>\n",
-       "      <td>156.695492</td>\n",
-       "      <td>188.565914</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">13</th>\n",
-       "      <th>0</th>\n",
-       "      <th>0</th>\n",
-       "      <td>29.523980</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <th>0</th>\n",
-       "      <td>31.098125</td>\n",
-       "      <td>67.095690</td>\n",
-       "      <td>148.884104</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"7\" valign=\"top\">14</th>\n",
-       "      <th>0</th>\n",
-       "      <th>0</th>\n",
-       "      <td>32.563100</td>\n",
-       "      <td>41.258266</td>\n",
-       "      <td>168.134535</td>\n",
-       "      <td>152.079839</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <th>0</th>\n",
-       "      <td>28.940105</td>\n",
-       "      <td>27.522238</td>\n",
-       "      <td>170.971118</td>\n",
-       "      <td>115.028001</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <th>0</th>\n",
-       "      <td>27.935260</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <th>0</th>\n",
-       "      <td>29.533568</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <th>0</th>\n",
-       "      <td>31.274453</td>\n",
-       "      <td>41.612607</td>\n",
-       "      <td>166.921202</td>\n",
-       "      <td>97.473849</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <th>0</th>\n",
-       "      <td>28.293434</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <th>0</th>\n",
-       "      <td>33.042608</td>\n",
-       "      <td>61.780070</td>\n",
-       "      <td>174.441777</td>\n",
-       "      <td>290.449040</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"17\" valign=\"top\">057B30G2</th>\n",
-       "      <th rowspan=\"4\" valign=\"top\">2</th>\n",
-       "      <th>0</th>\n",
-       "      <th>0</th>\n",
-       "      <td>18.968181</td>\n",
-       "      <td>17.610463</td>\n",
-       "      <td>166.291669</td>\n",
-       "      <td>7.722758</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <th>0</th>\n",
-       "      <td>28.534100</td>\n",
-       "      <td>44.234555</td>\n",
-       "      <td>165.797150</td>\n",
-       "      <td>58.574351</td>\n",
-       "      <td>28</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <th>0</th>\n",
-       "      <td>30.633345</td>\n",
-       "      <td>32.004088</td>\n",
-       "      <td>166.788177</td>\n",
-       "      <td>565.868529</td>\n",
-       "      <td>30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <th>0</th>\n",
-       "      <td>27.835786</td>\n",
-       "      <td>43.018873</td>\n",
-       "      <td>169.477370</td>\n",
-       "      <td>433.225682</td>\n",
-       "      <td>31</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <th>0</th>\n",
-       "      <th>0</th>\n",
-       "      <td>28.034553</td>\n",
-       "      <td>53.457678</td>\n",
-       "      <td>160.674130</td>\n",
-       "      <td>1002.391912</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <th>0</th>\n",
-       "      <th>0</th>\n",
-       "      <td>25.565483</td>\n",
-       "      <td>68.222377</td>\n",
-       "      <td>160.495419</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">12</th>\n",
-       "      <th>0</th>\n",
-       "      <th>0</th>\n",
-       "      <td>29.674439</td>\n",
-       "      <td>40.453873</td>\n",
-       "      <td>165.654194</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <th>0</th>\n",
-       "      <td>33.328252</td>\n",
-       "      <td>40.899023</td>\n",
-       "      <td>173.268236</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">16</th>\n",
-       "      <th>0</th>\n",
-       "      <th>0</th>\n",
-       "      <td>29.166927</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <th>0</th>\n",
-       "      <td>27.885092</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <th>0</th>\n",
-       "      <td>32.616679</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <th>0</th>\n",
-       "      <th>0</th>\n",
-       "      <td>29.180735</td>\n",
-       "      <td>30.049136</td>\n",
-       "      <td>174.280104</td>\n",
-       "      <td>170.293861</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">23</th>\n",
-       "      <th>0</th>\n",
-       "      <th>0</th>\n",
-       "      <td>31.895192</td>\n",
-       "      <td>41.637150</td>\n",
-       "      <td>149.514568</td>\n",
-       "      <td>8.084435</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <th>0</th>\n",
-       "      <td>29.498332</td>\n",
-       "      <td>50.533080</td>\n",
-       "      <td>171.461484</td>\n",
