diff --git a/docs/reference/map_neurons.rst b/docs/reference/map_neurons.rst
index 95e571fb5..9c3e37bfd 100644
--- a/docs/reference/map_neurons.rst
+++ b/docs/reference/map_neurons.rst
@@ -3,9 +3,6 @@ Mapping Neurons
 
 .. currentmodule:: brainlit.map_neurons
 
-Fragment Generation
--------------------
-
 .. autoapiclass:: DiffeomorphismAction
 	:members:
 .. autoapiclass:: Diffeomorphism_Transform
diff --git a/experiments/map_neurons/lddmm-figure.ipynb b/experiments/map_neurons/lddmm-figure.ipynb
index 6035f557b..734d2e92e 100644
--- a/experiments/map_neurons/lddmm-figure.ipynb
+++ b/experiments/map_neurons/lddmm-figure.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -9,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,7 +32,8 @@
     "from matplotlib.cm import ScalarMappable\n",
     "from mpl_toolkits.mplot3d import Axes3D\n",
     "import pandas as pd\n",
-    "from scipy.interpolate import splev, splprep\n",
+    "from scipy.ndimage import gaussian_filter\n",
+    "from scipy.interpolate import splev, splprep, RegularGridInterpolator\n",
     "from scipy.spatial.distance import cosine\n",
     "from scipy.stats import wilcoxon, pearsonr, linregress, norm\n",
     "from tqdm import tqdm\n",
@@ -50,10 +52,22 @@
     "from matplotlib.colors import ListedColormap\n",
     "from turtle import color\n",
     "from scipy.ndimage import distance_transform_edt, binary_dilation\n",
-    "from joblib import Parallel, delayed"
+    "from joblib import Parallel, delayed\n",
+    "import networkx as nx\n",
+    "from brainlit.map_neurons.diffeo_gen import diffeo_gen_ara\n",
+    "from brainlit.map_neurons.map_neurons import Diffeomorphism_Transform"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Jacobian"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -62,27 +76,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "integrating velocity field: 100%|██████████| 10/10 [00:23<00:00,  2.38s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "x 13300.0 y15100.0-7550.0 z9900.0\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "root_dir = Path(os.path.abspath(\"\"))\n",
-    "data_dir = os.path.join(root_dir, \"data\", \"mapping-files\")\n",
+    "data_dir = os.path.join(root_dir, \"data\")\n",
     "\n",
     "velocity_path = os.path.join(data_dir, \"downloop_1_v.mat\")\n",
     "affine_path = os.path.join(data_dir, \"downloop_1_A.mat\")\n",
@@ -104,6 +103,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -111,6 +111,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -119,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -156,48 +157,18 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Plot displacement field over atlas"
+    "### Plot displacement field over atlas"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "shp: (160, 3)\n",
-      "(1320, 800)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x13bdafe50>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "slice_num = 600\n",
     "\n",
@@ -256,65 +227,28 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Plot Jacobian over atlas border"
+    "### Plot Jacobian over atlas border"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 10201/10201 [01:17<00:00, 132.11it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(101, 1, 101)\n",
-      "shp: (81, 3)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0.98, 'Log Jacobian Determinant of Atlas to Target Mapping')"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1080x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "z = 600\n",
-    "plt.rcParams.update({\"font.size\": 22})\n",
+    "plt.rcParams.update({\"font.size\": 28})\n",
     "\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(15, 10))\n",
     "\n",
     "\n",
     "# Determinant jacobians\n",
-    "xs = np.array([z * 10 + 95])\n",
+    "xs = np.array([z])\n",
     "ys = np.arange(ymin, ymax + 1, (ymax - ymin) / 100)\n",
     "zs = np.arange(zmin, zmax + 1, (zmax - zmin) / 100)\n",
     "\n",
@@ -340,15 +274,13 @@
     "    np.squeeze(detJs).T,\n",
     "    extent=(-8000, 8000, -5000, 5000),\n",
     "    cmap=\"seismic\",\n",
-    "    vmin=-max_abs,\n",
-    "    vmax=max_abs,\n",
+    "    vmin=-max_abs * 2,\n",
+    "    vmax=max_abs * 2,\n",
     ")\n",
     "ax.axis(\"off\")\n",
-    "fig.colorbar(plt_ldjs, ax=ax)\n",
+    "# fig.colorbar(plt_ldjs, ax=ax)\n",
     "\n",
     "# Displacement\n",
-    "\n",
-    "xs = np.array([z * 10 + 95])\n",
     "ys = np.arange(ymin, ymax + 1, (ymax - ymin) / 8)\n",
     "zs = np.arange(zmin, zmax + 1, (zmax - zmin) / 8)\n",
     "og_coords, diff = get_displacements(xs, ys, zs, ct)\n",
@@ -374,11 +306,12 @@
     "border = binary_dilation(border, iterations=2)\n",
     "plt_border = ax.imshow(border, extent=(-8000, 8000, -5000, 5000), cmap=cmap_trans_white)\n",
     "\n",
-    "ax.legend(loc=\"lower right\", bbox_to_anchor=(1, -0.1))\n",
-    "fig.suptitle(\"Log Jacobian Determinant of Atlas to Target Mapping\")"
+    "# ax.legend(loc=\"lower right\", bbox_to_anchor=(1, -0.1))\n",
+    "# fig.suptitle(\"Log Jacobian Determinant of Atlas to Target Mapping\")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -387,20 +320,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "12"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import multiprocessing\n",
     "\n",
@@ -409,24 +331,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 132651/132651 [06:05<00:00, 362.78it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Max |J-I|: 0.0215624756966549, rad 1869.4116721578475 = 40.309143747946585\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Determinant jacobians\n",
     "grid = \"cust-full\"\n",
@@ -470,24 +377,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 132651/132651 [05:54<00:00, 374.00it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Max |ei-ei-1|: 0.0431249513933098\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def displacement(coord):\n",
     "    diff = ct.evaluate(coord) - coord\n",
@@ -503,39 +395,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Bound: Zeroth Order Mapping Error <= 0.01078123784832745L + 0.0215624756966549 \n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x146e02130>]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "xs = np.arange(0, 3000)\n",
     "ys = max_abs * xs / 2 + max_displacement\n",
@@ -546,6 +408,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -554,17 +417,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 17576/17576 [01:58<00:00, 148.41it/s]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# spectral norm jacobians\n",
     "xs = np.arange(xmin, xmax + 1, (xmax - xmin) / 25)\n",
@@ -583,91 +438,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([   31.,    69.,   111.,   180.,   508.,  3720., 10763.,  1715.,\n",
-       "          341.,   138.]),\n",
-       " array([1.71923854, 1.72126283, 1.72328712, 1.72531141, 1.7273357 ,\n",
-       "        1.72935998, 1.73138427, 1.73340856, 1.73543285, 1.73745714,\n",
-       "        1.73948142]),\n",
-       " <BarContainer object of 10 artists>)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.hist(normJs)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 121/121 [00:00<00:00, 10245.71it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(11, 1, 11)\n",
-      "shp: (121, 3)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.quiver.Quiver at 0x142b228e0>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "def Fx(pos):\n",
     "    pos = np.array(pos)\n",
@@ -752,6 +534,1881 @@
     "ct.diffeomorphism = og_diffeo"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Neuron Mapping"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "root_dir = Path(os.path.abspath(\"\"))\n",
+    "data_dir = os.path.join(root_dir, \"data\", \"mapping-files\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "velocity_path = os.path.join(data_dir, \"downloop_1_v.mat\")\n",
+    "affine_path = os.path.join(data_dir, \"downloop_1_A.mat\")\n",
+    "\n",
+    "ct = CloudReg_Transform(velocity_path, affine_path)\n",
+    "\n",
+    "im_path = \"precomputed://file://\" + os.path.join(data_dir, \"ch1_otsu_iso\")\n",
+    "vol_im = CloudVolume(im_path)\n",
+    "shp = np.array(vol_im.shape)\n",
+    "res_im = np.array(vol_im.resolution) / 1000\n",
+    "origin_im = (shp[:3] - 1) * res_im / 2\n",
+    "\n",
+    "axons_path = \"precomputed://file://\" + os.path.join(data_dir, \"axons\")\n",
+    "vol = CloudVolume(axons_path)\n",
+    "shp = np.array(vol.shape)\n",
+    "res_atlas = np.array(vol.resolution) / 1000\n",
+    "origin_atlas = (shp[:3] - 1) * res_atlas / 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1,22; 4,179\n",
+    "neuron_id = 1\n",
+    "branch_id = -1\n",
+    "\n",
+    "# get coords in proper cooordinates\n",
+    "skel = vol.skeleton.get(neuron_id)\n",
+    "coords = skel.vertices / 1000 - origin_im\n",
+    "\n",
+    "# apply affine transform\n",
+    "coords = ct.apply_affine(coords)\n",
+    "\n",
+    "G = GeometricGraph()\n",
+    "for id, coord in enumerate(coords):\n",
+    "    G.add_node(id, loc=coord)\n",
+    "for edge in skel.edges:\n",
+    "    G.add_edge(edge[0], edge[1])\n",
+    "\n",
+    "spline_tree = G.fit_spline_tree_invariant()\n",