-       "      <td>1201.192824</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">30</th>\n",
-       "      <th>0</th>\n",
-       "      <th>0</th>\n",
-       "      <td>17.554856</td>\n",
-       "      <td>51.042460</td>\n",
-       "      <td>168.864374</td>\n",
-       "      <td>5.343565</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <th>0</th>\n",
-       "      <td>29.404072</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <th>0</th>\n",
-       "      <td>32.240981</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\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",
-       "         8       0         0     26.332217     10.184926   171.030794   \n",
-       "         18      0         0     29.780962     58.197185   173.998510   \n",
-       "                 1         0     29.499533     49.947940   173.269796   \n",
-       "                 2         0     27.753017     59.075236   171.329462   \n",
-       "                 3         0     26.119723     52.913860   175.469543   \n",
-       "         20      0         0     27.564635     51.528415   171.741981   \n",
-       "                 1         0     29.986906     54.243535   173.332289   \n",
-       "         22      0         0     29.095342     65.611610   167.552579   \n",
-       "                 1         0     34.082424     45.047355   172.796835   \n",
-       "054B36G1 2       0         0     28.860724     50.738282   169.974732   \n",
-       "         3       0         0     32.918810           NaN          NaN   \n",
-       "         7       0         0     28.186633     28.223016   154.695752   \n",
-       "                 1         0     28.329459     48.747746   162.660341   \n",
-       "                 2         0     30.429561     47.480343   160.759520   \n",
-       "                 3         0     31.647358     45.451366   166.210177   \n",
-       "                 4         0     26.638607     48.631320   164.303959   \n",
-       "                 5         0     30.272416     53.902126   168.519110   \n",
-       "                 6         0     65.285148           NaN          NaN   \n",
-       "         11      0         0     48.298107     55.133918   102.926047   \n",
-       "                 1         0     27.684032     72.729038   152.148465   \n",
-       "                 2         0     30.399087     46.203811   156.695492   \n",
-       "         13      0         0     29.523980           NaN          NaN   \n",
-       "                 1         0     31.098125     67.095690   148.884104   \n",
-       "         14      0         0     32.563100     41.258266   168.134535   \n",
-       "                 1         0     28.940105     27.522238   170.971118   \n",
-       "                 2         0     27.935260           NaN          NaN   \n",
-       "                 3         0     29.533568           NaN          NaN   \n",
-       "                 4         0     31.274453     41.612607   166.921202   \n",
-       "                 5         0     28.293434           NaN          NaN   \n",
-       "                 6         0     33.042608     61.780070   174.441777   \n",
-       "057B30G2 2       0         0     18.968181     17.610463   166.291669   \n",
-       "                 1         0     28.534100     44.234555   165.797150   \n",
-       "                 2         0     30.633345     32.004088   166.788177   \n",
-       "                 3         0     27.835786     43.018873   169.477370   \n",
-       "         4       0         0     28.034553     53.457678   160.674130   \n",
-       "         10      0         0     25.565483     68.222377   160.495419   \n",
-       "         12      0         0     29.674439     40.453873   165.654194   \n",
-       "                 1         0     33.328252     40.899023   173.268236   \n",
-       "         16      0         0     29.166927           NaN          NaN   \n",
-       "                 1         0     27.885092           NaN          NaN   \n",
-       "                 2         0     32.616679           NaN          NaN   \n",
-       "         20      0         0     29.180735     30.049136   174.280104   \n",
-       "         23      0         0     31.895192     41.637150   149.514568   \n",
-       "                 1         0     29.498332     50.533080   171.461484   \n",
-       "         30      0         0     17.554856     51.042460   168.864374   \n",
-       "                 1         0     29.404072           NaN          NaN   \n",
-       "                 2         0     32.240981           NaN          NaN   \n",
-       "\n",