+    "\n",
+    "# Target space\n",
+    "\n",
+    "fig = plt.figure(figsize=(18, 10), dpi=300)\n",
+    "ax = fig.add_subplot(1, 2, 1, projection=\"3d\")\n",
+    "\n",
+    "G_transformed = deepcopy(G)\n",
+    "G_transformed = transform_geometricgraph(G_transformed, ct, deriv_method=\"two-sided\")\n",
+    "soma = np.array(G.nodes[G.root][\"loc\"])\n",
+    "spline_tree = G.spline_tree\n",
+    "\n",
+    "for i, node in enumerate(tqdm(spline_tree.nodes, desc=\"Target space\")):\n",
+    "    if node != branch_id and branch_id != -1:\n",
+    "        continue\n",
+    "    spline = spline_tree.nodes[node][\"spline\"]\n",
+    "    u = spline[1]\n",
+    "    tck = spline[0]\n",
+    "\n",
+    "    # trace points only\n",
+    "    pts = splev(u, tck)\n",
+    "    if i == 0 or node == branch_id:\n",
+    "        label = \"Neuron Branch\"\n",
+    "    else:\n",
+    "        label = None\n",
+    "    ax.plot(pts[0], pts[1], pts[2], linestyle=\"-\", label=label, color=\"red\")\n",
+    "    derivs = splev(u, tck, der=1)\n",
+    "\n",
+    "# Plot displacement field\n",
+    "xmin, xmax = ax.get_xlim()\n",
+    "ymin, ymax = ax.get_ylim()\n",
+    "zmin, zmax = ax.get_zlim()\n",
+    "xs = np.arange(xmin, xmax, (xmax - xmin) / 4)\n",
+    "ys = np.arange(ymin, ymax, (ymax - ymin) / 4)\n",
+    "zs = np.arange(zmin, zmax, (zmax - zmin) / 4)\n",
+    "\n",
+    "og_coords = np.meshgrid(xs, ys, zs, indexing=\"ij\")\n",
+    "og_coords = np.array(\n",
+    "    [og_coords[0].flatten(), og_coords[1].flatten(), og_coords[2].flatten()]\n",
+    ").T\n",
+    "new_coords = ct.evaluate(og_coords)\n",
+    "\n",
+    "displacements = new_coords - og_coords\n",
+    "ax.quiver(\n",
+    "    og_coords[:, 0],\n",
+    "    og_coords[:, 1],\n",
+    "    og_coords[:, 2],\n",
+    "    displacements[:, 0],\n",
+    "    displacements[:, 1],\n",
+    "    displacements[:, 2],\n",
+    "    length=20,\n",
+    "    label=\"Scaled Displacement Field\",\n",
+    "    alpha=0.75,\n",
+    ")  # length\n",
+    "\n",
+    "ax.w_xaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))\n",
+    "ax.w_yaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))\n",
+    "ax.w_zaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))\n",
+    "\n",
+    "# ax.legend(fontsize=12)\n",
+    "plt.axis(\"off\")\n",
+    "\n",
+    "ax = fig.add_subplot(1, 2, 2, projection=\"3d\")\n",
+    "\n",
+    "for i, node in enumerate(tqdm(spline_tree.nodes, desc=\"0th order mappings\")):\n",
+    "    if node != branch_id and branch_id != -1:\n",
+    "        continue\n",
+    "    spline = spline_tree.nodes[node][\"spline\"]\n",
+    "    u = spline[1]\n",
+    "    tck = spline[0]\n",
+    "    pts = splev(u, tck)\n",
+    "    pts = np.stack(pts, axis=1)\n",
+    "\n",
+    "    # dense line points\n",
+    "    tck_line, u_line = splprep(pts.T, k=1, s=0)\n",
+    "    u_line = np.arange(u_line[0], u_line[-1] + 0.01, 0.01)\n",
+    "    pts_line = splev(u_line, tck_line)\n",
+    "    pts_line = np.stack(pts_line, axis=1)\n",
+    "    trans_pts = ct.evaluate(pts_line)\n",
+    "    if i == 0 or node == branch_id:\n",
+    "        label = \"Continuous Mapping (Ground Truth)\"\n",
+    "    else:\n",
+    "        label = None\n",
+    "    # ax.plot(\n",
+    "    #     trans_pts[:, 0],\n",
+    "    #     trans_pts[:, 1],\n",
+    "    #     trans_pts[:, 2],\n",
+    "    #     linestyle=\"-\",\n",
+    "    #     color=\"red\",\n",
+    "    #     label=label,\n",
+    "    # )\n",
+    "\n",
+    "    # Transformed points\n",
+    "    trans_pts = ct.evaluate(pts)\n",
+    "    if i == 0 or node == branch_id:\n",
+    "        label = \"Discrete Mapping - 0th Order\"\n",
+    "    else:\n",
+    "        label = None\n",
+    "    # ax.plot(\n",
+    "    #     trans_pts[:, 0],\n",
+    "    #     trans_pts[:, 1],\n",
+    "    #     trans_pts[:, 2],\n",
+    "    #     linestyle=\"-\",\n",
+    "    #     label=label,\n",
+    "    #     color=\"blue\",\n",
+    "    #     alpha=0.5,\n",
+    "    # )\n",
+    "    derivs = splev(u, tck, der=1)\n",
+    "    derivs = np.stack(derivs, axis=1)\n",
+    "    trans_derivs = ct.D(pts, derivs)\n",
+    "\n",
+    "# act on derivatives\n",
+    "soma = np.array(G_transformed.nodes[G_transformed.root][\"loc\"])\n",
+    "spline_tree = G_transformed.spline_tree\n",
+    "for i, node in enumerate(tqdm(spline_tree.nodes, desc=\"1st order mappings\")):\n",
+    "    if node != branch_id and branch_id != -1:\n",
+    "        continue\n",
+    "    spline = spline_tree.nodes[node][\"spline\"]\n",
+    "    u = spline[1]\n",
+    "    u = np.arange(u[0], u[-1] + 0.01, 0.01)\n",
+    "    chspline = spline[0]\n",
+    "    pts = chspline(u)\n",
+    "\n",
+    "    if i == 0 or node == branch_id:\n",
+    "        label = \"Discrete Mapping - 1st Order\"\n",
+    "    else:\n",
+    "        label = None\n",
+    "    ax.plot(pts[:, 0], pts[:, 1], pts[:, 2], linestyle=\"-\", label=label, color=\"green\")\n",
+    "\n",
+    "ax.w_xaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))\n",
+    "ax.w_yaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))\n",
+    "ax.w_zaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))\n",
+    "ax.grid(True)\n",
+    "# ax.legend(fontsize=12)\n",
+    "plt.axis(\"off\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Soma Mapping"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Neuroglancer coordinates: in voxels (with voxel size printed)\n",
+    "\n",
+    "Cloudvolume skeleton coordinates: in nanometers\n",
+    "\n",
+    "SWC: in microns - add offset, then subtract origin of image (in transform.txt), which is in nanometers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "root_dir = Path(os.path.abspath(\"\"))\n",
+    "data_dir = os.path.join(root_dir, \"data\", \"mapping-files\")\n",
+    "swc_dir = os.path.join(data_dir, \"all-swcs\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "velocity_path = os.path.join(data_dir, \"downloop_1_v.mat\")\n",
+    "affine_path = os.path.join(data_dir, \"downloop_1_A.mat\")\n",
+    "\n",
+    "ct = CloudReg_Transform(velocity_path, affine_path)\n",
+    "\n",
+    "im_path = \"precomputed://file://\" + os.path.join(data_dir, \"ch1_otsu_iso\")\n",
+    "vol_im = CloudVolume(im_path)\n",
+    "shp = np.array(vol_im.shape)\n",
+    "res_im = np.array(vol_im.resolution) / 1000\n",
+    "origin_im = (shp[:3] - 1) * res_im / 2\n",
+    "\n",
+    "axons_path = \"precomputed://file://\" + os.path.join(data_dir, \"axons\")\n",
+    "vol = CloudVolume(\n",
+    "    \"precomputed://https://open-neurodata.s3.amazonaws.com/ara_2016/sagittal_10um/annotation_10um_2017\"\n",
+    ")\n",
+    "shp = np.array(vol.shape)\n",
+    "res_atlas = np.array(vol.resolution) / 1000\n",
+    "origin_atlas = (shp[:3] - 1) * res_atlas / 2\n",
+    "\n",
+    "image_origin = np.zeros(3)\n",
+    "coord_to_pos = {\"x\": 0, \"y\": 1, \"z\": 2}\n",
+    "with open(os.path.join(swc_dir, \"transform.txt\")) as f:\n",
+    "    for line in f.readlines():\n",
+    "        if \"o\" == line[0]:\n",
+    "            image_origin[coord_to_pos[line[1]]] = float(line.split(\":\")[1])\n",
+    "\n",
+    "image_origin /= 1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file_list = os.listdir(swc_dir)\n",
+    "\n",
+    "soma_coords = []\n",
+    "for file in file_list:\n",
+    "    if \".swc\" in file:\n",
+    "        trace = NeuronTrace(\n",
+    "            os.path.join(swc_dir, file), rounding=False, read_offset=True\n",
+    "        )\n",
+    "        point = trace.df.loc[0]\n",
+    "        if point[\"parent\"] >= 0:\n",
+    "            print(f\"Could not find root node in {file}\")\n",
+    "        else:\n",
+    "            soma_coord = np.array([point[\"x\"], point[\"y\"], point[\"z\"]])\n",
+    "            soma_coord = soma_coord - image_origin\n",
+    "            soma_coords.append(soma_coord)\n",
+    "            # print(f\"{file}: {soma_coord/[2.92,2.92,5]}\")\n",
+    "soma_coords_target_microns = np.array(soma_coords)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What is registration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vol = CloudVolume(\n",
+    "    \"precomputed://https://open-neurodata.s3.amazonaws.com/ara_2016/sagittal_25um/average_25um\"\n",
+    ")\n",
+    "im_og = np.array(vol[:, :, :])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d = np.arange(im_og.shape[0])\n",
+    "v = np.arange(im_og.shape[1])\n",
+    "h = np.arange(im_og.shape[2])\n",
+    "\n",
+    "image_interp = RegularGridInterpolator(\n",
+    "    (d, v, h), im_og, bounds_error=False, fill_value=0\n",
+    ")\n",
+    "\n",
+    "im_og_slice = im_og[280, :, :]\n",
+    "plt.imshow(im_og_slice, cmap=\"gray\")\n",
+    "print(im_og.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "im_og_slice_blur = gaussian_filter(im_og[29, :, :], sigma=15)\n",
+    "# plt.imshow(im_og[70, :, :], cmap='gray')\n",
+    "plt.imshow(im_og_slice_blur, cmap=\"gray\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# x is vertical component, y is horizontal\n",
+    "def deformation(h_atlas, v_atlas):\n",
+    "    y_disp = (\n",