-       "                              distance_to_closest_break  \\\n",
-       "ds       tomo_id object_id                                \n",
-       "053B40G2 4       0         0                        NaN   \n",
-       "         5       0         0                        NaN   \n",
-       "                 1         0                  10.670890   \n",
-       "                 2         0                        NaN   \n",
-       "                 3         0                 603.879757   \n",
-       "         8       0         0                        NaN   \n",
-       "         18      0         0                   3.937672   \n",
-       "                 1         0                   3.776994   \n",
-       "                 2         0                   5.644370   \n",
-       "                 3         0                1040.640748   \n",
-       "         20      0         0                   5.901630   \n",
-       "                 1         0                1091.114905   \n",
-       "         22      0         0                        NaN   \n",
-       "                 1         0                        NaN   \n",
-       "054B36G1 2       0         0                  13.976631   \n",
-       "         3       0         0                        NaN   \n",
-       "         7       0         0                 129.037390   \n",
-       "                 1         0                   9.551492   \n",
-       "                 2         0                  23.269485   \n",
-       "                 3         0                   7.781381   \n",
-       "                 4         0                  16.315059   \n",
-       "                 5         0                  97.418625   \n",
-       "                 6         0                        NaN   \n",
-       "         11      0         0                  15.566473   \n",
-       "                 1         0                  10.977328   \n",
-       "                 2         0                 188.565914   \n",
-       "         13      0         0                        NaN   \n",
-       "                 1         0                        NaN   \n",
-       "         14      0         0                 152.079839   \n",
-       "                 1         0                 115.028001   \n",
-       "                 2         0                        NaN   \n",
-       "                 3         0                        NaN   \n",
-       "                 4         0                  97.473849   \n",
-       "                 5         0                        NaN   \n",
-       "                 6         0                 290.449040   \n",
-       "057B30G2 2       0         0                   7.722758   \n",
-       "                 1         0                  58.574351   \n",
-       "                 2         0                 565.868529   \n",
-       "                 3         0                 433.225682   \n",
-       "         4       0         0                1002.391912   \n",
-       "         10      0         0                        NaN   \n",
-       "         12      0         0                        NaN   \n",
-       "                 1         0                        NaN   \n",
-       "         16      0         0                        NaN   \n",
-       "                 1         0                        NaN   \n",
-       "                 2         0                        NaN   \n",
-       "         20      0         0                 170.293861   \n",
-       "         23      0         0                   8.084435   \n",
-       "                 1         0                1201.192824   \n",
-       "         30      0         0                   5.343565   \n",
-       "                 1         0                        NaN   \n",
-       "                 2         0                        NaN   \n",
-       "\n",
-       "                              closest_break_contour_id  \n",
-       "ds       tomo_id object_id                              \n",
-       "053B40G2 4       0         0                      <NA>  \n",
-       "         5       0         0                         0  \n",
-       "                 1         0                         0  \n",
-       "                 2         0                         0  \n",
-       "                 3         0                         9  \n",
-       "         8       0         0                      <NA>  \n",
-       "         18      0         0                         4  \n",
-       "                 1         0                         9  \n",
-       "                 2         0                         6  \n",