+    "        np.exp(-1 * v_atlas / 200) * np.exp(-1 * (h_atlas - 228) ** 2 / 10000) * 100\n",
+    "    )\n",
+    "    return y_disp\n",
+    "\n",
+    "\n",
+    "k = 70\n",
+    "c2 = 0.01\n",
+    "m = 228\n",
+    "c1 = 0.0005\n",
+    "\n",
+    "\n",
+    "def deformation(h_atlas, v_atlas):\n",
+    "    v_target = v_atlas + k / (c2 * v_atlas + c1 * (h_atlas - m) ** 2 + 1)\n",
+    "    return v_target - v_atlas\n",
+    "\n",
+    "\n",
+    "def deform_inv(h_target, v_target):\n",
+    "    h_atlas = h_target\n",
+    "    c_combine = 1 + c1 * (h_atlas - m) ** 2\n",
+    "    b = c_combine - v_target * c2\n",
+    "    c = k - v_target * c_combine\n",
+    "    a = c2\n",
+    "    disc = b**2 - 4 * a * c\n",
+    "    v_atlas = (-b + np.sqrt(disc)) / (2 * a)\n",
+    "\n",
+    "    return v_target - v_atlas\n",
+    "\n",
+    "\n",
+    "k = 50\n",
+    "f = 1 / 50\n",
+    "\n",
+    "\n",
+    "def deformation(h_atlas, v_atlas):\n",
+    "    v_target = v_atlas + k * np.sin(f * h_atlas) * np.exp(\n",
+    "        -((h_atlas - 300) ** 2) / 20000\n",
+    "    )\n",
+    "    return v_target - v_atlas\n",
+    "\n",
+    "\n",
+    "def deform_inv(h_target, v_target):\n",
+    "    h_atlas = h_target\n",
+    "    v_atlas = v_target - k * np.sin(f * h_atlas) * np.exp(\n",
+    "        -((h_atlas - 300) ** 2) / 20000\n",
+    "    )\n",
+    "\n",
+    "    return v_target - v_atlas\n",
+    "\n",
+    "\n",
+    "print(deformation(228, 0))\n",
+    "deform_inv(228, 0 + deformation(228, 0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v_mat, h_mat = np.meshgrid(\n",
+    "    np.arange(0, im_og.shape[1], im_og.shape[1] / 11),\n",
+    "    np.arange(0, im_og.shape[2], im_og.shape[2] / 11),\n",
+    ")\n",
+    "v_flat = np.reshape(v_mat, (-1, 1))\n",
+    "h_flat = np.reshape(h_mat, (-1, 1))\n",
+    "y_displacements = deformation(h_flat, v_flat)\n",
+    "y_displacements = np.reshape(y_displacements, v_mat.shape)\n",
+    "\n",
+    "plt.imshow(im_og[280, :, :], cmap=\"gray\")\n",
+    "plt.quiver(\n",
+    "    h_mat,\n",
+    "    v_mat,\n",
+    "    0 * y_displacements,\n",
+    "    -y_displacements,\n",
+    "    scale=1,\n",
+    "    scale_units=\"x\",\n",
+    "    facecolor=\"red\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v_mat, h_mat, d_mat = np.meshgrid(v, h, d)\n",
+    "\n",
+    "print(v_mat.shape)\n",
+    "v_flat = np.reshape(v_mat, (-1))\n",
+    "h_flat = np.reshape(h_mat, (-1))\n",
+    "d_flat = np.reshape(d_mat, (-1))\n",
+    "\n",
+    "v_displacements = deformation(h_flat, v_flat)\n",
+    "new_v = v_flat - v_displacements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "points = np.stack([d_flat, new_v, h_flat], axis=1)\n",
+    "new_im_flat = image_interp(points)\n",
+    "new_im = np.reshape(new_im_flat, v_mat.shape)\n",
+    "new_im = np.swapaxes(new_im, 0, -1)\n",
+    "new_im = new_im.astype(\"uint16\")\n",
+    "plt.imshow(new_im[280, :, :], cmap=\"gray\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v_mat, h_mat = np.meshgrid(\n",
+    "    np.arange(0, im_og.shape[1] + 1, im_og.shape[1] / 11),\n",
+    "    np.arange(0, im_og.shape[2] + 1, im_og.shape[2] / 11),\n",
+    ")\n",
+    "v_flat = np.reshape(v_mat, (-1, 1))\n",
+    "h_flat = np.reshape(h_mat, (-1, 1))\n",
+    "y_displacements = deform_inv(h_flat, v_flat)\n",
+    "y_displacements = np.reshape(y_displacements, v_mat.shape)\n",
+    "\n",
+    "print(v_flat.shape)\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(im_og[280, :, :], cmap=\"gray\")\n",
+    "for row in np.arange(y_displacements.shape[0]):\n",
+    "    xs = h_mat[:, row]\n",
+    "    ys = v_mat[:, row] - y_displacements[:, row]\n",
+    "    plt.plot(xs, ys, c=\"red\")\n",
+    "for col in np.arange(y_displacements.shape[0]):\n",
+    "    xs = h_mat[col, :]\n",
+    "    ys = v_mat[col, :]\n",
+    "    plt.plot(xs, ys, c=\"red\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Techniques"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tools = [\n",
+    "    \"Generative Diffeomorphic Mapping\",\n",
+    "    \"mBrainAligner\",\n",
+    "    \"Multiscale Varifold LDDMM\",\n",
+    "    \"ANTs\",\n",
+    "    \"CloudReg\",\n",
+    "    \"Elastix\",\n",
+    "    \"Custom MATLAB scripts\",\n",
+    "    \"quickNII/Visualign\",\n",
+    "    \"TissueCyte\",\n",
+    "    \"Connection Lens/AlignTK\",\n",
+    "    \"nTracer2\",\n",
+    "]\n",
+    "models = [\n",
+    "    \"Affine\",\n",
+    "    \"Polynomial\",\n",
+    "    \"Spline\",\n",
+    "    \"Elastic\",\n",
+    "    \"Manual Pinning of Points\",\n",
+    "    \"LDDMM\",\n",
+    "    \"Piecewise Linear\",\n",
+    "]\n",
+    "metrics = [\n",
+    "    \"Squared Error (SE)\",\n",
+    "    \"SE with contrast change\",\n",
+    "    \"Landmark SE\",\n",
+    "    \"Varifold norm\",\n",
+    "    \"Correlation\",\n",
+    "    \"Mutual information\",\n",
+    "    \"Manual placement\",\n",
+    "]\n",
+    "\n",
+    "tools_models_edges = [\n",
+    "    (\"Generative Diffeomorphic Mapping\", \"Affine\"),\n",
+    "    (\"Generative Diffeomorphic Mapping\", \"LDDMM\"),\n",
+    "    (\"Elastix\", \"Affine\"),\n",
+    "    (\"Elastix\", \"Spline\"),\n",
+    "    (\"CloudReg\", \"Affine\"),\n",
+    "    (\"CloudReg\", \"LDDMM\"),\n",
+    "    (\"mBrainAligner\", \"Spline\"),\n",
+    "    (\"ANTs\", \"Affine\"),\n",
+    "    (\"ANTs\", \"Spline\"),\n",
+    "    (\"ANTs\", \"LDDMM\"),\n",
+    "    (\"TissueCyte\", \"Affine\"),\n",
+    "    (\"quickNII/Visualign\", \"Affine\"),\n",
+    "    (\"Custom MATLAB scripts\", \"Affine\"),\n",
+    "    (\"Custom MATLAB scripts\", \"Polynomial\"),\n",
+    "    (\"Custom MATLAB scripts\", \"Spline\"),\n",
+    "    (\"Multiscale Varifold LDDMM\", \"LDDMM\"),\n",
+    "    (\"Connection Lens/AlignTK\", \"Piecewise Linear\"),\n",
+    "    (\"nTracer2\", \"Affine\"),\n",
+    "    (\"nTracer2\", \"Spline\"),\n",
+    "]\n",
+    "tools_metrics_edges = [\n",
+    "    (\"Generative Diffeomorphic Mapping\", \"SE with contrast change\"),\n",
+    "    (\"Elastix\", \"Mutual information\"),\n",
+    "    (\"Elastix\", \"Correlation\"),\n",
+    "    (\"Elastix\", \"Squared Error (SE)\"),\n",
+    "    (\"CloudReg\", \"SE with contrast change\"),\n",
+    "    (\"mBrainAligner\", \"Squared Error (SE)\"),\n",
+    "    (\"mBrainAligner\", \"Correlation\"),\n",
+    "    (\"mBrainAligner\", \"Mutual information\"),\n",
+    "    (\"quickNII/Visualign\", \"Correlation\"),\n",
+    "    (\"quickNII/Visualign\", \"Manual placement\"),\n",
+    "    (\"Multiscale Varifold LDDMM\", \"Varifold norm\"),\n",
+    "    (\"ANTs\", \"Squared Error (SE)\"),\n",
+    "    (\"ANTs\", \"Correlation\"),\n",
+    "    (\"ANTs\", \"Mutual information\"),\n",
+    "    (\"Custom MATLAB scripts\", \"Squared Error (SE)\"),\n",
+    "    (\"Custom MATLAB scripts\", \"Mutual information\"),\n",
+    "    (\"Connection Lens/AlignTK\", \"Correlation\"),\n",
+    "    (\"nTracer2\", \"Squared Error (SE)\"),\n",
+    "    (\"nTracer2\", \"Mutual information\"),\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "BG = nx.Graph()\n",
+    "BG.add_nodes_from(models)\n",
+    "BG.add_nodes_from(tools)\n",
+    "BG.add_nodes_from(metrics)\n",
+    "\n",
+    "BG.add_edges_from(tools_models_edges)\n",
+    "BG.add_edges_from(tools_metrics_edges)\n",
+    "\n",
+    "pos = dict()\n",
+    "factor = 12\n",
+    "middle_vert_pos = [factor * (len(tools) - i) for i in range(len(tools))]\n",
+    "middle_vert_pos = [\n",
+    "    p + 2 * factor if i < 4 else p for i, p in enumerate(middle_vert_pos)\n",
+    "]\n",
+    "middle_max = np.amax(middle_vert_pos)\n",
+    "\n",
+    "models_shift = (middle_max - factor * (len(models) - 1)) / 2\n",
+    "pos.update(\n",
+    "    (n, (1, factor * (len(models) - i) + models_shift)) for i, n in enumerate(models)\n",
+    ")\n",
+    "pos.update((n, (2, p)) for n, p in zip(tools, middle_vert_pos))\n",
+    "metrics_shift = (middle_max - factor * (len(metrics) - 1)) / 2\n",
+    "pos.update(\n",
+    "    (n, (3, factor * (len(metrics) - i) + metrics_shift)) for i, n in enumerate(metrics)\n",
+    ")\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(12, 10))\n",
+    "labels = nx.draw_networkx_labels(\n",
+    "    BG, pos=pos, verticalalignment=\"center\", bbox=dict(facecolor=\"white\"), clip_on=False\n",
+    ")\n",
+    "nx.draw(BG, pos=pos, ax=ax, node_size=200, node_shape=\"s\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Citations\n",
+    "ntracer2 - ntracer2 is a platform which combines elastix, ants, and open3d affine stuff (excluded for clarify), add ntracer2 in the text?\n",
+    "\n",
+    "generative diffeomorphic mapping - daniel citation - Tward D, Brown T, Kageyama Y, Patel J, Hou Z, Mori S, Albert M, Troncoso J, Miller M. Diffeomorphic registration with intensity transformation and missing data: Application to 3D digital pathology of Alzheimer's disease. Frontiers in neuroscience. 2020 Feb 11;14:52.\n",
+    "Tward D, Li X, Huo B, Lee B, Miller MI, Mitra PP. Solving the where problem in neuroanatomy: a generative framework with learned mappings to register multimodal, incomplete data into a reference brain. bioRxiv. 2020 Mar 23:2020-03.\n",