-       "                 3         0                         0  \n",
-       "         20      0         0                         1  \n",
-       "                 1         0                         1  \n",
-       "         22      0         0                      <NA>  \n",
-       "                 1         0                      <NA>  \n",
-       "054B36G1 2       0         0                         3  \n",
-       "         3       0         0                      <NA>  \n",
-       "         7       0         0                         0  \n",
-       "                 1         0                         1  \n",
-       "                 2         0                         3  \n",
-       "                 3         0                         7  \n",
-       "                 4         0                        46  \n",
-       "                 5         0                        39  \n",
-       "                 6         0                         0  \n",
-       "         11      0         0                         3  \n",
-       "                 1         0                         0  \n",
-       "                 2         0                         1  \n",
-       "         13      0         0                      <NA>  \n",
-       "                 1         0                      <NA>  \n",
-       "         14      0         0                         7  \n",
-       "                 1         0                         3  \n",
-       "                 2         0                         0  \n",
-       "                 3         0                         0  \n",
-       "                 4         0                         2  \n",
-       "                 5         0                         0  \n",
-       "                 6         0                         2  \n",
-       "057B30G2 2       0         0                         1  \n",
-       "                 1         0                        28  \n",
-       "                 2         0                        30  \n",
-       "                 3         0                        31  \n",
-       "         4       0         0                         2  \n",
-       "         10      0         0                      <NA>  \n",
-       "         12      0         0                      <NA>  \n",
-       "                 1         0                      <NA>  \n",
-       "         16      0         0                         0  \n",
-       "                 1         0                         0  \n",
-       "                 2         0                         0  \n",
-       "         20      0         0                         2  \n",
-       "         23      0         0                         0  \n",
-       "                 1         0                         1  \n",
-       "         30      0         0                         4  \n",
-       "                 1         0                         0  \n",
-       "                 2         0                         0  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Perform measurements\n",
     "\n",
@@ -1115,7 +145,6 @@
     "    # get position of contours\n",
     "\n",
     "    tdf = gdf[gdf.type=='T3SS']\n",
-    "    # bdf = gdf[gdf.type=='break']\n",
     "\n",
     "    tdf = tdf.sort_values(by=['object_id', 'contour_id'])\n",
     "\n",
@@ -1132,51 +161,12 @@
     "    # 1) distance 1<->2\n",
     "    d12 = np.linalg.norm(positions[0] - positions[1])\n",
     "\n",
-    "    if d12 > 100:\n",
-    "        # log message indicating gdf\n",
-    "        msg = 'Distance between 1 and 2 is suspiciously large: {}. Maybe theres a measurement problem?'.format(d12)\n",
-    "        msg += '\\n\\tdataset: {}\\ttomo_id: {}\\tobject_id (starting at 0): {}'.format(gdf['ds'].values[0], gdf['tomo_id'].values[0], gdf['object_id'].values[0])\n",
-    "        log_msgs.append(msg)\n",
-    "\n",
     "    # 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",
-    "    v21 = positions[0] - positions[1]\n",
-    "    v23 = positions[2] - positions[1]\n",
-    "\n",
-    "    angle_rad = np.arccos(\n",
-    "        np.dot(v21, v23) / np.product([np.linalg.norm(v) for v in [v21, v23]]))\n",
-    "\n",
-    "    and_deg = np.degrees(angle_rad)\n",
-    "\n",
-    "    # # 4) for every 3 the closest membrane break point\n",
-    "    bdf = df[\\\n",
-    "         (df.type=='break') &\\\n",
-    "         (df.ds==gdf['ds'].values[0]) &\\\n",
-    "         (df.tomo_id==gdf['tomo_id'].values[0])]\n",
-    "\n",
-    "    bdf = bdf.sort_values(by=['contour_id'])\n",
-    "\n",
-    "    if len(bdf):\n",
-    "        b_positions = np.array([bdf['x'], bdf['y'], bdf['z']]).T\n",
-    "        b_dists = np.linalg.norm(np.array(positions[2]) - b_positions, axis=1)\n",
-    "        b_closest_ind = np.argmin(b_dists)\n",
-    "\n",
-    "        b_closest_contour_id = int(list(bdf['contour_id'])[b_closest_ind])\n",
-    "        b_distance = b_dists[b_closest_ind]\n",
-    "\n",
-    "    else:\n",
-    "        b_closest_contour_id = np.nan\n",
-    "        b_distance = np.nan\n",
-    "\n",
     "    ms = pd.DataFrame({\n",
     "        'distance_1_2': [d12],\n",
-    "        'distance_2_3': [d23],\n",
-    "        'angle_21_23': [and_deg],\n",
-    "        'distance_to_closest_break': [b_distance],\n",
-    "        'closest_break_contour_id': pd.array([b_closest_contour_id], dtype=pd.Int64Dtype()),\n",
-    "        \n",
+    "        'distance_2_3': [d23],        \n",
     "        })\n",
     "\n",
     "    return ms\n",
@@ -1190,7 +180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1211,40 +201,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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7rDKxd+9epaen66abbvJY5QAAAOA6t4LdlClTFB4ersTERLVp00Zt2rRRUlKSGjdurBdeeMHTdQQAAIAL3Jqg2Gq16uuvv9by5cu1bds2NWjQQF26dFHPnj09XT8AAAC4iHnsAAAA/Fh1soxbPXaStGLFCq1YsUJHjx6VzWZz2Pbmm2+6dczJkycrIyNDjzzyiP7xj39IkoqKipSenq558+apuLhY/fv312uvvabY2Fh3qw4AAGBKbt1j9/TTT6tfv35asWKFfv75Z504ccLh4Y7//Oc/ev3119WlSxeH8jFjxuiTTz7RggULtGbNGmVnZ+vWW2916xwAAABm5laP3YwZM/TWW2/prrvu8kglTp06paFDh+qNN97Qs88+ay/Pz8/X7NmzNWfOHF177bWSpMzMTCUlJWn9+vXq3r27R84PAABgBm712J07d06//e1vPVaJUaNG6YYbblDfvn0dyjdt2qSSkhKH8sTERMXHx2vdunUeOz8AAIAZuBXs/vSnP2nOnDkeqcC8efO0efNmTZo0qdK2nJwchYSEKDo62qE8NjZWOTk5To9XXFysgoIChwcAAEB94Nal2KKiIs2cOVNffPGFunTpouDgYIftL774okvHOXTokB555BEtX75cYWFh7lSlkkmTJunpp5/2yLEAAADqEreC3bfffquuXbtKkrZv3+6wzWKxuHycTZs26ejRo7rqqqvsZaWlpfryyy/1yiuvaOnSpTp37pxOnjzp0GuXm5urZs2aOT1mRkaGxo4da39eUFCguLg4l+sEAABQV7kV7FatWuWRk/fp00ffffedQ9nw4cOVmJioxx57THFxcQoODtaKFSs0cOBASdLu3buVlZWl1NRUp8cMDQ1VaGioR+oHAABQl7g9j50nREZGqnPnzg5l4eHhaty4sb18xIgRGjt2rBo1aqSoqCg99NBDSk1NZUQsAABABW4Hu40bN+qDDz5QVlaWzp0757Bt4cKFNa5YuZdeekkBAQEaOHCgwwTFAAAAcOTWkmLz5s3T3Xffrf79+2vZsmXq16+f9uzZo9zcXN1yyy3KzMz0Rl3dwpJiAACgLqtOlnFrupPnnntOL730kj755BOFhIRo2rRp2rVrlwYNGqT4+Hi3Kg0AAICacSvY7du3TzfccIMkKSQkRKdPn5bFYtGYMWM0c+ZMj1YQAAAArnEr2DVs2FCFhYWSpJYtW9qnPDl58qTOnDnjudoBAADAZW4NnujZs6eWL1+uyy+/XLfffrseeeQRrVy5UsuXL1efPn08XUcAAAC4wK1g98orr6ioqEiSNH78eAUHB+vrr7/WwIED9cQTT3i0ggAAAHCNW6Ni6xJGxQIAgLrM66NiAwMDdfTo0Urlx44dU2BgoDuHBAAAQA25Feyq6uQrLi5WSEhIjSoEAAAA91TrHruXX35ZkmSxWDRr1ixFRETYt5WWlurLL79UYmKiZ2sIAAAAl1Qr2L300kuSynrsZsyY4XDZNSQkRK1bt9aMGTM8W0MAAAC4pFrB7sCBA5Kk3/3ud1q4cKEaNmzolUoBAACg+ty6x27VqlUOoa60tFRbt27ViRMnPFYxAAAAVI9bwW706NGaPXu2pLJQ17NnT1111VWKi4vT6tWrPVk/AAAAuMitYLdgwQJdccUVkqRPPvlEBw8e1K5duzRmzBiNHz/eoxUEAACAa9wKdseOHVOzZs0kSZ999pluv/12dejQQffdd5++++47j1YQAAAArnEr2MXGxmrHjh0qLS3V559/ruuuu06SdObMGSYoBgAA8BG31oodPny4Bg0apObNm8tisahv376SpA0bNjCPHQAAgI+4Feyeeuopde7cWYcOHdLtt9+u0NBQSWVLjT3++OMerSAAAABcYzGqWh/MJKqzcC4AAIC/qU6WcbnH7uWXX9bIkSMVFhZmX1qsKg8//LCrhwUAAICHuNxj16ZNG23cuFGNGzdWmzZtqj6gxaL9+/d7rII1RY8dAACoy7zSY1e+nFjFfwMAAMA/uBzsxo4d69J+FotFU6dOdbtCAAAAcI/LwW7Lli0Ozzdv3qzz58+rY8eOkqQ9e/YoMDBQKSkpnq0hAAAAXOJysFu1apX93y+++KIiIyP19ttvq2HDhpKkEydOaPjw4UpLS/N8LQEAAHBJbk130rJlSy1btkzJyckO5du3b1e/fv2UnZ3tsQrWFIMnAABAXVadLOPWkmIFBQXKy8urVJ6Xl6fCwkJ3DgkAAIAacivY3XLLLRo+fLgWLlyow4cP6/Dhw/rwww81YsQI3XrrrZ6uIwAAAFzg1pJiM2bM0KOPPqohQ4aopKSk7EBBQRoxYoSef/55j1YQAAAArqnRkmKnT5/Wvn37JEmXXXaZwsPDPVYxT+EeOwAAUJd5ZYJiZ8LDw9WlS5eaHAIAAAAe4tY9dgAAAPA/BDsAAACTINgBAACYBMEOAADAJGo0eAImtHOxtHaqlLdLapoopaVLSTf6ulYAAMAF9Njhv3YuluYPlbI3SyVnyn7OH1ZWDgAA/B49dnVcUVGRsrKyPHKs+OUTFVap1NDZL57TocAOHjmHP4iPj1dYWOX/KQAAdR3Bro7LysrSyJEjPXKsJWm7pEAnG47u9Ng5/MHMmTPVoYN5gioAAOUIdnVcfHy8Zs6c6ZFjWT6/Rzq+o/KGmKRqn+PHH3/UxIkTNX78eCUkJHikfp4SHx/v6yoAAOAVBLs6LiwszHO9T6Xjy+6p04WrzFnU4Lrxbp8jISGB3jEAQBkG6HkdgyfwX0k3Sne8J7VMkYLDy37e+b6UeIOvawYAqOsYoFcr6LGDo6Qb+fYEAH7Ak4Pj/EFtDNBjcBzBDgD+qzqXibikBC/z5OA4f1AbA/QYHEewA4Ay5ZeJypVfJrrjvcqBrTr7Am7y5OA4T6nJwDhPDtCrCoPjCHYA6hBvXpqqzmWiujTnI5em6i6PDo7zMLcGxnlhgB4qI9gBqDO8eWmqOpeJlqbtrDNzPnJpCh6zc7Hil0/UkrRdZb1vpeOr10NdPkDvqxelo7ukmF9uYWCAnkcR7ADUGd68NOXqZaLwQ6sVunaL02Ocs7b1u0tnXJqCR/xy+0GYVPal5vgO924/YICe1xHsANQZXr005eplotX/4/TlhiGduuoBesdgTmunOik0ynrfCGp+hXnsAEByfR7HvF1OX37OZtHpVr1qoaKAD+Rud15+1Pn7Ab5Djx0AlHPlMlHTxLJRsBXsO92AD1SY087FUuk559tiEmu3LrgkeuwAoDrS0iVZHIoMWfT+j7G+qQ/gbU4vw/4iZ7s083esHuFHCHYAUB1OLtlm93xe/z4W7euaAd5Rxe0HkqTSYpYG8zNcOQCA6qpwyfb0nj2S3i97wooUMJsqbj9wxEAKf0GPHQB4SPih1SxyDvNxcvuBUwyk8Av02AFwKjc3V/n5+b6uRp3w448/SpIitrzuZKt/rkjhS1arVbGx3JNYZ1wwsbAtd4eKi4vVINBWeT8GUvgFgh2ASnJzczXsrrtVcq7Y11Xxez2anNTQ+FwtSStSaKHNeceGH65I4UvBIaF67913CHd1yS+3H+zds0fv/GWQ/tb5oCwV