+    "\n",
+    "multiscale varifold lddmm - https://arxiv.org/pdf/2208.08376.pdf\n",
+    "\n",
+    "cloudreg - https://www.nature.com/articles/s41592-021-01218-z - done\n",
+    "\n",
+    "mbrainaligner - https://academic.oup.com/bioinformatics/article/38/19/4654/6661343 Hanchuan - done\n",
+    "\n",
+    "ANTS - http://stnava.github.io/ANTs/ - done\n",
+    "\n",
+    "elastix - https://pubmed.ncbi.nlm.nih.gov/19923044/ - done\n",
+    "\n",
+    "matlab - https://www.mathworks.com/matlabcentral/answers/414438-how-do-i-cite-matlab-in-a-bibliography-or-a-published-journal#:~:text=Citing%20MATLAB%20software%3A&text=Citation%20in%20Vancouver%20style%3A,The%20MathWorks%20Inc.%3B%202022. https://www.mathworks.com/help/images/ref/imregister.html#d124e180774\n",
+    "\n",
+    "quickNII/Visualign - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0216796 https://www.nitrc.org/projects/visualign\n",
+    "\n",
+    "tissuecyte - cite software https://www.tissuevision.com/approach\n",
+    "\n",
+    "ConnectionLens/AlignTK - https://www.nature.com/articles/s41467-021-22915-5 dong -> greg hood done (https://mmbios.pitt.edu/aligntk-home)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# tools_metrics_edges += (\"Matlab\", \"Landmark SSE\")\n",
+    "\n",
+    "metrics += [\"Unknown metric\"]\n",
+    "models += [\"Unknown model\"]\n",
+    "tools_metrics_edges += [(tool, metrics[-1]) for tool in [\"TissueCyte\"]]"
+   ]
+  },
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+             [
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+          "table": [
+           {
+            "cells": {
+             "fill": {
+              "color": "#EBF0F8"
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+             "line": {
+              "color": "white"
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+            "header": {
+             "fill": {
+              "color": "#C8D4E3"
+             },
+             "line": {
+              "color": "white"
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+            "type": "table"
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+         "layout": {
+          "annotationdefaults": {
+           "arrowcolor": "#2a3f5f",
+           "arrowhead": 0,
+           "arrowwidth": 1
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+          "autotypenumbers": "strict",
+          "coloraxis": {
+           "colorbar": {
+            "outlinewidth": 0,
+            "ticks": ""
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+          },
+          "colorscale": {
+           "diverging": [
+            [
+             0,
+             "#8e0152"
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+            [
+             0.1,
+             "#c51b7d"
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+            [
+             0.2,
+             "#de77ae"
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+            [
+             0.3,
+             "#f1b6da"
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+            [
+             0.4,
+             "#fde0ef"
+            ],
+            [
+             0.5,
+             "#f7f7f7"
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+            [
+             0.6,
+             "#e6f5d0"
+            ],
+            [
+             0.7,
+             "#b8e186"
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+            [
+             0.8,
+             "#7fbc41"
+            ],
+            [
+             0.9,
+             "#4d9221"
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+            [
+             1,
+             "#276419"
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+           ],
+           "sequential": [
+            [
+             0,
+             "#0d0887"
+            ],
+            [
+             0.1111111111111111,
+             "#46039f"
+            ],
+            [
+             0.2222222222222222,
+             "#7201a8"
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+            [
+             0.3333333333333333,
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+            [
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+            [
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+            [
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+            [
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+             "#ed7953"
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+            [
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+             "#fb9f3a"