5nws69mDrxHs4D+4N8lv5Ofnq+Rcsc627SVbmNXX1fFbvUJ26dno+Zfcb5+tuX7dtYOGh69V26A87T/fVJmn07TmXP3r4Qgoypf2r1F+fj7Bro766udoZadNUcv981gazA8R7OAfflmuxq783qTqLlcDj7KFWWULb+Lravite8PWXXIfm2HRN0YnvXBBAEwOztYU6wcaVzxCq0uv8GYVAa84Hddb6kMvtD9i8AT8w8WWqwH81GUBOU7LSw2Lzhgh2l6aoEeL/6SrA3dX2ifAYmh48HJvVxFAPUOPHfxDVfMkMcoKfmyfrZk6B2ZVKt9pi9c9RY/an08Mfdvp69sGHPFa3QCf4bYan6LHDv6haRX3GjHKCn4ss6SfbIbjaAmbYdGbJf0cyvbZmjl9/X5bc6/VDfCJ8ttqmPLHZ+ixg39ISy978zPKCnXI6tIrNK54hIYHL1fbgCPab2uuN0v6aU1pF/s+vQO3KVJnZBiS5YIMeGEA7B24TcODl+mygBztszVTZkk/7r2rZUzv45ryqX3Kf1YUv3yiwiqV1t8pf3wxtQ/BDv7hgnmSGGWFumR16RVVhrDegds0NWyWQ5lhSFlGU60431X3BS/VpNBMhVrO27d3DszS8wGzGVhRi5jep/omTpzotHxJ2i4p0MmGejrljy+m9vFpsJs+fbqmT5+ugwcPSpKSk5P15JNP6vrrr5ckFRUVKT09XfPmzVNxcbH69++v1157jSHydUD4odWaftVutZufJsV2cu0eiwrLNAF13fDgZZXKLBZJhnRfSNUDJ8oHVhDsagfT+3jOfluukgOzK5XvszXX6U43+aBGvuOrqX18GuxatWqlyZMnq3379jIMQ2+//bZuvvlmbdmyRcnJyRozZow+/fRTLViwQFarVQ8++KBuvfVW/fvf//ZltXExOxdLy/+qlsf3q2WUpFIxdUkdFnD2pK+rUKdd9ivngyNaWX6+5GvbWrIVcPrS+9U1/tymmN6n5t4s/YOeD5qtAMt/b6uxGRa9WfoHfre1xKfBbsCAAQ7PJ06cqOnTp2v9+vVq1aqVZs+erTlz5ujaa6+VJGVmZiopKUnr169X9+7dfVFlXEzFuegcsEB0XdTgwJe+rkKddvCqYCVFlVQqd5yxv4rXngpW+I6PvVEtwGtcue8U3uU399iVlpZqwYIFOn36tFJTU7Vp0yaVlJSob9++9n0SExMVHx+vdevWVRnsiouLVVz83/skCgoKvF53/MLpXHQXYOqSOudsm56yNYj2dTXqrNm2XZpizFeAw6AJ6XBpQ8UHnajydTZDmm0boNOdzDcqPODsSb4wmNzF7juF9/k82H333XdKTU1VUVGRIiIi9NFHH6lTp07aunWrQkJCFB0d7bB/bGyscnKcTwoqSZMmTdLTTz/ttfoycqpq7XJ3XHT+nLNRrXVoz55aq09d4q+LotsaRHP5pAZWqYfGFUdW6r2wyNDzgY6XqwxDOqcg/WBrWdbDEdxFCvZh5WFqjMQ2L58Hu44dO2rr1q3Kz8/XP//5T91zzz1as2aN28fLyMjQ2LFj7c8LCgoUFxfniaoycuoSpl8VoKQo59sMQ3p29Rn9+8PKo6LKF1FvHV6kg6fDtPlEhK5qeMr+/P2sWH31c7R3K+9jLIruG7Xxx+3C3ovegdt0X/BSXRaQo8NGExk2qWlAPperUKsqjtb29EhsQqNv+TzYhYSEqF27dpKklJQU/ec//9G0adN0xx136Ny5czp58qRDr11ubq6aNXM+2ackhYaGKjQ01Ct1ZeTUxc227dLzxnyHubrKnVOQlsXeLVXILRUXUU+KOqOkqDMOz59JPqA/599h2gXTWRTdN7z9x+1S54u35MlmWJjWBLXO2WhtT43EfjB4kYaHfGF/zvQ9tc/nwa4im82m4uJipaSkKDg4WCtWrNDAgQMlSbt371ZWVpZSU1N9W0dGTjm1Sj2UZVuhhMDKI/l+sLV0+jtzZRH1AIt0b+R6rSrq4ZF6ApJ3/7j5w/mAqlS1xnHbgCMu9bZVtU/vwG0Ooa4c7bx2+TTYZWRk6Prrr1d8fLwKCws1Z84crV69WkuXLpXVatWIESM0duxYNWrUSFFRUXrooYeUmprq8xGx/jxc35d6heySJdTmZIZ9KbOwuwLOVQ58VU0HUZFZp36QaE++crE/bmY4H1CVqtY4zrNZL9mLfbGebmdfXsrRzmuPT4Pd0aNHdffdd+vIkSOyWq3q0qWLli5dquuuu06S9NJLLykgIEADBw50mKDY1xjRVVmPJif1bOcDDmWGIR0+G6IZ+1pq07E9ClflgRNVTQdRaT+mfoCHVfXHzRvrt/YO3FblBCesF+s/6suXrLdKUjXFmlVptLbFKK20b4DF0PDAJfqyoKUk6b6Gn1W5z2UBR6s85/7zTU375bwqvmpPPg12s2fPvuj2sLAwvfrqq3r11VdrqUauYQqIygY3fKNSmcUi5Qc3dXpvXTln00E46/Ez69QPEtM/+EpmST89H+BkItVf1m+tiQsvVR21RTm9PcGT54Nn1Jf34UZJTzZpoyHxuWoTXqQDp8P0/o+xeqKT8/VfLws4Yv9i3TbNec/zZQFHdLAwzOkXdcOQ5u4KUfgxvpzXBr+7x64uYAqIytoGOf/D1TYo76K/K2fTQXxT2kFXB+5xnNySqR/gYd6aSLXipaqqQt0ZI0RPFN/DSFg/Up++tC+VtLRIUtEvBbHSENsbl1wO7GJLhr1l6+H0i3rm6R5aFtunyi/4ZuWrL+0EO3hETS5rOZvM8tVLX50FaswbE6le7D6jigh1/qW+f2n/xuikTkZ2hSsmjsuBXWzJsDXBXZzO27jG0kUKr+3/Tf1FsINHePOyFlCXVDVIoiLurYM/cTai1TCkt0r6OnwBuVRPN6tO+B7BDh7B+oCoD1yZCqKq3usLXfilh8lc4Q+c9TRbLNLVgXsqXUEhvPk3gh08hjc7zMzVCY2d915Lh4ymamY5oVIFKFA23Re8VJcHHGAyV/gFpuMxD4IdgCoFFLEucrmLTfNQPhWEJH2plvpzyCDdG/6V2gYe1f7SGGWe7iGLpBcuWGWlc2CWkgMq9+w5O6aZ0Kb8kzv3SdPb7J8IdgAqsVqtCg4Jlfa7v25zXVBxneKLrUt8sWkeKs6xuFHSRsVIivmlZI+mX7W70mudLb9X1THNJDgkVFYryzL6E1fvky4Pc+0DshVqOW8vv1hvMwGwdhHs4DO82f1XbGys3nv3HeXnm7d3JfzQarVcO87+PCnqjP7W+aCy06bodFzvSvtbPr9HOr6j8oFikjR+/HhNnDhR48ePV0JCgtPztZufJlWe/9W5mCTNnDnTxZ3rHqvVyrrIfsaV+6Qr3o5QkbOlw2p7TWYQ7OAjVb3Z3y7po98E7iHs+YHY2Fhz//Fd/T+Viiwy1HL/PKnPyMr7l46X5g+THNaQsKjBdeOVEFAW5hISEtShQwfn54vtJGVvdqFiZces8jjwmvp+mfhLtdSXutehLED/nYfR2e0IFVVc/tHVWxjMyFftiWAHn6hyQXRuJEdtydvlvPxoFeVJN0p3vCd99WLZPjGJUlq6lHiDtKfycnmVpKU7CYZONGpbdkzUmvpy60FNVXU7woUqLv9YnVsYzMgXtx0Q7OATrs715axrH/CIponOe9BiLrJ0XdKNZQ93VAyG589Khq3yfoWuvTfgOfXh1gNPqPJ2hF8Yssh64zOa+UCvS7/G5LcblPPFbQcEO/iEK3N9lWO4PbzCaQ+apazcWy4MhjN/V/1gCa8x/a0HnlA6Xsb8YbJU7HUOCpViO8uSlq6WFXubL3ILA7cbeEeAryuA+imzpJ9shuOQQKOKK1TM0A+vKO9Ba5kiBYeX/bzz/dq7DJqWLqnisFgvB0ugJpJuVHbaFO0o+JVsQQ1+ec/MkZ44Kt2/0vl7x9fvs3qIHjv4hLMRWN+UdtC9wV+wLBlqT00urXri3FXdswf4qdNxvfW/mztq5syZrve4+fJ9Vg8R7OAzzlaq+N6WwLJkqD/4gwfAwwh28CssS4Z6Zediae3UshG6TX/psSPoAagB7rEDAF/YuViaP7RsAEXJmbKf84eVlQOAm+ixc0N9n8TSVb1Cdml4+Fq1DcrT/vNNlXk6TWvOMeKvItqT64qKipSV5dpo6tr0448/Ovx0RfzyiQqrVGro7BfP6VCg50YLxsfHKyys8pkAmBPBrhqYxNJ1PZqc1LOdD9ifJwdna4p1vp78vk2Va3HWZ6yd6ZqsrCyNHOlkVQg/MXHiRJf3XZK2Swp0suHoTo/+H6t1kzuAOo9gVw1MYum62I8HS6ccywIs0vhev9Kh/o6TUoYfWq3G32cqJH+/zlnb6ljycKdrdZoZa2e6Jj4+3jSTmtbWxK3x8fEeOxYA/0ewqyYmsXSN7exhp+UNCg469h7sXCxdsBB72PEdarn2z2XTQHATOSoICwszT+8TE7cC8AIGT8ArzlnbOt9QcVb9tVOd7GSUze0FmBkTtwLwAnrs4BXHkoer+ZfjFOAwsb6TWfWruxA7YCbMYwfAw+ixg1ecjuutJ79vo7ONky/eG9G0ilGyrJcJAN61c3HZmsUTm5f9ZKodU6DHDl7z1c/ROtT/EiPyfLEQOwDUd+XzKJYrn0eR+5vrPIIdfIv1MgHAKW/O2+juPIruzNlYm5i3kWAHf8B9RgBQiTfnbazpPIrVmbOxNjFvI8EOAAC/5M15G2trHsXaxryNBDsAAPySV+dtZB5F02JULPwLo7QAwPuYR9G06LGD/2CUFgDUHu5vNiWCXR3nzVFTNeHOyCl3R2lVF6OmAABmZTEMw7j0bnVXQUGBrFar8vPzFRUV5evqeNyePXu8Nmqqti1J26YGgbZK5WdLA3T92is8dh5GTQEA6pLqZBl67Oo4b46aqm21NUqLUVMAALMi2NVxXh01VdsYpQUAQI0wKhb+g1FaAADUCD128C+M0gIAwG302AEAAJgEwQ4AAMAkCHYAAAAmQbADAAAwCYIdAACASRDsAAAATIJgBwAAYBIEOwAAAJMg2AEAAJgEwQ4AAMAkCHYAAAAmQbADAAAwCYIdAACASRDsAAAATIJgBwAAYBIEOwAAAJMg2AEAAJgEwQ4AAMAkCHYAAAAmEeTrCqCO2blYWjtVytslNU2U0tKlpBt9XSsAACB67FAdOxdL84dK2ZulkjNlP+cPKysHAAA+R7CD69ZOdVJoSF+9WOtVAQAAlRHs4Lq8Xc7Lj1ZRDgAAahXBDq5rmui8PKaKcgAAUKsIdnBdWrokS4VCyy/lAADA1wh2cF3SjdId70ktU6Tg8LKfd74vJd7g65oBAAD5ONhNmjRJv/nNbxQZGamYmBj98Y9/1O7dux32KSoq0qhRo9S4cWNFRERo4MCBys3N9VGNoaQbpftXSuOzy34S6gAA8Bs+DXZr1qzRqFGjtH79ei1fvlwlJSXq16+fTp8+bd9nzJgx+uSTT7RgwQKtWbNG2dnZuvXWW31YawAAAP9kMQzD8HUlyuXl5SkmJkZr1qxRz549lZ+fr6ZNm2rOnDm67bbbJEm7du1SUlKS1q1bp+7du1/ymAUFBbJarcrPz1dUVJS3/wsAAAAeVZ0s41f32OXn50uSGjVqJEnatGmTSkpK1LdvX/s+iYmJio+P17p163xSRwAAAH/lN0uK2Ww2jR49Wtdcc406d+4sScrJyVFISIiio6Md9o2NjVVOTo7T4xQXF6u4uNj+vKCgwGt1BgAA8Cd+02M3atQobd++XfPmzavRcSZNmiSr1Wp/xMXFeaiGAAAA/s0vgt2DDz6oxYsXa9WqVWrVqpW9vFmzZjp37pxOnjzpsH9ubq6aNWvm9FgZGRnKz8+3Pw4dOuTNqgMAAPgNnwY7wzD04IMP6qOPPtLKlSvVpk0bh+0pKSkKDg7WihUr7GW7d+9WVlaWUlNTnR4zNDRUUVFRDg8AAID6wKf32I0aNUpz5szRokWLFBkZab9vzmq1qkGDBrJarRoxYoTGjh2rRo0aKSoqSg899JBSU1NdGhELAABQn/h0uhOLpeLyVGUyMzN17733SiqboDg9PV1z585VcXGx+vfvr9dee63KS7EVMd0JAKBe27lYWjtVyttVtuZ3WnrZZPOoM6qTZfxqHjtvINgBAOqtnYul+UMrFFrKlock3NUZdXYeOwAA4EFrpzopNKSvXqz1qqB2EOwAADCrvF3Oy49WUY46j2AHAIBZNU10Xh5TRTnqPIIdAABmlZYuqeJARcsv5TAjgh0AAGaVdGPZQImWKVJweNnPO9+XEm/wdc3gJX6zViwAAPCCpBsZAVuP0GMHAABgEgQ7AAAAkyDYAQAAmAT32AEAgDIsP1bn0WMHAAD+u/xY9map5EzZz/nDyspRZxDsAAAAy4+ZBMEOAACw/JhJEOwAAADLj5kEwQ4AALD8mEkQ7AAAAMuPmQTTnQAAgDIsP1bn0WMHAABgEgQ7AAAAkyDYAQAAmATBDgAAwCQIdgAAACZBsAMAADAJgh0AAIBJEOwAAABMgmAHAABgEgQ7AAAAkyDYAQAAmATBDgAAwCSCfF0BbzMMQ5JUUFDg45oAAABUX3mGKc80F2P6YFdYWChJiouL83FNAAAA3FdYWCir1XrRfSyGK/GvDrPZbMrOzlZkZKQsFouvq1NvFBQUKC4uTocOHVJUVJSvqwN4Fe0d9QVt3TcMw1BhYaFatGihgICL30Vn+h67gIAAtWrVytfVqLeioqJ486PeoL2jvqCt175L9dSVY/AEAACASRDsAAAATIJgB68IDQ3VhAkTFBoa6uuqAF5He0d9QVv3f6YfPAEAAFBf0GMHAABgEgQ7AAAAkyDYAQAAmATBzoReffVVtW7dWmFhYerWrZu++eYb+7bevXvLYrE4PB544AH79mPHjun3v/+9WrRoodDQUMXFxenBBx90WJLtrbfecnh9RESEUlJStHDhwirr9MADD8hisegf//iHQ/nx48c1dOhQRUVFKTo6WiNGjNCpU6cc9jEMQ2+88YZSU1MVFRWliIgIJScn65FHHtHevXvt+73xxhtKS0tTw4YN1bBhQ/Xt29fh/w5zqkl7v9CxY8fUqlUrWSwWnTx50l7uant/6qmnlJiYqPDwcHv727BhQ6XzfPrpp+rWrZsaNGighg0b6o9//GOlfT788ENde+21atiwoRo0aKCOHTvqvvvu05YtW+z7HDlyREOGDFGHDh0UEBCg0aNHV+8XhzqpJu29Ylu+8HH06FGn+9SkvfP57hsEO5OZP3++xo4dqwkTJmjz5s264oor1L9/f/ubVpLuv/9+HTlyxP6YMmWKfVtAQIBuvvlmffzxx9qzZ4/eeustffHFF5X+GEZFRdlfv2XLFvXv31+DBg3S7t27K9Xpo48+0vr169WiRYtK24YOHarvv/9ey5cv1+LFi/Xll19q5MiR9u2GYWjIkCF6+OGH9Yc//EHLli3Tjh07NHv2bIWFhenZZ5+177t69WoNHjxYq1at0rp16xQXF6d+/frpp59+qtHvFP6rpu39QiNGjFCXLl2cbnOlvXfo0EGvvPKKvvvuO3311Vdq3bq1+vXrp7y8PPs+H374oe666y4NHz5c27Zt07///W8NGTLE4VyPPfaY7rjjDnXt2lUff/yxdu/erTlz5qht27bKyMiw71dcXKymTZvqiSee0BVXXOHW7w91S03b+x133OGw7ciRI+rfv7969eqlmJgY+36eau98vvuIAVO5+uqrjVGjRtmfl5aWGi1atDAmTZpkGIZh9OrVy3jkkUeqdcxp06YZrVq1sj/PzMw0rFarwz6lpaVGcHCw8cEHHziUHz582GjZsqWxfft2IyEhwXjppZfs23bs2GFIMv7zn//Yy5YsWWJYLBbjp59+MgzDMObOnWtIMhYtWuS0bjabrcp6nz9/3oiMjDTefvttV/+rqGM81d5fe+01o1evXsaKFSsMScaJEyfs26rT3i+Un59vSDK++OILwzAMo6SkxGjZsqUxa9asKl+zbt06Q5Ixbdo0p9urau/uvK9R93j68/3o0aNGcHCw8c4779jLPNXe+Xz3HXrsTOTcuXPatGmT+vbtay8LCAhQ3759tW7dOnvZ+++/ryZNmqhz587KyMjQmTNnqjxmdna2Fi5cqF69elW5T2lpqd5++21J0lVXXWUvt9lsuuuuuzRu3DglJydXet26desUHR2tX//61/ayvn37KiAgwN6lP3fuXHXs2FE33XST03NfbP3fM2fOqKSkRI0aNapyH9RdnmrvO3bs0DPPPKN33nnnkmswSlW394p1mzlzpqxWq703bfPmzfrpp58UEBCgK6+8Us2bN9f111+v7du32183d+5cRURE6H//93+dHpf1rusvb3y+v/POO/rVr36l2267rcp93G3vfL77junXiq1Pfv75Z5WWlio2NtahPDY2Vrt27ZIkDRkyRAkJCWrRooW+/fZbPfbYY9q9e3el+ycGDx6sRYsW6ezZsxowYIBmzZrlsD0/P18RERGSpLNnzyo4OFgzZ87UZZddZt/n73//u4KCgvTwww87rW9OTo5D978kBQUFqVGjRsrJyZEk7dmzRx07dnTYZ/To0fb6REdH6/Dhw06P/9hjj6lFixYOH4QwD0+09+LiYg0ePFjPP/+84uPjtX//fqfncqW9S9LixYt155136syZM2revLmWL1+uJk2aSJL92E899ZRefPFFtW7dWlOnTlXv3r21Z88eNWrUSHv27FHbtm0VFPTfj+YXX3xRTz75pP35Tz/95PKakTAPT36+l5s9e7aGDBmiBg0aOJR7or3z+e47BLt65sL7Gy6//HI1b95cffr00b59+xzetC+99JImTJigPXv2KCMjQ2PHjtVrr71m3x4ZGanNmzdLKvvmVH4fXuPGjTVgwABt2rRJ06ZN0+bNmz3eyzB+/Hg9+OCDWrhwoZ577jmn+0yePFnz5s3T6tWrFRYW5tHzo+64VHvPyMhQUlKShg0bdtHjXKq9l/vd736nrVu36ueff9Ybb7yhQYMGacOGDYqJiZHNZpNU1n4HDhwoScrMzFSrVq20YMEC/c///I/Tc99333266aabtGHDBg0bNkwGc8qjCq5+vktlPWo7d+7Uu+++W+k4nmjv7uLzvea4FGsiTZo0UWBgoHJzcx3Kc3Nz1axZM6ev6datmyQ5jD6SpGbNmikxMVE33XSTXn/9dU2fPl1Hjhyxbw8ICFC7du3Url07denSRWPHjlXv3r3197//XZK0du1aHT16VPHx8QoKClJQUJB+/PFHpaenq3Xr1vZzXHjTrySdP39ex48ft9e3ffv2lQZkNG3aVO3atavyw+OFF17Q5MmTtWzZsipvhkfd54n2vnLlSi1YsMDeRvv06WM/9oQJE+yvu1R7LxceHq527dqpe/fumj17toKCgjR79mxJUvPmzSVJnTp1su8fGhqqtm3bKisrS1JZe9+/f79KSkrs+0RHR6tdu3Zq2bJl9X9JMA1Pfr5L0qxZs9S1a1elpKRU2uaJ9s7nu+8Q7EwkJCREKSkpWrFihb3MZrNpxYoVSk1NdfqarVu3SvrvHx1nynsaiouLL3r+wMBAnT17VpJ011136dtvv9XWrVvtjxYtWmjcuHFaunSpJCk1NVUnT57Upk2b7MdYuXKlbDab/QNp8ODB2r17txYtWnSJ/32ZKVOm6G9/+5s+//xzh3s7YD6eaO8ffvihtm3bZm+j5ZeA1q5dq1GjRl30/Be296rYbDb7+yYlJUWhoaEOf8hKSkp08OBBJSQkSCpr76dOnXLoHQckz36+nzp1Sh988IFGjBjh8vmr2975fPchX4/egGfNmzfPCA0NNd566y1jx44dxsiRI43o6GgjJyfH2Lt3r/HMM88YGzduNA4cOGAsWrTIaNu2rdGzZ0/76z/99FPjzTffNL777jvjwIEDxuLFi42kpCTjmmuuse+TmZlpREVFGUeOHDGOHDli7N+/33j99deNwMBA4+mnn66ybhVHxRqGYfz+9783rrzySmPDhg3GV199ZbRv394YPHiwfbvNZjNuu+02IywszHj66aeN9evXGwcOHDBWr15t/P73vzcaNWpk33fy5MlGSEiI8c9//tNetyNHjhiFhYUe+M3CH9W0vVe0atUqp6NiL9XeT506ZWRkZBjr1q0zDh48aGzcuNEYPny4ERoaamzfvt1+rEceecRo2bKlsXTpUmPXrl3GiBEjjJiYGOP48eP2fdLT043AwEBjzJgxxtq1a42DBw8a69atM4YNG2ZYLBYjPz/fvu+WLVuMLVu2GCkpKcaQIUOMLVu2GN9//70Hf8PwJ55q77NmzTLCwsIc2nk5T7Z3Pt99g2BnQv/3f/9nxMfHGyEhIcbVV19trF+/3jAMw8jKyjJ69uxpNGrUyAgNDTXatWtnjBs3zuEPxcqVK43U1FTDarUaYWFhRvv27Y3HHnus0h86SfZHaGio0aFDB2PixInG+fPnq6yXs2B37NgxY/DgwUZERIQRFRVlDB8+vNIbtbS01JgxY4bRrVs3Izw83AgJCTHatm1r3H///caOHTscjn9hvcofEyZMcP+XCb9Xk/ZeUVXB7lLt/ezZs8Ytt9xitGjRwggJCTGaN29u3HTTTcY333zjcPxz584Z6enpRkxMjBEZGWn07dvX4Q9hufnz5xu9e/c2rFarERwcbLRq1coYMmSI/f9Wzll7T0hIcPM3ibrAE+09NTXVGDJkiNPje7K98/nuGxbD4E5cAAAAM+AeOwAAAJMg2AEAAJgEwQ4AAMAkCHYAAAAmQbADAAAwCYJdPXfs2DHFxMTo4MGDvq7KJT3++ON66KGHfF0N1GG0d9QXtPX6i2BXz02cOFE333yzWrdurYMHD8pisSgmJkaFhYUO+3Xt2lVPPfWUW+fYtm2bBg8erLi4ODVo0EBJSUmaNm2awz5HjhzRkCFD1KFDBwUEBGj06NGVjvPoo4/q7bffrnKhduBSaqO9S9LDDz9sX2mia9eulbY/9dRTslgslR7h4eH2fWjvqAl/aesX2rt3ryIjIxUdHe1QTlv3LIJdPXbmzBnNnj270rIyhYWFeuGFFzx2nk2bNikmJkbvvfeevv/+e40fP14ZGRl65ZVX7PsUFxeradOmeuKJJ3TFFVc4PU6TJk3Uv39/TZ8+3WN1Q/1RW+293H333ac77rjD6bZHH31UR44ccXh06tRJt99+u30f2jvc5U9tvVxJSYkGDx6stLS0Stto655FsKvHPvvsM4WGhqp79+4O5Q899JBefPHFSgs4u+u+++7TtGnT1KtXL7Vt21bDhg3T8OHDtXDhQvs+rVu31rRp03T33XfLarVWeawBAwZo3rx5HqkX6pfaau+S9PLLL2vUqFFq27at0+0RERFq1qyZ/ZGbm6sdO3ZU+kNMe4c7/Kmtl3viiSeUmJioQYMGOd1OW/ccgl09tnbtWqWkpFQqHzx4sNq1a6dnnnmmytc+8MADioiIuOjjYvLz89WoUaNq1/nqq6/W4cOH68R9I/AvvmzvlzJr1ix16NChUm8G7R3u8Le2vnLlSi1YsECvvvpqlfvQ1j0nyNcVgO/8+OOPatGiRaVyi8WiyZMna8CAARozZowuu+yySvs888wzevTRR90679dff6358+fr008/rfZry+v7448/qnXr1m6dH/WTr9r7pRQVFen999/X448/Xmkb7R3u8Ke2fuzYMd1777167733FBUVVeV+tHXPIdjVY2fPnlVYWJjTbf3791ePHj3017/+VXPmzKm0PSYmRjExMdU+5/bt23XzzTdrwoQJ6tevX7Vf36BBA0ll95AA1eGL9u6Kjz76SIWFhbrnnnsqbaO9wx3+1Nbvv/9+DRkyRD179rzofrR1z+FSbD3WpEkTnThxosrtkydP1vz587Vly5ZK29zprt+xY4f69OmjkSNH6oknnnCrzsePH5ckNW3a1K3Xo/6q7fbuqlmzZunGG29UbGxspW20d7jDn9r6ypUr9cILLygoKEhBQUEaMWKE8vPzFRQUpDfffNO+H23dc+ixq8euvPJKvffee1Vuv/rqq3Xrrbc6vURU3e7677//Xtdee63uueceTZw40a36SmU9fsHBwUpOTnb7GKifarO9u+rAgQNatWqVPv74Y6fbae9whz+19XXr1qm0tNT+fNGiRfr73/+ur7/+Wi1btrSX09Y9h2BXj/Xv318ZGRk6ceKEGjZs6HSfiRMnKjk5WUFBjk2lOt3127dv17XXXqv+/ftr7NixysnJkSQFBgY6fDvbunWrJOnUqVPKy8vT1q1bFRISok6dOtn3Wbt2rdLS0uzd9oCraqu9S2XzdZ06dUo5OTk6e/asvW136tRJISEh9v3efPNNNW/eXNdff73T49De4Q5/autJSUkO+2/cuFEBAQHq3LmzQzlt3YMM1GtXX321MWPGDMMwDOPAgQOGJGPLli0O+4wcOdKQZEyYMMGtc0yYMMGQVOmRkJDgsJ8r+3Ts2NGYO3euW/UAaqO9G4Zh9OrVy2l7PnDggH2f0tJSo1WrVsZf/vKXKo9De4e7/KmtXygzM9OwWq2VymnrnmMxDMOorRAJ//Ppp59q3Lhx2r59uwIC/PuWyyVLlig9PV3ffvttpW+ZgCto76gvaOv1F7/Beu6GG27QDz/8oJ9++klxcXG+rs5FnT59WpmZmbzx4TbaO+oL2nr9RY8dAACASfh3/ywAAABcRrADAAAwCYIdAACASRDsAAAATIJgBwAAYBIEOwCogd69e2v06NG+rgYASCLYAQAAmAbBDgAAwCQIdgDgotOnT+vuu+9WRESEmjdvrqlTpzpsf+2119S+fXuFhYUpNjZWt912m49qCqC+Yv0OAHDRuHHjtGbNGi1atEgxMTH6y1/+os2bN6tr167auHGjHn74Yb377rv67W9/q+PHj2vt2rW+rjKAeoYlxQDABadOnVLjxo313nvv6fbbb5ckHT9+XK1atdLIkSPVs2dPDR8+XIcPH1ZkZKSPawugvuJSLAC4YN++fTp37py6detmL2vUqJE6duwoSbruuuuUkJCgtm3b6q677tL777+vM2fO+Kq6AOopgh0AeEBkZKQ2b96suXPnqnnz5nryySd1xRVX6OTJk76uGoB6hGAHAC647LLLFBwcrA0bNtjLTpw4oT179tifBwUFqW/fvpoyZYq+/fZbHTx4UCtXrvRFdQHUUwyeAAAXREREaMSIERo3bpwaN26smJgYjR8/XgEBZd+PFy9erP3796tnz55q2LChPvvsM9lsNvulWgCoDQQ7AHDR888/r1OnTmnAgAGKjIxUenq68vPzJUnR0dFauHChnnrqKRUVFal9+/aaO3eukpOTfVxrAPUJo2IBAABMgnvsAAAATIJgBwAAYBIEOwAAAJMg2AEAAJgEwQ4AAMAkCHYAAAAmQbADAAAwCYIdAACASRDsAAAATIJgBwAAYBIEOwAAAJMg2AEAAJjE/wdwry9s9ZYRwQAAAABJRU5ErkJggg==",
-      "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import seaborn as sns\n",
     "import matplotlib.pyplot as plt\n",
@@ -1252,7 +211,7 @@
     "pmdf = mdf.reset_index()\n",
     "\n",
     "xcol = 'ds'\n",
-    "for ycol in ['distance_1_2', 'distance_2_3', 'angle_21_23']:\n",
+    "for ycol in ['distance_1_2', 'distance_2_3']:\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",
@@ -1287,7 +246,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,