+            ],
+            [
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+             "#fdca26"
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+            [
+             1,
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+           ]
+          },
+          "colorway": [
+           "#636efa",
+           "#EF553B",
+           "#00cc96",
+           "#ab63fa",
+           "#FFA15A",
+           "#19d3f3",
+           "#FF6692",
+           "#B6E880",
+           "#FF97FF",
+           "#FECB52"
+          ],
+          "font": {
+           "color": "#2a3f5f"
+          },
+          "geo": {
+           "bgcolor": "white",
+           "lakecolor": "white",
+           "landcolor": "#E5ECF6",
+           "showlakes": true,
+           "showland": true,
+           "subunitcolor": "white"
+          },
+          "hoverlabel": {
+           "align": "left"
+          },
+          "hovermode": "closest",
+          "mapbox": {
+           "style": "light"
+          },
+          "paper_bgcolor": "white",
+          "plot_bgcolor": "#E5ECF6",
+          "polar": {
+           "angularaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "radialaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "zaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "width": 1150
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.graph_objects as go\n",
+    "\n",
+    "labels = tools + models + metrics\n",
+    "\n",
+    "\n",
+    "xs = [0.35 for t in tools] + [0.02 for m in models] + [0.9 for m in metrics]\n",
+    "ys = (\n",
+    "    list(np.arange(0.1, 0.09 * (len(tools) + 1), 0.09))\n",
+    "    + list(np.arange(0.1, 0.09 * (len(models) + 1), 0.09))\n",
+    "    + list(np.arange(0.1, 0.09 * (1 + len(metrics)), 0.09))\n",
+    ")\n",
+    "\n",
+    "color = [\n",
+    "    \"mistyrose\",\n",
+    "    \"moccasin\",\n",
+    "    \"khaki\",\n",
+    "    \"palegreen\",\n",
+    "    \"aquamarine\",\n",
+    "    \"powderblue\",\n",
+    "    \"cyan\",\n",
+    "    \"thistle\",\n",
+    "    \"orchid\",\n",
+    "    \"goldenrod\",\n",
+    "    \"silver\",\n",
+    "]\n",
+    "tool2color = {k: v for (k, v) in zip(tools, color)}\n",
+    "color += [\"black\" for t in models]\n",
+    "color += [\"black\" for m in metrics]\n",
+    "\n",
+    "source = []\n",
+    "target = []\n",
+    "e_color = []\n",
+    "for tool_model_edge in tools_models_edges:\n",
+    "    source.append(labels.index(tool_model_edge[1]))\n",
+    "    target.append(labels.index(tool_model_edge[0]))\n",
+    "    e_color.append(tool2color[tool_model_edge[0]])\n",
+    "\n",
+    "for tool_metric_edge in tools_metrics_edges:\n",
+    "    source.append(labels.index(tool_metric_edge[0]))\n",
+    "    target.append(labels.index(tool_metric_edge[1]))\n",
+    "    e_color.append(tool2color[tool_metric_edge[0]])\n",
+    "\n",
+    "value = [1 for s in source]\n",
+    "\n",
+    "fig = go.Figure(\n",
+    "    data=[\n",
+    "        go.Sankey(\n",
+    "            node=dict(\n",
+    "                pad=15,\n",
+    "                thickness=20,\n",
+    "                line=dict(color=\"black\", width=0.5),\n",
+    "                label=labels,\n",
+    "                color=color,\n",
+    "                # x=xs,\n",
+    "                # y=ys\n",
+    "            ),\n",
+    "            link=dict(\n",
+    "                source=source,  # indices correspond to labels, eg A1, A2, A1, B1, ...\n",
+    "                target=target,\n",
+    "                value=value,\n",
+    "                color=e_color,\n",
+    "            ),\n",
+    "        )\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "fig.update_layout(font_size=24)\n",
+    "fig.update_layout(width=1150, height=1000)\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ngauge import Neuron"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n = Neuron.from_swc(\n",
+    "    \"/Users/thomasathey/Documents/mimlab/mouselight/axon_mapping/mouselight-swcs/swcs-1/AA1087.swc\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.4080889430451775\n",
+      "0.034744588499389974\n",
+      "0.016900518965864735\n",
+      "0.03365819186590806\n",
+      "0.021338873390637995\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">34</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1;36m34\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for b in n.branches:\n",
+    "    print(np.amax(b.path_dist_to_ends()) / 10000)\n",
+    "n.total_bif_nodes()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,